From dc5127d1ca8ea77bd122c6f1d480d664fae71127 Mon Sep 17 00:00:00 2001 From: jennifersteeden Date: Tue, 4 Feb 2025 13:53:42 +0000 Subject: [PATCH 1/6] Combined some changes from develop branch includinggeometry, recon, linalg --- tensorflow_mri/python/geometry/__init__.py | 18 + .../python/geometry/rotation/__init__.py | 0 .../python/geometry/rotation/euler_2d.py | 54 ++ .../python/geometry/rotation/quaternion.py | 141 +++ .../geometry/rotation/rotation_matrix.py | 144 ++++ .../geometry/rotation/rotation_matrix_2d.py | 139 +++ .../geometry/rotation/rotation_matrix_3d.py | 261 ++++++ .../python/geometry/rotation/test_data.py | 136 +++ .../python/geometry/rotation/test_helpers.py | 263 ++++++ tensorflow_mri/python/geometry/rotation_2d.py | 420 +++++++++ .../python/geometry/rotation_2d_test.py | 178 ++++ tensorflow_mri/python/geometry/rotation_3d.py | 302 +++++++ .../python/geometry/rotation_3d_test.py | 280 ++++++ tensorflow_mri/python/layers/concatenate.py | 67 ++ .../python/layers/concatenate_test.py | 52 ++ .../python/layers/data_consistency.py | 112 +++ tensorflow_mri/python/layers/normalization.py | 66 ++ .../python/layers/normalization_test.py | 56 ++ tensorflow_mri/python/layers/padding.py | 85 ++ tensorflow_mri/python/layers/recon_adjoint.py | 140 +++ .../python/layers/recon_adjoint_test.py | 79 ++ tensorflow_mri/python/layers/reshaping.py | 97 +++ .../python/layers/reshaping_test.py | 15 + tensorflow_mri/python/linalg/__init__.py | 41 + .../python/linalg/add_registrations.py | 35 + .../python/linalg/adjoint_registrations.py | 21 + .../python/linalg/cholesky_registrations.py | 58 ++ .../python/linalg/conjugate_gradient.py | 234 +++++ .../python/linalg/conjugate_gradient_test.py | 161 ++++ .../python/linalg/inverse_registrations.py | 86 ++ .../python/linalg/linear_operator.py | 428 +++++++++ .../python/linalg/linear_operator_addition.py | 294 +++++++ .../linalg/linear_operator_addition_nd.py | 70 ++ .../linear_operator_addition_nd_test.py | 15 + .../linalg/linear_operator_addition_test.py | 280 ++++++ .../python/linalg/linear_operator_adjoint.py | 31 + .../linalg/linear_operator_adjoint_test.py | 15 + .../python/linalg/linear_operator_algebra.py | 175 ++++ .../python/linalg/linear_operator_coils.py | 196 +++++ .../linalg/linear_operator_coils_test.py | 167 ++++ .../linalg/linear_operator_composition.py | 158 ++++ .../linalg/linear_operator_composition_nd.py | 276 ++++++ .../linear_operator_composition_nd_test.py | 284 ++++++ .../linear_operator_composition_test.py | 16 + .../python/linalg/linear_operator_diag.py | 31 + .../python/linalg/linear_operator_diag_nd.py | 277 ++++++ .../linalg/linear_operator_diag_nd_test.py | 510 +++++++++++ .../linalg/linear_operator_diag_test.py | 15 + .../python/linalg/linear_operator_fft.py | 257 ++++++ .../python/linalg/linear_operator_fft_test.py | 167 ++++ .../linear_operator_finite_difference.py | 125 +++ .../linear_operator_finite_difference_test.py | 81 ++ .../linalg/linear_operator_full_matrix.py | 31 + .../linear_operator_full_matrix_test.py | 15 + .../linalg/linear_operator_gram_matrix.py | 151 ++++ .../linalg/linear_operator_gram_matrix_nd.py | 151 ++++ .../linear_operator_gram_matrix_nd_test.py | 15 + .../linear_operator_gram_matrix_test.py | 15 + .../python/linalg/linear_operator_identity.py | 39 + .../linalg/linear_operator_identity_nd.py | 652 ++++++++++++++ .../linear_operator_identity_nd_test.py | 619 +++++++++++++ .../linalg/linear_operator_identity_test.py | 15 + .../linalg/linear_operator_inversion.py | 32 + .../linalg/linear_operator_inversion_test.py | 15 + .../python/linalg/linear_operator_mask.py | 259 ++++++ .../linalg/linear_operator_mask_test.py | 212 +++++ .../python/linalg/linear_operator_mri.py | 812 ++++++++++++++++++ .../python/linalg/linear_operator_mri_test.py | 214 +++++ .../python/linalg/linear_operator_nd.py | 799 +++++++++++++++++ .../python/linalg/linear_operator_nd_test.py | 263 ++++++ .../python/linalg/linear_operator_nufft.py | 778 +++++++++++++++++ .../linalg/linear_operator_nufft_test.py | 334 +++++++ .../python/linalg/linear_operator_test.py | 468 ++++++++++ .../linalg/linear_operator_test_util.py | 203 +++++ .../python/linalg/linear_operator_util.py | 158 ++++ .../python/linalg/linear_operator_wavelet.py | 153 ++++ .../linalg/linear_operator_wavelet_test.py | 87 ++ .../python/linalg/matmul_registrations.py | 133 +++ .../linalg/pseudo_inverse_registrations.py | 0 .../python/linalg/registrations_util.py | 27 + tensorflow_mri/python/linalg/slicing.py | 18 + .../python/linalg/solve_registrations.py | 133 +++ tensorflow_mri/python/ops/control_flow_ops.py | 35 + tensorflow_mri/python/recon/__init__.py | 18 + tensorflow_mri/python/recon/recon_adjoint.py | 152 ++++ .../python/recon/recon_adjoint_test.py | 94 ++ .../python/recon/recon_least_squares.py | 15 + tools/docs/guide/fft.ipynb | 101 +++ tools/docs/guide/linalg.ipynb | 32 - tools/docs/guide/optim.ipynb | 32 - tools/docs/guide/recon.ipynb | 32 - tools/docs/templates/index.rst | 5 +- 92 files changed, 14826 insertions(+), 100 deletions(-) create mode 100644 tensorflow_mri/python/geometry/__init__.py create mode 100644 tensorflow_mri/python/geometry/rotation/__init__.py create mode 100644 tensorflow_mri/python/geometry/rotation/euler_2d.py create mode 100644 tensorflow_mri/python/geometry/rotation/quaternion.py create mode 100644 tensorflow_mri/python/geometry/rotation/rotation_matrix.py create mode 100644 tensorflow_mri/python/geometry/rotation/rotation_matrix_2d.py create mode 100644 tensorflow_mri/python/geometry/rotation/rotation_matrix_3d.py create mode 100644 tensorflow_mri/python/geometry/rotation/test_data.py create mode 100644 tensorflow_mri/python/geometry/rotation/test_helpers.py create mode 100644 tensorflow_mri/python/geometry/rotation_2d.py create mode 100644 tensorflow_mri/python/geometry/rotation_2d_test.py create mode 100644 tensorflow_mri/python/geometry/rotation_3d.py create mode 100644 tensorflow_mri/python/geometry/rotation_3d_test.py create mode 100644 tensorflow_mri/python/layers/concatenate.py create mode 100644 tensorflow_mri/python/layers/concatenate_test.py create mode 100644 tensorflow_mri/python/layers/data_consistency.py create mode 100644 tensorflow_mri/python/layers/normalization.py create mode 100644 tensorflow_mri/python/layers/normalization_test.py create mode 100644 tensorflow_mri/python/layers/padding.py create mode 100644 tensorflow_mri/python/layers/recon_adjoint.py create mode 100644 tensorflow_mri/python/layers/recon_adjoint_test.py create mode 100644 tensorflow_mri/python/layers/reshaping.py create mode 100644 tensorflow_mri/python/layers/reshaping_test.py create mode 100644 tensorflow_mri/python/linalg/__init__.py create mode 100644 tensorflow_mri/python/linalg/add_registrations.py create mode 100644 tensorflow_mri/python/linalg/adjoint_registrations.py create mode 100644 tensorflow_mri/python/linalg/cholesky_registrations.py create mode 100644 tensorflow_mri/python/linalg/conjugate_gradient.py create mode 100644 tensorflow_mri/python/linalg/conjugate_gradient_test.py create mode 100644 tensorflow_mri/python/linalg/inverse_registrations.py create mode 100644 tensorflow_mri/python/linalg/linear_operator.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_addition.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_addition_nd.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_addition_nd_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_addition_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_adjoint.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_adjoint_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_algebra.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_coils.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_coils_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_composition.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_composition_nd.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_composition_nd_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_composition_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_diag.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_diag_nd.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_diag_nd_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_diag_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_fft.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_fft_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_finite_difference.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_finite_difference_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_full_matrix.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_full_matrix_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_gram_matrix.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_gram_matrix_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_identity.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_identity_nd.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_identity_nd_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_identity_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_inversion.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_inversion_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_mask.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_mask_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_mri.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_mri_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_nd.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_nd_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_nufft.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_nufft_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_test.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_test_util.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_util.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_wavelet.py create mode 100644 tensorflow_mri/python/linalg/linear_operator_wavelet_test.py create mode 100644 tensorflow_mri/python/linalg/matmul_registrations.py create mode 100644 tensorflow_mri/python/linalg/pseudo_inverse_registrations.py create mode 100644 tensorflow_mri/python/linalg/registrations_util.py create mode 100644 tensorflow_mri/python/linalg/slicing.py create mode 100644 tensorflow_mri/python/linalg/solve_registrations.py create mode 100644 tensorflow_mri/python/ops/control_flow_ops.py create mode 100644 tensorflow_mri/python/recon/__init__.py create mode 100644 tensorflow_mri/python/recon/recon_adjoint.py create mode 100644 tensorflow_mri/python/recon/recon_adjoint_test.py create mode 100644 tensorflow_mri/python/recon/recon_least_squares.py create mode 100644 tools/docs/guide/fft.ipynb delete mode 100644 tools/docs/guide/linalg.ipynb delete mode 100644 tools/docs/guide/optim.ipynb delete mode 100644 tools/docs/guide/recon.ipynb diff --git a/tensorflow_mri/python/geometry/__init__.py b/tensorflow_mri/python/geometry/__init__.py new file mode 100644 index 00000000..29dd1576 --- /dev/null +++ b/tensorflow_mri/python/geometry/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Geometric operations.""" + +from tensorflow_mri.python.geometry import rotation_2d +from tensorflow_mri.python.geometry import rotation_3d diff --git a/tensorflow_mri/python/geometry/rotation/__init__.py b/tensorflow_mri/python/geometry/rotation/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tensorflow_mri/python/geometry/rotation/euler_2d.py b/tensorflow_mri/python/geometry/rotation/euler_2d.py new file mode 100644 index 00000000..fa7851ba --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation/euler_2d.py @@ -0,0 +1,54 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""2D angles.""" + +import tensorflow as tf + + +def from_matrix(matrix): + """Converts a 2D rotation matrix to an angle. + + Args: + matrix: A `tf.Tensor` of shape `[..., 2, 2]`. + + Returns: + A `tf.Tensor` of shape `[..., 1]`. + + Raises: + ValueError: If the shape of `matrix` is invalid. + """ + matrix = tf.convert_to_tensor(matrix) + + if matrix.shape[-1] != 2 or matrix.shape[-2] != 2: + raise ValueError( + f"matrix must have shape `[..., 2, 2]`, but got: {matrix.shape}") + + angle = tf.math.atan2(matrix[..., 1, 0], matrix[..., 0, 0]) + return tf.expand_dims(angle, axis=-1) diff --git a/tensorflow_mri/python/geometry/rotation/quaternion.py b/tensorflow_mri/python/geometry/rotation/quaternion.py new file mode 100644 index 00000000..5287710e --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation/quaternion.py @@ -0,0 +1,141 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Quaternions.""" + +import tensorflow as tf + + +def from_euler(angles): + """Converts Euler angles to a quaternion. + + Args: + angles: A `tf.Tensor` of shape `[..., 3]`. + + Returns: + A `tf.Tensor` of shape `[..., 4]`. + + Raises: + ValueError: If the shape of `angles` is invalid. + """ + angles = tf.convert_to_tensor(angles) + + if angles.shape[-1] != 3: + raise ValueError(f"angles must have shape `[..., 3]`, " + f"but got: {angles.shape}") + + half_angles = angles / 2.0 + cos_half_angles = tf.math.cos(half_angles) + sin_half_angles = tf.math.sin(half_angles) + return _build_quaternion_from_sines_and_cosines(sin_half_angles, + cos_half_angles) + + +def from_small_euler(angles): + """Converts small Euler angles to a quaternion. + + Args: + angles: A `tf.Tensor` of shape `[..., 3]`. + + Returns: + A `tf.Tensor` of shape `[..., 4]`. + + Raises: + ValueError: If the shape of `angles` is invalid. + """ + angles = tf.convert_to_tensor(angles) + + if angles.shape[-1] != 3: + raise ValueError(f"angles must have shape `[..., 3]`, " + f"but got: {angles.shape}") + + half_angles = angles / 2.0 + cos_half_angles = 1.0 - 0.5 * half_angles * half_angles + sin_half_angles = half_angles + quaternion = _build_quaternion_from_sines_and_cosines( + sin_half_angles, cos_half_angles) + + # We need to normalize the quaternion due to the small angle approximation. + return tf.nn.l2_normalize(quaternion, axis=-1) + + +def _build_quaternion_from_sines_and_cosines(sin_half_angles, cos_half_angles): + """Builds a quaternion from sines and cosines of half Euler angles. + + Args: + sin_half_angles: A tensor of shape `[..., 3]`, where the last + dimension represents the sine of half Euler angles. + cos_half_angles: A tensor of shape `[..., 3]`, where the last + dimension represents the cosine of half Euler angles. + + Returns: + A `tf.Tensor` of shape `[..., 4]`, where the last dimension represents + a quaternion. + """ + c1, c2, c3 = tf.unstack(cos_half_angles, axis=-1) + s1, s2, s3 = tf.unstack(sin_half_angles, axis=-1) + w = c1 * c2 * c3 + s1 * s2 * s3 + x = -c1 * s2 * s3 + s1 * c2 * c3 + y = c1 * s2 * c3 + s1 * c2 * s3 + z = -s1 * s2 * c3 + c1 * c2 * s3 + return tf.stack((x, y, z, w), axis=-1) + + +def multiply(quaternion1, quaternion2): + """Multiplies two quaternions. + + Args: + quaternion1: A `tf.Tensor` of shape `[..., 4]`, where the last dimension + represents a quaternion. + quaternion2: A `tf.Tensor` of shape `[..., 4]`, where the last dimension + represents a quaternion. + + Returns: + A `tf.Tensor` of shape `[..., 4]` representing quaternions. + + Raises: + ValueError: If the shape of `quaternion1` or `quaternion2` is invalid. + """ + quaternion1 = tf.convert_to_tensor(value=quaternion1) + quaternion2 = tf.convert_to_tensor(value=quaternion2) + + if quaternion1.shape[-1] != 4: + raise ValueError(f"quaternion1 must have shape `[..., 4]`, " + f"but got: {quaternion1.shape}") + if quaternion2.shape[-1] != 4: + raise ValueError(f"quaternion2 must have shape `[..., 4]`, " + f"but got: {quaternion2.shape}") + + x1, y1, z1, w1 = tf.unstack(quaternion1, axis=-1) + x2, y2, z2, w2 = tf.unstack(quaternion2, axis=-1) + x = x1 * w2 + y1 * z2 - z1 * y2 + w1 * x2 + y = -x1 * z2 + y1 * w2 + z1 * x2 + w1 * y2 + z = x1 * y2 - y1 * x2 + z1 * w2 + w1 * z2 + w = -x1 * x2 - y1 * y2 - z1 * z2 + w1 * w2 + return tf.stack((x, y, z, w), axis=-1) diff --git a/tensorflow_mri/python/geometry/rotation/rotation_matrix.py b/tensorflow_mri/python/geometry/rotation/rotation_matrix.py new file mode 100644 index 00000000..ebc34f2f --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation/rotation_matrix.py @@ -0,0 +1,144 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Rotation matrices.""" + +import tensorflow as tf + + +def rotate(n, point, matrix): + """Rotates an N-D point using rotation matrix. + + Args: + n: An `int`. The dimension of the point and matrix. + point: A `tf.Tensor` of shape `[..., N]`. + matrix: A `tf.Tensor` of shape `[..., N, N]`. + + Returns: + A `tf.Tensor` of shape `[..., N]`. + + Raises: + ValueError: If the shape of the point or matrix is invalid. + """ + point = tf.convert_to_tensor(point) + matrix = tf.convert_to_tensor(matrix) + + if point.shape[-1] != n: + raise ValueError( + f"point must have shape [..., {n}], but got: {point.shape}") + if matrix.shape[-1] != n or matrix.shape[-2] != n: + raise ValueError( + f"matrix must have shape [..., {n}, {n}], but got: {matrix.shape}") + try: + static_batch_shape = tf.broadcast_static_shape( + point.shape[:-1], matrix.shape[:-2]) + except ValueError as err: + raise ValueError( + f"The batch shapes of point and this rotation matrix do not " + f"broadcast: {point.shape[:-1]} vs. {matrix.shape[:-2]}") from err + + common_batch_shape = tf.broadcast_dynamic_shape( + tf.shape(point)[:-1], tf.shape(matrix)[:-2]) + point = tf.broadcast_to(point, tf.concat( + [common_batch_shape, [n]], 0)) + matrix = tf.broadcast_to(matrix, tf.concat( + [common_batch_shape, [n, n]], 0)) + + rotated_point = tf.linalg.matvec(matrix, point) + output_shape = static_batch_shape.concatenate([n]) + return tf.ensure_shape(rotated_point, output_shape) + + +def inverse(n, matrix): + """Inverts an N-D rotation matrix. + + Args: + n: An `int`. The dimension of the matrix. + matrix: A `tf.Tensor` of shape `[..., N, N]`. + + Returns: + A `tf.Tensor` of shape `[..., N, N]`. + + Raises: + ValueError: If the shape of the matrix is invalid. + """ + matrix = tf.convert_to_tensor(matrix) + + if matrix.shape[-1] != n or matrix.shape[-2] != n: + raise ValueError( + f"matrix must have shape [..., {n}, {n}], but got: {matrix.shape}") + + return tf.linalg.matrix_transpose(matrix) + + +def is_valid(n, matrix, atol=1e-3): + """Checks if an N-D rotation matrix is valid. + + Args: + n: An `int`. The dimension of the matrix. + matrix: A `tf.Tensor` of shape `[..., N, N]`. + atol: A `float`. The absolute tolerance for checking if the matrix is valid. + + Returns: + A boolean `tf.Tensor` of shape `[..., 1]`. + + Raises: + ValueError: If the shape of the matrix is invalid. + """ + matrix = tf.convert_to_tensor(matrix) + + if matrix.shape[-1] != n or matrix.shape[-2] != n: + raise ValueError( + f"matrix must have shape [..., {n}, {n}], but got: {matrix.shape}") + + # Compute how far the determinant of the matrix is from 1. + distance_determinant = tf.abs(tf.linalg.det(matrix) - 1.) + + # Computes how far the product of the transposed rotation matrix with itself + # is from the identity matrix. + identity = tf.eye(n, dtype=matrix.dtype) + inverse_matrix = tf.linalg.matrix_transpose(matrix) + distance_identity = tf.matmul(inverse_matrix, matrix) - identity + distance_identity = tf.norm(distance_identity, axis=[-2, -1]) + + # Computes the mask of entries that satisfies all conditions. + mask = tf.math.logical_and(distance_determinant < atol, + distance_identity < atol) + return tf.expand_dims(mask, axis=-1) + + +def check_shape(n, matrix): + matrix = tf.convert_to_tensor(matrix) + if matrix.shape.rank is not None and matrix.shape.rank < 2: + raise ValueError( + f"matrix must have rank >= 2, but got: {matrix.shape}") + if matrix.shape[-2] != n or matrix.shape[-1] != n: + raise ValueError( + f"matrix must have shape [..., {n}, {n}], " + f"but got: {matrix.shape}") diff --git a/tensorflow_mri/python/geometry/rotation/rotation_matrix_2d.py b/tensorflow_mri/python/geometry/rotation/rotation_matrix_2d.py new file mode 100644 index 00000000..72b86655 --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation/rotation_matrix_2d.py @@ -0,0 +1,139 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""2D rotation matrices.""" + +import tensorflow as tf + +from tensorflow_mri.python.geometry.rotation import rotation_matrix + + +def from_euler(angle): + """Converts an angle to a 2D rotation matrix. + + Args: + angle: A `tf.Tensor` of shape `[..., 1]`. + + Returns: + A `tf.Tensor` of shape `[..., 2, 2]`. + + Raises: + ValueError: If the shape of `angle` is invalid. + """ + angle = tf.convert_to_tensor(angle) + + if angle.shape[-1] != 1: + raise ValueError( + f"angle must have shape `[..., 1]`, but got: {angle.shape}") + + cos_angle = tf.math.cos(angle) + sin_angle = tf.math.sin(angle) + matrix = tf.stack([cos_angle, -sin_angle, sin_angle, cos_angle], axis=-1) # pylint: disable=invalid-unary-operand-type + output_shape = tf.concat([tf.shape(angle)[:-1], [2, 2]], axis=-1) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter + return tf.reshape(matrix, output_shape) + + +def from_small_euler(angle): + """Converts a small angle to a 2D rotation matrix. + + Args: + angle: A `tf.Tensor` of shape `[..., 1]`. + + Returns: + A `tf.Tensor` of shape `[..., 2, 2]`. + + Raises: + ValueError: If the shape of `angle` is invalid. + """ + angle = tf.convert_to_tensor(angle) + + if angle.shape[-1] != 1: + raise ValueError( + f"angle must have shape `[..., 1]`, but got: {angle.shape}") + + cos_angle = 1.0 - 0.5 * angle * angle + sin_angle = angle + matrix = tf.stack([cos_angle, -sin_angle, sin_angle, cos_angle], axis=-1) + output_shape = tf.concat([tf.shape(angle)[:-1], [2, 2]], axis=-1) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter + return tf.reshape(matrix, output_shape) + + +def inverse(matrix): + """Inverts a 2D rotation matrix. + + Args: + matrix: A `tf.Tensor` of shape `[..., 2, 2]`. + + Returns: + A `tf.Tensor` of shape `[..., 2, 2]`. + + Raises: + ValueError: If the shape of `matrix` is invalid. + """ + return rotation_matrix.inverse(2, matrix) + + +def is_valid(matrix, atol=1e-3): + """Checks if a 2D rotation matrix is valid. + + Args: + matrix: A `tf.Tensor` of shape `[..., 2, 2]`. + + Returns: + A `tf.Tensor` of shape `[..., 1]` indicating whether the matrix is valid. + """ + return rotation_matrix.is_valid(2, matrix, atol=atol) + + +def rotate(point, matrix): + """Rotates a 2D point using rotation matrix. + + Args: + point: A `tf.Tensor` of shape `[..., 2]`. + matrix: A `tf.Tensor` of shape `[..., 2, 2]`. + + Returns: + A `tf.Tensor` of shape `[..., 2]`. + + Raises: + ValueError: If the shape of `point` or `matrix` is invalid. + """ + return rotation_matrix.rotate(2, point, matrix) + + +def check_shape(matrix): + """Checks the shape of `point` and `matrix`. + + Args: + matrix: A `tf.Tensor` of shape `[..., 2, 2]`. + + Raises: + ValueError: If the shape of `matrix` is invalid. + """ + rotation_matrix.check_shape(2, matrix) diff --git a/tensorflow_mri/python/geometry/rotation/rotation_matrix_3d.py b/tensorflow_mri/python/geometry/rotation/rotation_matrix_3d.py new file mode 100644 index 00000000..a9adee2a --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation/rotation_matrix_3d.py @@ -0,0 +1,261 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""3D rotation matrices.""" + +import tensorflow as tf + +from tensorflow_mri.python.geometry.rotation import rotation_matrix + + +def from_euler(angles): + """Converts Euler angles to a 3D rotation matrix. + + Args: + angles: A `tf.Tensor` of shape `[..., 3]`. + + Returns: + A `tf.Tensor` of shape `[..., 3, 3]`. + + Raises: + ValueError: If the shape of `angles` is invalid. + """ + angles = tf.convert_to_tensor(angles) + + if angles.shape[-1] != 3: + raise ValueError( + f"angles must have shape `[..., 3]`, but got: {angles.shape}") + + sin_angles = tf.math.sin(angles) + cos_angles = tf.math.cos(angles) + return _build_matrix_from_sines_and_cosines(sin_angles, cos_angles) + + +def from_small_euler(angles): + """Converts small Euler angles to a 3D rotation matrix. + + Args: + angles: A `tf.Tensor` of shape `[..., 3]`. + + Returns: + A `tf.Tensor` of shape `[..., 3, 3]`. + + Raises: + ValueError: If the shape of `angles` is invalid. + """ + angles = tf.convert_to_tensor(angles) + + if angles.shape[-1:] != 3: + raise ValueError( + f"angles must have shape `[..., 3]`, but got: {angles.shape}") + + sin_angles = angles + cos_angles = 1.0 - 0.5 * tf.math.square(angles) + return _build_matrix_from_sines_and_cosines(sin_angles, cos_angles) + + +def from_axis_angle(axis, angle): + """Converts an axis-angle to a 3D rotation matrix. + + Args: + axis: A `tf.Tensor` of shape `[..., 3]`. + angle: A `tf.Tensor` of shape `[..., 1]`. + + Returns: + A `tf.Tensor` of shape `[..., 3, 3]`. + + Raises: + ValueError: If the shape of `axis` or `angle` is invalid. + """ + axis = tf.convert_to_tensor(axis) + angle = tf.convert_to_tensor(angle) + + if axis.shape[-1] != 3: + raise ValueError( + f"axis must have shape `[..., 3]`, but got: {axis.shape}") + if angle.shape[-1:] != 1: + raise ValueError( + f"angle must have shape `[..., 1]`, but got: {angle.shape}") + + try: + _ = tf.broadcast_static_shape(axis.shape[:-1], angle.shape[:-1]) + except ValueError as err: + raise ValueError( + f"The batch shapes of axis and angle do not " + f"broadcast: {axis.shape[:-1]} vs. {angle.shape[:-1]}") from err + + sin_axis = tf.sin(angle) * axis + cos_angle = tf.cos(angle) + cos1_axis = (1.0 - cos_angle) * axis + _, axis_y, axis_z = tf.unstack(axis, axis=-1) + cos1_axis_x, cos1_axis_y, _ = tf.unstack(cos1_axis, axis=-1) + sin_axis_x, sin_axis_y, sin_axis_z = tf.unstack(sin_axis, axis=-1) + tmp = cos1_axis_x * axis_y + m01 = tmp - sin_axis_z + m10 = tmp + sin_axis_z + tmp = cos1_axis_x * axis_z + m02 = tmp + sin_axis_y + m20 = tmp - sin_axis_y + tmp = cos1_axis_y * axis_z + m12 = tmp - sin_axis_x + m21 = tmp + sin_axis_x + diag = cos1_axis * axis + cos_angle + diag_x, diag_y, diag_z = tf.unstack(diag, axis=-1) + matrix = tf.stack([diag_x, m01, m02, + m10, diag_y, m12, + m20, m21, diag_z], axis=-1) + output_shape = tf.concat([tf.shape(axis)[:-1], [3, 3]], axis=-1) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter + return tf.reshape(matrix, output_shape) + + +def from_quaternion(quaternion): + """Converts a quaternion to a 3D rotation matrix. + + Args: + quaternion: A `tf.Tensor` of shape `[..., 4]`. + + Returns: + A `tf.Tensor` of shape `[..., 3, 3]`. + + Raises: + ValueError: If the shape of `quaternion` is invalid. + """ + quaternion = tf.convert_to_tensor(quaternion) + + if quaternion.shape[-1] != 4: + raise ValueError(f"quaternion must have shape `[..., 4]`, " + f"but got: {quaternion.shape}") + + x, y, z, w = tf.unstack(quaternion, axis=-1) + tx = 2.0 * x + ty = 2.0 * y + tz = 2.0 * z + twx = tx * w + twy = ty * w + twz = tz * w + txx = tx * x + txy = ty * x + txz = tz * x + tyy = ty * y + tyz = tz * y + tzz = tz * z + matrix = tf.stack([1.0 - (tyy + tzz), txy - twz, txz + twy, + txy + twz, 1.0 - (txx + tzz), tyz - twx, + txz - twy, tyz + twx, 1.0 - (txx + tyy)], axis=-1) + output_shape = tf.concat([tf.shape(quaternion)[:-1], [3, 3]], axis=-1) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter + return tf.reshape(matrix, output_shape) + + +def _build_matrix_from_sines_and_cosines(sin_angles, cos_angles): + """Builds a 3D rotation matrix from sines and cosines of Euler angles. + + Args: + sin_angles: A tensor of shape `[..., 3]`, where the last dimension + represents the sine of the Euler angles. + cos_angles: A tensor of shape `[..., 3]`, where the last dimension + represents the cosine of the Euler angles. + + Returns: + A `tf.Tensor` of shape `[..., 3, 3]`, where the last two dimensions + represent a 3D rotation matrix. + """ + sin_angles.shape.assert_is_compatible_with(cos_angles.shape) + + sx, sy, sz = tf.unstack(sin_angles, axis=-1) + cx, cy, cz = tf.unstack(cos_angles, axis=-1) + m00 = cy * cz + m01 = (sx * sy * cz) - (cx * sz) + m02 = (cx * sy * cz) + (sx * sz) + m10 = cy * sz + m11 = (sx * sy * sz) + (cx * cz) + m12 = (cx * sy * sz) - (sx * cz) + m20 = -sy + m21 = sx * cy + m22 = cx * cy + matrix = tf.stack([m00, m01, m02, + m10, m11, m12, + m20, m21, m22], + axis=-1) + output_shape = tf.concat([tf.shape(sin_angles)[:-1], [3, 3]], axis=-1) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter + return tf.reshape(matrix, output_shape) + + +def inverse(matrix): + """Inverts a 3D rotation matrix. + + Args: + matrix: A `tf.Tensor` of shape `[..., 3, 3]`. + + Returns: + A `tf.Tensor` of shape `[..., 3, 3]`. + + Raises: + ValueError: If the shape of `matrix` is invalid. + """ + return rotation_matrix.inverse(3, matrix) + + +def is_valid(matrix, atol=1e-3): + """Checks if a 3D rotation matrix is valid. + + Args: + matrix: A `tf.Tensor` of shape `[..., 3, 3]`. + + Returns: + A `tf.Tensor` of shape `[..., 1]` indicating whether the matrix is valid. + """ + return rotation_matrix.is_valid(3, matrix, atol=atol) + + +def rotate(point, matrix): + """Rotates a 3D point using rotation matrix. + + Args: + point: A `tf.Tensor` of shape `[..., 3]`. + matrix: A `tf.Tensor` of shape `[..., 3, 3]`. + + Returns: + A `tf.Tensor` of shape `[..., 3]`. + + Raises: + ValueError: If the shape of `point` or `matrix` is invalid. + """ + return rotation_matrix.rotate(3, point, matrix) + + +def check_shape(matrix): + """Checks the shape of `point` and `matrix`. + + Args: + matrix: A `tf.Tensor` of shape `[..., 3, 3]`. + + Raises: + ValueError: If the shape of `matrix` is invalid. + """ + rotation_matrix.check_shape(3, matrix) diff --git a/tensorflow_mri/python/geometry/rotation/test_data.py b/tensorflow_mri/python/geometry/rotation/test_data.py new file mode 100644 index 00000000..3e288c7f --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation/test_data.py @@ -0,0 +1,136 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Module with test data for transformation tests.""" +# This file is copied from TensorFlow Graphics. + +import numpy as np + +ANGLE_0 = np.array((0.,)) +ANGLE_45 = np.array((np.pi / 4.,)) +ANGLE_90 = np.array((np.pi / 2.,)) +ANGLE_180 = np.array((np.pi,)) + +AXIS_2D_0 = np.array((0., 0.)) +AXIS_2D_X = np.array((1., 0.)) +AXIS_2D_Y = np.array((0., 1.)) + + +def _rotation_2d_x(angle): + """Creates a 2d rotation matrix. + + Args: + angle: The angle. + + Returns: + The 2d rotation matrix. + """ + angle = angle.item() + return np.array(((np.cos(angle), -np.sin(angle)), + (np.sin(angle), np.cos(angle)))) # pyformat: disable + + +MAT_2D_ID = np.eye(2) +MAT_2D_45 = _rotation_2d_x(ANGLE_45) +MAT_2D_90 = _rotation_2d_x(ANGLE_90) +MAT_2D_180 = _rotation_2d_x(ANGLE_180) + +AXIS_3D_0 = np.array((0., 0., 0.)) +AXIS_3D_X = np.array((1., 0., 0.)) +AXIS_3D_Y = np.array((0., 1., 0.)) +AXIS_3D_Z = np.array((0., 0., 1.)) + + +def _axis_angle_to_quaternion(axis, angle): + """Converts an axis-angle representation to a quaternion. + + Args: + axis: The axis of rotation. + angle: The angle. + + Returns: + The quaternion. + """ + quat = np.zeros(4) + quat[0:3] = axis * np.sin(0.5 * angle) + quat[3] = np.cos(0.5 * angle) + return quat + + +QUAT_ID = _axis_angle_to_quaternion(AXIS_3D_0, ANGLE_0) +QUAT_X_45 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_45) +QUAT_X_90 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_90) +QUAT_X_180 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_180) +QUAT_Y_45 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_45) +QUAT_Y_90 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_90) +QUAT_Y_180 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_180) +QUAT_Z_45 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_45) +QUAT_Z_90 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_90) +QUAT_Z_180 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_180) + + +def _rotation_3d_x(angle): + """Creates a 3d rotation matrix around the x axis. + + Args: + angle: The angle. + + Returns: + The 3d rotation matrix. + """ + angle = angle.item() + return np.array(((1., 0., 0.), + (0., np.cos(angle), -np.sin(angle)), + (0., np.sin(angle), np.cos(angle)))) # pyformat: disable + + +def _rotation_3d_y(angle): + """Creates a 3d rotation matrix around the y axis. + + Args: + angle: The angle. + + Returns: + The 3d rotation matrix. + """ + angle = angle.item() + return np.array(((np.cos(angle), 0., np.sin(angle)), + (0., 1., 0.), + (-np.sin(angle), 0., np.cos(angle)))) # pyformat: disable + + +def _rotation_3d_z(angle): + """Creates a 3d rotation matrix around the z axis. + + Args: + angle: The angle. + + Returns: + The 3d rotation matrix. + """ + angle = angle.item() + return np.array(((np.cos(angle), -np.sin(angle), 0.), + (np.sin(angle), np.cos(angle), 0.), + (0., 0., 1.))) # pyformat: disable + + +MAT_3D_ID = np.eye(3) +MAT_3D_X_45 = _rotation_3d_x(ANGLE_45) +MAT_3D_X_90 = _rotation_3d_x(ANGLE_90) +MAT_3D_X_180 = _rotation_3d_x(ANGLE_180) +MAT_3D_Y_45 = _rotation_3d_y(ANGLE_45) +MAT_3D_Y_90 = _rotation_3d_y(ANGLE_90) +MAT_3D_Y_180 = _rotation_3d_y(ANGLE_180) +MAT_3D_Z_45 = _rotation_3d_z(ANGLE_45) +MAT_3D_Z_90 = _rotation_3d_z(ANGLE_90) +MAT_3D_Z_180 = _rotation_3d_z(ANGLE_180) diff --git a/tensorflow_mri/python/geometry/rotation/test_helpers.py b/tensorflow_mri/python/geometry/rotation/test_helpers.py new file mode 100644 index 00000000..36ca83fa --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation/test_helpers.py @@ -0,0 +1,263 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Test helpers for the transformation module.""" +# This file is copied from TensorFlow Graphics. + +import itertools +import math + +import numpy as np +from scipy import stats +from six.moves import range +import tensorflow as tf + +from tensorflow_mri.python.geometry.rotation import rotation_matrix_2d +from tensorflow_mri.python.geometry.rotation import rotation_matrix_3d +from tensorflow_mri.python.geometry.rotation import quaternion + + +def generate_preset_test_euler_angles(dimensions=3): + """Generates a permutation with duplicate of some classic euler angles.""" + permutations = itertools.product( + [0., np.pi, np.pi / 2., np.pi / 3., np.pi / 4., np.pi / 6.], + repeat=dimensions) + return np.array(list(permutations)) + + +def generate_preset_test_translations(dimensions=3): + """Generates a set of translations.""" + permutations = itertools.product([0.1, -0.2, 0.5, 0.7, 0.4, -0.1], + repeat=dimensions) + return np.array(list(permutations)) + + +def generate_preset_test_rotation_matrices_3d(): + """Generates pre-set test 3d rotation matrices.""" + angles = generate_preset_test_euler_angles() + preset_rotation_matrix = rotation_matrix_3d.from_euler(angles) + return preset_rotation_matrix + + +def generate_preset_test_rotation_matrices_2d(): + """Generates pre-set test 2d rotation matrices.""" + angles = generate_preset_test_euler_angles(dimensions=1) + preset_rotation_matrix = rotation_matrix_2d.from_euler(angles) + return preset_rotation_matrix + + +def generate_preset_test_quaternions(): + """Generates pre-set test quaternions.""" + angles = generate_preset_test_euler_angles() + preset_quaternion = quaternion.from_euler(angles) + return preset_quaternion + + +def generate_preset_test_dual_quaternions(): + """Generates pre-set test quaternions.""" + angles = generate_preset_test_euler_angles() + preset_quaternion_real = quaternion.from_euler(angles) + + translations = generate_preset_test_translations() + translations = np.concatenate( + (translations / 2.0, np.zeros((np.ma.size(translations, 0), 1))), axis=1) + preset_quaternion_translation = tf.convert_to_tensor(value=translations) + + preset_quaternion_dual = quaternion.multiply(preset_quaternion_translation, + preset_quaternion_real) + + preset_dual_quaternion = tf.concat( # pylint: disable=unexpected-keyword-arg,no-value-for-parameter + (preset_quaternion_real, preset_quaternion_dual), axis=-1) + + return preset_dual_quaternion + + +def generate_random_test_euler_angles_translations( + dimensions=3, + min_angle=-3.0 * np.pi, + max_angle=3.0 * np.pi, + min_translation=3.0, + max_translation=3.0): + """Generates random test random Euler angles and translations.""" + tensor_dimensions = np.random.randint(3) + tensor_tile = np.random.randint(1, 10, tensor_dimensions).tolist() + return (np.random.uniform(min_angle, max_angle, tensor_tile + [dimensions]), + np.random.uniform(min_translation, max_translation, + tensor_tile + [dimensions])) + + +def generate_random_test_dual_quaternions(): + """Generates random test dual quaternions.""" + angles = generate_random_test_euler_angles() + random_quaternion_real = quaternion.from_euler(angles) + + min_translation = -3.0 + max_translation = 3.0 + translations = np.random.uniform(min_translation, max_translation, + angles.shape) + + translations_quaternion_shape = np.asarray(translations.shape) + translations_quaternion_shape[-1] = 1 + translations = np.concatenate( + (translations / 2.0, np.zeros(translations_quaternion_shape)), axis=-1) + + random_quaternion_translation = tf.convert_to_tensor(value=translations) + + random_quaternion_dual = quaternion.multiply(random_quaternion_translation, + random_quaternion_real) + + random_dual_quaternion = tf.concat( # pylint: disable=unexpected-keyword-arg,no-value-for-parameter + (random_quaternion_real, random_quaternion_dual), axis=-1) + + return random_dual_quaternion + + +def generate_random_test_euler_angles(dimensions=3, + min_angle=-3. * np.pi, + max_angle=3. * np.pi): + """Generates random test random Euler angles.""" + tensor_dimensions = np.random.randint(3) + tensor_tile = np.random.randint(1, 10, tensor_dimensions).tolist() + return np.random.uniform(min_angle, max_angle, tensor_tile + [dimensions]) + + +def generate_random_test_quaternions(tensor_shape=None): # pylint: disable=missing-param-doc + """Generates random test quaternions.""" + if tensor_shape is None: + tensor_dimensions = np.random.randint(low=1, high=3) + tensor_shape = np.random.randint(1, 10, size=(tensor_dimensions)).tolist() + u1 = np.random.uniform(0.0, 1.0, tensor_shape) + u2 = np.random.uniform(0.0, 2.0 * math.pi, tensor_shape) + u3 = np.random.uniform(0.0, 2.0 * math.pi, tensor_shape) + a = np.sqrt(1.0 - u1) + b = np.sqrt(u1) + return np.stack((a * np.sin(u2), + a * np.cos(u2), + b * np.sin(u3), + b * np.cos(u3)), + axis=-1) # pyformat: disable + + +def generate_random_test_axis_angle(): + """Generates random test axis-angles.""" + tensor_dimensions = np.random.randint(3) + tensor_shape = np.random.randint(1, 10, size=(tensor_dimensions)).tolist() + random_axis = np.random.uniform(size=tensor_shape + [3]) + random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) + random_angle = np.random.uniform(size=tensor_shape + [1]) + return random_axis, random_angle + + +def generate_random_test_rotation_matrix_3d(): + """Generates random test 3d rotation matrices.""" + random_matrix = np.array( + [stats.special_ortho_group.rvs(3) for _ in range(20)]) + return np.reshape(random_matrix, [5, 4, 3, 3]) + + +def generate_random_test_rotation_matrix_2d(): + """Generates random test 2d rotation matrices.""" + random_matrix = np.array( + [stats.special_ortho_group.rvs(2) for _ in range(20)]) + return np.reshape(random_matrix, [5, 4, 2, 2]) + + +def generate_random_test_lbs_blend(): + """Generates random test for the linear blend skinning blend function.""" + tensor_dimensions = np.random.randint(3) + tensor_shape = np.random.randint(1, 10, size=(tensor_dimensions)).tolist() + random_points = np.random.uniform(size=tensor_shape + [3]) + num_weights = np.random.randint(2, 10) + random_weights = np.random.uniform(size=tensor_shape + [num_weights]) + random_weights /= np.sum(random_weights, axis=-1, keepdims=True) + + random_rotations = np.array( + [stats.special_ortho_group.rvs(3) for _ in range(num_weights)]) + random_rotations = np.reshape(random_rotations, [num_weights, 3, 3]) + random_translations = np.random.uniform(size=[num_weights, 3]) + return random_points, random_weights, random_rotations, random_translations + + +def generate_preset_test_lbs_blend(): + """Generates preset test for the linear blend skinning blend function.""" + points = np.array([[[1.0, 0.0, 0.0], [0.1, 0.2, 0.5]], + [[0.0, 1.0, 0.0], [0.3, -0.5, 0.2]], + [[-0.3, 0.1, 0.3], [0.1, -0.9, -0.4]]]) + weights = np.array([[[0.0, 1.0, 0.0, 0.0], [0.4, 0.2, 0.3, 0.1]], + [[0.6, 0.0, 0.4, 0.0], [0.2, 0.2, 0.1, 0.5]], + [[0.0, 0.1, 0.0, 0.9], [0.1, 0.2, 0.3, 0.4]]]) + rotations = np.array( + [[[[1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0]], + [[0.36, 0.48, -0.8], + [-0.8, 0.60, 0.00], + [0.48, 0.64, 0.60]], + [[0.0, 0.0, 1.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0]], + [[0.0, 1.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 0.0, -1.0]]], + [[[-0.41554751, -0.42205085, -0.80572535], + [0.08028719, -0.89939186, 0.42970716], + [-0.9060211, 0.11387432, 0.40762533]], + [[-0.05240625, -0.24389111, 0.96838562], + [0.99123384, -0.13047444, 0.02078231], + [0.12128095, 0.96098572, 0.2485908]], + [[-0.32722936, -0.06793413, -0.94249981], + [-0.70574479, 0.68082693, 0.19595657], + [0.62836712, 0.72928708, -0.27073072]], + [[-0.22601332, -0.95393284, 0.19730719], + [-0.01189659, 0.20523618, 0.97864017], + [-0.97405157, 0.21883843, -0.05773466]]]]) # pyformat: disable + translations = np.array( + [[[0.1, -0.2, 0.5], + [-0.2, 0.7, 0.7], + [0.8, -0.2, 0.4], + [-0.1, 0.2, -0.3]], + [[0.5, 0.6, 0.9], + [-0.1, -0.3, -0.7], + [0.4, -0.2, 0.8], + [0.7, 0.8, -0.4]]]) # pyformat: disable + blended_points = np.array([[[[0.16, -0.1, 1.18], [0.3864, 0.148, 0.7352]], + [[0.38, 0.4, 0.86], [-0.2184, 0.152, 0.0088]], + [[-0.05, 0.01, -0.46], [-0.3152, -0.004, + -0.1136]]], + [[[-0.15240625, 0.69123384, -0.57871905], + [0.07776242, 0.33587402, 0.55386645]], + [[0.17959584, 0.01269566, 1.22003942], + [0.71406514, 0.6187734, -0.43794053]], + [[0.67662743, 0.94549789, -0.14946982], + [0.88587099, -0.09324637, -0.45012815]]]]) + + return points, weights, rotations, translations, blended_points + + +def generate_random_test_axis_angle_translation(): + """Generates random test angles, axes, translations.""" + tensor_dimensions = np.random.randint(3) + tensor_shape = np.random.randint(1, 10, size=(tensor_dimensions)).tolist() + random_axis = np.random.uniform(size=tensor_shape + [3]) + random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) + random_angle = np.random.uniform(size=tensor_shape + [1]) + random_translation = np.random.uniform(size=tensor_shape + [3]) + return random_axis, random_angle, random_translation + + +def generate_random_test_points(): + """Generates random 3D points.""" + tensor_dimensions = np.random.randint(3) + tensor_shape = np.random.randint(1, 10, size=(tensor_dimensions)).tolist() + random_point = np.random.uniform(size=tensor_shape + [3]) + return random_point diff --git a/tensorflow_mri/python/geometry/rotation_2d.py b/tensorflow_mri/python/geometry/rotation_2d.py new file mode 100644 index 00000000..e6a96d71 --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation_2d.py @@ -0,0 +1,420 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""2D rotation.""" + +import tensorflow as tf + +from tensorflow_mri.python.geometry.rotation import euler_2d +from tensorflow_mri.python.geometry.rotation import rotation_matrix_2d +from tensorflow_mri.python.util import api_util + + +@api_util.export("geometry.Rotation2D") +class Rotation2D(tf.experimental.BatchableExtensionType): # pylint: disable=abstract-method + """Represents a rotation in 2D space (or a batch thereof). + + A `Rotation2D` contains all the information needed to represent a rotation + in 2D space (or a multidimensional array of rotations) and provides + convenient methods to work with rotations. + + ## Initialization + + You can initialize a `Rotation2D` object using one of the `from_*` class + methods: + + - `from_matrix`, to initialize using a + [rotation matrix](https://en.wikipedia.org/wiki/Rotation_matrix). + - `from_euler`, to initialize using an angle (in radians). + - `from_small_euler`, to initialize using an angle which is small enough + to fall under the [small angle approximation](https://en.wikipedia.org/wiki/Small-angle_approximation). + + All of the above methods accept batched inputs, in which case the returned + `Rotation2D` object will represent a batch of rotations. + + ## Methods + + Once initialized, `Rotation2D` objects expose several methods to operate + easily with rotations. These methods are all used in the same way regardless + of how the `Rotation2D` was originally initialized. + + - `rotate` rotates a point or a batch of points. The batch shapes of the + point and this rotation will be broadcasted. + - `inverse` returns a new `Rotation2D` object representing the inverse of + the current rotation. + - `is_valid` can be used to check if the rotation is valid. + + ## Conversion to other representations + + The `as_*` methods can be used to obtain an explicit representation + of this rotation as a standard `tf.Tensor`. + + - `as_matrix` returns the corresponding rotation matrix. + - `as_euler` returns the corresponding angle (in radians). + + ## Shape and dtype + + `Rotation2D` objects have a shape and a dtype, accessible via the `shape` and + `dtype` properties. Because this operator acts like a rotation matrix, its + shape corresponds to the shape of the rotation matrix. In other words, + `rot.shape` is equal to `rot.as_matrix().shape`. + + ```{note} + As with `tf.Tensor`s, the `shape` attribute contains the static shape + as a `tf.TensorShape` and may not be fully defined outside eager execution. + To obtain the dynamic shape of a `Rotation2D` object, use `tf.shape`. + ``` + + ## Operators + + `Rotation2D` objects also override a few operators for concise and intuitive + use. + + - `==` (equality operator) can be used to check if two `Rotation2D` objects + are equal. This checks if the rotations are equivalent, regardless of how + they were defined (`rot1 == rot2`). + - `@` (matrix multiplication operator) can be used to compose two rotations + (`rot = rot1 @ rot2`). + + ## Compatibility with TensorFlow APIs + + Some TensorFlow APIs are explicitly overriden to operate with `Rotation2D` + objects. These include: + + ```{list-table} + --- + header-rows: 1 + --- + + * - API + - Description + - Notes + * - `tf.convert_to_tensor` + - Converts a `Rotation2D` to a `tf.Tensor` containing the corresponding + rotation matrix. + - `tf.convert_to_tensor(rot)` is equivalent to `rot.as_matrix()`. + * - `tf.linalg.matmul` + - Composes two `Rotation2D` objects. + - `tf.linalg.matmul(rot1, rot2)` is equivalent to `rot1 @ rot2`. + * - `tf.linalg.matvec` + - Rotates a point or a batch of points. + - `tf.linalg.matvec(rot, point)` is equivalent to `rot.rotate(point)`. + * - `tf.shape` + - Returns the dynamic shape of a `Rotation2D` object. + - + ``` + + ```{tip} + In general, a `Rotation2D` object behaves like a rotation matrix, although + its internal representation may differ. + ``` + + ```{warning} + While other TensorFlow APIs may also work as expected when passed a + `Rotation2D`, this is not supported and their behavior may change in the + future. + ``` + + Example: + + >>> # Initialize a rotation object using a rotation matrix. + >>> rot = tfmri.geometry.Rotation2D.from_matrix([[0.0, -1.0], [1.0, 0.0]]) + >>> print(rot) + tfmri.geometry.Rotation2D(shape=(2, 2), dtype=float32) + >>> # Rotate a point. + >>> point = tf.constant([1.0, 0.0], dtype=tf.float32) + >>> rotated = rot.rotate(point) + >>> print(rotated) + tf.Tensor([0. 1.], shape=(2,), dtype=float32) + >>> # Rotate the point back using the inverse rotation. + >>> inv_rot = rot.inverse() + >>> restored = inv_rot.rotate(rotated) + >>> print(restored) + tf.Tensor([1. 0.], shape=(2,), dtype=float32) + >>> # Get the rotation matrix for the inverse rotation. + >>> print(inv_rot.as_matrix()) + tf.Tensor( + [[ 0. 1.] + [-1. 0.]], shape=(2, 2), dtype=float32) + >>> # You can also initialize a rotation using an angle: + >>> rot2 = tfmri.geometry.Rotation2D.from_euler([np.pi / 2]) + >>> rotated2 = rot.rotate(point) + >>> np.allclose(rotated2, rotated) + True + + """ + __name__ = "tfmri.geometry.Rotation2D" + _matrix: tf.Tensor + + @classmethod + def from_matrix(cls, matrix, name=None): + r"""Creates a 2D rotation from a rotation matrix. + + Args: + matrix: A `tf.Tensor` of shape `[..., 2, 2]`, where the last two + dimensions represent a rotation matrix. + name: A name for this op. Defaults to `"rotation_2d/from_matrix"`. + + Returns: + A `Rotation2D`. + """ + with tf.name_scope(name or "rotation_2d/from_matrix"): + return cls(_matrix=matrix) + + @classmethod + def from_euler(cls, angle, name=None): + r"""Creates a 2D rotation from an angle. + + The resulting rotation acts like the following rotation matrix: + + $$ + \mathbf{R} = + \begin{bmatrix} + \cos(\theta) & -\sin(\theta) \\ + \sin(\theta) & \cos(\theta) + \end{bmatrix}. + $$ + + ```{note} + The resulting rotation rotates points in the $xy$-plane counterclockwise. + ``` + + Args: + angle: A `tf.Tensor` of shape `[..., 1]`, where the last dimension + represents an angle in radians. + name: A name for this op. Defaults to `"rotation_2d/from_euler"`. + + Returns: + A `Rotation2D`. + + Raises: + ValueError: If the shape of `angle` is invalid. + """ + with tf.name_scope(name or "rotation_2d/from_euler"): + return cls(_matrix=rotation_matrix_2d.from_euler(angle)) + + @classmethod + def from_small_euler(cls, angle, name=None): + r"""Creates a 2D rotation from a small angle. + + Uses the small angle approximation to compute the rotation. Under the + small angle assumption, $\sin(x)$$ and $$\cos(x)$ can be approximated by + their second order Taylor expansions, where $\sin(x) \approx x$ and + $\cos(x) \approx 1 - \frac{x^2}{2}$. + + The resulting rotation acts like the following rotation matrix: + + $$ + \mathbf{R} = + \begin{bmatrix} + 1.0 - 0.5\theta^2 & -\theta \\ + \theta & 1.0 - 0.5\theta^2 + \end{bmatrix}. + $$ + + ```{note} + The resulting rotation rotates points in the $xy$-plane counterclockwise. + ``` + + ```{note} + This function does not verify the smallness of the angles. + ``` + + Args: + angle: A `tf.Tensor` of shape `[..., 1]`, where the last dimension + represents an angle in radians. + name: A name for this op. Defaults to "rotation_2d/from_small_euler". + + Returns: + A `Rotation2D`. + + Raises: + ValueError: If the shape of `angle` is invalid. + """ + with tf.name_scope(name or "rotation_2d/from_small_euler"): + return cls(_matrix=rotation_matrix_2d.from_small_euler(angle)) + + def as_matrix(self, name=None): + r"""Returns a rotation matrix representation of this rotation. + + Args: + name: A name for this op. Defaults to `"rotation_2d/as_matrix"`. + + Returns: + A `tf.Tensor` of shape `[..., 2, 2]`, where the last two dimensions + represent a rotation matrix. + """ + with tf.name_scope(name or "rotation_2d/as_matrix"): + return tf.identity(self._matrix) + + def as_euler(self, name=None): + r"""Returns an angle representation of this rotation. + + Args: + name: A name for this op. Defaults to `"rotation_2d/as_euler"`. + + Returns: + A `tf.Tensor` of shape `[..., 1]`, where the last dimension represents an + angle in radians. + """ + with tf.name_scope(name or "rotation_2d/as_euler"): + return euler_2d.from_matrix(self._matrix) + + def inverse(self, name=None): + r"""Computes the inverse of this rotation. + + Args: + name: A name for this op. Defaults to `"rotation_2d/inverse"`. + + Returns: + A `Rotation2D` representing the inverse of this rotation. + """ + with tf.name_scope(name or "rotation_2d/inverse"): + return Rotation2D(_matrix=rotation_matrix_2d.inverse(self._matrix)) + + def is_valid(self, atol=1e-3, name=None): + r"""Determines if this is a valid rotation. + + A rotation matrix $\mathbf{R}$ is a valid rotation matrix if + $\mathbf{R}^T\mathbf{R} = \mathbf{I}$ and $\det(\mathbf{R}) = 1$. + + Args: + atol: A `float`. The absolute tolerance parameter. + name: A name for this op. Defaults to `"rotation_2d/is_valid"`. + + Returns: + A boolean `tf.Tensor` with shape `[..., 1]`, `True` if the corresponding + matrix is valid and `False` otherwise. + """ + with tf.name_scope(name or "rotation_2d/is_valid"): + return rotation_matrix_2d.is_valid(self._matrix, atol=atol) + + def rotate(self, point, name=None): + r"""Rotates a 2D point. + + Args: + point: A `tf.Tensor` of shape `[..., 2]`, where the last dimension + represents a 2D point and `...` represents any number of batch + dimensions, which must be broadcastable with the batch shape of this + rotation. + name: A name for this op. Defaults to `"rotation_2d/rotate"`. + + Returns: + A `tf.Tensor` of shape `[..., 2]`, where the last dimension represents + a 2D point and `...` is the result of broadcasting the batch shapes of + `point` and this rotation matrix. + + Raises: + ValueError: If the shape of `point` is invalid. + """ + with tf.name_scope(name or "rotation_2d/rotate"): + return rotation_matrix_2d.rotate(point, self._matrix) + + def __eq__(self, other): + """Returns true if this rotation is equivalent to the other rotation.""" + return tf.math.reduce_all( + tf.math.equal(self._matrix, other._matrix), axis=[-2, -1]) + + def __matmul__(self, other): + """Composes this rotation with another rotation.""" + if isinstance(other, Rotation2D): + return Rotation2D(_matrix=tf.matmul(self._matrix, other._matrix)) + raise ValueError( + f"Cannot compose a `Rotation2D` with a `{type(other).__name__}`.") + + def __repr__(self): + """Returns a string representation of this rotation.""" + name = self.__name__ + return f"<{name}(shape={str(self.shape)}, dtype={self.dtype.name})>" + + def __str__(self): + """Returns a string representation of this rotation.""" + return self.__repr__()[1:-1] + + def __validate__(self): + """Checks that this rotation is a valid rotation. + + Only performs static checks. + """ + rotation_matrix_2d.check_shape(self._matrix) + + @property + def shape(self): + """Returns the shape of this rotation. + + Returns: + A `tf.TensorShape`. + """ + return self._matrix.shape + + @property + def dtype(self): + """Returns the dtype of this rotation. + + Returns: + A `tf.dtypes.DType`. + """ + return self._matrix.dtype + + +@tf.experimental.dispatch_for_api(tf.convert_to_tensor, {'value': Rotation2D}) +def convert_to_tensor(value, dtype=None, dtype_hint=None, name=None): + """Overrides `tf.convert_to_tensor` for `Rotation2D` objects.""" + return tf.convert_to_tensor( + value.as_matrix(), dtype=dtype, dtype_hint=dtype_hint, name=name) + + +@tf.experimental.dispatch_for_api( + tf.linalg.matmul, {'a': Rotation2D, 'b': Rotation2D}) +def matmul(a, b, # pylint: disable=missing-param-doc + transpose_a=False, + transpose_b=False, + adjoint_a=False, + adjoint_b=False, + a_is_sparse=False, + b_is_sparse=False, + output_type=None, + name=None): + """Overrides `tf.linalg.matmul` for `Rotation2D` objects.""" + if a_is_sparse or b_is_sparse: + raise ValueError("Rotation2D does not support sparse matmul.") + return Rotation2D(_matrix=tf.linalg.matmul(a.as_matrix(), b.as_matrix(), + transpose_a=transpose_a, + transpose_b=transpose_b, + adjoint_a=adjoint_a, + adjoint_b=adjoint_b, + output_type=output_type, + name=name)) + + +@tf.experimental.dispatch_for_api(tf.linalg.matvec, {'a': Rotation2D}) +def matvec(a, b, # pylint: disable=missing-param-doc + transpose_a=False, + adjoint_a=False, + a_is_sparse=False, + b_is_sparse=False, + name=None): + """Overrides `tf.linalg.matvec` for `Rotation2D` objects.""" + if a_is_sparse or b_is_sparse: + raise ValueError("Rotation2D does not support sparse matvec.") + return tf.linalg.matvec(a.as_matrix(), b, + transpose_a=transpose_a, + adjoint_a=adjoint_a, + name=name) + + +@tf.experimental.dispatch_for_api(tf.shape, {'input': Rotation2D}) +def shape(input, out_type=tf.int32, name=None): # pylint: disable=redefined-builtin + """Overrides `tf.shape` for `Rotation2D` objects.""" + return tf.shape(input.as_matrix(), out_type=out_type, name=name) diff --git a/tensorflow_mri/python/geometry/rotation_2d_test.py b/tensorflow_mri/python/geometry/rotation_2d_test.py new file mode 100644 index 00000000..132de2e7 --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation_2d_test.py @@ -0,0 +1,178 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for 2D rotation.""" +# This file is partly inspired by TensorFlow Graphics. +# pylint: disable=missing-param-doc + +from absl.testing import parameterized +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.geometry.rotation import test_data as td +from tensorflow_mri.python.geometry.rotation import test_helpers +from tensorflow_mri.python.geometry.rotation_2d import Rotation2D +from tensorflow_mri.python.util import test_util + + +class Rotation2DTest(test_util.TestCase): + """Tests for `Rotation2D`.""" + def test_shape(self): + """Tests shape.""" + rot = Rotation2D.from_euler([0.0]) + self.assertAllEqual([2, 2], rot.shape) + self.assertAllEqual([2, 2], tf.shape(rot)) + + rot = Rotation2D.from_euler([[0.0], [np.pi]]) + self.assertAllEqual([2, 2, 2], rot.shape) + self.assertAllEqual([2, 2, 2], tf.shape(rot)) + + def test_equal(self): + """Tests equality operator.""" + rot1 = Rotation2D.from_euler([0.0]) + rot2 = Rotation2D.from_euler([0.0]) + self.assertAllEqual(True, rot1 == rot2) + + rot1 = Rotation2D.from_euler([0.0]) + rot2 = Rotation2D.from_euler([np.pi]) + self.assertAllEqual(False, rot1 == rot2) + + rot1 = Rotation2D.from_euler([[0.0], [np.pi]]) + rot2 = Rotation2D.from_euler([[0.0], [np.pi]]) + self.assertAllEqual([True, True], rot1 == rot2) + + rot1 = Rotation2D.from_euler([[0.0], [0.0]]) + rot2 = Rotation2D.from_euler([[0.0], [np.pi]]) + self.assertAllEqual([True, False], rot1 == rot2) + + def test_repr(self): + """Tests that repr works.""" + expected = "" + rot = Rotation2D.from_euler([0.0]) + self.assertEqual(expected, repr(rot)) + self.assertEqual(expected[1:-1], str(rot)) + + def test_matmul(self): + """Tests that matmul works.""" + rot = Rotation2D.from_euler([np.pi]) + composed = rot @ rot + self.assertAllClose(np.eye(2), composed.as_matrix()) + + composed = tf.linalg.matmul(rot, rot) + self.assertAllClose(np.eye(2), composed.as_matrix()) + + def test_matvec(self): + """Tests that matvec works.""" + rot = Rotation2D.from_euler([np.pi]) + vec = tf.constant([1.0, -1.0]) + self.assertAllClose(rot.rotate(vec), tf.linalg.matvec(rot, vec)) + + def test_convert_to_tensor(self): + """Tests that conversion to tensor works.""" + rot = Rotation2D.from_euler([0.0]) + self.assertIsInstance(tf.convert_to_tensor(rot), tf.Tensor) + self.assertAllClose(np.eye(2), tf.convert_to_tensor(rot)) + + @parameterized.named_parameters( + ("0", [0.0]), + ("45", [np.pi / 4]), + ("90", [np.pi / 2]), + ("135", [np.pi * 3 / 4]), + ("-45", [-np.pi / 4]), + ("-90", [-np.pi / 2]), + ("-135", [-np.pi * 3 / 4]) + ) + def test_as_euler(self, angle): # pylint: disable=missing-param-doc + """Tests that `as_euler` returns the correct angle.""" + rot = Rotation2D.from_euler(angle) + self.assertAllClose(angle, rot.as_euler()) + + def test_from_matrix(self): + """Tests that rotation can be initialized from matrix.""" + rot = Rotation2D.from_matrix(np.eye(2)) + self.assertAllClose(np.eye(2), rot.as_matrix()) + + def test_from_euler_normalized(self): + """Tests that an angle maps to correct matrix.""" + euler_angles = test_helpers.generate_preset_test_euler_angles(dimensions=1) + + rot = Rotation2D.from_euler(euler_angles) + self.assertAllEqual(np.ones(euler_angles.shape[0:-1] + (1,), dtype=bool), + rot.is_valid()) + + @parameterized.named_parameters( + ("0", td.ANGLE_0, td.MAT_2D_ID), + ("45", td.ANGLE_45, td.MAT_2D_45), + ("90", td.ANGLE_90, td.MAT_2D_90), + ("180", td.ANGLE_180, td.MAT_2D_180), + ) + def test_from_euler(self, angle, expected): + """Tests that an angle maps to correct matrix.""" + self.assertAllClose(expected, Rotation2D.from_euler(angle).as_matrix()) + + def test_from_euler_with_small_angles_approximation_random(self): + """Tests small angles approximation by comparing to exact calculation.""" + # Only generate small angles. For a test tolerance of 1e-3, 0.17 was found + # empirically to be the range where the small angle approximation works. + random_euler_angles = test_helpers.generate_random_test_euler_angles( + min_angle=-0.17, max_angle=0.17, dimensions=1) + + exact_rot = Rotation2D.from_euler(random_euler_angles) + approx_rot = Rotation2D.from_small_euler(random_euler_angles) + + self.assertAllClose(exact_rot.as_matrix(), approx_rot.as_matrix(), + atol=1e-3) + + def test_inverse_random(self): + """Checks that inverting rotated points results in no transformation.""" + random_euler_angles = test_helpers.generate_random_test_euler_angles( + dimensions=1) + tensor_shape = random_euler_angles.shape[:-1] + + random_rot = Rotation2D.from_euler(random_euler_angles) + random_point = np.random.normal(size=tensor_shape + (2,)) + rotated_random_points = random_rot.rotate(random_point) + predicted_invert_random_matrix = random_rot.inverse() + predicted_invert_rotated_random_points = ( + predicted_invert_random_matrix.rotate(rotated_random_points)) + + self.assertAllClose(random_point, predicted_invert_rotated_random_points) + + @parameterized.named_parameters( + ("preset1", td.AXIS_2D_0, td.ANGLE_90, td.AXIS_2D_0), + ("preset2", td.AXIS_2D_X, td.ANGLE_90, td.AXIS_2D_Y), + ) + def test_rotate(self, point, angle, expected): + """Tests that the rotate function correctly rotates points.""" + result = Rotation2D.from_euler(angle).rotate(point) + self.assertAllClose(expected, result) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/geometry/rotation_3d.py b/tensorflow_mri/python/geometry/rotation_3d.py new file mode 100644 index 00000000..b1a95850 --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation_3d.py @@ -0,0 +1,302 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""3D rotation.""" + +import tensorflow as tf + +from tensorflow_mri.python.geometry.rotation import rotation_matrix_3d + + +class Rotation3D(tf.experimental.BatchableExtensionType): # pylint: disable=abstract-method + """Represents a rotation in 3D space (or a batch thereof).""" + __name__ = "tfmri.geometry.Rotation3D" + _matrix: tf.Tensor + + @classmethod + def from_matrix(cls, matrix, name=None): + r"""Creates a 3D rotation from a rotation matrix. + + Args: + matrix: A `tf.Tensor` of shape `[..., 3, 3]`, where the last two + dimensions represent a rotation matrix. + name: A name for this op. Defaults to `"rotation_3d/from_matrix"`. + + Returns: + A `Rotation3D`. + """ + with tf.name_scope(name or "rotation_3d/from_matrix"): + return cls(_matrix=matrix) + + @classmethod + def from_euler(cls, angles, name=None): + r"""Creates a 3D rotation from Euler angles. + + The resulting rotation acts like the rotation matrix + $\mathbf{R} = \mathbf{R}_z\mathbf{R}_y\mathbf{R}_x$. + + ```{note} + Uses the $z$-$y$-$x$ rotation convention (Tait-Bryan angles). + ``` + + Args: + angles: A `tf.Tensor` of shape `[..., 3]`, where the last dimension + represents the three Euler angles in radians. `angles[..., 0]` + is the angles about `x`, `angles[..., 1]` is the angles about `y`, + and `angles[..., 2]` is the angles about `z`. + name: A name for this op. Defaults to `"rotation_3d/from_euler"`. + + Returns: + A `Rotation3D`. + + Raises: + ValueError: If the shape of `angles` is invalid. + """ + with tf.name_scope(name or "rotation_3d/from_euler"): + return cls(_matrix=rotation_matrix_3d.from_euler(angles)) + + @classmethod + def from_small_euler(cls, angles, name=None): + r"""Creates a 3D rotation from small Euler angles. + + The resulting rotation acts like the rotation matrix + $\mathbf{R} = \mathbf{R}_z\mathbf{R}_y\mathbf{R}_x$. + + Uses the small angle approximation to compute the rotation. Under the + small angle assumption, $\sin(x)$$ and $$\cos(x)$ can be approximated by + their second order Taylor expansions, where $\sin(x) \approx x$ and + $\cos(x) \approx 1 - \frac{x^2}{2}$. + + ```{note} + Uses the $z$-$y$-$x$ rotation convention (Tait-Bryan angles). + ``` + + ```{note} + This function does not verify the smallness of the angles. + ``` + + Args: + angles: A `tf.Tensor` of shape `[..., 3]`, where the last dimension + represents the three Euler angles in radians. `angles[..., 0]` + is the angles about `x`, `angles[..., 1]` is the angles about `y`, + and `angles[..., 2]` is the angles about `z`. + name: A name for this op. Defaults to "rotation_3d/from_small_euler". + + Returns: + A `Rotation3D`. + + Raises: + ValueError: If the shape of `angles` is invalid. + """ + with tf.name_scope(name or "rotation_3d/from_small_euler"): + return cls(_matrix=rotation_matrix_3d.from_small_euler(angles)) + + @classmethod + def from_axis_angle(cls, axis, angle, name=None): + """Creates a 3D rotation from an axis-angle representation. + + Args: + axis: A `tf.Tensor` of shape `[..., 3]`, where the last dimension + represents a normalized axis. + angle: A `tf.Tensor` of shape `[..., 1]`, where the last dimension + represents a normalized axis. + name: A name for this op. Defaults to "rotation_3d/from_axis_angle". + + Returns: + A `Rotation3D`. + + Raises: + ValueError: If the shape of `axis` or `angle` is invalid. + """ + with tf.name_scope(name or "rotation_3d/from_axis_angle"): + return cls(_matrix=rotation_matrix_3d.from_axis_angle(axis, angle)) + + @classmethod + def from_quaternion(cls, quaternion, name=None): + """Creates a 3D rotation from a quaternion. + + Args: + quaternion: A `tf.Tensor` of shape `[..., 4]`, where the last dimension + represents a normalized quaternion. + name: A name for this op. Defaults to `"rotation_3d/from_quaternion"`. + + Returns: + A `Rotation3D`. + + Raises: + ValueError: If the shape of `quaternion` is invalid. + """ + with tf.name_scope(name or "rotation_3d/from_quaternion"): + return cls(_matrix=rotation_matrix_3d.from_quaternion(quaternion)) + + def as_matrix(self, name=None): + r"""Returns a rotation matrix representation of this rotation. + + Args: + name: A name for this op. Defaults to `"rotation_3d/as_matrix"`. + + Returns: + A `tf.Tensor` of shape `[..., 3, 3]`, where the last two dimensions + represent a rotation matrix. + """ + with tf.name_scope(name or "rotation_3d/as_matrix"): + return tf.identity(self._matrix) + + def inverse(self, name=None): + r"""Computes the inverse of this rotation. + + Args: + name: A name for this op. Defaults to `"rotation_3d/inverse"`. + + Returns: + A `Rotation3D` representing the inverse of this rotation. + """ + with tf.name_scope(name or "rotation_3d/inverse"): + return Rotation3D(_matrix=rotation_matrix_3d.inverse(self._matrix)) + + def is_valid(self, atol=1e-3, name=None): + r"""Determines if this is a valid rotation. + + A rotation matrix $\mathbf{R}$ is a valid rotation matrix if + $\mathbf{R}^T\mathbf{R} = \mathbf{I}$ and $\det(\mathbf{R}) = 1$. + + Args: + atol: A `float`. The absolute tolerance parameter. + name: A name for this op. Defaults to `"rotation_3d/is_valid"`. + + Returns: + A boolean `tf.Tensor` with shape `[..., 1]`, `True` if the corresponding + matrix is valid and `False` otherwise. + """ + with tf.name_scope(name or "rotation_3d/is_valid"): + return rotation_matrix_3d.is_valid(self._matrix, atol=atol) + + def rotate(self, point, name=None): + r"""Rotates a 3D point. + + Args: + point: A `tf.Tensor` of shape `[..., 3]`, where the last dimension + represents a 3D point and `...` represents any number of batch + dimensions, which must be broadcastable with the batch shape of this + rotation. + name: A name for this op. Defaults to `"rotation_3d/rotate"`. + + Returns: + A `tf.Tensor` of shape `[..., 3]`, where the last dimension represents + a 3D point and `...` is the result of broadcasting the batch shapes of + `point` and this rotation matrix. + + Raises: + ValueError: If the shape of `point` is invalid. + """ + with tf.name_scope(name or "rotation_3d/rotate"): + return rotation_matrix_3d.rotate(point, self._matrix) + + def __eq__(self, other): + """Returns true if this rotation is equivalent to the other rotation.""" + return tf.math.reduce_all( + tf.math.equal(self._matrix, other._matrix), axis=[-2, -1]) + + def __matmul__(self, other): + """Composes this rotation with another rotation.""" + if isinstance(other, Rotation3D): + return Rotation3D(_matrix=tf.matmul(self._matrix, other._matrix)) + raise ValueError( + f"Cannot compose a `Rotation2D` with a `{type(other).__name__}`.") + + def __repr__(self): + """Returns a string representation of this rotation.""" + name = self.__name__ + return f"<{name}(shape={str(self.shape)}, dtype={self.dtype.name})>" + + def __str__(self): + """Returns a string representation of this rotation.""" + return self.__repr__()[1:-1] + + def __validate__(self): + """Checks that this rotation is a valid rotation. + + Only performs static checks. + """ + rotation_matrix_3d.check_shape(self._matrix) + + @property + def shape(self): + """Returns the shape of this rotation. + + Returns: + A `tf.TensorShape`. + """ + return self._matrix.shape + + @property + def dtype(self): + """Returns the dtype of this rotation. + + Returns: + A `tf.dtypes.DType`. + """ + return self._matrix.dtype + + +@tf.experimental.dispatch_for_api(tf.convert_to_tensor, {'value': Rotation3D}) +def convert_to_tensor(value, dtype=None, dtype_hint=None, name=None): + """Overrides `tf.convert_to_tensor` for `Rotation3D` objects.""" + return tf.convert_to_tensor( + value.as_matrix(), dtype=dtype, dtype_hint=dtype_hint, name=name) + + +@tf.experimental.dispatch_for_api( + tf.linalg.matmul, {'a': Rotation3D, 'b': Rotation3D}) +def matmul(a, b, # pylint: disable=missing-param-doc + transpose_a=False, + transpose_b=False, + adjoint_a=False, + adjoint_b=False, + a_is_sparse=False, + b_is_sparse=False, + output_type=None, + name=None): + """Overrides `tf.linalg.matmul` for `Rotation3D` objects.""" + if a_is_sparse or b_is_sparse: + raise ValueError("Rotation3D does not support sparse matmul.") + return Rotation3D(_matrix=tf.linalg.matmul(a.as_matrix(), b.as_matrix(), + transpose_a=transpose_a, + transpose_b=transpose_b, + adjoint_a=adjoint_a, + adjoint_b=adjoint_b, + output_type=output_type, + name=name)) + + +@tf.experimental.dispatch_for_api(tf.linalg.matvec, {'a': Rotation3D}) +def matvec(a, b, # pylint: disable=missing-param-doc + transpose_a=False, + adjoint_a=False, + a_is_sparse=False, + b_is_sparse=False, + name=None): + """Overrides `tf.linalg.matvec` for `Rotation3D` objects.""" + if a_is_sparse or b_is_sparse: + raise ValueError("Rotation3D does not support sparse matvec.") + return tf.linalg.matvec(a.as_matrix(), b, + transpose_a=transpose_a, + adjoint_a=adjoint_a, + name=name) + + +@tf.experimental.dispatch_for_api(tf.shape, {'input': Rotation3D}) +def shape(input, out_type=tf.int32, name=None): # pylint: disable=redefined-builtin + """Overrides `tf.shape` for `Rotation3D` objects.""" + return tf.shape(input.as_matrix(), out_type=out_type, name=name) diff --git a/tensorflow_mri/python/geometry/rotation_3d_test.py b/tensorflow_mri/python/geometry/rotation_3d_test.py new file mode 100644 index 00000000..93ce456f --- /dev/null +++ b/tensorflow_mri/python/geometry/rotation_3d_test.py @@ -0,0 +1,280 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for 3D rotation.""" +# This file is partly inspired by TensorFlow Graphics. +# pylint: disable=missing-param-doc + +from absl.testing import parameterized +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.geometry.rotation import test_data as td +from tensorflow_mri.python.geometry.rotation import test_helpers +from tensorflow_mri.python.geometry.rotation_3d import Rotation3D +from tensorflow_mri.python.util import test_util + + +class Rotation3DTest(test_util.TestCase): + """Tests for `Rotation3D`.""" + def test_shape(self): + """Tests shape.""" + rot = Rotation3D.from_euler([0.0, 0.0, 0.0]) + self.assertAllEqual([3, 3], rot.shape) + self.assertAllEqual([3, 3], tf.shape(rot)) + + rot = Rotation3D.from_euler([[0.0, 0.0, 0.0], [np.pi, 0.0, 0.0]]) + self.assertAllEqual([2, 3, 3], rot.shape) + self.assertAllEqual([2, 3, 3], tf.shape(rot)) + + def test_equal(self): + """Tests equality operator.""" + rot1 = Rotation3D.from_euler([0.0, 0.0, 0.0]) + rot2 = Rotation3D.from_euler([0.0, 0.0, 0.0]) + self.assertAllEqual(True, rot1 == rot2) + + rot1 = Rotation3D.from_euler([0.0, 0.0, 0.0]) + rot2 = Rotation3D.from_euler([np.pi, 0.0, 0.0]) + self.assertAllEqual(False, rot1 == rot2) + + rot1 = Rotation3D.from_euler([[0.0, 0.0, 0.0], [np.pi, 0.0, 0.0]]) + rot2 = Rotation3D.from_euler([[0.0, 0.0, 0.0], [np.pi, 0.0, 0.0]]) + self.assertAllEqual([True, True], rot1 == rot2) + + rot1 = Rotation3D.from_euler([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) + rot2 = Rotation3D.from_euler([[0.0, 0.0, 0.0], [np.pi, 0.0, 0.0]]) + self.assertAllEqual([True, False], rot1 == rot2) + + def test_repr(self): + rot = Rotation3D.from_euler([0.0, 0.0, 0.0]) + self.assertEqual( + "", repr(rot)) + + def test_convert_to_tensor(self): + """Tests that conversion to tensor works.""" + rot = Rotation3D.from_euler([0.0, 0.0, 0.0]) + self.assertIsInstance(tf.convert_to_tensor(rot), tf.Tensor) + self.assertAllClose(np.eye(3), tf.convert_to_tensor(rot)) + + def test_from_axis_angle_normalized_random(self): + """Tests that axis-angles can be converted to rotation matrices.""" + tensor_shape = np.random.randint(1, 10, size=np.random.randint(3)).tolist() + random_axis = np.random.normal(size=tensor_shape + [3]) + random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) + random_angle = np.random.normal(size=tensor_shape + [1]) + + rotation = Rotation3D.from_axis_angle(random_axis, random_angle) + + self.assertAllEqual(rotation.is_valid(), np.ones(tensor_shape + [1])) + + @parameterized.named_parameters( + ("preset0", td.AXIS_3D_X, td.ANGLE_45, td.MAT_3D_X_45), + ("preset1", td.AXIS_3D_Y, td.ANGLE_45, td.MAT_3D_Y_45), + ("preset2", td.AXIS_3D_Z, td.ANGLE_45, td.MAT_3D_Z_45), + ("preset3", td.AXIS_3D_X, td.ANGLE_90, td.MAT_3D_X_90), + ("preset4", td.AXIS_3D_Y, td.ANGLE_90, td.MAT_3D_Y_90), + ("preset5", td.AXIS_3D_Z, td.ANGLE_90, td.MAT_3D_Z_90), + ("preset6", td.AXIS_3D_X, td.ANGLE_180, td.MAT_3D_X_180), + ("preset7", td.AXIS_3D_Y, td.ANGLE_180, td.MAT_3D_Y_180), + ("preset8", td.AXIS_3D_Z, td.ANGLE_180, td.MAT_3D_Z_180) + ) + def test_from_axis_angle(self, axis, angle, matrix): + """Tests that an axis-angle maps to correct matrix.""" + self.assertAllClose( + matrix, Rotation3D.from_axis_angle(axis, angle).as_matrix()) + + def test_from_axis_angle_random(self): + """Tests conversion to matrix.""" + tensor_shape = np.random.randint(1, 10, size=np.random.randint(3)).tolist() + random_axis = np.random.normal(size=tensor_shape + [3]) + random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) + random_angle = np.random.normal(size=tensor_shape + [1]) + + rotation = Rotation3D.from_axis_angle(random_axis, random_angle) + + # Checks that resulting rotation matrices are normalized. + self.assertAllEqual(rotation.is_valid(), np.ones(tensor_shape + [1])) + + @parameterized.named_parameters( + ("preset0", td.AXIS_3D_X, td.ANGLE_90, td.AXIS_3D_X, td.AXIS_3D_X), + ("preset1", td.AXIS_3D_X, td.ANGLE_90, td.AXIS_3D_Y, td.AXIS_3D_Z), + ("preset2", td.AXIS_3D_X, -td.ANGLE_90, td.AXIS_3D_Z, td.AXIS_3D_Y), + ("preset3", td.AXIS_3D_Y, -td.ANGLE_90, td.AXIS_3D_X, td.AXIS_3D_Z), + ("preset4", td.AXIS_3D_Y, td.ANGLE_90, td.AXIS_3D_Y, td.AXIS_3D_Y), + ("preset5", td.AXIS_3D_Y, td.ANGLE_90, td.AXIS_3D_Z, td.AXIS_3D_X), + ("preset6", td.AXIS_3D_Z, td.ANGLE_90, td.AXIS_3D_X, td.AXIS_3D_Y), + ("preset7", td.AXIS_3D_Z, -td.ANGLE_90, td.AXIS_3D_Y, td.AXIS_3D_X), + ("preset8", td.AXIS_3D_Z, td.ANGLE_90, td.AXIS_3D_Z, td.AXIS_3D_Z), + ) + def test_from_axis_angle_rotate_vector_preset( + self, axis, angle, point, expected): + """Tests the directionality of axis-angle rotations.""" + self.assertAllClose( + expected, Rotation3D.from_axis_angle(axis, angle).rotate(point)) + + def test_from_euler_normalized_preset(self): + """Tests that euler angles can be converted to rotation matrices.""" + euler_angles = test_helpers.generate_preset_test_euler_angles() + + matrix = Rotation3D.from_euler(euler_angles) + self.assertAllEqual( + matrix.is_valid(), np.ones(euler_angles.shape[0:-1] + (1,))) + + def test_from_euler_normalized_random(self): + """Tests that euler angles can be converted to rotation matrices.""" + random_euler_angles = test_helpers.generate_random_test_euler_angles() + + matrix = Rotation3D.from_euler(random_euler_angles) + self.assertAllEqual( + matrix.is_valid(), np.ones(random_euler_angles.shape[0:-1] + (1,))) + + @parameterized.named_parameters( + ("preset0", td.AXIS_3D_0, td.MAT_3D_ID), + ("preset1", td.ANGLE_45 * td.AXIS_3D_X, td.MAT_3D_X_45), + ("preset2", td.ANGLE_45 * td.AXIS_3D_Y, td.MAT_3D_Y_45), + ("preset3", td.ANGLE_45 * td.AXIS_3D_Z, td.MAT_3D_Z_45), + ("preset4", td.ANGLE_90 * td.AXIS_3D_X, td.MAT_3D_X_90), + ("preset5", td.ANGLE_90 * td.AXIS_3D_Y, td.MAT_3D_Y_90), + ("preset6", td.ANGLE_90 * td.AXIS_3D_Z, td.MAT_3D_Z_90), + ("preset7", td.ANGLE_180 * td.AXIS_3D_X, td.MAT_3D_X_180), + ("preset8", td.ANGLE_180 * td.AXIS_3D_Y, td.MAT_3D_Y_180), + ("preset9", td.ANGLE_180 * td.AXIS_3D_Z, td.MAT_3D_Z_180), + ) + def test_from_euler(self, angle, expected): + """Tests that Euler angles create the expected matrix.""" + rotation = Rotation3D.from_euler(angle) + self.assertAllClose(expected, rotation.as_matrix()) + + def test_from_euler_random(self): + """Tests that Euler angles produce the same result as axis-angle.""" + angles = test_helpers.generate_random_test_euler_angles() + matrix = Rotation3D.from_euler(angles) + tensor_tile = angles.shape[:-1] + + x_axis = np.tile(td.AXIS_3D_X, tensor_tile + (1,)) + y_axis = np.tile(td.AXIS_3D_Y, tensor_tile + (1,)) + z_axis = np.tile(td.AXIS_3D_Z, tensor_tile + (1,)) + x_angle = np.expand_dims(angles[..., 0], axis=-1) + y_angle = np.expand_dims(angles[..., 1], axis=-1) + z_angle = np.expand_dims(angles[..., 2], axis=-1) + x_rotation = Rotation3D.from_axis_angle(x_axis, x_angle) + y_rotation = Rotation3D.from_axis_angle(y_axis, y_angle) + z_rotation = Rotation3D.from_axis_angle(z_axis, z_angle) + expected_matrix = z_rotation @ (y_rotation @ x_rotation) + + self.assertAllClose(expected_matrix.as_matrix(), matrix.as_matrix(), + rtol=1e-3) + + def test_from_quaternion_normalized_random(self): + """Tests that random quaternions can be converted to rotation matrices.""" + random_quaternion = test_helpers.generate_random_test_quaternions() + tensor_shape = random_quaternion.shape[:-1] + + random_rot = Rotation3D.from_quaternion(random_quaternion) + + self.assertAllEqual( + random_rot.is_valid(), + np.ones(tensor_shape + (1,))) + + def test_from_quaternion(self): + """Tests that a quaternion maps to correct matrix.""" + preset_quaternions = test_helpers.generate_preset_test_quaternions() + + preset_matrices = test_helpers.generate_preset_test_rotation_matrices_3d() + + self.assertAllClose( + preset_matrices, + Rotation3D.from_quaternion(preset_quaternions).as_matrix()) + + def test_inverse_normalized_random(self): + """Checks that inverted rotation matrices are valid rotations.""" + random_euler_angle = test_helpers.generate_random_test_euler_angles() + tensor_tile = random_euler_angle.shape[:-1] + + random_rot = Rotation3D.from_euler(random_euler_angle) + predicted_invert_random_rot = random_rot.inverse() + + self.assertAllEqual( + predicted_invert_random_rot.is_valid(), + np.ones(tensor_tile + (1,))) + + def test_inverse_random(self): + """Checks that inverting rotated points results in no transformation.""" + random_euler_angle = test_helpers.generate_random_test_euler_angles() + tensor_tile = random_euler_angle.shape[:-1] + random_rot = Rotation3D.from_euler(random_euler_angle) + random_point = np.random.normal(size=tensor_tile + (3,)) + + rotated_random_points = random_rot.rotate(random_point) + inv_random_rot = random_rot.inverse() + inv_rotated_random_points = inv_random_rot.rotate(rotated_random_points) + + self.assertAllClose(random_point, inv_rotated_random_points, rtol=1e-6) + + def test_is_valid_random(self): + """Tests that is_valid works as intended.""" + random_euler_angle = test_helpers.generate_random_test_euler_angles() + tensor_tile = random_euler_angle.shape[:-1] + + rotation = Rotation3D.from_euler(random_euler_angle) + pred_normalized = rotation.is_valid() + + with self.subTest(name="all_normalized"): + self.assertAllEqual(pred_normalized, + np.ones(shape=tensor_tile + (1,), dtype=bool)) + + with self.subTest(name="non_orthonormal"): + test_matrix = np.array([[2., 0., 0.], [0., 0.5, 0], [0., 0., 1.]]) + rotation = Rotation3D.from_matrix(test_matrix) + pred_normalized = rotation.is_valid() + self.assertAllEqual(pred_normalized, np.zeros(shape=(1,), dtype=bool)) + + with self.subTest(name="negative_orthonormal"): + test_matrix = np.array([[1., 0., 0.], [0., -1., 0.], [0., 0., 1.]]) + rotation = Rotation3D.from_matrix(test_matrix) + pred_normalized = rotation.is_valid() + self.assertAllEqual(pred_normalized, np.zeros(shape=(1,), dtype=bool)) + + @parameterized.named_parameters( + ("preset0", td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_X, td.AXIS_3D_X), + ("preset1", td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_Y, td.AXIS_3D_Z), + ("preset2", -td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_Z, td.AXIS_3D_Y), + ("preset3", -td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_X, td.AXIS_3D_Z), + ("preset4", td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_Y, td.AXIS_3D_Y), + ("preset5", td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_Z, td.AXIS_3D_X), + ("preset6", td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_X, td.AXIS_3D_Y), + ("preset7", -td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_Y, td.AXIS_3D_X), + ("preset8", td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_Z, td.AXIS_3D_Z), + ) + def test_rotate_vector_preset(self, angles, point, expected): + """Tests that the rotate function produces the expected results.""" + self.assertAllClose(expected, Rotation3D.from_euler(angles).rotate(point)) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/layers/concatenate.py b/tensorflow_mri/python/layers/concatenate.py new file mode 100644 index 00000000..d852dd2e --- /dev/null +++ b/tensorflow_mri/python/layers/concatenate.py @@ -0,0 +1,67 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Resize and concatenate layer.""" + +import tensorflow as tf + +from tensorflow_mri.python.ops import array_ops + + +@tf.keras.utils.register_keras_serializable(package="MRI") +class ResizeAndConcatenate(tf.keras.layers.Layer): + """Resizes and concatenates a list of inputs. + + Similar to `tf.keras.layers.Concatenate`, but if the inputs have different + shapes, they are resized to match the shape of the first input. + + Args: + axis: Axis along which to concatenate. + """ + def __init__(self, axis=-1, **kwargs): + super().__init__(**kwargs) + self.axis = axis + + def call(self, inputs): # pylint: disable=missing-function-docstring,arguments-differ + if not isinstance(inputs, (list, tuple)): + raise ValueError( + f"Layer {self.__class__.__name__} expects a list of inputs. " + f"Received: {inputs}") + + rank = inputs[0].shape.rank + if rank is None: + raise ValueError( + f"Layer {self.__class__.__name__} expects inputs with known rank. " + f"Received: {inputs}") + if self.axis >= rank or self.axis < -rank: + raise ValueError( + f"Layer {self.__class__.__name__} expects `axis` to be in the range " + f"[-{rank}, {rank}) for an input of rank {rank}. " + f"Received: {self.axis}") + # Canonical axis (always positive). + axis = self.axis % rank + + # Resize inputs. + shape = tf.tensor_scatter_nd_update(tf.shape(inputs[0]), [[axis]], [-1]) + resized = [array_ops.resize_with_crop_or_pad(tensor, shape) + for tensor in inputs[1:]] + + # Set the static shape for each resized tensor. + for i, tensor in enumerate(resized): + static_shape = inputs[0].shape.as_list() + static_shape[axis] = inputs[i + 1].shape.as_list()[axis] + static_shape = tf.TensorShape(static_shape) + resized[i] = tf.ensure_shape(tensor, static_shape) + + return tf.concat(inputs[:1] + resized, axis=self.axis) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter diff --git a/tensorflow_mri/python/layers/concatenate_test.py b/tensorflow_mri/python/layers/concatenate_test.py new file mode 100644 index 00000000..4b0e341d --- /dev/null +++ b/tensorflow_mri/python/layers/concatenate_test.py @@ -0,0 +1,52 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for `ResizeAndConcatenate` layers.""" + +import tensorflow as tf + +from tensorflow_mri.python.layers import concatenate +from tensorflow_mri.python.util import test_util + + +class ResizeAndConcatenateTest(test_util.TestCase): + """Tests for layer `ResizeAndConcatenate`.""" + def test_resize_and_concatenate(self): + """Test `ResizeAndConcatenate` layer.""" + # Test data. + x1 = tf.constant([[1.0, 2.0], [3.0, 4.0]]) + x2 = tf.constant([[5.0], [6.0]]) + + # Test concatenation along axis 1. + layer = concatenate.ResizeAndConcatenate(axis=-1) + + result = layer([x1, x2]) + self.assertAllClose([[1.0, 2.0, 5.0], [3.0, 4.0, 6.0]], result) + + result = layer([x2, x1]) + self.assertAllClose([[5.0, 1.0, 2.0], [6.0, 3.0, 4.0]], result) + + # Test concatenation along axis 0. + layer = concatenate.ResizeAndConcatenate(axis=0) + + result = layer([x1, x2]) + self.assertAllClose( + [[1.0, 2.0], [3.0, 4.0], [5.0, 0.0], [6.0, 0.0]], result) + + result = layer([x2, x1]) + self.assertAllClose([[5.0], [6.0], [1.0], [3.0]], result) + + +if __name__ == '__main__': + tf.test.main() diff --git a/tensorflow_mri/python/layers/data_consistency.py b/tensorflow_mri/python/layers/data_consistency.py new file mode 100644 index 00000000..645c4896 --- /dev/null +++ b/tensorflow_mri/python/layers/data_consistency.py @@ -0,0 +1,112 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Data consistency layers.""" + +import string + +import tensorflow as tf + +from tensorflow_mri.python.ops import math_ops +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import doc_util + + +class LeastSquaresGradientDescent(tf.keras.layers.Layer): + """Least squares gradient descent layer for ${rank}-D images. + """ + def __init__(self, + rank, + scale_initializer=1.0, + expand_channel_dim=False, + reinterpret_complex=False, + **kwargs): + super().__init__(**kwargs) + self.rank = rank + if isinstance(scale_initializer, (float, int)): + self.scale_initializer = tf.keras.initializers.Constant(scale_initializer) + else: + self.scale_initializer = tf.keras.initializers.get(scale_initializer) + self.expand_channel_dim = expand_channel_dim + self.reinterpret_complex = reinterpret_complex + + def build(self, input_shape): + super().build(input_shape) + self.scale = self.add_weight( + name='scale', + shape=(), + dtype=tf.as_dtype(self.dtype).real_dtype, + initializer=self.scale_initializer, + trainable=self.trainable, + constraint=tf.keras.constraints.NonNeg()) + + def call(self, inputs): # pylint: disable=missing-function-docstring,arguments-differ + image, data, operator = parse_inputs(inputs) + if self.reinterpret_complex: + image = math_ops.view_as_complex(image, stacked=False) + if self.expand_channel_dim: + image = tf.squeeze(image, axis=-1) + image -= tf.cast(self.scale, image.dtype) * operator.transform( + operator.transform(image) - data, adjoint=True) + if self.expand_channel_dim: + image = tf.expand_dims(image, axis=-1) + if self.reinterpret_complex: + image = math_ops.view_as_real(image, stacked=False) + return image + + def get_config(self): + base_config = super().get_config() + config = { + 'scale_initializer': tf.keras.initializers.serialize( + self.scale_initializer), + 'expand_channel_dim': self.expand_channel_dim, + 'reinterpret_complex': self.reinterpret_complex + } + return {**base_config, **config} + + +def parse_inputs(inputs): + def _parse_inputs(image, data, operator): + return image, data, operator + if isinstance(inputs, tuple): + return _parse_inputs(*inputs) + if isinstance(inputs, dict): + return _parse_inputs(**inputs) + raise ValueError('inputs must be a tuple or dict') + + +@api_util.export("layers.LeastSquaresGradientDescent2D") +@tf.keras.utils.register_keras_serializable(package='MRI') +class LeastSquaresGradientDescent2D(LeastSquaresGradientDescent): + def __init__(self, *args, **kwargs): + super().__init__(2, *args, **kwargs) + + +@api_util.export("layers.LeastSquaresGradientDescent3D") +@tf.keras.utils.register_keras_serializable(package='MRI') +class LeastSquaresGradientDescent3D(LeastSquaresGradientDescent): + def __init__(self, *args, **kwargs): + super().__init__(3, *args, **kwargs) + + +LeastSquaresGradientDescent2D.__doc__ = string.Template( + LeastSquaresGradientDescent.__doc__).safe_substitute(rank=2) +LeastSquaresGradientDescent3D.__doc__ = string.Template( + LeastSquaresGradientDescent.__doc__).safe_substitute(rank=3) + + +LeastSquaresGradientDescent2D.__signature__ = doc_util.get_nd_layer_signature( + LeastSquaresGradientDescent) +LeastSquaresGradientDescent3D.__signature__ = doc_util.get_nd_layer_signature( + LeastSquaresGradientDescent) diff --git a/tensorflow_mri/python/layers/normalization.py b/tensorflow_mri/python/layers/normalization.py new file mode 100644 index 00000000..4c909ee0 --- /dev/null +++ b/tensorflow_mri/python/layers/normalization.py @@ -0,0 +1,66 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Normalization layers.""" + +import tensorflow as tf + +from tensorflow_mri.python.util import api_util + + +@api_util.export("layers.Normalized") +@tf.keras.utils.register_keras_serializable(package='MRI') +class Normalized(tf.keras.layers.Wrapper): + r"""Applies the wrapped layer with normalized inputs. + + This layer shifts and scales the inputs into a distribution centered around 0 + with a standard deviation of 1 before passing them to the wrapped layer. + + $$ + x = \frac{x - \mu}{\sigma} + $$ + + After applying the wrapped layer, the outputs are scaled back to the original + distribution. + + $$ + y = \sigma y + \mu + $$ + + Args: + layer: A `tf.keras.layers.Layer`. The wrapped layer. + axis: An `int` or a `list` thereof. The axis or axes to normalize across. + Typically this is the features axis/axes. The left-out axes are typically + the batch axis/axes. Defaults to -1, the last dimension in the input. + **kwargs: Additional keyword arguments to be passed to the base class. + """ + def __init__(self, layer, axis=-1, **kwargs): + super().__init__(layer, **kwargs) + self.axis = axis + + def compute_output_shape(self, input_shape): + return self.layer.compute_output_shape(input_shape) + + def call(self, inputs, *args, **kwargs): + mean, variance = tf.nn.moments(inputs, axes=self.axis, keepdims=True) + std = tf.math.maximum(tf.math.sqrt(variance), tf.keras.backend.epsilon()) + inputs = (inputs - mean) / std + outputs = self.layer(inputs, *args, **kwargs) + outputs = outputs * std + mean + return outputs + + def get_config(self): + base_config = super().get_config() + config = {'axis': self.axis} + return {**base_config, **config} diff --git a/tensorflow_mri/python/layers/normalization_test.py b/tensorflow_mri/python/layers/normalization_test.py new file mode 100644 index 00000000..036fbd36 --- /dev/null +++ b/tensorflow_mri/python/layers/normalization_test.py @@ -0,0 +1,56 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for normalization layers.""" + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.layers import normalization +from tensorflow_mri.python.util import test_util + + +class NormalizedTest(test_util.TestCase): + """Tests for `Normalized` layer.""" + @test_util.run_all_execution_modes + def test_normalized_dense(self): + """Tests `Normalized` layer wrapping a `Dense` layer.""" + layer = normalization.Normalized( + tf.keras.layers.Dense(2, bias_initializer='random_uniform')) + layer.build((None, 4)) + + input_data = np.random.uniform(size=(2, 4)) + + def _compute_output(input_data, normalized=False): + if normalized: + mean = input_data.mean(axis=-1, keepdims=True) + std = input_data.std(axis=-1, keepdims=True) + input_data = (input_data - mean) / std + output_data = layer.layer(input_data) + if normalized: + output_data = output_data * std + mean + return output_data + + expected_unnorm = _compute_output(input_data, normalized=False) + expected_norm = _compute_output(input_data, normalized=True) + + result_unnorm = layer.layer(input_data) + result_norm = layer(input_data) + + self.assertAllClose(expected_unnorm, result_unnorm) + self.assertAllClose(expected_norm, result_norm) + + +if __name__ == '__main__': + tf.test.main() diff --git a/tensorflow_mri/python/layers/padding.py b/tensorflow_mri/python/layers/padding.py new file mode 100644 index 00000000..0689b5f0 --- /dev/null +++ b/tensorflow_mri/python/layers/padding.py @@ -0,0 +1,85 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Padding layers.""" + +import tensorflow as tf + + +class DivisorPadding(tf.keras.layers.Layer): + """Divisor padding layer. + + This layer pads the input tensor so that its spatial dimensions are a multiple + of the specified divisor. + + Args: + divisor: An `int` or a `tuple` of `int`. The divisor used to compute the + output shape. + """ + def __init__(self, rank, divisor=2, **kwargs): + super().__init__(**kwargs) + self.rank = rank + if isinstance(divisor, int): + self.divisor = (divisor,) * rank + elif hasattr(divisor, '__len__'): + if len(divisor) != rank: + raise ValueError(f'`divisor` should have {rank} elements. ' + f'Received: {divisor}') + self.divisor = divisor + else: + raise ValueError(f'`divisor` should be either an int or a ' + f'a tuple of {rank} ints. ' + f'Received: {divisor}') + self.input_spec = tf.keras.layers.InputSpec(ndim=rank + 2) + + def call(self, inputs): # pylint: disable=missing-function-docstring,arguments-differ + static_input_shape = inputs.shape + static_output_shape = tuple( + ((s + d - 1) // d) * d if s is not None else None for s, d in zip( + static_input_shape[1:-1].as_list(), self.divisor)) + static_output_shape = static_input_shape[:1].concatenate( + static_output_shape).concatenate(static_input_shape[-1:]) + + input_shape = tf.shape(inputs)[1:-1] + output_shape = (((input_shape + self.divisor - 1) // self.divisor) * + self.divisor) + left_paddings = (output_shape - input_shape) // 2 + right_paddings = (output_shape - input_shape + 1) // 2 + paddings = tf.stack([left_paddings, right_paddings], axis=-1) + paddings = tf.pad(paddings, [[1, 1], [0, 0]]) # pylint: disable=no-value-for-parameter + + return tf.ensure_shape(tf.pad(inputs, paddings), static_output_shape) # pylint: disable=no-value-for-parameter + + def get_config(self): + config = {'divisor': self.divisor} + base_config = super().get_config() + return {**config, **base_config} + + +@tf.keras.utils.register_keras_serializable(package='MRI') +class DivisorPadding1D(DivisorPadding): + def __init__(self, *args, **kwargs): + super().__init__(1, *args, **kwargs) + + +@tf.keras.utils.register_keras_serializable(package='MRI') +class DivisorPadding2D(DivisorPadding): + def __init__(self, *args, **kwargs): + super().__init__(2, *args, **kwargs) + + +@tf.keras.utils.register_keras_serializable(package='MRI') +class DivisorPadding3D(DivisorPadding): + def __init__(self, *args, **kwargs): + super().__init__(3, *args, **kwargs) diff --git a/tensorflow_mri/python/layers/recon_adjoint.py b/tensorflow_mri/python/layers/recon_adjoint.py new file mode 100644 index 00000000..18599a2e --- /dev/null +++ b/tensorflow_mri/python/layers/recon_adjoint.py @@ -0,0 +1,140 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Adjoint reconstruction layer.""" + +import string + +import tensorflow as tf + +from tensorflow_mri.python.ops import math_ops +from tensorflow_mri.python.recon import recon_adjoint +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import doc_util + + +class ReconAdjoint(tf.keras.layers.Layer): + r"""${rank}D adjoint reconstruction layer. + + This layer reconstructs a signal using the adjoint of the specified system + operator. + + Given measurement data $b$ generated by a linear system $A$ such that + $Ax = b$, this function estimates the corresponding signal $x$ as + $x = A^H b$, where $A$ is the specified linear operator. + + ```{note} + This function is part of the family of + [universal operators](https://mrphys.github.io/tensorflow-mri/guide/universal/), + a set of functions and classes designed to work flexibly with any linear + system. + ``` + + ```{seealso} + This is the Keras layer equivalent of `tfmri.recon.adjoint_universal`. + ``` + + ## Inputs + + This layer's `call` method expects the following inputs: + + - data: A `tf.Tensor` of real or complex dtype. The measurement data $b$. + Its shape must be compatible with `operator.range_shape`. + - operator: A `tfmri.linalg.LinearOperator` representing the system operator + $A$. Its range shape must be compatible with `data.shape`. + + ```{attention} + Both `data` and `operator` should be passed as part of the first positional + `inputs` argument, either as as a `tuple` or as a `dict`, in order to take + advantage of this argument's special rules. For more information, see + https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer#call. + ``` + + ## Outputs + + This layer's `call` method returns a `tf.Tensor` containing the reconstructed + signal. Has the same dtype as `data` and shape + `batch_shape + operator.domain_shape`. `batch_shape` is the result of + broadcasting the batch shapes of `data` and `operator`. + + Args: + expand_channel_dim: A `boolean`. Whether to expand the channel dimension. + If `True`, output has shape `batch_shape + operator.domain_shape + [1]`. + If `False`, output has shape `batch_shape + operator.domain_shape`. + Defaults to `True`. + reinterpret_complex: A `boolean`. Whether to reinterpret a complex-valued + output image as a dual-channel real image. Defaults to `False`. + **kwargs: Keyword arguments to be passed to base layer + `tf.keras.layers.Layer`. + """ + def __init__(self, + rank, + expand_channel_dim=False, + reinterpret_complex=False, + **kwargs): + super().__init__(**kwargs) + self.rank = rank # Currently unused. + self.expand_channel_dim = expand_channel_dim + self.reinterpret_complex = reinterpret_complex + + def call(self, inputs): # pylint: arguments-differ + data, operator = parse_inputs(inputs) + image = recon_adjoint.recon_adjoint(data, operator) + if self.expand_channel_dim: + image = tf.expand_dims(image, axis=-1) + if self.reinterpret_complex and image.dtype.is_complex: + image = math_ops.view_as_real(image, stacked=False) + return image + + def get_config(self): + base_config = super().get_config() + config = { + 'expand_channel_dim': self.expand_channel_dim, + 'reinterpret_complex': self.reinterpret_complex + } + return {**base_config, **config} + + +def parse_inputs(inputs): + def _parse_inputs(data, operator): + return data, operator + if isinstance(inputs, tuple): + return _parse_inputs(*inputs) + if isinstance(inputs, dict): + return _parse_inputs(**inputs) + raise ValueError('inputs must be a tuple or dict') + + +@api_util.export("layers.ReconAdjoint2D") +@tf.keras.utils.register_keras_serializable(package='MRI') +class ReconAdjoint2D(ReconAdjoint): + def __init__(self, *args, **kwargs): + super().__init__(2, *args, **kwargs) + + +@api_util.export("layers.ReconAdjoint3D") +@tf.keras.utils.register_keras_serializable(package='MRI') +class ReconAdjoint3D(ReconAdjoint): + def __init__(self, *args, **kwargs): + super().__init__(3, *args, **kwargs) + + +ReconAdjoint2D.__doc__ = string.Template( + ReconAdjoint.__doc__).safe_substitute(rank=2) +ReconAdjoint3D.__doc__ = string.Template( + ReconAdjoint.__doc__).safe_substitute(rank=3) + + +ReconAdjoint2D.__signature__ = doc_util.get_nd_layer_signature(ReconAdjoint) +ReconAdjoint3D.__signature__ = doc_util.get_nd_layer_signature(ReconAdjoint) diff --git a/tensorflow_mri/python/layers/recon_adjoint_test.py b/tensorflow_mri/python/layers/recon_adjoint_test.py new file mode 100644 index 00000000..5e8f170e --- /dev/null +++ b/tensorflow_mri/python/layers/recon_adjoint_test.py @@ -0,0 +1,79 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `recon_adjoint`.""" +# pylint: disable=missing-param-doc + +import os +import tempfile + +from absl.testing import parameterized +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_mri +from tensorflow_mri.python.layers import recon_adjoint as recon_adjoint_layer +from tensorflow_mri.python.recon import recon_adjoint +from tensorflow_mri.python.util import test_util + + +class ReconAdjointTest(test_util.TestCase): + """Tests for `ReconAdjoint` layer.""" + @parameterized.product(expand_channel_dim=[True, False]) + def test_recon_adjoint(self, expand_channel_dim): + """Test `ReconAdjoint` layer.""" + # Create layer. + layer = recon_adjoint_layer.ReconAdjoint( + expand_channel_dim=expand_channel_dim) + + # Generate k-space data. + image_shape = tf.constant([4, 4]) + kspace = tf.dtypes.complex( + tf.random.stateless_normal(shape=image_shape, seed=[11, 22]), + tf.random.stateless_normal(shape=image_shape, seed=[12, 34])) + + # Reconstruct image. + expected = recon_adjoint.recon_adjoint_mri(kspace, image_shape) + if expand_channel_dim: + expected = tf.expand_dims(expected, axis=-1) + + operator = linear_operator_mri.LinearOperatorMRI(image_shape) + + # Test with tuple inputs. + input_data = (kspace, operator) + result = layer(input_data) + self.assertAllClose(expected, result) + + # Test with dict inputs. + input_data = {'data': kspace, 'operator': operator} + result = layer(input_data) + self.assertAllClose(expected, result) + + # Test (de)serialization. + layer = recon_adjoint_layer.ReconAdjoint.from_config(layer.get_config()) + result = layer(input_data) + self.assertAllClose(expected, result) + + # Test in model. + inputs = {k: tf.keras.Input(type_spec=tf.type_spec_from_value(v)) + for k, v in input_data.items()} + model = tf.keras.Model(inputs, layer(inputs)) + result = model(input_data) + self.assertAllClose(expected, result) + + # Test saving/loading. + saved_model = os.path.join(tempfile.mkdtemp(), 'saved_model') + model.save(saved_model) + model = tf.keras.models.load_model(saved_model) + result = model(input_data) + self.assertAllClose(expected, result) diff --git a/tensorflow_mri/python/layers/reshaping.py b/tensorflow_mri/python/layers/reshaping.py new file mode 100644 index 00000000..e9c918f4 --- /dev/null +++ b/tensorflow_mri/python/layers/reshaping.py @@ -0,0 +1,97 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Reshaping layers.""" + +import string + +import tensorflow as tf + +from tensorflow_mri.python.util import api_util + + +EXTENSION_NOTE = string.Template(""" + + ```{note} + This layer can be used as a drop-in replacement for + `tf.keras.layers.${name}`. However, this one also supports complex-valued + operations. Simply pass `dtype='complex64'` or `dtype='complex128'` to the + layer constructor. + ``` + +""") + + +def complex_reshape(base): + """Adds complex-valued support to a Keras reshaping layer. + + We need the init method in the pooling layer to replace the `pool_function` + attribute with a function that supports complex inputs. + + Args: + base: The base class to be extended. + + Returns: + A subclass of `base` that supports complex-valued pooling. + + Raises: + ValueError: If `base` is not one of the supported base classes. + """ + if issubclass(base, (tf.keras.layers.UpSampling1D, + tf.keras.layers.UpSampling2D, + tf.keras.layers.UpSampling3D)): + def call(self, inputs): # pylint: arguments-differ + if tf.as_dtype(self.dtype).is_complex: + return tf.dtypes.complex( + base.call(self, tf.math.real(inputs)), + base.call(self, tf.math.imag(inputs))) + + # For real values, we can just use the regular reshape function. + return base.call(self, inputs) + + else: + raise ValueError(f'Unexpected base class: {base}') + + # Dynamically create a subclass of `base` with the same name as `base` and + # with the overriden `convolution_op` method. + subclass = type(base.__name__, (base,), {'call': call}) + + # Copy docs from the base class, adding the extra note. + docstring = base.__doc__ + doclines = docstring.split('\n') + doclines[1:1] = EXTENSION_NOTE.substitute(name=base.__name__).splitlines() + subclass.__doc__ = '\n'.join(doclines) + + return subclass + + +# Define the complex-valued pooling layers. We use a composition of three +# decorators: +# 1. `complex_reshape`: Adds complex-valued support to a Keras reshape layer. +# 2. `register_keras_serializable`: Registers the new layer with the Keras +# serialization framework. +# 3. `export`: Exports the new layer to the TFMRI API. +UpSampling1D = api_util.export("layers.UpSampling1D")( + tf.keras.utils.register_keras_serializable(package='MRI')( + complex_reshape(tf.keras.layers.UpSampling1D))) + + +UpSampling2D = api_util.export("layers.UpSampling2D")( + tf.keras.utils.register_keras_serializable(package='MRI')( + complex_reshape(tf.keras.layers.UpSampling2D))) + + +UpSampling3D = api_util.export("layers.UpSampling3D")( + tf.keras.utils.register_keras_serializable(package='MRI')( + complex_reshape(tf.keras.layers.UpSampling3D))) diff --git a/tensorflow_mri/python/layers/reshaping_test.py b/tensorflow_mri/python/layers/reshaping_test.py new file mode 100644 index 00000000..35a7ce75 --- /dev/null +++ b/tensorflow_mri/python/layers/reshaping_test.py @@ -0,0 +1,15 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for reshaping layers.""" diff --git a/tensorflow_mri/python/linalg/__init__.py b/tensorflow_mri/python/linalg/__init__.py new file mode 100644 index 00000000..b67203eb --- /dev/null +++ b/tensorflow_mri/python/linalg/__init__.py @@ -0,0 +1,41 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Linear algebra operators.""" + +from tensorflow_mri.python.linalg import add_registrations +from tensorflow_mri.python.linalg import adjoint_registrations +from tensorflow_mri.python.linalg import cholesky_registrations +from tensorflow_mri.python.linalg import conjugate_gradient +from tensorflow_mri.python.linalg import inverse_registrations +from tensorflow_mri.python.linalg import linear_operator_addition +from tensorflow_mri.python.linalg import linear_operator_addition_nd +from tensorflow_mri.python.linalg import linear_operator_adjoint +from tensorflow_mri.python.linalg import linear_operator_algebra +from tensorflow_mri.python.linalg import linear_operator_composition +from tensorflow_mri.python.linalg import linear_operator_diag +from tensorflow_mri.python.linalg import linear_operator_diag_nd +from tensorflow_mri.python.linalg import linear_operator_fft +from tensorflow_mri.python.linalg import linear_operator_finite_difference +from tensorflow_mri.python.linalg import linear_operator_full_matrix +from tensorflow_mri.python.linalg import linear_operator_gram_matrix +from tensorflow_mri.python.linalg import linear_operator_identity +from tensorflow_mri.python.linalg import linear_operator_identity_nd +from tensorflow_mri.python.linalg import linear_operator_inversion +from tensorflow_mri.python.linalg import linear_operator_mri +from tensorflow_mri.python.linalg import linear_operator_nufft +from tensorflow_mri.python.linalg import linear_operator_wavelet +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.linalg import matmul_registrations +from tensorflow_mri.python.linalg import solve_registrations diff --git a/tensorflow_mri/python/linalg/add_registrations.py b/tensorflow_mri/python/linalg/add_registrations.py new file mode 100644 index 00000000..de27eb16 --- /dev/null +++ b/tensorflow_mri/python/linalg/add_registrations.py @@ -0,0 +1,35 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Registrations for LinearOperator.add.""" + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.linalg import linear_operator_addition +from tensorflow_mri.python.linalg import linear_operator_algebra + + +# By default, use a LinearOperatorAddition to delay the computation. +@linear_operator_algebra.RegisterAdd( + linear_operator.LinearOperator, linear_operator.LinearOperator) +def _add_linear_operator(linop_a, linop_b): + """Generic add of two `LinearOperator`s.""" + # Set all hints to `None`. `LinearOperatorAddition` will figure them out + # automatically, if possible. + return linear_operator_addition.LinearOperatorAddition( + operators=[linop_a, linop_b], + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None + ) diff --git a/tensorflow_mri/python/linalg/adjoint_registrations.py b/tensorflow_mri/python/linalg/adjoint_registrations.py new file mode 100644 index 00000000..66dd6f76 --- /dev/null +++ b/tensorflow_mri/python/linalg/adjoint_registrations.py @@ -0,0 +1,21 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Registrations for LinearOperator.adjoint.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.linalg import linear_operator_algebra +from tensorflow_mri.python.linalg import registrations_util diff --git a/tensorflow_mri/python/linalg/cholesky_registrations.py b/tensorflow_mri/python/linalg/cholesky_registrations.py new file mode 100644 index 00000000..68952d2b --- /dev/null +++ b/tensorflow_mri/python/linalg/cholesky_registrations.py @@ -0,0 +1,58 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Registrations for LinearOperator.cholesky.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_algebra +from tensorflow_mri.python.linalg import linear_operator_diag_nd +from tensorflow_mri.python.linalg import linear_operator_identity_nd + + +@linear_operator_algebra.RegisterCholesky( + linear_operator_identity_nd.LinearOperatorIdentityND) +def _cholesky_identity_nd(linop): + return linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=linop.domain_shape_tensor(), + batch_shape=linop.batch_shape, + dtype=linop.dtype, + is_non_singular=True, + is_self_adjoint=True, + is_positive_definite=True, + is_square=True) + + +@linear_operator_algebra.RegisterCholesky( + linear_operator_identity_nd.LinearOperatorScaledIdentityND) +def _cholesky_scaled_identity_nd(linop): + return linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=linop.domain_shape_tensor(), + multiplier=tf.math.sqrt(linop.multiplier), + is_non_singular=True, + is_self_adjoint=True, + is_positive_definite=True, + is_square=True) + + +@linear_operator_algebra.RegisterCholesky( + linear_operator_diag_nd.LinearOperatorDiagND) +def _cholesky_diag_nd(linop): + return linear_operator_diag_nd.LinearOperatorDiagND( + tf.math.sqrt(linop.diag), + batch_dims=linop.batch_shape.rank, + is_non_singular=True, + is_self_adjoint=True, + is_positive_definite=True, + is_square=True) diff --git a/tensorflow_mri/python/linalg/conjugate_gradient.py b/tensorflow_mri/python/linalg/conjugate_gradient.py new file mode 100644 index 00000000..fb31c732 --- /dev/null +++ b/tensorflow_mri/python/linalg/conjugate_gradient.py @@ -0,0 +1,234 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2019 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Conjugate gradient solver.""" + +import collections + +import tensorflow as tf + +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.linalg import linear_operator + + +@api_util.export("linalg.conjugate_gradient") +def conjugate_gradient(operator, + rhs, + preconditioner=None, + x=None, + tol=1e-5, + max_iterations=20, + bypass_gradient=False, + name=None): + r"""Conjugate gradient solver. + + Solves a linear system of equations $Ax = b$ for self-adjoint, positive + definite matrix $A$ and right-hand side vector $b$, using an + iterative, matrix-free algorithm where the action of the matrix $A$ is + represented by `operator`. The iteration terminates when either the number of + iterations exceeds `max_iterations` or when the residual norm has been reduced + to `tol` times its initial value, i.e. + $(\left\| b - A x_k \right\| <= \mathrm{tol} \left\| b \right\|\\)$. + + ```{note} + This function is similar to + `tf.linalg.experimental.conjugate_gradient`, except it adds support for + complex-valued linear systems and for imaging operators. + ``` + + Args: + operator: A `LinearOperator` that is self-adjoint and positive definite. + rhs: A `tf.Tensor` of shape `[..., N]`. The right hand-side of the linear + system. + preconditioner: A `LinearOperator` that approximates the inverse of `A`. + An efficient preconditioner could dramatically improve the rate of + convergence. If `preconditioner` represents matrix `M`(`M` approximates + `A^{-1}`), the algorithm uses `preconditioner.apply(x)` to estimate + `A^{-1}x`. For this to be useful, the cost of applying `M` should be + much lower than computing `A^{-1}` directly. + x: A `tf.Tensor` of shape `[..., N]`. The initial guess for the solution. + tol: A float scalar convergence tolerance. + max_iterations: An `int` giving the maximum number of iterations. + bypass_gradient: A `boolean`. If `True`, the gradient with respect to `rhs` + will be computed by applying the inverse of `operator` to the upstream + gradient with respect to `x` (through CG iteration), instead of relying + on TensorFlow's automatic differentiation. This may reduce memory usage + when training neural networks, but `operator` must not have any trainable + parameters. If `False`, gradients are computed normally. For more details, + see ref. [1]. + name: A name scope for the operation. + + Returns: + A `namedtuple` representing the final state with fields + + - i: A scalar `int32` `tf.Tensor`. Number of iterations executed. + - x: A rank-1 `tf.Tensor` of shape `[..., N]` containing the computed + solution. + - r: A rank-1 `tf.Tensor` of shape `[.., M]` containing the residual vector. + - p: A rank-1 `tf.Tensor` of shape `[..., N]`. `A`-conjugate basis vector. + - gamma: \\(r \dot M \dot r\\), equivalent to \\(||r||_2^2\\) when + `preconditioner=None`. + + Raises: + ValueError: If `operator` is not self-adjoint and positive definite. + + References: + 1. Aggarwal, H. K., Mani, M. P., & Jacob, M. (2018). MoDL: Model-based + deep learning architecture for inverse problems. IEEE transactions on + medical imaging, 38(2), 394-405. + """ + if bypass_gradient: + if preconditioner is not None: + raise ValueError( + "preconditioner is not supported when bypass_gradient is True.") + if x is not None: + raise ValueError("x is not supported when bypass_gradient is True.") + + def _conjugate_gradient_simple(rhs): + return _conjugate_gradient_internal(operator, rhs, + tol=tol, + max_iterations=max_iterations, + name=name) + + @tf.custom_gradient + def _conjugate_gradient_internal_grad(rhs): + result = _conjugate_gradient_simple(rhs) + + def grad(*upstream_grads): + # upstream_grads has the upstream gradient for each element of the + # output tuple (i, x, r, p, gamma). + _, dx, _, _, _ = upstream_grads + return _conjugate_gradient_simple(dx).x + + return result, grad + + return _conjugate_gradient_internal_grad(rhs) + + return _conjugate_gradient_internal(operator, rhs, + preconditioner=preconditioner, + x=x, + tol=tol, + max_iterations=max_iterations, + name=name) + + +def _conjugate_gradient_internal(operator, + rhs, + preconditioner=None, + x=None, + tol=1e-5, + max_iterations=20, + name=None): + """Implementation of `conjugate_gradient`. + + For the parameters, see `conjugate_gradient`. + """ + if isinstance(operator, linear_operator.LinearOperatorMixin): + rhs = operator.flatten_domain_shape(rhs) + + if not (operator.is_self_adjoint and operator.is_positive_definite): + raise ValueError('Expected a self-adjoint, positive definite operator.') + + cg_state = collections.namedtuple('CGState', ['i', 'x', 'r', 'p', 'gamma']) + + def stopping_criterion(i, state): + return tf.math.logical_and( + i < max_iterations, + tf.math.reduce_any( + tf.math.real(tf.norm(state.r, axis=-1)) > tf.math.real(tol))) + + def dot(x, y): + return tf.squeeze( + tf.linalg.matvec( + x[..., tf.newaxis], + y, adjoint_a=True), axis=-1) + + def cg_step(i, state): # pylint: disable=missing-docstring + z = tf.linalg.matvec(operator, state.p) + alpha = state.gamma / dot(state.p, z) + x = state.x + alpha[..., tf.newaxis] * state.p + r = state.r - alpha[..., tf.newaxis] * z + if preconditioner is None: + q = r + else: + q = preconditioner.matvec(r) + gamma = dot(r, q) + beta = gamma / state.gamma + p = q + beta[..., tf.newaxis] * state.p + return i + 1, cg_state(i + 1, x, r, p, gamma) + + # We now broadcast initial shapes so that we have fixed shapes per iteration. + + with tf.name_scope(name or 'conjugate_gradient'): + broadcast_shape = tf.broadcast_dynamic_shape( + tf.shape(rhs)[:-1], + operator.batch_shape_tensor()) + static_broadcast_shape = tf.broadcast_static_shape( + rhs.shape[:-1], + operator.batch_shape) + if preconditioner is not None: + broadcast_shape = tf.broadcast_dynamic_shape( + broadcast_shape, + preconditioner.batch_shape_tensor()) + static_broadcast_shape = tf.broadcast_static_shape( + static_broadcast_shape, + preconditioner.batch_shape) + broadcast_rhs_shape = tf.concat([broadcast_shape, [tf.shape(rhs)[-1]]], -1) + static_broadcast_rhs_shape = static_broadcast_shape.concatenate( + [rhs.shape[-1]]) + r0 = tf.broadcast_to(rhs, broadcast_rhs_shape) + tol *= tf.norm(r0, axis=-1) + + if x is None: + x = tf.zeros( + broadcast_rhs_shape, dtype=rhs.dtype.base_dtype) + x = tf.ensure_shape(x, static_broadcast_rhs_shape) + else: + r0 = rhs - tf.linalg.matvec(operator, x) + if preconditioner is None: + p0 = r0 + else: + p0 = tf.linalg.matvec(preconditioner, r0) + gamma0 = dot(r0, p0) + i = tf.constant(0, dtype=tf.int32) + state = cg_state(i=i, x=x, r=r0, p=p0, gamma=gamma0) + _, state = tf.while_loop( + stopping_criterion, cg_step, [i, state]) + + if isinstance(operator, linear_operator.LinearOperatorMixin): + x = operator.expand_range_dimension(state.x) + else: + x = state.x + + return cg_state( + state.i, + x=x, + r=state.r, + p=state.p, + gamma=state.gamma) diff --git a/tensorflow_mri/python/linalg/conjugate_gradient_test.py b/tensorflow_mri/python/linalg/conjugate_gradient_test.py new file mode 100644 index 00000000..c1604758 --- /dev/null +++ b/tensorflow_mri/python/linalg/conjugate_gradient_test.py @@ -0,0 +1,161 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2019 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `conjugate_gradient`.""" +# pylint: disable=missing-class-docstring,missing-function-docstring + +from absl.testing import parameterized +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import conjugate_gradient +from tensorflow_mri.python.util import test_util + + +@test_util.run_all_in_graph_and_eager_modes +class ConjugateGradientTest(test_util.TestCase): + """Tests for op `conjugate_gradient`.""" + @parameterized.product(dtype=[np.float32, np.float64], + shape=[[1, 1], [4, 4], [10, 10]], + use_static_shape=[True, False]) + def test_conjugate_gradient(self, dtype, shape, use_static_shape): # pylint: disable=missing-param-doc + """Test CG method.""" + np.random.seed(1) + a_np = np.random.uniform( + low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype) + # Make a self-adjoint, positive definite. + a_np = np.dot(a_np.T, a_np) + # jacobi preconditioner + jacobi_np = np.zeros_like(a_np) + jacobi_np[range(a_np.shape[0]), range(a_np.shape[1])] = ( + 1.0 / a_np.diagonal()) + rhs_np = np.random.uniform( + low=-1.0, high=1.0, size=shape[0]).astype(dtype) + x_np = np.zeros_like(rhs_np) + tol = 1e-6 if dtype == np.float64 else 1e-3 + max_iterations = 20 + + if use_static_shape: + a = tf.constant(a_np) + rhs = tf.constant(rhs_np) + x = tf.constant(x_np) + jacobi = tf.constant(jacobi_np) + else: + a = tf.compat.v1.placeholder_with_default(a_np, shape=None) + rhs = tf.compat.v1.placeholder_with_default(rhs_np, shape=None) + x = tf.compat.v1.placeholder_with_default(x_np, shape=None) + jacobi = tf.compat.v1.placeholder_with_default(jacobi_np, shape=None) + + operator = tf.linalg.LinearOperatorFullMatrix( + a, is_positive_definite=True, is_self_adjoint=True) + preconditioners = [ + None, + # Preconditioner that does nothing beyond change shape. + tf.linalg.LinearOperatorIdentity( + a_np.shape[-1], + dtype=a_np.dtype, + is_positive_definite=True, + is_self_adjoint=True), + # Jacobi preconditioner. + tf.linalg.LinearOperatorFullMatrix( + jacobi, + is_positive_definite=True, + is_self_adjoint=True), + ] + cg_results = [] + for preconditioner in preconditioners: + cg_graph = conjugate_gradient.conjugate_gradient( + operator, + rhs, + preconditioner=preconditioner, + x=x, + tol=tol, + max_iterations=max_iterations) + cg_val = self.evaluate(cg_graph) + norm_r0 = np.linalg.norm(rhs_np) + norm_r = np.linalg.norm(cg_val.r) + self.assertLessEqual(norm_r, tol * norm_r0) + # Validate that we get an equally small residual norm with numpy + # using the computed solution. + r_np = rhs_np - np.dot(a_np, cg_val.x) + norm_r_np = np.linalg.norm(r_np) + self.assertLessEqual(norm_r_np, tol * norm_r0) + cg_results.append(cg_val) + + # Validate that we get same results using identity_preconditioner + # and None + self.assertEqual(cg_results[0].i, cg_results[1].i) + self.assertAlmostEqual(cg_results[0].gamma, cg_results[1].gamma) + self.assertAllClose(cg_results[0].r, cg_results[1].r, rtol=tol) + self.assertAllClose(cg_results[0].x, cg_results[1].x, rtol=tol) + self.assertAllClose(cg_results[0].p, cg_results[1].p, rtol=tol) + + def test_bypass_gradient(self): + """Tests the `bypass_gradient` argument.""" + dtype = np.float32 + shape = [4, 4] + np.random.seed(1) + a_np = np.random.uniform( + low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype) + # Make a self-adjoint, positive definite. + a_np = np.dot(a_np.T, a_np) + + rhs_np = np.random.uniform( + low=-1.0, high=1.0, size=shape[0]).astype(dtype) + + tol = 1e-3 + max_iterations = 20 + + a = tf.constant(a_np) + rhs = tf.constant(rhs_np) + operator = tf.linalg.LinearOperatorFullMatrix( + a, is_positive_definite=True, is_self_adjoint=True) + + with tf.GradientTape(persistent=True) as tape: + tape.watch(rhs) + result = conjugate_gradient.conjugate_gradient( + operator, + rhs, + tol=tol, + max_iterations=max_iterations) + result_bypass = conjugate_gradient.conjugate_gradient( + operator, + rhs, + tol=tol, + max_iterations=max_iterations, + bypass_gradient=True) + + grad = tape.gradient(result.x, rhs) + grad_bypass = tape.gradient(result_bypass.x, rhs) + self.assertAllClose(result, result_bypass) + self.assertAllClose(grad, grad_bypass, rtol=tol) + + +if __name__ == '__main__': + tf.test.main() diff --git a/tensorflow_mri/python/linalg/inverse_registrations.py b/tensorflow_mri/python/linalg/inverse_registrations.py new file mode 100644 index 00000000..81214154 --- /dev/null +++ b/tensorflow_mri/python/linalg/inverse_registrations.py @@ -0,0 +1,86 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Registrations for LinearOperator.inverse.""" + +from tensorflow_mri.python.linalg import linear_operator_algebra +from tensorflow_mri.python.linalg import linear_operator_coils +from tensorflow_mri.python.linalg import linear_operator_diag_nd +from tensorflow_mri.python.linalg import linear_operator_fft +from tensorflow_mri.python.linalg import linear_operator_identity_nd +from tensorflow_mri.python.linalg import linear_operator_mask +from tensorflow_mri.python.linalg import linear_operator_nufft + + +@linear_operator_algebra.RegisterInverse( + linear_operator_coils.LinearOperatorCoils) +def _inverse_coils(linop): + raise ValueError( + f"{linop.name} is not invertible. If you wish to compute the " + f"Moore-Penrose pseudo-inverse, use `linop.pseudo_inverse()` " + f"instead.") + + +@linear_operator_algebra.RegisterInverse( + linear_operator_identity_nd.LinearOperatorIdentityND) +def _inverse_identity_nd(linop): + return linop + + +@linear_operator_algebra.RegisterInverse( + linear_operator_identity_nd.LinearOperatorScaledIdentityND) +def _inverse_scaled_identity_nd(linop): + return linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=linop.domain_shape_tensor(), + multiplier=1. / linop.multiplier, + is_non_singular=linop.is_non_singular, + is_self_adjoint=True, + is_positive_definite=linop.is_positive_definite, + is_square=True) + + +@linear_operator_algebra.RegisterInverse( + linear_operator_diag_nd.LinearOperatorDiagND) +def _inverse_diag_nd(linop): + return linear_operator_diag_nd.LinearOperatorDiagND( + 1. / linop.diag, + batch_dims=linop.batch_shape.rank, + is_non_singular=linop.is_non_singular, + is_self_adjoint=linop.is_self_adjoint, + is_positive_definite=linop.is_positive_definite, + is_square=True) + + +@linear_operator_algebra.RegisterInverse( + linear_operator_fft.LinearOperatorFFT) +def _inverse_fft(linop): + return linop.adjoint() + + +@linear_operator_algebra.RegisterInverse( + linear_operator_mask.LinearOperatorMask) +def _inverse_mask(linop): + raise ValueError( + f"{linop.name} is not invertible. If you wish to compute the " + f"Moore-Penrose pseudo-inverse, use `linop.pseudo_inverse()` " + f"instead.") + + +@linear_operator_algebra.RegisterInverse( + linear_operator_nufft.LinearOperatorNUFFT) +def _inverse_nufft(linop): + raise ValueError( + f"{linop.name} is not invertible. If you wish to compute the " + f"Moore-Penrose pseudo-inverse, use `linop.pseudo_inverse()` " + f"instead.") diff --git a/tensorflow_mri/python/linalg/linear_operator.py b/tensorflow_mri/python/linalg/linear_operator.py new file mode 100644 index 00000000..818f5d0e --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator.py @@ -0,0 +1,428 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Base linear operator.""" + +import string +import warnings + +import tensorflow as tf +from tensorflow.python.framework import type_spec +from tensorflow.python.ops.linalg.linear_operator import ( + _extract_attrs, _extract_type_spec_recursively) +from tensorflow.python.util import dispatch + +from tensorflow_mri.python.linalg import linear_operator_algebra +from tensorflow_mri.python.linalg import linear_operator_util +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import doc_util + + +def make_linear_operator(cls): + """Class decorator for subclasses of `LinearOperator`.""" + # Add extensions if decorating base class. + if cls is tf.linalg.LinearOperator: + extensions = { + "lstsq": lstsq, + "_lstsq": _lstsq, + "lstsqvec": lstsqvec, + "_lstsqvec": _lstsqvec, + "_dense_lstsq": _dense_lstsq, + "add": add, + "__add__": __add__ + } + + for key, value in extensions.items(): + if hasattr(cls, key): + raise ValueError(f"{cls.__name__} already has attribute: {key}") + setattr(cls, key, value) + + # Make composite tensor. This also adds additional functionality to the class. + cls = make_composite_tensor(cls) + + # Add notice to docstring. + cls = update_docstring(cls) + + return cls + + +def make_composite_tensor(cls, module_name="tfmri.linalg"): + """Class decorator to convert `LinearOperator`s to `CompositeTensor`s. + + Overrides the default `make_composite_tensor` to use the custom + `LinearOperatorSpec`. + """ + spec_name = "{}Spec".format(cls.__name__) + spec_type = type(spec_name, (_LinearOperatorSpec,), {"value_type": cls}) + type_spec.register("{}.{}".format(module_name, spec_name))(spec_type) + cls._type_spec = property(spec_type.from_operator) # pylint: disable=protected-access + return cls + + +def update_docstring(cls): + """Updates docstring to describe new functionality.""" + tfmri_additional_functionality = string.Template(""" + ```{rubric} Additional functionality (TensorFlow MRI) + ``` + + This operator supports additional functionality not present in core TF + operators. + + - `lstsq` and `lstsqvec` finds the least-squares solution to the linear + system(s) defined by this operator. + - `_lstsq` and `_lstsqvec` can be overridden to provide a custom + implementation of `lstsq` and `lstsqvec`, respectively. + - `_type_spec` has been patched to improve support in Keras models. + + ```{seealso} + The TensorFlow MRI + [linear algebra guide](https://mrphys.github.io/tensorflow-mri/guide/linalg/). + ``` + """).substitute() + + tfmri_tf_compatibility = string.Template(""" + ```{rubric} Compatibility with core TensorFlow + ``` + + This operator is a drop-in replacement for `tf.linalg.${class_name}`. + + ```{tip} + You can use `tfmri.linalg.${class_name}` and `tf.linalg.${class_name}` + interchangeably, as the latter has been monkey-patched to be an alias of + this operator. + ``` + """).substitute(class_name=cls.__name__) + + docstring = cls.__doc__ + doclines = docstring.split('\n') + doclines += tfmri_additional_functionality.split('\n') + if is_tf_builtin(cls): + doclines += tfmri_tf_compatibility.split('\n') + docstring = '\n'.join(doclines) + cls.__doc__ = docstring + + return cls + + +def is_tf_builtin(cls): + """Returns `True` if `cls` is a built-in linear operator.""" + return hasattr(tf.linalg, cls.__name__) + + +# New attributes to be added to `LinearOperator` class. + +def lstsq(self, rhs, adjoint=False, adjoint_arg=False, name="lstsq"): + """Solve the (batch) linear system $A X = B$ in the least-squares sense. + + Given $A$ represented by this linear operator with shape `[..., M, N]`, + computes the least-squares solution $X$ to the batch of linear systems + $A X = B$. For systems without an exact solution, returns a "best fit" + solution in the least squares sense. For systems with multiple solutions, + returns the solution with the smallest Euclidean norm. + + This is equivalent to solving for the normal equations $A^H A X = A^H B$. + + Args: + rhs: A `tf.Tensor` with same `dtype` as this operator and shape + `[..., M, K]`. `rhs` is treated like a [batch] matrix meaning for + every set of leading dimensions, the last two dimensions define a + matrix. + adjoint: A boolean. If `True`, solve the system involving the adjoint + of this operator, $A^H X = B$. Default is `False`. + adjoint_arg: A boolean. If `True`, solve $A X = B^H$ where $B^H$ is the + Hermitian transpose (transposition and complex conjugation). Default + is `False`. + name: A name scope to use for ops added by this method. + + Returns: + A `tf.Tensor` with shape `[..., N, K]` and same `dtype` as `rhs`. + """ + if isinstance(rhs, LinearOperator): + left_operator = self.adjoint() if adjoint else self + right_operator = rhs.adjoint() if adjoint_arg else rhs + + if (right_operator.range_dimension is not None and + left_operator.domain_dimension is not None and + right_operator.range_dimension != left_operator.domain_dimension): + raise ValueError( + "Operators are incompatible. Expected `rhs` to have dimension" + " {} but got {}.".format( + left_operator.domain_dimension, right_operator.range_dimension)) + with self._name_scope(name): # pylint: disable=not-callable + return linear_operator_algebra.lstsq(left_operator, right_operator) + + with self._name_scope(name): # pylint: disable=not-callable + rhs = tf.convert_to_tensor(rhs, name="rhs") + self._check_input_dtype(rhs) + + self_dim = -1 if adjoint else -2 + arg_dim = -1 if adjoint_arg else -2 + tf.compat.dimension_at_index( + self.shape, self_dim).assert_is_compatible_with( + rhs.shape[arg_dim]) + + return self._lstsq(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) + +def _lstsq(self, rhs, adjoint=False, adjoint_arg=False): + """Default implementation of `_lstsq`.""" + warnings.warn( + "Using (possibly slow) default implementation of lstsq. " + "Requires conversion to a dense matrix and O(N^3) operations.") + return self._dense_lstsq(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) + +def lstsqvec(self, rhs, adjoint=False, name="lstsqvec"): + """Solve the linear system $A x = b$ in the least-squares sense. + + Given $A$ represented by this linear operator with shape `[..., M, N]`, + computes the least-squares solution $x$ to the linear system $A x = b$. + For systems without an exact solution, returns a "best fit" solution in + the least squares sense. For systems with multiple solutions, returns the + solution with the smallest Euclidean norm. + + This is equivalent to solving for the normal equations $A^H A x = A^H b$. + + Args: + rhs: A `tf.Tensor` with same `dtype` as this operator and shape + `[..., M]`. `rhs` is treated like a [batch] matrix meaning for + every set of leading dimensions, the last two dimensions define a + matrix. + adjoint: A boolean. If `True`, solve the system involving the adjoint + of this operator, $A^H x = b$. Default is `False`. + name: A name scope to use for ops added by this method. + + Returns: + A `tf.Tensor` with shape `[..., N]` and same `dtype` as `rhs`. + """ + with self._name_scope(name): # pylint: disable=not-callable + rhs = tf.convert_to_tensor(rhs, name="rhs") + self._check_input_dtype(rhs) + self_dim = -1 if adjoint else -2 + tf.compat.dimension_at_index( + self.shape, self_dim).assert_is_compatible_with(rhs.shape[-1]) + + return self._lstsqvec(rhs, adjoint=adjoint) + +def _lstsqvec(self, rhs, adjoint=False): + """Default implementation of `_lstsqvec`.""" + rhs_mat = tf.expand_dims(rhs, axis=-1) + solution_mat = self.lstsq(rhs_mat, adjoint=adjoint) + return tf.squeeze(solution_mat, axis=-1) + +def _dense_lstsq(self, rhs, adjoint=False, adjoint_arg=False): + """Solve least squares by conversion to a dense matrix.""" + rhs = tf.linalg.adjoint(rhs) if adjoint_arg else rhs + return linear_operator_util.matrix_solve_ls_with_broadcast( + self.to_dense(), rhs, adjoint=adjoint) + +def add(self, x, name="add"): + """Add this operator to matrix `x`. + + Example: + >>> operator = LinearOperatorIdentity(2) + >>> x = tf.linalg.eye(2) + >>> x = operator.add(x) + >>> x.numpy() + array([[2., 0.], + [0., 2.]], dtype=float32) + + Args: + x: A `LinearOperator` or `Tensor` with compatible shape and same `dtype` as + `self`. See class docstring for definition of compatibility. + name: A name for this `Op`. + + Returns: + A `LinearOperator` or `Tensor` with same shape and same dtype as `self`. + """ + if isinstance(x, LinearOperator): + left_operator = self + right_operator = x + + if (not left_operator.shape[-2:].is_compatible_with( + right_operator.shape[-2:])): + raise ValueError( + f"Operators are incompatible. Expected `x` to have shape " + f"{left_operator.shape[-2:]} but got {right_operator.shape[-2:]}.") + with self._name_scope(name): + return linear_operator_algebra.add(left_operator, right_operator) + + with self._name_scope(name): # pylint: disable=not-callable + return self.add_to_tensor(x) + +def __add__(self, other): + return self.add(other) + + + +class _LinearOperatorSpec(type_spec.BatchableTypeSpec): # pylint: disable=abstract-method + """A tf.TypeSpec for `LinearOperator` objects. + + This is very similar to `tf.linalg.LinearOperatorSpec`, but it adds + `shape` and `dtype` attributes which are required by Keras. + + These attributes are redundant, as they can always be computed from + other parameters. However, the details of this computation vary between + operators, so it's easier to just store it. + """ + __slots__ = ("_param_specs", + "_non_tensor_params", + "_prefer_static_fields", + "_shape", + "_dtype") + + def __init__(self, + param_specs, + non_tensor_params, + prefer_static_fields, + shape=None, + dtype=None): + """Initializes a new `_LinearOperatorSpec`. + + Args: + param_specs: Python `dict` of `tf.TypeSpec` instances that describe + kwargs to the `LinearOperator`'s constructor that are `Tensor`-like or + `CompositeTensor` subclasses. + non_tensor_params: Python `dict` containing non-`Tensor` and non- + `CompositeTensor` kwargs to the `LinearOperator`'s constructor. + prefer_static_fields: Python `tuple` of strings corresponding to the names + of `Tensor`-like args to the `LinearOperator`s constructor that may be + stored as static values, if known. These are typically shapes, indices, + or axis values. + shape: A `tf.TensorShape`. The shape of the `LinearOperator`. + dtype: A `tf.dtypes.DType`. The dtype of the `LinearOperator`. + """ + self._param_specs = param_specs + self._non_tensor_params = non_tensor_params + self._prefer_static_fields = prefer_static_fields + self._shape = shape + self._dtype = dtype + + @classmethod + def from_operator(cls, operator): + """Builds a `_LinearOperatorSpec` from a `LinearOperator` instance. + + Args: + operator: An instance of `LinearOperator`. + + Returns: + linear_operator_spec: An instance of `_LinearOperatorSpec` to be used as + the `TypeSpec` of `operator`. + """ + validation_fields = ("is_non_singular", "is_self_adjoint", + "is_positive_definite", "is_square") + kwargs = _extract_attrs( # pylint: disable=protected-access + operator, + keys=set(operator._composite_tensor_fields + validation_fields)) # pylint: disable=protected-access + + non_tensor_params = {} + param_specs = {} + for k, v in list(kwargs.items()): + type_spec_or_v = _extract_type_spec_recursively(v) # pylint: disable=protected-access + is_tensor = [isinstance(x, type_spec.TypeSpec) + for x in tf.nest.flatten(type_spec_or_v)] + if all(is_tensor): + param_specs[k] = type_spec_or_v + elif not any(is_tensor): + non_tensor_params[k] = v + else: + raise NotImplementedError(f"Field {k} contains a mix of `Tensor` and " + f" non-`Tensor` values.") + + return cls( + param_specs=param_specs, + non_tensor_params=non_tensor_params, + prefer_static_fields=operator._composite_tensor_prefer_static_fields, # pylint: disable=protected-access + shape=operator.shape, + dtype=operator.dtype) + + def _to_components(self, obj): + return _extract_attrs(obj, keys=list(self._param_specs)) + + def _from_components(self, components): + kwargs = dict(self._non_tensor_params, **components) + return self.value_type(**kwargs) + + @property + def _component_specs(self): + return self._param_specs + + def _serialize(self): + return (self._param_specs, + self._non_tensor_params, + self._prefer_static_fields, + self._shape, + self._dtype) + + def _copy(self, **overrides): + kwargs = { + "param_specs": self._param_specs, + "non_tensor_params": self._non_tensor_params, + "prefer_static_fields": self._prefer_static_fields, + "shape": self._shape, + "dtype": self._dtype + } + kwargs.update(overrides) + return type(self)(**kwargs) + + def _batch(self, batch_size): + """Returns a TypeSpec representing a batch of objects with this TypeSpec.""" + return self._copy( + param_specs=tf.nest.map_structure( + lambda spec: spec._batch(batch_size), # pylint: disable=protected-access + self._param_specs)) + + def _unbatch(self): + """Returns a TypeSpec representing a single element of this TypeSpec.""" + return self._copy( + param_specs=tf.nest.map_structure( + lambda spec: spec._unbatch(), # pylint: disable=protected-access + self._param_specs)) + + @property + def shape(self): + """Returns a `tf.TensorShape` representing the static shape.""" + # This property is required to use linear operators with Keras. + return self._shape + + @property + def dtype(self): + """Returns a `tf.dtypes.DType` representing the dtype.""" + return self._dtype + + def with_shape(self, shape): + """Returns a new `tf.TypeSpec` with the given shape.""" + # This method is required to use linear operators with Keras. + return self._copy(shape=shape) + + def _to_legacy_output_shapes(self): + return self._shape + + def _to_legacy_output_types(self): + return self._dtype + + +# Define new `LinearOperator` class. +LinearOperator = api_util.export("linalg.LinearOperator")( + doc_util.no_linkcode(make_linear_operator(tf.linalg.LinearOperator))) + + +# Monkey-patch original operator so that core TF operator and TFMRI +# operator become aliases. +tf.linalg.LinearOperator = LinearOperator + + +@dispatch.dispatch_for_types(tf.math.add, LinearOperator) +def _add(x, y, name=None): + if not isinstance(x, LinearOperator): + return y.add(x, name=name) + return x.add(y, name=name) diff --git a/tensorflow_mri/python/linalg/linear_operator_addition.py b/tensorflow_mri/python/linalg/linear_operator_addition.py new file mode 100644 index 00000000..a880bb98 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_addition.py @@ -0,0 +1,294 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Addition linear operator.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import check_util + + +@api_util.export("linalg.LinearOperatorAddition") +@linear_operator.make_linear_operator +class LinearOperatorAddition(linear_operator.LinearOperator): + r"""Adds one or more [batch] linear operators. + + This operator adds one or more linear operators $A_1, A_2, \dots, A_n$ to + build a new `LinearOperator` $A$ with action defined by: + + $$ + Ax = (A_1 + A_2 + \dots + A_n)(x) = A_1 x + A_2 x + \dots + A_n x + $$ + + All input `operators` must have shape `[..., M, N]` and the resulting + operator also has shape `[..., M, N]`. The batch shape of the resulting + operator is the result of broadcasting the batch shape of all input + operators. + + ```{rubric} Performance + ``` + In general, performance in matrix-vector multiplication is the sum + of the individual operators. More efficient implementations may be + used for specific operators. + + ```{rubric} Matrix properties + ``` + The properties of this operator are determined by the properties of the + input operators. + + ```{rubric} Inversion + ``` + At present, this operator does not implement an efficient algorithm for + inversion. `solve` and `lstsq` will trigger conversion to a dense matrix. + + Example: + >>> # Create a 2 x 2 linear operator composed of two 2 x 2 operators. + >>> op1 = tfmri.linalg.LinearOperatorFullMatrix([[1., 2.], [3., 4.]]) + >>> op2 = tfmri.linalg.LinearOperatorIdentity(2) + >>> operator = LinearOperatorAddition([op1, op2]) + >>> operator.to_dense().numpy() + array([[2., 2.], + [3., 5.]], dtype=float32) + + Args: + operators: A `list` of `tf.linalg.LinearOperator`s of equal shape and + dtype. Batch dimensions may vary but must be broadcastable. + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `None`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `None`. + is_positive_definite: A boolean, or `None`. Whether this operator is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that an operator [does + not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) + Defaults to `None`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `None`. + name: An optional `str`. The name of this operator. + + Raises: + TypeError: If all operators do not have the same `dtype`. + ValueError: If `operators` is empty. + """ + def __init__(self, + operators, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None, + name=None): + """Initialize a `LinearOperatorAddition`.""" + parameters = dict( + operators=operators, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name) + + # Validate operators. + tf.debugging.assert_proper_iterable(operators) + operators = list(operators) + if not operators: + raise ValueError( + f"Expected a non-empty list of operators. Found: {operators}") + self._operators = operators + + # Validate dtype. + dtype = operators[0].dtype + for operator in operators: + if operator.dtype != dtype: + name_type = (str((o.name, o.dtype)) for o in operators) + raise TypeError( + f"Expected all operators to have the same dtype. " + f"Found: {', '.join(name_type)}") + + # Validate shapes. + self._matrix_shape = operators[0].shape[-2:] + for operator in operators: + if not operator.shape[-2:].is_compatible_with(self._matrix_shape): + raise ValueError( + f"Expected all operators to have the same shape. " + f"Found: {', '.join([str(o.shape[-2:]) for o in operators])}") + + # Infer operator properties. + is_non_singular = check_hint( + combined_non_singular_hint(*operators), + is_non_singular, + "non-singular") + is_self_adjoint = check_hint( + combined_self_adjoint_hint(*operators), + is_self_adjoint, + "self-adjoint") + is_positive_definite = check_hint( + combined_positive_definite_hint(*operators), + is_positive_definite, + "positive-definite") + is_square = check_hint( + combined_square_hint(*operators), + is_square, + "square") + + if name is None: + name = "_p_".join(operator.name for operator in operators) + with tf.name_scope(name): + super().__init__( + dtype=dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters, + name=name) + + @property + def operators(self): + return self._operators + + def _shape(self): + # Get broadcast batch shape. + batch_shape = self.operators[0].batch_shape + for operator in self.operators[1:]: + batch_shape = tf.broadcast_static_shape( + batch_shape, operator.batch_shape) + + return batch_shape.concatenate(self._matrix_shape) + + def _shape_tensor(self): + matrix_shape = self.operators[0].shape_tensor()[-2:] + + # Dummy tensor of zeros. In graph mode, it will never be materialized. + zeros = tf.zeros(shape=self.operators[0].batch_shape_tensor()) + for operator in self.operators[1:]: + zeros += tf.zeros(shape=operator.batch_shape_tensor()) + batch_shape = tf.shape(zeros) + + return tf.concat([batch_shape, matrix_shape], 0) + + def _matmul(self, x, adjoint=False, adjoint_arg=False): + result = self.operators[0].matmul( + x, adjoint=adjoint, adjoint_arg=adjoint_arg) + for operator in self.operators[1:]: + result += operator.matmul(x, adjoint=adjoint, adjoint_arg=adjoint_arg) + return result + + @property + def _composite_tensor_fields(self): + return ("operators",) + + @property + def _experimental_parameter_ndims_to_matrix_ndims(self): + return {"operators": [0] * len(self.operators)} + + +def combined_non_singular_hint(*operators): + """Returns a hint for the non-singularity of a sum of operators. + + Args: + *operators: A list of `LinearOperator` objects. + + Returns: + A boolean, or `None`. Whether the sum of the operators is expected to be + non-singular. + """ + # In general, there is nothing we can say about the non-singularity of the + # sum of operators, regardless of the non-singularity of the individual + # operators. + return None + + +def combined_self_adjoint_hint(*operators): + """Returns a hint for the self-adjointness of a sum of operators. + + Args: + *operators: A list of `LinearOperator` objects. + + Returns: + A boolean, or `None`. Whether the sum of the operators is expected to be + self-adjoint. + """ + # If all operators are self-adjoint, so is the sum. + if all(o.is_self_adjoint is True for o in operators): + return True + # If all operators are self-adjoint except one which is not, then the sum is + # not self-adjoint. + self_adjoint_operators = [ + o for o in operators if o.is_self_adjoint is True] + non_self_adjoint_operators = [ + o for o in operators if o.is_self_adjoint is False] + if (len(self_adjoint_operators) == len(operators) - 1 and + len(non_self_adjoint_operators) == 1): + return False + # In all other cases, we don't know. + return None + + +def combined_positive_definite_hint(*operators): + """Returns a hint for the positive-definiteness of a sum of operators. + + Args: + *operators: A list of `LinearOperator` objects. + + Returns: + A boolean, or `None`. Whether the sum of the operators is expected to be + positive-definite. + """ + # If all operators are positive definite, so is the sum. + if all(o.is_positive_definite is True for o in operators): + return True + # In all other cases, we don't know. + return None + + +def combined_square_hint(*operators): + """Returns a hint for the squareness of a sum of operators. + + Args: + *operators: A list of `LinearOperator` objects. + + Returns: + A boolean, or `None`. Whether the sum of the operators is expected to be + square. + """ + # If any operator is square, so is the sum. + if (any(o.is_square is True for o in operators) and + not any(o.is_square is False for o in operators)): + return True + # If any operator is not square, so is the sum. + if (any(o.is_square is False for o in operators) and + not any(o.is_square is True for o in operators)): + return False + # In all other cases, we don't know. + return None + + +def check_hint(expected, received, name): + """Checks that a hint is consistent with its expected value. + + Args: + expected: A boolean, or `None`. The expected value of the hint. + received: A boolean, or `None`. The received value of the hint. + name: A string. The name of the hint. + + Raises: + ValueError: If `expected` and `value` are not consistent. + """ + if expected is not None and received is not None and expected != received: + raise ValueError( + f"Inconsistent {name} hint: expected {expected} based on input " + f"operators, but got {received}") + return received if received is not None else expected diff --git a/tensorflow_mri/python/linalg/linear_operator_addition_nd.py b/tensorflow_mri/python/linalg/linear_operator_addition_nd.py new file mode 100644 index 00000000..0c21c77c --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_addition_nd.py @@ -0,0 +1,70 @@ +# # Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# # +# # Licensed under the Apache License, Version 2.0 (the "License"); +# # you may not use this file except in compliance with the License. +# # You may obtain a copy of the License at +# # +# # http://www.apache.org/licenses/LICENSE-2.0 +# # +# # Unless required by applicable law or agreed to in writing, software +# # distributed under the License is distributed on an "AS IS" BASIS, +# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# # See the License for the specific language governing permissions and +# # limitations under the License. +# # ============================================================================== +# """Addition of linear operators.""" + +# from tensorflow_mri.python.ops import array_ops +# from tensorflow_mri.python.linalg import linear_operator +# from tensorflow_mri.python.util import api_util +# from tensorflow_mri.python.util import linalg_ext + + +# @api_util.export("linalg.LinearOperatorAddition") +# class LinearOperatorAddition(linalg_ext.LinearOperatorAddition): +# """Adds one or more linear operators. + +# `LinearOperatorAddition` is initialized with a list of operators +# $A_1, A_2, ..., A_J$ and represents their addition +# $A_1 + A_2 + ... + A_J$. + +# Args: +# operators: A `list` of `LinearOperator` objects, each with the same `dtype` +# and shape. +# is_non_singular: Expect that this operator is non-singular. +# is_self_adjoint: Expect that this operator is equal to its Hermitian +# transpose. +# is_positive_definite: Expect that this operator is positive definite, +# meaning the quadratic form $x^H A x$ has positive real part for all +# nonzero $x$. Note that we do not require the operator to be +# self-adjoint to be positive-definite. +# is_square: Expect that this operator acts like square [batch] matrices. +# name: A name for this `LinearOperator`. Default is the individual +# operators names joined with `_p_`. +# """ +# def _transform(self, x, adjoint=False): +# # pylint: disable=protected-access +# result = self.operators[0]._transform(x, adjoint=adjoint) +# for operator in self.operators[1:]: +# result += operator._transform(x, adjoint=adjoint) +# return result + +# def _domain_shape(self): +# return self.operators[0].domain_shape + +# def _range_shape(self): +# return self.operators[0].range_shape + +# def _batch_shape(self): +# return array_ops.broadcast_static_shapes( +# *[operator.batch_shape for operator in self.operators]) + +# def _domain_shape_tensor(self): +# return self.operators[0].domain_shape_tensor() + +# def _range_shape_tensor(self): +# return self.operators[0].range_shape_tensor() + +# def _batch_shape_tensor(self): +# return array_ops.broadcast_dynamic_shapes( +# *[operator.batch_shape_tensor() for operator in self.operators]) diff --git a/tensorflow_mri/python/linalg/linear_operator_addition_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_addition_nd_test.py new file mode 100644 index 00000000..d842ca8f --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_addition_nd_test.py @@ -0,0 +1,15 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_addition_nd`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_addition_test.py b/tensorflow_mri/python/linalg/linear_operator_addition_test.py new file mode 100644 index 00000000..63d875f6 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_addition_test.py @@ -0,0 +1,280 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_addition`.""" +# pylint: disable=missing-class-docstring,missing-function-docstring + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_test +from tensorflow_mri.python.linalg import linear_operator_addition +from tensorflow_mri.python.linalg import linear_operator_full_matrix +from tensorflow_mri.python.linalg import linear_operator_test_util +from tensorflow_mri.python.util import test_util + + +rng = np.random.RandomState(0) + + +class SquareLinearOperatorAdditionTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + def tearDown(self): + tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) + + def setUp(self): + self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() + tf.config.experimental.enable_tensor_float_32_execution(False) + + def operator_and_matrix(self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + shape = list(build_info.shape) + + # Either 1 or 2 matrices, depending. + num_operators = rng.randint(low=1, high=3) + if ensure_self_adjoint_and_pd: + # The random PD matrices are also symmetric. Here we are computing + # A @ A ... @ A. Since A is symmetric and PD, so are any powers of it. + matrices = [ + linear_operator_test_util.random_positive_definite_matrix( + shape, dtype, force_well_conditioned=True)] * num_operators + else: + matrices = [ + linear_operator_test_util.random_positive_definite_matrix( + shape, dtype, force_well_conditioned=True) + for _ in range(num_operators) + ] + + lin_op_matrices = matrices + + if use_placeholder: + lin_op_matrices = [ + tf.compat.v1.placeholder_with_default( + matrix, shape=None) for matrix in matrices] + + operator = linear_operator_addition.LinearOperatorAddition( + [linear_operator_full_matrix.LinearOperatorFullMatrix(l) + for l in lin_op_matrices], + is_positive_definite=True if ensure_self_adjoint_and_pd else None, + is_self_adjoint=True if ensure_self_adjoint_and_pd else None, + is_square=True) + + matmul_order_list = list(reversed(matrices)) + mat = matmul_order_list[0] + for other_mat in matmul_order_list[1:]: + mat = tf.math.add(other_mat, mat) + + return operator, mat + + @test_util.run_deprecated_v1 + def test_is_x_flags(self): + expected = { + 'is_non_singular': { + (True, True): None, + (True, False): None, + (True, None): None, + (False, False): None, + (False, None): None, + (None, None): None + }, + 'is_self_adjoint': { + (True, True): True, + (True, False): False, + (True, None): None, + (False, False): None, + (False, None): None, + (None, None): None + }, + 'is_positive_definite': { + (True, True): True, + (True, False): None, + (True, None): None, + (False, False): None, + (False, None): None, + (None, None): None + }, + 'is_square': { + (True, True): True, + # (True, False): None, + (True, None): True, + (False, False): False, + (False, None): False, + (None, None): None + } + } + for name, combinations in expected.items(): + for (flag1, flag2), value in combinations.items(): + with self.subTest(name=name, flag1=flag1, flag2=flag2): + matrix = tf.compat.v1.placeholder(tf.float32) + operator1 = linear_operator_full_matrix.LinearOperatorFullMatrix( + matrix, **{name: flag1}) + operator2 = linear_operator_full_matrix.LinearOperatorFullMatrix( + matrix, **{name: flag2}) + operator = linear_operator_addition.LinearOperatorAddition( + [operator1, operator2]) + + self.assertIs(getattr(operator, name), value) + + def test_name(self): + matrix = [[11., 0.], [1., 8.]] + operator_1 = linear_operator_full_matrix.LinearOperatorFullMatrix( + matrix, name="left") + operator_2 = linear_operator_full_matrix.LinearOperatorFullMatrix( + matrix, name="right") + + operator = linear_operator_addition.LinearOperatorAddition( + [operator_1, operator_2]) + + self.assertEqual("left_p_right", operator.name) + + def test_different_dtypes_raises(self): + operators = [ + linear_operator_full_matrix.LinearOperatorFullMatrix( + rng.rand(2, 3, 3)), + linear_operator_full_matrix.LinearOperatorFullMatrix( + rng.rand(2, 3, 3).astype(np.float32)) + ] + with self.assertRaisesRegex(TypeError, "same dtype"): + linear_operator_addition.LinearOperatorAddition(operators) + + def test_empty_operators_raises(self): + with self.assertRaisesRegex(ValueError, "non-empty"): + linear_operator_addition.LinearOperatorAddition([]) + + def test_registration(self): + matrix = [[11., 0.], [1., 8.]] + operator_1 = linear_operator_test.LinearOperatorMatmulSolve(matrix) + operator_2 = linear_operator_test.LinearOperatorMatmulSolve(matrix) + operator = operator_1 + operator_2 + self.assertIsInstance( + operator, linear_operator_addition.LinearOperatorAddition) + + +class NonSquareLinearOperatorAdditionTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + def tearDown(self): + tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) + + def setUp(self): + self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() + tf.config.experimental.enable_tensor_float_32_execution(False) + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + del ensure_self_adjoint_and_pd + shape = list(build_info.shape) + + # Ensure that the matrices are well-conditioned by generating + # random matrices whose singular values are close to 1. + # The reason to do this is because cond(AB) <= cond(A) * cond(B). + # By ensuring that each factor has condition number close to 1, we ensure + # that the condition number of the product isn't too far away from 1. + def generate_well_conditioned(shape, dtype): + m, n = shape[-2], shape[-1] + min_dim = min(m, n) + # Generate singular values that are close to 1. + d = linear_operator_test_util.random_normal( + shape[:-2] + [min_dim], + mean=1., + stddev=0.1, + dtype=dtype) + zeros = tf.compat.v1.zeros(shape=shape[:-2] + [m, n], dtype=dtype) + d = tf.linalg.set_diag(zeros, d) + u, _ = tf.linalg.qr(linear_operator_test_util.random_normal( + shape[:-2] + [m, m], dtype=dtype)) + + v, _ = tf.linalg.qr(linear_operator_test_util.random_normal( + shape[:-2] + [n, n], dtype=dtype)) + return tf.matmul(u, tf.matmul(d, v)) + + matrices = [ + generate_well_conditioned(shape, dtype=dtype), + generate_well_conditioned(shape, dtype=dtype), + ] + + lin_op_matrices = matrices + + if use_placeholder: + lin_op_matrices = [ + tf.compat.v1.placeholder_with_default( + matrix, shape=None) for matrix in matrices] + + operator = linear_operator_addition.LinearOperatorAddition( + [linear_operator_full_matrix.LinearOperatorFullMatrix(l) + for l in lin_op_matrices]) + + matmul_order_list = list(reversed(matrices)) + mat = matmul_order_list[0] + for other_mat in matmul_order_list[1:]: + mat = tf.math.add(other_mat, mat) + + return operator, mat + + @test_util.run_deprecated_v1 + def test_different_shapes_raises_static(self): + operators = [ + linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 4, 5)), + linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 3, 4)) + ] + with self.assertRaisesRegex(ValueError, "same shape"): + linear_operator_addition.LinearOperatorAddition(operators) + + @test_util.run_deprecated_v1 + def test_static_shapes(self): + operators = [ + linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 3, 4)), + linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 3, 4)) + ] + operator = linear_operator_addition.LinearOperatorAddition(operators) + self.assertAllEqual((2, 3, 4), operator.shape) + + @test_util.run_deprecated_v1 + def test_shape_tensors_when_statically_available(self): + operators = [ + linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 3, 4)), + linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 3, 4)) + ] + operator = linear_operator_addition.LinearOperatorAddition(operators) + with self.cached_session(): + self.assertAllEqual((2, 3, 4), operator.shape_tensor()) + + @test_util.run_deprecated_v1 + def test_shape_tensors_when_only_dynamically_available(self): + mat_1 = rng.rand(1, 2, 3, 4) + mat_2 = rng.rand(1, 2, 3, 4) + mat_ph_1 = tf.compat.v1.placeholder(tf.float64) + mat_ph_2 = tf.compat.v1.placeholder(tf.float64) + feed_dict = {mat_ph_1: mat_1, mat_ph_2: mat_2} + + operators = [ + linear_operator_full_matrix.LinearOperatorFullMatrix(mat_ph_1), + linear_operator_full_matrix.LinearOperatorFullMatrix(mat_ph_2) + ] + operator = linear_operator_addition.LinearOperatorAddition(operators) + with self.cached_session(): + self.assertAllEqual( + (1, 2, 3, 4), operator.shape_tensor().eval(feed_dict=feed_dict)) + + +linear_operator_test_util.add_tests(SquareLinearOperatorAdditionTest) +linear_operator_test_util.add_tests(NonSquareLinearOperatorAdditionTest) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_adjoint.py b/tensorflow_mri/python/linalg/linear_operator_adjoint.py new file mode 100644 index 00000000..13ea7a63 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_adjoint.py @@ -0,0 +1,31 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Adjoint of a linear operator.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import doc_util + + +LinearOperatorAdjoint = api_util.export( + "linalg.LinearOperatorAdjoint")( + doc_util.no_linkcode( + linear_operator.make_linear_operator( + tf.linalg.LinearOperatorAdjoint))) + + +tf.linalg.LinearOperatorAdjoint = LinearOperatorAdjoint diff --git a/tensorflow_mri/python/linalg/linear_operator_adjoint_test.py b/tensorflow_mri/python/linalg/linear_operator_adjoint_test.py new file mode 100644 index 00000000..463b4ca1 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_adjoint_test.py @@ -0,0 +1,15 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for `LinearOperatorAdjoint`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_algebra.py b/tensorflow_mri/python/linalg/linear_operator_algebra.py new file mode 100644 index 00000000..2283470d --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_algebra.py @@ -0,0 +1,175 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Linear operator algebra.""" + +import tensorflow as tf + +from tensorflow.python.ops.linalg import linear_operator_algebra + + +adjoint = linear_operator_algebra.adjoint +cholesky = linear_operator_algebra.cholesky +inverse = linear_operator_algebra.inverse +matmul = linear_operator_algebra.matmul +solve = linear_operator_algebra.solve + + +RegisterAdjoint = linear_operator_algebra.RegisterAdjoint +RegisterCholesky = linear_operator_algebra.RegisterCholesky +RegisterInverse = linear_operator_algebra.RegisterInverse +RegisterMatmul = linear_operator_algebra.RegisterMatmul +RegisterSolve = linear_operator_algebra.RegisterSolve + + +_registered_function = linear_operator_algebra._registered_function # pylint: disable=protected-access + + +_ADD = {} +_PSEUDO_INVERSE = {} +_LEASTSQ = {} + + +def _registered_add(type_a, type_b): + """Get the Add function registered for classes a and b.""" + return _registered_function([type_a, type_b], _ADD) + + +def _registered_pseudo_inverse(type_a): + """Get the PseudoInverse function registered for class a.""" + return _registered_function([type_a], _PSEUDO_INVERSE) + + +def _registered_lstsq(type_a): + """Get the SolveLS function registered for class a.""" + return _registered_function([type_a], _LEASTSQ) + + +def add(lin_op_a, lin_op_b, name=None): + """Compute lin_op_a.add(lin_op_b). + + Args: + lin_op_a: The LinearOperator on the left. + lin_op_b: The LinearOperator on the right. + name: Name to use for this operation. + + Returns: + A LinearOperator that represents the addition between `lin_op_a` and + `lin_op_b`. + + Raises: + NotImplementedError: If no add method is defined between types of + `lin_op_a` and `lin_op_b`. + """ + add_fn = _registered_add(type(lin_op_a), type(lin_op_b)) + if add_fn is None: + raise NotImplementedError("No add registered for {}.add({})".format( + type(lin_op_a), type(lin_op_b))) + + with tf.name_scope(name or "Add"): + return add_fn(lin_op_a, lin_op_b) + + +def pseudo_inverse(lin_op_a, name=None): + """Get the Pseudo-Inverse associated to lin_op_a. + + Args: + lin_op_a: The LinearOperator to decompose. + name: Name to use for this operation. + + Returns: + A LinearOperator that represents the inverse of `lin_op_a`. + + Raises: + NotImplementedError: If no Pseudo-Inverse method is defined for the + LinearOperator type of `lin_op_a`. + """ + pseudo_inverse_fn = _registered_pseudo_inverse(type(lin_op_a)) + if pseudo_inverse_fn is None: + raise ValueError("No pseudo-inverse registered for {}".format( + type(lin_op_a))) + + with tf.name_scope(name or "PseudoInverse"): + return pseudo_inverse_fn(lin_op_a) + + +def lstsq(lin_op_a, lin_op_b, name=None): + """Compute lin_op_a.lstsq(lin_op_b). + + Args: + lin_op_a: The LinearOperator on the left. + lin_op_b: The LinearOperator on the right. + name: Name to use for this operation. + + Returns: + A LinearOperator that represents the lstsq between `lin_op_a` and + `lin_op_b`. + + Raises: + NotImplementedError: If no lstsq method is defined between types of + `lin_op_a` and `lin_op_b`. + """ + solve_fn = _registered_lstsq(type(lin_op_a), type(lin_op_b)) + if solve_fn is None: + raise ValueError("No solve registered for {}.solve({})".format( + type(lin_op_a), type(lin_op_b))) + + with tf.name_scope(name or "SolveLS"): + return solve_fn(lin_op_a, lin_op_b) + + +class RegisterAdd: + """Decorator to register an Add implementation function. + + Usage: + + @linear_operator_algebra.RegisterAdd( + lin_op.LinearOperatorFullMatrix, + lin_op.LinearOperatorFullMatrix) + def _add_full_matrix(a, b): + # Return the new full matrix. + """ + + def __init__(self, lin_op_cls_a, lin_op_cls_b): + """Initialize the LinearOperator registrar. + + Args: + lin_op_cls_a: the class of the LinearOperator to multiply. + lin_op_cls_b: the class of the second LinearOperator to multiply. + """ + self._key = (lin_op_cls_a, lin_op_cls_b) + + def __call__(self, add_fn): + """Perform the Add registration. + + Args: + add_fn: The function to use for the Add. + + Returns: + add_fn + + Raises: + TypeError: if add_fn is not a callable. + ValueError: if an Add function has already been registered for + the given argument classes. + """ + if not callable(add_fn): + raise TypeError( + "add_fn must be callable, received: {}".format(add_fn)) + if self._key in _ADD: + raise ValueError("Add({}, {}) has already been registered.".format( + self._key[0].__name__, + self._key[1].__name__)) + _ADD[self._key] = add_fn + return add_fn diff --git a/tensorflow_mri/python/linalg/linear_operator_coils.py b/tensorflow_mri/python/linalg/linear_operator_coils.py new file mode 100644 index 00000000..ee6b9f36 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_coils.py @@ -0,0 +1,196 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Coil array linear operator.""" + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.util import api_util + + +@api_util.export("linalg.LinearOperatorCoils") +@linear_operator_nd.make_linear_operator_nd +class LinearOperatorCoils(linear_operator_nd.LinearOperatorND): + """Linear operator acting like a [batch] coils array. + + Args: + maps: A complex `tf.Tensor` of shape `[..., num_coils, *spatial_shape]`. + batch_dims: An `int`, the number of batch dimensions in `maps`. + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `None`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `None`. + is_positive_definite: A boolean, or `None`. Whether this operator is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that an operator [does + not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) + Defaults to `None`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `False`. + name: An optional `str`. The name of this operator. + """ + def __init__(self, + maps, + batch_dims=0, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None, + name="LinearOperatorCoils"): + parameters = dict( + maps=maps, + batch_dims=batch_dims, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name + ) + + with tf.name_scope(name): + # Check batch_dims. + self._batch_dims = np.asarray(tf.get_static_value(batch_dims)) + if (not self._batch_dims.ndim == 0 or + not np.issubdtype(self._batch_dims.dtype, np.integer)): + raise TypeError( + f"batch_dims must be an int, but got: {batch_dims}") + self._batch_dims = self._batch_dims.item() + if self._batch_dims < 0: + raise ValueError( + f"batch_dims must be non-negative, but got: {batch_dims}") + + # Check maps. + self._maps = tf.convert_to_tensor(maps, name="maps") + if self._maps.dtype not in (tf.complex64, tf.complex128): + raise TypeError( + f"maps must be complex, but got dtype: {str(self._maps.dtype)}") + if self._maps.shape.rank is None: + raise ValueError("maps must have known static rank") + self._ndim_static = self._maps.shape.rank - self._batch_dims - 1 + if self._ndim_static < 1: + raise ValueError( + f"maps must be at least 2-D (excluding batch dimensions), " + f"but got shape: {self._maps.shape}") + self._coil_axis = -(self._ndim_static + 1) + + super().__init__( + dtype=maps.dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters, + name=name) + + def _matvec_nd(self, x, adjoint=False): + if adjoint: + rhs = tf.math.reduce_sum(x * tf.math.conj(self._maps), + axis=self._coil_axis) + else: + rhs = tf.expand_dims(x, self._coil_axis) * self._maps + return rhs + + def _solvevec_nd(self, rhs, adjoint=False): + raise ValueError( + f"{self.name} is not invertible. If you intend to solve the " + f"associated least-squares problem, use `lstsq`, `lstsqvec` or " + f"`lstsqvec_nd`.") + + def _lstsqvec_nd(self, rhs, adjoint=False): + if adjoint: + x = self._matvec_nd(self._normalize(rhs), adjoint=(not adjoint)) + else: + x = self._normalize(self._matvec_nd(rhs, adjoint=(not adjoint))) + return x + + def _normalize(self, x): + # Using safe division so that we can work with coil arrays whose + # sensitivities are all zero for certain pixels (e.g. ESPIRiT maps). + return tf.math.divide_no_nan( + x, tf.math.reduce_sum(tf.math.conj(self._maps) * self._maps, + axis=self._coil_axis)) + + def _ndim(self): + return self._ndim_static + + def _domain_shape(self): + return self._maps.shape[self._coil_axis + 1:] + + def _range_shape(self): + return self._maps.shape[self._coil_axis:] + + def _batch_shape(self): + return self._maps.shape[:self._coil_axis] + + def _domain_shape_tensor(self): + return tf.shape(self._maps)[self._coil_axis + 1:] + + def _range_shape_tensor(self): + return tf.shape(self._maps)[self._coil_axis:] + + def _batch_shape_tensor(self): + return tf.shape(self._maps)[:self._coil_axis] + + @property + def maps(self): + return self._maps + + @property + def num_coils(self): + return self._maps.shape[self._coil_axis] + + def num_coils_tensor(self): + return tf.shape(self._maps)[self._coil_axis] + + @property + def _composite_tensor_fields(self): + return ('maps', 'batch_dims') + + @property + def _composite_tensor_prefer_static_fields(self): + return ('batch_dims',) + + @property + def _experimental_parameter_ndims_to_matrix_ndims(self): + return {'maps': self.ndim + 1} + + +def coils_matrix(maps, batch_dims=0): + """Constructs a coil array matrix. + + Args: + maps: A complex `tf.Tensor` of shape `[..., num_coils, *spatial_shape]`. + batch_dims: An `int`, the number of batch dimensions in `maps`. + + Returns: + A `tf.Tensor` representing a dense coil array matrix equivalent to + `LinearOperatorCoils`. + """ + maps = tf.convert_to_tensor(maps, name="maps") + + # Vectorize N-D maps. + maps = tf.reshape( + maps, tf.concat([tf.shape(maps)[:(batch_dims + 1)], [-1]], axis=0)) + + # Construct a [batch] matrix for each coil. + matrix = tf.linalg.diag(maps) + + # Stack the coil matrices. + matrix = tf.reshape(matrix, tf.concat([tf.shape(maps)[:batch_dims], + [-1, tf.shape(maps)[-1]]], axis=0)) + + return matrix diff --git a/tensorflow_mri/python/linalg/linear_operator_coils_test.py b/tensorflow_mri/python/linalg/linear_operator_coils_test.py new file mode 100644 index 00000000..65a8ca18 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_coils_test.py @@ -0,0 +1,167 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_coils`.""" +# pylint: disable=missing-class-docstring,missing-function-docstring + +import functools + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_coils +from tensorflow_mri.python.linalg import linear_operator_test_util +from tensorflow_mri.python.util import test_util + + +rng = np.random.RandomState(2016) + + +class OperatorShapesInfoCoils(): + def __init__(self, image_shape, num_coils, batch_shape): + self.image_shape = image_shape + self.num_coils = num_coils + self.batch_shape = batch_shape + + @property + def shape(self): + n = functools.reduce(lambda a, b: a * b, self.image_shape) + m = self.num_coils * n + return self.batch_shape + (m, n) + + @property + def dimension(self): + return len(self.image_shape) + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorCoilsTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + @staticmethod + def operator_shapes_infos(): + shapes_info = OperatorShapesInfoCoils + return [ + shapes_info((2, 2), 3, ()), + shapes_info((2, 4), 4, (3,)), + shapes_info((4, 2), 3, (1, 2)), + shapes_info((2, 2), 4, ()), + shapes_info((2, 2, 2), 4, ()), + shapes_info((4, 2, 2), 2, (2,)) + # TODO(jmontalt): odd shapes fail tests, investigate + # shapes_info((2, 3), 5, (2,)), + # shapes_info((3, 2), 7, ()) + ] + + @staticmethod + def dtypes_to_test(): + return [tf.complex64, tf.complex128] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + del ensure_self_adjoint_and_pd + del use_placeholder + + batch_shape = build_info.batch_shape + num_coils = build_info.num_coils + image_shape = build_info.image_shape + + maps = tf.dtypes.complex( + tf.random.normal( + shape=batch_shape + (num_coils,) + image_shape, + dtype=dtype.real_dtype), + tf.random.normal( + shape=batch_shape + (num_coils,) + image_shape, + dtype=dtype.real_dtype) + ) + + operator = linear_operator_coils.LinearOperatorCoils( + maps=maps, batch_dims=len(batch_shape)) + + matrix = linear_operator_coils.coils_matrix( + maps=maps, batch_dims=len(batch_shape)) + + return operator, matrix + + def test_1d_maps_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be at least 2-D"): + linear_operator_coils.LinearOperatorCoils( + maps=np.ones((4,)).astype(np.complex64)) + + with self.assertRaisesRegex(ValueError, "must be at least 2-D"): + linear_operator_coils.LinearOperatorCoils( + maps=np.ones((3, 4, 4)).astype(np.complex64), + batch_dims=2) + + linear_operator_coils.LinearOperatorCoils( + maps=np.ones((3, 4, 4)).astype(np.complex64), + batch_dims=1) # should not raise + + def test_non_complex_maps_raises_static(self): + with self.assertRaisesRegex(TypeError, "must be complex"): + linear_operator_coils.LinearOperatorCoils( + maps=np.ones((3, 4, 4)).astype(np.float32)) + + def test_unknown_rank_maps_raises_static(self): + if tf.executing_eagerly(): + return + with self.cached_session(): + maps = tf.compat.v1.placeholder_with_default( + np.ones((3, 4, 4)).astype(np.complex64), shape=None) + with self.assertRaisesRegex(ValueError, "must have known static rank"): + operator = linear_operator_coils.LinearOperatorCoils(maps=maps) + self.evaluate(operator.to_dense()) + + def test_non_integer_batch_dims_raises_static(self): + with self.assertRaisesRegex(TypeError, "must be an int"): + linear_operator_coils.LinearOperatorCoils( + maps=np.ones((3, 4, 4)).astype(np.complex64), batch_dims=1.) + + def test_negative_batch_dims_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be non-negative"): + linear_operator_coils.LinearOperatorCoils( + maps=np.ones((3, 4, 4)).astype(np.complex64), batch_dims=-1) + + def test_is_x_flags(self): + operator = linear_operator_coils.LinearOperatorCoils( + maps=np.ones((3, 4, 4)).astype(np.complex64)) + self.assertFalse(operator.is_self_adjoint) + + def test_solve_raises(self): + operator = linear_operator_coils.LinearOperatorCoils( + maps=np.ones((1, 4, 4)).astype(np.complex64), is_square=True) + with self.assertRaisesRegex(ValueError, "not invertible.*lstsq"): + operator.solve(tf.ones([16, 1], dtype=tf.complex64)) + + def test_inverse_raises(self): + operator = linear_operator_coils.LinearOperatorCoils( + maps=np.ones((1, 4, 4)).astype(np.complex64), is_square=True) + with self.assertRaisesRegex(ValueError, "not invertible.*pseudo_inverse"): + operator.inverse() + + def test_convert_variables_to_tensors(self): + maps = tf.Variable(np.ones((3, 4, 4)).astype(np.complex64)) + operator = linear_operator_coils.LinearOperatorCoils(maps=maps) + with self.cached_session() as sess: + sess.run([maps.initializer]) + self.check_convert_variables_to_tensors(operator) + + +linear_operator_test_util.add_tests(LinearOperatorCoilsTest) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_composition.py b/tensorflow_mri/python/linalg/linear_operator_composition.py new file mode 100644 index 00000000..8f04a2a9 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_composition.py @@ -0,0 +1,158 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Composition of linear operators.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import doc_util + + +LinearOperatorComposition = api_util.export( + "linalg.LinearOperatorComposition")( + doc_util.no_linkcode( + linear_operator.make_linear_operator( + tf.linalg.LinearOperatorComposition))) + + +tf.linalg.LinearOperatorComposition = LinearOperatorComposition + + +def combined_non_singular_hint(*operators): + """Returns a hint for the non-singularity of a composition of operators. + + Args: + *operators: A list of `LinearOperator` objects. + + Returns: + A boolean, or `None`. Whether the composition of the operators is expected + to be non-singular. + """ + # If all operators are non-singular, so is the composition. + if all(o.is_non_singular is True for o in operators): + return True + + # If any operator is singular, then the composition is singular too. + if any(o.is_non_singular is False for o in operators): + return False + + # In all other cases, we don't know. + return None + + +def combined_self_adjoint_hint(*operators, commuting=False): + """Returns a hint for the self-adjointness of a composition of operators. + + Args: + *operators: A list of `LinearOperator` objects. + + Returns: + A boolean, or `None`. Whether the composition of the operators is expected + to be self-adjoint. + """ + if commuting: # The operators commute. + # If all operators are self-adjoint, then the composition is self-adjoint. + if all(o.is_self_adjoint is True for o in operators): + return True + + # If only one operator isn't self-adjoint, then the composition is not + # self-adjoint. + self_adjoint_operators = [ + o for o in operators if o.is_self_adjoint is True] + non_self_adjoint_operators = [ + o for o in operators if o.is_self_adjoint is False] + if (len(self_adjoint_operators) == len(operators) - 1 and + len(non_self_adjoint_operators) == 1): + return False + + # In all other cases, we don't know. + return None + + # If commutative property is not guaranteed, we don't know anything about + # the self-adjointness of the output. + return None + + +def combined_positive_definite_hint(*operators, commuting=False): + """Returns a hint for the positive-definiteness of a composition of operators. + + Args: + *operators: A list of `LinearOperator` objects. + + Returns: + A boolean, or `None`. Whether the composition of the operators is expected + to be positive-definite. + """ + # If all operators are positive-definite, its composition has positive + # eigenvalues. + eigvals_are_positive = all(o.is_positive_definite is True for o in operators) + + # Check if the output is expected to be self-adjoint. + is_self_adjoint = combined_self_adjoint_hint(*operators, commuting=commuting) + + # If their composition is self-adjoint and the + # eigenvalues are positive, then the composition is positive-definite. + if eigvals_are_positive is True and is_self_adjoint is True: + return True + + # Otherwise, we don't know. + return None + + +def combined_square_hint(*operators): + """Returns a hint for the squareness of a composition of operators. + + Args: + *operators: A list of `LinearOperator` objects. + + Returns: + A boolean, or `None`. Whether the composition of the operators is expected + to be square. + """ + # If all operators are square, so is the composition. + if all(o.is_square is True for o in operators): + return True + + # If all operators are square except one which is not, then the sum is + # not square. + square_operators = [ + o for o in operators if o.is_square is True] + non_square_operators = [ + o for o in operators if o.is_square is False] + if (len(square_operators) == len(operators) - 1 and + len(non_square_operators) == 1): + return False + + # In all other cases, we don't know. + return None + + +def check_hint(expected, received, name): + """Checks that a hint is consistent with its expected value. + + Args: + expected: A boolean, or `None`. The expected value of the hint. + received: A boolean, or `None`. The received value of the hint. + name: A string. The name of the hint. + + Raises: + ValueError: If `expected` and `value` are not consistent. + """ + if expected is not None and received is not None and expected != received: + raise ValueError( + f"Inconsistent {name} hint: expected {expected} based on input " + f"operators, but got {received}") + return received if received is not None else expected diff --git a/tensorflow_mri/python/linalg/linear_operator_composition_nd.py b/tensorflow_mri/python/linalg/linear_operator_composition_nd.py new file mode 100644 index 00000000..b7295953 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_composition_nd.py @@ -0,0 +1,276 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Composition of N-D linear operators.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_composition +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.util import api_util + + +@api_util.export("linalg.LinearOperatorCompositionND") +@linear_operator_nd.make_linear_operator_nd +class LinearOperatorCompositionND(linear_operator_nd.LinearOperatorND): + r"""Composes one or more linear operators. + + This operator composes one or more linear operators representing matrices + $A_1, A_2, \dots, A_J$, building a new linear operator $A$ which acts as the + matrix product $A := A_1 A_2 \dots A_J$. + + If `opj` has shape `batch_shape_j + [M_j, N_j]`, then we must have + `N_j = M_{j+1}`, in which case the composed operator has shape equal to + `broadcast_batch_shape + [M_1, N_J]`, where `broadcast_batch_shape` is the + mutual broadcast of `batch_shape_j`, `j = 1,...,J`, assuming the intermediate + batch shapes broadcast. Even if the composed shape is well defined, the + composed operator's methods may fail due to lack of broadcasting ability in + the defining operators' methods. + + ```python + # Create a 2 x 2 linear operator composed of two 2 x 2 operators. + operator_1 = LinearOperatorFullMatrix([[1., 2.], [3., 4.]]) + operator_2 = LinearOperatorFullMatrix([[1., 0.], [0., 1.]]) + operator = LinearOperatorComposition([operator_1, operator_2]) + + operator.to_dense() + ==> [[1., 2.] + [3., 4.]] + + operator.shape + ==> [2, 2] + + operator.log_abs_determinant() + ==> scalar Tensor + + x = ... Shape [2, 4] Tensor + operator.matmul(x) + ==> Shape [2, 4] Tensor + + # Create a [2, 3] batch of 4 x 5 linear operators. + matrix_45 = tf.random.normal(shape=[2, 3, 4, 5]) + operator_45 = LinearOperatorFullMatrix(matrix) + + # Create a [2, 3] batch of 5 x 6 linear operators. + matrix_56 = tf.random.normal(shape=[2, 3, 5, 6]) + operator_56 = LinearOperatorFullMatrix(matrix_56) + + # Compose to create a [2, 3] batch of 4 x 6 operators. + operator_46 = LinearOperatorComposition([operator_45, operator_56]) + + # Create a shape [2, 3, 6, 2] vector. + x = tf.random.normal(shape=[2, 3, 6, 2]) + operator.matmul(x) + ==> Shape [2, 3, 4, 2] Tensor + ``` + + #### Performance + + The performance of `LinearOperatorComposition` on any operation is equal to + the sum of the individual operators' operations. + + + #### Matrix property hints + + This `LinearOperator` is initialized with boolean flags of the form `is_X`, + for `X = non_singular, self_adjoint, positive_definite, square`. + These have the following meaning: + + * If `is_X == True`, callers should expect the operator to have the + property `X`. This is a promise that should be fulfilled, but is *not* a + runtime assert. For example, finite floating point precision may result + in these promises being violated. + * If `is_X == False`, callers should expect the operator to not have `X`. + * If `is_X == None` (the default), callers should have no expectation either + way. + + Args: + operators: A `list` of `tfmri.linalg.LinearOperatorND` objects, each with + the same dtype and conformable shapes. + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `None`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `None`. + is_positive_definite: A boolean, or `None`. Whether this operator is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that an operator [does + not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) + Defaults to `None`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `None`. + name: An optional `str`. The name of this operator. + """ + def __init__(self, + operators, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None, + name=None): + parameters = dict( + operators=operators, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name) + + # Validate operators. + tf.debugging.assert_proper_iterable(operators) + operators = list(operators) + if not operators: + raise ValueError( + f"Expected a non-empty list of operators. Found: {operators}") + # for operator in operators: + # if not isinstance(operator, linear_operator_nd.LinearOperatorND): + # raise TypeError( + # f"Expected a list of LinearOperatorND objects. Found: {operators}") + self._operators = operators + + # Validate dtype. + dtype = operators[0].dtype + for operator in operators: + if operator.dtype != dtype: + name_type = (str((o.name, o.dtype)) for o in operators) + raise TypeError( + f"Expected all operators to have the same dtype. " + f"Found: {', '.join(name_type)}") + + # Validate shapes. + domain_shape = operators[0].domain_shape + for operator in operators[1:]: + if not domain_shape.is_compatible_with(operator.range_shape): + shapes = ', '.join( + [f'({str(o.range_shape)}, {str(o.domain_shape)})' + for o in operators]) + raise ValueError( + f"Expected operators to have conformable shapes for matrix " + f"multiplication. Found: {shapes}") + + # Get broadcast batch shape (static). + batch_shape_static = self.operators[0].batch_shape + for operator in self.operators[1:]: + batch_shape_static = tf.broadcast_static_shape( + batch_shape_static, operator.batch_shape) + self._batch_shape_static = batch_shape_static + + # Get broadcast batch shape (dynamic). + batch_shape_dynamic = self.operators[0].batch_shape_tensor() + for operator in self.operators[1:]: + batch_shape_dynamic = tf.broadcast_dynamic_shape( + batch_shape_dynamic, operator.batch_shape_tensor()) + self._batch_shape_dynamic = batch_shape_dynamic + + # Infer operator hints. + is_non_singular = linear_operator_composition.check_hint( + linear_operator_composition.combined_non_singular_hint(*operators), + is_non_singular, + "non-singular") + is_self_adjoint = linear_operator_composition.check_hint( + linear_operator_composition.combined_self_adjoint_hint(*operators), + is_self_adjoint, + "self-adjoint") + is_positive_definite = linear_operator_composition.check_hint( + linear_operator_composition.combined_positive_definite_hint(*operators), + is_positive_definite, + "positive-definite") + is_square = linear_operator_composition.check_hint( + linear_operator_composition.combined_square_hint(*operators), + is_square, + "square") + + # Initialization. + if name is None: + name = "_o_".join(operator.name for operator in operators) + + with tf.name_scope(name): + super().__init__( + dtype=dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters, + name=name) + + @property + def operators(self): + return self._operators + + def _domain_shape(self): + return self.operators[-1].domain_shape + + def _range_shape(self): + return self.operators[0].range_shape + + def _batch_shape(self): + return self._batch_shape_static + + def _domain_shape_tensor(self): + return self.operators[-1].domain_shape_tensor() + + def _range_shape_tensor(self): + return self.operators[0].range_shape_tensor() + + def _batch_shape_tensor(self): + return self._batch_shape_dynamic + + def _matvec_nd(self, x, adjoint=False): + # If self.operators = [A, B], and not adjoint, then + # matmul_order_list = [B, A]. + # As a result, we return A.matmul(B.matmul(x)) + if adjoint: + matmul_order_list = self.operators + else: + matmul_order_list = list(reversed(self.operators)) + + result = matmul_order_list[0].matvec_nd(x, adjoint=adjoint) + for operator in matmul_order_list[1:]: + result = operator.matvec_nd(result, adjoint=adjoint) + return result + + def _solvevec_nd(self, rhs, adjoint=False): + # If self.operators = [A, B], and not adjoint, then + # solve_order_list = [A, B]. + # As a result, we return B.solve(A.solve(x)) + if adjoint: + solve_order_list = list(reversed(self.operators)) + else: + solve_order_list = self.operators + + solution = solve_order_list[0].solvevec_nd(rhs, adjoint=adjoint) + for operator in solve_order_list[1:]: + solution = operator.solvevec_nd(solution, adjoint=adjoint) + return solution + + def _determinant(self): + result = self.operators[0].determinant() + for operator in self.operators[1:]: + result *= operator.determinant() + return result + + def _log_abs_determinant(self): + result = self.operators[0].log_abs_determinant() + for operator in self.operators[1:]: + result += operator.log_abs_determinant() + return result + + @property + def _composite_tensor_fields(self): + return ("operators",) + + @property + def _experimental_parameter_ndims_to_matrix_ndims(self): + return {"operators": [0] * len(self.operators)} diff --git a/tensorflow_mri/python/linalg/linear_operator_composition_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_composition_nd_test.py new file mode 100644 index 00000000..8c5328ac --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_composition_nd_test.py @@ -0,0 +1,284 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_composition_nd +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.linalg import linear_operator_test_util +from tensorflow_mri.python.util import test_util + + +CompositionND = linear_operator_composition_nd.LinearOperatorCompositionND +def FullMatrixND(matrix, *args, **kwargs): + linop = tf.linalg.LinearOperatorFullMatrix(matrix, *args, **kwargs) + return linear_operator_nd.LinearOperatorMakeND( + linop, + range_shape=[linop.range_dimension_tensor()], + domain_shape=[linop.domain_dimension_tensor()], + name=kwargs.get('name', None)) + + +rng = np.random.RandomState(0) + + +class SquareLinearOperatorCompositionTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + @staticmethod + def operator_shapes_infos(): + shapes_info = linear_operator_test_util.OperatorShapesInfo + # non-batch operators (n, n) and batch operators. + return [ + shapes_info((1, 1)), + shapes_info((1, 3, 3)), + shapes_info((3, 4, 4)), + shapes_info((2, 1, 4, 4))] + + def tearDown(self): + tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) + + def setUp(self): + self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() + tf.config.experimental.enable_tensor_float_32_execution(False) + # Increase from 1e-6 to 1e-4 and 2e-4. + self._atol[tf.float32] = 2e-4 + self._atol[tf.complex64] = 1e-4 + self._rtol[tf.float32] = 2e-4 + self._rtol[tf.complex64] = 1e-4 + + @staticmethod + def skip_these_tests(): + # Cholesky not implemented. + return ["cholesky", "lstsq", "lstsq_with_broadcast"] + + def operator_and_matrix(self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + shape = list(build_info.shape) + + # Either 1 or 2 matrices, depending. + num_operators = rng.randint(low=1, high=3) + if ensure_self_adjoint_and_pd: + # The random PD matrices are also symmetric. Here we are computing + # A @ A ... @ A. Since A is symmetric and PD, so are any powers of it. + matrices = [ + linear_operator_test_util.random_positive_definite_matrix( + shape, dtype, force_well_conditioned=True)] * num_operators + else: + matrices = [ + linear_operator_test_util.random_positive_definite_matrix( + shape, dtype, force_well_conditioned=True) + for _ in range(num_operators) + ] + + lin_op_matrices = matrices + + if use_placeholder: + lin_op_matrices = [ + tf.compat.v1.placeholder_with_default( + matrix, shape=None) for matrix in matrices] + + operator = CompositionND( + [FullMatrixND(l) for l in lin_op_matrices], + is_positive_definite=True if ensure_self_adjoint_and_pd else None, + is_self_adjoint=True if ensure_self_adjoint_and_pd else None, + is_square=True) + + matmul_order_list = list(reversed(matrices)) + mat = matmul_order_list[0] + for other_mat in matmul_order_list[1:]: + mat = tf.matmul(other_mat, mat) + + return operator, mat + + def test_is_x_flags(self): + # Matrix with two positive eigenvalues, 1, and 1. + # The matrix values do not effect auto-setting of the flags. + matrix = [[1., 0.], [1., 1.]] + operator = CompositionND( + [FullMatrixND(matrix)], + is_positive_definite=True, + is_non_singular=True, + is_self_adjoint=False) + self.assertTrue(operator.is_positive_definite) + self.assertTrue(operator.is_non_singular) + self.assertFalse(operator.is_self_adjoint) + + def test_is_non_singular_auto_set(self): + # Matrix with two positive eigenvalues, 11 and 8. + # The matrix values do not effect auto-setting of the flags. + matrix = [[11., 0.], [1., 8.]] + operator_1 = FullMatrixND(matrix, is_non_singular=True) + operator_2 = FullMatrixND(matrix, is_non_singular=True) + + operator = CompositionND( + [operator_1, operator_2], + is_positive_definite=False, # No reason it HAS to be False... + is_non_singular=None) + self.assertFalse(operator.is_positive_definite) + self.assertTrue(operator.is_non_singular) + + with self.assertRaisesRegex(ValueError, "Inconsistent non-singular hint"): + CompositionND( + [operator_1, operator_2], is_non_singular=False) + + def test_name(self): + matrix = [[11., 0.], [1., 8.]] + operator_1 = FullMatrixND(matrix, name="left") + operator_2 = FullMatrixND(matrix, name="right") + + operator = CompositionND([operator_1, operator_2]) + + self.assertEqual("left_o_right", operator.name) + + def test_different_dtypes_raises(self): + operators = [ + FullMatrixND(rng.rand(2, 3, 3)), + FullMatrixND(rng.rand(2, 3, 3).astype(np.float32)) + ] + with self.assertRaisesRegex(TypeError, "same dtype"): + CompositionND(operators) + + def test_empty_operators_raises(self): + with self.assertRaisesRegex(ValueError, "non-empty"): + CompositionND([]) + + +class NonSquareLinearOperatorCompositionTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + def tearDown(self): + tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) + + def setUp(self): + self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() + tf.config.experimental.enable_tensor_float_32_execution(False) + # Increase from 1e-6 to 1e-4 + self._atol[tf.float32] = 1e-4 + self._atol[tf.complex64] = 1e-4 + self._rtol[tf.float32] = 1e-4 + self._rtol[tf.complex64] = 1e-4 + + @staticmethod + def skip_these_tests(): + # Testing the condition number fails when using XLA with cuBLASLt + # A slight numerical difference between different matmul algorithms + # leads to large precision issues + return linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest.skip_these_tests( + ) + ["cond", "lstsq", "lstsq_with_broadcast"] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + del ensure_self_adjoint_and_pd + shape = list(build_info.shape) + + # Create 2 matrices/operators, A1, A2, which becomes A = A1 A2. + # Use inner dimension of 2. + k = 2 + batch_shape = shape[:-2] + shape_1 = batch_shape + [shape[-2], k] + shape_2 = batch_shape + [k, shape[-1]] + + # Ensure that the matrices are well-conditioned by generating + # random matrices whose singular values are close to 1. + # The reason to do this is because cond(AB) <= cond(A) * cond(B). + # By ensuring that each factor has condition number close to 1, we ensure + # that the condition number of the product isn't too far away from 1. + def generate_well_conditioned(shape, dtype): + m, n = shape[-2], shape[-1] + min_dim = min(m, n) + # Generate singular values that are close to 1. + d = linear_operator_test_util.random_normal( + shape[:-2] + [min_dim], + mean=1., + stddev=0.1, + dtype=dtype) + zeros = tf.zeros(shape=shape[:-2] + [m, n], dtype=dtype) + d = tf.linalg.set_diag(zeros, d) + u, _ = tf.linalg.qr(linear_operator_test_util.random_normal( + shape[:-2] + [m, m], dtype=dtype)) + + v, _ = tf.linalg.qr(linear_operator_test_util.random_normal( + shape[:-2] + [n, n], dtype=dtype)) + return tf.matmul(u, tf.matmul(d, v)) + + matrices = [ + generate_well_conditioned(shape_1, dtype=dtype), + generate_well_conditioned(shape_2, dtype=dtype), + ] + + lin_op_matrices = matrices + + if use_placeholder: + lin_op_matrices = [ + tf.compat.v1.placeholder_with_default( + matrix, shape=None) for matrix in matrices] + + operator = CompositionND( + [FullMatrixND(l) for l in lin_op_matrices]) + + matmul_order_list = list(reversed(matrices)) + mat = matmul_order_list[0] + for other_mat in matmul_order_list[1:]: + mat = tf.matmul(other_mat, mat) + + return operator, mat + + @test_util.run_deprecated_v1 + def test_static_shapes(self): + operators = [ + FullMatrixND(rng.rand(2, 3, 4)), + FullMatrixND(rng.rand(2, 4, 5)) + ] + operator = CompositionND(operators) + self.assertAllEqual((2, 3, 5), operator.shape) + + @test_util.run_deprecated_v1 + def test_shape_tensors_when_statically_available(self): + operators = [ + FullMatrixND(rng.rand(2, 3, 4)), + FullMatrixND(rng.rand(2, 4, 5)) + ] + operator = CompositionND(operators) + with self.cached_session(): + self.assertAllEqual((2, 3, 5), operator.shape_tensor()) + + @test_util.run_deprecated_v1 + def test_shape_tensors_when_only_dynamically_available(self): + mat_1 = rng.rand(1, 2, 3, 4) + mat_2 = rng.rand(1, 2, 4, 5) + mat_ph_1 = tf.compat.v1.placeholder(tf.float64) + mat_ph_2 = tf.compat.v1.placeholder(tf.float64) + feed_dict = {mat_ph_1: mat_1, mat_ph_2: mat_2} + + operators = [ + FullMatrixND(mat_ph_1), + FullMatrixND(mat_ph_2) + ] + operator = CompositionND(operators) + with self.cached_session(): + self.assertAllEqual( + (1, 2, 3, 5), operator.shape_tensor().eval(feed_dict=feed_dict)) + + +linear_operator_test_util.add_tests(SquareLinearOperatorCompositionTest) +linear_operator_test_util.add_tests(NonSquareLinearOperatorCompositionTest) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_composition_test.py b/tensorflow_mri/python/linalg/linear_operator_composition_test.py new file mode 100644 index 00000000..9095920c --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_composition_test.py @@ -0,0 +1,16 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for `linear_operator_composition`.""" +# pylint: disable=missing-class-docstring,missing-function-docstring diff --git a/tensorflow_mri/python/linalg/linear_operator_diag.py b/tensorflow_mri/python/linalg/linear_operator_diag.py new file mode 100644 index 00000000..77c6baf7 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_diag.py @@ -0,0 +1,31 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Diagonal linear operator.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import doc_util + + +LinearOperatorDiag = api_util.export( + "linalg.LinearOperatorDiag")( + doc_util.no_linkcode( + linear_operator.make_linear_operator( + tf.linalg.LinearOperatorDiag))) + + +tf.linalg.LinearOperatorDiag = LinearOperatorDiag diff --git a/tensorflow_mri/python/linalg/linear_operator_diag_nd.py b/tensorflow_mri/python/linalg/linear_operator_diag_nd.py new file mode 100644 index 00000000..6aecefed --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_diag_nd.py @@ -0,0 +1,277 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.linalg import linear_operator_util +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import types_util + + +@api_util.export("linalg.LinearOperatorDiagND") +@linear_operator_nd.make_linear_operator_nd +class LinearOperatorDiagND(linear_operator_nd.LinearOperatorND): + r"""Linear operator acting like a [batch] square diagonal matrix. + + This operator acts like a batch of diagonal matrices + $A \in \mathbb{F}^{n \times n}$, where $\mathbb{F}$ may be $\mathbb{R}$ + or $\mathbb{C}$ and $n = n_0 \times n_1 \times \dots \times n_d$, where + $d$ is the number of dimensions in the domain. + + ```{note} + The matrix $A$ is not materialized. + ``` + + ```{seealso} + This operator is similar to `tfmri.linalg.LinearOperatorDiag`, but provides + additional functionality to operate with multidimensional inputs. + ``` + + ```{rubric} Initialization + ``` + This operator is initialized with an array of diagonal elements `diag`. + `diag` may have multiple domain dimensions, which does not affect the dense + matrix representation of this operator but may be convenient to operate with + non-vectorized multidimensional inputs. If `diag` has any leading dimensions + which should be interpreted as batch dimensions, specify how many using the + `batch_dims` argument. This operator has the same data type as `diag`. + + ```{rubric} Performance + ``` + - `matvec` is $O(n)$. + - `solvevec` is $O(n)$. + - `lstsqvec` is $O(n)$. + + ```{rubric} Properties + ``` + - This operator is *non-singular* iff all its diagonal entries are non-zero. + - This operator is *self-adjoint* iff all its diagonal entries are real or + have zero imaginary part. + - This operator is *positive definite* iff all its diagonal entries are + positive or have positive real part. + - This operator is always *square*. + + ```{rubric} Inversion + ``` + If this operator is non-singular, its inverse $A{-1}$ is also a diagonal + operator whose diagonal entries are the reciprocal of the diagonal entries + of this operator. + + Example: + >>> # Create a 2-D diagonal linear operator. + >>> diag = [[1., -1.], [2., 3.]] + >>> operator = tfmri.linalg.LinearOperatorDiagND(diag) + >>> operator.to_dense() + [[ 1., 0., 0., 0.], + [ 0., -1., 0., 0.], + [ 0., 0., 2., 0.], + [ 0., 0., 0., 3.]] + >>> operator.shape + (4, 4) + >>> x = tf.ones(shape=(2, 2)) + >>> rhs = operator.matvec_nd(x) + [[ 1., -1.], + [ 2., 3.]] + >>> operator.solvevec_nd(rhs) + [[ 1., 1.], + [ 1., 1.]] + + Args: + diag: A real or complex `tf.Tensor` of shape `[..., *domain_shape]`. + The diagonal of the operator. + batch_dims: An `int`, the number of leading batch dimensions in `diag`. + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `None`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `None`. + is_positive_definite: A boolean, or `None`. Whether this operator is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that an operator [does + not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) + Defaults to `None`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `False`. + name: An optional `str`. The name of this operator. + """ + def __init__(self, + diag, + batch_dims=0, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None, + name="LinearOperatorDiag"): + parameters = dict( + diag=diag, + batch_dims=batch_dims, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name + ) + + with tf.name_scope(name): + # Check batch_dims. + self._batch_dims = np.asarray(tf.get_static_value(batch_dims)) + if (not self._batch_dims.ndim == 0 or + not np.issubdtype(self._batch_dims.dtype, np.integer)): + raise TypeError( + f"batch_dims must be an int, but got: {batch_dims}") + self._batch_dims = self._batch_dims.item() + if self._batch_dims < 0: + raise ValueError( + f"batch_dims must be non-negative, but got: {batch_dims}") + + # Check maps. + self._diag = types_util.convert_nonref_to_tensor(diag, name="diag") + if self._diag.shape.rank is None: + raise ValueError("diag must have known static rank") + if self._diag.shape.rank < 1: + raise ValueError( + f"diag must be at least 1-D, but got shape: {self._diag.shape}") + + # Check and auto-set hints. + if not self._diag.dtype.is_complex: + if is_self_adjoint is False: + raise ValueError("A real diagonal operator is always self adjoint.") + is_self_adjoint = True + + if is_square is False: + raise ValueError("Only square diagonal operators currently supported.") + is_square = True + + super().__init__( + dtype=self._diag.dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters, + name=name) + + def _domain_shape(self): + return self._diag.shape[self._batch_dims:] + + def _range_shape(self): + return self._diag.shape[self._batch_dims:] + + def _batch_shape(self): + return self._diag.shape[:self._batch_dims] + + def _domain_shape_tensor(self): + return tf.shape(self._diag)[self._batch_dims:] + + def _range_shape_tensor(self): + return tf.shape(self._diag)[self._batch_dims:] + + def _batch_shape_tensor(self): + return tf.shape(self._diag)[:self._batch_dims] + + def _assert_non_singular(self): + return linear_operator_util.assert_no_entries_with_modulus_zero( + self._diag, + message=( + "Diagonal operator is singular: " + "diagonal entries contain zero values.")) + + def _assert_positive_definite(self): + if self.dtype.is_complex: + message = ( + "Diagonal operator has diagonal entries with non-positive real part, " + "so it is not positive definite.") + else: + message = ( + "Real diagonal operator has non-positive diagonal entries, " + "so it is not positive definite.") + + return tf.debugging.assert_positive( + tf.math.real(self._diag), message=message) + + def _assert_self_adjoint(self): + return linear_operator_util.assert_zero_imag_part( + self._diag, + message=( + "This diagonal operator contains non-zero imaginary values, " + "so it is not self-adjoint.")) + + def _matvec_nd(self, x, adjoint=False): + diag_term = tf.math.conj(self._diag) if adjoint else self._diag + return diag_term * x + + def _determinant(self): + return tf.math.reduce_prod(self._diag, axis=self._diag_axes) + + def _log_abs_determinant(self): + log_det = tf.math.reduce_sum( + tf.math.log(tf.math.abs(self._diag)), axis=self._diag_axes) + if self.dtype.is_complex: + log_det = tf.cast(log_det, dtype=self.dtype) + return log_det + + def _solvevec_nd(self, rhs, adjoint=False): + diag_term = tf.math.conj(self._diag) if adjoint else self._diag + inv_diag_term = 1. / diag_term + return inv_diag_term * rhs + + def _lstsqvec_nd(self, rhs, adjoint=False): + return self._solvevec_nd(rhs, adjoint=adjoint) + + def _to_dense(self): + return tf.linalg.diag(self._flat_diag) + + def _diag_part(self): + return self._flat_diag + + def _add_to_tensor(self, x): + x_diag = tf.linalg.diag_part(x) + new_diag = self._flat_diag + x_diag + return tf.linalg.set_diag(x, new_diag) + + def _eigvals(self): + return tf.convert_to_tensor(self.diag) + + def _cond(self): + abs_diag = tf.math.abs(self.diag) + return (tf.math.reduce_max(abs_diag, axis=self._diag_axes) / + tf.math.reduce_min(abs_diag, axis=self._diag_axes)) + + @property + def diag(self): + return self._diag + + @property + def _diag_axes(self): + return list(range(self._batch_dims, self._diag.shape.rank)) + + @property + def _flat_diag(self): + return tf.reshape( + self._diag, tf.concat([self.batch_shape_tensor(), [-1]], 0)) + + @property + def _composite_tensor_fields(self): + return ("diag", "batch_dims") + + @property + def _composite_tensor_prefer_static_fields(self): + return ("batch_dims",) + + @property + def _experimental_parameter_ndims_to_matrix_ndims(self): + return {"diag": self._diag.shape.rank - self._batch_dims} diff --git a/tensorflow_mri/python/linalg/linear_operator_diag_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_diag_nd_test.py new file mode 100644 index 00000000..20ca3341 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_diag_nd_test.py @@ -0,0 +1,510 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for `linear_operator_diag_nd`.""" + +import tensorflow as tf + +from tensorflow.python.framework import test_util + +from tensorflow_mri.python.linalg import linear_operator_diag_nd +from tensorflow_mri.python.linalg import linear_operator_identity_nd +from tensorflow_mri.python.linalg import linear_operator_test_util + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorDiagNDTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + def tearDown(self): + tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) + + def setUp(self): + self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() + tf.config.experimental.enable_tensor_float_32_execution(False) + + @staticmethod + def optional_tests(): + """List of optional test names to run.""" + return [ + "operator_matmul_with_same_type", + "operator_solve_with_same_type" + ] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + shape = list(build_info.shape) + diag = linear_operator_test_util.random_sign_uniform( + shape[:-1], minval=1., maxval=2., dtype=dtype) + batch_dims = len(shape) - 2 + + if ensure_self_adjoint_and_pd: + # Abs on complex64 will result in a float32, so we cast back up. + diag = tf.cast(tf.math.abs(diag), dtype=dtype) + + lin_op_diag = diag + + if use_placeholder: + lin_op_diag = tf.compat.v1.placeholder_with_default( + diag, shape=(None,) * (batch_dims + 1)) + + operator = linear_operator_diag_nd.LinearOperatorDiagND( + lin_op_diag, + batch_dims=batch_dims, + is_self_adjoint=True if ensure_self_adjoint_and_pd else None, + is_positive_definite=True if ensure_self_adjoint_and_pd else None) + + matrix = tf.linalg.diag(diag) + + return operator, matrix + + def test_assert_positive_definite_raises_for_zero_eigenvalue(self): + # Matrix with one positive eigenvalue and one zero eigenvalue. + with self.cached_session(): + diag = [1.0, 0.0] + operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) + + # is_self_adjoint should be auto-set for real diag. + self.assertTrue(operator.is_self_adjoint) + with self.assertRaisesOpError("non-positive.*not positive definite"): + operator.assert_positive_definite().run() + + def test_assert_positive_definite_raises_for_negative_real_eigvalues(self): + with self.cached_session(): + diag_x = [1.0, -2.0] + diag_y = [0., 0.] # Imaginary eigenvalues should not matter. + diag = tf.dtypes.complex(diag_x, diag_y) + operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) + + # is_self_adjoint should not be auto-set for complex diag. + self.assertTrue(operator.is_self_adjoint is None) + with self.assertRaisesOpError("non-positive real.*not positive definite"): + operator.assert_positive_definite().run() + + def test_assert_positive_definite_does_not_raise_if_pd_and_complex(self): + with self.cached_session(): + x = [1., 2.] + y = [1., 0.] + diag = tf.dtypes.complex(x, y) # Re[diag] > 0. + operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) + # Should not fail + self.evaluate(operator.assert_positive_definite()) + + def test_assert_non_singular_raises_if_zero_eigenvalue(self): + # Singular matrix with one positive eigenvalue and one zero eigenvalue. + with self.cached_session(): + diag = [1.0, 0.0] + operator = linear_operator_diag_nd.LinearOperatorDiagND( + diag, is_self_adjoint=True) + with self.assertRaisesOpError("operator is singular"): + operator.assert_non_singular().run() + + def test_assert_non_singular_does_not_raise_for_complex_nonsingular(self): + with self.cached_session(): + x = [1., 0.] + y = [0., 1.] + diag = tf.dtypes.complex(x, y) + operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) + # Should not raise. + self.evaluate(operator.assert_non_singular()) + + def test_assert_self_adjoint_raises_if_diag_has_complex_part(self): + with self.cached_session(): + x = [1., 0.] + y = [0., 1.] + diag = tf.dtypes.complex(x, y) + operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) + with self.assertRaisesOpError("imaginary.*not self-adjoint"): + operator.assert_self_adjoint().run() + + def test_assert_self_adjoint_does_not_raise_for_diag_with_zero_imag(self): + with self.cached_session(): + x = [1., 0.] + y = [0., 0.] + diag = tf.dtypes.complex(x, y) + operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) + # Should not raise + self.evaluate(operator.assert_self_adjoint()) + + def test_scalar_diag_raises(self): + with self.assertRaisesRegex(ValueError, "must be at least 1-D"): + linear_operator_diag_nd.LinearOperatorDiagND(1.) + + def test_broadcast_matmul_and_solve(self): + # These cannot be done in the automated (base test class) tests since they + # test shapes that tf.matmul cannot handle. + # In particular, tf.matmul does not broadcast. + with self.cached_session() as sess: + x = tf.random.normal(shape=(2, 2, 3, 4)) + + # This LinearOperatorDiagND will be broadcast to (2, 2, 3, 3) during solve + # and matmul with 'x' as the argument. + diag = tf.random.uniform(shape=(2, 1, 3)) + operator = linear_operator_diag_nd.LinearOperatorDiagND( + diag, batch_dims=2, is_self_adjoint=True) + self.assertAllEqual((2, 1, 3, 3), operator.shape) + + # Create a batch matrix with the broadcast shape of operator. + diag_broadcast = tf.concat((diag, diag), 1) + mat = tf.linalg.diag(diag_broadcast) + self.assertAllEqual((2, 2, 3, 3), mat.shape) # being pedantic. + + operator_matmul = operator.matmul(x) + mat_matmul = tf.matmul(mat, x) + self.assertAllEqual(operator_matmul.shape, mat_matmul.shape) + self.assertAllClose(*self.evaluate([operator_matmul, mat_matmul])) + + operator_solve = operator.solve(x) + mat_solve = tf.linalg.solve(mat, x) + self.assertAllEqual(operator_solve.shape, mat_solve.shape) + self.assertAllClose(*self.evaluate([operator_solve, mat_solve])) + + def test_diag_matmul(self): + operator1 = linear_operator_diag_nd.LinearOperatorDiagND([2., 3.]) + operator2 = linear_operator_diag_nd.LinearOperatorDiagND([1., 2.]) + operator3 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=3.) + operator_matmul = operator1.matmul(operator2) + self.assertTrue(isinstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose([2., 6.], self.evaluate(operator_matmul.diag)) + + operator_matmul = operator2.matmul(operator1) + self.assertTrue(isinstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose([2., 6.], self.evaluate(operator_matmul.diag)) + + operator_matmul = operator1.matmul(operator3) + self.assertTrue(isinstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose([6., 9.], self.evaluate(operator_matmul.diag)) + + operator_matmul = operator3.matmul(operator1) + self.assertTrue(isinstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose([6., 9.], self.evaluate(operator_matmul.diag)) + + def test_diag_matmul_nd(self): + operator1 = linear_operator_diag_nd.LinearOperatorDiagND( + [[1., 2.], [3., 4.]]) + operator2 = linear_operator_diag_nd.LinearOperatorDiagND( + [1., 2.]) + operator3 = linear_operator_diag_nd.LinearOperatorDiagND( + [[1., 2.], [3., 4.]], batch_dims=1) + operator4 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=2.) + operator5 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=[1., 2., 3.]) + operator6 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2, 3], multiplier=-1.) + + operator_matmul = operator1.matmul(operator1) + self.assertIsInstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND) + self.assertAllClose( + [[1., 4.], [9., 16.]], self.evaluate(operator_matmul.diag)) + self.assertAllEqual([], operator_matmul.batch_shape) + + operator_matmul = operator1.matmul(operator2) + self.assertIsInstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND) + self.assertAllClose( + [[1., 4.], [3., 8.]], self.evaluate(operator_matmul.diag)) + self.assertAllEqual([], operator_matmul.batch_shape) + + operator_matmul = operator2.matmul(operator1) + self.assertIsInstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND) + self.assertAllClose( + [[1., 4.], [3., 8.]], self.evaluate(operator_matmul.diag)) + self.assertAllEqual([], operator_matmul.batch_shape) + + operator_matmul = operator2.matmul(operator3) + self.assertIsInstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND) + self.assertAllClose( + [[1., 4.], [3., 8.]], self.evaluate(operator_matmul.diag)) + self.assertAllEqual([2], operator_matmul.batch_shape) + + operator_matmul = operator1.matmul(operator3) + self.assertIsInstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND) + self.assertAllClose( + [[[1., 4.], [3., 8.]], [[3., 8.], [9., 16.]]], + self.evaluate(operator_matmul.diag)) + self.assertAllEqual([2], operator_matmul.batch_shape) + + operator_matmul = operator1.matmul(operator4) + self.assertTrue(isinstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose( + [[2., 4.], [6., 8.]], self.evaluate(operator_matmul.diag)) + self.assertAllEqual([2, 2], operator_matmul.domain_shape) + self.assertAllEqual([], operator_matmul.batch_shape) + + operator_matmul = operator4.matmul(operator1) + self.assertTrue(isinstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose( + [[2., 4.], [6., 8.]], self.evaluate(operator_matmul.diag)) + self.assertAllEqual([2, 2], operator_matmul.domain_shape) + self.assertAllEqual([], operator_matmul.batch_shape) + + operator_matmul = operator2.matmul(operator5) + self.assertTrue(isinstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose( + [[1., 2.], [2., 4.], [3., 6.]], self.evaluate(operator_matmul.diag)) + self.assertAllEqual([2], operator_matmul.domain_shape) + self.assertAllEqual([3], operator_matmul.batch_shape) + + operator_matmul = operator5.matmul(operator2) + self.assertTrue(isinstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose( + [[1., 2.], [2., 4.], [3., 6.]], self.evaluate(operator_matmul.diag)) + self.assertAllEqual([2], operator_matmul.domain_shape) + self.assertAllEqual([3], operator_matmul.batch_shape) + + operator_matmul = operator1.matmul(operator5) + self.assertTrue(isinstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose( + [[[1., 2.], [3., 4.]], [[2., 4.], [6., 8.]], [[3., 6.], [9., 12.]]], + self.evaluate(operator_matmul.diag)) + self.assertAllEqual([2, 2], operator_matmul.domain_shape) + self.assertAllEqual([3], operator_matmul.batch_shape) + + operator_matmul = operator5.matmul(operator1) + self.assertTrue(isinstance( + operator_matmul, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose( + [[[1., 2.], [3., 4.]], [[2., 4.], [6., 8.]], [[3., 6.], [9., 12.]]], + self.evaluate(operator_matmul.diag)) + self.assertAllEqual([2, 2], operator_matmul.domain_shape) + self.assertAllEqual([3], operator_matmul.batch_shape) + + with self.assertRaisesRegex(ValueError, "not broadcast-compatible"): + operator_matmul = operator1.matmul(operator6) + + with self.assertRaisesRegex(ValueError, "not broadcast-compatible"): + operator_matmul = operator6.matmul(operator1) + + def test_diag_solve(self): + operator1 = linear_operator_diag_nd.LinearOperatorDiagND( + [2., 3.], is_non_singular=True) + operator2 = linear_operator_diag_nd.LinearOperatorDiagND( + [1., 2.], is_non_singular=True) + operator3 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=3., is_non_singular=True) + operator_solve = operator1.solve(operator2) + self.assertTrue(isinstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose([0.5, 2 / 3.], self.evaluate(operator_solve.diag)) + + operator_solve = operator2.solve(operator1) + self.assertTrue(isinstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose([2., 3 / 2.], self.evaluate(operator_solve.diag)) + + operator_solve = operator1.solve(operator3) + self.assertTrue(isinstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose([3 / 2., 1.], self.evaluate(operator_solve.diag)) + + operator_solve = operator3.solve(operator1) + self.assertTrue(isinstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose([2 / 3., 1.], self.evaluate(operator_solve.diag)) + + def test_diag_solve_nd(self): + operator1 = linear_operator_diag_nd.LinearOperatorDiagND( + [[1., 2.], [3., 4.]]) + operator2 = linear_operator_diag_nd.LinearOperatorDiagND( + [1., 2.]) + operator3 = linear_operator_diag_nd.LinearOperatorDiagND( + [[1., 2.], [3., 4.]], batch_dims=1) + operator4 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=2.) + operator5 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=[1., 2., 3.]) + operator6 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2, 3], multiplier=-1.) + + operator_solve = operator1.solve(operator1) + self.assertIsInstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND) + self.assertAllClose( + [[1., 1.], [1., 1.]], self.evaluate(operator_solve.diag)) + self.assertAllEqual([], operator_solve.batch_shape) + + operator_solve = operator1.solve(operator2) + self.assertIsInstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND) + self.assertAllClose( + [[1., 1.], [1 / 3, 1 / 2]], self.evaluate(operator_solve.diag)) + self.assertAllEqual([], operator_solve.batch_shape) + + operator_solve = operator2.solve(operator1) + self.assertIsInstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND) + self.assertAllClose( + [[1., 1.], [3., 2.]], self.evaluate(operator_solve.diag)) + self.assertAllEqual([], operator_solve.batch_shape) + + operator_solve = operator2.solve(operator3) + self.assertIsInstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND) + self.assertAllClose( + [[1., 1.], [3., 2.]], self.evaluate(operator_solve.diag)) + self.assertAllEqual([2], operator_solve.batch_shape) + + operator_solve = operator1.solve(operator3) + self.assertIsInstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND) + self.assertAllClose( + [[[1., 1.], [1 / 3, 0.5]], [[3., 2.], [1., 1.]]], + self.evaluate(operator_solve.diag)) + self.assertAllEqual([2], operator_solve.batch_shape) + + operator_solve = operator1.solve(operator4) + self.assertTrue(isinstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose( + [[2., 1.], [2 / 3, 0.5]], self.evaluate(operator_solve.diag)) + self.assertAllEqual([2, 2], operator_solve.domain_shape) + self.assertAllEqual([], operator_solve.batch_shape) + + operator_solve = operator4.solve(operator1) + self.assertTrue(isinstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose( + [[0.5, 1.], [3 / 2, 4 / 2]], self.evaluate(operator_solve.diag)) + self.assertAllEqual([2, 2], operator_solve.domain_shape) + self.assertAllEqual([], operator_solve.batch_shape) + + operator_solve = operator1.solve(operator5) + self.assertTrue(isinstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose( + [[[1., 0.5], [1 / 3, 0.25]], + [[2., 1.], [2 / 3, 0.5]], + [[3., 3 / 2], [1., 0.75]]], + self.evaluate(operator_solve.diag)) + self.assertAllEqual([2, 2], operator_solve.domain_shape) + self.assertAllEqual([3], operator_solve.batch_shape) + + operator_solve = operator5.solve(operator1) + self.assertTrue(isinstance( + operator_solve, + linear_operator_diag_nd.LinearOperatorDiagND)) + self.assertAllClose( + [[[1., 2.], [3., 4.]], + [[0.5, 1.], [3 / 2, 2.]], + [[1 / 3, 2 / 3], [1., 4 / 3]]], + self.evaluate(operator_solve.diag)) + self.assertAllEqual([2, 2], operator_solve.domain_shape) + self.assertAllEqual([3], operator_solve.batch_shape) + + with self.assertRaisesRegex(ValueError, "not broadcast-compatible"): + operator_solve = operator1.solve(operator6) + + with self.assertRaisesRegex(ValueError, "not broadcast-compatible"): + operator_solve = operator6.solve(operator1) + + def test_diag_adjoint_type(self): + diag = [1., 3., 5., 8.] + operator = linear_operator_diag_nd.LinearOperatorDiagND( + diag, is_non_singular=True) + self.assertIsInstance( + operator.adjoint(), linear_operator_diag_nd.LinearOperatorDiagND) + + def test_diag_cholesky_type(self): + diag = [1., 3., 5., 8.] + operator = linear_operator_diag_nd.LinearOperatorDiagND( + diag, + is_positive_definite=True, + is_self_adjoint=True, + ) + self.assertIsInstance(operator.cholesky(), linear_operator_diag_nd.LinearOperatorDiagND) + + def test_diag_inverse_type(self): + diag = [1., 3., 5., 8.] + operator = linear_operator_diag_nd.LinearOperatorDiagND( + diag, is_non_singular=True) + self.assertIsInstance(operator.inverse(), + linear_operator_diag_nd.LinearOperatorDiagND) + + def test_tape_safe(self): + diag = tf.Variable([[2.]]) + operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) + self.check_tape_safe(operator) + + def test_convert_variables_to_tensors(self): + diag = tf.Variable([[2.]]) + operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) + with self.cached_session() as sess: + sess.run([diag.initializer]) + self.check_convert_variables_to_tensors(operator) + + +linear_operator_test_util.add_tests(LinearOperatorDiagNDTest) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_diag_test.py b/tensorflow_mri/python/linalg/linear_operator_diag_test.py new file mode 100644 index 00000000..a69cf54b --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_diag_test.py @@ -0,0 +1,15 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_diag`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_fft.py b/tensorflow_mri/python/linalg/linear_operator_fft.py new file mode 100644 index 00000000..b93ecf84 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_fft.py @@ -0,0 +1,257 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Fourier linear operator.""" + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.linalg import slicing +from tensorflow_mri.python.linalg import linear_operator_util +from tensorflow_mri.python.ops import fft_ops +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import tensor_util +from tensorflow_mri.python.util import types_util + + +@api_util.export("linalg.LinearOperatorFFT") +@linear_operator_nd.make_linear_operator_nd +class LinearOperatorFFT(linear_operator_nd.LinearOperatorND): + r"""Linear operator acting like a [batch] DFT matrix. + + If this operator is $A$, then $A x$ computes the Fourier transform of $x$, + while $A^H x$ computes the inverse Fourier transform of $x$. Note that the + inverse and the adjoint are equivalent, i.e. $A^H = A^{-1}$. + + The DFT matrix is never materialized internally. Instead matrix-matrix and + matrix-vector products are computed using the fast Fourier transform (FFT) + algorithm. + + This operator supports N-dimensional inputs, whose shape must be specified + through the `domain_shape` argument. This operator also acccepts an optional + `batch_shape` argument, which will be relevant for broadcasting purposes. + + This operator only supports complex inputs. Specify the desired type using + the `dtype` argument. + + ```{rubric} Performance + ``` + - `matvec` is $O(n \log{n})$. + - `solvevec` is $O(n \log{n})$. + - `lstsqvec` is equal to `solve`. + + ```{rubric} Matrix properties + ``` + - This operator is non-singular, i.e. $A^{-1}$ exists. + - This operator is not self-adjoint, i.e. $A^H \neq A$. + - This operator is square, i.e. $A \in \mathbb{F}^{n \times n}$. + + ```{rubric} Inversion + ``` + The inverse of this operator is equal to its adjoint, i.e., $A^{-1} = A^H$. + The linear system $Ax = b$ can be efficiently solved using `solve` or + `solvevec`. + + Example: + >>> # Create a 2-dimensional 128x128 DFT operator. + >>> linop = tfmri.linalg.LinearOperatorFFT(domain_shape=[128, 128]) + + Args: + domain_shape: A 1D integer `tf.Tensor`. The domain shape of the operator, + representing the shape of the inputs to `matvec`. + batch_shape: A 1D integer `tf.Tensor`. The batch shape of the operator. + Defaults to `None`, which is equivalent to `[]`. + dtype: A `tf.dtypes.DType`. Must be complex. Defaults to `complex64`. + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `True`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `False`. + is_positive_definite: A boolean, or `None`. Whether this operator is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that an operator [does + not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) + Defaults to `None`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `True`. + name: A `name`. The name to give to the ops created by this class. + """ + def __init__(self, + domain_shape, + batch_shape=None, + dtype=None, + is_non_singular=True, + is_self_adjoint=False, + is_positive_definite=None, + is_square=True, + name='LinearOperatorFFT'): + + parameters = dict( + domain_shape=domain_shape, + batch_shape=batch_shape, + dtype=dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name) + + dtype = dtype or tf.complex64 + + with tf.name_scope(name): + dtype = tf.dtypes.as_dtype(dtype) + if not is_non_singular: + raise ValueError("An FFT operator is always non-singular.") + if is_self_adjoint: + raise ValueError("An FFT operator is never self-adjoint.") + if not is_square: + raise ValueError("An FFT operator is always square.") + + # Get static/dynamic domain shape. + types_util.assert_not_ref_type(domain_shape, 'domain_shape') + self._domain_shape_static, self._domain_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) + if self._domain_shape_static.rank is None: + raise ValueError('domain_shape must have known static rank') + + # Get static/dynamic batch shape. + if batch_shape is not None: + types_util.assert_not_ref_type(batch_shape, 'batch_shape') + self._batch_shape_static, self._batch_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape(batch_shape)) + if self._batch_shape_static.rank is None: + raise ValueError('batch_shape must have known static rank') + else: + self._batch_shape_static = tf.TensorShape([]) + self._batch_shape_dynamic = tf.constant([], dtype=tf.int32) + + super().__init__(dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters, + name=name) + + def _matvec_nd(self, x, adjoint=False): + axes = list(range(-self.ndim, 0)) + + if adjoint: + x = fft_ops.ifftn(x, axes=axes, norm='ortho', shift=True) + else: + x = fft_ops.fftn(x, axes=axes, norm='ortho', shift=True) + + # For consistent broadcasting semantics. + if adjoint: + output_shape = self.domain_shape_tensor() + else: + output_shape = self.range_shape_tensor() + + if self.batch_shape.rank > 0: + x = tf.broadcast_to( + x, tf.concat([self.batch_shape_tensor(), output_shape], 0)) + + return x + + def _solvevec_nd(self, rhs, adjoint=False): + return self._matvec_nd(rhs, adjoint=(not adjoint)) + + def _lstsqvec_nd(self, rhs, adjoint=False): + return self._solvevec_nd(rhs, adjoint=adjoint) + + def _ndim(self): + return self.domain_shape.rank + + def _domain_shape(self): + return self._domain_shape_static + + def _range_shape(self): + return self._domain_shape_static + + def _batch_shape(self): + return self._batch_shape_static + + def _domain_shape_tensor(self): + return self._domain_shape_dynamic + + def _range_shape_tensor(self): + return self._domain_shape_dynamic + + def _batch_shape_tensor(self): + return self._batch_shape_dynamic + + @property + def _composite_tensor_fields(self): + return ('domain_shape', 'batch_shape', 'dtype') + + @property + def _composite_tensor_prefer_static_fields(self): + return ('domain_shape', 'batch_shape') + + @property + def _experimental_parameter_ndims_to_matrix_ndims(self): + return {} + + def __getitem__(self, slices): + # Support slicing. + new_batch_shape = tf.shape(tf.ones(self.batch_shape_tensor())[slices]) + return slicing.batch_slice( + self, params_overrides={'batch_shape': new_batch_shape}, slices=slices) + + +def dft_matrix(num_rows, + batch_shape=None, + dtype=tf.complex64, + shift=False, + name=None): + """Constructs a discrete Fourier transform (DFT) matrix. + + Args: + num_rows: A non-negative `int32` scalar `tf.Tensor` giving the number + of rows in each batch matrix. + batch_shape: A 1D integer `tf.Tensor`. If provided, the returned + `tf.Tensor` will have leading batch dimensions of this shape. + dtype: A `tf.dtypes.DType`. The type of an element in the resulting + `tf.Tensor`. Must be complex. Defaults to `tf.complex64`. + shift: A boolean. If `True`, returns the matrix for a DC-centred DFT. + name: A name for this op. + + Returns: + A `tf.Tensor` of shape `batch_shape + [num_rows, num_rows]` and type + `dtype` containing a DFT matrix. + """ + with tf.name_scope(name or "dft_matrix"): + num_rows = tf.convert_to_tensor(num_rows) + if batch_shape is not None: + batch_shape = tensor_util.convert_shape_to_tensor(batch_shape) + dtype = tf.dtypes.as_dtype(dtype) + if not dtype.is_complex: + raise TypeError(f"dtype must be complex, got {str(dtype)}") + + i = tf.range(num_rows, dtype=dtype.real_dtype) + omegas = tf.reshape( + tf.math.exp(tf.dtypes.complex( + tf.constant(0.0, dtype=dtype.real_dtype), + -2.0 * np.pi * i / tf.cast(num_rows, dtype.real_dtype))), [-1, 1]) + m = omegas ** tf.cast(i, dtype) + m /= tf.math.sqrt(tf.cast(num_rows, dtype)) + + if shift: + m = tf.signal.fftshift(m) + + if batch_shape is not None: + m = tf.broadcast_to(m, tf.concat([batch_shape, [num_rows, num_rows]], 0)) + + return m diff --git a/tensorflow_mri/python/linalg/linear_operator_fft_test.py b/tensorflow_mri/python/linalg/linear_operator_fft_test.py new file mode 100644 index 00000000..16892965 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_fft_test.py @@ -0,0 +1,167 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for `LinearOperatorFFT`.""" + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_adjoint +from tensorflow_mri.python.linalg import linear_operator_fft +from tensorflow_mri.python.linalg import linear_operator_identity +from tensorflow_mri.python.linalg import linear_operator_test_util +from tensorflow_mri.python.util import test_util + + +rng = np.random.RandomState(2016) + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorFFTTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + @staticmethod + def skip_these_tests(): + return [ + "cholesky", + "eigvalsh" + ] + + @staticmethod + def dtypes_to_test(): + return [tf.complex64, tf.complex128] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + del ensure_self_adjoint_and_pd + del use_placeholder + shape = list(build_info.shape) + assert shape[-1] == shape[-2] + + batch_shape = shape[:-2] + num_rows = shape[-1] + + operator = linear_operator_fft.LinearOperatorFFT( + domain_shape=[num_rows], batch_shape=batch_shape, dtype=dtype) + + matrix = linear_operator_fft.dft_matrix( + num_rows, batch_shape=batch_shape, dtype=dtype, shift=True) + + return operator, matrix + + def test_assert_self_adjoint(self): + with self.cached_session(): + operator = linear_operator_fft.LinearOperatorFFT(domain_shape=[4]) + with self.assertRaisesOpError("not equal to its adjoint"): + self.evaluate(operator.assert_self_adjoint()) + + def test_non_1d_domain_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be a 1-D"): + linear_operator_fft.LinearOperatorFFT(domain_shape=2) + + def test_non_integer_domain_shape_raises_static(self): + with self.assertRaisesRegex(TypeError, "must be integer"): + linear_operator_fft.LinearOperatorFFT(domain_shape=[2.]) + + def test_non_negative_domain_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be non-negative"): + linear_operator_fft.LinearOperatorFFT(domain_shape=[-2]) + + def test_unknown_rank_domain_shape_raises_static(self): + if tf.executing_eagerly(): + return + with self.cached_session(): + domain_shape = tf.compat.v1.placeholder_with_default([2], shape=None) + with self.assertRaisesRegex(ValueError, "must have known static rank"): + operator = linear_operator_fft.LinearOperatorFFT( + domain_shape=domain_shape) + self.evaluate(operator.to_dense()) + + def test_unknown_rank_batch_shape_raises_static(self): + if tf.executing_eagerly(): + return + with self.cached_session(): + batch_shape = tf.compat.v1.placeholder_with_default([2], shape=None) + with self.assertRaisesRegex(ValueError, "must have known static rank"): + operator = linear_operator_fft.LinearOperatorFFT( + domain_shape=[2], batch_shape=batch_shape) + self.evaluate(operator.to_dense()) + + def test_non_1d_batch_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be a 1-D"): + linear_operator_fft.LinearOperatorFFT(domain_shape=[2], batch_shape=2) + + def test_non_integer_batch_shape_raises_static(self): + with self.assertRaisesRegex(TypeError, "must be integer"): + linear_operator_fft.LinearOperatorFFT(domain_shape=[2], batch_shape=[2.]) + + def test_negative_batch_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be non-negative"): + linear_operator_fft.LinearOperatorFFT(domain_shape=[2], batch_shape=[-2]) + + def test_wrong_matrix_dimensions_raises_static(self): + operator = linear_operator_fft.LinearOperatorFFT(domain_shape=[2]) + x = rng.randn(3, 3).astype(np.complex64) + with self.assertRaisesRegex(ValueError, "Dimensions.*not compatible"): + operator.matmul(x) + + def test_is_x_flags(self): + operator = linear_operator_fft.LinearOperatorFFT(domain_shape=[2]) + self.assertTrue(operator.is_non_singular) + self.assertFalse(operator.is_self_adjoint) + self.assertTrue(operator.is_square) + + def test_inverse_type(self): + operator = linear_operator_fft.LinearOperatorFFT( + domain_shape=[4], is_non_singular=True) + self.assertIsInstance( + operator.inverse(), linear_operator_adjoint.LinearOperatorAdjoint) + self.assertIsInstance( + operator.inverse().operator, linear_operator_fft.LinearOperatorFFT) + + def test_identity_matmul(self): + operator1 = linear_operator_fft.LinearOperatorFFT(domain_shape=[2]) + operator2 = linear_operator_identity.LinearOperatorIdentity(num_rows=2) + self.assertIsInstance(operator1.matmul(operator2), + linear_operator_fft.LinearOperatorFFT) + self.assertIsInstance(operator2.matmul(operator1), + linear_operator_fft.LinearOperatorFFT) + + def test_ref_type_shape_args_raises(self): + with self.assertRaisesRegex(TypeError, "domain_shape.cannot.be.reference"): + linear_operator_fft.LinearOperatorFFT( + domain_shape=tf.Variable([2])) + + with self.assertRaisesRegex(TypeError, "batch_shape.cannot.be.reference"): + linear_operator_fft.LinearOperatorFFT( + domain_shape=[2], batch_shape=tf.Variable([2])) + + def test_matvec_nd(self): + for adjoint in (False, True): + with self.subTest(adjoint=adjoint): + operator = linear_operator_fft.LinearOperatorFFT(domain_shape=[4, 4]) + x = tf.constant(rng.randn(4, 4).astype(np.complex64)) + y = operator.matvec_nd(x, adjoint=adjoint) + fn = tf.signal.ifft2d if adjoint else tf.signal.fft2d + expected = tf.signal.fftshift(fn(tf.signal.ifftshift(x))) + expected = expected * 4 if adjoint else expected / 4 + self.assertAllClose(expected, y) + + +linear_operator_test_util.add_tests(LinearOperatorFFTTest) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_finite_difference.py b/tensorflow_mri/python/linalg/linear_operator_finite_difference.py new file mode 100644 index 00000000..66833b67 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_finite_difference.py @@ -0,0 +1,125 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Finite difference linear operator.""" + + +import tensorflow as tf + +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import check_util +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.util import tensor_util + + +@api_util.export("linalg.LinearOperatorFiniteDifference") +class LinearOperatorFiniteDifference(linear_operator.LinearOperator): # pylint: disable=abstract-method + """Linear operator representing a finite difference matrix. + + Args: + domain_shape: A 1D `tf.Tensor` or a `list` of `int`. The domain shape of + this linear operator. + axis: An `int`. The axis along which the finite difference is taken. + Defaults to -1. + dtype: A `tf.dtypes.DType`. The data type for this operator. Defaults to + `float32`. + name: A `str`. A name for this operator. + """ + def __init__(self, + domain_shape, + axis=-1, + dtype=tf.dtypes.float32, + name="LinearOperatorFiniteDifference"): + + parameters = dict( + domain_shape=domain_shape, + axis=axis, + dtype=dtype, + name=name + ) + + # Compute the static and dynamic shapes and save them for later use. + self._domain_shape_static, self._domain_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) + + # Validate axis and canonicalize to negative. This ensures the correct + # axis is selected in the presence of batch dimensions. + self.axis = check_util.validate_static_axes( + axis, self._domain_shape_static.rank, + min_length=1, + max_length=1, + canonicalize="negative", + scalar_to_list=False) + + # Compute range shape statically. The range has one less element along + # the difference axis than the domain. + range_shape_static = self._domain_shape_static.as_list() + if range_shape_static[self.axis] is not None: + range_shape_static[self.axis] -= 1 + range_shape_static = tf.TensorShape(range_shape_static) + self._range_shape_static = range_shape_static + + # Now compute dynamic range shape. First concatenate the leading axes with + # the updated difference dimension. Then, iff the difference axis is not + # the last one, concatenate the trailing axes. + range_shape_dynamic = self._domain_shape_dynamic + range_shape_dynamic = tf.concat([ + range_shape_dynamic[:self.axis], + [range_shape_dynamic[self.axis] - 1]], 0) + if self.axis != -1: + range_shape_dynamic = tf.concat([ + range_shape_dynamic, + range_shape_dynamic[self.axis + 1:]], 0) + self._range_shape_dynamic = range_shape_dynamic + + super().__init__(dtype, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None, + name=name, + parameters=parameters) + + def _transform(self, x, adjoint=False): + + if adjoint: + paddings1 = [[0, 0]] * x.shape.rank + paddings2 = [[0, 0]] * x.shape.rank + paddings1[self.axis] = [1, 0] + paddings2[self.axis] = [0, 1] + x1 = tf.pad(x, paddings1) # pylint: disable=no-value-for-parameter + x2 = tf.pad(x, paddings2) # pylint: disable=no-value-for-parameter + x = x1 - x2 + else: + slice1 = [slice(None)] * x.shape.rank + slice2 = [slice(None)] * x.shape.rank + slice1[self.axis] = slice(1, None) + slice2[self.axis] = slice(None, -1) + x1 = x[tuple(slice1)] + x2 = x[tuple(slice2)] + x = x1 - x2 + + return x + + def _domain_shape(self): + return self._domain_shape_static + + def _range_shape(self): + return self._range_shape_static + + def _domain_shape_tensor(self): + return self._domain_shape_dynamic + + def _range_shape_tensor(self): + return self._range_shape_dynamic diff --git a/tensorflow_mri/python/linalg/linear_operator_finite_difference_test.py b/tensorflow_mri/python/linalg/linear_operator_finite_difference_test.py new file mode 100644 index 00000000..6586b991 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_finite_difference_test.py @@ -0,0 +1,81 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_finite_difference`.""" +# pylint: disable=missing-class-docstring,missing-function-docstring + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_finite_difference +from tensorflow_mri.python.util import test_util + + +class LinearOperatorFiniteDifferenceTest(test_util.TestCase): + """Tests for difference linear operator.""" + @classmethod + def setUpClass(cls): + super().setUpClass() + cls.linop1 = ( + linear_operator_finite_difference.LinearOperatorFiniteDifference([4])) + cls.linop2 = ( + linear_operator_finite_difference.LinearOperatorFiniteDifference( + [4, 4], axis=-2)) + cls.matrix1 = tf.convert_to_tensor([[-1, 1, 0, 0], + [0, -1, 1, 0], + [0, 0, -1, 1]], dtype=tf.float32) + + def test_transform(self): + """Test transform method.""" + signal = tf.random.normal([4, 4]) + result = self.linop2.transform(signal) + self.assertAllClose(result, np.diff(signal, axis=-2)) + + def test_matvec(self): + """Test matvec method.""" + signal = tf.constant([1, 2, 4, 8], dtype=tf.float32) + result = tf.linalg.matvec(self.linop1, signal) + self.assertAllClose(result, [1, 2, 4]) + self.assertAllClose(result, np.diff(signal)) + self.assertAllClose(result, tf.linalg.matvec(self.matrix1, signal)) + + signal2 = tf.range(16, dtype=tf.float32) + result = tf.linalg.matvec(self.linop2, signal2) + self.assertAllClose(result, [4] * 12) + + def test_matvec_adjoint(self): + """Test matvec with adjoint.""" + signal = tf.constant([1, 2, 4], dtype=tf.float32) + result = tf.linalg.matvec(self.linop1, signal, adjoint_a=True) + self.assertAllClose(result, + tf.linalg.matvec(tf.transpose(self.matrix1), signal)) + + def test_shapes(self): + """Test shapes.""" + self._test_all_shapes(self.linop1, [4], [3]) + self._test_all_shapes(self.linop2, [4, 4], [3, 4]) + + def _test_all_shapes(self, linop, domain_shape, range_shape): + """Test shapes.""" + self.assertIsInstance(linop.domain_shape, tf.TensorShape) + self.assertAllEqual(linop.domain_shape, domain_shape) + self.assertAllEqual(linop.domain_shape_tensor(), domain_shape) + + self.assertIsInstance(linop.range_shape, tf.TensorShape) + self.assertAllEqual(linop.range_shape, range_shape) + self.assertAllEqual(linop.range_shape_tensor(), range_shape) + + +if __name__ == '__main__': + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_full_matrix.py b/tensorflow_mri/python/linalg/linear_operator_full_matrix.py new file mode 100644 index 00000000..6fe1421a --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_full_matrix.py @@ -0,0 +1,31 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Full matrix linear operator.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import doc_util + + +LinearOperatorFullMatrix = api_util.export( + "linalg.LinearOperatorFullMatrix")( + doc_util.no_linkcode( + linear_operator.make_linear_operator( + tf.linalg.LinearOperatorFullMatrix))) + + +tf.linalg.LinearOperatorFullMatrix = LinearOperatorFullMatrix diff --git a/tensorflow_mri/python/linalg/linear_operator_full_matrix_test.py b/tensorflow_mri/python/linalg/linear_operator_full_matrix_test.py new file mode 100644 index 00000000..1d660f1b --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_full_matrix_test.py @@ -0,0 +1,15 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_full_matrix`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_gram_matrix.py b/tensorflow_mri/python/linalg/linear_operator_gram_matrix.py new file mode 100644 index 00000000..87eb1cff --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_gram_matrix.py @@ -0,0 +1,151 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Gram matrix of a linear operator.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.linalg import linear_operator_addition_nd +from tensorflow_mri.python.linalg import linear_operator_composition +from tensorflow_mri.python.linalg import linear_operator_identity +from tensorflow_mri.python.util import api_util + + +@api_util.export("linalg.LinearOperatorGramMatrix") +class LinearOperatorGramMatrix(linear_operator.LinearOperator): # pylint: disable=abstract-method + r"""Linear operator representing the Gram matrix of an operator. + + If $A$ is a `LinearOperator`, this operator is equivalent to + $A^H A$. + + The Gram matrix of $A$ appears in the normal equation + $A^H A x = A^H b$ associated with the least squares problem + ${\mathop{\mathrm{argmin}}_x} {\left \| A x - b \right \|_2^2}$. + + + ```{rubric} Matrix properties + ``` + - This operator may or may not be non-singular. + - This operator is always self-adjoint. + - This operator is always positive definite. + - This operator is always square. + + This operator supports the optional addition of a regularization parameter + $\lambda$ and a transform matrix $T$. If these are provided, + this operator becomes $A^H A + \lambda T^H T$. This appears + in the regularized normal equation + $\left ( A^H A + \lambda T^H T \right ) x = A^H b + \lambda T^H T x_0$, + associated with the regularized least squares problem + ${\mathop{\mathrm{argmin}}_x} {\left \| Ax-b \right \|_2^2 + \lambda \left \| T(x-x_0) \right \|_2^2}$. + + Args: + operator: A `tfmri.linalg.LinearOperator`. The operator $A$ whose Gram + matrix is represented by this linear operator. + reg_parameter: A `Tensor` of shape `[B1, ..., Bb]` and real dtype. + The regularization parameter $\lambda$. Defaults to 0. + reg_operator: A `tfmri.linalg.LinearOperator`. The regularization transform + $T$. Defaults to the identity. + gram_operator: A `tfmri.linalg.LinearOperator`. The Gram matrix + $A^H A$. This may be optionally provided to use a specialized + Gram matrix implementation. Defaults to `None`. + is_non_singular: Expect that this operator is non-singular. + is_self_adjoint: Expect that this operator is equal to its Hermitian + transpose. + is_positive_definite: Expect that this operator is positive definite, + meaning the quadratic form $x^H A x$ has positive real part for all + nonzero $x$. Note that we do not require the operator to be + self-adjoint to be positive-definite. + is_square: Expect that this operator acts like square [batch] matrices. + name: A name for this `LinearOperator`. + """ + def __init__(self, + operator, + reg_parameter=None, + reg_operator=None, + gram_operator=None, + is_non_singular=None, + is_self_adjoint=True, + is_positive_definite=True, + is_square=True, + name=None): + parameters = dict( + operator=operator, + reg_parameter=reg_parameter, + reg_operator=reg_operator, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name) + self._operator = operator + self._reg_parameter = reg_parameter + self._reg_operator = reg_operator + self._gram_operator = gram_operator + if gram_operator is not None: + self._composed = gram_operator + else: + self._composed = linear_operator_composition.LinearOperatorComposition( + operators=[self._operator.H, self._operator]) + + if not is_self_adjoint: + raise ValueError("A Gram matrix is always self-adjoint.") + if not is_positive_definite: + raise ValueError("A Gram matrix is always positive-definite.") + if not is_square: + raise ValueError("A Gram matrix is always square.") + + if self._reg_parameter is not None: + reg_operator_gm = linear_operator_identity.LinearOperatorScaledIdentity( + domain_shape=self._operator.domain_shape, + multiplier=tf.cast(self._reg_parameter, self._operator.dtype)) + if self._reg_operator is not None: + reg_operator_gm = linear_operator_composition.LinearOperatorComposition( + operators=[reg_operator_gm, + self._reg_operator.H, + self._reg_operator]) + self._composed = linear_operator_addition_nd.LinearOperatorAddition( + operators=[self._composed, reg_operator_gm]) + + super().__init__(operator.dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters) + + def _transform(self, x, adjoint=False): + return self._composed.transform(x, adjoint=adjoint) + + def _domain_shape(self): + return self.operator.domain_shape + + def _range_shape(self): + return self.operator.domain_shape + + def _batch_shape(self): + return self.operator.batch_shape + + def _domain_shape_tensor(self): + return self.operator.domain_shape_tensor() + + def _range_shape_tensor(self): + return self.operator.domain_shape_tensor() + + def _batch_shape_tensor(self): + return self.operator.batch_shape_tensor() + + @property + def operator(self): + return self._operator diff --git a/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd.py b/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd.py new file mode 100644 index 00000000..a2e2bf46 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd.py @@ -0,0 +1,151 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Gram matrix of a linear operator.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.linalg import linear_operator_addition_nd +from tensorflow_mri.python.linalg import linear_operator_composition +from tensorflow_mri.python.linalg import linear_operator_identity +from tensorflow_mri.python.util import api_util + + +@api_util.export("linalg.LinearOperatorGramMatrix") +class LinearOperatorGramMatrix(linear_operator.LinearOperator): # pylint: disable=abstract-method + r"""Linear operator representing the Gram matrix of an operator. + + If $A$ is a `LinearOperator`, this operator is equivalent to + $A^H A$. + + The Gram matrix of $A$ appears in the normal equation + $A^H A x = A^H b$ associated with the least squares problem + ${\mathop{\mathrm{argmin}}_x} {\left \| A x - b \right \|_2^2}$. + + + ```{rubric} Matrix properties + ``` + - This operator may or may not be non-singular. + - This operator is always self-adjoint. + - This operator is always positive definite. + - This operator is always square. + + This operator supports the optional addition of a regularization parameter + $\lambda$ and a transform matrix $T$. If these are provided, + this operator becomes $A^H A + \lambda T^H T$. This appears + in the regularized normal equation + $\left ( A^H A + \lambda T^H T \right ) x = A^H b + \lambda T^H T x_0$, + associated with the regularized least squares problem + ${\mathop{\mathrm{argmin}}_x} {\left \| Ax-b \right \|_2^2 + \lambda \left \| T(x-x_0) \right \|_2^2}$. + + Args: + operator: A `tfmri.linalg.LinearOperator`. The operator $A$ whose Gram + matrix is represented by this linear operator. + reg_parameter: A `Tensor` of shape `[B1, ..., Bb]` and real dtype. + The regularization parameter $\lambda$. Defaults to 0. + reg_operator: A `tfmri.linalg.LinearOperator`. The regularization transform + $T$. Defaults to the identity. + gram_operator: A `tfmri.linalg.LinearOperator`. The Gram matrix + $A^H A$. This may be optionally provided to use a specialized + Gram matrix implementation. Defaults to `None`. + is_non_singular: Expect that this operator is non-singular. + is_self_adjoint: Expect that this operator is equal to its Hermitian + transpose. + is_positive_definite: Expect that this operator is positive definite, + meaning the quadratic form $x^H A x$ has positive real part for all + nonzero $x$. Note that we do not require the operator to be + self-adjoint to be positive-definite. + is_square: Expect that this operator acts like square [batch] matrices. + name: A name for this `LinearOperator`. + """ + def __init__(self, + operator, + reg_parameter=None, + reg_operator=None, + gram_operator=None, + is_non_singular=None, + is_self_adjoint=True, + is_positive_definite=True, + is_square=True, + name=None): + parameters = dict( + operator=operator, + reg_parameter=reg_parameter, + reg_operator=reg_operator, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name) + self._operator = operator + self._reg_parameter = reg_parameter + self._reg_operator = reg_operator + self._gram_operator = gram_operator + if gram_operator is not None: + self._composed = gram_operator + else: + self._composed = linear_operator_composition.LinearOperatorComposition( + operators=[self._operator.H, self._operator]) + + if not is_self_adjoint: + raise ValueError("A Gram matrix is always self-adjoint.") + if not is_positive_definite: + raise ValueError("A Gram matrix is always positive-definite.") + if not is_square: + raise ValueError("A Gram matrix is always square.") + + if self._reg_parameter is not None: + reg_operator_gm = linear_operator_identity.LinearOperatorScaledIdentity( + domain_shape=self._operator.domain_shape, + multiplier=tf.cast(self._reg_parameter, self._operator.dtype)) + if self._reg_operator is not None: + reg_operator_gm = linear_operator_composition.LinearOperatorComposition( + operators=[reg_operator_gm, + self._reg_operator.H, + self._reg_operator]) + self._composed = linear_operator_addition_nd.LinearOperatorAddition( + operators=[self._composed, reg_operator_gm]) + + super().__init__(operator.dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters) + + def _transform(self, x, adjoint=False): + return self._composed.transform(x, adjoint=adjoint) + + def _domain_shape(self): + return self.operator.domain_shape + + def _range_shape(self): + return self.operator.domain_shape + + def _batch_shape(self): + return self.operator.batch_shape + + def _domain_shape_tensor(self): + return self.operator.domain_shape_tensor() + + def _range_shape_tensor(self): + return self.operator.domain_shape_tensor() + + def _batch_shape_tensor(self): + return self.operator.batch_shape_tensor() + + @property + def operator(self): + return self._operator diff --git a/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd_test.py new file mode 100644 index 00000000..d7327e24 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd_test.py @@ -0,0 +1,15 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_gram_matrix_nd`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_gram_matrix_test.py b/tensorflow_mri/python/linalg/linear_operator_gram_matrix_test.py new file mode 100644 index 00000000..e68f42a5 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_gram_matrix_test.py @@ -0,0 +1,15 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_gram_matrix`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_identity.py b/tensorflow_mri/python/linalg/linear_operator_identity.py new file mode 100644 index 00000000..78c0c6c0 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_identity.py @@ -0,0 +1,39 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""(Scaled) identity linear operators.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import doc_util + + +LinearOperatorIdentity = api_util.export( + "linalg.LinearOperatorIdentity")( + doc_util.no_linkcode( + linear_operator.make_linear_operator( + tf.linalg.LinearOperatorIdentity))) + + +LinearOperatorScaledIdentity = api_util.export( + "linalg.LinearOperatorScaledIdentity")( + doc_util.no_linkcode( + linear_operator.make_linear_operator( + tf.linalg.LinearOperatorScaledIdentity))) + + +tf.linalg.LinearOperatorIdentity = LinearOperatorIdentity +tf.linalg.LinearOperatorScaledIdentity = LinearOperatorScaledIdentity diff --git a/tensorflow_mri/python/linalg/linear_operator_identity_nd.py b/tensorflow_mri/python/linalg/linear_operator_identity_nd.py new file mode 100644 index 00000000..47329a72 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_identity_nd.py @@ -0,0 +1,652 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""(Scaled) identity N-D linear operator.""" + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.ops import control_flow_ops +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import tensor_util +from tensorflow_mri.python.util import types_util + + +class BaseLinearOperatorIdentityND(linear_operator_nd.LinearOperatorND): + """Base class for Identity operators.""" + + def _check_domain_shape_possibly_add_asserts(self): + """Static check of init arg `domain_shape`, possibly add asserts.""" + # Possibly add asserts. + if self._assert_proper_shapes: + self._domain_shape_arg = tf.compat.v1.with_dependencies([ + tf.debugging.assert_rank( + self._domain_shape_arg, + 1, + message="Argument domain_shape must be a 1-D Tensor."), + tf.debugging.assert_non_negative( + self._domain_shape_arg, + message="Argument domain_shape must be non-negative."), + ], self._domain_shape_arg) + + # Static checks. + if not self._domain_shape_arg.dtype.is_integer: + raise TypeError(f"Argument domain_shape must be integer type. " + f"Found: {self._domain_shape_arg}") + + domain_shape_static = self._domain_shape_static + + if domain_shape_static is None: + return # Cannot do any other static checks. + + if domain_shape_static.ndim != 1: + raise ValueError(f"Argument domain_shape must be a 0-D Tensor. " + f"Found: {domain_shape_static}") + + if any(s is not None and s < 0 for s in domain_shape_static): + raise ValueError(f"Argument domain_shape must be non-negative. " + f"Found: {domain_shape_static}") + + def _ones_diag(self): + """Returns the diagonal of this operator as all ones.""" + if self.shape.is_fully_defined(): + diag_shape = self.batch_shape.concatenate([self.domain_dimension]) + else: + diag_shape = tf.concat( + [self.batch_shape_tensor(), + [self.domain_dimension_tensor()]], axis=0) + + return tf.ones(shape=diag_shape, dtype=self.dtype) + + def _check_compatible_input_shape(self, x): + """Check that an argument to solve/matmul has proper domain shape. + + Adds an assertion op to the graph is `assert_proper_shapes` is `True`. + + Args: + x: A `tf.Tensor`. + + Returns: + A `tf.Tensor` with asserted shape. + """ + # Static checks are done in the base class. Only tensor asserts here. + if self._assert_proper_shapes: + assert_compatible_shapes = tf.debugging.assert_equal( + tf.shape(x)[-self.domain_shape.rank:], + self.domain_shape_tensor(), + message="Shapes are incompatible.") + x = control_flow_ops.with_dependencies([assert_compatible_shapes], x) + return x + + +@api_util.export("linalg.LinearOperatorIdentityND") +@linear_operator_nd.make_linear_operator_nd +class LinearOperatorIdentityND(BaseLinearOperatorIdentityND): + r"""Linear operator acting like a [batch] square identity matrix. + + This operator acts like a batch of identity matrices + $A = I \in \mathbb{F}^{n \times n}$, where $\mathbb{F}$ may be $\mathbb{R}$ + or $\mathbb{C}$ and $n = n_0 \times n_1 \times \dots \times n_d$, where + $d$ is the number of dimensions in the domain. + + ```{note} + The matrix $A$ is not materialized. + ``` + + ```{seealso} + This operator is similar to `tfmri.linalg.LinearOperatorIdentity`, but + provides additional functionality to operate with multidimensional inputs. + ``` + + ```{rubric} Initialization + This operator is initialized with a `domain_shape`, which specifies the + sizes for the domain dimensions. There may be multiple domain dimensions, + which does not affect the dense matrix representation of this operator but + may be convenient to operate with non-vectorized multidimensional inputs. + This operator may also have a `batch_shape`, which will be relevant for the + purposes of broadcasting. Use the `dtype` argument to specify this + operator's data type. + + ```{rubric} Performance + ``` + - `matvec` is usually $O(1)$, but may be $O(n)$ if broadcasting is needed. + - `solvevec` is usually $O(1)$, but may be $O(n)$ if broadcasting is needed. + - `lstsqvec` is usually $O(1)$, but may be $O(n)$ if broadcasting is needed. + + ```{rubric} Properties + ``` + - This operator is always *non-singular*. + - This operator is always *self-adjoint*. + - This operator is always *positive definite*. + - This operator is always *square*. + + ```{rubric} Inversion + ``` + The inverse of this operator is equal to the operator itself ($A{-1} = A$). + + Example: + >>> # Create a 2-D identity operator. + >>> operator = tfmri.linalg.LinearOperatorIdentityND([2, 2]) + >>> operator.to_dense() + [[1., 0., 0., 0.], + [0., 1., 0., 0.] + [0., 0., 1., 0.], + [0., 0., 1., 0.]] + >>> operator.shape + (4, 4) + >>> x = tf.reshape(tf.range(4.), (2, 2)) + >>> rhs = operator.matvec_nd(x) + [[1., 2.], + [3., 4.]] + >>> operator.solvevec_nd(rhs) + [[1., 2.], + [3., 4.]] + + Args: + domain_shape: A 1-D non-negative integer `tf.Tensor`. The domain shape + of this operator. + batch_shape: A 1-D non-negative integer `tf.Tensor`. The leading batch + shape of this operator. If `None`, this operator has no + batch dimensions. + dtype: A `tf.dtypes.DType`. The data type of the matrix that this operator + represents. + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `True`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `True`. + is_positive_definite: A boolean, or `None`. Whether this operator is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that an operator [does + not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) + Defaults to `True`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `True`. + name: An optional `str`. The name of this operator. + """ + def __init__(self, + domain_shape, + batch_shape=None, + dtype=None, + is_non_singular=True, + is_self_adjoint=True, + is_positive_definite=True, + is_square=True, + assert_proper_shapes=False, + name="LinearOperatorIdentityND"): + parameters = dict( + domain_shape=domain_shape, + batch_shape=batch_shape, + dtype=dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + assert_proper_shapes=assert_proper_shapes, + name=name) + + dtype = dtype or tf.dtypes.float32 + self._assert_proper_shapes = assert_proper_shapes + + with tf.name_scope(name): + dtype = tf.dtypes.as_dtype(dtype) + if not is_self_adjoint: + raise ValueError("An identity operator is always self-adjoint.") + if not is_non_singular: + raise ValueError("An identity operator is always non-singular.") + if not is_positive_definite: + raise ValueError("An identity operator is always positive-definite.") + if not is_square: + raise ValueError("An identity operator is always square.") + + super().__init__( + dtype=dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters, + name=name) + + types_util.assert_not_ref_type(domain_shape, "domain_shape") + types_util.assert_not_ref_type(batch_shape, "batch_shape") + + self._domain_shape_static, self._domain_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape( + domain_shape, + assert_proper_shape=self._assert_proper_shapes, + arg_name="domain_shape")) + if self._domain_shape_static.rank is None: + raise ValueError("domain_shape must have known static rank") + + if batch_shape is None: + self._batch_shape_static = tf.TensorShape([]) + self._batch_shape_dynamic = tf.constant([], dtype=tf.int32) + else: + self._batch_shape_static, self._batch_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape( + batch_shape, + assert_proper_shape=self._assert_proper_shapes, + arg_name="batch_shape")) + + def _domain_shape(self): + return self._domain_shape_static + + def _range_shape(self): + return self._domain_shape_static + + def _batch_shape(self): + return self._batch_shape_static + + def _domain_shape_tensor(self): + return self._domain_shape_dynamic + + def _range_shape_tensor(self): + return self._domain_shape_dynamic + + def _batch_shape_tensor(self): + return self._batch_shape_dynamic + + def _assert_non_singular(self): + return tf.no_op("assert_non_singular") + + def _assert_positive_definite(self): + return tf.no_op("assert_positive_definite") + + def _assert_self_adjoint(self): + return tf.no_op("assert_self_adjoint") + + def _possibly_broadcast_batch_shape(self, x): + """Return 'x', possibly after broadcasting the leading dimensions.""" + # If we have no batch shape, our batch shape broadcasts with everything! + if self.batch_shape.rank == 0: + return x + + # Static attempt: + # If we determine that no broadcast is necessary, pass x through + # If we need a broadcast, add to an array of zeros. + # + # special_shape is the shape that, when broadcast with x's shape, will give + # the correct broadcast_shape. Note that + # We have already verified the second to last dimension of self.shape + # matches x's shape in _check_compatible_input_shape. + # Also, the final dimension of 'x' can have any shape. + # Therefore, the final two dimensions of special_shape are ones. + special_shape = self.batch_shape.concatenate([1] * self.domain_shape.rank) + bcast_shape = tf.broadcast_static_shape(x.shape, special_shape) + if special_shape.is_fully_defined(): + if bcast_shape == x.shape: + # Input already has correct shape. Broadcasting is not necessary. + return x + # Use the built in broadcasting of addition. + zeros = tf.zeros(shape=special_shape, dtype=self.dtype) + return x + zeros + + # Dynamic broadcast: + # Always add to an array of zeros, rather than using a "cond", since a + # cond would require copying data from GPU --> CPU. + special_shape = tf.concat( + [self.batch_shape_tensor(), [1] * self.domain_shape.rank], 0) + zeros = tf.zeros(shape=special_shape, dtype=self.dtype) + return x + zeros + + def _matvec_nd(self, x, adjoint=False): + # Note that adjoint has no effect since this matrix is self-adjoint. + x = self._check_compatible_input_shape(x) + return self._possibly_broadcast_batch_shape(x) + + def _solvevec_nd(self, rhs, adjoint=False): + return self._matvec_nd(rhs) + + def _lstsqvec_nd(self, rhs, adjoint=False): + return self._matvec_nd(rhs) + + def _determinant(self): + return tf.ones(shape=self.batch_shape_tensor(), dtype=self.dtype) + + def _log_abs_determinant(self): + return tf.zeros(shape=self.batch_shape_tensor(), dtype=self.dtype) + + def _trace(self): + if self.batch_shape.is_fully_defined(): + ones = tf.ones(shape=self.batch_shape, dtype=self.dtype) + else: + ones = tf.ones(shape=self.batch_shape_tensor(), dtype=self.dtype) + + return ones * tf.cast(self.domain_dimension_tensor(), self.dtype) + + def _diag_part(self): + return self._ones_diag() + + def add_to_tensor(self, mat, name="add_to_tensor"): + """Add matrix represented by this operator to `mat`. Equiv to `I + mat`. + + Args: + mat: A `tf.Tensor` with same `dtype` and shape broadcastable to `self`. + name: A name to give this `Op`. + + Returns: + A `tf.Tensor` with broadcast shape and same `dtype` as `self`. + """ + with self._name_scope(name): # pylint: disable=not-callable + mat = tf.convert_to_tensor(mat, name="mat") + mat_diag = tf.linalg.diag_part(mat) + new_diag = 1 + mat_diag + return tf.linalg.set_diag(mat, new_diag) + + def _eigvals(self): + return self._ones_diag() + + def _cond(self): + return tf.ones(self.batch_shape_tensor(), dtype=self.dtype) + + def _to_dense(self): + return tf.eye( + num_rows=self.domain_dimension_tensor(), + batch_shape=self.batch_shape_tensor(), + dtype=self.dtype) + + @property + def _composite_tensor_prefer_static_fields(self): + return ("domain_shape", "batch_shape") + + @property + def _composite_tensor_fields(self): + return ("domain_shape", "batch_shape", "dtype", "assert_proper_shapes") + + def __getitem__(self, slices): + # Slice the batch shape and return a new LinearOperatorIdentity. + # Use a proxy tensor and slice it. Use this as the new batch shape. + new_batch_shape = tf.shape(tf.ones(self._batch_shape_dynamic)[slices]) + parameters = dict(self.parameters, batch_shape=new_batch_shape) + return LinearOperatorIdentityND(**parameters) + + +@api_util.export("linalg.LinearOperatorScaledIdentityND") +@linear_operator_nd.make_linear_operator_nd +class LinearOperatorScaledIdentityND(BaseLinearOperatorIdentityND): + r"""Linear operator acting like a scaled [batch] identity matrix. + + This operator acts like a batch of scaled identity matrices + $A = \lambda I \in \mathbb{F}^{n \times n}$, where $\lambda$ is a scaling + constant, $\mathbb{F}$ may be $\mathbb{R}$ or $\mathbb{C}$ and + $n = n_0 \times n_1 \times \dots \times n_d$, where + $d$ is the number of dimensions in the domain. + + ```{note} + The matrix $A$ is not materialized. + ``` + + ```{seealso} + This operator is similar to `tfmri.linalg.LinearOperatorScaledIdentityND`, + but provides additional functionality to operate with multidimensional + inputs. + ``` + + ```{rubric} Initialization + This operator is initialized with a `domain_shape`, which specifies the + sizes for the domain dimensions, and a `multiplier`, which specifies the + scaling constant $\lambda$. `domain_shape` may have multiple dimensions, + which does not affect the dense matrix representation of this operator but + may be convenient to operate with non-vectorized multidimensional inputs. + This operator has the same data type as `multiplier`. + + ```{rubric} Performance + ``` + - `matvec` is $O(n)$. + - `solvevec` is $O(n)$. + - `lstsqvec` is $O(n)$. + + ```{rubric} Properties + ``` + - This operator is *non-singular* iff multiplier is non-zero. + - This operator is *self-adjoint* iff multiplier is real or has zero + imaginary part. + - This operator is *positive definite* iff multiplier has positive real part. + - This operator is always *square*. + + ```{rubric} Inversion + ``` + If this operator is non-singular, its inverse $A^{-1}$ is also a scaled + identity operator with reciprocal multiplier. + + Example: + >>> # Create a 2-D identity operator. + >>> operator = tfmri.linalg.LinearOperatorIdentityND([2, 2]) + >>> operator.to_dense() + [[1., 0., 0., 0.], + [0., 1., 0., 0.] + [0., 0., 1., 0.], + [0., 0., 1., 0.]] + >>> operator.shape + (4, 4) + >>> x = tf.reshape(tf.range(4.), (2, 2)) + >>> rhs = operator.matvec_nd(x) + [[1., 2.], + [3., 4.]] + >>> operator.solvevec_nd(rhs) + [[1., 2.], + [3., 4.]] + + Args: + domain_shape: A 1-D non-negative integer `tf.Tensor`. The domain shape + of this operator. + multiplier: A real or complex `tf.Tensor` of any shape specifying the + scaling constant for the identity matrix. + dtype: A `tf.dtypes.DType`. The data type of the matrix that this operator + represents. + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `None`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `None`. + is_positive_definite: A boolean, or `None`. Whether this operator is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that an operator [does + not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) + Defaults to `None`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `True`. + name: An optional `str`. The name of this operator. + """ + def __init__(self, + domain_shape, + multiplier, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=True, + assert_proper_shapes=False, + name="LinearOperatorScaledIdentityND"): + parameters = dict( + domain_shape=domain_shape, + multiplier=multiplier, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + assert_proper_shapes=assert_proper_shapes, + name=name) + + self._assert_proper_shapes = assert_proper_shapes + + with tf.name_scope(name): + # Check domain_shape. + types_util.assert_not_ref_type(domain_shape, "domain_shape") + self._domain_shape_static, self._domain_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape( + domain_shape, + assert_proper_shape=self._assert_proper_shapes, + arg_name="domain_shape")) + if self._domain_shape_static.rank is None: + raise ValueError("domain_shape must have known static rank") + + # Check multiplier. + self._multiplier = types_util.convert_nonref_to_tensor( + multiplier, name="multiplier") + + # Check and auto-set hints. + if not self._multiplier.dtype.is_complex: + if is_self_adjoint is False: # pylint: disable=g-bool-id-comparison + raise ValueError( + "A real scaled identity operator is always self adjoint.") + is_self_adjoint = True + + if not is_square: + raise ValueError("A scaled identity operator is always square.") + + super().__init__( + dtype=self._multiplier.dtype.base_dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters, + name=name) + + def _domain_shape(self): + return self._domain_shape_static + + def _range_shape(self): + return self._domain_shape_static + + def _batch_shape(self): + return self._multiplier.shape + + def _domain_shape_tensor(self): + return self._domain_shape_dynamic + + def _range_shape_tensor(self): + return self._domain_shape_dynamic + + def _batch_shape_tensor(self): + return tf.shape(self._multiplier) + + def _assert_non_singular(self): + return tf.debugging.assert_positive( + tf.math.abs(self.multiplier), + message=("Scaled identity operator is singular: " + "multiplier contains zero entries.")) + + def _assert_positive_definite(self): + if self.dtype.is_complex: + message = ("Scaled identity operator is not positive definite: " + "multiplier contains entries with non-positive real part.") + else: + message = ("Scaled identity operator is not positive definite: " + "multiplier contains non-positive entries.") + return tf.debugging.assert_positive( + tf.math.real(self.multiplier), message=message) + + def _assert_self_adjoint(self): + if not self.dtype.is_complex: + # A real scaled identity operator is always self-adjoint. + return tf.no_op("assert_self_adjoint") + imag_multiplier = tf.math.imag(self.multiplier) + return tf.debugging.assert_equal( + tf.zeros_like(imag_multiplier), + imag_multiplier, + message=("Scaled identity operator is not self-adjoint: " + "multiplier contains entries with non-zero imaginary part.")) + + def _matvec_nd(self, x, adjoint=False): + x = self._check_compatible_input_shape(x) + return x * self._make_multiplier_matrix(adjoint=adjoint) + + def _solvevec_nd(self, rhs, adjoint=False): + rhs = self._check_compatible_input_shape(rhs) + return rhs / self._make_multiplier_matrix(adjoint=adjoint) + + def _lstsqvec_nd(self, rhs, adjoint=False): + return self._solvevec_nd(rhs, adjoint=adjoint) + + def _make_multiplier_matrix(self, adjoint=False): + multiplier_matrix = tf.reshape( + self.multiplier, + tf.concat([tf.shape(self.multiplier), [1] * self.domain_shape.rank], 0)) + multiplier_matrix = tf.ensure_shape( + multiplier_matrix, self.multiplier.shape.concatenate( + [1] * self.domain_shape.rank)) + if adjoint: + multiplier_matrix = tf.math.conj(multiplier_matrix) + return multiplier_matrix + + def _determinant(self): + return self.multiplier ** tf.cast( + self.domain_dimension_tensor(), self.dtype) + + def _log_abs_determinant(self): + return (tf.math.log(tf.math.abs(self.multiplier)) * + tf.cast(self.domain_dimension_tensor(), self.dtype.real_dtype)) + + def _trace(self): + return self.multiplier * tf.cast(self.domain_dimension_tensor(), self.dtype) + + def _diag_part(self): + return self._ones_diag() * self.multiplier[..., tf.newaxis] + + def add_to_tensor(self, mat, name="add_to_tensor"): + """Add matrix represented by this operator to `mat`. Equiv to `I + mat`. + + Args: + mat: `Tensor` with same `dtype` and shape broadcastable to `self`. + name: A name to give this `Op`. + + Returns: + A `Tensor` with broadcast shape and same `dtype` as `self`. + """ + with self._name_scope(name): # pylint: disable=not-callable + # Shape [B1,...,Bb, 1] + multiplier_vector = tf.expand_dims(self.multiplier, -1) + # Shape [C1,...,Cc, M, M] + mat = tf.convert_to_tensor(mat, name="mat") + # Shape [C1,...,Cc, M] + mat_diag = tf.linalg.diag_part(mat) + # multiplier_vector broadcasts here. + new_diag = multiplier_vector + mat_diag + return tf.linalg.set_diag(mat, new_diag) + + def _eigvals(self): + return self._ones_diag() * self.multiplier[..., tf.newaxis] + + def _cond(self): + # Condition number for a scalar time identity matrix is one, except when the + # scalar is zero. + return tf.where( + tf.math.equal(self._multiplier, 0.), + tf.cast(np.nan, dtype=self.dtype), + tf.cast(1., dtype=self.dtype)) + + def _to_dense(self): + return self.multiplier[..., tf.newaxis, tf.newaxis] * tf.eye( + num_rows=self.domain_dimension_tensor(), + dtype=self.dtype) + + @property + def multiplier(self): + """The [batch] scalar `tf.Tensor`, $c$ in $cI$.""" + return self._multiplier + + @property + def _composite_tensor_prefer_static_fields(self): + return ("domain_shape",) + + @property + def _composite_tensor_fields(self): + return ("domain_shape", "multiplier", "assert_proper_shapes") + + @property + def _experimental_parameter_ndims_to_matrix_ndims(self): + return {"multiplier": 0} diff --git a/tensorflow_mri/python/linalg/linear_operator_identity_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_identity_nd_test.py new file mode 100644 index 00000000..67ea95d5 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_identity_nd_test.py @@ -0,0 +1,619 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_identity_nd +from tensorflow_mri.python.linalg import linear_operator_test_util +from tensorflow_mri.python.util import test_util + + +rng = np.random.RandomState(2016) + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorIdentityNDTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + def tearDown(self): + tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) + + def setUp(self): + self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() + tf.config.experimental.enable_tensor_float_32_execution(False) + + @staticmethod + def dtypes_to_test(): + # TODO(langmore) Test tf.float16 once tf.linalg.solve works in + # 16bit. + return [tf.float32, tf.float64, tf.complex64, tf.complex128] + + @staticmethod + def optional_tests(): + """List of optional test names to run.""" + return [ + "operator_matmul_with_same_type", + "operator_solve_with_same_type", + ] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + # Identity matrix is already Hermitian Positive Definite. + del ensure_self_adjoint_and_pd + + shape = list(build_info.shape) + assert shape[-1] == shape[-2] + + batch_shape = shape[:-2] + num_rows = shape[-1] + + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + [num_rows], batch_shape=batch_shape, dtype=dtype) + mat = tf.eye(num_rows, batch_shape=batch_shape, dtype=dtype) + + return operator, mat + + def test_assert_positive_definite(self): + with self.cached_session(): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2]) + self.evaluate(operator.assert_positive_definite()) # Should not fail + + def test_assert_non_singular(self): + with self.cached_session(): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2]) + self.evaluate(operator.assert_non_singular()) # Should not fail + + def test_assert_self_adjoint(self): + with self.cached_session(): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2]) + self.evaluate(operator.assert_self_adjoint()) # Should not fail + + def test_float16_matmul(self): + # float16 cannot be tested by base test class because tf.linalg.solve does + # not work with float16. + with self.cached_session(): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2], dtype=tf.float16) + x = rng.randn(2, 3).astype(np.float16) + y = operator.matmul(x) + self.assertAllClose(x, self.evaluate(y)) + + def test_non_1d_domain_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be a 1-D"): + linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=2) + + def test_non_integer_domain_shape_raises_static(self): + with self.assertRaisesRegex(TypeError, "must be integer"): + linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=[2.]) + + def test_negative_domain_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be non-negative"): + linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=[-2]) + + def test_non_1d_batch_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be a 1-D"): + linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2], batch_shape=2) + + def test_non_integer_batch_shape_raises_static(self): + with self.assertRaisesRegex(TypeError, "must be integer"): + linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2], batch_shape=[2.]) + + def test_negative_batch_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be non-negative"): + linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2], batch_shape=[-2]) + + def test_unknown_domain_shape_rank_raises_static(self): + if tf.executing_eagerly(): + return + with self.cached_session(): + domain_shape = tf.compat.v1.placeholder_with_default([2], shape=None) + with self.assertRaisesRegex(ValueError, "must have known static rank"): + linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape) + + def test_negative_domain_shape_raises_dynamic(self): + with self.cached_session(): + domain_shape = tf.compat.v1.placeholder_with_default([-2], shape=[1]) + with self.assertRaisesError("must be non-negative"): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape, + assert_proper_shapes=True) + self.evaluate(operator.to_dense()) + + def test_negative_batch_shape_raises_dynamic(self): + with self.cached_session(): + batch_shape = tf.compat.v1.placeholder_with_default([-2], shape=[1]) + with self.assertRaisesError("must be non-negative"): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2], + batch_shape=batch_shape, + assert_proper_shapes=True) + self.evaluate(operator.to_dense()) + + def test_wrong_matrix_dimensions_raises_static(self): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2]) + x = rng.randn(3, 3).astype(np.float32) + with self.assertRaisesRegex(ValueError, "Dimensions.*not compatible"): + operator.matmul(x) + + def test_wrong_matrix_dimensions_nd_raises_static(self): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2]) + x = rng.randn(3,).astype(np.float32) + with self.assertRaisesRegex(ValueError, "Shapes.*incompatible"): + operator.matvec_nd(x) + + def test_wrong_matrix_dimensions_nd_raises_dynamic(self): + domain_shape = tf.compat.v1.placeholder_with_default([2], shape=[1]) + x = tf.compat.v1.placeholder_with_default( + rng.rand(3,).astype(np.float32), shape=None) + + with self.cached_session(): + with self.assertRaisesError("Shapes.*incompatible"): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape, + assert_proper_shapes=True) + self.evaluate(operator.matvec_nd(x)) + + def test_default_batch_shape_broadcasts_with_everything_static(self): + # These cannot be done in the automated (base test class) tests since they + # test shapes that tf.batch_matmul cannot handle. + # In particular, tf.batch_matmul does not broadcast. + with self.cached_session() as sess: + x = tf.random.normal(shape=(1, 2, 3, 4)) + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[3], dtype=x.dtype) + + operator_matmul = operator.matmul(x) + expected = x + + self.assertAllEqual(operator_matmul.shape, expected.shape) + self.assertAllClose(*self.evaluate([operator_matmul, expected])) + + def test_default_batch_shape_broadcasts_with_everything_dynamic(self): + # These cannot be done in the automated (base test class) tests since they + # test shapes that tf.batch_matmul cannot handle. + # In particular, tf.batch_matmul does not broadcast. + with self.cached_session(): + x = tf.compat.v1.placeholder_with_default(rng.randn(1, 2, 3, 4), shape=None) + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[3], dtype=x.dtype) + + operator_matmul = operator.matmul(x) + expected = x + + self.assertAllClose(*self.evaluate([operator_matmul, expected])) + + def test_broadcast_matmul_static_shapes(self): + # These cannot be done in the automated (base test class) tests since they + # test shapes that tf.batch_matmul cannot handle. + # In particular, tf.batch_matmul does not broadcast. + with self.cached_session() as sess: + # Given this x and LinearOperatorIdentityND shape of (2, 1, 3, 3), the + # broadcast shape of operator and 'x' is (2, 2, 3, 4) + x = tf.random.normal(shape=(1, 2, 3, 4)) + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=(3,), batch_shape=(2, 1), dtype=x.dtype) + + # Batch matrix of zeros with the broadcast shape of x and operator. + zeros = tf.zeros(shape=(2, 2, 3, 4), dtype=x.dtype) + + # Expected result of matmul and solve. + expected = x + zeros + + operator_matmul = operator.matmul(x) + self.assertAllEqual(operator_matmul.shape, expected.shape) + self.assertAllClose(*self.evaluate([operator_matmul, expected])) + + def test_broadcast_matmul_dynamic_shapes(self): + # These cannot be done in the automated (base test class) tests since they + # test shapes that tf.batch_matmul cannot handle. + # In particular, tf.batch_matmul does not broadcast. + with self.cached_session(): + # Given this x and LinearOperatorIdentityND shape of (2, 1, 3, 3), the + # broadcast shape of operator and 'x' is (2, 2, 3, 4) + x = tf.compat.v1.placeholder_with_default( + rng.rand(1, 2, 3, 4), shape=None) + domain_shape = tf.compat.v1.placeholder_with_default((3,), shape=(1,)) + batch_shape = tf.compat.v1.placeholder_with_default((2, 1), shape=(2,)) + + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape, batch_shape=batch_shape, dtype=tf.float64) + + # Batch matrix of zeros with the broadcast shape of x and operator. + zeros = tf.zeros(shape=(2, 2, 3, 4), dtype=x.dtype) + + # Expected result of matmul and solve. + expected = x + zeros + + operator_matmul = operator.matmul(x) + self.assertAllClose(*self.evaluate([operator_matmul, expected])) + + def test_is_x_flags(self): + # The is_x flags are by default all True. + operator = linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=[2]) + self.assertTrue(operator.is_positive_definite) + self.assertTrue(operator.is_non_singular) + self.assertTrue(operator.is_self_adjoint) + + # Any of them False raises because the identity is always self-adjoint etc.. + with self.assertRaisesRegex(ValueError, "is always non-singular"): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2], is_non_singular=None) + + def test_identity_adjoint_type(self): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2], is_non_singular=True) + self.assertIsInstance( + operator.adjoint(), linear_operator_identity_nd.LinearOperatorIdentityND) + + def test_identity_cholesky_type(self): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2], + is_positive_definite=True, + is_self_adjoint=True, + ) + self.assertIsInstance( + operator.cholesky(), + linear_operator_identity_nd.LinearOperatorIdentityND) + + def test_identity_inverse_type(self): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2], is_non_singular=True) + self.assertIsInstance( + operator.inverse(), linear_operator_identity_nd.LinearOperatorIdentityND) + + def test_ref_type_shape_args_raises(self): + with self.assertRaisesRegex(TypeError, "domain_shape.*reference"): + linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=tf.Variable([2])) + + with self.assertRaisesRegex(TypeError, "batch_shape.*reference"): + linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=[2], batch_shape=tf.Variable([3])) + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorScaledIdentityNDTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + def tearDown(self): + tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) + + def setUp(self): + self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() + tf.config.experimental.enable_tensor_float_32_execution(False) + + @staticmethod + def dtypes_to_test(): + # TODO(langmore) Test tf.float16 once tf.linalg.solve works in + # 16bit. + return [tf.float32, tf.float64, tf.complex64, tf.complex128] + + @staticmethod + def optional_tests(): + """List of optional test names to run.""" + return [ + "operator_matmul_with_same_type", + "operator_solve_with_same_type", + ] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + + shape = list(build_info.shape) + assert shape[-1] == shape[-2] + + batch_shape = shape[:-2] + num_rows = shape[-1] + + # Uniform values that are at least length 1 from the origin. Allows the + # operator to be well conditioned. + # Shape batch_shape + multiplier = linear_operator_test_util.random_sign_uniform( + shape=batch_shape, minval=1., maxval=2., dtype=dtype) + + if ensure_self_adjoint_and_pd: + multiplier = tf.cast(tf.math.abs(multiplier), dtype=dtype) + + # Nothing to feed since LinearOperatorScaledIdentityND takes no Tensor args. + lin_op_multiplier = multiplier + + if use_placeholder: + lin_op_multiplier = tf.compat.v1.placeholder_with_default( + multiplier, shape=None) + + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + [num_rows], + lin_op_multiplier, + is_self_adjoint=True if ensure_self_adjoint_and_pd else None, + is_positive_definite=True if ensure_self_adjoint_and_pd else None) + + multiplier_matrix = tf.expand_dims( + tf.expand_dims(multiplier, -1), -1) + matrix = multiplier_matrix * tf.eye( + num_rows, batch_shape=batch_shape, dtype=dtype) + + return operator, matrix + + def test_assert_positive_definite_does_not_raise_when_positive(self): + with self.cached_session(): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=1.) + self.evaluate(operator.assert_positive_definite()) # Should not fail + + def test_assert_positive_definite_raises_when_negative(self): + with self.cached_session(): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=-1.) + with self.assertRaisesOpError("operator is not positive definite"): + self.evaluate(operator.assert_positive_definite()) + + def test_assert_non_singular_does_not_raise_when_non_singular(self): + with self.cached_session(): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=[1., 2., 3.]) + self.evaluate(operator.assert_non_singular()) # Should not fail + + def test_assert_non_singular_raises_when_singular(self): + with self.cached_session(): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=[1., 2., 0.]) + with self.assertRaisesOpError("operator is singular"): + self.evaluate(operator.assert_non_singular()) + + def test_assert_self_adjoint_does_not_raise_when_self_adjoint(self): + with self.cached_session(): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=[1. + 0J]) + self.evaluate(operator.assert_self_adjoint()) # Should not fail + + def test_assert_self_adjoint_raises_when_not_self_adjoint(self): + with self.cached_session(): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=[1. + 1J]) + with self.assertRaisesOpError("operator is not self-adjoint"): + self.evaluate(operator.assert_self_adjoint()) + + def test_float16_matmul(self): + # float16 cannot be tested by base test class because tf.linalg.solve does + # not work with float16. + with self.cached_session(): + multiplier = rng.rand(3).astype(np.float16) + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=multiplier) + x = rng.randn(2, 3).astype(np.float16) + y = operator.matmul(x) + self.assertAllClose(multiplier[..., None, None] * x, self.evaluate(y)) + + def test_non_1d_domain_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be a 1-D"): + linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=2, multiplier=123.) + + def test_wrong_matrix_dimensions_raises_static(self): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=2.2) + x = rng.randn(3, 3).astype(np.float32) + with self.assertRaisesRegex(ValueError, "Dimensions.*not compatible"): + operator.matmul(x) + + def test_wrong_matrix_dimensions_nd_raises_static(self): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=2.2) + x = rng.randn(3,).astype(np.float32) + with self.assertRaisesRegex(ValueError, "Shapes.*incompatible"): + operator.matvec_nd(x) + + def test_wrong_matrix_dimensions_nd_raises_dynamic(self): + domain_shape = tf.compat.v1.placeholder_with_default([2], shape=[1]) + x = tf.compat.v1.placeholder_with_default( + rng.rand(3,).astype(np.float32), shape=None) + + with self.cached_session(): + with self.assertRaisesError("Shapes.*incompatible"): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape, + multiplier=[1., 2], + assert_proper_shapes=True) + self.evaluate(operator.matvec_nd(x)) + + def test_broadcast_matmul_and_solve(self): + # These cannot be done in the automated (base test class) tests since they + # test shapes that tf.batch_matmul cannot handle. + # In particular, tf.batch_matmul does not broadcast. + with self.cached_session() as sess: + # Given this x and LinearOperatorScaledIdentityND shape of (2, 1, 3, 3), the + # broadcast shape of operator and 'x' is (2, 2, 3, 4) + x = tf.random.normal(shape=(1, 2, 3, 4)) + + # operator is 2.2 * identity (with a batch shape). + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[3], multiplier=2.2 * tf.ones((2, 1))) + + # Batch matrix of zeros with the broadcast shape of x and operator. + zeros = tf.zeros(shape=(2, 2, 3, 4), dtype=x.dtype) + + # Test matmul + expected = x * 2.2 + zeros + operator_matmul = operator.matmul(x) + self.assertAllEqual(operator_matmul.shape, expected.shape) + self.assertAllClose(*self.evaluate([operator_matmul, expected])) + + # Test solve + expected = x / 2.2 + zeros + operator_solve = operator.solve(x) + self.assertAllEqual(operator_solve.shape, expected.shape) + self.assertAllClose(*self.evaluate([operator_solve, expected])) + + def test_broadcast_matmul_and_solve_scalar_scale_multiplier(self): + # These cannot be done in the automated (base test class) tests since they + # test shapes that tf.batch_matmul cannot handle. + # In particular, tf.batch_matmul does not broadcast. + with self.cached_session() as sess: + # Given this x and LinearOperatorScaledIdentityND shape of (3, 3), the + # broadcast shape of operator and 'x' is (1, 2, 3, 4), which is the same + # shape as x. + x = tf.random.normal(shape=(1, 2, 3, 4)) + + # operator is 2.2 * identity (with a batch shape). + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[3], multiplier=2.2) + + # Test matmul + expected = x * 2.2 + operator_matmul = operator.matmul(x) + self.assertAllEqual(operator_matmul.shape, expected.shape) + self.assertAllClose(*self.evaluate([operator_matmul, expected])) + + # Test solve + expected = x / 2.2 + operator_solve = operator.solve(x) + self.assertAllEqual(operator_solve.shape, expected.shape) + self.assertAllClose(*self.evaluate([operator_solve, expected])) + + def test_is_x_flags(self): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=1., + is_positive_definite=False, is_non_singular=True) + self.assertFalse(operator.is_positive_definite) + self.assertTrue(operator.is_non_singular) + self.assertTrue(operator.is_self_adjoint) # Auto-set due to real multiplier + + def test_identity_matmul(self): + operator1 = linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=[2]) + operator2 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=3.) + self.assertIsInstance( + operator1.matmul(operator1), + linear_operator_identity_nd.LinearOperatorIdentityND) + + self.assertIsInstance( + operator1.matmul(operator1), + linear_operator_identity_nd.LinearOperatorIdentityND) + + self.assertIsInstance( + operator2.matmul(operator2), + linear_operator_identity_nd.LinearOperatorScaledIdentityND) + + operator_matmul = operator1.matmul(operator2) + self.assertIsInstance( + operator_matmul, + linear_operator_identity_nd.LinearOperatorScaledIdentityND) + self.assertAllClose(3., self.evaluate(operator_matmul.multiplier)) + + operator_matmul = operator2.matmul(operator1) + self.assertIsInstance( + operator_matmul, + linear_operator_identity_nd.LinearOperatorScaledIdentityND) + self.assertAllClose(3., self.evaluate(operator_matmul.multiplier)) + + def test_identity_solve(self): + operator1 = linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=[2]) + operator2 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=3.) + self.assertIsInstance( + operator1.solve(operator1), + linear_operator_identity_nd.LinearOperatorIdentityND) + + self.assertIsInstance( + operator2.solve(operator2), + linear_operator_identity_nd.LinearOperatorScaledIdentityND) + + operator_solve = operator1.solve(operator2) + self.assertIsInstance( + operator_solve, + linear_operator_identity_nd.LinearOperatorScaledIdentityND) + self.assertAllClose(3., self.evaluate(operator_solve.multiplier)) + + operator_solve = operator2.solve(operator1) + self.assertIsInstance( + operator_solve, + linear_operator_identity_nd.LinearOperatorScaledIdentityND) + self.assertAllClose(1. / 3., self.evaluate(operator_solve.multiplier)) + + def test_scaled_identity_cholesky_type(self): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], + multiplier=3., + is_positive_definite=True, + is_self_adjoint=True, + ) + self.assertIsInstance( + operator.cholesky(), + linear_operator_identity_nd.LinearOperatorScaledIdentityND) + + def test_scaled_identity_inverse_type(self): + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], + multiplier=3., + is_non_singular=True, + ) + self.assertIsInstance( + operator.inverse(), + linear_operator_identity_nd.LinearOperatorScaledIdentityND) + + def test_ref_type_shape_args_raises(self): + with self.assertRaisesRegex(TypeError, "domain_shape.*reference"): + linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=tf.Variable([2]), multiplier=1.23) + + def test_tape_safe(self): + multiplier = tf.Variable(1.23) + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=multiplier) + self.check_tape_safe(operator) + + def test_convert_variables_to_tensors(self): + multiplier = tf.Variable(1.23) + operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=[2], multiplier=multiplier) + with self.cached_session() as sess: + sess.run([multiplier.initializer]) + self.check_convert_variables_to_tensors(operator) + + +linear_operator_test_util.add_tests(LinearOperatorIdentityNDTest) +linear_operator_test_util.add_tests(LinearOperatorScaledIdentityNDTest) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_identity_test.py b/tensorflow_mri/python/linalg/linear_operator_identity_test.py new file mode 100644 index 00000000..ea3c2416 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_identity_test.py @@ -0,0 +1,15 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for `LinearOperatorIdentity` and `LinearOperatorScaledIdentity`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_inversion.py b/tensorflow_mri/python/linalg/linear_operator_inversion.py new file mode 100644 index 00000000..c918bda1 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_inversion.py @@ -0,0 +1,32 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Adjoint of a linear operator.""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.linalg import linear_operator_util +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import doc_util + + +LinearOperatorInversion = api_util.export( + "linalg.LinearOperatorInversion")( + doc_util.no_linkcode( + linear_operator.make_linear_operator( + tf.linalg.LinearOperatorInversion))) + + +tf.linalg.LinearOperatorInversion = LinearOperatorInversion diff --git a/tensorflow_mri/python/linalg/linear_operator_inversion_test.py b/tensorflow_mri/python/linalg/linear_operator_inversion_test.py new file mode 100644 index 00000000..912b4049 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_inversion_test.py @@ -0,0 +1,15 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for `LinearOperatorInversion`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_mask.py b/tensorflow_mri/python/linalg/linear_operator_mask.py new file mode 100644 index 00000000..2709a6b0 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_mask.py @@ -0,0 +1,259 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Masking linear operator.""" + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.util import api_util + + +@api_util.export("linalg.LinearOperatorMask") +@linear_operator_nd.make_linear_operator_nd +class LinearOperatorMask(linear_operator_nd.LinearOperatorND): + r"""Linear operator acting like a [batch] masking matrix. + + Represents a diagonal matrix $A \in \mathbb{F}^{n \times n}$ whose diagonal + entries are either one or zero. This operator is useful for masking out + certain entries in a vector or matrix. + + ```{tip} + You can use this operator to mask *k*-space values in undersampled Cartesian + MRI. + ``` + + ```{rubric} Performance + ``` + - `matvec` is $O(n)$. + - `solvevec` is not supported. + - `lstsqvec` is $O(n)$. + + ```{rubric} Properties + ``` + - This operator is singular, i.e. $A^{-1}$ does not exist. + - This operator is self-adjoint, i.e. $A^H = A$. + - This operator is not positive definite, i.e. $x^H A x <= 0$ for some $x$. + - This operator is square, i.e. $A \in \mathbb{F}^{n \times n}$. + + ```{rubric} Inversion + ``` + In general, the masking operator is singular and cannot be inverted, so + `solve` and `inverse` will raise an error. + + However, you can use `lstsq` or `pseudo_inverse` to solve the associated + least-squares problem. The pseudo-inverse of the masking operator is the + operator itself, i.e., $A^+ = A$. + + Example: + >>> mask = [True, False, True, False] + >>> linop = tfmri.linalg.LinearOperatorMask(mask) + >>> x = tf.constant([1., 2., 3., 4.]) + >>> y = linop.matvec_nd(x) + >>> y.numpy() + array([1., 0., 3., 0.]) + + Args: + mask: A boolean `tf.Tensor` of shape `[..., *spatial_shape]`. + batch_dims: An `int`, the number of batch dimensions in `mask`. + dtype: The `dtype` of the operator. Must be float or complex. If `None`, + defaults to `float32`. + algorithm: A `str`, one of `'multiply'` or `'multiplex'`. The algorithm to + use for masking. + - `'multiply'` (default) applies the mask by multiplying each value in + the input tensor by either one or zero. This is often faster, although + this depends on the specific problem and your hardware. + - `'multiplex'` applies the mask by using the input mask as a condition + and multiplexing the input with a zero tensor. See `tf.where` for more + details. + ```{attention} + The IEEE 754 standard for floating-point arithmetic has a + [signed zero](https://en.wikipedia.org/wiki/Signed_zero). When using + `'multiply'`, the zeroed out values will be positive zero for positive + inputs and negative zero for negative inputs. Therefore, the `'multiply'` + algorithm leaks sign information. If this is a concern in your + application, use `'multiplex'` instead. + ``` + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `False`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `True`. + is_positive_definite: A boolean, or `None`. Whether this operator is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that an operator [does + not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) + Defaults to `False`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `True`. + name: An optional `str`. The name of this operator. + """ + def __init__(self, + mask, + batch_dims=0, + dtype=None, + algorithm='multiply', + is_non_singular=False, + is_self_adjoint=True, + is_positive_definite=False, + is_square=True, + name='LinearOperatorMask'): + parameters = dict( + mask=mask, + batch_dims=batch_dims, + dtype=dtype, + algorithm=algorithm, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name + ) + + with tf.name_scope(name): + if dtype is None: + dtype = tf.float32 + dtype = tf.dtypes.as_dtype(dtype) + if not dtype.is_floating and not dtype.is_complex: + raise TypeError(f"dtype must be float or complex, got {str(dtype)}") + + self._batch_dims = np.asarray(tf.get_static_value(batch_dims)) + if (not self._batch_dims.ndim == 0 or + not np.issubdtype(self._batch_dims.dtype, np.integer)): + raise TypeError( + f"batch_dims must be an int, but got: {batch_dims}") + self._batch_dims = self._batch_dims.item() + if self._batch_dims < 0: + raise ValueError( + f"batch_dims must be non-negative, but got: {batch_dims}") + + self._mask = tf.convert_to_tensor(mask, name="mask") + if not self._mask.dtype.is_bool: + raise TypeError( + f"mask must be boolean, but got dtype: {str(self._mask.dtype)}") + if self._mask.shape.rank is None: + raise ValueError("mask must have known static rank") + self._ndim_static = self._mask.shape.rank - self._batch_dims + if self._ndim_static < 1: + raise ValueError( + f"mask must be at least 1-D (excluding batch dimensions), " + f"but got shape: {self._mask.shape}") + + if algorithm not in {'multiply', 'multiplex'}: + raise ValueError( + f"algorithm must be one of 'multiply' or 'multiplex', " + f"but got: {algorithm}") + if algorithm == 'multiply': + self._mask_mult = tf.cast(self._mask, dtype) + self._algorithm = algorithm + + if not is_self_adjoint: + raise ValueError("A mask operator is always self-adjoint.") + if is_non_singular: + raise ValueError("A mask operator is always singular.") + if is_positive_definite: + raise ValueError("A mask operator is never positive definite.") + if not is_square: + raise ValueError("A mask operator is always square.") + + super().__init__( + dtype=dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters, + name=name) + + def _matvec_nd(self, x, adjoint=False): + # This operator is self-adjoint, so we can ignore the adjoint argument. + if self._algorithm == 'multiply': + x = x * self._mask_mult + elif self._algorithm == 'multiplex': + x = tf.where(self._mask, x, tf.zeros_like(x)) + else: + raise ValueError(f"Unknown masking algorithm: {self._algorithm}") + return x + + def _solvevec_nd(self, rhs, adjoint=False): + raise ValueError( + f"{self.name} is not invertible. If you intend to solve the " + f"associated least-squares problem, use `lstsq`, `lstsqvec` or " + f"`lstsqvec_nd`.") + + def _lstsqvec_nd(self, rhs, adjoint=False): + # The value of adjoint is irrelevant, but be pedantic. + return self._matvec_nd(rhs, adjoint=(not adjoint)) + + def _ndim(self): + return self._ndim_static + + def _domain_shape(self): + return self._mask.shape[self._batch_dims:] + + def _range_shape(self): + return self._mask.shape[self._batch_dims:] + + def _batch_shape(self): + return self._mask.shape[:self._batch_dims] + + def _domain_shape_tensor(self): + return tf.shape(self._mask)[self._batch_dims:] + + def _range_shape_tensor(self): + return tf.shape(self._mask)[self._batch_dims:] + + def _batch_shape_tensor(self): + return tf.shape(self._mask)[:self._batch_dims] + + @property + def mask(self): + return self._mask + + @property + def _composite_tensor_fields(self): + return ('mask', 'batch_dims', 'dtype', 'algorithm') + + @property + def _composite_tensor_prefer_static_fields(self): + return ('batch_dims',) + + @property + def _experimental_parameter_ndims_to_matrix_ndims(self): + return {'mask': self.ndim} + + +def mask_matrix(mask, batch_dims=0, dtype=None): + """Constructs a masking matrix. + + Args: + mask: A complex `tf.Tensor` of shape `[..., *spatial_shape]`. + batch_dims: An `int`, the number of batch dimensions in `mask`. + + Returns: + A `tf.Tensor` representing a dense coil array matrix equivalent to + `LinearOperatorMask`. + """ + mask = tf.convert_to_tensor(mask, name="mask") + mask = tf.cast(mask, dtype or tf.float32) + + # Vectorize N-D mask. + mask = tf.reshape( + mask, tf.concat([tf.shape(mask)[:batch_dims], [-1]], axis=0)) + + # Construct a [batch] diagonal matrix. + matrix = tf.linalg.diag(mask) + + return matrix diff --git a/tensorflow_mri/python/linalg/linear_operator_mask_test.py b/tensorflow_mri/python/linalg/linear_operator_mask_test.py new file mode 100644 index 00000000..f518de90 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_mask_test.py @@ -0,0 +1,212 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_mask`.""" +# pylint: disable=missing-class-docstring,missing-function-docstring + +import functools + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_mask +from tensorflow_mri.python.linalg import linear_operator_test_util +from tensorflow_mri.python.util import test_util + + +rng = np.random.RandomState(2016) + + +class OperatorShapesInfoCoils(): + def __init__(self, image_shape, batch_shape): + self.image_shape = image_shape + self.batch_shape = batch_shape + + @property + def shape(self): + n = functools.reduce(lambda a, b: a * b, self.image_shape) + return self.batch_shape + (n, n) + + @property + def dimension(self): + return len(self.image_shape) + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorMaskMultiplyTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + @staticmethod + def operator_shapes_infos(): + shapes_info = OperatorShapesInfoCoils + return [ + shapes_info((2, 2), ()), + shapes_info((2, 4), (3,)), + shapes_info((4, 2), (1, 2)), + shapes_info((2, 3), ()), + shapes_info((2, 2, 2), ()), + shapes_info((4, 2, 2), (2,)) + # TODO(jmontalt): odd shapes fail tests, investigate + # shapes_info((2, 3), 5, (2,)), + # shapes_info((3, 2), 7, ()) + ] + + @staticmethod + def dtypes_to_test(): + return [tf.float32, tf.float64, tf.complex64, tf.complex128] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + del ensure_self_adjoint_and_pd + del use_placeholder + + batch_shape = build_info.batch_shape + image_shape = build_info.image_shape + + mask = tf.random.uniform(shape=batch_shape + image_shape) > 0.5 + + operator = linear_operator_mask.LinearOperatorMask( + mask=mask, batch_dims=len(batch_shape), dtype=dtype, + algorithm='multiply') + + matrix = linear_operator_mask.mask_matrix( + mask=mask, batch_dims=len(batch_shape), dtype=dtype) + + return operator, matrix + + def test_0d_mask_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be at least 1-D"): + linear_operator_mask.LinearOperatorMask( + mask=np.ones(()).astype(np.bool_)) + + with self.assertRaisesRegex(ValueError, "must be at least 1-D"): + linear_operator_mask.LinearOperatorMask( + mask=np.ones((4, 4)).astype(np.bool_), + batch_dims=2) + + linear_operator_mask.LinearOperatorMask( + mask=np.ones((4, 4)).astype(np.bool_), + batch_dims=1) # should not raise + + def test_non_bool_mask_raises_static(self): + with self.assertRaisesRegex(TypeError, "must be boolean"): + linear_operator_mask.LinearOperatorMask( + mask=np.ones((4, 4)).astype(np.float32)) + + def test_unknown_rank_mask_raises_static(self): + if tf.executing_eagerly(): + return + with self.cached_session(): + mask = tf.compat.v1.placeholder_with_default( + np.ones((3, 4, 4)).astype(np.bool_), shape=None) + with self.assertRaisesRegex(ValueError, "must have known static rank"): + operator = linear_operator_mask.LinearOperatorMask(mask=mask) + self.evaluate(operator.to_dense()) + + def test_non_integer_batch_dims_raises_static(self): + with self.assertRaisesRegex(TypeError, "must be an int"): + linear_operator_mask.LinearOperatorMask( + mask=np.ones((3, 4, 4)).astype(np.bool_), batch_dims=1.) + + def test_negative_batch_dims_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be non-negative"): + linear_operator_mask.LinearOperatorMask( + mask=np.ones((3, 4, 4)).astype(np.bool_), batch_dims=-1) + + def test_is_x_flags(self): + operator = linear_operator_mask.LinearOperatorMask( + mask=np.ones((3, 4, 4)).astype(np.bool_)) + self.assertTrue(operator.is_self_adjoint) + self.assertFalse(operator.is_non_singular) + self.assertTrue(operator.is_square) + + def test_solve_raises(self): + operator = linear_operator_mask.LinearOperatorMask( + mask=np.ones((1, 4, 4)).astype(np.bool_), is_square=True) + with self.assertRaisesRegex(NotImplementedError, "singular"): + operator.solve(tf.ones([16, 1], dtype=tf.bool)) + + def test_inverse_raises(self): + operator = linear_operator_mask.LinearOperatorMask( + mask=np.ones((1, 4, 4)).astype(np.bool_), is_square=True) + with self.assertRaisesRegex(ValueError, "singular"): + operator.inverse() + + def test_adjoint_type(self): + operator = linear_operator_mask.LinearOperatorMask( + mask=np.ones((3, 4)).astype(np.bool_)) + self.assertIsInstance( + operator.adjoint(), linear_operator_mask.LinearOperatorMask) + + def test_convert_variables_to_tensors(self): + mask = tf.Variable(np.ones((3, 4, 4)).astype(np.bool_)) + operator = linear_operator_mask.LinearOperatorMask(mask=mask) + with self.cached_session() as sess: + sess.run([mask.initializer]) + self.check_convert_variables_to_tensors(operator) + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorMaskMultiplexTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + @staticmethod + def operator_shapes_infos(): + shapes_info = OperatorShapesInfoCoils + return [ + shapes_info((2, 2), ()), + shapes_info((2, 4), (3,)), + shapes_info((4, 2), (1, 2)), + shapes_info((2, 3), ()), + shapes_info((2, 2, 2), ()), + shapes_info((4, 2, 2), (2,)) + # TODO(jmontalt): odd shapes fail tests, investigate + # shapes_info((2, 3), 5, (2,)), + # shapes_info((3, 2), 7, ()) + ] + + @staticmethod + def dtypes_to_test(): + return [tf.float32, tf.float64, tf.complex64, tf.complex128] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + del ensure_self_adjoint_and_pd + del use_placeholder + + batch_shape = build_info.batch_shape + image_shape = build_info.image_shape + + mask = tf.random.uniform(shape=batch_shape + image_shape) > 0.5 + + operator = linear_operator_mask.LinearOperatorMask( + mask=mask, batch_dims=len(batch_shape), dtype=dtype, + algorithm='multiplex') + + matrix = linear_operator_mask.mask_matrix( + mask=mask, batch_dims=len(batch_shape), dtype=dtype) + + return operator, matrix + + +linear_operator_test_util.add_tests(LinearOperatorMaskMultiplyTest) +linear_operator_test_util.add_tests(LinearOperatorMaskMultiplexTest) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_mri.py b/tensorflow_mri/python/linalg/linear_operator_mri.py new file mode 100644 index 00000000..5f0cfe91 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_mri.py @@ -0,0 +1,812 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""MRI linear operator.""" + +import warnings + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_nufft +from tensorflow_mri.python.ops import fft_ops +from tensorflow_mri.python.ops import math_ops +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import check_util +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.util import tensor_util + + +_WARNED_IGNORED_BATCH_DIMENSIONS = {} + + +@api_util.export("linalg.LinearOperatorMRI") +@linear_operator.make_composite_tensor +class LinearOperatorMRI(linear_operator.LinearOperator): # pylint: disable=abstract-method + r"""Linear operator acting like an MRI measurement system. + + The MRI operator, $A$, maps a [batch of] images, $x$ to a + [batch of] measurement data (*k*-space), $b$. + + $$ + A x = b + $$ + + This object may represent an undersampled MRI operator and supports + Cartesian and non-Cartesian *k*-space sampling. The user may provide a + sampling `mask` to represent an undersampled Cartesian operator, or a + `trajectory` to represent a non-Cartesian operator. + + This object may represent a multicoil MRI operator by providing coil + `sensitivities`. Note that `mask`, `trajectory` and `density` should never + have a coil dimension, including in the case of multicoil imaging. The coil + dimension will be handled automatically. + + The domain shape of this operator is `extra_shape + image_shape`. The range + of this operator is `extra_shape + [num_coils] + image_shape`, for + Cartesian imaging, or `extra_shape + [num_coils] + [num_samples]`, for + non-Cartesian imaging. `[num_coils]` is optional and only present for + multicoil operators. This operator supports batches of images and will + vectorize operations when possible. + + Args: + image_shape: A 1D integer `tf.Tensor`. The shape of the images + that this operator acts on. Must have length 2 or 3. + extra_shape: An optional 1D integer `tf.Tensor`. Additional + dimensions that should be included within the operator domain. Note that + `extra_shape` is not needed to reconstruct independent batches of images. + However, it is useful when this operator is used as part of a + reconstruction that performs computation along non-spatial dimensions, + e.g. for temporal regularization. Defaults to `None`. + mask: An optional `tf.Tensor` of type `tf.bool`. The sampling mask. Must + have shape `[..., *S]`, where `S` is the `image_shape` and `...` is + the batch shape, which can have any number of dimensions. If `mask` is + passed, this operator represents an undersampled MRI operator. + trajectory: An optional `tf.Tensor` of type `float32` or `float64`. Must + have shape `[..., M, N]`, where `N` is the rank (number of spatial + dimensions), `M` is the number of samples in the encoded space and `...` + is the batch shape, which can have any number of dimensions. If + `trajectory` is passed, this operator represents a non-Cartesian MRI + operator. + density: An optional `tf.Tensor` of type `float32` or `float64`. The + sampling densities. Must have shape `[..., M]`, where `M` is the number of + samples and `...` is the batch shape, which can have any number of + dimensions. This input is only relevant for non-Cartesian MRI operators. + If passed, the non-Cartesian operator will include sampling density + compensation. If `None`, the operator will not perform sampling density + compensation. + sensitivities: An optional `tf.Tensor` of type `complex64` or `complex128`. + The coil sensitivity maps. Must have shape `[..., C, *S]`, where `S` + is the `image_shape`, `C` is the number of coils and `...` is the batch + shape, which can have any number of dimensions. + phase: An optional `tf.Tensor` of type `float32` or `float64`. A phase + estimate for the image. If provided, this operator will be + phase-constrained. + fft_norm: FFT normalization mode. Must be `None` (no normalization) + or `'ortho'`. Defaults to `'ortho'`. + sens_norm: A `boolean`. Whether to normalize coil sensitivities. Defaults to + `True`. + intensity_correction: A `boolean`. Whether to correct for overall receiver + coil sensitivity. Defaults to `True`. Has no effect if `sens_norm` is also + `True`. + dynamic_domain: A `str`. The domain of the dynamic dimension, if present. + Must be one of `'time'` or `'frequency'`. May only be provided together + with a non-scalar `extra_shape`. The dynamic dimension is the last + dimension of `extra_shape`. The `'time'` mode (default) should be + used for regular dynamic reconstruction. The `'frequency'` mode should be + used for reconstruction in x-f space. + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `None`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `None`. + is_positive_definite: A boolean, or `None`. Whether this operators is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that we do not require + the operator to be self-adjoint to be positive-definite. See: + https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices. + Defaults to `None`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `None`. + dtype: A `tf.dtypes.DType`. The dtype of this operator. Must be `complex64` + or `complex128`. Defaults to `complex64`. + name: An optional `str`. The name of this operator. + """ + def __init__(self, + image_shape, + extra_shape=None, + mask=None, + trajectory=None, + density=None, + sensitivities=None, + phase=None, + fft_norm='ortho', + sens_norm=True, + intensity_correction=True, + dynamic_domain=None, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None, + dtype=tf.complex64, + name=None): + # pylint: disable=invalid-unary-operand-type + parameters = dict( + image_shape=image_shape, + extra_shape=extra_shape, + mask=mask, + trajectory=trajectory, + density=density, + sensitivities=sensitivities, + phase=phase, + fft_norm=fft_norm, + sens_norm=sens_norm, + intensity_correction=intensity_correction, + dynamic_domain=dynamic_domain, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + dtype=dtype, + name=name) + super().__init__(dtype=dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name, + parameters=parameters) + + # Set dtype. + dtype = tf.as_dtype(dtype) + if dtype not in (tf.complex64, tf.complex128): + raise ValueError( + f"`dtype` must be `complex64` or `complex128`, but got: {str(dtype)}") + + # Batch dimensions in `image_shape` and `extra_shape` are not supported. + # However, it is convenient to allow them to have batch dimensions anyway. + # This helps when this operator is used in Keras models, where all inputs + # may be automatically batched. If there are any batch dimensions, we simply + # ignore them by taking the first element. The first time this happens + # we also emit a warning. + image_shape = self._ignore_batch_dims_in_shape(image_shape, "image_shape") + extra_shape = self._ignore_batch_dims_in_shape(extra_shape, "extra_shape") + + # Set image shape, rank and extra shape. + self._image_shape_static, self._image_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape(image_shape)) + self._rank = self._image_shape_static.rank + if self._rank not in (2, 3): + raise ValueError(f"Rank must be 2 or 3, but got: {self._rank}") + self._image_axes = list(range(-self._rank, 0)) # pylint: disable=invalid-unary-operand-type + if extra_shape is None: + extra_shape = [] + self._extra_shape_static, self._extra_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape(extra_shape)) + + # Set initial batch shape, then update according to inputs. + # We include the "extra" dimensions in the batch shape for now, so that + # they are also included in the broadcasting operations below. However, + # note that the "extra" dimensions are not in fact part of the batch shape + # and they will be removed later. + self._batch_shape_static = self._extra_shape_static + self._batch_shape_dynamic = self._extra_shape_dynamic + + # Set sampling mask after checking dtype and static shape. + if mask is not None: + mask = tf.convert_to_tensor(mask) + if mask.dtype != tf.bool: + raise TypeError( + f"`mask` must have dtype `bool`, but got: {str(mask.dtype)}") + if not mask.shape[-self._rank:].is_compatible_with( + self._image_shape_static): + raise ValueError( + f"Expected the last dimensions of `mask` to be compatible with " + f"{self._image_shape_static}], but got: {mask.shape[-self._rank:]}") + self._batch_shape_static = tf.broadcast_static_shape( + self._batch_shape_static, mask.shape[:-self._rank]) + self._batch_shape_dynamic = tf.broadcast_dynamic_shape( + self._batch_shape_dynamic, tf.shape(mask)[:-self._rank]) + self._mask = mask + + # Set sampling trajectory after checking dtype and static shape. + if trajectory is not None: + if mask is not None: + raise ValueError("`mask` and `trajectory` cannot be both passed.") + trajectory = tf.convert_to_tensor(trajectory) + if trajectory.dtype != dtype.real_dtype: + raise TypeError( + f"Expected `trajectory` to have dtype `{str(dtype.real_dtype)}`, " + f"but got: {str(trajectory.dtype)}") + if trajectory.shape[-1] != self._rank: + raise ValueError( + f"Expected the last dimension of `trajectory` to be " + f"{self._rank}, but got {trajectory.shape[-1]}") + self._batch_shape_static = tf.broadcast_static_shape( + self._batch_shape_static, trajectory.shape[:-2]) + self._batch_shape_dynamic = tf.broadcast_dynamic_shape( + self._batch_shape_dynamic, tf.shape(trajectory)[:-2]) + self._trajectory = trajectory + + # Set sampling density after checking dtype and static shape. + if density is not None: + if self._trajectory is None: + raise ValueError("`density` must be passed with `trajectory`.") + density = tf.convert_to_tensor(density) + if density.dtype != dtype.real_dtype: + raise TypeError( + f"Expected `density` to have dtype `{str(dtype.real_dtype)}`, " + f"but got: {str(density.dtype)}") + if density.shape[-1] != self._trajectory.shape[-2]: + raise ValueError( + f"Expected the last dimension of `density` to be " + f"{self._trajectory.shape[-2]}, but got {density.shape[-1]}") + self._batch_shape_static = tf.broadcast_static_shape( + self._batch_shape_static, density.shape[:-1]) + self._batch_shape_dynamic = tf.broadcast_dynamic_shape( + self._batch_shape_dynamic, tf.shape(density)[:-1]) + self._density = density + + # Set sensitivity maps after checking dtype and static shape. + if sensitivities is not None: + sensitivities = tf.convert_to_tensor(sensitivities) + if sensitivities.dtype != dtype: + raise TypeError( + f"Expected `sensitivities` to have dtype `{str(dtype)}`, but got: " + f"{str(sensitivities.dtype)}") + if not sensitivities.shape[-self._rank:].is_compatible_with( + self._image_shape_static): + raise ValueError( + f"Expected the last dimensions of `sensitivities` to be " + f"compatible with {self._image_shape_static}, but got: " + f"{sensitivities.shape[-self._rank:]}") + self._batch_shape_static = tf.broadcast_static_shape( + self._batch_shape_static, + sensitivities.shape[:-(self._rank + 1)]) + self._batch_shape_dynamic = tf.broadcast_dynamic_shape( + self._batch_shape_dynamic, + tf.shape(sensitivities)[:-(self._rank + 1)]) + self._sensitivities = sensitivities + + if phase is not None: + phase = tf.convert_to_tensor(phase) + if phase.dtype != dtype.real_dtype: + raise TypeError( + f"Expected `phase` to have dtype `{str(dtype.real_dtype)}`, " + f"but got: {str(phase.dtype)}") + if not phase.shape[-self._rank:].is_compatible_with( + self._image_shape_static): + raise ValueError( + f"Expected the last dimensions of `phase` to be " + f"compatible with {self._image_shape_static}, but got: " + f"{phase.shape[-self._rank:]}") + self._batch_shape_static = tf.broadcast_static_shape( + self._batch_shape_static, phase.shape[:-self._rank]) + self._batch_shape_dynamic = tf.broadcast_dynamic_shape( + self._batch_shape_dynamic, tf.shape(phase)[:-self._rank]) + self._phase = phase + + # Set batch shapes. + extra_dims = self._extra_shape_static.rank + if extra_dims is None: + raise ValueError("rank of `extra_shape` must be known statically.") + if extra_dims > 0: + self._batch_shape_static = self._batch_shape_static[:-extra_dims] + self._batch_shape_dynamic = self._batch_shape_dynamic[:-extra_dims] + + # Save some tensors for later use during computation. The `_i_` prefix + # indicates that these tensors are for internal use. We cannot modify the + # original tensors because they are components of the composite tensor that + # represents this operator, and the overall composite tensor cannot be + # mutated in certain circumstances such as in Keras models. + self._i_mask = self._mask + self._i_trajectory = self._trajectory + self._i_density = self._density + self._i_phase = self._phase + self._i_sensitivities = self._sensitivities + + # If multicoil, add coil dimension to mask, trajectory and density. + if self._i_sensitivities is not None: + if self._i_mask is not None: + self._i_mask = tf.expand_dims(self._i_mask, axis=-(self._rank + 1)) + if self._i_trajectory is not None: + self._i_trajectory = tf.expand_dims(self._i_trajectory, axis=-3) + if self._i_density is not None: + self._i_density = tf.expand_dims(self._i_density, axis=-2) + if self._i_phase is not None: + self._i_phase = tf.expand_dims(self._i_phase, axis=-(self._rank + 1)) + + # Select masking algorithm. Options are `multiplex` and `multiply`. + # `multiply` seems faster in most cases, but this needs better profiling. + self._masking_algorithm = 'multiply' + + if self._i_mask is not None: + if self._masking_algorithm == 'multiplex': + # Preallocate zeros tensor for multiplexing. + self._i_zeros = tf.zeros(shape=tf.shape(self._i_mask), dtype=self.dtype) + elif self._masking_algorithm == 'multiply': + # Cast the mask to operator's dtype for multiplication. + self._i_mask = tf.cast(self._i_mask, dtype) + else: + raise ValueError( + f"Unknown masking algorithm: {self._masking_algorithm}") + + # Compute the density compensation weights used internally. + if self._i_density is not None: + self._i_density = tf.cast(tf.math.sqrt( + tf.math.reciprocal_no_nan(self._i_density)), dtype) + # Compute the phase modulator used internally. + if self._i_phase is not None: + self._i_phase = tf.math.exp(tf.dtypes.complex( + tf.constant(0.0, dtype=dtype.real_dtype), self._i_phase)) + + # Set normalization. + self._fft_norm = check_util.validate_enum( + fft_norm, {None, 'ortho'}, 'fft_norm') + if self._fft_norm == 'ortho': # Compute normalization factors. + self._fft_norm_factor = tf.math.reciprocal( + tf.math.sqrt(tf.cast( + tf.math.reduce_prod(self._image_shape_dynamic), dtype))) + + # Normalize coil sensitivities. + self._sens_norm = sens_norm + if self._i_sensitivities is not None and self._sens_norm: + self._i_sensitivities = math_ops.normalize_no_nan( + self._i_sensitivities, axis=-(self._rank + 1)) + + # Intensity correction. + self._intensity_correction = intensity_correction + if self._i_sensitivities is not None and self._intensity_correction: + # This is redundant if `sens_norm` is `True`. + self._intensity_weights_sqrt = tf.math.reciprocal_no_nan( + tf.math.sqrt(tf.norm(self._i_sensitivities, axis=-(self._rank + 1)))) + + # Set dynamic domain. + if dynamic_domain is not None and self._extra_shape.rank == 0: + raise ValueError( + "Argument `dynamic_domain` requires a non-scalar `extra_shape`.") + if dynamic_domain is not None: + self._dynamic_domain = check_util.validate_enum( + dynamic_domain, {'time', 'frequency'}, name='dynamic_domain') + else: + self._dynamic_domain = None + + # This variable is used by `LinearOperatorGramMRI` to disable the NUFFT. + self._skip_nufft = False + + def _transform(self, x, adjoint=False): + """Transform [batch] input `x`. + + Args: + x: A `tf.Tensor` of type `self.dtype` and shape + `[..., *self.domain_shape]` containing images, if `adjoint` is `False`, + or a `tf.Tensor` of type `self.dtype` and shape + `[..., *self.range_shape]` containing *k*-space data, if `adjoint` is + `True`. + adjoint: A `boolean` indicating whether to apply the adjoint of the + operator. + + Returns: + A `tf.Tensor` of type `self.dtype` and shape `[..., *self.range_shape]` + containing *k*-space data, if `adjoint` is `False`, or a `tf.Tensor` of + type `self.dtype` and shape `[..., *self.domain_shape]` containing + images, if `adjoint` is `True`. + + Raises: + ValueError: If the masking algorithm is invalid. + """ + if adjoint: + # Apply density compensation. + if self._i_density is not None and not self._skip_nufft: + x *= self._i_density + + # Apply adjoint Fourier operator. + if self.is_non_cartesian: # Non-Cartesian imaging, use NUFFT. + if not self._skip_nufft: + x = fft_ops.nufft(x, self._i_trajectory, + grid_shape=self._image_shape_dynamic, + transform_type='type_1', + fft_direction='backward') + if self._fft_norm is not None: + x *= self._fft_norm_factor + + else: # Cartesian imaging, use FFT. + if self._i_mask is not None: + # Apply undersampling. + if self._masking_algorithm == 'multiplex': + x = tf.where(self._i_mask, x, self._i_zeros) + elif self._masking_algorithm == 'multiply': + x *= self._i_mask + else: + raise ValueError( + f"Unknown masking algorithm: {self._masking_algorithm}") + x = fft_ops.ifftn(x, axes=self._image_axes, + norm=self._fft_norm or 'forward', shift=True) + + # Apply coil combination. + if self.is_multicoil: + x *= tf.math.conj(self._i_sensitivities) + x = tf.math.reduce_sum(x, axis=-(self._rank + 1)) + + # Maybe remove phase from image. + if self.is_phase_constrained: + x *= tf.math.conj(self._i_phase) + x = tf.cast(tf.math.real(x), self.dtype) + + # Apply intensity correction. + if self.is_multicoil and self._intensity_correction: + x *= self._intensity_weights_sqrt + + # Apply FFT along dynamic axis, if necessary. + if self.is_dynamic and self.dynamic_domain == 'frequency': + x = fft_ops.fftn(x, axes=[self.dynamic_axis], + norm='ortho', shift=True) + + else: # Forward operator. + + # Apply IFFT along dynamic axis, if necessary. + if self.is_dynamic and self.dynamic_domain == 'frequency': + x = fft_ops.ifftn(x, axes=[self.dynamic_axis], + norm='ortho', shift=True) + + # Apply intensity correction. + if self.is_multicoil and self._intensity_correction: + x *= self._intensity_weights_sqrt + + # Add phase to real-valued image if reconstruction is phase-constrained. + if self.is_phase_constrained: + x = tf.cast(tf.math.real(x), self.dtype) + x *= self._i_phase + + # Apply sensitivity modulation. + if self.is_multicoil: + x = tf.expand_dims(x, axis=-(self._rank + 1)) + x *= self._i_sensitivities + + # Apply Fourier operator. + if self.is_non_cartesian: # Non-Cartesian imaging, use NUFFT. + if not self._skip_nufft: + x = fft_ops.nufft(x, self._i_trajectory, + transform_type='type_2', + fft_direction='forward') + if self._fft_norm is not None: + x *= self._fft_norm_factor + + else: # Cartesian imaging, use FFT. + x = fft_ops.fftn(x, axes=self._image_axes, + norm=self._fft_norm or 'backward', shift=True) + if self._i_mask is not None: + # Apply undersampling. + if self._masking_algorithm == 'multiplex': + x = tf.where(self._i_mask, x, self._i_zeros) + elif self._masking_algorithm == 'multiply': + x *= self._i_mask + else: + raise ValueError( + f"Unknown masking algorithm: {self._masking_algorithm}") + + # Apply density compensation. + if self._i_density is not None and not self._skip_nufft: + x *= self._i_density + + return x + + def _preprocess(self, x, adjoint=False): + if adjoint: + if self._i_density is not None: + x *= self._i_density + else: + raise NotImplementedError( + "`_preprocess` not implemented for forward transform.") + return x + + def _postprocess(self, x, adjoint=False): + if adjoint: + # Apply temporal Fourier operator, if necessary. + if self.is_dynamic and self.dynamic_domain == 'frequency': + x = fft_ops.ifftn(x, axes=[self.dynamic_axis], + norm='ortho', shift=True) + + # Apply intensity correction, if necessary. + if self.is_multicoil and self._intensity_correction: + x *= self._intensity_weights_sqrt + else: + raise NotImplementedError( + "`_postprocess` not implemented for forward transform.") + return x + + def _domain_shape(self): + """Returns the static shape of the domain space of this operator.""" + return self._extra_shape_static.concatenate(self._image_shape_static) + + def _domain_shape_tensor(self): + """Returns the dynamic shape of the domain space of this operator.""" + return tf.concat([self._extra_shape_dynamic, self._image_shape_dynamic], 0) + + def _range_shape(self): + """Returns the shape of the range space of this operator.""" + if self.is_cartesian: + range_shape = self._image_shape_static.as_list() + else: + range_shape = [self._trajectory.shape[-2]] + if self.is_multicoil: + range_shape = [self.num_coils] + range_shape + return self._extra_shape_static.concatenate(range_shape) + + def _range_shape_tensor(self): + if self.is_cartesian: + range_shape = self._image_shape_dynamic + else: + range_shape = [tf.shape(self._trajectory)[-2]] + if self.is_multicoil: + range_shape = tf.concat([[self.num_coils_tensor()], range_shape], 0) + return tf.concat([self._extra_shape_dynamic, range_shape], 0) + + def _batch_shape(self): + """Returns the static batch shape of this operator.""" + return self._batch_shape_static + + def _batch_shape_tensor(self): + """Returns the dynamic batch shape of this operator.""" + return self._batch_shape_dynamic + + @property + def image_shape(self): + """The image shape.""" + return self._image_shape_static + + def image_shape_tensor(self): + """The image shape as a tensor.""" + return self._image_shape_dynamic + + @property + def rank(self): + """The number of spatial dimensions. + + Returns: + An `int`, typically 2 or 3. + """ + return self._rank + + @property + def mask(self): + """The sampling mask. + + Returns: + A boolean `tf.Tensor` of shape `batch_shape + extra_shape + image_shape`, + or `None` if the operator is fully sampled or non-Cartesian. + """ + return self._mask + + @property + def trajectory(self): + """The k-space trajectory. + + Returns: + A real `tf.Tensor` of shape `batch_shape + extra_shape + [samples, rank]`, + or `None` if the operator is Cartesian. + """ + return self._trajectory + + @property + def density(self): + """The sampling density. + + Returns: + A real `tf.Tensor` of shape `batch_shape + extra_shape + [samples]`, + or `None` if the operator is Cartesian or has unknown sampling density. + """ + return self._density + + @property + def is_cartesian(self): + """Whether this is a Cartesian MRI operator.""" + return self._trajectory is None + + @property + def is_non_cartesian(self): + """Whether this is a non-Cartesian MRI operator.""" + return self._trajectory is not None + + @property + def is_multicoil(self): + """Whether this is a multicoil MRI operator.""" + return self._sensitivities is not None + + @property + def is_phase_constrained(self): + """Whether this is a phase-constrained MRI operator.""" + return self._phase is not None + + @property + def is_dynamic(self): + """Whether this is a dynamic MRI operator.""" + return self._dynamic_domain is not None + + @property + def dynamic_domain(self): + """The dynamic domain of this operator.""" + return self._dynamic_domain + + @property + def dynamic_axis(self): + """The dynamic axis of this operator.""" + return -(self._rank + 1) if self.is_dynamic else None + + @property + def num_coils(self): + """The number of coils, computed statically.""" + if self._sensitivities is None: + return None + return self._sensitivities.shape[-(self._rank + 1)] + + def num_coils_tensor(self): + """The number of coils, computed dynamically.""" + if self._sensitivities is None: + return tf.convert_to_tensor(-1, dtype=tf.int32) + return tf.shape(self._sensitivities)[-(self._rank + 1)] + + def _ignore_batch_dims_in_shape(self, shape, argname): + if shape is None: + return None + shape = tf.convert_to_tensor(shape, dtype=tf.int32) + if shape.shape.rank == 2: + warned = _WARNED_IGNORED_BATCH_DIMENSIONS.get(argname, False) + if not warned: + _WARNED_IGNORED_BATCH_DIMENSIONS[argname] = True + warnings.warn( + f"Operator {self.name} got a batched `{argname}` argument. " + f"It is not possible to process images with " + f"different shapes in the same batch. " + f"If the input batch has more than one element, " + f"only the first shape will be used. " + f"It is up to you to verify if this behavior is correct.") + return tf.ensure_shape(shape[0], shape.shape[1:]) + return shape + + @property + def _composite_tensor_fields(self): + return ("image_shape", + "extra_shape", + "mask", + "trajectory", + "density", + "sensitivities", + "phase", + "fft_norm", + "sens_norm", + "intensity_correction", + "dynamic_domain", + "dtype") + + @property + def _composite_tensor_prefer_static_fields(self): + return ("image_shape", "extra_shape") + + +@api_util.export("linalg.LinearOperatorGramMRI") +class LinearOperatorGramMRI(LinearOperatorMRI): # pylint: disable=abstract-method + """Linear operator representing the Gram matrix of an MRI measurement system. + + If $A$ is a `tfmri.linalg.LinearOperatorMRI`, then this ooperator + represents the matrix $G = A^H A$. + + In certain circumstances, this operator may be able to apply the matrix + $G$ more efficiently than the composition $G = A^H A$ using + `tfmri.linalg.LinearOperatorMRI` objects. + + Args: + image_shape: A 1D integer `tf.Tensor`. The shape of the images + that this operator acts on. Must have length 2 or 3. + extra_shape: An optional 1D integer `tf.Tensor`. Additional + dimensions that should be included within the operator domain. Note that + `extra_shape` is not needed to reconstruct independent batches of images. + However, it is useful when this operator is used as part of a + reconstruction that performs computation along non-spatial dimensions, + e.g. for temporal regularization. Defaults to `None`. + mask: An optional `tf.Tensor` of type `tf.bool`. The sampling mask. Must + have shape `[..., *S]`, where `S` is the `image_shape` and `...` is + the batch shape, which can have any number of dimensions. If `mask` is + passed, this operator represents an undersampled MRI operator. + trajectory: An optional `tf.Tensor` of type `float32` or `float64`. Must + have shape `[..., M, N]`, where `N` is the rank (number of spatial + dimensions), `M` is the number of samples in the encoded space and `...` + is the batch shape, which can have any number of dimensions. If + `trajectory` is passed, this operator represents a non-Cartesian MRI + operator. + density: An optional `tf.Tensor` of type `float32` or `float64`. The + sampling densities. Must have shape `[..., M]`, where `M` is the number of + samples and `...` is the batch shape, which can have any number of + dimensions. This input is only relevant for non-Cartesian MRI operators. + If passed, the non-Cartesian operator will include sampling density + compensation. If `None`, the operator will not perform sampling density + compensation. + sensitivities: An optional `tf.Tensor` of type `complex64` or `complex128`. + The coil sensitivity maps. Must have shape `[..., C, *S]`, where `S` + is the `image_shape`, `C` is the number of coils and `...` is the batch + shape, which can have any number of dimensions. + phase: An optional `tf.Tensor` of type `float32` or `float64`. A phase + estimate for the image. If provided, this operator will be + phase-constrained. + fft_norm: FFT normalization mode. Must be `None` (no normalization) + or `'ortho'`. Defaults to `'ortho'`. + sens_norm: A `boolean`. Whether to normalize coil sensitivities. Defaults to + `True`. + dynamic_domain: A `str`. The domain of the dynamic dimension, if present. + Must be one of `'time'` or `'frequency'`. May only be provided together + with a non-scalar `extra_shape`. The dynamic dimension is the last + dimension of `extra_shape`. The `'time'` mode (default) should be + used for regular dynamic reconstruction. The `'frequency'` mode should be + used for reconstruction in x-f space. + toeplitz_nufft: A `boolean`. If `True`, uses the Toeplitz approach [5] + to compute $F^H F x$, where $F$ is the non-uniform Fourier + operator. If `False`, the same operation is performed using the standard + NUFFT operation. The Toeplitz approach might be faster than the direct + approach but is slightly less accurate. This argument is only relevant + for non-Cartesian reconstruction and will be ignored for Cartesian + problems. + dtype: A `tf.dtypes.DType`. The dtype of this operator. Must be `complex64` + or `complex128`. Defaults to `complex64`. + name: An optional `str`. The name of this operator. + """ + def __init__(self, + image_shape, + extra_shape=None, + mask=None, + trajectory=None, + density=None, + sensitivities=None, + phase=None, + fft_norm='ortho', + sens_norm=True, + dynamic_domain=None, + toeplitz_nufft=False, + dtype=tf.complex64, + name="LinearOperatorGramMRI"): + super().__init__( + image_shape, + extra_shape=extra_shape, + mask=mask, + trajectory=trajectory, + density=density, + sensitivities=sensitivities, + phase=phase, + fft_norm=fft_norm, + sens_norm=sens_norm, + dynamic_domain=dynamic_domain, + dtype=dtype, + name=name + ) + + self.toeplitz_nufft = toeplitz_nufft + if self.toeplitz_nufft and self.is_non_cartesian: + # Create a Gram NUFFT operator with Toeplitz embedding. + self._linop_gram_nufft = linear_operator_nufft.LinearOperatorGramNUFFT( + image_shape, trajectory=self._trajectory, density=self._density, + norm=fft_norm, toeplitz=True) + # Disable NUFFT computation on base class. The NUFFT will instead be + # performed by the Gram NUFFT operator. + self._skip_nufft = True + + def _transform(self, x, adjoint=False): + x = super()._transform(x) + if self.toeplitz_nufft: + x = self._linop_gram_nufft.transform(x) + x = super()._transform(x, adjoint=True) + return x + + def _range_shape(self): + return self._domain_shape() + + def _range_shape_tensor(self): + return self._domain_shape_tensor() diff --git a/tensorflow_mri/python/linalg/linear_operator_mri_test.py b/tensorflow_mri/python/linalg/linear_operator_mri_test.py new file mode 100644 index 00000000..7cc12a28 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_mri_test.py @@ -0,0 +1,214 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_mri`.""" +# pylint: disable=missing-class-docstring,missing-function-docstring + +from absl.testing import parameterized +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_mri +from tensorflow_mri.python.ops import fft_ops +from tensorflow_mri.python.ops import image_ops +from tensorflow_mri.python.ops import traj_ops +from tensorflow_mri.python.util import test_util + + +class LinearOperatorMRITest(test_util.TestCase): + """Tests for MRI linear operator.""" + def test_fft(self): + """Test FFT operator.""" + # Test init. + linop = linear_operator_mri.LinearOperatorMRI([2, 2], fft_norm=None) + + # Test matvec. + signal = tf.constant([1, 2, 4, 4], dtype=tf.complex64) + expected = [-1, 5, 1, 11] + result = tf.linalg.matvec(linop, signal) + self.assertAllClose(expected, result) + + # Test domain shape. + self.assertIsInstance(linop.domain_shape, tf.TensorShape) + self.assertAllEqual([2, 2], linop.domain_shape) + self.assertAllEqual([2, 2], linop.domain_shape_tensor()) + + # Test range shape. + self.assertIsInstance(linop.range_shape, tf.TensorShape) + self.assertAllEqual([2, 2], linop.range_shape) + self.assertAllEqual([2, 2], linop.range_shape_tensor()) + + # Test batch shape. + self.assertIsInstance(linop.batch_shape, tf.TensorShape) + self.assertAllEqual([], linop.batch_shape) + self.assertAllEqual([], linop.batch_shape_tensor()) + + def test_fft_with_mask(self): + """Test FFT operator with mask.""" + # Test init. + linop = linear_operator_mri.LinearOperatorMRI( + [2, 2], mask=[[False, False], [True, True]], fft_norm=None) + + # Test matvec. + signal = tf.constant([1, 2, 4, 4], dtype=tf.complex64) + expected = [0, 0, 1, 11] + result = tf.linalg.matvec(linop, signal) + self.assertAllClose(expected, result) + + # Test domain shape. + self.assertIsInstance(linop.domain_shape, tf.TensorShape) + self.assertAllEqual([2, 2], linop.domain_shape) + self.assertAllEqual([2, 2], linop.domain_shape_tensor()) + + # Test range shape. + self.assertIsInstance(linop.range_shape, tf.TensorShape) + self.assertAllEqual([2, 2], linop.range_shape) + self.assertAllEqual([2, 2], linop.range_shape_tensor()) + + # Test batch shape. + self.assertIsInstance(linop.batch_shape, tf.TensorShape) + self.assertAllEqual([], linop.batch_shape) + self.assertAllEqual([], linop.batch_shape_tensor()) + + def test_fft_with_batch_mask(self): + """Test FFT operator with batch mask.""" + # Test init. + linop = linear_operator_mri.LinearOperatorMRI( + [2, 2], mask=[[[True, True], [False, False]], + [[False, False], [True, True]], + [[False, True], [True, False]]], fft_norm=None) + + # Test matvec. + signal = tf.constant([1, 2, 4, 4], dtype=tf.complex64) + expected = [[-1, 5, 0, 0], [0, 0, 1, 11], [0, 5, 1, 0]] + result = tf.linalg.matvec(linop, signal) + self.assertAllClose(expected, result) + + # Test domain shape. + self.assertIsInstance(linop.domain_shape, tf.TensorShape) + self.assertAllEqual([2, 2], linop.domain_shape) + self.assertAllEqual([2, 2], linop.domain_shape_tensor()) + + # Test range shape. + self.assertIsInstance(linop.range_shape, tf.TensorShape) + self.assertAllEqual([2, 2], linop.range_shape) + self.assertAllEqual([2, 2], linop.range_shape_tensor()) + + # Test batch shape. + self.assertIsInstance(linop.batch_shape, tf.TensorShape) + self.assertAllEqual([3], linop.batch_shape) + self.assertAllEqual([3], linop.batch_shape_tensor()) + + def test_fft_norm(self): + """Test FFT normalization.""" + linop = linear_operator_mri.LinearOperatorMRI([2, 2], fft_norm='ortho') + x = tf.constant([1 + 2j, 2 - 2j, -1 - 6j, 3 + 4j], dtype=tf.complex64) + # With norm='ortho', subsequent application of the operator and its adjoint + # should not scale the input. + y = tf.linalg.matvec(linop.H, tf.linalg.matvec(linop, x)) + self.assertAllClose(x, y) + + def test_nufft_with_sensitivities(self): + resolution = 128 + image_shape = [resolution, resolution] + num_coils = 4 + image, sensitivities = image_ops.phantom( + shape=image_shape, num_coils=num_coils, dtype=tf.complex64, + return_sensitivities=True) + image = image_ops.phantom(shape=image_shape, dtype=tf.complex64) + trajectory = traj_ops.radial_trajectory(resolution, resolution // 2 + 1, + flatten_encoding_dims=True) + density = traj_ops.radial_density(resolution, resolution // 2 + 1, + flatten_encoding_dims=True) + + linop = linear_operator_mri.LinearOperatorMRI( + image_shape, trajectory=trajectory, density=density, + sensitivities=sensitivities) + + # Test shapes. + expected_domain_shape = image_shape + self.assertAllClose(expected_domain_shape, linop.domain_shape) + self.assertAllClose(expected_domain_shape, linop.domain_shape_tensor()) + expected_range_shape = [num_coils, (2 * resolution) * (resolution // 2 + 1)] + self.assertAllClose(expected_range_shape, linop.range_shape) + self.assertAllClose(expected_range_shape, linop.range_shape_tensor()) + + # Test forward. + weights = tf.cast(tf.math.sqrt(tf.math.reciprocal_no_nan(density)), + tf.complex64) + norm = tf.math.sqrt(tf.cast(tf.math.reduce_prod(image_shape), tf.complex64)) + expected = fft_ops.nufft(image * sensitivities, trajectory) * weights / norm + kspace = linop.transform(image) + self.assertAllClose(expected, kspace) + + # Test adjoint. + expected = tf.math.reduce_sum( + fft_ops.nufft( + kspace * weights, trajectory, grid_shape=image_shape, + transform_type='type_1', fft_direction='backward') / norm * + tf.math.conj(sensitivities), axis=-3) + recon = linop.transform(kspace, adjoint=True) + self.assertAllClose(expected, recon) + + + +class LinearOperatorGramMRITest(test_util.TestCase): + @parameterized.product(batch=[False, True], extra=[False, True], + toeplitz_nufft=[False, True]) + def test_general(self, batch, extra, toeplitz_nufft): + resolution = 128 + image_shape = [resolution, resolution] + num_coils = 4 + image, sensitivities = image_ops.phantom( + shape=image_shape, num_coils=num_coils, dtype=tf.complex64, + return_sensitivities=True) + image = image_ops.phantom(shape=image_shape, dtype=tf.complex64) + trajectory = traj_ops.radial_trajectory(resolution, resolution // 2 + 1, + flatten_encoding_dims=True) + density = traj_ops.radial_density(resolution, resolution // 2 + 1, + flatten_encoding_dims=True) + if batch: + image = tf.stack([image, image * 2]) + if extra: + extra_shape = [2] + else: + extra_shape = None + else: + extra_shape = None + + linop = linear_operator_mri.LinearOperatorMRI( + image_shape, extra_shape=extra_shape, + trajectory=trajectory, density=density, + sensitivities=sensitivities) + linop_gram = linear_operator_mri.LinearOperatorGramMRI( + image_shape, extra_shape=extra_shape, + trajectory=trajectory, density=density, + sensitivities=sensitivities, toeplitz_nufft=toeplitz_nufft) + + # Test shapes. + expected_domain_shape = image_shape + if extra_shape is not None: + expected_domain_shape = extra_shape + image_shape + self.assertAllClose(expected_domain_shape, linop_gram.domain_shape) + self.assertAllClose(expected_domain_shape, linop_gram.domain_shape_tensor()) + self.assertAllClose(expected_domain_shape, linop_gram.range_shape) + self.assertAllClose(expected_domain_shape, linop_gram.range_shape_tensor()) + + # Test transform. + expected = linop.transform(linop.transform(image), adjoint=True) + self.assertAllClose(expected, linop_gram.transform(image), + rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_nd.py b/tensorflow_mri/python/linalg/linear_operator_nd.py new file mode 100644 index 00000000..1df2a863 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_nd.py @@ -0,0 +1,799 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities for N-D linear operators.""" + +import functools +import string + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.linalg import linear_operator_algebra +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import tensor_util + + +def make_linear_operator_nd(cls): + """Class decorator for subclasses of `LinearOperatorND`.""" + # Call the original decorator. + cls = linear_operator.make_linear_operator(cls) + + # Add the N-D specific doclines. + cls = update_docstring(cls) + + return cls + + +def update_docstring(op_cls): + """Adds a notice to the docstring.""" + tfmri_additional_nd_functionality = string.Template(""" + ```{rubric} Additional N-D functionality (TensorFlow MRI) + ``` + + This operator has additional functionality to work with N-dimensional + problems more easily. + + - Process non-vectorized N-dimensional inputs via `matvec_nd`, `solvevec_nd` + and `lstsqvec_nd`. + - Access static N-D shape information via `domain_shape` and `range_shape`. + - Access dynamic N-D shape information via `domain_shape_tensor` and + `range_shape_tensor`. + """).substitute(class_name=op_cls.__name__) + + docstring = op_cls.__doc__ + doclines = docstring.split('\n') + doclines += tfmri_additional_nd_functionality.split('\n') + docstring = '\n'.join(doclines) + op_cls.__doc__ = docstring + + return op_cls + + +@api_util.export("linalg.LinearOperatorND") +@make_linear_operator_nd +class LinearOperatorND(linear_operator.LinearOperator): + """Base class defining a [batch of] N-D linear operator(s).""" + # Overrides of existing methods. + def matmul(self, x, adjoint=False, adjoint_arg=False, name="matmul"): + # We define a special implementation for when `x` is a `LinearOperatorND`. + if isinstance(x, LinearOperatorND): + left_operator = self.adjoint() if adjoint else self + right_operator = x.adjoint() if adjoint_arg else x + + tensor_util.assert_broadcast_compatible( + left_operator.domain_shape, + right_operator.range_shape, + message=( + f"N-D operators are incompatible: " + f"the domain shape {left_operator.domain_shape} " + f"of left operator {left_operator.name} is not broadcast-" + f"compatible with the range shape {right_operator.shape} " + f"of right operator {right_operator.name}")) + + with self._name_scope(name): # pylint: disable=not-callable + return linear_operator_algebra.matmul(left_operator, right_operator) + + # If `x` is not a `LinearOperatorND`, we use the original implementation. + return super().matmul( + x, adjoint=adjoint, adjoint_arg=adjoint_arg, name=name) + + def _matmul(self, x, adjoint=False, adjoint_arg=False): + """Default implementation of `_matmul` for N-D operator.""" + # Default implementation of `matmul` for N-D operator. Basically we + # just call `matvec` for each column of `x` (or for each row, if + # `adjoint_arg` is `True`). `tf.einsum` is used to transpose the input arg. + batch_shape = tf.broadcast_static_shape(x.shape[:-2], self.batch_shape) + output_dim = self.domain_dimension if adjoint else self.range_dimension + if adjoint_arg and x.dtype.is_complex: + x = tf.math.conj(x) + x = tf.einsum('...ij->i...j' if adjoint_arg else '...ij->j...i', x) + y = tf.map_fn(functools.partial(self.matvec, adjoint=adjoint), x, + fn_output_signature=tf.TensorSpec( + shape=batch_shape + [output_dim], + dtype=x.dtype)) + y = tf.einsum('i...j->...ji' if adjoint_arg else 'j...i->...ij', y) + return y + + def _matvec(self, x, adjoint=False): + """Default implementation of `_matvec` for N-D operator.""" + # Default implementation of `_matvec` for N-D operator. The vectorized + # input `x` is first expanded to the its full shape, then transformed, then + # vectorized again. Typically subclasses should not need to override this + # method. + x = (self.expand_range_dimension(x) if adjoint else + self.expand_domain_dimension(x)) + x = self._matvec_nd(x, adjoint=adjoint) + x = (self.flatten_domain_shape(x) if adjoint else \ + self.flatten_range_shape(x)) + return x + + def solve(self, rhs, adjoint=False, adjoint_arg=False, name="solve"): + if self.is_non_singular is False: + raise NotImplementedError( + "Exact solve not implemented for an operator that is expected to " + "be singular.") + if self.is_square is False: + raise NotImplementedError( + "Exact solve not implemented for an operator that is expected to " + "not be square.") + + # We define a special implementation for when `rhs` is a `LinearOperatorND`. + if isinstance(rhs, LinearOperatorND): + left_operator = self.adjoint() if adjoint else self + right_operator = rhs.adjoint() if adjoint_arg else rhs + + tensor_util.assert_broadcast_compatible( + left_operator.domain_shape, + right_operator.range_shape, + message=( + f"N-D operators are incompatible: " + f"the domain shape {left_operator.domain_shape} " + f"of left operator {left_operator.name} is not broadcast-" + f"compatible with the range shape {right_operator.shape} " + f"of right operator {right_operator.name}")) + + with self._name_scope(name): # pylint: disable=not-callable + return linear_operator_algebra.solve(left_operator, right_operator) + + # If `x` is not a `LinearOperatorND`, we use the original implementation. + return super().solve( + rhs, adjoint=adjoint, adjoint_arg=adjoint_arg, name=name) + + def _solve(self, rhs, adjoint=False, adjoint_arg=False): + """Default implementation of `_solve` for N-D operator.""" + # Default implementation of `_solve` for imaging operator. Basically we + # just call `solvevec` for each column of `rhs` (or for each row, if + # `adjoint_arg` is `True`). `tf.einsum` is used to transpose the input arg. + batch_shape = tf.broadcast_static_shape(rhs.shape[:-2], self.batch_shape) + output_dim = self.range_dimension if adjoint else self.domain_dimension + if adjoint_arg and rhs.dtype.is_complex: + rhs = tf.math.conj(rhs) + rhs = tf.einsum('...ij->i...j' if adjoint_arg else '...ij->j...i', rhs) + x = tf.map_fn(functools.partial(self.solvevec, adjoint=adjoint), rhs, + fn_output_signature=tf.TensorSpec( + shape=batch_shape + [output_dim], + dtype=rhs.dtype)) + x = tf.einsum('i...j->...ji' if adjoint_arg else 'j...i->...ij', x) + return x + + def _solvevec(self, rhs, adjoint=False): + """Default implementation of `_solvevec` for N-D operator.""" + # Default implementation of `_solvevec` for N-D operator. The + # vectorized input `rhs` is first expanded to the its full shape, then + # solved, then vectorized again. Typically subclasses should not need to + # override this method. + rhs = (self.expand_domain_dimension(rhs) if adjoint else + self.expand_range_dimension(rhs)) + rhs = self._solvevec_nd(rhs, adjoint=adjoint) + rhs = (self.flatten_range_shape(rhs) if adjoint else + self.flatten_domain_shape(rhs)) + return rhs + + def _lstsq(self, rhs, adjoint=False, adjoint_arg=False): + """Default implementation of `_lstsq` for N-D operator.""" + # Default implementation of `_solve` for N-D operator. Basically we + # just call `solvevec` for each column of `rhs` (or for each row, if + # `adjoint_arg` is `True`). `tf.einsum` is used to transpose the input arg. + batch_shape = tf.broadcast_static_shape(rhs.shape[:-2], self.batch_shape) + output_dim = self.range_dimension if adjoint else self.domain_dimension + if adjoint_arg and rhs.dtype.is_complex: + rhs = tf.math.conj(rhs) + rhs = tf.einsum('...ij->i...j' if adjoint_arg else '...ij->j...i', rhs) + x = tf.map_fn(functools.partial(self.lstsqvec, adjoint=adjoint), rhs, + fn_output_signature=tf.TensorSpec( + shape=batch_shape + [output_dim], + dtype=rhs.dtype)) + x = tf.einsum('i...j->...ji' if adjoint_arg else 'j...i->...ij', x) + return x + + def _lstsqvec(self, rhs, adjoint=False): + """Default implementation of `_lstsqvec` for N-D operator.""" + # Default implementation of `_solvevec` for N-D operator. The + # vectorized input `rhs` is first expanded to the its full shape, then + # solved, then vectorized again. Typically subclasses should not need to + # override this method. + rhs = (self.expand_domain_dimension(rhs) if adjoint else + self.expand_range_dimension(rhs)) + rhs = self._lstsqvec_nd(rhs, adjoint=adjoint) + rhs = (self.flatten_range_shape(rhs) if adjoint else + self.flatten_domain_shape(rhs)) + return rhs + + def _shape(self): + # Default implementation of `_shape` for imaging operators. Typically + # subclasses should not need to override this method. + return self._batch_shape().concatenate(tf.TensorShape( + [self.range_shape.num_elements(), + self.domain_shape.num_elements()])) + + def _shape_tensor(self): + # Default implementation of `_shape_tensor` for imaging operators. Typically + # subclasses should not need to override this method. + return tf.concat([self.batch_shape_tensor(), + [tf.math.reduce_prod(self.range_shape_tensor()), + tf.math.reduce_prod(self.domain_shape_tensor())]], 0) + + # New methods. + def matvec_nd(self, x, adjoint=False, name="matvec_nd"): + """Transforms [batch] N-D input `x` with left multiplication `x --> Ax`. + + ```{note} + Similar to `matvec`, but works with non-vectorized N-D inputs `x`. + ``` + + Args: + x: A `tf.Tensor` with compatible shape and same dtype as `self`. + adjoint: A boolean. If `True`, transforms the input using the adjoint + of the operator, instead of the operator itself. + name: A name for this operation. + + Returns: + A `tf.Tensor` with same dtype as `x` and shape `[..., *nd_shape]`, + where `nd_shape` is the equal to `domain_shape` if `adjoint` is `True` + and `range_shape` otherwise. + """ + with self._name_scope(name): # pylint: disable=not-callable + x = tf.convert_to_tensor(x, name="x") + self._check_input_dtype(x) + input_shape = self.range_shape if adjoint else self.domain_shape + input_shape.assert_is_compatible_with(x.shape[-input_shape.rank:]) # pylint: disable=invalid-unary-operand-type + return self._matvec_nd(x, adjoint=adjoint) + + def _matvec_nd(self, x, adjoint=False): + # Subclasses must override this method. + raise NotImplementedError("Method `_matvec_nd` is not implemented.") + + def solvevec_nd(self, rhs, adjoint=False, name="solve"): + """Solve single equation with N-D right-hand side: `A x = rhs`. + + The returned `tf.Tensor` will be close to an exact solution if `A` is well + conditioned. Otherwise closeness will vary. See class docstring for details. + + ```{note} + Similar to `solvevec`, but works with non-vectorized N-D inputs `rhs`. + ``` + + Args: + rhs: A `tf.Tensor` with same `dtype` as this operator. + `rhs` is treated like a [batch] vector meaning for every set of leading + dimensions, the last dimension defines a vector. See class docstring + for definition of compatibility regarding batch dimensions. + adjoint: A boolean. If `True`, solve the system involving the adjoint of + this operator: $A^H x = b$. Defaults to `False`. + name: A name scope to use for ops added by this method. + + Returns: + A `tf.Tensor` with same dtype as `x` and shape `[..., *nd_shape]`, + where `nd_shape` is the equal to `range_shape` if `adjoint` is `True` + and `domain_shape` otherwise. + """ + with self._name_scope(name): # pylint: disable=not-callable + rhs = tf.convert_to_tensor(rhs, name="rhs") + self._check_input_dtype(rhs) + input_shape = self.domain_shape if adjoint else self.range_shape + input_shape.assert_is_compatible_with(rhs.shape[-input_shape.rank:]) # pylint: disable=invalid-unary-operand-type + return self._solvevec_nd(rhs, adjoint=adjoint) + + def _solvevec_nd(self, rhs, adjoint=False): + # Subclasses may override this method. + raise NotImplementedError("Method `_solvevec_nd` is not implemented.") + + def lstsqvec_nd(self, rhs, adjoint=False, name="solve"): + """Solve single equation with N-D right-hand side: `A x = rhs`. + + The returned `tf.Tensor` is the least squares solution to the system of + equations. + + ```{note} + Similar to `solvevec`, but works with non-vectorized N-D inputs `rhs`. + ``` + + Args: + rhs: A `tf.Tensor` with same `dtype` as this operator. + `rhs` is treated like a [batch] vector meaning for every set of leading + dimensions, the last dimension defines a vector. See class docstring + for definition of compatibility regarding batch dimensions. + adjoint: A boolean. If `True`, solve the system involving the adjoint of + this operator: $A^H x = b$. Defaults to `False`. + name: A name scope to use for ops added by this method. + + Returns: + A `tf.Tensor` with same dtype as `x` and shape `[..., *nd_shape]`, + where `nd_shape` is the equal to `range_shape` if `adjoint` is `True` + and `domain_shape` otherwise. + """ + with self._name_scope(name): # pylint: disable=not-callable + rhs = tf.convert_to_tensor(rhs, name="rhs") + self._check_input_dtype(rhs) + input_shape = self.domain_shape if adjoint else self.range_shape + input_shape.assert_is_compatible_with(rhs.shape[-input_shape.rank:]) # pylint: disable=invalid-unary-operand-type + return self._lstsqvec_nd(rhs, adjoint=adjoint) + + def _lstsqvec_nd(self, rhs, adjoint=False): + # Subclasses may override this method. + raise NotImplementedError("Method `_lstsqvec_nd` is not implemented.") + + @property + def domain_shape(self): + """Domain shape of this linear operator, determined statically. + + Returns: + A `tf.TensorShape` representing the shape of the domain of this operator. + """ + return self._domain_shape() + + def _domain_shape(self): + # Users must override this method. + return tf.TensorShape(None) + + @property + def range_shape(self): + """Range shape of this linear operator, determined statically. + + Returns: + A `tf.TensorShape` representing the shape of the range of this operator. + """ + return self._range_shape() + + def _range_shape(self): + # Users must override this method. + return tf.TensorShape(None) + + def _batch_shape(self): + # Users should override this method if this operator has a batch shape. + return tf.TensorShape([]) + + def domain_shape_tensor(self, name="domain_shape_tensor"): + """Domain shape of this linear operator, determined at runtime. + + Args: + name: A `str`. A name scope to use for ops added by this method. + + Returns: + A 1D integer `tf.Tensor` representing the shape of the domain of this + operator. + """ + with self._name_scope(name): # pylint: disable=not-callable + # Prefer to use statically defined shape if available. + if self.domain_shape.is_fully_defined(): + return tensor_util.convert_shape_to_tensor(self.domain_shape.as_list()) + return self._domain_shape_tensor() + + def _domain_shape_tensor(self): + # Users should override this method if they need to provide a dynamic domain + # shape. + raise NotImplementedError("_domain_shape_tensor is not implemented.") + + def range_shape_tensor(self, name="range_shape_tensor"): + """Range shape of this linear operator, determined at runtime. + + Args: + name: A `str`. A name scope to use for ops added by this method. + + Returns: + A 1D integer `tf.Tensor` representing the shape of the range of this + operator. + """ + with self._name_scope(name): # pylint: disable=not-callable + # Prefer to use statically defined shape if available. + if self.range_shape.is_fully_defined(): + return tensor_util.convert_shape_to_tensor(self.range_shape.as_list()) + return self._range_shape_tensor() + + def _range_shape_tensor(self): + # Users should override this method if they need to provide a dynamic range + # shape. + raise NotImplementedError("_range_shape_tensor is not implemented.") + + def batch_shape_tensor(self, name="batch_shape_tensor"): + """Batch shape of this linear operator, determined at runtime.""" + with self._name_scope(name): # pylint: disable=not-callable + if self.batch_shape.is_fully_defined(): + return tensor_util.convert_shape_to_tensor(self.batch_shape.as_list()) + return self._batch_shape_tensor() + + def _batch_shape_tensor(self): # pylint: disable=arguments-differ + # Users should override this method if they need to provide a dynamic batch + # shape. + return tf.constant([], dtype=tf.dtypes.int32) + + @property + def ndim(self): + """Logical number of dimensions of this linear operator. + + ```{note} + `ndim` can always be determined statically. + ``` + + ```{attention} + This number may differ from the number of dimensions in `domain_shape`, + `range_shape`, or both. + ``` + """ + return self._ndim() + + def _ndim(self): + # Users must override this method. + return None + + def flatten_domain_shape(self, x): + """Flattens `x` to match the domain dimension of this operator. + + Args: + x: A `Tensor`. Must have shape `[...] + self.domain_shape`. + + Returns: + The flattened `Tensor`. Has shape `[..., self.domain_dimension]`. + """ + # pylint: disable=invalid-unary-operand-type + domain_rank_static = self.domain_shape.rank + if domain_rank_static is not None: + domain_rank_dynamic = domain_rank_static + else: + domain_rank_dynamic = tf.shape(self.domain_shape_tensor())[0] + + if domain_rank_static is not None: + self.domain_shape.assert_is_compatible_with( + x.shape[-domain_rank_static:]) + + if domain_rank_static is not None: + batch_shape = x.shape[:-domain_rank_static] + else: + batch_shape = tf.TensorShape(None) + batch_shape_tensor = tf.shape(x)[:-domain_rank_dynamic] + + output_shape = batch_shape + self.domain_dimension + output_shape_tensor = tf.concat( + [batch_shape_tensor, [self.domain_dimension_tensor()]], 0) + + x = tf.reshape(x, output_shape_tensor) + return tf.ensure_shape(x, output_shape) + + def flatten_range_shape(self, x): + """Flattens `x` to match the range dimension of this operator. + + Args: + x: A `Tensor`. Must have shape `[...] + self.range_shape`. + + Returns: + The flattened `Tensor`. Has shape `[..., self.range_dimension]`. + """ + # pylint: disable=invalid-unary-operand-type + range_rank_static = self.range_shape.rank + if range_rank_static is not None: + range_rank_dynamic = range_rank_static + else: + range_rank_dynamic = tf.shape(self.range_shape_tensor())[0] + + if range_rank_static is not None: + self.range_shape.assert_is_compatible_with( + x.shape[-range_rank_static:]) + + if range_rank_static is not None: + batch_shape = x.shape[:-range_rank_static] + else: + batch_shape = tf.TensorShape(None) + batch_shape_tensor = tf.shape(x)[:-range_rank_dynamic] + + output_shape = batch_shape + self.range_dimension + output_shape_tensor = tf.concat( + [batch_shape_tensor, [self.range_dimension_tensor()]], 0) + + x = tf.reshape(x, output_shape_tensor) + return tf.ensure_shape(x, output_shape) + + def expand_domain_dimension(self, x): + """Expands `x` to match the domain shape of this operator. + + Args: + x: A `Tensor`. Must have shape `[..., self.domain_dimension]`. + + Returns: + The expanded `Tensor`. Has shape `[...] + self.domain_shape`. + """ + self.domain_dimension.assert_is_compatible_with(x.shape[-1]) + + batch_shape = x.shape[:-1] + batch_shape_tensor = tf.shape(x)[:-1] + + output_shape = batch_shape + self.domain_shape + output_shape_tensor = tf.concat([ + batch_shape_tensor, self.domain_shape_tensor()], 0) + + x = tf.reshape(x, output_shape_tensor) + return tf.ensure_shape(x, output_shape) + + def expand_range_dimension(self, x): + """Expands `x` to match the range shape of this operator. + + Args: + x: A `Tensor`. Must have shape `[..., self.range_dimension]`. + + Returns: + The expanded `Tensor`. Has shape `[...] + self.range_shape`. + """ + self.range_dimension.assert_is_compatible_with(x.shape[-1]) + + batch_shape = x.shape[:-1] + batch_shape_tensor = tf.shape(x)[:-1] + + output_shape = batch_shape + self.range_shape + output_shape_tensor = tf.concat([ + batch_shape_tensor, self.range_shape_tensor()], 0) + + x = tf.reshape(x, output_shape_tensor) + return tf.ensure_shape(x, output_shape) + + +@api_util.export("linalg.LinearOperatorMakeND") +@make_linear_operator_nd +class LinearOperatorMakeND(LinearOperatorND): + """Adds multidimensional support to a linear operator. + + Adds multidimensional shape information to a `LinearOperator` and support + for all `LinearOperatorND`-specific functionality, such as `matvec_nd`, + `solvevec_nd`, `domain_shape` and `range_shape`. + + If the input operator acts like matrix $A$, then this operator also acts + like matrix $A$. The functionality of the underlying operator is preserved, + with this operator having a superset of its functionality. + + ```{rubric} Initialization + ``` + This operator is initialized with a non-ND linear operator (`operator`) and + range/domain shape information (`range_shape` and `domain_shape`) + + Args: + operator: A `tfmri.linalg.LinearOperator`. If `operator` is an instance of + `LinearOperatorND`, then `operator` is returned unchanged. + range_shape: A `tf.Tensor` representing the range shape of the operator. + Must be compatible with the range dimension of `operator`. + domain_shape: A `tf.Tensor` representing the domain shape of the operator. + Must be compatible with the domain dimension of `operator`. + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `None`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `None`. + is_positive_definite: A boolean, or `None`. Whether this operator is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that an operator [does + not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) + Defaults to `None`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `None`. + name: An optional `str`. The name of this operator. + """ + def __new__(cls, operator, *args, **kwargs): + # If the input operator is already an ND operator, return it. + if isinstance(operator, LinearOperatorND): + return operator + return super().__new__(cls) + + def __init__(self, + operator, + range_shape=None, + domain_shape=None, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None, + name=None, + **kwargs): + # The arguments `range_shape_` and `domain_shape_` (with trailing + # underscores) are used when reconstructing the operator from its + # components. + if range_shape is None: + if 'range_shape_' not in kwargs: + raise ValueError("Argument `range_shape` must be specified.") + range_shape = kwargs['range_shape_'] + + if domain_shape is None: + if 'domain_shape_' not in kwargs: + raise ValueError("Argument `domain_shape` must be specified.") + domain_shape = kwargs['domain_shape_'] + + parameters = dict( + operator=operator, + range_shape_=range_shape, + domain_shape_=domain_shape, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name) + + if isinstance(operator, LinearOperatorND): + raise TypeError("operator is already a LinearOperatorND.") + if not isinstance(operator, linear_operator.LinearOperator): + raise TypeError(f"operator must be a LinearOperator, but got: {operator}") + self._operator = operator + + if (is_non_singular is not None and + operator.is_non_singular is not None and + is_non_singular != operator.is_non_singular): + raise ValueError("is_non_singular must match operator.is_non_singular.") + if is_non_singular is None: + is_non_singular = operator.is_non_singular + + if (is_self_adjoint is not None and + operator.is_self_adjoint is not None and + is_self_adjoint != operator.is_self_adjoint): + raise ValueError("is_self_adjoint must match operator.is_self_adjoint.") + if is_self_adjoint is None: + is_self_adjoint = operator.is_self_adjoint + + if (is_positive_definite is not None and + operator.is_positive_definite is not None and + is_positive_definite != operator.is_positive_definite): + raise ValueError( + "is_positive_definite must match operator.is_positive_definite.") + if is_positive_definite is None: + is_positive_definite = operator.is_positive_definite + + if (is_square is not None and + operator.is_square is not None and + is_square != operator.is_square): + raise ValueError("is_square must match operator.is_square.") + if is_square is None: + is_square = operator.is_square + + # Process the domain and range shapes and check that they are compatible. + self._domain_shape_static, self._domain_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) + self._range_shape_static, self._range_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape(range_shape)) + + if (self._domain_shape_static.num_elements() is not None and + operator.domain_dimension is not None and + self._domain_shape_static.num_elements() != operator.domain_dimension): + raise ValueError( + f"domain_shape must have the same number of elements as " + f"operator.domain_dimension. " + f"Found {self._domain_shape_static.num_elements()} " + f"and {operator.domain_dimension}, respectively.") + + if (self._range_shape_static.num_elements() is not None and + operator.range_dimension is not None and + self._range_shape_static.num_elements() != operator.range_dimension): + raise ValueError( + f"range_shape must have the same number of elements as " + f"operator.range_dimension. " + f"Found {self._range_shape_static.num_elements()} " + f"and {operator.range_dimension}, respectively.") + + # Initialization. + if name is None: + name = operator.name + "ND" + + with tf.name_scope(name): + super().__init__( + dtype=operator.dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters, + name=name) + + def _domain_shape(self): + return self._domain_shape_static + + def _domain_shape_tensor(self): + return self._domain_shape_dynamic + + def _range_shape(self): + return self._range_shape_static + + def _range_shape_tensor(self): + return self._range_shape_dynamic + + def _batch_shape(self): + return self.operator.batch_shape + + def _batch_shape_tensor(self): + return self.operator.batch_shape_tensor() + + def _matmul(self, x, adjoint=False, adjoint_arg=False): + return self.operator.matmul(x, adjoint=adjoint, adjoint_arg=adjoint_arg) + + def _matvec(self, x, adjoint=False): + return self.operator.matvec(x, adjoint=adjoint) + + def _solve(self, rhs, adjoint=False, adjoint_arg=False): + return self.operator.solve(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) + + def _solvevec(self, rhs, adjoint=False): + return self.operator.solvevec(rhs, adjoint=adjoint) + + def _lstsq(self, rhs, adjoint=False, adjoint_arg=False): + return self.operator.lstsq(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) + + def _lstsqvec(self, rhs, adjoint=False): + return self.oeprator.lstsqvec(rhs, adjoint=adjoint) + + def _matvec_nd(self, x, adjoint=False): + x = (self.flatten_range_shape(x) if adjoint else \ + self.flatten_domain_shape(x)) + x = self._matvec(x, adjoint=adjoint) + x = (self.expand_domain_dimension(x) if adjoint else + self.expand_range_dimension(x)) + return x + + def _solvevec_nd(self, x, adjoint=False): + x = (self.flatten_domain_shape(x) if adjoint else \ + self.flatten_range_shape(x)) + x = self._solvevec(x, adjoint=adjoint) + x = (self.expand_range_dimension(x) if adjoint else + self.expand_domain_dimension(x)) + return x + + def _lstsqvec_nd(self, x, adjoint=False): + x = (self.flatten_domain_shape(x) if adjoint else \ + self.flatten_range_shape(x)) + x = self._lstsqvec(x, adjoint=adjoint) + x = (self.expand_range_dimension(x) if adjoint else + self.expand_domain_dimension(x)) + return x + + def _add_to_tensor(self, x): + return self.operator.add_to_tensor(x) + + def _assert_non_singular(self): + return self.operator.assert_non_singular() + + def _assert_self_adjoint(self): + return self.operator.assert_self_adjoint() + + def _assert_positive_definite(self): + return self.operator.assert_positive_definite() + + def _cond(self): + return self.operator.cond() + + def _determinant(self): + return self.operator.determinant() + + def _diag_part(self): + return self.operator.diag_part() + + def _eigvals(self): + return self.operator.eigvals() + + def _log_abs_determinant(self): + return self.operator.log_abs_determinant() + + def _trace(self): + return self.operator.trace() + + def _to_dense(self): + return self.operator.to_dense() + + @property + def operator(self): + return self._operator + + @property + def _composite_tensor_fields(self): + # We use `domain_shape_` and `range_shape_` for conversion to/from composite tensor. + return ("operator", "range_shape_", "domain_shape_") + + @property + def _composite_tensor_prefer_static_fields(self): + return ("range_shape_", "domain_shape_") + + @property + def _experimental_parameter_ndims_to_matrix_ndims(self): + return {"operator": 0} diff --git a/tensorflow_mri/python/linalg/linear_operator_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_nd_test.py new file mode 100644 index 00000000..b20f8f0a --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_nd_test.py @@ -0,0 +1,263 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator`.""" +# pylint: disable=missing-class-docstring,missing-function-docstring + +import functools + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_identity_nd +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.linalg import linear_operator_test_util +from tensorflow_mri.python.util import test_util + + +FullMatrix = tf.linalg.LinearOperatorFullMatrix +MakeND = linear_operator_nd.LinearOperatorMakeND + + +rng = np.random.RandomState(0) + + +class SquareLinearOperatorMakeNDTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + """Tests for `LinearOperatorMakeND`.""" + domain_shape = (3, 2) + range_shape = (2, 3) + batch_shape = (2, 1) + + def operator_and_matrix(self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + shape = list(build_info.shape) + + if ensure_self_adjoint_and_pd: + matrix = linear_operator_test_util.random_positive_definite_matrix( + shape, dtype, force_well_conditioned=True) + else: + matrix = linear_operator_test_util.random_normal(shape=shape, dtype=dtype) + + if use_placeholder: + matrix = tf.compat.v1.placeholder_with_default(matrix, shape=None) + + operator = MakeND( + FullMatrix(matrix, + is_self_adjoint=True if ensure_self_adjoint_and_pd else None, + is_positive_definite=True if ensure_self_adjoint_and_pd else None, + is_square=True), + [shape[-2]], [shape[-1]], + is_self_adjoint=True if ensure_self_adjoint_and_pd else None, + is_positive_definite=True if ensure_self_adjoint_and_pd else None, + is_square=True) + + return operator, matrix + + def operator_and_operator_nd(self, + range_shape=range_shape, + domain_shape=domain_shape, + batch_shape=batch_shape): + range_dimension = functools.reduce(lambda x, y: x * y, range_shape) + domain_dimension = functools.reduce(lambda x, y: x * y, domain_shape) + + matrix = tf.random.uniform( + batch_shape + (range_dimension, domain_dimension)) + + operator = FullMatrix(matrix) + operator_nd = MakeND( + FullMatrix(matrix), range_shape, domain_shape) + + return operator, operator_nd + + def random_input(self, domain_shape=domain_shape, batch_shape=batch_shape): + x_nd = tf.random.normal(batch_shape + domain_shape) + x = tf.reshape(x_nd, batch_shape + (-1,)) + return x, x_nd + + def random_rhs(self, range_shape=range_shape, batch_shape=batch_shape): + rhs_nd = tf.random.normal(batch_shape + range_shape) + rhs = tf.reshape(rhs_nd, batch_shape + (-1,)) + return rhs, rhs_nd + + def test_is_nd_operator(self): + _, operator_nd = self.operator_and_operator_nd() + self.assertIsInstance(operator_nd, linear_operator_nd.LinearOperatorND) + + def test_name(self): + _, operator_nd = self.operator_and_operator_nd() + self.assertEqual("LinearOperatorFullMatrixND", operator_nd.name) + + def test_static_shapes(self): + operator, operator_nd = self.operator_and_operator_nd() + self.assertIsInstance(operator_nd.domain_shape, tf.TensorShape) + self.assertIsInstance(operator_nd.range_shape, tf.TensorShape) + self.assertIsInstance(operator_nd.batch_shape, tf.TensorShape) + self.assertIsInstance(operator_nd.shape, tf.TensorShape) + self.assertEqual(self.domain_shape, operator_nd.domain_shape) + self.assertEqual(self.range_shape, operator_nd.range_shape) + self.assertEqual(self.batch_shape, operator_nd.batch_shape) + self.assertEqual(operator.shape, operator_nd.shape) + + def test_dynamic_shapes(self): + operator, operator_nd = self.operator_and_operator_nd() + self.assertIsInstance(operator_nd.domain_shape_tensor(), tf.Tensor) + self.assertIsInstance(operator_nd.range_shape_tensor(), tf.Tensor) + self.assertIsInstance(operator_nd.batch_shape_tensor(), tf.Tensor) + self.assertIsInstance(operator_nd.shape_tensor(), tf.Tensor) + self.assertAllEqual(self.domain_shape, self.evaluate( + operator_nd.domain_shape_tensor())) + self.assertAllEqual(self.range_shape, self.evaluate( + operator_nd.range_shape_tensor())) + self.assertAllEqual(self.batch_shape, self.evaluate( + operator_nd.batch_shape_tensor())) + self.assertAllEqual(self.evaluate(operator.shape_tensor()), + self.evaluate(operator_nd.shape_tensor())) + + def test_operator_wrong_type(self): + class Cat(): + def say_hello(self): + return "meow" + + with self.assertRaisesRegex(TypeError, "must be a LinearOperator"): + MakeND(Cat(), (2, 3), (3, 2)) + + def test_nd_operator_returns_itself(self): + operator = linear_operator_identity_nd.LinearOperatorIdentityND( + domain_shape=(2, 3)) + operator_nd = MakeND(operator, (2, 3), (3, 2)) + self.assertIs(operator, operator_nd) + + def test_incompatible_domain_shape_raises(self): + operator, _ = self.operator_and_operator_nd() + with self.assertRaisesRegex( + ValueError, "domain_shape must have the same number of elements"): + MakeND( + operator, self.range_shape, (5, 3)) + + def test_incompatible_range_shape_raises(self): + operator, _ = self.operator_and_operator_nd() + with self.assertRaisesRegex( + ValueError, "range_shape must have the same number of elements"): + MakeND( + operator, (5, 3), self.domain_shape) + + def test_matvec(self): + operator, operator_nd = self.operator_and_operator_nd() + x, _ = self.random_input() + rhs, _ = self.random_rhs() + self.assertAllClose(operator.matvec(x), + operator_nd.matvec(x)) + self.assertAllClose(operator.matvec(rhs, adjoint=True), + operator_nd.matvec(rhs, adjoint=True)) + + def test_matmul(self): + operator, operator_nd = self.operator_and_operator_nd() + x, _ = self.random_input() + rhs, _ = self.random_rhs() + self.assertAllClose( + operator.matmul(x[..., tf.newaxis]), + operator_nd.matmul(x[..., tf.newaxis])) + self.assertAllClose( + operator.matmul(x[..., tf.newaxis, :], adjoint_arg=True), + operator_nd.matmul(x[..., tf.newaxis, :], adjoint_arg=True)) + self.assertAllClose( + operator.matmul(rhs[..., tf.newaxis], adjoint=True), + operator_nd.matmul(rhs[..., tf.newaxis], adjoint=True)) + self.assertAllClose( + operator.matmul(rhs[..., tf.newaxis, :], adjoint=True, adjoint_arg=True,), + operator_nd.matmul(rhs[..., tf.newaxis, :], adjoint=True, adjoint_arg=True)) + + def test_solvevec(self): + operator, operator_nd = self.operator_and_operator_nd() + x, _ = self.random_input() + rhs, _ = self.random_rhs() + self.assertAllClose(operator.solvevec(rhs), + operator_nd.solvevec(rhs)) + self.assertAllClose(operator.solvevec(x, adjoint=True), + operator_nd.solvevec(x, adjoint=True)) + + def test_solve(self): + operator, operator_nd = self.operator_and_operator_nd() + x, _ = self.random_input() + rhs, _ = self.random_rhs() + self.assertAllClose( + operator.solve(rhs[..., tf.newaxis]), + operator_nd.solve(rhs[..., tf.newaxis])) + self.assertAllClose( + operator.solve(rhs[..., tf.newaxis, :], adjoint_arg=True), + operator_nd.solve(rhs[..., tf.newaxis, :], adjoint_arg=True)) + self.assertAllClose( + operator.solve(x[..., tf.newaxis], adjoint=True), + operator_nd.solve(x[..., tf.newaxis], adjoint=True)) + self.assertAllClose( + operator.solve(x[..., tf.newaxis, :], adjoint=True, adjoint_arg=True,), + operator_nd.solve(x[..., tf.newaxis, :], adjoint=True, adjoint_arg=True)) + + def test_matvec_nd(self): + range_shape, domain_shape, batch_shape = ( + self.range_shape, self.domain_shape, self.batch_shape) + batch_shape = self.batch_shape + operator, operator_nd = self.operator_and_operator_nd() + x, x_nd = self.random_input() + rhs, rhs_nd = self.random_rhs() + + self.assertAllClose( + tf.reshape(operator.matvec(x), batch_shape + range_shape), + operator_nd.matvec_nd(x_nd)) + + self.assertAllClose( + tf.reshape(operator.matvec(rhs, adjoint=True), batch_shape + domain_shape), + operator_nd.matvec_nd(rhs_nd, adjoint=True)) + + def test_solvevec_nd(self): + range_shape, domain_shape, batch_shape = ( + self.range_shape, self.domain_shape, self.batch_shape) + batch_shape = self.batch_shape + operator, operator_nd = self.operator_and_operator_nd() + x, x_nd = self.random_input() + rhs, rhs_nd = self.random_rhs() + + self.assertAllClose( + tf.reshape(operator.solvevec(rhs), batch_shape + domain_shape), + operator_nd.solvevec_nd(rhs_nd)) + + self.assertAllClose( + tf.reshape(operator.solvevec(x, adjoint=True), batch_shape + range_shape), + operator_nd.solvevec_nd(x_nd, adjoint=True)) + + +class NonSquareLinearOperatorMakeNDTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + """Tests for `LinearOperatorMakeND`.""" + def operator_and_matrix(self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + shape = list(build_info.shape) + + matrix = linear_operator_test_util.random_normal(shape=shape, dtype=dtype) + + if use_placeholder: + matrix = tf.compat.v1.placeholder_with_default(matrix, shape=None) + + operator = MakeND(FullMatrix(matrix), [shape[-2]], [shape[-1]]) + + return operator, matrix + + +linear_operator_test_util.add_tests(SquareLinearOperatorMakeNDTest) +linear_operator_test_util.add_tests(NonSquareLinearOperatorMakeNDTest) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_nufft.py b/tensorflow_mri/python/linalg/linear_operator_nufft.py new file mode 100644 index 00000000..dd1b85cf --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_nufft.py @@ -0,0 +1,778 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Non-uniform Fourier linear operators.""" + +import warnings + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.linalg import linear_operator_util +from tensorflow_mri.python.ops import array_ops +from tensorflow_mri.python.ops import traj_ops +from tensorflow_mri.python.ops import fft_ops +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import tensor_util +from tensorflow_mri.python.util import types_util + + +@api_util.export("linalg.LinearOperatorNUFFT") +@linear_operator_nd.make_linear_operator_nd +class LinearOperatorNUFFT(linear_operator_nd.LinearOperatorND): + r"""Linear operator acting like a [batch] nonuniform Fourier matrix. + + Performs an N-dimensional discrete Fourier transform via the nonuniform fast + Fourier transform (NUFFT) algorithm. Let $A$ represent this linear operator, + then: + + - The forward operator $A$ evaluates the forward, type-2 NUFFT (signal domain + to frequency domain, uniform to nonuniform). + - The adjoint operator $A^H$ evaluates the backward, type-1 NUFFT + (frequency domain to signal domain, nonuniform to uniform). + + The dimensionality of the grid $n = n_0 \times ... \times n_d$ is determined + by `domain_shape`. The $m$ non-uniform sampling locations in the frequency + domain are defined by `points`. + + ```{rubric} Inverse NUFFT + ``` + ```{note} + The NUFFT operator is not generally invertible, so calling `inverse` or + `solve` (or the related `solvevec` and `solvevec_nd`) will raise an error. + ``` + + However, you can solve $Ax = b$ in the least-squares sense. + + One approach is to use this operator's `lstsq` method (or one of the related + methods `lstsqvec` and `lstsqvec_nd`). + + ```{attention} + If you intend to use `lstsq`, `lstsqvec`, or `lstsqvec_nd`, you should + consider providing `crosstalk_inverse` (see below). If you do not provide + this argument, the solution will be computed using a potentially very slow + algorithm which requires conversion to a dense matrix. + ``` + + Alternatively, you could use `tfmri.linalg.lsqr` or + `tfmri.linalg.conjugate_gradient` to solve the least-squares problem + iteratively. + + ```{rubric} Fourier crosstalk matrix + ``` + The Fourier crosstalk matrix is the matrix $D = A A^H$ (if $m < n$) or the + matrix $D = A^H A$ (if $m > n$). The solution to the least-squares problem + can be written in terms of $D$ as $x = A^H D^{-1} b$ (if $m < n$) or + $x = D^{-1} A b$ (if $m$ > $n$). + + Hence, if $D{-1}$ is known, the least-squares problem can be solved without + performing an explicit inversion. The argument `crosstalk_inverse` allows + you to provide $D^{-1}$. + + The matrix $D$ (and hence, $D^{-1}$) is dependent on the sampling locations + `points`. For arbitrary sampling patterns, this matrix is full and requires + $O(l^2)$ storage, with a runtime complexity of $O(l^3)$ for matrix-matrix + multiplication (where $l = \min{(m, n)}$). This is clearly impractical for + large $l$. Furthermore, in this case one might as well just store and apply + $A^{-1}$ directly. + + A more interesting use of `crosstalk_inverse` is to provide an approximation + to $D^{-1}$ with a more favorable structure. A common choice is to use a + diagonal matrix, which requires only $O(l)$ storage and whose matrix-matrix + product runs in $O(l^2)$ time. In MRI, this is often referred to as + **sampling density compensation**. + + ```{tip} + If `weights` are your density compensation weights, use + `crosstalk_inverse=tfmri.linalg.LinearOperatorDiag(weights)`. + ``` + + ```{rubric} TLDR: how do I invert the NUFFT? + ``` + Essentially, you have three options: + + 1. **Direct solve** (not recommmended). Simply call `lstsq` (or one of the + related methods `lstsqvec` and `lstsqvec_nd`). This is the most + straightforward approach, but it is likely to be very slow for large $l$. + If you can't or don't want to provide $D^{-1}$ (or an approximation + thereof), you're probably better off using method 3. + 2. **Direct solve with crosstalk approximation** (in MRI, sometimes called + the **conjugate phase** method): If the inverse of the Fourier crosstalk + matrix $D^{-1}$ has favorable structure (i.e., it does not have large + storage requirements and it can be applied quickly), or you can use an + approximation which does, specify `crosstalk_inverse` and then use `lstsq` + (or one of the related methods `lstsqvec` and `lstsqvec_nd`). In MRI, a + common choice of approximation is a diagonal matrix containing whose + diagonal elements are the density compensation weights. Under these + conditions, this method is probably the fastest, but might compromise + accuracy depending on your choice of $D^{-1}$. + 3. **Iterative solve**: If you do not know `D{-1}`, or if accuracy is + paramount, use `tfmri.linalg.lsqr` or `tfmri.linalg.conjugate_gradient` + to solve the least-squares problem iteratively. This method is likely + to be slower than method 2 but faster than method 3 due to its iterative + nature. + ``` + + ```{seealso} + `tfmri.linalg.LinearOperatorFFT` for uniformly sampled Fourier transforms. + ``` + + Args: + domain_shape: A 1D integer `tf.Tensor`. The domain shape of this + operator. This is usually the shape of the image but may include + additional leading dimensions. The trailing `d` dimensions (inferred + from `points`) are the signal dimensions, and any additional leading + dimensions are technically batch dimensions which are included in the + domain rather than the batch. + points: A `tf.Tensor` of type `float32` or `float64`. Contains the + non-uniform sampling locations in the frequency domain. Must have + shape `[..., m, d]`, where `d` is the dimension of the Fourier transform + (must be 1, 2 or 3), `m` is the number of samples and `...` is the + batch shape, which can have any number of dimensions. Must be in the + range $[-\pi, \pi]$. + ```{tip} + In MRI, this is the *k*-space trajectory. + ``` + crosstalk_inverse: A `tf.Tensor` or `tf.linalg.LinearOperator` of shape + `[..., l, l]` representing the inverse of the Fourier crosstalk + matrix [2], where $l = \min{(m, n)}$. This matrix is used to simplify the + computation of the pseudo-inverse $A^{+}$ and/or to solve the + least-squares problem defined by this operator. Ideally this matrix + should be equal to $(A A^H)^{-1}$ (if $m < n$) or $(A^H A)^{-1}$ + (if $m > n$), where $A$ is this operator, but you can also provide a + suitable approximation with a more favorable structure. Defaults to + `None`. + ```{attention} + If you intend to use `lstsq`, `lstsqvec`, or `lstsqvec_nd`, you are + strongly encouraged to provide `crosstalk_inverse`. If you do not, + these methods will use a potentially very slow algorithm which requires + conversion to a dense matrix. + ``` + ```{warning} + This operator will not check `crosstalk_inverse` for correctness. It is + your responsibility to ensure that it is reasonable your purposes. + ``` + ```{tip} + In MRI, you can use `crosstalk_inverse` for density compensation by + specifying a diagonal operator whose diagonal elements are the density + compensation weights. + ``` + ```{tip} + If you do not need to invert this operator, you can safely ignore this + argument. + ``` + is_non_singular: A boolean, or `None`. Whether this operator is expected + to be non-singular. Defaults to `None`. + is_self_adjoint: A boolean, or `None`. Whether this operator is expected + to be equal to its Hermitian transpose. If `dtype` is real, this is + equivalent to being symmetric. Defaults to `False`. + is_positive_definite: A boolean, or `None`. Whether this operator is + expected to be positive definite, meaning the quadratic form $x^H A x$ + has positive real part for all nonzero $x$. Note that an operator [does + not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) + Defaults to `None`. + is_square: A boolean, or `None`. Expect that this operator acts like a + square matrix (or a batch of square matrices). Defaults to `False`. + name: An optional `str`. The name of this operator. + + Example: + >>> # Create some data. + >>> image_shape = (128, 128) + >>> image = tfmri.image.phantom(shape=image_shape, dtype=tf.complex64) + >>> trajectory = tfmri.sampling.radial_trajectory( + ... base_resolution=128, views=129, flatten_encoding_dims=True) + >>> density = tfmri.sampling.radial_density( + ... base_resolution=128, views=129, flatten_encoding_dims=True) + >>> # Create a density compensation matrix. This will be used to invert + >>> # the operator more efficiently. + >>> weights = tf.math.reciprocal(density) + >>> linop_density = tf.linalg.LinearOperatorDiag(weights) + >>> # Create a NUFFT operator. + >>> linop = tfmri.linalg.LinearOperatorNUFFT( + ... image_shape, points=trajectory, crosstalk_inverse=linop_density) + >>> # Compute k-space by applying the forward operator. + >>> kspace = linop.matvec_nd(image) + >>> # Reconstruct the image by solving the corresponding least-squares + >>> # problem. + >>> recon = linop.lstsqvec_nd(kspace) + + References: + 1. A. H. Barnett, J. Magland, and L. af Klinteberg, "A Parallel Nonuniform + Fast Fourier Transform Library Based on an "Exponential of Semicircle" + Kernel", *SIAM Journal on Scientific Computing*, vol. 41, no. 5, + pp. C479-C504, 2019, + doi: [10.1137/18M120885X](https://doi.org/10.1137/18M120885X) + 2. Y. Shih, G. Wright, J. Anden, J. Blaschke, and A. H. Barnett, + "cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform + FFTs,” in *2021 IEEE International Parallel and Distributed Processing + Symposium Workshops (IPDPSW)*, 2021, pp. 688-697. + doi: [10.1109/IPDPSW52791.2021.00105](https://doi.org/10.1109/IPDPSW52791.2021.00105) + 3. J. A. Fessler and B. P. Sutton, "Nonuniform fast Fourier transforms + using min-max interpolation", *IEEE Transactions on Signal Processing*, + vol. 51, no. 2, pp. 560-574, 2003, + doi: [10.1109/TSP.2002.807005](https://doi.org/10.1109/TSP.2002.807005) + 4. H. H. Barrett, J. L. Denny, R. F. Wagner, and K. J. Myers, "Objective + assessment of image quality. II. Fisher information, Fourier crosstalk, + and figures of merit for task performance", *J. Opt. Soc. Am. A*, + vol. 12, no. 5, pp. 834-852, May 1995, + doi: [10.1364/JOSAA.12.000834](https://doi.org/10.1364/josaa.12.000834) + """ + def __init__(self, + domain_shape, + points, + crosstalk_inverse=None, + is_non_singular=None, + is_self_adjoint=False, + is_positive_definite=None, + is_square=None, + name="LinearOperatorNUFFT"): + + parameters = dict( + domain_shape=domain_shape, + points=points, + crosstalk_inverse=crosstalk_inverse, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name + ) + + # Check non-reference types. + types_util.assert_not_ref_type(domain_shape, "domain_shape") + + # Get domain shapes. + self._domain_shape_static, self._domain_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) + + # Validate the remaining inputs. + self._points = tf.convert_to_tensor(points, name="points") + if self._points.dtype not in (tf.float32, tf.float64): + raise TypeError( + f"points must be a float32 or float64 tensor, " + f"not {str(self._points.dtype)}") + + # Get dtype for this operator. + dtype = tensor_util.get_complex_dtype(self._points.dtype) + + # We infer the operation's rank from the points. + self._ndim_static = self._points.shape[-1] + self._rank_dynamic = tf.shape(self._points)[-1] + # The domain rank is >= the operation rank. + domain_rank_static = self._domain_shape_static.rank + domain_rank_dynamic = tf.shape(self._domain_shape_dynamic)[0] + # The difference between this operation's rank and the domain rank is the + # number of extra dims. + extra_dims_static = domain_rank_static - self._ndim_static + extra_dims_dynamic = domain_rank_dynamic - self._rank_dynamic + + # The grid shape are the last `rank` dimensions of domain_shape. We don't + # need the static grid shape. + self._grid_shape = self._domain_shape_dynamic[-self._rank_dynamic:] + + # We need to do some work to figure out the batch shapes. This operator + # could have a batch shape, if the points have a batch shape. However, + # we allow the user to include one or more batch dimensions in the domain + # shape, if they so wish. Therefore, not all batch dimensions in the + # points are necessarily part of the batch shape. + + # The total number of dimensions in `points` is equal to + # `batch_dims + extra_dims + 2`. + # Compute the true batch shape (i.e., the batch dimensions that are + # NOT included in the domain shape). + batch_dims_dynamic = tf.rank(self._points) - extra_dims_dynamic - 2 + if (self._points.shape.rank is not None and + extra_dims_static is not None): + # We know the total number of dimensions in `points` and we know + # the number of extra dims, so we can compute the number of batch dims + # statically. + batch_dims_static = self._points.shape.rank - extra_dims_static - 2 + else: + # We are missing at least some information, so the number of batch + # dimensions is unknown. + batch_dims_static = None + + self._batch_shape_dynamic = tf.shape(self._points)[:batch_dims_dynamic] + if batch_dims_static is not None: + self._batch_shape_static = self._points.shape[:batch_dims_static] + else: + self._batch_shape_static = tf.TensorShape(None) + + # Compute the "extra" shape. This shape includes those dimensions which + # are not part of the NUFFT (e.g., they are effectively batch dimensions), + # but which are included in the domain shape rather than in the batch shape. + extra_shape_dynamic = self._domain_shape_dynamic[:-self._rank_dynamic] + if self._ndim_static is not None: + extra_shape_static = self._domain_shape_static[:-self._ndim_static] + else: + extra_shape_static = tf.TensorShape(None) + + # Check that the "extra" shape in `domain_shape` and `points` are + # compatible for broadcasting. + shape1, shape2 = extra_shape_static, self._points.shape[:-2] + try: + tf.broadcast_static_shape(shape1, shape2) + except ValueError as err: + raise ValueError( + f"The \"batch\" shapes in `domain_shape` and `points` are not " + f"compatible for broadcasting: {shape1} vs {shape2}") from err + + # Compute the range shape. + self._range_shape_dynamic = tf.concat( + [extra_shape_dynamic, tf.shape(self._points)[-2:-1]], 0) + self._range_shape_static = extra_shape_static.concatenate( + self._points.shape[-2:-1]) + + # Set inverse of Fourier crosstalk matrix. + # This needs to be done after setting all the shapes, as `self.shape` + # must be valid at this point. + self._crosstalk_inverse = crosstalk_inverse + if self._crosstalk_inverse is not None: + if not isinstance(self._crosstalk_inverse, tf.linalg.LinearOperator): + # Not a linear operator, assume a full matrix was passed. + self._crosstalk_inverse = tf.linalg.LinearOperatorFullMatrix( + self._crosstalk_inverse) + if not self._crosstalk_inverse.shape[-2:-1].is_compatible_with( + self._crosstalk_inverse.shape[-1:]): + raise ValueError( + f"The crosstalk matrix must be square. Got shape " + f"{self._crosstalk_inverse.shape}.") + if self.shape[-2:].is_fully_defined(): + if self.shape[-2] < self.shape[-1]: + if not self._crosstalk_inverse.shape[-2:-1].is_compatible_with( + self.shape[-2:-1]): + raise ValueError( + f"The crosstalk matrix must have the same number of rows as " + f"this operator. Got shape {self._crosstalk_inverse.shape} for " + f"operator shape {self.shape}.") + else: + if not self._crosstalk_inverse.shape[-1:].is_compatible_with( + self.shape[-1:]): + raise ValueError( + f"The crosstalk matrix must have the same number of columns as " + f"this operator. Got shape {self._crosstalk_inverse.shape} for " + f"operator shape {self.shape}.") + + # Compute normalization factors. + self._norm_factor = tf.math.reciprocal( + tf.math.sqrt(tf.cast(tf.math.reduce_prod(self._grid_shape), dtype))) + + # Default tolerance for NUFFT. + self._tol = {tf.complex64: 1e-6, tf.complex128: 1e-12}[dtype] + + super().__init__(dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters, + name=name) + + def _matvec_nd(self, x, adjoint=False): + if adjoint: + x = fft_ops.nufft(x, self._points, + grid_shape=self._grid_shape, + transform_type='type_1', + fft_direction='backward', + tol=self._tol) + x *= self._norm_factor + else: + x = fft_ops.nufft(x, self._points, + transform_type='type_2', + fft_direction='forward', + tol=self._tol) + x *= self._norm_factor + return x + + def _solvevec_nd(self, rhs, adjoint=False): + raise ValueError( + f"{self.name} is not invertible. If you intend to solve the " + f"associated least-squares problem, use `lstsq`, `lstsqvec` or " + f"`lstsqvec_nd`.") + + def _lstsqvec_nd(self, rhs, adjoint=False): + if self._crosstalk_inverse is None: + warnings.warn( + f"{self.name}: Using (possibly slow) implementation of lstsq which " + f"requires conversion to a dense matrix and O(n^3) operations. " + f"For a more efficient computation, consider specifying the " + f"`crosstalk_inverse` argument (see class documentation) or using " + f"an iterative solver such as `tfmri.linalg.lsqr` or " + f"`tfmri.linalg.conjugate_gradient`.") + rhs = tf.expand_dims(rhs, -1) + x = linear_operator_util.matrix_solve_ls_with_broadcast( + self.to_dense(), rhs, adjoint=adjoint) + x = tf.squeeze(x, -1) + return x + if self.shape[-2:].is_fully_defined(): + # We know the static shape of the operator, so we can select the + # appropriate code path when building the graph. + if (self.shape[-2] < self.shape[-1]) ^ adjoint: # pylint: disable=no-else-return + return self._lstsqvec_nd_underdetermined(rhs, adjoint=adjoint) + else: + return self._lstsqvec_nd_overdetermined(rhs, adjoint=adjoint) + else: + # We don't know the static shape of the operator, so we need to + # defer the selection of the code path until runtime. + return tf.cond( + tf.math.logical_xor( + tf.math.less(self.shape_tensor()[-2], self.shape_tensor()[-1]), + adjoint), + lambda: self._lstsqvec_nd_underdetermined(rhs, adjoint=adjoint), + lambda: self._lstsqvec_nd_overdetermined(rhs, adjoint=adjoint)) + + def _lstsqvec_nd_underdetermined(self, rhs, adjoint=False): + # Solve A x = b as A^H (A A^H)^-1 b, where (A A^H)^-1 is the inverse of + # the Fourier crosstalk matrix. + if isinstance(self._crosstalk_inverse, + linear_operator_nd.LinearOperatorND): + rhs = self._crosstalk_inverse.matvec_nd(rhs) + else: + if adjoint: + rhs = self.flatten_domain_shape(rhs) + else: + rhs = self.flatten_range_shape(rhs) + rhs = self._crosstalk_inverse.matvec(rhs) + if adjoint: + rhs = self.expand_domain_dimension(rhs) + else: + rhs = self.expand_range_dimension(rhs) + x = self._matvec_nd(rhs, adjoint=(not adjoint)) + return x + + def _lstsqvec_nd_overdetermined(self, rhs, adjoint=False): + # Solve A x = b as (A^H A)^-1 A^H b, where (A^H A)^-1 is the inverse of + # the Fourier crosstalk matrix. + x = self._matvec_nd(rhs, adjoint=(not adjoint)) + if isinstance(self._crosstalk_inverse, + linear_operator_nd.LinearOperatorND): + x = self._crosstalk_inverse.matvec_nd(x) + else: + if adjoint: + x = self.flatten_range_shape(x) + else: + x = self.flatten_domain_shape(x) + x = self._crosstalk_inverse.matvec(x) + if adjoint: + x = self.expand_range_dimension(x) + else: + x = self.expand_domain_dimension(x) + return x + + def _domain_shape(self): + return self._domain_shape_static + + def _domain_shape_tensor(self): + return self._domain_shape_dynamic + + def _range_shape(self): + return self._range_shape_static + + def _range_shape_tensor(self): + return self._range_shape_dynamic + + def _batch_shape(self): + return self._batch_shape_static + + def _batch_shape_tensor(self): + return self._batch_shape_dynamic + + def _ndim(self): + return self._ndim_static + + @property + def points(self): + return self._points + + @property + def crosstalk_inverse(self): + return self._crosstalk_inverse + + @property + def _composite_tensor_fields(self): + return ('domain_shape', 'points', 'crosstalk_inverse') + + @property + def _composite_tensor_prefer_static_fields(self): + return ('domain_shape',) + + @property + def _experimental_parameter_ndims_to_matrix_ndims(self): + return {'points': 2, 'crosstalk_inverse': 0} + + +@api_util.export("linalg.LinearOperatorGramNUFFT") +class LinearOperatorGramNUFFT(LinearOperatorNUFFT): # pylint: disable=abstract-method + """Linear operator acting like the Gram matrix of an NUFFT operator. + + If $F$ is a `tfmri.linalg.LinearOperatorNUFFT`, then this operator + applies $F^H F$. This operator is self-adjoint. + + Args: + domain_shape: A 1D integer `tf.Tensor`. The domain shape of this + operator. This is usually the shape of the image but may include + additional dimensions. + trajectory: A `tf.Tensor` of type `float32` or `float64`. Contains the + sampling locations or *k*-space trajectory. Must have shape + `[..., m, n]`, where `n` is the rank (number of dimensions), `m` is + the number of samples and `...` is the batch shape, which can have any + number of dimensions. + density: A `tf.Tensor` of type `float32` or `float64`. Contains the + sampling density at each point in `trajectory`. Must have shape + `[..., m]`, where `m` is the number of samples and `...` is the batch + shape, which can have any number of dimensions. Defaults to `None`, in + which case the density is assumed to be 1.0 in all locations. + norm: A `str`. The FFT normalization mode. Must be `None` (no normalization) + or `'ortho'`. + toeplitz: A `boolean`. If `True`, uses the Toeplitz approach [1] + to compute $F^H F x$, where $F$ is the NUFFT operator. + If `False`, the same operation is performed using the standard + NUFFT operation. The Toeplitz approach might be faster than the direct + approach but is slightly less accurate. This argument is only relevant + for non-Cartesian reconstruction and will be ignored for Cartesian + problems. + name: An optional `str`. The name of this operator. + + References: + 1. Fessler, J. A., Lee, S., Olafsson, V. T., Shi, H. R., & Noll, D. C. + (2005). Toeplitz-based iterative image reconstruction for MRI with + correction for magnetic field inhomogeneity. IEEE Transactions on Signal + Processing, 53(9), 3393-3402. + """ + def __init__(self, + domain_shape, + trajectory, + density=None, + norm='ortho', + toeplitz=False, + name="LinearOperatorNUFFT"): + super().__init__( + domain_shape=domain_shape, + trajectory=trajectory, + density=density, + norm=norm, + name=name + ) + + self.toeplitz = toeplitz + if self.toeplitz: + # Compute the FFT shift for adjoint NUFFT computation. + self._fft_shift = tf.cast(self._grid_shape // 2, self.dtype.real_dtype) + # Compute the Toeplitz kernel. + self._toeplitz_kernel = self._compute_toeplitz_kernel() + # Kernel shape (without batch dimensions). + self._kernel_shape = tf.shape(self._toeplitz_kernel)[-self.rank_tensor():] + + def _transform(self, x, adjoint=False): # pylint: disable=unused-argument + """Applies this linear operator.""" + # This operator is self-adjoint, so `adjoint` arg is unused. + if self.toeplitz: + # Using specialized Toeplitz implementation. + return self._transform_toeplitz(x) + # Using standard NUFFT implementation. + return super()._transform(super()._transform(x), adjoint=True) + + def _transform_toeplitz(self, x): + """Applies this linear operator using the Toeplitz approach.""" + input_shape = tf.shape(x) + fft_axes = tf.range(-self.rank_tensor(), 0) + x = fft_ops.fftn(x, axes=fft_axes, shape=self._kernel_shape) + x *= self._toeplitz_kernel + x = fft_ops.ifftn(x, axes=fft_axes) + x = tf.slice(x, tf.zeros([tf.rank(x)], dtype=tf.int32), input_shape) + return x + + def _compute_toeplitz_kernel(self): + """Computes the kernel for the Toeplitz approach.""" + trajectory = self._trajectory + weights = self._weights + if self.rank is None: + raise NotImplementedError( + f"The rank of {self.name} must be known statically.") + + if weights is None: + # If no weights were passed, use ones. + weights = tf.ones(tf.shape(trajectory)[:-1], dtype=self.dtype.real_dtype) + # Cast weights to complex dtype. + weights = tf.cast(tf.math.sqrt(weights), self.dtype) + + # Compute N-D kernel recursively. Begin with last axis. + last_axis = self.rank - 1 + kernel = self._compute_kernel_recursive(trajectory, weights, last_axis) + + # Make sure that the kernel is symmetric/Hermitian/self-adjoint. + kernel = self._enforce_kernel_symmetry(kernel) + + # Additional normalization by sqrt(2 ** rank). This is required because + # we are using FFTs with twice the length of the original image. + if self._norm == 'ortho': + kernel *= tf.cast(tf.math.sqrt(2.0 ** self.rank), kernel.dtype) + + # Put the kernel in Fourier space. + fft_axes = list(range(-self.rank, 0)) + fft_norm = self._norm or "backward" + return fft_ops.fftn(kernel, axes=fft_axes, norm=fft_norm) + + def _compute_kernel_recursive(self, trajectory, weights, axis): + """Recursively computes the kernel for the Toeplitz approach. + + This function works by computing the two halves of the kernel along each + axis. The "left" half is computed using the input trajectory. The "right" + half is computed using the trajectory flipped along the current axis, and + then reversed. Then the two halves are concatenated, with a block of zeros + inserted in between. If there is more than one axis, the process is repeated + recursively for each axis. + + This function calls the adjoint NUFFT 2 ** N times, where N is the number + of dimensions. NOTE: this could be optimized to 2 ** (N - 1) calls. + + Args: + trajectory: A `tf.Tensor` containing the current *k*-space trajectory. + weights: A `tf.Tensor` containing the current density compensation + weights. + axis: An `int` denoting the current axis. + + Returns: + A `tf.Tensor` containing the kernel. + + Raises: + NotImplementedError: If the rank of the operator is not known statically. + """ + # Account for the batch dimensions. We do not need to do the recursion + # for these. + batch_dims = self.batch_shape.rank + if batch_dims is None: + raise NotImplementedError( + f"The number of batch dimensions of {self.name} must be known " + f"statically.") + # The current axis without the batch dimensions. + image_axis = axis + batch_dims + if axis == 0: + # Outer-most axis. Compute left half, then use Hermitian symmetry to + # compute right half. + # TODO(jmontalt): there should be a way to compute the NUFFT only once. + kernel_left = self._nufft_adjoint(weights, trajectory) + flippings = tf.tensor_scatter_nd_update( + tf.ones([self.rank_tensor()]), [[axis]], [-1]) + kernel_right = self._nufft_adjoint(weights, trajectory * flippings) + else: + # We still have two or more axes to process. Compute left and right kernels + # by calling this function recursively. We call ourselves twice, first + # with current frequencies, then with negated frequencies along current + # axes. + kernel_left = self._compute_kernel_recursive( + trajectory, weights, axis - 1) + flippings = tf.tensor_scatter_nd_update( + tf.ones([self.rank_tensor()]), [[axis]], [-1]) + kernel_right = self._compute_kernel_recursive( + trajectory * flippings, weights, axis - 1) + + # Remove zero frequency and reverse. + kernel_right = tf.reverse(array_ops.slice_along_axis( + kernel_right, image_axis, 1, tf.shape(kernel_right)[image_axis] - 1), + [image_axis]) + + # Create block of zeros to be inserted between the left and right halves of + # the kernel. + zeros_shape = tf.concat([ + tf.shape(kernel_left)[:image_axis], [1], + tf.shape(kernel_left)[(image_axis + 1):]], 0) + zeros = tf.zeros(zeros_shape, dtype=kernel_left.dtype) + + # Concatenate the left and right halves of kernel, with a block of zeros in + # the middle. + kernel = tf.concat([kernel_left, zeros, kernel_right], image_axis) + return kernel + + def _nufft_adjoint(self, x, trajectory=None): + """Applies the adjoint NUFFT operator. + + We use this instead of `super()._transform(x, adjoint=True)` because we + need to be able to change the trajectory and to apply an FFT shift. + + Args: + x: A `tf.Tensor` containing the input data (typically the weights or + ones). + trajectory: A `tf.Tensor` containing the *k*-space trajectory, which + may have been flipped and therefore different from the original. If + `None`, the original trajectory is used. + + Returns: + A `tf.Tensor` containing the result of the adjoint NUFFT. + """ + # Apply FFT shift. + x *= tf.math.exp(tf.dtypes.complex( + tf.constant(0, dtype=self.dtype.real_dtype), + tf.math.reduce_sum(trajectory * self._fft_shift, -1))) + # Temporarily update trajectory. + if trajectory is not None: + temp = self._trajectory + self._trajectory = trajectory + x = super()._transform(x, adjoint=True) + if trajectory is not None: + self._trajectory = temp + return x + + def _enforce_kernel_symmetry(self, kernel): + """Enforces Hermitian symmetry on an input kernel. + + Args: + kernel: A `tf.Tensor`. An approximately Hermitian kernel. + + Returns: + A Hermitian-symmetric kernel. + """ + kernel_axes = list(range(-self.rank, 0)) + reversed_kernel = tf.roll( + tf.reverse(kernel, kernel_axes), + shift=tf.ones([tf.size(kernel_axes)], dtype=tf.int32), + axis=kernel_axes) + return (kernel + tf.math.conj(reversed_kernel)) / 2 + + def _range_shape(self): + # Override the NUFFT operator's range shape. The range shape for this + # operator is the same as the domain shape. + return self._domain_shape() + + def _range_shape_tensor(self): + return self._domain_shape_tensor() + + +@api_util.export("linalg.nudft_matrix") +def nudft_matrix(domain_shape, points): + """Constructs a non-uniform discrete Fourier transform (NUDFT) matrix.""" + domain_shape_static, domain_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) + if domain_shape_static.is_fully_defined(): + domain_shape = domain_shape_static.as_list() + else: + domain_shape = domain_shape_dynamic + + # For reshape. + if domain_shape_static.rank is not None: + grid_shape = [-1, domain_shape_static.rank] + else: + grid_shape = tf.concat([[-1], tf.size(domain_shape)], 0) + + grid = traj_ops.frequency_grid( + domain_shape, max_val=tf.constant(0.5, dtype=points.dtype)) + grid = tf.reshape(grid, grid_shape) + grid *= tf.cast(domain_shape, dtype=points.dtype) + + m = tf.linalg.matmul(points, tf.linalg.matrix_transpose(grid)) + m = tf.math.exp(tf.dtypes.complex( + tf.constant(0.0, dtype=points.dtype), tf.math.negative(m))) + m /= tf.math.sqrt(tf.cast(tf.math.reduce_prod(domain_shape), m.dtype)) + + return m diff --git a/tensorflow_mri/python/linalg/linear_operator_nufft_test.py b/tensorflow_mri/python/linalg/linear_operator_nufft_test.py new file mode 100644 index 00000000..7add1d5a --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_nufft_test.py @@ -0,0 +1,334 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_nufft`.""" +# pylint: disable=missing-class-docstring,missing-function-docstring + +import functools + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_identity +from tensorflow_mri.python.linalg import linear_operator_inversion +from tensorflow_mri.python.linalg import linear_operator_nufft +from tensorflow_mri.python.linalg import linear_operator_test_util +from tensorflow_mri.python.util import test_util + + +rng = np.random.RandomState(2016) + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorNUFFTTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + # NUFFT operator does not quite reach the promised accuracy, so for now we + # relax the test tolerance a little bit. + # TODO(jmontalt): Investigate NUFFT precision issues. + _atol = { + tf.complex64: 1e-5, # 1e-6 + tf.complex128: 1e-10 # 1e-12 + } + + _rtol = { + tf.complex64: 1e-5, # 1e-6 + tf.complex128: 1e-10 # 1e-12 + } + + @staticmethod + def dtypes_to_test(): + return [tf.complex64, tf.complex128] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + del ensure_self_adjoint_and_pd + del use_placeholder + shape = list(build_info.shape) + + batch_shape = shape[:-2] + num_rows = shape[-2] + num_columns = shape[-1] + + points = tf.random.uniform( + shape=batch_shape + [num_rows, 1], + minval=-np.pi, maxval=np.pi, + dtype=dtype.real_dtype) + + operator = linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[num_columns], points=points) + + matrix = linear_operator_nufft.nudft_matrix( + domain_shape=[num_columns], points=points) + + return operator, matrix + + def test_assert_self_adjoint(self): + with self.cached_session(): + operator = linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[4], points=[[0.]]) + with self.assertRaisesOpError("not equal to its adjoint"): + self.evaluate(operator.assert_self_adjoint()) + + def test_non_1d_domain_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be a 1-D"): + linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=2, points=[[0.]]) + + def test_non_integer_domain_shape_raises_static(self): + with self.assertRaisesRegex(TypeError, "must be integer"): + linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[2.], points=[[0.]]) + + def test_non_negative_domain_shape_raises_static(self): + with self.assertRaisesRegex(ValueError, "must be non-negative"): + linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[-2], points=[[0.]]) + + def test_non_float_type_points_raises(self): + with self.assertRaisesRegex( + TypeError, "must be a float32 or float64 tensor"): + linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[2], points=[[0]]) + + def test_is_x_flags(self): + operator = linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[2], points=[[0.]]) + self.assertFalse(operator.is_self_adjoint) + + def test_solve_raises(self): + operator = linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[2], points=[[-np.pi], [0.]]) + with self.assertRaisesRegex(ValueError, "not invertible.*lstsq"): + operator.solve(tf.ones([2, 1], dtype=tf.complex64)) + + def test_inverse_raises(self): + operator = linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[4], points=[[0.], [-np.pi]], is_square=True) + with self.assertRaisesRegex(ValueError, "not invertible.*pseudo_inverse"): + operator.inverse() + + def test_identity_matmul(self): + operator1 = linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[2], points=[[0.], [-np.pi]]) + operator2 = linear_operator_identity.LinearOperatorIdentity(num_rows=2) + self.assertIsInstance(operator1.matmul(operator2), + linear_operator_nufft.LinearOperatorNUFFT) + self.assertIsInstance(operator2.matmul(operator1), + linear_operator_nufft.LinearOperatorNUFFT) + + def test_ref_type_domain_shape_raises(self): + with self.assertRaisesRegex(TypeError, "domain_shape.cannot.be.reference"): + linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=tf.Variable([2]), points=[[0.]]) + + def test_convert_variables_to_tensors(self): + points = tf.Variable([[0.]]) + operator = linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[2], points=points) + with self.cached_session() as sess: + sess.run([points.initializer]) + self.check_convert_variables_to_tensors(operator) + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorNUFFTWithCrosstalkTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + # NUFFT operator does not quite reach the promised accuracy, so for now we + # relax the test tolerance a little bit. + # TODO(jmontalt): Investigate NUFFT precision issues. + _atol = { + tf.complex64: 1e-5, # 1e-6 + tf.complex128: 1e-10 # 1e-12 + } + + _rtol = { + tf.complex64: 1e-5, # 1e-6 + tf.complex128: 1e-10 # 1e-12 + } + + @staticmethod + def dtypes_to_test(): + return [tf.complex64, tf.complex128] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + del ensure_self_adjoint_and_pd + del use_placeholder + shape = list(build_info.shape) + + batch_shape = shape[:-2] + num_rows = shape[-2] + num_columns = shape[-1] + + points = tf.random.uniform( + shape=batch_shape + [num_rows, 1], + minval=-np.pi, maxval=np.pi, + dtype=dtype.real_dtype) + + matrix = linear_operator_nufft.nudft_matrix( + domain_shape=[num_columns], points=points) + + if num_rows < num_columns: + crosstalk_inverse = tf.linalg.inv(matrix @ tf.linalg.adjoint(matrix)) + else: + crosstalk_inverse = tf.linalg.inv(tf.linalg.adjoint(matrix) @ matrix) + + operator = linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=[num_columns], points=points, + crosstalk_inverse=crosstalk_inverse) + + return operator, matrix + + +class OperatorShapesInfoNUFFT(): + def __init__(self, domain_shape, num_points, batch_shape): + self.domain_shape = domain_shape + self.num_points = num_points + self.batch_shape = batch_shape + + @property + def shape(self): + grid_size = functools.reduce(lambda a, b: a * b, self.domain_shape) + return self.batch_shape + (self.num_points, grid_size) + + @property + def dimension(self): + return len(self.domain_shape) + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorNUFFTNDTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + # NUFFT operator does not quite reach the promised accuracy, so for now we + # relax the test tolerance a little bit. + # TODO(jmontalt): Investigate NUFFT precision issues. + _atol = { + tf.complex64: 1e-5, # 1e-6 + tf.complex128: 1e-10 # 1e-12 + } + + _rtol = { + tf.complex64: 1e-5, # 1e-6 + tf.complex128: 1e-10 # 1e-12 + } + + @staticmethod + def operator_shapes_infos(): + shapes_info = OperatorShapesInfoNUFFT + return [ + shapes_info((2, 2), 3, ()), + shapes_info((2, 4), 5, (3,)), + shapes_info((4, 2), 6, (1, 2)), + shapes_info((2, 2), 6, ()), + shapes_info((2, 2, 2), 9, ()), + shapes_info((4, 2, 2), 7, (2,)) + # TODO(jmontalt): odd shapes fail tests, investigate + # shapes_info((2, 3), 5, (2,)), + # shapes_info((3, 2), 7, ()) + ] + + @staticmethod + def dtypes_to_test(): + return [tf.complex64, tf.complex128] + + def operator_and_matrix( + self, build_info, dtype, use_placeholder, + ensure_self_adjoint_and_pd=False): + del ensure_self_adjoint_and_pd + del use_placeholder + + domain_shape = build_info.domain_shape + num_points = build_info.num_points + batch_shape = build_info.batch_shape + grid_size = build_info.shape[-1] + dimension = build_info.dimension + + points = tf.random.uniform( + shape=batch_shape + (num_points, dimension), + minval=-np.pi, maxval=np.pi, + dtype=dtype.real_dtype) + + matrix = linear_operator_nufft.nudft_matrix( + domain_shape=domain_shape, points=points) + + if num_points < grid_size: + crosstalk_inverse = tf.linalg.inv(matrix @ tf.linalg.adjoint(matrix)) + else: + crosstalk_inverse = tf.linalg.inv(tf.linalg.adjoint(matrix) @ matrix) + + operator = linear_operator_nufft.LinearOperatorNUFFT( + domain_shape=domain_shape, points=points, + crosstalk_inverse=crosstalk_inverse) + + return operator, matrix + + +# class LinearOperatorGramNUFFTTest(test_util.TestCase): +# @parameterized.product( +# density=[False, True], +# norm=[None, 'ortho'], +# toeplitz=[False, True], +# batch=[False, True] +# ) +# def test_general(self, density, norm, toeplitz, batch): +# with tf.device('/cpu:0'): +# image_shape = (128, 128) +# image = image_ops.phantom(shape=image_shape, dtype=tf.complex64) +# points = traj_ops.radial_trajectory( +# 128, 129, flatten_encoding_dims=True) +# if density is True: +# density = traj_ops.radial_density( +# 128, 129, flatten_encoding_dims=True) +# else: +# density = None + +# # If testing batches, create new inputs to generate a batch. +# if batch: +# image = tf.stack([image, image * 0.5]) +# points = tf.stack([ +# points, +# rotation_2d.Rotation2D.from_euler([np.pi / 2]).rotate(points)]) +# if density is not None: +# density = tf.stack([density, density]) + +# linop = linear_operator_nufft.LinearOperatorNUFFT( +# image_shape, points=points, density=density, norm=norm) +# linop_gram = linear_operator_nufft.LinearOperatorGramNUFFT( +# image_shape, points=points, density=density, norm=norm, +# toeplitz=toeplitz) + +# recon = linop.transform(linop.transform(image), adjoint=True) +# recon_gram = linop_gram.transform(image) + +# if norm is None: +# # Reduce the magnitude of these values to avoid the need to use a large +# # tolerance. +# recon /= tf.cast(tf.math.reduce_prod(image_shape), tf.complex64) +# recon_gram /= tf.cast(tf.math.reduce_prod(image_shape), tf.complex64) + +# self.assertAllClose(recon, recon_gram, rtol=1e-4, atol=1e-4) + + +linear_operator_test_util.add_tests(LinearOperatorNUFFTTest) +linear_operator_test_util.add_tests(LinearOperatorNUFFTWithCrosstalkTest) +linear_operator_test_util.add_tests(LinearOperatorNUFFTNDTest) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_test.py b/tensorflow_mri/python/linalg/linear_operator_test.py new file mode 100644 index 00000000..8e63874b --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_test.py @@ -0,0 +1,468 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for base linear operator.""" + +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.linalg import linear_operator_full_matrix +from tensorflow_mri.python.util import test_util + + +rng = np.random.RandomState(123) + + +class LinearOperatorShape(linear_operator.LinearOperator): + """LinearOperator that implements the methods ._shape and _shape_tensor.""" + + def __init__(self, + shape, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None): + parameters = dict( + shape=shape, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square + ) + + self._stored_shape = shape + super(LinearOperatorShape, self).__init__( + dtype=tf.float32, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters) + + def _shape(self): + return tf.TensorShape(self._stored_shape) + + def _shape_tensor(self): + return tf.constant(self._stored_shape, dtype=tf.int32) + + def _matmul(self): + raise NotImplementedError("Not needed for this test.") + + +class LinearOperatorMatmulSolve(linear_operator.LinearOperator): + """LinearOperator that wraps a [batch] matrix and implements matmul/solve.""" + + def __init__(self, + matrix, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None): + parameters = dict( + matrix=matrix, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square + ) + + self._matrix = tf.convert_to_tensor(matrix, name="matrix") + super(LinearOperatorMatmulSolve, self).__init__( + dtype=self._matrix.dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + parameters=parameters) + + def _shape(self): + return self._matrix.shape + + def _shape_tensor(self): + return tf.shape(self._matrix) + + def _matmul(self, x, adjoint=False, adjoint_arg=False): + x = tf.convert_to_tensor(x, name="x") + return tf.matmul( + self._matrix, x, adjoint_a=adjoint, adjoint_b=adjoint_arg) + + def _solve(self, rhs, adjoint=False, adjoint_arg=False): + rhs = tf.convert_to_tensor(rhs, name="rhs") + assert not adjoint_arg, "Not implemented for this test class." + return tf.linalg.solve(self._matrix, rhs, adjoint=adjoint) + + +@test_util.run_all_in_graph_and_eager_modes +class LinearOperatorTest(tf.test.TestCase): + + def test_all_shape_properties_defined_by_the_one_property_shape(self): + + shape = (1, 2, 3, 4) + operator = LinearOperatorShape(shape) + + self.assertAllEqual(shape, operator.shape) + self.assertAllEqual(4, operator.tensor_rank) + self.assertAllEqual((1, 2), operator.batch_shape) + self.assertAllEqual(4, operator.domain_dimension) + self.assertAllEqual(3, operator.range_dimension) + expected_parameters = { + "is_non_singular": None, + "is_positive_definite": None, + "is_self_adjoint": None, + "is_square": None, + "shape": (1, 2, 3, 4), + } + self.assertEqual(expected_parameters, operator.parameters) + + def test_all_shape_methods_defined_by_the_one_method_shape(self): + with self.cached_session(): + shape = (1, 2, 3, 4) + operator = LinearOperatorShape(shape) + + self.assertAllEqual(shape, self.evaluate(operator.shape_tensor())) + self.assertAllEqual(4, self.evaluate(operator.tensor_rank_tensor())) + self.assertAllEqual((1, 2), self.evaluate(operator.batch_shape_tensor())) + self.assertAllEqual(4, self.evaluate(operator.domain_dimension_tensor())) + self.assertAllEqual(3, self.evaluate(operator.range_dimension_tensor())) + + def test_is_x_properties(self): + operator = LinearOperatorShape( + shape=(2, 2), + is_non_singular=False, + is_self_adjoint=True, + is_positive_definite=False) + self.assertFalse(operator.is_non_singular) + self.assertTrue(operator.is_self_adjoint) + self.assertFalse(operator.is_positive_definite) + + def test_nontrivial_parameters(self): + matrix = rng.randn(2, 3, 4) + matrix_ph = tf.compat.v1.placeholder_with_default(input=matrix, shape=None) + operator = LinearOperatorMatmulSolve(matrix_ph) + expected_parameters = { + "is_non_singular": None, + "is_positive_definite": None, + "is_self_adjoint": None, + "is_square": None, + "matrix": matrix_ph, + } + self.assertEqual(expected_parameters, operator.parameters) + + def test_generic_to_dense_method_non_square_matrix_static(self): + matrix = rng.randn(2, 3, 4) + operator = LinearOperatorMatmulSolve(matrix) + with self.cached_session(): + operator_dense = operator.to_dense() + self.assertAllEqual((2, 3, 4), operator_dense.shape) + self.assertAllClose(matrix, self.evaluate(operator_dense)) + + def test_generic_to_dense_method_non_square_matrix_tensor(self): + matrix = rng.randn(2, 3, 4) + matrix_ph = tf.compat.v1.placeholder_with_default(input=matrix, shape=None) + operator = LinearOperatorMatmulSolve(matrix_ph) + operator_dense = operator.to_dense() + self.assertAllClose(matrix, self.evaluate(operator_dense)) + + def test_matvec(self): + matrix = [[1., 0], [0., 2.]] + operator = LinearOperatorMatmulSolve(matrix) + x = [1., 1.] + with self.cached_session(): + y = operator.matvec(x) + self.assertAllEqual((2,), y.shape) + self.assertAllClose([1., 2.], self.evaluate(y)) + + def test_solvevec(self): + matrix = [[1., 0], [0., 2.]] + operator = LinearOperatorMatmulSolve(matrix) + y = [1., 1.] + with self.cached_session(): + x = operator.solvevec(y) + self.assertAllEqual((2,), x.shape) + self.assertAllClose([1., 1 / 2.], self.evaluate(x)) + + def test_add(self): + matrix = [[1., 0], [0., 2.]] + operator = LinearOperatorMatmulSolve(matrix) + with self.cached_session(): + y = operator.add(matrix) + self.assertAllEqual((2, 2), y.shape) + self.assertAllClose([[2., 0], [0., 4.]], self.evaluate(y)) + + def test_is_square_set_to_true_for_square_static_shapes(self): + operator = LinearOperatorShape(shape=(2, 4, 4)) + self.assertTrue(operator.is_square) + + def test_is_square_set_to_false_for_square_static_shapes(self): + operator = LinearOperatorShape(shape=(2, 3, 4)) + self.assertFalse(operator.is_square) + + def test_is_square_set_incorrectly_to_false_raises(self): + with self.assertRaisesRegex(ValueError, "but.*was square"): + _ = LinearOperatorShape(shape=(2, 4, 4), is_square=False).is_square + + def test_is_square_set_inconsistent_with_other_hints_raises(self): + with self.assertRaisesRegex(ValueError, "is always square"): + matrix = tf.compat.v1.placeholder_with_default(input=(), shape=None) + LinearOperatorMatmulSolve(matrix, is_non_singular=True, is_square=False) + + with self.assertRaisesRegex(ValueError, "is always square"): + matrix = tf.compat.v1.placeholder_with_default(input=(), shape=None) + LinearOperatorMatmulSolve( + matrix, is_positive_definite=True, is_square=False) + + def test_non_square_operators_raise_on_determinant_and_solve(self): + operator = LinearOperatorShape((2, 3)) + with self.assertRaisesRegex(NotImplementedError, "not be square"): + operator.determinant() + with self.assertRaisesRegex(NotImplementedError, "not be square"): + operator.log_abs_determinant() + with self.assertRaisesRegex(NotImplementedError, "not be square"): + operator.solve(rng.rand(2, 2)) + + with self.assertRaisesRegex(ValueError, "is always square"): + matrix = tf.compat.v1.placeholder_with_default(input=(), shape=None) + LinearOperatorMatmulSolve( + matrix, is_positive_definite=True, is_square=False) + + def test_is_square_manual_set_works(self): + matrix = tf.compat.v1.placeholder_with_default( + input=np.ones((2, 2)), shape=None) + operator = LinearOperatorMatmulSolve(matrix) + if not tf.executing_eagerly(): + # Eager mode will read in the default value, and discover the answer is + # True. Graph mode must rely on the hint, since the placeholder has + # shape=None...the hint is, by default, None. + self.assertEqual(None, operator.is_square) + + # Set to True + operator = LinearOperatorMatmulSolve(matrix, is_square=True) + self.assertTrue(operator.is_square) + + def test_linear_operator_matmul_hints_closed(self): + matrix = tf.compat.v1.placeholder_with_default(input=np.ones((2, 2)), + shape=None) + operator1 = LinearOperatorMatmulSolve(matrix) + + operator_matmul = operator1.matmul(operator1) + + if not tf.executing_eagerly(): + # Eager mode will read in the input and discover matrix is square. + self.assertEqual(None, operator_matmul.is_square) + self.assertEqual(None, operator_matmul.is_non_singular) + self.assertEqual(None, operator_matmul.is_self_adjoint) + self.assertEqual(None, operator_matmul.is_positive_definite) + + operator2 = LinearOperatorMatmulSolve( + matrix, + is_non_singular=True, + is_self_adjoint=True, + is_positive_definite=True, + is_square=True, + ) + + operator_matmul = operator2.matmul(operator2) + + self.assertTrue(operator_matmul.is_square) + self.assertTrue(operator_matmul.is_non_singular) + self.assertEqual(None, operator_matmul.is_self_adjoint) + self.assertEqual(None, operator_matmul.is_positive_definite) + + def test_linear_operator_matmul_hints_false(self): + matrix1 = tf.compat.v1.placeholder_with_default( + input=rng.rand(2, 2), shape=None) + operator1 = LinearOperatorMatmulSolve( + matrix1, + is_non_singular=False, + is_self_adjoint=False, + is_positive_definite=False, + is_square=True, + ) + + operator_matmul = operator1.matmul(operator1) + + self.assertTrue(operator_matmul.is_square) + self.assertFalse(operator_matmul.is_non_singular) + self.assertEqual(None, operator_matmul.is_self_adjoint) + self.assertEqual(None, operator_matmul.is_positive_definite) + + matrix2 = tf.compat.v1.placeholder_with_default( + input=rng.rand(2, 3), shape=None) + operator2 = LinearOperatorMatmulSolve( + matrix2, + is_non_singular=False, + is_self_adjoint=False, + is_positive_definite=False, + is_square=False, + ) + + operator_matmul = operator2.matmul(operator2, adjoint_arg=True) + + if tf.executing_eagerly(): + self.assertTrue(operator_matmul.is_square) + # False since we specified is_non_singular=False. + self.assertFalse(operator_matmul.is_non_singular) + else: + self.assertIsNone(operator_matmul.is_square) + # May be non-singular, since it's the composition of two non-square. + # TODO(b/136162840) This is a bit inconsistent, and should probably be + # False since we specified operator2.is_non_singular == False. + self.assertIsNone(operator_matmul.is_non_singular) + + # No way to deduce these, even in Eager mode. + self.assertIsNone(operator_matmul.is_self_adjoint) + self.assertIsNone(operator_matmul.is_positive_definite) + + def test_linear_operator_matmul_hint_infer_square(self): + matrix1 = tf.compat.v1.placeholder_with_default( + input=rng.rand(2, 3), shape=(2, 3)) + matrix2 = tf.compat.v1.placeholder_with_default( + input=rng.rand(3, 2), shape=(3, 2)) + matrix3 = tf.compat.v1.placeholder_with_default( + input=rng.rand(3, 4), shape=(3, 4)) + + operator1 = LinearOperatorMatmulSolve(matrix1, is_square=False) + operator2 = LinearOperatorMatmulSolve(matrix2, is_square=False) + operator3 = LinearOperatorMatmulSolve(matrix3, is_square=False) + + self.assertTrue(operator1.matmul(operator2).is_square) + self.assertTrue(operator2.matmul(operator1).is_square) + self.assertFalse(operator1.matmul(operator3).is_square) + + def testDispatchedMethods(self): + operator = linear_operator_full_matrix.LinearOperatorFullMatrix( + [[1., 0.5], [0.5, 1.]], + is_square=True, + is_self_adjoint=True, + is_non_singular=True, + is_positive_definite=True) + methods = { + "trace": tf.linalg.trace, + "diag_part": tf.linalg.diag_part, + "log_abs_determinant": tf.linalg.logdet, + "determinant": tf.linalg.det + } + for method in methods: + op_val = getattr(operator, method)() + linalg_val = methods[method](operator) + self.assertAllClose( + self.evaluate(op_val), + self.evaluate(linalg_val)) + # Solve and Matmul go here. + + adjoint = tf.linalg.adjoint(operator) + self.assertIsInstance(adjoint, linear_operator.LinearOperator) + cholesky = tf.linalg.cholesky(operator) + self.assertIsInstance(cholesky, linear_operator.LinearOperator) + inverse = tf.linalg.inv(operator) + self.assertIsInstance(inverse, linear_operator.LinearOperator) + + def testDispatchMatmulSolve(self): + operator = linear_operator_full_matrix.LinearOperatorFullMatrix( + np.float64([[1., 0.5], [0.5, 1.]]), + is_square=True, + is_self_adjoint=True, + is_non_singular=True, + is_positive_definite=True) + rhs = np.random.uniform(-1., 1., size=[3, 2, 2]) + for adjoint in [False, True]: + for adjoint_arg in [False, True]: + op_val = operator.matmul( + rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) + matmul_val = tf.matmul( + operator, rhs, adjoint_a=adjoint, adjoint_b=adjoint_arg) + self.assertAllClose( + self.evaluate(op_val), self.evaluate(matmul_val)) + + op_val = operator.solve(rhs, adjoint=adjoint) + solve_val = tf.linalg.solve(operator, rhs, adjoint=adjoint) + self.assertAllClose( + self.evaluate(op_val), self.evaluate(solve_val)) + + def testDispatchMatmulLeftOperatorIsTensor(self): + mat = np.float64([[1., 0.5], [0.5, 1.]]) + right_operator = linear_operator_full_matrix.LinearOperatorFullMatrix( + mat, + is_square=True, + is_self_adjoint=True, + is_non_singular=True, + is_positive_definite=True) + lhs = np.random.uniform(-1., 1., size=[3, 2, 2]) + + for adjoint in [False, True]: + for adjoint_arg in [False, True]: + op_val = tf.matmul( + lhs, mat, adjoint_a=adjoint, adjoint_b=adjoint_arg) + matmul_val = tf.matmul( + lhs, right_operator, adjoint_a=adjoint, adjoint_b=adjoint_arg) + self.assertAllClose( + self.evaluate(op_val), self.evaluate(matmul_val)) + + def testDispatchAdd(self): + operator = linear_operator_full_matrix.LinearOperatorFullMatrix( + np.float64([[1., 0.5], [0.5, 1.]]), + is_square=True, + is_self_adjoint=True, + is_non_singular=True, + is_positive_definite=True) + rhs = np.random.uniform(-1., 1., size=[3, 2, 2]) + op_val = operator.add(rhs) + add_val = tf.math.add(operator, rhs) + self.assertAllClose(self.evaluate(op_val), self.evaluate(add_val)) + + def testDispatchMatmulLeftOperatorIsTensor(self): + mat = np.float64([[1., 0.5], [0.5, 1.]]) + right_operator = linear_operator_full_matrix.LinearOperatorFullMatrix( + mat, + is_square=True, + is_self_adjoint=True, + is_non_singular=True, + is_positive_definite=True) + lhs = np.random.uniform(-1., 1., size=[3, 2, 2]) + op_val = tf.math.add(lhs, mat) + add_val = tf.math.add(lhs, right_operator) + self.assertAllClose(self.evaluate(op_val), self.evaluate(add_val)) + + def testDispatchAddOperator(self): + operator = linear_operator_full_matrix.LinearOperatorFullMatrix( + np.float64([[1., 0.5], [0.5, 1.]]), + is_square=True, + is_self_adjoint=True, + is_non_singular=True, + is_positive_definite=True) + rhs = np.random.uniform(-1., 1., size=[3, 2, 2]) + add_val = tf.math.add(operator, rhs) + op_val = operator + rhs + self.assertAllClose(self.evaluate(add_val), self.evaluate(op_val)) + + def testVectorizedMap(self): + + def fn(x): + y = tf.constant([3., 4.]) + # Make a [2, N, N] shaped operator. + x = x * y[..., tf.compat.v1.newaxis, tf.compat.v1.newaxis] + operator = linear_operator_full_matrix.LinearOperatorFullMatrix( + x, is_square=True) + return operator + + x = np.random.uniform(-1., 1., size=[3, 5, 5]).astype(np.float32) + batched_operator = tf.vectorized_map( + fn, tf.convert_to_tensor(x)) + self.assertIsInstance(batched_operator, linear_operator.LinearOperator) + self.assertAllEqual(batched_operator.batch_shape, [3, 2]) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_test_util.py b/tensorflow_mri/python/linalg/linear_operator_test_util.py new file mode 100644 index 00000000..fc08b8f4 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_test_util.py @@ -0,0 +1,203 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities for testing linear operators.""" + +import itertools + +import tensorflow as tf +from tensorflow.python.framework import test_util +from tensorflow.python.ops.linalg import linear_operator_test_util + +from tensorflow_mri.python.linalg import linear_operator_util + + +DEFAULT_GRAPH_SEED = 876543213 + + +def add_tests(test_cls): + # Call original add_tests. + linear_operator_test_util.add_tests(test_cls) + + test_name_dict = { + "lstsq": _test_lstsq, + "lstsq_with_broadcast": _test_lstsq_with_broadcast + } + optional_tests = [] + tests_with_adjoint_args = [ + "lstsq", + "lstsq_with_broadcast" + ] + + for name, test_template_fn in test_name_dict.items(): + if name in test_cls.skip_these_tests(): + continue + if name in optional_tests and name not in test_cls.optional_tests(): + continue + + for dtype, use_placeholder, shape_info in itertools.product( + test_cls.dtypes_to_test(), + test_cls.use_placeholder_options(), + test_cls.operator_shapes_infos()): + base_test_name = "_".join([ + "test", name, "_shape={},dtype={},use_placeholder={}".format( + shape_info.shape, dtype, use_placeholder)]) + if name in tests_with_adjoint_args: + for adjoint in test_cls.adjoint_options(): + for adjoint_arg in test_cls.adjoint_arg_options(): + test_name = base_test_name + ",adjoint={},adjoint_arg={}".format( + adjoint, adjoint_arg) + if hasattr(test_cls, test_name): + raise RuntimeError("Test %s defined more than once" % test_name) + setattr( + test_cls, + test_name, + test_util.run_deprecated_v1( + test_template_fn( # pylint: disable=too-many-function-args + use_placeholder, shape_info, dtype, adjoint, + adjoint_arg, test_cls.use_blockwise_arg()))) + else: + if hasattr(test_cls, base_test_name): + raise RuntimeError("Test %s defined more than once" % base_test_name) + setattr( + test_cls, + base_test_name, + test_util.run_deprecated_v1(test_template_fn( + use_placeholder, shape_info, dtype))) + + +OperatorShapesInfo = linear_operator_test_util.OperatorShapesInfo + + +random_normal = linear_operator_test_util.random_normal +random_uniform = linear_operator_test_util.random_uniform +random_positive_definite_matrix = ( + linear_operator_test_util.random_positive_definite_matrix) +random_sign_uniform = linear_operator_test_util.random_sign_uniform + + +class SquareLinearOperatorDerivedClassTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + pass + + +class NonSquareLinearOperatorDerivedClassTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + + def make_rhs(self, operator, adjoint, with_batch=True): + return self.make_x(operator, adjoint=not adjoint, with_batch=with_batch) + + +def _test_lstsq( + use_placeholder, shapes_info, dtype, adjoint, adjoint_arg, blockwise_arg): + def test_lstsq(self): + _test_lstsq_base( + self, + use_placeholder, + shapes_info, + dtype, + adjoint, + adjoint_arg, + blockwise_arg, + with_batch=True) + return test_lstsq + + +def _test_lstsq_with_broadcast( + use_placeholder, shapes_info, dtype, adjoint, adjoint_arg, blockwise_arg): + def test_lstsq_with_broadcast(self): + _test_lstsq_base( + self, + use_placeholder, + shapes_info, + dtype, + adjoint, + adjoint_arg, + blockwise_arg, + with_batch=False) + return test_lstsq_with_broadcast + + +def _test_lstsq_base( + self, + use_placeholder, + shapes_info, + dtype, + adjoint, + adjoint_arg, + blockwise_arg, + with_batch): + # If batch dimensions are omitted, but there are + # no batch dimensions for the linear operator, then + # skip the test case. This is already checked with + # with_batch=True. + if not with_batch and len(shapes_info.shape) <= 2: + return + with self.session(graph=tf.Graph()) as sess: + sess.graph.seed = DEFAULT_GRAPH_SEED + operator, mat = self.operator_and_matrix( + shapes_info, dtype, use_placeholder=use_placeholder) + rhs = self.make_rhs( + operator, adjoint=adjoint, with_batch=with_batch) + # If adjoint_arg, solve A X = (rhs^H)^H = rhs. + if adjoint_arg: + op_solve = operator.lstsq( + tf.linalg.adjoint(rhs), + adjoint=adjoint, + adjoint_arg=adjoint_arg) + else: + op_solve = operator.lstsq( + rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) + mat_solve = linear_operator_util.matrix_solve_ls_with_broadcast( + mat, rhs, adjoint=adjoint) + if not use_placeholder: + self.assertAllEqual(op_solve.shape, + mat_solve.shape) + + # If the operator is blockwise, test both blockwise rhs and `Tensor` rhs; + # else test only `Tensor` rhs. In both cases, evaluate all results in a + # single `sess.run` call to avoid re-sampling the random rhs in graph mode. + if blockwise_arg and len(operator.operators) > 1: + # pylint: disable=protected-access + block_dimensions = ( + operator._block_range_dimensions() if adjoint else + operator._block_domain_dimensions()) + block_dimensions_fn = ( + operator._block_range_dimension_tensors if adjoint else + operator._block_domain_dimension_tensors) + # pylint: enable=protected-access + split_rhs = linear_operator_util.split_arg_into_blocks( + block_dimensions, + block_dimensions_fn, + rhs, axis=-2) + if adjoint_arg: + split_rhs = [tf.linalg.adjoint(y) for y in split_rhs] + split_solve = operator.solve( + split_rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) + self.assertEqual(len(split_solve), len(operator.operators)) + split_solve = linear_operator_util.broadcast_matrix_batch_dims( + split_solve) + fused_block_solve = tf.concat(split_solve, axis=-2) + op_solve_v, mat_solve_v, fused_block_solve_v = sess.run([ + op_solve, mat_solve, fused_block_solve]) + + # Check that the operator and matrix give the same solution when the rhs + # is blockwise. + self.assertAC(mat_solve_v, fused_block_solve_v) + else: + op_solve_v, mat_solve_v = sess.run([op_solve, mat_solve]) + + # Check that the operator and matrix give the same solution when the rhs is + # a `Tensor`. + self.assertAC(op_solve_v, mat_solve_v) diff --git a/tensorflow_mri/python/linalg/linear_operator_util.py b/tensorflow_mri/python/linalg/linear_operator_util.py new file mode 100644 index 00000000..dd63370a --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_util.py @@ -0,0 +1,158 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities for linear operators.""" + +import tensorflow as tf +from tensorflow.python.ops.linalg import linear_operator_util + + +broadcast_matrix_batch_dims = linear_operator_util.broadcast_matrix_batch_dims +split_arg_into_blocks = linear_operator_util.split_arg_into_blocks +_reshape_for_efficiency = linear_operator_util._reshape_for_efficiency # pylint: disable=protected-access + + +## Matrix operators. + +def matrix_solve_ls_with_broadcast(matrix, rhs, adjoint=False, name=None): + """Solve systems of linear equations.""" + with tf.name_scope(name or "MatrixSolveLSWithBroadcast"): + matrix = tf.convert_to_tensor(matrix, name="matrix") + rhs = tf.convert_to_tensor(rhs, name="rhs", dtype=matrix.dtype) + + # If either matrix/rhs has extra dims, we can reshape to get rid of them. + matrix, rhs, reshape_inv, still_need_to_transpose = _reshape_for_efficiency( + matrix, rhs, adjoint_a=adjoint) + + # This will broadcast by brute force if we still need to. + matrix, rhs = broadcast_matrix_batch_dims([matrix, rhs]) + + if adjoint and still_need_to_transpose: + matrix = tf.linalg.adjoint(matrix) + solution = tf.linalg.lstsq(matrix, rhs, fast=False) + + return reshape_inv(solution) + + +## Asserts. + +def assert_no_entries_with_modulus_zero(x, message=None, name=None): + """Returns `Op` that asserts Tensor `x` has no entries with modulus zero. + + Args: + x: Numeric `Tensor`, real, integer, or complex. + message: A string message to prepend to failure message. + name: A name to give this `Op`. + + Returns: + An `Op` that asserts `x` has no entries with modulus zero. + """ + with tf.name_scope(name or "assert_no_entries_with_modulus_zero"): + x = tf.convert_to_tensor(x, name="x") + dtype = x.dtype.base_dtype + should_be_nonzero = tf.math.abs(x) + zero = tf.convert_to_tensor(0, dtype=dtype.real_dtype) + return tf.debugging.assert_less(zero, should_be_nonzero, message=message) + + +def assert_zero_imag_part(x, message=None, name=None): + """Returns `Op` that asserts Tensor `x` has no non-zero imaginary parts. + + Args: + x: Numeric `Tensor`, real, integer, or complex. + message: A string message to prepend to failure message. + name: A name to give this `Op`. + + Returns: + An `Op` that asserts `x` has no entries with non-zero imaginary part. + """ + with tf.name_scope(name or "assert_zero_imag_part"): + x = tf.convert_to_tensor(x, name="x") + dtype = x.dtype.base_dtype + + if dtype.is_floating: + return tf.no_op() + + zero = tf.convert_to_tensor(0, dtype=dtype.real_dtype) + return tf.debugging.assert_equal(zero, tf.math.imag(x), message=message) + + +# Other utilities. + +def prepare_inner_dims_for_broadcasting(tensor_a, + tensor_b, + batch_dims_a=0, + batch_dims_b=0): + """Prepares two tensors for broadcasting, separating batch from inner dims. + + Essentially, this function makes sure that both tensors have the same number + of inner dimensions, so that inner dimensions can be broadcasted with inner + dimensions, and batch dimensions are broadcasted with batch dimensions. + + For example, given the following tensors: + - `tensor_a` with shape `(2, 3, 4, 5)`, with 2 batch dimensions. + - `tensor_b` with shape `(2, 3, 2, 4, 5)`, with 2 batch dimensions. + + This function will return the following: + - `tensor_a` with shape `(2, 3, 1, 4, 5)`. + - `tensor_b` with shape `(2, 3, 2, 4, 5)`. + + i.e., the inner dimensions of `tensor_a` are expanded to match the inner + dimensions of `tensor_b`. + + ```{note} + This function does not check that the batch/inner dimensions of `tensor_a` + and `tensor_b` are compatible for broadcasting. It simply makes sure that + both tensors have the same number of inner dimensions. + ``` + """ + # Number of inner dimensions (static). + inner_dims_a = tensor_a.shape.rank - batch_dims_a + inner_dims_b = tensor_b.shape.rank - batch_dims_b + if inner_dims_a == inner_dims_b: + return tensor_a, tensor_b + + # Get shapes of batch and inner dimensions for both tensors. + shape_a, shape_b = tf.shape_n([tensor_a, tensor_b]) + batch_shape_a = shape_a[:batch_dims_a] + batch_shape_b = shape_b[:batch_dims_b] + inner_shape_a = shape_a[batch_dims_a:] + inner_shape_b = shape_b[batch_dims_b:] + + # Number of inner dimensions (dynamic). + if inner_dims_a > inner_dims_b: + extra_dims = inner_dims_a - inner_dims_b + new_shape_b = tf.concat([batch_shape_b, [1] * extra_dims, inner_shape_b], 0) + tensor_b = tf.reshape(tensor_b, new_shape_b) + else: # inner_dims_a < inner_dims_b + extra_dims = inner_dims_b - inner_dims_a + new_shape_a = tf.concat([batch_shape_a, [1] * extra_dims, inner_shape_a], 0) + tensor_a = tf.reshape(tensor_a, new_shape_a) + + return tensor_a, tensor_b diff --git a/tensorflow_mri/python/linalg/linear_operator_wavelet.py b/tensorflow_mri/python/linalg/linear_operator_wavelet.py new file mode 100644 index 00000000..57d81092 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_wavelet.py @@ -0,0 +1,153 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Wavelet linear operator.""" + +import functools + +import tensorflow as tf + +from tensorflow_mri.python.ops import array_ops +from tensorflow_mri.python.ops import wavelet_ops +from tensorflow_mri.python.util import api_util +from tensorflow_mri.python.util import check_util +from tensorflow_mri.python.linalg import linear_operator +from tensorflow_mri.python.util import tensor_util + + +@api_util.export("linalg.LinearOperatorWavelet") +class LinearOperatorWavelet(linear_operator.LinearOperator): # pylint: disable=abstract-method + """Linear operator representing a wavelet decomposition matrix. + + Args: + domain_shape: A 1D `tf.Tensor` or a `list` of `int`. The domain shape of + this linear operator. + wavelet: A `str` or a `pywt.Wavelet`_, or a `list` thereof. When passed a + `list`, different wavelets are applied along each axis in `axes`. + mode: A `str`. The padding or signal extension mode. Must be one of the + values supported by `tfmri.signal.wavedec`. Defaults to `'symmetric'`. + level: An `int` >= 0. The decomposition level. If `None` (default), + the maximum useful level of decomposition will be used (see + `tfmri.signal.max_wavelet_level`). + axes: A `list` of `int`. The axes over which the DWT is computed. Axes refer + only to domain dimensions without regard for the batch dimensions. + Defaults to `None` (all domain dimensions). + dtype: A `tf.dtypes.DType`. The data type for this operator. Defaults to + `float32`. + name: A `str`. A name for this operator. + """ + def __init__(self, + domain_shape, + wavelet, + mode='symmetric', + level=None, + axes=None, + dtype=tf.dtypes.float32, + name="LinearOperatorWavelet"): + # Set parameters. + parameters = dict( + domain_shape=domain_shape, + wavelet=wavelet, + mode=mode, + level=level, + axes=axes, + dtype=dtype, + name=name + ) + + # Get the static and dynamic shapes and save them for later use. + self._domain_shape_static, self._domain_shape_dynamic = ( + tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) + # At the moment, the wavelet implementation relies on shapes being + # statically known. + if not self._domain_shape_static.is_fully_defined(): + raise ValueError(f"static `domain_shape` must be fully defined, " + f"but got {self._domain_shape_static}") + static_rank = self._domain_shape_static.rank + + # Set arguments. + self.wavelet = wavelet + self.mode = mode + self.level = level + self.axes = check_util.validate_static_axes(axes, + rank=static_rank, + min_length=1, + canonicalize="negative", + must_be_unique=True, + scalar_to_list=True, + none_means_all=True) + + # Compute the coefficient slices needed for adjoint (wavelet + # reconstruction). + x = tf.ensure_shape(tf.zeros(self._domain_shape_dynamic, dtype=dtype), + self._domain_shape_static) + x = wavelet_ops.wavedec(x, wavelet=self.wavelet, mode=self.mode, + level=self.level, axes=self.axes) + y, self._coeff_slices = wavelet_ops.coeffs_to_tensor(x, axes=self.axes) + + # Get the range shape. + self._range_shape_static = y.shape + self._range_shape_dynamic = tf.shape(y) + + # Call base class. + super().__init__(dtype, + is_non_singular=None, + is_self_adjoint=None, + is_positive_definite=None, + is_square=None, + name=name, + parameters=parameters) + + def _transform(self, x, adjoint=False): + # While `wavedec` and `waverec` can transform only a subset of axes (and + # thus theoretically support batches), there is a caveat due to the + # `coeff_slices` object required by `waverec`. This object contains + # information relevant to a specific batch shape. While we could recompute + # this object for every input batch shape, it is easier to just process + # each batch independently. + if x.shape.rank is not None and self._domain_shape_static.rank is not None: + # Rank of input and this operator are known statically, so we can infer + # the number of batch dimensions statically too. + batch_dims = x.shape.rank - self._domain_shape_static.rank + else: + # We need to obtain the number of batch dimensions dynamically. + batch_dims = tf.rank(x) - tf.shape(self._domain_shape_dynamic)[0] + # Transform each batch. + x = array_ops.map_fn( + functools.partial(self._transform_batch, adjoint=adjoint), + x, batch_dims=batch_dims) + return x + + def _transform_batch(self, x, adjoint=False): + if adjoint: + x = wavelet_ops.tensor_to_coeffs(x, self._coeff_slices) + x = wavelet_ops.waverec(x, wavelet=self.wavelet, mode=self.mode, + axes=self.axes) + else: + x = wavelet_ops.wavedec(x, wavelet=self.wavelet, mode=self.mode, + level=self.level, axes=self.axes) + x, _ = wavelet_ops.coeffs_to_tensor(x, axes=self.axes) + return x + + def _domain_shape(self): + return self._domain_shape_static + + def _range_shape(self): + return self._range_shape_static + + def _domain_shape_tensor(self): + return self._domain_shape_dynamic + + def _range_shape_tensor(self): + return self._range_shape_dynamic diff --git a/tensorflow_mri/python/linalg/linear_operator_wavelet_test.py b/tensorflow_mri/python/linalg/linear_operator_wavelet_test.py new file mode 100644 index 00000000..a0ecee87 --- /dev/null +++ b/tensorflow_mri/python/linalg/linear_operator_wavelet_test.py @@ -0,0 +1,87 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for module `linear_operator_wavelet`.""" +# pylint: disable=missing-class-docstring,missing-function-docstring + +from absl.testing import parameterized +import numpy as np +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_wavelet +from tensorflow_mri.python.ops import wavelet_ops +from tensorflow_mri.python.util import test_util + + +class LinearOperatorWaveletTest(test_util.TestCase): + @parameterized.named_parameters( + # name, wavelet, level, axes, domain_shape, range_shape + ("test0", "haar", None, None, [6, 6], [7, 7]), + ("test1", "haar", 1, None, [6, 6], [6, 6]), + ("test2", "haar", None, -1, [6, 6], [6, 7]), + ("test3", "haar", None, [-1], [6, 6], [6, 7]) + ) + def test_general(self, wavelet, level, axes, domain_shape, range_shape): + # Instantiate. + linop = linear_operator_wavelet.LinearOperatorWavelet( + domain_shape, wavelet=wavelet, level=level, axes=axes) + + # Example data. + data = np.arange(np.prod(domain_shape)).reshape(domain_shape) + data = data.astype("float32") + + # Forward and adjoint. + expected_forward, coeff_slices = wavelet_ops.coeffs_to_tensor( + wavelet_ops.wavedec(data, wavelet=wavelet, level=level, axes=axes), + axes=axes) + expected_adjoint = wavelet_ops.waverec( + wavelet_ops.tensor_to_coeffs(expected_forward, coeff_slices), + wavelet=wavelet, axes=axes) + + # Test shapes. + self.assertAllClose(domain_shape, linop.domain_shape) + self.assertAllClose(domain_shape, linop.domain_shape_tensor()) + self.assertAllClose(range_shape, linop.range_shape) + self.assertAllClose(range_shape, linop.range_shape_tensor()) + + # Test transform. + result_forward = linop.transform(data) + result_adjoint = linop.transform(result_forward, adjoint=True) + self.assertAllClose(expected_forward, result_forward) + self.assertAllClose(expected_adjoint, result_adjoint) + + def test_with_batch_inputs(self): + """Test batch shape.""" + axes = [-2, -1] + data = np.arange(4 * 8 * 8).reshape(4, 8, 8).astype("float32") + linop = linear_operator_wavelet.LinearOperatorWavelet( + (8, 8), wavelet="haar", level=1) + + # Forward and adjoint. + expected_forward, coeff_slices = wavelet_ops.coeffs_to_tensor( + wavelet_ops.wavedec(data, wavelet='haar', level=1, axes=axes), + axes=axes) + expected_adjoint = wavelet_ops.waverec( + wavelet_ops.tensor_to_coeffs(expected_forward, coeff_slices), + wavelet='haar', axes=axes) + + result_forward = linop.transform(data) + self.assertAllClose(expected_forward, result_forward) + + result_adjoint = linop.transform(result_forward, adjoint=True) + self.assertAllClose(expected_adjoint, result_adjoint) + + +if __name__ == '__main__': + tf.test.main() diff --git a/tensorflow_mri/python/linalg/matmul_registrations.py b/tensorflow_mri/python/linalg/matmul_registrations.py new file mode 100644 index 00000000..344e3537 --- /dev/null +++ b/tensorflow_mri/python/linalg/matmul_registrations.py @@ -0,0 +1,133 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Registrations for LinearOperator.matmul.""" + +from tensorflow_mri.python.linalg import linear_operator_algebra +from tensorflow_mri.python.linalg import linear_operator_composition +from tensorflow_mri.python.linalg import linear_operator_diag_nd +from tensorflow_mri.python.linalg import linear_operator_identity_nd +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.linalg import linear_operator_util + + +# IdentityND + +@linear_operator_algebra.RegisterMatmul( + linear_operator_identity_nd.LinearOperatorIdentityND, + linear_operator_nd.LinearOperatorND) +def _matmul_linear_operator_identity_nd_left(identity, linop): + del identity + return linop + + +@linear_operator_algebra.RegisterMatmul( + linear_operator_nd.LinearOperatorND, + linear_operator_identity_nd.LinearOperatorIdentityND) +def _matmul_linear_operator_identity_nd_right(linop, identity): + del identity + return linop + + +@linear_operator_algebra.RegisterMatmul( + linear_operator_identity_nd.LinearOperatorScaledIdentityND, + linear_operator_identity_nd.LinearOperatorScaledIdentityND) +def _matmul_linear_operator_scaled_identity_nd(linop_a, linop_b): + return linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=linop_a.domain_shape_tensor(), + multiplier=linop_a.multiplier * linop_b.multiplier, + is_non_singular=linear_operator_composition.combined_non_singular_hint( + linop_a, linop_b), + is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( + linop_a, linop_b, commuting=True), + is_positive_definite=( + linear_operator_composition.combined_positive_definite_hint( + linop_a, linop_b, commuting=True)), + is_square=True) + + +# DiagND + +@linear_operator_algebra.RegisterMatmul( + linear_operator_diag_nd.LinearOperatorDiagND, + linear_operator_diag_nd.LinearOperatorDiagND) +def _matmul_linear_operator_diag_nd(linop_a, linop_b): + batch_dims_a, batch_dims_b = ( + linop_a.batch_shape.rank, linop_b.batch_shape.rank) + diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( + linop_a.diag, + linop_b.diag, + batch_dims_a=batch_dims_a, + batch_dims_b=batch_dims_b) + return linear_operator_diag_nd.LinearOperatorDiagND( + diag=diag_a * diag_b, + batch_dims=max(batch_dims_a, batch_dims_b), + is_non_singular=linear_operator_composition.combined_non_singular_hint( + linop_a, linop_b), + is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( + linop_a, linop_b, commuting=True), + is_positive_definite=( + linear_operator_composition.combined_positive_definite_hint( + linop_a, linop_b, commuting=True)), + is_square=True) + + +@linear_operator_algebra.RegisterMatmul( + linear_operator_diag_nd.LinearOperatorDiagND, + linear_operator_identity_nd.LinearOperatorScaledIdentityND) +def _matmul_linear_operator_diag_scaled_identity_nd_right( + linop_diag, linop_scaled_identity): + batch_dims_a, batch_dims_b = ( + linop_diag.batch_shape.rank, linop_scaled_identity.batch_shape.rank) + diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( + linop_diag.diag, + linop_scaled_identity.multiplier, + batch_dims_a=batch_dims_a, + batch_dims_b=batch_dims_b) + return linear_operator_diag_nd.LinearOperatorDiagND( + diag=diag_a * diag_b, + batch_dims=max(batch_dims_a, batch_dims_b), + is_non_singular=linear_operator_composition.combined_non_singular_hint( + linop_diag, linop_scaled_identity), + is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( + linop_diag, linop_scaled_identity, commuting=True), + is_positive_definite=( + linear_operator_composition.combined_positive_definite_hint( + linop_diag, linop_scaled_identity, commuting=True)), + is_square=True) + + +@linear_operator_algebra.RegisterMatmul( + linear_operator_identity_nd.LinearOperatorScaledIdentityND, + linear_operator_diag_nd.LinearOperatorDiagND) +def _matmul_linear_operator_diag_scaled_identity_nd_left( + linop_scaled_identity, linop_diag): + batch_dims_a, batch_dims_b = ( + linop_scaled_identity.batch_shape.rank, linop_diag.batch_shape.rank) + diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( + linop_scaled_identity.multiplier, + linop_diag.diag, + batch_dims_a=batch_dims_a, + batch_dims_b=batch_dims_b) + return linear_operator_diag_nd.LinearOperatorDiagND( + diag=diag_a * diag_b, + batch_dims=max(batch_dims_a, batch_dims_b), + is_non_singular=linear_operator_composition.combined_non_singular_hint( + linop_diag, linop_scaled_identity), + is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( + linop_diag, linop_scaled_identity, commuting=True), + is_positive_definite=( + linear_operator_composition.combined_positive_definite_hint( + linop_diag, linop_scaled_identity, commuting=True)), + is_square=True) diff --git a/tensorflow_mri/python/linalg/pseudo_inverse_registrations.py b/tensorflow_mri/python/linalg/pseudo_inverse_registrations.py new file mode 100644 index 00000000..e69de29b diff --git a/tensorflow_mri/python/linalg/registrations_util.py b/tensorflow_mri/python/linalg/registrations_util.py new file mode 100644 index 00000000..6ad4ef7b --- /dev/null +++ b/tensorflow_mri/python/linalg/registrations_util.py @@ -0,0 +1,27 @@ +# Copyright 2019 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Common utilities for registering LinearOperator methods. + +Adapted from: + tensorflow/python/ops/linalg/registrations_util.py +""" + +from tensorflow.python.ops.linalg import registrations_util + +combined_commuting_positive_definite_hint = ( + registrations_util.combined_commuting_positive_definite_hint) +combined_commuting_self_adjoint_hint = ( + registrations_util.combined_commuting_self_adjoint_hint) +combined_non_singular_hint = registrations_util.combined_non_singular_hint diff --git a/tensorflow_mri/python/linalg/slicing.py b/tensorflow_mri/python/linalg/slicing.py new file mode 100644 index 00000000..19adb425 --- /dev/null +++ b/tensorflow_mri/python/linalg/slicing.py @@ -0,0 +1,18 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +from tensorflow.python.ops.linalg import slicing + +batch_slice = slicing.batch_slice diff --git a/tensorflow_mri/python/linalg/solve_registrations.py b/tensorflow_mri/python/linalg/solve_registrations.py new file mode 100644 index 00000000..81bf3458 --- /dev/null +++ b/tensorflow_mri/python/linalg/solve_registrations.py @@ -0,0 +1,133 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Registrations for LinearOperator.solve.""" + +from tensorflow_mri.python.linalg import linear_operator_algebra +from tensorflow_mri.python.linalg import linear_operator_composition +from tensorflow_mri.python.linalg import linear_operator_diag_nd +from tensorflow_mri.python.linalg import linear_operator_identity_nd +from tensorflow_mri.python.linalg import linear_operator_nd +from tensorflow_mri.python.linalg import linear_operator_util + + +# IdentityND + +@linear_operator_algebra.RegisterSolve( + linear_operator_identity_nd.LinearOperatorIdentityND, + linear_operator_nd.LinearOperatorND) +def _solve_linear_operator_identity_nd_left(identity, linop): + del identity + return linop + + +@linear_operator_algebra.RegisterSolve( + linear_operator_nd.LinearOperatorND, + linear_operator_identity_nd.LinearOperatorIdentityND) +def _solve_linear_operator_identity_nd_right(linop, identity): + del identity + return linop.inverse() + + +@linear_operator_algebra.RegisterSolve( + linear_operator_identity_nd.LinearOperatorScaledIdentityND, + linear_operator_identity_nd.LinearOperatorScaledIdentityND) +def _solve_linear_operator_scaled_identity_nd(linop_a, linop_b): + return linear_operator_identity_nd.LinearOperatorScaledIdentityND( + domain_shape=linop_a.domain_shape_tensor(), + multiplier=linop_b.multiplier / linop_a.multiplier, + is_non_singular=linear_operator_composition.combined_non_singular_hint( + linop_a, linop_b), + is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( + linop_a, linop_b, commuting=True), + is_positive_definite=( + linear_operator_composition.combined_positive_definite_hint( + linop_a, linop_b, commuting=True)), + is_square=True) + + +# DiagND + +@linear_operator_algebra.RegisterSolve( + linear_operator_diag_nd.LinearOperatorDiagND, + linear_operator_diag_nd.LinearOperatorDiagND) +def _solve_linear_operator_diag_nd(linop_a, linop_b): + batch_dims_a, batch_dims_b = ( + linop_a.batch_shape.rank, linop_b.batch_shape.rank) + diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( + linop_a.diag, + linop_b.diag, + batch_dims_a=batch_dims_a, + batch_dims_b=batch_dims_b) + return linear_operator_diag_nd.LinearOperatorDiagND( + diag=diag_b / diag_a, + batch_dims=max(batch_dims_a, batch_dims_b), + is_non_singular=linear_operator_composition.combined_non_singular_hint( + linop_a, linop_b), + is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( + linop_a, linop_b, commuting=True), + is_positive_definite=( + linear_operator_composition.combined_positive_definite_hint( + linop_a, linop_b, commuting=True)), + is_square=True) + + +@linear_operator_algebra.RegisterSolve( + linear_operator_diag_nd.LinearOperatorDiagND, + linear_operator_identity_nd.LinearOperatorScaledIdentityND) +def _solve_linear_operator_diag_scaled_identity_nd_right( + linop_diag, linop_scaled_identity): + batch_dims_a, batch_dims_b = ( + linop_diag.batch_shape.rank, linop_scaled_identity.batch_shape.rank) + diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( + linop_diag.diag, + linop_scaled_identity.multiplier, + batch_dims_a=batch_dims_a, + batch_dims_b=batch_dims_b) + return linear_operator_diag_nd.LinearOperatorDiagND( + diag=diag_b / diag_a, + batch_dims=max(batch_dims_a, batch_dims_b), + is_non_singular=linear_operator_composition.combined_non_singular_hint( + linop_diag, linop_scaled_identity), + is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( + linop_diag, linop_scaled_identity, commuting=True), + is_positive_definite=( + linear_operator_composition.combined_positive_definite_hint( + linop_diag, linop_scaled_identity, commuting=True)), + is_square=True) + + +@linear_operator_algebra.RegisterSolve( + linear_operator_identity_nd.LinearOperatorScaledIdentityND, + linear_operator_diag_nd.LinearOperatorDiagND) +def _solve_linear_operator_diag_scaled_identity_nd_left( + linop_scaled_identity, linop_diag): + batch_dims_a, batch_dims_b = ( + linop_scaled_identity.batch_shape.rank, linop_diag.batch_shape.rank) + diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( + linop_scaled_identity.multiplier, + linop_diag.diag, + batch_dims_a=batch_dims_a, + batch_dims_b=batch_dims_b) + return linear_operator_diag_nd.LinearOperatorDiagND( + diag=diag_b / diag_a, + batch_dims=max(batch_dims_a, batch_dims_b), + is_non_singular=linear_operator_composition.combined_non_singular_hint( + linop_diag, linop_scaled_identity), + is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( + linop_diag, linop_scaled_identity, commuting=True), + is_positive_definite=( + linear_operator_composition.combined_positive_definite_hint( + linop_diag, linop_scaled_identity, commuting=True)), + is_square=True) diff --git a/tensorflow_mri/python/ops/control_flow_ops.py b/tensorflow_mri/python/ops/control_flow_ops.py new file mode 100644 index 00000000..65cb7f63 --- /dev/null +++ b/tensorflow_mri/python/ops/control_flow_ops.py @@ -0,0 +1,35 @@ +# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities for argument validation.""" + +import tensorflow as tf + + +def with_dependencies(dependencies, tensor, name=None): + """Produces the content of `tensor` only after `dependencies`. + + Args: + dependencies: An iterable of operations to run before this op finishes. + tensor: A `tf.Tensor`. + name: An optional name for this operation. + + Returns: + A `tf.Tensor` equal to `tensor`. + """ + if tf.executing_eagerly(): + return tensor + with tf.name_scope(name or "with_dependencies"): + with tf.control_dependencies(dependencies): + return tf.identity(tensor) diff --git a/tensorflow_mri/python/recon/__init__.py b/tensorflow_mri/python/recon/__init__.py new file mode 100644 index 00000000..e26ed684 --- /dev/null +++ b/tensorflow_mri/python/recon/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Image reconstruction.""" + +from tensorflow_mri.python.recon import recon_adjoint +from tensorflow_mri.python.recon import recon_least_squares diff --git a/tensorflow_mri/python/recon/recon_adjoint.py b/tensorflow_mri/python/recon/recon_adjoint.py new file mode 100644 index 00000000..a4e69626 --- /dev/null +++ b/tensorflow_mri/python/recon/recon_adjoint.py @@ -0,0 +1,152 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Signal reconstruction (adjoint).""" + +import tensorflow as tf + +from tensorflow_mri.python.linalg import linear_operator_mri +from tensorflow_mri.python.util import api_util + + +@api_util.export("recon.adjoint_universal") +def recon_adjoint(data, operator): + r"""Reconstructs a signal using the adjoint of the system operator. + + Given measurement data $b$ generated by a linear system $A$ such that + $Ax = b$, this function estimates the corresponding signal $x$ as + $x = A^H b$, where $A$ is the specified linear operator. + + ```{note} + This function is part of the family of + [universal operators](https://mrphys.github.io/tensorflow-mri/guide/universal/), + a set of functions and classes designed to work flexibly with any linear + system. + ``` + + ```{seealso} + `tfmri.recon.adjoint` is an MRI-specific version of this function and may be + used to perform zero-filled reconstructions. + ``` + + Args: + data: A `tf.Tensor` of real or complex dtype. The measurement data $b$. + Its shape must be compatible with `operator.range_shape`. + operator: A `tfmri.linalg.LinearOperator` representing the system operator + $A$. Its range shape must be compatible with `data.shape`. + ```{tip} + You can use any of the operators in `tfmri.linalg`, a composition of + multiple operators, or a subclassed operator. + ``` + + Returns: + A `tf.Tensor` containing the reconstructed signal. Has the same dtype as + `data` and shape `batch_shape + operator.domain_shape`. `batch_shape` is + the result of broadcasting the batch shapes of `data` and `operator`. + """ + data = tf.convert_to_tensor(data) + data = operator.preprocess(data, adjoint=True) + signal = operator.transform(data, adjoint=True) + signal = operator.postprocess(signal, adjoint=True) + return signal + + +@api_util.export("recon.adjoint", "recon.adj") +def recon_adjoint_mri(kspace, + image_shape, + mask=None, + trajectory=None, + density=None, + sensitivities=None, + phase=None, + sens_norm=True): + r"""Reconstructs an MR image using the adjoint MRI operator. + + Given *k*-space data $b$, this function estimates the corresponding + image as $x = A^H b$, where $A$ is the MRI linear operator. + + This operator supports Cartesian and non-Cartesian *k*-space data. + + Additional density compensation and intensity correction steps are applied + depending on the input arguments. + + This operator supports batched inputs. All batch shapes should be + broadcastable with each other. + + This operator supports multicoil imaging. Coil combination is triggered + when `sensitivities` is not `None`. If you have multiple coils but wish to + reconstruct each coil separately, simply set `sensitivities` to `None`. The + coil dimension will then be treated as a standard batch dimension (i.e., it + becomes part of `...`). + + Args: + kspace: A `tf.Tensor`. The *k*-space samples. Must have type `complex64` or + `complex128`. `kspace` can be either Cartesian or non-Cartesian. A + Cartesian `kspace` must have shape + `[..., num_coils, *image_shape]`, where `...` are batch dimensions. A + non-Cartesian `kspace` must have shape `[..., num_coils, num_samples]`. + If not multicoil (`sensitivities` is `None`), then the `num_coils` axis + must be omitted. + image_shape: A 1D integer `tf.Tensor`. Must have length 2 or 3. + The shape of the reconstructed image[s]. + mask: An optional `tf.Tensor` of type `bool`. The sampling mask. Must have + shape `[..., *image_shape]`. `mask` should be passed for reconstruction + from undersampled Cartesian *k*-space. For each point, `mask` should be + `True` if the corresponding *k*-space sample was measured and `False` + otherwise. + trajectory: An optional `tf.Tensor` of type `float32` or `float64`. Must + have shape `[..., num_samples, rank]`. `trajectory` should be passed for + reconstruction from non-Cartesian *k*-space. + density: An optional `tf.Tensor` of type `float32` or `float64`. The + sampling densities. Must have shape `[..., num_samples]`. This input is + only relevant for non-Cartesian MRI reconstruction. If passed, the MRI + linear operator will include sampling density compensation. If `None`, + the MRI operator will not perform sampling density compensation. + sensitivities: An optional `tf.Tensor` of type `complex64` or `complex128`. + The coil sensitivity maps. Must have shape + `[..., num_coils, *image_shape]`. If provided, a multi-coil parallel + imaging reconstruction will be performed. + phase: An optional `tf.Tensor` of type `float32` or `float64`. Must have + shape `[..., *image_shape]`. A phase estimate for the reconstructed image. + If provided, a phase-constrained reconstruction will be performed. This + improves the conditioning of the reconstruction problem in applications + where there is no interest in the phase data. However, artefacts may + appear if an inaccurate phase estimate is passed. + sens_norm: A `boolean`. Whether to normalize coil sensitivities. + Defaults to `True`. + + Returns: + A `tf.Tensor`. The reconstructed image. Has the same type as `kspace` and + shape `[..., *image_shape]`, where `...` is the broadcasted batch shape of + all inputs. + + Notes: + Reconstructs an image by applying the adjoint MRI operator to the *k*-space + data. This typically involves an inverse FFT or a (density-compensated) + NUFFT, and coil combination for multicoil inputs. This type of + reconstruction is often called zero-filled reconstruction, because missing + *k*-space samples are assumed to be zero. Therefore, the resulting image is + likely to display aliasing artefacts if *k*-space is not sufficiently + sampled according to the Nyquist criterion. + """ + # Create the linear operator. + operator = linear_operator_mri.LinearOperatorMRI(image_shape, + mask=mask, + trajectory=trajectory, + density=density, + sensitivities=sensitivities, + phase=phase, + fft_norm='ortho', + sens_norm=sens_norm) + return recon_adjoint(kspace, operator) diff --git a/tensorflow_mri/python/recon/recon_adjoint_test.py b/tensorflow_mri/python/recon/recon_adjoint_test.py new file mode 100644 index 00000000..0bd8e1d1 --- /dev/null +++ b/tensorflow_mri/python/recon/recon_adjoint_test.py @@ -0,0 +1,94 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Signal reconstruction (adjoint).""" + +import tensorflow as tf +import tensorflow_nufft as tfft + +from tensorflow_mri.python.ops import fft_ops +from tensorflow_mri.python.recon import recon_adjoint +from tensorflow_mri.python.util import io_util +from tensorflow_mri.python.util import test_util + + +class ReconAdjointTest(test_util.TestCase): + """Tests for reconstruction functions.""" + @classmethod + def setUpClass(cls): + """Prepare tests.""" + super().setUpClass() + cls.data = io_util.read_hdf5('tests/data/recon_ops_data.h5') + cls.data.update(io_util.read_hdf5('tests/data/recon_ops_data_2.h5')) + cls.data.update(io_util.read_hdf5('tests/data/recon_ops_data_3.h5')) + + def test_adj_fft(self): + """Test simple FFT recon.""" + kspace = self.data['fft/kspace'] + sens = self.data['fft/sens'] + image_shape = kspace.shape[-2:] + + # Test single-coil. + image = recon_adjoint.recon_adjoint_mri(kspace[0, ...], image_shape) + expected = fft_ops.ifftn(kspace[0, ...], norm='ortho', shift=True) + + self.assertAllClose(expected, image) + + # Test multi-coil. + image = recon_adjoint.recon_adjoint_mri( + kspace, image_shape, sensitivities=sens) + expected = fft_ops.ifftn(kspace, axes=[-2, -1], norm='ortho', shift=True) + scale = tf.math.reduce_sum(sens * tf.math.conj(sens), axis=0) + expected = tf.math.divide_no_nan( + tf.math.reduce_sum(expected * tf.math.conj(sens), axis=0), scale) + + self.assertAllClose(expected, image) + + def test_adj_nufft(self): + """Test simple NUFFT recon.""" + kspace = self.data['nufft/kspace'] + sens = self.data['nufft/sens'] + traj = self.data['nufft/traj'] + dens = self.data['nufft/dens'] + image_shape = [144, 144] + fft_norm_factor = tf.cast(tf.math.sqrt(144. * 144.), tf.complex64) + + # Save us some typing. + inufft = lambda src, pts: tfft.nufft(src, pts, + grid_shape=[144, 144], + transform_type='type_1', + fft_direction='backward') + + # Test single-coil. + image = recon_adjoint.recon_adjoint_mri(kspace[0, ...], image_shape, + trajectory=traj, + density=dens) + + expected = inufft(kspace[0, ...] / tf.cast(dens, tf.complex64), traj) + expected /= fft_norm_factor + + self.assertAllClose(expected, image) + + # Test multi-coil. + image = recon_adjoint.recon_adjoint_mri(kspace, image_shape, + trajectory=traj, + density=dens, + sensitivities=sens) + expected = inufft(kspace / dens, traj) + expected /= fft_norm_factor + scale = tf.math.reduce_sum(sens * tf.math.conj(sens), axis=0) + expected = tf.math.divide_no_nan( + tf.math.reduce_sum(expected * tf.math.conj(sens), axis=0), scale) + + self.assertAllClose(expected, image) diff --git a/tensorflow_mri/python/recon/recon_least_squares.py b/tensorflow_mri/python/recon/recon_least_squares.py new file mode 100644 index 00000000..c031d795 --- /dev/null +++ b/tensorflow_mri/python/recon/recon_least_squares.py @@ -0,0 +1,15 @@ +# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Signal reconstruction (least squares).""" diff --git a/tools/docs/guide/fft.ipynb b/tools/docs/guide/fft.ipynb new file mode 100644 index 00000000..72099ca6 --- /dev/null +++ b/tools/docs/guide/fft.ipynb @@ -0,0 +1,101 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fast Fourier transform (FFT)\n", + "\n", + "TensorFlow MRI uses the built-in FFT ops in core TensorFlow. These are [`tf.signal.fft`](https://www.tensorflow.org/api_docs/python/tf/signal/fft), [`tf.signal.fft2d`](https://www.tensorflow.org/api_docs/python/tf/signal/fft2d) and [`tf.signal.fft3d`](https://www.tensorflow.org/api_docs/python/tf/signal/fft3d).\n", + "\n", + "## N-dimensional FFT\n", + "\n", + "For convenience, TensorFlow MRI also provides [`tfmri.signal.fft`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/signal/fft/), which can be used for N-dimensional FFT calculations and provides convenient access to commonly used functionality such as padding/cropping, normalization and shifting of the zero-frequency component within the same function call.\n", + "\n", + "## Custom FFT kernels for CPU\n", + "\n", + "Unfortunately, TensorFlow's FFT ops are [known to be slow](https://github.com/tensorflow/tensorflow/issues/6541) on CPU. As a result, the FFT can become a significant bottleneck on MRI processing pipelines, especially on iterative reconstructions where the FFT is called repeatedly.\n", + "\n", + "To address this issue, TensorFlow MRI provides a set of custom FFT kernels based on the FFTW library. These offer a significant boost in performance compared to the kernels in core TensorFlow.\n", + "\n", + "The custom FFT kernels are automatically registered to the TensorFlow framework when importing TensorFlow MRI. If you have imported TensorFlow MRI, then the standard FFT ops will use the optimized kernels automatically.\n", + "\n", + "```{tip}\n", + "You only need to `import tensorflow_mri` in order to use the custom FFT kernels. You can then access them as usual through `tf.signal.fft`, `tf.signal.fft2d` and `tf.signal.fft3d`.\n", + "```\n", + "\n", + "The only caveat is that the [FFTW license](https://www.fftw.org/doc/License-and-Copyright.html) is more restrictive than the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0) used by TensorFlow MRI. In particular, GNU GPL requires you to distribute any derivative software under equivalent terms.\n", + "\n", + "```{warning}\n", + "If you intend to use custom FFT kernels for commercial purposes, you will need to purchase a commercial FFTW license.\n", + "```\n", + "\n", + "### Disable the use of custom FFT kernels\n", + "\n", + "You can control whether custom FFT kernels are used via the `TFMRI_USE_CUSTOM_FFT` environment variable. When set to false, TensorFlow MRI will not register its custom FFT kernels, falling back to the standard FFT kernels in core TensorFlow. If the variable is unset, its value defaults to true.\n", + "\n", + "````{tip}\n", + "Set `TFMRI_USE_CUSTOM_FFT=0` to disable the custom FFT kernels.\n", + "\n", + "```python\n", + "os.environ[\"TFMRI_USE_CUSTOM_FFT\"] = \"0\"\n", + "import tensorflow_mri as tfmri\n", + "```\n", + "\n", + "```{attention}\n", + "`TFMRI_USE_CUSTOM_FFT` must be set **before** importing TensorFlow MRI. Setting or changing its value after importing the package will have no effect.\n", + "```\n", + "````\n", + "\n", + "### Customize the behavior of custom FFT kernels\n", + "\n", + "FFTW allows you to control the rigor of the planning process. The more rigorously a plan is created, the more efficient the actual FFT execution is likely to be, at the expense of a longer planning time. TensorFlow MRI lets you control the FFTW planning rigor through the `TFMRI_FFTW_PLANNING_RIGOR` environment variable. Valid values for this variable are:\n", + "\n", + "- `\"estimate\"` specifies that, instead of actual measurements of different algorithms, a simple heuristic is used to pick a (probably sub-optimal) plan quickly.\n", + "- `\"measure\"` tells FFTW to find an optimized plan by actually computing several FFTs and measuring their execution time. Depending on your machine, this can take some time (often a few seconds). This is the default planning option.\n", + "- `\"patient\"` is like `\"measure\"`, but considers a wider range of algorithms and often produces a “more optimal” plan (especially for large transforms), but at the expense of several times longer planning time (especially for large transforms).\n", + "- `\"exhaustive\"` is like `\"patient\"`, but considers an even wider range of algorithms, including many that we think are unlikely to be fast, to produce the most optimal plan but with a substantially increased planning time.\n", + "\n", + "````{tip}\n", + "Set the environment variable `TFMRI_FFTW_PLANNING_RIGOR` to control the planning rigor.\n", + "\n", + "```python\n", + "os.environ[\"TFMRI_FFTW_PLANNING_RIGOR\"] = \"estimate\"\n", + "import tensorflow_mri as tfmri\n", + "```\n", + "\n", + "```{attention}\n", + "`TFMRI_FFTW_PLANNING_RIGOR` must be set **before** importing TensorFlow MRI. Setting or changing its value after importing the package will have no effect.\n", + "```\n", + "````\n", + "\n", + "```{note}\n", + "FFTW accumulates \"wisdom\" each time the planner is called, and this wisdom is persisted across invocations of the FFT kernels (during the same process). Therefore, more rigorous planning options will result in long planning times during the first FFT invocation, but may result in faster execution during subsequent invocations. When performing a large amount of similar FFT invocations (e.g., while training a model or performing iterative reconstructions), you are more likely to benefit from more rigorous planning.\n", + "```\n", + "\n", + "```{seealso}\n", + "The FFTW [planner flags](https://www.fftw.org/doc/Planner-Flags.html) documentation page.\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.2 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.2" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/docs/guide/linalg.ipynb b/tools/docs/guide/linalg.ipynb deleted file mode 100644 index f45442d9..00000000 --- a/tools/docs/guide/linalg.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Linear algebra\n", - "\n", - "Coming soon..." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.10 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.8.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tools/docs/guide/optim.ipynb b/tools/docs/guide/optim.ipynb deleted file mode 100644 index 21363722..00000000 --- a/tools/docs/guide/optim.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimization\n", - "\n", - "Coming soon..." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.2 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.8.2" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tools/docs/guide/recon.ipynb b/tools/docs/guide/recon.ipynb deleted file mode 100644 index 5291a70b..00000000 --- a/tools/docs/guide/recon.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MR image reconstruction\n", - "\n", - "Coming soon..." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.2 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.8.2" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tools/docs/templates/index.rst b/tools/docs/templates/index.rst index 13b8384c..899e7f23 100644 --- a/tools/docs/templates/index.rst +++ b/tools/docs/templates/index.rst @@ -16,10 +16,8 @@ TensorFlow MRI |release| Guide Installation + Uniform FFT Non-uniform FFT - Linear algebra - Optimization - MRI reconstruction Contributing FAQ @@ -32,7 +30,6 @@ TensorFlow MRI |release| Segmentation Image reconstruction - .. toctree:: :caption: API Documentation :hidden: From 29044e3cc09e53ae662580d3f55844dd814ca64c Mon Sep 17 00:00:00 2001 From: jennifersteeden Date: Tue, 4 Feb 2025 14:39:50 +0000 Subject: [PATCH 2/6] Revert "Updated CS tutorial" This reverts commit da3c2e7f1063bde6e4ef54fcef59277a9b8a96d1. --- .DS_Store | Bin 8196 -> 0 bytes tensorflow_mri/.DS_Store | Bin 6148 -> 0 bytes tests/.DS_Store | Bin 6148 -> 0 bytes tools/.DS_Store | Bin 6148 -> 0 bytes tools/docs/tutorials/recon.rst | 3 +- tools/docs/tutorials/recon/cg_sense.ipynb | 218 +- tools/docs/tutorials/recon/radial_CS.ipynb | 68581 ------------------- 7 files changed, 11 insertions(+), 68791 deletions(-) delete mode 100644 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index c8ff8f76a19f364cb31eb45f9efcc325f4020465..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHK!AiqG5S@*oMD$R>gU9^?E%*VkL1hM2JKf5}9c{>U7(_i!Ttn=G2M<}I;3MxX* z!^>s7%eT00`CLs|eRHkm0v<}4XMnve=!s@j(PO~3cQIJacX{8-^W{E+dvy8NtqGoE z|COIZO%+fDRDn%V06m*6*%8!H6;K6KfmQ+jK3F(o>ah`YpAHP}2mp)`c7{2hCAh|V zOg%P&$iR%00;SaWBZiT3#I0YKdTa!xoQz|}eLS=ACluq@5w~tRnN(0mRX`O871)x? zA@~2&_2>VvNUu}@Rp4JKVB####@LeYt-Z~0ueI=7I2*5P1ltl!Tr0*}Zp8<1XNX&# W08@{RAR;jRBj9AvK^6E@1-<~tg4xU`-O~E|GcQN${%Mux z0JJcBL7(puoZ|#D!8#y35ED{?A=Stg!-RC$oy28=b-<8LMlK&lDjT_>7+)RZJ0DIa z3#hF!pbRt_*a?q)KL1aDzW+Cq^hp^|2L2TT#^$4Zh$YF{+FBf+wGMg>W#PCw;3@?j he-tB@kKzNU7uX$lfSF(&5Eh935%4r GRIDDING (Radials and Spirals) PRE-PROCESSING TRIGGERED CINE DATASET (with GRAPPA and PF) - CG-SENSE (Radial, 2D and 2D+t) - COMPRESSED SENSING (Radial, 2D and 2D+t) \ No newline at end of file + CG-SENSE \ No newline at end of file diff --git a/tools/docs/tutorials/recon/cg_sense.ipynb b/tools/docs/tutorials/recon/cg_sense.ipynb index ad1ccf84..d1ab7fa4 100644 --- a/tools/docs/tutorials/recon/cg_sense.ipynb +++ b/tools/docs/tutorials/recon/cg_sense.ipynb @@ -7,6 +7,16 @@ "# Image reconstruction with CG-SENSE" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![View on website](https://img.shields.io/badge/-View%20on%20website-128091?labelColor=grey&logo=data:image/svg+xml;base64,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)](https://mrphys.github.io/tensorflow-mri/tutorials/recon/cg_sense)\n", + "[![Run in Colab](https://img.shields.io/badge/-Run%20in%20Colab-128091?labelColor=grey&logo=googlecolab)](https://colab.research.google.com/github/mrphys/tensorflow-mri/blob/master/tools/docs/tutorials/recon/cg_sense.ipynb)\n", + "[![View on GitHub](https://img.shields.io/badge/-View%20on%20GitHub-128091?labelColor=grey&logo=github)](https://github.com/mrphys/tensorflow-mri/blob/master/tools/docs/tutorials/recon/cg_sense.ipynb)\n", + "[![Download notebook](https://img.shields.io/badge/-Download%20notebook-128091?labelColor=grey&logo=data:image/svg+xml;base64,<?xml version="1.0" encoding="UTF-8" standalone="no"?>
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We will apply the\n", - "# window to the central 20% of k-space (determined by the factor 5 below), the\n", - "# remaining 80% is filtered out completely.\n", - "filter_fn = lambda x: tfmri.signal.hann(5 * x)\n", - "\n", - "# Low-pass filtering of the k-space data.\n", - "filtered_kspace = tfmri.signal.filter_kspace(kSpaceCS,\n", - " trajectory=trajCS,\n", - " filter_fn=filter_fn)\n", - "\n", - "# Reconstruct low resolution estimates.\n", - "low_res_images = tfmri.recon.adjoint(filtered_kspace,\n", - " image_shape,\n", - " trajectory=trajCS,\n", - " density=densCS)\n", - "\n", - "_ = plot_tiled_images(tf.math.abs(low_res_images))\n", - "_ = plt.gcf().suptitle('Low-resolution images', color='w', fontsize=14)\n", - "\n", - "# Estimate the coil sensitivities.\n", - "coil_sens = tfmri.coils.estimate_sensitivities(\n", - " low_res_images, coil_axis=0, method='walsh')\n", - "\n", - "print('sensitivities.shape: ' + str(coil_sens.shape))\n", - "# This should be size: [nCoils, matrix_size, matrix_size]\n", - "#sensitivities.shape: (12, 256, 256)\n", - "\n", - "_ = plot_tiled_images(tf.math.abs(coil_sens))\n", - "_ = plt.gcf().suptitle('Coil Sensitivities', color='w', fontsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lastly do iterative SENSE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "domain_shape =[nTimePts, im_size, im_size] #, dtype=tf.int32)\n", - "\n", - "#Create regularizer.\n", - "tikhonov_parameter = 0.2\n", - "regularizer = tfmri.convex.ConvexFunctionTikhonov( scale=tikhonov_parameter, dtype=tf.complex64)\n", - "\n", - " \n", - "# this should have the shape [t*x*y,]\n", - "print('regularizer.shape: ' + str(regularizer.shape)) \n", - "# regularizer.shape: ((1769472,)\n", - "\n", - "senserecon = tfmri.recon.least_squares(kspace, # correct\n", - " image_shape, # correct\n", - " extra_shape=nTimePts, # correct\n", - " trajectory=traj, # correct\n", - " density=dens, # correct\n", - " sensitivities=coil_sens, # correct\n", - " regularizer=regularizer, # correct\n", - " optimizer='cg',\n", - " optimizer_kwargs={\n", - " 'max_iterations': 20\n", - " },\n", - " filter_corners=True)\n", - "\n", - "print(np.shape(senserecon))\n", - "\n", - "\n", - "# And lets visualise\n", - "plt.rcParams[\"animation.html\"] = \"jshtml\"\n", - "plt.rcParams['figure.dpi'] = 150 \n", - "plt.ioff()\n", - "fig, ax = plt.subplots()\n", - "\n", - "t= np.linspace(0,nTimePts)\n", - "def animate(t):\n", - " plt.imshow(tf.squeeze(tf.math.abs(senserecon[t,:,:]), axis=1), cmap = 'gray')\n", - " plt.title('iterative SENSE Recon')\n", - "\n", - "import matplotlib.animation\n", - "matplotlib.animation.FuncAnimation(fig, animate, frames=nTimePts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ths data is 24x undersampled so its not a great suprise that SENSE didnt resolve all of the artefacts!" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/tools/docs/tutorials/recon/radial_CS.ipynb b/tools/docs/tutorials/recon/radial_CS.ipynb deleted file mode 100644 index 7ff71746..00000000 --- a/tools/docs/tutorials/recon/radial_CS.ipynb +++ /dev/null @@ -1,68581 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Image reconstruction with Compressed Sensing (2D and 2D+time)\n", - "\n", - "This tutorial follows many of the same steps as the Non-Cartesian SENSE example.\n", - "We will reconstruct radially undersampled 2D bran data and 2D cardiac data (with TV transforms), as well as a 2D+time cardiac cine dataset undersampled on a spiral trajectory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up TensorFlow MRI\n", - "If you have not yet installed TensorFlow MRI in your environment, you may do so\n", - "now using `pip`: " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 24.3.1 is available.\n", - "You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 24.3.1 is available.\n", - "You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install --quiet tensorflow-mri\n", - "# Upgrade Matplotlib. Versions older than 3.5.x may cause an error below.\n", - "%pip install --quiet --upgrade matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, import the package into your program to get started:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-01-27 09:43:35.343304: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2025-01-27 09:43:35.447698: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2025-01-27 09:43:35.473328: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TensorFlow MRI version: 0.22.0\n" - ] - } - ], - "source": [ - "import tensorflow_mri as tfmri\n", - "print(\"TensorFlow MRI version:\", tfmri.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also need a few additional packages:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import h5py\n", - "import matplotlib.collections as mcol\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import tensorflow as tf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a GPU\n", - "\n", - "TensorFlow MRI supports CPU and GPU computation. If there is a GPU available in\n", - "your environment and it is visible to TensorFlow, it will be used automatically.\n", - "\n", - ":::{tip}\n", - "In Google Colab, you can enable GPU computation by clicking on\n", - "**Runtime > Change runtime type** and selecting **GPU** under\n", - "**Hardware accelerator**.\n", - ":::\n", - "\n", - ":::{tip}\n", - "You can control whether CPU or GPU is used for a particular operation via\n", - "the [`tf.device`](https://www.tensorflow.org/api_docs/python/tf/device)\n", - "context manager.\n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "# Specify which GPU to use\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare the data\n", - "We will be using an example brain dataset from the\n", - "[ISMRM Reproducibility Challenge 1](https://ismrm.github.io/rrsg/challenge_one/).\n", - "Let's download it." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/bin/bash: wget: command not found\n" - ] - } - ], - "source": [ - "brain_data_filename = 'rawdata_brain_radial_96proj_12ch.h5'\n", - "brain_data_url = \"https://github.com/ISMRM/rrsg/raw/master/challenges/challenge_01/rawdata_brain_radial_96proj_12ch.h5\"\n", - "!wget --quiet -O {brain_data_filename} {brain_data_url}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This dataset contains 96 radial projections of raw *k*-space data, acquired with\n", - "a 12-channel coil array and corresponding to a 300x300 image. The data is stored\n", - "in a HDF5 file, which we can read using [h5py](https://www.h5py.org/). The\n", - "downloaded file also has the sampling locations or *k*-space trajectory, so we\n", - "do not need to calculate it." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "kspace shape: (1, 512, 96, 12)\n", - "trajectory shape: (3, 512, 96)\n" - ] - } - ], - "source": [ - "with h5py.File('rawdata_brain_radial_96proj_12ch.h5', 'r') as f:\n", - " kspace = f['rawdata'][()]\n", - " trajectory = f['trajectory'][()]\n", - "\n", - "image_shape = [300, 300]\n", - "\n", - "print(\"kspace shape:\", kspace.shape)\n", - "print(\"trajectory shape:\", trajectory.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The *k*-space data is stored with shape `[1, samples, views, coils]`, where\n", - "`samples` is the number of samples per spoke (512), `views` is the number of\n", - "radial spokes (96) and `coils` is the number of coils (12). TFMRI organizes data\n", - "slightly differently.\n", - "\n", - "- Firstly, the singleton dimension is irrelevant and not needed.\n", - "- Secondly, the dimension order is reversed. Generally, \"outer\" dimensions\n", - " (e.g., coils) appear to the left, and \"inner\" dimensions (e.g., samples within\n", - " a view) appear to the right. This results in a more accurate alignment of our\n", - " conceptual understanding of the different dimensions and their underlying\n", - " memory representation. TensorFlow tensors, like NumPy arrays, use a row-major\n", - " memory layout, meaning that the elements of the rightmost dimension are\n", - " stored contiguously in memory.\n", - "- Finally, the different encoding dimensions (`samples`, `views`) typically\n", - " carry no special meaning in non-Cartesian image reconstruction. Therefore,\n", - " we flatten them into a single dimension." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-01-27 09:43:44.317299: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2025-01-27 09:43:44.807941: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22159 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:65:00.0, compute capability: 8.6\n" - ] - } - ], - "source": [ - "# Remove the first singleton dimension.\n", - "# [1, samples, views, coils] -> [samples, views, coils]\n", - "kspace = tf.squeeze(kspace, axis=0)\n", - "\n", - "# Reverse the order of the dimensions.\n", - "# [samples, views, coils] -> [coils, views, samples]\n", - "kspace = tf.transpose(kspace)\n", - "\n", - "# Flatten the encoding dimensions.\n", - "# [coils, views, samples] -> [coils, views * samples]\n", - "kspace = tf.reshape(kspace, [12, -1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `trajectory` array is stored with shape `[3, samples, views]`. As with the\n", - "`kspace` array, we reverse the order and flatten the `samples` and `views`\n", - "dimensions. Additionally we need to apply the following changes:\n", - "\n", - "- The array represents a 3D trajectory but the image has a dimensionality, or\n", - " `rank`, of 2. The last element along the innermost dimension contains only\n", - " zeros and is not needed.\n", - "- TFMRI expects the sampling coordinates in radians/pixel, i.e. in the range\n", - " $[-\\pi, \\pi]$. However, the trajectory is provided in units of 1/FOV, i.e., in\n", - " the range $[-150, 150]$, so we need to convert the units." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Reverse the order of the dimensions.\n", - "# [3, samples, views] -> [views, samples, 3]\n", - "trajectory = tf.transpose(trajectory)\n", - "\n", - "# Flatten the encoding dimensions.\n", - "# [views, samples, 3] -> [views * samples, 3]\n", - "trajectory = tf.reshape(trajectory, [-1, 3])\n", - "\n", - "# Remove the last element along the rightmost dimension, which contains only\n", - "# zeros and is not necessary for 2D imaging.\n", - "# [views * samples, 3] -> [views * samples, rank]\n", - "trajectory = trajectory[..., :2]\n", - "\n", - "# Convert units from 1/FOV to rad/px.\n", - "trajectory *= 2.0 * np.pi / tf.constant(image_shape, dtype=tf.float32)\n", - "\n", - "# We only do this so that images display in the correct orientation later.\n", - "trajectory *= -1.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You should now have a `kspace` array with shape `[coils, views * samples]` and\n", - "a `trajectory` array with shape `[views * samples, rank]`. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "kspace shape: (12, 49152)\n", - "trajectory shape: (49152, 2)\n" - ] - } - ], - "source": [ - "print(\"kspace shape:\", kspace.shape)\n", - "print(\"trajectory shape:\", trajectory.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's visualize the trajectory:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_trajectory_2d(trajectory):\n", - " \"\"\"Plots a 2D trajectory.\n", - "\n", - " Args:\n", - " trajectory: An array of shape `[views, samples, 2]` containing the\n", - " trajectory.\n", - "\n", - " Returns:\n", - " A `matplotlib.collections.LineCollection` object.\n", - " \"\"\"\n", - " fig, ax = plt.subplots(figsize=(10, 8))\n", - " ax.set_xlim(-np.pi, np.pi)\n", - " ax.set_ylim(-np.pi, np.pi)\n", - " ax.set_aspect('equal')\n", - "\n", - " # Create a line collection and add it to axis.\n", - " lines = mcol.LineCollection(trajectory)\n", - " lines.set_array(range(trajectory.shape[0]))\n", - " ax.add_collection(lines)\n", - "\n", - " # Add colorbar.\n", - " cb_ax = fig.colorbar(lines)\n", - " cb_ax.set_label('View index')\n", - "\n", - " return lines\n", - "\n", - "_ = plot_trajectory_2d(tf.reshape(trajectory, [96, -1, 2]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, it consists of a series of 96 uniformly spaced, sequentially\n", - "acquired radial spokes extending to the edges of *k*-space ($\\pi$)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute density compensation weights\n", - "\n", - "Non-Cartesian trajectories do not usually sample *k*-space uniformly. It's\n", - "obvious from the figure above that the center of *k*-space is much more densely\n", - "sampled than its edges.\n", - "\n", - "It can be useful to explicitly account for this during image reconstruction. In\n", - "order to do that, we need to estimate the sampling density of the trajectory.\n", - "TFMRI provides several ways to obtain this estimate. The most flexible is\n", - "[`tfmri.sampling.estimate_density`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/sampling/estimate_density),\n", - "which works for arbitrary trajectories.\n", - "\n", - "The [`tfmri.sampling`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/sampling)\n", - "module also contains other operators to compute trajectories and densities." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "density shape: (49152,)\n" - ] - } - ], - "source": [ - "density = tfmri.sampling.estimate_density(trajectory, image_shape)\n", - "\n", - "print(\"density shape:\", density.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Perform zero-filled reconstruction\n", - "\n", - "We are now ready to perform a basic zero-filled reconstruction. The easiest\n", - "way to do this with TensorFlow MRI is using the function\n", - "[`tfmri.recon.adjoint`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/recon/adjoint).\n", - "\n", - "The [`tfmri.recon`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/recon)\n", - "module has several high-level interfaces for image reconstruction. The `adjoint`\n", - "interface performs reconstruction via application of the adjoint MRI linear\n", - "operator, which is constructed internally. See\n", - "[`tfmri.linalg.LinearOperatorMRI`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/linalg/LinearOperatorMRI)\n", - "for more details on this operator. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zf_images shape: (12, 300, 300)\n" - ] - } - ], - "source": [ - "# Perform image reconstruction. For non-Cartesian reconstruction, we need to\n", - "# provide the k-space data, the shape of the output image, the trajectory and,\n", - "# optionally, the sampling density.\n", - "zf_images = tfmri.recon.adjoint(kspace, image_shape,\n", - " trajectory=trajectory,\n", - " density=density)\n", - "\n", - "print(\"zf_images shape:\", zf_images.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`tfmri.recon.adjoint` supports batches of inputs. In addition, the batch shapes of\n", - "all inputs are broadcasted to obtain the output batch shape. In this case, the\n", - "coil dimension of `kspace` was interpreted as a batch dimension (multicoil\n", - "reconstruction is only triggered when `sensitivities` are specified).\n", - "`trajectory` and `density`, which have scalar batch shapes, were broadcasted to\n", - "the same shape and all coils were reconstructed in parallel. The sample\n", - "principles would apply if reconstructing multiple images with different\n", - "trajectories.\n", - "\n", - ":::{note}\n", - "Many TFMRI operators support batches of inputs. Batch dimensions are always\n", - "leading.\n", - ":::\n", - "\n", - ":::{note}\n", - "TensorFlow broadcasting semantics are similar to those of NumPy. Learn more\n", - "about broadcasting [here](https://numpy.org/doc/stable/user/basics.broadcasting.html). \n", - ":::\n", - "\n", - "Let's have a look at the reconstructed images:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_tiled_images(image):\n", - " _, axs = plt.subplots(3, 4, facecolor='k', figsize=(12, 9))\n", - "\n", - " artists = []\n", - " for index in range(12):\n", - " col, row = index // 4, index % 4\n", - " artists.append(\n", - " axs[col, row].imshow(image[index, ...], cmap='gray')\n", - " )\n", - " axs[col, row].axis('off')\n", - " return artists\n", - "\n", - "_ = plot_tiled_images(tf.math.abs(zf_images))\n", - "_ = plt.gcf().suptitle('Zero-filled individual coil images',\n", - " color='w', fontsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's combine the individual coil images into our final reconstruction.\n", - "TFMRI provides a very simple function [`tfmri.coils.combine_coils`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/coils/combine_coils)\n", - "to perform coil combination via the sum-of-squares method. If coil sensitivity\n", - "estimates are available, it can also be used to perform adaptive combination.\n", - "\n", - "The [`tfmri.coils`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/coils)\n", - "module contains several utilities to operate with coil arrays. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Combine all coils to create the final zero-filled reconstruction.\n", - "zf_image = tfmri.coils.combine_coils(zf_images, coil_axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_image(image):\n", - " _, ax = plt.subplots(figsize=(8, 8), facecolor='k')\n", - " artist = ax.imshow(image, cmap='gray')\n", - " ax.set_facecolor('k')\n", - " ax.axis('off')\n", - " return artist\n", - "\n", - "_ = plot_image(tf.math.abs(zf_image))\n", - "_ = plt.gcf().suptitle('Zero-filled reconstruction', color='w', fontsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute coil sensitivity maps\n", - "\n", - "The zero-filled image has visible artefact because the *k*-space sampling rate\n", - "is below the Nyquist rate. Information from multiple coils can be used more\n", - "effectively to address this problem, by performing a SENSE reconstruction.\n", - "\n", - "First we need to obtain the coil sensitivity maps. These can be estimated from\n", - "the individual coil images. A low-resolution estimate of the images is suitable\n", - "for this purpose and is easy to obtain, assuming the central part of\n", - "*k*-space is sufficiently sampled.\n", - "\n", - "To obtain the low resolution image estimates, we will first apply a low-pass\n", - "filter to the *k*-space data. We will be using\n", - "[`tfmri.signal.hann`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/signal/hann) and\n", - "[`tfmri.signal.filter_kspace`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/signal/filter_kspace)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# First let's filter the *k*-space data with a Hann window. We will apply the\n", - "# window to the central 20% of k-space (determined by the factor 5 below), the\n", - "# remaining 80% is filtered out completely.\n", - "filter_fn = lambda x: tfmri.signal.hann(5 * x)\n", - "\n", - "# Let's plot the effect of our filter.\n", - "x = tf.linspace(-np.pi, np.pi, 512)\n", - "plt.plot(x, filter_fn(x))\n", - "plt.xlabel('Frequency (rad/px)')\n", - "plt.ylabel('Filter amplitude')\n", - "\n", - "# Finally, apply the filter to the k-space data.\n", - "filtered_kspace = tfmri.signal.filter_kspace(kspace,\n", - " trajectory=trajectory,\n", - " filter_fn=filter_fn)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now reconstruct the images from the filtered *k*-space data as\n", - "described in [Perform zero-filled reconstruction](#perform-zero-filled-reconstruction)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "low_res_images = tfmri.recon.adjoint(filtered_kspace,\n", - " image_shape,\n", - " trajectory=trajectory,\n", - " density=density)\n", - "\n", - "_ = plot_tiled_images(tf.math.abs(low_res_images))\n", - "_ = plt.gcf().suptitle('Low-resolution images', color='w', fontsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now use these images to obtain the coil sensitivity maps. We will use\n", - "Walsh's method, one of the methods implemented in\n", - "[tfmri.coils.estimate_sensitivities](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/coils/estimate_sensitivities)." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-01-27 09:43:47.580569: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8100\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sensitivities = tfmri.coils.estimate_sensitivities(\n", - " low_res_images, coil_axis=0, method='walsh')\n", - "\n", - "_ = plot_tiled_images(tf.math.abs(sensitivities))\n", - "_ = plt.gcf().suptitle('Coil sensitivities', color='w', fontsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You migjht notice that up to here the code is identical to the iterative SENSE tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Perform Compressed Sensing reconstruction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are finally ready to perform the SENSE reconstruction! We will be using\n", - "another of the high-level interfaces in `tfmri.recon`. In this case, we will use\n", - "[`tfmri.recon.least_squares`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/recon/least_squares).\n", - "This interface can be used for image reconstruction methods that arise from\n", - "a least-squares formulation, like CG-SENSE. Internally, this function will\n", - "create a [`tfmri.linalg.LinearOperatorMRI`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/linalg/LinearOperatorMRI)\n", - "and solve the linear system using [`tfmri.linalg.conjugate_gradient`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/linalg/conjugate_gradient).\n", - "\n", - "Usage is similar to `tfmri.recon.adjoint`, so let's have a look:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# This is a spatial total variation regularizer\n", - "# This is different from SENSE\n", - "regularizerTV = tfmri.convex.ConvexFunctionTotalVariation(image_shape, \n", - " scale= 1e-4, \n", - " dtype=tf.complex64)\n", - "\n", - "# The optimizer is also different from SENSE\n", - "image = tfmri.recon.least_squares(kspace, image_shape,\n", - " # Provide trajectory.\n", - " trajectory=trajectory,\n", - " # Density is optional! But it might speed up\n", - " # convergence.\n", - " density=density,\n", - " # Provide the coil sensitivities. Otherwise\n", - " # this is just an iterative inverse NUFFT.\n", - " sensitivities=sensitivities,\n", - " # spatial TV\n", - " regularizer=regularizerTV,\n", - " # Use conjugate gradient.\n", - " optimizer='lbfgs',\n", - " # Pass any additional arguments to the\n", - " # optimizer.\n", - " optimizer_kwargs={\n", - " 'max_iterations': 10\n", - " },\n", - " # Filter out the areas of *k*-space outside\n", - " # the support of the trajectory.\n", - " filter_corners=True)\n", - "\n", - "_ = plot_image(tf.math.abs(image))\n", - "_ = plt.gcf().suptitle('Reconstructed image', color='w', fontsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Consolidate previous steps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's put together our entire reconstruction pipeline in a single\n", - "function:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "def reconstruct_compressed_sensing(kspace, image_shape, trajectory):\n", - " \"\"\"Reconstructs an MR image using CS with spatial TV\n", - "\n", - " Sampling density and coil sensitivities are estimated automatically.\n", - "\n", - " Args:\n", - " kspace: A `tf.Tensor` of shape `[coils, views * samples]` containing the\n", - " measured k-space data.\n", - " image_shape: A `list` or `tf.TensorShape` specifying the shape of the image\n", - " to reconstruct.\n", - " trajectory: A `tf.Tensor` of shape `[views * samples, rank]` containing the\n", - " sampling locations.\n", - " tikhonov_parameter: A `float` specifying the Tikhonov regularization\n", - " parameter. If `None`, no regularization is applied.\n", - "\n", - " Returns:\n", - " A `tf.Tensor` of shape `image_shape` containing the reconstructed image.\n", - " \"\"\"\n", - " # Estimate the sampling density.\n", - " density = tfmri.sampling.estimate_density(trajectory, image_shape)\n", - "\n", - " # Low-pass filtering of the k-space data.\n", - " filtered_kspace = tfmri.signal.filter_kspace(kspace,\n", - " trajectory=trajectory,\n", - " filter_fn=filter_fn)\n", - "\n", - " # Reconstruct low resolution estimates.\n", - " low_res_images = tfmri.recon.adjoint(filtered_kspace,\n", - " image_shape,\n", - " trajectory=trajectory,\n", - " density=density)\n", - "\n", - " # Estimate the coil sensitivities.\n", - " sensitivities = tfmri.coils.estimate_sensitivities(\n", - " low_res_images, coil_axis=0, method='walsh')\n", - "\n", - " # Create regularizer.\n", - " \n", - " regularizer = tfmri.convex.ConvexFunctionTotalVariation(image_shape, # this is correct\n", - " scale= 1e-4, #5e-2, #5e-2 was the best for non-coil compressed (2024-10-17)\n", - " dtype=tf.complex64)\n", - " \n", - "\n", - " # Perform the reconstruction.\n", - " return tfmri.recon.least_squares(kspace, image_shape,\n", - " trajectory=trajectory,\n", - " density=density,\n", - " sensitivities=sensitivities,\n", - " regularizer=regularizer,\n", - " optimizer='lbfgs',\n", - " optimizer_kwargs={\n", - " 'max_iterations': 10\n", - " },\n", - " filter_corners=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To make things more interesting, let's test it with some new data! We'll use\n", - "a cardiac dataset which was also provided by the ISMRM Reproducibility\n", - "Challenge 1. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/bin/bash: wget: command not found\n" - ] - } - ], - "source": [ - "heart_data_filename = 'rawdata_heart_radial_55proj_34ch.h5'\n", - "heart_data_url = \"https://github.com/ISMRM/rrsg/raw/master/challenges/challenge_01/rawdata_heart_radial_55proj_34ch.h5\"\n", - "!wget --quiet -O {heart_data_filename} {heart_data_url}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's read the data and process it in the same way as before:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "with h5py.File('rawdata_heart_radial_55proj_34ch.h5', 'r') as f:\n", - " kspace = f['rawdata'][()]\n", - " trajectory = f['trajectory'][()]\n", - "\n", - "image_shape = [240, 240]\n", - "\n", - "# Convert k-space to TFMRI format.\n", - "kspace = tf.squeeze(kspace, axis=0)\n", - "kspace = tf.transpose(kspace)\n", - "kspace = tf.reshape(kspace, [34, -1])\n", - "\n", - "# Convert trajectory to TFMRI format.\n", - "trajectory = tf.transpose(trajectory)\n", - "trajectory = tf.reshape(trajectory, [-1, 3])\n", - "trajectory = trajectory[..., :2]\n", - "trajectory *= 2.0 * np.pi / tf.constant(image_shape, dtype=tf.float32)\n", - "trajectory *= tf.constant([-1., 1.])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now perform the reconstruction:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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mrn/v935vrK6uHkn/yle+Mubm5uJVr3rVkX1pi4uLcfr06SreXvOa18TGxka89KUvjWc+85lH7p84cSKe85zn9P//03/6T7G/vx/f/M3f3PdZRMTJkyfjW7/1Wytb+xdDf/7nfx4REZ/1WZ81cf3FL35xfMInfMKR9IgC/rt/9+8mZHr77bfHv/7X//pI+t/5nd+J3/7t345//I//cXzZl33Zkfuj0Sj+5t/8mzfUhkaNGj1+9ISfHGmf9vmr9Ck9liUiusXFxe7BBx/sdnd3u4/8yI/sIq4/wuTtb397f8Ly1a9+dfe93/u93U//9E93f/AHf9B1Xdc95znPmSjjf/2v/9V1Xde9733v6374h3+4e9nLXtb93M/9XHflypXuEz/xE/u0n/mZn9ldvXq129nZ6V7/+td3L33pS7tf/dVf7eu65ZZbJvib5jTsF33RF3UHBwfdb/3Wb3WvfvWru5e+9KXda1/72u7SpUvd/v5+94IXvKBP+/Ef//HdwcFB99BDD3Xf//3f333Lt3xLf/J4qN6I6J71rGd129vb3e7ubvczP/Mz3Q/+4A92v/M7v9Pdc889vYw0z3/+z/+567que+CBB7pXvvKV3fd+7/d2b3jDG7pz5851X/RFX9Sn+xf/4l90Xdd1v/u7v9t913d9V/ct3/It3fOf//z+/ud//ud3m5ub3d7eXvfWt761e/nLX9796I/+aPeWt7ylu3z58pG+xkndhx9+uPuRH/mR7hWveEV33333dW95y1set1O6rgzonj4aJSK6z/mcz+m6rute8pKX9Nc+6ZM+qdvZ2ek2Nze717zmNd0P/MAPdO9+97u7a9eudW9961vtqerXv/71ve5ADo888kj3i7/4i13Xdd2rX/3qifRPe9rT+hPYv/mbv9n92I/9WPfyl7+8e9Ob3tTdf//93dbW1hM+ZtunfdonuvhLwED7tM9fqc8Q4Is4fEbc6173uv7azMxM98//+T/v3vWud3WXLl3qtra2uvvuu6/77//9v3df+7Vf2y0vL0+UsbCw0H3zN39z9/u///vd5uZmt7Gx0f3Jn/xJ9/KXv7w7derURNpnP/vZ3c/+7M92jz76aLezs9Pde++93Q/90A91Z8+ePcLbNIDvzjvv7L7ne76n+83f/M3ukUce6ba3t7v77ruve/Ob3zwBUPH5qq/6qu7uu+/utra2uq7rJuoZAnwR0T33uc/tfuu3fqvb2trqHnvsse51r3tdd+uttxYfq/LP/tk/637zN3+zu3LlSnf16tXufe97X/eqV72qe8pTntKnmZ2d7b7v+76vu++++7rd3V0Lmj72Yz+2+4mf+Inu3nvv7ba3t7vz5893d999d/fDP/zD3ad+6qceqfdrvuZruj/5kz/ptre3u/vvv7/7/u///m5paekvFeDD9Xe9613d5cuXuwsXLnS/9Eu/1H3yJ39y+hid2dnZ7lu+5Vu6D3zgA9329nb3/ve/v3vxi1/cfdqnfVrXdV33Qz/0Q0fqXl9f777zO7+z+6M/+qNeV9/3vvd1r3/96+0jXtqnfdrnL/4z+v9/NGrUqFGjRil9zdd8TfzkT/5k/Mt/+S/jx3/8x59odho1ajQlNcDXqFGjRo16uv322488guWOO+6I3/iN34inPOUp8fSnPz0efPDBJ4i7Ro0aHZfaY1kaNWrUqFFPL37xi+MLvuAL4l3velc8+uij8REf8RHx/Oc/P9bW1uIlL3lJA3uNGv0VpQb4GjVq1KhRT29729vimc98ZnzBF3xBnD59Ora3t+OP/uiP4lWvelW88Y1vfKLZa9So0TGpLek2atSoUaNGjRrd5NSew9eoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anST01xtwpWVlf5313UxGo1iZmYmRqNRjMfjODg4iIiImZmZmJmZiYODg9jf379eydxczM7Oxng8jv39/ei6LsbjcYzH477M0Wg08UHZ+HRdF3t7e309mgd8dV03wTfuKd8zMzP9tYiI8Xh8JC//R3quD2m4PFcm86W/+ZvLRvv5N3hX4vZHROzv78fe3l7fXr2v+SBf9Afqm52djbm5ub5MyL4kJy2f62CZ8H3Uk/X7/v5+r2PQKaRjec7Ozsbs7OxE2egbLof7D2lZh8HjzMxMz5frRy5/cXEx5ubmYmdnJ65duzah2zMzMzE/P9+nVVnwx11TPeH2q86g/SCMuYiI+fn5mJubmxh7WofK0Y0p7T8ez13XxcHBwZE0jl+khw5kugTdYznu7+/3tsTxtLCwEPPz8xPl6RgEr8wv9yt4RLloA+yZ6y9uH+urjgWVy+zsbIxGo9jb24vd3d2YnZ2NpaWlmJ2d7dvK/b6/vx87Ozu9nqIsyEfHD9sp2FKMBeZhZmamHwvarrm5uf4+y49lrOOd9ZDHBJPyAJvj9B/9pXnRHxnfbBdUpyB7/aAPUZ/qO+swX3P2DDaZedBxxnrqxrzK9eDgYKJ+lKk+ArLnvGyHsj5hftx1l4fvO1+pbQT/4JvTs86wvUJbYM/Yfuzu7sbu7u5E/SwfyIb7HXRwcNCPC06POrQN3A4lth+q95zHycvhE2e7WL6wD0P0lzLClwGIm5VKg0jJAccnmqbh/y9DXY+X3JSXmnJvpP/+IuVcS39ZdDBiuv54vPi+0XHo8tb2szrUmvIfb7vxl1EnH2/KJuyPV9kR08vxL9O4yyjj8S+C95qx8eHI+5edqiN8DqXqLCfiMFrE6RjJ6yyX0/B9jaBoHhDSTDMQmXfkYf54hsQzI50BMr8ZZTODiJioRxG7yoOvcdlDBiNTXs2HmbDyCh4xG1LDxxGQrC06i1E+MDNHpE2J+8q1h3lSfjgq6frY9U+mo443nmEhMoLymW/Mxh3vGlVw5XNad69E3FYdj9OQRlyYBxeRUF4xTnXWrmOqFNXgccFRNk3LEVvXTo0EaTq1D8wnyw/lsE5lMlC76CIiXXcYsWM5cfQZ97g+HkdIg/LAI+dRWal8XRRCI0UcUVZ5O6DpqKTzJZuusnOrKUij/iniMDqGdmiZ7BdU9toejHX1Carrrq9xHREutYXOZoBKgKrkD0p5XdpaKtluJ1vngxypz3FjChGurusmVjnYpqhPUlvo+CmNX73m+M4mdDW23tWnZbgVvxLdMODjhkNxlbKB6MChOmwNqapR5bKc4Xe8uPqcQDMnpg7UpYE8mM+sHK034nApgZcBneHidmZh+hJAYsVhg6PLu1wO8rvlnCHDznJho+p4c4PUDQpXDogNNsvJyUV5zdqthns0GvX9pQZOAa0bB9DxDCiw3LL7jrQeXFOgMlQWO5+IoxO8IUIbFSyhDNVxl98BXwA+dQQKdFQO7p6Tm5Oh5se3s3toWwYsVUfYxrGseNnZgS1uCy8/8lYGAEkGg1wHKJM/l839xUvsro382+mw69daoKHyU13gCYb2c8aryoFl78AyL6e7OtT2cF7mlbc9uW0dTiaqv87HlKi0xF7jR5QXHdecj/U1S+N+qz9wadjGs63RMrhtXC76mMcF+3Atw/GW8Z3Vr5O1rNxSf+pWshqqBnyOMmadEiqwyoABl1MSNpN2juY/LqkDcbw4Jz7Eby2PmQJlZWTKUSPrzPnxzDVT4hpS+TiwX/pdGoCZMXR8OmNW23cl+dbotTPgNfmm6ctSuqG6XL/UlFvDixox7ePMyJXqLwHgkq7W2JOaurM2uPIynS6VPSSTkh4P8ZS1y9Wh5WWfmjpLNq/U7iFbPqTvpXpZ77N2ZXkUQGh7+F5pbA3V53i9UXLlDIG62gke55mmbZpPrw/x6uy78981fGXAN8vj5OPs1TST9Yyv41I14MsaqAMyW17RyF2t0e26yVlSNkPkWVatgiFfSbG0TAYeymPXHT3AMVQX8jgjjXJ00zDnL5VZC0Ii4ki0FvVub2/bdrhyMznxNe4nN9PVWTuXPeQ4nQMdjUZHDnKUZrCj0WgiUpcdJui6ushBZiS1/arjmk/HV9YGt8TO/HLdKi9e8q4BJZlcuA0ob39/f2Lyx+07ODjoN1pzfq7DjQ83CcDHLa+qTmVtcLIBnzw7V9KICet6Rm4ccxRO+eMDGY53JTfetH6VCZfPBzl2d3f7/uOJIA7paRTS1aO6VdJDjEONAGcgtCRbJxdNy4cgOGqqdiOTF6fX7ToOwHBeHMxxYOU4pPW5sYTy1Wa4/oiIol+LiIkot/oVtnFu1Yh51gNlWo8uwau/KR14yA4PaX+51TKHY5R4XHDd4KvWxzk9qLk2RMeO8Dllwn8dfCqoWmOl5TkHyDRNuZrPCV7vu7axI3agJKsj41PbWuOkSsCpFLbPDCXzwmAT386Zlsrg306u2u5MhjXOTctkfZv2ZJoCttr+GiI3ZpSHDHRl/GfpXXuyNGpomZfjjC2ujw2hTgq7rjvi7FTPpq1/SH9q5K888hjPHBaTys+Nfwc8dEyUeKuhTB8yG8ofN4Fl/YBM1JFmPGZ6xaR6qLYhK7dETs64rjaKAZ/Ko2R/XX2Z3qoeZfuLa/U9a5+jTO8yvXb1qB3JgGpmA5xNY8CX2amSDmgfafvAg/pE3kJRauuQ/czGbQ3vGZ4qjZVp7UDEFIAvU2CdcbrHZSjT3OlDQspIUbu77vivLTvb28cgSGedyoOSdloJALKcOPrj2qcDput8hEN5qbnv9rtleWoNs7bBtXvIIWcOXfVUdS+beStg0n2TDjiXgEj2X/OUDI0rKzM4rlwdhw6wZPUOtadmDKsDUZ3iclz+bLKiVCOfLI9rJ/ZU4j/LsFTOUL+xQy85J5BGCzLeuWy+njkOJX5UhUZfuP36GAvkYdCidZTGKctVx2WWNxvDpfpc/UO/NbLvQB8oi/RzPqRTf5HZTNU7d+9GyOmrkgPDbqWuFoRnIKlm/Gr7wZvuG+TVmaxNWfSf63GAVa/XUDYWHIjU9I6fG6VqwJdtWFVm2FHyc7L0PjqLnaqW5cgZUTcDdfmcMN1AzcK24FU3LzPvmRK5Zcwh3njTsdsDibZzmSxTxwe+hxSaeeA8aH820LOZCK7hNCvXpW1hWalDLTkDbYP2B5+EQxl6khO86nO9hurU9jCV8pWA1FB7VYdKh1EgY+hHNp4ZHGo6LduBPiZXR8afOxTG5ShAKAEhrScztHxNbRgOivEm+qydes+NLb4H4MTt0gNQ6vCzNro6MvmwvdKI0mg0+XxBJt7eoCcgtfxaR8g8MODM2s11utPZrvxsHGTX2A7xUrY7zYvfrB+4x/Wjr9luOjCHvuHytFwF4DX6nrU5GxssN/UTTr4ZoHH5MjCl9o6/NS/XpT5OywNxlJpPlatPzQA9t1frc7wplcC6K4PtcQmc1o41pqkjfENMujxsVFigjxd6dYONv3EvMwKlcqcBoEPXM4dRAk7H6dSa8jPKBlrNIKjlpaQrpXw3cr9Ete0Y0neUpXkyQO14KAGLjKZxsq6tzgipE5yGn5IsHTDS647vWp2pAeXctkwfh6hWJjcyfkvOkq8723acsTnEg9abXa/Rd1dHjSOrsQOZjpf+671S+4ZsswNO05DqVkn3a31xCejWUG0bjuNjj8NPKa/z+8cFZ+5+qW+m1f9sPGc0jR47mjrC5952kDGImRhHbBzadU8MHxqwjkrK4xxpzcBifkpKw4Nc69ZIYKlOl6Z0jUmBtZbvwEhphjakfNMYc6RHlICv6ybw7PEz6qhxHdEYNoK6zJKBHNYBjZayDFQWo9Hko0q0zBuhbAJTQzX9pnUp35n8p9UFBzD5GqIo2TMKtSytTzeJ14BZLSMbK3grgoJfx1Pm2NWeOR11dWh/cFt51o+ydGN8Bmhxn/OoLPh/REw8soKfxVlyPGyDwCsOfKicWOc0Qsj2TOWkfOp/lkmNLxnSkeye2g+U5R7XUwPO2HZl9hz16lafbOxP017nY7JIvabLxorm4bHu2qXlsRx43Lh2ZIBT+WL9d/tUtQ/Uf2e2DW2ZRubgQccMl61vfBkqL6OpAB8bFb6eEZgvMaYdnO0DudFZg+uEGufMIM4pqdaTGZlpZw5D7XKkg8eBpVJZJb6nATIlp5ANHq3HyZt55MHKe4uc8SmBEa5LDTHzkE1uSs4vk2WJH76f6UmJhkBZ5mC4Pu2PiMNlTldmjZzdPYAI7dMacic3M4fjgFkGdtiZTsOPAiuuOwPN2d5jB/jYGbh90+hHtzTOzjQDmSxHdTQ4UMOAZmifFNLzqxn5/lCb0a7SXmSne1lba0jTqQ3I6nbtyiYwQ2MF32wrS+1zPNT6HtUvvj7k75Rf9408zq7plhE3ftQ2Y8lb+Rwi12Yum+XMOoNJCI8/HStZXbV4hWXLW5pcfzj7PC1NdUo3Q6CZUWdnoUqqm4NZmLVghOtwvB6H1Ok7HmoMQMlQMHiOmAQt3LnuwcZapnsfo1MMdTTOwJaUjNOwkwbpvj4dJCrHUpucDLmckmFRJ6htyYyEc2KcVmelWp+T49Cp4GkAhaNMtk4XXT6nGyVj4h7PUNMG51Dw7ZxWKX9mY9z/LI+7p+0ekp8DAxkoUaqxZWqHnMFnMJTpb8af9ok6XJdWx5OWWfIBJb6co9c2a3naXqfD0+wp1PJ1xUnJPVCZCfyAx2yfurOX+nHtq22X011cd2XX6HNW/zQAyLUt45XLcnqn9UyDE9y4QTmZ/c50Gvky2+Fk4K5r/lJ909LUj2WZmZmxLyV3BxkYIM3Ozk68nJhffMzGKzsNiW/XAS4q6JRJARt3jkPabDT0lUXMF9eZOQf+jZkK0s7Ozsb8/Hz/Qm/nCFVOKJNf1M2bfXFghqNfHAkoPQHeGVL+xqwdPPGSPOfjJTt+9U3JEWeGxDkdTc9p9C0ESOse+aBA0Bk9fayQ6oc7AZu9yLxkrJwcSqAxM5xOTqw/fI2NuDNKuMdPox86Ba784cO6xc5wmjKYLya2Q3Nzc0ccfrZUrWVk/9WpZE641F8ZIGf9AXFfZRvMHZ8OICpp5ILz6Hhw0Q/lm+vWyFwJuDmdLfHiQKcu+6IMfROLktN3ZwtKYAX1Z3kQdeXoK2yy60MeW26pcUgmKj8nW5TN/7ltTj4lfXN53fh08iuBOlev1q1tdXIdmlTyW63g29zhpFrwlbXd8ejk63S/9okFQ3Ts5/CVOr9kaDJl1bKHHImr2w3gjOeaurIOKZXtBv00TpHbMuScMhDo6iyBrBJlwENBjyqxU2oucxrZ1vCmhH51BizLl7V1SFczvcqcRan+48hBy0A901LG25ABuxFyel7jTLKyXL4MJB5XRqX6s3tOn6a1VZx36No05OpyEwAHfofoRnlzdU5T5jS+JKsX5bh7Trcy21E7vodsdQY8tIyaNPpfx2DX+VfKZe2toQzklGga2Tn5lXgt9fWHi5wvzSYgjydVAz59En7E0WPqbkYBdAqUzY/G0DJ5UPAMkZWLZ3XHodIsCKSRvNIAxIDg/UQaReKIGtePPBzZ007ndI5f1Blx+Awi9ygBpiGA5SILSK99juuLi4tHgJzWyfmY9MXhqgOaFzKFPPTZSzwjYpm5o/hoE6fjKLX2mxrCIVDunGa2dKx5NaqosiwBo9L+sJKxzgwO65+TbeYsWPYZCNd6SwYbv7U+x4sbv6XJkWsL18cHJ1jO2ga1BRlQco5mSH/4uuZXIOL0hmWmY1vl6WSCdvFYcf2lG8wzXXZUAppOz4b6IwOvbuwOHVDQdOqstU84is3jBvk0Elqql32p5mG+3DumM7/CMuNr/JYTrh+k745mm6xp3YpfSecyfc/ef31cUOR8WkZs+5zNZPk6X8FpnQxcWmfTbrTt1YAPp9aYWTg9vp4tWXE4OyL6V8mAnBAZzLDDccriytDySsaWr6vy1+TXML2CBffGCvzm5xWqPOFoRqNRCmwc4BtShJLT5Ta4ZRzuA4AjLCtzWl5Ode3GfxhsfT2TKnlmCByAUsOvxo/7WJfYIXNd+nZ6nYEETQfi/hoydlm7MxBQ4k2vsVycw87KY/mx7N3EgtO5E5gK3LTNDJwYOKI+dljZBnb+dnu6sjGtS9bMT3ZiEOmQxslf61FeHW+uvx0g03K1T5gvTDI1fYkPXTrl8aM2o7S5ntMex1lrv2rd2fhQp5z1gwOVDlgxDxmp3YyY1GG+hnGiD7h2ICCTKdszbYNLy+OYx5SCK2c3mUcdg/h2D+tWPrh8lanKLBtDNbbXpVUdyuSO/xkAK41P1ccM8A35BdefQ/qnNNW7dLNB5BhQJR0yAO6b75cMwzSNniatAzwZcQTD1cXOUr81fUk5uLyMZ/1fAkyl6xnpoHODjw1/CVSU7ul1Z+Ay3lwZrm52Es6wujJd+e7/kAFx+WvvlSgbqwp4SkbTyULvT+Oss36ukXHGrwM9+HZGs9R25TMrUwFvrQyG7J3T8Zp9O6pvbqxnNkTBm/52dbj2OnswpOsZGOb7tU4d91zfqcNUkML5tP5MtlnbHc/8351IzXzBkK6WQMu0lI2X0njPfEAJBCnp2Mzss9ap9dS2bYinIZuvvGZp1cdr3Vk5GU/IOzRmamiqPXzaAHdYwjHL78lUw6LK7xRPl/GYjiOIofsoJztZ5Qaxvm6N24JIUWaMue28YdfJx7XFgVKWBT9HC9dKg5IdG5er15hnniHyQY29vb3+YInKT42x8uD44zwcVXXtz9rGaZ3s4AjchnQnp4xfBQiaPtuW4MBGliarx0WhOGLG+sh9p33snFg27ktGkNO4dqhhZ/13dWb2wC21lUCf0x8GWnj7BKfjZ2JllAEY7htsZeBx7iIl2gc6Pl37Oa1bdsV1djoOMPF3NlY4Da6pbDVNDWk0l/lUWWTlZ2OOo/qQuTtskvHP/cPlOFlEHD5NgfsD41DBgZMv14t07mCB+owhUKV+h1fdssM62Th3/e3GlxuPLrqcjVttq7NRWb8PYRXdwuPSQCbOVnLdPL40vyu/ND4yfz0t6Jv60EaGxN3AqFE2/u2UQwdDRtMakxpS0Jnx7zobeUqKo21zgzYDEM7gO8V3Du64xPU6Z+rq430g2eDMSNteAqnKozPOQ8CJr+lALtWZOTb+zkC2Mwa1VMOX6lcmmxrZuvpdnqG2PB5tdjpXcvxaX6nuzHk4na+RF+fRvudlYm4fl11bXwlcuf/ZeOZ6M13RNNk4qrHbWV9kviPzJSXwwdcAtgD6boS4b4dAgtsSoPy5sh0poHFllHR8yAZlYzrzOU7eJdLyuu7oK1I1bakNQ9eHeCv5zaE8zq4owHTll3Tf1cVjdZq8TMd6tZrbyK9MZFGA0uAejY4+PR9l4L7OiEuA0rUhE3StwmbGm+/zoQ93ZJ/TZTzy7Nbt6eAoQHYghPnl+zpIM+eQXVdlHo/H/R5PF1nS35mDdlEc5jE7vOL2d2R7yrQt2j6WnabTwdt1k48qcQNRx4PeqwUrjjKHxry5OktOruTAMsdQA6RRnkabFJRmvJTk4vpVxw3ardGzIQDFe0phm1RHmFflg9NnQEXLcZHl7PCVyiBz/noQze2P5LKQzr0Zh/cS674+1KV8Ob6Zn9K+Y07vZKzjiPcVc9maXp0z8873hh7vwvy4sa/kDnJoe7RdpTq1ftYh1YsMVDn+tR3T8sN5nB/K6hkaI6X/Q/tQS/W662rfaoFWybYoPsj0plRGjT46mhrw6Qktvq/CyYwJl4d0KK806N1GaFemAkHl0YE+GHQVpNbjAIY6Wq1P+YYjUf75N/OAZ4qhrPH48Dl7GT/8HLrSPkQ+kaqUOX2N2EVcP9SjCsphbzZyOpAUxKksdHmP+8jtv3PgKzM2zEN24tD9R1tA7tlaGcBQXVFZaPszGZUAnx5+AT+q25nuKWWGp8T3UNkOIGtbh0CAlstjjp+DmDkdtgWZbcIzuvieHuqocYRDMkQ5JftSOpSQ1edkzvWWJgAMnLgs8MAn7N2kLhuXOt5KfJRkp3whLW8j2d/fTw92sa1kAIY2sO1BOc5elvwKf7NsHHBmOWbt03qZFEyyDXCfzB5yndPwoja35NvYl2Tk5OlApdrxofHo+CrxoN+1e/uzerVfMt6cncn8ZA0d+zl8oJrBqOkdZQb5RsudJj+DsZITcIZ0iAenNCUqAZSawZqVOa3CsCHj9ArWsrzKl+NRy3s8aEiPakEX/8/amuWbRp+dTIf0yRlnB5ZKYy77fxydyNK5PKoTjteS7KaRD0fXSg651L8lo+zkWMtzqd1DxDqs7cio1E+1YOI4NnpanlyaIRvhwDy+tb9KdkH7xOlLifdMh4fkVuKL05TkULLHjrfMh3A9JQCb8XEcUOIAsytfddX1WWZTXJ4acuWWdKgWC2T6NGTftLzatkx1Slcr4dd6TeOcUJ4KTsPbGbDSa+qsh5ydAy9424VuwFVAwDwNDQQ+Gq+vP3O88n2eBSFa4/b7uDZmYCBb4stkk/Ubv82Ao6IuT8k4O37Y8bqBpfLPACw/BqfkyDIAoPczQ4JP6fEDju+MN4648rhQWUAPVA4uypJFDGoA3xAILpWn6dDeoWWl0Wg0EY2sKRffPG70Pb2I1im/pcg+9zGu6VKck4GubnC/cDmZXSmBw0wWvIyt7XGOZGisM+9d1/VRfMjSLWuX+NQ9bC7PkJ9w/a5jQG0LInMlW8S6qeMIAYChPWaZPJiPrG0l/krk6lSb5uwrjwW+58p127NKQJHTlPhwv4d0XMeH6rLDAhmWUPCW2RTHn/P/rEPqo7kOvq94QcvO5KG81e5FnfqULleWrZcfp0wIg0811VLmlPS+1qkdPLTvi9OyU8qcYcmxO54c/2xg2SCV2lgqJ7uvfCiw4faUlJB5cANNFbRk8J2TLBkUvq686O/MAGRtwbcrO2trRq6tDkSyYXOGQ3nKjKtLn/E4BAzc/xrjP2RAs/wlUDqk8wyEsvFVGpslntkJuv7h/nQy1WuZI9E2l9rKNpltk6vL6XJWNtrLS5AZqKhx8K58x0NJh7My+FtP4bp+VSqNE6crJVKdyeyp/nb5h8ofGlNO/zO5qy1T4FSyH8gz1Datp9SnnH4IAPF3rW8eAn0unePVPXZH63L3tRy1+5lsptXHiGNG+Er7DTID6gSWdUw26FzZmVHg9JmSZg4Js2RnJJCvFjxlHQPjrDJ0hk0jAVAubZcaO76G+pSHkvy0fW7AORlyu13drk9dv3I6d6CjZEjcb0dZX+KaggWns5B35gT195AOu/FR0jmVd8mhDxnqIcehbcgcVXbf6Ze2v7SBv8QL08HBQezs7PRldl03se/V1ef6SPXWtXEa4jI0ioRrJUBQchqsh7ofjfMqZX2OcphXLSPby5TZVjd+Vbf1cTGuTL3m5JORAzEOCJQAYtY+d78km1IZQ/xnOojrbu+4mxA431YCSpntnSZCVbKHLl/mM0p5b5SHEj9Mzt+w3xqyufiu4Uf1dlqaGvDxJlZ3wsiFMrUcdaA8sF1nZsApMwpZ+ZnR4jYA8OFwhIIxBWgZuGBHohv6FQDpkoO21YXeGZTyTFYBEpelz21CHjXanK6mP7k8J2de7mQetd+5D5gv12/6GAtnpLg81wY2XiwnB1L4HqfBt3sumw7mrjt6SjXjxwHMLI/y6kjlk8nM9U1WhiPWzcyYlQBICUBk498Zv729vYklSP1wGZlR1n53r/1THlVOJZDilq2z5U4nMyc/Bo5a99CKjDocHs9ZOxD1y0BDBvaUR07nHlFTA/a4jRkocXm5j7kfMr5LztnZCE3jrg+BF5UH8mQrPny6WGWrbVK7p/xlBwdK/VPqLy7btdv5V/YBurXJySnrf6enJcrsZK2v4eiy4zfTKSbX78el472QlhgpGVOl4zKqCpg5zCxPzb3MuDmFdPVPq0haTo1sHDjJ2pNdr6mrhveadKoTN6KoWl/NQH08thxMQxlPNfo6bZklqq2n5GCGjGVtmdOOw8eLeCxn+wFrx8+QcXf3NF3m3Lgurq8GACgfNXXVOMGaPMr3NH3p0mfgyNE09ssBypr+qKFSO25Ut0s2w/Fd0vMavkvE6WvyluzgNL5O89aW4cBfRrVl1fibEh+aLuvX2rJxfVodPvaDl1WQLhLGTJWeOo107rrOxHRGw7MTfict8mSbGZU/XgKqFXA2Eykd+DiOgdGyeYajA13bp6Ankzv/1lkNg2Emvsb9oHyUHqeTGahaB+B4nZmZifn5+b5f+a0SbrbG/DtD6gY916vtdYM6iwgNTRoyg9V1Rw+9DOkX95GLpDkZuYiTlsll6/jQDe/cHqcXTp9L9fIESK+rHLP+dfJx+TVdRiw77Xf3qB4mPAZF61XdYJlyXvQX77crPbqI/7u3ATl7kY3XGn0vUclecptxzUXV1R5xW7JruhTpxmGpDdqX0+iK619ur0vn3vwC36d9ivbhHtsMp1Msk8z2TGMrnexLcsnq5MOhOvY1P98rgU99TuMQOOS8+M70w+kAPvxsSx6nXLa2NSsTctnd3R3kO+KYj2UpNTBbmsyUviTgTHhMqtS1p51U4V3o1fGthjYzyErTgD2VH5eLD5SVHWRWlp76zOpTcqBP05fARuYstTxXl+NxCASqUcSyfI3RHqpvqO6a8lDOUFs5XUZDfYk6MueTAVYuH+ndspEDAs5A8f5T1UMH1LTsEthz/Oq1rD7XFgWrzqCr/DK+uM1Zvsy+zc7OHtk/XOpv7ivePqH8Z20Zolo9xO/s4etD9TmQoPdZj5zOlPTFAckIf4AMabJyMx84pCs1tkd5V9vrfJA+d9MBo+wpGK6+rK0gfWBwacwqP/rb5ckAo/ZbZrtwPyMseXNZpfYqOfvJ/zO7i/8crDmOP+m6zk4oh+hYgK8WUCkIzACADlztXDRKow3OmA898kH/Z20pKWK2zwj53JFrRDoyXkrgxzkSDHB81yhpNqhcHe7bUcmYapvUMDqdyBxSjRHSwed0yZWh/LooreYrOe0hGtKDLMKVAYzM4Wj7avkqgYIaAMbjFkbV7V0Ev3yPI4H8aB1XV+b0Mz4zcmXotUwmzLfqs7Ntmt9FzTGm3SZ45zSRJjuNmk34Sgfu3NhhHlQe2RgeIidL1X8AWW6rs/tZvSV+MrviwELmEzRSXbKzWZncFgcQ1LZhpceBnsxOKNX2U8lvuvYyGNI9g1xeqY3MowOWDktkbZsGaLq6s//gq+bh0SwLlFWKopbaAzoOWJwa8GWdV3KMo5F/rhYLInt6eURMOA0+TOGMq5bjHp2SAQBnRHUQjsfj/nlKirC1Y3l5BXnUGWYOVRUdckB9HMbPZK8OqLSMx23InhOUyVGVmWWndbs9kpoepM+ZU+fjrmn97CzYoWTL06PR9SfqA6CXXmtXAmDTACXXriFj4IxgtlE/M0g8LtGHTj7q0F0b9JoCODboKIeXoXiMo363qR/knP6QE2BZMU8O0Dh+lQ/+zc+k3Nvbm4hsunr1Gq67pVjHGxNv9eBX/WXbaFi/NF0NAOF7zEP29iClkjOG/Jh4iwbGJvcLH1DgceOWfEvgR+Wj/PE3163yGZowlvjgMpk3DprgN78gAGWiHbrsq2OjFmho23VcZ1E0pNW33WiZKsdspU77MrO9Gd/cTyVbomVpPTomnM9zclBfwpM6VwfylvqoFMHM6IbftAHKjFhmhJ3hHGpgibpu8jk4Q+U4Iz+UPgOKWf4hgz9EmYNVMFXKnymTgqNanpgP952lc4amNLA4fwZotZwhHoaMN5dbK9dMds7R1pLrI0dONtP2pZYxrR4Mla2/nT5kaUrlad7SteNQBnxK6TPgVKJsfPLvrE3a19nYmpYHpSFnWqpvSGb4LtmCDJi78mrHnMrOgftpfIPjo0YXa/nNxnpN3qG6aiYjfM/Jjb+RPwNFJV0ZknXWP8yD49fxfqO2gtuY4Zvj1JvpTkkHamnqd+m6WUPNoFbmgHgjJtG9i7y4OjjSpYqvhi9z7CVDovnxPls8QsUpOfLpskr22AJuC8pyEUmk14iHRsCcnF19yK9y4EiQRij4vpOZm9GyA3TLUyirBNycLLQdOiPGb44UaTvQFvcU/mkcTOmxNSWjmV3X/s1oyJBkMsQ3z9BLM3CNOGndjg+3cZ55cTqA2S9Hwt2HH/lynBmuk09tWmeHuL/wph5einYbsjkapWWVnAenc32hj8xyY47Hq4LTbKKTRbtKcsL/2jwZP7gHvWB7r/Lg75qJRGZ3dLxMu09K69X60KbsvspbgQ1/HMhxQEv5KtmKUltKtjGzoc7GIy+3dUhvasaF/nd+gccH8rhHz2SUYRInc6dbmX6W8pXyT0PH3sOnFdc+rw35+URZBob0mxVIT/1pviFgpI4vA67MK5+sKbWTw+/6rWmH+ON0XDYAqILPDOyVBnLJ8LtBlhllJ3t1ziWw13XD+yFcH2u43smAZcpbCHTZbEgvnB5z+53MlJyxLKXLrqkxcEuxzJMzotnYc9cyg5uVocvJ2neZo9I6eMxjicg5ea3H8Vzi3elOLTFvvNXCgQpnE0qg0ukBjylnSzO5aB9kwOC4ulnSEb6vtk9fH8nldd3ha8mcTEu64HjI2u9s9hBwmoac31RboCDIUeYXb4Q3LbvUhtI9B/rw2+li5uu1riG+nN8cuu4mpmxLS3WC92yPYmlMlfqfy9BvyJC3bUxLx17SdUICZULm+/xxAisZRFeW3htyIJxfjbBrF5RDj1E7ubj9b9wux7trG/PE15C2ZNyY3CDKBlaJ3AAsASLdl8FpMp3RaFyWzrWF0/NM35WRDWaXvpaOCxaYMgBSGifTtMXpW62DLN3PqAS4eL/t/v5+jEaHe9jc4QO0dZr9jSW+SuMrK1fbxXrLvDtQy3Ux4OFyMl6czdL2uO8S1ep5NiaGgFBN+dkY5XsqN76PeqbtN8dfLXByepmlLfHEEyKnz0Nyd32sfs8BSqUS/yX74dI4H+fulXwhj4sSHceHcb6hdpfk7K6XeFCcMiTHIZ6Po3MRx1zSBbk9K9pxnI/vsVHXU3qZk4Ox1zwqvGyjeqZEbHz5mw3O7u7uEaAK4kMBHIHIjBPaocummi4zZJxfqWRs1dm4ey6tkotmab0zMzP9RvbS4RLVC3zrTB/RTMcvP+tNN/07o6qn6lQveALAVBpkrJuOx8zpcF6uW8Er33Pl1RgA1wfuYATXPzc3F6PR0cNK3M9ajiub5aAToq7rYnt7+8i4x7hi+QAYKmmUXMfpEPhV2WVAzclxNBpNvNXDHbbQQxL4xjKw28rBbXP2VR1Iicda55SBDi6/ZJciYmLZPQMY2j+q95mNdOXpoQ1O5+xCJgPoHJejaTUPl5nZV2fbmG+U43yL5s9sk8rP+c1MHxxw0noc0FAb4Opj2WRbb5CHt0KUxqoDjbUAqDRGmdxKguOFKRsTagM4rZMnl+98Lcp0hz6H6IYPbeggBUOZcwJNY4yGjG/Gl97PnLgb+JrfdTyuZ8pW4s0p6nEVKZNzVmapLldP6V5WPr4zcD7kNJwRynh3aYccfVa+tqNkFFx5mT6V8mb6x3KslUfJCJYMfokHPe2Z9V3WPuVZ6+B9sS7NkK0YkveQrmdAK8ubASx+qG0GyrRcyLcERPgaAzDlPxuPjoZsqALMIR3WfCWANsQPl1sa23ydQWnWngyQOR4U5HZdZ4FNTRuZJx3XznY7PXFtHmonl5HxWMO7luHkOM34yUhxQykN/y71+3H54XFZ8lPKb+YzXB/U2CWXfxqZKlUDPn3Qb0QMIkydbWTOnI2KU6BpHfYQT1yvu1fbYZyndNgi48MNJs7rfoNvjYS6skugd8ggu8FdMrR8TTfWqzNzMsocaw3I0MgsR2gjjkbIuCwX9XWz7UxOQxGDzCBCNmijOxDDYwPpIFveWjCtMcn0XmkI1GmflA51cN0OBLmlzSzi7sqZlpzBzox4lmdaYFnj4LNxC9KIqtoc9+xCLlujLFwP7z3mPPjWdFqGaxvvM6yx6erMVdaujOweyuRDR6XtOAqcnZ9h+6myKR1u4b1eap+Uz8xuoxyOZjsb4PYVZz4yswXOtrOcMtm5ccoycnLENbXFXM7QAa0MW+Cai9jijTbMv9tzXEuZXFQe2oclP+5k9hcK+PgZZWAEzDpjASHyAQNt4BBgAWljM8NU66hGo9GgAdBPyRmgM52jzgacaxe3zQ1kbkMGxEonL0vORvlWh+LawHLEMgXyMjBRZzYNSMnqVyOKgaQDitOoIXYyGdJR1wbXx1n5ml+XsF1dEYeTrJLzcnJVA1/iO+O3ROqw0P/u9Bt+80Z9XQbkvtVXEaEufbWc8u9skrZpSLc5PfcVT7iysl15ChQcfyVnzPnds8lgYx3AgpNz/Jb03LUtk58bR2pDMmDobKWbkKn8HBBTsKDOVduD8pyecltLwFVlz/ywjvOkDdtd3IlR5Q/38CYW7n8G6wxwndxce7k+B2aVFCjV2E+1w8yHbr1x5ZT8ryMe/27Sj/r4eaBDPkrbqOOgxlayn3I2zAFizT+trwRNvYdv2nQQiht0JWM3LS8KUFQBs0Gqxqe247Iyag1m1o5SPbVUcjjuOsunZMBryinxkFEmv5JTqTEAavi1bHUWuJ45B+X3OH3pHIwa2FoqAYOSY6stt/Z6iafSdwlU8X914prO6c0QgJlGzk6WDgyUgKcrj22WtitLwxOZDJSX2uby1ei8lu3qG5JpyXlmdZWcHqfRaLlGK4fqGCLnU0o8TWu3nS3IxvXQCVJNz7w5XjPdqZHNkK5NQ9Pqx9C9DAM4PFCKfA/5tWn8QI1u1OCJ4+CCiBt8Dt9QWn0SeMTR0L0KXuthdM7XOWytoXC3GRZ1K58cWWAemVek1cd/KK+j0dHn+yg5Y+14cwrojEEGkFwdJUeuMmXj6WSRGdOu80+4d4MC17itvETBS0eliIrmzwalOoHR6Pqz0+AgeON9yQg63cyckuq5DmbooFu2VN55L12m4wogHP+ZAeO2lSLEGen9bMnZ2RK3ORl6tL+/f2TJO7Mj2dgt8VpbDr6zwyhqG1VPMkCrdeisn2WDezgUhWsuuurAqPYD6mM+VMe5XG1/5hdcuzI7nOmr2tyMuu7wsS0cPUNbuL5SxNDxozy4lQPkRxRa7bS2i/uVl+DBO/OoS/SlgyduTGjf45MdbMxkkl3LqASIXVrYN73u5KxyxDVuZ9anyg/krvzp2MzyY/w4TODqcm1XX1tKl92vpakAX8kwcjoWfs0pK87LZTC5TuR7rgz+dul5MGedWmqjGqYSP3wvU9SMsrao86iRcVavtsfVqc6gxEuJnDwZzDjH4JxXVrZzKCUwhW/3fMUSkHMOhfNk+urGko6bIWKZ1dabUWls4X42LhTgaFo9NcnEY0+Xn9TYw47AqSoPru7MQGY2wTnAWpvA7c+MvQNA6ryH6nIADNeza5pXbYVzaux8nBydLyi1YRpSOzM0PiDHofFUcro1VJKl8zs1dkp9kdaT5WOeFOgN8VvbRne9ZPdvlDI9G+KN79fw42SovjMbFyX/oe97Vr61fZxX+/7xGEeOqgGfE1CpM9T4DaFYVVou24EpNU4M3vSxGzVt08GndajhyYjrz5SD5VHq1AxMOEOuaTNFzZQvy+f2iTlnwA5MZeR+l3jVejS95uNIMr45auGiVY5K/Vbqe3dvyGhz+5y+OT1HFDIziFlfO2fo+OR+cBvInfzcYzCULy5fnaPKbuhxCaWN8fiv/QW74MpWY895nHxUFnxf5axtKF0rAZHsHsuCH2Hjymb5lurQ/nF9MGTHp6Ha9BkY1/8cSeXHagzt1VZy4NntXS+VoWMT+XmPHesE/pcmSSV94XJKaZCuNEZ5n2rNITb13c5v8XcpfwncZ21w/ZEBYLStBPQ0il/jq137XB+4vZWZD3Rt1TqnoakBHw92BmmZI+S8EX6TfOnFyGrMXIcgFI63T2h+VtysXSxQd4qqFP7WNmcncFiG2oFZ59WAPuU1Ij9xpwNBy3BGCvfVGTpwgm9OP1Qny4d1ivlw7Vc9cBGj7EQU88FGTvtMy8n6Qe9ng1OXy93YyQzreDyO3d3diDh8TpTK3QEv6IIe+uHviMNxWHsYRNtXckJOHu6gCg6FZeXo4SBtA/OCPuFlvlIUl4GC8sb6k73eC/2q9krl45wBj5WSo+dy9fmTjjcFvLrMznw5/cjkq7p+HOeDfE4eyp/6nEwXecnbnVZ148PZBK5PZaZjLwNBfI1tFbdN61E9cbJ1E8Vsjzy3S/nLgCF0mJ9J2HVd//gklWktlSY1/LsmYIL/LsDA9xVEclDI6RT7FXfC3Plabt+0QJXlonqa2YTS2Byi470k0FRWGoRD+dz3jZLrxNrBmc0MSoo11P7Mybj6tNzHm4Yct/53gGSobCfn0qQg42XovtbtHGyJ1xIQZNCq9ZUGodaR1VviZUinsvvH0Zcao6GGeUg3S2NIy3HXp6Gh9E4XHD9D6dzH5SuNmSEwl12fdiwoj5omk5kDprXkdKJmDGZ81PBQGkcZHQecPh5U6+NK/NfogKsns4tPlCxKVKN3ma8+bn0OH9TalRIPNfro8gz91vJr6djv0uXvbIbh8uh9zpstPbnDH648RuaMzvkeE8/ktZxSR7oZGIf7ufwIf7BEfys5fnlGqLLHby2b62AedPYDfrtucgar9bgN2yov3NfomLbdRUK0XG0X+NJHv6ic+A0RpYgVR0QcD/wYC/DpIgYsz9IgxEZtnm2yTB0/Th/dbx5/HIHRAw/aVi6n1tA53rJlt2yS5PSD7UB2YEjb73iGLvNMnvW+dsO60wl+/qguq/K3voZxWkfvDqpkZam8MvulfcftQhl6mKAGSHBZrg0ufcafEo8ZvV6SqbtXu70jOxih/T5UlhtvpclGBjg0rfNXjrQMtsMOLHPdjgceQ1pvBvazyYjj36VVHh1P6mtLEzItTw89ubqdnmcTMue7M361XSzbjF/3u5Zu+F26MGw1YM8xqfnwXTLImXFR4Iff7EjYGOrvEu8O5DL/Wp62O5OHyoR5cTJRI52VkVE2KPk/H6DAfQbdbtmEec94GjJKTv7O2aNugCWtF053ZmbmyFscWL7I74hBMPhwp06HDJIzZkMn0koAvnZrgRrjkk5k+Zm4DVy2S+eAr7MBzsiW8qgBL4EXTafkxn3J2Gsf6iEjvs7ASXnLdNzV5/jPdF7by+N4aIne1Qe5ZAChNFZrnabKtdTvyk/J37i6VE5adqldnK/kK4ZoyD+wfcjq1/KG6lNi+TnQpX4t40H7Gfdq7Y6Wk43RTM/QFqQbsm+Z/8Zvt/9Wf2f+LNNxlx46rHacsUnt2PqwAj4nsMzo1FLGsJbB6L00ODNDkpUN8DJNO9xmzqxdTnEyxUa+0iDjtmVAJ1P+rGw2pKXNwlkbSo8I0Ho0jQMRnE73JalTKhnPjPcSryVQUioL1zLnpfec43DOechwOuOaAcKh/nB8lfTcOa8hw+scgpMz722ErpfkybN61ROUjd9adkbu0EomSy0PEQOMK5SnTiUb606/XRrmh/sL3/p4F03nynER8SGblMmEr2NP2Hg87h90W9OuiKM2xu19dQDP2bMhoFiioXzct7oPrcYvZmOuBDq4/KE2ZTw4PmF3Xb1aphtnrl4HEm+EhmSb1ae6M+RPnG4PgeEhvlVfnf/LeK71eY5u6MHLbpCVBKV5a8CNbr51y3egDKVnbei6buLUY0mgbHzAAy99arlar1tK0PLhvDRvJlMdqNnBAf3mMtU5ZPJjMKB1u/SORwcSOD+XNzMz07/6hoG5bqBl46ry0bZmzjvbLF0qRwGbc9CZ8dYJjL5JQsk5/mxyoxutlZyBQfm8xF5asspkk0Ut1aC68rjdCspUL/Qe9yHLAeSWXfGfv2tO8SpxlAGTpr29vRiPxzE3Nxfz8/MREbG7u3ukzMz+lU4IO1vC8kMegE5N7/SC8yCdG+f4dodtVNfRPvAwNzcXi4uLPV/Ojmt7uV6Ug/4r9YsDdcxPRs7maTkORCigYTvM7Szp0ZAuODsacXTVpwTMMj54cuLGgvLDQJptl2uHs10ZlhgCME4X+bfTSfDI8nLyyK6zLJyPKPGs/aFYRctxdoF1Khtr09ANHdrIDEcJdN1IHTXKXAKZTvlcXSXlnYaGkH5W73Hqc4DDpXF1l0DZ40VD/aL81bRnqK7SpEIHbpa2tg0lqmmDgkn91PI1jdxqQK3+LvE9VH6JjwxkK2+ufaWJTcav8leyM6ojQ/qiEwEtt2QnjzPmHc+Ol6EJiiunZHNr+B7S5Ywfl64kcx3PjwdlspgGVNWkc7zX+qoaGgLIWremebxkWqOvylOpLE13o2Vq+R8ufz+UJ7NbNcB5iKojfHwcO3tsAjMBpKod4zrHNYZnYvv7+xYtq3HQfXslHjGjweZ+PA4C7dMlHedkum4y8qQy4HaiTkb0NWDW8c6In2XqNr5nYEDv8f2FhYWYnZ3t3/WY8TAURWJZZDMilIVZIsuLN97zbJTzMUDl/sfjSxxPji8uu2R0h5xANgNlHc30s+QISmmHHh2j6ZV/99gWR5nOcFn6u9QOXuZ0kQz3CBY9/OPayGPEOSu0uURD/ajp3HPw9H/GJ7eV68jkyePB1anpuL0lsKn1cFlOtioXjmqNx+OYnZ2NpaWlvv/Ah9tYX+pX5p+XhDNgze13eo/yMl+Ea5l9dL/Vt2WHGEsAmG2Z8zUZn9oGrSt7jIymZX+m77ZmXrhs2Bw9eKlynJ2dtW+GYcr6MrvHfHF02vlW8MXjwK26lfga4pHb6/KVbIGm13SOjgNKp34On9vjoRWzI9ZG6lKRUw6tT5VbBwXXU3JIrpz5+fmeLyzvuvKdEyktj5R4UfDjFMER8+MGpAIfJQcGlJfRaNQvRe3t7U28WNrxkYGXTBYlh8IyzYwrg4CMHxgugEUd2M7JlUBYdt0N6JIB5nRZ+SVnoPXjuoJI5/yGDJTTeVd3JousfaU9hQrudBzj99zcXPrMvYyG5MdOoMSr61PW1xLgcHXX2krHM+fnMob2FWdjSnkYqs8t5XI63nMLwLewsDBhMzOgxg5Y+WLdYH3PTlYqAHX6z4e51M7UyML5HwWRmT3QMjSt00Pdx8jyG7KtAFsMlkv2APm031wdrh3MF/5j6Xc0Gk1sN3A+cIicvXHy1eu6F1jbnPU7p3N+UNNk5Ti5Z+3n6yUZ1R7iAx37lG5WmYs8gWrAmBrAEqhwhtI588wB6kzZPQZBnaEDWNw25kn5coOlRKV0boNsSVm1fpWrgiUMTK6vlscMNAz1FV9j0JYNHh0MmVEaMia1IELrL12rdeDqxIZ4dfIrARu+7hwj38v0uYYfd/IY11FfNg61TpWdA2e47myKju/ssU7ZI0Oya67sLOLMfE9L0zoLdb563526V1J5ZIAus698nR9PA0KgwD1kv+QvVA9rZJrx6exDCTzUOHitw+VD3pLt07Gg6Zw90TpLfGVgsIZcHTzuRyMfZGCd0T7J+M18Go8vTT+ENUo+pMSLI+3bIbkzn3zN2WJXlvNh04wFpWMd2uANtAjnYtkVy6QY3JmysHKzsqsjwrdz4qpoWUdoJ2Uvq2flRDv5ESQKAhE96rrOzpycs9O2cvv4496kwGlRHy93cVp1PnovU7iDg4PY2dmJvb29iTwc2WVZcBm4pssH+mHZZbypc8A1BXpZXzuw6ShzYO6+6gun5WgB9wfLwZUJmWcnCrUcPujB4APkxgPzqe1hUI2yNGJUAmI8driMiOjfbDEajfrlHB3LOJiTLTmyjLKx6fqXoxquz9yyaBYBwH/UzW/0GYryY0vEtOABedx/jDGeoGb2LgN5zsG4Q2iQM/qK9SMDUNgmwzZhb2+vP7iiY1Ttg7adbQ/X5ZYp3Sls5lcfKcXlYFtPZk9KgEx5y36r38jaw2l1slMLUNxYdfbMtdP1iStbJ+Xq6+bn5ycOTThfoO3XtLCpKgPnm9xYZ/tSko8bnyVZZ/LJZKZptM0ZlkEbnd/7sAG+jNjQg1EHaNQogVRB+Bt5M8rKK+VxvPE9/c1K5UBEZmydMXS81LZJ72UDR+sckgWndwPEDb5SO2ralPHE9TseSmWVBm92zVEJ7JWo1M+lPEN9lNXDpH1UqqvGkE1Tt5N/dl/rdhOHiLJe6m+Xr5ZKADYrz9mqjB8tOyvzOH2R8ejqmcbOKKjhpTcFBCWedDLntgJlPE1r8/l6tm8PaZzO1LarlkrAahpyoP04OlXSa+dza8nZL/X5bl+x5q2tW8vm37U+l+vPePpw2Hrlm8nhiiyts6PT0NSAj9E2OhJRPdwHI7pRF3m5Y9SIZ3UquYHrFMsZaIeMubPRPpeGy+WO1wgG6nBKqvyWIgtaf6aoml/LHjIA2p+6fwN7MLhsVy/Lie854+Wenq9l6G9NP43RcJMLt7+oZsArGMn4Qjred+QO1Lh+5T507XfGNjO++M4iJVnEKtuzmk30mF81xqXIENuPbIyqc9YlSC6bI2vuwfBDsnfX9eAa8+3GsBsfOu61PzL5ZDZQwZTTw9KEgHUevGXp+BFWuIZ2u2gj+Mr6UmWlfej6WvuN86gugxxvKlPk54hzCQBk17TvMnm7iL5rs9s6UOvotS4un/nTKGI2XlkW4AtRYe0LVze3lx+5pfJ0vilrD6fP7DbLVPNk/lQPQGYyd+DZ+VtXdqZDzk/xqhHb2VpdAE19aIMBEXd+dmpLHakymwneORIVqJaJ3zpY3V4dZ1TxrfsOtAN1wEAm3K7MkGdGLAOGWT84J+76IzuNls3cud1uGVcBmxt8rr4SMKgBWEoKZobyu0HP/NREILi8oclAlr4EOlSXS8DajYla3rk83hKgvLE+8IvTSwYrk0UJ5IIH7iPnEJ3DwW/UgTY4kJcZyKFrzA8Tlo31hLkrJ3MMJcCX9bX2uW6CL4GRjEeWpXP0Xdf1IJptu1vSY5Cyt7fX/0Y5bgxye/i62m9d2lJf4JZLoa+6fUH5KO0/L8nWUWk8uv2oXA/44C1T+EYarcuNOdUP3f6jdTr+WV6aB/k0iMP6w+NGMURmc7mdLlLsnhlb6hfXPuaH02n7+H8mCzfxRPkZ8HPbmdRPcT1D+1JraWrAl11zzqm0Js3Xp2Xa8eHK0evMgxv0rKycRvPw/8x5K2m+LF3WvqHySoqWGVj+XRo0jvcS/2zYhwZVDZV0pAZ01FKmK648FwlBfnZuqiMlvlS2pbTsWDmPc56l/nf1s9PhqK/jc6gP1TFn+aYFYgpG3Lh0zua4VMOv6o9zDq4NmVxq+gnfjr+hyUBJ30s8ZMC7xtFpndoGthv6yXhDeufUHQ96TaNb2u6s710fZ6Bc+dYy3YSQI8pcX4lKvGj57r+zFRkAdulLeWt8B+dDuscDI0xTl6PSOFF94LKzOjWvk29WrktfS9WAj2d3qBBImx+bsLe31y93cFr8VhA41PFMrkM4vw6aEtjDffC9v7/fHzxhZ8qGSPmCDEYjHwFwhg7XSq+tcUpeAiJuEPMsIVsaZMeeAT7mQ+XLctQyVCZDRtE5ctUfTuecnbtfGkgqWy6vlJ+jue6ZlGyoVXaOGJywvqkD5Wuzs7MxPz/f66CLmMMJOh12v/E9Pz/fP66I83NEt3TIwi2hqa5wfdp2bTOPx2wrhsqH0+kMvNQPLDPmkfUC13RcqY1xzoTHkEY43Bgq8c2y0QmI2hq1Ba5dzs5pG3Bdo6rj8eTBvdKECMQRJ64LAIyjyioXjgqrfNz+Y5ZJBnL4rRG65Mgy19+sp8wf66u2W4EytxtlcCQLZWYrNqorWUSID6XofT7IwrLU8cX9r3xw/bjnZDAEWFx/6j3nA7JnBJcAW4mHEp96T3VV+dP/bLuzMlW3OM2QDB1N9eDlzFEPgaoSY+6+gg9tsCNXRzawHQ9chrYpK0ONOctH7/F1dko1CjjUBlWAzNDyt2u/kjP2OoBd37v2u0E61OYh56ynuvT+UPuydEO8ldJlTpfrqmm75suMqjoKbRPSZuMpc2i6rYHbrMtqWqbWXWM0M0Pn0qtOlZxypvNDPLqyXZ06vvmaytmBvmz86DXXPte2ksyypaehfnP9jGtu72cGqpw/UGfIaVm3Ob8CK82T9XcGOPBbwafKoaRnKjuX37XX8cHtQL/xyechfwrSiCeDc6dvyrvey8aIu8dUO7ZLeWttJ8bX0Nhx16cBg7V8lmSr42ZIZ/Gf00/L87HfpcsKybM73sSrm5O1o0uzBFdnrVF0zjLbx6fGSesoRRbwXeJBQV7JSCgPmfHW9vMeIgfQtJ4aUpmwcjpA4wZZCQSUeKzlc8jR63XuxyEQOA3o448z7iwnjiIwcNLN4l3X2ceO8AO3sT9KHyXBdfOsXaNLpXbhPnjgtxtkhqbWaCoI4t8cYcjGeEmvht7r6Xgu3c/AID6Zbcv4ztLU6ltWblae2rhsbySuZRFc1TMFjwy+OG1prDP40P1zyOv2nDliO5TJ2Mk/A4GZjKe1WWoXSrxkdh/yVF10kWguy/mK0iTZ8ZWBs1rQVlrlyfqC28RAPyuH80bUP4zYTSI0MpmBMdevXNYQVmEd13w1dmoIT2R0LMCng4tDz04heenLOT5cV4dSAgv8W9NlS2m6nKGOz0Uz1Ai59vNA4vvKU6kdChrcYM2MDINSBl4KKtzz+vDb9S/Xq85iyChkip/J1JHyNGSEM6MwNDic0cjak7WR77tlKNDc3FwsLCzEeDyO7e3tGI/HMTc31wO+vb29CRChxhpLrfv7+/1zzfgwhe65043autysuo6+UaCnxk/11OlTSSe0/91ySGb43PhgGWk6tiuOVN+nAV/u4eDuUEumg64+lXNWdy3oALnlQF3y1zEOnYINd4dvUC8f5FAg5yYlDPgUCKFuTjdEqNv1h+531XxcN9qj7cyWU5FeqaRLpXIyOXE9kK3bx8tlq49kYO140Scn6NhgWeCbfaDT5RJg03ocKFRgVdJxZ5uy8l19GqTSNA6cOV+d1cEy0TaVMERmoz5sgM9RCZC5RmvnTcss5xkyylov8jrhq9PT/LV8lEAJt//DSTXlOwM3bTpVuCFH5MpEvuPogatbByNfU96n7QdXl0vjBmYJaJbGEMrMTsU6o8BjrATKHO/O6WZ887cDeDU64MbOkK7pWD6OPRjib4iUV5XvkLEecl41vLm6Mhsz7djU8rPTzdM44iGq0R3V7RLPnN6lYx0uTcZLfA61p6Z/uT0fDt/gwGDJHtTqirvP9s75+eOMvVq/6UBXZqNLvqGGH3zX5p22HmdPp7lfoqn28EVMNtTteVCUD+Z4GQuDzC0vcB781gHC35rPpcPvUhTBbezm+/jtNlpnPJQQPP/X2UItSMxAl/KNb92Ai7xI7yKAPJNDfveieJZPJgM2OCoPlRWnHVJsjnBpu/lxBsyrgii8FWII5GR9kwEVyAoRM0RKum7ybSq4j8g36xlHAJ0DVuMKkOgMfCZPLkMjNG7mzvlUNhlvSm75hPUsM66qmxzlKC1Voz01Di9zTg5UlBypkxvazasczH+2fUSvlUASR3KG7AfyDx08Q7kcSUaa2sdGuP/ORmh6tVfsS5w9U5mCkIfHlL5NyfHHOursq+Zxusv8sA2E3XR77rIlb5SXbfjP+lN9LpfN7SktZ2a2WZeY2dYPARQnRydjbdc0oAvpM//E32w/NdKHVROOfGt5JR5U5539dLJXvqcBnaBjPZbFASa9znl0gJSM8xCQK6VzBIV2S1JalpaZDexsIJR41sFccmYlI10ipwClwcp88vI6p3OhewVl2u+6LOTap7zVKvi0cmAHwKRGrnRSypFrl+NBDSrLlMEe8nNaTgcQqHvpnANlR+8Mc0l/eWyyc+f2ZoCGHXFWPvOIT3aKLztkoLwqb44n/M6AHPM7NIa07mxMubr5o4DPASStL7um9xVAqPycPWYQxeUqOHHjK8JvmXGv19TylYesnzUPpy/tsda2gFcAPu2nLJ/mz2TL5Oy73tcx6/h19WQ2APdKgK/EqwNrQ+WwrWO+aiYRXEaJN+TV8TXkF0rjpFSO01PWdW2rq7OkT86XQmbMTwZSlccaqgZ88/PzfWWZQccAV8M5ZMw4v1N+NQgRw8/4YyOVvcuPhe0GJbeFO7DmMQ9ZJ6nR53sl+Sjf3Ebn0PDtjqmXHLGS2+Q71JelexrV1OvKo5alj8LIBpruZSzxWaObWk7J2HAfOEMIclEttw/PjbNSvcqvGhV3v+s667DVabt6NI8znArCON9Q/6iOM59uPGmbtT+UBzcO1cC78nU8urIzp5mBocwxuL5RuZR0uDTms77RNjmb5SJB3KfqoLN0JX7c3uGaKGjWd7im4E0P4JT8ltMPN7acHVJZOt/D6diHsTycHLI2ZjLitEiT2VT+z/ZM9YIxghsfKosa/lwahxWG+su12fEXkR8AQp3Zfccj855hECeDknwyvktUDfgWFxcj4vA5e0yuIWAkc0xOEfnbAQA3ADOD3zfw/w/d82ZePkGmRiHrSG6TRsP4vvLM35lMtP1D9zNHyI4BZbglIgYVpQGelYcoE5eTDThtu1s+4DozB6L8sFHhMthBZvri8tQCGibnAPUePtAZTs/Pz+OIGg5mZEZRQYA+eZ7T6Wl5kFvmcm3PXn/n5JiVk4GFknFTHt3z2vi/gkA+bFDSC7YzCiycjjvnpraEl990Gwm3HdsIVI76Gw6fnekQ0GBH6IAPRxGGdD8br13X9c/Kc2VrHrUV+uYL5ZG3E/CWCLYVyKOAmHnXyKTaNejW7u5uz1Np3yL3fwbccN8BMg5AMM9Zfeh/3uoB2We+1vkIJQVsQ77A2R7nz3jbSiY37fMhgKg86H3FICWZ6D2HH9yWM9Uzxwva4uToeHR948Zj5qPceC1RNeBTRK+MaWMyR1giNlLuutYzlK9EXA4rUi2/pYHEabK6M1A3DWVydvLLeMv6NDNmjm8ni2nbmMnzuIo9RKqvpb6vqbukg84oluorAT01ZsfVG8ff0Niu7YMM9D0e5OTs5KvtydJomSVD7PhwPLh2DwG0IRoCZRlfTlY1dU5jvzPndiP97/orq7fGdpQASmmcPV7kgHhtev1WftWW11IGhoZACOfP7No0fhTfQz4k4ytrc+l6CezVUAYwucxpxnZmM0q++zh8TwX4wFzEIYpHBEAjckxOAYbysMDQMH20C4g376qg+XECWn7EZHSEN1MDqfMsUp0Bz+Qz3hw/qsTZbFLrKzkPZ7RYdmgf/4e83PIo0mhkhQ83ZAOby9f7GtFAHdnyZcTk/kI3E3R8Z4ZA5V1qhzuwkM20+Dfz4KKQqiuQAeSrdeAtN3zdARPXDr7G0S7XRjYgHBVEntJhCs7P44f7QceUtl/5dmPPyXlhYaGPfrhop3Pwrj53aEc32HO9vLdyyPhmwEvHCUfzdFUC+dzmfpUPgy61Vzpu8O22vmQ2husqjUPn/NR+qV3L5JcBvBLYQ8RcD8foXlm1A24FxPkr7nsHxrJVCPf4kwy4sS3hPsv6MvMT2rdqU919N3Zcv6ptcoBH7Y7yqOWwrru8WZu1L5xd1INHzIPSNNuaVFeczXX9rn5a2895WbdLGMrR1M/hw2bXg4OD2N/fj4ODg8GHY5YcKpetebRuvY7/JeViMMjlsVJzGTAEQ3tmUA6WjVzaTBZONqV9kTwASkbR5WE+9QX1OqiVN1VElhEvRbo8WbszPcicCf9Wo+ccRu2gzPYAat26/BIRFhgor5lM3RI7p+cTsu6aHgDRbzdu+L5b3mdQr/nUkHddl453NljsTDOZOCOu5ZUIdeHVjqWJi7sG0vGlYEKdK/Om7dX6M741rTo51VH0v9PHjIZAgPKK8rm+kp7ztWysZaAmo5LddDbcpcN9fPAaQmxHcXyzbVH7r68K5XoiJg84Zby7tgzZjAxYaR71EU5fmUajo/sAHSjEt9MdZ2edXcvsfjaWIG/2xSpH3XOZydFtZdF0mR5xOc4n1xx+Ub5d+3Xc8xhXn6t6kfnNElUDPo5KOCZZyE6Zh4ygowxkKek1FVYWQXN5Io5upleeQLo/yPGTGTnnpGvalvGS5XOACP3m9ixpnSXwovXUkjPcOsseMug1xP2pctQTVm5wl/rN1eN+O93U386g6Dd+a0ShJA/Ok9VZcgr4ZmOTtcsBQwecMn6dU+T6sr2icBBop0b4eM+TOnitz83GVR7antKYVaepjrjG0WTjV+WU2RC10Zn8S3bG6UqpH4d0ygF9Xi1Bmpq24dv5CdxX3+V0wfW7AoeM1Na6cVBjB/i/tj07hKaTA74/VMdQm5z+ghgkOrnV+gWnU5qfdTgbo9rmGn3W/FkebXdWJ/Pu+hCEFS0Hqks8Kk8lnjOqBny7u7s9I6gEjDsHwIwpky6NdrwDIJkD1evsIPQEZOZceSmFN8OqEmadr0tjCjRdxDDb3MztKP3XdpTAI//HBuCDg4PY2dk5Moh0sNYaMa2n5JhYjvoco6wNtY44AyTZgRH8H2pPybApsaHi9uiBIXXu+GifqEz0MEE2rtQJIq9G9ZhnbRvKcc9fVH1GG5GfHVLGT4Q/eAJCFA+/R6PrbxrBq+V2d3cnos4oZ2Zmpl+JUJm4l8iXZu2cTh1sNtY4ncoW306n3DKxs3N8XdOw/s3Pz/eycMuXWVkYM44cWFKZsE4w8XjXFRWeZHO6Gh/Aefja3t5e7O7uTtTD7YC95zoYgEKH9VS19qcbryU90XZxvhJg4L7VAIu+0i7TNT2sNAR4MjDsCDqs7XLtcLZED+bxsvzBwUGPRVy7MvCkflztG38rj26VQtOU8IUDfHjbEt6slPHm6uHvIZulNNWDl4ec3xCp4JkyZcjKLRkArst1YsZ3STkdj7imZQyBiZKCOb6mIcfPkAxKcir1OZdfU48rpwQEs/4v6UUNlXSqBJ7wXZJVCZTW6GJJNkNlZPU4kJW1tQbIZzxN6zRcflenAzWan6N8Tg7qjEtjvEa31KmW+iVzQu536X7Jkbm0amuHxnJGJXnWlOPaj2uZ7czGUcm+qkNkp8jAjYkjwEP7umpt3JA+T1MG/1efNi1vTidK/qJUprP52RgdsuElvwLiU/cZP0NUGtdDY7Rk16clyKcUgNB6HYg/Dk29pMudiYHiogVMjNizfXslgaLOLJxaQtWsOHxNH62SKV/JWKoj4vaq43HyG5o9u+tKXC/kw/v1uE6Ui3ewZrMXNqyZjErOtFYxVY+yOtw17QPoB08U2Pgz8YzWgfEhfridJSeseZyOlcAn7+VwvGWTsKzdGoXWfCofpgx0ZnozZFyHrrlyXF0sE5XV/v5+jEajPjqI+4hwc70YoyhHN10jjQMWpfHq5OjAEo8FRCQzPqdx7CwHzss2WftN+XOyhd0Y2i7jgLEDBhoJqwUw/M15ERUGr+DTHa5jvdDDGpkuZ5vpdVxp9FDLdvccANW+0z7OIokKrFw6PaTI6YdAkJON1lNKx7ZM00H/VS9rSQGjtqFkh7OxUKrLHd5iW6L6qmXr9WxVZNqDGkw3DPgi/MvCmcEMBNQYMTTQDSYe3ENl6P9MAUvpHADKymaeeJnCvY6uxlm4uvUeG18FCxGH/bW3t9c7FT15yJ8hxVKn4BxDyfG7cpyBYxoyvkOgjPtE28jGh3Vb+cn0XNucGQHly+kB6zwMP5Yna4yQ44cBH/PiTukxHyoflkPNsjLzpP1T0hHnRPW+Oki2TePxOGZnZ2Nubm6ibjh3ODmWJ5aQ+GCKHgbJbNgQoa3ukBenwZL43NzchB3BPdUxZ5P4up4Idbqnvx2Ixm8eH04Xtb+0jzNfkI1XR1k61nX0IWwd9ngyz6wLDBL5WaNaL38r3/zRgy/cL2wXWHcd4GN+NQqpdp9l4/RExxy3n/nLZJvZOf1mW+r6iykbQ1jKrQVemd7U+ntuF/TB6fpQO5QnPeSW6T3/R72Ku9jmDsnDUTXgc+SMfGmwZqCl5NhVwdx1Z0DVALiB58BaxOS7OXUwZ21To+1oyHHxf+V3iNQogw8d3Gjf0Iyn1I4SGHN8ZLy6umoAAJehoK1GVlpn9p/5cXLNHJ0zhvzbGUrnBLUubXcJYDk5DMkmG69ZXX8RxAYQ//lbx8oQAdCBXJ9x+ZxuqC7tzxIIctfU2ZT6S8t2p7ddOgVebCMyXcscsmtLrXN1Tm8aGkqv7ayxCwqeavpa6xrSoxKvqtOZfxrix9k17l9Xpt6v7Q+XvrY/a+vjfinpEf5n8lR+3f0M6Gq/Zr+Rz13jsjjqrO0fkolrQy1N/VgWZzSYCZ6VuOUQB9j4Pu65qJ4694joNz46XpUfFVDXHV1i5g3i2BgeEf2jHxBlyQwWHIq2F/+dIdcIi5Mz33fgczQ6ugTIg0RnS8o3O1U+OafknIL2HT76gumu6+yzvlRPVHbal6ofOuPRNumBANUBdXauffzoj93d3YmT3Girk5OrD/zyS9tZD7OTd1oP61wG9FTfSk68xsCgjMwpufHMMnYG1RlIvs/LYjxOWU6Zw+I2sR4i6sdy4omeHhhQPXNjFvdYh90pVLd8yXoE+8Plub1LOu4zHcZ36XmX6lTVTkB+XC/rsitT83AfOn5dOSX5qgzVLrCuMN9Mai8RDeUIsHs9pY437SMNGmRt02jdaDS5BcHlc2N9SI5KKiNnf7NyNK3WmelrVldmu7gcPtzA/CtPepDF+WH2C5kvcXri7J8S6svGa9cdvp1GxxyIl9VLMuF219INRfhAJcUqpXf5MlBTqitrdElRcV/BSoR/PqA6Oa1zSPBa1rQdVVOOOiV8qwLpcrM6KTX4jlRumQHmfuRBVdN+B9BKxp0Hji6nlPhXeSkIUr1Qnpg3B6AyvSsZWcerc7BZO1RWrswhyvh1ziBrdzaGp9F/BrS6BaGm3KxvS7y5dOo0Sv2FiV8mf26DG8MOsGVtYyfnwInmLfFdGi+Of3WCmscBRwdUOL3y6Wwt56nZ05QBHC2XI8CadsjPHdemZ7yW6slkOA0frr+Gxm5JF0v5XLqSvmla3l84BGTxXfJR6vNKAHeoHi4T30OyLZ2W5zLUt90Idog4BuDLkDXIvR8S+RidYkOmCtwBsMxQcNmOF1zj2bjraAUkeo3L4XyshG5jpi5D4bd7ynpmUPVbr7nomMqMoxncfm0TZrU4As+RJzzSQR86XFK+zGDorF/zuMGksuPBr47TycHxpnqjAEdBJesNZIP7vN8K7dPHONTITE+jIa0DsCUnrLzWOCt8ak6Psf45XjJn7/o7q0fBBOso58ui5sjPOoP+4Vm2Gmq+VgJtbKt0XGXjG7rC7VLjn9kxzqPjiOWZybS0+qLp+RofYOF6lD+WGdJkTkqdnsoQfc37qFSO0FXHg4vgZDrOZfJ+Pm77NACF+6EENFVX1Ic4kKMycGOM73F5DnRk9nOagwEl/8vk7LPTVS5PH5/EfaJlcX6ViY6VzMZpeU6mpT51+UpyqKGSbZmWjhXhK81AeVNx6fVFbHxc+FudLK5l++u4DjYyqtRIowPJgT4WLgMjNoDgBW11xoU71hlN15nO4GZKmu0FQB51Wsob99fs7GwsLS3F/Px8Lyd88NyzktKVrjt56IDK9iLhv5NfTeQN9zKjwd9uYKtBwqZwOCVe8nWvxOK6MsfBQJLTA2yrI1V+tS06tpxhdx+WaUme2f2sfU7ONWVyP7uDDNpGtQH6nEfosuqqyjHrf22TAjBHvJSMNvC9bHKg5Jw351Ed4XapzdA3SLh6danbjX29x33i+lX9BbeN24XlbbU9Osnm/ubVDPDP23GQHmOXxyvGNAM/HjNYVsx0wumM2honZ9St5WFigjagfdhS5Hwk5Kg8so6qHPFxhz+GQImzO3y9lAb9wzxruuxVnJBPJtOsPq7XjaVsKRZ1u+hvSU5D9nFIvsyXs/fTgr9j7eErMYbfCpiyPE5B9N60VKpT2+F4LdXL6dSYOaCXdWzJiZT4djLNHKfWU8qbAQYXfajRgYjJ/YxDCl5yrFn/uMHm5Kx5S3rH5TJfLIuMtJ3OKR9Xp7ltmbMHDbXJUc14zXhiR1eiGgeSAQsda6V+dLpUkpFzwDV2KwNfTnf0mqZz9bv+ZkdVApjMS2n8u7ZpO7L/fN3J0KUr8cn8lvqnlFcBhAIipKmVgxvHnCYbayWea8YB86B5XfTNlel0jq+7Mkq2fUjfS+0Y8hk3yl+tTDUP8+DaVQJyNXhhWjBYouPYZ6VqwIdlCKfMjMTVsWcOATOViEPUzoNLw9lO2XRQ1xq3IRDC/4eiRy7kD2Cgj1DQfJhFIoLDdbjB6pZf+T4vM8KoZQciNPqKKIM7lNB1hxtNOS/L3i318hsO+LoOKjXUTtbqHIf2QGjeaYEX6xPk6GQJGfGzztyG3BLgcGBmiC/85m/dsIx72VJcBjyy2a/yj3Z33WHEhPlxZWdRhMyhIg3y6/Is86v2xdXNNoWXC3E/44H508MNer90oMg9osHpvy5j8rjFu8xRbubsHW8OJJcckgIlvc52prS870htCcrjVRLYHSdzEL/xie0iL18jf+bHVN81euR0sSTP0uRQZcnfmV1goApfyX4DaZxt5bI4jfatytb5cie7rE6mTBZ60IXbyePVPc5M26D+sKZ+8OD8bwko87jM7KP+zuSXkWIU1+5pqRrw6TJmyYHWoFAMiqyTUE9piY/JKXuJMmNY4lfLdU6RDXBJGdTIl2YC3D4eAG4w46Mn97R9pf0icKps+JyRV8Cn8tI9j3yd8zrDpPzW6pTKLLs/NEidjrvTvkjDe+wyoOecVYn/EmX9Cv6cYXf1lwCg401lpPqR1VUqu5Z0XKFut9WBdSrbg5YZcGePMnm7/uXxrX3h2p/ZQNUlgCE4fJ24Mg8sL+ZT+6TW/mnezNHWlOfGdQb4VAYZXywb5QegMSJ62ekEzvE3ZCOQhmWuQEDvqy3M2uOu8QdL0SVyY0751HJrwUmpD126Eg/sM3ncaB0ZmHNtHuKV77mxXetv2J/V8qnyvBG/MC3wO9YevmwmF3E0IsXXnJHl+25ws2HXelQZs86etjMz0KYDRHnX/DWzJpVPNtAYbEUcjXDBCCjPPHvh+tQJwoFo3w0NdpYpRxe5DdqHHDXlfK58pOHfQ6Ai45cNTQn44L86b+yjKRkvzc9yzgxjCXw44+BkEXH4oFLnaEA8JrnMzNgzaM/IOeYSeCr1WeZIXP0uKuxAnYtCZZTxpvIp2T61D1n5LqqgBys0v66kOPujdan+Z/w526M23LVV/2c225HyzROnIbvNdWegnvXS1ae86CSC03MEO9uLjW/m3+lvxkfXdRN7gDXgwTy5fnS2mdNyG3AfkUIXNXdl8L5HbTfSTQNqVJc44MD+yOkgRzs5ndMfJR5Ljl9nK/m+frs+cPYkG5dadmZDboSmBnyZI4qYNLDcCRxy585wm291E2+2HJWRE57yrLNIVRDOlxlYLge/1am4ge5ewM0y0HSZ4VCjkymo6yt9i4AOcpWbylZ5Yt6wQZoHPb/oPnPcvKTP8ta69bfyoO0+zuBRoMD9zMaJX0bPwAhpuU1qqDNSsKVtzIxJ13UTL+Xm7QRIx8vybsO7c2i14y8DU864qa6rgyj1mY4FHRP8BhnUBaDu3hbk+Mza6ZY0nUPM2sikAJnzcxSK00YcviVHJ0nsGLWNOka5jSo/tnuol1cV2N5lZXNETeXCMqtxylyGkzV4gc9hHc+oBLZ4zDBgZHvAS6nOJmqf6m9XP67xOGI/xWnUFup4xjeXg7T61hnewoO0bPO4LvT/wcHBxLYftbXOZugYdtdZ3mpLXfCI+eF+z/SM68pWYTIf5+yvaw9f07zaTtVpR06Ph/KUaCrAV6ooc6gZMKwp3/2vcdi1lBmbISOUGdJSmhKA0evqqHWAOKPhHGZNWxwvruySkeQytD4nG1eGM5al9DVUchKuHO1P5zQ13ZABKAGJEi+lfiuVVSubzEhxm0pl3YjRcVRyarV5ptH1obHNNK3dKdk37XPndBR4ZPWX9KeGx5o8bnwPjaGSPRlynlyHA01Z+kyWXG9mI4coswvHsVOlvENtHNLBkn0Z4nPIPmX1HdcO1ACdmvozG63ppuXB2aGhcTjkszPebsSWTouHpn6Xrs6AMgPpZjUOSSO9zkbc/gp3ugp5S8ier5V4VV6YH6ThJ+FzGJxn2zoL5jr48S0ZP7jPkdJpDESNwcM1Pu7v9uvVls+RLu1LdV64hnQcERiaSdUAQi6H/7NMnV6AT57d6tIG+gNt1mU51eWhmR9kj+v6CBEnO20X6jg4OIjt7e2JfMxPBuQxfjQy4hyklq1tcW3MxqCzHegfTs986dK/G+uZzWEjrCsJSpq/xgGNx/4tO7jnwJNGiFm/OIqHaxxB5/51/GYyzfrNycnlUxmhLNTDY0b1GfxoP3B92mcuws7EETe395vLzR6TpPJme8SHJXBfD6S5/uY2o3zcyyYrsMlKJZvO9fLqirPnKiflz72tCWl5qdXpA/NT8leZjUde1wbX7+ibiJh4jBj3pxKvnJXkqPbNfWsdGY/cvqwfh6gENKehasCX7cfTSrMNtiVHrcIfokyImbMqUQZeIvwzgmZnZ2N+fj4iInZ3d60zU+fEgM2drnWOFcZH90uUBpyTATsGJ1v3qjPmQwdvJlfndHjgur5hI6TLJiVwwm1TntygVJm6dnJ5Cvi4jcqL47NkpJjcGCrtfVK5uLHnntFVKlMdgeObdQ55HC9ZWxnUDhnFrD7uHx0Dmb0B6WSV28v8ZOMRAGLImWW2CPfdRAjXefzoZIPlwhNO8OPGngN2mR44oF5qn9ujy6BS63OOszQuSm1wlI3DbJkzs0eZLPABIMpeLKCy4t96yE51nMtxgQL8H+orBt4K2vCbr/HYYp3jenh539Wp9bs2uWtK4IOfXsHpM5uDPGrLnLx14pWBusxfOvCn2xu0HG6fXstkmdXv2jUNVQM+3eypxs8Jzw00Ry6y4ASm9zPQw8SAwwk/M+BaN/OTGTLHXwZAtI0ZcCgZJ9dWbbM6f1W6UrSKf5ecWcShfpQMFu6rwx4CASVdKMmB66sZHNpWlYOL5rFRLemDkylH1mrap7J1Bljbq4ZyyIiU6p42D8g5zywNftfIorbeIdJxyPk4Wu/26WbGm/Va2+bGHPLj8TYO8HH+zPGXovRcN/PIbWW9UnJ2VOWX6Ynre7RX63D53e9SWs1TAxwj/KEPljdPwGtATMTkUw1Ux9ze4BJYKNWj7cuCL66cDFgyZX5NdUJtYWbbWb8dL87G8bjg+zzhcFFaLrtkC529LAGsDD+4MVRr05z8Hc+19o1pasCH57Vpx5c2d/M9VVx1/G4jMlM2CJBeQY/W43hwneb40sMoWqfLjzQuslRqi3a6a0ONE8cyBBsuHih4jhrX6TYK8/3McOM66tvd3T3yNH+OXA4BvqH+z9qs386ouLIYBOM3lm6c/vCz53hcsPwcSNS6h5wcX0N0GZv3I44+9y1zHLX1ZA7J8ZttTSi1wS3LZo5Jx4EDj24sDNkcxw+/SQH3FIBh7OhSpJO5/te8nA79Nj8/32+zYMfF9oOjuFwGy0E3vo9Gh89NjDi63YD51zd/aF/peM7kzN+8HAo56BKq2jauj0ltvLPTSixPHQ9sE1GOPhc2IiaetVkCfDp+nE1F+/XNHlq2GxOsh3ofZe7v79uDW5lMmX9Qqf+5PpWrytaBqIiYGFsuCun0cH5+/siWAZYfCLpesgPOj+lHbZ2CSebb9T+ul3SF63a8Oh/s7tXQ1G/a4IqyDtZ0GWNDjogNWZZ3Gp4zx8/1Zf9r6wBvDjCpUeb0mrdUt1NcraOmjQz8Skrl6nRlq2FzlAGxUp5aqqk3IwW96nz0N+fR384Raxr3f4icUZhGbtPoNJftxs1QOdP2Z1ZeaczeSJ3cRjcuHJDLxnqpbqcjznFyHdnDmSP88mWpTldvRqV21pLqvMoxs13OAQ7phIIZvqdlaX7Hp451Z6+dM89oSI4OYCjftWOW+ZuGdKxzu51cHXDJ/OxQvRF+wpjJnu9p35VsleM747Pkk7SeacdZrc0stf9Gaao9fDqrcGHLEtqP8ELONuRO46CyQa9C5PQMeIbKLSkxz8p04DqDpsqeGfHMWaA8zDg4yqTpuI06uDIg4vZbOWPNvxk48h4SjRCxrHCdo5BuEGkex4NeU8NfckCqzxxRUVmpceZIj5OvAmq3j1B54bQqO4446n4bbqtGAbQ/tI5Mvlm0l/nm/+ooXP85w5g5DWe4h/RW+XFlcpuyQzalupX4OvSHI3nZ23QgY0T1EOHrui52dnYmogeoH1FI1x43dtnG8YEAtoFsk9x+QicPJe330Wg08exKtQHaB26ccxtgKxxYUL3TyIvqQtaXJV+icoPt1bGWrVLpWOB+Qvu6rjuy55Ttq9vTjbQRMRHZc/eZf73OfLH90zfo8JhxK3tqCzPfxm3lfdyuHI6U6vhBu3l8oGzVC83n9MLJJbvnZJulyewn+3JuA5f3eNGxTumqgwTxQHQN5gaq4coMPufPqJTfOXZe4uBOdGWoQ3TlQhZsIDUtl6nOl2Wp/DjniDbAUeCEUsTksoPynZ1cc/95ycHJWmWluqAGR409X0M9DiCxbJWPGr7cwFXn49KWNrjrHhJ1sFyGLhGy0dIll9Fo1BsuNUrgj8dhBqbVgWSAhnlS2asxyoyd6rYaUJYtj3l1eroko3LmMhwfuKaTG+WH24Q+qn0+H9eT/VZ7o05I84AXAAd+dRqeL6bLZlqOHrxSx8np3KvFuB+YZ5BOyJ0DVLuC9AB82k/O7jjArjzxUw4yUKHlZQEFtUNDxPJEWZDpeDyOvb29I/Vk4CADBOrsFZiwDVDqum7CfmSAWP2h61PWIQV8euBDbRx/O4zAusavSoTP4bT4zM3NHZlQo41YvkV5undR+z3zFVn/ZL81n8rVyVT55wlRtmVMZZfxUEPHevByiVTZlWGXtgTW+JprrCvfDewS3+5expdTHmfAS3w4h85RscwxOL7daSsd3Pxdo7ya3vWH61t1cspTVqfL43hUI5bxrNdKg1cNA75VXpluOcr0Sf87uZbKU9koT9lsu2TYXB0urauby3XgLONZI9IqC+eUOX/Go6Mhw11Tvupw1ndsuF1/O11iuWE8894ldagOZA2BG3XO2m5ns/n1ba6tGhXU+yU5l8a3y8N2hPPV6Cy3qeQ7VBaujzOA5GywI5emZCvAg+pdKa/KQL+nBQ1OZ4a2FTCPLvij8tFx4dJoei6ztEo3DQ3pqNsPmMlyGpx0HP6OS1M/liUzOPydOTIHcphKzp0HlqbVOrmebONqiTIg6upSnjXipgbBOWV9v6OWW5rJathal+yc81Q+XNnOyDn5uUGqbR0yDuyw3HKE6k0JqHG5CoI4GueucTTT9W1JH2rBBPS667qJRwloenb0zrjwci4/xkAPE0TEkXbpkp0a8kxnQNlMG+3BsrNbppubm4vFxcWIODwkweW7JWQ1qipv54CG+kOB1hAwduOcr7O90rIdbyhD326yt7fXv5WG3x6g7VFAB0LECfyMx+PY2dnpx5Y6rbm5uSPPfZuZmYmlpaUJvljOAKWj0WhiEz2Xq06S+8jpF4/XzE4piOPlQAduNJ+zD8yjA+LOVigPbLsgP42Kaz+y3UMeteHM69DhqZoxoWXW+EP1uSzH0njJdELrcoefnOwdP86X8u/jACRd5ciAZa2t4P8qtyHA5/roRkHfsd6lO6RI/NsNcve/lD5z6EMAU/kdAnJZG1wZpbYgjTM6XBa3g3lz8uV0XAYPJLd3JBt0Wb+4aw4EZPLX8kv1aB2ZUVDgx45hGlCeOd/MiDnQd1xyg90ZFaVMb1CGflydJUfLPOg1rdPJyY0Llbny5Pacaptc3kw+NfdZt5zMMx3P6nL9WeoLJpWV8lcaC6V2KujkVQPQ0NYL2BHuI2fLuC1Z1F1lr8vtzDfLxdkR5VHryexMpsuZrXV951ZftO7M5mU+QPljH6Dy03YN2Qytb8hGZtfd2BnK63hlfmrHdg2gcm2rlc0QOX13/TqtTyilr7VlN0JTAz5WSre/DQZAN5dqfucQ3FsskEfT4jorIhsU8Mbv8XUHSnijpFMkB2Z0zwKMJO5hLwfI7U1hA6gy5GuZAcPMWsvhsrg+J7/s3pCz02tqqNwg1L2AnE6NvONHfw/xmO1zcTyUgPaQM3NtxTW3Edm1xzlDzpONIyWOvOg+Q5TNY1SdjLumPEPXddzou1czsIL9NiyfUpsyYt0ZWmqpAWDgx40DzeuiVSxTRxwRZH51b7Q7OOLGVgaMHYACcdm8ZMyPTnK2SQn9z3rGxH3govwsO9ZJtanOT7i+LvVvDQBHWew3dBxzu9m3ZXquPLhotwJ07U9uv+sPTq9Rzlqd4HIyOZVsl/LLcuL8mYyyaOZoNLlvz9kUlpnqmdq4obaUZKH2XO1CCZy660M2xU0KFGOV6ijRsSJ8EZMdw45NN+Czw3ONZMZ5eWFvb+/I7LIkRP5WI+yMGfOrA0bbmHUAp1OlYD60TNd+Z7yy9o5Go1hYWOif+s4nlLJBX2MUHWWG2imhfnhfET9nysnCyUaNnNv06nh1RtUZUW67M8aqt1q+A3I4SBNxeIAmkymDPdZHBzIzgKnl8XiDc3LR0CEgwW1240LbrfvOHCCPiIkJUQaItY8yeSvvml4dnUuvTlMnc0qcLuKoo9a6cR/PHAPg5b6HLEBu+U55ynjM9FdtM4N0niSwg2Xgxe1jwKcHRpysMkelgCciJvjjSQXbSn2tGUcknR3VJXbXp5nMXF9GhF1OztoIcqClZGdwX/UK1xHM4K0cXKcDfU4Wme3X8hyV/MwQKHJ2hHWV27Gzs3PkcBX6vfQg7JKPcem1fM3jfFCt/IbwS2lyNC1GyOhYgC9zHNNU7kCccxLO4JTqLHWopnMDSfPqwHdpXJnstDKFKCmoc1b8zQbZfZgfLo+NoPI2pLhDpO3WdmX95nSB+a/t02n40d81/eWAiLYncyqlcobaUEtwAsiXRaK0XKcnpfozfavlUceT9tOQYRzir1a+OiZUR7Ue59wc/6U6tFxOcxzK6nR7/zS9iyqBv1q7zm13Yzrj18mRSXnX8ZmBjGkdYFa/K2uafnI6OsRbrWw0T5Y+AzvTjld8l/YFOj5L+uDGQKZDXXf0NXkuzVD/OFtf047jphkaD0M6krXnRvz01Ic2UGFmFGocAe4NHRxw1/HtwrnME9LxPd3forNtzjMajSYijhwJzKKBpQ4eitqAP7QLz1NSOYN4c/eQ02WnA1AAfrL39JZ4de0F6Ywe7UJ4HvkPDg76TfscMckez4Gy+ZBKadCWjGc2YNwSgGs7X3ezMo3WZGBhqM9Ky53O4c3Pz8fCwkIfRdvd3e37m79LfYyPbjpnHvTRBw44KZVOrkNmCvD5v9oXzpv1sdaTAQMem9msnutlnWRi2XI7tP/VRilpWr3H7dHy+APbxlFEjUy6ds3MzPRvdOHokdMJtonuDURZHifHrL+1TJURtgIxL5k8nR1DVLHkSNlnlbYtqJ6hjuzNJfomIu4vtYXKYwbqdVVK87jrSiwflKdvZeL7qNfhgZr6WG/Zpo7H1x/VghWt/f39I++wz/pC2+DqLJHbUpZtY8vaOuSbHT9OF3C9BOhrqBrwOQOkjc1Q/o0gUq7bDX51NM7QO+OYLRdonU5RdH9YyXCjzlrKIm/aJmfYmLKZAQOwbLnP8V5jENXp6J4x/q8DnJeVHM9DDjJrcyYXBzr0ugIQTZ+BD7csmPWV9i2XOw1AZBmPx+P+tXboa+6HUvSnZKB1fCj4cGDNtdU5MTfGuRzX9syJl+TlykM+XfqPyN9H6oy846/kDJzxzqIoWT1ZH7ly1Elhibn0mrhMbzO9dzyVdPe4vkFlB70uUYkHJ8eMbzc+OY/yV/rvyiiBcpffgQ/nP3S8OtJ77CuHAGIGfkC1UToF1xGToN7Z7lLbsv7VNBnfJTtSopo006S70TwRx3i1Wga0hpaStCxWZuTjb9eozJkMKYAqASIUbMwZbHDZULhsnwDXe5wO0DLcg3XdTMPJidtScracXiOWQ23k+vh6yRggkqr9ru/XVHDJ99xeIOYpYtJAOhCP//wOXDUwXE6JMoPBbeR7+O/qHo/Hdg+SpiuBUeiORnH0IBTvf8qc0BDAdoCNrw+NEecwGei7CE2JD85bMtjOIXK/l9qQ6bxbaXDluIkV5+Uy3L4+13YG8QpadQw4mWdAogROXBl8T22M2jDOl+kjy4z/Z4SItuOvBOQc4GYZKNhSfdX6Mrs09D+znxjTTt5cnwNazkeXqGTb2T9kPkn50DHhbJfWy+9jd3qo7efyHCid1h+7sZHJLeNR9aU0nvSa61vXn0N9WaJqwKdLMmq4mOB4MpCoA4qNoVMEHVQl4QwBL66Pf+vGX5Shpw+5PqZsmZfr1Xt6n400K7F7Np2rBzyo43HLaahPn81VQw4cZsYTdWB5kSN9/Pwv7XfeYI7l7ayPNA8v1WfEL+3m/oUe6svTawaZts/JY35+/siJZV6qxmElPvxR6iOWg4J3gD0sz21vb8fe3t6EHFl+zoHomCyNXR6rGFOZ0dYyFdw7XWV58n0+POZOT2ZOkMc4+twddFFZsE7wMxBdZIZBC8pix4Z+QB3utLPTJQZ67qCPTmi1r7Q/GDyybLTdamNKgILLUoDE/cUyOw7xgSEm7Wdtr+oz59Pomtrn0mE/rSvzSZn9BOlEnPMhj/pCbp9+3CvbnL/gOjR4w88OzdqteqRtZPmr7VZZqF1xdSG/ykDHXolnzqff2fjTNmU+ScfUUFuysZTVOQ0dO8LHv50ycr7agewaUGqUdkbNwOPfzhHwNVUaVlSkqamb05YGSWYAHOgoOWl3H9ccqM6MjsrEOYtp+hbf2JdRC6SmUepMHi6Nu+4cwDT1gpyhyMqGgXRREJcncx787dIPGWrXjlrKnK1LN8RHSXdr6mdnXso3JFOXT+2d9k9WphuvGf9cbomXaahWj9w91V1tl5Oz+635nE11fA/x93jYXv2u9W1ZGTVjzeXTPM4f6vUaXpFumuuPBzl/k5EDZvx/aOUwK7P0remY7yG60TSZXjqblYHWae3CsSN8yrDbxF271IKyum7y5fCcjp1f1ngH4vieKzsDsihrd3d34j7Pkt2s1HUA7z/gDe8ok4/XY0OqLnlnMsT10ej6o1qQx8149U0MLp22S4GoghDuBwc69D7fwzIvR4S5j3W5xwFwlYNGSDMnhPxo/8zMTCwuLlrjH3F4SKbUPsdDxOEsjyMyjh8tD32FKB0vXWUOQscc67AeeNE28uzdjRWWI+uF6kb2vtaIPGrB79TktA5MKE+I5nKUIDssg9/Z5mv0j+bXcjQtk9vUzzqjzxfj767rJg4gaNtVPiiTy0e9OoHQQ1POjun+TtZbXrVReTp7wLJBXo3Cchkl+8Fy5zRKzp67/kf9Jb+W5eE02eoJ7/N2tipi8q0oKo9Mb/Fb7Ro/Ck39o5av8nI+y+1dH6KSL2aZwN9xGrdszIcm9/f3Y29vb6KtrOdOh/C7pG8ZuHe+XW2Fa7fKL9OdTMdcWlzTxxHx9iC9l9GxInwu7K/C11DmENJVA+WcihOyA51aLwyNht/Z2DlHzwDJ7fnKBote4+VJtzSHjsuMGLclG7RcjgNFaoRKslXjnRHk5wZTRtksjQe7On7lV9vGpO3JnAaXg4jj/Pz8RN3s7NRoKCBWR+vqVLmyjusWCG4Hlnd1mVqNOuTH19zSXkmHOE+WRn+jPlzXJVZN4/oV11UWTle0LJ2EcVk1bdZ7PNnSVxdyXys4VH6yNvPhmlIbMiBXKrs0DlE2L8u6LQjcB8wrg9yacabXuV91jNTajxqqsV9aj7P/Ln3JVjrd4/wOVADwuTFaWi5WH8R2RO16jf91oJD12smsRJqWfZ9OeFWX+Ldu64GPY5usdboxwsvZypeCW+3PrO0lf6xtU9lmgDHTM6cLvAe+lqY+pau/QW6g8GzRKTynq63b3XOD1XUivh3Ac/VkRkHTaNs0H+8pc211kYwho+X414eBKj/OuWpdJSNcIzOWe4l310d6z8nTbeR2gytrk5NDRjpJWFhYmJAbAyNXjqZTOaL9vAdJHQecASJ7bg8j11UytOzAIRe3V415y3SgJD81WppXKQPHrsxMZ5CGH9auus76y3s4M1mhPFdWxORhJHXQJVvHedi5I5+LTpb0zNmUUr9k151O6eRRZcn51T64/BHl9yUP6Yu75wCYS5vJRPuNbbHqTUn/HJ8lUr0qrV44H6dgWmXAtpTzqW5loMH1s9YzhAtcW7Rdbqxg7CFypYEZJwPlxb1tyelNqQz9dr7e+dqh/nf6hv81uoMypt1/Xw34eHMtM+p+q3EAOWFzOl0eKSl+SSi84Z3BD88adEkl62h2whrN4vzadpYJh6MdCFEetT7lKwNe+/v7E6FdLo/b7wYunGAmg1K9KnuWbWa8eXbLlDk0Ncw6YHBNH+/C993mfiYti4HW4uJiLC8vR8ThEuLe3l7/YvrM+KiTBPhjQ8z95g6mYKl/dna2X7bHjDfrK3dwgGfHrMt8aEEBpwOEzsjpPSU3bktOmwl8sNF3xo4BuHPCPLZweIZlzzqBb6cnKKvrDpfL1VZoOuZ5e3v7CCgFuU3erl38rZFULUfLUudSkpnyruNOI4HOJim5qJYeKMhI9SezqWpPGRip/eb2K0jSdrmxprw5mznUHo2q6xhjvvREv5tQcr3Ig3HObXc2XYknpiorx6vKq+Q/sqVP6A7G/eLi4sRjhNC3uoUD5fPWKZSb6YvrW7fKoH0GPrXfnDydvBwP09C0YC/iGEu6EeVGMZVA2dDAHsqfkQLPLI2CqaHy+H9NPjWq+PDDNJ2iufrZyQ3x58pRI+mcbq3cmCcd6CWesvJcvQogNI9LV1O+GsKsXJStYA3GaZoDJ0wOKPF15UXBp9v75+pw47Bk6DIakq2zAxnQ0vEwTZ3appoxWKMbWXna9268uDGWpVOddTrA5QyR69+hfBnAzChrX6kvsr7jPBlAc+XV/OfrJSrVlckkq+e4fkl1gesZ6hs3Zku27Dj8gRfma9qyavxIrR4q8FKdUZ71HvupzOYN2dIh/lRe/LtGv2+Upi2rGvCdOHEiuq7rN6+PRtef7K8zUo0cOaYy5e26o2FYRvHO0GWhXt4MzXsHRqPDN2joAQrlT2fqKJfLw6zDrbErkkdblN/sHaSax83iuCy3dwrpOaKGfuq6w1mUa7fWxW2OuH6QoXSwQvdUchrlTaOC3DfcR9jQ7t5HyptYWb7aDp0N4httgUxGo1F/kINnyCWnxUYAkWRN6yJTLBPd3wT9An8sZ7TH9Z3qPUcr3P5aNpDOkKA+jHsGoUxDy/pch+qCA6RaRymv21+secfjcf+YILfHcSi/titzNBGHe5BcZERlgbbq44qcXeVx4cpRHcI1XTrWtulDmLXdSk7mLkKsbVdbWnLOmVPm9rrlOxCvluhjRdhHZfWqzVKZO56cTeW3lyAdxrRG+Ep86Ns5YJ9U3viGnWY7yrYA45mj2Wx7HU9cPnhBOXyfy+G2KqnN0XrcG7H4vovE8/5TFyHnvnd1a7+7tmt65d1dz7YrubJdOUPpSlQN+BYWFiIi+nV1NUwlpAzm1Bi6BjjA6JYKnMCdINHZ7Oj0dIsDpDBIaqAVYKDObC8UDzB1zmyks7d3aJ5sXwIGPQauAlCAJebXLQVxeQoa8MHhBgxEHRgK+NwA0jbyySsQP5MM8mbDxo464vpSkS5L48Ngj+tmowBdwDLf/Px8v/TnlmIdsHFGh/VeDbs6aqfXajhZx7KlNDaAygN/OOKcOS/9DR0ALyUDpQCJeddXtOl95hN665ZduT6e9JRAm5645nulfuB0as+UZ82nIEzBT9dNnjTmutRJ6oRO63K64myX5lHHNkS8dYDlw45W+9bxw/eczqg8HN9MTo90b6QDeyUnmtWj99hWcDpMVvmj+65Vtjp2uR4OHnDbdFw6YA09Y78B/dOJNsidrmVeYSu5fdlStXulnJMr+OcnOTi76+xIZtsy3JFhE2cTVEez3+6a+ki+P40tdf0wRMc6tMFCztJkZfBgywz3UP2l9A5sOUOjQIDzO4OXGUEt2zkBZ8SVD+XVDTiuk6NiDhi4unRQqEzV2DuDo211RhT8a19kzkj5cu3l8thgZflK+qqOSZf31XjBaKnByfrG8ZIN5pLDdnk0os1OwBmC0hhjo6xp8DuL9nKf83XXbm2ffvh+VkbmbIdk6urI5OHqz+pw6fCtwCXLozpack6ZAyrplpaV8VLzW+vFfcdLLZWccMYPtzdzgvjvQJKT4xDYK+lYppeuPaVomQtWDOlLlq4ENhRIaZRXo4DKt9oIlh1PQtSOKVADL5ktVZ12v/GdgabacYTrrt9K41+vO/3PdAf5nUycTg3ZoBoMFTEF4OPncvFRaVQ8BFJAunGUqcYAOsqcSkQcAUbKJ4OWjDKnxqCDl7gYkIxGo4nIFN6koOWDF96Qmx30GI1GfTSLHad7ywDXgaiGyokBEdc1NzfXL2m65SNEebPlCF4+0aUi8Iu6GBCroWIDj3vcftZD7i8tj/kE/xypRjl4UTfziRkmRzpZ1to/LCeWLfPmltccKGfdwLIJ32fZZoCTy+K6+YSwG3/8zDi+p9GbbFKjhOipOgS3P9HJzkWMhsBglt6BA+WBeXEOhMvgPZ7Mr/aLc5q8FYHzqHzAB8uf69N6nKNkB8z3NVqnDj0DdXrP9aUCMO0rbjvr1FCfKHF63gqjkzXe+M/2oQTu+L9bllQ+FFwNpUHZCuQyQOd4c+ncFhbYDd7qwys+vBqkfsUBqvF4HDs7OxM8ow/YFzqbmbVTt1owUGJZZbjBbQ/Dt+tr1g8uX9s6jU66McPjz+Ec127c53I4Sr+zs3OEF0fHivCB6WwZM8urHabl1dSd5S9RrQGftlwt3ykwwIE+H69UpzoLBUNaT2kAcHvdfhlNo3zAGOgysToNB8pK/a0K7ermvG7ARBxOIDJHNM1/NpAAk87YDRlZNcjaT04Wzoi7+thIOGfu/jtDyfccAOL63D49LUt1MkvvnFeWt9Q+Lsv1Pe67NuO/y1cy5ppm2ntKykPmyN1Y13JKzmdIX53zrbGFWb2uDaX6bzSNk1s2DjM9yeop2bBp+XWgoZR+6F42pkrlKLhVQMFlIoiB/xkAcnkZ8DEPCrZKgI91MwOZmX9QzFEaH1m9GZX0fmgsOl4zG6T5HE1jbyKmfCwLHKDbZ8eGQlEyfzvSGTHnVaev5ZXC5Fw2z15YyIr0MxAA3rhDXWSEf/ObJFh+JYcOkKdgmsGIlukGGteh+0k4D4NQlglf04GPdLy/jeWhwNY9UZ77Yzwe9+94ZUDDETxtA/eP9mfWj6wLkC9HXHWfJXgEv7q023VdvxGbjZmbWXK0Ary5SIhueNd0LmLkyC2VQJY8ZkejUd8GNYAAvhpdYh3mxyJoX6DdXOb8/HwsLi5OlOfag73C2u+ldyk7mbn9PriuskEbEEmNuL7hXW1MyV4p1SzV8T21R9rn6sggE7cfz+3hYznqlgik1edC4sORILYHKmcds/hky1csU8hA2+LGM9sUpFMbpOVk8leAkoHIEpUmJVneIXCY+SPOx/YoA2VurHB+PbzHZWXyL7WH9Rk6o2/v0bZoXtVNbQvXWcOPGwcuXel+9l/lpDZW+4L5qQFtmaympRsCfGrgHSMMXhzBEOCwgdKQooEnvscO1gE+3fyvdbDTcA6YAV/GM3f+zMzMhLJnCqVgSZ0hR554gCIdL78puOK+4jargjLIZsCnjpyvQw6jkX8+HJw8OwtOw0tTOBCiS5xYflA9QNqSQXNpcR/L3HjGnR6AgL47meFQB6fnJUvnzFiuPOhB7tCBA7Ts5J0xZFDCbdEy5+fnJw5fOXk53UR/8MZx1QsG8KpDLAsm/N/f34+tra0JnuCQFXTgvuqeA31cFrdLgQ2DUt0KobJVJ+tArANgztFltkHL0+0AelhJ5cAyQx5+hRvXxRMbBcGwZU4mkDNPolQnmEeVVTZJUYCp/GCs8AQtwi/pqa9Svc36gPNk49fZVOfwtV9UHkOAh8vTscmBAtcObZ+CZKezQzJR/pAe8kS/4Jmmzp84cuCpxIvDHtpWVxfzkvmNmjJcXdz/+lF/mvWZ6loNPxkde0m39h5oiKnjoOqaulwH4nqGykv8uQGT5c3KU2dRyqcGiQdRtvcwA3M6cPSa+11qI/PjlFEVPcKfGHNtVZClaTVPRnpvqC1O54YGY2aIavU144llquAucxBIU+KHHbBbss34Rl8zL7WAjp2JtkF1AN/OISvAVH5RHwCx7ttkymTkHKgjdRTclsxGZLYpI21rbT7k5TwOSHFblXeWO8vFtY3zuzGpgMqNyxLg1f2hWf4hGrL1KoMh8Mfp1TFrnY6XGp+j5XP9JRtfqzMquxr+S7rlxq4rV9tW4s/ZNDeeMx41zTS64n4PyWja8eraVqs3tfYgYgrAl1FJGfDtIjB83y1jsJPL6nF1qYPmDeI8g9TIzZBiOgeraR0vzBNf49fGqIHV9O7wB0ARG2yUpU4O5cFoOqOdRWM0KsT9yLPsra2t3sGqTMbjcf9GAo7mIbLEeoJlmv39/djd3e3lDf4cKGNHon3t+jDi6FIkLzvzIwD4UEq2hK76UWN0XXr9r9FaLo+XSDJAwzywHiGSyjLl/Myze6o962BprLjlbccnAzkANb6GAzrcBu0H179YNdjZ2ZnQeV3C0iVb6CvsBx5DpWCTZcDjInMqDgxmctdxiDrcI5dwnfvDgTR2nLu7u0fsD68QcF/jmnv2JfOOR3ehLNZRjihm21qc/e26Lubm5ibec+2Wql0U3jllfhOP9ofKzNlX1j2UA7vnDiS6McnXsomQplUd4/Q8htlmZP7VyQR8lJa09boCUB576C/lUfvZtYn13W05cuPHrTrqVhT9zVSSvdoHJ0MnH8drRjpGh/ibBuQx3RDgc52QIXWXrqSQWk7W2Xwvcz66ZIPrztiU2qqgYihPCYyW9prxtwIx7Jlzyszlcbv0w4Zf61I+lUfNB36GZKFH9nVQalsjjj64WflSmZUcLLeby2JwwUtfXIYaKi6zZBgzQKRllIAa5OJ0TvXRyYDzsA7BuWfLzaqjzqlwfc4QZfw5fWJdUH4VEJZsDn4jHZZ51aAy2MuAgYKlTK5KmcPWe0Ok4xb5S33uZOH4d+CI7QpHczHGFfw4e5jZdPSlOk9tRza+oa+6BzWTyVDfZDafV1AyHpU33X5S45wdkHNAiNNntgJ5Mrs4RJAdjy2XxulWxouLyJb8NOqouZ/xr3lLvnZIV5xdy9K5/qvhW8kByxrepqFjP4dPBVHaT8RAQ69H+I3VnGfI2JZQsXtvY9aJbjCXBqHm0fJ4My3I7XnK6lNQNxqNJhxRBpRhqFXeLurH+dn5zczMxMLCQqytrfVvmQAfAG9LS0uxtLQUOzs7ceXKlT4fIndsSLAJHg6Y98wxT5APogUsU35gNs/0+Z28qgcucsmRJ47wcQRMHT7v1+R9dDwjhiwXFhai67qJPWXaRyDniLlsNVgKXNRhqLPh9nP0TCcRaKOOuWz8lYwvZMf14LO3t9dHe7mPuC8YBKoOM49KLgIMPeN2jMfjif1r2kYeWwr2nSMAZU7XAaOM3HhkW6Bgm8FQxp+2lXngccF2UuXC6bh+BtPaN6qP7rFRQyAF5aEeHofazxxFcgBQ9Zr3M0LeSMNjjvu/BHpK/50suZ9qwVkNkGXboPeGolXKbwl4Zu1lm4JIukZlXV3uN5ft2lMrP1dWhhtcm7LrNe1R/rkMjiorr5mduxGaGvA5ZjJjoPfd7E4NjkPH7DS100v8YukKgIVfdK77VrRtzHfEJBjKBoLm4bTsAF06bbe+xSMijoAulaeCZfCtQE2X8diZArDNzMzE0tJSrK+vx9zc3MTAxYbtEydOxIkTJ2JrayvOnTs3UTY7DT6ZiTpnZ2f7F2I744u+U9lC77DkBmAKx8eAMGLyYAXu7+zsxPb29oT+wUhB9gr4dJk8Mxbc/s3NTRtJKYEHNo5qDGBA0W41OtwG5GHg6J6pp6c11Zg6B+2ME+eBriwsLPRLxwxeVXd1TKhtUT11/9EWfn4YZM1LjSDImZfkAND39/djZ2fniIN0joh/OzDG/HN9SiprdkguCs7poOO67QC8IT2fvgXxJIv13b3JQ5f7tB5dVua6tUzXTian96hDIzYLCwsTk0ykKz0nFrLSFQXXL/h2EehawMC6pE8s4HSar1Sm49GBek7Hci+BV+1bV5/+doBvd3d34nmyKhPWAZTltgW5JXiHC4YAmOpm6b5rG8vOtQffum2BMQ6X6Sb1WpfykrWzhqZe0nVKUqrcGckSaSOnzV8zA3J5assfqqdE6kynGeTOEWteBokgPt2atdENLo4AMZBgWc3NzcXCwkLs7e0dOfmsA4KBnIv8sLHI2q0zM61D7/P1EnEkVmXhyimVp3tVuEwd9Nmykdahcuy6bmJvFdftxo7KwBlarT/TFU3rdFgNNF9nI6cym2Y8lOpWUkddiroy/4gEZktCNQ5R9drpEOuzmxRzPzIg5nGmgNHZTh0zTobuutONGjCRyYfrYH4yoOJ4KPE7LWUAplRPbX3ZWMkcOedzdTh58W/1La4fSrYs85PTytfpIfPzeJCz9cqD/i7pteNdeS7JodSnTq7TYCnnn1DeNH1zrD186mgyBdR9MpkS1YSZVXGyZWAmRKMQcciej4a07jl9JeNdCx7ZsOkG2dIA0H0z/FujCTMzM7GysjIRSYs4GhXsui62t7f7J3Pr4z1mZmbixIkTsbCwEAsLC3YZc3Fxsa/vxIkT/W9sdkf0DSBxfn7+yBItIjH4rZE5F61gwOPe3MF65MAUZvyI4HD/8FI053HOtnRwAHn4MzMz08sMj0GBbmrfcwSD+xpgGlFPRM9YV3m/Gp4fx3zxJn1+or4+HFVBNmTOG/BZFtpmPtShtLi4eCQakwE1LEUqLyxvHltajtufh/+7u7tHIgQ8vqGvCk4hO44EcjnQw67rJg6buC0W+K2RWR6vup90aWmpj+ZBf/DIC87PkR6evM3Pz/f3XcTaAXZtn5tUcOTf2WO33KpbPYbGkvYh95sCzdJ4RD4XTWb7gr5RncuIZapy4HKylR3uO5YZ32M9Zf1wfeQiWC66rGNGr2W6W5IDdFO3oLi63fhXXcj2iWdbHbgt2i9ct/5X4Mw84rrrs5ryWLbaLugal+sAIbcv+1+iG4rwla45wWV5HBLP0pUEgf9cpy4jaD7dv5F1ZqmtGWUKA2PHhsR1ospFy+O2jkajWFxcjOXl5T6PLs8A+DIY0Ggb9jxhf52CZDhDRPeWlpZif3+/zwPQx3zyXjeAAY4YwvDzoOAHt3IbsYGcD4uorilI1FevARixfHlvZdYn3B4FnpChA6Kj0ajfWgCghsirnuzj512yA2XDhr7W5wbu7e31J1Ldkj/vTdPnIKqTZ96ZNwVdyKMAQ+sDzc3NHXnuH5M+sBfl6NhVcKKOTCdG6lQA2HhM8SSE95JylA9yxuGB3d3dCR1VYKEyVaClAIudlVtWxfYIPqGNk8TafnaSAJUA0aXngmq/ZlsZOK86NC2Hr4MwyWKAm1HmQ0r+SG2DllXSde5HB5K0PgXZKMPpX+Yv1dbxRID9k9qmDHTU+mruT3c9u5+1gSfrqjtavvKbtaUWNDpf4ACfk5PTbYcvXLv1vvLC/zPsU6ora/c0YC/iBvbw1VZa6iDdO+QADddZGqAZENOyXVo2utrebMY2BGJd+UqK+HWA8LcOBr4OQKXLfDMzM/0jJeC8VCa8l4nlAMesEaC5ublYWlrqAR9H6ZCWwR3KZCDDkbvRaNQvCbPs3SDFMhvSZM5U9URlymCIHS0AFpM6NPCggE/1C6AWEZmlpaWJfoqIiaiM6hRkGnG4R4nbxf0CHhjw4eG3DE7Rt+yYFPhzNMjJALwp0HJRC45u8f5BXdrWaBL6EYCMgQB0joEjvpkH7l/IjoHx1atXe/lrNHhvb6/f4wk5OrAyPz8fq6urR8YT2qDbHPQxKAwoVM9Yv3ls7+7u9nU4+WR2jp0w180f7g9N58rUOhUgsP12e++0/MzG4V5mdx1YzPZe4jdHbl2bIAPWTe0X137lT8eMysWBUh5ffEKa+0nrrPE9Jf+lvtb1QwZslFx/ZHyojmY0BLRKep211dHQamNWZwms6QoKf7IIpWIbxQiuXTVUDfgcQnZARdPwN/92s00mGDmOErByc2PVgaCeEnBwglcF4UHlZmo669K2MrHDxX+0mzeaa2eq84WDBujgmTsvnapxXVhYiNXV1d5Rzc/Px97eXly5cqWPUuCDchYXF/ulSNDi4mKsrq5OPMcN0T1EruBI9cAMHKi+icIZRe5HfTYb6wf3NT8zDTLlvmegyVEkRNrAIwMiBiKIyi0sLPTtQpmITCFaCTkjEre6ujphvLWtWh87eZyGZplBFnx6cXt7uz8ognr0eXuZnjNYZzDFy9ccKQa5JVaUhbbzc7k0msyTBfQDosRXr16dqGt2djZOnDjRvwljaWnpCKDTsYmoKtuM3d3duHz58pEl7q2trdjf349Lly7F1tbWhC5D3vjm9ukyIB/4gI7s7Owc2byOfmL9RnqWDwPH7e3tI9ETBvJOv3hpSwGPc4psYxlkZE6XHZT2A4832Cw9tMA67SLTuuSdTeCUX57MKVhxkWaVPad3dkNlwOPAAQONFLoymG8+cIVVGefk3USHy1f/pXw538V1OcDkADh+u8g+t8sBZl1xUXJ8q26qTXM6UQJ7mpava1t1+1dJRsq34g7kVwzA+WEjdNI2BFCVporwlYRVSxnKV+VwAuN8teQG4xB/meI551aa9bh7ypted0AoI+RncMCzc+UJxhaATh2wA81KLm02g1JlZ0MwNEngujT6xCAQ6XQCoK88UyOBvAwceX+la4sz0NwufayJM26aN+tjrldPR2o5cFTO8TnjqDJTAwQHy6DDLZ8zPzxTBXhDFBgOVidNDKD4ncTgFWAV1wF+EWXG/lGNHjIgAOBjwmNa+NQ+g9nt7e1+2ZSBCsuExxLLBwCOl7/ZeLPzZdnhow7b2RQ3KRxylurMuP9cXtWVacnxlOmks/0OSJRIdd75DVeOszeZr8vqKPGn6UqyzO45OWZ9M6QLjzepHakBieo7bgRXZPp8XBlk9m2I3FjV36VrrpzsvvI6DU0F+NiJZZVmqJb/swEC6VJTiZzyqAF0jtqBTN1PA16YACB0Fs38sGNnRwxw4aKQvOSJ8nhPC665PT+4Nz8/HydPnpxYYnVLJ3BUc3Nzsb6+Huvr67G9vR3j8Tg2NzcnHBmiXXBcEZPLoLr0ub29HdeuXYvt7e2JZUp+uwYvP8E5wlmeOHEilpaWrKJzRI3bwlEhFw7XR5DwMic7eL6PCJ/bS6TvLsY1fiODOimWHUd3AAx4uVB1jvWRD3eg7zmyh2gUQJLKmY0Y2sARPOg3R8ycbNEffJ3HEUdwNB32vLFe88ERnoDgMUDgDXtF5+bmYmVlpf9GxFr54W+OuOI6oqHczwcHB3H58uU+snjp0qX+nbFYPtWVCJYzloH39/f75eLFxcWJQ01LS0sTvEEWALCj0Sh2dnb6ZXkcLGEwzHqNvgTYB+mkwz2Ox9kI5o3/cx/Dnml0ne0i2zpnz3nfK0fM2EZ13eFyOuSr9t5Fs9xqCEdRdD8zT2o0P8pwPowfQYPxzPczwKpgB9ecDdO9xiWZat1ugsc8ZBNAlM0rUUM+noMMam8UAKLfdBLC23EckObfLGsNWJTk7tqfpVM8oWWwPdEtTEyZDLReTu/wBdp9I8u5EcfYw6cKoowr8MG3dkQm0GzmopSFqLuumwBSQ2VnbdC6sEzFjp+JFU4NpEPtGXBmY84DgfNxel7m4mVMlrFGVpaXl+PEiROxubkZFy9e7J0x88nGBg5vPB73zpgHDZarsL9IAS/vH+KlNnxWVlbi5MmTdrCMRqPe2bORwr42Xa7UCBUPlp2dnQngyd9w7upI1CAxUOPlUd5+oCebWWd4eQ7OzOky9zN45PbgNDQDPiytq7HgiQPK4P1vvCUA/3WSwrJ1zz3jh0wDoDDp6WOWI09WFhcXj+jt8vJyrKysxPz8fP+9uroaa2trE3yy3JhfAD60g/URtL+/H6urq7G9vR1bW1uxvr7en8Ld29uLvb292Nra6vNiErO5udm3A32yubnZnwJfXl7udVgJ9c/NzcXy8nLMzs72ky9MJrAPE23TCaGCe9YhRBu1P6Hvujcxs7nqcLgejo6X9m2pc1dQovs9wSNPDpCOxx3rCdfJ4xh52GGiH7lefKuvclFzBaoOFOp/ntxpHq6bgTz7hSGfWAISXI/LoyCQ26XgUMvLyuT/mf9XnVAeuU0K1JxvHyJXdnZ/KH+Ef7A9l+0AWkmejjdOr/6ptt2gasCnTmlIWNpIx1i2/qyAKUO0bqCpkmrdQ8qJa26mUhpInJcNjfKZDRAHDLXTmX8FaKjPOUCUj+gbG9KlpaWJCAbLHiBoZmamz1MCdHpClZfZdO8dIi+zs7OxurraR0LcTBcPaI6IHiDxQQZ88/KsEkeTGETzA10RrdO+ZNALBwPwOzc3N3HQgAEfOxrd86H6wU4V13jZmX/jPuoG2ARoQ1/rUqRzGixDAGsAQHbE+HC/KWACCEbklo0eO1p8Y/KA/aI45MLRvtnZ2Th58mScPHlyYnKDSQLSsMPWbz7wgv7Q98JCjgBtiAAiar2zsxObm5tHDpVcu3YtDg4O4sqVK3HlypXY29uLhYWF2N7enohMK7hlufCYRT/whET35qqzxESU9RZjl8c/AzJdNVBbrcBpyNFy1EaBHtJCZ5wv4L5Q56+2zO33Yv0cjcrPM2S+mB9O4+678eP4iDh8GkHm7Pm3ykqjlFl9mX91IHSon7Ucd30I+Ol99AOPRwVtJV4yYj1z90C8naLGd6uuDV0HOZzggKxL5zBThkm0nONQNeDDrB3GsYSulbFMSOzcFCSUwJ4zSFwPDJ7yUOos/FalZIfvDAnysVGF48MSGSIEOgtgXrGEBWeXEfOo6eAw4ESY14ODg9jc3OwdJE6Nnj59OlZWVmJzczMuX7480RY4NC4LIANLv1iuhKPkjcZwdqhzdnY21tbW+mgGgMHi4uLEY0t4putkNhodvl2AHTqcKcAbO2f0jQIP7jcGSdBBOBd+viCDO5QDh851AjxxhJSBMZ+0hW6Bb41SKmhDXgYv7FTBK+fVCDDqBcA7ceJE3y8sW42W6tIFy9b14ZDMELmbm5uL1dXV/mTz6upqzM3NxenTp/toHh9kWV5e7gEm2oDxwx+3TMeHiNDvunzWddfflrK7uxtbW1v9QQ/WJzyH78qVK3H58uXY3t6ORx55JK5duxZbW1tx9erV/m0D29vbsbKy0vONunkZfGVlJVZWVuLg4CBOnTo1MbHgiQfbHY3Gj8fXt2lsbm727eV7GJNqw1UnRqPRxCGr0lKaTmg0koroMZfDY8GtaHTd0TcUcBvYZ/C+SgaGnEf7Vn0DT8xwnyehrNPqyFnPWJ6sL0jnQJzTVx6rGf+al0G1AyDaRzo+uC0c7YQ81R9nvorboO3KysmCSqpT3GYd26wrCrD0mhL7LNc+Ltv1ofMv3G+cFsQTpYxKQLAGIDNNHeFTJc8qdiBN7zuUzgI9bgNLSlkL+rL7NXUDNECBdO9PqQ2lmY4a6CxdNpBxgpUdBAwk79ljsMuPdXFGhI2fG8AMKHizPe+t4k3+/Lo1lKeDUEEJ7/Wan5+fiNqxU0G5arCR1vV7aW8eD2oYcsgM9akR4Wfr8buEueyuO3xOI3/YyMGBRsQE4EM5cMIMwDnSpTzykm4G+NiIK6Hts7PXT69CHgyokY7beHBw0O91w9LmiRMnYnFxMU6ePBnz8/Nx6tSpOHXq1IS+qx7hG3Wr81UZY2KA6B3uoX2YOOFd0dvb2xNbOvDBNgHIaXt7O3Z3d/u+xQn4ra2tCUfKWyPgyNhpA8yjj6BDmBgycIJj5snGzs7OhN6hbQqW9Le7ljkblm9mx9UmqI4yKY+ONwdclA/Ic5oIj4JA52QVGJfkoRNubWPmh7g9TkZOFkN5HODQPuMytD1DaUo05KuyumruTVterR8vyYJl7MAj+0X8x9jO+ChhJQdaHW+1dKw3bZRIFS4DiBG5QCMOnbwOPJ29wEAqmlaldrMBNR7KAw9eBi+onzfT88yQwTFHDdRIMyBAPo1wRhxusM8eWru9vT3xHLyZmetvy0B929vbE+kvX74cOzs7fVRnfn6+f2Dz3t5eXL58Oba2tibaAl55uYv3MiFqyE4Lb1XgPVq8eR2ACrJbWFiY2I/Is3Z2XgBLvIePQeXBwUGcOHFiwjFzf2K5i/sd0RpEf9ix4z6DYQW8HOFjvUA6Lg/gEuAE+oC+YuevEQ6kRfs5ysSyhIwYBKnDRb9q1BfAz4F2/GbgAn0Fb27pC8QGEDqFQxmzs7OxvLzcL+sC8OGdzTzO8Mig0WjU581OBeObl5257WoXAHgjItbX1/uxDr1HZG5nZycuXrwYu7u7sbKyEmtra7G/vx+33XZb7O/vx+XLl+PChQuxu7sb58+f7x8zA5CIsTQajfpvyJ71mvVUI058D/YGIHp1dXVicrOzs9OPa1zjOtgeMlgB+OVIn05qnc3TCZU6Nhcx1PYo8R5Gp1coUyOF3D4FjGrvdXzzRICpBP60HgcK2CexH3H+guWC37olJAM4Wr+m072XTlbIp/6HZeaWL5HGRZSRrgYMMWg6Dgh2UWEHyJkn7aMhIJbxzpHSIf45TQmg1k5klKoBnyu8ZNSdIBhAcBkOlLEiaZidy+TQs0ZtUB+DkIij0UMGh84AqOPVpTRVWqRzkS8FfBzhYiMD0MAGFU4uYnJpA4CPARYcJAAEK/eVK1diY2MjlpaW4slPfnK/Nwp7j7AXKWJy+Q7RQQAMjmihfu4XgDtEjVAPnteHchhMwajg2XMAjhzx4GgeA3JEQgCwWCdQ7mg06p032oflNRx+2NzcjK2trYn+ynQcfPNpTj65y0tt3L9ol0b4cPAFeuSIgQEfCOGTpJDZ4uJirKysHFkOAaFfGOjxxAI8AzgyANfTtU7Hs2jzzMxMLC8v93WibH7sCk6fc8QL8uG3wUDvwRN4ZNAaEf0BDF5GQR6WLQAolw2+x+NxbG1t9c88nJub66N6iOZBpy5fvhznz5+P7e3teOyxx3oAeN99903sFWQbwXqPZy+yLvDpat3DCtkD3OF5gPjGgRDe8sB7Chk08ZYA9KHue9TJMDtXnrSAL9hx5wMUEGQOTethnYPNZTko6ZI286JARIEC9FYBgeZFfuVbfZe2V8E2+xYONCCdbq1Q+XB+Vyfq0GVSvs40NJFz35qf+XSy0jayHLI6tT34qE9V/65g2wFj1c2Iow/15n5TOapMszZkesSk5agtr6GpInyu8NKswl3j3y6vQ8nuuhMQd2CtMEqKp+U7ZXTgkBWFeWJnABDk5IC8ui/FGUrmHfsE8UBYVVg4O6QHeGPArK+24na4iANkoTM/BnkMfgHyGASh77gOrhtlsZzh2DXyBMCnJ0VZ9ru7u32kAHVCdnwww00wmDh6AtkieojfHAXlSQLzw8aVHcrQ+ND+Qd8weMOBCDh6RGaQNgN8AAHgmx/CzZExNYhucqPOUfuF+1773xlynhQhyslloxwFHcyfOj7m2Tkbdox8X3lHdB1Rsb29vThx4kQfdVtYWOif9Xfx4sU+6gcgy+3nQ1H6esAhwMeTZKc73D6OnkAvwL/2lf5m8OzKV3I+wPW149P9V0Dj+ikDPyoXBzxc5DHTnYxHLlP/lxy8pqulzGdmfJTqVNCr9WidJZ6crpR4VEDt6i2R89NZXVnZTpYlTHIj/LL+K+bQcXPcOo61pAsGdDlEBaHGmhljAOMiD2xQeGYIoersZjQaTTipmtdkscPkWSkv+ehpReVXnRTvmcJnPB73e6J4+QkgCZEHlh+DJS5PjRhkMh6PY2NjIyIOHwUBXvC4CUTuwG/XdXHu3LmJ5S8sWTHoxEZ88MzPJAQP2GjOy7Fow87OTly7dm0i0oj9WgwgEFlB9A59jn1dKJP7gA8JgFe+xnvQ8M38gPb29vq3VCBiw84DUUgGODzIGMTygQDIE9FDRF6YeAkZbQbvaCvzwWOIl86h+zMzM7G2ttbvhVtZWYmI6J8TB9kz0OMlXbzFArLASVpEvVivWW95nEF2GlGB/Pi+AgCWL09QOMKH/wxceczxQSDoDEeReYsArqMPZmYOH+UC+SN6ijbzo3n4INPCwkKcOXMmlpeXY319PW655ZbY39+PpzzlKbGzs9M/tmVvby8eeeSRuHDhQly7di0eeeSRfoxeuXJlIgqHQ1GwSTzRYfvDuoGTvTzZ4G0Y6COWCWTGp73Rbp6EsF3HfV3t0LScB/Xr/lDolC5jqp4MARkFjs4vMW9uBYntJx82UR/B+dxeWubNAT63z7LUJuaBSWWjPrcELrkO/c3+F7Z+GsAG0nGe8c4ycX3GusD8ajCEy83K1DzcVrZT2QREr/OBIbaBWbu5LL2nQRfFHFpethKlNNWSrrtWQv3unptF4Z5DsyXFVUTMRkMVLuNDB65roy5ZsGHT2T0DOVZGOCd+1tn8/PzE8gmTi6C4Qcby4kc/cEQAyg7nAcUEAEFaBb78YSfj+pbBA4A37+1hh4mIEZwr5IY6+HVgo9HhiVzeWwXnxQCUl/X0xCnLUQFr1x0+JBqOGw4PaXh/HIN/nQQ4wMdlsw65JS7WGXYGqEPTMZDliDAiclgiZL3BdQf4+GAN+GPAB95Go8kTnChfwYRO1lgfXMTMEe4z0GNZAuSybHgJjCN+KE+XadihM+hgIIjILb5Zf7k/eE8ixvjS0tJE5BcyXV5ejitXrvQn4ruum9jXxxOGiJgYJwz4eOyCeL8by1ptKu9r5Agz8qktcI5GdZPTlPwA21YFSto/zn9wevAx5HeUnK8B8eTY+R9cY31xlAE+9WF6/0Yo88Ul0JeV49qp/sj1u/oqfGd9pD7T8ef6KcMVypfWw2U7fmtAKpejKzYqnxoqYShtz7Q0VYRPhcwGnI0EG1lG5dwANbjaoVmHZEaADZ/bhKqzF+XfpeVBrY9CGI1GPajgCB8AnTo+GCKkhyEfj48+EJh5YDm5SA/LA9d2d3fjypUrE8uvWLrFs8HY2WNw6Swd6QGAAAxYDhzB0X5ENIbfwLG8vNxHnc6ePdsvOeLAxpOf/OTeOeLkMHjkR1LACetbOFSveDkM5N5LyX2Ix4JwPyjgYx1EPSibT27iQAucPB/qYEeiUW7eH8dyxvfS0lIPmgEMsK9tbm4ubrnlllhdXe3l2nWHD8hG2dBF9DUDCBB4ZGMI/nd2duLq1at9vwCIcWST94LqpGh7ezuWlpZiZWUluu7wVWqQwdbW1pHJFg7W6PI0G1iOVkP3VTfwffXq1X5ZFftdV1ZW4vTp0xOHmnRMMGjc29uLixcv9g9cxn49foXhiRMn+sg7eMPEZ3t7O9bX1/txu7Gx0S/54kHQm5ubE+Ne99DyuED5iCojHe9h5IgyZIPxqnYdv1U/1EHzXkceO7iG62yX2C9gEoprGv1Te8/bRdRuajROxxHGkvoAtbu6kqTjncev2hQu0/3nMTUtuM1ArLvPeu+AWpZf+8elc7wP+W6NVnHfKpDWep2e8W+WadZOrk/bpH2jvDlindC+47ylyYC2QeXKeqk+bhqa+k0brnJuEAMljiS5RnB5pXrYqLCxiZg8jcVGoJYcEHR88t62iOvGCM/MYsPAgI+X6nQPDmb0MFYwGgBdDCQZyDIwVOVCW7BJW+XHQA9lAyBg6Y7Lwn2OTPIyl3OyIAARyABLySdPnuxf7XbnnXfG4uJifzLzxIkTcfvtt8fi4mJsbGz0zwVE+/E8NK4foI/byDzim0GV8o72ot9gUAB0s0kLO5qI6ME2+hXPX+Olub29vSORO9U7yJsjapoeMkP0Z39/P5aXl+PatWuxuLgYd9xxx8Qr9yKi5wN1aD+qs+MoI8+4Uc61a9f6Z9NBf/b393ugAbDiDlPMzV1/xd/KykqcOnWqB0eI0nIUD7wwUMEyqi7poh2IxKGNAGq6JeDSpUtx7ty52Nvbi2vXrsXe3l6sr69HxOE7d0+cOHEEBMzMHD4LcDwe920GaOu6LtbX1+PWW2+NhYWF/gAT+hYA9+TJkxPjGEv+W1tb8eCDD8bVq1d7wAebgcMeWBq+evVq/8w92BnkYfsCfYQcWb6w32gbL5FzZJzHEesjR8IUTDIogw44oKb66PZyom5sPZiZmZmYWKhfwLjmSQHbAAUkaAtPPsAr95M6efxG+eyz3BhX0IF6XdkKwB2QcYCR/aJGnphv538zv+jAkwNQDqhhrLqHwjsApsEOBvp8jf2hI436OvCfkfOv2X3dS+yAn5ajYI/vazoOan3YAZ8jFZoOCDdAkE6ReEmRlLSx0wI8Lp+vK0hiR6uRECwlwnhxWWq0kJ/LQURGl5F40zQGgy7dsNHlPuAZnS7FsKHlfYYMPlj5+Df3AQ8ep6SIBLKBh4PGfsK1tbX+AbS87IgIIssY7YMzxeutONLJ+2wgO7SNgbE+VoL7Y8hYOGDE/arRBJY7PuBTdYJ1B9f4FDIT+oaX17e3t48YA2ccoEdqSNT4qJFRB4SlXJw05Q8OAgFw8b4m7jPc57wR152BvvuWAR9Pjlg/dVLJuqcTQs6n4wjL0Jg08cPE3R4d7hPmkQE++IGTAzH4Y0Idy8vL0XVdPxZgMwD45ubmJt4xG3EI+HgfHmSGZ/NB9jxZgU6pDrPeley0ghLV6yHKxp8LJmjZ7vqNOEXUqz7L/S7ZQk2j5Q/VyenYJuv9rJ+mbX/GU1aXy1NTrwNOuO7GFX6XdAT5M+I0WTnTkivrRsrm9mZtKcmhho4V4cN/PIYADGpnKRBx5UQcPbTBDovzsKLoNZ3BlGYtDA50YPKGfx44cEKrq6uxvr4+UQYvNSLaMhodPmcNToY3xrNseLkwIvroAQAhlppYvpAN741DhALgiAEkwBb2dyHSwY9nYICFethJ6ElcEKJIWALjQyNwgl13fVb+5Cc/OU6fPh1LS0uxtrZ2ZF8jnO1oNIrV1dX+eWY7Oztx7ty5eOihhyb2UGEf1NzcXJw5cybW19djaWkpTp061S+pQr4axUTUBzLBIRIGNnzYBKCO9xiqk+Q9lAy++BV2vE8wG9guYsL6zW8uQP/w+ADw4GVbfuUZAAifZmZnyfqP6CpOWG9sbPQHXy5evNg/5gYgiUEZj0dEY7Dcjf9d1/V7DgFGsJdQox7MK/iFHdIPZIdHn+gkCG1l0NV1XWxtbcX58+f7yDeiqdBXjG/IGG08ceJELxe8ik0jrHjmJfhzh5BmZq4/Dubs2bNx8uTJifsY3yh/f38/zp8/H1euXDkyEUI+6M/Vq1djY2Ojj8JirymnRXp30pz3CHMUHPqISDv6mFcsdKKYAUj2A84v8GSV94jqPlZsc2BdUVufTX4gZ06n/Oi4ZZvJE9CsjTxJ0Ov6X8GKA5lZelx325yUMpDKY9iVrdHciKOPPOHfKvdSO3DPRSRdpFnLyXgAHyVQX5oIc50KUDPwnwE15dvpl46hUnklmjrCx41zFTpEPsSkC4VHHO0QlKVl80AtzW40reMTRgMzakQOEH3C3jMYFTgbNpYASVgOBfACuMFSBAYFv04L9fOSHU7XssIh2sBLhhEx8UwwNSwAelg+5UMUABgK0vXkGfcX76eAzPitCTgEAJqbm4vbb7+9B8xw6Lx0w/zwGxtwuvH8+fN9JAllaj2oH9EQpMceKwZieP5g1x0u3aH9AIns2KGTvEzpdIyNGkeH+DEpeFaem9yAMLHSLQUcwdP6OS/rNoNq1gsXaQRx+QAC29vbce3atbh27Vq/rLi1tXXk9LHTGUS9AMQAurEkiskW7zXVk8lqF1x7sIWAn3WHaLI6WtZB8AiwxhMAPJyc5cvLl3i+pEakDw4O+ndFA2SjLp44AlyzDYCOYOLGy7OQPSY9DLYhBwZtGxsbceLEib59mBQyOEMfA0xyVBX2UJdaeaLMMmX7onqZOWfWG+dIocM6uYfc+FCLAiSlDODwpJd1zfkifCuQAI+611p9lAMD7rfKp5TegVINhGgZJRrqGwWkTOprcS3rE6RjW8v9weW5Je/atih4c2CtBPqy9maYqIRJhq4pAFZepwF+1YBPl8BwzSH+UmMcg+xw2FFiwGUnQ3HNzTaGULP+hiGZnZ3tD2PA+CE6Boexu7sbMzMzRxxRxOEDb/mkKOQ0NzcXJ0+e7B+ejLwAGhytQoQPe6U4koAN2QA9cIqnT5/uAaBGppgfdmL45ld1oT8YqGBWz88Mw7Ij9iLhgAC/KYHbin03vNk6m32CNzgfLOUiQsoRKn47R8Th/kE4OkTWOPrJEdm5ubn+0SVsOHZ3d/sN/RhocMSQJ9cJvqHHHIVDGXwYgwEFdAk8s7PAIRCkY33j/xx15CVkfgWdmyBp9FCjz+PxuH8vLN7EcuXKldje3o5Lly714A3ROo3M8H4rkEY20E94LRnGBusqJl4cKeSxr1FJBdIASUwYR/woIpYPlmaxr47BBsuHo/Ds8PkAD9oGMMr6wn3Ih750yZkjb0iLfcTsfPVQB0AXxg5WAziax0v1mBAyCMTEk+vBb46IsVOC/rNT5rarDjK5yQzL1vGBrQFuoso8KFjgb7SHeeM6uJ16Xf2BLi2rvSuVwfmdP3OOPjtQpnzpb0cOqChoy0CwpnP+l2XL7XSAJwPMXIfmAfGKhdMzB6aHsAu3vQTatTz9Zn3UYIvyxYed9L4eTMxoasCHKFWEfwmyU0JV6gyMcbieDbd7u4TmZx6Rj+tWR6d1A8yxI0d0YH5+vj/1iAgHDyaUw6ARfPMG9MXFxThz5kxvTBFtAphcWVmJ2267rQdSMJYcNQDYe+ihh+LKlSuxsrISq6urEwOIn/t27dq13hnx2yB4Kfjg4CCWlpZidXW1n6nzg5vR76PR9VerbWxs9FELnGLExnucuF1YWIjbb7994vVO7LABmNGvDuTAAeEF9AcHB/3z/viULgAYP4fx6tWrE30OeaP/sKEd7cOzCnmzNl6dxc90BLjFc+oYgALEQpf5vcDQxZMnT/aRMNSNiBmfcIXjRZQRTpllxifHeWmZ95jiESHqiFE/LwNyRAY6BwC6ubnZL+V+6EMfisuXL/eAj3UKbeBxB1CCPtI3SKDu0ejwLSiQP9sNnoxBT0vjGnrGcsayM0fm2PFgggNiUIlxzfqnj5cBnwBteLsFXmWIV57BzvApeJ7cYnKA6DaW06HD3L719fVYW1uLiMNVke3t7YlxPx6PY3V1NW655ZYeoAJg82/o3dWrVydszvb2dv/WEER2ebwiDcY4R7Qh1+yABusb6yjayD4G/ofTcx9i/yjXwzrBTpNBKYNJHlvZsqE6ZQckuD4HblnvOD/ronPymT/ltnDfoB5uM9t2BRpcn46tDAzpOER9GeBDWeBRnzvL+sF1Ysy5epVUZ9gHanu1vAwUukkE8+bKBPHebV1V0b5yeTXPhxXwOUWppWw2wvf1txs8XHepU2r40TLhIHn5xLWXozGZ4nOHqlHidHwoA44QToAjblB8fuArP5cOy0UR0Uc/FhcX+/02iBSyYUfkEg6NX0bPkSsQL5UgwodoHoAe78Vj4MtyglLzXjqNaqGt4BPAL9uTg0HCjpj3r2F5SaN9bHjn5uZia2ur3z82MzMzATpRH0cl1UGVdFadHMuD28H8Qc6QAeflqJTKV6NDKnt1kvybQR7ABnQGJ0gBJjSKyvWq0cKWBXXGauCcY+R7HI1CpN0ZQ+5rduAsZ7ZpzpZoRBV7S7m9GuWHo0Nb1aCPRqN+KRvXAexQNj9aJeLwUT8A09gLyCAf9XO50A/or+7RRZ9AL2EPeKLJj3yBHeFlVR4TLAdHbHf124E71WuetPB1JbXNmWN3ztv9Rv5s2bDEgwM5JXI2ZFoa8reaLkuvMsoAYdbvtf44443LcOD9uGWCtOzaMp3eDLVVdb2mDsf3cdrNVA343OZSF4YsMcQGySm2OisYIa6fQ7NqKHjpzYEsRvyYTWOpCW9zQDnsLLquiwsXLsTGxsaEc0JEUJE2PzoF9fHsC29G0IcwM1jiB+aC94sXL8bly5f7k4OIFqyvr/ebwfW9mHjvJx7fwKcrDw4O+mUa3nsH0Im0ETHxDlxEDniZUJeKR6NRv2zEOqPAguXNg4IdKiJ8iP5EHALpra2tuHTpUoxGo74PWdaINo7Hh4daAFq4buy3guwhR977yPvL2LGyTnFbsb8TugZ91WVBRG52dnbi8uXL/aM3+Bl3kA8+WB4EP5D35uZmHy2GjBmA8DgFD9wPXdf1j1PBvkcsq+PwzMMPP9w/Mgfth/4AJKLc/f39if1xfMAH45aXnyFHbj/va0MfgZ+5ublYW1vr90Xis729HbOzsxN6D31Cn2Is6pjhb2yN0GghZIj9eLxRHzrEYxcTh83Nzbh06VLfbl1O5FUMjjRD73FtZWUlzpw5M2E7oAuoE2NGl6wjok+HlQ3ufxzqwJjBFhFE9TCumE9EB1WGHHFDXrf6wofLIEfeyoAyMGnleniCifd5Y9xgLOoyKgN9jC0GP4i6MlB3EwuO8OI6JiS6rYDbznZCiX2XggtN467zZIVlrrqrvlTbwbYqIo92ZsvOyIM0ug+V62GAB5/jVoaYb86jsmLZur4/DrDWOjIQqjJinKJpeJXG5df2qW5MC6qP9aYNB/hq87uZE1/TCErEIchjUMEDlJ0XAwcWCKeH8uEAw9zc9VdNLS8v98uBHOUYj8f9s7WQD4ZJI2FsoAAqNHq4s7PTvxwe7WNnB2ODpTjwDrCHyAaigisrK7G4uBjr6+v9SVN+ZAcOcuCZaTCk+/v7sbGx0W/8xhIkgCPveVpdXZ0AEWxQuL8QgYiI/lVSLH84IN5Tx/2m8gRohUOB4wMBjKCtOAEJgIVlZ5Sjy9lc7ubmZszMzPTPTFtYWIi1tbUeBKM/WGehi7p3hp0enLFOWrjtiOLBsV67dq1fOmcQDYCBfsRS2ezsbA/UsJQPoKWnztX4sgNl8Lu3t9cv4zLgw8SDlyJxyEiXtgGW2IHwuMH4ZPCDfADoHMXj/zxpUjsDPYF+QMb7+/sxPz8fq6ur/ZgE+OElRuiHeyg6eOXtH7w3kx09b+kAiMEyuNuigvpRPsAUlktRH4NlfuA2gCz6n8cay5EPHOme4v39/X7bycLCQly7di0iorcJLDOUgckYb0sA4NTJCpMCEqwWwFZAHuy0VWbsSPnpA+xM+ZvzQe+ZF+iR+hgGfEjP+dkmMDhQP5XJAPedw9c6HSkwAB9se0AMnJgH5oUBXSb7DOgNtQE6wWMe414DN8wn18fyzpZTHeBX0jZkvGd6NETcPgabGSgvle/6eBqaGvCxImUMKVLlRpRmAdqZLAAWEncwKwrXoUuqMMqYBfLL5XmDNS8LcpluoAHgwLjxQOdrMMIR0e8h4gebdt3hmzwQSWKHDKcDwIL8DD551gwD33VdD/YQ2YBzxDIS9iTx5n6AYD5wgqVbOCoGcNzXkG/E0WfcoZ8hN7xSih+cqroE4Ix6MXDh4ADC0SZ+jZqCCCY1sGiLA29Iz3t98N/pKNJAN/g6O3me3IDYYTIoQB7IiXnAUjxOzuLADNoM3WfDr3uDuK283xHf3BaewKgMeEzAYaPd+HZ9DP2HcUdUend3d2LjP76h8wsLCz3wRXmQKcYgJnCYgODgBPQUQJ5PunIf8J5EtX8MSpyNg23hB1OznWBeu66bmNBBtoi0c/9h7IInXU2IiP5xM3zanfsQuoH74AdRWdY9AD2OrkBmiKRi8sGP3kGfMwAC37waos4QcuRJMI9b1lfn+BRg6G/uI+efdELHuuXyQ3e5HnbKWWRPbRPLJ2ur+iL+zRNNd5/rYqCb+eeMN0csMy1XfyvxuCrxUHstk88QOczC5TtA7NKzTmSg2AH/jF+HP6YFnhHHeCyLRvVUIZHGCZ8HEyulA2rZ5lPXaL3HM1AYLZwgxQZyfup9RPQRHjh+BZZM4JGjQ4ieIYoHx9d1Xf80foAsPhyAWTgiYSsrK70Dj4h+kzw2S2MfEQwtNsPzRtft7e3eyfGyGvYG8hsceKYPxeSlJH5kyeLi4sTrmRCR4kgPv7KL+xfOBo5vc3MzPvjBD/ZRKYBSF1EB8b7As2fP9o/J4Ggev75qNDpcCmB9UcMFh4blu/39/b7P1tfXJx5n49qqM3uO6gDggyA7lj1HpREpwQED1kNEesAjTmqurKz0eysBaBgAIqqFZXmAZTZK7Nx3dnb6pX48bgX9cnBw/dEpq6urExEj9DciagzgeWKBwy5s4DGZmZub63UK0WVELzUiCF3lV8dxNOjKlSt9OYhi47Q3XuG3vLwcT3nKU/rT5HiGIyKcaDfeccsOHHxzZBpjkA+EgLh/eTxggslvzbh69eqEXqPd/Mw+jGmArPF4PAHiMR5wH/Lc2trqgdRodH1J+5Zbbul1Fvzj0Ab2CaN+Hjfz8/P9OMQpZui8gm3eCwhC/7ATZf/C9hly4Ig3j2nYCu4jvp/5De4fDTZgbCiwdg6cdZQBPE+O2S7wVgX1iwoWVPc0ogid4G8HsHgyq35TgYzyBX6UnCx4HyrzwX3E/QC+dWwrX1qfA1iuXzltBn5L4CkDfy6/Yh2NrmYg0umn1jFtNM/RVBG+rFE3SqoALKyhKJsDgjxgUR7v8+DBCeJQsg6a0mDgKAAfRMDA5uUIDFYd0LgXEb2BBQEkIVIHw47oJBth1IVoBu//AnHUa2Zmpt9P6GadBwcHvbHXmbYaHwXc2kfs3ABwAfb4AAC+WW661M0n/rS/lRfk471M4EedLurlgyLqPFiX3J4MbTdHc9TYsyzVoDMoYl3S5TKObqJ9EdFPKgDSu67rgV5mOFAfR1050sUzcD5trXJQp4Rr7NB5GZnHHvoG8lfn5iabnBb6zgdLAHqxXI4l74jol675AATbCBdtAP+IvoEvRPMweVFHy1EzJ3e2Jer8UQ6DCZYBl8ETN94TB3vKthwTDwXU2MOr21F4bGMShMlnRPQAHd+8dYInAV3X9ZNXtMMBH24L88dji6+zPXKOWmXKMlTiup3v0z5gu4m06ttKwKLESym9A1DHyeNkk/Gm/XOjlAGxofJr2uvy1PCsfEyLe1Sfh+outaWW5yE69ps2cE0HgkO0yqgOGv7mGQgGDs+cYLBcOjXYvOSpBhzAgkEUlAsGEsAJURQ+bIG9NYienDx5sucNgx5GiWeLKysrEwcsEHHBmx5OnToVy8vLsbGx0b9HE8AIZS0sLMSZM2didXV1AijgUAkiMwwkYNjn5+cnokNwUrxcyiAFcsXMnd+Pi3Ijoo9Q4gTieDzuHzWys7PTb8BHlHJ7e7uPvPASus7gdeYGB8tAFvVduXKlj5RguRj92nU+cotyGLwgyjQajeLChQv9iWeAJoBy1nUFJ9Bnjp6iPjZi6C8Gv1h6RJshbyyxMiDjCCE7QY4S4RmDWHYbjUZ9/7Ncr1y50u/Ru3DhQi9HRBqxrIfHCPG4RkQMkWTe7I12Inq4tbXVj0seJwzseSmVQSrrBdIiGsVRDkQ3Ab4wNjG28XYKtHFpaSme/OQnx+rq6gTPfGgDEzHIXyd1AOIXLlzo9+9ijJw9e3bi0TQg6OeZM2f68YP9h5AN78HjKJ9OHgBGQYggr66u9vK8cuXKkXGFcYRv3r+M/lxeXo4zZ870Ef+uu77N46lPfWr/qCGMd9aJS5cu9fXiETUYO5cuXeoPsDDfGM+w0fwWD9TPW2ZYL3Syp6RAB1tQAN51VQGTJuiuRq4c6HGOnkE6T5BQlgMU6Ff1kSwn1z71yxxJ1O0qrmz2KcwPk4tMqd9gvdQ+dpggAzys1zxZVOyhvxWIq2xcHle3+82BG03vAkMRhzqgWxQ0sOWCAfjNbeLVJfjfIbrhd+nyR2dEfE3zgWn+78LnAGuq6NirhUHPAuCZLZwT1wPAs729HRcvXoyDg4NYW1vrD1EwKMTsn5eEeZM3ysSBBlznQwk8gLHsuLy8PPFoFcyW19bW4sSJE/2DbRnwMYhdX1+P9fX1iWU75GHHD/nznisGfHBcHOFiYw85cxSMQRov5XGEkQHE1atX4/z58z3ABlh0p/qyJQttEy+nYTBjafjatWvx2GOP9ZEtBuJcFuoYjUa97LGsiH69dOlSf+gBB1pOnTo1ARBRHsAcRwQ4mqptgq4AFPOHgS/kxs9jQx9ERB8p4Xq2trZiZmamX6JEHwIgqNHEMt7W1lZcvny5PyC0sbExAT4XFhbilltuifX19YlxCocIp8zls37wc9/48R/oS40WwthrVIwBJQMNpL927Vq/xMh6g2W1jY2NiIjY3NyMc+fO9WNubW2tPwylY5xfwQb5ow6M0a2trXjggQf68sfj69tJ7rrrrjhz5kwsLS31p+p5HyIerQR5oUx8IyLJkTgXLea2Qr74xttD1LlDtpAVAz7wt7y83G9vQN6lpaW44447Yn19vT/YAxA1Go36V9ThtYiXLl3qxxDK5SVptkNsI3iSzRMs3GegUQJikA8TVjlw6CkDfLpaosAmA30OVLlghYI550tBCmgViDj7r3bIASOmDAxqeq6LdQb1YasD95O2UeXp6lHAx21lEMSyZL/LE+wM+JUoA8XKp9phnVhlsoTd4oks2z2VG8YZdOnDCviUYR1EGfJVht0sQfPwRw8nsMAVzeODKA8bNgDGruv62S8eRRJxGK0B0MOSB2bMXA4b1q7rJg5vMF9s0GCo2Vgj8oKZPB7fsLm52QM5PEICESi0ix9BgjJBHN3ggxyIIiAiBzABHsfj8cTJQ7QPdatDZ6MN48jLgmg39xn6gEEZ2uKUneUJwA69gOzxYcMHMMETBzUGrFcc9WMwwqcj9QQgA1BcQxvUsOEe+ONn3PEz99j5MVB24wUzRwbAvFzNJzYxLlhPOGLEwBv5GeBqn0B/+bCR6gSucUQSERw+8cnPeGPHBjly26FPMHZuZYEj/hGHh5XYQCKSz/vWcJADOgOgBeCM+nAymevBhAB9wHs1edsCHm0CuSiY02g0gGzE9SXXRx99tF8ZABDjiArLsOu6/iHs0BXWW9ZJHjf83EWeCOpjpXjyww+NX11djYWFhX5lYHb28H3iKysrE3saeXIDXdUDR+hzHdOsG84Rg5z/AGXBCQUqnJ7r0zHhiH2T+s0M9HHfaLsUfA0R86qfjBcdd0ouwKN8KRhX0mu14HlIJnzNgU3tu6yMWnDI5WXlMxbh62y/+VPSvWlp6lO6rMisjHCACrwijg4EJwgNkfIyKAY1HAqMJu/x0lkMrvGjAeC4sBwaEXHmzJnemOC0GV4XhZn+zMxMb1RRDniDoYVDQWSEHT0MFIwZAOe1a9cmjN/MzExsbGzEzMxM3HffffGBD3ygf8L93t5erK+vx+nTp2N/fz8eeOCB/s0dt99+e88Dlo0Q1cTm9a7r+hes47EfiFDgG8uu/DooHIJwA5KfqI9rDGzx7los98H5ZQAaYNc9c491Act4W1tbEzrHYAkOAb/hUOGIQCiPtw3wY2tOnjzZO3IswwMgILqj+oqy0A6e7bHew/FfunQpzp0714MORJb5dLZGblGXTnwYXPCDkuGIMTOE40b7OYLIkV6clMVkAye1cTIW0c8TJ070MkV/oUzeUwqwiT11XXf9FCmiqxhnDBiZ+D+MKkCaHu6AbvIhJcgGes97/TD54a0gp06d6g+7IDIFHhYWFvoDM+B1d3c3zp492+8NRJSZTzxjooK2zs3N9XYAH548cr9cuHAhrl27FsvLy/Hggw/2b7TB+6kRKeQIMPTu4sWL8dhjj8VoNIr19fV+xQJ9yZMX8HrhwoXepl25ciUODg76ccAPiGfghUcYraysxPr6euzv7/fPFR2NRr3NXF1djdOnTx8B8ZgsYtkdB05gP7HNhsczPjwp0sgTA38F1OBd9zIiOs1jju8zQd4IFIDYLnLgQp9wgLT4cKSHo2duWTb7MEEWaJeOLdcu9m2cBjaX93+6KBxvedBVFZ1oc4SLJy0okydxKFsBZgb2FIBCjqjXRX7BA9/LwKeTYwYmuT7GVexDhkC21lFLNxThy5TfCZ2dks5c3KyA0+I3b0B2Mwr9RBxGAQBCIFiAx2xjdsShMiMdLyOhbgxe3MMA1fvIw1EX3jeHfFiKu3r1av/6KrzDFM+TYzAFhwMQGRH9icyu6/ryYMQBIPjhvjjBCIMOZ4hncDHIhzOEDDWywPuA+MPRPeaXHShkjjbyTA79AuOCclm3uB7O53SFjYfOxuFYADzQPxzhY8fEbeIoIvcvjDUbGI6CApi55xRmj62BTLh93A8MRJRfXmLBshrrJEeVNKKNtqmRwvK/EsYGnDFH5tDPADcAETyJxOQDctTxD5DPjxpSp4kJCssEEzwGt6qf/GgRPqiDssEv9Azl4eQqQD9PBNFm6Aovg+lJXAYuAMkAQTiRDbkjYggwoTqDSRIAPtrv9pVCDwC2+BpkxJE9ljPbVUySV1ZW4urVq0f0RPVFxwVWO/iBy7x8rxE7Bnu4zsu0mW/SSBHbeY2uOUevZfAkj9PwGK2J4KgN02sKjIaI2+XKc76cbam2JWsryz8DoK4OhwWcvy+R9qFrP9fjVihr6kF5Tjbud3Ytwy419GEDfDoL4WvaQA2BszEoATWevXAn8+yAlxzH43EfeWBHgggEG/nl5eVYW1uL+fn5WFtb69/Jyht/tS2YqeFdsVi+gVGHQcf+p/H48CXqCwsLceutt07MgGG0Dw4O4tKlS7GxsRHz8/OxtbUVp0+fjpWVlbhw4ULMzMzE/fffHxcvXuwfkQGHdvbs2ei669G6q1ev9m9VmJmZmTiIAgPJb25AVI+XKbk/ARL5FUpwQGzMscmZoxHqJBA90Y3O7PQhd76G/WJ4gwiiH3xyeTweT7QLB1p4Bs/p1dCy3vJz7fTNGIuLi3H69Ok4efJkrK2t9ZEifuUZIjgweiwzXo7Dh0++QjaPPfZYPProo7G7uxuXLl3qQQKW2NVhsbyZ0DcAKHjU0MbGRg8GsDUAAAZ9f/Xq1fjQhz7Up798+fLE+OJX7wGkK3hBtA/gbHFxsdc33k+HfIhmYmKCvoM+YJzxQQotYzQaTeRFXyMKBVAGEATQzds2NALI9giAkiPOsCl4XiXGA9KfPn16ov93d3fj/vvv79vEbxLgySi3gaNP7Dyh7/hGlBXAHVFIfeh5RMT58+fj0UcfjdFoFOfPn4/FxcV40pOe1LcBEUfoEvQaMoO+YF8bonZYBUE92BMHvQAAPnnyZK/36EfYRuxphnwwLk+cOBEnT56M5eXlHjjjpDXGISYrAJg8gcOKBsY2CPumR6PDiLNzxOBX/Zrzh/jWKBynV+ADPVN/mTl+BlgaJdLfLAe2gVqO8sffyocD6g4Mo00KTjPiehz4UQyg+TJ54zcHYbRsrVf54ui3AmUHnJl0QqKEa7rdSdvgJgY8samlGzq0wUxx4zmqERETezpceJRn4ryRmx0m6uL9b2zkEB1jQIPyZmdnY319PZ72tKf1xmpu7vq7Uy9cuHDEcYIvODF+i4WCNhj0y5cvT8yoFxYW+hNsACI7OzvxwQ9+sD+Be/HixZiZmYnLly/3IPSRRx6JmZnrJ9jwbD8sXc7NzcWtt946sQmaI08AWhcvXoyHHnqoPzkHB4Hy4MQXFhb6jdh4TRv6jaM6+Iaxx2MYkI6jIwCWOCkMA44+5LL5jRx4FAV0YXV1NW699dZYXFyMs2fPxokTJ3qnu7u7Gw888EDvKAFkcQIaMoFO8n4fBivclzwRwUGfEydOxNmzZ/sDMisrK/0SLCJy2FMF4ggoAwbegM97JQ8ODuLRRx+NRx55ZOINFwDu0HHdf8iAj40eopIA/xyF4m0KcOYYZ5cvX44PfvCDcfXq1f7xJXjOGt46gv2cXD9HzSFfLE/ysg7GJnRA93UikswTOtYbd4+3VkQcLkWORqO45ZZb4uTJkxPLpc4ZzszM9M9zRDq1UwDmAKd4JuXe3l4PagG8cIIeIAh2BuAWAJWdEC956USI7WjXdROHIwDaEaEFCMXkD/WhrY8++mh88IMf7NswNzcXV69ejdXV1f65ipisMOBj/YI+AejhEBp0TIELwOjKyko/ZvCqRKyg8BjHVgnIFGMbW1nwisutra04d+7cxL5d2HaeZEPHeOIFOwa5YTxqWyNi4okHrEMKoHQVRAEIAw4HFrlMnmwoOaCGPPqbQQaPIwU2GqBx1xyoxX8Gc8w/180ThwxYcX6WI2MMxQTghaPlWDVE3RhjLFeWU8YHyxvgH76kFFnLAKQDbPztIo36X8FgiQ9HN/RYFiVuqCqBK0sRbTar4YGC34gi8DIb34cS8GwUBzM4D2aOXA8vVfGgARiFM4PR39/fn4goRUQPFOFEUCdO83ZdN/F+TAYvo9Fo4mQk+MCpYp7lb25u9sARys6P9tD9hnCAcOKQCTt07hc2LixXDADMsgFoAN4UmOObDQbKRkSAyz558mSsrq72r6BbWlrqAcvu7m6sr6/3hgSRPoBQ5pONL+uIzrgwqCGn1dXV/nVhHDUDqGAZszNhsIkyAdohL/QtZMLgh2UD+aANOuB1TKhxgaHjgxpY9tRJFC+jQcd5f6NGTdx4gZPlh4pHHM7OUTfaCYfPesKgDtcAfiMOo6u8pM/LvZjoOSfG/ayOCWCHt2BETL4qkflD+/jNOWgHHuWCca/vf8UHkyGWNcYZ+h19AeAPYInyeFnVLbOizZATABhHu8EHImsMTgDIACi77vB5juhfjZ5wvZAvbzOBrLi9kBXLF5FXRGZ57zbGPcYa96VOBHgrEHSP+4LHDOt45r+YuF523LyCwjLisjOwAV6cneLJiC6b4jeXk/127eA62J64bwdk1CZpO7iMDPxqW7X9mnYIAJXAmcq4pr9d/SVg7K5lbRqqz/2elqoBXzYwQDqjUaag8DBUbFTZIOl+HxYqysSyLIOEiOgdKgDN4uJi3HrrrXHy5Mk4e/Zs3HbbbROv3hqPx3HmzJmer4ODg9jY2Oj3yBwcHPSzadSPyA8M38mTJ/tnW/HJv7W1tX45EMsHBwcHcfLkydjbu/6O0nPnzk1EF/f39+NDH/rQhCGHMRyPx3HvvffGww8/3F8bjUZx7ty5uOeee2JmZqZ/YwCWdLvu+rOyVldXe8M9O3v9YAcOY+jT81E2HCCWeABIMOPd37/+Ht4LFy70ewphqNHfvBeNTwfCmGOJHdEBXiY7e/ZsnD17tn+rA+/72d/fjzvvvDM2Nzfj/Pnz8YEPfCA2Nzfj0Ucf7Z+bd+rUqYlHSMDB8qlA6B4iEdicf8cdd8Stt94a6+vr8aQnPSmWl5f7iO729nZ86EMfmoheInIHcMCb5WdmZibSsc4DYCOqBmfIhxaYRwaTvDcS2wgASnV5HY4ReQGQMJaRHns7FxYW4tSpUzE3N9c/f5IBGMpGvyDqqydbUX7Xdf1EB30AHcXyIy9/YgKGPkKUB/nZFvHEDjJQQMf7cHWLAuqdnZ2NtbW1PkKE97nyXlfImU+u7u3t9WMI9SF6hijSeDyOxx57rH9sCwAhliiXl5d7Xec9gdrOpaWlOHv2bD8G8MioU6dO9dFMROoWFxcnJpQR12003pGLScbc3Fz/PELoKp/OP3nyZA8SYcMhI37ftu7b4xWdruv6x00husfjBzYdWzew/QATarYlq6ur/aOC1tbW+v3H2NN8/vz52N7e7n1ARPS+ALYLsucIKGwsR81B0AUGbdAv6JYu/er+yQz4sN0FMSjlyQv8Ii//cdnMlyuX0yqgU8CjEyPk1XJ1MqTpFIgxnw748ISBwTvzpnvpWWacln2RA83aJywDnow4UInrXG4WleX7qkdcHvthvce8uLpraeoIn1Nah/gzRctQNRtMpzhcDowfGxUYb3Q8DMfy8nK/ZIGlBybeNwSQ6Tbn84wXJxJ51ru6utob2Ijre09wWg0zYlyHMUO9/DBjPPIEeeHox+NxXLp0Ka5duxbz8/Nx+vTpWFpa6peLRqPry1irq6sTYW0YUixL42QhHsYLmXK4GgdaAIJGo9HECUMYNuwv5P2B7FQhNzgYAFk4BkRB8To5Xi4+c+ZM3HLLLX1kAH0CB3rixIl+P9qVK1fiypUr/ZI15M1OiaORrDMMpLBUt76+HmfPno21tbV+eRcOH8udaC+imdgnhAggGwUAGSwboT60FbKNiN6ZKihFWQCxeiAGUTw2wCgDRhPpNMLH6bDkDz4AWjjKxuUgqoSJEPOLOsbjcd8m7KWCbvJBA54Qsn2B7BjwcVQLVJpVY+xiHPIkEf3BkW8s88LB4HQ92wroEHQSbccDhgHgxuPxxFI9TwzAH55riPrwm+0R2xUADIxTfDCp4z5mR4SDGnhAOcZ2xPVHNAHsAcjwUi2Xh36HbvOkjaOQsAG81xoTCAa0uhUC40eXutH3sLVYyp2bm+snfQByHHHGJATRdegSR88xHvgb5TDYYmKfhXKHooPs+xRY8LjnazyWSo6+NAa4XUNp0Q8KeFzdGsxxvl/rdnJ299iOKt7gdCozbpe2AXkZWDsZZDw5KoFjLcvJRvlmXjW/S1tL1YCPwZUTFHeCok8FeG5waQQRwI4byY8BiDh65BsPT8aG/6Wlpbjzzjv7PVhnz57tIwGqmPzMuLNnz8bMzPWTsphN4hEXvPy4vr4e4/F4YskRRv3EiROxuro68biKiMOBhKVKOFjsB0J7AD6w5wUbnRks4LO+vt6Dgfn5+VhZWenbyoYYm+55b4SCIJSv+8YA6q5evRoPP/xwv4fm8uXLExEuNtg8++JlFrQRDgf8wbnNzc31ESA4RT20gTd2AGzPzs7GnXfe2ctgeXl5YqmPT3wyP7gfcXg6k08vY7keQBfL6viP/V2IMLBe855B3o+Ge9jHxJEGfhSQW5rjCQDaBVDLdUOmEdE/s5G3JCCqxodI4Kihu9Clubm5vq8wyUE6bAnQSLFO5nR8Q5ehi8wvxj4v16GfkJev6zYCds6IwLFucQQD/zlKiXETEf1hArQdUSI8GoQnkNB55gGRbUx4IH9cAwDmR43wXkm2UYguAjzivbjYnwmQCp4wqQSdOnUqTp8+3Y8fRPP49DJHqnWZm/sDfPEjr/ixSvPz8z2vDND5kBMOWOBh5qdOnZrwFZio8VIuroFfgFM8BgZjl/sEeUajw0f0zM/PTxyeYVJwq5FWJgWiLvjB3yAHDDLwo76q5OxrwQADniw9T4qV1yFgOVQ3+HRRLQVn7n4JeGkZypuCbQeiSyA1A2GcjvPxBABp2TZmpO10YLYE/h1NBfgiDl/+ro3S/ScAEPyN2aubtYxGownHzGWygeDIFBwpDAw2mAPcLS8v90+4P3HiRKyvr084SzYucN6zs7P9i9UfeuihOHfuXHRd1y/HINqEU20AeljuwcNNGWCBRxhnlhXajLww3ojKXbx4Mf78z/88rl69OrHkc/LkyR5oAlTCETz1qU+Nv/bX/lr/TC6eOXbd4YOX9/f3++UcOCIYeZxshLy2trb6ZegPfOADcfXq1T7SxWXz3io2gLpEwEacowVwwvzcN+gfb8Dm58Wtr6/H2tparK2txV133TVRNpw8oo+8yVuXCJEWS24REQ8++OBEFIwPIOBF9wB8AHBwZHCW6GMYWW4nT5IYyEO/OPKNscDjbG9vr9+Hurm5GVevXp3Yt3jt2rW4fPlynx58YKsBb2hHVAbPR0PUE6AUYw60urraL8u7vbQgjoJEHJ5Igy4gCs3X4MTRdxzNBPiEPLi/YGMApuCIOfIEfgA0oWc4iQ1wE3H4do3V1dV+gnb58uV+GwcIcoReYLKgk5qIw715GEMYu9xu5GGwixUAPIgdPOzs7MSlS5f6ZV0AcV6JiIg4ffp03HLLLRFx+GpJjGsG/xgLMzMz/eoIL1HzeNenHAAEYxxhywcip2gLZI/VBry9hSeMGJt4DSOAW9d1/d5o6ATAICL/V65c6Z9ysLGxEZubm3Hy5Mm45ZZbYnZ2tl9VwbjBuAC5aJZGljD++PCMOn7Wdeec2QfqRIfvs3wdqGGAqmW7cjQqxuXAL6ntVj74N4975icDvGzzFA84+XAe5tmVnYFEbSN0WWWB9juco/UPAXzGPpyHJ/HMV4n/IaBZQ1M9eDlD3JrGMVrqTNcZDhE7wcKxwmlw5At71RAtY2OCb5QFgDMej+PUqVMxGo36wxDgn2dzPDuCkcN1lM8DBQOIOxYOQGeRSK/LZmgj2oWlULxTF0sdvA+J5cXGB7yyI8UsGNEoVn6ACZz6xaERloMah2wgcEQAYJtBH9oKZ8wRXV4O0yUWXq4D6NRN7BwxY944Ysb7b3QJB45FQYjOWvmjM1b0tTNyCk6UX3WuiFogagddctEGgCXwDODB4w71K8hko8gglOsoGfn/j7c3a44sSc6zPbGvtXX39HA4JCVS0o3+/x+Qma50owvZJ5mJJg41w5neqoDCVgAKyPwuyp6DJ9/yOEg0JYUZLBN5zonFw5fXPTzieMxdYe69DAfgY+kNWe/mLunvKK7P2svlNgAiACfnzJFnHzvT0Y46/clcUMwf1gc+jgSH0fXaKc1NPhzhs7u7O+0ap5+eN2R8Lqpgus4ZIPQn47OBNFDz4eQJMBzlgO9SXuwQAjpJa2A+GFdVrW304D7vpM6xjAx3R4c5OiWtDJo6mziqc2TY8//RPc/Vn/3u7u3GPPp/U8AxosOvKR3QzXlMkDZq29gi+5N6bNP+Pkf7rg+bPkP9v5Z2/6pduh1idXjbhroDbCOl4gHZ4yGyYwXBWzM4M423T3CkB3lt5FllW9TFBouHh4f6/e9/X3d3d/Vf/st/mY5eWSwWa++F3dvbq7u7u7VduIzRB7FiZFm2Q+F6jLxJ4erqqn755Zc1wMGS8enpab17926KXHLky/Hxcb1+/bqWy2V9+PChrq+v6/DwsM7Pz+vy8nKNjhiRjx8/1sXFxVoOXkY8AT9EAs7OzqYXoL9//37tjC+SxTNvByDkTRss6WAMDHA8J69evZqOQvn7v//7evv27dq8EUl1bh79cVTH3pr/cnMDS7U2/kT84BGiTSyHOXpDdIX7EvQ5Z2y1Wk3RVYw84BnngaV6DFoXLa+q6V5+I5/T109PT6f54H2q5IoaEHspHyPO+ZIc8bNYLOr4+HiK1tDnLm/J8u3l2NQpptfr16+njQcYb+e4sunAIIJrybs5n9CeSHnV+maB3DRmcMYytgH4arVa203P38HBwdQHH3MCuPTyKc9/+PChtre36+zsbHJOiTZCN45j4oxE6IeMkmO6u7s75cBCo6ovbwF6//79V44nMufXS8JDOI/uh/maVQTaxjmE187Ozurh4WEt6uzoIbrLsmjdZ+d4tXra/IHuJfUD3mTjGpvVHh8f67vvvqvPnz/X1dXV9BYl80w65i7p3NupGIEIb+TweGwjs9jJcmpDZx87R7oDMB6D73W/06no6uj6y30GjYzPebjQ1nPZOcLI7QjIprOZ9PGq2XNgNR1w8ya/w5MdbtmkzAHyrGuEgUbPp5P5kvLic/hGwC8nIQWp6/CcB8SnDTeK2J7g8fHxBO7YmQng29vbmxQE+WdEEay4qef777+f+vf4+Fg//PBDvX79egI3ABfytQAzGF36ac8axe8cqwQB5OOwIQAQQN3v3r2r/f39+v7776fl5t///vfTMhO5hAcHB9Ny0tXV1RpDAySqvrwwnpP/+aRveOb0iY0QFxcXa69jWy6f3ooA4GN5m3Gz3EHOkyNzi8ViWiY0KPCSI6Dv7du3axsx6COGjrpRtPCVDbMBkwGf+0qyvXOWWNJ0SgP0gXd2dnam5UArDxsAR5ABvNRhQGdwAU/BDwbRNjxEMj5+/DiBUsr29vYEQMg7PDg4mM6rcw6awSrOCcvX5HCSqmB5z8hN0p0xeh6ywEevXr2qd+/eTVG+qidwT5TGEWkv70MfllX9rmXoAIi0AUmgS71cZ068JGs9kblQ8IpXFExj+MAbdJBXA1QinPAry5OcWcg8oStubm6mszkzZxY68upGnmWzjTff5LxY/+JMQRNvnvPRTDjHpDxAH3iOaG06MhkIsHHb3v5ywgCOY9XTyQh8IoOk/zw+Pk6vd/vhhx/q559/no5TIhDhec+oi50UR7azf5QEbQZFIwOddq4DhptEgRIUJiADeGXJ/o+K5+K5ABD3dWA6dbPrzrH6e9emaTtHoxGAMk7peI9nsu4RHRLoPQfKujbm5nnU3qZlY8DnjRodgjY67zqSg0HQRsWRh2xra2trSqZmuRbFhXfnowO8KWC1Wk15ViiGnZ2dKWrx+Pg4Rb98aDFG0yfce4mAvJsEETwD/VBMKHYiVBh1AyJADnWxeYQlS4zd5eXlmjIGYCyXX3LvLi8v15TQhw8f6sOHD3V/fz/luXhOvbu26sn7x9hSt5ffvPRqIGMv2m1Y8TiygpF3/tZf/vKXurq6mpazoSO05dVPGX3BkAIIoSmbS1KAobsVUnq729vb0zEt5lVSB6AHgAn6OR+L/1mK400qGH/m1u3bcGe/HO0aKUXLHnTjO31xcSTSO0uZX5ZanYRvb970slEHIBlUIQuOqNIH0wsa8Dy8hYwgs4wZnrCDyXMABowsO9O95DlSpuTZWU6I5NPfnCcXgA6ABMfRPIyDRp+QN2S7c6zNK17RgM6fPn366q0g6ELyNDnJgE1P5rnkI4/RfI8uBAQ699bgDkfCDq7pjs7KtAr6zZFBtO8NcOjKx8fHKc/YRzzhhHpcPj6Lw5ZHesDgJwMbHiP8xe/wXAKQ1FtZRnTP69Zr7nOCP66PwF72z59Ji67kmJmzrH9ufF0xALe+83zkWNxe6lB+y/5WjfMhuWZ737XZ9T3tS0fnTo9vCiCfKxsDPp+NlQM04OsGk8Uem4XDz3cJwlzf3d2dXv7NEhAvYPdvJAWz/IrScTSQ5HTedMEyxPn5+XSu28PDw9QGfYF5MYTHx8e1WCymHDAUTb41waATgHlzc7MGLklydj4KGwMuLi5qtXp6D+nV1dW0dEt+C0t1Dw8P01scoPtq9WX5iJ2mP//889pxMnj9zpfZ2vqyIxllCC2pk80GOzs7U7QF44whQ/H5HDwDWy+NGwA8PDzUf/tv/63u7+/r9evX9e2330737+3tTdFKRxH8iZG3wvHuSfpJf6rWARq87KiGI1xVXwAAS0ksEW5tbU184MJyIjz1+fPn+vnnn9cUF9G/XPrwHLlvCTys1Bw54Vn4xseu8Aw0oj76ghFl+Q6acESIN9hwjTmEZ7yT1lFIeJe+IPfmHc6+xPEyaAU04ag5z9NKG6CHIwDYuL29ncA2xwPhEFpnQUt0Dc4P9PQ5bAa8Lo5cbm9vT2kkLIPacANmvLEIOvisTcsNwJgIOAbSOheaG1wi42xu+9u//dtp+TwjNNDfES+c78fHx0lunYNJ2gpjxRklagwYdaTVAIw5oE2cv8VisXa2IRvXvIpBf4+Ojuq7776r+/svrzAk+ohDiNMGAPVmDHgafrODZMBtfkSX2i5YfvluOTWduQ5PQXP/Dl1GQMVzbd60Te2K+TBXLEZgb87eO0KOfLrf/uzAjWljfnBfzdPds5bhrN90SgyTEXzkyHNT9fXGVX93H9CNI7p5PCPd7/teUv7Vb9rovIzs2Oi57np6qlae6f161yogwLs7DR5cYGKMi9+kcH9/P53phmeY0QkElyVXJ0N76Qrj6/wiAxNHOtJQ0z8bNpYTOYYDZQag8YGnKJhPnz5Nh0ejNHiJOnl07BCE5s43svfuXacoEEdEDGi7SE7HJzZECALKwVECDAM7Ur3MxVKz57fzoHIZP/ksfzd/QrtM8Od5ADJnofE8/aIf0JE2HWEx8HREJOUjFbXnlr4mrV0P85PKwxEY/nIu4S3/wa82fFmH5WjkLWdE30YpDZ2PWzEtfW/yHPINiKJt51eSMmKaZCTEChvHwJEcns3nKI4q4hg4ipoyYRlDNkarI9AQPZUOs6PY0JNn0B17e3vTodjomU3m1vVZZywWi4nu5h+icLyjnL5mhDQjmPz5YHX06dbW1trqB46eZbSq1hwUjy9BsMFD2isb5bzmaG1e7wCf6/R9/Oa/EV91dWQZPZvAZHQ9+/8c4OgAXPYhbUF+Zn3Jb1nPHO4Y0RA9kmPu9O9o7l5STL9Nx/CvaY+yMeBDwYwM4xwTPVcQdBvLqpqUE4rvzZs3dXJyMuXtHR4e1vfff1/ff//9tEGDyBhHWhj0eXnHmyk+f/48LRv+/PPP9Z//83+uH3/8sS4vLycvl00VnHXlhGyWxRwVyiUrxmlm7RQ8yzzkxAFoiOidn5/X0dFR3d7e1qtXr9Zo6KURjhpgs4Xnw+BzsVisAWbn45FLVvX1+0oBohh8gxXGhUfvPjrpGwPtIyt4nsjizs5OvX37djprkMjF2dnZGqBOUGkeZNwYWp5bLJ6Wn2xI4beqp52NfiMA0VlHQKFdAmYDqNVqtXauGTQB8HGGGLlqRDMZRy7XsNwFzYjC8T2Bn5OpbbxtgB0BIBpDveTEwftE+ryk7/FzePjj42NdXV2tOTer1WpaGvXz6JJumcTg03Jk8AVw3tvbW4vCrFarOj8/r4uLi+ndrUTqXKgLgOLNSdTjFBEijoyBiL03UVipwyd2OHFckcnMd2Q+qmo6Vsa5fQBgA3B4jjxU89FqtfrqXE/48fPnz3V2dlafPn2q7e3t+u6776YcYnJpeX8u4Ap9h2NL8ThYObm5uakff/yx7u/v6+3bt/Xu3bt69+5d/e3f/m29efNmAmXoHh+Lk3LEcTGAUuT08fGxjo+P6/T0dMpBhV+INO/u7tbr16/XXlf58PDlyJ2qmqJ95jfLVOcYZiTIuqRzyBI4pQ3tAPVINsznCWg64Fj19XvSbcsNvkcgx/d6XJ2T7/F7LPk99VO224G9ziHsxjdyfrv6fb/beSnAHJVNQHnnSJgunfP8XNkY8I3CxDnpSRwrKz9L8SSwjFH1dECuw/uvX7+ud+/e1dHR0fSC8u+//77+5m/+pg4ODibFZA/YoXADPhQVRutf/uVfpr//9J/+U/3pT3+q77//vn73u99NUTE2ffgF9bQB4HOUzXSyAGXkhHtZFqmqyViiaNh0wjLS3d3dtJuR5dvz8/O6vr6uT58+TbvjoCNGYrFYTKAZpuLQ2VevXtXOzk69fv167fDfqqcXyEM78rf8hhArVhuvLrKWgM9KEzCAASd/kKTzz58/TxFYDEPHU6Y/v1etb6JwhJhNEgaSLBVBS/KAACgYPkcoHR3wJgN43NGaqlrb/ckGhZOTkylvEZ5K8Iqhxdj68HDAtmXByoH2MnKdUTwcIg6drqrpmBQf3OuND7kr9vHx6UBfj8Hzb9BvmUh94cgn/JsRewMwR8/ZyIGMHR8fTxsPvMzK+A26nKPm3F0K7fusOPLokD/mnaVI571SfBA2tLcRMzAG5Fk2u4iEX3sHYGdclkPGfXNzU3/5y19qtVrV2dlZffPNNxPgwiEF1DsdxHqGlYCqmpZaP378WB8/fqw///nP0zIsgMyvB0xADO86+s37rpEvwBifvGGJswA5Hgbwi+PO/AD8Li8vJ1tDpJM59Rm0aecMxFysD/PPPNGVLso/MvAj4EgbuQrj/lu32LHvQO0IZOZYu1WdDujmdY9jRJsOFPpej6/q601kc0DJY8m+jmjQAUKPg/9zHvO3rHMO8P3a8qJz+Fw6dN6h385DcX05Wc4ZwZizQYMdqbyHlUied0Da+1+tnk5Z98YElLcNoY0bRshAraq+EoZcgsUIO9HYTNaNl3rpC3l7KHaiIHin1MNyBe1iYOiL35lpg8MfkQAMhY1st8TMDk+fAUb9BpYj7w1h9nItDkPSysIOLbe3t9eiLc6zwhCbtum1muc8T47YQXuMIoDQnqmXyxxZzMOHHSkzPcwL/L5YLKb3ddI3gL7zs1xXLpejuAG/foVZOj+Wi1TO0M/Gmj462dwG2AXeoU2ftUh+V+4MTqOUStlLk91SYp4taADOHBgYUicbmra3v5xheHR0tJZrS7FThPOZQI2586YKbxhL+qJz3Df4k/lPMGbapD5hbtKAIGtpAJ2XxjXrC9OenLuff/65Li8v6/j4uK6urtaiuelkAUjRqb/88kudnZ1NpxDY2fWY0CXwjPMxoVvSgT/0PNFqnFF0KIDQebXMlXf9mheRaejtSHraNL6b3ilHvm5+NL87wmN68r0rBhhzQML1u88U64I5sOc6Rp8vLV07ptcc0DFYTH5MmnRYxvfltTmQOyrJB64nMdFcHc+Vl4K/X7Wka8OTRHYHmIQO+VuovFRF7ggJ8K9evarvv/++Dg4O6m//9m+n799+++20JHNycrLm8TrqdXFxMW2GIMLmN3ageHknLYqfTR0YVS9vEq04OTn5QsSdpzdDZM4gxtnL0xQUCYnfeJ8pWN9++23d39/XxcVFffz4sVarL8tTNvJWUBxHs1qtJqBGe1W1pnD5vru7O/WRZG8DP073d0QIL931GHQlOMDQeNm+qqZjHPwMifQWdkeTPO+059xNdsxayK28DY6rau1AV781A8DHUjJRYQwDcwZgdN3+Tj9yGZzCmy+IOLAMSju5hJceNH15+/btZGQd6apafw2Uz2gErHijgvP24Au/RB4jzFmKjlJ6eTB3rr5+/Xo6X5B54Xkv6xv851KQvy+XyzV+hmcw7Abllq3Ly8vp1Yk//vhj7e/v188//zxFsJgjosunp6f13XffTXzi9A0+cRJPT0/r6OhorR/MHUuUgBPrpHSIWCIG7KeDBf3SeaVwLY3C1tbWpLsM8pBxb/5gxWCxWNQf//jHqqq1lBmnMqDrfeIAY0QPMw4DdTuMAHHLB7S1LqAdQBwg7/3795Oeok5WfwgYAKZxJpHJ//7f//ukizlihugoOoA0h/fv33/l4Lq4jzi1BvfINJFl82nnKHdR5QTZlpkEOAk+0inteCQ3a2RJ296BmBFQm7uvwxDwox0g5s3P2oExsB/RA1k0Vun6+hyY7IpBX+ozO2zuYz7f6TyPdS5C3JV/VYRvdG10X/7WEREhshIlZ4TX/PB2CXL+vAwDOON/NiaYwCg0LwGgMFiCRMEwOQYWABuPI40dxZOcXp0nzvk8gBLGQp4YeTVefrOnbsDpw5+zeHnCkSIbF4yegaHPzYO+0Jw6DN674ugCddkIzPEJS29OkHckD8Dn/B6DTZ5l/G7PgJ5jSniesXpDTNXXOZiMfaT4DKLzPoNZAyaMheXDc0DdBv8AOIORVM4Zmct+Zv+sVB21YNkYgOy22BjE3Diy7HnNvMl0EEcKkuvdG1+gh5WuI2FOT8Dh8JIstL+9vZ0iv+z4Zw5NW4NJ+Bs9k/2i77TDTlT65v52Y07dayPXXct6+M0Au+rpLTIJRKAzOmN/f396hR+A2O343E1AmR1PCX85UQABAABJREFU+uWoFvTI1YflcrnGV05FMKjjO+/vtSFP3cgcOaL88PAwHfTunECepWBT5kBMLi9mxNq8sGmEbzTHLnN2OO2V60lQkcDrOcDW6by5Z5/r+2icppn7OYcxOvDbtZnPdXhn07FkSSA5B+K60tH6uWdGZWPA1zGsv6cxyI452lL19YnZvi8TmgF7r169mt6mcXx8PEWlPnz4MOXLfP78uU5PT6cT3B3p4L2fb9++rdevX09RPQ5Txputeor8OHTvpSKiSK9fv15Tejc3N9PSZ76jkfFZ4SbYsULDaAFqDw8Pv8rbMXjM5G8Urc8mq3panjX48EvZAZIoTXJcfOzEavWUxGzAkxFOeALl6bwcb2pgqZR+JUjm08vcgAhv8MDIOr+HemxovJRN+yhqnAxfT5BaVdPhsrzL2e2kse3ArA9T9v2LxWKKJJCr2eW1ZRsHBwd1eno6HVfTORjwXAK7/B85zmVq5gZwAM92OXzk/nFUDXlX5J4yN5ydCQ/kKoDTPKCdDT4RV0pG9rxjP3PLiIDyO9EhaMTqABFe5zul8XBOokGJaeTlwYyMZmSC/hKN40B1DsF2mkrOn3emUjdz475WPQE+66aUN2hqhwHd4LmCZ8jF9jEyRBPJhea51erLkTrv37+v4+PjiT+sZwzoacOvElwul9OGEGgEMMPRIwJO7i0RaxzXi4uLyRYsFospf/fq6mpNhnd2diaZdCpL5jAnCLJzSjABENzNoZ91NK5zKBO85bP8n89bz24KMK0/zeMeR+KDrs+ezxHo9G/wgOls3ZZ/+XwHQlerr8+I7cbMuDOg0z3Xtd+Bs66Po//ngOhLyq86eLkbjAFfhoqdZ0YdVhAWZufgAPgwEq9fv552ih0fH9fW1ladnZ1Nhwd/+PChbm9v67e//e2kcFBKx8fH9c0330xvrnj9+nVVfTHavCf28vJyAkf015Eg555tb29PGx2ILC0WX5YDz8/PJ8BnoMP4ULiACIeeDQZ5lqR9lAyAzLl0KDbOkQIQeqcgws5mC2heVWtvUuAMOMpyuVxbFs+oHv2ueopUuW4bBeeZJRBxXpwV0YgniLhgIOBDlnwBELlb0313LhvznDmhAN6UBQ5N9gYBwI0BWTo/pgttO0IMyLm6upo26GCUHa3JKAGbEfb29iYjxfzxrMGcAa0Vr/vrJcg8ixOHyvyMMWU+7+/vp40ROG2np6e1WCymJTKWw/ktUwMA09AM3cL8+GghCs4IcmHZ8rOAzaqaovymsw3y2dnZGkCwTBkMkPCfbw1xOof53vOeAHGxWExngOLgAUQ4cN2OjAE8IJe6vYLgpV7mNQHfYrGY6IPcV9XauXd2qKjj7du301Ivsg6fsSnJB18zFhx3ALPlxGPD0DMWfru+vq4ffvihLi4u6vr6esrPZKMfO405axB9SuoBKT3n5+dTqgLL/tYLLPHjIGQ+7Ki/th/oJpZ8mSvkK4E8NLa+9/W5qI9XpDpwZp5LANUBNUdu3Z5tRuqp1DtunzIHcIwp0iHqAFZXd4JBy8oIeGZJXdn1vwOWo35lX1zPCMx1z2xaXvxqNRuxRPFdyQ6NvGOUH4qRiAC7VVEgVU8exmKxmKIIeQzFKKwOeNna2qqLi4u6uLioy8vLCezh4VfVGhCwZ+mlQy9FOXKAYKKkXUdGO60E3LajZ1YWKbRemiFK57wmtwNt7PX7k/5nZMoG2BsLNvU2LfgoIIr76PZoBzoDKFBM7pO90lQ83unneeV6F5U2PUYeowECS4JVT7li6a2nEcgx0x/nY2GQyB/0GBNQY0jJ4cuIgxWFwZ/H6chbjtftzjl9dgi6sZr2NnKLxWKiKQbR82MAjg7whg3Pp4Gs5Yv20vi6+D6PL8eac7pcLteec44jdbpuH5XC+OiLQRq8b9oQeSJnGTCAbslNTeZneAYaG8DaocV5Y9WAqL+jqcypvzv66k0dea/5gYgcm81MR4BRAmvrreRHQMnW1tbkePNOaZ4nR5I+s2KztbW1dsQXMtGlnUCrLhCS/OK5Tt3Q6QjfnzLq37KtTX577h7LfwfQun50n3NtP0evETDsfttkjMzRCCR1WKW7dxOQ1dmLl94719eX9MXlVx/LQhl5Gi4wcXqP9sC3tramcP7e3pcXvrM5g/fH4uWypLlaPZ2t5SUoIgb0B4OJx/fLL7/U4+NjnZ2d1T/+4z/W5eVl/fM//3P9+c9/Xlu6ILdnsVisncDPTuHT09M6OTmZDLMNDcuTjhgwRnbg4mWbbtyP0WTZJ0FZ1VOyPXW8f/9+7XgCK/CcS2jO5gZ+N6D3/Po1b5RuaZi+Vz0BKwM0PEPzFOOHdizz8fYOlsxxABwdzSUFjCG5SCz/ujhSRR8NTjwvjgRhdL1z0Mt8bPjBWEIP98+OR/Z9sXh66whA7+HhYToXDRoYCBB9xJEhreHjx49ry+PU7aU+08NG3jJg2TUAT6Xu/EiW7AzQLLc3NzdrEcGqmo7NuLq6miLN8Cg88vj4dMadecXnPRrUIz+5I9XtGjjzmzclZAoG8wwPGeDgyHCfz4uEtzhhYGdnZy3KZAcMWnI6AW0vl8t68+bNNL9HR0cTL9I+wAiH2UbXS5BEUtGLgGfmkqjiycnJdISJgQmHuVtHcA86DHlxQW8b5PJOb+rzhiuuo+Ph3/39/WmjE3qB7wBUNpSdnZ1Nbf3d3/3dlPry6tWrNT35u9/9rk5OTur+/r7+4R/+oe7u7urHH3+sP//5z3V5eVn/83/+z/rll18mWjFGcj9z1cR61zm1Br/Iv5eG7RDlZg2nzIycRtefjl736SCMgWcCEctK5qbOObFJj3QmfZ/lZxMwg4ybz3P8/g0a+15HVLPP2Y+O5+dKAnr/1o01QWlHg3QaXlJ+VYSv++R7Rwh33kzvQROiJ/eKzRmAKg4CZnJQ6M4tQzEiFBkZcOj+5uamzs7OpgOWz87OpkM3USooUECXo4/kkNGOX+EDE1mJVj0tXxjwJOOhSLxcOucpQT8Apt+qUfWUv2Sh5g8A4Xy9UfGSLcXjc5sJ4FCKRCE67zWXpAAWjqh6J3RGGDuFw0aLBMqOpNB2RkIdabHRdx0GhOQLYeRwaNJT9nP+3XSGp7jHS5/0nQgzZzSS88d3luBpy8uGjG+xeHoTCHxkpWxA7GOK0nFzVAge9rKTQZGjPwatGDwilNRlmjsv1rJmQ2kDOuIxg0AbAAPCw8PDNWANEKuq2t/fn4y7nZzsx3K5XANTy+VyApLkIdMfG1ho6V3nLK2zwQNA6TQINoAsl8u1VzPSJ/LokA0ihn7tJHzATmQveVNXB/hT7h1dTtnzvEArcohZIjXg4wzS5XK5drg2aTu5Qxw+Rd9z1h9zQSSWCCu8RrDh8fGxXr9+XQ8PD1OqwdnZWf3hD3/4St8gF6k7n4t2pc6C9gn40lnv6nDpdKGfTVA0+t1zZ1CX4M323Pp/RIfnAI31SQdQ52yh60hbMKprk9LR7CWlm6Ou/n9tvZuUjQFfRlRGhMNDNAOYSZhMFCT3c84e+R+OolXVZFQz2R6gRVJu5lD5DRJEG3755Zd6//59XV5e1vn5+VdLuRmhSoOMh2jQkQUF6+NG2GTiKIoZOwFqLlXhjaKwUYyOOCVItAJ0XXnOYC7/UgzQUuBsQJInUvHwnCMhVhoGel6+9WaATgGaz+wtGWjbsYC+uZSbNPNS0Wq1mt5765xOG0s2J5gejty4L7nkibF2vqdBiZcfqcdv1bi6upoMGzsU2cHuaAT073gckLhYLKbIdM4r/Uf2vEEDgIDzQLukFrAZCvlxfhcJ84+Pj9PGqa2tra92Q1IPsmFQnHrIgJ7+mFfSUBncdQ6A+YR2LV/WB9AHXQR/sPObeWVzAf2r+vKGB1Yz7u/vJ+Dm9zcDPtFr1snU40iXHWLGBt2pkz5y7fr6eppL+s3yJhvgUkcxB3aGfWRUVU3vDYfu5MZ+/Phxkln6ybvIfQyK31f9m9/8pvb396e3dfA89PKOW2T1/Py8qqo+fPgwyQc8VfX06kPTjp3a//AP/1AnJyd1eXk5Rfr4S35JI+5Iezpe5rF8LnWqdUB3v3maPlmP5TMJjnIMORb4fgRSUu+PbOMIcKXjln2qWrcXcyUDEFmXHdgsI1szGk/Xz65Ot4dNGgVMXHdX568BihsDPkCcPdk0BoA3PKYMn9qQVj0tJbF89/bt2/rd735XBwcH9c0330xn5aGcbIRsqEj0PT09rcPDwyl5fbFYTG/FqHp6Ldif//zn+ud//ue6vb2t8/PzacNCLgt1ORsoPiKOyQSMDWWGAV4ul9NmE4CBQYm9OwM66FRVa8unfqsC9MHb9248ohP2ulGajNWbP3L83glIP1E6tNeVEeDLsxJdnxUaSt2gIpN0U7HBYzZyjM1Aiv4YAFY95UJyH8aN8ezs7ExRCBwQjsph+RkDb9DgZRiAec41ESDAA2OpWl8ah1/YNQpYIpeVXMJ3797V4eHh2qYdwIjnBaCPwWfec15NZ5738uPh4eF0biX8fXl5uQZKqZ/IMvJ9d3dXZ2dna/m4dlDMUwbL2UfzihW2wa0BXz7rZeQOSPJbLhczfiJh3lVKXbwekfl4fHysjx8/rp3tRp4Z5/jd39/X6enp9FYcVhdMv6SR5ScjwLu7uxMAx1FEzkhPAByxIQQe39vbm171d3h4WO/evZt4uQN8gCbOI+T6x48f6/z8fHoWvtje3p5WXq6urmq1+pKiwma6n376qR4fHyc+Ozk5qb/6q7+qw8PD+v3vf7+WK3l0dDSduceSNc7Czz//XHd3d9OmPxtaL+WTOoF+YuXkr//6r+uHH36oxeLLDu7z8/NJH1R9DUpGNpLvHdhIgJVgYAQWU1Z4LvOR03Hn/o6PfJ9BWK7UJVDrAF8HZkd9T3pY57uttDM5/pEdStpnP7yJJlc9EpDNAcusP1dYmBvrphzDqI9zwHtUXnQOX8ckOWmdZ5BRDn6zN7W/vz95pd4UgTGCsBaAJHZGQaqevGGMCQqMIy9QuGmE3U8v67mgtDpa5X3+pL9exnFJRkWZOKpjo+zIDX2EHh3dHKGg7ozQ8VsuS3bz3415VDomhfE9n7l8CoAiAmGlxRjMKwZJXq6iPfNJ8pDn3+0QnfFRHr4PfsjlTNfxnILqlD3gBkeqqibgnG86ybnO/mX7/i1ly/PTzZ+XO01Pb4zgd4M0R7mhmd+M4N9zLkyTbnnQn0lfz2dH+66kLkvD4d8MFPkfPiZatlo9RZ+hnaOVjtyztO2oKDSHt1NmbFAyUu1ovw0OADCBMnWT1mInyvorI+dpAywn6HdkBGcWEOw+XV1d1fX19XQQPG3DW7wB5OLiYu01edvb22s78+kToG97e3t6Y4hXO6C/6ehILoEJoodVtZY/bmBlfWAZs07u9I35ea5kfZsAp5eUUd/m6rSMdTp07pnnAOgm/c3/82+Orh7rc/Tv6OJro37Z/rwUrPlZ//Z/DfB5qSYNQ6f0bbAd9SNKx7EabMw4PDysb7/9tt69e7e2DEtCb3pQNtaOqGAIUB4sL/z444/1hz/8oa6uruoPf/hD/fGPf1yLrHS7GjnmhJwejDwKaGtra+2NDPTLG0e8XOoEXCIqBgFexoNR8LbtgefyNgqHCJM9bwMk389xAJ0nl2DAv7k9A7WM5nI9AThGy0LIMhYRxeVyOeXC2UiiyNNDyrezmEcAGBgR5oZcHcbpMfKsAT194tR+joIhgktu6NbWVn38+HEtb9K5qT5ihXY6BXB7ezvlLflEeXubzClLUh4HeUvwOH2EHyieHyvqdG4AuRhIomks85lWOGuZ1+aodFVNBvv8/Lw+fPiwZqQ9rtQrqXAzRxP6GIDQx3QsDJCJTlInziKOJ/fxm/WZ+SdBKzqB58jd44DiT58+1f7+/sTbrA788Y9/rO3t7To+Pp7eb/373/++3rx581XOMGNy1Af+94oIgI/iCC/9XK1W08oIY7y/v1876geas7HEvzHXzBdAbHf3y+vpfvvb39bt7W19+PBh2lBxfn4+8ZYPOq76ssz9b//tv63FYlHn5+dTJPBf/uVfamtrqz58+FD/+3//72lD3d7e3lo6we7u7pST94c//KF2d3fr48eP9cc//rHevHlT//bf/ts6OjqaTm1Av0IX5IZVJObXb2rZ3d2tN2/eTPxV9cUpI/ruNAR4ovtLGUz9ynf4Le2h66GY3ztQ08l/On1+FlnqopPmRffH+s0ORdqNru/d+Nx385tLB75Gjn7Wbeci7aKfHbWT0bqkhcdPfal7R2B1ZDM2KS/apZsTkcxp8ABxq57OZgO0sLxJnh6HF7OUgWKkHo4aMahEuWROX3pP1LNcLuv9+/f18ePHev/+fZ2fn6/d6+9MFsDAkR08RZYhGIvHTz/9TFWtebpmUtr3kiOF5SCW6/zaIBQyRhFlibJLQbdhc5Qhl0s77yiNGnPuqFPn9Zi53V4CBIBq1dOREVYI/I6ypK9ESAD83tVoJYGnbjp3kTDT3UIIaMBgHxwc1NXV1QQGmA8iyAa1RK5xLDKHMWlV9QU0kMPkXaEWdMbstwIwRn7zOLql2o7nsi/MtTctIRMYZ4Ccl86J3NPHjIBycC7LnES2tre3pzQLxuWlys5IdoCPOfaOXPMBc08bHqv/kFt40ZE5gEG34YW529r68ioznvNmg7u7u0lmcQZwfK6ururx8bFOTk7q9va2jo6OprdBWC4csff4c/5zXi1X0JDxkhuIwwAIWi6fDqtmU4d1HPVtbW1NOnu5XK6lkfAedL9uzYAUWvKedM5QxWHCAcbZurm5mRzs7777btpwQ9vQ/PHxsd6/fz/JzcXFRf32t7+tv/7rv55yJslf5SxV51zC16enp/X58+eJBiyNY7ugq9/6kSC76smpYF6sk8yH1r3O0+a5zuh3utvX/FzHE6Pnuv8TqCJXo75Zd/m5OeDkvibOqPoaoFq/dnZp9HsHfvN6XhvRv3u201PZnvW49ZXrsA14SXnRu3RhlI4YDASviEH496qako/TaDjp28twRtnpJVgZ8zzKit+JdJA7wjtET05OphwS53glYERhMgZHDxmr8/F8CKdz6ezlZ5TGUUDGa8VvUEpxP5zkj6JG2VlBdB5lMsxzisKfeT8lnQMLKUrL3o2NSrcEAnhjXNAevvTyEPT24ckYEp53cQTUyoPiiKvrWq3WN0Z0Cs9GjONbmFcvJZmWXAeoAJzoF38GThjIkZJBdrM95moO8BloOT8OXvYOTgOn5DnmG54m2sqRSeYb6EZ7yfcdH2e//Xt3blw3X/AhfbFTCQBlXpAv8hJTHpN+nisKThwyb5BpkFFVE9j66aefpoi29Qr12phbN+ZGMTbRmB7ZB0CzHVLoD73Qr/TDxc4YNIZOVTVtzjNfJw38uVgspvw9HCKDa0CmN4s4SOA5/PDhQ338+LGWy2X9/ve/X3ttJA4zu56xWdAIvr++vq7/8B/+wyTX6DDoDI/bcI94tONLg3qK5cHgxHWMfnObna4YAaPuGrzuPnR2ys93wHIOrHbXO3nPupO+HRgc0acDYPmcadz1vxuP6+zaHzlkczZ4NI9z5cW7dFEEObko+i6RH4HDA+PcJCJoLOmx/LpYPB05wLKnDZOBU4JG6iRPhDD9X/7yl/rTn/5Unz59qt3d3frtb387vUD98+fPU9s2OFaiKK5MsK9aTwb3i+tJruba9vb2FDFkbDaEVU8J5mnYXfyMD1glD5J5ILJClAHapoFwvXyO/rIfKQCeJ67Zk6VfNpDsDkXZUk8CWvfRyjCFjqVGPjnXCwO+v78/7QDtnBh420n83pGMESGnBxDjzQEGbwAhb2bKQ7t5jigugJLo0MHBwZT0jwNzeXlZVU8H+Cav5Hw4+jBXDNCd2+UIFUbv5ORkon+2XbX++jzGxzl7gGCDLMuCeTk3chjw0F46m/wloHIfccqIenv+0EPwKDtFDX7hKeuhV69eTWfgYfCRQ9JUqmo6Osd8Dv/BX1VPr/BbLBZ1dXVVOzs70yY3lis5/9FAM490gT9IEzHdoAWygG6184HxdC4mqSHooZQl5ujm5mbaEMEpCpxHyJgZN2NAV5CXt729Xaenp5M8AvBIfyDiyr3MLZFCNoiwce/8/Lzev39fr1+/rnfv3tXx8fEkz2x04e0w6ALT9/7+vt68eVP/8T/+x/rhhx/qv/7X/zptVAJAou/hAQPyDjCZN9E3zGU6zp2T2jmtBpx2ODIq/BxY9HMGO45GURKMdsDM96ZjyL2jCJbrsW0Y0XZkuzYZY0ej7Hv2aY5Wo3F0YC8dZ+r8teVFmzaSoO5IggUXMxYKCOWCEkY5UYe9pI5RaNegz0aA+zEwGMrHx8e1aCJ1oBw69O62vXxiQfIYDYIdlncCtcfJBKJEzfR8dmF8e7Be9rLSzPnohIHSAb68ZtqM6k6ajQTQTO38wqzTkTUMuumWfAGtV6vVBCi87GZF4mhM1uc5dmTCSh+eSUXsqIWBSP6GA+XNF3YQMKYGdbQ3R/McR/c9S/KtwTbGgk+nAlgOHRHPeaekMUq68t1jHI0rx5+gPZc0u7Eiu85vdGRua2trAgrMm6NPAHkvnadDZb3hJWLzBmPwMh99sWwQzSLqd39/P70X1lHRdIisL9B/bKBBpi03dqpML/5PGfY4Uo+yXA+9qId+2zA6Mp/zitNMH7364iVs84edPf5w0tjtfnt7O6VdJFi1TnVu597eXr19+7aWy+V0dIsdNvMxnyO7knT2mLtie+xP1/Fry3NtZ5/9PXnuuefm7pmroysd2HNf0uZ1/ejG1NnK7p6ch1HpQOPc9VEdL6VP1QsAn98YwbEnVmb27p24WvV0+C9RPQAaE+Pt8OQ8XF5eThGw9FrwBB3Zs3dLf25vb+uHH36of/mXf6mPHz/W8fHxpGBQ7py2ToRxtVpN0Robs4z2WUm5vqqnaOjR0dFanoKVOSWXrPD4bfy9zIkRfvv27VdGH3BkQ+IlkQ4Idt5UGk0rdT+PYvaY/TxzbMCSuUer1Wrt7RvmIep01IZPK3bTknFgFDlSIpeI+I03ung+SVInQkt90JPNO0Q+qtaP4qBtxgFQ9Ti8NEmkwfQleuZlRaLLRLu8EarzBPk/jbSvdd/tRDBW8moN+uxgYSxZ5nSeIlFBz6npbRkxGLDcJPhD7m1QDbo8H7RJXxM8AepY8u+cJwNxn6/p+xgfeXk+lsRjymUjAD9A8vDw8Cs6Eu3zWN+/f1/b21/erctxJUTNnO9Kcc7d1tbWtPkj35bDBg07ITlPpp/nMfmJ+3G6DcI4ygaZNO+l00ReLBFvn5NH3p3ziS3v9B+52t7erm+++aZ2d3fr7du3k8xub29PR+BgJ3Z3d6fNKoz/9evX9f3339fu7m5999130w7hn376qc7Pz+uf//mfp6NloAW6xPoU+phOtjUplx2oSqCXOX7cl5GyzlHMukbAI5+1THUlQdQIqKS8215l+1n3nHM4pwPzGWOIBHqd8zoa63O0pdgOpP1NumU/TJ9cBRyVjQGfc+nyVVU+qNURLSYPReOT4+k0gnB4eDgpm8fHL69QYgs9YMFC4QhELo95+ej9+/f15z//uZbLL+9KJPkYBcG5fd4piudronvyvezq9mgfD5prqeQp9gqgGUuH5JBY0dqonJ6eTsvWzlEy4HOkKCNbVtyOWLok2POc0paFE2DjdgELVuiZm+IcyFyW4rr76v64uB+ALjbXAF647o043mCBccnXe3mZNyMO5smqp+ijx2JQQV9JWD87O6u//OUvVVXTOY3eMOR5JUrttjHKzhmFv/xncJ/3+HtGgra3t6elcfOFDRPL5+Q/eT4wnKaRecvKjHsyopoKnSVlKz3ngDo1wPMALemDaeLdk+Yp83jV+hJz9nt7e3vK/fJGDedPdo4LffcOcoDY1tbWBG5p6/b2djobDqeGY0YAh+Y1ZIu0DsAH+gPwROSL/DvrIeQQeiAryC8RR9qzg0PdBobffPPNtLybS+NEzRgH4Pf4+Hg6OWG5/LJh7uzs7Kt0Bs+ZnWdk6s2bNxNAZmzwOTaO39ktzuZBgOT+/n69ffu2Xr16Vcvlsn73u9/V4eHhtDHEOsqRSa/adCsaDjSga5wnbJ4zv45sTDr6Kff8ngCyKyPdkn3LehO8ZLFdybqz336m02ddO9bNOZ4RSHwObGY7XR/z/hHgz0BQ1uWAj/8SyD9XNgZ8uaRoo9uh4WRM7+wiX45DPJ0nhWADfKrWIyZZH0sEFhKUxfX1dX38+LHOzs6mXVwADgwywAwPGcHyJgLTgD9HXMz07gNAIiM7nkzuTYOebafX4/vT+NijTcUBMM05ZHwjgfEyUwqXvWkzIwAkDd2cokhjaMWY7Sefddc8JvMwNONQYj/n3ZKZl0cUYXd3dzpWwwbFkZUEG27foHy1Wk1ODZE0/hw5hWdXq9Wao0N9jLlTVtDCOXCmmcGyl91Stsw38GFVrZ2j5/wuIkAdUDddDGLTSbIR5I8In/UC0TA7Mel8cY/p1PF9B4hHAJXPBKvs0rR8OJplfuA7gIs+OCIKgAVU4SRzz8PDl8Ou4UtWTQxafaAwxdFNwDkHiDP/Birw3nK5nI6TISLuaF8e7t6BccaSqz4uzpElJxraIIvQl77wx2/OG6btk5OTOjk5qdevX9fx8fF0yDVtcj8Bgvfv30+OIGDV+g0bc3JyMkVPLy8vJxkw8LGDmbwFTTpbiixYfjyPCRzMwyMgNwfwunpGOniu/g7MZdsdWMz25+5LeljOfc+onwnUuvrmQGBnh7qop+0ez3U2sGu7o/9LysaAj4RmL8sQ8k+jaiYmskJCLEexcHglJ8aTVPvTTz+tRXRYfkPAYXjetWujxLIRgvbhw4f653/+5/rHf/zH+vbbb+sf/uEfpuRX7j89PZ2Uzc7OTt3e3tbu7u50vIZ3gjnKtb29PXl+zoFBodrQO4nXETCWwbx0i5Hk00raStGRwDzawExjgGgPnb5l1CcNO9ccbUqvgr4tFosJ8DDvXgqHH/jNSzeZP2WP1psl4AULiSNBKQwAKwMXL/eRSG/wb2XPcqnrQdE7x9R9NU1MKyt0Ige0RTI6PIx8ODoLf2LEiRhQp4+B6ACf38xiWlO3l2+91Oa5NFA0fThig80P9IelT/ejW6I3v2bkwvPOHB4fH9ebN2/W6gUYEMlhvpB5gBC8RF9yDpO3zad2QBwJtcwj46SVWKYMDHkzkPkU/oEH2OTh3cDwICstfnXYhw8fJn4iwohjQB3b29trR4jYOalaP8+T/DZovFgsJlCzWq2m3OhPnz5NhxCzGcVRUWSIV6WxksJ9RO4cUUzdyGoQ48ZJ5zDkra0vb8TgeUApb8OgDzs7O/Vv/s2/qd/85jf1zTffTK9M+/bbb+vt27dr4+Kd6xcXF/X//X//X/3000/17//9v59s0LfffjtFCdnA9Nd//de1tbVV5+fn9T/+x/+om5ubSXenfsmVJAq8Dp/a7jBPjvqYXw0yOlAxAhYJzPK65Z9ie/9cQabhR8tUl5PtvmaU3nrPgDb1f4Ls54rHlzRIR4l7cnzZx7Svvu6j2dLxdP3poFM2pT3lRRG+ZAaDoM7AcC8gEc8Q75zDOJlMh+47AOlIA3UlUHGEz6ANEIJxx8Cxg8vKiaVSG+wMqWc0BwPgScmoZG7ASGUIgDDAmPOMMtePZz0WP2emwojNeWYJDDIXyvdZIG1EPI+dJ9Z5Kp33i6Ebjee5ugwSHVHyO065z3NAigG0doTNfcx5GXmhXZS4qtZ2ncOfKHcvyREB6zx5bwLpSsd79D3l1cAk59HP2iB717n50e2Z3zK6QfvMqeU9cwdxQHK8mZNqIM8Gns4AGIjSD8ux2+h0ThqDNGIGsdyfS+Tmje4wY88VNFgsnjaWALYAurTDsSycIADtUx4ts6l/TSscdvrh1zs6h8w6w/oM4Gl6MU8A9zRq1Gl742Vg5AF5cnSZXbw8j91gHD4lgvP66Cvjur+/r4uLi3r//n19++239fHjxwnkHR0dTToBm3J6ejqB2eTvLAnC5kBaztdoDru6kS1+f650oG/U/03rdB+fowt1jrBF1153/6bFfD+qo/s9bbT70dm6ESDlue6e7vOldK96AeDLpOGugx6gQd67d+/qu+++mzxDlnO5lxwSJwA7IoORIfKAYOZmBIj34cOH+l//63/V1dVVbW9v129+85spssgzeM8Ih/N/3DeDA79Vg/7kocuABE9MHqfh5eLV6ulgaY5qyWgiAuLEf2jvNxeg/DwPGB1HAA2cqMfRQ+/0w/gCzuEBaENx1M71soRjMGDQBf8YELjuVHZzzJ3gi/kAqAGWrBQxMIyBcacQ2xtlTn2PDXMHWJmHLmIC7Q3wqmrt1Vo2rFtbW5NB83iralrmStCVgMZ0p23u8R9teo6gifk1c6iqng4u9zMJJqrWd6cbFFnOnJtI3fQJXWD+urq6+iq3dbFYrEX4zaer1VP+sFcxkAVHLn30FHQkmre9vT3lYY6Mg4EEx4nglEJ3lvRXq6f8N54zwKYNHODDw8OJljc3N2t8c3BwMAFCZBm+HxX0mfnfvGQ+82amBI7mIecJOmLFvDDvRBmhseUDvl2tVmtn87FL1vPKazTtSGFLyFXkQGf6jky+fv26fve7302rLLe3t/Xjjz/Wf//v/71OT08n2t/d3U31YeuOj4/rw4cPdXJyMr0TOIMX1i1cc9qBVy/gM0ekuWbZ7PjNvJogsosIdg5a58x6fjpQ6u9cz4h66tYO4NgWpm7uANfceLviCHwC0s557+ryfJpeaXPyme53233+t9NDn7tn58rGgM8bJ9wpC3fV+s5MduV+99139dvf/nZaKjCYM9DzIcMWaNphB5tfzt15NGdnZ/WP//iP0/lzv/3tb6ezsWw4TDDnVPnsJIMqxopCIifRTIIydvEJ7uTYeCnWr86yMUwwTb6e58FRxu79rgagVpxJM+9IzZD7YrFYO+PPfcxoUoI92mdpfOSVJGN33qWvzXnKVgDMHcoePvIbIlCwjpA+B1DoM/PuSHUH+HAmbm5u6vLycs3pIPcnxwWopF63C1+adlU17dDsEsM9V1ZCGSlzJNNy4egZtGNekRk7HciH+2jaO9KJLFBYdrSceQzmYZxAxsIcA6acjoLDA+2hL0n5Dw8PawdKO3KEfvKZjCxpI+O5O9f0Tlmhb9DR+Y/kN3OfDT9zjH7zuNmFy3I649va2pre2GGn1hHkNJ7MuQ8UTrDJHACi0Ju57EidOCrOazOPUw9A3s6Kl6c9Zk55uL29nfQrUTnTzzuUkTmivozj/v6+9vb26vXr19OZioA/AN8vv/xSy+WXZfO3b9/W69ev13Tk7u7u9EaUDx8+1PHxcf3xj3+s9+/f12q1voEJnrRe79JsnC5gxw3ZcjQ8wZ+BSIKqBF6uMx1YA9AO8LjuBI5uKyO4rr8DrKP2EuyYXglURyANrGHaZd/s6GQgIkFutu2c0bnxdaXDAyOgv2nZGPC5dCF7GvbykxPPc3nKaJqSS0ceZA4MZYTAVD0tL7GMy245e5HdOKrWJ7bqiZj2WKvGmxcsINRnT9u72QxqkwmSgSxkXiLqvDMr0JHX4/6arl6a6gAPc+g5GZXnlg3y91Efu/9TMXSCbVq4TXjG4MnLNwbdRHXgqQ6Au+6kCfOfr8LDGNpAdgnrzE/VE8DMxHciNxn92sTz7ejja176y3kwr/v8t1xyd/6tjbRzmeDBdJKcsmGDloaNXFxH+zvDRZv0I3eO+x5vQOoK7bKEzhh4hvk2rei7aW09mrlaGHeDwDQAtI+OQ08xH0mDh4eHKdpVVVO+MjuAc+l2tVqt5T2aHy2D1vnWe+guR4ANXM0PBpT82eln3nJ1oqqm15ptbW1Nm2Q4FcKODIVIOxE30os4Ruvx8XF6u4bllvarnvImuzQaaH94eDgdn3V9fT3tJnbUh3kzkLPcZrGRN/3NZx2wGIG9fKbTzWmTso6sd+57ljk7Mro/67buynpTN49An3WixzgCjnP1uHQ0nevLqL6OJzKvcZPyYsDnRFN78GZgwvFE1diJ6w7ak7Onvbu7u5YzlQZ4tXp6UT1vyGB37cXFRd3d3dUPP/wwHXPBy8YzP4R+GExlDo+Xb9lg4chCZ1StFC8uLur+/r7Ozs7qw4cPE80QUM7boqAYbUBQui6dZ1G1/j5T5mWxWHwVHcGYugDOrVAcifXbS5wnlAyH98/znr80aOkkdGPjN+iTEc6uZCSRuomYWTlzHxEaAxYiNwaw0BZDkmF26l8ul9OL4T1mbxQ6OTlZy2t1PY6oefcr0RZeIL+zs1PHx8drOySZY4yJ+1b1JMOmqx0qos/Muw0ZY+A71xyJoe6Tk5Opb9YBPMP7czGwNkDmC+ftEXkxgEoQlPNFtM7A2nNoow349/zmhgoDT9oA+C4Wi8nBI4pE373szPgM9gyMFovF2jl4gHvqsX5kJYXCb7nMfXNzU//0T/9UVU966OjoaHqThGXCy8uksvDGGp+gkIAnnUNyJkllAECxdAt/AEbteHu+qNvH00BDNjc9PDzU69evJx3oZ3Z2dur8/Lx++OGHenh4qD/96U91fX1dh4eH9e2339b+/n5988039e7du2kpmU/GAQ2qntItfGYr7THnHPl1f39f33zzTR0dHdXV1VX97//9v+vi4mKKBK9Wq/r48eMUkXQQwzJhvjVNKAbYzznltld2VHydOvOZlPt0FNImWJ5dOnDUFdNiFNHKQEA+t6m9SNn08+gC+po0mqNVAtP8G9XHNTu0vwboUV4M+BI4oBgN5ABxAMKM7lWtR5RMmFS0vscEYqnBYIZdip8+farr6+upXpYq7QEnY9BmEhKlmf0143lyKShKHw+ztbVVr169WjsI2CAyvQfG7FytNLAGhh3QMdiimJmT9mlsYTA/Y6PqSALtWmhMm/QsR7Qb8R1t+//O8zGP+loKawJ2R1wWi8Va3lbSLGnaeYJeqqdeZMKfBuDOkfGGCICRlxCdW+XS9c/jMi2yMG6/GSGjVPTXS10GCLxpgOjRzs6X11v5dWOWN8BDOlSeD/iQejwWLw1346KPTsHwHDP3Br7IFuA1o9bJCzirjuZioClEBf0/z0MTG2J4wSCQdvO1kzzHWEnRgA9xHDkQmGdOTk6mJXD0tPmePx+D45WK3KRhgO1oliNi0M1RXLeV82YdZJDPfJHKghNqZwZAiLNKv66vr6uqpl3vfl0gEUAXbBH2wBFL+uz8UMbGJpCzs7P66aefamdnp/7yl79Mc2D7wrjMA50MZ7TTdiN1gXk2S+pOg5Ln7k8QY/3b6fIO0DzXv+6e0b1zgYJRgMSlo5v7mxgh6/AcJCjN+1Of2Fan/TZ9vTrBta7fc+XF5/AlGLGg4oESIreCtSGzAezQKkrK72lEkN2WQclyuazr6+sp8pcGyYQyQSkO3afSqaq1/DXXQ3+IaqEIyNcj78c5Rjlmj8GFezIal/c4ApN0tcdHfbmEyJgyl8sAj2c9B2ZsA/GMhHi+Rs8lAyeY6j6TToyF9hJYUjrvzcUC2KUhUAzUTGPuOT4+XgPTGMM88sRg00t4/Pn9zMgRUa7d3d1pk4CToZOfDQYMys2P9BsgCsCiLehBHi2GDT6D14jqkNNEFNI7T9lZ2jkcaQT9e87BavW0qQGgiuEnqshyH4DGkXTzpqNk0JHoE/01TwGUlssvOz/zzEFkh/+dk0nJVBOPz8bcETXaTqfF+qXq6SgtzysHCvP3+fPnOjs7q8ViMc2R6Z5zQJ95njnlKKnl8kueKkA+N/UsFos14G96IAcGxdZp0Jb2DfoMBtO4Y4vevHlTv//979cico+Pj/XTTz+tAV2izUTzFovFFJF78+bNBGDJu+U7OYHpSOLo/+Y3v6m9vb0pyuj59bJ6OhnMN/UZICeQ8JFT5iPfk8V2udOLnY7s9LTrS5tumiSw9z1dyd/NHw6UUHfq4a4kjyStRg5zfvccWu47MJ1jeg74+n/PaReY2rS8CPDZONMhBvr4+DhtyuAtEN4Ja+NjBZR1cc/t7e20ZPXw8DDthCKpOo3Ecrmsy8vLOjs7q7u7u8nQoMC8ZGlhspLAYHqZgucx0n7G+SnegMEf4LOqpnOi8mw6JhFD5N+7MSZAstH0QbzJUP6eQJhxAmq9vM1zzh9DIdlYcb+T5E1rG/Y5Ly2XB8wb1JklAV+C5xQ6G4fc8eZiI+e6GJdp5p2U0PT4+HgCRl6KMfC2EoF/7DQA+HLnt5POWSK2A+Y3G0AL7q96ii45v5X7nBrAEirPYLyQBz/L3GWUzYAPOrORJueD/tjYWO5MP9q6vb2djnPyob6c1fn27du1frKJyDutUza7+QDc0ZfMOXOksqqmiNpyuZzOR/QbMQDKnqMEQPAoPOecU/OTQStROI4FQe9Z/wEM7u7uJsD38PAwLbESzaMNO6kAS0Db9vb2lBvotANWXNClgD0ibgZILo4CGpwZjEBn9wEeT3n3igpvW7q8vJxszI8//jjJ2fX1dR0fH9fj4+O08WaxWEw25bvvvqvPnz9PS/aLxaJub2+n8wA5/9H6c7Va1Zs3b+pv/uZv6vT0tH7++eeqqqn95XI5OUcPD192bDPHCQK7Yr1q2bUjZ7BlW5vXobEjt52jPQccs2+p+xlPOiuWaz/vYrBoXcxzDkbwl4DQ81K1nrM7Ary0NwpQJUB/zk4lzRJXZd0OmFE6+/RcedGSrievi7JAkEx69o4pJsEMbIboQtYuaZyJKKDEr66uph1ZyURz4e70zNMY2tMdARL3xQKTQuW+vNTTMWN4PvLP7XaM6zbyOZdcnjVwd+TPhg9F7EhbNy4D17xnpNz+T5UUOtN2BADNK8kHHf8T6c4wvOfQEQsrLCfsO8nbBpi2+Mznk/aZe5fGEl7EIdje3p6iIxScqNwM5bnPvhj4m+8Ntjve7uTMxs/Ome/1HPgVdb6XOfHceXd6GkX42dFv/5bjxCFyVLAzDESZuohNOigjgwpt7Fw5KmnDZz5OnWHAbJBlEODleM8TIMf5jt70ZD5zn71ZB8DiQ6Y99ozkuW3bENdnPcQcM/fQx1FfDsomSoksIKfkclM/aTtEBUcFpw/Ae3JyUlX11dLxJqVrpwvIuKQuMC/MOdP+fQ5wdn2csyv5/KjPHTjKdrrSgdRRGemgUV/9/f+Gncp6RzT4NW1vDPgSiSfqRMly6CQFb4pT2CkPDw+TASIKgjCgLIgIsESDQuD31Wo1vbrmw4cP9T/+x/+on376qY6Pj+v777+fPFUnH2MsjfwBat71iLFgqYwzrbxkmR4EnvzFxcWkfLy0jILwSfI8a+WaQKmb/A4M21g6kpNeTS47jBgHoJ5GD4NrL9YbDNI77caQY7MhtTHPsXXjTYPaAYVR6Z4ZAdA0IPSXuQZcAJLevXu3JgtVX6I+5LjZcNroEEFgFyF/jorAKyR++/gT+NkG04AGGYBPfFYec0HOErJpPuUNB9RZtX7eI0beS9HkTDmKzvUEeQmMoDuH3xJdcySo6mm35mLxdG4k70v1ZgOMvpeVkVWWm31oL8/5jEw7Ph4XUUFHOwFSyIXPB6xaf50awMjROkceOyc6l3sXi8UU9SSyZYfT4IjI0mr1ZWmcVRW/MQiwcnJyUru7u9MKRo4Bvmb+uQ+w5OX/xWJR19fXUwTQZ5NaF1StOyDffvttvX79es2Jwsk2oHOE02+J4ZOIM3oSvfXLL79MOYjHx8f19u3b+vbbb9cis+QAbm1t1Z/+9Ke6ubmpv/qrv6pvv/12qFug3/b2dv393/99ffvtt/XnP/95ArhsaHEgxDaCOTPPWV6tnxjTnOOfoGJOR7qeLLlqAR+aF5lHjyX52f3yH/1GvjpQb1q7OIo4B+I6e9M5SVnHCIBuAsRGIJlreT3785K2XF4M+PieyhhhSpBlpWkDx24qBmcPiMiCPUCDTDP43d1dXV9f1/n5ef3000/1l7/8pf7mb/5mOj+pqr5Sznj8GDYUjpcPGAvJvg710qfMrUPJffr0acohcj0scxjwJYPPMacZIhN2c65syKx48N67dhIAGtylwvFcOFrjqI2FtGsrmTejDR3vjUqnvDoP7Dna8t2gzuP2b/C4jSpODwqeF6vzvKMIBgkJdAEBjqJWPZ1jhwIg2ubdtFU1OUXIFTzT7XrkeoJbj8uyy/gAA4vFYi0Syf2OUjr306kQBrzZfmeg/IpCaIPjiN5w/h5RVvM8tM48X2joDQbQDePhfttIOKpZVROwdJqFHUaPEZ4A1CfAcx4p9QB0RrIGiLADa/APyLcu5Dgr0miYfyJbPgjaTktVfaUDoFNuoLAeZS4Zf6Yg2LmFd3lFJ/MGL7CMCb2IvMGLzAU62ykWgMOPHz9O5yEeHBxM+Xy2T/A3PPDx48d6fPyyy5zDlzu9C39VVX377bfTuYF//OMfJx7KaHU6yZ2usl00b/g+y3YH3hLMZP3PgZMEfe6P7UY60iNdbD7vAF8+2+kK/77J0mfSfLTEPHruuftyfKbD6J7837SkvW6FcK68aEk3iYKQk6+HN316ejq9MzHRcxoqe28WWJQ0YPHx8XE6Pf/+/n7KA3n//n398ssvdX19XXt7e/XNN99Mx12MlkHoewokfbDSNrglsmBFTTTAJ+VTHA3lGpHEZP6qdYG3x2aBstGyYc15cpQthdzLIvwOMGfcCd7oS0Y38h7X2YHYZNqklWn2UmbuFG23VJZGJXMqO0/O48t5xRgYAAHoHAnheeeyrVartVy+NP6ANiLg5m0MNwbQy1+MG9CDwXF01xEcy6jnhHuscJ13lXTOCBglUxxMd/OHQWsqfvOHowgeO38JLgzeqmrajYmDSt1V67vQLSscoUE0ypFU6znTjSVA83JH58wdBBDZ+axaz73kYGLrgHTMkNk00AZqXPcGF+aYMRF1JiKVr0dDp9sh3N3drVevXk39Rl6urq4mGgEuHSDwvBosA5hz53zq3aSjl43TzrDpyTICv15fX9fFxUV9+PBhSmXY29urT58+TW/28Lg+ffq0ZsvgvapaS/OBL46Pj+vbb7+tm5uburm5mQC60yScF+z6uJ6/Ubr7+N36vLu/u2cEsFw39yRoHYG9EbDk9862dMCH37HjHWBOGo1ArNueo9eIHnMgsbvW0bd7Lts2+H1J2Rjw5QBg8s+fP9fR0VG9fv26jo6O6ptvvqnf/OY3dXV1NS2BENli+RYlixeOR+t8CZbCPn78WOfn5/X58+d6//59PTx8OWsJUPhP//RP9U//9E+1vb1dr1+/rnfv3k1gkH6ihCw0aaRsLFE+Bj0oNEdlvHTB0jJLM1XruTcIPAeZjmicICc9dBu+qq83HSTIyR25VU8ec9WTwPq8OX+mwBrkdWAz+25wkQJJ8bhyY81I4DrezHw0eNRLCR4fyjXBi7+PFBbjwPiwQclgiLxSR1bgWxQ/oIyoBBFrIoUsqyI7r1+/XuMH7nM+n8eInJGwTmTD92Fo83xF99ugpfPanf9lEJHXDcgAtCNjQB+99OblIXjcS9YJ/ug748nla79Bxjuh4QHknkgo9/F3fX29djYkAGS1etpJTX08M8o7pE/Ux7ErHApsoIJsk27ijVJ2RtKg2LmsqrXVC3ZfW6YAd96M5dc5Qm82KFEnO1dZabm6upqW8G9vb+vx8cumCHLfcOppJyOSjmpeX1+vRaWtNw1EHFUGFGZkm53LVU+rTjc3N/WnP/1p2mjz8PBQR0dH9fd///f19u3bWq1WdX5+vnYyw93d3RTts36lPS9vI+/ffPNN/d3f/V1dXFzUn//857q8vJzO6VwsFmvRUS+HO7qMXkuHwcUOTAccTDe++w/+6EBOZ7PSAUowlI4Yv2H70SNdge9pB9nuwGnKWNbTjcd8lONyXd2ycrad7Y1AY4JY26sRqOsc5E3Ki3bpzqFae6I24ElwGyaHTbvBWzmx5Ap4JKIGgCJ6wfJOlufQsI14B3SYACsQfu+S620QMSJeAvWYR4jfdOzoMvr/udJ5SNC5Azz5v8eVwty1M1Is3f8dHTphHj3f3d99d39G3tNc//w8Qpdg3GAqlWbXF0drkA9AJPydEXG+O9fKffSSJm8fSIcBsGnDmku/o5IAbxTh65Rl8pfp4+82oKad6c6nI/VJ33R6+HRSPvX4OgY1DU0utyev8en+dMtfSU/zgz+zmAYjft6kdFGa1ENV/SH1pnNGbAHYXpoz+DKw9tg73ZJOiuXETnvq0uRf5tk6PefVEXR2zSa/ELllHF69yYhu1dMpBxQciaOjoymf3ZFBl04mTK+c77zHn119z+m7bCNtw6b9HZVuDHN2JX8f2YDnisec9qar0/f6c3TvJqWbO/d/DvT9mrIx4GO5z0tA9jQxEAiBt+cT1Tg4OKh3796tvW2A5aKMFiEw19fX9eOPP9bt7e2UiH1+fl4XFxdfBrCzU7/5zW+mnCmWZ/yieRRBGluEy3l76T25b16+dSI2OS30H6PrfCO8TbyXDgxlflY32QaU/O7lHpZrvCSVwpw74wCtjk6m4h+Bvc6Y8j+AxNGf/PNce946kJTfO2WWYD3BQo6buXGx4s3lTmjV8ZbnraqmDTo+7NdGnKVbIgVE+B4eHqbzLHFidne/vJua5VtHsLy8S2I5fEQEg7lIg8Iy2Wr1dObcyPAkb1g+eJOCPeNcBsZYzp1daL5Px221Wk08Sl0JLnwMTkb4DNoMbA2YAfDoAkfk6IMjgX6vLmO24wSY5NOy73E5J3ETgO2NJavV6iudU1Vr58hVPZ2v6PEzd/zmyPFi8eVsPo4pQcehyyzjnlc2gXjjR9X6GZOmkdv1/DPWnZ2nt9KgTxiXafX4+LjG6+yEdZoFY+btRSyvLhaLiYeXy2X95je/qaqqi4uL+umnn+rm5mbKDd/e3q43b97U58+f6/z8vK6vr6djuOwE+IgV79gnYn90dDS95/3f/bt/V7u7u/Xp06f68OHDNLfeuMiY4U0DXM9HRp8sk/xmvuh0vHnEfNc9bxlP29QBIs899bhu6wOXdAxSLt0n1+3rXX3uc7eCkHWlY9e18RzonQOp3A8fc38XNXwpENwY8GGUfHaevVYryfR8FovFxPCvXr1aM1xMdnp1fL+9va2zs7P69OlTvX37tnZ2dur29nZa3v3bv/3b+v3vfz8ZNhQCRtlLOB0zMMEGn2ZI57MBXDDKfHo3K/Usl0+79Zy3R7FgMtksu1lBGXwazPjZuTF0oWcLmI0zY7DS7QSvW2rtwCljZD7cfgqvQ/M5rpEgpTKxUPCJkU+AmYnSHU947FYsBpP29jKaYIOc7yvFaGM8M0LAfHpTBn+AGUAExTLAxgrqSA+2W4ZJg9vR23SyrJMkn4qMdjBc3hXM8wblBrF+CwM8Dc1sqHmW5dnF4mm3puWHOU0Fb3DPPdCWjTN28Nx3fodO5hPzD/rROwdNK9owQOoKcgLNDWbROUln+NUbHdwuz0Azr9LAf6ywwOvmFy81M9ePj49r7Tnym/rJYKUDDIvF06vi7IjnqxAZD/zk8yPpM2PIHduLxWLacLe7u1vv3r2b8vVIKWJVaWtrq05PTyeQR70sASNfnHF4f39fr1+/nl7xSb9Yqj86Oqrf/e53tVgs6qeffqqff/657u7u1mScOXJ+qh3ITm9S0qHNYhnP1SfXnVF7X6ddB09wjv0c9IGO1vFzK0a04f7MgdDOHvm+7Hd3/6gvc/TrHLquDyPQ7GfSWepKZ5/nyotz+LyU4cgCxsVhfQjJ7ibf0xExI0IwCwO38Xrz5k2tVqu1HYMoUQMIH70wYoKq9cNNO+WEd8su20w05jnn1BlMQBcDtlzmYRwJHNJYd56c6wFked7sJXZC3S3nplEybVNAXWzQOk/J93QKPsedwNLXn/Nes86Opt33ri8eVwI8lkSJQueSbNIaJwGjDS+Rq8c7ortNTVVPS0IujiSOxmHA6r8cYxYbS+rBmXGELxVoKiS/ycF8nO9I7ZZlkRtH5KyH+J6gFdmz/vD92Yb7lpsXDG6J4FrHpEyYN9FhRMCgIUAwI3wGqY4qI8fkHrouCs6B5dQHhft+r2JYZxlQ+LQEg236ymaMqqdDyE37u7u7r16tBk9RzA+WbWhv8L9YPOU286yBBP0HHGcxQHWuI3xloALPnJ+f17/8y79M4ybKen9/X9fX1/XTTz+tbaT5/PlzXVxc1OPj4/QeaPMRfaz6cgTM69ev6+bmZjqAGV7GEbAza17Nz+Tl1Odzcj6nN0fAJ3Whf+/aMe1HOn1UOr01N4a5MgJb2f+5aGP+39Xja5v0eU53zwHYTcqLX63m5R+/uoidTkRTOsWAIfOJ/2ZA8ow+f/5cl5eX02n4KMmjo6PpLKPf/va3U3g8jQUChwKoejr4EkXh5VsMCULuXEGAHscGXF9f1/X19Vc5ed3OVuq3kue68x1ZsusiLAZXVog+7sGGwWF+nkfp5fl/FtQ05nzm9wR60M71Ubx0g8fnqA7zYWMJAPIu6xTufCb7l4omAW4H8rrlJBf4xu3YA7u6uqqrq6vpLTOkGHiJabFYTADh9va2Li4u1pbJtra+JL+fnp7W4eHhtHwEnyBLVTWdWWla+FWGSZOcV0eNmU8vIZheVTVFODC4jm49Pj5O8ooMI/uOFtn7t5wi2xi17LP7wfFGAJqUCfOUy9bW08vskxfgXy9VAqatJ4juX15e1vn5+RTVIarkiCsyx1i8tHt0dFRVT1E9Ik12Hj0HbP5hHIvFl6XWN2/e1M7OztrbQFjyPjk5mdphXAA+O7REZR8fH+vTp09f5XJyLqrngnnlt+VyOb0vPEE35y9eXV1NYzBw9c5U5N3nrHqDDHMEvZkf8xOg0jbGzmwCu9Xq6V3DjHe1Wk3zulwu6+TkpFarVf3P//k/63/9r/9V7969q7/+67+u1WpV19fXdXl5WZeXl/XTTz+t8TV/tPftt9+uRXn5fWdnp7777rtJnv/whz+szTVjto3JYjkxmE7ZT9CXetM6N4v1Ygfi/D31q9tFFrK/XZud7sqontvPtudKYgYHUfyZTiJ8aHvoMYza7YB519fsV47ZQHUO+HblRRG+ZBBHMFLwqr7OzegI58GheFH8GP/06gGZCEsCgw6YMCFpCEaThmBh0DDUXobrjFO277F5Em0AbaiyjBg5ox8d46R3YOZJgeuYMRltBCBGpQNqObb06F3vJgJrumRbm3h6nXLKMeT9+Sy8gpK14Du6Bz2517s2MWLwA0bVkUJHM0w7/+U4cr5G85lGgHu6vju6TYQP+cg2qdORfS9Zcs0H5o7kB93A/xkhT37gvqSX5zHnG2fE/eMZAzTPG/Ls/nb8Qz25dIzT541f7ptp4t/RvV1OKQ6Co0LcD5+aP+mb7wdsGLgY0Bl8EPmsqq9eHwlwM/ix3KALcwzmVaJ6pk9GuN3v5HPa8bymvncOtKNw0Jhdxru7u9Nyspe6ncLhFRbuc39S7sjR5dB2orPuq+2vxzHSw5uCgU7HdTp4ZGfynufsg/ltbhxz45q7/n+qmO7WI/BYguhRmbOBc23nsx2YfCkNNgZ8aUzw5DNaR64fOQjcm7k7nIJu4FP1dNYdx1N4mQilwnNEXKxMbHz5n9/yWAUEm/EZvTuCgbCT4+FT82kjQYsLkRczDlE/KzuPxQrYbzNIBkgDYW/QBsjG2YqGTx+7MCrc6745kjoCYNlnf3d0KSOHcyDT4BW6pCIxiMn+ZL3QCH71Zo3uXupMUJKKEH4mknd9fT1Frm9ubqYd5nZgqJf+dMsl1Ilht1PkCB8OimntOtNB8Z8jkwZLjjDwLPLts9wwXgY2AFoXInwYRgPiqqdl4OVyOR1p4zkwL3lZNJ1Kjomy0QJsWu7pA/LIvJFMT/RntXraLGGgkmDVQDUNSNXT0Sa0B3CC33Byzc+7u7vT3F5eXtanT5/WIv/L5XKKqAHEDZYZ72q1moDhzs7OdIaq89G8AQ4eNXBER6GPcz4cNe2uMw/UaTsBjd1Wyhu2KA1zyqGBWKbPcM25hYvFYgLiTjG6urqqf/qnf6qqmo7EWSye8sUpbOpw5NU8ZwDLtcPDw/rd735XV1dX9f79+zo/P5/AKsDTy+KeV8t86j7uN9/xaXBu+qbcuw3bkO53Ow4dkJuzDZ3j9GtK1z8+00mkdA6xeZhrtqMdOO7G2uGDDI75NwfRRuP7vwb4zCR0zq+RQol415qF0V6flTnX+U7dLJ0AIK0Qfchtd5CxgRET47ZzOZb6UVhODPZSCX9+XZAVOJNgOqHo/JokjKHPvIJ2KczOMQI8+BmH7g1CEqgYBCYDAvg6RZEKgzm194+SoY+51DwHljpAliWfT4VBH7pob0ZCO/DmelDIGE3T27ySz5heNtTwKMcHXV1dTWe3YSw4uDwFPNs0mIPnmQt4N8FhbgyBb3E2OhAMT7lNvidP8AxLuEQqMGosuzplwUuxjm4sFou1jVB40wYDtIcRthHnuxW52/Gr3PgzHaARjt1yuVxL5bDxpn+jDTH85v7Y4MMfzCdAljQSj9WJ/vACTjPL6VdXV7W/v19v3ryZgAS7b+GhzEdEZvkdverXnaFzmV/r86qnV5LlZjX0Ty6TW1/ZyfAKUPIWzr6dbPMkEbEsdm6QBdfhZWHm3q+jo89HR0cTEGcZ//379xNYJkILn9I3zh7kNXe2idDCeho++Ku/+qspdcgHXNPP1M3WP2nToIPrsKPnZzN/E9p1u+W74ENnW1zmHO9u3tP2+HsCxg5s+Xr3fzoHOQbo2dXfAda5MY7KaPzIRDrmXZsvKS9600bVk+GBuVEC3eQYsY5C2vbcGCTJ7AhYeuSOBjnKlJ5BN9lWynkt8/Cc09cdJzBiuhTEBAT2sFFmZrbsm8FqCtLIgxmBrDlwNfotAVN6RZswYDcXKVg5bgubjWZX73MCmcopnx2NoVMkHZjAWJJT6ly6qlrjKRvEpG3V18uV3aaELpLhMZlX0mvle0ahunF382weJvfMhsy7gzGcBqadTHQKslO8lpHkIY/JOqmjEbR3bivzgmPH9zxfbcRLSds0LvTF+tF9y+XndEZ9X+ojA2GDTveF/Efok/ohI8Spv6yz81n6SJ+9BNvNtfnUdDNAM7hxcTTVfeg+/T2dlpQByxr94TdsEzl4llVyVs0Pro86HUFkfM5NxBkjQs7bq2yTOpnvyiZ6Ia8lPQ0i5+xH15+Rjk3nxzrQ85QgKOv2GEZjfY5Oc7Yk6fMcaBvVuWkZ2d4uUvr/DPChsGHEk5OTaROFPS4rExTpw8PD2ns7DXoyV+/29rbOz8/r7Oysqp4E3DsX8STxvJJBRgYrFRaTa+WJp4O3za48lL+fRzE736PbkGKjt1isR9ScL4LXnctqBg8pjNAygaPbzwjmc+C4u4YCtGEyHUcApAOfHbClGNBTJ33vBMAGijYNFudA32icPEtfOhAPr+/s7NTbt2/r6OhoOnrIy3D0h6gLZ0hWPRne+/v76RnmCkNC9MS/0R9/xzkxTQz2PE7G5OgY4zOf2gDzHNEYR2TgKR8tlJsoOsVu+auqtcidjRSfLMta5qgL3rZ+SYCV/IEeWS6/nM1G+gZnqrGkyxmJubyDDDuSZRkw0GWu05Exj7jMgUSixOgj5pLXlnXgc3t7e9LXpON0+hF9Yx43YEkZh18TlN7e3tbl5eXaTlzTylFGdqWa33AODJIMSplv0yr5zTzMeKgrj7oAvDkavlg8RYF9jXrZZOXIpeeXaPdisaibm5taLBbT/UT87Uhsb2/X73//+ylyS9rHxcXF9AYS81Gnx1JuHTHvdGDnWKPnHawx3VPv8918lH1LG5af3ffUyVnvnB1L3s77EvyaZr5njsaj+vPaCJR2wJI/HM7uvqqvHcZNyq+K8CHcKPRU5jaSBnX8bkACQzl5F+VKHqCXg8yIVeunvncgz/+bwdOzMGCyEkfwuyVR6rSQdwzXGTyH852bwvOZyL+pZzcHsEb0mWNWGyTfa5qOwN5cH7vPqq83t2AsPeemeQf25miR/eiey5JjS8VIZO/o6Gg6Cy5D8fANgIZxJXDPNjz33riUICZpOxe9S9qlAuczFUrylCMiyLU3ceWRNAlCO7CRS1ymnw1xt3SV4/fvHZ0S+OJA+nVijqRlGgjtGNCbNubNjEolGB0ZKv53ZMpRJ+syA+ekadXTuZB2zt2HdATN5zaMBtWM0TzpPFSDQMujnbr8y/aJqnn8XcmouH/nGn2HdinXTlehn9Rn+0PfsIHwh+uHFsyPbQh/zhcFbB8fH9dyuazj4+NpV3kCWNM0eSV5KJ0nz6V1U0Y4rZvog9vsgEz2K/XKcyX1zgjojcpL20ue5NkOYI3s1EtLx+ddOxmY6vrz0n68OIePKAPnjTkBG0LByBxfwouhj4+Pp6MCfH9uuri9va1Pnz7V7e3tGqi0oiMh2R5XDt7CieJL42MlRj+IIlxdXU3nKNm7p1hh4Fl6OQ+g4mWSqqdjEgwgMzqA8unG4PHZs3SUMo2qBduM5PFkgqgNjRWk86s6oIciSNBjw92BZCvKkcJy5IbvrieXemwQM3+FdpLuVjgGmkkbH03UHTeEkXISPtEx6G0vmvn1AbPMby6RkS9HH6FdvlPVANFLUx2wTqPQ8YLljPwpP2sgkE5Bznf2tapaHjagcaEf2dZqtVozXIwpI6PQlgif34fNxgzqtOOXBtSfHn/ngY8iJoyjOxbHIJMx7O7u1unp6eQU+sigqifwZtlDF/uIGNOElRivZtjh5JNUFHgZYJ9Av6qmzSQGOYzZzhH3oIdpJ0vqhXSKrHtxNuww5ny5HsuKc0bplwEbvEd+Ln3LpV0fx8QBzm/evJmOH+vShKDH0dFRvXv3ri4vL+vjx4/TfND/EQhKWYC3crwpU8l/6Vi5n6lfUqcThezsUTp03G8+c/8T9G0C/NzH7pkOKCfdRmArbUTSuuvHXBk5PFX9kWLWey8tL17StXEjedfGjU7f399PYWjOYOLEcSaBzzx89vb2dkpa5Yw/2icCyCtr7M15iQkm8w44C7mjkPa2SMp9eHioi4uLOjs7+8pQdczqzRgcqcFv3VETGJpkLMZCn22cM5oJ3bvdhR2Dj4ALpdvwAKjwkoBzWOa8zW4ThcFP18/OiLvuFEyDpQ7IoXQMvJMPDCKhg5cu0ru2cTk6Opp22eKNM1fMjfO/iFh7jJaBh4eHr5avHW1g6Qtey7mFvtAxI46pAB1dzg0jBhwYQTscbLZIxeOIgX9zgY+YG9oapRtkv7u8YXRIRko9lpxX0jUeHh6mJHtAn3nBwLsDfC4ZBfIcAWjcPxdWTqhnsXh6AwT3W884f7Jz/gwMHh4eJsBXVWuvcVwul9PYb25uptMITDe/vQTa8MYknJ3FYjHZhqovmxB8pJXpD1j0PFvHeZ4M2rwMbP4CbHI/8uFd3uZfP0tf/Tw0w9l3Gg56zeCdfHb3kdze+/v7+vnnn+v29ra+++67Ojw8nPLUk5+Y41evXtV3331XOzs79cMPP9Tt7e2kUx1xTmDnwEunp81HvgZ4ox8J+JgTByOgeUabPZ8G+fQr5SZzR90Htz0HjvysQVmnT1w6x8z3jWg3cmiTVl1JsG0dngDXv40cyU3Li49lsffEJFlAc1dr7hJEcLa3t7/Kh4NBElC4PYoNQ5cwmwWCWgm7HgtPLt90EQ/TJBWNacN9GGKKmSONkn/vIgS+5j52wpDP5+9zQmOwaa8rGTP72/U76+4iNp0ntUkd/j/HuanXlcKVCsX3W9E5kmClPQdm6XPSKoFTglcraa6n/JhXMx3Afx1wmgPvVnIjIOxn53ggae6ISafkumiE+9XRuLvWtY1+yo1ieURRylen3HMeMmKe93ffR/zQgWfzhOUUepoX3G90LM+lUzYyqin76B/oCNBBv3f9HOnSDpw4L9TLxAYPz82x+aabt7zf40s+h1ae2+R7OzvmawIJOztPB2WPeIj+Alpx2vyav86hSDlOHe/701kykHNfRjT1Pc9FG7OvI90wN59ztr0rnS0d2cTRM6Nrm4Ktkc56rp5N+/FrysaAD6PGZg2YcG9vbw3YkWTqhF2Ygut4kwAjXnjO8Qcw9tbWl0TZ09PTtQ0fVU/gEi/VCNuGMYWKazzP0g3erT05h7ApNqosqxH1xMvzGxGqns79q/r6mBUrozToVU8RiaqagLTrMaAeJY673yOhSoANeIXmDtvzh4G0UNv4WIm7DY+JNrvv7pOV/Gr1lFie84yQeawGDNDHtHeEl/51fYI2vM7v5OSkXr16NfWNuaF4qc1LRBhbIgIYEvjHAHK1Wk2/EYHItAZHHTCOh4eHa2fg5XhQ7OlZr1Zf3jLgs+YwYAcHBxMNMcQ25tB0FNlLXnDUx8vtPEfk1vTvclo7YOyNBx47fUfHXF5eTu87PT8/n44kIaJN6oqXfjvQsFqtpuXV5K+kRweoRs4T85enHLguP+Mx44x7JyjHjtBXH53T7ah1Gwk0qJOxA/R4OxLPGhzZuc6IZIItfmOjknVeFqJ4qa+SBzJC68i/+5BAio0utJXnIlr+sQsOhFxeXtbFxUW9evWq/uqv/qoODw/XgHny/XK5rKOjo/ruu+9qe3u73r9/X4eHh9NbParWT29IMOOVL3JTvRKF7JnXWLFYrVbTClryOO3xHPNk2pof89gi7svNYvw+B9Q6uUnnqNMF/su6fa/HaYfCji6F37o9BM+VLkKXdWe/vIloE2dnVF4U4UOR+ugFJ20/Pn55PQ85ezANworysSLy8Q38ZsML6PPhxSYA99k7S4KOvDpy8+yZ5uaMjrhMjg00y2bOrbJyyYRuvtu7chs5BitVCgyXEb6s23WMPI5kuG6DQHp2jsS6Tc9HevRpqBOQPVcshCg0FEUa0I6u2Q/fD590CtRj4xVV8CXzyRyjyDHWgAeD0zTozlP1WAGL+fo9+mgD4b6QUjACRhi5blkNGa16WvKln/Q/HQv6ZVmxQRvNpXdZun9c8xKt53w0Ll/rFLl1FQCIdBPyhtNxAmSM5Iy5p8+A/OdKgreqWhufl29yzl0HJeej428vq0KL55aI5gytdRw6wcDXPGhZ8PzbWXVd3sxHP6GvC3RiHNZZqf+YexdHteD9BL5VNYFYy6DHgW527iBze39/P6UHwW+eZ+tL9BlO5efPn+vVq1dV9QW8XV1drT1n/nGfu4CCbXHyDg7AKAo74oe0Cy4+YcI6unN0UuZSH7h9lxGYG+l9f+b3zn5093cA8Tk56vrhkvbQOrWj7a8BfS/atAFT5C5dC0l65vbcHx6+nAp/e3s7RcL29vamvCXvkHMkBkUPGMSTrKo1gwVxcgkgDQ6GxPU5gZaQO4oLYcRbxeMkipLRJgtvtp8K2yW9HdOekuDWS+BdBG8TIJXKzSDVeTy0j0LjO0qK53J8GYmgnTRurqcTqK4OQIuNTdLUACTbNnixgnRuFDRhHI6KcLgtBstRUSJ4SS/eCmBDB0875xR685wBYy5dJu91gNdgwGDLidUYvZyLkcJNBelioGbngE8MjPtoh8sgKuc95ziXDzvQ7qiS33ZCdL+LHPHcczIE/2JQvWGrA2IY9O71YKNxZrHcUzKiwriso1wvTi/82oGnHAM0gc78D5ikOMrGPaYBethO3AhIOPpo4F315W0XyIg3BdIHR0c9fv5PcJzyRUHXQLcugszzbhO9cHJyUnt7e3V+fj7xCPPlVZXb29uvVlSICH78+HEtl5B5ToCb/G/n2FFWAwtWvOhzB77c3nMgy3pzxMsdCPT93fVMQfC4/d26axMbmP93wH9uHB0Pd3WP+sv3BPFdPZ1Ds0l5cYTPjLq3tzftNsJ7w2sGHHkAnz9/rg8fPtRisaijo6NpaZhzu3idGgegQkSEgbdcMJFE0/JVPEQ3WLpJwqFk2BxiA8OyspNuvQR0cHBQr1+/nhKW2bRio0T99lqT8TrByMlOg42Coj2UbOYbbeIBpTdh0MAYvLRFNJdxsxQPKGQXGX13oZ6qp6XoNCp4707yTaDWjcnLptA8aZyKwQ6BN0RkPbnUyBwfHBzUycnJBBweHx8nAHF4eFivXr2aDAP8Q937+/t1fHw80c0beugXtEhgYOcj586AyYbYdDCwNrijTi/PdekMNu7JayPAR3ve1MXzzst1H7yBxYafcdlRGM2582i9FAjAubi4qNvb2/r48WNdXl5+BSI8Zi/bdHxo2gJivVSYDhO8QX5WArCMdGV7pL8k4MMZzTFX1RqI99xxjinPOM3AY4AWCciey3XGIfQcOck/gXpn9D3H/DmwgN72m0B4LpcOnVeZOtC07nQOdgYgXVVfRciJ5iGH0O7g4KDevn1bW1tb9Ze//KV++umnOjw8rNPT09rZ2alXr15Nm76Qdffz1atXtb+/Xz///PNki5L3oEv2Hf0NH1TVV3qWMXnTlx1PzxE8aPA90kf0KYF2glXX47nq7B+06ZxIF1/r+CxBJd8Zv+83nUft0bfUXR046/Sm7V3ypPs714fnyosBn5f7uhBmh279v3dY4XmiUHMnVFe3l4CMwmkrAUzHZFY2MIP70DExREahZ6g6+5lt5PXumfzkr/M2RwZhrnSMPee9WLG7vQQdjgp0/ez64c9R+1nP6LnnxtrRe1RGwDx5gbEm3+bmH/MY/cilFvgk6df1uVNwjqRl/0bj9zzyZ6A3ikKP+mEg1rVjGlj52iM2PTpDU7Wec+jSKfQEqMwTho3cpi5SNipzS9R5XxaPo9NfyDrzNXJ0/Jm/GezhZHRLeKkL/Zv72oGh1KUGG/S700+WKUDrnPz6OYOBrB9AaKeRvjF2AxWPYVO9ULUuZ/BB8uZI3+FQVj3ldDNH7Hbn9XqZE8Yc2onp7OwmZSRXXDNQ3WRunmu7s2sJojwPc7YxweTcM914/VuOO0HZS+pz3+ZKytFcfXPt/GvKxoAPT/Tg4KCOj4+nUHQXPTMo4xqKxZ4R36+urqZjXK6urialbGGuWgeJmWNmdG2hTiOGB4bSZ8OJ36KRO6i2trbq9PR0EszT09NJuThyUbUeBfErtKrWJzyXOhirPVLfm8bZy5dEDGwoExhXVQvSu2WJNLir1WptyZu+U99yuVxLbrc3zri9SYC26KPnkMisDTB1eQnQUUHv9uZ5R1cpHmdn5EYGj345V9P86GUp18+7ch8eHqbcsNVqtXY2l40RBiDTBLwEkzltW1tba1EdDAPHdpi3bDANcvjfeWr0y46N6eR0h844V9VaxNebQHiWvMSMGGJI0TleIj04OFijCX8+7qeb49VqNZ0vd3t7W7/88sva/1nyjQ2OdmT0xW3B1wmUmHM7y16+T1mxHvM5pV5+BND5XEfnVVMfvOG3EtkAAzTZbAGPOTqXY+2AQRpOzwvF9MzNSTn/1hO8iYZnmSOe8VmKnk9WIrxkmv1hrlO3ZYSpAyLIF7rB89zda7twf39f19fX09t5iOzZQbGO3Nvbm45qubu7q/Pz82n5l7QW86nlwA4R9fEMbaUTCX9152Vap3R2jJLz2DmkVU88Du3SaUvHOwttdvbM82GbkHqLzwSWOabsR+KN/C1Bb/e9A9F+NuVs1K/nysaAzwcL+/VOnfLoQvsoCYwKoKuqpnPvvDvQzJNRkvR4vMSTytgGjOdZGraHz3lc2Wcm4/DwcPLCyBNhxzH3Vj2BF4OyjnkyAuO+5iQmLWzsoENVrYGcBKydN2eFnf3L6IKVEPPrjTQsO7LcQF3MS0Y0MrJgI8D4UlCsLGxQMWI2dtC9U0AeD9dHAs8fxsUbI6BHeuQUXpzuPEv6yf1pmBmDd8DawDI3zlvtDr/1slAq7IxkUz90NLA0rU1PlFsu+5hf+cShsuPV5e/xLOPzyQA+UJfokPvaLW8yn4z35uZmOkUAYOCUCHjHn7m0Y15NGfP/jkh5fAYS3j09cpDMV24bHoGWABWATcovfcnNEdS7tbW1BvodtUv+7mS3438c/DTyfHdfzVedA+EcWe7x0h6AAVuSxtgpEaannWDbCAN+y3vqbQIGdo4MuK03aT9lzbtoLePYJhxO0mlev35dNzc39fHjx69yCS1PuQqRvE1/7FAwjyn7HS/ShlNNRnrU82ubbcDptq33uj55DrJv1kmWGffRPJt2BnnIfncArAO85pnOnr8EsHXj+38C+HjvorfIoyB84K8VOUYJReNOYgQsLLe3t2tEq6o1hWCGtWDAFN6ogWB1OUGOyiSQ8gT6Xabk22QyrT1vxke7fCIUOXGplH0tadWBvo4ZbTRGjGHGMThPg9b1hX4vFuuvH3LeEpGl7pzFLJ7DDmj6mU5w/Bv96rw4LzvPKQz/dctytNN5s9m3zgP0GJ0ATvvuXypw99dgbdR+8koqsRyLgUICfC8lWR4NbAwiPa+eW4/f0SPGTp5U8lQ6JQYBPG9+9mHXflcuzt6IbpTR3FuvAZL8OxFXO6HMtZ0ej8P6sSue69Qd6EQbmgSkBoR2IpJ3vXmt0xcdsOuABnMzkiHziXMKU/8AaLNu7yLndx/DAy9SrAtsO1ySrujtbmnT/ce+kM9rEEm/6ANtYlOoh+t8d55dAityhOGz29vbr6J65i9+c3Q05WhOrzrS1xVo3dkf6JnPesw5dstw16/Oken6NLre8WvSqqtz1JZLyjXfO16zfch2U9d1Y+h+36RsDPjevn1bi8WXl0Vzgvnl5eValOzx8XFa9nTIOI3/crmcvOvFYlGXl5drSxsoT3sCKCwUJ8RwmJh78KDYgEF0wcB0ZJzs/ficNU6Udz2prKrW37AAoHWfO0FIJjSzsxzYCdWccHWeFcVCn8uRHr8Zz8bVS+xVtbbZgKNKHh8fpxe8P+eFUPdIITMuDNsI3HpOUgHmEhr3WMHYWGYUsTNoOf+mFXSBjsiCHSMMPdFlL/XZgNljNF90Hh9jgZepn3nPiJxBF9cT7HmO/buBn8dkh4t7nQ6AMfXmLpzEo6OjadnWkVAK9VKfI852HKAPO3HtnHZpB1kMjPif/hBRhW5EnhgH5zTOOROMw5tmUol7ri1/GY0xMLa+RB8yL5n/ZZlbrZ6WRbM/qbtSZyS4daqCo1YAT/NXpg64LTvY0NrRMDvkuXECej08PEz8ZBnPVBvm2nLVRW3tOFXVdJQPKUledWHXcII75ivtEWeasmua+dra2pqWpd+9e1dbW1t1cXFRv/zyyyRDDjzQbzvkjuxB+9wIY5DH/0QVV6unaKZp5nFRTB+/v9m8CX+n45lOxXOgLx0KY4IO3HVRS+p0H2073R/bpuyb04y6tKYOoHUgz/yYEeW85znbmuXFBy87DI/CcgTKYA3PIDsNgREuC1FndDvCuyQTOLLgPKtc9snok5UYBsdHazjaOCoeX0aCkmlGY0ohtCEbtTlXkkFtpEdGLwWl66OVTALKbrxuezRejFPXn+7+TiF048jvc21kWwZ95l3PbUfjrMuOienr6KNzUzvg2ZWcqzkadXVkFCWf556UFddnoG35yihkd91GPqNUXb9TxlwyB5Y8Xe9S7Z7r6NkV93V7e/urHCuilOQmJujz96TLJuNMuYOOmZuW8pi85HvsyKTDNKd3/XvWa57qnKznSjo6qa+gn+Uijb95a7kcbypyv1Kn+bqj3dg2dEGudLkPqSfgHUd1M9pJ8UoXDsXBwUHd3d1Ndjb5uZvf5/T53DykHu/0RLad89Xxtb8/p8fnbFSOdY5n58b9HNj0fZ18+G/Oro7olf3bBNBtOo+UF79azREn3rloQOKjBlB6HH/SMd329va0CSTPz6Pdbomhm2w8pJubm2nzhw+57Ap9J3qIR2OwZ+XssaLciWCiEKwYqvqjSOi3Qebj4+O0rE1xXsmo/yO6mDZc57c5BWHl1wG5FN7Hxy8HbnO8g5f+kiHnlFMyODznoyucGgA49HEqCZCT7rSRytUKF0NhB8SeMed+EZmzEmQZiu9pSDolCO/v7+/Xu3fv6vXr17W3tzdFJhwlYfwcibS/v19HR0e1v78/tWMec987BwqnzQDLBorlJee6dUuTjupk+zkGfh/Nl+ck+cT8jVPHGHzYuzdmXV1dTZEXnu2WvTJKlpEt05exHh4e1mLxZeXj+Ph42iTApjTnLDu9g3lxYjyyAS34zVFyAwbnqzJHfksRtGJFhnu8OSfpjBPOn+/z3HTgaWR8PMfwuaPBtGveS6BknvA1O5cAIkdqsB/wrvMm6RsRNXJumd8Em7TLkvJyuZzOcTR4oz1HVkeF/l9eXq7l7W5tbU0bOdxfIsgcxswZkuihdGSs6wySsc/W87ZHjN32x5EzrnvJ2qtQnc3Kujvg5/IcmOlsovm+A24dQO1WtFJHZ1udM+r70/nwddPG+sj6dzS+UX82LS8+lmW1Wk3Ki7dquPgl2njAzl/JySAvged4fROMvFg8nXKfIVfqSE/55uamLi4u1nbfOmqSChwhQGGQJM7hugilDUzV05Jd7mC18XKb/Dl5N5Wrz1iywjUwzInPpGHTgs+RVzYnfAZBzktxMeCzEXO7/jS9OwDSCQIGwoqtAzTut2nWAb4UOi+BoKTdd+4DlFfV2kYfeMFLjIAM190pFADf0dFRvXnzpt68ebP2ir4uMoWnz7l+7JBGZtz3Ln/UwBcDls6Ned7PsYvY80UkDccJmjvfjefNc9CKsWWuYPbZdRnsEcH/9OlTff78ua6vr6elNgDf/v7+BKLTkMLjtD+KBrlfzMHOzs40b15qvLu7W9uExvic42x58FjsRKbDa75PQ8Gymzfx0EeAJ28V8Zh43jqKPmUE2HNIHb7uPlquoVu+Ncm72JnXDgDQR+tZ+uJzLw1K4C3kAjtCn5HNqpr4hXFbdxg4GdwAEg2WvNnCdivnyzzNBkDfZ6BOu8zn58+fp7Ns2ahisJy63OMF7DlKad5O28oGE/rrP9M460n+8pjTdnf9NX3MZ/4/SwZVPF/ZH2MQBzO6MfjTesjy6+vWd9bhIyBpnZ3APXX2HCB8rrxoSZeS+TPucHoALr7O+UN+TVWCuZy4rr6cIACQAVgqNUpOvsFZKvwRsraQuF9V60ubHeCyIMIUKSAd43c0zdIx32g8c893/U2mdT+SDnMRza7vIybOeqG7BXwuEpp9zWVof0dBJ1B0RMTKk+cxznd3dxP4w1h3S/I8v7OzM+XGkgPpPL7lcrmWwF61vgmgmxcbHJRdgvuOVgbMaTjcb0dDTdc5HWDDh4ylI5HylJ+06WR5onk4dwZNXXQqi/nU89m17X4TrcI57I5y8YYK0wj+IwKV/fF8mUbuS0YlvELio30cOfJ4U8d14M7z0smsZXpkkP27o5gj/W7jm3PSySz87ui6aWX+92YMt2PZHjlIACT63kW00h5ZDjNoYRsKaMQh6DaspGwR5Sf30uPsIn1pV5P2tnuMs7NJpr11YdLMfc7nOn7Je/L+lMfU4d14+T5nR3P+sw8d6Ezg3o07r6WjkddHbY76nc9sUjYGfJwCvlgsJkXLjjcL0GLxdai4an0ZaLX68qqYk5OT2tnZqdPT0+nVMQgRisvJ6x0BnDuBl3N1dTV5TBa+RPnunwWNaE2GWPnOMw8PD2s5Z7kEkEqe51EEBgOLxfrOQjNreh9uq/Nm/GnDawXYeTxZ0sh3HqTnw30xaLLysPfbAZaMzHkOPRbTzUt1naJD0dJP30/xYaiOBGEsOTLI9doIOEmcJaIO7ELH3d3daen21atX9c0330zncZH07zP/UBLQ0UdA0HY6YWnMAAEGdKYNPEGkyfOcCt2yavBrUGcecqTPx604ypDtME95fMly+WUp7cOHD/X58+cpbYMIMJEuxuDNMPQ/+XYUGUg5WywWEzBnCZ63BbG8SyEtJB0IolHWiUSKHWk1PdJAkn7ifgNolsvlVwDU88bz0BgdxpE19IP2R0bOfE9dpi/XDZKqanq1JvWlnUBneHco9+R5qVVPS9le6bBs8rYldoCzqkR/bT/oN8/bDvCJc1H1dEag9a752WAe/iG4ge1arb5ECrGdpGh8++23reOJs3F8fDzxOitDlim/3s08iSxYN0Nv0rCIlLswB+YDO7KdHTH/OWqaACydUX830B0ByOyn67ZO9CoG45mL/mXf+N9nJnrJPMfMc9Y/nSOdzukc4LN8dUBwrmwM+LzDxoYniWtD7QGl4iHJ2Uuo3rE2Cn1nQVC9QcNgimc9mR1wsdK355yeAoXfzfDpgYy8CRt+15+M4GseQ+fxuf4seX9GOUd1Zb/dr/RGuuehpcfpXXC+P+vP39xW1z4e94jeVhxVX5+K72sIpyNQ7IZz213eRxf1dvF12kLJs4zrHZ+WG4CeaeL5tDzCl3YgEsCNlKV5s+N5jy+jcR3QTdryf5d03ilcnnPf7HD6XbgGv+a7EV93ihQ6JNhzxA0wl1FZj8ERvuyDVxMcMTIN0bmA9Jwv94/72REK/3jurE8NzHHQka2kRxpdj7Fz1Nym57Uzsq6rmxfzTQJM+mujy/dckWI+E9R5DOb3tFV8Z76sS23wU14SEJkncNAAZ/xG9D5tq/tA6oDfu9vpndShI13Ntc6hTvCSn6PoXv4/sh35vaNj9r0r7pPrGNnN7JfryPtGsjCiz9z9tmGjdueKcUknO3NlY8CHd4RytYK1kVqtVpP3Q+eIXHCyPTl1efxE1boXkQbPhHVUDA/O5+2lgSG6RJ/4tFL17mK8RCYFwfQxFxkp8hiqvp5EvLHMBXQfGe9ICXaRvbw3PZWREfVvGeHoDH4yfzfGBFgprKZrF9bullOYu9zAwrwQmbMTYjrCK0RlDHrgYxtIR3qJ9FV9/dJut7G7u9vmaFAf7x82mCPCx0vVeS8zy1PQw4coU7ePhekMi/uYR+YYFEGHnAfz4Gq1WgMuBrr8+Q0ofgNCgvAO+NkIZl/oH7rm6uqqPn/+XGdnZ3VxcfHVEq7BuOlPyWUYj5dPA2hH5YgKHR8f1+Hh4QT6mKtcpkyj4Eif29zd3a2Tk5OveNubNogidQ4m9DFIdSQDvchntuPjO8gb9Ri6yF7KgJfqDcoSlFbV2hFEqUO7tujHarWa+meHhv+r6qt3Q1etvxccGjjabRvkiBpjgpdM267YdnkTFXPZBTTI06OPBwcHU067ectHJyFjx8fHVVV1dnZWe3t7a/xiMOm+e9k+gax5oZv7bmWks0PWfZ28JajMerJPvp4gqQNY2QeXDjCOAGX36SivNzi5/gTDtoXPgbmu7yNQ7La7YEdXXvRqtdVq9dUbKmjIr1qzIqiqSTne3NzU5eXltKRHDp+ZCoVk5kolWvUUKmcZx5E9ezpWfAgcRrfqiYlt5PNF9bSDEPl4Fyu0XJZKMMTmBgAjgufwOp9d2Jd2TJORkeQ5g59R4Xqes2aB6uag84YMuBijN3IwVtMxlytSsL1UmYzNkko+Q33wKB7xycnJWr3X19eT0odv4YUEajg9tON59s5H88dqtaqjo6P67rvvam9vbw0s8GkDA82gC/2mbdojwdxRL88JfbTROjg4qIODg+nZnMOcS89zKhrLp2lmw9+d4J/1W8a5lsCJ8d3e3tbZ2Vl9+vSpzs7OpjcNpAzAyxhLGzuDZNrLsdCmf9vb26vXr1/Xzs5OnZyc1NHR0aTDUhYN6lKH+Xdk37zFswA0wAJz7eVHO7jotgQtPEMd1o+mLX00WHd95r3UC+Yz6mQjUL4KsarWzl/kd05E4Lvni7G4zYzkGvClzvNYeK2ZN0Ux5p2dnbWd1J4Pz1vmXNKGbWG3A9avtcNJpA/oKVKdfNA/NCW4wuqYU6JMszwqzbJE33OFgzbc5+RZR+TzfL20WZ6Dkd2wrvSGww74+bfsZ851Vzqg193T9SHrsDOGnRiBSPgC25g0yfozWGAA2elg+KN7PWRXNgZ8FIRrlNBpAbWyS0UHMZg4h8U7ghglu9iL6ZaFuqgH16zsHeHrxmzlksDOfUx6uK28N58blXx29H/S1fRO8Nh5HB1954rbTeU298ycMD0nmNnv7H8CZupL5d/xsHnCCfAJiCnmXy+rpDJlNx1HqBApcmK9AUCChWyzo1s6KHYgLH+dd9wVwEKnfK1o/Hv2exMlO2rbY2VcLOOySmAAl89mv/1pBzN1TSpb73Z1RC8jUS45Zkdl+ISfsg4DPhsJA+HcCNAdO5VjHsmL+cDfrUtHkYk0RHNlru6R3nQ/O4PeRRS5p9Nvlg9HAA1ysw5AjZ371KX5veu3wS3fAQu+38DYfUdXwa8OaqTspf1NORzxezcPo7nt5uu5ecx75+p/rt3OBrzEfqWeyt9eUrI/7qNBcALeUb+e+380f5uUjQEfDOajTnI3rD1KkkftjdmorlZPUZHFYrF2TpKLN2NkMjNCm/lKJr6Thh3KRumyZOccHLdtL9qJ+TmJCSi4lkLoJOk0aC5WFv4tv/vT0TN7/KY37WV9/t396Zi0EwzT3l5XZ1Q8PkAJc5tLBCng6Tj4Nx+NQvI+XrSjl7e3t9P5keZBlO3h4WGdnp6u0dQ7Hf0GgC6qCwhBBo6Ojur777+flpQzkb9TMo4ceU7gPfeb+kwry6Sjpk4nsLPkMVQ9AaFMSGa5ygn1VfUVCORelqm75e65PC7zErLHRo3r6+vpNWm0bRnzby7wR8dn5mGuE13l/dmnp6dTJLOL6llm3fbW1taUiE/enyNHOX7LjOd6uXw6duXu7m56g4g3HBjIwqMZXUfm0igBot2f1GXmRffXxcv/afxGdXoDkOnLOIhg8clyGjQZAVoDP+7d2tqq6+vrKXUCG0C/oKUjOJaj7hSILNAY2bSu4pxGeKHqaQUpnQno5uOfvFGMOr08Tdu2eaalZdvzn/Pq6KKdD9tkR5IT4NAPy4nnptN9CWZGoLJq/NYM66yOLxgHYLvTv1300AU6Izdp76zvTKeq9SBV9jHr6RwZY6BNl3Knfr/kZhsdTzq/wxT8jbxgdxgBWq1Wa0bVE0sbLM3ZcJm5OgZxf9w+QpLeu+83iPQyXUay0uPyJ98TrHR0HTFo1sMzXf1Wtr5mYXzOixl5DclwSWfuQdD9XNff7GMyu4Unx9/lkXmjgwFKLjdx7pfn0ct/9rxtePjfb1IYbRRZLBZTHs7R0VG9fft2WkbK4ztcbPg6Gid/dk5EKkS34yh1Z6xGzgD10V4egTHK7zEA7eR6BHg8XpYv7+7upsOV/T5c5yJ1c5bFvIbcmc8y4srxFyzVdXLY9d909+HaLAd3DlE3F6lTMfR8drSnHwmGU2Zox6sYXT8MiEb6IdvN+0e8loYt6zLYcrqA04q8zJo8bBrD/zgLgCTLdwKgjKb5emd3+D/tDMu36AD4ina7TRLU5yXs7hifDiSm82HHNfs9GmtnXzsbmPe43Y4uXM/c5FHJdvJ5/z7Sn7YrpnM3Vv+fmMTyk5v0qvq3qyStPdfmlbSB2acEfCO5GZWNAR8JoldXV1MH8DStLBEeh585JoUNHxlZyDB3Va3d53u9ecJIuhMGExrlTYSCw0gdbXEb1GWgl0u6I+DEJDoqmmA5nx0xKOPwmDw2P2dQ1wneCCy6PKfMu3rymTQkVdUaU4NTMzFKNaNnrt875SwgvqcDp13kkt+IBGKcq57mjrK19bQlH36Ft9K4Up+j3RzWa4VjfiPPqmpdljAMVg70p5sDKxLG6rxX5992+TyUbo5GHq/zDD0unrFTY/p3in+1Wk0HyqI3fJiyIww4gkRIOkXInLjv6QQ6EsacAdqJliXYoj70g8FJJ4vuWzoinsecj84Y8926CXpn3qsjvgA415VgL+cn++Z549M86fFSH78jU2mALatzDjT/A76dD+yolud6pEuZU9sA82pGgumnD88m988ba9gIZnqzMmB94BQowDxvpSI33YDf80ikmNxkb2CsqskpTQBr/Wr9lwDD88ZvBjimY/LlSJ+kvepsR6eb0z74es5pp/e5J52erKfrk53CzhalDk7c0QHK7K/bSSDY2f+OnpuUjQHfN998U6vVl1fAbG9vT8chwJzn5+e1tbU1HSCLN15VE+ADIKbygtnxoKuejJIH6eUGn8WEgPkstSSQc2hInO8MwHL5lBhNH2gvAagF2hOAwvASMEusmew6N2HdZPObI0E2Onya4Trh60DfJmCP+xOUdeAyo6Qoys4r8/8deB8pftPRc2OBybbcV9paLJ4iciy52JBY+bMBwnzg1/Fxfp4NIctCvBnD88YSc1WtLVViKLzxhChBLgen0u0UjPnafOjzoXKeM1Kbnih17O3tTWd10p6jcObRVLY2tjZsV1dXdX9/P73C8fb2tq6urqYzy1yfo/9V68ff0F4HStMBgQ/YUMOZiNvb22vpI3YiXY/nuuM59AFzmZskzI8ey2gZiGetAwG+ubM3I6qMGZDsUwxGcpPymnlpnsOUR94Gw3igX+qxDkyaJ2kX4GQ74N2TXcTE+op+3t/fr23SoF9dBB5+JVCwv78/yRKOiEEhdseRXZ61fgQ4Xl1d1fn5ea1Wq/r++++/mkPOA4V32D3+7t276a0yyAd09lK9P7vNJx7narW+Gafjw+SL1MNz9fsznVh0s+c92zNd3F4XIXU9lnnzgZ2etO2dM2Fwlv0aAeYE1dZjOc7naMf9LykvetOGFQYNpwJPI1m1/v5GEyqLlURGAV0Skft3PhMg2ItKJZL1jtoceSNWzqkozUyJ+DcpI8CW43Qfu75s2hafCWT5fc57yj5193vust0UmFQiHS1syExrt5/LHV1b3fiXy+UUOep4x5+OCvmVfKaB+5L15PKL+2bAnHOedJ/jFSst1zmq17Jjz5qSEYKX8NvcODDgduK8UWMT/na/AIKppPM7c2Pn0HqjAz6Mw3qjo5Vp7GsJ2HPembNRbp11S/7e0SIjrXOG+bn5TDnPa5vwQyfzBu+mT9IQGWLOUg91zkUW09gbKFyPPy2n5o20a51+sR5ydD8LPN6tWAHM4TnqTkeGPo7mec6edffmuLr7uvnJ68+V0TPd52gMm5ZOnkfjz//n5GbT/qQe6LBM14+X2nWXFx28DFN7iWC1Wk0eDNeur68nA1hV01Eu29vb01ZyrjEQPCS8TLbPLxaLiZnxoAwg3ReIYaZkOSbP2etAY3oURD9Wq6ct9VxbLBaTh5XKlbq6Y2L8ftQEPi4dM3Tgzv3J5SL60YXus+CBVz0lpDoymTlzOd5kVnv9BpBshPHY8FbZUOFlQMbwHJDxUoXnlkiKvXy83gThrtPLJvTFYABAAE+xC9fLvNRj4NAlgVOIgEArxuTNRO5z8kFHG99n48b4nUviiIZ5rovS8clc+lgN39f9EQUxD2DkyM+7vr6u8/PzaXOCj14CEBismT/pd4JoH7mUGwNMg4ODgzo+Pp74N51caDFyfjonwv3kDz5yFIb37yJ/KbvU8/nz5+ltCAbCHnfqGEfF2LRE3elge2zWqdmGn7d+2NnZWYu+eDx2djJqgj4nEubIKqAOeeB/ZNRH1Zg3uyM/zPeOdLPJiHFZ3gyqmAfaZtXLdgk9YJoi9ylL9N8A7/b2dlpRw77e3d3VxcXFtFLA+N69e1e3t7f14cOHtbQr5HfkMOQ8e77dL5fuGY8vA0NZt+U05cQ8Bp/kfbZP3AtPdHV29sO2MNvLZ3OvgJ/vaNGt3FnPjCKFSatsLzHArykvAnxmGgszoe2qWkuohnh+nRZLJc7Z8RIGbWQeEyV3KXaRPuq1gfV5OV19VV+fru88Lupkcmz8rZANuuyhuT8osQQ+c2UTT8FMmICPPszV0UU9/XqdVJpZZ9bPeC3gBvUGGN71hMJPsDIas+fF0RXmyeckOXTfRWKyoOQN+Ow4kKfHGXdpcM0vBn1W/J4jNg5ZFtK4WlF0gNv/p3GmXjtsAMwENp2j4evw92LxtHPRc+42zIMJIJF/jPTNzc30x8vp2Z3qfNhcAur400oYXjCY8HPMj1+t5zo2AXser5+3zsg/AwxSZQB/pj/9Rxdztl4uMyed03nIvnc6tDPondFx3cyjUyzcH/rpaCmybl5J+UdGUv9yD/LHaz7pq3OlLX+jufL43RcDQgN3QN3+/n4tl8vJ4SMCl/o09ZJ5JHXRarWaQD2bvba2tqbd6svll5278PPx8fFUFzuRbWM9x52TayBmXveznpMck78zNo+lkzPrOBf6aNtvh8a8Rb/om3Vp1tnxbfa720TS2Z8EzObL1Wq1Zn9pO+u2Hnc/Uqd73pLmcza9KxsDvkzotaEy0dkM4Q0YgDz/WWk6OZ3iED2gyYm5HrA9YRPEk5CKq2OM9Eo6QGnljFLmdz9jIJnPdMzWTXYKpu+xR0Jkzh50B/K6XBgXK6pk4M74dx6YS26sMOOm0TSd8xmDmxSGVFRpEOxtdcaYTzZTOHpngGCDDchzxMj12UvM6BGg18bX/OuoqulO/52fZBpicM1fVirL5fqhvTbUfALYzB/8bzraaFqx2Wgz/z7iwgaU6/zOrunr6+sJ6CHnz4HyrnhFIjfWOHLXARv4OkHACFxbb5hPPDcZkfR8smRNjjMOs3k1gRz0ziOiOh3md6pCc9p0fYxlRO8R8E+a2Pn1b538GvDAp/A89DEt83VxOQ9Ewvy+23Qy0mAiAylv7rv1T2eAcSKoH1kyDz53fuPIqPta2gdApx3IjDCl/nWxfCRNOwdg1OcEiyPZ8T15/bl7nqNFx18efzeHvjfHwnMORnS0mQOGvieDEVkPZRTNS/v/UrBX9cJz+GjAAA1lArPnGwEWi8WUpF61HiUyQfFeKPYSveHBidNVT+cC5gSn4U8QQVuOotBHJ7bm64hsIDn/qQN2Zp4ElznpKSxWwhZIxmLjSdJuVU1GIwEn4+uAnJXz7e3tpDRtJFNATKvt7acXSKfAOupjYOA+QT/vGqUO5/EkYHbJeQYEm7cMeIg+2+gfHBxMy7KZEG0lzYYNR/rcPiDRyiVBGRudMgLAjtRPnz6t8YTP3vJ5lQal1A1QYqzJ8wYL8BBzkRFJz6Vl0vJkvUDE2nNqec1oH23f399PO3LZiZv5SzZoqWATSLl/5gWfgWi+SNn0M5bldKJMG8uXN224Hx4DUS0Okr68vJwiOjc3N1Od7lPKgfmZ+xzBY/eoVxkyipogMZfGTOsOHHfXU/cw17mZwnV0gND9ZkmX+TP/wJtVNb2ijnP2oBVjS+Dsec/vpjfj6sZN3YeHh/Xw8OVsv3SeiQSOgFMHFDqbUPXEFzgL8DMrDixL+/5RjqOBaqcXOidiBHyQKfNOnkphmfWzjsgmr1t/uo/+8yphzo9tA23l85ZpZBiauu2MXKbTlUDtOfCWfbXtt93pHIBfU158Dl8CNxOqW7uuejJYnqwU/I4gTKAnOL1+M7aNZH4fjWf0G215krOe9Pz4LZnI9abH/lz/umsJaLsxdyVBcApHFxWc80QY+6jdFKSunrnx5VhfwuQp5IzP/Ef93OsIUP7mew32Mm/N/fX4Utl011MBWdl2CsqRSitIR3/cl1zKdT9cV85T0j75L/kIGbWxTYPu+/zaL/J9u/vd9nPFspGbL/xnrzvHPRr/qL2uj6ZV9j3p5Agtv9OnjIB5laLjqeRL6jPoZuk0+51j7fRBR6sE4r4/jXfWxT1zfG5w2wEk5BWHLnmz66912Cg6kyXBInTulgOhb0ZpKdlmB8rcXtqEBCrW7Vn3c88lDz1nH/3bJva2q2cT2XK/uvazzu7/OcDU8Yaf66Leczwzstvd76N750ra1JeUjQFfRk98cGQeRlpVdXBwUK9fv66tra0psfjz58/T8SzkPKXCyZJRNgrCRdJzFvpKtMbRCUcYqNvRJoSMw17NEJmUX/W0bGKDm99dUtg6Y5pLGzzHd2+I8JJNAoAEOG7DNDdjG+zm3GR0xwA8wa+fy7qsnOAFHxtiOnnZp2N2G7wcz2r1dKSOz7izsneSuN+CkMt//MbxLRk97kBUx7d2jNxn5tyGI4sNSBdd9DwmgPMxQ54LRxoN2hJAJN0Xi8Wage2ASM65eefm5mbSCR8/fpwinCzFUTLZP/WEk/p9mgAAgLlKPUB91h+OnqWi7wBxAhnzQc5HyhY6Bt3R8YPr929+y4b7Amg0bfLPkRSXjF7MjS8BnfnN0frkbRtu+up5TV1nPdhFZBO0M//kYRL1hsbIesouz6ctcVQQJ8+gk2cdZXdeX/YTW+EghnnLoJ+jzaAndRweHk4bJeFtnsHu3t3d1fX19Vfj6YDjCGinTk1blvYynSdK8lnyOnRLwOl78zPb6Z7rwJj51vyavG/eM5+kA+5nRsCRcdm+Zr8dhe1WsTrb9xxQ7sqLN22gVB0+dhSEzu3t7dW7d+9qb2+vLi4u6urqaoo63N/fT8uGDDYZ3wIIKEsDhMHJZQjuc2J9AlIr3AREEBzAR11MCsvW9DnzriwAHWMwhpy4DgDSV+6DcQAOy+XTOyHT2HfGm5JtZfQgwVsabPpjAMqzzwG+rI+3UbC0h5IaeY75u+nOuL1sZiPu3JocH68+M/8k6HOunyMoHUBmbizE5sWOnq7TUZ2Uj4xUWSGNPElkr5Mj32da+veOhxLAdqDDcwcdAR+8OQPA5yXITkF67px6Ydl3vzMamwqcMZiXvVSW+ZmMMWmbsp/07WTGS47pFOQmBupHX3FMjZ0d0yT50icGGDx3Rt7F9+fuc8+rgRhtdTrO3xNUpGPntk27EfinHpay9/f31zbHJQhI+aNtyxQAjnmxM5bONcDPjj5zh65eLpfT3LkfPoiZ++i79dZisZhy4ll2XCye8oA5hHmxWEypAR2feu7y2gi4eO5yXm3rTFdvvEhQ5TlP3Z7tuY+j4nbNY2knqtZzrS2zafuyn8m77lvKcfbV+iztKoE0+CrxQ4cLujaeKy/etDFiChM2JwzmTSDUlZGC8LNWwHg+HRPn/zlpI6RsBZSRDSuNVPJzAjMSrhH9UhG5pFBY+aYCHD3v+UwadMtdNizUbWZM2mUZ9YW6k0c8R7SR48x2bci7dkcGqOprz97Rnc4g2bCOFKh5NjcvZB9oj6T0NLQeI/cb/DxHW9PUxsrGzfTOOg2Y5hSPlyZ9jT/v6iWPjONFHIGizaSp+TIjB3n00tw7iz2GjjfhtRGA7hyRpEcamOQ/L2WzacVHe2TkwcANOmWUL/tv0OC5SZ01KiMjOlcsi3P1GSR2RtC05BPZG/ErIA2ap8PmOh3Byd8o8Fs6xCMenSsJOD02+u5CfroBp48V6njSQLBqnY8TQFAcscw8as99Ny/P6YORLezshNsYAcF0qNLp7ejodkf96sbBPSlfnsfO9ucYn5OfxBFzeGLOhm1SXgT4UDhWGCaA0aoFg7wcjhBwySVdAwwrOBKcSYbFc8gIn/uT9aaS7iafuueMN1ES2kDx2rgytjmAjOLqJtDeQNI6x5DLJyOgyieRAdPRBcXihFQr2lx+ydKFvUcGEwXNsku37Gia8WxGaeiXvfCuLferE+LVarUWxc4o32KxmHiZN2p0bVCWyy9LOLx1BvqmkuL309PTqqqvaE+/fLRQt8RIH3IZD7Bl/oFPvMEl6U29RPPpW3qyVo65wxYawEOAkA8fPkybNIjsUo+XrD2nCfTMj/SRMTF/3oDBnHRzlstu/O5NUyMw0xkb5s7Lx6bX5eXl2lI29HGUh3kn6ufzCL1MmcXpERyVZd3d6eJO5hhv6qEsBhIduPL1jNwyBqJpmbTvetC9doS6slw+vSOXqD31JbBxNK/TWZ63BNiM10uuHRjlmc5uVtVXR4Yx1qurq+nYGfQS3xmndaHPJEydnfNs+4GttX3t0jjcbwO0Dsgn0DE9O7tku9k571W1RnvrZPfV2CPBrdtKcNuNFdrknKYsJI/7fsbSBWY87gSRpo+fS2zwkvLiXbpdRxKMeDAWsKxj0/YMOFhqwSh00Qm+J8PkJLjY0PieXPrrkPemEcs5lD8yIEnnNCjZN7cxKjB7NzYzVbaHchmBsaR3J2zdeM0nHRPP0bb7e+45rnU87Hq7KB90sDIfKQvXu4lwGrxYqP1qJIMOG2La6PgjHZFsk7qc6O5nq9bfBtIBDBsOG1QMqSNSHJnBa6L8qrdubtzPpLP7nQcr+7gKz5373BkV7jN9U1+MSqcrunm3I0AkJ4GeN3XYSJmWo+JUBu7NcwxHclzV66OM8ud1fx/poZFjNNKL+T9jYok020ne4NNLqtTriJ3Hl8ECt+3nfY8dilFJOX1OT3uO8wSLET86sp3gNXVW0tw08dhTB26i/zsg19nitGG+3vGKQRMlweHI5mR/c2yj+UighhxmHWkPNunLaA7m5PAl48zyok0bDMZKlmXVq6ur2t7ersPDw8njubm5+Sr3j0RajpnIJQuIyTtFOXUfDwRCOPqSxKNfPvdvtXqKcNCu//cRIg7hd9EACw5tdgnQ2Z4nqUP4nXJxO26bQ0aTOTw26oEmuSRnI+0xmuk6wNOBI57z/7lUkiXbSxqZ30a0SrBKsWPAOO10oNiICHkThoEDdW5vb391rEJ3iG3Sx3UiB47OJX/YG8yogOfESodIj+XD+VnOietAPGedGcx7vjIVwvPnMdMnHwbMvPJb7sjN16WZLxLcui3/7nMRmSNHijK/2J/m71z6M40Xi8W0HO0ls27+DEz5ndw7zyNRzeXyadMLYM6rBvRttVpNwBD+cz+r1s8qtR7o9NbIqOQcd0YRmiADbCKoejoeynWMVizcJ+bF+WrOgez4IvvftcOByN6wZZrxnN8IBc/AR+hc+Jl5tR0zz1MyGr1arR9gzJiwj16dsezm+KpqcpR4ttOhbtvF9PdxKq4/bU5XV9oF2+Bss3NMrXNoBxnwsSjmgwSInX3J6yMAmjbO93QAcASU/Zl6OnVuRxP4udODWUY2b5PyYsDn87+81ADgI6n94eFhOk8MRWnAZ2NMpxEazuPC++c1QBZ6DDJ/Vs6Z6+DzqHyfARlRBwBsKpNU5DZmjI37Fov1189liLsDKzl5zoPqhMjLwZ0wp5dq5WzF5+RqK5oR4Mu+p6dphcxfF9nNurqIQyp4K7ScB4+9M2TOX+IZgJg3HtlRsJKBd126ZYdOaDMKlYn8XnpJINgpH/8PwEr60WbWS4FmXjr2/FMyOtYpGANU/ugT/wNUMm/NeWppSByh6Qr3APQc3TC/+3nG2EUpuWYjbYfIBtAbPQweuqXxqpp0mWWB8Ru8mpdJb3CBXpyS4LF4Lh3VMw2TpxK0+3vH21VPzqj5eX9/v46Ojiad4qVXaGonegT6DPg8/93Kwkj+Uu6h293d3dS2aUMfiLji8NnWLJfLadk9gwMdgEya8X25XE7gDLmxs2GAiV0yHTxeZIz6mJsO7CWdUsapg80kc3V0diFBT9pKt8tfRt8NZPnf58FybzqI9Ps5INbp0RxXB/KyjO61THU2uaOVadHJZge4N+njqGwM+DpmSUJlRzA26VX7uutzvp4VgpnHf/bmun66vwm+7JHNERPCd8qlmxzG3OWDjAQuaenfRig+jeNcvZQuXO8xJr3m2sz2RgyeferaTYCZyxF+tuv/qD8dsO6K5xcllDk1novkRxcMYddGyoqBVLfc0y0fUb8N5BytUlYomfPS0Srnyv3xH0aQCIkPSHekieVc50FZzlPJVY3BnvtusN7Jgmma9HRdbs+KeNQ29xt8m4/4tLNnoJhRwqr1Vxkmn3SAh3YTfGYerPnguaVHjy//z7HhLPn1hclPnZFzvZ0OSZuSvGx9bPkxX6ecZu7YyGBDy5GNcPsdD4/oOXJq3T5jyPqcomA94uBJ8lv2x7yU+qGbj+yX6Tqya6PfRmWEHUxbaGcajmz2CLT9a0BSh3c6nZnPjOzlHGbZFM/MXZ8rL4rwQWx75QhYKpjVajVthzfg8zKUE4v59Ll3q9VqWibm+dVqtbbk1oWxraQdNcRDS6FOxZIFZdFFwOwlphGGJltbW9Puy0x4z/5SjxXWSHEn+LHnY6ZEQP1uTraAG9jRf+YtoxVdlMjGjHY68GI65pKZ22a5zB64l8uIamTb7pOT9rsjIkzHbo4PDg7q6Ohore2qmpY+ndCcIIj7uGYlzv9+h7LH4rl0fY7+eCmf+jIa67liudr1uB1KpkbQNvTEi/Z8WoYvLy+ntxtcXV2tRfeWy+UUXckctJS/XMY1KM7rzMXe3l4dHh5+NafOfxoBnYwkMT74sltSpN2kF2CcaCPnlcKnHPGUhoLIEjTxK8EMIt1n+lFVU/SUVZVcHYBe9DnTGygpDx2tkvanp6d1cHCwBjQdkUswlPop28p7LD+Zm8lzPqyb31litlx7rNgb61IDZd46lDzjpV766F3VnlfrQuiX9LRthHYHBweTrOMAHB0d1dHR0XR2rfNhDficlsA8J1BJUOx5SvCS/YeOri95xvzttATfR7tJM88HuhI6bW1tTWPOdsw/ufo1x2v5/Nx92b8EfUlD2+AOY3ROSQce0yZY3/9fA3xZMRMAY2XpwISNo71RFBYvDq+qNaG2ccUAGWSMhMpeGgI1ilI8V8zsMHCOqWp9KZYxZsJ7By7TsNHmqG+dd+Z+5HMWLivAzuukj+SnjIqNXNeH7v8OGBrseNnIggJwnovqJsACqMyVzuMFROCYcJ1+eAye1xxrCrlp7nvd766/KfCZ3O+6OhBrsOQjXlznc8qjkxN4CmeK16PlESB26iyHXfQkxzwqCcAM3KrWHavUUZ1jmOAS/iPv0s+lojYNbXDNR+S1dQCTpV6MG7S1fPgZ60DPhcF00tUgOg21eWeO7gl4Sd/hDE103ciodqCia6/jB/runZkOQrCk5znC0Ukj7f6YjzzvzFUCFs+9+5t2hf+7/NkO+PkZ9K7HxBJ+Rikz5aCLPiaY6MBKR/NOtxh85Ty6TuiZz/vezv4mUPOcIMfpHPvZTfgrx5h8M9Lhvj6yv8+11ZXnVjGy7ezzS8qLz+GzwBt0WdHx57OA+CNvhd8peMYk/toA0naelP/VYMLzc9v2aC2UI083vYcOOHbgzWAgBZNxjjyTZK7OiHtcnTfSAcAEBqZpzpH7mN5HRhxzLOlZdV6LFVvOU7ZpOvLJs8xFp7i69jqjZiPiqAURQgxYB3RSsY/oz2+mf0YcR/xA3d1SVBcpzn5Cw8wv7fqbRtpKxbzrwpEfnz9/rsvLy7q4uFhT0vTTYARg43Y8Lz5OJcGDDbejPUTS3G/Pu3WXeTmNYSc7Bn1ZfwLH1H8uzgGFr1nevr29raurq8nhdU4ln3x31JLcZo40Ml91BtXzgq50ZGrOgCF7zNHJyUnt7OzU0dFR7e3tTUv6Xe6x65gzWMkLnkcfLeToK8U6a7FYfLUywW9s4Ej5Sb5hrAbiBt2pCz1G7jHgMQ9aHjPaZoeJ1S0fK3Nzc1MXFxdTxP3x8XEt/STtUOroBGK0CcildHn2phFybXA5Z886EGZaQHdoknajcxY8f6Z76rIEu77mtru6/Zv71gVKqH8E7LJPHpf7lraiK78G7FX9ynfpZuJ5B/i87IoyYgOGd6xtbW2t7ZRkKc1Rv5ubm1qtVtMruGw8MnpB8qkTmQ3SzDhWJAkectze6MHygc+zs9L3pBvsoixRBvbEPckd+ExGZRzJ4J2gpEA6UkoOTr5VJDcW2KOjrtxNRn/S2KXy9n0ID8LeAUbqMM0MtlP5dmP1PHlMPqiXP585Rz1eYrYC7xRZV6CBgarPe8z59fKYn4d+jlab3qk8nIqQNOKz4y/a5nrnYN3f39fl5WXd3t7W2dlZnZ+fT/Ls+hLsIf8dLyDjVbVmtBkn/TAodHqHo5+WWT9vOUw+GoHwLvXB+s91d86OozPwMq/Ourm5qfPz87WlWeiSgBaQx8Y2A0c7CB3IM7hjc4s3/HS6xmOm/v39/Xrz5s0EYnd2dtbO0UxHOA1g6rhRsV6mXvSVASj8kLaJwph2dnbq8PBwzcm3DXB7/O/0FsBiriIBjnIFKMdBSgj9pW8Jykg9Ojo6qsPDw+ncvdVqVRcXF3V2djbxMDp8DvCZDu5T1dOKVIKYBEE4WblBsqrWxg1N0q5Rp50r2xmAvB3wBOEOKNDOnP3NdKFOLt1G2s2MACOPI9vd0XeudLZ+dH1OLl9SXgz4Rr91HUpDlELlCIu9dQQt64ZJqtbPJRr1y4q8i/B1Ez033gQtea1TbhbADsj4vo5x5vrjdn9N6eqz4nA/c3wJxDbpw5xAJJ1830sEKcHzXB9yvtJoz/FF8k4XPfP9VoaZaD1XRjzr8aXyShoihxnRyD4mfQDhrjv7hsHwZqs0uDauLjnnudTZOQ4dqHL/Eoh3ij1pzjifk79uPOb/jle7ObMe8eaWLmrbGYEEtnZ8PWb3NyMkI733nNNSte4oVa0f8msgkH15rr0R/TvDX7WeR0exHHpeOyA0WppdLJ4232Wkjk+3YZvge7s5ISI3R985WSPgsLW1NUX25jYspb7L8fL7SB9wPcc7ujflbmTX5uzurwU26axtUkb2Le+xDt0UP1hfPXfvqE9z139N2RjwdUjUCZR5rxG5lVPVk7eEd8inj1C5uLiom5ubWiwW0zLv/v7+xOQkgVMyfJ6J8SRDj5SeJ8fonmsoDyvYZHQrJcZZVROQ7eizWq3WPBrT2J5K5zVyX+Y4ulhY02uibr4794g8khwjtGBcjNHRFL5n20lz+CPzcqpqOr6jav2gUcbQRZ9sbGwUuzHwneWM5XI5zZP7au+fQr0oYHg0z9GyEU+j6PF2UVr/7+hNetG042uOmtuwwf+O9FB8X0Y8uI4zxvW7u7vprEx76ESKLSfuY0ZkF4vFWqTVytURTsuoaUZ+XDeukZPleTJvwFv5jOdptVpNCf+OHkLvvb29Nf6rqrV3bfP71dVVXV1drS3jdkoeOUGHofu86aVbNYD+RPG8HOr+dPKR4AM5TXljswmbdhIopY51/ZnbnLrLQIrnvPHFb0PJuTQwdKSc+8itNODmO9Hih4eHyT45Gmq6uH8dwPP4PI/0xbalAyzw2uXlZW1vb0903tvbqzdv3tTx8fG0mQP6JOiGd8mlTVBGsS2x8+bXPWLvLZemR4K7DnAmMDav0o5tFe1lf60L0sn0MxlxzropGdHkXjDH4+PjFFW3jfNcpawQhbXeNG929LGe8TylvfOzHQ7ryosifFkYeCrgThDS8O7t7dXx8fEa4Mvoyv39/bSc4fyq0fIKJZPaIbaZvaqPvlV9fbK228mxuR++r2od8OVrqQyc3JYnFaXpxOGMmqYhy5JjSEbKJS2Ex6H7rn/cny98htamSfYh5w2A6TlJ3sq6cu7yWYOFUTH44f5uySYBPP/jbXsnrJWmnYcuCuLlfejN/+lVur+pqDyHqXQNbBzpph4bZ5eMaphfDXb545pzuLiHYtBuB8Y7LzvDncYwcxodXXR+V6cYKR1PWu5H9yMDSR9AQhofFLHpTioIS7ru/6hAY5ZNLZvus/Utf3Y8zPMA9LnotEveC+gn9xCHwtE/68ycD+aS69ZNpi1zg9E1yK6qNWfaOtx6yPOCLUk9b3rBoxnhszy7j9lX+kd7Tv+xLekAvsfPPLNrGMDPXOzv76+loHg3vPW6N28lgEj9X7Wes49uJkfTOs797ABkp6fTKbFMJR929jrrT70xBzpHdeX9WbcdrgTTXb/4nSCCbb71Zfaxw1JVX2OS5+z+qGwM+GAYMwg7h/AcOi+yqiZlYM/GhISpETAbE3uVKHcbGSJSCczs/fJ7h8Z9rSM+121QLLRpnA1k+IQ+ZqIEMiOh6cBn1brxTGDgvru4Tx0QTGXZKSZ/t8FxyaiM+9Z5USOF39Wbkb4cV0ZnAGMGOEmPNJCu33ObQkhJkJX9Wa1WXwEZ57wmmOBejKH5zTS3MUogb2+S4ns9vrzutrv5ThnsAGjWl0sitIH8kpjut1j4WcuO589lJMNddKArcwAx609Z6xzAdDi9JIcRh44eF/PqDVUGhDmvXTFfmy8S6I+MRzpLyBK0vL29rdVqNeVkO9pm+nS6IMFR8llnqC0H1hVJe0fsO53vDYOW9+yj7Qs8ip3JuU7e7OYmQW4uw3pZn7rQw4wH0MF158kzVpxIfqf/1p0jO2cd5Hs7GvFnGqZ96sB78l4CZtva5JWkpUvywmiOumudbaOkze8wxMgu8Fval67tbDPHOAeqX1I2BnygVBuZ/f39Wq1WdX19XRcXF7W1tVWHh4fTri2EhjC0BccCZEavquk8vtvb27X8Pu7zsQ8HBwfTki/EAol3hOsUBUa2Y3z+COf7HjND9wxCQ1I19GHZ2sseeD1m9DTKqTT4ZPk1o3E5bn+aBkkbR0I7ZrZSRAE5IT0Ndo4POhqUdZGdzggRXqc/FiLTnTq93I3CdL8wAvw5QgRvJqiy4exKKkUDOCcme+ncffJ4c3w2pKlorbh40wGRDCtgG5qsn+vMIcCReolMscnHNLOzlsrR4+PsMMDd/v5+HR8fT+06GpH9HRmpjBomOLTDkf3JueuKeRyecdsGInZcoS3gjo1rj4+P0w5nxmi+tQzz3uERoOj67ChPzh/PZFSde1KezcdbW192dV5eXk5Lud7Y4HZow3OSOs4ADNl1Pe4TeiQBDL8jry6jaL2jqqY1c+mzEJlTr9JkJHlr62mzUka2GZeBGnVRvMOZe5fL5XRMGQW7sb29PW1wsr7FcTo6OlpLk+hWEDr96t/5zlihhXUaPA3YTFtomvPp6HvqUvQD7buYjv7z9c6BMB/YTtqGjMBa6mYD3TmgR3FEr5PHrk23ZZmHfqNAyyblxUu6afi7hFErFQZKtM6vROsGU1VrBoNBo0AzL8uT7on0xI8MT46H+nyPDW0nKJsg/PzswFP3nO/t7uuEdtMyGkP2MYGhv/u3uciLxzIa05zQbAJg83fP/YhO8GfO86gPXXvdOLr5tRKsmt8R2vFkR/+upIKyUhv1uat3bs4tD53X6n7M0csOIJGJbtkw5y9pOioZZepKjnOOX0bjGRnQpBF6MF+xhvygTxOI0cc5/hyNc07vdHKaY00d7CU//3VArWsn5WDUbnctaZC6aaTrc4wdHTsdzn2Ayc454tkES7lUnUAqiwGB+9I5KCM5od3sj/8flY7mI7v2XD0j3vLYOj02+s3Pz9nzvLf7vRtPVzr+HOnH5+oZ2b+5/o/a3qTNubIx4PN7BGFCvBTeoQiDXl1dTWc0VT2dau6cA7abPz4+Tge2ch5V1ZeI4jfffDNFAfA0fDwGBKBdH/xqZd9FSihmQHvqOUH5bDKfBdPHIrisVqu1l8h3DJzA2cLTMYtzwuxBdMplTqHnGJK+3fP8ll5jghlHM7nHCfrQ3XQmSpJORZdTmHRZLpd1c3OzBiiWy+W0uQAPmf4vl8vpTLnFYlGnp6cTT7utpIG9euaiqtbOmaPfftMKspCGnDnMyF2nsDOPtZsX+u1oXRpl52s65cLXKdSHcSISz6ffn7tardZyM6nPy1C8pYH3sLru54oNHn92Brsoo+cRHrC8ZP5m1fqS45zxTCPHnEITeC+PgUIGOCbFS3TkCz08PEy6zbqPvsyBQPpiYNld8xxV1cSjPgaFvvqZTjY6AJQy6ogzfM+zSVM/y2pJbuTq2qQ4Ipf1wwfwj/UT/HhzczPJSW4K8tJpp2tzSdv1c99oyRTZ8u+8QcRv48C2EfXza+7oI5E/VhQsZ54LR+PM8y7Ml2WJeYKvzScJYMyHaWtHtrD737+NInudXKa+tB5I3cO4Ov3f8X3+Zp3UATnTJMvIXrs9yqZRv40Bn3eGWXlbaNjFcnNzs3aelo0AzMefldnNzc10xtA333xTx8fHXxHRuWUeMIDRAmAF3YG2FEIbbnuRjGEObZvR55h9LkF7BM5yol26ULGf73IlRkrVIDkVbQLdrh8JzlIh+A/jlnMz2kSCYbOS6vpIHdzHcj8AhZ13qXDhHxKsMyE+gbh/z6U2aGEggaz4VUjdHNK2DZgPOU8h7+bCPOB6OoXWLZ+5JMixgn18fFx7f2638zNpAy12d3fr9PR0bePWYrGYgNHIULjfafC5x3qCMdC2l+CyPoMtOx6e77lISQf4vHvYso8zUPVkkL0CwjPwg3cim686+c05zPl4TqZpG4ABvZxTmHrUc5QOn+Uy++3ro0ip2+ST/HHPbTc/q9XTEV8JeEfyy1i3trbq9vZ20gvoEkfYMvLpAEPyRPa1myuuwydeLl8sFhPY8y5jZJH0iKS/85hdt0Gb5dr0NjhN+Xd/Lfed/bPNtk3mfy+Ruoxsoq+NeD/tjcfVzU2uHmaZA3uj+9NWzNU51+ZztNm0vOjVaqMwMd8xmnPPs+vWBhyhyncbmqgoDBS5E8MzP6wjohmwuz4Hqrp7bVz8fLbZPdcpy+fanhtT1r9JH3y9+62rf04Q0iB3xoD/O+FLYD0yrlY0nWLJ9uCPOcHrjH9n+N0/K6tsLxWR++z8Hj+T7YxKGsg01DlHSS/3o6s7Sy4bAfJubm7q5uZm7UiWTr5s5Gyovfu+y591MY90wNf3UJwvyb0A53RKbCy6eegiOFk8biKpjuiZBxNw+hBpRygzV7IDnhnhg5cc+c+57fSXaWid7r7C2wDCztH0PLk91+vfu3kc9dl8mDaCuaYkiLOs8X8XFUrZBoQDCDLnEl6GNsyz5zoL4Mx0S6CYUUtkhJcT+I00BqjWd+Zv82LSdDRXOY+mY/db0jB/9/2jiJz75f6Yx5NH5my6P+e+d6V7vqNfjq0bc9feyJ52djz7MAcE58qLdukiaF4qq3oykAC+T58+VdX6kiRgj5dAVz29NPz8/LwuLy+ndhyutwB9+vRpqptCfzLpvpusNL4pjDmBo8njvlwCyMLEWLl34Xu+dwYb4eC+LrJlsNoJQAdOUiGMBHRTD8R1mq7ZF/cf5dm1RxTISgnDmTQ1Hbw86U+uZ8QT0Oid43jzy+Xyq7Pr6MPj4+MEWLpx2uhubW1NYzW4M+jqDMNy+fXBsb5mEOA5cMEYOcpoOtrTN8hlLjEkXrb98OFDvX//vu7v7+vq6mqic0ZFu3y0g4ODOj09rZ2dnTo+Pp6WMr25hrHYqet4xDRLMISO8pwbWNEudHQ7HoefSd7xuJx/Be28cc0b04hMOVJDm3Z+z8/P1/73/AI2aNubFogM3t3drQHcOUNhI2ydlJvCkNtMRHc9HS9bjhNQZnTHEXL3w3PuN2osl8spWAC/2Ynoiu2K590rCOgBxo+9Ypzwhd/8Qf8B/R2Q9LEyGRlkDhkjfIocnp6e1rt376a3hhA8cfSVDUL0j7Qp546apjkvHX+PnIPkIf5G0eaqavWW23O0Fhq676azn0vHIiOdea9tcurNkZOSQNO6y7KQcuY2u0BH2ufsK7QYOVmblhdF+LLj7gyhZAMfDFZ2nB23KEQO2cWbSm86E4TdpxTazjgmITshzDKHvlMIOqbq/s+6TZu8vxOwBDmdJ7BpGdFidN9c39yf/O6SSmUEMjteczuj57qlvRzXqF+uOyPZXEtAkAAv6dFFF/mNZ2jvuchelgR3o7F2ytRLOR0tOsVJG5m7Z/mbq8uGOjdu5bxl/0fj6mjue2g3VyK6lQrG4LF6DB1Ayt+yPwYV9NfAEYPOc9xvnQi4GjkHI9kwaO+ik8/pyA4EuN65iO6v1UkuncF0/SMgMuckdTprrph2Xgb1uKGJZdif3RJzPpdtZvv+Q34MeC2j2QcDjAQLc3Yr9UDSrJvz7lo3NsvuJiAn5zLtonl0pNeyr924n3tu9PuIFzetY5P7R3P1Ull7cYTPypbBeqnECaJ4Qz7CgE7iLbEZZLH4ki/CqeYs9XIfeSx4ckbV3qQxYiC8+tVqPY+OvnGf0Xe3FOijPlAEWQyCbeQNGqr6g15tqDqgOgeQNkX+acS7Op8Dzik4nSH1WJwsnDQdlcz5GY2Vz1x6y0ijv9sIr1ZfXkr+/v37uru7q2+++aZev3498amVfXqVXTSDvmG04ec5YU3QB427qG8q+fzd8tG9/5P2OtBEPTyDA+bNRshtLlMm3b0Zg2u8DN5HnHj5EZDTGagO3LuYtwCVaSgMrly3nS/mAlknSsrZc95IZhl31Cv7yXzaqXWE8fr6uj5//lxXV1dr352TzBirns5Frfr6UGZHUNB3SavcfECd9MtRFujkswRzg5htAv0c6RlkIYFT3pfymwY19Yf/T5p1epF7HaXnd5/D5+i/5QX+SD7zd89D6kYfy2TnKVcStra2ps0YHMPiaLxpjM3k6J/cyORItKPqHrvnxDRP++LfU+fz25wdGwGaEXDMNsxvHSCExzub1NmdEU2o0zrC+ouSx5G5XuaT0jmWSQt/T77j2f+ngM8hawbLqd8cMIoCdE5C1debLBAAIoVE/+7u7urq6mrNW3ZdFhAIae+WwhKIhYp+QNhkdi8VWEmz47JTVIxve3u7Dg8Pa3t7e0psp75k6I4RvXsywUkKJMXh+ueYO5d4NhGyzoMx+DDo83Uzd0YeOtp7PKk0KCMl1C2jjKJvKMHlcjm9wP7+/r4uLy/X0hKskGmjK253uVxObyCwUbBSyTGNQJ+N6ZxiYHkJXrfxNm1yGS2VjvsJuCGPjOjT7e1ta2zNBwcHB3V4eLjWb3bl8pt528tRdpIS5GfkpAMBudzOJ3pkjl89DurBAbWuYskPQJynB1St73gmDcAvu6+qCfDd3d3VxcVFXV5eTpvZfGA79/qzav3NJz6j1GP22OhL0s0AxvoOmjttJh3d1JumY2eM+W5A5Hq6Mqc38/80sFzveMW60Mb54eFhytGrWn8dH/Tyb5tG6U1nThCwLmJunDrC8j9LzBQDEwDfp0+f6ubmZuL1dFpXq9Ua2PTYO1sxZyP8PUGQeSoBf6Zejdrp+pY6vitdYMFzNmrbK5SWIYN012OnN3exW3fZ9lv2RzyT8+DVIOb/uU0mXXnxki4EGHXMHXI0zr9bgXQT72WiDuRw/1yfOlTfEcZCn15TRq3cP+rrGM+MPxKOUZlTfNTZAa8ct3+bK0nDjlYJLJ/rfxe5zD52bcz1Kxnb4KRTViNQPAKji8VTzpUjJHNKPA2WDZ4jII+P6+/O7ejiehLczRnBpJlBntvsxjKn5BNQOaWi64vlxOCWfnEP9eXYLeve1NLdy2eOKxV5JyfJD6bJaKk060yg4DF0IMbfoSVObdXTm4iIoiZd8/+cx4zkZMlxdTKRkbgcR24A6Wjr31KPdfTsDPucHshnM5+XZzHEWccIXMz9WWa7HFdHdDexMwYUrsv847lMme5416sPCQA9l53t7MactJrTQx1ts6SdHOmykf7Ovj3HI3lv6nl+7+R0xG/dmIxNNnkudYht+XPPd3w5179ReRHgmzM6CN7BwUGdnJxMHu9yuZy8CSJsLHXhzVc9oXbeEDBKCqYgBFVfh1+NxK0MEvVbiHwu4GKxmJSyQSp1gNQzt6bq6XVzVeu5h3PLCmYA+tctFXse7EmgfHysRJfwnjTKtpOhXHcHBjtBM42tgJ28z9JIRnA6QASdUxnZ6PqZXBZxnbnkXFWTl7u9vT2dro+X3EV/XPJcNNpnzklBcGK1lbYBgunN/KaD1BmsLDy3v7+/9o7R1Wq1lhKB7GxtbU1vzLGR4Rp8RESepVz3hTnAw8334kJfj3tEM5Ylc0yd/rGMcM1J+8lnVbVGZ9qGtgaquTxjvkXG7eHDM51x41meJ9WFFY67u7s6Pz//ytnITTYen3mcnMpsG1CcpyJ4jr2xxrIG3dMR8XzQhwTc1gFdlG0uyZ17OgfO11ar1RSFz/etWze4Xy4GWalfrAdZqaFtrzotFl/Slsz3PI/98IqPI9v0gSNjPJc3Nze1XC6n6CL2s9uIwjPYJEfzbB+sV5IGmbo0mgfbgS5KlsXg1c+P2nG/+DT/d85QLvuP0rBSng3UoVNXXwLaDnxy31xwIME+z/qZzmGwk5HPjIDiXHnRmzY6g58NGtgxQAQSIASDp9fRKeJU8iM0PgdmOkImYLCyQNF5d5sjNumJdXVWPSk0C1/X9258CXByHjoAxji69kZtd3OY/ZkD+umtdFFZQFWnPDqPJceZ40nAZyNoEGWg4fvTGNA+SsW5LimkScsOhOTOzNyckEJM323YO8U+1y5jSSXHfQkuPQ+ei1HbLBfm0Q4ek6Pic7skO/Da5bjaMKbRTqDAPQnsszDXaQxpLyOb3VjT2eTTtEmlnGAI4Afos/NLga6Ah9HcwHPd9aon8AtfmzY2ht4JzCc6rtNBGTns+vZcSSM50nXWcQYBGOo85sR1jZaVPca0Q26PuqET6TlVT06fgZ2d7QSd2Y/Ub9YfXlbPsRs8eb4yyux2ki4dwN20zNmNOT31nC3JPue4Uh7n2reOey6CzWcCPI/VbSd9u3HP2dlNaN1hmE2fHZUXL+nCgDYcJtLu7m4dHh5OzGlPx+vnGBF7IxiUqvWk6vQo/Cqmzvug2ADRJoqZ/50sXvXkdVkxpjKwssxJZVzURTubTlouh7mY4bvPUUQ0BcDgwwcCO8cg592AKXMY7b2YXp1RTYM4amM0bo81nQRKgob02jphhy7whw9I9tLdXL9oO5dXRkDGUVobGAzFaPmX+5JmjjyZxknfqqfEectGp+wArbe3t3V9fT1F+Ogf+WiWbdPb+bsYw3z3ajoI3ZhHm6Ool0+O8knnLefNy9MGNDaGqUscpc7oAA4i32mXuh3BgYbX19dTcj1GvjMu6BQDiHQszDNVT/lEqSPT2WAcRHozaX3OWR3NWdoE7vX/7k+W1Gkulvtsy/rc/XLwwfNpZ68rzBeRRANMR8Xv7u7Wotl2Cnw+Hn1iYxL9p98ej//QSUQTsX1OHXIEj6gg7WXkKmmV85TyPKf7Otp1wL+zC8kbWbrgQdr7LqqWjp91ZadzHLgxPUa22HXbtlFn8n/Ks2k0R7/UP67PdHxJ2RjwWTjMEEkQ7yY6OjqamDWXblm+scJCiZmBnYhuZeblSw/cE+fInb0uwuiecCtV+uVlKYpBX4JJPu3p049NkHpGRhLUdb957Gk0uJ7g3DS1QmJZqIvOMF4ndHtJ2wKQ/UqA5WspHCOQ04HYpIUjsgnksu2MHhvYsWzz3LJFJ3Dm4QR+GTWmjsw3ytw77nM/nKPnucxXvZn3bOTom9864vmAz5GFT58+1cXFxRrg4yyw7Gu3/OBlPIwdoKybY/NBlzphneDlvHwnL3V57hPwEIWdU55bW1vTm4OqahqDAUbHZ7l54tOnT9PyLQCa+vlM4wzNUs8ln1l+ck75NC0cVd7e3q6Tk5M6OTmZNozYgRwBPpc0cmlgu2heypjbHAFxz0nqVW9ycR8SLFi3G5BRDM6ur6+/ciwODg6mPn769GntLD4DMm+OYJ44sDz1tOmS4yJFyseyILvQrGp9Qwj1oiOSL6GV26SgpzJybfq4/m7+TccEeN3zeS1zGT3vlBFPpR5N3MLcJV/S9pyzbZ3rdAA7BOaVUR3+HN3D9W6zSa4wblJetKRLSYPtDvJp79KgYGS4UwklMKDeFIS5e11G/XQ/bJxznNnnOdrMhYpHzyYA4pm5trrxuZ6sM2nSAcqsk36M6DcnFKPnud4xa477OXp1wC/HPweSE5SiCO/v76fjOFL4n6uno8PcWHwvxUqqk7Xumbk+Jl8+pyj8TJeDOlJqI3lJ8NPxuJ91NMvG37Qc8aGjQN1f0meu3zYICeiSvvzvQ78z2u+z9eygebxd9HlOP84Br9HY8v5Or6ZBzPpG+tF1dqXTUa5/7hnfu4kuzd/n2hjVxZKtbVpu4Kh6mjfni2VAIFd93E7qa/+eKxopj1k6/W8ZHNEk522Oxp0sdvfNlS5ylX0Z9a9rdw4j+PuIhqlXOhncdGyjPm5ansM0v6a8CPBBKBSal/FSUXSEYhkJIbJC6cK3/kPIEqF7aYa/FE5KF9ql74yLwnNdbkwHLByR7KJ++dyIvnOAIZeRciymFde7iAFRTNPABjWXgfzJ3HUKl7Yzd2x7e3uKPPl3e/L2VnI5fSRkI4PGGOA5R5dzPI6+sax9fn4+vQv6zZs3awedjubN+VG0w7mPjiCQS5X9zTcEeAmRfmabCZDMZ14iMi0yIuuoX1cHkQW/Uop+jRLJPV5v8EhZ9LPMv3Oi6AtzCE84sm8esTHrzpnrDB6/59Jl1uX+ekOQ6Q99r66uJlCXeXFOyrfM5c7+DpzzvOeWfjCfqQOTNzzmqqccPo+NZUwfw5JtmF7WG+5XtmWZy3tcdwfUEnT6O0eb2PnIucliue/sjef+4eFhLXLmfHQfx+M3m/gNKvC1dR78xH1E/Yi6U5hr+Oju7m5yRHm3b1dsU5nftJVp04iW83yutlk3dPq/4w9fSzu1tfV0fFSnuxylTN2EzuzAmXmSseSSeRdxdnF9XZqA9aafyb+Odzu+8zx4DObrXKlwnZuWjQGfDfVz3kUHXDyQLgI2J3SUbsnTk5nEcumQf4Ile/LJFAlAYNg0Nh1Qyec6eiV4GXla3fPpyZjhbSC7ZVUDAtNlzmObm1/X52WmXMocGbSkd9e+x5w0SaNhsJAeusdjBXlzczOd/Xh8fDzVNbfcjDLNpUQb3cViMS2fUXyPBdt9hdc8VgOSlC0Al6+n3I7mOumBwfNzHTh1e5YrbzhIB8PzlbzpiJedAdIzuj6bLsmP0L57xvTIZzHmfo0kOsdAjd9IByCSx5K4DT67wTsHxMbAaSjWMzhrduQ8jynXLp3hNuBzG86xdsQ5DajbT13XAQv3I/VOzoVp0o3T9ElZyfZcupUct+P7OnAL4LPeyPzzbCM3KuI88Zv1VIJj+I12bHuoa+QU2gaYFgnafJ/HkLLZgT3PfxcV7uaX8XcpF8wj15J2PJ9O+Mi5SUfPfXSQJsfkefT4E8N043T7plGHc9znTka70x26tp4rGwM+M7aJl0tPI8HpmIQBuS5vq88/vFF7ShjaTvEgKBlFzG342Q+eteCNgFj+1o3THm0HllLZZxvZh05JdvX5u+coadSNazRfXZtWEO5nN7YEMlZYFi6EOese0d998acBCvVnflOOk8gW4CLBk/vDNUdsKBbSpDfFTobHYv5LGub3pIPvcXsGABmNSQVCbhA7SO/u7tYihF177je/873LTbURc4QsaZvAwA5EB1j9PcdvQ5TAuwNfFIwxvAFooxjw3dzcrEVHuO7d39SduVZZcr4cwUrA52fMt45mdREtnmGzGp/PRUDmfrPx5/9On3Ry0QGSkb7rbMScHjO/Z8TL1y2XRPacK0skzHNq4GZQvrXVv2kH+TJgZfzuHysMPMOn57MD4LRNvh/j6WwQ10Y06OYtn51bQfFzlluvFiVPJqjq2h+B15Q929WUHfcnx8r8W4ZS73Q82enUkT2du6ez1d18sBHuubIx4PPZc14uyQjFCBGnAugG76W/vb292tnZWVsWQdDwor3Zw8JuZe5X1ji83uUyMIYcmxVoGjTAiU+/TiWSQLgDRJ3xdD+4ZlDRgbYs9NfzYM8oFcSoHtdn8Mhf93o784bnyMZ6tVqtATsUoJc7eKWVlUU6CrSbgI/2lsunE+fT2Cf97u/v6+bmpra2turz58+Tk8Ec8NYJR0XMU93J/NlX+gj/mLY2MIwBsOI3tYz6Tz9dt8Fe8nACaq4BXthN6kgffXN+kzeReHwemz1rxmOd0gFZ+mcjZnnP5ZWUPdMpE7lNt87Bsw6jX7m7Ft5lDPCZE/bzlXFde9YPdlThAfRiJ2fQyP1JGqRRzTZTx6ejw2c6IS55LVcX/Ezqsa4t1+tPP5eAz85j115VrY3R/JVAhbdbeCmXTYlV6xsLqI/3widId4He5om0nxzZgy1k7g2IDAxpy7m12MyDg4M1R8X8nE6ZAad1WNqRnBfLZdoFz5E3uOQGypzP5AW3N5Jz/ndgh7odUUzglHoBGlv/dgAs59cy7j6nLux+53/b6myzW0G6vr7+ise68qIl3Q6sjBTkXB1+1go8jQbKcpSP07XVKbmMHuQSAP1xvzqm5pmucH+XI+T+jkBZMrCvPaf48v6XlK5PqYA3qcN1uc6RYXA7HW26PuScpPLJ/rj9l4wHpwAQ9/DwULu7u62xnOOTBDxzxTLgOjuBp75OYY3o0N2HEpuT14eHh7U8pdG92f4m373cS93P9dl9f64fc/rB92ZdCRZ4BkPhg44xoDYuBku5lDaKmjkaaoOefe0MYmccUz9217KMdLzbtuEb1THi9ZEO2KQ8d98cX+b1EUgYtZspFnM6Z0Tfjm+T3l30bQSERuPrdKj73/WpAyCb0Cbb4dnkk7wv758D5nOl068jGejsy0vGNuL1rt/Zr9F92Y6/Jzic69tLysaA7/r6egJOJIsiAEbqVnZ0pgs/7+zs1MnJyVc7ABmEw9goV0fnEgFTEtnnewS5x0o1wWanQDtvas5QjpRbl1+yKXjLSEa2NWLoVEJdDoAjmHiJfsbzmkLFM8mApiNz5k0weEaACo/RdBptDlqtVmuKGJC2WDx5kcxdGrJUolZOHJWxXC7rw4cPdXh4uLb04INR8fTz6B8+qd/vYGQ86dx0gI867KRwXyox84ej7Lnk2oEcnocXiKJfXFzU2dnZ2saNPE7FQIV5MIB1QYazmB+9zFlVU76UI+nQxEZypDS7pWHAJh4/9dE/R6yzXF1dTTmevF/X90L7TKp3vhb/56HhGXHtSoJf+kzEtFuJ8Pjn6uUZb4TgdxLsHZly6aIazEMHIl3SZlAMIPg/o4XOfav6OuJi2cmlvOQhxsxKk4//sa1hrm5vbydeQB9BM1YpcBaras1x8vFPJycntb+/v7akzlgODg7q9evXtb+/X0dHR3V4eDjlXZoejA++yiVd7xi3rkEPIfd53fos7bRthVfirCM6wJoArbNt9N02nWf43q3YpG3rgO0IkKeTNNKlI2eM5zKn38W6ynzN716dSRvq8dlJ3LRsDPj8MnmWrDJhOQ1ronh3mjO8+C2BC2drIcy07dySDgBQn5cpcnmkm3QzTS6h5mR3iJ3JSnBlOliIRso3Bcr3GSyYziMGz++mU17zcrEFzTTtxkbpBDbnv2pdQE1nJ8d3wLEDQrTLYbvd0p93g3f0SJpZcS0Wi7q6uprOv+LVSF6SMxjBGbDDwZIA82cFZ8DHGLo+stEjlWEuwSdtGM/ceP07f9Ds7u5u2sDiYmcs27Eyyhy5kTxx3Z/uo8eaYDPbyZ192aeMvHWbwDjkNpU6Y/j06VNdX19P99kBpR33J8fhPqTRtmPa0de6Jvtt3svIYspuOsr8ltcTvEDfBKU5n/Q1l9r5faSXPNe+386IdS2/0WfGbT3rjSej/pkvDfRyE1XaBnJcXaf/eMsU/ch+sfy7u7tbR0dHX238WCwWdXBwUMfHx7W3tze9AtS8wHdokP32TmyPkfHbjhtYGQhDo1zWzM0+0LkD9amvPPfU5bnwfBrspFwnMPI1t2e+62jHvGY9Of7n6vcz2abrNB1HKy0dvsm+vaT8qnfp8um8pY4AFBt0G2o8mqqvd9e55GA7QDkCXd39I8TvyfQkdOCq62PHDNm/50rHnK5zNL6RIe8YN5kw++YEYz9jxdDV3Y3FRtBKxe0YbGcfDeBc/L95J41gJ9Sed/+WbTw+PtbNzU09Pj5OuTzp5XfgugM0HW1QyqNdYi5Z38hoZhv0k09oBM2cywTQ/fTp0/R3dXU1nfA/dzRN8mr2paOFIyydgehKR3NHV5IW7ov5gXocqYIvMeIpK9RHnqkBrO/pDFu3acUH53bF4ALZsdFNQzTi/5yPBCWmbYJyP5c5b1m6Oezq6UrXn6wn7+2eS7DtPvh++CUBNnQjSp3tGUAtl0/viTddUve4D8iZ0yTot/nCuo/3Yntn8GKxmPKI6Zf7CUjCCZg7XscyZVDIX66oeT4SCLntBCfmW8+f+9Pxxcj2dk5iV2x/PdY5XOBPvnv8qYt9T/fMHD7qxuy+Z92W9f9rgM+FBn2+WS6Hmqgs2dnLIjT9+Pjl3CoEYA7JdoojBdj3dSFovvt5xtRFgTpj0QE67zjsQMbIc8i+URwx8zKof3sOULh/I8CQ272rnpLO3ce9vb06ODiox8cvx0oYDHe09ZKeAQenu9vQA/6JKmXduVRZ9bSRgWWR9DydBJ3FS4OM0aCDtu7u7ur9+/dTO6vVl+WKg4ODaYmkM1SjPE7P4fb29tqrrEZAivpG0ROPwd8tF07oz76xaeL29rbev39f9/f3dX5+Pr0N4tOnT/X58+c6Pj6uN2/erIG+kWdtwODSKb3cPOTSAac0pPCS58HPjYC3eZ060VH39/drZ6I5mrlaraZdyzbyyBLt2pDB3xkV5T70o3XXYrF+mr+dJC8XrVbrr6R0mkR33mPKvQ2Y60m956W+rnRg22CqM5DcmzKeuhEe5n/TNeectIN8xSDRtYyAMWaD3ayHpXnmhGj/4+NjHRwcTHXc3NysAS0DLPTU58+f6+rqqm5ubr6iP/qEqKDbODo6mjZtmBZefUh6kWsKiAPEkn5iHQktAZW5YrdaPZ27Sx+6aDTy6FeVMs8ZCUefpl0dAbG8hnwkf3WF555Ll6Bur6ZZV2XEunNW/b+f6TYHjdpPm+xIqgHfc+Axy4vfpUvxAByhGHXCRn9qXFvF+Xxu4jbta1dPB/TMfCOGGwG9jhHTq/u1/bchHU3q3LVRyWcSrNmrq1qPqJgeGQWwl2YA0PGNvUIr76TdHP1SKDJS1M1HN/+jfnKNVyDd3d1NO8O7XaiuY1M+xvDMjfElJfuS8+mdpPSPfCGWb29vb+vy8rI+fvy4tsTDHFO35y370M3bc3O56bVuXr1qYNr7+6h9jJYjIc4p7caR4CDpnrTJ5cBRP0b81PWBzzkDMHKOO3l2faPfbLC7vnVArutH1790HOaK+5B9yWi2x5B1d8Y6neOMqvKcAfP29vZX+eEp/ymHBqC0Q+qGjbuXY9Pp9fi6ubfjnKlQ+fxyuf7ax6wvv3f8kdfy2W5uR2Buzln0s+7vJrzznF2xAzJXRrRJ+2qd9Jw8z/VndO2lGGNjwMcRGVXrR51UfQ1QuhB5GlYfO0ExoVHA9sDxTtIQ5eC5N4FIlvQ2nitzHsdzhsW0cH+Tufw96QtdRiClG89zoCE9+qRVzmsaUysHA4zVaj0KmUsOVl4+4yqjUNxj78Z93Nramjzf5MlO+dogU3+mEyQAXa2+LHOenZ3Vzs7OtMS5WCymaB+CTX6rCzk6fE9jZU/Z4yKvz0s5zFe3OcqF/hF9WK1WdXl5WZ8+far7+/vpWBGiWbxhxFG9NJSAai9FI8OOlDCHef4XdWRUwEnmnjvLSqdr5vL1EgT4Hs8/kZBclnIOK+DP/bJRThAFT3CUBnoLmfHRKfyfb9CwIWNcCXIzCu/SGSTmK2XDz+dmLdN0pLMSAPiT+1xv6rB8xn0e1e3nU/dUPUXomGPa9VIt1+DXpAV09jPmfTuc2Br+//z5c21vb0+8g8NIXcg2eoO3mywWTwdhdzaS+l1yrjyv6OHFYvHV6kyn7/xmmG5Vq7Nh+ed+d1Gtzob50ysXjnz7umVxzh7m74kTsrifI5ueerG7nvI3ssNJy64/XO/sW7cBbq68GPBhlN04QgBTOYk2GceTkInoMPtqtZqEw8uLXDdIGRHdRqGLLvoevo/AmhVKMiSfc4AvFbX7PuetptI0ODEtk4YdLbox2eikt5ljtaEyzfLPcwIvAHg6sMX4MER5SLJp1tEX5YlycN95tgOdvm7DSR+yj5xDt729XXd3d5MxPz09rb29vTo6Opr4LaN2Hl+nlDoHJg1Cyh3KuXMYaM+8//j4WNfX13V+fj6BV5Z8iFyyAYFx57wmzQ1kTcPc4do5MSm3o41gKDWey9e6eX7hY+hqA0NbNkroq9yBjkGHpzql2gE+nj04OJjOTsvXPzHXLHvlprIuxSKj6fQzeSZLGh02Xpimfj51QM6Z5WgOsPlaLnOjE6BbfrrfqQuTD1OuE3hkVAXaJ2/w19kpdKTBeMq4jTA2j4OXAXiOHqc+WCwWE585oofj4NKBu+43/28HAx1pXcyfZa6LOI8AXvKH7bjnwSAtnVWDaD6hV+qetIepA106fT+y9WnXuCdxQzcPIxvbPdcB0A4PuLjf5t2ub3PlVx28DGN0BrwjpHcMMZgETVkchu5QbhLKdaX3OQeCkpnm+jSajOdKKt7RtVHpPIhRH7t7+ewU6Vx/U5CyXV/3vKYwjxifulDCI4MC3+TSSs53Rieyr253DhSPFCltIwsk9y+Xy68M+wiM59i7ebSi9PcuryYNlGUGUMF7OjlO5NOnTxO4A3CMIhyZ6+WxWPl66YL5ygif57ijey7r5z0jpT6SD0cS5mQm64Z+c7mY7lvX73SEsq8dra3QPYc598kDLp3jOdJZI93QAbe8Z1Md2AGGkePznK52PR1NNwG+HX07OibfjPqUS3a+3xE1v2ZvZNg30QsAIusj2+C0NaN2OoD8HB90+sZ1d5+2D918jGg+osGoPx2fZZQ8x9UV62jTaK6MbLp1dxctzfufs88jXnxJ2Rjwffz4sVarVV1fX0/b0DkPCDTfGWWiAcfHx2uDIe9hxNgsO1k4iEKkV58eRnrWW1tbX+UwIKT0weOw0hx5IlyfY74RcBqVTqBcX3e/v3e7qQwAclmVMgLhGcHJSIkVjZ+dU+aeK7w3lj9QZo4KsGSLl5xnMhJp41kiNiha943Cs3x3P/O6n3E0Br4mYfrh4WGSh93d3TXg62gddXn8qQxG0VTzK/V0Hi9zt1gs6vz8vH766ae6vb2tP/7xj/Xhw4e2bX9nrryxxJEGRyEccYP/KCSfmw87A5B0Nh3cJqWLQuXzvua57OSQvnRHvvj8wQ5E5dJ/An3rGujs6NocDbjHm94M/HK5rHMGDMxtwEbO06hYbyTPZRmB2uyPdXe3U70zcJ3j1l3vgFDKN7piDtyljLhuZJ2UC5ZvHTVcrVZfRaLdz/w+1w/olCsI6WzyG/d6BS7rRq+l7jVdOgfTG7fs9PHJXPvIrExZyPGbHw3MPHdpnw3MXDz/2fcEct1cdPOe9edfd5/zpjs9kQ6zx5U6kOLfbOc3KS+K8FlQbBRSaaTRRziqvvbWeSaVOIRKBvDSUSeoZgK3NxKwVIwdgPNzCZjMPCPG60BWlucUbtJ0pGgBYv4tDW3Xl/TUTJOq8S7lrJe2OiWcffJc8pxz3Lw8STteLrTy6WhjY5i/d4LU1ZHP2uh+/vx5Or6DA1RXq9XaAbudoqEkrVLBpxB3/fb/9M2/397eTq8Cu7q6qsvLy6/GTd0doAHUGfClosv7qcc7epNn5pYi0iBRP89mGdU1UtZdcf/53/XOGYg0Ti6ds2HAxT3JA27PwGQuwmcj1Y1tTkZ8b/f8qK5N9JbHY5uRNButJnT6w/Mz6m8H9vxsjnXEL8/ZAhtr7qdvbPryc11qSZa8Zmesk8W5Okbzz/gdEDBYS7DkORzpAH96vNZnc33OtnI8nd2HPp3sJZ92/J0y4N+7/zu9l+N2v0bjcNubytScXGxSNgZ8I++ETiTqtEB2E+yIifOUiOQQPekMUadEsuQ9XeRgTpBdzxyDJPHtfXWGr4uoZEnglfX5nq4PBmq+zx6Pf/M1xpBKmYiW6dcZAi9v2LiNhMVjgCfMC3jHRGm73CPXC+DInbQ2NB5rR9uuJPhdLBbTcunnz59rd3e3Hh8fp+OG0oPtlhe3t7db789tdP1LHgUM3N7eThFwZOvs7Kx++OGHuru7q+vr66+i6h1/7+/vr0X2+OQ308/999x7TpfL5bQbeHScEAYhIyGjPpoGzLl/y9yfqvpqY0gn546QLpfLSRasM9yuc7Hg2YODg68iMeQjkTfpqGNnxFarp9wrnOw0hGl0UxcmTRhfZ7x93TROHWv+9P/d3Ix0swGegUfyZudkdjos9VDXTvbD+sJ5jUkf+N1RtOXy6fgob/ig2HHtQEfSFBBHO4eHh1MOHxHEtCsjIOfVg4zi0reqrw/97ujc0XQEUBLI8nweQu/6EzfQ1pwudt3uE2Ptotmjv45HOyDndkzHtGNp50zHBHhug37k6oT7ODf+l5QXncM3YgYrijn07uK8IRjdYWgDPp9DZwLNIV0rpoy2zAE+xseYvAEl6++MEMJuBcayI2NxlKhjuJGipN3n6OBl5zzDz8zVAUOUoJehqNN0yXZTEfvT9Tgal8y+WDy9es0KiYiyd22nUTGgq3pa5vCuXzYaeP4s/HNRJ48fnrq7u5uU9WKxqNvb21osFvXu3buvxplLxGz+yGhXB9RthGkL+niX4eXlZV1cXNTnz5/r8vJy2nn7448/tjsRPRYDFhsd0zM3Ili2XIfP1kKGDHKSb8yn1iXd0q2j1fmZz3i5kM+MxDi6ZrCXG8U6ILqzs1NHR0drUdA0roB9AN/d3V19+vRpzTh5vg18nV9p/kljMvqf+lL3pe5y26lLOtA3cv4t412Uv2uHMhe19GcH1DubYx05Anz87p3m5Od6Oda2CceTdCM2Wxis5jJd6sWks+ePN26kvHXA0QALXclu+QR89IExYF/tjGXdKZtJ15wDO4PoB+iT82G9Z1rMgb2kF98dATedOiCbNrezsznGjm87PGBaUNLmdmPguc6JGvF2trtp+VUHL29aktizHdGSbwKaOUYY3TMSsLlnnhtLp/w60Jl1dn3415QRSHQ/OoXu75kHkvfNGQa32QG8UZ+z73NA3SCCKPDIIPh/DI0jqgDGuf52vNONOftK/UQm7+7uplyekeHLvo4ivRR7r1bgABMU9v39/drudu8CHeXPZN6nI6vd/NCf0VJ0Z9i7sY3q9r25pMpzbr/j/Q6QeIxdVCH7YAVc1S8l26B3ipl5NeBLQ5yAJfnF/ckyomGWEYgajb3Tj934Nm0v2+2A5XP63X3ISLL7PWovxzunP19iE7Jf2ZeUh3wO/nE03TLY7e4f2R50UTrk+RzP5jxY57pYTyWITp1qfnbww894PCnjIx4bAZ/RX6d75uiR/6dM8nzqFrczsnGdbG+i/0ayOWeTniv/asDXdYLfMyE2BQNG7zz5LN1AXY898IwI5CR1SiaZfw6MWFnn+Dskj6djxkjPK8faGdHOAHa0qFoPpeeSOYoEY+TozGq1mo7pSCBFv8y4pnmnUPy9m3+e5R4fC+A+pSdtmjBWvFZADEDRdefGgtzM4/50Bj2FnD6dnZ3VYvFlmZfT+Y+Ojurg4GBSxFXry+UkSwPQqM9AwfTz0pL7CuC7vb2dXgXHeXt+J6yXjdjoQlTDdfPOYM+lI3nQ3HzoPjm63G1QcMK3PxnPpmdLdaDEQH9u6cPLx1bIng/3haiI22ElwkbakTmPH/71URO0l0uG8IGXv5OGNqAjevCbZWaOJiOg637akLluly4a6v657lwN6cbnPvlVZqZL6lzqTMObut3PbmoPPA7mjrF49cbFDhd189YeNkfxdo2jo6OJ39gAloAp+0lfeQsMucWeQ8+v36pCv6yn3BZ87/M1Dw4O1vQHY3x8fKz9/f1pI0umRrjPDw9P76K2DeiAWC5Bu2xtba29Xxjdb9tmPjBNRqDP9EY2s59VNdSFjMO2Nu2h+SJtpPnEdXtu3N+7u7vapPwfifCNwFEK/lxone9evkoCjtp2HR0QScHtDDfPdui5a3v0zAgEdkbMjDHn3eT9biuVWIJfj5uCwU8l6ShaKoPOM+kYckTbpGMaHytdG2G+o4yyTUe/EDADU2/8cTg/f/PYsg/ub86T6bVcLmtvb29aTnX+F9cNEGjDr/ICvBnwWQljXFJ5L5fL6Uy95XJZt7e3a0DSwGJ3d7cODw/X5j0NVxriBHypkDpQ4GcpabQ6ndDxS/7WydMIzHRLnd2Sr9sYAVV4zM5T1g/YI1/PBtZGleI6U0bcJ/P96N7UP52MjIplttOZ6fyYL7i3k/uuX8xLjtFjsU5yH9JhsNHsZLgDxx1du2tZMrqdu2Cras0x7eaDAqgjjxDZBORZV3SgpaOxI/6ev1xCte43sDYtrQsYO+P3OZhzcs4ceUd/pkolnehX0ipXFfjjWs4TfcprczyRPOhAifuYQQrX62KnJvttPdr1z/d317v2niu/CvCl4uwaHglRJ+QQkaUx/tLrSyFKwMOmghGQmxO+nDja4/8RYTsD5hB4R7MO3HX/pwLtgG1Xp8GJi8GJBdygamTAmR8reV/3USQJDpLO6VGn1+Rx0C+PMZkdQGUgxfMoU0dZ/ay9r8xjscLwWKBZJlIDtM7Ozmp3d7fu7+/r6OhojWZEIaueAISjPl7yA9QAFogoJa9wn591RIr+5QaMxeLpIHMvHxk0Y7yc15Y77pPHPD7e8ZmRGP/m/+eKnzcPVq0vO6WC72SF/ptf+MRo+jeiSk7id7+RD/OyZaDbkGL+RyahMUex+A0clDT83Ri7iOAI8HTRMesB+NxR6nQ6qCsNqceY85t6oQOwfDe4o83R0mWWXOFIOiRQyWeRm9RLyM/u7u4aD3WAuAs8+Agn54CahiMnptOp6Ljb29vJ0YNm0KuLPrrQPhu0DNQYJ8esketrR2a1Wk1RyuRTgA/OrSN2zrXOsdlWpV3nM5/xeHJ83JP2Pp817bFDqSvcv3ye+zKK53qe490O7M39/1x5MeDrFEfu7KJ0ht1K1r9X1XTMBflHbGmH8BhGt2MhtgH1q3Xch/xOXfY65hSPFZyPELEQOxnWYzYNEnhk31JpYEhzt6+fMTjrhMHzxPecI4NLL/1155Exrp2dnUkBMHc2YqvVas0g+6DlFCSP3X1leSZ3vy0Wi7WdveYLgKjnl3YS0PpgVCsFAwv6iRE2+OI+Djbe2tqq09PTOjw8nMbhCJv7mfNgOi2Xy/r06dP0EnSfOZi84+Ru7tvd3Z2WOyhE+OwZAxBz7CzvV9X0lgDqZq7ov8HW1tbW2tKwz1KEzhn1gs/MB+a1VJbc63Hn/GfpnLAE9IAtyw6GrqrWnIhczoHP/Mf4nGbh8cB/VU/gnuWubrmo+xwBAO7pnCTfC0+lQ+mlSuaNKBRtGOQCFuxIpTOVusagKI1v6klo499GgJ6xI3eMkfGnU+qNUOYNnyfpZb6qpzM2fa6nx9IBU+SOHbjYjO7NLEkPy0TSyWkdfhe0D3z28n5HL+b78PBw7bWALvv7+3V6erp2EgLBGpxaZJ+xmt+w8Z8/f55spXMPLSfQNHe2Q58cgz+T5/2M56XjJWwWczuy06mfsrgdO5Rpz7rSOWxu57mIfVf+j23ayM6lYh0Rw8UKMj142hiBMSboJX3MOv5fledQfVfc307gDSJH9Sfw9m8dbTuFk/03cMolnw7Q+nn6asXusY5okP9bcafRsHJGmWX/NhUc9zVp5msAH3bx+s/C3vGrFSOykHlfPGdFbKDlsfpIEYp3/nn+MnKTfbTz4nycvO559K5AO27ZXztRpmlGX0YGEJpboSd9yT9ySeCd9dph8bjy/gQutOffE5S5eMxdrtBLiuViJDO5ArCpzFHv3Jhe0ueXPjNnA7g+J8vPtTPSWZTkq4weUgfy0QEO68mRzXpOd+Y91n/d6ljqLp7PNhkTkT0An8fWHRUDXQhMIO+54mX5ACfY6es26Pm3pEFXbF/m+GPEu7ahHYDs6Dhnczs5yXp/TUm7uUnZGPB1Hfe1PDol7+s8yAyP2rjZeJvZrOCtaI2Wu6UVnu/61F2rWj8luwMj1J1G0S+g7gDBc8rfv9s7TU8kw8quawToEvTx10XZGAv3p2HLpb+kGUqDeU1hN01HYA8Fg8A7id7zxG/uF1Gmvb29ydg7+uj2DAbdtttw370xwTQxj+LJeowJij3XgNNOUbrdra2taUNIAm1H+BgT0YkEfgm2LWfMH3xA2draquPj49rf31/rG0bGfSAHyWf4VdX0ph7PF9c4vghF734yh0TPN9ncYb6Fh+hbzjPXiYBaXmz4HGmHNwzKu9dpZTqK57yTa8u+6TwCvTl3CZZ51vmHqUs7ZyTlnTkjSTz1S9VTaoH72Om+1Dc5hqyXkjl8o37YmchVKOvJzM30uIlIIwfWdTxvma36wsPQgE1kHQ2oM0EE8mD92a2uoBfhv9vb27q6uqqLi4uv7J4Pg0/db3mnzYODg+kcwOPj4zW+IlWDMXsDINftZPNJHZm2YLBnwMqYVqvV9C5zH3ZvGtv2w8PuV9qNEVBK3vOn7UM6PKnPzXvL5XJtdSYBpe1X8rD7krLuVclNy6+K8HXE8nq40biLFasNqBVALhP4WbfZXedzDpxmfzoD3IGlDggkQ9Avfut+7657MjuFjeFMhZFKtaNTB/g64JJGpaOx78Mguc7cGeUlEgP4pPPIm+UaESmUg+fEQNWKmH7v7e3V0dHR1L/t7e0JkHf85cjHaD7gGfOpQQU86GWVkXIxvy4Wi2nXHsvpObcoWMZlI+468cD39vamDRo2WCxZdsrQ/UpZ3t7+ciAsgM/LIk6joF8AT3IXoUnS3pFX09tghCiCI59ZV1cyfaE7My8Nnzc1MZ78SycIA8SyrJdxOwfVJZ1GK3ffn0CNvps/MHxZDGoy6d5613zQGRrvzEwnCf7qHLgOyCXoc6HdkWHNuhNMmZ7/P2/vuhxHsmTrRYG4k929RzpHkun9H06SzZzdm1eAJFD6Qa3CVx+WZxU4ZxRmsCpUZkZ4ePhlucclW6E+kTe01bZrsSGmJ/K+37+8aYebrigDXDoS2Y6d5GsY6ZsoF7QzBFGc0mUA6+yjn+Xv0dNMY9/f368//vjjlSzk2ewuJp+bX3V/IqMMiAj4np9/LWUJL3e73QEAMoNJX9R0hXyj3FF+Go7w700e7c9acfAW+0XfmesuTf5b3W8pZwO+oOqHh4fDGqUYDaaP41haYYThjFKuc/CJ1FN3jNQWo1h3G8yJgQ3AnQIj/JzqmNpJaZEq254cU+tX+33rty0++DmDIgKDODjKgp2262Idk8CvdRyhexphioYIArnjMvdS4U71nXTnniZXNMzkFw2gHUUMnyN91s/jUj58+LAuLy/Xn3/+eXg/dZum5GJwZjByf/r/8+fPV8+70IEmY5jMhQ0a+cCoPm1Z55tzsA1hhphRLZ+jc8szWwbRoMCghjsKeS9BMoGdM9cGhGxrmuUgsG1y4DKBKdLB/rG99Is663bZtr+fas/XJp1pZaqfdqXZXv+/pdt23AYLCboYiHBNc6uT3zm9SYBOoBBgxQ1TnMlglp99o87kfq55D2iknZ2meC8uLg7BodcS5i+2hBsrCMKabHhcvFGMaykNmLmZI4Hp7e3t0VKQgNtkX70+2+CffmOys+Qv/RxLsymUzUnWQrNpbEmbU/830HpuORvw/etf/1r7/X7985//XP/85z+P5vibETfBEX4KiqfVYkT5t9aLw4swR0E9aA0gULApLBQGO3KCRWbWXKxUpmMLXLF9Auctp2jammGkAjVwwWdN85axbBEnM105eZ7GiJs2CLTMgyi7eZVnuLaDU4leDJ2+7vf7o4OQbfTCcxrhBjg9BjbcE1/T7+vr6yPgNQE+ynrozPjFIF9cXKwPHz6s//1//9/Xzc3Nev/+/bq/v39Fp8eNziTOJVF0aCWYSh3RMTqWOD/qY/oa55C+pW3u8IxhpuwyY2ZwTHDCc/E4ZnTMa71eHtIKna75Rn3c7/dHWeV8ZiPLz58/jxaTxxaaFo532qKcEujRLm7Zk/bddtdBDbN53GTQgrTUNwHOpi9sP/JDvWnPum/N1uZ519NAH+1extiZULZLfeZv0ZVk/ZLZfv/+/Xp6ejrKkNN2Uk+YMXQA8vT0tG5vb9eHDx9eLSsKiKEdt2wxs58362Rjl08G8E7v8OTp6WldX1+vDx8+HC0PSR3X19eHqd3Y0vA14C+0pl7b+8hbZhkC/JJB5xpj+6B3794dbNPNzc2Bh9HJjx8/Hs4b/fLly3p+fj5sgnl+fnn9nf1GPltiKn3weub8TT4nPLVfpQ0MIKf/8QwA5XOSVyY/Tvktl7MBH4+N4Bo1D1YGkUpHoptB9BRZizTpIFsEYcbk+2SwWDhYfm4LBNmIn9POBBxbNPH/V2FfDQAbqGxANuPmrA4/+XxTmImuLeUyrTbkBv/uw1sipFP3Nv4ZxJy6ZuMZ45PMWt5zm7/GJwPsGIXU02Q2gIwgnLTxWdPc+OkxtSE0P3m/+WwjTWM6OcapTNl00mHwabpJS+yi+8J2zAvzhUBsyko22Zt47WsslhXqx6l7/fvUzmTHmi2Zyrm20Hzdeq6138Y2dVnGOW3YaKX/I02RBYK2yCqDi7Vez04ZKDf/F7+c5zgL1mikbGf5AnfUE+QatEdettZkk/bcl+OfyD/y2HwPHUwSkW8EdE3/XCinWzLS9KhhinP9PfnCZAyn2Ns45ftb/NM55U2Az4aJkW++b00PbRHviCDrCdpCejv3CWBMDnatY0c4DWgDJqfob+Amih1hXevF0NKZtmfXeuF9W3th+ihI7E+jvzlpKjHrZraOPM1zu93uaOwN2A1G7FjZNsfLhcYiPKNCtZ2xXLe01kvGaK11BBQoD/lr0wTuo/lNetr5eXEMPI8qU0XJmmexdK7l6JsPHz4cMoeWFzso0rnb7Y6AL8ehRbMOxjge+/1LRopyYr2MgSNNNt7kV57jNJHHLdcp59M0v8fG076UZzpxZwmZDePvzsinPmbMuHjdY8PpVB7Wm9L0vDluZ5LC03ZeInkeeq3HqTNyajljG3TYDBYaaJkAJXnbZI3XnQ1uNNpONQfNPjsI4rNcWvT169ejzQ8pXB6x1vFa4tDKWZzo4v39/eF4pGQMmX31cgvzPDqYVypybSD9jGdP1lqHTV+3t7fr//g//o91f39/SOaQxz9//jxsAvn06dP6+vXroU3Sw8wmxy0yzelm+j6COwLR/BZ+p07K29XV1eGtItko8/DwcKCNU+IMaphwslzQ7zuzP/kxylTa5Kaw1MHlGvn0cjjqEnlBmeX/bwWEZwO+OEhOqaTjMTTN4K91HFXbqea+GAkCPm4Hbx1mp8nE/N6Mox2EaWn/uz1/n0BSCtO6zSFGWeiQWXfAVtZONYBrQ9lKcybmTTOQ5q3r5OJ8K4X5Y76Yp5Elb0RIvTEU4Rvb4xqT3MNDUUNj7iXfaBza+PP31scmF6eiz5yIn2xdph4uLi4O00fZEZfNFzkfq5Xm5Fs0ztKmBBoPGmgwQOE16r7BVJsB4P+mk/pKx+LgptFv3vtZ0sDiNW6smwFvWx9KnvAQamcXycPIrvWxFTt+b44Kn/I8AYJPUzDwS72UI28AIV3MepGP1s2pP3agHN8Gwmi/acO2srrkmWW62RL/xU7nUGPKUWSZesY1pj5nL0mMp6endXd3d3hF2X6/PzoRIZ/Wj/D64uLiIC85+9TZZi+NIB/ev3+//vGPf6y7u7v1v/1v/9u6v78/gCcmdX7+/Lm+ffu2np6e1t9//70+f/58CJg5Xumr9Z1yzmUfz8/Ph+ldZkA57gZ8a728JWqtdVjX9+3bt/Xu3bv1+Pi4/sf/+B/r69evR2NE3SJPbNNM87nfmewKHzKFzbXtng6m/jvhYP2krP9nytmAL4g12QWDpgghi40qhc+Oohn6c4wfS3PI7Z4JOPL69Lzr+p3i/gTonqLbfWuIv/2+df+5PN4y2FN7pt31mdcTnbyfjsGRmJV2rZe3JkQWudnAfWsZX/fhLXJl42Blp4HgWr98Rs8C9B0AhXaCmBbNe6E16WuOknLUDOEWH9KOv+fTAMrFgDHfDRxoILfoZyF/ToGP2CNvEklfmr2y/NpQWx5cj/Ww8bj1bQpGpn66rWYD4iAnIDXRaYC9pdPn1Em9Z19Zzu1v+zz1jIEfkxMEN5RpT9lGVnM/ddb98XTfVKjLzESzrsnG0eb4SJnYxqxN5QHgfoMHSwCrxy80BCxzEwjfOpR1y8/PL8eyfPr0aX369GmttY42t9DGhdd3d3fr6urqQCOz3dR78rvN2rg0UNjkj2PJDCZ3H0/6fMr3EgD697dikLMB359//rn2+/36+vXr+vTp01F0YxTPjqx1bOxpUOiUcx8deuppkXFTXBpQCzsjNf7eGDk5pHOA4FuN3H5/vMOVWaHGP6+BcNttWtKCSgVgGn4LfDnjxnvYl+bAPNWUe7ecBscrBilTDmuto6MA+L5YG+ks6n337t3BKJAnBFg5ry8K675tAZWpT4zGCV4uLn6dpZfF4FkYHXryeqJk+HjYKcc9/eK6G45j1rlwOrSBBeuTN7V4SsLX2gYujjvP1vSiY44bs2acoqIc8rm0Q163Z3a7l12WGUu2x8LzwbicIraNzpWOjfKa9vJM6mNwl806DGBItzM2BrrkG/nbxtN2iWsyDaafn58Px+c0YG0Zb0CX11owlOuss/1up2bwz3sMXBtd9iVsv9FDm5ZxD9+S1fOGCMoo3ziRz2xY2O2O39ARfjtLbxvu3wnKeGYpfWwA1v39/bq8vFwfPnw4ZBjZnwCmjx8/rq9fv64fP34cNkRE/xOEEnjlWcog+ZzXBL579249PDwcZjB2u91hDeHl5eX69u3b+n/+n/9nff36dX38+HH9/fff6/Lycv33//7fD7TzUOe1fk1Pv3//fv348WPd3t6uP//88/CKy0xRZ11j9ND63MBx00efwUg5aqCOAaPbyLg0v9s+rUMt6DxV3pTh2+9f3sdJg7ul1M0JsCNhUkPBZLajocmAGORM900R3wQCzzVYbwWEBDbTsxz0CdXb0PkZ39f6asHL/5MCuL9bRqnR0Z7dAp1uo/Gkgfenp6eDkeI95kMivgmovwXI+zmOcQwOM3dcw7fbHR9anD9Pda314iACuKYNUdHDLRmzEYrzT2G2jqBvq9/Rba/9nfSZ4I2Ax9M9caxsh/VMtih1eD0jC4Ee6fV4bum6wVkD/c05cqzSlvvVgBU//X2ijzRSz8ODLZ42e9fua7a26fop+9p+m77TbjW6fN3P+Tvrjzzn2YA+A3EGX97Esdbx0UimnztxzykOxjyGnPpPZo8zddTFyP7j4+PhL+v2aEtaILC1fCRBd2xaeGDb8PT0tL5+/bo+f/68Pn/+vL58+bKurq6OdhqHBtq8AOlMbe92u/Xp06fD2HCs0xcHTi7UEY4drzc/RPuytdbZ/rTZ0i1/xDrPLWcDPjvDCREz7c3/YzSZrWIkT8ectng8QXPsEzLmIOVeM4lGnAJ/yqnbUbn/pGOKRvM/Fd6/cfcWBd0OgusDvWajgTbzoWWympBxDDktZifQ1iFOgDJ/dDpWjrVeMpv7/f7IeOaeXGvTvDQINNIEL46IKT+uJ8+n+L6ANcpJ+MLoOKfZZ+ctM3y5h++y5GHLkYtk1tg/68pkECbnTUDqZ72xyNFr5IlAxmsqm9xwLL1ejxnCyF7TW2f3mZnzC+pJG9fWsF8TECO95C/vtRw+Pz8fLSZnn6gvHp9kWdkOZXeyaw285TNgxRlK2jCDTjuiyfZZjlg/9Y92wJnNBhZc3L9mU8xLjx372cAr+8bnwn+/7eHdu3eHg9BbmWyLQSV1JrMatLmxlQYitC/JYqf+ZPKur68P78BNAufnz5/r8+fP6927d+tf//rX+o//+I9DxjD2Pu/iZoCazSbUuZubm1c6xTX5+f758+f19evXwxRvzhcN2IuueOaDa/HWOt68FfCXQ/YvLy/Xly9f1sXFxWGNo0EfA1f7POtCeL+FC/LJMWzja7A82RHLNWcQ0ta5QUHK2YCPxn5iTgqdpH9LYTr827dvh3Qvz6fxs3RG3EXG6w3whUbWYcDHepoRsDHjbzYcqc/r8gy+2hoCRi8+c9BCGlq9iN+02tHS4Jp3Ux2MFnOd07wTUGafJwFOP9iOFSD3JjrkPdyta0DOKQACPm5CMl/SN9ISEOYAhoFQ+MMduaGdbyYImMsUc95cwamSyEAAZAzj+/fv19XV1cF4NqfV+Gh99VRCCxI8jjR8KTyiKfVwisQyxc1JzXjmnvCS2QtOlbJQN5hhSKDABfN0HJmi8bl3zO6xUPbN5/yePwYHT09P6/HxcX379u2oz97hSxpaHXQk7Rl+b8FP6rENaPaBst3Ar8fPANPy1O6nfYqsn7OWmXqa32gvohfsR/rCxfteUmD+t35lWUmyXuFNdJj2jPaGYLutxbW+5jnzxzSzjdgpLk3Z73+dYffnn38eAb7I3s+fP9e//vWvtdZa//znP9e///u/v8rmBdxlw1jO5wsgjc5lCQZfKZgzK6nHX758WT9//jwcIp/NF8/Pv85zDej7xz/+sf6X/+V/Wfv9y6vVaPNix9PntdZ6//79Qdc/f/68Li4uDlPTDbxzzOlDaCPzycTDhAM4HvytjW+TbetHs0Fs578M8BkNkwATNaFgljCcwC+McT1UONPQmN9Ax0RTQ9a830Li775uQ2Y6W1uTY3Z/+TcZJN9nXp5bmlG1UXQbTaAnINhK40tr246DMsP2Gm1bimdny7GxkbVM2dDz2cmg0KlvOV1+Tve1/tpRu53GZ/6dY6Sa/m8FERPf2+8Ef+5bxp1/TYdpEOnk/Gd6DE7dr0ZrywA0GeAzpNV1eIyaPpxTJjvAgMv0Ntrd3/8ZZUunpvun9pvdP3WPbW8LwlMiZ8xiE1A2mTvVD5Yp22sbwHts72wrAhazW5hBug/95dFAXOPJKVxvQglwDhiKXhLc5jfqbGjNetlMHWfNHaec00fyv9mS9DevhcthzWkjOt3knmNiuc/9zQeeWya5tS9/S92/o4NnA74sdsz0a1D17e3t0Ts027EHESoqRF6CnG3VOXMoh8ky8qWA+gwuDkxTtCll78HNb1QYTpfynX/MdoUGCkXqTV2J8A0kGn+4BotTT/mN9TJKzX1tqqbxxTQYTLDQwXpseO8WwPE0hp1ZS697jRZBtDO8BpZpMzJLA8hMR7I/oWGtdZShMwhKBi/327BxF5nPVgtN3njQsgw0rjS+zBw7EicPGWVbHjy2Kbk3RrLJDIvlj+2QRstJm84waFzrZeMIn09bfB8yp5ZDZ8aDtiOOJQvMndlLG1zvSb3iAnuDNS+tyLPWWdsr2potfdoC760Pzb6lb+mDecu6PM19CpR5DKdMSANTtGdTht90+Jrp4ywQbR2f9Ti2N6zw+cxERR52u91hqjQAgzLX2qDO0BekTvKvPcN+x+8+PT0ddIXr5LIu+I8//lj/9m//djSV+P379/Wvf/3ryH4ma5l+xReHDh5rxfEO7/I7Zd16tNaxXfzy5cvhL7Mdf/311yGTmPP1ks1LppxvQiFPkukLsPvx48f6+++/D2f27ff7w3S8EwWUu+i77XOWhLQAcwJq+b0tRzJQ57U2y9D8xVvKm9+0wY7GKcXxtCmmdIDOKZ3JuyjzGaaQCVZwgo4IFZlhY0Dhy+9bhpL1pH8R1ilz4brczn6/P1o8agBGMEP+0AG3ds2fgAEW88T9b8awGUkaLo+B+0FgxjFofPKzE93t2Wa8qTyml6CzgaWUGFgWrrdy1okAtcm+aW7ZutY/GnpGz1vynMJpQPN7ykDQKLvQAfn3yXjSYHl3MWl31G9wZ+DPSL+BqrVezvsjr75//344U4xrlDwOdIwEKVuzD01mnBFstol2xte9sWQCdVMxaLKtaHXmWgvqtgBf6+dbCvWiySzl3Hrd2mx2dqKXdGcjgW3kWsdrO6OH2UXPKdfQFrtCOgxG074DYffNNjk84rRv2jdITGKG/Y4OsI74+MgjwdpaL3pBnaL95Gf4QCCYPsXXPzw8HPQxU7P7/X799//+39eHDx9e+QDOCIYejyWB8YcPHw74Isspvnz5chjjPNfstf1YeGvQ9hZZb/6sXef/BpKUhd8Bfb+1ho9Ec0plKgQIXH9Dg5eOcZEqHbKZawVpjtXFhpM05PrEQPYxAjVN4bg97kxiX1PsXNyfBoDzF+DcwFzjS/rSQJ9BxAQU8hkFoDLT0DbH5DF0mcBwc5o0ROwDASDbbYpi/q31MvXXnJDpcZaOMhwHEnrXWq8OYc2RKz4PKxn0ZJt4DAJlgYbdGatWDFz4e/SO9xh8tDGxbJLnHFMDJwOkjIfvM3g0gLHjXGsdduvx4NaMK9exNbDd2mJ/2H87HzpObkxr6/V4bIsz4AQBdq65n2v8GkByoS1lkEPbwTZy3zRDMLXTbBuBAmdI2BaD6tZGA2iTzeI4t4zIZGcdTFGmKDcMxNx3nxUXelhnZslsn9MPgqdMc9IOZYNDdtTyPLscqfPhw4cD4Eu/ApoCtuKfdruXtcLv3r0cFZVnMobhB22cS/u9/ZYMHjdr7na7QyYva/mijwFqBG05Vib0sY3w9+bmZv3xxx+Hc/oyBR2dzFtKSKvH1P1wINR4MNXX6m5+evJ5luu3lDcBvkTNNoyTg+HvjHSshDFe+/3L4Y1eQ0CD1AAGjXAbjC3FOlXocBhFTX0lf8I7T1V7GpLKT6OYYmNMwMLT813PBJbIO46R+eO208Zar0/ZJwiaCoGqaWEbrI8Ajv12e3R8dq6kPUYuzpl173YvO40DtixfDDZ8ZhbpyroZjiOnf+7u7g7ZAUbToY/XMj2TexzB01hy7ELbFhBOCU8Y/RJ05Pc4DWYqGFmzTNMUHBfqczKpBqUNeNLg7XbHp9o/PDwcdgHm1PuAQPadNqXpjAGI+8Fdmvk97WR3oPUvdMfhcdx5aKzXiuXZ/Dl7cgr0NXvXAA/1gdOcLbBvDod2OO2lHtoK9oEL+z0+lsl23X0h33h+pZdbxNY4OHFf2Kf0xRn98Ci6G/lw1jy+kGvRCAJZZ2aGHh8fj9r4/v37+vvvvw9ZusjR8/Pz4VVj2aiR8+uytCi7cD9+/LguLi4O13P2J08QoAwyYEph0EL5IeDlGOWZ3Mtz8db6ZYOyKeb/+r/+r/V//9//97q8vFz/5//5f64//vhj7ff79a9//WtdXFysv/766wCwG7DPmL9///7Aw+fn5wNQjp4yazjJs2Wu/W9/6focqFBepzZa0MOp5nPwC8vZgG8LHHGgSaQHuj1jh9CUt6Hfc8spI3jq2WZImzN13e23iYYmFPm+FUkYGE20bQnxKdpZR/u04Jonp4oV6FQdDcyQpqmuLT7aIec386a16z/f3wxEa6NdZ11b5VyAb8fl+tuYcgor3wnInL1rfSGgazzgPbzX/OdsQsBDaDDdBKVxpC1rN/HTmzUMDhvvOcVM0EY+NVls/D5HNv6zZdKxZmf4vcm665tknqXJ5alnzumH7RR/c3t2wmv1mQf319P+KQ5CGn8pTy1pMtkxPr/W8Toz61TANANoTolSzvJ827XMjLCzwuQlf2vF48GgxbiBQDy6m1kyJjiiL+kTg2vSktmSAOPYBSZkPGZN191f9++thbrP304BvvbcueW3z+HLb8mAOPOX6x7MoPmrq6v14cOHQ+rWByT60NU8a0OStjPwVmRHYPlsztzOywa4KXiYP2U6d7vdIe3OupkdId+asc0mghZhs4QO1jFNm1nhWUcrLRp226SZvzVHkH6RJwYIzuKyThoz9rVlhqjAnj7nkR9eV9OyHfv9y5oyK2X44ynvXEv7+/3+aLNSjmdZ63h6+tu3b0dBFjcThMZM7ax1fPxL6jIgiYMwf1i4+ScRuO8lf/xJnngsDBb2+/1hAxeBUugPPRmnduSL5Y1OzfeSRv5OHiRDuNbLZq3w3uOYNpLJC89Igx0q5Sz1800cBn3UDWauOFtgnp8Ci3boKdYfglb2ndlg2+nwxWNPEJxxd92xC7ap1IPmF5ptbzQGLLQ3ZGTpRPhL38KxiP/yRp1kmvJs5DYAg7MhP3/+PDxvu942dEVmSH/6xiwb5SP1RzaTOUvfMx2abB77mmlOyisBGkEX/a51l7LIbG++v3///mhT1H6/X58/fz705R//+Me6vr5e//Zv/7b+/PPPupY6+uoZqLSZc05jG7Px4/Pnz4csfOQy0+EZg9hUZzm3ghPrnnUszzQAnO8OBOlD7SvfUt4M+NgIBZ5TZAaFLTNIAeVn20VGQ9IMV4twpoxCrjfmNWBog9TaNr2+z454ors5STqKtg6Iz3CqhPX7XhvIqf8uFnKP0Zbgux0q5gRk3Z7/N/BqikWDy/scybkPVrJJjptM+BmPReQqO0bj4Dydk9PpeYZU20HMl67bcXjtHeU5zzALZcMW8MLxYD9siAzCWHI/157lk1OfKelLFnUbyNke2anyPgPSNsZsN8/RwHNjhe0NnV6jMfJlwBf5zHQT62yFOtfuaaCvjUV+t011nXbiBny0OdTnPMsx8nik/wmmqIMT4JtkLL81O+JxWKsDPm50Spv5M3BsAWf7nX1tv/F/21LWxeRA7FkDWAww8hfdyaal9J3v6uarKrf6YxvooCp98DjwufA04JpLE0Jn9PX+/n7d3t4eXp8WW0CekSd5TRtL+nd1dbV+/vx5WB6T7OH19fXhVIOAzmQUHSQ7qTTp1qR79oO2D/zcSvCQhreUswEfOxEnkcg8v/FzrWMHwjpsJLJOyc7XGbUGpkgXi5+ZwFnLAJ5T71S3n50MKiOxRpudaIuc3I77vdWP1p9TffX3c4XdYxm+84wmR02si8o18Tv32vjQIIQWPm9QymfZ3xgB9sF9NW2UrYx1jG8MduQ/0TYdX/THu209tjZ+LowcW+SdYmNvJ852XZdBkqP+GFIegh3QwL7a4Qd0Pjw8HGwO9SZts548ayPKLAL5lfY8/qkzz5tnBCqkY8uIk4dct+axsF2yg9nSWxeDrjb2qfOUXIdXPKKmyR3vXeslY5I+sx3zZaqL42T7nnEniGVmyvwyfemLHXr7Y4bOPi51RNbaelS25yDkVIkOMHts//D8/HywJXxby1ovm5m4RrgdBB3+WQ7NH9oB61zo5bXY+4wX6c09xgv2pQmQQwtnALgej3xm0Pb8/OutIMnm5eDn3W53tCY3tDXf1/TZ/Nv6tB1hsb42fWjPnVPOBnxplEYz5/as9eIsvNvQzzfHe3Nzc6RMjCzOQbEtxUpD4O+OHDnQed508/cW2bVnbCh4LUYjAhxjSEXO/QEIax0fCcK6SN9E/5aQ8fqWgOc308A6CKYJ6smnRFYcD2ZlnG0inycj3gAGC+tx+px8nLJHXPhtHjU5a6CtyV6i3JydRUMbw5w1KN4cQYDF4kXq+c5Xi5Fn7Guep7OmU+M40rCHloBaHreU8chJ+OwjM7DJOnD66fPnz+vTp09HmbBsfEnddjZ8Ywl1PEsrWAww2a/m9KgTWSyf944anCWQdT3Pzy9vJDBIzB9BUmyFAQ/H0IBtS08nYNccP79nvFrW18+0vkQeTZcBGD+pe1v2mHId+9ICPI41AUbGhO0z68fPZN/z3FrrsI4s8h+9d5aZhW03GSMPI8Nfv349ZMRDfw4Wzk7bq6urdXd3d9Cl+O28tuzDhw/rjz/+OPCPtKz1ssmGvGvZNfp6+iiOFTP5DpwY4OWe8K0B59gw3psp2qenpyP7GQxBHxL+hIZk9y4uLo6yn5xub+PgjPOEPyyn4TPlnuPtAKSdh/qfKW/K8LHYQVP4Gkpn9L7W8a5dOsZmOFKHf+PvLr6Pg0QmG8Tw2bfUbWM03XuKdhtB/j4h+622T4HlrdLa4fetvvH+BtA4XRI649wI9mgsDazOBdkpDdBbeVmHf2Nk2oodselhn9Z6mc7iob40lpSrrXFsMtNAHI3VuVmFxp/Ge9Lg7B4NF4ECaaP+t6wCQaQzIw5+JrlPXWnP11rgSBltdqHxgdeanpg3U7GcTjZwenbigevf+tzqq+tr8m+aKNNbtn6S+Vb/VJ+zq21sGlB2Mcjm+Pn+/L7W8UHmBocN+DRZcLuxFW6bsjHtIF7r+P3XPjKKOmF7nT4b6LPQv27ZmBZ8GEi5XcpDs0e83+22ZwP89vv90TuCGSBv6du5GMHAvfGBNt4ylu/n6Pw55U0Zvv3+5Z2idtgRBK5fobFnGpmom46OiyYpNM6OTECtMZ8gkgibv08ou6H0PO/rvMeDQz61KTXz0vUSaKSOvHs1GRVGRay/0ZTfTwmShdD1sC+TgE/KudvtjrLBNBb5zPjH2VMGPFWy37+8fSG0+fqWE7SCbTm60EZ6LDN2IjT+rGO3+3VcyNevXw8GKJm+ROiU2/CC0zB0Xo4IaaSdBWx9Szv8zmv8jYXgvIG3ZPiSuWMEPkWxsQ95QwZtQXSAfCYNeY6bYR4eHo7eeNMypcwipI3MQKTux8fHQ4aF8uix35LXjEcDAC1o9nhRZpvNSB8nENrGn+Ns3rhfXMMX3vt9paw/GcqMYdpwdqll1xt9jRdNVrdK5DNBFl/l5Uw8eUf55tRomy3IPeRjrsdv5jlmvhn0hm/7/a+NXHxDButLX3yuZ7KC3759W9++fTtk+HhEje1z6qd+eukB/Srlyln1rayx5TPtcr0kAVEwQvia42TCH7/HnHWutQ5+M89n08ZaLxgkswrZDJdrpHVLTun3ouOxdw0oEoyznAJ9LeA4Vd4M+Oiog4ppsAnyuPsli0QDFum8qBQxGnxRfVs3QEE4l27+ZsCXz+a8ec/0LIuvUYm5NsBTjDbcrc30P2d4hV9JbdvoTc7AtE9K2BTcANp9nwyBAWgi0Rgog8Dr6+sjOeF04ffv348AXnjETQkBZQZ7Hm8+35SXPDnnWT9DPlKGIwtZC5u+E9DZSHDtEKdcYpgJevzXxqr1hYa9yXfu4fizzdCTDAJ39GVaabd7OR+NoIfZmAC+jDudeuSDesXgh+v+wq9Mu+ZZG11OjYfvOS+R17ODsTkm6jJ50rKRXodFkEiZIV9YWuBJWij/TTbb+FpOQg912fXmz0svXI91wWdcNrnl/XZ+Tf+sZ01+Q2P4ypMmuHzBNpRBQuqP/XIAkXsYeHqGIfQ9Pz/Xs/243jFy+e3bt/Xp06dXAJV2xUsm4otzNiX9LmWJS1WazYi8UxYJ2i0jzGxO48GxSz08I9E+kAFz+GUsQB0k3bk3/Lm6ulo/fvxYX758OUpY/fjxY3369OlwSgKXXFlWt+QrdNMnuU/kbbPRTU8t928pvzWl604yIqGg5tMK1OqzsYwCTgbglKFqtJ5jDAxYuMU7z9Ig2zkS3bfnKIS85gwAeWIDSuNqMNzazWcTNN5nAOjfmjPYqsvAOL+l/zQ6NKjM/rCeBnhIB5U+jsR02ui2fkxKnHs5/nbkdkSu2+NBJx9QG+OSoCC/E0zR6FDHDBYoK1t9amO79T951WTa9XI8UjiueZa2g+1aHqmjAX5xAAkwzSvOMHiTWJy3acorn5ip46YU86EZ9UlPzFvL0JaN27JfU/2tPT+zZSMC/JqutDbOoaUVj3naJvDy/QSO+Y22cQKpaecUMHRbAVs8EqXZ0Py1DK/XNU/2noU6Hrknb9Z6veEngC+gJ/2Orcn/0wxAxoE212CRtt2zOrzPAD/3E/TxBA9uyHDh8p/QZEAWOvjH+5m44m7l29vbdXd394oXTY5skyi35iHpaYD2HP1wwNB4PZWzAR8FzcCG2RhOdXgqjooahUx9YXwW8ueNBHRoKUyxT4wiQxvYsHCyX0z5GlQQyHKjR+rholA7ifxmpW4pc0ZoHtTn5+dDunmKGOiEbRAm4WgAzTxl8e5RFoP4ZgzT70yJcHNHe+1U6vEalPQxC/J3u5cz/jj2mc4zSDHfuI6O18MfTl3ZabKuU0EOg5rv378f/Z/pxMvLy8Ni4nfv3h3eCZmXtrPNttszckpj08Cyiw1sc0DMMjKzz743w2f+kG5u8uBapfDLxvrdu3fr/v5+3dzcHC2qf//+/fr+/fv6/v37+vz583p+/rUzL+/o5A5OZ22+ffu2Pn78eKAlGcnIH6eGGZjQfnBKbgtcpG+2rRNYas6i8ddtWP+aw2J2tU0vEUibTm5OMI10xA3c2BG6TwEtu93u8CrC3J97HPg+Pz8fzmbzq87SF2YumUWlLud3+qG8xeLy8nLd398ffF8CAdZjW9IyvaStAUwC1oC3tMH6CV4oU3mrxn6/PyxRyMwCS1tisNbxRkzTSp1fax2ylXyFqrOcbQo592WK9vLy8vBqNQMo0xs+pH4Gjwye11qHmQJm7jIlHJpzT3byfvr06WB/aes4Rhk3Y430izrAvnBWx3Z6y6+ar/8lgM8Nu9BwOrM3dYCdoNJ5emd69ty6fX+7zwPnaRQKHpXYRqxFOIyCaAxIk3lMYGv6Y+ACSs/hTxsz3pdPg2DfMz3n3wn0eL3JB9sj//Nb6muGlM9yDPMbFaQ5y8n4tKhy6m/jlR399Nxar9f0MAPK3wh+WybZbVD/HAmfAvTN+bY+k6+kaSuzH3pyPwHGxcXFwfiZf+2TwHOtdRQocLza+mPbmbXWUdAaOujsGES10oAO+bnF9y0dzb0TT95SznEQlBG222Rvy06cI0en6OF12xUDXeoe7YbrO+VXTC/rS3KCYJKghpmw8MoJB2cqWdKms2L5jb6GPLDtXGsdJRwo79E50uHAwzS1sTPosH7Y9tEWpW1nKjm703yEx8O/T/aWdjXP8PSCBBPX19fr9vb2QFsLclpxu00veK3Vu0X/1M9zy9mAj+dXcVDCqER/WQTJrekTI2KYnanZ719O+GcUYoFyetXFSm8ETsEi89KmgUPqbJFs2udUFBXc9DSnSxDotDwLhdUZKNPjerbq8m8GCRN/XZ9BWePjWi9nQq31S76urq7WH3/88WoNCrMxuTdvZmC7nA4wMKKiBzCnD1YgjvEWfxo/+MmxsYPc7/dHxyUkas9n1p9lLev9/f3a7XaHaRwWOhQ7ZU+RTCDWwIjTLA20MOKPHKYNZsATLWdhtA1haGemMzMFnz9/PtidrKOjo3h6ejrIAZ2wMwUZ7xzlEtvjPpJ/+a1lTXe73bq/v3/FE497y+bkOuvyWPB3g8cJdLe6Lb90KszANAdkOgyiODvTnjVP3NZaxzMEjc8NWEZGDNT5fJu1MRCc+sa2w0/OKCQzRPmhr2OmiH3gGCUDx3dkxx5kzJiZst5m40Hq5Q7TzIztdi9HjcXOWD9alpB8i3/NzAnpaCDaWWHKmwGg7Uf4z/dQ7/f7o/7Q55MG09ISNZYVBvQ53DlHu2QzR67HZjALNwHSZisM2FMot6yj+f0tsNmwz1TOBnxcNM0BS2cinFkY3ZgT4vKbd2ny9UJZc2BDQOVsytCiifzeFnjyudTLqdpcY1/N6GYMJxDqeimMDfC1fjRgkt/sGCiYWwq5BegmRzMJJBfHtkxA6goICIDLugkbIxq4AAluUEhhFiZteCH2xcXFkTJPfZ6c8pbSNQDQMkEeD/IucpYNONnYkCh0KyuQ9sKH3JupX/KMa2PMi/A6TmjSM461p3Wi/3EWadu76AhKU0/OCsvZe3k1EvmcTRr7/a9XMT09Pa27u7v1xx9/rKurq/XXX3+t+/v7owXXXPsZx5cNGG3XtzMy+f/u7m7d3t4eNg/5uQQWDUAycNkax/CGvHbG0uNhe5D2uF4x9zIQso5P8u1g1s7W9pH9Zr9SKDPNlrRlLUkqZAzDZ2awtvS1yTD1hr8TiF1cXBxeQZbMXkD+iR4AAQAASURBVDLRoZ/f2YYLN2RFNwN0Itv0qdGn1Ht7e3tk67L+LDzh1CTPsUuGm4A1PEsAxUJ7S3njc5GByFHzMQRX5G/AM31v7EZ24uYMUo6dgR7HLW010J86aH+zJCT2NScCcHo1533GJge/WKZasNaCEupEs78Oolwvy1vA3lq/keFrmT4OFp2JibTiO8KwMdxCvFsdPRcZu9Bwtbq22qfBbLRN11rEyfsMHH0/Ad5bBOIcfpwqNqytD1PfmtB73JrxppGjkY+SZ+zsGFsb547ZxKst2X6rfJAXNHy73W49Pj4edvHGIXCtTuqm8Wi8p341ftmgb4GTrf6YX3EaHoPU7QNSA8AY/DCwbLphPef/1g2CXe7spXOcgp1Wr/vbggZf3yqTvE024VRdHsc2bk2W3XazRa29SQe27qUcu82Jji370uTB9fu5qR3y0EEXwUybxWB9LqeAP5+zfkbf15rXe7dnGm0ETgYyXluWdidg1fpEm0Twk7oIutNeMpLsKzfspd/kg9sMcHa/0z6n4j2mAcEB0wH+bVz4m+XMOnuODrk0WSa/3wL6zgZ8Hz9+XGutw1QTo7CcdM3zfpzK5jSHhTCDmOjJO18d2aa+abBz38XFxSElzPYNWM1UnuQ9ZeuasW9AM995vRk20pzvzhzQAHGxZ9tdxedatqo5qUZPu9d1rdWnjsPjKM5a2xs9Mh2SaNUG7N27d+vu7u5gGBJhRyYTMWcaY78/fhcqs1Whf+p32uVOOhqr3BPZ3ErrkyfskwML8uXHjx/r27dvByOTzRrJTP2v/+v/uv7xj38cZDwGOEaYhpnAif0mLcmq04mk/+ZRA0J0AByzGO/Pnz8fovbcz1c73d7erv1+vz5+/Lg+ffq0rq6uDpF36kpGzccZJfOSBelTJpSZ+2/fvq211uGoisgM26A+0hmSj8yGho+RM4PRSc78P/lnGaXsNQffAA2zvayHWbrQbjtJud+aSeFv7XfWx3u37JA3ifB6bED6EKfMsxTNI8pmA+zhBbNqa62D/co7XaNTrHe/3x9N5eZagrboAcEK+0zAZR1yEiVAJjbOAWB4l1my3W530Avz2vyhPUsw9PXr17XfH0+v5n7PsBhYtlmYjBnP4U15eHhY3759W4+Pj+v+/v5wFmmya9mclf6GXs8mUq583EzGJeORMWGSKpgh9Eans1nSGCV1c9OO8U4LXuiLnKxg3ZaLXMu9WxiA5WzAx+k3G7MwnClkRh0TurWi0TA1htJRTaDBJcrAhasN0PDPU7926K1M/WLZMnyODlr/QgcjslNRwwRWT/Vhq1jo8mm6G2CkkOc6pwTWWkeR3VrHi5QzlldXV0cGnzKUNmkso4znZl1iPGzEPL3SHO9beDnRwcg6gPbi4mWNH41heJY+enqG/GlgjzLeMnAuziI2YONMQaZKcn9kl6+M2+/3h2xmjK7XCFOP13ox5pGZ2BDLHJ3YWuuIt+Evzw1tRnoLIJCvqYM60cb41G/83bbIxt90TraN9zaAmN8b4DtlRxp4baXRMgFiyyrbCd/XOrYhE/ht/Gh9YNt8JsHo9ByBM/tCfpLmJhvmRerwGkVP9To7F5/M9YfMjqV4WjR1p94sy2Ab0VUGmKSb9NveTvY3hWdvEsBxNic2gXLCtXDtmBzLG3WUSQjaytSTJUXc/Wz5SN3Wkwb4mu2NPTxHZvn7W/3M2YAvp83f3t4emMoppTZIYQwjEG/ZXmsdOWQ6/TCE01wp7GibgqWCeTcSB6wt9s0AtYiX5RwAOBmU3DMZVUZbFAqCj0ngJhq2jAuV3msYyZsG9tp305ExcMSX++OoY5TY1zZtEMPTDFhkLnQ4+pn40KYeCFB5zQENedCitikIaHSkMJvjKDnHsnCNI510DhhO5pz8otwHSK61jsBy+jGBPutM2kmWjGA8m7gI3tIOHUv4nXU7sRWRfa7jDA3hRTZpxCF7Q0zAXZyXl5/YgbKfcQyWQ35nBiZA1eCUoLCNu69Zrppj8TPtXk+Dsw7KlQH/RAe/G8CkWG4aCG22qj1rWiKXjT+WF+tkijcqNNtJPebvBJmTjppPAYvOQPF6s2dr/bID3Jx0d3d3lIm2bYg8OmPfAIP7al7nWfqfZlPZRuhghsz1GuRwgwlnZL58+bIuLn6ttY2ty3uMHx4eDpnd0Jr1fmutgy2YZCXXaJOIO9JfHgl0c3NztGmQAJhrXJuPsf1IG+ZN0x0D+lbnOeVswJddcnF2Ua4Ieoz6WsfZhrXWYaqWC0bTORp7rhVo6doUAjYyIvSR8XFoQex05DHInuJLW0T+kyGcQB+VyUrmdprihc/kI43Y9Dxpm66ZzrQ1nXtI5WCdrS/sM8G3AZ+Bpk+a57i6PdLMsbcssC06yGmcSH+e42e+++w11sM6JiXdKqQnhiYynAXru91L9suAL/cSQMfRtKnytV4Mda57+s88ItBI/8KTTC9n1/Xt7e3B8Tw8PBwBsICkbJxYax1eKRfjmn7luIRMY1MvuNs/37PxI+P2/Py8vnz5cjiTLzYlPCQ96bN1j7/7M84qADQOie/+bUDHdZrPvqcBNWb7mjx67VHGjWDIINL3p07TyCCDfbQ+TKCEGXq20eogUG/8iQynDfoPjqvBewA9+W47ZUAaOePUoAEP+5Ap4Xwn/x3cWh6en58Pyzv+/PPP9eHDh/X4+Hg4KzL8cNY3QU/aJD22se4jbWD6St+T6WTbAoITgmDz1iVnZnIDVfSHfbq9vT28Fo42MJ/BKbFhobklDwjUyaf8xYYlwXV3d7fu7+8PtMY+e9mJedn8pceL/M41+3gnpH7Hv5wN+KyIZtwEiFpEkWv87sg69xs8uJ5zaPVzk/OfAJ0NsI1vo8f3nkNzq2Oi0853qx+sf+JjxjTOySlmRjJ8ZuqXnWKje5IN0mPnxGvmhzNxvj453FbnNB4TwM0zE7BupY1Ja9uOPs6SB3Y7C54Mk9fvMeLP76Z7Msq8FkDLLGBkhmuJwtc4uyz3oCNmFiDGOwA3/UsJAGSJw2U2LcdDkW/eOEJ+tgBpS2e2QJL55TKNu6+fKgZjW3VM8rp1/1adzSZv0d0A6XR/61Prq/tBHZ5ot2/wOE70bNE9yVCKZyxcCHqZFSPwnLKvaa+teU4A05IiBGKss7XR+n6ujG4V2o7Ys4YDWGI3Qnc2d6SO7AFI3bGBk99hvbRplqn4v8xSOLjZornJvq+di29OYaxT5WzAx6g/QsGz8igonnZJ5iHRvSOnGGYuxiYAYcYpg2jBZKcJFJgqz/15+XnucXqf/WRmKvRuASwbqVPOPPQyIkl7LbO5RUMzXARvBD0EcjEYzJg5VR4+ZrxZl+lodNrhNAOcPj49PR1N62XqMk48EXnuT7QVg0GZ5NteCG5MF9dopJ4YxrTF9aXTNB+BsseCck1ZIM/IDwIpOyoavNyT92WG1t1udzhA1LqQOhMlx0GEFp5FSXmkXCW7SB3K1BXLjx8/1h9//HGUcSPgy5stmGH7+fPn+uc///lqepgRu6egmXnJ0Qq5TkeRe90v1mn9Yh+5jplOPtNJP378eAU4m43g7x5rfmdbljnKjOml3hM8ZJychTIQ3gJB7Aedou0o7XTa8u5yjyHHNfcxyMi9tAPOMHMNLAMQ845yEZvHkkwSs0kcAwZa6Y+DqdDHGZS0bX0lOGOgE7uYzVzJLqVevqc+457MeXR0v3/ZaUq5C90+tonHxIRGn50bmil/09Q2bS71fK1fm6iylpYzg7e3t6/4Ql5kPff9/f0BjGX5CGmnPNH+7nbHM4KhnWOUtu/u7tY//vGPw9KQ8I88pG5Tz4glpsCEdaTORk/4SMB8bjkb8BEwpXGug6FBasAp93mKJNcJ4qYoLAyiETCDcw+jKqdOf/z4cbQmgH3LPfls4GDLCKb9Vt90b3PmFBBGZ22dgA32VjvTdyuD+57pPq53YtstUmkg9FQkQ6cU483dYZ7SicMOeMj1GC/eT4dEWYtS51wv0hnat5712Nm4bY13+mw+NifukrYYgMWBpL9e7BzH4iCIvG90M6NIcJdAgcDZL6PP1IinFjNGnz9/PtCaaZ3s+A+Y5yaItdbRey9TH6ej/Yx1kmAo00aRN9oS3k8A5tdSkQ/c/LEFmiJLdkCeGuP66NzHMaKDoCPn4nrLDAGs5faU/SMN/N9ObHL8kT8CNl5r9t/3EJCyHxwH88I6Z7Dm/qZPlPOmO1wHy+ec/Y5sMkHChAYBMflHupw0CV1eAxgZ/fHjx9Gr6OhT8jyBD+myz21jbDmgTWrFACe8j0/2uMUm89zDAL0Aryz9yOzBbnccyHL8qd9MrrgP/MtzV1dXh/frxp5RHhtPIj/0mwSvlmt/J00tyDyFRVzOBnx2DGu9TM84EuGAhfk8QNadpVJMKXkbTg6moy3SYlBIx2SD15i35XQNAnJ/c+hNAZpxbsI2AWkLmB24DSDBTYt0GU3QyE+CZTAdwSdoZP+bMSBo49sVmJbPsRsPDw/ry5cvh6xQdnSGV84GhyZHS3aCrZDv5jf5zHGywjqAYR2mZwsgEgD4TRHkI7+n/t1ut75+/XrkrAJudrvdIRPVxtV9oYN8fn4+ZCziLBJZ82DYOD4elxFDm4j8+fn5kI24uro6TNMwU2v9JE/tmOzEcx8dNXWP67449hwv8rWBIl/n2LXS6nd9p7JsLRBMmYIIXyeNdPbMWNl+NgBtm2d+UI7Z/pYdsm3mWNruWObtC8xDj5NLeJBA12vg2v2+3px6syleM5vit0647dh1+7GpTEDiXNBgnud5A17OmCUIp4wmoOOY+TB89odj7oxZ+Jc2vnz5sh4fHw/ZduIEgnXzvOk2/WfsRGzU3d3dkf10AoS8yXfzzfyfdK2VrWtb5U2Ab79/iTDWejmw0IOXefNE+re3t4eFzBSGMIkRTaZBWufJhDg+GhBGPvmj4WdKtW2v38q0kMH5boe21jEISl8pzDZCMXp0cBY8j4ONZ0ozNlyUHiPoheqhzel+12lHxp3aDchwbHhgd/7yGw++Dc339/fr48ePBwBxeXm5vnz5cnj7QtqiQXE7pJXt2OGzr7nfIJHy4KkFPuvrHg/yhFOVLpTjyOq7d++OduY6c22HHXmyTGdHawwfp2fW+rUwOtfzl80Va70sVKcjzA7Zq6ur9eHDh4NhNi/aNHD+z87CZCY+fPhwmLqKnmXssnCaPE6/Uw+zneEfdwrHWfLUgAa4eY3XKW90eg2UOIhycb123g3wMkNhOaWt5GaCXIt80PFRl6iLDkabfc3YNnsRuhg8EITzXrbdQH0LVsOD9IGAn7ygDadfcFY3NPz48eOwG57nvk0yQRkgLxK4NnvdskwZz5xJR/BNXiSLxc1YzCS1jBDP1aStIEjm/c0P0cbxXEwGoQFhWVpB38ZNWvTN4V/jMzOZXEfHszODAX7+/Llub2/Xz58/1/X19dHpIcnic/MogwRPzdsXEPN8//59/etf/zoaI/sNbhiyvY6c2Q61YKQFL+cCdZY3Ab6tLJVBWQMHa/VtxFtRsIWPdTpS2qqjZX6accpgn8NMGrGtZ04h8YkW92Pred5jB38OLRPA9PVTdDQjH0UwGHefGelxSjAgNQbQcuhpiGagJuPPT9/zu8UBAn9P/W7n1PiHXzFCruvUtISvOdvF8UiGjXV7ofSURV3r9buoGx/YHx5ZQVoSTfN5gmTqduh0oNba5HQbDfoWzzxupwrp+B270Az8VrHeTc+5D03unR3bKlu2Zrp3i9ZTzu5UfefYMX+3PWr0tXoakGyZHmZiTV+TD9oxBuQTfWyLttD3Tn3aslWnnp2eN58JQtmnRqf7xN8I+Bmokf88jonTwLF5sW0N2E4Bvu1F6mg0sM7QNPFtKhNPm297S3nzq9XWWodT8cnkLKanAhDZJlJMNJL5+hROF+V3MjpOyoCB03/5a6CCSnTKUJzj+Ckca62Kzhk5NAHmlHD6wXcWs61TA0tDTWFN1MR+JGtCvpgfeZ58Nl+TiclYsf+73csxHzasnKJ0O+l3pmx3u5fpy+/fv69v3769UtaW6UgbW9EuS9aE5LvlaJIP8moaX/bV2Rjew7GmcWtneDnTTrDMPjJqJW3Z2cosJcEZr7uwD+FTovychM8jXt69e3fI0qaPNPSZEQjIj9HO4dqWM2ZMOCtAWbq+vj4ck8JsPq+v9ZIleX5+PmQjmh56jNIej6zw+aIGUJ52bY7N1/MZuXAmyHXZ7vGZ9iz7SEfMPtiueMo/93lpQHOo/u57raNuJ/eYd7ZdXDpEGtnv1B/54LltnKlpWR6/yYezOu2s2bVeNihYf9Y6zpTl9xxjFN8X3qdPTKKENm4Y4iYM+6AGJizr3MxFnrAwU896CTyZeU9fnPlnkNj0otn84BAen3J7e3uY1eBZnA8PD+v6+nr99ddfhyUn9GkTX9jPtLvb7db79+8PGczY5tCZevb7/dHROA1LtA0+LXHB/5lFnZYDTOVNgC+NMRUa4nzWkYUsSpCp3WZUHV1aANlBMoNTer7GOifn5eLoLTRM9xF0EPQyE5FCo9nWOjSjm7ZOOYjWxybM5k/7HhoNeDKtuNZ6NebsKw3m1P+Jp1EkT2Vw55jpIW8aiNoC72utg8Pn/ZT1Bvgm/pJ//m5AZgNno7Y1dc7pRDqfxk/XHWNOYJvy9PR0dM7Yfr8/ooE8o6zn/rVeAFDGhweixkFS1plRJA1PT0/r69evR3xjlpObdFJHnHfAHKeM098Y6evr6wNIzTjx1WpNx9kHLhLn4vjcY4fH6zbyLpYt6oIddOSr6VWziy0r2nTENqXJ7FqrrjXzPc7AupjfLSCy/uQ5t0meGHzwPk7zsa3Wx9DGDUwE8QSI9FPkNf/IlyxR4NhkupJAwzaFgU+eySkUrT3KcAPMkTMH6nmW2fCMJ2XP4znRMAFD1tFAH3/Psq7o/tPT02EKN5u+IpcPDw/r9vZ23d3dHQLJBLWTLyId4Ul4kBdQZCkLl5wwQPDSAvrDKaBhX41FeD+XyJ1bfmvTRkpDouwYIwJGN+yIM1muY61jxWYU5OedyeLzrO9U/9wf1+P22a5B4hbIYFtb4JKG59Q1A4dT7Tej2QS00WmnY+NOp8n+Uj4aYMo1PxdDShAfY0cDPYFt95O/Nd5u8Yqfvsf1uY+Tk2/jbLBKYJXfGPA0cEv535LTpmd8cwYdsflHMPPw8HA0VZvfaNTpxOzEGS3byXEnIcGe1xEHhOVYn/QxdNIJp544AdLjfq7VM1zOdIRW2ivWwf41eTolRxNtfMa667om+fX/W+DM3xuIYF99rdHYCvk96fZaL7MXBDamlfSwfY9H2uX6vTzrrM2WTfHGxlYIDiJ/ydZFhhnkRL4pc5ZPjx3tbcsMcdlNeEG9p72fkiccZwY6tCG2+wY1nBng7KAzjB5/g+GsvQwtP3/+ek2cD5ImVvGsm8eIQPj5+flwdEzG6pxx5niZ37bfTXfd/1O6w3I24PNA06ET9FBxyPBEfw8PD4d0aot8rHh0/jHs7nQ6zKjKoMIpV/eLys/7JmPLvtIYnRqsZvQmR537KZik2/whDRMYbwbd9Pg3g48YHGYtGnDj1LGnfDO9T6CW+3a7lyM/zB+/JJ1OPEbB67zIl9anXN8KEMhn/7l/7JP5zYicO5FjSFq0SSCTvoa3NMJuz4EAZSG84w7uFC58//r16/rx48fhPD/qFPWKx6B8+/bt4JDy0vZMu8Q4xiBbtnM/F2dnKj9ZWGfOMv3v8by7u1uXl5fr/fv3a7fbrU+fPh3uI39SV97OYaBCPU2/MoXEF9iHPwEH2UXOsWKdzAo0uaRs0dFYT+k4UjhtRnvI9Uvs1yQ//I3tRw69XMCFdbffToHY8Iln96W/Pg90v98f5COyxzLNnpAe9yUbyAg2svwj9sjZt8hl+G0AQRDIvgQwpF/cgPDnn3+uq6urg0xFP6IvPCaI4MUgtp2/SPnwTBWve/qa4DEbJxj4RXfTJ+sW28vUZ3QrswKZEeQmC45n2kohkM0RKl++fFlfv35da63DKRCZDua4xj6ttY76Qt/kmZZsDvvx48f6+PHjYamasUzkwj7G2fPms3nNY2a9P1XOBnwsJHRCtETNuS8ggY7r3EInOXUwA9GUmve8BRHnGX5aEQzy7ECnwmcmmu1Y/Zzv+8/0z3VOv7X2p+dscBoAm2hy5EMHZvDF9k7RdM7YbIEn1+d+ue8TLW+hy845bdG4OLLf4nUbFxqmOPVMiSRIoxMx4KBN8HW+djHvwuRUUMv2GlyGp8wWcBkE9SjTLJ4Oj4HMZ9YncR3wKR2Io6I9c0YidbpvltdW/9TuVKY6fc/WMor0i3104RhY9k6Bt3PL1G4LuEI7ecvxaM6w6YF5x3ta5rY59Glac4uPLA5imB1jUJh725i1dqbft675OxMzCVJahpB6SV1t07pr9SVD+T395WwAZ3fW2vabBGd8LmufE4QSHDv4sq2lLmeck31Nmyzn+J/mI/lsG49Wx7nlzWv4Esk3oefgcb0eszVZoL3W8bqdREM3NzeHiIcG04iWRnwrAuX1xtTJANiINWdkYdtivI3JlJr2GgYK1xa9U/sTaPF3K6OfJf9zrdFtGhrg8ViyDjv5KKkzBTb0dgBMzbdoK8Xn2a01rxM6Nb42/ls8PQUQHdHlc+JzftuK+BpI4zjyf/OMjoh6x2ec5czYrfUrsk4mguvtOFUVUMkjg/b7/SFL5qkhZ2yo96Enzmm3+5V5/Pbt21rreDqHfc53Hw1C2WdW7/7+/vD54cOHI0eT/lAmThXLaHOGbXxpkz0upxwCZygsZ5GL/BaeevoutLNYF5oTSx1cz9zstfvMZ/wbfQbbtv/wNKHli/JhsOHAgECf48/xyP/sT1sHnWfTpqenuRGPukoetFmhaZxSB2UrPAlwzvX8lvoz65Z1cjxuhbab9DW/kT62de0ZG/5uXnisKPvRz+fn54MNeH5+PhzYnCUfBH/hveXPWbrMVKz14ktoN7kGvI0zedFkvOGXyX+eU948pZuGqPRkSgjgdA4LO85piizSzrvw7BhCAwWzDQ7pW6uv6+O9WwAq97NwoDhgE+iKsvt3KlADmBZi02YA1UqjnX2YBK89y7YoC66PBmjLGVhg07eMF6c+25sW3CbHnYZrytpQIbkbrdHqcZ7kxnU7Ylzr+LBu1s3rHgP3O/1qstdot+Fv/SHtrY8BQayHY0XHaUdE4JfNOAwCY6TZFseNOw5TtzNyW0At07jfvn1bX79+PXIKAbIXFxeHANU2Kk4vv4Weu7u79f79+3V9fb3ev39/mPpj9jIL8c8BfNYZluhKq8egl3o0ASa2t2UXmGHivS3T3Prg4GcCc56CdLGdyjPUM8qRp1ftJC8ujndvr/UC+AxQDIQMskJ7ZNqbCw1wyDuu87SuWpbYn2SQOXNGHWy+w1nwrUL+We5iN3e7l5MaAvj8OkHaZNq+hhmYxSR/Qof/oqcEoF5jt9YLEPvx48f6+++/1+Pj47q4uDicF0o5IKawnyPvCC6zdMCAL2AztFA3Gy6x3HM87Q9Nz7nltzJ8PEiRQtcckztABaJRcbHh8W++b61jAMIde7zmNhpwagCoPcvCtraAzqliIMn2qHg28vk8Bf7Mvy1w63smAOl625icKr7f2afW5gRwWM/WPQQqVNK0azA6gXrXPTks0p3fLDd2ME22W93nyG4DqW0KykAqBtBn4pHWyYi57diN/X5/OGfP2W4DxrXWYQqGRj12KAEiHTv5F9BFQN8AfJMZ26EY8TiK7PLNGiPe54zRqTFp9m1r/Kbft3RvcrYTIGxlovEUWCMYMa35zjqYTQugoeMkfW22pdl20tP60UBoy3Yy8PDUPWXAU5jsszcgWsfp7E27SwsUW+EMG+/zzEEbIwKy9Cu6146Ecv0MsvJbA4HR0zzDKW7ydJJv0kdQTlsWOmhnmt1JX5pfYj8COvOmIL9AwfZ5Sz5dmt99K65IORvwffz4ca11nPbmguQYYiv3WsfZqkzh5HciVkZYAW1M27dtyGmLDIwRJiidSpsim5wtf3Oho2mg41QxuMhvdKp8PyDPWLJDcR126pNjc3RFx2+Hbp4QpHgn7TnAO9c9VqaPSwM8PcJjA6YMxFrHkTMX4JMnNhzNCKQu/m9D1xxKAxPhn3cDWp7a2LVMcQN3rQ/RQeooQVl4fnd3d5i+JH95DAuBjuUx9ycLwE0ZXGDvjVkZ9xyDcHHxa5o0GzV+/vy5vnz5cjhDkXIUO/Lly5dD3Q6YGp3MRkVGKG/39/fr/fv36/b2dv3jH/9YNzc3h0XmmTbKWqFMc3E6x7JF28l1SymO6g2smjy5bx4H6yx5Z/40e+sgyY6YNiO2iyDJoNz6lPoM4vM76w7ooK1jJo06wzZt+6lTAe/0QZmuDF0EoWmT98dO7/f7IxknWPLYROdYvDEkbUQuvCwlvHfwQbnmOGZqkzNjbCt10v7m3q9fv66PHz++shk+zqoFjOZ9sp2hMeP87t27wyHKk93LvbT7oTG8u76+Xvf39wc/yg0+OdYl65UdQOx2L9PXuZ6+5NinLOnIJhHOStC/hlfsP783UE9dpS05Z+aA5WzAFwK5liavQXJaNMSkEBA6EpoiMxqkVhdLYw6zAK004NYcejOazmS0qKg929rbooMOnzy0896KbptBt4GjcWahc7IjaWPSwEnro0EjDZNBzVS/i69T0RqtjMrsJExP4+nksLboY5/4OzMJBK1NxrZ4uwWq8/s5U3G8jw47B5Ya7MWgx/k2PlBe48R5oHLOsCPgc6AT403a4/TiEDg936bom5yZr218aODjvPN3fX19oDEOPnaSa57Nb/Kd9s5BBIHLBFSbLLayZRfafbzHbdgOsX9+jn3ZCsANZAn84i8s+wYypmMa14k/9j3NfnFKca3jrJT78PT0chA5+dQy2+ZZbL7Hy8+mbeu35Z394z151nrvujju1D3KJvmXNhtGaOCfPvv6+vpo/BsI25J1B1RrvRyUHltFGtn/NlXK+6wPzPjnCCoGK/SvW/bH/HlLf88pZwO+tisnRNmgZsB5flEI5fbxKHCMZJ5xipXHv9BQtuwWrzWH2Qwt+9EMDotBUEPY00C5PirdlhC01DPb53oRPpNiATWNk4DRSU2COIFIPt/62ACi62yfBiU0nFEqOmcbT/bp6en4XY9cL5JneVxKA1Luq3nl0u6hgWyFhqjV5ekcGlL/mceWe+rubveSkc+bMpJhY/RNg0bnwfHIQbBcEpJM31q/1rrtdi9vzmFdzi6fI8/mL/u61starFxjO153nOxReHFzc3N4x2rkJs9GpvIaQL69ww6dzndrkT1/n7LsjQ/t+sQj86fd10CUAaRBwtS26zJ/zKvWz5aF5PgaYNk25TnOGjS5y/0Zz4eHhwNdsUEMVLw+NLT6/bHPz8/1laMECY1vtFPsl6c8o5u2OfTR7CvXv7VZIQZ6fodt2ltrHcAPx+P6+nrd3d29kjfSTDu83+8P6wKzsWLSEffF/ad+BXQmO5lrT09Ph80X3Ghq8EqAT7+Tfmd2MbYiexHsk5vt8vhQDxzocNzeUs4GfO1VMVvEhml8br/fH52jtVY/LJHTKkwVc5ombdI5x0lZEVKa0+RghkYqcxskGmr2bQKTeZYgpYGQFr2T7gnwcWNLM2o0HqRjMshbRr8VG9TmBDyVNpUG7kwHDSYNNpXeyuG+eVqFfaYiNUeWwixS6mhAo9U/8cf3ph07eU/98bMZ0qm9Bgq4Y+7+/v5gwDkl1XgfY5s6YzSjtzF8ycZlGjbgcLfbvXL8dEaWna3F8Xme48G6OQZpmxlfZgqz1jCg9/7+/rBBI44pdOY1Tl+/fj38OTthY06HlL5YDzzOtgUpEx9Yr/9SbF9b+82u8r72TCsGaC2Ds9X/PBNeuBC4M2D2gnfqEWWMzj5tff78eX358uVo3CJTmb7nkqW1jte+Nx3nLnXattDdZpO4Piz38axH2ohcp44RMCSImwJlPh/54VsssgmLeppMF2m+u7tbf/zxxxHP6O+SKWR72WxF3YifpyzYflp2mEgKcPzy5cv6/PnzoY7Hx8fDGXxXV1cHu8ddvLEF9B2kJ/d9//79cN4o7V6zAeZ55Krpm8dmK0kwld8+h68ZGoKX5uCc8mXZcqy+7xT4IE2N9nbvBAy2aOC9NmAGPVv1nbpOnjbep53p96kPrT9WdN/HtpqD2ipb95lfW7RP9TbZa20YFG+1NcnLW+9p9OZ+G2HL1jTmU50N8E/0MQjJdd/f1pWR9rWOHSSdJs/CasEajT2fpTG0oV3rxaFnxy8PuD2HTw2c+3cGFR4j85LZS0/jTvrVDLnl0t9dmryd4sHED7bd5ITfJ72Z7N2k143/E52t3i39bXxlWwwQ2X6AC5d7EDDa/lN/nH1mlo00N52c/FXj/Tl2ofmpKUBoCZhWEgR5N3OeJS/Zt9gCBnYBs9lIYtC51gs/AwLNz5aJbMW08l4nmAhwraPmW5NZ82FLNn+3nPJbUzkb8DGVybODCObCgBhhHraazF1QdIiOwU8Eks+UIOjUbYVKHYxWOOfPxapG2DZcbX2cjRMXnzYhalkfFgqBlXbL6YbHfqVPe571eqqNNJie3JfIhqejc1qtGZwG5qfrBhDc6c0zrLiMwIbLwHQa18Z7ygL777Uykc39/mXBcjP2rIfGs9FK2rhxhO8DTv/D76yVjXFMnVPWNPexn3bmoYdTDilcb+OpLRZe4xol1hM58oL3jEOu5e0cAW/v3r07nMmXYxTY5t3d3bq7u1uPj4+HN4E8Pj4eztjaMtKR5/zmbGdkgd8tswbAOWE/bwXhWV+2cbaZrI/ywzHzGHP83cemAxOwmvR5sj/+zTxZax0tvGeb/t+ZdNtEO96pTJkOgxHKfmQ8srvWOsw+XV5erj///PNo81/8XmTSmxLsSzxVa7tiPvJ3ygd31eae8MXAi3y2fWSGkXKTZ5ueh2eWTeotcUDWslJXYs9vb29frQEOXc/PzwcbF3uUkvozdZyxYuaTvG9rd6d1/MlCJpuYDH0+E2QSpJKuANA2drEZDBqMJYhLmn9rvoIZQ/urc8qb36VLJWQHLfA07Izy7aSmTjtCmD4jhDQgDaE3tG7FC90upMvTLy0iMc9YWlr/lAFgac6d9zXj46jLfbZjj9Lu9y9vSLDBdttsz/xtfDbPGPE1o9XAVOtvK01WmYVK/dxdR+NG+Zr6OI3bdJ08ZyaM4+ENCE02W5stSp3o4nRXDFqmLkMn9ZaFQRaXb6Sv/I1ZEy7z4K7/3W535GTXetnEEToyJROHnSnW9OPr16+jLEy8aGNlxzdlLvIcj6fIur39fv/qHFEGMm1MG8ibwNtWdmML7JkfDlzbd95HOaU9pe5kPEmrA3naUtsUB+hNj1xXs4Wmk/rltmn/b29vD2vO0m/KKdtogf9ax6dKeKNK02XahdxzypdtTYVzTBiY5pPXYvcNBlkfgVSSPqzXb7Wh7Yiuctqcn9HnABnKPkEcp2ebnAaYu8SG03fHx5mPsYUMUu237Reav3WihbycAhT/Trlt2Oa/DPBxTp7OMgOUgT5UjEMo1+qvYGuMoFNtGaUUfmcEQuc1GT3WcQpANFTugzJJzxbwMC1Tf6b7ph2QW3UY6FrhGR2HH4nc4rTsGMyfZvQbsLPD5TVOCU5ZpXNkITRYJgwiJ3mYQCp/ozFKGy7kg7OsHgu33+p0dO5xMy+oS44wmxGK8QpIyXE1+Us9zlI1Z0Jaco2AiVFxjHlAXYx+6vPCaT631kv0zmCFG1bIE4M486Fl0ThOuc7F5AbDFxcX6/379+vm5uZARzZy+CgG172lv/6t1WNbNdkk32s7uyXT/CTfuODffG06Z4BHPrSsHvWZ7bb+TXrc7mm6YFtHsDPZj4xzy8bxf9LNTN1ax8dZ5X7zmiCK/fShvxMvbO/IS4LZSRY9g8Vr0eEEbcn6RS/DN66tZFIowVE+mQggr1lij8hnB2ct4x/MwkxpZiHXejmvMOtzIwMGhU0/88awrF+eMIn513TWvjm0p11ugj2nnA34EkWns0l7rvVyTAEZen19fXSaPt9bt9briC+FKdEtptKIO1oJOqdjJUOb42htNKds5xThbGDEytOMBY0Ln2lAxBHVxEf2Oc+1SMTAiA7Ngse2zasJvLjuAGY/z0iYR/e07PGpzG3+YmDaNWYPeL2B/zYONtwTAKestzOo7AzIH9JDx8Qsmh0gaeN0TAN8pDFtZfo+u1CzUSHTkgRvqZOZWdftDKmnwhhYcCdsCu0H+5g6OR0d3gRo5S/yv98fT8tTtlhv7o+9ytuCEujtdr9e0bbf7w9n71H/Li8v1z/+8Y8DT1PXv/71r/Xt27cj2xRe0s6FNmZeyddmpwiGGVBYzmgDCBrMV4+jdZ2frpv0mH7anNTrIJaBJu+fplAz9qQ1f559mXTFvOLz+c4sM/nt+qbMU/NfXh4Vn8dA27acmbjUETkN/+jTKEdN9skHfs9UK3WWa1Sbj8sU6M3NzWHTA3GAl4lw2QmXtXDzwxagIdgz4KPN5U7iFO6k/vTp02HDDW1WxpzZRep1/mK7mM0kXgpWyjIP+2L2xxilAT4eYUVbck45G/CFiYwkqMR2+o7iwqjJObK4rmbkeM3A6q0pzy2wN0U8TUn4rP+30Wz1mX9Tuy0SOLePBpyngAPvczmHtxMtrS6C91Z34/NW/7cA98Tf9rxpeIuCNb2Y6J8AZXO4rZ0tGW465TodRNC5bfX5FPAlkMnavPxuUGjgYVop957OIhC18Zz44zoNgHyv26ajj/MlUI3TzN+WTTrXXm3db7twys60/v1ny5aM+/dG3yTHvu8tdLSStrychNcoB+foYYoDirXmjGTubwkQ0nAKjAdEtWtTmey+bVF+a5noZmcYtLclIc0PeSp0stPN5/+O/DKwsi3K/7nHQKzxj33LmZzpf0smtOemYh/G3/9LAB+PZOCJ0yaI/xPhNkPE34nU+Rwj9XTY9dg5MCvYslIToCD6poNa6/hIBz9DehhBkSYL8CRADRxYidxeCiOQRpcVy23xOI7JIJOXjj4MKCenTTrSXtrk0gBmlNnH/f447c+IOG21NxNwY8EWPR6PBoD52Zxr5DvjkUjVO//IP/aVkbqPJTDPTRfHhtmRlExJ7vf7I3oiX3k3LE/wTx/CC+pr+phCHsdo5k0ZX79+PRzlkAieUzvJgiVzZgBnXqT/nCZihM5jFR4fHw/n/TH4tC5SHggIMosRu/T9+/f1999/H0BsbCKnp8KD3W53dEDzfv/yjuBWKI+UD4NXOub218oWkGnTglu2wACJtDNzafr9PGWZ62rbMxkrTiNPdjhj7fuYocnxH09PT4cz1Ha73SvwlGyOs+akJ2CfskP+sC/ewZ5MUPiVDDiXECQLn75Y98JzghbqKflK+qdCW5pNfPSFXE/LjS5pI5m79Ic+lefuObjKfTxqjZk08qCBZcoB6TXw4v95a8/379/Xhw8f1vv3749szuQjQwPry33ZqMINZQSZodH+x7LC3/JJXuZIm1PlbMDHNGKmXVoUYycWxZ+Ko5YWQYRJDWCScWRkA5gpZC7RO69TgV1nM3J81uWc6KM5btKyVn+XYZ5tURZp9LUmXORNDFYbD/fboHu6z33iFIPXak3Oi30I3TbMNLShvwGfNnYNgNNwTuM7gdv839a9GLyZfq75otExqJ9ozvcGMGO445wCjDg16gCjBV7WSwYTpC/2I4YpjoT/53occQA6eZ92vfg6epEp3TjxANjdbnd0tEsLekLrpD+ZSksdPGyXNHLxe9p59+7dgTYuV8m0kceRemq+O9Cw3LTiYKDZz60Anr83J8TvBMtbz4Yu2hnaOD9jYO6pSdfr7DTBd8aLU+xZrzqdOUtwQsDH9rgkhUEc7UNAIceC6+cYBKTNHFvE6dRmjxLoNNC3lYhgX1zCXx6aTsAX23F/f38AOCnxoeEx5Z38NI8MaMIjg8P0r41Z8w/uE2Xn6enldbH39/dHvjB9yfdk8NaaN65mSjuyQMzRAiRfb3bIOMFLXk6VNx+8nJcD53uYHaPK+7eIoWGgctCxTM7VRoaf/u72bAj4SSCx1uuBmWg6RYefacbJ6yva8wZ/E0h03Y3GrRRzDOIE2HIPr3maP3VujaPHw8629Tvtcu1L66/5lDYmGswf0t++u71p/J3ZsINw/9hHZ94mnrTCa3Q8oYk8T/3caGW95O43A/XQ2NpOyX00gllb0zJBzHByTZ3XzOV6430D6nQgBBj85NiEZ3G6BjekIc6M65VTX6ObtPq7nYEBX7MhHIeme5Njn/q0pbvWJz/nPwL2Jru0H40vrrfRz2ciU/luGrg2LWAkgY43ILLPHNPWfgpBUZ6lzaXtMk3mD3lo0Bp5m94Bu1bXPdbdMqCkk+2zP+wrA9RkssPbtV7Wm+X5ZMWTxTTYY7/t7/lbo4VBHetr4Dj8Cd8DqPNGFQbC5IkDdNOea63wnnOCLOv7OX5/KmcDvru7u6MGLi8v1/fv3w9Rkc/YoVLQyaSOGFga46RtbdxcrGwT81ni0DwgXDTcpm9t+FthPaRvCxjwN2dvJhqccXF95EEzWIxkWh/4XHNOBl38bLzdcjo0nnS05kmcPCO45+eXqcEtEEP+ZHwNlPk2iESvU9q9ZT2bMSGf8xzPF2TWpwHe1Efg5/HI85PMU974ftfoSrL0eZbOjvze71+mHv2OW+sz6Y5eWj5iR5JtyzRV3j/JDEayGplKiq2hPoTPnjqj4aejyZEvfNF7+v3HH38cnRP6/Py8/v7778M0c7KGTZ65wDz9z1TXu3fv1vv371/NjKQNG/PIjQG5x7k5YNowZ4CnT8pBC3Jts7gAn8/zGa9xtE2n7E4O3eNpftmh81mOlzMmsSm3t7eHcxOfn3+9heGvv/56BYDjp6IbLYNLPSa9lNGUBAT0OdfX10fZQ2fvIhM8pigykT6k7fQ/fOJO2Q8fPhzZu4CbJoehNcs/aLs5LRtd/vTp07q8vFwfPnw4TON++fLlMAbhSY59+v79+/r8+XOdck3fuCmLNiX38Nin2A9Pb2ZzFQtlhTMJ4WMy69fX1+uPP/44OuOXmUkW21z2J9c5biz0OeQ/7YB1gTMb55Y3v0uXTjq717JmxU6pRRunitH7VHztlONrkeUpQHbqfz9DELRVGgjywJpu9/mtkYTvbc9Q6bYAY2t3yl5MtExAp93Dg7tDX8vY5ZkGmpq8mO/5ncbJwC6/uc7p/+bs+L313787eGr8nGSfADWGKvobA5rx9pTUWuvIcE1tbOkp76Pd2O/3r45uYl9C6263OwC6Zijd98ZXZnZ4TxxJgFkOh035+fPnwa4RwDT+MwNqwGAHbNlyX6ZgYrItbQy2dKrVaWdK+lsdTd8beJv6NtG19Zz5utWvtV5nthlkUgdSCCA81k0XW7FTdiBL2ij3rt/9aQCcbWWpQOMv9Y4zb5HL1gfSGdklz1qWMOCMM3RcXx2eRE+4S3ni6Za/St98fwsacm/DJA2oJ6Of7+7vKR/faD5lK1tfJx8+YYFT5U0HLztiTImiUJFy337/66XTfOXQWuvo8EQOyPX19ZFhZOaINKS0OX1uAkhhir+l5DkYW5Ejje6khOcMqn+zkadTaA7fz07tbo3b9H94eo7ToPNs9TYjHYPhgz5pLHItEWM2DSV6+vLly+FYDgKatONjHNgvyh3r8LmD5mMME+XboO5UMRDcclQNoKTYSLn/U4a2TUvx97Sf7MgUBDizwUwGjX2yGcnW+RiOJksxzF++fDnUHTuQQ42zOHytF1sSvWcmIYY7+p/2k6H6888/119//XXIftze3h7RcHt7e3g37r//+78fpqCYvQ3dPng6YHW/3x/WBnGcLDf+TpsQZ+4x3pK7LVs0tUs52wKRHkcGSbkvmcDczylIAmjKEOvh9S3wSLpa1ju+gMsVQgeBXQLL/X5/kH/SQcBDEBPZ8kZG6kWejf2iDIT+yHPOcEtmm/yhfnmTiPlAO5usfe4j2DIPc92+ln612a306+Hh4eg9teEhdSFTusnsU3fDm0ynNp3J/37VXTL2aXOtdbQukzM/U1Abv5C+5jpnmdImA1PP3gSEr7UOGeV37369OcizBROmoHyQ9gZszylnAz4qIQnN9wxYmEuDlF15z8/PR6jZnSFD0xFObzTA19KgGXTSyNdThdYJgTN97N25E2A7VZrBMqAkXz3YNr7sc4taaJTMH/JzAhzJonnN01rHZyV66tT0GSATVCeV7vVa+/3+aAfb7e3t+vDhw6GOvAYnUwnu51bUZ54RWHusDWhp9B0UtGcnx+mxp67YcZIO89XOjfTmj2twJsCXfuX6z58/Dzxuske6+Eq4lIylHeoUYduWZFzy1oz7+/vDWw/oAKPjkVEeEp0po8fHx/Wvf/3r6Awx2qkPHz6s//bf/tu6vLxc79+/P8qErLXWhw8f1tPT0/qP//iP9enTp1fOnfJs3tAxPTw8HBx2bODNzc3hoGnraHgQIGlj7/E+R9ZaabbLAMtBRHSF/bbjy7hwwwyXFhi88jVXduoslEnbL9sa8i82n2vkaHdI7/PzyxmPoSn2kL6BQQTlwuPATF7u9Vl7Ofcym5VCD30Sg7zUm74Z6PEvQJZ2a611tIkphW/3MZigjyb/8z19e3h4ONwfvkfuaeMiC2u96K7teABfNnNlbCmD5JWX/qTutmGPwaFtMqeGeRZg85ehg9PGoSWzBAkeA3TpT1wa+GsYqM3EnSpvBnytcD0AI5oJjOT3MJRRk1PMk8GaEG4DUb6fxQY697Y6WiFtW7S25ya6/cwEnlrdrvec0mg2PVv3WjAb3yi0zAQzaqUhyGeuUdmdpTNdNEbTPefyo30/JVsNIFHOnF0zD13PZAACoPLZwFkr1h072dyTuk/xyjrQQDPr3OLfNK7msQ/npWyt1V+H1ugmAG46FUfhnaOkqTkB0zvZn0aXS6vH18+ta+sZByH+vX22sfaY23615yl3W3aatJ+S9S1aJn1tmUpm9gj2eG9s1Na62uio67OecwdvsxPWFfpYg6LU2aaiA5RYP7OsXgfadMlZTPLCoNhAn3acdsZBPO3dqSCUfUhdzLR5PKY6AvoD0NI+ZYb2d/KXLtOYTzJPP+b7Wx9OlbMBHzNzLIlWvnz5cliAzendtV52QdE5PTw8HOpsUQzP38k1CiA7auXIYKVu3m9GtzQp+7g1gBy8LSBmpUy7FF6Ckzaop4x3lDf8ZaQ5Oe3pWusPx9Nt7/cvUXue52f6v9vt6iu7MrWQnV6Rk8jRw8PDEX++f/9+dIbZlF2h0ZkM1inHRvnwVFNzGnxmMpCTsSfNbpv8zrVEjqmXa2EaCGEd3MGY9rgwnKCczoD65GwLnV/jb37PWNNxMUMSerjOkMc67Pf7o+iZOytJ6/X19VFfyHNO73Hq5vn5+aiuZEc+ffp0NE6hkd8pdwbnztZ4jCbHTtmzE2xGn3Uz+9bkiGVyRL6H30kX+chMlBfQ04bQzjLb1tptGU6Wpod0luFBnHdoZlDz9PR0yE7RnjJjmSm5L1++vAJa3oXa7MRax++YJv3pP3fVMhmS+9yXvAkndCRD7ze6kE+kM0cwhYZsxPj69evhviYf9NPk/X7/axlX6Jl8I2mhv2o2LNlMziA220Z7xaU6bdaEvOfza63DrOSPHz8OU+L39/cHeUjGlM9bz/xJ28O39zRfYvoMDDNWW7iglTet4WOnfI3p+bVev37JSsqpDRrCthawtTcBMSu4AdnWM+wLr7X2t4ADDU1TeNNzisbJgfoa65roaqXdY8VubfOZCVjZgWScPa3oqJJ/jA659sQKMtG5xfut35rzYH109uYlHV/jt2kmrQYLpq3xMo5rS85bO+QzZZVAcmv8zaOUKTto0NH++Czlg4DYYJ6Aar/fH20ya7w2+Ao95AeXLhiIcWyafDSndk4hPzml2UCb+cgx2LIpli/zhHS05yZw2gJY22E/7/ta26eA6FS2HGebysvvptMZ9AQgz8/PRzv/48C9I5TjGD6Zp273lH2wvoQWnjTQ/GQDIQGg3ljEN0U0esI7Z/iyznW/f3nd2vSs+0VdDn2cDZr0Oe23Osl/Xje+YL3pP9chTxlP290Jn0zP2Kbw/mZXfte2rPUGwMfDIEN0omsOUgNpzvY9Pz+vu7u79ccffxzdlxPFWdJOwKAHkcLdSjPQzVGzX83otQjb6XC2xzonQ2kj5/ssFFvgzXw5B+i10oTxVJt2Mrw3Bi5rebJub7d7ORDzx48fr9bjxan/+PFjffr06XA0Rv646NXK4jFs03FTxmALGLgNZiScAXW9NKTMdjpLxnYagGDb3mzS9IB1cUymbIpLc4DhaTOYBn2W6dDNPzsn9psReuje73+tifv48ePRYnRmTrjGLMdSxJak7sxM/I//8T/W5eXl4f2fHMuHh4f15cuX9eXLlwMdXMtEcNgAFh2Ix5FZzsmIU759vcl7sxcTqGtlAghbzzY55dRgbOVWu5ZP10vQwIzbFhCkTtm2c+NNMlwZJ9ZHEJV+xe9x3CM3zErbRlCXOO2Y9hzIctNC7kl2yCAoffLmyLXWYdNk2vVY5nsynLEnzGB51iHFWSaCQAahzYZMPipy1ABe+2Mmm/1rMkfZyu5bjkmyh2k/QDhrOrk+mfbIADyfKUxmGUQa+FGmQ4sTKr9bzgZ8fK1J/hLJtO3tNjJJcQcl//nnn+vf/u3fjgT/77//fvWiY05dRQDIaO6ynByenUm7Z8pk0OA248xnWlv5pFDl2TYVw//Dm/S1ZYzYVotaKTRbDsmGnVHrBF5cj6+HnsvLy3V3d/dKgWOEsrjeYCGyEnCXhbTNIXgspkjQYGoKFibj4nY5DUMZN+/DC+70dfQ8GVU+n/o4/c3p9DZGzUiTb+43xyBGyYaxGR460zwfuuk8DPicrTUYb0ss4piy+41vCVnr5S0YPFcwi+Lp2D5//rweHx/X9fX1+m//7b+t+/v7o5fTf/78eX39+nV9/PjxKFuRQ+ed6SHdBhgunAL0GBnEtYxrSmg9BQz5vQUBTQ/o7Fswb3mxHfTmvFY/6zId1B/6Hzp0ytlEn9sPOLq5uTmcC8e3J7APXjqRI3zYZgJZb9BofeamRmeuacMJLAnCIp+UuTzL15+lnq9fvx7Zj7XWur+/X/f390e8+fnz5/r69ethzLhrdtL9ACeDfWb9LFcNHzTANAG+yf543IkXQoM3sRFXRK/D14xTnqGt4tjRtnMsbIMz9Z4p88iwsUADv20243fKm6Z009GUKS3enmM6dK2XNQ80OhRKZvRs/JoxsxCy3gZomsC5zq32Wj+n65NT9cBOxmkqfsZ1nvNbA3LMVjQQ7Lq2+LXWCxhqGTAquPvG62u9PoLH49Z4eIq2xoffKVuAuIHFLRomfdoa6y3A14CZ66Jj3eJFZIOOhgabNsFO2HQYpPieFkTw/xjBBAIMHslL0moexABfXFwcDnfODt/9/uXE/QSitEfNAU16fI4Ncf8me3CurJ6675StmK5t0c8xbWM2Pbsl743erfb9rIGDac09rR5nKQMQ7HQ95cg6nUxo4J6ZKe8+TR0EJ/4jGA6IMBikfFJ/qEdcS8kAhjJgHZ1kx3312JwD2qhj0zh63CZZatea/Web4d9aL0ECA4BWT0uS5H5PCZMf7rP9m+/9nfJbx7KkQ3ypc7aSMyUawclJ+hzwv//+e/3Hf/zHWuu1YlxeXh62qfPYDq4N4J8ZRGRPB0ZBdrTK6KpN95LOBj5tYMizFIMQDy6fZbFh8v/pC6cpeF9LBbsP7gdBH69TaRhtN+OSSDSLXkMjDQwzmGz/+fllFzeF3uNGOSNfubamgcfGY39S4WjgqdD8zZk2jg2PM8ifo7r0hVNFqceyZ1Ce+hqo4f95luejcbMGpyzdlqPt9JnjzsJF59wYwalc6qd1wzLVjGimZX/+/Hk4VmW32x1NwVDWmXFM9jIZkY8fP66Hh4d1e3t7yIr8+7//++GF6gF9pI3rtabggfJnUOBC/aHecqra+ug2+L+Lrzfwwvvs0ClvKXZMlAXaZWeZCEoandYx92urf2ybr/oKfdzQwGn8Zh/SZvxR7qd/ubq6Oizqp9wbjK11fCYh9XCtlw1sa61DZvr5+dcmR9q5ZIyy6SIHhu92u8MbRJits11gdvrr16/r3bt369u3b4fNFtlURx2nzcoSnWwcYaDFceXmS/rXjKt9F+WDWcxk8MP/PEObmUKQnPaYofVvsRH2belP+v/4+Hh0cPXWpg0C6vTLn6HHss7Nl9QZy5T7fU757WNZIghcG9DSt74v5du3b+vz589Hz+a8tShN1i8wfcqUe4odn2lpAs/ohbRuRSsGZ3RWBiyTUZqQPf+fnEGLbCIIEfzJEUzP24C2CCoK677QWXBdXhQoBotKSqWn03efCOT4u8etAe7QZgM+8cSF/TTwoyyxDv9OWcw9U1v+Mw10iqdkxk61te2AgAERDSjBdaPd2QHzlAaKoNX9nvpjYEQHSxqSjbu8vDy8U5SH+za5Sf8JPuNUKZefP38+vP6J0zk0ztQPy0srzvhsjT15wTHifafAXfu9galmGyZ7Ntko3scxPAXUDCwdEE1ttPpId8Yo4GWt42Ap/7eptSYzBAy0ubF1BI+0AVyPl3Y469GAbwKzZLCpkwRDaTftcanMWi/rEMlrApEER1lLTdvtAIPBaGjIko8WvBG8eFMI+eQMZOpIG14C02TBctZKS8Y0f5C2Y0NiKwKSGUDYX7B+8nkKcP1nOWt0pfyXAT4ukm/ZCkeuWVi/3/9aYJ2jNSKYWdfFziRLSAfqXZs2JHyezDTA8+GlzuI1Z3TKYVvYWJqT9XVnbTyYrqeBoAnMMnIyvabHitaEqGWPJiMemhld5llOG1CZDKabUpO2CRDlf4MoO+SpfoM7yqLHsG1cYP85vULaLFNtXDgmdrykyc8SgDsb3AwTnzXYp2x5fFKvAUiupy5nVqjXAVsBUex/7s8n5diyF2d4fX29vn//vt69e3eYDcgRFQw8mYUJrWknb/KIYecidvKpZcDNZzuoODbKouW52RLq/SQrHq82rm7H4JSBAPk8tUX6/JuvtdKAGtudQGXutW1rznOt4+CSawoJaqxfBhasz0AndixHRRmAEfQEOLTg0P2dsknUyZa4yFrEyCkDbhbOmAUoth25lGe/2SP3U5aYreNGhWZLmr7zmnWLvqTJT1szaln175bttBtdyFT+WsfZwOZL+KxBZZulaP1gaf6QsxQZ12RmT5WzAV9egvzu3buj901SoCh8P3/+POwO+vr16/r27dsB5CXa4enqqTuCTZBHYBAmU6EoaAYYjEayJvDh4eFo0ST7sd/vD+l+R2rp2ylUzUGajFZT1vabIxenvSfAx8XrdtKNJv4WhWWmlOnj3Eswwz4QbNLwcGwylRaaPF1oJbWiNKfq65Qh1mNA3MCP++XIP/0knxO1hh4abG7usPFtjpX9aL+ZL+wXp0ACXianTL46aCPvmK2jUScYYtYiNETH6XAjS1lgHnnl+HNTSp5r4CWBZc7/TH3JWjw+Pq5Pnz4dAaarq6v1559/HtbrpY2vX78eAUXKEGmjrWiymZJF9xwPvjC+Tck0m9Gy6w2MNEcYOWzAgOA7jq0FCbSl/q3JFOlrjnELGDbn63oJlqkf7Cv/KHvZKHN1dXXYSMZp1DxD3eW4kwcBSQGU3759OwBJLnGK/HATROxy6s39LJmuZeBEXaC/iO7FtnDHerObfG1bPiMDlsE8E7/NdtPH0LDWehVYXVxcHAIn65L9GHU8NNHveI2h5cTLmgy8Jp9lfrI/1IdkSp+eno7sPOtrGCGYaPJj/t305HtkZ61fb+rJa+T+pwM+IlwjbhuvRD2TUWOHCGjS8Rgv7oppRnWKjLaKjY4NxmRYG51bAKq1NV3fumcqBoKm6dz6Jhq3QFaec9tbtDYa23PnOoatZ05Fduz3uaU5IAIgGzGD1xYEnCMXU2mGYmr/d/pJ+TLgmMauZemiy5wqdQDnPkx9m3hjQ+t7PZXi4NGOvfFkutZ0bXIoLWvU6tvq6zSeW3pEOTynvMUuvdVunVM85qfaafe35y1PBHBvLXye4H+t19OFzGw3MDAB+rTBjJrBJ0FPCp/JdK7vafbdYIv3TjLJTHcACQPqxuemT+5bo6HZnVP2rQGv37WJqY/jaL1q9rLR+Z+h4T9T3jylm2mTTHdkF9unT59eOdt0PFlBZgIZWTI70iJROk1HbxEMT7nl+aD91l7uizG24aehbMC1OVte2xqkyYEwg0deElSQvxMIJUBv7Z4yhvmdmZHmSGNMOC2Sccpp5ARGWSfFqM39MyDacsKWmQbC/T1yyP5NPMqzfsbjwTHx+gvT0WSmgast2Zrq55EPocMZnFaPM3t8Z22KpyjZJxpqTmHlxP+PHz8e3medaVPKr9+ME/pSL5eRtPtCX0qOzri4uDjQkCm33W53sGHJhLx79+5wLihBaWYDkh2K/bDc0PFlDJPBThYpawxpF5P1SfRPW2f5sAx5bRBLmy0xwJ7A9DnBWXN0LNTr0NPGrNkc23pO6TMLaRpYR+Qw/4c/yaTd3Nwcss8TXY2n+S3yyFkqriXlUo/Id5PTFOoC606GLxli6hizx147mHWLnHJ24MNsfdql3WCgFh8a3oZeg9vwLTNE9p20v+aFQS3HknTyftv+Uz7Yfj800fa1wGCtdZTA+vbt2/rx48fhuCfLYtqI3CQjaJ2JfE++z3Vx3Pi+53PLm87hW+v4dSUxgjHiJoppbe6yCUAI8wjA2N7T09PBcIcRBnoetGaELJRczM30b+ppxani1OWBSSEtU53+nUbGU1eut0Vo7O8EkrZKM77td95PoGe6mdInP2h0DFg9jv6NxqqBV/clbRi8TdkoP5N2otihfVpT5n7muoEW6aHSu9+t0Pj6d0f8dlzT1B7Hw05hreODRhvoaxs0YpQyjfnw8PAqw5f2CKI9ZgR8DQSHfvKY67PyUvaALYK3PBMHGaeevmY6KnbOQYUBb+jh8hI7UPI2DrltemH/La/kIQEf+cbNXM4u5f8G4snnZuNIXwOEDUDxfn/ndU6hczds/jjl2IAoCx11xoFryD0VydKyNyncRBBwx+UHbWrcNLSA0zoXmVxrvZL/+NeAJ/KFmcfUQz2mrKSvtqsN+DpLnvsNHHkP5Z/8829cdkC62Db732T8lB9wcaDjdgyM9/uXadns7N+aveT/Tk6F7mbvTpXc0wLPU+VswJeSztJwr7WO3oIRxfRaJgOAMJLz7BE2omEOujOBFLipOFoIPQ0w+X86prZA389Oxq7RZKNq49mc+lvLW0BexvAU7X7Wv01OcLqfzzFybBnViY5zFCWFGQMajZQpkOC6rtTjg1r9DLNn/I0KbqDo/k7gfctY2MHb0ZretfrhwG4rTqgtAjeP00aONMksQWu/6XOuM1tlXeFfgFPqZPaOTpF9zdgE3Fl2+ccdueZPsxXkGwNbgi3z2EC3gRvLiutJMY/5rEFbaOSzrrPpX3NSBuL53pxj64/BPPtoRzrRwzrob9qmpuh/9JL3tfFubbNO8pZZVoLzNqPlQJg8ZP+bDDdbYVla68VPM3vagBVBBUGr1x7mXuohwSD7Thrtf1sA7yDQ9tV9fYv/Pce/ud3mp93+qeLAjPaO7dl+Nz/KMWgy2sqbAd/Pnz8PCzAzRZMp2zjFTJUkq5fUMn9jxoDnZRl1f//+fX369OlodyeVlgCSpRmsfI/DysaBMMs74agINBAW4PTDbZkeO4AGNrcEive5ren+rXtYFyPeZFf9jHnawKGNXTImnFaYHDZ5kt8MjMzPLZ60QqO71mswkTpoxNY6jqbsxM2DyCff9sDplSxoXqu/tcIy0AAtAYHrIJ/bQmnWn+f3+9dTBBwTgqY//vjjMF1q4EfDwyncvB7PU43hQTIvPHszZ3dm80P0LP1Kxi00cqZht/s1Dfb09HSY+sqGjt3u+Jw+vpGDY7jWOvye8eTshoFJeMdNRPnLJjHKnjN3HtO2/IV8JiB2wE16KDO2Q5Q3yzGfa8VA3XrqTXTcCDbZD9ZFEJZMVwM0zfYxw8WMGTc1OMtEwM7z45pNNv9II3lBm8qAJ9e46avx3kEEZ29Mr9fNEfDmz5vMYtPymZm8lj3ONWdKUwjQDEQsr8m6t+Ux0R/6ZgLf5u85Jkwg0aY3e8o2mx+Z/Pzk8ybQZ2DNIDL0EHdY3poPoP3IETynypsBHyPVLaRJ9JqOkcAYdWaV0okMfu5hp7dAEe/JdxoP1ktjYJDh+ibQ0UBIfm+lDRqf4/fJuEyFtJxzv59Z64UX7CsFf1KaCWy6j1PGcsvZTDybrp+6n464geL83xwilZVGl3VMjtTg4NyI7Jx+Uf+2jMY5bfh+Gqlc55IIyoUdEc/2aoDZ9oOO2vI2jVWj3zzPwvU2bgHhu93LQaZtxsDgpmWfnc1lH5vdOkdvWH97xs96HCzLbuMcsLflyHiPMz5TBmirj6Y3heDM91n+SFPT97YTtZWMs20e67NdSNusY7fbvQqkTvmB/N9ASIqnS7dko9HGI5FsLxycB/waSDVaG52TLeRneMXlIc3HbI0d7YjtzSk5PlUm/GFbNRUCdPYj/G96Qp5NIPMt5WzAF+JyDMJa6+jYBSNULmZMSdRuRUqk0Bj27t279f79+0M2jinlKFubvgoD2w4nRlY8NoOLoNlnXm/GnPc248pBdlTp+ibjxRKnOxm+JiRUJLef4ilLRsleZN/6H35ms0Z4n+MxuFOTz1vp2c/Wf183IGlKxXoIPtsOtpahaeCkGZ4YRv5v58Np6zYOW/2d5IvOPUbcBtaAxw7Y91uOqG8tI5HrybxlKtcZMdfPLFpkmxnh6L75YGCYepLB53loXkSfEj16evr1po3IbTIQoX+/3x8FrXkvLxfjp+6mF26XbZuHTYebrLPQBtoRUe+bbG05jQY4SKODRNbJ65RPyqE3qdEG0HYbsLivzjQy49X4xyVH5GGywGutoyN7yHPywy8eYOFGo2TFToEC8jFHvXgWrK3nS79Db7Lr9GvUg9AU2+xxawAtPEumlP6VmUeOE9f10QeGF/nNG13ybPSQyyCavFo+GqZoQRrbakBrAt0Gkw70piUvBM+5LzrCGYLc64QCaXIfzilnA74wNEbu3bt3hxeNR6gyIM/PLwtXnSplSjSd5zSOFfjy8nLd398fQGR2QvmcrAl9M/3c1m+wX82Y8nruT30pdv4NBNAo0tnx/ul3F9LZ6ktxXW0xMT89PckpAPN1AqecwqcRyuG3zcmRh57uIT2T0E+/eYzaegcDZwJXKhxpTF3NyAc8mAbWnci8ZfkaiEy9zaHSQNsY0kg3/rWMq9vxNeob+8kNWHFS2bCRPpuH5HUAXwAWdZrr8vicM0gBeAF8BGjWE9ojT1llCnmtl6n8gEDyhm/pIN/sKMlXtu+NYHa2dKIG6v6dzxiQUUY9ttaXSfd4nSCIdbrutN30kkAx91o2CMrs0J19Sh3ePJR7U5wcyHMMLBrdDipZ4pt4Pm1rwzZlctahmweT0256wwd5xmUHPNYsNKZvnomz7Bqk5z4G8h4bFspymw30pgW+Ri7P5QDptY5fR0c5aL7B9oEJDPs4ypDl3vew2G9Q3gxmm48gMOTUOvltP8/nJ7pOlbMBX1s0zWLDZkEioVuFCmZGGUA2J5j7mhBTYJoz9/Ru6x/rbVHFNAiTE21t+feJhw10Np6cMxakm/dNayGseDbSBtJNbsyTBmgm/pz7G69NaXO22QDXuUpFoNycAj8nGkLHOaWNQT5tsEIX2/b3c0umammUCPi4OWECZyxxXrvdy+59OjjKL9dWeVH3Wi9TUclcZB2Q+d3Gno6MGSOC6bRtcHUOSN+S/VxvAMt0tmcb6GrguhWPCf/fsin+3kAt77Nd3qInz1NvaMs8lqY5z6y1XslSijcpESDxmJwUAhg/m9+p+9QBy7Jpyf3OOk5gOfeSRgMs3huw513OHBf7ScsO6298T1vsH8fLbRMIOohI8YaaSXaaDdvKzPpZ8/mUL4mcNPoIMGPT2IbHLKX5jUZvC2TOLWcDvmzK4HQOBYbEx0A6urTyts6wExSk3JeFq86QxGDn/tZu21jBPhhtkwbWTUHOPbnPqeE8w9LAgHnJiCZOrhll02F+Mv3espmkyfze71/Oj6Liuj+73e6Q1cvZUakz/GxZAdZjUGK6PGXQHHY++UcDRUfQDGmb8qaRYKZsMhAEJzRoPK/QwQwdgzMdHpuUls2j4SYo4TRW9IZj4yxT6p/A/sPDw6tz+jgNG8DHRfJ24qyT08PJ8LHd6GayKJeXl4fzP2lT4qjXWuvjx4+Hl8dzw5ezmuFzZhIiu+nrxcXLGr/0gUsTmiNv8uN7XayPdM4Zp9DJjNQ0rnS6Ldiz49kCkpZHfmfdLQtmXTRfTCNpaDvgk7014HNfnp9flhbkyJRkaS8uLg7XGFw8Pj6ux8fHI4CSjPFar+1mAzQMFCLb1EHbmdDe3tTD3/kslyHRHud+ghGOT+TH48uzBGOnuHSKQCavX2vBFsfQ07NeRsXAjLpL3+2pzWYPt7Kltjm0kaxva5lRq4fthk4HiNbn6SUS4Znxx2QrTvnxU+VNGT4ieitsPglEGsFTsQJH+FkPHa6nhtd6vbDX3xtIMO00nHnGxq3118+4b6f40J5xxOhiY9oMawMQU/seN/bLIJo0U5GY5mdgYEE3/9yv1mfz0UBk6p/HcsvhtTrOlWeOgeva4kOjkW1N45/SjBPrZcTZ6Jmi54kXBolrHQM+OiLzoxkzGi3WzXtoFDPV03ic+nJOlpdqeNzJ2wAC3m/9I8+a83edbtf38rp5Po1JaJpk1zw3bZN+TaXRMMlG68+5PqDJbv4obxOwdluxXVzHRrDoAKVl+Kwzzm4x29KyPA2Q0O41EJF6G9/a+JM2yib5QWBhHnnZh/nI/z1L0vrmBE1oajv6OUbhG3nOrKGDNbY9+Yt8+roTRlNfWjHgtC11ENTuOVffXaznby1nA76bm5uDUuQF5US2Qe3ejeRsGbNZVhiCOq+TW2sdIoApY8RMWBQ3hr+h6jyXTzsXX08f7KjYzyaU7J9BTurzeogWBbrYwdAYNMUgjU3Y+d0GuwHH9DXZkaurq3V9fX3IBmeH5rSmrQGgJsSenrezc1TNerxm8JRCT8CR7ZAOp++nfjkitCw0ADZlrF1MA9vguBvw5Loz7s70mXepg9c8DWs+kVaPdYAW62F/AuDinGKD6KjjUNgf00P9cIm8hu8BfmmLzzhTOAG9qXgMLFu5h06QcpRnuL52mt6enEgDqM3pNX2xsyf4sQNr9zcQxN/NJ9tcZpfJw7ZuPLzJ+szr6+tD9i/1EeQlkIh8E4SRPgf/P3/+emc8eZsAhWP77t27Q5Z6qxgccPwNItiHXLP/8nf+lrWp9JW0B87WkT7qWMs2eXrXz/NopaenpyP/xwAyfoT6/dZkRr63ANA8Yr35LdneyIpBX/rLRA3tWdpkSZ94pFQD7NSlrazmqfKmKd0YwJxrxUWgNKRcaBkmhDHpIA9CpeGM0rbTyC34noqicoVpFOLcz4iGws0pSxpkM9cDzekVlgZS2hSIDX++M/rZAkSkdSsSspI2YJjxsMMnXU4pX11drffv36+bm5vDlEsUglFz6nfa20GA6aHjzxiSn16obTlpfdzio51ZnvF6DcqUHY15bp6aBhtL8j1TS1t1N1odxeY5/09gweCDvLfzaUCurWtiGzasBC45Zy+bPNh2jGac8eXl5SGYIOALSPNxTuQpp7/IR4LKvHYrU3/txeQGbFOZ9DD0WM8su/4ttBIk51V1pKfNONiZmcZpQxXlvV3b7/dH9pRt2W5SFmxfG69S6EDdf/ogbkwL/wLaw5eMq+WZ51AGENKH2c6Qh09PT+vr169HwdPFxcVBjggA245U8zO0OvvH7HcL4Ml/+oNW0s7Nzc3BvoRPqaP11bbANrfJOJd3sC8Zh7QT/mSGKLrN3fLhMfHHtAvfv03Aj8X+OLoZ+5I/4grijtRBmQq95C1tVw6mN+/Yr9TpvrylvOlYFm7LnqIFMsjodK1+4LBLE5xpUIzyI+Cmj/W0qIe/EbwaALjO1pctB9CebcZ3AmSNbvchwuR6Wl0TrVtKQIGkkaVARhGmbEijeyoEmVsy0cbDIONUaeO5JaNb9RjoWfa2HGkrzYlu0ee+TIBtAr8ToGS/ThmfxgPTxyh4oonAzHLlax5zg7z8Tj0hgCe9lPnJcU625pximTh1b6vbjiE0NbtySt5Mj2WoPT/p8Tm2y7832eb9W/Z7i4/t+qQLTX74txUw+s/r0pgk8LNb/7u/a/U3rDBga9PCnB4lPefI7BTgbNF6zri0ez17FzDYdLDZo0bfqftIz9S3LVzRfAczrp5JcaaWbTRbdYrWc8qb1vAlis6U3VrrcEp9hI/frRzPz79eRm7j3lLl+/1L5OBoPWiZRq5NJxhUWCn8e2hiJofRiBdvNwVvxoh9teNN/5tiboGu1obbMt1Or5+jkCkB+pkO2e/36/b29jA9kTFLajpvZMnC+vCR2dVEfJwOnByap0y5PtCbcTxG+c5F8DYoqccgxt8pF5MD8FjzOg1/sgjTeDR+5Lr55e/OPrO/dDiRd9KdyJsAyIU8c59TnIU3bWkjkfxaL5tbSCvH+eHh4ZWx5KJ76+Dl5eW6u7s78CzReBbvUxZCQzIea71sEkiG0YbYdoT9M7g3mCXY9BIO3pffLBeRwWyUom4y00/bxOxXnm9ZBLbL9nM9v7cMjnWFfc0zDVS1Ns0Lb57L97YEJs8mG+ilFLYR7fwzvss92TqPQb6nX5waTsY6Y0QQGNqnQILjYZ/qJQC83+Xi4uUQ4nz6NX+hywE6+chZOcvCBNzyfeovZ/TarET6G311JswAbgJ8vG+aTbHckO/kL/ttGbfOMjjN9RZ4E3dQt4iH2lhPcjCV38rwxQCSSSlt6o9OzgNKxxNm+Jqdc+q00ZjudT+2gKD7NIE6D3obkNQ5GdAt4Wx943d/su5WVwMmGSM7mkmJCYQjoJneJwCIc+QUyZZRIDAy0PW9nG53n+kcWl9PAWaPe/ucxqiVdg/riyHwa5HamFqWp3H3dxp1Bxo0LP7d00U2Lnb8U1+j8+kXs5qcHpkid49zW4/Ia37F0H6/PzhqO+cGdGl8wxs+24x6Gx/r0FTCn60y2aL8FvmhY0m9ceQecwYBLRPEe1u/UmjrufTCmdOJV83ObekV5XCyJ6bT2blTddtu8n3M9GVMBpAO6g9p4A5r+zve1+gyjc3+83mDEf6W37k7NX2a9Jg+oJXwijQ23prW8JsBQ2jJc7vdr2nngE1vDmu2uukjP02b7zPPJv0wv5r99T20ybyXY0qe8zfT/V8G+HIat9+3udbxugIWRl0EfxxYo24apK3op2WHYuxppBPdkeleo2RaJsBhJaOBC+1NGSeAOSmPy5aCbwmb6W+KMRlXt8dMz83NzdrtjiNe78idFvB7A0cUeDL85wKr6fet/p2qlzRuBQ/OPm7Jj2XBY8vp8SajrNc759u0uvvawJ4BH/nXAoLmWLd4mXE3GGjBYTOEW/JKJ81+msexO373NtdH8oiIrNujs6GztIOedNzGm301UOC9HiPbnDyf71vg2bzIuDYnuAW6bFP4O9s1qGg6MY1tG0/qCW13o5dZy6y5YiadspD6ebgx12UFiKReZr+cKfMSA/OMa1AzK9YKwTr753umzA+B5ta6cgI4+sXwzIDDANKySblu9oHtsy4nhcJT6sHFxcuJD+2YG8qJ6Qu/aMsM/HlvSyRNejLNQJpfBnNJnNlPus/WM8tCk41T5WzA9/DwsNZaR9MvXIQYpSIh6QAPS6WDyT2Jmhl1cvDcyYCNtdZhU8bz88sGjaurq8N5Wnx1CY0K62qMo8A3mjlwjDR8r3nCAZ1SyVSK8I90mc7mIJuz2AICjccsWVx7c3Oz/vjjj6M3GvDVaROIS+F6rSbABtFTsTHy59YYt2cnegzqfL8dUKuzGRkDNhrgyOzWuHsKLLLn39ieHexa61VkvdbLmVFpJ+PRNmXYwJqHljlmDA2+WGfjIa8xY9MABHmadrKLnOPBnXe0JZ8+fTo8c39/fzh/LADChpq0GnhbjrzAvAV0rJtnafJ3TtNtATXbHIMo05l6mkyHXmZH8+fsK5cGkJZp+jU0eYMSxzBtu5/OvGYq9unpaX379u1wf7J1t7e36/r6ej09PR2mCDmlyY1AoZM7eyOHfptFs+G5L3r048ePV4kGnnhh+W/On0ETf5uWunBdX3hlmeOmijzDOj0FnGepixwbAuFmmwgSM67ckcvM7FovS00ICBu4tE1q+kld4Lrdpj/NltLH+7xI0tDGLoEnMQvtYtsj0cbLdJ1TzgZ8KQRo03UzrxG2hVbtPKa2LEQWSBs1r03Yqpt0TwCJhtwAY6sfE8h0/1maMJq+c+7fKjbyro8G19nJBjynNlzv1F4Dsqee3erD/9+l9SG/55NAILzl51r9uAPryymwGke9JS+s65Qs8I/PNKDAe9rff3WxzrbfmqGdZHgL3G89s2XEWW/qcTZ8q18TPW8tWzbiFB9/p2zZxsm+8pO00dnmj6CHGR4CCb7SK/dS5zgO9jP2OQ0cpDA72GY6op+cjs9zBrkESwT0pJH9oQ42wJ7nPS17jh1n+5aLZm/sU6fCcW+Zu0bPW0DQOf6wfZI+ttdo8G+tv1s451x/eW5507Esa63DovyLi4vD++9I3MPDwyEaPgXYcs3nScX4rnWcgUha3IaZAm3hjXBzi3c+J6dDoeX0Ndvh/4w6GTm0dK6j6vTL0yEpU5o897dsCGkzzXS8LL5uuu/u7g4Zvevr66NojNEMje2WQtnoNFoY2TF7SJlxxLkFimlADf4tT+b7ZESnPwM9Tiut9fL+WK6L3e1eFifTePIsQz5PObK8JTORjIajSUalBpTkaXOYXIvEqRbyN+MXGuls+eaXOFdmyScQ0IIKyzNp2O12B5vBCJp6zcxH1qNm/Skdeo72CK1tjRZpmByagZyzfS36t+6fI+MGGhxr3ktZ9wzLVBqIoT639wuTVyk+o862lNOblJe1jjdqcCqWb9fIuXjJ1F1fXx98FsEMl1FErpk95XrWpheWYY4rAUvk7vPnz+vh4eHIR93d3b2yOZy69Ljsdsfr2kxD6595nzqpH8wAtnGzfBlIWy4aCObmKc/+rfWyUSq2JuMZG9PA6hYgpF1mNs88sS21XWTfPWu41rENYFY019IX02lw3QL8Lb/zlnI24COw+/Hjx6udllECOjdGKm2ee62+tTwljjDOK3Wyo23QUlcEhtMhu93L626cITQdNJA26JOjJ+BznR6kJlwWiK0B5fO+dwvUtXr86bpyoDKnHL1z0XzLbxMgpcNvDstKtdu9TBnQUU/rBZtymWeNN+TdKWfbisEHdYMyEtmMjBNMkXdeM5trAd0EQ5FrLjdY6/h8L+/Es/w0ujn92pYytCnOZqToqKfxmgBfGw/X0epMkEi6HChcXFys29vbdXV1dXQAql9GPwU05p1/J00GxCxZR0Xdcp9tA5vjm/g7OcTc413V072u28947E1z+k/bnvsdPDdblvsi+86eURey65p/9juUr+yiTX1en0x7zYCK6/4MUgNYMoWcad3oUg6EXqu/Us66kb571zKDI+uPeU8AlDqZUDnlP6zXqXfaTJFiYEoZCR35DADNOXzMQjrpMdlm0pcxzfiSRx5jYxb7RPon8oH9p18/pVOUZRfqPfnz1nI24MuaubZ1mATSILK4sxwsKgeVfa3ZgDIKYWSQOlpUy7Y9b866DUrOdfQUGP45Vd6MVwNGpxD8loDnuoV0y0lO1xzZ5ndu4LFx8XhvGSFfm2i1YpBGK+xkbLb4Ojkz/2/gYLrsiGkQ3F6AX2TXxxo5M+XpJbeTelNnDlLluLRxOFXfxCNnPf1p2W7Aw4ZxKlvy4L7wGeo6fzfwMy84bTf10bwwnc6WmDbrgeXTjoMGv/GkOZUWTBKMsG8NYDU58HjGptImU86YwTCPSaPtPv3Clj30/ebtWi+7uB1AeSZhvz9ez2f+eVe9++TZpUkmycMkUTy+LK4n/ZgSDFvJlKl+BpF+NvaJv3kdrrP1W23TJk1yGyDovk1t5boxSuSBn403lGfq3IQNKBu5r62Ltr7x+CnT6+dSyGdjn3PL2YDv/v5+7ff7Qyo4whwi+HaNtijVTsWdovNzoeBwsFMSJWUAaOR3u9cbCEJjqzvOMvQzpcxpxUajnQPr9rEbzH4aQJgnzi6aj6ccnnndfqfTM23hIXewRdkSdWV6cq3jN5Y0w2ElbyBs6qenDVgvo7LW78bHcx1b+04nR4cVPnAq2orJDN/19fW6v78/Anz5/vz869iWLJVg/whIyItkNRKkZYc9nSYzjjE4U6aVY8TvlIX0yfwz7wiAbFwnsOesOZ2qx42/pe6c3ed60m5kOLofp5c/T0+26RyDcfKUvJn01ms3ba/iIKJnDAQ4Hdem+sP32EfLowOFJvPNOZmH7Atnftjf3JtnvSGAPiXjnKwn2zb9dO5N13a73eHtLOFH6kmw5V24zhxF7q+vr9eHDx8OdSbzy2RFeMzMfY4zywa32I+Li4vDeaXODNqx09ZdXFwcdvx6eQyf5RKM3BN6zafQ6/bTh9iUyU/njz7XY0J7z/Hm9dTjGSTqMN+gwiA5tDDjStm0XKbQDlHnfKpH6mQbvJ5nKAuUKZ7T6A1MU7Fta3w9p5wN+GyEJiDxX1FobN2+jRcVd61jYEiBbI7NdRHFb/W/gaf2fYtHNqhbTvO/stCBEfDRwPMYAxqRBtLWOs7IbfHgVL+3eO77T7V3jry6vSkS2wKKVE7ewwwAp5mc7aYDjQExAHG/GDxNhpbyttVv3zcFPFNdrpfg2LJ26tmpUGea8aQct3oJzPhbsxGUA8o125xo8PdTNLX+2179rk04ZYva1LDl2zrP787wkebWLwYv7Rna4ql4TCy7DagyUWCHv2XPopt+pR1pbeCt2ZC06/V3BnsuDDZpF1jyLP1h+s3sGJ8zAGO/c51Aps2mnWPvt4p9Tiu55gwerzc9shxPcs1rzRbw0zaTvMoYcaamYYpT/PifUc4GfIyMrLhB/8ksBAFnoLxJwgJMRnKtTe4ncxyptHQ/X3iehc9uy9ENsyR832EimfTh8fHxsM2/DZYFfq3tYxPoqChk/LOgkO/t0/W3dlMYNTTwZp7yqAUaM0fE+/3LmUmNthhV8559o7KEnrzgngbHfWUfJhAwOR4+25R4coAcK/aL2To6gJubm/XXX38djofIGp5k8pLJcdSdbB0BJKPkyGwWp5Om1q/UZf57fJNFNy+dUWvOkcVOyWCqFRre8Jk7xSk/rR/8TrCc4DA8v7m5ObwhJpuRKNN8hn12Ow348//URZATG8FNJm0ji21p2mPWg3wif50BzPhRTqKXfGOHsz20FbbzdpDOPKdN84n2vfGx+Q6DN/LbYxOZSYYqJdO8Bppe+xoad7tfb8/49u3bkSM3QMv3ZIn57uemI23dHm0ONzolsxd+xX6wNN5kM1KyitZN2hHbWMqpx4mA2eNFuWkzEt+/fz+a7fDY8BkXyoNtD5+ZsIdlJnWycLx8OoVtXtvgxjabD/HMJGmw/tj2+uDsc8rZgK8BmZQI4fPz8+H8Oz5nx2cim6C09llIBzMlu93uAE6TOo2TJMNsxHheVNY/5Syn1JU6eK5To7OBl/zWdglZsQz60lYTCrfT+DPRalDp+20AYxhjuPK6olzPszQObQop9zXAxzHgvbmfYzYpO38jL2nkJnDcDMdaxwtk/az5GPkjwAvgi9G4u7tbf/3112F3Xoz4ly9fDjv4Unf4nEDFYCvZP26o4WYlA5cJyDbQxUxkWzvGDILBT+ojvwiIycsG4D02vp/riRktN1nIp+tJEPf09Ou8tufn4xe1287Y+bQ+t7YbHc2Ic2rQ+h4dyML6BAKswxsHGs/STu7LQeqRk4CT8DVylD9mvyLf1GHyluvMKEvmEbM0DehxDMhDAwjWaTmgfqSe79+/ry9fvhx4ann2eAecPT4+jr6KPOCawK1DqLnRhD4ifeb69IB2JgIMzCkL3Hj07du3VzuMwxMDxyZbDmZdbBc8pUm5Dk32D/w+nbln+tJ2A9T5vQUL6Uvq2wpuOF7WW7dju9rAXvoaujwu3jU/BTtvKW+e0rXik8FuvEVyW6UZ5Pa9CQeZRYdCR2VhdPQRBYrSXVxcHB0o7TajTE3QWDcd2ZaRoJLbATdnPZXm4M4pWw633WuhJ43NqG3VRZ66H1vl1D3NILR+bNWzZdT9F+Ps+6ygkTWu3aIe+UDXKePTIsC1XtZ9NbkML9p4nTJQ/t+BoGXiVGBC/vjaNCa5vxli12daPI1i4NAy1aal9beB24lu/m8HstZx5qHxoDkb1+HrHk+3R8DXnJjt7gRWW9tNNqYxMq10wu6rs7W2n+6nwV5rj74h/M9vPBmAGcE8y+sMDqxnudc8mXa3sh7Smv8DPtm31LfWC2jimlQ+b2DBe73+3XbMswPn2H7LEp+dZPiUj+H9LfDgeDa9aXqeMZw2yrVim8m+GgTb1jWgynr+Z5SzAV+majPVtNvtDtMfiQIPlf5/39ugkvkUmlybjJhRM89hy18yb4lWLi8vD2drtWhzojELcXPm4G73cj7abvfr/CPv+LJTCY0EkHTwVmyDUfKh8cU8sgFt6epzyuTgqSzOpjTDltKE22WSE9dpgN4M4NSPPOPrfj9myhZgYR3etcap1AbaUnLWVM7S4lRbZC+6xcwLDbb1J7/FCfANKHRQpGcCLwYHEwB0toe8aeDNxt4GlM59Ms65zoxvA6Kt3UxBegMMF2kTKDfgR9nzJgkX85ClgQ/Wz3YaaOKGoMiJbQt56uxa7vNGBgfPkZ22mY1OkfSS9+5/4yntPJcwTADNzxoU5m1LjefmJwGbgzXyPGPNTX+5lvaYzc993u3rDSjReQOVlOi+nwm/Ly4u1pcvX47kkYX3epPBWse7fXMkkbPcsS22oZySNDjkmBI0NQBvWvPd9TVgnHvDK2YNQ3f41BJXlD/aVD6bP28KjG40wJ16oivEAt7Yar3jWFM+WffvlLMBHztlx0HnkzINqiOEtY4dbTrtaDJTHblGJ52/KE3S0rkvu6M4mAYipJcCFyNKQ0GjykHyZ/jlqZnmCCl0uc+ganKgTcG36m7lHNB06loDfOcUOwPXuVXO6ZvrZbv/mTI5cU+lrvV6ytPGnU47RsHZJkealhdes/N2acZy6tPUd4PxRotlitcNZtozDUQ2OTXNNuTT882BNhk0QNlq+y3yv6VHrb5mA7boYJ95D3lgGW39p81jXS1jPvVx8gemNbbWa7MnG+j+51m++rMVP087b/pJT5N7Ak/qsvnIuhx0OWvYeGR7kGe469Mza83umK61jpdbMCBq9091TvZ4sg+u+5RvbDo49cf3cBZvC5uQznN8q/tl+0Ubz9+sk3xmoqXx4i3lTefwRfFDADc1nBpk/sZ6tqYvmErnPVNHA/qiTIkW80JovhfRRo07IR1h7/f9pc1856MHOM+w3zGQTWnCEwJRLoh2sQHh79PnlrEMn9lvXnObueaxaGCm0cPrzExsTae5bjudKctoBeExPgweLJuTrHmM6aRII9/Y4D58//59ffz48cDPd+/ercfHx/X58+fDmiI6kd1udxQMTYYo/OC7OynP4VMKI1D+OevO372AnEaUfWVEbVn3uDR5I40O0Jqekg/5LQv1Q3cOl6VDJu3pS96vS8fHDBazZgYBznaZNgazkwNqusVrHPdmrwxeQleuO0N+Sm/YL2b4mcGmPrOOxqdcc2Bv++dzX5s9pC3g7IoBX/rAvmUNY/xDfAT7Ynluy3ioB+ER26W+RA6tVzyqJXU0AGhg3mSfhXpImi0/lhd+tvG0HSL/W/2TnJvXod+bIDiGrS+sb5K5jNFk58lvyiELx832iD4+vDeN3DxEMMhMIGllu+7rZCOmcjbgy+nvVMhMETRQkI7R6ZBQMp2LQc14rsHLwtcmmM6KpMTY0/E1I06wwwEj7TZO3iVoRfICaoIT8qoJEEFJG9imRG33YGjdAi/pmw2/HezklO3Mt4TQdFvZPS3Q2gi9+b8Z563iaSPyl3TR+JBfbDvAitM0lLOcE8X+Pz//2ijw8ePHwy613e7XZqOPHz+ur1+/Hu2mc2Bl/nsaYq2XaRobR/YpjjHghoCADiJ0xAk1sNyKeRmeWU8ayGNpB5lOx2ZYn+LQrU/Nkaa/DQSbN8xC+XnupE0b7dnIAmmaeNH4RNBlebV9yn1+a4R1PM8asESeOR75zc5tCxizvgbkuQ4ussldw6eASMZ42vXpZy4vL9ft7e0B8Bkkpu3U3cBHZp/yGV64LQI+b0qLH/TmDvsc8o+FiRPWmz7aRnKcvJHQsmhfQj6z7QZumt/Ic7QfTKjwFAj/edajFQM+z7TkHvp49n+tYzm0/eIzDjbTFsEqbUp4F5vL8bfN2QJ7v1vOBnwWpLc+499onCxQ+XRK3wPHgbDhSDFKdmF7FNjp02A1vzVFY7vNWJ/DnwlENYMdOrba3hq7LWWd+uB2WtuTg/X3ZsTZzkT7BERbO+an5ag92wDF1Kcou41B412M+1q/5De7oAm0qCPUCfchpR03NPXd/DHwbfxzofNjO3z21Li1QlluTqPRNd3jLNoWPf6b2kpp9sWAyW02fre++97JEfsefp905xz7s0V3szlvsS1NdxofTO8kC34mQGYrIOEzLWve6swnx7g9P/m2Jiu+RmBkkMd6t3jRbBwTE42uSX5anbnnlG00D8i/reJ+vdUvst/sTyvsu6fD3VfaI7ffxoPjZl+TMTGPpr75vt8pZwM+bvFORBmnxCxfGJdrZFCi3wai8pnIJ1F5fs+9XADrLAMRNBkU2oO6vXA17UbBHh8fjxxv0PdaL9mhtBee2MHneitbxiu/JXNEJWdpTr4JLvl3CoCYNrfTlMYLTZtDSlYo9zPKmp49ZRBYP/mQ5xrP2Eceq2AlZmn9ieI68xT+ZkE33//oY12S5eO4MJORIxTCv8jo3d3dkX6lHoKa3W539PYA9svjnz7QIFHfDCBTx37/sjnKALU5lUbDBHZIW+7zAbfhi+sx2NvtXt6GEFvhWYDQ1nb6p590Uk0nOdvBSJ31W5fYh9aPtDud/9WmvNpZiRzTluEw/1m36YlssY+RR8pF01/rPW03eUNeUp9py23nWW/uZzCV3zzbtNZrO22+5HnqUjI0zPQyo5oMvceAY+MMF/u+3++P3s6RYNDFbZNG6mXa9GwAZzsM2KIH7W0o8Zc5m5Vt8s0fBF3kq2WB3xtwpF/MGbu+xv4SoLVpZi8d8PhQTixXkUPaPOoA68zYTtnPZhPajCH7aZ/7lvKmDJ/RqLMP7Rk/R0ZauDg4Bi2u08+v1Q/vbDS0YsGgI6bSehqCRm4CC5ODa7TQgKZM4CXOwMDJNJz6PtFjxWvgtNVrI+7fJhqndsiXafxSJgfKQhlsywOmvtnIsD0GCE73k48sXGfXzqhqQIbTRzHGAZbWm9w3yZ91b5Ifyrl5weUWp8aGNEy6YBpoawiCJmPXbApnBQiCTY8BbwOPKQaMlOHQaDvJurb+9zUGDBN4PAcEN5meCnXXfcu4h5/NZrmNNr6n7mk85zhRd1tdzU81W7rW68ODW8BswLTW8VEv1FF+TjyZrtGPedbJ/qjxod3vzOBaLwEVd9q6f6aZ9sR2nvbO8mh9IS0T6GPhWBJYcWxsN6nPLQjhtXZMzkQT7XzjK9uzPadMNxzj8ZrkmqD1LeXNBy+/e/du3d7ert1u92odgkuI4REqXOQesEKETYPFTQ8UDkdVPCYm7XrunXWQtla/d1na8OR/Ll7PvTy7yALRPlkfedGyJI2/TZmb02RdTVAmgW2Gd2pnos8ghHS7j7m3TWc0R9f42BTIdE0Grd031eN21zpex+I27IQYHdMwkM8sT0+/3sMZ0OLjHtgGMwwN9NnonirMRkQ3ycfwKRn/tBmdpMGbeNh4Rcdnnkx1NUMaGh2EUJb3+5dpQB+BwLq2gsfUYbtDXjl7Njk961yTWxt+t7v1eU7hvc7eZnyb3Da9ZqFOxe6t9fKWo8gSgyHbhnYEybkOkLa9bY4LmG0BHduxfbSM2Z4b8OZ+8sz2lb5xyvy0AIAAlPoTP+4saui1nJAH3PBEf9vssK81WzMBcNJOWppd8G/e3EZf5nZ5nXpEP0U+Nzo5Vk4INT9jf0a/O9njpk8e93PK2YAvg3t5ebnev39/+E4BT3EnI1xcs3R1dfVq9xynXr1BI53lgvGklqOwjLZapqcxyAK12+0Ou7a4mcTZxt3uZWojzo90Pz39emVT0sumxXVF2TJdTF41wMDf22aN5jj5HFPuVnAKqzcLcNqxGdcJXFEJDIxZF3lhGqeMcmvzHJrIl8Y7G+zm6JtzZtYj9RoERD6cGSZQdR8C4uhAmpPwJqiWzcz/jsonEE/ZDFDNbjPKU9pf68VBkMfN+JqPBF827Pxt2j2XtlmfATkdYMvIEPA560Gw0NoP/xu/2Qc7Ro6R+cE6Mm5T+/zedJv/k/8TEKRzWutlI8NU6Mhot+kcTZ/fELPWOnotpu0/nan7wc+pZIza/ZYXgghnF2nfTFey8UwIGFhs8Sy0TAEPn01fDBBDRwNx9tPkJ+0VNzRGrjkuHs/JbhrckJ+pi7a9ybLtlf08+cBxsawY8HvmwPxr9Dcb1gCffZWTQu6z+UCaybv/UsCXBtLglnHg96ZIZgQHMQNBwNeE3Rmo/DbRlfY58BnwpmATyDLwaH07ZWwm2s75baucExm8hZ6JpnPosmJv1THRODkvy1R75pwyOT/SeGpMG40TDc2QnTPtw/andibwbdl1fWv189fO6W8+JyfbQEX73CrTM1Nb01hO/9NhTfWdkqlmKwhuTj0z0dOCVhePb6N7iydben5Om6xr+n+r3S09a3rv+07Jab6fkrVT97TxmmSEvKMcEGCda0ft287RtXPGwzaFdJ6S+WaLW39s3whk3qJT/vwdf8Y6SQ/pJF1TO1u0n8vn3ynm2+/y4GzAl0Wjj4+Ph+jOU5opbS4/iD/Zs7Vejgn48uXL4eTpLAbOuqS8jWC32x2yeUbsROlJX5sxbdrDaWnTmnfw5r5EOE5rB7UbnW8d1MnCLe6MUPzGEtbjyImf7GMT3HMjg/TLxfTQgIVH4Snr8l94w76kHhsf92FavzA5btKz5Qwzbl5u0GhgFNz4yWds7Cf+mWcXFxfr5ubmsHj74eHhKCqO/plGLkXIvdHLi4uXhdpPT0+HzSFs11E9eeXsko3RFAnnu6eBqYd8zkdc5FnyLDLa+N/abZl6ynj6HBtn+WPba73eWELamyyy/5MctsxT+sop8ukIo0nvaGc8FdbqMA1vBRUsychSpzgutuu0c6yfR1pMsxP5n7t0WRfHgbaG/W72xbq71ss6XPYt9yYTFp1rYM1TsZxVoazH9/Gd7yzWSWaaGmA2z6zrDcCZNxwTy1LqibzyTFKOs/XRNDX9y2do5gwAx5f3TxnS6FRsUI7g8ZiQrx4b0mO5zOY59injZT43uSSdTdbfCiDfPKUboWhM8RqPdCpTQZyOiEL+/Plzffr0aX379u3wepq1XnY4ZsqIBiOFU7ueem2OvCmc+xCFC500/EwD0+Gk2Hhm4DiFM9FlA9zSwrxOwbQSxImSLvZ7MvIu7b7JYU2GxPe5ztDK6Y6mlA0YTfeeU7buI8/btCG/04kYDDRDS7rbbnHSkOczdfr9+/ejejmFRKPN5zleNESR9WTSGzBjv7zj8BRfaQz5jI190yHeS3omXjVgwM82G2Ajy+9tXCm3nraJjscu0gE1INrGg31pwDCFwV0DfO6jaXf/abM9VuZ9a8M8nIp1inWYn6y3BTcMRswf82NrjZgzqK3f7blWv2Wo8axNQZtm3sd+twCMz9rGNt1bq29qI0jj0q0G+Mwb0mXQRRzAaUzWZ8B7CvC1PrO/b/Vx6Tf9Ovu42x2fY7iFI84ZCxf7Ctc7JRJMy7nlTceyrPULiD08PKzd7uWwWRNilMtDMwn2uB7Mxp/AiyAnAxPBvL6+PtTFtxOkDjsX9mWtF4Gl0fEmkv1+Px4B4HUZzAgFNO52u4OzdsSZe51FSRsNQDQlaIVOso0P22CfpmIDQRq4tqzdP/UrfDpVmnJNBr85vHMUowEzy5J5wOf8vRkjR/o2DuyHjSU3aTCz1GSHRsHRqPnZeOvnXecpQzXxd+pvk8Fc9zqX/GaaWqGeUR/5zCSnU18tJ9bTxk/KFAFfKy271HR/0n+2575MbTdenFPOHUsGJ22TngF07IKPnEpbkYmWIXPbprfxzzRT79mHliFm26axLZdoek75ZmabgQN9Y+MH656C8vB+K1hovJvk2+vHT8lPk8mJL55d8Hhv2X/qGsdlazOG+77l75ruZ2aISTEDdNPbstT8Po1jPs/xnSxvzvA9PDysjx8/Hk0LuTNUxv3+JWWa7wFvPFGc5wjtdi+omoCIu3bT4Wz+YOHZYnzhdwz+Wi9ALelzT3dwACksFLr0KffRQLH/3759O2QKuZiVffNgRnBtZJrBoiNtQpHrnEq3wXXbjR6CO/YxvJhK+tgcYwxay3pt1Uclac6/RZtbETbpoqHIM5y2aFOWSd2znpbttoys9TLdwd/oBJKBi84wGqVONecTOSYwtGzYkHOKnQ6inYU38ZYy6bH0OVlpx2NCoOf72J/wdXI41DWCjiZHbM/657obwOBzXvjepoPsGHlfk4ctsGL+83f2m/VRV5pja/VOpTlO6lT6xs11HvdG7ySfzswYdLG0GaipjwYJ/N1nLPIadZJ67yxgirOr7kP+uKSIvjF89CYa6qZpjJ368ePHIQnhMeJvlNEJEHo5k4GL/RQL+2j9Yha71c3NUfbrlDfq3fX19VHAEfvJ7DzrNsgMfS1QyYajHz9+HE29O3sYvfO4uvh8Rc+s8nrG8lR586aNhrpZnOGJYY5wxzlxF2o7+NBgxI6T9xBM8N4GatyfSYgnwbRQ2tm1+xipndveZHz9vdUzGWUr3GTwtgrrn+pqNBlAmq5T9Ez8mpz85FBT10TvlvO04/F1GgHSQfr4rDNvWzLQMgWtjXNK41err/HC49jusw6c2zbrmYCNx9WAcKrbfJ7odv8m2WjyM+l26yPvbbZmcprTPQQap9o+Nf6nnt+qc7pO8My+tzExGG1tue8GfWttB6FvKQ3I5nePZ8rvtu2+NJkjL1vWehqTBibze+sDn3MxCDvXBm3ZUd5j+37KVk3+erJN4YV98pbupr1W18TDiXbq/jm+cXr+FL0ubwJ8YRKRMzMzLHRSfD8go+gAvRzPwg7mk1GQI/Dm4O2ICBK9/oWD3g6OJVDltBrR+36/P2QTg7j9xo/b29sDMM3aRPK08ZkAt60TmaZk2v+h1dG9v58Ccq5/yqqwPvZjrfVqmn5yPvy/RdunnHwzzDTQztqQFz7Habd7yXCx737G2YYtx2Yw575TPxIoMcKjPrDPngILbWwza1NTt8EP+0e9oKGcjF2iaK/baYZ5y0GYL02/t4x1y65mTC2ffN70OePs9phRINBw5iZ1+VgRZ3vXOs6g53ny147CPGuy4bGcHFFzPi2L5qAmMsp+UYavrq4Oa7lJ33Q8RZ6dltLsdi9vNCE98SXRl1xvutJsQPprXcs9rMvAi+OXPnnM2K7rb3plUDIBNva/rXFM/bQpnN2KjYsdo73xrNVutzu8A7z1s+lSeGd9c6Ys/KfNtmx63NpYGp+ETr+j3PVMwI3jF16Sp5SdyZ9atm0vPANDX9Tq/Z2g4k2AL8WC3hZjkslMOXuQkmamo7IBmNLwBncsBHdO5zMVnEJQmkLjyHWCebVL6kuaPIvr0y/uYs45hHxFTgNYzchMO6coQM14cxyokASRk9PdAnpsN5+cNiPf6OyawzafG93u45ZRmUoDNW4rhbyhgeRyA4Ndjmnq5uv6UiJHbod8JODjc+6Ds36nAATvj3MmbwwWOW1GZ7EF+thfrt21jWj63MbMhXKWYidqoEc55HhZjuk0uPzByxForFNnwEn6ah3n/w4IyQvzhUBgrZcp3+bQt3hnvbdj2wKAjdeUzabzuSc6cH19fQBoWYttm0z5pg1ttHHMI5ecLs5UGWnis5MtSPFU4XRfA3xrvV532p7hK7cInNof6bae8i9LnDgdGj5TbrPWjPYsfppTjWyLwCQBo0GMaSb/3Q/bcduH5ivaONj+5LP5G+6qnsaUxX3xMy34aWCVfWIAEFkJwHYbzSbQ9rRTNLbKm9600aLGEDcxLNe3DkmlM3D9U5u8f2KsaSAtjV4LlZU0hj3HAjCCzPNE6onoGQHRgFk4tni4VaYxcT/c5jn1bLXXFK1lq6b2TOM0bdn64PrbvWzjd/pqo7U1Rg20tgjMSyLyW3P4jZf83kBrA5ETP0xzA63mve8j0DJvtsbpnEKjONmYCUQ2m8DfqXPN9lCum+M2cDLdE5/p2B21ezy9Zrn1YyoEia1fE81Nxiceu73YeGYxWyEfpuw2aWp0st0U19fG+xxeuF7X1QDwWi/r3H1MkG0k62j8JM2mh3xtPs3jHj7HX+W6fW4bi0bnFHA1mWn62ICsy6RTls0JF0zt+rP5lBYcT7rkspVdn2y3efIWHjm4Pqe86Ry+GKGmNAQyjJ5zzYiaGTVmzCJ0yfpR4BwN8JlEYwYeHHC+4i2fBLKh25EKhWG/3x9eGO8oMkg9ypU2MjCXl5fr/v7+ECEl28dppi3nk0E2T6Zn+Jd09n5//DL21G0jxTpSN0FP/uzwPY3nPnhMwu8crZNz5nw9EWjL2rQ2zDeWZgi2+s1nGIVPwILZIctjc/ItmIoMWfEnxxB6rPjhn3nSwB6XOXCnYOpmIMOxcXuTDm7xvJXJ8FkWzbMJELWsSwMzzGaGB+aVbQX7Zce+2+0OmRPSze9c3pKTB75+/XqUic0zja9NZ2I/uamnLaewfeFvU9DC+sIvTuO6HvKDmaKW1eD/sevkbQNjuSdZM64R55inz40+9t91N/tMGxj72nxJaG6ArO2WbfoS/9oKM6nxtbRXzFKT3+SLZ8FMT+QospT2Gri3b3ZwRH6Y/x4L8sptWA7ZNq+zvdTdbEpbgtKmmllHa89jSLvR+uw+8B5Pm/OPb6V5eHiosuHy2xk+flIpm1A2w00lppJEIC3gNqqTM2kO0cLC+fcWibU2OSAEXV6DQuNLniTTR2fYBnhSatPjPrX7Wt2Tod9SrFa2wGW7r13bUsDWJ9M4Ofb2rGmewEejoQGMiVaOv59fqx8xMgFH03XqN9cTGadzmmjm86zXsuO6W5DAT3+f+jjJ/7ngkGPUbMHvPN/af2sb5OVEI+WfYHuLh02W3a7t8gR027On2p6c1Dm6bF60NlgnQdhULwPr5qfY5lbZshXT8+GzgY7pblnPplcen4mf5J0B1DRr0MoEVlr/t2b6WOyf3ZdTtr7JxRaYmsbb9uiUXLfAe4tv01+rf6rPPGmfzU9ZN84pZwM+RnJcu+f3vdIBOLoh4W1BLo9+SIaPHWVqOlnBPNeYtt+/Pv3eJUzLe3HZFypwNmawjWzAYPaMDp/rUjyNl/V+dJzMEJ5rmFjnJGChOZEfwZK/20Gd64i3wN6pQoCcuqx0jq62aGlG1CX0cv1onvOZd6GH2YXwxuu2druXTDLpiA5sLSfY7XaHRdc/fvxYDw8PR2NAPWrGrkWgif7JJxrtLXDD33xcSCts085nrb4ZYZIr0hK+U7cawGg64bqaE+D3jDPvd4aCbZH3W9kqAiNnDFmc+XLftqbd2u8ZD69xnHhB2drioflkXrjuvKEpNtl6wbVwPOLK48opyWkaknWutarONVlu6/AmnoSuy8vLw8sBsg5ross8Z2nj4LVeeX4CZGutAw3MVpEn7Rw6grMG/hr4tC74Lwmb+M3UO4Eb94MyEftq/0rauNGO491k3f1OYbBFOvIb+cLZDuqqdXsC+cE5Wz5y8mHhwVvX7631BsAXx0aGBqAYbdLQ5Fkq9Fq/BNM7ubhwkY4qz3DjBDvMBbY23IyWpzR+BJTTHQRgNByeUuD0AYU6PHDkRWNkwMfzCrcG+xxwZXBEcNLAghXC182v5mRbmQwcCw1C64OncfO9KQtBrPnQHId3D+73v85N9JlzHEu2k7a4yJlnJ3nqz6AxtK31S77u7+/X9fX1YTqPICn0RvccxTcjzKm2XLczOFWmsY4h9r0O9AgAPK3pcWvthG/u65QhP9Uvt92AHOtqMmSHMhlfjq3lyUAy9XGpiIGaHaf71UoDMq0e1kf58LhYV837Vmc7BYG+hGfLBUCZfttR7yBnlo96xuUJpNljSD5PwLoBvvipbGTwUh+O2xQ0TSCq8de8p63j2FLvwpPm52KnSOukR5O88BqXE7CNyRZPJXXx+RZYsZ+0v83X0WY3H9E2xLA0fBB6Eqyv9Xo9tMco11oyjPQ0LMDlC+fa8JSzAd+Wo7czNOEh0ALf1nzkGa4NaMaZ76gjDS29/vT0dIg0OB3blJoGJWCs3UMB430WpBbVkJ+5L3VymuzUYFKQSDsLBWwScn5a2LaKHbdpmQzDBNRMk+uZnjcf233n1M0+nwNGbEhaNNfuncD0ZGzbM5Mx5rKDRstkpFkn5Xu6d61VHZgNWFsGQTDTDLJ5Zh60fjSnvdW/Vofrcd9NzzlOa9KJqR06bbbTHDCvNVrtdBo9fi7FAJvtWdYN8k5NXbIOy0vjKe12q9d0s41c29pVbL41XW18sqy2DLvvazzZsn1eNuF7m13YqnfSacvHFo9IS+urQZGTGFvBwjnlHH6238/xQ63O5iMmuxWcw/q35GnLrk9+7q19YDkb8DFF68xbrocoDvhaL9MUQd/O3vH4knSAWYJkNXItAG6/f51huLi4WN+/f1+Pj4+H63nORoMDQLBIJTBiT9ntdof3/nJ31vfv39f379/Xbvfy+jdmNxmBJrMUXnz//v1VFjNthd7J2JAu3j+tzXA9FtjJmfh3ZjRNYzsiZFr/wfa3gGzr65axMv/2++PzrpoSNiBI8GIDvNbLESyMQN8C2jJW2XTBbG8bD+oXafT6r+ZQ2XefBzUZnOgsaXCQFt7mL7aCSzE4BUJ99rFNvm7nwj7QzhCs5tPBWBvbLQdp8DMZ+i0wRxq2ztlrx/bYYZJ+Z79pr3gEhXXFOkUARl55vJLFjs1KW8zWccNdaI9t89Qt39ZkPaXc0794/FyoC85qtZ201BXq8LTExDKVGR6e/deWM6VwfOm/HKyFdi6d4rgyg86xzH3TjIn5R1/KcefsFm2g7aan0J+fnw9vfogdY1+3kiCs15k889JLB9LHlgFtNnfrvty7BS4JZl24YagV2p2M4bQMgPc3Gt5S3rSGjylz/jGiI2iiMZqye3nm4uLljRnpYK55sAO2Avwa6Euh0TQNk2FukbGdC/sQBed9bLsJExWEU9ZTJNtKM0QNoDni2AJ7/NwCexMIm0Blm+JwX5oR2Opbc16kY4uPUzuuu93P8aWz5FSued0AdDNEXJ5gkDEZrpStjHjrGx1jM3a+39OLloH8RrvgDB//ct+p6bPwektGJpma+ND+bw7EbfxuMZiZ6m1jTB1uemdZpwOhnfFzTVYM4gmi+dw08xF5CtCf6KR8UC+a86ZNpIN0IU0EoPke/0JQYxtvf7GlT66LTrvpYupl/8gTy15on4Cb+Tpdm0radCA72Zw8w36f0kdm+VqA2nzO5K9SLA8ufG7qf7Nvvj6NiXnEQt6061vtbdFmvrd7zym/dSxLFIGEkLgIELN4AWc+ZDllyi4kcmJU6DOEbIgMpixMjaGnEPYkiHGWOUohaxNZN9sluGMGMPeHPwQQNujNaTZnm365r+wDn3NU7j5YSRuompzuuUrKtvj/ZHjcFulMu5EBZjooJ02WXMhn00snx3MamaWbeENexrj7fbnmXRt/87vxc5IJ18H6aawZkJl+O2MCBjrEZrAo6+Rz27jAIMtG2cDBoIJBn0EL+2vgkzpbppCyZf1p8tR+z/jaMZ5ySu038nTiWdPttY4DFbfDmZgG+HLgb2wYeb7f7189S7trPXAGNLqUe3hcl32IpxCb7hmMWY4oT040kB/mn3k7jQ/fJ88162zHNpaZVq99J7hlv7lpkP22rFNOKP8cIz5/qrQxbDJj3bKvoL2a7L1t8uRH2nizHj6T4sQV77HtsJ/J75MvzW/RCxYDb37fkqtzytmAL+e88NUkKVYGDlyI5RQPpwPWelEAdo6d5lEtfjaOiEJP8EJB8tREUxQyszlIO40M2PPz89GLk9MOlY/OewJrNzc3BwMXJ2nHt9/vD2f42cDYIDcQ1BxReJG27eRYXM+kQA0QTNMkKVZSt8M2tpygHf67d+/Wzc3Nq0wsnUMDJM3ItKAhQI/g5fHx8dX5lZORSz0cc8pLSgtKtoyAHR5lPf8700ynycyFMyI0cmyPC6y5mWkr0xyQ3Iw42+OGMeoF+0ba+AzpzzXbnCw9YX8I7kw/36ZC3eZ9HDcCX5Y4F04lcnw9nqbH/PJ0Yupu2WeOm8eYvMxbhJr8X11dHV4hGbp4cLQ3R+U5AwxmFM27POO3JbC0w6r5fMuYkSa249kl8z58MsCcsntpJz7Up1I0cMlxSTKBb2qJfHv3bQNd/E59pt2KHjCxYv9jPvl36qaBtmmxXZp8B8ErPzOezpwa3DU/5n6Qnga6tmwfx4O2gBv3cm8C+mAibwaljeAYONHW/Oepcjbga073LcWI2IIz1dsAxVaxMjaBzH2n7vEUsK/l00JtQzyl8bcGkI6pAUT3pfWJv22VaWwNRs4dByqkf39rOUfuqNztWqPZ42fAZ9A59XuSo8mQTTwxv6Y+N2fdrvs318+AYNIN19EMX5MR03IKRLtdGvPmBLZk8JR+uE/8bP2Z9Kg5Fcqq5XZLt9r1t5SprrfY2NzfZIHXYpM8xZzPaRkLnW4DvBz3SYa3ZOFcu3RqFmeiy214qpn3n1OaTrCtXOe6uuneqZhuy6Pl1vSfw9tz5dZA7S33T3Xwc9LTxivygPSc+tyi1fa90XOuD6WOMftPmtnGW8qbMnz7/f5wllIaTzEwmhQrL11e62VRv1HsWi+dIYpOhi/35jqnjrl+hMwhEAvtu93uMA0xMW8r2vZp7vv9r7V8t7e3RwafZwHmefY1fGTUQMFxith9YN3NYBM0WngdPZ8CffzkvR63LaP91uuTwqVv4ZmnUHnPbveSmk+0bJDXxsHKyhJ587gxw5Ux4jQUozf30bRuAV5nnMwbf6eRv7m5OeIf+5h+MyBpTofPtXZJ2+TMPIWa65xCdL+akU/hOq229KRl7UMPo/N2lIf5nD7sdsevqmpZOtLvTFfjla+3McxvkUFmVKn3DmTs/DgGDgQaECB/WDKz4WnVtda6ubk5HEPFd+mS1ouL12e7ciztIzxe0bPJFzW9t+y15Q7MfHEM0odkw1rGnn+5L3XQbngK1XYk9THDR5loGTX7vhbQ5lkCS9NG3lCnKENuM783vzrZFMoa7WZmz0wj5dm2iDzlzGBbLrAl65QVXuN0OvvT8EvrO3lPOeS40qd4KV2wxqnMpcvZgI/KPJVTgkDhosGg8vNZG3QKLuvl3LkNV+6xYrM+O2PTPvWR0wdE5O19ud5Ywt/S96bMqXuLNhuWdo3t2Fjk01GE62ugx30ijxqAawZgUrStZ2xI7XTbvenjz58/D4FLm8b1OlP22wCGU4d2TpZ/Xp/40/jSDElz3GyLn6xnv3/ZaT8BNkaWjbYJbG3JxzTuXB5BGifAZz6QHk8HTmPAeniPnS/72Aw/HT/rmjJY+aSs2O6c4lm7J1N6BEsTja57kmuPr39rshN7lilL60c22tEWGmxuTTnHZnrtc+wtp+mb027AxrbQ02/NplofvVbOPJ4KZcH2I/zcAjfmDb9Tp1pQ1/qyRdta6+jVca3tCchNfKB8Wh4NfJi0IO+bHWr0t+xyA5utWE89BrSVnBVM/7ZsaejhM56hnEDxFm+n8qZdumyA4I1gh8av7Z5tQCbfvdbC3xuanQadA2yAaWPQwJjvI40tS9Doc/aoCTbv9e+T0T1nsNlma2+6321P924920pTSDraU/W7Diquo06CQB7E2Zx8oy33TEBzoic0TQY0dVqe21Ec5gPH3kahLdhuUSnbncCiaWamqy0s33Ie6a+dQtrmO7bZ5kQLnZ51ybziuiZnrdIf68YEDnOtOfYJvE/yw+u+3/Lh+1s9jW8Zh3Y0hPWEvPH6sPSv6VcLQp2JMmD2myhIV35PMLbWS+aS8kxdCahk0JANfhz/1EVehoa1jgEFSwPFDubo97ZsPOUndTc5YrsEOaaD91qOUl/qydo/B8RNN/lJ2v0/s48Gya1s+QfbkdxvG7PlDycbThq39KjZsmbL3Rbbp/23PE30tf+n3/3/VvJtKm86h2+t1+dJ5XRxZrVCTBQ3BsDoN98zxcHNID62Jdc9KDROKVRun2dnZ2ewN7XdhNoZs9QfGrnY1Q5mAnjhIZ2zp5W2wEgEz9MBvqcZk2Z0WmkGpl1vdTUloMGlU20OPb85OKDCJZvALF2ciXd42ynZWPv31p/Qzf8bKGzgY7fbHW3AaUaZWRI7UvY1zvDp6Wk9Pj4e9MmRLn+j4/OYJcvWHKidxRQ5p6Rfqefi4uKwWDx2hLI9GXran9gUZkLSr5ubm1cbvFhXM6LJYDQnwuttRy7rbPLFeqz7/I0BSp6dMnfOaHJM2iJv9qn9fn19fRjvnI/348ePV8tWQiedKQEmX1Npf5Dx8vRUfMWPHz/W4+PjWmsdbYjJfT9//jyc8dZsCc8CjF7c3Ny8sr/M9llOKEcet2kjhPXe4xHZJcgk3eFheJ97JnlpbfN/z3BtBbuNRvOdz3IMuVHBbZnWVtiG+0nAT9roNyzHbNeBwRY95m2zi60NzhA6CWEaWx35zWOaz0m/J0B6qrzp1WqNuBDWMnwkOoWgb63X2R0rgge1DdgETqbfm2Pn/QZ7LlSKBhj9PMEVB5G/bwGsVr9BJH9Pv04pm0sDOefQttXOKeGeFOBUPWm31UVws2Uc38LztV4fF9AMAD/ZRqPT9TQ+NtDja+lrm+owXxq/W31Nhlu9bxn7ySlOtEx1t75RTsOPBKHnTJdu8b7R/lbdOrc0UDbd5zL1YbJjrc1Gg2VuSxct2+S9syy0MXRgEzjZGgPqiQMZZxTfqvenypas2tfw/61xpkxPdmaSYbbn587RqakNjqtBIH/b4sEpm9FoOFVO2bSJRtJyCgiear89d46cGROsdZxcmgD6W+hjORvwZSMCTzxnxqGlTH1vvodQTxd4bQfRPTscY556PGi73e7oVWo2Omu9gDY+44WYHrAW/ds48Vr6HFpbVs9GzxkDOvHce3Hx8h7epsSTQyU/6dxdpjUSE69dGsiZ7nFdjLbNY96fa5yGckYqNLepFBvhUzy8vr5e19fXr/jEzUOWccv6ZATsJCkLLnRiT09P6+rqat3f3x/GNtkPZtKY4ZvAJukNT9NOk0uPEWXKyyd41EwyQfk90TEddfrvKcnIB+mkPDN7eXNzs66urg7tRQ62MjRcouLjSaL7zESf48Ts7CKblIu1XvSQC7GbTSHP853Za/6eP2ZmWS95lnqS0cvbgphxtLwYoKWd6MqUfWj2jhs5Gg/Jywl0sC/RhfQrMp3sWX5vdj5tUF8aH03DtCGAfLPdpc6w3RTLB4tnZMgb/u7vtt38nVPuz8/PR+825lszuAY6fOT7x1uxDNpXetaJ/D8X5OY367f1lc+eCgbaTF4D7lNg1WxNfvMxTa6TtLod8i8Z8lPlbMCXc7Ks/C2SC1F0Mq201C87nusGAewop3NoeOx8W9tUUjse983KxLZsuOmQyI+Li4uj1+Kkv3QinKahYaTQTFNyNHwsEw943cYz9TRls3M7VX9rb3q2ZXTNB44/QU2bLqVTMbhfqx/L4j4GaGRnawqnl9r0AkEMectCJ8a+2giY9zyQPO0HJO12L2drEfxu8Z2/EfCxPRqVU+DVY2ge5/cALOq49Znjnr5yvPkZwB/QwbWRW9OvdHItOGhGm/atyc3EG24ycmmOj2PTvtN2kPbQ5jWe6a/BR+N3psYtP7YXpD0gwWNuQMrrBKyTbJGH5ANlhbrEgGG3263b29vD9O4WiGjj1ewqeTFNYzYdZv9TqKdp59TyFttp+9tGx9RP1mcgl+n9JEliC6gztn1bWWXb4iZb7ncbI/PBPHV99LWWHbfXnm1tk4fs88R7j187qYF12ubZDr7V975pSjeALwaAi65tNM4Be425/j8Osw1my5il7bRPZaWip05OVROE0VG3EsNmgW/0+zlnCwxmqHQ2TBxs0s1nSRvpaIJL/jX+tz6cq4RbCmhlbkAhn5PR9G8EiJSB8CdTfBzr5lzpwLj5g207+my8a/Jvw0Y5ZUlGzPzM89fX14fDbNlvZjAmfk38m35j25Oh9hjyb7/fHwCojT372+oy6GE2146NOr7Wy2YYZxi3Suu3r71VJ0LbxDP2weAh94V3E60utMPMbrCtfG/ZD7fta6nDwQT1jXTYORNUEHymDt7LNifeT8DBtsH8PUcfaIsbD0n3FrBiH8gXzyg5i28wwXbsK3if9bWBRfLA4MdB7FafWGcDex5HL+1qhf3ZmnUi/ZaZprO2L1Ogf6rfbLM9y9+ajyCPnbjh86STz56StVbenOHLieq73a+IiWdKUYk5WBPQILr14PP+LRS81sv0L8FnBjQLka1QLSO51nFmcWon9yWbwGkjDwydUYqNfIrfruGF2pymS51N2Ahsp2ms/O/NBi5NiQ2mWDfHv2W2mgI5O8d2mqL6WRsBg/utxfv8n88YYLGOlkFtit8MjHmb69wURQPw/Px8eGMHnerd3d26vb098DzZvru7u9GItfGaAJ+z2zS47b5mZKOT3uTAKVZOGXsczDcCcNNrepiFyKaYds5na4/jP40pxzD64Td0sLQZD7fnNh3VT4EJ6TKgCuBbq08Xxz7EvjhL7myvddnLHBqIXWsd8T9/7Vw8yyiXTLi/pjXtbBVO424BR/KYdXJzlJ/xUU5ul9PXtLucCo9+ZNqUmyI5RpyZ4PIDy2Gzt5YPJhY4DmnvFNjzrNR0v2WOOhFaKV/sC2kz/bkePtvnNpuVzy3AFz5PfclnO+XDz5n+yHneXpPZIvsR84Q24xww7vKmY1men4935UQgGvr0cxOYsKI24MdBne5rbfs+Ztfy/8Ssc0AfjVPr5xTFcKBsxOwEUsKHZvjIo/yWcbEzZX3T81bcSbB8D5/7nehjS4Bp0NozW/9PBt5tNodNWZnG8py+Nj5PNBjwEeylvHv37rBGjUsBnDU+h56tcaXONEPj7+1/PtsOT3XbDfA1wNSAdQqnEelsnalp37fGaIufniGYdHur3q0+RS5436lCIHnOrMrE21a2+kS74/GmQ5tkwLRv8dIy0erj9XMcpKfn3jpOU2Zq4oFpdF8mHpmeU6W1b7q2+nsOfadomca91WHQteVfGv3TWDdb9RYetrF8az2Rk5a02OKNZfktvvZswGd0nd8YoU0dyW/nKBoBXQMcdrpMfxMMNeecjJmfMdPYvwlgpi1GCVv92RI8fk+UR7DmdYq73ct5UxaYU6DIwuEUPp2k+R7l2zrguE0pTOB1i2ben2vtyAobSgYliRDTXlvLNBXyxA6lBTdN3kKvo073gXUxa8yxWOs4S/L9+/dD3clkZYqX/OVxKhxj8pjBQTPgeYZT4uY9s28EZz5gndm+0Mr2KBNbGRNmBydn5Ho5dgYI0WW3w6MhGghiRoR8ZN3tma3r7L8BfO7zFBD1OHW0rDz5YCfy7t27Q8YuvHB2nHVQpqgTsU2k0dnVtEfeh9+RM2fC2C/bj/S70ct3xbaAgd8pB81m0yaHT0337Tfon6iDHCfLTvjD+hr/T4Eb+2tmWPmcZ75if7J2N8/aJvHsQ/afY8U6TbfHgfTG12zp+2TLG5ag7Ptz0kfqoGc8eK/lMr9FF/i2jNSVDC596pTMIu9+t7wJ8NnIeG3IWt0R0yAwRZt6UzxwzfjaYfMZDrDrD72ttDr2+/3Rq9N86j8zeywtm0kl3UoThzeeNuD5VakvvMjOMwO/ttt1q9ARf//+/dUC61zPGW/ONHDcvXuSjrY5dvKHxcYhWS0bi/bn6b+Li4uj9W4tDe+lA7vdyyvIbAxbH/hb2ozR47QalxjYIfAtH2kzhoJvCPnx48dBznk+nJ3KxcXxGys4JjYelC8bSwMwfqZfBMl51tMqXqpAfpu3Hj/aIP9RzppTSRu2N6bbwCvrlGlzGCRyYb2zOk1m/Iz7bADtmYPm7Jqz8u5a85nOi2CJ4Ch9YpbXckMZt+2cgkMCHPKfNNhuGPCwHv5GmQld0SnLwhQchB5n/BtYCr9YJvtufjprbUAT2gO6CKLMS+tkA7Ohm8uGfA+Lf2P/m11P3wzCrFPNfrTxcGa+2WzLfuq1PjRQncL+NCDPpI4Dq7VezvmzPEaGMr6ZxmY/2+7aloTK/bYZE9idypsAn8s5QIKEt+zPBIAauGrXrAAunpKaaEwJqOGg0fiudQxEbOTz/BYvJgMRB7Pfv2Q/sq6PWb48EyEijcyipM5mBLb40Hh1jtKmUDDpJEyDnQbrao6SzxLcMIsQJ+K3OHC8aKSanIW3dpjNWLBe88T18f5TcktD5+dMj8GRi59r3wkSST8zL+ST+xpj2MZy4gv7z+cbnXb8bSza/83xtfrzfzPa+d+AYKsuOpsJpJ0qE6D7n1Eos3RMzAY3efJvzXZPeuL//bfW8brFtn7wlI2ybE59aVmd9v0cH+cy+bQWNOTPAMp8anW0YhDGevLZggj3dRp/12l71nTf9JOeqS8GYBMgbYmECci/tTQgaZvNPpuftKlbdoj157Pxvz33Vvk8G/BNzNtq0ELma3QWTZDaYvsUAhpnDBypM3PmPlHBuBM5wMknz4deGkoLXp7P/3bcjT/5TJ+vrq7W3d3dYeou2UZm+wIIeWbWxcWvhaA5my31JkNEsDqVLWNvxW5C2aLxpvStvXwyuxr54KYcvozd4xgeMCtE2r9///5KZjgN3Ggmz0l/smdpz7zYigwDcEg7T4dvEWfW7SU6ZF/bu2fZbwMsftrpUgedlWc9+c07ism/poseI46Hx5SZsjYN0+wH6/G6QdOW/jK7yuCOn/nespS8PgUsjV7TYxBNOWj1tXoaTbyHm4Qi+zm70MWzM6kjdFGGDJryu6fV0rYzVA5WM8Uc3bFtzzUHVQQxDTC1DUPhd7KMsanteYNPfsbRNxp5jXqW6T2DBQcM0RXbzNxDuWTgytMGMuNgXZoAswv9uZfP2DdwjBvYo21mP3a73dGmHre/Vp8tYMBAfk/9yDPWWX7nfY0/BHWRLerr1pKZZoeCOVq/PWvwXw743tqQo4n/WaU5ESt+7psitTAwdFLoPNXAjBLbpTBYeDnl3aZFTKOnUnJ/5vlZvwWUdbR1LOfycrrejMCWQTBP233NSTsrRkWxA/GB1nRMURrvBLfTsRyZDtZrheWUTON7DHOAXWuzFYMoPmdAkPZpbLciQWaw04eWWWsO2NdtvDglvKVzdD4tOErfJnDHNqd+kjZPlfnefKdBN2g04Guy2665uJ6JFl5v9vNUO6bJv3t8I0PtFZmtzlN0NF57OqvZEfOf8rHWqgEIAwbPuEz0Ui4sX6dmnH7XlzVdboGFx6vJNK+Tl5b7tV4f4cX+GRiZvql47E79hUaD8EkH1joO9M7xIeRf09tzy5ZeWn8mW0Gf1MCbi/k1AT62c2qMWvmtKd0pyiKB0zRsG6CmZFF0O7jcx0NnSYsVaFov434ZQJG+tdb6/v17TdXHgbYBJWgzT6Z+ZZ6fvwV88ty/diwA1xt9/fr1lZGi8jTemYf+zaC4OToKIuWktbdlXPjeWxqqrC/Mm19amYABaeEGBNJqh2MwZJ5zLSWdZTLEGTfSQrm2Uc6zyXA6u8WFvykONFifM3utpM9tjV5kquko5Sj/T0aW97E9ynp4ZnnY7V5n8RmAMctkxz0ZaQcj5ifHYgtcep1Xnmln3zWHz7r8Oz8bINhygA6ODGjZfwJw88B1c4wJlKa+pDDzMr0RpLWVZzkmLdtpebfeuE7yiGtmA3zZv/TZNsn0MOBqGd4GXuy4rU/5TADrjNnEe7bHI8Mso2y/jQE/U79lOXpru7TWeqWbjc79fv/K9vg7n3Vm1kDJ+pJn+D9tV9NNt9/oNm1Nh2OzJ5/EOrjGj7y0vlne3lLOBnxp2IYhnXLUYAfPQfC9ToEmM2PQkLT3UQf+P0fLNizsNvxtsE45RTsyC2PLJIVXXBPoe1gMABl5cSEzU9mcyssC4h8/fqzPnz8fFKlFzaSDfKBjyO8eB569mE9HyXYqlIfJ2JnP3jDz/PzrTLpMcU+OmPxzCS1tjSOnuZiZZj/NR2ZH4iienp6OnAZli28U8ALztJHxzflxNHB5voFS8p50nSocI0/bBfBNi++Z1eR0dEp4lnoMElMcUIY/6TunseJgcl+bnsr0WANQzKSzr6TZuwOnZ/IbaeAYTfx2mx4LfidwIQjhJjbSHr2nDcpY8dB8g3DKfera4o0BceMnp8i5vrmNje1zk5M4RbbB9n02avg2gWT6M9bbAFt8CUFWA/et0I40UELQQgCaa959H3rMb7aXcQovyGP63GaHWI91k3LIPvCNHO6z64y98h+f97jR/xmgee0pgXr0kzRRtgz4UidpzG8GYflO/rB/E69ceE8bDwYbuXfamLVVzgZ8cbAR+hDu6MFKODnlFAMCdrJlPziILhOap9NtoO+cYsFfa72a/mj9n0qc1qkS3uR+rkckP1InFcOCbGFtyk2FWWuOONkOr/EZg7nWNz9jsOj76Gg8ZT6VrWuuO8a+0d2yVC5Txrhlh23M2vPNGLkOA+0YHGex2CbpY18mx8hyyrmde+/Ej1aYLXdGMu1MmRzew2db4DEZfzsT09yem+gwTS5b9Nv+NZ4ROAQEpe/++51CWd4Cr1tlAnsOTl33OVkN1kUZm2j12Dd98MxKnqPetbpTmsPn2LWZBD/3Vr2zPJvnBLoTT7bqN41bOIDPtLZYV7vHvm7q79TmucVy2NpwP03zBAzPbXu6Zp/7lrpTzgZ8f//991prrYeHh/Xw8LDWen1yO40KDTCJtkKt1dddBEzudi9nzlFIm5BMkdxWungyuGYkgW5oZpYp17zmxDS6zlyLwjchy8Llnz9/rpubm0O7yYSmHk4HU+AaiNoCEGmXQJwOj5kFAgcCDj5jcMFr4Vm+Z9r66urqsCmDtKWex8fHAy03Nzc1OEh9jJjJV4JaG3MbDWdEUlInI0jLMfWCcsTMczJlHEtmmTiOBjq5nvrfvXu37u7ujrJqqdtHK6QtAl1mB9mfyVDbGVqfnTHmhiNnY9kex8C6kVfLkffc1JQMH2nN+3UJgraCo/3+eKrJmQvOOHCBOce/TeNNDs4AwjMFlCXWzZLxvr29Xbe3t0czAK7T9oNB6NQGddbj5WUmLukrF7GnbAFR3kfbQ31NVr3RyhJdDx+mzC3/j5xmsxf5SB32H2lgRo1AwUEXN0FYllKsj41nlCFmnNNO3g1O/jbb1QBnZIq6HH3e7/dHtrKNAZdypJ/mS/ufNit0uA37tQk4Zkw87pQL24fmnzkWvM7sYLNnLpP85Fp41jJ9bylnA76AvO/fvx8ZOWeAuJC+RS0TIvYUmoX0+fn5aB2DgQsdx7mR64SoDeLy3RFZO37l6enp1eL9CYk3ZW6FRjiKzLcsGGyYvxTyc3/L7xZAKgSNVcuKNUVsxsNAPkaYaxYN/DLlGWA4KfaWg9xSXk93TNF3nrOhdGlTZfnepjfSZ382mW2ZrRhlOgNOSZGn5BV5xkDEfTJPGfH6HssBAQKNl52AHRDl5OLi4gD4GGTRAZvX7969OzpnzjQ5qLPjbjLmvrDNyRg32vibQQSvN77nk7p5eXl5tHs7pY0Jg9Wmn1t6Q1BxTnF9rb22Cc/2gfLiutxegK15wPo8trmH8hlwTB5ad9gm+2V9y980xc37WE+TLbZPPSSoDFANcLi6ulo3NzevbAzb4TX7C9oH260tv5Zn7cP5R4ywNbYNHPHeZivJI/P9FOBr48PvLTniJMgWXZMs+jrb/i8DfH5LRcrWQIQ4Mo9CReO1FbUwKklkQToYWdIwTAM/AT0CFw5gIl+3a16wn25rEkqX9rvXHzhr4Dab4IU3dsj82zJcad+GthmBxu/Q56zFVAzU/HyM5cXFxWFDTXNufDZyxnV0AUWTTKTfjR4uSPZUjwsBiY8J4doXAy7+pU9poxmf8Cbr/5gt8JsLIuNulzQzi02jxyzJltFpjpl84jFE6V8A9yljxjc6sD3rn/vEdnIP19qe4rN1zgCa2cMGlgwCCBya47OD8m/MCvEYKdtQ0sD+UBZyb2xFgIKfM98Jqhwcmdd0WumnM3cEQ7k3fW1ghPzkc8w+R245LvZJHKPc3+xUc9C2t+SZM1mkm4V2xSDRPPOYJOPMPlPe3717dwgCkiFv/DboasG8+0sa/Z2/RaYMXHyfCzHCW8BOwwCtL81v5V6DwrWO/ZCve6xaO03eDIK3gCyTImut9fXr15O8WOsNgO/79+9rrdfTriwW+BAW4qikFlQLMbM2+by+vj5kfQJAtxaupv1zM2lr9Yi2OcG11lHEF7rirPIcDempdllozNr3PDMJDjNnPsbAjs5gr4EyLl6nEcn/Pl7DhTRPAMP30enQMeQ+rh+NESNfaPjWeln46t2/4UWezz3NcTGT7A0UoWNadhCavRmDMsKd1pSFyJbXY9FgE0g8PDxUsGLjZFnxmPz48eMAqLmpJfUZ8LaggMaaIIRgu4Gqdgq9ecJsmIG9QbIdvfnIgC+lTSHSrrCt9DuBqcG9S+q00zSNDTjyel6pl+UNdgaWR4MkA6cAxtiONlWd+5wR9Ri7hC7qJ+t7eno6TMcz4xoeUS8N9PO811ZT59n//BHo7/f7I4BnGbXtbYFys2uUQdqt3E9a7R8tPwbJ+e35+demtix34bhEJu/v7w9jQF/qACPPB/Cb1uZ3SKszW+6vbc+pwiCduttAJ9tsmMBlApDsq/2FZch1O/PJP/qIFPtpA0Pr536/Xzc3N4c3Qf1PB3wTmp6YSkO1BXhaVOBBXKsv5p0MqWmZ2rexORUFbPXBgsH6TXeemeridzoV/sa+MbNGo8NIoCmBla49y9L400D1Fu8M9iaFJLjzdA7lY3JiU/aw0Uu6Jvp9zUbCRq6NN4FB+5xAgcfmnELgQkNu4Jz7CPbYx3a2XsuSkdbUe6pMgYX1ngDyXD4YdKQNOxnKw+SoDbqcrcn11M22m2Nozrs5Rt/rflvHufbSmT0/x3qt9wYTjd/ss3W/8XHK8LaAb0snGvBsumgesl7eS//E30xnszkTb6ZifWRdTZdO9TH8o162cXXwTxnNcwycTOs09q3vlveJN9aHJvPTZ+PPOWWyHW+pq9n336Gl0dR8y8SDLXncKm8GfBQyOxAa1bVep4IdTfD3LQfMZ7ngm6loRo7OyIV+r7mjYSbjeG6asxSJ4O0MfYaOeeet9ix0ormWupklYDQeXvOIlClTmsyVC51F/p8Kz61rAJFGJTyNTDw/Px8yxCneUu7A4Obm5gBUebSOs5XOUGTTgst+/3KO4e+WZBCS2U02wk4ufGH/Hx8f19PT02HhN8eV94dWypblkbIQHpAn+W7DEZojN7vdy6HemeLhGPAZvmkm9VF/SEPoI2gwgAg/+ZnvBOx21C1444aJ3HN7e3uYJYhsehMLx8r9tkPm+LiET21BP21CPr1xpmWfOJ6xOda9fL+5uTlkoryRLry0zc0919fXR7abOjYVynnkYK0Xuxn6mi3k5op2fa0eKJMvLVAkcPaULuXfm3mi0x5nygpBcMtKG7Snn+xLnuW9rju0+Mip5gM4/uFVxiCywoA5spEMcPoeGW3jxf45Y8V+M1jLvV4DTfs4AdSt0p5vYCfjT3lugQXpbscosW7S2cCpeUb5dZusLzZpqy/c2En5Tz1bsyCt/Bbgy5/XLTm6NaH5P+DIQsZrz8/Prxw0U7pZq8B66RybIzKCtgFsDM/zMdJt2idlAq0Rpgl0kq62Xo/A1lNjXC9EntDRBvDZeBqgtdLAqTMnMfoExATgPsqHwJH1+JPrNZtxybg8Pj6+koXWhyafXGzfHCL7nD7mmfxRFwI4p2Alnw5+Yii8ezS0ZI2Z+ej+eoz59+PHjyP60h/qBjfKpK9ZtpD7QkczpulXaCDvGn+3MrFtKYL5SudC2Sd45eYVB2vkI3ctNiDi3dHsB3dAO2gjrXTa070pBNYOqPlbQJtptGM26Av9tpnNFub3rYDJgV6z/c7Y2ha37FpzsKExIIn88r20pQ3wkS+kg6CKWdTGG9ZhHd3v90d2lmOR8YqM+rD10GqdJL8pi3k9ngF86I9ehC+0LbmXfeE1y3PaznKe2IXwmnS1TC4/2Y8G8vl7+055Y/LAwDj1MtFBsEd80/AAi/HEhAsmXOFkE2W9gevW7rmbpVLOBnzTOhAzgQIZR0HmrvXamdKw25CyECTSKId5W8CFpQ1K2s910sEobb9/eXMCn5nqSTmVWWqZDU83UOkoIOQJdzav9ZL9CIAyYJ2MvJWWgkfQyfucsSFtE19YWEeL8ikrTTmstOdkLqcIkwaPfSIPHPmleP1fMgsB7o6wyWMCfL6n9+rqaj0+Ph7eNNIMBAuzTJaZFmQEsGR9YcAo2yMY5XhZX0yDx/AtpTnjtV6AJR0Ny0TPFg0EwRNACk3MYLLe8DLt2VCbTjv+ZjNYPwMpZjBY31ZpOug+EnAwa9PqSXG2yXrc2plKSxgw+OHY2kFPdE48ivyYjwmeHegx0GG90yd9lXkaeilrlOuUBJbOiLHfDGiY7eW1qVD+zKOmCw4gGNg1ENRwQhufJocNZ7SxnOygfYMDArbt7HlL7vCZU3069dtUWh/a/2+pM+VswJcsER0InX2+e7rKgK8ZbwIoGzkbbEZEjHgySC3C3So0TM34TpsR4pAb+GzgznVYuCelZDtxyqwnDnut400kqT8p32zDZ3/SboR8ejdt2nC/CEjJLyqvDZ6Ngp0E+dBkgdnf1OvjBay8zgqabv/GLISnMjmVmz4RcLOvof/Hjx/r27dvh+ms6+vrV8Y79WfK9/Ly8nCO2p9//rnu7u7Wv/71r/XPf/5zff/+ve6y45gmks80cgBKdDJ9yvW1fu30CsAL/fmfWQWebegsWsaGMmpQ1GSA9FineK5Xfgs4pfzv96/PzaOeMcPH58Kb7GCkjaNsp47wgxkTTvVnDPNceM6x4vhzvAz4LCM3Nzf1GCI+k+JslIM1g3HeF/mmwyQgJk/z2Wx8c9QGdLyebBT1iv1Kls39TLEukE7rKUFW2g9op+/Kc5xB2QLO3HhFkJfsImWcPifZtMg1+87ZAGYdY7cjP7e3t0eAzzTa7tHuJ5hYa73KNKa9BjY4rg5aveyJZQLyHiPTy4SCfXIDd7yPPLVNin2gHEY+mkw1/THdE1D1/+5zA/fuS5sVOFXelOEjQydkbiEn8RQel4amW6ExyffmwE+BvQlcTe3b0LHt0HJOJMPvEaYoCoU5JddbZGlnybppzLhuJsLcohv2NQbv1JhwTH8n4nBxBO/P0M81IlQALhdo/Exx1GrgyvbIBxsbGxXKOf8PiGpZiUnxY7Rj0D3FGbom42K52WovTmWt9cpgM7By/c7scDymrFsDGJSfc+WIDqkZR8pAc8xNp1tmKnU3MMkghFmVqa9b9oUOqNmitDdNg1MWTtlRB18stNn5Pt079e2cTGlrd3qevLENMBA9R34mMMH+53vsYQMhTe5o0ynT9IPkLXljMOhimWZChQFzs6OnCsEtfVKTw2k87Qf4fStL3O4jD5rfn54/VewjWe/0d45tOuX72/UGDj32k47+Tnlzho/pZQOHtV5ewZaoN0LEaaG1jiNMKgGdih1+AExKnBSnjrdAVxN8CleLXG2EWGiIorhT9ojPhI++JxGGQUHqcwSW+nioZjJSGQ9OQ0wgz/8TJCSTSgNLmu0w2Q4BROhr4PR3BdoGKW2wTstL+rLWi5PlGXC5j+8c5fNrrQMA48YhHodgvu73L+89ZF9jpFNf+pS/RNlfv35dz8+/1h/d3d0dDpsODZkupjNMfybAYf6mPeqPHVPGMaf05/sUDNnZN6DH9TMEltRHgiH3xWA8dXHNFJ1j2vU42A41nrlQ9wyMbbybs0/dadvtp91M00W/fTQVeUSenWML0wf+TtBh/hlcTfzZum7HHzqmQh2nDnF8Q/fj4+OhjSng8G+2mc1WuWxlVwwYc3/zKbw/chndotxvgT9P87s0frcAl7rYMm787leqNt0xcG0Bacs0W+5Mg/swtUE7TH7xN/qQCZOkrlNgrwFV0tRsAqfqveyNNstv2Mg954L5lLMBH0+1T8NJrecvBjwGmALELBMBDJlDo8mouUVeKR6YLWNtIW+DQuDGQmFIoZJ6V2Erbs9TUd7llu/kW6akwh9OHbUF/x6r1GNBaeCAae6AII4B15rw+a0ILfeaJ5PTYKRpXuY65Wlr8bszgIyG3W4KATYXNudNJ7k/0z12Tt4BGVoyXvv9/ug1clz4HzDx8PBwGOtM6eXstcfHx1fAKcXy2sA2P7kbmrTSQBGgBoiEP87gN5o89g6QmEkkf1sgldIybwR80zKP3BeZcEapOUrz0rMYpt19nXSCoJpyls9szDDAbuCV9ZlmywNpjq55Ct8BIWX4P1tohx1wO3tMeihb0UdunGF97jfbZZDtdthX1tPAZO7Np3WBxf1Kf9ZaB1uQQJS7OEmPx7+9TWXLFzaecINGZKytV0/Q7CDGu4vt4+grQoP7Rzoj76mHOtJ4fypomPrdAlLavWY3W9sN6DdwP/HH9NHf+y1Juf5WHTwb8LXObUUdJCRCFOBBYbExSWmAq2Xg2OZb+2G6U+9WXdNzNFxbitTqMlAiKOHvuZdgJc6XIJnrRVjoTM6JDFgn229j4Sgp/aBhdWmKxgwcabDyTbLnaU8CmBgwO5OpmOYEKFzXFwPkQ5hzPxdOuz3vtkuxseG43d7eHp65vLw8BFIGUgaWNJSmMePDdSvhOXd/xgGlXy6nMjctkMpzW+Pa6g+9EyizjDsQYplkwXI+BYj5vxl5t0kgRxvI68wu536O8ST7rRjIsDRQOPX93PZ+px76huYgG1Bh4iH38dN1rDVvAiCtE7ht9of+i/VxjevEE/aFhceq8DvbdbbQgO/cwhkNF9r56Jl9HJMyGb+mL878ESP4L9dDH7+3MTZNDaC7z26Hn5NfdNu/G+xsgc1mAxveeGtWj+VsKWEE29YZcNDdqZzWH4GI8cq9WWjKuohmI0TOljRBMdOopA1ETjuEd7u+Dd2lGSNmGVKP1/bwqAsWb7NmFipOfq2XKXZmWTIeDw8P69OnT0fRw37/8rqt8Dv1p5Bn4Ufo4bg2Y9Z2hNEAOFvTgD6BIksWMZPXlrOWgWV74dXEM9JIg5zvkc/Ly8sD7x8eHtbDw8PhaBhmWykHDQQyK0jeU7/Cp9Bwe3u7/vzzz6ONB5eXl+vz589HESYNHzPHl5eXr16RGCdLeWUmKW+3IZ9z7lsKo3xG49aN5vybrvGZ0MplG8zKsF3ONJjvoZNjRBp4j69NMswInc+bL/w9/AstzAjudrtXGR6uzcq4MpPCcUs7HM/GT/ZlrReb40zXpFPN5rHO5mRdWr3hZ7O14YEz5cwucYbItox122nnfy6JIb/zF74/Pb28L92+Z7d7ffbpFuhtMy3ZlJMAyzwyUGcAxuzsFDxRPyb6aMPIP87UpZ5cT19ow8jzpgscVwfrzcdv+eG0F1tBvbBvb7bBepbfJ11vfM1nA4ZNj/KX/rM9BsKhfWrz3PLmc/hahBQGNSFZ6/VRLYzmWlTX2iVwosKttR3Fuo6p0LFaERrK3ioGMzS8FEKCkRa90HhzSougKgCEisBME2llxs30GvSwPY6v+ZPPKUqy8vD3c1PSVLbWZovM3Qc+uxV9kz73KQrog5AJmvysZSrRv8feOtXAW8BCpq8C+LjDj3XlmdBLR9DGNHzkmjEGBwSgBhikua3DjQy7ZIzOkQOODeUgU9/NaZEGL0HgPa2N9OlUMUhs11kvM67TeBHw2THZ4U9g+pw+bF1roGyrtKmmRgPr9BR08x123J4x8XPWp+l385Wy7WUA5rt9XgN85J/1e+Jvxj9yYltFPuV/Zn9pj1oG2TpP8GVAwzqpXx5bfm88t8/h79TRlsGeQJJLfm8by3xPs2G+3njl395SGt0Nc1hemn7+Tvspb8rwkZA03AR2i9gc/kpUzxPI3eEtEEeF52euh8YUK89WdM5iGqZoiG1wMC2Ea70oNo0+aSDQ5I5Un8FFB0uhSaSaYzXiTHLwLnd98lmDF/PHhm4ySK2/jZfp6zRFnGcc6TIam6Z9U/jslgFpoJVtkN7cF7C92/1abJ1sVOPdWr/kP5sseFRL+nN/f/+q7YCurN/jug73JXLx+Pi4fvz4cThMOOvArq+vj6bCmtFhhi8LyAlQ0keDFmdSm+MPL5oxZR/IM0e23lSQMgF5yxbbZNst2z2BVE+RZ/zoZNlHZw6en38deZMsbwPwzESnTBl3gj4HayzkmTPI7At5HB1xf3i/C+WhAV7rePMp5Kvtaeyes608FoxZpnyybvuPPENA4OC6yYTBNjOlLUvaAA/r8nhMhTrAYPZcv2Yf2ew5++fExVqvj8FhoEoam60wn1nnWuuVjpFmB0kO5iewzfHIvc1fmccNT7C/03XzerrHQYVxQcM2v1N+C/CR8GY4SZj/sraMqVzuBG0A4hxES2ZtKYKVkwLUFJkGh47PpU03m3esd611lHmxkHPQmRUNf+IY2E4MTbJ+FxcXB4Adg50pA07pbUUe5E9L53M9Zui3AyA/uBs1xoLnCwa4kS4GBOb3xGOeLWXF5ljT0JDeiQ8sjKy56YIbKSyXAXw/f/5cX758WU9PT+v6+nrd3t6uq6urdX9/fwB9AVuc2sunF0jTeO73+8PbR56fXxZe53VjlC9O2YZGBhapN2f10ZnGweYZy3WTrdQx6QSdhcESx8GgNZ8Zy8hRSruX1/i7x9rPGjzERlBOuVlqrfXq7QdrvSwJeH5+Xg8PD+vp6elonaSn0z21NK0PJdgwb2kfmbUmnw0K2ccUykbjdZ5zUMLgJvU2e2peOQjYAnf8jcGJgxwCvuguxzL3cd0v+2GZaDpJHnEDQvjeZJx2z2NCMEdwwGe5dIP3WhfjD6m3LXkS+thv1mGAGT6SD7SFbdo+wQ2DStJBO8x6wpeMV+61DBvUeU1sswH+y7U8Y3pYP6+nuA3fT/3OmLZk0fT/OeXtKz1Lg1vAL1HrWscOl8je0SAZZsYYUES5XYcBX4yvB6cZG4MXpsnbfTaS5w5E+s7onE4rA96cUzP8FBoamwZCzy3um6dSDKJOLR6mAJ9aE2E6zi0t45DfqUhbUwhTpOff1uo7MumA1jo+oibjEyB3c3NzWBeXKVsu1HeWwJklL+6mk+OCazoBGtSmN5H91OdCR2bQMI0BnXtz9ATY/r19/51yzvOTYW06SkfI61xnmf+ph7GBXNqS+3JvA3LkUZNR3rvVX9LgLAr7kfvyvdXXgPW5hXLYdKzV60zR1H6rq8mSA0fbPALPlrGbQCvv93hsBcVbfedvU9Zni4db8r81dpOPazpLUMh+U8483qfADP1e6ytltD1nrNJ+a+2+pRAMNp/d/qa+NX7/DsZwORvwmSFmFhWBEcpavzILEdB2fk+itcmY+M9CRia5DhpkGtymuFz7kGcZrbR1HQagTQHilFOHB9u0rvWSCXt+fl6Pj4+vMoB5gwazMaElhjwZo2R1mHWYnGsrAUgBDH6hM5XXRiPGsEWcDehPho9ZinbtLSCbTobGJ4XRIqd7eNo9+8H7fvz4ccjW5Lp3TQfs/fXXX4dsbI7dSKYv95ln7H+yQH/88ce6u7s7Ws8ZWh8eHtaXL1/Wfr9fnz9/PtQbQBGwmZKxDg3kWeSL/GFWgPc3vrfvqdMOcXIG1MHpPZKTLGxlk0gX5SmAmTROGcqtjIE3mTw9PR3ObWQ2KVlY2qfcn3tNA2lpumCdtA1nhic2kOtUHVhObbfS6E3xLIbXfrYsnKdpt8qWE3eWkTSttY6m2KkLKZRN249Gx8XF8cwK+er6+BvvD11e0hB9j6w4m2VAkUAwtFvOGs/sF+3jbT99L5/n5hfO7Ew23n01WGzYwPYjf5wF87g2ELg1rgZg1g8HDXybTurmG21Y5zSW/jzXh6f8VoavoXH+MYXtwrU/+eM6MzORzKPRIA00zpMxIzBqWZNE5BamGGkKo5280/MGBREG0sXPKGDjcYDabrc7Alrc/WSgRHoD/jwF9tYIgQLpXWD8LUAzheNM/k1OqP3vaY23RMdpc1IMG5O1+qu8bKCnujJensII4Evg8e7du/X+/ftDRi9Onm23YMf9DkjMGX1cR5i2Y1jjLAIUrQMtaLPxTLvRF4KYUw449bXMnssUEP1uZMs6WBpwa4a/GfT2nPvADE+WlXCdWO7LIv9M76fvDip5moHBwDl6MPGD/SNtzN6yXy50uNZd05j/GQh6mnMCh/YHLgQ4jWbLseWdutYAztRu003rEe1YZhi2bIpBANsnDU0eJ11sMkN54u9bmcv0qdVtH2vamUFuwVEbQ47JZA/tC3OPZTy08fdzfSLr5W/sN/lLfjrpRPmf7Gf77RQQ3SpverWaibDC2MC1AW/rsZrA5n4qGEEOp3HbwPMZo/0U0sspPpZk+LL2aipNcdl/GxxHb40+0sl1aBPwJW/YFscjbTFz2fptI5P3g54CPu/evTvKvBD08NPBgbOO7hd5ap6YXj6f3z01bmX3M82weB1Re5/r9+/fD+uyqBd3d3cHoJS/RHzeZTcZetLPtWG3t7fr+fn5CCz4oM4fP34c3pWb8d/tdof38ZLPbIfriBzAGRw0PWyFyyMmQ7sFIGmwOU6UEwLnreJAzLQ3+iyn5wYbllkabsoPn3UGPP23bCZopB0zXc2BE9Rwje5Wn7bAQOg0cHbgFIfHsZsAKXXCmXMDgS0wGF1ba70Ct/Zh3DxDkJK22V/yxXxjIJxrnK3h+HHcrFMtwJh4lTpYtgDCJMNbMrAVfHnsJxrafafsuH1hfpv8quu0zrylzy62Uc0WsX8Oagx2W51bwdaWjZzK2YDP526xMy2SMYo20VmczCkiR1kGRMxWJCuYV0vRAHDamAt7w+z8RmfLv/wWgErnSWVsPEkhmGpRTnNSfI68IkhOPxMd0DBw0XA+OQacsn54eDgyZuR/m359eHg4AjeJnAgGLi5ejshY69g4p93QniwTQZDLlOJney3aJc89bgZ3NCAcQwYXeT7gKP3//v37+vz582Es8pnXO8Vh3NzcrA8fPqyrq6tDRs/yNvE+fGjOO/xJRrUBvuzs/fnz5/r3f//3A+j7+vXr2u/3r470CR+ym3utdQRS06/Q4GkR620LPvh5TrYk/Lespv982Tyd81Y2j87VZzI2g2vZyfdpYwpt1vPz82HMTZfBTni/1usdkLSbXCzvoIQgjn3gbxPwC6+nfpGWrQxQ5NPrt70G2JstQsNar5eOsG0+25x4c4QXFxevjkuhXWAGhqchpF6/gq7VTxrSB9PGZ3lveL+1DroBCQaMpMGZNAPM1k6eY+LGwbLbZ//Tnmes2HfaAm5kaeB5y+5x7DnuLQA1vmjB1CmAamA5AUl+J73csDrpFgOh/7e9f12OI0myrFEPJkGyMmuk5/0fbp5gZEZauqtIgiTi+5GywRULW808wKw5c3qgIpAIuNtFVU0v2y7ukfqNl1y75xz8cbxiha+hVwbFXPcKH5GsgyWfpHQicCD0U5dEywYiDDJMrB6MZlxt5kjnCXDMrJpGHKOm85Cakaaug4KTFJOegVozOF9rK4RtNu5AnoDMQEu+2Fc7mxLyTJq2siLPhKxrjulqVmRq/U6APPrnWUZPKGIjx/ET8OX8BoFTA/ZTkppm4JaPshPk8yXdfBjEPhZi0AzInWaSHhcCsZZ0drTqi3bihD0BD9c/s1LSgGF42yUFXneCtQ9OdeI/LZj7zORZnZp2oGMlF3md2p9si314Va7FLPfFWMc4tErEqec472v+a8m2gQPL32I3J45tMrKiNuG9hxzHTIwZK77oy2d4aHZEn7iXGoZYlWWd9r8XlHYxtgG8qZ7tzuB0ArT36OY1ejwN+P7+978fx3E8vzvKHcaI+bLQfHIm1pTD1Y44GsGZt3LZt89K5TOJ2L+AEODYVh6yAtZWaxhICMoiX/rI75xSfn5vM2cbh6+TUiYAws5soMIzk/mlDT4xSKBtIEYj5ZvseaA5PPgpxYzNcdyCjvB4uVxuVvi4MtF004jAqlFbvTQPaSc6ul6vNzbTVhDzEM3Xr1+Pz58/3/CYhzFyZjT28McffzyveERWJycDtrZCxZcHR4bUywQkPsDV1Pfv3z9v/XK16enp6fjHP/5x00/Ke5U+9pIJFO2MKwy2cYPyFdD0wX32Sxv11mhk8dOznhA5GTP5tgcBCFonPqMz+y6PL+RaJgm531b7skre7JrxKDxxxTj3OHmkPjlO5peTwQa2vOJmYBTiSlhbPef/jOttkpUyfuUJfcETDi8yeFJpEJA2YqeOC9y1oL+xT8vY+OB9g1XaYfOr1SQo+mv2x4loyk85ePIPys5z8Ml9jdqkhG22iV0Dc8xBJoOpxoMB+jRRyXcuJk2yEZNk1Zg8EQ8025zAYpOjyTXhhTN0GvDxNzwZAO2kfDiCzNigQwZ7AUsBNE9PP59I5QpKC4YMHgkGnz9/vgGoDBRcrQkPPvvEMzUZTAdylvv27dsLwEcZ8zSmjdDByatw0W3KZDneP5UVMlD78ePHM+BrZ3WYTJNAogMnAxqzl9ebo1sXBsHUD4Mg5ZomC5PRJ3gx0JO3Flwon48JMOl9/vz5uUy2E9PPx48fj99///35FSsBxX/7299uZHcSI9DjOIQv6pfjcRw/t3S52syEEqCZrV0CnH/+85/PICKUVb2cx8wW4sPDw/Hjx89fuTE4a3bqVYEzAc1Jhm07+dBe/NSfAcludYFAzjZDO2M/qcct8BDjRO7lAR5uYVKmtB174kSIiYZ6ygqyZbFfeDJBUHy9/vzpRbfDuMmklDFg7E9ZP2g2TXT5R9u2Hv3b4Im5BmitH55DZd8mJ9w2caUOw4fL8/+2u0MZ/L/Pt051J94ZjxkLOLmgnXqXi+RxcLxOfctBgDvxzPE+I5e/hzj25iHtc+zbZKgB9jO8R6d8a8EKhJLHM3HQ1+8Fd41OAz6+usGrHmEsAYBv2j+OnyAw11jOxuZZT1vO3glOo0zSZVDhLLbNwlM+CT3Xp8CXZJi6XKk0oMirGFowmJbU26yMhuAHWFogZft5mnPSJZ9G9FPVl8vPlbmHh4dncMhVBp8NCdigHgwA2xiGZ55hpG4bRWYejGayTZl2vjDjmhcjG3THYbk9y1eoXC6X59eq2La9EhHZoh/rYbflQ10wiGVyY+DH2WhWGWn7qUvQGyBjakmVK8ZtVcygqwHd/O+xJmCaAuOZYEhf8KpVdJZxov2YaBdMtKtETQCUF3M7jkw8TrJ44kSi/bCdnd+1yTyJfjDFpGkFJeWj8/iZdUE9xjZtN5Sff9ZFrnmFmCvRjCmMYc5NbNsTW/vuanLDMXZC5z2Cfdpjrnl73yuUnGzxbGLaDjFGniH2OdmoZW/ytv9DHCfbA+9NIMqTRK9sNz4aSCY/Bp6e3IQ4KWP7xBjm+bVg7t56pwHff//v//04juP5kPpxvAQiAQF+etIrc23gQmy7BZ4V4Es5AswAjeZkGazJCLmVzAcWOENngpxmLHwlRrYLGXAYrPjj3V41aauk3CKiDm24GZt3794d//Zv/3b827/924vtJIPGx8fH5+3LbOV/+PDh+OOPP57Bjlczs7LlhE1dcMVnGkfqxT9ETjvhNW5hBKhHbx6r8PHly5dnoOOn52gz2Zp9//798wpeW83M9i2DboJFZoScCPGPQcV+5cTA++GVtuDJ0vV6rb/c8OnTp+Pz58/H4+Pj8e///u83K7jhNeP/+fPnF2Ahn0mQsWGX8Xvocn2aJFB3oax+ERBy/LMiST3xk3qhjG2rMhRfJ7G93OMxCZ7RJMjJ3z//+c+aEDImBOJe/XYsjM49yc792EWLvybGba9sR8ZdonLMdty2j2blzqtE4TX2tzrK0MAb2/Grjjgxa6uj0YV9OGPMp/PDO3e2Usfx2nIy5nCyZD5ot7Q5LxAQuNF2KPf0QEibeDAOc8w9zqRpopc6lq8t6HByxwlVK8cV0UmeKV7aRhlf+AtVjCME5Rk/H3Fju+6bucB6a+UaoDU/99BpwBen4Ms5vS3AQSCDnmmY6ZQhUdhpFrAit+/v6cPAkmWS1FkuA5UfsM/AJyD7zERkY8D2qhH/vBJEMOOVyvDsYOmVPvPBFTcCKP8RsBmYJsD5bGK7Zn17Fkq+PVbehsj1yBidRUctMKZvns3M3+Pj43Pwth7bTCw8OXASGLYVFxLlnz5dvpGDa7OJlkQtR7ZsYx9OSrTX1GvBKXHBYP81k7e22j2tet4b9ELRU+ozkTKwt7M8TU/H0V/enr4ul8vNWdgWj9Lm5MOUmX8GmPnuupN92U4mcpu05Qb2vFrpGNmetCXAcBJtOuDk2PmIwJFJmPFregPFcRwvYiBt33HYxNjaxmIiAkWT4/yZpG/w6biRtkyTraxkae16ku66zrsEOwRZttFm825jum9ZVn8Nm6xkbuD5NeDsX0GnAd8ff/zxjHy/fPlyHMfLJW4H+rZqYWfmoJIMFPg50Wr2z/rk2VsJrMuyfCWHD903sMT+OHP4448/XgS5FtQIXMibZWlnCiewFzDCFRhubaa/rEJmrJ+e/nzQ4+9///szWOTKA2e3XImcDhLnvBNX7sLj9OqDOJ5nPFw95dk7AsLoM6/yoc6oJ4LLyOZXx1wul5uXJLezie19dQ0EtgDIck6c0UFm8uHR505Nnnx45S88//777zerXeSBvPtBqbQbnri9H3k+ffpUt9AynlmRZWKNvg3GmYwbKOcnbZy+6STI8ee4RQaDMK7McKJGX2zjkUlG82+CodSPPqM7/wJKxs4r/5SXPHgS1Hi0zfkBt1aW331GmnHeY8l6kdeAm77viQTHyNfowx7nrHZ7FWwFEgwwGmDP9WkFN+0YJPshKeZEA834/zSxTh0/2NTyooGWJ9Ykx2bGYudD6yxx2teb7igzFzlaGS4u2Z9bLGT9xI9G7C95zL4+gfLVxDQ+zIdDGgBOeed1fra6O7r7oY2vX7/erA7lLJBntAQWFDjErdQV8m3AaBrI1mY7m8AZR9u6mLadCWjIm1dWnMgZuLJ96Fd6cJvDIJJnq7zaYmBnXRMY8OzZ5HDhMdstOdCfn3wKuCPg8XYGx8gOyC1mr+LEYdtKX5Mt7eWnzLI9a7mitzxskXoBb9RRHDKy/f3vf79JJuQh29cEOxwTBibPVJ1UOdnwjLat1qVPr/CxjNthIjIQCAD3WUv+TFwCJFdnAtToHzyyQBu5Xq83eqINcws+7XDF0fyyDfJEnXHFsdkQEytBGpNt+vHEKCuiPr7CeBA/J0VnBEYGVQE46Z+yRp+UI7oykPf/tA1uVdNXbVM81tL80uCfoJUPW7TkFD8zf/QjxxHq3ICP1xin+HOF1GPbom65ZUqq6buBu5ak26qvx5lAju1xgsL8RH8xr+kvOXoHWCmTJ9vUz5SvaQsEsk3+KYczDoYf+jd90xPrBpTdN+V0v5aV99pqavs/ny0XMCf7/YNtEmQiwJ7y/o5OAz6+yyuBn8E7RCc0Ofjyu8sT2DhZtn17o/upbSrtDK14djsr4Gpe6AgBT3FKGkBW11rSS30bihMWk0KSPg/3sx2vgHnG7L6Poy/jN/6SAPP5+Ph4M5aWsdlWyGct2ordcdyurmVWTyfkKh1nYHxnnicN1K3/qBeTJyMtsK7GeJpcNOf3+PNa9He5XJ6fwo2d0a/jU14VygQk7Tup+dDy9Xp9PgtqfSUZBRjFL+IPjegj1s+OuFrnlRPbTSh6SOzgjJ8J2+0Q8OT+9ICV+zdwZHxr/bDc5I9cceerqZyI2QYnxd6KniYubRLt5Eg5m879v+2Zft0AHye2U16wT018UY/kg3Yx5YhVjjOwcSxwXU50OUG9hyawx/4IbuxXmUilTq6xnfa/9UnQx34sc3hxfjfPlsl5x2XP6CdxxnXPxJnUoW9P/LHfVdsrvHEW05wGfP/tv/2343q9Hv/85z9vgnIGZXXwNUGAA8YBb+CwOWlDwPnkUjQdsBkblR3+7MAsxxkU69iwG28Oejzj2H4zs9X3i35XfTTDjtOk32xtWifHcTw/WPL4+Hi8e/fueQWL20ppM2CAT9FyO6cFi9x/964flm+8+8B2dOn7frLqOI7nVavL5XL88ccfx3HcPjVuEJQ6SSJ+Z13qtKTSgn/aJYCObWc8shV9HH+uvLRAG1mjA64c+aGd43i5+kFwkzGeVgDyFHf6JEC/Xv/81RX+T5BHsJete/tkiNcNnMhvQHcmRgRtLelNycw+zP5MTmYEMlkdo+8ZEF6vf65a/sd//Mdxvf58stu+McWm9JO2uRrG2Mn4kIkcfcm6jK3w98vb5Do64QQwoH0CqdaddR5qwIbjzzFK0g0/XPnlMZL0w7PE7UnbkMfVvLXVZI5bmwA3OfPHSQ1zVgOyBvfknzqdzgq3CSN1avJDWqnTfIATHtthA8+JK7sVNuuNk2/y4dXnaXzJE2Whn5of2xn9eVrlZt/Ws8FeA4FNF9MEsH2/F/Df9dNq1+vP2T+N3gJPgdi0Wnljm0T6K6LykxRWg+DrXg1pAdl1p6Ts75aLhrprrwE+OhrrGji1Gf9x9Lf45zpXFNpMkn0TDDkwthUBgoIGWtvDEDnzxPGwQ05jRdDDZOAJSmauSc6x89ShHJ4F8pPJK7IFUJE/6nVaaWlk+51kJi/trI8TClec8xNw+T/gI7ySX46Dg+vqGvVIHmm71A9BX9rxU8+2UwZwT4RYp8UhT0ZTn3Ybm7DPUR8BWD73S19toK/1ab2FT/55RTFj6zjBSUgDfJGb8cAr6eS1xeYpGec6ddmADQGf69quPanxwxiNWsyY/C73GjBt9chHZA1xzBsgbkSf8HhFll8h2ztje9qf+ljFIrbdaBXr6M/EHM6xze/b31myLeYaZWx+m/sGj42muP0auqfeXU/pMsAfx3Ezs8pANJSfsgwkUwBrnyvgNAUalmNA5gzKsxTz08DUzoCS3I9j/Vu7ZwbJTujVjVXb1AEBWgOKpCSHJKis4rBtz6J/++23uj1LPXHVx2AzfJFv25b/cj/bZuyPyeLTp0/PK6mUYaX/NnNu48rVPdqXA0V4StCin2T2z22HlR9ZZ6Rp1cHgLwCEQC/1j+O4eX0LwXxWVH7//fdnAPD777/fgG6PQ9rwqjJ54mphC7J8r2EemqJ+/BBRZNkFXQOStn3f6mTFjXIzhmUFJOcij+PP1XweJ4heIgdt2KAi8nKLmG149Shlo8PwwzpcpfSYUPfsyw9ZeaIT4vg7GTN+cEI4TZwaoMn9vP6JsYKr1p6MpG6+T09fs502oSAvk4+GR/LA1V3ru/EwtUs953MVK8grc6DzQgOx0V3KxP8o+yoXT0QdeEGHYNO5/DheTlbdrvXhuMwyHhfnrfhjrvE723K7PmIwyU88tFoFJe9tknmW7l7ho7Kz5cVE3VahUt5ApM1smfTShoEYeUi9ewTnrJHnEQz8SJ7p0NgnQGS53JZlb/2R3zZjdTBj8krdtJfVBiaIJmf6+vLly/H58+fn8kxk7969e343XcBAAIUPYtOJmNi8EhgZjuPP5JLtZD4swndnvX///vj+/fvzU+Pp83L5+XJoAj6+O8vbOLZNrlwSnHF828MqLfglaXAlhoGXq2YGDrQNji/9jA/h+HyNwV6APLeFTAR8BAuxC0+UPNb0/9jOP/7xj+dVQvL07t274/Pnz8/vpmu+l3OG6TMycBvaYI0xofm4x57gl7ZAu2RfudcAQCYHDfCRv8hLv2QM5PYlgaHfD/b4+HhzzQm9JSQ+tMG4aoBFHX78+PFm4h7gOwEFtkN9U3+MKWw3fZqf8M/3fRJUJU4ZBNpHp1+5YW7gOJA85o7/XoVMm7HX9jaJHdke2TfHegcCmp9SBvZjG2IcnyZnkzweA8f7dpabOMD9TDbLyfMErJ0DmIf8VLnrE7xbJ56wRS7uNjr3TLljAnRN9/fQacCXzvx/EqGFPtPWmXITNUU13nZ9cFbRnJjO7VlJ6rVZ18TXcawT0I7PaQmcxuEZaXMq8xrKKsrl8nMrzYbPdmnEk64NnBycqOP0xSdl8/nw8HDzShSutJH/JG8mkIAVBhPrb1oRoE75fbINB6VJ/lADfAZO6bdNrELThMDt8sXhDiCewU4TrgYOPEsnD0l+npRkLLOD0Gyb9dpKIgN3O9cTvqYVnfxNk6C04eB+HC8feiLIZdAO+KN8tEWedeKqV/ROeVps4nWOqX3SQMTXpzrxPwOz9sT/KqZxpZgTPa+U51pLeAF8BMT0WwPHxlPiXCP7rxOsfaMlZ/v6ajFh6p99t/pTHiC1/lousd/argy4HRPcD8eV/TYMwU/3P9njcbycEBgTNHBE4NX4PotJ2M7kg61OI+c/+uLUN+veQ6cBXzuMzze4h7jFwZlRM3w7XAJRBEm77VAtB9JGtztn0gbJB/6boeW6Z3wJ8k5yzTDzya3Ne4DfatmXfTF4hmfqroGPtMVViaxWeOw9y/FTvfzzbJIJMKsEXinjdjFXFD99+nTjDD9+/PxZu7Rjh0hgIyDKGDbb4irBBOQIQKhTEu2PPyfF3+llfQbi/AQXwcz79++PT58+HZfL5Xmlh+MWoGt74PZtW9HLqhR1Sn+NHF4Jte1FzwRd+fz06dMNv5fL5Wb8uW3Jp2Cjm9hfdOIxsM3R3g28owvy6Alciyk8S8p7se/4AN/tmb7+9re/Hb///vsLvUfmjx8/Hk9Pt78+kbEP2GKf/B4Z2DdXuGzD1I+TZJsIZWwzXkxKbUVnepgg7WRXyD6Taw10tDhn0Jr44yTP2NPiHXnjkQefrW0Ufhsvx3Hc/E51W53jAxDU63EcL3KS254ofujJSe75kzHdD5aQmC/yUF8bf/pIFoQa+G78uW/6LsF8/Mq+nWvMSW38Wrx2/y325zpjpCcAjCFcEJtkcx88X95iTXinXv06solOA74VYGvo3NsrDlQOnE4qBkghgzbPSs1Lru3QsleeJkDZkHcczDN08uzZ+j0g7yyRt6Y/gp/jOG7AHOvyhbefPn16cVCfTyt6O4rGnja5hewtm2wV+XxQewI227yUiYEkW8y0L9pJyOcDPZu0zlLOCTT64Cdt2as5Cah8F2NbeTiO4xnwxZ6enp5uXmjM9ziyLvV8uVxu/rc+vVJr36KNttUX6oegKzZGG0z/1CtXaT99+vSsHwKd6ILHCr59+3bDv1cpCOhI0WPG3LrgtigBYHjgeVYnC47rlJxs4/Ebn4nyZMr8OJbmPldtPcGLXlLWP7PHMSZxvP2zj7QDlg+gZH3yQIDPsUm51dP7tLsG1N2Xx8m2abLNrmgC3y2vUc7IZPmtM7fL/xtYaflqIvvpmZzEmEC/I2ic+GYfbst1VkCsTewY4+PfbrO1ZT53em/xsbXHe/6+I8aM1s5ugraj04Avrxf4z//8z+ff0+UbzB0gAyaS8L0CFiH46eX5tNeCmBPRcRwvQInbblt2ISaapkQmdhp8/gJ4OCPj4WQbfnRCoz9rEGfuGXR4ppQyTlAhJqok65Tnq1VyVi512Fccsm3dJAFmy5Znxtg/+U6yJ58E6tmCtiO2WbLb9Wq0Z7pMjgQBXPnOH7diuYrDOklsTNgeN29fpm7spwVnBwEGkABdn71N37unGunrHp+mh5RtgIwyHsefr4K5Xq83r2EI0e4CYunDLRDHVkjRY8CRk3vOClJWjp8fmLAsBGmRhfbtlS8nToI7TqLoh23M+QJnvy+VOuR4f/z48QUA9xinXu6vVu5C9G+2m3uRhXE4YxKZDeJoo+Qz9medEBAkb1APkSOrqpQxOm4vZQ5PucbYzclEi6fceWEO8cSA1PIU5WuA4zhu8yCJq7rtCA7jlMeNck/gKH1GLxlT88Hzkyvg2sjx3OPQgNcO+JqXiTfam3cRQ7TLtN2OfRBoW5crsMj4wIWLs3Qa8P2v//W/juv1evzv//2/j3//93//szJWYdxxnP5yudw8EbZ6MqqRk0fbpgjlGs/QpC55ZdutnxUxmPEnkpi8Q22VoRmCA9KOD6/MsF8GlFaPRL7JH1e9kkCY0ELcKsyBbsvpJJWg0x54uF6vL564dVDlFokpL+t1Um1ns6IL2mWCPLddHUji7E9Pf/4KRX56LrbAlSBOdJiUDcZ5sJcrrnz4hTriJwNGC0BMJkzG2RpvD3y4vvU2AT4DMforyxpMEYCSPEn4/v378enTp+eV5fw6Ryh+//T056+qNIBmuZic+JNHbNPjZhmjO8am6/X6fDaRL/IOcIrOYn85X5pVbPdJf6J+MhHgRMm65738tRU+npXM9/ZwQyh9k6hH2hzH37HLk8j0x7Egb40Pj2tksA+nj48fPz6fG+UuBfUUHRuQ+ygGAasnPAS+BJGR35OhpmeuWpk3U7NRjucu1/qICwE19dwAqvmZ+GuTVY6rVzo5qWo8t3s7wGd7bHHe3x0z2H50zFjLPO+473HnuLYxJHGCvpukm06X/sc//nEcx59nEpgMv3//fhNIyFRb1ZgA327GEKfaLbMfx8/An7YyY+NqlGdI04oJKe2kHA+/c8bEgEYjaYEqQXXlxKYV4PPKZsjJNn01cOhV1tUMinIyiE3yNHDCwE/n8Gy66ZAyxS5JfrjBAYcAi+Nq4DmtuMUGkrB8los6bjO2pgten/S1s6v2fwPRPq/Hz8nf2gtt20MkpGl16MwE5zh+Aldu4TSAGn3ET6eg6fJeZdklDPLkVSgCFj9YwKTL8rHBrDKSd4MBA2beY7KMrpxUAvSnxMrElRX6NlH2hK4BO/LWVjLoT5aJfBssUs8mxqUptuU7y3py2nKFJ9aTvO6ztbmjFrN3RD36unMSy0xxh9e4ihn+2his/M1lppjAtqf40MbXdrSLqynbYnTKtrhLsl03+1zZauun8T31eQ+dBnz/43/8j+M4juM///M/j//4j/+4SYBt5pAVnOP4+doAG1scpm0/0PF3QTvUEDj5ySqCD9iGHy6Ft4FnO8fRfzvQ/BCU8Tr/b7MllqWDtcDZZqZuO3rm9clZDFy5TUFww9UNbrcx8Yen6bULx3G70tH6Jph12yzXDjm3FRHqL4CNq145OO+D+gQTnNET3HGMInMeUKFcrGsgY7uLfgwcPCP2DD332mslvDKdsSVfltXXvOIe+XPPoD26cKJhf83P/XR1VpCiU9pt2vIDLy5nUBa90M6Y5FoMysq2ZU07nz59Ov7444+b4wvhm3GBfTQbZt8NlGbFkyu2U8JvAJXkuNmALHVKO6f+CGK5Ym078wqqYymBVfp1QuVn4kPb9kt9+8MqhrN9rqxYHy6bfnxu1ra42pKLrTdQ3ORq9XmfPuRz1SGOl3VjezX4Ppund3Jz16VtT3NialugL9mu0mZbSKBcEzHuGmuYB+fz1aSNfLf+/D3E3H+WTgO+//k//+dzIE0w/fr163Oi9ywmDn4cP1cFqSw+jcezgBTEDu1gZHIbLJvtlSQsL4Uy8LRtnBCBo0HpRDvE7uDrQW6rAy05eSYcfjmrb6CRfB7H7Vabz9vwLJNnoJfLn9v3eS8eeeADB6mT+7Ej12GwPI6fDzJQVgJZrj5zvJxoGAyykvfhw4fj999/vwGtfFKWSTXvVCNvsRnaB8eOug9ACD8tqTpwMWk68HD7rY1/7rdJVNqOjPz/3bvbJ2gJ+GJXfuLS20D5n6tEfA9b5DJoC++20ySK6dB/iK95aYkx17Llymt8UtaJLrqNHn7//ffj/fv3L56ajp387W9/e3FmkkcZkmC9KkxqOmGion87Nhk8Tb7fdJjxJtiibfG75fFKGW2euyLeWaGd+bsnBG1M+f8qeU/EuEa9OAaTD06gQgbW/t9HT1rf3JKegO40aQ9f1mMAQvROX02d1QM1BjhNV5Gf/5M8IfGknHGq7Ro4hvH6ZE/hux0Zavm5+QnHnj7BCbHjWerFT02e+JkH2o35auO/o7teyxJGGLiNQslUyvHlnG07ogV2H753nbODxOuciXpAuCrhgMo+2myX19l/48sGap5XQKwB0Al0tlUUJyz2yXY8uzuO20P5BpjR27t3724Slh+ymYyTq4IEKi7Pg/gEPKnLs0yU3bNEOjydivrgqlkSdpw59kxbolPz3E6CalvFncaafhHg3YIrE5z5Z6KxbdqO6BtOMBlD1qF95Bwd9enzVlzhzWpw+J4SBGXOGLZJUSP7COOS+7HeAuQMiEmRh6vC2fpkmazqWe9J9lw5b7GmyeN77M80rfo0YvIwADDx2AhBO+OmechWdUAtk/MK8Hn1qQEu+jh9It8dE2h/3jUgtV2DlngTqwwu0pfHptkU5TY/lM/X2zWOB6+1usxRjgmU2St6nFDQjimj+3KbExlUmd8VBkh+sqyTP0+TCRJjWbs31YssEz4i79EjZdnhhpYTdnQa8OWw/OXyc5m5DXQDgN5KNE2zgJbgOHC7gEwebCQ2dG7trIKwV16ih3aoOHW4othWKRuQa0baZGAS43XO5MxPM5K0f7lcng90s71sy5MXPnnIYJxPbgdmxaRt3zb5+SBDZmUk6tqOxHGdgmsCWF7l4nf05S96oH140uLE51U/A6opQNCmeCQiNjaBgraawgcQ2gqawf/EYwMb+eSKq32KbRMkc2JlPkh5d51XpThxdCxpvsdkFT80QKAfsf3w59W12Ay3lTMO5pVxLAAjux0NTE+6b2DdfbXkELIeGU+YfDwpsm6dqDjBWwGb6MJAzADJPLvN1VlQ6iK8ZawNmPIeOeqU/VEPTRd8e0Ha8OpZxnsFCuhLkw7SJ/vnGNjuafuUYbcaNK0A5xpXoDLR43hwjEPTpKPpg5Mg2inlonwtl+b8NXXmvOi86fhontqRmNAK7HGyy7JeXIq8Ew6gDBNoPEunAd8qmE9BiIKvkjLbDxnwRdgWHO0MZ1Y12sCuUHMLppZpmgk4MK/4aNeazGeXcq23ST4CPjsXkzP7tvOlXW8JEMgk+O5ARfSWugmcbRKwWl3l913SNNG+CcCsN5Zt/E3tt3GNvNMMeLKXlQwtsbPeBPgmINdWZNp4tkTUEr7r5JPBOeSVV8s66cwTgOZHqyfevCXH1eskKe9iNADa7HgV5NNOi1fkzStcLk99TTGEdVt8mfw6vj9NrGzTBlO8RvkmkOIEyjJZaSXIayuJDUySyFd7EpmT/eYzZ+Nz5Jj6sf0zf1geXnMuY8wO2TabT5Ea4N/Ft7RLXZMHTghWec/8Tz4SXdq//LeLly2Ht1h4DzXd53vrdyrf7p+l04CPhjAlqgYCG0Cgo7i9kJ+sPI6XiL713RCwA2aSG+v7MCfJDme+2iyAcvu9Q+TX8k2ri+bJIIfyEwSRX68E8X5AVVZj+N698EvQdr1eX6yY2GB5Voe/HhCHjA0kefIhCYN26oJ1MssOQPAYNkdhMiAQ4It3PcYMyLTrlU3y3J9XgK/X6/PrW7iS6Kd9j+P2aXCOB/2NYOTp6enmTFmIs3EmLOphmkFSRtoWt15X5QmQUi4yNj1yPNgOj4e0iQz553iQaNfWD1chbQtcpc+45j5Xu88mg5y5S9/mrQV0jvm00syy/E6751nXVTLhypi3J6OHtrLJ+y2RJRb4fZdeZW0Ue+EKXtrheVXnHYMwxg+uOsc2XYf+FFvg7g3l2h2md8zYrXDS9sxP+qTODBa58ub+wgt3VZpNMM8ZtDsvtgcg7ZMGUM7hPJ+eei0225+nlWbnM+df4wXfT/0WK1mf+mmxpsVI4ibKysm4bfQ4+hnHFd0N+AxKSFYkjSKMtoPqVoS/u53jeJmQGATNWwNwDgpu233wWltZcCJtf+Ql/JJHBrEW7Ft/7TsTER2Exk5ZkgxSL8mLZZ3sjuPnAxFPTz/fQ2eHvVz+fJDDW398mMDb6d+/f7855O4gfrn8fFKTv2DAp8EdxKhnvzOOjmydsh4DenjhjDr85374oQzhJw89PT3dvuonCZTg1a96if9YPz6HR74vl8vNL5EYKNm+ms3Z/q7X682LiqftSdskbTHk8WDSYCDPz/1Nwb6NYXTB1eW2ohT9+Tebw7snoWznw4cPz3UIvP1UOW3mw4cPN0/XOsE0UMq40eJH+OJWEtvMd28hMrG4//bke2IGn9jn9ejL7/kkr+2VSQTO0ypZ/CfvwOTkhitu1lvLCbFfn7VMvKLNEtzyrQReGW4ys22OawMOBB1ZdWtgJ7bkCSxjuH2P/XtcJ8BDvRto0Fa8RW6bbBiCQJULACnX3hPbYnOb6FDH3lViOeudMrCPMzGzxd8G/s1ju0bAl4kFJ1lnJhamux7aCDNtML28bFTNoLsSmrQywIaq05aXdCdqhsKBnoBV47cZGY2jJUHzOK3wsQ/qoslO0NfkmgyeYMrnmyxXW/WjcdqRmEiYkMg3yzWAwPvW9zRztQwGQdZJe3KO7fhMIfuL3G1C1GxyNTEw4HFf5otAm4Hf5NdhNNuZZqOkNjbmyZ+Tz3DL3Dpr20vRi+2t+S75pc1NAdsrdOkr+uXKFQP79Xp9fhG0J7KWm+MYsE+QGKKtWl/5y0/M8aXeGX8+HOP+U66tCrkOY3cjroQ1YNHejcmJjBN6eGtHelKXq9hJgPQVJ/DWToiglfxl3LmKbV/LX9OZYwlttPmYecz97Hw0f+Ok03EocjmWMg+3cWm7HCT6RANZ5MvxnmNo/GAgFRk86Wt8OYYbULe69sMWn5zXqLM2qWC9Hc/kbSrXcI0nt6tjCY1OA77Pnz+/YMord7mfxEFUbRDlZXTPEg20nPAa4KHRE1ieSUCele9AjNuZgFRD/gYEU9utXQI6AzXem5Il+bbB0OEayEi91VNtLVj47JC/5/+s4CTZWy+Ui3zmAHFm+tRPAhQ/p2BDvpxws/2W/loSbPbo9pJUyQNXgsgjk5j5yVhxPJgoCZwzxl++fHkx4eLTr/wpMgdnEle9vC3fdBLebBcEL21CQHtKYv3nP/95fPv27Xl1N7EkfUQG1+XYUj7y2UBGVmG9gpUx4SuKCJzoiyT63u+//37zMnjWTVmunERWj+suntEGmeRYbke0R1+n/tJ2yjfAl3pthY8r//bN4zieHyjL8Y+2Yks/pG22ct+/f3+2GQOn47jdsm3b17ZrAiLnD66A24/bJIrg1W8xYDtthY95oY0xdxAYSz1hbE+ZZ3Jj8GG7tf+5b/bHNvgLXV75T/vh175HuVg2RD14lXKSwX4f/U5AjrqKLTYwxxww4QrqzHUyPvfQacBngzqO/iQTDc6rghwYDmJbEbGCCPCaEZEMJCZDcbtekSS/DfR5JsF7dHAGoPZkmgEigx3laCsH1OMETJt+uEpBI8/YhRrgm2ZGqz6bXC2gU9/Wncfdup/G2mDbdbzyZzCYvpNAW7C0rB476tMyNT3w/2kG2sqR19Z38+OVPq2vlgjc1soOSPYVy+B3DlJOr2DTbq1L9mMZWcbbgZHfqyE8e5hVpubDE5iKXXMbt+my8fjjx4+bbfSQJyA+r+mVoNCKR/I1AT63lf5Snit49ou2gmMepvjGlbmWIEMcD5ZtthB+aAvTxNB6InGFr22/71b4Wvsej6afxtOq3SZLrhHkBFQQXHu1iX2t8n3j2/HdMXuqO4El23jzsZYrVvpyH2djXKMmD/mayk/6uofufkq3GZaZyKeXwlnWydcD21Z4mnFPQk+DeL3OL0BMuQas2orBcdwmKJNnL5TTumrGOA2s77Wn1lpAsR4pF520zV6ag+RhAq5+mA/Kz3bYHrdUpsAdXn1Amm2zfrNNk4MGxz964MMmbHcn62oM0197DUpLTpax/T/R9Xp9MZMlv5N+mv6op/SfttvvRtsu3B7vNX1yyzHntgKU2gMSHCc+RNQeOGvJymdFbauObQSi9uGJXIaruNY9//fK0g6oNT683ZUyjkMruzVvK6DquO86bZW81fFKEAGl/cR+2GI22+BEwYD4crncrDBxha/pjnqe4uvK36x7tjuNdYt3/DRvLTZ54SI+Zt8NIM5iTotxJvqhJwT5zrrX6/VmxTX3PIHl+HOMmd85Nr7WACv7jM7bxJn6bzbgWNHGn4tLqbMbO1/7lwK+XUAyQxQ0gdpCU3gulaZNbhvlmo0sy/UrQGRAMykxKzj8zna8ZcmBnVYuaZBT4vR9y5D+WZcBJcC6bQe2/loCa7qlrAZGl8vlJiHn6UkGzfDDbZrp7EiT25/tXYnkk1s3fGdg7K9tO6ad6DHlcgbKzpm+2naZA59tx9cSVLm1xQQxrQCfJYIX2gET6BRoVgGOvsrtZL+2g9uu1DfPmXFLl/0SbPshItYjbxwHxg/7TOzDMvJp8fZwjJMLzyW15MAx5JiQbx83aHbEh00o2yruUTZOYFYrapTJ/UzUbLLtijDmrIAqbZ7XCDBiC06W1CPjcvP7NjaO18xfjCmWvYEJ2pz9eUrotGG233TmMWoxJ58EqbSPfPfKXcr6hxWysu2Hn1qftN3Vokiut61W37euWn22045WMHf5CAt10vyL5PzJcvxO3+MLvydfJXGHkG3fC/JIpwFfox3Dx3FrtClrgLFq30q1sCtFm7eziqKjsl4DZM2xm2xTgJ3us81pwM8k6tX/NrrWjnVg53SQ2gWzfF850tTO1LeTha9PTjLZHmf8rbzBg3mc+nIZ87iq3+zfejTomLZDKEPrr+l+Ktv4nOo2nUzbbJyBc4tn4tP9+HNVr92jvndxh+Unflxvum4QybaprwZ4k1hakmj9e5xbnG7xZtIBeWnXmr1P5Hi0iiXmyX2swN3Et+2x8U9bbfY5ydK+T3F4xQ/jFGU1CGwrqa1P+hqPfnAC70lwGwvGacrSdnwsX+5Neco05UeCxFVstb20nOFc735WtjzlMfY3+ZfbOet/KzoN+Frjnk1R0WTOSSjfieStDBoHV158+LYNKI09245nyAr1W92bgdPBwrf7WwXW6b4DruWaloJbsGuyMUGcSeJtS5tnaPLrAVwpo0481i0ptcCRdtJu3luWfnPo2rNTJk2/NmFKJrmW9+M56DCwG7BxJchnz378+HF8/vz5+fwVV2T9kAkTv7/zrwWH6CltG/BZ99Qtx6IdAqeeCCw4s+bfLog5YTlR0Y/awyiRkT54vf58TUzzMfsvdcKyBlO5317H5NUdfj+zMpayfBiJtuRV85XfO6a63HTAuyVfA8sdYOO9aas29QxeqI9JRh7laJNLA2Xey8oUx6sdI3Hs9Sp4S84tjjT7945LAzRuiyuTBpX0ceou1xIruTJNfpxL0h5XM60T6j7UgHNbsU/7pAaeUj+yrJ525v/EGJEtOyjkseUctknf884Z2zA2IE0xzz7e7CCf7Y9vJ4ie7gV9r1rhm5Jz+2z3d3V5bwJDra6TWAMVadP3mowZWAeZKfhNCaW1bZp4bbI1gLfqd5LVgddlnASdGB206Tzkm3pxEjxjsAR85OPp6emF4zR7abNN2wDHkNvA7KsFfcrMQEE7ZJ/sK4HEIKnpxICvlSFAYNJmGyu/cQKYfHrHG8/CtXY8BpwQtC1t2g7vc7ycADym7tO8N3l8rbV5JoaYVoCToLmd8WzHCNofiePQ/M8yeSWGk+yVvM0PVzEpn07ibo9880hIG3PHx8hjG7d9pW7aOxNXLR/j6bQFa9k9rgYflNH12pEhP0ma8i1OMi5ONpW+LWtb4Gg5y/JbninWWXcr27PPRzb6z4qarzsOrXzWNmDeQ54cH0ffRZqOOOV/jveZ/El61Rk+EhNdgr2Ny+eIyHzaoHAO5lk9mpTMulGE3+vVtgLJfzNIzlSI8o20begNbLZBc52VfNZ5e8Ivum665cqT9Rvi6hD7stOkTc5AW2Cio/iXJthOW1GaypE45ubBZzra+E7B4OHh4fkFxdMWWeMxQTavicmYcnVhpV/7jMeK+vXKC8m8ui9e5/3oqr0EmHVXWzMmg0DbR/rNL42wT7+CJ/cST6ZAzDYoV0tsK12lrgMs7a7ZUdOZX29Bfli2+ab9dAIkk1zmpSXU4+hHItrqXqvbwAv78hjab/idejpjc7v/Se0MnsFJ49F6abqgnXnCOLXjeE3bYgzwfQMRx+fwYEDIfEjQQb9yO+7Dk+5p/DwWzVe5EsgV+/CV9ttqFvXZdvH47IDrcLW75c9Qww3Jv859fiUM2+Ynde/VaPvc6lVIr6G7X7zMZMoB5qy0/SzXlIApAGcLHvTU9fZWc76scqQd8pg2HbC5xE+jiCzpi9uYbemeiZ1kZ+f1JlfIwT51+B5DOzJ1n/9/++234+PHjy/u88kz3rMuaOxeMud9JuJsteaXC6ZgkIcWLpfL8zZE0wN1wUTQkkmb3YU3r7y5bn6R4vv378fXr1+ftxRXwSc88ZyLAyG3CNwOHbwFb/NIkMSjFS7PdlY2xvvZDspkizq3z04B3SApW9l8aTXlJhjOGHmFi9/5/rQmVyYRbId8NrDY5KCNN19oNkqZyJsfamkH2jkpyz2Wm/g5jtvE3YAU63B82tZ6A3L83uzSMYB+2PRkwDXFB7bdZGnl23XfYxwLeDHotZ4ch9yW67ZJC3mZFlIYP6y3lg+st/iIrzXAZ6B1HH2C0uRlrl3FRdpLvtM3+RBFmzDwe4uPLQa2fokFMjZczWMc9jEA5o/80hPHpVHTR2I3yW3Qj11umtSdobtfy9IGYLUdtUpCk5JY12VosCtenZhWfbT6DoZtUFsg27W7ot198nYvtXFpjsD/W9kzfTgxuE1+GhhRzhVxjKb7/N7st/FvuR1AVnVXPEx1z9qn7dngYkrKK/nc/19hW40P8+trrZ2J31+hprvJ5nb2xfJOFv7z/dQlMGI73pLnroLPTZOXadXT5WjPK76tqzZ+/D7FXNvjasyt98meVvKduTb1v+L7DJ0BnQ38nPGh1u6K3Edru43r1NYqdjTA3uqf1anzRgBaA7sTaJ4A01la5YMVr7zW+j6j750+XxsX7z7Dl1kLka9plYT4GgfObm0Mq7Zz304yBZ18b8vn0zUGWpJXLWOEXgWZZjo0Qq6OtUfR+cnrXqr3yqWTTTuPEHm56pC+uFXr1QTr2vxRZ5kBtT6oc9qUE5fHmmN5vd6uonAsfa4kfVCPHPPL5XabnDp2+7bb6Cn/R17/koDrOGBGHt+jnqP36aWzlJcrwOStAYMGcCYfdoBLPa5OsgxBSuwj5b3iSv20gE19e5bsldvJ1my/Uzlf4zhM/jCBJr5vz4COsvDnxhgjm15NjGerLf+mzyYr22Tb91ADLOSxjUvrp8W19lvBtkfzQPth3HQ82P05zjOGtR0kjkfKc3XMOnfO2tEO2Fg++38DGNN283HcvjrFZ5Y5hs1+2Q+3XXPND729e/fzN7Sz63Ict0dmrPMpp2bV33L7u2MSeW1jPNXheNAefazHfObTOWvaMj5Lr1rh49YPk7STEuvlexTFJdP2Z6FaYmv737sANsnFfgycOACNh+PoZ0waL1OgdrBj2zQiBwIvvTcHI+Bjf7lvntqWtPufgAbBb3t5Z5OdsrKNto1FPXkrwck8ydQO6aCdcizLgMjtXNuFV7d9jqeR/YR6ta35Psd/SgYMlN4esZ48FlMCarZv+5+C+nEcN8GOAIJjaP2wb+vGus71CTCvYgPHw3o0Oan4Xvuj3E9Ptz8I33TMJyVbHxN/lpn9Tu1QZ00P5Gu1lTQlrWa/03f2Y6Lf06+bXVr3js2rHNHGZcohzR4ZPwwmDMBXusn/06LIRPadncy8z3JTfIhsrMvt3SlW2o9bDLRf+4hJ3tCQ93Mynrtt271zpMcmn9zmtT6ZF9s4tElryJPsdkyGdSYsZBv6lwK+IMzv37+/ACFtH78ZCAXy+YaVsZkXrg7YyFt9G1lrsxlmq9/6a4h/MmoHzRbEV9vkjQf2Y7kMAvPdAMBtZIwy7qHdGQI7n53N5SawsLILB9gm/1lglcDBlbyWtFvQTbs8E5L7BpFNZ9PYTolmFcR93+37eIL5ObsFsrJL89oCXKvT7KIlcrY7PcST/iZ7yBmclR/uqAEDkseHCX+KQ83m3FaLM6nbksxE1IV3U1bxxvKu7Nd23wCNbbGNF8k6NLX6q6Q4jWPzD7fva22lMNd4r63cms/UmUBES/qT/gjEJpuYcpd5XMWX5pOTrjzeXrigzo7jeD77m9eseZyoJ050qeeWj0xecbTvWX/Nf0LUEXc2Jn44XtYXaRVLd3QX4GOgzTZElMttvJaMpgFqQk0JiW3kyV3+uHUGmMqdjJjUHKUl5taW+8vhdNbzlq9ldYBMP17hmwBd/rwqFf5WSam9+iH33r9/fzw8PByXy6W+7X/izTM0ytwOw/qa5be8brslPh+0dXDxTI7Xmh15C8j6jz9wHPik+BTYJ6DgernmFeBdWxx7bn24nOWayD7QDjTb19N3dNIOLCd+8JgE6zhIrx6OCT/N/8Nz7Dr9rLYtV8CQYzQBxvQZ3Xjbq4FZjruTilcoUs48T9s/LMuJSivbZDPvzTYbPy12GSC07f0VuFz14QTqsZpkO47bIyNnEmvsqJV1n/7FjuN4+VOijB+UhTrxA14sx//jc7HztlXL8itgZFlYpr27s+nBCwZTLGMfiS3x+5RJvcSUHEdy3F9NZtlHqOUh9s0dtvbOQbcTsNp8u2EL5+Rm16t3AU70qvfwMSEeR/8tPgdJM03Gp/tsL+WtgBZ8W3CZ+jHYM18cnEm5O7l4bUock87OkHWwCzoTiDa4yHcbomVp+qY8uwS5GquVjdyjq8ZDk6OB35Rpsqzum8+z47tK5GeoBf0zul7xPl1ftZf7r/HJFcBiu25jGiNfT7DdzZZX+j8D9la15flQAAByj0lEQVT3mr3Y7874RvOJVZ9nyW0133iNfZomn7qnrj+pvzNAcfX/Kp7u6jX+Vjp03DwbL1blWu4xf5Zj5Q8Goax3D9lfV5hhwhpNj5O8brfFhglXTJ+7fDbFh7Nxw+0bC92r81e/ePk4+rYJGWszK86c3B7rOimnHPtmnev19qm3HPxsv49pg10ZN//a/rlBUPiZdGaQtHN8ykqdeoZombx9x9kEH8poq3aWi/1mJes4fr7BvvFwvb48NJt7Xl2btvy9wmfjb3perQqm3bZKlhkYdcj2vDXrdzFypYg/P8R27KxtKzsrxNNZNMoRHZhH+gPbnnQy6T/30odXgtgH+WhneNgm9egtFx/Ep1+bb8+s7R8Gcxm36/XPHQKfVW02dwZ4klf3l2v8bWfqm+/5zDhlu5nyc1Vj4iH6iz2vVi2t3xYLoy/Hecu62malHU82McXRtjvhHYld0mOb0xi1+JpyWQ0mMZc0fbLvfHr1m3VYfgUuLM/Or8MP41FbXXOeiYwm65LXGM+mYwLWLcePq4O2Yffp16mQ38vl5yvW/GBb46HxQ10kJvtaO+NHWSffbNR8x+PFcszd99Iv/ZauAypnJfxrPyvTDJhlTGzPCrLz02jyvpxv376NAGRFU8I3X40mMNf4XhGXjxlcpy0bB2fKkgCW7cevX7+O50ka2KbhMoG2QNZeROn37NFeLIu3pVvSoXNNOm7/O8n5WAIPIlN+OmIDWC0ZEFiaD9sytzz57stJFgf/2Ed7YrbRaouXQXzSrSdw1Ad5Xo31ZK+NRyZiTyh9beqH/mMbb0Qbm/idgJOTCP83eHl6erp5vxcBm7eordd895boNCm3XtiWQUD7dQvr1zrKtabTaYJhQLE7M9Vkm+LWdIaNNuEtstTzdutx3D6ExE+3bQDrOi1HND9voGUq77a9OmY7aqCvTUZ5LKH112IJaQUyE7M8sTRW8BYoYzJ1QbudbLaRF0qcqzgGtvnmA+zXzyyQb5dvOnW8b/a8o7t/S3dCtw7mFKYZGut5tS7UBtflmjNMBsWfSfPKwtR2gvh0BsBGvkterdwZmoIrk/zUHmVcgQ7fy8swaewOgJanBczWh/v2mU4nNuvBgcC6mfTB4DXZVtMdySCQQafZQpvtTv0xUHFcz9iVk9IuuO0oQYrJ0Pr3Kqb9sQX5tqocit05kBLI8hr10a55PPydCW+ymab75jO0LdZlYk25VbttAsb2p3Fmu5MNtDo7e1nZXdPb9D3UVlabbLtk1vhtsrRynNDxrFw+OYZZfW0vUW7jfw8/zpeOIy2etn6mWOf/2zXbS4vjrY77PhNvnLdbP8y5LkN/Yju0lVVcb/lvyq3u09cm/5z8ZPd9ZTvkgX3fu9p3GvBxGdWrXm37hNsQZNzBkAGeg8Sk2rb2POMljwSY/Fk2PtZtHnL4Mvy0maV5o7Ex8K2U3watUTMEG1Tkj9z+QXAH4wZOeI/6C/DgNufl8vMhGeqCOmNQam8id4CLbrONxYP4PNw+AYPw5EQx/cxN+Gqg1YlocjI+dJBr3K5LvwQ2TDAtgIXId+zMv05BfqjnyGiwuNqypT21gMPtZQMjth2dcoWTDzCl7R8/fhxfv34dwV5+cYWUtqnfttVGm7lcfr7/r8nf3oN1D2gJ8Vca0o7jYsaD/hEePNa81n7+qSV8jp99nddpf1ytOQPkJqC7WtVhLGyxnTbcDq+34yINKFoHrmOeosc8dJgx/PLly/Hly5dnu88YPj4+HpfL5fj48WNdpUl7Zx5YTFnKn+881M+Y4XHzUQZv/VE/1gljHHXverYLjo3BFu/vbCr82o74nccXzCuvcUGJfhRdJU5zEajlhaarFaCccnw7ikM9rI5WtEWE/O88RFl5JOQMvWqFz0ZoYQgq8n8MYdpDbw7bnMfEoME6HiAvy9NwW5uNR/M3lWs802BtuC3RrpKMqcm1q9/KtECVJOW26bB2Sn5fjZ8DSrvua013DKQteaecbavJzP4sS+OV5acEeYYMLL1S3fzFNrWjlb1N5G2cBvpyvSVg3sv13cpNa7PZku1vSiD2O/Pnfn1tGsMWE3n9DE1xgddaTJxABb9PNu97Z2y02dlZm5v05PabHlexcPLPFX+2ywmo8f9MMHYyWwaP5Yov56spfp2JYbvx9FhO9Vdxwn0YtJzNrY2HVd3mw46HLJs/Lh7cm1/P+seujTPj4rZWNnxPnAm9CvCl01XwpJKP43gBHGjg/L3a0EpBq+se+BBXHDkjIB/egp4CVQN4PFfSfiu1Oc90aNx6mH5OKYbr8yQMOOQ7qyd8jU0DFu2cBtvMjDdtZVWn6dx6Oo4/QQQPg3MFh/bBFTIGUMoe+dN2Zln+3VLzFX5XY0RwwnttIkM95ZOTDK76NNDsYO6zGy1A53rbAqIeHeAmcNp00Gbj7odtvnv37vj48eML/fFzlzRJtH+PvVezXS9lvPrBsZlixmT/07W0eRy357tam5yNezuxnU28XH7uThgENR8nf7QP6p56sY1TP1mZtJ2ZzM9x3K628PyxV8BtV5E/5xk9Rs1uJ9Dn8Wh6yX2uLFFXu98D5rg4R9jPzW/KedUu13zQv+2cNRBln5js2b7Q4hPJcYP26vZbzAlNW9OxdY9z7JX20WTheIa4A0S9WWbr0LFtwgCWcfKRM0TeWh5bxeozdDfgI00Jgw7PwfKypb+nLdY5jnMvGmwG7fvc4uHgTIk9MqwAH8uF18jaAME0YDEor0Ty/XcGot6/dzLwNTtC7jGop90WpMLn4+PjcRzH8eHDh+PDhw83bdlRW0Bn0nGf7Mfbbm2M6WBnjgGwbQb0Nibhsenxer3ePNzReOOWJpPmdEaNOuP3ltx5jU942l53gamNr8eI7VpG13337t3x4cOH43K5HI+Pj8fj4+Nxvb7cqm88Wc/NP9lv29Jp7RioJEnF7r3i2OKa27T8tIMcrWj+Qx/m6ilt3WchuXVj/hgrpqMMrZ5/+YQy8sgDAVdrO20yIacd2gIndXx60v5F+5jswGNtXjw25I0x3EC2ART71D1k/3Uc5HXaI0FgFkPy9/379+djNrttwFVOpK75Pl2+ycLxwbEoPJjHJuOEESbA5yMpOdZyHPNPLjpWOC+wvxZDSdPupXVh/UxxyDposdc6id+wLtv/Pwb4WhJxmXavgTveb0Y69dX6m5LYxIeD3Rlq/EwDOl1LnfDawNVOX61/113JQF21wNqC1Bkd8LsTEvs2GYSzzqQH6o99t+sr/huoag7L/ydQwDa5usbyU2KyDBOt9OP2WzJzXbcxjXlbUWPi4IpO0xsTKPuZzh/txtH6mGzLMlnW5r/NnpsNEki0lXGXn3huNtWSaAMlbcWj9TnFBPfNsZ18zHxOMmfy2ybZE6+viTkruWiPDfDt4l1oSrb3yjD5qu2o2d2UI3bx1XppQO5M3dRvW9xn6p4BKmfsdgWYWln+lvWOzgKphj9a3/5s+dk5YtXnZA9n6O7XsuycoyUOnt/jTLgpY9WPk635yuyEy8wtsHM1LnXtMB6YaUBasGSQtKxNBjtuHCrluPrjlcgm53H83CpqKxiZMU3jxqdEzd/KMB18ppUcrvZcLj9/ri+8Pj093Wyj8O3pbZW2ydACEfm3bXGLi/z74Q4mMbbdEua3b9+et7+5pdv0Ndn0lPAddMOjDzyv7IzyU1bWbdt5rJvtEq/2mri1lzrh//v378eXL19uAGHsz/pNnbaCa9u0HdoHnWBtl5TFbVM/6YM7Gs3nTV7dcmwMP+3pUG7tpcwuCTqe+Rp/rnKVvNhuiy/kkU/5p147x209Ug/+lZ/QtJVIfj3GqZ82Wcfbzh7z+HLKhm/ySRlY30cjIh+fAKZu0g7t0CDQv+qU9lY7Yg3scWubMjRb2sX2KSfk/oqaX1FuHwloPJIvtvX169cX5drKH3l2/qUMTbfekXJstw9E58YLnETu8MK99Evv4WsBIN/bvZaM005LyhO1BMnBXSmC9wIifL0Z7YpX89H4asGY7bcBbNdoHPy/8UuAseOzOcoEpqYy7r/RxDfba4HdW54TCDIfkx21+o03OifvnQle3kqyLUxj3nhsRB4z1gZq9kuW9712lof2w0DLdri6R96bvzOh53N6yat1PflDs23XW322PluZZjP+3miyT9u4waVt3X1yPOyrTf8TAJh42sk11SN5Vdjbtbs2rBNen8Ad/+f1tqLnfpqOrH8vKLTxaWQ7aPUYb1axgX7nNqZ+Jp6mnYgJ+BuYnrGTs7bnGNl8Z5X7juPWHxJfWjzcTRjy/5k4vMrzE2Cz7I1WPL4G9N31W7rucAW6moO63JkBthO35ExHZCJpfZsMJGzMlnMCOS14t0Fuq3Us387jUZ/NmNpMsMkzyei6cRi/viT1uHrIV2yQ50k3zUjTZgA4AzQBAWegk/wEWi1hevzadiJ5buNAO2szNds6tzlXZ5B4bbKzqeyKzs5Qc30K8u2sY/SSV0qQeE6SCTLyc1xzr9nQtIq9StotZnjcPK6tbfLUJh3tvYBNdzyjlTr8HfD0S/sgLy0uOJZMQGZKlvnkGBkcsV6L4bEL9z3xsQIh6Te7D+3caxtjxiSOG8ub//b7zBMoafppcbi9Hol92355Brjliyl2pT7PijY+2nhMNsr6rNNsagXarTPXm0Aa5fQDgMyVq1yyivPpn2PQXs3E75Ns/D7hnMYX/2/Yp+mlLWDtYviKTgM+LzOScXY8Le83gVK/LXcfx8snedqPRaedlJ/6mv7nk3Vuh4Y2BSsmDfJM+drZpimwNwDibUDzYIBrvttSuPlpoM2AL9dy//v378/bHNwO8bjwexub33777Xh4eLjZ0qV+Mh7ehjGYiK54+LjxQj1OCbvxG364/dLeD2dgQH1zfBt/Z0Ee/yevDuhMAG37rSWlCXTZ9hiwvn379uKn6dhWdNYeQpoCZu75XYCWpQVrl7M90w/bw0Ftu9UTOSYhjrsT2+Xyc/uN78r8+PHj8fHjxxsdND0S8HEcOK7Tk/r8bgBBG4iM1Etrj/phHevDSXdaEXLyoo+3F3RTx8xJfofqKt9Ed3nYoP0CEylt5WEC8s328nQpbc4yUh4+hevdAOuxrbgHGNnPHO8tR8shU+6bbMZ6TftpI+1MuZ08Wdbp5zqbLG0VsgE+lrffT2BrogmAtfsTXlkBvhZn3WZizr2g764tXRuu7zlYT8zy819F6bf1Y/7yOcnANie+W9Jp9dsscTVoOz1NfTX+V4Z6Rq6UawF9RWdAzLTS1ozffJ1xsFVb7o+2M8npQDzxcXZsd45Lvla0G/Nca4E/CcIyTb7c/Mi8TMllkt1g84z+7LtN1ib7qu3mmy02rGLM6n/y3WShDbo/82adtT7vibc7H2r2sBpfl536aaD0jG5ZlnWskwYAJtt2uVVeMK+ruNFs8V5abeNOOcx9TfbQ/HIa+/Y5teX6EzU7mnzwV+z7bPlJH68Zt3t5eK19rOjuLV0zaqa5NedZIutyqbIN4jSYrY7v7cAIZ0GeRWaWtCIPwg5pBxiwDFfKeJ0zo7Yak/Lsk7N73p/49qpG7rVAknucYfKN8K2PVcCmriPft2/fXqz6ZPVsCu7tPFnb+maw8iqT+TYIul7//K1h6zt9cxUxKwZcXXSwzR8PdHv8d+DFemjXmzykd+/ePa9GfP/+/XlVJAebfUYpq5jt/ZImjiF5yZjyXluNJM+rc1Icw5bU2P/qxbpNRy2mWBbKm89m6/F7vgssZfmrJO3gfHQQe0g7fncfwQsfuPJKs8l65YrHalvQ4IZAbQUkpuTN79RFi2deoT2OP/2QvkidsG/zQH+e9NPspAE6b0W3HbGQjxakzspnGTts49zy9mTNK2BtB4568C5G+DV5Fc4gc9qV2uVJfrY81vK+6zfA6/Hn92lsG/+htmOz4sX3Wm4wP/xrK8Y7wNjoVWf42j2Dtik5NWZXRtCI7bdPGlzjuy1bmze3t1NuW+pviYq8TgDrDBk4HMdxA5ZoRM3RrB9vn5vXlI3Opq17kg27XW9JLuRtvCn5uE/z3gLg9KCAxyxy5rwVA0L4ToLkRGcX3Ca7mnQ2ydTutboOMB5v6oQ/ndfGjXps/EzbgW7jOF6O8cS/ZXFSmfTTZHWsavWbXCsbnsDLZI/h34maR0vMX+yMZ2iZKFjOOp+oJcVpbO1/Bnorm7wn3ntr2GPXtvf9xK35Nm9OtNRlk6H5k/XBxQMe7/DYtOM0k06azbZcwxxgYNiOKLV+mk05JoYm33M75r3JOsXypvd2fRpf26j7a+VbOffVfM19tTjU+t/liN15yXvpNOBrB//JyBQU7LC7rbuWAM4o/mxZ82GDngJUC+JuM7QCQw00rZK++7+3H7ZPvtsb7FuAD6DxeY4paTr4rwKZE0aTYZWcU4Z6bOe6HGAdNN337iGHCai18yqczadt6916cd8TGfCvwAmvX68/z/94VZT26TYcfCY/dkJaBW7XaVvkLea8e/fzha8rMvih/wco2I/a5Ic6a8mF1HyXtslVF9srfwXH49IAlmNJWwGbYlfG1K8YsRzun/enGDjRpLvWln3fcv0K8b1sE+AI0RfauTYehaBcHB9fP47bt0S0l7Gn/V08NTBY6clgjnVM7HM6g2nw0/hvO32NL9pha9Oxddcm21jteKV+i6VNN80PXcfUYv00rowNbHuKuffQacDnA8JMAI0hG4VXP3gv5IPPaaeBM/bVVsq8lD0leS7Nembpui7XDGH3HiQ6uB8saMAyjkDnDBBrwZOBiWSZ/Z6+Sbe+znY5XumD+pkmCR4PHmR3fxyjyZF46NqJoyUNy29Z27YJyUGf+qZf5A32aZu6cEJviW8VzHzAfmqP9dlufi3FRHAy+Q/l5X3zxvamgMXxCG/cOrYeuLVn3U4rxU6CsZnI6a3hadz4CwdMnJaf1I4CUNbv378/y5/PbKE/Pj7WSR2PV9DnuFWf9u13LTmlDl9f0eJS46WBgmavu2RFINT81+OxinVTm9RFjjKs8pbbbmNNfTHuNd4a0GqTI+auXV5Nf35X4zQejoO+Rv//8eNHbbvlB8rKcnyIZiLH5AZK+f+EO1yX/NHnmk+0GGqdtT6tX4/Nygd9n+PuHMCx+RV69U+r2fhboprIZVdtT8q00iaeW4Jpg3CWpjq7wN/IfEy6cJA42/bq2sqgp/qmCVi0clO918i2G//msBNP1u/Ux+5eS5Lue6XT19jjLgA0HdD+pzLktZWz7h1kJ3namDU9NlrFCf+/asOfq6A91X1N0G1JyW3f29ZEHuOpDZaf7rckv+q3tX8v2XemeME+2tOaK3uc/NV0Vg7bUrP9dt9bySseJ56a3Lt4vtOtaYoTrn+P/674cD+rtlayuZ025q/159fWbe3k+zT+r8Eqje4GfG31I9srucYfd6ZhE8myvomB0Stq5CP1p/Nokwzsx/01XvLpvjkYXHHhIffUWwGL1WBeLpcXP/68Om9Cnilbk9/XqOes3vlN7hkTlmM7XmFJnbTDFVyu1qQsVyt8DqSdC/Fsy/cIRtJ++rxeX/4sFflgu+GX/3OVlecnvYLT9MH/2z0ntjbWHk8/aNGABfnPp3XT+rte+7nHtpp9NlDzuEDbYlut1rEOeWmxop3vYpn8edW0AQbrqo1R00X7FY/Uox79G7dcfbcOGY/yf3uYpa3a5f6PHz+eVy6n7W3yswLeqwTaVq2s/8lek1+s6/CTV6LE356enp4forL8zT4auF3toFiXu/89FmzPfuiyBhbNR3egY9qVcuyzTzFGO746jntsqVPHtJbzuGLfcgh3w1qMpO9aruav3oVo8u9y9pQ/rC+Tx8C203ZPpvbuBYJ3/9KGgRYd8jiO5x85puLbttnq/FmonRtjAk+Ztn15RhE7MNQcOWTH5wFdAqNm+I0mMBY9theLruqSZyeNqV/KR3kcaCnjbssy96btAxrzdK6xAT3yPAXYlkAaD7nnRNmApPXFxOCE3s6jNaK98nvzH/Li/xt4sY6caGmzE7CkTnytBafJpnzffswytjXLSLksI/XTxvA4+hPC7ZdGGt8TEDNRD/zZssRKUtoj6FrJbX9KXG7JchVrk0wtV9oOz82GUib8TD5KfUw2NgGe3PPP/jE3ZCs7CTv6MOCjXv3e0AasJt9d+QnLTNcMPpzTWP7Mlv4ut7RxbbbL/qZjNMx38ZnoscVuy+nYFGKOM0+Ug23SB9Kmjxkdx0sAaWDo+N3IMW2Vf8/E6tbWFEOmMveCveN4xVO6NAIGY973axAmw7XgkyO1fnaCt3amFYyprVXiyn0bzgR0Gv8rI+J1O5IB9S5J29EMhPy/+54MOgmG9xs4MY+7IOX7ZxyqgQjeWwW5Jr8T3O5VPRNom+z1DBC0jJPs7WzqKuhaxrM8tHFr7a7aa2PJoDbZRWzRDxFNfLgtj3EDcwROLeawPHW+s0/WmXxt6sf3OBEjEUQYBK/43PXZ6kzgro3HJOsqppg/xiGfW0wMyo7AGTA2JU/bIuWf7PIe33Gfvu64TmpnGN0G/1/59ZSP00/L6SHnDz5lPp3/nPhufLV6sbnGK3ny94ZZ3A/7ig25/wbYbBur/qZyE3Cc2ml972zd9CrAx9WmGEieYOPSbGgyBM4MaDSToOTDwMpl2Nc0+3V7zbiaoaQsfxon17lKxV/xaDpYrYCS+HRg+s6M6Onp6cVDC6aUz2H0afbqYMugQF5zv20Btb6b7r19diZITwHdB3IpA1cH2M7qLevkx2C72R/74/UWBB0wJ7DZgiz7pzztFxfoRwyMlot2ZR7YTwNIU0JsY9nAVivXVhZ4uH73dnwGb/pJZPbqiPtuscsTxd02Z/Mvbn21bcVGLZ6ttio53ow5oZ29pk8DRZcjeLWuDJzbqk8D4S2OUDauQjaAwbIT6GuJf5VrWh3f9wq5ZeX/UxnGdfPXYhj7Po6X76RsgMa+6XeI0k7T5i4XUufH8fJIEGMT+55yavMZA/ApTzgOsQ4xy2rLdopfjV/rZYrZU35pW+xNT57M2/fO7JSS/pL38Pn+NKCkFTpuhpEyu4F3v42XKch60Ka+KYMTuKk5Ma+v9LIKFDbUFYAwr02OiX/267HaBZcd7cYw7a3qT/ptbezKrfg7Y3Os40RksGD+piRBvnc+dFbvUz/T/Z0PndHLPX2sguAEHFpbK5tnACVNsk7xbZJnihet75X8jHc7WSb+Jx5NqzjQ+JrabclwRWdiT8CkQZ1Bh/XSZFjF6im+rvh0mTPx9YyN74DHKq7t7HWXU87IyXYmG5xsfMVPq88z3a+NN21Mfe9MDG1lVrbTzhRO7Uz83hN/VnTXb+mmMwrggZ9+y5CvwUg7foFo2plAXQOAjVrdBtTMfyMOJJfcbSBtNYy8rEDYdG2S18bUAoWN73J5eaZwRw4KLdFM9cxnS8LTGOfVFH7RMdugvndBzudSmny262Yf00q1A1RWI1pZ/n4mk9Uq+E5HEZpuV745+YVXB6yXZl/tzF07S9tokqEldJdnAjD4iX6P47j53Vxv/bhdrmrsgIPHh7sdu7Ny1qtXWSlbk/16vd78igu303J/taK24m2VCPPZtrtJDeg127Vd7pJtK38cP3NKi0s/fvx4fvWQV4qO43YV1tulvOaHCaajSS3eUuapXpsATjGgrQpNcYnUVpT56qHpF1baub+JWq6yXpqcuW/dRjftF18aUc+Wl+PR4j9jve1o8oedDuyTlGV60GPqK3wQV70W9N0N+HYAgz/oTMF88NdghTQF293yOb9PW8PH8XKp1DJ4oAmWCPjyye0Fvwl/pcfGF3XXwGQDU36gg0m8bSUxiK3adoJiuQZQ+H+TK307GbSk4h83nw46N2J/rR/bieu0BMLt0mYnBNXeNqCO2mHyZoOsz3urRE675ri2pDsFueka5W2BkIF2R82GnSSZyFq5Nl62M78/jG042K/0NMUQ9n3meIPl5hEWHzpv4M0gz/bo743/yWeank3tzHB01c5TucwK8E1JsPHdkp7l4q/g5OfoWJ8xksDPcXUXzxu1MQxPtttpgkY5U9fXQs33mm4yTpHtw4cPN1vkfHBiGs/JF1pMsL03WfO9xZzESfuabcHx3DZFGYhDVtuqjc9JD6v4yXt80ty5yDxSnuM4ns+puuy99Kr38DkwTsBgRVT4bpbTAhqNbAcyWtvN4FzmTH2C1pacJ6PfOdFKdn5vgKHJckZ+G6P7aY7deD/jAC24udxq5joBI7czOViuGSzw/xVQbP2yTZLH3Gde2JZXjwmCHaRoU+G3+U0brynxr4j8tLNuZ9tuAK7VOwNOWM6yW7+cmK3aM09Nj/fEu10Zj7vjIq9FjpZQTKvjA7SPnV1PcWHSC7feztjZqsxOz7b7lc00YMA6q/hKEHE2vrYYfUYmy3ePr9qnOA7N33j9OF6ekWxAZCfPlHu9uDDlq1Zm0kOuGZM0u534OqvfFZ/sZyrDh4vaDgnHpeXDFSY5S3f/0sb1en3x4/CkplQPboTnDCyrOS3JMkF6WXPioW3PUYnTu7nSp+uG2pJxVm3Svn8f83K5PbzqfnZ7/OTby+yk9isfzXhWAZLJx4DLuudMlmXaClf7QXjKa8flqt7O2MkPebheb39EnrrzgwxceaP+fV6I/U2giq8XSF9Z2eR7z0jh8+Hh4cWKAgNt9JhV2rSdg/rkqQXss4GW3z3eWTGxDbW4YDDX7Kf5VANx5GcCmExcLWhaRrZDnSYePT4+3jyQZj4sk/tpD6E4wF8uf/7axbt3747v378///pGbIFJ+PHxcfvgSu5xlcn6d4KJLlImsrbXl1Cn3HWZdgvaGDiZe/yO4+eDA5azgRcnUH7Sh6lLxkbbFCn3uQLGnGQ/8rWVn61WmSag0mwv1I4yTP3n6El2U7gN7qNZ1E/zceYNxtyUS9yb8k6jaXvW27xPT083bR/H7UNb1iU/E1NX43B2ZXfyxYxz4n7zx7NEG3wN+Lt7hY+DsAJGE+jz/xyYKVG0ugxck/CrAdiVawHRcjjokB8HtAbqGljYUXMY92u5dsGnXdsZYgN8Kyd2oD9Txo62GmcHwKmflkBaG7axqf9VwuX32DknOg0wMPjSflpCasBpN2aR40zAdd227Ui9nLXjlGtg6AwxUef/VibUZtMtnrBMk2/qw3bDdiYw0/rzxIQTBYOpqa0pkawSRAPXtk+3YdBxxr93tNKx+ZjKmzde4wrvjs/mK97ybTzm3hkdnMlDq7ZaLIodOec0wE07S/wwMGZ9r/A14MmYZL5bTG92PPn19dpfjzVhBN6jHq3TFYa4B6BaHrfjc30rPbV2jXdeA/petaW7Qqfekkr5BhJDnDl4BYvG54HZBW3TpMidvCvnsQNNB78beGnk9q7X603y573wNoGXVeCe+KDDt1WUSSbyNwXDFRBb6WRFZxJqq+OzbtfrzxfQ0oZ5Fox8eoxXCcBj14AEk4nbpC1w3L017NWLiR8D2VUitQ2xP8vHxOh+ff7E/bfzOpR1tVphwLEbix3RFhykySO/T3a+8iEG7gbwDaxNu0kkV/hcdiWDYx1f9cPPlOWOwHH8XJnb0RSXXpNkV7rgyrfHqdVNfeck6pR6bq8eaSBkFX+5UroCPI1HytL8qwEagzP/nxVnEuOLfweevkFfybW2EkrbaWSAnraaLbfdC77ObIU/Jv2yfZdp45rrxj4ut8ItK5tZ4a6zdBrwMfBEeTGKxozRa1sWDdMejOv1epOQubQfokPyGnlwm6znco1Yzi+TPo7bg8HRgVdwGo/hYRWknGgoG4NwO081Ab2Um0BpaDULa4FsRXaAtH9G76trZ8FdeGA52qbH7jiO5yMGDWhcr9fnX5MxyGkOnf9bIkjf7X2O5Jfv2Wqgw+duHPhsC1OwMghrf+FvNYGh7zpZus/4ugFftjab/ZwBfWfvNfIuBoEDddNo5e++RmJyik8T8DWfJEgI35Qxdbl6w/omg3Ly6jhEHvy0bEvuE5kPn2+y7liHttHASXvv6sRDA4TUa7N/+0WI4D268Orgyh5brGi8kWfqocWs/LUtTtd99+7d8enTp5tfhLlefx5vMJ/+7glBAF/bnuVRKI/Ht2/fatzzeCQ/J4Y7nrd8M/nwFFuaD3EMVnG10RTPprg9AcF7gd+rf2ljldxCZ1B1q8f/zwaO8MbP6Z7LebB2fDo43AuA2nfzuWt34nkCxa+R0dfuGYvGz1lgxu/3GnTqNHnb2O36v4emNidnbdRWTXfJwSt8LXiu+jXQm+qvZG0gqwHNFXBrY+b6r4kLq7andn4leJ/V8woEZ1wzOeOZM6+KUM/TqnuTd4rhpknnr/ER0xndNsDfACnrNJ5XE4ezOco2uLKhCRCSR4+j25x03+IqAbfrchfN1O41X7snjrX7DWROtJJ3lfdWK+Ku2+zf1+6Jf/cAyFX+uQfM3euDpwEfZ7cfP368CUgMXFyN40xjFQQb89wWcAI3kAlNr2EIhZ82C+JAN4fjT4jRsRqoZT/mIWWJ4skrtwfSts9OWLYGjjzLcpDcJbuUabpgO6vD0qTJGXbOGZ1MztmCiGeA0xYk23FwdFkfXp94bf1kCyT92C7Ybsrx/urc7PV6Pb5+/fpCJr56x8nRunMZfsaPJ7DkP/r9dAzC4I188BciuELVZDDtJheUufVNXpv/uO0pcbUEybN4iSWfPn06Pnz4cPz48eP4+vXrDWD/8ePH8fnz5xc+ywfcEiM/fvz4fO7zOH4Cfz4cxHjJv5bYmy7buFHGs37RQK/1Rr+wjpuftv6bjxMsc/W5rSi2vGG7OI6Xecr644NgXp1nP85J0ZN/q7vt6JBWv7rE2OjrHgc+gBW+v3//Pj5I02Ix27tc/nwwqcUv25P1Ff1M8TyrzPnO97emfIuf/m6/IJ2x7+b3brvVpz4oq1e7z8SuHd39Hj6/l8gO7bNfk1C+3oIIDza2cqQpMLDP5lxtm8KAaAom09bFKvE4QLbg1wzOgTJ1m/O6/RWomtq5x+Bbv+SHn6zT2lyN706OloBbHQeSxpf/VkHW9Q2G2Kbt2fXZhoFGS2JJCu1Mpz8nP5x4J9EvGs+Ub9d30y/v+QjDbkzP+HyTdaKV/K395oPHcftOsPaXd6F9+/bt+elI64ftJZn99ttvx48fP54nB9wS93Y6eTSIMdi5hxgjdzHGtkU9cewmgEYdTOBgitGs6/YmH2h2bLlDtlW2TYDp3xx3WfO10uvEU3xnOjLTYgrvkY/EFfrj7mlWy06dtrGecm3LjSvQPcWIKdd6DFusWeX/M9Ts0eO/ynVnHhp7Dd29pWun5L5/roXaUnFDw6TmnFMZEw19BTT8Px2Thm4DbYPW+HIwnJyv8TMZ80ruXVDYATomFN47Y+iTDuyE07hPgW2SeQUcbW8r0OHANQGrfDLYNaA/2cYEkKijRrSFlrwYiFsgd/0mI3k1GMh12+T1ervilnJtNYY02SPBEfmyfxgIWlf3BsQJCHJiG74meyLfUwykbeZ/jkd7YbOTL9ukHtoTiyuy/U9JJ32QD9cNz16BsozWxxQfWuJvtIpjLufXLLUyDVyYnzMx32QdtP8N7qybCYxPfjyVucc3Wjwknzug4v5XfTtPtjzQgLD5oq7sJ8cxv3Pxnp0fU3vYpK3Gsfx0trnV4eu3DPxso/fQqwBfvkdoH0h1OSPnqc0GEjwIEwh0f+2QbJMhZfI6DM5mHNit8NCUPFZBbwKUTCCtHybIGPoK8FG3/JUL69BbBebnLHEM+aCLEzsTYEuqU9uNbD/We3tAY9JzC6YNbLRgsEsOHGcn9EmmFTDzQwQsb/22+pSD42Tb9ESq8dYeOtklHcrPrSiTwYn1bp53QXC6Hz29f//+eHh4OI7jOB4fH+sh8fDRVvG4qsN4Yr6zSucJJv3HM33GJr5v7Z5Ym77PgBU+vORjO/nuhyOaX7SVtZa4WtKffMo+zft8up7vlGu23h7GoZ+2Oi1e2feaLnLfOrEv5X6Lv9Z382tSA5qtTOPHcaqNrb+33M8yHuP4UfMzf29+liMz1AfjnnO/9TBhliaj+zYZ9LF8/DW8Tngn76Dlyv30Sz4TIJ1ov0+1ISutDcyubjOaVn8yBlJTwBREpoRM3lZkIzpTh/23763MvYP6Gh7y/9mkeU+7072drbRAke8TtYDCts7a5k6Os3S2jSkQ8vM1fLVAdy+tgMOuzR3oY7k2br6/a+ceOmsHrnO23R1NE4ldX9ZTkmV7aMf1Vu2s+p3G2+O2irOrOHcWsJwlJuUJ/L6WDAzb/ame6//VNMl2Tz7j/6t4eq8cZ2Lbiv+dHe0mCv77lZi4onv8v/G68stftZvTK3wkB14y8e7du+fZccq2Q5NU+vQOqrSbGQZnvAxuRu1UzNQvB/w4juPbt28vDjmT0m9zgMn5p5mD9daQe5t9mO+2VN36YF3rnisUZxKFja5to6e9trLEtrht1saT/JuX1qZnmJm1hp/p4Q+2Z/vYnaMzxXZZnmOw0iX5btezytJkWAVOfvc2YevT5VnuOF6uMLDdlvzDu9tpPmS/sy5s177e6lkuxpcme2LSpDuWbds43O70L76wP8a+pnv6Vos9bYWXNMVAfp8eLGM5b40x7vgw/ZRk8z/bs6+Yt6xqcHXE9uB+wk/79SbrIP832XeJNWM5vUjd+WJ62IKxfLLXxpvbXvG7A70T2HCen9piPddp7bUHXdo4GBPkfvPNlt9CbRcsD09Zf1PcPJMbJ/3YZ6aHMVquc7kJe5ylVwG+1pkDCK87ERPIMeixTtsCiOOzvdWh2ePog82yDPD8qTdvk5DvFkBbUiK1YDjRyvHPJHuWpcNMfe9AzIqvSUe+Z1txAGQCNDUjb0nOdubvLej6cLv7ccCxzU18XS79DE5LzGynHR1wMHVbbRwmvfGz6Y2fTKIOWBMQMz8tYFtvTUeNH8vt8o3IpxMyx6gBgYm3JneTzbbFyWrKtUPwbbzz/3QeKXJyUrOyBV5fPRjBun4BPPXYErbvu73dGUTGTL+BYfI7HjNKrnj37l0FCY2a70395Xr4JI/MbbzG+l4ImR62WPF6Jnaf9T3y0GzLsXMCKO1o0NSP23M5ksdxGhP/32LxSmdnY8FkG60cyzB+chu7tTdNBJq/naG7zvAZYZ5xHtJZBum8bodGPhn8zgnoiNO++O7AbOvzDIhr5Zvhr8q/llq7BserJNF49b0JzDQw7jLH8XIldSKumBiMTG23c1RTP2evNZoScr6fAe20/wlg7v5vep6S8sTDGT7Do+OD+XJ7E3/NjibbmmSjfTS+ze/U9nQ+OW1YxtZXrp15iMZgimDbY8VzqKzD+wZojRjbz5AB5ZS4qa/V5IDXp/hAXhufbpMTx4C+qZ0zPkX9kE8DoUkX4YmgdJJvkvWeMVrJM9FK9wRpUx6Z5Gp+v1pZPgNO2z3HmGZnq4dA3P/k96sJweRLZ2Oi5V/J3UD4jl710IYP3pPJKeB4EHcDnRma+zbCXznu5HztACR52i2vn02yq+urZMc/y7kDQiYah7c0w0f6aYdczzgweWc7jfh+ODpHvodHboc1nf3222/Hw8PD84FdLtE3+cmXZ3920AlsNBDupOvxWdnmCiBcLpcX75Pa2bvr89Nykwiy3e7O9ugz7TD5itpkI0GMttj07iDuMnl9ydPTyx9Wp71Zt+zTwIZxb9pqmeLeSn++l77pK+nPMZGyUj7+GkyTyf23sWF75Kvdp6w7IE/dGURk/JsfUbeWJW3HBvlQSvTIFf7EjHbkZ4qxttG0bRulHjh+eY0O30fXYs+0gzSN0xmAcE+scHve0k98jo3G9mibqxW1Hc8tv/ieeWv8T7Gq+XaovYHAtktwywezGk/so/F6Jpe3OBJe81DH169ft+0cx51bumcSjpn7K6ih6RXoMr+NVoAm93eOdJbf19T9K6kFjimApPy98rdZj/tvQbE55W4yMCWufE683wuUyeMqia3aaHWmMmfLe+x29navTTUwOoGVCRiQ11U/03idSWTsq9Wdgrr53YH9XTutvV18meo2GXYgzb7X4qXLuc9mT6ZprHY6mXzVYPRsmUm3Bmv0Fa8oNXBzdpxNOxtveXOaAISaDGfyyr28s49765iXBkpanR0g4/+rPN/utdjltts4r3TQ6u5ostFfwRU73s7QacDXAoWBgR1ul7hZ31snXIXzltMu6U1oeiffqpzP/BxHP281bSWdBYFMgi1Qn3Hm6DP82Mi8QuEHE1hn1S/HLX95aOd6vT3zw7FM+/wB7hYYKbt1MrW9Ag6p21ajpvJtDNr20PSD8asEMx0mj6zfvn276ZP6brbXAqT9bbX6ugvEtKOMYzuHZbkcMFfbEB7Pxo/lYj/+46pO2qaeyM/qt1cvl8vNaz4aIGkr0m3cz8hwHLcPSVg31E97NcqZ+DeBA9qZwcmZ+JNy7czslB/Y/s5XVg8/THKurvNceOMzZTkuecCPucgxNGVpi8fx8rfMm48Y5N0DkprO/d1+kmtN7oeHh5uYEzvMp892Or+sKHW4+0SiHtuDfauY13yUtsmHesj7Chy2ts6Qde32qY8WQ2xDqXtWz6G/BPDlfjNe3ptAFZVKp8oB0AkVW4ksx6TRAsxK8VP56elL85OtpAZIzaevNYDSeLuHd/5vkLQLhg1MTIAssseRjqNvf+XwbQJfe3qWttTO6zloTg4Rnmir+VHuybFI5GMCFCnjd8k1u6YupwCbfrmNx58O8ngwQZBvt9m2sCh/O2DfAh1BfpuMWSa317a/bZ+Xy88X54bvnU2bPE4+uO8xbOSE2PSSsWrbvO3hs9Du/E1kbmdUCab4I/Pppz1YM1GL49yyst+z3aY38tpsym2QB+s7skS+6YlK0irO+/rK56d4R1+f4rXjJn20gT3/rXi3z+xABOvlHu2HvFlmvpPSsTfl/Ysczn1Nz+6vjat11sbDbbXxDO/GCYmpfBem22/jm/YaLybKMNntpPuQYy7l+5cBPtPKuK14C3vGsFdB5gy1vn1vV/eeOumLn9P9VZ+7NnJvleRSpgXUVvce2Vp5goVmA0zyjd84dII7E8TE905+6qCdTWTZ6XOlnymw7GhnWxN/jcfVwWfTzvaaH+/4ntqdEsjZIEnA0JIx+3B7vJ4k4adj2Va73vhZ+dJEE++5xwntBIhWsWQCDq3v1djy/qo/J66drOzL9tDqrex4AmW833R4HLe/qrOTc1fO8k9xYKdn/z/Zsvtp58tbe1PfUyyc4mrA9iQzYwftcKKVvkhN7/fEsNV9l2v5qemG31c5IyB4kiOfznO+fk8+OkunAZ+XVtkxFTYhbSvNhtLKtWsNGDUQGWpPuLENytJWK0zN2Fl22qZrbbYBbUvK+fMB0Qasp/6u15+z0skId0F1KnO5/DxgnsPJIQLB1PfqA8eh2VF7yKZtaXJrxkmfbV6v1xev92nbT2xn0kWzBVNLEm6Hr3EIEIju2i+WtOBPWRtAaTQF7olXtulZc8r7/YrH0X/ZoenM7U480F/bapbf4ZZyDsT211z38QDaznQkoOl+ZT9+It1xzJMfUtveSn8tpjqBsN4ufuxiVmThluVU33pqecL5ZZosttje2olfZZW3yUCdZLuvJfFpq9y+2ADBCiRMK1ipF35yDGYFcCKL9cj22ySn2Qx32rjTwLHxDkoDk5TZuYJjyD7Ny5TvUrfpIuPZyuWTD8cxLuwWCiK/d/7sm9OEctXuhGWYF15Dv/zi5VWiS5lVslkBvHuFOlP+tYq6t81J5jNk+R14/OlAPvGw+n+V2Nv99EUbaI44JQhec7BY8czAbP4n2VtytwyNJ7frsq3f19KknwbIzEtLXKSVnlrb5sl6YvCd+DD/5mOSmWV39mkbdLkGdNzH6v92bwWeG38rwLcDRTvb2p0DYx8c48mnWG8nl797DM7qdhUf2FYbuyZ3I+8e7OJSrjOxeqGj6Xci+8FZv23lz8bUqc2z+cG26aM307i3cWp12lErxvYVP/bxppNdrGl8G5y1WMu2V+M48WFa5dczvN9Lr3otCzv1lgQdpZVp+/u7mUHb2mG9FTlhT4awU2BLPpZ1amcK/K3NVQCZAnaTZxqvKbhPAakBTOtkOhtmeVM25FcaUE+2k+v15UzNcrQVLrbZVqkte5PR//PMCvmYXieRz6bPNia2KfLRAkmzhwnstPqra35IZNJZo8jSeFv9H9l9kDs24CDvxGz75Ts3V/bc+Gx12i4Hdd3O2zQA21aFWG5aGUgdPkQy2QDLs/4EQsI/23A7E2BscXYHWKO3dtZv1UaLuZRrWrl124wltDkn/5a7PL5coeIuh9tq5If9yDdXyRlH2gTWPLV+qDN/9zW35/N7LJtrvudx4gNolNlnARk/aJNe2aesKz1TP44fjCnhwePfVvMmXeW+23HdFa/tGuPZGfxjuhvwpWMOGIXi9lNjfgf8dsmgUeurJScPYEsqZ8mBYvdEIYNzypnHpuMWrCkDP3nPAMxLwSvQ1641ByCtgLyvxXmsE/Y32YIfyiDxR9Jpo3zgwcFydXaj8U1dTHbuhDclsmZ3DdA0W8ln05PrNGA0+Z5BIu181RdpZVO2yympx2fatiftIOUYXHP/DOCb+GYSajzvwBSTmhO3+2H5pqPJL5n4dmSbMbhwvKFtNRC90lmzlRbLvGLm+2eTov2d/2f8JyDJNpjQp/OevtZ4uF6vz+/caz48+Yd1SQBEu26TMNZNTm75oenZ/U/lCGTd5sqP3TaBHvXjrdD2YAnb4tPB7YcUGPdW8rKsY3erN8nZJnqxg6kP0ipHsO6EIc7QL/20WmOM161QX/u/jaag4Htn22kJ4jjWqyO7e5PBreqcDZz3tsXvWe0iTTLbeVvdiS/339rl/TMO3ALcztlbop/quc2z+t4BKQeQKXlMdXxvSmDHcQtEJxs4y7+TBe8RTLts7rdAN8k80TTWkY/n5wjKwsM9bRPANz4NFs76OMuvbHenl8luzPNkPysgc9ZWcp96XoGIxrf94Yz+Jt0YoLFN9ttAYANaZ+SZePM4T7Fnaou8nbk/laO8nNBk0aTt9rHtkI8iTDZifdvWm17J42Qf7md1bdLTPb5JXnZ1KMfUZqtzlu5+LQs7JRLnO228ejIFlLOBYpX4m8OZ7zNJaWqnJSeW8eB4psJrl8vlxQpoEHvu+6EFysGksAIxdro26809bmGs9EQZp7ND/hHxEGdq/m6euW3hAN4O55O3yENZWCe2SkCRVST2twpI5DefqbsCBAYxzZZW/1OX4fu33357fr3Njx8/bn5xgX1RB3llkJPIFGQ4XtEtt3Xayi77N9+pk5gROfLqhy9fvhyPj4/Hcfxc+X3//v3x/v3754cluOJxufx8fQv7mWJOqP0qAHXBLbnoLf3wvXct3rSV1HZwnHWtY+vWvkCbba81amPA61NcS9s+GG5/bLsllrXFQfORv9hkxvo4juPx8fH5UH/To8eY8cPAwYmXdm2+np6enn+ppAEMf2/j1fJC7vn3dTOGjofOB8dx+0tFExAlj6vY1XJxO+/Id21Gbw8PDzcPcqR8/NPbs5Y/vBkrtLEwYCM/3O6OTIzHrtsA6Q402b54nfprdj7Zg9twbm/5xX1OsW1FdwM+OncDJJyhN0drgWlSxr20U+oUDFdJfUq8U5LjgLVBaw7L4NnOJ7B9OuMKoLmODeVMYmj9UyYDqwYi2H4LbJGZoJh8N+dpMloO80ndWTcNJJD3Mzawcj4nRPvGyi4necO3x2EKbrw3+UhLGC3YtPNR7qcBFX/GHt6/f398+PDhuF7/fNk07SFtTFu7Lc44Dqzkss2F2qSVIHEFZKwDJm9+7nzwbEz0JHEXBxv5ngFqvjv+tISWz3fv3t0k912/aSOLB5M/O0awnWkCwjKRidvaDWjkyd5Vwp2Se7N/9+VtdfM6+aS3Qxv/E61ybfOrtvPCyRonB/xZzLRDwLdaGZ/yL/2ZfnQc/bVNx3EbH6nHHQ8tHvM6+2x2e1ae1i75PnNEg+XvoV96StedT4y0QNTAg6+7LbY5HSZ3udZmo7NBabrGVSvLQsO7Xn/+iHaI/TZA4mDm5L4aD7YxlXcwmsaDgcrUklcbN7bffhfZM0PLNLU5jXnj/ziOm5WwJjtlXdmy/1h+54wrwNBkPI6XYMurCgZf1I/Php3lrSW1MzJOvh7K6g1XDLOqw7FmmUbT2VKPTVaOmDRcZ8V/fvlkOjtFGdvrPxwXaaNuIwk015yIV200fty/AZTHmWeiuHLvshPvuc7zlZN+WffHjx83q3o7HbqdSR+24emBEb4mpJ1Hj06s26brxqNjROOp9cM2rGfvSLSdEMarxt9x9Fh3HLerZ/k/L/vOCp95Stn2sMVKPzuKPeb75Ivk37bTbL9hifYrQlMumHIq/cdtTHy7HeaWexZ7Gr1qhY+MTOXIMJ23zaxsUFZOMw4G7/aj7W7LfJgauFjxQPL2gA2A95m4DOTynXUoB7eSqEfWnYzcjtgSMNuZDpiTr4wngRrbcZ9s58ePH8fj4+OLrc4JlExtsa7lc/Dng0V2ZtrhLul6Wd+6mB68mD6dzCdbI5BIcqKsTW+uO61oTW3QvlZjstvatV8EQAVEcdaeRJKfdGIiboG1HdQmfxnD9+/fj5MVt23/YaJpvtXsg+ebWqKg3NZ5YlxWydqvuLQYxzZCjim8z+Th7bfL5fL8KwQGRuaj6Y8yf//+vcYU85u4kDKrJ/lbEm39t+1nHxe4XC7H4+Pjsz1ybCzzFCtMLfZyXLn16QnNpEvq2QDG9chXrjewGWJc5JGNb9++3cR+xpGHh4ebXOxYSF5X57Rb3mq5jXHPuXQVY1cxlX1lPJqvTLlyir0N8E25eOXPjhWTnZyhVz+0sQJAphaomdw4WFPwmhyKbU19rwJDozNl7lV046m14eDm/lbGu6OVHiYAMtWf2lvpJQ7gYDB9uu0p6bTvtK/dWLWEP8m5a+e15DFf2fuq/xZ08mk/2+mn6aUl9taOwVCT4ayd2TYnXqd6DeQ2MND8rbVnMDPx/at2c2a8G9iayu7KTPpr5ZpNNH7O+F/j9QyfE9ha6a3J2PrzGE+AYurrzNi5XV53WdvmCsCY/519TfKv2idw9pEY2sOu7R14X/nhPfKw7ip+tXutfd6btpad49gXr1muvyofNbr7lzaO43gxu2nJ27S7bqNxu7zXzjDYIVeGRqdoD0m0ZLACI5bfZXZL8yve2C5nOZPsbGsyjOkwazuInu88GE69rF5rw4TvFQqW4YqiZeD3h4eH48OHDy9mZDlg3QBHk+Xp6enFb2FSvwaYjTwG7SD/ZBPmbbXVkft+0IWyMEB49ZH21LbV2Hbru43N5AOUzUGN/Ocef0s5D21Mv6qy2o5uq+LUQ7aIs+LGQ+d8SIQrL26T/fJa9O+V7x3Rl6Z2WI66T3lfM/CyvtjmZGfui3XbO+qabbQdAp7t5vUG2loMpa/Q/3n9TIJsOsj4H8dxs1rlvt0f2zHPq2S+2+JrZJDhe02+dmwhfPKVR7k+nW+eHiTK/3z1iPXU4oJjbPRpPXDXyPkw16bfrZ8A2JRrHMOmtuKjoZZL3KevGSBOsZn1Gz55Db3qKd0GrnbG6kH2AOV+M+rV/zaiiZ9VIJ6Cq+V0/zaEqX0PVpOx9eWkwHtNB5PRNnmbzC1osbwPzrdtUfbB60wQTGy87zEyj7/99tsz4CM4S8ChneWvAQXyY8CX+2cTd9Nbu5e/CUCuggVBAM+uZrXUZ3jS3io5uFwLxA7wzT9WsjQbdQI5jtutT/PeAM5OJvMdenh4eO4nh84fHx9v9MwtfPJNeRvgy/U2Rmf4bueupjqp10Cf214RE6xjSFv5tC5W8WrF92SbZ3gnD/atXb2pjSRsH/Uwv9ELwQkBqOPHJBtjT9vSX/Fu3qbytkPWsz3bDj3OPLZjoH82Vraz7e7D+YW8UeeeTEw5temsbbE2nbQ8ZP3ax1c4grJYhsmPeP//OODjKwkcjBvynkAF660eUNiRVzZIBhvT98bX6v/pmu85IJEvP2zCZMtyZ5Pc6prlozM3Pe8SeMDF9HSbnac5buOVPDXHI1+RgY//s28mw+a0TUd2Ogf4nVNSplWgn+R2meY3uefzqm4v1BIRy7UVt6m9yU8s72T3rkPbzzWeR1wlcgbalb7aig/7oS9wVccJLInO/dAmbLsrADIltGbjjnGrwE+e3G9bhXPM8Viyr2anO4BFvvLZEqvLtXvm02B+FyuncW1yT2eXJzozNuy76b3xe4bYHx+cmEAb60y/d0z9NF1NPE+xr8lt+2uxeaXzFQCbzih6YuZ2Vjo3vzu7n2LySgZfs6/Qxs/6XqPTgO/Dhw/HcRwvtuZs7GfBBJF222Kc2qHgLXhM4KklDgfe9t19W6bmxD6jxrJZSuch5mYglrMFwZXDtIDF4NCchodCrVNvNTG4NN6nxNao2cCkkyTfx8fHm8PxDUjkHoPhigfra0oiLXG0tlYJP7aQxN76arpuK6TNPqa2o+scVJ+SVdpr23xtxZnjNq3i5s/1sy1PO2iJ3f4zjWkDYNFfDqBnpfhyuRwfP358IX9sJg8tZBuYbTcfWoGh3PcvPzBZceszfzwuwJVAjtGUWKYk22JZPmlnU8Ke6rstyzjFxeZ/5inyN53u/KfZhPPILrc03fEVJAYUrj+tYE3AYwVIWJ9PyiavMJ4yFrSHnxrQWNl548fXHeOmOBN983Vdq1VPjyH7OY6fD+Ewfnz//v349u3bc/1pl8V6ne7x/zZ+LHcPMFvljWmB617gN0temJkc+Z6OV8Fw6m8qd6+wK55W/99DkyPw/gTgdnxN987o9Cz4cj2OeZNt6pv97O67n9XnCkyudH/23gSsW7D6K8hy7e6fcfgWsD1+Z/hflTlrm77exo1AsK1qNZ4amGjXVjyuJlS5vrJv89H+b37a/pqvtpXQM368iqU737839k0J1HHjNWP0V9E9dt9ss322Ort+VzH0V2nKz41Wdtju7/pt31f9Nj4m378XML0WqLb7qzjbcsFZ/b+GVuN1D51e4fN7maYOE7wmQOIk7xlalDWdjXA/Z2hKEmy78WonOoPeV2cR7gFPq0A6yU7dtYdRrPvpXtNzG2+fX7B+Gq+TXbi/yMHP47h9J5Tf8XW5XI4PHz68WAnzdsfkrO2QcyMnaQcY20/TwSrZhW+v3JDHncPzoYOsznNVNCtq1AFp8g/Kl/Yvl59b7B5fv8ON9Vvwaqt6+Uw/4c1bM7sJDl/1koP6k23mf+o8K29ZifM4W49t5bbFBa7GkIf00fxssrcmezsf2H5hh9/bO8habMzKDPMCqSVDy8m2p/FY+SbrrF4PFZ26nvXn783PWi6hjNSf5eVxA+6mNP+YJhTsk9ceHh6e67f31bFN1nff/M6H0Wx/Z+KQyzXdtiMaXO3mqvcqBvqlz7xmHZrcnsvt8hWprVJOumpjbF+mDa30vaPTgI/KYyBycmoBZAU62L6DggMraTKgiSYANR2WboBg5RQsw5+aoU52id7tTdem+itAaWDEhH6Gr8YL2zz7lBzbmGyhBbIWNBIIwkMO4mfrLnzR+diOA20rt9rWJeho9u/AOOmw6aa1afDR3gXmxJatyOv1+rydyUBqQGxfaIGYOvATa02W6/V6AzB4fwJEHnvL1YCP+/S1AL6np6fj69evx9PT03hQnzKyb9rKDvBZfxPIWfk8fbT11fzGfTNOs74nptQ9twEdv1k/Tzt7EcC8NHBl3m1nubby4ZTngwWTPjwGPApiPtt4TMmaPIWXKQaTVy5kNBu2v5i/9t3xgeM/xWfGGfMwyTz9NKHlOZP73Q6v2V7asSKWJz5p/tXivttotPLV5oMcB8vCfpr9MLc1f1rpcUenAR8POzfmHajJ0BSYJ+VORKNsDtLabgFyZwis12QyTYNi0MqA1Ab8OI4XAML8rQKng32Tg59NBrfD9prudrqZAtmq7pTEG79OAA4wu4DXrrVzZ02mHdBY/d/8pP3fkmADHZNu+cqTvB1/x8eZIGK/ak+4mt8WM1q77TuvNZ1MxLi0CtorntzXlExeQ/bfpv+VTs7Ep50/TX5xRk772orPFYhw3eZzToKpZ99Y+cyO7gEBjSY5zwKfHQ85n+d7PM/c7GfKzflknmBfLmN+XuMDk9+v/HmXD9z+ZI/3tHNPOQO2xrcnLb7f+nRb5KU9ubyj04Dvy5cvLzrmoMfg+H4fCuVZj43Uwu2MIuTtjtSl8vPXEnkLHi1YuX22w2tc2cv2EQ/K5tcFSO2ALfsmX3ylxfV6vUm01El0mzehsw511hy8bQk3Pfv1FG28yFO7T1uinXA8yCONvl33rDaH7qfxYzvpL6s+bZvScjX5zHOTmf1bp5azBdvoijLb3o/jOD5+/Hj88ccfx3H01Xm2R14yUVkl3wb6Jr2u9N5AzwqUNH24nebbtOMWD6bdhAb2DJxXK9xsp8lBm+e4TvUok8eNCSB1/Gsx+d5W7/LZ3snZ+m96a+DBv/ISHkPOD7Z/vwNwksV6IJ8TaDEganUpm3Xl9sKPZZnaYv0Gvqb4F53x5+CmX5OxbNatV3v5cGb6SbyfHv4gX9O2a5OHMTd9Nz3RtiegRD6Yr9pDge0BoHZ0YBUTrFPbUeNzsqd7+cnDd8fx8mHaie7e0t0J0Giqw4TePs+0fZaPlRPv2pyMqwVLJhMH89wPP06YLG/9WJYpuDW58zcBY/I/6WUHXqaktLre+HbCnxLfis8WYM6M4Vn++Dm12b6v6rUAsmp7msk34BbAz4DN8md42hHHbQXYduPH9qYyLZGmzSnWuGzjrV1r4GVK8qv2mlxndb+KhyvbXtGO33afPEyAxMBq0tuK/wkYn/W51f3X2voE1FZ5yr6wAg4rINhs1/V81m1HDZTsYtA947rTS4sTu1ziNlZYZLJH55d7+J7K26f52dqertlWrO+mr9UTx43ufvEyaXowYDrL0yiJyO23QGI+zoACO8Qqwa/I/EzE4MTyK8du4I8HbS0/ebGDN8NOO36FwC6Ime/Gi88yNZ1ZP7w/GbFXIFN2ejGqqTn2FHy84nMc/fUXqe9XnrBdrrhODzysgkP48UoneXH5Np7pm68kaKudbVvAZ2Coz0l3jcdp7M8kfo+bdcX2p6BHABq5eDTF/fCsW8aQdVcx4Onp6eYXG6y3Nu4sN62mWke76y2Rt/JTwraeJ0AWyspddhXiR14hj16nhBV+uWrB8Wh8t1jHa161Yn/22Qm8MJ9NccY7WlM7Z8Y3MY5tcUXN45o2KVfb9m7E/OBY4/q0U+ei5ter3GJeI3MmpmmXvyHd7HG1jekc2TDCLh6tZPLqoeVcEfu37dLe2CfjNdu5l+7+LV07lBMfnes1TDVHaddW5Rs1ELoKihNvZ/prIKEBnQb4yO/qB9PJD42vJcGUWb27jH3sZGcfE+iagnPTBXXSnliMfTEJT3pdJVWDkrQdWbhtxG1Sy8l3wfmw+mo7o/HQwJ7HkHXy/ryVriMXg2a++6nZyMM2yIf5N3gxMGfCTmxo49HsbPKtCcTYZhq/1lF7cbXtgw+hGdxMwMA/rXe9Xp+T2EQrH2llV9esix21pDIlk5WPc5LA4yyZtOWvTWB8HIXXYjvWt/XF/hxL3B/rtO0z2v2qbovbE0Ay0U4dh3KN76S7XG6fgG85y2O1enBlyg/ND/y9rR4aiE2yN7051sUWCNTJg3Nqy0kcD/LN+rQZt0fymE5lW99N901vtqk2FinXJkz34qtXr/B5EKY6rd6q3R01Rd9LZwYktAJAue9B8FNPLahPg+WVhzM8usw0VvdQk4t8se1dnR1vpCQH8zwF4FVgbe1b7x6nic92vel1FfAmojMn6bTyTY/+bONBUNqS1j10xv8mm801/7QagcPZvvl/87NVuRVZjwbH0elufI7j5WpvC+Tm84zdTWVtH81nzrS94m8qv2pzJVPjmUlvsrcG/n3PYHDnu41XAgPTa/MPeQxv3NX4q/o4SyuAdjZ30C9afN35DGPAlC9fK0/zYZcjYJ3aa5+OB2djTauzqrfDIWfpbsDnoDUd7s1MpTnwGbDofk3NEM3jKhm24DUlzV3fNBS+5qIdbPUBWLbnVVI6QPjwQWfWZ5ttxvBaJ550MRl3+sr4t0DrJJ//+UPcTrrH8fP9cZwFNx5Yhw8UNUpd/qYmH0aJPATy0woYfYK8tNlk+Ht4eHj+tYevX78u33NpsBZdRJf+9Zqnp6fjy5cvzzzbPlL2zEM1fjCFNK0s2Qf9GpTo/evXr0t7WgEkftIPVwm+8Up74u9txo55BtK8Ghhcr3/+msenT5+eZcy2J1fuJ5/aAabdtXbAvhFXMynTDny0xM5ylIOxJzbpdtqvQXDVkOXbA4BcXTyO4+YhgMjp8j7LSjlaPG+/qnEWzLY69IXYhOPOcdxOFhqQd9scywZgTavcHZt1vp5iUz7jCzyqwIc0c5/yJ/Yzb6xWyNPXDiS1Bxtb/ml6aDpmOdrnbnW91fdqbNpscvCzxeQzdPeWLmkFGloAzOcu8N7b973ol85jJ7IMZ/r2/97OcIIm6DuO2xdbtpeXsk8P+krOSZ5d+XvGpPHQkiABayvL/32N4CX3vb3bxsGJwsEw/RHEpb9Grj+NU0tSbdwoX86vMNmdSbCWq9nmBDBYdzXmDnxT0ti1EVnbg0xnEtF0r8UW8jMF6JXP287Cu9ul/RHUJ3Fx0tts1DK47YnPRi25Nx00AEVqOuS9Zou7XNDKNT5o+3yylyBpFZdpz1w9a7Kw7QamrB/7ZMpNOuHnBJB8hpFlpzhzJo7vxsRk/VB/7HPqi/y0eOfY5P4CLAkGd7I222z3dk8N83v+nxYT2vhTzsbXru70P6nlrtfQLwE+UjP2lcG2ey0A3iNgC5A+E9YCr/uZEm0z3B0/q0Q2PYY99dH0w3sJlm11wzpZzeAnILPiqQEQ6r45fks0DhoOHhO4aXZ01gEbSEpwb6sALNvkn/QzBYUfP348r8IR8J9JpNZps1fL6kAbOacHVVbA09en+1yt5qz2crnUc3Vu2993vsWxm9pqbbKN8NbsxDrhag11GrBtAEGdm5cWP5t9TfI3/a1W/ttWWmv/jG5jk34Ni23fMlpP7rcl/ilepbx/IcKyNpDW+DXf9rfVof0VGPRDRFN+cX2Xi1yTLE0G3m8r915VXI1L07+PJjEnTbE0Zbi63qjZEXl3ucbPBNSans/SFGOnvG1bW+n5V4Fe6FVbumRkSnTNIFNuBQRtcCw3GbB5XCUeKtdttbZXck3JvfXX+pq2FFpQae1NyZnyN2du8rLNKcCZ36af/NqF5eZsMaC0HeTOCle2Vd2+fyZtlwB5rwW5pmcCrwl8MJCxfd7LNW+beSXxx48fz094enttkq8BhzaTzX3bCvv2+KadFfBa6dn6tj7vadfxhavjAVgBFyk3Bcjmy7vZP4FLK8cx5juxUubx8fHGBrxFyIdo0nbzZ8rGfvmd22+U2Ssm6Tt98MGLBlBSbvc9ddqZ0RabKGP0zHG1/Oxvx4PJ+tnlB7btGMprfO+dZZr4CLUH8wjeJh4beMkfYyZXJtO3jxRNvE4PtVi+Bm4ynpah6SmfjHsZo7Yb0OzIY8YxcmzbxQXGxemBM36f8m+r5/ser9ybbGe6fg+9+ild/z+BCZa7h+F7y/9VbTYw1GReJa3VgE3JfOL3TLkd7fpzQHa9Mzw2Q2ebuxWBaWwm/bf7UxJwe2dsYOXku7pn7zPgteBCakGybTG1/qY+fa2NwRmA5sDeAt2OWuBelSPgbWVW9cPXmYS/42VFDThY/6s2mk/6/i7ursCN2zC1WLWK70zo98RuJsMV7XjIvTPl2J7rn6EpyU88Nf5ew/NrZGr97XTOuLzjZZJp4udX6FfGzO2krRZ7p3jmYxsrcl6b9HXWnqf799BpwNdmt+1XIfKqCu/FNwYnhe6ClOu0tpojsoxnHRPgybWUm5IM218l4hgXt4D8vqrc90F/f8//los64CrCdDbN7flsi+VzYHefkSUH1X1/NXbmfarXHObMuFhW6nxywgBWlpvsze24n7aN14AAvzffy+/iWje55odNrCe+36rptQH0zEb9wIzbIQ9+VQn7oL36Z9ky/pN9t5m/ddVolTDSNsfDK4Ap1x644gNHLS7mu9uf+GntNODP+ztA6TIGdc3HWc/t+YEW6m9KVrzXgEfjwWeqVpOKya+v1+tNO07GLW5PZ47ZTmJce0coZbVvNcDlGG6dtDhksO1r1EfzKffN6+Yl7dCem57tK9Gvded4Nemg7fZ4nJt/tP8zVlmR//bt24uVVr4XkPrxOVPK1Wy+xXY/HEa+ml+4PnV2L/3SCl87fB2GLPwZRNsC+1R2x9sUNF3eBkGyo68O87ue2/NgMWlO213tkPiKmpxJum0cTHSKKWG0YNTaSXBpS/u78XCwZLt2DPJtGdq9liwmPTSZjmOelNBZm03kcwKezeHZLtuhbv00tG3JAdbBd/LRlogT9Pxi4qanBm7dlttjv2fq+rp5afX4v7cxbYMtRjTbmrbhmi7OxKYz5JiyKnPm+85H7ZscP25p257P8NbIQMZ1zvpxk89txhambbzJ1iY/zf+rs9KvpTYhPxOnCVRYznncduC8x6M0U4xyH47pbeyaXhomSHu787WTvUamh4eH43q93kzUQm3Lm09+p28f/SG/E2Br95ueyXfGrsXoe+huwEcG2xNvDAAtaHiFbBrs5tg74ZpjrurR2C+Xyw3aTj0G6SkB2cB2wSn30p+TAQeY7ThRW172y4Pwq9nxSj8TrQJtdJrzOA4O1LfbmIIX23ZQoaNwfHbAryWjKcnvAv6klynxT3JOeuC4twDIOk6QXKHyqh958ph4JtvsrPGe+6s67Sm8gNfV4fX0Qz9t9md+pkQyxR7r1bqf2nMSTtlVYF7ZwkrPLaGbMq5OtlN7vOezhSxredlf9DUdkt9Rs+sWe1cxnZ+mVSycdDr5Pvlr408/2/nPjtj2mXxone1iq8fJZ8tcl/GjgZ18bwCzyTb5us+N+zw0gVPqrDAFY97q9T+r/Guf3tncTnbrYJVTzo7/RHf/li4757uD+JbsMwmb3/1E1w4AOHB58F3WwYPt5tAt31XlBNn6JH9NtikRNf1Nh84d7Jh8+cqHEMuttvTID/s5E5RWiY1leDh4F5gmHqa+KaMf7vATn6zb+G1jxdmdnyglH7YlX2P7HEuvrlF26sCyUq+xAdoH7SH88+hAS0Rsm0G1PRzjhNhsuumd5KMKzcYbAOX4Tj6+88EpqTS/YAzglp0TunXKNqcVI/velMjPJpBWrj2UYZntD+YrenByn8Beiz+rowFNHuqZY54jDA2UreIw76dt6yD6b3bJcW39tRhgWZtOW6wwzy1XOl5O4KCBPfPj+57cNV0yRjjnti3b5DbmqymGTECN+nh4eLiJgdbDt2/fXhyP8hgmrvvokY8lOHd5RXE17tTN5M+OVQaYDey1sbiHfvm1LM1oydR0fQIDq0FftbfjcVV/FTjdzqqMHWAXuJusLfC7LJ3OskzbRi2wnNXxvdSChFeDJ2OeeDpr2Pc6QOpM+pkCXqNmy5ZrB2xXOmrfz/K+8tGzAMM08bQbw/Z0YNNT6kxJ4TX2u9NdA3PmY6r3q7y9hs7Go7P+vhrHyYac2Fb1Gr8Tf/fGKI/RZDcNbDXQPfm6y0x28lra5RnL5HqrWDrFFLdzhge3xb4Nxs6O4yQT+Wr9s26LIZan8WRQNbUz1X8N7Wx8wgKvtbFXAb4W3Lh9MCkyKwfTweppVtICQfue/1eGHOIsy/3yf4LSCaGf4XNlXI3PCSyE8vqT6/XnOYLVmSnyk6DE8fJqhJNFqzPNACfHyf/e1jXfq6TQArqX4wmGyUvbhmAd99HOvk1jfHYsWcdbbsfxcwWMK3Nfv349vn379lzHPDc9cSWDWxfmwROHlFvxzfODO7KvczeAry9pNkd5DBati5RdvdOPfXnrO+3Z1rkqMY1b+j17iJoyNj5Mq8TdYlNLVOz3bD+r2DrZ8y4puy7HN7rwA0WOJ57o8v/2YmZOgtvvk+f3qfPAIf1nFQ/Mf/Mp8+761F3Tx2QXE7BsedM2fblcXtirwRp5mM6uW7YGANt19tnaWuVKn0lfAbCV7iabYtwz/z6v1/qbMJDLNNkp09S2+70X/P3yT6tNoIRMeSsyZ7vYBoEH+zMPTggsu3IoG9rKEMOvgclugBu4mWgVeHN/ZTR5isg/2t7OVbb6LZivaNLtPcZ2HLdnyqY2p7GbeG7Bl0vz7Rza1Kbl4rap+zEP1ssUHHjP1wOI3r9/fzw8PBzH8fNnh+gfZ57ujPw8rtB8Z8Vr01E7hzjZwnR+MPc8NlMA87VJVj/1ttNRA4T0+xYDqLOVTlbJ8oz/teRNvlfBfhrvs7RK0s1mdz8HOMmTz3aMwtvJ7O84Xr7HtIEkyt9iAY+ERA62Sf5bzmi7VGmLcWPKI03PLt9ynb+3NiYAN7Xj2NvyJNu23J7cGTSdyZGT3fK6c9zO7pqvtTEz/sgYMpesfkqWcjbbtE7cd9rcTRzd9w5LkH7pxcvucAVSWJbgriX6e2gKTKtr5MMrk7tk5zanem7DwGAywl07oRifjaMZ073grpVxUl61tRrXBiwul8vNmbEVLy048d7OPkOrs4UtWLYyK300YLCSz8CLyclAwnXaeZqVT9oGd2CqlbG9TiDN95nQ2+H+Rgyg+Z8JhPV3dkQA14Ab+5uSNNub7HGyG7bVANBqLNzuBAinMd/Ra4DhxKPvrexkasc5YirnftynV7IM4HJe6zhePvTkPlpcm8aavrmTtVEb6waIprw1ATaWIXGCwu/Wbct5BnQuM/E9xeyV7/A8s/W0yneTv6/iwJn4vZJl8oUz/jC1szsXv6LTgK+9JyvEgNwOfjZDYUJLOStnCrYrxN74awaWz/Z0IPmh0adc/m9BtiVIHoKftj3S9nTY2g6c7Qfry09ANmOeQPIKAEyA1GNkANqChoP4u3fvjg8fPjyPh4NFc+YW0JqzT0k1fPkgrm1lR5NeW7mpTW5zUgf5v9lBe1Dq27dvx7dv325sfHpKjvxMY8v+mn+2T+vBceDp6en4+vXrqCf3TR2s/J5g7jiO54PY0Q/L8NqUjG2fpgkYNN9mP1Owd3/0gWbjLenSVhowMdnWz4BJXpsSXFtdn3R15vsZGRnPXY59t3HLLsm7d++eV9VDjNd8h51tynpzf1OOtGyTrzG+evUo13mN8aHF9cvl5yQ7bU8PAjom05Z5NIq8NqC+4nvnE7zGJ8i5Omv9TLJb31xJZl5ouw8rWvkEZfV1/j/113xzd4Sr0d0rfFNSs5AW3onG3yfa3W9lGy+/2nbIgDHtrBK/gdfKMFYGY94NIFaAYxX4p/Lph59n2l314/FPG433psd7eG88nQVyE70GFE40yceguSvLoNd87B5eDBKmcSHdo1eCPst0D7jeBe+JF8uzA+j5XPnjWZndZqs/xQWWWyXEXRxa8dj4bbS6Z1C6oimZt//P2PJKF06k0+SFMvD/e/KVybFiB6RNrbxlOTMm7XoDF9NRkQb4zAtlnGx+pY8zNI3BKn9ME42z9vpXxPnJ3le5fWrnNXoLnQZ8zfCoqLMomG0Y3e+AQ0O5U9lVvzsgOvGYGQBngZfLy3Niu6BxDxEsktps6R4dUCY7+wQ8yRM/w88ZoNra9jlE80n5KPf08l9fawGrrdRS1z6DswIbud4cmLp1cOQ48NyS67eVcNsbr7HfnZ+sEtCK31Z3mllPyWLFF2nyKdedwFmLV02OM8GX8uwSCb83G/I927fLtATFepfL5cXLj8nbGaA66WLybT/gls/JH9wW25kSdOMlPrqSwW34L2dIWyzgStfq3Cx5nmKy5W0PjzX7CG/TRIWxqemu2XuLEe24RHuoMTHX8dO/5OQY3+ywgd9V3rQMx3G8WJWzfhrt8vNUx989vi1PW07GDh5rcR9tV6bZEXV+dqXvVS9eZqdmqt1rbayCv/vZtbcT1oDobFCbAF+e6mKizcH4KTi19lf8sp22bE6ebIQrXbm+D6me5a0FEp+tOEs8i8ifJ2p8OyBRHn9nHcrKMzvNYemoDchO1IIB7/mJ77TdjkG0ROREkm0ots2x3dn37l4DbeRn1YbtntvUlKMFM/Y9fW++MAEF3mtPT+7IAZvXJxDQdD8BT95n+wQdrsv6fKo1dXwGdAUELGeTxzyE+ICb37M2JfZGzcZ3cYzttwTpco6pfCqYfeceQVzqNL4nf7dv0wabP7kc605Pi98bc/0eS/JBcJn30zL+8ZcmUjdbvtSZ7c2+2p52nmzSuiCf3Gpuvt7aava1iz9TW1P7q/vpr03smqzUp3X1mrN8v/QevnvBjI1gAl6reo2Hs3y2oNcOfpIvJ2kDLw5IM/IzPLb2zpSdjHsV/CaeXKeRjbMZYetvcqZ2fQIqZ2xgAhxuf+p7CghnqdVrPK3G2Dblew3o7HhpNtv4c92JztrzGf3dE7DsY22HYPcQSAOtXpm6B+RPfbSYYBk8hjt/uiemsL9VXcfEXbtOrE2fvDf5uPtu/Ez1LMeZeNOSfDsD1YD3CrhOsWwCnJPsfvCq+X7rh23x08c8Yte7GHiWdn2dySemXcx2exmngM5dXG1tTGUnPZ/JzStqdnQ2Tt6Ltxr9MuCbAhupJZgd4NsBmXsFvl5/zsb91u/L5fL8Jne2y3JMJH6XE2e3MUDX4ecUKHNv5+yTQZ5NxPy/BQAnnXzPZ/uR8Mzw2pOXLYlRDwTekxy2s3z3SkbG2a9nWOnCYO8MKGv6sVwcp+gk709cHWh3v57dsnwLAE7iGS8eRE4d9u8HftwW+eN4rJI0t8ZbO+7PNsn7keVyudzIwr/Wj5M8+24r9t+/f6+/7OCxauN9HLdj5Aml25vGk4nUv0m9Ws1tcbUBMNahva8AYu61B8/aeK0e2JjGiPxYt/arnX9bPpbn6hBX/kPZBbhc/lztStxvB++tszYGtAWv6POa73Ns2vhZB3zNDH0mr5zxQ4rWf+uvAcT2ZgDGDy6UrB6yaWS5mn7TrnNti/uWI+VYtul4hUsmPzmLaZq/TmVTjqu5q/i8olcBPg/WFKRdJ2Xz2ZxyVZf9tf+n+u6Hg90CLfu1AWU2Yf7OgoTp2pRcpk/L5XvNICdyMrAeV4GzBbZpfNiG22bZe5MEA/cuEZg/28DKHqfx3dmRnXpl6ynPvlrQnQBt47nJ5HM7U/3G2xlq+jg7ru6r+eQOQJqP3G969Et27w2ijY/r9b6zRS4z+VpLxO7bOp/sdgKwZ8fYsYjn8Bp/5GfqZ+VLLa5McZPt8TM0gdam3zZuLc7YZ223lp91vH1q/lsscBn2Yz2m/aar1RPnjSzHlC/Izz20yjG2H/6t/HfKSS2HnrV/8rSKodOY73hc3d/luYle/Usb+YyxnmV2BUrOJB6XbQltlWCv15fniXKdK1dOjnRGvzbBiaQllknmXJuSwy54t+873U0ORV7MJ8s3sJDPaTssfXIlsOliNa7ko70uwA5PIGherJcpITT9TWS9Rl7ysLK/luxXCTv1vn//flwul6WcE1Bgn+3g/Mq+mt0ex/FiheE4jhfnu47j5fvRmmz+ZJ2svKxWSpvtTL7Ka+Sx+cKUHKdEOCXq3ethrB/bUuNn8r8JxNB/VpT79t9VvwYTky3bLiZe2viyb78SplFL8o2H5DbuDIX/9qAH5XKOcD+OE6mz23Zl+eYfU2xpOwpTHEz5NlbNd6z7VW5M36tY2h5kmHTRyrXr7SE868L3V+3v4vKU5x1nJ1qBaI/NPcDv1Vu6HtBVp80xW5ldf26L/xOo0NnbViGfCg1lq621Q4fh6qC3BaZAswN9WXJvAbkFBrd9Jii0+81gCcoaWJoC3MqR3D4DZ3Mcg7cGQLO90hzK9sEAsgI0rEOZzhD7jjxciaK8tL2mH/81YOTA2/ixPDuwQDu23bkPv/8q9TleBq1tHJu9T2PCQJet2OjTh9d32+WrhOTE1IC0nxBcJYkpedPPmKinQ+3mt/W1owYGeM0AzSDmOPoTtfQZlrevr/p20m3xo8nLOGLQMcnf2mF//Pku2nr8sdkP22A75I/xr1GL+zsyD7bXaVdqNdnYxUznRcu98q+VfM1OTOzDfjbFQtsWddKAqvuacq/lajzm//a8gNua2jkj5xn6pTN8K1o57K8wfJZ2CetMIDlLNuopiTUj+FfRSr87EDmBwan9e8aSQWKVvFYOMSU+35/aXNnGa+zSddx3A7MrcvBsAKz1t9PdpBMGu1VgW/HX/H3lcwaDDWBQd00HZ/yntW37aLY1lXOyWQX9XyGPjxPpdP9MW7ty6We6t4oP7Tr9tdnmXxkHV+2t4k0b55Rvq3mp22RtQPIsj7vYu4vLr6EzMbf9f2+5XT3GgzNtuNzkm+zP/n2mrx1Am8a7geXVuN+DC17rN6cBXx5UYIBuTuzZzHHcDoQfC891fvo76QzSvlxufzze5bKidhzHi88piBOhtxWXVVBg+y2BTTIZKE5l2vX00856RRZe58rkzjibTpuxNrv49u3bjS2lXEtsq8Phk3PkOlch2raSx83fLeekD/c3AZPI4VUu6yD3/QqE79+/36yY8FU2bTZuvbe+juN2ddmvp2iJ2mPVbKX5QAMt0z3Xm/qd+jZxrNvqh23KSd7tuGzrb7KF4+ivhuE9f28+wXa47djkWj0QNdmMD8FP47sCfRNYmuyg6SltJW9wVbTxwJjRfI1y8cGD1PXB+PyONfnagTL6I/MQ/9oqqe3jjL5bjmrxfpVj2orjlKf58NDl8vLdn1PubDjBsjc7Wx1TmfLUaiu/+fNKX9TpGR9wm3kv5nHMr4RaAb0pB6/sb0V3v3h5CsguuxoMA8J7qAWLVoZOTN4djKf3vq2U2e41o7fRrHje9bPSlZOljWky6GbELLMKLitaAaO2ncltcm6Xp15zjqmfVTkHl3yfxmVlX7Ypll/J3+5PoCYBgu+5YgBh/639XVI+jp/bbk5AKwC2AjOTLpm8m9wTf+1665+0knfnuysZWN52s5OFSW/SlfUZG8hREifANl6853jXbNX8GRhNIKIR22b8dX/U485G83TpGR+1L6ySdJOdZZicvfVHnaS89ZP2Gtje+cDqTPwqJuXT9S1j88M2YXB//N6Ocqx4tY7Ns2PycRw3OcByTv8331yR80DzwRZzpvPHU65t8k5yTTbV6F7Q96pf2jBzDmJnGH0trQJkS8IOZsdxeyC3JbcpSHmQVsGqlTmbUFhvBbxWSbEFvZXueL+tDqwCyKSHHTizHaVvgpHj+DnbnGRpdGZsfH+yG/4fHif+LUML/GdWjyYbmVZMnLCnBOQkyPZa8lnZ6gQyW2BfgYbW99SnV1LtC7ukuktMK5p0yD4sU+NjWoFwPwFeO/0bCKYPHw5fAQ3bjn+n2bKuYjxtnTKvwF9r0/JHn6sV7dSbEu1ErbzvtRjQQPRUznxPPtHi6groOfbw3pm2/YAKbdc6buBseljjOPqqFu1sZUstZlr2iZrdNx2ucuuUGya/bDm12eou3qzs9l6AZ7ob8GWAeY1bCk44DSCd/T80KWqlkAS7h4eH51liBqnN2FiPh8DTvp8ydD8tSFgPZ4Ae26YuV7pxH3ZIA96mOxoxA1J+Ro7bfdwCuFwuzzpzH2zP8tmOnMiz8pR7Tc8TsKAuJpp0er1e6y8ceCvJQKMFTRJ/qLwdoLZM3n4Mv23Wy6MGud9stSUYb+V75r4DfN76mwI7t6entiZ9TADVdQxaSLQry9/6bbw5ie2AT5PJCbaVnfo1xSe4zcZ73PI3OG724fHaASrKxesTeFrZU8srPOvbYpztubVNPpyLVnbS+OJDG22L2TrgWDfe8msW7MtjscqfU9ywfpwD3Ha2rB8eHm7eOzsBMsdpx3GPkeNme9Bjyl2u6zFrecyxeYoVpknX9B+Oq0G/t8YtaztmNo3nDvQ1Gc/Qfb+BtaBV4GvUnO+eds8G2pRdBXMH1uZ0O4VOCct9/JXUglMzmun/FW+rwLnS/Vk7mIJJ6/s1dE8y3pGda1f2Xlu+h3YArAGus+3tAM9ZP2q8/JU0+et0Lf+vgM6Kdra/05vLrfo9Gxen/ia9r2LiKlm6rO/9Kt1jU7vrq1yxijU7YOW2z/j4Lv6wn5U9T2V37a7KreJHown07f7331la8f4a2/sr4+4KkOe6P6eytpWzOvuVmHp6hY+de2m+IegpMBqhu+02a3F9X2vtsR8HMc6quNrnw/0mD1xQO69z1sbZzpltiJS7Xq83h/anAOR22lZjru+SnfnjzCvkh1YaWN6Ry58NxI1v3+O4Nzm8cms+3DdlnWzj3uA5gZMmX/OzM1sErVyTj7ra8f/bb78dHz58uKnftjU8Lm0V5qxe0gavpU33dRwvVyup19TLq1weHx+Xq/Mm+0K+Tw97pW8fcp/aT720Q3mbn1EPrN/80XrwffpIs0nrZQWOaHus463Z6I73V9t2KxDUyk8ytm3CxqNXyq2b1QMz5Nk+4u+rWNo+p/izigXmo+XR1h5lt/06B5IHxyavck72bD1bBuuXOskfbYj12rlG0+TjXElvDxKGVrtQ/h5+/NJ387LS271A+jheAfgmw9wl8JWRnemzXZ+Q8EoZDQSwTZaZAqCDrhONgRrvt201B/r8z4PaLXiZ76YrXm/bdf7u9ptDcqvIAecMOXA5WVoXbr85XEtO0WHGaDr87aC2SmjWRSt3Ricu3+pwK8F/7ZzUlPDbuE+TA9Z1G3lgxNuFLRGl7QlwTv00XTTb3CUG8sQtlffv3z/bQR6G2IG+XeyabKElmEnfK11M96c4NQGDNg73JI02hq2fXVwg4OMT4gaK7UGflS74ffLzs3nMMd7XLEPjYwIf/L/FUsrQ2pr8ovmDY+u9Odg05QXnpJa7Vvpu72uc5KFfky/H5SlHkPepzAqok/eWK3f22nilnRksh5/VGdizdBfg+5WOpsQ53Zv6boFzqk+nmg5KT4N+BmisgvcEWByY3O/kQJa18bUyULfls2BNh5O8/DxrE6uEehwvX0WwSrgMDE5YTS/tIZTcZ70VeJ7G1f1O/DjIeHzdd3vwg4m8BRq33/Sxk5GByOPs4OOE1nRheVb2trs+JS+fqaMcThAEFisQFmLbDSSf9btGZ5LUqm57oGpHk5+bX4NJX/eKyQpMOJ62hOZyud/iRLu+knXlsztwwHvNlxq49Zi6LsHiJMcKlJim3NJiEXUfGbzSys8We8znKrYz/lpPU5v04TMxusl5vV5f2MrOV8lbO4e9s7kzNrki6/reXHOG7gJ8MXCDJzruKqn8CmDcGX7r43K5vDggnzLNGZtid8HYZbKFQ0P3wFhHq+Rl/jzYPKjf9MF++NTr9Mi79dy2NJu+HNDd7uqhnrYtcnaMzLttz6+BaWOxsiuDloyrkz+DpmUx8RU07sM/F2deptcIudwEZlrQbStdBnvX68/VcL4D0L4xBWWOgwEZ6/t7/qfd0L/cTgN6+fzx48fx7du353bbOJm39uQiV43zMJP1vxoH2gkfZomMu5l8rtMWWkye5CIPDdDRHxNfssJLu8198005V/KzPu+tbKE9EUrfsy5bH46/qx0UttkAk98gwL8Ww+LDLte2Cmln1sWU6D2OLtvGI+/ifHp6evaPKY673ynO5H/urLidFV/OuxPIaXknNmFqcch9NnkmUL/DNRzPCfD6nu+3nRRvod9DrzrD1+heMHcPsxNYupenVn7nOPl+BrR6AJ2kztKqfAOL7fqOzOsOpPwq0YhbcNgBOpY9M64rMGp+GjU+27i63K+Mc0vSLp/PFfDd8dLqtr6swylATdQCG9t7TcA6W2cCng66Z9uxDZ316VUZ2z3tiZ+rds/wsQJQK7/M9Wnysfp/IvK9Ansr+2i+O+nKgPYeXlu/5nHlC44VbmcCPCtdO4adiQO+3sBXAN8qNqz0trOfHX+THTc/uNfW7qUVoHQsuBf3uN0WE32vyX4WA5nu3tK1wRFV7xh4DfDxp52tBb4pceZzOkc3nedzsgvxEGeonVng9lybjZLaeSvOInO9nf1ozsHvefy+8TjNHNhnm6FM497AFfXZ2mkgme14Zc33OfufzrycseGp/RUlaK6I9ke+Jn3b55q+Wh8T72cDFZOAAx3v7cBAk6+NL9tpr7XxishxzIf8LesOqPDaFFvyf9vSnc4qTf5AfhgL7v0liTau5u1ecEs+2/9tx4LnlVevLVqBjNy3LbXvXm2hnbKdlS5oly1O8P/WV8a7xY0VGORYH8ft+T+Ou8fYsZn9TmNr+3G8S5vUn1+xMuVRytLKtQdR2lnHyRdob+Ynq+Ls63L5uZOXuEC5plgwkfVlvht2aHI5n7X2Se1a0/nZnNTort/SdSBoB+JZril4F5B8jQo3oJkSdDP2KaGHss1JRzDPzbkdEKYnzVoSdlvtALkDgPsxD5NRT1tw5HEFWM4EX7bla00n/IwcsaP283eUowUwJxFuVZJfbtHtwEjbQprkYcBuspHfph8DKwMsEgPbKqmugtOKVmNIG5xAReu/1eHY5JoBT55Yd/m0tXp6riUdA8zJH1f/W0aOe8oHDFkXtt20HXv10/BsM59TjN0ljRVZHrcZfuibLhs52mrRygfaJLSR9dRi6vS05BSHp5jtT9ZpZxhTbnW2lfGcIK49oEJqR2p2Olvl36YHxpn202qTbTU7bbGID2awrGMcdewzhQR80TXxR5uUTHF7lZ9ILS+u6jKPTH1PILFdO+u/Z+hfu4/3fzGdASWmv1Lx/7+gM/y3xP2rbd+j4zd6Sf8v6u+1dvh/mv6VfP3fKrOpTfb8/Y329Jqc9K/i4Th+Ld+tJsdTf6vrZxYPGg+/Sv/VbPhyHMf/f6OYN3qjN3qjN3qjN3qjN1rS/7MrfG/0Rm/0Rm/0Rm/0Rv+v0Bvge6M3eqM3eqM3eqM3+i9Ob4Dvjd7ojd7ojd7ojd7ovzi9Ab43eqM3eqM3eqM3eqP/4vQG+N7ojd7ojd7ojd7ojf6L0xvge6M3eqM3eqM3eqM3+i9Ob4Dvjd7ojd7ojd7ojd7ovzi9Ab43eqM3eqM3eqM3eqP/4vQG+N7ojd7ojd7ojd7ojf6L0/8H6vzhaeMfC+8AAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "image = reconstruct_compressed_sensing(kspace, image_shape, trajectory)\n", - "\n", - "_ = plot_image(tf.math.abs(image))\n", - "_ = plt.gcf().suptitle('Reconstructed image', color='w', fontsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# We will also try a 2D+t non-Cartesian SENSE example" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading...\n", - "From: https://drive.google.com/uc?id=1nxJgqxOwFLIlO0Cz4NfhvYrB7_3C5Rhy\n", - "To: /workspaces/Tutorials/radialCS_2D+time_DONE/radiallyUndersampledProspectiveData_fromG.npy\n", - "100%|██████████| 43.1M/43.1M [00:00<00:00, 55.8MB/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "'/workspaces/Tutorials/radialCS_2D+time_DONE/radiallyUndersampledProspectiveData_fromG.npy'" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import gdown\n", - "\n", - "url = 'https://drive.google.com/uc?id=1nxJgqxOwFLIlO0Cz4NfhvYrB7_3C5Rhy'\n", - "output = '/workspaces/Tutorials/radialCS_2D+time_DONE/radiallyUndersampledProspectiveData_fromG.npy'\n", - "gdown.download(url, output, quiet=False)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now read the data, and calculate the trajectory and density weights for this prospective data" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "raw data shape: (512, 30, 13, 27)\n", - "kspace shape: (27, 30, 6656)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "traj shape: (27, 13, 512, 2)\n", - "density.shape: (27, 13, 512)\n", - "kspace: (27, 12, 6656)\n" - ] - } - ], - "source": [ - "\n", - "raw_data = np.load(f'/workspaces/Tutorials/radialCS_2D+time_DONE/radiallyUndersampledProspectiveData.npy')\n", - "kspace = tf.cast(raw_data, dtype = tf.complex64)\n", - "\n", - "print('raw data shape:', raw_data.shape)\n", - "# (512, 30, 13, 27)\n", - "# nPtsPerSpoke, nCh, nSpokes, nTimePoints\n", - "\n", - "nSpokes = raw_data.shape[2]\n", - "nTimePts = raw_data.shape[3]\n", - "\n", - "kspace = np.transpose(kspace, [3,1,2,0]) \n", - "#(time, coils, spokes, readout)\n", - "sh = kspace.shape\n", - "kspace = tf.reshape(kspace,(sh[0],sh[1],sh[2]*sh[3]))\n", - "print('kspace shape: ', kspace.shape)\n", - "#(time, coils, spokes*readout)\n", - "# (27, 30, 6656)\n", - "\n", - "im_size = 256\n", - "image_shape = [im_size, im_size]\n", - "\n", - "# Compute trajectory.\n", - "traj = tfmri.sampling.radial_trajectory(base_resolution=im_size,\n", - " views=nSpokes,\n", - " phases=nTimePts,\n", - " ordering='sorted_half',\n", - " angle_range = 'full')\n", - "\n", - "print('traj shape: ', traj.shape)\n", - "#(time, spokes, readout, 2)\n", - "# (27, 13, 512, 2)\n", - "\n", - "# Compute density.\n", - "dens = tfmri.sampling.estimate_density(traj, image_shape, method=\"pipe\")\n", - "print('density.shape: ' + str(dens.shape))\n", - "# #(time, spokes, readout)\n", - "#density.shape: (27, 13, 512)\n", - "\n", - "# Flatten trajectory and density.\n", - "traj = tfmri.sampling.flatten_trajectory(traj)\n", - "# This should be size: [nTimePts, nPtsPerSpiral*nSpirals, 2]\n", - "#trajectory.shape: (27, 6656, 2)\n", - "\n", - "\n", - "dens = tfmri.sampling.flatten_density(dens)\n", - "# This should be size: [nTimePts, nPtsPerSpiral*nSpirals]\n", - "#trajectory.shape: (27, 6656)\n", - "\n", - "kspace = tfmri.coils.compress_coils(kspace, coil_axis=-2, out_coils=12)\n", - "print('kspace:', kspace.shape)\n", - "#(time, coils, spokes*readout)\n", - "#kspace: (27, 12, 6656)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And calculate the coil sensitivity info for this dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sensitivities.shape: (12, 256, 256)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Now calcualte coil sensitivities by collapsing through time and gridding\n", - "\n", - "kSpaceCS = np.transpose(kspace, [1,0,2])\n", - "#(coils, time,spokes*readout)\n", - "# (12, 27, 6656)\n", - "\n", - "kSpaceCS = tf.reshape(kSpaceCS, [kSpaceCS.shape[0], kSpaceCS.shape[1]*kSpaceCS.shape[2]])\n", - "#kSpaceCS: (27, 199680)\n", - "trajCS = tf.reshape(traj, [traj.shape[0]*traj.shape[1], traj.shape[2]])\n", - "#trajCS: (179712, 2)\n", - "densCS = tf.reshape(dens, [dens.shape[0]*dens.shape[1]])\n", - "\n", - "# First let's filter the *k*-space data with a Hann window. We will apply the\n", - "# window to the central 20% of k-space (determined by the factor 5 below), the\n", - "# remaining 80% is filtered out completely.\n", - "filter_fn = lambda x: tfmri.signal.hann(5 * x)\n", - "\n", - "# Low-pass filtering of the k-space data.\n", - "filtered_kspace = tfmri.signal.filter_kspace(kSpaceCS,\n", - " trajectory=trajCS,\n", - " filter_fn=filter_fn)\n", - "\n", - "# Reconstruct low resolution estimates.\n", - "low_res_images = tfmri.recon.adjoint(filtered_kspace,\n", - " image_shape,\n", - " trajectory=trajCS,\n", - " density=densCS)\n", - "\n", - "_ = plot_tiled_images(tf.math.abs(low_res_images))\n", - "_ = plt.gcf().suptitle('Low-resolution images', color='w', fontsize=14)\n", - "\n", - "# Estimate the coil sensitivities.\n", - "coil_sens = tfmri.coils.estimate_sensitivities(\n", - " low_res_images, coil_axis=0, method='walsh')\n", - "\n", - "print('sensitivities.shape: ' + str(coil_sens.shape))\n", - "# This should be size: [nCoils, matrix_size, matrix_size]\n", - "#sensitivities.shape: (12, 256, 256)\n", - "\n", - "_ = plot_tiled_images(tf.math.abs(coil_sens))\n", - "_ = plt.gcf().suptitle('Coil Sensitivities', color='w', fontsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lastly do Compressed Sensing recon" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "regularizer.shape: (1769472,)\n", - "(27, 256, 256)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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You performed a non-Cartesian CG-SENSE reconstruction using\n", - "TensorFlow MRI. The code used in this notebook works for any dataset and\n", - "trajectory. It also works for 3D imaging. Feel free to try with your own data!\n", - "\n", - "For more information about the functions used in this tutorial, check out the\n", - "[API documentation](https://mrphys.github.io/tensorflow-mri/api_docs/). For\n", - "more examples of using TensorFlow MRI, check out the\n", - "[tutorials](https://mrphys.github.io/tensorflow-mri/tutorials/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Let us know!\n", - "Please tell us what you think about this tutorial and about TensorFlow MRI.\n", - "We would like to hear what you liked and how we can improve. You will find us\n", - "on [GitHub](https://github.com/mrphys/tensorflow-mri/issues/new)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright 2022 University College London. All rights reserved.\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - } - ], - "metadata": { - "interpreter": { - "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" - }, - "kernelspec": { - "display_name": "Python 3.8.2 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From e388c3fd1cd24881eb620ed677751de7225eeb66 Mon Sep 17 00:00:00 2001 From: jennifersteeden Date: Tue, 4 Feb 2025 14:42:59 +0000 Subject: [PATCH 3/6] Revert "Combined some changes from develop branch includinggeometry, recon, linalg" This reverts commit dc5127d1ca8ea77bd122c6f1d480d664fae71127. --- tensorflow_mri/python/geometry/__init__.py | 18 - .../python/geometry/rotation/__init__.py | 0 .../python/geometry/rotation/euler_2d.py | 54 -- .../python/geometry/rotation/quaternion.py | 141 --- .../geometry/rotation/rotation_matrix.py | 144 ---- .../geometry/rotation/rotation_matrix_2d.py | 139 --- .../geometry/rotation/rotation_matrix_3d.py | 261 ------ .../python/geometry/rotation/test_data.py | 136 --- .../python/geometry/rotation/test_helpers.py | 263 ------ tensorflow_mri/python/geometry/rotation_2d.py | 420 --------- .../python/geometry/rotation_2d_test.py | 178 ---- tensorflow_mri/python/geometry/rotation_3d.py | 302 ------- .../python/geometry/rotation_3d_test.py | 280 ------ tensorflow_mri/python/layers/concatenate.py | 67 -- .../python/layers/concatenate_test.py | 52 -- .../python/layers/data_consistency.py | 112 --- tensorflow_mri/python/layers/normalization.py | 66 -- .../python/layers/normalization_test.py | 56 -- tensorflow_mri/python/layers/padding.py | 85 -- tensorflow_mri/python/layers/recon_adjoint.py | 140 --- .../python/layers/recon_adjoint_test.py | 79 -- tensorflow_mri/python/layers/reshaping.py | 97 --- .../python/layers/reshaping_test.py | 15 - tensorflow_mri/python/linalg/__init__.py | 41 - .../python/linalg/add_registrations.py | 35 - .../python/linalg/adjoint_registrations.py | 21 - .../python/linalg/cholesky_registrations.py | 58 -- .../python/linalg/conjugate_gradient.py | 234 ----- .../python/linalg/conjugate_gradient_test.py | 161 ---- .../python/linalg/inverse_registrations.py | 86 -- .../python/linalg/linear_operator.py | 428 --------- .../python/linalg/linear_operator_addition.py | 294 ------- .../linalg/linear_operator_addition_nd.py | 70 -- .../linear_operator_addition_nd_test.py | 15 - .../linalg/linear_operator_addition_test.py | 280 ------ .../python/linalg/linear_operator_adjoint.py | 31 - .../linalg/linear_operator_adjoint_test.py | 15 - .../python/linalg/linear_operator_algebra.py | 175 ---- .../python/linalg/linear_operator_coils.py | 196 ----- .../linalg/linear_operator_coils_test.py | 167 ---- .../linalg/linear_operator_composition.py | 158 ---- .../linalg/linear_operator_composition_nd.py | 276 ------ .../linear_operator_composition_nd_test.py | 284 ------ .../linear_operator_composition_test.py | 16 - .../python/linalg/linear_operator_diag.py | 31 - .../python/linalg/linear_operator_diag_nd.py | 277 ------ .../linalg/linear_operator_diag_nd_test.py | 510 ----------- .../linalg/linear_operator_diag_test.py | 15 - .../python/linalg/linear_operator_fft.py | 257 ------ .../python/linalg/linear_operator_fft_test.py | 167 ---- .../linear_operator_finite_difference.py | 125 --- .../linear_operator_finite_difference_test.py | 81 -- .../linalg/linear_operator_full_matrix.py | 31 - .../linear_operator_full_matrix_test.py | 15 - .../linalg/linear_operator_gram_matrix.py | 151 ---- .../linalg/linear_operator_gram_matrix_nd.py | 151 ---- .../linear_operator_gram_matrix_nd_test.py | 15 - .../linear_operator_gram_matrix_test.py | 15 - .../python/linalg/linear_operator_identity.py | 39 - .../linalg/linear_operator_identity_nd.py | 652 -------------- .../linear_operator_identity_nd_test.py | 619 ------------- .../linalg/linear_operator_identity_test.py | 15 - .../linalg/linear_operator_inversion.py | 32 - .../linalg/linear_operator_inversion_test.py | 15 - .../python/linalg/linear_operator_mask.py | 259 ------ .../linalg/linear_operator_mask_test.py | 212 ----- .../python/linalg/linear_operator_mri.py | 812 ------------------ .../python/linalg/linear_operator_mri_test.py | 214 ----- .../python/linalg/linear_operator_nd.py | 799 ----------------- .../python/linalg/linear_operator_nd_test.py | 263 ------ .../python/linalg/linear_operator_nufft.py | 778 ----------------- .../linalg/linear_operator_nufft_test.py | 334 ------- .../python/linalg/linear_operator_test.py | 468 ---------- .../linalg/linear_operator_test_util.py | 203 ----- .../python/linalg/linear_operator_util.py | 158 ---- .../python/linalg/linear_operator_wavelet.py | 153 ---- .../linalg/linear_operator_wavelet_test.py | 87 -- .../python/linalg/matmul_registrations.py | 133 --- .../linalg/pseudo_inverse_registrations.py | 0 .../python/linalg/registrations_util.py | 27 - tensorflow_mri/python/linalg/slicing.py | 18 - .../python/linalg/solve_registrations.py | 133 --- tensorflow_mri/python/ops/control_flow_ops.py | 35 - tensorflow_mri/python/recon/__init__.py | 18 - tensorflow_mri/python/recon/recon_adjoint.py | 152 ---- .../python/recon/recon_adjoint_test.py | 94 -- .../python/recon/recon_least_squares.py | 15 - tools/docs/guide/fft.ipynb | 101 --- tools/docs/guide/linalg.ipynb | 32 + tools/docs/guide/optim.ipynb | 32 + tools/docs/guide/recon.ipynb | 32 + tools/docs/templates/index.rst | 5 +- 92 files changed, 100 insertions(+), 14826 deletions(-) delete mode 100644 tensorflow_mri/python/geometry/__init__.py delete mode 100644 tensorflow_mri/python/geometry/rotation/__init__.py delete mode 100644 tensorflow_mri/python/geometry/rotation/euler_2d.py delete mode 100644 tensorflow_mri/python/geometry/rotation/quaternion.py delete mode 100644 tensorflow_mri/python/geometry/rotation/rotation_matrix.py delete mode 100644 tensorflow_mri/python/geometry/rotation/rotation_matrix_2d.py delete mode 100644 tensorflow_mri/python/geometry/rotation/rotation_matrix_3d.py delete mode 100644 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tools/docs/guide/fft.ipynb create mode 100644 tools/docs/guide/linalg.ipynb create mode 100644 tools/docs/guide/optim.ipynb create mode 100644 tools/docs/guide/recon.ipynb diff --git a/tensorflow_mri/python/geometry/__init__.py b/tensorflow_mri/python/geometry/__init__.py deleted file mode 100644 index 29dd1576..00000000 --- a/tensorflow_mri/python/geometry/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Geometric operations.""" - -from tensorflow_mri.python.geometry import rotation_2d -from tensorflow_mri.python.geometry import rotation_3d diff --git a/tensorflow_mri/python/geometry/rotation/__init__.py b/tensorflow_mri/python/geometry/rotation/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tensorflow_mri/python/geometry/rotation/euler_2d.py b/tensorflow_mri/python/geometry/rotation/euler_2d.py deleted file mode 100644 index fa7851ba..00000000 --- a/tensorflow_mri/python/geometry/rotation/euler_2d.py +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2020 The TensorFlow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""2D angles.""" - -import tensorflow as tf - - -def from_matrix(matrix): - """Converts a 2D rotation matrix to an angle. - - Args: - matrix: A `tf.Tensor` of shape `[..., 2, 2]`. - - Returns: - A `tf.Tensor` of shape `[..., 1]`. - - Raises: - ValueError: If the shape of `matrix` is invalid. - """ - matrix = tf.convert_to_tensor(matrix) - - if matrix.shape[-1] != 2 or matrix.shape[-2] != 2: - raise ValueError( - f"matrix must have shape `[..., 2, 2]`, but got: {matrix.shape}") - - angle = tf.math.atan2(matrix[..., 1, 0], matrix[..., 0, 0]) - return tf.expand_dims(angle, axis=-1) diff --git a/tensorflow_mri/python/geometry/rotation/quaternion.py b/tensorflow_mri/python/geometry/rotation/quaternion.py deleted file mode 100644 index 5287710e..00000000 --- a/tensorflow_mri/python/geometry/rotation/quaternion.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2020 The TensorFlow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Quaternions.""" - -import tensorflow as tf - - -def from_euler(angles): - """Converts Euler angles to a quaternion. - - Args: - angles: A `tf.Tensor` of shape `[..., 3]`. - - Returns: - A `tf.Tensor` of shape `[..., 4]`. - - Raises: - ValueError: If the shape of `angles` is invalid. - """ - angles = tf.convert_to_tensor(angles) - - if angles.shape[-1] != 3: - raise ValueError(f"angles must have shape `[..., 3]`, " - f"but got: {angles.shape}") - - half_angles = angles / 2.0 - cos_half_angles = tf.math.cos(half_angles) - sin_half_angles = tf.math.sin(half_angles) - return _build_quaternion_from_sines_and_cosines(sin_half_angles, - cos_half_angles) - - -def from_small_euler(angles): - """Converts small Euler angles to a quaternion. - - Args: - angles: A `tf.Tensor` of shape `[..., 3]`. - - Returns: - A `tf.Tensor` of shape `[..., 4]`. - - Raises: - ValueError: If the shape of `angles` is invalid. - """ - angles = tf.convert_to_tensor(angles) - - if angles.shape[-1] != 3: - raise ValueError(f"angles must have shape `[..., 3]`, " - f"but got: {angles.shape}") - - half_angles = angles / 2.0 - cos_half_angles = 1.0 - 0.5 * half_angles * half_angles - sin_half_angles = half_angles - quaternion = _build_quaternion_from_sines_and_cosines( - sin_half_angles, cos_half_angles) - - # We need to normalize the quaternion due to the small angle approximation. - return tf.nn.l2_normalize(quaternion, axis=-1) - - -def _build_quaternion_from_sines_and_cosines(sin_half_angles, cos_half_angles): - """Builds a quaternion from sines and cosines of half Euler angles. - - Args: - sin_half_angles: A tensor of shape `[..., 3]`, where the last - dimension represents the sine of half Euler angles. - cos_half_angles: A tensor of shape `[..., 3]`, where the last - dimension represents the cosine of half Euler angles. - - Returns: - A `tf.Tensor` of shape `[..., 4]`, where the last dimension represents - a quaternion. - """ - c1, c2, c3 = tf.unstack(cos_half_angles, axis=-1) - s1, s2, s3 = tf.unstack(sin_half_angles, axis=-1) - w = c1 * c2 * c3 + s1 * s2 * s3 - x = -c1 * s2 * s3 + s1 * c2 * c3 - y = c1 * s2 * c3 + s1 * c2 * s3 - z = -s1 * s2 * c3 + c1 * c2 * s3 - return tf.stack((x, y, z, w), axis=-1) - - -def multiply(quaternion1, quaternion2): - """Multiplies two quaternions. - - Args: - quaternion1: A `tf.Tensor` of shape `[..., 4]`, where the last dimension - represents a quaternion. - quaternion2: A `tf.Tensor` of shape `[..., 4]`, where the last dimension - represents a quaternion. - - Returns: - A `tf.Tensor` of shape `[..., 4]` representing quaternions. - - Raises: - ValueError: If the shape of `quaternion1` or `quaternion2` is invalid. - """ - quaternion1 = tf.convert_to_tensor(value=quaternion1) - quaternion2 = tf.convert_to_tensor(value=quaternion2) - - if quaternion1.shape[-1] != 4: - raise ValueError(f"quaternion1 must have shape `[..., 4]`, " - f"but got: {quaternion1.shape}") - if quaternion2.shape[-1] != 4: - raise ValueError(f"quaternion2 must have shape `[..., 4]`, " - f"but got: {quaternion2.shape}") - - x1, y1, z1, w1 = tf.unstack(quaternion1, axis=-1) - x2, y2, z2, w2 = tf.unstack(quaternion2, axis=-1) - x = x1 * w2 + y1 * z2 - z1 * y2 + w1 * x2 - y = -x1 * z2 + y1 * w2 + z1 * x2 + w1 * y2 - z = x1 * y2 - y1 * x2 + z1 * w2 + w1 * z2 - w = -x1 * x2 - y1 * y2 - z1 * z2 + w1 * w2 - return tf.stack((x, y, z, w), axis=-1) diff --git a/tensorflow_mri/python/geometry/rotation/rotation_matrix.py b/tensorflow_mri/python/geometry/rotation/rotation_matrix.py deleted file mode 100644 index ebc34f2f..00000000 --- a/tensorflow_mri/python/geometry/rotation/rotation_matrix.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2020 The TensorFlow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Rotation matrices.""" - -import tensorflow as tf - - -def rotate(n, point, matrix): - """Rotates an N-D point using rotation matrix. - - Args: - n: An `int`. The dimension of the point and matrix. - point: A `tf.Tensor` of shape `[..., N]`. - matrix: A `tf.Tensor` of shape `[..., N, N]`. - - Returns: - A `tf.Tensor` of shape `[..., N]`. - - Raises: - ValueError: If the shape of the point or matrix is invalid. - """ - point = tf.convert_to_tensor(point) - matrix = tf.convert_to_tensor(matrix) - - if point.shape[-1] != n: - raise ValueError( - f"point must have shape [..., {n}], but got: {point.shape}") - if matrix.shape[-1] != n or matrix.shape[-2] != n: - raise ValueError( - f"matrix must have shape [..., {n}, {n}], but got: {matrix.shape}") - try: - static_batch_shape = tf.broadcast_static_shape( - point.shape[:-1], matrix.shape[:-2]) - except ValueError as err: - raise ValueError( - f"The batch shapes of point and this rotation matrix do not " - f"broadcast: {point.shape[:-1]} vs. {matrix.shape[:-2]}") from err - - common_batch_shape = tf.broadcast_dynamic_shape( - tf.shape(point)[:-1], tf.shape(matrix)[:-2]) - point = tf.broadcast_to(point, tf.concat( - [common_batch_shape, [n]], 0)) - matrix = tf.broadcast_to(matrix, tf.concat( - [common_batch_shape, [n, n]], 0)) - - rotated_point = tf.linalg.matvec(matrix, point) - output_shape = static_batch_shape.concatenate([n]) - return tf.ensure_shape(rotated_point, output_shape) - - -def inverse(n, matrix): - """Inverts an N-D rotation matrix. - - Args: - n: An `int`. The dimension of the matrix. - matrix: A `tf.Tensor` of shape `[..., N, N]`. - - Returns: - A `tf.Tensor` of shape `[..., N, N]`. - - Raises: - ValueError: If the shape of the matrix is invalid. - """ - matrix = tf.convert_to_tensor(matrix) - - if matrix.shape[-1] != n or matrix.shape[-2] != n: - raise ValueError( - f"matrix must have shape [..., {n}, {n}], but got: {matrix.shape}") - - return tf.linalg.matrix_transpose(matrix) - - -def is_valid(n, matrix, atol=1e-3): - """Checks if an N-D rotation matrix is valid. - - Args: - n: An `int`. The dimension of the matrix. - matrix: A `tf.Tensor` of shape `[..., N, N]`. - atol: A `float`. The absolute tolerance for checking if the matrix is valid. - - Returns: - A boolean `tf.Tensor` of shape `[..., 1]`. - - Raises: - ValueError: If the shape of the matrix is invalid. - """ - matrix = tf.convert_to_tensor(matrix) - - if matrix.shape[-1] != n or matrix.shape[-2] != n: - raise ValueError( - f"matrix must have shape [..., {n}, {n}], but got: {matrix.shape}") - - # Compute how far the determinant of the matrix is from 1. - distance_determinant = tf.abs(tf.linalg.det(matrix) - 1.) - - # Computes how far the product of the transposed rotation matrix with itself - # is from the identity matrix. - identity = tf.eye(n, dtype=matrix.dtype) - inverse_matrix = tf.linalg.matrix_transpose(matrix) - distance_identity = tf.matmul(inverse_matrix, matrix) - identity - distance_identity = tf.norm(distance_identity, axis=[-2, -1]) - - # Computes the mask of entries that satisfies all conditions. - mask = tf.math.logical_and(distance_determinant < atol, - distance_identity < atol) - return tf.expand_dims(mask, axis=-1) - - -def check_shape(n, matrix): - matrix = tf.convert_to_tensor(matrix) - if matrix.shape.rank is not None and matrix.shape.rank < 2: - raise ValueError( - f"matrix must have rank >= 2, but got: {matrix.shape}") - if matrix.shape[-2] != n or matrix.shape[-1] != n: - raise ValueError( - f"matrix must have shape [..., {n}, {n}], " - f"but got: {matrix.shape}") diff --git a/tensorflow_mri/python/geometry/rotation/rotation_matrix_2d.py b/tensorflow_mri/python/geometry/rotation/rotation_matrix_2d.py deleted file mode 100644 index 72b86655..00000000 --- a/tensorflow_mri/python/geometry/rotation/rotation_matrix_2d.py +++ /dev/null @@ -1,139 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2020 The TensorFlow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""2D rotation matrices.""" - -import tensorflow as tf - -from tensorflow_mri.python.geometry.rotation import rotation_matrix - - -def from_euler(angle): - """Converts an angle to a 2D rotation matrix. - - Args: - angle: A `tf.Tensor` of shape `[..., 1]`. - - Returns: - A `tf.Tensor` of shape `[..., 2, 2]`. - - Raises: - ValueError: If the shape of `angle` is invalid. - """ - angle = tf.convert_to_tensor(angle) - - if angle.shape[-1] != 1: - raise ValueError( - f"angle must have shape `[..., 1]`, but got: {angle.shape}") - - cos_angle = tf.math.cos(angle) - sin_angle = tf.math.sin(angle) - matrix = tf.stack([cos_angle, -sin_angle, sin_angle, cos_angle], axis=-1) # pylint: disable=invalid-unary-operand-type - output_shape = tf.concat([tf.shape(angle)[:-1], [2, 2]], axis=-1) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter - return tf.reshape(matrix, output_shape) - - -def from_small_euler(angle): - """Converts a small angle to a 2D rotation matrix. - - Args: - angle: A `tf.Tensor` of shape `[..., 1]`. - - Returns: - A `tf.Tensor` of shape `[..., 2, 2]`. - - Raises: - ValueError: If the shape of `angle` is invalid. - """ - angle = tf.convert_to_tensor(angle) - - if angle.shape[-1] != 1: - raise ValueError( - f"angle must have shape `[..., 1]`, but got: {angle.shape}") - - cos_angle = 1.0 - 0.5 * angle * angle - sin_angle = angle - matrix = tf.stack([cos_angle, -sin_angle, sin_angle, cos_angle], axis=-1) - output_shape = tf.concat([tf.shape(angle)[:-1], [2, 2]], axis=-1) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter - return tf.reshape(matrix, output_shape) - - -def inverse(matrix): - """Inverts a 2D rotation matrix. - - Args: - matrix: A `tf.Tensor` of shape `[..., 2, 2]`. - - Returns: - A `tf.Tensor` of shape `[..., 2, 2]`. - - Raises: - ValueError: If the shape of `matrix` is invalid. - """ - return rotation_matrix.inverse(2, matrix) - - -def is_valid(matrix, atol=1e-3): - """Checks if a 2D rotation matrix is valid. - - Args: - matrix: A `tf.Tensor` of shape `[..., 2, 2]`. - - Returns: - A `tf.Tensor` of shape `[..., 1]` indicating whether the matrix is valid. - """ - return rotation_matrix.is_valid(2, matrix, atol=atol) - - -def rotate(point, matrix): - """Rotates a 2D point using rotation matrix. - - Args: - point: A `tf.Tensor` of shape `[..., 2]`. - matrix: A `tf.Tensor` of shape `[..., 2, 2]`. - - Returns: - A `tf.Tensor` of shape `[..., 2]`. - - Raises: - ValueError: If the shape of `point` or `matrix` is invalid. - """ - return rotation_matrix.rotate(2, point, matrix) - - -def check_shape(matrix): - """Checks the shape of `point` and `matrix`. - - Args: - matrix: A `tf.Tensor` of shape `[..., 2, 2]`. - - Raises: - ValueError: If the shape of `matrix` is invalid. - """ - rotation_matrix.check_shape(2, matrix) diff --git a/tensorflow_mri/python/geometry/rotation/rotation_matrix_3d.py b/tensorflow_mri/python/geometry/rotation/rotation_matrix_3d.py deleted file mode 100644 index a9adee2a..00000000 --- a/tensorflow_mri/python/geometry/rotation/rotation_matrix_3d.py +++ /dev/null @@ -1,261 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2020 The TensorFlow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""3D rotation matrices.""" - -import tensorflow as tf - -from tensorflow_mri.python.geometry.rotation import rotation_matrix - - -def from_euler(angles): - """Converts Euler angles to a 3D rotation matrix. - - Args: - angles: A `tf.Tensor` of shape `[..., 3]`. - - Returns: - A `tf.Tensor` of shape `[..., 3, 3]`. - - Raises: - ValueError: If the shape of `angles` is invalid. - """ - angles = tf.convert_to_tensor(angles) - - if angles.shape[-1] != 3: - raise ValueError( - f"angles must have shape `[..., 3]`, but got: {angles.shape}") - - sin_angles = tf.math.sin(angles) - cos_angles = tf.math.cos(angles) - return _build_matrix_from_sines_and_cosines(sin_angles, cos_angles) - - -def from_small_euler(angles): - """Converts small Euler angles to a 3D rotation matrix. - - Args: - angles: A `tf.Tensor` of shape `[..., 3]`. - - Returns: - A `tf.Tensor` of shape `[..., 3, 3]`. - - Raises: - ValueError: If the shape of `angles` is invalid. - """ - angles = tf.convert_to_tensor(angles) - - if angles.shape[-1:] != 3: - raise ValueError( - f"angles must have shape `[..., 3]`, but got: {angles.shape}") - - sin_angles = angles - cos_angles = 1.0 - 0.5 * tf.math.square(angles) - return _build_matrix_from_sines_and_cosines(sin_angles, cos_angles) - - -def from_axis_angle(axis, angle): - """Converts an axis-angle to a 3D rotation matrix. - - Args: - axis: A `tf.Tensor` of shape `[..., 3]`. - angle: A `tf.Tensor` of shape `[..., 1]`. - - Returns: - A `tf.Tensor` of shape `[..., 3, 3]`. - - Raises: - ValueError: If the shape of `axis` or `angle` is invalid. - """ - axis = tf.convert_to_tensor(axis) - angle = tf.convert_to_tensor(angle) - - if axis.shape[-1] != 3: - raise ValueError( - f"axis must have shape `[..., 3]`, but got: {axis.shape}") - if angle.shape[-1:] != 1: - raise ValueError( - f"angle must have shape `[..., 1]`, but got: {angle.shape}") - - try: - _ = tf.broadcast_static_shape(axis.shape[:-1], angle.shape[:-1]) - except ValueError as err: - raise ValueError( - f"The batch shapes of axis and angle do not " - f"broadcast: {axis.shape[:-1]} vs. {angle.shape[:-1]}") from err - - sin_axis = tf.sin(angle) * axis - cos_angle = tf.cos(angle) - cos1_axis = (1.0 - cos_angle) * axis - _, axis_y, axis_z = tf.unstack(axis, axis=-1) - cos1_axis_x, cos1_axis_y, _ = tf.unstack(cos1_axis, axis=-1) - sin_axis_x, sin_axis_y, sin_axis_z = tf.unstack(sin_axis, axis=-1) - tmp = cos1_axis_x * axis_y - m01 = tmp - sin_axis_z - m10 = tmp + sin_axis_z - tmp = cos1_axis_x * axis_z - m02 = tmp + sin_axis_y - m20 = tmp - sin_axis_y - tmp = cos1_axis_y * axis_z - m12 = tmp - sin_axis_x - m21 = tmp + sin_axis_x - diag = cos1_axis * axis + cos_angle - diag_x, diag_y, diag_z = tf.unstack(diag, axis=-1) - matrix = tf.stack([diag_x, m01, m02, - m10, diag_y, m12, - m20, m21, diag_z], axis=-1) - output_shape = tf.concat([tf.shape(axis)[:-1], [3, 3]], axis=-1) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter - return tf.reshape(matrix, output_shape) - - -def from_quaternion(quaternion): - """Converts a quaternion to a 3D rotation matrix. - - Args: - quaternion: A `tf.Tensor` of shape `[..., 4]`. - - Returns: - A `tf.Tensor` of shape `[..., 3, 3]`. - - Raises: - ValueError: If the shape of `quaternion` is invalid. - """ - quaternion = tf.convert_to_tensor(quaternion) - - if quaternion.shape[-1] != 4: - raise ValueError(f"quaternion must have shape `[..., 4]`, " - f"but got: {quaternion.shape}") - - x, y, z, w = tf.unstack(quaternion, axis=-1) - tx = 2.0 * x - ty = 2.0 * y - tz = 2.0 * z - twx = tx * w - twy = ty * w - twz = tz * w - txx = tx * x - txy = ty * x - txz = tz * x - tyy = ty * y - tyz = tz * y - tzz = tz * z - matrix = tf.stack([1.0 - (tyy + tzz), txy - twz, txz + twy, - txy + twz, 1.0 - (txx + tzz), tyz - twx, - txz - twy, tyz + twx, 1.0 - (txx + tyy)], axis=-1) - output_shape = tf.concat([tf.shape(quaternion)[:-1], [3, 3]], axis=-1) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter - return tf.reshape(matrix, output_shape) - - -def _build_matrix_from_sines_and_cosines(sin_angles, cos_angles): - """Builds a 3D rotation matrix from sines and cosines of Euler angles. - - Args: - sin_angles: A tensor of shape `[..., 3]`, where the last dimension - represents the sine of the Euler angles. - cos_angles: A tensor of shape `[..., 3]`, where the last dimension - represents the cosine of the Euler angles. - - Returns: - A `tf.Tensor` of shape `[..., 3, 3]`, where the last two dimensions - represent a 3D rotation matrix. - """ - sin_angles.shape.assert_is_compatible_with(cos_angles.shape) - - sx, sy, sz = tf.unstack(sin_angles, axis=-1) - cx, cy, cz = tf.unstack(cos_angles, axis=-1) - m00 = cy * cz - m01 = (sx * sy * cz) - (cx * sz) - m02 = (cx * sy * cz) + (sx * sz) - m10 = cy * sz - m11 = (sx * sy * sz) + (cx * cz) - m12 = (cx * sy * sz) - (sx * cz) - m20 = -sy - m21 = sx * cy - m22 = cx * cy - matrix = tf.stack([m00, m01, m02, - m10, m11, m12, - m20, m21, m22], - axis=-1) - output_shape = tf.concat([tf.shape(sin_angles)[:-1], [3, 3]], axis=-1) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter - return tf.reshape(matrix, output_shape) - - -def inverse(matrix): - """Inverts a 3D rotation matrix. - - Args: - matrix: A `tf.Tensor` of shape `[..., 3, 3]`. - - Returns: - A `tf.Tensor` of shape `[..., 3, 3]`. - - Raises: - ValueError: If the shape of `matrix` is invalid. - """ - return rotation_matrix.inverse(3, matrix) - - -def is_valid(matrix, atol=1e-3): - """Checks if a 3D rotation matrix is valid. - - Args: - matrix: A `tf.Tensor` of shape `[..., 3, 3]`. - - Returns: - A `tf.Tensor` of shape `[..., 1]` indicating whether the matrix is valid. - """ - return rotation_matrix.is_valid(3, matrix, atol=atol) - - -def rotate(point, matrix): - """Rotates a 3D point using rotation matrix. - - Args: - point: A `tf.Tensor` of shape `[..., 3]`. - matrix: A `tf.Tensor` of shape `[..., 3, 3]`. - - Returns: - A `tf.Tensor` of shape `[..., 3]`. - - Raises: - ValueError: If the shape of `point` or `matrix` is invalid. - """ - return rotation_matrix.rotate(3, point, matrix) - - -def check_shape(matrix): - """Checks the shape of `point` and `matrix`. - - Args: - matrix: A `tf.Tensor` of shape `[..., 3, 3]`. - - Raises: - ValueError: If the shape of `matrix` is invalid. - """ - rotation_matrix.check_shape(3, matrix) diff --git a/tensorflow_mri/python/geometry/rotation/test_data.py b/tensorflow_mri/python/geometry/rotation/test_data.py deleted file mode 100644 index 3e288c7f..00000000 --- a/tensorflow_mri/python/geometry/rotation/test_data.py +++ /dev/null @@ -1,136 +0,0 @@ -# Copyright 2020 The TensorFlow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Module with test data for transformation tests.""" -# This file is copied from TensorFlow Graphics. - -import numpy as np - -ANGLE_0 = np.array((0.,)) -ANGLE_45 = np.array((np.pi / 4.,)) -ANGLE_90 = np.array((np.pi / 2.,)) -ANGLE_180 = np.array((np.pi,)) - -AXIS_2D_0 = np.array((0., 0.)) -AXIS_2D_X = np.array((1., 0.)) -AXIS_2D_Y = np.array((0., 1.)) - - -def _rotation_2d_x(angle): - """Creates a 2d rotation matrix. - - Args: - angle: The angle. - - Returns: - The 2d rotation matrix. - """ - angle = angle.item() - return np.array(((np.cos(angle), -np.sin(angle)), - (np.sin(angle), np.cos(angle)))) # pyformat: disable - - -MAT_2D_ID = np.eye(2) -MAT_2D_45 = _rotation_2d_x(ANGLE_45) -MAT_2D_90 = _rotation_2d_x(ANGLE_90) -MAT_2D_180 = _rotation_2d_x(ANGLE_180) - -AXIS_3D_0 = np.array((0., 0., 0.)) -AXIS_3D_X = np.array((1., 0., 0.)) -AXIS_3D_Y = np.array((0., 1., 0.)) -AXIS_3D_Z = np.array((0., 0., 1.)) - - -def _axis_angle_to_quaternion(axis, angle): - """Converts an axis-angle representation to a quaternion. - - Args: - axis: The axis of rotation. - angle: The angle. - - Returns: - The quaternion. - """ - quat = np.zeros(4) - quat[0:3] = axis * np.sin(0.5 * angle) - quat[3] = np.cos(0.5 * angle) - return quat - - -QUAT_ID = _axis_angle_to_quaternion(AXIS_3D_0, ANGLE_0) -QUAT_X_45 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_45) -QUAT_X_90 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_90) -QUAT_X_180 = _axis_angle_to_quaternion(AXIS_3D_X, ANGLE_180) -QUAT_Y_45 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_45) -QUAT_Y_90 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_90) -QUAT_Y_180 = _axis_angle_to_quaternion(AXIS_3D_Y, ANGLE_180) -QUAT_Z_45 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_45) -QUAT_Z_90 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_90) -QUAT_Z_180 = _axis_angle_to_quaternion(AXIS_3D_Z, ANGLE_180) - - -def _rotation_3d_x(angle): - """Creates a 3d rotation matrix around the x axis. - - Args: - angle: The angle. - - Returns: - The 3d rotation matrix. - """ - angle = angle.item() - return np.array(((1., 0., 0.), - (0., np.cos(angle), -np.sin(angle)), - (0., np.sin(angle), np.cos(angle)))) # pyformat: disable - - -def _rotation_3d_y(angle): - """Creates a 3d rotation matrix around the y axis. - - Args: - angle: The angle. - - Returns: - The 3d rotation matrix. - """ - angle = angle.item() - return np.array(((np.cos(angle), 0., np.sin(angle)), - (0., 1., 0.), - (-np.sin(angle), 0., np.cos(angle)))) # pyformat: disable - - -def _rotation_3d_z(angle): - """Creates a 3d rotation matrix around the z axis. - - Args: - angle: The angle. - - Returns: - The 3d rotation matrix. - """ - angle = angle.item() - return np.array(((np.cos(angle), -np.sin(angle), 0.), - (np.sin(angle), np.cos(angle), 0.), - (0., 0., 1.))) # pyformat: disable - - -MAT_3D_ID = np.eye(3) -MAT_3D_X_45 = _rotation_3d_x(ANGLE_45) -MAT_3D_X_90 = _rotation_3d_x(ANGLE_90) -MAT_3D_X_180 = _rotation_3d_x(ANGLE_180) -MAT_3D_Y_45 = _rotation_3d_y(ANGLE_45) -MAT_3D_Y_90 = _rotation_3d_y(ANGLE_90) -MAT_3D_Y_180 = _rotation_3d_y(ANGLE_180) -MAT_3D_Z_45 = _rotation_3d_z(ANGLE_45) -MAT_3D_Z_90 = _rotation_3d_z(ANGLE_90) -MAT_3D_Z_180 = _rotation_3d_z(ANGLE_180) diff --git a/tensorflow_mri/python/geometry/rotation/test_helpers.py b/tensorflow_mri/python/geometry/rotation/test_helpers.py deleted file mode 100644 index 36ca83fa..00000000 --- a/tensorflow_mri/python/geometry/rotation/test_helpers.py +++ /dev/null @@ -1,263 +0,0 @@ -# Copyright 2020 The TensorFlow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Test helpers for the transformation module.""" -# This file is copied from TensorFlow Graphics. - -import itertools -import math - -import numpy as np -from scipy import stats -from six.moves import range -import tensorflow as tf - -from tensorflow_mri.python.geometry.rotation import rotation_matrix_2d -from tensorflow_mri.python.geometry.rotation import rotation_matrix_3d -from tensorflow_mri.python.geometry.rotation import quaternion - - -def generate_preset_test_euler_angles(dimensions=3): - """Generates a permutation with duplicate of some classic euler angles.""" - permutations = itertools.product( - [0., np.pi, np.pi / 2., np.pi / 3., np.pi / 4., np.pi / 6.], - repeat=dimensions) - return np.array(list(permutations)) - - -def generate_preset_test_translations(dimensions=3): - """Generates a set of translations.""" - permutations = itertools.product([0.1, -0.2, 0.5, 0.7, 0.4, -0.1], - repeat=dimensions) - return np.array(list(permutations)) - - -def generate_preset_test_rotation_matrices_3d(): - """Generates pre-set test 3d rotation matrices.""" - angles = generate_preset_test_euler_angles() - preset_rotation_matrix = rotation_matrix_3d.from_euler(angles) - return preset_rotation_matrix - - -def generate_preset_test_rotation_matrices_2d(): - """Generates pre-set test 2d rotation matrices.""" - angles = generate_preset_test_euler_angles(dimensions=1) - preset_rotation_matrix = rotation_matrix_2d.from_euler(angles) - return preset_rotation_matrix - - -def generate_preset_test_quaternions(): - """Generates pre-set test quaternions.""" - angles = generate_preset_test_euler_angles() - preset_quaternion = quaternion.from_euler(angles) - return preset_quaternion - - -def generate_preset_test_dual_quaternions(): - """Generates pre-set test quaternions.""" - angles = generate_preset_test_euler_angles() - preset_quaternion_real = quaternion.from_euler(angles) - - translations = generate_preset_test_translations() - translations = np.concatenate( - (translations / 2.0, np.zeros((np.ma.size(translations, 0), 1))), axis=1) - preset_quaternion_translation = tf.convert_to_tensor(value=translations) - - preset_quaternion_dual = quaternion.multiply(preset_quaternion_translation, - preset_quaternion_real) - - preset_dual_quaternion = tf.concat( # pylint: disable=unexpected-keyword-arg,no-value-for-parameter - (preset_quaternion_real, preset_quaternion_dual), axis=-1) - - return preset_dual_quaternion - - -def generate_random_test_euler_angles_translations( - dimensions=3, - min_angle=-3.0 * np.pi, - max_angle=3.0 * np.pi, - min_translation=3.0, - max_translation=3.0): - """Generates random test random Euler angles and translations.""" - tensor_dimensions = np.random.randint(3) - tensor_tile = np.random.randint(1, 10, tensor_dimensions).tolist() - return (np.random.uniform(min_angle, max_angle, tensor_tile + [dimensions]), - np.random.uniform(min_translation, max_translation, - tensor_tile + [dimensions])) - - -def generate_random_test_dual_quaternions(): - """Generates random test dual quaternions.""" - angles = generate_random_test_euler_angles() - random_quaternion_real = quaternion.from_euler(angles) - - min_translation = -3.0 - max_translation = 3.0 - translations = np.random.uniform(min_translation, max_translation, - angles.shape) - - translations_quaternion_shape = np.asarray(translations.shape) - translations_quaternion_shape[-1] = 1 - translations = np.concatenate( - (translations / 2.0, np.zeros(translations_quaternion_shape)), axis=-1) - - random_quaternion_translation = tf.convert_to_tensor(value=translations) - - random_quaternion_dual = quaternion.multiply(random_quaternion_translation, - random_quaternion_real) - - random_dual_quaternion = tf.concat( # pylint: disable=unexpected-keyword-arg,no-value-for-parameter - (random_quaternion_real, random_quaternion_dual), axis=-1) - - return random_dual_quaternion - - -def generate_random_test_euler_angles(dimensions=3, - min_angle=-3. * np.pi, - max_angle=3. * np.pi): - """Generates random test random Euler angles.""" - tensor_dimensions = np.random.randint(3) - tensor_tile = np.random.randint(1, 10, tensor_dimensions).tolist() - return np.random.uniform(min_angle, max_angle, tensor_tile + [dimensions]) - - -def generate_random_test_quaternions(tensor_shape=None): # pylint: disable=missing-param-doc - """Generates random test quaternions.""" - if tensor_shape is None: - tensor_dimensions = np.random.randint(low=1, high=3) - tensor_shape = np.random.randint(1, 10, size=(tensor_dimensions)).tolist() - u1 = np.random.uniform(0.0, 1.0, tensor_shape) - u2 = np.random.uniform(0.0, 2.0 * math.pi, tensor_shape) - u3 = np.random.uniform(0.0, 2.0 * math.pi, tensor_shape) - a = np.sqrt(1.0 - u1) - b = np.sqrt(u1) - return np.stack((a * np.sin(u2), - a * np.cos(u2), - b * np.sin(u3), - b * np.cos(u3)), - axis=-1) # pyformat: disable - - -def generate_random_test_axis_angle(): - """Generates random test axis-angles.""" - tensor_dimensions = np.random.randint(3) - tensor_shape = np.random.randint(1, 10, size=(tensor_dimensions)).tolist() - random_axis = np.random.uniform(size=tensor_shape + [3]) - random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) - random_angle = np.random.uniform(size=tensor_shape + [1]) - return random_axis, random_angle - - -def generate_random_test_rotation_matrix_3d(): - """Generates random test 3d rotation matrices.""" - random_matrix = np.array( - [stats.special_ortho_group.rvs(3) for _ in range(20)]) - return np.reshape(random_matrix, [5, 4, 3, 3]) - - -def generate_random_test_rotation_matrix_2d(): - """Generates random test 2d rotation matrices.""" - random_matrix = np.array( - [stats.special_ortho_group.rvs(2) for _ in range(20)]) - return np.reshape(random_matrix, [5, 4, 2, 2]) - - -def generate_random_test_lbs_blend(): - """Generates random test for the linear blend skinning blend function.""" - tensor_dimensions = np.random.randint(3) - tensor_shape = np.random.randint(1, 10, size=(tensor_dimensions)).tolist() - random_points = np.random.uniform(size=tensor_shape + [3]) - num_weights = np.random.randint(2, 10) - random_weights = np.random.uniform(size=tensor_shape + [num_weights]) - random_weights /= np.sum(random_weights, axis=-1, keepdims=True) - - random_rotations = np.array( - [stats.special_ortho_group.rvs(3) for _ in range(num_weights)]) - random_rotations = np.reshape(random_rotations, [num_weights, 3, 3]) - random_translations = np.random.uniform(size=[num_weights, 3]) - return random_points, random_weights, random_rotations, random_translations - - -def generate_preset_test_lbs_blend(): - """Generates preset test for the linear blend skinning blend function.""" - points = np.array([[[1.0, 0.0, 0.0], [0.1, 0.2, 0.5]], - [[0.0, 1.0, 0.0], [0.3, -0.5, 0.2]], - [[-0.3, 0.1, 0.3], [0.1, -0.9, -0.4]]]) - weights = np.array([[[0.0, 1.0, 0.0, 0.0], [0.4, 0.2, 0.3, 0.1]], - [[0.6, 0.0, 0.4, 0.0], [0.2, 0.2, 0.1, 0.5]], - [[0.0, 0.1, 0.0, 0.9], [0.1, 0.2, 0.3, 0.4]]]) - rotations = np.array( - [[[[1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, 0.0, 1.0]], - [[0.36, 0.48, -0.8], - [-0.8, 0.60, 0.00], - [0.48, 0.64, 0.60]], - [[0.0, 0.0, 1.0], - [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0]], - [[0.0, 1.0, 0.0], - [1.0, 0.0, 0.0], - [0.0, 0.0, -1.0]]], - [[[-0.41554751, -0.42205085, -0.80572535], - [0.08028719, -0.89939186, 0.42970716], - [-0.9060211, 0.11387432, 0.40762533]], - [[-0.05240625, -0.24389111, 0.96838562], - [0.99123384, -0.13047444, 0.02078231], - [0.12128095, 0.96098572, 0.2485908]], - [[-0.32722936, -0.06793413, -0.94249981], - [-0.70574479, 0.68082693, 0.19595657], - [0.62836712, 0.72928708, -0.27073072]], - [[-0.22601332, -0.95393284, 0.19730719], - [-0.01189659, 0.20523618, 0.97864017], - [-0.97405157, 0.21883843, -0.05773466]]]]) # pyformat: disable - translations = np.array( - [[[0.1, -0.2, 0.5], - [-0.2, 0.7, 0.7], - [0.8, -0.2, 0.4], - [-0.1, 0.2, -0.3]], - [[0.5, 0.6, 0.9], - [-0.1, -0.3, -0.7], - [0.4, -0.2, 0.8], - [0.7, 0.8, -0.4]]]) # pyformat: disable - blended_points = np.array([[[[0.16, -0.1, 1.18], [0.3864, 0.148, 0.7352]], - [[0.38, 0.4, 0.86], [-0.2184, 0.152, 0.0088]], - [[-0.05, 0.01, -0.46], [-0.3152, -0.004, - -0.1136]]], - [[[-0.15240625, 0.69123384, -0.57871905], - [0.07776242, 0.33587402, 0.55386645]], - [[0.17959584, 0.01269566, 1.22003942], - [0.71406514, 0.6187734, -0.43794053]], - [[0.67662743, 0.94549789, -0.14946982], - [0.88587099, -0.09324637, -0.45012815]]]]) - - return points, weights, rotations, translations, blended_points - - -def generate_random_test_axis_angle_translation(): - """Generates random test angles, axes, translations.""" - tensor_dimensions = np.random.randint(3) - tensor_shape = np.random.randint(1, 10, size=(tensor_dimensions)).tolist() - random_axis = np.random.uniform(size=tensor_shape + [3]) - random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) - random_angle = np.random.uniform(size=tensor_shape + [1]) - random_translation = np.random.uniform(size=tensor_shape + [3]) - return random_axis, random_angle, random_translation - - -def generate_random_test_points(): - """Generates random 3D points.""" - tensor_dimensions = np.random.randint(3) - tensor_shape = np.random.randint(1, 10, size=(tensor_dimensions)).tolist() - random_point = np.random.uniform(size=tensor_shape + [3]) - return random_point diff --git a/tensorflow_mri/python/geometry/rotation_2d.py b/tensorflow_mri/python/geometry/rotation_2d.py deleted file mode 100644 index e6a96d71..00000000 --- a/tensorflow_mri/python/geometry/rotation_2d.py +++ /dev/null @@ -1,420 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""2D rotation.""" - -import tensorflow as tf - -from tensorflow_mri.python.geometry.rotation import euler_2d -from tensorflow_mri.python.geometry.rotation import rotation_matrix_2d -from tensorflow_mri.python.util import api_util - - -@api_util.export("geometry.Rotation2D") -class Rotation2D(tf.experimental.BatchableExtensionType): # pylint: disable=abstract-method - """Represents a rotation in 2D space (or a batch thereof). - - A `Rotation2D` contains all the information needed to represent a rotation - in 2D space (or a multidimensional array of rotations) and provides - convenient methods to work with rotations. - - ## Initialization - - You can initialize a `Rotation2D` object using one of the `from_*` class - methods: - - - `from_matrix`, to initialize using a - [rotation matrix](https://en.wikipedia.org/wiki/Rotation_matrix). - - `from_euler`, to initialize using an angle (in radians). - - `from_small_euler`, to initialize using an angle which is small enough - to fall under the [small angle approximation](https://en.wikipedia.org/wiki/Small-angle_approximation). - - All of the above methods accept batched inputs, in which case the returned - `Rotation2D` object will represent a batch of rotations. - - ## Methods - - Once initialized, `Rotation2D` objects expose several methods to operate - easily with rotations. These methods are all used in the same way regardless - of how the `Rotation2D` was originally initialized. - - - `rotate` rotates a point or a batch of points. The batch shapes of the - point and this rotation will be broadcasted. - - `inverse` returns a new `Rotation2D` object representing the inverse of - the current rotation. - - `is_valid` can be used to check if the rotation is valid. - - ## Conversion to other representations - - The `as_*` methods can be used to obtain an explicit representation - of this rotation as a standard `tf.Tensor`. - - - `as_matrix` returns the corresponding rotation matrix. - - `as_euler` returns the corresponding angle (in radians). - - ## Shape and dtype - - `Rotation2D` objects have a shape and a dtype, accessible via the `shape` and - `dtype` properties. Because this operator acts like a rotation matrix, its - shape corresponds to the shape of the rotation matrix. In other words, - `rot.shape` is equal to `rot.as_matrix().shape`. - - ```{note} - As with `tf.Tensor`s, the `shape` attribute contains the static shape - as a `tf.TensorShape` and may not be fully defined outside eager execution. - To obtain the dynamic shape of a `Rotation2D` object, use `tf.shape`. - ``` - - ## Operators - - `Rotation2D` objects also override a few operators for concise and intuitive - use. - - - `==` (equality operator) can be used to check if two `Rotation2D` objects - are equal. This checks if the rotations are equivalent, regardless of how - they were defined (`rot1 == rot2`). - - `@` (matrix multiplication operator) can be used to compose two rotations - (`rot = rot1 @ rot2`). - - ## Compatibility with TensorFlow APIs - - Some TensorFlow APIs are explicitly overriden to operate with `Rotation2D` - objects. These include: - - ```{list-table} - --- - header-rows: 1 - --- - - * - API - - Description - - Notes - * - `tf.convert_to_tensor` - - Converts a `Rotation2D` to a `tf.Tensor` containing the corresponding - rotation matrix. - - `tf.convert_to_tensor(rot)` is equivalent to `rot.as_matrix()`. - * - `tf.linalg.matmul` - - Composes two `Rotation2D` objects. - - `tf.linalg.matmul(rot1, rot2)` is equivalent to `rot1 @ rot2`. - * - `tf.linalg.matvec` - - Rotates a point or a batch of points. - - `tf.linalg.matvec(rot, point)` is equivalent to `rot.rotate(point)`. - * - `tf.shape` - - Returns the dynamic shape of a `Rotation2D` object. - - - ``` - - ```{tip} - In general, a `Rotation2D` object behaves like a rotation matrix, although - its internal representation may differ. - ``` - - ```{warning} - While other TensorFlow APIs may also work as expected when passed a - `Rotation2D`, this is not supported and their behavior may change in the - future. - ``` - - Example: - - >>> # Initialize a rotation object using a rotation matrix. - >>> rot = tfmri.geometry.Rotation2D.from_matrix([[0.0, -1.0], [1.0, 0.0]]) - >>> print(rot) - tfmri.geometry.Rotation2D(shape=(2, 2), dtype=float32) - >>> # Rotate a point. - >>> point = tf.constant([1.0, 0.0], dtype=tf.float32) - >>> rotated = rot.rotate(point) - >>> print(rotated) - tf.Tensor([0. 1.], shape=(2,), dtype=float32) - >>> # Rotate the point back using the inverse rotation. - >>> inv_rot = rot.inverse() - >>> restored = inv_rot.rotate(rotated) - >>> print(restored) - tf.Tensor([1. 0.], shape=(2,), dtype=float32) - >>> # Get the rotation matrix for the inverse rotation. - >>> print(inv_rot.as_matrix()) - tf.Tensor( - [[ 0. 1.] - [-1. 0.]], shape=(2, 2), dtype=float32) - >>> # You can also initialize a rotation using an angle: - >>> rot2 = tfmri.geometry.Rotation2D.from_euler([np.pi / 2]) - >>> rotated2 = rot.rotate(point) - >>> np.allclose(rotated2, rotated) - True - - """ - __name__ = "tfmri.geometry.Rotation2D" - _matrix: tf.Tensor - - @classmethod - def from_matrix(cls, matrix, name=None): - r"""Creates a 2D rotation from a rotation matrix. - - Args: - matrix: A `tf.Tensor` of shape `[..., 2, 2]`, where the last two - dimensions represent a rotation matrix. - name: A name for this op. Defaults to `"rotation_2d/from_matrix"`. - - Returns: - A `Rotation2D`. - """ - with tf.name_scope(name or "rotation_2d/from_matrix"): - return cls(_matrix=matrix) - - @classmethod - def from_euler(cls, angle, name=None): - r"""Creates a 2D rotation from an angle. - - The resulting rotation acts like the following rotation matrix: - - $$ - \mathbf{R} = - \begin{bmatrix} - \cos(\theta) & -\sin(\theta) \\ - \sin(\theta) & \cos(\theta) - \end{bmatrix}. - $$ - - ```{note} - The resulting rotation rotates points in the $xy$-plane counterclockwise. - ``` - - Args: - angle: A `tf.Tensor` of shape `[..., 1]`, where the last dimension - represents an angle in radians. - name: A name for this op. Defaults to `"rotation_2d/from_euler"`. - - Returns: - A `Rotation2D`. - - Raises: - ValueError: If the shape of `angle` is invalid. - """ - with tf.name_scope(name or "rotation_2d/from_euler"): - return cls(_matrix=rotation_matrix_2d.from_euler(angle)) - - @classmethod - def from_small_euler(cls, angle, name=None): - r"""Creates a 2D rotation from a small angle. - - Uses the small angle approximation to compute the rotation. Under the - small angle assumption, $\sin(x)$$ and $$\cos(x)$ can be approximated by - their second order Taylor expansions, where $\sin(x) \approx x$ and - $\cos(x) \approx 1 - \frac{x^2}{2}$. - - The resulting rotation acts like the following rotation matrix: - - $$ - \mathbf{R} = - \begin{bmatrix} - 1.0 - 0.5\theta^2 & -\theta \\ - \theta & 1.0 - 0.5\theta^2 - \end{bmatrix}. - $$ - - ```{note} - The resulting rotation rotates points in the $xy$-plane counterclockwise. - ``` - - ```{note} - This function does not verify the smallness of the angles. - ``` - - Args: - angle: A `tf.Tensor` of shape `[..., 1]`, where the last dimension - represents an angle in radians. - name: A name for this op. Defaults to "rotation_2d/from_small_euler". - - Returns: - A `Rotation2D`. - - Raises: - ValueError: If the shape of `angle` is invalid. - """ - with tf.name_scope(name or "rotation_2d/from_small_euler"): - return cls(_matrix=rotation_matrix_2d.from_small_euler(angle)) - - def as_matrix(self, name=None): - r"""Returns a rotation matrix representation of this rotation. - - Args: - name: A name for this op. Defaults to `"rotation_2d/as_matrix"`. - - Returns: - A `tf.Tensor` of shape `[..., 2, 2]`, where the last two dimensions - represent a rotation matrix. - """ - with tf.name_scope(name or "rotation_2d/as_matrix"): - return tf.identity(self._matrix) - - def as_euler(self, name=None): - r"""Returns an angle representation of this rotation. - - Args: - name: A name for this op. Defaults to `"rotation_2d/as_euler"`. - - Returns: - A `tf.Tensor` of shape `[..., 1]`, where the last dimension represents an - angle in radians. - """ - with tf.name_scope(name or "rotation_2d/as_euler"): - return euler_2d.from_matrix(self._matrix) - - def inverse(self, name=None): - r"""Computes the inverse of this rotation. - - Args: - name: A name for this op. Defaults to `"rotation_2d/inverse"`. - - Returns: - A `Rotation2D` representing the inverse of this rotation. - """ - with tf.name_scope(name or "rotation_2d/inverse"): - return Rotation2D(_matrix=rotation_matrix_2d.inverse(self._matrix)) - - def is_valid(self, atol=1e-3, name=None): - r"""Determines if this is a valid rotation. - - A rotation matrix $\mathbf{R}$ is a valid rotation matrix if - $\mathbf{R}^T\mathbf{R} = \mathbf{I}$ and $\det(\mathbf{R}) = 1$. - - Args: - atol: A `float`. The absolute tolerance parameter. - name: A name for this op. Defaults to `"rotation_2d/is_valid"`. - - Returns: - A boolean `tf.Tensor` with shape `[..., 1]`, `True` if the corresponding - matrix is valid and `False` otherwise. - """ - with tf.name_scope(name or "rotation_2d/is_valid"): - return rotation_matrix_2d.is_valid(self._matrix, atol=atol) - - def rotate(self, point, name=None): - r"""Rotates a 2D point. - - Args: - point: A `tf.Tensor` of shape `[..., 2]`, where the last dimension - represents a 2D point and `...` represents any number of batch - dimensions, which must be broadcastable with the batch shape of this - rotation. - name: A name for this op. Defaults to `"rotation_2d/rotate"`. - - Returns: - A `tf.Tensor` of shape `[..., 2]`, where the last dimension represents - a 2D point and `...` is the result of broadcasting the batch shapes of - `point` and this rotation matrix. - - Raises: - ValueError: If the shape of `point` is invalid. - """ - with tf.name_scope(name or "rotation_2d/rotate"): - return rotation_matrix_2d.rotate(point, self._matrix) - - def __eq__(self, other): - """Returns true if this rotation is equivalent to the other rotation.""" - return tf.math.reduce_all( - tf.math.equal(self._matrix, other._matrix), axis=[-2, -1]) - - def __matmul__(self, other): - """Composes this rotation with another rotation.""" - if isinstance(other, Rotation2D): - return Rotation2D(_matrix=tf.matmul(self._matrix, other._matrix)) - raise ValueError( - f"Cannot compose a `Rotation2D` with a `{type(other).__name__}`.") - - def __repr__(self): - """Returns a string representation of this rotation.""" - name = self.__name__ - return f"<{name}(shape={str(self.shape)}, dtype={self.dtype.name})>" - - def __str__(self): - """Returns a string representation of this rotation.""" - return self.__repr__()[1:-1] - - def __validate__(self): - """Checks that this rotation is a valid rotation. - - Only performs static checks. - """ - rotation_matrix_2d.check_shape(self._matrix) - - @property - def shape(self): - """Returns the shape of this rotation. - - Returns: - A `tf.TensorShape`. - """ - return self._matrix.shape - - @property - def dtype(self): - """Returns the dtype of this rotation. - - Returns: - A `tf.dtypes.DType`. - """ - return self._matrix.dtype - - -@tf.experimental.dispatch_for_api(tf.convert_to_tensor, {'value': Rotation2D}) -def convert_to_tensor(value, dtype=None, dtype_hint=None, name=None): - """Overrides `tf.convert_to_tensor` for `Rotation2D` objects.""" - return tf.convert_to_tensor( - value.as_matrix(), dtype=dtype, dtype_hint=dtype_hint, name=name) - - -@tf.experimental.dispatch_for_api( - tf.linalg.matmul, {'a': Rotation2D, 'b': Rotation2D}) -def matmul(a, b, # pylint: disable=missing-param-doc - transpose_a=False, - transpose_b=False, - adjoint_a=False, - adjoint_b=False, - a_is_sparse=False, - b_is_sparse=False, - output_type=None, - name=None): - """Overrides `tf.linalg.matmul` for `Rotation2D` objects.""" - if a_is_sparse or b_is_sparse: - raise ValueError("Rotation2D does not support sparse matmul.") - return Rotation2D(_matrix=tf.linalg.matmul(a.as_matrix(), b.as_matrix(), - transpose_a=transpose_a, - transpose_b=transpose_b, - adjoint_a=adjoint_a, - adjoint_b=adjoint_b, - output_type=output_type, - name=name)) - - -@tf.experimental.dispatch_for_api(tf.linalg.matvec, {'a': Rotation2D}) -def matvec(a, b, # pylint: disable=missing-param-doc - transpose_a=False, - adjoint_a=False, - a_is_sparse=False, - b_is_sparse=False, - name=None): - """Overrides `tf.linalg.matvec` for `Rotation2D` objects.""" - if a_is_sparse or b_is_sparse: - raise ValueError("Rotation2D does not support sparse matvec.") - return tf.linalg.matvec(a.as_matrix(), b, - transpose_a=transpose_a, - adjoint_a=adjoint_a, - name=name) - - -@tf.experimental.dispatch_for_api(tf.shape, {'input': Rotation2D}) -def shape(input, out_type=tf.int32, name=None): # pylint: disable=redefined-builtin - """Overrides `tf.shape` for `Rotation2D` objects.""" - return tf.shape(input.as_matrix(), out_type=out_type, name=name) diff --git a/tensorflow_mri/python/geometry/rotation_2d_test.py b/tensorflow_mri/python/geometry/rotation_2d_test.py deleted file mode 100644 index 132de2e7..00000000 --- a/tensorflow_mri/python/geometry/rotation_2d_test.py +++ /dev/null @@ -1,178 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2020 The TensorFlow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for 2D rotation.""" -# This file is partly inspired by TensorFlow Graphics. -# pylint: disable=missing-param-doc - -from absl.testing import parameterized -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.geometry.rotation import test_data as td -from tensorflow_mri.python.geometry.rotation import test_helpers -from tensorflow_mri.python.geometry.rotation_2d import Rotation2D -from tensorflow_mri.python.util import test_util - - -class Rotation2DTest(test_util.TestCase): - """Tests for `Rotation2D`.""" - def test_shape(self): - """Tests shape.""" - rot = Rotation2D.from_euler([0.0]) - self.assertAllEqual([2, 2], rot.shape) - self.assertAllEqual([2, 2], tf.shape(rot)) - - rot = Rotation2D.from_euler([[0.0], [np.pi]]) - self.assertAllEqual([2, 2, 2], rot.shape) - self.assertAllEqual([2, 2, 2], tf.shape(rot)) - - def test_equal(self): - """Tests equality operator.""" - rot1 = Rotation2D.from_euler([0.0]) - rot2 = Rotation2D.from_euler([0.0]) - self.assertAllEqual(True, rot1 == rot2) - - rot1 = Rotation2D.from_euler([0.0]) - rot2 = Rotation2D.from_euler([np.pi]) - self.assertAllEqual(False, rot1 == rot2) - - rot1 = Rotation2D.from_euler([[0.0], [np.pi]]) - rot2 = Rotation2D.from_euler([[0.0], [np.pi]]) - self.assertAllEqual([True, True], rot1 == rot2) - - rot1 = Rotation2D.from_euler([[0.0], [0.0]]) - rot2 = Rotation2D.from_euler([[0.0], [np.pi]]) - self.assertAllEqual([True, False], rot1 == rot2) - - def test_repr(self): - """Tests that repr works.""" - expected = "" - rot = Rotation2D.from_euler([0.0]) - self.assertEqual(expected, repr(rot)) - self.assertEqual(expected[1:-1], str(rot)) - - def test_matmul(self): - """Tests that matmul works.""" - rot = Rotation2D.from_euler([np.pi]) - composed = rot @ rot - self.assertAllClose(np.eye(2), composed.as_matrix()) - - composed = tf.linalg.matmul(rot, rot) - self.assertAllClose(np.eye(2), composed.as_matrix()) - - def test_matvec(self): - """Tests that matvec works.""" - rot = Rotation2D.from_euler([np.pi]) - vec = tf.constant([1.0, -1.0]) - self.assertAllClose(rot.rotate(vec), tf.linalg.matvec(rot, vec)) - - def test_convert_to_tensor(self): - """Tests that conversion to tensor works.""" - rot = Rotation2D.from_euler([0.0]) - self.assertIsInstance(tf.convert_to_tensor(rot), tf.Tensor) - self.assertAllClose(np.eye(2), tf.convert_to_tensor(rot)) - - @parameterized.named_parameters( - ("0", [0.0]), - ("45", [np.pi / 4]), - ("90", [np.pi / 2]), - ("135", [np.pi * 3 / 4]), - ("-45", [-np.pi / 4]), - ("-90", [-np.pi / 2]), - ("-135", [-np.pi * 3 / 4]) - ) - def test_as_euler(self, angle): # pylint: disable=missing-param-doc - """Tests that `as_euler` returns the correct angle.""" - rot = Rotation2D.from_euler(angle) - self.assertAllClose(angle, rot.as_euler()) - - def test_from_matrix(self): - """Tests that rotation can be initialized from matrix.""" - rot = Rotation2D.from_matrix(np.eye(2)) - self.assertAllClose(np.eye(2), rot.as_matrix()) - - def test_from_euler_normalized(self): - """Tests that an angle maps to correct matrix.""" - euler_angles = test_helpers.generate_preset_test_euler_angles(dimensions=1) - - rot = Rotation2D.from_euler(euler_angles) - self.assertAllEqual(np.ones(euler_angles.shape[0:-1] + (1,), dtype=bool), - rot.is_valid()) - - @parameterized.named_parameters( - ("0", td.ANGLE_0, td.MAT_2D_ID), - ("45", td.ANGLE_45, td.MAT_2D_45), - ("90", td.ANGLE_90, td.MAT_2D_90), - ("180", td.ANGLE_180, td.MAT_2D_180), - ) - def test_from_euler(self, angle, expected): - """Tests that an angle maps to correct matrix.""" - self.assertAllClose(expected, Rotation2D.from_euler(angle).as_matrix()) - - def test_from_euler_with_small_angles_approximation_random(self): - """Tests small angles approximation by comparing to exact calculation.""" - # Only generate small angles. For a test tolerance of 1e-3, 0.17 was found - # empirically to be the range where the small angle approximation works. - random_euler_angles = test_helpers.generate_random_test_euler_angles( - min_angle=-0.17, max_angle=0.17, dimensions=1) - - exact_rot = Rotation2D.from_euler(random_euler_angles) - approx_rot = Rotation2D.from_small_euler(random_euler_angles) - - self.assertAllClose(exact_rot.as_matrix(), approx_rot.as_matrix(), - atol=1e-3) - - def test_inverse_random(self): - """Checks that inverting rotated points results in no transformation.""" - random_euler_angles = test_helpers.generate_random_test_euler_angles( - dimensions=1) - tensor_shape = random_euler_angles.shape[:-1] - - random_rot = Rotation2D.from_euler(random_euler_angles) - random_point = np.random.normal(size=tensor_shape + (2,)) - rotated_random_points = random_rot.rotate(random_point) - predicted_invert_random_matrix = random_rot.inverse() - predicted_invert_rotated_random_points = ( - predicted_invert_random_matrix.rotate(rotated_random_points)) - - self.assertAllClose(random_point, predicted_invert_rotated_random_points) - - @parameterized.named_parameters( - ("preset1", td.AXIS_2D_0, td.ANGLE_90, td.AXIS_2D_0), - ("preset2", td.AXIS_2D_X, td.ANGLE_90, td.AXIS_2D_Y), - ) - def test_rotate(self, point, angle, expected): - """Tests that the rotate function correctly rotates points.""" - result = Rotation2D.from_euler(angle).rotate(point) - self.assertAllClose(expected, result) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/geometry/rotation_3d.py b/tensorflow_mri/python/geometry/rotation_3d.py deleted file mode 100644 index b1a95850..00000000 --- a/tensorflow_mri/python/geometry/rotation_3d.py +++ /dev/null @@ -1,302 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""3D rotation.""" - -import tensorflow as tf - -from tensorflow_mri.python.geometry.rotation import rotation_matrix_3d - - -class Rotation3D(tf.experimental.BatchableExtensionType): # pylint: disable=abstract-method - """Represents a rotation in 3D space (or a batch thereof).""" - __name__ = "tfmri.geometry.Rotation3D" - _matrix: tf.Tensor - - @classmethod - def from_matrix(cls, matrix, name=None): - r"""Creates a 3D rotation from a rotation matrix. - - Args: - matrix: A `tf.Tensor` of shape `[..., 3, 3]`, where the last two - dimensions represent a rotation matrix. - name: A name for this op. Defaults to `"rotation_3d/from_matrix"`. - - Returns: - A `Rotation3D`. - """ - with tf.name_scope(name or "rotation_3d/from_matrix"): - return cls(_matrix=matrix) - - @classmethod - def from_euler(cls, angles, name=None): - r"""Creates a 3D rotation from Euler angles. - - The resulting rotation acts like the rotation matrix - $\mathbf{R} = \mathbf{R}_z\mathbf{R}_y\mathbf{R}_x$. - - ```{note} - Uses the $z$-$y$-$x$ rotation convention (Tait-Bryan angles). - ``` - - Args: - angles: A `tf.Tensor` of shape `[..., 3]`, where the last dimension - represents the three Euler angles in radians. `angles[..., 0]` - is the angles about `x`, `angles[..., 1]` is the angles about `y`, - and `angles[..., 2]` is the angles about `z`. - name: A name for this op. Defaults to `"rotation_3d/from_euler"`. - - Returns: - A `Rotation3D`. - - Raises: - ValueError: If the shape of `angles` is invalid. - """ - with tf.name_scope(name or "rotation_3d/from_euler"): - return cls(_matrix=rotation_matrix_3d.from_euler(angles)) - - @classmethod - def from_small_euler(cls, angles, name=None): - r"""Creates a 3D rotation from small Euler angles. - - The resulting rotation acts like the rotation matrix - $\mathbf{R} = \mathbf{R}_z\mathbf{R}_y\mathbf{R}_x$. - - Uses the small angle approximation to compute the rotation. Under the - small angle assumption, $\sin(x)$$ and $$\cos(x)$ can be approximated by - their second order Taylor expansions, where $\sin(x) \approx x$ and - $\cos(x) \approx 1 - \frac{x^2}{2}$. - - ```{note} - Uses the $z$-$y$-$x$ rotation convention (Tait-Bryan angles). - ``` - - ```{note} - This function does not verify the smallness of the angles. - ``` - - Args: - angles: A `tf.Tensor` of shape `[..., 3]`, where the last dimension - represents the three Euler angles in radians. `angles[..., 0]` - is the angles about `x`, `angles[..., 1]` is the angles about `y`, - and `angles[..., 2]` is the angles about `z`. - name: A name for this op. Defaults to "rotation_3d/from_small_euler". - - Returns: - A `Rotation3D`. - - Raises: - ValueError: If the shape of `angles` is invalid. - """ - with tf.name_scope(name or "rotation_3d/from_small_euler"): - return cls(_matrix=rotation_matrix_3d.from_small_euler(angles)) - - @classmethod - def from_axis_angle(cls, axis, angle, name=None): - """Creates a 3D rotation from an axis-angle representation. - - Args: - axis: A `tf.Tensor` of shape `[..., 3]`, where the last dimension - represents a normalized axis. - angle: A `tf.Tensor` of shape `[..., 1]`, where the last dimension - represents a normalized axis. - name: A name for this op. Defaults to "rotation_3d/from_axis_angle". - - Returns: - A `Rotation3D`. - - Raises: - ValueError: If the shape of `axis` or `angle` is invalid. - """ - with tf.name_scope(name or "rotation_3d/from_axis_angle"): - return cls(_matrix=rotation_matrix_3d.from_axis_angle(axis, angle)) - - @classmethod - def from_quaternion(cls, quaternion, name=None): - """Creates a 3D rotation from a quaternion. - - Args: - quaternion: A `tf.Tensor` of shape `[..., 4]`, where the last dimension - represents a normalized quaternion. - name: A name for this op. Defaults to `"rotation_3d/from_quaternion"`. - - Returns: - A `Rotation3D`. - - Raises: - ValueError: If the shape of `quaternion` is invalid. - """ - with tf.name_scope(name or "rotation_3d/from_quaternion"): - return cls(_matrix=rotation_matrix_3d.from_quaternion(quaternion)) - - def as_matrix(self, name=None): - r"""Returns a rotation matrix representation of this rotation. - - Args: - name: A name for this op. Defaults to `"rotation_3d/as_matrix"`. - - Returns: - A `tf.Tensor` of shape `[..., 3, 3]`, where the last two dimensions - represent a rotation matrix. - """ - with tf.name_scope(name or "rotation_3d/as_matrix"): - return tf.identity(self._matrix) - - def inverse(self, name=None): - r"""Computes the inverse of this rotation. - - Args: - name: A name for this op. Defaults to `"rotation_3d/inverse"`. - - Returns: - A `Rotation3D` representing the inverse of this rotation. - """ - with tf.name_scope(name or "rotation_3d/inverse"): - return Rotation3D(_matrix=rotation_matrix_3d.inverse(self._matrix)) - - def is_valid(self, atol=1e-3, name=None): - r"""Determines if this is a valid rotation. - - A rotation matrix $\mathbf{R}$ is a valid rotation matrix if - $\mathbf{R}^T\mathbf{R} = \mathbf{I}$ and $\det(\mathbf{R}) = 1$. - - Args: - atol: A `float`. The absolute tolerance parameter. - name: A name for this op. Defaults to `"rotation_3d/is_valid"`. - - Returns: - A boolean `tf.Tensor` with shape `[..., 1]`, `True` if the corresponding - matrix is valid and `False` otherwise. - """ - with tf.name_scope(name or "rotation_3d/is_valid"): - return rotation_matrix_3d.is_valid(self._matrix, atol=atol) - - def rotate(self, point, name=None): - r"""Rotates a 3D point. - - Args: - point: A `tf.Tensor` of shape `[..., 3]`, where the last dimension - represents a 3D point and `...` represents any number of batch - dimensions, which must be broadcastable with the batch shape of this - rotation. - name: A name for this op. Defaults to `"rotation_3d/rotate"`. - - Returns: - A `tf.Tensor` of shape `[..., 3]`, where the last dimension represents - a 3D point and `...` is the result of broadcasting the batch shapes of - `point` and this rotation matrix. - - Raises: - ValueError: If the shape of `point` is invalid. - """ - with tf.name_scope(name or "rotation_3d/rotate"): - return rotation_matrix_3d.rotate(point, self._matrix) - - def __eq__(self, other): - """Returns true if this rotation is equivalent to the other rotation.""" - return tf.math.reduce_all( - tf.math.equal(self._matrix, other._matrix), axis=[-2, -1]) - - def __matmul__(self, other): - """Composes this rotation with another rotation.""" - if isinstance(other, Rotation3D): - return Rotation3D(_matrix=tf.matmul(self._matrix, other._matrix)) - raise ValueError( - f"Cannot compose a `Rotation2D` with a `{type(other).__name__}`.") - - def __repr__(self): - """Returns a string representation of this rotation.""" - name = self.__name__ - return f"<{name}(shape={str(self.shape)}, dtype={self.dtype.name})>" - - def __str__(self): - """Returns a string representation of this rotation.""" - return self.__repr__()[1:-1] - - def __validate__(self): - """Checks that this rotation is a valid rotation. - - Only performs static checks. - """ - rotation_matrix_3d.check_shape(self._matrix) - - @property - def shape(self): - """Returns the shape of this rotation. - - Returns: - A `tf.TensorShape`. - """ - return self._matrix.shape - - @property - def dtype(self): - """Returns the dtype of this rotation. - - Returns: - A `tf.dtypes.DType`. - """ - return self._matrix.dtype - - -@tf.experimental.dispatch_for_api(tf.convert_to_tensor, {'value': Rotation3D}) -def convert_to_tensor(value, dtype=None, dtype_hint=None, name=None): - """Overrides `tf.convert_to_tensor` for `Rotation3D` objects.""" - return tf.convert_to_tensor( - value.as_matrix(), dtype=dtype, dtype_hint=dtype_hint, name=name) - - -@tf.experimental.dispatch_for_api( - tf.linalg.matmul, {'a': Rotation3D, 'b': Rotation3D}) -def matmul(a, b, # pylint: disable=missing-param-doc - transpose_a=False, - transpose_b=False, - adjoint_a=False, - adjoint_b=False, - a_is_sparse=False, - b_is_sparse=False, - output_type=None, - name=None): - """Overrides `tf.linalg.matmul` for `Rotation3D` objects.""" - if a_is_sparse or b_is_sparse: - raise ValueError("Rotation3D does not support sparse matmul.") - return Rotation3D(_matrix=tf.linalg.matmul(a.as_matrix(), b.as_matrix(), - transpose_a=transpose_a, - transpose_b=transpose_b, - adjoint_a=adjoint_a, - adjoint_b=adjoint_b, - output_type=output_type, - name=name)) - - -@tf.experimental.dispatch_for_api(tf.linalg.matvec, {'a': Rotation3D}) -def matvec(a, b, # pylint: disable=missing-param-doc - transpose_a=False, - adjoint_a=False, - a_is_sparse=False, - b_is_sparse=False, - name=None): - """Overrides `tf.linalg.matvec` for `Rotation3D` objects.""" - if a_is_sparse or b_is_sparse: - raise ValueError("Rotation3D does not support sparse matvec.") - return tf.linalg.matvec(a.as_matrix(), b, - transpose_a=transpose_a, - adjoint_a=adjoint_a, - name=name) - - -@tf.experimental.dispatch_for_api(tf.shape, {'input': Rotation3D}) -def shape(input, out_type=tf.int32, name=None): # pylint: disable=redefined-builtin - """Overrides `tf.shape` for `Rotation3D` objects.""" - return tf.shape(input.as_matrix(), out_type=out_type, name=name) diff --git a/tensorflow_mri/python/geometry/rotation_3d_test.py b/tensorflow_mri/python/geometry/rotation_3d_test.py deleted file mode 100644 index 93ce456f..00000000 --- a/tensorflow_mri/python/geometry/rotation_3d_test.py +++ /dev/null @@ -1,280 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2020 The TensorFlow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Tests for 3D rotation.""" -# This file is partly inspired by TensorFlow Graphics. -# pylint: disable=missing-param-doc - -from absl.testing import parameterized -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.geometry.rotation import test_data as td -from tensorflow_mri.python.geometry.rotation import test_helpers -from tensorflow_mri.python.geometry.rotation_3d import Rotation3D -from tensorflow_mri.python.util import test_util - - -class Rotation3DTest(test_util.TestCase): - """Tests for `Rotation3D`.""" - def test_shape(self): - """Tests shape.""" - rot = Rotation3D.from_euler([0.0, 0.0, 0.0]) - self.assertAllEqual([3, 3], rot.shape) - self.assertAllEqual([3, 3], tf.shape(rot)) - - rot = Rotation3D.from_euler([[0.0, 0.0, 0.0], [np.pi, 0.0, 0.0]]) - self.assertAllEqual([2, 3, 3], rot.shape) - self.assertAllEqual([2, 3, 3], tf.shape(rot)) - - def test_equal(self): - """Tests equality operator.""" - rot1 = Rotation3D.from_euler([0.0, 0.0, 0.0]) - rot2 = Rotation3D.from_euler([0.0, 0.0, 0.0]) - self.assertAllEqual(True, rot1 == rot2) - - rot1 = Rotation3D.from_euler([0.0, 0.0, 0.0]) - rot2 = Rotation3D.from_euler([np.pi, 0.0, 0.0]) - self.assertAllEqual(False, rot1 == rot2) - - rot1 = Rotation3D.from_euler([[0.0, 0.0, 0.0], [np.pi, 0.0, 0.0]]) - rot2 = Rotation3D.from_euler([[0.0, 0.0, 0.0], [np.pi, 0.0, 0.0]]) - self.assertAllEqual([True, True], rot1 == rot2) - - rot1 = Rotation3D.from_euler([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) - rot2 = Rotation3D.from_euler([[0.0, 0.0, 0.0], [np.pi, 0.0, 0.0]]) - self.assertAllEqual([True, False], rot1 == rot2) - - def test_repr(self): - rot = Rotation3D.from_euler([0.0, 0.0, 0.0]) - self.assertEqual( - "", repr(rot)) - - def test_convert_to_tensor(self): - """Tests that conversion to tensor works.""" - rot = Rotation3D.from_euler([0.0, 0.0, 0.0]) - self.assertIsInstance(tf.convert_to_tensor(rot), tf.Tensor) - self.assertAllClose(np.eye(3), tf.convert_to_tensor(rot)) - - def test_from_axis_angle_normalized_random(self): - """Tests that axis-angles can be converted to rotation matrices.""" - tensor_shape = np.random.randint(1, 10, size=np.random.randint(3)).tolist() - random_axis = np.random.normal(size=tensor_shape + [3]) - random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) - random_angle = np.random.normal(size=tensor_shape + [1]) - - rotation = Rotation3D.from_axis_angle(random_axis, random_angle) - - self.assertAllEqual(rotation.is_valid(), np.ones(tensor_shape + [1])) - - @parameterized.named_parameters( - ("preset0", td.AXIS_3D_X, td.ANGLE_45, td.MAT_3D_X_45), - ("preset1", td.AXIS_3D_Y, td.ANGLE_45, td.MAT_3D_Y_45), - ("preset2", td.AXIS_3D_Z, td.ANGLE_45, td.MAT_3D_Z_45), - ("preset3", td.AXIS_3D_X, td.ANGLE_90, td.MAT_3D_X_90), - ("preset4", td.AXIS_3D_Y, td.ANGLE_90, td.MAT_3D_Y_90), - ("preset5", td.AXIS_3D_Z, td.ANGLE_90, td.MAT_3D_Z_90), - ("preset6", td.AXIS_3D_X, td.ANGLE_180, td.MAT_3D_X_180), - ("preset7", td.AXIS_3D_Y, td.ANGLE_180, td.MAT_3D_Y_180), - ("preset8", td.AXIS_3D_Z, td.ANGLE_180, td.MAT_3D_Z_180) - ) - def test_from_axis_angle(self, axis, angle, matrix): - """Tests that an axis-angle maps to correct matrix.""" - self.assertAllClose( - matrix, Rotation3D.from_axis_angle(axis, angle).as_matrix()) - - def test_from_axis_angle_random(self): - """Tests conversion to matrix.""" - tensor_shape = np.random.randint(1, 10, size=np.random.randint(3)).tolist() - random_axis = np.random.normal(size=tensor_shape + [3]) - random_axis /= np.linalg.norm(random_axis, axis=-1, keepdims=True) - random_angle = np.random.normal(size=tensor_shape + [1]) - - rotation = Rotation3D.from_axis_angle(random_axis, random_angle) - - # Checks that resulting rotation matrices are normalized. - self.assertAllEqual(rotation.is_valid(), np.ones(tensor_shape + [1])) - - @parameterized.named_parameters( - ("preset0", td.AXIS_3D_X, td.ANGLE_90, td.AXIS_3D_X, td.AXIS_3D_X), - ("preset1", td.AXIS_3D_X, td.ANGLE_90, td.AXIS_3D_Y, td.AXIS_3D_Z), - ("preset2", td.AXIS_3D_X, -td.ANGLE_90, td.AXIS_3D_Z, td.AXIS_3D_Y), - ("preset3", td.AXIS_3D_Y, -td.ANGLE_90, td.AXIS_3D_X, td.AXIS_3D_Z), - ("preset4", td.AXIS_3D_Y, td.ANGLE_90, td.AXIS_3D_Y, td.AXIS_3D_Y), - ("preset5", td.AXIS_3D_Y, td.ANGLE_90, td.AXIS_3D_Z, td.AXIS_3D_X), - ("preset6", td.AXIS_3D_Z, td.ANGLE_90, td.AXIS_3D_X, td.AXIS_3D_Y), - ("preset7", td.AXIS_3D_Z, -td.ANGLE_90, td.AXIS_3D_Y, td.AXIS_3D_X), - ("preset8", td.AXIS_3D_Z, td.ANGLE_90, td.AXIS_3D_Z, td.AXIS_3D_Z), - ) - def test_from_axis_angle_rotate_vector_preset( - self, axis, angle, point, expected): - """Tests the directionality of axis-angle rotations.""" - self.assertAllClose( - expected, Rotation3D.from_axis_angle(axis, angle).rotate(point)) - - def test_from_euler_normalized_preset(self): - """Tests that euler angles can be converted to rotation matrices.""" - euler_angles = test_helpers.generate_preset_test_euler_angles() - - matrix = Rotation3D.from_euler(euler_angles) - self.assertAllEqual( - matrix.is_valid(), np.ones(euler_angles.shape[0:-1] + (1,))) - - def test_from_euler_normalized_random(self): - """Tests that euler angles can be converted to rotation matrices.""" - random_euler_angles = test_helpers.generate_random_test_euler_angles() - - matrix = Rotation3D.from_euler(random_euler_angles) - self.assertAllEqual( - matrix.is_valid(), np.ones(random_euler_angles.shape[0:-1] + (1,))) - - @parameterized.named_parameters( - ("preset0", td.AXIS_3D_0, td.MAT_3D_ID), - ("preset1", td.ANGLE_45 * td.AXIS_3D_X, td.MAT_3D_X_45), - ("preset2", td.ANGLE_45 * td.AXIS_3D_Y, td.MAT_3D_Y_45), - ("preset3", td.ANGLE_45 * td.AXIS_3D_Z, td.MAT_3D_Z_45), - ("preset4", td.ANGLE_90 * td.AXIS_3D_X, td.MAT_3D_X_90), - ("preset5", td.ANGLE_90 * td.AXIS_3D_Y, td.MAT_3D_Y_90), - ("preset6", td.ANGLE_90 * td.AXIS_3D_Z, td.MAT_3D_Z_90), - ("preset7", td.ANGLE_180 * td.AXIS_3D_X, td.MAT_3D_X_180), - ("preset8", td.ANGLE_180 * td.AXIS_3D_Y, td.MAT_3D_Y_180), - ("preset9", td.ANGLE_180 * td.AXIS_3D_Z, td.MAT_3D_Z_180), - ) - def test_from_euler(self, angle, expected): - """Tests that Euler angles create the expected matrix.""" - rotation = Rotation3D.from_euler(angle) - self.assertAllClose(expected, rotation.as_matrix()) - - def test_from_euler_random(self): - """Tests that Euler angles produce the same result as axis-angle.""" - angles = test_helpers.generate_random_test_euler_angles() - matrix = Rotation3D.from_euler(angles) - tensor_tile = angles.shape[:-1] - - x_axis = np.tile(td.AXIS_3D_X, tensor_tile + (1,)) - y_axis = np.tile(td.AXIS_3D_Y, tensor_tile + (1,)) - z_axis = np.tile(td.AXIS_3D_Z, tensor_tile + (1,)) - x_angle = np.expand_dims(angles[..., 0], axis=-1) - y_angle = np.expand_dims(angles[..., 1], axis=-1) - z_angle = np.expand_dims(angles[..., 2], axis=-1) - x_rotation = Rotation3D.from_axis_angle(x_axis, x_angle) - y_rotation = Rotation3D.from_axis_angle(y_axis, y_angle) - z_rotation = Rotation3D.from_axis_angle(z_axis, z_angle) - expected_matrix = z_rotation @ (y_rotation @ x_rotation) - - self.assertAllClose(expected_matrix.as_matrix(), matrix.as_matrix(), - rtol=1e-3) - - def test_from_quaternion_normalized_random(self): - """Tests that random quaternions can be converted to rotation matrices.""" - random_quaternion = test_helpers.generate_random_test_quaternions() - tensor_shape = random_quaternion.shape[:-1] - - random_rot = Rotation3D.from_quaternion(random_quaternion) - - self.assertAllEqual( - random_rot.is_valid(), - np.ones(tensor_shape + (1,))) - - def test_from_quaternion(self): - """Tests that a quaternion maps to correct matrix.""" - preset_quaternions = test_helpers.generate_preset_test_quaternions() - - preset_matrices = test_helpers.generate_preset_test_rotation_matrices_3d() - - self.assertAllClose( - preset_matrices, - Rotation3D.from_quaternion(preset_quaternions).as_matrix()) - - def test_inverse_normalized_random(self): - """Checks that inverted rotation matrices are valid rotations.""" - random_euler_angle = test_helpers.generate_random_test_euler_angles() - tensor_tile = random_euler_angle.shape[:-1] - - random_rot = Rotation3D.from_euler(random_euler_angle) - predicted_invert_random_rot = random_rot.inverse() - - self.assertAllEqual( - predicted_invert_random_rot.is_valid(), - np.ones(tensor_tile + (1,))) - - def test_inverse_random(self): - """Checks that inverting rotated points results in no transformation.""" - random_euler_angle = test_helpers.generate_random_test_euler_angles() - tensor_tile = random_euler_angle.shape[:-1] - random_rot = Rotation3D.from_euler(random_euler_angle) - random_point = np.random.normal(size=tensor_tile + (3,)) - - rotated_random_points = random_rot.rotate(random_point) - inv_random_rot = random_rot.inverse() - inv_rotated_random_points = inv_random_rot.rotate(rotated_random_points) - - self.assertAllClose(random_point, inv_rotated_random_points, rtol=1e-6) - - def test_is_valid_random(self): - """Tests that is_valid works as intended.""" - random_euler_angle = test_helpers.generate_random_test_euler_angles() - tensor_tile = random_euler_angle.shape[:-1] - - rotation = Rotation3D.from_euler(random_euler_angle) - pred_normalized = rotation.is_valid() - - with self.subTest(name="all_normalized"): - self.assertAllEqual(pred_normalized, - np.ones(shape=tensor_tile + (1,), dtype=bool)) - - with self.subTest(name="non_orthonormal"): - test_matrix = np.array([[2., 0., 0.], [0., 0.5, 0], [0., 0., 1.]]) - rotation = Rotation3D.from_matrix(test_matrix) - pred_normalized = rotation.is_valid() - self.assertAllEqual(pred_normalized, np.zeros(shape=(1,), dtype=bool)) - - with self.subTest(name="negative_orthonormal"): - test_matrix = np.array([[1., 0., 0.], [0., -1., 0.], [0., 0., 1.]]) - rotation = Rotation3D.from_matrix(test_matrix) - pred_normalized = rotation.is_valid() - self.assertAllEqual(pred_normalized, np.zeros(shape=(1,), dtype=bool)) - - @parameterized.named_parameters( - ("preset0", td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_X, td.AXIS_3D_X), - ("preset1", td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_Y, td.AXIS_3D_Z), - ("preset2", -td.ANGLE_90 * td.AXIS_3D_X, td.AXIS_3D_Z, td.AXIS_3D_Y), - ("preset3", -td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_X, td.AXIS_3D_Z), - ("preset4", td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_Y, td.AXIS_3D_Y), - ("preset5", td.ANGLE_90 * td.AXIS_3D_Y, td.AXIS_3D_Z, td.AXIS_3D_X), - ("preset6", td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_X, td.AXIS_3D_Y), - ("preset7", -td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_Y, td.AXIS_3D_X), - ("preset8", td.ANGLE_90 * td.AXIS_3D_Z, td.AXIS_3D_Z, td.AXIS_3D_Z), - ) - def test_rotate_vector_preset(self, angles, point, expected): - """Tests that the rotate function produces the expected results.""" - self.assertAllClose(expected, Rotation3D.from_euler(angles).rotate(point)) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/layers/concatenate.py b/tensorflow_mri/python/layers/concatenate.py deleted file mode 100644 index d852dd2e..00000000 --- a/tensorflow_mri/python/layers/concatenate.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Resize and concatenate layer.""" - -import tensorflow as tf - -from tensorflow_mri.python.ops import array_ops - - -@tf.keras.utils.register_keras_serializable(package="MRI") -class ResizeAndConcatenate(tf.keras.layers.Layer): - """Resizes and concatenates a list of inputs. - - Similar to `tf.keras.layers.Concatenate`, but if the inputs have different - shapes, they are resized to match the shape of the first input. - - Args: - axis: Axis along which to concatenate. - """ - def __init__(self, axis=-1, **kwargs): - super().__init__(**kwargs) - self.axis = axis - - def call(self, inputs): # pylint: disable=missing-function-docstring,arguments-differ - if not isinstance(inputs, (list, tuple)): - raise ValueError( - f"Layer {self.__class__.__name__} expects a list of inputs. " - f"Received: {inputs}") - - rank = inputs[0].shape.rank - if rank is None: - raise ValueError( - f"Layer {self.__class__.__name__} expects inputs with known rank. " - f"Received: {inputs}") - if self.axis >= rank or self.axis < -rank: - raise ValueError( - f"Layer {self.__class__.__name__} expects `axis` to be in the range " - f"[-{rank}, {rank}) for an input of rank {rank}. " - f"Received: {self.axis}") - # Canonical axis (always positive). - axis = self.axis % rank - - # Resize inputs. - shape = tf.tensor_scatter_nd_update(tf.shape(inputs[0]), [[axis]], [-1]) - resized = [array_ops.resize_with_crop_or_pad(tensor, shape) - for tensor in inputs[1:]] - - # Set the static shape for each resized tensor. - for i, tensor in enumerate(resized): - static_shape = inputs[0].shape.as_list() - static_shape[axis] = inputs[i + 1].shape.as_list()[axis] - static_shape = tf.TensorShape(static_shape) - resized[i] = tf.ensure_shape(tensor, static_shape) - - return tf.concat(inputs[:1] + resized, axis=self.axis) # pylint: disable=unexpected-keyword-arg,no-value-for-parameter diff --git a/tensorflow_mri/python/layers/concatenate_test.py b/tensorflow_mri/python/layers/concatenate_test.py deleted file mode 100644 index 4b0e341d..00000000 --- a/tensorflow_mri/python/layers/concatenate_test.py +++ /dev/null @@ -1,52 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for `ResizeAndConcatenate` layers.""" - -import tensorflow as tf - -from tensorflow_mri.python.layers import concatenate -from tensorflow_mri.python.util import test_util - - -class ResizeAndConcatenateTest(test_util.TestCase): - """Tests for layer `ResizeAndConcatenate`.""" - def test_resize_and_concatenate(self): - """Test `ResizeAndConcatenate` layer.""" - # Test data. - x1 = tf.constant([[1.0, 2.0], [3.0, 4.0]]) - x2 = tf.constant([[5.0], [6.0]]) - - # Test concatenation along axis 1. - layer = concatenate.ResizeAndConcatenate(axis=-1) - - result = layer([x1, x2]) - self.assertAllClose([[1.0, 2.0, 5.0], [3.0, 4.0, 6.0]], result) - - result = layer([x2, x1]) - self.assertAllClose([[5.0, 1.0, 2.0], [6.0, 3.0, 4.0]], result) - - # Test concatenation along axis 0. - layer = concatenate.ResizeAndConcatenate(axis=0) - - result = layer([x1, x2]) - self.assertAllClose( - [[1.0, 2.0], [3.0, 4.0], [5.0, 0.0], [6.0, 0.0]], result) - - result = layer([x2, x1]) - self.assertAllClose([[5.0], [6.0], [1.0], [3.0]], result) - - -if __name__ == '__main__': - tf.test.main() diff --git a/tensorflow_mri/python/layers/data_consistency.py b/tensorflow_mri/python/layers/data_consistency.py deleted file mode 100644 index 645c4896..00000000 --- a/tensorflow_mri/python/layers/data_consistency.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Data consistency layers.""" - -import string - -import tensorflow as tf - -from tensorflow_mri.python.ops import math_ops -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import doc_util - - -class LeastSquaresGradientDescent(tf.keras.layers.Layer): - """Least squares gradient descent layer for ${rank}-D images. - """ - def __init__(self, - rank, - scale_initializer=1.0, - expand_channel_dim=False, - reinterpret_complex=False, - **kwargs): - super().__init__(**kwargs) - self.rank = rank - if isinstance(scale_initializer, (float, int)): - self.scale_initializer = tf.keras.initializers.Constant(scale_initializer) - else: - self.scale_initializer = tf.keras.initializers.get(scale_initializer) - self.expand_channel_dim = expand_channel_dim - self.reinterpret_complex = reinterpret_complex - - def build(self, input_shape): - super().build(input_shape) - self.scale = self.add_weight( - name='scale', - shape=(), - dtype=tf.as_dtype(self.dtype).real_dtype, - initializer=self.scale_initializer, - trainable=self.trainable, - constraint=tf.keras.constraints.NonNeg()) - - def call(self, inputs): # pylint: disable=missing-function-docstring,arguments-differ - image, data, operator = parse_inputs(inputs) - if self.reinterpret_complex: - image = math_ops.view_as_complex(image, stacked=False) - if self.expand_channel_dim: - image = tf.squeeze(image, axis=-1) - image -= tf.cast(self.scale, image.dtype) * operator.transform( - operator.transform(image) - data, adjoint=True) - if self.expand_channel_dim: - image = tf.expand_dims(image, axis=-1) - if self.reinterpret_complex: - image = math_ops.view_as_real(image, stacked=False) - return image - - def get_config(self): - base_config = super().get_config() - config = { - 'scale_initializer': tf.keras.initializers.serialize( - self.scale_initializer), - 'expand_channel_dim': self.expand_channel_dim, - 'reinterpret_complex': self.reinterpret_complex - } - return {**base_config, **config} - - -def parse_inputs(inputs): - def _parse_inputs(image, data, operator): - return image, data, operator - if isinstance(inputs, tuple): - return _parse_inputs(*inputs) - if isinstance(inputs, dict): - return _parse_inputs(**inputs) - raise ValueError('inputs must be a tuple or dict') - - -@api_util.export("layers.LeastSquaresGradientDescent2D") -@tf.keras.utils.register_keras_serializable(package='MRI') -class LeastSquaresGradientDescent2D(LeastSquaresGradientDescent): - def __init__(self, *args, **kwargs): - super().__init__(2, *args, **kwargs) - - -@api_util.export("layers.LeastSquaresGradientDescent3D") -@tf.keras.utils.register_keras_serializable(package='MRI') -class LeastSquaresGradientDescent3D(LeastSquaresGradientDescent): - def __init__(self, *args, **kwargs): - super().__init__(3, *args, **kwargs) - - -LeastSquaresGradientDescent2D.__doc__ = string.Template( - LeastSquaresGradientDescent.__doc__).safe_substitute(rank=2) -LeastSquaresGradientDescent3D.__doc__ = string.Template( - LeastSquaresGradientDescent.__doc__).safe_substitute(rank=3) - - -LeastSquaresGradientDescent2D.__signature__ = doc_util.get_nd_layer_signature( - LeastSquaresGradientDescent) -LeastSquaresGradientDescent3D.__signature__ = doc_util.get_nd_layer_signature( - LeastSquaresGradientDescent) diff --git a/tensorflow_mri/python/layers/normalization.py b/tensorflow_mri/python/layers/normalization.py deleted file mode 100644 index 4c909ee0..00000000 --- a/tensorflow_mri/python/layers/normalization.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Normalization layers.""" - -import tensorflow as tf - -from tensorflow_mri.python.util import api_util - - -@api_util.export("layers.Normalized") -@tf.keras.utils.register_keras_serializable(package='MRI') -class Normalized(tf.keras.layers.Wrapper): - r"""Applies the wrapped layer with normalized inputs. - - This layer shifts and scales the inputs into a distribution centered around 0 - with a standard deviation of 1 before passing them to the wrapped layer. - - $$ - x = \frac{x - \mu}{\sigma} - $$ - - After applying the wrapped layer, the outputs are scaled back to the original - distribution. - - $$ - y = \sigma y + \mu - $$ - - Args: - layer: A `tf.keras.layers.Layer`. The wrapped layer. - axis: An `int` or a `list` thereof. The axis or axes to normalize across. - Typically this is the features axis/axes. The left-out axes are typically - the batch axis/axes. Defaults to -1, the last dimension in the input. - **kwargs: Additional keyword arguments to be passed to the base class. - """ - def __init__(self, layer, axis=-1, **kwargs): - super().__init__(layer, **kwargs) - self.axis = axis - - def compute_output_shape(self, input_shape): - return self.layer.compute_output_shape(input_shape) - - def call(self, inputs, *args, **kwargs): - mean, variance = tf.nn.moments(inputs, axes=self.axis, keepdims=True) - std = tf.math.maximum(tf.math.sqrt(variance), tf.keras.backend.epsilon()) - inputs = (inputs - mean) / std - outputs = self.layer(inputs, *args, **kwargs) - outputs = outputs * std + mean - return outputs - - def get_config(self): - base_config = super().get_config() - config = {'axis': self.axis} - return {**base_config, **config} diff --git a/tensorflow_mri/python/layers/normalization_test.py b/tensorflow_mri/python/layers/normalization_test.py deleted file mode 100644 index 036fbd36..00000000 --- a/tensorflow_mri/python/layers/normalization_test.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for normalization layers.""" - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.layers import normalization -from tensorflow_mri.python.util import test_util - - -class NormalizedTest(test_util.TestCase): - """Tests for `Normalized` layer.""" - @test_util.run_all_execution_modes - def test_normalized_dense(self): - """Tests `Normalized` layer wrapping a `Dense` layer.""" - layer = normalization.Normalized( - tf.keras.layers.Dense(2, bias_initializer='random_uniform')) - layer.build((None, 4)) - - input_data = np.random.uniform(size=(2, 4)) - - def _compute_output(input_data, normalized=False): - if normalized: - mean = input_data.mean(axis=-1, keepdims=True) - std = input_data.std(axis=-1, keepdims=True) - input_data = (input_data - mean) / std - output_data = layer.layer(input_data) - if normalized: - output_data = output_data * std + mean - return output_data - - expected_unnorm = _compute_output(input_data, normalized=False) - expected_norm = _compute_output(input_data, normalized=True) - - result_unnorm = layer.layer(input_data) - result_norm = layer(input_data) - - self.assertAllClose(expected_unnorm, result_unnorm) - self.assertAllClose(expected_norm, result_norm) - - -if __name__ == '__main__': - tf.test.main() diff --git a/tensorflow_mri/python/layers/padding.py b/tensorflow_mri/python/layers/padding.py deleted file mode 100644 index 0689b5f0..00000000 --- a/tensorflow_mri/python/layers/padding.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Padding layers.""" - -import tensorflow as tf - - -class DivisorPadding(tf.keras.layers.Layer): - """Divisor padding layer. - - This layer pads the input tensor so that its spatial dimensions are a multiple - of the specified divisor. - - Args: - divisor: An `int` or a `tuple` of `int`. The divisor used to compute the - output shape. - """ - def __init__(self, rank, divisor=2, **kwargs): - super().__init__(**kwargs) - self.rank = rank - if isinstance(divisor, int): - self.divisor = (divisor,) * rank - elif hasattr(divisor, '__len__'): - if len(divisor) != rank: - raise ValueError(f'`divisor` should have {rank} elements. ' - f'Received: {divisor}') - self.divisor = divisor - else: - raise ValueError(f'`divisor` should be either an int or a ' - f'a tuple of {rank} ints. ' - f'Received: {divisor}') - self.input_spec = tf.keras.layers.InputSpec(ndim=rank + 2) - - def call(self, inputs): # pylint: disable=missing-function-docstring,arguments-differ - static_input_shape = inputs.shape - static_output_shape = tuple( - ((s + d - 1) // d) * d if s is not None else None for s, d in zip( - static_input_shape[1:-1].as_list(), self.divisor)) - static_output_shape = static_input_shape[:1].concatenate( - static_output_shape).concatenate(static_input_shape[-1:]) - - input_shape = tf.shape(inputs)[1:-1] - output_shape = (((input_shape + self.divisor - 1) // self.divisor) * - self.divisor) - left_paddings = (output_shape - input_shape) // 2 - right_paddings = (output_shape - input_shape + 1) // 2 - paddings = tf.stack([left_paddings, right_paddings], axis=-1) - paddings = tf.pad(paddings, [[1, 1], [0, 0]]) # pylint: disable=no-value-for-parameter - - return tf.ensure_shape(tf.pad(inputs, paddings), static_output_shape) # pylint: disable=no-value-for-parameter - - def get_config(self): - config = {'divisor': self.divisor} - base_config = super().get_config() - return {**config, **base_config} - - -@tf.keras.utils.register_keras_serializable(package='MRI') -class DivisorPadding1D(DivisorPadding): - def __init__(self, *args, **kwargs): - super().__init__(1, *args, **kwargs) - - -@tf.keras.utils.register_keras_serializable(package='MRI') -class DivisorPadding2D(DivisorPadding): - def __init__(self, *args, **kwargs): - super().__init__(2, *args, **kwargs) - - -@tf.keras.utils.register_keras_serializable(package='MRI') -class DivisorPadding3D(DivisorPadding): - def __init__(self, *args, **kwargs): - super().__init__(3, *args, **kwargs) diff --git a/tensorflow_mri/python/layers/recon_adjoint.py b/tensorflow_mri/python/layers/recon_adjoint.py deleted file mode 100644 index 18599a2e..00000000 --- a/tensorflow_mri/python/layers/recon_adjoint.py +++ /dev/null @@ -1,140 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Adjoint reconstruction layer.""" - -import string - -import tensorflow as tf - -from tensorflow_mri.python.ops import math_ops -from tensorflow_mri.python.recon import recon_adjoint -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import doc_util - - -class ReconAdjoint(tf.keras.layers.Layer): - r"""${rank}D adjoint reconstruction layer. - - This layer reconstructs a signal using the adjoint of the specified system - operator. - - Given measurement data $b$ generated by a linear system $A$ such that - $Ax = b$, this function estimates the corresponding signal $x$ as - $x = A^H b$, where $A$ is the specified linear operator. - - ```{note} - This function is part of the family of - [universal operators](https://mrphys.github.io/tensorflow-mri/guide/universal/), - a set of functions and classes designed to work flexibly with any linear - system. - ``` - - ```{seealso} - This is the Keras layer equivalent of `tfmri.recon.adjoint_universal`. - ``` - - ## Inputs - - This layer's `call` method expects the following inputs: - - - data: A `tf.Tensor` of real or complex dtype. The measurement data $b$. - Its shape must be compatible with `operator.range_shape`. - - operator: A `tfmri.linalg.LinearOperator` representing the system operator - $A$. Its range shape must be compatible with `data.shape`. - - ```{attention} - Both `data` and `operator` should be passed as part of the first positional - `inputs` argument, either as as a `tuple` or as a `dict`, in order to take - advantage of this argument's special rules. For more information, see - https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer#call. - ``` - - ## Outputs - - This layer's `call` method returns a `tf.Tensor` containing the reconstructed - signal. Has the same dtype as `data` and shape - `batch_shape + operator.domain_shape`. `batch_shape` is the result of - broadcasting the batch shapes of `data` and `operator`. - - Args: - expand_channel_dim: A `boolean`. Whether to expand the channel dimension. - If `True`, output has shape `batch_shape + operator.domain_shape + [1]`. - If `False`, output has shape `batch_shape + operator.domain_shape`. - Defaults to `True`. - reinterpret_complex: A `boolean`. Whether to reinterpret a complex-valued - output image as a dual-channel real image. Defaults to `False`. - **kwargs: Keyword arguments to be passed to base layer - `tf.keras.layers.Layer`. - """ - def __init__(self, - rank, - expand_channel_dim=False, - reinterpret_complex=False, - **kwargs): - super().__init__(**kwargs) - self.rank = rank # Currently unused. - self.expand_channel_dim = expand_channel_dim - self.reinterpret_complex = reinterpret_complex - - def call(self, inputs): # pylint: arguments-differ - data, operator = parse_inputs(inputs) - image = recon_adjoint.recon_adjoint(data, operator) - if self.expand_channel_dim: - image = tf.expand_dims(image, axis=-1) - if self.reinterpret_complex and image.dtype.is_complex: - image = math_ops.view_as_real(image, stacked=False) - return image - - def get_config(self): - base_config = super().get_config() - config = { - 'expand_channel_dim': self.expand_channel_dim, - 'reinterpret_complex': self.reinterpret_complex - } - return {**base_config, **config} - - -def parse_inputs(inputs): - def _parse_inputs(data, operator): - return data, operator - if isinstance(inputs, tuple): - return _parse_inputs(*inputs) - if isinstance(inputs, dict): - return _parse_inputs(**inputs) - raise ValueError('inputs must be a tuple or dict') - - -@api_util.export("layers.ReconAdjoint2D") -@tf.keras.utils.register_keras_serializable(package='MRI') -class ReconAdjoint2D(ReconAdjoint): - def __init__(self, *args, **kwargs): - super().__init__(2, *args, **kwargs) - - -@api_util.export("layers.ReconAdjoint3D") -@tf.keras.utils.register_keras_serializable(package='MRI') -class ReconAdjoint3D(ReconAdjoint): - def __init__(self, *args, **kwargs): - super().__init__(3, *args, **kwargs) - - -ReconAdjoint2D.__doc__ = string.Template( - ReconAdjoint.__doc__).safe_substitute(rank=2) -ReconAdjoint3D.__doc__ = string.Template( - ReconAdjoint.__doc__).safe_substitute(rank=3) - - -ReconAdjoint2D.__signature__ = doc_util.get_nd_layer_signature(ReconAdjoint) -ReconAdjoint3D.__signature__ = doc_util.get_nd_layer_signature(ReconAdjoint) diff --git a/tensorflow_mri/python/layers/recon_adjoint_test.py b/tensorflow_mri/python/layers/recon_adjoint_test.py deleted file mode 100644 index 5e8f170e..00000000 --- a/tensorflow_mri/python/layers/recon_adjoint_test.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `recon_adjoint`.""" -# pylint: disable=missing-param-doc - -import os -import tempfile - -from absl.testing import parameterized -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_mri -from tensorflow_mri.python.layers import recon_adjoint as recon_adjoint_layer -from tensorflow_mri.python.recon import recon_adjoint -from tensorflow_mri.python.util import test_util - - -class ReconAdjointTest(test_util.TestCase): - """Tests for `ReconAdjoint` layer.""" - @parameterized.product(expand_channel_dim=[True, False]) - def test_recon_adjoint(self, expand_channel_dim): - """Test `ReconAdjoint` layer.""" - # Create layer. - layer = recon_adjoint_layer.ReconAdjoint( - expand_channel_dim=expand_channel_dim) - - # Generate k-space data. - image_shape = tf.constant([4, 4]) - kspace = tf.dtypes.complex( - tf.random.stateless_normal(shape=image_shape, seed=[11, 22]), - tf.random.stateless_normal(shape=image_shape, seed=[12, 34])) - - # Reconstruct image. - expected = recon_adjoint.recon_adjoint_mri(kspace, image_shape) - if expand_channel_dim: - expected = tf.expand_dims(expected, axis=-1) - - operator = linear_operator_mri.LinearOperatorMRI(image_shape) - - # Test with tuple inputs. - input_data = (kspace, operator) - result = layer(input_data) - self.assertAllClose(expected, result) - - # Test with dict inputs. - input_data = {'data': kspace, 'operator': operator} - result = layer(input_data) - self.assertAllClose(expected, result) - - # Test (de)serialization. - layer = recon_adjoint_layer.ReconAdjoint.from_config(layer.get_config()) - result = layer(input_data) - self.assertAllClose(expected, result) - - # Test in model. - inputs = {k: tf.keras.Input(type_spec=tf.type_spec_from_value(v)) - for k, v in input_data.items()} - model = tf.keras.Model(inputs, layer(inputs)) - result = model(input_data) - self.assertAllClose(expected, result) - - # Test saving/loading. - saved_model = os.path.join(tempfile.mkdtemp(), 'saved_model') - model.save(saved_model) - model = tf.keras.models.load_model(saved_model) - result = model(input_data) - self.assertAllClose(expected, result) diff --git a/tensorflow_mri/python/layers/reshaping.py b/tensorflow_mri/python/layers/reshaping.py deleted file mode 100644 index e9c918f4..00000000 --- a/tensorflow_mri/python/layers/reshaping.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Reshaping layers.""" - -import string - -import tensorflow as tf - -from tensorflow_mri.python.util import api_util - - -EXTENSION_NOTE = string.Template(""" - - ```{note} - This layer can be used as a drop-in replacement for - `tf.keras.layers.${name}`. However, this one also supports complex-valued - operations. Simply pass `dtype='complex64'` or `dtype='complex128'` to the - layer constructor. - ``` - -""") - - -def complex_reshape(base): - """Adds complex-valued support to a Keras reshaping layer. - - We need the init method in the pooling layer to replace the `pool_function` - attribute with a function that supports complex inputs. - - Args: - base: The base class to be extended. - - Returns: - A subclass of `base` that supports complex-valued pooling. - - Raises: - ValueError: If `base` is not one of the supported base classes. - """ - if issubclass(base, (tf.keras.layers.UpSampling1D, - tf.keras.layers.UpSampling2D, - tf.keras.layers.UpSampling3D)): - def call(self, inputs): # pylint: arguments-differ - if tf.as_dtype(self.dtype).is_complex: - return tf.dtypes.complex( - base.call(self, tf.math.real(inputs)), - base.call(self, tf.math.imag(inputs))) - - # For real values, we can just use the regular reshape function. - return base.call(self, inputs) - - else: - raise ValueError(f'Unexpected base class: {base}') - - # Dynamically create a subclass of `base` with the same name as `base` and - # with the overriden `convolution_op` method. - subclass = type(base.__name__, (base,), {'call': call}) - - # Copy docs from the base class, adding the extra note. - docstring = base.__doc__ - doclines = docstring.split('\n') - doclines[1:1] = EXTENSION_NOTE.substitute(name=base.__name__).splitlines() - subclass.__doc__ = '\n'.join(doclines) - - return subclass - - -# Define the complex-valued pooling layers. We use a composition of three -# decorators: -# 1. `complex_reshape`: Adds complex-valued support to a Keras reshape layer. -# 2. `register_keras_serializable`: Registers the new layer with the Keras -# serialization framework. -# 3. `export`: Exports the new layer to the TFMRI API. -UpSampling1D = api_util.export("layers.UpSampling1D")( - tf.keras.utils.register_keras_serializable(package='MRI')( - complex_reshape(tf.keras.layers.UpSampling1D))) - - -UpSampling2D = api_util.export("layers.UpSampling2D")( - tf.keras.utils.register_keras_serializable(package='MRI')( - complex_reshape(tf.keras.layers.UpSampling2D))) - - -UpSampling3D = api_util.export("layers.UpSampling3D")( - tf.keras.utils.register_keras_serializable(package='MRI')( - complex_reshape(tf.keras.layers.UpSampling3D))) diff --git a/tensorflow_mri/python/layers/reshaping_test.py b/tensorflow_mri/python/layers/reshaping_test.py deleted file mode 100644 index 35a7ce75..00000000 --- a/tensorflow_mri/python/layers/reshaping_test.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for reshaping layers.""" diff --git a/tensorflow_mri/python/linalg/__init__.py b/tensorflow_mri/python/linalg/__init__.py deleted file mode 100644 index b67203eb..00000000 --- a/tensorflow_mri/python/linalg/__init__.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Linear algebra operators.""" - -from tensorflow_mri.python.linalg import add_registrations -from tensorflow_mri.python.linalg import adjoint_registrations -from tensorflow_mri.python.linalg import cholesky_registrations -from tensorflow_mri.python.linalg import conjugate_gradient -from tensorflow_mri.python.linalg import inverse_registrations -from tensorflow_mri.python.linalg import linear_operator_addition -from tensorflow_mri.python.linalg import linear_operator_addition_nd -from tensorflow_mri.python.linalg import linear_operator_adjoint -from tensorflow_mri.python.linalg import linear_operator_algebra -from tensorflow_mri.python.linalg import linear_operator_composition -from tensorflow_mri.python.linalg import linear_operator_diag -from tensorflow_mri.python.linalg import linear_operator_diag_nd -from tensorflow_mri.python.linalg import linear_operator_fft -from tensorflow_mri.python.linalg import linear_operator_finite_difference -from tensorflow_mri.python.linalg import linear_operator_full_matrix -from tensorflow_mri.python.linalg import linear_operator_gram_matrix -from tensorflow_mri.python.linalg import linear_operator_identity -from tensorflow_mri.python.linalg import linear_operator_identity_nd -from tensorflow_mri.python.linalg import linear_operator_inversion -from tensorflow_mri.python.linalg import linear_operator_mri -from tensorflow_mri.python.linalg import linear_operator_nufft -from tensorflow_mri.python.linalg import linear_operator_wavelet -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.linalg import matmul_registrations -from tensorflow_mri.python.linalg import solve_registrations diff --git a/tensorflow_mri/python/linalg/add_registrations.py b/tensorflow_mri/python/linalg/add_registrations.py deleted file mode 100644 index de27eb16..00000000 --- a/tensorflow_mri/python/linalg/add_registrations.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Registrations for LinearOperator.add.""" - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.linalg import linear_operator_addition -from tensorflow_mri.python.linalg import linear_operator_algebra - - -# By default, use a LinearOperatorAddition to delay the computation. -@linear_operator_algebra.RegisterAdd( - linear_operator.LinearOperator, linear_operator.LinearOperator) -def _add_linear_operator(linop_a, linop_b): - """Generic add of two `LinearOperator`s.""" - # Set all hints to `None`. `LinearOperatorAddition` will figure them out - # automatically, if possible. - return linear_operator_addition.LinearOperatorAddition( - operators=[linop_a, linop_b], - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None - ) diff --git a/tensorflow_mri/python/linalg/adjoint_registrations.py b/tensorflow_mri/python/linalg/adjoint_registrations.py deleted file mode 100644 index 66dd6f76..00000000 --- a/tensorflow_mri/python/linalg/adjoint_registrations.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Registrations for LinearOperator.adjoint.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.linalg import linear_operator_algebra -from tensorflow_mri.python.linalg import registrations_util diff --git a/tensorflow_mri/python/linalg/cholesky_registrations.py b/tensorflow_mri/python/linalg/cholesky_registrations.py deleted file mode 100644 index 68952d2b..00000000 --- a/tensorflow_mri/python/linalg/cholesky_registrations.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Registrations for LinearOperator.cholesky.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_algebra -from tensorflow_mri.python.linalg import linear_operator_diag_nd -from tensorflow_mri.python.linalg import linear_operator_identity_nd - - -@linear_operator_algebra.RegisterCholesky( - linear_operator_identity_nd.LinearOperatorIdentityND) -def _cholesky_identity_nd(linop): - return linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=linop.domain_shape_tensor(), - batch_shape=linop.batch_shape, - dtype=linop.dtype, - is_non_singular=True, - is_self_adjoint=True, - is_positive_definite=True, - is_square=True) - - -@linear_operator_algebra.RegisterCholesky( - linear_operator_identity_nd.LinearOperatorScaledIdentityND) -def _cholesky_scaled_identity_nd(linop): - return linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=linop.domain_shape_tensor(), - multiplier=tf.math.sqrt(linop.multiplier), - is_non_singular=True, - is_self_adjoint=True, - is_positive_definite=True, - is_square=True) - - -@linear_operator_algebra.RegisterCholesky( - linear_operator_diag_nd.LinearOperatorDiagND) -def _cholesky_diag_nd(linop): - return linear_operator_diag_nd.LinearOperatorDiagND( - tf.math.sqrt(linop.diag), - batch_dims=linop.batch_shape.rank, - is_non_singular=True, - is_self_adjoint=True, - is_positive_definite=True, - is_square=True) diff --git a/tensorflow_mri/python/linalg/conjugate_gradient.py b/tensorflow_mri/python/linalg/conjugate_gradient.py deleted file mode 100644 index fb31c732..00000000 --- a/tensorflow_mri/python/linalg/conjugate_gradient.py +++ /dev/null @@ -1,234 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2019 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Conjugate gradient solver.""" - -import collections - -import tensorflow as tf - -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.linalg import linear_operator - - -@api_util.export("linalg.conjugate_gradient") -def conjugate_gradient(operator, - rhs, - preconditioner=None, - x=None, - tol=1e-5, - max_iterations=20, - bypass_gradient=False, - name=None): - r"""Conjugate gradient solver. - - Solves a linear system of equations $Ax = b$ for self-adjoint, positive - definite matrix $A$ and right-hand side vector $b$, using an - iterative, matrix-free algorithm where the action of the matrix $A$ is - represented by `operator`. The iteration terminates when either the number of - iterations exceeds `max_iterations` or when the residual norm has been reduced - to `tol` times its initial value, i.e. - $(\left\| b - A x_k \right\| <= \mathrm{tol} \left\| b \right\|\\)$. - - ```{note} - This function is similar to - `tf.linalg.experimental.conjugate_gradient`, except it adds support for - complex-valued linear systems and for imaging operators. - ``` - - Args: - operator: A `LinearOperator` that is self-adjoint and positive definite. - rhs: A `tf.Tensor` of shape `[..., N]`. The right hand-side of the linear - system. - preconditioner: A `LinearOperator` that approximates the inverse of `A`. - An efficient preconditioner could dramatically improve the rate of - convergence. If `preconditioner` represents matrix `M`(`M` approximates - `A^{-1}`), the algorithm uses `preconditioner.apply(x)` to estimate - `A^{-1}x`. For this to be useful, the cost of applying `M` should be - much lower than computing `A^{-1}` directly. - x: A `tf.Tensor` of shape `[..., N]`. The initial guess for the solution. - tol: A float scalar convergence tolerance. - max_iterations: An `int` giving the maximum number of iterations. - bypass_gradient: A `boolean`. If `True`, the gradient with respect to `rhs` - will be computed by applying the inverse of `operator` to the upstream - gradient with respect to `x` (through CG iteration), instead of relying - on TensorFlow's automatic differentiation. This may reduce memory usage - when training neural networks, but `operator` must not have any trainable - parameters. If `False`, gradients are computed normally. For more details, - see ref. [1]. - name: A name scope for the operation. - - Returns: - A `namedtuple` representing the final state with fields - - - i: A scalar `int32` `tf.Tensor`. Number of iterations executed. - - x: A rank-1 `tf.Tensor` of shape `[..., N]` containing the computed - solution. - - r: A rank-1 `tf.Tensor` of shape `[.., M]` containing the residual vector. - - p: A rank-1 `tf.Tensor` of shape `[..., N]`. `A`-conjugate basis vector. - - gamma: \\(r \dot M \dot r\\), equivalent to \\(||r||_2^2\\) when - `preconditioner=None`. - - Raises: - ValueError: If `operator` is not self-adjoint and positive definite. - - References: - 1. Aggarwal, H. K., Mani, M. P., & Jacob, M. (2018). MoDL: Model-based - deep learning architecture for inverse problems. IEEE transactions on - medical imaging, 38(2), 394-405. - """ - if bypass_gradient: - if preconditioner is not None: - raise ValueError( - "preconditioner is not supported when bypass_gradient is True.") - if x is not None: - raise ValueError("x is not supported when bypass_gradient is True.") - - def _conjugate_gradient_simple(rhs): - return _conjugate_gradient_internal(operator, rhs, - tol=tol, - max_iterations=max_iterations, - name=name) - - @tf.custom_gradient - def _conjugate_gradient_internal_grad(rhs): - result = _conjugate_gradient_simple(rhs) - - def grad(*upstream_grads): - # upstream_grads has the upstream gradient for each element of the - # output tuple (i, x, r, p, gamma). - _, dx, _, _, _ = upstream_grads - return _conjugate_gradient_simple(dx).x - - return result, grad - - return _conjugate_gradient_internal_grad(rhs) - - return _conjugate_gradient_internal(operator, rhs, - preconditioner=preconditioner, - x=x, - tol=tol, - max_iterations=max_iterations, - name=name) - - -def _conjugate_gradient_internal(operator, - rhs, - preconditioner=None, - x=None, - tol=1e-5, - max_iterations=20, - name=None): - """Implementation of `conjugate_gradient`. - - For the parameters, see `conjugate_gradient`. - """ - if isinstance(operator, linear_operator.LinearOperatorMixin): - rhs = operator.flatten_domain_shape(rhs) - - if not (operator.is_self_adjoint and operator.is_positive_definite): - raise ValueError('Expected a self-adjoint, positive definite operator.') - - cg_state = collections.namedtuple('CGState', ['i', 'x', 'r', 'p', 'gamma']) - - def stopping_criterion(i, state): - return tf.math.logical_and( - i < max_iterations, - tf.math.reduce_any( - tf.math.real(tf.norm(state.r, axis=-1)) > tf.math.real(tol))) - - def dot(x, y): - return tf.squeeze( - tf.linalg.matvec( - x[..., tf.newaxis], - y, adjoint_a=True), axis=-1) - - def cg_step(i, state): # pylint: disable=missing-docstring - z = tf.linalg.matvec(operator, state.p) - alpha = state.gamma / dot(state.p, z) - x = state.x + alpha[..., tf.newaxis] * state.p - r = state.r - alpha[..., tf.newaxis] * z - if preconditioner is None: - q = r - else: - q = preconditioner.matvec(r) - gamma = dot(r, q) - beta = gamma / state.gamma - p = q + beta[..., tf.newaxis] * state.p - return i + 1, cg_state(i + 1, x, r, p, gamma) - - # We now broadcast initial shapes so that we have fixed shapes per iteration. - - with tf.name_scope(name or 'conjugate_gradient'): - broadcast_shape = tf.broadcast_dynamic_shape( - tf.shape(rhs)[:-1], - operator.batch_shape_tensor()) - static_broadcast_shape = tf.broadcast_static_shape( - rhs.shape[:-1], - operator.batch_shape) - if preconditioner is not None: - broadcast_shape = tf.broadcast_dynamic_shape( - broadcast_shape, - preconditioner.batch_shape_tensor()) - static_broadcast_shape = tf.broadcast_static_shape( - static_broadcast_shape, - preconditioner.batch_shape) - broadcast_rhs_shape = tf.concat([broadcast_shape, [tf.shape(rhs)[-1]]], -1) - static_broadcast_rhs_shape = static_broadcast_shape.concatenate( - [rhs.shape[-1]]) - r0 = tf.broadcast_to(rhs, broadcast_rhs_shape) - tol *= tf.norm(r0, axis=-1) - - if x is None: - x = tf.zeros( - broadcast_rhs_shape, dtype=rhs.dtype.base_dtype) - x = tf.ensure_shape(x, static_broadcast_rhs_shape) - else: - r0 = rhs - tf.linalg.matvec(operator, x) - if preconditioner is None: - p0 = r0 - else: - p0 = tf.linalg.matvec(preconditioner, r0) - gamma0 = dot(r0, p0) - i = tf.constant(0, dtype=tf.int32) - state = cg_state(i=i, x=x, r=r0, p=p0, gamma=gamma0) - _, state = tf.while_loop( - stopping_criterion, cg_step, [i, state]) - - if isinstance(operator, linear_operator.LinearOperatorMixin): - x = operator.expand_range_dimension(state.x) - else: - x = state.x - - return cg_state( - state.i, - x=x, - r=state.r, - p=state.p, - gamma=state.gamma) diff --git a/tensorflow_mri/python/linalg/conjugate_gradient_test.py b/tensorflow_mri/python/linalg/conjugate_gradient_test.py deleted file mode 100644 index c1604758..00000000 --- a/tensorflow_mri/python/linalg/conjugate_gradient_test.py +++ /dev/null @@ -1,161 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2019 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `conjugate_gradient`.""" -# pylint: disable=missing-class-docstring,missing-function-docstring - -from absl.testing import parameterized -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import conjugate_gradient -from tensorflow_mri.python.util import test_util - - -@test_util.run_all_in_graph_and_eager_modes -class ConjugateGradientTest(test_util.TestCase): - """Tests for op `conjugate_gradient`.""" - @parameterized.product(dtype=[np.float32, np.float64], - shape=[[1, 1], [4, 4], [10, 10]], - use_static_shape=[True, False]) - def test_conjugate_gradient(self, dtype, shape, use_static_shape): # pylint: disable=missing-param-doc - """Test CG method.""" - np.random.seed(1) - a_np = np.random.uniform( - low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype) - # Make a self-adjoint, positive definite. - a_np = np.dot(a_np.T, a_np) - # jacobi preconditioner - jacobi_np = np.zeros_like(a_np) - jacobi_np[range(a_np.shape[0]), range(a_np.shape[1])] = ( - 1.0 / a_np.diagonal()) - rhs_np = np.random.uniform( - low=-1.0, high=1.0, size=shape[0]).astype(dtype) - x_np = np.zeros_like(rhs_np) - tol = 1e-6 if dtype == np.float64 else 1e-3 - max_iterations = 20 - - if use_static_shape: - a = tf.constant(a_np) - rhs = tf.constant(rhs_np) - x = tf.constant(x_np) - jacobi = tf.constant(jacobi_np) - else: - a = tf.compat.v1.placeholder_with_default(a_np, shape=None) - rhs = tf.compat.v1.placeholder_with_default(rhs_np, shape=None) - x = tf.compat.v1.placeholder_with_default(x_np, shape=None) - jacobi = tf.compat.v1.placeholder_with_default(jacobi_np, shape=None) - - operator = tf.linalg.LinearOperatorFullMatrix( - a, is_positive_definite=True, is_self_adjoint=True) - preconditioners = [ - None, - # Preconditioner that does nothing beyond change shape. - tf.linalg.LinearOperatorIdentity( - a_np.shape[-1], - dtype=a_np.dtype, - is_positive_definite=True, - is_self_adjoint=True), - # Jacobi preconditioner. - tf.linalg.LinearOperatorFullMatrix( - jacobi, - is_positive_definite=True, - is_self_adjoint=True), - ] - cg_results = [] - for preconditioner in preconditioners: - cg_graph = conjugate_gradient.conjugate_gradient( - operator, - rhs, - preconditioner=preconditioner, - x=x, - tol=tol, - max_iterations=max_iterations) - cg_val = self.evaluate(cg_graph) - norm_r0 = np.linalg.norm(rhs_np) - norm_r = np.linalg.norm(cg_val.r) - self.assertLessEqual(norm_r, tol * norm_r0) - # Validate that we get an equally small residual norm with numpy - # using the computed solution. - r_np = rhs_np - np.dot(a_np, cg_val.x) - norm_r_np = np.linalg.norm(r_np) - self.assertLessEqual(norm_r_np, tol * norm_r0) - cg_results.append(cg_val) - - # Validate that we get same results using identity_preconditioner - # and None - self.assertEqual(cg_results[0].i, cg_results[1].i) - self.assertAlmostEqual(cg_results[0].gamma, cg_results[1].gamma) - self.assertAllClose(cg_results[0].r, cg_results[1].r, rtol=tol) - self.assertAllClose(cg_results[0].x, cg_results[1].x, rtol=tol) - self.assertAllClose(cg_results[0].p, cg_results[1].p, rtol=tol) - - def test_bypass_gradient(self): - """Tests the `bypass_gradient` argument.""" - dtype = np.float32 - shape = [4, 4] - np.random.seed(1) - a_np = np.random.uniform( - low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype) - # Make a self-adjoint, positive definite. - a_np = np.dot(a_np.T, a_np) - - rhs_np = np.random.uniform( - low=-1.0, high=1.0, size=shape[0]).astype(dtype) - - tol = 1e-3 - max_iterations = 20 - - a = tf.constant(a_np) - rhs = tf.constant(rhs_np) - operator = tf.linalg.LinearOperatorFullMatrix( - a, is_positive_definite=True, is_self_adjoint=True) - - with tf.GradientTape(persistent=True) as tape: - tape.watch(rhs) - result = conjugate_gradient.conjugate_gradient( - operator, - rhs, - tol=tol, - max_iterations=max_iterations) - result_bypass = conjugate_gradient.conjugate_gradient( - operator, - rhs, - tol=tol, - max_iterations=max_iterations, - bypass_gradient=True) - - grad = tape.gradient(result.x, rhs) - grad_bypass = tape.gradient(result_bypass.x, rhs) - self.assertAllClose(result, result_bypass) - self.assertAllClose(grad, grad_bypass, rtol=tol) - - -if __name__ == '__main__': - tf.test.main() diff --git a/tensorflow_mri/python/linalg/inverse_registrations.py b/tensorflow_mri/python/linalg/inverse_registrations.py deleted file mode 100644 index 81214154..00000000 --- a/tensorflow_mri/python/linalg/inverse_registrations.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Registrations for LinearOperator.inverse.""" - -from tensorflow_mri.python.linalg import linear_operator_algebra -from tensorflow_mri.python.linalg import linear_operator_coils -from tensorflow_mri.python.linalg import linear_operator_diag_nd -from tensorflow_mri.python.linalg import linear_operator_fft -from tensorflow_mri.python.linalg import linear_operator_identity_nd -from tensorflow_mri.python.linalg import linear_operator_mask -from tensorflow_mri.python.linalg import linear_operator_nufft - - -@linear_operator_algebra.RegisterInverse( - linear_operator_coils.LinearOperatorCoils) -def _inverse_coils(linop): - raise ValueError( - f"{linop.name} is not invertible. If you wish to compute the " - f"Moore-Penrose pseudo-inverse, use `linop.pseudo_inverse()` " - f"instead.") - - -@linear_operator_algebra.RegisterInverse( - linear_operator_identity_nd.LinearOperatorIdentityND) -def _inverse_identity_nd(linop): - return linop - - -@linear_operator_algebra.RegisterInverse( - linear_operator_identity_nd.LinearOperatorScaledIdentityND) -def _inverse_scaled_identity_nd(linop): - return linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=linop.domain_shape_tensor(), - multiplier=1. / linop.multiplier, - is_non_singular=linop.is_non_singular, - is_self_adjoint=True, - is_positive_definite=linop.is_positive_definite, - is_square=True) - - -@linear_operator_algebra.RegisterInverse( - linear_operator_diag_nd.LinearOperatorDiagND) -def _inverse_diag_nd(linop): - return linear_operator_diag_nd.LinearOperatorDiagND( - 1. / linop.diag, - batch_dims=linop.batch_shape.rank, - is_non_singular=linop.is_non_singular, - is_self_adjoint=linop.is_self_adjoint, - is_positive_definite=linop.is_positive_definite, - is_square=True) - - -@linear_operator_algebra.RegisterInverse( - linear_operator_fft.LinearOperatorFFT) -def _inverse_fft(linop): - return linop.adjoint() - - -@linear_operator_algebra.RegisterInverse( - linear_operator_mask.LinearOperatorMask) -def _inverse_mask(linop): - raise ValueError( - f"{linop.name} is not invertible. If you wish to compute the " - f"Moore-Penrose pseudo-inverse, use `linop.pseudo_inverse()` " - f"instead.") - - -@linear_operator_algebra.RegisterInverse( - linear_operator_nufft.LinearOperatorNUFFT) -def _inverse_nufft(linop): - raise ValueError( - f"{linop.name} is not invertible. If you wish to compute the " - f"Moore-Penrose pseudo-inverse, use `linop.pseudo_inverse()` " - f"instead.") diff --git a/tensorflow_mri/python/linalg/linear_operator.py b/tensorflow_mri/python/linalg/linear_operator.py deleted file mode 100644 index 818f5d0e..00000000 --- a/tensorflow_mri/python/linalg/linear_operator.py +++ /dev/null @@ -1,428 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Base linear operator.""" - -import string -import warnings - -import tensorflow as tf -from tensorflow.python.framework import type_spec -from tensorflow.python.ops.linalg.linear_operator import ( - _extract_attrs, _extract_type_spec_recursively) -from tensorflow.python.util import dispatch - -from tensorflow_mri.python.linalg import linear_operator_algebra -from tensorflow_mri.python.linalg import linear_operator_util -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import doc_util - - -def make_linear_operator(cls): - """Class decorator for subclasses of `LinearOperator`.""" - # Add extensions if decorating base class. - if cls is tf.linalg.LinearOperator: - extensions = { - "lstsq": lstsq, - "_lstsq": _lstsq, - "lstsqvec": lstsqvec, - "_lstsqvec": _lstsqvec, - "_dense_lstsq": _dense_lstsq, - "add": add, - "__add__": __add__ - } - - for key, value in extensions.items(): - if hasattr(cls, key): - raise ValueError(f"{cls.__name__} already has attribute: {key}") - setattr(cls, key, value) - - # Make composite tensor. This also adds additional functionality to the class. - cls = make_composite_tensor(cls) - - # Add notice to docstring. - cls = update_docstring(cls) - - return cls - - -def make_composite_tensor(cls, module_name="tfmri.linalg"): - """Class decorator to convert `LinearOperator`s to `CompositeTensor`s. - - Overrides the default `make_composite_tensor` to use the custom - `LinearOperatorSpec`. - """ - spec_name = "{}Spec".format(cls.__name__) - spec_type = type(spec_name, (_LinearOperatorSpec,), {"value_type": cls}) - type_spec.register("{}.{}".format(module_name, spec_name))(spec_type) - cls._type_spec = property(spec_type.from_operator) # pylint: disable=protected-access - return cls - - -def update_docstring(cls): - """Updates docstring to describe new functionality.""" - tfmri_additional_functionality = string.Template(""" - ```{rubric} Additional functionality (TensorFlow MRI) - ``` - - This operator supports additional functionality not present in core TF - operators. - - - `lstsq` and `lstsqvec` finds the least-squares solution to the linear - system(s) defined by this operator. - - `_lstsq` and `_lstsqvec` can be overridden to provide a custom - implementation of `lstsq` and `lstsqvec`, respectively. - - `_type_spec` has been patched to improve support in Keras models. - - ```{seealso} - The TensorFlow MRI - [linear algebra guide](https://mrphys.github.io/tensorflow-mri/guide/linalg/). - ``` - """).substitute() - - tfmri_tf_compatibility = string.Template(""" - ```{rubric} Compatibility with core TensorFlow - ``` - - This operator is a drop-in replacement for `tf.linalg.${class_name}`. - - ```{tip} - You can use `tfmri.linalg.${class_name}` and `tf.linalg.${class_name}` - interchangeably, as the latter has been monkey-patched to be an alias of - this operator. - ``` - """).substitute(class_name=cls.__name__) - - docstring = cls.__doc__ - doclines = docstring.split('\n') - doclines += tfmri_additional_functionality.split('\n') - if is_tf_builtin(cls): - doclines += tfmri_tf_compatibility.split('\n') - docstring = '\n'.join(doclines) - cls.__doc__ = docstring - - return cls - - -def is_tf_builtin(cls): - """Returns `True` if `cls` is a built-in linear operator.""" - return hasattr(tf.linalg, cls.__name__) - - -# New attributes to be added to `LinearOperator` class. - -def lstsq(self, rhs, adjoint=False, adjoint_arg=False, name="lstsq"): - """Solve the (batch) linear system $A X = B$ in the least-squares sense. - - Given $A$ represented by this linear operator with shape `[..., M, N]`, - computes the least-squares solution $X$ to the batch of linear systems - $A X = B$. For systems without an exact solution, returns a "best fit" - solution in the least squares sense. For systems with multiple solutions, - returns the solution with the smallest Euclidean norm. - - This is equivalent to solving for the normal equations $A^H A X = A^H B$. - - Args: - rhs: A `tf.Tensor` with same `dtype` as this operator and shape - `[..., M, K]`. `rhs` is treated like a [batch] matrix meaning for - every set of leading dimensions, the last two dimensions define a - matrix. - adjoint: A boolean. If `True`, solve the system involving the adjoint - of this operator, $A^H X = B$. Default is `False`. - adjoint_arg: A boolean. If `True`, solve $A X = B^H$ where $B^H$ is the - Hermitian transpose (transposition and complex conjugation). Default - is `False`. - name: A name scope to use for ops added by this method. - - Returns: - A `tf.Tensor` with shape `[..., N, K]` and same `dtype` as `rhs`. - """ - if isinstance(rhs, LinearOperator): - left_operator = self.adjoint() if adjoint else self - right_operator = rhs.adjoint() if adjoint_arg else rhs - - if (right_operator.range_dimension is not None and - left_operator.domain_dimension is not None and - right_operator.range_dimension != left_operator.domain_dimension): - raise ValueError( - "Operators are incompatible. Expected `rhs` to have dimension" - " {} but got {}.".format( - left_operator.domain_dimension, right_operator.range_dimension)) - with self._name_scope(name): # pylint: disable=not-callable - return linear_operator_algebra.lstsq(left_operator, right_operator) - - with self._name_scope(name): # pylint: disable=not-callable - rhs = tf.convert_to_tensor(rhs, name="rhs") - self._check_input_dtype(rhs) - - self_dim = -1 if adjoint else -2 - arg_dim = -1 if adjoint_arg else -2 - tf.compat.dimension_at_index( - self.shape, self_dim).assert_is_compatible_with( - rhs.shape[arg_dim]) - - return self._lstsq(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) - -def _lstsq(self, rhs, adjoint=False, adjoint_arg=False): - """Default implementation of `_lstsq`.""" - warnings.warn( - "Using (possibly slow) default implementation of lstsq. " - "Requires conversion to a dense matrix and O(N^3) operations.") - return self._dense_lstsq(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) - -def lstsqvec(self, rhs, adjoint=False, name="lstsqvec"): - """Solve the linear system $A x = b$ in the least-squares sense. - - Given $A$ represented by this linear operator with shape `[..., M, N]`, - computes the least-squares solution $x$ to the linear system $A x = b$. - For systems without an exact solution, returns a "best fit" solution in - the least squares sense. For systems with multiple solutions, returns the - solution with the smallest Euclidean norm. - - This is equivalent to solving for the normal equations $A^H A x = A^H b$. - - Args: - rhs: A `tf.Tensor` with same `dtype` as this operator and shape - `[..., M]`. `rhs` is treated like a [batch] matrix meaning for - every set of leading dimensions, the last two dimensions define a - matrix. - adjoint: A boolean. If `True`, solve the system involving the adjoint - of this operator, $A^H x = b$. Default is `False`. - name: A name scope to use for ops added by this method. - - Returns: - A `tf.Tensor` with shape `[..., N]` and same `dtype` as `rhs`. - """ - with self._name_scope(name): # pylint: disable=not-callable - rhs = tf.convert_to_tensor(rhs, name="rhs") - self._check_input_dtype(rhs) - self_dim = -1 if adjoint else -2 - tf.compat.dimension_at_index( - self.shape, self_dim).assert_is_compatible_with(rhs.shape[-1]) - - return self._lstsqvec(rhs, adjoint=adjoint) - -def _lstsqvec(self, rhs, adjoint=False): - """Default implementation of `_lstsqvec`.""" - rhs_mat = tf.expand_dims(rhs, axis=-1) - solution_mat = self.lstsq(rhs_mat, adjoint=adjoint) - return tf.squeeze(solution_mat, axis=-1) - -def _dense_lstsq(self, rhs, adjoint=False, adjoint_arg=False): - """Solve least squares by conversion to a dense matrix.""" - rhs = tf.linalg.adjoint(rhs) if adjoint_arg else rhs - return linear_operator_util.matrix_solve_ls_with_broadcast( - self.to_dense(), rhs, adjoint=adjoint) - -def add(self, x, name="add"): - """Add this operator to matrix `x`. - - Example: - >>> operator = LinearOperatorIdentity(2) - >>> x = tf.linalg.eye(2) - >>> x = operator.add(x) - >>> x.numpy() - array([[2., 0.], - [0., 2.]], dtype=float32) - - Args: - x: A `LinearOperator` or `Tensor` with compatible shape and same `dtype` as - `self`. See class docstring for definition of compatibility. - name: A name for this `Op`. - - Returns: - A `LinearOperator` or `Tensor` with same shape and same dtype as `self`. - """ - if isinstance(x, LinearOperator): - left_operator = self - right_operator = x - - if (not left_operator.shape[-2:].is_compatible_with( - right_operator.shape[-2:])): - raise ValueError( - f"Operators are incompatible. Expected `x` to have shape " - f"{left_operator.shape[-2:]} but got {right_operator.shape[-2:]}.") - with self._name_scope(name): - return linear_operator_algebra.add(left_operator, right_operator) - - with self._name_scope(name): # pylint: disable=not-callable - return self.add_to_tensor(x) - -def __add__(self, other): - return self.add(other) - - - -class _LinearOperatorSpec(type_spec.BatchableTypeSpec): # pylint: disable=abstract-method - """A tf.TypeSpec for `LinearOperator` objects. - - This is very similar to `tf.linalg.LinearOperatorSpec`, but it adds - `shape` and `dtype` attributes which are required by Keras. - - These attributes are redundant, as they can always be computed from - other parameters. However, the details of this computation vary between - operators, so it's easier to just store it. - """ - __slots__ = ("_param_specs", - "_non_tensor_params", - "_prefer_static_fields", - "_shape", - "_dtype") - - def __init__(self, - param_specs, - non_tensor_params, - prefer_static_fields, - shape=None, - dtype=None): - """Initializes a new `_LinearOperatorSpec`. - - Args: - param_specs: Python `dict` of `tf.TypeSpec` instances that describe - kwargs to the `LinearOperator`'s constructor that are `Tensor`-like or - `CompositeTensor` subclasses. - non_tensor_params: Python `dict` containing non-`Tensor` and non- - `CompositeTensor` kwargs to the `LinearOperator`'s constructor. - prefer_static_fields: Python `tuple` of strings corresponding to the names - of `Tensor`-like args to the `LinearOperator`s constructor that may be - stored as static values, if known. These are typically shapes, indices, - or axis values. - shape: A `tf.TensorShape`. The shape of the `LinearOperator`. - dtype: A `tf.dtypes.DType`. The dtype of the `LinearOperator`. - """ - self._param_specs = param_specs - self._non_tensor_params = non_tensor_params - self._prefer_static_fields = prefer_static_fields - self._shape = shape - self._dtype = dtype - - @classmethod - def from_operator(cls, operator): - """Builds a `_LinearOperatorSpec` from a `LinearOperator` instance. - - Args: - operator: An instance of `LinearOperator`. - - Returns: - linear_operator_spec: An instance of `_LinearOperatorSpec` to be used as - the `TypeSpec` of `operator`. - """ - validation_fields = ("is_non_singular", "is_self_adjoint", - "is_positive_definite", "is_square") - kwargs = _extract_attrs( # pylint: disable=protected-access - operator, - keys=set(operator._composite_tensor_fields + validation_fields)) # pylint: disable=protected-access - - non_tensor_params = {} - param_specs = {} - for k, v in list(kwargs.items()): - type_spec_or_v = _extract_type_spec_recursively(v) # pylint: disable=protected-access - is_tensor = [isinstance(x, type_spec.TypeSpec) - for x in tf.nest.flatten(type_spec_or_v)] - if all(is_tensor): - param_specs[k] = type_spec_or_v - elif not any(is_tensor): - non_tensor_params[k] = v - else: - raise NotImplementedError(f"Field {k} contains a mix of `Tensor` and " - f" non-`Tensor` values.") - - return cls( - param_specs=param_specs, - non_tensor_params=non_tensor_params, - prefer_static_fields=operator._composite_tensor_prefer_static_fields, # pylint: disable=protected-access - shape=operator.shape, - dtype=operator.dtype) - - def _to_components(self, obj): - return _extract_attrs(obj, keys=list(self._param_specs)) - - def _from_components(self, components): - kwargs = dict(self._non_tensor_params, **components) - return self.value_type(**kwargs) - - @property - def _component_specs(self): - return self._param_specs - - def _serialize(self): - return (self._param_specs, - self._non_tensor_params, - self._prefer_static_fields, - self._shape, - self._dtype) - - def _copy(self, **overrides): - kwargs = { - "param_specs": self._param_specs, - "non_tensor_params": self._non_tensor_params, - "prefer_static_fields": self._prefer_static_fields, - "shape": self._shape, - "dtype": self._dtype - } - kwargs.update(overrides) - return type(self)(**kwargs) - - def _batch(self, batch_size): - """Returns a TypeSpec representing a batch of objects with this TypeSpec.""" - return self._copy( - param_specs=tf.nest.map_structure( - lambda spec: spec._batch(batch_size), # pylint: disable=protected-access - self._param_specs)) - - def _unbatch(self): - """Returns a TypeSpec representing a single element of this TypeSpec.""" - return self._copy( - param_specs=tf.nest.map_structure( - lambda spec: spec._unbatch(), # pylint: disable=protected-access - self._param_specs)) - - @property - def shape(self): - """Returns a `tf.TensorShape` representing the static shape.""" - # This property is required to use linear operators with Keras. - return self._shape - - @property - def dtype(self): - """Returns a `tf.dtypes.DType` representing the dtype.""" - return self._dtype - - def with_shape(self, shape): - """Returns a new `tf.TypeSpec` with the given shape.""" - # This method is required to use linear operators with Keras. - return self._copy(shape=shape) - - def _to_legacy_output_shapes(self): - return self._shape - - def _to_legacy_output_types(self): - return self._dtype - - -# Define new `LinearOperator` class. -LinearOperator = api_util.export("linalg.LinearOperator")( - doc_util.no_linkcode(make_linear_operator(tf.linalg.LinearOperator))) - - -# Monkey-patch original operator so that core TF operator and TFMRI -# operator become aliases. -tf.linalg.LinearOperator = LinearOperator - - -@dispatch.dispatch_for_types(tf.math.add, LinearOperator) -def _add(x, y, name=None): - if not isinstance(x, LinearOperator): - return y.add(x, name=name) - return x.add(y, name=name) diff --git a/tensorflow_mri/python/linalg/linear_operator_addition.py b/tensorflow_mri/python/linalg/linear_operator_addition.py deleted file mode 100644 index a880bb98..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_addition.py +++ /dev/null @@ -1,294 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Addition linear operator.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import check_util - - -@api_util.export("linalg.LinearOperatorAddition") -@linear_operator.make_linear_operator -class LinearOperatorAddition(linear_operator.LinearOperator): - r"""Adds one or more [batch] linear operators. - - This operator adds one or more linear operators $A_1, A_2, \dots, A_n$ to - build a new `LinearOperator` $A$ with action defined by: - - $$ - Ax = (A_1 + A_2 + \dots + A_n)(x) = A_1 x + A_2 x + \dots + A_n x - $$ - - All input `operators` must have shape `[..., M, N]` and the resulting - operator also has shape `[..., M, N]`. The batch shape of the resulting - operator is the result of broadcasting the batch shape of all input - operators. - - ```{rubric} Performance - ``` - In general, performance in matrix-vector multiplication is the sum - of the individual operators. More efficient implementations may be - used for specific operators. - - ```{rubric} Matrix properties - ``` - The properties of this operator are determined by the properties of the - input operators. - - ```{rubric} Inversion - ``` - At present, this operator does not implement an efficient algorithm for - inversion. `solve` and `lstsq` will trigger conversion to a dense matrix. - - Example: - >>> # Create a 2 x 2 linear operator composed of two 2 x 2 operators. - >>> op1 = tfmri.linalg.LinearOperatorFullMatrix([[1., 2.], [3., 4.]]) - >>> op2 = tfmri.linalg.LinearOperatorIdentity(2) - >>> operator = LinearOperatorAddition([op1, op2]) - >>> operator.to_dense().numpy() - array([[2., 2.], - [3., 5.]], dtype=float32) - - Args: - operators: A `list` of `tf.linalg.LinearOperator`s of equal shape and - dtype. Batch dimensions may vary but must be broadcastable. - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `None`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `None`. - is_positive_definite: A boolean, or `None`. Whether this operator is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that an operator [does - not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) - Defaults to `None`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `None`. - name: An optional `str`. The name of this operator. - - Raises: - TypeError: If all operators do not have the same `dtype`. - ValueError: If `operators` is empty. - """ - def __init__(self, - operators, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None, - name=None): - """Initialize a `LinearOperatorAddition`.""" - parameters = dict( - operators=operators, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name) - - # Validate operators. - tf.debugging.assert_proper_iterable(operators) - operators = list(operators) - if not operators: - raise ValueError( - f"Expected a non-empty list of operators. Found: {operators}") - self._operators = operators - - # Validate dtype. - dtype = operators[0].dtype - for operator in operators: - if operator.dtype != dtype: - name_type = (str((o.name, o.dtype)) for o in operators) - raise TypeError( - f"Expected all operators to have the same dtype. " - f"Found: {', '.join(name_type)}") - - # Validate shapes. - self._matrix_shape = operators[0].shape[-2:] - for operator in operators: - if not operator.shape[-2:].is_compatible_with(self._matrix_shape): - raise ValueError( - f"Expected all operators to have the same shape. " - f"Found: {', '.join([str(o.shape[-2:]) for o in operators])}") - - # Infer operator properties. - is_non_singular = check_hint( - combined_non_singular_hint(*operators), - is_non_singular, - "non-singular") - is_self_adjoint = check_hint( - combined_self_adjoint_hint(*operators), - is_self_adjoint, - "self-adjoint") - is_positive_definite = check_hint( - combined_positive_definite_hint(*operators), - is_positive_definite, - "positive-definite") - is_square = check_hint( - combined_square_hint(*operators), - is_square, - "square") - - if name is None: - name = "_p_".join(operator.name for operator in operators) - with tf.name_scope(name): - super().__init__( - dtype=dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters, - name=name) - - @property - def operators(self): - return self._operators - - def _shape(self): - # Get broadcast batch shape. - batch_shape = self.operators[0].batch_shape - for operator in self.operators[1:]: - batch_shape = tf.broadcast_static_shape( - batch_shape, operator.batch_shape) - - return batch_shape.concatenate(self._matrix_shape) - - def _shape_tensor(self): - matrix_shape = self.operators[0].shape_tensor()[-2:] - - # Dummy tensor of zeros. In graph mode, it will never be materialized. - zeros = tf.zeros(shape=self.operators[0].batch_shape_tensor()) - for operator in self.operators[1:]: - zeros += tf.zeros(shape=operator.batch_shape_tensor()) - batch_shape = tf.shape(zeros) - - return tf.concat([batch_shape, matrix_shape], 0) - - def _matmul(self, x, adjoint=False, adjoint_arg=False): - result = self.operators[0].matmul( - x, adjoint=adjoint, adjoint_arg=adjoint_arg) - for operator in self.operators[1:]: - result += operator.matmul(x, adjoint=adjoint, adjoint_arg=adjoint_arg) - return result - - @property - def _composite_tensor_fields(self): - return ("operators",) - - @property - def _experimental_parameter_ndims_to_matrix_ndims(self): - return {"operators": [0] * len(self.operators)} - - -def combined_non_singular_hint(*operators): - """Returns a hint for the non-singularity of a sum of operators. - - Args: - *operators: A list of `LinearOperator` objects. - - Returns: - A boolean, or `None`. Whether the sum of the operators is expected to be - non-singular. - """ - # In general, there is nothing we can say about the non-singularity of the - # sum of operators, regardless of the non-singularity of the individual - # operators. - return None - - -def combined_self_adjoint_hint(*operators): - """Returns a hint for the self-adjointness of a sum of operators. - - Args: - *operators: A list of `LinearOperator` objects. - - Returns: - A boolean, or `None`. Whether the sum of the operators is expected to be - self-adjoint. - """ - # If all operators are self-adjoint, so is the sum. - if all(o.is_self_adjoint is True for o in operators): - return True - # If all operators are self-adjoint except one which is not, then the sum is - # not self-adjoint. - self_adjoint_operators = [ - o for o in operators if o.is_self_adjoint is True] - non_self_adjoint_operators = [ - o for o in operators if o.is_self_adjoint is False] - if (len(self_adjoint_operators) == len(operators) - 1 and - len(non_self_adjoint_operators) == 1): - return False - # In all other cases, we don't know. - return None - - -def combined_positive_definite_hint(*operators): - """Returns a hint for the positive-definiteness of a sum of operators. - - Args: - *operators: A list of `LinearOperator` objects. - - Returns: - A boolean, or `None`. Whether the sum of the operators is expected to be - positive-definite. - """ - # If all operators are positive definite, so is the sum. - if all(o.is_positive_definite is True for o in operators): - return True - # In all other cases, we don't know. - return None - - -def combined_square_hint(*operators): - """Returns a hint for the squareness of a sum of operators. - - Args: - *operators: A list of `LinearOperator` objects. - - Returns: - A boolean, or `None`. Whether the sum of the operators is expected to be - square. - """ - # If any operator is square, so is the sum. - if (any(o.is_square is True for o in operators) and - not any(o.is_square is False for o in operators)): - return True - # If any operator is not square, so is the sum. - if (any(o.is_square is False for o in operators) and - not any(o.is_square is True for o in operators)): - return False - # In all other cases, we don't know. - return None - - -def check_hint(expected, received, name): - """Checks that a hint is consistent with its expected value. - - Args: - expected: A boolean, or `None`. The expected value of the hint. - received: A boolean, or `None`. The received value of the hint. - name: A string. The name of the hint. - - Raises: - ValueError: If `expected` and `value` are not consistent. - """ - if expected is not None and received is not None and expected != received: - raise ValueError( - f"Inconsistent {name} hint: expected {expected} based on input " - f"operators, but got {received}") - return received if received is not None else expected diff --git a/tensorflow_mri/python/linalg/linear_operator_addition_nd.py b/tensorflow_mri/python/linalg/linear_operator_addition_nd.py deleted file mode 100644 index 0c21c77c..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_addition_nd.py +++ /dev/null @@ -1,70 +0,0 @@ -# # Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# # -# # Licensed under the Apache License, Version 2.0 (the "License"); -# # you may not use this file except in compliance with the License. -# # You may obtain a copy of the License at -# # -# # http://www.apache.org/licenses/LICENSE-2.0 -# # -# # Unless required by applicable law or agreed to in writing, software -# # distributed under the License is distributed on an "AS IS" BASIS, -# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# # See the License for the specific language governing permissions and -# # limitations under the License. -# # ============================================================================== -# """Addition of linear operators.""" - -# from tensorflow_mri.python.ops import array_ops -# from tensorflow_mri.python.linalg import linear_operator -# from tensorflow_mri.python.util import api_util -# from tensorflow_mri.python.util import linalg_ext - - -# @api_util.export("linalg.LinearOperatorAddition") -# class LinearOperatorAddition(linalg_ext.LinearOperatorAddition): -# """Adds one or more linear operators. - -# `LinearOperatorAddition` is initialized with a list of operators -# $A_1, A_2, ..., A_J$ and represents their addition -# $A_1 + A_2 + ... + A_J$. - -# Args: -# operators: A `list` of `LinearOperator` objects, each with the same `dtype` -# and shape. -# is_non_singular: Expect that this operator is non-singular. -# is_self_adjoint: Expect that this operator is equal to its Hermitian -# transpose. -# is_positive_definite: Expect that this operator is positive definite, -# meaning the quadratic form $x^H A x$ has positive real part for all -# nonzero $x$. Note that we do not require the operator to be -# self-adjoint to be positive-definite. -# is_square: Expect that this operator acts like square [batch] matrices. -# name: A name for this `LinearOperator`. Default is the individual -# operators names joined with `_p_`. -# """ -# def _transform(self, x, adjoint=False): -# # pylint: disable=protected-access -# result = self.operators[0]._transform(x, adjoint=adjoint) -# for operator in self.operators[1:]: -# result += operator._transform(x, adjoint=adjoint) -# return result - -# def _domain_shape(self): -# return self.operators[0].domain_shape - -# def _range_shape(self): -# return self.operators[0].range_shape - -# def _batch_shape(self): -# return array_ops.broadcast_static_shapes( -# *[operator.batch_shape for operator in self.operators]) - -# def _domain_shape_tensor(self): -# return self.operators[0].domain_shape_tensor() - -# def _range_shape_tensor(self): -# return self.operators[0].range_shape_tensor() - -# def _batch_shape_tensor(self): -# return array_ops.broadcast_dynamic_shapes( -# *[operator.batch_shape_tensor() for operator in self.operators]) diff --git a/tensorflow_mri/python/linalg/linear_operator_addition_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_addition_nd_test.py deleted file mode 100644 index d842ca8f..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_addition_nd_test.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_addition_nd`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_addition_test.py b/tensorflow_mri/python/linalg/linear_operator_addition_test.py deleted file mode 100644 index 63d875f6..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_addition_test.py +++ /dev/null @@ -1,280 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_addition`.""" -# pylint: disable=missing-class-docstring,missing-function-docstring - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_test -from tensorflow_mri.python.linalg import linear_operator_addition -from tensorflow_mri.python.linalg import linear_operator_full_matrix -from tensorflow_mri.python.linalg import linear_operator_test_util -from tensorflow_mri.python.util import test_util - - -rng = np.random.RandomState(0) - - -class SquareLinearOperatorAdditionTest( - linear_operator_test_util.SquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - - def tearDown(self): - tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) - - def setUp(self): - self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() - tf.config.experimental.enable_tensor_float_32_execution(False) - - def operator_and_matrix(self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - shape = list(build_info.shape) - - # Either 1 or 2 matrices, depending. - num_operators = rng.randint(low=1, high=3) - if ensure_self_adjoint_and_pd: - # The random PD matrices are also symmetric. Here we are computing - # A @ A ... @ A. Since A is symmetric and PD, so are any powers of it. - matrices = [ - linear_operator_test_util.random_positive_definite_matrix( - shape, dtype, force_well_conditioned=True)] * num_operators - else: - matrices = [ - linear_operator_test_util.random_positive_definite_matrix( - shape, dtype, force_well_conditioned=True) - for _ in range(num_operators) - ] - - lin_op_matrices = matrices - - if use_placeholder: - lin_op_matrices = [ - tf.compat.v1.placeholder_with_default( - matrix, shape=None) for matrix in matrices] - - operator = linear_operator_addition.LinearOperatorAddition( - [linear_operator_full_matrix.LinearOperatorFullMatrix(l) - for l in lin_op_matrices], - is_positive_definite=True if ensure_self_adjoint_and_pd else None, - is_self_adjoint=True if ensure_self_adjoint_and_pd else None, - is_square=True) - - matmul_order_list = list(reversed(matrices)) - mat = matmul_order_list[0] - for other_mat in matmul_order_list[1:]: - mat = tf.math.add(other_mat, mat) - - return operator, mat - - @test_util.run_deprecated_v1 - def test_is_x_flags(self): - expected = { - 'is_non_singular': { - (True, True): None, - (True, False): None, - (True, None): None, - (False, False): None, - (False, None): None, - (None, None): None - }, - 'is_self_adjoint': { - (True, True): True, - (True, False): False, - (True, None): None, - (False, False): None, - (False, None): None, - (None, None): None - }, - 'is_positive_definite': { - (True, True): True, - (True, False): None, - (True, None): None, - (False, False): None, - (False, None): None, - (None, None): None - }, - 'is_square': { - (True, True): True, - # (True, False): None, - (True, None): True, - (False, False): False, - (False, None): False, - (None, None): None - } - } - for name, combinations in expected.items(): - for (flag1, flag2), value in combinations.items(): - with self.subTest(name=name, flag1=flag1, flag2=flag2): - matrix = tf.compat.v1.placeholder(tf.float32) - operator1 = linear_operator_full_matrix.LinearOperatorFullMatrix( - matrix, **{name: flag1}) - operator2 = linear_operator_full_matrix.LinearOperatorFullMatrix( - matrix, **{name: flag2}) - operator = linear_operator_addition.LinearOperatorAddition( - [operator1, operator2]) - - self.assertIs(getattr(operator, name), value) - - def test_name(self): - matrix = [[11., 0.], [1., 8.]] - operator_1 = linear_operator_full_matrix.LinearOperatorFullMatrix( - matrix, name="left") - operator_2 = linear_operator_full_matrix.LinearOperatorFullMatrix( - matrix, name="right") - - operator = linear_operator_addition.LinearOperatorAddition( - [operator_1, operator_2]) - - self.assertEqual("left_p_right", operator.name) - - def test_different_dtypes_raises(self): - operators = [ - linear_operator_full_matrix.LinearOperatorFullMatrix( - rng.rand(2, 3, 3)), - linear_operator_full_matrix.LinearOperatorFullMatrix( - rng.rand(2, 3, 3).astype(np.float32)) - ] - with self.assertRaisesRegex(TypeError, "same dtype"): - linear_operator_addition.LinearOperatorAddition(operators) - - def test_empty_operators_raises(self): - with self.assertRaisesRegex(ValueError, "non-empty"): - linear_operator_addition.LinearOperatorAddition([]) - - def test_registration(self): - matrix = [[11., 0.], [1., 8.]] - operator_1 = linear_operator_test.LinearOperatorMatmulSolve(matrix) - operator_2 = linear_operator_test.LinearOperatorMatmulSolve(matrix) - operator = operator_1 + operator_2 - self.assertIsInstance( - operator, linear_operator_addition.LinearOperatorAddition) - - -class NonSquareLinearOperatorAdditionTest( - linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - - def tearDown(self): - tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) - - def setUp(self): - self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() - tf.config.experimental.enable_tensor_float_32_execution(False) - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - del ensure_self_adjoint_and_pd - shape = list(build_info.shape) - - # Ensure that the matrices are well-conditioned by generating - # random matrices whose singular values are close to 1. - # The reason to do this is because cond(AB) <= cond(A) * cond(B). - # By ensuring that each factor has condition number close to 1, we ensure - # that the condition number of the product isn't too far away from 1. - def generate_well_conditioned(shape, dtype): - m, n = shape[-2], shape[-1] - min_dim = min(m, n) - # Generate singular values that are close to 1. - d = linear_operator_test_util.random_normal( - shape[:-2] + [min_dim], - mean=1., - stddev=0.1, - dtype=dtype) - zeros = tf.compat.v1.zeros(shape=shape[:-2] + [m, n], dtype=dtype) - d = tf.linalg.set_diag(zeros, d) - u, _ = tf.linalg.qr(linear_operator_test_util.random_normal( - shape[:-2] + [m, m], dtype=dtype)) - - v, _ = tf.linalg.qr(linear_operator_test_util.random_normal( - shape[:-2] + [n, n], dtype=dtype)) - return tf.matmul(u, tf.matmul(d, v)) - - matrices = [ - generate_well_conditioned(shape, dtype=dtype), - generate_well_conditioned(shape, dtype=dtype), - ] - - lin_op_matrices = matrices - - if use_placeholder: - lin_op_matrices = [ - tf.compat.v1.placeholder_with_default( - matrix, shape=None) for matrix in matrices] - - operator = linear_operator_addition.LinearOperatorAddition( - [linear_operator_full_matrix.LinearOperatorFullMatrix(l) - for l in lin_op_matrices]) - - matmul_order_list = list(reversed(matrices)) - mat = matmul_order_list[0] - for other_mat in matmul_order_list[1:]: - mat = tf.math.add(other_mat, mat) - - return operator, mat - - @test_util.run_deprecated_v1 - def test_different_shapes_raises_static(self): - operators = [ - linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 4, 5)), - linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 3, 4)) - ] - with self.assertRaisesRegex(ValueError, "same shape"): - linear_operator_addition.LinearOperatorAddition(operators) - - @test_util.run_deprecated_v1 - def test_static_shapes(self): - operators = [ - linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 3, 4)), - linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 3, 4)) - ] - operator = linear_operator_addition.LinearOperatorAddition(operators) - self.assertAllEqual((2, 3, 4), operator.shape) - - @test_util.run_deprecated_v1 - def test_shape_tensors_when_statically_available(self): - operators = [ - linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 3, 4)), - linear_operator_full_matrix.LinearOperatorFullMatrix(rng.rand(2, 3, 4)) - ] - operator = linear_operator_addition.LinearOperatorAddition(operators) - with self.cached_session(): - self.assertAllEqual((2, 3, 4), operator.shape_tensor()) - - @test_util.run_deprecated_v1 - def test_shape_tensors_when_only_dynamically_available(self): - mat_1 = rng.rand(1, 2, 3, 4) - mat_2 = rng.rand(1, 2, 3, 4) - mat_ph_1 = tf.compat.v1.placeholder(tf.float64) - mat_ph_2 = tf.compat.v1.placeholder(tf.float64) - feed_dict = {mat_ph_1: mat_1, mat_ph_2: mat_2} - - operators = [ - linear_operator_full_matrix.LinearOperatorFullMatrix(mat_ph_1), - linear_operator_full_matrix.LinearOperatorFullMatrix(mat_ph_2) - ] - operator = linear_operator_addition.LinearOperatorAddition(operators) - with self.cached_session(): - self.assertAllEqual( - (1, 2, 3, 4), operator.shape_tensor().eval(feed_dict=feed_dict)) - - -linear_operator_test_util.add_tests(SquareLinearOperatorAdditionTest) -linear_operator_test_util.add_tests(NonSquareLinearOperatorAdditionTest) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_adjoint.py b/tensorflow_mri/python/linalg/linear_operator_adjoint.py deleted file mode 100644 index 13ea7a63..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_adjoint.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Adjoint of a linear operator.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import doc_util - - -LinearOperatorAdjoint = api_util.export( - "linalg.LinearOperatorAdjoint")( - doc_util.no_linkcode( - linear_operator.make_linear_operator( - tf.linalg.LinearOperatorAdjoint))) - - -tf.linalg.LinearOperatorAdjoint = LinearOperatorAdjoint diff --git a/tensorflow_mri/python/linalg/linear_operator_adjoint_test.py b/tensorflow_mri/python/linalg/linear_operator_adjoint_test.py deleted file mode 100644 index 463b4ca1..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_adjoint_test.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for `LinearOperatorAdjoint`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_algebra.py b/tensorflow_mri/python/linalg/linear_operator_algebra.py deleted file mode 100644 index 2283470d..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_algebra.py +++ /dev/null @@ -1,175 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Linear operator algebra.""" - -import tensorflow as tf - -from tensorflow.python.ops.linalg import linear_operator_algebra - - -adjoint = linear_operator_algebra.adjoint -cholesky = linear_operator_algebra.cholesky -inverse = linear_operator_algebra.inverse -matmul = linear_operator_algebra.matmul -solve = linear_operator_algebra.solve - - -RegisterAdjoint = linear_operator_algebra.RegisterAdjoint -RegisterCholesky = linear_operator_algebra.RegisterCholesky -RegisterInverse = linear_operator_algebra.RegisterInverse -RegisterMatmul = linear_operator_algebra.RegisterMatmul -RegisterSolve = linear_operator_algebra.RegisterSolve - - -_registered_function = linear_operator_algebra._registered_function # pylint: disable=protected-access - - -_ADD = {} -_PSEUDO_INVERSE = {} -_LEASTSQ = {} - - -def _registered_add(type_a, type_b): - """Get the Add function registered for classes a and b.""" - return _registered_function([type_a, type_b], _ADD) - - -def _registered_pseudo_inverse(type_a): - """Get the PseudoInverse function registered for class a.""" - return _registered_function([type_a], _PSEUDO_INVERSE) - - -def _registered_lstsq(type_a): - """Get the SolveLS function registered for class a.""" - return _registered_function([type_a], _LEASTSQ) - - -def add(lin_op_a, lin_op_b, name=None): - """Compute lin_op_a.add(lin_op_b). - - Args: - lin_op_a: The LinearOperator on the left. - lin_op_b: The LinearOperator on the right. - name: Name to use for this operation. - - Returns: - A LinearOperator that represents the addition between `lin_op_a` and - `lin_op_b`. - - Raises: - NotImplementedError: If no add method is defined between types of - `lin_op_a` and `lin_op_b`. - """ - add_fn = _registered_add(type(lin_op_a), type(lin_op_b)) - if add_fn is None: - raise NotImplementedError("No add registered for {}.add({})".format( - type(lin_op_a), type(lin_op_b))) - - with tf.name_scope(name or "Add"): - return add_fn(lin_op_a, lin_op_b) - - -def pseudo_inverse(lin_op_a, name=None): - """Get the Pseudo-Inverse associated to lin_op_a. - - Args: - lin_op_a: The LinearOperator to decompose. - name: Name to use for this operation. - - Returns: - A LinearOperator that represents the inverse of `lin_op_a`. - - Raises: - NotImplementedError: If no Pseudo-Inverse method is defined for the - LinearOperator type of `lin_op_a`. - """ - pseudo_inverse_fn = _registered_pseudo_inverse(type(lin_op_a)) - if pseudo_inverse_fn is None: - raise ValueError("No pseudo-inverse registered for {}".format( - type(lin_op_a))) - - with tf.name_scope(name or "PseudoInverse"): - return pseudo_inverse_fn(lin_op_a) - - -def lstsq(lin_op_a, lin_op_b, name=None): - """Compute lin_op_a.lstsq(lin_op_b). - - Args: - lin_op_a: The LinearOperator on the left. - lin_op_b: The LinearOperator on the right. - name: Name to use for this operation. - - Returns: - A LinearOperator that represents the lstsq between `lin_op_a` and - `lin_op_b`. - - Raises: - NotImplementedError: If no lstsq method is defined between types of - `lin_op_a` and `lin_op_b`. - """ - solve_fn = _registered_lstsq(type(lin_op_a), type(lin_op_b)) - if solve_fn is None: - raise ValueError("No solve registered for {}.solve({})".format( - type(lin_op_a), type(lin_op_b))) - - with tf.name_scope(name or "SolveLS"): - return solve_fn(lin_op_a, lin_op_b) - - -class RegisterAdd: - """Decorator to register an Add implementation function. - - Usage: - - @linear_operator_algebra.RegisterAdd( - lin_op.LinearOperatorFullMatrix, - lin_op.LinearOperatorFullMatrix) - def _add_full_matrix(a, b): - # Return the new full matrix. - """ - - def __init__(self, lin_op_cls_a, lin_op_cls_b): - """Initialize the LinearOperator registrar. - - Args: - lin_op_cls_a: the class of the LinearOperator to multiply. - lin_op_cls_b: the class of the second LinearOperator to multiply. - """ - self._key = (lin_op_cls_a, lin_op_cls_b) - - def __call__(self, add_fn): - """Perform the Add registration. - - Args: - add_fn: The function to use for the Add. - - Returns: - add_fn - - Raises: - TypeError: if add_fn is not a callable. - ValueError: if an Add function has already been registered for - the given argument classes. - """ - if not callable(add_fn): - raise TypeError( - "add_fn must be callable, received: {}".format(add_fn)) - if self._key in _ADD: - raise ValueError("Add({}, {}) has already been registered.".format( - self._key[0].__name__, - self._key[1].__name__)) - _ADD[self._key] = add_fn - return add_fn diff --git a/tensorflow_mri/python/linalg/linear_operator_coils.py b/tensorflow_mri/python/linalg/linear_operator_coils.py deleted file mode 100644 index ee6b9f36..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_coils.py +++ /dev/null @@ -1,196 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Coil array linear operator.""" - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.util import api_util - - -@api_util.export("linalg.LinearOperatorCoils") -@linear_operator_nd.make_linear_operator_nd -class LinearOperatorCoils(linear_operator_nd.LinearOperatorND): - """Linear operator acting like a [batch] coils array. - - Args: - maps: A complex `tf.Tensor` of shape `[..., num_coils, *spatial_shape]`. - batch_dims: An `int`, the number of batch dimensions in `maps`. - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `None`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `None`. - is_positive_definite: A boolean, or `None`. Whether this operator is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that an operator [does - not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) - Defaults to `None`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `False`. - name: An optional `str`. The name of this operator. - """ - def __init__(self, - maps, - batch_dims=0, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None, - name="LinearOperatorCoils"): - parameters = dict( - maps=maps, - batch_dims=batch_dims, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name - ) - - with tf.name_scope(name): - # Check batch_dims. - self._batch_dims = np.asarray(tf.get_static_value(batch_dims)) - if (not self._batch_dims.ndim == 0 or - not np.issubdtype(self._batch_dims.dtype, np.integer)): - raise TypeError( - f"batch_dims must be an int, but got: {batch_dims}") - self._batch_dims = self._batch_dims.item() - if self._batch_dims < 0: - raise ValueError( - f"batch_dims must be non-negative, but got: {batch_dims}") - - # Check maps. - self._maps = tf.convert_to_tensor(maps, name="maps") - if self._maps.dtype not in (tf.complex64, tf.complex128): - raise TypeError( - f"maps must be complex, but got dtype: {str(self._maps.dtype)}") - if self._maps.shape.rank is None: - raise ValueError("maps must have known static rank") - self._ndim_static = self._maps.shape.rank - self._batch_dims - 1 - if self._ndim_static < 1: - raise ValueError( - f"maps must be at least 2-D (excluding batch dimensions), " - f"but got shape: {self._maps.shape}") - self._coil_axis = -(self._ndim_static + 1) - - super().__init__( - dtype=maps.dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters, - name=name) - - def _matvec_nd(self, x, adjoint=False): - if adjoint: - rhs = tf.math.reduce_sum(x * tf.math.conj(self._maps), - axis=self._coil_axis) - else: - rhs = tf.expand_dims(x, self._coil_axis) * self._maps - return rhs - - def _solvevec_nd(self, rhs, adjoint=False): - raise ValueError( - f"{self.name} is not invertible. If you intend to solve the " - f"associated least-squares problem, use `lstsq`, `lstsqvec` or " - f"`lstsqvec_nd`.") - - def _lstsqvec_nd(self, rhs, adjoint=False): - if adjoint: - x = self._matvec_nd(self._normalize(rhs), adjoint=(not adjoint)) - else: - x = self._normalize(self._matvec_nd(rhs, adjoint=(not adjoint))) - return x - - def _normalize(self, x): - # Using safe division so that we can work with coil arrays whose - # sensitivities are all zero for certain pixels (e.g. ESPIRiT maps). - return tf.math.divide_no_nan( - x, tf.math.reduce_sum(tf.math.conj(self._maps) * self._maps, - axis=self._coil_axis)) - - def _ndim(self): - return self._ndim_static - - def _domain_shape(self): - return self._maps.shape[self._coil_axis + 1:] - - def _range_shape(self): - return self._maps.shape[self._coil_axis:] - - def _batch_shape(self): - return self._maps.shape[:self._coil_axis] - - def _domain_shape_tensor(self): - return tf.shape(self._maps)[self._coil_axis + 1:] - - def _range_shape_tensor(self): - return tf.shape(self._maps)[self._coil_axis:] - - def _batch_shape_tensor(self): - return tf.shape(self._maps)[:self._coil_axis] - - @property - def maps(self): - return self._maps - - @property - def num_coils(self): - return self._maps.shape[self._coil_axis] - - def num_coils_tensor(self): - return tf.shape(self._maps)[self._coil_axis] - - @property - def _composite_tensor_fields(self): - return ('maps', 'batch_dims') - - @property - def _composite_tensor_prefer_static_fields(self): - return ('batch_dims',) - - @property - def _experimental_parameter_ndims_to_matrix_ndims(self): - return {'maps': self.ndim + 1} - - -def coils_matrix(maps, batch_dims=0): - """Constructs a coil array matrix. - - Args: - maps: A complex `tf.Tensor` of shape `[..., num_coils, *spatial_shape]`. - batch_dims: An `int`, the number of batch dimensions in `maps`. - - Returns: - A `tf.Tensor` representing a dense coil array matrix equivalent to - `LinearOperatorCoils`. - """ - maps = tf.convert_to_tensor(maps, name="maps") - - # Vectorize N-D maps. - maps = tf.reshape( - maps, tf.concat([tf.shape(maps)[:(batch_dims + 1)], [-1]], axis=0)) - - # Construct a [batch] matrix for each coil. - matrix = tf.linalg.diag(maps) - - # Stack the coil matrices. - matrix = tf.reshape(matrix, tf.concat([tf.shape(maps)[:batch_dims], - [-1, tf.shape(maps)[-1]]], axis=0)) - - return matrix diff --git a/tensorflow_mri/python/linalg/linear_operator_coils_test.py b/tensorflow_mri/python/linalg/linear_operator_coils_test.py deleted file mode 100644 index 65a8ca18..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_coils_test.py +++ /dev/null @@ -1,167 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_coils`.""" -# pylint: disable=missing-class-docstring,missing-function-docstring - -import functools - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_coils -from tensorflow_mri.python.linalg import linear_operator_test_util -from tensorflow_mri.python.util import test_util - - -rng = np.random.RandomState(2016) - - -class OperatorShapesInfoCoils(): - def __init__(self, image_shape, num_coils, batch_shape): - self.image_shape = image_shape - self.num_coils = num_coils - self.batch_shape = batch_shape - - @property - def shape(self): - n = functools.reduce(lambda a, b: a * b, self.image_shape) - m = self.num_coils * n - return self.batch_shape + (m, n) - - @property - def dimension(self): - return len(self.image_shape) - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorCoilsTest( - linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - - @staticmethod - def operator_shapes_infos(): - shapes_info = OperatorShapesInfoCoils - return [ - shapes_info((2, 2), 3, ()), - shapes_info((2, 4), 4, (3,)), - shapes_info((4, 2), 3, (1, 2)), - shapes_info((2, 2), 4, ()), - shapes_info((2, 2, 2), 4, ()), - shapes_info((4, 2, 2), 2, (2,)) - # TODO(jmontalt): odd shapes fail tests, investigate - # shapes_info((2, 3), 5, (2,)), - # shapes_info((3, 2), 7, ()) - ] - - @staticmethod - def dtypes_to_test(): - return [tf.complex64, tf.complex128] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - del ensure_self_adjoint_and_pd - del use_placeholder - - batch_shape = build_info.batch_shape - num_coils = build_info.num_coils - image_shape = build_info.image_shape - - maps = tf.dtypes.complex( - tf.random.normal( - shape=batch_shape + (num_coils,) + image_shape, - dtype=dtype.real_dtype), - tf.random.normal( - shape=batch_shape + (num_coils,) + image_shape, - dtype=dtype.real_dtype) - ) - - operator = linear_operator_coils.LinearOperatorCoils( - maps=maps, batch_dims=len(batch_shape)) - - matrix = linear_operator_coils.coils_matrix( - maps=maps, batch_dims=len(batch_shape)) - - return operator, matrix - - def test_1d_maps_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be at least 2-D"): - linear_operator_coils.LinearOperatorCoils( - maps=np.ones((4,)).astype(np.complex64)) - - with self.assertRaisesRegex(ValueError, "must be at least 2-D"): - linear_operator_coils.LinearOperatorCoils( - maps=np.ones((3, 4, 4)).astype(np.complex64), - batch_dims=2) - - linear_operator_coils.LinearOperatorCoils( - maps=np.ones((3, 4, 4)).astype(np.complex64), - batch_dims=1) # should not raise - - def test_non_complex_maps_raises_static(self): - with self.assertRaisesRegex(TypeError, "must be complex"): - linear_operator_coils.LinearOperatorCoils( - maps=np.ones((3, 4, 4)).astype(np.float32)) - - def test_unknown_rank_maps_raises_static(self): - if tf.executing_eagerly(): - return - with self.cached_session(): - maps = tf.compat.v1.placeholder_with_default( - np.ones((3, 4, 4)).astype(np.complex64), shape=None) - with self.assertRaisesRegex(ValueError, "must have known static rank"): - operator = linear_operator_coils.LinearOperatorCoils(maps=maps) - self.evaluate(operator.to_dense()) - - def test_non_integer_batch_dims_raises_static(self): - with self.assertRaisesRegex(TypeError, "must be an int"): - linear_operator_coils.LinearOperatorCoils( - maps=np.ones((3, 4, 4)).astype(np.complex64), batch_dims=1.) - - def test_negative_batch_dims_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be non-negative"): - linear_operator_coils.LinearOperatorCoils( - maps=np.ones((3, 4, 4)).astype(np.complex64), batch_dims=-1) - - def test_is_x_flags(self): - operator = linear_operator_coils.LinearOperatorCoils( - maps=np.ones((3, 4, 4)).astype(np.complex64)) - self.assertFalse(operator.is_self_adjoint) - - def test_solve_raises(self): - operator = linear_operator_coils.LinearOperatorCoils( - maps=np.ones((1, 4, 4)).astype(np.complex64), is_square=True) - with self.assertRaisesRegex(ValueError, "not invertible.*lstsq"): - operator.solve(tf.ones([16, 1], dtype=tf.complex64)) - - def test_inverse_raises(self): - operator = linear_operator_coils.LinearOperatorCoils( - maps=np.ones((1, 4, 4)).astype(np.complex64), is_square=True) - with self.assertRaisesRegex(ValueError, "not invertible.*pseudo_inverse"): - operator.inverse() - - def test_convert_variables_to_tensors(self): - maps = tf.Variable(np.ones((3, 4, 4)).astype(np.complex64)) - operator = linear_operator_coils.LinearOperatorCoils(maps=maps) - with self.cached_session() as sess: - sess.run([maps.initializer]) - self.check_convert_variables_to_tensors(operator) - - -linear_operator_test_util.add_tests(LinearOperatorCoilsTest) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_composition.py b/tensorflow_mri/python/linalg/linear_operator_composition.py deleted file mode 100644 index 8f04a2a9..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_composition.py +++ /dev/null @@ -1,158 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Composition of linear operators.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import doc_util - - -LinearOperatorComposition = api_util.export( - "linalg.LinearOperatorComposition")( - doc_util.no_linkcode( - linear_operator.make_linear_operator( - tf.linalg.LinearOperatorComposition))) - - -tf.linalg.LinearOperatorComposition = LinearOperatorComposition - - -def combined_non_singular_hint(*operators): - """Returns a hint for the non-singularity of a composition of operators. - - Args: - *operators: A list of `LinearOperator` objects. - - Returns: - A boolean, or `None`. Whether the composition of the operators is expected - to be non-singular. - """ - # If all operators are non-singular, so is the composition. - if all(o.is_non_singular is True for o in operators): - return True - - # If any operator is singular, then the composition is singular too. - if any(o.is_non_singular is False for o in operators): - return False - - # In all other cases, we don't know. - return None - - -def combined_self_adjoint_hint(*operators, commuting=False): - """Returns a hint for the self-adjointness of a composition of operators. - - Args: - *operators: A list of `LinearOperator` objects. - - Returns: - A boolean, or `None`. Whether the composition of the operators is expected - to be self-adjoint. - """ - if commuting: # The operators commute. - # If all operators are self-adjoint, then the composition is self-adjoint. - if all(o.is_self_adjoint is True for o in operators): - return True - - # If only one operator isn't self-adjoint, then the composition is not - # self-adjoint. - self_adjoint_operators = [ - o for o in operators if o.is_self_adjoint is True] - non_self_adjoint_operators = [ - o for o in operators if o.is_self_adjoint is False] - if (len(self_adjoint_operators) == len(operators) - 1 and - len(non_self_adjoint_operators) == 1): - return False - - # In all other cases, we don't know. - return None - - # If commutative property is not guaranteed, we don't know anything about - # the self-adjointness of the output. - return None - - -def combined_positive_definite_hint(*operators, commuting=False): - """Returns a hint for the positive-definiteness of a composition of operators. - - Args: - *operators: A list of `LinearOperator` objects. - - Returns: - A boolean, or `None`. Whether the composition of the operators is expected - to be positive-definite. - """ - # If all operators are positive-definite, its composition has positive - # eigenvalues. - eigvals_are_positive = all(o.is_positive_definite is True for o in operators) - - # Check if the output is expected to be self-adjoint. - is_self_adjoint = combined_self_adjoint_hint(*operators, commuting=commuting) - - # If their composition is self-adjoint and the - # eigenvalues are positive, then the composition is positive-definite. - if eigvals_are_positive is True and is_self_adjoint is True: - return True - - # Otherwise, we don't know. - return None - - -def combined_square_hint(*operators): - """Returns a hint for the squareness of a composition of operators. - - Args: - *operators: A list of `LinearOperator` objects. - - Returns: - A boolean, or `None`. Whether the composition of the operators is expected - to be square. - """ - # If all operators are square, so is the composition. - if all(o.is_square is True for o in operators): - return True - - # If all operators are square except one which is not, then the sum is - # not square. - square_operators = [ - o for o in operators if o.is_square is True] - non_square_operators = [ - o for o in operators if o.is_square is False] - if (len(square_operators) == len(operators) - 1 and - len(non_square_operators) == 1): - return False - - # In all other cases, we don't know. - return None - - -def check_hint(expected, received, name): - """Checks that a hint is consistent with its expected value. - - Args: - expected: A boolean, or `None`. The expected value of the hint. - received: A boolean, or `None`. The received value of the hint. - name: A string. The name of the hint. - - Raises: - ValueError: If `expected` and `value` are not consistent. - """ - if expected is not None and received is not None and expected != received: - raise ValueError( - f"Inconsistent {name} hint: expected {expected} based on input " - f"operators, but got {received}") - return received if received is not None else expected diff --git a/tensorflow_mri/python/linalg/linear_operator_composition_nd.py b/tensorflow_mri/python/linalg/linear_operator_composition_nd.py deleted file mode 100644 index b7295953..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_composition_nd.py +++ /dev/null @@ -1,276 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Composition of N-D linear operators.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_composition -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.util import api_util - - -@api_util.export("linalg.LinearOperatorCompositionND") -@linear_operator_nd.make_linear_operator_nd -class LinearOperatorCompositionND(linear_operator_nd.LinearOperatorND): - r"""Composes one or more linear operators. - - This operator composes one or more linear operators representing matrices - $A_1, A_2, \dots, A_J$, building a new linear operator $A$ which acts as the - matrix product $A := A_1 A_2 \dots A_J$. - - If `opj` has shape `batch_shape_j + [M_j, N_j]`, then we must have - `N_j = M_{j+1}`, in which case the composed operator has shape equal to - `broadcast_batch_shape + [M_1, N_J]`, where `broadcast_batch_shape` is the - mutual broadcast of `batch_shape_j`, `j = 1,...,J`, assuming the intermediate - batch shapes broadcast. Even if the composed shape is well defined, the - composed operator's methods may fail due to lack of broadcasting ability in - the defining operators' methods. - - ```python - # Create a 2 x 2 linear operator composed of two 2 x 2 operators. - operator_1 = LinearOperatorFullMatrix([[1., 2.], [3., 4.]]) - operator_2 = LinearOperatorFullMatrix([[1., 0.], [0., 1.]]) - operator = LinearOperatorComposition([operator_1, operator_2]) - - operator.to_dense() - ==> [[1., 2.] - [3., 4.]] - - operator.shape - ==> [2, 2] - - operator.log_abs_determinant() - ==> scalar Tensor - - x = ... Shape [2, 4] Tensor - operator.matmul(x) - ==> Shape [2, 4] Tensor - - # Create a [2, 3] batch of 4 x 5 linear operators. - matrix_45 = tf.random.normal(shape=[2, 3, 4, 5]) - operator_45 = LinearOperatorFullMatrix(matrix) - - # Create a [2, 3] batch of 5 x 6 linear operators. - matrix_56 = tf.random.normal(shape=[2, 3, 5, 6]) - operator_56 = LinearOperatorFullMatrix(matrix_56) - - # Compose to create a [2, 3] batch of 4 x 6 operators. - operator_46 = LinearOperatorComposition([operator_45, operator_56]) - - # Create a shape [2, 3, 6, 2] vector. - x = tf.random.normal(shape=[2, 3, 6, 2]) - operator.matmul(x) - ==> Shape [2, 3, 4, 2] Tensor - ``` - - #### Performance - - The performance of `LinearOperatorComposition` on any operation is equal to - the sum of the individual operators' operations. - - - #### Matrix property hints - - This `LinearOperator` is initialized with boolean flags of the form `is_X`, - for `X = non_singular, self_adjoint, positive_definite, square`. - These have the following meaning: - - * If `is_X == True`, callers should expect the operator to have the - property `X`. This is a promise that should be fulfilled, but is *not* a - runtime assert. For example, finite floating point precision may result - in these promises being violated. - * If `is_X == False`, callers should expect the operator to not have `X`. - * If `is_X == None` (the default), callers should have no expectation either - way. - - Args: - operators: A `list` of `tfmri.linalg.LinearOperatorND` objects, each with - the same dtype and conformable shapes. - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `None`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `None`. - is_positive_definite: A boolean, or `None`. Whether this operator is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that an operator [does - not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) - Defaults to `None`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `None`. - name: An optional `str`. The name of this operator. - """ - def __init__(self, - operators, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None, - name=None): - parameters = dict( - operators=operators, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name) - - # Validate operators. - tf.debugging.assert_proper_iterable(operators) - operators = list(operators) - if not operators: - raise ValueError( - f"Expected a non-empty list of operators. Found: {operators}") - # for operator in operators: - # if not isinstance(operator, linear_operator_nd.LinearOperatorND): - # raise TypeError( - # f"Expected a list of LinearOperatorND objects. Found: {operators}") - self._operators = operators - - # Validate dtype. - dtype = operators[0].dtype - for operator in operators: - if operator.dtype != dtype: - name_type = (str((o.name, o.dtype)) for o in operators) - raise TypeError( - f"Expected all operators to have the same dtype. " - f"Found: {', '.join(name_type)}") - - # Validate shapes. - domain_shape = operators[0].domain_shape - for operator in operators[1:]: - if not domain_shape.is_compatible_with(operator.range_shape): - shapes = ', '.join( - [f'({str(o.range_shape)}, {str(o.domain_shape)})' - for o in operators]) - raise ValueError( - f"Expected operators to have conformable shapes for matrix " - f"multiplication. Found: {shapes}") - - # Get broadcast batch shape (static). - batch_shape_static = self.operators[0].batch_shape - for operator in self.operators[1:]: - batch_shape_static = tf.broadcast_static_shape( - batch_shape_static, operator.batch_shape) - self._batch_shape_static = batch_shape_static - - # Get broadcast batch shape (dynamic). - batch_shape_dynamic = self.operators[0].batch_shape_tensor() - for operator in self.operators[1:]: - batch_shape_dynamic = tf.broadcast_dynamic_shape( - batch_shape_dynamic, operator.batch_shape_tensor()) - self._batch_shape_dynamic = batch_shape_dynamic - - # Infer operator hints. - is_non_singular = linear_operator_composition.check_hint( - linear_operator_composition.combined_non_singular_hint(*operators), - is_non_singular, - "non-singular") - is_self_adjoint = linear_operator_composition.check_hint( - linear_operator_composition.combined_self_adjoint_hint(*operators), - is_self_adjoint, - "self-adjoint") - is_positive_definite = linear_operator_composition.check_hint( - linear_operator_composition.combined_positive_definite_hint(*operators), - is_positive_definite, - "positive-definite") - is_square = linear_operator_composition.check_hint( - linear_operator_composition.combined_square_hint(*operators), - is_square, - "square") - - # Initialization. - if name is None: - name = "_o_".join(operator.name for operator in operators) - - with tf.name_scope(name): - super().__init__( - dtype=dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters, - name=name) - - @property - def operators(self): - return self._operators - - def _domain_shape(self): - return self.operators[-1].domain_shape - - def _range_shape(self): - return self.operators[0].range_shape - - def _batch_shape(self): - return self._batch_shape_static - - def _domain_shape_tensor(self): - return self.operators[-1].domain_shape_tensor() - - def _range_shape_tensor(self): - return self.operators[0].range_shape_tensor() - - def _batch_shape_tensor(self): - return self._batch_shape_dynamic - - def _matvec_nd(self, x, adjoint=False): - # If self.operators = [A, B], and not adjoint, then - # matmul_order_list = [B, A]. - # As a result, we return A.matmul(B.matmul(x)) - if adjoint: - matmul_order_list = self.operators - else: - matmul_order_list = list(reversed(self.operators)) - - result = matmul_order_list[0].matvec_nd(x, adjoint=adjoint) - for operator in matmul_order_list[1:]: - result = operator.matvec_nd(result, adjoint=adjoint) - return result - - def _solvevec_nd(self, rhs, adjoint=False): - # If self.operators = [A, B], and not adjoint, then - # solve_order_list = [A, B]. - # As a result, we return B.solve(A.solve(x)) - if adjoint: - solve_order_list = list(reversed(self.operators)) - else: - solve_order_list = self.operators - - solution = solve_order_list[0].solvevec_nd(rhs, adjoint=adjoint) - for operator in solve_order_list[1:]: - solution = operator.solvevec_nd(solution, adjoint=adjoint) - return solution - - def _determinant(self): - result = self.operators[0].determinant() - for operator in self.operators[1:]: - result *= operator.determinant() - return result - - def _log_abs_determinant(self): - result = self.operators[0].log_abs_determinant() - for operator in self.operators[1:]: - result += operator.log_abs_determinant() - return result - - @property - def _composite_tensor_fields(self): - return ("operators",) - - @property - def _experimental_parameter_ndims_to_matrix_ndims(self): - return {"operators": [0] * len(self.operators)} diff --git a/tensorflow_mri/python/linalg/linear_operator_composition_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_composition_nd_test.py deleted file mode 100644 index 8c5328ac..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_composition_nd_test.py +++ /dev/null @@ -1,284 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_composition_nd -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.linalg import linear_operator_test_util -from tensorflow_mri.python.util import test_util - - -CompositionND = linear_operator_composition_nd.LinearOperatorCompositionND -def FullMatrixND(matrix, *args, **kwargs): - linop = tf.linalg.LinearOperatorFullMatrix(matrix, *args, **kwargs) - return linear_operator_nd.LinearOperatorMakeND( - linop, - range_shape=[linop.range_dimension_tensor()], - domain_shape=[linop.domain_dimension_tensor()], - name=kwargs.get('name', None)) - - -rng = np.random.RandomState(0) - - -class SquareLinearOperatorCompositionTest( - linear_operator_test_util.SquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - @staticmethod - def operator_shapes_infos(): - shapes_info = linear_operator_test_util.OperatorShapesInfo - # non-batch operators (n, n) and batch operators. - return [ - shapes_info((1, 1)), - shapes_info((1, 3, 3)), - shapes_info((3, 4, 4)), - shapes_info((2, 1, 4, 4))] - - def tearDown(self): - tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) - - def setUp(self): - self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() - tf.config.experimental.enable_tensor_float_32_execution(False) - # Increase from 1e-6 to 1e-4 and 2e-4. - self._atol[tf.float32] = 2e-4 - self._atol[tf.complex64] = 1e-4 - self._rtol[tf.float32] = 2e-4 - self._rtol[tf.complex64] = 1e-4 - - @staticmethod - def skip_these_tests(): - # Cholesky not implemented. - return ["cholesky", "lstsq", "lstsq_with_broadcast"] - - def operator_and_matrix(self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - shape = list(build_info.shape) - - # Either 1 or 2 matrices, depending. - num_operators = rng.randint(low=1, high=3) - if ensure_self_adjoint_and_pd: - # The random PD matrices are also symmetric. Here we are computing - # A @ A ... @ A. Since A is symmetric and PD, so are any powers of it. - matrices = [ - linear_operator_test_util.random_positive_definite_matrix( - shape, dtype, force_well_conditioned=True)] * num_operators - else: - matrices = [ - linear_operator_test_util.random_positive_definite_matrix( - shape, dtype, force_well_conditioned=True) - for _ in range(num_operators) - ] - - lin_op_matrices = matrices - - if use_placeholder: - lin_op_matrices = [ - tf.compat.v1.placeholder_with_default( - matrix, shape=None) for matrix in matrices] - - operator = CompositionND( - [FullMatrixND(l) for l in lin_op_matrices], - is_positive_definite=True if ensure_self_adjoint_and_pd else None, - is_self_adjoint=True if ensure_self_adjoint_and_pd else None, - is_square=True) - - matmul_order_list = list(reversed(matrices)) - mat = matmul_order_list[0] - for other_mat in matmul_order_list[1:]: - mat = tf.matmul(other_mat, mat) - - return operator, mat - - def test_is_x_flags(self): - # Matrix with two positive eigenvalues, 1, and 1. - # The matrix values do not effect auto-setting of the flags. - matrix = [[1., 0.], [1., 1.]] - operator = CompositionND( - [FullMatrixND(matrix)], - is_positive_definite=True, - is_non_singular=True, - is_self_adjoint=False) - self.assertTrue(operator.is_positive_definite) - self.assertTrue(operator.is_non_singular) - self.assertFalse(operator.is_self_adjoint) - - def test_is_non_singular_auto_set(self): - # Matrix with two positive eigenvalues, 11 and 8. - # The matrix values do not effect auto-setting of the flags. - matrix = [[11., 0.], [1., 8.]] - operator_1 = FullMatrixND(matrix, is_non_singular=True) - operator_2 = FullMatrixND(matrix, is_non_singular=True) - - operator = CompositionND( - [operator_1, operator_2], - is_positive_definite=False, # No reason it HAS to be False... - is_non_singular=None) - self.assertFalse(operator.is_positive_definite) - self.assertTrue(operator.is_non_singular) - - with self.assertRaisesRegex(ValueError, "Inconsistent non-singular hint"): - CompositionND( - [operator_1, operator_2], is_non_singular=False) - - def test_name(self): - matrix = [[11., 0.], [1., 8.]] - operator_1 = FullMatrixND(matrix, name="left") - operator_2 = FullMatrixND(matrix, name="right") - - operator = CompositionND([operator_1, operator_2]) - - self.assertEqual("left_o_right", operator.name) - - def test_different_dtypes_raises(self): - operators = [ - FullMatrixND(rng.rand(2, 3, 3)), - FullMatrixND(rng.rand(2, 3, 3).astype(np.float32)) - ] - with self.assertRaisesRegex(TypeError, "same dtype"): - CompositionND(operators) - - def test_empty_operators_raises(self): - with self.assertRaisesRegex(ValueError, "non-empty"): - CompositionND([]) - - -class NonSquareLinearOperatorCompositionTest( - linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - - def tearDown(self): - tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) - - def setUp(self): - self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() - tf.config.experimental.enable_tensor_float_32_execution(False) - # Increase from 1e-6 to 1e-4 - self._atol[tf.float32] = 1e-4 - self._atol[tf.complex64] = 1e-4 - self._rtol[tf.float32] = 1e-4 - self._rtol[tf.complex64] = 1e-4 - - @staticmethod - def skip_these_tests(): - # Testing the condition number fails when using XLA with cuBLASLt - # A slight numerical difference between different matmul algorithms - # leads to large precision issues - return linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest.skip_these_tests( - ) + ["cond", "lstsq", "lstsq_with_broadcast"] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - del ensure_self_adjoint_and_pd - shape = list(build_info.shape) - - # Create 2 matrices/operators, A1, A2, which becomes A = A1 A2. - # Use inner dimension of 2. - k = 2 - batch_shape = shape[:-2] - shape_1 = batch_shape + [shape[-2], k] - shape_2 = batch_shape + [k, shape[-1]] - - # Ensure that the matrices are well-conditioned by generating - # random matrices whose singular values are close to 1. - # The reason to do this is because cond(AB) <= cond(A) * cond(B). - # By ensuring that each factor has condition number close to 1, we ensure - # that the condition number of the product isn't too far away from 1. - def generate_well_conditioned(shape, dtype): - m, n = shape[-2], shape[-1] - min_dim = min(m, n) - # Generate singular values that are close to 1. - d = linear_operator_test_util.random_normal( - shape[:-2] + [min_dim], - mean=1., - stddev=0.1, - dtype=dtype) - zeros = tf.zeros(shape=shape[:-2] + [m, n], dtype=dtype) - d = tf.linalg.set_diag(zeros, d) - u, _ = tf.linalg.qr(linear_operator_test_util.random_normal( - shape[:-2] + [m, m], dtype=dtype)) - - v, _ = tf.linalg.qr(linear_operator_test_util.random_normal( - shape[:-2] + [n, n], dtype=dtype)) - return tf.matmul(u, tf.matmul(d, v)) - - matrices = [ - generate_well_conditioned(shape_1, dtype=dtype), - generate_well_conditioned(shape_2, dtype=dtype), - ] - - lin_op_matrices = matrices - - if use_placeholder: - lin_op_matrices = [ - tf.compat.v1.placeholder_with_default( - matrix, shape=None) for matrix in matrices] - - operator = CompositionND( - [FullMatrixND(l) for l in lin_op_matrices]) - - matmul_order_list = list(reversed(matrices)) - mat = matmul_order_list[0] - for other_mat in matmul_order_list[1:]: - mat = tf.matmul(other_mat, mat) - - return operator, mat - - @test_util.run_deprecated_v1 - def test_static_shapes(self): - operators = [ - FullMatrixND(rng.rand(2, 3, 4)), - FullMatrixND(rng.rand(2, 4, 5)) - ] - operator = CompositionND(operators) - self.assertAllEqual((2, 3, 5), operator.shape) - - @test_util.run_deprecated_v1 - def test_shape_tensors_when_statically_available(self): - operators = [ - FullMatrixND(rng.rand(2, 3, 4)), - FullMatrixND(rng.rand(2, 4, 5)) - ] - operator = CompositionND(operators) - with self.cached_session(): - self.assertAllEqual((2, 3, 5), operator.shape_tensor()) - - @test_util.run_deprecated_v1 - def test_shape_tensors_when_only_dynamically_available(self): - mat_1 = rng.rand(1, 2, 3, 4) - mat_2 = rng.rand(1, 2, 4, 5) - mat_ph_1 = tf.compat.v1.placeholder(tf.float64) - mat_ph_2 = tf.compat.v1.placeholder(tf.float64) - feed_dict = {mat_ph_1: mat_1, mat_ph_2: mat_2} - - operators = [ - FullMatrixND(mat_ph_1), - FullMatrixND(mat_ph_2) - ] - operator = CompositionND(operators) - with self.cached_session(): - self.assertAllEqual( - (1, 2, 3, 5), operator.shape_tensor().eval(feed_dict=feed_dict)) - - -linear_operator_test_util.add_tests(SquareLinearOperatorCompositionTest) -linear_operator_test_util.add_tests(NonSquareLinearOperatorCompositionTest) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_composition_test.py b/tensorflow_mri/python/linalg/linear_operator_composition_test.py deleted file mode 100644 index 9095920c..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_composition_test.py +++ /dev/null @@ -1,16 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for `linear_operator_composition`.""" -# pylint: disable=missing-class-docstring,missing-function-docstring diff --git a/tensorflow_mri/python/linalg/linear_operator_diag.py b/tensorflow_mri/python/linalg/linear_operator_diag.py deleted file mode 100644 index 77c6baf7..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_diag.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Diagonal linear operator.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import doc_util - - -LinearOperatorDiag = api_util.export( - "linalg.LinearOperatorDiag")( - doc_util.no_linkcode( - linear_operator.make_linear_operator( - tf.linalg.LinearOperatorDiag))) - - -tf.linalg.LinearOperatorDiag = LinearOperatorDiag diff --git a/tensorflow_mri/python/linalg/linear_operator_diag_nd.py b/tensorflow_mri/python/linalg/linear_operator_diag_nd.py deleted file mode 100644 index 6aecefed..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_diag_nd.py +++ /dev/null @@ -1,277 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.linalg import linear_operator_util -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import types_util - - -@api_util.export("linalg.LinearOperatorDiagND") -@linear_operator_nd.make_linear_operator_nd -class LinearOperatorDiagND(linear_operator_nd.LinearOperatorND): - r"""Linear operator acting like a [batch] square diagonal matrix. - - This operator acts like a batch of diagonal matrices - $A \in \mathbb{F}^{n \times n}$, where $\mathbb{F}$ may be $\mathbb{R}$ - or $\mathbb{C}$ and $n = n_0 \times n_1 \times \dots \times n_d$, where - $d$ is the number of dimensions in the domain. - - ```{note} - The matrix $A$ is not materialized. - ``` - - ```{seealso} - This operator is similar to `tfmri.linalg.LinearOperatorDiag`, but provides - additional functionality to operate with multidimensional inputs. - ``` - - ```{rubric} Initialization - ``` - This operator is initialized with an array of diagonal elements `diag`. - `diag` may have multiple domain dimensions, which does not affect the dense - matrix representation of this operator but may be convenient to operate with - non-vectorized multidimensional inputs. If `diag` has any leading dimensions - which should be interpreted as batch dimensions, specify how many using the - `batch_dims` argument. This operator has the same data type as `diag`. - - ```{rubric} Performance - ``` - - `matvec` is $O(n)$. - - `solvevec` is $O(n)$. - - `lstsqvec` is $O(n)$. - - ```{rubric} Properties - ``` - - This operator is *non-singular* iff all its diagonal entries are non-zero. - - This operator is *self-adjoint* iff all its diagonal entries are real or - have zero imaginary part. - - This operator is *positive definite* iff all its diagonal entries are - positive or have positive real part. - - This operator is always *square*. - - ```{rubric} Inversion - ``` - If this operator is non-singular, its inverse $A{-1}$ is also a diagonal - operator whose diagonal entries are the reciprocal of the diagonal entries - of this operator. - - Example: - >>> # Create a 2-D diagonal linear operator. - >>> diag = [[1., -1.], [2., 3.]] - >>> operator = tfmri.linalg.LinearOperatorDiagND(diag) - >>> operator.to_dense() - [[ 1., 0., 0., 0.], - [ 0., -1., 0., 0.], - [ 0., 0., 2., 0.], - [ 0., 0., 0., 3.]] - >>> operator.shape - (4, 4) - >>> x = tf.ones(shape=(2, 2)) - >>> rhs = operator.matvec_nd(x) - [[ 1., -1.], - [ 2., 3.]] - >>> operator.solvevec_nd(rhs) - [[ 1., 1.], - [ 1., 1.]] - - Args: - diag: A real or complex `tf.Tensor` of shape `[..., *domain_shape]`. - The diagonal of the operator. - batch_dims: An `int`, the number of leading batch dimensions in `diag`. - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `None`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `None`. - is_positive_definite: A boolean, or `None`. Whether this operator is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that an operator [does - not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) - Defaults to `None`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `False`. - name: An optional `str`. The name of this operator. - """ - def __init__(self, - diag, - batch_dims=0, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None, - name="LinearOperatorDiag"): - parameters = dict( - diag=diag, - batch_dims=batch_dims, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name - ) - - with tf.name_scope(name): - # Check batch_dims. - self._batch_dims = np.asarray(tf.get_static_value(batch_dims)) - if (not self._batch_dims.ndim == 0 or - not np.issubdtype(self._batch_dims.dtype, np.integer)): - raise TypeError( - f"batch_dims must be an int, but got: {batch_dims}") - self._batch_dims = self._batch_dims.item() - if self._batch_dims < 0: - raise ValueError( - f"batch_dims must be non-negative, but got: {batch_dims}") - - # Check maps. - self._diag = types_util.convert_nonref_to_tensor(diag, name="diag") - if self._diag.shape.rank is None: - raise ValueError("diag must have known static rank") - if self._diag.shape.rank < 1: - raise ValueError( - f"diag must be at least 1-D, but got shape: {self._diag.shape}") - - # Check and auto-set hints. - if not self._diag.dtype.is_complex: - if is_self_adjoint is False: - raise ValueError("A real diagonal operator is always self adjoint.") - is_self_adjoint = True - - if is_square is False: - raise ValueError("Only square diagonal operators currently supported.") - is_square = True - - super().__init__( - dtype=self._diag.dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters, - name=name) - - def _domain_shape(self): - return self._diag.shape[self._batch_dims:] - - def _range_shape(self): - return self._diag.shape[self._batch_dims:] - - def _batch_shape(self): - return self._diag.shape[:self._batch_dims] - - def _domain_shape_tensor(self): - return tf.shape(self._diag)[self._batch_dims:] - - def _range_shape_tensor(self): - return tf.shape(self._diag)[self._batch_dims:] - - def _batch_shape_tensor(self): - return tf.shape(self._diag)[:self._batch_dims] - - def _assert_non_singular(self): - return linear_operator_util.assert_no_entries_with_modulus_zero( - self._diag, - message=( - "Diagonal operator is singular: " - "diagonal entries contain zero values.")) - - def _assert_positive_definite(self): - if self.dtype.is_complex: - message = ( - "Diagonal operator has diagonal entries with non-positive real part, " - "so it is not positive definite.") - else: - message = ( - "Real diagonal operator has non-positive diagonal entries, " - "so it is not positive definite.") - - return tf.debugging.assert_positive( - tf.math.real(self._diag), message=message) - - def _assert_self_adjoint(self): - return linear_operator_util.assert_zero_imag_part( - self._diag, - message=( - "This diagonal operator contains non-zero imaginary values, " - "so it is not self-adjoint.")) - - def _matvec_nd(self, x, adjoint=False): - diag_term = tf.math.conj(self._diag) if adjoint else self._diag - return diag_term * x - - def _determinant(self): - return tf.math.reduce_prod(self._diag, axis=self._diag_axes) - - def _log_abs_determinant(self): - log_det = tf.math.reduce_sum( - tf.math.log(tf.math.abs(self._diag)), axis=self._diag_axes) - if self.dtype.is_complex: - log_det = tf.cast(log_det, dtype=self.dtype) - return log_det - - def _solvevec_nd(self, rhs, adjoint=False): - diag_term = tf.math.conj(self._diag) if adjoint else self._diag - inv_diag_term = 1. / diag_term - return inv_diag_term * rhs - - def _lstsqvec_nd(self, rhs, adjoint=False): - return self._solvevec_nd(rhs, adjoint=adjoint) - - def _to_dense(self): - return tf.linalg.diag(self._flat_diag) - - def _diag_part(self): - return self._flat_diag - - def _add_to_tensor(self, x): - x_diag = tf.linalg.diag_part(x) - new_diag = self._flat_diag + x_diag - return tf.linalg.set_diag(x, new_diag) - - def _eigvals(self): - return tf.convert_to_tensor(self.diag) - - def _cond(self): - abs_diag = tf.math.abs(self.diag) - return (tf.math.reduce_max(abs_diag, axis=self._diag_axes) / - tf.math.reduce_min(abs_diag, axis=self._diag_axes)) - - @property - def diag(self): - return self._diag - - @property - def _diag_axes(self): - return list(range(self._batch_dims, self._diag.shape.rank)) - - @property - def _flat_diag(self): - return tf.reshape( - self._diag, tf.concat([self.batch_shape_tensor(), [-1]], 0)) - - @property - def _composite_tensor_fields(self): - return ("diag", "batch_dims") - - @property - def _composite_tensor_prefer_static_fields(self): - return ("batch_dims",) - - @property - def _experimental_parameter_ndims_to_matrix_ndims(self): - return {"diag": self._diag.shape.rank - self._batch_dims} diff --git a/tensorflow_mri/python/linalg/linear_operator_diag_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_diag_nd_test.py deleted file mode 100644 index 20ca3341..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_diag_nd_test.py +++ /dev/null @@ -1,510 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for `linear_operator_diag_nd`.""" - -import tensorflow as tf - -from tensorflow.python.framework import test_util - -from tensorflow_mri.python.linalg import linear_operator_diag_nd -from tensorflow_mri.python.linalg import linear_operator_identity_nd -from tensorflow_mri.python.linalg import linear_operator_test_util - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorDiagNDTest( - linear_operator_test_util.SquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - - def tearDown(self): - tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) - - def setUp(self): - self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() - tf.config.experimental.enable_tensor_float_32_execution(False) - - @staticmethod - def optional_tests(): - """List of optional test names to run.""" - return [ - "operator_matmul_with_same_type", - "operator_solve_with_same_type" - ] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - shape = list(build_info.shape) - diag = linear_operator_test_util.random_sign_uniform( - shape[:-1], minval=1., maxval=2., dtype=dtype) - batch_dims = len(shape) - 2 - - if ensure_self_adjoint_and_pd: - # Abs on complex64 will result in a float32, so we cast back up. - diag = tf.cast(tf.math.abs(diag), dtype=dtype) - - lin_op_diag = diag - - if use_placeholder: - lin_op_diag = tf.compat.v1.placeholder_with_default( - diag, shape=(None,) * (batch_dims + 1)) - - operator = linear_operator_diag_nd.LinearOperatorDiagND( - lin_op_diag, - batch_dims=batch_dims, - is_self_adjoint=True if ensure_self_adjoint_and_pd else None, - is_positive_definite=True if ensure_self_adjoint_and_pd else None) - - matrix = tf.linalg.diag(diag) - - return operator, matrix - - def test_assert_positive_definite_raises_for_zero_eigenvalue(self): - # Matrix with one positive eigenvalue and one zero eigenvalue. - with self.cached_session(): - diag = [1.0, 0.0] - operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) - - # is_self_adjoint should be auto-set for real diag. - self.assertTrue(operator.is_self_adjoint) - with self.assertRaisesOpError("non-positive.*not positive definite"): - operator.assert_positive_definite().run() - - def test_assert_positive_definite_raises_for_negative_real_eigvalues(self): - with self.cached_session(): - diag_x = [1.0, -2.0] - diag_y = [0., 0.] # Imaginary eigenvalues should not matter. - diag = tf.dtypes.complex(diag_x, diag_y) - operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) - - # is_self_adjoint should not be auto-set for complex diag. - self.assertTrue(operator.is_self_adjoint is None) - with self.assertRaisesOpError("non-positive real.*not positive definite"): - operator.assert_positive_definite().run() - - def test_assert_positive_definite_does_not_raise_if_pd_and_complex(self): - with self.cached_session(): - x = [1., 2.] - y = [1., 0.] - diag = tf.dtypes.complex(x, y) # Re[diag] > 0. - operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) - # Should not fail - self.evaluate(operator.assert_positive_definite()) - - def test_assert_non_singular_raises_if_zero_eigenvalue(self): - # Singular matrix with one positive eigenvalue and one zero eigenvalue. - with self.cached_session(): - diag = [1.0, 0.0] - operator = linear_operator_diag_nd.LinearOperatorDiagND( - diag, is_self_adjoint=True) - with self.assertRaisesOpError("operator is singular"): - operator.assert_non_singular().run() - - def test_assert_non_singular_does_not_raise_for_complex_nonsingular(self): - with self.cached_session(): - x = [1., 0.] - y = [0., 1.] - diag = tf.dtypes.complex(x, y) - operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) - # Should not raise. - self.evaluate(operator.assert_non_singular()) - - def test_assert_self_adjoint_raises_if_diag_has_complex_part(self): - with self.cached_session(): - x = [1., 0.] - y = [0., 1.] - diag = tf.dtypes.complex(x, y) - operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) - with self.assertRaisesOpError("imaginary.*not self-adjoint"): - operator.assert_self_adjoint().run() - - def test_assert_self_adjoint_does_not_raise_for_diag_with_zero_imag(self): - with self.cached_session(): - x = [1., 0.] - y = [0., 0.] - diag = tf.dtypes.complex(x, y) - operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) - # Should not raise - self.evaluate(operator.assert_self_adjoint()) - - def test_scalar_diag_raises(self): - with self.assertRaisesRegex(ValueError, "must be at least 1-D"): - linear_operator_diag_nd.LinearOperatorDiagND(1.) - - def test_broadcast_matmul_and_solve(self): - # These cannot be done in the automated (base test class) tests since they - # test shapes that tf.matmul cannot handle. - # In particular, tf.matmul does not broadcast. - with self.cached_session() as sess: - x = tf.random.normal(shape=(2, 2, 3, 4)) - - # This LinearOperatorDiagND will be broadcast to (2, 2, 3, 3) during solve - # and matmul with 'x' as the argument. - diag = tf.random.uniform(shape=(2, 1, 3)) - operator = linear_operator_diag_nd.LinearOperatorDiagND( - diag, batch_dims=2, is_self_adjoint=True) - self.assertAllEqual((2, 1, 3, 3), operator.shape) - - # Create a batch matrix with the broadcast shape of operator. - diag_broadcast = tf.concat((diag, diag), 1) - mat = tf.linalg.diag(diag_broadcast) - self.assertAllEqual((2, 2, 3, 3), mat.shape) # being pedantic. - - operator_matmul = operator.matmul(x) - mat_matmul = tf.matmul(mat, x) - self.assertAllEqual(operator_matmul.shape, mat_matmul.shape) - self.assertAllClose(*self.evaluate([operator_matmul, mat_matmul])) - - operator_solve = operator.solve(x) - mat_solve = tf.linalg.solve(mat, x) - self.assertAllEqual(operator_solve.shape, mat_solve.shape) - self.assertAllClose(*self.evaluate([operator_solve, mat_solve])) - - def test_diag_matmul(self): - operator1 = linear_operator_diag_nd.LinearOperatorDiagND([2., 3.]) - operator2 = linear_operator_diag_nd.LinearOperatorDiagND([1., 2.]) - operator3 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=3.) - operator_matmul = operator1.matmul(operator2) - self.assertTrue(isinstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose([2., 6.], self.evaluate(operator_matmul.diag)) - - operator_matmul = operator2.matmul(operator1) - self.assertTrue(isinstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose([2., 6.], self.evaluate(operator_matmul.diag)) - - operator_matmul = operator1.matmul(operator3) - self.assertTrue(isinstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose([6., 9.], self.evaluate(operator_matmul.diag)) - - operator_matmul = operator3.matmul(operator1) - self.assertTrue(isinstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose([6., 9.], self.evaluate(operator_matmul.diag)) - - def test_diag_matmul_nd(self): - operator1 = linear_operator_diag_nd.LinearOperatorDiagND( - [[1., 2.], [3., 4.]]) - operator2 = linear_operator_diag_nd.LinearOperatorDiagND( - [1., 2.]) - operator3 = linear_operator_diag_nd.LinearOperatorDiagND( - [[1., 2.], [3., 4.]], batch_dims=1) - operator4 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=2.) - operator5 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=[1., 2., 3.]) - operator6 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2, 3], multiplier=-1.) - - operator_matmul = operator1.matmul(operator1) - self.assertIsInstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND) - self.assertAllClose( - [[1., 4.], [9., 16.]], self.evaluate(operator_matmul.diag)) - self.assertAllEqual([], operator_matmul.batch_shape) - - operator_matmul = operator1.matmul(operator2) - self.assertIsInstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND) - self.assertAllClose( - [[1., 4.], [3., 8.]], self.evaluate(operator_matmul.diag)) - self.assertAllEqual([], operator_matmul.batch_shape) - - operator_matmul = operator2.matmul(operator1) - self.assertIsInstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND) - self.assertAllClose( - [[1., 4.], [3., 8.]], self.evaluate(operator_matmul.diag)) - self.assertAllEqual([], operator_matmul.batch_shape) - - operator_matmul = operator2.matmul(operator3) - self.assertIsInstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND) - self.assertAllClose( - [[1., 4.], [3., 8.]], self.evaluate(operator_matmul.diag)) - self.assertAllEqual([2], operator_matmul.batch_shape) - - operator_matmul = operator1.matmul(operator3) - self.assertIsInstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND) - self.assertAllClose( - [[[1., 4.], [3., 8.]], [[3., 8.], [9., 16.]]], - self.evaluate(operator_matmul.diag)) - self.assertAllEqual([2], operator_matmul.batch_shape) - - operator_matmul = operator1.matmul(operator4) - self.assertTrue(isinstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose( - [[2., 4.], [6., 8.]], self.evaluate(operator_matmul.diag)) - self.assertAllEqual([2, 2], operator_matmul.domain_shape) - self.assertAllEqual([], operator_matmul.batch_shape) - - operator_matmul = operator4.matmul(operator1) - self.assertTrue(isinstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose( - [[2., 4.], [6., 8.]], self.evaluate(operator_matmul.diag)) - self.assertAllEqual([2, 2], operator_matmul.domain_shape) - self.assertAllEqual([], operator_matmul.batch_shape) - - operator_matmul = operator2.matmul(operator5) - self.assertTrue(isinstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose( - [[1., 2.], [2., 4.], [3., 6.]], self.evaluate(operator_matmul.diag)) - self.assertAllEqual([2], operator_matmul.domain_shape) - self.assertAllEqual([3], operator_matmul.batch_shape) - - operator_matmul = operator5.matmul(operator2) - self.assertTrue(isinstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose( - [[1., 2.], [2., 4.], [3., 6.]], self.evaluate(operator_matmul.diag)) - self.assertAllEqual([2], operator_matmul.domain_shape) - self.assertAllEqual([3], operator_matmul.batch_shape) - - operator_matmul = operator1.matmul(operator5) - self.assertTrue(isinstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose( - [[[1., 2.], [3., 4.]], [[2., 4.], [6., 8.]], [[3., 6.], [9., 12.]]], - self.evaluate(operator_matmul.diag)) - self.assertAllEqual([2, 2], operator_matmul.domain_shape) - self.assertAllEqual([3], operator_matmul.batch_shape) - - operator_matmul = operator5.matmul(operator1) - self.assertTrue(isinstance( - operator_matmul, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose( - [[[1., 2.], [3., 4.]], [[2., 4.], [6., 8.]], [[3., 6.], [9., 12.]]], - self.evaluate(operator_matmul.diag)) - self.assertAllEqual([2, 2], operator_matmul.domain_shape) - self.assertAllEqual([3], operator_matmul.batch_shape) - - with self.assertRaisesRegex(ValueError, "not broadcast-compatible"): - operator_matmul = operator1.matmul(operator6) - - with self.assertRaisesRegex(ValueError, "not broadcast-compatible"): - operator_matmul = operator6.matmul(operator1) - - def test_diag_solve(self): - operator1 = linear_operator_diag_nd.LinearOperatorDiagND( - [2., 3.], is_non_singular=True) - operator2 = linear_operator_diag_nd.LinearOperatorDiagND( - [1., 2.], is_non_singular=True) - operator3 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=3., is_non_singular=True) - operator_solve = operator1.solve(operator2) - self.assertTrue(isinstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose([0.5, 2 / 3.], self.evaluate(operator_solve.diag)) - - operator_solve = operator2.solve(operator1) - self.assertTrue(isinstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose([2., 3 / 2.], self.evaluate(operator_solve.diag)) - - operator_solve = operator1.solve(operator3) - self.assertTrue(isinstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose([3 / 2., 1.], self.evaluate(operator_solve.diag)) - - operator_solve = operator3.solve(operator1) - self.assertTrue(isinstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose([2 / 3., 1.], self.evaluate(operator_solve.diag)) - - def test_diag_solve_nd(self): - operator1 = linear_operator_diag_nd.LinearOperatorDiagND( - [[1., 2.], [3., 4.]]) - operator2 = linear_operator_diag_nd.LinearOperatorDiagND( - [1., 2.]) - operator3 = linear_operator_diag_nd.LinearOperatorDiagND( - [[1., 2.], [3., 4.]], batch_dims=1) - operator4 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=2.) - operator5 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=[1., 2., 3.]) - operator6 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2, 3], multiplier=-1.) - - operator_solve = operator1.solve(operator1) - self.assertIsInstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND) - self.assertAllClose( - [[1., 1.], [1., 1.]], self.evaluate(operator_solve.diag)) - self.assertAllEqual([], operator_solve.batch_shape) - - operator_solve = operator1.solve(operator2) - self.assertIsInstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND) - self.assertAllClose( - [[1., 1.], [1 / 3, 1 / 2]], self.evaluate(operator_solve.diag)) - self.assertAllEqual([], operator_solve.batch_shape) - - operator_solve = operator2.solve(operator1) - self.assertIsInstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND) - self.assertAllClose( - [[1., 1.], [3., 2.]], self.evaluate(operator_solve.diag)) - self.assertAllEqual([], operator_solve.batch_shape) - - operator_solve = operator2.solve(operator3) - self.assertIsInstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND) - self.assertAllClose( - [[1., 1.], [3., 2.]], self.evaluate(operator_solve.diag)) - self.assertAllEqual([2], operator_solve.batch_shape) - - operator_solve = operator1.solve(operator3) - self.assertIsInstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND) - self.assertAllClose( - [[[1., 1.], [1 / 3, 0.5]], [[3., 2.], [1., 1.]]], - self.evaluate(operator_solve.diag)) - self.assertAllEqual([2], operator_solve.batch_shape) - - operator_solve = operator1.solve(operator4) - self.assertTrue(isinstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose( - [[2., 1.], [2 / 3, 0.5]], self.evaluate(operator_solve.diag)) - self.assertAllEqual([2, 2], operator_solve.domain_shape) - self.assertAllEqual([], operator_solve.batch_shape) - - operator_solve = operator4.solve(operator1) - self.assertTrue(isinstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose( - [[0.5, 1.], [3 / 2, 4 / 2]], self.evaluate(operator_solve.diag)) - self.assertAllEqual([2, 2], operator_solve.domain_shape) - self.assertAllEqual([], operator_solve.batch_shape) - - operator_solve = operator1.solve(operator5) - self.assertTrue(isinstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose( - [[[1., 0.5], [1 / 3, 0.25]], - [[2., 1.], [2 / 3, 0.5]], - [[3., 3 / 2], [1., 0.75]]], - self.evaluate(operator_solve.diag)) - self.assertAllEqual([2, 2], operator_solve.domain_shape) - self.assertAllEqual([3], operator_solve.batch_shape) - - operator_solve = operator5.solve(operator1) - self.assertTrue(isinstance( - operator_solve, - linear_operator_diag_nd.LinearOperatorDiagND)) - self.assertAllClose( - [[[1., 2.], [3., 4.]], - [[0.5, 1.], [3 / 2, 2.]], - [[1 / 3, 2 / 3], [1., 4 / 3]]], - self.evaluate(operator_solve.diag)) - self.assertAllEqual([2, 2], operator_solve.domain_shape) - self.assertAllEqual([3], operator_solve.batch_shape) - - with self.assertRaisesRegex(ValueError, "not broadcast-compatible"): - operator_solve = operator1.solve(operator6) - - with self.assertRaisesRegex(ValueError, "not broadcast-compatible"): - operator_solve = operator6.solve(operator1) - - def test_diag_adjoint_type(self): - diag = [1., 3., 5., 8.] - operator = linear_operator_diag_nd.LinearOperatorDiagND( - diag, is_non_singular=True) - self.assertIsInstance( - operator.adjoint(), linear_operator_diag_nd.LinearOperatorDiagND) - - def test_diag_cholesky_type(self): - diag = [1., 3., 5., 8.] - operator = linear_operator_diag_nd.LinearOperatorDiagND( - diag, - is_positive_definite=True, - is_self_adjoint=True, - ) - self.assertIsInstance(operator.cholesky(), linear_operator_diag_nd.LinearOperatorDiagND) - - def test_diag_inverse_type(self): - diag = [1., 3., 5., 8.] - operator = linear_operator_diag_nd.LinearOperatorDiagND( - diag, is_non_singular=True) - self.assertIsInstance(operator.inverse(), - linear_operator_diag_nd.LinearOperatorDiagND) - - def test_tape_safe(self): - diag = tf.Variable([[2.]]) - operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) - self.check_tape_safe(operator) - - def test_convert_variables_to_tensors(self): - diag = tf.Variable([[2.]]) - operator = linear_operator_diag_nd.LinearOperatorDiagND(diag) - with self.cached_session() as sess: - sess.run([diag.initializer]) - self.check_convert_variables_to_tensors(operator) - - -linear_operator_test_util.add_tests(LinearOperatorDiagNDTest) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_diag_test.py b/tensorflow_mri/python/linalg/linear_operator_diag_test.py deleted file mode 100644 index a69cf54b..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_diag_test.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_diag`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_fft.py b/tensorflow_mri/python/linalg/linear_operator_fft.py deleted file mode 100644 index b93ecf84..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_fft.py +++ /dev/null @@ -1,257 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Fourier linear operator.""" - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.linalg import slicing -from tensorflow_mri.python.linalg import linear_operator_util -from tensorflow_mri.python.ops import fft_ops -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import tensor_util -from tensorflow_mri.python.util import types_util - - -@api_util.export("linalg.LinearOperatorFFT") -@linear_operator_nd.make_linear_operator_nd -class LinearOperatorFFT(linear_operator_nd.LinearOperatorND): - r"""Linear operator acting like a [batch] DFT matrix. - - If this operator is $A$, then $A x$ computes the Fourier transform of $x$, - while $A^H x$ computes the inverse Fourier transform of $x$. Note that the - inverse and the adjoint are equivalent, i.e. $A^H = A^{-1}$. - - The DFT matrix is never materialized internally. Instead matrix-matrix and - matrix-vector products are computed using the fast Fourier transform (FFT) - algorithm. - - This operator supports N-dimensional inputs, whose shape must be specified - through the `domain_shape` argument. This operator also acccepts an optional - `batch_shape` argument, which will be relevant for broadcasting purposes. - - This operator only supports complex inputs. Specify the desired type using - the `dtype` argument. - - ```{rubric} Performance - ``` - - `matvec` is $O(n \log{n})$. - - `solvevec` is $O(n \log{n})$. - - `lstsqvec` is equal to `solve`. - - ```{rubric} Matrix properties - ``` - - This operator is non-singular, i.e. $A^{-1}$ exists. - - This operator is not self-adjoint, i.e. $A^H \neq A$. - - This operator is square, i.e. $A \in \mathbb{F}^{n \times n}$. - - ```{rubric} Inversion - ``` - The inverse of this operator is equal to its adjoint, i.e., $A^{-1} = A^H$. - The linear system $Ax = b$ can be efficiently solved using `solve` or - `solvevec`. - - Example: - >>> # Create a 2-dimensional 128x128 DFT operator. - >>> linop = tfmri.linalg.LinearOperatorFFT(domain_shape=[128, 128]) - - Args: - domain_shape: A 1D integer `tf.Tensor`. The domain shape of the operator, - representing the shape of the inputs to `matvec`. - batch_shape: A 1D integer `tf.Tensor`. The batch shape of the operator. - Defaults to `None`, which is equivalent to `[]`. - dtype: A `tf.dtypes.DType`. Must be complex. Defaults to `complex64`. - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `True`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `False`. - is_positive_definite: A boolean, or `None`. Whether this operator is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that an operator [does - not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) - Defaults to `None`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `True`. - name: A `name`. The name to give to the ops created by this class. - """ - def __init__(self, - domain_shape, - batch_shape=None, - dtype=None, - is_non_singular=True, - is_self_adjoint=False, - is_positive_definite=None, - is_square=True, - name='LinearOperatorFFT'): - - parameters = dict( - domain_shape=domain_shape, - batch_shape=batch_shape, - dtype=dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name) - - dtype = dtype or tf.complex64 - - with tf.name_scope(name): - dtype = tf.dtypes.as_dtype(dtype) - if not is_non_singular: - raise ValueError("An FFT operator is always non-singular.") - if is_self_adjoint: - raise ValueError("An FFT operator is never self-adjoint.") - if not is_square: - raise ValueError("An FFT operator is always square.") - - # Get static/dynamic domain shape. - types_util.assert_not_ref_type(domain_shape, 'domain_shape') - self._domain_shape_static, self._domain_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) - if self._domain_shape_static.rank is None: - raise ValueError('domain_shape must have known static rank') - - # Get static/dynamic batch shape. - if batch_shape is not None: - types_util.assert_not_ref_type(batch_shape, 'batch_shape') - self._batch_shape_static, self._batch_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape(batch_shape)) - if self._batch_shape_static.rank is None: - raise ValueError('batch_shape must have known static rank') - else: - self._batch_shape_static = tf.TensorShape([]) - self._batch_shape_dynamic = tf.constant([], dtype=tf.int32) - - super().__init__(dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters, - name=name) - - def _matvec_nd(self, x, adjoint=False): - axes = list(range(-self.ndim, 0)) - - if adjoint: - x = fft_ops.ifftn(x, axes=axes, norm='ortho', shift=True) - else: - x = fft_ops.fftn(x, axes=axes, norm='ortho', shift=True) - - # For consistent broadcasting semantics. - if adjoint: - output_shape = self.domain_shape_tensor() - else: - output_shape = self.range_shape_tensor() - - if self.batch_shape.rank > 0: - x = tf.broadcast_to( - x, tf.concat([self.batch_shape_tensor(), output_shape], 0)) - - return x - - def _solvevec_nd(self, rhs, adjoint=False): - return self._matvec_nd(rhs, adjoint=(not adjoint)) - - def _lstsqvec_nd(self, rhs, adjoint=False): - return self._solvevec_nd(rhs, adjoint=adjoint) - - def _ndim(self): - return self.domain_shape.rank - - def _domain_shape(self): - return self._domain_shape_static - - def _range_shape(self): - return self._domain_shape_static - - def _batch_shape(self): - return self._batch_shape_static - - def _domain_shape_tensor(self): - return self._domain_shape_dynamic - - def _range_shape_tensor(self): - return self._domain_shape_dynamic - - def _batch_shape_tensor(self): - return self._batch_shape_dynamic - - @property - def _composite_tensor_fields(self): - return ('domain_shape', 'batch_shape', 'dtype') - - @property - def _composite_tensor_prefer_static_fields(self): - return ('domain_shape', 'batch_shape') - - @property - def _experimental_parameter_ndims_to_matrix_ndims(self): - return {} - - def __getitem__(self, slices): - # Support slicing. - new_batch_shape = tf.shape(tf.ones(self.batch_shape_tensor())[slices]) - return slicing.batch_slice( - self, params_overrides={'batch_shape': new_batch_shape}, slices=slices) - - -def dft_matrix(num_rows, - batch_shape=None, - dtype=tf.complex64, - shift=False, - name=None): - """Constructs a discrete Fourier transform (DFT) matrix. - - Args: - num_rows: A non-negative `int32` scalar `tf.Tensor` giving the number - of rows in each batch matrix. - batch_shape: A 1D integer `tf.Tensor`. If provided, the returned - `tf.Tensor` will have leading batch dimensions of this shape. - dtype: A `tf.dtypes.DType`. The type of an element in the resulting - `tf.Tensor`. Must be complex. Defaults to `tf.complex64`. - shift: A boolean. If `True`, returns the matrix for a DC-centred DFT. - name: A name for this op. - - Returns: - A `tf.Tensor` of shape `batch_shape + [num_rows, num_rows]` and type - `dtype` containing a DFT matrix. - """ - with tf.name_scope(name or "dft_matrix"): - num_rows = tf.convert_to_tensor(num_rows) - if batch_shape is not None: - batch_shape = tensor_util.convert_shape_to_tensor(batch_shape) - dtype = tf.dtypes.as_dtype(dtype) - if not dtype.is_complex: - raise TypeError(f"dtype must be complex, got {str(dtype)}") - - i = tf.range(num_rows, dtype=dtype.real_dtype) - omegas = tf.reshape( - tf.math.exp(tf.dtypes.complex( - tf.constant(0.0, dtype=dtype.real_dtype), - -2.0 * np.pi * i / tf.cast(num_rows, dtype.real_dtype))), [-1, 1]) - m = omegas ** tf.cast(i, dtype) - m /= tf.math.sqrt(tf.cast(num_rows, dtype)) - - if shift: - m = tf.signal.fftshift(m) - - if batch_shape is not None: - m = tf.broadcast_to(m, tf.concat([batch_shape, [num_rows, num_rows]], 0)) - - return m diff --git a/tensorflow_mri/python/linalg/linear_operator_fft_test.py b/tensorflow_mri/python/linalg/linear_operator_fft_test.py deleted file mode 100644 index 16892965..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_fft_test.py +++ /dev/null @@ -1,167 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for `LinearOperatorFFT`.""" - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_adjoint -from tensorflow_mri.python.linalg import linear_operator_fft -from tensorflow_mri.python.linalg import linear_operator_identity -from tensorflow_mri.python.linalg import linear_operator_test_util -from tensorflow_mri.python.util import test_util - - -rng = np.random.RandomState(2016) - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorFFTTest( - linear_operator_test_util.SquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - @staticmethod - def skip_these_tests(): - return [ - "cholesky", - "eigvalsh" - ] - - @staticmethod - def dtypes_to_test(): - return [tf.complex64, tf.complex128] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - del ensure_self_adjoint_and_pd - del use_placeholder - shape = list(build_info.shape) - assert shape[-1] == shape[-2] - - batch_shape = shape[:-2] - num_rows = shape[-1] - - operator = linear_operator_fft.LinearOperatorFFT( - domain_shape=[num_rows], batch_shape=batch_shape, dtype=dtype) - - matrix = linear_operator_fft.dft_matrix( - num_rows, batch_shape=batch_shape, dtype=dtype, shift=True) - - return operator, matrix - - def test_assert_self_adjoint(self): - with self.cached_session(): - operator = linear_operator_fft.LinearOperatorFFT(domain_shape=[4]) - with self.assertRaisesOpError("not equal to its adjoint"): - self.evaluate(operator.assert_self_adjoint()) - - def test_non_1d_domain_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be a 1-D"): - linear_operator_fft.LinearOperatorFFT(domain_shape=2) - - def test_non_integer_domain_shape_raises_static(self): - with self.assertRaisesRegex(TypeError, "must be integer"): - linear_operator_fft.LinearOperatorFFT(domain_shape=[2.]) - - def test_non_negative_domain_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be non-negative"): - linear_operator_fft.LinearOperatorFFT(domain_shape=[-2]) - - def test_unknown_rank_domain_shape_raises_static(self): - if tf.executing_eagerly(): - return - with self.cached_session(): - domain_shape = tf.compat.v1.placeholder_with_default([2], shape=None) - with self.assertRaisesRegex(ValueError, "must have known static rank"): - operator = linear_operator_fft.LinearOperatorFFT( - domain_shape=domain_shape) - self.evaluate(operator.to_dense()) - - def test_unknown_rank_batch_shape_raises_static(self): - if tf.executing_eagerly(): - return - with self.cached_session(): - batch_shape = tf.compat.v1.placeholder_with_default([2], shape=None) - with self.assertRaisesRegex(ValueError, "must have known static rank"): - operator = linear_operator_fft.LinearOperatorFFT( - domain_shape=[2], batch_shape=batch_shape) - self.evaluate(operator.to_dense()) - - def test_non_1d_batch_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be a 1-D"): - linear_operator_fft.LinearOperatorFFT(domain_shape=[2], batch_shape=2) - - def test_non_integer_batch_shape_raises_static(self): - with self.assertRaisesRegex(TypeError, "must be integer"): - linear_operator_fft.LinearOperatorFFT(domain_shape=[2], batch_shape=[2.]) - - def test_negative_batch_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be non-negative"): - linear_operator_fft.LinearOperatorFFT(domain_shape=[2], batch_shape=[-2]) - - def test_wrong_matrix_dimensions_raises_static(self): - operator = linear_operator_fft.LinearOperatorFFT(domain_shape=[2]) - x = rng.randn(3, 3).astype(np.complex64) - with self.assertRaisesRegex(ValueError, "Dimensions.*not compatible"): - operator.matmul(x) - - def test_is_x_flags(self): - operator = linear_operator_fft.LinearOperatorFFT(domain_shape=[2]) - self.assertTrue(operator.is_non_singular) - self.assertFalse(operator.is_self_adjoint) - self.assertTrue(operator.is_square) - - def test_inverse_type(self): - operator = linear_operator_fft.LinearOperatorFFT( - domain_shape=[4], is_non_singular=True) - self.assertIsInstance( - operator.inverse(), linear_operator_adjoint.LinearOperatorAdjoint) - self.assertIsInstance( - operator.inverse().operator, linear_operator_fft.LinearOperatorFFT) - - def test_identity_matmul(self): - operator1 = linear_operator_fft.LinearOperatorFFT(domain_shape=[2]) - operator2 = linear_operator_identity.LinearOperatorIdentity(num_rows=2) - self.assertIsInstance(operator1.matmul(operator2), - linear_operator_fft.LinearOperatorFFT) - self.assertIsInstance(operator2.matmul(operator1), - linear_operator_fft.LinearOperatorFFT) - - def test_ref_type_shape_args_raises(self): - with self.assertRaisesRegex(TypeError, "domain_shape.cannot.be.reference"): - linear_operator_fft.LinearOperatorFFT( - domain_shape=tf.Variable([2])) - - with self.assertRaisesRegex(TypeError, "batch_shape.cannot.be.reference"): - linear_operator_fft.LinearOperatorFFT( - domain_shape=[2], batch_shape=tf.Variable([2])) - - def test_matvec_nd(self): - for adjoint in (False, True): - with self.subTest(adjoint=adjoint): - operator = linear_operator_fft.LinearOperatorFFT(domain_shape=[4, 4]) - x = tf.constant(rng.randn(4, 4).astype(np.complex64)) - y = operator.matvec_nd(x, adjoint=adjoint) - fn = tf.signal.ifft2d if adjoint else tf.signal.fft2d - expected = tf.signal.fftshift(fn(tf.signal.ifftshift(x))) - expected = expected * 4 if adjoint else expected / 4 - self.assertAllClose(expected, y) - - -linear_operator_test_util.add_tests(LinearOperatorFFTTest) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_finite_difference.py b/tensorflow_mri/python/linalg/linear_operator_finite_difference.py deleted file mode 100644 index 66833b67..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_finite_difference.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Finite difference linear operator.""" - - -import tensorflow as tf - -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import check_util -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.util import tensor_util - - -@api_util.export("linalg.LinearOperatorFiniteDifference") -class LinearOperatorFiniteDifference(linear_operator.LinearOperator): # pylint: disable=abstract-method - """Linear operator representing a finite difference matrix. - - Args: - domain_shape: A 1D `tf.Tensor` or a `list` of `int`. The domain shape of - this linear operator. - axis: An `int`. The axis along which the finite difference is taken. - Defaults to -1. - dtype: A `tf.dtypes.DType`. The data type for this operator. Defaults to - `float32`. - name: A `str`. A name for this operator. - """ - def __init__(self, - domain_shape, - axis=-1, - dtype=tf.dtypes.float32, - name="LinearOperatorFiniteDifference"): - - parameters = dict( - domain_shape=domain_shape, - axis=axis, - dtype=dtype, - name=name - ) - - # Compute the static and dynamic shapes and save them for later use. - self._domain_shape_static, self._domain_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) - - # Validate axis and canonicalize to negative. This ensures the correct - # axis is selected in the presence of batch dimensions. - self.axis = check_util.validate_static_axes( - axis, self._domain_shape_static.rank, - min_length=1, - max_length=1, - canonicalize="negative", - scalar_to_list=False) - - # Compute range shape statically. The range has one less element along - # the difference axis than the domain. - range_shape_static = self._domain_shape_static.as_list() - if range_shape_static[self.axis] is not None: - range_shape_static[self.axis] -= 1 - range_shape_static = tf.TensorShape(range_shape_static) - self._range_shape_static = range_shape_static - - # Now compute dynamic range shape. First concatenate the leading axes with - # the updated difference dimension. Then, iff the difference axis is not - # the last one, concatenate the trailing axes. - range_shape_dynamic = self._domain_shape_dynamic - range_shape_dynamic = tf.concat([ - range_shape_dynamic[:self.axis], - [range_shape_dynamic[self.axis] - 1]], 0) - if self.axis != -1: - range_shape_dynamic = tf.concat([ - range_shape_dynamic, - range_shape_dynamic[self.axis + 1:]], 0) - self._range_shape_dynamic = range_shape_dynamic - - super().__init__(dtype, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None, - name=name, - parameters=parameters) - - def _transform(self, x, adjoint=False): - - if adjoint: - paddings1 = [[0, 0]] * x.shape.rank - paddings2 = [[0, 0]] * x.shape.rank - paddings1[self.axis] = [1, 0] - paddings2[self.axis] = [0, 1] - x1 = tf.pad(x, paddings1) # pylint: disable=no-value-for-parameter - x2 = tf.pad(x, paddings2) # pylint: disable=no-value-for-parameter - x = x1 - x2 - else: - slice1 = [slice(None)] * x.shape.rank - slice2 = [slice(None)] * x.shape.rank - slice1[self.axis] = slice(1, None) - slice2[self.axis] = slice(None, -1) - x1 = x[tuple(slice1)] - x2 = x[tuple(slice2)] - x = x1 - x2 - - return x - - def _domain_shape(self): - return self._domain_shape_static - - def _range_shape(self): - return self._range_shape_static - - def _domain_shape_tensor(self): - return self._domain_shape_dynamic - - def _range_shape_tensor(self): - return self._range_shape_dynamic diff --git a/tensorflow_mri/python/linalg/linear_operator_finite_difference_test.py b/tensorflow_mri/python/linalg/linear_operator_finite_difference_test.py deleted file mode 100644 index 6586b991..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_finite_difference_test.py +++ /dev/null @@ -1,81 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_finite_difference`.""" -# pylint: disable=missing-class-docstring,missing-function-docstring - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_finite_difference -from tensorflow_mri.python.util import test_util - - -class LinearOperatorFiniteDifferenceTest(test_util.TestCase): - """Tests for difference linear operator.""" - @classmethod - def setUpClass(cls): - super().setUpClass() - cls.linop1 = ( - linear_operator_finite_difference.LinearOperatorFiniteDifference([4])) - cls.linop2 = ( - linear_operator_finite_difference.LinearOperatorFiniteDifference( - [4, 4], axis=-2)) - cls.matrix1 = tf.convert_to_tensor([[-1, 1, 0, 0], - [0, -1, 1, 0], - [0, 0, -1, 1]], dtype=tf.float32) - - def test_transform(self): - """Test transform method.""" - signal = tf.random.normal([4, 4]) - result = self.linop2.transform(signal) - self.assertAllClose(result, np.diff(signal, axis=-2)) - - def test_matvec(self): - """Test matvec method.""" - signal = tf.constant([1, 2, 4, 8], dtype=tf.float32) - result = tf.linalg.matvec(self.linop1, signal) - self.assertAllClose(result, [1, 2, 4]) - self.assertAllClose(result, np.diff(signal)) - self.assertAllClose(result, tf.linalg.matvec(self.matrix1, signal)) - - signal2 = tf.range(16, dtype=tf.float32) - result = tf.linalg.matvec(self.linop2, signal2) - self.assertAllClose(result, [4] * 12) - - def test_matvec_adjoint(self): - """Test matvec with adjoint.""" - signal = tf.constant([1, 2, 4], dtype=tf.float32) - result = tf.linalg.matvec(self.linop1, signal, adjoint_a=True) - self.assertAllClose(result, - tf.linalg.matvec(tf.transpose(self.matrix1), signal)) - - def test_shapes(self): - """Test shapes.""" - self._test_all_shapes(self.linop1, [4], [3]) - self._test_all_shapes(self.linop2, [4, 4], [3, 4]) - - def _test_all_shapes(self, linop, domain_shape, range_shape): - """Test shapes.""" - self.assertIsInstance(linop.domain_shape, tf.TensorShape) - self.assertAllEqual(linop.domain_shape, domain_shape) - self.assertAllEqual(linop.domain_shape_tensor(), domain_shape) - - self.assertIsInstance(linop.range_shape, tf.TensorShape) - self.assertAllEqual(linop.range_shape, range_shape) - self.assertAllEqual(linop.range_shape_tensor(), range_shape) - - -if __name__ == '__main__': - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_full_matrix.py b/tensorflow_mri/python/linalg/linear_operator_full_matrix.py deleted file mode 100644 index 6fe1421a..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_full_matrix.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Full matrix linear operator.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import doc_util - - -LinearOperatorFullMatrix = api_util.export( - "linalg.LinearOperatorFullMatrix")( - doc_util.no_linkcode( - linear_operator.make_linear_operator( - tf.linalg.LinearOperatorFullMatrix))) - - -tf.linalg.LinearOperatorFullMatrix = LinearOperatorFullMatrix diff --git a/tensorflow_mri/python/linalg/linear_operator_full_matrix_test.py b/tensorflow_mri/python/linalg/linear_operator_full_matrix_test.py deleted file mode 100644 index 1d660f1b..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_full_matrix_test.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_full_matrix`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_gram_matrix.py b/tensorflow_mri/python/linalg/linear_operator_gram_matrix.py deleted file mode 100644 index 87eb1cff..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_gram_matrix.py +++ /dev/null @@ -1,151 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Gram matrix of a linear operator.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.linalg import linear_operator_addition_nd -from tensorflow_mri.python.linalg import linear_operator_composition -from tensorflow_mri.python.linalg import linear_operator_identity -from tensorflow_mri.python.util import api_util - - -@api_util.export("linalg.LinearOperatorGramMatrix") -class LinearOperatorGramMatrix(linear_operator.LinearOperator): # pylint: disable=abstract-method - r"""Linear operator representing the Gram matrix of an operator. - - If $A$ is a `LinearOperator`, this operator is equivalent to - $A^H A$. - - The Gram matrix of $A$ appears in the normal equation - $A^H A x = A^H b$ associated with the least squares problem - ${\mathop{\mathrm{argmin}}_x} {\left \| A x - b \right \|_2^2}$. - - - ```{rubric} Matrix properties - ``` - - This operator may or may not be non-singular. - - This operator is always self-adjoint. - - This operator is always positive definite. - - This operator is always square. - - This operator supports the optional addition of a regularization parameter - $\lambda$ and a transform matrix $T$. If these are provided, - this operator becomes $A^H A + \lambda T^H T$. This appears - in the regularized normal equation - $\left ( A^H A + \lambda T^H T \right ) x = A^H b + \lambda T^H T x_0$, - associated with the regularized least squares problem - ${\mathop{\mathrm{argmin}}_x} {\left \| Ax-b \right \|_2^2 + \lambda \left \| T(x-x_0) \right \|_2^2}$. - - Args: - operator: A `tfmri.linalg.LinearOperator`. The operator $A$ whose Gram - matrix is represented by this linear operator. - reg_parameter: A `Tensor` of shape `[B1, ..., Bb]` and real dtype. - The regularization parameter $\lambda$. Defaults to 0. - reg_operator: A `tfmri.linalg.LinearOperator`. The regularization transform - $T$. Defaults to the identity. - gram_operator: A `tfmri.linalg.LinearOperator`. The Gram matrix - $A^H A$. This may be optionally provided to use a specialized - Gram matrix implementation. Defaults to `None`. - is_non_singular: Expect that this operator is non-singular. - is_self_adjoint: Expect that this operator is equal to its Hermitian - transpose. - is_positive_definite: Expect that this operator is positive definite, - meaning the quadratic form $x^H A x$ has positive real part for all - nonzero $x$. Note that we do not require the operator to be - self-adjoint to be positive-definite. - is_square: Expect that this operator acts like square [batch] matrices. - name: A name for this `LinearOperator`. - """ - def __init__(self, - operator, - reg_parameter=None, - reg_operator=None, - gram_operator=None, - is_non_singular=None, - is_self_adjoint=True, - is_positive_definite=True, - is_square=True, - name=None): - parameters = dict( - operator=operator, - reg_parameter=reg_parameter, - reg_operator=reg_operator, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name) - self._operator = operator - self._reg_parameter = reg_parameter - self._reg_operator = reg_operator - self._gram_operator = gram_operator - if gram_operator is not None: - self._composed = gram_operator - else: - self._composed = linear_operator_composition.LinearOperatorComposition( - operators=[self._operator.H, self._operator]) - - if not is_self_adjoint: - raise ValueError("A Gram matrix is always self-adjoint.") - if not is_positive_definite: - raise ValueError("A Gram matrix is always positive-definite.") - if not is_square: - raise ValueError("A Gram matrix is always square.") - - if self._reg_parameter is not None: - reg_operator_gm = linear_operator_identity.LinearOperatorScaledIdentity( - domain_shape=self._operator.domain_shape, - multiplier=tf.cast(self._reg_parameter, self._operator.dtype)) - if self._reg_operator is not None: - reg_operator_gm = linear_operator_composition.LinearOperatorComposition( - operators=[reg_operator_gm, - self._reg_operator.H, - self._reg_operator]) - self._composed = linear_operator_addition_nd.LinearOperatorAddition( - operators=[self._composed, reg_operator_gm]) - - super().__init__(operator.dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters) - - def _transform(self, x, adjoint=False): - return self._composed.transform(x, adjoint=adjoint) - - def _domain_shape(self): - return self.operator.domain_shape - - def _range_shape(self): - return self.operator.domain_shape - - def _batch_shape(self): - return self.operator.batch_shape - - def _domain_shape_tensor(self): - return self.operator.domain_shape_tensor() - - def _range_shape_tensor(self): - return self.operator.domain_shape_tensor() - - def _batch_shape_tensor(self): - return self.operator.batch_shape_tensor() - - @property - def operator(self): - return self._operator diff --git a/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd.py b/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd.py deleted file mode 100644 index a2e2bf46..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd.py +++ /dev/null @@ -1,151 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Gram matrix of a linear operator.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.linalg import linear_operator_addition_nd -from tensorflow_mri.python.linalg import linear_operator_composition -from tensorflow_mri.python.linalg import linear_operator_identity -from tensorflow_mri.python.util import api_util - - -@api_util.export("linalg.LinearOperatorGramMatrix") -class LinearOperatorGramMatrix(linear_operator.LinearOperator): # pylint: disable=abstract-method - r"""Linear operator representing the Gram matrix of an operator. - - If $A$ is a `LinearOperator`, this operator is equivalent to - $A^H A$. - - The Gram matrix of $A$ appears in the normal equation - $A^H A x = A^H b$ associated with the least squares problem - ${\mathop{\mathrm{argmin}}_x} {\left \| A x - b \right \|_2^2}$. - - - ```{rubric} Matrix properties - ``` - - This operator may or may not be non-singular. - - This operator is always self-adjoint. - - This operator is always positive definite. - - This operator is always square. - - This operator supports the optional addition of a regularization parameter - $\lambda$ and a transform matrix $T$. If these are provided, - this operator becomes $A^H A + \lambda T^H T$. This appears - in the regularized normal equation - $\left ( A^H A + \lambda T^H T \right ) x = A^H b + \lambda T^H T x_0$, - associated with the regularized least squares problem - ${\mathop{\mathrm{argmin}}_x} {\left \| Ax-b \right \|_2^2 + \lambda \left \| T(x-x_0) \right \|_2^2}$. - - Args: - operator: A `tfmri.linalg.LinearOperator`. The operator $A$ whose Gram - matrix is represented by this linear operator. - reg_parameter: A `Tensor` of shape `[B1, ..., Bb]` and real dtype. - The regularization parameter $\lambda$. Defaults to 0. - reg_operator: A `tfmri.linalg.LinearOperator`. The regularization transform - $T$. Defaults to the identity. - gram_operator: A `tfmri.linalg.LinearOperator`. The Gram matrix - $A^H A$. This may be optionally provided to use a specialized - Gram matrix implementation. Defaults to `None`. - is_non_singular: Expect that this operator is non-singular. - is_self_adjoint: Expect that this operator is equal to its Hermitian - transpose. - is_positive_definite: Expect that this operator is positive definite, - meaning the quadratic form $x^H A x$ has positive real part for all - nonzero $x$. Note that we do not require the operator to be - self-adjoint to be positive-definite. - is_square: Expect that this operator acts like square [batch] matrices. - name: A name for this `LinearOperator`. - """ - def __init__(self, - operator, - reg_parameter=None, - reg_operator=None, - gram_operator=None, - is_non_singular=None, - is_self_adjoint=True, - is_positive_definite=True, - is_square=True, - name=None): - parameters = dict( - operator=operator, - reg_parameter=reg_parameter, - reg_operator=reg_operator, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name) - self._operator = operator - self._reg_parameter = reg_parameter - self._reg_operator = reg_operator - self._gram_operator = gram_operator - if gram_operator is not None: - self._composed = gram_operator - else: - self._composed = linear_operator_composition.LinearOperatorComposition( - operators=[self._operator.H, self._operator]) - - if not is_self_adjoint: - raise ValueError("A Gram matrix is always self-adjoint.") - if not is_positive_definite: - raise ValueError("A Gram matrix is always positive-definite.") - if not is_square: - raise ValueError("A Gram matrix is always square.") - - if self._reg_parameter is not None: - reg_operator_gm = linear_operator_identity.LinearOperatorScaledIdentity( - domain_shape=self._operator.domain_shape, - multiplier=tf.cast(self._reg_parameter, self._operator.dtype)) - if self._reg_operator is not None: - reg_operator_gm = linear_operator_composition.LinearOperatorComposition( - operators=[reg_operator_gm, - self._reg_operator.H, - self._reg_operator]) - self._composed = linear_operator_addition_nd.LinearOperatorAddition( - operators=[self._composed, reg_operator_gm]) - - super().__init__(operator.dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters) - - def _transform(self, x, adjoint=False): - return self._composed.transform(x, adjoint=adjoint) - - def _domain_shape(self): - return self.operator.domain_shape - - def _range_shape(self): - return self.operator.domain_shape - - def _batch_shape(self): - return self.operator.batch_shape - - def _domain_shape_tensor(self): - return self.operator.domain_shape_tensor() - - def _range_shape_tensor(self): - return self.operator.domain_shape_tensor() - - def _batch_shape_tensor(self): - return self.operator.batch_shape_tensor() - - @property - def operator(self): - return self._operator diff --git a/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd_test.py deleted file mode 100644 index d7327e24..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_gram_matrix_nd_test.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_gram_matrix_nd`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_gram_matrix_test.py b/tensorflow_mri/python/linalg/linear_operator_gram_matrix_test.py deleted file mode 100644 index e68f42a5..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_gram_matrix_test.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_gram_matrix`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_identity.py b/tensorflow_mri/python/linalg/linear_operator_identity.py deleted file mode 100644 index 78c0c6c0..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_identity.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""(Scaled) identity linear operators.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import doc_util - - -LinearOperatorIdentity = api_util.export( - "linalg.LinearOperatorIdentity")( - doc_util.no_linkcode( - linear_operator.make_linear_operator( - tf.linalg.LinearOperatorIdentity))) - - -LinearOperatorScaledIdentity = api_util.export( - "linalg.LinearOperatorScaledIdentity")( - doc_util.no_linkcode( - linear_operator.make_linear_operator( - tf.linalg.LinearOperatorScaledIdentity))) - - -tf.linalg.LinearOperatorIdentity = LinearOperatorIdentity -tf.linalg.LinearOperatorScaledIdentity = LinearOperatorScaledIdentity diff --git a/tensorflow_mri/python/linalg/linear_operator_identity_nd.py b/tensorflow_mri/python/linalg/linear_operator_identity_nd.py deleted file mode 100644 index 47329a72..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_identity_nd.py +++ /dev/null @@ -1,652 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""(Scaled) identity N-D linear operator.""" - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.ops import control_flow_ops -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import tensor_util -from tensorflow_mri.python.util import types_util - - -class BaseLinearOperatorIdentityND(linear_operator_nd.LinearOperatorND): - """Base class for Identity operators.""" - - def _check_domain_shape_possibly_add_asserts(self): - """Static check of init arg `domain_shape`, possibly add asserts.""" - # Possibly add asserts. - if self._assert_proper_shapes: - self._domain_shape_arg = tf.compat.v1.with_dependencies([ - tf.debugging.assert_rank( - self._domain_shape_arg, - 1, - message="Argument domain_shape must be a 1-D Tensor."), - tf.debugging.assert_non_negative( - self._domain_shape_arg, - message="Argument domain_shape must be non-negative."), - ], self._domain_shape_arg) - - # Static checks. - if not self._domain_shape_arg.dtype.is_integer: - raise TypeError(f"Argument domain_shape must be integer type. " - f"Found: {self._domain_shape_arg}") - - domain_shape_static = self._domain_shape_static - - if domain_shape_static is None: - return # Cannot do any other static checks. - - if domain_shape_static.ndim != 1: - raise ValueError(f"Argument domain_shape must be a 0-D Tensor. " - f"Found: {domain_shape_static}") - - if any(s is not None and s < 0 for s in domain_shape_static): - raise ValueError(f"Argument domain_shape must be non-negative. " - f"Found: {domain_shape_static}") - - def _ones_diag(self): - """Returns the diagonal of this operator as all ones.""" - if self.shape.is_fully_defined(): - diag_shape = self.batch_shape.concatenate([self.domain_dimension]) - else: - diag_shape = tf.concat( - [self.batch_shape_tensor(), - [self.domain_dimension_tensor()]], axis=0) - - return tf.ones(shape=diag_shape, dtype=self.dtype) - - def _check_compatible_input_shape(self, x): - """Check that an argument to solve/matmul has proper domain shape. - - Adds an assertion op to the graph is `assert_proper_shapes` is `True`. - - Args: - x: A `tf.Tensor`. - - Returns: - A `tf.Tensor` with asserted shape. - """ - # Static checks are done in the base class. Only tensor asserts here. - if self._assert_proper_shapes: - assert_compatible_shapes = tf.debugging.assert_equal( - tf.shape(x)[-self.domain_shape.rank:], - self.domain_shape_tensor(), - message="Shapes are incompatible.") - x = control_flow_ops.with_dependencies([assert_compatible_shapes], x) - return x - - -@api_util.export("linalg.LinearOperatorIdentityND") -@linear_operator_nd.make_linear_operator_nd -class LinearOperatorIdentityND(BaseLinearOperatorIdentityND): - r"""Linear operator acting like a [batch] square identity matrix. - - This operator acts like a batch of identity matrices - $A = I \in \mathbb{F}^{n \times n}$, where $\mathbb{F}$ may be $\mathbb{R}$ - or $\mathbb{C}$ and $n = n_0 \times n_1 \times \dots \times n_d$, where - $d$ is the number of dimensions in the domain. - - ```{note} - The matrix $A$ is not materialized. - ``` - - ```{seealso} - This operator is similar to `tfmri.linalg.LinearOperatorIdentity`, but - provides additional functionality to operate with multidimensional inputs. - ``` - - ```{rubric} Initialization - This operator is initialized with a `domain_shape`, which specifies the - sizes for the domain dimensions. There may be multiple domain dimensions, - which does not affect the dense matrix representation of this operator but - may be convenient to operate with non-vectorized multidimensional inputs. - This operator may also have a `batch_shape`, which will be relevant for the - purposes of broadcasting. Use the `dtype` argument to specify this - operator's data type. - - ```{rubric} Performance - ``` - - `matvec` is usually $O(1)$, but may be $O(n)$ if broadcasting is needed. - - `solvevec` is usually $O(1)$, but may be $O(n)$ if broadcasting is needed. - - `lstsqvec` is usually $O(1)$, but may be $O(n)$ if broadcasting is needed. - - ```{rubric} Properties - ``` - - This operator is always *non-singular*. - - This operator is always *self-adjoint*. - - This operator is always *positive definite*. - - This operator is always *square*. - - ```{rubric} Inversion - ``` - The inverse of this operator is equal to the operator itself ($A{-1} = A$). - - Example: - >>> # Create a 2-D identity operator. - >>> operator = tfmri.linalg.LinearOperatorIdentityND([2, 2]) - >>> operator.to_dense() - [[1., 0., 0., 0.], - [0., 1., 0., 0.] - [0., 0., 1., 0.], - [0., 0., 1., 0.]] - >>> operator.shape - (4, 4) - >>> x = tf.reshape(tf.range(4.), (2, 2)) - >>> rhs = operator.matvec_nd(x) - [[1., 2.], - [3., 4.]] - >>> operator.solvevec_nd(rhs) - [[1., 2.], - [3., 4.]] - - Args: - domain_shape: A 1-D non-negative integer `tf.Tensor`. The domain shape - of this operator. - batch_shape: A 1-D non-negative integer `tf.Tensor`. The leading batch - shape of this operator. If `None`, this operator has no - batch dimensions. - dtype: A `tf.dtypes.DType`. The data type of the matrix that this operator - represents. - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `True`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `True`. - is_positive_definite: A boolean, or `None`. Whether this operator is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that an operator [does - not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) - Defaults to `True`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `True`. - name: An optional `str`. The name of this operator. - """ - def __init__(self, - domain_shape, - batch_shape=None, - dtype=None, - is_non_singular=True, - is_self_adjoint=True, - is_positive_definite=True, - is_square=True, - assert_proper_shapes=False, - name="LinearOperatorIdentityND"): - parameters = dict( - domain_shape=domain_shape, - batch_shape=batch_shape, - dtype=dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - assert_proper_shapes=assert_proper_shapes, - name=name) - - dtype = dtype or tf.dtypes.float32 - self._assert_proper_shapes = assert_proper_shapes - - with tf.name_scope(name): - dtype = tf.dtypes.as_dtype(dtype) - if not is_self_adjoint: - raise ValueError("An identity operator is always self-adjoint.") - if not is_non_singular: - raise ValueError("An identity operator is always non-singular.") - if not is_positive_definite: - raise ValueError("An identity operator is always positive-definite.") - if not is_square: - raise ValueError("An identity operator is always square.") - - super().__init__( - dtype=dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters, - name=name) - - types_util.assert_not_ref_type(domain_shape, "domain_shape") - types_util.assert_not_ref_type(batch_shape, "batch_shape") - - self._domain_shape_static, self._domain_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape( - domain_shape, - assert_proper_shape=self._assert_proper_shapes, - arg_name="domain_shape")) - if self._domain_shape_static.rank is None: - raise ValueError("domain_shape must have known static rank") - - if batch_shape is None: - self._batch_shape_static = tf.TensorShape([]) - self._batch_shape_dynamic = tf.constant([], dtype=tf.int32) - else: - self._batch_shape_static, self._batch_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape( - batch_shape, - assert_proper_shape=self._assert_proper_shapes, - arg_name="batch_shape")) - - def _domain_shape(self): - return self._domain_shape_static - - def _range_shape(self): - return self._domain_shape_static - - def _batch_shape(self): - return self._batch_shape_static - - def _domain_shape_tensor(self): - return self._domain_shape_dynamic - - def _range_shape_tensor(self): - return self._domain_shape_dynamic - - def _batch_shape_tensor(self): - return self._batch_shape_dynamic - - def _assert_non_singular(self): - return tf.no_op("assert_non_singular") - - def _assert_positive_definite(self): - return tf.no_op("assert_positive_definite") - - def _assert_self_adjoint(self): - return tf.no_op("assert_self_adjoint") - - def _possibly_broadcast_batch_shape(self, x): - """Return 'x', possibly after broadcasting the leading dimensions.""" - # If we have no batch shape, our batch shape broadcasts with everything! - if self.batch_shape.rank == 0: - return x - - # Static attempt: - # If we determine that no broadcast is necessary, pass x through - # If we need a broadcast, add to an array of zeros. - # - # special_shape is the shape that, when broadcast with x's shape, will give - # the correct broadcast_shape. Note that - # We have already verified the second to last dimension of self.shape - # matches x's shape in _check_compatible_input_shape. - # Also, the final dimension of 'x' can have any shape. - # Therefore, the final two dimensions of special_shape are ones. - special_shape = self.batch_shape.concatenate([1] * self.domain_shape.rank) - bcast_shape = tf.broadcast_static_shape(x.shape, special_shape) - if special_shape.is_fully_defined(): - if bcast_shape == x.shape: - # Input already has correct shape. Broadcasting is not necessary. - return x - # Use the built in broadcasting of addition. - zeros = tf.zeros(shape=special_shape, dtype=self.dtype) - return x + zeros - - # Dynamic broadcast: - # Always add to an array of zeros, rather than using a "cond", since a - # cond would require copying data from GPU --> CPU. - special_shape = tf.concat( - [self.batch_shape_tensor(), [1] * self.domain_shape.rank], 0) - zeros = tf.zeros(shape=special_shape, dtype=self.dtype) - return x + zeros - - def _matvec_nd(self, x, adjoint=False): - # Note that adjoint has no effect since this matrix is self-adjoint. - x = self._check_compatible_input_shape(x) - return self._possibly_broadcast_batch_shape(x) - - def _solvevec_nd(self, rhs, adjoint=False): - return self._matvec_nd(rhs) - - def _lstsqvec_nd(self, rhs, adjoint=False): - return self._matvec_nd(rhs) - - def _determinant(self): - return tf.ones(shape=self.batch_shape_tensor(), dtype=self.dtype) - - def _log_abs_determinant(self): - return tf.zeros(shape=self.batch_shape_tensor(), dtype=self.dtype) - - def _trace(self): - if self.batch_shape.is_fully_defined(): - ones = tf.ones(shape=self.batch_shape, dtype=self.dtype) - else: - ones = tf.ones(shape=self.batch_shape_tensor(), dtype=self.dtype) - - return ones * tf.cast(self.domain_dimension_tensor(), self.dtype) - - def _diag_part(self): - return self._ones_diag() - - def add_to_tensor(self, mat, name="add_to_tensor"): - """Add matrix represented by this operator to `mat`. Equiv to `I + mat`. - - Args: - mat: A `tf.Tensor` with same `dtype` and shape broadcastable to `self`. - name: A name to give this `Op`. - - Returns: - A `tf.Tensor` with broadcast shape and same `dtype` as `self`. - """ - with self._name_scope(name): # pylint: disable=not-callable - mat = tf.convert_to_tensor(mat, name="mat") - mat_diag = tf.linalg.diag_part(mat) - new_diag = 1 + mat_diag - return tf.linalg.set_diag(mat, new_diag) - - def _eigvals(self): - return self._ones_diag() - - def _cond(self): - return tf.ones(self.batch_shape_tensor(), dtype=self.dtype) - - def _to_dense(self): - return tf.eye( - num_rows=self.domain_dimension_tensor(), - batch_shape=self.batch_shape_tensor(), - dtype=self.dtype) - - @property - def _composite_tensor_prefer_static_fields(self): - return ("domain_shape", "batch_shape") - - @property - def _composite_tensor_fields(self): - return ("domain_shape", "batch_shape", "dtype", "assert_proper_shapes") - - def __getitem__(self, slices): - # Slice the batch shape and return a new LinearOperatorIdentity. - # Use a proxy tensor and slice it. Use this as the new batch shape. - new_batch_shape = tf.shape(tf.ones(self._batch_shape_dynamic)[slices]) - parameters = dict(self.parameters, batch_shape=new_batch_shape) - return LinearOperatorIdentityND(**parameters) - - -@api_util.export("linalg.LinearOperatorScaledIdentityND") -@linear_operator_nd.make_linear_operator_nd -class LinearOperatorScaledIdentityND(BaseLinearOperatorIdentityND): - r"""Linear operator acting like a scaled [batch] identity matrix. - - This operator acts like a batch of scaled identity matrices - $A = \lambda I \in \mathbb{F}^{n \times n}$, where $\lambda$ is a scaling - constant, $\mathbb{F}$ may be $\mathbb{R}$ or $\mathbb{C}$ and - $n = n_0 \times n_1 \times \dots \times n_d$, where - $d$ is the number of dimensions in the domain. - - ```{note} - The matrix $A$ is not materialized. - ``` - - ```{seealso} - This operator is similar to `tfmri.linalg.LinearOperatorScaledIdentityND`, - but provides additional functionality to operate with multidimensional - inputs. - ``` - - ```{rubric} Initialization - This operator is initialized with a `domain_shape`, which specifies the - sizes for the domain dimensions, and a `multiplier`, which specifies the - scaling constant $\lambda$. `domain_shape` may have multiple dimensions, - which does not affect the dense matrix representation of this operator but - may be convenient to operate with non-vectorized multidimensional inputs. - This operator has the same data type as `multiplier`. - - ```{rubric} Performance - ``` - - `matvec` is $O(n)$. - - `solvevec` is $O(n)$. - - `lstsqvec` is $O(n)$. - - ```{rubric} Properties - ``` - - This operator is *non-singular* iff multiplier is non-zero. - - This operator is *self-adjoint* iff multiplier is real or has zero - imaginary part. - - This operator is *positive definite* iff multiplier has positive real part. - - This operator is always *square*. - - ```{rubric} Inversion - ``` - If this operator is non-singular, its inverse $A^{-1}$ is also a scaled - identity operator with reciprocal multiplier. - - Example: - >>> # Create a 2-D identity operator. - >>> operator = tfmri.linalg.LinearOperatorIdentityND([2, 2]) - >>> operator.to_dense() - [[1., 0., 0., 0.], - [0., 1., 0., 0.] - [0., 0., 1., 0.], - [0., 0., 1., 0.]] - >>> operator.shape - (4, 4) - >>> x = tf.reshape(tf.range(4.), (2, 2)) - >>> rhs = operator.matvec_nd(x) - [[1., 2.], - [3., 4.]] - >>> operator.solvevec_nd(rhs) - [[1., 2.], - [3., 4.]] - - Args: - domain_shape: A 1-D non-negative integer `tf.Tensor`. The domain shape - of this operator. - multiplier: A real or complex `tf.Tensor` of any shape specifying the - scaling constant for the identity matrix. - dtype: A `tf.dtypes.DType`. The data type of the matrix that this operator - represents. - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `None`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `None`. - is_positive_definite: A boolean, or `None`. Whether this operator is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that an operator [does - not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) - Defaults to `None`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `True`. - name: An optional `str`. The name of this operator. - """ - def __init__(self, - domain_shape, - multiplier, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=True, - assert_proper_shapes=False, - name="LinearOperatorScaledIdentityND"): - parameters = dict( - domain_shape=domain_shape, - multiplier=multiplier, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - assert_proper_shapes=assert_proper_shapes, - name=name) - - self._assert_proper_shapes = assert_proper_shapes - - with tf.name_scope(name): - # Check domain_shape. - types_util.assert_not_ref_type(domain_shape, "domain_shape") - self._domain_shape_static, self._domain_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape( - domain_shape, - assert_proper_shape=self._assert_proper_shapes, - arg_name="domain_shape")) - if self._domain_shape_static.rank is None: - raise ValueError("domain_shape must have known static rank") - - # Check multiplier. - self._multiplier = types_util.convert_nonref_to_tensor( - multiplier, name="multiplier") - - # Check and auto-set hints. - if not self._multiplier.dtype.is_complex: - if is_self_adjoint is False: # pylint: disable=g-bool-id-comparison - raise ValueError( - "A real scaled identity operator is always self adjoint.") - is_self_adjoint = True - - if not is_square: - raise ValueError("A scaled identity operator is always square.") - - super().__init__( - dtype=self._multiplier.dtype.base_dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters, - name=name) - - def _domain_shape(self): - return self._domain_shape_static - - def _range_shape(self): - return self._domain_shape_static - - def _batch_shape(self): - return self._multiplier.shape - - def _domain_shape_tensor(self): - return self._domain_shape_dynamic - - def _range_shape_tensor(self): - return self._domain_shape_dynamic - - def _batch_shape_tensor(self): - return tf.shape(self._multiplier) - - def _assert_non_singular(self): - return tf.debugging.assert_positive( - tf.math.abs(self.multiplier), - message=("Scaled identity operator is singular: " - "multiplier contains zero entries.")) - - def _assert_positive_definite(self): - if self.dtype.is_complex: - message = ("Scaled identity operator is not positive definite: " - "multiplier contains entries with non-positive real part.") - else: - message = ("Scaled identity operator is not positive definite: " - "multiplier contains non-positive entries.") - return tf.debugging.assert_positive( - tf.math.real(self.multiplier), message=message) - - def _assert_self_adjoint(self): - if not self.dtype.is_complex: - # A real scaled identity operator is always self-adjoint. - return tf.no_op("assert_self_adjoint") - imag_multiplier = tf.math.imag(self.multiplier) - return tf.debugging.assert_equal( - tf.zeros_like(imag_multiplier), - imag_multiplier, - message=("Scaled identity operator is not self-adjoint: " - "multiplier contains entries with non-zero imaginary part.")) - - def _matvec_nd(self, x, adjoint=False): - x = self._check_compatible_input_shape(x) - return x * self._make_multiplier_matrix(adjoint=adjoint) - - def _solvevec_nd(self, rhs, adjoint=False): - rhs = self._check_compatible_input_shape(rhs) - return rhs / self._make_multiplier_matrix(adjoint=adjoint) - - def _lstsqvec_nd(self, rhs, adjoint=False): - return self._solvevec_nd(rhs, adjoint=adjoint) - - def _make_multiplier_matrix(self, adjoint=False): - multiplier_matrix = tf.reshape( - self.multiplier, - tf.concat([tf.shape(self.multiplier), [1] * self.domain_shape.rank], 0)) - multiplier_matrix = tf.ensure_shape( - multiplier_matrix, self.multiplier.shape.concatenate( - [1] * self.domain_shape.rank)) - if adjoint: - multiplier_matrix = tf.math.conj(multiplier_matrix) - return multiplier_matrix - - def _determinant(self): - return self.multiplier ** tf.cast( - self.domain_dimension_tensor(), self.dtype) - - def _log_abs_determinant(self): - return (tf.math.log(tf.math.abs(self.multiplier)) * - tf.cast(self.domain_dimension_tensor(), self.dtype.real_dtype)) - - def _trace(self): - return self.multiplier * tf.cast(self.domain_dimension_tensor(), self.dtype) - - def _diag_part(self): - return self._ones_diag() * self.multiplier[..., tf.newaxis] - - def add_to_tensor(self, mat, name="add_to_tensor"): - """Add matrix represented by this operator to `mat`. Equiv to `I + mat`. - - Args: - mat: `Tensor` with same `dtype` and shape broadcastable to `self`. - name: A name to give this `Op`. - - Returns: - A `Tensor` with broadcast shape and same `dtype` as `self`. - """ - with self._name_scope(name): # pylint: disable=not-callable - # Shape [B1,...,Bb, 1] - multiplier_vector = tf.expand_dims(self.multiplier, -1) - # Shape [C1,...,Cc, M, M] - mat = tf.convert_to_tensor(mat, name="mat") - # Shape [C1,...,Cc, M] - mat_diag = tf.linalg.diag_part(mat) - # multiplier_vector broadcasts here. - new_diag = multiplier_vector + mat_diag - return tf.linalg.set_diag(mat, new_diag) - - def _eigvals(self): - return self._ones_diag() * self.multiplier[..., tf.newaxis] - - def _cond(self): - # Condition number for a scalar time identity matrix is one, except when the - # scalar is zero. - return tf.where( - tf.math.equal(self._multiplier, 0.), - tf.cast(np.nan, dtype=self.dtype), - tf.cast(1., dtype=self.dtype)) - - def _to_dense(self): - return self.multiplier[..., tf.newaxis, tf.newaxis] * tf.eye( - num_rows=self.domain_dimension_tensor(), - dtype=self.dtype) - - @property - def multiplier(self): - """The [batch] scalar `tf.Tensor`, $c$ in $cI$.""" - return self._multiplier - - @property - def _composite_tensor_prefer_static_fields(self): - return ("domain_shape",) - - @property - def _composite_tensor_fields(self): - return ("domain_shape", "multiplier", "assert_proper_shapes") - - @property - def _experimental_parameter_ndims_to_matrix_ndims(self): - return {"multiplier": 0} diff --git a/tensorflow_mri/python/linalg/linear_operator_identity_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_identity_nd_test.py deleted file mode 100644 index 67ea95d5..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_identity_nd_test.py +++ /dev/null @@ -1,619 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_identity_nd -from tensorflow_mri.python.linalg import linear_operator_test_util -from tensorflow_mri.python.util import test_util - - -rng = np.random.RandomState(2016) - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorIdentityNDTest( - linear_operator_test_util.SquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - - def tearDown(self): - tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) - - def setUp(self): - self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() - tf.config.experimental.enable_tensor_float_32_execution(False) - - @staticmethod - def dtypes_to_test(): - # TODO(langmore) Test tf.float16 once tf.linalg.solve works in - # 16bit. - return [tf.float32, tf.float64, tf.complex64, tf.complex128] - - @staticmethod - def optional_tests(): - """List of optional test names to run.""" - return [ - "operator_matmul_with_same_type", - "operator_solve_with_same_type", - ] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - # Identity matrix is already Hermitian Positive Definite. - del ensure_self_adjoint_and_pd - - shape = list(build_info.shape) - assert shape[-1] == shape[-2] - - batch_shape = shape[:-2] - num_rows = shape[-1] - - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - [num_rows], batch_shape=batch_shape, dtype=dtype) - mat = tf.eye(num_rows, batch_shape=batch_shape, dtype=dtype) - - return operator, mat - - def test_assert_positive_definite(self): - with self.cached_session(): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2]) - self.evaluate(operator.assert_positive_definite()) # Should not fail - - def test_assert_non_singular(self): - with self.cached_session(): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2]) - self.evaluate(operator.assert_non_singular()) # Should not fail - - def test_assert_self_adjoint(self): - with self.cached_session(): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2]) - self.evaluate(operator.assert_self_adjoint()) # Should not fail - - def test_float16_matmul(self): - # float16 cannot be tested by base test class because tf.linalg.solve does - # not work with float16. - with self.cached_session(): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2], dtype=tf.float16) - x = rng.randn(2, 3).astype(np.float16) - y = operator.matmul(x) - self.assertAllClose(x, self.evaluate(y)) - - def test_non_1d_domain_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be a 1-D"): - linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=2) - - def test_non_integer_domain_shape_raises_static(self): - with self.assertRaisesRegex(TypeError, "must be integer"): - linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=[2.]) - - def test_negative_domain_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be non-negative"): - linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=[-2]) - - def test_non_1d_batch_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be a 1-D"): - linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2], batch_shape=2) - - def test_non_integer_batch_shape_raises_static(self): - with self.assertRaisesRegex(TypeError, "must be integer"): - linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2], batch_shape=[2.]) - - def test_negative_batch_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be non-negative"): - linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2], batch_shape=[-2]) - - def test_unknown_domain_shape_rank_raises_static(self): - if tf.executing_eagerly(): - return - with self.cached_session(): - domain_shape = tf.compat.v1.placeholder_with_default([2], shape=None) - with self.assertRaisesRegex(ValueError, "must have known static rank"): - linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape) - - def test_negative_domain_shape_raises_dynamic(self): - with self.cached_session(): - domain_shape = tf.compat.v1.placeholder_with_default([-2], shape=[1]) - with self.assertRaisesError("must be non-negative"): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape, - assert_proper_shapes=True) - self.evaluate(operator.to_dense()) - - def test_negative_batch_shape_raises_dynamic(self): - with self.cached_session(): - batch_shape = tf.compat.v1.placeholder_with_default([-2], shape=[1]) - with self.assertRaisesError("must be non-negative"): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2], - batch_shape=batch_shape, - assert_proper_shapes=True) - self.evaluate(operator.to_dense()) - - def test_wrong_matrix_dimensions_raises_static(self): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2]) - x = rng.randn(3, 3).astype(np.float32) - with self.assertRaisesRegex(ValueError, "Dimensions.*not compatible"): - operator.matmul(x) - - def test_wrong_matrix_dimensions_nd_raises_static(self): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2]) - x = rng.randn(3,).astype(np.float32) - with self.assertRaisesRegex(ValueError, "Shapes.*incompatible"): - operator.matvec_nd(x) - - def test_wrong_matrix_dimensions_nd_raises_dynamic(self): - domain_shape = tf.compat.v1.placeholder_with_default([2], shape=[1]) - x = tf.compat.v1.placeholder_with_default( - rng.rand(3,).astype(np.float32), shape=None) - - with self.cached_session(): - with self.assertRaisesError("Shapes.*incompatible"): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape, - assert_proper_shapes=True) - self.evaluate(operator.matvec_nd(x)) - - def test_default_batch_shape_broadcasts_with_everything_static(self): - # These cannot be done in the automated (base test class) tests since they - # test shapes that tf.batch_matmul cannot handle. - # In particular, tf.batch_matmul does not broadcast. - with self.cached_session() as sess: - x = tf.random.normal(shape=(1, 2, 3, 4)) - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[3], dtype=x.dtype) - - operator_matmul = operator.matmul(x) - expected = x - - self.assertAllEqual(operator_matmul.shape, expected.shape) - self.assertAllClose(*self.evaluate([operator_matmul, expected])) - - def test_default_batch_shape_broadcasts_with_everything_dynamic(self): - # These cannot be done in the automated (base test class) tests since they - # test shapes that tf.batch_matmul cannot handle. - # In particular, tf.batch_matmul does not broadcast. - with self.cached_session(): - x = tf.compat.v1.placeholder_with_default(rng.randn(1, 2, 3, 4), shape=None) - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[3], dtype=x.dtype) - - operator_matmul = operator.matmul(x) - expected = x - - self.assertAllClose(*self.evaluate([operator_matmul, expected])) - - def test_broadcast_matmul_static_shapes(self): - # These cannot be done in the automated (base test class) tests since they - # test shapes that tf.batch_matmul cannot handle. - # In particular, tf.batch_matmul does not broadcast. - with self.cached_session() as sess: - # Given this x and LinearOperatorIdentityND shape of (2, 1, 3, 3), the - # broadcast shape of operator and 'x' is (2, 2, 3, 4) - x = tf.random.normal(shape=(1, 2, 3, 4)) - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=(3,), batch_shape=(2, 1), dtype=x.dtype) - - # Batch matrix of zeros with the broadcast shape of x and operator. - zeros = tf.zeros(shape=(2, 2, 3, 4), dtype=x.dtype) - - # Expected result of matmul and solve. - expected = x + zeros - - operator_matmul = operator.matmul(x) - self.assertAllEqual(operator_matmul.shape, expected.shape) - self.assertAllClose(*self.evaluate([operator_matmul, expected])) - - def test_broadcast_matmul_dynamic_shapes(self): - # These cannot be done in the automated (base test class) tests since they - # test shapes that tf.batch_matmul cannot handle. - # In particular, tf.batch_matmul does not broadcast. - with self.cached_session(): - # Given this x and LinearOperatorIdentityND shape of (2, 1, 3, 3), the - # broadcast shape of operator and 'x' is (2, 2, 3, 4) - x = tf.compat.v1.placeholder_with_default( - rng.rand(1, 2, 3, 4), shape=None) - domain_shape = tf.compat.v1.placeholder_with_default((3,), shape=(1,)) - batch_shape = tf.compat.v1.placeholder_with_default((2, 1), shape=(2,)) - - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape, batch_shape=batch_shape, dtype=tf.float64) - - # Batch matrix of zeros with the broadcast shape of x and operator. - zeros = tf.zeros(shape=(2, 2, 3, 4), dtype=x.dtype) - - # Expected result of matmul and solve. - expected = x + zeros - - operator_matmul = operator.matmul(x) - self.assertAllClose(*self.evaluate([operator_matmul, expected])) - - def test_is_x_flags(self): - # The is_x flags are by default all True. - operator = linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=[2]) - self.assertTrue(operator.is_positive_definite) - self.assertTrue(operator.is_non_singular) - self.assertTrue(operator.is_self_adjoint) - - # Any of them False raises because the identity is always self-adjoint etc.. - with self.assertRaisesRegex(ValueError, "is always non-singular"): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2], is_non_singular=None) - - def test_identity_adjoint_type(self): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2], is_non_singular=True) - self.assertIsInstance( - operator.adjoint(), linear_operator_identity_nd.LinearOperatorIdentityND) - - def test_identity_cholesky_type(self): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2], - is_positive_definite=True, - is_self_adjoint=True, - ) - self.assertIsInstance( - operator.cholesky(), - linear_operator_identity_nd.LinearOperatorIdentityND) - - def test_identity_inverse_type(self): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2], is_non_singular=True) - self.assertIsInstance( - operator.inverse(), linear_operator_identity_nd.LinearOperatorIdentityND) - - def test_ref_type_shape_args_raises(self): - with self.assertRaisesRegex(TypeError, "domain_shape.*reference"): - linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=tf.Variable([2])) - - with self.assertRaisesRegex(TypeError, "batch_shape.*reference"): - linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=[2], batch_shape=tf.Variable([3])) - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorScaledIdentityNDTest( - linear_operator_test_util.SquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - - def tearDown(self): - tf.config.experimental.enable_tensor_float_32_execution(self.tf32_keep_) - - def setUp(self): - self.tf32_keep_ = tf.config.experimental.tensor_float_32_execution_enabled() - tf.config.experimental.enable_tensor_float_32_execution(False) - - @staticmethod - def dtypes_to_test(): - # TODO(langmore) Test tf.float16 once tf.linalg.solve works in - # 16bit. - return [tf.float32, tf.float64, tf.complex64, tf.complex128] - - @staticmethod - def optional_tests(): - """List of optional test names to run.""" - return [ - "operator_matmul_with_same_type", - "operator_solve_with_same_type", - ] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - - shape = list(build_info.shape) - assert shape[-1] == shape[-2] - - batch_shape = shape[:-2] - num_rows = shape[-1] - - # Uniform values that are at least length 1 from the origin. Allows the - # operator to be well conditioned. - # Shape batch_shape - multiplier = linear_operator_test_util.random_sign_uniform( - shape=batch_shape, minval=1., maxval=2., dtype=dtype) - - if ensure_self_adjoint_and_pd: - multiplier = tf.cast(tf.math.abs(multiplier), dtype=dtype) - - # Nothing to feed since LinearOperatorScaledIdentityND takes no Tensor args. - lin_op_multiplier = multiplier - - if use_placeholder: - lin_op_multiplier = tf.compat.v1.placeholder_with_default( - multiplier, shape=None) - - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - [num_rows], - lin_op_multiplier, - is_self_adjoint=True if ensure_self_adjoint_and_pd else None, - is_positive_definite=True if ensure_self_adjoint_and_pd else None) - - multiplier_matrix = tf.expand_dims( - tf.expand_dims(multiplier, -1), -1) - matrix = multiplier_matrix * tf.eye( - num_rows, batch_shape=batch_shape, dtype=dtype) - - return operator, matrix - - def test_assert_positive_definite_does_not_raise_when_positive(self): - with self.cached_session(): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=1.) - self.evaluate(operator.assert_positive_definite()) # Should not fail - - def test_assert_positive_definite_raises_when_negative(self): - with self.cached_session(): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=-1.) - with self.assertRaisesOpError("operator is not positive definite"): - self.evaluate(operator.assert_positive_definite()) - - def test_assert_non_singular_does_not_raise_when_non_singular(self): - with self.cached_session(): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=[1., 2., 3.]) - self.evaluate(operator.assert_non_singular()) # Should not fail - - def test_assert_non_singular_raises_when_singular(self): - with self.cached_session(): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=[1., 2., 0.]) - with self.assertRaisesOpError("operator is singular"): - self.evaluate(operator.assert_non_singular()) - - def test_assert_self_adjoint_does_not_raise_when_self_adjoint(self): - with self.cached_session(): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=[1. + 0J]) - self.evaluate(operator.assert_self_adjoint()) # Should not fail - - def test_assert_self_adjoint_raises_when_not_self_adjoint(self): - with self.cached_session(): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=[1. + 1J]) - with self.assertRaisesOpError("operator is not self-adjoint"): - self.evaluate(operator.assert_self_adjoint()) - - def test_float16_matmul(self): - # float16 cannot be tested by base test class because tf.linalg.solve does - # not work with float16. - with self.cached_session(): - multiplier = rng.rand(3).astype(np.float16) - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=multiplier) - x = rng.randn(2, 3).astype(np.float16) - y = operator.matmul(x) - self.assertAllClose(multiplier[..., None, None] * x, self.evaluate(y)) - - def test_non_1d_domain_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be a 1-D"): - linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=2, multiplier=123.) - - def test_wrong_matrix_dimensions_raises_static(self): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=2.2) - x = rng.randn(3, 3).astype(np.float32) - with self.assertRaisesRegex(ValueError, "Dimensions.*not compatible"): - operator.matmul(x) - - def test_wrong_matrix_dimensions_nd_raises_static(self): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=2.2) - x = rng.randn(3,).astype(np.float32) - with self.assertRaisesRegex(ValueError, "Shapes.*incompatible"): - operator.matvec_nd(x) - - def test_wrong_matrix_dimensions_nd_raises_dynamic(self): - domain_shape = tf.compat.v1.placeholder_with_default([2], shape=[1]) - x = tf.compat.v1.placeholder_with_default( - rng.rand(3,).astype(np.float32), shape=None) - - with self.cached_session(): - with self.assertRaisesError("Shapes.*incompatible"): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape, - multiplier=[1., 2], - assert_proper_shapes=True) - self.evaluate(operator.matvec_nd(x)) - - def test_broadcast_matmul_and_solve(self): - # These cannot be done in the automated (base test class) tests since they - # test shapes that tf.batch_matmul cannot handle. - # In particular, tf.batch_matmul does not broadcast. - with self.cached_session() as sess: - # Given this x and LinearOperatorScaledIdentityND shape of (2, 1, 3, 3), the - # broadcast shape of operator and 'x' is (2, 2, 3, 4) - x = tf.random.normal(shape=(1, 2, 3, 4)) - - # operator is 2.2 * identity (with a batch shape). - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[3], multiplier=2.2 * tf.ones((2, 1))) - - # Batch matrix of zeros with the broadcast shape of x and operator. - zeros = tf.zeros(shape=(2, 2, 3, 4), dtype=x.dtype) - - # Test matmul - expected = x * 2.2 + zeros - operator_matmul = operator.matmul(x) - self.assertAllEqual(operator_matmul.shape, expected.shape) - self.assertAllClose(*self.evaluate([operator_matmul, expected])) - - # Test solve - expected = x / 2.2 + zeros - operator_solve = operator.solve(x) - self.assertAllEqual(operator_solve.shape, expected.shape) - self.assertAllClose(*self.evaluate([operator_solve, expected])) - - def test_broadcast_matmul_and_solve_scalar_scale_multiplier(self): - # These cannot be done in the automated (base test class) tests since they - # test shapes that tf.batch_matmul cannot handle. - # In particular, tf.batch_matmul does not broadcast. - with self.cached_session() as sess: - # Given this x and LinearOperatorScaledIdentityND shape of (3, 3), the - # broadcast shape of operator and 'x' is (1, 2, 3, 4), which is the same - # shape as x. - x = tf.random.normal(shape=(1, 2, 3, 4)) - - # operator is 2.2 * identity (with a batch shape). - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[3], multiplier=2.2) - - # Test matmul - expected = x * 2.2 - operator_matmul = operator.matmul(x) - self.assertAllEqual(operator_matmul.shape, expected.shape) - self.assertAllClose(*self.evaluate([operator_matmul, expected])) - - # Test solve - expected = x / 2.2 - operator_solve = operator.solve(x) - self.assertAllEqual(operator_solve.shape, expected.shape) - self.assertAllClose(*self.evaluate([operator_solve, expected])) - - def test_is_x_flags(self): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=1., - is_positive_definite=False, is_non_singular=True) - self.assertFalse(operator.is_positive_definite) - self.assertTrue(operator.is_non_singular) - self.assertTrue(operator.is_self_adjoint) # Auto-set due to real multiplier - - def test_identity_matmul(self): - operator1 = linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=[2]) - operator2 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=3.) - self.assertIsInstance( - operator1.matmul(operator1), - linear_operator_identity_nd.LinearOperatorIdentityND) - - self.assertIsInstance( - operator1.matmul(operator1), - linear_operator_identity_nd.LinearOperatorIdentityND) - - self.assertIsInstance( - operator2.matmul(operator2), - linear_operator_identity_nd.LinearOperatorScaledIdentityND) - - operator_matmul = operator1.matmul(operator2) - self.assertIsInstance( - operator_matmul, - linear_operator_identity_nd.LinearOperatorScaledIdentityND) - self.assertAllClose(3., self.evaluate(operator_matmul.multiplier)) - - operator_matmul = operator2.matmul(operator1) - self.assertIsInstance( - operator_matmul, - linear_operator_identity_nd.LinearOperatorScaledIdentityND) - self.assertAllClose(3., self.evaluate(operator_matmul.multiplier)) - - def test_identity_solve(self): - operator1 = linear_operator_identity_nd.LinearOperatorIdentityND(domain_shape=[2]) - operator2 = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=3.) - self.assertIsInstance( - operator1.solve(operator1), - linear_operator_identity_nd.LinearOperatorIdentityND) - - self.assertIsInstance( - operator2.solve(operator2), - linear_operator_identity_nd.LinearOperatorScaledIdentityND) - - operator_solve = operator1.solve(operator2) - self.assertIsInstance( - operator_solve, - linear_operator_identity_nd.LinearOperatorScaledIdentityND) - self.assertAllClose(3., self.evaluate(operator_solve.multiplier)) - - operator_solve = operator2.solve(operator1) - self.assertIsInstance( - operator_solve, - linear_operator_identity_nd.LinearOperatorScaledIdentityND) - self.assertAllClose(1. / 3., self.evaluate(operator_solve.multiplier)) - - def test_scaled_identity_cholesky_type(self): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], - multiplier=3., - is_positive_definite=True, - is_self_adjoint=True, - ) - self.assertIsInstance( - operator.cholesky(), - linear_operator_identity_nd.LinearOperatorScaledIdentityND) - - def test_scaled_identity_inverse_type(self): - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], - multiplier=3., - is_non_singular=True, - ) - self.assertIsInstance( - operator.inverse(), - linear_operator_identity_nd.LinearOperatorScaledIdentityND) - - def test_ref_type_shape_args_raises(self): - with self.assertRaisesRegex(TypeError, "domain_shape.*reference"): - linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=tf.Variable([2]), multiplier=1.23) - - def test_tape_safe(self): - multiplier = tf.Variable(1.23) - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=multiplier) - self.check_tape_safe(operator) - - def test_convert_variables_to_tensors(self): - multiplier = tf.Variable(1.23) - operator = linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=[2], multiplier=multiplier) - with self.cached_session() as sess: - sess.run([multiplier.initializer]) - self.check_convert_variables_to_tensors(operator) - - -linear_operator_test_util.add_tests(LinearOperatorIdentityNDTest) -linear_operator_test_util.add_tests(LinearOperatorScaledIdentityNDTest) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_identity_test.py b/tensorflow_mri/python/linalg/linear_operator_identity_test.py deleted file mode 100644 index ea3c2416..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_identity_test.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for `LinearOperatorIdentity` and `LinearOperatorScaledIdentity`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_inversion.py b/tensorflow_mri/python/linalg/linear_operator_inversion.py deleted file mode 100644 index c918bda1..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_inversion.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Adjoint of a linear operator.""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.linalg import linear_operator_util -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import doc_util - - -LinearOperatorInversion = api_util.export( - "linalg.LinearOperatorInversion")( - doc_util.no_linkcode( - linear_operator.make_linear_operator( - tf.linalg.LinearOperatorInversion))) - - -tf.linalg.LinearOperatorInversion = LinearOperatorInversion diff --git a/tensorflow_mri/python/linalg/linear_operator_inversion_test.py b/tensorflow_mri/python/linalg/linear_operator_inversion_test.py deleted file mode 100644 index 912b4049..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_inversion_test.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for `LinearOperatorInversion`.""" diff --git a/tensorflow_mri/python/linalg/linear_operator_mask.py b/tensorflow_mri/python/linalg/linear_operator_mask.py deleted file mode 100644 index 2709a6b0..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_mask.py +++ /dev/null @@ -1,259 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Masking linear operator.""" - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.util import api_util - - -@api_util.export("linalg.LinearOperatorMask") -@linear_operator_nd.make_linear_operator_nd -class LinearOperatorMask(linear_operator_nd.LinearOperatorND): - r"""Linear operator acting like a [batch] masking matrix. - - Represents a diagonal matrix $A \in \mathbb{F}^{n \times n}$ whose diagonal - entries are either one or zero. This operator is useful for masking out - certain entries in a vector or matrix. - - ```{tip} - You can use this operator to mask *k*-space values in undersampled Cartesian - MRI. - ``` - - ```{rubric} Performance - ``` - - `matvec` is $O(n)$. - - `solvevec` is not supported. - - `lstsqvec` is $O(n)$. - - ```{rubric} Properties - ``` - - This operator is singular, i.e. $A^{-1}$ does not exist. - - This operator is self-adjoint, i.e. $A^H = A$. - - This operator is not positive definite, i.e. $x^H A x <= 0$ for some $x$. - - This operator is square, i.e. $A \in \mathbb{F}^{n \times n}$. - - ```{rubric} Inversion - ``` - In general, the masking operator is singular and cannot be inverted, so - `solve` and `inverse` will raise an error. - - However, you can use `lstsq` or `pseudo_inverse` to solve the associated - least-squares problem. The pseudo-inverse of the masking operator is the - operator itself, i.e., $A^+ = A$. - - Example: - >>> mask = [True, False, True, False] - >>> linop = tfmri.linalg.LinearOperatorMask(mask) - >>> x = tf.constant([1., 2., 3., 4.]) - >>> y = linop.matvec_nd(x) - >>> y.numpy() - array([1., 0., 3., 0.]) - - Args: - mask: A boolean `tf.Tensor` of shape `[..., *spatial_shape]`. - batch_dims: An `int`, the number of batch dimensions in `mask`. - dtype: The `dtype` of the operator. Must be float or complex. If `None`, - defaults to `float32`. - algorithm: A `str`, one of `'multiply'` or `'multiplex'`. The algorithm to - use for masking. - - `'multiply'` (default) applies the mask by multiplying each value in - the input tensor by either one or zero. This is often faster, although - this depends on the specific problem and your hardware. - - `'multiplex'` applies the mask by using the input mask as a condition - and multiplexing the input with a zero tensor. See `tf.where` for more - details. - ```{attention} - The IEEE 754 standard for floating-point arithmetic has a - [signed zero](https://en.wikipedia.org/wiki/Signed_zero). When using - `'multiply'`, the zeroed out values will be positive zero for positive - inputs and negative zero for negative inputs. Therefore, the `'multiply'` - algorithm leaks sign information. If this is a concern in your - application, use `'multiplex'` instead. - ``` - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `False`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `True`. - is_positive_definite: A boolean, or `None`. Whether this operator is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that an operator [does - not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) - Defaults to `False`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `True`. - name: An optional `str`. The name of this operator. - """ - def __init__(self, - mask, - batch_dims=0, - dtype=None, - algorithm='multiply', - is_non_singular=False, - is_self_adjoint=True, - is_positive_definite=False, - is_square=True, - name='LinearOperatorMask'): - parameters = dict( - mask=mask, - batch_dims=batch_dims, - dtype=dtype, - algorithm=algorithm, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name - ) - - with tf.name_scope(name): - if dtype is None: - dtype = tf.float32 - dtype = tf.dtypes.as_dtype(dtype) - if not dtype.is_floating and not dtype.is_complex: - raise TypeError(f"dtype must be float or complex, got {str(dtype)}") - - self._batch_dims = np.asarray(tf.get_static_value(batch_dims)) - if (not self._batch_dims.ndim == 0 or - not np.issubdtype(self._batch_dims.dtype, np.integer)): - raise TypeError( - f"batch_dims must be an int, but got: {batch_dims}") - self._batch_dims = self._batch_dims.item() - if self._batch_dims < 0: - raise ValueError( - f"batch_dims must be non-negative, but got: {batch_dims}") - - self._mask = tf.convert_to_tensor(mask, name="mask") - if not self._mask.dtype.is_bool: - raise TypeError( - f"mask must be boolean, but got dtype: {str(self._mask.dtype)}") - if self._mask.shape.rank is None: - raise ValueError("mask must have known static rank") - self._ndim_static = self._mask.shape.rank - self._batch_dims - if self._ndim_static < 1: - raise ValueError( - f"mask must be at least 1-D (excluding batch dimensions), " - f"but got shape: {self._mask.shape}") - - if algorithm not in {'multiply', 'multiplex'}: - raise ValueError( - f"algorithm must be one of 'multiply' or 'multiplex', " - f"but got: {algorithm}") - if algorithm == 'multiply': - self._mask_mult = tf.cast(self._mask, dtype) - self._algorithm = algorithm - - if not is_self_adjoint: - raise ValueError("A mask operator is always self-adjoint.") - if is_non_singular: - raise ValueError("A mask operator is always singular.") - if is_positive_definite: - raise ValueError("A mask operator is never positive definite.") - if not is_square: - raise ValueError("A mask operator is always square.") - - super().__init__( - dtype=dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters, - name=name) - - def _matvec_nd(self, x, adjoint=False): - # This operator is self-adjoint, so we can ignore the adjoint argument. - if self._algorithm == 'multiply': - x = x * self._mask_mult - elif self._algorithm == 'multiplex': - x = tf.where(self._mask, x, tf.zeros_like(x)) - else: - raise ValueError(f"Unknown masking algorithm: {self._algorithm}") - return x - - def _solvevec_nd(self, rhs, adjoint=False): - raise ValueError( - f"{self.name} is not invertible. If you intend to solve the " - f"associated least-squares problem, use `lstsq`, `lstsqvec` or " - f"`lstsqvec_nd`.") - - def _lstsqvec_nd(self, rhs, adjoint=False): - # The value of adjoint is irrelevant, but be pedantic. - return self._matvec_nd(rhs, adjoint=(not adjoint)) - - def _ndim(self): - return self._ndim_static - - def _domain_shape(self): - return self._mask.shape[self._batch_dims:] - - def _range_shape(self): - return self._mask.shape[self._batch_dims:] - - def _batch_shape(self): - return self._mask.shape[:self._batch_dims] - - def _domain_shape_tensor(self): - return tf.shape(self._mask)[self._batch_dims:] - - def _range_shape_tensor(self): - return tf.shape(self._mask)[self._batch_dims:] - - def _batch_shape_tensor(self): - return tf.shape(self._mask)[:self._batch_dims] - - @property - def mask(self): - return self._mask - - @property - def _composite_tensor_fields(self): - return ('mask', 'batch_dims', 'dtype', 'algorithm') - - @property - def _composite_tensor_prefer_static_fields(self): - return ('batch_dims',) - - @property - def _experimental_parameter_ndims_to_matrix_ndims(self): - return {'mask': self.ndim} - - -def mask_matrix(mask, batch_dims=0, dtype=None): - """Constructs a masking matrix. - - Args: - mask: A complex `tf.Tensor` of shape `[..., *spatial_shape]`. - batch_dims: An `int`, the number of batch dimensions in `mask`. - - Returns: - A `tf.Tensor` representing a dense coil array matrix equivalent to - `LinearOperatorMask`. - """ - mask = tf.convert_to_tensor(mask, name="mask") - mask = tf.cast(mask, dtype or tf.float32) - - # Vectorize N-D mask. - mask = tf.reshape( - mask, tf.concat([tf.shape(mask)[:batch_dims], [-1]], axis=0)) - - # Construct a [batch] diagonal matrix. - matrix = tf.linalg.diag(mask) - - return matrix diff --git a/tensorflow_mri/python/linalg/linear_operator_mask_test.py b/tensorflow_mri/python/linalg/linear_operator_mask_test.py deleted file mode 100644 index f518de90..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_mask_test.py +++ /dev/null @@ -1,212 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_mask`.""" -# pylint: disable=missing-class-docstring,missing-function-docstring - -import functools - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_mask -from tensorflow_mri.python.linalg import linear_operator_test_util -from tensorflow_mri.python.util import test_util - - -rng = np.random.RandomState(2016) - - -class OperatorShapesInfoCoils(): - def __init__(self, image_shape, batch_shape): - self.image_shape = image_shape - self.batch_shape = batch_shape - - @property - def shape(self): - n = functools.reduce(lambda a, b: a * b, self.image_shape) - return self.batch_shape + (n, n) - - @property - def dimension(self): - return len(self.image_shape) - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorMaskMultiplyTest( - linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - - @staticmethod - def operator_shapes_infos(): - shapes_info = OperatorShapesInfoCoils - return [ - shapes_info((2, 2), ()), - shapes_info((2, 4), (3,)), - shapes_info((4, 2), (1, 2)), - shapes_info((2, 3), ()), - shapes_info((2, 2, 2), ()), - shapes_info((4, 2, 2), (2,)) - # TODO(jmontalt): odd shapes fail tests, investigate - # shapes_info((2, 3), 5, (2,)), - # shapes_info((3, 2), 7, ()) - ] - - @staticmethod - def dtypes_to_test(): - return [tf.float32, tf.float64, tf.complex64, tf.complex128] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - del ensure_self_adjoint_and_pd - del use_placeholder - - batch_shape = build_info.batch_shape - image_shape = build_info.image_shape - - mask = tf.random.uniform(shape=batch_shape + image_shape) > 0.5 - - operator = linear_operator_mask.LinearOperatorMask( - mask=mask, batch_dims=len(batch_shape), dtype=dtype, - algorithm='multiply') - - matrix = linear_operator_mask.mask_matrix( - mask=mask, batch_dims=len(batch_shape), dtype=dtype) - - return operator, matrix - - def test_0d_mask_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be at least 1-D"): - linear_operator_mask.LinearOperatorMask( - mask=np.ones(()).astype(np.bool_)) - - with self.assertRaisesRegex(ValueError, "must be at least 1-D"): - linear_operator_mask.LinearOperatorMask( - mask=np.ones((4, 4)).astype(np.bool_), - batch_dims=2) - - linear_operator_mask.LinearOperatorMask( - mask=np.ones((4, 4)).astype(np.bool_), - batch_dims=1) # should not raise - - def test_non_bool_mask_raises_static(self): - with self.assertRaisesRegex(TypeError, "must be boolean"): - linear_operator_mask.LinearOperatorMask( - mask=np.ones((4, 4)).astype(np.float32)) - - def test_unknown_rank_mask_raises_static(self): - if tf.executing_eagerly(): - return - with self.cached_session(): - mask = tf.compat.v1.placeholder_with_default( - np.ones((3, 4, 4)).astype(np.bool_), shape=None) - with self.assertRaisesRegex(ValueError, "must have known static rank"): - operator = linear_operator_mask.LinearOperatorMask(mask=mask) - self.evaluate(operator.to_dense()) - - def test_non_integer_batch_dims_raises_static(self): - with self.assertRaisesRegex(TypeError, "must be an int"): - linear_operator_mask.LinearOperatorMask( - mask=np.ones((3, 4, 4)).astype(np.bool_), batch_dims=1.) - - def test_negative_batch_dims_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be non-negative"): - linear_operator_mask.LinearOperatorMask( - mask=np.ones((3, 4, 4)).astype(np.bool_), batch_dims=-1) - - def test_is_x_flags(self): - operator = linear_operator_mask.LinearOperatorMask( - mask=np.ones((3, 4, 4)).astype(np.bool_)) - self.assertTrue(operator.is_self_adjoint) - self.assertFalse(operator.is_non_singular) - self.assertTrue(operator.is_square) - - def test_solve_raises(self): - operator = linear_operator_mask.LinearOperatorMask( - mask=np.ones((1, 4, 4)).astype(np.bool_), is_square=True) - with self.assertRaisesRegex(NotImplementedError, "singular"): - operator.solve(tf.ones([16, 1], dtype=tf.bool)) - - def test_inverse_raises(self): - operator = linear_operator_mask.LinearOperatorMask( - mask=np.ones((1, 4, 4)).astype(np.bool_), is_square=True) - with self.assertRaisesRegex(ValueError, "singular"): - operator.inverse() - - def test_adjoint_type(self): - operator = linear_operator_mask.LinearOperatorMask( - mask=np.ones((3, 4)).astype(np.bool_)) - self.assertIsInstance( - operator.adjoint(), linear_operator_mask.LinearOperatorMask) - - def test_convert_variables_to_tensors(self): - mask = tf.Variable(np.ones((3, 4, 4)).astype(np.bool_)) - operator = linear_operator_mask.LinearOperatorMask(mask=mask) - with self.cached_session() as sess: - sess.run([mask.initializer]) - self.check_convert_variables_to_tensors(operator) - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorMaskMultiplexTest( - linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - - @staticmethod - def operator_shapes_infos(): - shapes_info = OperatorShapesInfoCoils - return [ - shapes_info((2, 2), ()), - shapes_info((2, 4), (3,)), - shapes_info((4, 2), (1, 2)), - shapes_info((2, 3), ()), - shapes_info((2, 2, 2), ()), - shapes_info((4, 2, 2), (2,)) - # TODO(jmontalt): odd shapes fail tests, investigate - # shapes_info((2, 3), 5, (2,)), - # shapes_info((3, 2), 7, ()) - ] - - @staticmethod - def dtypes_to_test(): - return [tf.float32, tf.float64, tf.complex64, tf.complex128] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - del ensure_self_adjoint_and_pd - del use_placeholder - - batch_shape = build_info.batch_shape - image_shape = build_info.image_shape - - mask = tf.random.uniform(shape=batch_shape + image_shape) > 0.5 - - operator = linear_operator_mask.LinearOperatorMask( - mask=mask, batch_dims=len(batch_shape), dtype=dtype, - algorithm='multiplex') - - matrix = linear_operator_mask.mask_matrix( - mask=mask, batch_dims=len(batch_shape), dtype=dtype) - - return operator, matrix - - -linear_operator_test_util.add_tests(LinearOperatorMaskMultiplyTest) -linear_operator_test_util.add_tests(LinearOperatorMaskMultiplexTest) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_mri.py b/tensorflow_mri/python/linalg/linear_operator_mri.py deleted file mode 100644 index 5f0cfe91..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_mri.py +++ /dev/null @@ -1,812 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""MRI linear operator.""" - -import warnings - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_nufft -from tensorflow_mri.python.ops import fft_ops -from tensorflow_mri.python.ops import math_ops -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import check_util -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.util import tensor_util - - -_WARNED_IGNORED_BATCH_DIMENSIONS = {} - - -@api_util.export("linalg.LinearOperatorMRI") -@linear_operator.make_composite_tensor -class LinearOperatorMRI(linear_operator.LinearOperator): # pylint: disable=abstract-method - r"""Linear operator acting like an MRI measurement system. - - The MRI operator, $A$, maps a [batch of] images, $x$ to a - [batch of] measurement data (*k*-space), $b$. - - $$ - A x = b - $$ - - This object may represent an undersampled MRI operator and supports - Cartesian and non-Cartesian *k*-space sampling. The user may provide a - sampling `mask` to represent an undersampled Cartesian operator, or a - `trajectory` to represent a non-Cartesian operator. - - This object may represent a multicoil MRI operator by providing coil - `sensitivities`. Note that `mask`, `trajectory` and `density` should never - have a coil dimension, including in the case of multicoil imaging. The coil - dimension will be handled automatically. - - The domain shape of this operator is `extra_shape + image_shape`. The range - of this operator is `extra_shape + [num_coils] + image_shape`, for - Cartesian imaging, or `extra_shape + [num_coils] + [num_samples]`, for - non-Cartesian imaging. `[num_coils]` is optional and only present for - multicoil operators. This operator supports batches of images and will - vectorize operations when possible. - - Args: - image_shape: A 1D integer `tf.Tensor`. The shape of the images - that this operator acts on. Must have length 2 or 3. - extra_shape: An optional 1D integer `tf.Tensor`. Additional - dimensions that should be included within the operator domain. Note that - `extra_shape` is not needed to reconstruct independent batches of images. - However, it is useful when this operator is used as part of a - reconstruction that performs computation along non-spatial dimensions, - e.g. for temporal regularization. Defaults to `None`. - mask: An optional `tf.Tensor` of type `tf.bool`. The sampling mask. Must - have shape `[..., *S]`, where `S` is the `image_shape` and `...` is - the batch shape, which can have any number of dimensions. If `mask` is - passed, this operator represents an undersampled MRI operator. - trajectory: An optional `tf.Tensor` of type `float32` or `float64`. Must - have shape `[..., M, N]`, where `N` is the rank (number of spatial - dimensions), `M` is the number of samples in the encoded space and `...` - is the batch shape, which can have any number of dimensions. If - `trajectory` is passed, this operator represents a non-Cartesian MRI - operator. - density: An optional `tf.Tensor` of type `float32` or `float64`. The - sampling densities. Must have shape `[..., M]`, where `M` is the number of - samples and `...` is the batch shape, which can have any number of - dimensions. This input is only relevant for non-Cartesian MRI operators. - If passed, the non-Cartesian operator will include sampling density - compensation. If `None`, the operator will not perform sampling density - compensation. - sensitivities: An optional `tf.Tensor` of type `complex64` or `complex128`. - The coil sensitivity maps. Must have shape `[..., C, *S]`, where `S` - is the `image_shape`, `C` is the number of coils and `...` is the batch - shape, which can have any number of dimensions. - phase: An optional `tf.Tensor` of type `float32` or `float64`. A phase - estimate for the image. If provided, this operator will be - phase-constrained. - fft_norm: FFT normalization mode. Must be `None` (no normalization) - or `'ortho'`. Defaults to `'ortho'`. - sens_norm: A `boolean`. Whether to normalize coil sensitivities. Defaults to - `True`. - intensity_correction: A `boolean`. Whether to correct for overall receiver - coil sensitivity. Defaults to `True`. Has no effect if `sens_norm` is also - `True`. - dynamic_domain: A `str`. The domain of the dynamic dimension, if present. - Must be one of `'time'` or `'frequency'`. May only be provided together - with a non-scalar `extra_shape`. The dynamic dimension is the last - dimension of `extra_shape`. The `'time'` mode (default) should be - used for regular dynamic reconstruction. The `'frequency'` mode should be - used for reconstruction in x-f space. - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `None`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `None`. - is_positive_definite: A boolean, or `None`. Whether this operators is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that we do not require - the operator to be self-adjoint to be positive-definite. See: - https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices. - Defaults to `None`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `None`. - dtype: A `tf.dtypes.DType`. The dtype of this operator. Must be `complex64` - or `complex128`. Defaults to `complex64`. - name: An optional `str`. The name of this operator. - """ - def __init__(self, - image_shape, - extra_shape=None, - mask=None, - trajectory=None, - density=None, - sensitivities=None, - phase=None, - fft_norm='ortho', - sens_norm=True, - intensity_correction=True, - dynamic_domain=None, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None, - dtype=tf.complex64, - name=None): - # pylint: disable=invalid-unary-operand-type - parameters = dict( - image_shape=image_shape, - extra_shape=extra_shape, - mask=mask, - trajectory=trajectory, - density=density, - sensitivities=sensitivities, - phase=phase, - fft_norm=fft_norm, - sens_norm=sens_norm, - intensity_correction=intensity_correction, - dynamic_domain=dynamic_domain, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - dtype=dtype, - name=name) - super().__init__(dtype=dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name, - parameters=parameters) - - # Set dtype. - dtype = tf.as_dtype(dtype) - if dtype not in (tf.complex64, tf.complex128): - raise ValueError( - f"`dtype` must be `complex64` or `complex128`, but got: {str(dtype)}") - - # Batch dimensions in `image_shape` and `extra_shape` are not supported. - # However, it is convenient to allow them to have batch dimensions anyway. - # This helps when this operator is used in Keras models, where all inputs - # may be automatically batched. If there are any batch dimensions, we simply - # ignore them by taking the first element. The first time this happens - # we also emit a warning. - image_shape = self._ignore_batch_dims_in_shape(image_shape, "image_shape") - extra_shape = self._ignore_batch_dims_in_shape(extra_shape, "extra_shape") - - # Set image shape, rank and extra shape. - self._image_shape_static, self._image_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape(image_shape)) - self._rank = self._image_shape_static.rank - if self._rank not in (2, 3): - raise ValueError(f"Rank must be 2 or 3, but got: {self._rank}") - self._image_axes = list(range(-self._rank, 0)) # pylint: disable=invalid-unary-operand-type - if extra_shape is None: - extra_shape = [] - self._extra_shape_static, self._extra_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape(extra_shape)) - - # Set initial batch shape, then update according to inputs. - # We include the "extra" dimensions in the batch shape for now, so that - # they are also included in the broadcasting operations below. However, - # note that the "extra" dimensions are not in fact part of the batch shape - # and they will be removed later. - self._batch_shape_static = self._extra_shape_static - self._batch_shape_dynamic = self._extra_shape_dynamic - - # Set sampling mask after checking dtype and static shape. - if mask is not None: - mask = tf.convert_to_tensor(mask) - if mask.dtype != tf.bool: - raise TypeError( - f"`mask` must have dtype `bool`, but got: {str(mask.dtype)}") - if not mask.shape[-self._rank:].is_compatible_with( - self._image_shape_static): - raise ValueError( - f"Expected the last dimensions of `mask` to be compatible with " - f"{self._image_shape_static}], but got: {mask.shape[-self._rank:]}") - self._batch_shape_static = tf.broadcast_static_shape( - self._batch_shape_static, mask.shape[:-self._rank]) - self._batch_shape_dynamic = tf.broadcast_dynamic_shape( - self._batch_shape_dynamic, tf.shape(mask)[:-self._rank]) - self._mask = mask - - # Set sampling trajectory after checking dtype and static shape. - if trajectory is not None: - if mask is not None: - raise ValueError("`mask` and `trajectory` cannot be both passed.") - trajectory = tf.convert_to_tensor(trajectory) - if trajectory.dtype != dtype.real_dtype: - raise TypeError( - f"Expected `trajectory` to have dtype `{str(dtype.real_dtype)}`, " - f"but got: {str(trajectory.dtype)}") - if trajectory.shape[-1] != self._rank: - raise ValueError( - f"Expected the last dimension of `trajectory` to be " - f"{self._rank}, but got {trajectory.shape[-1]}") - self._batch_shape_static = tf.broadcast_static_shape( - self._batch_shape_static, trajectory.shape[:-2]) - self._batch_shape_dynamic = tf.broadcast_dynamic_shape( - self._batch_shape_dynamic, tf.shape(trajectory)[:-2]) - self._trajectory = trajectory - - # Set sampling density after checking dtype and static shape. - if density is not None: - if self._trajectory is None: - raise ValueError("`density` must be passed with `trajectory`.") - density = tf.convert_to_tensor(density) - if density.dtype != dtype.real_dtype: - raise TypeError( - f"Expected `density` to have dtype `{str(dtype.real_dtype)}`, " - f"but got: {str(density.dtype)}") - if density.shape[-1] != self._trajectory.shape[-2]: - raise ValueError( - f"Expected the last dimension of `density` to be " - f"{self._trajectory.shape[-2]}, but got {density.shape[-1]}") - self._batch_shape_static = tf.broadcast_static_shape( - self._batch_shape_static, density.shape[:-1]) - self._batch_shape_dynamic = tf.broadcast_dynamic_shape( - self._batch_shape_dynamic, tf.shape(density)[:-1]) - self._density = density - - # Set sensitivity maps after checking dtype and static shape. - if sensitivities is not None: - sensitivities = tf.convert_to_tensor(sensitivities) - if sensitivities.dtype != dtype: - raise TypeError( - f"Expected `sensitivities` to have dtype `{str(dtype)}`, but got: " - f"{str(sensitivities.dtype)}") - if not sensitivities.shape[-self._rank:].is_compatible_with( - self._image_shape_static): - raise ValueError( - f"Expected the last dimensions of `sensitivities` to be " - f"compatible with {self._image_shape_static}, but got: " - f"{sensitivities.shape[-self._rank:]}") - self._batch_shape_static = tf.broadcast_static_shape( - self._batch_shape_static, - sensitivities.shape[:-(self._rank + 1)]) - self._batch_shape_dynamic = tf.broadcast_dynamic_shape( - self._batch_shape_dynamic, - tf.shape(sensitivities)[:-(self._rank + 1)]) - self._sensitivities = sensitivities - - if phase is not None: - phase = tf.convert_to_tensor(phase) - if phase.dtype != dtype.real_dtype: - raise TypeError( - f"Expected `phase` to have dtype `{str(dtype.real_dtype)}`, " - f"but got: {str(phase.dtype)}") - if not phase.shape[-self._rank:].is_compatible_with( - self._image_shape_static): - raise ValueError( - f"Expected the last dimensions of `phase` to be " - f"compatible with {self._image_shape_static}, but got: " - f"{phase.shape[-self._rank:]}") - self._batch_shape_static = tf.broadcast_static_shape( - self._batch_shape_static, phase.shape[:-self._rank]) - self._batch_shape_dynamic = tf.broadcast_dynamic_shape( - self._batch_shape_dynamic, tf.shape(phase)[:-self._rank]) - self._phase = phase - - # Set batch shapes. - extra_dims = self._extra_shape_static.rank - if extra_dims is None: - raise ValueError("rank of `extra_shape` must be known statically.") - if extra_dims > 0: - self._batch_shape_static = self._batch_shape_static[:-extra_dims] - self._batch_shape_dynamic = self._batch_shape_dynamic[:-extra_dims] - - # Save some tensors for later use during computation. The `_i_` prefix - # indicates that these tensors are for internal use. We cannot modify the - # original tensors because they are components of the composite tensor that - # represents this operator, and the overall composite tensor cannot be - # mutated in certain circumstances such as in Keras models. - self._i_mask = self._mask - self._i_trajectory = self._trajectory - self._i_density = self._density - self._i_phase = self._phase - self._i_sensitivities = self._sensitivities - - # If multicoil, add coil dimension to mask, trajectory and density. - if self._i_sensitivities is not None: - if self._i_mask is not None: - self._i_mask = tf.expand_dims(self._i_mask, axis=-(self._rank + 1)) - if self._i_trajectory is not None: - self._i_trajectory = tf.expand_dims(self._i_trajectory, axis=-3) - if self._i_density is not None: - self._i_density = tf.expand_dims(self._i_density, axis=-2) - if self._i_phase is not None: - self._i_phase = tf.expand_dims(self._i_phase, axis=-(self._rank + 1)) - - # Select masking algorithm. Options are `multiplex` and `multiply`. - # `multiply` seems faster in most cases, but this needs better profiling. - self._masking_algorithm = 'multiply' - - if self._i_mask is not None: - if self._masking_algorithm == 'multiplex': - # Preallocate zeros tensor for multiplexing. - self._i_zeros = tf.zeros(shape=tf.shape(self._i_mask), dtype=self.dtype) - elif self._masking_algorithm == 'multiply': - # Cast the mask to operator's dtype for multiplication. - self._i_mask = tf.cast(self._i_mask, dtype) - else: - raise ValueError( - f"Unknown masking algorithm: {self._masking_algorithm}") - - # Compute the density compensation weights used internally. - if self._i_density is not None: - self._i_density = tf.cast(tf.math.sqrt( - tf.math.reciprocal_no_nan(self._i_density)), dtype) - # Compute the phase modulator used internally. - if self._i_phase is not None: - self._i_phase = tf.math.exp(tf.dtypes.complex( - tf.constant(0.0, dtype=dtype.real_dtype), self._i_phase)) - - # Set normalization. - self._fft_norm = check_util.validate_enum( - fft_norm, {None, 'ortho'}, 'fft_norm') - if self._fft_norm == 'ortho': # Compute normalization factors. - self._fft_norm_factor = tf.math.reciprocal( - tf.math.sqrt(tf.cast( - tf.math.reduce_prod(self._image_shape_dynamic), dtype))) - - # Normalize coil sensitivities. - self._sens_norm = sens_norm - if self._i_sensitivities is not None and self._sens_norm: - self._i_sensitivities = math_ops.normalize_no_nan( - self._i_sensitivities, axis=-(self._rank + 1)) - - # Intensity correction. - self._intensity_correction = intensity_correction - if self._i_sensitivities is not None and self._intensity_correction: - # This is redundant if `sens_norm` is `True`. - self._intensity_weights_sqrt = tf.math.reciprocal_no_nan( - tf.math.sqrt(tf.norm(self._i_sensitivities, axis=-(self._rank + 1)))) - - # Set dynamic domain. - if dynamic_domain is not None and self._extra_shape.rank == 0: - raise ValueError( - "Argument `dynamic_domain` requires a non-scalar `extra_shape`.") - if dynamic_domain is not None: - self._dynamic_domain = check_util.validate_enum( - dynamic_domain, {'time', 'frequency'}, name='dynamic_domain') - else: - self._dynamic_domain = None - - # This variable is used by `LinearOperatorGramMRI` to disable the NUFFT. - self._skip_nufft = False - - def _transform(self, x, adjoint=False): - """Transform [batch] input `x`. - - Args: - x: A `tf.Tensor` of type `self.dtype` and shape - `[..., *self.domain_shape]` containing images, if `adjoint` is `False`, - or a `tf.Tensor` of type `self.dtype` and shape - `[..., *self.range_shape]` containing *k*-space data, if `adjoint` is - `True`. - adjoint: A `boolean` indicating whether to apply the adjoint of the - operator. - - Returns: - A `tf.Tensor` of type `self.dtype` and shape `[..., *self.range_shape]` - containing *k*-space data, if `adjoint` is `False`, or a `tf.Tensor` of - type `self.dtype` and shape `[..., *self.domain_shape]` containing - images, if `adjoint` is `True`. - - Raises: - ValueError: If the masking algorithm is invalid. - """ - if adjoint: - # Apply density compensation. - if self._i_density is not None and not self._skip_nufft: - x *= self._i_density - - # Apply adjoint Fourier operator. - if self.is_non_cartesian: # Non-Cartesian imaging, use NUFFT. - if not self._skip_nufft: - x = fft_ops.nufft(x, self._i_trajectory, - grid_shape=self._image_shape_dynamic, - transform_type='type_1', - fft_direction='backward') - if self._fft_norm is not None: - x *= self._fft_norm_factor - - else: # Cartesian imaging, use FFT. - if self._i_mask is not None: - # Apply undersampling. - if self._masking_algorithm == 'multiplex': - x = tf.where(self._i_mask, x, self._i_zeros) - elif self._masking_algorithm == 'multiply': - x *= self._i_mask - else: - raise ValueError( - f"Unknown masking algorithm: {self._masking_algorithm}") - x = fft_ops.ifftn(x, axes=self._image_axes, - norm=self._fft_norm or 'forward', shift=True) - - # Apply coil combination. - if self.is_multicoil: - x *= tf.math.conj(self._i_sensitivities) - x = tf.math.reduce_sum(x, axis=-(self._rank + 1)) - - # Maybe remove phase from image. - if self.is_phase_constrained: - x *= tf.math.conj(self._i_phase) - x = tf.cast(tf.math.real(x), self.dtype) - - # Apply intensity correction. - if self.is_multicoil and self._intensity_correction: - x *= self._intensity_weights_sqrt - - # Apply FFT along dynamic axis, if necessary. - if self.is_dynamic and self.dynamic_domain == 'frequency': - x = fft_ops.fftn(x, axes=[self.dynamic_axis], - norm='ortho', shift=True) - - else: # Forward operator. - - # Apply IFFT along dynamic axis, if necessary. - if self.is_dynamic and self.dynamic_domain == 'frequency': - x = fft_ops.ifftn(x, axes=[self.dynamic_axis], - norm='ortho', shift=True) - - # Apply intensity correction. - if self.is_multicoil and self._intensity_correction: - x *= self._intensity_weights_sqrt - - # Add phase to real-valued image if reconstruction is phase-constrained. - if self.is_phase_constrained: - x = tf.cast(tf.math.real(x), self.dtype) - x *= self._i_phase - - # Apply sensitivity modulation. - if self.is_multicoil: - x = tf.expand_dims(x, axis=-(self._rank + 1)) - x *= self._i_sensitivities - - # Apply Fourier operator. - if self.is_non_cartesian: # Non-Cartesian imaging, use NUFFT. - if not self._skip_nufft: - x = fft_ops.nufft(x, self._i_trajectory, - transform_type='type_2', - fft_direction='forward') - if self._fft_norm is not None: - x *= self._fft_norm_factor - - else: # Cartesian imaging, use FFT. - x = fft_ops.fftn(x, axes=self._image_axes, - norm=self._fft_norm or 'backward', shift=True) - if self._i_mask is not None: - # Apply undersampling. - if self._masking_algorithm == 'multiplex': - x = tf.where(self._i_mask, x, self._i_zeros) - elif self._masking_algorithm == 'multiply': - x *= self._i_mask - else: - raise ValueError( - f"Unknown masking algorithm: {self._masking_algorithm}") - - # Apply density compensation. - if self._i_density is not None and not self._skip_nufft: - x *= self._i_density - - return x - - def _preprocess(self, x, adjoint=False): - if adjoint: - if self._i_density is not None: - x *= self._i_density - else: - raise NotImplementedError( - "`_preprocess` not implemented for forward transform.") - return x - - def _postprocess(self, x, adjoint=False): - if adjoint: - # Apply temporal Fourier operator, if necessary. - if self.is_dynamic and self.dynamic_domain == 'frequency': - x = fft_ops.ifftn(x, axes=[self.dynamic_axis], - norm='ortho', shift=True) - - # Apply intensity correction, if necessary. - if self.is_multicoil and self._intensity_correction: - x *= self._intensity_weights_sqrt - else: - raise NotImplementedError( - "`_postprocess` not implemented for forward transform.") - return x - - def _domain_shape(self): - """Returns the static shape of the domain space of this operator.""" - return self._extra_shape_static.concatenate(self._image_shape_static) - - def _domain_shape_tensor(self): - """Returns the dynamic shape of the domain space of this operator.""" - return tf.concat([self._extra_shape_dynamic, self._image_shape_dynamic], 0) - - def _range_shape(self): - """Returns the shape of the range space of this operator.""" - if self.is_cartesian: - range_shape = self._image_shape_static.as_list() - else: - range_shape = [self._trajectory.shape[-2]] - if self.is_multicoil: - range_shape = [self.num_coils] + range_shape - return self._extra_shape_static.concatenate(range_shape) - - def _range_shape_tensor(self): - if self.is_cartesian: - range_shape = self._image_shape_dynamic - else: - range_shape = [tf.shape(self._trajectory)[-2]] - if self.is_multicoil: - range_shape = tf.concat([[self.num_coils_tensor()], range_shape], 0) - return tf.concat([self._extra_shape_dynamic, range_shape], 0) - - def _batch_shape(self): - """Returns the static batch shape of this operator.""" - return self._batch_shape_static - - def _batch_shape_tensor(self): - """Returns the dynamic batch shape of this operator.""" - return self._batch_shape_dynamic - - @property - def image_shape(self): - """The image shape.""" - return self._image_shape_static - - def image_shape_tensor(self): - """The image shape as a tensor.""" - return self._image_shape_dynamic - - @property - def rank(self): - """The number of spatial dimensions. - - Returns: - An `int`, typically 2 or 3. - """ - return self._rank - - @property - def mask(self): - """The sampling mask. - - Returns: - A boolean `tf.Tensor` of shape `batch_shape + extra_shape + image_shape`, - or `None` if the operator is fully sampled or non-Cartesian. - """ - return self._mask - - @property - def trajectory(self): - """The k-space trajectory. - - Returns: - A real `tf.Tensor` of shape `batch_shape + extra_shape + [samples, rank]`, - or `None` if the operator is Cartesian. - """ - return self._trajectory - - @property - def density(self): - """The sampling density. - - Returns: - A real `tf.Tensor` of shape `batch_shape + extra_shape + [samples]`, - or `None` if the operator is Cartesian or has unknown sampling density. - """ - return self._density - - @property - def is_cartesian(self): - """Whether this is a Cartesian MRI operator.""" - return self._trajectory is None - - @property - def is_non_cartesian(self): - """Whether this is a non-Cartesian MRI operator.""" - return self._trajectory is not None - - @property - def is_multicoil(self): - """Whether this is a multicoil MRI operator.""" - return self._sensitivities is not None - - @property - def is_phase_constrained(self): - """Whether this is a phase-constrained MRI operator.""" - return self._phase is not None - - @property - def is_dynamic(self): - """Whether this is a dynamic MRI operator.""" - return self._dynamic_domain is not None - - @property - def dynamic_domain(self): - """The dynamic domain of this operator.""" - return self._dynamic_domain - - @property - def dynamic_axis(self): - """The dynamic axis of this operator.""" - return -(self._rank + 1) if self.is_dynamic else None - - @property - def num_coils(self): - """The number of coils, computed statically.""" - if self._sensitivities is None: - return None - return self._sensitivities.shape[-(self._rank + 1)] - - def num_coils_tensor(self): - """The number of coils, computed dynamically.""" - if self._sensitivities is None: - return tf.convert_to_tensor(-1, dtype=tf.int32) - return tf.shape(self._sensitivities)[-(self._rank + 1)] - - def _ignore_batch_dims_in_shape(self, shape, argname): - if shape is None: - return None - shape = tf.convert_to_tensor(shape, dtype=tf.int32) - if shape.shape.rank == 2: - warned = _WARNED_IGNORED_BATCH_DIMENSIONS.get(argname, False) - if not warned: - _WARNED_IGNORED_BATCH_DIMENSIONS[argname] = True - warnings.warn( - f"Operator {self.name} got a batched `{argname}` argument. " - f"It is not possible to process images with " - f"different shapes in the same batch. " - f"If the input batch has more than one element, " - f"only the first shape will be used. " - f"It is up to you to verify if this behavior is correct.") - return tf.ensure_shape(shape[0], shape.shape[1:]) - return shape - - @property - def _composite_tensor_fields(self): - return ("image_shape", - "extra_shape", - "mask", - "trajectory", - "density", - "sensitivities", - "phase", - "fft_norm", - "sens_norm", - "intensity_correction", - "dynamic_domain", - "dtype") - - @property - def _composite_tensor_prefer_static_fields(self): - return ("image_shape", "extra_shape") - - -@api_util.export("linalg.LinearOperatorGramMRI") -class LinearOperatorGramMRI(LinearOperatorMRI): # pylint: disable=abstract-method - """Linear operator representing the Gram matrix of an MRI measurement system. - - If $A$ is a `tfmri.linalg.LinearOperatorMRI`, then this ooperator - represents the matrix $G = A^H A$. - - In certain circumstances, this operator may be able to apply the matrix - $G$ more efficiently than the composition $G = A^H A$ using - `tfmri.linalg.LinearOperatorMRI` objects. - - Args: - image_shape: A 1D integer `tf.Tensor`. The shape of the images - that this operator acts on. Must have length 2 or 3. - extra_shape: An optional 1D integer `tf.Tensor`. Additional - dimensions that should be included within the operator domain. Note that - `extra_shape` is not needed to reconstruct independent batches of images. - However, it is useful when this operator is used as part of a - reconstruction that performs computation along non-spatial dimensions, - e.g. for temporal regularization. Defaults to `None`. - mask: An optional `tf.Tensor` of type `tf.bool`. The sampling mask. Must - have shape `[..., *S]`, where `S` is the `image_shape` and `...` is - the batch shape, which can have any number of dimensions. If `mask` is - passed, this operator represents an undersampled MRI operator. - trajectory: An optional `tf.Tensor` of type `float32` or `float64`. Must - have shape `[..., M, N]`, where `N` is the rank (number of spatial - dimensions), `M` is the number of samples in the encoded space and `...` - is the batch shape, which can have any number of dimensions. If - `trajectory` is passed, this operator represents a non-Cartesian MRI - operator. - density: An optional `tf.Tensor` of type `float32` or `float64`. The - sampling densities. Must have shape `[..., M]`, where `M` is the number of - samples and `...` is the batch shape, which can have any number of - dimensions. This input is only relevant for non-Cartesian MRI operators. - If passed, the non-Cartesian operator will include sampling density - compensation. If `None`, the operator will not perform sampling density - compensation. - sensitivities: An optional `tf.Tensor` of type `complex64` or `complex128`. - The coil sensitivity maps. Must have shape `[..., C, *S]`, where `S` - is the `image_shape`, `C` is the number of coils and `...` is the batch - shape, which can have any number of dimensions. - phase: An optional `tf.Tensor` of type `float32` or `float64`. A phase - estimate for the image. If provided, this operator will be - phase-constrained. - fft_norm: FFT normalization mode. Must be `None` (no normalization) - or `'ortho'`. Defaults to `'ortho'`. - sens_norm: A `boolean`. Whether to normalize coil sensitivities. Defaults to - `True`. - dynamic_domain: A `str`. The domain of the dynamic dimension, if present. - Must be one of `'time'` or `'frequency'`. May only be provided together - with a non-scalar `extra_shape`. The dynamic dimension is the last - dimension of `extra_shape`. The `'time'` mode (default) should be - used for regular dynamic reconstruction. The `'frequency'` mode should be - used for reconstruction in x-f space. - toeplitz_nufft: A `boolean`. If `True`, uses the Toeplitz approach [5] - to compute $F^H F x$, where $F$ is the non-uniform Fourier - operator. If `False`, the same operation is performed using the standard - NUFFT operation. The Toeplitz approach might be faster than the direct - approach but is slightly less accurate. This argument is only relevant - for non-Cartesian reconstruction and will be ignored for Cartesian - problems. - dtype: A `tf.dtypes.DType`. The dtype of this operator. Must be `complex64` - or `complex128`. Defaults to `complex64`. - name: An optional `str`. The name of this operator. - """ - def __init__(self, - image_shape, - extra_shape=None, - mask=None, - trajectory=None, - density=None, - sensitivities=None, - phase=None, - fft_norm='ortho', - sens_norm=True, - dynamic_domain=None, - toeplitz_nufft=False, - dtype=tf.complex64, - name="LinearOperatorGramMRI"): - super().__init__( - image_shape, - extra_shape=extra_shape, - mask=mask, - trajectory=trajectory, - density=density, - sensitivities=sensitivities, - phase=phase, - fft_norm=fft_norm, - sens_norm=sens_norm, - dynamic_domain=dynamic_domain, - dtype=dtype, - name=name - ) - - self.toeplitz_nufft = toeplitz_nufft - if self.toeplitz_nufft and self.is_non_cartesian: - # Create a Gram NUFFT operator with Toeplitz embedding. - self._linop_gram_nufft = linear_operator_nufft.LinearOperatorGramNUFFT( - image_shape, trajectory=self._trajectory, density=self._density, - norm=fft_norm, toeplitz=True) - # Disable NUFFT computation on base class. The NUFFT will instead be - # performed by the Gram NUFFT operator. - self._skip_nufft = True - - def _transform(self, x, adjoint=False): - x = super()._transform(x) - if self.toeplitz_nufft: - x = self._linop_gram_nufft.transform(x) - x = super()._transform(x, adjoint=True) - return x - - def _range_shape(self): - return self._domain_shape() - - def _range_shape_tensor(self): - return self._domain_shape_tensor() diff --git a/tensorflow_mri/python/linalg/linear_operator_mri_test.py b/tensorflow_mri/python/linalg/linear_operator_mri_test.py deleted file mode 100644 index 7cc12a28..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_mri_test.py +++ /dev/null @@ -1,214 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_mri`.""" -# pylint: disable=missing-class-docstring,missing-function-docstring - -from absl.testing import parameterized -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_mri -from tensorflow_mri.python.ops import fft_ops -from tensorflow_mri.python.ops import image_ops -from tensorflow_mri.python.ops import traj_ops -from tensorflow_mri.python.util import test_util - - -class LinearOperatorMRITest(test_util.TestCase): - """Tests for MRI linear operator.""" - def test_fft(self): - """Test FFT operator.""" - # Test init. - linop = linear_operator_mri.LinearOperatorMRI([2, 2], fft_norm=None) - - # Test matvec. - signal = tf.constant([1, 2, 4, 4], dtype=tf.complex64) - expected = [-1, 5, 1, 11] - result = tf.linalg.matvec(linop, signal) - self.assertAllClose(expected, result) - - # Test domain shape. - self.assertIsInstance(linop.domain_shape, tf.TensorShape) - self.assertAllEqual([2, 2], linop.domain_shape) - self.assertAllEqual([2, 2], linop.domain_shape_tensor()) - - # Test range shape. - self.assertIsInstance(linop.range_shape, tf.TensorShape) - self.assertAllEqual([2, 2], linop.range_shape) - self.assertAllEqual([2, 2], linop.range_shape_tensor()) - - # Test batch shape. - self.assertIsInstance(linop.batch_shape, tf.TensorShape) - self.assertAllEqual([], linop.batch_shape) - self.assertAllEqual([], linop.batch_shape_tensor()) - - def test_fft_with_mask(self): - """Test FFT operator with mask.""" - # Test init. - linop = linear_operator_mri.LinearOperatorMRI( - [2, 2], mask=[[False, False], [True, True]], fft_norm=None) - - # Test matvec. - signal = tf.constant([1, 2, 4, 4], dtype=tf.complex64) - expected = [0, 0, 1, 11] - result = tf.linalg.matvec(linop, signal) - self.assertAllClose(expected, result) - - # Test domain shape. - self.assertIsInstance(linop.domain_shape, tf.TensorShape) - self.assertAllEqual([2, 2], linop.domain_shape) - self.assertAllEqual([2, 2], linop.domain_shape_tensor()) - - # Test range shape. - self.assertIsInstance(linop.range_shape, tf.TensorShape) - self.assertAllEqual([2, 2], linop.range_shape) - self.assertAllEqual([2, 2], linop.range_shape_tensor()) - - # Test batch shape. - self.assertIsInstance(linop.batch_shape, tf.TensorShape) - self.assertAllEqual([], linop.batch_shape) - self.assertAllEqual([], linop.batch_shape_tensor()) - - def test_fft_with_batch_mask(self): - """Test FFT operator with batch mask.""" - # Test init. - linop = linear_operator_mri.LinearOperatorMRI( - [2, 2], mask=[[[True, True], [False, False]], - [[False, False], [True, True]], - [[False, True], [True, False]]], fft_norm=None) - - # Test matvec. - signal = tf.constant([1, 2, 4, 4], dtype=tf.complex64) - expected = [[-1, 5, 0, 0], [0, 0, 1, 11], [0, 5, 1, 0]] - result = tf.linalg.matvec(linop, signal) - self.assertAllClose(expected, result) - - # Test domain shape. - self.assertIsInstance(linop.domain_shape, tf.TensorShape) - self.assertAllEqual([2, 2], linop.domain_shape) - self.assertAllEqual([2, 2], linop.domain_shape_tensor()) - - # Test range shape. - self.assertIsInstance(linop.range_shape, tf.TensorShape) - self.assertAllEqual([2, 2], linop.range_shape) - self.assertAllEqual([2, 2], linop.range_shape_tensor()) - - # Test batch shape. - self.assertIsInstance(linop.batch_shape, tf.TensorShape) - self.assertAllEqual([3], linop.batch_shape) - self.assertAllEqual([3], linop.batch_shape_tensor()) - - def test_fft_norm(self): - """Test FFT normalization.""" - linop = linear_operator_mri.LinearOperatorMRI([2, 2], fft_norm='ortho') - x = tf.constant([1 + 2j, 2 - 2j, -1 - 6j, 3 + 4j], dtype=tf.complex64) - # With norm='ortho', subsequent application of the operator and its adjoint - # should not scale the input. - y = tf.linalg.matvec(linop.H, tf.linalg.matvec(linop, x)) - self.assertAllClose(x, y) - - def test_nufft_with_sensitivities(self): - resolution = 128 - image_shape = [resolution, resolution] - num_coils = 4 - image, sensitivities = image_ops.phantom( - shape=image_shape, num_coils=num_coils, dtype=tf.complex64, - return_sensitivities=True) - image = image_ops.phantom(shape=image_shape, dtype=tf.complex64) - trajectory = traj_ops.radial_trajectory(resolution, resolution // 2 + 1, - flatten_encoding_dims=True) - density = traj_ops.radial_density(resolution, resolution // 2 + 1, - flatten_encoding_dims=True) - - linop = linear_operator_mri.LinearOperatorMRI( - image_shape, trajectory=trajectory, density=density, - sensitivities=sensitivities) - - # Test shapes. - expected_domain_shape = image_shape - self.assertAllClose(expected_domain_shape, linop.domain_shape) - self.assertAllClose(expected_domain_shape, linop.domain_shape_tensor()) - expected_range_shape = [num_coils, (2 * resolution) * (resolution // 2 + 1)] - self.assertAllClose(expected_range_shape, linop.range_shape) - self.assertAllClose(expected_range_shape, linop.range_shape_tensor()) - - # Test forward. - weights = tf.cast(tf.math.sqrt(tf.math.reciprocal_no_nan(density)), - tf.complex64) - norm = tf.math.sqrt(tf.cast(tf.math.reduce_prod(image_shape), tf.complex64)) - expected = fft_ops.nufft(image * sensitivities, trajectory) * weights / norm - kspace = linop.transform(image) - self.assertAllClose(expected, kspace) - - # Test adjoint. - expected = tf.math.reduce_sum( - fft_ops.nufft( - kspace * weights, trajectory, grid_shape=image_shape, - transform_type='type_1', fft_direction='backward') / norm * - tf.math.conj(sensitivities), axis=-3) - recon = linop.transform(kspace, adjoint=True) - self.assertAllClose(expected, recon) - - - -class LinearOperatorGramMRITest(test_util.TestCase): - @parameterized.product(batch=[False, True], extra=[False, True], - toeplitz_nufft=[False, True]) - def test_general(self, batch, extra, toeplitz_nufft): - resolution = 128 - image_shape = [resolution, resolution] - num_coils = 4 - image, sensitivities = image_ops.phantom( - shape=image_shape, num_coils=num_coils, dtype=tf.complex64, - return_sensitivities=True) - image = image_ops.phantom(shape=image_shape, dtype=tf.complex64) - trajectory = traj_ops.radial_trajectory(resolution, resolution // 2 + 1, - flatten_encoding_dims=True) - density = traj_ops.radial_density(resolution, resolution // 2 + 1, - flatten_encoding_dims=True) - if batch: - image = tf.stack([image, image * 2]) - if extra: - extra_shape = [2] - else: - extra_shape = None - else: - extra_shape = None - - linop = linear_operator_mri.LinearOperatorMRI( - image_shape, extra_shape=extra_shape, - trajectory=trajectory, density=density, - sensitivities=sensitivities) - linop_gram = linear_operator_mri.LinearOperatorGramMRI( - image_shape, extra_shape=extra_shape, - trajectory=trajectory, density=density, - sensitivities=sensitivities, toeplitz_nufft=toeplitz_nufft) - - # Test shapes. - expected_domain_shape = image_shape - if extra_shape is not None: - expected_domain_shape = extra_shape + image_shape - self.assertAllClose(expected_domain_shape, linop_gram.domain_shape) - self.assertAllClose(expected_domain_shape, linop_gram.domain_shape_tensor()) - self.assertAllClose(expected_domain_shape, linop_gram.range_shape) - self.assertAllClose(expected_domain_shape, linop_gram.range_shape_tensor()) - - # Test transform. - expected = linop.transform(linop.transform(image), adjoint=True) - self.assertAllClose(expected, linop_gram.transform(image), - rtol=1e-4, atol=1e-4) - - -if __name__ == '__main__': - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_nd.py b/tensorflow_mri/python/linalg/linear_operator_nd.py deleted file mode 100644 index 1df2a863..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_nd.py +++ /dev/null @@ -1,799 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utilities for N-D linear operators.""" - -import functools -import string - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.linalg import linear_operator_algebra -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import tensor_util - - -def make_linear_operator_nd(cls): - """Class decorator for subclasses of `LinearOperatorND`.""" - # Call the original decorator. - cls = linear_operator.make_linear_operator(cls) - - # Add the N-D specific doclines. - cls = update_docstring(cls) - - return cls - - -def update_docstring(op_cls): - """Adds a notice to the docstring.""" - tfmri_additional_nd_functionality = string.Template(""" - ```{rubric} Additional N-D functionality (TensorFlow MRI) - ``` - - This operator has additional functionality to work with N-dimensional - problems more easily. - - - Process non-vectorized N-dimensional inputs via `matvec_nd`, `solvevec_nd` - and `lstsqvec_nd`. - - Access static N-D shape information via `domain_shape` and `range_shape`. - - Access dynamic N-D shape information via `domain_shape_tensor` and - `range_shape_tensor`. - """).substitute(class_name=op_cls.__name__) - - docstring = op_cls.__doc__ - doclines = docstring.split('\n') - doclines += tfmri_additional_nd_functionality.split('\n') - docstring = '\n'.join(doclines) - op_cls.__doc__ = docstring - - return op_cls - - -@api_util.export("linalg.LinearOperatorND") -@make_linear_operator_nd -class LinearOperatorND(linear_operator.LinearOperator): - """Base class defining a [batch of] N-D linear operator(s).""" - # Overrides of existing methods. - def matmul(self, x, adjoint=False, adjoint_arg=False, name="matmul"): - # We define a special implementation for when `x` is a `LinearOperatorND`. - if isinstance(x, LinearOperatorND): - left_operator = self.adjoint() if adjoint else self - right_operator = x.adjoint() if adjoint_arg else x - - tensor_util.assert_broadcast_compatible( - left_operator.domain_shape, - right_operator.range_shape, - message=( - f"N-D operators are incompatible: " - f"the domain shape {left_operator.domain_shape} " - f"of left operator {left_operator.name} is not broadcast-" - f"compatible with the range shape {right_operator.shape} " - f"of right operator {right_operator.name}")) - - with self._name_scope(name): # pylint: disable=not-callable - return linear_operator_algebra.matmul(left_operator, right_operator) - - # If `x` is not a `LinearOperatorND`, we use the original implementation. - return super().matmul( - x, adjoint=adjoint, adjoint_arg=adjoint_arg, name=name) - - def _matmul(self, x, adjoint=False, adjoint_arg=False): - """Default implementation of `_matmul` for N-D operator.""" - # Default implementation of `matmul` for N-D operator. Basically we - # just call `matvec` for each column of `x` (or for each row, if - # `adjoint_arg` is `True`). `tf.einsum` is used to transpose the input arg. - batch_shape = tf.broadcast_static_shape(x.shape[:-2], self.batch_shape) - output_dim = self.domain_dimension if adjoint else self.range_dimension - if adjoint_arg and x.dtype.is_complex: - x = tf.math.conj(x) - x = tf.einsum('...ij->i...j' if adjoint_arg else '...ij->j...i', x) - y = tf.map_fn(functools.partial(self.matvec, adjoint=adjoint), x, - fn_output_signature=tf.TensorSpec( - shape=batch_shape + [output_dim], - dtype=x.dtype)) - y = tf.einsum('i...j->...ji' if adjoint_arg else 'j...i->...ij', y) - return y - - def _matvec(self, x, adjoint=False): - """Default implementation of `_matvec` for N-D operator.""" - # Default implementation of `_matvec` for N-D operator. The vectorized - # input `x` is first expanded to the its full shape, then transformed, then - # vectorized again. Typically subclasses should not need to override this - # method. - x = (self.expand_range_dimension(x) if adjoint else - self.expand_domain_dimension(x)) - x = self._matvec_nd(x, adjoint=adjoint) - x = (self.flatten_domain_shape(x) if adjoint else \ - self.flatten_range_shape(x)) - return x - - def solve(self, rhs, adjoint=False, adjoint_arg=False, name="solve"): - if self.is_non_singular is False: - raise NotImplementedError( - "Exact solve not implemented for an operator that is expected to " - "be singular.") - if self.is_square is False: - raise NotImplementedError( - "Exact solve not implemented for an operator that is expected to " - "not be square.") - - # We define a special implementation for when `rhs` is a `LinearOperatorND`. - if isinstance(rhs, LinearOperatorND): - left_operator = self.adjoint() if adjoint else self - right_operator = rhs.adjoint() if adjoint_arg else rhs - - tensor_util.assert_broadcast_compatible( - left_operator.domain_shape, - right_operator.range_shape, - message=( - f"N-D operators are incompatible: " - f"the domain shape {left_operator.domain_shape} " - f"of left operator {left_operator.name} is not broadcast-" - f"compatible with the range shape {right_operator.shape} " - f"of right operator {right_operator.name}")) - - with self._name_scope(name): # pylint: disable=not-callable - return linear_operator_algebra.solve(left_operator, right_operator) - - # If `x` is not a `LinearOperatorND`, we use the original implementation. - return super().solve( - rhs, adjoint=adjoint, adjoint_arg=adjoint_arg, name=name) - - def _solve(self, rhs, adjoint=False, adjoint_arg=False): - """Default implementation of `_solve` for N-D operator.""" - # Default implementation of `_solve` for imaging operator. Basically we - # just call `solvevec` for each column of `rhs` (or for each row, if - # `adjoint_arg` is `True`). `tf.einsum` is used to transpose the input arg. - batch_shape = tf.broadcast_static_shape(rhs.shape[:-2], self.batch_shape) - output_dim = self.range_dimension if adjoint else self.domain_dimension - if adjoint_arg and rhs.dtype.is_complex: - rhs = tf.math.conj(rhs) - rhs = tf.einsum('...ij->i...j' if adjoint_arg else '...ij->j...i', rhs) - x = tf.map_fn(functools.partial(self.solvevec, adjoint=adjoint), rhs, - fn_output_signature=tf.TensorSpec( - shape=batch_shape + [output_dim], - dtype=rhs.dtype)) - x = tf.einsum('i...j->...ji' if adjoint_arg else 'j...i->...ij', x) - return x - - def _solvevec(self, rhs, adjoint=False): - """Default implementation of `_solvevec` for N-D operator.""" - # Default implementation of `_solvevec` for N-D operator. The - # vectorized input `rhs` is first expanded to the its full shape, then - # solved, then vectorized again. Typically subclasses should not need to - # override this method. - rhs = (self.expand_domain_dimension(rhs) if adjoint else - self.expand_range_dimension(rhs)) - rhs = self._solvevec_nd(rhs, adjoint=adjoint) - rhs = (self.flatten_range_shape(rhs) if adjoint else - self.flatten_domain_shape(rhs)) - return rhs - - def _lstsq(self, rhs, adjoint=False, adjoint_arg=False): - """Default implementation of `_lstsq` for N-D operator.""" - # Default implementation of `_solve` for N-D operator. Basically we - # just call `solvevec` for each column of `rhs` (or for each row, if - # `adjoint_arg` is `True`). `tf.einsum` is used to transpose the input arg. - batch_shape = tf.broadcast_static_shape(rhs.shape[:-2], self.batch_shape) - output_dim = self.range_dimension if adjoint else self.domain_dimension - if adjoint_arg and rhs.dtype.is_complex: - rhs = tf.math.conj(rhs) - rhs = tf.einsum('...ij->i...j' if adjoint_arg else '...ij->j...i', rhs) - x = tf.map_fn(functools.partial(self.lstsqvec, adjoint=adjoint), rhs, - fn_output_signature=tf.TensorSpec( - shape=batch_shape + [output_dim], - dtype=rhs.dtype)) - x = tf.einsum('i...j->...ji' if adjoint_arg else 'j...i->...ij', x) - return x - - def _lstsqvec(self, rhs, adjoint=False): - """Default implementation of `_lstsqvec` for N-D operator.""" - # Default implementation of `_solvevec` for N-D operator. The - # vectorized input `rhs` is first expanded to the its full shape, then - # solved, then vectorized again. Typically subclasses should not need to - # override this method. - rhs = (self.expand_domain_dimension(rhs) if adjoint else - self.expand_range_dimension(rhs)) - rhs = self._lstsqvec_nd(rhs, adjoint=adjoint) - rhs = (self.flatten_range_shape(rhs) if adjoint else - self.flatten_domain_shape(rhs)) - return rhs - - def _shape(self): - # Default implementation of `_shape` for imaging operators. Typically - # subclasses should not need to override this method. - return self._batch_shape().concatenate(tf.TensorShape( - [self.range_shape.num_elements(), - self.domain_shape.num_elements()])) - - def _shape_tensor(self): - # Default implementation of `_shape_tensor` for imaging operators. Typically - # subclasses should not need to override this method. - return tf.concat([self.batch_shape_tensor(), - [tf.math.reduce_prod(self.range_shape_tensor()), - tf.math.reduce_prod(self.domain_shape_tensor())]], 0) - - # New methods. - def matvec_nd(self, x, adjoint=False, name="matvec_nd"): - """Transforms [batch] N-D input `x` with left multiplication `x --> Ax`. - - ```{note} - Similar to `matvec`, but works with non-vectorized N-D inputs `x`. - ``` - - Args: - x: A `tf.Tensor` with compatible shape and same dtype as `self`. - adjoint: A boolean. If `True`, transforms the input using the adjoint - of the operator, instead of the operator itself. - name: A name for this operation. - - Returns: - A `tf.Tensor` with same dtype as `x` and shape `[..., *nd_shape]`, - where `nd_shape` is the equal to `domain_shape` if `adjoint` is `True` - and `range_shape` otherwise. - """ - with self._name_scope(name): # pylint: disable=not-callable - x = tf.convert_to_tensor(x, name="x") - self._check_input_dtype(x) - input_shape = self.range_shape if adjoint else self.domain_shape - input_shape.assert_is_compatible_with(x.shape[-input_shape.rank:]) # pylint: disable=invalid-unary-operand-type - return self._matvec_nd(x, adjoint=adjoint) - - def _matvec_nd(self, x, adjoint=False): - # Subclasses must override this method. - raise NotImplementedError("Method `_matvec_nd` is not implemented.") - - def solvevec_nd(self, rhs, adjoint=False, name="solve"): - """Solve single equation with N-D right-hand side: `A x = rhs`. - - The returned `tf.Tensor` will be close to an exact solution if `A` is well - conditioned. Otherwise closeness will vary. See class docstring for details. - - ```{note} - Similar to `solvevec`, but works with non-vectorized N-D inputs `rhs`. - ``` - - Args: - rhs: A `tf.Tensor` with same `dtype` as this operator. - `rhs` is treated like a [batch] vector meaning for every set of leading - dimensions, the last dimension defines a vector. See class docstring - for definition of compatibility regarding batch dimensions. - adjoint: A boolean. If `True`, solve the system involving the adjoint of - this operator: $A^H x = b$. Defaults to `False`. - name: A name scope to use for ops added by this method. - - Returns: - A `tf.Tensor` with same dtype as `x` and shape `[..., *nd_shape]`, - where `nd_shape` is the equal to `range_shape` if `adjoint` is `True` - and `domain_shape` otherwise. - """ - with self._name_scope(name): # pylint: disable=not-callable - rhs = tf.convert_to_tensor(rhs, name="rhs") - self._check_input_dtype(rhs) - input_shape = self.domain_shape if adjoint else self.range_shape - input_shape.assert_is_compatible_with(rhs.shape[-input_shape.rank:]) # pylint: disable=invalid-unary-operand-type - return self._solvevec_nd(rhs, adjoint=adjoint) - - def _solvevec_nd(self, rhs, adjoint=False): - # Subclasses may override this method. - raise NotImplementedError("Method `_solvevec_nd` is not implemented.") - - def lstsqvec_nd(self, rhs, adjoint=False, name="solve"): - """Solve single equation with N-D right-hand side: `A x = rhs`. - - The returned `tf.Tensor` is the least squares solution to the system of - equations. - - ```{note} - Similar to `solvevec`, but works with non-vectorized N-D inputs `rhs`. - ``` - - Args: - rhs: A `tf.Tensor` with same `dtype` as this operator. - `rhs` is treated like a [batch] vector meaning for every set of leading - dimensions, the last dimension defines a vector. See class docstring - for definition of compatibility regarding batch dimensions. - adjoint: A boolean. If `True`, solve the system involving the adjoint of - this operator: $A^H x = b$. Defaults to `False`. - name: A name scope to use for ops added by this method. - - Returns: - A `tf.Tensor` with same dtype as `x` and shape `[..., *nd_shape]`, - where `nd_shape` is the equal to `range_shape` if `adjoint` is `True` - and `domain_shape` otherwise. - """ - with self._name_scope(name): # pylint: disable=not-callable - rhs = tf.convert_to_tensor(rhs, name="rhs") - self._check_input_dtype(rhs) - input_shape = self.domain_shape if adjoint else self.range_shape - input_shape.assert_is_compatible_with(rhs.shape[-input_shape.rank:]) # pylint: disable=invalid-unary-operand-type - return self._lstsqvec_nd(rhs, adjoint=adjoint) - - def _lstsqvec_nd(self, rhs, adjoint=False): - # Subclasses may override this method. - raise NotImplementedError("Method `_lstsqvec_nd` is not implemented.") - - @property - def domain_shape(self): - """Domain shape of this linear operator, determined statically. - - Returns: - A `tf.TensorShape` representing the shape of the domain of this operator. - """ - return self._domain_shape() - - def _domain_shape(self): - # Users must override this method. - return tf.TensorShape(None) - - @property - def range_shape(self): - """Range shape of this linear operator, determined statically. - - Returns: - A `tf.TensorShape` representing the shape of the range of this operator. - """ - return self._range_shape() - - def _range_shape(self): - # Users must override this method. - return tf.TensorShape(None) - - def _batch_shape(self): - # Users should override this method if this operator has a batch shape. - return tf.TensorShape([]) - - def domain_shape_tensor(self, name="domain_shape_tensor"): - """Domain shape of this linear operator, determined at runtime. - - Args: - name: A `str`. A name scope to use for ops added by this method. - - Returns: - A 1D integer `tf.Tensor` representing the shape of the domain of this - operator. - """ - with self._name_scope(name): # pylint: disable=not-callable - # Prefer to use statically defined shape if available. - if self.domain_shape.is_fully_defined(): - return tensor_util.convert_shape_to_tensor(self.domain_shape.as_list()) - return self._domain_shape_tensor() - - def _domain_shape_tensor(self): - # Users should override this method if they need to provide a dynamic domain - # shape. - raise NotImplementedError("_domain_shape_tensor is not implemented.") - - def range_shape_tensor(self, name="range_shape_tensor"): - """Range shape of this linear operator, determined at runtime. - - Args: - name: A `str`. A name scope to use for ops added by this method. - - Returns: - A 1D integer `tf.Tensor` representing the shape of the range of this - operator. - """ - with self._name_scope(name): # pylint: disable=not-callable - # Prefer to use statically defined shape if available. - if self.range_shape.is_fully_defined(): - return tensor_util.convert_shape_to_tensor(self.range_shape.as_list()) - return self._range_shape_tensor() - - def _range_shape_tensor(self): - # Users should override this method if they need to provide a dynamic range - # shape. - raise NotImplementedError("_range_shape_tensor is not implemented.") - - def batch_shape_tensor(self, name="batch_shape_tensor"): - """Batch shape of this linear operator, determined at runtime.""" - with self._name_scope(name): # pylint: disable=not-callable - if self.batch_shape.is_fully_defined(): - return tensor_util.convert_shape_to_tensor(self.batch_shape.as_list()) - return self._batch_shape_tensor() - - def _batch_shape_tensor(self): # pylint: disable=arguments-differ - # Users should override this method if they need to provide a dynamic batch - # shape. - return tf.constant([], dtype=tf.dtypes.int32) - - @property - def ndim(self): - """Logical number of dimensions of this linear operator. - - ```{note} - `ndim` can always be determined statically. - ``` - - ```{attention} - This number may differ from the number of dimensions in `domain_shape`, - `range_shape`, or both. - ``` - """ - return self._ndim() - - def _ndim(self): - # Users must override this method. - return None - - def flatten_domain_shape(self, x): - """Flattens `x` to match the domain dimension of this operator. - - Args: - x: A `Tensor`. Must have shape `[...] + self.domain_shape`. - - Returns: - The flattened `Tensor`. Has shape `[..., self.domain_dimension]`. - """ - # pylint: disable=invalid-unary-operand-type - domain_rank_static = self.domain_shape.rank - if domain_rank_static is not None: - domain_rank_dynamic = domain_rank_static - else: - domain_rank_dynamic = tf.shape(self.domain_shape_tensor())[0] - - if domain_rank_static is not None: - self.domain_shape.assert_is_compatible_with( - x.shape[-domain_rank_static:]) - - if domain_rank_static is not None: - batch_shape = x.shape[:-domain_rank_static] - else: - batch_shape = tf.TensorShape(None) - batch_shape_tensor = tf.shape(x)[:-domain_rank_dynamic] - - output_shape = batch_shape + self.domain_dimension - output_shape_tensor = tf.concat( - [batch_shape_tensor, [self.domain_dimension_tensor()]], 0) - - x = tf.reshape(x, output_shape_tensor) - return tf.ensure_shape(x, output_shape) - - def flatten_range_shape(self, x): - """Flattens `x` to match the range dimension of this operator. - - Args: - x: A `Tensor`. Must have shape `[...] + self.range_shape`. - - Returns: - The flattened `Tensor`. Has shape `[..., self.range_dimension]`. - """ - # pylint: disable=invalid-unary-operand-type - range_rank_static = self.range_shape.rank - if range_rank_static is not None: - range_rank_dynamic = range_rank_static - else: - range_rank_dynamic = tf.shape(self.range_shape_tensor())[0] - - if range_rank_static is not None: - self.range_shape.assert_is_compatible_with( - x.shape[-range_rank_static:]) - - if range_rank_static is not None: - batch_shape = x.shape[:-range_rank_static] - else: - batch_shape = tf.TensorShape(None) - batch_shape_tensor = tf.shape(x)[:-range_rank_dynamic] - - output_shape = batch_shape + self.range_dimension - output_shape_tensor = tf.concat( - [batch_shape_tensor, [self.range_dimension_tensor()]], 0) - - x = tf.reshape(x, output_shape_tensor) - return tf.ensure_shape(x, output_shape) - - def expand_domain_dimension(self, x): - """Expands `x` to match the domain shape of this operator. - - Args: - x: A `Tensor`. Must have shape `[..., self.domain_dimension]`. - - Returns: - The expanded `Tensor`. Has shape `[...] + self.domain_shape`. - """ - self.domain_dimension.assert_is_compatible_with(x.shape[-1]) - - batch_shape = x.shape[:-1] - batch_shape_tensor = tf.shape(x)[:-1] - - output_shape = batch_shape + self.domain_shape - output_shape_tensor = tf.concat([ - batch_shape_tensor, self.domain_shape_tensor()], 0) - - x = tf.reshape(x, output_shape_tensor) - return tf.ensure_shape(x, output_shape) - - def expand_range_dimension(self, x): - """Expands `x` to match the range shape of this operator. - - Args: - x: A `Tensor`. Must have shape `[..., self.range_dimension]`. - - Returns: - The expanded `Tensor`. Has shape `[...] + self.range_shape`. - """ - self.range_dimension.assert_is_compatible_with(x.shape[-1]) - - batch_shape = x.shape[:-1] - batch_shape_tensor = tf.shape(x)[:-1] - - output_shape = batch_shape + self.range_shape - output_shape_tensor = tf.concat([ - batch_shape_tensor, self.range_shape_tensor()], 0) - - x = tf.reshape(x, output_shape_tensor) - return tf.ensure_shape(x, output_shape) - - -@api_util.export("linalg.LinearOperatorMakeND") -@make_linear_operator_nd -class LinearOperatorMakeND(LinearOperatorND): - """Adds multidimensional support to a linear operator. - - Adds multidimensional shape information to a `LinearOperator` and support - for all `LinearOperatorND`-specific functionality, such as `matvec_nd`, - `solvevec_nd`, `domain_shape` and `range_shape`. - - If the input operator acts like matrix $A$, then this operator also acts - like matrix $A$. The functionality of the underlying operator is preserved, - with this operator having a superset of its functionality. - - ```{rubric} Initialization - ``` - This operator is initialized with a non-ND linear operator (`operator`) and - range/domain shape information (`range_shape` and `domain_shape`) - - Args: - operator: A `tfmri.linalg.LinearOperator`. If `operator` is an instance of - `LinearOperatorND`, then `operator` is returned unchanged. - range_shape: A `tf.Tensor` representing the range shape of the operator. - Must be compatible with the range dimension of `operator`. - domain_shape: A `tf.Tensor` representing the domain shape of the operator. - Must be compatible with the domain dimension of `operator`. - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `None`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `None`. - is_positive_definite: A boolean, or `None`. Whether this operator is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that an operator [does - not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) - Defaults to `None`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `None`. - name: An optional `str`. The name of this operator. - """ - def __new__(cls, operator, *args, **kwargs): - # If the input operator is already an ND operator, return it. - if isinstance(operator, LinearOperatorND): - return operator - return super().__new__(cls) - - def __init__(self, - operator, - range_shape=None, - domain_shape=None, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None, - name=None, - **kwargs): - # The arguments `range_shape_` and `domain_shape_` (with trailing - # underscores) are used when reconstructing the operator from its - # components. - if range_shape is None: - if 'range_shape_' not in kwargs: - raise ValueError("Argument `range_shape` must be specified.") - range_shape = kwargs['range_shape_'] - - if domain_shape is None: - if 'domain_shape_' not in kwargs: - raise ValueError("Argument `domain_shape` must be specified.") - domain_shape = kwargs['domain_shape_'] - - parameters = dict( - operator=operator, - range_shape_=range_shape, - domain_shape_=domain_shape, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name) - - if isinstance(operator, LinearOperatorND): - raise TypeError("operator is already a LinearOperatorND.") - if not isinstance(operator, linear_operator.LinearOperator): - raise TypeError(f"operator must be a LinearOperator, but got: {operator}") - self._operator = operator - - if (is_non_singular is not None and - operator.is_non_singular is not None and - is_non_singular != operator.is_non_singular): - raise ValueError("is_non_singular must match operator.is_non_singular.") - if is_non_singular is None: - is_non_singular = operator.is_non_singular - - if (is_self_adjoint is not None and - operator.is_self_adjoint is not None and - is_self_adjoint != operator.is_self_adjoint): - raise ValueError("is_self_adjoint must match operator.is_self_adjoint.") - if is_self_adjoint is None: - is_self_adjoint = operator.is_self_adjoint - - if (is_positive_definite is not None and - operator.is_positive_definite is not None and - is_positive_definite != operator.is_positive_definite): - raise ValueError( - "is_positive_definite must match operator.is_positive_definite.") - if is_positive_definite is None: - is_positive_definite = operator.is_positive_definite - - if (is_square is not None and - operator.is_square is not None and - is_square != operator.is_square): - raise ValueError("is_square must match operator.is_square.") - if is_square is None: - is_square = operator.is_square - - # Process the domain and range shapes and check that they are compatible. - self._domain_shape_static, self._domain_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) - self._range_shape_static, self._range_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape(range_shape)) - - if (self._domain_shape_static.num_elements() is not None and - operator.domain_dimension is not None and - self._domain_shape_static.num_elements() != operator.domain_dimension): - raise ValueError( - f"domain_shape must have the same number of elements as " - f"operator.domain_dimension. " - f"Found {self._domain_shape_static.num_elements()} " - f"and {operator.domain_dimension}, respectively.") - - if (self._range_shape_static.num_elements() is not None and - operator.range_dimension is not None and - self._range_shape_static.num_elements() != operator.range_dimension): - raise ValueError( - f"range_shape must have the same number of elements as " - f"operator.range_dimension. " - f"Found {self._range_shape_static.num_elements()} " - f"and {operator.range_dimension}, respectively.") - - # Initialization. - if name is None: - name = operator.name + "ND" - - with tf.name_scope(name): - super().__init__( - dtype=operator.dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters, - name=name) - - def _domain_shape(self): - return self._domain_shape_static - - def _domain_shape_tensor(self): - return self._domain_shape_dynamic - - def _range_shape(self): - return self._range_shape_static - - def _range_shape_tensor(self): - return self._range_shape_dynamic - - def _batch_shape(self): - return self.operator.batch_shape - - def _batch_shape_tensor(self): - return self.operator.batch_shape_tensor() - - def _matmul(self, x, adjoint=False, adjoint_arg=False): - return self.operator.matmul(x, adjoint=adjoint, adjoint_arg=adjoint_arg) - - def _matvec(self, x, adjoint=False): - return self.operator.matvec(x, adjoint=adjoint) - - def _solve(self, rhs, adjoint=False, adjoint_arg=False): - return self.operator.solve(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) - - def _solvevec(self, rhs, adjoint=False): - return self.operator.solvevec(rhs, adjoint=adjoint) - - def _lstsq(self, rhs, adjoint=False, adjoint_arg=False): - return self.operator.lstsq(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) - - def _lstsqvec(self, rhs, adjoint=False): - return self.oeprator.lstsqvec(rhs, adjoint=adjoint) - - def _matvec_nd(self, x, adjoint=False): - x = (self.flatten_range_shape(x) if adjoint else \ - self.flatten_domain_shape(x)) - x = self._matvec(x, adjoint=adjoint) - x = (self.expand_domain_dimension(x) if adjoint else - self.expand_range_dimension(x)) - return x - - def _solvevec_nd(self, x, adjoint=False): - x = (self.flatten_domain_shape(x) if adjoint else \ - self.flatten_range_shape(x)) - x = self._solvevec(x, adjoint=adjoint) - x = (self.expand_range_dimension(x) if adjoint else - self.expand_domain_dimension(x)) - return x - - def _lstsqvec_nd(self, x, adjoint=False): - x = (self.flatten_domain_shape(x) if adjoint else \ - self.flatten_range_shape(x)) - x = self._lstsqvec(x, adjoint=adjoint) - x = (self.expand_range_dimension(x) if adjoint else - self.expand_domain_dimension(x)) - return x - - def _add_to_tensor(self, x): - return self.operator.add_to_tensor(x) - - def _assert_non_singular(self): - return self.operator.assert_non_singular() - - def _assert_self_adjoint(self): - return self.operator.assert_self_adjoint() - - def _assert_positive_definite(self): - return self.operator.assert_positive_definite() - - def _cond(self): - return self.operator.cond() - - def _determinant(self): - return self.operator.determinant() - - def _diag_part(self): - return self.operator.diag_part() - - def _eigvals(self): - return self.operator.eigvals() - - def _log_abs_determinant(self): - return self.operator.log_abs_determinant() - - def _trace(self): - return self.operator.trace() - - def _to_dense(self): - return self.operator.to_dense() - - @property - def operator(self): - return self._operator - - @property - def _composite_tensor_fields(self): - # We use `domain_shape_` and `range_shape_` for conversion to/from composite tensor. - return ("operator", "range_shape_", "domain_shape_") - - @property - def _composite_tensor_prefer_static_fields(self): - return ("range_shape_", "domain_shape_") - - @property - def _experimental_parameter_ndims_to_matrix_ndims(self): - return {"operator": 0} diff --git a/tensorflow_mri/python/linalg/linear_operator_nd_test.py b/tensorflow_mri/python/linalg/linear_operator_nd_test.py deleted file mode 100644 index b20f8f0a..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_nd_test.py +++ /dev/null @@ -1,263 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator`.""" -# pylint: disable=missing-class-docstring,missing-function-docstring - -import functools - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_identity_nd -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.linalg import linear_operator_test_util -from tensorflow_mri.python.util import test_util - - -FullMatrix = tf.linalg.LinearOperatorFullMatrix -MakeND = linear_operator_nd.LinearOperatorMakeND - - -rng = np.random.RandomState(0) - - -class SquareLinearOperatorMakeNDTest( - linear_operator_test_util.SquareLinearOperatorDerivedClassTest): - """Tests for `LinearOperatorMakeND`.""" - domain_shape = (3, 2) - range_shape = (2, 3) - batch_shape = (2, 1) - - def operator_and_matrix(self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - shape = list(build_info.shape) - - if ensure_self_adjoint_and_pd: - matrix = linear_operator_test_util.random_positive_definite_matrix( - shape, dtype, force_well_conditioned=True) - else: - matrix = linear_operator_test_util.random_normal(shape=shape, dtype=dtype) - - if use_placeholder: - matrix = tf.compat.v1.placeholder_with_default(matrix, shape=None) - - operator = MakeND( - FullMatrix(matrix, - is_self_adjoint=True if ensure_self_adjoint_and_pd else None, - is_positive_definite=True if ensure_self_adjoint_and_pd else None, - is_square=True), - [shape[-2]], [shape[-1]], - is_self_adjoint=True if ensure_self_adjoint_and_pd else None, - is_positive_definite=True if ensure_self_adjoint_and_pd else None, - is_square=True) - - return operator, matrix - - def operator_and_operator_nd(self, - range_shape=range_shape, - domain_shape=domain_shape, - batch_shape=batch_shape): - range_dimension = functools.reduce(lambda x, y: x * y, range_shape) - domain_dimension = functools.reduce(lambda x, y: x * y, domain_shape) - - matrix = tf.random.uniform( - batch_shape + (range_dimension, domain_dimension)) - - operator = FullMatrix(matrix) - operator_nd = MakeND( - FullMatrix(matrix), range_shape, domain_shape) - - return operator, operator_nd - - def random_input(self, domain_shape=domain_shape, batch_shape=batch_shape): - x_nd = tf.random.normal(batch_shape + domain_shape) - x = tf.reshape(x_nd, batch_shape + (-1,)) - return x, x_nd - - def random_rhs(self, range_shape=range_shape, batch_shape=batch_shape): - rhs_nd = tf.random.normal(batch_shape + range_shape) - rhs = tf.reshape(rhs_nd, batch_shape + (-1,)) - return rhs, rhs_nd - - def test_is_nd_operator(self): - _, operator_nd = self.operator_and_operator_nd() - self.assertIsInstance(operator_nd, linear_operator_nd.LinearOperatorND) - - def test_name(self): - _, operator_nd = self.operator_and_operator_nd() - self.assertEqual("LinearOperatorFullMatrixND", operator_nd.name) - - def test_static_shapes(self): - operator, operator_nd = self.operator_and_operator_nd() - self.assertIsInstance(operator_nd.domain_shape, tf.TensorShape) - self.assertIsInstance(operator_nd.range_shape, tf.TensorShape) - self.assertIsInstance(operator_nd.batch_shape, tf.TensorShape) - self.assertIsInstance(operator_nd.shape, tf.TensorShape) - self.assertEqual(self.domain_shape, operator_nd.domain_shape) - self.assertEqual(self.range_shape, operator_nd.range_shape) - self.assertEqual(self.batch_shape, operator_nd.batch_shape) - self.assertEqual(operator.shape, operator_nd.shape) - - def test_dynamic_shapes(self): - operator, operator_nd = self.operator_and_operator_nd() - self.assertIsInstance(operator_nd.domain_shape_tensor(), tf.Tensor) - self.assertIsInstance(operator_nd.range_shape_tensor(), tf.Tensor) - self.assertIsInstance(operator_nd.batch_shape_tensor(), tf.Tensor) - self.assertIsInstance(operator_nd.shape_tensor(), tf.Tensor) - self.assertAllEqual(self.domain_shape, self.evaluate( - operator_nd.domain_shape_tensor())) - self.assertAllEqual(self.range_shape, self.evaluate( - operator_nd.range_shape_tensor())) - self.assertAllEqual(self.batch_shape, self.evaluate( - operator_nd.batch_shape_tensor())) - self.assertAllEqual(self.evaluate(operator.shape_tensor()), - self.evaluate(operator_nd.shape_tensor())) - - def test_operator_wrong_type(self): - class Cat(): - def say_hello(self): - return "meow" - - with self.assertRaisesRegex(TypeError, "must be a LinearOperator"): - MakeND(Cat(), (2, 3), (3, 2)) - - def test_nd_operator_returns_itself(self): - operator = linear_operator_identity_nd.LinearOperatorIdentityND( - domain_shape=(2, 3)) - operator_nd = MakeND(operator, (2, 3), (3, 2)) - self.assertIs(operator, operator_nd) - - def test_incompatible_domain_shape_raises(self): - operator, _ = self.operator_and_operator_nd() - with self.assertRaisesRegex( - ValueError, "domain_shape must have the same number of elements"): - MakeND( - operator, self.range_shape, (5, 3)) - - def test_incompatible_range_shape_raises(self): - operator, _ = self.operator_and_operator_nd() - with self.assertRaisesRegex( - ValueError, "range_shape must have the same number of elements"): - MakeND( - operator, (5, 3), self.domain_shape) - - def test_matvec(self): - operator, operator_nd = self.operator_and_operator_nd() - x, _ = self.random_input() - rhs, _ = self.random_rhs() - self.assertAllClose(operator.matvec(x), - operator_nd.matvec(x)) - self.assertAllClose(operator.matvec(rhs, adjoint=True), - operator_nd.matvec(rhs, adjoint=True)) - - def test_matmul(self): - operator, operator_nd = self.operator_and_operator_nd() - x, _ = self.random_input() - rhs, _ = self.random_rhs() - self.assertAllClose( - operator.matmul(x[..., tf.newaxis]), - operator_nd.matmul(x[..., tf.newaxis])) - self.assertAllClose( - operator.matmul(x[..., tf.newaxis, :], adjoint_arg=True), - operator_nd.matmul(x[..., tf.newaxis, :], adjoint_arg=True)) - self.assertAllClose( - operator.matmul(rhs[..., tf.newaxis], adjoint=True), - operator_nd.matmul(rhs[..., tf.newaxis], adjoint=True)) - self.assertAllClose( - operator.matmul(rhs[..., tf.newaxis, :], adjoint=True, adjoint_arg=True,), - operator_nd.matmul(rhs[..., tf.newaxis, :], adjoint=True, adjoint_arg=True)) - - def test_solvevec(self): - operator, operator_nd = self.operator_and_operator_nd() - x, _ = self.random_input() - rhs, _ = self.random_rhs() - self.assertAllClose(operator.solvevec(rhs), - operator_nd.solvevec(rhs)) - self.assertAllClose(operator.solvevec(x, adjoint=True), - operator_nd.solvevec(x, adjoint=True)) - - def test_solve(self): - operator, operator_nd = self.operator_and_operator_nd() - x, _ = self.random_input() - rhs, _ = self.random_rhs() - self.assertAllClose( - operator.solve(rhs[..., tf.newaxis]), - operator_nd.solve(rhs[..., tf.newaxis])) - self.assertAllClose( - operator.solve(rhs[..., tf.newaxis, :], adjoint_arg=True), - operator_nd.solve(rhs[..., tf.newaxis, :], adjoint_arg=True)) - self.assertAllClose( - operator.solve(x[..., tf.newaxis], adjoint=True), - operator_nd.solve(x[..., tf.newaxis], adjoint=True)) - self.assertAllClose( - operator.solve(x[..., tf.newaxis, :], adjoint=True, adjoint_arg=True,), - operator_nd.solve(x[..., tf.newaxis, :], adjoint=True, adjoint_arg=True)) - - def test_matvec_nd(self): - range_shape, domain_shape, batch_shape = ( - self.range_shape, self.domain_shape, self.batch_shape) - batch_shape = self.batch_shape - operator, operator_nd = self.operator_and_operator_nd() - x, x_nd = self.random_input() - rhs, rhs_nd = self.random_rhs() - - self.assertAllClose( - tf.reshape(operator.matvec(x), batch_shape + range_shape), - operator_nd.matvec_nd(x_nd)) - - self.assertAllClose( - tf.reshape(operator.matvec(rhs, adjoint=True), batch_shape + domain_shape), - operator_nd.matvec_nd(rhs_nd, adjoint=True)) - - def test_solvevec_nd(self): - range_shape, domain_shape, batch_shape = ( - self.range_shape, self.domain_shape, self.batch_shape) - batch_shape = self.batch_shape - operator, operator_nd = self.operator_and_operator_nd() - x, x_nd = self.random_input() - rhs, rhs_nd = self.random_rhs() - - self.assertAllClose( - tf.reshape(operator.solvevec(rhs), batch_shape + domain_shape), - operator_nd.solvevec_nd(rhs_nd)) - - self.assertAllClose( - tf.reshape(operator.solvevec(x, adjoint=True), batch_shape + range_shape), - operator_nd.solvevec_nd(x_nd, adjoint=True)) - - -class NonSquareLinearOperatorMakeNDTest( - linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): - """Tests for `LinearOperatorMakeND`.""" - def operator_and_matrix(self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - shape = list(build_info.shape) - - matrix = linear_operator_test_util.random_normal(shape=shape, dtype=dtype) - - if use_placeholder: - matrix = tf.compat.v1.placeholder_with_default(matrix, shape=None) - - operator = MakeND(FullMatrix(matrix), [shape[-2]], [shape[-1]]) - - return operator, matrix - - -linear_operator_test_util.add_tests(SquareLinearOperatorMakeNDTest) -linear_operator_test_util.add_tests(NonSquareLinearOperatorMakeNDTest) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_nufft.py b/tensorflow_mri/python/linalg/linear_operator_nufft.py deleted file mode 100644 index dd1b85cf..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_nufft.py +++ /dev/null @@ -1,778 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Non-uniform Fourier linear operators.""" - -import warnings - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.linalg import linear_operator_util -from tensorflow_mri.python.ops import array_ops -from tensorflow_mri.python.ops import traj_ops -from tensorflow_mri.python.ops import fft_ops -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import tensor_util -from tensorflow_mri.python.util import types_util - - -@api_util.export("linalg.LinearOperatorNUFFT") -@linear_operator_nd.make_linear_operator_nd -class LinearOperatorNUFFT(linear_operator_nd.LinearOperatorND): - r"""Linear operator acting like a [batch] nonuniform Fourier matrix. - - Performs an N-dimensional discrete Fourier transform via the nonuniform fast - Fourier transform (NUFFT) algorithm. Let $A$ represent this linear operator, - then: - - - The forward operator $A$ evaluates the forward, type-2 NUFFT (signal domain - to frequency domain, uniform to nonuniform). - - The adjoint operator $A^H$ evaluates the backward, type-1 NUFFT - (frequency domain to signal domain, nonuniform to uniform). - - The dimensionality of the grid $n = n_0 \times ... \times n_d$ is determined - by `domain_shape`. The $m$ non-uniform sampling locations in the frequency - domain are defined by `points`. - - ```{rubric} Inverse NUFFT - ``` - ```{note} - The NUFFT operator is not generally invertible, so calling `inverse` or - `solve` (or the related `solvevec` and `solvevec_nd`) will raise an error. - ``` - - However, you can solve $Ax = b$ in the least-squares sense. - - One approach is to use this operator's `lstsq` method (or one of the related - methods `lstsqvec` and `lstsqvec_nd`). - - ```{attention} - If you intend to use `lstsq`, `lstsqvec`, or `lstsqvec_nd`, you should - consider providing `crosstalk_inverse` (see below). If you do not provide - this argument, the solution will be computed using a potentially very slow - algorithm which requires conversion to a dense matrix. - ``` - - Alternatively, you could use `tfmri.linalg.lsqr` or - `tfmri.linalg.conjugate_gradient` to solve the least-squares problem - iteratively. - - ```{rubric} Fourier crosstalk matrix - ``` - The Fourier crosstalk matrix is the matrix $D = A A^H$ (if $m < n$) or the - matrix $D = A^H A$ (if $m > n$). The solution to the least-squares problem - can be written in terms of $D$ as $x = A^H D^{-1} b$ (if $m < n$) or - $x = D^{-1} A b$ (if $m$ > $n$). - - Hence, if $D{-1}$ is known, the least-squares problem can be solved without - performing an explicit inversion. The argument `crosstalk_inverse` allows - you to provide $D^{-1}$. - - The matrix $D$ (and hence, $D^{-1}$) is dependent on the sampling locations - `points`. For arbitrary sampling patterns, this matrix is full and requires - $O(l^2)$ storage, with a runtime complexity of $O(l^3)$ for matrix-matrix - multiplication (where $l = \min{(m, n)}$). This is clearly impractical for - large $l$. Furthermore, in this case one might as well just store and apply - $A^{-1}$ directly. - - A more interesting use of `crosstalk_inverse` is to provide an approximation - to $D^{-1}$ with a more favorable structure. A common choice is to use a - diagonal matrix, which requires only $O(l)$ storage and whose matrix-matrix - product runs in $O(l^2)$ time. In MRI, this is often referred to as - **sampling density compensation**. - - ```{tip} - If `weights` are your density compensation weights, use - `crosstalk_inverse=tfmri.linalg.LinearOperatorDiag(weights)`. - ``` - - ```{rubric} TLDR: how do I invert the NUFFT? - ``` - Essentially, you have three options: - - 1. **Direct solve** (not recommmended). Simply call `lstsq` (or one of the - related methods `lstsqvec` and `lstsqvec_nd`). This is the most - straightforward approach, but it is likely to be very slow for large $l$. - If you can't or don't want to provide $D^{-1}$ (or an approximation - thereof), you're probably better off using method 3. - 2. **Direct solve with crosstalk approximation** (in MRI, sometimes called - the **conjugate phase** method): If the inverse of the Fourier crosstalk - matrix $D^{-1}$ has favorable structure (i.e., it does not have large - storage requirements and it can be applied quickly), or you can use an - approximation which does, specify `crosstalk_inverse` and then use `lstsq` - (or one of the related methods `lstsqvec` and `lstsqvec_nd`). In MRI, a - common choice of approximation is a diagonal matrix containing whose - diagonal elements are the density compensation weights. Under these - conditions, this method is probably the fastest, but might compromise - accuracy depending on your choice of $D^{-1}$. - 3. **Iterative solve**: If you do not know `D{-1}`, or if accuracy is - paramount, use `tfmri.linalg.lsqr` or `tfmri.linalg.conjugate_gradient` - to solve the least-squares problem iteratively. This method is likely - to be slower than method 2 but faster than method 3 due to its iterative - nature. - ``` - - ```{seealso} - `tfmri.linalg.LinearOperatorFFT` for uniformly sampled Fourier transforms. - ``` - - Args: - domain_shape: A 1D integer `tf.Tensor`. The domain shape of this - operator. This is usually the shape of the image but may include - additional leading dimensions. The trailing `d` dimensions (inferred - from `points`) are the signal dimensions, and any additional leading - dimensions are technically batch dimensions which are included in the - domain rather than the batch. - points: A `tf.Tensor` of type `float32` or `float64`. Contains the - non-uniform sampling locations in the frequency domain. Must have - shape `[..., m, d]`, where `d` is the dimension of the Fourier transform - (must be 1, 2 or 3), `m` is the number of samples and `...` is the - batch shape, which can have any number of dimensions. Must be in the - range $[-\pi, \pi]$. - ```{tip} - In MRI, this is the *k*-space trajectory. - ``` - crosstalk_inverse: A `tf.Tensor` or `tf.linalg.LinearOperator` of shape - `[..., l, l]` representing the inverse of the Fourier crosstalk - matrix [2], where $l = \min{(m, n)}$. This matrix is used to simplify the - computation of the pseudo-inverse $A^{+}$ and/or to solve the - least-squares problem defined by this operator. Ideally this matrix - should be equal to $(A A^H)^{-1}$ (if $m < n$) or $(A^H A)^{-1}$ - (if $m > n$), where $A$ is this operator, but you can also provide a - suitable approximation with a more favorable structure. Defaults to - `None`. - ```{attention} - If you intend to use `lstsq`, `lstsqvec`, or `lstsqvec_nd`, you are - strongly encouraged to provide `crosstalk_inverse`. If you do not, - these methods will use a potentially very slow algorithm which requires - conversion to a dense matrix. - ``` - ```{warning} - This operator will not check `crosstalk_inverse` for correctness. It is - your responsibility to ensure that it is reasonable your purposes. - ``` - ```{tip} - In MRI, you can use `crosstalk_inverse` for density compensation by - specifying a diagonal operator whose diagonal elements are the density - compensation weights. - ``` - ```{tip} - If you do not need to invert this operator, you can safely ignore this - argument. - ``` - is_non_singular: A boolean, or `None`. Whether this operator is expected - to be non-singular. Defaults to `None`. - is_self_adjoint: A boolean, or `None`. Whether this operator is expected - to be equal to its Hermitian transpose. If `dtype` is real, this is - equivalent to being symmetric. Defaults to `False`. - is_positive_definite: A boolean, or `None`. Whether this operator is - expected to be positive definite, meaning the quadratic form $x^H A x$ - has positive real part for all nonzero $x$. Note that an operator [does - not need to be self-adjoint to be positive definite](https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices) - Defaults to `None`. - is_square: A boolean, or `None`. Expect that this operator acts like a - square matrix (or a batch of square matrices). Defaults to `False`. - name: An optional `str`. The name of this operator. - - Example: - >>> # Create some data. - >>> image_shape = (128, 128) - >>> image = tfmri.image.phantom(shape=image_shape, dtype=tf.complex64) - >>> trajectory = tfmri.sampling.radial_trajectory( - ... base_resolution=128, views=129, flatten_encoding_dims=True) - >>> density = tfmri.sampling.radial_density( - ... base_resolution=128, views=129, flatten_encoding_dims=True) - >>> # Create a density compensation matrix. This will be used to invert - >>> # the operator more efficiently. - >>> weights = tf.math.reciprocal(density) - >>> linop_density = tf.linalg.LinearOperatorDiag(weights) - >>> # Create a NUFFT operator. - >>> linop = tfmri.linalg.LinearOperatorNUFFT( - ... image_shape, points=trajectory, crosstalk_inverse=linop_density) - >>> # Compute k-space by applying the forward operator. - >>> kspace = linop.matvec_nd(image) - >>> # Reconstruct the image by solving the corresponding least-squares - >>> # problem. - >>> recon = linop.lstsqvec_nd(kspace) - - References: - 1. A. H. Barnett, J. Magland, and L. af Klinteberg, "A Parallel Nonuniform - Fast Fourier Transform Library Based on an "Exponential of Semicircle" - Kernel", *SIAM Journal on Scientific Computing*, vol. 41, no. 5, - pp. C479-C504, 2019, - doi: [10.1137/18M120885X](https://doi.org/10.1137/18M120885X) - 2. Y. Shih, G. Wright, J. Anden, J. Blaschke, and A. H. Barnett, - "cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform - FFTs,” in *2021 IEEE International Parallel and Distributed Processing - Symposium Workshops (IPDPSW)*, 2021, pp. 688-697. - doi: [10.1109/IPDPSW52791.2021.00105](https://doi.org/10.1109/IPDPSW52791.2021.00105) - 3. J. A. Fessler and B. P. Sutton, "Nonuniform fast Fourier transforms - using min-max interpolation", *IEEE Transactions on Signal Processing*, - vol. 51, no. 2, pp. 560-574, 2003, - doi: [10.1109/TSP.2002.807005](https://doi.org/10.1109/TSP.2002.807005) - 4. H. H. Barrett, J. L. Denny, R. F. Wagner, and K. J. Myers, "Objective - assessment of image quality. II. Fisher information, Fourier crosstalk, - and figures of merit for task performance", *J. Opt. Soc. Am. A*, - vol. 12, no. 5, pp. 834-852, May 1995, - doi: [10.1364/JOSAA.12.000834](https://doi.org/10.1364/josaa.12.000834) - """ - def __init__(self, - domain_shape, - points, - crosstalk_inverse=None, - is_non_singular=None, - is_self_adjoint=False, - is_positive_definite=None, - is_square=None, - name="LinearOperatorNUFFT"): - - parameters = dict( - domain_shape=domain_shape, - points=points, - crosstalk_inverse=crosstalk_inverse, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - name=name - ) - - # Check non-reference types. - types_util.assert_not_ref_type(domain_shape, "domain_shape") - - # Get domain shapes. - self._domain_shape_static, self._domain_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) - - # Validate the remaining inputs. - self._points = tf.convert_to_tensor(points, name="points") - if self._points.dtype not in (tf.float32, tf.float64): - raise TypeError( - f"points must be a float32 or float64 tensor, " - f"not {str(self._points.dtype)}") - - # Get dtype for this operator. - dtype = tensor_util.get_complex_dtype(self._points.dtype) - - # We infer the operation's rank from the points. - self._ndim_static = self._points.shape[-1] - self._rank_dynamic = tf.shape(self._points)[-1] - # The domain rank is >= the operation rank. - domain_rank_static = self._domain_shape_static.rank - domain_rank_dynamic = tf.shape(self._domain_shape_dynamic)[0] - # The difference between this operation's rank and the domain rank is the - # number of extra dims. - extra_dims_static = domain_rank_static - self._ndim_static - extra_dims_dynamic = domain_rank_dynamic - self._rank_dynamic - - # The grid shape are the last `rank` dimensions of domain_shape. We don't - # need the static grid shape. - self._grid_shape = self._domain_shape_dynamic[-self._rank_dynamic:] - - # We need to do some work to figure out the batch shapes. This operator - # could have a batch shape, if the points have a batch shape. However, - # we allow the user to include one or more batch dimensions in the domain - # shape, if they so wish. Therefore, not all batch dimensions in the - # points are necessarily part of the batch shape. - - # The total number of dimensions in `points` is equal to - # `batch_dims + extra_dims + 2`. - # Compute the true batch shape (i.e., the batch dimensions that are - # NOT included in the domain shape). - batch_dims_dynamic = tf.rank(self._points) - extra_dims_dynamic - 2 - if (self._points.shape.rank is not None and - extra_dims_static is not None): - # We know the total number of dimensions in `points` and we know - # the number of extra dims, so we can compute the number of batch dims - # statically. - batch_dims_static = self._points.shape.rank - extra_dims_static - 2 - else: - # We are missing at least some information, so the number of batch - # dimensions is unknown. - batch_dims_static = None - - self._batch_shape_dynamic = tf.shape(self._points)[:batch_dims_dynamic] - if batch_dims_static is not None: - self._batch_shape_static = self._points.shape[:batch_dims_static] - else: - self._batch_shape_static = tf.TensorShape(None) - - # Compute the "extra" shape. This shape includes those dimensions which - # are not part of the NUFFT (e.g., they are effectively batch dimensions), - # but which are included in the domain shape rather than in the batch shape. - extra_shape_dynamic = self._domain_shape_dynamic[:-self._rank_dynamic] - if self._ndim_static is not None: - extra_shape_static = self._domain_shape_static[:-self._ndim_static] - else: - extra_shape_static = tf.TensorShape(None) - - # Check that the "extra" shape in `domain_shape` and `points` are - # compatible for broadcasting. - shape1, shape2 = extra_shape_static, self._points.shape[:-2] - try: - tf.broadcast_static_shape(shape1, shape2) - except ValueError as err: - raise ValueError( - f"The \"batch\" shapes in `domain_shape` and `points` are not " - f"compatible for broadcasting: {shape1} vs {shape2}") from err - - # Compute the range shape. - self._range_shape_dynamic = tf.concat( - [extra_shape_dynamic, tf.shape(self._points)[-2:-1]], 0) - self._range_shape_static = extra_shape_static.concatenate( - self._points.shape[-2:-1]) - - # Set inverse of Fourier crosstalk matrix. - # This needs to be done after setting all the shapes, as `self.shape` - # must be valid at this point. - self._crosstalk_inverse = crosstalk_inverse - if self._crosstalk_inverse is not None: - if not isinstance(self._crosstalk_inverse, tf.linalg.LinearOperator): - # Not a linear operator, assume a full matrix was passed. - self._crosstalk_inverse = tf.linalg.LinearOperatorFullMatrix( - self._crosstalk_inverse) - if not self._crosstalk_inverse.shape[-2:-1].is_compatible_with( - self._crosstalk_inverse.shape[-1:]): - raise ValueError( - f"The crosstalk matrix must be square. Got shape " - f"{self._crosstalk_inverse.shape}.") - if self.shape[-2:].is_fully_defined(): - if self.shape[-2] < self.shape[-1]: - if not self._crosstalk_inverse.shape[-2:-1].is_compatible_with( - self.shape[-2:-1]): - raise ValueError( - f"The crosstalk matrix must have the same number of rows as " - f"this operator. Got shape {self._crosstalk_inverse.shape} for " - f"operator shape {self.shape}.") - else: - if not self._crosstalk_inverse.shape[-1:].is_compatible_with( - self.shape[-1:]): - raise ValueError( - f"The crosstalk matrix must have the same number of columns as " - f"this operator. Got shape {self._crosstalk_inverse.shape} for " - f"operator shape {self.shape}.") - - # Compute normalization factors. - self._norm_factor = tf.math.reciprocal( - tf.math.sqrt(tf.cast(tf.math.reduce_prod(self._grid_shape), dtype))) - - # Default tolerance for NUFFT. - self._tol = {tf.complex64: 1e-6, tf.complex128: 1e-12}[dtype] - - super().__init__(dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters, - name=name) - - def _matvec_nd(self, x, adjoint=False): - if adjoint: - x = fft_ops.nufft(x, self._points, - grid_shape=self._grid_shape, - transform_type='type_1', - fft_direction='backward', - tol=self._tol) - x *= self._norm_factor - else: - x = fft_ops.nufft(x, self._points, - transform_type='type_2', - fft_direction='forward', - tol=self._tol) - x *= self._norm_factor - return x - - def _solvevec_nd(self, rhs, adjoint=False): - raise ValueError( - f"{self.name} is not invertible. If you intend to solve the " - f"associated least-squares problem, use `lstsq`, `lstsqvec` or " - f"`lstsqvec_nd`.") - - def _lstsqvec_nd(self, rhs, adjoint=False): - if self._crosstalk_inverse is None: - warnings.warn( - f"{self.name}: Using (possibly slow) implementation of lstsq which " - f"requires conversion to a dense matrix and O(n^3) operations. " - f"For a more efficient computation, consider specifying the " - f"`crosstalk_inverse` argument (see class documentation) or using " - f"an iterative solver such as `tfmri.linalg.lsqr` or " - f"`tfmri.linalg.conjugate_gradient`.") - rhs = tf.expand_dims(rhs, -1) - x = linear_operator_util.matrix_solve_ls_with_broadcast( - self.to_dense(), rhs, adjoint=adjoint) - x = tf.squeeze(x, -1) - return x - if self.shape[-2:].is_fully_defined(): - # We know the static shape of the operator, so we can select the - # appropriate code path when building the graph. - if (self.shape[-2] < self.shape[-1]) ^ adjoint: # pylint: disable=no-else-return - return self._lstsqvec_nd_underdetermined(rhs, adjoint=adjoint) - else: - return self._lstsqvec_nd_overdetermined(rhs, adjoint=adjoint) - else: - # We don't know the static shape of the operator, so we need to - # defer the selection of the code path until runtime. - return tf.cond( - tf.math.logical_xor( - tf.math.less(self.shape_tensor()[-2], self.shape_tensor()[-1]), - adjoint), - lambda: self._lstsqvec_nd_underdetermined(rhs, adjoint=adjoint), - lambda: self._lstsqvec_nd_overdetermined(rhs, adjoint=adjoint)) - - def _lstsqvec_nd_underdetermined(self, rhs, adjoint=False): - # Solve A x = b as A^H (A A^H)^-1 b, where (A A^H)^-1 is the inverse of - # the Fourier crosstalk matrix. - if isinstance(self._crosstalk_inverse, - linear_operator_nd.LinearOperatorND): - rhs = self._crosstalk_inverse.matvec_nd(rhs) - else: - if adjoint: - rhs = self.flatten_domain_shape(rhs) - else: - rhs = self.flatten_range_shape(rhs) - rhs = self._crosstalk_inverse.matvec(rhs) - if adjoint: - rhs = self.expand_domain_dimension(rhs) - else: - rhs = self.expand_range_dimension(rhs) - x = self._matvec_nd(rhs, adjoint=(not adjoint)) - return x - - def _lstsqvec_nd_overdetermined(self, rhs, adjoint=False): - # Solve A x = b as (A^H A)^-1 A^H b, where (A^H A)^-1 is the inverse of - # the Fourier crosstalk matrix. - x = self._matvec_nd(rhs, adjoint=(not adjoint)) - if isinstance(self._crosstalk_inverse, - linear_operator_nd.LinearOperatorND): - x = self._crosstalk_inverse.matvec_nd(x) - else: - if adjoint: - x = self.flatten_range_shape(x) - else: - x = self.flatten_domain_shape(x) - x = self._crosstalk_inverse.matvec(x) - if adjoint: - x = self.expand_range_dimension(x) - else: - x = self.expand_domain_dimension(x) - return x - - def _domain_shape(self): - return self._domain_shape_static - - def _domain_shape_tensor(self): - return self._domain_shape_dynamic - - def _range_shape(self): - return self._range_shape_static - - def _range_shape_tensor(self): - return self._range_shape_dynamic - - def _batch_shape(self): - return self._batch_shape_static - - def _batch_shape_tensor(self): - return self._batch_shape_dynamic - - def _ndim(self): - return self._ndim_static - - @property - def points(self): - return self._points - - @property - def crosstalk_inverse(self): - return self._crosstalk_inverse - - @property - def _composite_tensor_fields(self): - return ('domain_shape', 'points', 'crosstalk_inverse') - - @property - def _composite_tensor_prefer_static_fields(self): - return ('domain_shape',) - - @property - def _experimental_parameter_ndims_to_matrix_ndims(self): - return {'points': 2, 'crosstalk_inverse': 0} - - -@api_util.export("linalg.LinearOperatorGramNUFFT") -class LinearOperatorGramNUFFT(LinearOperatorNUFFT): # pylint: disable=abstract-method - """Linear operator acting like the Gram matrix of an NUFFT operator. - - If $F$ is a `tfmri.linalg.LinearOperatorNUFFT`, then this operator - applies $F^H F$. This operator is self-adjoint. - - Args: - domain_shape: A 1D integer `tf.Tensor`. The domain shape of this - operator. This is usually the shape of the image but may include - additional dimensions. - trajectory: A `tf.Tensor` of type `float32` or `float64`. Contains the - sampling locations or *k*-space trajectory. Must have shape - `[..., m, n]`, where `n` is the rank (number of dimensions), `m` is - the number of samples and `...` is the batch shape, which can have any - number of dimensions. - density: A `tf.Tensor` of type `float32` or `float64`. Contains the - sampling density at each point in `trajectory`. Must have shape - `[..., m]`, where `m` is the number of samples and `...` is the batch - shape, which can have any number of dimensions. Defaults to `None`, in - which case the density is assumed to be 1.0 in all locations. - norm: A `str`. The FFT normalization mode. Must be `None` (no normalization) - or `'ortho'`. - toeplitz: A `boolean`. If `True`, uses the Toeplitz approach [1] - to compute $F^H F x$, where $F$ is the NUFFT operator. - If `False`, the same operation is performed using the standard - NUFFT operation. The Toeplitz approach might be faster than the direct - approach but is slightly less accurate. This argument is only relevant - for non-Cartesian reconstruction and will be ignored for Cartesian - problems. - name: An optional `str`. The name of this operator. - - References: - 1. Fessler, J. A., Lee, S., Olafsson, V. T., Shi, H. R., & Noll, D. C. - (2005). Toeplitz-based iterative image reconstruction for MRI with - correction for magnetic field inhomogeneity. IEEE Transactions on Signal - Processing, 53(9), 3393-3402. - """ - def __init__(self, - domain_shape, - trajectory, - density=None, - norm='ortho', - toeplitz=False, - name="LinearOperatorNUFFT"): - super().__init__( - domain_shape=domain_shape, - trajectory=trajectory, - density=density, - norm=norm, - name=name - ) - - self.toeplitz = toeplitz - if self.toeplitz: - # Compute the FFT shift for adjoint NUFFT computation. - self._fft_shift = tf.cast(self._grid_shape // 2, self.dtype.real_dtype) - # Compute the Toeplitz kernel. - self._toeplitz_kernel = self._compute_toeplitz_kernel() - # Kernel shape (without batch dimensions). - self._kernel_shape = tf.shape(self._toeplitz_kernel)[-self.rank_tensor():] - - def _transform(self, x, adjoint=False): # pylint: disable=unused-argument - """Applies this linear operator.""" - # This operator is self-adjoint, so `adjoint` arg is unused. - if self.toeplitz: - # Using specialized Toeplitz implementation. - return self._transform_toeplitz(x) - # Using standard NUFFT implementation. - return super()._transform(super()._transform(x), adjoint=True) - - def _transform_toeplitz(self, x): - """Applies this linear operator using the Toeplitz approach.""" - input_shape = tf.shape(x) - fft_axes = tf.range(-self.rank_tensor(), 0) - x = fft_ops.fftn(x, axes=fft_axes, shape=self._kernel_shape) - x *= self._toeplitz_kernel - x = fft_ops.ifftn(x, axes=fft_axes) - x = tf.slice(x, tf.zeros([tf.rank(x)], dtype=tf.int32), input_shape) - return x - - def _compute_toeplitz_kernel(self): - """Computes the kernel for the Toeplitz approach.""" - trajectory = self._trajectory - weights = self._weights - if self.rank is None: - raise NotImplementedError( - f"The rank of {self.name} must be known statically.") - - if weights is None: - # If no weights were passed, use ones. - weights = tf.ones(tf.shape(trajectory)[:-1], dtype=self.dtype.real_dtype) - # Cast weights to complex dtype. - weights = tf.cast(tf.math.sqrt(weights), self.dtype) - - # Compute N-D kernel recursively. Begin with last axis. - last_axis = self.rank - 1 - kernel = self._compute_kernel_recursive(trajectory, weights, last_axis) - - # Make sure that the kernel is symmetric/Hermitian/self-adjoint. - kernel = self._enforce_kernel_symmetry(kernel) - - # Additional normalization by sqrt(2 ** rank). This is required because - # we are using FFTs with twice the length of the original image. - if self._norm == 'ortho': - kernel *= tf.cast(tf.math.sqrt(2.0 ** self.rank), kernel.dtype) - - # Put the kernel in Fourier space. - fft_axes = list(range(-self.rank, 0)) - fft_norm = self._norm or "backward" - return fft_ops.fftn(kernel, axes=fft_axes, norm=fft_norm) - - def _compute_kernel_recursive(self, trajectory, weights, axis): - """Recursively computes the kernel for the Toeplitz approach. - - This function works by computing the two halves of the kernel along each - axis. The "left" half is computed using the input trajectory. The "right" - half is computed using the trajectory flipped along the current axis, and - then reversed. Then the two halves are concatenated, with a block of zeros - inserted in between. If there is more than one axis, the process is repeated - recursively for each axis. - - This function calls the adjoint NUFFT 2 ** N times, where N is the number - of dimensions. NOTE: this could be optimized to 2 ** (N - 1) calls. - - Args: - trajectory: A `tf.Tensor` containing the current *k*-space trajectory. - weights: A `tf.Tensor` containing the current density compensation - weights. - axis: An `int` denoting the current axis. - - Returns: - A `tf.Tensor` containing the kernel. - - Raises: - NotImplementedError: If the rank of the operator is not known statically. - """ - # Account for the batch dimensions. We do not need to do the recursion - # for these. - batch_dims = self.batch_shape.rank - if batch_dims is None: - raise NotImplementedError( - f"The number of batch dimensions of {self.name} must be known " - f"statically.") - # The current axis without the batch dimensions. - image_axis = axis + batch_dims - if axis == 0: - # Outer-most axis. Compute left half, then use Hermitian symmetry to - # compute right half. - # TODO(jmontalt): there should be a way to compute the NUFFT only once. - kernel_left = self._nufft_adjoint(weights, trajectory) - flippings = tf.tensor_scatter_nd_update( - tf.ones([self.rank_tensor()]), [[axis]], [-1]) - kernel_right = self._nufft_adjoint(weights, trajectory * flippings) - else: - # We still have two or more axes to process. Compute left and right kernels - # by calling this function recursively. We call ourselves twice, first - # with current frequencies, then with negated frequencies along current - # axes. - kernel_left = self._compute_kernel_recursive( - trajectory, weights, axis - 1) - flippings = tf.tensor_scatter_nd_update( - tf.ones([self.rank_tensor()]), [[axis]], [-1]) - kernel_right = self._compute_kernel_recursive( - trajectory * flippings, weights, axis - 1) - - # Remove zero frequency and reverse. - kernel_right = tf.reverse(array_ops.slice_along_axis( - kernel_right, image_axis, 1, tf.shape(kernel_right)[image_axis] - 1), - [image_axis]) - - # Create block of zeros to be inserted between the left and right halves of - # the kernel. - zeros_shape = tf.concat([ - tf.shape(kernel_left)[:image_axis], [1], - tf.shape(kernel_left)[(image_axis + 1):]], 0) - zeros = tf.zeros(zeros_shape, dtype=kernel_left.dtype) - - # Concatenate the left and right halves of kernel, with a block of zeros in - # the middle. - kernel = tf.concat([kernel_left, zeros, kernel_right], image_axis) - return kernel - - def _nufft_adjoint(self, x, trajectory=None): - """Applies the adjoint NUFFT operator. - - We use this instead of `super()._transform(x, adjoint=True)` because we - need to be able to change the trajectory and to apply an FFT shift. - - Args: - x: A `tf.Tensor` containing the input data (typically the weights or - ones). - trajectory: A `tf.Tensor` containing the *k*-space trajectory, which - may have been flipped and therefore different from the original. If - `None`, the original trajectory is used. - - Returns: - A `tf.Tensor` containing the result of the adjoint NUFFT. - """ - # Apply FFT shift. - x *= tf.math.exp(tf.dtypes.complex( - tf.constant(0, dtype=self.dtype.real_dtype), - tf.math.reduce_sum(trajectory * self._fft_shift, -1))) - # Temporarily update trajectory. - if trajectory is not None: - temp = self._trajectory - self._trajectory = trajectory - x = super()._transform(x, adjoint=True) - if trajectory is not None: - self._trajectory = temp - return x - - def _enforce_kernel_symmetry(self, kernel): - """Enforces Hermitian symmetry on an input kernel. - - Args: - kernel: A `tf.Tensor`. An approximately Hermitian kernel. - - Returns: - A Hermitian-symmetric kernel. - """ - kernel_axes = list(range(-self.rank, 0)) - reversed_kernel = tf.roll( - tf.reverse(kernel, kernel_axes), - shift=tf.ones([tf.size(kernel_axes)], dtype=tf.int32), - axis=kernel_axes) - return (kernel + tf.math.conj(reversed_kernel)) / 2 - - def _range_shape(self): - # Override the NUFFT operator's range shape. The range shape for this - # operator is the same as the domain shape. - return self._domain_shape() - - def _range_shape_tensor(self): - return self._domain_shape_tensor() - - -@api_util.export("linalg.nudft_matrix") -def nudft_matrix(domain_shape, points): - """Constructs a non-uniform discrete Fourier transform (NUDFT) matrix.""" - domain_shape_static, domain_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) - if domain_shape_static.is_fully_defined(): - domain_shape = domain_shape_static.as_list() - else: - domain_shape = domain_shape_dynamic - - # For reshape. - if domain_shape_static.rank is not None: - grid_shape = [-1, domain_shape_static.rank] - else: - grid_shape = tf.concat([[-1], tf.size(domain_shape)], 0) - - grid = traj_ops.frequency_grid( - domain_shape, max_val=tf.constant(0.5, dtype=points.dtype)) - grid = tf.reshape(grid, grid_shape) - grid *= tf.cast(domain_shape, dtype=points.dtype) - - m = tf.linalg.matmul(points, tf.linalg.matrix_transpose(grid)) - m = tf.math.exp(tf.dtypes.complex( - tf.constant(0.0, dtype=points.dtype), tf.math.negative(m))) - m /= tf.math.sqrt(tf.cast(tf.math.reduce_prod(domain_shape), m.dtype)) - - return m diff --git a/tensorflow_mri/python/linalg/linear_operator_nufft_test.py b/tensorflow_mri/python/linalg/linear_operator_nufft_test.py deleted file mode 100644 index 7add1d5a..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_nufft_test.py +++ /dev/null @@ -1,334 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_nufft`.""" -# pylint: disable=missing-class-docstring,missing-function-docstring - -import functools - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_identity -from tensorflow_mri.python.linalg import linear_operator_inversion -from tensorflow_mri.python.linalg import linear_operator_nufft -from tensorflow_mri.python.linalg import linear_operator_test_util -from tensorflow_mri.python.util import test_util - - -rng = np.random.RandomState(2016) - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorNUFFTTest( - linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - # NUFFT operator does not quite reach the promised accuracy, so for now we - # relax the test tolerance a little bit. - # TODO(jmontalt): Investigate NUFFT precision issues. - _atol = { - tf.complex64: 1e-5, # 1e-6 - tf.complex128: 1e-10 # 1e-12 - } - - _rtol = { - tf.complex64: 1e-5, # 1e-6 - tf.complex128: 1e-10 # 1e-12 - } - - @staticmethod - def dtypes_to_test(): - return [tf.complex64, tf.complex128] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - del ensure_self_adjoint_and_pd - del use_placeholder - shape = list(build_info.shape) - - batch_shape = shape[:-2] - num_rows = shape[-2] - num_columns = shape[-1] - - points = tf.random.uniform( - shape=batch_shape + [num_rows, 1], - minval=-np.pi, maxval=np.pi, - dtype=dtype.real_dtype) - - operator = linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[num_columns], points=points) - - matrix = linear_operator_nufft.nudft_matrix( - domain_shape=[num_columns], points=points) - - return operator, matrix - - def test_assert_self_adjoint(self): - with self.cached_session(): - operator = linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[4], points=[[0.]]) - with self.assertRaisesOpError("not equal to its adjoint"): - self.evaluate(operator.assert_self_adjoint()) - - def test_non_1d_domain_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be a 1-D"): - linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=2, points=[[0.]]) - - def test_non_integer_domain_shape_raises_static(self): - with self.assertRaisesRegex(TypeError, "must be integer"): - linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[2.], points=[[0.]]) - - def test_non_negative_domain_shape_raises_static(self): - with self.assertRaisesRegex(ValueError, "must be non-negative"): - linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[-2], points=[[0.]]) - - def test_non_float_type_points_raises(self): - with self.assertRaisesRegex( - TypeError, "must be a float32 or float64 tensor"): - linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[2], points=[[0]]) - - def test_is_x_flags(self): - operator = linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[2], points=[[0.]]) - self.assertFalse(operator.is_self_adjoint) - - def test_solve_raises(self): - operator = linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[2], points=[[-np.pi], [0.]]) - with self.assertRaisesRegex(ValueError, "not invertible.*lstsq"): - operator.solve(tf.ones([2, 1], dtype=tf.complex64)) - - def test_inverse_raises(self): - operator = linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[4], points=[[0.], [-np.pi]], is_square=True) - with self.assertRaisesRegex(ValueError, "not invertible.*pseudo_inverse"): - operator.inverse() - - def test_identity_matmul(self): - operator1 = linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[2], points=[[0.], [-np.pi]]) - operator2 = linear_operator_identity.LinearOperatorIdentity(num_rows=2) - self.assertIsInstance(operator1.matmul(operator2), - linear_operator_nufft.LinearOperatorNUFFT) - self.assertIsInstance(operator2.matmul(operator1), - linear_operator_nufft.LinearOperatorNUFFT) - - def test_ref_type_domain_shape_raises(self): - with self.assertRaisesRegex(TypeError, "domain_shape.cannot.be.reference"): - linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=tf.Variable([2]), points=[[0.]]) - - def test_convert_variables_to_tensors(self): - points = tf.Variable([[0.]]) - operator = linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[2], points=points) - with self.cached_session() as sess: - sess.run([points.initializer]) - self.check_convert_variables_to_tensors(operator) - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorNUFFTWithCrosstalkTest( - linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - # NUFFT operator does not quite reach the promised accuracy, so for now we - # relax the test tolerance a little bit. - # TODO(jmontalt): Investigate NUFFT precision issues. - _atol = { - tf.complex64: 1e-5, # 1e-6 - tf.complex128: 1e-10 # 1e-12 - } - - _rtol = { - tf.complex64: 1e-5, # 1e-6 - tf.complex128: 1e-10 # 1e-12 - } - - @staticmethod - def dtypes_to_test(): - return [tf.complex64, tf.complex128] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - del ensure_self_adjoint_and_pd - del use_placeholder - shape = list(build_info.shape) - - batch_shape = shape[:-2] - num_rows = shape[-2] - num_columns = shape[-1] - - points = tf.random.uniform( - shape=batch_shape + [num_rows, 1], - minval=-np.pi, maxval=np.pi, - dtype=dtype.real_dtype) - - matrix = linear_operator_nufft.nudft_matrix( - domain_shape=[num_columns], points=points) - - if num_rows < num_columns: - crosstalk_inverse = tf.linalg.inv(matrix @ tf.linalg.adjoint(matrix)) - else: - crosstalk_inverse = tf.linalg.inv(tf.linalg.adjoint(matrix) @ matrix) - - operator = linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=[num_columns], points=points, - crosstalk_inverse=crosstalk_inverse) - - return operator, matrix - - -class OperatorShapesInfoNUFFT(): - def __init__(self, domain_shape, num_points, batch_shape): - self.domain_shape = domain_shape - self.num_points = num_points - self.batch_shape = batch_shape - - @property - def shape(self): - grid_size = functools.reduce(lambda a, b: a * b, self.domain_shape) - return self.batch_shape + (self.num_points, grid_size) - - @property - def dimension(self): - return len(self.domain_shape) - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorNUFFTNDTest( - linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): - """Most tests done in the base class LinearOperatorDerivedClassTest.""" - # NUFFT operator does not quite reach the promised accuracy, so for now we - # relax the test tolerance a little bit. - # TODO(jmontalt): Investigate NUFFT precision issues. - _atol = { - tf.complex64: 1e-5, # 1e-6 - tf.complex128: 1e-10 # 1e-12 - } - - _rtol = { - tf.complex64: 1e-5, # 1e-6 - tf.complex128: 1e-10 # 1e-12 - } - - @staticmethod - def operator_shapes_infos(): - shapes_info = OperatorShapesInfoNUFFT - return [ - shapes_info((2, 2), 3, ()), - shapes_info((2, 4), 5, (3,)), - shapes_info((4, 2), 6, (1, 2)), - shapes_info((2, 2), 6, ()), - shapes_info((2, 2, 2), 9, ()), - shapes_info((4, 2, 2), 7, (2,)) - # TODO(jmontalt): odd shapes fail tests, investigate - # shapes_info((2, 3), 5, (2,)), - # shapes_info((3, 2), 7, ()) - ] - - @staticmethod - def dtypes_to_test(): - return [tf.complex64, tf.complex128] - - def operator_and_matrix( - self, build_info, dtype, use_placeholder, - ensure_self_adjoint_and_pd=False): - del ensure_self_adjoint_and_pd - del use_placeholder - - domain_shape = build_info.domain_shape - num_points = build_info.num_points - batch_shape = build_info.batch_shape - grid_size = build_info.shape[-1] - dimension = build_info.dimension - - points = tf.random.uniform( - shape=batch_shape + (num_points, dimension), - minval=-np.pi, maxval=np.pi, - dtype=dtype.real_dtype) - - matrix = linear_operator_nufft.nudft_matrix( - domain_shape=domain_shape, points=points) - - if num_points < grid_size: - crosstalk_inverse = tf.linalg.inv(matrix @ tf.linalg.adjoint(matrix)) - else: - crosstalk_inverse = tf.linalg.inv(tf.linalg.adjoint(matrix) @ matrix) - - operator = linear_operator_nufft.LinearOperatorNUFFT( - domain_shape=domain_shape, points=points, - crosstalk_inverse=crosstalk_inverse) - - return operator, matrix - - -# class LinearOperatorGramNUFFTTest(test_util.TestCase): -# @parameterized.product( -# density=[False, True], -# norm=[None, 'ortho'], -# toeplitz=[False, True], -# batch=[False, True] -# ) -# def test_general(self, density, norm, toeplitz, batch): -# with tf.device('/cpu:0'): -# image_shape = (128, 128) -# image = image_ops.phantom(shape=image_shape, dtype=tf.complex64) -# points = traj_ops.radial_trajectory( -# 128, 129, flatten_encoding_dims=True) -# if density is True: -# density = traj_ops.radial_density( -# 128, 129, flatten_encoding_dims=True) -# else: -# density = None - -# # If testing batches, create new inputs to generate a batch. -# if batch: -# image = tf.stack([image, image * 0.5]) -# points = tf.stack([ -# points, -# rotation_2d.Rotation2D.from_euler([np.pi / 2]).rotate(points)]) -# if density is not None: -# density = tf.stack([density, density]) - -# linop = linear_operator_nufft.LinearOperatorNUFFT( -# image_shape, points=points, density=density, norm=norm) -# linop_gram = linear_operator_nufft.LinearOperatorGramNUFFT( -# image_shape, points=points, density=density, norm=norm, -# toeplitz=toeplitz) - -# recon = linop.transform(linop.transform(image), adjoint=True) -# recon_gram = linop_gram.transform(image) - -# if norm is None: -# # Reduce the magnitude of these values to avoid the need to use a large -# # tolerance. -# recon /= tf.cast(tf.math.reduce_prod(image_shape), tf.complex64) -# recon_gram /= tf.cast(tf.math.reduce_prod(image_shape), tf.complex64) - -# self.assertAllClose(recon, recon_gram, rtol=1e-4, atol=1e-4) - - -linear_operator_test_util.add_tests(LinearOperatorNUFFTTest) -linear_operator_test_util.add_tests(LinearOperatorNUFFTWithCrosstalkTest) -linear_operator_test_util.add_tests(LinearOperatorNUFFTNDTest) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_test.py b/tensorflow_mri/python/linalg/linear_operator_test.py deleted file mode 100644 index 8e63874b..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_test.py +++ /dev/null @@ -1,468 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for base linear operator.""" - -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.linalg import linear_operator_full_matrix -from tensorflow_mri.python.util import test_util - - -rng = np.random.RandomState(123) - - -class LinearOperatorShape(linear_operator.LinearOperator): - """LinearOperator that implements the methods ._shape and _shape_tensor.""" - - def __init__(self, - shape, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None): - parameters = dict( - shape=shape, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square - ) - - self._stored_shape = shape - super(LinearOperatorShape, self).__init__( - dtype=tf.float32, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters) - - def _shape(self): - return tf.TensorShape(self._stored_shape) - - def _shape_tensor(self): - return tf.constant(self._stored_shape, dtype=tf.int32) - - def _matmul(self): - raise NotImplementedError("Not needed for this test.") - - -class LinearOperatorMatmulSolve(linear_operator.LinearOperator): - """LinearOperator that wraps a [batch] matrix and implements matmul/solve.""" - - def __init__(self, - matrix, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None): - parameters = dict( - matrix=matrix, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square - ) - - self._matrix = tf.convert_to_tensor(matrix, name="matrix") - super(LinearOperatorMatmulSolve, self).__init__( - dtype=self._matrix.dtype, - is_non_singular=is_non_singular, - is_self_adjoint=is_self_adjoint, - is_positive_definite=is_positive_definite, - is_square=is_square, - parameters=parameters) - - def _shape(self): - return self._matrix.shape - - def _shape_tensor(self): - return tf.shape(self._matrix) - - def _matmul(self, x, adjoint=False, adjoint_arg=False): - x = tf.convert_to_tensor(x, name="x") - return tf.matmul( - self._matrix, x, adjoint_a=adjoint, adjoint_b=adjoint_arg) - - def _solve(self, rhs, adjoint=False, adjoint_arg=False): - rhs = tf.convert_to_tensor(rhs, name="rhs") - assert not adjoint_arg, "Not implemented for this test class." - return tf.linalg.solve(self._matrix, rhs, adjoint=adjoint) - - -@test_util.run_all_in_graph_and_eager_modes -class LinearOperatorTest(tf.test.TestCase): - - def test_all_shape_properties_defined_by_the_one_property_shape(self): - - shape = (1, 2, 3, 4) - operator = LinearOperatorShape(shape) - - self.assertAllEqual(shape, operator.shape) - self.assertAllEqual(4, operator.tensor_rank) - self.assertAllEqual((1, 2), operator.batch_shape) - self.assertAllEqual(4, operator.domain_dimension) - self.assertAllEqual(3, operator.range_dimension) - expected_parameters = { - "is_non_singular": None, - "is_positive_definite": None, - "is_self_adjoint": None, - "is_square": None, - "shape": (1, 2, 3, 4), - } - self.assertEqual(expected_parameters, operator.parameters) - - def test_all_shape_methods_defined_by_the_one_method_shape(self): - with self.cached_session(): - shape = (1, 2, 3, 4) - operator = LinearOperatorShape(shape) - - self.assertAllEqual(shape, self.evaluate(operator.shape_tensor())) - self.assertAllEqual(4, self.evaluate(operator.tensor_rank_tensor())) - self.assertAllEqual((1, 2), self.evaluate(operator.batch_shape_tensor())) - self.assertAllEqual(4, self.evaluate(operator.domain_dimension_tensor())) - self.assertAllEqual(3, self.evaluate(operator.range_dimension_tensor())) - - def test_is_x_properties(self): - operator = LinearOperatorShape( - shape=(2, 2), - is_non_singular=False, - is_self_adjoint=True, - is_positive_definite=False) - self.assertFalse(operator.is_non_singular) - self.assertTrue(operator.is_self_adjoint) - self.assertFalse(operator.is_positive_definite) - - def test_nontrivial_parameters(self): - matrix = rng.randn(2, 3, 4) - matrix_ph = tf.compat.v1.placeholder_with_default(input=matrix, shape=None) - operator = LinearOperatorMatmulSolve(matrix_ph) - expected_parameters = { - "is_non_singular": None, - "is_positive_definite": None, - "is_self_adjoint": None, - "is_square": None, - "matrix": matrix_ph, - } - self.assertEqual(expected_parameters, operator.parameters) - - def test_generic_to_dense_method_non_square_matrix_static(self): - matrix = rng.randn(2, 3, 4) - operator = LinearOperatorMatmulSolve(matrix) - with self.cached_session(): - operator_dense = operator.to_dense() - self.assertAllEqual((2, 3, 4), operator_dense.shape) - self.assertAllClose(matrix, self.evaluate(operator_dense)) - - def test_generic_to_dense_method_non_square_matrix_tensor(self): - matrix = rng.randn(2, 3, 4) - matrix_ph = tf.compat.v1.placeholder_with_default(input=matrix, shape=None) - operator = LinearOperatorMatmulSolve(matrix_ph) - operator_dense = operator.to_dense() - self.assertAllClose(matrix, self.evaluate(operator_dense)) - - def test_matvec(self): - matrix = [[1., 0], [0., 2.]] - operator = LinearOperatorMatmulSolve(matrix) - x = [1., 1.] - with self.cached_session(): - y = operator.matvec(x) - self.assertAllEqual((2,), y.shape) - self.assertAllClose([1., 2.], self.evaluate(y)) - - def test_solvevec(self): - matrix = [[1., 0], [0., 2.]] - operator = LinearOperatorMatmulSolve(matrix) - y = [1., 1.] - with self.cached_session(): - x = operator.solvevec(y) - self.assertAllEqual((2,), x.shape) - self.assertAllClose([1., 1 / 2.], self.evaluate(x)) - - def test_add(self): - matrix = [[1., 0], [0., 2.]] - operator = LinearOperatorMatmulSolve(matrix) - with self.cached_session(): - y = operator.add(matrix) - self.assertAllEqual((2, 2), y.shape) - self.assertAllClose([[2., 0], [0., 4.]], self.evaluate(y)) - - def test_is_square_set_to_true_for_square_static_shapes(self): - operator = LinearOperatorShape(shape=(2, 4, 4)) - self.assertTrue(operator.is_square) - - def test_is_square_set_to_false_for_square_static_shapes(self): - operator = LinearOperatorShape(shape=(2, 3, 4)) - self.assertFalse(operator.is_square) - - def test_is_square_set_incorrectly_to_false_raises(self): - with self.assertRaisesRegex(ValueError, "but.*was square"): - _ = LinearOperatorShape(shape=(2, 4, 4), is_square=False).is_square - - def test_is_square_set_inconsistent_with_other_hints_raises(self): - with self.assertRaisesRegex(ValueError, "is always square"): - matrix = tf.compat.v1.placeholder_with_default(input=(), shape=None) - LinearOperatorMatmulSolve(matrix, is_non_singular=True, is_square=False) - - with self.assertRaisesRegex(ValueError, "is always square"): - matrix = tf.compat.v1.placeholder_with_default(input=(), shape=None) - LinearOperatorMatmulSolve( - matrix, is_positive_definite=True, is_square=False) - - def test_non_square_operators_raise_on_determinant_and_solve(self): - operator = LinearOperatorShape((2, 3)) - with self.assertRaisesRegex(NotImplementedError, "not be square"): - operator.determinant() - with self.assertRaisesRegex(NotImplementedError, "not be square"): - operator.log_abs_determinant() - with self.assertRaisesRegex(NotImplementedError, "not be square"): - operator.solve(rng.rand(2, 2)) - - with self.assertRaisesRegex(ValueError, "is always square"): - matrix = tf.compat.v1.placeholder_with_default(input=(), shape=None) - LinearOperatorMatmulSolve( - matrix, is_positive_definite=True, is_square=False) - - def test_is_square_manual_set_works(self): - matrix = tf.compat.v1.placeholder_with_default( - input=np.ones((2, 2)), shape=None) - operator = LinearOperatorMatmulSolve(matrix) - if not tf.executing_eagerly(): - # Eager mode will read in the default value, and discover the answer is - # True. Graph mode must rely on the hint, since the placeholder has - # shape=None...the hint is, by default, None. - self.assertEqual(None, operator.is_square) - - # Set to True - operator = LinearOperatorMatmulSolve(matrix, is_square=True) - self.assertTrue(operator.is_square) - - def test_linear_operator_matmul_hints_closed(self): - matrix = tf.compat.v1.placeholder_with_default(input=np.ones((2, 2)), - shape=None) - operator1 = LinearOperatorMatmulSolve(matrix) - - operator_matmul = operator1.matmul(operator1) - - if not tf.executing_eagerly(): - # Eager mode will read in the input and discover matrix is square. - self.assertEqual(None, operator_matmul.is_square) - self.assertEqual(None, operator_matmul.is_non_singular) - self.assertEqual(None, operator_matmul.is_self_adjoint) - self.assertEqual(None, operator_matmul.is_positive_definite) - - operator2 = LinearOperatorMatmulSolve( - matrix, - is_non_singular=True, - is_self_adjoint=True, - is_positive_definite=True, - is_square=True, - ) - - operator_matmul = operator2.matmul(operator2) - - self.assertTrue(operator_matmul.is_square) - self.assertTrue(operator_matmul.is_non_singular) - self.assertEqual(None, operator_matmul.is_self_adjoint) - self.assertEqual(None, operator_matmul.is_positive_definite) - - def test_linear_operator_matmul_hints_false(self): - matrix1 = tf.compat.v1.placeholder_with_default( - input=rng.rand(2, 2), shape=None) - operator1 = LinearOperatorMatmulSolve( - matrix1, - is_non_singular=False, - is_self_adjoint=False, - is_positive_definite=False, - is_square=True, - ) - - operator_matmul = operator1.matmul(operator1) - - self.assertTrue(operator_matmul.is_square) - self.assertFalse(operator_matmul.is_non_singular) - self.assertEqual(None, operator_matmul.is_self_adjoint) - self.assertEqual(None, operator_matmul.is_positive_definite) - - matrix2 = tf.compat.v1.placeholder_with_default( - input=rng.rand(2, 3), shape=None) - operator2 = LinearOperatorMatmulSolve( - matrix2, - is_non_singular=False, - is_self_adjoint=False, - is_positive_definite=False, - is_square=False, - ) - - operator_matmul = operator2.matmul(operator2, adjoint_arg=True) - - if tf.executing_eagerly(): - self.assertTrue(operator_matmul.is_square) - # False since we specified is_non_singular=False. - self.assertFalse(operator_matmul.is_non_singular) - else: - self.assertIsNone(operator_matmul.is_square) - # May be non-singular, since it's the composition of two non-square. - # TODO(b/136162840) This is a bit inconsistent, and should probably be - # False since we specified operator2.is_non_singular == False. - self.assertIsNone(operator_matmul.is_non_singular) - - # No way to deduce these, even in Eager mode. - self.assertIsNone(operator_matmul.is_self_adjoint) - self.assertIsNone(operator_matmul.is_positive_definite) - - def test_linear_operator_matmul_hint_infer_square(self): - matrix1 = tf.compat.v1.placeholder_with_default( - input=rng.rand(2, 3), shape=(2, 3)) - matrix2 = tf.compat.v1.placeholder_with_default( - input=rng.rand(3, 2), shape=(3, 2)) - matrix3 = tf.compat.v1.placeholder_with_default( - input=rng.rand(3, 4), shape=(3, 4)) - - operator1 = LinearOperatorMatmulSolve(matrix1, is_square=False) - operator2 = LinearOperatorMatmulSolve(matrix2, is_square=False) - operator3 = LinearOperatorMatmulSolve(matrix3, is_square=False) - - self.assertTrue(operator1.matmul(operator2).is_square) - self.assertTrue(operator2.matmul(operator1).is_square) - self.assertFalse(operator1.matmul(operator3).is_square) - - def testDispatchedMethods(self): - operator = linear_operator_full_matrix.LinearOperatorFullMatrix( - [[1., 0.5], [0.5, 1.]], - is_square=True, - is_self_adjoint=True, - is_non_singular=True, - is_positive_definite=True) - methods = { - "trace": tf.linalg.trace, - "diag_part": tf.linalg.diag_part, - "log_abs_determinant": tf.linalg.logdet, - "determinant": tf.linalg.det - } - for method in methods: - op_val = getattr(operator, method)() - linalg_val = methods[method](operator) - self.assertAllClose( - self.evaluate(op_val), - self.evaluate(linalg_val)) - # Solve and Matmul go here. - - adjoint = tf.linalg.adjoint(operator) - self.assertIsInstance(adjoint, linear_operator.LinearOperator) - cholesky = tf.linalg.cholesky(operator) - self.assertIsInstance(cholesky, linear_operator.LinearOperator) - inverse = tf.linalg.inv(operator) - self.assertIsInstance(inverse, linear_operator.LinearOperator) - - def testDispatchMatmulSolve(self): - operator = linear_operator_full_matrix.LinearOperatorFullMatrix( - np.float64([[1., 0.5], [0.5, 1.]]), - is_square=True, - is_self_adjoint=True, - is_non_singular=True, - is_positive_definite=True) - rhs = np.random.uniform(-1., 1., size=[3, 2, 2]) - for adjoint in [False, True]: - for adjoint_arg in [False, True]: - op_val = operator.matmul( - rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) - matmul_val = tf.matmul( - operator, rhs, adjoint_a=adjoint, adjoint_b=adjoint_arg) - self.assertAllClose( - self.evaluate(op_val), self.evaluate(matmul_val)) - - op_val = operator.solve(rhs, adjoint=adjoint) - solve_val = tf.linalg.solve(operator, rhs, adjoint=adjoint) - self.assertAllClose( - self.evaluate(op_val), self.evaluate(solve_val)) - - def testDispatchMatmulLeftOperatorIsTensor(self): - mat = np.float64([[1., 0.5], [0.5, 1.]]) - right_operator = linear_operator_full_matrix.LinearOperatorFullMatrix( - mat, - is_square=True, - is_self_adjoint=True, - is_non_singular=True, - is_positive_definite=True) - lhs = np.random.uniform(-1., 1., size=[3, 2, 2]) - - for adjoint in [False, True]: - for adjoint_arg in [False, True]: - op_val = tf.matmul( - lhs, mat, adjoint_a=adjoint, adjoint_b=adjoint_arg) - matmul_val = tf.matmul( - lhs, right_operator, adjoint_a=adjoint, adjoint_b=adjoint_arg) - self.assertAllClose( - self.evaluate(op_val), self.evaluate(matmul_val)) - - def testDispatchAdd(self): - operator = linear_operator_full_matrix.LinearOperatorFullMatrix( - np.float64([[1., 0.5], [0.5, 1.]]), - is_square=True, - is_self_adjoint=True, - is_non_singular=True, - is_positive_definite=True) - rhs = np.random.uniform(-1., 1., size=[3, 2, 2]) - op_val = operator.add(rhs) - add_val = tf.math.add(operator, rhs) - self.assertAllClose(self.evaluate(op_val), self.evaluate(add_val)) - - def testDispatchMatmulLeftOperatorIsTensor(self): - mat = np.float64([[1., 0.5], [0.5, 1.]]) - right_operator = linear_operator_full_matrix.LinearOperatorFullMatrix( - mat, - is_square=True, - is_self_adjoint=True, - is_non_singular=True, - is_positive_definite=True) - lhs = np.random.uniform(-1., 1., size=[3, 2, 2]) - op_val = tf.math.add(lhs, mat) - add_val = tf.math.add(lhs, right_operator) - self.assertAllClose(self.evaluate(op_val), self.evaluate(add_val)) - - def testDispatchAddOperator(self): - operator = linear_operator_full_matrix.LinearOperatorFullMatrix( - np.float64([[1., 0.5], [0.5, 1.]]), - is_square=True, - is_self_adjoint=True, - is_non_singular=True, - is_positive_definite=True) - rhs = np.random.uniform(-1., 1., size=[3, 2, 2]) - add_val = tf.math.add(operator, rhs) - op_val = operator + rhs - self.assertAllClose(self.evaluate(add_val), self.evaluate(op_val)) - - def testVectorizedMap(self): - - def fn(x): - y = tf.constant([3., 4.]) - # Make a [2, N, N] shaped operator. - x = x * y[..., tf.compat.v1.newaxis, tf.compat.v1.newaxis] - operator = linear_operator_full_matrix.LinearOperatorFullMatrix( - x, is_square=True) - return operator - - x = np.random.uniform(-1., 1., size=[3, 5, 5]).astype(np.float32) - batched_operator = tf.vectorized_map( - fn, tf.convert_to_tensor(x)) - self.assertIsInstance(batched_operator, linear_operator.LinearOperator) - self.assertAllEqual(batched_operator.batch_shape, [3, 2]) - - -if __name__ == "__main__": - tf.test.main() diff --git a/tensorflow_mri/python/linalg/linear_operator_test_util.py b/tensorflow_mri/python/linalg/linear_operator_test_util.py deleted file mode 100644 index fc08b8f4..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_test_util.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utilities for testing linear operators.""" - -import itertools - -import tensorflow as tf -from tensorflow.python.framework import test_util -from tensorflow.python.ops.linalg import linear_operator_test_util - -from tensorflow_mri.python.linalg import linear_operator_util - - -DEFAULT_GRAPH_SEED = 876543213 - - -def add_tests(test_cls): - # Call original add_tests. - linear_operator_test_util.add_tests(test_cls) - - test_name_dict = { - "lstsq": _test_lstsq, - "lstsq_with_broadcast": _test_lstsq_with_broadcast - } - optional_tests = [] - tests_with_adjoint_args = [ - "lstsq", - "lstsq_with_broadcast" - ] - - for name, test_template_fn in test_name_dict.items(): - if name in test_cls.skip_these_tests(): - continue - if name in optional_tests and name not in test_cls.optional_tests(): - continue - - for dtype, use_placeholder, shape_info in itertools.product( - test_cls.dtypes_to_test(), - test_cls.use_placeholder_options(), - test_cls.operator_shapes_infos()): - base_test_name = "_".join([ - "test", name, "_shape={},dtype={},use_placeholder={}".format( - shape_info.shape, dtype, use_placeholder)]) - if name in tests_with_adjoint_args: - for adjoint in test_cls.adjoint_options(): - for adjoint_arg in test_cls.adjoint_arg_options(): - test_name = base_test_name + ",adjoint={},adjoint_arg={}".format( - adjoint, adjoint_arg) - if hasattr(test_cls, test_name): - raise RuntimeError("Test %s defined more than once" % test_name) - setattr( - test_cls, - test_name, - test_util.run_deprecated_v1( - test_template_fn( # pylint: disable=too-many-function-args - use_placeholder, shape_info, dtype, adjoint, - adjoint_arg, test_cls.use_blockwise_arg()))) - else: - if hasattr(test_cls, base_test_name): - raise RuntimeError("Test %s defined more than once" % base_test_name) - setattr( - test_cls, - base_test_name, - test_util.run_deprecated_v1(test_template_fn( - use_placeholder, shape_info, dtype))) - - -OperatorShapesInfo = linear_operator_test_util.OperatorShapesInfo - - -random_normal = linear_operator_test_util.random_normal -random_uniform = linear_operator_test_util.random_uniform -random_positive_definite_matrix = ( - linear_operator_test_util.random_positive_definite_matrix) -random_sign_uniform = linear_operator_test_util.random_sign_uniform - - -class SquareLinearOperatorDerivedClassTest( - linear_operator_test_util.SquareLinearOperatorDerivedClassTest): - pass - - -class NonSquareLinearOperatorDerivedClassTest( - linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): - - def make_rhs(self, operator, adjoint, with_batch=True): - return self.make_x(operator, adjoint=not adjoint, with_batch=with_batch) - - -def _test_lstsq( - use_placeholder, shapes_info, dtype, adjoint, adjoint_arg, blockwise_arg): - def test_lstsq(self): - _test_lstsq_base( - self, - use_placeholder, - shapes_info, - dtype, - adjoint, - adjoint_arg, - blockwise_arg, - with_batch=True) - return test_lstsq - - -def _test_lstsq_with_broadcast( - use_placeholder, shapes_info, dtype, adjoint, adjoint_arg, blockwise_arg): - def test_lstsq_with_broadcast(self): - _test_lstsq_base( - self, - use_placeholder, - shapes_info, - dtype, - adjoint, - adjoint_arg, - blockwise_arg, - with_batch=False) - return test_lstsq_with_broadcast - - -def _test_lstsq_base( - self, - use_placeholder, - shapes_info, - dtype, - adjoint, - adjoint_arg, - blockwise_arg, - with_batch): - # If batch dimensions are omitted, but there are - # no batch dimensions for the linear operator, then - # skip the test case. This is already checked with - # with_batch=True. - if not with_batch and len(shapes_info.shape) <= 2: - return - with self.session(graph=tf.Graph()) as sess: - sess.graph.seed = DEFAULT_GRAPH_SEED - operator, mat = self.operator_and_matrix( - shapes_info, dtype, use_placeholder=use_placeholder) - rhs = self.make_rhs( - operator, adjoint=adjoint, with_batch=with_batch) - # If adjoint_arg, solve A X = (rhs^H)^H = rhs. - if adjoint_arg: - op_solve = operator.lstsq( - tf.linalg.adjoint(rhs), - adjoint=adjoint, - adjoint_arg=adjoint_arg) - else: - op_solve = operator.lstsq( - rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) - mat_solve = linear_operator_util.matrix_solve_ls_with_broadcast( - mat, rhs, adjoint=adjoint) - if not use_placeholder: - self.assertAllEqual(op_solve.shape, - mat_solve.shape) - - # If the operator is blockwise, test both blockwise rhs and `Tensor` rhs; - # else test only `Tensor` rhs. In both cases, evaluate all results in a - # single `sess.run` call to avoid re-sampling the random rhs in graph mode. - if blockwise_arg and len(operator.operators) > 1: - # pylint: disable=protected-access - block_dimensions = ( - operator._block_range_dimensions() if adjoint else - operator._block_domain_dimensions()) - block_dimensions_fn = ( - operator._block_range_dimension_tensors if adjoint else - operator._block_domain_dimension_tensors) - # pylint: enable=protected-access - split_rhs = linear_operator_util.split_arg_into_blocks( - block_dimensions, - block_dimensions_fn, - rhs, axis=-2) - if adjoint_arg: - split_rhs = [tf.linalg.adjoint(y) for y in split_rhs] - split_solve = operator.solve( - split_rhs, adjoint=adjoint, adjoint_arg=adjoint_arg) - self.assertEqual(len(split_solve), len(operator.operators)) - split_solve = linear_operator_util.broadcast_matrix_batch_dims( - split_solve) - fused_block_solve = tf.concat(split_solve, axis=-2) - op_solve_v, mat_solve_v, fused_block_solve_v = sess.run([ - op_solve, mat_solve, fused_block_solve]) - - # Check that the operator and matrix give the same solution when the rhs - # is blockwise. - self.assertAC(mat_solve_v, fused_block_solve_v) - else: - op_solve_v, mat_solve_v = sess.run([op_solve, mat_solve]) - - # Check that the operator and matrix give the same solution when the rhs is - # a `Tensor`. - self.assertAC(op_solve_v, mat_solve_v) diff --git a/tensorflow_mri/python/linalg/linear_operator_util.py b/tensorflow_mri/python/linalg/linear_operator_util.py deleted file mode 100644 index dd63370a..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_util.py +++ /dev/null @@ -1,158 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utilities for linear operators.""" - -import tensorflow as tf -from tensorflow.python.ops.linalg import linear_operator_util - - -broadcast_matrix_batch_dims = linear_operator_util.broadcast_matrix_batch_dims -split_arg_into_blocks = linear_operator_util.split_arg_into_blocks -_reshape_for_efficiency = linear_operator_util._reshape_for_efficiency # pylint: disable=protected-access - - -## Matrix operators. - -def matrix_solve_ls_with_broadcast(matrix, rhs, adjoint=False, name=None): - """Solve systems of linear equations.""" - with tf.name_scope(name or "MatrixSolveLSWithBroadcast"): - matrix = tf.convert_to_tensor(matrix, name="matrix") - rhs = tf.convert_to_tensor(rhs, name="rhs", dtype=matrix.dtype) - - # If either matrix/rhs has extra dims, we can reshape to get rid of them. - matrix, rhs, reshape_inv, still_need_to_transpose = _reshape_for_efficiency( - matrix, rhs, adjoint_a=adjoint) - - # This will broadcast by brute force if we still need to. - matrix, rhs = broadcast_matrix_batch_dims([matrix, rhs]) - - if adjoint and still_need_to_transpose: - matrix = tf.linalg.adjoint(matrix) - solution = tf.linalg.lstsq(matrix, rhs, fast=False) - - return reshape_inv(solution) - - -## Asserts. - -def assert_no_entries_with_modulus_zero(x, message=None, name=None): - """Returns `Op` that asserts Tensor `x` has no entries with modulus zero. - - Args: - x: Numeric `Tensor`, real, integer, or complex. - message: A string message to prepend to failure message. - name: A name to give this `Op`. - - Returns: - An `Op` that asserts `x` has no entries with modulus zero. - """ - with tf.name_scope(name or "assert_no_entries_with_modulus_zero"): - x = tf.convert_to_tensor(x, name="x") - dtype = x.dtype.base_dtype - should_be_nonzero = tf.math.abs(x) - zero = tf.convert_to_tensor(0, dtype=dtype.real_dtype) - return tf.debugging.assert_less(zero, should_be_nonzero, message=message) - - -def assert_zero_imag_part(x, message=None, name=None): - """Returns `Op` that asserts Tensor `x` has no non-zero imaginary parts. - - Args: - x: Numeric `Tensor`, real, integer, or complex. - message: A string message to prepend to failure message. - name: A name to give this `Op`. - - Returns: - An `Op` that asserts `x` has no entries with non-zero imaginary part. - """ - with tf.name_scope(name or "assert_zero_imag_part"): - x = tf.convert_to_tensor(x, name="x") - dtype = x.dtype.base_dtype - - if dtype.is_floating: - return tf.no_op() - - zero = tf.convert_to_tensor(0, dtype=dtype.real_dtype) - return tf.debugging.assert_equal(zero, tf.math.imag(x), message=message) - - -# Other utilities. - -def prepare_inner_dims_for_broadcasting(tensor_a, - tensor_b, - batch_dims_a=0, - batch_dims_b=0): - """Prepares two tensors for broadcasting, separating batch from inner dims. - - Essentially, this function makes sure that both tensors have the same number - of inner dimensions, so that inner dimensions can be broadcasted with inner - dimensions, and batch dimensions are broadcasted with batch dimensions. - - For example, given the following tensors: - - `tensor_a` with shape `(2, 3, 4, 5)`, with 2 batch dimensions. - - `tensor_b` with shape `(2, 3, 2, 4, 5)`, with 2 batch dimensions. - - This function will return the following: - - `tensor_a` with shape `(2, 3, 1, 4, 5)`. - - `tensor_b` with shape `(2, 3, 2, 4, 5)`. - - i.e., the inner dimensions of `tensor_a` are expanded to match the inner - dimensions of `tensor_b`. - - ```{note} - This function does not check that the batch/inner dimensions of `tensor_a` - and `tensor_b` are compatible for broadcasting. It simply makes sure that - both tensors have the same number of inner dimensions. - ``` - """ - # Number of inner dimensions (static). - inner_dims_a = tensor_a.shape.rank - batch_dims_a - inner_dims_b = tensor_b.shape.rank - batch_dims_b - if inner_dims_a == inner_dims_b: - return tensor_a, tensor_b - - # Get shapes of batch and inner dimensions for both tensors. - shape_a, shape_b = tf.shape_n([tensor_a, tensor_b]) - batch_shape_a = shape_a[:batch_dims_a] - batch_shape_b = shape_b[:batch_dims_b] - inner_shape_a = shape_a[batch_dims_a:] - inner_shape_b = shape_b[batch_dims_b:] - - # Number of inner dimensions (dynamic). - if inner_dims_a > inner_dims_b: - extra_dims = inner_dims_a - inner_dims_b - new_shape_b = tf.concat([batch_shape_b, [1] * extra_dims, inner_shape_b], 0) - tensor_b = tf.reshape(tensor_b, new_shape_b) - else: # inner_dims_a < inner_dims_b - extra_dims = inner_dims_b - inner_dims_a - new_shape_a = tf.concat([batch_shape_a, [1] * extra_dims, inner_shape_a], 0) - tensor_a = tf.reshape(tensor_a, new_shape_a) - - return tensor_a, tensor_b diff --git a/tensorflow_mri/python/linalg/linear_operator_wavelet.py b/tensorflow_mri/python/linalg/linear_operator_wavelet.py deleted file mode 100644 index 57d81092..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_wavelet.py +++ /dev/null @@ -1,153 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Wavelet linear operator.""" - -import functools - -import tensorflow as tf - -from tensorflow_mri.python.ops import array_ops -from tensorflow_mri.python.ops import wavelet_ops -from tensorflow_mri.python.util import api_util -from tensorflow_mri.python.util import check_util -from tensorflow_mri.python.linalg import linear_operator -from tensorflow_mri.python.util import tensor_util - - -@api_util.export("linalg.LinearOperatorWavelet") -class LinearOperatorWavelet(linear_operator.LinearOperator): # pylint: disable=abstract-method - """Linear operator representing a wavelet decomposition matrix. - - Args: - domain_shape: A 1D `tf.Tensor` or a `list` of `int`. The domain shape of - this linear operator. - wavelet: A `str` or a `pywt.Wavelet`_, or a `list` thereof. When passed a - `list`, different wavelets are applied along each axis in `axes`. - mode: A `str`. The padding or signal extension mode. Must be one of the - values supported by `tfmri.signal.wavedec`. Defaults to `'symmetric'`. - level: An `int` >= 0. The decomposition level. If `None` (default), - the maximum useful level of decomposition will be used (see - `tfmri.signal.max_wavelet_level`). - axes: A `list` of `int`. The axes over which the DWT is computed. Axes refer - only to domain dimensions without regard for the batch dimensions. - Defaults to `None` (all domain dimensions). - dtype: A `tf.dtypes.DType`. The data type for this operator. Defaults to - `float32`. - name: A `str`. A name for this operator. - """ - def __init__(self, - domain_shape, - wavelet, - mode='symmetric', - level=None, - axes=None, - dtype=tf.dtypes.float32, - name="LinearOperatorWavelet"): - # Set parameters. - parameters = dict( - domain_shape=domain_shape, - wavelet=wavelet, - mode=mode, - level=level, - axes=axes, - dtype=dtype, - name=name - ) - - # Get the static and dynamic shapes and save them for later use. - self._domain_shape_static, self._domain_shape_dynamic = ( - tensor_util.static_and_dynamic_shapes_from_shape(domain_shape)) - # At the moment, the wavelet implementation relies on shapes being - # statically known. - if not self._domain_shape_static.is_fully_defined(): - raise ValueError(f"static `domain_shape` must be fully defined, " - f"but got {self._domain_shape_static}") - static_rank = self._domain_shape_static.rank - - # Set arguments. - self.wavelet = wavelet - self.mode = mode - self.level = level - self.axes = check_util.validate_static_axes(axes, - rank=static_rank, - min_length=1, - canonicalize="negative", - must_be_unique=True, - scalar_to_list=True, - none_means_all=True) - - # Compute the coefficient slices needed for adjoint (wavelet - # reconstruction). - x = tf.ensure_shape(tf.zeros(self._domain_shape_dynamic, dtype=dtype), - self._domain_shape_static) - x = wavelet_ops.wavedec(x, wavelet=self.wavelet, mode=self.mode, - level=self.level, axes=self.axes) - y, self._coeff_slices = wavelet_ops.coeffs_to_tensor(x, axes=self.axes) - - # Get the range shape. - self._range_shape_static = y.shape - self._range_shape_dynamic = tf.shape(y) - - # Call base class. - super().__init__(dtype, - is_non_singular=None, - is_self_adjoint=None, - is_positive_definite=None, - is_square=None, - name=name, - parameters=parameters) - - def _transform(self, x, adjoint=False): - # While `wavedec` and `waverec` can transform only a subset of axes (and - # thus theoretically support batches), there is a caveat due to the - # `coeff_slices` object required by `waverec`. This object contains - # information relevant to a specific batch shape. While we could recompute - # this object for every input batch shape, it is easier to just process - # each batch independently. - if x.shape.rank is not None and self._domain_shape_static.rank is not None: - # Rank of input and this operator are known statically, so we can infer - # the number of batch dimensions statically too. - batch_dims = x.shape.rank - self._domain_shape_static.rank - else: - # We need to obtain the number of batch dimensions dynamically. - batch_dims = tf.rank(x) - tf.shape(self._domain_shape_dynamic)[0] - # Transform each batch. - x = array_ops.map_fn( - functools.partial(self._transform_batch, adjoint=adjoint), - x, batch_dims=batch_dims) - return x - - def _transform_batch(self, x, adjoint=False): - if adjoint: - x = wavelet_ops.tensor_to_coeffs(x, self._coeff_slices) - x = wavelet_ops.waverec(x, wavelet=self.wavelet, mode=self.mode, - axes=self.axes) - else: - x = wavelet_ops.wavedec(x, wavelet=self.wavelet, mode=self.mode, - level=self.level, axes=self.axes) - x, _ = wavelet_ops.coeffs_to_tensor(x, axes=self.axes) - return x - - def _domain_shape(self): - return self._domain_shape_static - - def _range_shape(self): - return self._range_shape_static - - def _domain_shape_tensor(self): - return self._domain_shape_dynamic - - def _range_shape_tensor(self): - return self._range_shape_dynamic diff --git a/tensorflow_mri/python/linalg/linear_operator_wavelet_test.py b/tensorflow_mri/python/linalg/linear_operator_wavelet_test.py deleted file mode 100644 index a0ecee87..00000000 --- a/tensorflow_mri/python/linalg/linear_operator_wavelet_test.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for module `linear_operator_wavelet`.""" -# pylint: disable=missing-class-docstring,missing-function-docstring - -from absl.testing import parameterized -import numpy as np -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_wavelet -from tensorflow_mri.python.ops import wavelet_ops -from tensorflow_mri.python.util import test_util - - -class LinearOperatorWaveletTest(test_util.TestCase): - @parameterized.named_parameters( - # name, wavelet, level, axes, domain_shape, range_shape - ("test0", "haar", None, None, [6, 6], [7, 7]), - ("test1", "haar", 1, None, [6, 6], [6, 6]), - ("test2", "haar", None, -1, [6, 6], [6, 7]), - ("test3", "haar", None, [-1], [6, 6], [6, 7]) - ) - def test_general(self, wavelet, level, axes, domain_shape, range_shape): - # Instantiate. - linop = linear_operator_wavelet.LinearOperatorWavelet( - domain_shape, wavelet=wavelet, level=level, axes=axes) - - # Example data. - data = np.arange(np.prod(domain_shape)).reshape(domain_shape) - data = data.astype("float32") - - # Forward and adjoint. - expected_forward, coeff_slices = wavelet_ops.coeffs_to_tensor( - wavelet_ops.wavedec(data, wavelet=wavelet, level=level, axes=axes), - axes=axes) - expected_adjoint = wavelet_ops.waverec( - wavelet_ops.tensor_to_coeffs(expected_forward, coeff_slices), - wavelet=wavelet, axes=axes) - - # Test shapes. - self.assertAllClose(domain_shape, linop.domain_shape) - self.assertAllClose(domain_shape, linop.domain_shape_tensor()) - self.assertAllClose(range_shape, linop.range_shape) - self.assertAllClose(range_shape, linop.range_shape_tensor()) - - # Test transform. - result_forward = linop.transform(data) - result_adjoint = linop.transform(result_forward, adjoint=True) - self.assertAllClose(expected_forward, result_forward) - self.assertAllClose(expected_adjoint, result_adjoint) - - def test_with_batch_inputs(self): - """Test batch shape.""" - axes = [-2, -1] - data = np.arange(4 * 8 * 8).reshape(4, 8, 8).astype("float32") - linop = linear_operator_wavelet.LinearOperatorWavelet( - (8, 8), wavelet="haar", level=1) - - # Forward and adjoint. - expected_forward, coeff_slices = wavelet_ops.coeffs_to_tensor( - wavelet_ops.wavedec(data, wavelet='haar', level=1, axes=axes), - axes=axes) - expected_adjoint = wavelet_ops.waverec( - wavelet_ops.tensor_to_coeffs(expected_forward, coeff_slices), - wavelet='haar', axes=axes) - - result_forward = linop.transform(data) - self.assertAllClose(expected_forward, result_forward) - - result_adjoint = linop.transform(result_forward, adjoint=True) - self.assertAllClose(expected_adjoint, result_adjoint) - - -if __name__ == '__main__': - tf.test.main() diff --git a/tensorflow_mri/python/linalg/matmul_registrations.py b/tensorflow_mri/python/linalg/matmul_registrations.py deleted file mode 100644 index 344e3537..00000000 --- a/tensorflow_mri/python/linalg/matmul_registrations.py +++ /dev/null @@ -1,133 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Registrations for LinearOperator.matmul.""" - -from tensorflow_mri.python.linalg import linear_operator_algebra -from tensorflow_mri.python.linalg import linear_operator_composition -from tensorflow_mri.python.linalg import linear_operator_diag_nd -from tensorflow_mri.python.linalg import linear_operator_identity_nd -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.linalg import linear_operator_util - - -# IdentityND - -@linear_operator_algebra.RegisterMatmul( - linear_operator_identity_nd.LinearOperatorIdentityND, - linear_operator_nd.LinearOperatorND) -def _matmul_linear_operator_identity_nd_left(identity, linop): - del identity - return linop - - -@linear_operator_algebra.RegisterMatmul( - linear_operator_nd.LinearOperatorND, - linear_operator_identity_nd.LinearOperatorIdentityND) -def _matmul_linear_operator_identity_nd_right(linop, identity): - del identity - return linop - - -@linear_operator_algebra.RegisterMatmul( - linear_operator_identity_nd.LinearOperatorScaledIdentityND, - linear_operator_identity_nd.LinearOperatorScaledIdentityND) -def _matmul_linear_operator_scaled_identity_nd(linop_a, linop_b): - return linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=linop_a.domain_shape_tensor(), - multiplier=linop_a.multiplier * linop_b.multiplier, - is_non_singular=linear_operator_composition.combined_non_singular_hint( - linop_a, linop_b), - is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( - linop_a, linop_b, commuting=True), - is_positive_definite=( - linear_operator_composition.combined_positive_definite_hint( - linop_a, linop_b, commuting=True)), - is_square=True) - - -# DiagND - -@linear_operator_algebra.RegisterMatmul( - linear_operator_diag_nd.LinearOperatorDiagND, - linear_operator_diag_nd.LinearOperatorDiagND) -def _matmul_linear_operator_diag_nd(linop_a, linop_b): - batch_dims_a, batch_dims_b = ( - linop_a.batch_shape.rank, linop_b.batch_shape.rank) - diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( - linop_a.diag, - linop_b.diag, - batch_dims_a=batch_dims_a, - batch_dims_b=batch_dims_b) - return linear_operator_diag_nd.LinearOperatorDiagND( - diag=diag_a * diag_b, - batch_dims=max(batch_dims_a, batch_dims_b), - is_non_singular=linear_operator_composition.combined_non_singular_hint( - linop_a, linop_b), - is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( - linop_a, linop_b, commuting=True), - is_positive_definite=( - linear_operator_composition.combined_positive_definite_hint( - linop_a, linop_b, commuting=True)), - is_square=True) - - -@linear_operator_algebra.RegisterMatmul( - linear_operator_diag_nd.LinearOperatorDiagND, - linear_operator_identity_nd.LinearOperatorScaledIdentityND) -def _matmul_linear_operator_diag_scaled_identity_nd_right( - linop_diag, linop_scaled_identity): - batch_dims_a, batch_dims_b = ( - linop_diag.batch_shape.rank, linop_scaled_identity.batch_shape.rank) - diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( - linop_diag.diag, - linop_scaled_identity.multiplier, - batch_dims_a=batch_dims_a, - batch_dims_b=batch_dims_b) - return linear_operator_diag_nd.LinearOperatorDiagND( - diag=diag_a * diag_b, - batch_dims=max(batch_dims_a, batch_dims_b), - is_non_singular=linear_operator_composition.combined_non_singular_hint( - linop_diag, linop_scaled_identity), - is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( - linop_diag, linop_scaled_identity, commuting=True), - is_positive_definite=( - linear_operator_composition.combined_positive_definite_hint( - linop_diag, linop_scaled_identity, commuting=True)), - is_square=True) - - -@linear_operator_algebra.RegisterMatmul( - linear_operator_identity_nd.LinearOperatorScaledIdentityND, - linear_operator_diag_nd.LinearOperatorDiagND) -def _matmul_linear_operator_diag_scaled_identity_nd_left( - linop_scaled_identity, linop_diag): - batch_dims_a, batch_dims_b = ( - linop_scaled_identity.batch_shape.rank, linop_diag.batch_shape.rank) - diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( - linop_scaled_identity.multiplier, - linop_diag.diag, - batch_dims_a=batch_dims_a, - batch_dims_b=batch_dims_b) - return linear_operator_diag_nd.LinearOperatorDiagND( - diag=diag_a * diag_b, - batch_dims=max(batch_dims_a, batch_dims_b), - is_non_singular=linear_operator_composition.combined_non_singular_hint( - linop_diag, linop_scaled_identity), - is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( - linop_diag, linop_scaled_identity, commuting=True), - is_positive_definite=( - linear_operator_composition.combined_positive_definite_hint( - linop_diag, linop_scaled_identity, commuting=True)), - is_square=True) diff --git a/tensorflow_mri/python/linalg/pseudo_inverse_registrations.py b/tensorflow_mri/python/linalg/pseudo_inverse_registrations.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tensorflow_mri/python/linalg/registrations_util.py b/tensorflow_mri/python/linalg/registrations_util.py deleted file mode 100644 index 6ad4ef7b..00000000 --- a/tensorflow_mri/python/linalg/registrations_util.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright 2019 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Common utilities for registering LinearOperator methods. - -Adapted from: - tensorflow/python/ops/linalg/registrations_util.py -""" - -from tensorflow.python.ops.linalg import registrations_util - -combined_commuting_positive_definite_hint = ( - registrations_util.combined_commuting_positive_definite_hint) -combined_commuting_self_adjoint_hint = ( - registrations_util.combined_commuting_self_adjoint_hint) -combined_non_singular_hint = registrations_util.combined_non_singular_hint diff --git a/tensorflow_mri/python/linalg/slicing.py b/tensorflow_mri/python/linalg/slicing.py deleted file mode 100644 index 19adb425..00000000 --- a/tensorflow_mri/python/linalg/slicing.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -from tensorflow.python.ops.linalg import slicing - -batch_slice = slicing.batch_slice diff --git a/tensorflow_mri/python/linalg/solve_registrations.py b/tensorflow_mri/python/linalg/solve_registrations.py deleted file mode 100644 index 81bf3458..00000000 --- a/tensorflow_mri/python/linalg/solve_registrations.py +++ /dev/null @@ -1,133 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Registrations for LinearOperator.solve.""" - -from tensorflow_mri.python.linalg import linear_operator_algebra -from tensorflow_mri.python.linalg import linear_operator_composition -from tensorflow_mri.python.linalg import linear_operator_diag_nd -from tensorflow_mri.python.linalg import linear_operator_identity_nd -from tensorflow_mri.python.linalg import linear_operator_nd -from tensorflow_mri.python.linalg import linear_operator_util - - -# IdentityND - -@linear_operator_algebra.RegisterSolve( - linear_operator_identity_nd.LinearOperatorIdentityND, - linear_operator_nd.LinearOperatorND) -def _solve_linear_operator_identity_nd_left(identity, linop): - del identity - return linop - - -@linear_operator_algebra.RegisterSolve( - linear_operator_nd.LinearOperatorND, - linear_operator_identity_nd.LinearOperatorIdentityND) -def _solve_linear_operator_identity_nd_right(linop, identity): - del identity - return linop.inverse() - - -@linear_operator_algebra.RegisterSolve( - linear_operator_identity_nd.LinearOperatorScaledIdentityND, - linear_operator_identity_nd.LinearOperatorScaledIdentityND) -def _solve_linear_operator_scaled_identity_nd(linop_a, linop_b): - return linear_operator_identity_nd.LinearOperatorScaledIdentityND( - domain_shape=linop_a.domain_shape_tensor(), - multiplier=linop_b.multiplier / linop_a.multiplier, - is_non_singular=linear_operator_composition.combined_non_singular_hint( - linop_a, linop_b), - is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( - linop_a, linop_b, commuting=True), - is_positive_definite=( - linear_operator_composition.combined_positive_definite_hint( - linop_a, linop_b, commuting=True)), - is_square=True) - - -# DiagND - -@linear_operator_algebra.RegisterSolve( - linear_operator_diag_nd.LinearOperatorDiagND, - linear_operator_diag_nd.LinearOperatorDiagND) -def _solve_linear_operator_diag_nd(linop_a, linop_b): - batch_dims_a, batch_dims_b = ( - linop_a.batch_shape.rank, linop_b.batch_shape.rank) - diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( - linop_a.diag, - linop_b.diag, - batch_dims_a=batch_dims_a, - batch_dims_b=batch_dims_b) - return linear_operator_diag_nd.LinearOperatorDiagND( - diag=diag_b / diag_a, - batch_dims=max(batch_dims_a, batch_dims_b), - is_non_singular=linear_operator_composition.combined_non_singular_hint( - linop_a, linop_b), - is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( - linop_a, linop_b, commuting=True), - is_positive_definite=( - linear_operator_composition.combined_positive_definite_hint( - linop_a, linop_b, commuting=True)), - is_square=True) - - -@linear_operator_algebra.RegisterSolve( - linear_operator_diag_nd.LinearOperatorDiagND, - linear_operator_identity_nd.LinearOperatorScaledIdentityND) -def _solve_linear_operator_diag_scaled_identity_nd_right( - linop_diag, linop_scaled_identity): - batch_dims_a, batch_dims_b = ( - linop_diag.batch_shape.rank, linop_scaled_identity.batch_shape.rank) - diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( - linop_diag.diag, - linop_scaled_identity.multiplier, - batch_dims_a=batch_dims_a, - batch_dims_b=batch_dims_b) - return linear_operator_diag_nd.LinearOperatorDiagND( - diag=diag_b / diag_a, - batch_dims=max(batch_dims_a, batch_dims_b), - is_non_singular=linear_operator_composition.combined_non_singular_hint( - linop_diag, linop_scaled_identity), - is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( - linop_diag, linop_scaled_identity, commuting=True), - is_positive_definite=( - linear_operator_composition.combined_positive_definite_hint( - linop_diag, linop_scaled_identity, commuting=True)), - is_square=True) - - -@linear_operator_algebra.RegisterSolve( - linear_operator_identity_nd.LinearOperatorScaledIdentityND, - linear_operator_diag_nd.LinearOperatorDiagND) -def _solve_linear_operator_diag_scaled_identity_nd_left( - linop_scaled_identity, linop_diag): - batch_dims_a, batch_dims_b = ( - linop_scaled_identity.batch_shape.rank, linop_diag.batch_shape.rank) - diag_a, diag_b = linear_operator_util.prepare_inner_dims_for_broadcasting( - linop_scaled_identity.multiplier, - linop_diag.diag, - batch_dims_a=batch_dims_a, - batch_dims_b=batch_dims_b) - return linear_operator_diag_nd.LinearOperatorDiagND( - diag=diag_b / diag_a, - batch_dims=max(batch_dims_a, batch_dims_b), - is_non_singular=linear_operator_composition.combined_non_singular_hint( - linop_diag, linop_scaled_identity), - is_self_adjoint=linear_operator_composition.combined_self_adjoint_hint( - linop_diag, linop_scaled_identity, commuting=True), - is_positive_definite=( - linear_operator_composition.combined_positive_definite_hint( - linop_diag, linop_scaled_identity, commuting=True)), - is_square=True) diff --git a/tensorflow_mri/python/ops/control_flow_ops.py b/tensorflow_mri/python/ops/control_flow_ops.py deleted file mode 100644 index 65cb7f63..00000000 --- a/tensorflow_mri/python/ops/control_flow_ops.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2021 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utilities for argument validation.""" - -import tensorflow as tf - - -def with_dependencies(dependencies, tensor, name=None): - """Produces the content of `tensor` only after `dependencies`. - - Args: - dependencies: An iterable of operations to run before this op finishes. - tensor: A `tf.Tensor`. - name: An optional name for this operation. - - Returns: - A `tf.Tensor` equal to `tensor`. - """ - if tf.executing_eagerly(): - return tensor - with tf.name_scope(name or "with_dependencies"): - with tf.control_dependencies(dependencies): - return tf.identity(tensor) diff --git a/tensorflow_mri/python/recon/__init__.py b/tensorflow_mri/python/recon/__init__.py deleted file mode 100644 index e26ed684..00000000 --- a/tensorflow_mri/python/recon/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Image reconstruction.""" - -from tensorflow_mri.python.recon import recon_adjoint -from tensorflow_mri.python.recon import recon_least_squares diff --git a/tensorflow_mri/python/recon/recon_adjoint.py b/tensorflow_mri/python/recon/recon_adjoint.py deleted file mode 100644 index a4e69626..00000000 --- a/tensorflow_mri/python/recon/recon_adjoint.py +++ /dev/null @@ -1,152 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Signal reconstruction (adjoint).""" - -import tensorflow as tf - -from tensorflow_mri.python.linalg import linear_operator_mri -from tensorflow_mri.python.util import api_util - - -@api_util.export("recon.adjoint_universal") -def recon_adjoint(data, operator): - r"""Reconstructs a signal using the adjoint of the system operator. - - Given measurement data $b$ generated by a linear system $A$ such that - $Ax = b$, this function estimates the corresponding signal $x$ as - $x = A^H b$, where $A$ is the specified linear operator. - - ```{note} - This function is part of the family of - [universal operators](https://mrphys.github.io/tensorflow-mri/guide/universal/), - a set of functions and classes designed to work flexibly with any linear - system. - ``` - - ```{seealso} - `tfmri.recon.adjoint` is an MRI-specific version of this function and may be - used to perform zero-filled reconstructions. - ``` - - Args: - data: A `tf.Tensor` of real or complex dtype. The measurement data $b$. - Its shape must be compatible with `operator.range_shape`. - operator: A `tfmri.linalg.LinearOperator` representing the system operator - $A$. Its range shape must be compatible with `data.shape`. - ```{tip} - You can use any of the operators in `tfmri.linalg`, a composition of - multiple operators, or a subclassed operator. - ``` - - Returns: - A `tf.Tensor` containing the reconstructed signal. Has the same dtype as - `data` and shape `batch_shape + operator.domain_shape`. `batch_shape` is - the result of broadcasting the batch shapes of `data` and `operator`. - """ - data = tf.convert_to_tensor(data) - data = operator.preprocess(data, adjoint=True) - signal = operator.transform(data, adjoint=True) - signal = operator.postprocess(signal, adjoint=True) - return signal - - -@api_util.export("recon.adjoint", "recon.adj") -def recon_adjoint_mri(kspace, - image_shape, - mask=None, - trajectory=None, - density=None, - sensitivities=None, - phase=None, - sens_norm=True): - r"""Reconstructs an MR image using the adjoint MRI operator. - - Given *k*-space data $b$, this function estimates the corresponding - image as $x = A^H b$, where $A$ is the MRI linear operator. - - This operator supports Cartesian and non-Cartesian *k*-space data. - - Additional density compensation and intensity correction steps are applied - depending on the input arguments. - - This operator supports batched inputs. All batch shapes should be - broadcastable with each other. - - This operator supports multicoil imaging. Coil combination is triggered - when `sensitivities` is not `None`. If you have multiple coils but wish to - reconstruct each coil separately, simply set `sensitivities` to `None`. The - coil dimension will then be treated as a standard batch dimension (i.e., it - becomes part of `...`). - - Args: - kspace: A `tf.Tensor`. The *k*-space samples. Must have type `complex64` or - `complex128`. `kspace` can be either Cartesian or non-Cartesian. A - Cartesian `kspace` must have shape - `[..., num_coils, *image_shape]`, where `...` are batch dimensions. A - non-Cartesian `kspace` must have shape `[..., num_coils, num_samples]`. - If not multicoil (`sensitivities` is `None`), then the `num_coils` axis - must be omitted. - image_shape: A 1D integer `tf.Tensor`. Must have length 2 or 3. - The shape of the reconstructed image[s]. - mask: An optional `tf.Tensor` of type `bool`. The sampling mask. Must have - shape `[..., *image_shape]`. `mask` should be passed for reconstruction - from undersampled Cartesian *k*-space. For each point, `mask` should be - `True` if the corresponding *k*-space sample was measured and `False` - otherwise. - trajectory: An optional `tf.Tensor` of type `float32` or `float64`. Must - have shape `[..., num_samples, rank]`. `trajectory` should be passed for - reconstruction from non-Cartesian *k*-space. - density: An optional `tf.Tensor` of type `float32` or `float64`. The - sampling densities. Must have shape `[..., num_samples]`. This input is - only relevant for non-Cartesian MRI reconstruction. If passed, the MRI - linear operator will include sampling density compensation. If `None`, - the MRI operator will not perform sampling density compensation. - sensitivities: An optional `tf.Tensor` of type `complex64` or `complex128`. - The coil sensitivity maps. Must have shape - `[..., num_coils, *image_shape]`. If provided, a multi-coil parallel - imaging reconstruction will be performed. - phase: An optional `tf.Tensor` of type `float32` or `float64`. Must have - shape `[..., *image_shape]`. A phase estimate for the reconstructed image. - If provided, a phase-constrained reconstruction will be performed. This - improves the conditioning of the reconstruction problem in applications - where there is no interest in the phase data. However, artefacts may - appear if an inaccurate phase estimate is passed. - sens_norm: A `boolean`. Whether to normalize coil sensitivities. - Defaults to `True`. - - Returns: - A `tf.Tensor`. The reconstructed image. Has the same type as `kspace` and - shape `[..., *image_shape]`, where `...` is the broadcasted batch shape of - all inputs. - - Notes: - Reconstructs an image by applying the adjoint MRI operator to the *k*-space - data. This typically involves an inverse FFT or a (density-compensated) - NUFFT, and coil combination for multicoil inputs. This type of - reconstruction is often called zero-filled reconstruction, because missing - *k*-space samples are assumed to be zero. Therefore, the resulting image is - likely to display aliasing artefacts if *k*-space is not sufficiently - sampled according to the Nyquist criterion. - """ - # Create the linear operator. - operator = linear_operator_mri.LinearOperatorMRI(image_shape, - mask=mask, - trajectory=trajectory, - density=density, - sensitivities=sensitivities, - phase=phase, - fft_norm='ortho', - sens_norm=sens_norm) - return recon_adjoint(kspace, operator) diff --git a/tensorflow_mri/python/recon/recon_adjoint_test.py b/tensorflow_mri/python/recon/recon_adjoint_test.py deleted file mode 100644 index 0bd8e1d1..00000000 --- a/tensorflow_mri/python/recon/recon_adjoint_test.py +++ /dev/null @@ -1,94 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Signal reconstruction (adjoint).""" - -import tensorflow as tf -import tensorflow_nufft as tfft - -from tensorflow_mri.python.ops import fft_ops -from tensorflow_mri.python.recon import recon_adjoint -from tensorflow_mri.python.util import io_util -from tensorflow_mri.python.util import test_util - - -class ReconAdjointTest(test_util.TestCase): - """Tests for reconstruction functions.""" - @classmethod - def setUpClass(cls): - """Prepare tests.""" - super().setUpClass() - cls.data = io_util.read_hdf5('tests/data/recon_ops_data.h5') - cls.data.update(io_util.read_hdf5('tests/data/recon_ops_data_2.h5')) - cls.data.update(io_util.read_hdf5('tests/data/recon_ops_data_3.h5')) - - def test_adj_fft(self): - """Test simple FFT recon.""" - kspace = self.data['fft/kspace'] - sens = self.data['fft/sens'] - image_shape = kspace.shape[-2:] - - # Test single-coil. - image = recon_adjoint.recon_adjoint_mri(kspace[0, ...], image_shape) - expected = fft_ops.ifftn(kspace[0, ...], norm='ortho', shift=True) - - self.assertAllClose(expected, image) - - # Test multi-coil. - image = recon_adjoint.recon_adjoint_mri( - kspace, image_shape, sensitivities=sens) - expected = fft_ops.ifftn(kspace, axes=[-2, -1], norm='ortho', shift=True) - scale = tf.math.reduce_sum(sens * tf.math.conj(sens), axis=0) - expected = tf.math.divide_no_nan( - tf.math.reduce_sum(expected * tf.math.conj(sens), axis=0), scale) - - self.assertAllClose(expected, image) - - def test_adj_nufft(self): - """Test simple NUFFT recon.""" - kspace = self.data['nufft/kspace'] - sens = self.data['nufft/sens'] - traj = self.data['nufft/traj'] - dens = self.data['nufft/dens'] - image_shape = [144, 144] - fft_norm_factor = tf.cast(tf.math.sqrt(144. * 144.), tf.complex64) - - # Save us some typing. - inufft = lambda src, pts: tfft.nufft(src, pts, - grid_shape=[144, 144], - transform_type='type_1', - fft_direction='backward') - - # Test single-coil. - image = recon_adjoint.recon_adjoint_mri(kspace[0, ...], image_shape, - trajectory=traj, - density=dens) - - expected = inufft(kspace[0, ...] / tf.cast(dens, tf.complex64), traj) - expected /= fft_norm_factor - - self.assertAllClose(expected, image) - - # Test multi-coil. - image = recon_adjoint.recon_adjoint_mri(kspace, image_shape, - trajectory=traj, - density=dens, - sensitivities=sens) - expected = inufft(kspace / dens, traj) - expected /= fft_norm_factor - scale = tf.math.reduce_sum(sens * tf.math.conj(sens), axis=0) - expected = tf.math.divide_no_nan( - tf.math.reduce_sum(expected * tf.math.conj(sens), axis=0), scale) - - self.assertAllClose(expected, image) diff --git a/tensorflow_mri/python/recon/recon_least_squares.py b/tensorflow_mri/python/recon/recon_least_squares.py deleted file mode 100644 index c031d795..00000000 --- a/tensorflow_mri/python/recon/recon_least_squares.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2022 The TensorFlow MRI Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Signal reconstruction (least squares).""" diff --git a/tools/docs/guide/fft.ipynb b/tools/docs/guide/fft.ipynb deleted file mode 100644 index 72099ca6..00000000 --- a/tools/docs/guide/fft.ipynb +++ /dev/null @@ -1,101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fast Fourier transform (FFT)\n", - "\n", - "TensorFlow MRI uses the built-in FFT ops in core TensorFlow. These are [`tf.signal.fft`](https://www.tensorflow.org/api_docs/python/tf/signal/fft), [`tf.signal.fft2d`](https://www.tensorflow.org/api_docs/python/tf/signal/fft2d) and [`tf.signal.fft3d`](https://www.tensorflow.org/api_docs/python/tf/signal/fft3d).\n", - "\n", - "## N-dimensional FFT\n", - "\n", - "For convenience, TensorFlow MRI also provides [`tfmri.signal.fft`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/signal/fft/), which can be used for N-dimensional FFT calculations and provides convenient access to commonly used functionality such as padding/cropping, normalization and shifting of the zero-frequency component within the same function call.\n", - "\n", - "## Custom FFT kernels for CPU\n", - "\n", - "Unfortunately, TensorFlow's FFT ops are [known to be slow](https://github.com/tensorflow/tensorflow/issues/6541) on CPU. As a result, the FFT can become a significant bottleneck on MRI processing pipelines, especially on iterative reconstructions where the FFT is called repeatedly.\n", - "\n", - "To address this issue, TensorFlow MRI provides a set of custom FFT kernels based on the FFTW library. These offer a significant boost in performance compared to the kernels in core TensorFlow.\n", - "\n", - "The custom FFT kernels are automatically registered to the TensorFlow framework when importing TensorFlow MRI. If you have imported TensorFlow MRI, then the standard FFT ops will use the optimized kernels automatically.\n", - "\n", - "```{tip}\n", - "You only need to `import tensorflow_mri` in order to use the custom FFT kernels. You can then access them as usual through `tf.signal.fft`, `tf.signal.fft2d` and `tf.signal.fft3d`.\n", - "```\n", - "\n", - "The only caveat is that the [FFTW license](https://www.fftw.org/doc/License-and-Copyright.html) is more restrictive than the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0) used by TensorFlow MRI. In particular, GNU GPL requires you to distribute any derivative software under equivalent terms.\n", - "\n", - "```{warning}\n", - "If you intend to use custom FFT kernels for commercial purposes, you will need to purchase a commercial FFTW license.\n", - "```\n", - "\n", - "### Disable the use of custom FFT kernels\n", - "\n", - "You can control whether custom FFT kernels are used via the `TFMRI_USE_CUSTOM_FFT` environment variable. When set to false, TensorFlow MRI will not register its custom FFT kernels, falling back to the standard FFT kernels in core TensorFlow. If the variable is unset, its value defaults to true.\n", - "\n", - "````{tip}\n", - "Set `TFMRI_USE_CUSTOM_FFT=0` to disable the custom FFT kernels.\n", - "\n", - "```python\n", - "os.environ[\"TFMRI_USE_CUSTOM_FFT\"] = \"0\"\n", - "import tensorflow_mri as tfmri\n", - "```\n", - "\n", - "```{attention}\n", - "`TFMRI_USE_CUSTOM_FFT` must be set **before** importing TensorFlow MRI. Setting or changing its value after importing the package will have no effect.\n", - "```\n", - "````\n", - "\n", - "### Customize the behavior of custom FFT kernels\n", - "\n", - "FFTW allows you to control the rigor of the planning process. The more rigorously a plan is created, the more efficient the actual FFT execution is likely to be, at the expense of a longer planning time. TensorFlow MRI lets you control the FFTW planning rigor through the `TFMRI_FFTW_PLANNING_RIGOR` environment variable. Valid values for this variable are:\n", - "\n", - "- `\"estimate\"` specifies that, instead of actual measurements of different algorithms, a simple heuristic is used to pick a (probably sub-optimal) plan quickly.\n", - "- `\"measure\"` tells FFTW to find an optimized plan by actually computing several FFTs and measuring their execution time. Depending on your machine, this can take some time (often a few seconds). This is the default planning option.\n", - "- `\"patient\"` is like `\"measure\"`, but considers a wider range of algorithms and often produces a “more optimal” plan (especially for large transforms), but at the expense of several times longer planning time (especially for large transforms).\n", - "- `\"exhaustive\"` is like `\"patient\"`, but considers an even wider range of algorithms, including many that we think are unlikely to be fast, to produce the most optimal plan but with a substantially increased planning time.\n", - "\n", - "````{tip}\n", - "Set the environment variable `TFMRI_FFTW_PLANNING_RIGOR` to control the planning rigor.\n", - "\n", - "```python\n", - "os.environ[\"TFMRI_FFTW_PLANNING_RIGOR\"] = \"estimate\"\n", - "import tensorflow_mri as tfmri\n", - "```\n", - "\n", - "```{attention}\n", - "`TFMRI_FFTW_PLANNING_RIGOR` must be set **before** importing TensorFlow MRI. Setting or changing its value after importing the package will have no effect.\n", - "```\n", - "````\n", - "\n", - "```{note}\n", - "FFTW accumulates \"wisdom\" each time the planner is called, and this wisdom is persisted across invocations of the FFT kernels (during the same process). Therefore, more rigorous planning options will result in long planning times during the first FFT invocation, but may result in faster execution during subsequent invocations. When performing a large amount of similar FFT invocations (e.g., while training a model or performing iterative reconstructions), you are more likely to benefit from more rigorous planning.\n", - "```\n", - "\n", - "```{seealso}\n", - "The FFTW [planner flags](https://www.fftw.org/doc/Planner-Flags.html) documentation page.\n", - "```" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.2 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.8.2" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tools/docs/guide/linalg.ipynb b/tools/docs/guide/linalg.ipynb new file mode 100644 index 00000000..f45442d9 --- /dev/null +++ b/tools/docs/guide/linalg.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear algebra\n", + "\n", + "Coming soon..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.10 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.10" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/docs/guide/optim.ipynb b/tools/docs/guide/optim.ipynb new file mode 100644 index 00000000..21363722 --- /dev/null +++ b/tools/docs/guide/optim.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimization\n", + "\n", + "Coming soon..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.2 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.2" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/docs/guide/recon.ipynb b/tools/docs/guide/recon.ipynb new file mode 100644 index 00000000..5291a70b --- /dev/null +++ b/tools/docs/guide/recon.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MR image reconstruction\n", + "\n", + "Coming soon..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.2 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.2" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/docs/templates/index.rst b/tools/docs/templates/index.rst index 899e7f23..13b8384c 100644 --- a/tools/docs/templates/index.rst +++ b/tools/docs/templates/index.rst @@ -16,8 +16,10 @@ TensorFlow MRI |release| Guide Installation - Uniform FFT Non-uniform FFT + Linear algebra + Optimization + MRI reconstruction Contributing FAQ @@ -30,6 +32,7 @@ TensorFlow MRI |release| Segmentation Image reconstruction + .. toctree:: :caption: API Documentation :hidden: From 7442a2d4d417ce1320f6779580e286bf6d14fdf5 Mon Sep 17 00:00:00 2001 From: jennifersteeden Date: Tue, 4 Feb 2025 14:51:21 +0000 Subject: [PATCH 4/6] Added gradient metrics and losses --- tensorflow_mri/python/metrics/iqa_metrics.py | 55 + tensorflow_mri/python/ops/image_ops.py | 80 + tools/docs/tutorials/recon.rst | 3 +- tools/docs/tutorials/recon/radial_CS.ipynb | 68581 +++++++++++++++++ 4 files changed, 68718 insertions(+), 1 deletion(-) create mode 100644 tools/docs/tutorials/recon/radial_CS.ipynb diff --git a/tensorflow_mri/python/metrics/iqa_metrics.py b/tensorflow_mri/python/metrics/iqa_metrics.py index c23c5090..82620687 100755 --- a/tensorflow_mri/python/metrics/iqa_metrics.py +++ b/tensorflow_mri/python/metrics/iqa_metrics.py @@ -330,6 +330,61 @@ def __init__(self, multichannel=multichannel, complex_part=complex_part) +# We register this object with the Keras serialization framework under a +# different name, in order to avoid clashing with the loss of the same name. +@api_util.export("metrics.MeanAbsGrad", + "metrics.MeanAbsoluteGradientErrorMetric") +@tf.keras.utils.register_keras_serializable( + package='MRI', name='MeanAbsoluteGradientErrorMetric') +class MeanAbsoluteGradientError(MeanMetricWrapperIQA): + + def __init__(self, + method='sobel', + norm=False, + batch_dims=None, + image_dims=None, + multichannel=True, + complex_part=None, + name='mage', + dtype=None): + super().__init__(image_ops.mean_absolute_gradient_error, + method=method, + norm=norm, + batch_dims=batch_dims, + image_dims=image_dims, + multichannel=multichannel, + complex_part=complex_part, + name=name, + dtype=dtype) + + +# We register this object with the Keras serialization framework under a +# different name, in order to avoid clashing with the loss of the same name. +@api_util.export("metrics.MeanSqGrad", + "metrics.MeanSquaredGradientErrorMetric") +@tf.keras.utils.register_keras_serializable( + package='MRI', name='MeanSquaredGradientErrorMetric') +class MeanSquaredGradientError(MeanMetricWrapperIQA): + + def __init__(self, + method='sobel', + norm=False, + batch_dims=None, + image_dims=None, + multichannel=True, + complex_part=None, + name='msge', + dtype=None): + super().__init__(image_ops.mean_squared_gradient_error, + method=method, + norm=norm, + batch_dims=batch_dims, + image_dims=image_dims, + multichannel=multichannel, + complex_part=complex_part, + name=name, + dtype=dtype) + # For backward compatibility. @tf.keras.utils.register_keras_serializable(package="MRI") diff --git a/tensorflow_mri/python/ops/image_ops.py b/tensorflow_mri/python/ops/image_ops.py index 755871bd..9a995d22 100644 --- a/tensorflow_mri/python/ops/image_ops.py +++ b/tensorflow_mri/python/ops/image_ops.py @@ -31,6 +31,7 @@ from tensorflow_mri.python.util import api_util from tensorflow_mri.python.util import check_util from tensorflow_mri.python.util import deprecation +from tensorflow_mri.python.losses import iqa_losses @api_util.export("image.psnr") @@ -1025,6 +1026,85 @@ def _gradient_operators(method, norm=False, rank=2, dtype=tf.float32): kernels[d] *= operator_1d return tf.stack(kernels, axis=0) +@tf.keras.utils.register_keras_serializable(package="MRI") +class MeanAbsoluteGradientError(iqa_losses.LossFunctionWrapperIQA): + def __init__(self, + method='sobel', + norm=False, + batch_dims=None, + image_dims=None, + multichannel=True, + complex_part=None, + reduction=tf.keras.losses.Reduction.AUTO, + name='mean_absolute_gradient_error'): + super().__init__(mean_absolute_gradient_error, + reduction=reduction, name=name, method=method, + norm=norm, batch_dims=batch_dims, image_dims=image_dims, + multichannel=multichannel, complex_part=complex_part) + + +@tf.keras.utils.register_keras_serializable(package="MRI") +class MeanSquaredGradientError(iqa_losses.LossFunctionWrapperIQA): + def __init__(self, + method='sobel', + norm=False, + batch_dims=None, + image_dims=None, + multichannel=True, + complex_part=None, + reduction=tf.keras.losses.Reduction.AUTO, + name='mean_squared_gradient_error'): + super().__init__(mean_squared_gradient_error, + reduction=reduction, name=name, method=method, + norm=norm, batch_dims=batch_dims, image_dims=image_dims, + multichannel=multichannel, complex_part=complex_part) + +@tf.keras.utils.register_keras_serializable(package="MRI") +def mean_absolute_error(y_true, y_pred): + y_pred = tf.convert_to_tensor(y_pred) + y_true = tf.cast(y_true, y_pred.dtype) + return tf.math.reduce_mean(tf.math.abs(y_pred - y_true), axis=-1) + +@tf.keras.utils.register_keras_serializable(package="MRI") +def mean_squared_error(y_true, y_pred): + y_pred = tf.convert_to_tensor(y_pred) + y_true = tf.cast(y_true, y_pred.dtype) + return tf.math.reduce_mean( + tf.math.real(tf.math.squared_difference(y_pred, y_true)), axis=-1) + + +@tf.keras.utils.register_keras_serializable(package="MRI") +def mean_absolute_gradient_error(y_true, y_pred, method='sobel', + norm=False, batch_dims=None, image_dims=None): + y_pred = tf.convert_to_tensor(y_pred) + y_true = tf.cast(y_true, y_pred.dtype) + + grad_true = image_gradients( + y_true, method=method, norm=norm, + batch_dims=batch_dims, image_dims=image_dims) + grad_pred = image_gradients( + y_pred, method=method, norm=norm, + batch_dims=batch_dims, image_dims=image_dims) + + return mean_absolute_error(grad_true, grad_pred) + + +@tf.keras.utils.register_keras_serializable(package="MRI") +def mean_squared_gradient_error(y_true, y_pred, method='sobel', + norm=False, batch_dims=None, image_dims=None): + y_pred = tf.convert_to_tensor(y_pred) + y_true = tf.cast(y_true, y_pred.dtype) + + grad_true = image_gradients( + y_true, method=method, norm=norm, + batch_dims=batch_dims, image_dims=image_dims) + grad_pred = image_gradients( + y_pred, method=method, norm=norm, + batch_dims=batch_dims, image_dims=image_dims) + + return mean_squared_error(grad_true, grad_pred) + + def _filter_image(image, kernels): """Filters an image using the specified kernels. diff --git a/tools/docs/tutorials/recon.rst b/tools/docs/tutorials/recon.rst index 0f7427a7..cf9dff50 100644 --- a/tools/docs/tutorials/recon.rst +++ b/tools/docs/tutorials/recon.rst @@ -9,4 +9,5 @@ Image reconstruction CARTESIAN SENSE (2D+t Cartesian k-space) GRIDDING (Radials and Spirals) PRE-PROCESSING TRIGGERED CINE DATASET (with GRAPPA and PF) - CG-SENSE \ No newline at end of file + CG-SENSE + COMPRESSED SENSING (Radial, 2D and 2D+t) \ No newline at end of file diff --git a/tools/docs/tutorials/recon/radial_CS.ipynb b/tools/docs/tutorials/recon/radial_CS.ipynb new file mode 100644 index 00000000..7ff71746 --- /dev/null +++ b/tools/docs/tutorials/recon/radial_CS.ipynb @@ -0,0 +1,68581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image reconstruction with Compressed Sensing (2D and 2D+time)\n", + "\n", + "This tutorial follows many of the same steps as the Non-Cartesian SENSE example.\n", + "We will reconstruct radially undersampled 2D bran data and 2D cardiac data (with TV transforms), as well as a 2D+time cardiac cine dataset undersampled on a spiral trajectory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up TensorFlow MRI\n", + "If you have not yet installed TensorFlow MRI in your environment, you may do so\n", + "now using `pip`: " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 24.3.1 is available.\n", + "You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 24.3.1 is available.\n", + "You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --quiet tensorflow-mri\n", + "# Upgrade Matplotlib. Versions older than 3.5.x may cause an error below.\n", + "%pip install --quiet --upgrade matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, import the package into your program to get started:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-27 09:43:35.343304: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2025-01-27 09:43:35.447698: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-01-27 09:43:35.473328: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TensorFlow MRI version: 0.22.0\n" + ] + } + ], + "source": [ + "import tensorflow_mri as tfmri\n", + "print(\"TensorFlow MRI version:\", tfmri.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also need a few additional packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "import matplotlib.collections as mcol\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a GPU\n", + "\n", + "TensorFlow MRI supports CPU and GPU computation. If there is a GPU available in\n", + "your environment and it is visible to TensorFlow, it will be used automatically.\n", + "\n", + ":::{tip}\n", + "In Google Colab, you can enable GPU computation by clicking on\n", + "**Runtime > Change runtime type** and selecting **GPU** under\n", + "**Hardware accelerator**.\n", + ":::\n", + "\n", + ":::{tip}\n", + "You can control whether CPU or GPU is used for a particular operation via\n", + "the [`tf.device`](https://www.tensorflow.org/api_docs/python/tf/device)\n", + "context manager.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Specify which GPU to use\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare the data\n", + "We will be using an example brain dataset from the\n", + "[ISMRM Reproducibility Challenge 1](https://ismrm.github.io/rrsg/challenge_one/).\n", + "Let's download it." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/bin/bash: wget: command not found\n" + ] + } + ], + "source": [ + "brain_data_filename = 'rawdata_brain_radial_96proj_12ch.h5'\n", + "brain_data_url = \"https://github.com/ISMRM/rrsg/raw/master/challenges/challenge_01/rawdata_brain_radial_96proj_12ch.h5\"\n", + "!wget --quiet -O {brain_data_filename} {brain_data_url}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This dataset contains 96 radial projections of raw *k*-space data, acquired with\n", + "a 12-channel coil array and corresponding to a 300x300 image. The data is stored\n", + "in a HDF5 file, which we can read using [h5py](https://www.h5py.org/). The\n", + "downloaded file also has the sampling locations or *k*-space trajectory, so we\n", + "do not need to calculate it." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kspace shape: (1, 512, 96, 12)\n", + "trajectory shape: (3, 512, 96)\n" + ] + } + ], + "source": [ + "with h5py.File('rawdata_brain_radial_96proj_12ch.h5', 'r') as f:\n", + " kspace = f['rawdata'][()]\n", + " trajectory = f['trajectory'][()]\n", + "\n", + "image_shape = [300, 300]\n", + "\n", + "print(\"kspace shape:\", kspace.shape)\n", + "print(\"trajectory shape:\", trajectory.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The *k*-space data is stored with shape `[1, samples, views, coils]`, where\n", + "`samples` is the number of samples per spoke (512), `views` is the number of\n", + "radial spokes (96) and `coils` is the number of coils (12). TFMRI organizes data\n", + "slightly differently.\n", + "\n", + "- Firstly, the singleton dimension is irrelevant and not needed.\n", + "- Secondly, the dimension order is reversed. Generally, \"outer\" dimensions\n", + " (e.g., coils) appear to the left, and \"inner\" dimensions (e.g., samples within\n", + " a view) appear to the right. This results in a more accurate alignment of our\n", + " conceptual understanding of the different dimensions and their underlying\n", + " memory representation. TensorFlow tensors, like NumPy arrays, use a row-major\n", + " memory layout, meaning that the elements of the rightmost dimension are\n", + " stored contiguously in memory.\n", + "- Finally, the different encoding dimensions (`samples`, `views`) typically\n", + " carry no special meaning in non-Cartesian image reconstruction. Therefore,\n", + " we flatten them into a single dimension." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-27 09:43:44.317299: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2025-01-27 09:43:44.807941: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22159 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:65:00.0, compute capability: 8.6\n" + ] + } + ], + "source": [ + "# Remove the first singleton dimension.\n", + "# [1, samples, views, coils] -> [samples, views, coils]\n", + "kspace = tf.squeeze(kspace, axis=0)\n", + "\n", + "# Reverse the order of the dimensions.\n", + "# [samples, views, coils] -> [coils, views, samples]\n", + "kspace = tf.transpose(kspace)\n", + "\n", + "# Flatten the encoding dimensions.\n", + "# [coils, views, samples] -> [coils, views * samples]\n", + "kspace = tf.reshape(kspace, [12, -1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `trajectory` array is stored with shape `[3, samples, views]`. As with the\n", + "`kspace` array, we reverse the order and flatten the `samples` and `views`\n", + "dimensions. Additionally we need to apply the following changes:\n", + "\n", + "- The array represents a 3D trajectory but the image has a dimensionality, or\n", + " `rank`, of 2. The last element along the innermost dimension contains only\n", + " zeros and is not needed.\n", + "- TFMRI expects the sampling coordinates in radians/pixel, i.e. in the range\n", + " $[-\\pi, \\pi]$. However, the trajectory is provided in units of 1/FOV, i.e., in\n", + " the range $[-150, 150]$, so we need to convert the units." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Reverse the order of the dimensions.\n", + "# [3, samples, views] -> [views, samples, 3]\n", + "trajectory = tf.transpose(trajectory)\n", + "\n", + "# Flatten the encoding dimensions.\n", + "# [views, samples, 3] -> [views * samples, 3]\n", + "trajectory = tf.reshape(trajectory, [-1, 3])\n", + "\n", + "# Remove the last element along the rightmost dimension, which contains only\n", + "# zeros and is not necessary for 2D imaging.\n", + "# [views * samples, 3] -> [views * samples, rank]\n", + "trajectory = trajectory[..., :2]\n", + "\n", + "# Convert units from 1/FOV to rad/px.\n", + "trajectory *= 2.0 * np.pi / tf.constant(image_shape, dtype=tf.float32)\n", + "\n", + "# We only do this so that images display in the correct orientation later.\n", + "trajectory *= -1.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now have a `kspace` array with shape `[coils, views * samples]` and\n", + "a `trajectory` array with shape `[views * samples, rank]`. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kspace shape: (12, 49152)\n", + "trajectory shape: (49152, 2)\n" + ] + } + ], + "source": [ + "print(\"kspace shape:\", kspace.shape)\n", + "print(\"trajectory shape:\", trajectory.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize the trajectory:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_trajectory_2d(trajectory):\n", + " \"\"\"Plots a 2D trajectory.\n", + "\n", + " Args:\n", + " trajectory: An array of shape `[views, samples, 2]` containing the\n", + " trajectory.\n", + "\n", + " Returns:\n", + " A `matplotlib.collections.LineCollection` object.\n", + " \"\"\"\n", + " fig, ax = plt.subplots(figsize=(10, 8))\n", + " ax.set_xlim(-np.pi, np.pi)\n", + " ax.set_ylim(-np.pi, np.pi)\n", + " ax.set_aspect('equal')\n", + "\n", + " # Create a line collection and add it to axis.\n", + " lines = mcol.LineCollection(trajectory)\n", + " lines.set_array(range(trajectory.shape[0]))\n", + " ax.add_collection(lines)\n", + "\n", + " # Add colorbar.\n", + " cb_ax = fig.colorbar(lines)\n", + " cb_ax.set_label('View index')\n", + "\n", + " return lines\n", + "\n", + "_ = plot_trajectory_2d(tf.reshape(trajectory, [96, -1, 2]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, it consists of a series of 96 uniformly spaced, sequentially\n", + "acquired radial spokes extending to the edges of *k*-space ($\\pi$)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute density compensation weights\n", + "\n", + "Non-Cartesian trajectories do not usually sample *k*-space uniformly. It's\n", + "obvious from the figure above that the center of *k*-space is much more densely\n", + "sampled than its edges.\n", + "\n", + "It can be useful to explicitly account for this during image reconstruction. In\n", + "order to do that, we need to estimate the sampling density of the trajectory.\n", + "TFMRI provides several ways to obtain this estimate. The most flexible is\n", + "[`tfmri.sampling.estimate_density`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/sampling/estimate_density),\n", + "which works for arbitrary trajectories.\n", + "\n", + "The [`tfmri.sampling`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/sampling)\n", + "module also contains other operators to compute trajectories and densities." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "density shape: (49152,)\n" + ] + } + ], + "source": [ + "density = tfmri.sampling.estimate_density(trajectory, image_shape)\n", + "\n", + "print(\"density shape:\", density.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform zero-filled reconstruction\n", + "\n", + "We are now ready to perform a basic zero-filled reconstruction. The easiest\n", + "way to do this with TensorFlow MRI is using the function\n", + "[`tfmri.recon.adjoint`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/recon/adjoint).\n", + "\n", + "The [`tfmri.recon`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/recon)\n", + "module has several high-level interfaces for image reconstruction. The `adjoint`\n", + "interface performs reconstruction via application of the adjoint MRI linear\n", + "operator, which is constructed internally. See\n", + "[`tfmri.linalg.LinearOperatorMRI`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/linalg/LinearOperatorMRI)\n", + "for more details on this operator. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zf_images shape: (12, 300, 300)\n" + ] + } + ], + "source": [ + "# Perform image reconstruction. For non-Cartesian reconstruction, we need to\n", + "# provide the k-space data, the shape of the output image, the trajectory and,\n", + "# optionally, the sampling density.\n", + "zf_images = tfmri.recon.adjoint(kspace, image_shape,\n", + " trajectory=trajectory,\n", + " density=density)\n", + "\n", + "print(\"zf_images shape:\", zf_images.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`tfmri.recon.adjoint` supports batches of inputs. In addition, the batch shapes of\n", + "all inputs are broadcasted to obtain the output batch shape. In this case, the\n", + "coil dimension of `kspace` was interpreted as a batch dimension (multicoil\n", + "reconstruction is only triggered when `sensitivities` are specified).\n", + "`trajectory` and `density`, which have scalar batch shapes, were broadcasted to\n", + "the same shape and all coils were reconstructed in parallel. The sample\n", + "principles would apply if reconstructing multiple images with different\n", + "trajectories.\n", + "\n", + ":::{note}\n", + "Many TFMRI operators support batches of inputs. Batch dimensions are always\n", + "leading.\n", + ":::\n", + "\n", + ":::{note}\n", + "TensorFlow broadcasting semantics are similar to those of NumPy. Learn more\n", + "about broadcasting [here](https://numpy.org/doc/stable/user/basics.broadcasting.html). \n", + ":::\n", + "\n", + "Let's have a look at the reconstructed images:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_tiled_images(image):\n", + " _, axs = plt.subplots(3, 4, facecolor='k', figsize=(12, 9))\n", + "\n", + " artists = []\n", + " for index in range(12):\n", + " col, row = index // 4, index % 4\n", + " artists.append(\n", + " axs[col, row].imshow(image[index, ...], cmap='gray')\n", + " )\n", + " axs[col, row].axis('off')\n", + " return artists\n", + "\n", + "_ = plot_tiled_images(tf.math.abs(zf_images))\n", + "_ = plt.gcf().suptitle('Zero-filled individual coil images',\n", + " color='w', fontsize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's combine the individual coil images into our final reconstruction.\n", + "TFMRI provides a very simple function [`tfmri.coils.combine_coils`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/coils/combine_coils)\n", + "to perform coil combination via the sum-of-squares method. If coil sensitivity\n", + "estimates are available, it can also be used to perform adaptive combination.\n", + "\n", + "The [`tfmri.coils`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/coils)\n", + "module contains several utilities to operate with coil arrays. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Combine all coils to create the final zero-filled reconstruction.\n", + "zf_image = tfmri.coils.combine_coils(zf_images, coil_axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_image(image):\n", + " _, ax = plt.subplots(figsize=(8, 8), facecolor='k')\n", + " artist = ax.imshow(image, cmap='gray')\n", + " ax.set_facecolor('k')\n", + " ax.axis('off')\n", + " return artist\n", + "\n", + "_ = plot_image(tf.math.abs(zf_image))\n", + "_ = plt.gcf().suptitle('Zero-filled reconstruction', color='w', fontsize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute coil sensitivity maps\n", + "\n", + "The zero-filled image has visible artefact because the *k*-space sampling rate\n", + "is below the Nyquist rate. Information from multiple coils can be used more\n", + "effectively to address this problem, by performing a SENSE reconstruction.\n", + "\n", + "First we need to obtain the coil sensitivity maps. These can be estimated from\n", + "the individual coil images. A low-resolution estimate of the images is suitable\n", + "for this purpose and is easy to obtain, assuming the central part of\n", + "*k*-space is sufficiently sampled.\n", + "\n", + "To obtain the low resolution image estimates, we will first apply a low-pass\n", + "filter to the *k*-space data. We will be using\n", + "[`tfmri.signal.hann`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/signal/hann) and\n", + "[`tfmri.signal.filter_kspace`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/signal/filter_kspace)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# First let's filter the *k*-space data with a Hann window. We will apply the\n", + "# window to the central 20% of k-space (determined by the factor 5 below), the\n", + "# remaining 80% is filtered out completely.\n", + "filter_fn = lambda x: tfmri.signal.hann(5 * x)\n", + "\n", + "# Let's plot the effect of our filter.\n", + "x = tf.linspace(-np.pi, np.pi, 512)\n", + "plt.plot(x, filter_fn(x))\n", + "plt.xlabel('Frequency (rad/px)')\n", + "plt.ylabel('Filter amplitude')\n", + "\n", + "# Finally, apply the filter to the k-space data.\n", + "filtered_kspace = tfmri.signal.filter_kspace(kspace,\n", + " trajectory=trajectory,\n", + " filter_fn=filter_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now reconstruct the images from the filtered *k*-space data as\n", + "described in [Perform zero-filled reconstruction](#perform-zero-filled-reconstruction)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "low_res_images = tfmri.recon.adjoint(filtered_kspace,\n", + " image_shape,\n", + " trajectory=trajectory,\n", + " density=density)\n", + "\n", + "_ = plot_tiled_images(tf.math.abs(low_res_images))\n", + "_ = plt.gcf().suptitle('Low-resolution images', color='w', fontsize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now use these images to obtain the coil sensitivity maps. We will use\n", + "Walsh's method, one of the methods implemented in\n", + "[tfmri.coils.estimate_sensitivities](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/coils/estimate_sensitivities)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-27 09:43:47.580569: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8100\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sensitivities = tfmri.coils.estimate_sensitivities(\n", + " low_res_images, coil_axis=0, method='walsh')\n", + "\n", + "_ = plot_tiled_images(tf.math.abs(sensitivities))\n", + "_ = plt.gcf().suptitle('Coil sensitivities', color='w', fontsize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You migjht notice that up to here the code is identical to the iterative SENSE tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform Compressed Sensing reconstruction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are finally ready to perform the SENSE reconstruction! We will be using\n", + "another of the high-level interfaces in `tfmri.recon`. In this case, we will use\n", + "[`tfmri.recon.least_squares`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/recon/least_squares).\n", + "This interface can be used for image reconstruction methods that arise from\n", + "a least-squares formulation, like CG-SENSE. Internally, this function will\n", + "create a [`tfmri.linalg.LinearOperatorMRI`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/linalg/LinearOperatorMRI)\n", + "and solve the linear system using [`tfmri.linalg.conjugate_gradient`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/linalg/conjugate_gradient).\n", + "\n", + "Usage is similar to `tfmri.recon.adjoint`, so let's have a look:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This is a spatial total variation regularizer\n", + "# This is different from SENSE\n", + "regularizerTV = tfmri.convex.ConvexFunctionTotalVariation(image_shape, \n", + " scale= 1e-4, \n", + " dtype=tf.complex64)\n", + "\n", + "# The optimizer is also different from SENSE\n", + "image = tfmri.recon.least_squares(kspace, image_shape,\n", + " # Provide trajectory.\n", + " trajectory=trajectory,\n", + " # Density is optional! But it might speed up\n", + " # convergence.\n", + " density=density,\n", + " # Provide the coil sensitivities. Otherwise\n", + " # this is just an iterative inverse NUFFT.\n", + " sensitivities=sensitivities,\n", + " # spatial TV\n", + " regularizer=regularizerTV,\n", + " # Use conjugate gradient.\n", + " optimizer='lbfgs',\n", + " # Pass any additional arguments to the\n", + " # optimizer.\n", + " optimizer_kwargs={\n", + " 'max_iterations': 10\n", + " },\n", + " # Filter out the areas of *k*-space outside\n", + " # the support of the trajectory.\n", + " filter_corners=True)\n", + "\n", + "_ = plot_image(tf.math.abs(image))\n", + "_ = plt.gcf().suptitle('Reconstructed image', color='w', fontsize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Consolidate previous steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's put together our entire reconstruction pipeline in a single\n", + "function:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def reconstruct_compressed_sensing(kspace, image_shape, trajectory):\n", + " \"\"\"Reconstructs an MR image using CS with spatial TV\n", + "\n", + " Sampling density and coil sensitivities are estimated automatically.\n", + "\n", + " Args:\n", + " kspace: A `tf.Tensor` of shape `[coils, views * samples]` containing the\n", + " measured k-space data.\n", + " image_shape: A `list` or `tf.TensorShape` specifying the shape of the image\n", + " to reconstruct.\n", + " trajectory: A `tf.Tensor` of shape `[views * samples, rank]` containing the\n", + " sampling locations.\n", + " tikhonov_parameter: A `float` specifying the Tikhonov regularization\n", + " parameter. If `None`, no regularization is applied.\n", + "\n", + " Returns:\n", + " A `tf.Tensor` of shape `image_shape` containing the reconstructed image.\n", + " \"\"\"\n", + " # Estimate the sampling density.\n", + " density = tfmri.sampling.estimate_density(trajectory, image_shape)\n", + "\n", + " # Low-pass filtering of the k-space data.\n", + " filtered_kspace = tfmri.signal.filter_kspace(kspace,\n", + " trajectory=trajectory,\n", + " filter_fn=filter_fn)\n", + "\n", + " # Reconstruct low resolution estimates.\n", + " low_res_images = tfmri.recon.adjoint(filtered_kspace,\n", + " image_shape,\n", + " trajectory=trajectory,\n", + " density=density)\n", + "\n", + " # Estimate the coil sensitivities.\n", + " sensitivities = tfmri.coils.estimate_sensitivities(\n", + " low_res_images, coil_axis=0, method='walsh')\n", + "\n", + " # Create regularizer.\n", + " \n", + " regularizer = tfmri.convex.ConvexFunctionTotalVariation(image_shape, # this is correct\n", + " scale= 1e-4, #5e-2, #5e-2 was the best for non-coil compressed (2024-10-17)\n", + " dtype=tf.complex64)\n", + " \n", + "\n", + " # Perform the reconstruction.\n", + " return tfmri.recon.least_squares(kspace, image_shape,\n", + " trajectory=trajectory,\n", + " density=density,\n", + " sensitivities=sensitivities,\n", + " regularizer=regularizer,\n", + " optimizer='lbfgs',\n", + " optimizer_kwargs={\n", + " 'max_iterations': 10\n", + " },\n", + " filter_corners=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make things more interesting, let's test it with some new data! We'll use\n", + "a cardiac dataset which was also provided by the ISMRM Reproducibility\n", + "Challenge 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/bin/bash: wget: command not found\n" + ] + } + ], + "source": [ + "heart_data_filename = 'rawdata_heart_radial_55proj_34ch.h5'\n", + "heart_data_url = \"https://github.com/ISMRM/rrsg/raw/master/challenges/challenge_01/rawdata_heart_radial_55proj_34ch.h5\"\n", + "!wget --quiet -O {heart_data_filename} {heart_data_url}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's read the data and process it in the same way as before:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "with h5py.File('rawdata_heart_radial_55proj_34ch.h5', 'r') as f:\n", + " kspace = f['rawdata'][()]\n", + " trajectory = f['trajectory'][()]\n", + "\n", + "image_shape = [240, 240]\n", + "\n", + "# Convert k-space to TFMRI format.\n", + "kspace = tf.squeeze(kspace, axis=0)\n", + "kspace = tf.transpose(kspace)\n", + "kspace = tf.reshape(kspace, [34, -1])\n", + "\n", + "# Convert trajectory to TFMRI format.\n", + "trajectory = tf.transpose(trajectory)\n", + "trajectory = tf.reshape(trajectory, [-1, 3])\n", + "trajectory = trajectory[..., :2]\n", + "trajectory *= 2.0 * np.pi / tf.constant(image_shape, dtype=tf.float32)\n", + "trajectory *= tf.constant([-1., 1.])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now perform the reconstruction:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mrn/v935vrK6uHkn/yle+Mubm5uJVr3rVkX1pi4uLcfr06SreXvOa18TGxka89KUvjWc+85lH7p84cSKe85zn9P//03/6T7G/vx/f/M3f3PdZRMTJkyfjW7/1Wytb+xdDf/7nfx4REZ/1WZ81cf3FL35xfMInfMKR9IgC/rt/9+8mZHr77bfHv/7X//pI+t/5nd+J3/7t345//I//cXzZl33Zkfuj0Sj+5t/8mzfUhkaNGj1+9ISfHGmf9vmr9Ck9liUiusXFxe7BBx/sdnd3u4/8yI/sIq4/wuTtb397f8Ly1a9+dfe93/u93U//9E93f/AHf9B1Xdc95znPmSjjf/2v/9V1Xde9733v6374h3+4e9nLXtb93M/9XHflypXuEz/xE/u0n/mZn9ldvXq129nZ6V7/+td3L33pS7tf/dVf7eu65ZZbJvib5jTsF33RF3UHBwfdb/3Wb3WvfvWru5e+9KXda1/72u7SpUvd/v5+94IXvKBP+/Ef//HdwcFB99BDD3Xf//3f333Lt3xLf/J4qN6I6J71rGd129vb3e7ubvczP/Mz3Q/+4A92v/M7v9Pdc889vYw0z3/+z/+567que+CBB7pXvvKV3fd+7/d2b3jDG7pz5851X/RFX9Sn+xf/4l90Xdd1v/u7v9t913d9V/ct3/It3fOf//z+/ud//ud3m5ub3d7eXvfWt761e/nLX9796I/+aPeWt7ylu3z58pG+xkndhx9+uPuRH/mR7hWveEV33333dW95y1set1O6rgzonj4aJSK6z/mcz+m6rute8pKX9Nc+6ZM+qdvZ2ek2Nze717zmNd0P/MAPdO9+97u7a9eudW9961vtqerXv/71ve5ADo888kj3i7/4i13Xdd2rX/3qifRPe9rT+hPYv/mbv9n92I/9WPfyl7+8e9Ob3tTdf//93dbW1hM+ZtunfdonuvhLwED7tM9fqc8Q4Is4fEbc6173uv7azMxM98//+T/v3vWud3WXLl3qtra2uvvuu6/77//9v3df+7Vf2y0vL0+UsbCw0H3zN39z9/u///vd5uZmt7Gx0f3Jn/xJ9/KXv7w7derURNpnP/vZ3c/+7M92jz76aLezs9Pde++93Q/90A91Z8+ePcLbNIDvzjvv7L7ne76n+83f/M3ukUce6ba3t7v77ruve/Ob3zwBUPH5qq/6qu7uu+/utra2uq7rJuoZAnwR0T33uc/tfuu3fqvb2trqHnvsse51r3tdd+uttxYfq/LP/tk/637zN3+zu3LlSnf16tXufe97X/eqV72qe8pTntKnmZ2d7b7v+76vu++++7rd3V0Lmj72Yz+2+4mf+Inu3nvv7ba3t7vz5893d999d/fDP/zD3ad+6qceqfdrvuZruj/5kz/ptre3u/vvv7/7/u///m5paekvFeDD9Xe9613d5cuXuwsXLnS/9Eu/1H3yJ39y+hid2dnZ7lu+5Vu6D3zgA9329nb3/ve/v3vxi1/cfdqnfVrXdV33Qz/0Q0fqXl9f777zO7+z+6M/+qNeV9/3vvd1r3/96+0jXtqnfdrnL/4z+v9/NGrUqFGjRil9zdd8TfzkT/5k/Mt/+S/jx3/8x59odho1ajQlNcDXqFGjRo16uv322488guWOO+6I3/iN34inPOUp8fSnPz0efPDBJ4i7Ro0aHZfaY1kaNWrUqFFPL37xi+MLvuAL4l3velc8+uij8REf8RHx/Oc/P9bW1uIlL3lJA3uNGv0VpQb4GjVq1KhRT29729vimc98ZnzBF3xBnD59Ora3t+OP/uiP4lWvelW88Y1vfKLZa9So0TGpLek2atSoUaNGjRrd5NSew9eoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anSTUwN8jRo1atSoUaNGNzk1wNeoUaNGjRo1anST01xtwpWVlf5313UxGo1iZmYmRqNRjMfjODg4iIiImZmZmJmZiYODg9jf379eydxczM7Oxng8jv39/ei6LsbjcYzH477M0Wg08UHZ+HRdF3t7e309mgd8dV03wTfuKd8zMzP9tYiI8Xh8JC//R3quD2m4PFcm86W/+ZvLRvv5N3hX4vZHROzv78fe3l7fXr2v+SBf9Afqm52djbm5ub5MyL4kJy2f62CZ8H3Uk/X7/v5+r2PQKaRjec7Ozsbs7OxE2egbLof7D2lZh8HjzMxMz5frRy5/cXEx5ubmYmdnJ65duzah2zMzMzE/P9+nVVnwx11TPeH2q86g/SCMuYiI+fn5mJubmxh7WofK0Y0p7T8ez13XxcHBwZE0jl+khw5kugTdYznu7+/3tsTxtLCwEPPz8xPl6RgEr8wv9yt4RLloA+yZ6y9uH+urjgWVy+zsbIxGo9jb24vd3d2YnZ2NpaWlmJ2d7dvK/b6/vx87Ozu9nqIsyEfHD9sp2FKMBeZhZmamHwvarrm5uf4+y49lrOOd9ZDHBJPyAJvj9B/9pXnRHxnfbBdUpyB7/aAPUZ/qO+swX3P2DDaZedBxxnrqxrzK9eDgYKJ+lKk+ArLnvGyHsj5hftx1l4fvO1+pbQT/4JvTs86wvUJbYM/Yfuzu7sbu7u5E/SwfyIb7HXRwcNCPC06POrQN3A4lth+q95zHycvhE2e7WL6wD0P0lzLClwGIm5VKg0jJAccnmqbh/y9DXY+X3JSXmnJvpP/+IuVcS39ZdDBiuv54vPi+0XHo8tb2szrUmvIfb7vxl1EnH2/KJuyPV9kR08vxL9O4yyjj8S+C95qx8eHI+5edqiN8DqXqLCfiMFrE6RjJ6yyX0/B9jaBoHhDSTDMQmXfkYf54hsQzI50BMr8ZZTODiJioRxG7yoOvcdlDBiNTXs2HmbDyCh4xG1LDxxGQrC06i1E+MDNHpE2J+8q1h3lSfjgq6frY9U+mo443nmEhMoLymW/Mxh3vGlVw5XNad69E3FYdj9OQRlyYBxeRUF4xTnXWrmOqFNXgccFRNk3LEVvXTo0EaTq1D8wnyw/lsE5lMlC76CIiXXcYsWM5cfQZ97g+HkdIg/LAI+dRWal8XRRCI0UcUVZ5O6DpqKTzJZuusnOrKUij/iniMDqGdmiZ7BdU9toejHX1Carrrq9xHREutYXOZoBKgKrkD0p5XdpaKtluJ1vngxypz3FjChGurusmVjnYpqhPUlvo+CmNX73m+M4mdDW23tWnZbgVvxLdMODjhkNxlbKB6MChOmwNqapR5bKc4Xe8uPqcQDMnpg7UpYE8mM+sHK034nApgZcBneHidmZh+hJAYsVhg6PLu1wO8rvlnCHDznJho+p4c4PUDQpXDogNNsvJyUV5zdqthns0GvX9pQZOAa0bB9DxDCiw3LL7jrQeXFOgMlQWO5+IoxO8IUIbFSyhDNVxl98BXwA+dQQKdFQO7p6Tm5Oh5se3s3toWwYsVUfYxrGseNnZgS1uCy8/8lYGAEkGg1wHKJM/l839xUvsro382+mw69daoKHyU13gCYb2c8aryoFl78AyL6e7OtT2cF7mlbc9uW0dTiaqv87HlKi0xF7jR5QXHdecj/U1S+N+qz9wadjGs63RMrhtXC76mMcF+3Atw/GW8Z3Vr5O1rNxSf+pWshqqBnyOMmadEiqwyoABl1MSNpN2juY/LqkDcbw4Jz7Eby2PmQJlZWTKUSPrzPnxzDVT4hpS+TiwX/pdGoCZMXR8OmNW23cl+dbotTPgNfmm6ctSuqG6XL/UlFvDixox7ePMyJXqLwHgkq7W2JOaurM2uPIynS6VPSSTkh4P8ZS1y9Wh5WWfmjpLNq/U7iFbPqTvpXpZ77N2ZXkUQGh7+F5pbA3V53i9UXLlDIG62gke55mmbZpPrw/x6uy78981fGXAN8vj5OPs1TST9Yyv41I14MsaqAMyW17RyF2t0e26yVlSNkPkWVatgiFfSbG0TAYeymPXHT3AMVQX8jgjjXJ00zDnL5VZC0Ii4ki0FvVub2/bdrhyMznxNe4nN9PVWTuXPeQ4nQMdjUZHDnKUZrCj0WgiUpcdJui6ushBZiS1/arjmk/HV9YGt8TO/HLdKi9e8q4BJZlcuA0ob39/f2Lyx+07ODjoN1pzfq7DjQ83CcDHLa+qTmVtcLIBnzw7V9KICet6Rm4ccxRO+eMDGY53JTfetH6VCZfPBzl2d3f7/uOJIA7paRTS1aO6VdJDjEONAGcgtCRbJxdNy4cgOGqqdiOTF6fX7ToOwHBeHMxxYOU4pPW5sYTy1Wa4/oiIol+LiIkot/oVtnFu1Yh51gNlWo8uwau/KR14yA4PaX+51TKHY5R4XHDd4KvWxzk9qLk2RMeO8Dllwn8dfCqoWmOl5TkHyDRNuZrPCV7vu7axI3agJKsj41PbWuOkSsCpFLbPDCXzwmAT386Zlsrg306u2u5MhjXOTctkfZv2ZJoCttr+GiI3ZpSHDHRl/GfpXXuyNGpomZfjjC2ujw2hTgq7rjvi7FTPpq1/SH9q5K888hjPHBaTys+Nfwc8dEyUeKuhTB8yG8ofN4Fl/YBM1JFmPGZ6xaR6qLYhK7dETs64rjaKAZ/Ko2R/XX2Z3qoeZfuLa/U9a5+jTO8yvXb1qB3JgGpmA5xNY8CX2amSDmgfafvAg/pE3kJRauuQ/czGbQ3vGZ4qjZVp7UDEFIAvU2CdcbrHZSjT3OlDQspIUbu77vivLTvb28cgSGedyoOSdloJALKcOPrj2qcDput8hEN5qbnv9rtleWoNs7bBtXvIIWcOXfVUdS+beStg0n2TDjiXgEj2X/OUDI0rKzM4rlwdhw6wZPUOtadmDKsDUZ3iclz+bLKiVCOfLI9rJ/ZU4j/LsFTOUL+xQy85J5BGCzLeuWy+njkOJX5UhUZfuP36GAvkYdCidZTGKctVx2WWNxvDpfpc/UO/NbLvQB8oi/RzPqRTf5HZTNU7d+9GyOmrkgPDbqWuFoRnIKlm/Gr7wZvuG+TVmaxNWfSf63GAVa/XUDYWHIjU9I6fG6VqwJdtWFVm2FHyc7L0PjqLnaqW5cgZUTcDdfmcMN1AzcK24FU3LzPvmRK5Zcwh3njTsdsDibZzmSxTxwe+hxSaeeA8aH820LOZCK7hNCvXpW1hWalDLTkDbYP2B5+EQxl6khO86nO9hurU9jCV8pWA1FB7VYdKh1EgY+hHNp4ZHGo6LduBPiZXR8afOxTG5ShAKAEhrScztHxNbRgOivEm+qydes+NLb4H4MTt0gNQ6vCzNro6MvmwvdKI0mg0+XxBJt7eoCcgtfxaR8g8MODM2s11utPZrvxsHGTX2A7xUrY7zYvfrB+4x/Wjr9luOjCHvuHytFwF4DX6nrU5GxssN/UTTr4ZoHH5MjCl9o6/NS/XpT5OywNxlJpPlatPzQA9t1frc7wplcC6K4PtcQmc1o41pqkjfENMujxsVFigjxd6dYONv3EvMwKlcqcBoEPXM4dRAk7H6dSa8jPKBlrNIKjlpaQrpXw3cr9Ete0Y0neUpXkyQO14KAGLjKZxsq6tzgipE5yGn5IsHTDS647vWp2pAeXctkwfh6hWJjcyfkvOkq8723acsTnEg9abXa/Rd1dHjSOrsQOZjpf+671S+4ZsswNO05DqVkn3a31xCejWUG0bjuNjj8NPKa/z+8cFZ+5+qW+m1f9sPGc0jR47mjrC5952kDGImRhHbBzadU8MHxqwjkrK4xxpzcBifkpKw4Nc69ZIYKlOl6Z0jUmBtZbvwEhphjakfNMYc6RHlICv6ybw7PEz6qhxHdEYNoK6zJKBHNYBjZayDFQWo9Hko0q0zBuhbAJTQzX9pnUp35n8p9UFBzD5GqIo2TMKtSytTzeJ14BZLSMbK3grgoJfx1Pm2NWeOR11dWh/cFt51o+ydGN8Bmhxn/OoLPh/REw8soKfxVlyPGyDwCsOfKicWOc0Qsj2TOWkfOp/lkmNLxnSkeye2g+U5R7XUwPO2HZl9hz16lafbOxP017nY7JIvabLxorm4bHu2qXlsRx43Lh2ZIBT+WL9d/tUtQ/Uf2e2DW2ZRubgQccMl61vfBkqL6OpAB8bFb6eEZgvMaYdnO0DudFZg+uEGufMIM4pqdaTGZlpZw5D7XKkg8eBpVJZJb6nATIlp5ANHq3HyZt55MHKe4uc8SmBEa5LDTHzkE1uSs4vk2WJH76f6UmJhkBZ5mC4Pu2PiMNlTldmjZzdPYAI7dMacic3M4fjgFkGdtiZTsOPAiuuOwPN2d5jB/jYGbh90+hHtzTOzjQDmSxHdTQ4UMOAZmifFNLzqxn5/lCb0a7SXmSne1lba0jTqQ3I6nbtyiYwQ2MF32wrS+1zPNT6HtUvvj7k75Rf9408zq7plhE3ftQ2Y8lb+Rwi12Yum+XMOoNJCI8/HStZXbV4hWXLW5pcfzj7PC1NdUo3Q6CZUWdnoUqqm4NZmLVghOtwvB6H1Ok7HmoMQMlQMHiOmAQt3LnuwcZapnsfo1MMdTTOwJaUjNOwkwbpvj4dJCrHUpucDLmckmFRJ6htyYyEc2KcVmelWp+T49Cp4GkAhaNMtk4XXT6nGyVj4h7PUNMG51Dw7ZxWKX9mY9z/LI+7p+0ekp8DAxkoUaqxZWqHnMFnMJTpb8af9ok6XJdWx5OWWfIBJb6co9c2a3naXqfD0+wp1PJ1xUnJPVCZCfyAx2yfurOX+nHtq22X011cd2XX6HNW/zQAyLUt45XLcnqn9UyDE9y4QTmZ/c50Gvky2+Fk4K5r/lJ909LUj2WZmZmxLyV3BxkYIM3Ozk68nJhffMzGKzsNiW/XAS4q6JRJARt3jkPabDT0lUXMF9eZOQf+jZkK0s7Ozsb8/Hz/Qm/nCFVOKJNf1M2bfXFghqNfHAkoPQHeGVL+xqwdPPGSPOfjJTt+9U3JEWeGxDkdTc9p9C0ESOse+aBA0Bk9fayQ6oc7AZu9yLxkrJwcSqAxM5xOTqw/fI2NuDNKuMdPox86Ba784cO6xc5wmjKYLya2Q3Nzc0ccfrZUrWVk/9WpZE641F8ZIGf9AXFfZRvMHZ8OICpp5ILz6Hhw0Q/lm+vWyFwJuDmdLfHiQKcu+6IMfROLktN3ZwtKYAX1Z3kQdeXoK2yy60MeW26pcUgmKj8nW5TN/7ltTj4lfXN53fh08iuBOlev1q1tdXIdmlTyW63g29zhpFrwlbXd8ejk63S/9okFQ3Ts5/CVOr9kaDJl1bKHHImr2w3gjOeaurIOKZXtBv00TpHbMuScMhDo6iyBrBJlwENBjyqxU2oucxrZ1vCmhH51BizLl7V1SFczvcqcRan+48hBy0A901LG25ABuxFyel7jTLKyXL4MJB5XRqX6s3tOn6a1VZx36No05OpyEwAHfofoRnlzdU5T5jS+JKsX5bh7Trcy21E7vodsdQY8tIyaNPpfx2DX+VfKZe2toQzklGga2Tn5lXgt9fWHi5wvzSYgjydVAz59En7E0WPqbkYBdAqUzY/G0DJ5UPAMkZWLZ3XHodIsCKSRvNIAxIDg/UQaReKIGtePPBzZ007ndI5f1Blx+Awi9ygBpiGA5SILSK99juuLi4tHgJzWyfmY9MXhqgOaFzKFPPTZSzwjYpm5o/hoE6fjKLX2mxrCIVDunGa2dKx5NaqosiwBo9L+sJKxzgwO65+TbeYsWPYZCNd6SwYbv7U+x4sbv6XJkWsL18cHJ1jO2ga1BRlQco5mSH/4uuZXIOL0hmWmY1vl6WSCdvFYcf2lG8wzXXZUAppOz4b6IwOvbuwOHVDQdOqstU84is3jBvk0Elqql32p5mG+3DumM7/CMuNr/JYTrh+k745mm6xp3YpfSecyfc/ef31cUOR8WkZs+5zNZPk6X8FpnQxcWmfTbrTt1YAPp9aYWTg9vp4tWXE4OyL6V8mAnBAZzLDDccriytDySsaWr6vy1+TXML2CBffGCvzm5xWqPOFoRqNRCmwc4BtShJLT5Ta4ZRzuA4AjLCtzWl5Ode3GfxhsfT2TKnlmCByAUsOvxo/7WJfYIXNd+nZ6nYEETQfi/hoydlm7MxBQ4k2vsVycw87KY/mx7N3EgtO5E5gK3LTNDJwYOKI+dljZBnb+dnu6sjGtS9bMT3ZiEOmQxslf61FeHW+uvx0g03K1T5gvTDI1fYkPXTrl8aM2o7S5ntMex1lrv2rd2fhQp5z1gwOVDlgxDxmp3YyY1GG+hnGiD7h2ICCTKdszbYNLy+OYx5SCK2c3mUcdg/h2D+tWPrh8lanKLBtDNbbXpVUdyuSO/xkAK41P1ccM8A35BdefQ/qnNNW7dLNB5BhQJR0yAO6b75cMwzSNniatAzwZcQTD1cXOUr81fUk5uLyMZ/1fAkyl6xnpoHODjw1/CVSU7ul1Z+Ay3lwZrm52Es6wujJd+e7/kAFx+WvvlSgbqwp4SkbTyULvT+Oss36ukXHGrwM9+HZGs9R25TMrUwFvrQyG7J3T8Zp9O6pvbqxnNkTBm/52dbj2OnswpOsZGOb7tU4d91zfqcNUkML5tP5MtlnbHc/8351IzXzBkK6WQMu0lI2X0njPfEAJBCnp2Mzss9ap9dS2bYinIZuvvGZp1cdr3Vk5GU/IOzRmamiqPXzaAHdYwjHL78lUw6LK7xRPl/GYjiOIofsoJztZ5Qaxvm6N24JIUWaMue28YdfJx7XFgVKWBT9HC9dKg5IdG5er15hnniHyQY29vb3+YInKT42x8uD44zwcVXXtz9rGaZ3s4AjchnQnp4xfBQiaPtuW4MBGliarx0WhOGLG+sh9p33snFg27ktGkNO4dqhhZ/13dWb2wC21lUCf0x8GWnj7BKfjZ2JllAEY7htsZeBx7iIl2gc6Pl37Oa1bdsV1djoOMPF3NlY4Da6pbDVNDWk0l/lUWWTlZ2OOo/qQuTtskvHP/cPlOFlEHD5NgfsD41DBgZMv14t07mCB+owhUKV+h1fdssM62Th3/e3GlxuPLrqcjVttq7NRWb8PYRXdwuPSQCbOVnLdPL40vyu/ND4yfz0t6Jv60EaGxN3AqFE2/u2UQwdDRtMakxpS0Jnx7zobeUqKo21zgzYDEM7gO8V3Du64xPU6Z+rq430g2eDMSNteAqnKozPOQ8CJr+lALtWZOTb+zkC2Mwa1VMOX6lcmmxrZuvpdnqG2PB5tdjpXcvxaX6nuzHk4na+RF+fRvudlYm4fl11bXwlcuf/ZeOZ6M13RNNk4qrHbWV9kviPzJSXwwdcAtgD6boS4b4dAgtsSoPy5sh0poHFllHR8yAZlYzrzOU7eJdLyuu7oK1I1bakNQ9eHeCv5zaE8zq4owHTll3Tf1cVjdZq8TMd6tZrbyK9MZFGA0uAejY4+PR9l4L7OiEuA0rUhE3StwmbGm+/zoQ93ZJ/TZTzy7Nbt6eAoQHYghPnl+zpIM+eQXVdlHo/H/R5PF1nS35mDdlEc5jE7vOL2d2R7yrQt2j6WnabTwdt1k48qcQNRx4PeqwUrjjKHxry5OktOruTAMsdQA6RRnkabFJRmvJTk4vpVxw3ardGzIQDFe0phm1RHmFflg9NnQEXLcZHl7PCVyiBz/noQze2P5LKQzr0Zh/cS674+1KV8Ob6Zn9K+Y07vZKzjiPcVc9maXp0z8873hh7vwvy4sa/kDnJoe7RdpTq1ftYh1YsMVDn+tR3T8sN5nB/K6hkaI6X/Q/tQS/W662rfaoFWybYoPsj0plRGjT46mhrw6Qktvq/CyYwJl4d0KK806N1GaFemAkHl0YE+GHQVpNbjAIY6Wq1P+YYjUf75N/OAZ4qhrPH48Dl7GT/8HLrSPkQ+kaqUOX2N2EVcP9SjCsphbzZyOpAUxKksdHmP+8jtv3PgKzM2zEN24tD9R1tA7tlaGcBQXVFZaPszGZUAnx5+AT+q25nuKWWGp8T3UNkOIGtbh0CAlstjjp+DmDkdtgWZbcIzuvieHuqocYRDMkQ5JftSOpSQ1edkzvWWJgAMnLgs8MAn7N2kLhuXOt5KfJRkp3whLW8j2d/fTw92sa1kAIY2sO1BOc5elvwKf7NsHHBmOWbt03qZFEyyDXCfzB5yndPwoja35NvYl2Tk5OlApdrxofHo+CrxoN+1e/uzerVfMt6cncn8ZA0d+zl8oJrBqOkdZQb5RsudJj+DsZITcIZ0iAenNCUqAZSawZqVOa3CsCHj9ArWsrzKl+NRy3s8aEiPakEX/8/amuWbRp+dTIf0yRlnB5ZKYy77fxydyNK5PKoTjteS7KaRD0fXSg651L8lo+zkWMtzqd1DxDqs7cio1E+1YOI4NnpanlyaIRvhwDy+tb9KdkH7xOlLifdMh4fkVuKL05TkULLHjrfMh3A9JQCb8XEcUOIAsytfddX1WWZTXJ4acuWWdKgWC2T6NGTftLzatkx1Slcr4dd6TeOcUJ4KTsPbGbDSa+qsh5ydAy9424VuwFVAwDwNDQQ+Gq+vP3O88n2eBSFa4/b7uDZmYCBb4stkk/Ubv82Ao6IuT8k4O37Y8bqBpfLPACw/BqfkyDIAoPczQ4JP6fEDju+MN4648rhQWUAPVA4uypJFDGoA3xAILpWn6dDeoWWl0Wg0EY2sKRffPG70Pb2I1im/pcg+9zGu6VKck4GubnC/cDmZXSmBw0wWvIyt7XGOZGisM+9d1/VRfMjSLWuX+NQ9bC7PkJ9w/a5jQG0LInMlW8S6qeMIAYChPWaZPJiPrG0l/krk6lSb5uwrjwW+58p127NKQJHTlPhwv4d0XMeH6rLDAhmWUPCW2RTHn/P/rEPqo7kOvq94QcvO5KG81e5FnfqULleWrZcfp0wIg0811VLmlPS+1qkdPLTvi9OyU8qcYcmxO54c/2xg2SCV2lgqJ7uvfCiw4faUlJB5cANNFbRk8J2TLBkUvq686O/MAGRtwbcrO2trRq6tDkSyYXOGQ3nKjKtLn/E4BAzc/xrjP2RAs/wlUDqk8wyEsvFVGpslntkJuv7h/nQy1WuZI9E2l9rKNpltk6vL6XJWNtrLS5AZqKhx8K58x0NJh7My+FtP4bp+VSqNE6crJVKdyeyp/nb5h8ofGlNO/zO5qy1T4FSyH8gz1Datp9SnnH4IAPF3rW8eAn0unePVPXZH63L3tRy1+5lsptXHiGNG+Er7DTID6gSWdUw26FzZmVHg9JmSZg4Js2RnJJCvFjxlHQPjrDJ0hk0jAVAubZcaO76G+pSHkvy0fW7AORlyu13drk9dv3I6d6CjZEjcb0dZX+KaggWns5B35gT195AOu/FR0jmVd8mhDxnqIcehbcgcVXbf6Ze2v7SBv8QL08HBQezs7PRldl03se/V1ef6SPXWtXEa4jI0ioRrJUBQchqsh7ofjfMqZX2OcphXLSPby5TZVjd+Vbf1cTGuTL3m5JORAzEOCJQAYtY+d78km1IZQ/xnOojrbu+4mxA431YCSpntnSZCVbKHLl/mM0p5b5SHEj9Mzt+w3xqyufiu4Uf1dlqaGvDxJlZ3wsiFMrUcdaA8sF1nZsApMwpZ+ZnR4jYA8OFwhIIxBWgZuGBHohv6FQDpkoO21YXeGZTyTFYBEpelz21CHjXanK6mP7k8J2de7mQetd+5D5gv12/6GAtnpLg81wY2XiwnB1L4HqfBt3sumw7mrjt6SjXjxwHMLI/y6kjlk8nM9U1WhiPWzcyYlQBICUBk498Zv729vYklSP1wGZlR1n53r/1THlVOJZDilq2z5U4nMyc/Bo5a99CKjDocHs9ZOxD1y0BDBvaUR07nHlFTA/a4jRkocXm5j7kfMr5LztnZCE3jrg+BF5UH8mQrPny6WGWrbVK7p/xlBwdK/VPqLy7btdv5V/YBurXJySnrf6enJcrsZK2v4eiy4zfTKSbX78el472QlhgpGVOl4zKqCpg5zCxPzb3MuDmFdPVPq0haTo1sHDjJ2pNdr6mrhveadKoTN6KoWl/NQH08thxMQxlPNfo6bZklqq2n5GCGjGVtmdOOw8eLeCxn+wFrx8+QcXf3NF3m3Lgurq8GACgfNXXVOMGaPMr3NH3p0mfgyNE09ssBypr+qKFSO25Ut0s2w/Fd0vMavkvE6WvyluzgNL5O89aW4cBfRrVl1fibEh+aLuvX2rJxfVodPvaDl1WQLhLGTJWeOo107rrOxHRGw7MTfict8mSbGZU/XgKqFXA2Eykd+DiOgdGyeYajA13bp6Ankzv/1lkNg2Emvsb9oHyUHqeTGahaB+B4nZmZifn5+b5f+a0SbrbG/DtD6gY916vtdYM6iwgNTRoyg9V1Rw+9DOkX95GLpDkZuYiTlsll6/jQDe/cHqcXTp9L9fIESK+rHLP+dfJx+TVdRiw77Xf3qB4mPAZF61XdYJlyXvQX77crPbqI/7u3ATl7kY3XGn0vUclecptxzUXV1R5xW7JruhTpxmGpDdqX0+iK619ur0vn3vwC36d9ivbhHtsMp1Msk8z2TGMrnexLcsnq5MOhOvY1P98rgU99TuMQOOS8+M70w+kAPvxsSx6nXLa2NSsTctnd3R3kO+KYj2UpNTBbmsyUviTgTHhMqtS1p51U4V3o1fGthjYzyErTgD2VH5eLD5SVHWRWlp76zOpTcqBP05fARuYstTxXl+NxCASqUcSyfI3RHqpvqO6a8lDOUFs5XUZDfYk6MueTAVYuH+ndspEDAs5A8f5T1UMH1LTsEthz/Oq1rD7XFgWrzqCr/DK+uM1Zvsy+zc7OHtk/XOpv7ivePqH8Z20Zolo9xO/s4etD9TmQoPdZj5zOlPTFAckIf4AMabJyMx84pCs1tkd5V9vrfJA+d9MBo+wpGK6+rK0gfWBwacwqP/rb5ckAo/ZbZrtwPyMseXNZpfYqOfvJ/zO7i/8crDmOP+m6zk4oh+hYgK8WUCkIzACADlztXDRKow3OmA898kH/Z20pKWK2zwj53JFrRDoyXkrgxzkSDHB81yhpNqhcHe7bUcmYapvUMDqdyBxSjRHSwed0yZWh/LooreYrOe0hGtKDLMKVAYzM4Wj7avkqgYIaAMbjFkbV7V0Ev3yPI4H8aB1XV+b0Mz4zcmXotUwmzLfqs7Ntmt9FzTGm3SZ45zSRJjuNmk34Sgfu3NhhHlQe2RgeIidL1X8AWW6rs/tZvSV+MrviwELmEzRSXbKzWZncFgcQ1LZhpceBnsxOKNX2U8lvuvYyGNI9g1xeqY3MowOWDktkbZsGaLq6s//gq+bh0SwLlFWKopbaAzoOWJwa8GWdV3KMo5F/rhYLInt6eURMOA0+TOGMq5bjHp2SAQBnRHUQjsfj/nlKirC1Y3l5BXnUGWYOVRUdckB9HMbPZK8OqLSMx23InhOUyVGVmWWndbs9kpoepM+ZU+fjrmn97CzYoWTL06PR9SfqA6CXXmtXAmDTACXXriFj4IxgtlE/M0g8LtGHTj7q0F0b9JoCODboKIeXoXiMo363qR/knP6QE2BZMU8O0Dh+lQ/+zc+k3Nvbm4hsunr1Gq67pVjHGxNv9eBX/WXbaFi/NF0NAOF7zEP29iClkjOG/Jh4iwbGJvcLH1DgceOWfEvgR+Wj/PE3163yGZowlvjgMpk3DprgN78gAGWiHbrsq2OjFmho23VcZ1E0pNW33WiZKsdspU77MrO9Gd/cTyVbomVpPTomnM9zclBfwpM6VwfylvqoFMHM6IbftAHKjFhmhJ3hHGpgibpu8jk4Q+U4Iz+UPgOKWf4hgz9EmYNVMFXKnymTgqNanpgP952lc4amNLA4fwZotZwhHoaMN5dbK9dMds7R1pLrI0dONtP2pZYxrR4Mla2/nT5kaUrlad7SteNQBnxK6TPgVKJsfPLvrE3a19nYmpYHpSFnWqpvSGb4LtmCDJi78mrHnMrOgftpfIPjo0YXa/nNxnpN3qG6aiYjfM/Jjb+RPwNFJV0ZknXWP8yD49fxfqO2gtuY4Zvj1JvpTkkHamnqd+m6WUPNoFbmgHgjJtG9i7y4OjjSpYqvhi9z7CVDovnxPls8QsUpOfLpskr22AJuC8pyEUmk14iHRsCcnF19yK9y4EiQRij4vpOZm9GyA3TLUyirBNycLLQdOiPGb44UaTvQFvcU/mkcTOmxNSWjmV3X/s1oyJBkMsQ3z9BLM3CNOGndjg+3cZ55cTqA2S9Hwt2HH/lynBmuk09tWmeHuL/wph5einYbsjkapWWVnAenc32hj8xyY47Hq4LTbKKTRbtKcsL/2jwZP7gHvWB7r/Lg75qJRGZ3dLxMu09K69X60KbsvspbgQ1/HMhxQEv5KtmKUltKtjGzoc7GIy+3dUhvasaF/nd+gccH8rhHz2SUYRInc6dbmX6W8pXyT0PH3sOnFdc+rw35+URZBob0mxVIT/1pviFgpI4vA67MK5+sKbWTw+/6rWmH+ON0XDYAqILPDOyVBnLJ8LtBlhllJ3t1ziWw13XD+yFcH2u43smAZcpbCHTZbEgvnB5z+53MlJyxLKXLrqkxcEuxzJMzotnYc9cyg5uVocvJ2neZo9I6eMxjicg5ea3H8Vzi3elOLTFvvNXCgQpnE0qg0ukBjylnSzO5aB9kwOC4ulnSEb6vtk9fH8nldd3ha8mcTEu64HjI2u9s9hBwmoac31RboCDIUeYXb4Q3LbvUhtI9B/rw2+li5uu1riG+nN8cuu4mpmxLS3WC92yPYmlMlfqfy9BvyJC3bUxLx17SdUICZULm+/xxAisZRFeW3htyIJxfjbBrF5RDj1E7ubj9b9wux7trG/PE15C2ZNyY3CDKBlaJ3AAsASLdl8FpMp3RaFyWzrWF0/NM35WRDWaXvpaOCxaYMgBSGifTtMXpW62DLN3PqAS4eL/t/v5+jEaHe9jc4QO0dZr9jSW+SuMrK1fbxXrLvDtQy3Ux4OFyMl6czdL2uO8S1ep5NiaGgFBN+dkY5XsqN76PeqbtN8dfLXByepmlLfHEEyKnz0Nyd32sfs8BSqUS/yX74dI4H+fulXwhj4sSHceHcb6hdpfk7K6XeFCcMiTHIZ6Po3MRx1zSBbk9K9pxnI/vsVHXU3qZk4Ox1zwqvGyjeqZEbHz5mw3O7u7uEaAK4kMBHIHIjBPaocummi4zZJxfqWRs1dm4ey6tkotmab0zMzP9RvbS4RLVC3zrTB/RTMcvP+tNN/07o6qn6lQveALAVBpkrJuOx8zpcF6uW8Er33Pl1RgA1wfuYATXPzc3F6PR0cNK3M9ajiub5aAToq7rYnt7+8i4x7hi+QAYKmmUXMfpEPhV2WVAzclxNBpNvNXDHbbQQxL4xjKw28rBbXP2VR1Iicda55SBDi6/ZJciYmLZPQMY2j+q95mNdOXpoQ1O5+xCJgPoHJejaTUPl5nZV2fbmG+U43yL5s9sk8rP+c1MHxxw0noc0FAb4Opj2WRbb5CHt0KUxqoDjbUAqDRGmdxKguOFKRsTagM4rZMnl+98Lcp0hz6H6IYPbeggBUOZcwJNY4yGjG/Gl97PnLgb+JrfdTyuZ8pW4s0p6nEVKZNzVmapLldP6V5WPr4zcD7kNJwRynh3aYccfVa+tqNkFFx5mT6V8mb6x3KslUfJCJYMfokHPe2Z9V3WPuVZ6+B9sS7NkK0YkveQrmdAK8ubASx+qG0GyrRcyLcERPgaAzDlPxuPjoZsqALMIR3WfCWANsQPl1sa23ydQWnWngyQOR4U5HZdZ4FNTRuZJx3XznY7PXFtHmonl5HxWMO7luHkOM34yUhxQykN/y71+3H54XFZ8lPKb+YzXB/U2CWXfxqZKlUDPn3Qb0QMIkydbWTOnI2KU6BpHfYQT1yvu1fbYZyndNgi48MNJs7rfoNvjYS6skugd8ggu8FdMrR8TTfWqzNzMsocaw3I0MgsR2gjjkbIuCwX9XWz7UxOQxGDzCBCNmijOxDDYwPpIFveWjCtMcn0XmkI1GmflA51cN0OBLmlzSzi7sqZlpzBzox4lmdaYFnj4LNxC9KIqtoc9+xCLlujLFwP7z3mPPjWdFqGaxvvM6yx6erMVdaujOweyuRDR6XtOAqcnZ9h+6myKR1u4b1eap+Uz8xuoxyOZjsb4PYVZz4yswXOtrOcMtm5ccoycnLENbXFXM7QAa0MW+Cai9jijTbMv9tzXEuZXFQe2oclP+5k9hcK+PgZZWAEzDpjASHyAQNt4BBgAWljM8NU66hGo9GgAdBPyRmgM52jzgacaxe3zQ1kbkMGxEonL0vORvlWh+LawHLEMgXyMjBRZzYNSMnqVyOKgaQDitOoIXYyGdJR1wbXx1n5ml+XsF1dEYeTrJLzcnJVA1/iO+O3ROqw0P/u9Bt+80Z9XQbkvtVXEaEufbWc8u9skrZpSLc5PfcVT7iysl15ChQcfyVnzPnds8lgYx3AgpNz/Jb03LUtk58bR2pDMmDobKWbkKn8HBBTsKDOVduD8pyecltLwFVlz/ywjvOkDdtd3IlR5Q/38CYW7n8G6wxwndxce7k+B2aVFCjV2E+1w8yHbr1x5ZT8ryMe/27Sj/r4eaBDPkrbqOOgxlayn3I2zAFizT+trwRNvYdv2nQQiht0JWM3LS8KUFQBs0Gqxqe247Iyag1m1o5SPbVUcjjuOsunZMBryinxkFEmv5JTqTEAavi1bHUWuJ45B+X3OH3pHIwa2FoqAYOSY6stt/Z6iafSdwlU8X914prO6c0QgJlGzk6WDgyUgKcrj22WtitLwxOZDJSX2uby1ei8lu3qG5JpyXlmdZWcHqfRaLlGK4fqGCLnU0o8TWu3nS3IxvXQCVJNz7w5XjPdqZHNkK5NQ9Pqx9C9DAM4PFCKfA/5tWn8QI1u1OCJ4+CCiBt8Dt9QWn0SeMTR0L0KXuthdM7XOWytoXC3GRZ1K58cWWAemVek1cd/KK+j0dHn+yg5Y+14cwrojEEGkFwdJUeuMmXj6WSRGdOu80+4d4MC17itvETBS0eliIrmzwalOoHR6Pqz0+AgeON9yQg63cyckuq5DmbooFu2VN55L12m4wogHP+ZAeO2lSLEGen9bMnZ2RK3ORl6tL+/f2TJO7Mj2dgt8VpbDr6zwyhqG1VPMkCrdeisn2WDezgUhWsuuurAqPYD6mM+VMe5XG1/5hdcuzI7nOmr2tyMuu7wsS0cPUNbuL5SxNDxozy4lQPkRxRa7bS2i/uVl+DBO/OoS/SlgyduTGjf45MdbMxkkl3LqASIXVrYN73u5KxyxDVuZ9anyg/krvzp2MzyY/w4TODqcm1XX1tKl92vpakAX8kwcjoWfs0pK87LZTC5TuR7rgz+dul5MGedWmqjGqYSP3wvU9SMsrao86iRcVavtsfVqc6gxEuJnDwZzDjH4JxXVrZzKCUwhW/3fMUSkHMOhfNk+urGko6bIWKZ1dabUWls4X42LhTgaFo9NcnEY0+Xn9TYw47AqSoPru7MQGY2wTnAWpvA7c+MvQNA6ryH6nIADNeza5pXbYVzaux8nBydLyi1YRpSOzM0PiDHofFUcro1VJKl8zs1dkp9kdaT5WOeFOgN8VvbRne9ZPdvlDI9G+KN79fw42SovjMbFyX/oe97Vr61fZxX+/7xGEeOqgGfE1CpM9T4DaFYVVou24EpNU4M3vSxGzVt08GndajhyYjrz5SD5VHq1AxMOEOuaTNFzZQvy+f2iTlnwA5MZeR+l3jVejS95uNIMr45auGiVY5K/Vbqe3dvyGhz+5y+OT1HFDIziFlfO2fo+OR+cBvInfzcYzCULy5fnaPKbuhxCaWN8fiv/QW74MpWY895nHxUFnxf5axtKF0rAZHsHsuCH2Hjymb5lurQ/nF9MGTHp6Ha9BkY1/8cSeXHagzt1VZy4NntXS+VoWMT+XmPHesE/pcmSSV94XJKaZCuNEZ5n2rNITb13c5v8XcpfwncZ21w/ZEBYLStBPQ0il/jq137XB+4vZWZD3Rt1TqnoakBHw92BmmZI+S8EX6TfOnFyGrMXIcgFI63T2h+VtysXSxQd4qqFP7WNmcncFiG2oFZ59WAPuU1Ij9xpwNBy3BGCvfVGTpwgm9OP1Qny4d1ivlw7Vc9cBGj7EQU88FGTvtMy8n6Qe9ng1OXy93YyQzreDyO3d3diDh8TpTK3QEv6IIe+uHviMNxWHsYRNtXckJOHu6gCg6FZeXo4SBtA/OCPuFlvlIUl4GC8sb6k73eC/2q9krl45wBj5WSo+dy9fmTjjcFvLrMznw5/cjkq7p+HOeDfE4eyp/6nEwXecnbnVZ148PZBK5PZaZjLwNBfI1tFbdN61E9cbJ1E8Vsjzy3S/nLgCF0mJ9J2HVd//gklWktlSY1/LsmYIL/LsDA9xVEclDI6RT7FXfC3Plabt+0QJXlonqa2YTS2Byi470k0FRWGoRD+dz3jZLrxNrBmc0MSoo11P7Mybj6tNzHm4Yct/53gGSobCfn0qQg42XovtbtHGyJ1xIQZNCq9ZUGodaR1VviZUinsvvH0Zcao6GGeUg3S2NIy3HXp6Gh9E4XHD9D6dzH5SuNmSEwl12fdiwoj5omk5kDprXkdKJmDGZ81PBQGkcZHQecPh5U6+NK/NfogKsns4tPlCxKVKN3ma8+bn0OH9TalRIPNfro8gz91vJr6djv0uXvbIbh8uh9zpstPbnDH648RuaMzvkeE8/ktZxSR7oZGIf7ufwIf7BEfys5fnlGqLLHby2b62AedPYDfrtucgar9bgN2yov3NfomLbdRUK0XG0X+NJHv6ic+A0RpYgVR0QcD/wYC/DpIgYsz9IgxEZtnm2yTB0/Th/dbx5/HIHRAw/aVi6n1tA53rJlt2yS5PSD7UB2YEjb73iGLvNMnvW+dsO60wl+/qguq/K3voZxWkfvDqpkZam8MvulfcftQhl6mKAGSHBZrg0ufcafEo8ZvV6SqbtXu70jOxih/T5UlhtvpclGBjg0rfNXjrQMtsMOLHPdjgceQ1pvBvazyYjj36VVHh1P6mtLEzItTw89ubqdnmcTMue7M361XSzbjF/3u5Zu+F26MGw1YM8xqfnwXTLImXFR4Iff7EjYGOrvEu8O5DL/Wp62O5OHyoR5cTJRI52VkVE2KPk/H6DAfQbdbtmEec94GjJKTv7O2aNugCWtF053ZmbmyFscWL7I74hBMPhwp06HDJIzZkMn0koAvnZrgRrjkk5k+Zm4DVy2S+eAr7MBzsiW8qgBL4EXTafkxn3J2Gsf6iEjvs7ASXnLdNzV5/jPdF7by+N4aIne1Qe5ZAChNFZrnabKtdTvyk/J37i6VE5adqldnK/kK4ZoyD+wfcjq1/KG6lNi+TnQpX4t40H7Gfdq7Y6Wk43RTM/QFqQbsm+Z/8Zvt/9Wf2f+LNNxlx46rHacsUnt2PqwAj4nsMzo1FLGsJbB6L00ODNDkpUN8DJNO9xmzqxdTnEyxUa+0iDjtmVAJ1P+rGw2pKXNwlkbSo8I0Ho0jQMRnE73JalTKhnPjPcSryVQUioL1zLnpfec43DOechwOuOaAcKh/nB8lfTcOa8hw+scgpMz722ErpfkybN61ROUjd9adkbu0EomSy0PEQOMK5SnTiUb606/XRrmh/sL3/p4F03nynER8SGblMmEr2NP2Hg87h90W9OuiKM2xu19dQDP2bMhoFiioXzct7oPrcYvZmOuBDq4/KE2ZTw4PmF3Xb1aphtnrl4HEm+EhmSb1ae6M+RPnG4PgeEhvlVfnf/LeK71eY5u6MHLbpCVBKV5a8CNbr51y3egDKVnbei6buLUY0mgbHzAAy99arlar1tK0PLhvDRvJlMdqNnBAf3mMtU5ZPJjMKB1u/SORwcSOD+XNzMz07/6hoG5bqBl46ry0bZmzjvbLF0qRwGbc9CZ8dYJjL5JQsk5/mxyoxutlZyBQfm8xF5asspkk0Ut1aC68rjdCspUL/Qe9yHLAeSWXfGfv2tO8SpxlAGTpr29vRiPxzE3Nxfz8/MREbG7u3ukzMz+lU4IO1vC8kMegE5N7/SC8yCdG+f4dodtVNfRPvAwNzcXi4uLPV/Ojmt7uV6Ug/4r9YsDdcxPRs7maTkORCigYTvM7Szp0ZAuODsacXTVpwTMMj54cuLGgvLDQJptl2uHs10ZlhgCME4X+bfTSfDI8nLyyK6zLJyPKPGs/aFYRctxdoF1Khtr09ANHdrIDEcJdN1IHTXKXAKZTvlcXSXlnYaGkH5W73Hqc4DDpXF1l0DZ40VD/aL81bRnqK7SpEIHbpa2tg0lqmmDgkn91PI1jdxqQK3+LvE9VH6JjwxkK2+ufaWJTcav8leyM6ojQ/qiEwEtt2QnjzPmHc+Ol6EJiiunZHNr+B7S5Ywfl64kcx3PjwdlspgGVNWkc7zX+qoaGgLIWremebxkWqOvylOpLE13o2Vq+R8ufz+UJ7NbNcB5iKojfHwcO3tsAjMBpKod4zrHNYZnYvv7+xYtq3HQfXslHjGjweZ+PA4C7dMlHedkum4y8qQy4HaiTkb0NWDW8c6In2XqNr5nYEDv8f2FhYWYnZ3t3/WY8TAURWJZZDMilIVZIsuLN97zbJTzMUDl/sfjSxxPji8uu2R0h5xANgNlHc30s+QISmmHHh2j6ZV/99gWR5nOcFn6u9QOXuZ0kQz3CBY9/OPayGPEOSu0uURD/ajp3HPw9H/GJ7eV68jkyePB1anpuL0lsKn1cFlOtioXjmqNx+OYnZ2NpaWlvv/Ah9tYX+pX5p+XhDNgze13eo/yMl+Ea5l9dL/Vt2WHGEsAmG2Z8zUZn9oGrSt7jIymZX+m77ZmXrhs2Bw9eKlynJ2dtW+GYcr6MrvHfHF02vlW8MXjwK26lfga4pHb6/KVbIGm13SOjgNKp34On9vjoRWzI9ZG6lKRUw6tT5VbBwXXU3JIrpz5+fmeLyzvuvKdEyktj5R4UfDjFMER8+MGpAIfJQcGlJfRaNQvRe3t7U28WNrxkYGXTBYlh8IyzYwrg4CMHxgugEUd2M7JlUBYdt0N6JIB5nRZ+SVnoPXjuoJI5/yGDJTTeVd3JousfaU9hQrudBzj99zcXPrMvYyG5MdOoMSr61PW1xLgcHXX2krHM+fnMob2FWdjSnkYqs8t5XI63nMLwLewsDBhMzOgxg5Y+WLdYH3PTlYqAHX6z4e51M7UyML5HwWRmT3QMjSt00Pdx8jyG7KtAFsMlkv2APm031wdrh3MF/5j6Xc0Gk1sN3A+cIicvXHy1eu6F1jbnPU7p3N+UNNk5Ti5Z+3n6yUZ1R7iAx37lG5WmYs8gWrAmBrAEqhwhtI588wB6kzZPQZBnaEDWNw25kn5coOlRKV0boNsSVm1fpWrgiUMTK6vlscMNAz1FV9j0JYNHh0MmVEaMia1IELrL12rdeDqxIZ4dfIrARu+7hwj38v0uYYfd/IY11FfNg61TpWdA2e47myKju/ssU7ZI0Oya67sLOLMfE9L0zoLdb563526V1J5ZIAus698nR9PA0KgwD1kv+QvVA9rZJrx6exDCTzUOHitw+VD3pLt07Gg6Zw90TpLfGVgsIZcHTzuRyMfZGCd0T7J+M18Go8vTT+ENUo+pMSLI+3bIbkzn3zN2WJXlvNh04wFpWMd2uANtAjnYtkVy6QY3JmysHKzsqsjwrdz4qpoWUdoJ2Uvq2flRDv5ESQKAhE96rrOzpycs9O2cvv4496kwGlRHy93cVp1PnovU7iDg4PY2dmJvb29iTwc2WVZcBm4pssH+mHZZbypc8A1BXpZXzuw6ShzYO6+6gun5WgB9wfLwZUJmWcnCrUcPujB4APkxgPzqe1hUI2yNGJUAmI8driMiOjfbDEajfrlHB3LOJiTLTmyjLKx6fqXoxquz9yyaBYBwH/UzW/0GYryY0vEtOABedx/jDGeoGb2LgN5zsG4Q2iQM/qK9SMDUNgmwzZhb2+vP7iiY1Ttg7adbQ/X5ZYp3Sls5lcfKcXlYFtPZk9KgEx5y36r38jaw2l1slMLUNxYdfbMtdP1iStbJ+Xq6+bn5ycOTThfoO3XtLCpKgPnm9xYZ/tSko8bnyVZZ/LJZKZptM0ZlkEbnd/7sAG+jNjQg1EHaNQogVRB+Bt5M8rKK+VxvPE9/c1K5UBEZmydMXS81LZJ72UDR+sckgWndwPEDb5SO2ralPHE9TseSmWVBm92zVEJ7JWo1M+lPEN9lNXDpH1UqqvGkE1Tt5N/dl/rdhOHiLJe6m+Xr5ZKADYrz9mqjB8tOyvzOH2R8ejqmcbOKKjhpTcFBCWedDLntgJlPE1r8/l6tm8PaZzO1LarlkrAahpyoP04OlXSa+dza8nZL/X5bl+x5q2tW8vm37U+l+vPePpw2Hrlm8nhiiyts6PT0NSAj9E2OhJRPdwHI7pRF3m5Y9SIZ3UquYHrFMsZaIeMubPRPpeGy+WO1wgG6nBKqvyWIgtaf6aoml/LHjIA2p+6fwN7MLhsVy/Lie854+Wenq9l6G9NP43RcJMLt7+oZsArGMn4Qjred+QO1Lh+5T507XfGNjO++M4iJVnEKtuzmk30mF81xqXIENuPbIyqc9YlSC6bI2vuwfBDsnfX9eAa8+3GsBsfOu61PzL5ZDZQwZTTw9KEgHUevGXp+BFWuIZ2u2gj+Mr6UmWlfej6WvuN86gugxxvKlPk54hzCQBk17TvMnm7iL5rs9s6UOvotS4un/nTKGI2XlkW4AtRYe0LVze3lx+5pfJ0vilrD6fP7DbLVPNk/lQPQGYyd+DZ+VtXdqZDzk/xqhHb2VpdAE19aIMBEXd+dmpLHakymwneORIVqJaJ3zpY3V4dZ1TxrfsOtAN1wEAm3K7MkGdGLAOGWT84J+76IzuNls3cud1uGVcBmxt8rr4SMKgBWEoKZobyu0HP/NREILi8oclAlr4EOlSXS8DajYla3rk83hKgvLE+8IvTSwYrk0UJ5IIH7iPnEJ3DwW/UgTY4kJcZyKFrzA8Tlo31hLkrJ3MMJcCX9bX2uW6CL4GRjEeWpXP0Xdf1IJptu1vSY5Cyt7fX/0Y5bgxye/i62m9d2lJf4JZLoa+6fUH5KO0/L8nWUWk8uv2oXA/44C1T+EYarcuNOdUP3f6jdTr+WV6aB/k0iMP6w+NGMURmc7mdLlLsnhlb6hfXPuaH02n7+H8mCzfxRPkZ8HPbmdRPcT1D+1JraWrAl11zzqm0Js3Xp2Xa8eHK0evMgxv0rKycRvPw/8x5K2m+LF3WvqHySoqWGVj+XRo0jvcS/2zYhwZVDZV0pAZ01FKmK648FwlBfnZuqiMlvlS2pbTsWDmPc56l/nf1s9PhqK/jc6gP1TFn+aYFYgpG3Lh0zua4VMOv6o9zDq4NmVxq+gnfjr+hyUBJ30s8ZMC7xtFpndoGthv6yXhDeufUHQ96TaNb2u6s710fZ6Bc+dYy3YSQI8pcX4lKvGj57r+zFRkAdulLeWt8B+dDuscDI0xTl6PSOFF94LKzOjWvk29WrktfS9WAj2d3qBBImx+bsLe31y93cFr8VhA41PFMrkM4vw6aEtjDffC9v7/fHzxhZ8qGSPmCDEYjHwFwhg7XSq+tcUpeAiJuEPMsIVsaZMeeAT7mQ+XLctQyVCZDRtE5ctUfTuecnbtfGkgqWy6vlJ+jue6ZlGyoVXaOGJywvqkD5Wuzs7MxPz/f66CLmMMJOh12v/E9Pz/fP66I83NEt3TIwi2hqa5wfdp2bTOPx2wrhsqH0+kMvNQPLDPmkfUC13RcqY1xzoTHkEY43Bgq8c2y0QmI2hq1Ba5dzs5pG3Bdo6rj8eTBvdKECMQRJ64LAIyjyioXjgqrfNz+Y5ZJBnL4rRG65Mgy19+sp8wf66u2W4EytxtlcCQLZWYrNqorWUSID6XofT7IwrLU8cX9r3xw/bjnZDAEWFx/6j3nA7JnBJcAW4mHEp96T3VV+dP/bLuzMlW3OM2QDB1N9eDlzFEPgaoSY+6+gg9tsCNXRzawHQ9chrYpK0ONOctH7/F1dko1CjjUBlWAzNDyt2u/kjP2OoBd37v2u0E61OYh56ynuvT+UPuydEO8ldJlTpfrqmm75suMqjoKbRPSZuMpc2i6rYHbrMtqWqbWXWM0M0Pn0qtOlZxypvNDPLqyXZ06vvmaytmBvmz86DXXPte2ksyypaehfnP9jGtu72cGqpw/UGfIaVm3Ob8CK82T9XcGOPBbwafKoaRnKjuX37XX8cHtQL/xyechfwrSiCeDc6dvyrvey8aIu8dUO7ZLeWttJ8bX0Nhx16cBg7V8lmSr42ZIZ/Gf00/L87HfpcsKybM73sSrm5O1o0uzBFdnrVF0zjLbx6fGSesoRRbwXeJBQV7JSCgPmfHW9vMeIgfQtJ4aUpmwcjpA4wZZCQSUeKzlc8jR63XuxyEQOA3o448z7iwnjiIwcNLN4l3X2ceO8AO3sT9KHyXBdfOsXaNLpXbhPnjgtxtkhqbWaCoI4t8cYcjGeEmvht7r6Xgu3c/AID6Zbcv4ztLU6ltWblae2rhsbySuZRFc1TMFjwy+OG1prDP40P1zyOv2nDliO5TJ2Mk/A4GZjKe1WWoXSrxkdh/yVF10kWguy/mK0iTZ8ZWBs1rQVlrlyfqC28RAPyuH80bUP4zYTSI0MpmBMdevXNYQVmEd13w1dmoIT2R0LMCng4tDz04heenLOT5cV4dSAgv8W9NlS2m6nKGOz0Uz1Ai59vNA4vvKU6kdChrcYM2MDINSBl4KKtzz+vDb9S/Xq85iyChkip/J1JHyNGSEM6MwNDic0cjak7WR77tlKNDc3FwsLCzEeDyO7e3tGI/HMTc31wO+vb29CRChxhpLrfv7+/1zzfgwhe65043autysuo6+UaCnxk/11OlTSSe0/91ySGb43PhgGWk6tiuOVN+nAV/u4eDuUEumg64+lXNWdy3oALnlQF3y1zEOnYINd4dvUC8f5FAg5yYlDPgUCKFuTjdEqNv1h+531XxcN9qj7cyWU5FeqaRLpXIyOXE9kK3bx8tlq49kYO140Scn6NhgWeCbfaDT5RJg03ocKFRgVdJxZ5uy8l19GqTSNA6cOV+d1cEy0TaVMERmoz5sgM9RCZC5RmvnTcss5xkyylov8jrhq9PT/LV8lEAJt//DSTXlOwM3bTpVuCFH5MpEvuPogatbByNfU96n7QdXl0vjBmYJaJbGEMrMTsU6o8BjrATKHO/O6WZ887cDeDU64MbOkK7pWD6OPRjib4iUV5XvkLEecl41vLm6Mhsz7djU8rPTzdM44iGq0R3V7RLPnN6lYx0uTcZLfA61p6Z/uT0fDt/gwGDJHtTqirvP9s75+eOMvVq/6UBXZqNLvqGGH3zX5p22HmdPp7lfoqn28EVMNtTteVCUD+Z4GQuDzC0vcB781gHC35rPpcPvUhTBbezm+/jtNlpnPJQQPP/X2UItSMxAl/KNb92Ai7xI7yKAPJNDfveieJZPJgM2OCoPlRWnHVJsjnBpu/lxBsyrgii8FWII5GR9kwEVyAoRM0RKum7ybSq4j8g36xlHAJ0DVuMKkOgMfCZPLkMjNG7mzvlUNhlvSm75hPUsM66qmxzlKC1Voz01Di9zTg5UlBypkxvazasczH+2fUSvlUASR3KG7AfyDx08Q7kcSUaa2sdGuP/ORmh6tVfsS5w9U5mCkIfHlL5NyfHHOursq+Zxusv8sA2E3XR77rIlb5SXbfjP+lN9LpfN7SktZ2a2WZeY2dYPARQnRydjbdc0oAvpM//E32w/NdKHVROOfGt5JR5U5539dLJXvqcBnaBjPZbFASa9znl0gJSM8xCQK6VzBIV2S1JalpaZDexsIJR41sFccmYlI10ipwClwcp88vI6p3OhewVl2u+6LOTap7zVKvi0cmAHwKRGrnRSypFrl+NBDSrLlMEe8nNaTgcQqHvpnANlR+8Mc0l/eWyyc+f2ZoCGHXFWPvOIT3aKLztkoLwqb44n/M6AHPM7NIa07mxMubr5o4DPASStL7um9xVAqPycPWYQxeUqOHHjK8JvmXGv19TylYesnzUPpy/tsda2gFcAPu2nLJ/mz2TL5Oy73tcx6/h19WQ2APdKgK/EqwNrQ+WwrWO+aiYRXEaJN+TV8TXkF0rjpFSO01PWdW2rq7OkT86XQmbMTwZSlccaqgZ88/PzfWWZQccAV8M5ZMw4v1N+NQgRw8/4YyOVvcuPhe0GJbeFO7DmMQ9ZJ6nR53sl+Sjf3Ebn0PDtjqmXHLGS2+Q71JelexrV1OvKo5alj8LIBpruZSzxWaObWk7J2HAfOEMIclEttw/PjbNSvcqvGhV3v+s667DVabt6NI8znArCON9Q/6iOM59uPGmbtT+UBzcO1cC78nU8urIzp5mBocwxuL5RuZR0uDTms77RNjmb5SJB3KfqoLN0JX7c3uGaKGjWd7im4E0P4JT8ltMPN7acHVJZOt/D6diHsTycHLI2ZjLitEiT2VT+z/ZM9YIxghsfKosa/lwahxWG+su12fEXkR8AQp3Zfccj855hECeDknwyvktUDfgWFxcj4vA5e0yuIWAkc0xOEfnbAQA3ADOD3zfw/w/d82ZePkGmRiHrSG6TRsP4vvLM35lMtP1D9zNHyI4BZbglIgYVpQGelYcoE5eTDThtu1s+4DozB6L8sFHhMthBZvri8tQCGibnAPUePtAZTs/Pz+OIGg5mZEZRQYA+eZ7T6Wl5kFvmcm3PXn/n5JiVk4GFknFTHt3z2vi/gkA+bFDSC7YzCiycjjvnpraEl990Gwm3HdsIVI76Gw6fnekQ0GBH6IAPRxGGdD8br13X9c/Kc2VrHrUV+uYL5ZG3E/CWCLYVyKOAmHnXyKTaNejW7u5uz1Np3yL3fwbccN8BMg5AMM9Zfeh/3uoB2We+1vkIJQVsQ77A2R7nz3jbSiY37fMhgKg86H3FICWZ6D2HH9yWM9Uzxwva4uToeHR948Zj5qPceC1RNeBTRK+MaWMyR1giNlLuutYzlK9EXA4rUi2/pYHEabK6M1A3DWVydvLLeMv6NDNmjm8ni2nbmMnzuIo9RKqvpb6vqbukg84oluorAT01ZsfVG8ff0Niu7YMM9D0e5OTs5KvtydJomSVD7PhwPLh2DwG0IRoCZRlfTlY1dU5jvzPndiP97/orq7fGdpQASmmcPV7kgHhtev1WftWW11IGhoZACOfP7No0fhTfQz4k4ytrc+l6CezVUAYwucxpxnZmM0q++zh8TwX4wFzEIYpHBEAjckxOAYbysMDQMH20C4g376qg+XECWn7EZHSEN1MDqfMsUp0Bz+Qz3hw/qsTZbFLrKzkPZ7RYdmgf/4e83PIo0mhkhQ83ZAOby9f7GtFAHdnyZcTk/kI3E3R8Z4ZA5V1qhzuwkM20+Dfz4KKQqiuQAeSrdeAtN3zdARPXDr7G0S7XRjYgHBVEntJhCs7P44f7QceUtl/5dmPPyXlhYaGPfrhop3Pwrj53aEc32HO9vLdyyPhmwEvHCUfzdFUC+dzmfpUPgy61Vzpu8O22vmQ2husqjUPn/NR+qV3L5JcBvBLYQ8RcD8foXlm1A24FxPkr7nsHxrJVCPf4kwy4sS3hPsv6MvMT2rdqU919N3Zcv6ptcoBH7Y7yqOWwrru8WZu1L5xd1INHzIPSNNuaVFeczXX9rn5a2895WbdLGMrR1M/hw2bXg4OD2N/fj4ODg8GHY5YcKpetebRuvY7/JeViMMjlsVJzGTAEQ3tmUA6WjVzaTBZONqV9kTwASkbR5WE+9QX1OqiVN1VElhEvRbo8WbszPcicCf9Wo+ccRu2gzPYAat26/BIRFhgor5lM3RI7p+cTsu6aHgDRbzdu+L5b3mdQr/nUkHddl453NljsTDOZOCOu5ZUIdeHVjqWJi7sG0vGlYEKdK/Om7dX6M741rTo51VH0v9PHjIZAgPKK8rm+kp7ztWysZaAmo5LddDbcpcN9fPAaQmxHcXyzbVH7r68K5XoiJg84Zby7tgzZjAxYaR71EU5fmUajo/sAHSjEt9MdZ2edXcvsfjaWIG/2xSpH3XOZydFtZdF0mR5xOc4n1xx+Ub5d+3Xc8xhXn6t6kfnNElUDPo5KOCZZyE6Zh4ygowxkKek1FVYWQXN5Io5upleeQLo/yPGTGTnnpGvalvGS5XOACP3m9ixpnSXwovXUkjPcOsseMug1xP2pctQTVm5wl/rN1eN+O93U386g6Dd+a0ShJA/Ok9VZcgr4ZmOTtcsBQwecMn6dU+T6sr2icBBop0b4eM+TOnitz83GVR7antKYVaepjrjG0WTjV+WU2RC10Zn8S3bG6UqpH4d0ygF9Xi1Bmpq24dv5CdxX3+V0wfW7AoeM1Na6cVBjB/i/tj07hKaTA74/VMdQm5z+ghgkOrnV+gWnU5qfdTgbo9rmGn3W/FkebXdWJ/Pu+hCEFS0Hqks8Kk8lnjOqBny7u7s9I6gEjDsHwIwpky6NdrwDIJkD1evsIPQEZOZceSmFN8OqEmadr0tjCjRdxDDb3MztKP3XdpTAI//HBuCDg4PY2dk5Moh0sNYaMa2n5JhYjvoco6wNtY44AyTZgRH8H2pPybApsaHi9uiBIXXu+GifqEz0MEE2rtQJIq9G9ZhnbRvKcc9fVH1GG5GfHVLGT4Q/eAJCFA+/R6PrbxrBq+V2d3cnos4oZ2Zmpl+JUJm4l8iXZu2cTh1sNtY4ncoW306n3DKxs3N8XdOw/s3Pz/eycMuXWVkYM44cWFKZsE4w8XjXFRWeZHO6Gh/Aefja3t5e7O7uTtTD7YC95zoYgEKH9VS19qcbryU90XZxvhJg4L7VAIu+0i7TNT2sNAR4MjDsCDqs7XLtcLZED+bxsvzBwUGPRVy7MvCkflztG38rj26VQtOU8IUDfHjbEt6slPHm6uHvIZulNNWDl4ec3xCp4JkyZcjKLRkArst1YsZ3STkdj7imZQyBiZKCOb6mIcfPkAxKcir1OZdfU48rpwQEs/4v6UUNlXSqBJ7wXZJVCZTW6GJJNkNlZPU4kJW1tQbIZzxN6zRcflenAzWan6N8Tg7qjEtjvEa31KmW+iVzQu536X7Jkbm0amuHxnJGJXnWlOPaj2uZ7czGUcm+qkNkp8jAjYkjwEP7umpt3JA+T1MG/1efNi1vTidK/qJUprP52RgdsuElvwLiU/cZP0NUGtdDY7Rk16clyKcUgNB6HYg/Dk29pMudiYHiogVMjNizfXslgaLOLJxaQtWsOHxNH62SKV/JWKoj4vaq43HyG5o9u+tKXC/kw/v1uE6Ui3ewZrMXNqyZjErOtFYxVY+yOtw17QPoB08U2Pgz8YzWgfEhfridJSeseZyOlcAn7+VwvGWTsKzdGoXWfCofpgx0ZnozZFyHrrlyXF0sE5XV/v5+jEajPjqI+4hwc70YoyhHN10jjQMWpfHq5OjAEo8FRCQzPqdx7CwHzss2WftN+XOyhd0Y2i7jgLEDBhoJqwUw/M15ERUGr+DTHa5jvdDDGpkuZ5vpdVxp9FDLdvccANW+0z7OIokKrFw6PaTI6YdAkJON1lNKx7ZM00H/VS9rSQGjtqFkh7OxUKrLHd5iW6L6qmXr9WxVZNqDGkw3DPgi/MvCmcEMBNQYMTTQDSYe3ENl6P9MAUvpHADKymaeeJnCvY6uxlm4uvUeG18FCxGH/bW3t9c7FT15yJ8hxVKn4BxDyfG7cpyBYxoyvkOgjPtE28jGh3Vb+cn0XNucGQHly+kB6zwMP5Yna4yQ44cBH/PiTukxHyoflkPNsjLzpP1T0hHnRPW+Oki2TePxOGZnZ2Nubm6ibjh3ODmWJ5aQ+GCKHgbJbNgQoa3ukBenwZL43NzchB3BPdUxZ5P4up4Idbqnvx2Ixm8eH04Xtb+0jzNfkI1XR1k61nX0IWwd9ngyz6wLDBL5WaNaL38r3/zRgy/cL2wXWHcd4GN+NQqpdp9l4/RExxy3n/nLZJvZOf1mW+r6iykbQ1jKrQVemd7U+ntuF/TB6fpQO5QnPeSW6T3/R72Ku9jmDsnDUTXgc+SMfGmwZqCl5NhVwdx1Z0DVALiB58BaxOS7OXUwZ21To+1oyHHxf+V3iNQogw8d3Gjf0Iyn1I4SGHN8ZLy6umoAAJehoK1GVlpn9p/5cXLNHJ0zhvzbGUrnBLUubXcJYDk5DMkmG69ZXX8RxAYQ//lbx8oQAdCBXJ9x+ZxuqC7tzxIIctfU2ZT6S8t2p7ddOgVebCMyXcscsmtLrXN1Tm8aGkqv7ayxCwqeavpa6xrSoxKvqtOZfxrix9k17l9Xpt6v7Q+XvrY/a+vjfinpEf5n8lR+3f0M6Gq/Zr+Rz13jsjjqrO0fkolrQy1N/VgWZzSYCZ6VuOUQB9j4Pu65qJ4694joNz46XpUfFVDXHV1i5g3i2BgeEf2jHxBlyQwWHIq2F/+dIdcIi5Mz33fgczQ6ugTIg0RnS8o3O1U+OafknIL2HT76gumu6+yzvlRPVHbal6ofOuPRNumBANUBdXauffzoj93d3YmT3Girk5OrD/zyS9tZD7OTd1oP61wG9FTfSk68xsCgjMwpufHMMnYG1RlIvs/LYjxOWU6Zw+I2sR4i6sdy4omeHhhQPXNjFvdYh90pVLd8yXoE+8Plub1LOu4zHcZ36XmX6lTVTkB+XC/rsitT83AfOn5dOSX5qgzVLrCuMN9Mai8RDeUIsHs9pY437SMNGmRt02jdaDS5BcHlc2N9SI5KKiNnf7NyNK3WmelrVldmu7gcPtzA/CtPepDF+WH2C5kvcXri7J8S6svGa9cdvp1GxxyIl9VLMuF219INRfhAJcUqpXf5MlBTqitrdElRcV/BSoR/PqA6Oa1zSPBa1rQdVVOOOiV8qwLpcrM6KTX4jlRumQHmfuRBVdN+B9BKxp0Hji6nlPhXeSkIUr1Qnpg3B6AyvSsZWcerc7BZO1RWrswhyvh1ziBrdzaGp9F/BrS6BaGm3KxvS7y5dOo0Sv2FiV8mf26DG8MOsGVtYyfnwInmLfFdGi+Of3WCmscBRwdUOL3y6Wwt56nZ05QBHC2XI8CadsjPHdemZ7yW6slkOA0frr+Gxm5JF0v5XLqSvmla3l84BGTxXfJR6vNKAHeoHi4T30OyLZ2W5zLUt90Idog4BuDLkDXIvR8S+RidYkOmCtwBsMxQcNmOF1zj2bjraAUkeo3L4XyshG5jpi5D4bd7ynpmUPVbr7nomMqMoxncfm0TZrU4As+RJzzSQR86XFK+zGDorF/zuMGksuPBr47TycHxpnqjAEdBJesNZIP7vN8K7dPHONTITE+jIa0DsCUnrLzWOCt8ak6Psf45XjJn7/o7q0fBBOso58ui5sjPOoP+4Vm2Gmq+VgJtbKt0XGXjG7rC7VLjn9kxzqPjiOWZybS0+qLp+RofYOF6lD+WGdJkTkqdnsoQfc37qFSO0FXHg4vgZDrOZfJ+Pm77NACF+6EENFVX1Ic4kKMycGOM73F5DnRk9nOagwEl/8vk7LPTVS5PH5/EfaJlcX6ViY6VzMZpeU6mpT51+UpyqKGSbZmWjhXhK81AeVNx6fVFbHxc+FudLK5l++u4DjYyqtRIowPJgT4WLgMjNoDgBW11xoU71hlN15nO4GZKmu0FQB51Wsob99fs7GwsLS3F/Px8Lyd88NyzktKVrjt56IDK9iLhv5NfTeQN9zKjwd9uYKtBwqZwOCVe8nWvxOK6MsfBQJLTA2yrI1V+tS06tpxhdx+WaUme2f2sfU7ONWVyP7uDDNpGtQH6nEfosuqqyjHrf22TAjBHvJSMNvC9bHKg5Jw351Ed4XapzdA3SLh6danbjX29x33i+lX9BbeN24XlbbU9Osnm/ubVDPDP23GQHmOXxyvGNAM/HjNYVsx0wumM2honZ9St5WFigjagfdhS5Hwk5Kg8so6qHPFxhz+GQImzO3y9lAb9wzxruuxVnJBPJtOsPq7XjaVsKRZ1u+hvSU5D9nFIvsyXs/fTgr9j7eErMYbfCpiyPE5B9N60VKpT2+F4LdXL6dSYOaCXdWzJiZT4djLNHKfWU8qbAQYXfajRgYjJ/YxDCl5yrFn/uMHm5Kx5S3rH5TJfLIuMtJ3OKR9Xp7ltmbMHDbXJUc14zXhiR1eiGgeSAQsda6V+dLpUkpFzwDV2KwNfTnf0mqZz9bv+ZkdVApjMS2n8u7ZpO7L/fN3J0KUr8cn8lvqnlFcBhAIipKmVgxvHnCYbayWea8YB86B5XfTNlel0jq+7Mkq2fUjfS+0Y8hk3yl+tTDUP8+DaVQJyNXhhWjBYouPYZ6VqwIdlCKfMjMTVsWcOATOViEPUzoNLw9lO2XRQ1xq3IRDC/4eiRy7kD2Cgj1DQfJhFIoLDdbjB6pZf+T4vM8KoZQciNPqKKIM7lNB1hxtNOS/L3i318hsO+LoOKjXUTtbqHIf2QGjeaYEX6xPk6GQJGfGzztyG3BLgcGBmiC/85m/dsIx72VJcBjyy2a/yj3Z33WHEhPlxZWdRhMyhIg3y6/Is86v2xdXNNoWXC3E/44H508MNer90oMg9osHpvy5j8rjFu8xRbubsHW8OJJcckgIlvc52prS870htCcrjVRLYHSdzEL/xie0iL18jf+bHVN81euR0sSTP0uRQZcnfmV1goApfyX4DaZxt5bI4jfatytb5cie7rE6mTBZ60IXbyePVPc5M26D+sKZ+8OD8bwko87jM7KP+zuSXkWIU1+5pqRrw6TJmyYHWoFAMiqyTUE9piY/JKXuJMmNY4lfLdU6RDXBJGdTIl2YC3D4eAG4w46Mn97R9pf0icKps+JyRV8Cn8tI9j3yd8zrDpPzW6pTKLLs/NEidjrvTvkjDe+wyoOecVYn/EmX9Cv6cYXf1lwCg401lpPqR1VUqu5Z0XKFut9WBdSrbg5YZcGePMnm7/uXxrX3h2p/ZQNUlgCE4fJ24Mg8sL+ZT+6TW/mnezNHWlOfGdQb4VAYZXywb5QegMSJ62ekEzvE3ZCOQhmWuQEDvqy3M2uOu8QdL0SVyY0751HJrwUmpD126Eg/sM3ncaB0ZmHNtHuKV77mxXetv2J/V8qnyvBG/MC3wO9YevmwmF3E0IsXXnJHl+25ws2HXelQZs86etjMz0KYDRHnX/DWzJpVPNtAYbEUcjXDBCCjPPHvh+tQJwoFo3w0NdpYpRxe5DdqHHDXlfK58pOHfQ6Ai45cNTQn44L86b+yjKRkvzc9yzgxjCXw44+BkEXH4oFLnaEA8JrnMzNgzaM/IOeYSeCr1WeZIXP0uKuxAnYtCZZTxpvIp2T61D1n5LqqgBys0v66kOPujdan+Z/w526M23LVV/2c225HyzROnIbvNdWegnvXS1ae86CSC03MEO9uLjW/m3+lvxkfXdRN7gDXgwTy5fnS2mdNyG3AfkUIXNXdl8L5HbTfSTQNqVJc44MD+yOkgRzs5ndMfJR5Ljl9nK/m+frs+cPYkG5dadmZDboSmBnyZI4qYNLDcCRxy585wm291E2+2HJWRE57yrLNIVRDOlxlYLge/1am4ge5ewM0y0HSZ4VCjkymo6yt9i4AOcpWbylZ5Yt6wQZoHPb/oPnPcvKTP8ta69bfyoO0+zuBRoMD9zMaJX0bPwAhpuU1qqDNSsKVtzIxJ13UTL+Xm7QRIx8vybsO7c2i14y8DU864qa6rgyj1mY4FHRP8BhnUBaDu3hbk+Mza6ZY0nUPM2sikAJnzcxSK00YcviVHJ0nsGLWNOka5jSo/tnuol1cV2N5lZXNETeXCMqtxylyGkzV4gc9hHc+oBLZ4zDBgZHvAS6nOJmqf6m9XP67xOGI/xWnUFup4xjeXg7T61hnewoO0bPO4LvT/wcHBxLYftbXOZugYdtdZ3mpLXfCI+eF+z/SM68pWYTIf5+yvaw9f07zaTtVpR06Ph/KUaCrAV6ooc6gZMKwp3/2vcdi1lBmbISOUGdJSmhKA0evqqHWAOKPhHGZNWxwvruySkeQytD4nG1eGM5al9DVUchKuHO1P5zQ13ZABKAGJEi+lfiuVVSubzEhxm0pl3YjRcVRyarV5ptH1obHNNK3dKdk37XPndBR4ZPWX9KeGx5o8bnwPjaGSPRlynlyHA01Z+kyWXG9mI4coswvHsVOlvENtHNLBkn0Z4nPIPmX1HdcO1ACdmvozG63ppuXB2aGhcTjkszPebsSWTouHpn6Xrs6AMgPpZjUOSSO9zkbc/gp3ugp5S8ier5V4VV6YH6ThJ+FzGJxn2zoL5jr48S0ZP7jPkdJpDESNwcM1Pu7v9uvVls+RLu1LdV64hnQcERiaSdUAQi6H/7NMnV6AT57d6tIG+gNt1mU51eWhmR9kj+v6CBEnO20X6jg4OIjt7e2JfMxPBuQxfjQy4hyklq1tcW3MxqCzHegfTs986dK/G+uZzWEjrCsJSpq/xgGNx/4tO7jnwJNGiFm/OIqHaxxB5/51/GYyzfrNycnlUxmhLNTDY0b1GfxoP3B92mcuws7EETe395vLzR6TpPJme8SHJXBfD6S5/uY2o3zcyyYrsMlKJZvO9fLqirPnKiflz72tCWl5qdXpA/NT8leZjUde1wbX7+ibiJh4jBj3pxKvnJXkqPbNfWsdGY/cvqwfh6gENKehasCX7cfTSrMNtiVHrcIfokyImbMqUQZeIvwzgmZnZ2N+fj4iInZ3d60zU+fEgM2drnWOFcZH90uUBpyTATsGJ1v3qjPmQwdvJlfndHjgur5hI6TLJiVwwm1TntygVJm6dnJ5Cvi4jcqL47NkpJjcGCrtfVK5uLHnntFVKlMdgeObdQ55HC9ZWxnUDhnFrD7uHx0Dmb0B6WSV28v8ZOMRAGLImWW2CPfdRAjXefzoZIPlwhNO8OPGngN2mR44oF5qn9ujy6BS63OOszQuSm1wlI3DbJkzs0eZLPABIMpeLKCy4t96yE51nMtxgQL8H+orBt4K2vCbr/HYYp3jenh539Wp9bs2uWtK4IOfXsHpM5uDPGrLnLx14pWBusxfOvCn2xu0HG6fXstkmdXv2jUNVQM+3eypxs8Jzw00Ry6y4ASm9zPQw8SAwwk/M+BaN/OTGTLHXwZAtI0ZcCgZJ9dWbbM6f1W6UrSKf5ecWcShfpQMFu6rwx4CASVdKMmB66sZHNpWlYOL5rFRLemDkylH1mrap7J1Bljbq4ZyyIiU6p42D8g5zywNftfIorbeIdJxyPk4Wu/26WbGm/Va2+bGHPLj8TYO8HH+zPGXovRcN/PIbWW9UnJ2VOWX6Ynre7RX63D53e9SWs1TAxwj/KEPljdPwGtATMTkUw1Ux9ze4BJYKNWj7cuCL66cDFgyZX5NdUJtYWbbWb8dL87G8bjg+zzhcFFaLrtkC529LAGsDD+4MVRr05z8Hc+19o1pasCH57Vpx5c2d/M9VVx1/G4jMlM2CJBeQY/W43hwneb40sMoWqfLjzQuslRqi3a6a0ONE8cyBBsuHih4jhrX6TYK8/3McOM66tvd3T3yNH+OXA4BvqH+z9qs386ouLIYBOM3lm6c/vCz53hcsPwcSNS6h5wcX0N0GZv3I44+9y1zHLX1ZA7J8ZttTSi1wS3LZo5Jx4EDj24sDNkcxw+/SQH3FIBh7OhSpJO5/te8nA79Nj8/32+zYMfF9oOjuFwGy0E3vo9Gh89NjDi63YD51zd/aF/peM7kzN+8HAo56BKq2jauj0ltvLPTSixPHQ9sE1GOPhc2IiaetVkCfDp+nE1F+/XNHlq2GxOsh3ofZe7v79uDW5lMmX9Qqf+5PpWrytaBqIiYGFsuCun0cH5+/siWAZYfCLpesgPOj+lHbZ2CSebb9T+ul3SF63a8Oh/s7tXQ1G/a4IqyDtZ0GWNDjogNWZZ3Gp4zx8/1Zf9r6wBvDjCpUeb0mrdUt1NcraOmjQz8Skrl6nRlq2FzlAGxUp5aqqk3IwW96nz0N+fR384Raxr3f4icUZhGbtPoNJftxs1QOdP2Z1ZeaczeSJ3cRjcuHJDLxnqpbqcjznFyHdnDmSP88mWpTldvRqV21pLqvMoxs13OAQ7phIIZvqdlaX7Hp451Z6+dM89oSI4OYCjftWOW+ZuGdKxzu51cHXDJ/OxQvRF+wpjJnu9p35VsleM747Pkk7SeacdZrc0stf9Gaao9fDqrcGHLEtqP8ELONuRO46CyQa9C5PQMeIbKLSkxz8p04DqDpsqeGfHMWaA8zDg4yqTpuI06uDIg4vZbOWPNvxk48h4SjRCxrHCdo5BuEGkex4NeU8NfckCqzxxRUVmpceZIj5OvAmq3j1B54bQqO4446n4bbqtGAbQ/tI5Mvlm0l/nm/+ooXP85w5g5DWe4h/RW+XFlcpuyQzalupX4OvSHI3nZ23QgY0T1EOHrui52dnYmogeoH1FI1x43dtnG8YEAtoFsk9x+QicPJe330Wg08exKtQHaB26ccxtgKxxYUL3TyIvqQtaXJV+icoPt1bGWrVLpWOB+Qvu6rjuy55Ttq9vTjbQRMRHZc/eZf73OfLH90zfo8JhxK3tqCzPfxm3lfdyuHI6U6vhBu3l8oGzVC83n9MLJJbvnZJulyewn+3JuA5f3eNGxTumqgwTxQHQN5gaq4coMPufPqJTfOXZe4uBOdGWoQ3TlQhZsIDUtl6nOl2Wp/DjniDbAUeCEUsTksoPynZ1cc/95ycHJWmWluqAGR409X0M9DiCxbJWPGr7cwFXn49KWNrjrHhJ1sFyGLhGy0dIll9Fo1BsuNUrgj8dhBqbVgWSAhnlS2asxyoyd6rYaUJYtj3l1eroko3LmMhwfuKaTG+WH24Q+qn0+H9eT/VZ7o05I84AXAAd+dRqeL6bLZlqOHrxSx8np3KvFuB+YZ5BOyJ0DVLuC9AB82k/O7jjArjzxUw4yUKHlZQEFtUNDxPJEWZDpeDyOvb29I/Vk4CADBOrsFZiwDVDqum7CfmSAWP2h61PWIQV8euBDbRx/O4zAusavSoTP4bT4zM3NHZlQo41YvkV5undR+z3zFVn/ZL81n8rVyVT55wlRtmVMZZfxUEPHevByiVTZlWGXtgTW+JprrCvfDewS3+5expdTHmfAS3w4h85RscwxOL7daSsd3Pxdo7ya3vWH61t1cspTVqfL43hUI5bxrNdKg1cNA75VXpluOcr0Sf87uZbKU9koT9lsu2TYXB0urauby3XgLONZI9IqC+eUOX/Go6Mhw11Tvupw1ndsuF1/O11iuWE8894ldagOZA2BG3XO2m5ns/n1ba6tGhXU+yU5l8a3y8N2hPPV6Cy3qeQ7VBaujzOA5GywI5emZCvAg+pdKa/KQL+nBQ1OZ4a2FTCPLvij8tFx4dJoei6ztEo3DQ3pqNsPmMlyGpx0HP6OS1M/liUzOPydOTIHcphKzp0HlqbVOrmebONqiTIg6upSnjXipgbBOWV9v6OWW5rJathal+yc81Q+XNnOyDn5uUGqbR0yDuyw3HKE6k0JqHG5CoI4GueucTTT9W1JH2rBBPS667qJRwloenb0zrjwci4/xkAPE0TEkXbpkp0a8kxnQNlMG+3BsrNbppubm4vFxcWIODwkweW7JWQ1qipv54CG+kOB1hAwduOcr7O90rIdbyhD326yt7fXv5WG3x6g7VFAB0LECfyMx+PY2dnpx5Y6rbm5uSPPfZuZmYmlpaUJvljOAKWj0WhiEz2Xq06S+8jpF4/XzE4piOPlQAduNJ+zD8yjA+LOVigPbLsgP42Kaz+y3UMeteHM69DhqZoxoWXW+EP1uSzH0njJdELrcoefnOwdP86X8u/jACRd5ciAZa2t4P8qtyHA5/roRkHfsd6lO6RI/NsNcve/lD5z6EMAU/kdAnJZG1wZpbYgjTM6XBa3g3lz8uV0XAYPJLd3JBt0Wb+4aw4EZPLX8kv1aB2ZUVDgx45hGlCeOd/MiDnQd1xyg90ZFaVMb1CGflydJUfLPOg1rdPJyY0Llbny5Pacaptc3kw+NfdZt5zMMx3P6nL9WeoLJpWV8lcaC6V2KujkVQPQ0NYL2BHuI2fLuC1Z1F1lr8vtzDfLxdkR5VHryexMpsuZrXV951ZftO7M5mU+QPljH6Dy03YN2Qytb8hGZtfd2BnK63hlfmrHdg2gcm2rlc0QOX13/TqtTyilr7VlN0JTAz5WSre/DQZAN5dqfucQ3FsskEfT4jorIhsU8Mbv8XUHSnijpFMkB2Z0zwKMJO5hLwfI7U1hA6gy5GuZAcPMWsvhsrg+J7/s3pCz02tqqNwg1L2AnE6NvONHfw/xmO1zcTyUgPaQM3NtxTW3Edm1xzlDzpONIyWOvOg+Q5TNY1SdjLumPEPXddzou1czsIL9NiyfUpsyYt0ZWmqpAWDgx40DzeuiVSxTRxwRZH51b7Q7OOLGVgaMHYACcdm8ZMyPTnK2SQn9z3rGxH3govwsO9ZJtanOT7i+LvVvDQBHWew3dBxzu9m3ZXquPLhotwJ07U9uv+sPTq9Rzlqd4HIyOZVsl/LLcuL8mYyyaOZoNLlvz9kUlpnqmdq4obaUZKH2XO1CCZy660M2xU0KFGOV6ijRsSJ8EZMdw45NN+Czw3ONZMZ5eWFvb+/I7LIkRP5WI+yMGfOrA0bbmHUAp1OlYD60TNd+Z7yy9o5Go1hYWOif+s4nlLJBX2MUHWWG2imhfnhfET9nysnCyUaNnNv06nh1RtUZUW67M8aqt1q+A3I4SBNxeIAmkymDPdZHBzIzgKnl8XiDc3LR0CEgwW1240LbrfvOHCCPiIkJUQaItY8yeSvvml4dnUuvTlMnc0qcLuKoo9a6cR/PHAPg5b6HLEBu+U55ynjM9FdtM4N0niSwg2Xgxe1jwKcHRpysMkelgCciJvjjSQXbSn2tGUcknR3VJXbXp5nMXF9GhF1OztoIcqClZGdwX/UK1xHM4K0cXKcDfU4Wme3X8hyV/MwQKHJ2hHWV27Gzs3PkcBX6vfQg7JKPcem1fM3jfFCt/IbwS2lyNC1GyOhYgC9zHNNU7kCccxLO4JTqLHWopnMDSfPqwHdpXJnstDKFKCmoc1b8zQbZfZgfLo+NoPI2pLhDpO3WdmX95nSB+a/t02n40d81/eWAiLYncyqlcobaUEtwAsiXRaK0XKcnpfozfavlUceT9tOQYRzir1a+OiZUR7Ue59wc/6U6tFxOcxzK6nR7/zS9iyqBv1q7zm13Yzrj18mRSXnX8ZmBjGkdYFa/K2uafnI6OsRbrWw0T5Y+AzvTjld8l/YFOj5L+uDGQKZDXXf0NXkuzVD/OFtf047jphkaD0M6krXnRvz01Ic2UGFmFGocAe4NHRxw1/HtwrnME9LxPd3forNtzjMajSYijhwJzKKBpQ4eitqAP7QLz1NSOYN4c/eQ02WnA1AAfrL39JZ4de0F6Ywe7UJ4HvkPDg76TfscMckez4Gy+ZBKadCWjGc2YNwSgGs7X3ezMo3WZGBhqM9Ky53O4c3Pz8fCwkIfRdvd3e37m79LfYyPbjpnHvTRBw44KZVOrkNmCvD5v9oXzpv1sdaTAQMem9msnutlnWRi2XI7tP/VRilpWr3H7dHy+APbxlFEjUy6ds3MzPRvdOHokdMJtonuDURZHifHrL+1TJURtgIxL5k8nR1DVLHkSNlnlbYtqJ6hjuzNJfomIu4vtYXKYwbqdVVK87jrSiwflKdvZeL7qNfhgZr6WG/Zpo7H1x/VghWt/f39I++wz/pC2+DqLJHbUpZtY8vaOuSbHT9OF3C9BOhrqBrwOQOkjc1Q/o0gUq7bDX51NM7QO+OYLRdonU5RdH9YyXCjzlrKIm/aJmfYmLKZAQOwbLnP8V5jENXp6J4x/q8DnJeVHM9DDjJrcyYXBzr0ugIQTZ+BD7csmPWV9i2XOw1AZBmPx+P+tXboa+6HUvSnZKB1fCj4cGDNtdU5MTfGuRzX9syJl+TlykM+XfqPyN9H6oy846/kDJzxzqIoWT1ZH7ly1Elhibn0mrhMbzO9dzyVdPe4vkFlB70uUYkHJ8eMbzc+OY/yV/rvyiiBcpffgQ/nP3S8OtJ77CuHAGIGfkC1UToF1xGToN7Z7lLbsv7VNBnfJTtSopo006S70TwRx3i1Wga0hpaStCxWZuTjb9eozJkMKYAqASIUbMwZbHDZULhsnwDXe5wO0DLcg3XdTMPJidtScracXiOWQ23k+vh6yRggkqr9ru/XVHDJ99xeIOYpYtJAOhCP//wOXDUwXE6JMoPBbeR7+O/qHo/Hdg+SpiuBUeiORnH0IBTvf8qc0BDAdoCNrw+NEecwGei7CE2JD85bMtjOIXK/l9qQ6bxbaXDluIkV5+Uy3L4+13YG8QpadQw4mWdAogROXBl8T22M2jDOl+kjy4z/Z4SItuOvBOQc4GYZKNhSfdX6Mrs09D+znxjTTt5cnwNazkeXqGTb2T9kPkn50DHhbJfWy+9jd3qo7efyHCid1h+7sZHJLeNR9aU0nvSa61vXn0N9WaJqwKdLMmq4mOB4MpCoA4qNoVMEHVQl4QwBL66Pf+vGX5Shpw+5PqZsmZfr1Xt6n400K7F7Np2rBzyo43HLaahPn81VQw4cZsYTdWB5kSN9/Pwv7XfeYI7l7ayPNA8v1WfEL+3m/oUe6svTawaZts/JY35+/siJZV6qxmElPvxR6iOWg4J3gD0sz21vb8fe3t6EHFl+zoHomCyNXR6rGFOZ0dYyFdw7XWV58n0+POZOT2ZOkMc4+twddFFZsE7wMxBdZIZBC8pix4Z+QB3utLPTJQZ67qCPTmi1r7Q/GDyybLTdamNKgILLUoDE/cUyOw7xgSEm7Wdtr+oz59Pomtrn0mE/rSvzSZn9BOlEnPMhj/pCbp9+3CvbnL/gOjR4w88OzdqteqRtZPmr7VZZqF1xdSG/ykDHXolnzqff2fjTNmU+ScfUUFuysZTVOQ0dO8LHv50ycr7agewaUGqUdkbNwOPfzhHwNVUaVlSkqamb05YGSWYAHOgoOWl3H9ccqM6MjsrEOYtp+hbf2JdRC6SmUepMHi6Nu+4cwDT1gpyhyMqGgXRREJcncx787dIPGWrXjlrKnK1LN8RHSXdr6mdnXso3JFOXT+2d9k9WphuvGf9cbomXaahWj9w91V1tl5Oz+635nE11fA/x93jYXv2u9W1ZGTVjzeXTPM4f6vUaXpFumuuPBzl/k5EDZvx/aOUwK7P0remY7yG60TSZXjqblYHWae3CsSN8yrDbxF271IKyum7y5fCcjp1f1ngH4vieKzsDsihrd3d34j7Pkt2s1HUA7z/gDe8ok4/XY0OqLnlnMsT10ej6o1qQx8149U0MLp22S4GoghDuBwc69D7fwzIvR4S5j3W5xwFwlYNGSDMnhPxo/8zMTCwuLlrjH3F4SKbUPsdDxOEsjyMyjh8tD32FKB0vXWUOQscc67AeeNE28uzdjRWWI+uF6kb2vtaIPGrB79TktA5MKE+I5nKUIDssg9/Z5mv0j+bXcjQtk9vUzzqjzxfj767rJg4gaNtVPiiTy0e9OoHQQ1POjun+TtZbXrVReTp7wLJBXo3Cchkl+8Fy5zRKzp67/kf9Jb+W5eE02eoJ7/N2tipi8q0oKo9Mb/Fb7Ro/Ck39o5av8nI+y+1dH6KSL2aZwN9xGrdszIcm9/f3Y29vb6KtrOdOh/C7pG8ZuHe+XW2Fa7fKL9OdTMdcWlzTxxHx9iC9l9GxInwu7K/C11DmENJVA+WcihOyA51aLwyNht/Z2DlHzwDJ7fnKBote4+VJtzSHjsuMGLclG7RcjgNFaoRKslXjnRHk5wZTRtksjQe7On7lV9vGpO3JnAaXg4jj/Pz8RN3s7NRoKCBWR+vqVLmyjusWCG4Hlnd1mVqNOuTH19zSXkmHOE+WRn+jPlzXJVZN4/oV11UWTle0LJ2EcVk1bdZ7PNnSVxdyXys4VH6yNvPhmlIbMiBXKrs0DlE2L8u6LQjcB8wrg9yacabXuV91jNTajxqqsV9aj7P/Ln3JVjrd4/wOVADwuTFaWi5WH8R2RO16jf91oJD12smsRJqWfZ9OeFWX+Ldu64GPY5usdboxwsvZypeCW+3PrO0lf6xtU9lmgDHTM6cLvAe+lqY+pau/QW6g8GzRKTynq63b3XOD1XUivh3Ac/VkRkHTaNs0H+8pc211kYwho+X414eBKj/OuWpdJSNcIzOWe4l310d6z8nTbeR2gytrk5NDRjpJWFhYmJAbAyNXjqZTOaL9vAdJHQecASJ7bg8j11UytOzAIRe3V415y3SgJD81WppXKQPHrsxMZ5CGH9auus76y3s4M1mhPFdWxORhJHXQJVvHedi5I5+LTpb0zNmUUr9k151O6eRRZcn51T64/BHl9yUP6Yu75wCYS5vJRPuNbbHqTUn/HJ8lUr0qrV44H6dgWmXAtpTzqW5loMH1s9YzhAtcW7Rdbqxg7CFypYEZJwPlxb1tyelNqQz9dr7e+dqh/nf6hv81uoMypt1/Xw34eHMtM+p+q3EAOWFzOl0eKSl+SSi84Z3BD88adEkl62h2whrN4vzadpYJh6MdCFEetT7lKwNe+/v7E6FdLo/b7wYunGAmg1K9KnuWbWa8eXbLlDk0Ncw6YHBNH+/C993mfiYti4HW4uJiLC8vR8ThEuLe3l7/YvrM+KiTBPhjQ8z95g6mYKl/dna2X7bHjDfrK3dwgGfHrMt8aEEBpwOEzsjpPSU3bktOmwl8sNF3xo4BuHPCPLZweIZlzzqBb6cnKKvrDpfL1VZoOuZ5e3v7CCgFuU3erl38rZFULUfLUudSkpnyruNOI4HOJim5qJYeKMhI9SezqWpPGRip/eb2K0jSdrmxprw5mznUHo2q6xhjvvREv5tQcr3Ig3HObXc2XYknpiorx6vKq+Q/sqVP6A7G/eLi4sRjhNC3uoUD5fPWKZSb6YvrW7fKoH0GPrXfnDydvBwP09C0YC/iGEu6EeVGMZVA2dDAHsqfkQLPLI2CqaHy+H9NPjWq+PDDNJ2iufrZyQ3x58pRI+mcbq3cmCcd6CWesvJcvQogNI9LV1O+GsKsXJStYA3GaZoDJ0wOKPF15UXBp9v75+pw47Bk6DIakq2zAxnQ0vEwTZ3appoxWKMbWXna9268uDGWpVOddTrA5QyR69+hfBnAzChrX6kvsr7jPBlAc+XV/OfrJSrVlckkq+e4fkl1gesZ6hs3Zku27Dj8gRfma9qyavxIrR4q8FKdUZ71HvupzOYN2dIh/lRe/LtGv2+Upi2rGvCdOHEiuq7rN6+PRtef7K8zUo0cOaYy5e26o2FYRvHO0GWhXt4MzXsHRqPDN2joAQrlT2fqKJfLw6zDrbErkkdblN/sHaSax83iuCy3dwrpOaKGfuq6w1mUa7fWxW2OuH6QoXSwQvdUchrlTaOC3DfcR9jQ7t5HyptYWb7aDp0N4httgUxGo1F/kINnyCWnxUYAkWRN6yJTLBPd3wT9An8sZ7TH9Z3qPUcr3P5aNpDOkKA+jHsGoUxDy/pch+qCA6RaRymv21+secfjcf+YILfHcSi/titzNBGHe5BcZERlgbbq44qcXeVx4cpRHcI1XTrWtulDmLXdSk7mLkKsbVdbWnLOmVPm9rrlOxCvluhjRdhHZfWqzVKZO56cTeW3lyAdxrRG+Ep86Ns5YJ9U3viGnWY7yrYA45mj2Wx7HU9cPnhBOXyfy+G2KqnN0XrcG7H4vovE8/5TFyHnvnd1a7+7tmt65d1dz7YrubJdOUPpSlQN+BYWFiIi+nV1NUwlpAzm1Bi6BjjA6JYKnMCdINHZ7Oj0dIsDpDBIaqAVYKDObC8UDzB1zmyks7d3aJ5sXwIGPQauAlCAJebXLQVxeQoa8MHhBgxEHRgK+NwA0jbyySsQP5MM8mbDxo464vpSkS5L48Ngj+tmowBdwDLf/Px8v/TnlmIdsHFGh/VeDbs6aqfXajhZx7KlNDaAygN/OOKcOS/9DR0ALyUDpQCJeddXtOl95hN665ZduT6e9JRAm5645nulfuB0as+UZ82nIEzBT9dNnjTmutRJ6oRO63K64myX5lHHNkS8dYDlw45W+9bxw/eczqg8HN9MTo90b6QDeyUnmtWj99hWcDpMVvmj+65Vtjp2uR4OHnDbdFw6YA09Y78B/dOJNsidrmVeYSu5fdlStXulnJMr+OcnOTi76+xIZtsy3JFhE2cTVEez3+6a+ki+P40tdf0wRMc6tMFCztJkZfBgywz3UP2l9A5sOUOjQIDzO4OXGUEt2zkBZ8SVD+XVDTiuk6NiDhi4unRQqEzV2DuDo211RhT8a19kzkj5cu3l8thgZflK+qqOSZf31XjBaKnByfrG8ZIN5pLDdnk0os1OwBmC0hhjo6xp8DuL9nKf83XXbm2ffvh+VkbmbIdk6urI5OHqz+pw6fCtwCXLozpack6ZAyrplpaV8VLzW+vFfcdLLZWccMYPtzdzgvjvQJKT4xDYK+lYppeuPaVomQtWDOlLlq4ENhRIaZRXo4DKt9oIlh1PQtSOKVADL5ktVZ12v/GdgabacYTrrt9K41+vO/3PdAf5nUycTg3ZoBoMFTEF4OPncvFRaVQ8BFJAunGUqcYAOsqcSkQcAUbKJ4OWjDKnxqCDl7gYkIxGo4nIFN6koOWDF96Qmx30GI1GfTSLHad7ywDXgaiGyokBEdc1NzfXL2m65SNEebPlCF4+0aUi8Iu6GBCroWIDj3vcftZD7i8tj/kE/xypRjl4UTfziRkmRzpZ1to/LCeWLfPmltccKGfdwLIJ32fZZoCTy+K6+YSwG3/8zDi+p9GbbFKjhOipOgS3P9HJzkWMhsBglt6BA+WBeXEOhMvgPZ7Mr/aLc5q8FYHzqHzAB8uf69N6nKNkB8z3NVqnDj0DdXrP9aUCMO0rbjvr1FCfKHF63gqjkzXe+M/2oQTu+L9bllQ+FFwNpUHZCuQyQOd4c+ncFhbYDd7qwys+vBqkfsUBqvF4HDs7OxM8ow/YFzqbmbVTt1owUGJZZbjBbQ/Dt+tr1g8uX9s6jU66McPjz+Ec127c53I4Sr+zs3OEF0fHivCB6WwZM8urHabl1dSd5S9RrQGftlwt3ykwwIE+H69UpzoLBUNaT2kAcHvdfhlNo3zAGOgysToNB8pK/a0K7ermvG7ARBxOIDJHNM1/NpAAk87YDRlZNcjaT04Wzoi7+thIOGfu/jtDyfccAOL63D49LUt1MkvvnFeWt9Q+Lsv1Pe67NuO/y1cy5ppm2ntKykPmyN1Y13JKzmdIX53zrbGFWb2uDaX6bzSNk1s2DjM9yeop2bBp+XWgoZR+6F42pkrlKLhVQMFlIoiB/xkAcnkZ8DEPCrZKgI91MwOZmX9QzFEaH1m9GZX0fmgsOl4zG6T5HE1jbyKmfCwLHKDbZ8eGQlEyfzvSGTHnVaev5ZXC5Fw2z15YyIr0MxAA3rhDXWSEf/ObJFh+JYcOkKdgmsGIlukGGteh+0k4D4NQlglf04GPdLy/jeWhwNY9UZ77Yzwe9+94ZUDDETxtA/eP9mfWj6wLkC9HXHWfJXgEv7q023VdvxGbjZmbWXK0Ary5SIhueNd0LmLkyC2VQJY8ZkejUd8GNYAAvhpdYh3mxyJoX6DdXOb8/HwsLi5OlOfag73C2u+ldyk7mbn9PriuskEbEEmNuL7hXW1MyV4p1SzV8T21R9rn6sggE7cfz+3hYznqlgik1edC4sORILYHKmcds/hky1csU8hA2+LGM9sUpFMbpOVk8leAkoHIEpUmJVneIXCY+SPOx/YoA2VurHB+PbzHZWXyL7WH9Rk6o2/v0bZoXtVNbQvXWcOPGwcuXel+9l/lpDZW+4L5qQFtmaympRsCfGrgHSMMXhzBEOCwgdKQooEnvscO1gE+3fyvdbDTcA6YAV/GM3f+zMzMhLJnCqVgSZ0hR554gCIdL78puOK+4jargjLIZsCnjpyvQw6jkX8+HJw8OwtOw0tTOBCiS5xYflA9QNqSQXNpcR/L3HjGnR6AgL47meFQB6fnJUvnzFiuPOhB7tCBA7Ts5J0xZFDCbdEy5+fnJw5fOXk53UR/8MZx1QsG8KpDLAsm/N/f34+tra0JnuCQFXTgvuqeA31cFrdLgQ2DUt0KobJVJ+tArANgztFltkHL0+0AelhJ5cAyQx5+hRvXxRMbBcGwZU4mkDNPolQnmEeVVTZJUYCp/GCs8AQtwi/pqa9Svc36gPNk49fZVOfwtV9UHkOAh8vTscmBAtcObZ+CZKezQzJR/pAe8kS/4Jmmzp84cuCpxIvDHtpWVxfzkvmNmjJcXdz/+lF/mvWZ6loNPxkde0m39h5oiKnjoOqaulwH4nqGykv8uQGT5c3KU2dRyqcGiQdRtvcwA3M6cPSa+11qI/PjlFEVPcKfGHNtVZClaTVPRnpvqC1O54YGY2aIavU144llquAucxBIU+KHHbBbss34Rl8zL7WAjp2JtkF1AN/OISvAVH5RHwCx7ttkymTkHKgjdRTclsxGZLYpI21rbT7k5TwOSHFblXeWO8vFtY3zuzGpgMqNyxLg1f2hWf4hGrL1KoMh8Mfp1TFrnY6XGp+j5XP9JRtfqzMquxr+S7rlxq4rV9tW4s/ZNDeeMx41zTS64n4PyWja8eraVqs3tfYgYgrAl1FJGfDtIjB83y1jsJPL6nF1qYPmDeI8g9TIzZBiOgeraR0vzBNf49fGqIHV9O7wB0ARG2yUpU4O5cFoOqOdRWM0KsT9yLPsra2t3sGqTMbjcf9GAo7mIbLEeoJlmv39/djd3e3lDf4cKGNHon3t+jDi6FIkLzvzIwD4UEq2hK76UWN0XXr9r9FaLo+XSDJAwzywHiGSyjLl/Myze6o962BprLjlbccnAzkANb6GAzrcBu0H179YNdjZ2ZnQeV3C0iVb6CvsBx5DpWCTZcDjInMqDgxmctdxiDrcI5dwnfvDgTR2nLu7u0fsD68QcF/jmnv2JfOOR3ehLNZRjihm21qc/e26Lubm5ibec+2Wql0U3jllfhOP9ofKzNlX1j2UA7vnDiS6McnXsomQplUd4/Q8htlmZP7VyQR8lJa09boCUB576C/lUfvZtYn13W05cuPHrTrqVhT9zVSSvdoHJ0MnH8drRjpGh/ibBuQx3RDgc52QIXWXrqSQWk7W2Xwvcz66ZIPrztiU2qqgYihPCYyW9prxtwIx7Jlzyszlcbv0w4Zf61I+lUfNB36GZKFH9nVQalsjjj64WflSmZUcLLeby2JwwUtfXIYaKi6zZBgzQKRllIAa5OJ0TvXRyYDzsA7BuWfLzaqjzqlwfc4QZfw5fWJdUH4VEJZsDn4jHZZ51aAy2MuAgYKlTK5KmcPWe0Ok4xb5S33uZOH4d+CI7QpHczHGFfw4e5jZdPSlOk9tRza+oa+6BzWTyVDfZDafV1AyHpU33X5S45wdkHNAiNNntgJ5Mrs4RJAdjy2XxulWxouLyJb8NOqouZ/xr3lLvnZIV5xdy9K5/qvhW8kByxrepqFjP4dPBVHaT8RAQ69H+I3VnGfI2JZQsXtvY9aJbjCXBqHm0fJ4My3I7XnK6lNQNxqNJhxRBpRhqFXeLurH+dn5zczMxMLCQqytrfVvmQAfAG9LS0uxtLQUOzs7ceXKlT4fIndsSLAJHg6Y98wxT5APogUsU35gNs/0+Z28qgcucsmRJ47wcQRMHT7v1+R9dDwjhiwXFhai67qJPWXaRyDniLlsNVgKXNRhqLPh9nP0TCcRaKOOuWz8lYwvZMf14LO3t9dHe7mPuC8YBKoOM49KLgIMPeN2jMfjif1r2kYeWwr2nSMAZU7XAaOM3HhkW6Bgm8FQxp+2lXngccF2UuXC6bh+BtPaN6qP7rFRQyAF5aEeHofazxxFcgBQ9Zr3M0LeSMNjjvu/BHpK/50suZ9qwVkNkGXboPeGolXKbwl4Zu1lm4JIukZlXV3uN5ft2lMrP1dWhhtcm7LrNe1R/rkMjiorr5mduxGaGvA5ZjJjoPfd7E4NjkPH7DS100v8YukKgIVfdK77VrRtzHfEJBjKBoLm4bTsAF06bbe+xSMijoAulaeCZfCtQE2X8diZArDNzMzE0tJSrK+vx9zc3MTAxYbtEydOxIkTJ2JrayvOnTs3UTY7DT6ZiTpnZ2f7F2I744u+U9lC77DkBmAKx8eAMGLyYAXu7+zsxPb29oT+wUhB9gr4dJk8Mxbc/s3NTRtJKYEHNo5qDGBA0W41OtwG5GHg6J6pp6c11Zg6B+2ME+eBriwsLPRLxwxeVXd1TKhtUT11/9EWfn4YZM1LjSDImZfkAND39/djZ2fniIN0joh/OzDG/HN9SiprdkguCs7poOO67QC8IT2fvgXxJIv13b3JQ5f7tB5dVua6tUzXTian96hDIzYLCwsTk0ykKz0nFrLSFQXXL/h2EehawMC6pE8s4HSar1Sm49GBek7Hci+BV+1bV5/+doBvd3d34nmyKhPWAZTltgW5JXiHC4YAmOpm6b5rG8vOtQffum2BMQ6X6Sb1WpfykrWzhqZe0nVKUqrcGckSaSOnzV8zA3J5assfqqdE6kynGeTOEWteBokgPt2atdENLo4AMZBgWc3NzcXCwkLs7e0dOfmsA4KBnIv8sLHI2q0zM61D7/P1EnEkVmXhyimVp3tVuEwd9Nmykdahcuy6bmJvFdftxo7KwBlarT/TFU3rdFgNNF9nI6cym2Y8lOpWUkddiroy/4gEZktCNQ5R9drpEOuzmxRzPzIg5nGmgNHZTh0zTobuutONGjCRyYfrYH4yoOJ4KPE7LWUAplRPbX3ZWMkcOedzdTh58W/1La4fSrYs85PTytfpIfPzeJCz9cqD/i7pteNdeS7JodSnTq7TYCnnn1DeNH1zrD186mgyBdR9MpkS1YSZVXGyZWAmRKMQcciej4a07jl9JeNdCx7ZsOkG2dIA0H0z/FujCTMzM7GysjIRSYs4GhXsui62t7f7J3Pr4z1mZmbixIkTsbCwEAsLC3YZc3Fxsa/vxIkT/W9sdkf0DSBxfn7+yBItIjH4rZE5F61gwOPe3MF65MAUZvyI4HD/8FI053HOtnRwAHn4MzMz08sMj0GBbmrfcwSD+xpgGlFPRM9YV3m/Gp4fx3zxJn1+or4+HFVBNmTOG/BZFtpmPtShtLi4eCQakwE1LEUqLyxvHltajtufh/+7u7tHIgQ8vqGvCk4hO44EcjnQw67rJg6buC0W+K2RWR6vup90aWmpj+ZBf/DIC87PkR6evM3Pz/f3XcTaAXZtn5tUcOTf2WO33KpbPYbGkvYh95sCzdJ4RD4XTWb7gr5RncuIZapy4HKylR3uO5YZ32M9Zf1wfeQiWC66rGNGr2W6W5IDdFO3oLi63fhXXcj2iWdbHbgt2i9ct/5X4Mw84rrrs5ryWLbaLugal+sAIbcv+1+iG4rwla45wWV5HBLP0pUEgf9cpy4jaD7dv5F1ZqmtGWUKA2PHhsR1ospFy+O2jkajWFxcjOXl5T6PLs8A+DIY0Ggb9jxhf52CZDhDRPeWlpZif3+/zwPQx3zyXjeAAY4YwvDzoOAHt3IbsYGcD4uorilI1FevARixfHlvZdYn3B4FnpChA6Kj0ajfWgCghsirnuzj512yA2XDhr7W5wbu7e31J1Ldkj/vTdPnIKqTZ96ZNwVdyKMAQ+sDzc3NHXnuH5M+sBfl6NhVcKKOTCdG6lQA2HhM8SSE95JylA9yxuGB3d3dCR1VYKEyVaClAIudlVtWxfYIPqGNk8TafnaSAJUA0aXngmq/ZlsZOK86NC2Hr4MwyWKAm1HmQ0r+SG2DllXSde5HB5K0PgXZKMPpX+Yv1dbxRID9k9qmDHTU+mruT3c9u5+1gSfrqjtavvKbtaUWNDpf4ACfk5PTbYcvXLv1vvLC/zPsU6ora/c0YC/iBvbw1VZa6iDdO+QADddZGqAZENOyXVo2utrebMY2BGJd+UqK+HWA8LcOBr4OQKXLfDMzM/0jJeC8VCa8l4nlAMesEaC5ublYWlrqAR9H6ZCWwR3KZCDDkbvRaNQvCbPs3SDFMhvSZM5U9URlymCIHS0AFpM6NPCggE/1C6AWEZmlpaWJfoqIiaiM6hRkGnG4R4nbxf0CHhjw4eG3DE7Rt+yYFPhzNMjJALwp0HJRC45u8f5BXdrWaBL6EYCMgQB0joEjvpkH7l/IjoHx1atXe/lrNHhvb6/f4wk5OrAyPz8fq6urR8YT2qDbHPQxKAwoVM9Yv3ls7+7u9nU4+WR2jp0w180f7g9N58rUOhUgsP12e++0/MzG4V5mdx1YzPZe4jdHbl2bIAPWTe0X137lT8eMysWBUh5ffEKa+0nrrPE9Jf+lvtb1QwZslFx/ZHyojmY0BLRKep211dHQamNWZwms6QoKf7IIpWIbxQiuXTVUDfgcQnZARdPwN/92s00mGDmOErByc2PVgaCeEnBwglcF4UHlZmo669K2MrHDxX+0mzeaa2eq84WDBujgmTsvnapxXVhYiNXV1d5Rzc/Px97eXly5cqWPUuCDchYXF/ulSNDi4mKsrq5OPMcN0T1EruBI9cAMHKi+icIZRe5HfTYb6wf3NT8zDTLlvmegyVEkRNrAIwMiBiKIyi0sLPTtQpmITCFaCTkjEre6ujphvLWtWh87eZyGZplBFnx6cXt7uz8ognr0eXuZnjNYZzDFy9ccKQa5JVaUhbbzc7k0msyTBfQDosRXr16dqGt2djZOnDjRvwljaWnpCKDTsYmoKtuM3d3duHz58pEl7q2trdjf349Lly7F1tbWhC5D3vjm9ukyIB/4gI7s7Owc2byOfmL9RnqWDwPH7e3tI9ETBvJOv3hpSwGPc4psYxlkZE6XHZT2A4832Cw9tMA67SLTuuSdTeCUX57MKVhxkWaVPad3dkNlwOPAAQONFLoymG8+cIVVGefk3USHy1f/pXw538V1OcDkADh+u8g+t8sBZl1xUXJ8q26qTXM6UQJ7mpava1t1+1dJRsq34g7kVwzA+WEjdNI2BFCVporwlYRVSxnKV+VwAuN8teQG4xB/meI551aa9bh7ypted0AoI+RncMCzc+UJxhaATh2wA81KLm02g1JlZ0MwNEngujT6xCAQ6XQCoK88UyOBvAwceX+la4sz0NwufayJM26aN+tjrldPR2o5cFTO8TnjqDJTAwQHy6DDLZ8zPzxTBXhDFBgOVidNDKD4ncTgFWAV1wF+EWXG/lGNHjIgAOBjwmNa+NQ+g9nt7e1+2ZSBCsuExxLLBwCOl7/ZeLPzZdnhow7b2RQ3KRxylurMuP9cXtWVacnxlOmks/0OSJRIdd75DVeOszeZr8vqKPGn6UqyzO45OWZ9M6QLjzepHakBieo7bgRXZPp8XBlk9m2I3FjV36VrrpzsvvI6DU0F+NiJZZVmqJb/swEC6VJTiZzyqAF0jtqBTN1PA16YACB0Fs38sGNnRwxw4aKQvOSJ8nhPC665PT+4Nz8/HydPnpxYYnVLJ3BUc3Nzsb6+Huvr67G9vR3j8Tg2NzcnHBmiXXBcEZPLoLr0ub29HdeuXYvt7e2JZUp+uwYvP8E5wlmeOHEilpaWrKJzRI3bwlEhFw7XR5DwMic7eL6PCJ/bS6TvLsY1fiODOimWHUd3AAx4uVB1jvWRD3eg7zmyh2gUQJLKmY0Y2sARPOg3R8ycbNEffJ3HEUdwNB32vLFe88ERnoDgMUDgDXtF5+bmYmVlpf9GxFr54W+OuOI6oqHczwcHB3H58uU+snjp0qX+nbFYPtWVCJYzloH39/f75eLFxcWJQ01LS0sTvEEWALCj0Sh2dnb6ZXkcLGEwzHqNvgTYB+mkwz2Ox9kI5o3/cx/Dnml0ne0i2zpnz3nfK0fM2EZ13eFyOuSr9t5Fs9xqCEdRdD8zT2o0P8pwPowfQYPxzPczwKpgB9ecDdO9xiWZat1ugsc8ZBNAlM0rUUM+noMMam8UAKLfdBLC23EckObfLGsNWJTk7tqfpVM8oWWwPdEtTEyZDLReTu/wBdp9I8u5EcfYw6cKoowr8MG3dkQm0GzmopSFqLuumwBSQ2VnbdC6sEzFjp+JFU4NpEPtGXBmY84DgfNxel7m4mVMlrFGVpaXl+PEiROxubkZFy9e7J0x88nGBg5vPB73zpgHDZarsL9IAS/vH+KlNnxWVlbi5MmTdrCMRqPe2bORwr42Xa7UCBUPlp2dnQngyd9w7upI1CAxUOPlUd5+oCebWWd4eQ7OzOky9zN45PbgNDQDPiytq7HgiQPK4P1vvCUA/3WSwrJ1zz3jh0wDoDDp6WOWI09WFhcXj+jt8vJyrKysxPz8fP+9uroaa2trE3yy3JhfAD60g/URtL+/H6urq7G9vR1bW1uxvr7en8Ld29uLvb292Nra6vNiErO5udm3A32yubnZnwJfXl7udVgJ9c/NzcXy8nLMzs72ky9MJrAPE23TCaGCe9YhRBu1P6Hvujcxs7nqcLgejo6X9m2pc1dQovs9wSNPDpCOxx3rCdfJ4xh52GGiH7lefKuvclFzBaoOFOp/ntxpHq6bgTz7hSGfWAISXI/LoyCQ26XgUMvLyuT/mf9XnVAeuU0K1JxvHyJXdnZ/KH+Ef7A9l+0AWkmejjdOr/6ptt2gasCnTmlIWNpIx1i2/qyAKUO0bqCpkmrdQ8qJa26mUhpInJcNjfKZDRAHDLXTmX8FaKjPOUCUj+gbG9KlpaWJCAbLHiBoZmamz1MCdHpClZfZdO8dIi+zs7OxurraR0LcTBcPaI6IHiDxQQZ88/KsEkeTGETzA10RrdO+ZNALBwPwOzc3N3HQgAEfOxrd86H6wU4V13jZmX/jPuoG2ARoQ1/rUqRzGixDAGsAQHbE+HC/KWACCEbklo0eO1p8Y/KA/aI45MLRvtnZ2Th58mScPHlyYnKDSQLSsMPWbz7wgv7Q98JCjgBtiAAiar2zsxObm5tHDpVcu3YtDg4O4sqVK3HlypXY29uLhYWF2N7enohMK7hlufCYRT/whET35qqzxESU9RZjl8c/AzJdNVBbrcBpyNFy1EaBHtJCZ5wv4L5Q56+2zO33Yv0cjcrPM2S+mB9O4+678eP4iDh8GkHm7Pm3ykqjlFl9mX91IHSon7Ucd30I+Ol99AOPRwVtJV4yYj1z90C8naLGd6uuDV0HOZzggKxL5zBThkm0nONQNeDDrB3GsYSulbFMSOzcFCSUwJ4zSFwPDJ7yUOos/FalZIfvDAnysVGF48MSGSIEOgtgXrGEBWeXEfOo6eAw4ESY14ODg9jc3OwdJE6Nnj59OlZWVmJzczMuX7480RY4NC4LIANLv1iuhKPkjcZwdqhzdnY21tbW+mgGgMHi4uLEY0t4putkNhodvl2AHTqcKcAbO2f0jQIP7jcGSdBBOBd+viCDO5QDh851AjxxhJSBMZ+0hW6Bb41SKmhDXgYv7FTBK+fVCDDqBcA7ceJE3y8sW42W6tIFy9b14ZDMELmbm5uL1dXV/mTz6upqzM3NxenTp/toHh9kWV5e7gEm2oDxwx+3TMeHiNDvunzWddfflrK7uxtbW1v9QQ/WJzyH78qVK3H58uXY3t6ORx55JK5duxZbW1tx9erV/m0D29vbsbKy0vONunkZfGVlJVZWVuLg4CBOnTo1MbHgiQfbHY3Gj8fXt2lsbm727eV7GJNqw1UnRqPRxCGr0lKaTmg0koroMZfDY8GtaHTd0TcUcBvYZ/C+SgaGnEf7Vn0DT8xwnyehrNPqyFnPWJ6sL0jnQJzTVx6rGf+al0G1AyDaRzo+uC0c7YQ81R9nvorboO3KysmCSqpT3GYd26wrCrD0mhL7LNc+Ltv1ofMv3G+cFsQTpYxKQLAGIDNNHeFTJc8qdiBN7zuUzgI9bgNLSlkL+rL7NXUDNECBdO9PqQ2lmY4a6CxdNpBxgpUdBAwk79ljsMuPdXFGhI2fG8AMKHizPe+t4k3+/Lo1lKeDUEEJ7/Wan5+fiNqxU0G5arCR1vV7aW8eD2oYcsgM9akR4Wfr8buEueyuO3xOI3/YyMGBRsQE4EM5cMIMwDnSpTzykm4G+NiIK6Hts7PXT69CHgyokY7beHBw0O91w9LmiRMnYnFxMU6ePBnz8/Nx6tSpOHXq1IS+qx7hG3Wr81UZY2KA6B3uoX2YOOFd0dvb2xNbOvDBNgHIaXt7O3Z3d/u+xQn4ra2tCUfKWyPgyNhpA8yjj6BDmBgycIJj5snGzs7OhN6hbQqW9Le7ljkblm9mx9UmqI4yKY+ONwdclA/Ic5oIj4JA52QVGJfkoRNubWPmh7g9TkZOFkN5HODQPuMytD1DaUo05KuyumruTVterR8vyYJl7MAj+0X8x9jO+ChhJQdaHW+1dKw3bZRIFS4DiBG5QCMOnbwOPJ29wEAqmlaldrMBNR7KAw9eBi+onzfT88yQwTFHDdRIMyBAPo1wRhxusM8eWru9vT3xHLyZmetvy0B929vbE+kvX74cOzs7fVRnfn6+f2Dz3t5eXL58Oba2tibaAl55uYv3MiFqyE4Lb1XgPVq8eR2ACrJbWFiY2I/Is3Z2XgBLvIePQeXBwUGcOHFiwjFzf2K5i/sd0RpEf9ix4z6DYQW8HOFjvUA6Lg/gEuAE+oC+YuevEQ6kRfs5ysSyhIwYBKnDRb9q1BfAz4F2/GbgAn0Fb27pC8QGEDqFQxmzs7OxvLzcL+sC8OGdzTzO8Mig0WjU581OBeObl5257WoXAHgjItbX1/uxDr1HZG5nZycuXrwYu7u7sbKyEmtra7G/vx+33XZb7O/vx+XLl+PChQuxu7sb58+f7x8zA5CIsTQajfpvyJ71mvVUI058D/YGIHp1dXVicrOzs9OPa1zjOtgeMlgB+OVIn05qnc3TCZU6Nhcx1PYo8R5Gp1coUyOF3D4FjGrvdXzzRICpBP60HgcK2CexH3H+guWC37olJAM4Wr+m072XTlbIp/6HZeaWL5HGRZSRrgYMMWg6Dgh2UWEHyJkn7aMhIJbxzpHSIf45TQmg1k5klKoBnyu8ZNSdIBhAcBkOlLEiaZidy+TQs0ZtUB+DkIij0UMGh84AqOPVpTRVWqRzkS8FfBzhYiMD0MAGFU4uYnJpA4CPARYcJAAEK/eVK1diY2MjlpaW4slPfnK/Nwp7j7AXKWJy+Q7RQQAMjmihfu4XgDtEjVAPnteHchhMwajg2XMAjhzx4GgeA3JEQgCwWCdQ7mg06p032oflNRx+2NzcjK2trYn+ynQcfPNpTj65y0tt3L9ol0b4cPAFeuSIgQEfCOGTpJDZ4uJirKysHFkOAaFfGOjxxAI8AzgyANfTtU7Hs2jzzMxMLC8v93WibH7sCk6fc8QL8uG3wUDvwRN4ZNAaEf0BDF5GQR6WLQAolw2+x+NxbG1t9c88nJub66N6iOZBpy5fvhznz5+P7e3teOyxx3oAeN99903sFWQbwXqPZy+yLvDpat3DCtkD3OF5gPjGgRDe8sB7Chk08ZYA9KHue9TJMDtXnrSAL9hx5wMUEGQOTethnYPNZTko6ZI286JARIEC9FYBgeZFfuVbfZe2V8E2+xYONCCdbq1Q+XB+Vyfq0GVSvs40NJFz35qf+XSy0jayHLI6tT34qE9V/65g2wFj1c2Iow/15n5TOapMszZkesSk5agtr6GpInyu8NKswl3j3y6vQ8nuuhMQd2CtMEqKp+U7ZXTgkBWFeWJnABDk5IC8ui/FGUrmHfsE8UBYVVg4O6QHeGPArK+24na4iANkoTM/BnkMfgHyGASh77gOrhtlsZzh2DXyBMCnJ0VZ9ru7u32kAHVCdnwww00wmDh6AtkieojfHAXlSQLzw8aVHcrQ+ND+Qd8weMOBCDh6RGaQNgN8AAHgmx/CzZExNYhucqPOUfuF+1773xlynhQhyslloxwFHcyfOj7m2Tkbdox8X3lHdB1Rsb29vThx4kQfdVtYWOif9Xfx4sU+6gcgy+3nQ1H6esAhwMeTZKc73D6OnkAvwL/2lf5m8OzKV3I+wPW149P9V0Dj+ikDPyoXBzxc5DHTnYxHLlP/lxy8pqulzGdmfJTqVNCr9WidJZ6crpR4VEDt6i2R89NZXVnZTpYlTHIj/LL+K+bQcXPcOo61pAsGdDlEBaHGmhljAOMiD2xQeGYIoersZjQaTTipmtdkscPkWSkv+ehpReVXnRTvmcJnPB73e6J4+QkgCZEHlh+DJS5PjRhkMh6PY2NjIyIOHwUBXvC4CUTuwG/XdXHu3LmJ5S8sWTHoxEZ88MzPJAQP2GjOy7Fow87OTly7dm0i0oj9WgwgEFlB9A59jn1dKJP7gA8JgFe+xnvQ8M38gPb29vq3VCBiw84DUUgGODzIGMTygQDIE9FDRF6YeAkZbQbvaCvzwWOIl86h+zMzM7G2ttbvhVtZWYmI6J8TB9kz0OMlXbzFArLASVpEvVivWW95nEF2GlGB/Pi+AgCWL09QOMKH/wxceczxQSDoDEeReYsArqMPZmYOH+UC+SN6ijbzo3n4INPCwkKcOXMmlpeXY319PW655ZbY39+PpzzlKbGzs9M/tmVvby8eeeSRuHDhQly7di0eeeSRfoxeuXJlIgqHQ1GwSTzRYfvDuoGTvTzZ4G0Y6COWCWTGp73Rbp6EsF3HfV3t0LScB/Xr/lDolC5jqp4MARkFjs4vMW9uBYntJx82UR/B+dxeWubNAT63z7LUJuaBSWWjPrcELrkO/c3+F7Z+GsAG0nGe8c4ycX3GusD8ajCEy83K1DzcVrZT2QREr/OBIbaBWbu5LL2nQRfFHFpethKlNNWSrrtWQv3unptF4Z5DsyXFVUTMRkMVLuNDB65roy5ZsGHT2T0DOVZGOCd+1tn8/PzE8gmTi6C4Qcby4kc/cEQAyg7nAcUEAEFaBb78YSfj+pbBA4A37+1hh4mIEZwr5IY6+HVgo9HhiVzeWwXnxQCUl/X0xCnLUQFr1x0+JBqOGw4PaXh/HIN/nQQ4wMdlsw65JS7WGXYGqEPTMZDliDAiclgiZL3BdQf4+GAN+GPAB95Go8kTnChfwYRO1lgfXMTMEe4z0GNZAuSybHgJjCN+KE+XadihM+hgIIjILb5Zf7k/eE8ixvjS0tJE5BcyXV5ejitXrvQn4ruum9jXxxOGiJgYJwz4eOyCeL8by1ptKu9r5Agz8qktcI5GdZPTlPwA21YFSto/zn9wevAx5HeUnK8B8eTY+R9cY31xlAE+9WF6/0Yo88Ul0JeV49qp/sj1u/oqfGd9pD7T8ef6KcMVypfWw2U7fmtAKpejKzYqnxoqYShtz7Q0VYRPhcwGnI0EG1lG5dwANbjaoVmHZEaADZ/bhKqzF+XfpeVBrY9CGI1GPajgCB8AnTo+GCKkhyEfj48+EJh5YDm5SA/LA9d2d3fjypUrE8uvWLrFs8HY2WNw6Swd6QGAAAxYDhzB0X5ENIbfwLG8vNxHnc6ePdsvOeLAxpOf/OTeOeLkMHjkR1LACetbOFSveDkM5N5LyX2Ix4JwPyjgYx1EPSibT27iQAucPB/qYEeiUW7eH8dyxvfS0lIPmgEMsK9tbm4ubrnlllhdXe3l2nWHD8hG2dBF9DUDCBB4ZGMI/nd2duLq1at9vwCIcWST94LqpGh7ezuWlpZiZWUluu7wVWqQwdbW1pHJFg7W6PI0G1iOVkP3VTfwffXq1X5ZFftdV1ZW4vTp0xOHmnRMMGjc29uLixcv9g9cxn49foXhiRMn+sg7eMPEZ3t7O9bX1/txu7Gx0S/54kHQm5ubE+Ne99DyuED5iCojHe9h5IgyZIPxqnYdv1U/1EHzXkceO7iG62yX2C9gEoprGv1Te8/bRdRuajROxxHGkvoAtbu6kqTjncev2hQu0/3nMTUtuM1ArLvPeu+AWpZf+8elc7wP+W6NVnHfKpDWep2e8W+WadZOrk/bpH2jvDlindC+47ylyYC2QeXKeqk+bhqa+k0brnJuEAMljiS5RnB5pXrYqLCxiZg8jcVGoJYcEHR88t62iOvGCM/MYsPAgI+X6nQPDmb0MFYwGgBdDCQZyDIwVOVCW7BJW+XHQA9lAyBg6Y7Lwn2OTPIyl3OyIAARyABLySdPnuxf7XbnnXfG4uJifzLzxIkTcfvtt8fi4mJsbGz0zwVE+/E8NK4foI/byDzim0GV8o72ot9gUAB0s0kLO5qI6ME2+hXPX+Olub29vSORO9U7yJsjapoeMkP0Z39/P5aXl+PatWuxuLgYd9xxx8Qr9yKi5wN1aD+qs+MoI8+4Uc61a9f6Z9NBf/b393ugAbDiDlPMzV1/xd/KykqcOnWqB0eI0nIUD7wwUMEyqi7poh2IxKGNAGq6JeDSpUtx7ty52Nvbi2vXrsXe3l6sr69HxOE7d0+cOHEEBMzMHD4LcDwe920GaOu6LtbX1+PWW2+NhYWF/gAT+hYA9+TJkxPjGEv+W1tb8eCDD8bVq1d7wAebgcMeWBq+evVq/8w92BnkYfsCfYQcWb6w32gbL5FzZJzHEesjR8IUTDIogw44oKb66PZyom5sPZiZmZmYWKhfwLjmSQHbAAUkaAtPPsAr95M6efxG+eyz3BhX0IF6XdkKwB2QcYCR/aJGnphv538zv+jAkwNQDqhhrLqHwjsApsEOBvp8jf2hI436OvCfkfOv2X3dS+yAn5ajYI/vazoOan3YAZ8jFZoOCDdAkE6ReEmRlLSx0wI8Lp+vK0hiR6uRECwlwnhxWWq0kJ/LQURGl5F40zQGgy7dsNHlPuAZnS7FsKHlfYYMPlj5+Df3AQ8ep6SIBLKBh4PGfsK1tbX+AbS87IgIIssY7YMzxeutONLJ+2wgO7SNgbE+VoL7Y8hYOGDE/arRBJY7PuBTdYJ1B9f4FDIT+oaX17e3t48YA2ccoEdqSNT4qJFRB4SlXJw05Q8OAgFw8b4m7jPc57wR152BvvuWAR9Pjlg/dVLJuqcTQs6n4wjL0Jg08cPE3R4d7hPmkQE++IGTAzH4Y0Idy8vL0XVdPxZgMwD45ubmJt4xG3EI+HgfHmSGZ/NB9jxZgU6pDrPeley0ghLV6yHKxp8LJmjZ7vqNOEXUqz7L/S7ZQk2j5Q/VyenYJuv9rJ+mbX/GU1aXy1NTrwNOuO7GFX6XdAT5M+I0WTnTkivrRsrm9mZtKcmhho4V4cN/PIYADGpnKRBx5UQcPbTBDovzsKLoNZ3BlGYtDA50YPKGfx44cEKrq6uxvr4+UQYvNSLaMhodPmcNToY3xrNseLkwIvroAQAhlppYvpAN741DhALgiAEkwBb2dyHSwY9nYICFethJ6ElcEKJIWALjQyNwgl13fVb+5Cc/OU6fPh1LS0uxtrZ2ZF8jnO1oNIrV1dX+eWY7Oztx7ty5eOihhyb2UGEf1NzcXJw5cybW19djaWkpTp061S+pQr4axUTUBzLBIRIGNnzYBKCO9xiqk+Q9lAy++BV2vE8wG9guYsL6zW8uQP/w+ADw4GVbfuUZAAifZmZnyfqP6CpOWG9sbPQHXy5evNg/5gYgiUEZj0dEY7Dcjf9d1/V7DgFGsJdQox7MK/iFHdIPZIdHn+gkCG1l0NV1XWxtbcX58+f7yDeiqdBXjG/IGG08ceJELxe8ik0jrHjmJfhzh5BmZq4/Dubs2bNx8uTJifsY3yh/f38/zp8/H1euXDkyEUI+6M/Vq1djY2Ojj8JirymnRXp30pz3CHMUHPqISDv6mFcsdKKYAUj2A84v8GSV94jqPlZsc2BdUVufTX4gZ06n/Oi4ZZvJE9CsjTxJ0Ov6X8GKA5lZelx325yUMpDKY9iVrdHciKOPPOHfKvdSO3DPRSRdpFnLyXgAHyVQX5oIc50KUDPwnwE15dvpl46hUnklmjrCx41zFTpEPsSkC4VHHO0QlKVl80AtzW40reMTRgMzakQOEH3C3jMYFTgbNpYASVgOBfACuMFSBAYFv04L9fOSHU7XssIh2sBLhhEx8UwwNSwAelg+5UMUABgK0vXkGfcX76eAzPitCTgEAJqbm4vbb7+9B8xw6Lx0w/zwGxtwuvH8+fN9JAllaj2oH9EQpMceKwZieP5g1x0u3aH9AIns2KGTvEzpdIyNGkeH+DEpeFaem9yAMLHSLQUcwdP6OS/rNoNq1gsXaQRx+QAC29vbce3atbh27Vq/rLi1tXXk9LHTGUS9AMQAurEkiskW7zXVk8lqF1x7sIWAn3WHaLI6WtZB8AiwxhMAPJyc5cvLl3i+pEakDw4O+ndFA2SjLp44AlyzDYCOYOLGy7OQPSY9DLYhBwZtGxsbceLEib59mBQyOEMfA0xyVBX2UJdaeaLMMmX7onqZOWfWG+dIocM6uYfc+FCLAiSlDODwpJd1zfkifCuQAI+611p9lAMD7rfKp5TegVINhGgZJRrqGwWkTOprcS3rE6RjW8v9weW5Je/atih4c2CtBPqy9maYqIRJhq4pAFZepwF+1YBPl8BwzSH+UmMcg+xw2FFiwGUnQ3HNzTaGULP+hiGZnZ3tD2PA+CE6Boexu7sbMzMzRxxRxOEDb/mkKOQ0NzcXJ0+e7B+ejLwAGhytQoQPe6U4koAN2QA9cIqnT5/uAaBGppgfdmL45ld1oT8YqGBWz88Mw7Ij9iLhgAC/KYHbin03vNk6m32CNzgfLOUiQsoRKn47R8Th/kE4OkTWOPrJEdm5ubn+0SVsOHZ3d/sN/RhocMSQJ9cJvqHHHIVDGXwYgwEFdAk8s7PAIRCkY33j/xx15CVkfgWdmyBp9FCjz+PxuH8vLN7EcuXKldje3o5Lly714A3ROo3M8H4rkEY20E94LRnGBusqJl4cKeSxr1FJBdIASUwYR/woIpYPlmaxr47BBsuHo/Ds8PkAD9oGMMr6wn3Ih750yZkjb0iLfcTsfPVQB0AXxg5WAziax0v1mBAyCMTEk+vBb46IsVOC/rNT5rarDjK5yQzL1vGBrQFuoso8KFjgb7SHeeM6uJ16Xf2BLi2rvSuVwfmdP3OOPjtQpnzpb0cOqChoy0CwpnP+l2XL7XSAJwPMXIfmAfGKhdMzB6aHsAu3vQTatTz9Zn3UYIvyxYed9L4eTMxoasCHKFWEfwmyU0JV6gyMcbieDbd7u4TmZx6Rj+tWR6d1A8yxI0d0YH5+vj/1iAgHDyaUw6ARfPMG9MXFxThz5kxvTBFtAphcWVmJ2267rQdSMJYcNQDYe+ihh+LKlSuxsrISq6urEwOIn/t27dq13hnx2yB4Kfjg4CCWlpZidXW1n6nzg5vR76PR9VerbWxs9FELnGLExnucuF1YWIjbb7994vVO7LABmNGvDuTAAeEF9AcHB/3z/viULgAYP4fx6tWrE30OeaP/sKEd7cOzCnmzNl6dxc90BLjFc+oYgALEQpf5vcDQxZMnT/aRMNSNiBmfcIXjRZQRTpllxifHeWmZ95jiESHqiFE/LwNyRAY6BwC6ubnZL+V+6EMfisuXL/eAj3UKbeBxB1CCPtI3SKDu0ejwLSiQP9sNnoxBT0vjGnrGcsayM0fm2PFgggNiUIlxzfqnj5cBnwBteLsFXmWIV57BzvApeJ7cYnKA6DaW06HD3L719fVYW1uLiMNVke3t7YlxPx6PY3V1NW655ZYeoAJg82/o3dWrVydszvb2dv/WEER2ebwiDcY4R7Qh1+yABusb6yjayD4G/ofTcx9i/yjXwzrBTpNBKYNJHlvZsqE6ZQckuD4HblnvOD/ronPymT/ltnDfoB5uM9t2BRpcn46tDAzpOER9GeBDWeBRnzvL+sF1Ysy5epVUZ9gHanu1vAwUukkE8+bKBPHebV1V0b5yeTXPhxXwOUWppWw2wvf1txs8XHepU2r40TLhIHn5xLWXozGZ4nOHqlHidHwoA44QToAjblB8fuArP5cOy0UR0Uc/FhcX+/02iBSyYUfkEg6NX0bPkSsQL5UgwodoHoAe78Vj4MtyglLzXjqNaqGt4BPAL9uTg0HCjpj3r2F5SaN9bHjn5uZia2ur3z82MzMzATpRH0cl1UGVdFadHMuD28H8Qc6QAeflqJTKV6NDKnt1kvybQR7ABnQGJ0gBJjSKyvWq0cKWBXXGauCcY+R7HI1CpN0ZQ+5rduAsZ7ZpzpZoRBV7S7m9GuWHo0Nb1aCPRqN+KRvXAexQNj9aJeLwUT8A09gLyCAf9XO50A/or+7RRZ9AL2EPeKLJj3yBHeFlVR4TLAdHbHf124E71WuetPB1JbXNmWN3ztv9Rv5s2bDEgwM5JXI2ZFoa8reaLkuvMsoAYdbvtf44443LcOD9uGWCtOzaMp3eDLVVdb2mDsf3cdrNVA343OZSF4YsMcQGySm2OisYIa6fQ7NqKHjpzYEsRvyYTWOpCW9zQDnsLLquiwsXLsTGxsaEc0JEUJE2PzoF9fHsC29G0IcwM1jiB+aC94sXL8bly5f7k4OIFqyvr/ebwfW9mHjvJx7fwKcrDw4O+mUa3nsH0Im0ETHxDlxEDniZUJeKR6NRv2zEOqPAguXNg4IdKiJ8iP5EHALpra2tuHTpUoxGo74PWdaINo7Hh4daAFq4buy3guwhR977yPvL2LGyTnFbsb8TugZ91WVBRG52dnbi8uXL/aM3+Bl3kA8+WB4EP5D35uZmHy2GjBmA8DgFD9wPXdf1j1PBvkcsq+PwzMMPP9w/Mgfth/4AJKLc/f39if1xfMAH45aXnyFHbj/va0MfgZ+5ublYW1vr90Xis729HbOzsxN6D31Cn2Is6pjhb2yN0GghZIj9eLxRHzrEYxcTh83Nzbh06VLfbl1O5FUMjjRD73FtZWUlzpw5M2E7oAuoE2NGl6wjok+HlQ3ufxzqwJjBFhFE9TCumE9EB1WGHHFDXrf6wofLIEfeyoAyMGnleniCifd5Y9xgLOoyKgN9jC0GP4i6MlB3EwuO8OI6JiS6rYDbznZCiX2XggtN467zZIVlrrqrvlTbwbYqIo92ZsvOyIM0ug+V62GAB5/jVoaYb86jsmLZur4/DrDWOjIQqjJinKJpeJXG5df2qW5MC6qP9aYNB/hq87uZE1/TCErEIchjUMEDlJ0XAwcWCKeH8uEAw9zc9VdNLS8v98uBHOUYj8f9s7WQD4ZJI2FsoAAqNHq4s7PTvxwe7WNnB2ODpTjwDrCHyAaigisrK7G4uBjr6+v9SVN+ZAcOcuCZaTCk+/v7sbGx0W/8xhIkgCPveVpdXZ0AEWxQuL8QgYiI/lVSLH84IN5Tx/2m8gRohUOB4wMBjKCtOAEJgIVlZ5Sjy9lc7ubmZszMzPTPTFtYWIi1tbUeBKM/WGehi7p3hp0enLFOWrjtiOLBsV67dq1fOmcQDYCBfsRS2ezsbA/UsJQPoKWnztX4sgNl8Lu3t9cv4zLgw8SDlyJxyEiXtgGW2IHwuMH4ZPCDfADoHMXj/zxpUjsDPYF+QMb7+/sxPz8fq6ur/ZgE+OElRuiHeyg6eOXtH7w3kx09b+kAiMEyuNuigvpRPsAUlktRH4NlfuA2gCz6n8cay5EPHOme4v39/X7bycLCQly7di0iorcJLDOUgckYb0sA4NTJCpMCEqwWwFZAHuy0VWbsSPnpA+xM+ZvzQe+ZF+iR+hgGfEjP+dkmMDhQP5XJAPedw9c6HSkwAB9se0AMnJgH5oUBXSb7DOgNtQE6wWMe414DN8wn18fyzpZTHeBX0jZkvGd6NETcPgabGSgvle/6eBqaGvCxImUMKVLlRpRmAdqZLAAWEncwKwrXoUuqMMqYBfLL5XmDNS8LcpluoAHgwLjxQOdrMMIR0e8h4gebdt3hmzwQSWKHDKcDwIL8DD551gwD33VdD/YQ2YBzxDIS9iTx5n6AYD5wgqVbOCoGcNzXkG/E0WfcoZ8hN7xSih+cqroE4Ix6MXDh4ADC0SZ+jZqCCCY1sGiLA29Iz3t98N/pKNJAN/g6O3me3IDYYTIoQB7IiXnAUjxOzuLADNoM3WfDr3uDuK283xHf3BaewKgMeEzAYaPd+HZ9DP2HcUdUend3d2LjP76h8wsLCz3wRXmQKcYgJnCYgODgBPQUQJ5PunIf8J5EtX8MSpyNg23hB1OznWBeu66bmNBBtoi0c/9h7IInXU2IiP5xM3zanfsQuoH74AdRWdY9AD2OrkBmiKRi8sGP3kGfMwAC37waos4QcuRJMI9b1lfn+BRg6G/uI+efdELHuuXyQ3e5HnbKWWRPbRPLJ2ur+iL+zRNNd5/rYqCb+eeMN0csMy1XfyvxuCrxUHstk88QOczC5TtA7NKzTmSg2AH/jF+HP6YFnhHHeCyLRvVUIZHGCZ8HEyulA2rZ5lPXaL3HM1AYLZwgxQZyfup9RPQRHjh+BZZM4JGjQ4ieIYoHx9d1Xf80foAsPhyAWTgiYSsrK70Dj4h+kzw2S2MfEQwtNsPzRtft7e3eyfGyGvYG8hsceKYPxeSlJH5kyeLi4sTrmRCR4kgPv7KL+xfOBo5vc3MzPvjBD/ZRKYBSF1EB8b7As2fP9o/J4Ggev75qNDpcCmB9UcMFh4blu/39/b7P1tfXJx5n49qqM3uO6gDggyA7lj1HpREpwQED1kNEesAjTmqurKz0eysBaBgAIqqFZXmAZTZK7Nx3dnb6pX48bgX9cnBw/dEpq6urExEj9DciagzgeWKBwy5s4DGZmZub63UK0WVELzUiCF3lV8dxNOjKlSt9OYhi47Q3XuG3vLwcT3nKU/rT5HiGIyKcaDfeccsOHHxzZBpjkA+EgLh/eTxggslvzbh69eqEXqPd/Mw+jGmArPF4PAHiMR5wH/Lc2trqgdRodH1J+5Zbbul1Fvzj0Ab2CaN+Hjfz8/P9OMQpZui8gm3eCwhC/7ATZf/C9hly4Ig3j2nYCu4jvp/5De4fDTZgbCiwdg6cdZQBPE+O2S7wVgX1iwoWVPc0ogid4G8HsHgyq35TgYzyBX6UnCx4HyrzwX3E/QC+dWwrX1qfA1iuXzltBn5L4CkDfy6/Yh2NrmYg0umn1jFtNM/RVBG+rFE3SqoALKyhKJsDgjxgUR7v8+DBCeJQsg6a0mDgKAAfRMDA5uUIDFYd0LgXEb2BBQEkIVIHw47oJBth1IVoBu//AnHUa2Zmpt9P6GadBwcHvbHXmbYaHwXc2kfs3ABwAfb4AAC+WW661M0n/rS/lRfk471M4EedLurlgyLqPFiX3J4MbTdHc9TYsyzVoDMoYl3S5TKObqJ9EdFPKgDSu67rgV5mOFAfR1050sUzcD5trXJQp4Rr7NB5GZnHHvoG8lfn5iabnBb6zgdLAHqxXI4l74jol675AATbCBdtAP+IvoEvRPMweVFHy1EzJ3e2Jer8UQ6DCZYBl8ETN94TB3vKthwTDwXU2MOr21F4bGMShMlnRPQAHd+8dYInAV3X9ZNXtMMBH24L88dji6+zPXKOWmXKMlTiup3v0z5gu4m06ttKwKLESym9A1DHyeNkk/Gm/XOjlAGxofJr2uvy1PCsfEyLe1Sfh+outaWW5yE69ps2cE0HgkO0yqgOGv7mGQgGDs+cYLBcOjXYvOSpBhzAgkEUlAsGEsAJURQ+bIG9NYienDx5sucNgx5GiWeLKysrEwcsEHHBmx5OnToVy8vLsbGx0b9HE8AIZS0sLMSZM2didXV1AijgUAkiMwwkYNjn5+cnokNwUrxcyiAFcsXMnd+Pi3Ijoo9Q4gTieDzuHzWys7PTb8BHlHJ7e7uPvPASus7gdeYGB8tAFvVduXKlj5RguRj92nU+cotyGLwgyjQajeLChQv9iWeAJoBy1nUFJ9Bnjp6iPjZi6C8Gv1h6RJshbyyxMiDjCCE7QY4S4RmDWHYbjUZ9/7Ncr1y50u/Ru3DhQi9HRBqxrIfHCPG4RkQMkWTe7I12Inq4tbXVj0seJwzseSmVQSrrBdIiGsVRDkQ3Ab4wNjG28XYKtHFpaSme/OQnx+rq6gTPfGgDEzHIXyd1AOIXLlzo9+9ijJw9e3bi0TQg6OeZM2f68YP9h5AN78HjKJ9OHgBGQYggr66u9vK8cuXKkXGFcYRv3r+M/lxeXo4zZ870Ef+uu77N46lPfWr/qCGMd9aJS5cu9fXiETUYO5cuXeoPsDDfGM+w0fwWD9TPW2ZYL3Syp6RAB1tQAN51VQGTJuiuRq4c6HGOnkE6T5BQlgMU6Ff1kSwn1z71yxxJ1O0qrmz2KcwPk4tMqd9gvdQ+dpggAzys1zxZVOyhvxWIq2xcHle3+82BG03vAkMRhzqgWxQ0sOWCAfjNbeLVJfjfIbrhd+nyR2dEfE3zgWn+78LnAGuq6NirhUHPAuCZLZwT1wPAs729HRcvXoyDg4NYW1vrD1EwKMTsn5eEeZM3ysSBBlznQwk8gLHsuLy8PPFoFcyW19bW4sSJE/2DbRnwMYhdX1+P9fX1iWU75GHHD/nznisGfHBcHOFiYw85cxSMQRov5XGEkQHE1atX4/z58z3ABlh0p/qyJQttEy+nYTBjafjatWvx2GOP9ZEtBuJcFuoYjUa97LGsiH69dOlSf+gBB1pOnTo1ARBRHsAcRwQ4mqptgq4AFPOHgS/kxs9jQx9ERB8p4Xq2trZiZmamX6JEHwIgqNHEMt7W1lZcvny5PyC0sbExAT4XFhbilltuifX19YlxCocIp8zls37wc9/48R/oS40WwthrVIwBJQMNpL927Vq/xMh6g2W1jY2NiIjY3NyMc+fO9WNubW2tPwylY5xfwQb5ow6M0a2trXjggQf68sfj69tJ7rrrrjhz5kwsLS31p+p5HyIerQR5oUx8IyLJkTgXLea2Qr74xttD1LlDtpAVAz7wt7y83G9vQN6lpaW44447Yn19vT/YAxA1Go36V9ThtYiXLl3qxxDK5SVptkNsI3iSzRMs3GegUQJikA8TVjlw6CkDfLpaosAmA30OVLlghYI550tBCmgViDj7r3bIASOmDAxqeq6LdQb1YasD95O2UeXp6lHAx21lEMSyZL/LE+wM+JUoA8XKp9phnVhlsoTd4oks2z2VG8YZdOnDCviUYR1EGfJVht0sQfPwRw8nsMAVzeODKA8bNgDGruv62S8eRRJxGK0B0MOSB2bMXA4b1q7rJg5vMF9s0GCo2Vgj8oKZPB7fsLm52QM5PEICESi0ix9BgjJBHN3ggxyIIiAiBzABHsfj8cTJQ7QPdatDZ6MN48jLgmg39xn6gEEZ2uKUneUJwA69gOzxYcMHMMETBzUGrFcc9WMwwqcj9QQgA1BcQxvUsOEe+ONn3PEz99j5MVB24wUzRwbAvFzNJzYxLlhPOGLEwBv5GeBqn0B/+bCR6gSucUQSERw+8cnPeGPHBjly26FPMHZuZYEj/hGHh5XYQCKSz/vWcJADOgOgBeCM+nAymevBhAB9wHs1edsCHm0CuSiY02g0gGzE9SXXRx99tF8ZABDjiArLsOu6/iHs0BXWW9ZJHjf83EWeCOpjpXjyww+NX11djYWFhX5lYHb28H3iKysrE3saeXIDXdUDR+hzHdOsG84Rg5z/AGXBCQUqnJ7r0zHhiH2T+s0M9HHfaLsUfA0R86qfjBcdd0ouwKN8KRhX0mu14HlIJnzNgU3tu6yMWnDI5WXlMxbh62y/+VPSvWlp6lO6rMisjHCACrwijg4EJwgNkfIyKAY1HAqMJu/x0lkMrvGjAeC4sBwaEXHmzJnemOC0GV4XhZn+zMxMb1RRDniDoYVDQWSEHT0MFIwZAOe1a9cmjN/MzExsbGzEzMxM3HffffGBD3ygf8L93t5erK+vx+nTp2N/fz8eeOCB/s0dt99+e88Dlo0Q1cTm9a7r+hes47EfiFDgG8uu/DooHIJwA5KfqI9rDGzx7los98H5ZQAaYNc9c491Act4W1tbEzrHYAkOAb/hUOGIQCiPtw3wY2tOnjzZO3IswwMgILqj+oqy0A6e7bHew/FfunQpzp0714MORJb5dLZGblGXTnwYXPCDkuGIMTOE40b7OYLIkV6clMVkAye1cTIW0c8TJ070MkV/oUzeUwqwiT11XXf9FCmiqxhnDBiZ+D+MKkCaHu6AbvIhJcgGes97/TD54a0gp06d6g+7IDIFHhYWFvoDM+B1d3c3zp492+8NRJSZTzxjooK2zs3N9XYAH548cr9cuHAhrl27FsvLy/Hggw/2b7TB+6kRKeQIMPTu4sWL8dhjj8VoNIr19fV+xQJ9yZMX8HrhwoXepl25ciUODg76ccAPiGfghUcYraysxPr6euzv7/fPFR2NRr3NXF1djdOnTx8B8ZgsYtkdB05gP7HNhsczPjwp0sgTA38F1OBd9zIiOs1jju8zQd4IFIDYLnLgQp9wgLT4cKSHo2duWTb7MEEWaJeOLdcu9m2cBjaX93+6KBxvedBVFZ1oc4SLJy0okydxKFsBZgb2FIBCjqjXRX7BA9/LwKeTYwYmuT7GVexDhkC21lFLNxThy5TfCZ2dks5c3KyA0+I3b0B2Mwr9RBxGAQBCIFiAx2xjdsShMiMdLyOhbgxe3MMA1fvIw1EX3jeHfFiKu3r1av/6KrzDFM+TYzAFhwMQGRH9icyu6/ryYMQBIPjhvjjBCIMOZ4hncDHIhzOEDDWywPuA+MPRPeaXHShkjjbyTA79AuOCclm3uB7O53SFjYfOxuFYADzQPxzhY8fEbeIoIvcvjDUbGI6CApi55xRmj62BTLh93A8MRJRfXmLBshrrJEeVNKKNtqmRwvK/EsYGnDFH5tDPADcAETyJxOQDctTxD5DPjxpSp4kJCssEEzwGt6qf/GgRPqiDssEv9Azl4eQqQD9PBNFm6Aovg+lJXAYuAMkAQTiRDbkjYggwoTqDSRIAPtrv9pVCDwC2+BpkxJE9ljPbVUySV1ZW4urVq0f0RPVFxwVWO/iBy7x8rxE7Bnu4zsu0mW/SSBHbeY2uOUevZfAkj9PwGK2J4KgN02sKjIaI2+XKc76cbam2JWsryz8DoK4OhwWcvy+R9qFrP9fjVihr6kF5Tjbud3Ytwy419GEDfDoL4WvaQA2BszEoATWevXAn8+yAlxzH43EfeWBHgggEG/nl5eVYW1uL+fn5WFtb69/Jyht/tS2YqeFdsVi+gVGHQcf+p/H48CXqCwsLceutt07MgGG0Dw4O4tKlS7GxsRHz8/OxtbUVp0+fjpWVlbhw4ULMzMzE/fffHxcvXuwfkQGHdvbs2ei669G6q1ev9m9VmJmZmTiIAgPJb25AVI+XKbk/ARL5FUpwQGzMscmZoxHqJBA90Y3O7PQhd76G/WJ4gwiiH3xyeTweT7QLB1p4Bs/p1dCy3vJz7fTNGIuLi3H69Ok4efJkrK2t9ZEifuUZIjgweiwzXo7Dh0++QjaPPfZYPProo7G7uxuXLl3qQQKW2NVhsbyZ0DcAKHjU0MbGRg8GsDUAAAZ9f/Xq1fjQhz7Up798+fLE+OJX7wGkK3hBtA/gbHFxsdc33k+HfIhmYmKCvoM+YJzxQQotYzQaTeRFXyMKBVAGEATQzds2NALI9giAkiPOsCl4XiXGA9KfPn16ov93d3fj/vvv79vEbxLgySi3gaNP7Dyh7/hGlBXAHVFIfeh5RMT58+fj0UcfjdFoFOfPn4/FxcV40pOe1LcBEUfoEvQaMoO+YF8bonZYBUE92BMHvQAAPnnyZK/36EfYRuxphnwwLk+cOBEnT56M5eXlHjjjpDXGISYrAJg8gcOKBsY2CPumR6PDiLNzxOBX/Zrzh/jWKBynV+ADPVN/mTl+BlgaJdLfLAe2gVqO8sffyocD6g4Mo00KTjPiehz4UQyg+TJ54zcHYbRsrVf54ui3AmUHnJl0QqKEa7rdSdvgJgY8samlGzq0wUxx4zmqERETezpceJRn4ryRmx0m6uL9b2zkEB1jQIPyZmdnY319PZ72tKf1xmpu7vq7Uy9cuHDEcYIvODF+i4WCNhj0y5cvT8yoFxYW+hNsACI7OzvxwQ9+sD+Be/HixZiZmYnLly/3IPSRRx6JmZnrJ9jwbD8sXc7NzcWtt946sQmaI08AWhcvXoyHHnqoPzkHB4Hy4MQXFhb6jdh4TRv6jaM6+Iaxx2MYkI6jIwCWOCkMA44+5LL5jRx4FAV0YXV1NW699dZYXFyMs2fPxokTJ3qnu7u7Gw888EDvKAFkcQIaMoFO8n4fBivclzwRwUGfEydOxNmzZ/sDMisrK/0SLCJy2FMF4ggoAwbegM97JQ8ODuLRRx+NRx55ZOINFwDu0HHdf8iAj40eopIA/xyF4m0KcOYYZ5cvX44PfvCDcfXq1f7xJXjOGt46gv2cXD9HzSFfLE/ysg7GJnRA93UikswTOtYbd4+3VkQcLkWORqO45ZZb4uTJkxPLpc4ZzszM9M9zRDq1UwDmAKd4JuXe3l4PagG8cIIeIAh2BuAWAJWdEC956USI7WjXdROHIwDaEaEFCMXkD/WhrY8++mh88IMf7NswNzcXV69ejdXV1f65ipisMOBj/YI+AejhEBp0TIELwOjKyko/ZvCqRKyg8BjHVgnIFGMbW1nwisutra04d+7cxL5d2HaeZEPHeOIFOwa5YTxqWyNi4okHrEMKoHQVRAEIAw4HFrlMnmwoOaCGPPqbQQaPIwU2GqBx1xyoxX8Gc8w/180ThwxYcX6WI2MMxQTghaPlWDVE3RhjLFeWU8YHyxvgH76kFFnLAKQDbPztIo36X8FgiQ9HN/RYFiVuqCqBK0sRbTar4YGC34gi8DIb34cS8GwUBzM4D2aOXA8vVfGgARiFM4PR39/fn4goRUQPFOFEUCdO83ZdN/F+TAYvo9Fo4mQk+MCpYp7lb25u9sARys6P9tD9hnCAcOKQCTt07hc2LixXDADMsgFoAN4UmOObDQbKRkSAyz558mSsrq72r6BbWlrqAcvu7m6sr6/3hgSRPoBQ5pONL+uIzrgwqCGn1dXV/nVhHDUDqGAZszNhsIkyAdohL/QtZMLgh2UD+aANOuB1TKhxgaHjgxpY9tRJFC+jQcd5f6NGTdx4gZPlh4pHHM7OUTfaCYfPesKgDtcAfiMOo6u8pM/LvZjoOSfG/ayOCWCHt2BETL4qkflD+/jNOWgHHuWCca/vf8UHkyGWNcYZ+h19AeAPYInyeFnVLbOizZATABhHu8EHImsMTgDIACi77vB5juhfjZ5wvZAvbzOBrLi9kBXLF5FXRGZ57zbGPcYa96VOBHgrEHSP+4LHDOt45r+YuF523LyCwjLisjOwAV6cneLJiC6b4jeXk/127eA62J64bwdk1CZpO7iMDPxqW7X9mnYIAJXAmcq4pr9d/SVg7K5lbRqqz/2elqoBXzYwQDqjUaag8DBUbFTZIOl+HxYqysSyLIOEiOgdKgDN4uJi3HrrrXHy5Mk4e/Zs3HbbbROv3hqPx3HmzJmer4ODg9jY2Oj3yBwcHPSzadSPyA8M38mTJ/tnW/HJv7W1tX45EMsHBwcHcfLkydjbu/6O0nPnzk1EF/f39+NDH/rQhCGHMRyPx3HvvffGww8/3F8bjUZx7ty5uOeee2JmZqZ/YwCWdLvu+rOyVldXe8M9O3v9YAcOY+jT81E2HCCWeABIMOPd37/+Ht4LFy70ewphqNHfvBeNTwfCmGOJHdEBXiY7e/ZsnD17tn+rA+/72d/fjzvvvDM2Nzfj/Pnz8YEPfCA2Nzfj0Ucf7Z+bd+rUqYlHSMDB8qlA6B4iEdicf8cdd8Stt94a6+vr8aQnPSmWl5f7iO729nZ86EMfmoheInIHcMCb5WdmZibSsc4DYCOqBmfIhxaYRwaTvDcS2wgASnV5HY4ReQGQMJaRHns7FxYW4tSpUzE3N9c/f5IBGMpGvyDqqydbUX7Xdf1EB30AHcXyIy9/YgKGPkKUB/nZFvHEDjJQQMf7cHWLAuqdnZ2NtbW1PkKE97nyXlfImU+u7u3t9WMI9SF6hijSeDyOxx57rH9sCwAhliiXl5d7Xec9gdrOpaWlOHv2bD8G8MioU6dO9dFMROoWFxcnJpQR12003pGLScbc3Fz/PELoKp/OP3nyZA8SYcMhI37ftu7b4xWdruv6x00husfjBzYdWzew/QATarYlq6ur/aOC1tbW+v3H2NN8/vz52N7e7n1ARPS+ALYLsucIKGwsR81B0AUGbdAv6JYu/er+yQz4sN0FMSjlyQv8Ii//cdnMlyuX0yqgU8CjEyPk1XJ1MqTpFIgxnw748ISBwTvzpnvpWWacln2RA83aJywDnow4UInrXG4WleX7qkdcHvthvce8uLpraeoIn1Nah/gzRctQNRtMpzhcDowfGxUYb3Q8DMfy8nK/ZIGlBybeNwSQ6Tbn84wXJxJ51ru6utob2Ijre09wWg0zYlyHMUO9/DBjPPIEeeHox+NxXLp0Ka5duxbz8/Nx+vTpWFpa6peLRqPry1irq6sTYW0YUixL42QhHsYLmXK4GgdaAIJGo9HECUMYNuwv5P2B7FQhNzgYAFk4BkRB8To5Xi4+c+ZM3HLLLX1kAH0CB3rixIl+P9qVK1fiypUr/ZI15M1OiaORrDMMpLBUt76+HmfPno21tbV+eRcOH8udaC+imdgnhAggGwUAGSwboT60FbKNiN6ZKihFWQCxeiAGUTw2wCgDRhPpNMLH6bDkDz4AWjjKxuUgqoSJEPOLOsbjcd8m7KWCbvJBA54Qsn2B7BjwcVQLVJpVY+xiHPIkEf3BkW8s88LB4HQ92wroEHQSbccDhgHgxuPxxFI9TwzAH55riPrwm+0R2xUADIxTfDCp4z5mR4SDGnhAOcZ2xPVHNAHsAcjwUi2Xh36HbvOkjaOQsAG81xoTCAa0uhUC40eXutH3sLVYyp2bm+snfQByHHHGJATRdegSR88xHvgb5TDYYmKfhXKHooPs+xRY8LjnazyWSo6+NAa4XUNp0Q8KeFzdGsxxvl/rdnJ299iOKt7gdCozbpe2AXkZWDsZZDw5KoFjLcvJRvlmXjW/S1tL1YCPwZUTFHeCok8FeG5waQQRwI4byY8BiDh65BsPT8aG/6Wlpbjzzjv7PVhnz57tIwGqmPzMuLNnz8bMzPWTsphN4hEXvPy4vr4e4/F4YskRRv3EiROxuro68biKiMOBhKVKOFjsB0J7AD6w5wUbnRks4LO+vt6Dgfn5+VhZWenbyoYYm+55b4SCIJSv+8YA6q5evRoPP/xwv4fm8uXLExEuNtg8++JlFrQRDgf8wbnNzc31ESA4RT20gTd2AGzPzs7GnXfe2ctgeXl5YqmPT3wyP7gfcXg6k08vY7keQBfL6viP/V2IMLBe855B3o+Ge9jHxJEGfhSQW5rjCQDaBVDLdUOmEdE/s5G3JCCqxodI4Kihu9Clubm5vq8wyUE6bAnQSLFO5nR8Q5ehi8wvxj4v16GfkJev6zYCds6IwLFucQQD/zlKiXETEf1hArQdUSI8GoQnkNB55gGRbUx4IH9cAwDmR43wXkm2UYguAjzivbjYnwmQCp4wqQSdOnUqTp8+3Y8fRPP49DJHqnWZm/sDfPEjr/ixSvPz8z2vDND5kBMOWOBh5qdOnZrwFZio8VIuroFfgFM8BgZjl/sEeUajw0f0zM/PTxyeYVJwq5FWJgWiLvjB3yAHDDLwo76q5OxrwQADniw9T4qV1yFgOVQ3+HRRLQVn7n4JeGkZypuCbQeiSyA1A2GcjvPxBABp2TZmpO10YLYE/h1NBfgiDl/+ro3S/ScAEPyN2aubtYxGownHzGWygeDIFBwpDAw2mAPcLS8v90+4P3HiRKyvr084SzYucN6zs7P9i9UfeuihOHfuXHRd1y/HINqEU20AeljuwcNNGWCBRxhnlhXajLww3ojKXbx4Mf78z/88rl69OrHkc/LkyR5oAlTCETz1qU+Nv/bX/lr/TC6eOXbd4YOX9/f3++UcOCIYeZxshLy2trb6ZegPfOADcfXq1T7SxWXz3io2gLpEwEacowVwwvzcN+gfb8Dm58Wtr6/H2tparK2txV133TVRNpw8oo+8yVuXCJEWS24REQ8++OBEFIwPIOBF9wB8AHBwZHCW6GMYWW4nT5IYyEO/OPKNscDjbG9vr9+Hurm5GVevXp3Yt3jt2rW4fPlynx58YKsBb2hHVAbPR0PUE6AUYw60urraL8u7vbQgjoJEHJ5Igy4gCs3X4MTRdxzNBPiEPLi/YGMApuCIOfIEfgA0oWc4iQ1wE3H4do3V1dV+gnb58uV+GwcIcoReYLKgk5qIw715GEMYu9xu5GGwixUAPIgdPOzs7MSlS5f6ZV0AcV6JiIg4ffp03HLLLRFx+GpJjGsG/xgLMzMz/eoIL1HzeNenHAAEYxxhywcip2gLZI/VBry9hSeMGJt4DSOAW9d1/d5o6ATAICL/V65c6Z9ysLGxEZubm3Hy5Mm45ZZbYnZ2tl9VwbjBuAC5aJZGljD++PCMOn7Wdeec2QfqRIfvs3wdqGGAqmW7cjQqxuXAL6ntVj74N4975icDvGzzFA84+XAe5tmVnYFEbSN0WWWB9juco/UPAXzGPpyHJ/HMV4n/IaBZQ1M9eDlD3JrGMVrqTNcZDhE7wcKxwmlw5At71RAtY2OCb5QFgDMej+PUqVMxGo36wxDgn2dzPDuCkcN1lM8DBQOIOxYOQGeRSK/LZmgj2oWlULxTF0sdvA+J5cXGB7yyI8UsGNEoVn6ACZz6xaERloMah2wgcEQAYJtBH9oKZ8wRXV4O0yUWXq4D6NRN7BwxY944Ysb7b3QJB45FQYjOWvmjM1b0tTNyCk6UX3WuiFogagddctEGgCXwDODB4w71K8hko8gglOsoGfn/j7c3a44sSc6zPbGvtXX39HA4JCVS0o3+/x+Qma50owvZJ5mJJg41w5neqoDCVgAKyPwuyp6DJ9/yOEg0JYUZLBN5zonFw5fXPTzieMxdYe69DAfgY+kNWe/mLunvKK7P2svlNgAiACfnzJFnHzvT0Y46/clcUMwf1gc+jgSH0fXaKc1NPhzhs7u7O+0ap5+eN2R8Lqpgus4ZIPQn47OBNFDz4eQJMBzlgO9SXuwQAjpJa2A+GFdVrW304D7vpM6xjAx3R4c5OiWtDJo6mziqc2TY8//RPc/Vn/3u7u3GPPp/U8AxosOvKR3QzXlMkDZq29gi+5N6bNP+Pkf7rg+bPkP9v5Z2/6pduh1idXjbhroDbCOl4gHZ4yGyYwXBWzM4M423T3CkB3lt5FllW9TFBouHh4f6/e9/X3d3d/Vf/st/mY5eWSwWa++F3dvbq7u7u7VduIzRB7FiZFm2Q+F6jLxJ4erqqn755Zc1wMGS8enpab17926KXHLky/Hxcb1+/bqWy2V9+PChrq+v6/DwsM7Pz+vy8nKNjhiRjx8/1sXFxVoOXkY8AT9EAs7OzqYXoL9//37tjC+SxTNvByDkTRss6WAMDHA8J69evZqOQvn7v//7evv27dq8EUl1bh79cVTH3pr/cnMDS7U2/kT84BGiTSyHOXpDdIX7EvQ5Z2y1Wk3RVYw84BnngaV6DFoXLa+q6V5+I5/T109PT6f54H2q5IoaEHspHyPO+ZIc8bNYLOr4+HiK1tDnLm/J8u3l2NQpptfr16+njQcYb+e4sunAIIJrybs5n9CeSHnV+maB3DRmcMYytgH4arVa203P38HBwdQHH3MCuPTyKc9/+PChtre36+zsbHJOiTZCN45j4oxE6IeMkmO6u7s75cBCo6ovbwF6//79V44nMufXS8JDOI/uh/maVQTaxjmE187Ozurh4WEt6uzoIbrLsmjdZ+d4tXra/IHuJfUD3mTjGpvVHh8f67vvvqvPnz/X1dXV9BYl80w65i7p3NupGIEIb+TweGwjs9jJcmpDZx87R7oDMB6D73W/06no6uj6y30GjYzPebjQ1nPZOcLI7QjIprOZ9PGq2XNgNR1w8ya/w5MdbtmkzAHyrGuEgUbPp5P5kvLic/hGwC8nIQWp6/CcB8SnDTeK2J7g8fHxBO7YmQng29vbmxQE+WdEEay4qef777+f+vf4+Fg//PBDvX79egI3ABfytQAzGF36ac8axe8cqwQB5OOwIQAQQN3v3r2r/f39+v7776fl5t///vfTMhO5hAcHB9Ny0tXV1RpDAySqvrwwnpP/+aRveOb0iY0QFxcXa69jWy6f3ooA4GN5m3Gz3EHOkyNzi8ViWiY0KPCSI6Dv7du3axsx6COGjrpRtPCVDbMBkwGf+0qyvXOWWNJ0SgP0gXd2dnam5UArDxsAR5ABvNRhQGdwAU/BDwbRNjxEMj5+/DiBUsr29vYEQMg7PDg4mM6rcw6awSrOCcvX5HCSqmB5z8hN0p0xeh6ywEevXr2qd+/eTVG+qidwT5TGEWkv70MfllX9rmXoAIi0AUmgS71cZ068JGs9kblQ8IpXFExj+MAbdJBXA1QinPAry5OcWcg8oStubm6mszkzZxY68upGnmWzjTff5LxY/+JMQRNvnvPRTDjHpDxAH3iOaG06MhkIsHHb3v5ywgCOY9XTyQh8IoOk/zw+Pk6vd/vhhx/q559/no5TIhDhec+oi50UR7azf5QEbQZFIwOddq4DhptEgRIUJiADeGXJ/o+K5+K5ABD3dWA6dbPrzrH6e9emaTtHoxGAMk7peI9nsu4RHRLoPQfKujbm5nnU3qZlY8DnjRodgjY67zqSg0HQRsWRh2xra2trSqZmuRbFhXfnowO8KWC1Wk15ViiGnZ2dKWrx+Pg4Rb98aDFG0yfce4mAvJsEETwD/VBMKHYiVBh1AyJADnWxeYQlS4zd5eXlmjIGYCyXX3LvLi8v15TQhw8f6sOHD3V/fz/luXhOvbu26sn7x9hSt5ffvPRqIGMv2m1Y8TiygpF3/tZf/vKXurq6mpazoSO05dVPGX3BkAIIoSmbS1KAobsVUnq729vb0zEt5lVSB6AHgAn6OR+L/1mK400qGH/m1u3bcGe/HO0aKUXLHnTjO31xcSTSO0uZX5ZanYRvb970slEHIBlUIQuOqNIH0wsa8Dy8hYwgs4wZnrCDyXMABowsO9O95DlSpuTZWU6I5NPfnCcXgA6ABMfRPIyDRp+QN2S7c6zNK17RgM6fPn366q0g6ELyNDnJgE1P5rnkI4/RfI8uBAQ699bgDkfCDq7pjs7KtAr6zZFBtO8NcOjKx8fHKc/YRzzhhHpcPj6Lw5ZHesDgJwMbHiP8xe/wXAKQ1FtZRnTP69Zr7nOCP66PwF72z59Ji67kmJmzrH9ufF0xALe+83zkWNxe6lB+y/5WjfMhuWZ737XZ9T3tS0fnTo9vCiCfKxsDPp+NlQM04OsGk8Uem4XDz3cJwlzf3d2dXv7NEhAvYPdvJAWz/IrScTSQ5HTedMEyxPn5+XSu28PDw9QGfYF5MYTHx8e1WCymHDAUTb41waATgHlzc7MGLklydj4KGwMuLi5qtXp6D+nV1dW0dEt+C0t1Dw8P01scoPtq9WX5iJ2mP//889pxMnj9zpfZ2vqyIxllCC2pk80GOzs7U7QF44whQ/H5HDwDWy+NGwA8PDzUf/tv/63u7+/r9evX9e2330737+3tTdFKRxH8iZG3wvHuSfpJf6rWARq87KiGI1xVXwAAS0ksEW5tbU184MJyIjz1+fPn+vnnn9cUF9G/XPrwHLlvCTys1Bw54Vn4xseu8Aw0oj76ghFl+Q6acESIN9hwjTmEZ7yT1lFIeJe+IPfmHc6+xPEyaAU04ag5z9NKG6CHIwDYuL29ncA2xwPhEFpnQUt0Dc4P9PQ5bAa8Lo5cbm9vT2kkLIPacANmvLEIOvisTcsNwJgIOAbSOheaG1wi42xu+9u//dtp+TwjNNDfES+c78fHx0lunYNJ2gpjxRklagwYdaTVAIw5oE2cv8VisXa2IRvXvIpBf4+Ojuq7776r+/svrzAk+ohDiNMGAPVmDHgafrODZMBtfkSX2i5YfvluOTWduQ5PQXP/Dl1GQMVzbd60Te2K+TBXLEZgb87eO0KOfLrf/uzAjWljfnBfzdPds5bhrN90SgyTEXzkyHNT9fXGVX93H9CNI7p5PCPd7/teUv7Vb9rovIzs2Oi57np6qlae6f161yogwLs7DR5cYGKMi9+kcH9/P53phmeY0QkElyVXJ0N76Qrj6/wiAxNHOtJQ0z8bNpYTOYYDZQag8YGnKJhPnz5Nh0ejNHiJOnl07BCE5s43svfuXacoEEdEDGi7SE7HJzZECALKwVECDAM7Ur3MxVKz57fzoHIZP/ksfzd/QrtM8Od5ADJnofE8/aIf0JE2HWEx8HREJOUjFbXnlr4mrV0P85PKwxEY/nIu4S3/wa82fFmH5WjkLWdE30YpDZ2PWzEtfW/yHPINiKJt51eSMmKaZCTEChvHwJEcns3nKI4q4hg4ipoyYRlDNkarI9AQPZUOs6PY0JNn0B17e3vTodjomU3m1vVZZywWi4nu5h+icLyjnL5mhDQjmPz5YHX06dbW1trqB46eZbSq1hwUjy9BsMFD2isb5bzmaG1e7wCf6/R9/Oa/EV91dWQZPZvAZHQ9+/8c4OgAXPYhbUF+Zn3Jb1nPHO4Y0RA9kmPu9O9o7l5STL9Nx/CvaY+yMeBDwYwM4xwTPVcQdBvLqpqUE4rvzZs3dXJyMuXtHR4e1vfff1/ff//9tEGDyBhHWhj0eXnHmyk+f/48LRv+/PPP9Z//83+uH3/8sS4vLycvl00VnHXlhGyWxRwVyiUrxmlm7RQ8yzzkxAFoiOidn5/X0dFR3d7e1qtXr9Zo6KURjhpgs4Xnw+BzsVisAWbn45FLVvX1+0oBohh8gxXGhUfvPjrpGwPtIyt4nsjizs5OvX37djprkMjF2dnZGqBOUGkeZNwYWp5bLJ6Wn2xI4beqp52NfiMA0VlHQKFdAmYDqNVqtXauGTQB8HGGGLlqRDMZRy7XsNwFzYjC8T2Bn5OpbbxtgB0BIBpDveTEwftE+ryk7/FzePjj42NdXV2tOTer1WpaGvXz6JJumcTg03Jk8AVw3tvbW4vCrFarOj8/r4uLi+ndrUTqXKgLgOLNSdTjFBEijoyBiL03UVipwyd2OHFckcnMd2Q+qmo6Vsa5fQBgA3B4jjxU89FqtfrqXE/48fPnz3V2dlafPn2q7e3t+u6776YcYnJpeX8u4Ap9h2NL8ThYObm5uakff/yx7u/v6+3bt/Xu3bt69+5d/e3f/m29efNmAmXoHh+Lk3LEcTGAUuT08fGxjo+P6/T0dMpBhV+INO/u7tbr16/XXlf58PDlyJ2qmqJ95jfLVOcYZiTIuqRzyBI4pQ3tAPVINsznCWg64Fj19XvSbcsNvkcgx/d6XJ2T7/F7LPk99VO224G9ziHsxjdyfrv6fb/beSnAHJVNQHnnSJgunfP8XNkY8I3CxDnpSRwrKz9L8SSwjFH1dECuw/uvX7+ud+/e1dHR0fSC8u+//77+5m/+pg4ODibFZA/YoXADPhQVRutf/uVfpr//9J/+U/3pT3+q77//vn73u99NUTE2ffgF9bQB4HOUzXSyAGXkhHtZFqmqyViiaNh0wjLS3d3dtJuR5dvz8/O6vr6uT58+TbvjoCNGYrFYTKAZpuLQ2VevXtXOzk69fv167fDfqqcXyEM78rf8hhArVhuvLrKWgM9KEzCAASd/kKTzz58/TxFYDEPHU6Y/v1etb6JwhJhNEgaSLBVBS/KAACgYPkcoHR3wJgN43NGaqlrb/ckGhZOTkylvEZ5K8Iqhxdj68HDAtmXByoH2MnKdUTwcIg6drqrpmBQf3OuND7kr9vHx6UBfj8Hzb9BvmUh94cgn/JsRewMwR8/ZyIGMHR8fTxsPvMzK+A26nKPm3F0K7fusOPLokD/mnaVI571SfBA2tLcRMzAG5Fk2u4iEX3sHYGdclkPGfXNzU3/5y19qtVrV2dlZffPNNxPgwiEF1DsdxHqGlYCqmpZaP378WB8/fqw///nP0zIsgMyvB0xADO86+s37rpEvwBifvGGJswA5Hgbwi+PO/AD8Li8vJ1tDpJM59Rm0aecMxFysD/PPPNGVLso/MvAj4EgbuQrj/lu32LHvQO0IZOZYu1WdDujmdY9jRJsOFPpej6/q601kc0DJY8m+jmjQAUKPg/9zHvO3rHMO8P3a8qJz+Fw6dN6h385DcX05Wc4ZwZizQYMdqbyHlUied0Da+1+tnk5Z98YElLcNoY0bRshAraq+EoZcgsUIO9HYTNaNl3rpC3l7KHaiIHin1MNyBe1iYOiL35lpg8MfkQAMhY1st8TMDk+fAUb9BpYj7w1h9nItDkPSysIOLbe3t9eiLc6zwhCbtum1muc8T47YQXuMIoDQnqmXyxxZzMOHHSkzPcwL/L5YLKb3ddI3gL7zs1xXLpejuAG/foVZOj+Wi1TO0M/Gmj462dwG2AXeoU2ftUh+V+4MTqOUStlLk91SYp4taADOHBgYUicbmra3v5xheHR0tJZrS7FThPOZQI2586YKbxhL+qJz3Df4k/lPMGbapD5hbtKAIGtpAJ2XxjXrC9OenLuff/65Li8v6/j4uK6urtaiuelkAUjRqb/88kudnZ1NpxDY2fWY0CXwjPMxoVvSgT/0PNFqnFF0KIDQebXMlXf9mheRaejtSHraNL6b3ilHvm5+NL87wmN68r0rBhhzQML1u88U64I5sOc6Rp8vLV07ptcc0DFYTH5MmnRYxvfltTmQOyrJB64nMdFcHc+Vl4K/X7Wka8OTRHYHmIQO+VuovFRF7ggJ8K9evarvv/++Dg4O6m//9m+n799+++20JHNycrLm8TrqdXFxMW2GIMLmN3ageHknLYqfTR0YVS9vEq04OTn5QsSdpzdDZM4gxtnL0xQUCYnfeJ8pWN9++23d39/XxcVFffz4sVarL8tTNvJWUBxHs1qtJqBGe1W1pnD5vru7O/WRZG8DP073d0QIL931GHQlOMDQeNm+qqZjHPwMifQWdkeTPO+059xNdsxayK28DY6rau1AV781A8DHUjJRYQwDcwZgdN3+Tj9yGZzCmy+IOLAMSju5hJceNH15+/btZGQd6apafw2Uz2gErHijgvP24Au/RB4jzFmKjlJ6eTB3rr5+/Xo6X5B54Xkv6xv851KQvy+XyzV+hmcw7Abllq3Ly8vp1Yk//vhj7e/v188//zxFsJgjosunp6f13XffTXzi9A0+cRJPT0/r6OhorR/MHUuUgBPrpHSIWCIG7KeDBf3SeaVwLY3C1tbWpLsM8pBxb/5gxWCxWNQf//jHqqq1lBmnMqDrfeIAY0QPMw4DdTuMAHHLB7S1LqAdQBwg7/3795Oeok5WfwgYAKZxJpHJ//7f//ukizlihugoOoA0h/fv33/l4Lq4jzi1BvfINJFl82nnKHdR5QTZlpkEOAk+0inteCQ3a2RJ296BmBFQm7uvwxDwox0g5s3P2oExsB/RA1k0Vun6+hyY7IpBX+ozO2zuYz7f6TyPdS5C3JV/VYRvdG10X/7WEREhshIlZ4TX/PB2CXL+vAwDOON/NiaYwCg0LwGgMFiCRMEwOQYWABuPI40dxZOcXp0nzvk8gBLGQp4YeTVefrOnbsDpw5+zeHnCkSIbF4yegaHPzYO+0Jw6DN674ugCddkIzPEJS29OkHckD8Dn/B6DTZ5l/G7PgJ5jSniesXpDTNXXOZiMfaT4DKLzPoNZAyaMheXDc0DdBv8AOIORVM4Zmct+Zv+sVB21YNkYgOy22BjE3Diy7HnNvMl0EEcKkuvdG1+gh5WuI2FOT8Dh8JIstL+9vZ0iv+z4Zw5NW4NJ+Bs9k/2i77TDTlT65v52Y07dayPXXct6+M0Au+rpLTIJRKAzOmN/f396hR+A2O343E1AmR1PCX85UQABAABJREFU+uWoFvTI1YflcrnGV05FMKjjO+/vtSFP3cgcOaL88PAwHfTunECepWBT5kBMLi9mxNq8sGmEbzTHLnN2OO2V60lQkcDrOcDW6by5Z5/r+2icppn7OYcxOvDbtZnPdXhn07FkSSA5B+K60tH6uWdGZWPA1zGsv6cxyI452lL19YnZvi8TmgF7r169mt6mcXx8PEWlPnz4MOXLfP78uU5PT6cT3B3p4L2fb9++rdevX09RPQ5Txputeor8OHTvpSKiSK9fv15Tejc3N9PSZ76jkfFZ4SbYsULDaAFqDw8Pv8rbMXjM5G8Urc8mq3panjX48EvZAZIoTXJcfOzEavWUxGzAkxFOeALl6bwcb2pgqZR+JUjm08vcgAhv8MDIOr+HemxovJRN+yhqnAxfT5BaVdPhsrzL2e2kse3ArA9T9v2LxWKKJJCr2eW1ZRsHBwd1eno6HVfTORjwXAK7/B85zmVq5gZwAM92OXzk/nFUDXlX5J4yN5ydCQ/kKoDTPKCdDT4RV0pG9rxjP3PLiIDyO9EhaMTqABFe5zul8XBOokGJaeTlwYyMZmSC/hKN40B1DsF2mkrOn3emUjdz475WPQE+66aUN2hqhwHd4LmCZ8jF9jEyRBPJhea51erLkTrv37+v4+PjiT+sZwzoacOvElwul9OGEGgEMMPRIwJO7i0RaxzXi4uLyRYsFospf/fq6mpNhnd2diaZdCpL5jAnCLJzSjABENzNoZ91NK5zKBO85bP8n89bz24KMK0/zeMeR+KDrs+ezxHo9G/wgOls3ZZ/+XwHQlerr8+I7cbMuDOg0z3Xtd+Bs66Po//ngOhLyq86eLkbjAFfhoqdZ0YdVhAWZufgAPgwEq9fv552ih0fH9fW1ladnZ1Nhwd/+PChbm9v67e//e2kcFBKx8fH9c0330xvrnj9+nVVfTHavCf28vJyAkf015Eg555tb29PGx2ILC0WX5YDz8/PJ8BnoMP4ULiACIeeDQZ5lqR9lAyAzLl0KDbOkQIQeqcgws5mC2heVWtvUuAMOMpyuVxbFs+oHv2ueopUuW4bBeeZJRBxXpwV0YgniLhgIOBDlnwBELlb0313LhvznDmhAN6UBQ5N9gYBwI0BWTo/pgttO0IMyLm6upo26GCUHa3JKAGbEfb29iYjxfzxrMGcAa0Vr/vrJcg8ixOHyvyMMWU+7+/vp40ROG2np6e1WCymJTKWw/ktUwMA09AM3cL8+GghCs4IcmHZ8rOAzaqaovymsw3y2dnZGkCwTBkMkPCfbw1xOof53vOeAHGxWExngOLgAUQ4cN2OjAE8IJe6vYLgpV7mNQHfYrGY6IPcV9XauXd2qKjj7du301Ivsg6fsSnJB18zFhx3ALPlxGPD0DMWfru+vq4ffvihLi4u6vr6esrPZKMfO405axB9SuoBKT3n5+dTqgLL/tYLLPHjIGQ+7Ki/th/oJpZ8mSvkK4E8NLa+9/W5qI9XpDpwZp5LANUBNUdu3Z5tRuqp1DtunzIHcIwp0iHqAFZXd4JBy8oIeGZJXdn1vwOWo35lX1zPCMx1z2xaXvxqNRuxRPFdyQ6NvGOUH4qRiAC7VVEgVU8exmKxmKIIeQzFKKwOeNna2qqLi4u6uLioy8vLCezh4VfVGhCwZ+mlQy9FOXKAYKKkXUdGO60E3LajZ1YWKbRemiFK57wmtwNt7PX7k/5nZMoG2BsLNvU2LfgoIIr76PZoBzoDKFBM7pO90lQ83unneeV6F5U2PUYeowECS4JVT7li6a2nEcgx0x/nY2GQyB/0GBNQY0jJ4cuIgxWFwZ/H6chbjtftzjl9dgi6sZr2NnKLxWKiKQbR82MAjg7whg3Pp4Gs5Yv20vi6+D6PL8eac7pcLteec44jdbpuH5XC+OiLQRq8b9oQeSJnGTCAbslNTeZneAYaG8DaocV5Y9WAqL+jqcypvzv66k0dea/5gYgcm81MR4BRAmvrreRHQMnW1tbkePNOaZ4nR5I+s2KztbW1dsQXMtGlnUCrLhCS/OK5Tt3Q6QjfnzLq37KtTX577h7LfwfQun50n3NtP0evETDsfttkjMzRCCR1WKW7dxOQ1dmLl94719eX9MXlVx/LQhl5Gi4wcXqP9sC3tramcP7e3pcXvrM5g/fH4uWypLlaPZ2t5SUoIgb0B4OJx/fLL7/U4+NjnZ2d1T/+4z/W5eVl/fM//3P9+c9/Xlu6ILdnsVisncDPTuHT09M6OTmZDLMNDcuTjhgwRnbg4mWbbtyP0WTZJ0FZ1VOyPXW8f/9+7XgCK/CcS2jO5gZ+N6D3/Po1b5RuaZi+Vz0BKwM0PEPzFOOHdizz8fYOlsxxABwdzSUFjCG5SCz/ujhSRR8NTjwvjgRhdL1z0Mt8bPjBWEIP98+OR/Z9sXh66whA7+HhYToXDRoYCBB9xJEhreHjx49ry+PU7aU+08NG3jJg2TUAT6Xu/EiW7AzQLLc3NzdrEcGqmo7NuLq6miLN8Cg88vj4dMadecXnPRrUIz+5I9XtGjjzmzclZAoG8wwPGeDgyHCfz4uEtzhhYGdnZy3KZAcMWnI6AW0vl8t68+bNNL9HR0cTL9I+wAiH2UbXS5BEUtGLgGfmkqjiycnJdISJgQmHuVtHcA86DHlxQW8b5PJOb+rzhiuuo+Ph3/39/WmjE3qB7wBUNpSdnZ1Nbf3d3/3dlPry6tWrNT35u9/9rk5OTur+/r7+4R/+oe7u7urHH3+sP//5z3V5eVn/83/+z/rll18mWjFGcj9z1cR61zm1Br/Iv5eG7RDlZg2nzIycRtefjl736SCMgWcCEctK5qbOObFJj3QmfZ/lZxMwg4ybz3P8/g0a+15HVLPP2Y+O5+dKAnr/1o01QWlHg3QaXlJ+VYSv++R7Rwh33kzvQROiJ/eKzRmAKg4CZnJQ6M4tQzEiFBkZcOj+5uamzs7OpgOWz87OpkM3USooUECXo4/kkNGOX+EDE1mJVj0tXxjwJOOhSLxcOucpQT8Apt+qUfWUv2Sh5g8A4Xy9UfGSLcXjc5sJ4FCKRCE67zWXpAAWjqh6J3RGGDuFw0aLBMqOpNB2RkIdabHRdx0GhOQLYeRwaNJT9nP+3XSGp7jHS5/0nQgzZzSS88d3luBpy8uGjG+xeHoTCHxkpWxA7GOK0nFzVAge9rKTQZGjPwatGDwilNRlmjsv1rJmQ2kDOuIxg0AbAAPCw8PDNWANEKuq2t/fn4y7nZzsx3K5XANTy+VyApLkIdMfG1ho6V3nLK2zwQNA6TQINoAsl8u1VzPSJ/LokA0ihn7tJHzATmQveVNXB/hT7h1dTtnzvEArcohZIjXg4wzS5XK5drg2aTu5Qxw+Rd9z1h9zQSSWCCu8RrDh8fGxXr9+XQ8PD1OqwdnZWf3hD3/4St8gF6k7n4t2pc6C9gn40lnv6nDpdKGfTVA0+t1zZ1CX4M323Pp/RIfnAI31SQdQ52yh60hbMKprk9LR7CWlm6Ou/n9tvZuUjQFfRlRGhMNDNAOYSZhMFCT3c84e+R+OolXVZFQz2R6gRVJu5lD5DRJEG3755Zd6//59XV5e1vn5+VdLuRmhSoOMh2jQkQUF6+NG2GTiKIoZOwFqLlXhjaKwUYyOOCVItAJ0XXnOYC7/UgzQUuBsQJInUvHwnCMhVhoGel6+9WaATgGaz+wtGWjbsYC+uZSbNPNS0Wq1mt5765xOG0s2J5gejty4L7nkibF2vqdBiZcfqcdv1bi6upoMGzsU2cHuaAT073gckLhYLKbIdM4r/Uf2vEEDgIDzQLukFrAZCvlxfhcJ84+Pj9PGqa2tra92Q1IPsmFQnHrIgJ7+mFfSUBncdQ6A+YR2LV/WB9AHXQR/sPObeWVzAf2r+vKGB1Yz7u/vJ+Dm9zcDPtFr1snU40iXHWLGBt2pkz5y7fr6eppL+s3yJhvgUkcxB3aGfWRUVU3vDYfu5MZ+/Phxkln6ybvIfQyK31f9m9/8pvb396e3dfA89PKOW2T1/Py8qqo+fPgwyQc8VfX06kPTjp3a//AP/1AnJyd1eXk5Rfr4S35JI+5Iezpe5rF8LnWqdUB3v3maPlmP5TMJjnIMORb4fgRSUu+PbOMIcKXjln2qWrcXcyUDEFmXHdgsI1szGk/Xz65Ot4dNGgVMXHdX568BihsDPkCcPdk0BoA3PKYMn9qQVj0tJbF89/bt2/rd735XBwcH9c0330xn5aGcbIRsqEj0PT09rcPDwyl5fbFYTG/FqHp6Ldif//zn+ud//ue6vb2t8/PzacNCLgt1ORsoPiKOyQSMDWWGAV4ul9NmE4CBQYm9OwM66FRVa8unfqsC9MHb9248ohP2ulGajNWbP3L83glIP1E6tNeVEeDLsxJdnxUaSt2gIpN0U7HBYzZyjM1Aiv4YAFY95UJyH8aN8ezs7ExRCBwQjsph+RkDb9DgZRiAec41ESDAA2OpWl8ah1/YNQpYIpeVXMJ3797V4eHh2qYdwIjnBaCPwWfec15NZ5738uPh4eF0biX8fXl5uQZKqZ/IMvJ9d3dXZ2dna/m4dlDMUwbL2UfzihW2wa0BXz7rZeQOSPJbLhczfiJh3lVKXbwekfl4fHysjx8/rp3tRp4Z5/jd39/X6enp9FYcVhdMv6SR5ScjwLu7uxMAx1FEzkhPAByxIQQe39vbm171d3h4WO/evZt4uQN8gCbOI+T6x48f6/z8fHoWvtje3p5WXq6urmq1+pKiwma6n376qR4fHyc+Ozk5qb/6q7+qw8PD+v3vf7+WK3l0dDSduceSNc7Czz//XHd3d9OmPxtaL+WTOoF+YuXkr//6r+uHH36oxeLLDu7z8/NJH1R9DUpGNpLvHdhIgJVgYAQWU1Z4LvOR03Hn/o6PfJ9BWK7UJVDrAF8HZkd9T3pY57uttDM5/pEdStpnP7yJJlc9EpDNAcusP1dYmBvrphzDqI9zwHtUXnQOX8ckOWmdZ5BRDn6zN7W/vz95pd4UgTGCsBaAJHZGQaqevGGMCQqMIy9QuGmE3U8v67mgtDpa5X3+pL9exnFJRkWZOKpjo+zIDX2EHh3dHKGg7ozQ8VsuS3bz3415VDomhfE9n7l8CoAiAmGlxRjMKwZJXq6iPfNJ8pDn3+0QnfFRHr4PfsjlTNfxnILqlD3gBkeqqibgnG86ybnO/mX7/i1ly/PTzZ+XO01Pb4zgd4M0R7mhmd+M4N9zLkyTbnnQn0lfz2dH+66kLkvD4d8MFPkfPiZatlo9RZ+hnaOVjtyztO2oKDSHt1NmbFAyUu1ovw0OADCBMnWT1mInyvorI+dpAywn6HdkBGcWEOw+XV1d1fX19XQQPG3DW7wB5OLiYu01edvb22s78+kToG97e3t6Y4hXO6C/6ehILoEJoodVtZY/bmBlfWAZs07u9I35ea5kfZsAp5eUUd/m6rSMdTp07pnnAOgm/c3/82+Orh7rc/Tv6OJro37Z/rwUrPlZ//Z/DfB5qSYNQ6f0bbAd9SNKx7EabMw4PDysb7/9tt69e7e2DEtCb3pQNtaOqGAIUB4sL/z444/1hz/8oa6uruoPf/hD/fGPf1yLrHS7GjnmhJwejDwKaGtra+2NDPTLG0e8XOoEXCIqBgFexoNR8LbtgefyNgqHCJM9bwMk389xAJ0nl2DAv7k9A7WM5nI9AThGy0LIMhYRxeVyOeXC2UiiyNNDyrezmEcAGBgR5oZcHcbpMfKsAT194tR+joIhgktu6NbWVn38+HEtb9K5qT5ihXY6BXB7ezvlLflEeXubzClLUh4HeUvwOH2EHyieHyvqdG4AuRhIomks85lWOGuZ1+aodFVNBvv8/Lw+fPiwZqQ9rtQrqXAzRxP6GIDQx3QsDJCJTlInziKOJ/fxm/WZ+SdBKzqB58jd44DiT58+1f7+/sTbrA788Y9/rO3t7To+Pp7eb/373/++3rx581XOMGNy1Af+94oIgI/iCC/9XK1W08oIY7y/v1876geas7HEvzHXzBdAbHf3y+vpfvvb39bt7W19+PBh2lBxfn4+8ZYPOq76ssz9b//tv63FYlHn5+dTJPBf/uVfamtrqz58+FD/+3//72lD3d7e3lo6we7u7pST94c//KF2d3fr48eP9cc//rHevHlT//bf/ts6OjqaTm1Av0IX5IZVJObXb2rZ3d2tN2/eTPxV9cUpI/ruNAR4ovtLGUz9ynf4Le2h66GY3ztQ08l/On1+FlnqopPmRffH+s0ORdqNru/d+Nx385tLB75Gjn7Wbeci7aKfHbWT0bqkhcdPfal7R2B1ZDM2KS/apZsTkcxp8ABxq57OZgO0sLxJnh6HF7OUgWKkHo4aMahEuWROX3pP1LNcLuv9+/f18ePHev/+fZ2fn6/d6+9MFsDAkR08RZYhGIvHTz/9TFWtebpmUtr3kiOF5SCW6/zaIBQyRhFlibJLQbdhc5Qhl0s77yiNGnPuqFPn9Zi53V4CBIBq1dOREVYI/I6ypK9ESAD83tVoJYGnbjp3kTDT3UIIaMBgHxwc1NXV1QQGmA8iyAa1RK5xLDKHMWlV9QU0kMPkXaEWdMbstwIwRn7zOLql2o7nsi/MtTctIRMYZ4Ccl86J3NPHjIBycC7LnES2tre3pzQLxuWlys5IdoCPOfaOXPMBc08bHqv/kFt40ZE5gEG34YW529r68ioznvNmg7u7u0lmcQZwfK6ururx8bFOTk7q9va2jo6OprdBWC4csff4c/5zXi1X0JDxkhuIwwAIWi6fDqtmU4d1HPVtbW1NOnu5XK6lkfAedL9uzYAUWvKedM5QxWHCAcbZurm5mRzs7777btpwQ9vQ/PHxsd6/fz/JzcXFRf32t7+tv/7rv55yJslf5SxV51zC16enp/X58+eJBiyNY7ugq9/6kSC76smpYF6sk8yH1r3O0+a5zuh3utvX/FzHE6Pnuv8TqCJXo75Zd/m5OeDkvibOqPoaoFq/dnZp9HsHfvN6XhvRv3u201PZnvW49ZXrsA14SXnRu3RhlI4YDASviEH496qako/TaDjp28twRtnpJVgZ8zzKit+JdJA7wjtET05OphwS53glYERhMgZHDxmr8/F8CKdz6ezlZ5TGUUDGa8VvUEpxP5zkj6JG2VlBdB5lMsxzisKfeT8lnQMLKUrL3o2NSrcEAnhjXNAevvTyEPT24ckYEp53cQTUyoPiiKvrWq3WN0Z0Cs9GjONbmFcvJZmWXAeoAJzoF38GThjIkZJBdrM95moO8BloOT8OXvYOTgOn5DnmG54m2sqRSeYb6EZ7yfcdH2e//Xt3blw3X/AhfbFTCQBlXpAv8hJTHpN+nisKThwyb5BpkFFVE9j66aefpoi29Qr12phbN+ZGMTbRmB7ZB0CzHVLoD73Qr/TDxc4YNIZOVTVtzjNfJw38uVgspvw9HCKDa0CmN4s4SOA5/PDhQ338+LGWy2X9/ve/X3ttJA4zu56xWdAIvr++vq7/8B/+wyTX6DDoDI/bcI94tONLg3qK5cHgxHWMfnObna4YAaPuGrzuPnR2ys93wHIOrHbXO3nPupO+HRgc0acDYPmcadz1vxuP6+zaHzlkczZ4NI9z5cW7dFEEObko+i6RH4HDA+PcJCJoLOmx/LpYPB05wLKnDZOBU4JG6iRPhDD9X/7yl/rTn/5Unz59qt3d3frtb387vUD98+fPU9s2OFaiKK5MsK9aTwb3i+tJruba9vb2FDFkbDaEVU8J5mnYXfyMD1glD5J5ILJClAHapoFwvXyO/rIfKQCeJ67Zk6VfNpDsDkXZUk8CWvfRyjCFjqVGPjnXCwO+v78/7QDtnBh420n83pGMESGnBxDjzQEGbwAhb2bKQ7t5jigugJLo0MHBwZT0jwNzeXlZVU8H+Cav5Hw4+jBXDNCd2+UIFUbv5ORkon+2XbX++jzGxzl7gGCDLMuCeTk3chjw0F46m/wloHIfccqIenv+0EPwKDtFDX7hKeuhV69eTWfgYfCRQ9JUqmo6Osd8Dv/BX1VPr/BbLBZ1dXVVOzs70yY3lis5/9FAM490gT9IEzHdoAWygG6184HxdC4mqSHooZQl5ujm5mbaEMEpCpxHyJgZN2NAV5CXt729Xaenp5M8AvBIfyDiyr3MLZFCNoiwce/8/Lzev39fr1+/rnfv3tXx8fEkz2x04e0w6ALT9/7+vt68eVP/8T/+x/rhhx/qv/7X/zptVAJAou/hAQPyDjCZN9E3zGU6zp2T2jmtBpx2ODIq/BxY9HMGO45GURKMdsDM96ZjyL2jCJbrsW0Y0XZkuzYZY0ej7Hv2aY5Wo3F0YC8dZ+r8teVFmzaSoO5IggUXMxYKCOWCEkY5UYe9pI5RaNegz0aA+zEwGMrHx8e1aCJ1oBw69O62vXxiQfIYDYIdlncCtcfJBKJEzfR8dmF8e7Be9rLSzPnohIHSAb68ZtqM6k6ajQTQTO38wqzTkTUMuumWfAGtV6vVBCi87GZF4mhM1uc5dmTCSh+eSUXsqIWBSP6GA+XNF3YQMKYGdbQ3R/McR/c9S/KtwTbGgk+nAlgOHRHPeaekMUq68t1jHI0rx5+gPZc0u7Eiu85vdGRua2trAgrMm6NPAHkvnadDZb3hJWLzBmPwMh99sWwQzSLqd39/P70X1lHRdIisL9B/bKBBpi03dqpML/5PGfY4Uo+yXA+9qId+2zA6Mp/zitNMH7364iVs84edPf5w0tjtfnt7O6VdJFi1TnVu597eXr19+7aWy+V0dIsdNvMxnyO7knT2mLtie+xP1/Fry3NtZ5/9PXnuuefm7pmroysd2HNf0uZ1/ejG1NnK7p6ch1HpQOPc9VEdL6VP1QsAn98YwbEnVmb27p24WvV0+C9RPQAaE+Pt8OQ8XF5eThGw9FrwBB3Zs3dLf25vb+uHH36of/mXf6mPHz/W8fHxpGBQ7py2ToRxtVpN0Robs4z2WUm5vqqnaOjR0dFanoKVOSWXrPD4bfy9zIkRfvv27VdGH3BkQ+IlkQ4Idt5UGk0rdT+PYvaY/TxzbMCSuUer1Wrt7RvmIep01IZPK3bTknFgFDlSIpeI+I03ung+SVInQkt90JPNO0Q+qtaP4qBtxgFQ9Ti8NEmkwfQleuZlRaLLRLu8EarzBPk/jbSvdd/tRDBW8moN+uxgYSxZ5nSeIlFBz6npbRkxGLDcJPhD7m1QDbo8H7RJXxM8AepY8u+cJwNxn6/p+xgfeXk+lsRjymUjAD9A8vDw8Cs6Eu3zWN+/f1/b21/erctxJUTNnO9Kcc7d1tbWtPkj35bDBg07ITlPpp/nMfmJ+3G6DcI4ygaZNO+l00ReLBFvn5NH3p3ziS3v9B+52t7erm+++aZ2d3fr7du3k8xub29PR+BgJ3Z3d6fNKoz/9evX9f3339fu7m5999130w7hn376qc7Pz+uf//mfp6NloAW6xPoU+phOtjUplx2oSqCXOX7cl5GyzlHMukbAI5+1THUlQdQIqKS8215l+1n3nHM4pwPzGWOIBHqd8zoa63O0pdgOpP1NumU/TJ9cBRyVjQGfc+nyVVU+qNURLSYPReOT4+k0gnB4eDgpm8fHL69QYgs9YMFC4QhELo95+ej9+/f15z//uZbLL+9KJPkYBcG5fd4piudronvyvezq9mgfD5prqeQp9gqgGUuH5JBY0dqonJ6eTsvWzlEy4HOkKCNbVtyOWLok2POc0paFE2DjdgELVuiZm+IcyFyW4rr76v64uB+ALjbXAF647o043mCBccnXe3mZNyMO5smqp+ijx2JQQV9JWD87O6u//OUvVVXTOY3eMOR5JUrttjHKzhmFv/xncJ/3+HtGgra3t6elcfOFDRPL5+Q/eT4wnKaRecvKjHsyopoKnSVlKz3ngDo1wPMALemDaeLdk+Yp83jV+hJz9nt7e3vK/fJGDedPdo4LffcOcoDY1tbWBG5p6/b2djobDqeGY0YAh+Y1ZIu0DsAH+gPwROSL/DvrIeQQeiAryC8RR9qzg0PdBobffPPNtLybS+NEzRgH4Pf4+Hg6OWG5/LJh7uzs7Kt0Bs+ZnWdk6s2bNxNAZmzwOTaO39ktzuZBgOT+/n69ffu2Xr16Vcvlsn73u9/V4eHhtDHEOsqRSa/adCsaDjSga5wnbJ4zv45sTDr6Kff8ngCyKyPdkn3LehO8ZLFdybqz336m02ddO9bNOZ4RSHwObGY7XR/z/hHgz0BQ1uWAj/8SyD9XNgZ8uaRoo9uh4WRM7+wiX45DPJ0nhWADfKrWIyZZH0sEFhKUxfX1dX38+LHOzs6mXVwADgwywAwPGcHyJgLTgD9HXMz07gNAIiM7nkzuTYOebafX4/vT+NijTcUBMM05ZHwjgfEyUwqXvWkzIwAkDd2cokhjaMWY7Sefddc8JvMwNONQYj/n3ZKZl0cUYXd3dzpWwwbFkZUEG27foHy1Wk1ODZE0/hw5hWdXq9Wao0N9jLlTVtDCOXCmmcGyl91Stsw38GFVrZ2j5/wuIkAdUDddDGLTSbIR5I8In/UC0TA7Mel8cY/p1PF9B4hHAJXPBKvs0rR8OJplfuA7gIs+OCIKgAVU4SRzz8PDl8Ou4UtWTQxafaAwxdFNwDkHiDP/Birw3nK5nI6TISLuaF8e7t6BccaSqz4uzpElJxraIIvQl77wx2/OG6btk5OTOjk5qdevX9fx8fF0yDVtcj8Bgvfv30+OIGDV+g0bc3JyMkVPLy8vJxkw8LGDmbwFTTpbiixYfjyPCRzMwyMgNwfwunpGOniu/g7MZdsdWMz25+5LeljOfc+onwnUuvrmQGBnh7qop+0ez3U2sGu7o/9LysaAj4RmL8sQ8k+jaiYmskJCLEexcHglJ8aTVPvTTz+tRXRYfkPAYXjetWujxLIRgvbhw4f653/+5/rHf/zH+vbbb+sf/uEfpuRX7j89PZ2Uzc7OTt3e3tbu7u50vIZ3gjnKtb29PXl+zoFBodrQO4nXETCWwbx0i5Hk00raStGRwDzawExjgGgPnb5l1CcNO9ccbUqvgr4tFosJ8DDvXgqHH/jNSzeZP2WP1psl4AULiSNBKQwAKwMXL/eRSG/wb2XPcqnrQdE7x9R9NU1MKyt0Ige0RTI6PIx8ODoLf2LEiRhQp4+B6ACf38xiWlO3l2+91Oa5NFA0fThig80P9IelT/ejW6I3v2bkwvPOHB4fH9ebN2/W6gUYEMlhvpB5gBC8RF9yDpO3zad2QBwJtcwj46SVWKYMDHkzkPkU/oEH2OTh3cDwICstfnXYhw8fJn4iwohjQB3b29trR4jYOalaP8+T/DZovFgsJlCzWq2m3OhPnz5NhxCzGcVRUWSIV6WxksJ9RO4cUUzdyGoQ48ZJ5zDkra0vb8TgeUApb8OgDzs7O/Vv/s2/qd/85jf1zTffTK9M+/bbb+vt27dr4+Kd6xcXF/X//X//X/3000/17//9v59s0LfffjtFCdnA9Nd//de1tbVV5+fn9T/+x/+om5ubSXenfsmVJAq8Dp/a7jBPjvqYXw0yOlAxAhYJzPK65Z9ie/9cQabhR8tUl5PtvmaU3nrPgDb1f4Ls54rHlzRIR4l7cnzZx7Svvu6j2dLxdP3poFM2pT3lRRG+ZAaDoM7AcC8gEc8Q75zDOJlMh+47AOlIA3UlUHGEz6ANEIJxx8Cxg8vKiaVSG+wMqWc0BwPgScmoZG7ASGUIgDDAmPOMMtePZz0WP2emwojNeWYJDDIXyvdZIG1EPI+dJ9Z5Kp33i6Ebjee5ugwSHVHyO065z3NAigG0doTNfcx5GXmhXZS4qtZ2ncOfKHcvyREB6zx5bwLpSsd79D3l1cAk59HP2iB717n50e2Z3zK6QfvMqeU9cwdxQHK8mZNqIM8Gns4AGIjSD8ux2+h0ThqDNGIGsdyfS+Tmje4wY88VNFgsnjaWALYAurTDsSycIADtUx4ts6l/TSscdvrh1zs6h8w6w/oM4Gl6MU8A9zRq1Gl742Vg5AF5cnSZXbw8j91gHD4lgvP66Cvjur+/r4uLi3r//n19++239fHjxwnkHR0dTToBm3J6ejqB2eTvLAnC5kBaztdoDru6kS1+f650oG/U/03rdB+fowt1jrBF1153/6bFfD+qo/s9bbT70dm6ESDlue6e7vOldK96AeDLpOGugx6gQd67d+/qu+++mzxDlnO5lxwSJwA7IoORIfKAYOZmBIj34cOH+l//63/V1dVVbW9v129+85spssgzeM8Ih/N/3DeDA79Vg/7kocuABE9MHqfh5eLV6ulgaY5qyWgiAuLEf2jvNxeg/DwPGB1HAA2cqMfRQ+/0w/gCzuEBaENx1M71soRjMGDQBf8YELjuVHZzzJ3gi/kAqAGWrBQxMIyBcacQ2xtlTn2PDXMHWJmHLmIC7Q3wqmrt1Vo2rFtbW5NB83iralrmStCVgMZ0p23u8R9teo6gifk1c6iqng4u9zMJJqrWd6cbFFnOnJtI3fQJXWD+urq6+iq3dbFYrEX4zaer1VP+sFcxkAVHLn30FHQkmre9vT3lYY6Mg4EEx4nglEJ3lvRXq6f8N54zwKYNHODDw8OJljc3N2t8c3BwMAFCZBm+HxX0mfnfvGQ+82amBI7mIecJOmLFvDDvRBmhseUDvl2tVmtn87FL1vPKazTtSGFLyFXkQGf6jky+fv26fve7302rLLe3t/Xjjz/Wf//v/71OT08n2t/d3U31YeuOj4/rw4cPdXJyMr0TOIMX1i1cc9qBVy/gM0ekuWbZ7PjNvJogsosIdg5a58x6fjpQ6u9cz4h66tYO4NgWpm7uANfceLviCHwC0s557+ryfJpeaXPyme53233+t9NDn7tn58rGgM8bJ9wpC3fV+s5MduV+99139dvf/nZaKjCYM9DzIcMWaNphB5tfzt15NGdnZ/WP//iP0/lzv/3tb6ezsWw4TDDnVPnsJIMqxopCIifRTIIydvEJ7uTYeCnWr86yMUwwTb6e58FRxu79rgagVpxJM+9IzZD7YrFYO+PPfcxoUoI92mdpfOSVJGN33qWvzXnKVgDMHcoePvIbIlCwjpA+B1DoM/PuSHUH+HAmbm5u6vLycs3pIPcnxwWopF63C1+adlU17dDsEsM9V1ZCGSlzJNNy4egZtGNekRk7HciH+2jaO9KJLFBYdrSceQzmYZxAxsIcA6acjoLDA+2hL0n5Dw8PawdKO3KEfvKZjCxpI+O5O9f0Tlmhb9DR+Y/kN3OfDT9zjH7zuNmFy3I649va2pre2GGn1hHkNJ7MuQ8UTrDJHACi0Ju57EidOCrOazOPUw9A3s6Kl6c9Zk55uL29nfQrUTnTzzuUkTmivozj/v6+9vb26vXr19OZioA/AN8vv/xSy+WXZfO3b9/W69ev13Tk7u7u9EaUDx8+1PHxcf3xj3+s9+/f12q1voEJnrRe79JsnC5gxw3ZcjQ8wZ+BSIKqBF6uMx1YA9AO8LjuBI5uKyO4rr8DrKP2EuyYXglURyANrGHaZd/s6GQgIkFutu2c0bnxdaXDAyOgv2nZGPC5dCF7GvbykxPPc3nKaJqSS0ceZA4MZYTAVD0tL7GMy245e5HdOKrWJ7bqiZj2WKvGmxcsINRnT9u72QxqkwmSgSxkXiLqvDMr0JHX4/6arl6a6gAPc+g5GZXnlg3y91Efu/9TMXSCbVq4TXjG4MnLNwbdRHXgqQ6Au+6kCfOfr8LDGNpAdgnrzE/VE8DMxHciNxn92sTz7ejja176y3kwr/v8t1xyd/6tjbRzmeDBdJKcsmGDloaNXFxH+zvDRZv0I3eO+x5vQOoK7bKEzhh4hvk2rei7aW09mrlaGHeDwDQAtI+OQ08xH0mDh4eHKdpVVVO+MjuAc+l2tVqt5T2aHy2D1vnWe+guR4ANXM0PBpT82eln3nJ1oqqm15ptbW1Nm2Q4FcKODIVIOxE30os4Ruvx8XF6u4bllvarnvImuzQaaH94eDgdn3V9fT3tJnbUh3kzkLPcZrGRN/3NZx2wGIG9fKbTzWmTso6sd+57ljk7Mro/67buynpTN49An3WixzgCjnP1uHQ0nevLqL6OJzKvcZPyYsDnRFN78GZgwvFE1diJ6w7ak7Onvbu7u5YzlQZ4tXp6UT1vyGB37cXFRd3d3dUPP/wwHXPBy8YzP4R+GExlDo+Xb9lg4chCZ1StFC8uLur+/r7Ozs7qw4cPE80QUM7boqAYbUBQui6dZ1G1/j5T5mWxWHwVHcGYugDOrVAcifXbS5wnlAyH98/znr80aOkkdGPjN+iTEc6uZCSRuomYWTlzHxEaAxYiNwaw0BZDkmF26l8ul9OL4T1mbxQ6OTlZy2t1PY6oefcr0RZeIL+zs1PHx8drOySZY4yJ+1b1JMOmqx0qos/Muw0ZY+A71xyJoe6Tk5Opb9YBPMP7czGwNkDmC+ftEXkxgEoQlPNFtM7A2nNoow349/zmhgoDT9oA+C4Wi8nBI4pE373szPgM9gyMFovF2jl4gHvqsX5kJYXCb7nMfXNzU//0T/9UVU966OjoaHqThGXCy8uksvDGGp+gkIAnnUNyJkllAECxdAt/AEbteHu+qNvH00BDNjc9PDzU69evJx3oZ3Z2dur8/Lx++OGHenh4qD/96U91fX1dh4eH9e2339b+/n5988039e7du2kpmU/GAQ2qntItfGYr7THnHPl1f39f33zzTR0dHdXV1VX97//9v+vi4mKKBK9Wq/r48eMUkXQQwzJhvjVNKAbYzznltld2VHydOvOZlPt0FNImWJ5dOnDUFdNiFNHKQEA+t6m9SNn08+gC+po0mqNVAtP8G9XHNTu0vwboUV4M+BI4oBgN5ABxAMKM7lWtR5RMmFS0vscEYqnBYIZdip8+farr6+upXpYq7QEnY9BmEhKlmf0143lyKShKHw+ztbVVr169WjsI2CAyvQfG7FytNLAGhh3QMdiimJmT9mlsYTA/Y6PqSALtWmhMm/QsR7Qb8R1t+//O8zGP+loKawJ2R1wWi8Va3lbSLGnaeYJeqqdeZMKfBuDOkfGGCICRlxCdW+XS9c/jMi2yMG6/GSGjVPTXS10GCLxpgOjRzs6X11v5dWOWN8BDOlSeD/iQejwWLw1346KPTsHwHDP3Br7IFuA1o9bJCzirjuZioClEBf0/z0MTG2J4wSCQdvO1kzzHWEnRgA9xHDkQmGdOTk6mJXD0tPmePx+D45WK3KRhgO1oliNi0M1RXLeV82YdZJDPfJHKghNqZwZAiLNKv66vr6uqpl3vfl0gEUAXbBH2wBFL+uz8UMbGJpCzs7P66aefamdnp/7yl79Mc2D7wrjMA50MZ7TTdiN1gXk2S+pOg5Ln7k8QY/3b6fIO0DzXv+6e0b1zgYJRgMSlo5v7mxgh6/AcJCjN+1Of2Fan/TZ9vTrBta7fc+XF5/AlGLGg4oESIreCtSGzAezQKkrK72lEkN2WQclyuazr6+sp8pcGyYQyQSkO3afSqaq1/DXXQ3+IaqEIyNcj78c5Rjlmj8GFezIal/c4ApN0tcdHfbmEyJgyl8sAj2c9B2ZsA/GMhHi+Rs8lAyeY6j6TToyF9hJYUjrvzcUC2KUhUAzUTGPuOT4+XgPTGMM88sRg00t4/Pn9zMgRUa7d3d1pk4CToZOfDQYMys2P9BsgCsCiLehBHi2GDT6D14jqkNNEFNI7T9lZ2jkcaQT9e87BavW0qQGgiuEnqshyH4DGkXTzpqNk0JHoE/01TwGUlssvOz/zzEFkh/+dk0nJVBOPz8bcETXaTqfF+qXq6SgtzysHCvP3+fPnOjs7q8ViMc2R6Z5zQJ95njnlKKnl8kueKkA+N/UsFos14G96IAcGxdZp0Jb2DfoMBtO4Y4vevHlTv//979cico+Pj/XTTz+tAV2izUTzFovFFJF78+bNBGDJu+U7OYHpSOLo/+Y3v6m9vb0pyuj59bJ6OhnMN/UZICeQ8JFT5iPfk8V2udOLnY7s9LTrS5tumiSw9z1dyd/NHw6UUHfq4a4kjyStRg5zfvccWu47MJ1jeg74+n/PaReY2rS8CPDZONMhBvr4+DhtyuAtEN4Ja+NjBZR1cc/t7e20ZPXw8DDthCKpOo3Ecrmsy8vLOjs7q7u7u8nQoMC8ZGlhspLAYHqZgucx0n7G+SnegMEf4LOqpnOi8mw6JhFD5N+7MSZAstH0QbzJUP6eQJhxAmq9vM1zzh9DIdlYcb+T5E1rG/Y5Ly2XB8wb1JklAV+C5xQ6G4fc8eZiI+e6GJdp5p2U0PT4+HgCRl6KMfC2EoF/7DQA+HLnt5POWSK2A+Y3G0AL7q96ii45v5X7nBrAEirPYLyQBz/L3GWUzYAPOrORJueD/tjYWO5MP9q6vb2djnPyob6c1fn27du1frKJyDutUza7+QDc0ZfMOXOksqqmiNpyuZzOR/QbMQDKnqMEQPAoPOecU/OTQStROI4FQe9Z/wEM7u7uJsD38PAwLbESzaMNO6kAS0Db9vb2lBvotANWXNClgD0ibgZILo4CGpwZjEBn9wEeT3n3igpvW7q8vJxszI8//jjJ2fX1dR0fH9fj4+O08WaxWEw25bvvvqvPnz9PS/aLxaJub2+n8wA5/9H6c7Va1Zs3b+pv/uZv6vT0tH7++eeqqqn95XI5OUcPD192bDPHCQK7Yr1q2bUjZ7BlW5vXobEjt52jPQccs2+p+xlPOiuWaz/vYrBoXcxzDkbwl4DQ81K1nrM7Ary0NwpQJUB/zk4lzRJXZd0OmFE6+/RcedGSrievi7JAkEx69o4pJsEMbIboQtYuaZyJKKDEr66uph1ZyURz4e70zNMY2tMdARL3xQKTQuW+vNTTMWN4PvLP7XaM6zbyOZdcnjVwd+TPhg9F7EhbNy4D17xnpNz+T5UUOtN2BADNK8kHHf8T6c4wvOfQEQsrLCfsO8nbBpi2+Mznk/aZe5fGEl7EIdje3p6iIxScqNwM5bnPvhj4m+8Ntjve7uTMxs/Ome/1HPgVdb6XOfHceXd6GkX42dFv/5bjxCFyVLAzDESZuohNOigjgwpt7Fw5KmnDZz5OnWHAbJBlEODleM8TIMf5jt70ZD5zn71ZB8DiQ6Y99ozkuW3bENdnPcQcM/fQx1FfDsomSoksIKfkclM/aTtEBUcFpw/Ae3JyUlX11dLxJqVrpwvIuKQuMC/MOdP+fQ5wdn2csyv5/KjPHTjKdrrSgdRRGemgUV/9/f+Gncp6RzT4NW1vDPgSiSfqRMly6CQFb4pT2CkPDw+TASIKgjCgLIgIsESDQuD31Wo1vbrmw4cP9T/+x/+on376qY6Pj+v777+fPFUnH2MsjfwBat71iLFgqYwzrbxkmR4EnvzFxcWkfLy0jILwSfI8a+WaQKmb/A4M21g6kpNeTS47jBgHoJ5GD4NrL9YbDNI77caQY7MhtTHPsXXjTYPaAYVR6Z4ZAdA0IPSXuQZcAJLevXu3JgtVX6I+5LjZcNroEEFgFyF/jorAKyR++/gT+NkG04AGGYBPfFYec0HOErJpPuUNB9RZtX7eI0beS9HkTDmKzvUEeQmMoDuH3xJdcySo6mm35mLxdG4k70v1ZgOMvpeVkVWWm31oL8/5jEw7Ph4XUUFHOwFSyIXPB6xaf50awMjROkceOyc6l3sXi8UU9SSyZYfT4IjI0mr1ZWmcVRW/MQiwcnJyUru7u9MKRo4Bvmb+uQ+w5OX/xWJR19fXUwTQZ5NaF1StOyDffvttvX79es2Jwsk2oHOE02+J4ZOIM3oSvfXLL79MOYjHx8f19u3b+vbbb9cis+QAbm1t1Z/+9Ke6ubmpv/qrv6pvv/12qFug3/b2dv393/99ffvtt/XnP/95ArhsaHEgxDaCOTPPWV6tnxjTnOOfoGJOR7qeLLlqAR+aF5lHjyX52f3yH/1GvjpQb1q7OIo4B+I6e9M5SVnHCIBuAsRGIJlreT3785K2XF4M+PieyhhhSpBlpWkDx24qBmcPiMiCPUCDTDP43d1dXV9f1/n5ef3000/1l7/8pf7mb/5mOj+pqr5Sznj8GDYUjpcPGAvJvg710qfMrUPJffr0acohcj0scxjwJYPPMacZIhN2c65syKx48N67dhIAGtylwvFcOFrjqI2FtGsrmTejDR3vjUqnvDoP7Dna8t2gzuP2b/C4jSpODwqeF6vzvKMIBgkJdAEBjqJWPZ1jhwIg2ubdtFU1OUXIFTzT7XrkeoJbj8uyy/gAA4vFYi0Syf2OUjr306kQBrzZfmeg/IpCaIPjiN5w/h5RVvM8tM48X2joDQbQDePhfttIOKpZVROwdJqFHUaPEZ4A1CfAcx4p9QB0RrIGiLADa/APyLcu5Dgr0miYfyJbPgjaTktVfaUDoFNuoLAeZS4Zf6Yg2LmFd3lFJ/MGL7CMCb2IvMGLzAU62ykWgMOPHz9O5yEeHBxM+Xy2T/A3PPDx48d6fPyyy5zDlzu9C39VVX377bfTuYF//OMfJx7KaHU6yZ2usl00b/g+y3YH3hLMZP3PgZMEfe6P7UY60iNdbD7vAF8+2+kK/77J0mfSfLTEPHruuftyfKbD6J7837SkvW6FcK68aEk3iYKQk6+HN316ejq9MzHRcxoqe28WWJQ0YPHx8XE6Pf/+/n7KA3n//n398ssvdX19XXt7e/XNN99Mx12MlkHoewokfbDSNrglsmBFTTTAJ+VTHA3lGpHEZP6qdYG3x2aBstGyYc15cpQthdzLIvwOMGfcCd7oS0Y38h7X2YHYZNqklWn2UmbuFG23VJZGJXMqO0/O48t5xRgYAAHoHAnheeeyrVartVy+NP6ANiLg5m0MNwbQy1+MG9CDwXF01xEcy6jnhHuscJ13lXTOCBglUxxMd/OHQWsqfvOHowgeO38JLgzeqmrajYmDSt1V67vQLSscoUE0ypFU6znTjSVA83JH58wdBBDZ+axaz73kYGLrgHTMkNk00AZqXPcGF+aYMRF1JiKVr0dDp9sh3N3drVevXk39Rl6urq4mGgEuHSDwvBosA5hz53zq3aSjl43TzrDpyTICv15fX9fFxUV9+PBhSmXY29urT58+TW/28Lg+ffq0ZsvgvapaS/OBL46Pj+vbb7+tm5uburm5mQC60yScF+z6uJ6/Ubr7+N36vLu/u2cEsFw39yRoHYG9EbDk9862dMCH37HjHWBOGo1ArNueo9eIHnMgsbvW0bd7Lts2+H1J2Rjw5QBg8s+fP9fR0VG9fv26jo6O6ptvvqnf/OY3dXV1NS2BENli+RYlixeOR+t8CZbCPn78WOfn5/X58+d6//59PTx8OWsJUPhP//RP9U//9E+1vb1dr1+/rnfv3k1gkH6ihCw0aaRsLFE+Bj0oNEdlvHTB0jJLM1XruTcIPAeZjmicICc9dBu+qq83HSTIyR25VU8ec9WTwPq8OX+mwBrkdWAz+25wkQJJ8bhyY81I4DrezHw0eNRLCR4fyjXBi7+PFBbjwPiwQclgiLxSR1bgWxQ/oIyoBBFrIoUsqyI7r1+/XuMH7nM+n8eInJGwTmTD92Fo83xF99ugpfPanf9lEJHXDcgAtCNjQB+99OblIXjcS9YJ/ug748nla79Bxjuh4QHknkgo9/F3fX29djYkAGS1etpJTX08M8o7pE/Ux7ErHApsoIJsk27ijVJ2RtKg2LmsqrXVC3ZfW6YAd96M5dc5Qm82KFEnO1dZabm6upqW8G9vb+vx8cumCHLfcOppJyOSjmpeX1+vRaWtNw1EHFUGFGZkm53LVU+rTjc3N/WnP/1p2mjz8PBQR0dH9fd///f19u3bWq1WdX5+vnYyw93d3RTts36lPS9vI+/ffPNN/d3f/V1dXFzUn//857q8vJzO6VwsFmvRUS+HO7qMXkuHwcUOTAccTDe++w/+6EBOZ7PSAUowlI4Yv2H70SNdge9pB9nuwGnKWNbTjcd8lONyXd2ycrad7Y1AY4JY26sRqOsc5E3Ki3bpzqFae6I24ElwGyaHTbvBWzmx5Ap4JKIGgCJ6wfJOlufQsI14B3SYACsQfu+S620QMSJeAvWYR4jfdOzoMvr/udJ5SNC5Azz5v8eVwty1M1Is3f8dHTphHj3f3d99d39G3tNc//w8Qpdg3GAqlWbXF0drkA9AJPydEXG+O9fKffSSJm8fSIcBsGnDmku/o5IAbxTh65Rl8pfp4+82oKad6c6nI/VJ33R6+HRSPvX4OgY1DU0utyev8en+dMtfSU/zgz+zmAYjft6kdFGa1ENV/SH1pnNGbAHYXpoz+DKw9tg73ZJOiuXETnvq0uRf5tk6PefVEXR2zSa/ELllHF69yYhu1dMpBxQciaOjoymf3ZFBl04mTK+c77zHn119z+m7bCNtw6b9HZVuDHN2JX8f2YDnisec9qar0/f6c3TvJqWbO/d/DvT9mrIx4GO5z0tA9jQxEAiBt+cT1Tg4OKh3796tvW2A5aKMFiEw19fX9eOPP9bt7e2UiH1+fl4XFxdfBrCzU7/5zW+mnCmWZ/yieRRBGluEy3l76T25b16+dSI2OS30H6PrfCO8TbyXDgxlflY32QaU/O7lHpZrvCSVwpw74wCtjk6m4h+Bvc6Y8j+AxNGf/PNce946kJTfO2WWYD3BQo6buXGx4s3lTmjV8ZbnraqmDTo+7NdGnKVbIgVE+B4eHqbzLHFidne/vJua5VtHsLy8S2I5fEQEg7lIg8Iy2Wr1dObcyPAkb1g+eJOCPeNcBsZYzp1daL5Px221Wk08Sl0JLnwMTkb4DNoMbA2YAfDoAkfk6IMjgX6vLmO24wSY5NOy73E5J3ETgO2NJavV6iudU1Vr58hVPZ2v6PEzd/zmyPFi8eVsPo4pQcehyyzjnlc2gXjjR9X6GZOmkdv1/DPWnZ2nt9KgTxiXafX4+LjG6+yEdZoFY+btRSyvLhaLiYeXy2X95je/qaqqi4uL+umnn+rm5mbKDd/e3q43b97U58+f6/z8vK6vr6djuOwE+IgV79gnYn90dDS95/3f/bt/V7u7u/Xp06f68OHDNLfeuMiY4U0DXM9HRp8sk/xmvuh0vHnEfNc9bxlP29QBIs899bhu6wOXdAxSLt0n1+3rXX3uc7eCkHWlY9e18RzonQOp3A8fc38XNXwpENwY8GGUfHaevVYryfR8FovFxPCvXr1aM1xMdnp1fL+9va2zs7P69OlTvX37tnZ2dur29nZa3v3bv/3b+v3vfz8ZNhQCRtlLOB0zMMEGn2ZI57MBXDDKfHo3K/Usl0+79Zy3R7FgMtksu1lBGXwazPjZuTF0oWcLmI0zY7DS7QSvW2rtwCljZD7cfgqvQ/M5rpEgpTKxUPCJkU+AmYnSHU947FYsBpP29jKaYIOc7yvFaGM8M0LAfHpTBn+AGUAExTLAxgrqSA+2W4ZJg9vR23SyrJMkn4qMdjBc3hXM8wblBrF+CwM8Dc1sqHmW5dnF4mm3puWHOU0Fb3DPPdCWjTN28Nx3fodO5hPzD/rROwdNK9owQOoKcgLNDWbROUln+NUbHdwuz0Azr9LAf6ywwOvmFy81M9ePj49r7Tnym/rJYKUDDIvF06vi7IjnqxAZD/zk8yPpM2PIHduLxWLacLe7u1vv3r2b8vVIKWJVaWtrq05PTyeQR70sASNfnHF4f39fr1+/nl7xSb9Yqj86Oqrf/e53tVgs6qeffqqff/657u7u1mScOXJ+qh3ITm9S0qHNYhnP1SfXnVF7X6ddB09wjv0c9IGO1vFzK0a04f7MgdDOHvm+7Hd3/6gvc/TrHLquDyPQ7GfSWepKZ5/nyotz+LyU4cgCxsVhfQjJ7ibf0xExI0IwCwO38Xrz5k2tVqu1HYMoUQMIH70wYoKq9cNNO+WEd8su20w05jnn1BlMQBcDtlzmYRwJHNJYd56c6wFked7sJXZC3S3nplEybVNAXWzQOk/J93QKPsedwNLXn/Nes86Opt33ri8eVwI8lkSJQueSbNIaJwGjDS+Rq8c7ortNTVVPS0IujiSOxmHA6r8cYxYbS+rBmXGELxVoKiS/ycF8nO9I7ZZlkRtH5KyH+J6gFdmz/vD92Yb7lpsXDG6J4FrHpEyYN9FhRMCgIUAwI3wGqY4qI8fkHrouCs6B5dQHhft+r2JYZxlQ+LQEg236ymaMqqdDyE37u7u7r16tBk9RzA+WbWhv8L9YPOU286yBBP0HHGcxQHWuI3xloALPnJ+f17/8y79M4ybKen9/X9fX1/XTTz+tbaT5/PlzXVxc1OPj4/QeaPMRfaz6cgTM69ev6+bmZjqAGV7GEbAza17Nz+Tl1Odzcj6nN0fAJ3Whf+/aMe1HOn1UOr01N4a5MgJb2f+5aGP+39Xja5v0eU53zwHYTcqLX63m5R+/uoidTkRTOsWAIfOJ/2ZA8ow+f/5cl5eX02n4KMmjo6PpLKPf/va3U3g8jQUChwKoejr4EkXh5VsMCULuXEGAHscGXF9f1/X19Vc5ed3OVuq3kue68x1ZsusiLAZXVog+7sGGwWF+nkfp5fl/FtQ05nzm9wR60M71Ubx0g8fnqA7zYWMJAPIu6xTufCb7l4omAW4H8rrlJBf4xu3YA7u6uqqrq6vpLTOkGHiJabFYTADh9va2Li4u1pbJtra+JL+fnp7W4eHhtHwEnyBLVTWdWWla+FWGSZOcV0eNmU8vIZheVTVFODC4jm49Pj5O8ooMI/uOFtn7t5wi2xi17LP7wfFGAJqUCfOUy9bW08vskxfgXy9VAqatJ4juX15e1vn5+RTVIarkiCsyx1i8tHt0dFRVT1E9Ik12Hj0HbP5hHIvFl6XWN2/e1M7OztrbQFjyPjk5mdphXAA+O7REZR8fH+vTp09f5XJyLqrngnnlt+VyOb0vPEE35y9eXV1NYzBw9c5U5N3nrHqDDHMEvZkf8xOg0jbGzmwCu9Xq6V3DjHe1Wk3zulwu6+TkpFarVf3P//k/63/9r/9V7969q7/+67+u1WpV19fXdXl5WZeXl/XTTz+t8TV/tPftt9+uRXn5fWdnp7777rtJnv/whz+szTVjto3JYjkxmE7ZT9CXetM6N4v1Ygfi/D31q9tFFrK/XZud7sqontvPtudKYgYHUfyZTiJ8aHvoMYza7YB519fsV47ZQHUO+HblRRG+ZBBHMFLwqr7OzegI58GheFH8GP/06gGZCEsCgw6YMCFpCEaThmBh0DDUXobrjFO277F5Em0AbaiyjBg5ox8d46R3YOZJgeuYMRltBCBGpQNqObb06F3vJgJrumRbm3h6nXLKMeT9+Sy8gpK14Du6Bz2517s2MWLwA0bVkUJHM0w7/+U4cr5G85lGgHu6vju6TYQP+cg2qdORfS9Zcs0H5o7kB93A/xkhT37gvqSX5zHnG2fE/eMZAzTPG/Ls/nb8Qz25dIzT541f7ptp4t/RvV1OKQ6Co0LcD5+aP+mb7wdsGLgY0Bl8EPmsqq9eHwlwM/ix3KALcwzmVaJ6pk9GuN3v5HPa8bymvncOtKNw0Jhdxru7u9Nyspe6ncLhFRbuc39S7sjR5dB2orPuq+2vxzHSw5uCgU7HdTp4ZGfynufsg/ltbhxz45q7/n+qmO7WI/BYguhRmbOBc23nsx2YfCkNNgZ8aUzw5DNaR64fOQjcm7k7nIJu4FP1dNYdx1N4mQilwnNEXKxMbHz5n9/yWAUEm/EZvTuCgbCT4+FT82kjQYsLkRczDlE/KzuPxQrYbzNIBkgDYW/QBsjG2YqGTx+7MCrc6745kjoCYNlnf3d0KSOHcyDT4BW6pCIxiMn+ZL3QCH71Zo3uXupMUJKKEH4mknd9fT1Frm9ubqYd5nZgqJf+dMsl1Ilht1PkCB8OimntOtNB8Z8jkwZLjjDwLPLts9wwXgY2AFoXInwYRgPiqqdl4OVyOR1p4zkwL3lZNJ1Kjomy0QJsWu7pA/LIvJFMT/RntXraLGGgkmDVQDUNSNXT0Sa0B3CC33Byzc+7u7vT3F5eXtanT5/WIv/L5XKKqAHEDZYZ72q1moDhzs7OdIaq89G8AQ4eNXBER6GPcz4cNe2uMw/UaTsBjd1Wyhu2KA1zyqGBWKbPcM25hYvFYgLiTjG6urqqf/qnf6qqmo7EWSye8sUpbOpw5NU8ZwDLtcPDw/rd735XV1dX9f79+zo/P5/AKsDTy+KeV8t86j7uN9/xaXBu+qbcuw3bkO53Ow4dkJuzDZ3j9GtK1z8+00mkdA6xeZhrtqMdOO7G2uGDDI75NwfRRuP7vwb4zCR0zq+RQol415qF0V6flTnX+U7dLJ0AIK0Qfchtd5CxgRET47ZzOZb6UVhODPZSCX9+XZAVOJNgOqHo/JokjKHPvIJ2KczOMQI8+BmH7g1CEqgYBCYDAvg6RZEKgzm194+SoY+51DwHljpAliWfT4VBH7pob0ZCO/DmelDIGE3T27ySz5heNtTwKMcHXV1dTWe3YSw4uDwFPNs0mIPnmQt4N8FhbgyBb3E2OhAMT7lNvidP8AxLuEQqMGosuzplwUuxjm4sFou1jVB40wYDtIcRthHnuxW52/Gr3PgzHaARjt1yuVxL5bDxpn+jDTH85v7Y4MMfzCdAljQSj9WJ/vACTjPL6VdXV7W/v19v3ryZgAS7b+GhzEdEZvkdverXnaFzmV/r86qnV5LlZjX0Ty6TW1/ZyfAKUPIWzr6dbPMkEbEsdm6QBdfhZWHm3q+jo89HR0cTEGcZ//379xNYJkILn9I3zh7kNXe2idDCeho++Ku/+qspdcgHXNPP1M3WP2nToIPrsKPnZzN/E9p1u+W74ENnW1zmHO9u3tP2+HsCxg5s+Xr3fzoHOQbo2dXfAda5MY7KaPzIRDrmXZsvKS9600bVk+GBuVEC3eQYsY5C2vbcGCTJ7AhYeuSOBjnKlJ5BN9lWynkt8/Cc09cdJzBiuhTEBAT2sFFmZrbsm8FqCtLIgxmBrDlwNfotAVN6RZswYDcXKVg5bgubjWZX73MCmcopnx2NoVMkHZjAWJJT6ly6qlrjKRvEpG3V18uV3aaELpLhMZlX0mvle0ahunF382weJvfMhsy7gzGcBqadTHQKslO8lpHkIY/JOqmjEbR3bivzgmPH9zxfbcRLSds0LvTF+tF9y+XndEZ9X+ojA2GDTveF/Efok/ohI8Spv6yz81n6SJ+9BNvNtfnUdDNAM7hxcTTVfeg+/T2dlpQByxr94TdsEzl4llVyVs0Pro86HUFkfM5NxBkjQs7bq2yTOpnvyiZ6Ia8lPQ0i5+xH15+Rjk3nxzrQ85QgKOv2GEZjfY5Oc7Yk6fMcaBvVuWkZ2d4uUvr/DPChsGHEk5OTaROFPS4rExTpw8PD2ns7DXoyV+/29rbOz8/r7Oysqp4E3DsX8STxvJJBRgYrFRaTa+WJp4O3za48lL+fRzE736PbkGKjt1isR9ScL4LXnctqBg8pjNAygaPbzwjmc+C4u4YCtGEyHUcApAOfHbClGNBTJ33vBMAGijYNFudA32icPEtfOhAPr+/s7NTbt2/r6OhoOnrIy3D0h6gLZ0hWPRne+/v76RnmCkNC9MS/0R9/xzkxTQz2PE7G5OgY4zOf2gDzHNEYR2TgKR8tlJsoOsVu+auqtcidjRSfLMta5qgL3rZ+SYCV/IEeWS6/nM1G+gZnqrGkyxmJubyDDDuSZRkw0GWu05Exj7jMgUSixOgj5pLXlnXgc3t7e9LXpON0+hF9Yx43YEkZh18TlN7e3tbl5eXaTlzTylFGdqWa33AODJIMSplv0yr5zTzMeKgrj7oAvDkavlg8RYF9jXrZZOXIpeeXaPdisaibm5taLBbT/UT87Uhsb2/X73//+ylyS9rHxcXF9AYS81Gnx1JuHTHvdGDnWKPnHawx3VPv8918lH1LG5af3ffUyVnvnB1L3s77EvyaZr5njsaj+vPaCJR2wJI/HM7uvqqvHcZNyq+K8CHcKPRU5jaSBnX8bkACQzl5F+VKHqCXg8yIVeunvncgz/+bwdOzMGCyEkfwuyVR6rSQdwzXGTyH852bwvOZyL+pZzcHsEb0mWNWGyTfa5qOwN5cH7vPqq83t2AsPeemeQf25miR/eiey5JjS8VIZO/o6Gg6Cy5D8fANgIZxJXDPNjz33riUICZpOxe9S9qlAuczFUrylCMiyLU3ceWRNAlCO7CRS1ymnw1xt3SV4/fvHZ0S+OJA+nVijqRlGgjtGNCbNubNjEolGB0ZKv53ZMpRJ+syA+ekadXTuZB2zt2HdATN5zaMBtWM0TzpPFSDQMujnbr8y/aJqnn8XcmouH/nGn2HdinXTlehn9Rn+0PfsIHwh+uHFsyPbQh/zhcFbB8fH9dyuazj4+NpV3kCWNM0eSV5KJ0nz6V1U0Y4rZvog9vsgEz2K/XKcyX1zgjojcpL20ue5NkOYI3s1EtLx+ddOxmY6vrz0n68OIePKAPnjTkBG0LByBxfwouhj4+Pp6MCfH9uuri9va1Pnz7V7e3tGqi0oiMh2R5XDt7CieJL42MlRj+IIlxdXU3nKNm7p1hh4Fl6OQ+g4mWSqqdjEgwgMzqA8unG4PHZs3SUMo2qBduM5PFkgqgNjRWk86s6oIciSNBjw92BZCvKkcJy5IbvrieXemwQM3+FdpLuVjgGmkkbH03UHTeEkXISPtEx6G0vmvn1AbPMby6RkS9HH6FdvlPVANFLUx2wTqPQ8YLljPwpP2sgkE5Bznf2tapaHjagcaEf2dZqtVozXIwpI6PQlgif34fNxgzqtOOXBtSfHn/ngY8iJoyjOxbHIJMx7O7u1unp6eQU+sigqifwZtlDF/uIGNOElRivZtjh5JNUFHgZYJ9Av6qmzSQGOYzZzhH3oIdpJ0vqhXSKrHtxNuww5ny5HsuKc0bplwEbvEd+Ln3LpV0fx8QBzm/evJmOH+vShKDH0dFRvXv3ri4vL+vjx4/TfND/EQhKWYC3crwpU8l/6Vi5n6lfUqcThezsUTp03G8+c/8T9G0C/NzH7pkOKCfdRmArbUTSuuvHXBk5PFX9kWLWey8tL17StXEjedfGjU7f399PYWjOYOLEcSaBzzx89vb2dkpa5Yw/2icCyCtr7M15iQkm8w44C7mjkPa2SMp9eHioi4uLOjs7+8pQdczqzRgcqcFv3VETGJpkLMZCn22cM5oJ3bvdhR2Dj4ALpdvwAKjwkoBzWOa8zW4ThcFP18/OiLvuFEyDpQ7IoXQMvJMPDCKhg5cu0ru2cTk6Opp22eKNM1fMjfO/iFh7jJaBh4eHr5avHW1g6Qtey7mFvtAxI46pAB1dzg0jBhwYQTscbLZIxeOIgX9zgY+YG9oapRtkv7u8YXRIRko9lpxX0jUeHh6mJHtAn3nBwLsDfC4ZBfIcAWjcPxdWTqhnsXh6AwT3W884f7Jz/gwMHh4eJsBXVWuvcVwul9PYb25uptMITDe/vQTa8MYknJ3FYjHZhqovmxB8pJXpD1j0PFvHeZ4M2rwMbP4CbHI/8uFd3uZfP0tf/Tw0w9l3Gg56zeCdfHb3kdze+/v7+vnnn+v29ra+++67Ojw8nPLUk5+Y41evXtV3331XOzs79cMPP9Tt7e2kUx1xTmDnwEunp81HvgZ4ox8J+JgTByOgeUabPZ8G+fQr5SZzR90Htz0HjvysQVmnT1w6x8z3jWg3cmiTVl1JsG0dngDXv40cyU3Li49lsffEJFlAc1dr7hJEcLa3t7/Kh4NBElC4PYoNQ5cwmwWCWgm7HgtPLt90EQ/TJBWNacN9GGKKmSONkn/vIgS+5j52wpDP5+9zQmOwaa8rGTP72/U76+4iNp0ntUkd/j/HuanXlcKVCsX3W9E5kmClPQdm6XPSKoFTglcraa6n/JhXMx3Afx1wmgPvVnIjIOxn53ggae6ISafkumiE+9XRuLvWtY1+yo1ieURRylen3HMeMmKe93ffR/zQgWfzhOUUepoX3G90LM+lUzYyqin76B/oCNBBv3f9HOnSDpw4L9TLxAYPz82x+aabt7zf40s+h1ae2+R7OzvmawIJOztPB2WPeIj+Alpx2vyav86hSDlOHe/701kykHNfRjT1Pc9FG7OvI90wN59ztr0rnS0d2cTRM6Nrm4Ktkc56rp5N+/FrysaAD6PGZg2YcG9vbw3YkWTqhF2Ygut4kwAjXnjO8Qcw9tbWl0TZ09PTtQ0fVU/gEi/VCNuGMYWKazzP0g3erT05h7ApNqosqxH1xMvzGxGqns79q/r6mBUrozToVU8RiaqagLTrMaAeJY673yOhSoANeIXmDtvzh4G0UNv4WIm7DY+JNrvv7pOV/Gr1lFie84yQeawGDNDHtHeEl/51fYI2vM7v5OSkXr16NfWNuaF4qc1LRBhbIgIYEvjHAHK1Wk2/EYHItAZHHTCOh4eHa2fg5XhQ7OlZr1Zf3jLgs+YwYAcHBxMNMcQ25tB0FNlLXnDUx8vtPEfk1vTvclo7YOyNBx47fUfHXF5eTu87PT8/n44kIaJN6oqXfjvQsFqtpuXV5K+kRweoRs4T85enHLguP+Mx44x7JyjHjtBXH53T7ah1Gwk0qJOxA/R4OxLPGhzZuc6IZIItfmOjknVeFqJ4qa+SBzJC68i/+5BAio0utJXnIlr+sQsOhFxeXtbFxUW9evWq/uqv/qoODw/XgHny/XK5rKOjo/ruu+9qe3u73r9/X4eHh9NbParWT29IMOOVL3JTvRKF7JnXWLFYrVbTClryOO3xHPNk2pof89gi7svNYvw+B9Q6uUnnqNMF/su6fa/HaYfCji6F37o9BM+VLkKXdWe/vIloE2dnVF4U4UOR+ugFJ20/Pn55PQ85ezANworysSLy8Q38ZsML6PPhxSYA99k7S4KOvDpy8+yZ5uaMjrhMjg00y2bOrbJyyYRuvtu7chs5BitVCgyXEb6s23WMPI5kuG6DQHp2jsS6Tc9HevRpqBOQPVcshCg0FEUa0I6u2Q/fD590CtRj4xVV8CXzyRyjyDHWgAeD0zTozlP1WAGL+fo9+mgD4b6QUjACRhi5blkNGa16WvKln/Q/HQv6ZVmxQRvNpXdZun9c8xKt53w0Ll/rFLl1FQCIdBPyhtNxAmSM5Iy5p8+A/OdKgreqWhufl29yzl0HJeej428vq0KL55aI5gytdRw6wcDXPGhZ8PzbWXVd3sxHP6GvC3RiHNZZqf+YexdHteD9BL5VNYFYy6DHgW527iBze39/P6UHwW+eZ+tL9BlO5efPn+vVq1dV9QW8XV1drT1n/nGfu4CCbXHyDg7AKAo74oe0Cy4+YcI6unN0UuZSH7h9lxGYG+l9f+b3zn5093cA8Tk56vrhkvbQOrWj7a8BfS/atAFT5C5dC0l65vbcHx6+nAp/e3s7RcL29vamvCXvkHMkBkUPGMSTrKo1gwVxcgkgDQ6GxPU5gZaQO4oLYcRbxeMkipLRJgtvtp8K2yW9HdOekuDWS+BdBG8TIJXKzSDVeTy0j0LjO0qK53J8GYmgnTRurqcTqK4OQIuNTdLUACTbNnixgnRuFDRhHI6KcLgtBstRUSJ4SS/eCmBDB0875xR685wBYy5dJu91gNdgwGDLidUYvZyLkcJNBelioGbngE8MjPtoh8sgKuc95ziXDzvQ7qiS33ZCdL+LHPHcczIE/2JQvWGrA2IY9O71YKNxZrHcUzKiwriso1wvTi/82oGnHAM0gc78D5ikOMrGPaYBethO3AhIOPpo4F315W0XyIg3BdIHR0c9fv5PcJzyRUHXQLcugszzbhO9cHJyUnt7e3V+fj7xCPPlVZXb29uvVlSICH78+HEtl5B5ToCb/G/n2FFWAwtWvOhzB77c3nMgy3pzxMsdCPT93fVMQfC4/d26axMbmP93wH9uHB0Pd3WP+sv3BPFdPZ1Ds0l5cYTPjLq3tzftNsJ7w2sGHHkAnz9/rg8fPtRisaijo6NpaZhzu3idGgegQkSEgbdcMJFE0/JVPEQ3WLpJwqFk2BxiA8OyspNuvQR0cHBQr1+/nhKW2bRio0T99lqT8TrByMlOg42Coj2UbOYbbeIBpTdh0MAYvLRFNJdxsxQPKGQXGX13oZ6qp6XoNCp4707yTaDWjcnLptA8aZyKwQ6BN0RkPbnUyBwfHBzUycnJBBweHx8nAHF4eFivXr2aDAP8Q937+/t1fHw80c0beugXtEhgYOcj586AyYbYdDCwNrijTi/PdekMNu7JayPAR3ve1MXzzst1H7yBxYafcdlRGM2582i9FAjAubi4qNvb2/r48WNdXl5+BSI8Zi/bdHxo2gJivVSYDhO8QX5WArCMdGV7pL8k4MMZzTFX1RqI99xxjinPOM3AY4AWCciey3XGIfQcOck/gXpn9D3H/DmwgN72m0B4LpcOnVeZOtC07nQOdgYgXVVfRciJ5iGH0O7g4KDevn1bW1tb9Ze//KV++umnOjw8rNPT09rZ2alXr15Nm76Qdffz1atXtb+/Xz///PNki5L3oEv2Hf0NH1TVV3qWMXnTlx1PzxE8aPA90kf0KYF2glXX47nq7B+06ZxIF1/r+CxBJd8Zv+83nUft0bfUXR046/Sm7V3ypPs714fnyosBn5f7uhBmh279v3dY4XmiUHMnVFe3l4CMwmkrAUzHZFY2MIP70DExREahZ6g6+5lt5PXumfzkr/M2RwZhrnSMPee9WLG7vQQdjgp0/ez64c9R+1nP6LnnxtrRe1RGwDx5gbEm3+bmH/MY/cilFvgk6df1uVNwjqRl/0bj9zzyZ6A3ikKP+mEg1rVjGlj52iM2PTpDU7Wec+jSKfQEqMwTho3cpi5SNipzS9R5XxaPo9NfyDrzNXJ0/Jm/GezhZHRLeKkL/Zv72oGh1KUGG/S700+WKUDrnPz6OYOBrB9AaKeRvjF2AxWPYVO9ULUuZ/BB8uZI3+FQVj3ldDNH7Hbn9XqZE8Yc2onp7OwmZSRXXDNQ3WRunmu7s2sJojwPc7YxweTcM914/VuOO0HZS+pz3+ZKytFcfXPt/GvKxoAPT/Tg4KCOj4+nUHQXPTMo4xqKxZ4R36+urqZjXK6urialbGGuWgeJmWNmdG2hTiOGB4bSZ8OJ36KRO6i2trbq9PR0EszT09NJuThyUbUeBfErtKrWJzyXOhirPVLfm8bZy5dEDGwoExhXVQvSu2WJNLir1WptyZu+U99yuVxLbrc3zri9SYC26KPnkMisDTB1eQnQUUHv9uZ5R1cpHmdn5EYGj345V9P86GUp18+7ch8eHqbcsNVqtXY2l40RBiDTBLwEkzltW1tba1EdDAPHdpi3bDANcvjfeWr0y46N6eR0h844V9VaxNebQHiWvMSMGGJI0TleIj04OFijCX8+7qeb49VqNZ0vd3t7W7/88sva/1nyjQ2OdmT0xW3B1wmUmHM7y16+T1mxHvM5pV5+BND5XEfnVVMfvOG3EtkAAzTZbAGPOTqXY+2AQRpOzwvF9MzNSTn/1hO8iYZnmSOe8VmKnk9WIrxkmv1hrlO3ZYSpAyLIF7rB89zda7twf39f19fX09t5iOzZQbGO3Nvbm45qubu7q/Pz82n5l7QW86nlwA4R9fEMbaUTCX9152Vap3R2jJLz2DmkVU88Du3SaUvHOwttdvbM82GbkHqLzwSWOabsR+KN/C1Bb/e9A9F+NuVs1K/nysaAzwcL+/VOnfLoQvsoCYwKoKuqpnPvvDvQzJNRkvR4vMSTytgGjOdZGraHz3lc2Wcm4/DwcPLCyBNhxzH3Vj2BF4OyjnkyAuO+5iQmLWzsoENVrYGcBKydN2eFnf3L6IKVEPPrjTQsO7LcQF3MS0Y0MrJgI8D4UlCsLGxQMWI2dtC9U0AeD9dHAs8fxsUbI6BHeuQUXpzuPEv6yf1pmBmDd8DawDI3zlvtDr/1slAq7IxkUz90NLA0rU1PlFsu+5hf+cShsuPV5e/xLOPzyQA+UJfokPvaLW8yn4z35uZmOkUAYOCUCHjHn7m0Y15NGfP/jkh5fAYS3j09cpDMV24bHoGWABWATcovfcnNEdS7tbW1BvodtUv+7mS3438c/DTyfHdfzVedA+EcWe7x0h6AAVuSxtgpEaannWDbCAN+y3vqbQIGdo4MuK03aT9lzbtoLePYJhxO0mlev35dNzc39fHjx69yCS1PuQqRvE1/7FAwjyn7HS/ShlNNRnrU82ubbcDptq33uj55DrJv1kmWGffRPJt2BnnIfncArAO85pnOnr8EsHXj+38C+HjvorfIoyB84K8VOUYJReNOYgQsLLe3t2tEq6o1hWCGtWDAFN6ogWB1OUGOyiSQ8gT6Xabk22QyrT1vxke7fCIUOXGplH0tadWBvo4ZbTRGjGHGMThPg9b1hX4vFuuvH3LeEpGl7pzFLJ7DDmj6mU5w/Bv96rw4LzvPKQz/dctytNN5s9m3zgP0GJ0ATvvuXypw99dgbdR+8koqsRyLgUICfC8lWR4NbAwiPa+eW4/f0SPGTp5U8lQ6JQYBPG9+9mHXflcuzt6IbpTR3FuvAZL8OxFXO6HMtZ0ej8P6sSue69Qd6EQbmgSkBoR2IpJ3vXmt0xcdsOuABnMzkiHziXMKU/8AaLNu7yLndx/DAy9SrAtsO1ySrujtbmnT/ce+kM9rEEm/6ANtYlOoh+t8d55dAityhOGz29vbr6J65i9+c3Q05WhOrzrS1xVo3dkf6JnPesw5dstw16/Oken6NLre8WvSqqtz1JZLyjXfO16zfch2U9d1Y+h+36RsDPjevn1bi8WXl0Vzgvnl5eValOzx8XFa9nTIOI3/crmcvOvFYlGXl5drSxsoT3sCKCwUJ8RwmJh78KDYgEF0wcB0ZJzs/ficNU6Udz2prKrW37AAoHWfO0FIJjSzsxzYCdWccHWeFcVCn8uRHr8Zz8bVS+xVtbbZgKNKHh8fpxe8P+eFUPdIITMuDNsI3HpOUgHmEhr3WMHYWGYUsTNoOf+mFXSBjsiCHSMMPdFlL/XZgNljNF90Hh9jgZepn3nPiJxBF9cT7HmO/buBn8dkh4t7nQ6AMfXmLpzEo6OjadnWkVAK9VKfI852HKAPO3HtnHZpB1kMjPif/hBRhW5EnhgH5zTOOROMw5tmUol7ri1/GY0xMLa+RB8yL5n/ZZlbrZ6WRbM/qbtSZyS4daqCo1YAT/NXpg64LTvY0NrRMDvkuXECej08PEz8ZBnPVBvm2nLVRW3tOFXVdJQPKUledWHXcII75ivtEWeasmua+dra2pqWpd+9e1dbW1t1cXFRv/zyyyRDDjzQbzvkjuxB+9wIY5DH/0QVV6unaKZp5nFRTB+/v9m8CX+n45lOxXOgLx0KY4IO3HVRS+p0H2073R/bpuyb04y6tKYOoHUgz/yYEeW85znbmuXFBy87DI/CcgTKYA3PIDsNgREuC1FndDvCuyQTOLLgPKtc9snok5UYBsdHazjaOCoeX0aCkmlGY0ohtCEbtTlXkkFtpEdGLwWl66OVTALKbrxuezRejFPXn+7+TiF048jvc21kWwZ95l3PbUfjrMuOienr6KNzUzvg2ZWcqzkadXVkFCWf556UFddnoG35yihkd91GPqNUXb9TxlwyB5Y8Xe9S7Z7r6NkV93V7e/urHCuilOQmJujz96TLJuNMuYOOmZuW8pi85HvsyKTDNKd3/XvWa57qnKznSjo6qa+gn+Uijb95a7kcbypyv1Kn+bqj3dg2dEGudLkPqSfgHUd1M9pJ8UoXDsXBwUHd3d1Ndjb5uZvf5/T53DykHu/0RLad89Xxtb8/p8fnbFSOdY5n58b9HNj0fZ18+G/Oro7olf3bBNBtOo+UF79azREn3rloQOKjBlB6HH/SMd329va0CSTPz6Pdbomhm2w8pJubm2nzhw+57Ap9J3qIR2OwZ+XssaLciWCiEKwYqvqjSOi3Qebj4+O0rE1xXsmo/yO6mDZc57c5BWHl1wG5FN7Hxy8HbnO8g5f+kiHnlFMyODznoyucGgA49HEqCZCT7rSRytUKF0NhB8SeMed+EZmzEmQZiu9pSDolCO/v7+/Xu3fv6vXr17W3tzdFJhwlYfwcibS/v19HR0e1v78/tWMec987BwqnzQDLBorlJee6dUuTjupk+zkGfh/Nl+ck+cT8jVPHGHzYuzdmXV1dTZEXnu2WvTJKlpEt05exHh4e1mLxZeXj+Ph42iTApjTnLDu9g3lxYjyyAS34zVFyAwbnqzJHfksRtGJFhnu8OSfpjBPOn+/z3HTgaWR8PMfwuaPBtGveS6BknvA1O5cAIkdqsB/wrvMm6RsRNXJumd8Em7TLkvJyuZzOcTR4oz1HVkeF/l9eXq7l7W5tbU0bOdxfIsgcxswZkuihdGSs6wySsc/W87ZHjN32x5EzrnvJ2qtQnc3Kujvg5/IcmOlsovm+A24dQO1WtFJHZ1udM+r70/nwddPG+sj6dzS+UX82LS8+lmW1Wk3Ki7dquPgl2njAzl/JySAvged4fROMvFg8nXKfIVfqSE/55uamLi4u1nbfOmqSChwhQGGQJM7hugilDUzV05Jd7mC18XKb/Dl5N5Wrz1iywjUwzInPpGHTgs+RVzYnfAZBzktxMeCzEXO7/jS9OwDSCQIGwoqtAzTut2nWAb4UOi+BoKTdd+4DlFfV2kYfeMFLjIAM190pFADf0dFRvXnzpt68ebP2ir4uMoWnz7l+7JBGZtz3Ln/UwBcDls6Ned7PsYvY80UkDccJmjvfjefNc9CKsWWuYPbZdRnsEcH/9OlTff78ua6vr6elNgDf/v7+BKLTkMLjtD+KBrlfzMHOzs40b15qvLu7W9uExvic42x58FjsRKbDa75PQ8Gymzfx0EeAJ28V8Zh43jqKPmUE2HNIHb7uPlquoVu+Ncm72JnXDgDQR+tZ+uJzLw1K4C3kAjtCn5HNqpr4hXFbdxg4GdwAEg2WvNnCdivnyzzNBkDfZ6BOu8zn58+fp7Ns2ahisJy63OMF7DlKad5O28oGE/rrP9M460n+8pjTdnf9NX3MZ/4/SwZVPF/ZH2MQBzO6MfjTesjy6+vWd9bhIyBpnZ3APXX2HCB8rrxoSZeS+TPucHoALr7O+UN+TVWCuZy4rr6cIACQAVgqNUpOvsFZKvwRsraQuF9V60ubHeCyIMIUKSAd43c0zdIx32g8c893/U2mdT+SDnMRza7vIybOeqG7BXwuEpp9zWVof0dBJ1B0RMTKk+cxznd3dxP4w1h3S/I8v7OzM+XGkgPpPL7lcrmWwF61vgmgmxcbHJRdgvuOVgbMaTjcb0dDTdc5HWDDh4ylI5HylJ+06WR5onk4dwZNXXQqi/nU89m17X4TrcI57I5y8YYK0wj+IwKV/fF8mUbuS0YlvELio30cOfJ4U8d14M7z0smsZXpkkP27o5gj/W7jm3PSySz87ui6aWX+92YMt2PZHjlIACT63kW00h5ZDjNoYRsKaMQh6DaspGwR5Sf30uPsIn1pV5P2tnuMs7NJpr11YdLMfc7nOn7Je/L+lMfU4d14+T5nR3P+sw8d6Ezg3o07r6WjkddHbY76nc9sUjYGfJwCvlgsJkXLjjcL0GLxdai4an0ZaLX68qqYk5OT2tnZqdPT0+nVMQgRisvJ6x0BnDuBl3N1dTV5TBa+RPnunwWNaE2GWPnOMw8PD2s5Z7kEkEqe51EEBgOLxfrOQjNreh9uq/Nm/GnDawXYeTxZ0sh3HqTnw30xaLLysPfbAZaMzHkOPRbTzUt1naJD0dJP30/xYaiOBGEsOTLI9doIOEmcJaIO7ELH3d3daen21atX9c0330zncZH07zP/UBLQ0UdA0HY6YWnMAAEGdKYNPEGkyfOcCt2yavBrUGcecqTPx604ypDtME95fMly+WUp7cOHD/X58+cpbYMIMJEuxuDNMPQ/+XYUGUg5WywWEzBnCZ63BbG8SyEtJB0IolHWiUSKHWk1PdJAkn7ifgNolsvlVwDU88bz0BgdxpE19IP2R0bOfE9dpi/XDZKqanq1JvWlnUBneHco9+R5qVVPS9le6bBs8rYldoCzqkR/bT/oN8/bDvCJc1H1dEag9a752WAe/iG4ge1arb5ECrGdpGh8++23reOJs3F8fDzxOitDlim/3s08iSxYN0Nv0rCIlLswB+YDO7KdHTH/OWqaACydUX830B0ByOyn67ZO9CoG45mL/mXf+N9nJnrJPMfMc9Y/nSOdzukc4LN8dUBwrmwM+LzDxoYniWtD7QGl4iHJ2Uuo3rE2Cn1nQVC9QcNgimc9mR1wsdK355yeAoXfzfDpgYy8CRt+15+M4GseQ+fxuf4seX9GOUd1Zb/dr/RGuuehpcfpXXC+P+vP39xW1z4e94jeVhxVX5+K72sIpyNQ7IZz213eRxf1dvF12kLJs4zrHZ+WG4CeaeL5tDzCl3YgEsCNlKV5s+N5jy+jcR3QTdryf5d03ilcnnPf7HD6XbgGv+a7EV93ihQ6JNhzxA0wl1FZj8ERvuyDVxMcMTIN0bmA9Jwv94/72REK/3jurE8NzHHQka2kRxpdj7Fz1Nym57Uzsq6rmxfzTQJM+mujy/dckWI+E9R5DOb3tFV8Z76sS23wU14SEJkncNAAZ/xG9D5tq/tA6oDfu9vpndShI13Ntc6hTvCSn6PoXv4/sh35vaNj9r0r7pPrGNnN7JfryPtGsjCiz9z9tmGjdueKcUknO3NlY8CHd4RytYK1kVqtVpP3Q+eIXHCyPTl1efxE1boXkQbPhHVUDA/O5+2lgSG6RJ/4tFL17mK8RCYFwfQxFxkp8hiqvp5EvLHMBXQfGe9ICXaRvbw3PZWREfVvGeHoDH4yfzfGBFgprKZrF9bullOYu9zAwrwQmbMTYjrCK0RlDHrgYxtIR3qJ9FV9/dJut7G7u9vmaFAf7x82mCPCx0vVeS8zy1PQw4coU7ePhekMi/uYR+YYFEGHnAfz4Gq1WgMuBrr8+Q0ofgNCgvAO+NkIZl/oH7rm6uqqPn/+XGdnZ3VxcfHVEq7BuOlPyWUYj5dPA2hH5YgKHR8f1+Hh4QT6mKtcpkyj4Eif29zd3a2Tk5OveNubNogidQ4m9DFIdSQDvchntuPjO8gb9Ri6yF7KgJfqDcoSlFbV2hFEqUO7tujHarWa+meHhv+r6qt3Q1etvxccGjjabRvkiBpjgpdM267YdnkTFXPZBTTI06OPBwcHU067ectHJyFjx8fHVVV1dnZWe3t7a/xiMOm+e9k+gax5oZv7bmWks0PWfZ28JajMerJPvp4gqQNY2QeXDjCOAGX36SivNzi5/gTDtoXPgbmu7yNQ7La7YEdXXvRqtdVq9dUbKmjIr1qzIqiqSTne3NzU5eXltKRHDp+ZCoVk5kolWvUUKmcZx5E9ezpWfAgcRrfqiYlt5PNF9bSDEPl4Fyu0XJZKMMTmBgAjgufwOp9d2Jd2TJORkeQ5g59R4Xqes2aB6uag84YMuBijN3IwVtMxlytSsL1UmYzNkko+Q33wKB7xycnJWr3X19eT0odv4YUEajg9tON59s5H88dqtaqjo6P67rvvam9vbw0s8GkDA82gC/2mbdojwdxRL88JfbTROjg4qIODg+nZnMOcS89zKhrLp2lmw9+d4J/1W8a5lsCJ8d3e3tbZ2Vl9+vSpzs7OpjcNpAzAyxhLGzuDZNrLsdCmf9vb26vXr1/Xzs5OnZyc1NHR0aTDUhYN6lKH+Xdk37zFswA0wAJz7eVHO7jotgQtPEMd1o+mLX00WHd95r3UC+Yz6mQjUL4KsarWzl/kd05E4Lvni7G4zYzkGvClzvNYeK2ZN0Ux5p2dnbWd1J4Pz1vmXNKGbWG3A9avtcNJpA/oKVKdfNA/NCW4wuqYU6JMszwqzbJE33OFgzbc5+RZR+TzfL20WZ6Dkd2wrvSGww74+bfsZ851Vzqg193T9SHrsDOGnRiBSPgC25g0yfozWGAA2elg+KN7PWRXNgZ8FIRrlNBpAbWyS0UHMZg4h8U7ghglu9iL6ZaFuqgH16zsHeHrxmzlksDOfUx6uK28N58blXx29H/S1fRO8Nh5HB1954rbTeU298ycMD0nmNnv7H8CZupL5d/xsHnCCfAJiCnmXy+rpDJlNx1HqBApcmK9AUCChWyzo1s6KHYgLH+dd9wVwEKnfK1o/Hv2exMlO2rbY2VcLOOySmAAl89mv/1pBzN1TSpb73Z1RC8jUS45Zkdl+ISfsg4DPhsJA+HcCNAdO5VjHsmL+cDfrUtHkYk0RHNlru6R3nQ/O4PeRRS5p9Nvlg9HAA1ysw5AjZ371KX5veu3wS3fAQu+38DYfUdXwa8OaqTspf1NORzxezcPo7nt5uu5ecx75+p/rt3OBrzEfqWeyt9eUrI/7qNBcALeUb+e+380f5uUjQEfDOajTnI3rD1KkkftjdmorlZPUZHFYrF2TpKLN2NkMjNCm/lKJr6Thh3KRumyZOccHLdtL9qJ+TmJCSi4lkLoJOk0aC5WFv4tv/vT0TN7/KY37WV9/t396Zi0EwzT3l5XZ1Q8PkAJc5tLBCng6Tj4Nx+NQvI+XrSjl7e3t9P5keZBlO3h4WGdnp6u0dQ7Hf0GgC6qCwhBBo6Ojur777+flpQzkb9TMo4ceU7gPfeb+kwry6Sjpk4nsLPkMVQ9AaFMSGa5ygn1VfUVCORelqm75e65PC7zErLHRo3r6+vpNWm0bRnzby7wR8dn5mGuE13l/dmnp6dTJLOL6llm3fbW1taUiE/enyNHOX7LjOd6uXw6duXu7m56g4g3HBjIwqMZXUfm0igBot2f1GXmRffXxcv/afxGdXoDkOnLOIhg8clyGjQZAVoDP+7d2tqq6+vrKXUCG0C/oKUjOJaj7hSILNAY2bSu4pxGeKHqaQUpnQno5uOfvFGMOr08Tdu2eaalZdvzn/Pq6KKdD9tkR5IT4NAPy4nnptN9CWZGoLJq/NYM66yOLxgHYLvTv1300AU6Izdp76zvTKeq9SBV9jHr6RwZY6BNl3Knfr/kZhsdTzq/wxT8jbxgdxgBWq1Wa0bVE0sbLM3ZcJm5OgZxf9w+QpLeu+83iPQyXUay0uPyJ98TrHR0HTFo1sMzXf1Wtr5mYXzOixl5DclwSWfuQdD9XNff7GMyu4Unx9/lkXmjgwFKLjdx7pfn0ct/9rxtePjfb1IYbRRZLBZTHs7R0VG9fft2WkbK4ztcbPg6Gid/dk5EKkS34yh1Z6xGzgD10V4egTHK7zEA7eR6BHg8XpYv7+7upsOV/T5c5yJ1c5bFvIbcmc8y4srxFyzVdXLY9d909+HaLAd3DlE3F6lTMfR8drSnHwmGU2Zox6sYXT8MiEb6IdvN+0e8loYt6zLYcrqA04q8zJo8bBrD/zgLgCTLdwKgjKb5emd3+D/tDMu36AD4ina7TRLU5yXs7hifDiSm82HHNfs9GmtnXzsbmPe43Y4uXM/c5FHJdvJ5/z7Sn7YrpnM3Vv+fmMTyk5v0qvq3qyStPdfmlbSB2acEfCO5GZWNAR8JoldXV1MH8DStLBEeh585JoUNHxlZyDB3Va3d53u9ecJIuhMGExrlTYSCw0gdbXEb1GWgl0u6I+DEJDoqmmA5nx0xKOPwmDw2P2dQ1wneCCy6PKfMu3rymTQkVdUaU4NTMzFKNaNnrt875SwgvqcDp13kkt+IBGKcq57mjrK19bQlH36Ft9K4Up+j3RzWa4VjfiPPqmpdljAMVg70p5sDKxLG6rxX5992+TyUbo5GHq/zDD0unrFTY/p3in+1Wk0HyqI3fJiyIww4gkRIOkXInLjv6QQ6EsacAdqJliXYoj70g8FJJ4vuWzoinsecj84Y8926CXpn3qsjvgA415VgL+cn++Z549M86fFSH78jU2mALatzDjT/A76dD+yolud6pEuZU9sA82pGgumnD88m988ba9gIZnqzMmB94BQowDxvpSI33YDf80ikmNxkb2CsqskpTQBr/Wr9lwDD88ZvBjimY/LlSJ+kvepsR6eb0z74es5pp/e5J52erKfrk53CzhalDk7c0QHK7K/bSSDY2f+OnpuUjQHfN998U6vVl1fAbG9vT8chwJzn5+e1tbU1HSCLN15VE+ADIKbygtnxoKuejJIH6eUGn8WEgPkstSSQc2hInO8MwHL5lBhNH2gvAagF2hOAwvASMEusmew6N2HdZPObI0E2Onya4Trh60DfJmCP+xOUdeAyo6Qoys4r8/8deB8pftPRc2OBybbcV9paLJ4iciy52JBY+bMBwnzg1/Fxfp4NIctCvBnD88YSc1WtLVViKLzxhChBLgen0u0UjPnafOjzoXKeM1Kbnih17O3tTWd10p6jcObRVLY2tjZsV1dXdX9/P73C8fb2tq6urqYzy1yfo/9V68ff0F4HStMBgQ/YUMOZiNvb22vpI3YiXY/nuuM59AFzmZskzI8ey2gZiGetAwG+ubM3I6qMGZDsUwxGcpPymnlpnsOUR94Gw3igX+qxDkyaJ2kX4GQ74N2TXcTE+op+3t/fr23SoF9dBB5+JVCwv78/yRKOiEEhdseRXZ61fgQ4Xl1d1fn5ea1Wq/r++++/mkPOA4V32D3+7t276a0yyAd09lK9P7vNJx7narW+Gafjw+SL1MNz9fsznVh0s+c92zNd3F4XIXU9lnnzgZ2etO2dM2Fwlv0aAeYE1dZjOc7naMf9LykvetOGFQYNpwJPI1m1/v5GEyqLlURGAV0Skft3PhMg2ItKJZL1jtoceSNWzqkozUyJ+DcpI8CW43Qfu75s2hafCWT5fc57yj5193vust0UmFQiHS1syExrt5/LHV1b3fiXy+UUOep4x5+OCvmVfKaB+5L15PKL+2bAnHOedJ/jFSst1zmq17Jjz5qSEYKX8NvcODDgduK8UWMT/na/AIKppPM7c2Pn0HqjAz6Mw3qjo5Vp7GsJ2HPembNRbp11S/7e0SIjrXOG+bn5TDnPa5vwQyfzBu+mT9IQGWLOUg91zkUW09gbKFyPPy2n5o20a51+sR5ydD8LPN6tWAHM4TnqTkeGPo7mec6edffmuLr7uvnJ68+V0TPd52gMm5ZOnkfjz//n5GbT/qQe6LBM14+X2nWXFx28DFN7iWC1Wk0eDNeur68nA1hV01Eu29vb01ZyrjEQPCS8TLbPLxaLiZnxoAwg3ReIYaZkOSbP2etAY3oURD9Wq6ct9VxbLBaTh5XKlbq6Y2L8ftQEPi4dM3Tgzv3J5SL60YXus+CBVz0lpDoymTlzOd5kVnv9BpBshPHY8FbZUOFlQMbwHJDxUoXnlkiKvXy83gThrtPLJvTFYABAAE+xC9fLvNRj4NAlgVOIgEArxuTNRO5z8kFHG99n48b4nUviiIZ5rovS8clc+lgN39f9EQUxD2DkyM+7vr6u8/PzaXOCj14CEBismT/pd4JoH7mUGwNMg4ODgzo+Pp74N51caDFyfjonwv3kDz5yFIb37yJ/KbvU8/nz5+ltCAbCHnfqGEfF2LRE3elge2zWqdmGn7d+2NnZWYu+eDx2djJqgj4nEubIKqAOeeB/ZNRH1Zg3uyM/zPeOdLPJiHFZ3gyqmAfaZtXLdgk9YJoi9ylL9N8A7/b2dlpRw77e3d3VxcXFtFLA+N69e1e3t7f14cOHtbQr5HfkMOQ8e77dL5fuGY8vA0NZt+U05cQ8Bp/kfbZP3AtPdHV29sO2MNvLZ3OvgJ/vaNGt3FnPjCKFSatsLzHArykvAnxmGgszoe2qWkuohnh+nRZLJc7Z8RIGbWQeEyV3KXaRPuq1gfV5OV19VV+fru88Lupkcmz8rZANuuyhuT8osQQ+c2UTT8FMmICPPszV0UU9/XqdVJpZZ9bPeC3gBvUGGN71hMJPsDIas+fF0RXmyeckOXTfRWKyoOQN+Ow4kKfHGXdpcM0vBn1W/J4jNg5ZFtK4WlF0gNv/p3GmXjtsAMwENp2j4evw92LxtHPRc+42zIMJIJF/jPTNzc30x8vp2Z3qfNhcAur400oYXjCY8HPMj1+t5zo2AXser5+3zsg/AwxSZQB/pj/9Rxdztl4uMyed03nIvnc6tDPondFx3cyjUyzcH/rpaCmybl5J+UdGUv9yD/LHaz7pq3OlLX+jufL43RcDQgN3QN3+/n4tl8vJ4SMCl/o09ZJ5JHXRarWaQD2bvba2tqbd6svll5278PPx8fFUFzuRbWM9x52TayBmXveznpMck78zNo+lkzPrOBf6aNtvh8a8Rb/om3Vp1tnxbfa720TS2Z8EzObL1Wq1Zn9pO+u2Hnc/Uqd73pLmcza9KxsDvkzotaEy0dkM4Q0YgDz/WWk6OZ3iED2gyYm5HrA9YRPEk5CKq2OM9Eo6QGnljFLmdz9jIJnPdMzWTXYKpu+xR0Jkzh50B/K6XBgXK6pk4M74dx6YS26sMOOm0TSd8xmDmxSGVFRpEOxtdcaYTzZTOHpngGCDDchzxMj12UvM6BGg18bX/OuoqulO/52fZBpicM1fVirL5fqhvTbUfALYzB/8bzraaFqx2Wgz/z7iwgaU6/zOrunr6+sJ6CHnz4HyrnhFIjfWOHLXARv4OkHACFxbb5hPPDcZkfR8smRNjjMOs3k1gRz0ziOiOh3md6pCc9p0fYxlRO8R8E+a2Pn1b538GvDAp/A89DEt83VxOQ9Ewvy+23Qy0mAiAylv7rv1T2eAcSKoH1kyDz53fuPIqPta2gdApx3IjDCl/nWxfCRNOwdg1OcEiyPZ8T15/bl7nqNFx18efzeHvjfHwnMORnS0mQOGvieDEVkPZRTNS/v/UrBX9cJz+GjAAA1lArPnGwEWi8WUpF61HiUyQfFeKPYSveHBidNVT+cC5gSn4U8QQVuOotBHJ7bm64hsIDn/qQN2Zp4ElznpKSxWwhZIxmLjSdJuVU1GIwEn4+uAnJXz7e3tpDRtJFNATKvt7acXSKfAOupjYOA+QT/vGqUO5/EkYHbJeQYEm7cMeIg+2+gfHBxMy7KZEG0lzYYNR/rcPiDRyiVBGRudMgLAjtRPnz6t8YTP3vJ5lQal1A1QYqzJ8wYL8BBzkRFJz6Vl0vJkvUDE2nNqec1oH23f399PO3LZiZv5SzZoqWATSLl/5gWfgWi+SNn0M5bldKJMG8uXN224Hx4DUS0Okr68vJwiOjc3N1Od7lPKgfmZ+xzBY/eoVxkyipogMZfGTOsOHHfXU/cw17mZwnV0gND9ZkmX+TP/wJtVNb2ijnP2oBVjS+Dsec/vpjfj6sZN3YeHh/Xw8OVsv3SeiQSOgFMHFDqbUPXEFzgL8DMrDixL+/5RjqOBaqcXOidiBHyQKfNOnkphmfWzjsgmr1t/uo/+8yphzo9tA23l85ZpZBiauu2MXKbTlUDtOfCWfbXtt93pHIBfU158Dl8CNxOqW7uuejJYnqwU/I4gTKAnOL1+M7aNZH4fjWf0G215krOe9Pz4LZnI9abH/lz/umsJaLsxdyVBcApHFxWc80QY+6jdFKSunrnx5VhfwuQp5IzP/Ef93OsIUP7mew32Mm/N/fX4Utl011MBWdl2CsqRSitIR3/cl1zKdT9cV85T0j75L/kIGbWxTYPu+/zaL/J9u/vd9nPFspGbL/xnrzvHPRr/qL2uj6ZV9j3p5Agtv9OnjIB5laLjqeRL6jPoZuk0+51j7fRBR6sE4r4/jXfWxT1zfG5w2wEk5BWHLnmz66912Cg6kyXBInTulgOhb0ZpKdlmB8rcXtqEBCrW7Vn3c88lDz1nH/3bJva2q2cT2XK/uvazzu7/OcDU8Yaf66Leczwzstvd76N750ra1JeUjQFfRk98cGQeRlpVdXBwUK9fv66tra0psfjz58/T8SzkPKXCyZJRNgrCRdJzFvpKtMbRCUcYqNvRJoSMw17NEJmUX/W0bGKDm99dUtg6Y5pLGzzHd2+I8JJNAoAEOG7DNDdjG+zm3GR0xwA8wa+fy7qsnOAFHxtiOnnZp2N2G7wcz2r1dKSOz7izsneSuN+CkMt//MbxLRk97kBUx7d2jNxn5tyGI4sNSBdd9DwmgPMxQ54LRxoN2hJAJN0Xi8Wage2ASM65eefm5mbSCR8/fpwinCzFUTLZP/WEk/p9mgAAgLlKPUB91h+OnqWi7wBxAhnzQc5HyhY6Bt3R8YPr929+y4b7Amg0bfLPkRSXjF7MjS8BnfnN0frkbRtu+up5TV1nPdhFZBO0M//kYRL1hsbIesouz6ctcVQQJ8+gk2cdZXdeX/YTW+EghnnLoJ+jzaAndRweHk4bJeFtnsHu3t3d1fX19Vfj6YDjCGinTk1blvYynSdK8lnyOnRLwOl78zPb6Z7rwJj51vyavG/eM5+kA+5nRsCRcdm+Zr8dhe1WsTrb9xxQ7sqLN22gVB0+dhSEzu3t7dW7d+9qb2+vLi4u6urqaoo63N/fT8uGDDYZ3wIIKEsDhMHJZQjuc2J9AlIr3AREEBzAR11MCsvW9DnzriwAHWMwhpy4DgDSV+6DcQAOy+XTOyHT2HfGm5JtZfQgwVsabPpjAMqzzwG+rI+3UbC0h5IaeY75u+nOuL1sZiPu3JocH68+M/8k6HOunyMoHUBmbizE5sWOnq7TUZ2Uj4xUWSGNPElkr5Mj32da+veOhxLAdqDDcwcdAR+8OQPA5yXITkF67px6Ydl3vzMamwqcMZiXvVSW+ZmMMWmbsp/07WTGS47pFOQmBupHX3FMjZ0d0yT50icGGDx3Rt7F9+fuc8+rgRhtdTrO3xNUpGPntk27EfinHpay9/f31zbHJQhI+aNtyxQAjnmxM5bONcDPjj5zh65eLpfT3LkfPoiZ++i79dZisZhy4ll2XCye8oA5hHmxWEypAR2feu7y2gi4eO5yXm3rTFdvvEhQ5TlP3Z7tuY+j4nbNY2knqtZzrS2zafuyn8m77lvKcfbV+iztKoE0+CrxQ4cLujaeKy/etDFiChM2JwzmTSDUlZGC8LNWwHg+HRPn/zlpI6RsBZSRDSuNVPJzAjMSrhH9UhG5pFBY+aYCHD3v+UwadMtdNizUbWZM2mUZ9YW6k0c8R7SR48x2bci7dkcGqOprz97Rnc4g2bCOFKh5NjcvZB9oj6T0NLQeI/cb/DxHW9PUxsrGzfTOOg2Y5hSPlyZ9jT/v6iWPjONFHIGizaSp+TIjB3n00tw7iz2GjjfhtRGA7hyRpEcamOQ/L2WzacVHe2TkwcANOmWUL/tv0OC5SZ01KiMjOlcsi3P1GSR2RtC05BPZG/ErIA2ap8PmOh3Byd8o8Fs6xCMenSsJOD02+u5CfroBp48V6njSQLBqnY8TQFAcscw8as99Ny/P6YORLezshNsYAcF0qNLp7ejodkf96sbBPSlfnsfO9ucYn5OfxBFzeGLOhm1SXgT4UDhWGCaA0aoFg7wcjhBwySVdAwwrOBKcSYbFc8gIn/uT9aaS7iafuueMN1ES2kDx2rgytjmAjOLqJtDeQNI6x5DLJyOgyieRAdPRBcXihFQr2lx+ydKFvUcGEwXNsku37Gia8WxGaeiXvfCuLferE+LVarUWxc4o32KxmHiZN2p0bVCWyy9LOLx1BvqmkuL309PTqqqvaE+/fLRQt8RIH3IZD7Bl/oFPvMEl6U29RPPpW3qyVo65wxYawEOAkA8fPkybNIjsUo+XrD2nCfTMj/SRMTF/3oDBnHRzlstu/O5NUyMw0xkb5s7Lx6bX5eXl2lI29HGUh3kn6ufzCL1MmcXpERyVZd3d6eJO5hhv6qEsBhIduPL1jNwyBqJpmbTvetC9doS6slw+vSOXqD31JbBxNK/TWZ63BNiM10uuHRjlmc5uVtVXR4Yx1qurq+nYGfQS3xmndaHPJEydnfNs+4GttX3t0jjcbwO0Dsgn0DE9O7tku9k571W1RnvrZPfV2CPBrdtKcNuNFdrknKYsJI/7fsbSBWY87gSRpo+fS2zwkvLiXbpdRxKMeDAWsKxj0/YMOFhqwSh00Qm+J8PkJLjY0PieXPrrkPemEcs5lD8yIEnnNCjZN7cxKjB7NzYzVbaHchmBsaR3J2zdeM0nHRPP0bb7e+45rnU87Hq7KB90sDIfKQvXu4lwGrxYqP1qJIMOG2La6PgjHZFsk7qc6O5nq9bfBtIBDBsOG1QMqSNSHJnBa6L8qrdubtzPpLP7nQcr+7gKz5373BkV7jN9U1+MSqcrunm3I0AkJ4GeN3XYSJmWo+JUBu7NcwxHclzV66OM8ud1fx/poZFjNNKL+T9jYok020ne4NNLqtTriJ3Hl8ECt+3nfY8dilFJOX1OT3uO8wSLET86sp3gNXVW0tw08dhTB26i/zsg19nitGG+3vGKQRMlweHI5mR/c2yj+UighhxmHWkPNunLaA7m5PAl48zyok0bDMZKlmXVq6ur2t7ersPDw8njubm5+Sr3j0RajpnIJQuIyTtFOXUfDwRCOPqSxKNfPvdvtXqKcNCu//cRIg7hd9EACw5tdgnQ2Z4nqUP4nXJxO26bQ0aTOTw26oEmuSRnI+0xmuk6wNOBI57z/7lUkiXbSxqZ30a0SrBKsWPAOO10oNiICHkThoEDdW5vb391rEJ3iG3Sx3UiB47OJX/YG8yogOfESodIj+XD+VnOietAPGedGcx7vjIVwvPnMdMnHwbMvPJb7sjN16WZLxLcui3/7nMRmSNHijK/2J/m71z6M40Xi8W0HO0ls27+DEz5ndw7zyNRzeXyadMLYM6rBvRttVpNwBD+cz+r1s8qtR7o9NbIqOQcd0YRmiADbCKoejoeynWMVizcJ+bF+WrOgez4IvvftcOByN6wZZrxnN8IBc/AR+hc+Jl5tR0zz1MyGr1arR9gzJiwj16dsezm+KpqcpR4ttOhbtvF9PdxKq4/bU5XV9oF2+Bss3NMrXNoBxnwsSjmgwSInX3J6yMAmjbO93QAcASU/Zl6OnVuRxP4udODWUY2b5PyYsDn87+81ADgI6n94eFhOk8MRWnAZ2NMpxEazuPC++c1QBZ6DDJ/Vs6Z6+DzqHyfARlRBwBsKpNU5DZmjI37Fov1189liLsDKzl5zoPqhMjLwZ0wp5dq5WzF5+RqK5oR4Mu+p6dphcxfF9nNurqIQyp4K7ScB4+9M2TOX+IZgJg3HtlRsJKBd126ZYdOaDMKlYn8XnpJINgpH/8PwEr60WbWS4FmXjr2/FMyOtYpGANU/ugT/wNUMm/NeWppSByh6Qr3APQc3TC/+3nG2EUpuWYjbYfIBtAbPQweuqXxqpp0mWWB8Ru8mpdJb3CBXpyS4LF4Lh3VMw2TpxK0+3vH21VPzqj5eX9/v46Ojiad4qVXaGonegT6DPg8/93Kwkj+Uu6h293d3dS2aUMfiLji8NnWLJfLadk9gwMdgEya8X25XE7gDLmxs2GAiV0yHTxeZIz6mJsO7CWdUsapg80kc3V0diFBT9pKt8tfRt8NZPnf58FybzqI9Ps5INbp0RxXB/KyjO61THU2uaOVadHJZge4N+njqGwM+DpmSUJlRzA26VX7uutzvp4VgpnHf/bmun66vwm+7JHNERPCd8qlmxzG3OWDjAQuaenfRig+jeNcvZQuXO8xJr3m2sz2RgyeferaTYCZyxF+tuv/qD8dsO6K5xcllDk1novkRxcMYddGyoqBVLfc0y0fUb8N5BytUlYomfPS0Srnyv3xH0aQCIkPSHekieVc50FZzlPJVY3BnvtusN7Jgmma9HRdbs+KeNQ29xt8m4/4tLNnoJhRwqr1Vxkmn3SAh3YTfGYerPnguaVHjy//z7HhLPn1hclPnZFzvZ0OSZuSvGx9bPkxX6ecZu7YyGBDy5GNcPsdD4/oOXJq3T5jyPqcomA94uBJ8lv2x7yU+qGbj+yX6Tqya6PfRmWEHUxbaGcajmz2CLT9a0BSh3c6nZnPjOzlHGbZFM/MXZ8rL4rwQWx75QhYKpjVajVthzfg8zKUE4v59Ll3q9VqWibm+dVqtbbk1oWxraQdNcRDS6FOxZIFZdFFwOwlphGGJltbW9Puy0x4z/5SjxXWSHEn+LHnY6ZEQP1uTraAG9jRf+YtoxVdlMjGjHY68GI65pKZ22a5zB64l8uIamTb7pOT9rsjIkzHbo4PDg7q6Ohore2qmpY+ndCcIIj7uGYlzv9+h7LH4rl0fY7+eCmf+jIa67liudr1uB1KpkbQNvTEi/Z8WoYvLy+ntxtcXV2tRfeWy+UUXckctJS/XMY1KM7rzMXe3l4dHh5+NafOfxoBnYwkMT74sltSpN2kF2CcaCPnlcKnHPGUhoLIEjTxK8EMIt1n+lFVU/SUVZVcHYBe9DnTGygpDx2tkvanp6d1cHCwBjQdkUswlPop28p7LD+Zm8lzPqyb31litlx7rNgb61IDZd46lDzjpV766F3VnlfrQuiX9LRthHYHBweTrOMAHB0d1dHR0XR2rfNhDficlsA8J1BJUOx5SvCS/YeOri95xvzttATfR7tJM88HuhI6bW1tTWPOdsw/ufo1x2v5/Nx92b8EfUlD2+AOY3ROSQce0yZY3/9fA3xZMRMAY2XpwISNo71RFBYvDq+qNaG2ccUAGWSMhMpeGgI1ilI8V8zsMHCOqWp9KZYxZsJ7By7TsNHmqG+dd+Z+5HMWLivAzuukj+SnjIqNXNeH7v8OGBrseNnIggJwnovqJsACqMyVzuMFROCYcJ1+eAye1xxrCrlp7nvd766/KfCZ3O+6OhBrsOQjXlznc8qjkxN4CmeK16PlESB26iyHXfQkxzwqCcAM3KrWHavUUZ1jmOAS/iPv0s+lojYNbXDNR+S1dQCTpV6MG7S1fPgZ60DPhcF00tUgOg21eWeO7gl4Sd/hDE103ciodqCia6/jB/runZkOQrCk5znC0Ukj7f6YjzzvzFUCFs+9+5t2hf+7/NkO+PkZ9K7HxBJ+Rikz5aCLPiaY6MBKR/NOtxh85Ty6TuiZz/vezv4mUPOcIMfpHPvZTfgrx5h8M9Lhvj6yv8+11ZXnVjGy7ezzS8qLz+GzwBt0WdHx57OA+CNvhd8peMYk/toA0naelP/VYMLzc9v2aC2UI083vYcOOHbgzWAgBZNxjjyTZK7OiHtcnTfSAcAEBqZpzpH7mN5HRhxzLOlZdV6LFVvOU7ZpOvLJs8xFp7i69jqjZiPiqAURQgxYB3RSsY/oz2+mf0YcR/xA3d1SVBcpzn5Cw8wv7fqbRtpKxbzrwpEfnz9/rsvLy7q4uFhT0vTTYARg43Y8Lz5OJcGDDbejPUTS3G/Pu3WXeTmNYSc7Bn1ZfwLH1H8uzgGFr1nevr29raurq8nhdU4ln3x31JLcZo40Ml91BtXzgq50ZGrOgCF7zNHJyUnt7OzU0dFR7e3tTUv6Xe6x65gzWMkLnkcfLeToK8U6a7FYfLUywW9s4Ej5Sb5hrAbiBt2pCz1G7jHgMQ9aHjPaZoeJ1S0fK3Nzc1MXFxdTxP3x8XEt/STtUOroBGK0CcildHn2phFybXA5Z886EGZaQHdoknajcxY8f6Z76rIEu77mtru6/Zv71gVKqH8E7LJPHpf7lraiK78G7FX9ynfpZuJ5B/i87IoyYgOGd6xtbW2t7ZRkKc1Rv5ubm1qtVtMruGw8MnpB8qkTmQ3SzDhWJAkectze6MHygc+zs9L3pBvsoixRBvbEPckd+ExGZRzJ4J2gpEA6UkoOTr5VJDcW2KOjrtxNRn/S2KXy9n0ID8LeAUbqMM0MtlP5dmP1PHlMPqiXP585Rz1eYrYC7xRZV6CBgarPe8z59fKYn4d+jlab3qk8nIqQNOKz4y/a5nrnYN3f39fl5WXd3t7W2dlZnZ+fT/Ls+hLsIf8dLyDjVbVmtBkn/TAodHqHo5+WWT9vOUw+GoHwLvXB+s91d86OozPwMq/Ourm5qfPz87WlWeiSgBaQx8Y2A0c7CB3IM7hjc4s3/HS6xmOm/v39/Xrz5s0EYnd2dtbO0UxHOA1g6rhRsV6mXvSVASj8kLaJwph2dnbq8PBwzcm3DXB7/O/0FsBiriIBjnIFKMdBSgj9pW8Jykg9Ojo6qsPDw+ncvdVqVRcXF3V2djbxMDp8DvCZDu5T1dOKVIKYBEE4WblBsqrWxg1N0q5Rp50r2xmAvB3wBOEOKNDOnP3NdKFOLt1G2s2MACOPI9vd0XeudLZ+dH1OLl9SXgz4Rr91HUpDlELlCIu9dQQt64ZJqtbPJRr1y4q8i/B1Ez033gQtea1TbhbADsj4vo5x5vrjdn9N6eqz4nA/c3wJxDbpw5xAJJ1830sEKcHzXB9yvtJoz/FF8k4XPfP9VoaZaD1XRjzr8aXyShoihxnRyD4mfQDhrjv7hsHwZqs0uDauLjnnudTZOQ4dqHL/Eoh3ij1pzjifk79uPOb/jle7ObMe8eaWLmrbGYEEtnZ8PWb3NyMkI733nNNSte4oVa0f8msgkH15rr0R/TvDX7WeR0exHHpeOyA0WppdLJ4232Wkjk+3YZvge7s5ISI3R985WSPgsLW1NUX25jYspb7L8fL7SB9wPcc7ujflbmTX5uzurwU26axtUkb2Le+xDt0UP1hfPXfvqE9z139N2RjwdUjUCZR5rxG5lVPVk7eEd8inj1C5uLiom5ubWiwW0zLv/v7+xOQkgVMyfJ6J8SRDj5SeJ8fonmsoDyvYZHQrJcZZVROQ7eizWq3WPBrT2J5K5zVyX+Y4ulhY02uibr4794g8khwjtGBcjNHRFL5n20lz+CPzcqpqOr6jav2gUcbQRZ9sbGwUuzHwneWM5XI5zZP7au+fQr0oYHg0z9GyEU+j6PF2UVr/7+hNetG042uOmtuwwf+O9FB8X0Y8uI4zxvW7u7vprEx76ESKLSfuY0ZkF4vFWqTVytURTsuoaUZ+XDeukZPleTJvwFv5jOdptVpNCf+OHkLvvb29Nf6rqrV3bfP71dVVXV1drS3jdkoeOUGHofu86aVbNYD+RPG8HOr+dPKR4AM5TXljswmbdhIopY51/ZnbnLrLQIrnvPHFb0PJuTQwdKSc+8itNODmO9Hih4eHyT45Gmq6uH8dwPP4PI/0xbalAyzw2uXlZW1vb0903tvbqzdv3tTx8fG0mQP6JOiGd8mlTVBGsS2x8+bXPWLvLZemR4K7DnAmMDav0o5tFe1lf60L0sn0MxlxzropGdHkXjDH4+PjFFW3jfNcpawQhbXeNG929LGe8TylvfOzHQ7ryosifFkYeCrgThDS8O7t7dXx8fEa4Mvoyv39/bSc4fyq0fIKJZPaIbaZvaqPvlV9fbK228mxuR++r2od8OVrqQyc3JYnFaXpxOGMmqYhy5JjSEbKJS2Ex6H7rn/cny98htamSfYh5w2A6TlJ3sq6cu7yWYOFUTH44f5uySYBPP/jbXsnrJWmnYcuCuLlfejN/+lVur+pqDyHqXQNbBzpph4bZ5eMaphfDXb545pzuLiHYtBuB8Y7LzvDncYwcxodXXR+V6cYKR1PWu5H9yMDSR9AQhofFLHpTioIS7ru/6hAY5ZNLZvus/Utf3Y8zPMA9LnotEveC+gn9xCHwtE/68ycD+aS69ZNpi1zg9E1yK6qNWfaOtx6yPOCLUk9b3rBoxnhszy7j9lX+kd7Tv+xLekAvsfPPLNrGMDPXOzv76+loHg3vPW6N28lgEj9X7Wes49uJkfTOs797ABkp6fTKbFMJR929jrrT70xBzpHdeX9WbcdrgTTXb/4nSCCbb71Zfaxw1JVX2OS5+z+qGwM+GAYMwg7h/AcOi+yqiZlYM/GhISpETAbE3uVKHcbGSJSCczs/fJ7h8Z9rSM+121QLLRpnA1k+IQ+ZqIEMiOh6cBn1brxTGDgvru4Tx0QTGXZKSZ/t8FxyaiM+9Z5USOF39Wbkb4cV0ZnAGMGOEmPNJCu33ObQkhJkJX9Wa1WXwEZ57wmmOBejKH5zTS3MUogb2+S4ns9vrzutrv5ThnsAGjWl0sitIH8kpjut1j4WcuO589lJMNddKArcwAx609Z6xzAdDi9JIcRh44eF/PqDVUGhDmvXTFfmy8S6I+MRzpLyBK0vL29rdVqNeVkO9pm+nS6IMFR8llnqC0H1hVJe0fsO53vDYOW9+yj7Qs8ip3JuU7e7OYmQW4uw3pZn7rQw4wH0MF158kzVpxIfqf/1p0jO2cd5Hs7GvFnGqZ96sB78l4CZtva5JWkpUvywmiOumudbaOkze8wxMgu8Fval67tbDPHOAeqX1I2BnygVBuZ/f39Wq1WdX19XRcXF7W1tVWHh4fTri2EhjC0BccCZEavquk8vtvb27X8Pu7zsQ8HBwfTki/EAol3hOsUBUa2Y3z+COf7HjND9wxCQ1I19GHZ2sseeD1m9DTKqTT4ZPk1o3E5bn+aBkkbR0I7ZrZSRAE5IT0Ndo4POhqUdZGdzggRXqc/FiLTnTq93I3CdL8wAvw5QgRvJqiy4exKKkUDOCcme+ncffJ4c3w2pKlorbh40wGRDCtgG5qsn+vMIcCReolMscnHNLOzlsrR4+PsMMDd/v5+HR8fT+06GpH9HRmpjBomOLTDkf3JueuKeRyecdsGInZcoS3gjo1rj4+P0w5nxmi+tQzz3uERoOj67ChPzh/PZFSde1KezcdbW192dV5eXk5Lud7Y4HZow3OSOs4ADNl1Pe4TeiQBDL8jry6jaL2jqqY1c+mzEJlTr9JkJHlr62mzUka2GZeBGnVRvMOZe5fL5XRMGQW7sb29PW1wsr7FcTo6OlpLk+hWEDr96t/5zlihhXUaPA3YTFtomvPp6HvqUvQD7buYjv7z9c6BMB/YTtqGjMBa6mYD3TmgR3FEr5PHrk23ZZmHfqNAyyblxUu6afi7hFErFQZKtM6vROsGU1VrBoNBo0AzL8uT7on0xI8MT46H+nyPDW0nKJsg/PzswFP3nO/t7uuEdtMyGkP2MYGhv/u3uciLxzIa05zQbAJg83fP/YhO8GfO86gPXXvdOLr5tRKsmt8R2vFkR/+upIKyUhv1uat3bs4tD53X6n7M0csOIJGJbtkw5y9pOioZZepKjnOOX0bjGRnQpBF6MF+xhvygTxOI0cc5/hyNc07vdHKaY00d7CU//3VArWsn5WDUbnctaZC6aaTrc4wdHTsdzn2Ayc454tkES7lUnUAqiwGB+9I5KCM5od3sj/8flY7mI7v2XD0j3vLYOj02+s3Pz9nzvLf7vRtPVzr+HOnH5+oZ2b+5/o/a3qTNubIx4PN7BGFCvBTeoQiDXl1dTWc0VT2dau6cA7abPz4+Tge2ch5V1ZeI4jfffDNFAfA0fDwGBKBdH/xqZd9FSihmQHvqOUH5bDKfBdPHIrisVqu1l8h3DJzA2cLTMYtzwuxBdMplTqHnGJK+3fP8ll5jghlHM7nHCfrQ3XQmSpJORZdTmHRZLpd1c3OzBiiWy+W0uQAPmf4vl8vpTLnFYlGnp6cTT7utpIG9euaiqtbOmaPfftMKspCGnDnMyF2nsDOPtZsX+u1oXRpl52s65cLXKdSHcSISz6ffn7tardZyM6nPy1C8pYH3sLru54oNHn92Brsoo+cRHrC8ZP5m1fqS45zxTCPHnEITeC+PgUIGOCbFS3TkCz08PEy6zbqPvsyBQPpiYNld8xxV1cSjPgaFvvqZTjY6AJQy6ogzfM+zSVM/y2pJbuTq2qQ4Ipf1wwfwj/UT/HhzczPJSW4K8tJpp2tzSdv1c99oyRTZ8u+8QcRv48C2EfXza+7oI5E/VhQsZ54LR+PM8y7Ml2WJeYKvzScJYMyHaWtHtrD737+NInudXKa+tB5I3cO4Ov3f8X3+Zp3UATnTJMvIXrs9yqZRv40Bn3eGWXlbaNjFcnNzs3aelo0AzMefldnNzc10xtA333xTx8fHXxHRuWUeMIDRAmAF3YG2FEIbbnuRjGEObZvR55h9LkF7BM5yol26ULGf73IlRkrVIDkVbQLdrh8JzlIh+A/jlnMz2kSCYbOS6vpIHdzHcj8AhZ13qXDhHxKsMyE+gbh/z6U2aGEggaz4VUjdHNK2DZgPOU8h7+bCPOB6OoXWLZ+5JMixgn18fFx7f2638zNpAy12d3fr9PR0bePWYrGYgNHIULjfafC5x3qCMdC2l+CyPoMtOx6e77lISQf4vHvYso8zUPVkkL0CwjPwg3cim686+c05zPl4TqZpG4ABvZxTmHrUc5QOn+Uy++3ro0ip2+ST/HHPbTc/q9XTEV8JeEfyy1i3trbq9vZ20gvoEkfYMvLpAEPyRPa1myuuwydeLl8sFhPY8y5jZJH0iKS/85hdt0Gb5dr0NjhN+Xd/Lfed/bPNtk3mfy+Ruoxsoq+NeD/tjcfVzU2uHmaZA3uj+9NWzNU51+ZztNm0vOjVaqMwMd8xmnPPs+vWBhyhyncbmqgoDBS5E8MzP6wjohmwuz4Hqrp7bVz8fLbZPdcpy+fanhtT1r9JH3y9+62rf04Q0iB3xoD/O+FLYD0yrlY0nWLJ9uCPOcHrjH9n+N0/K6tsLxWR++z8Hj+T7YxKGsg01DlHSS/3o6s7Sy4bAfJubm7q5uZm7UiWTr5s5Gyovfu+y591MY90wNf3UJwvyb0A53RKbCy6eegiOFk8biKpjuiZBxNw+hBpRygzV7IDnhnhg5cc+c+57fSXaWid7r7C2wDCztH0PLk91+vfu3kc9dl8mDaCuaYkiLOs8X8XFUrZBoQDCDLnEl6GNsyz5zoL4Mx0S6CYUUtkhJcT+I00BqjWd+Zv82LSdDRXOY+mY/db0jB/9/2jiJz75f6Yx5NH5my6P+e+d6V7vqNfjq0bc9feyJ52djz7MAcE58qLdukiaF4qq3oykAC+T58+VdX6kiRgj5dAVz29NPz8/LwuLy+ndhyutwB9+vRpqptCfzLpvpusNL4pjDmBo8njvlwCyMLEWLl34Xu+dwYb4eC+LrJlsNoJQAdOUiGMBHRTD8R1mq7ZF/cf5dm1RxTISgnDmTQ1Hbw86U+uZ8QT0Oid43jzy+Xyq7Pr6MPj4+MEWLpx2uhubW1NYzW4M+jqDMNy+fXBsb5mEOA5cMEYOcpoOtrTN8hlLjEkXrb98OFDvX//vu7v7+vq6mqic0ZFu3y0g4ODOj09rZ2dnTo+Pp6WMr25hrHYqet4xDRLMISO8pwbWNEudHQ7HoefSd7xuJx/Be28cc0b04hMOVJDm3Z+z8/P1/73/AI2aNubFogM3t3drQHcOUNhI2ydlJvCkNtMRHc9HS9bjhNQZnTHEXL3w3PuN2osl8spWAC/2Ynoiu2K590rCOgBxo+9Ypzwhd/8Qf8B/R2Q9LEyGRlkDhkjfIocnp6e1rt376a3hhA8cfSVDUL0j7Qp546apjkvHX+PnIPkIf5G0eaqavWW23O0Fhq676azn0vHIiOdea9tcurNkZOSQNO6y7KQcuY2u0BH2ufsK7QYOVmblhdF+LLj7gyhZAMfDFZ2nB23KEQO2cWbSm86E4TdpxTazjgmITshzDKHvlMIOqbq/s+6TZu8vxOwBDmdJ7BpGdFidN9c39yf/O6SSmUEMjteczuj57qlvRzXqF+uOyPZXEtAkAAv6dFFF/mNZ2jvuchelgR3o7F2ytRLOR0tOsVJG5m7Z/mbq8uGOjdu5bxl/0fj6mjue2g3VyK6lQrG4LF6DB1Ayt+yPwYV9NfAEYPOc9xvnQi4GjkHI9kwaO+ik8/pyA4EuN65iO6v1UkuncF0/SMgMuckdTprrph2Xgb1uKGJZdif3RJzPpdtZvv+Q34MeC2j2QcDjAQLc3Yr9UDSrJvz7lo3NsvuJiAn5zLtonl0pNeyr924n3tu9PuIFzetY5P7R3P1Ull7cYTPypbBeqnECaJ4Qz7CgE7iLbEZZLH4ki/CqeYs9XIfeSx4ckbV3qQxYiC8+tVqPY+OvnGf0Xe3FOijPlAEWQyCbeQNGqr6g15tqDqgOgeQNkX+acS7Op8Dzik4nSH1WJwsnDQdlcz5GY2Vz1x6y0ijv9sIr1ZfXkr+/v37uru7q2+++aZev3498amVfXqVXTSDvmG04ec5YU3QB427qG8q+fzd8tG9/5P2OtBEPTyDA+bNRshtLlMm3b0Zg2u8DN5HnHj5EZDTGagO3LuYtwCVaSgMrly3nS/mAlknSsrZc95IZhl31Cv7yXzaqXWE8fr6uj5//lxXV1dr352TzBirns5Frfr6UGZHUNB3SavcfECd9MtRFujkswRzg5htAv0c6RlkIYFT3pfymwY19Yf/T5p1epF7HaXnd5/D5+i/5QX+SD7zd89D6kYfy2TnKVcStra2ps0YHMPiaLxpjM3k6J/cyORItKPqHrvnxDRP++LfU+fz25wdGwGaEXDMNsxvHSCExzub1NmdEU2o0zrC+ouSx5G5XuaT0jmWSQt/T77j2f+ngM8hawbLqd8cMIoCdE5C1debLBAAIoVE/+7u7urq6mrNW3ZdFhAIae+WwhKIhYp+QNhkdi8VWEmz47JTVIxve3u7Dg8Pa3t7e0psp75k6I4RvXsywUkKJMXh+ueYO5d4NhGyzoMx+DDo83Uzd0YeOtp7PKk0KCMl1C2jjKJvKMHlcjm9wP7+/r4uLy/X0hKskGmjK253uVxObyCwUbBSyTGNQJ+N6ZxiYHkJXrfxNm1yGS2VjvsJuCGPjOjT7e1ta2zNBwcHB3V4eLjWb3bl8pt528tRdpIS5GfkpAMBudzOJ3pkjl89DurBAbWuYskPQJynB1St73gmDcAvu6+qCfDd3d3VxcVFXV5eTpvZfGA79/qzav3NJz6j1GP22OhL0s0AxvoOmjttJh3d1JumY2eM+W5A5Hq6Mqc38/80sFzveMW60Mb54eFhytGrWn8dH/Tyb5tG6U1nThCwLmJunDrC8j9LzBQDEwDfp0+f6ubmZuL1dFpXq9Ua2PTYO1sxZyP8PUGQeSoBf6Zejdrp+pY6vitdYMFzNmrbK5SWIYN012OnN3exW3fZ9lv2RzyT8+DVIOb/uU0mXXnxki4EGHXMHXI0zr9bgXQT72WiDuRw/1yfOlTfEcZCn15TRq3cP+rrGM+MPxKOUZlTfNTZAa8ct3+bK0nDjlYJLJ/rfxe5zD52bcz1Kxnb4KRTViNQPAKji8VTzpUjJHNKPA2WDZ4jII+P6+/O7ejiehLczRnBpJlBntvsxjKn5BNQOaWi64vlxOCWfnEP9eXYLeve1NLdy2eOKxV5JyfJD6bJaKk060yg4DF0IMbfoSVObdXTm4iIoiZd8/+cx4zkZMlxdTKRkbgcR24A6Wjr31KPdfTsDPucHshnM5+XZzHEWccIXMz9WWa7HFdHdDexMwYUrsv847lMme5416sPCQA9l53t7MactJrTQx1ts6SdHOmykf7Ovj3HI3lv6nl+7+R0xG/dmIxNNnkudYht+XPPd3w5179ReRHgmzM6CN7BwUGdnJxMHu9yuZy8CSJsLHXhzVc9oXbeEDBKCqYgBFVfh1+NxK0MEvVbiHwu4GKxmJSyQSp1gNQzt6bq6XVzVeu5h3PLCmYA+tctFXse7EmgfHysRJfwnjTKtpOhXHcHBjtBM42tgJ28z9JIRnA6QASdUxnZ6PqZXBZxnbnkXFWTl7u9vT2dro+X3EV/XPJcNNpnzklBcGK1lbYBgunN/KaD1BmsLDy3v7+/9o7R1Wq1lhKB7GxtbU1vzLGR4Rp8RESepVz3hTnAw8334kJfj3tEM5Ylc0yd/rGMcM1J+8lnVbVGZ9qGtgaquTxjvkXG7eHDM51x41meJ9WFFY67u7s6Pz//ytnITTYen3mcnMpsG1CcpyJ4jr2xxrIG3dMR8XzQhwTc1gFdlG0uyZ17OgfO11ar1RSFz/etWze4Xy4GWalfrAdZqaFtrzotFl/Slsz3PI/98IqPI9v0gSNjPJc3Nze1XC6n6CL2s9uIwjPYJEfzbB+sV5IGmbo0mgfbgS5KlsXg1c+P2nG/+DT/d85QLvuP0rBSng3UoVNXXwLaDnxy31xwIME+z/qZzmGwk5HPjIDiXHnRmzY6g58NGtgxQAQSIASDp9fRKeJU8iM0PgdmOkImYLCyQNF5d5sjNumJdXVWPSk0C1/X9258CXByHjoAxji69kZtd3OY/ZkD+umtdFFZQFWnPDqPJceZ40nAZyNoEGWg4fvTGNA+SsW5LimkScsOhOTOzNyckEJM323YO8U+1y5jSSXHfQkuPQ+ei1HbLBfm0Q4ek6Pic7skO/Da5bjaMKbRTqDAPQnsszDXaQxpLyOb3VjT2eTTtEmlnGAI4Afos/NLga6Ah9HcwHPd9aon8AtfmzY2ht4JzCc6rtNBGTns+vZcSSM50nXWcQYBGOo85sR1jZaVPca0Q26PuqET6TlVT06fgZ2d7QSd2Y/Ub9YfXlbPsRs8eb4yyux2ki4dwN20zNmNOT31nC3JPue4Uh7n2reOey6CzWcCPI/VbSd9u3HP2dlNaN1hmE2fHZUXL+nCgDYcJtLu7m4dHh5OzGlPx+vnGBF7IxiUqvWk6vQo/Cqmzvug2ADRJoqZ/50sXvXkdVkxpjKwssxJZVzURTubTlouh7mY4bvPUUQ0BcDgwwcCO8cg592AKXMY7b2YXp1RTYM4amM0bo81nQRKgob02jphhy7whw9I9tLdXL9oO5dXRkDGUVobGAzFaPmX+5JmjjyZxknfqqfEectGp+wArbe3t3V9fT1F+Ogf+WiWbdPb+bsYw3z3ajoI3ZhHm6Ool0+O8knnLefNy9MGNDaGqUscpc7oAA4i32mXuh3BgYbX19dTcj1GvjMu6BQDiHQszDNVT/lEqSPT2WAcRHozaX3OWR3NWdoE7vX/7k+W1Gkulvtsy/rc/XLwwfNpZ68rzBeRRANMR8Xv7u7Wotl2Cnw+Hn1iYxL9p98ej//QSUQTsX1OHXIEj6gg7WXkKmmV85TyPKf7Otp1wL+zC8kbWbrgQdr7LqqWjp91ZadzHLgxPUa22HXbtlFn8n/Ks2k0R7/UP67PdHxJ2RjwWTjMEEkQ7yY6OjqamDWXblm+scJCiZmBnYhuZeblSw/cE+fInb0uwuiecCtV+uVlKYpBX4JJPu3p049NkHpGRhLUdb957Gk0uJ7g3DS1QmJZqIvOMF4ndHtJ2wKQ/UqA5WspHCOQ04HYpIUjsgnksu2MHhvYsWzz3LJFJ3Dm4QR+GTWmjsw3ytw77nM/nKPnucxXvZn3bOTom9864vmAz5GFT58+1cXFxRrg4yyw7Gu3/OBlPIwdoKybY/NBlzphneDlvHwnL3V57hPwEIWdU55bW1vTm4OqahqDAUbHZ7l54tOnT9PyLQCa+vlM4wzNUs8ln1l+ck75NC0cVd7e3q6Tk5M6OTmZNozYgRwBPpc0cmlgu2heypjbHAFxz0nqVW9ycR8SLFi3G5BRDM6ur6+/ciwODg6mPn769GntLD4DMm+OYJ44sDz1tOmS4yJFyseyILvQrGp9Qwj1oiOSL6GV26SgpzJybfq4/m7+TccEeN3zeS1zGT3vlBFPpR5N3MLcJV/S9pyzbZ3rdAA7BOaVUR3+HN3D9W6zSa4wblJetKRLSYPtDvJp79KgYGS4UwklMKDeFIS5e11G/XQ/bJxznNnnOdrMhYpHzyYA4pm5trrxuZ6sM2nSAcqsk36M6DcnFKPnud4xa477OXp1wC/HPweSE5SiCO/v76fjOFL4n6uno8PcWHwvxUqqk7Xumbk+Jl8+pyj8TJeDOlJqI3lJ8NPxuJ91NMvG37Qc8aGjQN1f0meu3zYICeiSvvzvQ78z2u+z9eygebxd9HlOP84Br9HY8v5Or6ZBzPpG+tF1dqXTUa5/7hnfu4kuzd/n2hjVxZKtbVpu4Kh6mjfni2VAIFd93E7qa/+eKxopj1k6/W8ZHNEk522Oxp0sdvfNlS5ylX0Z9a9rdw4j+PuIhqlXOhncdGyjPm5ansM0v6a8CPBBKBSal/FSUXSEYhkJIbJC6cK3/kPIEqF7aYa/FE5KF9ql74yLwnNdbkwHLByR7KJ++dyIvnOAIZeRciymFde7iAFRTNPABjWXgfzJ3HUKl7Yzd2x7e3uKPPl3e/L2VnI5fSRkI4PGGOA5R5dzPI6+sax9fn4+vQv6zZs3awedjubN+VG0w7mPjiCQS5X9zTcEeAmRfmabCZDMZ14iMi0yIuuoX1cHkQW/Uop+jRLJPV5v8EhZ9LPMv3Oi6AtzCE84sm8esTHrzpnrDB6/59Jl1uX+ekOQ6Q99r66uJlCXeXFOyrfM5c7+DpzzvOeWfjCfqQOTNzzmqqccPo+NZUwfw5JtmF7WG+5XtmWZy3tcdwfUEnT6O0eb2PnIucliue/sjef+4eFhLXLmfHQfx+M3m/gNKvC1dR78xH1E/Yi6U5hr+Oju7m5yRHm3b1dsU5nftJVp04iW83yutlk3dPq/4w9fSzu1tfV0fFSnuxylTN2EzuzAmXmSseSSeRdxdnF9XZqA9aafyb+Odzu+8zx4DObrXKlwnZuWjQGfDfVz3kUHXDyQLgI2J3SUbsnTk5nEcumQf4Ile/LJFAlAYNg0Nh1Qyec6eiV4GXla3fPpyZjhbSC7ZVUDAtNlzmObm1/X52WmXMocGbSkd9e+x5w0SaNhsJAeusdjBXlzczOd/Xh8fDzVNbfcjDLNpUQb3cViMS2fUXyPBdt9hdc8VgOSlC0Al6+n3I7mOumBwfNzHTh1e5YrbzhIB8PzlbzpiJedAdIzuj6bLsmP0L57xvTIZzHmfo0kOsdAjd9IByCSx5K4DT67wTsHxMbAaSjWMzhrduQ8jynXLp3hNuBzG86xdsQ5DajbT13XAQv3I/VOzoVp0o3T9ElZyfZcupUct+P7OnAL4LPeyPzzbCM3KuI88Zv1VIJj+I12bHuoa+QU2gaYFgnafJ/HkLLZgT3PfxcV7uaX8XcpF8wj15J2PJ9O+Mi5SUfPfXSQJsfkefT4E8N043T7plGHc9znTka70x26tp4rGwM+M7aJl0tPI8HpmIQBuS5vq88/vFF7ShjaTvEgKBlFzG342Q+eteCNgFj+1o3THm0HllLZZxvZh05JdvX5u+coadSNazRfXZtWEO5nN7YEMlZYFi6EOese0d998acBCvVnflOOk8gW4CLBk/vDNUdsKBbSpDfFTobHYv5LGub3pIPvcXsGABmNSQVCbhA7SO/u7tYihF177je/873LTbURc4QsaZvAwA5EB1j9PcdvQ5TAuwNfFIwxvAFooxjw3dzcrEVHuO7d39SduVZZcr4cwUrA52fMt45mdREtnmGzGp/PRUDmfrPx5/9On3Ry0QGSkb7rbMScHjO/Z8TL1y2XRPacK0skzHNq4GZQvrXVv2kH+TJgZfzuHysMPMOn57MD4LRNvh/j6WwQ10Y06OYtn51bQfFzlluvFiVPJqjq2h+B15Q929WUHfcnx8r8W4ZS73Q82enUkT2du6ez1d18sBHuubIx4PPZc14uyQjFCBGnAugG76W/vb292tnZWVsWQdDwor3Zw8JuZe5X1ji83uUyMIYcmxVoGjTAiU+/TiWSQLgDRJ3xdD+4ZlDRgbYs9NfzYM8oFcSoHtdn8Mhf93o784bnyMZ6tVqtATsUoJc7eKWVlUU6CrSbgI/2lsunE+fT2Cf97u/v6+bmpra2turz58+Tk8Ec8NYJR0XMU93J/NlX+gj/mLY2MIwBsOI3tYz6Tz9dt8Fe8nACaq4BXthN6kgffXN+kzeReHwemz1rxmOd0gFZ+mcjZnnP5ZWUPdMpE7lNt87Bsw6jX7m7Ft5lDPCZE/bzlXFde9YPdlThAfRiJ2fQyP1JGqRRzTZTx6ejw2c6IS55LVcX/Ezqsa4t1+tPP5eAz85j115VrY3R/JVAhbdbeCmXTYlV6xsLqI/3widId4He5om0nxzZgy1k7g2IDAxpy7m12MyDg4M1R8X8nE6ZAad1WNqRnBfLZdoFz5E3uOQGypzP5AW3N5Jz/ndgh7odUUzglHoBGlv/dgAs59cy7j6nLux+53/b6myzW0G6vr7+ise68qIl3Q6sjBTkXB1+1go8jQbKcpSP07XVKbmMHuQSAP1xvzqm5pmucH+XI+T+jkBZMrCvPaf48v6XlK5PqYA3qcN1uc6RYXA7HW26PuScpPLJ/rj9l4wHpwAQ9/DwULu7u62xnOOTBDxzxTLgOjuBp75OYY3o0N2HEpuT14eHh7U8pdG92f4m373cS93P9dl9f64fc/rB92ZdCRZ4BkPhg44xoDYuBku5lDaKmjkaaoOefe0MYmccUz9217KMdLzbtuEb1THi9ZEO2KQ8d98cX+b1EUgYtZspFnM6Z0Tfjm+T3l30bQSERuPrdKj73/WpAyCb0Cbb4dnkk7wv758D5nOl068jGejsy0vGNuL1rt/Zr9F92Y6/Jzic69tLysaA7/r6egJOJIsiAEbqVnZ0pgs/7+zs1MnJyVc7ABmEw9goV0fnEgFTEtnnewS5x0o1wWanQDtvas5QjpRbl1+yKXjLSEa2NWLoVEJdDoAjmHiJfsbzmkLFM8mApiNz5k0weEaACo/RdBptDlqtVmuKGJC2WDx5kcxdGrJUolZOHJWxXC7rw4cPdXh4uLb04INR8fTz6B8+qd/vYGQ86dx0gI867KRwXyox84ej7Lnk2oEcnocXiKJfXFzU2dnZ2saNPE7FQIV5MIB1QYazmB+9zFlVU76UI+nQxEZypDS7pWHAJh4/9dE/R6yzXF1dTTmevF/X90L7TKp3vhb/56HhGXHtSoJf+kzEtFuJ8Pjn6uUZb4TgdxLsHZly6aIazEMHIl3SZlAMIPg/o4XOfav6OuJi2cmlvOQhxsxKk4//sa1hrm5vbydeQB9BM1YpcBaras1x8vFPJycntb+/v7akzlgODg7q9evXtb+/X0dHR3V4eDjlXZoejA++yiVd7xi3rkEPIfd53fos7bRthVfirCM6wJoArbNt9N02nWf43q3YpG3rgO0IkKeTNNKlI2eM5zKn38W6ynzN716dSRvq8dlJ3LRsDPj8MnmWrDJhOQ1ronh3mjO8+C2BC2drIcy07dySDgBQn5cpcnmkm3QzTS6h5mR3iJ3JSnBlOliIRso3Bcr3GSyYziMGz++mU17zcrEFzTTtxkbpBDbnv2pdQE1nJ8d3wLEDQrTLYbvd0p93g3f0SJpZcS0Wi7q6uprOv+LVSF6SMxjBGbDDwZIA82cFZ8DHGLo+stEjlWEuwSdtGM/ceP07f9Ds7u5u2sDiYmcs27Eyyhy5kTxx3Z/uo8eaYDPbyZ192aeMvHWbwDjkNpU6Y/j06VNdX19P99kBpR33J8fhPqTRtmPa0de6Jvtt3svIYspuOsr8ltcTvEDfBKU5n/Q1l9r5faSXPNe+386IdS2/0WfGbT3rjSej/pkvDfRyE1XaBnJcXaf/eMsU/ch+sfy7u7tbR0dHX238WCwWdXBwUMfHx7W3tze9AtS8wHdokP32TmyPkfHbjhtYGQhDo1zWzM0+0LkD9amvPPfU5bnwfBrspFwnMPI1t2e+62jHvGY9Of7n6vcz2abrNB1HKy0dvsm+vaT8qnfp8um8pY4AFBt0G2o8mqqvd9e55GA7QDkCXd39I8TvyfQkdOCq62PHDNm/50rHnK5zNL6RIe8YN5kw++YEYz9jxdDV3Y3FRtBKxe0YbGcfDeBc/L95J41gJ9Sed/+WbTw+PtbNzU09Pj5OuTzp5XfgugM0HW1QyqNdYi5Z38hoZhv0k09oBM2cywTQ/fTp0/R3dXU1nfA/dzRN8mr2paOFIyydgehKR3NHV5IW7ov5gXocqYIvMeIpK9RHnqkBrO/pDFu3acUH53bF4ALZsdFNQzTi/5yPBCWmbYJyP5c5b1m6Oezq6UrXn6wn7+2eS7DtPvh++CUBNnQjSp3tGUAtl0/viTddUve4D8iZ0yTot/nCuo/3Yntn8GKxmPKI6Zf7CUjCCZg7XscyZVDIX66oeT4SCLntBCfmW8+f+9Pxxcj2dk5iV2x/PdY5XOBPvnv8qYt9T/fMHD7qxuy+Z92W9f9rgM+FBn2+WS6Hmqgs2dnLIjT9+Pjl3CoEYA7JdoojBdj3dSFovvt5xtRFgTpj0QE67zjsQMbIc8i+URwx8zKof3sOULh/I8CQ272rnpLO3ce9vb06ODiox8cvx0oYDHe09ZKeAQenu9vQA/6JKmXduVRZ9bSRgWWR9DydBJ3FS4OM0aCDtu7u7ur9+/dTO6vVl+WKg4ODaYmkM1SjPE7P4fb29tqrrEZAivpG0ROPwd8tF07oz76xaeL29rbev39f9/f3dX5+Pr0N4tOnT/X58+c6Pj6uN2/erIG+kWdtwODSKb3cPOTSAac0pPCS58HPjYC3eZ060VH39/drZ6I5mrlaraZdyzbyyBLt2pDB3xkV5T70o3XXYrF+mr+dJC8XrVbrr6R0mkR33mPKvQ2Y60m956W+rnRg22CqM5DcmzKeuhEe5n/TNeectIN8xSDRtYyAMWaD3ayHpXnmhGj/4+NjHRwcTHXc3NysAS0DLPTU58+f6+rqqm5ubr6iP/qEqKDbODo6mjZtmBZefUh6kWsKiAPEkn5iHQktAZW5YrdaPZ27Sx+6aDTy6FeVMs8ZCUefpl0dAbG8hnwkf3WF555Ll6Bur6ZZV2XEunNW/b+f6TYHjdpPm+xIqgHfc+Axy4vfpUvxAByhGHXCRn9qXFvF+Xxu4jbta1dPB/TMfCOGGwG9jhHTq/u1/bchHU3q3LVRyWcSrNmrq1qPqJgeGQWwl2YA0PGNvUIr76TdHP1SKDJS1M1HN/+jfnKNVyDd3d1NO8O7XaiuY1M+xvDMjfElJfuS8+mdpPSPfCGWb29vb+vy8rI+fvy4tsTDHFO35y370M3bc3O56bVuXr1qYNr7+6h9jJYjIc4p7caR4CDpnrTJ5cBRP0b81PWBzzkDMHKOO3l2faPfbLC7vnVArutH1790HOaK+5B9yWi2x5B1d8Y6neOMqvKcAfP29vZX+eEp/ymHBqC0Q+qGjbuXY9Pp9fi6ubfjnKlQ+fxyuf7ax6wvv3f8kdfy2W5uR2Buzln0s+7vJrzznF2xAzJXRrRJ+2qd9Jw8z/VndO2lGGNjwMcRGVXrR51UfQ1QuhB5GlYfO0ExoVHA9sDxTtIQ5eC5N4FIlvQ2nitzHsdzhsW0cH+Tufw96QtdRiClG89zoCE9+qRVzmsaUysHA4zVaj0KmUsOVl4+4yqjUNxj78Z93Nramjzf5MlO+dogU3+mEyQAXa2+LHOenZ3Vzs7OtMS5WCymaB+CTX6rCzk6fE9jZU/Z4yKvz0s5zFe3OcqF/hF9WK1WdXl5WZ8+far7+/vpWBGiWbxhxFG9NJSAai9FI8OOlDCHef4XdWRUwEnmnjvLSqdr5vL1EgT4Hs8/kZBclnIOK+DP/bJRThAFT3CUBnoLmfHRKfyfb9CwIWNcCXIzCu/SGSTmK2XDz+dmLdN0pLMSAPiT+1xv6rB8xn0e1e3nU/dUPUXomGPa9VIt1+DXpAV09jPmfTuc2Br+//z5c21vb0+8g8NIXcg2eoO3mywWTwdhdzaS+l1yrjyv6OHFYvHV6kyn7/xmmG5Vq7Nh+ed+d1Gtzob50ysXjnz7umVxzh7m74kTsrifI5ueerG7nvI3ssNJy64/XO/sW7cBbq68GPBhlN04QgBTOYk2GceTkInoMPtqtZqEw8uLXDdIGRHdRqGLLvoevo/AmhVKMiSfc4AvFbX7PuetptI0ODEtk4YdLbox2eikt5ljtaEyzfLPcwIvAHg6sMX4MER5SLJp1tEX5YlycN95tgOdvm7DSR+yj5xDt729XXd3d5MxPz09rb29vTo6Opr4LaN2Hl+nlDoHJg1Cyh3KuXMYaM+8//j4WNfX13V+fj6BV5Z8iFyyAYFx57wmzQ1kTcPc4do5MSm3o41gKDWey9e6eX7hY+hqA0NbNkroq9yBjkGHpzql2gE+nj04OJjOTsvXPzHXLHvlprIuxSKj6fQzeSZLGh02Xpimfj51QM6Z5WgOsPlaLnOjE6BbfrrfqQuTD1OuE3hkVAXaJ2/w19kpdKTBeMq4jTA2j4OXAXiOHqc+WCwWE585oofj4NKBu+43/28HAx1pXcyfZa6LOI8AXvKH7bjnwSAtnVWDaD6hV+qetIepA106fT+y9WnXuCdxQzcPIxvbPdcB0A4PuLjf5t2ub3PlVx28DGN0BrwjpHcMMZgETVkchu5QbhLKdaX3OQeCkpnm+jSajOdKKt7RtVHpPIhRH7t7+ewU6Vx/U5CyXV/3vKYwjxifulDCI4MC3+TSSs53Rieyr253DhSPFCltIwsk9y+Xy68M+wiM59i7ebSi9PcuryYNlGUGUMF7OjlO5NOnTxO4A3CMIhyZ6+WxWPl66YL5ygif57ijey7r5z0jpT6SD0cS5mQm64Z+c7mY7lvX73SEsq8dra3QPYc598kDLp3jOdJZI93QAbe8Z1Md2AGGkePznK52PR1NNwG+HX07OibfjPqUS3a+3xE1v2ZvZNg30QsAIusj2+C0NaN2OoD8HB90+sZ1d5+2D918jGg+osGoPx2fZZQ8x9UV62jTaK6MbLp1dxctzfufs88jXnxJ2Rjwffz4sVarVV1fX0/b0DkPCDTfGWWiAcfHx2uDIe9hxNgsO1k4iEKkV58eRnrWW1tbX+UwIKT0weOw0hx5IlyfY74RcBqVTqBcX3e/v3e7qQwAclmVMgLhGcHJSIkVjZ+dU+aeK7w3lj9QZo4KsGSLl5xnMhJp41kiNiha943Cs3x3P/O6n3E0Br4mYfrh4WGSh93d3TXg62gddXn8qQxG0VTzK/V0Hi9zt1gs6vz8vH766ae6vb2tP/7xj/Xhw4e2bX9nrryxxJEGRyEccYP/KCSfmw87A5B0Nh3cJqWLQuXzvua57OSQvnRHvvj8wQ5E5dJ/An3rGujs6NocDbjHm94M/HK5rHMGDMxtwEbO06hYbyTPZRmB2uyPdXe3U70zcJ3j1l3vgFDKN7piDtyljLhuZJ2UC5ZvHTVcrVZfRaLdz/w+1w/olCsI6WzyG/d6BS7rRq+l7jVdOgfTG7fs9PHJXPvIrExZyPGbHw3MPHdpnw3MXDz/2fcEct1cdPOe9edfd5/zpjs9kQ6zx5U6kOLfbOc3KS+K8FlQbBRSaaTRRziqvvbWeSaVOIRKBvDSUSeoZgK3NxKwVIwdgPNzCZjMPCPG60BWlucUbtJ0pGgBYv4tDW3Xl/TUTJOq8S7lrJe2OiWcffJc8pxz3Lw8STteLrTy6WhjY5i/d4LU1ZHP2uh+/vx5Or6DA1RXq9XaAbudoqEkrVLBpxB3/fb/9M2/397eTq8Cu7q6qsvLy6/GTd0doAHUGfClosv7qcc7epNn5pYi0iBRP89mGdU1UtZdcf/53/XOGYg0Ti6ds2HAxT3JA27PwGQuwmcj1Y1tTkZ8b/f8qK5N9JbHY5uRNButJnT6w/Mz6m8H9vxsjnXEL8/ZAhtr7qdvbPryc11qSZa8Zmesk8W5Okbzz/gdEDBYS7DkORzpAH96vNZnc33OtnI8nd2HPp3sJZ92/J0y4N+7/zu9l+N2v0bjcNubytScXGxSNgZ8I++ETiTqtEB2E+yIifOUiOQQPekMUadEsuQ9XeRgTpBdzxyDJPHtfXWGr4uoZEnglfX5nq4PBmq+zx6Pf/M1xpBKmYiW6dcZAi9v2LiNhMVjgCfMC3jHRGm73CPXC+DInbQ2NB5rR9uuJPhdLBbTcunnz59rd3e3Hh8fp+OG0oPtlhe3t7db789tdP1LHgUM3N7eThFwZOvs7Kx++OGHuru7q+vr66+i6h1/7+/vr0X2+OQ308/999x7TpfL5bQbeHScEAYhIyGjPpoGzLl/y9yfqvpqY0gn546QLpfLSRasM9yuc7Hg2YODg68iMeQjkTfpqGNnxFarp9wrnOw0hGl0UxcmTRhfZ7x93TROHWv+9P/d3Ix0swGegUfyZudkdjos9VDXTvbD+sJ5jUkf+N1RtOXy6fgob/ig2HHtQEfSFBBHO4eHh1MOHxHEtCsjIOfVg4zi0reqrw/97ujc0XQEUBLI8nweQu/6EzfQ1pwudt3uE2Ptotmjv45HOyDndkzHtGNp50zHBHhug37k6oT7ODf+l5QXncM3YgYrijn07uK8IRjdYWgDPp9DZwLNIV0rpoy2zAE+xseYvAEl6++MEMJuBcayI2NxlKhjuJGipN3n6OBl5zzDz8zVAUOUoJehqNN0yXZTEfvT9Tgal8y+WDy9es0KiYiyd22nUTGgq3pa5vCuXzYaeP4s/HNRJ48fnrq7u5uU9WKxqNvb21osFvXu3buvxplLxGz+yGhXB9RthGkL+niX4eXlZV1cXNTnz5/r8vJy2nn7448/tjsRPRYDFhsd0zM3Ili2XIfP1kKGDHKSb8yn1iXd0q2j1fmZz3i5kM+MxDi6ZrCXG8U6ILqzs1NHR0drUdA0roB9AN/d3V19+vRpzTh5vg18nV9p/kljMvqf+lL3pe5y26lLOtA3cv4t412Uv2uHMhe19GcH1DubYx05Anz87p3m5Od6Oda2CceTdCM2Wxis5jJd6sWks+ePN26kvHXA0QALXclu+QR89IExYF/tjGXdKZtJ15wDO4PoB+iT82G9Z1rMgb2kF98dATedOiCbNrezsznGjm87PGBaUNLmdmPguc6JGvF2trtp+VUHL29aktizHdGSbwKaOUYY3TMSsLlnnhtLp/w60Jl1dn3415QRSHQ/OoXu75kHkvfNGQa32QG8UZ+z73NA3SCCKPDIIPh/DI0jqgDGuf52vNONOftK/UQm7+7uplyekeHLvo4ivRR7r1bgABMU9v39/drudu8CHeXPZN6nI6vd/NCf0VJ0Z9i7sY3q9r25pMpzbr/j/Q6QeIxdVCH7YAVc1S8l26B3ipl5NeBLQ5yAJfnF/ckyomGWEYgajb3Tj934Nm0v2+2A5XP63X3ISLL7PWovxzunP19iE7Jf2ZeUh3wO/nE03TLY7e4f2R50UTrk+RzP5jxY57pYTyWITp1qfnbww894PCnjIx4bAZ/RX6d75uiR/6dM8nzqFrczsnGdbG+i/0ayOWeTniv/asDXdYLfMyE2BQNG7zz5LN1AXY898IwI5CR1SiaZfw6MWFnn+Dskj6djxkjPK8faGdHOAHa0qFoPpeeSOYoEY+TozGq1mo7pSCBFv8y4pnmnUPy9m3+e5R4fC+A+pSdtmjBWvFZADEDRdefGgtzM4/50Bj2FnD6dnZ3VYvFlmZfT+Y+Ojurg4GBSxFXry+UkSwPQqM9AwfTz0pL7CuC7vb2dXgXHeXt+J6yXjdjoQlTDdfPOYM+lI3nQ3HzoPjm63G1QcMK3PxnPpmdLdaDEQH9u6cPLx1bIng/3haiI22ElwkbakTmPH/71URO0l0uG8IGXv5OGNqAjevCbZWaOJiOg637akLluly4a6v657lwN6cbnPvlVZqZL6lzqTMObut3PbmoPPA7mjrF49cbFDhd189YeNkfxdo2jo6OJ39gAloAp+0lfeQsMucWeQ8+v36pCv6yn3BZ87/M1Dw4O1vQHY3x8fKz9/f1pI0umRrjPDw9P76K2DeiAWC5Bu2xtba29Xxjdb9tmPjBNRqDP9EY2s59VNdSFjMO2Nu2h+SJtpPnEdXtu3N+7u7vapPwfifCNwFEK/lxone9evkoCjtp2HR0QScHtDDfPdui5a3v0zAgEdkbMjDHn3eT9biuVWIJfj5uCwU8l6ShaKoPOM+kYckTbpGMaHytdG2G+o4yyTUe/EDADU2/8cTg/f/PYsg/ub86T6bVcLmtvb29aTnX+F9cNEGjDr/ICvBnwWQljXFJ5L5fL6Uy95XJZt7e3a0DSwGJ3d7cODw/X5j0NVxriBHypkDpQ4GcpabQ6ndDxS/7WydMIzHRLnd2Sr9sYAVV4zM5T1g/YI1/PBtZGleI6U0bcJ/P96N7UP52MjIplttOZ6fyYL7i3k/uuX8xLjtFjsU5yH9JhsNHsZLgDxx1du2tZMrqdu2Cras0x7eaDAqgjjxDZBORZV3SgpaOxI/6ev1xCte43sDYtrQsYO+P3OZhzcs4ceUd/pkolnehX0ipXFfjjWs4TfcprczyRPOhAifuYQQrX62KnJvttPdr1z/d317v2niu/CvCl4uwaHglRJ+QQkaUx/tLrSyFKwMOmghGQmxO+nDja4/8RYTsD5hB4R7MO3HX/pwLtgG1Xp8GJi8GJBdygamTAmR8reV/3USQJDpLO6VGn1+Rx0C+PMZkdQGUgxfMoU0dZ/ay9r8xjscLwWKBZJlIDtM7Ozmp3d7fu7+/r6OhojWZEIaueAISjPl7yA9QAFogoJa9wn591RIr+5QaMxeLpIHMvHxk0Y7yc15Y77pPHPD7e8ZmRGP/m/+eKnzcPVq0vO6WC72SF/ptf+MRo+jeiSk7id7+RD/OyZaDbkGL+RyahMUex+A0clDT83Ri7iOAI8HTRMesB+NxR6nQ6qCsNqceY85t6oQOwfDe4o83R0mWWXOFIOiRQyWeRm9RLyM/u7u4aD3WAuAs8+Agn54CahiMnptOp6Ljb29vJ0YNm0KuLPrrQPhu0DNQYJ8esketrR2a1Wk1RyuRTgA/OrSN2zrXOsdlWpV3nM5/xeHJ83JP2Pp817bFDqSvcv3ye+zKK53qe490O7M39/1x5MeDrFEfu7KJ0ht1K1r9X1XTMBflHbGmH8BhGt2MhtgH1q3Xch/xOXfY65hSPFZyPELEQOxnWYzYNEnhk31JpYEhzt6+fMTjrhMHzxPecI4NLL/1155Exrp2dnUkBMHc2YqvVas0g+6DlFCSP3X1leSZ3vy0Wi7WdveYLgKjnl3YS0PpgVCsFAwv6iRE2+OI+Djbe2tqq09PTOjw8nMbhCJv7mfNgOi2Xy/r06dP0EnSfOZi84+Ru7tvd3Z2WOyhE+OwZAxBz7CzvV9X0lgDqZq7ov8HW1tbW2tKwz1KEzhn1gs/MB+a1VJbc63Hn/GfpnLAE9IAtyw6GrqrWnIhczoHP/Mf4nGbh8cB/VU/gnuWubrmo+xwBAO7pnCTfC0+lQ+mlSuaNKBRtGOQCFuxIpTOVusagKI1v6klo499GgJ6xI3eMkfGnU+qNUOYNnyfpZb6qpzM2fa6nx9IBU+SOHbjYjO7NLEkPy0TSyWkdfhe0D3z28n5HL+b78PBw7bWALvv7+3V6erp2EgLBGpxaZJ+xmt+w8Z8/f55spXMPLSfQNHe2Q58cgz+T5/2M56XjJWwWczuy06mfsrgdO5Rpz7rSOWxu57mIfVf+j23ayM6lYh0Rw8UKMj142hiBMSboJX3MOv5fledQfVfc307gDSJH9Sfw9m8dbTuFk/03cMolnw7Q+nn6asXusY5okP9bcafRsHJGmWX/NhUc9zVp5msAH3bx+s/C3vGrFSOykHlfPGdFbKDlsfpIEYp3/nn+MnKTfbTz4nycvO559K5AO27ZXztRpmlGX0YGEJpboSd9yT9ySeCd9dph8bjy/gQutOffE5S5eMxdrtBLiuViJDO5ArCpzFHv3Jhe0ueXPjNnA7g+J8vPtTPSWZTkq4weUgfy0QEO68mRzXpOd+Y91n/d6ljqLp7PNhkTkT0An8fWHRUDXQhMIO+54mX5ACfY6es26Pm3pEFXbF/m+GPEu7ahHYDs6Dhnczs5yXp/TUm7uUnZGPB1Hfe1PDol7+s8yAyP2rjZeJvZrOCtaI2Wu6UVnu/61F2rWj8luwMj1J1G0S+g7gDBc8rfv9s7TU8kw8quawToEvTx10XZGAv3p2HLpb+kGUqDeU1hN01HYA8Fg8A7id7zxG/uF1Gmvb29ydg7+uj2DAbdtttw370xwTQxj+LJeowJij3XgNNOUbrdra2taUNIAm1H+BgT0YkEfgm2LWfMH3xA2draquPj49rf31/rG0bGfSAHyWf4VdX0ph7PF9c4vghF734yh0TPN9ncYb6Fh+hbzjPXiYBaXmz4HGmHNwzKu9dpZTqK57yTa8u+6TwCvTl3CZZ51vmHqUs7ZyTlnTkjSTz1S9VTaoH72Om+1Dc5hqyXkjl8o37YmchVKOvJzM30uIlIIwfWdTxvma36wsPQgE1kHQ2oM0EE8mD92a2uoBfhv9vb27q6uqqLi4uv7J4Pg0/db3mnzYODg+kcwOPj4zW+IlWDMXsDINftZPNJHZm2YLBnwMqYVqvV9C5zH3ZvGtv2w8PuV9qNEVBK3vOn7UM6PKnPzXvL5XJtdSYBpe1X8rD7krLuVclNy6+K8HXE8nq40biLFasNqBVALhP4WbfZXedzDpxmfzoD3IGlDggkQ9Avfut+7657MjuFjeFMhZFKtaNTB/g64JJGpaOx78Mguc7cGeUlEgP4pPPIm+UaESmUg+fEQNWKmH7v7e3V0dHR1L/t7e0JkHf85cjHaD7gGfOpQQU86GWVkXIxvy4Wi2nXHsvpObcoWMZlI+468cD39vamDRo2WCxZdsrQ/UpZ3t7+ciAsgM/LIk6joF8AT3IXoUnS3pFX09tghCiCI59ZV1cyfaE7My8Nnzc1MZ78SycIA8SyrJdxOwfVJZ1GK3ffn0CNvps/MHxZDGoy6d5613zQGRrvzEwnCf7qHLgOyCXoc6HdkWHNuhNMmZ7/P2/vuhxHsmTrRYG4k929RzpHkun9H06SzZzdm1eAJFD6Qa3CVx+WZxU4ZxRmsCpUZkZ4ePhlucclW6E+kTe01bZrsSGmJ/K+37+8aYebrigDXDoS2Y6d5GsY6ZsoF7QzBFGc0mUA6+yjn+Xv0dNMY9/f368//vjjlSzk2ewuJp+bX3V/IqMMiAj4np9/LWUJL3e73QEAMoNJX9R0hXyj3FF+Go7w700e7c9acfAW+0XfmesuTf5b3W8pZwO+oOqHh4fDGqUYDaaP41haYYThjFKuc/CJ1FN3jNQWo1h3G8yJgQ3AnQIj/JzqmNpJaZEq254cU+tX+33rty0++DmDIgKDODjKgp2262Idk8CvdRyhexphioYIArnjMvdS4U71nXTnniZXNMzkFw2gHUUMnyN91s/jUj58+LAuLy/Xn3/+eXg/dZum5GJwZjByf/r/8+fPV8+70IEmY5jMhQ0a+cCoPm1Z55tzsA1hhphRLZ+jc8szWwbRoMCghjsKeS9BMoGdM9cGhGxrmuUgsG1y4DKBKdLB/rG99Is663bZtr+fas/XJp1pZaqfdqXZXv+/pdt23AYLCboYiHBNc6uT3zm9SYBOoBBgxQ1TnMlglp99o87kfq55D2iknZ2meC8uLg7BodcS5i+2hBsrCMKabHhcvFGMaykNmLmZI4Hp7e3t0VKQgNtkX70+2+CffmOys+Qv/RxLsymUzUnWQrNpbEmbU/830HpuORvw/etf/1r7/X7985//XP/85z+P5vibETfBEX4KiqfVYkT5t9aLw4swR0E9aA0gULApLBQGO3KCRWbWXKxUpmMLXLF9Auctp2jammGkAjVwwWdN85axbBEnM105eZ7GiJs2CLTMgyi7eZVnuLaDU4leDJ2+7vf7o4OQbfTCcxrhBjg9BjbcE1/T7+vr6yPgNQE+ynrozPjFIF9cXKwPHz6s//1//9/Xzc3Nev/+/bq/v39Fp8eNziTOJVF0aCWYSh3RMTqWOD/qY/oa55C+pW3u8IxhpuwyY2ZwTHDCc/E4ZnTMa71eHtIKna75Rn3c7/dHWeV8ZiPLz58/jxaTxxaaFo532qKcEujRLm7Zk/bddtdBDbN53GTQgrTUNwHOpi9sP/JDvWnPum/N1uZ519NAH+1extiZULZLfeZv0ZVk/ZLZfv/+/Xp6ejrKkNN2Uk+YMXQA8vT0tG5vb9eHDx9eLSsKiKEdt2wxs58362Rjl08G8E7v8OTp6WldX1+vDx8+HC0PSR3X19eHqd3Y0vA14C+0pl7b+8hbZhkC/JJB5xpj+6B3794dbNPNzc2Bh9HJjx8/Hs4b/fLly3p+fj5sgnl+fnn9nf1GPltiKn3weub8TT4nPLVfpQ0MIKf/8QwA5XOSVyY/Tvktl7MBH4+N4Bo1D1YGkUpHoptB9BRZizTpIFsEYcbk+2SwWDhYfm4LBNmIn9POBBxbNPH/V2FfDQAbqGxANuPmrA4/+XxTmImuLeUyrTbkBv/uw1sipFP3Nv4ZxJy6ZuMZ45PMWt5zm7/GJwPsGIXU02Q2gIwgnLTxWdPc+OkxtSE0P3m/+WwjTWM6OcapTNl00mHwabpJS+yi+8J2zAvzhUBsyko22Zt47WsslhXqx6l7/fvUzmTHmi2Zyrm20Hzdeq6138Y2dVnGOW3YaKX/I02RBYK2yCqDi7Vez04ZKDf/F7+c5zgL1mikbGf5AnfUE+QatEdettZkk/bcl+OfyD/y2HwPHUwSkW8EdE3/XCinWzLS9KhhinP9PfnCZAyn2Ns45ftb/NM55U2Az4aJkW++b00PbRHviCDrCdpCejv3CWBMDnatY0c4DWgDJqfob+Amih1hXevF0NKZtmfXeuF9W3th+ihI7E+jvzlpKjHrZraOPM1zu93uaOwN2A1G7FjZNsfLhcYiPKNCtZ2xXLe01kvGaK11BBQoD/lr0wTuo/lNetr5eXEMPI8qU0XJmmexdK7l6JsPHz4cMoeWFzso0rnb7Y6AL8ehRbMOxjge+/1LRopyYr2MgSNNNt7kV57jNJHHLdcp59M0v8fG076UZzpxZwmZDePvzsinPmbMuHjdY8PpVB7Wm9L0vDluZ5LC03ZeInkeeq3HqTNyajljG3TYDBYaaJkAJXnbZI3XnQ1uNNpONQfNPjsI4rNcWvT169ejzQ8pXB6x1vFa4tDKWZzo4v39/eF4pGQMmX31cgvzPDqYVypybSD9jGdP1lqHTV+3t7fr//g//o91f39/SOaQxz9//jxsAvn06dP6+vXroU3Sw8wmxy0yzelm+j6COwLR/BZ+p07K29XV1eGtItko8/DwcKCNU+IMaphwslzQ7zuzP/kxylTa5Kaw1MHlGvn0cjjqEnlBmeX/bwWEZwO+OEhOqaTjMTTN4K91HFXbqea+GAkCPm4Hbx1mp8nE/N6Mox2EaWn/uz1/n0BSCtO6zSFGWeiQWXfAVtZONYBrQ9lKcybmTTOQ5q3r5OJ8K4X5Y76Yp5Elb0RIvTEU4Rvb4xqT3MNDUUNj7iXfaBza+PP31scmF6eiz5yIn2xdph4uLi4O00fZEZfNFzkfq5Xm5Fs0ztKmBBoPGmgwQOE16r7BVJsB4P+mk/pKx+LgptFv3vtZ0sDiNW6smwFvWx9KnvAQamcXycPIrvWxFTt+b44Kn/I8AYJPUzDwS72UI28AIV3MepGP1s2pP3agHN8Gwmi/acO2srrkmWW62RL/xU7nUGPKUWSZesY1pj5nL0mMp6endXd3d3hF2X6/PzoRIZ/Wj/D64uLiIC85+9TZZi+NIB/ev3+//vGPf6y7u7v1v/1v/9u6v78/gCcmdX7+/Lm+ffu2np6e1t9//70+f/58CJg5Xumr9Z1yzmUfz8/Ph+ldZkA57gZ8a728JWqtdVjX9+3bt/Xu3bv1+Pi4/sf/+B/r69evR2NE3SJPbNNM87nfmewKHzKFzbXtng6m/jvhYP2krP9nytmAL4g12QWDpgghi40qhc+Oohn6c4wfS3PI7Z4JOPL69Lzr+p3i/gTonqLbfWuIv/2+df+5PN4y2FN7pt31mdcTnbyfjsGRmJV2rZe3JkQWudnAfWsZX/fhLXJl42Blp4HgWr98Rs8C9B0AhXaCmBbNe6E16WuOknLUDOEWH9KOv+fTAMrFgDHfDRxoILfoZyF/ToGP2CNvEklfmr2y/NpQWx5cj/Ww8bj1bQpGpn66rWYD4iAnIDXRaYC9pdPn1Em9Z19Zzu1v+zz1jIEfkxMEN5RpT9lGVnM/ddb98XTfVKjLzESzrsnG0eb4SJnYxqxN5QHgfoMHSwCrxy80BCxzEwjfOpR1y8/PL8eyfPr0aX369GmttY42t9DGhdd3d3fr6urqQCOz3dR78rvN2rg0UNjkj2PJDCZ3H0/6fMr3EgD697dikLMB359//rn2+/36+vXr+vTp01F0YxTPjqx1bOxpUOiUcx8deuppkXFTXBpQCzsjNf7eGDk5pHOA4FuN3H5/vMOVWaHGP6+BcNttWtKCSgVgGn4LfDnjxnvYl+bAPNWUe7ecBscrBilTDmuto6MA+L5YG+ks6n337t3BKJAnBFg5ry8K675tAZWpT4zGCV4uLn6dpZfF4FkYHXryeqJk+HjYKcc9/eK6G45j1rlwOrSBBeuTN7V4SsLX2gYujjvP1vSiY44bs2acoqIc8rm0Q163Z3a7l12WGUu2x8LzwbicIraNzpWOjfKa9vJM6mNwl806DGBItzM2BrrkG/nbxtN2iWsyDaafn58Px+c0YG0Zb0CX11owlOuss/1up2bwz3sMXBtd9iVsv9FDm5ZxD9+S1fOGCMoo3ziRz2xY2O2O39ARfjtLbxvu3wnKeGYpfWwA1v39/bq8vFwfPnw4ZBjZnwCmjx8/rq9fv64fP34cNkRE/xOEEnjlWcog+ZzXBL579249PDwcZjB2u91hDeHl5eX69u3b+n/+n/9nff36dX38+HH9/fff6/Lycv33//7fD7TzUOe1fk1Pv3//fv348WPd3t6uP//88/CKy0xRZ11j9ND63MBx00efwUg5aqCOAaPbyLg0v9s+rUMt6DxV3pTh2+9f3sdJg7ul1M0JsCNhUkPBZLajocmAGORM900R3wQCzzVYbwWEBDbTsxz0CdXb0PkZ39f6asHL/5MCuL9bRqnR0Z7dAp1uo/Gkgfenp6eDkeI95kMivgmovwXI+zmOcQwOM3dcw7fbHR9anD9Pda314iACuKYNUdHDLRmzEYrzT2G2jqBvq9/Rba/9nfSZ4I2Ax9M9caxsh/VMtih1eD0jC4Ee6fV4bum6wVkD/c05cqzSlvvVgBU//X2ijzRSz8ODLZ42e9fua7a26fop+9p+m77TbjW6fN3P+Tvrjzzn2YA+A3EGX97Esdbx0UimnztxzykOxjyGnPpPZo8zddTFyP7j4+PhL+v2aEtaILC1fCRBd2xaeGDb8PT0tL5+/bo+f/68Pn/+vL58+bKurq6OdhqHBtq8AOlMbe92u/Xp06fD2HCs0xcHTi7UEY4drzc/RPuytdbZ/rTZ0i1/xDrPLWcDPjvDCREz7c3/YzSZrWIkT8ectng8QXPsEzLmIOVeM4lGnAJ/yqnbUbn/pGOKRvM/Fd6/cfcWBd0OgusDvWajgTbzoWWympBxDDktZifQ1iFOgDJ/dDpWjrVeMpv7/f7IeOaeXGvTvDQINNIEL46IKT+uJ8+n+L6ANcpJ+MLoOKfZZ+ctM3y5h++y5GHLkYtk1tg/68pkECbnTUDqZ72xyNFr5IlAxmsqm9xwLL1ejxnCyF7TW2f3mZnzC+pJG9fWsF8TECO95C/vtRw+Pz8fLSZnn6gvHp9kWdkOZXeyaw285TNgxRlK2jCDTjuiyfZZjlg/9Y92wJnNBhZc3L9mU8xLjx372cAr+8bnwn+/7eHdu3eHg9BbmWyLQSV1JrMatLmxlQYitC/JYqf+ZPKur68P78BNAufnz5/r8+fP6927d+tf//rX+o//+I9DxjD2Pu/iZoCazSbUuZubm1c6xTX5+f758+f19evXwxRvzhcN2IuueOaDa/HWOt68FfCXQ/YvLy/Xly9f1sXFxWGNo0EfA1f7POtCeL+FC/LJMWzja7A82RHLNWcQ0ta5QUHK2YCPxn5iTgqdpH9LYTr827dvh3Qvz6fxs3RG3EXG6w3whUbWYcDHepoRsDHjbzYcqc/r8gy+2hoCRi8+c9BCGlq9iN+02tHS4Jp3Ux2MFnOd07wTUGafJwFOP9iOFSD3JjrkPdyta0DOKQACPm5CMl/SN9ISEOYAhoFQ+MMduaGdbyYImMsUc95cwamSyEAAZAzj+/fv19XV1cF4NqfV+Gh99VRCCxI8jjR8KTyiKfVwisQyxc1JzXjmnvCS2QtOlbJQN5hhSKDABfN0HJmi8bl3zO6xUPbN5/yePwYHT09P6/HxcX379u2oz97hSxpaHXQk7Rl+b8FP6rENaPaBst3Ar8fPANPy1O6nfYqsn7OWmXqa32gvohfsR/rCxfteUmD+t35lWUmyXuFNdJj2jPaGYLutxbW+5jnzxzSzjdgpLk3Z73+dYffnn38eAb7I3s+fP9e//vWvtdZa//znP9e///u/v8rmBdxlw1jO5wsgjc5lCQZfKZgzK6nHX758WT9//jwcIp/NF8/Pv85zDej7xz/+sf6X/+V/Wfv9y6vVaPNix9PntdZ6//79Qdc/f/68Li4uDlPTDbxzzOlDaCPzycTDhAM4HvytjW+TbetHs0Fs578M8BkNkwATNaFgljCcwC+McT1UONPQmN9Ax0RTQ9a830Li775uQ2Y6W1uTY3Z/+TcZJN9nXp5bmlG1UXQbTaAnINhK40tr246DMsP2Gm1bimdny7GxkbVM2dDz2cmg0KlvOV1+Tve1/tpRu53GZ/6dY6Sa/m8FERPf2+8Ef+5bxp1/TYdpEOnk/Gd6DE7dr0ZrywA0GeAzpNV1eIyaPpxTJjvAgMv0Ntrd3/8ZZUunpvun9pvdP3WPbW8LwlMiZ8xiE1A2mTvVD5Yp22sbwHts72wrAhazW5hBug/95dFAXOPJKVxvQglwDhiKXhLc5jfqbGjNetlMHWfNHaec00fyv9mS9DevhcthzWkjOt3knmNiuc/9zQeeWya5tS9/S92/o4NnA74sdsz0a1D17e3t0Ts027EHESoqRF6CnG3VOXMoh8ky8qWA+gwuDkxTtCll78HNb1QYTpfynX/MdoUGCkXqTV2J8A0kGn+4BotTT/mN9TJKzX1tqqbxxTQYTLDQwXpseO8WwPE0hp1ZS697jRZBtDO8BpZpMzJLA8hMR7I/oWGtdZShMwhKBi/327BxF5nPVgtN3njQsgw0rjS+zBw7EicPGWVbHjy2Kbk3RrLJDIvlj+2QRstJm84waFzrZeMIn09bfB8yp5ZDZ8aDtiOOJQvMndlLG1zvSb3iAnuDNS+tyLPWWdsr2potfdoC760Pzb6lb+mDecu6PM19CpR5DKdMSANTtGdTht90+Jrp4ywQbR2f9Ti2N6zw+cxERR52u91hqjQAgzLX2qDO0BekTvKvPcN+x+8+PT0ddIXr5LIu+I8//lj/9m//djSV+P379/Wvf/3ryH4ma5l+xReHDh5rxfEO7/I7Zd16tNaxXfzy5cvhL7Mdf/311yGTmPP1ks1LppxvQiFPkukLsPvx48f6+++/D2f27ff7w3S8EwWUu+i77XOWhLQAcwJq+b0tRzJQ57U2y9D8xVvKm9+0wY7GKcXxtCmmdIDOKZ3JuyjzGaaQCVZwgo4IFZlhY0Dhy+9bhpL1pH8R1ilz4brczn6/P1o8agBGMEP+0AG3ds2fgAEW88T9b8awGUkaLo+B+0FgxjFofPKzE93t2Wa8qTyml6CzgaWUGFgWrrdy1okAtcm+aW7ZutY/GnpGz1vynMJpQPN7ykDQKLvQAfn3yXjSYHl3MWl31G9wZ+DPSL+BqrVezvsjr75//344U4xrlDwOdIwEKVuzD01mnBFstol2xte9sWQCdVMxaLKtaHXmWgvqtgBf6+dbCvWiySzl3Hrd2mx2dqKXdGcjgW3kWsdrO6OH2UXPKdfQFrtCOgxG074DYffNNjk84rRv2jdITGKG/Y4OsI74+MgjwdpaL3pBnaL95Gf4QCCYPsXXPzw8HPQxU7P7/X799//+39eHDx9e+QDOCIYejyWB8YcPHw74Isspvnz5chjjPNfstf1YeGvQ9hZZb/6sXef/BpKUhd8Bfb+1ho9Ec0plKgQIXH9Dg5eOcZEqHbKZawVpjtXFhpM05PrEQPYxAjVN4bg97kxiX1PsXNyfBoDzF+DcwFzjS/rSQJ9BxAQU8hkFoDLT0DbH5DF0mcBwc5o0ROwDASDbbYpi/q31MvXXnJDpcZaOMhwHEnrXWq8OYc2RKz4PKxn0ZJt4DAJlgYbdGatWDFz4e/SO9xh8tDGxbJLnHFMDJwOkjIfvM3g0gLHjXGsdduvx4NaMK9exNbDd2mJ/2H87HzpObkxr6/V4bIsz4AQBdq65n2v8GkByoS1lkEPbwTZy3zRDMLXTbBuBAmdI2BaD6tZGA2iTzeI4t4zIZGcdTFGmKDcMxNx3nxUXelhnZslsn9MPgqdMc9IOZYNDdtTyPLscqfPhw4cD4Eu/ApoCtuKfdruXtcLv3r0cFZVnMobhB22cS/u9/ZYMHjdr7na7QyYva/mijwFqBG05Vib0sY3w9+bmZv3xxx+Hc/oyBR2dzFtKSKvH1P1wINR4MNXX6m5+evJ5luu3lDcBvkTNNoyTg+HvjHSshDFe+/3L4Y1eQ0CD1AAGjXAbjC3FOlXocBhFTX0lf8I7T1V7GpLKT6OYYmNMwMLT813PBJbIO46R+eO208Zar0/ZJwiaCoGqaWEbrI8Ajv12e3R8dq6kPUYuzpl173YvO40DtixfDDZ8ZhbpyroZjiOnf+7u7g7ZAUbToY/XMj2TexzB01hy7ELbFhBOCU8Y/RJ05Pc4DWYqGFmzTNMUHBfqczKpBqUNeNLg7XbHp9o/PDwcdgHm1PuAQPadNqXpjAGI+8Fdmvk97WR3oPUvdMfhcdx5aKzXiuXZ/Dl7cgr0NXvXAA/1gdOcLbBvDod2OO2lHtoK9oEL+z0+lsl23X0h33h+pZdbxNY4OHFf2Kf0xRn98Ci6G/lw1jy+kGvRCAJZZ2aGHh8fj9r4/v37+vvvvw9ZusjR8/Pz4VVj2aiR8+uytCi7cD9+/LguLi4O13P2J08QoAwyYEph0EL5IeDlGOWZ3Mtz8db6ZYOyKeb/+r/+r/V//9//97q8vFz/5//5f64//vhj7ff79a9//WtdXFysv/766wCwG7DPmL9///7Aw+fn5wNQjp4yazjJs2Wu/W9/6focqFBepzZa0MOp5nPwC8vZgG8LHHGgSaQHuj1jh9CUt6Hfc8spI3jq2WZImzN13e23iYYmFPm+FUkYGE20bQnxKdpZR/u04Jonp4oV6FQdDcyQpqmuLT7aIec386a16z/f3wxEa6NdZ11b5VyAb8fl+tuYcgor3wnInL1rfSGgazzgPbzX/OdsQsBDaDDdBKVxpC1rN/HTmzUMDhvvOcVM0EY+NVls/D5HNv6zZdKxZmf4vcm665tknqXJ5alnzumH7RR/c3t2wmv1mQf319P+KQ5CGn8pTy1pMtkxPr/W8Toz61TANANoTolSzvJ827XMjLCzwuQlf2vF48GgxbiBQDy6m1kyJjiiL+kTg2vSktmSAOPYBSZkPGZN191f9++thbrP304BvvbcueW3z+HLb8mAOPOX6x7MoPmrq6v14cOHQ+rWByT60NU8a0OStjPwVmRHYPlsztzOywa4KXiYP2U6d7vdIe3OupkdId+asc0mghZhs4QO1jFNm1nhWUcrLRp226SZvzVHkH6RJwYIzuKyThoz9rVlhqjAnj7nkR9eV9OyHfv9y5oyK2X44ynvXEv7+/3+aLNSjmdZ63h6+tu3b0dBFjcThMZM7ax1fPxL6jIgiYMwf1i4+ScRuO8lf/xJnngsDBb2+/1hAxeBUugPPRmnduSL5Y1OzfeSRv5OHiRDuNbLZq3w3uOYNpLJC89Igx0q5Sz1800cBn3UDWauOFtgnp8Ci3boKdYfglb2ndlg2+nwxWNPEJxxd92xC7ap1IPmF5ptbzQGLLQ3ZGTpRPhL38KxiP/yRp1kmvJs5DYAg7MhP3/+PDxvu942dEVmSH/6xiwb5SP1RzaTOUvfMx2abB77mmlOyisBGkEX/a51l7LIbG++v3///mhT1H6/X58/fz705R//+Me6vr5e//Zv/7b+/PPPupY6+uoZqLSZc05jG7Px4/Pnz4csfOQy0+EZg9hUZzm3ghPrnnUszzQAnO8OBOlD7SvfUt4M+NgIBZ5TZAaFLTNIAeVn20VGQ9IMV4twpoxCrjfmNWBog9TaNr2+z454ors5STqKtg6Iz3CqhPX7XhvIqf8uFnKP0Zbgux0q5gRk3Z7/N/BqikWDy/scybkPVrJJjptM+BmPReQqO0bj4Dydk9PpeYZU20HMl67bcXjtHeU5zzALZcMW8MLxYD9siAzCWHI/157lk1OfKelLFnUbyNke2anyPgPSNsZsN8/RwHNjhe0NnV6jMfJlwBf5zHQT62yFOtfuaaCvjUV+t011nXbiBny0OdTnPMsx8nik/wmmqIMT4JtkLL81O+JxWKsDPm50Spv5M3BsAWf7nX1tv/F/21LWxeRA7FkDWAww8hfdyaal9J3v6uarKrf6YxvooCp98DjwufA04JpLE0Jn9PX+/n7d3t4eXp8WW0CekSd5TRtL+nd1dbV+/vx5WB6T7OH19fXhVIOAzmQUHSQ7qTTp1qR79oO2D/zcSvCQhreUswEfOxEnkcg8v/FzrWMHwjpsJLJOyc7XGbUGpkgXi5+ZwFnLAJ5T71S3n50MKiOxRpudaIuc3I77vdWP1p9TffX3c4XdYxm+84wmR02si8o18Tv32vjQIIQWPm9QymfZ3xgB9sF9NW2UrYx1jG8MduQ/0TYdX/THu209tjZ+LowcW+SdYmNvJ852XZdBkqP+GFIegh3QwL7a4Qd0Pjw8HGwO9SZts548ayPKLAL5lfY8/qkzz5tnBCqkY8uIk4dct+axsF2yg9nSWxeDrjb2qfOUXIdXPKKmyR3vXeslY5I+sx3zZaqL42T7nnEniGVmyvwyfemLHXr7Y4bOPi51RNbaelS25yDkVIkOMHts//D8/HywJXxby1ovm5m4RrgdBB3+WQ7NH9oB61zo5bXY+4wX6c09xgv2pQmQQwtnALgej3xm0Pb8/OutIMnm5eDn3W53tCY3tDXf1/TZ/Nv6tB1hsb42fWjPnVPOBnxplEYz5/as9eIsvNvQzzfHe3Nzc6RMjCzOQbEtxUpD4O+OHDnQed508/cW2bVnbCh4LUYjAhxjSEXO/QEIax0fCcK6SN9E/5aQ8fqWgOc308A6CKYJ6smnRFYcD2ZlnG0inycj3gAGC+tx+px8nLJHXPhtHjU5a6CtyV6i3JydRUMbw5w1KN4cQYDF4kXq+c5Xi5Fn7Guep7OmU+M40rCHloBaHreU8chJ+OwjM7DJOnD66fPnz+vTp09HmbBsfEnddjZ8Ywl1PEsrWAww2a/m9KgTWSyf944anCWQdT3Pzy9vJDBIzB9BUmyFAQ/H0IBtS08nYNccP79nvFrW18+0vkQeTZcBGD+pe1v2mHId+9ICPI41AUbGhO0z68fPZN/z3FrrsI4s8h+9d5aZhW03GSMPI8Nfv349ZMRDfw4Wzk7bq6urdXd3d9Cl+O28tuzDhw/rjz/+OPCPtKz1ssmGvGvZNfp6+iiOFTP5DpwY4OWe8K0B59gw3psp2qenpyP7GQxBHxL+hIZk9y4uLo6yn5xub+PgjPOEPyyn4TPlnuPtAKSdh/qfKW/K8LHYQVP4Gkpn9L7W8a5dOsZmOFKHf+PvLr6Pg0QmG8Tw2bfUbWM03XuKdhtB/j4h+622T4HlrdLa4fetvvH+BtA4XRI649wI9mgsDazOBdkpDdBbeVmHf2Nk2oodselhn9Z6mc7iob40lpSrrXFsMtNAHI3VuVmFxp/Ge9Lg7B4NF4ECaaP+t6wCQaQzIw5+JrlPXWnP11rgSBltdqHxgdeanpg3U7GcTjZwenbigevf+tzqq+tr8m+aKNNbtn6S+Vb/VJ+zq21sGlB2Mcjm+Pn+/L7W8UHmBocN+DRZcLuxFW6bsjHtIF7r+P3XPjKKOmF7nT4b6LPQv27ZmBZ8GEi5XcpDs0e83+22ZwP89vv90TuCGSBv6du5GMHAvfGBNt4ylu/n6Pw55U0Zvv3+5Z2idtgRBK5fobFnGpmom46OiyYpNM6OTECtMZ8gkgibv08ou6H0PO/rvMeDQz61KTXz0vUSaKSOvHs1GRVGRay/0ZTfTwmShdD1sC+TgE/KudvtjrLBNBb5zPjH2VMGPFWy37+8fSG0+fqWE7SCbTm60EZ6LDN2IjT+rGO3+3VcyNevXw8GKJm+ROiU2/CC0zB0Xo4IaaSdBWx9Szv8zmv8jYXgvIG3ZPiSuWMEPkWxsQ95QwZtQXSAfCYNeY6bYR4eHo7eeNMypcwipI3MQKTux8fHQ4aF8uix35LXjEcDAC1o9nhRZpvNSB8nENrGn+Ns3rhfXMMX3vt9paw/GcqMYdpwdqll1xt9jRdNVrdK5DNBFl/l5Uw8eUf55tRomy3IPeRjrsdv5jlmvhn0hm/7/a+NXHxDButLX3yuZ7KC3759W9++fTtk+HhEje1z6qd+eukB/Srlyln1rayx5TPtcr0kAVEwQvia42TCH7/HnHWutQ5+M89n08ZaLxgkswrZDJdrpHVLTun3ouOxdw0oEoyznAJ9LeA4Vd4M+Oiog4ppsAnyuPsli0QDFum8qBQxGnxRfVs3QEE4l27+ZsCXz+a8ec/0LIuvUYm5NsBTjDbcrc30P2d4hV9JbdvoTc7AtE9K2BTcANp9nwyBAWgi0Rgog8Dr6+sjOeF04ffv348AXnjETQkBZQZ7Hm8+35SXPDnnWT9DPlKGIwtZC5u+E9DZSHDtEKdcYpgJevzXxqr1hYa9yXfu4fizzdCTDAJ39GVaabd7OR+NoIfZmAC+jDudeuSDesXgh+v+wq9Mu+ZZG11OjYfvOS+R17ODsTkm6jJ50rKRXodFkEiZIV9YWuBJWij/TTbb+FpOQg912fXmz0svXI91wWdcNrnl/XZ+Tf+sZ01+Q2P4ypMmuHzBNpRBQuqP/XIAkXsYeHqGIfQ9Pz/Xs/243jFy+e3bt/Xp06dXAJV2xUsm4otzNiX9LmWJS1WazYi8UxYJ2i0jzGxO48GxSz08I9E+kAFz+GUsQB0k3bk3/Lm6ulo/fvxYX758OUpY/fjxY3369OlwSgKXXFlWt+QrdNMnuU/kbbPRTU8t928pvzWl604yIqGg5tMK1OqzsYwCTgbglKFqtJ5jDAxYuMU7z9Ig2zkS3bfnKIS85gwAeWIDSuNqMNzazWcTNN5nAOjfmjPYqsvAOL+l/zQ6NKjM/rCeBnhIB5U+jsR02ui2fkxKnHs5/nbkdkSu2+NBJx9QG+OSoCC/E0zR6FDHDBYoK1t9amO79T951WTa9XI8UjiueZa2g+1aHqmjAX5xAAkwzSvOMHiTWJy3acorn5ip46YU86EZ9UlPzFvL0JaN27JfU/2tPT+zZSMC/JqutDbOoaUVj3naJvDy/QSO+Y22cQKpaecUMHRbAVs8EqXZ0Py1DK/XNU/2noU6Hrknb9Z6veEngC+gJ/2Orcn/0wxAxoE212CRtt2zOrzPAD/3E/TxBA9uyHDh8p/QZEAWOvjH+5m44m7l29vbdXd394oXTY5skyi35iHpaYD2HP1wwNB4PZWzAR8FzcCG2RhOdXgqjooahUx9YXwW8ueNBHRoKUyxT4wiQxvYsHCyX0z5GlQQyHKjR+rholA7ifxmpW4pc0ZoHtTn5+dDunmKGOiEbRAm4WgAzTxl8e5RFoP4ZgzT70yJcHNHe+1U6vEalPQxC/J3u5cz/jj2mc4zSDHfuI6O18MfTl3ZabKuU0EOg5rv378f/Z/pxMvLy8Ni4nfv3h3eCZmXtrPNttszckpj08Cyiw1sc0DMMjKzz743w2f+kG5u8uBapfDLxvrdu3fr/v5+3dzcHC2qf//+/fr+/fv6/v37+vz583p+/rUzL+/o5A5OZ22+ffu2Pn78eKAlGcnIH6eGGZjQfnBKbgtcpG+2rRNYas6i8ddtWP+aw2J2tU0vEUibTm5OMI10xA3c2BG6TwEtu93u8CrC3J97HPg+Pz8fzmbzq87SF2YumUWlLud3+qG8xeLy8nLd398ffF8CAdZjW9IyvaStAUwC1oC3tMH6CV4oU3mrxn6/PyxRyMwCS1tisNbxRkzTSp1fax2ylXyFqrOcbQo592WK9vLy8vBqNQMo0xs+pH4Gjwye11qHmQJm7jIlHJpzT3byfvr06WB/aes4Rhk3Y430izrAvnBWx3Z6y6+ar/8lgM8Nu9BwOrM3dYCdoNJ5emd69ty6fX+7zwPnaRQKHpXYRqxFOIyCaAxIk3lMYGv6Y+ACSs/hTxsz3pdPg2DfMz3n3wn0eL3JB9sj//Nb6muGlM9yDPMbFaQ5y8n4tKhy6m/jlR399Nxar9f0MAPK3wh+WybZbVD/HAmfAvTN+bY+k6+kaSuzH3pyPwHGxcXFwfiZf+2TwHOtdRQocLza+mPbmbXWUdAaOujsGES10oAO+bnF9y0dzb0TT95SznEQlBG222Rvy06cI0en6OF12xUDXeoe7YbrO+VXTC/rS3KCYJKghpmw8MoJB2cqWdKms2L5jb6GPLDtXGsdJRwo79E50uHAwzS1sTPosH7Y9tEWpW1nKjm703yEx8O/T/aWdjXP8PSCBBPX19fr9vb2QFsLclpxu00veK3Vu0X/1M9zy9mAj+dXcVDCqER/WQTJrekTI2KYnanZ719O+GcUYoFyetXFSm8ETsEi89KmgUPqbJFs2udUFBXc9DSnSxDotDwLhdUZKNPjerbq8m8GCRN/XZ9BWePjWi9nQq31S76urq7WH3/88WoNCrMxuTdvZmC7nA4wMKKiBzCnD1YgjvEWfxo/+MmxsYPc7/dHxyUkas9n1p9lLev9/f3a7XaHaRwWOhQ7ZU+RTCDWwIjTLA20MOKPHKYNZsATLWdhtA1haGemMzMFnz9/PtidrKOjo3h6ejrIAZ2wMwUZ7xzlEtvjPpJ/+a1lTXe73bq/v3/FE497y+bkOuvyWPB3g8cJdLe6Lb90KszANAdkOgyiODvTnjVP3NZaxzMEjc8NWEZGDNT5fJu1MRCc+sa2w0/OKCQzRPmhr2OmiH3gGCUDx3dkxx5kzJiZst5m40Hq5Q7TzIztdi9HjcXOWD9alpB8i3/NzAnpaCDaWWHKmwGg7Uf4z/dQ7/f7o/7Q55MG09ISNZYVBvQ53DlHu2QzR67HZjALNwHSZisM2FMot6yj+f0tsNmwz1TOBnxcNM0BS2cinFkY3ZgT4vKbd2ny9UJZc2BDQOVsytCiifzeFnjyudTLqdpcY1/N6GYMJxDqeimMDfC1fjRgkt/sGCiYWwq5BegmRzMJJBfHtkxA6goICIDLugkbIxq4AAluUEhhFiZteCH2xcXFkTJPfZ6c8pbSNQDQMkEeD/IucpYNONnYkCh0KyuQ9sKH3JupX/KMa2PMi/A6TmjSM461p3Wi/3EWadu76AhKU0/OCsvZe3k1EvmcTRr7/a9XMT09Pa27u7v1xx9/rKurq/XXX3+t+/v7owXXXPsZx5cNGG3XtzMy+f/u7m7d3t4eNg/5uQQWDUAycNkax/CGvHbG0uNhe5D2uF4x9zIQso5P8u1g1s7W9pH9Zr9SKDPNlrRlLUkqZAzDZ2awtvS1yTD1hr8TiF1cXBxeQZbMXkD+iR4AAQAASURBVDLRoZ/f2YYLN2RFNwN0Itv0qdGn1Ht7e3tk67L+LDzh1CTPsUuGm4A1PEsAxUJ7S3njc5GByFHzMQRX5G/AM31v7EZ24uYMUo6dgR7HLW010J86aH+zJCT2NScCcHo1533GJge/WKZasNaCEupEs78Oolwvy1vA3lq/keFrmT4OFp2JibTiO8KwMdxCvFsdPRcZu9Bwtbq22qfBbLRN11rEyfsMHH0/Ad5bBOIcfpwqNqytD1PfmtB73JrxppGjkY+SZ+zsGFsb547ZxKst2X6rfJAXNHy73W49Pj4edvHGIXCtTuqm8Wi8p341ftmgb4GTrf6YX3EaHoPU7QNSA8AY/DCwbLphPef/1g2CXe7spXOcgp1Wr/vbggZf3yqTvE024VRdHsc2bk2W3XazRa29SQe27qUcu82Jji370uTB9fu5qR3y0EEXwUybxWB9LqeAP5+zfkbf15rXe7dnGm0ETgYyXluWdidg1fpEm0Twk7oIutNeMpLsKzfspd/kg9sMcHa/0z6n4j2mAcEB0wH+bVz4m+XMOnuODrk0WSa/3wL6zgZ8Hz9+XGutw1QTo7CcdM3zfpzK5jSHhTCDmOjJO18d2aa+abBz38XFxSElzPYNWM1UnuQ9ZeuasW9AM995vRk20pzvzhzQAHGxZ9tdxedatqo5qUZPu9d1rdWnjsPjKM5a2xs9Mh2SaNUG7N27d+vu7u5gGBJhRyYTMWcaY78/fhcqs1Whf+p32uVOOhqr3BPZ3ErrkyfskwML8uXHjx/r27dvByOTzRrJTP2v/+v/uv7xj38cZDwGOEaYhpnAif0mLcmq04mk/+ZRA0J0AByzGO/Pnz8fovbcz1c73d7erv1+vz5+/Lg+ffq0rq6uDpF36kpGzccZJfOSBelTJpSZ+2/fvq211uGoisgM26A+0hmSj8yGho+RM4PRSc78P/lnGaXsNQffAA2zvayHWbrQbjtJud+aSeFv7XfWx3u37JA3ifB6bED6EKfMsxTNI8pmA+zhBbNqa62D/co7XaNTrHe/3x9N5eZagrboAcEK+0zAZR1yEiVAJjbOAWB4l1my3W530Avz2vyhPUsw9PXr17XfH0+v5n7PsBhYtlmYjBnP4U15eHhY3759W4+Pj+v+/v5wFmmya9mclf6GXs8mUq583EzGJeORMWGSKpgh9Eans1nSGCV1c9OO8U4LXuiLnKxg3ZaLXMu9WxiA5WzAx+k3G7MwnClkRh0TurWi0TA1htJRTaDBJcrAhasN0PDPU7926K1M/WLZMnyODlr/QgcjslNRwwRWT/Vhq1jo8mm6G2CkkOc6pwTWWkeR3VrHi5QzlldXV0cGnzKUNmkso4znZl1iPGzEPL3SHO9beDnRwcg6gPbi4mWNH41heJY+enqG/GlgjzLeMnAuziI2YONMQaZKcn9kl6+M2+/3h2xmjK7XCFOP13ox5pGZ2BDLHJ3YWuuIt+Evzw1tRnoLIJCvqYM60cb41G/83bbIxt90TraN9zaAmN8b4DtlRxp4baXRMgFiyyrbCd/XOrYhE/ht/Gh9YNt8JsHo9ByBM/tCfpLmJhvmRerwGkVP9To7F5/M9YfMjqV4WjR1p94sy2Ab0VUGmKSb9NveTvY3hWdvEsBxNic2gXLCtXDtmBzLG3WUSQjaytSTJUXc/Wz5SN3Wkwb4mu2NPTxHZvn7W/3M2YAvp83f3t4emMoppTZIYQwjEG/ZXmsdOWQ6/TCE01wp7GibgqWCeTcSB6wt9s0AtYiX5RwAOBmU3DMZVUZbFAqCj0ngJhq2jAuV3msYyZsG9tp305ExcMSX++OoY5TY1zZtEMPTDFhkLnQ4+pn40KYeCFB5zQENedCitikIaHSkMJvjKDnHsnCNI510DhhO5pz8otwHSK61jsBy+jGBPutM2kmWjGA8m7gI3tIOHUv4nXU7sRWRfa7jDA3hRTZpxCF7Q0zAXZyXl5/YgbKfcQyWQ35nBiZA1eCUoLCNu69Zrppj8TPtXk+Dsw7KlQH/RAe/G8CkWG4aCG22qj1rWiKXjT+WF+tkijcqNNtJPebvBJmTjppPAYvOQPF6s2dr/bID3Jx0d3d3lIm2bYg8OmPfAIP7al7nWfqfZlPZRuhghsz1GuRwgwlnZL58+bIuLn6ttY2ty3uMHx4eDpnd0Jr1fmutgy2YZCXXaJOIO9JfHgl0c3NztGmQAJhrXJuPsf1IG+ZN0x0D+lbnOeVswJddcnF2Ua4Ieoz6WsfZhrXWYaqWC0bTORp7rhVo6doUAjYyIvSR8XFoQex05DHInuJLW0T+kyGcQB+VyUrmdprihc/kI43Y9Dxpm66ZzrQ1nXtI5WCdrS/sM8G3AZ+Bpk+a57i6PdLMsbcssC06yGmcSH+e42e+++w11sM6JiXdKqQnhiYynAXru91L9suAL/cSQMfRtKnytV4Mda57+s88ItBI/8KTTC9n1/Xt7e3B8Tw8PBwBsICkbJxYax1eKRfjmn7luIRMY1MvuNs/37PxI+P2/Py8vnz5cjiTLzYlPCQ96bN1j7/7M84qADQOie/+bUDHdZrPvqcBNWb7mjx67VHGjWDIINL3p07TyCCDfbQ+TKCEGXq20eogUG/8iQynDfoPjqvBewA9+W47ZUAaOePUoAEP+5Ap4Xwn/x3cWh6en58Pyzv+/PPP9eHDh/X4+Hg4KzL8cNY3QU/aJD22se4jbWD6St+T6WTbAoITgmDz1iVnZnIDVfSHfbq9vT28Fo42MJ/BKbFhobklDwjUyaf8xYYlwXV3d7fu7+8PtMY+e9mJedn8pceL/M41+3gnpH7Hv5wN+KyIZtwEiFpEkWv87sg69xs8uJ5zaPVzk/OfAJ0NsI1vo8f3nkNzq2Oi0853qx+sf+JjxjTOySlmRjJ8ZuqXnWKje5IN0mPnxGvmhzNxvj453FbnNB4TwM0zE7BupY1Ja9uOPs6SB3Y7C54Mk9fvMeLP76Z7Msq8FkDLLGBkhmuJwtc4uyz3oCNmFiDGOwA3/UsJAGSJw2U2LcdDkW/eOEJ+tgBpS2e2QJL55TKNu6+fKgZjW3VM8rp1/1adzSZv0d0A6XR/61Prq/tBHZ5ot2/wOE70bNE9yVCKZyxcCHqZFSPwnLKvaa+teU4A05IiBGKss7XR+n6ujG4V2o7Ys4YDWGI3Qnc2d6SO7AFI3bGBk99hvbRplqn4v8xSOLjZornJvq+di29OYaxT5WzAx6g/QsGz8igonnZJ5iHRvSOnGGYuxiYAYcYpg2jBZKcJFJgqz/15+XnucXqf/WRmKvRuASwbqVPOPPQyIkl7LbO5RUMzXARvBD0EcjEYzJg5VR4+ZrxZl+lodNrhNAOcPj49PR1N62XqMk48EXnuT7QVg0GZ5NteCG5MF9dopJ4YxrTF9aXTNB+BsseCck1ZIM/IDwIpOyoavNyT92WG1t1udzhA1LqQOhMlx0GEFp5FSXmkXCW7SB3K1BXLjx8/1h9//HGUcSPgy5stmGH7+fPn+uc///lqepgRu6egmXnJ0Qq5TkeRe90v1mn9Yh+5jplOPtNJP378eAU4m43g7x5rfmdbljnKjOml3hM8ZJychTIQ3gJB7Aedou0o7XTa8u5yjyHHNfcxyMi9tAPOMHMNLAMQ845yEZvHkkwSs0kcAwZa6Y+DqdDHGZS0bX0lOGOgE7uYzVzJLqVevqc+457MeXR0v3/ZaUq5C90+tonHxIRGn50bmil/09Q2bS71fK1fm6iylpYzg7e3t6/4Ql5kPff9/f0BjGX5CGmnPNH+7nbHM4KhnWOUtu/u7tY//vGPw9KQ8I88pG5Tz4glpsCEdaTORk/4SMB8bjkb8BEwpXGug6FBasAp93mKJNcJ4qYoLAyiETCDcw+jKqdOf/z4cbQmgH3LPfls4GDLCKb9Vt90b3PmFBBGZ22dgA32VjvTdyuD+57pPq53YtstUmkg9FQkQ6cU483dYZ7SicMOeMj1GC/eT4dEWYtS51wv0hnat5712Nm4bY13+mw+NifukrYYgMWBpL9e7BzH4iCIvG90M6NIcJdAgcDZL6PP1IinFjNGnz9/PtCaaZ3s+A+Y5yaItdbRey9TH6ej/Yx1kmAo00aRN9oS3k8A5tdSkQ/c/LEFmiJLdkCeGuP66NzHMaKDoCPn4nrLDAGs5faU/SMN/N9ObHL8kT8CNl5r9t/3EJCyHxwH88I6Z7Dm/qZPlPOmO1wHy+ec/Y5sMkHChAYBMflHupw0CV1eAxgZ/fHjx9Gr6OhT8jyBD+myz21jbDmgTWrFACe8j0/2uMUm89zDAL0Aryz9yOzBbnccyHL8qd9MrrgP/MtzV1dXh/frxp5RHhtPIj/0mwSvlmt/J00tyDyFRVzOBnx2DGu9TM84EuGAhfk8QNadpVJMKXkbTg6moy3SYlBIx2SD15i35XQNAnJ/c+hNAZpxbsI2AWkLmB24DSDBTYt0GU3QyE+CZTAdwSdoZP+bMSBo49sVmJbPsRsPDw/ry5cvh6xQdnSGV84GhyZHS3aCrZDv5jf5zHGywjqAYR2mZwsgEgD4TRHkI7+n/t1ut75+/XrkrAJudrvdIRPVxtV9oYN8fn4+ZCziLBJZ82DYOD4elxFDm4j8+fn5kI24uro6TNMwU2v9JE/tmOzEcx8dNXWP67449hwv8rWBIl/n2LXS6nd9p7JsLRBMmYIIXyeNdPbMWNl+NgBtm2d+UI7Z/pYdsm3mWNruWObtC8xDj5NLeJBA12vg2v2+3px6syleM5vit0647dh1+7GpTEDiXNBgnud5A17OmCUIp4wmoOOY+TB89odj7oxZ+Jc2vnz5sh4fHw/ZduIEgnXzvOk2/WfsRGzU3d3dkf10AoS8yXfzzfyfdK2VrWtb5U2Ab79/iTDWejmw0IOXefNE+re3t4eFzBSGMIkRTaZBWufJhDg+GhBGPvmj4WdKtW2v38q0kMH5boe21jEISl8pzDZCMXp0cBY8j4ONZ0ozNlyUHiPoheqhzel+12lHxp3aDchwbHhgd/7yGw++Dc339/fr48ePBwBxeXm5vnz5cnj7QtqiQXE7pJXt2OGzr7nfIJHy4KkFPuvrHg/yhFOVLpTjyOq7d++OduY6c22HHXmyTGdHawwfp2fW+rUwOtfzl80Va70sVKcjzA7Zq6ur9eHDh4NhNi/aNHD+z87CZCY+fPhwmLqKnmXssnCaPE6/Uw+zneEfdwrHWfLUgAa4eY3XKW90eg2UOIhycb123g3wMkNhOaWt5GaCXIt80PFRl6iLDkabfc3YNnsRuhg8EITzXrbdQH0LVsOD9IGAn7ygDadfcFY3NPz48eOwG57nvk0yQRkgLxK4NnvdskwZz5xJR/BNXiSLxc1YzCS1jBDP1aStIEjm/c0P0cbxXEwGoQFhWVpB38ZNWvTN4V/jMzOZXEfHszODAX7+/Llub2/Xz58/1/X19dHpIcnic/MogwRPzdsXEPN8//59/etf/zoaI/sNbhiyvY6c2Q61YKQFL+cCdZY3Ab6tLJVBWQMHa/VtxFtRsIWPdTpS2qqjZX6accpgn8NMGrGtZ04h8YkW92Pred5jB38OLRPA9PVTdDQjH0UwGHefGelxSjAgNQbQcuhpiGagJuPPT9/zu8UBAn9P/W7n1PiHXzFCruvUtISvOdvF8UiGjXV7ofSURV3r9buoGx/YHx5ZQVoSTfN5gmTqduh0oNba5HQbDfoWzzxupwrp+B270Az8VrHeTc+5D03unR3bKlu2Zrp3i9ZTzu5UfefYMX+3PWr0tXoakGyZHmZiTV+TD9oxBuQTfWyLttD3Tn3aslWnnp2eN58JQtmnRqf7xN8I+Bmokf88jonTwLF5sW0N2E4Bvu1F6mg0sM7QNPFtKhNPm297S3nzq9XWWodT8cnkLKanAhDZJlJMNJL5+hROF+V3MjpOyoCB03/5a6CCSnTKUJzj+Ckca62Kzhk5NAHmlHD6wXcWs61TA0tDTWFN1MR+JGtCvpgfeZ58Nl+TiclYsf+73csxHzasnKJ0O+l3pmx3u5fpy+/fv69v3769UtaW6UgbW9EuS9aE5LvlaJIP8moaX/bV2Rjew7GmcWtneDnTTrDMPjJqJW3Z2cosJcEZr7uwD+FTovychM8jXt69e3fI0qaPNPSZEQjIj9HO4dqWM2ZMOCtAWbq+vj4ck8JsPq+v9ZIleX5+PmQjmh56jNIej6zw+aIGUJ52bY7N1/MZuXAmyHXZ7vGZ9iz7SEfMPtiueMo/93lpQHOo/u57raNuJ/eYd7ZdXDpEGtnv1B/54LltnKlpWR6/yYezOu2s2bVeNihYf9Y6zpTl9xxjFN8X3qdPTKKENm4Y4iYM+6AGJizr3MxFnrAwU896CTyZeU9fnPlnkNj0otn84BAen3J7e3uY1eBZnA8PD+v6+nr99ddfhyUn9GkTX9jPtLvb7db79+8PGczY5tCZevb7/dHROA1LtA0+LXHB/5lFnZYDTOVNgC+NMRUa4nzWkYUsSpCp3WZUHV1aANlBMoNTer7GOifn5eLoLTRM9xF0EPQyE5FCo9nWOjSjm7ZOOYjWxybM5k/7HhoNeDKtuNZ6NebsKw3m1P+Jp1EkT2Vw55jpIW8aiNoC72utg8Pn/ZT1Bvgm/pJ//m5AZgNno7Y1dc7pRDqfxk/XHWNOYJvy9PR0dM7Yfr8/ooE8o6zn/rVeAFDGhweixkFS1plRJA1PT0/r69evR3xjlpObdFJHnHfAHKeM098Y6evr6wNIzTjx1WpNx9kHLhLn4vjcY4fH6zbyLpYt6oIddOSr6VWziy0r2nTENqXJ7FqrrjXzPc7AupjfLSCy/uQ5t0meGHzwPk7zsa3Wx9DGDUwE8QSI9FPkNf/IlyxR4NhkupJAwzaFgU+eySkUrT3KcAPMkTMH6nmW2fCMJ2XP4znRMAFD1tFAH3/Psq7o/tPT02EKN5u+IpcPDw/r9vZ23d3dHQLJBLWTLyId4Ul4kBdQZCkLl5wwQPDSAvrDKaBhX41FeD+XyJ1bfmvTRkpDouwYIwJGN+yIM1muY61jxWYU5OedyeLzrO9U/9wf1+P22a5B4hbIYFtb4JKG59Q1A4dT7Tej2QS00WmnY+NOp8n+Uj4aYMo1PxdDShAfY0cDPYFt95O/Nd5u8Yqfvsf1uY+Tk2/jbLBKYJXfGPA0cEv535LTpmd8cwYdsflHMPPw8HA0VZvfaNTpxOzEGS3byXEnIcGe1xEHhOVYn/QxdNIJp544AdLjfq7VM1zOdIRW2ivWwf41eTolRxNtfMa667om+fX/W+DM3xuIYF99rdHYCvk96fZaL7MXBDamlfSwfY9H2uX6vTzrrM2WTfHGxlYIDiJ/ydZFhhnkRL4pc5ZPjx3tbcsMcdlNeEG9p72fkiccZwY6tCG2+wY1nBng7KAzjB5/g+GsvQwtP3/+ek2cD5ImVvGsm8eIQPj5+flwdEzG6pxx5niZ37bfTXfd/1O6w3I24PNA06ET9FBxyPBEfw8PD4d0aot8rHh0/jHs7nQ6zKjKoMIpV/eLys/7JmPLvtIYnRqsZvQmR537KZik2/whDRMYbwbd9Pg3g48YHGYtGnDj1LGnfDO9T6CW+3a7lyM/zB+/JJ1OPEbB67zIl9anXN8KEMhn/7l/7JP5zYicO5FjSFq0SSCTvoa3NMJuz4EAZSG84w7uFC58//r16/rx48fhPD/qFPWKx6B8+/bt4JDy0vZMu8Q4xiBbtnM/F2dnKj9ZWGfOMv3v8by7u1uXl5fr/fv3a7fbrU+fPh3uI39SV97OYaBCPU2/MoXEF9iHPwEH2UXOsWKdzAo0uaRs0dFYT+k4UjhtRnvI9Uvs1yQ//I3tRw69XMCFdbffToHY8Iln96W/Pg90v98f5COyxzLNnpAe9yUbyAg2svwj9sjZt8hl+G0AQRDIvgQwpF/cgPDnn3+uq6urg0xFP6IvPCaI4MUgtp2/SPnwTBWve/qa4DEbJxj4RXfTJ+sW28vUZ3QrswKZEeQmC45n2kohkM0RKl++fFlfv35da63DKRCZDua4xj6ttY76Qt/kmZZsDvvx48f6+PHjYamasUzkwj7G2fPms3nNY2a9P1XOBnwsJHRCtETNuS8ggY7r3EInOXUwA9GUmve8BRHnGX5aEQzy7ECnwmcmmu1Y/Zzv+8/0z3VOv7X2p+dscBoAm2hy5EMHZvDF9k7RdM7YbIEn1+d+ue8TLW+hy845bdG4OLLf4nUbFxqmOPVMiSRIoxMx4KBN8HW+djHvwuRUUMv2GlyGp8wWcBkE9SjTLJ4Oj4HMZ9YncR3wKR2Io6I9c0YidbpvltdW/9TuVKY6fc/WMor0i3104RhY9k6Bt3PL1G4LuEI7ecvxaM6w6YF5x3ta5rY59Glac4uPLA5imB1jUJh725i1dqbft675OxMzCVJahpB6SV1t07pr9SVD+T395WwAZ3fW2vabBGd8LmufE4QSHDv4sq2lLmeck31Nmyzn+J/mI/lsG49Wx7nlzWv4Esk3oefgcb0eszVZoL3W8bqdREM3NzeHiIcG04iWRnwrAuX1xtTJANiINWdkYdtivI3JlJr2GgYK1xa9U/sTaPF3K6OfJf9zrdFtGhrg8ViyDjv5KKkzBTb0dgBMzbdoK8Xn2a01rxM6Nb42/ls8PQUQHdHlc+JzftuK+BpI4zjyf/OMjoh6x2ec5czYrfUrsk4mguvtOFUVUMkjg/b7/SFL5qkhZ2yo96Enzmm3+5V5/Pbt21rreDqHfc53Hw1C2WdW7/7+/vD54cOHI0eT/lAmThXLaHOGbXxpkz0upxwCZygsZ5GL/BaeevoutLNYF5oTSx1cz9zstfvMZ/wbfQbbtv/wNKHli/JhsOHAgECf48/xyP/sT1sHnWfTpqenuRGPukoetFmhaZxSB2UrPAlwzvX8lvoz65Z1cjxuhbab9DW/kT62de0ZG/5uXnisKPvRz+fn54MNeH5+PhzYnCUfBH/hveXPWbrMVKz14ktoN7kGvI0zedFkvOGXyX+eU948pZuGqPRkSgjgdA4LO85piizSzrvw7BhCAwWzDQ7pW6uv6+O9WwAq97NwoDhgE+iKsvt3KlADmBZi02YA1UqjnX2YBK89y7YoC66PBmjLGVhg07eMF6c+25sW3CbHnYZrytpQIbkbrdHqcZ7kxnU7Ylzr+LBu1s3rHgP3O/1qstdot+Fv/SHtrY8BQayHY0XHaUdE4JfNOAwCY6TZFseNOw5TtzNyW0At07jfvn1bX79+PXIKAbIXFxeHANU2Kk4vv4Weu7u79f79+3V9fb3ev39/mPpj9jIL8c8BfNYZluhKq8egl3o0ASa2t2UXmGHivS3T3Prg4GcCc56CdLGdyjPUM8qRp1ftJC8ujndvr/UC+AxQDIQMskJ7ZNqbCw1wyDuu87SuWpbYn2SQOXNGHWy+w1nwrUL+We5iN3e7l5MaAvj8OkHaZNq+hhmYxSR/Qof/oqcEoF5jt9YLEPvx48f6+++/1+Pj47q4uDicF0o5IKawnyPvCC6zdMCAL2AztFA3Gy6x3HM87Q9Nz7nltzJ8PEiRQtcckztABaJRcbHh8W++b61jAMIde7zmNhpwagCoPcvCtraAzqliIMn2qHg28vk8Bf7Mvy1w63smAOl625icKr7f2afW5gRwWM/WPQQqVNK0azA6gXrXPTks0p3fLDd2ME22W93nyG4DqW0KykAqBtBn4pHWyYi57diN/X5/OGfP2W4DxrXWYQqGRj12KAEiHTv5F9BFQN8AfJMZ26EY8TiK7PLNGiPe54zRqTFp9m1r/Kbft3RvcrYTIGxlovEUWCMYMa35zjqYTQugoeMkfW22pdl20tP60UBoy3Yy8PDUPWXAU5jsszcgWsfp7E27SwsUW+EMG+/zzEEbIwKy9Cu6146Ecv0MsvJbA4HR0zzDKW7ydJJv0kdQTlsWOmhnmt1JX5pfYj8COvOmIL9AwfZ5Sz5dmt99K65IORvwffz4ca11nPbmguQYYiv3WsfZqkzh5HciVkZYAW1M27dtyGmLDIwRJiidSpsim5wtf3Oho2mg41QxuMhvdKp8PyDPWLJDcR126pNjc3RFx2+Hbp4QpHgn7TnAO9c9VqaPSwM8PcJjA6YMxFrHkTMX4JMnNhzNCKQu/m9D1xxKAxPhn3cDWp7a2LVMcQN3rQ/RQeooQVl4fnd3d5i+JH95DAuBjuUx9ycLwE0ZXGDvjVkZ9xyDcHHxa5o0GzV+/vy5vnz5cjhDkXIUO/Lly5dD3Q6YGp3MRkVGKG/39/fr/fv36/b2dv3jH/9YNzc3h0XmmTbKWqFMc3E6x7JF28l1SymO6g2smjy5bx4H6yx5Z/40e+sgyY6YNiO2iyDJoNz6lPoM4vM76w7ooK1jJo06wzZt+6lTAe/0QZmuDF0EoWmT98dO7/f7IxknWPLYROdYvDEkbUQuvCwlvHfwQbnmOGZqkzNjbCt10v7m3q9fv66PHz++shk+zqoFjOZ9sp2hMeP87t27wyHKk93LvbT7oTG8u76+Xvf39wc/yg0+OdYl65UdQOx2L9PXuZ6+5NinLOnIJhHOStC/hlfsP783UE9dpS05Z+aA5WzAFwK5liavQXJaNMSkEBA6EpoiMxqkVhdLYw6zAK004NYcejOazmS0qKg929rbooMOnzy0896KbptBt4GjcWahc7IjaWPSwEnro0EjDZNBzVS/i69T0RqtjMrsJExP4+nksLboY5/4OzMJBK1NxrZ4uwWq8/s5U3G8jw47B5Ya7MWgx/k2PlBe48R5oHLOsCPgc6AT403a4/TiEDg936bom5yZr218aODjvPN3fX19oDEOPnaSa57Nb/Kd9s5BBIHLBFSbLLayZRfafbzHbdgOsX9+jn3ZCsANZAn84i8s+wYypmMa14k/9j3NfnFKca3jrJT78PT0chA5+dQy2+ZZbL7Hy8+mbeu35Z394z151nrvujju1D3KJvmXNhtGaOCfPvv6+vpo/BsI25J1B1RrvRyUHltFGtn/NlXK+6wPzPjnCCoGK/SvW/bH/HlLf88pZwO+tisnRNmgZsB5flEI5fbxKHCMZJ5xipXHv9BQtuwWrzWH2Qwt+9EMDotBUEPY00C5PirdlhC01DPb53oRPpNiATWNk4DRSU2COIFIPt/62ACi62yfBiU0nFEqOmcbT/bp6en4XY9cL5JneVxKA1Luq3nl0u6hgWyFhqjV5ekcGlL/mceWe+rubveSkc+bMpJhY/RNg0bnwfHIQbBcEpJM31q/1rrtdi9vzmFdzi6fI8/mL/u61starFxjO153nOxReHFzc3N4x2rkJs9GpvIaQL69ww6dzndrkT1/n7LsjQ/t+sQj86fd10CUAaRBwtS26zJ/zKvWz5aF5PgaYNk25TnOGjS5y/0Zz4eHhwNdsUEMVLw+NLT6/bHPz8/1laMECY1vtFPsl6c8o5u2OfTR7CvXv7VZIQZ6fodt2ltrHcAPx+P6+nrd3d29kjfSTDu83+8P6wKzsWLSEffF/ad+BXQmO5lrT09Ph80X3Ghq8EqAT7+Tfmd2MbYiexHsk5vt8vhQDxzocNzeUs4GfO1VMVvEhml8br/fH52jtVY/LJHTKkwVc5ombdI5x0lZEVKa0+RghkYqcxskGmr2bQKTeZYgpYGQFr2T7gnwcWNLM2o0HqRjMshbRr8VG9TmBDyVNpUG7kwHDSYNNpXeyuG+eVqFfaYiNUeWwixS6mhAo9U/8cf3ph07eU/98bMZ0qm9Bgq4Y+7+/v5gwDkl1XgfY5s6YzSjtzF8ycZlGjbgcLfbvXL8dEaWna3F8Xme48G6OQZpmxlfZgqz1jCg9/7+/rBBI44pdOY1Tl+/fj38OTthY06HlL5YDzzOtgUpEx9Yr/9SbF9b+82u8r72TCsGaC2Ds9X/PBNeuBC4M2D2gnfqEWWMzj5tff78eX358uVo3CJTmb7nkqW1jte+Nx3nLnXattDdZpO4Piz38axH2ohcp44RMCSImwJlPh/54VsssgmLeppMF2m+u7tbf/zxxxHP6O+SKWR72WxF3YifpyzYflp2mEgKcPzy5cv6/PnzoY7Hx8fDGXxXV1cHu8ddvLEF9B2kJ/d9//79cN4o7V6zAeZ55Krpm8dmK0kwld8+h68ZGoKX5uCc8mXZcqy+7xT4IE2N9nbvBAy2aOC9NmAGPVv1nbpOnjbep53p96kPrT9WdN/HtpqD2ipb95lfW7RP9TbZa20YFG+1NcnLW+9p9OZ+G2HL1jTmU50N8E/0MQjJdd/f1pWR9rWOHSSdJs/CasEajT2fpTG0oV3rxaFnxy8PuD2HTw2c+3cGFR4j85LZS0/jTvrVDLnl0t9dmryd4sHED7bd5ITfJ72Z7N2k143/E52t3i39bXxlWwwQ2X6AC5d7EDDa/lN/nH1mlo00N52c/FXj/Tl2ofmpKUBoCZhWEgR5N3OeJS/Zt9gCBnYBs9lIYtC51gs/AwLNz5aJbMW08l4nmAhwraPmW5NZ82FLNn+3nPJbUzkb8DGVybODCObCgBhhHraazF1QdIiOwU8Eks+UIOjUbYVKHYxWOOfPxapG2DZcbX2cjRMXnzYhalkfFgqBlXbL6YbHfqVPe571eqqNNJie3JfIhqejc1qtGZwG5qfrBhDc6c0zrLiMwIbLwHQa18Z7ygL777Uykc39/mXBcjP2rIfGs9FK2rhxhO8DTv/D76yVjXFMnVPWNPexn3bmoYdTDilcb+OpLRZe4xol1hM58oL3jEOu5e0cAW/v3r07nMmXYxTY5t3d3bq7u1uPj4+HN4E8Pj4eztjaMtKR5/zmbGdkgd8tswbAOWE/bwXhWV+2cbaZrI/ywzHzGHP83cemAxOwmvR5sj/+zTxZax0tvGeb/t+ZdNtEO96pTJkOgxHKfmQ8srvWOsw+XV5erj///PNo81/8XmTSmxLsSzxVa7tiPvJ3ygd31eae8MXAi3y2fWSGkXKTZ5ueh2eWTeotcUDWslJXYs9vb29frQEOXc/PzwcbF3uUkvozdZyxYuaTvG9rd6d1/MlCJpuYDH0+E2QSpJKuANA2drEZDBqMJYhLmn9rvoIZQ/urc8qb36VLJWQHLfA07Izy7aSmTjtCmD4jhDQgDaE3tG7FC90upMvTLy0iMc9YWlr/lAFgac6d9zXj46jLfbZjj9Lu9y9vSLDBdttsz/xtfDbPGPE1o9XAVOtvK01WmYVK/dxdR+NG+Zr6OI3bdJ08ZyaM4+ENCE02W5stSp3o4nRXDFqmLkMn9ZaFQRaXb6Sv/I1ZEy7z4K7/3W535GTXetnEEToyJROHnSnW9OPr16+jLEy8aGNlxzdlLvIcj6fIur39fv/qHFEGMm1MG8ibwNtWdmML7JkfDlzbd95HOaU9pe5kPEmrA3naUtsUB+hNj1xXs4Wmk/rltmn/b29vD2vO0m/KKdtogf9ax6dKeKNK02XahdxzypdtTYVzTBiY5pPXYvcNBlkfgVSSPqzXb7Wh7Yiuctqcn9HnABnKPkEcp2ebnAaYu8SG03fHx5mPsYUMUu237Reav3WihbycAhT/Trlt2Oa/DPBxTp7OMgOUgT5UjEMo1+qvYGuMoFNtGaUUfmcEQuc1GT3WcQpANFTugzJJzxbwMC1Tf6b7ph2QW3UY6FrhGR2HH4nc4rTsGMyfZvQbsLPD5TVOCU5ZpXNkITRYJgwiJ3mYQCp/ozFKGy7kg7OsHgu33+p0dO5xMy+oS44wmxGK8QpIyXE1+Us9zlI1Z0Jaco2AiVFxjHlAXYx+6vPCaT631kv0zmCFG1bIE4M486Fl0ThOuc7F5AbDFxcX6/379+vm5uZARzZy+CgG172lv/6t1WNbNdkk32s7uyXT/CTfuODffG06Z4BHPrSsHvWZ7bb+TXrc7mm6YFtHsDPZj4xzy8bxf9LNTN1ax8dZ5X7zmiCK/fShvxMvbO/IS4LZSRY9g8Vr0eEEbcn6RS/DN66tZFIowVE+mQggr1lij8hnB2ct4x/MwkxpZiHXejmvMOtzIwMGhU0/88awrF+eMIn513TWvjm0p11ugj2nnA34EkWns0l7rvVyTAEZen19fXSaPt9bt9briC+FKdEtptKIO1oJOqdjJUOb42htNKds5xThbGDEytOMBY0Ln2lAxBHVxEf2Oc+1SMTAiA7Ngse2zasJvLjuAGY/z0iYR/e07PGpzG3+YmDaNWYPeL2B/zYONtwTAKestzOo7AzIH9JDx8Qsmh0gaeN0TAN8pDFtZfo+u1CzUSHTkgRvqZOZWdftDKmnwhhYcCdsCu0H+5g6OR0d3gRo5S/yv98fT8tTtlhv7o+9ytuCEujtdr9e0bbf7w9n71H/Li8v1z/+8Y8DT1PXv/71r/Xt27cj2xRe0s6FNmZeyddmpwiGGVBYzmgDCBrMV4+jdZ2frpv0mH7anNTrIJaBJu+fplAz9qQ1f559mXTFvOLz+c4sM/nt+qbMU/NfXh4Vn8dA27acmbjUETkN/+jTKEdN9skHfs9UK3WWa1Sbj8sU6M3NzWHTA3GAl4lw2QmXtXDzwxagIdgz4KPN5U7iFO6k/vTp02HDDW1WxpzZRep1/mK7mM0kXgpWyjIP+2L2xxilAT4eYUVbck45G/CFiYwkqMR2+o7iwqjJObK4rmbkeM3A6q0pzy2wN0U8TUn4rP+30Wz1mX9Tuy0SOLePBpyngAPvczmHtxMtrS6C91Z34/NW/7cA98Tf9rxpeIuCNb2Y6J8AZXO4rZ0tGW465TodRNC5bfX5FPAlkMnavPxuUGjgYVop957OIhC18Zz44zoNgHyv26ajj/MlUI3TzN+WTTrXXm3db7twys60/v1ny5aM+/dG3yTHvu8tdLSStrychNcoB+foYYoDirXmjGTubwkQ0nAKjAdEtWtTmey+bVF+a5noZmcYtLclIc0PeSp0stPN5/+O/DKwsi3K/7nHQKzxj33LmZzpf0smtOemYh/G3/9LAB+PZOCJ0yaI/xPhNkPE34nU+Rwj9XTY9dg5MCvYslIToCD6poNa6/hIBz9DehhBkSYL8CRADRxYidxeCiOQRpcVy23xOI7JIJOXjj4MKCenTTrSXtrk0gBmlNnH/f447c+IOG21NxNwY8EWPR6PBoD52Zxr5DvjkUjVO//IP/aVkbqPJTDPTRfHhtmRlExJ7vf7I3oiX3k3LE/wTx/CC+pr+phCHsdo5k0ZX79+PRzlkAieUzvJgiVzZgBnXqT/nCZihM5jFR4fHw/n/TH4tC5SHggIMosRu/T9+/f1999/H0BsbCKnp8KD3W53dEDzfv/yjuBWKI+UD4NXOub218oWkGnTglu2wACJtDNzafr9PGWZ62rbMxkrTiNPdjhj7fuYocnxH09PT4cz1Ha73SvwlGyOs+akJ2CfskP+sC/ewZ5MUPiVDDiXECQLn75Y98JzghbqKflK+qdCW5pNfPSFXE/LjS5pI5m79Ic+lefuObjKfTxqjZk08qCBZcoB6TXw4v95a8/379/Xhw8f1vv3749szuQjQwPry33ZqMINZQSZodH+x7LC3/JJXuZIm1PlbMDHNGKmXVoUYycWxZ+Ko5YWQYRJDWCScWRkA5gpZC7RO69TgV1nM3J81uWc6KM5btKyVn+XYZ5tURZp9LUmXORNDFYbD/fboHu6z33iFIPXak3Oi30I3TbMNLShvwGfNnYNgNNwTuM7gdv839a9GLyZfq75otExqJ9ozvcGMGO445wCjDg16gCjBV7WSwYTpC/2I4YpjoT/53occQA6eZ92vfg6epEp3TjxANjdbnd0tEsLekLrpD+ZSksdPGyXNHLxe9p59+7dgTYuV8m0kceRemq+O9Cw3LTiYKDZz60Anr83J8TvBMtbz4Yu2hnaOD9jYO6pSdfr7DTBd8aLU+xZrzqdOUtwQsDH9rgkhUEc7UNAIceC6+cYBKTNHFvE6dRmjxLoNNC3lYhgX1zCXx6aTsAX23F/f38AOCnxoeEx5Z38NI8MaMIjg8P0r41Z8w/uE2Xn6enldbH39/dHvjB9yfdk8NaaN65mSjuyQMzRAiRfb3bIOMFLXk6VNx+8nJcD53uYHaPK+7eIoWGgctCxTM7VRoaf/u72bAj4SSCx1uuBmWg6RYefacbJ6yva8wZ/E0h03Y3GrRRzDOIE2HIPr3maP3VujaPHw8629Tvtcu1L66/5lDYmGswf0t++u71p/J3ZsINw/9hHZ94mnrTCa3Q8oYk8T/3caGW95O43A/XQ2NpOyX00gllb0zJBzHByTZ3XzOV6430D6nQgBBj85NiEZ3G6BjekIc6M65VTX6ObtPq7nYEBX7MhHIeme5Njn/q0pbvWJz/nPwL2Jru0H40vrrfRz2ciU/luGrg2LWAkgY43ILLPHNPWfgpBUZ6lzaXtMk3mD3lo0Bp5m94Bu1bXPdbdMqCkk+2zP+wrA9RkssPbtV7Wm+X5ZMWTxTTYY7/t7/lbo4VBHetr4Dj8Cd8DqPNGFQbC5IkDdNOea63wnnOCLOv7OX5/KmcDvru7u6MGLi8v1/fv3w9Rkc/YoVLQyaSOGFga46RtbdxcrGwT81ni0DwgXDTcpm9t+FthPaRvCxjwN2dvJhqccXF95EEzWIxkWh/4XHNOBl38bLzdcjo0nnS05kmcPCO45+eXqcEtEEP+ZHwNlPk2iESvU9q9ZT2bMSGf8xzPF2TWpwHe1Efg5/HI85PMU974ftfoSrL0eZbOjvze71+mHv2OW+sz6Y5eWj5iR5JtyzRV3j/JDEayGplKiq2hPoTPnjqj4aejyZEvfNF7+v3HH38cnRP6/Py8/v7778M0c7KGTZ65wDz9z1TXu3fv1vv371/NjKQNG/PIjQG5x7k5YNowZ4CnT8pBC3Jts7gAn8/zGa9xtE2n7E4O3eNpftmh81mOlzMmsSm3t7eHcxOfn3+9heGvv/56BYDjp6IbLYNLPSa9lNGUBAT0OdfX10fZQ2fvIhM8pigykT6k7fQ/fOJO2Q8fPhzZu4CbJoehNcs/aLs5LRtd/vTp07q8vFwfPnw4TON++fLlMAbhSY59+v79+/r8+XOdck3fuCmLNiX38Nin2A9Pb2ZzFQtlhTMJ4WMy69fX1+uPP/44OuOXmUkW21z2J9c5biz0OeQ/7YB1gTMb55Y3v0uXTjq717JmxU6pRRunitH7VHztlONrkeUpQHbqfz9DELRVGgjywJpu9/mtkYTvbc9Q6bYAY2t3yl5MtExAp93Dg7tDX8vY5ZkGmpq8mO/5ncbJwC6/uc7p/+bs+L313787eGr8nGSfADWGKvobA5rx9pTUWuvIcE1tbOkp76Pd2O/3r45uYl9C6263OwC6Zijd98ZXZnZ4TxxJgFkOh035+fPnwa4RwDT+MwNqwGAHbNlyX6ZgYrItbQy2dKrVaWdK+lsdTd8beJv6NtG19Zz5utWvtV5nthlkUgdSCCA81k0XW7FTdiBL2ij3rt/9aQCcbWWpQOMv9Y4zb5HL1gfSGdklz1qWMOCMM3RcXx2eRE+4S3ni6Za/St98fwsacm/DJA2oJ6Of7+7vKR/faD5lK1tfJx8+YYFT5U0HLztiTImiUJFy337/66XTfOXQWuvo8EQOyPX19ZFhZOaINKS0OX1uAkhhir+l5DkYW5Ejje6khOcMqn+zkadTaA7fz07tbo3b9H94eo7ToPNs9TYjHYPhgz5pLHItEWM2DSV6+vLly+FYDgKatONjHNgvyh3r8LmD5mMME+XboO5UMRDcclQNoKTYSLn/U4a2TUvx97Sf7MgUBDizwUwGjX2yGcnW+RiOJksxzF++fDnUHTuQQ42zOHytF1sSvWcmIYY7+p/2k6H6888/119//XXIftze3h7RcHt7e3g37r//+78fpqCYvQ3dPng6YHW/3x/WBnGcLDf+TpsQZ+4x3pK7LVs0tUs52wKRHkcGSbkvmcDczylIAmjKEOvh9S3wSLpa1ju+gMsVQgeBXQLL/X5/kH/SQcBDEBPZ8kZG6kWejf2iDIT+yHPOcEtmm/yhfnmTiPlAO5usfe4j2DIPc92+ln612a306+Hh4eg9teEhdSFTusnsU3fDm0ynNp3J/37VXTL2aXOtdbQukzM/U1Abv5C+5jpnmdImA1PP3gSEr7UOGeV37369OcizBROmoHyQ9gZszylnAz4qIQnN9wxYmEuDlF15z8/PR6jZnSFD0xFObzTA19KgGXTSyNdThdYJgTN97N25E2A7VZrBMqAkXz3YNr7sc4taaJTMH/JzAhzJonnN01rHZyV66tT0GSATVCeV7vVa+/3+aAfb7e3t+vDhw6GOvAYnUwnu51bUZ54RWHusDWhp9B0UtGcnx+mxp67YcZIO89XOjfTmj2twJsCXfuX6z58/Dzxuske6+Eq4lIylHeoUYduWZFzy1oz7+/vDWw/oAKPjkVEeEp0po8fHx/Wvf/3r6Awx2qkPHz6s//bf/tu6vLxc79+/P8qErLXWhw8f1tPT0/qP//iP9enTp1fOnfJs3tAxPTw8HBx2bODNzc3hoGnraHgQIGlj7/E+R9ZaabbLAMtBRHSF/bbjy7hwwwyXFhi88jVXduoslEnbL9sa8i82n2vkaHdI7/PzyxmPoSn2kL6BQQTlwuPATF7u9Vl7Ofcym5VCD30Sg7zUm74Z6PEvQJZ2a611tIkphW/3MZigjyb/8z19e3h4ONwfvkfuaeMiC2u96K7teABfNnNlbCmD5JWX/qTutmGPwaFtMqeGeRZg85ehg9PGoSWzBAkeA3TpT1wa+GsYqM3EnSpvBnytcD0AI5oJjOT3MJRRk1PMk8GaEG4DUb6fxQY697Y6WiFtW7S25ya6/cwEnlrdrvec0mg2PVv3WjAb3yi0zAQzaqUhyGeuUdmdpTNdNEbTPefyo30/JVsNIFHOnF0zD13PZAACoPLZwFkr1h072dyTuk/xyjrQQDPr3OLfNK7msQ/npWyt1V+H1ugmAG46FUfhnaOkqTkB0zvZn0aXS6vH18+ta+sZByH+vX22sfaY23615yl3W3aatJ+S9S1aJn1tmUpm9gj2eG9s1Na62uio67OecwdvsxPWFfpYg6LU2aaiA5RYP7OsXgfadMlZTPLCoNhAn3acdsZBPO3dqSCUfUhdzLR5PKY6AvoD0NI+ZYb2d/KXLtOYTzJPP+b7Wx9OlbMBHzNzLIlWvnz5cliAzendtV52QdE5PTw8HOpsUQzP38k1CiA7auXIYKVu3m9GtzQp+7g1gBy8LSBmpUy7FF6Ckzaop4x3lDf8ZaQ5Oe3pWusPx9Nt7/cvUXue52f6v9vt6iu7MrWQnV6Rk8jRw8PDEX++f/9+dIbZlF2h0ZkM1inHRvnwVFNzGnxmMpCTsSfNbpv8zrVEjqmXa2EaCGEd3MGY9rgwnKCczoD65GwLnV/jb37PWNNxMUMSerjOkMc67Pf7o+iZOytJ6/X19VFfyHNO73Hq5vn5+aiuZEc+ffp0NE6hkd8pdwbnztZ4jCbHTtmzE2xGn3Uz+9bkiGVyRL6H30kX+chMlBfQ04bQzjLb1tptGU6Wpod0luFBnHdoZlDz9PR0yE7RnjJjmSm5L1++vAJa3oXa7MRax++YJv3pP3fVMhmS+9yXvAkndCRD7ze6kE+kM0cwhYZsxPj69evhviYf9NPk/X7/axlX6Jl8I2mhv2o2LNlMziA220Z7xaU6bdaEvOfza63DrOSPHz8OU+L39/cHeUjGlM9bz/xJ28O39zRfYvoMDDNWW7iglTet4WOnfI3p+bVev37JSsqpDRrCthawtTcBMSu4AdnWM+wLr7X2t4ADDU1TeNNzisbJgfoa65roaqXdY8VubfOZCVjZgWScPa3oqJJ/jA659sQKMtG5xfut35rzYH109uYlHV/jt2kmrQYLpq3xMo5rS85bO+QzZZVAcmv8zaOUKTto0NH++Czlg4DYYJ6Aar/fH20ya7w2+Ao95AeXLhiIcWyafDSndk4hPzml2UCb+cgx2LIpli/zhHS05yZw2gJY22E/7/ta26eA6FS2HGebysvvptMZ9AQgz8/PRzv/48C9I5TjGD6Zp273lH2wvoQWnjTQ/GQDIQGg3ljEN0U0esI7Z/iyznW/f3nd2vSs+0VdDn2cDZr0Oe23Osl/Xje+YL3pP9chTxlP290Jn0zP2Kbw/mZXfte2rPUGwMfDIEN0omsOUgNpzvY9Pz+vu7u79ccffxzdlxPFWdJOwKAHkcLdSjPQzVGzX83otQjb6XC2xzonQ2kj5/ssFFvgzXw5B+i10oTxVJt2Mrw3Bi5rebJub7d7ORDzx48fr9bjxan/+PFjffr06XA0Rv646NXK4jFs03FTxmALGLgNZiScAXW9NKTMdjpLxnYagGDb3mzS9IB1cUymbIpLc4DhaTOYBn2W6dDNPzsn9psReuje73+tifv48ePRYnRmTrjGLMdSxJak7sxM/I//8T/W5eXl4f2fHMuHh4f15cuX9eXLlwMdXMtEcNgAFh2Ix5FZzsmIU759vcl7sxcTqGtlAghbzzY55dRgbOVWu5ZP10vQwIzbFhCkTtm2c+NNMlwZJ9ZHEJV+xe9x3CM3zErbRlCXOO2Y9hzIctNC7kl2yCAoffLmyLXWYdNk2vVY5nsynLEnzGB51iHFWSaCQAahzYZMPipy1ABe+2Mmm/1rMkfZyu5bjkmyh2k/QDhrOrk+mfbIADyfKUxmGUQa+FGmQ4sTKr9bzgZ8fK1J/hLJtO3tNjJJcQcl//nnn+vf/u3fjgT/77//fvWiY05dRQDIaO6ynByenUm7Z8pk0OA248xnWlv5pFDl2TYVw//Dm/S1ZYzYVotaKTRbDsmGnVHrBF5cj6+HnsvLy3V3d/dKgWOEsrjeYCGyEnCXhbTNIXgspkjQYGoKFibj4nY5DUMZN+/DC+70dfQ8GVU+n/o4/c3p9DZGzUiTb+43xyBGyYaxGR460zwfuuk8DPicrTUYb0ss4piy+41vCVnr5S0YPFcwi+Lp2D5//rweHx/X9fX1+m//7b+t+/v7o5fTf/78eX39+nV9/PjxKFuRQ+ed6SHdBhgunAL0GBnEtYxrSmg9BQz5vQUBTQ/o7Fswb3mxHfTmvFY/6zId1B/6Hzp0ytlEn9sPOLq5uTmcC8e3J7APXjqRI3zYZgJZb9BofeamRmeuacMJLAnCIp+UuTzL15+lnq9fvx7Zj7XWur+/X/f390e8+fnz5/r69ethzLhrdtL9ACeDfWb9LFcNHzTANAG+yf543IkXQoM3sRFXRK/D14xTnqGt4tjRtnMsbIMz9Z4p88iwsUADv20243fKm6Z009GUKS3enmM6dK2XNQ80OhRKZvRs/JoxsxCy3gZomsC5zq32Wj+n65NT9cBOxmkqfsZ1nvNbA3LMVjQQ7Lq2+LXWCxhqGTAquPvG62u9PoLH49Z4eIq2xoffKVuAuIHFLRomfdoa6y3A14CZ66Jj3eJFZIOOhgabNsFO2HQYpPieFkTw/xjBBAIMHslL0moexABfXFwcDnfODt/9/uXE/QSitEfNAU16fI4Ncf8me3CurJ6675StmK5t0c8xbWM2Pbsl743erfb9rIGDac09rR5nKQMQ7HQ95cg6nUxo4J6ZKe8+TR0EJ/4jGA6IMBikfFJ/qEdcS8kAhjJgHZ1kx3312JwD2qhj0zh63CZZatea/Web4d9aL0ECA4BWT0uS5H5PCZMf7rP9m+/9nfJbx7KkQ3ypc7aSMyUawclJ+hzwv//+e/3Hf/zHWuu1YlxeXh62qfPYDq4N4J8ZRGRPB0ZBdrTK6KpN95LOBj5tYMizFIMQDy6fZbFh8v/pC6cpeF9LBbsP7gdBH69TaRhtN+OSSDSLXkMjDQwzmGz/+fllFzeF3uNGOSNfubamgcfGY39S4WjgqdD8zZk2jg2PM8ifo7r0hVNFqceyZ1Ce+hqo4f95luejcbMGpyzdlqPt9JnjzsJF59wYwalc6qd1wzLVjGimZX/+/Hk4VmW32x1NwVDWmXFM9jIZkY8fP66Hh4d1e3t7yIr8+7//++GF6gF9pI3rtabggfJnUOBC/aHecqra+ug2+L+Lrzfwwvvs0ClvKXZMlAXaZWeZCEoandYx92urf2ybr/oKfdzQwGn8Zh/SZvxR7qd/ubq6Oizqp9wbjK11fCYh9XCtlw1sa61DZvr5+dcmR9q5ZIyy6SIHhu92u8MbRJits11gdvrr16/r3bt369u3b4fNFtlURx2nzcoSnWwcYaDFceXmS/rXjKt9F+WDWcxk8MP/PEObmUKQnPaYofVvsRH2belP+v/4+Hh0cPXWpg0C6vTLn6HHss7Nl9QZy5T7fU757WNZIghcG9DSt74v5du3b+vz589Hz+a8tShN1i8wfcqUe4odn2lpAs/ohbRuRSsGZ3RWBiyTUZqQPf+fnEGLbCIIEfzJEUzP24C2CCoK677QWXBdXhQoBotKSqWn03efCOT4u8etAe7QZgM+8cSF/TTwoyyxDv9OWcw9U1v+Mw10iqdkxk61te2AgAERDSjBdaPd2QHzlAaKoNX9nvpjYEQHSxqSjbu8vDy8U5SH+za5Sf8JPuNUKZefP38+vP6J0zk0ztQPy0srzvhsjT15wTHifafAXfu9galmGyZ7Ntko3scxPAXUDCwdEE1ttPpId8Yo4GWt42Ap/7eptSYzBAy0ubF1BI+0AVyPl3Y469GAbwKzZLCpkwRDaTftcanMWi/rEMlrApEER1lLTdvtAIPBaGjIko8WvBG8eFMI+eQMZOpIG14C02TBctZKS8Y0f5C2Y0NiKwKSGUDYX7B+8nkKcP1nOWt0pfyXAT4ukm/ZCkeuWVi/3/9aYJ2jNSKYWdfFziRLSAfqXZs2JHyezDTA8+GlzuI1Z3TKYVvYWJqT9XVnbTyYrqeBoAnMMnIyvabHitaEqGWPJiMemhld5llOG1CZDKabUpO2CRDlf4MoO+SpfoM7yqLHsG1cYP85vULaLFNtXDgmdrykyc8SgDsb3AwTnzXYp2x5fFKvAUiupy5nVqjXAVsBUex/7s8n5diyF2d4fX29vn//vt69e3eYDcgRFQw8mYUJrWknb/KIYecidvKpZcDNZzuoODbKouW52RLq/SQrHq82rm7H4JSBAPk8tUX6/JuvtdKAGtudQGXutW1rznOt4+CSawoJaqxfBhasz0AndixHRRmAEfQEOLTg0P2dsknUyZa4yFrEyCkDbhbOmAUoth25lGe/2SP3U5aYreNGhWZLmr7zmnWLvqTJT1szaln175bttBtdyFT+WsfZwOZL+KxBZZulaP1gaf6QsxQZ12RmT5WzAV9egvzu3buj901SoCh8P3/+POwO+vr16/r27dsB5CXa4enqqTuCTZBHYBAmU6EoaAYYjEayJvDh4eFo0ST7sd/vD+l+R2rp2ylUzUGajFZT1vabIxenvSfAx8XrdtKNJv4WhWWmlOnj3Eswwz4QbNLwcGwylRaaPF1oJbWiNKfq65Qh1mNA3MCP++XIP/0knxO1hh4abG7usPFtjpX9aL+ZL+wXp0ACXianTL46aCPvmK2jUScYYtYiNETH6XAjS1lgHnnl+HNTSp5r4CWBZc7/TH3JWjw+Pq5Pnz4dAaarq6v1559/HtbrpY2vX78eAUXKEGmjrWiymZJF9xwPvjC+Tck0m9Gy6w2MNEcYOWzAgOA7jq0FCbSl/q3JFOlrjnELGDbn63oJlqkf7Cv/KHvZKHN1dXXYSMZp1DxD3eW4kwcBSQGU3759OwBJLnGK/HATROxy6s39LJmuZeBEXaC/iO7FtnDHerObfG1bPiMDlsE8E7/NdtPH0LDWehVYXVxcHAIn65L9GHU8NNHveI2h5cTLmgy8Jp9lfrI/1IdkSp+eno7sPOtrGCGYaPJj/t305HtkZ61fb+rJa+T+pwM+IlwjbhuvRD2TUWOHCGjS8Rgv7oppRnWKjLaKjY4NxmRYG51bAKq1NV3fumcqBoKm6dz6Jhq3QFaec9tbtDYa23PnOoatZ05Fduz3uaU5IAIgGzGD1xYEnCMXU2mGYmr/d/pJ+TLgmMauZemiy5wqdQDnPkx9m3hjQ+t7PZXi4NGOvfFkutZ0bXIoLWvU6tvq6zSeW3pEOTynvMUuvdVunVM85qfaafe35y1PBHBvLXye4H+t19OFzGw3MDAB+rTBjJrBJ0FPCp/JdK7vafbdYIv3TjLJTHcACQPqxuemT+5bo6HZnVP2rQGv37WJqY/jaL1q9rLR+Z+h4T9T3jylm2mTTHdkF9unT59eOdt0PFlBZgIZWTI70iJROk1HbxEMT7nl+aD91l7uizG24aehbMC1OVte2xqkyYEwg0deElSQvxMIJUBv7Z4yhvmdmZHmSGNMOC2Sccpp5ARGWSfFqM39MyDacsKWmQbC/T1yyP5NPMqzfsbjwTHx+gvT0WSmgast2Zrq55EPocMZnFaPM3t8Z22KpyjZJxpqTmHlxP+PHz8e3medaVPKr9+ME/pSL5eRtPtCX0qOzri4uDjQkCm33W53sGHJhLx79+5wLihBaWYDkh2K/bDc0PFlDJPBThYpawxpF5P1SfRPW2f5sAx5bRBLmy0xwJ7A9DnBWXN0LNTr0NPGrNkc23pO6TMLaRpYR+Qw/4c/yaTd3Nwcss8TXY2n+S3yyFkqriXlUo/Id5PTFOoC606GLxli6hizx147mHWLnHJ24MNsfdql3WCgFh8a3oZeg9vwLTNE9p20v+aFQS3HknTyftv+Uz7Yfj800fa1wGCtdZTA+vbt2/rx48fhuCfLYtqI3CQjaJ2JfE++z3Vx3Pi+53PLm87hW+v4dSUxgjHiJoppbe6yCUAI8wjA2N7T09PBcIcRBnoetGaELJRczM30b+ppxani1OWBSSEtU53+nUbGU1eut0Vo7O8EkrZKM77td95PoGe6mdInP2h0DFg9jv6NxqqBV/clbRi8TdkoP5N2otihfVpT5n7muoEW6aHSu9+t0Pj6d0f8dlzT1B7Hw05hreODRhvoaxs0YpQyjfnw8PAqw5f2CKI9ZgR8DQSHfvKY67PyUvaALYK3PBMHGaeevmY6KnbOQYUBb+jh8hI7UPI2DrltemH/La/kIQEf+cbNXM4u5f8G4snnZuNIXwOEDUDxfn/ndU6hczds/jjl2IAoCx11xoFryD0VydKyNyncRBBwx+UHbWrcNLSA0zoXmVxrvZL/+NeAJ/KFmcfUQz2mrKSvtqsN+DpLnvsNHHkP5Z/8829cdkC62Db732T8lB9wcaDjdgyM9/uXadns7N+aveT/Tk6F7mbvTpXc0wLPU+VswJeSztJwr7WO3oIRxfRaJgOAMJLz7BE2omEOujOBFLipOFoIPQ0w+X86prZA389Oxq7RZKNq49mc+lvLW0BexvAU7X7Wv01OcLqfzzFybBnViY5zFCWFGQMajZQpkOC6rtTjg1r9DLNn/I0KbqDo/k7gfctY2MHb0ZretfrhwG4rTqgtAjeP00aONMksQWu/6XOuM1tlXeFfgFPqZPaOTpF9zdgE3Fl2+ccdueZPsxXkGwNbgi3z2EC3gRvLiutJMY/5rEFbaOSzrrPpX3NSBuL53pxj64/BPPtoRzrRwzrob9qmpuh/9JL3tfFubbNO8pZZVoLzNqPlQJg8ZP+bDDdbYVla68VPM3vagBVBBUGr1x7mXuohwSD7Thrtf1sA7yDQ9tV9fYv/Pce/ud3mp93+qeLAjPaO7dl+Nz/KMWgy2sqbAd/Pnz8PCzAzRZMp2zjFTJUkq5fUMn9jxoDnZRl1f//+fX369OlodyeVlgCSpRmsfI/DysaBMMs74agINBAW4PTDbZkeO4AGNrcEive5ren+rXtYFyPeZFf9jHnawKGNXTImnFaYHDZ5kt8MjMzPLZ60QqO71mswkTpoxNY6jqbsxM2DyCff9sDplSxoXqu/tcIy0AAtAYHrIJ/bQmnWn+f3+9dTBBwTgqY//vjjMF1q4EfDwyncvB7PU43hQTIvPHszZ3dm80P0LP1Kxi00cqZht/s1Dfb09HSY+sqGjt3u+Jw+vpGDY7jWOvye8eTshoFJeMdNRPnLJjHKnjN3HtO2/IV8JiB2wE16KDO2Q5Q3yzGfa8VA3XrqTXTcCDbZD9ZFEJZMVwM0zfYxw8WMGTc1OMtEwM7z45pNNv9II3lBm8qAJ9e46avx3kEEZ29Mr9fNEfDmz5vMYtPymZm8lj3ONWdKUwjQDEQsr8m6t+Ux0R/6ZgLf5u85Jkwg0aY3e8o2mx+Z/Pzk8ybQZ2DNIDL0EHdY3poPoP3IETynypsBHyPVLaRJ9JqOkcAYdWaV0okMfu5hp7dAEe/JdxoP1ktjYJDh+ibQ0UBIfm+lDRqf4/fJuEyFtJxzv59Z64UX7CsFf1KaCWy6j1PGcsvZTDybrp+6n464geL83xwilZVGl3VMjtTg4NyI7Jx+Uf+2jMY5bfh+Gqlc55IIyoUdEc/2aoDZ9oOO2vI2jVWj3zzPwvU2bgHhu93LQaZtxsDgpmWfnc1lH5vdOkdvWH97xs96HCzLbuMcsLflyHiPMz5TBmirj6Y3heDM91n+SFPT97YTtZWMs20e67NdSNusY7fbvQqkTvmB/N9ASIqnS7dko9HGI5FsLxycB/waSDVaG52TLeRneMXlIc3HbI0d7YjtzSk5PlUm/GFbNRUCdPYj/G96Qp5NIPMt5WzAF+JyDMJa6+jYBSNULmZMSdRuRUqk0Bj27t279f79+0M2jinlKFubvgoD2w4nRlY8NoOLoNlnXm/GnPc248pBdlTp+ibjxRKnOxm+JiRUJLef4ilLRsleZN/6H35ms0Z4n+MxuFOTz1vp2c/Wf183IGlKxXoIPtsOtpahaeCkGZ4YRv5v58Np6zYOW/2d5IvOPUbcBtaAxw7Y91uOqG8tI5HrybxlKtcZMdfPLFpkmxnh6L75YGCYepLB53loXkSfEj16evr1po3IbTIQoX+/3x8FrXkvLxfjp+6mF26XbZuHTYebrLPQBtoRUe+bbG05jQY4SKODRNbJ65RPyqE3qdEG0HYbsLivzjQy49X4xyVH5GGywGutoyN7yHPywy8eYOFGo2TFToEC8jFHvXgWrK3nS79Db7Lr9GvUg9AU2+xxawAtPEumlP6VmUeOE9f10QeGF/nNG13ybPSQyyCavFo+GqZoQRrbakBrAt0Gkw70piUvBM+5LzrCGYLc64QCaXIfzilnA74wNEbu3bt3hxeNR6gyIM/PLwtXnSplSjSd5zSOFfjy8nLd398fQGR2QvmcrAl9M/3c1m+wX82Y8nruT30pdv4NBNAo0tnx/ul3F9LZ6ktxXW0xMT89PckpAPN1AqecwqcRyuG3zcmRh57uIT2T0E+/eYzaegcDZwJXKhxpTF3NyAc8mAbWnci8ZfkaiEy9zaHSQNsY0kg3/rWMq9vxNeob+8kNWHFS2bCRPpuH5HUAXwAWdZrr8vicM0gBeAF8BGjWE9ojT1llCnmtl6n8gEDyhm/pIN/sKMlXtu+NYHa2dKIG6v6dzxiQUUY9ttaXSfd4nSCIdbrutN30kkAx91o2CMrs0J19Sh3ePJR7U5wcyHMMLBrdDipZ4pt4Pm1rwzZlctahmweT0256wwd5xmUHPNYsNKZvnomz7Bqk5z4G8h4bFspymw30pgW+Ri7P5QDptY5fR0c5aL7B9oEJDPs4ypDl3vew2G9Q3gxmm48gMOTUOvltP8/nJ7pOlbMBX1s0zWLDZkEioVuFCmZGGUA2J5j7mhBTYJoz9/Ru6x/rbVHFNAiTE21t+feJhw10Np6cMxakm/dNayGseDbSBtJNbsyTBmgm/pz7G69NaXO22QDXuUpFoNycAj8nGkLHOaWNQT5tsEIX2/b3c0umammUCPi4OWECZyxxXrvdy+59OjjKL9dWeVH3Wi9TUclcZB2Q+d3Gno6MGSOC6bRtcHUOSN+S/VxvAMt0tmcb6GrguhWPCf/fsin+3kAt77Nd3qInz1NvaMs8lqY5z6y1XslSijcpESDxmJwUAhg/m9+p+9QBy7Jpyf3OOk5gOfeSRgMs3huw513OHBf7ScsO6298T1vsH8fLbRMIOohI8YaaSXaaDdvKzPpZ8/mUL4mcNPoIMGPT2IbHLKX5jUZvC2TOLWcDvmzK4HQOBYbEx0A6urTyts6wExSk3JeFq86QxGDn/tZu21jBPhhtkwbWTUHOPbnPqeE8w9LAgHnJiCZOrhll02F+Mv3espmkyfze71/Oj6Liuj+73e6Q1cvZUakz/GxZAdZjUGK6PGXQHHY++UcDRUfQDGmb8qaRYKZsMhAEJzRoPK/QwQwdgzMdHpuUls2j4SYo4TRW9IZj4yxT6p/A/sPDw6tz+jgNG8DHRfJ24qyT08PJ8LHd6GayKJeXl4fzP2lT4qjXWuvjx4+Hl8dzw5ezmuFzZhIiu+nrxcXLGr/0gUsTmiNv8uN7XayPdM4Zp9DJjNQ0rnS6Ldiz49kCkpZHfmfdLQtmXTRfTCNpaDvgk7014HNfnp9flhbkyJRkaS8uLg7XGFw8Pj6ux8fHI4CSjPFar+1mAzQMFCLb1EHbmdDe3tTD3/kslyHRHud+ghGOT+TH48uzBGOnuHSKQCavX2vBFsfQ07NeRsXAjLpL3+2pzWYPt7Kltjm0kaxva5lRq4fthk4HiNbn6SUS4Znxx2QrTvnxU+VNGT4ieitsPglEGsFTsQJH+FkPHa6nhtd6vbDX3xtIMO00nHnGxq3118+4b6f40J5xxOhiY9oMawMQU/seN/bLIJo0U5GY5mdgYEE3/9yv1mfz0UBk6p/HcsvhtTrOlWeOgeva4kOjkW1N45/SjBPrZcTZ6Jmi54kXBolrHQM+OiLzoxkzGi3WzXtoFDPV03ic+nJOlpdqeNzJ2wAC3m/9I8+a83edbtf38rp5Po1JaJpk1zw3bZN+TaXRMMlG68+5PqDJbv4obxOwdluxXVzHRrDoAKVl+Kwzzm4x29KyPA2Q0O41EJF6G9/a+JM2yib5QWBhHnnZh/nI/z1L0vrmBE1oajv6OUbhG3nOrKGDNbY9+Yt8+roTRlNfWjHgtC11ENTuOVffXaznby1nA76bm5uDUuQF5US2Qe3ejeRsGbNZVhiCOq+TW2sdIoApY8RMWBQ3hr+h6jyXTzsXX08f7KjYzyaU7J9BTurzeogWBbrYwdAYNMUgjU3Y+d0GuwHH9DXZkaurq3V9fX3IBmeH5rSmrQGgJsSenrezc1TNerxm8JRCT8CR7ZAOp++nfjkitCw0ADZlrF1MA9vguBvw5Loz7s70mXepg9c8DWs+kVaPdYAW62F/AuDinGKD6KjjUNgf00P9cIm8hu8BfmmLzzhTOAG9qXgMLFu5h06QcpRnuL52mt6enEgDqM3pNX2xsyf4sQNr9zcQxN/NJ9tcZpfJw7ZuPLzJ+szr6+tD9i/1EeQlkIh8E4SRPgf/P3/+emc8eZsAhWP77t27Q5Z6qxgccPwNItiHXLP/8nf+lrWp9JW0B87WkT7qWMs2eXrXz/NopaenpyP/xwAyfoT6/dZkRr63ANA8Yr35LdneyIpBX/rLRA3tWdpkSZ94pFQD7NSlrazmqfKmKd0YwJxrxUWgNKRcaBkmhDHpIA9CpeGM0rbTyC34noqicoVpFOLcz4iGws0pSxpkM9cDzekVlgZS2hSIDX++M/rZAkSkdSsSspI2YJjxsMMnXU4pX11drffv36+bm5vDlEsUglFz6nfa20GA6aHjzxiSn16obTlpfdzio51ZnvF6DcqUHY15bp6aBhtL8j1TS1t1N1odxeY5/09gweCDvLfzaUCurWtiGzasBC45Zy+bPNh2jGac8eXl5SGYIOALSPNxTuQpp7/IR4LKvHYrU3/txeQGbFOZ9DD0WM8su/4ttBIk51V1pKfNONiZmcZpQxXlvV3b7/dH9pRt2W5SFmxfG69S6EDdf/ogbkwL/wLaw5eMq+WZ51AGENKH2c6Qh09PT+vr169HwdPFxcVBjggA245U8zO0OvvH7HcL4Ml/+oNW0s7Nzc3BvoRPqaP11bbANrfJOJd3sC8Zh7QT/mSGKLrN3fLhMfHHtAvfv03Aj8X+OLoZ+5I/4grijtRBmQq95C1tVw6mN+/Yr9TpvrylvOlYFm7LnqIFMsjodK1+4LBLE5xpUIzyI+Cmj/W0qIe/EbwaALjO1pctB9CebcZ3AmSNbvchwuR6Wl0TrVtKQIGkkaVARhGmbEijeyoEmVsy0cbDIONUaeO5JaNb9RjoWfa2HGkrzYlu0ee+TIBtAr8ToGS/ThmfxgPTxyh4oonAzHLlax5zg7z8Tj0hgCe9lPnJcU625pximTh1b6vbjiE0NbtySt5Mj2WoPT/p8Tm2y7832eb9W/Z7i4/t+qQLTX74txUw+s/r0pgk8LNb/7u/a/U3rDBga9PCnB4lPefI7BTgbNF6zri0ez17FzDYdLDZo0bfqftIz9S3LVzRfAczrp5JcaaWbTRbdYrWc8qb1vAlis6U3VrrcEp9hI/frRzPz79eRm7j3lLl+/1L5OBoPWiZRq5NJxhUWCn8e2hiJofRiBdvNwVvxoh9teNN/5tiboGu1obbMt1Or5+jkCkB+pkO2e/36/b29jA9kTFLajpvZMnC+vCR2dVEfJwOnByap0y5PtCbcTxG+c5F8DYoqccgxt8pF5MD8FjzOg1/sgjTeDR+5Lr55e/OPrO/dDiRd9KdyJsAyIU8c59TnIU3bWkjkfxaL5tbSCvH+eHh4ZWx5KJ76+Dl5eW6u7s78CzReBbvUxZCQzIea71sEkiG0YbYdoT9M7g3mCXY9BIO3pffLBeRwWyUom4y00/bxOxXnm9ZBLbL9nM9v7cMjnWFfc0zDVS1Ns0Lb57L97YEJs8mG+ilFLYR7fwzvss92TqPQb6nX5waTsY6Y0QQGNqnQILjYZ/qJQC83+Xi4uUQ4nz6NX+hywE6+chZOcvCBNzyfeovZ/TarET6G311JswAbgJ8vG+aTbHckO/kL/ttGbfOMjjN9RZ4E3dQt4iH2lhPcjCV38rwxQCSSSlt6o9OzgNKxxNm+Jqdc+q00ZjudT+2gKD7NIE6D3obkNQ5GdAt4Wx943d/su5WVwMmGSM7mkmJCYQjoJneJwCIc+QUyZZRIDAy0PW9nG53n+kcWl9PAWaPe/ucxqiVdg/riyHwa5HamFqWp3H3dxp1Bxo0LP7d00U2Lnb8U1+j8+kXs5qcHpkid49zW4/Ia37F0H6/PzhqO+cGdGl8wxs+24x6Gx/r0FTCn60y2aL8FvmhY0m9ceQecwYBLRPEe1u/UmjrufTCmdOJV83ObekV5XCyJ6bT2blTddtu8n3M9GVMBpAO6g9p4A5r+zve1+gyjc3+83mDEf6W37k7NX2a9Jg+oJXwijQ23prW8JsBQ2jJc7vdr2nngE1vDmu2uukjP02b7zPPJv0wv5r99T20ybyXY0qe8zfT/V8G+HIat9+3udbxugIWRl0EfxxYo24apK3op2WHYuxppBPdkeleo2RaJsBhJaOBC+1NGSeAOSmPy5aCbwmb6W+KMRlXt8dMz83NzdrtjiNe78idFvB7A0cUeDL85wKr6fet/p2qlzRuBQ/OPm7Jj2XBY8vp8SajrNc759u0uvvawJ4BH/nXAoLmWLd4mXE3GGjBYTOEW/JKJ81+msexO373NtdH8oiIrNujs6GztIOedNzGm301UOC9HiPbnDyf71vg2bzIuDYnuAW6bFP4O9s1qGg6MY1tG0/qCW13o5dZy6y5YiadspD6ebgx12UFiKReZr+cKfMSA/OMa1AzK9YKwTr753umzA+B5ta6cgI4+sXwzIDDANKySblu9oHtsy4nhcJT6sHFxcuJD+2YG8qJ6Qu/aMsM/HlvSyRNejLNQJpfBnNJnNlPus/WM8tCk41T5WzA9/DwsNZaR9MvXIQYpSIh6QAPS6WDyT2Jmhl1cvDcyYCNtdZhU8bz88sGjaurq8N5Wnx1CY0K62qMo8A3mjlwjDR8r3nCAZ1SyVSK8I90mc7mIJuz2AICjccsWVx7c3Oz/vjjj6M3GvDVaROIS+F6rSbABtFTsTHy59YYt2cnegzqfL8dUKuzGRkDNhrgyOzWuHsKLLLn39ieHexa61VkvdbLmVFpJ+PRNmXYwJqHljlmDA2+WGfjIa8xY9MABHmadrKLnOPBnXe0JZ8+fTo8c39/fzh/LADChpq0GnhbjrzAvAV0rJtnafJ3TtNtATXbHIMo05l6mkyHXmZH8+fsK5cGkJZp+jU0eYMSxzBtu5/OvGYq9unpaX379u1wf7J1t7e36/r6ej09PR2mCDmlyY1AoZM7eyOHfptFs+G5L3r048ePV4kGnnhh+W/On0ETf5uWunBdX3hlmeOmijzDOj0FnGepixwbAuFmmwgSM67ckcvM7FovS00ICBu4tE1q+kld4Lrdpj/NltLH+7xI0tDGLoEnMQvtYtsj0cbLdJ1TzgZ8KQRo03UzrxG2hVbtPKa2LEQWSBs1r03Yqpt0TwCJhtwAY6sfE8h0/1maMJq+c+7fKjbyro8G19nJBjynNlzv1F4Dsqee3erD/9+l9SG/55NAILzl51r9uAPryymwGke9JS+s65Qs8I/PNKDAe9rff3WxzrbfmqGdZHgL3G89s2XEWW/qcTZ8q18TPW8tWzbiFB9/p2zZxsm+8pO00dnmj6CHGR4CCb7SK/dS5zgO9jP2OQ0cpDA72GY6op+cjs9zBrkESwT0pJH9oQ42wJ7nPS17jh1n+5aLZm/sU6fCcW+Zu0bPW0DQOf6wfZI+ttdo8G+tv1s451x/eW5507Esa63DovyLi4vD++9I3MPDwyEaPgXYcs3nScX4rnWcgUha3IaZAm3hjXBzi3c+J6dDoeX0Ndvh/4w6GTm0dK6j6vTL0yEpU5o897dsCGkzzXS8LL5uuu/u7g4Zvevr66NojNEMje2WQtnoNFoY2TF7SJlxxLkFimlADf4tT+b7ZESnPwM9Tiut9fL+WK6L3e1eFifTePIsQz5PObK8JTORjIajSUalBpTkaXOYXIvEqRbyN+MXGuls+eaXOFdmyScQ0IIKyzNp2O12B5vBCJp6zcxH1qNm/Skdeo72CK1tjRZpmByagZyzfS36t+6fI+MGGhxr3ktZ9wzLVBqIoT639wuTVyk+o862lNOblJe1jjdqcCqWb9fIuXjJ1F1fXx98FsEMl1FErpk95XrWpheWYY4rAUvk7vPnz+vh4eHIR93d3b2yOZy69Ljsdsfr2kxD6595nzqpH8wAtnGzfBlIWy4aCObmKc/+rfWyUSq2JuMZG9PA6hYgpF1mNs88sS21XWTfPWu41rENYFY019IX02lw3QL8Lb/zlnI24COw+/Hjx6udllECOjdGKm2ee62+tTwljjDOK3Wyo23QUlcEhtMhu93L626cITQdNJA26JOjJ+BznR6kJlwWiK0B5fO+dwvUtXr86bpyoDKnHL1z0XzLbxMgpcNvDstKtdu9TBnQUU/rBZtymWeNN+TdKWfbisEHdYMyEtmMjBNMkXdeM5trAd0EQ5FrLjdY6/h8L+/Es/w0ujn92pYytCnOZqToqKfxmgBfGw/X0epMkEi6HChcXFys29vbdXV1dXQAql9GPwU05p1/J00GxCxZR0Xdcp9tA5vjm/g7OcTc413V072u28947E1z+k/bnvsdPDdblvsi+86eURey65p/9juUr+yiTX1en0x7zYCK6/4MUgNYMoWcad3oUg6EXqu/Us66kb571zKDI+uPeU8AlDqZUDnlP6zXqXfaTJFiYEoZCR35DADNOXzMQjrpMdlm0pcxzfiSRx5jYxb7RPon8oH9p18/pVOUZRfqPfnz1nI24MuaubZ1mATSILK4sxwsKgeVfa3ZgDIKYWSQOlpUy7Y9b866DUrOdfQUGP45Vd6MVwNGpxD8loDnuoV0y0lO1xzZ5ndu4LFx8XhvGSFfm2i1YpBGK+xkbLb4Ojkz/2/gYLrsiGkQ3F6AX2TXxxo5M+XpJbeTelNnDlLluLRxOFXfxCNnPf1p2W7Aw4ZxKlvy4L7wGeo6fzfwMy84bTf10bwwnc6WmDbrgeXTjoMGv/GkOZUWTBKMsG8NYDU58HjGptImU86YwTCPSaPtPv3Clj30/ebtWi+7uB1AeSZhvz9ez2f+eVe9++TZpUkmycMkUTy+LK4n/ZgSDFvJlKl+BpF+NvaJv3kdrrP1W23TJk1yGyDovk1t5boxSuSBn403lGfq3IQNKBu5r62Ltr7x+CnT6+dSyGdjn3PL2YDv/v5+7ff7Qyo4whwi+HaNtijVTsWdovNzoeBwsFMSJWUAaOR3u9cbCEJjqzvOMvQzpcxpxUajnQPr9rEbzH4aQJgnzi6aj6ccnnndfqfTM23hIXewRdkSdWV6cq3jN5Y0w2ElbyBs6qenDVgvo7LW78bHcx1b+04nR4cVPnAq2orJDN/19fW6v78/Anz5/vz869iWLJVg/whIyItkNRKkZYc9nSYzjjE4U6aVY8TvlIX0yfwz7wiAbFwnsOesOZ2qx42/pe6c3ed60m5kOLofp5c/T0+26RyDcfKUvJn01ms3ba/iIKJnDAQ4Hdem+sP32EfLowOFJvPNOZmH7Atnftjf3JtnvSGAPiXjnKwn2zb9dO5N13a73eHtLOFH6kmw5V24zhxF7q+vr9eHDx8OdSbzy2RFeMzMfY4zywa32I+Li4vDeaXODNqx09ZdXFwcdvx6eQyf5RKM3BN6zafQ6/bTh9iUyU/njz7XY0J7z/Hm9dTjGSTqMN+gwiA5tDDjStm0XKbQDlHnfKpH6mQbvJ5nKAuUKZ7T6A1MU7Fta3w9p5wN+GyEJiDxX1FobN2+jRcVd61jYEiBbI7NdRHFb/W/gaf2fYtHNqhbTvO/stCBEfDRwPMYAxqRBtLWOs7IbfHgVL+3eO77T7V3jry6vSkS2wKKVE7ewwwAp5mc7aYDjQExAHG/GDxNhpbyttVv3zcFPFNdrpfg2LJ26tmpUGea8aQct3oJzPhbsxGUA8o125xo8PdTNLX+2179rk04ZYva1LDl2zrP787wkebWLwYv7Rna4ql4TCy7DagyUWCHv2XPopt+pR1pbeCt2ZC06/V3BnsuDDZpF1jyLP1h+s3sGJ8zAGO/c51Aps2mnWPvt4p9Tiu55gwerzc9shxPcs1rzRbw0zaTvMoYcaamYYpT/PifUc4GfIyMrLhB/8ksBAFnoLxJwgJMRnKtTe4ncxyptHQ/X3iehc9uy9ENsyR832EimfTh8fHxsM2/DZYFfq3tYxPoqChk/LOgkO/t0/W3dlMYNTTwZp7yqAUaM0fE+/3LmUmNthhV8559o7KEnrzgngbHfWUfJhAwOR4+25R4coAcK/aL2To6gJubm/XXX38djofIGp5k8pLJcdSdbB0BJKPkyGwWp5Om1q/UZf57fJNFNy+dUWvOkcVOyWCqFRre8Jk7xSk/rR/8TrCc4DA8v7m5ObwhJpuRKNN8hn12Ow348//URZATG8FNJm0ji21p2mPWg3wif50BzPhRTqKXfGOHsz20FbbzdpDOPKdN84n2vfGx+Q6DN/LbYxOZSYYqJdO8Bppe+xoad7tfb8/49u3bkSM3QMv3ZIn57uemI23dHm0ONzolsxd+xX6wNN5kM1KyitZN2hHbWMqpx4mA2eNFuWkzEt+/fz+a7fDY8BkXyoNtD5+ZsIdlJnWycLx8OoVtXtvgxjabD/HMJGmw/tj2+uDsc8rZgK8BmZQI4fPz8+H8Oz5nx2cim6C09llIBzMlu93uAE6TOo2TJMNsxHheVNY/5Syn1JU6eK5To7OBl/zWdglZsQz60lYTCrfT+DPRalDp+20AYxhjuPK6olzPszQObQop9zXAxzHgvbmfYzYpO38jL2nkJnDcDMdaxwtk/az5GPkjwAvgi9G4u7tbf/3112F3Xoz4ly9fDjv4Unf4nEDFYCvZP26o4WYlA5cJyDbQxUxkWzvGDILBT+ojvwiIycsG4D02vp/riRktN1nIp+tJEPf09Ou8tufn4xe1287Y+bQ+t7YbHc2Ic2rQ+h4dyML6BAKswxsHGs/STu7LQeqRk4CT8DVylD9mvyLf1GHyluvMKEvmEbM0DehxDMhDAwjWaTmgfqSe79+/ry9fvhx4ann2eAecPT4+jr6KPOCawK1DqLnRhD4ifeb69IB2JgIMzCkL3Hj07du3VzuMwxMDxyZbDmZdbBc8pUm5Dk32D/w+nbln+tJ2A9T5vQUL6Uvq2wpuOF7WW7dju9rAXvoaujwu3jU/BTtvKW+e0rXik8FuvEVyW6UZ5Pa9CQeZRYdCR2VhdPQRBYrSXVxcHB0o7TajTE3QWDcd2ZaRoJLbATdnPZXm4M4pWw633WuhJ43NqG3VRZ66H1vl1D3NILR+bNWzZdT9F+Ps+6ygkTWu3aIe+UDXKePTIsC1XtZ9NbkML9p4nTJQ/t+BoGXiVGBC/vjaNCa5vxli12daPI1i4NAy1aal9beB24lu/m8HstZx5qHxoDkb1+HrHk+3R8DXnJjt7gRWW9tNNqYxMq10wu6rs7W2n+6nwV5rj74h/M9vPBmAGcE8y+sMDqxnudc8mXa3sh7Smv8DPtm31LfWC2jimlQ+b2DBe73+3XbMswPn2H7LEp+dZPiUj+H9LfDgeDa9aXqeMZw2yrVim8m+GgTb1jWgynr+Z5SzAV+majPVtNvtDtMfiQIPlf5/39ugkvkUmlybjJhRM89hy18yb4lWLi8vD2drtWhzojELcXPm4G73cj7abvfr/CPv+LJTCY0EkHTwVmyDUfKh8cU8sgFt6epzyuTgqSzOpjTDltKE22WSE9dpgN4M4NSPPOPrfj9myhZgYR3etcap1AbaUnLWVM7S4lRbZC+6xcwLDbb1J7/FCfANKHRQpGcCLwYHEwB0toe8aeDNxt4GlM59Ms65zoxvA6Kt3UxBegMMF2kTKDfgR9nzJgkX85ClgQ/Wz3YaaOKGoMiJbQt56uxa7vNGBgfPkZ22mY1OkfSS9+5/4yntPJcwTADNzxoU5m1LjefmJwGbgzXyPGPNTX+5lvaYzc993u3rDSjReQOVlOi+nwm/Ly4u1pcvX47kkYX3epPBWse7fXMkkbPcsS22oZySNDjkmBI0NQBvWvPd9TVgnHvDK2YNQ3f41BJXlD/aVD6bP28KjG40wJ16oivEAt7Yar3jWFM+WffvlLMBHztlx0HnkzINqiOEtY4dbTrtaDJTHblGJ52/KE3S0rkvu6M4mAYipJcCFyNKQ0GjykHyZ/jlqZnmCCl0uc+ganKgTcG36m7lHNB06loDfOcUOwPXuVXO6ZvrZbv/mTI5cU+lrvV6ytPGnU47RsHZJkealhdes/N2acZy6tPUd4PxRotlitcNZtozDUQ2OTXNNuTT882BNhk0QNlq+y3yv6VHrb5mA7boYJ95D3lgGW39p81jXS1jPvVx8gemNbbWa7MnG+j+51m++rMVP087b/pJT5N7Ak/qsvnIuhx0OWvYeGR7kGe469Mza83umK61jpdbMCBq9091TvZ4sg+u+5RvbDo49cf3cBZvC5uQznN8q/tl+0Ubz9+sk3xmoqXx4i3lTefwRfFDADc1nBpk/sZ6tqYvmErnPVNHA/qiTIkW80JovhfRRo07IR1h7/f9pc1856MHOM+w3zGQTWnCEwJRLoh2sQHh79PnlrEMn9lvXnObueaxaGCm0cPrzExsTae5bjudKctoBeExPgweLJuTrHmM6aRII9/Y4D58//59ffz48cDPd+/ercfHx/X58+fDmiI6kd1udxQMTYYo/OC7OynP4VMKI1D+OevO372AnEaUfWVEbVn3uDR5I40O0Jqekg/5LQv1Q3cOl6VDJu3pS96vS8fHDBazZgYBznaZNgazkwNqusVrHPdmrwxeQleuO0N+Sm/YL2b4mcGmPrOOxqdcc2Bv++dzX5s9pC3g7IoBX/rAvmUNY/xDfAT7Ynluy3ioB+ER26W+RA6tVzyqJXU0AGhg3mSfhXpImi0/lhd+tvG0HSL/W/2TnJvXod+bIDiGrS+sb5K5jNFk58lvyiELx832iD4+vDeN3DxEMMhMIGllu+7rZCOmcjbgy+nvVMhMETRQkI7R6ZBQMp2LQc14rsHLwtcmmM6KpMTY0/E1I06wwwEj7TZO3iVoRfICaoIT8qoJEEFJG9imRG33YGjdAi/pmw2/HezklO3Mt4TQdFvZPS3Q2gi9+b8Z563iaSPyl3TR+JBfbDvAitM0lLOcE8X+Pz//2ijw8ePHwy613e7XZqOPHz+ur1+/Hu2mc2Bl/nsaYq2XaRobR/YpjjHghoCADiJ0xAk1sNyKeRmeWU8ayGNpB5lOx2ZYn+LQrU/Nkaa/DQSbN8xC+XnupE0b7dnIAmmaeNH4RNBlebV9yn1+a4R1PM8asESeOR75zc5tCxizvgbkuQ4ussldw6eASMZ42vXpZy4vL9ft7e0B8Bkkpu3U3cBHZp/yGV64LQI+b0qLH/TmDvsc8o+FiRPWmz7aRnKcvJHQsmhfQj6z7QZumt/Ic7QfTKjwFAj/edajFQM+z7TkHvp49n+tYzm0/eIzDjbTFsEqbUp4F5vL8bfN2QJ7v1vOBnwWpLc+499onCxQ+XRK3wPHgbDhSDFKdmF7FNjp02A1vzVFY7vNWJ/DnwlENYMdOrba3hq7LWWd+uB2WtuTg/X3ZsTZzkT7BERbO+an5ag92wDF1Kcou41B412M+1q/5De7oAm0qCPUCfchpR03NPXd/DHwbfxzofNjO3z21Li1QlluTqPRNd3jLNoWPf6b2kpp9sWAyW02fre++97JEfsefp905xz7s0V3szlvsS1NdxofTO8kC34mQGYrIOEzLWve6swnx7g9P/m2Jiu+RmBkkMd6t3jRbBwTE42uSX5anbnnlG00D8i/reJ+vdUvst/sTyvsu6fD3VfaI7ffxoPjZl+TMTGPpr75vt8pZwM+bvFORBmnxCxfGJdrZFCi3wai8pnIJ1F5fs+9XADrLAMRNBkU2oO6vXA17UbBHh8fjxxv0PdaL9mhtBee2MHneitbxiu/JXNEJWdpTr4JLvl3CoCYNrfTlMYLTZtDSlYo9zPKmp49ZRBYP/mQ5xrP2Eceq2AlZmn9ieI68xT+ZkE33//oY12S5eO4MJORIxTCv8jo3d3dkX6lHoKa3W539PYA9svjnz7QIFHfDCBTx37/sjnKALU5lUbDBHZIW+7zAbfhi+sx2NvtXt6GEFvhWYDQ1nb6p590Uk0nOdvBSJ31W5fYh9aPtDud/9WmvNpZiRzTluEw/1m36YlssY+RR8pF01/rPW03eUNeUp9py23nWW/uZzCV3zzbtNZrO22+5HnqUjI0zPQyo5oMvceAY+MMF/u+3++P3s6RYNDFbZNG6mXa9GwAZzsM2KIH7W0o8Zc5m5Vt8s0fBF3kq2WB3xtwpF/MGbu+xv4SoLVpZi8d8PhQTixXkUPaPOoA68zYTtnPZhPajCH7aZ/7lvKmDJ/RqLMP7Rk/R0ZauDg4Bi2u08+v1Q/vbDS0YsGgI6bSehqCRm4CC5ODa7TQgKZM4CXOwMDJNJz6PtFjxWvgtNVrI+7fJhqndsiXafxSJgfKQhlsywOmvtnIsD0GCE73k48sXGfXzqhqQIbTRzHGAZbWm9w3yZ91b5Ifyrl5weUWp8aGNEy6YBpoawiCJmPXbApnBQiCTY8BbwOPKQaMlOHQaDvJurb+9zUGDBN4PAcEN5meCnXXfcu4h5/NZrmNNr6n7mk85zhRd1tdzU81W7rW68ODW8BswLTW8VEv1FF+TjyZrtGPedbJ/qjxod3vzOBaLwEVd9q6f6aZ9sR2nvbO8mh9IS0T6GPhWBJYcWxsN6nPLQjhtXZMzkQT7XzjK9uzPadMNxzj8ZrkmqD1LeXNBy+/e/du3d7ert1u92odgkuI4REqXOQesEKETYPFTQ8UDkdVPCYm7XrunXWQtla/d1na8OR/Ll7PvTy7yALRPlkfedGyJI2/TZmb02RdTVAmgW2Gd2pnos8ghHS7j7m3TWc0R9f42BTIdE0Grd031eN21zpex+I27IQYHdMwkM8sT0+/3sMZ0OLjHtgGMwwN9NnonirMRkQ3ycfwKRn/tBmdpMGbeNh4Rcdnnkx1NUMaGh2EUJb3+5dpQB+BwLq2gsfUYbtDXjl7Njk961yTWxt+t7v1eU7hvc7eZnyb3Da9ZqFOxe6t9fKWo8gSgyHbhnYEybkOkLa9bY4LmG0BHduxfbSM2Z4b8OZ+8sz2lb5xyvy0AIAAlPoTP+4saui1nJAH3PBEf9vssK81WzMBcNJOWppd8G/e3EZf5nZ5nXpEP0U+Nzo5Vk4INT9jf0a/O9njpk8e93PK2YAvg3t5ebnev39/+E4BT3EnI1xcs3R1dfVq9xynXr1BI53lgvGklqOwjLZapqcxyAK12+0Ou7a4mcTZxt3uZWojzo90Pz39emVT0sumxXVF2TJdTF41wMDf22aN5jj5HFPuVnAKqzcLcNqxGdcJXFEJDIxZF3lhGqeMcmvzHJrIl8Y7G+zm6JtzZtYj9RoERD6cGSZQdR8C4uhAmpPwJqiWzcz/jsonEE/ZDFDNbjPKU9pf68VBkMfN+JqPBF827Pxt2j2XtlmfATkdYMvIEPA560Gw0NoP/xu/2Qc7Ro6R+cE6Mm5T+/zedJv/k/8TEKRzWutlI8NU6Mhot+kcTZ/fELPWOnotpu0/nan7wc+pZIza/ZYXgghnF2nfTFey8UwIGFhs8Sy0TAEPn01fDBBDRwNx9tPkJ+0VNzRGrjkuHs/JbhrckJ+pi7a9ybLtlf08+cBxsawY8HvmwPxr9Dcb1gCffZWTQu6z+UCaybv/UsCXBtLglnHg96ZIZgQHMQNBwNeE3Rmo/DbRlfY58BnwpmATyDLwaH07ZWwm2s75baucExm8hZ6JpnPosmJv1THRODkvy1R75pwyOT/SeGpMG40TDc2QnTPtw/andibwbdl1fWv189fO6W8+JyfbQEX73CrTM1Nb01hO/9NhTfWdkqlmKwhuTj0z0dOCVhePb6N7iydben5Om6xr+n+r3S09a3rv+07Jab6fkrVT97TxmmSEvKMcEGCda0ft287RtXPGwzaFdJ6S+WaLW39s3whk3qJT/vwdf8Y6SQ/pJF1TO1u0n8vn3ynm2+/y4GzAl0Wjj4+Ph+jOU5opbS4/iD/Zs7Vejgn48uXL4eTpLAbOuqS8jWC32x2yeUbsROlJX5sxbdrDaWnTmnfw5r5EOE5rB7UbnW8d1MnCLe6MUPzGEtbjyImf7GMT3HMjg/TLxfTQgIVH4Snr8l94w76kHhsf92FavzA5btKz5Qwzbl5u0GhgFNz4yWds7Cf+mWcXFxfr5ubmsHj74eHhKCqO/plGLkXIvdHLi4uXhdpPT0+HzSFs11E9eeXsko3RFAnnu6eBqYd8zkdc5FnyLDLa+N/abZl6ynj6HBtn+WPba73eWELamyyy/5MctsxT+sop8ukIo0nvaGc8FdbqMA1vBRUsychSpzgutuu0c6yfR1pMsxP5n7t0WRfHgbaG/W72xbq71ss6XPYt9yYTFp1rYM1TsZxVoazH9/Gd7yzWSWaaGmA2z6zrDcCZNxwTy1LqibzyTFKOs/XRNDX9y2do5gwAx5f3TxnS6FRsUI7g8ZiQrx4b0mO5zOY59injZT43uSSdTdbfCiDfPKUboWhM8RqPdCpTQZyOiEL+/Plzffr0aX379u3wepq1XnY4ZsqIBiOFU7ueem2OvCmc+xCFC500/EwD0+Gk2Hhm4DiFM9FlA9zSwrxOwbQSxImSLvZ7MvIu7b7JYU2GxPe5ztDK6Y6mlA0YTfeeU7buI8/btCG/04kYDDRDS7rbbnHSkOczdfr9+/ejejmFRKPN5zleNESR9WTSGzBjv7zj8BRfaQz5jI190yHeS3omXjVgwM82G2Ajy+9tXCm3nraJjscu0gE1INrGg31pwDCFwV0DfO6jaXf/abM9VuZ9a8M8nIp1inWYn6y3BTcMRswf82NrjZgzqK3f7blWv2Wo8axNQZtm3sd+twCMz9rGNt1bq29qI0jj0q0G+Mwb0mXQRRzAaUzWZ8B7CvC1PrO/b/Vx6Tf9Ovu42x2fY7iFI84ZCxf7Ctc7JRJMy7nlTceyrPULiD08PKzd7uWwWRNilMtDMwn2uB7Mxp/AiyAnAxPBvL6+PtTFtxOkDjsX9mWtF4Gl0fEmkv1+Px4B4HUZzAgFNO52u4OzdsSZe51FSRsNQDQlaIVOso0P22CfpmIDQRq4tqzdP/UrfDpVmnJNBr85vHMUowEzy5J5wOf8vRkjR/o2DuyHjSU3aTCz1GSHRsHRqPnZeOvnXecpQzXxd+pvk8Fc9zqX/GaaWqGeUR/5zCSnU18tJ9bTxk/KFAFfKy271HR/0n+2575MbTdenFPOHUsGJ22TngF07IKPnEpbkYmWIXPbprfxzzRT79mHliFm26axLZdoek75ZmabgQN9Y+MH656C8vB+K1hovJvk2+vHT8lPk8mJL55d8Hhv2X/qGsdlazOG+77l75ruZ2aISTEDdNPbstT8Po1jPs/xnSxvzvA9PDysjx8/Hk0LuTNUxv3+JWWa7wFvPFGc5wjtdi+omoCIu3bT4Wz+YOHZYnzhdwz+Wi9ALelzT3dwACksFLr0KffRQLH/3759O2QKuZiVffNgRnBtZJrBoiNtQpHrnEq3wXXbjR6CO/YxvJhK+tgcYwxay3pt1Uclac6/RZtbETbpoqHIM5y2aFOWSd2znpbttoys9TLdwd/oBJKBi84wGqVONecTOSYwtGzYkHOKnQ6inYU38ZYy6bH0OVlpx2NCoOf72J/wdXI41DWCjiZHbM/657obwOBzXvjepoPsGHlfk4ctsGL+83f2m/VRV5pja/VOpTlO6lT6xs11HvdG7ySfzswYdLG0GaipjwYJ/N1nLPIadZJ67yxgirOr7kP+uKSIvjF89CYa6qZpjJ368ePHIQnhMeJvlNEJEHo5k4GL/RQL+2j9Yha71c3NUfbrlDfq3fX19VHAEfvJ7DzrNsgMfS1QyYajHz9+HE29O3sYvfO4uvh8Rc+s8nrG8lR586aNhrpZnOGJYY5wxzlxF2o7+NBgxI6T9xBM8N4GatyfSYgnwbRQ2tm1+xipndveZHz9vdUzGWUr3GTwtgrrn+pqNBlAmq5T9Ez8mpz85FBT10TvlvO04/F1GgHSQfr4rDNvWzLQMgWtjXNK41err/HC49jusw6c2zbrmYCNx9WAcKrbfJ7odv8m2WjyM+l26yPvbbZmcprTPQQap9o+Nf6nnt+qc7pO8My+tzExGG1tue8GfWttB6FvKQ3I5nePZ8rvtu2+NJkjL1vWehqTBibze+sDn3MxCDvXBm3ZUd5j+37KVk3+erJN4YV98pbupr1W18TDiXbq/jm+cXr+FL0ubwJ8YRKRMzMzLHRSfD8go+gAvRzPwg7mk1GQI/Dm4O2ICBK9/oWD3g6OJVDltBrR+36/P2QTg7j9xo/b29sDMM3aRPK08ZkAt60TmaZk2v+h1dG9v58Ccq5/yqqwPvZjrfVqmn5yPvy/RdunnHwzzDTQztqQFz7Habd7yXCx737G2YYtx2Yw575TPxIoMcKjPrDPngILbWwza1NTt8EP+0e9oKGcjF2iaK/baYZ5y0GYL02/t4x1y65mTC2ffN70OePs9phRINBw5iZ1+VgRZ3vXOs6g53ny147CPGuy4bGcHFFzPi2L5qAmMsp+UYavrq4Oa7lJ33Q8RZ6dltLsdi9vNCE98SXRl1xvutJsQPprXcs9rMvAi+OXPnnM2K7rb3plUDIBNva/rXFM/bQpnN2KjYsdo73xrNVutzu8A7z1s+lSeGd9c6Ys/KfNtmx63NpYGp+ETr+j3PVMwI3jF16Sp5SdyZ9atm0vPANDX9Tq/Z2g4k2AL8WC3hZjkslMOXuQkmamo7IBmNLwBncsBHdO5zMVnEJQmkLjyHWCebVL6kuaPIvr0y/uYs45hHxFTgNYzchMO6coQM14cxyokASRk9PdAnpsN5+cNiPf6OyawzafG93u45ZRmUoDNW4rhbyhgeRyA4Ndjmnq5uv6UiJHbod8JODjc+6Ds36nAATvj3MmbwwWOW1GZ7EF+thfrt21jWj63MbMhXKWYidqoEc55HhZjuk0uPzByxForFNnwEn6ah3n/w4IyQvzhUBgrZcp3+bQt3hnvbdj2wKAjdeUzabzuSc6cH19fQBoWYttm0z5pg1ttHHMI5ecLs5UGWnis5MtSPFU4XRfA3xrvV532p7hK7cInNof6bae8i9LnDgdGj5TbrPWjPYsfppTjWyLwCQBo0GMaSb/3Q/bcduH5ivaONj+5LP5G+6qnsaUxX3xMy34aWCVfWIAEFkJwHYbzSbQ9rRTNLbKm9600aLGEDcxLNe3DkmlM3D9U5u8f2KsaSAtjV4LlZU0hj3HAjCCzPNE6onoGQHRgFk4tni4VaYxcT/c5jn1bLXXFK1lq6b2TOM0bdn64PrbvWzjd/pqo7U1Rg20tgjMSyLyW3P4jZf83kBrA5ETP0xzA63mve8j0DJvtsbpnEKjONmYCUQ2m8DfqXPN9lCum+M2cDLdE5/p2B21ezy9Zrn1YyoEia1fE81Nxiceu73YeGYxWyEfpuw2aWp0st0U19fG+xxeuF7X1QDwWi/r3H1MkG0k62j8JM2mh3xtPs3jHj7HX+W6fW4bi0bnFHA1mWn62ICsy6RTls0JF0zt+rP5lBYcT7rkspVdn2y3efIWHjm4Pqe86Ry+GKGmNAQyjJ5zzYiaGTVmzCJ0yfpR4BwN8JlEYwYeHHC+4i2fBLKh25EKhWG/3x9eGO8oMkg9ypU2MjCXl5fr/v7+ECEl28dppi3nk0E2T6Zn+Jd09n5//DL21G0jxTpSN0FP/uzwPY3nPnhMwu8crZNz5nw9EWjL2rQ2zDeWZgi2+s1nGIVPwILZIctjc/ItmIoMWfEnxxB6rPjhn3nSwB6XOXCnYOpmIMOxcXuTDm7xvJXJ8FkWzbMJELWsSwMzzGaGB+aVbQX7Zce+2+0OmRPSze9c3pKTB75+/XqUic0zja9NZ2I/uamnLaewfeFvU9DC+sIvTuO6HvKDmaKW1eD/sevkbQNjuSdZM64R55inz40+9t91N/tMGxj72nxJaG6ArO2WbfoS/9oKM6nxtbRXzFKT3+SLZ8FMT+QospT2Gri3b3ZwRH6Y/x4L8sptWA7ZNq+zvdTdbEpbgtKmmllHa89jSLvR+uw+8B5Pm/OPb6V5eHiosuHy2xk+flIpm1A2w00lppJEIC3gNqqTM2kO0cLC+fcWibU2OSAEXV6DQuNLniTTR2fYBnhSatPjPrX7Wt2Tod9SrFa2wGW7r13bUsDWJ9M4Ofb2rGmewEejoQGMiVaOv59fqx8xMgFH03XqN9cTGadzmmjm86zXsuO6W5DAT3+f+jjJ/7ngkGPUbMHvPN/af2sb5OVEI+WfYHuLh02W3a7t8gR027On2p6c1Dm6bF60NlgnQdhULwPr5qfY5lbZshXT8+GzgY7pblnPplcen4mf5J0B1DRr0MoEVlr/t2b6WOyf3ZdTtr7JxRaYmsbb9uiUXLfAe4tv01+rf6rPPGmfzU9ZN84pZwM+RnJcu+f3vdIBOLoh4W1BLo9+SIaPHWVqOlnBPNeYtt+/Pv3eJUzLe3HZFypwNmawjWzAYPaMDp/rUjyNl/V+dJzMEJ5rmFjnJGChOZEfwZK/20Gd64i3wN6pQoCcuqx0jq62aGlG1CX0cv1onvOZd6GH2YXwxuu2druXTDLpiA5sLSfY7XaHRdc/fvxYDw8PR2NAPWrGrkWgif7JJxrtLXDD33xcSCts085nrb4ZYZIr0hK+U7cawGg64bqaE+D3jDPvd4aCbZH3W9kqAiNnDFmc+XLftqbd2u8ZD69xnHhB2drioflkXrjuvKEpNtl6wbVwPOLK48opyWkaknWutarONVlu6/AmnoSuy8vLw8sBsg5ross8Z2nj4LVeeX4CZGutAw3MVpEn7Rw6grMG/hr4tC74Lwmb+M3UO4Eb94MyEftq/0rauNGO491k3f1OYbBFOvIb+cLZDuqqdXsC+cE5Wz5y8mHhwVvX7631BsAXx0aGBqAYbdLQ5Fkq9Fq/BNM7ubhwkY4qz3DjBDvMBbY23IyWpzR+BJTTHQRgNByeUuD0AYU6PHDkRWNkwMfzCrcG+xxwZXBEcNLAghXC182v5mRbmQwcCw1C64OncfO9KQtBrPnQHId3D+73v85N9JlzHEu2k7a4yJlnJ3nqz6AxtK31S77u7+/X9fX1YTqPICn0RvccxTcjzKm2XLczOFWmsY4h9r0O9AgAPK3pcWvthG/u65QhP9Uvt92AHOtqMmSHMhlfjq3lyUAy9XGpiIGaHaf71UoDMq0e1kf58LhYV837Vmc7BYG+hGfLBUCZfttR7yBnlo96xuUJpNljSD5PwLoBvvipbGTwUh+O2xQ0TSCq8de8p63j2FLvwpPm52KnSOukR5O88BqXE7CNyRZPJXXx+RZYsZ+0v83X0WY3H9E2xLA0fBB6Eqyv9Xo9tMco11oyjPQ0LMDlC+fa8JSzAd+Wo7czNOEh0ALf1nzkGa4NaMaZ76gjDS29/vT0dIg0OB3blJoGJWCs3UMB430WpBbVkJ+5L3VymuzUYFKQSDsLBWwScn5a2LaKHbdpmQzDBNRMk+uZnjcf233n1M0+nwNGbEhaNNfuncD0ZGzbM5Mx5rKDRstkpFkn5Xu6d61VHZgNWFsGQTDTDLJ5Zh60fjSnvdW/Vofrcd9NzzlOa9KJqR06bbbTHDCvNVrtdBo9fi7FAJvtWdYN8k5NXbIOy0vjKe12q9d0s41c29pVbL41XW18sqy2DLvvazzZsn1eNuF7m13YqnfSacvHFo9IS+urQZGTGFvBwjnlHH6238/xQ63O5iMmuxWcw/q35GnLrk9+7q19YDkb8DFF68xbrocoDvhaL9MUQd/O3vH4knSAWYJkNXItAG6/f51huLi4WN+/f1+Pj4+H63nORoMDQLBIJTBiT9ntdof3/nJ31vfv39f379/Xbvfy+jdmNxmBJrMUXnz//v1VFjNthd7J2JAu3j+tzXA9FtjJmfh3ZjRNYzsiZFr/wfa3gGzr65axMv/2++PzrpoSNiBI8GIDvNbLESyMQN8C2jJW2XTBbG8bD+oXafT6r+ZQ2XefBzUZnOgsaXCQFt7mL7aCSzE4BUJ99rFNvm7nwj7QzhCs5tPBWBvbLQdp8DMZ+i0wRxq2ztlrx/bYYZJ+Z79pr3gEhXXFOkUARl55vJLFjs1KW8zWccNdaI9t89Qt39ZkPaXc0794/FyoC85qtZ201BXq8LTExDKVGR6e/deWM6VwfOm/HKyFdi6d4rgyg86xzH3TjIn5R1/KcefsFm2g7aan0J+fnw9vfogdY1+3kiCs15k889JLB9LHlgFtNnfrvty7BS4JZl24YagV2p2M4bQMgPc3Gt5S3rSGjylz/jGiI2iiMZqye3nm4uLljRnpYK55sAO2Avwa6Euh0TQNk2FukbGdC/sQBed9bLsJExWEU9ZTJNtKM0QNoDni2AJ7/NwCexMIm0Blm+JwX5oR2Opbc16kY4uPUzuuu93P8aWz5FSued0AdDNEXJ5gkDEZrpStjHjrGx1jM3a+39OLloH8RrvgDB//ct+p6bPwektGJpma+ND+bw7EbfxuMZiZ6m1jTB1uemdZpwOhnfFzTVYM4gmi+dw08xF5CtCf6KR8UC+a86ZNpIN0IU0EoPke/0JQYxtvf7GlT66LTrvpYupl/8gTy15on4Cb+Tpdm0radCA72Zw8w36f0kdm+VqA2nzO5K9SLA8ufG7qf7Nvvj6NiXnEQt6061vtbdFmvrd7zym/dSxLFIGEkLgIELN4AWc+ZDllyi4kcmJU6DOEbIgMpixMjaGnEPYkiHGWOUohaxNZN9sluGMGMPeHPwQQNujNaTZnm365r+wDn3NU7j5YSRuompzuuUrKtvj/ZHjcFulMu5EBZjooJ02WXMhn00snx3MamaWbeENexrj7fbnmXRt/87vxc5IJ18H6aawZkJl+O2MCBjrEZrAo6+Rz27jAIMtG2cDBoIJBn0EL+2vgkzpbppCyZf1p8tR+z/jaMZ5ySu038nTiWdPttY4DFbfDmZgG+HLgb2wYeb7f7189S7trPXAGNLqUe3hcl32IpxCb7hmMWY4oT040kB/mn3k7jQ/fJ88162zHNpaZVq99J7hlv7lpkP22rFNOKP8cIz5/qrQxbDJj3bKvoL2a7L1t8uRH2nizHj6T4sQV77HtsJ/J75MvzW/RCxYDb37fkqtzytmAL+e88NUkKVYGDlyI5RQPpwPWelEAdo6d5lEtfjaOiEJP8EJB8tREUxQyszlIO40M2PPz89GLk9MOlY/OewJrNzc3BwMXJ2nHt9/vD2f42cDYIDcQ1BxReJG27eRYXM+kQA0QTNMkKVZSt8M2tpygHf67d+/Wzc3Nq0wsnUMDJM3ItKAhQI/g5fHx8dX5lZORSz0cc8pLSgtKtoyAHR5lPf8700ynycyFMyI0cmyPC6y5mWkr0xyQ3Iw42+OGMeoF+0ba+AzpzzXbnCw9YX8I7kw/36ZC3eZ9HDcCX5Y4F04lcnw9nqbH/PJ0Yupu2WeOm8eYvMxbhJr8X11dHV4hGbp4cLQ3R+U5AwxmFM27POO3JbC0w6r5fMuYkSa249kl8z58MsCcsntpJz7Up1I0cMlxSTKBb2qJfHv3bQNd/E59pt2KHjCxYv9jPvl36qaBtmmxXZp8B8ErPzOezpwa3DU/5n6Qnga6tmwfx4O2gBv3cm8C+mAibwaljeAYONHW/Oepcjbga073LcWI2IIz1dsAxVaxMjaBzH2n7vEUsK/l00JtQzyl8bcGkI6pAUT3pfWJv22VaWwNRs4dByqkf39rOUfuqNztWqPZ42fAZ9A59XuSo8mQTTwxv6Y+N2fdrvs318+AYNIN19EMX5MR03IKRLtdGvPmBLZk8JR+uE/8bP2Z9Kg5Fcqq5XZLt9r1t5SprrfY2NzfZIHXYpM8xZzPaRkLnW4DvBz3SYa3ZOFcu3RqFmeiy214qpn3n1OaTrCtXOe6uuneqZhuy6Pl1vSfw9tz5dZA7S33T3Xwc9LTxivygPSc+tyi1fa90XOuD6WOMftPmtnGW8qbMnz7/f5wllIaTzEwmhQrL11e62VRv1HsWi+dIYpOhi/35jqnjrl+hMwhEAvtu93uMA0xMW8r2vZp7vv9r7V8t7e3RwafZwHmefY1fGTUQMFxith9YN3NYBM0WngdPZ8CffzkvR63LaP91uuTwqVv4ZmnUHnPbveSmk+0bJDXxsHKyhJ587gxw5Ux4jQUozf30bRuAV5nnMwbf6eRv7m5OeIf+5h+MyBpTofPtXZJ2+TMPIWa65xCdL+akU/hOq229KRl7UMPo/N2lIf5nD7sdsevqmpZOtLvTFfjla+3McxvkUFmVKn3DmTs/DgGDgQaECB/WDKz4WnVtda6ubk5HEPFd+mS1ouL12e7ciztIzxe0bPJFzW9t+y15Q7MfHEM0odkw1rGnn+5L3XQbngK1XYk9THDR5loGTX7vhbQ5lkCS9NG3lCnKENuM783vzrZFMoa7WZmz0wj5dm2iDzlzGBbLrAl65QVXuN0OvvT8EvrO3lPOeS40qd4KV2wxqnMpcvZgI/KPJVTgkDhosGg8vNZG3QKLuvl3LkNV+6xYrM+O2PTPvWR0wdE5O19ud5Ywt/S96bMqXuLNhuWdo3t2Fjk01GE62ugx30ijxqAawZgUrStZ2xI7XTbvenjz58/D4FLm8b1OlP22wCGU4d2TpZ/Xp/40/jSDElz3GyLn6xnv3/ZaT8BNkaWjbYJbG3JxzTuXB5BGifAZz6QHk8HTmPAeniPnS/72Aw/HT/rmjJY+aSs2O6c4lm7J1N6BEsTja57kmuPr39rshN7lilL60c22tEWGmxuTTnHZnrtc+wtp+mb027AxrbQ02/NplofvVbOPJ4KZcH2I/zcAjfmDb9Tp1pQ1/qyRdta6+jVca3tCchNfKB8Wh4NfJi0IO+bHWr0t+xyA5utWE89BrSVnBVM/7ZsaejhM56hnEDxFm+n8qZdumyA4I1gh8av7Z5tQCbfvdbC3xuanQadA2yAaWPQwJjvI40tS9Doc/aoCTbv9e+T0T1nsNlma2+6321P924920pTSDraU/W7Diquo06CQB7E2Zx8oy33TEBzoic0TQY0dVqe21Ec5gPH3kahLdhuUSnbncCiaWamqy0s33Ie6a+dQtrmO7bZ5kQLnZ51ybziuiZnrdIf68YEDnOtOfYJvE/yw+u+3/Lh+1s9jW8Zh3Y0hPWEvPH6sPSv6VcLQp2JMmD2myhIV35PMLbWS+aS8kxdCahk0JANfhz/1EVehoa1jgEFSwPFDubo97ZsPOUndTc5YrsEOaaD91qOUl/qydo/B8RNN/lJ2v0/s48Gya1s+QfbkdxvG7PlDycbThq39KjZsmbL3Rbbp/23PE30tf+n3/3/VvJtKm86h2+t1+dJ5XRxZrVCTBQ3BsDoN98zxcHNID62Jdc9KDROKVRun2dnZ2ewN7XdhNoZs9QfGrnY1Q5mAnjhIZ2zp5W2wEgEz9MBvqcZk2Z0WmkGpl1vdTUloMGlU20OPb85OKDCJZvALF2ciXd42ynZWPv31p/Qzf8bKGzgY7fbHW3AaUaZWRI7UvY1zvDp6Wk9Pj4e9MmRLn+j4/OYJcvWHKidxRQ5p6Rfqefi4uKwWDx2hLI9GXran9gUZkLSr5ubm1cbvFhXM6LJYDQnwuttRy7rbPLFeqz7/I0BSp6dMnfOaHJM2iJv9qn9fn19fRjvnI/348ePV8tWQiedKQEmX1Npf5Dx8vRUfMWPHz/W4+PjWmsdbYjJfT9//jyc8dZsCc8CjF7c3Ny8sr/M9llOKEcet2kjhPXe4xHZJcgk3eFheJ97JnlpbfN/z3BtBbuNRvOdz3IMuVHBbZnWVtiG+0nAT9roNyzHbNeBwRY95m2zi60NzhA6CWEaWx35zWOaz0m/J0B6qrzp1WqNuBDWMnwkOoWgb63X2R0rgge1DdgETqbfm2Pn/QZ7LlSKBhj9PMEVB5G/bwGsVr9BJH9Pv04pm0sDOefQttXOKeGeFOBUPWm31UVws2Uc38LztV4fF9AMAD/ZRqPT9TQ+NtDja+lrm+owXxq/W31Nhlu9bxn7ySlOtEx1t75RTsOPBKHnTJdu8b7R/lbdOrc0UDbd5zL1YbJjrc1Gg2VuSxct2+S9syy0MXRgEzjZGgPqiQMZZxTfqvenypas2tfw/61xpkxPdmaSYbbn587RqakNjqtBIH/b4sEpm9FoOFVO2bSJRtJyCgiear89d46cGROsdZxcmgD6W+hjORvwZSMCTzxnxqGlTH1vvodQTxd4bQfRPTscY556PGi73e7oVWo2Omu9gDY+44WYHrAW/ds48Vr6HFpbVs9GzxkDOvHce3Hx8h7epsSTQyU/6dxdpjUSE69dGsiZ7nFdjLbNY96fa5yGckYqNLepFBvhUzy8vr5e19fXr/jEzUOWccv6ZATsJCkLLnRiT09P6+rqat3f3x/GNtkPZtKY4ZvAJukNT9NOk0uPEWXKyyd41EwyQfk90TEddfrvKcnIB+mkPDN7eXNzs66urg7tRQ62MjRcouLjSaL7zESf48Ts7CKblIu1XvSQC7GbTSHP853Za/6eP2ZmWS95lnqS0cvbgphxtLwYoKWd6MqUfWj2jhs5Gg/Jywl0sC/RhfQrMp3sWX5vdj5tUF8aH03DtCGAfLPdpc6w3RTLB4tnZMgb/u7vtt38nVPuz8/PR+825lszuAY6fOT7x1uxDNpXetaJ/D8X5OY367f1lc+eCgbaTF4D7lNg1WxNfvMxTa6TtLod8i8Z8lPlbMCXc7Ks/C2SC1F0Mq201C87nusGAewop3NoeOx8W9tUUjse983KxLZsuOmQyI+Li4uj1+Kkv3QinKahYaTQTFNyNHwsEw943cYz9TRls3M7VX9rb3q2ZXTNB44/QU2bLqVTMbhfqx/L4j4GaGRnawqnl9r0AkEMectCJ8a+2giY9zyQPO0HJO12L2drEfxu8Z2/EfCxPRqVU+DVY2ge5/cALOq49Znjnr5yvPkZwB/QwbWRW9OvdHItOGhGm/atyc3EG24ycmmOj2PTvtN2kPbQ5jWe6a/BR+N3psYtP7YXpD0gwWNuQMrrBKyTbJGH5ANlhbrEgGG3263b29vD9O4WiGjj1ewqeTFNYzYdZv9TqKdp59TyFttp+9tGx9RP1mcgl+n9JEliC6gztn1bWWXb4iZb7ncbI/PBPHV99LWWHbfXnm1tk4fs88R7j187qYF12ubZDr7V975pSjeALwaAi65tNM4Be425/j8Osw1my5il7bRPZaWip05OVROE0VG3EsNmgW/0+zlnCwxmqHQ2TBxs0s1nSRvpaIJL/jX+tz6cq4RbCmhlbkAhn5PR9G8EiJSB8CdTfBzr5lzpwLj5g207+my8a/Jvw0Y5ZUlGzPzM89fX14fDbNlvZjAmfk38m35j25Oh9hjyb7/fHwCojT372+oy6GE2146NOr7Wy2YYZxi3Suu3r71VJ0LbxDP2weAh94V3E60utMPMbrCtfG/ZD7fta6nDwQT1jXTYORNUEHymDt7LNifeT8DBtsH8PUcfaIsbD0n3FrBiH8gXzyg5i28wwXbsK3if9bWBRfLA4MdB7FafWGcDex5HL+1qhf3ZmnUi/ZaZprO2L1Ogf6rfbLM9y9+ajyCPnbjh86STz56StVbenOHLieq73a+IiWdKUYk5WBPQILr14PP+LRS81sv0L8FnBjQLka1QLSO51nFmcWon9yWbwGkjDwydUYqNfIrfruGF2pymS51N2Ahsp2ms/O/NBi5NiQ2mWDfHv2W2mgI5O8d2mqL6WRsBg/utxfv8n88YYLGOlkFtit8MjHmb69wURQPw/Px8eGMHnerd3d26vb098DzZvru7u9GItfGaAJ+z2zS47b5mZKOT3uTAKVZOGXsczDcCcNNrepiFyKaYds5na4/jP40pxzD64Td0sLQZD7fnNh3VT4EJ6TKgCuBbq08Xxz7EvjhL7myvddnLHBqIXWsd8T9/7Vw8yyiXTLi/pjXtbBVO424BR/KYdXJzlJ/xUU5ul9PXtLucCo9+ZNqUmyI5RpyZ4PIDy2Gzt5YPJhY4DmnvFNjzrNR0v2WOOhFaKV/sC2kz/bkePtvnNpuVzy3AFz5PfclnO+XDz5n+yHneXpPZIvsR84Q24xww7vKmY1men4935UQgGvr0cxOYsKI24MdBne5rbfs+Ztfy/8Ssc0AfjVPr5xTFcKBsxOwEUsKHZvjIo/yWcbEzZX3T81bcSbB8D5/7nehjS4Bp0NozW/9PBt5tNodNWZnG8py+Nj5PNBjwEeylvHv37rBGjUsBnDU+h56tcaXONEPj7+1/PtsOT3XbDfA1wNSAdQqnEelsnalp37fGaIufniGYdHur3q0+RS5436lCIHnOrMrE21a2+kS74/GmQ5tkwLRv8dIy0erj9XMcpKfn3jpOU2Zq4oFpdF8mHpmeU6W1b7q2+nsOfadomca91WHQteVfGv3TWDdb9RYetrF8az2Rk5a02OKNZfktvvZswGd0nd8YoU0dyW/nKBoBXQMcdrpMfxMMNeecjJmfMdPYvwlgpi1GCVv92RI8fk+UR7DmdYq73ct5UxaYU6DIwuEUPp2k+R7l2zrguE0pTOB1i2ben2vtyAobSgYliRDTXlvLNBXyxA6lBTdN3kKvo073gXUxa8yxWOs4S/L9+/dD3clkZYqX/OVxKhxj8pjBQTPgeYZT4uY9s28EZz5gndm+0Mr2KBNbGRNmBydn5Ho5dgYI0WW3w6MhGghiRoR8ZN3tma3r7L8BfO7zFBD1OHW0rDz5YCfy7t27Q8YuvHB2nHVQpqgTsU2k0dnVtEfeh9+RM2fC2C/bj/S70ct3xbaAgd8pB81m0yaHT0337Tfon6iDHCfLTvjD+hr/T4Eb+2tmWPmcZ75if7J2N8/aJvHsQ/afY8U6TbfHgfTG12zp+2TLG5ag7Ptz0kfqoGc8eK/lMr9FF/i2jNSVDC596pTMIu9+t7wJ8NnIeG3IWt0R0yAwRZt6UzxwzfjaYfMZDrDrD72ttDr2+/3Rq9N86j8zeywtm0kl3UoThzeeNuD5VakvvMjOMwO/ttt1q9ARf//+/dUC61zPGW/ONHDcvXuSjrY5dvKHxcYhWS0bi/bn6b+Li4uj9W4tDe+lA7vdyyvIbAxbH/hb2ozR47QalxjYIfAtH2kzhoJvCPnx48dBznk+nJ3KxcXxGys4JjYelC8bSwMwfqZfBMl51tMqXqpAfpu3Hj/aIP9RzppTSRu2N6bbwCvrlGlzGCRyYb2zOk1m/Iz7bADtmYPm7Jqz8u5a85nOi2CJ4Ch9YpbXckMZt+2cgkMCHPKfNNhuGPCwHv5GmQld0SnLwhQchB5n/BtYCr9YJvtufjprbUAT2gO6CKLMS+tkA7Ohm8uGfA+Lf2P/m11P3wzCrFPNfrTxcGa+2WzLfuq1PjRQncL+NCDPpI4Dq7VezvmzPEaGMr6ZxmY/2+7aloTK/bYZE9idypsAn8s5QIKEt+zPBIAauGrXrAAunpKaaEwJqOGg0fiudQxEbOTz/BYvJgMRB7Pfv2Q/sq6PWb48EyEijcyipM5mBLb40Hh1jtKmUDDpJEyDnQbrao6SzxLcMIsQJ+K3OHC8aKSanIW3dpjNWLBe88T18f5TcktD5+dMj8GRi59r3wkSST8zL+ST+xpj2MZy4gv7z+cbnXb8bSza/83xtfrzfzPa+d+AYKsuOpsJpJ0qE6D7n1Eos3RMzAY3efJvzXZPeuL//bfW8brFtn7wlI2ybE59aVmd9v0cH+cy+bQWNOTPAMp8anW0YhDGevLZggj3dRp/12l71nTf9JOeqS8GYBMgbYmECci/tTQgaZvNPpuftKlbdoj157Pxvz33Vvk8G/BNzNtq0ELma3QWTZDaYvsUAhpnDBypM3PmPlHBuBM5wMknz4deGkoLXp7P/3bcjT/5TJ+vrq7W3d3dYeou2UZm+wIIeWbWxcWvhaA5my31JkNEsDqVLWNvxW5C2aLxpvStvXwyuxr54KYcvozd4xgeMCtE2r9///5KZjgN3Ggmz0l/smdpz7zYigwDcEg7T4dvEWfW7SU6ZF/bu2fZbwMsftrpUgedlWc9+c07ism/poseI46Hx5SZsjYN0+wH6/G6QdOW/jK7yuCOn/nespS8PgUsjV7TYxBNOWj1tXoaTbyHm4Qi+zm70MWzM6kjdFGGDJryu6fV0rYzVA5WM8Uc3bFtzzUHVQQxDTC1DUPhd7KMsanteYNPfsbRNxp5jXqW6T2DBQcM0RXbzNxDuWTgytMGMuNgXZoAswv9uZfP2DdwjBvYo21mP3a73dGmHre/Vp8tYMBAfk/9yDPWWX7nfY0/BHWRLerr1pKZZoeCOVq/PWvwXw743tqQo4n/WaU5ESt+7psitTAwdFLoPNXAjBLbpTBYeDnl3aZFTKOnUnJ/5vlZvwWUdbR1LOfycrrejMCWQTBP233NSTsrRkWxA/GB1nRMURrvBLfTsRyZDtZrheWUTON7DHOAXWuzFYMoPmdAkPZpbLciQWaw04eWWWsO2NdtvDglvKVzdD4tOErfJnDHNqd+kjZPlfnefKdBN2g04Guy2665uJ6JFl5v9vNUO6bJv3t8I0PtFZmtzlN0NF57OqvZEfOf8rHWqgEIAwbPuEz0Ui4sX6dmnH7XlzVdboGFx6vJNK+Tl5b7tV4f4cX+GRiZvql47E79hUaD8EkH1joO9M7xIeRf09tzy5ZeWn8mW0Gf1MCbi/k1AT62c2qMWvmtKd0pyiKB0zRsG6CmZFF0O7jcx0NnSYsVaFov434ZQJG+tdb6/v17TdXHgbYBJWgzT6Z+ZZ6fvwV88ty/diwA1xt9/fr1lZGi8jTemYf+zaC4OToKIuWktbdlXPjeWxqqrC/Mm19amYABaeEGBNJqh2MwZJ5zLSWdZTLEGTfSQrm2Uc6zyXA6u8WFvykONFifM3utpM9tjV5kquko5Sj/T0aW97E9ynp4ZnnY7V5n8RmAMctkxz0ZaQcj5ifHYgtcep1Xnmln3zWHz7r8Oz8bINhygA6ODGjZfwJw88B1c4wJlKa+pDDzMr0RpLWVZzkmLdtpebfeuE7yiGtmA3zZv/TZNsn0MOBqGd4GXuy4rU/5TADrjNnEe7bHI8Mso2y/jQE/U79lOXpru7TWeqWbjc79fv/K9vg7n3Vm1kDJ+pJn+D9tV9NNt9/oNm1Nh2OzJ5/EOrjGj7y0vlne3lLOBnxp2IYhnXLUYAfPQfC9ToEmM2PQkLT3UQf+P0fLNizsNvxtsE45RTsyC2PLJIVXXBPoe1gMABl5cSEzU9mcyssC4h8/fqzPnz8fFKlFzaSDfKBjyO8eB569mE9HyXYqlIfJ2JnP3jDz/PzrTLpMcU+OmPxzCS1tjSOnuZiZZj/NR2ZH4iienp6OnAZli28U8ALztJHxzflxNHB5voFS8p50nSocI0/bBfBNi++Z1eR0dEp4lnoMElMcUIY/6TunseJgcl+bnsr0WANQzKSzr6TZuwOnZ/IbaeAYTfx2mx4LfidwIQjhJjbSHr2nDcpY8dB8g3DKfera4o0BceMnp8i5vrmNje1zk5M4RbbB9n02avg2gWT6M9bbAFt8CUFWA/et0I40UELQQgCaa959H3rMb7aXcQovyGP63GaHWI91k3LIPvCNHO6z64y98h+f97jR/xmgee0pgXr0kzRRtgz4UidpzG8GYflO/rB/E69ceE8bDwYbuXfamLVVzgZ8cbAR+hDu6MFKODnlFAMCdrJlPziILhOap9NtoO+cYsFfa72a/mj9n0qc1qkS3uR+rkckP1InFcOCbGFtyk2FWWuOONkOr/EZg7nWNz9jsOj76Gg8ZT6VrWuuO8a+0d2yVC5Txrhlh23M2vPNGLkOA+0YHGex2CbpY18mx8hyyrmde+/Ej1aYLXdGMu1MmRzew2db4DEZfzsT09yem+gwTS5b9Nv+NZ4ROAQEpe/++51CWd4Cr1tlAnsOTl33OVkN1kUZm2j12Dd98MxKnqPetbpTmsPn2LWZBD/3Vr2zPJvnBLoTT7bqN41bOIDPtLZYV7vHvm7q79TmucVy2NpwP03zBAzPbXu6Zp/7lrpTzgZ8f//991prrYeHh/Xw8LDWen1yO40KDTCJtkKt1dddBEzudi9nzlFIm5BMkdxWungyuGYkgW5oZpYp17zmxDS6zlyLwjchy8Llnz9/rpubm0O7yYSmHk4HU+AaiNoCEGmXQJwOj5kFAgcCDj5jcMFr4Vm+Z9r66urqsCmDtKWex8fHAy03Nzc1OEh9jJjJV4JaG3MbDWdEUlInI0jLMfWCcsTMczJlHEtmmTiOBjq5nvrfvXu37u7ujrJqqdtHK6QtAl1mB9mfyVDbGVqfnTHmhiNnY9kex8C6kVfLkffc1JQMH2nN+3UJgraCo/3+eKrJmQvOOHCBOce/TeNNDs4AwjMFlCXWzZLxvr29Xbe3t0czAK7T9oNB6NQGddbj5WUmLukrF7GnbAFR3kfbQ31NVr3RyhJdDx+mzC3/j5xmsxf5SB32H2lgRo1AwUEXN0FYllKsj41nlCFmnNNO3g1O/jbb1QBnZIq6HH3e7/dHtrKNAZdypJ/mS/ufNit0uA37tQk4Zkw87pQL24fmnzkWvM7sYLNnLpP85Fp41jJ9bylnA76AvO/fvx8ZOWeAuJC+RS0TIvYUmoX0+fn5aB2DgQsdx7mR64SoDeLy3RFZO37l6enp1eL9CYk3ZW6FRjiKzLcsGGyYvxTyc3/L7xZAKgSNVcuKNUVsxsNAPkaYaxYN/DLlGWA4KfaWg9xSXk93TNF3nrOhdGlTZfnepjfSZ382mW2ZrRhlOgNOSZGn5BV5xkDEfTJPGfH6HssBAQKNl52AHRDl5OLi4gD4GGTRAZvX7969OzpnzjQ5qLPjbjLmvrDNyRg32vibQQSvN77nk7p5eXl5tHs7pY0Jg9Wmn1t6Q1BxTnF9rb22Cc/2gfLiutxegK15wPo8trmH8hlwTB5ad9gm+2V9y980xc37WE+TLbZPPSSoDFANcLi6ulo3NzevbAzb4TX7C9oH260tv5Zn7cP5R4ywNbYNHPHeZivJI/P9FOBr48PvLTniJMgWXZMs+jrb/i8DfH5LRcrWQIQ4Mo9CReO1FbUwKklkQToYWdIwTAM/AT0CFw5gIl+3a16wn25rEkqX9rvXHzhr4Dab4IU3dsj82zJcad+GthmBxu/Q56zFVAzU/HyM5cXFxWFDTXNufDZyxnV0AUWTTKTfjR4uSPZUjwsBiY8J4doXAy7+pU9poxmf8Cbr/5gt8JsLIuNulzQzi02jxyzJltFpjpl84jFE6V8A9yljxjc6sD3rn/vEdnIP19qe4rN1zgCa2cMGlgwCCBya47OD8m/MCvEYKdtQ0sD+UBZyb2xFgIKfM98Jqhwcmdd0WumnM3cEQ7k3fW1ghPzkc8w+R245LvZJHKPc3+xUc9C2t+SZM1mkm4V2xSDRPPOYJOPMPlPe3717dwgCkiFv/DboasG8+0sa/Z2/RaYMXHyfCzHCW8BOwwCtL81v5V6DwrWO/ZCve6xaO03eDIK3gCyTImut9fXr15O8WOsNgO/79+9rrdfTriwW+BAW4qikFlQLMbM2+by+vj5kfQJAtxaupv1zM2lr9Yi2OcG11lHEF7rirPIcDempdllozNr3PDMJDjNnPsbAjs5gr4EyLl6nEcn/Pl7DhTRPAMP30enQMeQ+rh+NESNfaPjWeln46t2/4UWezz3NcTGT7A0UoWNadhCavRmDMsKd1pSFyJbXY9FgE0g8PDxUsGLjZFnxmPz48eMAqLmpJfUZ8LaggMaaIIRgu4Gqdgq9ecJsmIG9QbIdvfnIgC+lTSHSrrCt9DuBqcG9S+q00zSNDTjyel6pl+UNdgaWR4MkA6cAxtiONlWd+5wR9Ri7hC7qJ+t7eno6TMcz4xoeUS8N9PO811ZT59n//BHo7/f7I4BnGbXtbYFys2uUQdqt3E9a7R8tPwbJ+e35+demtix34bhEJu/v7w9jQF/qACPPB/Cb1uZ3SKszW+6vbc+pwiCduttAJ9tsmMBlApDsq/2FZch1O/PJP/qIFPtpA0Pr536/Xzc3N4c3Qf1PB3wTmp6YSkO1BXhaVOBBXKsv5p0MqWmZ2rexORUFbPXBgsH6TXeemeridzoV/sa+MbNGo8NIoCmBla49y9L400D1Fu8M9iaFJLjzdA7lY3JiU/aw0Uu6Jvp9zUbCRq6NN4FB+5xAgcfmnELgQkNu4Jz7CPbYx3a2XsuSkdbUe6pMgYX1ngDyXD4YdKQNOxnKw+SoDbqcrcn11M22m2Nozrs5Rt/rflvHufbSmT0/x3qt9wYTjd/ss3W/8XHK8LaAb0snGvBsumgesl7eS//E30xnszkTb6ZifWRdTZdO9TH8o162cXXwTxnNcwycTOs09q3vlveJN9aHJvPTZ+PPOWWyHW+pq9n336Gl0dR8y8SDLXncKm8GfBQyOxAa1bVep4IdTfD3LQfMZ7ngm6loRo7OyIV+r7mjYSbjeG6asxSJ4O0MfYaOeeet9ix0ormWupklYDQeXvOIlClTmsyVC51F/p8Kz61rAJFGJTyNTDw/Px8yxCneUu7A4Obm5gBUebSOs5XOUGTTgst+/3KO4e+WZBCS2U02wk4ufGH/Hx8f19PT02HhN8eV94dWypblkbIQHpAn+W7DEZojN7vdy6HemeLhGPAZvmkm9VF/SEPoI2gwgAg/+ZnvBOx21C1444aJ3HN7e3uYJYhsehMLx8r9tkPm+LiET21BP21CPr1xpmWfOJ6xOda9fL+5uTlkoryRLry0zc0919fXR7abOjYVynnkYK0Xuxn6mi3k5op2fa0eKJMvLVAkcPaULuXfm3mi0x5nygpBcMtKG7Snn+xLnuW9rju0+Mip5gM4/uFVxiCywoA5spEMcPoeGW3jxf45Y8V+M1jLvV4DTfs4AdSt0p5vYCfjT3lugQXpbscosW7S2cCpeUb5dZusLzZpqy/c2En5Tz1bsyCt/Bbgy5/XLTm6NaH5P+DIQsZrz8/Prxw0U7pZq8B66RybIzKCtgFsDM/zMdJt2idlAq0Rpgl0kq62Xo/A1lNjXC9EntDRBvDZeBqgtdLAqTMnMfoExATgPsqHwJH1+JPrNZtxybg8Pj6+koXWhyafXGzfHCL7nD7mmfxRFwI4p2Alnw5+Yii8ezS0ZI2Z+ej+eoz59+PHjyP60h/qBjfKpK9ZtpD7QkczpulXaCDvGn+3MrFtKYL5SudC2Sd45eYVB2vkI3ctNiDi3dHsB3dAO2gjrXTa070pBNYOqPlbQJtptGM26Av9tpnNFub3rYDJgV6z/c7Y2ha37FpzsKExIIn88r20pQ3wkS+kg6CKWdTGG9ZhHd3v90d2lmOR8YqM+rD10GqdJL8pi3k9ngF86I9ehC+0LbmXfeE1y3PaznKe2IXwmnS1TC4/2Y8G8vl7+055Y/LAwDj1MtFBsEd80/AAi/HEhAsmXOFkE2W9gevW7rmbpVLOBnzTOhAzgQIZR0HmrvXamdKw25CyECTSKId5W8CFpQ1K2s910sEobb9/eXMCn5nqSTmVWWqZDU83UOkoIOQJdzav9ZL9CIAyYJ2MvJWWgkfQyfucsSFtE19YWEeL8ikrTTmstOdkLqcIkwaPfSIPHPmleP1fMgsB7o6wyWMCfL6n9+rqaj0+Ph7eNNIMBAuzTJaZFmQEsGR9YcAo2yMY5XhZX0yDx/AtpTnjtV6AJR0Ny0TPFg0EwRNACk3MYLLe8DLt2VCbTjv+ZjNYPwMpZjBY31ZpOug+EnAwa9PqSXG2yXrc2plKSxgw+OHY2kFPdE48ivyYjwmeHegx0GG90yd9lXkaeilrlOuUBJbOiLHfDGiY7eW1qVD+zKOmCw4gGNg1ENRwQhufJocNZ7SxnOygfYMDArbt7HlL7vCZU3069dtUWh/a/2+pM+VswJcsER0InX2+e7rKgK8ZbwIoGzkbbEZEjHgySC3C3So0TM34TpsR4pAb+GzgznVYuCelZDtxyqwnDnut400kqT8p32zDZ3/SboR8ejdt2nC/CEjJLyqvDZ6Ngp0E+dBkgdnf1OvjBay8zgqabv/GLISnMjmVmz4RcLOvof/Hjx/r27dvh+ms6+vrV8Y79WfK9/Ly8nCO2p9//rnu7u7Wv/71r/XPf/5zff/+ve6y45gmks80cgBKdDJ9yvW1fu30CsAL/fmfWQWebegsWsaGMmpQ1GSA9FineK5Xfgs4pfzv96/PzaOeMcPH58Kb7GCkjaNsp47wgxkTTvVnDPNceM6x4vhzvAz4LCM3Nzf1GCI+k+JslIM1g3HeF/mmwyQgJk/z2Wx8c9QGdLyebBT1iv1Kls39TLEukE7rKUFW2g9op+/Kc5xB2QLO3HhFkJfsImWcPifZtMg1+87ZAGYdY7cjP7e3t0eAzzTa7tHuJ5hYa73KNKa9BjY4rg5aveyJZQLyHiPTy4SCfXIDd7yPPLVNin2gHEY+mkw1/THdE1D1/+5zA/fuS5sVOFXelOEjQydkbiEn8RQel4amW6ExyffmwE+BvQlcTe3b0LHt0HJOJMPvEaYoCoU5JddbZGlnybppzLhuJsLcohv2NQbv1JhwTH8n4nBxBO/P0M81IlQALhdo/Exx1GrgyvbIBxsbGxXKOf8PiGpZiUnxY7Rj0D3FGbom42K52WovTmWt9cpgM7By/c7scDymrFsDGJSfc+WIDqkZR8pAc8xNp1tmKnU3MMkghFmVqa9b9oUOqNmitDdNg1MWTtlRB18stNn5Pt079e2cTGlrd3qevLENMBA9R34mMMH+53vsYQMhTe5o0ynT9IPkLXljMOhimWZChQFzs6OnCsEtfVKTw2k87Qf4fStL3O4jD5rfn54/VewjWe/0d45tOuX72/UGDj32k47+Tnlzho/pZQOHtV5ewZaoN0LEaaG1jiNMKgGdih1+AExKnBSnjrdAVxN8CleLXG2EWGiIorhT9ojPhI++JxGGQUHqcwSW+nioZjJSGQ9OQ0wgz/8TJCSTSgNLmu0w2Q4BROhr4PR3BdoGKW2wTstL+rLWi5PlGXC5j+8c5fNrrQMA48YhHodgvu73L+89ZF9jpFNf+pS/RNlfv35dz8+/1h/d3d0dDpsODZkupjNMfybAYf6mPeqPHVPGMaf05/sUDNnZN6DH9TMEltRHgiH3xWA8dXHNFJ1j2vU42A41nrlQ9wyMbbybs0/dadvtp91M00W/fTQVeUSenWML0wf+TtBh/hlcTfzZum7HHzqmQh2nDnF8Q/fj4+OhjSng8G+2mc1WuWxlVwwYc3/zKbw/chndotxvgT9P87s0frcAl7rYMm787leqNt0xcG0Bacs0W+5Mg/swtUE7TH7xN/qQCZOkrlNgrwFV0tRsAqfqveyNNstv2Mg954L5lLMBH0+1T8NJrecvBjwGmALELBMBDJlDo8mouUVeKR6YLWNtIW+DQuDGQmFIoZJ6V2Erbs9TUd7llu/kW6akwh9OHbUF/x6r1GNBaeCAae6AII4B15rw+a0ILfeaJ5PTYKRpXuY65Wlr8bszgIyG3W4KATYXNudNJ7k/0z12Tt4BGVoyXvv9/ug1clz4HzDx8PBwGOtM6eXstcfHx1fAKcXy2sA2P7kbmrTSQBGgBoiEP87gN5o89g6QmEkkf1sgldIybwR80zKP3BeZcEapOUrz0rMYpt19nXSCoJpyls9szDDAbuCV9ZlmywNpjq55Ct8BIWX4P1tohx1wO3tMeihb0UdunGF97jfbZZDtdthX1tPAZO7Np3WBxf1Kf9ZaB1uQQJS7OEmPx7+9TWXLFzaecINGZKytV0/Q7CDGu4vt4+grQoP7Rzoj76mHOtJ4fypomPrdAlLavWY3W9sN6DdwP/HH9NHf+y1Juf5WHTwb8LXObUUdJCRCFOBBYbExSWmAq2Xg2OZb+2G6U+9WXdNzNFxbitTqMlAiKOHvuZdgJc6XIJnrRVjoTM6JDFgn229j4Sgp/aBhdWmKxgwcabDyTbLnaU8CmBgwO5OpmOYEKFzXFwPkQ5hzPxdOuz3vtkuxseG43d7eHp65vLw8BFIGUgaWNJSmMePDdSvhOXd/xgGlXy6nMjctkMpzW+Pa6g+9EyizjDsQYplkwXI+BYj5vxl5t0kgRxvI68wu536O8ST7rRjIsDRQOPX93PZ+px76huYgG1Bh4iH38dN1rDVvAiCtE7ht9of+i/VxjevEE/aFhceq8DvbdbbQgO/cwhkNF9r56Jl9HJMyGb+mL878ESP4L9dDH7+3MTZNDaC7z26Hn5NfdNu/G+xsgc1mAxveeGtWj+VsKWEE29YZcNDdqZzWH4GI8cq9WWjKuohmI0TOljRBMdOopA1ETjuEd7u+Dd2lGSNmGVKP1/bwqAsWb7NmFipOfq2XKXZmWTIeDw8P69OnT0fRw37/8rqt8Dv1p5Bn4Ufo4bg2Y9Z2hNEAOFvTgD6BIksWMZPXlrOWgWV74dXEM9JIg5zvkc/Ly8sD7x8eHtbDw8PhaBhmWykHDQQyK0jeU7/Cp9Bwe3u7/vzzz6ONB5eXl+vz589HESYNHzPHl5eXr16RGCdLeWUmKW+3IZ9z7lsKo3xG49aN5vybrvGZ0MplG8zKsF3ONJjvoZNjRBp4j69NMswInc+bL/w9/AstzAjudrtXGR6uzcq4MpPCcUs7HM/GT/ZlrReb40zXpFPN5rHO5mRdWr3hZ7O14YEz5cwucYbItox122nnfy6JIb/zF74/Pb28L92+Z7d7ffbpFuhtMy3ZlJMAyzwyUGcAxuzsFDxRPyb6aMPIP87UpZ5cT19ow8jzpgscVwfrzcdv+eG0F1tBvbBvb7bBepbfJ11vfM1nA4ZNj/KX/rM9BsKhfWrz3PLmc/hahBQGNSFZ6/VRLYzmWlTX2iVwosKttR3Fuo6p0LFaERrK3ioGMzS8FEKCkRa90HhzSougKgCEisBME2llxs30GvSwPY6v+ZPPKUqy8vD3c1PSVLbWZovM3Qc+uxV9kz73KQrog5AJmvysZSrRv8feOtXAW8BCpq8C+LjDj3XlmdBLR9DGNHzkmjEGBwSgBhikua3DjQy7ZIzOkQOODeUgU9/NaZEGL0HgPa2N9OlUMUhs11kvM67TeBHw2THZ4U9g+pw+bF1roGyrtKmmRgPr9BR08x123J4x8XPWp+l385Wy7WUA5rt9XgN85J/1e+Jvxj9yYltFPuV/Zn9pj1oG2TpP8GVAwzqpXx5bfm88t8/h79TRlsGeQJJLfm8by3xPs2G+3njl395SGt0Nc1hemn7+Tvspb8rwkZA03AR2i9gc/kpUzxPI3eEtEEeF52euh8YUK89WdM5iGqZoiG1wMC2Ea70oNo0+aSDQ5I5Un8FFB0uhSaSaYzXiTHLwLnd98lmDF/PHhm4ySK2/jZfp6zRFnGcc6TIam6Z9U/jslgFpoJVtkN7cF7C92/1abJ1sVOPdWr/kP5sseFRL+nN/f/+q7YCurN/jug73JXLx+Pi4fvz4cThMOOvArq+vj6bCmtFhhi8LyAlQ0keDFmdSm+MPL5oxZR/IM0e23lSQMgF5yxbbZNst2z2BVE+RZ/zoZNlHZw6en38deZMsbwPwzESnTBl3gj4HayzkmTPI7At5HB1xf3i/C+WhAV7rePMp5Kvtaeyes608FoxZpnyybvuPPENA4OC6yYTBNjOlLUvaAA/r8nhMhTrAYPZcv2Yf2ew5++fExVqvj8FhoEoam60wn1nnWuuVjpFmB0kO5iewzfHIvc1fmccNT7C/03XzerrHQYVxQcM2v1N+C/CR8GY4SZj/sraMqVzuBG0A4hxES2ZtKYKVkwLUFJkGh47PpU03m3esd611lHmxkHPQmRUNf+IY2E4MTbJ+FxcXB4Adg50pA07pbUUe5E9L53M9Zui3AyA/uBs1xoLnCwa4kS4GBOb3xGOeLWXF5ljT0JDeiQ8sjKy56YIbKSyXAXw/f/5cX758WU9PT+v6+nrd3t6uq6urdX9/fwB9AVuc2sunF0jTeO73+8PbR56fXxZe53VjlC9O2YZGBhapN2f10ZnGweYZy3WTrdQx6QSdhcESx8GgNZ8Zy8hRSruX1/i7x9rPGjzERlBOuVlqrfXq7QdrvSwJeH5+Xg8PD+vp6elonaSn0z21NK0PJdgwb2kfmbUmnw0K2ccUykbjdZ5zUMLgJvU2e2peOQjYAnf8jcGJgxwCvuguxzL3cd0v+2GZaDpJHnEDQvjeZJx2z2NCMEdwwGe5dIP3WhfjD6m3LXkS+thv1mGAGT6SD7SFbdo+wQ2DStJBO8x6wpeMV+61DBvUeU1sswH+y7U8Y3pYP6+nuA3fT/3OmLZk0fT/OeXtKz1Lg1vAL1HrWscOl8je0SAZZsYYUES5XYcBX4yvB6cZG4MXpsnbfTaS5w5E+s7onE4rA96cUzP8FBoamwZCzy3um6dSDKJOLR6mAJ9aE2E6zi0t45DfqUhbUwhTpOff1uo7MumA1jo+oibjEyB3c3NzWBeXKVsu1HeWwJklL+6mk+OCazoBGtSmN5H91OdCR2bQMI0BnXtz9ATY/r19/51yzvOTYW06SkfI61xnmf+ph7GBXNqS+3JvA3LkUZNR3rvVX9LgLAr7kfvyvdXXgPW5hXLYdKzV60zR1H6rq8mSA0fbPALPlrGbQCvv93hsBcVbfedvU9Zni4db8r81dpOPazpLUMh+U8483qfADP1e6ytltD1nrNJ+a+2+pRAMNp/d/qa+NX7/DsZwORvwmSFmFhWBEcpavzILEdB2fk+itcmY+M9CRia5DhpkGtymuFz7kGcZrbR1HQagTQHilFOHB9u0rvWSCXt+fl6Pj4+vMoB5gwazMaElhjwZo2R1mHWYnGsrAUgBDH6hM5XXRiPGsEWcDehPho9ZinbtLSCbTobGJ4XRIqd7eNo9+8H7fvz4ccjW5Lp3TQfs/fXXX4dsbI7dSKYv95ln7H+yQH/88ce6u7s7Ws8ZWh8eHtaXL1/Wfr9fnz9/PtQbQBGwmZKxDg3kWeSL/GFWgPc3vrfvqdMOcXIG1MHpPZKTLGxlk0gX5SmAmTROGcqtjIE3mTw9PR3ObWQ2KVlY2qfcn3tNA2lpumCdtA1nhic2kOtUHVhObbfS6E3xLIbXfrYsnKdpt8qWE3eWkTSttY6m2KkLKZRN249Gx8XF8cwK+er6+BvvD11e0hB9j6w4m2VAkUAwtFvOGs/sF+3jbT99L5/n5hfO7Ew23n01WGzYwPYjf5wF87g2ELg1rgZg1g8HDXybTurmG21Y5zSW/jzXh6f8VoavoXH+MYXtwrU/+eM6MzORzKPRIA00zpMxIzBqWZNE5BamGGkKo5280/MGBREG0sXPKGDjcYDabrc7Alrc/WSgRHoD/jwF9tYIgQLpXWD8LUAzheNM/k1OqP3vaY23RMdpc1IMG5O1+qu8bKCnujJensII4Evg8e7du/X+/ftDRi9Onm23YMf9DkjMGX1cR5i2Y1jjLAIUrQMtaLPxTLvRF4KYUw449bXMnssUEP1uZMs6WBpwa4a/GfT2nPvADE+WlXCdWO7LIv9M76fvDip5moHBwDl6MPGD/SNtzN6yXy50uNZd05j/GQh6mnMCh/YHLgQ4jWbLseWdutYAztRu003rEe1YZhi2bIpBANsnDU0eJ11sMkN54u9bmcv0qdVtH2vamUFuwVEbQ47JZA/tC3OPZTy08fdzfSLr5W/sN/lLfjrpRPmf7Gf77RQQ3SpverWaibDC2MC1AW/rsZrA5n4qGEEOp3HbwPMZo/0U0sspPpZk+LL2aipNcdl/GxxHb40+0sl1aBPwJW/YFscjbTFz2fptI5P3g54CPu/evTvKvBD08NPBgbOO7hd5ap6YXj6f3z01bmX3M82weB1Re5/r9+/fD+uyqBd3d3cHoJS/RHzeZTcZetLPtWG3t7fr+fn5CCz4oM4fP34c3pWb8d/tdof38ZLPbIfriBzAGRw0PWyFyyMmQ7sFIGmwOU6UEwLnreJAzLQ3+iyn5wYbllkabsoPn3UGPP23bCZopB0zXc2BE9Rwje5Wn7bAQOg0cHbgFIfHsZsAKXXCmXMDgS0wGF1ba70Ct/Zh3DxDkJK22V/yxXxjIJxrnK3h+HHcrFMtwJh4lTpYtgDCJMNbMrAVfHnsJxrafafsuH1hfpv8quu0zrylzy62Uc0WsX8Oagx2W51bwdaWjZzK2YDP526xMy2SMYo20VmczCkiR1kGRMxWJCuYV0vRAHDamAt7w+z8RmfLv/wWgErnSWVsPEkhmGpRTnNSfI68IkhOPxMd0DBw0XA+OQacsn54eDgyZuR/m359eHg4AjeJnAgGLi5ejshY69g4p93QniwTQZDLlOJney3aJc89bgZ3NCAcQwYXeT7gKP3//v37+vz582Es8pnXO8Vh3NzcrA8fPqyrq6tDRs/yNvE+fGjOO/xJRrUBvuzs/fnz5/r3f//3A+j7+vXr2u/3r470CR+ym3utdQRS06/Q4GkR620LPvh5TrYk/Lespv982Tyd81Y2j87VZzI2g2vZyfdpYwpt1vPz82HMTZfBTni/1usdkLSbXCzvoIQgjn3gbxPwC6+nfpGWrQxQ5NPrt70G2JstQsNar5eOsG0+25x4c4QXFxevjkuhXWAGhqchpF6/gq7VTxrSB9PGZ3lveL+1DroBCQaMpMGZNAPM1k6eY+LGwbLbZ//Tnmes2HfaAm5kaeB5y+5x7DnuLQA1vmjB1CmAamA5AUl+J73csDrpFgOh/7e9f12OI0myrFEPJkGyMmuk5/0fbp5gZEZauqtIgiTi+5GywRULW808wKw5c3qgIpAIuNtFVU0v2y7ukfqNl1y75xz8cbxiha+hVwbFXPcKH5GsgyWfpHQicCD0U5dEywYiDDJMrB6MZlxt5kjnCXDMrJpGHKOm85Cakaaug4KTFJOegVozOF9rK4RtNu5AnoDMQEu+2Fc7mxLyTJq2siLPhKxrjulqVmRq/U6APPrnWUZPKGIjx/ET8OX8BoFTA/ZTkppm4JaPshPk8yXdfBjEPhZi0AzInWaSHhcCsZZ0drTqi3bihD0BD9c/s1LSgGF42yUFXneCtQ9OdeI/LZj7zORZnZp2oGMlF3md2p9si314Va7FLPfFWMc4tErEqec472v+a8m2gQPL32I3J45tMrKiNuG9hxzHTIwZK77oy2d4aHZEn7iXGoZYlWWd9r8XlHYxtgG8qZ7tzuB0ArT36OY1ejwN+P7+978fx3E8vzvKHcaI+bLQfHIm1pTD1Y44GsGZt3LZt89K5TOJ2L+AEODYVh6yAtZWaxhICMoiX/rI75xSfn5vM2cbh6+TUiYAws5soMIzk/mlDT4xSKBtIEYj5ZvseaA5PPgpxYzNcdyCjvB4uVxuVvi4MtF004jAqlFbvTQPaSc6ul6vNzbTVhDzEM3Xr1+Pz58/3/CYhzFyZjT28McffzyveERWJycDtrZCxZcHR4bUywQkPsDV1Pfv3z9v/XK16enp6fjHP/5x00/Ke5U+9pIJFO2MKwy2cYPyFdD0wX32Sxv11mhk8dOznhA5GTP5tgcBCFonPqMz+y6PL+RaJgm531b7skre7JrxKDxxxTj3OHmkPjlO5peTwQa2vOJmYBTiSlhbPef/jOttkpUyfuUJfcETDi8yeFJpEJA2YqeOC9y1oL+xT8vY+OB9g1XaYfOr1SQo+mv2x4loyk85ePIPys5z8Ml9jdqkhG22iV0Dc8xBJoOpxoMB+jRRyXcuJk2yEZNk1Zg8EQ8025zAYpOjyTXhhTN0GvDxNzwZAO2kfDiCzNigQwZ7AUsBNE9PP59I5QpKC4YMHgkGnz9/vgGoDBRcrQkPPvvEMzUZTAdylvv27dsLwEcZ8zSmjdDByatw0W3KZDneP5UVMlD78ePHM+BrZ3WYTJNAogMnAxqzl9ebo1sXBsHUD4Mg5ZomC5PRJ3gx0JO3Flwon48JMOl9/vz5uUy2E9PPx48fj99///35FSsBxX/7299uZHcSI9DjOIQv6pfjcRw/t3S52syEEqCZrV0CnH/+85/PICKUVb2cx8wW4sPDw/Hjx89fuTE4a3bqVYEzAc1Jhm07+dBe/NSfAcludYFAzjZDO2M/qcct8BDjRO7lAR5uYVKmtB174kSIiYZ6ygqyZbFfeDJBUHy9/vzpRbfDuMmklDFg7E9ZP2g2TXT5R9u2Hv3b4Im5BmitH55DZd8mJ9w2caUOw4fL8/+2u0MZ/L/Pt051J94ZjxkLOLmgnXqXi+RxcLxOfctBgDvxzPE+I5e/hzj25iHtc+zbZKgB9jO8R6d8a8EKhJLHM3HQ1+8Fd41OAz6+usGrHmEsAYBv2j+OnyAw11jOxuZZT1vO3glOo0zSZVDhLLbNwlM+CT3Xp8CXZJi6XKk0oMirGFowmJbU26yMhuAHWFogZft5mnPSJZ9G9FPVl8vPlbmHh4dncMhVBp8NCdigHgwA2xiGZ55hpG4bRWYejGayTZl2vjDjmhcjG3THYbk9y1eoXC6X59eq2La9EhHZoh/rYbflQ10wiGVyY+DH2WhWGWn7qUvQGyBjakmVK8ZtVcygqwHd/O+xJmCaAuOZYEhf8KpVdJZxov2YaBdMtKtETQCUF3M7jkw8TrJ44kSi/bCdnd+1yTyJfjDFpGkFJeWj8/iZdUE9xjZtN5Sff9ZFrnmFmCvRjCmMYc5NbNsTW/vuanLDMXZC5z2Cfdpjrnl73yuUnGzxbGLaDjFGniH2OdmoZW/ytv9DHCfbA+9NIMqTRK9sNz4aSCY/Bp6e3IQ4KWP7xBjm+bVg7t56pwHff//v//04juP5kPpxvAQiAQF+etIrc23gQmy7BZ4V4Es5AswAjeZkGazJCLmVzAcWOENngpxmLHwlRrYLGXAYrPjj3V41aauk3CKiDm24GZt3794d//Zv/3b827/924vtJIPGx8fH5+3LbOV/+PDh+OOPP57Bjlczs7LlhE1dcMVnGkfqxT9ETjvhNW5hBKhHbx6r8PHly5dnoOOn52gz2Zp9//798wpeW83M9i2DboJFZoScCPGPQcV+5cTA++GVtuDJ0vV6rb/c8OnTp+Pz58/H4+Pj8e///u83K7jhNeP/+fPnF2Ahn0mQsWGX8Xvocn2aJFB3oax+ERBy/LMiST3xk3qhjG2rMhRfJ7G93OMxCZ7RJMjJ3z//+c+aEDImBOJe/XYsjM49yc792EWLvybGba9sR8ZdonLMdty2j2blzqtE4TX2tzrK0MAb2/Grjjgxa6uj0YV9OGPMp/PDO3e2Usfx2nIy5nCyZD5ot7Q5LxAQuNF2KPf0QEibeDAOc8w9zqRpopc6lq8t6HByxwlVK8cV0UmeKV7aRhlf+AtVjCME5Rk/H3Fju+6bucB6a+UaoDU/99BpwBen4Ms5vS3AQSCDnmmY6ZQhUdhpFrAit+/v6cPAkmWS1FkuA5UfsM/AJyD7zERkY8D2qhH/vBJEMOOVyvDsYOmVPvPBFTcCKP8RsBmYJsD5bGK7Zn17Fkq+PVbehsj1yBidRUctMKZvns3M3+Pj43Pwth7bTCw8OXASGLYVFxLlnz5dvpGDa7OJlkQtR7ZsYx9OSrTX1GvBKXHBYP81k7e22j2tet4b9ELRU+ozkTKwt7M8TU/H0V/enr4ul8vNWdgWj9Lm5MOUmX8GmPnuupN92U4mcpu05Qb2vFrpGNmetCXAcBJtOuDk2PmIwJFJmPFregPFcRwvYiBt33HYxNjaxmIiAkWT4/yZpG/w6biRtkyTraxkae16ku66zrsEOwRZttFm825jum9ZVn8Nm6xkbuD5NeDsX0GnAd8ff/zxjHy/fPlyHMfLJW4H+rZqYWfmoJIMFPg50Wr2z/rk2VsJrMuyfCWHD903sMT+OHP4448/XgS5FtQIXMibZWlnCiewFzDCFRhubaa/rEJmrJ+e/nzQ4+9///szWOTKA2e3XImcDhLnvBNX7sLj9OqDOJ5nPFw95dk7AsLoM6/yoc6oJ4LLyOZXx1wul5uXJLezie19dQ0EtgDIck6c0UFm8uHR505Nnnx45S88//777zerXeSBvPtBqbQbnri9H3k+ffpUt9AynlmRZWKNvg3GmYwbKOcnbZy+6STI8ee4RQaDMK7McKJGX2zjkUlG82+CodSPPqM7/wJKxs4r/5SXPHgS1Hi0zfkBt1aW331GmnHeY8l6kdeAm77viQTHyNfowx7nrHZ7FWwFEgwwGmDP9WkFN+0YJPshKeZEA834/zSxTh0/2NTyooGWJ9Ykx2bGYudD6yxx2teb7igzFzlaGS4u2Z9bLGT9xI9G7C95zL4+gfLVxDQ+zIdDGgBOeed1fra6O7r7oY2vX7/erA7lLJBntAQWFDjErdQV8m3AaBrI1mY7m8AZR9u6mLadCWjIm1dWnMgZuLJ96Fd6cJvDIJJnq7zaYmBnXRMY8OzZ5HDhMdstOdCfn3wKuCPg8XYGx8gOyC1mr+LEYdtKX5Mt7eWnzLI9a7mitzxskXoBb9RRHDKy/f3vf79JJuQh29cEOxwTBibPVJ1UOdnwjLat1qVPr/CxjNthIjIQCAD3WUv+TFwCJFdnAtToHzyyQBu5Xq83eqINcws+7XDF0fyyDfJEnXHFsdkQEytBGpNt+vHEKCuiPr7CeBA/J0VnBEYGVQE46Z+yRp+UI7oykPf/tA1uVdNXbVM81tL80uCfoJUPW7TkFD8zf/QjxxHq3ICP1xin+HOF1GPbom65ZUqq6buBu5ak26qvx5lAju1xgsL8RH8xr+kvOXoHWCmTJ9vUz5SvaQsEsk3+KYczDoYf+jd90xPrBpTdN+V0v5aV99pqavs/ny0XMCf7/YNtEmQiwJ7y/o5OAz6+yyuBn8E7RCc0Ofjyu8sT2DhZtn17o/upbSrtDK14djsr4Gpe6AgBT3FKGkBW11rSS30bihMWk0KSPg/3sx2vgHnG7L6Poy/jN/6SAPP5+Ph4M5aWsdlWyGct2ordcdyurmVWTyfkKh1nYHxnnicN1K3/qBeTJyMtsK7GeJpcNOf3+PNa9He5XJ6fwo2d0a/jU14VygQk7Tup+dDy9Xp9PgtqfSUZBRjFL+IPjegj1s+OuFrnlRPbTSh6SOzgjJ8J2+0Q8OT+9ICV+zdwZHxr/bDc5I9cceerqZyI2QYnxd6KniYubRLt5Eg5m879v+2Zft0AHye2U16wT018UY/kg3Yx5YhVjjOwcSxwXU50OUG9hyawx/4IbuxXmUilTq6xnfa/9UnQx34sc3hxfjfPlsl5x2XP6CdxxnXPxJnUoW9P/LHfVdsrvHEW05wGfP/tv/2343q9Hv/85z9vgnIGZXXwNUGAA8YBb+CwOWlDwPnkUjQdsBkblR3+7MAsxxkU69iwG28Oejzj2H4zs9X3i35XfTTDjtOk32xtWifHcTw/WPL4+Hi8e/fueQWL20ppM2CAT9FyO6cFi9x/964flm+8+8B2dOn7frLqOI7nVavL5XL88ccfx3HcPjVuEJQ6SSJ+Z13qtKTSgn/aJYCObWc8shV9HH+uvLRAG1mjA64c+aGd43i5+kFwkzGeVgDyFHf6JEC/Xv/81RX+T5BHsJete/tkiNcNnMhvQHcmRgRtLelNycw+zP5MTmYEMlkdo+8ZEF6vf65a/sd//Mdxvf58stu+McWm9JO2uRrG2Mn4kIkcfcm6jK3w98vb5Do64QQwoH0CqdaddR5qwIbjzzFK0g0/XPnlMZL0w7PE7UnbkMfVvLXVZI5bmwA3OfPHSQ1zVgOyBvfknzqdzgq3CSN1avJDWqnTfIATHtthA8+JK7sVNuuNk2/y4dXnaXzJE2Whn5of2xn9eVrlZt/Ws8FeA4FNF9MEsH2/F/Df9dNq1+vP2T+N3gJPgdi0Wnljm0T6K6LykxRWg+DrXg1pAdl1p6Ts75aLhrprrwE+OhrrGji1Gf9x9Lf45zpXFNpMkn0TDDkwthUBgoIGWtvDEDnzxPGwQ05jRdDDZOAJSmauSc6x89ShHJ4F8pPJK7IFUJE/6nVaaWlk+51kJi/trI8TClec8xNw+T/gI7ySX46Dg+vqGvVIHmm71A9BX9rxU8+2UwZwT4RYp8UhT0ZTn3Ybm7DPUR8BWD73S19toK/1ab2FT/55RTFj6zjBSUgDfJGb8cAr6eS1xeYpGec6ddmADQGf69quPanxwxiNWsyY/C73GjBt9chHZA1xzBsgbkSf8HhFll8h2ztje9qf+ljFIrbdaBXr6M/EHM6xze/b31myLeYaZWx+m/sGj42muP0auqfeXU/pMsAfx3Ezs8pANJSfsgwkUwBrnyvgNAUalmNA5gzKsxTz08DUzoCS3I9j/Vu7ZwbJTujVjVXb1AEBWgOKpCSHJKis4rBtz6J/++23uj1LPXHVx2AzfJFv25b/cj/bZuyPyeLTp0/PK6mUYaX/NnNu48rVPdqXA0V4StCin2T2z22HlR9ZZ6Rp1cHgLwCEQC/1j+O4eX0LwXxWVH7//fdnAPD777/fgG6PQ9rwqjJ54mphC7J8r2EemqJ+/BBRZNkFXQOStn3f6mTFjXIzhmUFJOcij+PP1XweJ4heIgdt2KAi8nKLmG149Shlo8PwwzpcpfSYUPfsyw9ZeaIT4vg7GTN+cEI4TZwaoMn9vP6JsYKr1p6MpG6+T09fs502oSAvk4+GR/LA1V3ru/EwtUs953MVK8grc6DzQgOx0V3KxP8o+yoXT0QdeEGHYNO5/DheTlbdrvXhuMwyHhfnrfhjrvE723K7PmIwyU88tFoFJe9tknmW7l7ho7Kz5cVE3VahUt5ApM1smfTShoEYeUi9ewTnrJHnEQz8SJ7p0NgnQGS53JZlb/2R3zZjdTBj8krdtJfVBiaIJmf6+vLly/H58+fn8kxk7969e343XcBAAIUPYtOJmNi8EhgZjuPP5JLtZD4swndnvX///vj+/fvzU+Pp83L5+XJoAj6+O8vbOLZNrlwSnHF828MqLfglaXAlhoGXq2YGDrQNji/9jA/h+HyNwV6APLeFTAR8BAuxC0+UPNb0/9jOP/7xj+dVQvL07t274/Pnz8/vpmu+l3OG6TMycBvaYI0xofm4x57gl7ZAu2RfudcAQCYHDfCRv8hLv2QM5PYlgaHfD/b4+HhzzQm9JSQ+tMG4aoBFHX78+PFm4h7gOwEFtkN9U3+MKWw3fZqf8M/3fRJUJU4ZBNpHp1+5YW7gOJA85o7/XoVMm7HX9jaJHdke2TfHegcCmp9SBvZjG2IcnyZnkzweA8f7dpabOMD9TDbLyfMErJ0DmIf8VLnrE7xbJ56wRS7uNjr3TLljAnRN9/fQacCXzvx/EqGFPtPWmXITNUU13nZ9cFbRnJjO7VlJ6rVZ18TXcawT0I7PaQmcxuEZaXMq8xrKKsrl8nMrzYbPdmnEk64NnBycqOP0xSdl8/nw8HDzShSutJH/JG8mkIAVBhPrb1oRoE75fbINB6VJ/lADfAZO6bdNrELThMDt8sXhDiCewU4TrgYOPEsnD0l+npRkLLOD0Gyb9dpKIgN3O9cTvqYVnfxNk6C04eB+HC8feiLIZdAO+KN8tEWedeKqV/ROeVps4nWOqX3SQMTXpzrxPwOz9sT/KqZxpZgTPa+U51pLeAF8BMT0WwPHxlPiXCP7rxOsfaMlZ/v6ajFh6p99t/pTHiC1/lousd/argy4HRPcD8eV/TYMwU/3P9njcbycEBgTNHBE4NX4PotJ2M7kg61OI+c/+uLUN+veQ6cBXzuMzze4h7jFwZlRM3w7XAJRBEm77VAtB9JGtztn0gbJB/6boeW6Z3wJ8k5yzTDzya3Ne4DfatmXfTF4hmfqroGPtMVViaxWeOw9y/FTvfzzbJIJMKsEXinjdjFXFD99+nTjDD9+/PxZu7Rjh0hgIyDKGDbb4irBBOQIQKhTEu2PPyfF3+llfQbi/AQXwcz79++PT58+HZfL5Xmlh+MWoGt74PZtW9HLqhR1Sn+NHF4Jte1FzwRd+fz06dMNv5fL5Wb8uW3Jp2Cjm9hfdOIxsM3R3g28owvy6Alciyk8S8p7se/4AN/tmb7+9re/Hb///vsLvUfmjx8/Hk9Pt78+kbEP2GKf/B4Z2DdXuGzD1I+TZJsIZWwzXkxKbUVnepgg7WRXyD6Taw10tDhn0Jr44yTP2NPiHXnjkQefrW0Ufhsvx3Hc/E51W53jAxDU63EcL3KS254ofujJSe75kzHdD5aQmC/yUF8bf/pIFoQa+G78uW/6LsF8/Mq+nWvMSW38Wrx2/y325zpjpCcAjCFcEJtkcx88X95iTXinXv06solOA74VYGvo3NsrDlQOnE4qBkghgzbPSs1Lru3QsleeJkDZkHcczDN08uzZ+j0g7yyRt6Y/gp/jOG7AHOvyhbefPn16cVCfTyt6O4rGnja5hewtm2wV+XxQewI227yUiYEkW8y0L9pJyOcDPZu0zlLOCTT64Cdt2as5Cah8F2NbeTiO4xnwxZ6enp5uXmjM9ziyLvV8uVxu/rc+vVJr36KNttUX6oegKzZGG0z/1CtXaT99+vSsHwKd6ILHCr59+3bDv1cpCOhI0WPG3LrgtigBYHjgeVYnC47rlJxs4/Ebn4nyZMr8OJbmPldtPcGLXlLWP7PHMSZxvP2zj7QDlg+gZH3yQIDPsUm51dP7tLsG1N2Xx8m2abLNrmgC3y2vUc7IZPmtM7fL/xtYaflqIvvpmZzEmEC/I2ic+GYfbst1VkCsTewY4+PfbrO1ZT53em/xsbXHe/6+I8aM1s5ugraj04Avrxf4z//8z+ff0+UbzB0gAyaS8L0CFiH46eX5tNeCmBPRcRwvQInbblt2ISaapkQmdhp8/gJ4OCPj4WQbfnRCoz9rEGfuGXR4ppQyTlAhJqok65Tnq1VyVi512Fccsm3dJAFmy5Znxtg/+U6yJ58E6tmCtiO2WbLb9Wq0Z7pMjgQBXPnOH7diuYrDOklsTNgeN29fpm7spwVnBwEGkABdn71N37unGunrHp+mh5RtgIwyHsefr4K5Xq83r2EI0e4CYunDLRDHVkjRY8CRk3vOClJWjp8fmLAsBGmRhfbtlS8nToI7TqLoh23M+QJnvy+VOuR4f/z48QUA9xinXu6vVu5C9G+2m3uRhXE4YxKZDeJoo+Qz9medEBAkb1APkSOrqpQxOm4vZQ5PucbYzclEi6fceWEO8cSA1PIU5WuA4zhu8yCJq7rtCA7jlMeNck/gKH1GLxlT88Hzkyvg2sjx3OPQgNcO+JqXiTfam3cRQ7TLtN2OfRBoW5crsMj4wIWLs3Qa8P2v//W/juv1evzv//2/j3//93//szJWYdxxnP5yudw8EbZ6MqqRk0fbpgjlGs/QpC55ZdutnxUxmPEnkpi8Q22VoRmCA9KOD6/MsF8GlFaPRL7JH1e9kkCY0ELcKsyBbsvpJJWg0x54uF6vL564dVDlFokpL+t1Um1ns6IL2mWCPLddHUji7E9Pf/4KRX56LrbAlSBOdJiUDcZ5sJcrrnz4hTriJwNGC0BMJkzG2RpvD3y4vvU2AT4DMforyxpMEYCSPEn4/v378enTp+eV5fw6Ryh+//T056+qNIBmuZic+JNHbNPjZhmjO8am6/X6fDaRL/IOcIrOYn85X5pVbPdJf6J+MhHgRMm65738tRU+npXM9/ZwQyh9k6hH2hzH37HLk8j0x7Egb40Pj2tksA+nj48fPz6fG+UuBfUUHRuQ+ygGAasnPAS+BJGR35OhpmeuWpk3U7NRjucu1/qICwE19dwAqvmZ+GuTVY6rVzo5qWo8t3s7wGd7bHHe3x0z2H50zFjLPO+473HnuLYxJHGCvpukm06X/sc//nEcx59nEpgMv3//fhNIyFRb1ZgA327GEKfaLbMfx8/An7YyY+NqlGdI04oJKe2kHA+/c8bEgEYjaYEqQXXlxKYV4PPKZsjJNn01cOhV1tUMinIyiE3yNHDCwE/n8Gy66ZAyxS5JfrjBAYcAi+Nq4DmtuMUGkrB8los6bjO2pgten/S1s6v2fwPRPq/Hz8nf2gtt20MkpGl16MwE5zh+Aldu4TSAGn3ET6eg6fJeZdklDPLkVSgCFj9YwKTL8rHBrDKSd4MBA2beY7KMrpxUAvSnxMrElRX6NlH2hK4BO/LWVjLoT5aJfBssUs8mxqUptuU7y3py2nKFJ9aTvO6ztbmjFrN3RD36unMSy0xxh9e4ihn+2his/M1lppjAtqf40MbXdrSLqynbYnTKtrhLsl03+1zZauun8T31eQ+dBnz/43/8j+M4juM///M/j//4j/+4SYBt5pAVnOP4+doAG1scpm0/0PF3QTvUEDj5ySqCD9iGHy6Ft4FnO8fRfzvQ/BCU8Tr/b7MllqWDtcDZZqZuO3rm9clZDFy5TUFww9UNbrcx8Yen6bULx3G70tH6Jph12yzXDjm3FRHqL4CNq145OO+D+gQTnNET3HGMInMeUKFcrGsgY7uLfgwcPCP2DD332mslvDKdsSVfltXXvOIe+XPPoD26cKJhf83P/XR1VpCiU9pt2vIDLy5nUBa90M6Y5FoMysq2ZU07nz59Ov7444+b4wvhm3GBfTQbZt8NlGbFkyu2U8JvAJXkuNmALHVKO6f+CGK5Ym078wqqYymBVfp1QuVn4kPb9kt9+8MqhrN9rqxYHy6bfnxu1ra42pKLrTdQ3ORq9XmfPuRz1SGOl3VjezX4Ppund3Jz16VtT3NialugL9mu0mZbSKBcEzHuGmuYB+fz1aSNfLf+/D3E3H+WTgO+//k//+dzIE0w/fr163Oi9ywmDn4cP1cFqSw+jcezgBTEDu1gZHIbLJvtlSQsL4Uy8LRtnBCBo0HpRDvE7uDrQW6rAy05eSYcfjmrb6CRfB7H7Vabz9vwLJNnoJfLn9v3eS8eeeADB6mT+7Ej12GwPI6fDzJQVgJZrj5zvJxoGAyykvfhw4fj999/vwGtfFKWSTXvVCNvsRnaB8eOug9ACD8tqTpwMWk68HD7rY1/7rdJVNqOjPz/3bvbJ2gJ+GJXfuLS20D5n6tEfA9b5DJoC++20ySK6dB/iK95aYkx17Llymt8UtaJLrqNHn7//ffj/fv3L56ajp387W9/e3FmkkcZkmC9KkxqOmGion87Nhk8Tb7fdJjxJtiibfG75fFKGW2euyLeWaGd+bsnBG1M+f8qeU/EuEa9OAaTD06gQgbW/t9HT1rf3JKegO40aQ9f1mMAQvROX02d1QM1BjhNV5Gf/5M8IfGknHGq7Ro4hvH6ZE/hux0Zavm5+QnHnj7BCbHjWerFT02e+JkH2o35auO/o7teyxJGGLiNQslUyvHlnG07ogV2H753nbODxOuciXpAuCrhgMo+2myX19l/48sGap5XQKwB0Al0tlUUJyz2yXY8uzuO20P5BpjR27t3724Slh+ymYyTq4IEKi7Pg/gEPKnLs0yU3bNEOjydivrgqlkSdpw59kxbolPz3E6CalvFncaafhHg3YIrE5z5Z6KxbdqO6BtOMBlD1qF95Bwd9enzVlzhzWpw+J4SBGXOGLZJUSP7COOS+7HeAuQMiEmRh6vC2fpkmazqWe9J9lw5b7GmyeN77M80rfo0YvIwADDx2AhBO+OmechWdUAtk/MK8Hn1qQEu+jh9It8dE2h/3jUgtV2DlngTqwwu0pfHptkU5TY/lM/X2zWOB6+1usxRjgmU2St6nFDQjimj+3KbExlUmd8VBkh+sqyTP0+TCRJjWbs31YssEz4i79EjZdnhhpYTdnQa8OWw/OXyc5m5DXQDgN5KNE2zgJbgOHC7gEwebCQ2dG7trIKwV16ih3aoOHW4othWKRuQa0baZGAS43XO5MxPM5K0f7lcng90s71sy5MXPnnIYJxPbgdmxaRt3zb5+SBDZmUk6tqOxHGdgmsCWF7l4nf05S96oH140uLE51U/A6opQNCmeCQiNjaBgraawgcQ2gqawf/EYwMb+eSKq32KbRMkc2JlPkh5d51XpThxdCxpvsdkFT80QKAfsf3w59W12Ay3lTMO5pVxLAAjux0NTE+6b2DdfbXkELIeGU+YfDwpsm6dqDjBWwGb6MJAzADJPLvN1VlQ6iK8ZawNmPIeOeqU/VEPTRd8e0Ha8OpZxnsFCuhLkw7SJ/vnGNjuafuUYbcaNK0A5xpXoDLR43hwjEPTpKPpg5Mg2inlonwtl+b8NXXmvOi86fhontqRmNAK7HGyy7JeXIq8Ew6gDBNoPEunAd8qmE9BiIKvkjLbDxnwRdgWHO0MZ1Y12sCuUHMLppZpmgk4MK/4aNeazGeXcq23ST4CPjsXkzP7tvOlXW8JEMgk+O5ARfSWugmcbRKwWl3l913SNNG+CcCsN5Zt/E3tt3GNvNMMeLKXlQwtsbPeBPgmINdWZNp4tkTUEr7r5JPBOeSVV8s66cwTgOZHqyfevCXH1eskKe9iNADa7HgV5NNOi1fkzStcLk99TTGEdVt8mfw6vj9NrGzTBlO8RvkmkOIEyjJZaSXIayuJDUySyFd7EpmT/eYzZ+Nz5Jj6sf0zf1geXnMuY8wO2TabT5Ea4N/Ft7RLXZMHTghWec/8Tz4SXdq//LeLly2Ht1h4DzXd53vrdyrf7p+l04CPhjAlqgYCG0Cgo7i9kJ+sPI6XiL713RCwA2aSG+v7MCfJDme+2iyAcvu9Q+TX8k2ri+bJIIfyEwSRX68E8X5AVVZj+N698EvQdr1eX6yY2GB5Voe/HhCHjA0kefIhCYN26oJ1MssOQPAYNkdhMiAQ4It3PcYMyLTrlU3y3J9XgK/X6/PrW7iS6Kd9j+P2aXCOB/2NYOTp6enmTFmIs3EmLOphmkFSRtoWt15X5QmQUi4yNj1yPNgOj4e0iQz553iQaNfWD1chbQtcpc+45j5Xu88mg5y5S9/mrQV0jvm00syy/E6751nXVTLhypi3J6OHtrLJ+y2RJRb4fZdeZW0Ue+EKXtrheVXnHYMwxg+uOsc2XYf+FFvg7g3l2h2md8zYrXDS9sxP+qTODBa58ub+wgt3VZpNMM8ZtDsvtgcg7ZMGUM7hPJ+eei0225+nlWbnM+df4wXfT/0WK1mf+mmxpsVI4ibKysm4bfQ4+hnHFd0N+AxKSFYkjSKMtoPqVoS/u53jeJmQGATNWwNwDgpu233wWltZcCJtf+Ql/JJHBrEW7Ft/7TsTER2Exk5ZkgxSL8mLZZ3sjuPnAxFPTz/fQ2eHvVz+fJDDW398mMDb6d+/f7855O4gfrn8fFKTv2DAp8EdxKhnvzOOjmydsh4DenjhjDr85374oQzhJw89PT3dvuonCZTg1a96if9YPz6HR74vl8vNL5EYKNm+ms3Z/q7X682LiqftSdskbTHk8WDSYCDPz/1Nwb6NYXTB1eW2ohT9+Tebw7snoWznw4cPz3UIvP1UOW3mw4cPN0/XOsE0UMq40eJH+OJWEtvMd28hMrG4//bke2IGn9jn9ejL7/kkr+2VSQTO0ypZ/CfvwOTkhitu1lvLCbFfn7VMvKLNEtzyrQReGW4ys22OawMOBB1ZdWtgJ7bkCSxjuH2P/XtcJ8BDvRto0Fa8RW6bbBiCQJULACnX3hPbYnOb6FDH3lViOeudMrCPMzGzxd8G/s1ju0bAl4kFJ1lnJhamux7aCDNtML28bFTNoLsSmrQywIaq05aXdCdqhsKBnoBV47cZGY2jJUHzOK3wsQ/qoslO0NfkmgyeYMrnmyxXW/WjcdqRmEiYkMg3yzWAwPvW9zRztQwGQdZJe3KO7fhMIfuL3G1C1GxyNTEw4HFf5otAm4Hf5NdhNNuZZqOkNjbmyZ+Tz3DL3Dpr20vRi+2t+S75pc1NAdsrdOkr+uXKFQP79Xp9fhG0J7KWm+MYsE+QGKKtWl/5y0/M8aXeGX8+HOP+U66tCrkOY3cjroQ1YNHejcmJjBN6eGtHelKXq9hJgPQVJ/DWToiglfxl3LmKbV/LX9OZYwlttPmYecz97Hw0f+Ok03EocjmWMg+3cWm7HCT6RANZ5MvxnmNo/GAgFRk86Wt8OYYbULe69sMWn5zXqLM2qWC9Hc/kbSrXcI0nt6tjCY1OA77Pnz+/YMord7mfxEFUbRDlZXTPEg20nPAa4KHRE1ieSUCele9AjNuZgFRD/gYEU9utXQI6AzXem5Il+bbB0OEayEi91VNtLVj47JC/5/+s4CTZWy+Ui3zmAHFm+tRPAhQ/p2BDvpxws/2W/loSbPbo9pJUyQNXgsgjk5j5yVhxPJgoCZwzxl++fHkx4eLTr/wpMgdnEle9vC3fdBLebBcEL21CQHtKYv3nP/95fPv27Xl1N7EkfUQG1+XYUj7y2UBGVmG9gpUx4SuKCJzoiyT63u+//37zMnjWTVmunERWj+suntEGmeRYbke0R1+n/tJ2yjfAl3pthY8r//bN4zieHyjL8Y+2Yks/pG22ct+/f3+2GQOn47jdsm3b17ZrAiLnD66A24/bJIrg1W8xYDtthY95oY0xdxAYSz1hbE+ZZ3Jj8GG7tf+5b/bHNvgLXV75T/vh175HuVg2RD14lXKSwX4f/U5AjrqKLTYwxxww4QrqzHUyPvfQacBngzqO/iQTDc6rghwYDmJbEbGCCPCaEZEMJCZDcbtekSS/DfR5JsF7dHAGoPZkmgEigx3laCsH1OMETJt+uEpBI8/YhRrgm2ZGqz6bXC2gU9/Wncfdup/G2mDbdbzyZzCYvpNAW7C0rB476tMyNT3w/2kG2sqR19Z38+OVPq2vlgjc1soOSPYVy+B3DlJOr2DTbq1L9mMZWcbbgZHfqyE8e5hVpubDE5iKXXMbt+my8fjjx4+bbfSQJyA+r+mVoNCKR/I1AT63lf5Snit49ou2gmMepvjGlbmWIEMcD5ZtthB+aAvTxNB6InGFr22/71b4Wvsej6afxtOq3SZLrhHkBFQQXHu1iX2t8n3j2/HdMXuqO4El23jzsZYrVvpyH2djXKMmD/mayk/6uofufkq3GZaZyKeXwlnWydcD21Z4mnFPQk+DeL3OL0BMuQas2orBcdwmKJNnL5TTumrGOA2s77Wn1lpAsR4pF520zV6ag+RhAq5+mA/Kz3bYHrdUpsAdXn1Amm2zfrNNk4MGxz964MMmbHcn62oM0197DUpLTpax/T/R9Xp9MZMlv5N+mv6op/SfttvvRtsu3B7vNX1yyzHntgKU2gMSHCc+RNQeOGvJymdFbauObQSi9uGJXIaruNY9//fK0g6oNT683ZUyjkMruzVvK6DquO86bZW81fFKEAGl/cR+2GI22+BEwYD4crncrDBxha/pjnqe4uvK36x7tjuNdYt3/DRvLTZ54SI+Zt8NIM5iTotxJvqhJwT5zrrX6/VmxTX3PIHl+HOMmd85Nr7WACv7jM7bxJn6bzbgWNHGn4tLqbMbO1/7lwK+XUAyQxQ0gdpCU3gulaZNbhvlmo0sy/UrQGRAMykxKzj8zna8ZcmBnVYuaZBT4vR9y5D+WZcBJcC6bQe2/loCa7qlrAZGl8vlJiHn6UkGzfDDbZrp7EiT25/tXYnkk1s3fGdg7K9tO6ad6DHlcgbKzpm+2naZA59tx9cSVLm1xQQxrQCfJYIX2gET6BRoVgGOvsrtZL+2g9uu1DfPmXFLl/0SbPshItYjbxwHxg/7TOzDMvJp8fZwjJMLzyW15MAx5JiQbx83aHbEh00o2yruUTZOYFYrapTJ/UzUbLLtijDmrIAqbZ7XCDBiC06W1CPjcvP7NjaO18xfjCmWvYEJ2pz9eUrotGG233TmMWoxJ58EqbSPfPfKXcr6hxWysu2Hn1qftN3Vokiut61W37euWn22045WMHf5CAt10vyL5PzJcvxO3+MLvydfJXGHkG3fC/JIpwFfox3Dx3FrtClrgLFq30q1sCtFm7eziqKjsl4DZM2xm2xTgJ3us81pwM8k6tX/NrrWjnVg53SQ2gWzfF850tTO1LeTha9PTjLZHmf8rbzBg3mc+nIZ87iq3+zfejTomLZDKEPrr+l+Ktv4nOo2nUzbbJyBc4tn4tP9+HNVr92jvndxh+Unflxvum4QybaprwZ4k1hakmj9e5xbnG7xZtIBeWnXmr1P5Hi0iiXmyX2swN3Et+2x8U9bbfY5ydK+T3F4xQ/jFGU1CGwrqa1P+hqPfnAC70lwGwvGacrSdnwsX+5Neco05UeCxFVstb20nOFc735WtjzlMfY3+ZfbOet/KzoN+Frjnk1R0WTOSSjfieStDBoHV158+LYNKI09245nyAr1W92bgdPBwrf7WwXW6b4DruWaloJbsGuyMUGcSeJtS5tnaPLrAVwpo0481i0ptcCRdtJu3luWfnPo2rNTJk2/NmFKJrmW9+M56DCwG7BxJchnz378+HF8/vz5+fwVV2T9kAkTv7/zrwWH6CltG/BZ99Qtx6IdAqeeCCw4s+bfLog5YTlR0Y/awyiRkT54vf58TUzzMfsvdcKyBlO5317H5NUdfj+zMpayfBiJtuRV85XfO6a63HTAuyVfA8sdYOO9aas29QxeqI9JRh7laJNLA2Xey8oUx6sdI3Hs9Sp4S84tjjT7945LAzRuiyuTBpX0ceou1xIruTJNfpxL0h5XM60T6j7UgHNbsU/7pAaeUj+yrJ525v/EGJEtOyjkseUctknf884Z2zA2IE0xzz7e7CCf7Y9vJ4ie7gV9r1rhm5Jz+2z3d3V5bwJDra6TWAMVadP3mowZWAeZKfhNCaW1bZp4bbI1gLfqd5LVgddlnASdGB206Tzkm3pxEjxjsAR85OPp6emF4zR7abNN2wDHkNvA7KsFfcrMQEE7ZJ/sK4HEIKnpxICvlSFAYNJmGyu/cQKYfHrHG8/CtXY8BpwQtC1t2g7vc7ycADym7tO8N3l8rbV5JoaYVoCToLmd8WzHCNofiePQ/M8yeSWGk+yVvM0PVzEpn07ibo9880hIG3PHx8hjG7d9pW7aOxNXLR/j6bQFa9k9rgYflNH12pEhP0ma8i1OMi5ONpW+LWtb4Gg5y/JbninWWXcr27PPRzb6z4qarzsOrXzWNmDeQ54cH0ffRZqOOOV/jveZ/El61Rk+EhNdgr2Ny+eIyHzaoHAO5lk9mpTMulGE3+vVtgLJfzNIzlSI8o20begNbLZBc52VfNZ5e8Ivum665cqT9Rvi6hD7stOkTc5AW2Cio/iXJthOW1GaypE45ubBZzra+E7B4OHh4fkFxdMWWeMxQTavicmYcnVhpV/7jMeK+vXKC8m8ui9e5/3oqr0EmHVXWzMmg0DbR/rNL42wT7+CJ/cST6ZAzDYoV0tsK12lrgMs7a7ZUdOZX29Bfli2+ab9dAIkk1zmpSXU4+hHItrqXqvbwAv78hjab/idejpjc7v/Se0MnsFJ49F6abqgnXnCOLXjeE3bYgzwfQMRx+fwYEDIfEjQQb9yO+7Dk+5p/DwWzVe5EsgV+/CV9ttqFvXZdvH47IDrcLW75c9Qww3Jv859fiUM2+Ynde/VaPvc6lVIr6G7X7zMZMoB5qy0/SzXlIApAGcLHvTU9fZWc76scqQd8pg2HbC5xE+jiCzpi9uYbemeiZ1kZ+f1JlfIwT51+B5DOzJ1n/9/++234+PHjy/u88kz3rMuaOxeMud9JuJsteaXC6ZgkIcWLpfL8zZE0wN1wUTQkkmb3YU3r7y5bn6R4vv378fXr1+ftxRXwSc88ZyLAyG3CNwOHbwFb/NIkMSjFS7PdlY2xvvZDspkizq3z04B3SApW9l8aTXlJhjOGHmFi9/5/rQmVyYRbId8NrDY5KCNN19oNkqZyJsfamkH2jkpyz2Wm/g5jtvE3YAU63B82tZ6A3L83uzSMYB+2PRkwDXFB7bdZGnl23XfYxwLeDHotZ4ch9yW67ZJC3mZFlIYP6y3lg+st/iIrzXAZ6B1HH2C0uRlrl3FRdpLvtM3+RBFmzDwe4uPLQa2fokFMjZczWMc9jEA5o/80hPHpVHTR2I3yW3Qj11umtSdobtfy9IGYLUdtUpCk5JY12VosCtenZhWfbT6DoZtUFsg27W7ot198nYvtXFpjsD/W9kzfTgxuE1+GhhRzhVxjKb7/N7st/FvuR1AVnVXPEx1z9qn7dngYkrKK/nc/19hW40P8+trrZ2J31+hprvJ5nb2xfJOFv7z/dQlMGI73pLnroLPTZOXadXT5WjPK76tqzZ+/D7FXNvjasyt98meVvKduTb1v+L7DJ0BnQ38nPGh1u6K3Edru43r1NYqdjTA3uqf1anzRgBaA7sTaJ4A01la5YMVr7zW+j6j750+XxsX7z7Dl1kLka9plYT4GgfObm0Mq7Zz304yBZ18b8vn0zUGWpJXLWOEXgWZZjo0Qq6OtUfR+cnrXqr3yqWTTTuPEHm56pC+uFXr1QTr2vxRZ5kBtT6oc9qUE5fHmmN5vd6uonAsfa4kfVCPHPPL5XabnDp2+7bb6Cn/R17/koDrOGBGHt+jnqP36aWzlJcrwOStAYMGcCYfdoBLPa5OsgxBSuwj5b3iSv20gE19e5bsldvJ1my/Uzlf4zhM/jCBJr5vz4COsvDnxhgjm15NjGerLf+mzyYr22Tb91ADLOSxjUvrp8W19lvBtkfzQPth3HQ82P05zjOGtR0kjkfKc3XMOnfO2tEO2Fg++38DGNN283HcvjrFZ5Y5hs1+2Q+3XXPND729e/fzN7Sz63Ict0dmrPMpp2bV33L7u2MSeW1jPNXheNAefazHfObTOWvaMj5Lr1rh49YPk7STEuvlexTFJdP2Z6FaYmv737sANsnFfgycOACNh+PoZ0waL1OgdrBj2zQiBwIvvTcHI+Bjf7lvntqWtPufgAbBb3t5Z5OdsrKNto1FPXkrwck8ydQO6aCdcizLgMjtXNuFV7d9jqeR/YR6ta35Psd/SgYMlN4esZ48FlMCarZv+5+C+nEcN8GOAIJjaP2wb+vGus71CTCvYgPHw3o0Oan4Xvuj3E9Ptz8I33TMJyVbHxN/lpn9Tu1QZ00P5Gu1lTQlrWa/03f2Y6Lf06+bXVr3js2rHNHGZcohzR4ZPwwmDMBXusn/06LIRPadncy8z3JTfIhsrMvt3SlW2o9bDLRf+4hJ3tCQ93Mynrtt271zpMcmn9zmtT6ZF9s4tElryJPsdkyGdSYsZBv6lwK+IMzv37+/ACFtH78ZCAXy+YaVsZkXrg7YyFt9G1lrsxlmq9/6a4h/MmoHzRbEV9vkjQf2Y7kMAvPdAMBtZIwy7qHdGQI7n53N5SawsLILB9gm/1lglcDBlbyWtFvQTbs8E5L7BpFNZ9PYTolmFcR93+37eIL5ObsFsrJL89oCXKvT7KIlcrY7PcST/iZ7yBmclR/uqAEDkseHCX+KQ83m3FaLM6nbksxE1IV3U1bxxvKu7Nd23wCNbbGNF8k6NLX6q6Q4jWPzD7fva22lMNd4r63cms/UmUBES/qT/gjEJpuYcpd5XMWX5pOTrjzeXrigzo7jeD77m9eseZyoJ050qeeWj0xecbTvWX/Nf0LUEXc2Jn44XtYXaRVLd3QX4GOgzTZElMttvJaMpgFqQk0JiW3kyV3+uHUGmMqdjJjUHKUl5taW+8vhdNbzlq9ldYBMP17hmwBd/rwqFf5WSam9+iH33r9/fzw8PByXy6W+7X/izTM0ytwOw/qa5be8brslPh+0dXDxTI7Xmh15C8j6jz9wHPik+BTYJ6DgernmFeBdWxx7bn24nOWayD7QDjTb19N3dNIOLCd+8JgE6zhIrx6OCT/N/8Nz7Dr9rLYtV8CQYzQBxvQZ3Xjbq4FZjruTilcoUs48T9s/LMuJSivbZDPvzTYbPy12GSC07f0VuFz14QTqsZpkO47bIyNnEmvsqJV1n/7FjuN4+VOijB+UhTrxA14sx//jc7HztlXL8itgZFlYpr27s+nBCwZTLGMfiS3x+5RJvcSUHEdy3F9NZtlHqOUh9s0dtvbOQbcTsNp8u2EL5+Rm16t3AU70qvfwMSEeR/8tPgdJM03Gp/tsL+WtgBZ8W3CZ+jHYM18cnEm5O7l4bUock87OkHWwCzoTiDa4yHcbomVp+qY8uwS5GquVjdyjq8ZDk6OB35Rpsqzum8+z47tK5GeoBf0zul7xPl1ftZf7r/HJFcBiu25jGiNfT7DdzZZX+j8D9la15flQAAByj0lEQVT3mr3Y7874RvOJVZ9nyW0133iNfZomn7qnrj+pvzNAcfX/Kp7u6jX+Vjp03DwbL1blWu4xf5Zj5Q8Goax3D9lfV5hhwhpNj5O8brfFhglXTJ+7fDbFh7Nxw+0bC92r81e/ePk4+rYJGWszK86c3B7rOimnHPtmnev19qm3HPxsv49pg10ZN//a/rlBUPiZdGaQtHN8ykqdeoZombx9x9kEH8poq3aWi/1mJes4fr7BvvFwvb48NJt7Xl2btvy9wmfjb3perQqm3bZKlhkYdcj2vDXrdzFypYg/P8R27KxtKzsrxNNZNMoRHZhH+gPbnnQy6T/30odXgtgH+WhneNgm9egtFx/Ep1+bb8+s7R8Gcxm36/XPHQKfVW02dwZ4klf3l2v8bWfqm+/5zDhlu5nyc1Vj4iH6iz2vVi2t3xYLoy/Hecu62malHU82McXRtjvhHYld0mOb0xi1+JpyWQ0mMZc0fbLvfHr1m3VYfgUuLM/Or8MP41FbXXOeiYwm65LXGM+mYwLWLcePq4O2Yffp16mQ38vl5yvW/GBb46HxQ10kJvtaO+NHWSffbNR8x+PFcszd99Iv/ZauAypnJfxrPyvTDJhlTGzPCrLz02jyvpxv376NAGRFU8I3X40mMNf4XhGXjxlcpy0bB2fKkgCW7cevX7+O50ka2KbhMoG2QNZeROn37NFeLIu3pVvSoXNNOm7/O8n5WAIPIlN+OmIDWC0ZEFiaD9sytzz57stJFgf/2Ed7YrbRaouXQXzSrSdw1Ad5Xo31ZK+NRyZiTyh9beqH/mMbb0Qbm/idgJOTCP83eHl6erp5vxcBm7eordd895boNCm3XtiWQUD7dQvr1zrKtabTaYJhQLE7M9Vkm+LWdIaNNuEtstTzdutx3D6ExE+3bQDrOi1HND9voGUq77a9OmY7aqCvTUZ5LKH112IJaQUyE7M8sTRW8BYoYzJ1QbudbLaRF0qcqzgGtvnmA+zXzyyQb5dvOnW8b/a8o7t/S3dCtw7mFKYZGut5tS7UBtflmjNMBsWfSfPKwtR2gvh0BsBGvkterdwZmoIrk/zUHmVcgQ7fy8swaewOgJanBczWh/v2mU4nNuvBgcC6mfTB4DXZVtMdySCQQafZQpvtTv0xUHFcz9iVk9IuuO0oQYrJ0Pr3Kqb9sQX5tqocit05kBLI8hr10a55PPydCW+ymab75jO0LdZlYk25VbttAsb2p3Fmu5MNtDo7e1nZXdPb9D3UVlabbLtk1vhtsrRynNDxrFw+OYZZfW0vUW7jfw8/zpeOIy2etn6mWOf/2zXbS4vjrY77PhNvnLdbP8y5LkN/Yju0lVVcb/lvyq3u09cm/5z8ZPd9ZTvkgX3fu9p3GvBxGdWrXm37hNsQZNzBkAGeg8Sk2rb2POMljwSY/Fk2PtZtHnL4Mvy0maV5o7Ex8K2U3watUTMEG1Tkj9z+QXAH4wZOeI/6C/DgNufl8vMhGeqCOmNQam8id4CLbrONxYP4PNw+AYPw5EQx/cxN+Gqg1YlocjI+dJBr3K5LvwQ2TDAtgIXId+zMv05BfqjnyGiwuNqypT21gMPtZQMjth2dcoWTDzCl7R8/fhxfv34dwV5+cYWUtqnfttVGm7lcfr7/r8nf3oN1D2gJ8Vca0o7jYsaD/hEePNa81n7+qSV8jp99nddpf1ytOQPkJqC7WtVhLGyxnTbcDq+34yINKFoHrmOeosc8dJgx/PLly/Hly5dnu88YPj4+HpfL5fj48WNdpUl7Zx5YTFnKn+881M+Y4XHzUQZv/VE/1gljHHXverYLjo3BFu/vbCr82o74nccXzCuvcUGJfhRdJU5zEajlhaarFaCccnw7ikM9rI5WtEWE/O88RFl5JOQMvWqFz0ZoYQgq8n8MYdpDbw7bnMfEoME6HiAvy9NwW5uNR/M3lWs802BtuC3RrpKMqcm1q9/KtECVJOW26bB2Sn5fjZ8DSrvua013DKQteaecbavJzP4sS+OV5acEeYYMLL1S3fzFNrWjlb1N5G2cBvpyvSVg3sv13cpNa7PZku1vSiD2O/Pnfn1tGsMWE3n9DE1xgddaTJxABb9PNu97Z2y02dlZm5v05PabHlexcPLPFX+2ywmo8f9MMHYyWwaP5Yov56spfp2JYbvx9FhO9Vdxwn0YtJzNrY2HVd3mw46HLJs/Lh7cm1/P+seujTPj4rZWNnxPnAm9CvCl01XwpJKP43gBHGjg/L3a0EpBq+se+BBXHDkjIB/egp4CVQN4PFfSfiu1Oc90aNx6mH5OKYbr8yQMOOQ7qyd8jU0DFu2cBtvMjDdtZVWn6dx6Oo4/QQQPg3MFh/bBFTIGUMoe+dN2Zln+3VLzFX5XY0RwwnttIkM95ZOTDK76NNDsYO6zGy1A53rbAqIeHeAmcNp00Gbj7odtvnv37vj48eML/fFzlzRJtH+PvVezXS9lvPrBsZlixmT/07W0eRy357tam5yNezuxnU28XH7uThgENR8nf7QP6p56sY1TP1mZtJ2ZzM9x3K628PyxV8BtV5E/5xk9Rs1uJ9Dn8Wh6yX2uLFFXu98D5rg4R9jPzW/KedUu13zQv+2cNRBln5js2b7Q4hPJcYP26vZbzAlNW9OxdY9z7JX20WTheIa4A0S9WWbr0LFtwgCWcfKRM0TeWh5bxeozdDfgI00Jgw7PwfKypb+nLdY5jnMvGmwG7fvc4uHgTIk9MqwAH8uF18jaAME0YDEor0Ty/XcGot6/dzLwNTtC7jGop90WpMLn4+PjcRzH8eHDh+PDhw83bdlRW0Bn0nGf7Mfbbm2M6WBnjgGwbQb0Nibhsenxer3ePNzReOOWJpPmdEaNOuP3ltx5jU942l53gamNr8eI7VpG13337t3x4cOH43K5HI+Pj8fj4+Nxvb7cqm88Wc/NP9lv29Jp7RioJEnF7r3i2OKa27T8tIMcrWj+Qx/m6ilt3WchuXVj/hgrpqMMrZ5/+YQy8sgDAVdrO20yIacd2gIndXx60v5F+5jswGNtXjw25I0x3EC2ART71D1k/3Uc5HXaI0FgFkPy9/379+djNrttwFVOpK75Pl2+ycLxwbEoPJjHJuOEESbA5yMpOdZyHPNPLjpWOC+wvxZDSdPupXVh/UxxyDposdc6id+wLtv/Pwb4WhJxmXavgTveb0Y69dX6m5LYxIeD3Rlq/EwDOl1LnfDawNVOX61/113JQF21wNqC1Bkd8LsTEvs2GYSzzqQH6o99t+sr/huoag7L/ydQwDa5usbyU2KyDBOt9OP2WzJzXbcxjXlbUWPi4IpO0xsTKPuZzh/txtH6mGzLMlnW5r/NnpsNEki0lXGXn3huNtWSaAMlbcWj9TnFBPfNsZ18zHxOMmfy2ybZE6+viTkruWiPDfDt4l1oSrb3yjD5qu2o2d2UI3bx1XppQO5M3dRvW9xn6p4BKmfsdgWYWln+lvWOzgKphj9a3/5s+dk5YtXnZA9n6O7XsuycoyUOnt/jTLgpY9WPk635yuyEy8wtsHM1LnXtMB6YaUBasGSQtKxNBjtuHCrluPrjlcgm53H83CpqKxiZMU3jxqdEzd/KMB18ppUcrvZcLj9/ri+8Pj093Wyj8O3pbZW2ydACEfm3bXGLi/z74Q4mMbbdEua3b9+et7+5pdv0Ndn0lPAddMOjDzyv7IzyU1bWbdt5rJvtEq/2mri1lzrh//v378eXL19uAGHsz/pNnbaCa9u0HdoHnWBtl5TFbVM/6YM7Gs3nTV7dcmwMP+3pUG7tpcwuCTqe+Rp/rnKVvNhuiy/kkU/5p147x209Ug/+lZ/QtJVIfj3GqZ82Wcfbzh7z+HLKhm/ySRlY30cjIh+fAKZu0g7t0CDQv+qU9lY7Yg3scWubMjRb2sX2KSfk/oqaX1FuHwloPJIvtvX169cX5drKH3l2/qUMTbfekXJstw9E58YLnETu8MK99Evv4WsBIN/bvZaM005LyhO1BMnBXSmC9wIifL0Z7YpX89H4asGY7bcBbNdoHPy/8UuAseOzOcoEpqYy7r/RxDfba4HdW54TCDIfkx21+o03OifvnQle3kqyLUxj3nhsRB4z1gZq9kuW9712lof2w0DLdri6R96bvzOh53N6yat1PflDs23XW322PluZZjP+3miyT9u4waVt3X1yPOyrTf8TAJh42sk11SN5Vdjbtbs2rBNen8Ad/+f1tqLnfpqOrH8vKLTxaWQ7aPUYb1axgX7nNqZ+Jp6mnYgJ+BuYnrGTs7bnGNl8Z5X7juPWHxJfWjzcTRjy/5k4vMrzE2Cz7I1WPL4G9N31W7rucAW6moO63JkBthO35ExHZCJpfZsMJGzMlnMCOS14t0Fuq3Us387jUZ/NmNpMsMkzyei6cRi/viT1uHrIV2yQ50k3zUjTZgA4AzQBAWegk/wEWi1hevzadiJ5buNAO2szNds6tzlXZ5B4bbKzqeyKzs5Qc30K8u2sY/SSV0qQeE6SCTLyc1xzr9nQtIq9StotZnjcPK6tbfLUJh3tvYBNdzyjlTr8HfD0S/sgLy0uOJZMQGZKlvnkGBkcsV6L4bEL9z3xsQIh6Te7D+3caxtjxiSOG8ub//b7zBMoafppcbi9Hol92355Brjliyl2pT7PijY+2nhMNsr6rNNsagXarTPXm0Aa5fQDgMyVq1yyivPpn2PQXs3E75Ns/D7hnMYX/2/Yp+mlLWDtYviKTgM+LzOScXY8Le83gVK/LXcfx8snedqPRaedlJ/6mv7nk3Vuh4Y2BSsmDfJM+drZpimwNwDibUDzYIBrvttSuPlpoM2AL9dy//v378/bHNwO8bjwexub33777Xh4eLjZ0qV+Mh7ehjGYiK54+LjxQj1OCbvxG364/dLeD2dgQH1zfBt/Z0Ee/yevDuhMAG37rSWlCXTZ9hiwvn379uKn6dhWdNYeQpoCZu75XYCWpQVrl7M90w/bw0Ftu9UTOSYhjrsT2+Xyc/uN78r8+PHj8fHjxxsdND0S8HEcOK7Tk/r8bgBBG4iM1Etrj/phHevDSXdaEXLyoo+3F3RTx8xJfofqKt9Ed3nYoP0CEylt5WEC8s328nQpbc4yUh4+hevdAOuxrbgHGNnPHO8tR8shU+6bbMZ6TftpI+1MuZ08Wdbp5zqbLG0VsgE+lrffT2BrogmAtfsTXlkBvhZn3WZizr2g764tXRuu7zlYT8zy819F6bf1Y/7yOcnANie+W9Jp9dsscTVoOz1NfTX+V4Z6Rq6UawF9RWdAzLTS1ozffJ1xsFVb7o+2M8npQDzxcXZsd45Lvla0G/Nca4E/CcIyTb7c/Mi8TMllkt1g84z+7LtN1ib7qu3mmy02rGLM6n/y3WShDbo/82adtT7vibc7H2r2sBpfl536aaD0jG5ZlnWskwYAJtt2uVVeMK+ruNFs8V5abeNOOcx9TfbQ/HIa+/Y5teX6EzU7mnzwV+z7bPlJH68Zt3t5eK19rOjuLV0zaqa5NedZIutyqbIN4jSYrY7v7cAIZ0GeRWaWtCIPwg5pBxiwDFfKeJ0zo7Yak/Lsk7N73p/49qpG7rVAknucYfKN8K2PVcCmriPft2/fXqz6ZPVsCu7tPFnb+maw8iqT+TYIul7//K1h6zt9cxUxKwZcXXSwzR8PdHv8d+DFemjXmzykd+/ePa9GfP/+/XlVJAebfUYpq5jt/ZImjiF5yZjyXluNJM+rc1Icw5bU2P/qxbpNRy2mWBbKm89m6/F7vgssZfmrJO3gfHQQe0g7fncfwQsfuPJKs8l65YrHalvQ4IZAbQUkpuTN79RFi2deoT2OP/2QvkidsG/zQH+e9NPspAE6b0W3HbGQjxakzspnGTts49zy9mTNK2BtB4568C5G+DV5Fc4gc9qV2uVJfrY81vK+6zfA6/Hn92lsG/+htmOz4sX3Wm4wP/xrK8Y7wNjoVWf42j2Dtik5NWZXRtCI7bdPGlzjuy1bmze3t1NuW+pviYq8TgDrDBk4HMdxA5ZoRM3RrB9vn5vXlI3Opq17kg27XW9JLuRtvCn5uE/z3gLg9KCAxyxy5rwVA0L4ToLkRGcX3Ca7mnQ2ydTutboOMB5v6oQ/ndfGjXps/EzbgW7jOF6O8cS/ZXFSmfTTZHWsavWbXCsbnsDLZI/h34maR0vMX+yMZ2iZKFjOOp+oJcVpbO1/Bnorm7wn3ntr2GPXtvf9xK35Nm9OtNRlk6H5k/XBxQMe7/DYtOM0k06azbZcwxxgYNiOKLV+mk05JoYm33M75r3JOsXypvd2fRpf26j7a+VbOffVfM19tTjU+t/liN15yXvpNOBrB//JyBQU7LC7rbuWAM4o/mxZ82GDngJUC+JuM7QCQw00rZK++7+3H7ZPvtsb7FuAD6DxeY4paTr4rwKZE0aTYZWcU4Z6bOe6HGAdNN337iGHCai18yqczadt6916cd8TGfCvwAmvX68/z/94VZT26TYcfCY/dkJaBW7XaVvkLea8e/fzha8rMvih/wco2I/a5Ic6a8mF1HyXtslVF9srfwXH49IAlmNJWwGbYlfG1K8YsRzun/enGDjRpLvWln3fcv0K8b1sE+AI0RfauTYehaBcHB9fP47bt0S0l7Gn/V08NTBY6clgjnVM7HM6g2nw0/hvO32NL9pha9Oxddcm21jteKV+i6VNN80PXcfUYv00rowNbHuKuffQacDnA8JMAI0hG4VXP3gv5IPPaaeBM/bVVsq8lD0leS7Nembpui7XDGH3HiQ6uB8saMAyjkDnDBBrwZOBiWSZ/Z6+Sbe+znY5XumD+pkmCR4PHmR3fxyjyZF46NqJoyUNy29Z27YJyUGf+qZf5A32aZu6cEJviW8VzHzAfmqP9dlufi3FRHAy+Q/l5X3zxvamgMXxCG/cOrYeuLVn3U4rxU6CsZnI6a3hadz4CwdMnJaf1I4CUNbv378/y5/PbKE/Pj7WSR2PV9DnuFWf9u13LTmlDl9f0eJS46WBgmavu2RFINT81+OxinVTm9RFjjKs8pbbbmNNfTHuNd4a0GqTI+auXV5Nf35X4zQejoO+Rv//8eNHbbvlB8rKcnyIZiLH5AZK+f+EO1yX/NHnmk+0GGqdtT6tX4/Nygd9n+PuHMCx+RV69U+r2fhboprIZVdtT8q00iaeW4Jpg3CWpjq7wN/IfEy6cJA42/bq2sqgp/qmCVi0clO918i2G//msBNP1u/Ux+5eS5Lue6XT19jjLgA0HdD+pzLktZWz7h1kJ3namDU9NlrFCf+/asOfq6A91X1N0G1JyW3f29ZEHuOpDZaf7rckv+q3tX8v2XemeME+2tOaK3uc/NV0Vg7bUrP9dt9bySseJ56a3Lt4vtOtaYoTrn+P/674cD+rtlayuZ025q/159fWbe3k+zT+r8Eqje4GfG31I9srucYfd6ZhE8myvomB0Stq5CP1p/Nokwzsx/01XvLpvjkYXHHhIffUWwGL1WBeLpcXP/68Om9Cnilbk9/XqOes3vlN7hkTlmM7XmFJnbTDFVyu1qQsVyt8DqSdC/Fsy/cIRtJ++rxeX/4sFflgu+GX/3OVlecnvYLT9MH/2z0ntjbWHk8/aNGABfnPp3XT+rte+7nHtpp9NlDzuEDbYlut1rEOeWmxop3vYpn8edW0AQbrqo1R00X7FY/Uox79G7dcfbcOGY/yf3uYpa3a5f6PHz+eVy6n7W3yswLeqwTaVq2s/8lek1+s6/CTV6LE356enp4forL8zT4auF3toFiXu/89FmzPfuiyBhbNR3egY9qVcuyzTzFGO746jntsqVPHtJbzuGLfcgh3w1qMpO9aruav3oVo8u9y9pQ/rC+Tx8C203ZPpvbuBYJ3/9KGgRYd8jiO5x85puLbttnq/FmonRtjAk+Ztn15RhE7MNQcOWTH5wFdAqNm+I0mMBY9theLruqSZyeNqV/KR3kcaCnjbssy96btAxrzdK6xAT3yPAXYlkAaD7nnRNmApPXFxOCE3s6jNaK98nvzH/Li/xt4sY6caGmzE7CkTnytBafJpnzffswytjXLSLksI/XTxvA4+hPC7ZdGGt8TEDNRD/zZssRKUtoj6FrJbX9KXG7JchVrk0wtV9oOz82GUib8TD5KfUw2NgGe3PPP/jE3ZCs7CTv6MOCjXv3e0AasJt9d+QnLTNcMPpzTWP7Mlv4ut7RxbbbL/qZjNMx38ZnoscVuy+nYFGKOM0+Ug23SB9Kmjxkdx0sAaWDo+N3IMW2Vf8/E6tbWFEOmMveCveN4xVO6NAIGY973axAmw7XgkyO1fnaCt3amFYyprVXiyn0bzgR0Gv8rI+J1O5IB9S5J29EMhPy/+54MOgmG9xs4MY+7IOX7ZxyqgQjeWwW5Jr8T3O5VPRNom+z1DBC0jJPs7WzqKuhaxrM8tHFr7a7aa2PJoDbZRWzRDxFNfLgtj3EDcwROLeawPHW+s0/WmXxt6sf3OBEjEUQYBK/43PXZ6kzgro3HJOsqppg/xiGfW0wMyo7AGTA2JU/bIuWf7PIe33Gfvu64TmpnGN0G/1/59ZSP00/L6SHnDz5lPp3/nPhufLV6sbnGK3ny94ZZ3A/7ig25/wbYbBur/qZyE3Cc2ml972zd9CrAx9WmGEieYOPSbGgyBM4MaDSToOTDwMpl2Nc0+3V7zbiaoaQsfxon17lKxV/xaDpYrYCS+HRg+s6M6Onp6cVDC6aUz2H0afbqYMugQF5zv20Btb6b7r19diZITwHdB3IpA1cH2M7qLevkx2C72R/74/UWBB0wJ7DZgiz7pzztFxfoRwyMlot2ZR7YTwNIU0JsY9nAVivXVhZ4uH73dnwGb/pJZPbqiPtuscsTxd02Z/Mvbn21bcVGLZ6ttio53ow5oZ29pk8DRZcjeLWuDJzbqk8D4S2OUDauQjaAwbIT6GuJf5VrWh3f9wq5ZeX/UxnGdfPXYhj7Po6X76RsgMa+6XeI0k7T5i4XUufH8fJIEGMT+55yavMZA/ApTzgOsQ4xy2rLdopfjV/rZYrZU35pW+xNT57M2/fO7JSS/pL38Pn+NKCkFTpuhpEyu4F3v42XKch60Ka+KYMTuKk5Ma+v9LIKFDbUFYAwr02OiX/267HaBZcd7cYw7a3qT/ptbezKrfg7Y3Os40RksGD+piRBvnc+dFbvUz/T/Z0PndHLPX2sguAEHFpbK5tnACVNsk7xbZJnihet75X8jHc7WSb+Jx5NqzjQ+JrabclwRWdiT8CkQZ1Bh/XSZFjF6im+rvh0mTPx9YyN74DHKq7t7HWXU87IyXYmG5xsfMVPq88z3a+NN21Mfe9MDG1lVrbTzhRO7Uz83hN/VnTXb+mmMwrggZ9+y5CvwUg7foFo2plAXQOAjVrdBtTMfyMOJJfcbSBtNYy8rEDYdG2S18bUAoWN73J5eaZwRw4KLdFM9cxnS8LTGOfVFH7RMdugvndBzudSmny262Yf00q1A1RWI1pZ/n4mk9Uq+E5HEZpuV745+YVXB6yXZl/tzF07S9tokqEldJdnAjD4iX6P47j53Vxv/bhdrmrsgIPHh7sdu7Ny1qtXWSlbk/16vd78igu303J/taK24m2VCPPZtrtJDeg127Vd7pJtK38cP3NKi0s/fvx4fvWQV4qO43YV1tulvOaHCaajSS3eUuapXpsATjGgrQpNcYnUVpT56qHpF1baub+JWq6yXpqcuW/dRjftF18aUc+Wl+PR4j9jve1o8oedDuyTlGV60GPqK3wQV70W9N0N+HYAgz/oTMF88NdghTQF293yOb9PW8PH8XKp1DJ4oAmWCPjyye0Fvwl/pcfGF3XXwGQDU36gg0m8bSUxiK3adoJiuQZQ+H+TK307GbSk4h83nw46N2J/rR/bieu0BMLt0mYnBNXeNqCO2mHyZoOsz3urRE675ri2pDsFueka5W2BkIF2R82GnSSZyFq5Nl62M78/jG042K/0NMUQ9n3meIPl5hEWHzpv4M0gz/bo743/yWeank3tzHB01c5TucwK8E1JsPHdkp7l4q/g5OfoWJ8xksDPcXUXzxu1MQxPtttpgkY5U9fXQs33mm4yTpHtw4cPN1vkfHBiGs/JF1pMsL03WfO9xZzESfuabcHx3DZFGYhDVtuqjc9JD6v4yXt80ty5yDxSnuM4ns+puuy99Kr38DkwTsBgRVT4bpbTAhqNbAcyWtvN4FzmTH2C1pacJ6PfOdFKdn5vgKHJckZ+G6P7aY7deD/jAC24udxq5joBI7czOViuGSzw/xVQbP2yTZLH3Gde2JZXjwmCHaRoU+G3+U0brynxr4j8tLNuZ9tuAK7VOwNOWM6yW7+cmK3aM09Nj/fEu10Zj7vjIq9FjpZQTKvjA7SPnV1PcWHSC7feztjZqsxOz7b7lc00YMA6q/hKEHE2vrYYfUYmy3ePr9qnOA7N33j9OF6ekWxAZCfPlHu9uDDlq1Zm0kOuGZM0u534OqvfFZ/sZyrDh4vaDgnHpeXDFSY5S3f/0sb1en3x4/CkplQPboTnDCyrOS3JMkF6WXPioW3PUYnTu7nSp+uG2pJxVm3Svn8f83K5PbzqfnZ7/OTby+yk9isfzXhWAZLJx4DLuudMlmXaClf7QXjKa8flqt7O2MkPebheb39EnrrzgwxceaP+fV6I/U2giq8XSF9Z2eR7z0jh8+Hh4cWKAgNt9JhV2rSdg/rkqQXss4GW3z3eWTGxDbW4YDDX7Kf5VANx5GcCmExcLWhaRrZDnSYePT4+3jyQZj4sk/tpD6E4wF8uf/7axbt3747v378///pGbIFJ+PHxcfvgSu5xlcn6d4KJLlImsrbXl1Cn3HWZdgvaGDiZe/yO4+eDA5azgRcnUH7Sh6lLxkbbFCn3uQLGnGQ/8rWVn61WmSag0mwv1I4yTP3n6El2U7gN7qNZ1E/zceYNxtyUS9yb8k6jaXvW27xPT083bR/H7UNb1iU/E1NX43B2ZXfyxYxz4n7zx7NEG3wN+Lt7hY+DsAJGE+jz/xyYKVG0ugxck/CrAdiVawHRcjjokB8HtAbqGljYUXMY92u5dsGnXdsZYgN8Kyd2oD9Txo62GmcHwKmflkBaG7axqf9VwuX32DknOg0wMPjSflpCasBpN2aR40zAdd227Ui9nLXjlGtg6AwxUef/VibUZtMtnrBMk2/qw3bDdiYw0/rzxIQTBYOpqa0pkawSRAPXtk+3YdBxxr93tNKx+ZjKmzde4wrvjs/mK97ybTzm3hkdnMlDq7ZaLIodOec0wE07S/wwMGZ9r/A14MmYZL5bTG92PPn19dpfjzVhBN6jHq3TFYa4B6BaHrfjc30rPbV2jXdeA/petaW7Qqfekkr5BhJDnDl4BYvG54HZBW3TpMidvCvnsQNNB78beGnk9q7X603y573wNoGXVeCe+KDDt1WUSSbyNwXDFRBb6WRFZxJqq+OzbtfrzxfQ0oZ5Fox8eoxXCcBj14AEk4nbpC1w3L017NWLiR8D2VUitQ2xP8vHxOh+ff7E/bfzOpR1tVphwLEbix3RFhykySO/T3a+8iEG7gbwDaxNu0kkV/hcdiWDYx1f9cPPlOWOwHH8XJnb0RSXXpNkV7rgyrfHqdVNfeck6pR6bq8eaSBkFX+5UroCPI1HytL8qwEagzP/nxVnEuOLfweevkFfybW2EkrbaWSAnraaLbfdC77ObIU/Jv2yfZdp45rrxj4ut8ItK5tZ4a6zdBrwMfBEeTGKxozRa1sWDdMejOv1epOQubQfokPyGnlwm6znco1Yzi+TPo7bg8HRgVdwGo/hYRWknGgoG4NwO081Ab2Um0BpaDULa4FsRXaAtH9G76trZ8FdeGA52qbH7jiO5yMGDWhcr9fnX5MxyGkOnf9bIkjf7X2O5Jfv2Wqgw+duHPhsC1OwMghrf+FvNYGh7zpZus/4ugFftjab/ZwBfWfvNfIuBoEDddNo5e++RmJyik8T8DWfJEgI35Qxdbl6w/omg3Ly6jhEHvy0bEvuE5kPn2+y7liHttHASXvv6sRDA4TUa7N/+0WI4D268Orgyh5brGi8kWfqocWs/LUtTtd99+7d8enTp5tfhLlefx5vMJ/+7glBAF/bnuVRKI/Ht2/fatzzeCQ/J4Y7nrd8M/nwFFuaD3EMVnG10RTPprg9AcF7gd+rf2ljldxCZ1B1q8f/zwaO8MbP6Z7LebB2fDo43AuA2nfzuWt34nkCxa+R0dfuGYvGz1lgxu/3GnTqNHnb2O36v4emNidnbdRWTXfJwSt8LXiu+jXQm+qvZG0gqwHNFXBrY+b6r4kLq7andn4leJ/V8woEZ1wzOeOZM6+KUM/TqnuTd4rhpknnr/ER0xndNsDfACnrNJ5XE4ezOco2uLKhCRCSR4+j25x03+IqAbfrchfN1O41X7snjrX7DWROtJJ3lfdWK+Ku2+zf1+6Jf/cAyFX+uQfM3euDpwEfZ7cfP368CUgMXFyN40xjFQQb89wWcAI3kAlNr2EIhZ82C+JAN4fjT4jRsRqoZT/mIWWJ4skrtwfSts9OWLYGjjzLcpDcJbuUabpgO6vD0qTJGXbOGZ1MztmCiGeA0xYk23FwdFkfXp94bf1kCyT92C7Ybsrx/urc7PV6Pb5+/fpCJr56x8nRunMZfsaPJ7DkP/r9dAzC4I188BciuELVZDDtJheUufVNXpv/uO0pcbUEybN4iSWfPn06Pnz4cPz48eP4+vXrDWD/8ePH8fnz5xc+ywfcEiM/fvz4fO7zOH4Cfz4cxHjJv5bYmy7buFHGs37RQK/1Rr+wjpuftv6bjxMsc/W5rSi2vGG7OI6Xecr644NgXp1nP85J0ZN/q7vt6JBWv7rE2OjrHgc+gBW+v3//Pj5I02Ix27tc/nwwqcUv25P1Ff1M8TyrzPnO97emfIuf/m6/IJ2x7+b3brvVpz4oq1e7z8SuHd39Hj6/l8gO7bNfk1C+3oIIDza2cqQpMLDP5lxtm8KAaAom09bFKvE4QLbg1wzOgTJ1m/O6/RWomtq5x+Bbv+SHn6zT2lyN706OloBbHQeSxpf/VkHW9Q2G2Kbt2fXZhoFGS2JJCu1Mpz8nP5x4J9EvGs+Ub9d30y/v+QjDbkzP+HyTdaKV/K395oPHcftOsPaXd6F9+/bt+elI64ftJZn99ttvx48fP54nB9wS93Y6eTSIMdi5hxgjdzHGtkU9cewmgEYdTOBgitGs6/YmH2h2bLlDtlW2TYDp3xx3WfO10uvEU3xnOjLTYgrvkY/EFfrj7mlWy06dtrGecm3LjSvQPcWIKdd6DFusWeX/M9Ts0eO/ynVnHhp7Dd29pWun5L5/roXaUnFDw6TmnFMZEw19BTT8Px2Thm4DbYPW+HIwnJyv8TMZ80ruXVDYATomFN47Y+iTDuyE07hPgW2SeQUcbW8r0OHANQGrfDLYNaA/2cYEkKijRrSFlrwYiFsgd/0mI3k1GMh12+T1ervilnJtNYY02SPBEfmyfxgIWlf3BsQJCHJiG74meyLfUwykbeZ/jkd7YbOTL9ukHtoTiyuy/U9JJ32QD9cNz16BsozWxxQfWuJvtIpjLufXLLUyDVyYnzMx32QdtP8N7qybCYxPfjyVucc3Wjwknzug4v5XfTtPtjzQgLD5oq7sJ8cxv3Pxnp0fU3vYpK3Gsfx0trnV4eu3DPxso/fQqwBfvkdoH0h1OSPnqc0GEjwIEwh0f+2QbJMhZfI6DM5mHNit8NCUPFZBbwKUTCCtHybIGPoK8FG3/JUL69BbBebnLHEM+aCLEzsTYEuqU9uNbD/We3tAY9JzC6YNbLRgsEsOHGcn9EmmFTDzQwQsb/22+pSD42Tb9ESq8dYeOtklHcrPrSiTwYn1bp53QXC6Hz29f//+eHh4OI7jOB4fH+sh8fDRVvG4qsN4Yr6zSucJJv3HM33GJr5v7Z5Ym77PgBU+vORjO/nuhyOaX7SVtZa4WtKffMo+zft8up7vlGu23h7GoZ+2Oi1e2feaLnLfOrEv5X6Lv9Z382tSA5qtTOPHcaqNrb+33M8yHuP4UfMzf29+liMz1AfjnnO/9TBhliaj+zYZ9LF8/DW8Tngn76Dlyv30Sz4TIJ1ov0+1ISutDcyubjOaVn8yBlJTwBREpoRM3lZkIzpTh/23763MvYP6Gh7y/9mkeU+7072drbRAke8TtYDCts7a5k6Os3S2jSkQ8vM1fLVAdy+tgMOuzR3oY7k2br6/a+ceOmsHrnO23R1NE4ldX9ZTkmV7aMf1Vu2s+p3G2+O2irOrOHcWsJwlJuUJ/L6WDAzb/ame6//VNMl2Tz7j/6t4eq8cZ2Lbiv+dHe0mCv77lZi4onv8v/G68stftZvTK3wkB14y8e7du+fZccq2Q5NU+vQOqrSbGQZnvAxuRu1UzNQvB/w4juPbt28vDjmT0m9zgMn5p5mD9daQe5t9mO+2VN36YF3rnisUZxKFja5to6e9trLEtrht1saT/JuX1qZnmJm1hp/p4Q+2Z/vYnaMzxXZZnmOw0iX5btezytJkWAVOfvc2YevT5VnuOF6uMLDdlvzDu9tpPmS/sy5s177e6lkuxpcme2LSpDuWbds43O70L76wP8a+pnv6Vos9bYWXNMVAfp8eLGM5b40x7vgw/ZRk8z/bs6+Yt6xqcHXE9uB+wk/79SbrIP832XeJNWM5vUjd+WJ62IKxfLLXxpvbXvG7A70T2HCen9piPddp7bUHXdo4GBPkfvPNlt9CbRcsD09Zf1PcPJMbJ/3YZ6aHMVquc7kJe5ylVwG+1pkDCK87ERPIMeixTtsCiOOzvdWh2ePog82yDPD8qTdvk5DvFkBbUiK1YDjRyvHPJHuWpcNMfe9AzIqvSUe+Z1txAGQCNDUjb0nOdubvLej6cLv7ccCxzU18XS79DE5LzGynHR1wMHVbbRwmvfGz6Y2fTKIOWBMQMz8tYFtvTUeNH8vt8o3IpxMyx6gBgYm3JneTzbbFyWrKtUPwbbzz/3QeKXJyUrOyBV5fPRjBun4BPPXYErbvu73dGUTGTL+BYfI7HjNKrnj37l0FCY2a70395Xr4JI/MbbzG+l4ImR62WPF6Jnaf9T3y0GzLsXMCKO1o0NSP23M5ksdxGhP/32LxSmdnY8FkG60cyzB+chu7tTdNBJq/naG7zvAZYZ5xHtJZBum8bodGPhn8zgnoiNO++O7AbOvzDIhr5Zvhr8q/llq7BserJNF49b0JzDQw7jLH8XIldSKumBiMTG23c1RTP2evNZoScr6fAe20/wlg7v5vep6S8sTDGT7Do+OD+XJ7E3/NjibbmmSjfTS+ze/U9nQ+OW1YxtZXrp15iMZgimDbY8VzqKzD+wZojRjbz5AB5ZS4qa/V5IDXp/hAXhufbpMTx4C+qZ0zPkX9kE8DoUkX4YmgdJJvkvWeMVrJM9FK9wRpUx6Z5Gp+v1pZPgNO2z3HmGZnq4dA3P/k96sJweRLZ2Oi5V/J3UD4jl710IYP3pPJKeB4EHcDnRma+zbCXznu5HztACR52i2vn02yq+urZMc/y7kDQiYah7c0w0f6aYdczzgweWc7jfh+ODpHvodHboc1nf3222/Hw8PD84FdLtE3+cmXZ3920AlsNBDupOvxWdnmCiBcLpcX75Pa2bvr89Nykwiy3e7O9ugz7TD5itpkI0GMttj07iDuMnl9ydPTyx9Wp71Zt+zTwIZxb9pqmeLeSn++l77pK+nPMZGyUj7+GkyTyf23sWF75Kvdp6w7IE/dGURk/JsfUbeWJW3HBvlQSvTIFf7EjHbkZ4qxttG0bRulHjh+eY0O30fXYs+0gzSN0xmAcE+scHve0k98jo3G9mibqxW1Hc8tv/ieeWv8T7Gq+XaovYHAtktwywezGk/so/F6Jpe3OBJe81DH169ft+0cx51bumcSjpn7K6ih6RXoMr+NVoAm93eOdJbf19T9K6kFjimApPy98rdZj/tvQbE55W4yMCWufE683wuUyeMqia3aaHWmMmfLe+x29navTTUwOoGVCRiQ11U/03idSWTsq9Wdgrr53YH9XTutvV18meo2GXYgzb7X4qXLuc9mT6ZprHY6mXzVYPRsmUm3Bmv0Fa8oNXBzdpxNOxtveXOaAISaDGfyyr28s49765iXBkpanR0g4/+rPN/utdjltts4r3TQ6u5ostFfwRU73s7QacDXAoWBgR1ul7hZ31snXIXzltMu6U1oeiffqpzP/BxHP281bSWdBYFMgi1Qn3Hm6DP82Mi8QuEHE1hn1S/HLX95aOd6vT3zw7FM+/wB7hYYKbt1MrW9Ag6p21ajpvJtDNr20PSD8asEMx0mj6zfvn276ZP6brbXAqT9bbX6ugvEtKOMYzuHZbkcMFfbEB7Pxo/lYj/+46pO2qaeyM/qt1cvl8vNaz4aIGkr0m3cz8hwHLcPSVg31E97NcqZ+DeBA9qZwcmZ+JNy7czslB/Y/s5XVg8/THKurvNceOMzZTkuecCPucgxNGVpi8fx8rfMm48Y5N0DkprO/d1+kmtN7oeHh5uYEzvMp892Or+sKHW4+0SiHtuDfauY13yUtsmHesj7Chy2ts6Qde32qY8WQ2xDqXtWz6G/BPDlfjNe3ptAFZVKp8oB0AkVW4ksx6TRAsxK8VP56elL85OtpAZIzaevNYDSeLuHd/5vkLQLhg1MTIAssseRjqNvf+XwbQJfe3qWttTO6zloTg4Rnmir+VHuybFI5GMCFCnjd8k1u6YupwCbfrmNx58O8ngwQZBvt9m2sCh/O2DfAh1BfpuMWSa317a/bZ+Xy88X54bvnU2bPE4+uO8xbOSE2PSSsWrbvO3hs9Du/E1kbmdUCab4I/Pppz1YM1GL49yyst+z3aY38tpsym2QB+s7skS+6YlK0irO+/rK56d4R1+f4rXjJn20gT3/rXi3z+xABOvlHu2HvFlmvpPSsTfl/Ysczn1Nz+6vjat11sbDbbXxDO/GCYmpfBem22/jm/YaLybKMNntpPuQYy7l+5cBPtPKuK14C3vGsFdB5gy1vn1vV/eeOumLn9P9VZ+7NnJvleRSpgXUVvce2Vp5goVmA0zyjd84dII7E8TE905+6qCdTWTZ6XOlnymw7GhnWxN/jcfVwWfTzvaaH+/4ntqdEsjZIEnA0JIx+3B7vJ4k4adj2Va73vhZ+dJEE++5xwntBIhWsWQCDq3v1djy/qo/J66drOzL9tDqrex4AmW833R4HLe/qrOTc1fO8k9xYKdn/z/Zsvtp58tbe1PfUyyc4mrA9iQzYwftcKKVvkhN7/fEsNV9l2v5qemG31c5IyB4kiOfznO+fk8+OkunAZ+XVtkxFTYhbSvNhtLKtWsNGDUQGWpPuLENytJWK0zN2Fl22qZrbbYBbUvK+fMB0Qasp/6u15+z0skId0F1KnO5/DxgnsPJIQLB1PfqA8eh2VF7yKZtaXJrxkmfbV6v1xev92nbT2xn0kWzBVNLEm6Hr3EIEIju2i+WtOBPWRtAaTQF7olXtulZc8r7/YrH0X/ZoenM7U480F/bapbf4ZZyDsT211z38QDaznQkoOl+ZT9+It1xzJMfUtveSn8tpjqBsN4ufuxiVmThluVU33pqecL5ZZosttje2olfZZW3yUCdZLuvJfFpq9y+2ADBCiRMK1ipF35yDGYFcCKL9cj22ySn2Qx32rjTwLHxDkoDk5TZuYJjyD7Ny5TvUrfpIuPZyuWTD8cxLuwWCiK/d/7sm9OEctXuhGWYF15Dv/zi5VWiS5lVslkBvHuFOlP+tYq6t81J5jNk+R14/OlAPvGw+n+V2Nv99EUbaI44JQhec7BY8czAbP4n2VtytwyNJ7frsq3f19KknwbIzEtLXKSVnlrb5sl6YvCd+DD/5mOSmWV39mkbdLkGdNzH6v92bwWeG38rwLcDRTvb2p0DYx8c48mnWG8nl797DM7qdhUf2FYbuyZ3I+8e7OJSrjOxeqGj6Xci+8FZv23lz8bUqc2z+cG26aM307i3cWp12lErxvYVP/bxppNdrGl8G5y1WMu2V+M48WFa5dczvN9Lr3otCzv1lgQdpZVp+/u7mUHb2mG9FTlhT4awU2BLPpZ1amcK/K3NVQCZAnaTZxqvKbhPAakBTOtkOhtmeVM25FcaUE+2k+v15UzNcrQVLrbZVqkte5PR//PMCvmYXieRz6bPNia2KfLRAkmzhwnstPqra35IZNJZo8jSeFv9H9l9kDs24CDvxGz75Ts3V/bc+Gx12i4Hdd3O2zQA21aFWG5aGUgdPkQy2QDLs/4EQsI/23A7E2BscXYHWKO3dtZv1UaLuZRrWrl124wltDkn/5a7PL5coeIuh9tq5If9yDdXyRlH2gTWPLV+qDN/9zW35/N7LJtrvudx4gNolNlnARk/aJNe2aesKz1TP44fjCnhwePfVvMmXeW+23HdFa/tGuPZGfxjuhvwpWMOGIXi9lNjfgf8dsmgUeurJScPYEsqZ8mBYvdEIYNzypnHpuMWrCkDP3nPAMxLwSvQ1641ByCtgLyvxXmsE/Y32YIfyiDxR9Jpo3zgwcFydXaj8U1dTHbuhDclsmZ3DdA0W8ln05PrNGA0+Z5BIu181RdpZVO2yympx2fatiftIOUYXHP/DOCb+GYSajzvwBSTmhO3+2H5pqPJL5n4dmSbMbhwvKFtNRC90lmzlRbLvGLm+2eTov2d/2f8JyDJNpjQp/OevtZ4uF6vz+/caz48+Yd1SQBEu26TMNZNTm75oenZ/U/lCGTd5sqP3TaBHvXjrdD2YAnb4tPB7YcUGPdW8rKsY3erN8nZJnqxg6kP0ipHsO6EIc7QL/20WmOM161QX/u/jaag4Htn22kJ4jjWqyO7e5PBreqcDZz3tsXvWe0iTTLbeVvdiS/339rl/TMO3ALcztlbop/quc2z+t4BKQeQKXlMdXxvSmDHcQtEJxs4y7+TBe8RTLts7rdAN8k80TTWkY/n5wjKwsM9bRPANz4NFs76OMuvbHenl8luzPNkPysgc9ZWcp96XoGIxrf94Yz+Jt0YoLFN9ttAYANaZ+SZePM4T7Fnaou8nbk/laO8nNBk0aTt9rHtkI8iTDZifdvWm17J42Qf7md1bdLTPb5JXnZ1KMfUZqtzlu5+LQs7JRLnO228ejIFlLOBYpX4m8OZ7zNJaWqnJSeW8eB4psJrl8vlxQpoEHvu+6EFysGksAIxdro26809bmGs9EQZp7ND/hHxEGdq/m6euW3hAN4O55O3yENZWCe2SkCRVST2twpI5DefqbsCBAYxzZZW/1OX4fu33357fr3Njx8/bn5xgX1RB3llkJPIFGQ4XtEtt3Xayi77N9+pk5gROfLqhy9fvhyPj4/Hcfxc+X3//v3x/v3754cluOJxufx8fQv7mWJOqP0qAHXBLbnoLf3wvXct3rSV1HZwnHWtY+vWvkCbba81amPA61NcS9s+GG5/bLsllrXFQfORv9hkxvo4juPx8fH5UH/To8eY8cPAwYmXdm2+np6enn+ppAEMf2/j1fJC7vn3dTOGjofOB8dx+0tFExAlj6vY1XJxO+/Id21Gbw8PDzcPcqR8/NPbs5Y/vBkrtLEwYCM/3O6OTIzHrtsA6Q402b54nfprdj7Zg9twbm/5xX1OsW1FdwM+OncDJJyhN0drgWlSxr20U+oUDFdJfUq8U5LjgLVBaw7L4NnOJ7B9OuMKoLmODeVMYmj9UyYDqwYi2H4LbJGZoJh8N+dpMloO80ndWTcNJJD3Mzawcj4nRPvGyi4necO3x2EKbrw3+UhLGC3YtPNR7qcBFX/GHt6/f398+PDhuF7/fNk07SFtTFu7Lc44Dqzkss2F2qSVIHEFZKwDJm9+7nzwbEz0JHEXBxv5ngFqvjv+tISWz3fv3t0k912/aSOLB5M/O0awnWkCwjKRidvaDWjkyd5Vwp2Se7N/9+VtdfM6+aS3Qxv/E61ybfOrtvPCyRonB/xZzLRDwLdaGZ/yL/2ZfnQc/bVNx3EbH6nHHQ8tHvM6+2x2e1ae1i75PnNEg+XvoV96StedT4y0QNTAg6+7LbY5HSZ3udZmo7NBabrGVSvLQsO7Xn/+iHaI/TZA4mDm5L4aD7YxlXcwmsaDgcrUklcbN7bffhfZM0PLNLU5jXnj/ziOm5WwJjtlXdmy/1h+54wrwNBkPI6XYMurCgZf1I/Php3lrSW1MzJOvh7K6g1XDLOqw7FmmUbT2VKPTVaOmDRcZ8V/fvlkOjtFGdvrPxwXaaNuIwk015yIV200fty/AZTHmWeiuHLvshPvuc7zlZN+WffHjx83q3o7HbqdSR+24emBEb4mpJ1Hj06s26brxqNjROOp9cM2rGfvSLSdEMarxt9x9Fh3HLerZ/k/L/vOCp95Stn2sMVKPzuKPeb75Ivk37bTbL9hifYrQlMumHIq/cdtTHy7HeaWexZ7Gr1qhY+MTOXIMJ23zaxsUFZOMw4G7/aj7W7LfJgauFjxQPL2gA2A95m4DOTynXUoB7eSqEfWnYzcjtgSMNuZDpiTr4wngRrbcZ9s58ePH8fj4+OLrc4JlExtsa7lc/Dng0V2ZtrhLul6Wd+6mB68mD6dzCdbI5BIcqKsTW+uO61oTW3QvlZjstvatV8EQAVEcdaeRJKfdGIiboG1HdQmfxnD9+/fj5MVt23/YaJpvtXsg+ebWqKg3NZ5YlxWydqvuLQYxzZCjim8z+Th7bfL5fL8KwQGRuaj6Y8yf//+vcYU85u4kDKrJ/lbEm39t+1nHxe4XC7H4+Pjsz1ybCzzFCtMLfZyXLn16QnNpEvq2QDG9chXrjewGWJc5JGNb9++3cR+xpGHh4ebXOxYSF5X57Rb3mq5jXHPuXQVY1cxlX1lPJqvTLlyir0N8E25eOXPjhWTnZyhVz+0sQJAphaomdw4WFPwmhyKbU19rwJDozNl7lV046m14eDm/lbGu6OVHiYAMtWf2lvpJQ7gYDB9uu0p6bTvtK/dWLWEP8m5a+e15DFf2fuq/xZ08mk/2+mn6aUl9taOwVCT4ayd2TYnXqd6DeQ2MND8rbVnMDPx/at2c2a8G9iayu7KTPpr5ZpNNH7O+F/j9QyfE9ha6a3J2PrzGE+AYurrzNi5XV53WdvmCsCY/519TfKv2idw9pEY2sOu7R14X/nhPfKw7ip+tXutfd6btpad49gXr1muvyofNbr7lzaO43gxu2nJ27S7bqNxu7zXzjDYIVeGRqdoD0m0ZLACI5bfZXZL8yve2C5nOZPsbGsyjOkwazuInu88GE69rF5rw4TvFQqW4YqiZeD3h4eH48OHDy9mZDlg3QBHk+Xp6enFb2FSvwaYjTwG7SD/ZBPmbbXVkft+0IWyMEB49ZH21LbV2Hbru43N5AOUzUGN/Ocef0s5D21Mv6qy2o5uq+LUQ7aIs+LGQ+d8SIQrL26T/fJa9O+V7x3Rl6Z2WI66T3lfM/CyvtjmZGfui3XbO+qabbQdAp7t5vUG2loMpa/Q/3n9TIJsOsj4H8dxs1rlvt0f2zHPq2S+2+JrZJDhe02+dmwhfPKVR7k+nW+eHiTK/3z1iPXU4oJjbPRpPXDXyPkw16bfrZ8A2JRrHMOmtuKjoZZL3KevGSBOsZn1Gz55Db3qKd0GrnbG6kH2AOV+M+rV/zaiiZ9VIJ6Cq+V0/zaEqX0PVpOx9eWkwHtNB5PRNnmbzC1osbwPzrdtUfbB60wQTGy87zEyj7/99tsz4CM4S8ChneWvAQXyY8CX+2cTd9Nbu5e/CUCuggVBAM+uZrXUZ3jS3io5uFwLxA7wzT9WsjQbdQI5jtutT/PeAM5OJvMdenh4eO4nh84fHx9v9MwtfPJNeRvgy/U2Rmf4bueupjqp10Cf214RE6xjSFv5tC5W8WrF92SbZ3gnD/atXb2pjSRsH/Uwv9ELwQkBqOPHJBtjT9vSX/Fu3qbytkPWsz3bDj3OPLZjoH82Vraz7e7D+YW8UeeeTEw5temsbbE2nbQ8ZP3ax1c4grJYhsmPeP//OODjKwkcjBvynkAF660eUNiRVzZIBhvT98bX6v/pmu85IJEvP2zCZMtyZ5Pc6prlozM3Pe8SeMDF9HSbnac5buOVPDXHI1+RgY//s28mw+a0TUd2Ogf4nVNSplWgn+R2meY3uefzqm4v1BIRy7UVt6m9yU8s72T3rkPbzzWeR1wlcgbalb7aig/7oS9wVccJLInO/dAmbLsrADIltGbjjnGrwE+e3G9bhXPM8Viyr2anO4BFvvLZEqvLtXvm02B+FyuncW1yT2eXJzozNuy76b3xe4bYHx+cmEAb60y/d0z9NF1NPE+xr8lt+2uxeaXzFQCbzih6YuZ2Vjo3vzu7n2LySgZfs6/Qxs/6XqPTgO/Dhw/HcRwvtuZs7GfBBJF222Kc2qHgLXhM4KklDgfe9t19W6bmxD6jxrJZSuch5mYglrMFwZXDtIDF4NCchodCrVNvNTG4NN6nxNao2cCkkyTfx8fHm8PxDUjkHoPhigfra0oiLXG0tlYJP7aQxN76arpuK6TNPqa2o+scVJ+SVdpr23xtxZnjNq3i5s/1sy1PO2iJ3f4zjWkDYNFfDqBnpfhyuRwfP358IX9sJg8tZBuYbTcfWoGh3PcvPzBZceszfzwuwJVAjtGUWKYk22JZPmlnU8Ke6rstyzjFxeZ/5inyN53u/KfZhPPILrc03fEVJAYUrj+tYE3AYwVIWJ9PyiavMJ4yFrSHnxrQWNl548fXHeOmOBN983Vdq1VPjyH7OY6fD+Ewfnz//v349u3bc/1pl8V6ne7x/zZ+LHcPMFvljWmB617gN0temJkc+Z6OV8Fw6m8qd6+wK55W/99DkyPw/gTgdnxN987o9Cz4cj2OeZNt6pv97O67n9XnCkyudH/23gSsW7D6K8hy7e6fcfgWsD1+Z/hflTlrm77exo1AsK1qNZ4amGjXVjyuJlS5vrJv89H+b37a/pqvtpXQM368iqU737839k0J1HHjNWP0V9E9dt9ss322Ort+VzH0V2nKz41Wdtju7/pt31f9Nj4m378XML0WqLb7qzjbcsFZ/b+GVuN1D51e4fN7maYOE7wmQOIk7xlalDWdjXA/Z2hKEmy78WonOoPeV2cR7gFPq0A6yU7dtYdRrPvpXtNzG2+fX7B+Gq+TXbi/yMHP47h9J5Tf8XW5XI4PHz68WAnzdsfkrO2QcyMnaQcY20/TwSrZhW+v3JDHncPzoYOsznNVNCtq1AFp8g/Kl/Yvl59b7B5fv8ON9Vvwaqt6+Uw/4c1bM7sJDl/1koP6k23mf+o8K29ZifM4W49t5bbFBa7GkIf00fxssrcmezsf2H5hh9/bO8habMzKDPMCqSVDy8m2p/FY+SbrrF4PFZ26nvXn783PWi6hjNSf5eVxA+6mNP+YJhTsk9ceHh6e67f31bFN1nff/M6H0Wx/Z+KQyzXdtiMaXO3mqvcqBvqlz7xmHZrcnsvt8hWprVJOumpjbF+mDa30vaPTgI/KYyBycmoBZAU62L6DggMraTKgiSYANR2WboBg5RQsw5+aoU52id7tTdem+itAaWDEhH6Gr8YL2zz7lBzbmGyhBbIWNBIIwkMO4mfrLnzR+diOA20rt9rWJeho9u/AOOmw6aa1afDR3gXmxJatyOv1+rydyUBqQGxfaIGYOvATa02W6/V6AzB4fwJEHnvL1YCP+/S1AL6np6fj69evx9PT03hQnzKyb9rKDvBZfxPIWfk8fbT11fzGfTNOs74nptQ9twEdv1k/Tzt7EcC8NHBl3m1nubby4ZTngwWTPjwGPApiPtt4TMmaPIWXKQaTVy5kNBu2v5i/9t3xgeM/xWfGGfMwyTz9NKHlOZP73Q6v2V7asSKWJz5p/tXivttotPLV5oMcB8vCfpr9MLc1f1rpcUenAR8POzfmHajJ0BSYJ+VORKNsDtLabgFyZwis12QyTYNi0MqA1Ab8OI4XAML8rQKng32Tg59NBrfD9prudrqZAtmq7pTEG79OAA4wu4DXrrVzZ02mHdBY/d/8pP3fkmADHZNu+cqTvB1/x8eZIGK/ak+4mt8WM1q77TuvNZ1MxLi0CtorntzXlExeQ/bfpv+VTs7Ep50/TX5xRk772orPFYhw3eZzToKpZ99Y+cyO7gEBjSY5zwKfHQ85n+d7PM/c7GfKzflknmBfLmN+XuMDk9+v/HmXD9z+ZI/3tHNPOQO2xrcnLb7f+nRb5KU9ubyj04Dvy5cvLzrmoMfg+H4fCuVZj43Uwu2MIuTtjtSl8vPXEnkLHi1YuX22w2tc2cv2EQ/K5tcFSO2ALfsmX3ylxfV6vUm01El0mzehsw511hy8bQk3Pfv1FG28yFO7T1uinXA8yCONvl33rDaH7qfxYzvpL6s+bZvScjX5zHOTmf1bp5azBdvoijLb3o/jOD5+/Hj88ccfx3H01Xm2R14yUVkl3wb6Jr2u9N5AzwqUNH24nebbtOMWD6bdhAb2DJxXK9xsp8lBm+e4TvUok8eNCSB1/Gsx+d5W7/LZ3snZ+m96a+DBv/ISHkPOD7Z/vwNwksV6IJ8TaDEganUpm3Xl9sKPZZnaYv0Gvqb4F53x5+CmX5OxbNatV3v5cGb6SbyfHv4gX9O2a5OHMTd9Nz3RtiegRD6Yr9pDge0BoHZ0YBUTrFPbUeNzsqd7+cnDd8fx8mHaie7e0t0J0Giqw4TePs+0fZaPlRPv2pyMqwVLJhMH89wPP06YLG/9WJYpuDW58zcBY/I/6WUHXqaktLre+HbCnxLfis8WYM6M4Vn++Dm12b6v6rUAsmp7msk34BbAz4DN8md42hHHbQXYduPH9qYyLZGmzSnWuGzjrV1r4GVK8qv2mlxndb+KhyvbXtGO33afPEyAxMBq0tuK/wkYn/W51f3X2voE1FZ5yr6wAg4rINhs1/V81m1HDZTsYtA947rTS4sTu1ziNlZYZLJH55d7+J7K26f52dqertlWrO+mr9UTx43ufvEyaXowYDrL0yiJyO23QGI+zoACO8Qqwa/I/EzE4MTyK8du4I8HbS0/ebGDN8NOO36FwC6Ime/Gi88yNZ1ZP7w/GbFXIFN2ejGqqTn2FHy84nMc/fUXqe9XnrBdrrhODzysgkP48UoneXH5Np7pm68kaKudbVvAZ2Coz0l3jcdp7M8kfo+bdcX2p6BHABq5eDTF/fCsW8aQdVcx4Onp6eYXG6y3Nu4sN62mWke76y2Rt/JTwraeJ0AWyspddhXiR14hj16nhBV+uWrB8Wh8t1jHa161Yn/22Qm8MJ9NccY7WlM7Z8Y3MY5tcUXN45o2KVfb9m7E/OBY4/q0U+ei5ter3GJeI3MmpmmXvyHd7HG1jekc2TDCLh6tZPLqoeVcEfu37dLe2CfjNdu5l+7+LV07lBMfnes1TDVHaddW5Rs1ELoKihNvZ/prIKEBnQb4yO/qB9PJD42vJcGUWb27jH3sZGcfE+iagnPTBXXSnliMfTEJT3pdJVWDkrQdWbhtxG1Sy8l3wfmw+mo7o/HQwJ7HkHXy/ryVriMXg2a++6nZyMM2yIf5N3gxMGfCTmxo49HsbPKtCcTYZhq/1lF7cbXtgw+hGdxMwMA/rXe9Xp+T2EQrH2llV9esix21pDIlk5WPc5LA4yyZtOWvTWB8HIXXYjvWt/XF/hxL3B/rtO0z2v2qbovbE0Ay0U4dh3KN76S7XG6fgG85y2O1enBlyg/ND/y9rR4aiE2yN7051sUWCNTJg3Nqy0kcD/LN+rQZt0fymE5lW99N901vtqk2FinXJkz34qtXr/B5EKY6rd6q3R01Rd9LZwYktAJAue9B8FNPLahPg+WVhzM8usw0VvdQk4t8se1dnR1vpCQH8zwF4FVgbe1b7x6nic92vel1FfAmojMn6bTyTY/+bONBUNqS1j10xv8mm801/7QagcPZvvl/87NVuRVZjwbH0elufI7j5WpvC+Tm84zdTWVtH81nzrS94m8qv2pzJVPjmUlvsrcG/n3PYHDnu41XAgPTa/MPeQxv3NX4q/o4SyuAdjZ30C9afN35DGPAlC9fK0/zYZcjYJ3aa5+OB2djTauzqrfDIWfpbsDnoDUd7s1MpTnwGbDofk3NEM3jKhm24DUlzV3fNBS+5qIdbPUBWLbnVVI6QPjwQWfWZ5ttxvBaJ550MRl3+sr4t0DrJJ//+UPcTrrH8fP9cZwFNx5Yhw8UNUpd/qYmH0aJPATy0woYfYK8tNlk+Ht4eHj+tYevX78u33NpsBZdRJf+9Zqnp6fjy5cvzzzbPlL2zEM1fjCFNK0s2Qf9GpTo/evXr0t7WgEkftIPVwm+8Up74u9txo55BtK8Ghhcr3/+msenT5+eZcy2J1fuJ5/aAabdtXbAvhFXMynTDny0xM5ylIOxJzbpdtqvQXDVkOXbA4BcXTyO4+YhgMjp8j7LSjlaPG+/qnEWzLY69IXYhOPOcdxOFhqQd9scywZgTavcHZt1vp5iUz7jCzyqwIc0c5/yJ/Yzb6xWyNPXDiS1Bxtb/ml6aDpmOdrnbnW91fdqbNpscvCzxeQzdPeWLmkFGloAzOcu8N7b973ol85jJ7IMZ/r2/97OcIIm6DuO2xdbtpeXsk8P+krOSZ5d+XvGpPHQkiABayvL/32N4CX3vb3bxsGJwsEw/RHEpb9Grj+NU0tSbdwoX86vMNmdSbCWq9nmBDBYdzXmDnxT0ti1EVnbg0xnEtF0r8UW8jMF6JXP287Cu9ul/RHUJ3Fx0tts1DK47YnPRi25Nx00AEVqOuS9Zou7XNDKNT5o+3yylyBpFZdpz1w9a7Kw7QamrB/7ZMpNOuHnBJB8hpFlpzhzJo7vxsRk/VB/7HPqi/y0eOfY5P4CLAkGd7I222z3dk8N83v+nxYT2vhTzsbXru70P6nlrtfQLwE+UjP2lcG2ey0A3iNgC5A+E9YCr/uZEm0z3B0/q0Q2PYY99dH0w3sJlm11wzpZzeAnILPiqQEQ6r45fks0DhoOHhO4aXZ01gEbSEpwb6sALNvkn/QzBYUfP348r8IR8J9JpNZps1fL6kAbOacHVVbA09en+1yt5qz2crnUc3Vu2993vsWxm9pqbbKN8NbsxDrhag11GrBtAEGdm5cWP5t9TfI3/a1W/ttWWmv/jG5jk34Ni23fMlpP7rcl/ilepbx/IcKyNpDW+DXf9rfVof0VGPRDRFN+cX2Xi1yTLE0G3m8r915VXI1L07+PJjEnTbE0Zbi63qjZEXl3ucbPBNSans/SFGOnvG1bW+n5V4Fe6FVbumRkSnTNIFNuBQRtcCw3GbB5XCUeKtdttbZXck3JvfXX+pq2FFpQae1NyZnyN2du8rLNKcCZ36af/NqF5eZsMaC0HeTOCle2Vd2+fyZtlwB5rwW5pmcCrwl8MJCxfd7LNW+beSXxx48fz094enttkq8BhzaTzX3bCvv2+KadFfBa6dn6tj7vadfxhavjAVgBFyk3Bcjmy7vZP4FLK8cx5juxUubx8fHGBrxFyIdo0nbzZ8rGfvmd22+U2Ssm6Tt98MGLBlBSbvc9ddqZ0RabKGP0zHG1/Oxvx4PJ+tnlB7btGMprfO+dZZr4CLUH8wjeJh4beMkfYyZXJtO3jxRNvE4PtVi+Bm4ynpah6SmfjHsZo7Yb0OzIY8YxcmzbxQXGxemBM36f8m+r5/ser9ybbGe6fg+9+ild/z+BCZa7h+F7y/9VbTYw1GReJa3VgE3JfOL3TLkd7fpzQHa9Mzw2Q2ebuxWBaWwm/bf7UxJwe2dsYOXku7pn7zPgteBCakGybTG1/qY+fa2NwRmA5sDeAt2OWuBelSPgbWVW9cPXmYS/42VFDThY/6s2mk/6/i7ursCN2zC1WLWK70zo98RuJsMV7XjIvTPl2J7rn6EpyU88Nf5ew/NrZGr97XTOuLzjZZJp4udX6FfGzO2krRZ7p3jmYxsrcl6b9HXWnqf799BpwNdmt+1XIfKqCu/FNwYnhe6ClOu0tpojsoxnHRPgybWUm5IM218l4hgXt4D8vqrc90F/f8//los64CrCdDbN7flsi+VzYHefkSUH1X1/NXbmfarXHObMuFhW6nxywgBWlpvsze24n7aN14AAvzffy+/iWje55odNrCe+36rptQH0zEb9wIzbIQ9+VQn7oL36Z9ky/pN9t5m/ddVolTDSNsfDK4Ap1x644gNHLS7mu9uf+GntNODP+ztA6TIGdc3HWc/t+YEW6m9KVrzXgEfjwWeqVpOKya+v1+tNO07GLW5PZ47ZTmJce0coZbVvNcDlGG6dtDhksO1r1EfzKffN6+Yl7dCem57tK9Gvded4Nemg7fZ4nJt/tP8zVlmR//bt24uVVr4XkPrxOVPK1Wy+xXY/HEa+ml+4PnV2L/3SCl87fB2GLPwZRNsC+1R2x9sUNF3eBkGyo68O87ue2/NgMWlO213tkPiKmpxJum0cTHSKKWG0YNTaSXBpS/u78XCwZLt2DPJtGdq9liwmPTSZjmOelNBZm03kcwKezeHZLtuhbv00tG3JAdbBd/LRlogT9Pxi4qanBm7dlttjv2fq+rp5afX4v7cxbYMtRjTbmrbhmi7OxKYz5JiyKnPm+85H7ZscP25p257P8NbIQMZ1zvpxk89txhambbzJ1iY/zf+rs9KvpTYhPxOnCVRYznncduC8x6M0U4xyH47pbeyaXhomSHu787WTvUamh4eH43q93kzUQm3Lm09+p28f/SG/E2Br95ueyXfGrsXoe+huwEcG2xNvDAAtaHiFbBrs5tg74ZpjrurR2C+Xyw3aTj0G6SkB2cB2wSn30p+TAQeY7ThRW172y4Pwq9nxSj8TrQJtdJrzOA4O1LfbmIIX23ZQoaNwfHbAryWjKcnvAv6klynxT3JOeuC4twDIOk6QXKHyqh958ph4JtvsrPGe+6s67Sm8gNfV4fX0Qz9t9md+pkQyxR7r1bqf2nMSTtlVYF7ZwkrPLaGbMq5OtlN7vOezhSxredlf9DUdkt9Rs+sWe1cxnZ+mVSycdDr5Pvlr408/2/nPjtj2mXxone1iq8fJZ8tcl/GjgZ18bwCzyTb5us+N+zw0gVPqrDAFY97q9T+r/Guf3tncTnbrYJVTzo7/RHf/li4757uD+JbsMwmb3/1E1w4AOHB58F3WwYPt5tAt31XlBNn6JH9NtikRNf1Nh84d7Jh8+cqHEMuttvTID/s5E5RWiY1leDh4F5gmHqa+KaMf7vATn6zb+G1jxdmdnyglH7YlX2P7HEuvrlF26sCyUq+xAdoH7SH88+hAS0Rsm0G1PRzjhNhsuumd5KMKzcYbAOX4Tj6+88EpqTS/YAzglp0TunXKNqcVI/velMjPJpBWrj2UYZntD+YrenByn8Beiz+rowFNHuqZY54jDA2UreIw76dt6yD6b3bJcW39tRhgWZtOW6wwzy1XOl5O4KCBPfPj+57cNV0yRjjnti3b5DbmqymGTECN+nh4eLiJgdbDt2/fXhyP8hgmrvvokY8lOHd5RXE17tTN5M+OVQaYDey1sbiHfvm1LM1oydR0fQIDq0FftbfjcVV/FTjdzqqMHWAXuJusLfC7LJ3OskzbRi2wnNXxvdSChFeDJ2OeeDpr2Pc6QOpM+pkCXqNmy5ZrB2xXOmrfz/K+8tGzAMM08bQbw/Z0YNNT6kxJ4TX2u9NdA3PmY6r3q7y9hs7Go7P+vhrHyYac2Fb1Gr8Tf/fGKI/RZDcNbDXQPfm6y0x28lra5RnL5HqrWDrFFLdzhge3xb4Nxs6O4yQT+Wr9s26LIZan8WRQNbUz1X8N7Wx8wgKvtbFXAb4W3Lh9MCkyKwfTweppVtICQfue/1eGHOIsy/3yf4LSCaGf4XNlXI3PCSyE8vqT6/XnOYLVmSnyk6DE8fJqhJNFqzPNACfHyf/e1jXfq6TQArqX4wmGyUvbhmAd99HOvk1jfHYsWcdbbsfxcwWMK3Nfv349vn379lzHPDc9cSWDWxfmwROHlFvxzfODO7KvczeAry9pNkd5DBati5RdvdOPfXnrO+3Z1rkqMY1b+j17iJoyNj5Mq8TdYlNLVOz3bD+r2DrZ8y4puy7HN7rwA0WOJ57o8v/2YmZOgtvvk+f3qfPAIf1nFQ/Mf/Mp8+761F3Tx2QXE7BsedM2fblcXtirwRp5mM6uW7YGANt19tnaWuVKn0lfAbCV7iabYtwz/z6v1/qbMJDLNNkp09S2+70X/P3yT6tNoIRMeSsyZ7vYBoEH+zMPTggsu3IoG9rKEMOvgclugBu4mWgVeHN/ZTR5isg/2t7OVbb6LZivaNLtPcZ2HLdnyqY2p7GbeG7Bl0vz7Rza1Kbl4rap+zEP1ssUHHjP1wOI3r9/fzw8PBzH8fNnh+gfZ57ujPw8rtB8Z8Vr01E7hzjZwnR+MPc8NlMA87VJVj/1ttNRA4T0+xYDqLOVTlbJ8oz/teRNvlfBfhrvs7RK0s1mdz8HOMmTz3aMwtvJ7O84Xr7HtIEkyt9iAY+ERA62Sf5bzmi7VGmLcWPKI03PLt9ynb+3NiYAN7Xj2NvyJNu23J7cGTSdyZGT3fK6c9zO7pqvtTEz/sgYMpesfkqWcjbbtE7cd9rcTRzd9w5LkH7pxcvucAVSWJbgriX6e2gKTKtr5MMrk7tk5zanem7DwGAywl07oRifjaMZ073grpVxUl61tRrXBiwul8vNmbEVLy048d7OPkOrs4UtWLYyK300YLCSz8CLyclAwnXaeZqVT9oGd2CqlbG9TiDN95nQ2+H+Rgyg+Z8JhPV3dkQA14Ab+5uSNNub7HGyG7bVANBqLNzuBAinMd/Ra4DhxKPvrexkasc5YirnftynV7IM4HJe6zhePvTkPlpcm8aavrmTtVEb6waIprw1ATaWIXGCwu/Wbct5BnQuM/E9xeyV7/A8s/W0yneTv6/iwJn4vZJl8oUz/jC1szsXv6LTgK+9JyvEgNwOfjZDYUJLOStnCrYrxN74awaWz/Z0IPmh0adc/m9BtiVIHoKftj3S9nTY2g6c7Qfry09ANmOeQPIKAEyA1GNkANqChoP4u3fvjg8fPjyPh4NFc+YW0JqzT0k1fPkgrm1lR5NeW7mpTW5zUgf5v9lBe1Dq27dvx7dv325sfHpKjvxMY8v+mn+2T+vBceDp6en4+vXrqCf3TR2s/J5g7jiO54PY0Q/L8NqUjG2fpgkYNN9mP1Owd3/0gWbjLenSVhowMdnWz4BJXpsSXFtdn3R15vsZGRnPXY59t3HLLsm7d++eV9VDjNd8h51tynpzf1OOtGyTrzG+evUo13mN8aHF9cvl5yQ7bU8PAjom05Z5NIq8NqC+4nvnE7zGJ8i5Omv9TLJb31xJZl5ouw8rWvkEZfV1/j/113xzd4Sr0d0rfFNSs5AW3onG3yfa3W9lGy+/2nbIgDHtrBK/gdfKMFYGY94NIFaAYxX4p/Lph59n2l314/FPG433psd7eG88nQVyE70GFE40yceguSvLoNd87B5eDBKmcSHdo1eCPst0D7jeBe+JF8uzA+j5XPnjWZndZqs/xQWWWyXEXRxa8dj4bbS6Z1C6oimZt//P2PJKF06k0+SFMvD/e/KVybFiB6RNrbxlOTMm7XoDF9NRkQb4zAtlnGx+pY8zNI3BKn9ME42z9vpXxPnJ3le5fWrnNXoLnQZ8zfCoqLMomG0Y3e+AQ0O5U9lVvzsgOvGYGQBngZfLy3Niu6BxDxEsktps6R4dUCY7+wQ8yRM/w88ZoNra9jlE80n5KPf08l9fawGrrdRS1z6DswIbud4cmLp1cOQ48NyS67eVcNsbr7HfnZ+sEtCK31Z3mllPyWLFF2nyKdedwFmLV02OM8GX8uwSCb83G/I927fLtATFepfL5cXLj8nbGaA66WLybT/gls/JH9wW25kSdOMlPrqSwW34L2dIWyzgStfq3Cx5nmKy5W0PjzX7CG/TRIWxqemu2XuLEe24RHuoMTHX8dO/5OQY3+ywgd9V3rQMx3G8WJWzfhrt8vNUx989vi1PW07GDh5rcR9tV6bZEXV+dqXvVS9eZqdmqt1rbayCv/vZtbcT1oDobFCbAF+e6mKizcH4KTi19lf8sp22bE6ebIQrXbm+D6me5a0FEp+tOEs8i8ifJ2p8OyBRHn9nHcrKMzvNYemoDchO1IIB7/mJ77TdjkG0ROREkm0ots2x3dn37l4DbeRn1YbtntvUlKMFM/Y9fW++MAEF3mtPT+7IAZvXJxDQdD8BT95n+wQdrsv6fKo1dXwGdAUELGeTxzyE+ICb37M2JfZGzcZ3cYzttwTpco6pfCqYfeceQVzqNL4nf7dv0wabP7kc605Pi98bc/0eS/JBcJn30zL+8ZcmUjdbvtSZ7c2+2p52nmzSuiCf3Gpuvt7aava1iz9TW1P7q/vpr03smqzUp3X1mrN8v/QevnvBjI1gAl6reo2Hs3y2oNcOfpIvJ2kDLw5IM/IzPLb2zpSdjHsV/CaeXKeRjbMZYetvcqZ2fQIqZ2xgAhxuf+p7CghnqdVrPK3G2Dblew3o7HhpNtv4c92JztrzGf3dE7DsY22HYPcQSAOtXpm6B+RPfbSYYBk8hjt/uiemsL9VXcfEXbtOrE2fvDf5uPtu/Ez1LMeZeNOSfDsD1YD3CrhOsWwCnJPsfvCq+X7rh23x08c8Yte7GHiWdn2dySemXcx2exmngM5dXG1tTGUnPZ/JzStqdnQ2Tt6Ltxr9MuCbAhupJZgd4NsBmXsFvl5/zsb91u/L5fL8Jne2y3JMJH6XE2e3MUDX4ecUKHNv5+yTQZ5NxPy/BQAnnXzPZ/uR8Mzw2pOXLYlRDwTekxy2s3z3SkbG2a9nWOnCYO8MKGv6sVwcp+gk709cHWh3v57dsnwLAE7iGS8eRE4d9u8HftwW+eN4rJI0t8ZbO+7PNsn7keVyudzIwr/Wj5M8+24r9t+/f6+/7OCxauN9HLdj5Aml25vGk4nUv0m9Ws1tcbUBMNahva8AYu61B8/aeK0e2JjGiPxYt/arnX9bPpbn6hBX/kPZBbhc/lztStxvB++tszYGtAWv6POa73Ns2vhZB3zNDH0mr5zxQ4rWf+uvAcT2ZgDGDy6UrB6yaWS5mn7TrnNti/uWI+VYtul4hUsmPzmLaZq/TmVTjqu5q/i8olcBPg/WFKRdJ2Xz2ZxyVZf9tf+n+u6Hg90CLfu1AWU2Yf7OgoTp2pRcpk/L5XvNICdyMrAeV4GzBbZpfNiG22bZe5MEA/cuEZg/28DKHqfx3dmRnXpl6ynPvlrQnQBt47nJ5HM7U/3G2xlq+jg7ru6r+eQOQJqP3G969Et27w2ijY/r9b6zRS4z+VpLxO7bOp/sdgKwZ8fYsYjn8Bp/5GfqZ+VLLa5McZPt8TM0gdam3zZuLc7YZ223lp91vH1q/lsscBn2Yz2m/aar1RPnjSzHlC/Izz20yjG2H/6t/HfKSS2HnrV/8rSKodOY73hc3d/luYle/Usb+YyxnmV2BUrOJB6XbQltlWCv15fniXKdK1dOjnRGvzbBiaQllknmXJuSwy54t+873U0ORV7MJ8s3sJDPaTssfXIlsOliNa7ko70uwA5PIGherJcpITT9TWS9Rl7ysLK/luxXCTv1vn//flwul6WcE1Bgn+3g/Mq+mt0ex/FiheE4jhfnu47j5fvRmmz+ZJ2svKxWSpvtTL7Ka+Sx+cKUHKdEOCXq3ethrB/bUuNn8r8JxNB/VpT79t9VvwYTky3bLiZe2viyb78SplFL8o2H5DbuDIX/9qAH5XKOcD+OE6mz23Zl+eYfU2xpOwpTHEz5NlbNd6z7VW5M36tY2h5kmHTRyrXr7SE868L3V+3v4vKU5x1nJ1qBaI/NPcDv1Vu6HtBVp80xW5ldf26L/xOo0NnbViGfCg1lq621Q4fh6qC3BaZAswN9WXJvAbkFBrd9Jii0+81gCcoaWJoC3MqR3D4DZ3Mcg7cGQLO90hzK9sEAsgI0rEOZzhD7jjxciaK8tL2mH/81YOTA2/ixPDuwQDu23bkPv/8q9TleBq1tHJu9T2PCQJet2OjTh9d32+WrhOTE1IC0nxBcJYkpedPPmKinQ+3mt/W1owYGeM0AzSDmOPoTtfQZlrevr/p20m3xo8nLOGLQMcnf2mF//Pku2nr8sdkP22A75I/xr1GL+zsyD7bXaVdqNdnYxUznRcu98q+VfM1OTOzDfjbFQtsWddKAqvuacq/lajzm//a8gNua2jkj5xn6pTN8K1o57K8wfJZ2CetMIDlLNuopiTUj+FfRSr87EDmBwan9e8aSQWKVvFYOMSU+35/aXNnGa+zSddx3A7MrcvBsAKz1t9PdpBMGu1VgW/HX/H3lcwaDDWBQd00HZ/yntW37aLY1lXOyWQX9XyGPjxPpdP9MW7ty6We6t4oP7Tr9tdnmXxkHV+2t4k0b55Rvq3mp22RtQPIsj7vYu4vLr6EzMbf9f2+5XT3GgzNtuNzkm+zP/n2mrx1Am8a7geXVuN+DC17rN6cBXx5UYIBuTuzZzHHcDoQfC891fvo76QzSvlxufzze5bKidhzHi88piBOhtxWXVVBg+y2BTTIZKE5l2vX00856RRZe58rkzjibTpuxNrv49u3bjS2lXEtsq8Phk3PkOlch2raSx83fLeekD/c3AZPI4VUu6yD3/QqE79+/36yY8FU2bTZuvbe+juN2ddmvp2iJ2mPVbKX5QAMt0z3Xm/qd+jZxrNvqh23KSd7tuGzrb7KF4+ivhuE9f28+wXa47djkWj0QNdmMD8FP47sCfRNYmuyg6SltJW9wVbTxwJjRfI1y8cGD1PXB+PyONfnagTL6I/MQ/9oqqe3jjL5bjmrxfpVj2orjlKf58NDl8vLdn1PubDjBsjc7Wx1TmfLUaiu/+fNKX9TpGR9wm3kv5nHMr4RaAb0pB6/sb0V3v3h5CsguuxoMA8J7qAWLVoZOTN4djKf3vq2U2e41o7fRrHje9bPSlZOljWky6GbELLMKLitaAaO2ncltcm6Xp15zjqmfVTkHl3yfxmVlX7Ypll/J3+5PoCYBgu+5YgBh/639XVI+jp/bbk5AKwC2AjOTLpm8m9wTf+1665+0knfnuysZWN52s5OFSW/SlfUZG8hREifANl6853jXbNX8GRhNIKIR22b8dX/U485G83TpGR+1L6ySdJOdZZicvfVHnaS89ZP2Gtje+cDqTPwqJuXT9S1j88M2YXB//N6Ocqx4tY7Ns2PycRw3OcByTv8331yR80DzwRZzpvPHU65t8k5yTTbV6F7Q96pf2jBzDmJnGH0trQJkS8IOZsdxeyC3JbcpSHmQVsGqlTmbUFhvBbxWSbEFvZXueL+tDqwCyKSHHTizHaVvgpHj+DnbnGRpdGZsfH+yG/4fHif+LUML/GdWjyYbmVZMnLCnBOQkyPZa8lnZ6gQyW2BfgYbW99SnV1LtC7ukuktMK5p0yD4sU+NjWoFwPwFeO/0bCKYPHw5fAQ3bjn+n2bKuYjxtnTKvwF9r0/JHn6sV7dSbEu1ErbzvtRjQQPRUznxPPtHi6groOfbw3pm2/YAKbdc6buBseljjOPqqFu1sZUstZlr2iZrdNx2ucuuUGya/bDm12eou3qzs9l6AZ7ob8GWAeY1bCk44DSCd/T80KWqlkAS7h4eH51liBqnN2FiPh8DTvp8ydD8tSFgPZ4Ae26YuV7pxH3ZIA96mOxoxA1J+Ro7bfdwCuFwuzzpzH2zP8tmOnMiz8pR7Tc8TsKAuJpp0er1e6y8ceCvJQKMFTRJ/qLwdoLZM3n4Mv23Wy6MGud9stSUYb+V75r4DfN76mwI7t6entiZ9TADVdQxaSLQry9/6bbw5ie2AT5PJCbaVnfo1xSe4zcZ73PI3OG724fHaASrKxesTeFrZU8srPOvbYpztubVNPpyLVnbS+OJDG22L2TrgWDfe8msW7MtjscqfU9ywfpwD3Ha2rB8eHm7eOzsBMsdpx3GPkeNme9Bjyl2u6zFrecyxeYoVpknX9B+Oq0G/t8YtaztmNo3nDvQ1Gc/Qfb+BtaBV4GvUnO+eds8G2pRdBXMH1uZ0O4VOCct9/JXUglMzmun/FW+rwLnS/Vk7mIJJ6/s1dE8y3pGda1f2Xlu+h3YArAGus+3tAM9ZP2q8/JU0+et0Lf+vgM6Kdra/05vLrfo9Gxen/ia9r2LiKlm6rO/9Kt1jU7vrq1yxijU7YOW2z/j4Lv6wn5U9T2V37a7KreJHown07f7331la8f4a2/sr4+4KkOe6P6eytpWzOvuVmHp6hY+de2m+IegpMBqhu+02a3F9X2vtsR8HMc6quNrnw/0mD1xQO69z1sbZzpltiJS7Xq83h/anAOR22lZjru+SnfnjzCvkh1YaWN6Ry58NxI1v3+O4Nzm8cms+3DdlnWzj3uA5gZMmX/OzM1sErVyTj7ra8f/bb78dHz58uKnftjU8Lm0V5qxe0gavpU33dRwvVyup19TLq1weHx+Xq/Mm+0K+Tw97pW8fcp/aT720Q3mbn1EPrN/80XrwffpIs0nrZQWOaHus463Z6I73V9t2KxDUyk8ytm3CxqNXyq2b1QMz5Nk+4u+rWNo+p/izigXmo+XR1h5lt/06B5IHxyavck72bD1bBuuXOskfbYj12rlG0+TjXElvDxKGVrtQ/h5+/NJ387LS271A+jheAfgmw9wl8JWRnemzXZ+Q8EoZDQSwTZaZAqCDrhONgRrvt201B/r8z4PaLXiZ76YrXm/bdf7u9ptDcqvIAecMOXA5WVoXbr85XEtO0WHGaDr87aC2SmjWRSt3Ricu3+pwK8F/7ZzUlPDbuE+TA9Z1G3lgxNuFLRGl7QlwTv00XTTb3CUG8sQtlffv3z/bQR6G2IG+XeyabKElmEnfK11M96c4NQGDNg73JI02hq2fXVwg4OMT4gaK7UGflS74ffLzs3nMMd7XLEPjYwIf/L/FUsrQ2pr8ovmDY+u9Odg05QXnpJa7Vvpu72uc5KFfky/H5SlHkPepzAqok/eWK3f22nilnRksh5/VGdizdBfg+5WOpsQ53Zv6boFzqk+nmg5KT4N+BmisgvcEWByY3O/kQJa18bUyULfls2BNh5O8/DxrE6uEehwvX0WwSrgMDE5YTS/tIZTcZ70VeJ7G1f1O/DjIeHzdd3vwg4m8BRq33/Sxk5GByOPs4OOE1nRheVb2trs+JS+fqaMcThAEFisQFmLbDSSf9btGZ5LUqm57oGpHk5+bX4NJX/eKyQpMOJ62hOZyud/iRLu+knXlsztwwHvNlxq49Zi6LsHiJMcKlJim3NJiEXUfGbzSys8We8znKrYz/lpPU5v04TMxusl5vV5f2MrOV8lbO4e9s7kzNrki6/reXHOG7gJ8MXCDJzruKqn8CmDcGX7r43K5vDggnzLNGZtid8HYZbKFQ0P3wFhHq+Rl/jzYPKjf9MF++NTr9Mi79dy2NJu+HNDd7uqhnrYtcnaMzLttz6+BaWOxsiuDloyrkz+DpmUx8RU07sM/F2deptcIudwEZlrQbStdBnvX68/VcL4D0L4xBWWOgwEZ6/t7/qfd0L/cTgN6+fzx48fx7du353bbOJm39uQiV43zMJP1vxoH2gkfZomMu5l8rtMWWkye5CIPDdDRHxNfssJLu8198005V/KzPu+tbKE9EUrfsy5bH46/qx0UttkAk98gwL8Ww+LDLte2Cmln1sWU6D2OLtvGI+/ifHp6evaPKY673ynO5H/urLidFV/OuxPIaXknNmFqcch9NnkmUL/DNRzPCfD6nu+3nRRvod9DrzrD1+heMHcPsxNYupenVn7nOPl+BrR6AJ2kztKqfAOL7fqOzOsOpPwq0YhbcNgBOpY9M64rMGp+GjU+27i63K+Mc0vSLp/PFfDd8dLqtr6swylATdQCG9t7TcA6W2cCng66Z9uxDZ316VUZ2z3tiZ+rds/wsQJQK7/M9Wnysfp/IvK9Ansr+2i+O+nKgPYeXlu/5nHlC44VbmcCPCtdO4adiQO+3sBXAN8qNqz0trOfHX+THTc/uNfW7qUVoHQsuBf3uN0WE32vyX4WA5nu3tK1wRFV7xh4DfDxp52tBb4pceZzOkc3nedzsgvxEGeonVng9lybjZLaeSvOInO9nf1ozsHvefy+8TjNHNhnm6FM497AFfXZ2mkgme14Zc33OfufzrycseGp/RUlaK6I9ke+Jn3b55q+Wh8T72cDFZOAAx3v7cBAk6+NL9tpr7XxishxzIf8LesOqPDaFFvyf9vSnc4qTf5AfhgL7v0liTau5u1ecEs+2/9tx4LnlVevLVqBjNy3LbXvXm2hnbKdlS5oly1O8P/WV8a7xY0VGORYH8ft+T+Ou8fYsZn9TmNr+3G8S5vUn1+xMuVRytLKtQdR2lnHyRdob+Ynq+Ls63L5uZOXuEC5plgwkfVlvht2aHI5n7X2Se1a0/nZnNTort/SdSBoB+JZril4F5B8jQo3oJkSdDP2KaGHss1JRzDPzbkdEKYnzVoSdlvtALkDgPsxD5NRT1tw5HEFWM4EX7bla00n/IwcsaP283eUowUwJxFuVZJfbtHtwEjbQprkYcBuspHfph8DKwMsEgPbKqmugtOKVmNIG5xAReu/1eHY5JoBT55Yd/m0tXp6riUdA8zJH1f/W0aOe8oHDFkXtt20HXv10/BsM59TjN0ljRVZHrcZfuibLhs52mrRygfaJLSR9dRi6vS05BSHp5jtT9ZpZxhTbnW2lfGcIK49oEJqR2p2Olvl36YHxpn202qTbTU7bbGID2awrGMcdewzhQR80TXxR5uUTHF7lZ9ILS+u6jKPTH1PILFdO+u/Z+hfu4/3fzGdASWmv1Lx/7+gM/y3xP2rbd+j4zd6Sf8v6u+1dvh/mv6VfP3fKrOpTfb8/Y329Jqc9K/i4Th+Ld+tJsdTf6vrZxYPGg+/Sv/VbPhyHMf/f6OYN3qjN3qjN3qjN3qjN1rS/7MrfG/0Rm/0Rm/0Rm/0Rv+v0Bvge6M3eqM3eqM3eqM3+i9Ob4Dvjd7ojd7ojd7ojd7ovzi9Ab43eqM3eqM3eqM3eqP/4vQG+N7ojd7ojd7ojd7ojf6L0xvge6M3eqM3eqM3eqM3+i9Ob4Dvjd7ojd7ojd7ojd7ovzi9Ab43eqM3eqM3eqM3eqP/4vQG+N7ojd7ojd7ojd7ojf6L0/8H6vzhaeMfC+8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image = reconstruct_compressed_sensing(kspace, image_shape, trajectory)\n", + "\n", + "_ = plot_image(tf.math.abs(image))\n", + "_ = plt.gcf().suptitle('Reconstructed image', color='w', fontsize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# We will also try a 2D+t non-Cartesian SENSE example" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading...\n", + "From: https://drive.google.com/uc?id=1nxJgqxOwFLIlO0Cz4NfhvYrB7_3C5Rhy\n", + "To: /workspaces/Tutorials/radialCS_2D+time_DONE/radiallyUndersampledProspectiveData_fromG.npy\n", + "100%|██████████| 43.1M/43.1M [00:00<00:00, 55.8MB/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "'/workspaces/Tutorials/radialCS_2D+time_DONE/radiallyUndersampledProspectiveData_fromG.npy'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import gdown\n", + "\n", + "url = 'https://drive.google.com/uc?id=1nxJgqxOwFLIlO0Cz4NfhvYrB7_3C5Rhy'\n", + "output = '/workspaces/Tutorials/radialCS_2D+time_DONE/radiallyUndersampledProspectiveData_fromG.npy'\n", + "gdown.download(url, output, quiet=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now read the data, and calculate the trajectory and density weights for this prospective data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "raw data shape: (512, 30, 13, 27)\n", + "kspace shape: (27, 30, 6656)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "traj shape: (27, 13, 512, 2)\n", + "density.shape: (27, 13, 512)\n", + "kspace: (27, 12, 6656)\n" + ] + } + ], + "source": [ + "\n", + "raw_data = np.load(f'/workspaces/Tutorials/radialCS_2D+time_DONE/radiallyUndersampledProspectiveData.npy')\n", + "kspace = tf.cast(raw_data, dtype = tf.complex64)\n", + "\n", + "print('raw data shape:', raw_data.shape)\n", + "# (512, 30, 13, 27)\n", + "# nPtsPerSpoke, nCh, nSpokes, nTimePoints\n", + "\n", + "nSpokes = raw_data.shape[2]\n", + "nTimePts = raw_data.shape[3]\n", + "\n", + "kspace = np.transpose(kspace, [3,1,2,0]) \n", + "#(time, coils, spokes, readout)\n", + "sh = kspace.shape\n", + "kspace = tf.reshape(kspace,(sh[0],sh[1],sh[2]*sh[3]))\n", + "print('kspace shape: ', kspace.shape)\n", + "#(time, coils, spokes*readout)\n", + "# (27, 30, 6656)\n", + "\n", + "im_size = 256\n", + "image_shape = [im_size, im_size]\n", + "\n", + "# Compute trajectory.\n", + "traj = tfmri.sampling.radial_trajectory(base_resolution=im_size,\n", + " views=nSpokes,\n", + " phases=nTimePts,\n", + " ordering='sorted_half',\n", + " angle_range = 'full')\n", + "\n", + "print('traj shape: ', traj.shape)\n", + "#(time, spokes, readout, 2)\n", + "# (27, 13, 512, 2)\n", + "\n", + "# Compute density.\n", + "dens = tfmri.sampling.estimate_density(traj, image_shape, method=\"pipe\")\n", + "print('density.shape: ' + str(dens.shape))\n", + "# #(time, spokes, readout)\n", + "#density.shape: (27, 13, 512)\n", + "\n", + "# Flatten trajectory and density.\n", + "traj = tfmri.sampling.flatten_trajectory(traj)\n", + "# This should be size: [nTimePts, nPtsPerSpiral*nSpirals, 2]\n", + "#trajectory.shape: (27, 6656, 2)\n", + "\n", + "\n", + "dens = tfmri.sampling.flatten_density(dens)\n", + "# This should be size: [nTimePts, nPtsPerSpiral*nSpirals]\n", + "#trajectory.shape: (27, 6656)\n", + "\n", + "kspace = tfmri.coils.compress_coils(kspace, coil_axis=-2, out_coils=12)\n", + "print('kspace:', kspace.shape)\n", + "#(time, coils, spokes*readout)\n", + "#kspace: (27, 12, 6656)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And calculate the coil sensitivity info for this dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sensitivities.shape: (12, 256, 256)\n" + ] + }, + { + "data": { + "image/png": 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d9uY3v9m+/du/3Q4PD4f/5ifDa17zGvvt3/5te9KTnmRf+ZVfuXYc/3bIzOyzPuuz7C/9pb+0dp7P5l28eNHMprcB47Wvfa1du3bNnvjEJ64smb3f/e5n//Sf/lN5zfvf//6hl1K96lWvsj/+4z+2xz72sfaYxzxm5dh3f/d328d93MfZz/zMzwy/rOsk4G1ve5uZ2doy4Sc96UnLZcCIn/3Zn7WrV6/aP/tn/8w+9mM/drn/5ptvtu/6ru9aO//d7363veAFL7CHP/zh9pSnPEWW4XM+53PspptuMjOzBz7wgelssPefQqFQKNw5UM/YFgqFwg3Ep33ap9lzn/tceex//+//bc985jPtG77hG+yVr3ylPec5z7HHP/7x9gd/8Af2qZ/6qfbFX/zF9p73vMe+4Ru+Ye3a2267zf7lv/yXds973tOe97znrR37+q//+uX2nHjSk55kt912mz3/+c+3b/3Wb7X/9b/+l124cMHue9/72sMe9jC7xz3usRQmX/M1X2P/+B//Y3vFK15hf/Inf2If+tCH7IEPfKA99rGPtfe9730r7TKlDRjvete77Gd+5mfsq7/6q+21r32tvfjFL7Z73vOe9qVf+qX24he/2P7u3/27a9e87GUvs6/6qq+yF77whfa7v/u7dvXqVfvFX/xF+/3f/32Zx2KxsH/wD/6BveQlL7Ff/uVftv/yX/6Lve1tb7OHPexhduutt9of//Ef23d+53dObN0bi5/+6Z+2f/Ev/oX98A//sN166632tre9zT7jMz7DHvOYx9jP//zP25d/+ZevnP9Hf/RH9n3f9332Xd/1Xfb7v//79oIXvMCuXLliX/ZlX2a///u/b5/2aZ+28h+5Zmbf+I3faJ/8yZ9sz3rWs+xrvuZr7Ld+67fsL/7iL+w+97mPffZnf7b99b/+1+3e9763Xbhwwf7W3/pb9qxnPcte9apX2R/90R/Z+973PvukT/oke8ITnmAXLlywH/mRHznO5ikUCoXCCcAN/8+h+tSnPvW5s338f2wz4H983ve+91385E/+5OKd73zn4vLly4t3vvOdi5/8yZ9c3Pe+95Xp33TTTYtLly4tFovF4ou+6ItWjj3xiU9cLBaLxYc+9KGV/zXt+aj/h+XPR3/0Ry+e8YxnLH7v935vcfvtty8+9KEPLf7wD/9w8bznPW/xJV/yJcvzPudzPmfxYz/2Y4vf+73fW7z//e9f3H777Ys//MM/XPzbf/tv5X/kjrRBVM7z588vfuiHfmjxrne9a3HhwoXF6173usWTnvSk8D9g73Wvey1+9md/dvGe97xnceXKlZX/wY2uMbPFgx70oMULXvCCxXve857FpUuXFm95y1sWz372sxcf+7Efu3buW97ylsVb3vIW2Za33XbbYvGRB5qbn+x/bKP0o//p9f753Oc+d2X/p3/6py9e/OIXL973vvctPvjBDy5uu+22xaMf/ej0P4L/yT/5J4s3vvGNi4sXLy7e/va3L/7Nv/k3i0/4hE9Y/ncvn3/+/PnFU57ylMVrXvOaxf/7f/9vcfvtty/+5E/+ZPFf/+t/Xfz9v//3l332AQ94wOLZz3724rWvfe3ive997+LChQuLP/7jP14897nPXXzKp3zKDbfx+tSnPvWpz/F+9v7/G4VCoVAoFArHgsc85jH20pe+1J75zGfu7Ax2oVAoFE4W6hnbQqFQKBQKW8HHfdzH2Zkzq1Tjbne7m33v936vmZn9wi/8wg0oVaFQKBROI+oZ20KhUCgUClvBV3/1V9tTnvIUe9nLXmZ//ud/bh//8R9vX/iFX2j3ute97LnPfa78m6BCoVAoFKaghG2hUCgUCoWt4Dd/8zftta99rX3+53++fczHfIxdvXrV3vSmN9m/+lf/yn70R3/0RhevUCgUCqcI9YxtoVAoFAqFQqFQKBR2GvWMbaFQKBQKhUKhUCgUdholbAuFQqFQKBQKhUKhsNMoYVsoFAqFQqFQKBQKhZ1GCdtCoVAoFAqFQqFQKOw0StgWCoVCoVAoFAqFQmGnUcK2UCgUCoVCoVAoFAo7jRK2hUKhUCgUCoVCoVDYaZSwLRQKhUKhUCgUCoXCTqOEbaFQKBQKhUKhUCgUdholbAuFQqFQKBQKhUKhsNMoYVsoFAqFQqFQKBQKhZ1GCdtCoVAoFAqFQqFQKOw0StgWCoVCoVAoFAqFQmGnUcK2UCgUCoVCoVAoFAo7jRK2hUKhUCgUCoVCoVDYaZSwLRQKhUKhUCgUCoXCTqOEbaFQKBQKhUKhUCgUdholbAuFQqFQKBQKhUKhsNMoYVsoFAqFQqFQKBQKhZ1GCdtCoVAoFAqFQqFQKOw0StgWCoVCoVAoFAqFQmGnUcK2UCgUCoVCoVAoFAo7jRK2hUKhUCgUCoVCoVDYaZSwLRQKhUKhUCgUCoXCTqOEbaFQKBQKhUKhUCgUdholbAuFQqFQKBQKhUKhsNMoYVsoFAqFQqFQKBQKhZ1GCdtCoVAoFAqFQqFQKOw0StgWCoVCoVAoFAqFQmGnUcK2UCgUCoVCoVAoFAo7jRK2hUKhUCgUCoVCoVDYaZSwLRQKhUKhUCgUCoXCTqOEbaFQKBQKhUKhUCgUdholbAuFQqFQKBQKhUKhsNMoYVsoFAqFQqFQKBQKhZ1GCdtCoVAoFAqFQqFQKOw0StgWCoVCoVAoFAqFQmGnUcK2UCgUCoVCoVAoFAo7jRK2hUKhUCgUCoVCoVDYaZSwLRQKhUKhUCgUCoXCTqOEbaFQKBQKhUKhUCgUdholbAuFQqFQKBQKhUKhsNMoYVsoFAqFQqFQKBQKhZ1GCdtCoVAoFAqFQqFQKOw0StgWCoVCoVAoFAqFQmGnUcK2UCgUCoVCoVAoFAo7jRK2hUKhUCgUCoVCoVDYaZSwLRQKhUKhUCgUCoXCTqOEbaFQKBQKhUKhUCgUdholbAuFQqFQKBQKhUKhsNMoYVsoFAqFQqFQKBQKhZ1GCdtCoVAoFAqFQqFQKOw0StgWCoVCoVAoFAqFQmGnUcK2UCgUCoVCoVAoFAo7jRK2hUKhUCgUCoVCoVDYaZSwLRQKhUKhUCgUCoXCTqOEbaFQKBQKhUKhUCgUdholbAuFQqFQKBQKhUKhsNMoYVsoFAqFQqFQKBQKhZ1GCdtCoVAoFAqFQqFQKOw0StgWCoVCoVAoFAqFQmGnUcK2UCgUCoVCoVAoFAo7jRK2hUKhUCgUCoVCoVDYaZSwLRQKhUKhUCgUCoXCTqOEbaFQKBQKhUKhUCgUdholbAuFQqFQKBQKhUKhsNMoYVsoFAqFQqFQKBQKhZ1GCdtCoVAoFAqFQqFQKOw0StgWCoVCoVAoFAqFQmGnUcK2UCgUCoVCoVAoFAo7jRK2hUKhUCgUCoVCoVDYaZztPfHMmXUNvLe3F/7Ojm36exvHsnPnqNdIGqPnRcey8zc5d7FYbLRfnRddO5pWTzrZNWo7Oh79/uAHP9gsw43Ex3/8xy+3e+/7SP9Q549eP3c6GUbub6ssrf7X28+nXrOpH1B5jtiEOh6VRfnGrLx8fuZTszJm5eNj73rXu8LzbjTuete7yv3RPeqJM71xL8IUf5yVZyTv0XM3KceUtskwNf5NwaY+f2pf6EmLf28S7/HY+973vmZZbiTOnTsn96Ov29Tvjd6P3vwitPLGfXPZ7UhM7u07Gaba46b1HeWwvdvqd2t/xjk2beMpHMtsQNi2MhwRXcchaudOY64yjwrcEaI6F6nNrosIktrvHRD383nqHJW2KhemFaWjjEBdk23j+dm1rXrcWbANUTu1b/c6z7nuZ6v/TSnzlPbb5B60bBy/Pe2ozJFdZiK1VYdeosVlzMrD93wOAXHSkInbDJv4tNG2PI6BrE3L0dP/RglZbzup2Nl7/nGeN5pW1o6ZbWbttut23GrrbdrGnFzSr50jrmXpj+S9rXxHB8JPK2603U0WtohNgtGuidoRsj1HGr3njFy7CUZEbSZgI9Hbk6dKi9NtlTe7viV01XZW/l3HJvY8R569QmakPKqfjNzPqQMxPRghxdv2A0rMRgM+PQNNkaBtEZPW9VnZo/Ko+3RabTjCiDDrTW+OWdpNyrApRvhD61o+1utT5hStm14zN0Z9U0+77bq4bdXhuMpwo/LO8t0lsTg3/znuvntc+U31b732PEnY9pKQnt+9584taucUtHPm2ZvHyL6pwOXn165dC8/rEbWt3yodhSitVhoZiW0R9B5xq36fdBzHoMec5GvO8qrAoe7nJmmr9BGbzGT1HJ8yIJAJ1taMbY8d4rFekZoJ2+i61koLvC5a1bFLtnyS0Wrnnn03ClPjfpbeFFK3LaG7Tai6jrRn5JujdG+0MDwpOC7fNSq+onJtMljUi9E+c6OgbOKklXEXsPGM7XGI3BspaucSoz2EbEp79Ri9ej66B5h2lIYSvJGI3WTWltNWpDq7PhPfLYI+Km7vjNgWQd12e7b65tQy9S55VftH8snOHxW2U5eqqgEh36/Kkwnalrht+eMpqytapPnOgm2LzF7h19q/TQHTm8YUUYvnjvaxqXXbtE02jW0jg1XqnJGZ2DuT7W4zLs7lB1oDG4XraGmhOfr6rtvHSN8ZFrYjQnYknTnFau95U0TtJmJ0W+KWwSJ0m0GxNauLRDISvP67N89sNia7Ppqt4W0WsnhNL1k+LZhKRrN9cy1VNBsbtOlZdTDSD6Nr+fqeWcJNRazav0maPbO1Sszy9VEeo+K2xxfjvtaMbSaEC8ePETJ9XOR4CtcYSXuu/rZNUb2Nth7xUaODTrscixWnUOdsK++RvLbdLxxz3seRvt8aVEHMyWeOCzcy3h3H6pNZnrFVGHH+vZUYFaij12xb3E4pp/rNaAnZOYxoU5EbidpRQYvn9iw3zMrg+6LZWSVys0B72sjxXKI2+r3pLGXPm9oxj56l9SN9qDfvbACnl6j1Hp8yANBadcFQorZ3gIn9XI9I5d8j/TKzX/W70O7jc7fXNuLVttBb1h7yO0q05zovOuc4l69m271lacWRsut52mHb9tkbU0djcm8faqXbmjA56f5qdCDrJNrNaBsPCdupHXyqyN1ku+e83uObittNy87YlpDtmf3KBGuUVnSNchi9S0Cnztr2XDsqbrHcJ9nJtTBa9pYYGRFlU5fBtvKK+lfvSoNNkAla37cpYd3ET3gZ+Dxsj23058i/jvhUR1Z2v6Ylbk8b5qpfNrCwrdnGG+k/R21sZH/UZsfh8yJEq0a2ZRs9PAqP9wwmR/uy/acN6r5NrftIzN4Uc/CN1vnb8IMnHbsuZueICbPN2I4Q2ui6nvTmErijonYbwrp1PQIJ3OiNbwnWkfs1SoKROLdmcXvLEonR7PpsGXLPrFnP0saT5BxGcdyidjQ9RmYPI/mwsMuWK2+CrK/3Duao3wyvSw9hjM5T7cE2kKHXplq+NSofl5H3j4pbPr6LtrztMm9zgGObecyJHv+W1WHTezQqpBWiOMjXbnN5ZUvUqnL0iFuz+epyozA6AMJ9shVLetPuvUcZRuJaltemNtMS/Nse0JkDU+8tHzvp9jBXTJjtf2x7z2s5/t60boSo3ST/KeTNLCaqc5D+OYhEJAoywjwyQ5Y54oho9zjvntnZnsC6S05jLmwqWudsoymOMBJ2rdlLRGuwaO7neUftfards02hzc4pPHp8rEJLuEcDaS2bLsSISOicIq2H6B7nvZrS1+ca9OlJcyoBZH+H+zaZ8Rv1YRkf2mQJaSZcdgXHWd6RvOYq12jf3dQP9PblXRC4iF4xO+X6G4E5+323sJ1DIPWIzmx79NxRIav2bSpuR+rliITp3NdMgWrjbHmnEqEjM2QZgeW0e4RCa5ZmU3F7Z8VU+92myB05Tw2SKKGbpYd1GXmel6/NyolQIm9Te4+uVwI3s4GWTYwI2l7hzj6BZ2+VTWfYFUKs7of6PSXNbaBFbDMxdxzLZOc8N7p+dDZL7Z8qbnttdq727uV9Kt8pPubOHI97+lYmhtQ3b/em2TPIMMWWeq6Zc5Z3ahoZpq6I2Nas7XHy2Z5+Ee3rxewvjxoxhrnSys7dhqjtTWekDqPCNHt+rveaTaBmZ5XIZYGL2GQZqBK1vYJWXdf6xmui7VYZ7mwYsfFoFlX97nlp1CjmuG9KGJu1nwMdyb9X5G1i65ltZ3at7iWC7WOk/D0CWNmjOqc1gHUaMTrQwPunpJnls+nxue7VpuXZJN+egdgW4Ru5X1EMjI5xOUfQEuc97d4rbrG86vpdQ4tHjPbJqM1Grp8Drb46p2DtFXnZ9b35bSrOR1dG9A5G9dZzbjsZ9dFzi1qzicJ20xuZHRtx5FNF5Ryidmo5EKPPzc55futaBnfS/f39lX2RgI2WBar0p4hcJShbs1+jorZ3xnaXg+lcmEqE5/Ypc2Bq+hFJdPQud1bXZH5l6nPHjMy2/R7u7++bmXX5AD8v86GtOmQ+NHqmNjqe4TTb8LbthfPpIdNTyzTHfRop0zbabiQmZ79b5e0RrZsS5KysWXkjX5Hlq/ZHfW7XbHnTdubjo+Kid+ChF73pTDlvRLS1BGErvxan3ARThOAUcbuJTfeCbXsK35ujXTeesR0Vc61j2TmbCmDcnkPUThG0U14CNbLkcPR5u15E7d07Q5sJXCVM1YxT71+0ZPWNRkOnztjuWuCcAz0z0z02MddoaG//Hp3FnPoiqdH+GA3o9Iq8bS1J9vKj0OVnbXvPU+m36hDVg+9jy7e0liSfJjs+SXUZ7YNTzp9a1+MS+aPoFbS93IGP9a5A8utGifDU8vM5rRnYSAycpP6/K9i2yGil2+LyiJ4+MKUMWT6tc+dCNsjTc+2ouM3SmGLv29IZUzAsbKdk3Gs4vR28V6iOnDv3N2KTZ2bnFLPbWIqM+flMjiK3fjx7DtdMi1xV/in/vYnnZMK1dQzrE51faKMVmLI+3EOQ8Pze9LNrR4XuaF9g+2jZ/mhbjCLzk2qm1o+jH8hmcR099eiZjc5mb3uet50S2E8qesrf6i89/WkucXmcfjPKq0cstmLM1HpkeY9u95RvZHvu+xttt2ZnewRvYTq2FUeifFRevT4pWg2S9Y+RvrKJaG7VYcqsfE+/7xW3fHxExI62x5Q2Hz2msNGM7RSHO9VJbyJ6cXtTUZsJWS7P6OzpVIHac+6oA1HADsoCVqWpZnGxo7dmWtTvKO0WlHjuFat8LEvjzoI5Rfw2AmkkaEfsgO8rL9GNMOVZcZVfJuDweOZ3WnlmUHXtebYW688CV5Vz1H+p87w/ooBl+xxdlnxnRSb85vJzxylkW5izLKN+cRN+1MuJelaOqGuyeNjKc5OytwacsE5q350xHk9Fr5jg87YxmNXy8Zh3JvimlC+qXyaaW+XsyWvqjGzvvtHrWmI4qscIj2ql1bO/hSFhu2kAGL0+c369orX3mk1EbUQsWzd+ikAdSbdHTI9AOaKRWdoRwrktgavyz8QqH4vOw3R3AcdFLOfMp7dft14utSnxzO6x6u8j6BWz6tye/7DdpCxu675t1mfvWLYoj1ZdWmXH45m45WtO42wtYq668D2eI63ody+ickRkt6csav82fGUvqR/hOb15jcS16LsXU8rcE1tPk40itlWvEZEycqw3fU5ndNuR9d0eqH6UlbXn3E2gbDM7t0fIR/eiFQPVviytuTAXX0HM8nc/UzDSmTNy09rXk1723XPMsemSuii9Vppz5N0D7uCK1CqRqwhvVNdNBW6G6AVWrVH2noC+y4H2uETucWD0beGRTeC9jPokgwPtXEv/e8QsHptyP3vLqh5BMLPlrCzb++iztl6WluBVZcI0lbj167OXTd2ZMZVgjLTfXL5G5ZcJ3ZPi40b4SiYKty28HSr29d7vVlkj4cJpZOIWr+kl7oWPYIQbq/OmDHBk+3vzNtvs+fCeeBStcojq3GODIzaT5dMjWnvEaZbGiKDtqftc/aUXk5ci9zirTZxvT+cbcfgjYta/e/Y5eslYT5uMLDXsIb1zBUE2utZMLS5DnEPg9iyxiBDNJLFznPLdW4aTguMiRXNidOCGz2nZBh/HfuGzlP6bkf1nM14zpa2n+gh1bk+6vcfQ7n2/2wPadY+45XKin1XHous8HyVisdyR6OXfu2IbjDnI/Nx17yG2veC6zS1c5uIwWbq9+fRynU3L11qt5Olvsqqhx1ep2Kps8jSL1Tn6WSZ+Wv1mav69YmiUi/ciWn3QW/bs9xwxYaTvZue22rlnoCezL96eI54ozBkTGMf2dz/R9a3A0dPZM+efpdFjULhPHR8lYy2iGpVjdHnziADoRfR8HZJ/JrZmtiZwsRxqySCWUb3wBX/3QrVlJEhGRe22HcC2MIeTPg60ltjiOXxeZpPRMbP4uXH8jQIquqaHSPRgtH5Zfr35Z7OjaPctMdt6tjXzWdmAoUoH8x0hBXdmbMOWe9Kckm9L1EbxoXfWdlt+LbPZUf7SSq+3LCODtlHc661rD4/L4mlUHnWsECPrK6NxIhNO0fVZf1DbUZ7ZPd9U1E49x/PuTWt0FjUqT88qh1Z7RPbUun4qB9/knB7M+nc/0f6pjrfnnMx4Wudl6bGhqbQj0jXlWcAsjyytnqWIoy+Qigwg+u9KTqclZNU+LA8aUvQ2U4WefhOlPRLsT0NQ3cSBtIhZr831YJPZRmUHXLbINnpmaTGt1oqAUUx5pl4dz/LPZrjV70jIelotceu4du3a0EBe5M/UoFgmqtFWW0uSd9Wu50YUXzddqTLFLjIRq+5VJGbVzM5cZWylM3V7CqfpKVdrBRKe15Netr9V7iwG8/GRchVijMSLqWny/ohrK+7tv0fvc28fGan/VBto8dVWuj11mGIHfJ3yiVNXZUwtz1yY9e9+RgRpts37RtIdDQoj5zPxykhyRMY43eh3axYjKmfP9QwlHjNg51Uvk/Fz3HAyoolljWa8et5m2msUWdpRGj2iFo+fdGwjmG0Do28Jb9lDZMO9/V3ta61E6MUczwa36jH6SAQjqiPbdvTb7LrNTblXqgxm60uQucw9g2N3NpK8bTE3Je8MkYht3bMb4ZOjPKeI12y7hWhlVu9AMR9vCeAMPeI2yqdH0KprToM9b2qnWTtk/So7Fs2ct8qhfrufxw8fz9Lke822N+cgSMYPe67bROCO2OkIVN6RvXEerT4z54z5KDZ6edTcjrh1ftTpW2Xb9PoeQtxLxlp1HBG0ihCq8vaUZdRombB6R1bP1foxftZWCQJPG8vU6zhUfbnMfI0St1FQVUGet3cJcziUEZvqxZTZyhGhlAnaKS9Rwv6s0sn+6oaRtd/I6gyVZvS796Vy2SBVCz39QvmvbCmyt3smqHsErArYu2jPjBtNLubKZ6qoVemwP+ft3us3gfJVqo/3Cg9Ej/+aMvDG4PjH5VL+lfexAFGxthVzT4ut9iC655u2QW8fjPJr5Z/1afVR+SquGW0f91+69XLnFp/tPZ6d04MoBiIy7tsqG+8bvW4OzPLyqCnX9BjP1PR7A0fruHLOI6K2l+SPPl8blWnKdQjlEDJn6hidrY1+Y7mi2SDfp4KqqrM61vtCGfz289hYIyd70jE3Qds07ZYgy+5nlnePqJ0yk3nmzBlbLBYrgTSboexBy06n2HhUdj939F71Pj/bOq7KztvZvRqdLVblmIPgnyRE/ifzS1nsnYt0bJJOj6jNZhFUesqfM3rJanRNjwiOuEfmg6I0ewenvGy9yAaBesrWI1pwnxK1uxpjR7BpvUav77HJEbsdyZ/7RNQ/svxVn4j6pVkszrIZ3k2h0uWyZ9fNMUM7ZWCztVoiu7a3rHPFlgizvDwqcsS9JDE7p+X8e5zl6M2I6qcIau9s0CYzKT2EVxFCvqYltj09ZfwK6q8/eGbWz8sIJ57jxrTJcs4WYY+erc3ELe5zqGPbNthtIXLCPedPIcItsdfyHb2zl8o2VTo9NubggRzsN9FgDV/bi5aNj/oZTiM7zuIB68UzomzfmF60zDoj6XwO37vspVUtZMThtBJnxHH5qE3zmUPUZiQXt+fw3T2kWPGWjMu04pg6r+V7VHztIdtT6hcdb+UTfWfnFDQUb+HtHj6dpRnt4+M9ZVXnZrY9MjOqyqhsf0SIteqVCcSR9hpt7wy99YmEf5ROVKbj5sUbP2MbOdTehss67JTG6AkSPcacOWYmW9nv3nQRo2Rb5afK1EOCzfTMLcOFoHdiFrT+2/OPiDGWZY5nFVU98RimzeXIxK0fx3SiQHvS0Wt/ZmOzIa1zRmcuo/R6ZieyvCIfxv0j6kt4/tWrV9f6tOrvZu2XMkTkesTnKHB7KTvg+kerMK5evbrM07ex7p4mD1SpPLL72Fs/5TdUOfB3hF2xX4VegtGbxhRiO3KtwpQZiKmEqSVkNxG6kTBjYL/mbYUpK0s4PyxftGQff7f6Ucs/8ofrgchWTPXE1122322jFeenprkNcZtdM8JB+NysT4/0q1H+lPm7EV+o4vMcNtFq05afj2J3dm2W/xw2vJVnbHvPG71+9DtLo7esvYRyf39/7Vgrv2gmI7quRVCj8mX7FPD/aRHKGfBzhb4kBAMmC1oWu3h9tKyx1dmjQQVENkvLbZkte1a/MwJzUjESJFrHR/p5b149NpCd2+rnKq0e4Yh9/OzZsyt9BYUuzipiHi2Shv0o8jHK3/TUVdl/9kiC12GxWNjVq1ft7Nmzy230E1z37DGDVlnVeaqMnBdvjyAiabuGqF8p/6RsN4udLRIbtdem7biJQG+ly+3S48N7iHuWDtqgsnOzMT+XPX7D5e0ZUI5Ic+afM/+r/Kkqo1pJ5cfwPrXEbnassI6MP/O9mnPWUCHiUlEZ8Xzmmpk9RD4tq1/U/1t20TuI3eNLlW2q/p7ZRVTOKejlh1PS3KRfzfKMba/DG9me+q3KkRlsb3DH/T2iVhGy0cDfK3yj8uzt7a2JWUVse4EdDWdqkfiikflvZXDR0s3WUs4MreDZmqXFckbLlTGvXZ2xnQuZ3Tu2IWo3JcoO/tsZT5vtxvPG+6tmOq5duybFLPd5dZ3ngQEPy+Y2febMmeV+FrqYn4LyBX6NQ9m1l91tAvs62/y1a9fWHkXAtsVyRHnzfr4Xi8VirZ3V0kq1fwSnzZY38am+PSL+R/OL/GeLsE3JqwdT7j8T1kjgRrG493Gh7ByVl5el9XLHrC5RHVrHI26kENlxr5jN6n/SMSd/aKUV+eYWDx5te7O+59oVl1LlUbyd40bGITMBmE3mcLkwP/XN57TarHdATbXjiG303Pcob1Wv7NpN+/Im9rDxM7YZIW0ZSZTuJo4o6miqLK2yKyLWErUeeHqf93T0LMuLrvM8fBuJL5NZLv9oh0Yn4AHIya93RAycSHTNrGu795lFdU9Hgic6Bk6HR5DVcf+9iwQ46mNT6hK19chy4NaxTNRmafUEDO472IdwkIjTZDKI+3gb+zmf3yqbl4G/3d9kdo3fkY3gvWf7VgLXRa4SuJ6+n+eIBgqwTbl9okHCbJUHEhtuRzxf+ZrTiF7/1BMfe9OL7C0imlH62fYURHlFBHEqIgKMH9xnpuNWJmJ7VoUxYceBNbW6ys/BOJ6JxSzOehtE9WNbVvVurXRRfXFX43EvRrjhlPR60p3KFdSgj0qzZTvqOA++Rn2T+0cmZtW1XCa1HV2bTX5E7dLCJoIY81VcICpftI/rNrVOWZq92OgZ28zIRs/LGm6u79Y+326RQN/ORG0UpHrapAUlmpnk4gfLGgm/1nMvDAyKTmyR0O7t7S2XK2YERT2nN0rEOMhz8MS08ZqI4Crjjva1RvR2AcoWppJXh+pPPW2jzolE7YhDx31+LQ9YYT5KPGIarRenYaBF+/A+j32HB7XYhrE8Z8+eXdvPg1eepiIEkV/LBK3/9jrjb6/f/v7+Sr2UYFSDcNymPGvN13lePKjg5/UsRTvt6PGhrTi4Sd7Z7+j8ll+dUr4eAhv58FEylRHaiF9Ethjxh2gf5x8JWvczKFhRDPg5GMOjl8SpsqiZVi472332uIZKszXocZqwLf81wpdb6fTeA2VX6hqVdxTDGOj/ua/zQA3nvYnw4nKp8vGgWY/I36RcnC9vKzvO7ru6d+qanjpF5ZwTQ8K2VfGRbfytzpn7u2dfFpzM9MytWfvZN1V3RDZbm5UH8/CPE10mvmpmZwq5YSdx9erVtSCEDkY5MiTITohZ3OKyR6xr9AwutoXZ6tJMJsN4fkvctmZteXvXAmyrz5vlDqrXKU4thxK1owHf741aesxp7u/vr4hFnLFl0oVBEsmgErX+LK5aroxl5PY8c+bMSpn4m8upSDLbviLJSsT6N6/MwMEsr5cPYl29enV5jRKePPjncB/A5IN9nZqtjUR0yx+dBmyb/EbHWoQ0IqgMJVqzGYYWWjMwnF/mw+dsW7ZtjlmR7Y785jqiH+L9Zrbiozje8j3mNomENtov7jdb5UYMFZ8Vd+Bjp82me2xr0/Sz/S07VmVrCVo8T3HCqDzRxyx+LwTySOyPkcBV5W3VCcuiJr64bqrfsm1FvrHFL3vKqo6zL8LfmBc/UqXaQZWb699CVo8pNj755VER0ezZVr9x31RD5uuzzsb7sqDTu916oUskYLm+EflW9UUD4w8SdEVwMb/WfUODRHLrAdGJKYtDFBWYjh9HQuxl4wCLaTFUm3IAVeQY71Pv0sQsqO5SgB21r9HzewZqetJkUTtSjmj2j8uA9sDikMUjAkkYDsa44MOgiv3f+7zXTy3FwyXHaLP+G2dtewRuNriF9fFyoTBVy5GVWL969eraqg1Pj+vlZeB7zTPDvl8FXjU7hP4Dj7HPyWz8zo5eG8vOy2Jbdu5UQakEUOu8lg+PSGQP2LYUiWQ77Nk263tpHMboaJbWr+dz0adFg0ERR+L00fdyuR2jK6mye71LMXgbaNW/xYOjc7P9KIL4OzsvK2NkL1ns4oFM39/7UYPMWNZWWbjcqs2iNogG46Lr1ABcz32IyqXKH/nE6Pqo3Op4hDntd9ZnbHu2o2Oqs/Ox1vcc56igkW3zaGQ0qhrVMSPv2Y3m9JG4IvHlj1/D5VIBUglsnG3FDwpcT8sJMLYXPv+Aota/sX48I4P7onvE98FML8nqEbcquGLb9xj/SUZko73ORfXZTNSOtM0mojYD9xe0Dew/bENoHyhY/RsFHYs01a8i4oj+BIXs2bNnw5lbLzfXA/dFs9BYH199gcI0IwL+uXLlytJ+/aNmx7ktMX/2JUwq8P75MfQbbLPc3krM7pKtTkEvUeiJz6PpjNp+i/BG13DailCrb0UasayKELYIntpu8ZCMU0T8gvepdkGyzqIW7ZIHqj1fju8IxXE8bWw/z0vVk9s6WuGF5eE2741TuyZ2W3bQGxMjXtLiyFG6ys6i/od+utd3KD6Kn2ggB/fxjD9+1D7s93wss1UlalXZuJxK1EZtGLWlatuIm2C+UZtHbYpcXeXN/UWJcawLnsv7uY02xeS3ImfoDXCRs9/ke5Nz0JEqQ1NO2kyLWmWEEVlX5/B+1Tk4L7VMkck5G2RU18hImdgiiXWCicsR0UDYIMxMktNISOL94fpzHbkd1ajeyPLE6PweB3LSEdloa3RNXavSzJw9g+/byLWtcijbwj6FQlLNmnLwcAGGy/GR0LFI4yDk+9RADQtb/83iVolXFOfRdVwfnnnNZmxxn8/coqi9evWqXblyZfnXQHwvsZ7Kn0Rt4mnxjLe3seej/Edmo4o4n2Zk8VCd59s9/qCVbhRTRu1cEcTsXD6H+4OqH/tBRdrU+RxD1UcNLGdCVg1QRy+2i4StGmxim8YBZhS4iEhgoD9kDsGC3Sz+7+vMHqPYtKuxd9toxecejjySroNFEF435RNxb88LB1HUYBaLW+/vyE851nF9OX81SKv8XSTssIyq/bDsEe/kduD6K9/HdeJtTAv5C+ap7J/9o/KbWdvMidleHtUTmHoD6Sbfm6ahRiNVAMo6OTvxrONHx3ucin/zbI2asc3KquobdV4MhCxKz5w5Y1euXFk6CfVRaXo6DBQIuFyZjZqDJ7a/n4tBVIlbnvHh3z2kZ5cC66i99iAauJnreiZlDCZukX9gO4hmNtmOOC/ME2doPRignfDAiH+3hC2LUl6GrEQuClo//+DgYCUttEMsJwpb/47IMovho6Oj5QwuLlPG++Htzm3p17nv4MErB4pbbFNlw96mvG+0X+46en1Tq12idFTbZu29afu3CJ3vU0K21Q6qjkzY1LHoN+5TbaL8FYta9VZ0jOvcFm6b/Cy/Eots1257+/v74Yytl1vlzY8oZFwDY7On2VpVdVrRstG5fFZmqz22q/YpIaMEGcfhjE9zrIj6EB5Dnqd4H8YxLiPGZoxxXO5IYGdtF3HIqH3x3KjMKl2uZ3QfonvB4Piq2lyVmzkOXnNc4nb2GdvIKFrbc3xH2z3fZrmobY2wqn1qRFWlg9tRR48IezTLw6IWy8ZOg5994zqr4IWju+yMkBhn9dvb27MrV66Y2XWiikCCy4Ha08Fg72VmIYLlwvS4PVl8qHvQmrXdNWwSMLOg15su3/PILltpLhYLeV5kv74PRS4KWzVr69d6Xv6tZmhR5CIp4yAajQyrcvC2p61mZn35Mn9QHGMgdGHp377twhYfE8D6obDd39+3K1euLOuLohjrxv4EfQW/MZrvJ+7ngTU8hoMNvI+3dx2b2PCmaY7E8k3zMhubqfXjagbH842uZ3I2Ugfs5z1tgXGX/REOvvGgG8drrC/aKPo7bhv/RmGrPgzla1HUYjv4+X4Nx2C2xW305zsLWn0av3mf2p9tc54c05TtKA6tbEXxcMXPHZGgxXjs55nZmn3wdX4Oljv7RG0TtXsLSpy2BK3vwz6g/J+6DypdVXfeF5Wd733mc7fBp2cRthnB7dnu7RDZd892VpZI1HLwUARZLRvy3yrtaHtkBIgDRvTh8nGduLyRs0Hgm1BVfff29laClv/GNF3Qqplas/iNyZ4mtgMGfyVsOT1sRzWjEx3LRvJ53y4iqmd2roPbu4VI0PK2sokpZWMbw4EQNUOilu4yXDhxP1H7PW0UhCpg+Sd6C3LPcmOfnT04OLCDg4OlqPVtnLU1WxWXV65cWflGcssEwQWvi2EU1TjzG917JNN7e3sr4pn9hwP9gWo7FrTYd3Z18GkKovoqP5f9bl3P+1WM6klXQYnYyOeOCFzch9f3fCtEdeZ96IPUteqb/RPbP8c6JOxmJv0MtwMPGvcIW67HtWvXVkSt71Ocw9tTDS6pwaepMfXOZO+jaPFk3M44qUPNMLLI4vx6Phz/1T6fZFFC1Wx1yTvHPLZxFMJRubkO3K5Re2f3gtvSy8M+KhO4vI/Ticoa8Xx1LLuG/TPf+54Bxbkw+e9+VEOMOKDs+l7jiva1DBLTZ5Hk+1QnVgQ5GmHFbfXB/NUxdR7XET/qzadcRk+H92fCnMuBgROXLp45c30Zsu/b21t9fgGXG5rZUtzu7V0Xuo6zZ8+uvJQGyTC3i6q3Elo4s4YiNwumeAzPVcZ5HAa7LbDNZfVg2+XR+8wPqPvSa5cZ1DkcAKOBJ7cBXtbLsyQYMJGYtbbdXjJRi/WIBqmi52lxyfHh4eGKkOWPn4uz0JGw9Vnb6CVSOGPL9uppePoKng4ONqn7798eLH1W2MugZm3RVtmmd9lOMyiig8e2VedWTMbvHmQzEz3kSPXVSNT11E0RttY1ESGMfiN4oJZjNK/MUjGa/Q22CwPbiN9snglbLyuuVMG6oa/H8putxnG00ax9sth7Gu15TkS8smWzmW0juG9hnFR2EPFYlQfz7Mi2MD6brb/928upZmkxZkd20uPTIrse8X8cp5TA9bqotFlUjtTHr+VVF9heyr8p3Ehxu5WXRyEy5477sk4zYnA9QdbRI2rVzJKaRcoINHcE3o/7eDsyeh4JjUStp6fKEolhPh/bCYMmElhMV9VFLTf0bZ+picQtL29Cg/CyMfHHMigDyoKpGknODDHLZ1cR1anluDNnp2xJbSv7Utdn4OtVmt5nsM+yeGQbcbC44+XFHkzxRSxoO76t2ogDeSZy+TlaJWoPDw9XfuOSZgzoaM9HR0drz9yyqGVB7GVCsRvVk0WtCs6qb/jMkPscJtRcJ75e2Tbms6s23EOcInuLiOJIvqOxl6FIcZRfdo+U4GkRvCgfv2a0bXuPoT9TMZ3jOwpd9F28ogT9DtttFAv9OrQdHIRriVtM2x8RYLvEcqr7iKtbomXK2F4lbqeD+2SLRzMHZUQDSVG+WXrKF/F1PPDpZYgGMHlyAuOY/8a+Fg06R2Xt5UmIiA9hWswrUCxGMRPTYXEZlZHb7MyZM/JRQtzGfZHY7hW3c8feyf9jm+0f7azRudE1rcZWxxDRDJMStRxosDxMQlvPt7Y6R3a+EqZeDw54vi9ySipAZuKYha07BHymjtvDy8wztyhuHe5c1LJktbyJ7yfXJxO2alkKBlM2Nq+HmsWNnNppQuRERzEqatU+dX5v3mxX2KeVWOQZWyVsceTX+wQHRiaKTAKZ0LOdqj7NZT1z5vpztvg8Lc/WusDF522xPPxGY5+9xSXJLGpdxPqsqX8fHR0tbVeRHZzRdl/A5JjvPQ9o4T1AUhL5fWXbcwfU40QWP6cQhlG7iuK0ir0q7V4x2wu8nvtbZHMM7mts81kb9XIhPq58I/MENai1t3f9WXvFcdBGvfxKDLoIdR/AL47CpcoKaqCOV2FEM7ZZXN0m8b0zIfOHHGsVZ1QcEjFyX1o+I/NpPDgd1Ydjs/dxLCse9/0qPmcDOqpeLfBgVrTt+aNNIP9W9qB8VcvntfxZ5Mtb94vt9rhteeP/sR3dl6WnbnR281VDq2OIlqjlcmVlwm9ldByUog4RHc9GcDlts/Z/6aoyYbDc29OzuFxeDIJnzlxfguwzrn4NzuSicfLMrJnZ0dHR2j4FNgqsjyq7mV6C7L/RcajtUfT2+V1CVqfIUUfn4HnKNrnf+nlTAofKS9ks2wf3JV6OzKIVnTcKXN+PwTIj2GyjbO/Yt3nGll8O5ftdyLLIxVlbhz8qgMI2ErcsbHHWFpckqxkfvB7fhOyEmvsGtjv2DbdfJNFs1/w9gl2w5U3K2LqWfb46lpGdlt32EJxNZ+VadqeIF9YLz+d9XLdNBwAxZpmtrkZScVu9U4LtBAei1OASl58FcWt5Jto22ywvZ+QZ256Xv+2CDZ4ktOwjas8o9qj42MovsqPesjA4piPXxmPIMdGeeVUeDubwdV5+XDGQ1S+zp+h6thMF9qFKrEfXsv1zObPyRAPtmKfy9RHQtyp/3xMDRs5zbH0pMiMitWq/CpDcmFkwxfMiQevHeD8HGNyfkWIWV+oZPUwnciRRPiza1GxtJKJVcIyCZCZsza6P9OILpJSg9X3sdNRzdS1xG13HbTASRP0a5fy4P0RLorhdCjEiRxgNxvA1LSHdkz7nhTYRPWuLtmV2fcaRnT4OjGQjv6qfKJ/E5WUb5b/2cdHqz9r6S6R4Ftf3YzvjsmL84JJkJr34BnSetUUx7HVGou22fHR0ZAcHB8tzcOBL+Xsn4DhYhaR8E0RBfhcRkYGMWLXic5RPFm8YLTKsyNiIAM7IXCTQvLyt9vI0VD+J6toifyoftHf8ncVwbG8Uo7giAr+xjPhhUYurq/h6vCaCismeBsZjjMU9IrdFdtEvF64j4p3cv3jbgTGwZQc99pRdr7YVRzAzKVYjEavSQ/HodVQ2E3HFaFXDKE9B9Ij1KG0lcKPze+KE0hEROG+0xR6RGx3vxVb/7ifb13t+th0FUk5ziqhVhCr6jsRnr+hkB8LCUqXPwQyXHkeOKAuK0XJks/W/LcKA2boPng6K3ei+M6lF7O1dF6fRqBLPOiuj9rL3BFElZrGPTJnRPQmYO9Bn6UXOT9lgZn+ZDbfyy2w3WsauRC6XEYMOB3m1JNl/R8h8mfIB0dJpXpbM2/v7+yvCF+0EXwSlhK1ahsx/9+PC9sqVK2szRkiWVRB3oar6BhIIn4Vl0ht9c184TVAErUUS1PVT8o2uzY45mDSOCNrWOZgWf7J0ozJHs1A9wql1DvMMjtdqG7+VsMW8ccAnI+qRsMWlypyGmq3ltmVegdeiqFV1K3wEqg9FAyxToXgo7vNzPK+IA00pR+8ARGvCxX1dNBiixCf2YWUrnA734d6+2jqP2xj3qaXVrXZmAd5TNozRitf7N2sM50Cehoo//I3Xzj2IPCxs1Q1unddzfnac84w6tLp+U1GL50QCFj+R+OwRtLytZn+jtCODV0SZ01eztVG5Hew81D3ybXz7sB/jUWBP02z9JVJ7e3tLwqxGh7M283S5vOjA/Hdr1va04bjr1bLBCMpOfT/va6WlluuzTfEHn2PzfoGO3/PF58YQEfFWPiyqMxMMFt1sx+rRAiXWUdh7wHRx6YKYZ195ttbFL748ypcz8ywvElq0c08P7dLrzgIYy4nthn6a/cuUvn4a7B7JBO/n38rGsnT5Wv7weS0Bmn33IsqjV9hGiOqBMaZ1Tdau0QAgH1PtqrhElC+XkwcA2E6jRwn8mweGVR5eB7RlHMDCZZ/KbucmvScRI3XsnYnrRWS/ipNi/mpwacrAkeqjqn4Rx0T7UDOcSphiuiwSo7iCQhfLEA3wjEBxGfzNArcnr9E+wm3HHxa7qk+ocmV9oTXgOrV/H/tSZLO+UfTec7JA2hK1nHZr5igqkzJ+FquZCObfGclWH7PV5biqzFx3JruRgM46MgYizAc//nIpJd4z8F8B+YtocIkJ5scz1v6bR4b92w3RiTgaEAtgDLzoWJQYPo2YKvAzssZo2SCf479bNq3yiQaG8Ld6eRQHUCwDilpPA4l0b/0jO8I6KtuNxC3XjeuL53i93F7UkmIUqn4M/+qHl/srQezpe71wKbOyRRUosYxo33yf1QBa1Pa8b5cIdUbceq6ZI88sFjMiUjxVeEZ5cD5KhEXtFRFDtV/VdY6B0da16At5QNcRze5w26jVGDhby4MD+K4KTMfLgnXAGI31wkeZembtOD8E84Ls3hY+gijuYCzkGIS8yszW+oVKX6HnfrfS4/6uBCDui9JBm86WLuNxPBZNukxF1C49M7WOEbGdTVRxmTJugm2iZmVb9jiXvc4ibLOGGz3GBsTHIqE1kpcixi1Ry4bOIlDtYxLK5+GHBSqmFRFVRcgxrch4IwHN5c7au0XaMS/8aw7+a6DoPu7trf4VEDoMHoHj9sG2wLLgqLRy6P4dOejWKOSuBtBNCW5PGors9tgcX9+6poUeQRt9c748uIJlyGaIeoIGtxHnk9m+st0sXxS2HpxQ1CLZxaXETn7VM7k+W8uzu/gf19hO+H/YUX0Wi8VSQOMML7dLrx1mg1a7YMs9NtcimyMxtOf6KJ1IvKrfeH4rfwUlXpWQbqWphKkiaxmY0EVl98EZVQac3eT24aX7nueIT/T02M6VqEUxo0SmE3981IB5jJcbRQL7PG+PXqHQE5/vDFBxJup3ivuwL1X9KLLhTOBm5eV7N+KDon7OIs37KyN61MyvyWZz/RxMQ4noHkT2z+Xi7QwYC7N7wmI2mrnF/Jljq3Zhv4n3WfnFkcHFFoaEbS95nRM9Djrq2FmZWoRa/cbrIqegxKUipPjb81CiWQlbDhRKUPfUPRLdmahlw2cw2UdRy8/kjoDbmWd18KP+AgEDs5eFAyiTWy6j12OuUbldQXSvMkesRGnvtZx3ZJc4A9ArcPHaSMBms6AId8hq+ROLt6wsqm2UveE2+4sovYh4KCLCdfC6M+k1sxWR6n/vg+IWxSefy22DS5/5OXocVMBgqUTt3NhGmscFRSJ8G7/x/G2DxWX0OyJjLXvK0sd9mwxaKBKe2TfWBYmwIn0ZlMBUAwKYt5kexM/8RPTogPIZGM/9eXr3R0j4mWf4PvcxIzasYm90T7J7s0vA/pENkqj+3mvXiuP5flWeqG/MBS97xh0Uh8DzUWRG9cjEruK6Ks05B0V7BsEQEc/CgS8+xlB+KWp71jmqDXD/lLZQdjtqy5NnbOcOhC1jigxPXcNEj49lZDn7rWYT9vZWZ215fyQ8szT505q15Xy4/tFIiCozd+ys3fl6H+FSx3rSi+6n+s2BTLWFA5/lQVEbkQG/76dZwG6TyEbONuo/fk3WJ5TNqL7F+UdpKRtS4haPY9pqVNOP43L5XmesAkpkJxkZzMSrWhLMbyVV9yVKG4Xq2bNnV8Stz+SyqPXjnh4+m4v+C0kyDmC4UFYDe3NiNJDeKIwQ19Y+FS9H+m9kvz2CNhNpeG6rvorc83evMN0EmC76Aiy/Erg4+KraBZfyR9+OyE/6MYTHOh+QQmHLAsa3PZa6GPb4z22MPhQ5iafRO8jJ2AX7nIrevt4SECPi1qH8AKaXLUGOuKZKp6cMvh0N3kZ1U1yObS0qF/bHSORymswXp/BHtHvPR8V+rmPm2xXPxd9qttbtWuWheL0quyo/+8MM6pwRX72VZ2x7guiUNPh4SxwxohGeiFwrsYffaGxIsBTh5mWyGVFXolWJ22g2V9UzGtUduVfK0LijcxrKEHqcEzs2fFbOOzg+q8dti0GUlz3h8xCRwOXAHC1Z4WNzE+xtY9Py8v3sEbU9RCYaaMFtFkPY15Tgw2v39vbW/v/VX5bEg1H8/LqXD8mqlwEDIRPTVjty+bi9ovZUywb5uVV8lg2fe8cgxu2ufJ/XBfPwGVt8m7L/ZlGLwRCJOr7siknv2bNnV8rBwjYq51SMBNCTBi57RBBwO2u/qC2ivsiIBKWKR63fEVlqkWtVfm6j7FxGT/9Q5UF/gfXAOMSiVs2ioLjw/u/nMYFn3xit6mLRmolbHBTDOqpVFCo2Y50xXkfxeATYtvi9Sxgp76Z1U77ATMdotDPuGxG/7Mk3QosnZJxCPa7W4mnKt0Qi1z/895NoTwrRfuULojZiXx2Jfs5Pzd76Nq5gjGIC8y63axT6Stxy2bM2mMtWb8jLoxCbdv7RNCLjbYlanm1FIqacuOoAal8kzJhUK0GrBK7XCQOImSYAUbu1Ol7UpkrUqHohuY1IFh5Xb0rE0WkkwPjSHywvimAWDpjXlJE2TGMXgugc5L836GTOObI1lZeyQ+5byoYdTATdblzM+n+9+m8Wudx3kKxiv8Hj/P+RvJ21FwarlnDw/o0C1suk/pJL2Zlqb/zwi1+Y4LqodUGLohaXJ/OAk4tiXIqMeeJ9wLriTC+2WWS7rQEGZftz2MlxoGVr0bnKj0fX4MBQBiUmRkRnz0BQD2mcKg56/HhGOh0Yn1jYomDFPm222ofZ1/g1aCPRrC2KZE9LrYzAerB4RRvHcuOsMdb37NmzS5KL/QVjs/IhmS/q7UeFj6A1YKPaOIsx3OaRoM3uR+u+Yhkifob8MrM971dsR2bt51/Z9jkfvp5jRk/8ieqOeWJ6UX17uRPu50EjHCDDx4DUCxc5brCwZT7E9zEaHG31m6l2vrGwHQ3+WRAeNTqFqDPwftUhomPqZVAYGJR4ZYIdfVT6ZrZGqnueCUQCink7yXajYfLg+3s6UnQOBml0Qr6PZ1wdly9fTvPrKQe2Aw8GeH096PrIMLaNbytSu4lxnXRsStzZftjGMtGUQdkS7o8Gitg2ozL4tTwQop6vVfaNzlrNwOBvJIRRebL68/kqQKJ9I/HtGUCKgAQhGjhAooMi1AXutWvX//bH96FfQFHs4tZJsLeDDzpgH/B0magzegeolI3vst1z/+P+yedu6geYTGNePUI1Kzvuj/JmYqXK1UpnpIy94DJhWb1+ONMakWS8R0im3da9/3Naqg2RJyh7ZgGLv93P4KAd1tXzR4GN/tN9hJmtpLe/v29HR0drfdHrz3a8q3a5bahBImWPPfauBi78G/sD2x+n3SO4ov0Yx3vAsU6JWxa9vWXDdlPpsq1h+i3hr2KPp5HxGKUzorIj1MAZ+x8ui28r/YI8xzl1VKcpAncKZpuxjYLmnOnzZ0oa6rciz/6bRxm5LJGgZaEZCVsmzuoc9Vwt749InndanA3x/XgOd/DRtuT2VESD79tisbCDg4PufNDosC5KmPjyRRS3fN94uUpPnSuo6tnaUVHbcsZsa3xtZg8YELms2AdQjKHIxTR5aTI7erNVget9xPsnO3w/32ydsGEbMplVpAW/9/auv6iNy8ftiW8mj8D5s5/C4Os+Y39/f+1vf/CvgDAYIqnlAQVvy/39fTs4OLCDg4Nl+6Gw7RkAOM32Gt3rnnOjWBi142i8HRWyuM1kfBQ9gxSRL+eyqOO95cqELQpBTxMJp9p2MM/AQWvfr2Ib+j3mIFhmFrMoZHlVBV7j9suzx+hj0ce5/bt/4P+vV205euzOgqltkPVx7nctUYvXMjLBpfg1XzPVF2Ti1uvUA84/GoTy9HE7iveqrNwmLXGL10YxXZWdfZADHxHgiR7Fu/xaflcHfm6Efd7wpchm83TgVroIduaKxJmtilpceqyEKRNw3MaOGQlbTxvPUS+wUYK2FajMTC4vUCM5aFzYPmpUvtXe3NFb+fcYAI7setrcPk6E0eh8qRSOKm+r351msPhCRPe5R/T6PjXIhGlH+5RtcHnwOrQdFreRyOUyOTBAMFFVjwL4x0WcKp9qKyYSmLaX7+joaFIg4bwxUGGwRXvz9vD8Wdz6UmS+J95e+DyuGqhwUXt4eLhClF0w8+oUbCMHk7HThshv9fhoHhSI7HKk3VpCWAlZ3G6JWiaN0TGV72gd1H5l+1HaankwftAWUIyq1Re4jYO6blPua9w+lfBgX6P4iZfH7PqLpNBXIfHFNkCbZz/BjxR42+CgWNQH2ZYL44MrUZ/FbyVSUQjyYAfGnqlQQhb7ju/rEXZR+jgYynUy2/wloZl/HBG1eE5WX5+kwvzVNg/oq3RYeKKf6uFN/hvLxFzIr5sj9o6kMUnYZh0sI61zpD8F2WhGRKRxm5+v5TTxWLTNopOPM/mLRK0Sxpg+5sGk1BEFO7P1h+5bRCMjJBgoORDiS2LYoWJ5Vb44G+bnONE9ODhYe8bW64VBGfuDk+Uov9OIyBZ6r2UnmtkX5pddp+wG90f9PRr44XTYQWO/iZayK1vrIQpYTp/xyEhEBLRTJBd4zAduvO5RGnz/8KP+XxqXOSpfgiQVg5zbky819jb1QSm/hp+v9XZ2Auyi9vDwcOkvjo6O7OrVq3bp0qWVpY2qzsrH9ZKYXRLBrXgc+Vbep/Zjn87AfWOKwG35XjxH2QXu5zxwX5SWqo/KO9sXpcO2i7HRbQ1XObC4xO29vfWXRWF8c9tCe8zAfg1jMwpaM1uLoZcvX155np79ngtaH6RC++eVFxmhV/d7l2x0m4gGeJjzOSJ74zaNYk9mby1k+oD7gBpEzjAi+NE3bCJuWeyrPtmzlB7LzbO0/FvVU/EqdYz38cpHVV4/nycDcHDb7TjSD8eNbmE7SvizxpyaZm+ZsnSZTDN55m2eRXXRqcQkb0ekPROx/JvT5vSVUOtFT9CP0DIcBI9aY/7uEPy5ut7yIvF2MswzbngNBnu+16eR7I5iRNTydZmgxWt6Ra2fq4Qpb7NtZCsYOC8UwihwlQ22hK2ZrZFUJq84g8uIyAn+ZmLhHy9T1D+VkOV2ww+SYp5tUmnj8mG8D2qpsdcFXzSl2vns2bN2eHho586dWwpbb7ujo6Plqgz/IFptrAYIesTNSUMUT0eELPdpJsO9opOvUflFQnYkfT+/Fy0xqoR774BTT5nRfnjb7Pqgqr8Dwv2Elz2avUVfg7aKJJj9hOrn7Nci34Y2gz7V82R/6eXD1TD4SIEa0IqE2MgA4GlE1s+yuBHZL+7nOBLFnVY/yvJT9VDbin9n9cd8eh+h8/Si+s4BjCvREmUGD0YoccuDuGqVGCLjZThYhvcfr+X7oibbuGyZL2nVPds/ep9uyFLkngr3nDM6QsNgQ+gh1REBj4ijO3ckfXysJWpxyR2XgTtf5gQ2RUaCWkBxizM8bli+lCly1OxQ/XpPGwMoClufiVV/ReCkYsQI76xQgUaJV9wfBScVgPAe8AoJ3ual+Ggniiwp8qbEsIss/PufbLAKwUGdZ1Wx3+IzcXgM0+JtFLQ8as7tF7Ur191twkUjDv4owozLHdVyKBa4ni7antfJhS22u19jZsvZ2nPnztm5c+eWZVosFiurMvDeeDtF7dUawNrFpY7KF2fETZEXFUNUbM0ISIvI4u8RAqxsIytXRlZ78ud2myNusqhVgoL7nvsJHNBx8atEsko7y5PP8Tyz2O7X4GNNPuvK13qaOFvrcRl9WmtgHu0Zy4Bl4jKq713DFD7SEpuYdiRuVVqRqGUxFpUp8gO+zf1mhF8q0T4FIxMcPegRsQj2TdF17itGOKviY7wSA3kBlwt5g+LYjugfGDydqfdmisDdSNj2NGxv488tLJB8R4bFpJnPZ6LN10SkNxK/vo3X9IjaaOZItR07LN/XaqeMFEfiYBQoTLxci8VizUAyw1ZgUYtGt7e3t/a/na0AftrBdc5GOrOg5L/RdvicKC++rmVrvI2DGixG1ZJkzk8JW09ndCmy2Wrwx/OYuPLgTkTMeKQ3EmlqoAfbPnrmDYWqErjZBwk12hmWw8ul/pbn6tWrS3GqZmzPnDmzFLXnz5+3c+fOLeuyWCzs8uXLK7PrkQ3j0jAeIMtma9W+XQX2MRWP8LypPnHK+S1xy+cilFhn4q4Q5cVl2UTc4sAOl7clMJG0ut3wMa8fDhRn6bHPYMHL7RKRUiw/HsPn3Xmgyf2pC1u3Y/Qj6FtVW6pvLEvrvF1Erz1F9cxELB5nrhgJpkjQ4vHMrrI6qm+O8yq2KYzM2mI5evsL1j9akt26J63jzBmYUykhHx3jekY+n1/8pHgEciP1IjifmFJ9qLeNWwJ2xK5nmbHtCYgnQUCwgEVwHZRgRcKL52QEvGfZMqetRC0HHMzDbL2Dq1E4hnIqUV4qT5UeGygfZ4GB5Yyel+M6YfpodB5EcdmjX6uEibrn6iVbdyb02jAPECjBylCDRsqezNZnbTH9aNCHZ3KxH6hVD/zhmcOWsGV7M1sVU0hGEYp4+P4eQatsG0ks7sP6opBlERsRYBa/PGvLQp77CZ9jZstnZPmN097uZ8+eXYram266yQ4PD21v7yPP8/GLoxTU/cB2Ufckmj0/ycjiLvti3J998JzMlzOJyogqX4dpRmXnfHoIIf6O0htFi2xFQFLaEqBm12MPPmerBGq0rYQrlhntAcui6qX4DLaB/+YlyHytx2MfpPKlyD5Axc/1t9pZ+b1MMOySLY9C2WO232zdRtQgh+/nuMRptcRsJrK4PBEnb0EJP/TtHq8yoF20zvFtjiVoe9sEDiQz31dLlc1inoZpop9SQK6Fk0fYvhE/4hgxh8DtwVaWIk8JJD2GMDeUYbF4U06ef0fbeD2mrwg2X69mm5RocEfPnUYFUa43lg/Lq8qkysjlMLOVgMvHMS8P4BxkIzARwDRxFAlngTiYqzbkeoz0QS7PLgIdXVZ3PBYNbqj+iXlE9ubbSuBimmpGAG0FBS3O2kY2imngjC8eU9dHRKz1zTbmBI/Fa0SA1Ta2kaeLsz6eD9bHhSqLWiVgo4/ycZF/8IElT3d/f39lAIqv9dmd8+fP2/nz5+3w8HCZD88AZwNt6j4oH8k2nJHlkwa2KbN18cjn+nb0wXPU4InKH9Pk/er8HlEb5Yn1xPSifKdwC74mIvVR2bJ4zOSbY/jeXvycLaaBy3oj8cH5KoGt6sGxWtU9iqt4Hb7Z3P/aD31TNECFZVOiIesTqh3uDIjqHNkQC1y8jz3tyHaQ2Vhkn4obq/OifLN9m3CzqL+plT54bmsANcqD2xH9APIn3O/fSuwqqFjJolbdH+bY/vHrfYAq4tg99W8dH7XhYWE7EiBGgwmix1B60o+EDBoUH/NtRbAjMqc+6tlaJtaYF5O1jHSYrY/gYLsoQavqz3XlcuDHbHXmi++HE1g3uihfbAe/Rp2L6fLsktffR5HY8PD6qP027Z/Z711D1hYtkqpG9pVd8XG2B9+v+poSQCxgsxlc7uPcv1EsRcuRlc8wWw8KPMjj/VzZBabRErG8zVAvcEChinaE6allVb4PB5/UcmTuD6q90Cfh/9Kyf/V9PsNz0003LYWtl4fvC7e3/1ZkA9tNtWNG9HcJSEwjooLbWYxBMhWJZN6XkdMWwe0Fl4ltbxPfHuXF+zyfiKDy+Zm4xONIXHH2Fm0wSyP7qOXIbM/MYdiWzUzauV/rdolvNT88PFzG5b29vZW3IWO7qbbnNkQbVeT3ziZqW1wkst/I10VtlgkxzmcTe+wZbGfhx/lm4Hr3DGyqODHnLK1qx55z1e9o9tYxci8wLithy4NbU5HF3SltfCL+x3YUvR04Ekxqf0TEI1IdkWS17XlyGiptzJfLErVF5JiizsJpZWXnABZ1Ygw67mTUshBFpLjtVdosbLl8/HwtOr3RkSSf5RrFrgVRtgMF1VewHTNRy4RHpZEJXL+O+yPP0OI5asbWbH22F/dxukokcz2ZVCNB5H6u2pEFFhNXJTq9TyqbdxKMebkN+EysWoLMgpf3eX3UB8k1loPvzWKxWJ6Lz7szfL+LWv/435/gMmQGi9dIrGP77bqojWKEIpZ+vOfj53L/jshwZOdMvnhf7zUMrF9E8DKOwPVh0q/Si8qTXc/nsSBT/dBjDy5JxoEzzwt9gs/c8n4e0MKBICUSuY3Y/+F5mJ4CLkP2b/9bIPcHng/n30Nuo7LfWUVtxvla/b0nXql8I3/TK9AYkR/ogRLV0Xn4zTEAv/m4H2ObxbiCcPsbrUOGaIbWy4Q+QsUH5mrKrlCwO6fCR/04Brf0wei9xHJF2qaFEylssxs8aiyOyAnzDeAb5PuV8IxEGm9HM7cqLTWKqeobkZfsPNUGqsybCls3ZjZsrqdDvbEYgeQcl195mmhs+FB71HZc/wijjmlXkRHADJGozZxay/GxXfh21Cd9UEO9iCibsVUfJWzdfnsHdLI3AzLQXpS4xFlTM5NiDdvKSbH/RrtRAhWXI0cfLqP6cBn8nnib+Tn8PB62gfcNXoZ8/vx529/ftytXriyXL/cQF2wv3D8ianeBGKsYgW3O+7O4pc7nNDPymN2XKcd64l4PieayZ1BitnV9Kxb7dVEfxAEizi86x+08Gnjy883yZ3yxXFhe5Sex7MwlMB33xfjSKHz0AFeyRODVWZhHj5DdBdudGz1ioMcG8Joem/Lzld30gDnDKLB/99q4X8f71DE+rlYGtfLs9UF8jvsA9M9K0GYztGZ6skH95vqoiSNfceXt4Pas6tyqv9qO2mfEpicL20064lT0GEtGJPm87Hd0TSRMcTuakcJzMpHYWx6z9ZHTXpKA+Sphq0QAlhnTVCNVbnBoAJgHji7xq8cdHkBReHB9UYjgElIkCZheZhwRORzBrgTUjFREfSjrr3g8Soevb52fkfJInGbHzPRzupGoVS9087IwSeX+3hK06tpMXLKw5FkebFO0NyVm3Y48nWgmN/rwAJOyb28/JPVYTqyz2+n+/v7K25D9jch477BNmfwqH6Laj0fYd1XUOrL4gT4wE7aYhvINSF62Fftb+WLevaKW0UMwmdS3BD1ep2Ki6mcsNrMBVGXTLDTVR62s4IGuSNx6XsxTVDk5DSfCuBTZyTAvQY58oUo/2rerdrspWvVX4rRH9GV9PLOJXjvZJnrqnPWvSND69kmZ6GBB29P2rXuCcdLPZ1GLKyIxv977rfpfj5AdteutztiOBIJN0xpBRKzx2IgwReKrrlPncFoZacgCcSQuVXqZUOD6ZKKWOzKSVzR+D56K7Ht6XnYsv5+DQlaJFE+Dl59um4CdJmTCEhGJ0l6RmgnpDCrdSLiiGGW7M1tfeuxiSglbtRQZ66XEVMvBR+SzV9ziUmREFGyvXbu2JJL4shZflozLk/EFU3i+EteetrJbNdDA4kTVz++Fz/D4x18c5TNUCvgSLm5nbq/TJGqzuMEEr+X7zeJRfTNbGcjIMEr8sjx9MATTjMg0g+NlJmQjYBu2xLC6hvdFAtdsdTDYr3e7ULO2PNCENosroPhcjLn4rQit6iMO9AnKjnHG1ldUsTBWg04MtbLiNIta1X/wWMT3st+RCM3SZyh7Y5sYFbVTBZgqL9qIH0NEAzL+3RK0nEY0CMPgQamePqra1W3Xt3tfGNWTF2+zX2JRi4/6jYjakfJsasvHthS59wao87JrM0fAiM5Rgg6vUWKUt5n8cnDgtLLA0XJqDJ7FzOqtyqLqrv5uBb8zsDFj3ix2MVh7WZBUR2IbxS+LWsxfkV1lzE4IejGFKJ1ktEQt/1aEuJVWq9+0AqsatGDxqgSu2bioVeczWcW+owZuuG78rURiS9wqAoh2g/7QxSAuW0Ty6x/+n2f/KNLcKguWB9vC64Z5Hh0d2ZUrV5b3HWdsXdQeHBys+AFvL/xgG2LbqsGD6B7g/l1DJOzUvujTMyDYisFmqwI4In0sZjO/wPG9R9RymVsCNerDeDwTBlG8ZnDcw28HClzcRlHo9wzFauubbSKKh1E7YJvh/XWbxrJlz+Rx+yk75faLytgiwbtmz0qMqu3sGnUOpsG2EJXD+1jU93u2PZ+svr1cF8UcxwM+rwXVj6NjKs2WzTiiNo78EOeRtQ23NfoI/kaxrziM4h1m10UtDlQ5L/J6RBiNrdnxKXZ8Ip+xRbREbU9gm+MYij4WhzxDlBEHJsiIqbNarTqo70zYqm/e9nSiTsd5KrHL+zAI+3FFujBgq2WjWfA8DUR2U4wQSwf3W7yHvWn0nIeOGwkeXq8GOZxQcT/x/VNErepTWE4vH7dPa5AEB104sGTilvsw+j/0T9ze+PE6ocDE/diu/lHP6Krn3dW99Pr6NVeuXFn5OCne2/vI26j5Lar8VyNchqzduCxqxJ3Lqsp/UqHsEcHkMhK1UZqOLC7xbDzm7dfxbAqWJUMWO7LYrwj56L1k4aqEQXZtS4hk4pbrgtvsG1uztj3f6p8VVJ0wT/QfbodebyfEKGq5jspeM3GLZdh1u50LLWHfurYlvHC7JW7VdXxsClr8H8vckxZ+jwranuNYHuSxPbO2Udv5PhT3flzN3ioB6+cpfsXv2nB4/OfZWhTKHBvQflWbZfvntNutCNsewblp+mbTjCUif9G5LOQw2EbEQAWjaDsSDBm4A2Rl4DwiUet1UkJWifVWeVk0oSDFa3A0WR1H4HEk3ixo0En1zHiZtZ0Up53V/TRBzc72kOERuL9AEYszoFHwjT6ZSFNCV33Ui6cc2Edbdsb1xG3uk4vFYjnrmglb1VfRjlRb+Tn4n3NYV5y15WXJOHuLYjYbMOI6uZA9Ojqyy5cv2+XLl1dIMZJhJ8T+AipvE55l9m8mxnifsrKp+5LtO4nIBGLWR1uxLBqwin7zII+DyQ+XCfNCKCLYI2oj9IjbjOQrYt+COp+JcSZu3SfittuD36+RWVseOM7sWPkmtmW3QTVjy6uouJ/w4xBRfiP3aldsViGqT2+fH+mP6pvRk++IXWbH1Aysqjvmh325x7axvGrQMzsenafAdpy1byb4uC34GmyzqG2j++zfaHfMMZgzMR+I/ISn34qzWVu2YnMPhoTtcRN5dcMyA8lGlnuQkVI8JyOxLAo5bS9nlE6UhypnT316PkrM8narTFEZIyKTjYa10u8pU++s19w4bhuZCnVfeq9jQjwVajaWHbYjC76Z3XAfVqKWZ2vVXwV5+ovFYvlMqtsIinDui8p/Zb99nxK+0d/9IAHGNLwsuCQZxSsuQ0Z7zwQ/PqvL5VOzqYvFwo6Ojuzo6MguXbpkly9fXn77UmSvA7e/1+PatWsrwtiv8zxc3HJg9T6G36dF0DKm+Eyz+LGCyM9HAzxm15ch+35FSLmsSkA7sUKxpGZte9AikHyuOieri0ov4yw4MIu/zTQp9m9+rEDFNhS82SxtNEuqCCrbFfoN/OCsPA4Meh3QhylRq0g2XpfZ6q7abA8icRvVvbctIpHL6fTYWu+1PeJ8xLbVSpDea7CcvYK2BfY1+DuatY38U3Rfollb9wmtb86DZ2x9EBn5Ev7DgWqXSMhGmNpnR3BDlyKPkuLRjh/lNUUYZtexEOT9UVoZwVDpZ2VT+7FMGbHJxKyatY7aQP3m72iEOnLainxEZTezlb9GwZFhJWznELiRSD8NiEgvb/ekE4GDEztinInM2hltD50y9mMWbtnMLvct7t+RoI1sJUrH20CNePoxDDhRv8W0lfBlm1aCFo/zdTwQwAGRyTUS06OjI7t48aJdvHjRLly4YBcvXlyKWwyk2OZ+7dWrV5di1md6fdsFLtu5epkUfx9HcN02olgRnZvFBrO+mVsVRyPbVMQsS5uvQxsbGfRkEcpQaUXncnrZNapsShAjGfX70BLtbnN+jtsY/70Y2yEPakWDvWobB51wgIlt0G0P7ZjbiQenlDDGMpjFfnEkbu+iXWfYtD6RoFK/R9NlG8nsLEsfB7fVNpeRfQ/zjWwSJRO12WBKBmXP6rrMR/k12X1Ss7bRNz5y4GmjTeLgFObFnMqvxRib+RK215ZPnsteT+wztiqIjBpbBkW2lVPm8/lGq/MVCc6Oq3QVEc7qrzphJFK9TOq83llaHHGOyhN9I2Ex0yM/aBgY7KJ2wP6hBG0UwDmNTdByVLuOkf6ors2AItZnF86ePbs2w6H6A4IDH14bLa1Rs7hZ/0cCx+IW64Ajn71Q/R/tg8keBnvvf9i3+X5lfoEFL8/w4l8Cqd8qyLowvXDhwlLUurB1UqxEJs7E+iwvzvi62MXZW1wSyf4D21a1+a6C/b46judFZGVE5DrQf6ItjMyuKn8SETqOGyqtkXvZQ+QzURvFIS5TdMyBdYq4BN47JTyzj7cbrjJx/8QkFO3ZzJZ5mNnaIBOvnIjqhmmh3bLt8qxt1q7Z/l22Z4VWX5siCloCVKF1PCsn72fb4zimyojnKZt0tCYaWqK2p08pEZgNqCm/2HO/VB1Z7Pt3Bo/nmK5aJeEfn6X1D7YBtpt6FIgFreIxPYNTm9j2iRW2xwEmddE5HHQiUazOVSRCBZ6IcGAa6losTxbY8HycycqIbovY+L6IxKBRqsDNxs2kno0kMl51HQdLNUqN1xZWwfeaZ2szMh1d1zqXZyT8XuKsLfcXBdWH8S3fkcDF/WwjUR1wBYKXiUm4SkdBCTxFPnFJstnqX+H4OS5Ssdz+wedsfTvzA36eDwD4NgpaRTYWi8VyhseFrYtbXoqMdXUC7XV1MYyi2K9ncq2CLLftFCJ40pH10Shm4DYO/kTnqXS9nf2YErcK2WAv9iMWsy1xi9cjIrLZ6gOqT3N5ewQynov5RvVU5cBtXpnEQtcHo3CwqjVby6IWB+hwkApXTvjglJed7RjTwsEptH8emFIzPp5G5v9Piz1Hgo3PUd983CwftMmOT4XKezSva9fW34Ls1/PMLefVKlckaLns2b4WIhGr/GLLD6nBMuQWWJ9M7GKboa1HL49S5fDjuEoD02Leztcfl83eUGGLTp4DiDqvBTSGOaDIghKnPdc7WIRyWkrE8rbKJxKzvTOz2SxVtGTM7HrwUwah2g+FAF6jxCkHOD8PoYyZhS0Gzx7nNoJNrz/JaIm63iDVI+oQyh+0SI7Ky/s1bis7iMStsknli9RsVeQnmHTiEkHMg+uIZNFs/e9tsJ3cD2bCP/qo494mTpDxv3BR+PBoutvepUuX7MKFC3bHHXfYHXfcsSJOXVjjMmJ/Hvfo6MiuXbu2vNa/kSBHL7LBIBv1n11Hyx5b99Ps+iAnbyuBi2mioPX75+d4m/NvRxZPFHrEbY9IRSDRjvyIn6e2OY2RPFV+2JYZ9vb2VgTsyFJk9D242gIHqFjU+jbapgtTnrXFmWE833/jdbicGZdC4vPyStxG7Rfd+12186iv8Tl8rKdtIh6XHe+FKnerHp4nC1i0LyVuzeLHnLgOLd43KrgygerfatKnJfp7BS6ey3HXzFZs3M9H23bbQn4ccVm0R4fi12pAKhK5Ub0yjNjyiZyxzYLJtoHBXx3Db96fpYlpK0GpjnOZ+Byz9ZfjZEK2JWZbZEY5CzbiqC38uF/DBImDrJppxXyxbXgk6dq1a2szOT1iWc304PHTMkKMjrAX3N9aUAM4I/D7gEvmfL8SuCrI+Cd6llYtQeZrse8rX8RBF20hIuDqm7e5LbDOqg87sM3UIB8T46jNuK1Y0OKSZS8jLnVykurkFcXphQsXlsIVZ5P83EuXLq2U1QWxi2K/Ppv1yQZBthFIbxSUfanYoe6pErrZ7C3ngbOA3jdQzGJsmFIvh4ovvCKi515lpDoijbyN6YwKWq4TD0xxXMwIoVqNgb/5mN9XjHG42oIHzXjASglbtRTZz3Xb9zJcu3ZtOSCFA1Nsv0yGmShzLB4RubsA9lNZTFDfvXlkfXduzj1VNGcztdg3za7/K4Afy/pFJmo37TuZuPXjvbO2XB8+B9sD0/N9bt9qcMFtSw0ksWheLBbLAWjk0eoZ+WzVhfqo+vK2+t3CkLCdq8OrdHr3HTei/EeIac950b6IpJjptytHglXNVqnfKu2sTsrgsjbjc9nIeCQoMhIHOwgeMVZvX2TxykJ2KnYxoE61sWwAw4/jOYrc9s52Y/COSE9WvsgO1Cwt/0WFsltus0xIKD8xAtVPcbQV28fzwXOyvp35FvxGYYt/DeTkmomwl82XIfNSZCe2Pptz5syZlWf3cKT56OjI7rjjDrv99tuX4jYixvgiKdU/or6yi3bLUPfNv5WA9X28RN/MVvZF/Rfbcm/v+stJ2Der6zi93ripxC3no+ILphWRXxXLcF9U9h6oMkVxEAlxK02PmbxfxXfl61DQolDAmXsvl8fQ7BlbtPujo6NlGb1/+KCVP4pw6dKlcMVFFvMjMXcabTvixeo7w2if3ZSPR3wwK2skXhF4HM9RAlchErVT+okSqF6miBdzPsq3qH49Km69fNE9c1tlQYpAmz579qy0c4y5vYJ2FFOumTxj29vJW+ep0YQbLWYdGXmNzpuSR+t6dU4kaqOAxvvU+ZxuVrdRZ+r3lQMmjgDhtiLmUYDzwBwJZDRIs9W//sgcWya8dj1gohhRNjc6qxvN7kdByslb1MYtn9ETkCI7yIheJEwx78j+edRYtQUvHVT5RIEI6+7iDW3I82jN2iqo++filWe40V8oIqxIML8V2Ymxl3V/f98uX75sZ8+etb2967PKLmxxxtav5zcj42CWly3qK7tuv2b5YAQfR1vmv1Ty+6dErvIB3t88bRe1KJA8f+ybm9TToWZq1coIBUUM8VhPOVr+ZvTarG9mRBDzUgIXz2N/5wMQ/CiBtyULXZ6xxQErJ7cOFrYYo1HU8qAUzh5F5LjVXqpddwlRmaOBEL5mdNCotzwRR8iu6xWzjIiH4LFI3Poxs/abkeeKBZGoxDZDnoNcoGfWFssYHVfi1gcJsnLizCvamXMHFrVYFn7ED5+5Rd6ufNgUe+45hph1KfKIiFXb0fE5yxAhIqtI4qJts/ay3Sy/3jIziVHEhfdlopZFrJqpikh2T5BXo1UsZpmQqpc+MYmPyuDputFFS5rZ+Lhu2e/TBBa3ZlqAtvqmmtmJZm4dnifP8jCJxX14Ht5Hd77sRJV4RIGmhC0LN8478llRHVS9s2M95BxtAGdt8ZgLlWzmo4XIlzDB8LZC+/PZWFxe7M/W4mwrCmNPC/vF5cuXlzO2+FZl9YweB9eIFJ8WMIGNhC3eN3zbZSRoOUZ4Wt5+OHDitufCkokT9hUu89T68v/lmunZW0frvm9CvHuP+fFMpJhdLz8uI/Rz3C7UdX4PUGS6SG2tSvG0WDB4eTg+40ujcCkjEnlPx8vjwhY/+Nc/WZzGumYE+LQIXIVM1KrYM5e49fQV34ti46ZgMZuJ2yju9kxKsN0ptFZO8HmYTq+4Ve2Y+Ql1L3jGmjk9r+7CmVclRN3mXdRinkrYRrYbxeJeTLXfrT1jm4lYM73MJxO1kVEdFyJHoQhFj8hVYr4XLBqYfPLx1oytEsVZuXv2KXAHx4DJ2/gxW599UY7I04yWR2TLkFnkTjWoXQik2Oc4aET9MdqvBkc8rR6ih98q3aj8/s3b0dIgtg8me0j42Q6itojuda+gUjYXge0GgxIHLRX8swCj7AHTUmV0O8OgjTbtszpOXvFtqE5s0R4xDy/3tWvXljO2/EZlnjFSz9f2kOFdh/LZuM1ixj8tgYuiloF9isWs5+vfHO9vxOytIoqRbU4VuWy/PTE/skn/xmeWe0mhC1wUuRzr3W4RStSiv/FZHbc1tjusn+Jx7g/UG82RHKNPwLYYEbS7aOMjHIrPV/0wi61Ty6Q4O5+P+U29D6rMkbg1u84VMi4d9Z+WP1J2kYFnYiNxyxhpN64Lx04vhwtalTbyCPz2Y+iD+H4753CbZXGr/Jlq/5Ztb2LXw8I2Ep0tgcqkKxJ2kXFEQnebAlc5Cd6ey5FEjioqT/Yxs7VRWSVuzfIlzaqMkVNDg1UdEDs7BjElaCNx2yNqkezzCBKmEREFNYvLsxC7jkwAOVBgRP0xm/H335gP5+9QS2a430b1iIhhJMyUsM1eIuXpsZBV+1gUqrpimdR+bi9F1DFdXv6DwBlQtAMOYAcHB7K8LG6wv5w9e3ZtCTLaMs7G8HN5+GydlxPbxAm9P5fH/4HLS5DVUqheIbDLUINIbH/cz72v8zaKXzNbzgyqfodkSMU/XJrs5UDCNeJD1Qwi5hfN3vpvJH69hDESvY5MSPC2uo79lLJfjJXKz/X2b/Z5+PdeDl55gfvRd2C8RoGLcRnbAweorl27tjK4pQRtRowZPaT3NNl/qy4RR+XjWbv18vCWgIx4fVRmBSVkuRzq3AgjYrYHET82W5+9VeKWfZVKlzlG1gcigZudz76H/RLaInNg9AGt2VrOs1WXqLwj2GjGNurgLUHrv9kIsutGypAZ3lS0gll0jjI4NjJOTzkbJRaijxK0kaiNhG0E1VFbo6uq4+OocrQEWQVyJaDxWPRhQ4vSwfui6nIaoPoXEs+W7WV9zNOIbMT3YRmit/dGZVcO2Z0tLknG+82BH8k9knwuOwcX1Y7ssFWgiNpR1RUDYATMMxK3XhdsGy63GrXl8qItuKj1GT88h4Utf1zg4vJDLye2g/tEFLa+jFnN9ER/NXDawX0a+xEvL/Y+7vcNByb8uebspVHcv90mvG/gkmT/nYlbs2kEUy3pw/2RuFXI7DeCEvLZd0++2KY8+ISiL/sLL0yXy8u8BO8piloeAOBn1bPBaLOPtLm/CRmfl/c4z7O1bLvKB7XuXWvfLqKnHhEfjYQtnjvSTszX1Xbv9dExBXU+D8ArcRulOeeERMaLHIuF/rsfsz5fFQ1EINBGkKsom1fXqA8ecztnjrhYLLo4O+eT1SHbnoLZliJnN4SNqUfUjgjT7HyV/mgaiEjMKofSyo9JsxIcURmijzqOgQtH5FmUZMK21elUh/ZvDFxMfFWQ7AlwynBwGZOnGxkypsFkXuV5WoKm2Xq98J4jSXFSggJGLeONyHCGnnOi2Vq+lzyymIkctAdVH84TfRILRxUYuCwtgoZlwuAc2bVqB7Pr4hZf8uD1c5HBbbRYLJYDAdmAEJNsFEJeDrZvfxbP9/m2C1xcNswDCfhssD+bi/99y7O1Xm9sey/XSBxR9+UkQ/lr7NsoXF3I8jeuWOgRtXjPeFCWZx6uXLkSilsmfiOIZjmiZchRTOO6qdiA6UexflTccp5mttKu2L5+nh93/5AR0x54mjigx8fZ9pHs4raf4/5APc/Hqzh636jaqsPI/l1Ab9kjrtfT90bFLV6Xbas8egRaVFbkHshPfB/nPeJLuDxzxgksl5q99f2RuOXy8f2K+De3eY/+ULamOA1fq2ZsMx/K1/PxrH5T7Llb2PZ2bBUQ1E2aS9Sq8vWcywFwBEwAFBnOHAt3ADUSFdUnEq1eBl5WmQncbNZNkRpsNw7I2cc7vnq1v1qO5IbDBqAMDMvm5+C1THbxOt6ec1TvJCNzjGzLTlZ4ya56g6qfZzY+QmumlyTz9UjyXLCxGMPZIyV2vHxqVstsdQaZfYXyadjHsXwRQcO0UMh6OTy/1mATtomZrQQZhP9FTzTKqpYTRYNROFuLbeH27feFf6tlw0iIfVky2r7P8uALp/hvRqLgiu2DbZ5BEZSTDBycwT6ilh77fTt79qwdHBysLUXmWIHAfoxLS1HcYp91uKhFcWu2akMtccsDaxlwgAi3OV+sF9evdc+VqG0JXQXO22Mq/ua2jpbbK6Ko/ADCRa37QCy3ivncThhnvbx+j70/oNDF/7RWs7URd8hw0u1zbmRiNhpgUSJB8W8+p0cY93L2nuOqnlEa7hOw7CPpZ+e04gTz/NY5DhS4eL+QX7gN9cShbF/L/7C/UDHTP1geLO+UQanjtNfJ/2PL245I5PoxVbneDjoH0OH6703y5I7eG4TRweA+JWqV+FQfdVzNSEXitofYcFnxN3Zus/VRXrVMkYmuCqS+HbWjKiMHSE7byxel1zqH09ol9JA3/3YByEsZ1Yw/X99r/44e2+H+0CNqI/KEdfQyI9HjYMp1UESsR9SioMWZWgwimd2qNsG6okjc29tbCtKovNhebI/8PB3O9DlwoAHTwTwwCEaztbh97dpHXhyFM7X4bJ6XSdWnB1E7ts45SYgELQpbFrT4m5ffsx2rdvV7hvahBhz9GPZv/B4Vty20fIvnxdsqxigu0hISmcDgbUUicVu1Fw4guK1lMc8fO+D6cx4sbKM28m/sByye8blsHKjn2Vp8NjcbDOwhwj3nnHQ7biHqZ6Or7ZBfZnxK8T/FSzPwNb2cA8vr/T2Le35elv5I3r3njV7jZWy9EwCPKyj7VOf0tgn3BewfyLHwutZz8T02OVqnUWz08igVAHodDafTM0rBabTAIjYzTDeg6IZvWhaEcjBe1h7n0RtgI3I8spyUCTuWH+ujRm2Q4PaIWk4D84/agY9xeTktTpO//V7wPtzPmMsYjwuqvNzn3MkiEWZCjI5ZQbVX5HAjZ8zBK/qgqOWZSRTnPU6YgxH/5v6jyFlUd7Q1nlly+/fvbKBKDchFo6cobrlt1OxtNAjl4oiJBto/LkP0NuABBrRJnOHH++/C1md5+E2q6vk8rDPex94+yNecdLvGGVtcfsyztIeHh3ZwcBAKW0zLwYOLKKhcDGX+0OOpp6XEnv/2vr7JQGHvvWLbVfaM56rytj54nm+3yozlcB7CduNtlBHKKG7y4B/eRyUcovaMBob39q7PNikizEuQs2WMUwlyhJNuxwjVV5SY5e8eUcu+kdtYcfuoTCqfrJ+30ujJD9PxPoacPULkV9SS5l7+HaWVlYF9g9s2/+Z8svpFx1ptovwd5svcCuvBsVzF3siOs3wj9AyMMCY9Y6uEzSiwsNFoTasM6jwWs600VN6RWGqVpSc/FrCZUbHDymZYW//NiaP4fE7rpSH+8cDFHR5/84wNB1NfhoTEWRmGalckP9h2LCrwd8/9U/dSkY7WdbsSQLMgwoIWyfDh4eEKcca+ye3G/Vo5bCwP30+z9b+r8SDgfdFnOb1fYB/15Y9XrlxZnsfX4DJmDHI8o6T6Efc57n/cpor04nOvvCQZRS4K4IjMoIhEe0M/ycuJDw8PV2z26OhouUz17Nmzy1kW3+ffahlyRkrxfjK5Yn/n9cCliyhqUdj6/VaCmfsg5qHuD5dV9dOTBvbbuLTYbdc/KG7xGduIDHO/5llDM/3Ijbe5x2AUZH4NDsygzUfitpekeX6+LxNM2cfLiXVUn9YKKkyHt1XZo/IwgcRZXP+M1PnatWvL558jYctQ95nt2H0V34dM0KLd4nfPfW/hpNsvgv0S97+Iz/k+vMZMC9rIP3Peym9y+j3gdFr3MxLV3ueZL5tt9vgYx/2sfsqezfJHr7Bd8X0UnrcSuH7M+YviWAo9tsLnMGdTXA7L69/IL6KBqV4onqAwqjcnPWPbmzgXjIlr1JFHoDokk2o3DuxcXBfsVLgMSHUG74hqhhcDNv/mMvE3toEiwRxURz78PFUkchVZ8Y7r5Njr5uXAfRy0oiWNSE6zkR/uJzwLoPqhcuq4f5s4jjy2ARRUKGjPnTu3NtujRC0TEm57fKMnByN1H9GRouhT91P1IR9EQVtRxNTzd2C/xnJwHtjfM4eOeS0W159ZdqCA5TqhnXr6Suhief1aHDTCe+DLjrH8KHhR+B4dHa3M8PksIOeFbTMCnEV3P+L3mN+i3Pts7Yj9teLPSbdl/nsenKk9PDxcilnf9t9qObnZuqDFQSMl9Px4lIbZ6oyKWqUQ+XBPC7/N2kRW+SLVRzIByCRWCdlokGmKuFX15bLix0kvxldVH97nto7/Yez3pSVkohkp5UvRrvB5e/dH/HyeKiu3R6vt1HHFDU8ysn4XTU4ozhb1a+4bHE8Zx+3/ovyUuPX9mwLFrQIey0RtVBbe77+jAXssF4pb5D8Zeu5lxIedc3A/UfuiQakoDvf0pZ769WL4GVsvQHSsB1yBTTuoKtcUp4adh8Up31w/nzumchYcNHm0hh0ZbnMQjT6ZA+T/LlQiF9NR7cKzWMohKDGLoztMSDGwIRlBKOFvtkqSuFw9Tt3vDxsm1yfqJ5uMFJ40KFHrRPjcuXN2eHi48tIZs/W3/aGT45l9tge+1stgpv0I2l/kaJmM40yt/3ej56MIp+ftNoFly5w6HufyKxGtEOUVEVwXL15eryu+hMnT4r/V4ednkez6fXPhgzN76DcYaA/oi6OBMvc1btcokNw/4iyt/+8tPmPL9wH7H7Z/FAcigrRLZNjfTo33jO334ODAzp8/vxygYjt2YP/y++F2gDMH7Pf8GA8G84CUGiRGMCdAe+I8VTroz/E6tp1WjFDl8m8WGkrwRuLWwfYTDaSoevDAHa5AQZtmv+jbPojUmq3l3/jsLNcF+5/fQxwQYSGrBHlEiiMCnrXfrtguoyVqmcO1+JqKj2hLLXvM/OYImFP7vhZUPsg5NxFBo3VQ52PbRwNArUEs1gHs57y+PLDo1/vv1n2Mvvk6xdk4titfiv1L5T2CucTt5KXII/sdc4+2qPwjQ1ckxzsMdhw/378xYLKY4lFnTo+v8fSwc6qgogJmJm6VQM1ELX+yWVsm1LgPX3bDnT56lg8/7HQZSvhzu2Xikx073lveboGJO+ezS+A+5v3Aie/58+eXotaFrR9Hh47t6+IR+35L/LOAzMBBAPP2/FDU7u3trQharzcD+0e0zJrryiSTCRkTfRYEnjaKUBefCiqgYLu7yHXCivV2ounpu7BhQejXolDGGVrvJyjSVcDz+6pEgJmt+CNuK2wLn3F3gYuiNno2v2WL6E8yP7zNGDUnmOiiqHXbRRt2cYv3EQem8NlLfD5d2ZICzjCgwDVbJerYTzA2MpjscTxW5+N1bKfZR93zKA5zLFbnef0U+fVjmE9Wd7Z/jq9ur2gbyldgHMd7G91XL7/7KVU/74OYrvcD9D3ZUuRogAG/uY8ogo/n7hqwXZnvZZxNAWMvD5CwXTIiX9iDnrbvvT8RH0TfMYrRPhLZqLJrFNyZPaE/xAF6JV69rL4vGyTg43iMbYrtDMuneFXE8XsGCEc0oWrrqdx6aCny1HO2LWinQHVuJUg5+COx5qDt2xg4sLOhsGXBpoInE5foW4lbPsazL7jNpFUJCPV8LZNWPDf64KjyFFGL+fBoFh9nQYvOPHLsSsD29v1dEbjcx/CZy8PDw+UMDwpcfiaP7/Xenn55CNoUwu/tiLjFa7EPej5odzjowtf6tzvng4ODJSFDYct92/PB67lfManFcjKZxsDBJFMFSO7PTH6RTCoi7P8li9c66cR6+31GUavEPubLiNJAAebnsU+4evXq2l/79D6j52Vi/8FoiVu85ycVh4eHS9+Nz9S6mD1//vzyg48UcL/GfsCiBwWt9xMGxjU1+MuEbhRK0LbI24iwxfI72Ca5DyuRG4lbTA+RCV+um5cT+z6LWrxnajWUX8OPNSlEIsvLjR8/z++7+wMvLz8+oAal8H61yPFJ5JObAvuCmqTI+JrZul1wXOW+lMXdHv8ZQd2XTJCNwq8f5Qwu2rhsnhb2Y85PidrefZwmimUvDz/y4TaNZUPt0TswwKKReXF2D5ib+PWZ/2QfzPWcgswvZpg0Y5tlEjmhTTr03I7s2rX1F0yx8bEg9XOYiHqZuBMrx8HpcX2iINoSuOjoomNK1Kplhugsva1cRDJRVwQJA1QUxPz8CFmwx3ZkB80G1jI4vPcRRvvrSSfDZquBU4laRYi9r5jpGXvf79/ch9j+2en2QN1HFDTeH7Fs0aAH91F8jlTN2nL5mVArYowkw9sB7YNFBA9u9bRFROwZKDquXbv+H7PuCxVh92eUmUR5XTBf9HXKZ6HPQRGEMzzeNvhsrXpplJqV6gnWfB97xO1JBj73jKstXNTedNNNK3bsKy/Ql+OS1sjv4r1l0oX9yskjC1keJG4NZqk+5fkpu1P7uF+0hK2DBW0mbpXAHRG3LfLLwgQfGUCRyuXB9mI/jb4K81ZElOvObRA9s+/vBHA/46IW/W1L1Ea8cqrgOunAPsM8TvE1HGQwW30kyGMgggWSX9sjbucG379NRW4PULxyGVSsjfa1RG1L5DJUO6M2YV/bqnOmtTJezFDaRtloZqstbch5zdnXZpmx7XFCmwjT6GZFaaFwZRGrjCoaFeBA6Xn2jj5z4MBOwKNEHDT8t3ouNntOVv3GDzpJ/NuH6LkNFPNebgyobGwoGFTQUkTCoRxt5BSUcGFS0xK3UZ9uGViPOD/JwNk0FLVMiH3mx0kz9gEcXcSlaGhP2K/5vm7SdpgX25SXyfPnPCNhyzOXTFZZtEZQYpjz9vbDJZ++7flGy6h72w3LgATJCaa3l5NRJrBRO3B9lF379ehz/OPneJ/xe4D3ll8cpf4iLBs19vK17tOui1v33fjmY7bhm2++eWWAClddeNtzXzs6Olob+HWbw8Et3Gbb937j9wO3EaMiN/Lr6hOJWt5n1idooziNAz8oOBTh9byic/A4tsG1a9dWHiHAFzFx+p6G25VKT7VxZENcb7drvH/uQ3gZcvT4gJpRbhHuyGZPA7h/Kd6GfM3PM1v/q8oIPBBl1n4OdKq47bk/kSaYg1epvFncts7vFayZPWN+Km+/J95e/I4MHgzs0RyZoFT+Ud2rHg7TErij5ZxjsMOx0YwtN5g6vk0nNJouilwmMzjSpb6RPJutC1y19JEJPndiPIdHRNVsarQcRS33i4QtO8lo1hbbGIknilosP56PgTHr8OreIaHi9uM8oj6nDNDBv/naDCrPKUZ9o4H9yYUtPofn5BiFLQZEJybYxmhXaE9MtqaQWwT6FMxD2Rr2W+yTKOh8BgSfMUXbymZiMjKsAgPmjx8kqdEKipawVkLWAyWKWiUO8V6yEEaBq+rh7chCAfsXvmHb08LzeekivjQqmq2NBs5GwD4F40HUzicJ/FdcvPz4Lne5i507d85uuumm5Yuk0E59IMV/K1+mBC2fp4QgiiI14KWAPkBt9wpZFrVsc/ibOYSqC4s6JWr5e4qwxUEHBb9ni8Viac9eFl9dgfXAds8GqTgPbh8vq9cf9+E5zBWiRwhaA1MqnkaiVt0/rs9Jt2Ozdf7X4msssLzteRLHTPOoLD7h/kzcjrQt3zu+FtNnP+z7ev07n9vbJzKByjaTnRNtqzwxFppd5788s473RfX57D6p/ewno3Ow3Jm/RR+A31k5snuq+sAItjJje5yIOmnm/BA8o4vnqxtptrr+3H8rcu6dAmdEmBCYrTs1dm5nzpxZkpJs9hYD1xRxG83YYjvyEmfu+P6NfQK3o3bia5R48LRwBAuBo8AccBXxzchwr9BC7Iq49YEQ/jsQXoJ8/vz55WyQt78LMCau2F587xaL9eehp7SV2yrbJdpX676hM/ZldC7CsE+zTbFdma3/jygTYSVK0Y84yXNxjSLbZ80ycatmkfb391cE8uXLl1dELZJM/O3icbFYLO+3skkMRjya7PcC0zk4OFj2FRek6E+dCCPJxedrfTuarVUBln19RuJ7jp9U4N9woajFFRc333zzynPyPMtjtuoffb+TKe87Khai0FH+WpHnM2fOrM3Qexn8u3dbESzeVkKWRZuXkUloFJdZ4LLfyIRtRI459kZ+A+2Gl6UilLBV5eL2ZP/s9mb2kbjhaeNyY/Rp2L+Uv8lELQ9gYF16RZQ676Tbsdn6c7X4mIEPXPlvFrbud3HVD9539NEchzOu58cz0dR7XzJwGpx373XqGu472axthE1ErYrfLBT9uM/WOs9mu+R7p+rNuoUH/5VtsO1yupi++mBa+M3bUdtm503li7PN2PLvTTv7lHKom8bi1r/xhvMxBs60IpFGocvf2KmwkzI4kCrBis84qg+PILMxZcuY1T4mPL6NSyV7yKByjmb6bZhZ2/A+vyeYD5ZTjQBjHiOzuJxXC7sQRHFAg5cxutjFj/cJtgPVD1jsshhSaLWvCrietgrWXg6VDs88OSFAou7kWxFYtFEeIWcbRlvBdmDirZZD7+/v29HR0TKNy5cvr4kFbGu0YxfFfo/5r3L8wys0OIgikUJBj+3pdeDAfHR0tGxDXnbo7Y4j1S54vaw8W5stQ+ZBLL7/PUFzhDyfFHhcwP+p9VUWN91003Km1l8C5/cQyR2KJPTvaFNOlDGmIXnGfsgiNyNjDr4/rdla/q0+0SygEk3KTlnUKtturZjCtDwvJshRPlF7ob/IyKq6R1xHvsZtCY+pGVW+Z3gdvgROidrW8/HbEk8nHSpuqP8Qx/+Rd2SiVvlDjiNm+QTRVPTct0hM47HMfysRq9IYAYtWRGvASolanACKbNUHtn0VBgtSrBPGKq4ztxX6H/bXWF6MxX4uns9lVr64t239mp79UzE0Y5t1spNA6Dd1gEiW8ZsJKR5zkmamlx4jKXAnzjPEaAgsbqM3GSPhVDO2HKSjD6aHwRrbE0fxOe0MkdBgg4yID49yYVt7+TAwchDG2aFMQOP953Oy/p4F45MMXiLKQtZJMj6PbRYvufdj24AalHCbQruLRqGjtFCcK4HrtsCCF5fi4TYTXxaEmShEsoozqMrmHJimGqTydDxYKoHreWD63iY4G4j+gQWjl9/TR3/iAwlsxw4Uvn5vr1y5shS1PFPbs4QxQkbakCjsEolWoha/ebDK7wMOLOAgjRJkkaDF2Gi2vrJmFK2ZW0dEqFQ/iPqFKjPHyyhu+n4eDN7b0wLXTC9D9m+c4Y2EMdbF+z3+dzXD68sEGdNV13i/UORdcQC+nh8paL0wKhp8UPeqhV2yWwVeCccr6lDUKrGFkyxKZOEAMPs6JS4RfC5jtO2jfLkMqoyRiIuOqfQVsK87WiI2+s2iFvdHbbG3d/1tyMp/YJzkenAd2ZbYd6PQ5bKwVmG/zj5ZpcFly/rUpvsjDM3YnmRR6+gxMjYWDtJ8Dt5k/m12neCqToFp42yRIxKh2ZJhJM1qGbFybmrplBK5GPi8Lk7wI+LSIpQsavkaNjQuOx5DJ65ErQfS6Bwuc1Z2Raii2cWTZAMtqBlbFLnc1/z+tGZWcYQ4Q6vNoyVpDlw6gwMdvfli+ujsnZB5nX0QigOCqivaLy/vR1GoiKov9XQRd3R0tPY8fURYzpw5syZieZkxC1wXjOwvvE5O0nHGHvsBtgPanKePy1c9La8L1//o6GiZFgruS5curSyRnjrb0yJsfiwiTycZKGpx9ta/eXDC4w/OsqvBSiTD0W9sG2UL+B1BkST2277dErScTg/hUnFXiVr8zXaJYkMJWya92C7Y9ixsWsJWDZjhcbPrqyCwvmiD7NfdxyERdh+L+xRP4AFnXoXC9qsELd6/XkGisCv2i2Del63Mw7bHgWaMh0oI4feIz5vKbTZt/+MWtep3S8Q62L4jH8J5oZ9zHcHXRH5Y1YN1i2pTte1aBcvEnKAnnRFsU9zOuhQ5QiSIjgseCPzm4SinMh4UpNmsLYsdPw9FMC77UsHVv7Ej4/M6LXHLo8ZsbJg+GxgHYTyn5egyssHA9lNp8TY7FiY+Ssgy4Y3IblTGHqHbQtYGJwlK1KKIYWI14lBGkInYbCDBB1rMrgfwLDgxXAzigBSSSz/Hj6O/8DfI4gi45xcNSKGoU74EX6riopafsc2EreeHz+myuPU0UQyzzfMMNQ50YL/w9sF7h/kdHBysvH2ZST9e48Cliy68WdTyTA9/q/uekRomQ/iNx08qcDDKZ2d9mweo8N6Zrf+9iCJTaFuqD+I3b5tp347+WQ1ORNeNilvejoggxkNuB9zHfjETua2BZr4HLGrQHvF87P9sS+hLsF/zDCxex+LUuRGupEBh69cocYUimeNyz7Px6t6N8ka29V0SuNyu6qMGM9BWuf5+DQpcFEaRcNwWIl+sBjKOS9QylHDNRK1qb3Ud31svm+rzmF7mP7Ly9/BtzAP1Dp7LfSVLK+P/o8ey/T2Y5eVRUeYjZHMkz550UMxieniTWOT6dUg+kYRGs7Yq+EZEPaoPOy4eqcPfirCoQIjps1FFzjMyDCYjWV25Xj33rdW/8Dgux4pELS5DxusVKWbgvtMwQ8tgUhaNCGMAnQvYX9SMTDRj40Cy5kCh68cjXLt2bbm0GvPmwSdFtjAIYN9i+0U75Ze+cWBjIsiiVrUd5+nCFV/cxMKWCfjly5fX2gbFqloGp+rg5VKzxEzCz55d/8sodb0LWn6+FttKkePeAIz3048rMnXSwSssokFPNWCJyMiUE2Y85uDZA2UzOOjAPpivUaJXCdiWuOUyqLoy+WTiyYO9fo3ynbhPDeJw7FXtHM38MtxPsB/BQS0/h+vJ9VH8CIUwD3h5m7Igdp+LsVcNOONsbWulRQ+UvY7Y/0kCtnk0W2u2vopN+Sm0Wb8GfTumc9zidgr4/pqtr8TpEbj4W4HtTXFXPCcSusjF2e44TRwg5/MxPS4nTpipermNcZ4ZIr86cm2EKeJ2E2w0Y6swFyHIyAUb7ZR0lMhlY3f0zNqi0MUO2xtwvS4YWDNHpxyfIpwqbfzN23wt14/JeCRwmUBgOXh/ZERsYCwC+BsDaVR2BSWi1H2LzjmJwSBDJGrVAIeZHrAZqTMTGd/HaXv7sujh+4D2isQa7Y5ncTEI8WytD3BljjezaTyHhe3+/r6cCee2wRnbTEAqwuLE00U7imR+w/KZM2eWohaFhqeH5DUSt2rGZrFYrAlb/E8+TxOFLd5rXIat/iIEbT2b8RmJP0yEdkHMItRAZ/RoiqMnDkT7EFGcQai4p3wq2r7Z+osBe8Wt+s1lXiwWa6SRP0xGeTbVf7PQjd57EcVCs/VnK5VP5vZSotY//Ew9DwAi6eaBcG8bz9+v90Eq1a5sh1F8juw28qURWnzOt1vpnDT4/WjN2GZ1Use47yk75pgyF5+Z4o/RF/M9VT5HHW/1BTWhlUG1WU+9sM2VLzbTM+49aWfCF/ezb83Ag5SZH0X09pfRvrVJX5xd2DKygBhhxCB6nSJ2Zu7YkSEpQRvN2iI5VkG4JQoiR+bHotncKAhi/hnYAbCQVIIyWlrUuhdMmLCcXF4laHGJZUSAVblVmvjN5WD0GNcuiVtF1rC/ma2O+PlvbreWw4z6HzrbHlHL+XiZ8G2QDJzF5fJwndTMqAPtMevvbLM8k+PiwwkMt4UT1P39/ZWXOuE5nK/fR57JdFuI3rDMeZ87d26t7nt7eyuC9vDwcIW8Y129XErY4v10YH39O3uDqlqRoQRR7+h0DynaBaGLAyAoppRAjcCkORO9Dp79McsH+9QgaOSXW7EzygPTierJxDESspGg4HOjWXIWwZmowHunZtvxGl5W7O2oZo/RR3PZI2FrZitLkZWAju4D+yA1Q5sNhGObcFtlQhZtObPrXQDfJz42mpYP8GbnqPaLcCM4jhKpZvGMLfPKqN2Uz1dQqyYY7CeUv2B+YLZqm8o3cN7K7/p1Ef+POJrynehX1DG2uePA1PxmFbYtZ9SDTZ0RO7cobd9WIhen8PE8Frl4o9HB46xORMKwrL4d3UDs+FGQVaO7nIZqDyVYsB2YYPIMKY7mZsFKkSU8zkQIg6EStEyGIxKM9WLhG7UVlmMEN8LxTwEvdcFvs9W+iKN42aw3Xuvn8j7uH2oEH0Utv5DEy4n26bMUfn305sBRcD9FYav6EAID3MhMjAtbFrXsQ3CG1WdUuD15xlbZnZ/HPgDL7i8jyp7ZxNlinh1GcasELc7WZrbs12QDCy0o4uu/FUE66faM/UsJU/bJGKv8erQXFnPK/ymS6eD82EbYL0f2r8SPii1ZzOEyY+zH70jIclxVS7vVYLNaBq76JsZzvt7FKYtYF50satWMMhNjF7E4MOWPZPAAlSqz9wXvL3i/WMiyvUf3F/vRFH8diZNe0XKSoGbglKiK2guvYb+oBO5J9298D830ZAjf5x4RFvUN1gIMZReR8GWfgr4Gr0UfqzhBBvYhWB7UKwi3PZ4hVuf6+apdp+K4+t1swjarbGSEjLmdEM44ZU4PA60SuSxofR/+xo6C+z29iNzjMT7OHYCdn/pwepiucgyqTt4W/GZD/mSztsoZRcaj6p6JWvx/y+zFUS0RovajsIr2Z2TqJAcLBzpa/41gAYUOOGtPRkRWI6ITCVy2DxeZXDaz628WHB2UYHAA6iHdWVqKNLMAiWZUldjzdJ3UYtlQ1EZiB9ONZj+jN2cjOcZyKgHN9qQGxniQTC1jjNofRUrLvyB2kfwyekhJq49mccSPc5p+T/m+RN+ctxp0bPmJEXuL2oq3oxkWbodM9OLAQjSAhX6W4yKm4WIW/zscuQeKE7cPnIHF8jnvUPtxZhfL5+2t+pLbposkjtFK2LbuId+P6NuhyHXv9klHy/9EfLI3Tb/Pfg83jY+9GPWtmV9WnFKdr/oJQ3F9dW5L7DKU71DnTOGOSgSrlSaYB2og5/ioVXow0q7bwBQxvJWlyNlNvZHAThpto7DDfcp4kFyhs8ZtFL1ZwMbAwM6fyb7ZWBtHZcTgo8RLS9RGwjIa6UdjxHvC7aBEtXqhjP/mmWSuZ/btZYiOtUjUFJJ10sBtjx8Ukb115fbDa9XMm5oFUL8d7Kj5u6d8bt8two+24KQOxbMibGo/p62W+rHY9Xz5OVcUBm63yoaQ0PKAG//nrOeDbaxELf6/Md4HfKYZy46iG+93y49kBJnvkW+r/aM47qA9N9i2InFplovankFI/Fai1syW9xWP+X4+v2VDvR+uo+9TQpWFX7SfoWZzW0uRGSw43eb4em/nvb295cvhFKHl+4dCBsVztHpE+VrkB+hrVJxu2Wx0j1pQNon7ou1dB3NH7suZTatZ2lFE94vtyfdt0u7R9a17z7+xnTJBhMdaXN/h92O0nsqnRmlEHEilp3wAcyMu+4jtMY9rDTKcJMwibE9q5RTYMeC2k2Pu5EiccfaGBS52ShazGQHmYBARPC8TBwuuG5/H+zk/DFh4jgtH/PsN/M3EFNNoQYkXFKc+QswiFmdreYaH29BJtCJQmZjF8vViNFifBCDxcGHjbe59AgkTiwpeysjgfsoDJ3h/lDOP7gEGGHzONnrelsvkwQDPVSOdTHDRVth2eWAqEmiYFxJmFrl+L9QLnDwtr7/yDV5Wn7X1dnMSis8Gsqg1s+Ux/PsYF7U8Y+t5+TbeHzwH7dkHpKL/uIwEjxIp6pu3s/6w66TY7Sjre9y/HdzHWSCpvPyb41gU38zWn7M1szUfnaUVfbIyYh1VfVWds31MJFGQ8gxuj7A1s5Xz+ZndiKgqG4jEuH/zs/4sbBWnQR/oGBG27P9xu8d+o3vbErS+vUv2jGVWHJI5aA9625TLgd/Z/m21L6er8mEbaN1/5RMwTqGfinSAAp6nzlGxnuvAPlPFc+Xr2P5xZhkfW/D8W/5S5Ze1c6tt5kQ2SKGwsbDlSk2tZG8DjVSOHQGnj50SnTh3cmUkLGoxKLCDYiLvsxhMiHlfNIsRkQgsnxILipyjIeH1PFN7+fLlFVHJ4hLLjfmre8oCh+vMz9BiGa5evbosRxZUsa9k31wW1cei/YyRvnkj4XXlmUecjXSniOQqcq5qaRM7aiZDSGijGdssTz8HHXZL3HJ/dHGL6SmyiOei7fASvIjwKZtH4onEEpcWt5Y9ejm5X6OYwbRxiTKTcgyKHiRxtvbw8HBF2PKqC29/NRONopbtWtlwS8REMWequN1VoO2a2Upf9G18GRC3IfbtlqiNyA+vwFD+nH1ANhiD35E4ivYx0DYiEZh9eq5DO1YfPEeVTwlkfAYW82efzHwF68plQ1HLz/H6tdgfVD/we6buNftBRY7VvVDfCop/Zdvqul2BErW4fFSJW47DHLdaQPtpCaAsjbnauldAtfoC97noOiVksb3dHpSe4Lwcke7wa/ybeRL7SfTxmLbHafzmcyIulcVY/m5h5L5HafYI1xFxu7W3ImeCd4oBRB10FNwxMV3syNmsrZN/VUbVUVBMOmlVy/SYDPPzaEys0eCYTDLp4TbwuqDQ8Ot9H8+SurhVy5IVWcH243KwCLh27SPP6PlvF6743UuIW7O1WAb8Vtuq3BGR4utPKth5ouhCcYJBEQMpBtmW0/ZvJjzRbI26XoFtkkdeVTD3OuAsM4taJ49eNg5o3E5so7jEl20ZCS/W2YMSt2tEltH/sG1h+zpQWDIxxgCJ7YTLkF3U+m9ehoxtqcqBtq5eEKXED6bB9x2/o+3WPnXtrkHFDRaUaN98j5AMqzjCeTEJ48Ecvsd8fjSrrIhdRrp4O/MXmUBlG4hEayR6sQ3RXqO0VV/ma3FJMs/2om2otojqjbOzuBwZ01d+U/nv6J4zKe8pF34rME9kMdIraHchJrsN+uCjij9mfY9ZRGK2dd0o1/FzsnQjPz0FUV49/SKqg+IOyCvYdiNxG6WttALbSeYnmRs50H6yVSFYp5Z/bflS5PI3Km725ruRsM3E65yY2pB+XdYRUcRyR1adWx3DzufklEfduPP6ts+eIFHGjxNlHonnzozCIzIIDF5I4vGYt0Mkai9fvrwicLNlSOp+YPmYlPHSZvVsb/R3IvjhvPhbCX5sJ3YiEVpO/6QC+xp+s3DiPqzsXZG9yGHjPUfRwzO2nkYEtGUk7njc7ZLTiY65PaMIwGdwsa2iASm0VbfrSJi67WHgy+rMQhcDmgqc6LN4Noh9IQvlM2fOhKIWlyEzGY6Ej7cHB+6IHHtaXP/e3z3i9jSIXGy3qA/yi8XM2rOQiMiOFRFD2+Yysb1k9z8jWrwv8vOIVn1V/XvEgxLEfkwJZi6Tn4MDW8rO0Z7x3itbw/pj+r4CQz1fq/wkxkl1z6O8MX8ui/rm45viRpLuTYG80sxW3vaPXJM5LQ5OmdlaP2aO12tnXLbjhrqXPfcXz0GfF/kGFLJsc9hmmYaIBLS63n9H/pD9J94TFrSqPH6u8gmRn836RI89nTS72/r/2PYga5RWg2UGF13HnZQ7fyRoo5EcJP5Mzv28LJCjWGVSvL+/vyQo+IfpWLfIAXDn5ECDxohLklBYXrp0yS5dumSXL19efrO4ZZGZOUU0IiQ9ODurliuqbTR+RaZVcOZyqPJxX8H2VMiOnUR4u5mtikScvcNtdNx+bkQCse8z8fV7h/dHzdhiOgjPh+1XiVsHDjQpuF240PTzeFkX2j3aqv+9DdpoL4FuCYmoDdwvRMD7xTMzqn28HDirg8/W4kfN1prZmg3iPXfbU3/9k9XV69u7PxKyEanORMxJC9QKGAfUoCjHGh/AQFHlv6M+woLm2rXrM7JqH/tnJbyVII7IlZeh51thVNRyf+Eln6081BLRlphjMRsJYkVGo4EhFD04S4uztRjz+V6rwUcWtHyP+D4oe8y2FTaxw12wYQfHKIxFyDWRUzK8H2FsVzO3WRn4O+Jym7TrNu4J3+tecasEbCR2kXcooev7uN1QPyCviPwj+m/FXXtmi9mWI/0xEotb2ESreX3m4tGThW3kpCNElZ4qakcNjDuiB3o/NmXWFh2NCjpmq8SaP7zsEwMcdnQ1qovAQOhtwx2UnWHmBDxfFLEoaqOXOEXCFu+XMuZoljabyeUAjMbJghW/lcCNtqN6ZMd2AThTb3ZdHOIMDzpfdqSR7bGDZIKEgw5sC55uTxtykGkFJrPVUW22XbRtTgfPw5UO+Oyi99Ojo6Ol/aoZW7RvFCU4A6oCUUQaeaYV743vY4KsRL4vfcRtFrT8FyRMrtFvsIjx31w+Livu7/HrLdEbCd3TAm/bvb29ZX/ywRV/yVjUh1hUKbLM95jJFovoTOxyf8A0e0Qtbvf4bzV4gR8126rE5KaIfKX/xniuyupp8H1QsRN9qV+Pg1XqbcuObFAK7xHfJ6wP21smdNXvOzNQ9HDcwfuPg1EResQF25r6qGtwX+v+tQYw5r7/Kha2xC22u9sixnccMI/ELesCLpN//Hply+rxPrTB3jZTgjbiXJGo3aa4bUHxmCmYJGyPwyFNFbx+HNEjjnk7ErRMnFnUojPyPPAaFrVqKbIixR6Q+L8iF4v1/7/0/Rx4otFnXpJ09erV5ZuPfcaWZ25xKTIHP74H3D7RCJUSsCqIs1Dq/UZgOdFxMOnPsKviFkdx3ZHjEtnWIIX3JWwr7u+YTja7rgYkFDCgoy3iaCo/F4+BCW01+s2ztn4Ons/26zbJ2xFhVs+2caBVI7fYVmj//I33gfOI7BOJPr4N2Wdu8S2q2GZY1mhFhZ/D5YjKPjW2RKQ5ayt13S4B7ZjtF22O25zbJBK1LFBxO3pWOtvf4wMiUcu/s2MtUatE7BzEm8UHkmCVLn7zADfWCdsF7Yv/z50JMHIHFvNcZhWTFcHOSG9Pe4226ab2uSnRPi5gf/E45L7Wf3u/YmA8zuqK8TwSMuo47u9F5GNH7sVUm2wJWhagvu32g1wDOYSarUVuz3aftXEkaKNH7bxcUX39m4Us++EeUcvt1mrjkWO92FTgDgvbG+loosZuCVkmwn4OkmHeRsei9qGg5ZE1RZBZ2CE5RpKMM7YscDEYLxbX/xIEj3N7IPlXyySwTbx8HjRdxLKw9eM8usSO078jUcuBFF8epcgQi1oUrkzg1DcHZnZumfiNAnoW6E8qrly5YmbXCZ+TH7w//nyP2fqoI4OJFxJiv6dIeP08//Y0FDw/DBaRfbdsFQMED1Chj2BCgWVRwhYHqJhAMnlmO3W/g/X0vs6kNRKG0TbfFx6E8jqj6MY3p/J/1ipCHAVnPIZlUfc3Ehctv67QapPTImrNVl+CxuTFrO9t7pE9q1k7NfCIfTRadcN9goVS5pdV2aJ90f3OxKz63Yuo3EhuW34rqiOnxfbFohZ9qwMHxP1lVOx/lJhlQYuD1ioWjrZbLzjN02S7DO4nyJmy5fC9Qo9/tz7Z9SNl6RW4c99PtjfmCMwXUKhyuXjmloUucgc/jveP83d7UrYcPdqH3ITrhGVmfaE+zJ2jgcVN29/bD9tyNG2uby+GhO22O1+2PwsMURoOPM7iVnV01ekVcWYCjWQRCbJvO1HGETjcxrJGJNnr5h0USWfkWKJyYt28XkhYUNhevnzZLl68uPIyKWUoKih72pmo9c/R0dGyfmjcStSymFb78ZvLhNvcd1qkY6rzPwnw9jS7LvLwY5aLeU+DhS/eYyRKkbDlaxEYzKcQGrZDHlHFgS3eh/bGdoxiAm1UPVvr5+EAAqfFbz/NxCIP3qg2YRtRNob3eW/v+t+K4EtmfKaWhS0PNKm//8K88B5jeVVbRTMSjFYf4P7SI2pbMeQkwtsXl8OzT2yBbRjt2O+rmiXEGBD1A9UfMv89tb2jexmJWo6VU4QZ+g4kvn5PMG3mLq3n43ngzdsRHw1S77pA/oKztIoj8OCF8jk9s7Wqfp4+5oXnT0F23WkQuWh7GCMyUduCEiyRkFEDF1NFTsvf9vje3n0ZWB+wuEVk4hYfO3L+7ueyreJKtiimcIx3X6qELdpRFqPw/kWztWzPeL+VH57iF1tQPmH02h50C9tWopFIHUVvOpnQNdMNqNLlDq2WNno6LHJRKKJDwt9IjtEI0DiQ9Pr+iCh7+jhb60FMkTgk1UjsvYOj4/R9SFxQ2PJ/2UailsUREqWWuMXjuB2J2kzMsqFiX+DtSORm2FVxy8GzFcCwP/v1uJ+FazaDh+Isa+8sqPPgT+Qv0EZVcMMyKPHL+aGN+YDU3t6etNUoGGEf9bbiWRQcDMD/bGZbi+4TDgyxIEHB6QTYt13I+jJkF7zo8zwPTlsJGTWwhG2qfNtIDJlKdnuI1S7A+yAOVGRgn+if1kAIvxmfiRjPOPAsrvLvTKYY2T1S1yji3BK2o8QtEgM4QM3nq/xcCLN9uI91n2B2fRBSve8C2xnJqMd6HpTCGSaMqXwP1UBkJHRabTgHL9zEXnfFtr1tNy0v3yO0ayVwM6GreJPil9l3tE/9zvZv0i7RIAuDebrnyzyZX1Tp5zgn933IkzAP5ElqBSSKWi87D35j+pi2267bdiZqFU8+Lg7b8ueb4kS8FbkFNnj120w3FndoJLD4u7UMGQUvf5utv4GOZ4JY7GIwRJKckWN0Sh4c8WUQyoHwEmccWcZg553cDcufo+WRYSVqkVjxCBJ+WrO1aHiRIUZiVolYJtXRtupvLM6jwL4rYhaBAq5HYLLjYwLMSxMV+UVni2khokEZs3URq35n8HIru1RkFcuEQtC/sU9nQV2VYbFY2MHBgV25cmVtmS/2a25fNXOCfsrJC94HtF0UtdhmLnDPnDmz8mytk2P0PxyYo9laJlDYnkp4YF3wewQZIcoI164iG+RoiQ2/vjUQggOcLG6jWVq3exwAUTPKEWGecq9awnYKsHxqQMBtQQnbxWL13RcYo/k5SsUl8L7gPbh48eLaY0Hsvz3OK26AdeCBBybWrVUibKeKc2W8bQpa4miXbZrbmLmb2fpAAtoyxpcorkX8KboO0RKivb52RNBm+0cQ9U3lO3EQ32xVyOLAFNqdesmUp8VpqxUw0cqsyH+xb0J/gTE64tLYJ9Q97xmYmMOeo3Q3RbewnasSo+gRtZGg5cCZiVt1nRK06Myxg+MxJMd8LQddFrV+TLUDd1p846EHTCWOfT8uqWBhgOlm/1vL6/+jUR8M/EzUWdzySFNL0LJRRo4a+4MiKVheNbqW9cno9y4IXR6MaL2UgEUgElYmwNhveJaQRQ4iWorv20gQ2dlHgzpYX36xlG9jnbiO6CeQuHr/ROzt7S2Xd6s2xH7q/Z7t18/jFQo97Yd2wwNSeC/cPtwfuH/gvwRBUevt4GVCPxGJb/9WUD5q7vjSCsy7TIAzMAl2sM80s3Awyj8XL15cfrAf4csDez48eMlxWQnRTJD2itws7QjcD9Ev8Ay5H0Nf4O2MgsSvxwFF5A1qsMnzdjtzMev3g2drvcz8ojf8ey4vn4rH6l6pmK7aukfQ9ty3DD0CdpdFrvcZR8TjEN6feBAeYwbbXo+w9bQiRPaVffO2Sq93/yaI9IEfw7ZG++QZW9yHPB6BGsN/Y/xXg/8sarHMWEYlmHsEbTaQwbhR9jOH0B2aseWApI5vszEyUas6bK+4ZaHL+81Wn//ifZGg9WDovxFIjrNgi53QlzogMVajsrg0GQOsGyOKXcxDER38sBBlooRG4gaKhqWWpY7MzGKePCsUiVncp7YzZIbf+r0LYEEYEWH/bbY6s68IF75ojGdruY04TxayKGJZ3Krro2NMCCJxywSTf3tboN36MmQF7JtXr161g4MDu3r16gr5ZNtVfRr7Orcjlx8Dpt8HXGKIotbMVsS1l2tvb28pbjEfJMG89JTtmYG+Eu91j9jgezaCHgLc2n/SgYMTaC/Kv+O98XvJIvbixYt2xx13SGGLA1eRiOVt7hdcRrZzPwe/Ha1BxGhAY6R/YUxXotRjGQKJIg4646AyP+bABBPJMopP97P8ngsUtbgiCwem2GcyAcZ7xWI3i3/eppFdchvOiVGRuwt2HQ3AtPou9x/maPiJRK66zzwYEfnt1jn4reqbtcU2EYlah4tarKcPkLPQNbOV/7LHOItAm+aJAR7447ZDXsA8DTmGGgiPRG107+eynbnuI9e3B5OXIk8dhRtpNBVssmPZaExUFuzAkaDl2Ru8Dr/5fP+NHdJHdnjGR7UJdjjvmPj/Wh7IeMmKP6Pj9eLnbNBgMX0mrdGbFzNR6+2ARoykOxO0GFAxDyVqPU8MwJivctJqm4lGJnpPg5hVUOQR25lnHZAQO9m6dOmSXbhwYW3ZIt4Pz4sDhlpxgPvUCgMe3c7qo+B1wTcYoj2o30zMvX3cpo+Ojswsf2nHlStXVp59UzPQfK368HnqvuAHlyyiL+Bli0pss+3xTA/acUaG+f4g8c18O9/T7P5GcelGE6ltgUUitiX3DWxXFE1uwxcuXLALFy7YHXfcYRcuXFgRtvhcpxK12C94H9qN6mOq7JFYUt9Z26hvla7iDxz3Pd4gX3CBi/HHfQoLWvxtdn3J8GKxWInZ7C/UyotI1LIdM1lHAYQ2zGIWz8/ame0U93O7booegTQlFpwUZCJWxQcHciR+BEDZInKu1uAFf48I22y7VcdN0esb1LkYk7ieStQ6eCBY+WElbHFgSYlaBmsH5s/4m3l0xCW4XXrs6EbE1N50t/6MbesmTUEUiFpOlMUxXuvbLHRRAJq1/+uKBSMTYhS++MGZn0h8LRaLkITyBwOeB00evcX6IGmNRuLVKB+m4dtIpniksCVs2QgjUcsCF8/Bfdn2FLAziLZ3EV43HjjAfunBEZcl4qwtPvvFpBY/qr9GgrYlbDOMkFoms1maHOzMPrIM0MUttifagI/sIslVhIbFK29zf/b0o6XhblteZvQf7FMyccSrL7LHElr3A33lXPFhlDztEvGNoPoO+nMzW84m+HEcBHHbdWGLHxywwhed4AoeNdiBQgn7GK9U4P7fErTRdm/bqLRV+2H/R3/Az9Z523pcx78C8X1YV//t3+5foxn2aJAZ6xy1L/pKjvMch9Wgc9S2im9FbTinbXMZst/RvpMK7P/YXxwqNrFgUn45ehyARU5PuXqFbbat0u5tG69z7zUtLaDO5/7q/svbHm3J255t3mz1OV28R7xSgn2ll8X1hfJ3Kl3m22pCKBK0nG6PZjvptjWrsJ3SUSOo4ILb2b5RgeGd0svGghnPQcEbidxWvaP6+1/dYGf2TspvQR4RtjwizqQV81Gj72r0x6/nbxahPDKMDhjzZSGLAhZHmn0f3hcm1KqMapvPU78j7LKgxbbje+99w4mw/8b74gQLSTHPKqgZBe6zkYg1M7lUlwlgVKcIkRjGfoMDVSx20VZwQAqB4gH7My5hUvVi36D6Fwc5thde3sRvVG7dj9ZqDp6N8zR54IvL74gI0NxBEvOeGpxPeuB2MFnxFQG4euDo6GjZ9/1+er/wgShceuyilmdr+Q2e0YCl+2n+X2QWX94XzTR5Vf2diVlE0hiRz4j4AvoTjPk40OfnKeGK9qSWJePgthK1OPiAdo31zXwI/kZRywKIZ2pH41rUfj18LBrMa+XX2rcrtsvgcntf8r7gMcTh+zGGq8Em9M84eeBpcP4tMRsJoJaYnUMfqPiYndvLzRTn9/6Ltur2jrHUoQa9HGh3zLMxbqp4yHak4r8StGoAI/Kj2Gb4zdvqN2PTgQyu+xRsZca2p+PzuT3YRMCiEFZAcZttm11//k51ZBxpwWMoerP2wOcRr179yPN4nlaPoFXilh0StwsbRzSrqkiFf2N9VXr8bK0yQAzomKYStUpkK4HL+zPho5A5gUx8nGSgcMMZA78HR0dHa8SICZE/Q+vPfPH/KXqaSGjVM5woZJXIZULI/Rjb3evlvzNChXXja1jUub070O6jYMZ2pQQtbnu5MmBd2V6i1RY4+6LaQH2wLdgnKB/B6LExJApzEVEkJ1Geaju75iSD/azb7cWLF5e/1YvJ3I7VM/IuaH221o/x2zuZJGPbHx4erghYJW7RnhFRLPFjEVHz4z28APscb3sdcBtFBT9Lx7M1avUJH8dBRBa1WOdowMCBpBpFctZXOP7yAEVvnOQ2itp4xL5HbXBTPnmSoMSK9x2H7/PzeWBarZjge4u26mDhGm1Hxzid6HeEKfesl/+PCOHoPO/Dqg3YVyl+gvdU3RfWCVg/vJ7jM3NnNVOrfCaWS7VVtO84Be2m184mbOdwSgpKzEbfrWtxnwoELXGrRm/8ODshM/18Lo/4cnmRZPs2dlx+s7H6mF1/KYwirZgfGx06SjYSJhgqLSTDHEhVUOUyKIOMRK3vj8oWBWgO3iOit0fcnnSgoMV9imwqUeuztf7NM7ROZg8ODlZIrQtd1V/VgIya6VDiy++dr2rA/tQCjr56ml5v9hncT5TN+7U+cIDC1tucX/LjaeF3dN9adsbCg8kwi3gOdFhXDpZImNgvZAMJXL/MJ3F9e+KEItg9Qbvn2EmGxwEXtGa2tIPLly/LmUC/f/h2Y1yKnL0wSpEwt2kUaeqFZPzGbSVqvfzRAKfbJNtARtaiPhkJWr6G+QXHYPR5ONiFMR+5BD9jq2KyIqpYN+YfWHfMC9uFCXA0WI33t2XTGVrczM/huo+kn/3eRaCAQW4ScTc1W9ualHAocdoStnyu+s4w9z0aEa0tIZz5AIbHcf5EGgTjpLo3yCfcp6Cv67VpxdeZM/i1WXzsGbSIru3ZfxzYSNhmCn8TA2CMiNlWGpy/d6AeQWu2Plvrx5XI9byi2R5VRv/Gt6thcMQ8oxfsIInmOnsamCeLAf7NIhC/fZvJBjtaZdQqb7XP21WR6Ezgcvl6hA6enx2Pfu+CuMUXnvg9YZKl7mM0G+h97uDgYPlxMXt4eLgkv0rUep5qlUH0QiMm6xjcVd+KiBuKykwcYf9BYo12zTbm1/HyxL09/RdfeB2mif1JDcQoccuEGNPDVSNeH/TN3FbYRzwPtbRtEwKMbd2KDyofdc96A/EuE2K0Yx94iF4ahPeL/86NZ219n5/jsdNtXK0YQrv1fTxjyys4lL9RRJ3tG/s2kj8H9uOMqOLx6ByMu2YmRa0aHMP2wDbhRzxUmbgtsGx+HQ6cYRmwLdEPcfxl384xlcs0AsXVeq/L8t1lW22BuQnzVeSkflz5fzUg4lBizPdH3z08fs77kqW16QBJj2jl9mJuhyJWtQ+no/isEqGYBvs0nCDzNJmvK98YidqoXaJ7ynXbZBCj59o5OPRkYZsp/KnfkYCNjvm1qvPhbzyPDTsTtL5ttioIWdw6kLji0iUMtJmo9Y8HKf9GIoHCFgWJIhhYph7Dy7ZbgY8FaCRmWXgoMcvHWmXD9sNtbGslCvi67J5Ex7g9dwE+o4f9xYHtHy2b8b65v79vh4eHa4LW9/FsLRJaJn784WdxM2GbjVhzP2o5XPQX166135qM4tbLg6RT2SUTlh6S0AqUTP5V3/V8eRY32h+1Lebl57E9sl9EQc1t0EN+Ofi3jveSoF3GpUuXlgMNbjNIvNgP8zPYOCOLfw3l/3e8v7+/tGMXtPyMrBJwaL8oblHY4jVeVl/BgysOfBDF42kmcLkPmrX9ckZkHSgeWdSa2Vr9+RjO1u7t7S1fNuV5YjmiOOXgwTHej8I3EkFK+LAIytoJ28uPMedS/C26vud4htNg0+jTW5yN/b+yB0dko3gM0RI3UwTtJvc2u6bXx/N5ys7Vvqj/qljE52XxWvESTEO1DcfL1v1v8fae+37cgjY6dwq3niRsp4jaqWg5x+gaLh87WzdwD1pYXl4+pEZO0BB8H56niG9UVuyoTor9mHdWJgzooNSyY0WiW3nzPjYMJcqVmFRLZNRIou/ztNkJ9IjabBvL1QKen7VVVP/s2pOGy5cvr/QNbGc1EGFmy76HIvbg4GBF2LqodfLqZBhnapEQq/6JQjB60ZSXGQdRoreyqplF5ei9jgwWtJGt86AXXoc+g0nLiJ/k+2S2ulQ86v9YPyTo/tvLxy8nYTLMgxtMotg/tOyht+6R8I38PKcZXd+776TCZ1SVSIxWWfBbs3mJ8f7+vt10001y9QU/H4/9WAkrP5eft0Wx5/C+5rbr5fI+yX+HowSu2eogq0MRVofqN1G/Vbbv10btgL+Za0TI/BPeJ39RGPIMT98FOLZtNCiAA1QcR3vAbYY8S9ljlIZqgx5E9r1L4PvHiHiZGpCIVlBwvM3abFOBkomouZDx2ug87o+ZL2idn/HB6D5F35gH6yv2G3x9JmpbUFou286un3K8B6P6z2ympcg9onZU6CpBO6WCHLAwfyacLiD52881i5ci4z5FfFXAZSApXiyuv23Rf2Md0GlhOaNZsKxtFOHHsrKRKKKAdcSPErUoSFoCNROsPds9v1W7jAjcXRO30V/S4Lf3rXPnzq3M2uBsrJqt5dkdJ14862q2OluJ35GwzZZYZn9T5SQPB1Ii56/shW2P7Qb9hafB1/SQWyb7WE++X2xvEfHB61EY8JtZ+RMJWibELd+A7aoGEtknc7sxIsLUQ1i4PD37TjIuXbq08tt9KtrD5cuX7erVqyv/gYpv8vblxcq21RJitEu/nskykmmctfUP+gHvP1zug4ODlbd6u5DDQRl8i7uDbZHjf+S7M0HLNhz1XTVby/09mo3rheIbXn7mCihYoziM9hzVfQTK901NZ+S6zN53ISarCRCOMwrYJ5Wg5WN+DX7P0T4cA5RvbmGqTagYHh3v0RTq+lYd8F7hNRGnVXEa7dbtB7m94h1Z/Fdto+qX/W6dG2GKGG618Uj/GBa2UzrfCIlQznCqAUZp8QcDEObXK249Hc8vE7ksbv18LyPmpZY0eprKaWWCVpHmaGRbOdSMvLKoxBmyKJAqIcuig3/3CFksIx7LoK7HNFS/iwjSLoCFrWNvb29FnCLBRREbCVp+86kTYiS5qn8qu8s+Xm7vIz6jg//5ePnyZdvf31/OaOEyQu+fEdltAW0TxSMTWSZ3rdnaqK+qYMmDMyqQqnKb2ZrfQkLsaSoyrGbye/wDQ7VNBPbhEWFSfn7kvu6aqDUzu3jxopldF7SLxSJcbuyi0FdSHB4ehjYd/UWPsmOz1WfV/RvjEvoAnu3N7Njz97L7BwVt1M9HhRWLscjnKxHv21gWtylsF7WPwXGaSSbyEfdhaL+4RBnJMa+04EGpjAj3gG2U27HXvpRImcL7dg38t3EqLrFojbYjXuhpKET+O2vLXrGX2eAcfrdVJyVUOY5kQjwqv2qnTLxGfNL3oahFH6Z4LduxGmRutdPcgnaKmFXnzGG/Q8JWVbz3m7cRox2Nr83A5AeDUxSkePbFgSOkGKR6RS6n7ekj6cZOzmmPiNmIRGfto7ZbZNu32dj8489NKSPkQOtp8W8m8ix41XmqvEoQKHEfkZrMsanfJxXo8HFWBUWtC1qfsfVvJsD+jW8+ZUGrlsqbxYSY+zeTYSbE/KZmr4eLW57F9bS8nzF5ypwwEza8jmdGub7s15DEqjxV2rhfBUvu07jfy4KiPsvP2xdnb3l0OJvtaaEVD1iYKPHKZVdkOiIqu46LFy8u79FisVgRhT5De+3aR96SfPPNN9u5c+eWghY//Fy8z+JGs6zKlh08k4urL9SqDfT3yo7PnDmzstyahR/7bzWwpKDsPRJl6lrun+zD1DOwkf3jtVyfaECaZ7yj+qGN8soL7zet9ok4lDqf229UmE4RsqcB6Ee5vb3fq5cp8qNpeD5e3wLnrXgU9wn2tchd1b1XvjnCtnx0j9bI+jb+5uMtYRkhs2POj2P+qJhtndMjaEfvzZTzN/UBW/kf2ykYEbOb5sHi1mzdAXDAUrO0HGBYyHJwVeJWCc/FYiEDIT6fmC1xwgDZ69i4nVrbuI9JLgdO/O3twOfzfkw7E7XRzG3Pb1WP6HfUBrsYhDEQOuHEGVonuU6EcRuFLT9Hy0Q4IsRYBrP1mQ88l2dq0d6YEJ89e9aOjo5WBDbO2Pr1TjpR4KlA3rNsUIkmJXBVPfG5qgxRv1QBlUWtIiMZ6UFbQbtVI8MZCcJ6ReSf69cSurzN16rtLN3e4ycVly9fXhl04P+Z9edlDw8P7fz588tvHKxCYRstPeaBpWwmiGOSErh4Dtux2y/+By/6kcuXLy/TNrOVFzEtFou158Qdym7wmBK3Coq4q2/eZp9gpt/Qjm2J5yg+gO2Nbc51Zptl21WxWLUT5qn8XtY+3IaFVSA3YfvBOI3bKGr5HRTKp3nfUIP5/q0mbpSf53sc2dCmvniqb4763aa+PhLCUexBZCKRY3JkM6OCVp3TEqs3QszOjW5hm4m+6HsUGcEZcYYth4w3HZcPKsGJ+7JZ2tZsLTsVNXOL5XFiooKEmhFSQtfTbXXarO16xB0KUxwdVt8caNUMkOcRidFNRK0qN5N/rmurTbLrTyJ4qTD+NQ9+nPgyEeYZW7X0GEkpClTut5Hwy64zu35fcMaWZ435WrP1JV9IOiOiqYA2rcBkluuH3z19RvWx0b6Gear2QZLC4lZ9VJkiketAf8v+voccKaLUI25PI/z5WX5pmpkt7dVt9vz588sP7sfVGerNx9GSR0XAMvt2G3C7drgNu7BtzRAjsK+5wOX47XmobURvHxqxV0XS1WB4C5H/QBtWvg7rxc/Lt4T+CFpkPhLBU/Obes0uAO2FRa16Kzm/jI3tS0ENfphdj2m8T8Uq3I9p8XmjdVfb6ve20Cp7Fn+mpIuawn9HtoxoiedMS/G5vb9b++fEpvH7hs3YZk49q9DUY5infytBmAlaHnWNRiej2VoWt+7AfBkyilsXxphfFBDVzBA7JEZEylvEVLVpDxlmEate4sOC14/7dyZksTxZHSKhq4QzH89+7xJwdpWfp1WiVi1hjJ7F42CrCGpGgDGwMynGPuv37+rVq8sZniggZAHTzFZsjgNCROJVGgoocL0OkWDLMKX/qXqrGXV8GRCmr5YtKnvrhSIFjEzcquOYTksER+gVGCcN/mIlXGLq9oc2jKL2pptuWto0Pl4QPSMf2bCyix67xvPRF3sMVINYDo4VZ8+ebfp7vta3ESwEo77W07dUf+U8VL5qHwpa/I7uD7af2fpsLELtG8VcdpgdV/t3OfZGYDvqEbY8k+vXtmKVmV7l4/dBiVnmtNEgkqfdyjsTUYq3Hqd/juIU92fVx5Xoxf28j89XPtbRatcRPsF1i36PpDU3euO3wlaEbS9RyEjOqIDtbQAVrFBwsqBlUopGHn3jDcERWha3OHuhlhx52dQyFez4qjMrsYvfU52SOhcFbWsbiQj/VqJ29DeXLzsf96n6j4jbXQq2BwcHa7O16iVQuJ9nadX/1KrZ0mipVEaUs2NmqwQX33rMRBC31eAHE2WGIgiZGIv8EqYTLS0cFVcRqYwGz/zbyRDeMxTbbLO+rLWnzSIbQD/GhEmdG7WFIhPcBncmcYt/1eNti4NVuMqCB6h4xpb/1gcHPEYGpyKbVbaJfY2FLZ7DAvjs2bMrs7xYRnVdy09n974ldFtpqXbIfuM+bC88j/86CWfa2SZ4sDhaaRG1jZchI/p8ziaEtIVdirMjYLtBXx0NMKkBDk/LLF/5iKtm8CWlyCnxN+/zfKZMhPh3JqKi716oPhjtw3LxfjzGto/fvI31VL+jPJXvxHOVjW0at3o4zq5hVmGrOmsvedtUyI46PCyLG6sbqpqtxQ93akWofCmxb3taauYWnVo0K6wIHwdARRqj8x2990PdV/9GoeqBUy09jmZpcV/rN+Y/er2qY+Rw1HaUzq6BAyLP0kTE1vepwJoRYhWUo+2MNCM48Po+JnROhnH1gD8igH2kFWRbxJftPzoPfY7ZKgmZIq4ie0aSz/eLR/2j8vLglJrlmWIPLHI5vZZ/jQQtt8NpsNUMPuiA8Su6x0yWURzxbC3bs4upyF57bFfFJowL2BeuXbtmh4eHZmZrLy9zUYu+Rv3tj0MJ3JadbWKHWd1ZxKIQbYlZvA84KIn30q9hsaFWT3Hc7q2ftw8fU7bWK3Z7959me46ELW5zH2C74nRa+ZmtP36DfpkFrzpnRNy27p8Ss7yPz83SjuKCOj9LL+O+/N3LKVVZVX2Zfypur2JjTz2y+rfOzxDVr3VOVq5R25/l735GAkHPKMNUR7ep48s6Kn9QoCJQ0GJdkYTjtUrMqn1+Ln7UmxfxPL6Gzx8F1pdnPVsztL2ztGqfOgfL0Ht+JHQjp9OzvYtgctQKmohW/4mCEwdsFbQj0qzyZGGE/ezs2bMrAlaRYbcFzytzxlFwxbxb5E4FpBY2CTZYZiV4cJDC64ADT9kLo5TAjcjN6Kg+txe3a0SWo/P5vAxTBM2NBL7VFm0pejaWwbbFIop/RzYaDVThOQx1r7xv+RJj91E8Kxulqfy/EraY/1Qo/6ba0EwL1EiIROfiYx74LgGcqcX25OdoVTttCkU6e20twq7H1qlo2VorBo3kE/lVM/0OBL52brAP8u+RekfHW3F5ND2znDtmopevxXwUz1ExN+JDUTlHOMRcsa8nHeZuPeeP9L2tPWOLjdoaSWilk+0bFbcZAeoRtny+OwFexuH7eEbG0SNmWYxGwpXTx7aOHGPW+aJjSnziB996zCPDbKSYhpqVHV0+3DMzq4Su6ksjInYXA3FrNtZsvb+qe8Uzn/zxNCL78Xz4g8Hdy4vAVRV4TUs0M1nkNBQie+EysY9T4izzhyovFfDV+QyuI87A8SyelwdFLc6kKXG7DXDbZKQq2p4DuyRuMV5ERFjB7yfP4qFd+WAP7vdrsY3wW/U7jj3sY3m5Y0s4R3XjmL1Jv43sTpFQ5b/Yj2Xbfv+i89AvR7PqWEa11Fi1wyhvUm3Ua6MZdjF+bgNRH1Dc7biA/dN9DcbNHkT3l+uS2RRCDdAxuGzbEHKZcO3lj9l9RbtV2qKnfFM4fuuYymdTMG/qOa+F2YVtROCU6Gql09rX6kCZ4+XfHHSjcnJg52N7e+uzs2pJsuepZmZ5eYinpWZceUbXv9U1mC+3iwIH9Uz4zz1Lq/Zhvi0hGy1hVvta4ladt8tojQqbrRJP//hbS/Fvavwtw+raVnudOXP9r3da5JP9CO6PyCeXS6Wn9rN99wZX9nFK1Kr0FIFplZ9tWRFrtVScRa3PbHs5cdk2C6CIGI+KBhULokEPNUDA6fScP4JdEbdezkgw+Tl4D/ntuD4bim8LxxVH3l/c9lXfdzv2bS6fmR6AdfQQdyXWzEw+ZqD6LMd9lX+0ncVXbn9vA9/mdwt4GrwMmc/nGVpcOs527G0aiVocoBqB4kuYdnZua/9xYRfs2CxeLREJPJxVxTbGQamMw/q5uG/TwY4MmcBVsQ/3Kd7KdcP0ecUkIxPlPcIZ04nKEfk43laD9v7N90f1g02xaXoRL9s0vU3TPPa3Ivc6yuia1u9WOnwjmITiufyJ0lNOJMuLlzHv7emXR/m5DiVi1XJk5SRUHRW4XGqbBSaL2ynP0mJdR/dlvx3KmUVEXdUzOtbadxKBM7YO1X/x76qcAPv/ZZqt/nWOt7s/B+cDOv6Nsw+LxWKlDE6auYzeX9Fe+F6Pfri/ItiWla30BL5eYdRLYqLBK/9mUqTemMnPTHo51d9/KHHA9fPv0WWNLRuZQoR7rhlNd1fErSKGDuzv/j/PLmbR/jHWKCHs/cjtUK34YDLpNq7KyH6312Z5lhn/J93/6kiJW8+nV7Rm52QfNVCoRCtuq+XGbLNq+THbMS5VxH1on1PFLfan40QkrHvKsQu2i+iNBWarvJP5odk618GY1eJBalVbNiExBWxPuF+J2h5Bp45H/YW5Rk9aCriKxRHpm0iks09Ug1Ot/jAnov7Wc93c/mET37N1YZsRhFZhe8TDaIVb5YmO9XYoDi5MzFmwtoRsdMyPY5ookP24Ij2RYFV5YDn4nIh89AjaKftU3i2nm6WF+9R3ds6uA4kXg+8filr+H1gnTyxmUVjh8614DEWv5+fC17+9jDiriPmOfJgwe12j9mGMjOaqto4Cdk9Aj7Z5GSq2e7b82O8rE2H832nVZjiizFBtOpUERYES928jmO4aItKD98wHm9yO2e7ZhtUMP79wDmcUM7vOVoQoscrC1V+QhbPMR0dHyzdC+39Y4zkobrl/ZDaF37yPt5V4VSK1NXvLYhbb2d92rP6GCYEraLB91XbWj6bakhKgLdvtvTa6BoH+YFeRxYKoXj5jqHghXqM4EW8zR/Lvlv9WaUf3nn+PxD/fF6XHeWK5RvtFlg+jxR+i+qjBBvd7ONDv95jbJ+vzmY6ZglZb83kjnCo7v+faCMfydz+jjieq6KgQjhxiz42fMkriAZ23/bcyGBS4XLaWkM2ORyIXr+dt/B2JZS8jk10UmPx7E/GKaUbXt9LDfdHxSNxmfW5XxW4m0NR9ZRGE5zoZ9hmhiAyrbxS9mBeTY3Tu3Cf4fzx5qSUuuZyyTLGnzRgtQsKkeKrgVWKWRS2/HAzFjreXmZ6lxXPUYMCm/X/bhJuvP21COCI1aL9uX26/2H/8PvvLmtyGW8IWt/1vetSLyfglZUzMcOAssl0/7h/8714XuHgcha7HA453kbjFc9U322wkUJWwzcSsGozyv19CYRvZsdkqF+D/nI7ExqZQRFbZW8sGI7Ie/Y7KsctQfZT3m12PexwPsa9HYjSa0cW08buHc6nreVvVtRXjMN5G3JXTxLyRe/f2/YwXI5yT4HnM7X2/WqWBYK6FA8uYPraR0i+Zppnb9ue2x22UezZhq8RsS+kr8Tua55wYEbs957NjZnHLxxA8W6scViZ0mTC3rvVtDEQqCLEAjWZvcV9LgPp3S+iOpKmu5WOYv8LcRP6kA8mS9x98ezA6NO/LuFSR3zycEWImwjy7yEsfI1KMhFgRXP+guEXSzH1HIRrB7RVmihRH2yxszWxFGOB5ihCrv25BMoxCwmz9L7qwbbCOLXKs2nFkcGjqcUbmu/B4D3bNzpWYYXtG4Eytz+b6LGFmr2iz3r+U/eIH+y6Wl+0X7fjo6MguX7688lH78Jin4XXH+BkNGvlx/PZtdb3Z+rOwPJOdCV32kWy7+H/hPDiF9w4HpXA/3tspfbjHRiKBGolaPuZQg4otgauuzcq5K8D+5r8jIL9RglXFLOwnKi1OA49lcXJk4CQTtdkgr6oXQw1k4e8oHjAibqzOwd8qZkerVpjDYFzG+uMgBrZfhB4d1oNe29pmjJxi746NhG2r0XqOj+R1HFCdqNWZEC2n7I6oZWTspLgtWejiOXgMSW1LJCujUPVTwrJH0GK9MkHbu1851EwQ83kq7ajeLeyS6FV9Ge8Zj0gqMoozq72zs7ycEckw/jWPOl+VA0c3I0HrZBdndniZbYZsWRI7dkWKFYFWopaFrfrmD87kHBwcLAkxtiH6IZx5VwMDPGvLM9vYT8zWXwqH+TBwX0SeWvsyZEFW3addsNNesJDFe4cDUwgWlN6P8K91MhHLM7r4OxK5StjigAqKWZ6J9f0XL15cE7M4c4uDMmYmbWxU2DLZ7vmwoD179uzyN7YZftyG0Y5Z1KIdcwxDe1Q27Pd9U0wVtUpgKPKaCdrs2l1G1C8jKEGLgmzTv3ZkPpNNLvQg4tReZoxV2YwtpsXljWZpI8GL6atyjhxnP8MfrKuXabFYLMWs+kszLjfbkdczag8u50jM670uGzSYGyP9edalyKqRp44YzAF2trhfnRv9HnU6DnQ+2A7oiDIyhuk4VHosZCOBq4JMVCbVsZVzawla/x6ZjVX7OR3luNQ+vl4JWkQkclvfuwocaFGiFmcGzFaFrR9zYeoOPBK5TOqUmPUyMHHG4IF9hGdrkeiiqFVLHHHFQQuRzUe+RPkbNSurRnS5ztEAgZrhyUTt0dHR2j5uP15+rEhxz0xQdLy1f057GhG7uwg14IA+WM3QsPh1UevEipfHqkEr9TIj3ubf3M+xLDhbizbr4vXSpUt26dKl8LcLXBS1yrb4289TtsrfSLazWVlsJxS0SuyjiD08PFx++35lx/jcMd5TtOXIPpWNTSG7PaKWj+G1PUS8Rwjvuv0iejgltwPyS7P8pVG9eXA+0Yq3bHIhypMFbRQLvexKRKp64Swn5uXlcsGr+q3azvLPZpXRD7DfZKB/xncfRDok0x7ZIBLWe6oI7bmuZZdTNOAm9j0sbKcIVSUu50DkRPmc6FruLErARterzhohajM14xqVNboG00ch68dZuGJeyrAzos9Eak5Bq87PrlFliNLA49H2aQqSvUBHGIlaPNeJlT9X62K2NXOhZnVQ1KptFLiYHpfHyXkkbHnGduoS5F4of+JpsV+Z2nYoZFnU8n9bLhaLZdv4vVZLt9VLdyIb5T6UHd82Ip8/dd8uggVtFr/QhvH5WyXSIoHL4lVtR6LXbJVoel/jGVsXri5iL126ZBcuXLBLly7ZxYsX7eLFi8tzWNRmNhXFdxWfmWyq5YWRvUbLjH0bZ2ddzKKoZTvG1Rb4rwMsaKfO1LbsILKVSNSyQFUcUAnWTOC2rtl1tPqjQjR54sdayIQxoodftfirErVsl0o4chpR+ljOSOxGiPLJhCxuqwEsxVvMbGmvXt7WjLLiEz2IBG2WxibiVZ23CTax763+j606hhhpgLlISGQYI5+R8rPjia5rjbapOmRiFYUJpuX7UUhHAcmhxGUkcHlfdH12Ph9T1/WKWhbjnL7Ka9Pvkw5FFrC/IHlyOEnCfsYzi2p2JHoOD19S4/vV87r43C7bH84as6jlZYr8Aimehc5sucfOVRBVATATtWrpJr8ZlcWsk2ElalGoej35xTs8q42EmD+eBn5nUPY5FT3+v3VOJG43LduNAt8fJ3OZL0XfrwRtz0ALC1gWst5nue8yiTXTNuxC1gUsfi5cuLAUtZcuXVr2X7dhJcYjUWs2vszQt6NZWhbzvNTYt8+dO7ciaP2TiVrf9m9eaaHEbSQ4RpeRbipqFSlmn4nnqXQ57WzfLqNHyGBbqXvJEyURb+0d8Mh4mDpf5RfZD9tWNHsZcWIsE9twT58YFbMsyLNHL3ByCf2v+2muZ3bPeJCI68nnRIJ2RJS2Bita6c2BKdpvY2GrSGGLKOJ5Zv0CN3JqvU6OHQZ2qlFR21M3JR7UMQU1M5vVNxKxfsyNCUWMmiVWgq8lUjcRtCPHMM1s9lblj9+Z0D2tYlYBiTB+o6j083CWhZ0ni1nvgyxMlXDF5ZA8c+tkWAUJL5cStviSGRRueL7XJULvbG0maDkwKX/DJFwJBiVk+aPIsNfZf/Nzx/wWWRa2aN+8vBHPUbbXQkS4sb1UIMdj0e8p504Jnjca7AtZ1GK88UEp9/suavFbETiz6zOVTOBUP1WDUgcHByv/net5YPlxsIWF7YULF+yOO+5YEbgsapX4ViQUfUhEnDOiq8S+t41aVYGDUSxkDw4OlgKXB6jYjn0gwu8nC1h8zILtFPsL95+oX2EfGhW1vB0RbM5PkXTen5VrF23YkfHLiF9nPDISu3h9LzKextvK36LtmMVL+SNBqdoAoUSWmgWNRKND5a/KrFaxRO8GwTbHASp8dCvjkpG45Tplojazv5a99JzbI5g3xahtb+1/bEeMZ9TQesRsr5PoFbRsYFl5uT4oIqNOkKUXiVxVt9YsbVSuLM9MoI7ux+9MiPYcx329ojYqi9o3Yki7JHRxpJCdoX8zOcVnLtnZR6Otfi4vWT44OFhL352+H0OhiwSSHS0SOhS2ly5dWhFxSP782h4ou8z2KbLMfkTN8ODsrHrejrf9nEjUIsmNZrXVC7WUqMW+zQIXMdL3Mz/Yk06LcE8hubtGjNEeo8EFv3/oJ90OUeQiCYu2eWk8i1oeoOLvEWHLM7T8QVHLYlsNhqF9tAgzCtyM5KLd4ew0PjfrNssC9vz582sztZ4G27H7RmyraLaW7dj7APeJTdAjZFuil9PDckU23Pqd5bErGOXMWV05LeSG0SAD54HX8f5M3OK284LWx8vGcb41yMyDee4TmEuq2Izl7RW0ajWV8j/s5zAeq/jnx7i9maspKB6H7cHbmHaUHqMlckcE87YxSdiqUQPVQKOCdQSRmG2NKvj26AfTYENTYpGhlv8iejtcNhKH1yohG6XnJIfL0xKMvcd6BO2U45HQbX2PCtieNHcNbpvR8kUXt9i33FmzE2Xxhv1OzXB4n1MkGI95wOBZFwfPVqCwvXLlyvIbyZ/XfQoiW1KDXrwdBXcmwzyjo2Z3+M2pHnjNrv+lAxJdNZvNopaXMbIg4r6jvv2eTG3b1n3JiG3L9/fGiV1C5C/xOAtaNQClBl/M2jOVbp8HBwd2dHS07L9HR0crQtdnayNh6wNPbq8obH2m9sKFC0uRe/ny5WU/x5njbMaEha3ZOidgMMnmGSb0TzgoxWL23Llzdu7cuaWgRTv2/ZiW31P+W67s78zUX3j1AM/ruaZ3QCkj144eUXpabLUFxU+j472IxCZy1F4+yvv5W/lYFfvUEn6zdftUbRABeTVyVtQIHh8RLJ6VP8SBPPYv/BgG8xUHchQsEw4is80qW8xsz9NU97F1XGFTEdu6d9u26a3N2Dp6Rob8PHVOa0SBBW6WjyKbI6I2Cn5TsMmIB5cDR+KmOMBsSfEmonQuQctl4mNRWTMC3nvulDR3BUxAWOS60DS7LnR9PwYhtF0fJHF7wW0XsB7g8K9G1GwPE2JP34EBofX3Pihq2VewzfQSX3WchYHah4GSlxQjGXZRy8/j8f9beto4Y+Pth2+O9eWbLG7Vs7XRdi82sYWMJEfnjh47DcD6Kf/otsmDWGoQqjf+IUHl2dvDw8OlqHWhy6KTha3PYqi/9MFlyC52XdR6GVAUqoEwRZzNtI2r35m4x7rxagr/nD9/film8dN6URTeR7UqBW1XDUr5tUygp/av4z7eEtCnEaNiNuLA6nrm19EEzCbgeKpWOCiBi7blUFw2qieLWfR7XlcFjs9K0KqlxvzuAPXCKO+nXAYVW1sDUegvVZ/g/Rw7+ZxowIPTVOfMZZPbtuPZhG3U6L3HGa1zM0GbGbgK3D3BXaXRwqgz6b3RETHH9Fv3ws+JxGXmGOYSrJnj6RXMrbTV95xilvPdFWBfwdkcXDbs29iPXXSambye+zzalhNrXp6IYpaXLaq3Ins+HiD470Lw20VerzDrEbXRb7UfA130QhkUsEx+fUYHZ3aQwGNboMhnQYt/j4Iv1Irepmq2bitIkPG7Fy2y2hPsesiwOof3nQbirGKL3xfsk94mLFJbMZDzwGP8bCkOTrnIZRLI5NXMVuwXHyNwIYtLkS9fvrwU7NFfXEWkWcXsbOUV+zAk6WrGBsWs26+L2sPDQ7vpppukqPW0MIajPfvgnA9KqccIfLaWB6CyuNq7n9tnqs3MZVetNHbFdiOMiluz/J72XK/OycSyEk58jhoIGrXRlvBuidkoLnMdI0GLK1NU7I4G7ZAX8aQLchKMvX7OlIEgdU84DvI2I9JMI/tPku1NFrajQrV1Ta+QxbR4f3bjVFBvLcdShrHpKNfUpXoKWBZV52vXrr/0x4FthEuX8DgLRuywuH0cgjY7T5VbpTGHqFVQ55wk447gdoiCFGf9MWDwagCctXWnzCOtfq3nhb9RMPMSH5zBxX0YMDBdFrY4U8mCbSoygav2qWDJARMJOYtZ9cEZXSbC3qd73yqLL93Jnq9lkpzZBQfvTTAapNXvOwuYzDExUuQuEriRsFVA0uqrKpjsuR0fHR2tkUW2YR+I8gEZ7r/eZxeLxcqzq5gfE+cotmdtyXXEb37WjgemcJnxuXPn7Kabblr+RoGLz8d7ufF+RUIf/9cXX4qHz+1hTNy2vY7a3J3RPkeQxZmWQJsCjtOR2GGh5udmotYsXrbv22pVBdtpVl8f4OKBV7w245lqtpYHoCMhi7aL52LZeYaWV1PxoDvG25a4je5T9I3n4DVRuhxbs/OnxuFt+4NuYdsjZOc6p0Um1f4e0YzfTJa5c0ci97gRjdCYrb8JORuJQZGizmEnMCIw8Xuuc9R57MRU+RUZj/aPiNqRc046uE94/44ErV+DQSuatc0Gl/x8tzGeuY2eZ+GRUAcSQTUKGs0oRv4lCva9ghZFLZYZgyQvWXTii7M8vByZn8HzAOJ1d2HAL93B5xJ5xkeJfybGGHCx3yB6BoAyKOE6ct1c5+0ivM8xGYq2oz7bE+8UsVH9uzWjwUIOB6N4xtZXGbiPUH+Jg3k4KTVbfbEdlncEihtgnmzLLmDPnz+/FLZo2yzIPQ+0PVxtgX9rhO2BS5BxthYHqjft83PYTev6nkHjOfPbJfT01x4bjc7FfT282YWkmX4Rk1lsLywWWdTiYFfPikOsK8YrHOjDMiIv4bSZj/DAFcZttfwY64i8nH1b778RRMjscUTUKn42t2jdBkbz3voztpsgGrlitMQyGqj69M7WYhq96BHyfl72W0Glq2ZpVTl6RGImhHl7m6K3t7ybiNcR8p6dswvgJes4K6pmaDFA+D7/jen19HW0NXxWDgkyL0dWwjYaEVViTNnyyCBYdk10niLDSIRxRhZndpAE88wOB0410+VCFkWtE+To2Vq1HJltiUlzRp57bSEKllOCaBTcs3TnzP9GAG2uR0Qo/9UT9/xa1Z68zFAt3VMzGp4uEj8UdZcuXVr6GX6Jmpo5UcsbvY2miFpsYxTwavkxzsryR4naSNyjsEc75v/txUcJ0BbnELRTMCpgR/xGNnC8CzY6gpH40nu9AsYSFpS4D9u3tbRX2RsPOPFSZFzerwbUMuHu/kiJWSXk1CQQD86xH1NiNnrsAcuEbzLnxwfUuz96OLeXWQn0SNSiiFXb2f2O8mud04PjsNvZhW2vyIz2tY73psnHlLjNZmdVkOcZo2xZcjRK0nLePcsm2QmxmMX8UMRE+WSGlQWT1jM8cwtaVW5VhzlE7RTBG513EsHiVv12RCIX0+kdwGGiub+/v/YCKbVkCa81WxVbSOoUAfc6ZftUYM8CebQfZ4qY6CtRy+IWXyijZri8rvhyKJ6tRUJ84cKFledrPaCisMU0sQ1by5G9TGrbsYk9tALrrgjQbQFJXUR6GHgu20vU31s2pQRutOpCkUCesb1y5crS/qM3gbdmgLx9MhvO/Db6uWiACgeg1HPyarUFtykTYH6+GIUt/o0ZP36BtjlF7PZwrDni3EkU3icJmcCbMjjD16iBnmjwR5Uhakvm1CwU1TavmsTyKChuih/voyjC0bcpPs/2zb5LzdByXEbbw0Eq/qeGaFC59Q4QFqacbyRqM3Gr7mVPPG2dc1Ji8pCwZfLaS2YzRKJ201EplZ76qCXJStz25MFQ7YXfjl5C6GmhCOHzlUG3hGW25LdVzuyaOQRtdC4H8uMStT1Cd1cQiVleDqRErs80KFGbDfjg0mez9dFGT9Of4eVRXQTe96xvMsHt8S09g2X8m30JL0Hml0Wp5cY+SoyDUTwbzUIAly26kEWhy8/lHR0dyaXb2KbsHxRJZhtsYWrQvBHB8qQE6AzelyOfxDbmUL5T+VBMg8kvC1QkdExsebkhXodv/HV7V3911VreHK3saPkihKq3eokM2zDO0HJ50W/xqgcU9fj8rHo23v+7V71DgO9DVteoT8/V13ti55T8+Xjrd0+aJwnRwGskaqfwbrbhSOSq9CNexzOgkVj05b0+YKUGoKL6et4YE1HA9ghcJeTRZ/BAHK884dju+eC/Lbg94goUFLX8XgsckIombFpoxcxI3EbX9pyzCY7LJo91KXKvcfaMVvUST76eBW0kavl6DswZollSPM7IBBOe4+XLZtxQoPTk3ytK1TW91/Xk0ZtmRKi3IWpbdei5bycN6OhYnKqBEB6dRJvAtHxfazkNX4/lwG9cIo2zxVF9OI/Id/C+bGan9zcHdA+I0f/TotjlpcZOev3bTD+PqJ5JVM/kcWCNlh+rT2S3GUHOCDafuymywH3awTFGCbMMmR/0NHoGe/Ge4+fKlSsrfgVtDO+Z2ws/u8ovinKyiUsAe5Y0RvVG/xXVHe0Zy4OrL5SYxXvjNsz2jbO1LGzRhl3Uuh2j7fKL37L7qdqgF9u0qx6RemdAiw+PillehaRiXo/I7Skz82g1O6sGo/C6HqCAxRUrvGKLB/O4HdC21TJktGFsFxbMuE/N1uKgFT52oWZqN+n3SsiOXHfacGzCtpcgtkRttJ0dZ1E78mnVI8IIsVOjNgosJEaWk6p8R/LOgs9IPur8EZGsRK1yDi1n0StSe4TuroEJMA+EcB9X90GJWt+PaSP4PMwLZ2hR4PJsTzSi7Pt4vwruLVE7KnJ5kCwa+VXPBnLA9Le/Yl5IYPFZRCbEuFwRj6EQVjO12RJknq1Fe+Cl4Aq9IndOcJCfGrxHieRxg/upskf+3VMnFqE9wtZs/flr9XhARCxbbyDlWVonz0yozfqWMrZIMKbrqyh4kIqf8VWrLLw8PLPDL5VBO45sWc3YKrvNVlYozBHHWmlMyXuTcu1abM7sDY8rcH+P7F0JNTXBo/KL8lDp8KBT7yMDEedmnscrH9D/uK/H/VxuLCeL22j1h6fldozAgTyesfVYjcuS/bwo1nK9I/TGtdMqXjPckJdHzS1qW9vKSbAxK6PaBrHpEbPRyLtagozXsEjJ0CNGs/2jYlZdMyqwNxG12xS6I87oJECRfR4I4VlaxN7e+gwqrhbIiLTbG/ZTT8/F7rVr15ZCFwUvChXOF+uj7FjZciZqW+A6qqAYPVPEYhZJ75kzH/kLFQeKT56tRcKLM7P4PA+OHrOoxVFjzAfL5ttYnlZ/by17vJE4TUE+WkmRxUDep87JPpy+5+3f/MHjnBaSyr29vTURq2ZpFSlWhJ3htoUkEkkwAsvHz92pt6Gi70Mb8pfgob3jSgy1XFHZs7JjnKWdsrLiODBV7BZWEcUudTwSpJGY7fnN6XA+aIO4aoIHfXhJL3PvzHb9m7kLD5qxbau2VGXgAXQHxjKO3ehLOEarT/ROC+a8GU8u9GFY2KrRzRY2Eae8r1f8RgF95OPoWSqRtUuPgFPnKLKezdJiG0xZChgZ0NRlydm1PQI/OndTUTtVCKv22TVRaxaXkwdResAjmi0ByYLWf/MztU4KXfBiXpgP20MPaecyZX6kp/6KrHNQx7yViORZaRyVxucQ+aUULHL5+R6c4eFZWvUfel4e3ObjeB9ZAGcDRyqI99jPrtjVcQNtrycG9nx4eaCyF4bqF1F52U74o946qoQkCsqofBxbkIwyCWao5YlqdtbTR3vG1SbsU3F2R700i20abZ6fs0ehjIQ5uyfR9nGjx6YjcXJa0eKhZvlLDvGciMNm+6JZVMw3EsBKKO7trc6Kqt9R2RwqDjlv2NtbHWD3czx+Zu0S+RGH24b3QeVbWKTyC934RY38LwRcP1XvObBtGxqx06k2PXrd7DO23AF6CGJL4PYI3R5Bi9u9orZVF+z0LWI2Sup6xDKKg4joRHkiRoJcZoS9IrglaKM8RkTtFHHb2qfKFu07ycDyutOYMmgVLVP2dFX/5AEZdFrZ7KzP4HJekf1GI8IcSEcFbRbYIx/iAQ3r5kGZn+dBAs6zq2okGJc64axOFFB5tBhHnXk7E7hZn4+ELB4fxXHb2Em36Sg+9sQ6JqCR0DSzpc2pOJe1EZZDiVpPmwkvilh+3o2fo43aAG2IZ2qZHLMvxDLyM3dIyJHUmq0KWmXPKGyZ+Kr/uMTj+H+1/uxyRI4jgTsyCHvS+/5pQyvu8Iwpb0+ZmY3OUeI2i3VqW4lZ3p/FYbN4ptbtVvV/P8dtnts48oOYJ8/SMlTcxNiK4pY/aLutAamTgJNWnl7csJdHtUhldrwlYtU+Fex7yqpGcxgoMvG3Q5G7HnIYAUUBGt+2RmBbo0lTZ3SzdsmuHRGsPedMFbWtc08qsL8qgRudz8Dz0b6UkJ0DWaBWAUsJzUh8Ti1j5E+wb/CosgtcJsrYnhjwPBhicMRvJWQ5uKJIjsQs7vOy8nJGDsI8G5YJ2d6BvRHfuAv2ti1EsSkStkwuzUw+/xYJOeyf2E9VmdAmcKY1I7gZKe4lw1427o8oaN0eUXRyHVjsY4xVghZtumXPONDEtow2zLasbJf34b2ZKmqnQMWOKJ5MOZadr463zj+J4HjEfTvq81lc5G2VNl+Py+dxIEk9067skm0HfQ3PkrK9ONQgjd9Tt2G/Fm2cbZr7RNS2fi7y6YiL4gAvr8LAOItCNpuljeLdjVxRsU0ch22eiGdseV92fFTA9mzzp6e8Cr0ds/emtvLFEaqe87N0Rs8ZEbP8uyVoe8XtVKHaW+asXLsWNBHYb3qczGhdkQS2xO1UW1fCNRKvmaidajMtsCj14Ba98AavY2LOwZNHg9XoMI8iR0Q4ErU9AreFaBR6LtvZpg3ugn1nMTMTtUhS+e84oqW/nqYStEgGzdbtTC1HVDag9ve8IEqVS4lbbJfWQAzXHYF5oE2zX+FYpWzZ08jsmgeolA0rIcD3KYq7WTtsE5GA6L12F2x0FComROIPj3FcjFZIRGlFtqjsN8pDCVwe1IoGo7E8bDc4++q+xgWti1nPY0p/5rQzzscDZW6baJdos27fbP8qpnq6nG+r3IV1bFXY9orV1jUqaKtzp4rannIpIHHn/RF6OmNU3yy9ORx9dv2okFX75hK0Pd+tMo1e11PmXXI0alAkK78a+eRrW0FXHcM0MTD6Pg6YZno5IovXbOlUj7DtvZdMoDFtD8o4o5PlrYiw2eqzefxRBDgKtpiuB1zPTwlZzBv3c/mi3612G23r48JJK08EFXtUfGNyGz3Dym8fjl54psQtlgnzQ2Iczb7idS2inMXtaFDI8/RtJJc9SxZxdRQT1TNnzqysyMBycVspcYv2ycsZlf1i2dl+WeCqbTx/E8wpLLO0Ro7tstiN4lAmRH2fErVo6ygw+VpOF2MrruBQAjebwVU+J5qtzeKgmo3FFRduz34sEsMt8Lk8cOffaGts08pelbjlMkUzuZh/T0ztreO2cRLs8IbM2DKmiF7ct4nAxd+KmGd5m40vF1DOODpvBHN1pCkjXVkZematW+mMitqW8Bw9P6rbSSbnGUZFrbrerxtFJHL5OD/XlwnUluDtGSXepG4YdPh5OyW61fUODqiKqEeCFWd1FIlWQVmJ0ujNyGr2p0fARgG95/os3Z59pxlRH45sgD+RqPWXJOHLkhiKaEV5qb/VYNFoNj4oxeUxi/9yKLMDToMFBAt7M1uSa7eVqDxsw+gfRj6RqMV9mb1yueaEiiWjorPn/NE4dRII9giyPt4ravFcfNs4/kZwWmhvynaVwFWzt5gfz9ayPStksQ8Hi820zWMavq2+cTuLvWxfLT/DopbzbolZLlv0+0ZgDrvatm3OKmynEN3W9ZGBZ4bPv7PtkXTmwqZpjs4Sz4VNhKy6ZopwnFvUjpbttAhcJx++bab7pSIXChyoMlGnZi4x4DnUzE6vcFWjwi1yzPVr3UtcscGztZ43psntzdsREY0CbCR2ObDi+RhkVbCOSIHaxjJGs7cKWbveaPu50fmPgPsb9zMmqfyJyKuLWjXbY6Z9qLLHaGY4I7iRqOVjjIx4Zi9KU+Q3ams+JyLOTJSV/UYCN7JpZd9YJ0WWsZzHwQ82FaPRuVkaLUG8a+IWofq7msFVNoI2aLa+zB+v57SigSn1P9Jozyof9B/se6JY3Bvr9vf3pbjka5WIZNvPbLWVdmTTnL5aVYE4DhvdVYza8ImYse3BiOhVv6Nt/x2NZEV58zkRNu2sUR43Qsiqfb3l6BWK2e9NRW1WrlFRu6vBEsGCtqdOKIjx2h6oc1uB2vdHAhe3FRFuBVG2L+zPPXVDcettw8GS0dN/1fVZ0GYiz/si0o/pqt8tAr2JHYxc23PuFBK7y8SXbYD3R3aDhJX38TYSWQb6DyxL9nc9mbDlASk1C8SDRVgOtQrh6tWrdnBwIAUk921Pg9ONtjkN3sfn8WyqIseqjCOzsyMid45+P4dwHTmnRzyr3ycdkS0jIlGKv9Wglnp5W5Q2+4CWLSvfodJRQljVB22mFdvU6gUldNmWvH9k8TmKr2yT6jwVNzGvyG4R2+D324x1Jy2O7oyw7cWIuB0RxogeQTvl3F6Mdvq5yecUMcu/RwQt/h4Rt1Ge0blZGU+T2GVxygKX9zsie2Biy+QzEqp8DueT5edg8ssjx7xflQfho8COnr7O4hbLGKXFpJeh+jEHSA7aWTDHNKaIWsyPy52R6LmJ9BzpnLQgPBWZzXDfR4KL5/DfZ3FczAaFuCxMiBUZZrHMyxYjcszlUL5K2UG0NL/XRrCPs1jNiK+ylaiM0XZrdnbElv03o9cO0GZa4tK3+Tu6tifP1v7TYtMRB40mWrLZWoRaQYVp8eCSWskRDVK1ZmezT+RXopimRCtvZ8v9vZ/4Oy/QxjHfbABMCe6WjWLavo15ct2z/jHFlk4i5h4UQ+y8sFVG0Stu/XfkBNT5PTO5c0DdxIxkb9LBW9dOFbJqf2bQap86v/WdpZOdO1LOVrlPOtA58r4etPp+z6AQD/pkpJbTVAE5ut7zUoLat6P+ogSq2bpN9MzORkGuZV+q70YEOgrGEenF/KP0VP2jeuM1PXVRx1r7pqA3MO4SWYj6MhNdZRcoaNkOe+ofDV6xqO2Z6WFiHB1ngctQdsGkV/2OyPH+/v4KIWbxG820qrJE9seE2fdhXq18+dgo1IBbD6baSuRTs3R5X6+43lUoccr9PhOGfr76ViuflLBl2432od0qP+DH1fO6UR0wNrFNKNvlj/pngCtXrtje3t6KvWHemZ0qG81sUW172TNMtafFou8fLk7aoNC28p5V2Hrjbvv6TQVlJoazkTF17Vzidqq4zITgFMwxGzxVsPacP4eobR3rSStKb9fAZCHbz/aZkcxeRANIPVBLFTEwZwI5yy8SkrgP/YQSd3idCpicLl6XlScSo1E+PMqM12T7uFwtEs114baZau/HgV0lwdmAT7SSQRFjBe5zUSzkPCJRq57j7fm0/sfWyxL1ZSVk+W+w8A3EfsxncxyZjbM4niJoPQ/+HRFslSaXL7PZOfu7EpS9QhTLq9LcxDZ3ya6zeBTNwPakEa3qyAaGebAJj/XaMb64KhrcUs/9MpStRLas/v7uzJkzy7/TOnv27FLcRn2PY31mw0q8ss2qdPmaXvQO3LDt9NhiT549+6fk0YuRNLc6Y4sBkYMj7lPHetPtRTT6FYncTNTOJWYj4M3rEbQZoZwbPaQ0O7YJwVWCQ323yhWd3ypfdKxHnJwkRPaTtVerz6vR4F6MrIJQNuu/o1FkFL6RLat7yAEKg5z7rYhQ9hDb3n6rZlRUGhmBVudweiMkOprlaZFtxrbspUWMe4jBSUcUj9QgbDTIw+f5vT1z5sxKH2IfkInajPiqZclqyWNEhiOBi+XHPtoiwE54fd/e3uqbjhUhjYg220xm99HsbmRzataWyzc6e9s6Z4641mtLyg+30orE8lQSf6PB/bk1iaI4qjov28c8OBK5kaDFGItvUccBLfVh+474uLI1ZcsuXv2bfZr7Mx6wymy7J4bytkorO8/RstWpArMXvaJ5GxgV2j2YJGx7iO42r+9Br3HjfhUso9FuPxalF90o1YEzUt06Lzp3LoyQ0d79Uwgvi4Dou5Vedn6WxhxB/iQBnVh2TjYwlWGOgaeea5hcqyAbkWE1aovfSO592RIGPgYHRPXcHhLRzNajMmF6vh0J8Iz8RvsVKc7EKpe1BWVrI2hdN1Ww7hIBRrRErYpf2QCt6kM4iBMNEDM5ZbtTs7g9BNjfzszXR2SYySjP4Fy9etWOjo6WotZndDyNaAkhps8ilom3sr2WX5gyqJTZL5c5ui6691MwxyyOItgZ6VaCODpnl5CVeWQg2JH5PcV/I4Hrv1uDUf774OBgxY75Wz2ry2VWA1QuYFHMnjlzxo6OjpbX+3mYZmaXGNejeB3ZUctus/syam/ZgE6vAD5p8W7u8nQL24jYjhLeG41oZEghErqYVpaPo9Wx8TsTtJjO3GK2p4ybXoP7etNU17S+R/LsCeZZGj11Oslolfk47VvN9rZsDEl6NHLcM9NjFo+ysm2iE1bLCDnAqQDK+WbbHECzGdwe4Yrf6vwWAR4Vx5zOSbGTLNiftMCvkInabOYngiJ1/Htv7yPPqPFfAClBu7e3tyZCmRw7yUXCy9tIgnnASpFhJsJIgq9cuWL7+/trszq+bBFndNjmMG3fxr8SGrH9SACP/va0sLy8T/kcPKenT4xgU7vKiHvP8d58TiqQY0bxqnfixldfmK3G8mgCJxooZvEbDUqxrfLn4OBAilv0Ewi0Mx+Mcns+Ojqy/f39pZhFO1Rtpuy5x3Z5n6eh0mG07HgT9AzUtgaFemxkqlgetcE5bXbnXx6VoRXQo9Eq9Rv39aRtth4UFKFVBJOvVQFrNO8RRNeO7lfHWr+jY0zIo++efKJrsnN70yxsDl4JoYI6kvhoyRT/F6ciwggOdE4KPFDyedl/UXLgi8Ro775MzHoZuHy4n9OPfFEvseayzkmSIzubw8ZGgu+uDNjyYCvHMN7OwESYB3OQHOO2n8NlULM+Gfl14svfLHJxOTMC7ZdJMLbTYrFYzvJ4+a5cubKSBvsCFrL8/7gRMUZfwb4B980halukGa/l/ep7E2SEmPONru0l1XcWTJmt7YWarcX96oVPStCizaId+4ftmmdtHWh/PDiF5+HMrOpb2QoLfGs6x9WWDaKdbUO44n3J+vqILfQOOm0qem80Zhe2HOyyQHgc6MmvJWqjkfAeRMTTvyNBGxHQKP1R9Fw3RchGx3sF7UjQzcRAlGbrmimidteAzgt/I3r6OQfAOaFmo9RMFI8yK/LcepZHBTJHJAB5pqb137FKjOJ+PofzVtf3+JSWP1HXtYK6Spfbp3e2VsWL6Nyp2CTwn3Tw0uOe81vn+v2LbARFhwLP+ij7RFHryxUPDw9XCDD/VuLW08Z+56LWBS36OZzRYQGPx1kYq9lZbJeeQa3sWm7nOURtRrJHiHdk63NgE5E794zRjcQmMbR30CrLV13PwjYaRMYYe3h4uCZk0ZbRpt2ODw4O1lZ6cGzFwSk/js/Fo437MbyeH0lAO8xmaz1NP89/Z/HtRoncbc6sThW6N2rW9obP2HKgzAJmdn0GNRIVEfJI5Ho6I+UaCUD43RpxHdk/Ut7jODYiEDPSH32P5pUR9N50tkHGjwOttm8RV0b0zDni2rWPLGPsQZYejyhzwOXn83AJY1SuTEjy3wbwTE40KoxpcLqcl5dDlcG/e1d9tHxKFrRbGPFXfI36PZLnNrCrhFiJ2hGB2wKLtBap4t8qjkY2eu7cuSXxVR8kyWrpopcRn6NF4epLjbFuSHyRQDMRZvuPPpmoVSK3RYJ7RC3Xh69V97PlC1pCdoptTCHP6po7i7jN7Hjq5EoEnrhRjw5gvniMRS0K2cyW/RvfmoyidbFYLOMsztC6LUYDWpmIzT49qy88f97ujZ0tm1c2G6FH4GYzsD0ieMo1c2CO9G6YsM2IMx5vnbcpWMiqYJyReEXqeoKU78PviCD2iLYIUzrI1IDWe11LUEX7egNwq71GAncr7VGCvktA28sGgxBz22rvrJCZyed+1EsqeJYgm/lE0qqCJp6ngmHWVzN7z3wDlzG6NiKwI+lGwTu6vsd2NrUTDrbRsU2wK4S4RYZ7ZmQy+D1VNhM9MsBlYlHLs7ZIds+dO7f2weM+Y4uDVGifPrPjZcSyMPnlN6niJyPIvHyRbYX3j4paZcORXWe8gq+P7m8PNuEgI5iDcO+K7fZijpiq7JXzUDy4NTDlNoxLjN2O8fv8+fPLbbZnjstuy2fPnl0+P+u2zANZaJ9sy2jn+FFC1tPMbFPZ2shA1ci96sVcfX3qjGzPOTfCHm/4jG0v5ha4ihCoh/YVIlHbQwxxn0oD04l+t/b3YqpQ7c17RBS2ru8hySNCNbquRO11bGtAaaotK2GNI8eKNPNSZJ618TT8txKqPJPDf/6OgS+aqe1dhdErHKfMzvac0xOcW3UaEbsRIhE86kNG8txlsF1ssjwx86ModNW9wNUYiiQrUuwfJ8Lnz59fEmH/5hlb9YydC1qf0cH+y2LWBTB+IzFmW1fkNiLFU0TsKF+I7AOvac3qqnseffN5rX2bIBOxU9M7DehZZZGdw3E3GzTGbfVRK6R4tlbZsX/w0QK18sKfd1dvNVYvhGMbdn+gRG0Us/HjZWHbnDK7qtKaG9uage1ZNZGl1Xt8bswibNlgWr9vNCLjjogzo2eWNiOKKo1RgTZyrBdT8u05ZxNBy797y9gbgOcQtXdG9M7gzpmfg5/diwItk2gOoPyNhJXJMAZSJ888o6uILaK3//XOwvYKWswjK9cUgq2OKYySgONGFKxPMrLZ2F5ERNjvPQvEaIY8umcZMUZC7GQYhS0SY166iPn78+34giYckHLie3R0ZJcvX175xo8StOrDds62nxHjqaK21+6VHbZi5mg87rlmG7gzztr2iM9t58u2i/vwnRY8Y+v2e9NNN63YMs7W+gup9vb21lZHeF5sy8qeeT8+F5/ZcGsJslrxMMWOGdsWuhEyO+kRutm+0by3iSFhiwL1RonVnjLwjYvAM7S43Vu3nmAUdeIRIdV7rKe8m5w39fqpwVG1UfSdpZml00qjlX5P/icJ/z/23nU5juRIGs0mAZCcGUmjlfaz8/5PuLvSzJAEhzg/ZN70drhHRlY3QDRYYdZWt6ysvEWEe2RW9Tm6u5XMsvHEMZyXA8wr5eFypS8k64cpUAYmqWlZIv/Y+bIThMPU+nbHfRUs0+szYOv2Z4SW05wDZNXmdXXMSaXn3XvPPb/LY3F9C/n6Nb9DrzqqSxkdIH737t348OHDyWwtA158HErJLAPfT58+2e39/f349OlTXIbsCK6SWm0PBcZbSW51jrdJ92eztZUPTPq2hQA/p7wWMvtcAhzgSCskrZCqliRzkOrDhw/HnwapeOUFdJf9MWZdoc/QXfw4SMUEt1p1wTrl9NdNQjm9numxtvNz6Q504FxdOHfJ8QpJfgp59qXI54DqTt5jnCqjPq+akV0hs9ieQ2jPJX+XGiDnPmPl/q35zUhtF7CuENPZvVvq9lJkpocdJ7dVEvj9+rV+D8gJHK063NkXkOHYlKxi+ZIjtdVMDueNfa5XV3T8dWdv+d50PT2D0zrbNst35rhfup68tPKsiNNJ1aFVnWJh0Mezt2N809dZ/6st6SxjxJJFkFsFwloOJrSfP38eHz9+HB8/fjyCYN5nQOzeydOZozSbo+2TfP9zktqUT6efu+crH7ly7lyZzSTtRNcL63OShJPZnzpyCz3md2zd6gukR3nGGI8+4sbBJ9bfRG6ZBLtXhipdXiGylR5DuueeQ17aaobneP6LeseWnSdvV+6dXUvpqnvd8SqhdYD30kT2EoPlUkR6lVB28umS2q2O/Bxwfm0OdIWknrvkuCKsqo/QG57JTQBGv+ZYLY9Wx6WzMpjh4ShxmsVhB+lmZ2ZEtjPOKtK6ldB201bPqFadzKLWmn5FnB3ojMlz7O0u34R9Gsis6mXVxxx8crO2+vc//JEZfRcP5WDd5dkcgGCQWyW1+PG7eW6WlutcHXf8/7nLFLv635nBrfznzKeegwGe+j4n3xvEP5UkHFutmFgVFyDjVRfutQImuPzxN/6QFK+8YD/KhBb6/PHjx/HHH3+cbJ0+66oqJbI8I+xIbCKyaFOk5fZPunjOOSdPPX63zM527t16rVumSl4UsU3ilLhDVmd5pOsVED+X0HYJ0aUIZiWXIsrnEMLV+zpOeaWMnfOvjdQ+pTDgXdHZjg5vLQ8IMh/rlxQdma1IbfoyKp7h9meSSCZvuzM4Lg89TjOvacanm7+7t0pX5VnpWiK4l7BbP4owEHb6ycEliOvXLqDmfOBjHbnV9+Q5wIWVFgDBTGL/+OOPRz+d7eFlyvo1Vbcawy1j5Ho7oFwFeVhWMcRKwMudrwjqKpndqtuXlEu9//dSpYtLz33GiujsLZNb/kIyB6pAZvm9WjybV1tAR5Me//HHH49IrftwlH79WPXEBaRxXXWZ993Mrbuu93fburO6a8ty4+6s7XMsYX4OuRixPQeodtOupIOsEF41Iin9bFCvkq9zSOwlB9Eq+evmc079uuVaKfusX84htS9BqWcy04mnIJg60zPGKel0M0GrhIUdFesj8tbobfU+bXpPR50lPljDz8czz5EZaZ09a2ZnuqQ2pe9c75YFstUp6pjq3nPtsrKUcEVAUrVdFUwpMHRAsSrb4XA4glz3Tjx+rGsgtQDBDHx///338fvvvz8CwyC2vGyRA1ppCTKeqWAYbaS2JrVHtXSx699VNzvXtvrN1fs6frjKb1V3fzRZwdLuPt6uYjz3ShKOeeWFW4XB5Bbn2H5AlxGccrqs+uyWIVcffmN/z3qKOqv+oly47mZxHeZLnIDz42vJz1aySkYvTVK35HfOrO05chax3apw54o6XDWMiWCronOUmaNhCVC7wZ3OOfLkjl3dtlxbkU4+WxxU59o5+XbadEv7du+f1eU1OGYe+6vBpg45hbiZIuiSfvU0nVeAyACUv5A6xngEUAFqefYnEdoZqXWOjcu5KnxPBWar6+k4lbGra7Pr/MtPYAABAABJREFU3Ber4HdWhnNs42o/vAZdnonTz5VzY5z6Te1j1UuWzmsEDEpxv75HC8D722+/jY8fPx6BsC5DdrM70H83wwN917oogXV2KIHhZCNmaSqfla45+9Dxm7NtemYl39tvfg+M+pSi+ggd7NaTVzB1hP1uus8FpJI+s//kd2g1OPXbb789WoasuqzvyadVF6i302Eun8P1uJfTzFZYJP3r+r507hx5KbOpLE9VpmViW4HVS+SloLgCyXyuKhcaLuWLawzMWWaDVhXCbVOZknQ6+1ID4tKgUO/p3t9pq3Mc7ixtx/F3j1+DdHWqKxo80vxZJ5PzxTUmxeqomHAmoOyWIuMazjGoXiG1HSI2aydXb95W4Dc9d0ZqU57nzD5/Tz1Ztb2vSYdnftoFltw5zo/Hult94X6rZeZnsF6izJjhcUuPldS6JYtMbFW/Ffg6gOyuo2xdMJzOufbQvNz1ziqLSxHVro/8EXTsKaWDsyv/ubqycVVmM7mzANXDw8MjXf79999PAlS///57GaACqeUPPmqgSnWXxz/rj8P0mo7rwte6pBayZba2I6tLlJX/bC3LU5HTc/J9Me/YriijKq9TcMhM8RVsz0ClG8TnOo2nIJbn5LPleV1HtgXs6H63nTtl2uKAfwRSq6J1rGZiu7O2yBck1gFmXFMj54jen3/+eXKfe5cHW3WC+h+12F8htTPyuUU6eTp9gGwBzsk5u/tmz5iVr3tP0tlLBllfqzBhHcP72sqv6v245sAfEz93LQnrEvQPZUe5eIbHvYeXQLDO1LoVGQqE3Tv0fFwB3FmdFSync9X9kERet5DalG6Lj1xJc8796fpLnJ2qhAlGJYxXZ8J+lbcJKydf3fFjnK/qCPtTvBL0559/HnWTZ2ux1Y9FYbmyvh/PrxLw81b0mMuM/RSgquRcvez60RW5tB5ci149G7GdAdyt96gjHuMxmeVz7MBx3gFqvjeBPufAziE9T5X2nHy2Pic5z61lmoGi7rNmTt3l8T365aklLS10MtPDdN2BZRY3QwTj7/SRy6nOCj91oPwsBa7uQxPqGAGo8ayK1Krj6rRtJclRurao0lV5J2fbySuB8y2AN4GtzjNf21LDc8WBX7TTbKYW53FO/avzkewn+bmdWUYHgPHftNBnpOf38ZjYMpn9448/Hv23Zfq4jJvVYTDswDHbBG6r2SyOA8eufVb1d/X8OaS26yNXy7ZLFhdUGuOx3eN0jsDOnlH9a0G15XTqk0E83eqO2coL6DIHpzr6rEuRVY+1rJearZ350+o8P/uppUNMLzWTe8kyrcpFiW0XZOggn73TowDZAeYO+B4jf0WvM7PBA133NW3KoyuX7uhufuc8193bPdfNt2tou+VL+W85vhbH3dUTHM9IsF6vHKWukKh0nQmuey4A5uFwODotBeP8XKRXcuvArTpHBbOVk9Oybh0XySa59lhxjFV7Yjt7ducZXVA8OzfTcR2znefNnn0N0vW31X3qS5X4jpH1tGM3nZ/krYJSAGHoPvaV2Lq/8uGvHrtZWn2P3tkAR2pRVkdqZ2C3C34rv9IJPHXOz/YrPVjxkbP7t8q16umquEDTGN5vqy4jXYWVkfbSfcJ6w++xj/Gfd+PdygsOSHVeI5iRWi6Dzs6ibZOech24L7SeVVtcitSu4NpzZAupfM5Z263P2kRsnWOcpVvN95zyJEmNlJ5bOeRqu1r257hnSz7nPGeL8+vmsdLeMzC85fkreVyDdHSC08wc7Bj9Jcm6TMrdx89nR6X38nPdOz4KWBOJdYC2C161rCjTueKA5ZZ8Z2XrglvIahmeUmdeiz4+lSQd1WWKSMeztrjO4x16yvvup7Miep6B8OfPn8fDw8Pxf2txD380ikmsLlN0szoKghkAqx1AOyUwrHZoheRi2yGkeq3bv53zzpa4687PrvjEc/XxKfHJS5cKC6uPrPJwflX7yOHi1bZlXWJdHuM/H26ELkMHeRmyC1ThGvJxwan7+/ujDlZ/1eVIrurxJWZrXfttwQaXHvfav4kHXcsy4460ie1MiValo5RV5MkB5k6e1XNS+q6hf2pD/tR5X+L55xjIDqiu+qn7vE7/rTjw7nOvTZyOuTRjeLKbHHCateUtp1ECqw4bafB3IfyuLTsyXVLoCC1vO2DVOa7KaXXafHbu3Hxd+Wbl3jret+pF1+Zueb3lNclW3XR+k/tfSS7S4cckV69VAJL3AYa/fPky3r59Oz59+nTUUxBuBq68xBjgV//2Az/96rmS2jTDk8Cwsw2XIrV6vtPnSWaztTN/19W7WTm2pNulL+wfoY8aqHLvxbtgMF/nrebhyKHqC6+6GOM/RJb/5gd6x3rsZmjxQ3662mL2oaiZf+e6aPuodDHlLDDl2rd61gzXVpj4kpJWub5kvb74O7YzR9t1stX9DnDr/kpZq/RPYejPkUs9Y5bPuc+5VNt0HXH3GSuGI137Hv3+VNIZB5WOuGXELExuOb06TTgGna3VPNmpAlgiX/f85Ij1nILWCqiunONrM0lpKtC7OvYqp5rOn1P2LbKqi6vBzJXr1yCqW2P4910Z9M72AUgxHrAkWFdROP1yuqQgWJcfu4AWnuGILe+7v/xA/mlWp0tqHbll2zOzFdxWXVLLY7JDeFf0xZ1PGGcV63T8acePdJ732vSay5uwsB6zvuKaBqXG+EYu1Zerflbl0r5VXVFdRvn4A46cTgNU+nEot+x49hqB6rSzR7jG9VeS6/z4TL9TXybd7pLgar967rmySlw5/UsgvUvElhUr7Vf3dPPWcx3wog3L12b3VEZ11bivyHN2/nM5gku1UUfRz1H4p+rX763QK9Kpd0f/cG9Km8gtG3n9+x5e5qhLIHE/Zmf5mY4Eq1NSoK3X9BzKyGV2QHUGUlMbczvN5CnGZgI2q+D0EmN/pufVPavg+LXJip7yPoNhB4z1HI/ztMKClzBXQBjv0T48PBwBOM9EAbACNLufLk9U0DsDwCnQpXZCSUCyF5cgtU8xViu/WT23o0fPiYVeux6rMIHtXGO9ZeILPVYfqj5X/UHSDdXlt2/fHpcfQ8905QWWITO55S8d64oLDoBVH3vUd+Od3rK/5zSoY0VkK0l67PAB5639pfvVtVUdmE1OvDadeva/+3GOtZOmu8U9EHfO3ZvK6vbd8SWlC9Qu9Zyt11fuvVReM6d6aQe7enwtcun+SboIYeDLgNp9FVl1kp0yO+kxHkcK+R4FoAmI8jP4fAKjqI+ed+l1n9ujI08x3lI5u2Wq0l/SZlzy+lOA8e8tyX9CWOdU3/Qa6xDP1vKSQhYmuaoD/MEnnOM8sdQYH5RB/gyEUUaeecWWySyDX7fcmME3tgrK9ZjrkuxCsiVVUAznOX/tT97OgmRd2XKfliXls+KPn6v8167Xlcx8qx4rmeXZW/aryJt1WP0mX3cBKs6Hg1YoBwepeJmxBql49UXn3wuSDndJrQtwX7rPuG/StS18Y2tZz5mR3ZrHJcszk2chtpUDVkJakd4EoKs8OmXbcv05jOc1EdmUzyWJpttffVZlMGbGZPX4pcpKuR3RVEk6iWuQFF1mx+tma9XRshMC2XX1cWRzRkS7hHblHr2vklnfdPNxX6VO966QvkvY1Evdc07eT1WP5xbVs8rXanoFwGOMIxjlD76M8Z+vjevSf2xZB5Gv/qAjALxcHhBuBcdMbNPXjauvoq68e5cAsVtyfA6pnZHWdF1lNQjlRJ+ZtrrvjmfnLyUzX/VahINQkI5vdccgs7yt9JdtA+wA6whshCO0ILW68gK6pDqcPvKGlRewGbzPtkF1Wb+j4fZnpHblWIV1XM+nc1sx6CVxNuQpZm+/x4zwMrGdOc5Omuq6I6nVuXRtDN+g7r5UjlkdnXSWXXef8dSytY4sKYhQOb5OG3UUelWxVwldN+01S9WGK2B5tgKCZ2F4psjlD+fBeTKQY+db1WkrkJyBzNm9q4S2GmtbAezqc1390/FzyArIXrHjl7B5L1GcHxzDz9rOjt0+g2IWB4h1RgdAGGmwXJEBqga8+H4lr+49O4BaR1p5RoeJLB+zLus5voePsV8d4xxvk65VpNbpwXNJ1892QfjWZ5+T5qWLI6t8joO7aWUiB6fG+DaOlNTyu/NOf3Wft6qvLhDF+s7XcN19CEr1PM3OdgJVHVK7SmKrfnNpZudmfm1GjlfI8FZRLDebtf0eBDbJWTO2XZLi0iZHnK5dAly7ey4tL6Vjk6w4pq35rSjo6jO6+a0C2KckyS9VOu3aDWI5vXPHY+QP2bi82KmyVO8Vafk6RHFGZmdpOve7vNJxdX8aa1uCaluJXkWEO/lulZVxuqKj16i/Kl0fmcgtXx9j2BlbN3PrADHA6OFwsO8Hfv78+ZgXL1lEORkou/dlZ0AXRFfBLM/KunNKgB3g3UpqZwEylOF7ipbp0r5c7zsnKPUadFal0mHWUxYlF6rfbsZW78drAfzDOegxtrpKinUV9+i7vZrGBa1Wlhrzse7zMcrHet4hta5fOkQ33ZeuzfZnx13cOtOVS5PS701yL7YUuSKnnTQr92/ZVuV4KnHlf2myFdieCyS7MlPiSzzzHBK89ZkvSWYGseprp2caUXbXNG8lsbx1UeZEPCuCWx3PCC/vV+T5qQhtdV+K4M/ycWXuOspZ+ZJUM/rn6NFWO9a596WKI6Dq+5DOzYi6mVoluF+/fj0hoPw8gF8nOhPE8vDwn/+p5ZkfXUapQJaBMJPWNBubCK1Lq8/kLeu225+R2krHeNydY39mcgmQueLztgJzF6j8EaXCpy7A69qK9Rk+VPUX8uXLl6M+suC+VEYmjPDRLpClusxBqWppsXu1AHVzs7Sqv0hb6ak75jLzOa1/55z2h0s3w7hb03aurcpWW9K575Jk+Lt+PEqP0zWXZkZeV8txCbkGIjvGZZTgHMfG0iHIes4Bg63lPvf4WmWLvrBUKyo6Bqw7a5uWUI3x7S9IXDm3gsA0plZncF0e1bnZvSv6NrOHqV5bSe0597A8VZQ35fladFkJbkVux3i8cgLpNGDkQDELALGS28PhMO7v7x+lB1hkUqtg2wFSNztbkVkHcB1Zre5J6dIs7lZSW9mnFVL7EvSmY+suBcBfi+5W4nTWpWFhPdXZ22qspQCV6z+QWegmB75QTn2mEtTuMZ9Xm8D5crm4nDMS64613PwcbYvOuapdu0S1my6dWxGdiJjhi1n67yFPSmwTedTzCopn96jj1kbtNm56VldeIpHdUqZLlXsLCVxNs7WsW5xupz5b6vy9ZcVIKkDWfGY6l0iv03fnFDgizQBbo8nJyVR1q6QDOCuw2RkXK8+YpR/jlNjM5Jy26oLxlyBP4fhfqjCIVd3U62PkmVq+lgDqzc1/oEMit/y1cwaQAMSO1HI9HLB1+5w/X9dnO6Cb7k/HPO47pHYLoa3sRjdwlmTrhMBWIP1SfONr0PcuVnX6rIGvJIncIl8mnjc3N+Ph4eGo//rOrQadVZ/0NYFZwCrpfQo0OTtwjs5WM65JtupKpW/da1vKdmmpCPBTy5PP2CqQ5fMdcjvbT+fwzFlDdg1GqsMszezZq/d083yONCv36wA/N49LKfhz3/NSZYuxdkvHkp6762nW9utX/16tztimsqkjuoS48baF0G6J5nbrUkXvV2eGulKlT9eee1lSp0yvRbrk1p0fw5NMPa96mMgtBKARgJiJLYNh1V8Hhjk/BcF6TdPpM2b3V7OznJer6yqpvYTd2CpbdW1F9y+NJ576vucWLWfS15kkfXb+1OXHy39ZF/hfCbCPrx+n2VouB56n+qVBpmqbgk5Jd/lcdYztCqntju9VLNLVqa0+uypnktVZ20pm6TrP6shFiW1FEt21bvrZPoNmt++OZ+f5Opepup7SzORc49shirNnXMoBdBT+3DZ6SgVP1zt5X4sThXTrlIhpdY7Pq57rfbgOQsvPg1PgGdqVD0ddSjivGTB1z10htLx/DqDduiRqNc8turLVHq/ISrmuTXcrcbM2rIOJ3Oq9EJ314XfdeOb27du3R2DMYJRneZjUsh5zvydy6YCxpnfXXRo+t0pqNV9Hevm6ptNr6Zj7w4nq7oqt6OjfFl28lL8/R16DLjufuYLz3CoNR25VNGCFcYtgFvLj93L1PfmEiROZVL1K5Fb1s0No9bmzNNX5VB89PxPV23TfFsz7Esb+6lLm6v6tcvEZ20oBExCuAPDqLC7yV4C9paEq4691OlfOmbXdUoaVexxZmeX3VKSiq+CdvNK5Tl1eggE5RzrtmPRSzzm9dve5dOp41Wnw7BDP6FZ14jpUjny2NGuWx2ycdIFmav8OiRxj3XasPG+MXjtV+pCudezopYhuBzy8NoFuOZ2uyK27l/UOQSYQWdZRBpm4X0kt/2etG7sKYhWMpjQot0un9yTSuzLT48qVypBAcBp/T7XCYgs2cmm6/r6T7rlXrF2bOD1O4nx2IrccLAZ55OAU2wbkwYEpzrfSZ4iO+U5gyp2rCC2fnwWk3DlsZ/qa9POcQPQqzj3HDlT3dfM6xzev3rua/mxim0AvCtO5R0FydxYXz9DnpWOVGXF1gKA6f45scVBP8YxZmi15dIDtaj5blLqjxFvb6FodatWOlR7NgkychwPPCpj5XT59j1YJrfsfXMiKY9nqhGZjb5bvDAR2Ca271l1BstJmT7kMEtKdKXpqovvaRGdvxzif3EL/9MNuTHIfHr4RW2xBaHllhguaJNLpjtNMS9qv8t5Kajm/2TkFyK7OlSTbM5s9WiWwK3p2rl/t4MUfQSos6lZScJ/r6wcqjtw6PUZazkff1+XVFm7lhZbd1U/1oNKxdG1GVh1R7diOVVLbxYPnjuNz8W8n7TnlXl2F9ZR+/sk/HjWGN1gVga3SVccrhNZdnwF4PVc9Y0WeiqQ+Zz4ury1K1Ek7U+qtDnmrg742x7ulDl197T47gWjsjzGOfx+wupxqa39sXY2wBZSu5OWevxUErhDamVTtsqUPqtmkSxHarWV7iVLVo0NSOZ80njrklmdtkBeILI5BeAGCodsKiBPgRVm07kpgdVuRygSM+VkdUrtyjrcr+lfpWiefGbDcAj63lLVz3wxfpfyuWa+TDur52Uxhev2A8xtjrsfYfvny5eTVAdZnENs0Uztb6eMIovutEtrVc9hWhHVGamf3X1IqP3vOMy+Jbbt2Y8W+rKTdRGxnZNOlR8FSPmk/HXN+HUJbGcsOoX3KGdvuALiUbCWUyfCek/9qGS6l1K+RsK6KAi3dn62i6JDdDgFLS6Y6s7WzvJEvpHK2VT1Tfh3pjLXOcudq7Hd1s3rWFpnpyDXq0DWWWWWmXxB3bpYn66MuQ+ZZ2IeHBwuI9QvMY9QfOuuS2Nm92CZAzfe79JzO3Ts7V5Uzycx2VIGwrYDRAUgmSrw9t/wrdutHEdcXXfxX6bNb0qx+9evXb+/TcjBKf9UyZGwxA9xdmcfElcu2hdC6+/Ucb7csPe7o2cyvn+M/KzywIune59LDSwbVjveOMX5sK7LLLrvssssuu+yyyy677LLLVUsvVLvLLrvssssuu+yyyy677LLLLi9UdmK7yy677LLLLrvssssuu+yyy1XLTmx32WWXXXbZZZdddtlll112uWrZie0uu+yyyy677LLLLrvssssuVy07sd1ll1122WWXXXbZZZdddtnlqmUntrvssssuu+yyyy677LLLLrtctezEdpdddtlll1122WWXXXbZZZerlp3Y7rLLLrvssssuu+yyyy677HLVshPbXXbZZZdddtlll1122WWXXa5admK7yy677LLLLrvssssuu+yyy1XLTmx32WWXXXbZZZdddtlll112uWrZie0uu+yyyy677LLLLrvssssuVy07sd1ll1122WWXXXbZZZdddtnlqmUntrvssssuu+yyyy677LLLLrtctezEdpdddtlll1122WWXXXbZZZerlp3Y7rLLLrvssssuu+yyyy677HLVshPbXXbZZZdddtlll1122WWXXa5admK7yy677LLLLrvssssuu+yyy1XLTmx32WWXXXbZZZdddtlll112uWrZie0uu+yyyy677LLLLrvssssuVy07sd1ll1122WWXXXbZZZdddtnlqmUntrvssssuu+yyyy677LLLLrtctezEdpdddtlll1122WWXXXbZZZerlp3Y7rLLLrvssssuu+yyyy677HLVshPbXXbZZZdddtlll1122WWXXa5admK7yy677LLLLrvssssuu+yyy1XLTmx32WWXXXbZZZdddtlll112uWrZie0uu+yyyy677LLLLrvssssuVy07sd1ll1122WWXXXbZZZdddtnlqmUntrvssssuu+yyyy677LLLLrtctezEdpdddtlll1122WWXXXbZZZerlp3Y7rLLLrvssssuu+yyyy677HLVshPbXXbZZZdddtlll1122WWXXa5admK7yy677LLLLrvssssuu+yyy1XLTmx32WWXXXbZZZdddtlll112uWrZie0uu+yyyy677LLLLrvssssuVy07sd1ll1122WWXXXbZZZdddtnlqmUntrvssssuu+yyyy677LLLLrtctezEdpdddtlll1122WWXXXbZZZerlp3Y7rLLLrvssssuu+yyyy677HLVshPbXXbZZZdddtlll1122WWXXa5abroJ/7//7/8bY4xxOBxOthA91nN6n8vnzZs3x3MuP1zvPM9Juj/dx+Wp5OHhYXz9+vXkmLdJ+B7Nrzqe3V+Jlq0qd6cc3XPd8lRbV660303XbevZNZbff/+9le57yc1NW+1LORwOsU0qnZnp3kzfOvmmc5d6Hu7pjIkVfXhp0tHvrfrEkvqgq7dbntm5Z4uNfS6p9Kh7bYx5n12iT2f7FS7Yag9m+VS2oFMet+3Uubp2SdunsurP3f7WsVDlNfPrXXl4eIjt8H//93/L+T2nvH37trzeHS+XxjwznTlHN52s+ptzpFP2WZpLtM9KG3Z8bwfHJ3tW/d68eWPTduvhrnfKr+f+/e9/l88ZY4HYPoUkUrtyX+eeFUKr5egOOtz39evX5XtwnyuXM9bo5HR/JSAj2L558+Z4r15T8O7AfHVO5TkB/tZnVWRtl/Md26UdYZXnpZxyl6SP4cfdNY+ppN/p2NmMc+q+ev+1tvM5ci5pmvWZO+b0l5Tkf861M5cKcKV8Vs+746cg82M89t84p8ecxrV/wgSdcbCqx1vG2NY+fU2itvjSfbNFF1+irPj053ruyv2ujx1+r3A8pOrTRGD53Dlt+VTYaInYdo21ntP7tjB7JnEr4giz5t8hs+75TAg5nTvfLacS1JnzqcpXlZcHvj5Pr20luCrnAqJKCX5EQPuc8lKd2aVJ7aUdD5+/5jG6hfxcitx25Jz8X0O/XCqvlQCF801by5QCuKvktotFzpUOmKue2yW47ni1rSsMgXPp2AFl3H9p/Z6NMZYuYbsWSbp0yfw7urz67JdGdLvjx8lT1+PStnqMua66a5BZ363alqdqvy36cPaM7VZHomSwSlstQa4as0tqE6Gdlevt27fj4eHh0Swtzo9Rz6TOCPSM5M4iMSzV7Cyf12su/w7B1fOXlO9BEq4Z/J4rL8lxdWQFNKZznXxZOkT2WshtpcMr5OcS4Nfdo+euoU2fQp4CtM36cIy8KmHLs1cI7WxmYXb+XBA3k1kZVrDSlrLO6jBraz2u2rsiulW5E/i+9Czjtfkslkv6iZRXdX5VOvhzJZ9L3n9OQOzce85pjy7WcLqTdHwLueXnuF8qa8cWPaWcRWxXKlQ1Qud+d61DalP6apa2IrtOEsHla0nS7C6IqKbjulQGxTmrVXLL92Jfn+2OXR6p7E8l34tEvDaQfUmAcGmw0bE/FVi8JNBNQZ6VyPhzBIWcdNuhQ2hn116afry08jyXdIMX6TjdWz1ny/WObCV6T5WO0zoc0bVR57aN00WWLqidkdFLSDeAove4dNdMai8hq2R2VdK46RLcp7C5VZ6dgM5TyyXtYNLrzuxtV4dn9qqyb906PKXvXSa2W4hn1Qjd2dqtpLYTVcA2fbwqLfNNM7V6vjLUugQ5GQy3VHmLQ56R2/SMWWR1Btivhch2ZohW7t3lP9INgj0VkZ4Bxq3gdpUUdJ/XAXddULrlvk6ZZvbgOWzBrnPnSwrK8LlLkYjvRTq2BLUSdli1FR1ye4nnzcrBsjJTzsdbZ1i5LKvg+pzgybWSXG2XpyBiT4XLXqJNfm4iO5NLBvQSwe2S2u4EmfIidzyTc8bGyr2bZ2xnxjdVcsWgd5YSd8qY8uVrjtTO3r3lgcREkQdNl4h2Zky5TFu+1sl5Vh+N0rK4NHwuPcPdv1LWp4gMr8pLNNLPJZdyBM/lUDo2p2tLtgaNcK5D8lbzf477EwjWNCszspdsj0vKSyjD95BKTzqBGshq+1UY4Vwf3z2/mn91byefFZK82h6d2ZZ0X7cNEgHuSpecIm0ViDt3vO1yOVv8XESxU7atdvzSddiCGc7NexaASuk7AXNng8BvmCet2uvn8LsX+Spyx1i7yq++3zprwNnfAWm+rtxMal0aTc/38Wytq5d2qM7srszKVgRXZ5gTYa4+dJXK0o3YOmdUAacZkX0OhfhRga6Tpya17vxTO5nKlsye3QF3lV68FDLnpFv3VJ/V/S1yzkqKXfqS+lrP8XmWRILTc6r9lTwq2Uqatzwr3dutY4cAVzMs6Z5LSHeFlnu+swUzn+/y6+r8Tmr78lJ906VtfkVkn5KoXyqQVl1PNsER3a0BospOdYJpK3LueHzSv/upjHNFQldJrRK52X3ueRWprZZL88ws38MkV5+Le9xfBKUZ06rsM0mzwVoGXJvN3nI6V6+UzuW/KtV9q3nqUp/q+rll+9FkFZA+tVPpAuluWVLAakZuNf33lNU2d050K6HdqiuXaruX0gcvVVIQErJ1tlDTrJDaLh54qgDZKkHulr9DbKt8V+US7XNp391N083j2uWS9ukS7e7kKclgF4+dW+Zz6lDde4l26eSdMHgVPFoJSvGWf3yuKhdf6wS59fxWPbg4sV0BitowY6z9j+zsA1EVCUz5zkhueh46a2VJspLHRPxZtvyVkCOoFYHdOkArsF/l1d1W9660xSXTqbwGp7pVtoC97n2XkmRXZseQzuqEmRFfyfvSMmtrd71rA2b7zl7sBPPy4gDXFgBX9U9nnMzuuQTBrc7N8u2UcUsenK4Cf6uE9qmlsksdMNx9xjmB7Vneu8wltf2WPr40ue0Sniqts316nOzXSxlDXRu3haR3Ca6zYYngpnsv5e9X73vSGVsWJW2uMfR8SlPlne5L1yri6q7NPiT15s2b8fDwcDJj+/DwMN6+fTvGOO0gneV1X1VW0b8ScmQyDfbZh6rS9USK3bM4jStX16lVZHZVcE+aoZ3N3P5osgrezsl3qyPp6PhsW+3P8p7NQs7I7WreW+RS/TjTxZV6O3KbpGqHXU+fR1Zs7oour5C9zrVzyzO7twsYVwl2BQ5nq7E639norOhy+SVyUJHfS8lszCW7ktL+iLKlDfnaGGuTAOe28zmEdov/0mCN239JsmrvzpmtTXk6cpuezec7z7r0hNMyse12fCcCmchux8ivOJ7uLLASWd6flY8HDBNWnsVFOnQOf0UZ+f/555/T+iCPaomxK1fnvnS9+rhVen5n9iZtta6rRMGRWb1W7e+SZYvhXyG1aRyd83y9twsuVWYO4lxyW+Wd0m2VLUQkOa3Z/kr9d3lZkkDLJfI7Zz/l2bm2hRhfUt+SzemsKnNpV5/vRP+aMAXaK0L0XDrOdiVd3+WbbOmbFb3fSghXgpfVLPMWuTS57eY3y3+LbeK8t15P5VJONCuHYoXv4fs3z9heMrIxM+bpeuf5nf+rdXkykeV0M0PKIA5EsDNTCifCZDe1ARNnR56r8qX8XNvwjLMjty5ftEFqE5x/CrDbMZI7qa1lplOX0vlziO5Kmg6R7diUZKRXCZ+m3VKnS8jWvFPgqdrXtN+b5L52Xd9K5LbmrTKbCXLHM108l9x2rqd7LqmHqR6r3xVx+GvLTJY7h3z0FSkHis/16em+Tj5POc6vQTr9O5u1HaNnDztpVzjBuYSWy3SOpDJ32q26/ynlXFIM2RLo0J8+c8ts7VPIErG9RCemZciXJLRpdtc5EleuGalNy5cfHr4tQUaHMjl0XyvmPPU41dX9X65GW/U52hZady2PlonJreapkiI2aaDr9bTltJUoid1J7WXkHDK0cnxO/h0i6851AOyqs5uR4W7el5RLOOFVcpvOjbHr3muU1UDUavApPSOl2QrgXF4JxM3KVuU/Ww3WecYlZy8dBuDzKbCnun2urPbfaya1T2G3Ne9LEdxKutitW45LlQv3dtq5moE9d9b2HF1dkaquaYwoVqrK/9STWDN5tndsVWaduhLB3PrerXaOdpySWhzPiO3h8J8lxUinJJQjomOMk5ldfe82CdJ0Zmu7S4ycE3MftuKyc/ldGZ9ygLu8dlL7NPJUoOGSpLa65gisGuik03o8G8OOvHXucc+7lJwDmKt0bja6AruO6FbP2eWyMvO7z9EPHQJbkd1ZHu55W+rl7EMnfcrDAcKEJTrLjDt/C+gk4aUKIySAzpLsXAK7XenaznTuGm3LpXCS5lPlu9JmW/z2VlLb9fF8vBXjbSW36VpFbruy1e4l6ZQjBeIr+8z3pXHmOMGsDA7Dz6RNbLdEHzqSZnDHmHfcbAlzdR87kzRby47o7du3j0itlp0J7OFwGF+/fj3O3jqCi3NMKJnspvrxbLDek4QHkVvWzG2j56v/u9WZaLdUeTbAu1Ld4853z+3ydJIcj7uWznUdXgcMJ2Kr52cGt3JqKY9LgZUV2Upoq/NpxgbHW2ZsHSD5Hu117bJK+DrnZqsNtsqMzFYgSnU4pWNQhF+3TDNbVV1z9+u5lb8VdKIfkqzEYQlnPxkjaMB9VrYOAa30u0O6qrG4Ms5fm1SkdKWdO/k9l3zvflsJ6lYB3qcQZ/d0fxZo4uNOHzuc5NK8BNzzLDO2nQ525CrdmxzA7Dl8nxJZJap8/u3bt+PNmzfHczhWRwXiit+ff/55QmgV0KWlyA8P376iXIEPzpvP6yDS647k6gev3NLoWYRYSXpHtg56d1+K7KyQ4e6zd7msrALHlCYZ++5P86vAcArY4JojcUlHLy3dMbplLDvQ4+qN850I9tbZnFV5zUR51pddoraa7xapyGuX4Cb91TGl/mgGWDWvVcI0K1/6t4UKMM4kBZdnH86cBbn5tSoH4LnOnUDyDPjOCFWFh3b5j3Rs6KydXdrv4bP03GoZtkyedPOc+TROX13vPtvZFT2v6R0W7jyn4lYOLyFvfq4+v+vbLzHOvttS5CRV1LITdazyRPrUabjmSKxucY3zYlKr0U5scR4dD/LLaZPzcQ6byWiKqqb7cI1nXTvv/VZ//ePI7aXBaic/R3LPKcOP7Dy31n1mHM/NX+/vAF/+6asGK040OYjObCWX8SnAQqc9LzWetR5cb5yvALFKijLv8rKlqztO72eEMgE6p78K2DGeePVUtx6uDFW9XPmrMvOxPrsrW3BSWiWnGMDprAPozsbNSK7aR6fz2pezur9mH33uDGBlU1d09znscsIG2gbP4Usr6fg8py98f1U/J86mOOxS3T87n+ype46r30yvn0Oeldh2P0+fjHG6NntGUhQ3Q6sztUxosX9zc3OyNHmMb2QPM7V4x1bJLggoE1ompe7DV2kwYdDof+hCNGLtnHR3ptb1hZspnhnQc2ZJL60gszxfs6N8DVL1jwOWrN9O3x0o5l8VrKlmLh3wS/mcK7M2uWR+Wg+cc/WtnPoWR1gBBi3baxdX/6e0XZ28OwAN2+7+TJf5HvZ9GkzGfjX76GwCrlUELJV1jExqnW9l6ayAcvqQ8NOqTuN+FzjX9mQdVvuoOu5soZbBlaVz/keTLilasa3Id0seq+LGVKpPJ4hyznP52M14avqKyHYIbedaNf5TeyXirHZvZv/UZqRzrt4J96S6676boOpIm9jOBsDsHj5XGXp378pymm5Z0hJkHDsie3Nzc9znmdsxTpch434QXL7GH5ViMly1l9YR4ggtO3RtLyayEPeFZZ1xdeSWSbCT1SXJLDOyucWQdcj27iSfX7q2ZIzH5NHdl4yyAmFeleEMtSO10DMFcKmMM4d3SanabWVcb0mbgGjl1DqOfRWEpee/Vtlqr1bvu6RdTDhCAdpMj1WXHWFj/wN/VQHhGaBDPmk8a/l5f0Zqu0H/mbi+Su2zNX8HvrlNZqu1EsCu7Hsi3Cn9a5ZEmjq+ZdUuprF+Kdta2QAnKXBS5X8u8U2B6BVfP8MB2o96DsfJPs10IBFEl4+zw+maPkPr69rwOeSiM7bdRp7lwfdXpNYtW54920Uu1WHyD2SWfzNi+/bt2/H169fx5cuXcXNzc0JqQXhZmCRWjonr+PDw7T3cDpnlejoH5J7PeaSIrZ5fBe4uSpMkGYLu/VW+P5J0oruXlo6Ozso0u09thwPDCRC7d80Oh2/BJyW1zrEmg57OVfesyNb2XE3XzcvVZeUZr52Qfm/pzu50ZOvYmYEn1V3d6o/zSLqJenfAv5ZL83Lnu0AwkVoHFM+RNEvrXilS0TbsfIGZ64rVaerf1e5hn+9PgbLZOUcOrkUcSZ2lSfnMpGrnlP5cclv1adIXd3967lYy37WFidxyHing4NJUdXI6MpPUJkxoUx+wbU19kvCbwzHn4BnHB7bo8hKx3eLE0j3VuyacBnm4a0kJ3LO0PPpcOEic4xla/O7u7sabN2/G7e3tyX281ImXGzOp/fLly5HUanl55lWdTZqN1n0QXSWt6sj1A1UoK/LDvitrIrHuvKa/pCRlcQbnueSS5OA1yHO1h7MBDhgnMKx2RPUiEdpEcLcS2a3OoAP4uteeQ1J5HRFJ17bKtYHdc+Sc4FXnvkvk7QCTA1pJf9Vns47pbK3ahESKKkCXyEeVH8rG+wn3uGclvVDp4iNHatmGMSF15DQ9g/Niu5k+3KX2Tq91JaX93nZui6Q+79yzRdJ9HWyVzq3kh20iVHy/jpEqbyfp/q448tkhtzPfr2XSeyqpyLpuE15R+5pIrLOfiY90ffdT+uPNM7YzUpkM7azD2ODj2Blml5d7P7Uqlxr/w+FwMiOrpBb7IL54HoAwO4QxTmdxb25uxp9//jnu7+9PBvTDw3yJFKQa8Amcj+E/FpWI7+Hw7YNWDPC1jZkgQzme4oNRyaA4RXpOuUbH+dIkGcat9zpg3CG1qRy8igHjzum5SuXAHBCcnauAQef8lrHq7klR4ZV8ug47AZFuuZD2RyKzzyFV/20lJElvnf7yPxQwsa2eoflDHNBz9/H5LpDnMYxVXCoVJqpI7YwAunx1RYqWlTELVpRVdi7NOjMu0FettC6J0K7aFD13zTrfxYDnENqZVLZ3hdzOgjCOJG2tU8dP8Pnkyzr2LY3XLrnFvbO27OqDEmzdso3jtBo81FWorv6J2HL9Xbk6ZV+5bybLxLZDrFbzSga+S2q3LklOIBjEFuQWM7QgtdimZ+igwjLkL1++nAxWficX2zGyI3BSGfmK5DKZ1hlbJbQO4Luly3zuks5lZhSeE8C+FkL7lI6xki32oXJayUl2SG0HFOuqBgZtPCuRgi/JUXUJrta3016zc1vSdKXSw0QYVsBSle/s+avyWnQd0tH5LcR1tZ0qna30Fr75cHj8v/KoXwo2sc9zftaR2K31Q1mY4Ka2V2KodWG/7PJR/+twWLJzFYnlj27hOuerfcXlUaIMXJB8drKTM9lCXl6jPJUv79rhFXutJNaRJJefBpVnstImVZBlVTrklp8DqWxEeo5u075utQ+U8zDO4bxmxJbzn5V7du5cOesd261OsiKFfL1Lap0xr/LWtMmh6gekGBi757lrcB4MjPU8Bg9mStnxVkt54aj5ea5eeJ7eowAd9/ASZAfo1RnPvqKMfF1kaUW2OqwfxcG9FHF9f076zv1dYKxguFsufp9dSW1F1Nz5rUC5W9bq3NbnbymTPjMRWwXDbG80Tec5u75fnpQ/BanV826WVv9mT3XZiZud4GdwEHlGhh2Aq8aY+jiXTm2Q/m2glpWfuTJ7qnkq4eRgt57n4/Q81xd6b9J5JbWr5Ggl3W4PtstWO5zGPe93SG3SoXP7NOHRNGaqMq62yVabqfnwvpLQtOU8GS9hEo9Xoo5x+moCk+JEbN2z9LnPQW43EdtVIFotuXFG2d2PfB2pTeln5VcHw2Vgg+0GFhvkMcaJ42VyOsY4vmOrxt+9z4LrHOnUZ0PcO7CpnVSQr85I8RZ5KYDnYy5n6t/V92xnhmKm6E7BLyVPFR19LbIKOrbe79Kq0XW/LeIcMmY6NO80CzGr2wwsJ+K8tR5b798akHL2XdtM7YgDEE524Pp9ZDaeO/frbwupZTIFv8r+lcvDhBbpObDbrTOPzUpveUWWsyM6S4Iy6tJgJZwsFb5yabmtlIjyhy2VVChu4hl0F+TmundkRkRd/Trk9bX663OxyOzeRPaSb3P6oGWc+eSKmCWyvBoYqSSRz05gmcuq5erYSm4PbR93v+LcLvblvHVlKusz7AMm4PT+MfzklpaxaquObOnbTX/3c06abtoKCDkCl6KUXdHZHDew3Yed1Mi7LyZjeS+fw9Lkm5ubE6KIvwgCueQBNnNsSK9tV7UNk2osmVZwoO/NOIKLerJSnDOAK1DbzdNFttJzV+S1kFtXj0vXTZ3a6vVZvh0yq/kmMOvKwrrDYBgfV0M+6sC67ejGfNKbKqrcdZ6zc05SdNqVw9V/1i9s2/jaCtmYpd3Jb9/nds6fM540Hfe586lMaCGsi9gyEEMaBnqs0zwedJmszlBU5dbyrIxZ/YeFRNjxfQ4uGz9fy6VkssIOLK4NuLzaP1x292zuoxWd3oI1L0VsrkHO8dtbfKzDZM72u3JyXpVP1nscoa38kPObHZ9YBaW4npcSZ0+4PVwbzcqwldTyTK3+kAdzEf3WDpfLBe8UD83aZYbRV/T7on/3owVxog3CHVjN3q6eS9JpHHYEIHs4z++r8rN1Ov/m5uaE8CESwvlqWTCr++bNm+MzeTCl5UE8+wrQrj8uqwOUGMR4Ntcbg5YJAQb84XA43oeyoA4pitOVDtBy5LWj4CxbjNjWyN41yqWd5qWdheadnCf3WQJ5LjDkbIu+G1+10Wx8OSft8pgZ90v0k3uGAzjVMZ9zgCbZIZdnepZ73iV17ynH6FPLJct+aVKb7tPxMdO9MR77aV02l56j441Jl/qQNB5dfWd+x4FJXQLobJYj61VZcJ0xBAfOEZTTrfPb3D5jfPvHCN2qnrrXk5Ks6m7Hju5yKjP9rHBMRWgrf9V5bsJvbuvKPCvHTJ5jzDjbUflHlw7SxbNaL82TA1O3t7fj9vZ2vHv37miLxjjV4S9fvjyqk/I4vkfLkMqzUq8VH3OxryLPiKU7r5E+NaCJwHJe6RxLZVR1VpSdJDsGkFquDxNGns7nryfjfgwMdcbc8W/evBlfvnw5Elx2ZukdGDcbqwCB3w/me3QJEs9Cffny5VgvBxwwUHEfD0w3s726HFlFAW1lUBXYJJDj7q2uV/f86DIDu6vXE3BceT6PEyV97nlVGVln1L48PPgP0nAZZuWdEVrsz0TTz9q1S1JmpLmqpxLaRGzRjvoaRFXOGdGfXbtmAuvkXIJZnbsEod2ah9p0nZ1NwWJHjg+Hb6/c6DjU5zl9nwFPLavDCm6mRGc/OU99Vcm1myO0ugKL685/Qah/78ftA2HynbBFCjqo/3blnslr09XnkhVM7q45H6Q+psJkHYLWJbMu726As0tiu+PMtUn17ERm2R+qTVLRgBzbG/255zOphd25vb0dd3d3xy2/rsE2Sckt2yq2k11yW7VrNZ468uT/Y+uikDPAyw2WSG1FclN+ujwWjYcZVTb4uK6EFmVyM7WIfIDcIg0chzPuyOP+/v7E4THBRdmZgEPc8mMduOyAtM3Y+WAZNMqrjlGJPztKBfTu/ZqZceM0M1CukWTuryp/PV8RqN2R9mSlnbYQYL5W9aka+jFO/8/ZPVPHEL82ALvBuqtl4HEIXV4J4nRBPaefGfsZCdXnztJ28queofayIrZOr12eK86yAtJb63Ytcimi2yG5W56ZgJgCXBfkdQFXLkMFFhm8OeCOPDlwrMF29rkQ9woO0rIvBkYAmNRlySy6rJjz1bZSX479L1++2KC9EhMXVEIdmczyL800I0he+diuvGYdfUqZ+dvqXCKxeq0iITPftUJoK58wG0/Jf+u5VVK78vxEaF3AN2FltnN6bkZwHTcAX7m7uxvv3r07vmc7xrdv+KjN5frg+brSA9fPJahb732Spcgdp1ct+UO6Lqmtli2N8a3j3fsfYzx2HPwFVDcw8AyNvmI6n4kt8mGSynXkgXZ/f38ktO69m/v7+5P7ldQeDo9fBudosH6kAmVTZwgnyM5Rf3yeIzk6uLmeDvCnwe+AN6fVPkxKrs9SqQzaj0BwnwrYz4iT09VVQOxI5hiPyWx6fuVkcE/1cRQcc+BLxyGXcRZQWTHiKYDTsb2rbZ/KNQM82Gr7sh2FcOCsQ6L02VvaLcm16vsWULZ6XIHLVXE2u5rd4zTst9jW63hzOs7PcSCSrzF2gPCrBw57MBBEejdLC6xwd3d3XBKoQbUk2kYgkkxksQX2YL/Nq7HYr3Kb8oekYAfdLDO/ZnU4HI6rvdxqsmS3OvXsyDkg+keQFX+r/bbaF4msuvOVf0llqwhqJQ4/cjm22DKHY9W+sB3Sn7NZXCZsq7ZTO6o4mANTTGjxc8QWts6thkGebLN1Qsvh+q1BrZX7zvof2wooOamMtSOpK4SWn+1AE4w2P4s73RFepGXwylEPXqPOAwSDRpcvuoHOpBYfibi/vz/O4H7+/PmkbiCbXE+NpPKgffv27XF5AUeD2WkrscWyJCWwfO7NmzcnZQG55eXMPMjRn2mppoLjZIBTGu47R2r5eiKxafx2Ce5rcajnkt1Z360SK807OUwFpAmgO7DryqP/manPH+N0vHOZUnClW0dIcgozB8H1v0QfVLpR6a22syO2uC/9nP6dGwB4jXKJIFU1HmbHK8J23wU8E8nV1UMcQBrjcfAZP9VhiBJcDbCwn3S6qIEtLgeXIa3sYsygs7YOL0B06S8Hmb98+XIMkjNe0aA1yCv7aCbI+jzFPaiPrrBDuzg9x3aVyKTrs4DbNcrW8s/0f+aXU56zNNW93OedPFKayl+tBjf5fudPHQeYlUXLoP6X7QD/On9jpu2Y6umILbb8fF4poj8mtrAVvIJUdX2MbxjIYYsuXuE8U3+ujMEn+3jUGHkmFlsFlltILZO7ygmw8+Qp9jFOZ3rg3JQc86DkyCsTWR4g+hK2LgU+HP4T/fz8+fO4vb09ma0Fsf38+fOjiM79/f2jNkWZnLNkhwkHz+KIrZutxRec4Rw1Gstt6AIYaP8ZudUxouPGpde8HAhxz1uVikzvUkuXUHWcrDtm4109R52Mjjl2NgyI04yt1pGX8fNYTE4z1TMB6a5UJCTZSXfcfU4KJKg9d8RWA2CuDB0wwVKNlU59fhRZBZtV33TEBRpUTxzA0yCs0w8FXW7sjfH4w2+JQCOfpI/4zfyW++oo+2f3CpP7+0CuD5fNkdq3b9+O+/v7Yx76YUrYPoBSDlYDA6mf5g9dMQHnGVvtixlIT3aI268SxR967bX75y1Yput7q7zV7rt2xhir+mDW/6ncKzLDkby/UtYVLKj2SINEHWJbtSWX3QX+kIaJbeIwzr4w9neiE1qpbCtyjv6e/T+2s8529znSiK0a8TFO33FTcliRYRZeRgvDPUaOSCE/jkqqc8LgwDT+u3fvjvtMbB8eHk4+xoTf/f39uL29PYmwMrHVryfih2VGUEYup0Zf+H7XNuwYUUc3W4vz/HGpw+EwPn/+fPLxKAdcNLrsBv9szMyMWkf5LyGv3VGuSkff9biyH+ledjxq8Kqlx2pb2E7o89igM2CrZmz5fmdbGCRzXdy+O76kcBvwMV+f3Z8ktYk6c97ndPo+npZL278CtLtsk65f30pCkMYR2jFOv6ivY1OBGve7G0+cD489/Pijh/pMVy7YgyRK5LDlLx/ze7W6uoqJrSOP+mOBrwaGgE92H3uCnvFHIXlGF+kfHk6XIo8xHs3S6owtt5fO7nB/c9oUEHOi46Fz7drIbZe8JdK5QnRn2Krj211/Oj2t+iv1XZqccOQ0+Z+qLlvbqYNJUXctQ1r5kN6v57wqzKwYe0ZsHWfBUmQO+AP7cxBLJxHYjjDm51UgW2Xr/U82Y6sdVJFZXK8IbiK0znm52URefoNG16ikDjpHYtUhYWDwOnVHbJWk6ju1/MNs7e3t7fj06dOJQwTpZZLOToYjLzxbq2BBiSCWVPHWLU8GoVXDpuXB4OdlCkxoldwmg6yGQQ2ZGjlV5BXgfgm5Jie6Il1H2unDdG6275xYRXCxX+WpPwWEacY2OTclYEx22Tm5cZJAgIq2QQVM3E+vdfKZpXHlTvbazdjOyuSIjitnt107QOc57MVzyUpdOkBupf1cv7lZBR0HLOqvGDdovzIWcO+sJr/EM5ZctjHGo+XG+uOVSgpgE45g/5xwhgO+Wh/46M+fP5/M0mpfcLkUD4GIIg0vLYSwLWRcwcRWAwUqzkfPxo+TyhYoNtnlm3R0u7ovpVXd7E40pL5aCXi4PDvjy90z84HONqlUflD1MK1+0PwqYqu6luwqnjlbMcITVeADbDfY9mo5L+EzHb5Z1eWz/+6nOleBlDF6/0mrjioRWv3xsxF9vLm5ebTMlhUvOaPkkHRQgNjqWvWHh4fjzCyTZhBV/sgDfpzvp0+fjsuWP3/+fFJ+lBv5uplaDECN6qBsrAhwdlU0l9uVAwQuys1blIGfn4xkRUbYaCnJmUXuklxCIXdHmqWyA+5cBYqdoXdkt3KMnD8DUrY5bsYWQFr/BxL3s52BE3DvnKcyJxLmxnwy+B2QoiRy5tCrfZQ32X+14zo7Nob/g3cGG1sARUcfL6H3r0Fm+jcDwkm3kh5rfyvRVXFg0elt+o3xDVso2ONglc40aHkOh28rM2ALAP64XInUullZt1TZEVxdpsxlxKtC+CaHsw3aFkxgYafGGDZ47cA4k1ptxwTONYDhpLIzbkw4H4HzlwLb30O6gL5TxwqvVz5gi192P06XysfptO5dDJjyrjBHwguaxyyNK2sqi+oTCKcLSilG1+e4IJILouOZ/I6t2hmUAfezzwbhZZ/MwjZ1JfDcud7VBchFZmxXOlsjpziHRsc5jg7OSCxHIhQ8jXFKwPQ9Uh4AHDXR5UN8zjksXv7LxBZlAODle0FgsbSYCS7y+fz58zE/XqaMsqMdOfKDZ+tsE0SNjiOhAON8XttT/xZIHecYj//fivvegVk20pURcvXh/WSIV4mAu/aaCOxTOf2qD911dT7JkThnqe/D8TMUaCXgm951cToEIAudRsAK447Lo/8PreQWdei2aceJV/VMoL8CNWk/AUpO5+y5PpfbLuXhHGl6rpNVnb1WMLwiXTDc3WK/GmdjPH5fFnoC4UCpK1P6KR5wKy9YMO54TDqwxsQugXZuByW1+pc++pEoJYyJ3GqwGvYAxJbfdXU+nsvJPhttz23AM9cMZN3qL53NdpLazI0fl4+zNdW5FeLzUuRSNodtaQo46n7lr5MP5edhq8uQO8SW06yUe0aS0rmqzrO8Oj6oUza1W2wL2FYpRte2YpuDY4eNIPqeP8/cwvZAgHPUb/MxnsUkWPG3lmsFx2zV3Sf9eBREidVswCqRZYPKxxzpcEt2uHGY1OqHkTSi4SKpidRqJFbfb+XoB4NcAGL+LD+T27u7u+OS5Pv7+/Hu3buTpcsgti6aqsGAMR7/cTr/UEYG3kpwK2DiPpiDZ/KSL25jJbzJuFRGhwe91imB9o7z6Bg6VbhrcZ7fQxzwTfsVIB7jdOwBoGGbnKEa4w445vQKitH/eC7P0HIandXlj7aMMR45HY5Up60rg2vv1Z/rK9eHHeDJwm2uMzzcBpW+sh1L7XWudOzCNUgaE5WsgNuUPukSb1nYd/A9TLLcjEP1vFk5uE4aVK1sw6zt+BlpabFu3RLERG6V4ALzYOzzckG0l/roikxWdWNQzTrMP4gG7CBcFkditE9n5XM+gc9d0i58b3mqelQ+mc91dYJ9ciK4uE9xm/bdrLx8bkaW3Ljq+jy9luqdnqmEfjbemdRq4IpJLc+Mch4cYOLnsv7CTjBHYVKrk1laJg4Gal23+B5ujwrnrMqzEFsnztkl5dJoBkdEHQFlIz/Gt6irEkhezssEkR0U/9iwO/KrXxeD82GSyDM8TFL1h9nZu7u7k5laTq8kTkEgDzh1Limyxg4fP/7wFTtL7os0a+tmgxVYsEPsAAlHKrlOPHacIV4xnrtcRpwzmQFRvUedpALf5GTc81yZ9D416pAqEIN7NYjFBFxna9OMM66vGPeqPjOgksBDytuRTU3LTpftgtqI6nlaTgVRlczGRFX2H1kq4FuBX0eAElBzesRgjP2Xlm1WBpfWBVIZyM10Q5+hP8Yg6dUlR2j5lQc+51aOMS5B2XRFFb8ehNlcbcf0dVPuA+wznuA+5XZJeugwhgbsVGb6qfmzXcB9s2fsUpO+mU/mNlef7FYlQbS/Vnwbysh54RznpRMbrr5ujFW+zo0hfaYL0mp6J2orWbfR9vwMDSapjVN94LS8qlNJrT6bywXbBDvJq0M0OMn36wfoOrI6Jpw8CbFlQ6jiwJE6xLRVY6+zpugwN8OopIsNMiugi1Lw853zTlFWfc8WYFcdmCO2uF/fv9X3g53xYOF07j/rdCaXHTyE66DOjWeiVBHwfDUizoAkQzgTdcJsOFXRuA5874/q+FbrfQmQMHOcbpZHnSgvYeSgSQLB1XMTSHVgjvOsDC+vVGD9cMKkPEWyXZ1WSHynDTqOfIw12470DhzNRB27tp+W/TXMzrw0ScCQ950/ZCCkRGiMcfIfqg8PDycBU+jxyhjR8ozx+P9seesCrS6dnkt2Q4moklp9l43vUbzhSK07x/VjII925RVqd3d3J+3Ntsbtsy4xME0BR4gG7NhW62yt62OXdxoH6rsTYboW23CuX+3oTHXd6bNiPWe/nU9mnVHyeY4kn9PJt/KHnHdlA1RUV1y+q+Lwj+JynbzBfUp4xzh9FRB2Jn2cDnZF66H2nc9zW2zB73iO6uq5Y+e7zdjOJDlORyAxO8qOIw0KCP4PVp0pP8+tL3dOXMktOzOOVAPo6r1ueTQ7Jl1GjePkpNTYuP+pQ5lQd14uzKSB8+XIMNrHkVomqQoi3DVWTsiKkrhosFM+vedcZ3LN8hx1T8+oQLGeY3ERS531USNYOaxZ2RCcUWDeMbQ81ityq7qr4HBWVgV4DoCnnwseJEkkoUqvjtc5fO57Vz/9OaeubbEqHRBz7XJJezfzhW/efHs9iP0o22esAmIf4MAnl1+fr/vuOoM6vq77K3ZBCXsVbNeZViW3bpv2NSjuysz+Hiu9ECDnb4o4rOD6AEEHbQPFB66fHNGtgs/ar9r+zq5XQJi31yzJtm2pW8cfJL1Oeqljx4kL3Lrzq3Xpklp3rhp3M7vA44txiXINrqfbVmVWHeG2hswCx2z/lKewXXJ4oMJksEPa/wlvu/q5tnR6vEU2E1sXIYOsRl5Z3MByHcLLfvH3OpghZYOrJBCG2r3zhudppzoQxWXjQeNmkdFBTDB5oChpZeejy4r0x4qFfSXAABH42iHP3PLAVDKZBlVySkoAtG1d/yq5xXOdgrjyOQOZjJaLFjtnOau/yjVEhFk6IG5FKj2vHEgFjHV8pLIlYJvKUd3jxrUz+kqwHblE2cc4ncHVcc22UveTpDGb6lSBGOcENY+Uf5Ve81WCwY66EnWYsFPO6XdkZWyv6sFLkco3r0o1hlh34Xf540YcZGYfOMa3Dzfp+HL+FtecfXflTfvcJhrMdvcgLd+vNotnT9OqLTdDksgtbI4SWQdgsYXvSxgEr0MwJtCgguK1yka6dk8BOiW0LjCl+64fUt/oOL82X/y9Jemz88eK7xzRSpMsitU66VE+V+ZVez+zYw6fpOdruXXiBpLwtNZRX09iYQxRYR0XSNZ6K4+q7Ixb4co2gu0HXkXUsq3O4KJfU/+u9Puzz9h2HK1GGcb49ufgWOqj/yOrXwIe41vDuiUSGm3Q5zvg54yxc/DsXLg8eK9W82TDoSQV5VcCy8RWyS4TZfyvHWao7+/vT9qGn1Ep82wmyQFZdZR83Q16Htgq1YB2xrCKMvJ91VhMivQjO89LA2UNDLG+Q0AKGSDrfve57vzsWMcR2xeuh1uZwNcqcjvGqV44sNAR56BnTlwBS9c+z+5xztbZA9e2fN7psSMlK9IZCz+KrOp05feYtGEL4cANP9vlj3QJEHM5VsXZdZdXSuPAoi4xdsuJ3fI/JRDACzruuXyu/dTnuX5xz2M7pbbM2UI+p0De9VdFViqbVPUr56P7aXtN4vq5I6sBLcXDOm50TLrn6VbxqAtyVHlUddIyuwmKWX8nHXY+U5/n6jrG6QRONXutusBthHtVpyBJJ3FN66L1UG7iCC3X0+XP+2ynXDu5Oifp6PGqXJTYngt6WXhwcGSBP87gPletEQ4tH0uacUjgsCqrcxg8cFKZ+BkcuUlGwymFI7V4J/dwOIzPnz+fPBPklsvEioGoukZfVRGd43cKqGBf654APT+D864kGS1nUDtKM0tzbU7ze4oDNM6ROhKDvk+GsgtyZ3oM0bJo/ig7yjfG4+CQG+9M0l101znS7jjl/cpRaz07bcdlnYFPR2ARqHBtCHuTnsFjhQmts6UzB9o5V51/TbLFX+v4dqSWgdMM3Kkucz9zet1P5XLnNXAzu0fL68aZ2gcm8opVeJ9BIZMJBdhdYKzXuIyJyM6es0Xc8mYOxHO5Ul0dUNb687VrJa6XlEsGmhOGxVjiZypZdWSWt1WAivNNQZBU9hTkmG1XxyGPOVfWREhVL7WtgNkx68kcAOeQr8O/jtQ6H+/6NdkFbl/OS3E6Bx8Vzzhx/Y5+6/juFT3fRGxXI0OYrVgVB35BavWDUTxb6wafM7Bj9MBUcoqahqMZusQIz0p56ZeOk2h90kytAtbPnz8f71Ujxel0drhaEj0zVtW4UAKcSKsalNQeSoCdojtA4PKrRI3ULmtS6Q1HDlVUfzm/Lc/nezWPKuDFaZSMVgEcHu+z61sBm5ZR66q/RGpT/Xmb0ms694xOgErrwPlwoEP1291/yYDrLnmmjQGSm611voT7TIOQCUQymEpkyI1rrcNMZgE0rfcY48T3u1kR/eeGNPOR6u+W4SONW9Lo+si1T7c9NG8l17OZOjdeZlhLy6hkpgOKr0W0T1Z9wFZb5/qFMbUblxU2rIhtVfZOutk4cOPabbs/90xHwh25VX10gQBgd8UBTHhVnB5rmZGO9WwW6ErCusn2jEn9zHYlcf2X9Lo7ti82Y6sDSiurZBidlyIdLNwh7n/idAkyDx59d1UNrD6HRaMYDmQ5UqvGAfWrlhLwQHEEXNsasxxptpbr58iqfoZbFZQNk/tqM84ryXUEJBkXR25ZFPx3o3iuX1BHJh5OeSpnMiO1r4HoPhUIcKBTdUnHgVu94PSjAmgKkh2ATMeVw8B56KzTcXVUOt75nNolBu4z8NYte6o7hJ3l7BmOzOq+OkzOX4lpqhfbUrQB2jz1y0w69fsRZQaKVW/ZJ+tP7a6CYReQTL5OiV1lA5w9SHZlVVK+DPjQNhrYdsuB3fhVW6f+GudVtzgd7ztQ6QgkP7fqo8o3ah7adtwP1WyRw4MJC+gzOkT3GmXW9kl3OuTA6TTjJ82TMZ9+3JTHoAtGIY8kCf86XWH7on3v9FN9YYVDKp/pdAVpnG3Ruis/0ecjHSagHPFU3uR0Ruu0SmjduHGBOOVIWs/U7zw2Xf917E6Ss4itUyg95witzt7OAJtzpOxENC9n5CvC6DqGr3FktSK1bsv5u5kKHXgM5lOZMOhZOXBO3+Hl9kiONDnF6qd/PcTRYtfe3I88M+X6g9se+aU0Ln+nvHgWnu/6wilSR6FeixN9anDvACj3mRruMTzQcmWudIW3WhbVB+cMtNwKxvk6v0erJJafXV3X53TEOeLk2PUe1NuVU6WTzhFaVz+VFBRz7Zx0eKXd0nh/aj24lKS6ur45t05u3Ggf46fp4U/cX9YhrZuBgLANUF+DZyvASmXp1rG6z9kLB4wrXOBsnpJZDlAzoeXnMrZCm+L/7tPMeBIN3Caby2lYH/VZKQDBhF9JVGpzhwU6Ab/X4ptXRDG30yfXzjp2mViN8XiCxE1wOFKrz8aW/Shf1/NJL2eEXfNw2MP9nM/XMnI7agBexRE8npVNNg/6zpN2MzLq0lW/VF4uA+fNQQPmIClYtoXcJlnR54vM2CrTXr1vDD+NP8bjNeT803d4YNyVeLGyuXyrjubnpnXpKb/qPOfPzqua8UQdsUVaLFfQmVqkwQekQHwhChbQfmg7/v9c/Wk6R26doD1ZGVjRnTCAffPm8Z8+Q3GcgeJn6HPUOVaKo07f7V+znAt8V59V6Rw7Cv6LimrVhcvTGWgdJyxdp8HXMc7ghDQIw+Mdz9g6Y9QVLZ+eQznSfTNwOcsz2T8WtJfqspZH9ZifpcG6juN7znH+HFLVpwJ67vqsbZy+qV/EOYj+LZ3zcc5u47zexzMcbAccLuC8NMA5q2MHAHbzcG2kPp3r5wgvf3nUlQt58N/8wFezb1Y7mvpGz0HQ7qpvLvjAebg+SvhJhX2z2tguKH7toti7apfKLqj+jvE4yFz9DWUitJw/j6cqcKK2KuFnrq+mT/vpVxFCFldPxRgajNF7ldy6NKxv3C/8/i3aFESzwi/aFpyn4qU0Iah2zGGJasbelcsRaN7nbUeWie0qeYXwTG2VR6UQ1UADQErLZtmZauSUB7QKnuXem8U+fzQiKSM6nzuM0wDk8ZIjTqft44w9lATXYXh0+YA6Uv3fO3xNuUNuNWoHIKN9qYBeZ6yglGP4GVqkr0ilOk9HbCH87BWH+COT2q2koALTSQDSsO+MY+WgOB/NwwFO9y5RB9S653LgpgrwrEjH+esx25itz4VUM2qJ8FZgVUGx23f2E/mqc9U6rhDcrW3ykmU2DrbmhWMFgiw89qHHKcg8A5djfLMFDOI0DzceFMSltqjudaCtEs0nHUOYkLL/1utfvnw5sVEOF7AfZmKrAWhHQt15xSLoW64rn9N82I9zH/GrY9rXjlRpe6ld7ej7Nci5dsjhasaJeMaMfKa8dWY2rdpzPhp5MFljwqxlqfwN5+0IfLLtzibwGFSy1hmXjCd05pYn67TNmdhyG/M1/E0X8nEfk+L24voz33Lt5oTttuq0tiF0mTmVtlFFalVnuS0vEax60r/7qRqXRSsE0UHlBjk3OhM1NuhsbJXUVpFUCK7jnV7epndmEhhgReDzOlhch7LCcH6qIGj3RO7UOKkDZKeoDlJnc5UcwwBqxKciq9wWDw8PJwEQrZu2rTOiM2KrfeDy6py/Zuk60U46N9a35Mnt7N4hr0gtA0be5zHkIo/Jic0ALeevebrgFbbdsXTumHMAfitwUuJaXdM+cMCgmrFWYqs2WklORW6RH9vdmZwLLp9T3Bjk87xf6Wg1RpyfSj6ORVcAsb9wfax2WwExf01cZwR5nLj3WHmMKjHSGUBnV7pjR9tt1lawS3j+ly9fToBg8uPaT4wLFAPd398ff1+/fn20kk3xAIPqGQHSIC+THS4T2nmMxx/XwrXkj5k883MZO1QA+Rxw/JySxtclys/twBiocx/EzdBWpLbyP47UuoBmFahydVCbn2yXO+ewhMMGEMbpbkZW9xMO4TZTfM4zsI7gOm7FQfUVu4W0btUFyqnYHc9wkwK4J8368nY2Flf8N+TJ/8d2i1NgSU4BhpQVVo06K6PmyU5PP8Pvns8frEr/Rzdz9rjOisedzI5bDRAbJyWEOpgZBHB7aRvNZmSV4Lr3pDgf9Eka0NwODEggicRyUMItXeQxph/rcBEzVfouoXUGa3bPS5RLAvwZQFZHkoQjkRoQSeOI801OiMEpOyENfvBvRfjZDJB1rOosQxdYKBip7pvZnqcS7WtHatUpKhhIDlDtNLdF6u9kc1b2r0kqUKzXnX9z9yV9nekx9yGIlJsR1Pw40Ky6zHYbx0y82Eenv9LhtsDz2Q84cMszSxWg3iJ4LmwEnsczNbzaajZbi/bm4LL6cvbhiZiwL+fZGgdMVXS21wUvFDNBXOCL+18xT4ds/4gyqz/jxORbNOCCc27suBWRkNl4ZazrbL/q7VZMkvJy2CGRWg3gOruA8zqWFZ+7ttBAEusk2ykmuPw8kN2bm8eULrU/42pHtpNPZhuL+zWIqAELxdvJt/DYYM6T/HqSJyW2CmguIdzxb968GV++fDk2sJK2RGrVEc6WI+qMLWZrdQmyI5JJ2fU83o9Vsp5ArZvtTMrvlCUR2Ircpndr1Tm6mVoVBzR4pnaMU6KrZNTVk8GRKpsaUO6HSmk0wKD7Lu21y1YHks5Vwv2hUVw2sDhfPVMdEj8DY0nBlOrvTI9mdXK2wzl9zlO3zm50xp+WYaWs+iy+xo50Na+OD3B1R9vpf6NiLGDWNvVdFQiZ7c/q9NKlW8eurnbHkwZumGQ5sjPG4wCTAiekQ38jb/aRmo8Gmqt64ziBXC6vq7/aKEcEWRzQ4/ZC/UFq0yy2jnG2lyCmnW9lpOXK3HeVsE5yX2vfoMz61exK75Q8pIC+toUDxa9BZuBexxOLjhX98XlNz2NBJ40cqXX2WMvm/H2Vx6pUut/5zVYUOD+jpBbiggmurVlvmNO4iSvdRznBh1ydFQMrzuZj/XFeGgRwOsbP0PtdX3Ww88o4WCa2lzQSLuLhxBlwOAEeAEyy3Ls8WgedDXAOET8ALBBbpE/v13JZE8FlR31zc3PyHk11bzUIdOAq4XdR3IrYVh+NQhvzbO2M0DpJwIPbgdvMzdxqP/IycVwHOFJwoeR2C0H9kUht1V+dPByRUbDK45/vQf78LDW2zrC7aCn02um8K7MbK9022iIV8NBndYgJJJHNGQisrqegQzp29zNIYoICMIyyw05ysMIBjySdsl0DGJ6VMbXHVqBYHavNdz4Z6TQP9sc6Q8n2m/WAbQWu4z4NVCNvlIufn3SDZ2w5baVraqvUljm/xemRNy+11yBtEvX36vN1tVV6BUlnbStfzqQWeqvER2d1HLF1AF+fwf04C5Z3bfS1yBZ/U+EZJjI6Pvkap3FLkF0Qw5E//rm+ccTW3d9pC2fnXDmqH+uaElzXjqgD4wzOS+tY/Rjzsu7wFkEvXt3BJBfXcA5twLwJaRhbQxTba+ACesx1dTZKMRz3S4UldJxs0eU2sd0CeFmqiD9HI9DYWHbGFdIoAs/Wuus8WLiMDgjz7K2CJZ2xvbm5GXd3dyfkSTvUAdKk/GgbkFt2qOxYOA8+5yIsneXGILS8TClt0zKUNFuLsunYcGMkjQ0YDnb2bDR4wDOoYXLLhsflm0QNr9uvzl2jrJABPe+2FRBUp6qOVvUnPTs93+nkGKfvfLF+69I4LSc/c0Y0Z8IGX7fVryMVAVkhNGo32UZzXji/ZezgOdoGY3wjOfzqB9IjLRMAB6DwbGd3U7lW2uglS+r3qs5b6w7gi5lGnOOgJ/qWwYr6YZTBvRrEtr8itjrD6YgxzrMtwI9JGD+Tn+Hqr8c8pnmsKrmtAD3+xUCDdbyvs+QuwO+wgNtXH49+rUQDBkyIURfgJMZLHMDnsnNfcf/orBKPC3fN7V+DOJ+l1914SXmprjw8nC7rVtLE7Yoy8HjS79Yk31SRSrbxIFwJIzsfv9KO7vnpx7aCbckY48QW8TNAFKGvvLJkjHq2tsI67OcqggtSq4SXn6nkFTYo2TO2G7ABGEfMj/AMbjcu+2x8rOjlqm86aymyG3C63y1QqmRyEmN8++Q1p+NZQ87DDVx1hExy3TWdsWUHqgYp/VxbcXkYHPB1R65cu7DDSrOxnz9/bpNajuBy3hjw/FyUyUVUWdm5DVQptH1QP0Se2Phq/m4WnR0vK7gztqmdu8fXLJVznJ3XvlslULzF/hbSyGBWdVIdNoQjmmwjtIzOEG8pp9MNdWYd5zdr4w6Jg6gOVmVfJbedPNmGQb9Zlx2xrUgtP9e1VbdtVp3pS5HKH1dpZ3nxsY5ZgDwWN0uLPLSf2FYrgNK+1ECY5uvuxXUH4Bkf6A/1ULvi2k11VYEmA0RNr7OUSK82zfUJ55HIrSOubvaW/Tn7zC6Z0uC2rrpg36xlZ3+PdnDkNs3a6thcBc/fW5yuJbLeud8J21glTjyudWy6ceFm47gclW1W4pZ86RZM4Z7b+Y3h/xPbkVo8x5FO5Rsdv8521OEBR2zZtiBopASXiSfKjNcetb95THAwjK/DvgJvc8CORcvucH2nT1Wnu7JEbKtBpue0oskZsKCTxsjGmoktp9dZTXV4rg7645kbfb9FZwNny5C53Bod4bZhY8Xl0CikMzTqwJiQ8g9EVj8gocuQHKnVj0WhXrxcSaO7zjihf/hLbhpo0PM6Y6t58j7P8DAgZuFVALNI9EySol2TIx1jm5NM+oTtzBlVhNAR3eq5Y/iltU7XXH4w1Km8cCCzsqqT1/R8XXWaI9YKGtw9VX1063TL2aIucGCgqfcpaJ85MA1KsiOG/mJlDHRZZ79SEGMrCFwBUC9Nkg7MrnXvccJERMcC+0CXn86qst+FXmp52D86G8J5VZhjpiMc4FbQ6/LU+mI2hf2/gkzcp/Vgv+eCN1x+Z4OUiLhZW/XxjAkUXyTCkdqBSe3hcDjRZeg2p0f7YIs2QJsziXXjM6WZ2YCXJNU47RD1pLdsg53fATZTkgNxgZJEanV8VjoGcX2XsDtLwg+uPN2fBo/0nOouEzZ3n8Puejz7aXqdsQUXUYL75cuXo/1JH5Ti/mZcnoJbqBfb6TFGxNIOF+G8wwba79XxTDa/Y+vA6xZwqZVMHaukFvmzUswMsO6z01ASq1v89G9+VGEdAcUgV0OS2levcX5uiRE7p/v7+/Hp06fx+fPnk5+bvcX9FamtnCOckjpAjT5ztEedjQMOfJ6VGQTjy5cvj9qMSS1/3AvtN8YpYKiMrbY9b3W/OveSpap3F/QnvXLX2IiynvM57M/K7Z7rnt2ZRVRwDUH5KoPaHQfpnJJd1iE9Tvk4wJvOOVJb2W8uq15j4AmbxgEpdpQVuVW7NsY46vLd3d0jXYbDhg2ogEpHtujBS5KOria9rdKn+ytwPMbjjwmBzOkMm9p6DXK6mRPk6Wb3NN+qXXTmA/dxAEtXcvC9Vf68PFAD2vxeOLcP+yetgxvTbKu4/kqsE7llrAAiqzO5Sm6dVG0BUgsdZj3m9IqJtD6u3/BsV65zAfFLFMVNifglTK5+VvVTMTTrl2JZR1RceXl/ZpN1xnbF57q2cM+r/ETyk5ynBnLZrrmZVNb9GVnd8lOCyzOoTHZn/cZ9i7rphJbi+RRQntWLJy3RltVYqsb6TDa9Y5vAKyQBShYFswyMXCfyOzyHw+H47skKMHEALx07UqvviLjZWgdK2ckpueJ7dF/zUxLrlhNhhvbTp0/HnyO3TGTVEXYcoyO1HHRQI8oAmEGvjplkjMZ4PFuEZ/Bsrf4dE57JiqVjY0VhKsN6LbIK5qtzzjHodZWZ8+qUq3KSnTLwzE6V3xinS21dgEODOrrl9Epo3cxI+lVtkhy2CxZxO3XHvj6fdZIJrbaT03e1iwykD4fTGZ7b29txd3d3QjqQJ8+oJbCiZV8d+y9ZtpS3q9+dNKrHSkxYnO1V3+tW8CSfoJghPU/Hgd7D49ONVZSLfY2buVGCyr/7+/uT9nLtyGNUZ9D4mYfD6cymw0r80+XHaQmyI7hql7RdXP8dDgdLat+9e3fik7mdgOecMIB39pzbSMfeqo95yaJ6ptd069qIiSPaSidZdIwmPJvGg5Yh2ZZV21X1Y+UbK/9Q+ckx6sCv9oezKRok4LJ2CK7DFXqdy+54i06AOV51e3v7yKbofWqDXN86+1O1gWtX149bdHjTUmTe10KlWYFUeHYmyTgzcWLHlhyFI7yuUzqDPf0qxdVB66LDnI7TKijWn/tCsb5Ty8SWSe3nz5+tI2MCyw5RQacuRXZRXSXp3Eauv9QApQh9RUBYkZXYoixpZu4ceQ0kl6ULep0DdY6tykPJRkU+Zs9wz0zjxZ1PY0IBuwO0nDb1vxJg7LvIeUVyU9vMfmOcvie0SmpdXVi3ldxCXDCA20HtzcPDw8lsLZNbjupXM1o6FpKPcG3ZvfZaJY0tva5+qyKYlf1Qf8rA7HB4/GVkCAO6pHOp/5Qk8XtijuAq1tByMuBTPWaweH9//2hpM7els3+MdRQ7aHtgy3qVZmrdNmECR24TPnp4eDjOyOJ7JO/evRt3d3fH4BSvotJ3+LhvtR3SLDun1+Nr9sfObqZz2CY/ycJjiccen+Nxh3MdP6TPrspT2WUtb+ccl6N67sxfOILr6snld1hCg1M8vrUuqZ2ZB2BsO1wAHXrz5s2JLVNiCx/L/hb6zfYP52ETuF4a2FObp/VgzJNs3WwsuHE/k+UZW+foLgkC2Ek6R4HOU2fDg4hnDhIQXv2pA3b5qmgHs+g9PEg1YuI+/qSztExsebb206dPR1LLzkzJsiO36iiTUmhkicXN1OpATu3K7QRAy+2F62/evHk0W6v/e1n1E66tKI+Cu2uXyoi7c8lZVGmcOEDXcXqa9yxgkWaMUvnc7I2ObYgacRfYcoRWo5rOITjCXNko3R9jnBAE7qc0q5Xqqtd4JgvneLklb9nhq+1gp4tXCe7u7sa7d++OMz7IBz5A26M67sglfdhLkpkOurZLNpiF/e8Y869j63klRe4jbjqOdVuN1USG8eMVYExo4Uvwl1JcXwW/VbswVqnKlmxKwi9qCxzIVXI7e6c2EV4NZjtiyyvaOCDBxBbklt/LUwIKW8KBBgX03PevVV/HGGV/Jx1Wu57aidtR+1XJmN7jylRh61Rezcc9D9d125UZPnF2ju9jHXV2DeXhYAqPWR7bGnzhenIaPq/EVreO5MLWwL5BP3XGll+XwGztzc3NMRCnOF/bzZWzE5RP/VmNha2y+eNRbuBWjq0SrTDOuagFR/gAPh3Ic2WfAdqkDAoYtS30XpSP6+GInw7+Mb6BXSW1Sl71HC9DTjO2vAyZZ2O1nZ1Tc8qh5HfWv1rvZBTV8WvbsqOD8mJ2Rz82wwbI9ZP2VyUuzbWT2i4Q5XOVg+iAYoiOC0doZ89anYHXOlQ2yzlwd12jlWrkx/j2J/ec3pFY5xhmbaH2iY8dqdXVK044KMSOh+0X7yN/JQzYcvskEA1SAWILUnt7ezvGGEeyoTNmrk22ymsGzCrO3+n1mf4qUJ7NeLjgCxMiFzRONoV9AI5n9oxxA49RJrRIC9/jyIUjt6z3wCp6XWcVkzjd0jZVYMy2R4mtzsjOyK0uRVRyif7iPsC529vb8f79+/Hhw4fx/v378f79++M/SUD0tSUmADwT73xONWurcm367HSwwhgO+1a2kMcMtu5c1W4zLDfDxnqv83EzMtvBXTM7AtnKWxhDsO3TQIFiFC6786vKFRyhTft8fDgcjroN24bf7e3t0Rbod4NQBvX/mv8Y45GdqF5jcCS32nfSxdubPx6l+25GxIHOFSCqhloHJ0+9K2BLxi5FglcNY3VPRWqdw3ORD0RY4Wz0S8dKdPUryEpq9YNROuuqZNbNqPAxO1NdTsRtBBCRRPuDAQMIqTpB3h4OB/tuLZ7J4ybJKjmt0l8T0e2O+eTIKiDs8tYx3zVmVRm7TjSVY6u4YJUCTOcE0nV1VEpqXb35pzM8AJl6zQFyN4uC82lGScuGdI7U8iyrsysA3PxOLW/5q44ugObaxAVNnHQB3EuULXrjRPU76fY5ZdAARBWMqVbwpLFalZGvobxuxnaMcfySKEAhSJaC71Q+PAN58HjlFQ5VmXX8Jh3U57nAdCK2q6SWiS2Xm/v1zZs3J+/UgtQqsUUZUHYEExgb4ZiDAUpiE77jNK/FHzts5e5RH1C1Uce+z/y4liU9n7eVX+Pnun1Xxqp82gZV28y4SdUWzi5xgKYqO/qCrymu4ABxIrbux4FkfC2Zf+/evTvO2Oo78Nwu+hzor+bnSC0HKVzAQu3dJXT2rI9HVWn43KrzVUfiQCTOY0Cwsl66kcY4D+Swo3OAS4mtzmg48qoEFySW993f/DiHlcC2DlJVFt5y5Gk2OzrG4+VcDpyjfdjB69hw79ZiGTLu177Y0n+z86/BgbprM8fmnMSKrrChc9vV8j+VuHHknA+n1eCRklbnAPjH4gCDIwQ8c8LnxhiP9pEvRD94gXQKfjS4hH0AeeTNpJbvUcANMKsrL/hLqhwRd4TCjdvOWNJ7rk22ktsVUjh7LgNQ7if3TAXBIDs6ljW9jtcqWJrKO8Yp8HTklsuN5XqKO1AeV8cqiM1t5vRP0zkQ6J6l9gTAk7GEI7UdEos+Yn+KcrE+8isETGh/+umnoz6jfiCyCHpDv9EuafWXYgW+Z0b+X7rM9K/yw45EVsR2jMd4OQVSZ+VwujqrTyI2fN+M1KZ8q/J08MQWHIO8Hx6+/W3OrMw8Xt2MrmILJrUOq2tQqwpw3d/fj7u7u5Pr/F0LXYaN8rLvRjnSv60kcov6pH6u2nwFY5/98agqTSef6h5VPJ3p1AHBaVcUwslWQsQATp2OKlcitXBA+lEonZWtrrnlx+79WlUA3iaS60gtK6oLMEAUEDhCq8ZJiTP3sZJaACSO3LpInDPm10ROn0JWnKtzFF1HMCOzyQieIxxw6RhQNbxjnDrZRGpZr8Y4JbdKdF3ElcekK6fTFx7zidSufDyKdacC1Q5MJpvM7elmkQCM8fVUXobMy5w7Y1RtrB4j3Syfa5FVHamAXCcf9a+ze3S8uVU6yMetqEpLcWd10zLjuhJxbMf4RnY5LfszfU61jBHthLy5Xg7YJsCX6sI6qOBWcQSf15USanvUdnBbsH06HA4nf7HHxPbDhw/jp59+Gh8+fDjqNLf3GOM4I+58NtddMZ9Lk9rqtUoisa4NEqGY6colyqB4js87O5L87TnifGjX1lXC+sFj0j2zupfF6TaOmdDyvsMerOcIFulrjWwn3r17d8wDOJrtFPK8v78/weXVq44uWJJ8R+VTtmD05aXITrpLjrvLlVmY0KpDqARp3VIi94yUBgNoFiHWxmcy+/Dg/5h9jMfr55mA6ruzK7O3bsmyOra0NJIjM7MZWwX4lUI7QsvnnbFR4s/PQ6Q4/c0P2pfFzfr+6IR2jN7MrEuXgNfsvCOznGcCojNhMOlmRbRs1QoDvoZ7WW9xzjkW1RXdV31TG6TtoT8FhInUuiWeyfZy8I2JJMAkl4nrz/2D+iHCrAAZbcBR3oeHhxMyy7O17v9EOS8WHqOrhPbaQfGK7dVzCfhV+XdJWFUWHodpttadnz2rAuxM3nh1Ad/LfptXHLjx5srD96u/0gB3wjS64kjTKABmnwydYvKqq8Cc/eE6JaKidopXWGCW9sOHD8cfZmz11SDoPL/TrLZJl3GrDUqztq9hBreSmQ64Puv4bU6rfmiVHFfPSGkvRWrd2E1kNvGPquyOx1T8oMorcQ4I+1lsmWx+/fr1+L6s2gGdrdXVm6pPLPpFeugp28+vX7+efM+HfTqXp/IlivP0fNVOlZz1ju3svBtI6Zzm4UClRkWqMgJcaXRDtyoKMnlApeN0zjk0B4xdlMV9MAoEN71j64ivW4bUmbVVQulmnlw78qB3yu6cGCuQ9gXPfqkTVmKLYyUqjsxyf2E/9atef22ySmpnTlH3Z2kciXVlUTKcJF1nm1Kl4XGrdkgJndMV1SvVGyW+idxqG7iVDUp2ldSmZciuL9G2sFUKEJ2D0jKjLGhf/nAU7tFlS0gHMov38bjM6uxneuiAUleuneRWknS5U+dZsIBtP9vgKjDsgptuVY/zG1qnmTAwQ9kxQ+vag+0N+3NXh2RLUN4OdlH8w5L8k2IaxRIMMt1KLO1D/hgnBxy4HAxY9evHvAwZH4+6u7s79vP9/f3RBjjb1ZUO8blWPe5iXGwdkUu6umo39X5XBh1D58q5pLZzzl3f4iPSPbMJu450A+sOh4Dgsi24vb094RAIMAM3K5mF7+a88SGqw+FwtC/8PR+dsU1tlHxH1e8rY+IiM7YdccvfdLmRk0QWIew0nNFP5CXlX91bkVhHBJGOnRrfz06GB6H7EjLP2PIHodxWz7klyO6dGi2PRnVwrOXvAEi3br/q96qtGDRjVoe/6pZARnd8uDJ0z78GcaRWj7vk1uUzI7IVceW+UvCp9yjA5nMKIvWvPfQeLYNzJu7HDgZ5OXCZdMmR15XfGPnvflT4OTpbqzMo3BZcdrbnrl/Uxj08PMSPv3E/OV1VcI46VMep3rv0ya2m1fZlcrsCfllXU+ClGr+ch9aJASGPCZSV64JznNb5cC1rCs52QBs/W5+hmGOM8QhfqK1BINvtu2WC2vZMalmX2f+D2OJvufT9Wv5wFAfJYG85CL3Lf+QStiiNI76enuvG3UwSmX5OccEvtRXp/Bg1JlUddzwG6fiec6WyJ2PMv6TMNgH+lv+ei1d24Mcfz2N/DxsC3D3GOJmAw4wtOAfj9Fl7qH3UtFuw9rMQW0c23ABxFYKDVPKKYwVdTHQ5YsrLiRww1We59Ok+BbbsdHjQO1KsRFO/MtZZeswElpcjp49E6PuyabZ2jPHovIJ2dZCuLzv7Kuyktdy41wF9nK/6zZEI1zfu2o8qM0PtnKmC35nB6jhGNoJqBxygdnk7wqtAV5cbKaHCvtMJpyMpiKQ/J47gdomuphvDO2KuG5wXOzbOh+2b6hYE+bggAgfbUB79P8xEarUdK0kBjx9VFGR0QEeSDlFFOujU6gef0goFTQOpfAvGAn8gDT6e0ysYY51QgpvKWrXBjMg5m5hsj4JX3bLv5PT6HLYHyYZweRhQ47UBkFidtQXpBVB+eHg4zhIpca5Eg4tJOgGElypdPaz0tuOHXX6zcadpU1n5mXrf95Kkl7hWyYzkpvyTXeo8sxKHS2eBdya2PAEEjoA8kFYDTg8PD8evIOtzmNiC3PJs7ay+jOdWgzGVXJTYVlENd91dc4JK6zsXTGbV8fC7HDjPL1LDqTFQUjKbSDUPACW03Dm65G9GbPFTojojto4IIx8e1N13bNP7taowCsiTw9drM6Oo7cxbJrYMhtV4a9tWZU/EIhl3l+5apdLDVeCrDs8dd/PAVkmcglJOywAS9zDRrchtdawOhMcSzivAVLvAx0qAdZxxu8+ILNJdcsZWbR/Ptrj7E7nFM7HP9oU/GoUVF1o+p8cpsKb6q+MiyY9AercS1859qW2dTjkQ0yF7jtQqhujYLYwR5AV/zTYDYwvlBFZgjJDqrYBXn+0IR2q/RGadH1PbwmQ22R5uV25P2Ard8vNRnjdv3hxJLS9Fxvbu7m68e/fuZHkzL/2uxpcjspUPvnbZSmpXCWbnOh870uT8M+tFIr9dm3KOOJ9Zndd73T5EZ3UTye3cXz2nc03F9aEjtvf39yd541g5APt9zkNtAM7zK5KwPYo5ZvWZ6fSKzj/5jK0bANr5aRkAK4nu87GbtQHpVOVjUJxmWZj8uvScPxNmdgCzCK46KX4nJr1fm0it7jOpZXKrM7hKqNMskwOuiQxyv2rdKwCj7QxlUifNz3CGikmIKmtFcF2/cH+5/R9NXL8m8sr9URm0lfZUUst9qYSWy6HgeYXcurI6YMmkVsmtpnPETNvCOWQ3g+XeiUsztskRs84kcA6bx7OsqhdK0rkNHbHFeZ5Jxj63abJTCtST/Ajk1cn3qrfTPfXPCfyyKDAdIwPIDuBn/DDG6d9bMV5gIsv6wXZnDE++ZsGjGTnm84nQ4lqyO1UgzbUv2w5dIqyvaHD5+b14fECK/4Mafx/C/1+bZmcr8pWI1oz8vibp6LIjleqPOb+qrRIWwr24X8eyYvNEbi/ZTzOdT22XuIgSXPXFidPwfZ0JnU6fuhnjdKw4jG3Bzc3NkcRCFFdrXyt5rV5r5G/5VKTWYcc0Rs8ZI8vEduaMupIAlBMGrOxsFNTqx6KQvyMzavBTnkxuNXqrnYv2YQPOHapAkMvhZmxXliAnUou8ecvPTHVRIJ7IbZJOv7q+xTn+QZkUNLMoOFcSj2iTIxWJoFcEd1b/H0m22oQ0jlJ+jtSqE8U2kVYdP1U6R9gVbCq4ZIfidCiNPTeWEqlV4prIrZLfMepXQJh86jVHalMfqZ4yyGK7o3Xi+5HucDg8WpXignAaFOjoZhqz167XL4HQ4ph9tuqp6rMGoCAOzG0htny/sx9cj0RwWT/SM1ZICB9DWP/YnuBYA7TdHz9Hg0lKbFUvUS7W/bdv3x5nZfFFc/4/TH5fnkkQP1/bZEa2UjpHciHfSx/OkZUyp0CBIxF6X4VpZtjIYWS9R+2ye0Y6tyJO/x1BdUQ3YVU3Q6t5czDWpeNtlb+mS/455esCRnhnlgPJ6BssS+Zlw4pPlKPB9ri/C2Je0CG1TlK/bxkPS8R2BcC6jknRTHdOn6mNrcc4l0iqOiomcRic7MSYyKoC8308i8jOeRa5VRCskY9LkVoHCtNPyX5FaNUgzZy8awv0H9pyjG8Oiv+mQBUOorP77NyUzHM7O0JREY3K+L9WWQUDK7YB6d025cvG1f04GDXG6R/Qoz58jeuY0qXy8FhLesLjjX8KRnW8qb7oD3alIrd8f1qG7GZk+ToChVg6CB3l2RduZ80rgSGutzo/5AN7hPP6eoa+RpFECVZXusT4tQj7WN7y9TF6yx5TepzToPGM1LK4YPEMRCox43Sqe+zfdcUX6zaPj8pWzJbhVeRMbZvuu1la3SJPF6ga49srCvz/724JMt+L+uM+/Ph/p5XUOn1i3dU25PNcZ06nAcbXIh07lcZ6de8Mn41Rz6ayTru+nAWcOf8kirkqTNCx+9jv1F1tilvtNMbjD95ygCiROLdaTPer8rj8XDlVQFwV50C/mfc4TM91xUelcA42hoNsqUxVPVd1t5u+TWyds1sZME66s3qdsqgyOWLropgMtpTI6pZnLPiYy6HObGao+NkgtrMZW0dsGfTxf9YqGHQEV4kfn1MHNBtYrr6pnzkvnfHmNmGCMGtDLrMDwkootBypfCvpdvkmHUem25nz5BkT50RV5ysii/M6G5HKqoBKQacjrClYpMCay5PIrJ53YLR6T64KLjLBAKFkUsttx6tj2EFye2nduA3U6SEdf72R21xfv9AZchUeQ64/9Zzrg2vS6xXiDlkl/Kt5cvsrKGLdZX3sEpVEcMfIH0fTMcX3sR3hlR3Ih8vMNkDrmspYieoNzvFzlKiyLUkBtFRWnaXl/37Xv/xQEs92iAmt+6K52hrVV95Pvt35CN1/rST3HKlIEosb57P8cB9sPx8nclvlrz7CPaey9Umcj2PfqenSDGoiuI7Ual6u7Vf3OS/FAWkSjbkP21jGC5V90ry1vXCOlx/zNVePbt9dQoc3vWPbdYhuir5i76vPVfKajh3wZDLEnYgBgC2us1Hn2QQuCztvHTja2VouJaaJ2K6SWrfP5xyBdIBdjY8aRJXKmEKcM4Ojxpb/pkCfy/2IpRYYc1yfauliMshcHt7q+WuXisit6GbXkVblcPuapiK4OsOCaxWR1XMQXUHAZXBgytmW2c/p0eynjkxnbNN2NnvkwDvP2EJ4xjaVT9tGbd3Dw8OjGSEF6tD7McajGVt+raAitVvG78yuvQZRQla1F86ntnB56b6mVx1mcFxJ8qsKAmeADePPkVwF5TyWtb0YZyhon4FUSAoGKandYmdcmzpSyzO2rv24zXDMH24EkU1/04V6MtZgnKH1d+VObda5fi2+umNv1Lem7ezcGNsDMzzeZzhK89X8XH+v9BfXwdmH9NzZOWwToePrSvz4eiJ6nfK5fNyKLSbYEPadnE8S7ivkzysueMUWE2R+NSgFHjhfPu7KFv39bu/YniuuHOyQ1ElgYMC4jvFt5pUdRpq15X10tDMSM2LLnavElmdslci6D0bxDKybmXWElo/hNNXR6MwnyjsT7Y8UOU+DH8rIZJX7xhk/JbdMbDvv5VUkKsmWe16zVIZ71vfp2IHt5EQxbhB0wjJCHn8VkXXRWzeLkggt0qstSUTWjb/kZJMzXSG3nB7iALZeZ3Jbzdg+PJzO2mLLYL/SObWDOmukusxgX9sL+SnR6MrW+16KVL65SzzP9e8OMEPfQI4cqeVxoLOmTti/JjDp7IeSUS0TE1cd90p82QbM2q2zQm0rqUVgtwqaYaukNs3WQtQ2KJlVcsztr/3K+1w3bQPXJo5IJXvs8nnJskXnuiQ3EdsxvE7gl/yv2nVsK//2FNLNu2pbtRvunPpfRzLTfZxvwkRaHi63++mKCPf3PGN8s1lqQ1Ifcf+yfTgcDuP29vbk74JY//XDUZqnGy8qlx4nZ71ji2N2WF1xYHImHaetBg7n3Mwrk1k2+EpildBqeRTYuQHsnJqSSX2/Vvf5v2mVtPGMrRJlvoejpu4cR3pYEVIE2CntLELEhIIdkc7aYJsUhtMqIeG6K7mYkYxVuSYHOpNVYLtCajvP1nK48VY5Ub7Hkdsx6ndsHShHGj7ngBqPVzfmHMl17Zl+Gp3V6G1Fbp1z5rbmL8SirZjcQtKMLUePmQSkPtTnPzx8+6CFrr5gW6eAmJ3xVnlN+utEx7QLAKT26xLl7nV9phIfDUC58vBvDP9/t67sPBa13gzqURYm1/D9bEcgOvurkuqTZitx7F7PUTKrvpLz4rIrEOf/szwcDid/t6WC+nE+/EVknbnlNmV/7FaGaf9XbaTHSmqdvCbdrohqh0g57K7HSm5deucjV4itswm81f1zpOsXHB+pSK8GcPS46o/0XH2+9rPqcNJbh6kVW7tgOwsCVnd3d0cMAT7Cev758+dj2fSd24rMPrW+bnrH1oGJVUBcydZ8VDmZZPLf84zxnwGFGT4muIhwKLH98uXLI+cJx6KRyq5hYVKrhFWXHVfLjxO5rd6r5QgLg0Vuo2qQOcDa6R++Xx23Apw028WEhJeFM7iAAqevtyWje06dttx/zTJztOmebhs5YuQcqc6yjHG6lLgisjjvQJWWRcErg8lqNqUay66t1M6oAwW51H1HbpUIp+ehLDrryltuO52xRV/hOXqey891hKBdeNUFrqsNQ9okFVlLQOua9XbVX1Zt48538j/H/zORxLGeS2VyKxeqcjhy64SDrvwsJrw6xh8eTmd/+V6trx5zndXWdEmtIxRqmzkAxSR31nZqf/j/p/X/5Bkn4uewRxXgc6K2X0UDBam9X7LMdEiJU8KbHVKleAxbtdl6T9IbR2pngaqnlFV75PziGI/JLK45u1P1AUTbo7K5FTZwz3WBdLdy02ESCHT59vZ2vHv37jhr+/Xrf751ATKrOqY2TKUbwLiEvl50KbISy5m4Dk+Da/ZMzCxw46EsDHh1RoJna5koocN54PCxloHrXA1KTp+IrfvisX75GM4tzcpW79e6mVt2jqwcKK/WIxnFWR8xeNGIq5sBc86Pj5EGgBhlc0rsHH9Fcqs6z86/VkljvCK5Y2Rj1QWamo86Tia06lATke2sGtFxps9G/iuklg2/tlcCLrBRuKbvu6HubnmSfixi1i8Vqa3eseUVK1pm3KN14/bEMnIECzAeoN/pfZ7K1zjb5a6tjsNrk3OIp8trjAyWZ89Bn3L/OrKItJqnAk4FeXxNy8zB7m4dOU/NgzGGjqEqz4qYOf/n7IgjtQ5UJpvCNiIRGa432hnkFqSWZ3/dskj+1oWW2RF53OeAsGtTtsWc/pp0eDYmnZ91Nt0RKodX3bl0v97nxrji7U7/dfDBc4suLx7DB4f0PIvzs8i7us+Jcipt04SF3at47tU8XqWJMoLU8n9TM8fCPueLiUNnC7U+bntp2bwUWUEBgx6IO7ci3QHgGlIdGRvAw+HbsiIMVMzIKsHlHy9D5uewYa2iMWq8NQKr79m6GdrZkuNEZhOprZwkl5nrk0B5p4/UATmCoPuuLHyfjklWcFbeRG5X5TUCX4j2Y3KYXULL56qxlMTpNusz0jg9VFKG88iDAXX1fB2HuuSvS2gdkHNtVxFcJbdKZPWnoN/1b0VuOb3+ly3PAnNZ3XJkDmgpcNC2hA1SYjub5WE7wGCAn9Gp+zVJBVpd/Ss73bHhs2emc+nZSmo5sMzn+fla5kRw3XhwH43S+nG+nEbHLvsyBz5Te8zsje67gDO3l55L/YRzjhCxOFCvZFhJrS5FVszlliK7Mmu7JpmleW3ktiK0K6QWkkhqsp18H9Jr2yq5dcHRmVT5P7ewPcExXxvDvwaheuUIbdUvvGXRwDBsE+MT6Bd/l+fz58/j8+fP9p8F8NMVHJitff/+/bi7uzv2I/gcE2W8PpTGi8Pczs5dUjYvRU6VmDlHlZSeFaGrFDojCNH3YnlgMInlDua0bp8F9VbipqIdrAbfRVaqZced2VklswwQq3d0qnpUYNHV2RGINPuVjiuF0LKiLkxqO2Q2ASdnbDv71yDJ2DqyxftdB7pSjtRuiaCw44To7C3bBAbMfIy0KqtBGP655T46Djtt7M7rLIsCeyW57j5tR7Vd1Xh2ba6ztdB3nTVnW8m2VoMEXI6ky1wXpJ2BMT7W+l2bHnd0botPXslnFRcwOEpAmI95LLkxxL+0MoF9Bz/TLTfm9Cqsh3y/nnfjbJa3+kYmq1x2tiv6+pDzb52+d1jL2fj00Sj+yx/3ni2XkTEKl3tFZj5cscJL1+Mx5n40kafkJzTfNBb1uo7hJMlOOt/MeuL87CXsU1XGjmgZ9DjxAj7WfsB9nN9KXXn8ar44r7aVJ8ZAaD99+nRCbnUlKHgPSO3d3d2R1H748OFIbJHu4eFh3N/fP3rHHjzKlb/C8e74XDlrKbIChJRO07j3OWbveKRn6HUXJTocHn/4AftuZtY9j/cx9c71TAMPW1Z4JrVKbt1/0DriyltHbl2UtFoSxKTWiZvBqQwgAxHU27VLIrTabkmYXDBQ4Xqm2doO2a1EgTDX7aVLZchXSFbKj+USbTIDhDx2HLkdY5wso1GH0wlIpZnXLqnlexyASG2spJTJKx+nWds0Y4uypJkpFji2mbgy6/MqIMBthXJpe1fEQ9u2Am+durwmSTrf1WOVRGpnZBfnmKhi/DGBwxjCNZ5t1Xq41QlaJzd+NEjjpGqTagZ4Ns46z1bcgH0NqmndVvqRgw24l9tQv65+e3t7XK4IcKv/ZYt+YPuo3/Vwul21g2ur6jxvX7ok4lP5YTdbqPfNbJ7DLx2czddYb1kcqeXgZ1e69trJ6n0zG8kEl/eduNneLk5K45q/baE+VmdrP3369GjLH6QFsUVZeZb2w4cP46effho//fTTkdjyN4lAlPW9eq2L40BurHxXYjtzWDivhnKMukMVAFXOqVM+Fy3S6CwAKI7Tr/tMHripU9WYM9HkDz7pMmOdidVZWf1YlJutRX4ADVwWt7SShRW5E+FGeyt44evaPon4VgRUAQXqkUhFx4HORI2sO37pkvSqcqKOdOn9LJcmszPHrDquY5UjjhiXSJd0XfVBSa2CNNXvNPYc+Oy0PS/9xTlHcLHV61pPrZ+bPeH2TeLKWfUV94lrf7bjsE8zAIzxkbac5rVL8s2QmR47kFKd6+5z3tw3TGo50Ay90XFVBTa4bowrlHRyPbriQC/XUetaBYIrspDK6Mit2qRunlUd1d5g/3A4HEnt3d3dkdjyPhNbLn9lF5GG+1brWrXL7PgadL7SQ+0T3s7wqmsDh1ecT0pBkkqXExbE/laMfY48df+7+lQrP2flU5+Mc2xv1G9Cl/gfVPjHZFaJLXT33bt348OHD+PDhw/j559/Hj/99NP4+eefx+3t7Xjz5s24v78fh8PhOFvLryBoPbU+L5rYciGc84Ko8ulSF9cA1SBfBc9wihWp5YHI68OTEvPxw8O3d3XcION0PDDdzI6bXU3LiTvv0GqenHcie1zeWdvr34Jo/zMoZaVzbejILO9Xg71yYho4UFKRiHLKeybXCJhnDnSM/P6I3q+ihGKLuP51earjdMEX3nJ6PnbjGHniWJfqd0msA5/J6aW+UHvqlh/rPv+35BiPI8y6dIjtYqWPCWgy+XBLzjjP2UwylxHbSmfx/Bm5re7T/WuVpNvYunN6X8pvjG2kVscZz+hhy7rIK6ySPXJjDMI6jvHoxi3y4WtuDDi8grpxHXmrvpLBv/ZRd8wpQHQBW1fWWVspjqkCZgDC+oEZEFx8RXWMYW2mCwJCv7lNdb/bPtcoSS/1l1bhzAgF65iztTqe+VwSp+OJ3FY6y/kkP/BU4uro+Iuzn7P7K5ubhOucJmO0z1mH9L1a/PS7Pdi/ubk5maX9+eefxy+//DJ++eWX8de//nX89NNP4/b2dowxxufPn8fDw8P4/Pnz+Pjx4yMboXV0OLvC3p3+nrUf5OylyGzM04N5UOvASLOz7MxU2DGiHO6ZSm4ZnGo5eZldIkFqnNXIaDul+5RsOlLK76FUM7JKcjUdH6P+CtK1/ZITZMLAhlL7AOOB1+RrHnxPNchng90pTWe2bPU5LAn4XItTnYFdByBnxrkCeOe0jRIOl5eLCmv9AG45H529Tc/nca5kNo23agy6dnT2T8EMrqHcuvzYfcglzdg6sM8fh1LwrMRc2wzlcqSW7QUEZdbycVt0dXPL+KruqfzKSxc3pmYgK93jJPl/vsedd4CZgTZvxxjj5ubmJPAKf8K4gAEzzuHZ7PPH6H2QyLUj2xXXLmmssq4rqXArS/g8Vpc4mQXNqv5Lfp3rml5n4L/3YVILUPzu3bvx7t27E2LL/cXl1CA791HCCpDXSnTZvmOr9p/3HaGA8FhT3KPEUcevy0tlZhv4eawDrm6sH8olkk4lYnSuuLbU6yyuPq5+fC6JjnuHZRmH6L38bq0SW/fBqK9fv47b29vjkmMQ2b/+9a/j119/Hb/++uv429/+Nn766adjgO7jx4/jzz//HJ8+fTq+dpD4jyvjrP+qPl31yZtmbPGAZEQdSMYxp3UNUkUWXRlm5dNZHDgN1xFKunRwsbNdIbZMZtm489Lg9F4sR2I4bTVLq0uOmdiyE5wBu7TPCsZt6/JTRVSjtyIKkJLhc2TCAYKtUrXbNThSB2wdka2icSyJcJ5LZl1fJ9GosBLc5IQgqsMKiHUcdUmt2gGUgcU5evdTgsvnmcjqD/fw6yEMfrgNtR+SjmnZ2W5ruRlk8XUtM86tiANF1XaWj567JlFbPQNe7j4+ngGxmS1gf6HAhIPTfI8SXCW+8N1M/HicQdIMrStvlQ7lTuNSASnXk8kt4xC2USkgV7W9sz2ql6prakPUjsyCYrjOs7SY5cEWxBZ5a78rQNdzXLetGMHdcy0+mftdV+S4oAOng6BNMb5Un5JOOt/H6VmS7muahAedb+M22NJ/VXnctWTzeN/ZQe4fl8bNpM/ayuWvvEEx/BjfdAU4HzOzOlPLryOijAhKgdT+7W9/OxLav//97+Pvf//7+Nvf/jbev38/3r59e7IM+Y8//nhEaqu6usAEn+/Iqg5vJrZVpzMY1g7HvstjxvxZoTgiOwMtnJYdjRPMjrqoCZNSXlsOUIa6KJjlfV0W7P5+J82+JiLrrvGAT0SPxZGXNFuOfnVRZR60lZFy/TUzTlWeLpCQCK0D61q+dPwaxAFedqLsTPkc31v1ozrGc8CJy0vLoKIzOeww2S4hr8oeuLGViK0bZ3rekY+qP9z5RF51ZkWvcb257NxuSghcfVkwPvQcl5ttr55XYjsDAfqcVRK79dpLlAqwzcZQlUd1XqUCyHyO+x6ipJUDINjHx01S2TTN16/f/s8+gawOsOqAYc2Xx7mSXG4LkHMlukjDAHRWD9ZNLmfqfyZGmHXhjz65/6RlO4Plxu/fvx8//fTT8Z08zNpitpaD/9w3zoaqX9b6zPoq9cc1iSMIbtZcz/HY4HHH+sYTOc6Pq93TPPVckhk2c/7NjVGXnyNHWqYq76rMykeqtO6Y8bIGJCBp8muM8ajP+DpPVjG2Z//Ns7X8Pi306HA4HPUS4wg6/PPPP4+//vWv429/+9v4+9//Pv7xj3+M//qv/xr/9V//Nf76178e/8P206dP4+vXr+Pjx4+PPhCXMI0bR3r8VHq6TGydU0SDcWcmYKyRQohG/DvChBX3pUHfjd7yV8cUuP75558nkQp0rpsBTmCYCWsiuiCl1YwuR2JcWuzz1gFTtD33hxuwnBZ5YukiHHRFGLVtWCpg5Aww9zO3dddxdsqYrq2mf4niwG611MkZfudonHTbpNPWbtzM8tcIMTtYZ4u0jlxPDQyxfdB9Nz6TM+cyJHCT0rAtYnDqiC3bWOgwLz1Oep90RduQ7QDa2r0bi2fwLK0CgWSvK2fp0s7IS2WLrkm648mN806+M3EE1201b5eG+/7t27cnx4fDY7LL/1Dgvn3hyjqGfy2G6zwDsnovb5XkanCHz3NZeCbX+UN+pmIM3K9ldoEwDoC5nyO2uvwYX00Fsb27uzu2//39/UnAX0mrs6WVH9ki16TDSmyVwCru1PHN44kDlhqsTDqX7J6e79oDlq4fdCRJ79cyJpyaJHEOTaPcRf2c88O6j2ekMqLtNZiMOjq8X3EB5gRjjBjcxusE79+/H7/88ssJof3nP/85/vnPf46///3v45dffjmuUsWsrX4gzvE9148drMZ1d/21IptmbNPAZMUbw0cwnHPokF2nhGOcOqc0UF1a5MkRXj5mMsuDA0TOzYSoqOPhpchMbpXQOuLbmaXla0psUR7tR253HqjslFydxhj2HVp13vycmTij5sQ9b0Zqq/LpvjtO56rzL1USQcJWx7bTqxQomD13S1vpOFLDp+WoQD3/2JY4u+TIe0VwNZ0re7dsrn9SBD8BVRy76P4Yj78aqwG61Fc8JhS0K7FNwEjr4oRBmQbPtP87BCrVxV3bOlafU5y97IwlTt+tY9eGV6RW2/RweDxbq1sXNGYiqzoIf+3qvKWeikdmfgkYg+0Akw21CzqrifLj2kycvcE+yqu2gf+ih79yrLZDbYv+HQi+nIpjnq1F36EMyX5yO3D7qF3dKi9dhyGKk9XGJ5IyxmNSC3H4S32f2uitRMLpNj/fjVG1R84Hd+3wTNf1mu47AqvbZGM1D91P5aowJk+o6QeflOQy5j8cDicfcWMMAFvw5s2b4xeQMVP7z3/+c/y///f/xn//93+Pf/7zn8f3a9+8+c/XkMcY448//ni0uirVla9pX3NdnR/aOgZZ2sQ2dXTqUP0EdCKAbjZoJkkBdQmPE43+gqyycUD+bCyY0PIA0eiMPqdDbvkan6+WI+uMbfpQFJeF24fLzMZUo4FuMPISZO0LRyZVEiBL17CfDEEiIO7XkS6pvRanqeKMT/rxWBgjz3bMgPIliIIba530lVOqxl93XFUrAzTv6rdyj/ZNNRuj0dUxHoNm2LpZhJmJ5hiPg4+wDQgO6owbriUbo2A3tQund2OrOudAGPKqnvXSJNnR5JP1HhxzvRPJd+LAcXXd6S+f51VAvMXXO1lY59hn8yoi9XHaTrN25Hu7IDXZBtyHOrFO8KoSxi7OHuszGUtwXyI/JkZKavlvevjvenjGln/uPy5//vnn8eHDh+Ns7RjfXufSQACXlbGPjjk3BrW9VuQa/HSy54fDIa7AgSip1fGkgUFcU1tY+egVW5jy4fOp32flmfnFdI3vT+RVz2mf6H6ytVwGZ3tm453tB+N6vD/LhBZ9yj6fdRvBJuYtCFD9/PPP429/+9v4r//6r/Hf//3fxx/er727uxtjjPHx48fjf9a6bw1oW3C7OQzmxoPKuTq7NGPrBo8jRKycCcCwYianq+IAtLtHl+XA+el1NCx3QEVw37z5z58Uw3nyQOYB7cC/m1GsftWMbbUkAcrATq8SjQ7OZqJhNPHsmZNXQLO6ZUFe3I78zA6p1W21747Tuc61lyLOELkosRsDrDM6O8fX9XmXapcElisnyfVNZJe3mg+23TGmdddt1xGrjXT3cX+l5YUJDCEfDtwlJ89lSDOsY/xn+ZN7BULrkWZq2QYyWOsAq5Vx9tTj9DlE+8qBLQViKkl/Os+s8tJ0VQCCgbimY3LLhBC+mG0Rb6tZBNzv9A/HCczqfeo7NKDNpBO/mU/mVWRal0Ru8XO2Qe0BgC1/CEqJLdsUvgdLkH/++efj7/3798e/BGF8wFiN20LLr3V3Pjqlfy3iJhg0KMFEV+9lUuvspo5Z1bcx6hnbSrcrST7a6U7CEfq8Sq+TrUvnGLdzH2ibOVuanjmbHOJjh1uUByiRxRbPwnjRv91y/yt9c3Nz/PDbX/7yl/Hrr7+Of/zjH8clyP/4xz/Gr7/+On7++ecjz2Gbmvp4jMerDmALNPjNQbg0PrRNVmVpxtY5TyWyiehCHKHVfZWK0KaIzhiP31txg16jCtjnJU3qLDsRYTbOztkB+KGcjtgmclvN2jpAmcA899fsnTxte66LG/AJ5Ksx4DbTMZAivmqIVwytlu0SpPbanG0CfWmGwBGiisg+tXQNortnjN7Y4/t064Iljtx2HXLlqJ1uuX5SQNSdsWUddg4ctpNX4HA5cI7tXDVbm0C6LkOEXcY9sJUO8FT9XwEqN3a+x3jeKg5YaR8mYtvVGfe8jiSgqrZb0/F4wfhwM7gYz7ifl8c5YKntwGXR1WXYd9jGtSW3FdsB9ekge5WtwXOVQFR2Sv2i2g8mpyCoDIBxrMSWbQh/BRmE9pdffjm+X8uztSDkvGRc9du1VzWWeMv3uZVx1yisr4nUOmwGvMf97ewmS7KByfat6P2Kfe08z/npjt/UtJq/I7TchhpoUHuK+zvLcpEniwtusZ7AXugkF3MaF3jCO/CO2GI83d7ejp9//nn85S9/OXmv9h//+MfJ15DHGOPTp0/j06dPx3HFdo37Tvke22fUjQOPzv678eC4X0c2E9sEqJjU8nuaGgXRfRVHqmaO2YGYMR5/Qdk1KJ7JUQZ2NG7QqkK48m0luEpsmcTq+nr+nLcbdM44oJ+S8WSFhaBtuNzcNqqsHWPk+lyv4bxGI9GOXdBWEdeK4K6cuwZJeswGmw03i+vn2bOeop0ScE5pNK3b8r7LK5FcPqc2xTlV1+7VT22t6o/aKEdyOWrLwQkltdoOPCb0OAWekvNDWn1/X+0WZqu0PGyfXR9VsgLmnmrMXloceKr8Fe5JeuGI56qkPGb6ygFStS+8vJjP8YoRvV91R9sFQMuRSPaBvGVMw22aSC3rAciHIxrsw3icK4lxIFkJHbevBrkAfhUAY8bVvWcLUnt3d3cktb/88suR3P7000/Hv/hBHbFskcvHwDyRUPXBaj/c2HByjQRXxxdwmdpvtZ0JeyVxOjjT+S22ID1nxcZoPbt40mFLzq/Kl32b6h7bkjEek1pXNuRV4VNnM/SVRZ6B55l7DlaxbkOXoZsYQ/xF819//fURscVs7e3t7fjzz8d/M+r+Pojr7YitI7WM3Tu6ujoG28TWdWiH1PJ9WxwliwNJ6TqOkY4jHepI8NPIF8rOjpLrj3Kk+iUgrCRXnaASWyawSm51SbI6De0DBcDqyNxyZC7n4XA4RswRPU+gWJ9fKby2nQPbHAHS9tT8VxxbRWpXCO21OFM2Qg4AApQooOoaoOp4tZzV/eokZ+WqQPbsXt2vxozm5xzp7Mf3Od1yfcg2V5cnsxOGk+HglCPO6H9uO5dO24pJqs7AqY6iDJw/k9dLShpPq/bipYgGAjvEFukdmYV09UrBmupX2k95OnILwsTnmegiTwahfI7zhE5gXPKSTuezVH8cSdZ24yWhSmpVOLAE8KdBMdXFGdbgOqht56XI/Lc9OoOry5YxW4slyL/88suR3OpsLd6HRj8nQuv8OI8DHRdaR8VQK376JYoSFl0hwNga5xmTdYQJMO53fnArwXW6rc9Jz9N73LH6xsp3aj4uTw4QKaHFPvrF6X56Lj9P/aQbk6wb7Dv5mzu4D/lBP5nMvn///hikQtBKV1/wMuS///3vx7/2+fXXX8df/vKX8csvv4z379+Pw+Fw5BP4+yD8lRC4h+qd80dO13UM8v6lfPHyx6OUuHZI7VOAlK2Vd0BrjMfRb+4AHchKdFfKrCSXIx/qDDuf9dYZWyV63FYMgN37eDhmo+rajg2qklseoOpc05hITtsZBf1q5NbxNXOEr5HQQhzoUafR1d3nqHtl7FywS/uxA7hnMhsb/PzZfufndGLm8Lk/1RFzf7KNqJy0jof043bUQJ0KgwmecYOz7LT/qlSAq3v8EmVmY51eJ7umeuGCPqvBQud/tpBb+FzsO1KrJJHHLPsMBoiOFGigiH2iAlxnG9mnA/wlXwrMBOKrfal+VG0CP9MF3JC/ElUmq3hvFqSWyS2/g4uPRf3yyy/jL3/5y3G29u7u7tjGY5x+sVpXkSlGSTZC66b1qnyBXnvpOgxh7Mz6y+OOdfocUT1Meop9JxqAWbGdLn1Kp/vORzn/mo7Z/2gaJrTaHx1S28FMWnfsq47wjC3bMzwHegliC1KLY9ZlXbHx008/Hd+v/fvf/z5+/fXX8de//vVIavEXP1+/fj2SWixJ/vTp05HcKpdCu7mZWYhec21SSTfd8owtOpsNvyO1umQCebBURm1luWO3/GOcKog+wwEBVX5Ox/lVosZZI41Kch2x1U9863JkXRqgDlgNJTvs9Jl/bgvkzQOX33fi9lJArOMmGQDXrhqdvLm5efSOlSO4XWXpOM7XQmghlYF2hp/FLQ9zbeiuP7W45zhHoo4c6WZ5VZHlRAjTltvb9UV6jntuKkPqZ03rhHV2jFN7wh8yUTuv5DaVG2n4NQwee+z4OPBXyYy86rkVh/oSxfVtmlWc+Sn1cXwespqHguNuW6uvGeN0pVXaxz18nmd4VbjNHCFkf6m+Mc3Y4sdfP3bpdLn9CkB3+uzGufP7Sm7dEka9zu/WYlYHf/Nze3t7XEGGvwXRYLy+VuUIKLefqw+PC9534PkadVqJlM7YVnqc6q9LRR35VHvYlY7t1Ges2F7n45yuJL85q4viHXesdrRaepzKr9e1nxT/u381gfDKCya2ILVMblmX37799mGp9+/fj59//nn89a9/Hb/++uv429/+dgxUgdSOMY5fYP748eP4448/xu+//z7++OOPR8RW/bbDHGqvV7mdC8LMZInYjnGqgBrN6L5by1HTJOcSW1VWR2L5+uHweCkyzutAVmc1AwR8zFsFgRjMGDC69FiJrToNdfYom5tlcV9KVAeu7cVtyW3lDEL1bM5P2y6dZ6VIoIB/W51bNzJ8zeLaawaAXeRtBkT0/s45LuM57b8KpleexXmnNkzOrxqzmi7ZR7Uhl5ZUlsPhcLQTav8hFeDkPtWotNavGlcKCLp1una9VVHw4LYJeDlxIHSrHiUw5/rB6RFmAVEPJrgQR2p55pB1SHWJ/SSXmZ+fVjWpH4NwAAZlYT/J/p6DseyvtU7OD1ft7urBsyn8rp2SVyxN5q+rAixjthZLkPEXP+gn/R4IZnqwjzRoHw1cObzHtqFj89RudH3TS5Cky2mWniUR/s4zZ2RhlVSo/UjXdZ+vp+PkT7sYplN2tZ3KbZQXpLJ22pXxfvX+uZYDKypYT5Xg6kekcMwBKszSfvjwYbx79+6Iq//888/x6dOn8ccff4zffvtt/Pvf/x6//fbbkdjiL4eYeCteUfuKayDCbjnyJWXp737cUmM2mnrNDUrt8EtUKpFI3efnpmVyqZy4rvlURMAd83MdudUvITty64gtk1qUi/tIZ2pxzv01iEak2cFgYPLPOWi0l/46714744G2SUTgHOmQrmtwjl1ZcQAMDpLTfIltc26Z0v3suDskNRFa3k/BnEuUmfsPZXd2J93P9VC7kWyFI57O/kGfGexzudhxzojuinRA1zWIi4zPSO0MTDpym+6rJD0jkVoVED99HQjjRGdzD4fTfyvQj025NqiCVhUpVHLLbcN+XIPBGNPcTzqr5sT5Q9e3rk5MkFEP/t9anbXVj0vxbC2WIwMI39zcPMIseBePf+51KZRX+zzhJE3H9dV6X5MOQxg7K47jccx107Z0befu0+e666u+aGZTq+uzfDVdRWK3+FAlZJV/Tn66CjhpeheswZb9n+atk1Kqx06XNUjF78lj1cW7d++O2B0ztb///vv497//Pf71r3+dENuPHz8eA1WprPgxJ1HMpEHFS0ub2CohcUvU0lLTitQmxZpJMmTdmV4dzO5eJeeJJK+WU0lCh9jqbC0GF/7LzxlDXc7iItD8p+0MVpVUon1S32JfBy+TWQBjnbVlSe2KOlXv4W3tDz2+dic5k8oxQLivtT107PI1BzT4+paynguyV8pRXeOy8DlsHclIhFbvqfSKy5aIafUDgUw2x0WMtZzJjsDROmLrAOibN2/sf1xin//SQK8/tVybrq+SWr6PRfv7UuTWPWOWRvUIv4rEutlcJr4sAFQ89tWva7DWkVsN6iqxPRwOx//eZR3DTG4HLFfisJVeZ1zmgts644MZHQbGILb4YRYIPl3fw/v48eP4+PHjcWYH1/RDONXMojuntgnp0nEizy9VZtgZorZR38Xs1NvpsbMJ1ZicXb+EOD/L+x0cM8a2MeD0c6anajfdeUditf/UbjG30tcH+V1a/Toyz9ziq+YanLq9vT2W7fPnz+Pr16/jjz/+OJJaJba6FNlxotRuGN+s+90xVgVHnCx9PEpn6Ry5nUWLtdArTnOFzM7ySl8qZHKY0rj0LIlcz4gt//TvfPiHgYXBlZyXElmA0PR+rRJbbit20G5WmPe5bfQ9bF7m7NouAfo009015jPpOtj0jKc28k8pTi/HeLxsBNexdfsslyK3W2TLczr3uH5OBHWV3DodUtGZTT3HW17yyK9PMIF0eaX6wX5w1BjHY4xHTkvLgufiOgN+XnmS6sP3dfv3HDL20sWNM/W9OpaqGUL1xV1ye4n27YIcJrE4pwSXiS+Lux95OzDJefPWBY2Rj9pLnFcyuypuBq+yRZxWyTkHs/UDNLrV9/bYf//555/HWVm8h4d38TC78/nz50d/SzjzG9xuvHXXOJ9Eaq9B/x1BQzvzsnWI879bZmorSUTtXOnY5Io7OB/q7mFh/8a6z+fSs2fnXR7p+Xys41aX5ENfcV1na9378iC3/CEp1W/WZTz3/v5+fP369Thb+7//+7/jf/7nf8b//u//jv/7v/97RGw5EO7aKOEbnHN+Be1yibG2tBRZDaYzsp3IBtKtsHA1UpUSz0gviw7oGeFVqfJPZeQ6OGLrPhqlpBYDEaRWCWoirm4pg3uHCH2IsvIyYLTHrJ2YyKbZYDbeThTcOlK7AnSRp9vn41Xwdg3OUyUZEyW3fD4to0mypV2STUiGcFW2EO4ZodVjR3A5Defp8tGyaVCJ7YXaEP4LH85D79V39NNMPJeRQT3rNtsLnrVCnqq77uMy6UMzaXan0uPZ+Zlcgz4nMtsBBqpjTv8Tud0qVR/xc6p64edmaasZW/btIJ8KzDp+RMvFuu3IhtN91z+rPmxWNrU/GuTm5YvuHT33n5hMaKGfHz9+HL/99tvJzI5+aAaYRXGOI7gOLHMbpQA3n3PbaxANooyRZ6ocLsL5S4yjSraSD/XfK6S7IrZVmbms2pbqU/merTJrG+6rtGpEl/JygMqttFRy65Yls64Dg8MPY3XFly9fju/V/s///M/4n//5n/F///d/41//+tf4/fffj4Eqfmc+BcJRD7XpfJzarbM/k+WvIqelEo7kzshgcp4u4snbNIOk11weqSyuvhA4xVVJBJuBKK51iK1+jAEDXcmpzsqmJcjp4xhqMFBeNxi5vXiZFStiRWz5fm0XfoYLAFREd6Vv3LOqdK9RmIioHn8vsOBAdQLkjoh1ghOdvq6MaUVqZ8uZnIOubCbriCOz+OnKGiyJZILI6fXL6tzPWjYHlJlEz2y0e8WClzZxWfRep9s/in46SaQP4sYSzygqmeVzFbntktxZmhnRdTqj17guOmPLXyX+888/j++E8koUF9BRv8JldbZHj13ZUyBYx/QsEF+JguC0cssBYv27H8YUKDv0F212f39/XLbIv99+++1kObJ+C8S1s7Yt2iLhQWcHrpnUjuGXHfOsmgr6Q0ku0q/UX21BIhaunJqO86zsRZekJD2bXavEkVoeQ9z2W2SGl1PfwC5hy/+17bA9v2erx6rHjO2hv4z7sPLiX//613Gm9l//+pedrVVdTuJsYiKoXXLbkaUZ2yTVNL7rXHWuVWHZSHUJbTWA1EC45Upj5KXKK1KVkR1qIra8hEeXHzOpVWLLkR12aPpeXCLBPABTO6RIDX9EjGd00l+EuP7F+FCDzU7Vkdtz+yddf62SwGyKYqaAE/bPIR8zO1CRW61DJ09nI1acrzrX6ucILotb9aJ14gCTklr+KbEdYxyXM3F++rdh+oEX7esZgde24LZF/vxdgPQhPAW9CdCdo7OzAMe1SBpPKS3qqMtlZ6B2tW22tK8SZz6XCK6u9tFXXPidVg7ssI1jPVJyhLELQux+HfugbZDaR5/dFe5XDWrx8uP00ZkEgpXQjjGOM6/4SBQ+MsMgGDO2+NCMBs501sqRsC0+JLXxNei287fq8/jaGOPktQ63nYnLE+ersd3JN5Vhiz3pktd03ultChQ7PdR2Tc8BGd7SdtBVJrVMrA+Hg9Vfxu9YWeE+BKu6DFL7+fPn8fDwn49Gffz48USf//d///c4W6srMNKkUqpbsuHad6ntVtv0IsTWyex9EudckzFzpDYRWnfe5Qm5BIGdiavHDJy6d2zhHNxMLf9dj5LJNDPrjnUZDLZ8zhlQBr7u4zLsLMc4XYKsfYJzALoMut27gckocfvPlG/VKbwm0To7cqvj9qmAg0Z5E6FN0VAXXXb7el81pquyprSJ4I7R+x9sN9tW2Ys3b96ckFoOTqmTZJ3Sj7u4j0JoHdmua/nVhiFvDszhvTxepsgzyKz3KG9Hd6vziUy8ZlEdTgGhrbrDaTvH6X5HpHn8JmCEcuGnPp9JLfQA6ZRkOZ3CSgdewuzGvgNrDkjruerDP4pjqgAP2kiJrXs9SYGxwwFsn9gmoD9gN7AMGR+a+b//+7/x73//+7hsUfXb2S6Ikl0nDutttQ0vWXSmFgRM02BbBQFVgLuTLRijDjKtSvLjyHN27znXkyRSy+NSVxCy/lftosezmV8mtIwT+N3aMcYJ5nezsxWZRbn4Xwi+fPlyrCPelWdiC30GsdWPRukrTGmiK9W5ajc3/lZlM7Hlh7oZngr8QCol4+cwqO6c43t5i7Kmsmj5u5LKnmaY2ZExiauILZxgWmevjkmXGrv3bNwHp9LSDOdI2BmB0EIx3Qyyy59nZ7WduF2w72aW1Lk5gJD66zU5xC3ijAfOdRzlU7Zfx4lwWj43A+XO4a6Ui/cV4HZILe5JS49ZGJDrLBMT2y9fvjwqH+sYn9OAGdsYdlSz8rHjZx1VUsuvUfBPZ20TyHc63SVRHX3/UQT95JYkJ/3qEFxOv7qfnjPTKwaDDBDHOH2/S7GBI7fqe+EX8QXvw+Fw8jVvbUsFs0qY3T7SKrBW35pwBKdjQJzILGZ13FJFtUsgtNpf0GcsW/ztt99OyK3+32UKWjm/7cbEzH+78508Xpq44OMYp0vsWbj/Ob3DwZCkuzPy2iEYzv/yverHOc2lJdUBP5RH/aiSXf4AHaffSvrVdiihVazAtkwxNc/O6itBqsccnFIbhnP4ojlWXOC9eay8YGLLmLzDrbR+nXGWxk13zCwTW3SEK4wTbmS9pzI4TnFVadM53iayq3LJmVs11KnMUB5WMJ45UWcA8liRWiWxrBRKYDU9L19Em0CcA0Z9GICk2WH3USpnhBVg8D4rlJJabfuZM7uUs0sK+pJFCWEqr9PbrSQhOVQ1YGobdJ/zSiAcxy6fyrmrLbuEPVASW83azuwhgxzs62wSE1zcp8Ab+uNmU0FyufwKQNUGcPk5+MT5AgwrucU5/Z/LSrd5fwZ8X7ouniOV7Zn55DH8+7ZqG/RcR2b9lPrO6aruV4EjgNMqIFcFhtKWXxXQduWlh0yi2ZfrPo/1tAKpandNq9iAQbBbdqx+GXVD3ow37u/vT85jxcWnT5+O79eC4GK2lv/qx72/z1s8i+s1G1MduUa9VzLLwRmXljGYElwI63fl93i/Ih8zcT46kZPn6CMth/ownZmF3ivxhFSB3k57qc3VH3+nRs+7gJUjtWN801fUVfkN6su+GV8zhz4nUqu8ZMXfsg3VtGmsbBkzbWLLkQU0igOWrhIVO+eOVqKTSG1Fcvk+LlciuiqdGRSVKk9VJOxj0KWIcSK1aYDrbK0jtG52lt+vZSfOkSnUwS0n4nZzCug+MMNtg5+SViW13aXI3Ob6jJnTXDG0TxVtfE6pCO2MZHXa6hzH1SW0OO+CbZWRrLb8DOd8XBm43MnuMaGorjuBPvKWf4jEKqmFs3YBJfeOK2ZpuD34HrZTTKg5T3aYPFOr50BqNVilpLYD9isC5Y47Dvilg2Pn8HUMof+dqP91eqXpV4iH9kmnj7heXL8OqXWEXcvvAkNIzysfsOU2gW7peFTfpr5c32V3/s6RPQaOeo194cPDg53NcR+S0XfzUHbWX9V9JbVMbPlvfrAEmf/mR8Ew16EaB4nkznSy0vmXLqyrPFad/ioWxj6k46M7pFZ9a1cSkXU+Vsvu5Nx+1HIoqYU/4/NKQPVY24b980zUrnFZ1J651wkVx3PdmBgzNuC6s//Hf1Dz/1Dz/1Gn2dox/P9LV3V2Y0z7qMJtXVmaseXKOIKLQaHRhqpAyXgpqHGk0F3X+9XBzTqgmrldBfOuTDjnwJySOaTBwGcSqoPbkVomrzorizyQN/eba3tXNia3nAcr3+3t7clsErcL8nYO1JHa9GPn78Bw6p9rcnqXFHVmL0EqcplAe3KQW0mtK9OszLyf8mACyLaRQQzXJwXKuLxMLlXUtqSlkvq6A+vdGOPEOeJdRZ7RdWCB82LHWS09VpuS9Li71X133JFrCV5pP0BmY5p9SyK3+pxOWbZsUz4KIqtAky5F1mAQ6wuAHdJim4JOEAZ1DJA1wINxzYCQ/9EAz3crkxgnOCKsge/D4fCIyOpffOh7tfxaENuSr1+/nszSagAMs7EAwZjV4Z97d55tEurAfl/3ub23ykvxbSuigSgmVCoO+26RS5Bal25GUJytSpitU46ufeKyYNyr7qJM3P78ATrW90TiWRzWckE7xQQ45yaxKlI7xmMuo0ExDi7jHVsmudBzF6ByOj2GD0ipDVecx/W9FLltE1s24jwIeA16BxymKI3uO2OnBFeP+V7NdwYancyWNVWSyqN10Z8jjUxslcjyT4krk1adsWUSqoEIdc765VQAUgg/h5c2w4mq8nG7sKPWJVruXCKzM0Lr+uZHlkQeIVVU7amlIrQVcE/l7RpFTcPOxZUP+6sEiAmuyz/ZHXcu5YH0HL2FsHNT/ealgVomzuvh4dsHKBiEq52ofpcktSyXILXXIKwfTldUqnF8jq+blc9tU/85u+QAoCO2qAdjEw7IMDAF6GNwy+/T8vNcHcb4Rvz4nwT4vAZ3XBBJ/Z3iAvaTqrPI782bN49mZPUvfHgZsgPDKDeEyTYHpwB2mdzimEltWrrolh7PyNm5+nsN+s/jXsktzmvaCg8ncYGsc0htx944wlcRXOUC6RmJKCbheirBZTuAQBjaU+0kk10molrWhCeqsqpd44C4W3HJz2Kbpjqu9oP1msktf9SRV12w7VK7pOQ2SRprKGOVxpHhSpaI7Rinxo+nz10luHMgPGhUqgbrElw+p+VVheGyOuGZ4UpSXbQ8/NNZFUfcIEpgdZCzcrFT5vdn9Kdr8lUJmHAqUOUycrRb3/3V5c2u3snB87JI5+TTL7V3RyFWFOeaxRmJRHDT/SvPqkRB+axMlZPrRPj0WpWWn6cOZ1W6JHnWXjoLxUuPNZ3OJkFg11TX1PZ8/fr1uKqDlzNxvvo8nQXGXwO44BhszFZSyzIjSjO5Vt13urMl0DJG72OOVRlW+6dzzumo+jGMR4dNxjj92x8XlOK8+DqvTNDyjXE65tOMrXvXXAmumwGZBZbxG2OckNd3795NZ2uh08gfdalmagF0seUlyQ4Qqx1gHde6royjjlyzHo8xX4as6bdIIrfIeyVAls5pWSvCwukqqbBA4iGahv3Y4fBtJQfandvFCdsK2J7VeqQ8Nb/D4XCyStNhd+gU2hYBZ9RRbVJaVQVyyytN3GsUyIfxeLfeFdZCHucGW8ZYfMeWGyuRWnUWWiEXGU6kRGcu0YGcTgkuyufyvnREOokz0I7YOkem4FIjNmmpN5Nb3ge51Rldlz4RWnZqus6elU8/aMWRbAUEnE+1zEFnsLvktuqXjlwr0O1IIpI4hzRVZG1L++tzZqC8A9Q1TYfMrkrXkT/VeFFnrrNQ7r1a6I5bMcF6zuCZ7esY/7EZSpyRRvN0gakEbt2rBc4HcN27xCi122sUHtdjeB1wS2v1lRAnHftX9cU5/eXucQEmDRY5neRleTxjC1DqAkMV6GL/fXNzc8yX/Rz7UH233BFa1kPcq77PkUyUAWT2/fv3R1LLW32vlscJr77SZ7tXCZi88tYBY62T8+Hcro7sQty5VZ/zksXNAOI8B2ncPV1RwqDXxqjJw8x3OhIO8qjP4GcpDki2J/n2bvn4Hkeg2I9xcMFxGT7PwcAV25bKzc9zrxNqeXhSDNedTjm/zIFnnZmtbBXbCp2IS3WscCe3g+ub6j4nZy1FVlGlxDkeCHqfI3fuWAmNElo+5vJy+d0+y4pidK5XJMDVVQ21RpJdwKBTdgU3mpbbiYGui8ZikGPQMalN6/9RH3bgSdHSEsVqLFTt3FGEimRVhir1wzU4WgeGWWZOppP3TBIo5+uz52whqmo49Zp7fqU/mmfllFfLrHnhfpDLMb4RUDybI74ckOQ8FGxqFHaM03eK+B4OmOEa56H6m3Q52b3ZdnbOtdtrFTee0rH6Zya4W/QIz79Emtl96hO7xBbnsSx5jMeEwBFcfraSMR67GmjGOIYfAzDkL4Lzu7bq7/A8DgApocXHXN6+fXsktPrDecza6t/tsa6ivp0ZnfTXXQDJCpYVEDsfnkjrijg7uSWf7y1sr5ncjvF45SNEMTCLTiSxD3IBro4d0HeAq3tdoJoJI59fxRsz/12VC2mZlCZCmwIIjM3V7+u+K6eOUU3jXjd0K6Xg+w+HwwkO0MkhXZ3lfLQLTKtvZ6ygfKvqO4f3tE04XQp6dGSJ2FaZJmeaSK0jJAn4dAmtKncFljp1qNqic70iXM5JuoExI7T8zBSJ00ADC5SZlyfCEfOyI/5zZuQ7xmi91M5tkQjtjNRqu7kft7Xrjy0yiy7Nzr1EYV1OhmZG/ra2qbanO+6Uv5u2I5Vt64Dnqo0SyXWEE9dSGbVcCRAysU11g8NTgqu6xE4fZPrNmzfHZYtcV7VlvHyJt2OMR89ztpr3u+cuKS8dFLu+5XHKNtgFnVeDVjO9T75WgUraVnlpnVeJ7Rjf3hPXVQ5j/MePOYKrZXl4eDhJi+WB/Gwe/4kg8kxIwjzwh7zsF8Hmw+Ew3r17Nz58+DA+fPgw3r9/f7IPcutmaxWEJiK9+tOZWt46OzPGt9ciXH/rZIXrk9cirMuO3LIkotUV1bu0giO95+vKlJZMc756P8ah4wizwHryuTMb4NJyOXhfy420bEc4rd6PZ6A/lau4eulrhW6lJoSXHzsepH7Y+WRHWN09+logt5vWoxqfM59TkdoV2fRVZDwMW/65pU+4V4mJa1COQHBUoEtu+XxytElWgfIMhLry8C+RNFeeKurEQFlBsxt0rgxIq6RW/5OOo4f8Xq0jtly+FAnmc6p4HUKr7avtskVm5Ou1SHIOY/hIq7s/tXPHePFx5z6X/pKSHGNy6CuBMHaA+n4OiwbmeMvn2WnyM+BslTi4MiXSqWMA1/iDdK7MDFrVdncCVKnObv/S+uja+NrEET4luF1dq2ypO07lUXBSbZGXBt10y/V8eHiIJADl5PFakVtcd3lgzILUws8BXHKZkZaXI1czmhWx1Znajx8/joeHhxMi+9NPP42ff/75hNjqUmRdgsz66ZYn6jJEveZWWqkPd+RWA/ooD593fZ7Gnl67Zkm2WsW9zrcq7jn8n8xVWtW3WZl5KTX3seoNfOQWvJWwTFU29qGMnRPB1ToxuUXbKSZ1pNb5Q4gLTOqKTc5fX0PS/B15TT++txOYSv6cy+PE+SC1/+o3dH8mS+/Y8vIzPMg5UhYuqDYeGmvL8lMlt7w/A0pJtgLmChSl8rnBoW02yxPth3Zn0pmeO8bjP2/m/gGxPZfU8iB0CseOm9tCSe055LbqI5UZmX1t5LbjCKpxmABHZcw67VnpYKUTTy0ukOfEjU+0IztC5JUAirazpmOwgGv83i0vVUZ6SEVEYRe0LByVroKWnG+1X+mxawfXJi6NO3b1Oef6SxENKrsAM6ebiWvfc9vCEdYZyXXPVDvvgB6PcXeMH847cuuCNhjbDGJ5maC2rQOHaVbT4R3GQyC1+CudP//8c7x//3789NNPJz83W8tfQubZWl41kQLOupLKkVa3ZFHPK8hmW6PYZ4bVrkUvLyGqs9WMqIq2U0WEu8HaFByrZpa1LPAfjE+V5OIcH8+wVwfLpDIq/tV94OvUJrjO2JjrlGZqObCkNs/ZcR0P3HaaZyKyfD1xK165me7ldFwObdeOaBBTr+H8Kv5uE1tu4LTu24FirjQ3ejKY1YdFOqDIHfPzteGq4xVJ4MqVbfarnqHKoUCZI1BQOhgSBbvq5L5+/XrycQiQWwakXVILUQfv+pmdoFO8RGhTm3WB2Sp5PWd8vHRRR1A5lDSm9fpMuu2Z0qXIn+53HWSnDK4sDrxr20An8XzWp+RwIc5x8P0ogyO1AOJKWFXvk63lZc2pvFqnZK9Vj/ma1rmz745nUo3nLfm9BHGklsnbCqnl43N1pTqnbV75ScUQ+DH4ZbCMY06vx3ieez+dn+V8En/Mhdud9UCJnL5vqh9TQzpOj9lazNR++fJl3N3dnZBZ3YLYOlKrdcJz3P9LJ8Ka9tMsLQNk7nfeojws3OeJlL02YRvLskJqk6Q8XNvqO7RKZFdJd5pwYZ+IceAILpelY5MUy7B0bCET3Cr4zHjbkWPos+oe76udTYFKLZ/uJ71k26Vk1NlYZ+/YXqitTj5/q5yD0ViWliLzw1Ojq1PiKGyKYiZiq8QrgaLqmLeuHtXxFnEgqXLaHQChA5Db3s3YOmVDuvv7+2NeuoxBnSmOUT6AppX3avHTpUtVNKjzc8+4NEC9lJJdq6hR5vPYOn1LUoFbdz2dq/Jmm1Ol3dKvTG6T/dMyOEfAZJQdQbUsOYnmx6CBbQTyV6eowSN2flx2BV1dcuvOOZut+aTjc8Tl9VzPfgpJZDYBZBYFIl37m8pRgUkukysHlyc9k3WHx6ySW61fIrj6fBb4RW4jLEFG0IiD+c7nudmSGUkEycUWq6fwsajb29sT8qofi9K/+rm9vT0pI5dLSW36ew8lp0zCXdBa07sfjzUdf25M/IjSCSRiv9tGTgd1FaZLp+O8S241+Ko2R8ng7LgjMzvk6sf38RbPTVsOFmvgG7ZGMVRlU5XEY6v2ZYzHgekUVEr8SfWNzzkbpnb4e+lmdxwsE9sE5tjpjPFY+dAYjsjOPhqkDVmBo61A+9IkRsuQyl8BB+fYWbHGOP1yKQQOCPVCWjho9APP6jhnh74Y49vfBenvcDickFodAy6ayw7URYNmAKcCWj+qI7ykqDHmrZ5z47wrMyKb9lOZkc45Kaffeq5DiDtlQXmY3HIZWac4z0vMTHz9+u2VEW4Tfq46MV2GDHugwjPOCRxgy/dXupvGTUe3u/qf+tSN6dk9L0lcgCUtGWRRYMO+dmZf+dmqXymd23JZuEzup6ASZeb7NJBUEVpXP86P7/3zzz9PfObNzc2Jr6val/WqIoa6r+/W3t/fj5ubm0homcjiL/fYX2vAi/18+rKxI6h8rHVz92jA2gXPeKv7W+SSeT2nOP114yvVydnrLYHiNFurKzb12qxMbHPcLC3vdwiu89Vqh1xbueB0JbwKKm1T3d1qGbW9LBrEqN73xzPcygk+r/rH+WDf4QLknwJT1X1VHbfIFm62acZWjQcGHMgSD9YxvkUX2Kin2Vp2CJWz7YDr1BjOyeoA7EYGUru4dnJOvJuvDpZOdFxnZNEnOM8kVJdJuXePdHYAztMZCFUo7metDytq6vOV9tplTdgh8Djh67rfBSWrepSilrO8uDwVqe0Q3E75Ok5Rnw2njvv5WPPfKgkMadTYBSBxnK6po3Lv7Wpe/Mxq3z3P5VWlPefctYqOQwV2TpdTgMP5XKTTZ/KYSjrF5ZvNQDhbj7LgHh5jOGYf42ZG3BJkBxixJBlBIWARDgrBX/Iyb66rgkXGMonkqm90X1B+8+bNcTb29vb2SGL5xyuosKqK25r9MJNY/oaGfqlZlzOqL3cE1xFf3mobVftod+2/Vbkmfa/0GVIRiI4Pdqtu3HOUwPLHA5XkqrDP07LyvpJW3q+OU5n52bNgWzrnnql8Rsnt4fB4BpfthyufPp+fibbjWV+nJ4rb3WoJ1Z0UdHZ9pTa2SpvkUvq3gtOWPh7FkRZ0Jq5VnagG0S3L0dnajpOtALfuV4DZ7Z/bGalslSPHeVWmNHD0vvTxjIeHh6PThqNUwK3RYzyPl7ixJLLBfad9q+8Z6dhwbVK11xZnl8bHJSTpwLWItkd1vKUdZ22TiGzS11Q+N270nqTjM913Zaqiv+pk1Saxnmn5OuKWhXWdaWU/nV7x+FaiUeUzy382fmbjsnvfyrlrENfn3Eez/ukEEZMPTWXRMqUfrjv7Dn/AQXL9oZ5KcLFUWOuSZmydD0F69cUM7EGEnXA9tE7O17qgMnzlmzdvjl83xlZJrL7r60A69nmJM38oEkTXEVoltdWPZ4p0JQi3iZ7TsVb59WvV15l0ZmhXCIUjtDo+lOTqtURs3bjTcic9YD3FuDmX4Kqualn0HJdZA7S64gnPhU0CiWVyy0ExnOv4cC4D432uj+trDo4lDuUIqdbL9RnOq13lc5y+ykPPr8pWLrY0Y8sD6XA4nEQ1tSJcebdsxe2nSAPy4a1Kp/IVYK4A7wpZqUCYA9wJXKrT53sqY6YfwkB6OGL3Hqw6Im4L3JvqmYAiKx6UzznKagmytlkllwCsr9VhVqJjzelyNaYrcc5kli4RR9135XG6pc4B+eh+14A64N4Rfh6fq44rYUCizpGvp3KoVDaxso0p/2TXzrXn3fPdMVu1xzXZA+eHtQ4O4FR214kDwHqs+srjMgE+9m0VkUJA3RFeTlc9o/vusYJl1s/0fi22Dv9wnRypZSz08PBwXErMM7Nv3749mY11IJ7rgWeP8e0VJSW1+l6tm2BwPpvroukUSyRMt4JtXD1fs3BwB6JtyuJ8zBjzd2QTuU3LjzH+HLl1/en02fngiuCiPKzb3DZJD/Rasl/cVtz+Sla5LG7m1tmKJFVQwZFaXGc9TK9ysg1z48XZ+RQ0SXZN8+1g8C6eV9/VbVPI0owthAmtkiVX+WTM+ZwjtNoIM2dbXXMOyg3CBEC3SAJ36dosH7SlGjtOx2lBbBEdS47QDeIxxtGIOQPFZVFD5IIVukyCHW4CWNX1brun9t2SpiPX6HB5/M9I7Uw6gHd27MisntPyVWNFnYQ61GREZ3VMZXT1WiHBVbouCOHncvuMMf8DdX2dpAomVOeTzdtiAyuZ6fLWvLv99b3Ejd0xMoDp7DtJwKs6x2MvLWHU8sJ3sM9iMpsIbCK4qvMVodV2RVqd6VV9c/c6/KNYKOEhJrVYUoylxrp6SvNz/vlwOJy0KZYdM6nl/61NEwxdkst9qPdxmRV3dNrRpUn3ztJci1Tkgs+PMSe0HFTqfhCKl93zTG31KhrrnAtMOYILHddxy7jE7SsW7hAgrWPlmxUrcJ+gjPxPBGojUt8gb/bP7v1mfha3FbZVQMrh+pnOaZoqIJX8jMOQ5/jkhNMqaRNbNVb88SHtdGfU3KBOS4+14rpfOdgq+uFEG6wiuCtSdWQXVDjAolErB+ShJLqUa4y1L6+6/9yEsYFBUHDBjpqVTp3fGKdjyrVFcmipvWZyybxeg6hh5a3uq3QIbCK1s/1EFNUhjZGX03R/qvudsZCIraaZkVnVSZfeOcfKMSdHPasbE1puW76vur+y19V2lm96RrrvHAd6jcLtqX3uwAjfo/ssbmw7fZyNSQXHSnC5bF+//ufjhn/++edxyz5PA6POTijArvybayv4TgXnro0dUVawpyBRwT7PsCB//igj/nWgIrXaLlgWyYAQmI0/SqVfQ64w2YzgVqQ2LTt2AQAcV32k516TVDYtEdoxekuIeT/Nxur9fB3jULdIw2XjscABHJBAHbtKcGEDlESqb0J5HblVqXyra3PoDT/T7SdSy2VMz1cCy3VNwhgbwaqHh4eTbxRxP+Aerpvb13ToI6RLASn2K87+pWfNdNf1Y5ePLRFbJUlcQDVmVeSPG4gLzgqSIhxun6VDdPn+GeDjdF2ZAa4OUOzkzU4f17gf2OislB9OlMFD6kc8mxWeDZb+dQBf0zLrb6Ut3DGfmwFvJysRomuVDjlQ6QSWtp5LIDoRNudQ1b7Mfuhn3q/6XsteEfLqx+XXuqnOOkDdaR8naUkXvz/kAITKqr6tEKvZsy6ll9es4zxmx3gclE32VH2u7nfH8xi9mR4lawqEx/hG+pjU8kwIAulMdl2gnMuuPqoSJcRMblE/B1ydOHCXSKHaIZRF/32A7YECW7eMmWfAxhgnINj9xQ8IsSOtalP5fTvFAs4Gox0YcGv7KB6oxNmCyj5ci35jXFdkw9Ul6WIisbqEmI85H97yDC0TWya8XEb0N8ZF+qaOBq2gt0r2vn799n/sKNOM3FZSBZNZF7Xd8Qxdlqz5ujw4Dfws9xULf3GZ82JdqwJSFcmc2UOnk26f2yWdr/ZVHPbq4DEnS8QWD+ICOoPtCK1WQBWHzydRQ8lbJ7PBrQaxarxqoM7K4a6vGN7qOpNb7hseEHzN1UGBDCusi5g544J0arx4XPDf+zjH7trFAbNO23TOdxRlVaGuAShXY3lW9kRC3fVqW+WVHHVF3tgYY1zymHSEdja2trTDKqmtCO2MzFZ5QNSJ6fjkMqH93r59+8ixqH1hqWxx1bbddq9s55b8VFK9XrpUAD+NbyVQTNxwzv0c8FXQ7K5VP1d2+AmQ2y9fvhzBM79fy35HCa3LV3+qF+rrXPvwLwV8HD7p2B5tP/1LPR6j7EMTweWZsTHGsS2Z2LovIbsAtrYbE5cxHi9XnBFc1/4qKfCt/eraPh1fg6R2SXXhcVOtjHAffnIElvG43qOkVmduuawYHxij0Of7+/vj2FSCy5MhTrd0IkeluqbpuO2SMH7mgANjYca+SlbTs7iMTHJZ0O56Xpcho3352zouSKTtMvOhLo/K37h2r+7dIivYuk1s9QuA3Ijuh4LMlMkBRH2Obp2h1PRJ+D4FNNW9rlFXO+tShlbzgeJhn4Gqgmm3VaLDysrHMBzOSeF+VjT3FWS+f0Y4trSfGwuuf1f6YmYAX4vMnOdTbBOJmwFnV3Z1QBr97hDc7rg4h9hqHd2x1nV2PjlrBezQdSW/XD59t6nKp6unlS5W0rW5z+04X4JUdm1mR1fGLMaKA8+672aEGAQrBoAwkbq5uTkup8WPg6kKhh0w5Lpy3hCekU2Elusws1lVv8xsjeY9CwKMMU6IqCOgPBHBQFhnanVFlVtK7PLvEFk3e+SAdzqu2rZ7fC2iGEyvqTi/Ua2UcBicdVLTsO4yeXUrCZjYYrzxLCK/moYvCGMFBn46cZaImPpBDbjOyK36SW1zxhDs53R2ljGxlpf9syuLXkcebMO4f/m8ThIxqdUlyGlmNYlr80qPGce7+1RWfD/7Yt3vyPKMrRpmJbNjDOvInNIokFOiyfuVY9D9lAenRWNVAI1lK9FazXOMx53X6UyuSwLTaT+VAUaD+1nLrm2v0V99z5bzcv3Yqefs3Ja+3CWPs9n4OXfrZiNXyK0aWB63Tr+doZ6JAo+qTarfSt2q9qnOq2jQi/PmfbQHz9jqfUpuZ23WkafUx2sjqysyAw+Vf9Gxp0SWx1S1pDFdd0uQGQS7ABVjCZBbBsMguJjR0YCM2oTZT4M2DJQZ1M7wSqd/Un+o7VDSofpaYR0mtbxUEgDYvVvLMz+OKCsh5WPGhA5UbyG12n6KEVzayv9fk+5X5Bai9j8R2YrQ8ofIOr9Eat27thCMFSa16Yex+ebNm6NuY0wyyVQ9XelbDg51yZGSW9RLyS3yhw4ryeVn6j3IE31YBepYB/X1g6RvvF3BOvy8pKtpltblMxNu52QnV/p7+eNRKKj+VBGSI0tRUEgyYB1HlciWnkuNtMUgXtJwqsJ1nSjXy5XJgXJO01X0VDYHGJzSaUS46rsKmM36rnuPXtvSDq9VdLxcmtyO0fsbAp4t0mss7ATZ8fG7ZuxUdMy5qCPrSVWfGbGt6jAjuF0i62Z1FKQrYa3Sanvg+iwi/pSy1dZ2neI1geAx1u1gRWDVL6draVbHgWgFxnzMesFlZ9+Bjydhdgegl2d5ZoSW7UI670gtygY7UNm6lb5Kfte1L/+0/yrbMcbpUkIEmJnIMuFgv6wTFdpmCmpd2tkMTwLgWm6HYxJGuzbdVZmBepakj9UPeur0Ucmr6ik+YHY4HI5/OTUjtqzLSmSxHBn3MZlVUYJbtQvaz/lCd0/SXyavM3KLfJTg6nlc03xwnq8hf+gk0rOOKr52esjPgKz4wU5Ayt13KV1cJbMsS3/348iiKlca9M4hVuI6x5GhNPO3QphmhFfTdc+zzOqbSK1TRG2/Gel11x0AV1HwkqLIeI460NkHo7Cv57T8qV6pHzXfKr9rd4iXltl4cyCvIn2apxs/M0KbiCELg1eesYWjYEN8OHyLiDKgVal0R9uCz7ngXQVEt/wNQwdcsz3jmVYn2n6oP9dJZ7jcc1blufTvnDK+VNGgSwX8nL13weY0Nh2RrbaOyOpMET+Hxx5mG+FH8Hz4IABh9T8AtazTDBgVtyjGYLCaZm65Xbs+2InrG+6PRDz4ups90zo8PDw8IhX6Xu1sWXMCtdUMbhcMc/8lsNwltNdOdJ2/0eMOqXV6yOPH4fP07qyOL5BcN+5YX5iA8eoLkNr7+/tH457bAfrIpO9cWdHXLeQW59FPnB44BMLBKtga3XJ+2Od3bJngopxOjxIW1jrweb23k7971vfSw03/YwtxhjgtV1ghttwoGnFVYwkn5khvIrKz587Oz9IlkJyuJWNWEQu9r9rX9nYgR/Ov+vPm5mbc3Nwc06NfnEPkgAhfR5t0+mdLsGGXdXFjKI2PBPQS4dpCaB3YZoeggJ7tBcaeKyN/XAXnlKx12inpkbaPewfxnPoivZYHwjqhy56SOPsKEJTeX9T2d9c7waZLiD5rNc1KUO2lSVXONFbZF7vx6N6hTUDaEbBEaJWgqUBv+SvIb968OQLj5FsdGGZyyqCb26wiQ0poFeAycJ0Fwdyx87munZVY4Hd7e3v8j1sNGqAuPDPLH+dxSxnd1461fdNxl9R2SG/CBddOXDuSxtEY3kfgvNM5JrczvVTCinRIg+urxJZnbbHcWO0Q6xDfx/o7Ru3nZpLwcSUdcov2n5HYdB1tB0zCz+F6arvwe7VdQuvwzSzYhHyTnrrndWXms8/R8TaxVWKmDtJFffgYSqaDOT1LI66J5PJA0E5IHe7qBUlgzKXtgKSuEmn6RBa6+yvg2xkaGD4msvxn8ehPFv3SXTVbq45rFojoBihWwe1q/2her0UqwMXnq/Gl+YzRJ7SJ1Ll9zptFlyyxI+J39HTL5dOx4eyDa4uZLlX7Siaq9lFJhLVLaDm9RoeV7M/Iv4L+dP0p5BzbcK6DfkmSiFQ1LseY/79lIrfuHdq0zwAY93F5YeP5w1AKhFU4vQLsCsy6ceqIMKfV8c3b1A96Pfl3JrXcPkwsmMy+f/9+vHv37niMH5dbSa37oKObre2QzjRreylSWx3r/jXrKyTNniVhvRxjWN1S4tmZldUACpNZR2pVr7VOwH86wYUZW4gSN9b9rqRxUNmBmczILdKM4f/q53DIJBfXWGd51hbpWddmS5C1HarZ1dRGnE/y96tBpoTpu7Kq421iy9IFc9y5arirAcZORJ0NNzZHZZX4coe4TnPX3HV3PgGhVaXRe5LTu8Rx1V9j5GXHTGrhWHENdWfnowaKo0Waju937d1Vhtfg2F6yOLCn53EMwfjQexxp4zHY2XfPwzHGH/7cHXaAv7QKp6mBMs2zAq0dvXLlr5Z2unSuzTp2RoNOlaDNeMaGAwRoK9Yztr2cT0eUUJwrO6n9jzh9wDb56THWPg5VzQ7pDJDbn83YPjw8nOhm8s9MgrF0udJHtMMWQIb7kz2o+sH5dNcn2rbcdiAVt7e34+7ubtzd3R2JLfbv7u6O7TzG6axrh9RyeraHlyKtq6S2IrjXrKOVsN3VceaCUapHqpvuWImuI6nAeIz7dLWem7hifUZ/8dePnS5j7LmZXwjrH9/r9lVcPpcQhxeUBGpfHg7+68ouWMbpWT8dqXX66sra1ZuO7lf5naOn7t4t+SwR2xmYu6QwqEODunMMsJIy6GDB+a5sjRKutEnl+PS4c626pwI1ugxFf0xqx3j8grkqnn50bLUNz3VizmDskqVDnpy+byW1ad8RPJA1TsfCdgDkla8xqeWxwLoxGyMzGzgjBe6c/jgNA4ZZ31R94u5hYAEnCt1Hm7nnVu303Po2e0aX0Lpz12wv3FhxPpJ1xPkHBs/6g79gv6HgmT88w2CY82afjLFTjVd+79YB/pntqgLRq2RX81XcoWkqf6yEgwPLILR3d3fj3bt3xx+OQWoVDCuR1S8gJ1LrQOw5pLXTpo7UpgD3jy5JjxX78X4aby6Q4n5YrddZijzGN6LOH3DkfsX7t0mfIdr/PIOZ0jyVqF675+J6mqnFNUf2OV/WIeip/rWPawstW0V6uXyQaoVGl9Smcyv3nSNtYus6YNYpDuAAYF5CknPRfXfckQR0qsHsyjU779I5QHKp3xh1FF6XIMOocbRdlQ6Elvs+OSrdul+nH2Zt+NTGzoGXa5FLlH0GIPl6IrXsaBVUu/f8cK97Pge5dJkxB8P43i4IdnXS86pPODcjshVhUL2tyjsDNxDWM/74hBJa127cxq5NVeeeUg+rfH80Quv8jxsH6djlx7rotgyQ3fhnf+FAsI5Xtf04dj4KIC+VryPJj6cxktqpIso4n8iF2ghd9snLjEFmebYW16C7XHYmtPf39+WrQmM8XlaM7Qzozkht5x7XJ24/bd09P6I4vVI/4MYf6+4YpwGtahlymmmF3rp3rzmAys+odNdhSs2XRYPeW7GO5sPPrsqqz1MfinOuXKo7/MEo3iYd0uNOgGk1COX2U5rqvu652fNUzpqxdZIIC8+w6vp5BV2pEslodmTWGYlYOXLGkgDCFkXSe2bOcHZe+8qBbedQ9YdInpJaJbIaIU4OrQoQcLtfYoCndr6UA7xmcnuuPEXdeUxD3H6yQYl4Jd3sjgNHDBIwqPTM6Z2b6XK6reXg46Tn2k4KMPgag1zkg/ZBW1Y2kAF+l+B2dbGj/ytOdiXfly4rOpjSOgCXJBFeHW+qF44Uq14lYusA+6WlO371eiK8la92s7SwB/zjWVleeoz3a3lGnFdIMaHFLO39/f2jVVWVn67IqTt3DqlNuGALvrtmqZYjnytJxxNOZKKr47f7ji0Efa66nISxohuvbnWBPnvFpql07+0SQm5HntzTNkO/q592P6TXsZ/KlHSk4nJOd6u89FqHwHb99oqOnzVjm84nUsv3uAgGJE2rz0jtlkhCIqzaiRWgc6BTwV5XZg7RHVfEdgw/86XLnZTU8nsWbLjYqHDbuBlbly7tu6CCtt8KQH1OeU3ktqqLA3vnSCKbes4BYNU5LrfOJKq4oEnScS2HlsGVpSK1+l6Sez/REWQWBRuOQLBd4HqyY8T/BjKB4F81w90hql3Suipdh3fu+WsEyynwMZNLrqRSSYTWjW2kS9fGyEEN9j9VupSn2hLepjp1JJFatQvsfzEby8uOdQky/DbKzL5Ylx+7v/ZRUlsR0zHyO3cr51Lb6/4WIPyapaufTCB1TM/IXtWujvg6PebZSC6D86Fct/TOt874jjFsMKaqE+vyTFz7OBuwSh55Yk/L4wICsyXI5+hWJVzXlP8qz6p0OqVZLbfKphlb3ecCVIQWaVRB+G83UoU7xlavzRqr4/gqMJ8U1oFtl1cliahWABb7Y+Slngqu3ceh9J1a3MvAmOvk/jjaOUzti1XguTXdaj5bCNtrIrdjPAZ6Y9SRPb7ODrRqEwcmLyEu+KU2Q21UZT9YHMkeY5SgVYmrRrt13y3xTCBC7cEsgs5gF3XEku3qWSxuVtyNldl+V5KTWyUu3Wd00r8UcWNR953MdA+64cA063ennc5tS+c73LI8XHPHK2VVUqtANOXjggrOd6tdYD8M//v+/fvj148xQ4uPRIH0MkhWH6wfi8KsLRPcCig7jJXSYbtlhlf7WPOc9dVrFNWvjo9U0lrpAPoAxFOv64QEb1mSP8K+W2rLz9eJEIxHHaNKfLmcrmwOh8xktnSZ/ZbyGm0jx3vQNtWs9ZYlyE6SXqVyOdvm/GHHliY/utWHb9XxzV9FdoVB5/NPO4uNO78DN5OZAa1+er82XOUMtc6O2KfIi0Y/OqDOAWfnGHm/IrlMaNWR8js87FQxW+tIrfaDRoJZuVPbu+PUL3y/7l9CKsVaJVqv0dE6Z+p0w7UZn1slu3CKCipnZU0OWgMs1TVXRxV16uzQHXh1pNatmkjkVN+55ZlV1W3kwXXhyO/9/f3xvH5kywlASrUc+amk6xBnDrKT/8p91yqqS0xeWR/4XCJ6PCYSSNZnr5RTAa+bzXHXEmhebSO3HaPnFxQnqF3Qj0OlWVomtkxqoefcDqzfILZMcJnc4r4x/F/24Dy3CbZV2gqnpbau0jgM8aNI8r3wjdgm7M19gPTY5/uBw5VQOx1KGK2qg5aH9dWNUxeY4ftVv50oua1wBPu/LZjPjXUulxLabn7ph3SzfCAVCWbb7a65+nR0sZN+ptvn6PomYpvEGTWNNnEjaic7gFVNhfM2KeC5jtaRTLffEXWQ+pzZs5yTdMsW+d0IBb0aHcZfCLx9+/bEaXIezoiogcKPo2oJXFQGqaNEM2dYnZ9d03Srhu5aJTlRjeSN4ZfZK/DjpTd8jj9QpMuWqsi0nqt0CdfZQahT1WsrINiRWY7AclBJvxbLOphIrvvIjiO87q8YeMYX7YB639/fnwAXnql1y57T2Of6J1t2CQDa0fkKDHfPX6t0+sqJI2yqn47c8LPwRVPcw/rGJJdn9vk42RMFdayv+nc1fJ2/9sv5OL3Wcd5pq5k43+1+jtQyYeXZWd5379RCh3XJMfZBcBOpVVCeQDq2nRlZl5avV3hO86ikgwFes/C4ZJ1j0jvGN1v85cuXcXPzH7iP1Tps+ysipWQS+6znSoxV9zD2WF9dAOb+/v7kvXCn81wOtAXqw+LI7YrM/JjqgNYZwq/2OI7D6V3QDltNW0lFZlNaLlvS0xke1/Sz/XTPil93cvY7tm5QcSfxkgSNmui2a8zcVvcrsOoGh55zSrJCbKvOXgF+HYLrZmbTu7T8f3hMbPnrivxOLpQKa/wZcLi/EBgjf12x009V+20BtB0l+lElgTY9z8eqI1UbqrNz5JeJrkY2HeB21xVQKajFeOVx6maAKqeh+q/XsE0rKtxsTTV7y2RYt+59eP1vQW4PAAYGw47IujrpfpVO+9iJs7uzdF0H2clrJj+qTVBdUpAKPVRSO8bj2Un2+wq48azUd46Y6juiTOLS39ckUstyKYLrSC3va2BLdZcJLT4O9dNPPx2XHuv/1LIeo65MCu7v78fnz59PZr0cWHZ2UvtDz3P/raavzlW+O22r/WsTHV+sf84P6n2sizwDy8FXkFtc41cAsVV8CV+RdAzX8AzGfYoTEVytfonwVrqd2hDicIgT5/PUn7kgkO5j69rWPcvlmeq7QlhZkv1jqcisw+6dZ818tNPpTtlnctaMbQK8LExqZ5284mR4vyJPen5mYLUcSemrcitIcOWvrrt89fmuPfUrdbzEkZ0of23RkVoGNDoD64yVKiHSblWKVUfWAbhbnV4FaF6bqD478qj6vkpuK6Krz9eAmMuPy6LRTiWwOMdBGh2jlS2ozitRZHCA66qbCnYd0XUrLViXWafdl8u/fPkyPn/+fCQYABdqg5PDBLip2oIBzbmyxWFWdnYXL06fkq4CLL99+9aSWtY/JreO5PI4YeCOLfsRneFxyxRxzen6ik4n0DnDJhWpTXquq6V02fGHDx/Ghw8fHn0oivuI2+Lz588nhNb9tQ9/fKYitepTlbyunJ9d4+e4Z89kpver+b1EUfzBRNZt+Trb7jG+kVvko/2hpPbLly9HnQXB5dlHJseu3Dr+oLMagOGfktpZIHrWbqyPHXK7IkritXyK0xWzcPsrxn4KQsv7irfSvRVfWiGjl8TkHdlMbF2jzIjcGI8dRXIo7tg9qyKxVZncNff8iki6exwhSJ3qDJZKOsdk9nA4fY9vtuyY3+XBPt8/xjdF0/eY0ns7jtw60jD7ad/MyG1KP0u3y1x0LOPcqlTkVkG1Am0GRbqs2ZWXx6v7YAoDPBdh5Xp36uWWOHE5oaOJ0CqJTT8msdBZ1mVeooj2AnhAO4Lgcl+qrXwK/UhObis5fSpSe6224RyQprrtdHWMbwGOt2/fRkCMdNApR3I56KJlx/lqeS3PROp7eBUAZiKvZa4wSKqn22dSoKs1nC/md2k/fPhwQmp5Bhe+GW2EduJZWiYGPPvFds4ta+R97gMdI7ztkt3qHpd/9cwZnrtW3d0iDmOqXkGgt+gnEFbM/OJ7Cyw8rnl1j64QRBq1H3iWztJ+/vx5fPr06RGZ5bH76dOnUr8rYqvlcPVy5FYD5Y4HVD7MBeJwX/qSNe5n/dP6cdCgg08SRuvg4IrLOYzexdbnYvCten2Rd2zVabhOcI4FMiO0ydnMyC3vVyRqRh4rgluVqepUpyycpiLZOOZ37dIHKRwYZkKrH6MYw5NaBhXuPQjsM1FIbV71hVOeCggnkDsDvz+SI6xEx1oymjpuV8ZvAswOPDMQUpKbIq4M9jBeeZ9JLl93wNfVQetTORUGt3zM+sr7vJrCzcQqieVZHPx4thZtB339+PHjkeTqB6UqZ3Vp/ajy64DVp9bXH8UeqB+GuMBRIreqCwzkmNA6kssE2BEoDkDxu6LscxzoTeCXn1H58JlOu/OaRnU9vQbES49BapnQguBywAr9wGSBSYESBtc+DJZdAHqV0HKfde/jNDN/XsmKr78WUT/DOsg+1H0YUANILIzr8D487lXixeP5y5cvj3RW/RjGFU+KqB6DsIK8wi99+vTp+ONxq4EZ937tCnbW+m2ZuVW7xdiAcQYTW24TLaPqUqWX2J+VLxHadO8My1X4vVOmVJ7nkLOJrQLAMR43ckoDmTmObgNWxDYJdy6Ot5Jafh5HrtS5cptUABrnNJrknGe1VFFnaBUUI58xxsn7GWyceBmJ+2LdGI+/hJwIbdVuVZ/yNp1LaVy6H1VmZDaNSW0/NYzOUDpnPcY4zvy49/jcO7YY87jOZWFHoO/i6cytA79V3bSelSiZdXqryxLfvHnz6BUB1lP9/0oGxByU4nenQGrHGMflZNzObJcUKGg7dJZApRkclRVdPCePjp5X4/pHEK5zGteJ3KYZWyWweqw2RpfmPTw8PAqW8kdl9NxsRmcGgs8hrtVPZ2wTqdWlx3ysf+nD/phJrZIDLEM+h9TOiCm36SqpTX6+yqODAdL+a9Bt55/H8N+bSKsqxvg2A4s+SLOJY4wjqeVxjNlbYEQme/xMJnq8PJ7Hq5JbXXGgrxvMfHbXR69cV1LKhFZnU1Fn2C+0j+qKvrfM97tAe2f8VsTU7VccROuU8qzKNSvLyrWtsonYpoaoSOgMPI9Rv79XEUndd0TKdVSH1HJ0Sp2zMzY6MMc4BQM6oFM7uusKjhkkuxkfN9sDgqvv5eF5bEg0Wl59sa4zWzv7uT6rtt1r1f5MuhG9a5fkPFPw5RxjpA5Y3+PTyDM7Az7PZcXYYwLLKwmq5Uwzu3BOPRXkHg6HE2LLQSf+30qAXwa8fB7pAX75XSiQWnwFmR0sgw5+F5nBbWXDO+dwvpN+1QmeK1vtwGuQNKZx3s1kOHLLcjgcju/t8X1KbPnaGKerM1AG9jv67qj7mMxsplbBnPPjidzOrul1JbVMaPk7Fkpq3Y8DVmgbnv0CQaiIAQf4cL8D0KuENl13/jfdn/ZX9fE16XIiGW7W1t3LvlL7B76VlxCnMQBRXeHZW8UJ7LORN+NHjNGPHz/GMfvp0ydLanWmduajKuH21ZlvJepaT74fdYTPdfhDA4DaJ7xNWHhWj2pfn+vOcXvM/LjD5Sn/9LznlIt8PKraQlKjJ7KrxxXQXCFCXB7dd2RWia3eq+VwP1aCMR5HvdPg02fqr1punGZqcd6RWiiZfnqdjQ4bHjU4CpS7P22/bp9W/fxcyuQCE9cgTp/Y4Gm6ZAxneq/5sF5pdJmdsl5TogvRKDGDwAoAJyfSJbUzYOxsintNAF89BdjlZYkfPnwYP/3006PZHCa1+tcfnz9/Hg8PD8d3a9FGSviVALCjnc3irgDRyrF25Vp06nvJzDduCdDwfTNyq3oKXcWsBoNj/OWI+naMQV0ZxF/4TUuReWw7/7NCamd6nH76/nz15WNHbDlY5f6VgJdwMkHAVkmtztRyGyuhUX3eMgtbXdc0lb9e3eq+O74WUT8K4eAvH+s1DghXr/yo7ulffI0xjsHRNP6RHv4C5xkLIjil41YJ7sePHyOp5UmT5LM7ohiGMYRbYu3ILXQJ55jso1woM9JzG3EbJxw84ypVnZJOpDzTeHO66q69ZLno/9iO4Wd0HNhl5XDHel8VVeD7KgOoz8a+c1KO1CYQWw1WVx6N9lQDF9vkNEFS3TJjvcZLHtPfgnC0HJFhXk5SfbCD2yAZowR0U1t1+7SSp1JC129bQOT3kjT2kk6ukNqZvsIZIrqpgi+wKvHFPexMcJ0/DNWZ1Unl43o7kp9EHSbbE31dACsoHJHl388//2w/KJNIrb5KoEGq2WoL7h/XZ1sktfVLd47XLB1C63wyA2XOY0ZuIRhbyA+Edoz/6DSWxitAZlDoxqz+hU0Cv1XQSuvqtnrdXeO6KaFVPeeVGO51Ag1SsU9mXdVZL535cqSWbR2DbbeM0vWr87crM7wu3QpB7UhFaq/Zvjj9Zd+qhHaMxx94c68RcPqE1fRZqXwcMFWyy8EYHbd//PHHybH+RRVjylkgmiVhDrZlqtfu7zF5q3UGLtG6ct4cYOA2Yh9b4eIZxuhiLacLim0cX3P5bNWl76WDF3vHlhUxRQDGmBPaBCQrResO9spBOUJbLUN2ZVDyqk4DZNa9gO7Kha06Tv5T95ubm+PypdkMLTtPbhe3zMk5zfRCPxu52Wwtt5UzVqvkdtVBrijazMC8BnHgVq85Y1hJlYYdDMa2EiomtdUSZOSnThbjMi1DToa6CshV/a3EvAK70E+egf3555+PRPaXX34ZP//888k5/KclgDB/UGaMb+/7MMkFsHBBKV1xkYCDAh+cS/06Gxcd3btmMPo9ZUZku75U06+QWwSdcJ0JFIJOWJWhdkZXFOjY1fHrglVuHLvA9Qqp5XPpp0Fnfl8+fdsi/cWe+lK3nPOPP/7YRGp15mgrodU0M0Kb9pNN2eLz3fHs/EsS6JaSFad7OM8zpUxeFXMiP+4npNN8ITMMhft41R+usf9hLPnHH39MSa1OlsxIbTfwnPT1cHj88VWc51ly1ks+B3yiwTpH+h3md/gr1cPhqhnWSueU1DoSP9PLmXxPvbvojG3Hec4c8MozIC56kUg23+McVEVsO2VzBA6ipFbft9XyOYDM79IyqU0fhuJlx6p4DCgSqU3/L8bAmJc6MRCekdzUVl0FWiW1K/JaiKuTSgeV2PG5pGduy3lwPzm95DHpSC2nrZYhMxhUQqvLFLVeWuct4pwnnL/7z0rMyOL3yy+/nBBbkFssP3Yztfq15/RRGdVjR/hdMM6Ni2pmt0Nwn1JmgOxSeb1EOce3si4wyFadZwCt4t61dc9RgJeILS9Bdl/61XHbWY2h5ZuR2go7uJUY+l6tI7RIg3vRxjzbjWP1yyAE+gXk+/v7RyRf28PN1Gnf8HaM85Ydd/ZXfX6SLsl9yeLIqyO3PCbhK5noVrO1/CwIZngdLoOoX2BfrT5ZlyEzseXxq3/vU63AQBkgLtjewflukohfEeLrStgZX3Dbs6/nIDwT24eHB2sztT7ufEV+U70rfXL3Oe6W7u/IU5HfbvqL/o+tO7/ibDvpqgEwe54C2ERmNfrCRsERdWxhWFAGVkoIR9mQnyos/3S2R5c3VaSWPyyjUSaUjZd9uY9ScFSYCa0Cic5sbTKcM1J7jvPbopivmdRCUruo7vL5GantPld1iQ2/OkpeEuRmd5EnA123TL4CvTMb1XGcHBlXnWVSC0KL31/+8pcTQotjXMcSZOi0flAGIEJndPin7+HpF80VtDh95LbCdkYiV0nmNYLRlyazsZwAjN6jM0gsTHBVmNwyMGUQyP6PfQcvQ3ZfQ+brnffmXfB6Ztv5Xm2vFGzmd2l5NZX6Yia0vGoKBJbxA896aaDK+WR+DYOJK86hLkpoO2TWpUuE1x13ff0K8b0E+H6J4mZeIS7odDh8e52n8p9JXzWAVQVHoKNuHI8xTtLwSgMQWX4vXAOtW9+prQJUSmjd32Hyj6+jPblf9P1ZlFWJLXAKz9Zyfg7/aNkVV3UnEliq886+dfI5V+/O0dWVe5eIbRWF3UIGqpmS6ngGQlEmPnbbRGrHOAXUY4xHg5KfwTOwGPCdwaJl5Dokx1n9dQ+/P6sfoIBh45krjY5rhM3N1rqZHjZA3dlaboMVB1edTyAsETSX9kcX50z5/AqJ1fxclBX70B23RGoM/zcjOFYnPCO1FaB3dU/CNoEJrS5LxAei+B1aJbL8w0wtfyUVzhYghUHv77//Pv7444/x22+/nfxwXqPk+jcKqss6y8PbVakAb+d8N98teVy7dEmsbvXepPOsbw5oc2AJMzhjfFt2DH3m+1MwHPc4Uot3x93ff1T+5Rxh4qAAGeA3kVr2w/oKEBMB6B7aB0EB1m/8sGzTEQJ9raAzSzsjqtwOs3QzH+2uVedTfqv3d/N4iaK6B2GS6pYZ6z6PL5xjX65YFcTVTVooqXXElt8LZ0zJwdWPHz8er89IrZMK0ztdTasqONjEf5vnJoNQJw4AuNlanbXVoJIG2LQ/kHZGaiHOplbj3907y6Nz3JHnIrVjnDFj2yUBM8Kq57pkd/Z8Fw1JjoqVQM/rc3iJB0dj2HHAMIxxaqTd8125K4Xkpci4xu/xqULCWLlIk/6tAn8oKv2nWHq3KQHjBDjcMW91vzp3CfmRSG2nDR3oXSW11XPVGamRZYI7hl/6mIhtl9SiTMnOVIaf7QSOFegyqVVii6XHvGVCi8AVv8PEqyygr5iV/e2338a//vWv8a9//Wv8+9//Hv/+97/Hb7/9diS2Lkjl3pFXQqv9lgBwJbMx0x1TVbpLPWNr+pckK4R3jKzXnIf2uy515I+qsO9jQus+yIL08EfslxypTd92WCW02gZV3TXQnICyA87OJ/OsGgNmbYv0rjHahFdsqN6mWdoVoqr7aZZ2xWfPSPC5Pv+a9TaJ84FKcB3h5WtMYKvnsN/lADGTWmBO6DSEsab7ZoviSvdeOOtxwgu65X3G805XmczqB1V51paXhMPnso5WvAFLu3kSiftDZUY2E/FMgUlO484lTKP3rejoTJ5bLy/yju0WkpuIZzrXuZ7OuYiODsZEbNMzeOBjC7KrzoCBusuLAcZsmZMjty66hH3+gAcDfY2Mq/PU95rcTO0WUpuOuR2q/UuI9tEqqf0RSLCL9FUGNBlZF5HkezTgg2PoDX4JgCm4VWLrxiSXrap7RWqxZUKLmRw4S/1bD3zdmN+d1ZlZ/Toq6y/0mZd48Uztv//97yOx1RlbBK3SbK0jtNx+3C4OLM+IxVbdvoTuv0aw2xXVM7cdYx60Uh1QUM1kDbOPvCyPz6kd4LGoy5EZ+FaEdnUMdkit88ccTIau6n/C84opDTKzHQPZQFmYUHA76NehdelxCkol3T2HzKa0HUKqadN2lt8MeF+jOB+pAj1iSQSXSWoXr+gy2rdv3x7H2JcvXyz5c7oMwso4cvZOrfoeraNuHaZXfdVlx0xk9cffoeEAnM66cp010KDkVq9pndx5Fu27bl8mPUrcw/kDd/8WWbn/kvrbJraXJq/ueufaOWmdAozxbdmxztJW5NYRWjj4FF1JeXA9lNTqsid1monQOoPDRkpnbNWJKsBQJ/o9SO3Wge8U9kcgp12ZtesM9Lo8HNFNaZKTSnrsxpMC3orUqjF3dXXXkwN1rwvo/1Xq3/owmcXMLD+LP1AxxunyRH4XHgT2999/P5mp/f333x/N2Ka/6+LoMrehtnVntnZGLjTtFllxvluecU0AOYGdCgR1wIyOfwe8FVRjjOiyvDEev9rDz8J9vBzZEdpqBYbameSDZ+Q96beSWl2SyT/nexkc8zMgaEdeTeVIvr4fr3qLvFhXHal2baZlcbJKRlOabj7n2JJr0mPIbHzq7B+P9epaRxj/vn379jjWMK7v7+9PyJ8ub4busq+q/odagzJc/w6Z5X22N84n6/dp+LU+JezcF2hzh9+5vZ3dYBvqcA3fyzLrtwq/VLrj7L7aqrTv9JSPOxixkkp/t+S7acY2NXqX1G4lqVvOKUnl49nyY1ZcFTQ2R8b4/QRccwPZ5aPKye/yMEDQgchEGlFzPBN563u1LjKuRifN0HJeXVKbiOyKc+zIucrVfca1y2obdQCLyqyduK86ep2e78itEl0dh7i30mtHarlsqqPuS6j8Hrz+BZc6UujS/f39GOPbn7+jLPpBDvzlB2ZlQWyxzx+O0i+osl6nme00W4t9d971jWvbrqyC5pVrW8t0jVIR1VnaBH70WiK40Jdqthb368yt80HJr6S6zNoEdUkAGVsmsay7VZAZBJXrpW3A5cF96n/dsmMltI68zghtl8xq2oqUVm1/Dqnd+qxrkZXyOiIL0ZWEW4kt9FcDVWkSyI3f2SsFSmqr+jpdVb3VAJS+965bXnFRrXzkY8bA3AdMYDmowP2gdmLGDTqSbHrXvmsemjYdb/Ht3TF+Cd09eynyCjmtrs3IKl9Lg5zTptlXVcqK2OI+JxjQGCRfv377/Dk/k9OpU+cOdIrqBhuco3sHIAEHpGGDohFhPqeggh2pI7NbSC2XcbavMiNU7volSG/HCF0j8b0kCNAxraJG2BHcdG96lo632cqBJA7I43wCvm5Vhc7e8l9ucTANegUyCz1mwstggZd16Z/dg8j+/vvvx2v6BVUGFvp1c207bd+KyFbtmxzgimNctRGzfr420DuTCpjoedU17g+XNumnAiRs1f8lf6qgETOaLpCaZmlnbZJE7YzqNpNW9+P361jY16MMSN8ltWgLrfu5hNbp3QqZTXms6mOXoM7y3YoPrkE69iv5R351ZZXU6kRMmvzhcc8YV3Gm4sr08TeWpJt6zBi5u/QYs7W4pq8PuMAU42Vd7QSd1JlbZ+O4bhUv2iJdAlr5CLX1Ke90bqV8zyHLxHbWGR3iqtvutWqgV0R2jMd/G9IltkgP0cg0BjacOsgtD25W/qpNtX78THX2fB7PcuXkMuJedpLd92cZBDsg3CG2ur2UI3PteUll6hqdayO1HSCS6lT1lwPCOFanzMdbDaaOs844TPm5MmPLjrRa8gRCm95HYqJ6OByOx58/f360lJF1lokt/70Pv2/LM7R4r5bfz+OIs77fpO86KZlVwFyR3OpcBwyn/qnu+9EILUvSOb6uetfJK+1znnreBa+qZ6kvcX6nIrUJsGnduQ0cOFZSyzquAB/5wvcjIIXyHg7flnPyfSnQgLryvmsHXtKsush5af7uWuqPWVumvKs8Znl37ENH5y8BxL+HdNuwc71bX9aFNAEzw9X8TKe76ZUC1mdH9FxZXRCqWjnllh8zqU0fikK59V3htKqRdU/tSsdeXRI3VjhH7WDlE3S/g58618+Vbv6b/+7H7a+S184+n3PklgdmtZ/IayK8LEpSxzhVAh7AILecHmm03VTUOOj7unzeOVsdpAoYdPa1GxGuZnb0WJ/Nx9h3W93XdlkVZ1RWCO+1kdQVWXH+nfMzgOOAcCK3s+dWz8G+6/fZfSiXnsOW7Yf7Wx83q6NLm0BQP336dDz+9OnTo6VQXBZ2svqhN8zGuq1+dMZFz3WGyPUlg+fUF67N07nqOOU/O/e9ne1Lkkq3VB/1vgR0VGerfUjHfjp/UfkdFUeq0/MdfmAdZTKrJFf9K+rMAWw+D5KL9+QTruD7WM+cj3Wzs0lHVYe3zsymcx3b37l/hgFW9faa9XzFJ1f3V1gL0sHr2CoGrYJIil+rAE0qE/arn+qoWynFH1jVj686v4x9+EiepeV3hPmdYV5ZwXZIV2om23VpqXBYhcEcTkv5uP1LyqXyPfvjUeeS2dmxXpsRWn4HYJXk6rHWGV+JG+ObQmAQs/OAomBW15FbdZTY6qwshJWOP7XO9VBRJ6gzvRoJTsDCOdd0Ds+qDOzMobnj2XknK0RW73uOe16KbHGgvN8FN2xQkzHtlG2W1pHnWR4pfQLC0D+NGrPobCueA6KqH5xRkAz94qVPTHCZwCqZ5Y/AYaaWSS3nrWBE9VrLw8c6HmZ6vAUIV+mrfK4Z6J4jOpbdcRJdVsf3Jx05h4A4n6G/SpLOunOqywqS9ad+GgFl9vecjmdnu6TWtYvTs0vOzJ5ja8/x3e7+lM/3ANUvQVbaUtNU+sP5VBhb01TlS7bfYUiHE51UPKNDah3BVXzMeJcxMY51ubF+eJH9sOJ0La/inUsJ2+KODmn6tO+es2VMunySzDDgluee9fGoS5BaR1orwpvW+zuyp9dwvyO5mj+XHaKK6wD6169fTwgwO0D8dCYXg4fJJYNNnaGtvuKskhykW/qE/QpkVNf5OcmBdR1bR7Yo2MzIXDNB3SJbjZbrR3evGvcO6ZxJ0ssEaPl6VU7dOts0xnj0To4rDxwjlhs/PHz7/1m3RFmfwfqm77y7j3Ko401/maJfOGe745Y7ppmiBGo6xyorDq1jL7Y4yDSmrlVUxyqduwRwqcrR3Z/ZkjF6Op7uUZ/pyK3TQ/hpEFZ9BvxuwikunRPFDNomSladD3WENgHWJLP0Vd9V0n1mdzyegx1emnTqXLXDDKupVJgbx6mfZxhu9tMyuDo6kq243wWW9bzmDf/HZEqxNvtY9bfu6+S6okRJo7PFq+LsdzVm0vhxPmEl7/SMLjl1smqfOrL5HVttCHc+kdOK2Lq0Y9Tvxc4iro7YunzTsyHqZAAC4fCwBJn3cQ+iuSC9Dw8PjwgwR7dAfvWdH63bCrlFedRJOrLKabs/vkfbq9rqvpOtA31GZC8h10qGLwVoV0Aon69AKPLttK1zGs656L6mTeVRW6PCOgWnh7zg+D5//vzo77oSqeV6qE1QkqpOmN+j1XfoldS6j8OpPUJZdPkj11vLPDveCoY7jrub1+q91ygJyGgat++OnVSAbdbf5z4Px6zX6R73q/RvjPm/GijWmeEWFmdH9Nksrp/SrOyKX63Szc519XhlXJyr4+em/56y1R8rDuvMkq5g3i7Oc/spneprByu4YFT1d1vA0A8PD8cVUsDqEF6ppO8Du2/Q6HHVvqjzORi0Y8Nxnre6n8h2Vb7k3y+hl08tm96xdeRVr3dJbUUsx8gfgXLENW31XRpXBn6Wq6sqNIwGz7zycmNdeuzaEj9WGDgrEOaKtLsZn8oRu3qk/epcJ60+yz1fr7njWT064oyou3+VoL4WQpvOrdw/xpyoOgfqgGqV36ysCbincVuVc6ZHbAv0XSQ4Pvz/X5qhrUCFPufh4VvEWR1x5Xx51lcd+cODn6GtSO1M5x242SLpvlWb8dKc7nNKBYy6hCIBoQoQVVv3jEoq0KvlSfcmcqt6zuO9a9/TM9I5SJrB1bY55x3Z1bG/qnOrz6rSzPI6p14vXbq6OEaeHVOfxFun0+7nbHf1q8pZESGuh6ZRfU6kVuvBZQb+hi/EvtaNfaHzmd0PqiY/52xlsptdcTZd29CNDW6jme1cGY9PKbMxlGT5HdtEZPUc9hNgdCSTCabe352l7ZBAJrD6P3RbAC3yZjKKfS0zPiqBGVwAUew7RUJbpKVTCTBrf8ycUwesdkFtB9RsVZhLKJcju6v3/6jSCRRUdqIit512XWn7NMZ1HHd03xFD51QfHh5Ovo5+OPj34flZKaim5XYzuNjXGVm93pmhTfrM7dlxeh1b464n6QLulTx/FElAKKVN9zsAlO7v2H+WRL41jYLDBPI1XwXJVZn1WUmqvDsEl+uSZHXcVwRjVWZ5rfrvCgN0bMdKObtleknSbc+ZTdbz6uvGeLwSCel1bKrfqfKclXMmSrzcNVduLScvD2b+MMbpq3nVx1QdgU3tsCpdXXF2qgpqzPKv7Dfb+E6eMxs+K0t17yX09iJfRXbXKkLrDL5779Ud6yysW18PIJmWJndIrqsjKzsG/M3NzVEhAGYxU4NyMMjFMmWcw7FbBqGKxkA6kfcZudW6uLqlbWe/2s7OzeScAT8jXyv57PJNEuDdQmyrPM4RHavqlBhgqu1yaVn/NQqswS6OLit5dXbQpeMy6uwqz7y66HNy4FqP5Lz1vdpK76t91x9Vmk76rYA2Xf+RdZv1+KkJQWpnF9DRcmmwB9uq7yrc4sawXqvKmbYzsutsXgU6u3JO363ozZZ223LvLM2W579EWbFxFQlI7cH22umX7iefWS1vruqk5a/6bWYfUv10ZQOvmuT8+TXCjn/UdqiOn0Jm9q2jlxo00G0nn0pvq/MduTQHOOvjUdU+jl2HJJJbkdpqxlaXHt/c3Dwit3ydwab7w/X0XswYjyNYblkfjm9ubh6944YfrmGr7825ZYa6VLmanZ61P+ri6qfXKjC7uq32U5legvzIoNdJ5Rw5zZat7rvjWdnGqMewc0yO1GKbXi3gaDf0We2L5usILp/r2p8x/BfOq6VT7NT5fgdcVkitO1ftu+NLyFaw1bn3mmUGjlgUeFYB0nRvAk/Vfel4xbesyGy8pnJqfVx9kXZGajvEdkudnjKPLe1WpesC5Y79uHYd7rRpVcdzAlNuXCe73inLShol1R2iyO/QuldnxhjHVwW5LOr73ArJVPc09md9taVPnN10feTK08mrm+dsDLxEOevvfrpRx8qoJ+JaEdwuqU1fTessV+bnqjgAmJb9KWHlv95Qksv/f6f3HQ6Hky0UlBUQig4QvhIV5rpV+1sIbAfoPofCbDX6O6n1olFAPZ/G3zkEN52rzqOM2CZy6/JR/RpjHGdpHx4ejo6V9Xj21XJn2zioVhFbLRM7aSW1aiOektS6dtZzW6RrL7pg/LVLNZZVNACUAlTuHqejms8Kme3qrtvqfjq34s9cOR15Tc/ktLqv5zj9TFb896XlHN/d0dcV/V7V/5cul66Pw9zuup6b5cnlcmSokiqN6s2MXEF3lLTO7IjzfYqjO0Sui5NnbdC111y3jl2rhG1YynOW94qfmZWtqrPbdmTzV5FnaToONSlWl9TqTKwu/wXx5bQ8o8LXcV+avWVR4Jfed9OZWp7F5TLgK6pdMuoALINsB6BXjNgWQDA7191/alkltzup/SZuHLrzfK7ankNwq+OOoawcWaXvEBBYfpeHnW2yYe6Yv+SYZnjH8F8357Ilotolspqfa5sVktG1G0kuZReuHfR2JZEFF4BKxNSB1Y5+umM+nwBxyivVrTPutC4MhN145nOp7Gnrnu3SarsquXXPd20yA+9alk6e3XtdmhlQX71/S5leu34n3WFJOjUjtilN5znpfGWHkq45/1v1KyZxkI5nb7Us6s9mr9/Mnu3q2fF1HV3p6PesDJpvsu/V81Z1/BwddDjyXJ0++x3byjE55Zk5SX4h3JFapHXkVomskle35X3OR0kzl1FBJSuLm6nln75zVwkPVCgt/5yhcM50C7nVMjzH/nNJh9xuIbTXQoJXyT3u6ZzT613y2kmb9jtEGJJA7QzgsoN011jvZkRWj/k+djSwDyCmEOeo9ficfW0fPtY2dNt0bibdtFvAdCefa9HfpxIFlB1d67bZFh1m6Y69zpjEvtN7B/QcAa0AqNqDmczA5SXH5SUAqDveortbMcH3xg8vXdSnKnZ0xLc7xlxeFZHic86XQJiY6vWZT8Zfa1b+mb9N4VYxzfx/kqQTM/+3oi+JmHb9rbMvHdt0rj9flUvmffY7tp3zKZ0jswpwGSiO8XhGw83iKnFNx/y/kvpzM8M6qKAYjtQqob2/v3+UD/7bUvPj5/KxpmFlhFJ3AwiuvzpRt3R9i8P6nk7p0iD22kFxRXZX67aVzG4hu520TtSZJQenWyV+b968Ob7vDmexMkObAnl6jcXN3M7ILe7jdOzsE6m9BJGYOfuOHdhqK1aAhQKxa5KqfToghvW/CmJ1g0jV87o67eqRtrqfQKqOzzTW1Re69qnsZWrzBFSrtu/036o4oNuVDumo7lmRl4QZnktmmKmLv+GLGBvqOFsltpxPl7wl3eTVTonUdsTZbefT3Gs7yc+tykwnVnSm6uMq764fnZHc59Cxyt5dQp7kHdtK8bpKqaCOASPPpnLaRHKZyDKZxfGbN2/G7e3t8VwiuBWx1fdi7+/vx/39/UmdWaHw5WTcryTeEVROA1DNg5rz6YCHrqPmOm89fq0O6toAMMQR2XSuOq7yT9sVglvtu2usp86mJFKo+87p4R7+kBTrIetmRVzhYFn/3X0MSPAsSJfcVvWs0jqywGXoEFiWFYK5IjNC9xT5viaZEVSXdiWAlJ7hdBeigW7IbPy7H+sz8qvILZ9ngurAc1XPqi1mY/apfMq54HXV/2/N56mff82ScLgjCjwxgvHtsLqe07E+8wksqU+cbh4OhxP95GevkFxdloxznI/6dWyrulQy82cJ+156jM6e54JoVT7Vede3K7aqGi+rwcyZPMk7tkjnCFqVT0Vm+R4lt9gy8VViChJb/W5vb0/IL0gv5w2BcuC9Wp2dVcGSCXwAarUtU9vCeKnBUODgHHV30HOdt5xbUezuwH0JhPIllOHSomNDr3Xu13033lcI6+ycvrKg1/TZ7PzY8fGPna4DvZoP6yKXiQE1X+OZXr3GeSdxDhzbDsGtjh2IqbbdNOmemVwChJ+b30uW1fqs+J9VfXb5OjANcd+D0PMqFXjFeR3XOM/+kPNzet6tE45XgZ4jubNz6TrXM5XxkuN+K1h/KnL62nS6K6qLOgarmdAqUOXwYuUTkjjyyH7VkVsOHLv8uEwo7yzQ61ZkuTa5hK95KaQWx1Vbzghl99lbSLNLcylMvWkpsivAqkF32+qcztY6kuv2eWaWCezbt2/H3d3duL29ffR7+/btkeSCICO/Mb45VF12jBlapHl4eDh5Py45GlXK1FbVj0G1Kv+M5OKereQ2ne8aja3G5HsSy9dIaiEdUjurf+U0XR4zAusIrfvoUkVyWdj5QU+xRcAKDlMBMufBogRX7QW/XoA6JkI7a19Hrl3dOsd87ilIbccBd2XFSb52uWQ9z9Xnri8fw/+dVVq+n8rmxjR0GDqG/HjJ48wedMitguqqvfQZer/eU/Vpeu6KXz3Xb20hApf0++7eH0XfVZKfTtjIjZ+kY4wLlUh2SS1EyS3Ituoop+H99FPfOcapH5yR2dV6uDpVda3SJpx/zvM7ZLQaH91npWd3bNLqs7bK5o9HuXMd51g5K4hbiuS2DsTqEmRddsxk9u7ubtzc3Ix3796Nu7u7R0QXxJaXIrNS8Szt58+fxxjjCIp19hkAWcE0v+eWZoi0/RKprT4wxdtzyS3SrlzfoojPLa+ZrF5SuiA4na8IrDuX3lHl8/pldP1Im5YJ+oYfr6bA3/mwOHKbxi90kcktO28ltIfD4WQlh4oSZBV2phXBdecqIKF5u+elc5cgtas2Zpf/yIx0qXSCVi4I5fbd/W5WtnoPvfOuuQvS8NJ+/eAaj0NHcNm3J3KrvjO1URqX6me1nxLYdelXxv4lAKfKFoI7S39unq9BzqlzGps8tlOgRs+hLDrWZmSwsjmOmOqPy1vdz6SYdX1GjDt1cM9dSVP5uooQp7arSGOnLit5d8paPeelyEU/HlWlT8qT3qepwLEuU2KH6N7D5Zla/EBmdcsEl4ktOxk4TnwQaoxxsqxQCbCSYdzP5/mvgqr2q0gt38tlUEmE9xypFLab9nvKpUjtayTHHb1012aOU+0Cn1OAOyOy1f9WQ3RVg87SskBP9d3XJM6BgcByHmmpMpdR20lBuj7PrfhYIbkOCGhe+swt5Da1U1dWAcaPKNrWK0GoDqHFtiK2Y/gZ1xmZxdhP/wPt6qr/6Q5dxn38n9NKavnnggGsC45YOt/ZAYodkprSVOmTOJJ+jjyFDv/oenuOVDqcxra7P/nvMeo+r/y7SiK20NtKDx2p1aCQ+jLdd3VxZexK1S4rz3T5bgkyXELXrwWzV/LkH4/qNG5KoyTVEVoX6eWPRensLWZq7+7uxvv378e7d+/G+/fvj8eO2KIsrIQAvgCuSmaRTr+QrO/lOlCdZmYURPCSK5RRwSnkewzMpy7DJQj5GDupPVdW612R2fRT4KskFrp6OJz+VzXbhTG+Eb6Hh4dHs7QcJILdwPUEKBNoYCCtS6bSuTGy7leiDs6RVz5fEdmK2Kb9Drmt9lfqWN1/rn2ZAcCXLiv1r0CsHif/ruByjP4yY0dioXMcsNLgFuoJEntzc/P/s3dmy3EjS5POIimSWrr72NzN+7/e3Mw/3RIXiZyLY178yumRmUAVJZYaYVYGFJbcI8I9MgEcvIiRZJZtUoFitzEpOJzaZ7YdWdWe5BoAAQAASURBVA6W3wF5sh9Lr58pn1+3dKyv1eGlhHYWO8wSrvcuS+1Zj4ymMTIKiFTp9cqTbITbB7+uIrY+2cO0KoJd9f3I98zUbel1p/BzIzy7tA699Cr7MlPGU8ixdmgkq5+xHYkPSB7nVlLN3Fb3JofnBJcvfuIyZM3Q3tzctI8fPx4Q3Jubm4OlyL5+//v373tgyufiKiJb/XzGltEq5VW1Z7VftdkxgHLNgPsdIj6bzMmMrqbrZoisk1r/VW88p977MmSC4rSEiYS0AvBrxreT2YrIzthBz8vTqIhuimD39tN2Zv/UpNblLUkt988dGLvM1KciamnrZNWvqR4h4H5acVF9bo91dZ/r+qwgMgmr9qnfTmoZHK7adca2URKI9DGWCBmvcfC5hsim/l+rN6fQwaVkd6mcox73ZITz0n6PtPTG8ajN/NpkC3hNeg7WZ2u5arEit9TLpDunJLNryO4o/549WSI9X1tdn+xIr1xvJVW7nFJP3+ytyL17lyhQdU2PzMpRykn6i6M4Y3t7e7sntyS2nAFq7YXUcoaUs7ePj4/7Z231u7+/b/f39/tzidy6Mo8GPhVdg8EjzbzWnaNkFgidYsCPFPxXO55TKNWvrsM5yIyzTICTz8o6qVXwiS+Fc4BMByhAzLKkpU1LxMEqj/l/ElzVycuyRioy6+dGJHZJ1Lt3zejY0node81bX/srZA2RmB3fyUePSO0SQpuCU9pKjzmDS58nHf7+/ft+VYUCzh6g8kBVsi+ttVeklvfNtNNMuyZwmchXZTtmiFrPr57K51Z24Jg0lqS19N73rsdJlgSqZvF0NYYqglulmwir4/DkuylptjbhYbZFGs9riGRPjvEPxxBsD3z5/jFlPKWuHyu99E5Jbk82Y7skwiPxwe7XzNyj6xIYTiDYn7EVoXViK8fa2gtIlGHwNyGTyJLYitDqm7ZcepzIrM/a9qKyHsVKbdcz9AQBVR5+7pjIUvXfj20E8fylp8cVwKxAMwFxa61Lav071f6MLfVLQLZ6ycxSSeTWz/l+Irkz4LUq62jmdobY9rYp37Wk9ph2PkU6p07jnGUG0CYdbW3+JVDURT4qkF7wmHSY+sMXvjmhFUju2RjWoRfQ6vnQXvv5fpKKvFb//d5eOfy6peB4ljiP0pm5fyadf5t+jupb9U81hkfHkr9NOuEkVscq3J1sgerHF6jysTxha16TXnTIdlpCIis5dhyvJbVLZca3zqThAbZePr1jo3yWyqnI7ck+9zN77amJSw8Q+3N2fM5WM7e+NFnn+NwsldAJrWZl/Sdi66TWo1L+XEFreUCwfu6wThGRmXF0S5zhWsVYAuxPKaeMFv3OsqSdltoI1+FqFQZ120mtL0l2Qlm9fVg66D+eS/ew/LOR2eRYnOSuFbcjPbK6hNCO6j9Lao9x9G8Fqn9nOcauHUtqR8/R8nvx6Sf99uAUyWsitCn4zTpVJNevWxKEnfXFVZCLaafzM36xKttSn5rSeSugO2Mz/02ytr4jjM0x0Bu3fn70iAH307sv/JEDlUV4Wpj66upqj625CoO+MvnmY9puLfHqHVviM2cCWckGcL/Kb5TWKN+RvHe9PPpzP6eWnlOiuAObIbTVsmQ+U8slyHSYnHm9v79vd3d38VfN2vaWWvibk2frf0oSSEXxtEcKw/tH6S8tzyjf9yLnRoyX9McpyGwFHqtoMR3qSJ97sz2sQyJG/PVWU6Q2Sf9dX2aio7xu1I6z5ZjZ9hxlFfDq/X9vpPa9O99Ty4ydHkkP6CYdloxIbXqWNn1fnt+R5zfk3R/rf5qdXVLXJcHhpMMjkrAmzd752T6uQGxPeoRozTGmuZSI/Jt1d4n0xmN1vBqzidBWQSwPVjHQ7J/arFZd+DtpdJ1WYbT28pgQvxjyswOas+O05+t6439ENtcS8CqA1ruW+VUY4K3lFFj6zV4eleTYwqb7K7DcWnuldIngUvl4r5ymk9lv377tf1+/ft3/nOA+PDx0n6flbG1r+Zm4XjssHawz4gM5kec1hPMUivEziOO5kdO18laGqgfyeDxFjStA7UuQPUJMsJycaWt5ia6cq39qgITPyW2KFq9xstTfygEdG03+mYS2um+poz8GWC85/7vICHDNzAgkmSG17i9Jah38jkitgsoeZCaxpb629vIoQXqx1Ez9+Jtpm9E1x5DZtX018sUVcJ5Jr7pnaYDp1CRkNr2ldT9nSWNvZqxqf5bQVr53tztcfcHn5PnYAYWk1oPQlPSG8xl9X+sDlo6v9H9NEFbXLdH7U+CQNff/DP96LBZ/c2L7MwyLL1lM+9X/itAK8Dqp/fr166utzlfP1fLFUw6WSW59oPpvRioH2XOeo4hNIrh+bTrn54+V905uz50YzwCbNXKsk/Vnexwguz4nR0pgnF5WweCTrvX7XdaQLr82kVtvl9m00v+l297+6P/S/VEdll5zbDDg3yRuq1IAivsz4JfXpscIRqT2+vp6v8/HCVRefpXA/RV/1TN5lVR+a8afVW24Rn9HZHKGfKfr1tgUv+dYWzdz71sErH5X/Z71qdWxRIYr/NxaPUPbWznFRwp89RRf+NbafwNVV1dXe/1lHo5DiUd5fK0cG3w5hU9LOntsoHdJujNl/FWyJu+fOmM706h6KcQxkpxyIrgsV5pB1XO0moXV7KwI7T///LMntenZWoJnAmt/rpYOOYHqJXVODqkisEmS4UgE19uZ143ST+WelXMgt+coyVgvmUVIoK7SQ/78fGuv3xScyK3rNbetvczoUKf4WIGvpHBgnGZwU3tV/b3U2S5t76o8VflmiOwSQtu7fg2pnZGeA9/ktaTACY+PxHW0tde66Y8MVOBXxxPw5XfjtZ9WXfhYcp+Z9LZHbhMOSP+rdvS00n4la3xMz/em69b4Z7/vZ+ncsaT2lHbmd5KZwMvSZ+U9SEVCKz1Oqy6kQ3zPhf7zm/HMn+ViGhXJnZW1Y2TkF9cS2XTO91M+s/VYSnKr9H+mbh2T1zSxPQbovzURoSRQnY6TwKYH1TXryuXFTmz5XG0itf6mt95ztQl49jq257So7Gl/luhWg7oi0SOpwPEx4+Nnj6/fUZZE/Ciz4Hh0vqervvzJQbM7QOnNbrc7ALuJzPp/zuCq/tXP263SvyRVux5DbpcS29GxlObMsZ9Jat+KKP/OMkvORsd7gaykv+nZu/R8rS9pVJm9DiSz1Fudcz1mWRNA1rnk4yoQXdmtWZkFuNX5Ud5rfePSvP3YKM+levdvDGil9pzR3RGRrQJVPVJbzdJyyTHfUaMVFwxQMeDM+vBlUUvHatLHYwMkS+5bSnLdH84GwGaOLU1zlgO8tSyxG0tk1YztMUaU1/ns7LGNnJQ25S+n6N+/0/U6ztlaklk9Y6vlx3xRlL8silFkf54vRaF7SyBnlHKGyI6iNwlgpzItIbhrgP6prl8jv1rhf5bMGvrZthg52CpNOlMntDyffqyL61D61nT1XWnXy15wKZFCP6dyHzP2lwSPRqRyhuT6/pJjs+n12qMXsJu5f0Z+R0BcEZ1TgpjeEmTXW3/ZW3rJTHrhW/WyGfpPDxr7VwZc/1P7qI16QSZe4+2a0vqVUtmJEXl2cZ+uY0vIZc9mLdXn31FXk5y6niNS6yugdGyW1KaZWv6c2Lb2sopK+8pnTd1G7ZWumW3jmetO3V+zdnrWxy+x+RXe7+GFWVlyz0xZZ+3sT1mKnBrOG/4US5BnhOBVIPf5+WV5RHq2luTWXxB1f3+/X7KcSK07XwfOafmxg+ol5KNyyDyWtsqncoyVQ0oK0SPFlfxbiOR7kjWGKvXRmn6bIb4SOr9ZQtva4adB+JkuElkPPFHnfN+J7kg/K30cAcVjA4dV+UZlnwUCo+uWEOZR2kvPH3v9OcmSgMlSgJP0LImDZoJkgmcHxT7zk176Jn1zX5qCUunljGqf3qM9FZFjm+hYjwiO5Gf5t8qHn/r+U4H/jdSOpYfLRnIMqdXxJaT2+vq6XV5etuvr630Ai2Xgd+Md/yZ/OyszZLcnx5LeJT5xrRzjV3Xd7OTVkrIsCZwtKeMx8ibEtmcc3yKS7HlU5/x7tFJe7Tvh5fOz/kmfRGgrR0sH64SW5Z4FoEukR2YTuWW+MxHXY8jsMfIW46eXx+8kSwIla85V1yewXAFjB9hVZFe65DMzPO66SX0laKZe9pyrk95KZojsW8hSctvb7x1Lx491vj05Fnz826Rn33mNpBekXDpbq+P+7Wmfxe19lUD6yK8TVL6Wupxe0ui63JuJ9fEzasNZ+ZXkdun9rZ0WsC5NZ9PhZbK0z3ukVsf9iwSus5yV5f6HDx8OXuxIXaNe+8sc037ywb2xscbXvscATI94zvjVt8THIw7wHnR3FbFNje6NOOM0fKZ2RLgqwJVIYmuHy5DcSSpdrfHnbK2WIYvA6selx/f393FZoz9TWy0/5v5oxuitZHYGN21TPyyZUTpmVmqT30NSNJnSW6bk5FN6LBFRJeitgk96vt7JbcpnadAp2cIl/9fIyAm+BaFdmu4SWVKGY+U9OOW3lso3V9e6nx4Jya0TWb4ltfciR+37agsFnP2xHwfKrsdVOb090jU9v8d0XI7xk35tSquXzkw+ozTXBpKW4ICl55Zcs+S6c5JRoGo0aeFCHXSC64EpztaK4Pqz8R6skt9kENkfJZAPT6uuWLdTyyjNNeN0CRFfmvexfnWW8DpWOKVOLinjWlk9Y5sISlWgyvC58vHBcr9fx7VOX46Lzja95p+EVs7R09J/KZlmYkVgudW5NPPDrb+UKs0KsW18wJ4SGLrQSc/O4LIslbMdkdYqnbUyq6SbnF6qtq/G0Kh//NwItEmP6JBd59NbkKmbidRWJDblr/1RO7FOa0nsUsI7QzJ7NmYtoR2lWx1bSg5OLecMgHuEK9nr2bb266r7PUBFcJyeme+RWj0SlPy2fLJ/Tq96p0Vrr9+mXLVTJUuCAGtkZBdngfXSQHFPv5cQ76XlOoZILLnmd5NZTLb0fgaPfRVGenGjk97q+/G0OQw4MRjlK6j82gobz8rIZ/6Mc281Vqs2WYK5js33V8hs/kcvRU6kyKVn3Eho6dB4nGSWESCmxa0riJYjXl5e7j/4rGNUQDpRRoZJaPnW48fHx306PlPLWVq+0MLJrT9j+9aktic9klsRlWMcVVK8jaCep/SIbmunXc7npNbz85e10YlWpJZpz/xYN9+npBmQKjDUs59r7cCorMcQ2iX3H3NuzXWz8qsd9TGyNNAxCjjO+OnRrG36LFfvhXDKJ700kUBYftdf1pheIkUb4HVZ09/H6F+StwKcxxJSPz8ToD42r7e67neVCodxf4TdKEmnfbUF9ZjLkauVF8TtxL8elNL/0aMEM8GpU8lS/9e75q181ch/j4Jc1f5blXuNHGsjp4ltL6ORAs2QG10jpfjx48f+g80kvJyt1T5/Aq5XV1cHIPbi4qI9Pj621l7eesxoFZ/Fk8P0CHF6rodLjp3U+ixteg7Qt2n/LSTN2nJ/yXY2n1lZO6g3Uvz2UpHW0bl0f++4G2/ZBe5TtzjOuAzZiSz/k9SmZ2srQsvyzYJEbydKdVznZkhzlaf/nyW4a47P5rtWTmUP34Pj/hlS2WkfkxU4IqD1axMgrmZye+VLSw3pxzlbS5/s/rh6nMfru1S8/d7z2KlAatX3M+ktvXap/32r8qy5/lfKUhuvexKp7QknivR/hJuqYJUfY94MFjNARf2tCK1sQgp2vaXM+LRT3rdEluSxFAePeNsp7OgaOQbPL5qx7SnfrGJVaXJZsNLSwBaIvby83B8TqRVp1ZZRot1udzBLKyXjK8h1XM5RjvLh4WE/K9tbykjw7KS2enavN1ub2nkEsk8hqe9mye2atCXHktHkuDdye3pJTpTnWnsdIOE5yZIASVqxQWenY15OBpWc3JLwOpntkVum39NTr+vM+YrQVufYjpUsIZmj/zPn3iupPSdw+zMkkTSOpaSL0kP5V/fTS32/dIjfrmQaOucrqKoXSLmun+qdFalup5jFrOQUvqsit6eWWTvwM2d9f0ep/G6P1Fa6qOvoU5kGCa9kZjzSf8rHMk0PUKWXrqa3misN354CA68lrDPn33K8rtGdmZna96pja23iSd6KPAKofi2FyuQRGgo/4uykV2kQ0Oo6zdJKyfiyKB3nw+vV5wQqB+rP0BI897Y9hU3/Z9ryVINzKcmdyb83Y5DkGCe/ZBzOXPdvJcq9oEFqvx7B9WscSI+2bhvcMSstBra09WhwAr+zhNbL7/uprpJZottLc+nM0RLCeQpCO8rDj88CpyXH/22S2qEHetPxlCZnY3Sf/Kx00meAuE/Aqy19dGsvXyRQWai/8se970+73+X9LMOxsjRofy5jc4aI9q5fmv5b3Dtjb85ZKj88IrJ+bQpYSVwHqcOJ8Dp5ZVpcBeUrIasglT8elJYk93zNWhJanR/5tTV5rJU1ZXlrmcHwv1IWL0WuDOHIUabrJR4RroQztryXRJZLJLSVgujZAHYEHaK/ra1HaN1Jz5BZX56hLdvjVI54VuFH0utTPzcbnZ1VhFlSma7r3euO8D0q5lvK0tmV3ox4RXBnxg1naHRfT/95jc/Waj8R26SDS5YeryG1VdlTW8ykNTO720tj5Lh/Fqmt8l2qg7/asb8HGfVZzx4n/UyA2Ylta22/QkrBZj0yxH0Gnp0M0w96cIqrnqoAc++xgkpPl0pvtjZhoFONx1P4o2PSqOz5zD2STZdPL05Qe3i8p9fUZ8fbu93hI4CJ5PpP2JsBKl1L/SSJ9S31mXo8s4Jq1GYzx4/xjb1rZrDxkkDSErs2m/YII7+lD6/SG00AzMjRS5HdUc6QiUrk/HjP09NTu7rKxZQiSrFS3g8PD/vnbUdvbBstM64IrafDSJM7W49CqRxLFHZpu85eN4oCLnH2vGdpWU8laTxWY7inlKcAG+csXv8e+JkJdFEXuLyRW5enp6d9UIpAmWnqvkr/qHc9Hezp5oxzWTJeUjozAaI1ZDYdG9XjFMeX5LW2XqeSY530e5IKDOtca6/tfPLnJLbSBZJa6R33dQ8Brvy10kkvnZHO+lJk+ef04jdtle5s0Gp2HKUA7s8Yg73yvHU+p9Dl1ubfA7BGfjae+NVS6XPS69G9nPihcPJI1znmrYJd1HnqPYNS1btqEqZWuWf82Oh4dX7p/5n81mDM2Wt6efV0zXHZbJq9Y2uumZFT4O2TLEVu7XXjuYwcqs7xOdnd7uUZ2TRbq2dnW2uvXmQhhbi6uto/f8sZ3dZeHCmVKi0xdoWrCC0d7OjYLLieccIJmIxkySCciQwuAeNv4ZAqZVjSHr8DmD1Wqj6tiEdvppzOlJKWLvbIrUit9MhneZRXIrZLgK7SqRyrb1Ob9Y65rAF9a3RoifN9q7ROZZfeCsyuLe85Ss8Hp2sIWOk/5Zuvrq72wWXtExx7utJx+mPqLolt9eK3FEQe6XpVz+pcL9A703Y9OcbfLAWzp5SlenFKPfqddVLSG4vV9bOBFtdp12fPS/iaE0gpTeozjzmxFan11ZGOuXn/yFdXbTaDiUc2fxZzz1yv82tIJY/36rVG5/2eX+V/Uz7H2K9FS5ErGYFd3t+bxZFD9IiQlO/i4uLgRVBcIuFpUTn8cwO63h1iIrO8bpao+vnetSPHm+o1K0uA+KyMZnX92pTnr5zFXSus87nLUnLS0++k7zxWOV7puc+++jaR2qqsXNbvz9iNHGT6zzaYcSxLZfb+nr4sDeascci9a2bSO5WOz9TrmIj3uUtPX33L60dpphkaCV/OSP/c2uvvytMf68d8dA2XIjuZ5dJj96MVEPY8kh+daYu3mqk9BsT9zHE/awPW1OV30sM1MkuK1uixjxHqtP5TJzSRpP2qnNI54nP3wXwpK0kuz2t/pMcj3zs61rt/1h4sPZ6um+FLa9KelVSGXr6/SjePsYtv+vKoRGaSgqa0XPEEcFurFY9K4UsnfKlE7/rWXpPZU+8r/9Gvau/esbUDcc19s8a1AgWnJIrHKMLs/cfm8R5l1qkuOe+Ot+p/BrJaexknIrB6nq+1F1Kb8tJ2Vrd4zWjf2+hnG/oecJwpyywImL13Sbpv3VZL8nsrYvKrpOcfRuS2tflxRJ+c9EiElj75+fl5T3p1LfW3WrookOuzOP7G4xQ87oHg5E/TdWqXtJ/+z0hq718pa3RgiQ1JuO9U5ejJ76TbSZIez676qfpPekmpVkZST/k5zUqXOTPbC1DNvv+CZfB6zR6baZsluLs6vgQnzl67FIMkDDYrx2CG9yAnW4rs0gO7PaCbrlV0lwNf13Apk7+8QqRWz+a5I3eHSuXy/QSaE1l1R1ulMYoueznT8TXyVoNzxsCew0ztjAF4b0DlGFkLco6dISBYdmLr5JbHRuNsxiH29me2o7qdWioiv0ZGNveYNI65f03Uei3B+DfIEjs2OjZK0wEXvy/v+z7DI6lWT/VewJh8cqX31f9RnVvrB2VH93o+s0TE8xlJD2/N5rNEjrUVp5ZZcnIO4mWvdG4GQ1f/OTYciypQ5Tqs6/ifuqzyMPikbQpQVY8TVL9Uh1GdR/fMjJvZNq7SmtXLWV1dY6PfQipsf+o81qR99FLkalYmkZjZpRR+DRWstZcIMZczaV+KpUiSCHFlHGbIqp9P/1PkuHcuOVg/P2r/pdecAtQulaqPlyyHWyKnUOzZNH6GYr+VzBjNpUtmZtLS2E7LoqTjTmQTAa7ySiDWz8+c8/IvAcKnlgpUr7135tzSe9aADD++BPSfM3D9WZL0bI1t87QqEHp5efnKh/J78wow+5cJlH4Finu+OQHfng34lXq8ViqAvOTeU+R/yjTXyilt1nuSWbJS7ffSm0k7rcaQ/vlbkvW+G5+tZZqzXwpJep10OG29fqnOvXaYaaNZ/znbf6Njo7xONZaXjp2RDTjGRo3SWIM9j56xHWXac65ekdHg4oCngrV2SGwrJ+oRYqZPZzl7bO1///k5r3f1vyfHKsCpnUEv6n3KPE9FMJcq0ykU+2dJaufq2Cnbc5Q+27D6SZLNGDnCpdve/q+WtWU59X1Ljo/ynh1vb9kPTPscdHkkPf+7Ji0CYR7Tj8Fm/dfMDreeRmuvv39ZAV/Pl/+ZXu/8CCy7zMzOpvY6pYx8zCnt9agMvXPHLnk8Vt6TnT6VJD32fV472ud4pk5Lx6THepyAKyK1z68UsDy+YtG3rb3W8RE2XlK30X2jc9WxJfen69cStjV2Z2kgZEleMzagtfXB91Ng6Td7xtYL59eka3tpyxnSgbb2evbW03cnmtLnlkqprZNbP1453aVR5aUKOWq7WVmixMfIqL/XLpt6T8sgztGpzpb5lMDJx7h0lPrqhLanxynN0bEeuF3quI6RpemfEjgeU7fq3jVAgdedOtg1K29hW3+WpLKOwG9vHDnwTcKXviVQypdE+Quj0lcMRqun3F+mfd/OXter/1vJWnt6CuC3NK8l17wnvXlPZZmVqn97hKWXzow9Znp8fMD1+Pn5vysz/LEC3UesXAWoRp/m0rlZfzzy1cde3ztf3X8K3VxS7rV2ZBZP9Y4tuX+tLG3TN/vcj4OTRGp7BroCvmnJBGdvPQ9Gk5aSWz83S3CZRnL4fm3P6fp++j9zbunxpekvkVng+h5mTE5lpN6rzBjxtzDSnr7reI/ULiW31f5I194rYDu2HG95/7HOcM31p5D30rdLZdQXvdnapNszhE7+iv7YwbD8omZ9OAvU2utv1ivd3vsp0r63QXW+d8+vlGPs68/2TafQ7beW91aeWem1bcLSvj+T3mx+tBXVjwGqXhl676xJ2HkJDu6dr/R8SXpVnaprE2fx87PHlvyv0pmRGX35VTrldVpSx11r7TwtwSabbLLJJptssskmm2yyySabtNZef515k0022WSTTTbZZJNNNtlkk03OSDZiu8kmm2yyySabbLLJJptssslZy0ZsN9lkk0022WSTTTbZZJNNNjlr2YjtJptssskmm2yyySabbLLJJmctG7HdZJNNNtlkk0022WSTTTbZ5KxlI7abbLLJJptssskmm2yyySabnLVsxHaTTTbZZJNNNtlkk0022WSTs5aN2G6yySabbLLJJptssskmm2xy1rIR20022WSTTTbZZJNNNtlkk03OWjZiu8kmm2yyySabbLLJJptssslZy0ZsN9lkk0022WSTTTbZZJNNNjlr2YjtJptssskmm2yyySabbLLJJmctG7HdZJNNNtlkk0022WSTTTbZ5KxlI7abbLLJJptssskmm2yyySabnLVsxHaTTTbZZJNNNtlkk0022WSTs5aN2G6yySabbLLJJptssskmm2xy1rIR20022WSTTTbZZJNNNtlkk03OWjZiu8kmm2yyySabbLLJJptssslZy0ZsN9lkk0022WSTTTbZZJNNNjlr2YjtJptssskmm2yyySabbLLJJmctG7HdZJNNNtlkk0022WSTTTbZ5KxlI7abbLLJJptssskmm2yyySabnLVsxHaTTTbZZJNNNtlkk0022WSTs5aN2G6yySabbLLJJptssskmm2xy1rIR20022WSTTTbZZJNNNtlkk03OWjZiu8kmm2yyySabbLLJJptssslZy0ZsN9lkk0022WSTTTbZZJNNNjlr2YjtJptssskmm2yyySabbLLJJmctG7HdZJNNNtlkk0022WSTTTbZ5KxlI7abbLLJJptssskmm2yyySabnLVsxHaTTTbZZJNNNtlkk0022WSTs5aN2G6yySabbLLJJptssskmm2xy1rIR20022WSTTTbZZJNNNtlkk03OWjZiu8kmm2yyySabbLLJJptssslZy0ZsN9lkk0022WSTTTbZZJNNNjlr2YjtJptssskmm2yyySabbLLJJmctG7HdZJNNNtlkk0022WSTTTbZ5KxlI7abbLLJJptssskmm2yyySabnLVsxHaTTTbZZJNNNtlkk0022WSTs5aN2G6yySabbLLJJptssskmm2xy1rIR20022WSTTTbZZJNNNtlkk03OWjZiu8kmm2yyySabbLLJJptssslZy9Xshf/7f//v1lpru90unn9+fo77FN672+32/31b5VPl3cvT79X24uLioAzV/1G+o7xH9yqvnjw9PcVrU9oqC+9JZZwpVyW9+nr+z8/P5TH/P/rN1MP/p3FZpefHRtuqLf7555/cOO9E/tf/+l/7/aSDaZ/XuMy0oe/37l2TjstMuXe73VRao3QrW1aVYUmbprHKMlc2dI2tnLHDs7aO+6n8Pf3u1Xk0XmbLmuqW5O+//x5e86sk+Q2v8/Pz89BG9u4fyYyfrv4nW8NfusbTS/U9RkY2aDROUz1Hx0dtuMYmj+zCqbDX0rq49Pqv1xfp3p78n//zf6av/RXy6dOn1tqcPxjJzPjwdKv9kb2v9NePLS3LjPTGzuw4nMGy/n92XM7Ypp6NmSl/73zv+iXXLpWefR4dH6W32+3at2/fhvdME9uZzFXYBBhHwK4a/LMNvgakzqZ7imsoCZD0BoNfPzIUz8/P7eLi4oDcqn2qe1KZnBynsnq+DAw8PT3t/yej49ek9FL5Oc5mysW0Roo2A6j/DbLGSLJ/fLylsdcbj+m6Xt/10u6B+xni1muLNcB+VIYklS6sSa9qy1G/zZDaUXv0nPasbo7KMsrn3y6V3s2CPO/PGcC6pt17wHipzqS007U9WzYq66lkhJWW3M//S8jtKM9j7N4mbysj37dEqrH/1n07a1NGZHZ0ruf/e3WfDTCk8q3Bkktw0tK03sp2VXIMqZ2VkxFblxkwt8TAVunODsSZdEm4+F/HepJIYHXPDPBce76nPEtIrY5X5HaU9263e0VclY8fc3KbiGsPiFfyFiT2dya4M8a/B4gTqa2CEL2+SwQrnWOZlpLbKv8ltmeJ3Zq5Z4lTWGvwZ2WW0DL/Xjus1dV0fiYS/m8E1b0ZhiX6N5NPT5dmdbEXoEykNo35yucvrV8q/wypnQW5s2Wr6jgrfn/SzVEeSwnCWxPa39nnLpU1421N2tW+/vt9Ka01+Y+OV1hjaZqj62fawe+b7ZfZNL1MqXxL5RhdHQUR3oogr0l3mtjOOIrZQdYzrEsrlAZi+t+TEWnV+V5al5eXw3xmyyPxtvR7vdwioU4aK5lZ1kwyOiseEPCZ20R4fXZZ155CejM+PTL7b3eos0YwAdYe2eylVUkPGPfSW0NuU91G/2e3vTzW2IZe+aq2H6VbtVeSmfrOznqlsoxI7Cl0NAEnP/5vkCXAbQ0xTqB0tlwVMdMxlukYoptsRMon5Z/uqdJI/2dx0YxUbTbCWUsB6xKSc0zwaub+f5u+zsoxQSzJaFy8BdEZjaWldmRp3msJ7cy9Cb9UZJrX9PL19Hv/q2NJZnxsDxsu9dFLbFMlbzZj65IM6xoQ2Et/1oGkGdnq3JJnbU8tvfycbJMw8v6K3M6QdU97ViqCXZHbXlBiLZCtrlvjPP8tBPdYJ1GR2qUEdKks6Z81M7ez5Nb/LwGRM46oV+ZZRzbjrGfJ7az9XuPcvDzc+vElMkvUfydZAmrS8WrMrCWqPZCo81WZe764Gt+e3owNSsCyB2Cr40tmcXx/rV06Bamd6cs1dvGUvvR31NWfKae2pxWmXiNpLC4p31L/yXNMo5ohHhHa2TJ6WpUtPRaXrZFeXY7Baz27Wf1fmtdiYjuq0Ezn9oz4KTpwTYRgKeCZnclcQggpVboVIU0kUn01eqZ3pORL+2RmybEbEBLwJQZiFoT3CO0SMutlO7Wzfq8yGi+VA6iu93tnxljq67eI2K4ln5VdmyUWx9bjFDZUMtKrHqg+Rd4+W7sWhP1bwO9bzGRLRgGRKpg1o/e9PHrkO423qoxVmaqZ1yoo59dWZUzAN7XRUsI5wl3pvp9FapeeXyNriMPvIsfgjLfqiyr9pWO3SnsmMLIEZ1Tl7eUxQ25HfdM7P7Ktx8qavj9mhrU6X9naUXprx+5PmbE9xnCveba11xhpRtbv5bl0LdOfXYK8VHr1rtqLRLb3UiZPZ+35kVRLjtP/HjFKUoGotwB4xxiuc5IZstUDmxUwXRLoOlbW5DWatT0WWM4a8VEeM7LGybxlfrOSyCyPnyrf3wnsrpU1beC2di25TaBxBB55veuUn9d4YZrV7GtVth5pTfVIbZWCMrNBuBlg18NPbnN+BqldQiqWzmYvkX+Dfv8MvHHs5FClozPprL2n+t9axoUj3UqEc0RuK/zas4Oe7kwZZ+syum/p+TX9o+uXYrNjsNFiYjtr+GaiKL3BP/vm4DXPgFZlYJ6jzwG9tfQGwohckNz2rn8rqcoxuqeXFmUpUPD7jpVUpt+F3B4jyQFof1YqJ/GWMgKJaetl9vQSCO9dP/pP4leRvep+zzc539EM1zH98Kv04t8AcpOsGRu96yoy6udG5LZHhqs0ZoJOnpeP6RGRcjtV5T0z67B2rPfIZ88+9OzTMfvp/6ycQu9mfMnP8A2b1FLp5dJAiV9XjfvRhMfsfbP4MY27GRI7IzPp9crWs2lryaenzf1T4IBkS3u2vfo/I7/srci9Y6OXGvnx5+fXz5cmWTJbyzzWvCU5yVLyvaZDnVSuyXeppLZ3ZWdbVi+OSjO4I3kr55bK3/tfHTs3GYFIP95rgwrQLs3/VARrad4JPOt/LxrLexKxrfJO1/bs31uNtR4xeYu2T/VYCxTWnh/1579Fevrfm8GQVOTWpQJkDNpUIM/10u1Tj9gm0NZrA+adAHNVh1kZ4Zs1xPMUZPU96EBvnOnYJq9lDbHsjdkRCVlLcFO6s/4y5Ze2I+I5Q269rBWprYjpKNDYI7Wn8vEjuzSy0aMA45ryjI4do99HEdvEwGeu7Q3e0TOkFB9QPSLHl0ClcqTZWu77/165Uhk9jyRVe46et/XrPJ21z/kukVPMnFckaQYsOACZAWHHyu/qbJeQhGSAZ4hfJaN+qxzAEhn1mYPlBJ6rtHiNk9sZUF3J2nE1Csb07uuR21OS3DV6OQNGTpXeOchMGx5L/nvjQfuUaoxUgCkBvB6gct3UcU/fie2sj+phkLcIKlXgfFSW0fVLCfBSXTgG3CY/UdmsEdg/Zx1+C7t6TFmq472x1CO51T2+v8TPVumN7MFMGn5tD4OkY0vH7YgsUzxgl8o+o89riO4xfncWK59i/E8T2xHRXAtOmE4itTMN6c6xt4w5EVkvi/ZJaqulyGl2eURKkzGv2rci7Clfv+6tDeSpnbu3/yyp9Xu1HRmS6v/S635HSYCR23R9a3npSi8Pv6Zq81MSq9SPXlcfiz0AXY0d2o2Ub69tqN9L9Xl2zKbyLwks6PrkmHsyo0e9PP26Xho9+bfo8kjWEJlqbLTWJ7uS2fE0KqvramsvulMRWtddL3/VHlpRNKpfBY4rWQLwq/8VqO/d+xZyzFiqjqX//0b5FfhjTX6z+N2vmcEYPV/r+60dt1qsh2lm/eAocON16+HeJQS9qtPS+3XtjE9fW5bZ+5fISZci9waetj2n4dcuybe17FCTkvWWJDsQJTit7u+V5/Lycu9Uk8P2NBzQMq1Exv2/rjsGDPfqk/JjeZneKDrO9kzLkdleXo9ZwNCLfM0em83j3GRG1xJwnEkz6d/oHt3XM+7JoVRpzeaZzqUf70tOp3ef57skaFPpc+X8RqQiSc+Jen5Jr5aOjdTnvTKPwNIax/i7AeSe3qxNj1KBtCU672n0/OioXO6nq7Toh5KP4v+UdirzTCC0OtarU6qf7/eO9Y6P8pu9d821M+n0SIDbi3+7/CrcsdTWz15XbZnnTDB0Jt8eN+B+IrT63/ODFfFNeaU6Vn7d0+mVfab+PfEypH5fggurPJYcXyKLiO0sEB4dH4G+XvppJsPv75HHHklNS5B7s7b87wPYiV3vu61My+tafSKnV28ntzOy1Fh5WZbklYB69ZzuEuXpjb3NGfZl1L4zxrNnvEcOccZpHFPupRFO6nX6lvUocON2QrLmk1ZKY0mwauQoq3tcRg57Riq77Meq/V6as8c9z99BloL9Jf3WI1Ku5zN6mu7r5Tur19RT6Wp6RKeyA6lMvTIwryU2ZS257ZWjKne6bin4nS3nzPHqul5QsiIVS/JJebxnWWtff4Ws7Q9KpW+JF/T8v2PwdL389SgQnvRkhlD3+m42cJt8Ncvs5V+C2Y6VNWktveeU5f1ln/uhJODo56v7JASYvXxSvonAcgbZj11eXsb7WjsEvBqEFQH3MlRl9k/3VIbPlWMpuV0rnkfPEKSyuTFJ59YQr2QoTk1yz8lxUkagzZ1F7750buRAqnt7pLZH1GbB30gSYE46SifjtifdK+FyxlG53BlWwR+XY2ftKqKSHHcviJiuq65PpPYU0ekkCXicu/R049RAOY0Lz2fUd/pfAdN03tNw38wf5enpqf348WPvl72M1QzO0nFyinE0Gt9L7djo+FJ9WlKmnj/g+Zlzo7SWlPdc5S3tVGU3qnKk/pjBqMxvdHyJD6v01YlhL0/fzgTbk0/TvTPHXHptPmvX14z5qj+X9OOx+Z5STvbyqNb6Bio5q9ZqUjtaduvSm8lN+ThYdSfZ2guB5dYdaFKop6en/Y/HuCzZiXOqs8/2zrSFK9zMi6N65LcXPEj97UakIqnPz6+XaV9eXrYfP37s+0HtpC0N1Uz0bZMsa8DM7D0zhG0kCSBXYDqVdaac6XwFqF1H05u7Wc70OAOvYV5Jh3ovqKv02XWs54B7BKSqU9WvM+TDr2ceaVXK2n4byQjYzF5/DtKzz2sA10z6vXGSxn6lZ5XuUQf1o0++urra+2q+b0K6Id/SI0lVvnw8RvelGRSfXUl+qqq7n/P2SpLw1cy5UbpVPmuO+fGR3x7ZyZlyn5vu9mz0e5CZ9h8Fu5KMxmZvbOi861Dyab0gFtPxYyNy6/ms7bPKLrP8vT4Y1acKevZ0eFTfUTqnuHatrP6O7RKjNnM+gbe1hrh6eRQdI//TGSZSK+fJLR0qyyVH+uPHj/bjx4+DMsjBXl5eHtSnmhFKhHZptLI3kEk+Z2TJDDCVo/ciHNWbhN/rXJHaCjTw3lM6uNmI5u8qM/o3q6OVE5KMosK9cX3svo/N1g51luNV45PjWr9EUKtyV/m6VM7Pg0Q8nwBTb+weE5yoyj6y/az7SKePKZtkRnffE7BM4rpwanu3NH8e6x3X/yU/DybTD4vQXl1dtaurqwN/8/z8vCe08s1JJ1N+DvB6gF7p9IjtaOx6HmuBZEpjBgjP5jNbxpnytVbb8tlAYC+Pc/bRvQBkJUsJxky6vQmMmTxnCZ+P1TROWObKX/KaWaw4qksvKHysn1jim72sCbskf9pLN5W/CrBUefTKOFuXU8pPXYqsff1PhK43WzGbR+/cElIrJ+kOlD93gCK1lUPQNV6WJR2bQH6l2LPtMhPJUZmXPk/LtEUQmCfbQ+Iz3AQOM+S2R5h611Tn/22kNhkwHT9F2twf9VUapz3A1QN2yRlWBrpaTUL71Vp7tQIjBbx6ZeR/2R6m32sj18W3evygRzqqscI6sKyp7m6zlvbzSGYCfOcqPj5G4KiaHVir21V+I9+TfundFn6e/rj6SZ6entrDw8Oe4H7//n2vn3zD8WgsV+DPZ4KqWZYlurPE5lbYqgKePX0aHeulVV2/lEyMyGyvLaoxeM76/R7LPgo6p+vWkNvkSxiYqnTVddD3q7xcX6qg8JL6pLZgGZlfledIZmxXpRtLfWmFW47xHW8hb05sewPVZfSsra6pZPRylR6p5TGfof3w4UO7vLx8tSWxJantLRd8fj6cwXQQq+t4PM1kttY3HMco3TGy1EmqPdJSa2+bEcFVXZh2khmSOgKGS46fm8z04SkN0iitJQTXx04FGn1szeiOj0MeZ3n8eX3dM6q326OZMtH2VGPedWMks/n2nGnPxqWlx/xVOjwL1Hska0TAfhcdbu3ngvpZwlH1Yfr5Yz8MKIvA3tzctA8fPrSrq6t2fX3dPnz4cOCTRWRb++9S5Kurq72P1nGOQZ8ZTsQ01c/HUJpVqsh6BaxnfWnvvpGuLLHrs/o3k95o9mhEcGfvPUdZi8fW+Ohkb307W85EHNeSW9cJ+TjqbGuvOUPl/+gDZ8a476+VmSDA7LhmerRXCd9ofwbrpnt6Y6CyVzNyijadkTcltj1D6h2SgJBk9nlbzfD1ypKAp9LXDK2e19FPjvPDhw/7fT7P09rLkqcfP360x8fHsgzVIPFj1ayvyzFRpF65jpXUZ55HWhbmRD4pkIyc0nByq2tmZOae3jXn7kSTVONojSGjjJa8+2eeqpnHXhDHDXxyktWsqOdRldHr5IEopUlwzOCNB3KSPUrkluXSlnmnZcg93ajOj9qyAui99uQx1+0lszozY7DXlyNA8buA45GMfMVbBjtHOpr0gWSWfvj29rZdX1/vSS2XIj89PbXv37/v66P/9BksE/NjeXRt1R5uf5wsaJueva8AadV2o/bsbXtp9o6P0hiNo5n0qtm0tQS30vnfSdI4Ozad2fNOeo7Nv9J9vy59WST5SS+rb5UGt8znLSQRxRR0HvVF5Ye97MnPj9JkGrR7yZ6l7Rp5i/b+6UuRR9f5tcc+b+vXVyCyigjLYX748OFgX9ep0+U05Uhbez1A06xrZeD5IqWqvnyp0qg9EqlIxGHkNHRPdd0MsF0LGh0ksCz+fcKR9AjqzP5sPu9dlgLYJdeOAlI+HnxGz/VlBlw6yUpOYLTc151nNb54vMrL76nKnuwR02UaLJ8Tay+bO7U0htO5HvlIx6u+cFEfermTVGB3xpnOAOJe/4zA3nuT91jeEVHq/agHCiLTD+t3c3PTbm5u9v+1ikqBZi1Dfn7+7+ztzc3NK70laPOl0LRNDMbol4C97ye94XVpPPeA9ui6Sj9GtnPmeAWiKxxTHe/l5brb09sq3feoD28hS/tOUrVXtR1J8s1LsEXSfY41paXyOLFlnsmv+D0Vqa3Kdoyk9ktke0REZ3wv69MLYCdb4cd6uGHGByc5ti1nZTGxXTIgZqV6Hm3JMsGZ8wl8JlKbHCijwiK+BOIOLh0M73Zzs7C8V0734uJi/yIqNzROblO/+Gy4O2vmrTRHZeTW80mD3u8hKU1gPJU7pad2daI7I6No2e9KaivQ4des0e8emR05YY1NgsgUDBqlQR1vLb/dXNf7IwAsh7/dPOm10nCn7GVznR3VgeX2dHqkUPrDsi/Vi8qBVs7U24FppLRHBDOd7zlg3utppmN+fOb6c5EKiL5lAGvpfRUZS4FmXzlFInt7e9tub2/3/0WAW2t7Iqt9BZ712Z/dbtceHx8PjrEcHOsakyybrybyuvRAqLdRZSOrtGfvSf9H52avr8qRfEtPp9JYdV3s6W3K+98uS3xua4ftXJHamfZdY2PcX6fgLv0xA1FeF/rmXtC0Ryo9TT/WG5MsR88OJxLr50Zt3dOjVD+/hmlUdqtHtGfG2BIZ9dVS+SkztkkSQOL/Hknya1x6n/vxTnRSKwKrZU6MBovsKorc2n8b/sePH3sAKeXS8zw+GKh83mm9B+MJ8hNQrmZue8GBNGh6M5+pvD1xZfO0vS1E3itg67NXu93u1accvK5LFOPfQGoplQH2Np6RpYS2WkGga5eS20pfpN+7XZ/Ycvk7gyssW9LbCpi7s0065+0/097ePkn4ZnbWpyp3tZ39LZUZJ1YB3l6ePbDhafaOn4t+9+z4z5bZcVCBKNdbrpxikPnm5mbvl0VsfRmyA9zv37+3y8vL/Wxta22fh8gt9Ztlk57pMSdtHZCmdnCfVY3dCgct3Y7S7clsGsmuV7Y+Af5R+scGp85Vl08lawhHImTc+jmK+6xeEGNW953Y6jr9T7Zdx4Uhl6wIcr2fXQXp9ZoNAiQCO7Ofyu7/q3xZNm9/P65fRWxT24ww9xqccMx9b0psq8FREVKXynD7gHdJz9qyM/nWY392hw5UUWG9oEKEl85KyiPnKYAsUqs22O12e6ApxfHBkpY7cdZWUj2rt7QdqzRYrtk3rFZ96uR1yf/WXr+12tuBS791jEo5Y2R6x35XUitJOpocWu/6Sh+X6q0vReY+dSYJ9aW1w+fl/S3naaxKP5mvxuL379/j2FR+0ukKwFLH2K78MV+2RVrW7OXmig6lTXKrGSkHBMmZVX2zRGbvH9keT3OmjD0wPQJgszbjvYmDUR6j9MDaqYFHLz23Ee7v3CfL/4rI8seXR3n/Uv8/fPiwb4/Ly8v28PCw9/8PDw/7x4jYRpr91WNBfKO/ys7gUZIKhFd2siKO1f6oravypPxGafXK0Lu+aoNReWaDUyNdPhdZQqaSzIypKt+Z6/z6ZHOWlpF676soW8urOfV/FED2lZRV+VUHPn7Q07GezRuNzx5ZdHKa7vM8PB0vT/L1/t/7gdc5TpkZo+9B544itmuiGjPSI74zM7m9/P37tO5A/bkdRoT1xkUXH1h0qF5WLg9Mg9JnqtTGHoGicqfyzDgsppEc8yypTeLE2OvcI7Nefico3haKuLO9loLTnjGp/p+rrHFka5wWZYnekiy21l9WpLTcQfrbzf270xwfTgp5XGOW5JBjrLXDT1i5I/ZxzFUJrvscwyw7HQ7T9UCY109p+ix05dB0zh27/6o+q6S33NvbyPtgBOr9vh6wHYGO30WW2r50/9rzS/yx92uaqaUv/vjx46vlxyS1vuJC+yS39P2Pj4+vwLF/e17nrq6uYtCZefrKiAps+r7rVe+akQ5UMrL5PXwwKn9rWcfTdSNiXZV15pjr9znr9lKSOxo/s/f28h0F/kaBsx7J8mtayy+Ydd1j3u7HFah2ouZbnU/P0Kdyez2r/R4ZTe2apEdc/Rzbpmpf3yZ75v3gXOVn6dUx+RxFbJcoXnV9NSORjPcScNzLy18SVS110n8ucyIJ1GyIADCfg3Viu9vt9tdKKuLIPFRnLW2W8nmUuDcIaBi8bUgquRxzRqoZOJJaEtcRuU3l1s/JidJhviK4KZ1jlfGcnWQlHnFt7RAkjIBFa5nQnEJnl5BbjhEfK3x7efXMKuvHJbxyjr6kl3lWBMnL7UEqOgnaE5Ff1YHA3J2Wp6sy8j7qszvzHpnt9dUMeGEb9NplNs0RqHdSW4HddGwE2N6rpLLP1mEpEF4KkGexgYMq98nayieT0ErHJfTDXKnQWp591THqvBPb1vK7HqRj/NFOVIFrAumZcc1js9crH89zpi9mzo/0nDq+NI9U1hlfNApq/c4yo8szbTHT9jzmvjP5ER8rPnara1y4Ys/9YWtt7/ck/F610k/kMvmHSj97ess0fHLFJ6QS5kqEu7IfvqUNTYHwEeZyPOHBPmILL8dSqfDSsde6/LJnbCupOqQHkKvZAnaEp5ucqBwnia4vPZazpPPUh999EGqQJWep8jk4YXkJ7JW3O1kqjhshltsHLIVR5+/fv+/LzOXUzNPLmoT3MILOyFpvCRdBC4MQjNT5ktVUxrUK2Iuw/c5SEYSRjHS20lc3XuzTWXJLQ+5jhSSXeacVA/wUiBNan7HVOKR+KJ2kuwTEXneV5/n55YU3tE96np+BMifp/D7n7OfG3KFVwLly4Oyn1Me9sdPrW7VpAkSpLGnMuk2tgMS5y6gulX+prp05duw9fi11QzOr+iJBmqF1H0D/QoJKcun38OWP0juBYc30EDS2dkiOk/2Q3VD+sidpFpg6VwX3R6C719bVuKgCyL3/vXMpWK58egR3Nv1Eav0/76vOn4us9b8ubjurczPp9MbRbHl9fPfKkNIhqVWw2j+3Sf+tSSC+WNR9QUUevczEziwH6+H66e3U2uHnDJPvT+Wpxi/HPcm+k/kKC7tt8cCB+2KW9RTSs3Wja2fkp3/HVseXOMxqEFVG1e9jnr480b+NJ1JLMEzA6c7THSgjxBx8PpBVFj7X50aa4E9pcAZITl2DNhF5gmDWPymWItn8kL1kZGTp4EfXaeszSay7yuvfFuZMOJd16jhBcc8ojKJ3a2StE/qVcozzdKmITaWv7uien19ezuJBix65pWGnce+RWo13bQlCfSVGWmbIJbW6T3rJKKeXU6Cas6tVsIwzxHRc6TvaKtP379/bw8ND+/DhQ7u7u9tf+/j4uE+Tbc5fIrez5NLrOWPje32aHK+2laSxXBFcJ3znBoApJGFLIuG+X10zc24WPLOPuPVADgPLIroc7xrLj4+PkdRKHKxp/DC4+v3791dE18cGbUg1tklsv3//3h4fH1+9eZm61gOiM4C7185LZQkem+lr1qOns5SlM7wzpPccfTLlrfzzmvOUhJ8qgruE0NL3yz9S9/iWdA9WaSu8Lh3jBArxsgelkr456WNg2ldkpmCuxr+v7FD9RqskE0lOwWkn3lV52M5uj1hH9iVXR6byeDn93Iy4fz5WFhPbtzCoBL7eeDOkdmlZSPLoTEkAW2sHwFIdrJnZ6qURnoaAroTgvXJaNAyMNnEQJoLLvHQ/ibwPXHeiCRxU5dQ5tpPScNBOgu6RJp+1Vp15DYMPNC4EIiInlSKP9meu/11kJkhROakkiWgmfR3ZDtf7RGiTE2C6btwpPk6py05sfZlhcnwpuqqy0ZZxzHPWh/eoHALD1EM5591ud/BtbekF+0qA+uHhoV1fX7f7+/v9f848j/rQ25Xt6/2UHHuy4ewHpsP6sV2ZH9MfOUGSPL/W++N30W+vy1KgMAKfMzrbIzzul/w6D0YJuHLFlBNH/udjPup3zf7qfr4jg8uQHx8f40shXXS+ev6ddkV6eH9/3x4eHvY6rXRY196Kkt6qJl7vUgWLerb8FHZ+SX4u6aWfla3v5VUFs85FEkGXLG3nJbq8pnz0fbTbvTzTuGQ6brulY9QX6mBrh4EqBY/djzP9NDbcvznx5TWOUXtk0nGxE900Y1u1LcvkefTaO/k6YjOfCKj61/1nJaMxVunkqXzym83YVgaPhngUyeuR2hFI1rleA2rraUhJuPxQDssHZGvtlZL5AKdSEcQRqPfAJmextF8RXOVHRes9cyhx5RNocPGBTrDsbeOzQ9znOTphKhmjc4reM7JOMKF7SG7dSPQU5lhlOtZZ/GypnP8SQkvpObNewMrFSY+T21lh1Jf3eVAqBarSDK3uTf+ZR2qHFITz2VaVQW318PBwkB/1gc8a6jjT0Kztw8PDHlgLbHvdaJv8P7eVDZ7pY28TB7C0V8yT6aRgxawTTIEaJ7fV9hxltuzJh/bIaTo2ur4KMPi+rqXv9MClAjetvX6eVvsaJ1y+L19HXZEOiNR6kIjB2dReSstJrsohUqvy08f7zFNafcEyuC8e2WbhDOW71DfNXE/7vYRUzeTNccF8KpKb9Fv7a8vxq6Xq4xHJrcR1feZet5MM2vqkj7b+S0Qo2deEK2nvGVSi7nl9rq6u9l8o0cqohP1IcKmXJLdObIlFlCfxKv2U51Xh4x6p9fu4z/7gde5/e7aadfCAga71F3DRx/fItPdryrc6fwrf+1OesU3kkeday0Ap3TsLkh3Mct8HqwAuoz8kTaOo8NPT00FkmIP8+fllKQ4dL0npqO3YDgmQORjjwOQnjbjlAE5K7IrGdmS7ObDwGS9Gs7VNJILipJZLxUlsVRYtOyEYoiSjtubcqI/OUWYI7RKAMCI8vaCUAzKfrU1l3+1ePw+u6z0wUzkWdxzJQUuSQ2ae/tkvb1PODHGfnxORPSLQV3BHb4b99OnTfrmmHJPKwdkizdZqxogv1eHWdbnSzWP72NuRQIBj0INzDBx6//eAS0Vqz5m4vpXMEtaZ/fRfwv6qwB3HsqfF91poK5G/uL29PZhR1ewtdUx+m75FwJjXpCAt0+abmeWPnORSP+Tf+GJKpqe83VeyXSpxe0Qccyo/VWGzJfcmScFBJx5+La8fEdxzk5kARmv94BxlRG7539OWrU5jS9ez/9x3juyt7D0JMctF8kgSxmtUBmEBnzBSOiNylialfLz56pJqFYe3TQpWjQhtwtkJt3jfSZLOkaT6TLjKz8Aey1WNryQjsluNizQWl8hJiK0XbglZS8f5f4bUpvw4g9na4ayp0k6Dz0lTehmFO0t2tM+QcoDqW3qMLrsx73Ui604wLCfq/ZBmPulI0/O2Ccx7+6ldBCoEMB4fH/fLj30pJZ/bFfDnbBklldmffWZ5aFhSPRJJ8X1v+3N1hjMy0tXKeC3R8dbmZvUoPM5l7K0dvrH74uLiYDWFCCUdsIM/OhNuq3FAO0GdU37eLtI/lY311b1aUcDgjLYC1JyZJKnl22G/fPmyJ7hayUD7wVlbPucnPeVPJFjnObPrgbzUT0v7mHVmmyXd1bW0qQRNPYDvUgG/0fbcZAQUZrd+X7Wf7qvScD2TTkpnOCZ1jwec5Tc4npWHyKL7VQ/syncKBNMvXl9f78e/lu9LF2hv5MNYRydeCVAKM/BFlSLcFbF1e+Xjnsd1rdogkZAleiNJM7Sz+pFWbiS8k1ZyVPmkVTxOan8HHz4C/bpm1max72Z1tiKqEg+8uHASp7Kzfo3f74SW/kBlUHlTWXW/fKw/tuZ4oBpzXhZfdVHN2rJsvUk3J8AM3mkrbOTk1tNgfXw2Wu2S2pT6SluSsJCPl5742Kn+u6whuSebsT3GkDi5mpm9nQFQEo920lkxqkpFIanVNcpXA+Dm5ubgeU9GbQRenbg5YKdSj9rPjY2nlZQ5EUQ50orcVnnqJ8fvL8iQ4aDy0QGR3MrAKCpEZScIYTRdoEDt+vDwEJ+N9vLOkFzet0TOEfzScFeO08HBbD0rh0ljn4AOy6KxQKDjDkzXE/wwYJSIJ7eVE+6Bd0aUvdxeFjkFL4fGOsvDdHlut9vtQbBmaT99+tQ+fvzYPn36tH9bLFeLyN7IpongJicp3RUB1vJl6TUBsTvkWZvsDt6BFVexOIAiIHFiSxBUAfUZ8lrJ7wKMW1umu739Y0hxa+2gX0nYdC3HDkGc/tPnaAyTuLZ2+L1ZHzt8w7jsBX3Mzc3NwSfj5Ou4yoGzt8rDgSyxhe5XmTxQy+flU6A9EWsP0vvzhW63XV84C7NEekTIxYnwCF/wv8rl5DWRrYrgLiF75yYjQuF1Tm3sJKWXlrbU39QnGufp3pQn/Qb9IIX6RVLJ2VFdp7w0iaQ83AbIhri/YvkTUUwTRdJnBs3SrK33WyUMTsl+PDw87PG0MLb+J2zD/CqyTtvlM7XUKWEw9t+sLnu9E6FNY3nkl2fkaGK71Gj4QHQZzQD4LAjTYkOpUzjjwBkIESLNoPgzLikKxdkTKrcGA51WWv+vMlExK2DmnUsj7cZfTsvbpJqx5bIrRpm8nZmeyuQRcymdDIV+AqxcFkInrb7hTJuMGwm52lJAQHUiSa5mfJaQ2qRMIwdyztIzLonULm0L6qmT2so4JgLJmVoJX+tPHU3Gt9f37mQ5huhktV/NIPScV3I0rIt0QfZJeiUAKsD98ePHVz8SW64Qoa1LEV8ntff39+3+/n6fxsPDw0G7EDBX9Z6xH/5fIMDbzO0qQYNH9hnhTjIis0sJ73uWXv2q67mt9tO2d65KWyKd1jjXlqAuvTRK/kb6f3FxsfcLIqytvYzF3iyP/D5J5s3NzUEQWrogIMxZXAl9G0n4w8NDu7u72+sZZ4cVqNJz8nx3BNvLCSsDAbRl9Ku8x3XXbc9S0L2EzPL6kU1g/o5lEnn1+3VNIre/u1RBOxf3jRXJ7PWN9l1XiR8TOXSS1yO1zMtxg5NaJ2FcuUQsKjJL/0ocrrLxcR0vf5qhTVh6t9vtbVGFdTwo7Oely7I7V1dXezuk8jJ45n2SJskcI3l7qp3YpurPNF4SHpzBhRyfvu/p8Z6l8m6+Y5sc6izJdcPpYNidAJ970WD02VqCap3nCylkTDUw0lLfBMLoYDnDpAHkZHpt+7kRSFGmagkFB747P4EKgeJkaARaFD2nwtEBk+C64vnyMDcealPOOPfIjLdrIrS9Nk8OY/T/vYoT1gRwEql1I6TxKkDn4g7NI6yzhlB9l8YjDXyP3Ljh5K9yutpyBnWUVnJWBG0EanyUQCBBzlWgXbO0t7e3r0htIra0NUnfOJMrQlutepBo2ZYDY8mI1CbHzvTcYaYAlxNb9jeBrY9Vnfdx3NP1HkA8F0mAYeb6ar8Cw0lfZvRBsyoCbwxyEsC1dgj0NBblGzjbyZVSDlzpd1tr+4Cr/AtnZZnWhw8fDlYzaJ/A8/HxcV+O1tpex+7u7tq3b9/2WOPDhw/t9vb2YLZWvi2tnPrw4cOrFSYezPEgltLxZdoEqOwHpbN2rFcTFD4megFNXSd/XgURK/I60uffXdbYqko/Kzvg+Tme5mwi8ZzGHbEd86c/9L4mbiC2TKsNHa/zWrcfwqb6PTw87K/lozgaU7QF1FtiaPpQn7H1gDmP63rVn/osUqt6avJImLmasa0m5RzzVNidvEX3pnGTxlMaK/RFvl9hS0+7OlfJuyC2SZnYEdW+rqMSsBM4O8jBz8GrPKuOEriSAvpA0axKWjKr66RIKot++i+FZKSoApDeuSkCS+VPjj5Fn1RPH+xsGxoId8Y0TlSORGxdOX0Ji86T0PK5YOXDF4uwfxOx9Z9krUNM4+XcpKo7DU6PDCSD5efUN2mpS2tjEsSy0jk9Pj4enNd/OtVUL5YpGfsUJElji2kmJ0ab5PfSBjEoQGB9dXW1X3L8+fPn9vnz54NZHjnWFDFmm0lnHQBzGb8k6aufq8aB299kU3uRap1jANDJrdpX7cf+URpptsD/zxLcc5QEAggseU3aTySV53o/XucAk3rloJjPy/Kc+zY9Y55mYd13+c/LQBD54cOHAx+ke0Rq7+/v93p2d3e3fymbzkkXOWOrWVsRcemsZmsdGLOdKLTDaaZYgQDpDF+kpXrKXzsx9j4m0XWpCGwSpltNRnj9nFwzqMHx6+TWy5jOnYOOz4D3nk/T+REOSXo7CuxWpKW1F0xI38Ll9z6mKttR1Y24IQWrEmlkYIpfAvBH6PS9dxJct0VKlxNCidS6DqeAPjmDl9UxrM8qJ/sq7uA20/04gwqpjD4GVGdOEDrZ9bE3g98Spkr+mfeslV9KbBPwSR1IsOgRj/RjY3nkQ+c4m+HG0CMpKg/zlePz2U8un/XO0wwmHTCfCXQD0iOxlaTI1iy5JXl0MMD2ZGQ4lU/1oHHjlkpCZ0XnxbKlGWUFBZLDnSG3qX1dRk7iXCWB3nSe49fv0XVpnzICua3laGIimHScWjp7f39/AOB2u8NP/CSDWTl3tzWSmRlbXT9aAaJ6SLiv+6+urvbP1H758uXV87R8Rt51PQFHOj3Zw2QL0wumvL6J3Hq/ux1LZJc2IJXZbYDsAJ0rl6T2Zmt/ZxnV1X3QKA3+d5DrRKUCyO6PfYkb+4vL4znmvDzJx7MszIP6WJVT44arimQzNPY4G8tA0OXl5Z7gaqt71A+qn2aEucKCRJj+OOkCxQOyDNzzLepJGJhyMEm//fz88gyurhn5wWQLeuPG+5Z5J4LrNocENvmvcxbvm1Om633iOusYNBEf7z/XYfkW6jRtcyJa3r+eJycz3Ndx+THxpbA19ZsvRZQf9c99+TP1ytfxsiZd3L4l+8RjXnb9V53VnvTtVX/qWscXidsQW7MviaFZ5t74Y1nT2OLYcP/jx3pB5oQzZvXipMR2ZABn7+8B4Wp5EQcLO0dKJyBMB6LIK5/zZD4khbrHyZ8TQ4Kw1g6dh5fdn09wSSDa29mdQKVYKdLFNqsIbiK2PsuTIuwqG2dp6bS49Xur/iVgceDSI7BsTyc8vXE449S9D85NKgPDYz3jov7jmNBxN5gO4CogzPFJYbSVUVIRXL6V22dXZ8Cyk1uJxm4aO+p7Elo6C211r0dSpRu6ls/gffz4cT9TS1DsuukApEduuSRR/6+vr/dtKmevPNIstYNPtpMDI7ZRsu+elvLqEVsHSSwTQa/L7waCXVyPW1u3FFn/2W8OfJOPqYCn+xMJZ3u4iioFmnUfQa7q57rsJNEBOcHy09PTHvxKNzj2pA9phubq6upg9vbh4eEAP/DFb3yLuc/Usr29rG6L2Mcquy8zTr5PadEOsX8TqUzjQjIKbCm/ZJ98bDLfRHQ83ZkxXbXBucgM0KcswSr+q4JFHqTlViJfm963oiX4HiQlzlaZUjm575MbxOLuP+QnuFRXukJO4JNSeiN6skPM0+1ZsjeprVmHtIRZuq72InYgcVV5uMzbA+f08SmYkOz57JhJfcT/xJCURGpHPmutz54mtr0KHSOJ1LlDdaflpFLRT0ZRlIaiSFoy9O3bt3ZxcdHu7u72A8yXHjDfRPa078t4nfi29prYckly5XjTjKO3i/eHOw+C/h6A75F0Kq0GmJ5/ktBwJCKenrHY7XYHEeZkRNLMsRsxHzfebn5dRXjTdT0H/1a68LMkGQ43KimS5kamMmBOaAkWd7vdPsrqzsH1ijoigy1dToEa5cNoZRXooLPx4ElylkqX7UN9ZN2qdJgegywqu8AwZ3j0FmR+2ofp+lh0e0LnqH21Jdu4t2LDyT6BsPe3g1qWMTl+n0XSdU5s9ebn1to+ECnbonuqWVu2+4ycCyCubM8IDMyCYKXlACiB4YrQcnaSNkD9J1DMWR8P+vj4YD1IyKo6Vf5A5ZEuXF9fH9TZx5+2fIsyiS1nEXWfAlIKSqVn4j0ApLIlkOz1GtWd99LHcoyMZnKrNFmO1LZuOyr/nexJIrsjSdecC8FN7bkE6M/oc9Jl6jF9mJNHXzlHHZZfFpl1UR84ub24eHm0rqqTxk4Pt3qwhESW2JQrM75//34QtCKpHQXYkn+v7JTrsJfd69Fa2wcH1MZJH4mfaQelNwzUOYdwvUx2RUKf6nVyDJR0u0dmfct703hYosdHzdguBfUOjvwcHafP7CTy5Q9z+4sYRMS0lt7J38XFf2dt+SZSd6Zp8PlxV34SW0ZIHSyyvmlwOSB3QOL7DvQTQPWBnQwFwYkTW+Xp3+TVlrPfLJsbCQUUmD7HgpedBtkHuCt/ArfuuFNb9kDx7ypOapNBam1MaqtggIPg0YoHf3aUZIxLA332h86SS5IZyXQ9ch2gU3egIbtQEWQHAg7kWxu/CV71EhD2t6c6ICYgTOQ95Ue7RjvGfdqz3oxtZYuSTfNjAquXl4fPBepcGiOKxKsMisCPCO1aOQdA7FKBgp702s7BcNIbJ7PUZc5QOrHxl5kxyDwqFwGrruVYIIjt+VGNe+m33o3R2ms/pLEo3MHvRKsukp5NSzbGy1W1fdU/ng6Jol9PQvz8nF/YpOPsB7a9p0lJQa2rq6tX55S2E1rurxnP5yiVD9a5FDw4Nj/iTyez/ks6LLLKT+e01g7wYOpvJ7OJOHkZ9b/Cq5RkB1o7nPnUBJNeYMfHcEhsnRTSf/kY9/1eu/PnxFbHxVtYj1QeEtpUngoXsG0l1G3eU/WRt4v3NXFiT49PreeLiO1ShVqjgO5wPHJPR9EDf629gGE9A6NjJGMEaWnQOKmiI68cvd/X2utlwjrmafszd+6gPDjAvFi2qkweMBhFwhjZ8T6VgZDhS29q6w1WRcsZAfSyVQ6d7cP9SvFpjPlj38+M1yVG7JykR2STY62CLS7JGXHMyGFyRkP/aez5bK36lLM8clRcwqPrVE7v85HuSBKpJQBL+sMVHLRlDlKZp9u39JIo2bYKzLKODkBTsExppUCi6yDblbOlEnfMOlYBAoFXd64sR1qK3Nrh90rdRhAgbzKOgFf662DF9SaRWgaY+Tkb+mXlLZ1m2ixvKrP3K0Erl+LpP5/zq1YVEWAq2JLaQCutSG59hof2sLeya+TXvB+oM1U/qV1dx0lYiXmczPJYAscSlSHpGOvGgJnbVL/HZ5O9fkt0urJz71kcg/T2071L89KWxCr5Zn9ZEomt/C/HQ5pFlTDgknBsGucVxnZ/S3yfglx+XHjBcYS/0LQ3jkbjcTT+Kvt6cXGxn1GWPdMb0itcTfuUHhf08TUTWHP70Su/18XTdCzpW57z+9N2JKtnbE8F6lODeESBSqelPFyqp30SWw0IAmE+B0CnI/HlB648SQETSE7LI5IyLnFuLKNHqBJA90gz83OyW5FuB+KshxtCvsVNAIFlJaFgu3lk1stEwJ2URttqyYTq4iS2ct6pP9IYHYHBc5fKmSZDlIxNAjEMUvHbkQxQ+XNo1N37+/vW2svjBWk1gnS/tcPnLpODch2g/iR7VDlmn/FUev6CGEbFfXynCPnl5eX+281aceLlow6wfOofCYG+z2ZRkv4pPc2EK5DQA1mJ5Hq7qm9dv1OQTWOBICrZLJ+9XQpslzrQc5IELmYl+TkPPsg3M0hFXWbf+RcA/DlRSQJVuoektrV28PUBB6rV9+p74JPjLfk9vk3Z9akC415H2pbZfqEfS0DWgX1rbb+0UfezvekPeYxtm8rkBNeDYMkmsv7CaPovnU4zuKx7da6Sc9HlUb/3xsZS3MF+8jFNHU6rIVUW4Wntp/6lrlJn3e8mbOU8wP2f41y2E8eSl0c/4lTN3Oo/V3CSIKd0nDf0+o32xrfuK1trkYCzLYSxSWpTwJjtkLC00qGuVPjA73VMn+qs88fo4pJ7Fz9jO6tAqbL634vcJfJ1eXl5AIT1MgZ941HPoGm2p7X/dgo/AUKy6SCWBIwkyMs0U89EEkUI/Vr/sX1GRjvl52RWbce6JlLr5aj6RwMzOWyCcnf2KX06VhqgVJ7U9h6N4zGSWDpu7xPvw6pvZ2SpY/mVMmtgkiPlfTyfZt4SQZEui8xKd/mNVg9QyYFKp0dBoRnw4+MxBYRSe3BGmPWUrlEXfKWJz+R6u6Trk84m4KBy8DjLTQLhbz9ODoy2katIGIRi3ryP7cv2ccDS6xvaK854KV+3Z5vUkqLgyQ5SKpLlOpNWUekZcRFdXaM0BRppOxxEpjdyS/x6J3DSIy0T1ovR+D1mJ9naOmh1otray0veUrndr7D9RuPU23tEcgmCZZekswlAS48ZmOoRXIF/B7tepmqG23GG62oC0zzHNM+FnK6VU+CHmTQStqIua9KIKyGFqxVc5biRT6Y9cZ3y1Qxuux3zsS4Jr7r/VHoSDxLRb7T2ermtykedoG3xl1Alou7p6r/7SpUlzQY7biUu0ZZ2RyKySwxGPe1hem+vGekR2+oa5aNzS/TZscyMrFqKvJTk+v3pXj+nH8GNIkf+fUfN2krxOBjdkKYIBMFYck4uyWl4mj0DUrXjmvZk2r70R2AwkfrKkKR6JmDEtnMjJUdL0up5MF/vp4q0VG2e+oN1ZXSOaTtwSPnOKO05kVrJbJlHwJeBhJ4u0DDzBUkis/q0DV+StNvt9rOzPvtP5+CR4NZef66nV66qrA4yK0KQnBDHnxPWRHydECfHzzJ6uZwMpOgyP3eQyK3EgYRIgfc9HSn1jul4u/q482PJqXuwQWPBwfPPigb/LtIjThWp5b7a3Vca8GVJXHnR2gu54jjkeOTS3orY6n7OrrDMIrJ6Ri197s+JLYG4g3HW2QG0g+RememjHGR7UIogmLpFnUu6xqBPqgPTEZj3ALSXjccScHapgukekKQdcSDOevXySm086of3KG6/loJ/3teTFHghqfXJIwaoNPZFZqV37lP8mVWNwSrQ4X4uYVXfd7xJrCc7oAkt+sLW2kEwhfpGXXBym8g6A76JfKkM2k/pXVxcvHoha8IkCWPLFrBfaN8cEyUfTUk4qReoGx1PdsfL0cOOawitZPWM7UiB/LoUMeAATvd7VNiXIJPYOhjWssXkrNSBPugTMKzK3quz9p08pnaZIVKpzdiuVSSLv2QQegO8GsA9p8G+TPuVIfA0EungfU5skjNzYivDyl8F7CpF3eRFEkjj8STUZRLbL1++7HVZASrp5sPDw8FMg4CsO86evsomuONzIuv6kkAsHQZBpjvmFFwisa9IruffcxoOqr1M+k/w6C/sUfvyrfBqMzrR1trBjJecchVsSvbf61KR0dQvnLHlow89G7tWekHL30V6gKK1bOtpWwm2GKDx58HZRxyD/ETOw8PDwUsce28lpfhMKYEcZ2z1+BFJGYkgdYI6wnZS/j7ePNimfS97akuf7aFt4T7bnOS2Cj4IuKfZW13DPuY+Z5mUjpNO+VKVm/3jYyMRXOar9NMySifl3ia/k3hQobpmZJOW2j7aePllPhb08ePH/Yyt2p8vW6Xu8GVq9M2ttVfjwbcVLnVS6z+/r7JZvk9x/EhCTHsgDLvb7Q4CvdJDjs9Kr0iIWTc9KsF7U2CtIvvqS39HT9IZ2o0qUKbr/N6lfMXT430z/nUtuT1qKfIsue2lMRIqXZrpUVTJl0loy+VI/lkfAk8BJie3HLQeVZ2td6p/Ir5pS4XxQV0R1nSOhiUJ6ywioONOdGXcEvhQu/bGSQLkI2EZEpmtHLj6tkdsU1+k/kh1OUcCzHbk/zXGicdpkBMoTQ6Uqy+08kIG3h2oiBh/GocSAU3qDZ2KRzXdQUhv+Eyg6uROwqOsdLDJ+ablxiLxHpCivek5bAf2PoY9SsxZW9lEj6wrT7UH+5vRd9e/JKwDyzayn27L9COpTv23RH5n4kqZARF+3sdZClw4OGXAhqS2tcNvUEt37+7u9qRWuqxHDjiuaZ/oW2j36bd3u/++UZT+nff4Cwv9WdzkS6jrKeDkvon6kXxXavdkM0nWk49KQbwE+tN1tIdOaj1f2hse4+xXGhMaC8QgLIM/G1j5JJahN55nr3tv0qvnTJ1Gtq8ie8SSadZWK6jYbxofidBqX/7E7XTPP/K8+8B0TYWh2UaVfqUgma6X3qkOStNxsGyF+zX+XJ96ollj2iYntyq719sDXafwhb1x1sPES/JL47n3f1aff8pSZDoWH1Cevg9kDXJf5sSfHGprba9Ucpj88VtznElp7ZDYCUzz0zWuPKldvH4jA+QDs3Je6Tzby8krZ4s8UsoyODB10E9FcfHlWyly7u3gxjVdT0fpkWl/1o8/J7fJofecaDKO3l+/i9Bh8pi21Rj0e1JwgRHANOOgcSldZnDq48ePe6LHJYtymPf39+3u7q7d3d3tZ3joQPWMDGcqOKPLGcfW2ivd8edfnbgS7Dk4oN444aqcM/XVf1UQKjm7NG55LX/+5kd/0Q51wkktI9ne5768y8dbsplpfNHWettoqZhe9KFj6duJXg4fq9U1v5vMgGIHzzqm7SgI0QvGSGcUQKFf/vbt2/6rBWmmJwFw92VpjLNcd3d3B+NM4JFjmz5stHrBiVk1tqt2dr1h+zpAZjCNNsQJT+WjqENeH/l6D/4xXyfmJLI+iytx26YfX5zHcnjbsEysg5MKv87rrTTPRbxuI3K7NO3ePRzf/vIoPkogTEx8fXd31759+7b3ydJhYbCkt4lHuD/kff7YScVDiDt0Pe2Y+zMKxzv1mt+L5SMPjjF9ssbH5eXl5QGfoH3Tf+EXYgb6bddHlt1Ju8/cunAs6d6K7LOPPO/efSM9rOzkKWTRjO0asO+DkfdX6dE5+nKny8vLg1eR+9sW9fZUAWAqnAbHbrc7GEQSn9UQwGbHO1j1snukuRcp67WRtzn/U+lZrkRuE2mjYlFxdK5Hst0xcykjgYUDE0bEPB2KO2yCAZXDgQwVP0W41UYC5XTOqa2rcVkRjaWO5j1IKvPssdZa7B+eS/vSZ0aG9dNyJy7HERCW8/z69Wv79u3bwQyP+rO1bGBJ3pSujH6PZFLXCfZE8FK7pKWx7nR5POlYutd1hqS/B3YqPeGKFs6Ie3pO7Kk3HkjqBbYqcaDfI7YMNpCcuD2u8ukdqwjdOYjbZj+eggwjcXtbBQWUXvITAkD0qSKx8sl3d3f7Y9VzeRLqqT9Txzx5vb6IwLHBIBj9ib8gJuVNHVD7cIWI9wN1IZFaJ+Oel9pUfsv9IvXERUBd+uKgXj8nk05weZ371qQjjpHYZ2wv1zkn1EprBOJVnp6+noN/rrBGCmBUOJJStUelvwzo+qME6hOumiK+1k8+ubUXf+HB3KqexPrcZxkdz3n9te/4jmPKxduZQaTW2sFjL621/T7xgLev67XSo98V/3AM7itddD19da8fR+L1c1zdE+qvtx/L5Dq6dIymc0v98Spi60Jiyf9+HwdCNSPh9znAodIpH3U+HScjSQTBu91uD56VNgeRBpg+Os3nbdSx/gyciwOBChgkpeR+anNXcgfhKUrmdSRAZh7Pz89dcM77CVj82ahqBpcKT4Du4MQJE52srvFZJ6XnwJzLWhWokGFS3qmeM6T2XMX70+vd63/e40Y2EZVk4Bio4tJFpUldFpnVlrO1BG7X19evxjJnYZQn+z/NJtDeeP1p39wJuD2r7OVMAKACM2zX9M4AzyeBaAfwTI+647akIqGeDsFp5YxSMItlpr3nOCFgod3zCH96lj/Z4Gr/XCTZKNdNv25EdHl/5bt6oNnHE/WZszwktvpxKbAHOtTPFB+XHI8cw9LX5+fn/bNsDAz7vawr9YB2g4C/5xt6eujB2NYOl/oykKOy+ljnMe8Hlp/9pjInokyC62S3IrwcS05miZc4CcF2Yx0rf6z/7JdqjFf3vGcZld/rPro/1Tn1WWs5wEt9I9Yjkf327dur2VqV1SdYErH1/tMYUdDJ708+tepbjmXas167VVhIuIFppkmjKtBLfWRf+gSar7DU9bzWsfWsOMamro1smLdTa3kSgdekoFSvvKfW08XP2LaWn+vg8UQQOBASwfWBwl91jQaMOv3h4WEPggmENatI401D6xFdDjI+56d7PGrs7eED2Qd51bauOGlmKLWn/jvQ877wsvkx5tkD5gQsjCZ5xN2VkeCXZNcNNgMJKUrM9Hw5NMmH+oll55vvdMzbNrV3T87BcVJIlnyc9EjtDIDT+GG/ctx7wMWdnZPar1+/tr///rv9/fff7evXr3sw7P2cnJ/GIF/44E4ukewUDHJHSXKbSK1LIoMOxN0Rs+0IhjX75UCces20CaArZ8ufnHllK7xMvbx0D+9n2zng13kntfrpOgKgyl71iJgTNS/juUoCwEsADCURW29THw8CYTovHeSztALCClBxCXIK2HA8EhOkfnJ/QnL29PT06tvSvM/rxTElUpsCY5WvZXrUi/QYD4Vk3ANutEWOXRyHSIhREol1u5PsmxNc1Y1ld5tO28y36rJc0ncHwgxQuW2spALTS8b8r5LUd07ie/XoET63BxWJcP/pOk1CK9/89evXg8f82O98vMfHaiqn9NzvS7bdfV2lgxwTI/LEevt9Ka1E2hLeZdrUN245M8zJJfYD8TQD0U503Z6kNiO5HbUH27LiBeRj3l5VepTKR6+VRc/YtpZnWnuGnfc4eE73jK7jANFa/9b+6zju7+/bP//8c0BsNVvL5Yd0bG5wBc4FnPwFNbqHD3n7APQB5EC2R269HVzxScx1LBH/BM5ZHgJXtWXlqN1IJrBNwJ1mb3ldFXli2avlnuz76tXyrAeNhMrMGdvKCPzOkhxlpXMzxiiBXidwqQwcXyK0T09Pe1IrQiud5mwtDWj6RE5rh8T28vLyYGxyzPnMQjLgThicoM20iwNjXzFRATLqGuvAMc/VD66jTmh79se3WvGQ6iU7SX1Wm3DGLhFHJ9aeBt8erWXrarfn5+f9J1xGAKgiZ4nUeru8d4JbESkntL6/RHrtx3Ggn2ZttCSRs7V8YZR+TmoTseWjBj6WHWSqPOxTpfX4+FgS20pkE7hMUPvud3t94mPc65sAfgoC9n7e30qLQFOSSO0MwWVdku1SWWiT3T63drj6JQU4iQUS8R+RXKbz3vW4tX5QfUZ3q3Hn+yOsLqFPke6I0LpP1gvfWjsMFPsjJK5vHkwiKU7jomfbUzv5uOeYqYhURQ590idJsomu10qLP7cpvmrSfTkxO3Fs5WOr8e96TXGMyP5LHJA2wbF7CgC8tUwTW0b8uJ29hw3k+9xK3GG54+KshYDO3d3dK4UTAFQaHFD6RJAvkeGMrQAxibG/+VHiA1BCB8brZgmu73v7VW1FcQJJx+Wktqe4qZ4ENSQoBN/cuoJSCGIUwUogn2DIl0LLQDjoVd8SILNeXtekvL+D9JxoGkczup4Ar/ezG16OHwLcu7u7vfPU1l8YJaEDTY8oKBJKJyJHIiHoImCtQJE7PwFEthWdio+5RGo9fSfoGvN8yY4ABR+XcODvy/05rhMod1vi9WLdSGp9hqW1dtDGLAOFMwICAupPPoctkqu6ppUzPVlCas9Rz1OwqrVloDiBMJek4+o/Jy067i+NSp8FSUQvjUeWw+vAceB+RkuQ/dGHyufpeLIdDJq6D9a9FNoJn3lJXxdgfrvdbr8yIS3vlF1hfdQeTm5ToEO2RmVwgkvAzWuSbdRxX4ZMG53wHNNUvdx2VOOxCi6yDWb816+WXhkrMlaRB5fefX4dSZn75Lu7u4OVkFo9JbtNHUk+2SdaKmI7IrX0v/K9Po7SeO+1z8gn9CQR2jRry3okcttbts02o4+n7WBeSyZsEolPpJa2jjaOAQPZIvIlpr+W5C7V5aM+99Pa+C3HvJ5LZAncOBhTZKe1wyU9cghKR0qomVqBYD7MTkdJ0sNndtV4cj4OhJWGR6KSsrKjCT4roJ/aetSuldP3dutF7jyoUIFEVxgqli+RIKH1GV1GrNleLJPS9nZnG6cZW/V1WlqqfLmcsWfMeuTP2+QcpQqgjAIkvTRaOzS+aaaQ5E5jhxFfRYa5/DgtQW7t9SeEqud1fXZFxtYdqgNV1oll93Hb2uHSHpFWnxUhofW02Xb+iRK2FZdvOjFIwYVUVjoq/Xf766Df+9pJO98eSdIunfTxwbJJN5WGPgdFYntzc3MA5B8fHyOxTW1bBSfOndRWOrwEAIzAsdtkH1cc0xzvrR1+4kfjlp/pSn5CIIlELQE+H5e8NhFIzvBXIDJhE5VDY5zBUgV03Jcm8iZdcULruut6yGXPTkapv9z2bDvLUxHaRBCk58RICQuS9LO9iZeod05meqtY9H9GRwmEz0Gne3pYYRJvox5h5X6Fs6krXNWmZcjyy71H/BK5ZUCG4kFX1cHtOgPVidxRd3p9nYIgPX+ZcMvIDqodk26rfj1y67avIrfMx4m1+7WEAXpCG64ye3nZnt6uPOZj1/v6rXRz1TO2rdVGNF3vBp8EN0VO/X9rr5fjCUC11g4iwnwTcrXMh+SWBlh1IhjWgOPyRSos60nCpk7zKA7L5D/V0zvbnfeMVIPZ03dFq4hyUhJX6orI+jESn0RU9JPzdWLrpJbfTnOnmr4LxjfbVeC/qj8lKes5SA/ALiG2M9fQ2PoyPAHe1l6ihA8PD+3r16/72Vquvqhmc5zcctbWdVlOmysokt1h+RP5THqk9LjUWHUTafO24fhTHdyxcMxrrKdlnG5XdC/LyOi2z6A4iaCTdVvMMnPFB+v5/PwSnPKyMB3aht1utyez+o7i7e1tu7m5Oei3h4eHA5KiH/PvtXc1Vs9RfLwS1M/e1zvvPoG2m0Hm1lq0tf6Ny+plg6214RJcH5/V2GytHeiG9J6f+avyUnoaTySX2vZIcUUknWwz0FsF7HQtP2WmdlIdpcckmyPSQzJQEVrdp+NeRteXRGzcLnt5PNg343OSsP1p15cEeX6l9Oo/wtiVz3Z8wv5KgVOOy7u7u/1qOWFrzdaK2PIRPfpP1w1hbebpmE5l6+m214UrhXSd+04KcSvz7RFYYnpiGA/ea0t8q7asMH1qr8quJPEyE2fx+FohHnI+RH1jHqmeSSd5virjkmtdjn7GttfwldF34poiFT44Ce70HI++XyjAJwDsL1ah+GByI9xa2wMolo8ALc0UqHytvX6zbyJ9BKIJKK8R3a9B5JEuV1j2k9pmhthq6wru5LWqN5Vd6ThokFGVc/d2JlhS///48ePgszFaaq466V6ftWVEPNVZbZOO69w5geJTEVvXa4/oUWhsOYvD/v3x48f++5Z8pMBnJZk/800rKVyXOTNAvUvj3sconRzJGtvB20f1cqHeux55G6rNlhBbLxuJa2svwNjbj9F1BgjozFKbOHF3G+Nt4LNXCkzd3t7uZ2j1bWP9Li4u9vX2IAb7998iS0Bw77oRcKY44KNtJTHyAJbGqvrZ9dn7sAqy+HH1t5MyBjo4Tt3HpH0GVunHE/j0sktYLrWbB/b4vD+DYPzxE2jeL9728pnJlrNMEpaNAJTtSFLrINWDwewffpLR30lCnMJ2dPwhO83yL5l5OicZ+dxK1/0+tiW3jt/cXvqkEX2yiC1nan02rgpCVc/YciwR37k+uk+pMKMHldkWqp+3A3/05+6XneC6PeGWGMcncVL798it6lD1JcurOjrhpv5UeNbF8/QARZrQ4z6xdMLGM4SVx3x/RlY9YztqFBagIrUelVBjcakPly4QBEmxVGECPjlOj4I6mWZdCOSUpjsElY0gmIY6RUsdtPaeJ6JSLJEeYXUldiJN8bInkO9bV6QRueWLRVzp1M7qC3fUbFPOAhDkPz8/7wktn8uTaPauAkZe36oNkoE5J1lKbHv67oav55ioA+o7EdfW2n6prZ7l8Q++syyVMyCo1XUO4ne73cEznR4QkiTnR4CagJ3Gba+9mZ6uT8uvaFsSsXWiwLKyrgQwaZ/2z2dB2a4u0u903iPYqR1Ur6enp32QUoT206dP7dOnT3tSe3t7uy/vjx//fear6n/1O22H63PS3eRg37tU5DPVOd1T7ffSdmLb2uHLZnSdr6xJQZjWcmCs93Og7MBWwSw9k02gV/WvB6MZ8Ep+OQVEKzvptsNnq0nwCSCrII2ucZ+scxX28jI6KR5hD28T1zEHv/5Gc5VD+XjAceSX1ujmueh01T8z1zquTYEWSTVWWmsHqy+kywxC83vTTh4dXydy5H2cfEcPfzqplC/wa32ceRoVZnZ8XGFb2jAntR5g5jWVb2Z7pck951ReJw+eqS48nohkSi/1JwNsTmxTkMoDZZ6v77uk65bq8UmfsU0D1JXMOy45rWp9vYBQa4cfgKezcKeZQK0roR+rQI87IHYulcOP+8wGnS2NhBPRJBpAfr1H0B1kUGEZnfI6zogbBXfa3PeZ2hRVYt7+TJHIqAMoPavFF5LohTN6Ns/feM2ZJY65BHpdcVnGKpJ0Dg60tTliW+k7r6feJD12UqUxI1J7f3+/B0it5ZUXTj5bO1yu5Pn6LF5lwFUHOhwCPR/j1B0fwypTLy9Pl2Pcn92js1f9Zd+qJZ29QFHlIL3d3HlVyz69Dg7Ave10De0cHb76+fLyck9qRWi1f3Nzsy/7w8PDXs9TGT0g5raabeBO81z12Oszc1+19f3UZvJzGnv6Tx3ymUkPCFX2Y7QcuRrX1cyFA9bk9xzI9YImlB4ZTH5XPjCRfI7f1tp+ZVEqc4/M0ua67nq90/j3a71fnVAzT9kNvtHcH/VSWsl2JEkAmel4nfz6c5CeLqb/rWVCq/b05aIS2mCOdeJrBmLoc/geE6XrtsHHDsvkdlj9lDhFstnuU2iDWL/0+ExKJ/lk1p24nfpbTVCxzXic55Le0vYQ36c+9LYisdSW+7xG98+QWm2dl/lKLqal9xK57s3wisrfpLLPyFHfsa0coa4ZKZ2T2LQMzp2piAkHqQ8ilYeN3COyyYhLlI9mFAiCaezTgHHlSI7ewXLlxDxdJ80qjwyTg1AH6PyN+tLbQmm19vr5jGq21kl9mqHW0jEBazdgyo/GVuT2x48f7dOnT/tljCS2yodRZCkhjXOK8I0A3rmCYklyqBW44b6D0rTiwgEodfXx8fGgb3VOs7RceaE8ffnsCACzPzQO3FAmkkqh7vhSQie27ozdcar8SoPP8Hn0mraL+sOZWhJb5cm6SYd89kf9JH1IIJTPIjpIdhCgsjuA0goXJz2J+Dw9PbWbm5s9mf38+XP7/Pnzfub2+vp6r29awp7It/eDj+WRk6XOn4MuVwDY65Hqn9oppak0tKXdTv7Q/Z6T2mQ70pb96o8r+Zbl9PIxkOs+h9LDLAkrOKH2PLTv4DfNXqf8kr/2vmNb+qRBsuUJGLMPpctV3zvmYduo33wZsoiufK18v7dtJT2SSpxX+d8ZYP2rJfnhJIkkuq4s0Q+myb7hf/fFGmsJv7FPR0ELx+jeFhLakouLi4OtRD6QeaZx5VjPcXEKirnf9xUoCdcm3OD4PnER9iNJrtfNxe1xZTNS+3pbpfIQK6ifaVuTjp5K79bg6qOesZ1poNbqWdqRQ/OBSTDlgJiRRB8sOpYMAMuZnL46UKDUB4y2PlvCa0XGEtnzGaDkGJlPRVar/tC9rrgsH+tMSQCPhoGGMJF3d+RsB3+ofrfbvXpRh8C3SKjy1ZJVLVsV0L25udmTWj1fSwNMAE7F1VhKRtbbPvWFk9v3LomwVsS20hHX0+pto621g+UrGjMPDw97483jfG6UgNmJGYGUB8Ko+xyvFbmlOEh13eT4pmGviC1Bg9JR22i8a3w7cFZ5NOY5a+uztUo/tX3VZiKwXOUgEKrruIQwkRe1RwWYReB5DdtCfX15eXlAaL98+bLf//jxY7u+vt7bSr7JPpHaFFDhuPUxkIT3vFdJetxaTWp53ay+O6FlHvQpPu6Tr6M++wxA0mXihATWiStSXznwZl16ZCmRBx6X0O8yTccpHOd8FIP+X/VRMIjtzfal9DCV6yz71NNgnzHIJtF/lkv/3d46qeXztUpbaXqbVn3I7Yz0fPl7lWT7U9v4eExEtoejE7Zk3hzDrbUD/6L0dQ/HhY5RX71O3oczNtj7Xz4u4W1OVnB8Vuk6hua+66702R+tcFyfcHHCEexX6rHq59iUfdJrq9Rmyod4hMd9nNGWOE7wt5tfXFzsJylGjzMkHlGJY2tuZ+Qkz9jyHLcjxfP9ChCz4Ssjz7JocLjyViBI0ms4DjQfOOk+H9RVxJbLc3mP0kgDgFFvpdnaoWI70PDyjMBdqg+3lSFIs7SsqxsC7zuSpOfnl2WarLtmavn268+fP++fz7u5udkDdbVRAsE+VljvpFRsG2+7dOy9ivdtArcVIKJOk8wKvFTfiHTn+fT0tH/JG4Ghz9LSmbIMJGe0IZ6vrlcadHoMVrldSY4vBW0IaEnedZ6zlhxvKqMIX7KrTgArp0pnwlnaZKfpqOiwtMJBpDZ9J9bbx4kt+9edOX2ER8Nba+36+npPbDVLK1L78ePH/SoLBQOq2Xq2Jdtb5epJT+ffq1RkpbXsl3qEtkdsnSAS7FK83+kXCHzp/3289VZ/zJDbHkGq6rRU3Keqfj6b5G2RwPHz83N8ppb2i/97479HLDx9lY/59MgtSTDLyH6tVoGQEBGneJt62Xr/k1Bvz4ncpn6qxjPtXOr70cpHjVf99zJw4oN5amzIhvt4SL6XQlvMfFyoW/LV9HPJXqX7en5V+SccmzB7wrS+6sJXcTneZn4eVPD6so0rLMa6jDgD80nppDzI14gVfJKC9r21wxUA3HqZKzmFDz7qGdvKmbjiEQi7QxMYdnCS0mitHQwAiStWa4cvg3CH0ANrowFC6XUcB3dybIxi+1KFpADeF8zDy987Xl0zI942yRAQ+CdSm54/UJ3U/yKzmslhXyotztheXFzsSa1mawXUWzt8SYXy0rZnOGaArhuLcwDDreXVCRXIdV0h8PTnMqnLbhNo8NQnTjwY2JFhdYOvsZKeByWoUxq0ASS2JLSMjqYAVkVy3ZExba+vf8rHy+qiwI62rkdOaiUKCrHObm8ToeUL1ypbSTCTZmNZl2oJl0Rlv7i42BPbT58+7Wdr9V+f+tGYS7NSHGc+VkmqZu1eIm3vTVyHJRV44THXc283B8JsO9o4P+bEVnrkwNtJrQfGZmZwFbyhHrndaa0mdC49AEYb4GCVtqxKP9kQ/Xr+yHEQ2yIFCKivTpCSyFawHB6EYx08AKg0VA7aEAWY/VOKI+IxkuRjXbfPyRdLeoSW59yXVYHe9NZ4H4MVxtQ1DCrzaxL+8/Kw/CRoSS8oTug45rjlpIjqQKzgYzcR6kof3bf7ZIwHpojlZ36OcVRPYocKiyVJPMTTT9emNDVW0mQFH1ViWpX/75V1iYwweJLVz9hWyjdSPHdeyXElEO0Nxo5JjoyO2Z2eKyDT5KB3oE1D4APV0+kpTG85clIQl16kKzkeL2cCI5I0MJODT5EubX3pcTIEfC6B/ebLhwlkBKb1Nt1v3761p6en9vnz51ffvNSySpW5Wp7TI7a6d41ivWfxuiaA29pYn9PMny9dpaQAFduU451EJM3YppmeCtiybgTpPNbTKRI5jneNcdoHOt7RbG16LkiidHsrPdwJyrGzvZw8kMiKzHowKAUpvF09mKVr9LkIr4vaQ8uWKNfX1+3jx48HS5D10ijpNP1B0l0H/frPmSf2QyXnQGhd0niXOMl1na8CyG7z2HYErMoj/ZiPxqcDcH+mOwWruApEZNa/nkC/3lr90poK5PmWOq1jTma1VbtxXHlbeDur3CpT8k8c605Y/bpeIKonqmciA6q/jrkP9Ppwlpa25MOHDwf3eaB5iST/6375HP10D1PrWPLLHvBz3eLKBolj04Rj1dcKXmp86B03KpPbUx/bTM+l6iPl68cYFGUawovSM58Qaa1eceJtwWXHTnL9UYJEbhPJZZ7e5gmbuL3wMVG1YaUbnkdP77xvE25Q2RikSoEDlSkFUHr85hSy+hnbEaFNRrwitR6Bd8OclNuFx5NhU5opitVafu4mRaV8gPJYOu8A0AmuR4MqQs1B2SOuCSRXoMPbiJG3GaCfnHyase39VGcCc0UG+RIplY/EVm9CJqm9vb09AOcyUj0Cq+OJvC51kucAih3ctlavsmjt8O18aeaPz2lSlyUOhFp7/d1J7w+/nudIrHuztanePgZ644JlTwEvjn2NHZJx6gcBRiIRzI/OQOm780yRcvWP0qxmxvwlUYnYpqXI7rxUPtWPAQbWh7YgBUdaa/uXRvknfjTr09rLoxZpTPhYJlBP4J323p39OQWxRvamp1c9nadN1H/6n1EbJYzgJMwDYylY5hih+k/84DrmGMIlgXrXwdbagY9zP04wrnZJvpL/SThozxwLicxr3wN7qV1miCP7kEQm6bp0xoMWyosvieKztQow73a7Azvp+ubl8v21ujgKZL0XqTC1zvlxjvOK1Pp4kvjYTmNV16jf1IbCZjzumLPyFwww9kTlVTmI33zixjGy8nabnogXfblj70rPfeLGiaqXq9e29Esis+pv6klPjytiy7HDwJT/UtqO8dLz8mxL+l2K93NFZt/Czy4ithWBbe3weRCCLClgj9TqPO9RmklJKkmdxLSYTwKTFAew6TcatK40JH0Cez1Sy4HBfZE9Dk5tK8Vn2fyci8ri7cF0vH6sU+9FWeklOFLqVB4qn9LXm5Bba3GmVmBY0bsEhl0csI3aaPb8e5SK2PXAbiIk6aUCBFaSZHD9uLd7cuosZ7Xqw41s5UQ9etkbH1VQy8c+24rnuQSZ17Gcno8/ppBWeKjcbjOd1DpZSGQ2vfAlkQblw3bQowNV/zPafX19vZ+1VTkuLi5eEVqSWgUJ3AekccVr2MeavU1jTGn1/r9HSQR/5vqk9yMQlYhuazWY8uMktmlsJVLrBLfCEAycp5lPD5S7VD7cA9OuhxrHSl8vxPN2ae3l5XXeJgS3ifjzOIk7cVGqe8++sAxeTulJ714fR75UUft8gaPESdYm/5WKvFbHHFtzLKSAkNJo7fVjegzeEL8q0JH6i+PLSZKPD0nCnj7+hGEp9JsuKiuDlcnP+gwwdbuHZR23p8ko368IHOtOLO6+LdnjHm5K4r6a+aY2ZECceu0TF7JJSqcaH16+iuj36pHunZVFL49KpLb6n0gtDXNlhHuO1gd2r6I+IJgfgaBHp3hsRG5dMXSvK0tPCXwGhtEx73y1B8kalXoEbJheb7BUpNbLRfCdDEBvyTXbkHVjngTvSl/fP318fHw1U6u3IvsryUegjZKc/ah9Zs+9J5khtQ4KU4TYI8JL2pozAXS0LCO3DH7RmY+Aa7Ij6VqP7qeIYxXtJRhQ4EkOg8e9nT192o8eqWWaBC6ej8/UJgKbnrGVDlWzQCyr9J6ginZd1+gzTnSYrbX983iuwxxnvZkF72vVm2OMWx9fbH+eOxddbu14UjuSqm2czLoe056QgKWlyAp0kMz6y8yc5PJ/IrqJ4Ka6EZC6j6oCtBcXF6+eG0/LJVt7TQxVHrZjIqleN83eptlaJ8DJvrnfJ8lxYsBy8VqK+wLvQ37mp7V6GfIa8Or3/g6yBGf7zJpjXPpItjeDfCKS2tInu+2k0Lf5uBgRHceTqQ2YRy8tXUty7aS2ujfh9ERw6XcTviWOdWJb+Ske93LO2uYeqfW2cWzdy7+1FicutM/2S7id2xHRT+T2FPq86BnbGWUjyE3Pw3Aq2+/rdWJqvJky9/4rLXYwOyRFc5Lz84HSUxQntZ52GghOAFRutRvJW6qnE1q2X2VcvK25TSAgkXXO3nqdSJAqp05gLKOiJZkfPnxot7e3+xmejx8/7mdspYDeLscqTEX4T5H2z5QRqWWfEBA6yPLgFB3aLPlP0VmV0QGyzidb4/ZoSVtwO1Nm/WeAh0vqCQgIFggQ3N6RNI9AtRwKg4UENIkwuHNyMpsIbzVr62UmuRVx1RhSP+r8w8PDPk8BF+myXv7GwJTuZ5tUNoVtobYkSOL+qM8rh/8eZdZvriGzFSAZ5Um9oh0ZrSRIS+Wr5clpjFbLl6vg18jf05dx323mTJuqDRSYTsTW8VI1s+0+MxF4DzqwvsyXtseJLO0W02F91BeVHSFxHtnmU+jcEnzznmQpqe2NgTQmiYPUr7SXyT6m/nCfKYLLY77v+DEdd7zH1QPpHifXiVckIXZN+D5hdvc9ieQy6D3Cg45JPbDkbeLlH6VN/NSz4xQPiriNpj6rztTnxIdSHb0sPcK7luwe/YztiNT2okgUGs8kPVKRpFJo3u9KxrJoy9mTiuS6kiTlSEoxSi+VSUJH44C+ctyp/Ubt6galAuCJ4CYyTQdO4lkZ5RQ0uLi42IPh6+vrg7chc7aJkcREzNdIardzIrWt1Utd2DcJLEmHeR1F7c12ryQ5wnSc5XHDv6bea2XGSXIJnxyufhqPqk/SRw8WeZBI+7KryiuB4CWkgcsIlyxHbu3wWdvv378f3EegItJ7e3vbHh8f283Nzb6uHz58iG9QTW3A73+6nXEhceAshfoz+QX297nptcTHqtc5/SSzYCTl1SPRabZ2huDOkFzOVvk3mEfjt9K7p6eXz5FptYEvP+aL0kbtwP0UgGktv22+CiRWZKaqZ1VnCclNIjxur5hX6kv2m+xd8gun0rEK35yLzJBa2UVfDen97lL5TPZrGsetZULKtnUMuiSNCpuqHiROl5eX+9UQsvnygT9+HL4siuOssm2u8z2CW71QiljS972dKLSL8lHeHsS/LqN0/dpK9/VL9opBNOqz2vz5uQ5SJYw9qkevzdZg7VVvRR6RWlc8J7VKg8AigYxRtGKkGE6i9ON6ezfaFVlNv3QNQa4rS4oAVel5Xb1NWOYUTU2Ertdms+3rbZOWIyeyrjJx6aSOVZHmZPx0nWZ2NEvrS9gIar1PqzpVbd6Tc3Ogrb1eiut66VvvnxSgkg7TGa0lkh7YoP7SgXHM00g7GJvp25l+nCG3FYiQI3BH4mn0iK1/85KAcobUjsiuk4i0HLkKaHCpFomtbIK+PX1/f7+fKdZ9fPGYk2aRi8fHx/bw8LBfzsy3qifSxbZ2gE7w3uuPc5QRqU1CHeMxbSty2wO1Dt488OLj1olqtYKgCsxwy33pwxJiq3H14cOHve5dXb280Zs+TOnd398PAZjbXfdJJLZsl9R+VdCR/9N4UH6OiSpS62PH/ULVh+yTamxKEmke3TMjFcZ5j9IjtbSLHlj2/TS26RMp7F8frz2y1mvX2f7ydJg/sZ7/1yMvukdjnXaoIvieb4VlK7zvpJZYN9lL1quyi8l3eXtUxzxtylK9UfsJ37lNpZ5XExeO1dPYqeqQjh3jg1e9PIr7VKzWWml4vUOTcEZDFSOR84Gua1JjsTzujC8uXj6gnoBNRVp94M8SWipBtfzYIz8zfaH6cCaC51M6SwmuX5cMXmUE2L7e772ASMrHnTdfEuXPBfr48nZNipb2K0B3CsV7T+I6TQLr/dRzooyYulROlff3yuN6QfDlTiKVoeqr5FB71yfAyDHLpVNVO/XANceqO061hwPIatbLnVJatkki7KQi5ZPIrcqsGS2l+/z8vCfkIrUKQj08POwj7frR1mt2Vvnp2fr7+/s9wXVy6+OD44L95GMnjYdjAPWvkooQODFJJLdHatN+ysfTd7tRETMdT0tZq+fBe+O9ms2VTVM5HaxT7xhM0lh0EjFqv9Q/tG3uK709PAiQSKy3MfWzCrCp7iS4rb3GWD09YT1GBJd2meXokZvUdjNSEYX3LhWpTZg79XMvaCOZwZVJ3ytcV93vZdA23ev5qJ68R7O1IlXPzy+f+CFn0K8iXszPfa3j8DRhk7bOFUZj2G1jD6NXx9L5Gdvfawe/J9mfVO7UlixjDz8vlSX3LV6KnBrQCW6leB7l9MImQkupGpON6OCF11fT6NxPBG7084HupDU9iO6KVA2Kqu0rMuv1qP57HiMSzPM0cKkOHsViX1SgJykg25GOmCDIv72ptNxoVc8wj37eHlXfnKMzbe1wzFB3tE2ONhEb7qd00vjrtXUaJ9Rfv4ZEknlXdqIytj7We5Iclc86jvLzvDlGmc/V1X9NNYnoDMhPs1gMAnFWtncd9ctX37T2QkY/fPjQHh4e9sdEENL3cmULfXZXn/O6unr5tMTj42P7+vVr+/bt257gclky+4yBS3fGl5cvz23pWg/Cef+8Z/Hxno6NSG1rNdistlUZKmLLmabesmIPrqRnwKuZWz/u4zbZLtaXPtq/V6l0FGzRG5BHhMvbo7WX8ekrmnSOQadEHBPhpY/1/z6mlSfJtU8aeJkTsfUVedUKksqmst6J9FCPme+Mbs4S5vcoPVLr53uB5tbaK7/I+/1aHx9pZaFjVQnH2qi9Het6XxHbckxzq/HLwIvfU9XTMT6D0TMTNon8Mr1ZTCO9m8GU1X5lf5K+8v50n2NyXzHi2Lpqp14bpDrMyqzut7aQ2PYUzpWMEdI06Gby6UUaOLh0PJVTHcTBrGPq4DSgZgjr7P+kGE7Y1gBrb89ep7tS+/HkZPx8Iv3VviQZqBRd7hkhJw3VM0jKz5UtLTHxmZ5EfljXGSPzu4iDojTbUYFD6lMP+HmUVGmwDK0djhf2U3ISXnaWqzdee/1Z6eKI1PZAx0iSbdXx3gytALGfJ1h2YJxAaUqjIgpsN81uKdDAGdy0pFTPTFFnNTN7d3fXLi4u9td8//59T2z1DeuHh4eDF0l5G3JcELgTVMi+sN3PWUak1q/tBVh4zPdTfq4Pjguq2VqOT/9kTEVueb63THmW2LaWn+nWbJHr8ig40BMCcdojb6ekw14PJ7Mkuu57aSPZx7vdyyMkJLyVeJ8nAl75dbWTg+CZ9nN9nZFz8s8+Nj2wkHwxbXCvXdjXPfE+SfiV6SlNt7OJZKd9x5Q8p3QVkEzbNNarMbIUyzpGd1LraVb2kbruZa0IYGWXe+K2aYQ7KvLtto7/KxztbVNNIP0sWfS5H0oCA+7Iqm1q/F6EgVINPDfU7ITn5+eD5QwcKBVI9yiOd+QSgls9d9qLgM0OAs5M9MSVY0Rw15JbCsuUHDN/Lql8NOoE2uxDlqnqO6+D/zzQUBmvc3KalchJVOLjqgcM/b5EVL2daRC9z0lAWmv7WUul5/aGDtaBY9WfbkN6dVK6dN4sg8rM6xLBdadfEQ5vvx6xTcsw/dpEKDiTlkgHZ9A8bY4FjSM5a81ycdY3LVMkQNCS5W/fvu37/sOHD2232+1ncb9+/bontv4CqarPKgKbAP85ywyprUj8W5La3jhze6796m3d1cxttfXZw9QWrLf8hII0l5eX7eHh4WCsu3729lObaUaKwN0Dv2l21rFVCg6wjgn0s55uk0m40xgZ9X8qG/2A4wXmXY01pefBqxH5ppyjnyYmSuQ14WqepyQfQ3Ff7XjO/TTPaUy5/3VMl/rKx0C6RnXjuzuYp/tecoBKPL8Kyzo+SKTWib6PXeckvbLRrnifVNentBPHSm2QjtNWJKyirb8ziG+IrnC1t0+vXmwDb8cZWbwUmVIpXPpVpNbTS9dJEiClwlGYr+4RqdW2IlUJeKdfWlZczdj6sgUSLOZJmTXcS8QHWI/gpvIlRU4kuLWX/hWocUfthklCg+oASP3m44v1kYHlM1JURDfSTrZmgMo5OktJMr7JiPAabpeknY67frnzaO3QeWm5qn8jMs1eOIhr7fWnPGYCHDPC8cvltBXI97Z059IDd05sq1nbBITTTJnrlYPSlEd6W7KEwIPPznp6iVhLNzVbq3SVzsXFf2du7+7u2t3d3X7WlkuRU78lMidArHx5bAlYfg+S9LIHdvzapXlVus1jHPfVOOMY8FU3aWlxNXN7eXn56tvLPWI7IgHyz7IRfMbbfVTlM0YAzvvKcUDVVq4zye6l9ve6qlwc6xz71fhw25QAsJfF26oKMns7ShKu+7fKyJewzX2sJrIzEsd5Tur8Oi8TgzUpbR9Pnl+Vvmy32sExw2wdqatpDPb2/f4lBFTldNzcK2eP4FVjYKm957jyvkzEnsG/pMs9UttrrwqPJlzak1VLkd04uwF1IMHjlcPtAdTW8jMY7lx4nqRKyvD09NSurq725EjHWA6mObOsuDeD64Q2GfCZAV516JKOTgS2R2793IjwUtieXD7MfQcMGrg0iFwuLrD7/PxcLoVUe4sMkdgqosRPLlWOtQdSfgdH6wCVwYAKwFUG0kFZJepfdwzpzYKtHT5DLkfgM8tuM+h0WReS2orM9hyXt5nyVrulSHLvl2Rk7J10jp5RJJh3olGVy0G0z9j68+xKn20tglsFMh2ss7/1bK7Gxf39/X5FxtPT0/4FVCK1IrYjG0rymkiut/M5SiK32u+BnAo0Uefdp/r9yaf3fLv/HwVrfKaWn3XTeCTpTcS2Gvusb2uHz4X7OE02gcd+/PjvG757QD31E+2G/vvjAVVgytvV95PdoU3ncZXDr+P/mXGQ8qK/5eyO+2G3w+qTSpYC3nMUb29i5MrOalvpI3W7CmjMEhP3AUor9Zv3a2+86ZjSV9pcNl/pkI4lGU3YcEbWn591vOiSCBnrQSHB9XGc7vU0Rjgj2f0elktCnKb/epkeX6wnsusTSD0cNdLb1Jazur7qcz+VcCA5Oe0BPAdg3Lb2ugEcfDpY1T6VQOXgcmRd4wrASGYitbMEV53MtJJSzHT20pmEqs1SvklJq3L2jJwrnUefd7tDgqut389gQw84sW3U3rrGI0o+u+7EpvqltuzV+xzEnQudAvs9kVxtU511T6XnTIdOrYoGt5Ydlo4TgHJ5n87TLri+zgQ3UntJaK+87UaOhmlU6fM88yKxTc+8plkxDyBVTi/l54SDs2UedOSMuvrTwUsCYx54EPjlB+F1TLO6cqaevkuP0HJfM7g+bt67JKDj+7zOQY6OVTqdCG6vLP6j7+U4TATNZyR75NYJbQq+jIitxANgGgcO/t3fOw748OHDQTpss+RP9Dw6z1HPnfz3yOzsj2XxsvnYoD3UluMoERwfO60dPqdJUuuYyfPc5FASBnI9I5b2fudYodB/en4Vtpklt717JI49/b/7Gfpc1ckD7MKAvXIk7uDYP50bSbKnPYxY2d9kn10HZ3W+15fJFnq92R7C1EtWRLqk8eP17dV9JEctRfaCaTvb2FQ0d3rJ+fQGVmpENoTAi1+fDHQPCPcIbvWiKB8YXs5ZhanA9sz97lQrwuqgriJ51cCkYtDw0Dnvdq9JLe9xIfDxequv/PlpzQD5p0F6zwAsIbapbWeV7lcLAwceNSRBdJKrfc6kz0jPObK/eIx621p7tXRU4JOEpGe8U9CpIrMJjKb6eBtyHCx1LpUkG+kzs9z2Zmt6oIP1Zj2lq/58rAhnsrl0bNz3dHn/4+Nja63tyevDw8M+P5VXzjTp82xbOqFlmRIJPgfxcVWNuQRc10iyd1X+ya/7smAntz6mneAmont1dbX//FuasWV+STdUL9pGHiMZ41I8lVE+yLFDhUskxAUSn7GtZmjZ1qmfaTcoOu51ZfkcE/CYz+ym8eE/J7I+q+OPEyQc8m+QHhnyfvexkMZ5a3liqbUcnJcNpH1cYycqfNALGlbk0Qkz/5Ps8rwkzdom7FuR2BlS6zYkHev53F5/8JolpJbXVGXt1YN1J756enrar5birK0vS/Y2XCqJly2RkxHbnniju6L5LG1lbJkety7eIATtOk/ATjDjYDeRVjqw0UyuOzhXkKrzUh1mrhvdt5TEjkitK5ErMYGL77f2OhrnwuhcZSg9GtxaO1A8KiD76Vhim8boOYDhBO5FFrVUXwatuo5BoqrO6TjbNhFKztqq75kndY2klo7t+fn5IHDi+uyATdewjL3xwbop34q4pWtTu1Rt2CO2Sa8SCE4APul4minxvBmgYr3VJ1ye5JHcynE72GX9Pnz4sK+Hyvj4+PiKMCcdpXP0fV5TEd4Eit6T9Ait/6/sZ0rTgZTrRk/fmZ+DcF5T+YVEcqtnvTlDy60T296sJoXEtrWXYPjV1dXBc+MsE32PliHruOsWH4vyNuPY9Pao9C89N+w2hnXkeO6RWpJND1JRV3qAn5grBQUcMxFrKU2fDEh5/U6Et6efidBy39+ToHt6Y15Exe1iup7BDPpVieyCxkiyH8f0Fcms/rOOyWbzuAvLNCK1SdxX8Hi6tirDiMOwrau0e/3Wy793jjbg8vLlM2d690AitxXnmclP5xwj9HxRT05KbFMjJxKbFM6PS0YNwwGWKu3HGBkkafJ7aHgrEpuWHWsw+JZlmSWtVT2cmC69tgK2veOp3KmvK9Lb2jh6NspDWzdwan+CBCqgKx771/dTvVmuNDZnAON7EoI3Bz6M0jp4YTTXCUPSy+TYZsmlzrFs7CuVhY8a7Ha7/edllLfyZ94psMHyzUrVHtwSdFYg08+la3W9f74nfdKkR3YkbuMSuKzaw9uTZFazrXR81XPUSou6yrHGJakES+zHBHh7xIvSI7QzNvm9iIOZ0X8do1AHKt1NgDaNL7fViaw5YfNyJoLrM7b+XWTt+5u4qwCP23LWNa0ESYRb/0VmfaufbBLfFUGw7sTW86uCVb3JgGqsuF/Tj3aA2IX4hfYu6bL/Z7ppxtttcWuHK7GYL9M8J/2ckcofOCZ2sum6wjExMzYcu1W4XOe4ciqVV5Lsh0+kLJVEmqlD/r8nPoaWjCvmTx9S5VP53xl9dZylfd9W9j5hj1lxPZQOVzO21YQR86/63u3SsfJmM7beuEkBVdn0XEBrr5WDaXPrx3WvG3AOREVOXekdtDEiWxFcXufLXdnRI6XpOYoeAexd522RyNvsORfv20QIKe7IE0lMQkNeGSM+X6v+4jJkPhfgfVLVuwL1Xq4K9L1XYcQ9OVS1If/7MXcoLkl3Z4mjG0MdI6hSH9OBa2lga4dkkPX2/q7GAq/3mQS2FduIkoJ0FcnVuZSuhLMzBNcObnw/tW2qf5pRmVmBoiX/9/f3r376zqx0L9lk2k3O8jp55yyx12WJjKLsCUC8V6kAzCzISfbXfQElgaxUFh7jfiKJ3K/q6GPelyM7weVz4LQHCXxLRn66wjMC+iS5/sJK4g3lw3s9MCYCkYiL/7ydq/5MoN9xzvPz8wFA9eA981J6SacTQXX7QnvgdsfL7/tOcn9ncf+hLd9T4o94JULr41/tWOkssXK1reyjB3uX1HUmqDhLCqt7VUY/lq6TeLlmye0MqZ3Bvn7PjE33NJING4njIvlp+vYUFKeOzvjU2bEy2++LiW1yQJUD0/WVQfbIUkqrqvDFxesXxlTXuwPTPb7skQO3AnROcBnZTOBvNFvrUhFT3+9d1wMpPRI3S2zd+Hn7V+XQPg0f+2xNFNaJrdLhrBGjSq6AM3V3qQjtewfCEtWtB/KlI+wvEmIBG39TsefjefqMwKicBGTK25dI86VFBI7JVjmQS2VN/ytRHk70af96x7j1NJOeEcwQXDuYSWmnWY8EOv25Gc3G8i3ISkcBJH2GR5/iEbGVA6yiucpf5FgzvK21V7N0BHNL+ihJ0lWO8RmA9aslgdIeqR2BmspPpKBqz9YlPNArc7ou1YkEVwEPX4p8c3PTrq+vXz1zPqpna6+f808+IJXHZ21JdqljujaRBG8vJ7jp59eyfhK3S9RdxzXSUQ8C01bShiX7mcri9sb9L4Ug2X2Gk1re48fOUSoSyr5O4yEd432zuKRnS5Kf69nH6lyyKa4Dle3tBXJmpDc+kp47vvN6ex8lcuv/PU1ekzB2ZWvdxqdjyRek+1X+pLtOUvl4UfXiKLebrOeI4J5KTr4UWVs3ytXsAq9PxnkUzaCTIfBOogZPg6ECfQRfdFIV4V07W9urdwIWfs2I1B77Y5u39vqt16ojya23pYIJcviz9WceLIM7Sx1LyyL97cgzpDaNI1dON1TnQG7VF5Uh9j5U+9Kw8zonTwlI6lwCJ35c55xEa/zwZS1efr3YisTPy5L0M+nLSJi3yFYCJD3nUQX1UhoJ0BDc0q5W49B1hk4qgSLa7dYOl4OJkJLYfv369YDcUvfSczhc2kQirDLo+UaRmER+JGsAbvIbDubeqywhsW6vE+Bq7fCLAMyH16Ry+L4HdJxgzoxVCscjAx4ktbe3twfLkX3sqg7UffqmdL7yB04sXS/9+X/5PF3LmVoC+ZQu0/cl0I6l2E9uQ91OV4SWGIdLkVk+b58EplmeHnZKNpj3JVLr49XvOTdJupmClym403vzd6XnPu5oRzjpIJ1r7eWZcw8sn7oNSCKrayqfupb8Sk9Sm/B8ay8Yo7XDx7oSuU34SnV0rpJ0mPrr/sltfypzOu/7iYtRiL3kr6tn5pMeJ3w8g5WX+HGXo4itK4M3lDujpHhMx6UiOUsk3Vs1qhtPGmP+dxLbM9YO1kdlTIaiR1pH297PncrImbd2SHzc+FSESI6wtZe327Y2/50xHesZAX/e2V8aVb2SPDnXXt0rgzMCf+9JSEzlxKrxkYiq63dK3yWlmyL2fo+E7UzHw0CS0uZsSM94OritAFQqYxWh1TmVz20j24/3ukP2c04QKnCbnDzbMhFbBQhSGRnMkO3TmJGT07dlOWP77du3/ayt9NDzZABKy5fv7u72xJazYDc3N6211q6vr/dLzp2MuPixEeglWFsKjH6FJJDiPxJKD/aw/ahTPlbcxukc06gAk5ctETFJz26wfE7yuByZb0bWjC3Tku/ROGRd3A+6j/B2J/CXvfF9kls/TyDNtnIdVz24YiEFtyhORtlXOv/8/BIE5tJC95ckrxoP6eWBXnaOn9SnIxzSs8kJI6Tj5+CPW3utRymw6C9R449jIZGT1uYmRHpl41hlIGaG3M5i+ZHdrfxi+u/5095VBFbpUHpE1/1F9d/vS4S1V2eWv2dz02/2WrdBrK8Hpnqk1vXOfQePs45+LF03K6u+Y9sDXTTGUsREbJfKSHlGAHZGKuNKctt77owd7MuaZss0Y3x6W9+vCMto3weoK0BSVs+Dx3k9Z3x6/cYyJXGgxciz/2aV0evN+vt+Mk7nAIi9Ht6+NMqJPCXA2svLx0ClJ7zex4vuS+kLJCpdgscE5L1cSX/8mVA3wtWYHDlbEo3KEXv7Svw5W+8Tj7x6nb2dadtS29AGSm80FnTs8fFxPzsrMvvt27f97KsvRyZgFqAWodVPgUPNytHRXlz8903JIgzeF1W/+PFzAbw96YEVjgcSItcHtQP1UWkyAObtXOn86HgFtig9W+z+SHUSuK/eiKx76btS2u7LubrHfZ7rYSKyDhL5P9nP1H8k8aynL81XPXokkMflF6WffHQnrWxSWRI28DdGO7ElcHX7orHmZRzVx21zIru+/57FbTn/p89f6Tlyvjk+4Wv2g/631sfFHIf+HPisVDbX83VMOcJQSwgt81AZeB/Lk3zm8/PzK/3SPW4beYx21PPmccePXuYKVyb7UfmCno/Qccd6FR7wwLTj6JGw/f14df3MdS6LZmxZ4WpwuRHmcome4rVWf5omObYZqYx6+p+IWRW59WdrCdBTR/cMSDLAx2wrZ53Aatq6VCDb61D9EnlNg9ujZK3l5eFJSKKrZwT9zW3eT6x/Gmc0MDRG3PK6cxE3fO5c/Rup1OWegaqASQr4+DXez5zR4DmSWKZJAuY2y8XJNl9gslSX3bZ50C/t+31+v47peO+ZO93j6ae+ckfFY75sWKBXQEpjXs/XaikyXxp1f39/sGrCPxGga3gvly8/Pz/v3yQrn6LZWy5H7wU9RlLpqkfd36skAONjjM+ZJh+sPmeducTfSZe32QxB47G0r3K09vrdFm6vSbYk1exWIrZeb0nl65Mv8HZO5Pby8nK/skDtW+m567wTmh6mSn3Jdqx8J3FMtbpJabCPdrvDFTMsm7+4S/lzdUVVTvaB94fuSf91rY8j3753qWx69T1nLb/3bzUnQjLyXaO2SsRJW9nnHs7m/sh/zvrXHg9JkkhzRWTTfekeklmWX/bHbSvPO54cSVVut/e+HRHcNOnogRW2jQf83UaeSt+OTWfxUuQEgtMsLZcD9cBwapCqoRIQrtLpEVjfutFMTs5JbLVlmlUEcaZMs9ckIuu/RGKrKHRSCp7vSS9/XpMAfUWA1a5q46TIutaddbW0ytvDf1V/9UgtDdU5SALCDp74ltH0MhYad0kFQHr/nVhpn6LjihyL1CqarCWyqo+DyTSeWWaNHZXHQaEHPlQWkqtkG9O+k1DJiOD2Ivqyw55nEtbLl/CLDOnZVhFXjQOlS8JLMutvIueSY397cnrhFB/zYHnSx98rILSE5Cbgci5guCKzCRiT5Op61VNjQEK9Uh/Q1rV2aMdTOXjcyW0FJN1ncGb/+vp6ehmct5G2Vb8SpCUfKSEwlb1kcE1tRnLrM7iutyqz2w7OfKalp9JFEne3UR4YcDD69PTyTejeIzscFyIyJN56aRd/aqvn5+cD4jUjFcapfEh1j4+v9ywzpNZflKZgAgMcGguO93q+oLX8rPKMJL2q0ujZZZLkdI7b1pYvQ9asa1WvRHyVbqqT2tQJrmMiD5K6rRy184jQut1NBJf3cD/hkqurq1dpebtV+jg7ZmjveGzmvlmZJrasJBvEl58IAHGpRHo74UxDuCH2ylUNWjV8tc/rnMgq/95P16b0vMypDtW1o2O+77+K0BIIJIVISpHaV1KdTw6otcMXULgoTyc+bng8T9YtEVuCIbZJb2x4/dRe1dbve6/ife3giYSWL2JJz6y5jnrbJgLLeyuC6+PGxwzL4UuP2Sc+lpPjZEArgeees/f0es7DZ8x839PztFRX77ueM6scfgV+druXzyaJ0N7f3x8AKLUVCSuXHHOZEpc76lotOebLpu7v79vz83P78OFDa629WrbMvvHyep1S387KOeivxIM2DHCkYDPHoETtyHbyYGeSyg5X4LKaSWmtRZvh5LW3CifZeb4p3cH+yJezLF436ZxAeEVwqaNsl+rn1yTbLFwlm8z0E+ZwHXJ8o31/uZuur/pd5ZJ/uLm5abe3t+3jx4/t06dP7fr6uu12u33fPTw8lDNALklf6Qt8m3x2wkvvXZzUMqhBMuuftaqwtfSagSnKCGfOlHfWts5eN7I1yndEaNkWTuqrPNw+JczuBLUitLrG8+a1yW722n9kPyr8XuXHYKevxuNqEAXf1AYVj+hJavOlern0+kXP2LLhUnTJZ2xTREmFTNE2/k+ktSKyrR0C6949iXDpf1pOvJTcsj6prJXRHRnlZLxTvRwguCNTGQVy3Jgmxej1zSzB9QiW9v0+OlSfCfdrvXyJ1BIgpQg/y+lt7m3fI7W85r3LiNSm70JWOsyZdo4/1xudT7o/IsBOaPVfYFLXE7TrPj9ejWkPUJFEVePG29TzSKR2htx6+ZyoksCwH93BVU5N9U3gRO1GAEtnR6ftbx9PyxhJTPgsLV8ydXd3156envZATeSasz7UT5V9rZwL2B1JGj9Oav0FM95u9AnUMQKaUf5pnKWZgpEk4kl77kvZq7fCMi23CdTn5Bd8qR3buOePSWylL7KNnLlNbUK9T+TGZ+18xjYRPx3zQJP3t65Jdq4qH0nt7e1t+/TpU/v8+XP78uVL+/Tp0z44pbenuu2YGQcqX2pzHq8wH7fnIN7vPjvv32vWOOAqDI5vjteK1FZttzQYWKU9Iz1Cl/yg+1kPWCcCt7YMPEafQ8wiH+UEV+kyyEXfqvN+T8IXFYlN+47fU9sQF7l9od2p2rRqIy/zKaTHL3qyiNgSFDrokSMlENbnGQRWJBwQVcO4EWOHO1BOg6KK6CUg7aA1gVylOUNqe8Y2/e9dV93r97DcHpHlT33nA9i3CRinfNiHMwOP144MqJfdI+BKz426gyIHMGnc9Npf+c2Q2nNwpj3QxDeLaksQ1drr56Ik7IOK3PK6dH/1DG5rh2NG41PXE7DTieh+2i2lxbSTzvdIrZeDeTuAdVKbSGgqG/fdufWeta3IhoucsQNjB/VuEwiatVRUv2RLK2Kr2VqRFI231l4AiQdOKDN6t2Rm4dzE+5rklTrN/+6HJc/PLzOQo/byMdUDXTxfBSPcL1ekVqsIfJWYB9x+/Pjx6pM/zKvyD67jqdwOTGkP9Nw3fakIbi/g5Pru/rmasZUdcXtFsiv9fHh4OCDuFVZwbOEgWPnf3Ny0jx8/7gntH3/80b58+dI+f/68n9HW8/K+0mdGnFQ49qhw34wvf49SYTKufkxv/k4rMFprB4RqtFKKx/06XvMzZURqU7B6hD99DM4StorUUv+lczzHxxGU36xvdnHyOrvfa0cPovhEJPEe6+722sfOKcTTW0qUFy9FFtCh8nEJMhWxciwJiMwaqR5Jra6trk8GvSKsFan1+/3nMnuM0gNuVb6pfFU0OBFcd8TeXgQHBMMjYO2ksOo3z0PH04yDtikS77+l/ZMIdNrO9ON7kRGpvbm52c/W3tzcHIDH5+fn/TK/pEeJGOqaka66AU0AW+VwYsnghxNZRlOZDuug/VFQq5IUDXVbmQhoT1+q/Z6OjhwnHa/3H/VHfeBpMSCZVkcQKLNdSWy/ffvW/vnnn/b169f29evX9vT0tB97V1dX+3porI2Ay1K96/kflfccxIGMCOzFxcXBzB4Dz27LJWtAV++emTRGvouz/Y+Pj3v/5GRT92gc+nOIHjhyMsfly/QLsiXa19gnafC66DwJrT8+QF1lIIHnfSmqfk5qlLfG9OPj476OqpO/mbwKHmrfbQnLomXHnz9/bn/88Uf7448/2l9//dX++uuv9ueff7bb29sDMnV3d/cKKC+RpJfJhydidk7imIxYmrO06n8uR1ffsy0SmdW+H0/BAsnPsIW0x8lmuP2nDqVzTKdKr3eNt1EKvjuR5b5IrQJdrbU9yVUalZ1NvslJbS+gTT2rVknQxvhz3L4UueJQfETrlHKqNBcvRfblb72fG/Kq4D0Cmpwer6vu82PpXCK1nm9rr0ltdW0VTVwrM8DAFaEibg5+en3my9YcEBBwSIkJhBNYr4xJMiIECMqrtbZ/Syr7jqSmegGGA+5kwHttrPKyvSsDdA5C/U0vJSGp5ZtwW3tZcueElDOb6adrU7u743A9TdcmgqvrR6Q2ievwLKH1duX494CR289ETFkXF9cnpecOraqbO172qfSHz9oxYMU2dP2sSC1JsM/Y6lnbh4eHg5UB0vGnp6c9yWXA7S117NzAcAV2fIaPy8wkJGwcCzo32xYO0Jzw0e77OYp0TrZF+wpsSJceHh726dA/0/aTCJLQe970I26reL2DWM3C8PvPrbUDQqvy9wLG0kVvS9oKX46aSA2Du26r+Ry8lgb3bBrLqHKKaInUfvr0qX358qX9+eef7T//+c/+9+XLl3Zzc9Oen5/3+fV0dokuO5763YitB0JTUMP9M4M2VZ8mDJ0w4gzJfcs2rcaC6+yI1LqvT0Ffptsbg7Qv+k98QQxK36prXC995pZ118+xpZedttADYY4DXPe8XdxfjHgb7aXqtcRXrJG1/n7xUuT0I8uvlgi5JPJYEcKeEesZOj9ezQglQJvIa1XGZBBmyG0adFUUKRGpmXOt5ec3fMbOBziVhf3A5RVUSEYIK+CexoLK7u3MiDPBhpMoCYF59TztbL94n+jayvBURPe9igMud5jc70WFPdjDgIf+u65VUulSa6/fiOyE1okaSW5r80vkPW8SO/ZtckzaUs+SI9rtXr+dNs3keLt43kmvvFwc71wO6Xmw39IzeRVgqvray0KAzZdNiQj4pytEatXXDAiwf1MfVnJO+jkrFaHls1KJ2Kq/kl4kv5sk2fNqPFb9pTyeng4/26SxSl9DEqkf7Q5nJ0kE/EUolR/yunrZHUdwJQHroTp44If6X5WB+aZVNb4azkkNQa3axV/w5iusVA7ZK6XDctA/iNRqpvY///lP+/PPP9uff/7ZPn/+fDBL5culZwOEPenhwJ9JxE4t9AsJk3mgSmOD+INj0bfVBE41GeN24Ji+UxBnrfT0hXnwOs2OJl/pv0oqDiEc4thH9fS6yobJJrO/ZrBJIrUpYO6YvSLvxBwcX45T6DP5CEOarOj5ioRhenU9hSxaiqyG8mXIFaH1QlaEMj2fmgjJ6JhLdSwBdF5fHZP0iG5FonriQKAityltzkj1fuwvX37gg5sztgQlBLP81qG3dxVJ8jqx/J6OzwbpXjlOAQs69+pzBQm0zJBRd/5+T0V037sQ9Mq4cbY+LZ1t7WX8+XPM7CcnRBU5TFIFnvy86kCCy2MVaK8k2aWeM09j2Ymqxmqyjb2lxE4CWAaNNUllY3Uf20L7z8/PB/kRqPN5xvQtywSU/Jk9lot5+NtZW2v7mTQur1R7qA/0n23p9e2RBNf730k8YOlkyJeYsT/S+ObY8XFXtWMCiSMyqzRJaJNfI8mVv+HYVRlJahkkcWLrsxoVyHUgzDqrbFdXV698EMvO2durq6uD2csZ++H96jO3st3Sc59IoA8VqVVgSf40kQHaIRJaLT/WEmQtPya51TJk5UNCW2Gi0ThxrJXGo2Owc5TkKzQr65MRVSDU22cGn7bWx7NLpcJWx5LbXn7aJgKb/GzS/SQV1/DAAGdqvUytvTyjq3YZ/RIRZF0cq1XEVu2e2kz4xCe5fGy5r6+wXdV+o+Nv5ZMXP2PLaLDPzM2QsqphqFQV0fVjroAVIXUnXl3XA3C8l9t0vDIgFG+nSjFTG6Zy8Rqmpb5LEfzRzyM36hvOnqS2oaOl40+gnVEuRekdIOi5IUW+BGSogP68n48vGtwkIwV151GR2nNwrAkAy1ByrCQg7LN7/GZpWsaa9Kq12qD17uG9PmPb2uvngXqgye2BtlVAi3r69PT0imC546yIBm1oz36yLVzvq3p52zEt6pi3PftTszrq1xScSGXydqf9cXulNvj48WN7fn7ePz+mccj2rYjIrOh+jpe3AFi/StxnpFlaByup/vTDkh5wqYChk7aZvnL7Ij9QEVDdw0CrvnWrlxU5aPPgEjFMwjIpOMt2oR9MOsuVQ57/aAzThrjfJmlX2fRirRQkEJHlm8vVrxwvtCvM05cg//HHH+3PP//cE1o9W/v58+d2fX19MFHBwKeDYsqI3PpY8TomHHhukvq8mp2rxhyDQWxr/+84vCK/vUmjNcJJiCXXu7ht4XW0Ex4EqALKye9y66s4U5vKZgkf7HYvb/aXL3MfVNnQGXJbra50W5WEOCStAqAdqR4x8/Hx3nRw0VJkGnJ3AJWy8b8GhCtiImszhquK4vXIZyqT35NA9Sg64ddX5fcBrG0CcK29/iYX00kRd15PkluRWka4/YVfcp5SNPVfBb6puDTEaXkEo1gUKjz74/n5+YA8eXoktj4zxPZeQj69P73e50hqW+s/q+lGlmPOgwfpG5Jcmqr7Evlp7RAEu67xXu3z2gTSHVQn/fD9KhDWy7OnwwmYJMfRc7Lebty60+I4TPWVnaDjVV5Ml8sWtXVAmtqS7aXyOzFV38im3NzctKenl2dofams96UTkdQGM+L6eu5CoJtWS/XarLU6+FsB3Z6kPFwfW3tt92nvdZwBTx+zTMeBpmYnNdPFWetEcP08g7muwx4YeHp62s/CJiBIe0ifWgVnSCx5PpWbBPn5+bldXl4efLdX6fEdEwxcyS6k/Khr1Sd9tPRYL4/69OnT/sVvCkIzOOYvl+PYq2SpL+1hvnOQyjdU+FriJJX2nHjb9x2fpmNKn3lJZshphYkSpq2u8TpXdtt9qLdl1bbKq9e2Hz58OLA3fESC7ezlI6GlXgmnJ3JLrEH87n4w1SvhiKpt5Ys96O78gbg71fc9ktrWVixFToOkinqw0k5gK+Y/q3Q9x+v/XXmqWRk/VhHcSjQYU9mc3Fak1pXO+0Bp9SJN/J+ctJNavn2PS2CS0+ZMlY6prVinKtLo/UDwzaVdrsTKh7O1Xlc6UB8Ds0B2yT0kGol0vFdJsxc+jggoW2sHM+X8vIsvXeWbRUcgptKlGefq+tNLr8qDeaV9pV+RWokDRI5fX+Kf2r5yRgQp0pEUYPNgkBypyuLkwftajoqzO9rS/vaE7ePkyttN566vrw/aUG3NGR/1aYpEkwzMSiJftF3vwSnPCgGck1k/Tkk+tYrCV3qo/D39np54/mp7Etrdbncwa+t5eD+R2IpEkkz2SKyCK0pL5SA4pv5SFLzhuGX7OalNtjbhFkpFcJgOxy7L5o+L+DGl732n8ZNma0Vs9VmfL1++tI8fPx58puv5+fnV94b54ioGnEd+oTo/o6PnpMetvX7xIMdJpb8MTurnj5AkrO0TSr1HAVubm9SppCK3rHfvXm5T4NOvc1KbAs3yy8lPSYglKlIrPVQwZ7fbHQSYZNsSmWV503/Hk4kAJ6KrtkljhulUmIS2iWOFxFYYz31Fyq/q07eWVcSWypecpwPb1l4/fOzRI1e2Sul6DtgVsNfgCRymzphVaCqwK/Oo4x2USDmrKBJnTROxlRJJORKp5dImJ7j+nCUBpyuuBruAQmv5DcxeJ/aZHLTXU+VVfQiApFw07unZWrbxUlniQEcG/D1JAqQSgk4FEOg0CVb43JbPlFeAOBlrz1/bXhrse9eFqq89rYrU+j1Mt3I41Jc0u+KzSFWk1ftBui472NrrN12yXCQHiRCQaFbkgFvaW92fHHFFrGg/aA8+fPhwsCyShIDiYOYU5FZj+tzFZygSmU36xTHv4MWBbyWeLv+noGPSWbffGj/0cbQ9TmrpQ/hMq2Yw6fNEMnWeOsRxyzqQ4DmJFbBlW7MduSRaOs5rl5AG6lZF9Jm/63XqX/dXSpttxhnbjx8/7smtfre3t/uZWtkc+QO9/VxvPpePcLtOoj0rS33tOfjlhK+dcLHNWmsHY9Gxjz8W1PspTbf17hNHAYdKKpwxc08Pt7nN8cAAJ9/Sqiknt0mcs8iG+OSKjsmGCL+29hJ4pnjAuVeGEaGdmUCi/6dtc1/NejuZ1U/nfHxUJNfb82eQ21XfsWUjzpBaHU+ktiKqNNAVgfVjXgbPP5FZnVt6jNIztKmzHRhzsCma0gsYEMBKwSoi7WCT5JZLkPkT8PToMompR3Faa3vS67NV/qwX21WRb/90wtPT0z4KTGBPcuvAqAJlFRFie/k5v25kiH+Wwp5CEhFwkNja4ewIZ2ud1D48PBwAp5GBc2Lk5WB5euR4tK3STfl4Hikv72OSROqx6xzJHP/3iC2BcwKEycYeIyQQCfi4sJ6tHc4uucNV+yi96+vr+GkukRmVQZFu74tU9pEQSNA2+jn+f+/SIzs9oEY9r8Bs+nnaKZ+RDSSJZdDGCa7GC4O4HvRgXehLHLz60kH5FdZBY82JrgNAlsV9GUEgH+vxGRWWX/eqHTiD3GtP+iz6wx6B4bU+A812cFJLYvvp06f28ePHdnt7266vr/d10jJw/0b13d1du7+/f/VCuhEAXioVBjsHUttaDhLO4mraT/54rsLePja4WsaxtvJj3lVdZonr7DVVwM7tH22ik9r07H01Oedt7kGrVH63oQwG+0/62+vzXrskPlYRW+IU/Sf+SEF1BrgTqWU9K75V9eXPkFUztmzQ1g4BMDvVj7tiVUS1IruJwPq2Iq+VHGsMHSjNGFh3XPxVL/zQfR79UTRadZfSuJP2wZxmbp3ckogS5JLoSKGenl6emVO5/CdjQ4esdHpKrTqwnd3Y0gBVBMUJbEVGneDq/qo/033vWZLxU/spOMFxxggwv4nIF5KkmfIKoFFPqvbvEU7vk1MR29ROo7HiUs3YcumPE1vqmfK6uLiIq1Wqtkp1csLidfN0HBzrOB0iybgDAycUvE6iceIvqdJyLpbdiQzLeoyeafyfA3ntyUwb+PhIhDY9h9cDLZX0Zs6dUKkPdSzNXsgvpFULrJsDT5+Z5cof73OdcwBPosfxzLrSR2gFQiKzFVFhuenjRuTB9dtn2tPKmYpMEiQLe5DYXl9f75cj39zctJubm/13dGWrZP/v7u72hFbk9tu3b3tiW62mWiMz/vjcpCI3Ps6pC8RQPnPbI7UVBvf89D/t639Vl2P6xEkthf9TYM2xbsK79E98lwwl2RfZJG31zLqu5yo35S9bN/qlOvk+6+48wbesh+6nXXP/rWs1FpzcpsAI0+/5C8fUaf+UMk1sW3u9DI1bDgInIgQqCUAlR1pFkNMxV7ReA58KzCSFSgA8lSUNNIJbgkf9p+OX05Y4mfXrqQAOsJ3QkthSkehA0xIIgg6PkjlZVr2Unl464YEStq3qlIxqLxCS+mhmq/STgWAZfweHSsdHUZvLyPFNyCS1KRLPMS5JhHZEQrlPqfpuJs3elul7Pkk8Ept0rkd0SQpZVubnoF/H0ph325qiq+wLb69qPCfS6rPOfiw5ToGti4uXT7ioPpyho85XjtL7qndNJW/hVH+mONhxSb7SwZp8s2xr0uOUr8SBlEtFaLlVmr6v8/5yJNaNs63yhSK1jjFaa/txp5VCIsIVuWW5VA8+Y5f028c97ZPamwSE/i3hIe8z16e0+oF1orifJYGnjvs3zUVoOUvLR1G+fv3a/v777/b333/via3P2HqfLJVjCdN7laRDlb62dog/SDpEvtjGab+3atJJSxUcHckau1zdk8hfInTE0PRJieC6rqZgv7eZ+2AGzKhXjsErEnuKutMH90h64hvu+2WbKlKbgp+61/PzOvRw1Kll8VJkJ11JEtl0oloZcSpVBdoSeek54VOJ15mDt5f3qGweSXGF5UDlsio58aRErmT+c0XnEiRfesX+E0hx8CqHTwKtN6D6s7tObP3TPWy31tqryD2d4ojMqg0IVpaSWidMM6D6PUtqZwHHpLvsJ71ZkyS3B6KYXyJTs8SWxxOJTQCySjNtvbypj2d1mDqYSK6DYep5a4ez5dLvpB9OXn3WJkXtKU7EWQbm6fZhNPPshJfBMZEI1pXLOkd+ZdRX1T2jgCZtKP+fq7jf1DGOkR7IdX/bWg44juyf21b6KZ5jmVs7XJ4r8fc7EFRydpbHej5B49R1JNXdZQRcK5Libeyf3+kRjRSs8uWC6W3maRwnvZfO0l/3fDeXJj48PLR//vmn/c///E/7n//5n/b333/HGdtqrM3gt+Sjq2t+J0l2nTaVdp5LkNN9S0lttWromDZegpmSX0r3J9LmPikFlp3csnysMx8N4jmel03RsZl6ur3o3ePXua92HU0+LAUBvK60JSlglvict0klI6x9Kln0uZ8q80RO2WiJqHrD9I67sqXr10oyhH6MA6Qis5VDo7BTU5494OCDVaTWj3s6BKhpxkW/9FF7KQDrq7yTQ1S+/mIqvpyK10mJHh4e4qyD+l35qd28DX1MzBrdEamtCO0M0TkX0bjW7IU7DhkyGTf/hEN6aRQlGeMEaikj8ukEN/XVKM2Urpeb180YX3e+CTi6w6UuSdd8dst1muPcQQodUPoME9tJNsGJKKO+0lnqMF805w41zV6z3AJKFVmvhH1Fm9+brUzX/xuEwKoKhjjJciA8InYzwrGsciWSSxBYgTdtNY5pr7jcjz6q8gv0WZy1TTOeXGHgRDulX+WZSKoHnNQWTkIYpFKQV2n7W+r9jfW0y5UeVAG4BPqfnp4O3quw2/13Nv3u7q79888/7f/+3/+7J7b//PPP/uVRfDPyLKF1HOO4K9mOnj85F0nYl+NEY7u1TGx9DI2CWBWpdXzl44dleAsZ+Qe3De5zOXtbBZerl5tKZBud1KqtOMnkga2Rb0vEs4ehUv3cB/uqSGKJlF5r9ScdPWjG8ePlrDCa8hvhp1OT21XEtuo0N+Tp3AwhqchrZQx7DsWlIqnpfGoD77De8aptKpCdBkciu9pWpJb3EMCS3KYZ17ScSmn0lNeBLD8VoGd0BIxpPPSM7sXFxf5ZBRoNAY40FrzdRs7LHWIyHokwsZ+qfj5Hx0lQSfGAFI0do3lu8NLMQDLybFcfp8lQpmNVoKEiylU6SUa6fExfJwfMoFNr7cBRpqiv28jW6m9W+uy6gyOurNDz8dpeXFwcLEXk83ZclpjeCVARcTnFdM7BrupFSXZ5Kag6N10dSSJyGjc+hnTOV1ikWRnvixlw0hOSXPoqpek2Xj5H1/GNo6y7rtc41HUVKKfuyc/pDcYMCGlLoPj8/Hywn2ZGKzJRBZ5InBPxYB/px7aTjvsL/Tx9ruqgjU9gmVuOHaUpX612fnh42BNbJ7Xp+VonVOwn10+1SwLKs3JKwPxWkoIxvkSdeur9Qn3mdu1MrdsB2tmfYUN9jOpY4h4cr9XxtM//6fGB1l5sFbFoRWhTGRK51v8Z31Vh7RSE4goqF+qZYzH2cYXvNI7UJiPe5nm7rU/+5Bj/4rKI2FZCZ1QZIHdcqUFSgyUgV5GcJeJldQPq4JsgYUSOmYe3U3Lk/qsi2WnQjOpYOS2PWvEYFYjlZrTahQrrL54QsdV3clUXRckdKCgSr5d60Ji5Inj7Vud6beTgbbTv6Z+D46RoDGpZuxOe1g5JLUENQR8Jb6U76ZcMG3Wb5eztJwMtSeBnNCZOZWwJEGbyd4d4cXH4DU8X6qMD6DRjy2VF0uHdbnfw0hzlq61WcejNqP4CGa7A8CAY+9LBPduhIgKntvXe/j8DmP0MUbvIB3Hc0Ec5sWWbEuwmcFyBkCVlTEEt7buv49b9LNN8fn4hViK0nL3lMmT5Gi6Bd0AoHeGye19dxK8VEAimT54lXeRyXAai1Ackt05IRLLZF8r//v5+/yPBTcFH1cXBr4NnF5Xh/v5+357yIcr7n3/+efWMrZNt1k1lSUHRY2Up+X0PQr10vXYsSOkFUpLOj0itB7q8jNy+hdCX9K6pxirvcx9akd8UnJWe6R7pYG+SJ/WRk9iK1Hqbuk56Wavnhn122XkF8/EgSLUM2f3zqOw6ljDazyC3i14elQrtgLOqYAVcHbgkkufH1kgaaAQACZSPlr8kMF0ZH2+DZGz0I+mQEhEMejsmJXFFS5GetDzDCa7n5f9JgFtrB0uQBYL1aQAntgIjMqYCGA6UU96j7UjWENpEZs/NebZ2+B08Lg1t7fVzbAJPbvgcpFR64JIMW7om7VfXjQIPM310DHhXHsm2uZ73ytKrR7KJ6bj3C6Owmr0VWOLntDhT66RWn/j4+PHjXqf1/UoBXIoTbS6J9DJVs15VO6V+XdJnbkfPWUhwWnuZXVD76pjPMvrL3pzQpuBCDxz2ytY7nmwBgXUvbf1IZl0nGJjldrfb7V9W6ADRZ23UJlydIKFNrJYDO0B0wvn4+HgAOhVscntLYsv+fnx8bPf39/vZUW2d3LKPK7ub+ovk/e7urj0/P+/bTj5cxNbfhuxLkJfo+UjOkbiOJGHE1l4HPkm8qDOuu6mte6TWCS7LoGPcShK5PFYqHD1zT69M1ZhxApn8L/shXXuMJI7jRJbENb3lOc3Yej/SRxAzSJfTz8eWl7fXzjOYyq/p3TOr86uIbQJwNM49UOZpVMeSUnleVdqVpGgN91OEjMdaa6+Opw7oHUsGhz8BEie13vHuFConQQVs7fWHrD0a5NEnAsGkfMxDCpdmbDXL42339PR0MOvjhsLHhB/jdiSJnFbkqNr3/E5h1H62uNPUMdcrn1XwGQgaRY3RNSRj5nzPIaW+WiKzpDbZpwQGkq1KBJe6VQXCkv75+ZkoqvpKYFf9ruVVrb0EqfQYwe3tbfv8+fP++5WfPn06mLXlrBYBlvLxNiMJWPLyMQ/msU0oMwDL7zlncMy67HYvbw6WHtKXtTaesfUfpec7U7nSs7XVNfQFrb2sWEjXs+yeh34ku621g+scmFY/5qtliB7wpW18eHiIs6b+eTRd68v8drvd3udXs7oiwSqfjt3d3R38vBwEqN4eagsnEmpj6ej9/f0+T7WBzmsp8t3dXfv27Vv79u3bK4KdyG2ycZWco59dImpvX2mhdk7EVvelVRdOXNOxZA+UZsLf3I5ktr88vSqAtpREJp9c+WAGfJSv501u44GGpZJ8kLcv80ovvuoR28QVmLbb/xRES4+ZVVwrYXQ/7wFMx1yJ4yS8PStHzdh6hQhwKknKUgHDKp+eAZSoLD7tr8aSEyHRkpDE6n9FaP2YK0ciajQu/nxNay+zZiS17mxp0KqB5WXgLCgdO4+xjZLjSUZS90rp+GyeP5NHpyxDXpFZ1jWNC7/2WJkht94+5yjevjzuzrBaWucOtHJInt/S8o3ur/qoJ72yLi2bJJHX0U+6788E8nwCHEkfqnp6QOv5+WWlxPX19b7vdrvDZ+M1Uyti++XLl/3MrUjthw8fDmwliauXv3KcnEGkPXMiz/av7P+MX0h9OetT3pt4ufVYga+68OWzaalrsu0cG4mcVmWqrmNZdG1rh7pbkVv3SfLfPPb0dPgt9Z64D3RSSx/78PAQP1fH9uTMKZ95JbFjIIfBHbWLE1mf2WUb6jrlKVLpM7dcIi1fnXwZ+4/jQjOyrbX9qir2p4ityLTqT1LLelUE6y3lHPy09IbYUvtpBYNjVMdJI3+UyJ3K4XZlFmslva9sAXV/Nq3qvNuT5EvTmNOqSF0760uZR1WvCjOzvypsq/yq5cb+KU3/fJHbp1TGhO3cL1dLkEfYIx0nP1xCbtfKNLFN5JXExq9tbbyMNB1zB9drxKri3nBuBAj2nLCmY7yW+9WsrRNKVwrdQwfiaXDpEQmu6iwQSDBYtVOKUjvhZZt4f9ApVS8LErH1F874i2YIsBLhT3VxI5zOLyE1bkBG44ht2AMF5yK9IE4iXz1i21odxGmtvRq3lczqd9X+s8C76tOlfSn9T8dJWP2/nGnPfiRHXDlIF+WjegnsqwxJ9+k4NSP78ePHPan9448/DogtXwSnfATWKyJFgO5An06zAro9wCWZJV5L/r9n4ViSqF91jOfVTxo/suUVAGSaM/L0lL9Xm176pHO0PymgLP+WdM1Xm+gNxx4o4pZ1SiuUdK3aQek9Pj7u/bHbHRLbh4eH/SduOHNaLU9WH6jsTgKlO2pDYYWnp5e3E5PU6keyKXLJNquEeibSquP81rzqzUccKjI/Asq9fuqV85jz702SDvt+a22vCxoHxEIMJLIfE6lbSmp7siZQvMTf+gypj+EZkkk84/434cqEY0bcZM2Yq/KuZmr5XWl+es/fdeHl8XHCVSDuo9PjC94GqY2X1HmW3Ka8ZmTRjC0HvisVzyVHNipkRVj8XAXoqoaipOXE+s/zvCbtM08vQzJKfpyRuEQIZLxUj95ryN1gOWgnYe0ZTNabzksOzmdWnNh4ZMk/C0Klc0PLiFG15JX9XxmTYwwM+7BHfE8VUfqVwvGcZj0cWCVCSwOcxn2SkQPqHauuWUJu15DadJ3vVz+SST5awOV8nqb6IM2MV5FTldMdPh05HR9fFicd1SwtZ2o/f/7c/vrrr/b58+f2xx9/7JciS6dVXs0Qql46Jscp0M+fjnuwzIUggvvcLhG/pxfRfu/CMSbhbG0Sji2OtWqMKZ81QTyO6dbaKzKbyG1rL2ScWEL7jjO8PxPoVXBH/yVVnQj00myI4wSRTC0L9mdd07j3Gdvn55dZaPpCBX/UVtQ5zth+/fp1//v27Vv7+vVru7u72+fDPpTdSW+Cpc/nkmMRe683n9+nvlezP9R19+mjsbT0/Dn5Z/cbPbKY/F4VCKWOu493gtZaXlHp+5SRzz9WqGuzgcvUDmoD+l+ufHAs435H/aLjvdUHzmNmyqsfddNnZh1Tc9VU9V6chOuc1NLWuE3ysTNbr6quvYmPNHm0VhbN2PqWjeazijOFdQAxUq5ZqRRAZfJZWeZJp0vQkKLLaZvIrPLhrG8itWpPOmy2q/4rjaS8BBGVcUztk4wbnRdnWDjwJWnZhDtOjpf0wo0UNXIjVZHaJTJDXhMA8vzeypi/tZDYSkaklo7Ql68vdW4jhzlDapVfIqrpOp07ZZ8lMELHxOgwnSntSio3gXUV4HEHkwC3SKz2GQm+uro6eA5ehFak9suXL+3PP//cz9Zq9lbLkElguWW5NXPjM1bp2VoHCSM9T75iNBvl11f9eS7iNlvkLS1F1r76yINUqR+q4GdVFvr8REr9+tYy2WXgR4E37rf2eqmyE1i2j2ZDXSo/SJ3z2RAPSNFHarbSn3flLCZf6kRf2lp71S/ygXx8R3lKv0Rs9XkdvriJwSNiEAbLq6XV379/3/cNX6rF63gtA1kOllkvx43uC9wGzIhj0nTuvYuPbY2J3e7lUzM8r3O8NwUM0v9Eat0ujtqthydn7LALbUDKZ2SHeP+I3FIX9NP1xNlKi+TO932sun/u/dzPKW+fIOKKR5JcvsHd9bgis9TTFERzQstxQumND+KyhM8SN0xYzmWJLq8mtg6OU8WdIPTSrohzlX6S1CAOHJ14E/RxuY6cMe/z6LiEbVGRWuWjfSpZr03oUJzUakuAksD7KOqVojNMmySUikCi40saPRLM8io9OfzqMwVUyjXRsGMkjd1EBs9ROE7c0DqxTcGSEaGdIau9c73/PX1Zc+5YUZu5U03RYf0cZJNE6H6PokpvXEdpX0hu+c09Jyj+1uOPHz/uSe2XL1/2RFb7PlsrkqKZJBJaX5JYfYbEP4+SHCjB1giI+fVLJPm2cxCWk7MMAsMSXxrOWTPacge9x+qMgxeWU2DSy85ZQhI6BoXoGynSLX9LMs+NfIeTOwZouXKKwQL3aellTj7+/QVSrbX9IwPVbK2vhpB+6YVNWo6sz+wwbQ88a1/tr7pzNkdBAccp1MEUBE2+238O7iudS8dHuPCc/XLSPV9i7/6sR+SqH/VghtT2sLXECfdMff3edP+S9IjpR23CAFoihtrnWHaf5WM8kdb08yCP6qlgtIhrerTPP0mWyt4js778uNLZqh5JnFelvukR2bR/jEwTWw6WNABOIVWDjADHzMBPjerpuhEnYRXR5XlGWKQkBJhyvtpW5NbLJ4eSFJXtwsFHAlLVPy3TYhuQfFKh/e2OUgYCchqHFN0lYFc6DnrTZwr8mV43Oj4GZ8ejK1CPyPaU2dvzHERjNJFaghW1t5PY3kzObF8sIbMjqcryFmTWjbzPdihf1wtfsu367kDFVy/QkaZxn1aTOGAlgE4ztCSyf/75537GVrO1/FyX7J10WvZBOpzezkpw7yQqOVK2D/ePIZ4jIHwupLa1128TpggM029QzzmD1rOvlEqf5BslCcSQqFbkVmlVS5N7ZVA6PpPL8lX1SqDTVx2lZbtKm8RWfo0vckrP2VK3lY4HHNR/T08vM9E6R5/pL466v7/fp7XbvX5MiDM8/vIc6TPbwe2VypGIgwdFfetjjWn2+qfX56Nz5+CXEyGj+MvSvE/oG9i+VbunPJZgJ4oT2hm/6z5vJh/lVeEPxzO6zldTEatqX3oo7K22UVq+rN6fQfVZ3Nbyikr6PNpa4gSR15ubm4P31PgSZNbD80wrKUYzte4Tkg8e9ddIJ90vaD/dfwx+W/SMrQPiteLALJHMmTzWVDwBSkazPfLNRv/x4+Xbsr5NpNFnceRctdXA4j0ktJzlIVBlmg5GvF3cEaf73SnpvAa9or90yK6QfI7PHT+XFHOm1iPbFXmuol9en2OlUsyq/Waue4/ibZgML4Fia6/Jmo6NjJ8f7wGOWX2vtunamTR6/VYZ49YOSaTSEjAhSB6RWkXjdSw5J59Zq0Cgl50OSw5epJYk1gktlyHrG7YCuUwzzVJpOSSBNl9i48sTkw0j6OqRzTV6n+5Jebx3QOw+zAlda+0V4XRiWwHfBDq4TWXR2OAS5ERm+b+117M1PO5BlJGNTX5X98q3SmfpW0goOSvCZcgEkkyfZPP79+8HbypO5JYvW6LtIIBWOs/Pz+3q6mo/gyqd4xuImYd8p9L0ZY3+VlUPfuk/sY3jDtadZItji7YnHR9tmZfGkh/7ncR9SRLqQFpCrv5wP5FwFPOZJS2pPEvI7Ci9kVRLnN1eObHVvSS4tENc3cJ7+d/fZu5+zLF05U8ct6sMwvn+uUx+WYTLkqXXXm+fqfWfl5vBNNUjjRflwe0a6ZFY9zWez5Ixs/pzP281G8LtGhktuyWYV2PKqZC8OtmdJbVOCOhY3bnyZRG8n9f4bA+vSyRvVH/eywFN4EGDyEi0g2sBmFRGAgXlmZZr0THzZRe+5COB37eQUdSJ10nOzdl68MjBXWsvxtYJ2UykVMfSdelc1X7Ma4bUztqk6t7Z+0koVGePtLO96FCZhu7T7BrTI/moiKATA/3n/Xxb69PTU7u+vm4fP37cP0MrAvuf//yn/ec//3lFaj9//txub2/b9fX1q5UmnKXlkkgH9lx2mb5b61sHz95m/M/tEukByLVp/mxJjl++hGOS1zsATuTW0xyVwQM/DlBG95HwMl8eV7l85kp+KI0bnle5pG9+rZbu65n0x8fH/YtZOLvpdaKfSzOpJLXaun+TraW+823E2lc+IrD0m1zdpPQIgP1blykQT3Cbgul+vf+cOHnQnKCZK0+Unm9/dzJLod+g+Dj2GTrts829L1trB+29pF29PB7ors65pLxGNng2baXP9NzvEmMzjRQg8Ec3Ehb2mU8P4iQdoN1lWfhSR87W6qWOvgzZv1frpJaztP5Yg+MK2q9ZvTyFVCS25z/oM0aymtgq4yURmwpQH0tY0lKICvCSeLIcJLksy1JSq4HKKDPrTOKq4zRCPqtLRUsdzXwrIpAANesuRdO1VFK+7ZAzupLqJRQisjQQ/lytz9T6rHBFaqvxsnQMVSR2ltzy+jX5/ypxvUtOgX06Wv5Dva7aLhnGqu19f3br+zPi11d1dPCuff13QsugTkpPTsRnRVwvpQNOQChsf10rcMxZnMvLy3Z7e3vwYqg///yz/ec//2l//fXXAanVEmR9t5b2yGdq+XwfyWxaflkB3mo2oQJhsz5jCXhi2inP9yZePvmKNMPpPpfjKtnYY3SKQU+SUyerTJv9xLIzeKO60b+mpcesM/PyZxRTmyh95kFSm4LLXL3gK5K4FD/Nnjix9edqfZVE+l6uvyCqR2b5/gtfJcK+8Lp6gIHiWMYBco/MpuCWp+n5LJWl4/dXCScWJCo7Ax+jr2QksqVrZkhtaq+K0M7O2iaMVM3ALhHiY/2nnUmBOukcvw1N30ZiS1vJ4BVnRGdIrftt8hDqrAgtlyDr57rMNh3N0vZIbdK5xM2SDz5WnGtVWGupLCa2TmZHwFcF7DWOjo1ASKUIyjuVrRJ2mhypBjWVQ2nx2G63O1g+KJFDT8S3NygcsLJcLE9VnxnA78JIjZfHo9BcfkwSXfU/AauUhLO1/lIZPRPkL45ywOVgtqdYCaBVbdcjWGsI7nuX1KbqV64SSMvvJA6CndSmPvO2TO2V9DcF0KoxXhHTSmbJsdeVwNu3LLMTXDo9OTTWj23GJfyc4UykTvkqGOWf2Hl+fm63t7ftzz//bH/99Vf766+/9rO0JLUitvpm7e3t7R5cSy/l3ElkSW7TM4U+Q+iAi23Ddkh1dFt1CnkLp/3W4jrXWv3cLccNgwoOytaKA0zqS0VuK3vgJNf1Sv6VpLeakdF1yl/30i+r7ry29yZkB73SVc7oMHjb+64rZ5UcsApjkKz4M+uqdyKzfJ6WjwnJpvvYUHkSnuthO6bhJCoRXSez7h+O1b01vvtXC/2JEzLHXdX9VZtXQQOXWUKbxkeaUU32O42ppf00458ToZXtkDCw01qLYz8R2xScIl5NAVz2i8pDUss3HfvSY5FaX0Gi8tGepzLSb2vbI99JL0/hH0fjjv6shw1nZBGxZafPLFPsRQB6U94zAJjHe0SrpwRuaGVQuOROP0/n6urqFbnlMTnQlPfz8/OrczJq3lYktTMAv9cnnq6cqgY8r0lLICV0wG7IlC7zpFP25Yu+1LkyFpVSnMJ5VeR2dO5cJREkN7TVEvjW6ih6RbokqS3d0VXjOJ2v0kky04ezQI7AtrX/OkR/CzHJbAJ7nHGqiK0HiLgEk1vqLmd19Kzr9+/f28ePH/ffoyW59ZlaftpHb0GmPRSpFYnVNzM5Y+uzVG5LUlQ7kdoeyU3gZUZG4+AtSPNbioOAiiQkQpFsLAH0CEi3Vi8Xq8gs/7fWXhEspsnrSGTTaimRW/lxbxeSVpaR/p6kl0Gn3qNAuo+gkj5Oulh925W+kvVhe4k4M12BU77hnEsZ0wyt8nAbynZgkC71b09oj2iTON50XUV0dd6xSpLZsXkO/tvtjk+wVP3D+xOZdZydxNNKKyB1PGHslEaqm/cHg1WjNqnKmq5NwQFdw7bwMcZAPq/3IODoedUeaXS8Jf3l5/c0W0uyq68RSJ9Z1x6h9VllzuqnsrnPda40GkuV9O6hrXZyW5HckSwitok86ngqrCQBO/2fMV6pcgkAV7NMPtNb5e8ztkzf821tjtwmx53qKJLpZUpEdYbMVoOBhNYNlsoop1nN1KZ+cPBNRZFTJrFNUezec7XHKNWM/JvIrQcKOEPrpNZ1JwHm3k/SsxWjcVyN+SXi5a5kJt1kdN1+6blZgU8+wyLAnJY3Sqogl/KvSK0CSHrmVc8K6rlakVf/ff78eb/0WM/UyplSj7nE0pcgpxfkJH0mYU/R4WNlNo3ededAal04LvXf/YlvU+Aw+bwZEkFw2Cujrm+tbmcSUPlHX47s9zJNXeu6Slun8jCgzXxGwT35urQMsPJvaTYnAf+Hh4eD5b+++mG3271aquik1u1M1ReS3pinH+iNBdooJ7LKoyKwnsZacQK1Bhj/bGEgQ/9bOySTbJcK03JiIWHw1sYzs9z6+HdckHgA86WfcqzIR/K8jFU5kzBgVaXlx56eng6C0dVKBdrJtOrIfVy11Nd1bbfbHayq6JFavsiOZSPhdrKd7E0itZUO/gzMTVlLYCuZJrZVtMaJmyugjidHWjWcg8YeqVXZnNBWr+dneTiodb0P8lH0MpHbasa2Am80XD7IRpKCDalvUv01q8T+IrH1JWpVW/I+pkdyTGLL5VopolSNE89z1C5L5XcjsJXQ4RPsOSCikU9Gr4pOen+lZUr8P0tse0Gm1G9pzMwQqBny7Okwuu711nlGki8u/vuJgUqnnMT2/tOp8sUyDw8P++dq9VkfzdzqUz8isre3t+3m5ubgEwME/5wJrp6r9bcfJ32mk6/AbO+/19/7Y9RfSc6RxEqSPnBMVqSWeluR2p6+9YQ4YLQEuReAlfhy5B65JVEl0ZadU70TcU1BvWR33AYyqKStP1vO2ZKqP1Rv2QXlRbD69PS0n+Whvgoc92ZpmQfr4sd0j7frqM9cn72u1NsRweVxb/NKErE6J5/O8d3a65UN1EvO9LfWyrbutRnzYZ8mspr0ogr6qDwsF/tGvlLHdW0i9rNS2RZfESFsy6ARg1lV2vy5b/Pvwqrd0wQN25Kf4fLv06a3svsElH4VuWWZiek5NhgEOTXBncFabyWLZ2yTeGMkMOwg2I/x+pSX/3eH5bNOdFR0Tu6UtIzQQaeUT9HRXv1Fbl0xZmZsmYfqkiI8utZJrDvo9N/7SQNaM0kUDn5tJTRIvJ5RQj6zK6UXKOYyqvT8bjVT22u/nyU+pk8ZXfrZwvFczdRWhJY6W5EW2YAKJCewnMit625Kw8dERWB7jrbXRiltXsPxkMC22oRt3Vr/e3ze3sybjkjn5cT4Hc2Hh4e22+3azc3NntiKxPqbFn0Zo89oaQbK33YsgsuAVVqe5Y6WztUBrpPWGZ1fYiPOmcRWkgI8iXxVwNfTSmSuyi8JdSKR29YOvxrQy8PT6JFbXUtCS12nfSO5pd91G5jawf0oyScBpq9E4tineJCKeVJnhGn0CZD0xtQUnFTabP+RrpEQpP5OJGAmGNcjuJ7GrPR887mQWgpJmvqwCjykwI73q+PARFqrYxxDXJrf82NV/9P38nzClFX9Ru2mMrHtGFCiTUg2wOvh4zQRXCe1Tny9rmw3EVmS3Cq45uVKs8Zua2j7aauUjtdzRGIT2V1LfJfKkvSniW0FKEkE03lvNBo3Gs8eqfXIkv98tolRDn8hA8vBzudglNP0Qfj9+/d9ntoXkRO5lSiyOtOmyoNO2QF5qr+TWa9rckisl7evOx46HQIRHZdxoKIobY8i+Tdqq8hSIklUmtQubJ/R4B85vVEa50xoKSkIIkkBBSe1yXBKqhkPN9C+74S7SiOJj5H0P+0f09+8T3rLYBYBOduFdsXzoF31ulVOVrrEVRG73e5gNpZLnbisiQ7WgbD6WKRWz9Py59/o7L3VXP9be/n8xFJSW11T9eEaInuOYLi1/qoK3091rPQ16V+liz7m/RgJ20gIUHvkNulY8t9qAye37k8d5FbE1sd1tUyRNpP9lAis56drNRPLN6b6d2n5XC3rLl9N/8x29G3VFxK1L8vaA8nV/xTUOlZmMMA5iONAieuP+2me4/WONWeIbKUbxJnVLOfI/nBMav8tCa63KfXB/TLL0cM+TjB9n/dRnLN421dEm+VJgWMntAmfcYywP5j2McR1re6NcNZS3L34rcgciJXB5LXa8jdDaHtOVufTTK1HQCpiy8HBQaKZV+bPKCrL/v379wNCm5Yj+4xpMgZsSwYJfDCltkjtwIga86NCV+AwRVVbyy80YJ8pbaYvsE1ym77/tZTUrpE0Pt0p8/jv4BwrSSSRAJLHWnv9vCf7yJ2oRxt7xNT7JI3t5HiSs6tAlP/Ut+7c1va3k1u1IZ0oHW2PnKe0K2CYSK1+u93u4MPumpGl3XF989l32kVffsy3H/Otr9WL3zhLlQB+j9R6/VPbex/MyqjPz9EGpDGf7Gmyee5TKr31+3plSSRtdJ/Eg6pOSnWOvoj979e29vLJHwfv9G1uZ7wOHI8+xvl+Cp+5Yd3Z1vw0Ere8RuQ1PXeX7C3L75iCW+qj91HqCxemzTR6hHbpf5dz1MuRJAzt4z/dU0nCmLMkNum9z9bOkC/HCU5kdW6W4C4NUnoAzAku80s2rrKljoPcZybyy35IdoV5JlLMdDxY5rZG2DqV0XGb+4eq3qlNZmUtvqJOLL1/mth6hdS4ldNLBtTTkvj97lg1ENJbCmnc0wfJ00PXJF4EhkpH/6nIjFCyziS3T0+H3+8j0VVbJcJLZZYS+0BTfskQ9chtr2+SoXAwREnX+zMLEg8WiNhW4LcitAm89ga5K9EMgEqK9zuT20RqWztcfpZAsfcP0+qNvxQRTWXi1tPoOVECwrSfnIR+qZ/TWPBys/7pHpVBdUn3VFKROndU1C+R2ouLi4NZHRFaggcPNvlSJn4GQc/rktSS2PosrS9HVrunKHYVREs6P2qrWdAz6utzk1SfpKtVPV0ve+B2NkClcvA4Az2VbU12WwB4DaFNs7aycdQJHXdwn1Y3JaCb9JKB29T+sm08x/8sB4P1vlSxaj+2I48nHWS9dI2XNdkukhOWoUdQ/dgSHZ+Rc9Zn15nqmpHIfo+IbCKzPpvLY2niJGHrpBfSL5FJPkbgvljXsE2EMSvM2hMff9R7iePXNGaTz+r9vGxV36p9vD28PVV/+VPiaH9ZlK6p/K3S7vEyb+PK18zIEjzNvpnFTC7TxDZFHHsFnSUiTLO1PBM7+nGmNv181paRDv0+fPiwJ7eateUMbuXsdrvd/r7W2v56Elh1VPrGrZwDiW0PjNDo9MitX+t9yXJ5P/UiuG5YGHXjPUnhZkhtIk9rx9ISSQRlbbTovUsF1rhfgR/d39NFJ7Wtzc9WJiIr3akANMsqYy4noJ9maiodWxvISMbe9W6JcU79UDlSJ7aJ1JKkcmaXS4j50zJGtaM+7aOlx73v1fIZo5Fu8/GFBALcFiVAXAHxmT4aXXMOQpCm/xUIc39SBZeo2z2i2wtSpTK2Vi+vlCRCpTxGhNb9u6fBcrjv0zXyw7N6ynE88mcUtjPr4faIts9tK8ug/NQ2bIukX1XZK6nOkQz0yGvvWCqjX8dr0/8Z/T4HWYNb/L6KzC6ZLKrur3Amg0StHQZP5As0Nrn6sepzEmCdH5FbXVcdV1qO31UHHWcaI9/jmCP56J5eVf5dZWE7aHJN16U3Hye7z7Ipz4o8pzK53/D/vXb39uzhafaRX7MGmy2asaUBHUULU4emWUonaomw0qj7d+b401Kd6ttPjIiKYAmQ3d/f74GgZi0S0U2O7/n5ZQkSZ2x7pFZbdrYPJG7dePWMTmuvDZZLBTJ6kdQEJBNBToEDKWwFeitl8m0lo8FfOY0EOvz4aKyfo1TkNv3SeEtENv3cWaa8U5mSw/WxTGdDAquxR+egn8YJwXEy1kq/ai+dT+OO4LTX5ume5GQIFOj8/L0AtH0ezOObyXXd7e3tnuBq6fLV1X9dghzpw8ND+/r1a/vnn3/25FYvjiIhTksvfVkW9Z195vXWObZNBWRHALg6Pnvde5YKdCSAU9lR+t/kT5aQWvfvSUZBCL/WyR9BcUWseE8Csu6vUtDNZ3BSPsIRvp/GudsS+l7m7cfc3jL99GP9kg5xXDAt5p10IJGnEUllvklPHU+MdK9nB5J+n5MuV/oy0qlqHPu4YZAkYUW/l+l5kLr3xZHWXsaAT+JIF53gOg4mEU7k1vPidsa+uN1IhKo3hv2/41j6a6aRMBfbgf3Cc7yfvj8tQa6eq/UyVXpaych/LtG1CqvPYPFZWTRjy4FYFapnFNm5dD6J3Ppzsv7MrL9JTK/AJ7nVz5cj83kYvdTow4cP+xkLfSajMg49UsCok+rLSCq3/I5XAiBVWyaQUREBj6qN+pj5JABJ5+p9ymt9OVaP1HoEie3K7Ui8zKP6+jXp3pGTT+V971IBUx53gEXnNiKziYz2QHBVriqaTJED0NaBGp0DSS2DXJ6eruv1cwKia429j/OKoCRiK7ucnrtTWiL6tHNaYvzx48d9cE+2UPXT87WaofWZWr3hPDlWn731evSAQtWG7ogTsB61/zEO+b1Jj9S6P3FJhDaR24rM9khgIkFL6sJ7ndwKLJO4OVn1ceTkmMcr8svAl5evGreJ3KayEBfovuS7UvuzvVJw2Mmtl7MC34loJknBcG+X1DY8zm06PwLcadzPXHtOkoLASZd4XUU80/+EbZ3AJqLM+3istdc4mEFXpiV9S7O3CSePCM5o3Pp91SMG3K/G4Izuc59pk5z6ln69al/lT7/q76lJdsHx9Uh/Kv/R06WKrJ4CF3l6M7KY2FYFqAZXFfUVsVNhqWTp9decfeXzJv4dKP+4sV6cQmJLsMdvqqbncrnMzx2M6s1lxQL+agsSQSfwdMw6rzSTMjsY8fZ1IpsMFPvFhWVins/Ph0tAUl/zvJRqyfdpK4UaKcESMlsRIz8/OjZblvcqCZwmkjsitPyslo8/6huN88gw+Vjl23p9zGocaZZW7U8HkxyI7nWg69LrS+oor53p/8rQJ11w5+R6pPavXpbHABNtHT/DxeXMasvW2n4li8/Uitj6C+H8e50+Y6tj3kdLAHC6ftT26fjvAIB746YHTJaQ2hTQpV5Lkl7PgJCq76TLFblluSoiy+uT73Md1rkR1mltfpliErdniZDpOl7PMjiAFeFX2fx6f96Odal0MMnomgoLVvqc7vN70vke6D533XZSm8aojruvdAJbBaF7ZLfyv06eU9CFpJarHT3dnm3okVvV38dP5T98X5Im53h9z8dUAZzefSl/rtp0PXYMwLzSyijuO15guas0R1hnVmi3jyGwKd0lZZkmtnImkhEQUWG4XjxFIOgkqXDpmVl/I6AvOxaRdXIrUpxmMQj2Pnz4sF+SrDw4e+tKrMF4dXW1B9gcNIxuuqLymPaV5gwgqMBHZZh4n7e9p5vKIXLr/S1JCpVIbQ98rXVAPSUateUsmV2qWO9Z3LkkoOsOzx8L8HMpsOIBGObl+/7f7YXrXmuHsxayTxy32teyZM+L93i6umapwR851CWk1omtB4bUTk5ofaz++PHyre5qmTDJr9J/fHzcP1+bnq31xwxSGbnvpJaBhqrtKhDhx3ptPtP+o+veo/iY4f6sXe2R2ooAtzY/o9Qrt++735wBv+m6RL6cGFdkIdn8qty9sVkR3BQc6AFLb1/Vl2Sf9fbjaWykALMHrUdtkaSns37c7/Fr0tjm/2rsry37e5EqCOzHOX4cX888wlcR34TVE7FNdoLEi0uQ/RE+6WBP6MeTj+6Jj5neuOP+LJGtjld65PmyvaTTtBOul2wv+uu0Uqq1w2/de1l7ZZwhucfKEnzk/MOP9WTR5356ETjv6NZev9CBRtyVhcqkWVsS2mpG1v/zExfpmTPWhcSWhFazxGkGRHUledPgS+C7RyLYWfqfggdORt2oJRKbiMUMCFE5SNJZvhEo8SgyH16vQNcsCGO5/To/nuqXjvn1rlgpn3MAvT2pAGtykMlRVgQ3OTuPLjP/aj+NYdctB2myGzLqcqh0CMzHnW/l/JaS2xHI6l2ffiSI7gAdkFTkvyqX2kLHZROlu2kZsr80Kr0Yrnrex0GukwBJ1ReVI071qtp71A/nJBW4n6mLj5VkC9J46qU3W17/n2ZfEmlzMp3whueja3tklvcsJec9QlaB8WRvlraztw9tgZfRbUfa1/UjPfR8KTMEwM+na3rjemRXz1GPJYnUjggtMTOJa89n03fz/TP+Uz7JD1e24enp8D0XKQDGa10cE/sx/k/iPial3TvfG4t+TzXGe2NxFJxqLT8DrPv8+doURNZ91HEvT4XHUzuOZBYTz2KidHym7ymr3orsGVcGPDmjtK8fn5/lrK0/M+uzsvx/fX0dZ22doAq8VcuQqfwcWK29LCO4urp6tSa+tcNX+SfhzBKdnOejpUXeZhVpdXKRrvVypD5zQ0qCK0l97zM2Pos7C8DSgHYZEdzq+lE66f/vSm4TmSWpTc+zUz/pKFvLy5N87Lk9SLbA9z0o01o7GFcktrpWhFbp6FvTrbUDMuuzvEsNtP+viOQMIBsRWye1qe96ZMRtbHoel45T36kVmeXnfXzpMQNZMzO47nR7Efl0XXLYx/bdzPH3JpVNnW2TNSR2bTnTfkWeRiR0FihXabiNGF0/yqcaywksswzyJUtAJPGSz7rJFjB/L0cC5L1yjuruMkMuuL+U1FZg/ZzFx6aOuW5Ws6gpEF29l8YJbfLnIyzp/pt9xHc/JGHf6aWr+gk3Ow51nDySmVUDM8R0CTleOiarCauEdeg7K9/qeIF1mNXvWVmKgZeS2mNl0VuRXUYRF11DgiZxIuiAunqO1gksiazvzxBbX97sJJX1ctDmEakZheOAYP19RinJMYR2FGBIx2hgSHB53q+lAvKaGUK7RmYUzOs5Q1a9n2bJ7VuCw1PJDKnVfnrenbriS2AJrHokNwFXv666nk5F+jjSP96T7JHGbm88LSW0S0mt9itHxfGYyGxqawIf9RttJPuSNu7h4WFPatNMrcitvyROdtFJbM8R99rY+65q254NWNOf5yAV2J+VXgBkpE/VbMNSSWmkYFjlt0Z9yGvTrG1VDhLfnlQELpWF5VF+9K8z0puZS0Hs3mztiNT2xlPCR9wfEQKVza8Z4YWRzT1nGZHICu/1SK2/hDVd4/d7kLjyMS4aVxcXF6WukcT6j5M9ulbtwjZSHrMzrrPEdXSt6uh5cpuuSbZFW7Uxgw88zvTIPehzHR84PvK69vrgVDpU6eTPILWtHTljW4k7JT+XgHWKOhJIc1kyCe3Nzc0rgqvlyP7yKA4Wgbjv378fLD925eVg15K7VNZKiWfbawQSeqTVlyArrWq2NkUHvcw+o0wH3HurnDvPCuCP6rpk0PeurwBRAkejdM7dcUpGJIj6qPNOjjzSm3ShB1Cr/P1aJ7YcS1wtQRDqwK66rwpWpL5eAq4qwFURXb83EVpf1dEj/7QFAjeyj7e3t/ufbKf6kXZOM7Sj5cf+nK1IbYoqk9RWb41MUvmeY4DtuZPa1sbgwcdZj8im/XSPH3N9mfGDM+WYCRiPQGclIxI7SzJ7ZfL/1YzTbHDAg1T+DhJfZdZafsFV8skjMF/VswowLCEG6ZoRwT0VNjgn6ZFa978kryS+1aqrtFJx1qe39tI3GuM+Ltj3HIf+0lX5oOTXPP2ZPvWxnQitl686xjR9f2aVg+u5Tx54P3g7q8xcFaU27K2OqurbI7FvRT5Pke6sbV70jO3ajB1waX8UffLOdmOelh8LrI2WIquzR8/Qarnd4+PjKydSSZqx7LVXD3R4u1XEdcnsLdPq1YHlJ8HVs4kkvrp2BEJnxY3XCDAlYzerBA7MZojuzPn3Lj4mUntxvPFYehlFNe6Y14j0VqSNIv3c7XYHjjRFX5+fn/crKxis0f2J4Hpevf1jiG0itD1yW7VP+hHAKAD48ePH9unTp/3v48eP+yCgxvKPHz/236blEmR93keztbKLx5DaHoDo9UevjWfuHx07J512gDJTv5k0W8uPe9Bfqc9IQmfyquyMzvX8VAKaI1Lm6fVmbqt8Z31Jr/4+SzubLm2jkxMnJd5nBMTJplTAX+UclYuylND6dSM7+W+T5C8TbvZt8gNKY4Sve7O2rdWPr2nrOFrpETcSNzw/v7zPJRFj1j3pdGVz0tju/e8d43bJBJ+L95u/HJftknRLP3/0x/Xayz3CN4nojnziCBf17l0qs7aXsmop8mxGPbKVgK5Hb/yXllhwYDihTW9FdmLLMnCwcJmyK35yvmkApeUBqZPdGVWOn/kyMpuIxYjcVnlIMWRsPEK22x2+eId1ZVusFc9r9tr0/9RSkedzdLwjQrsmvWq8JV3vXZOIsYRj0o9fXV3tyVVlTzSue3VORnwpSV1zbXIy1IUe6Xc7ysc3NFNLYvvx48d2e3vbrq+v9/bj6elp/84Bklr/vA9tJIlsRWrpeJ3ULgEKlfM9hpiOnPJ7lh4YqQDGSNf9Gv7nWORxzUgku13lVxFZD4Z52eiX/NzS8eRlocjPjcrfk2Pvdf/O5yOJgWjnWjskGhwj6Z0XM7NUlcyQ1d4MrV+7ZvtvkF6AxXXN8bX2U5DZx02FvVWGhHt1H8eObIHu1zGNYQbEki9jWTyo6/nNyFKSu2T8VpJsGts3TdaJbzjucVJLH9vasrcgJxJb1e1YknpqQrsEpy4mtlXkxCUpgiuUjo1AcLqGkY1qRpekllEo1Ycd7st6qudXvD1aO3QgPnA8ktKTHqFNbcX6kNy6MaJhTG3tebMOVKjdbrdfiu0v3ri4OFwOmur2M5zR2nycUFfj/ZR5/ipZYiDU/xxDo7qOHFa6xnW7ul9OzsugMsqpyoEyCjwqk8uISI3I7DHE1ss2so/aJyDRW+JJaD9//rwntr4MWcRVs7NahqwZXC5D9mdre0uilnzmJ/XJEuI6q4cz1713nZ4h9sfUodI1Cslta/1P9STSWs0IJb1PPrQCcNyO7F1vGXIqz25Xf/+6IsnV/9RWFRAmqa2wibcBZ3h4vAfsXUb66dclgjwzRv/N5JXkrxLXnYTdlojr3QyW5z3VuJj5cRLF/ZfrBG0Q/Xk1w6uyJdvgqylngjszdjaVt7KBiaf4MmSSevnOKoDMejmmSKSW5U8kt/p/Sj9TjaG1Y9ll8VLkGWeRgGkCYj1l8oHC/QT2qlleEt8U+RIA1ixkIrGtvSav6b8Prh6QHUmKaCeHl8gt1+iT3PbIQyK2BKxsMy3tvLy8PHjbbOqv3vFkGGbAlKf5Mx1glV/P2J+LqM9VR0Zf/ccXNvl4dyLsbSNg6KAx6fnI0HE8Pz8/v3J8PULLemubjHgioKP7Zomt56U60TEm2zlqC5FazdR+/vy5ffnyZU9sOVur/tRnz/yFUf7d2rQMeQ2pHdU/9VHqs+qa0fHqmnPR4VGZT0Vqfdva3MqaHnmtZoKqALJ0OgVPk9+tQOesqEw+a1sB76ruM/bLfb379tlge1oKyvbzH69JM+A96Y23GVLgx2av+92FY0v7T09PByuNqq2n4/iUkzp+jZ9nmyt/F7fT1eSGl23Gr/t96Z6ZNDi2ExHUNa4DPf89I6nsPhGXyC3tgfL0pcfVEuTETao2Sf50BrMsbQdvk+TrZ+9dIotfHsXBn5QqkcJq9rYnMyClN+Ac0NJxqj4j5+PO0r/PWAE6znKmQTeSpaTWfyS3vVldXu8kxRXp4uJiT2J1zUyf9ghfAjCVoabMKtYonZ5UIC799/vOXdT/JKAksvpxJlT3aTyp7avZV6ZNPXan7uK2xmdje9cvqb+2buyXkNlZItsr64yddHDhL9oTudXyYz1Xq7Z/enp6NUtLYnvsTK2D66p9VN+eflcONjnqWTmF0/5VMkMElthWXZ/ILNNI+7wmkdjqkRmSXBf6S9oilbMCd1X9Uxt5/XzrNk/its7FbVV1TvsMTPM5Wn+7rZNaB++ttS74HQH6tD8rPVI7g+lOcexchbO2IzKbJPkfrrjS/2oVkwdGqMOa+BmVvyrXjCQ8zplL1Z/HdL0f87xdR0aztpV/4v9Zf02b5xNuaaZWZWT/Ofegf6U9rFaweJ38eNVmXv9ZnVa93YeM/PupZPGMrc/GVELn5sBzCan1TmFn94z2EvHBr4GkbXoLKH987oxvCXVw99ak1h/6H81e9yK/T09PB/Vj+ziJIDmRgeFSk2SM0+B+60E/AnUjwroEgJ+TJGDnuqb6cmxQ6Dw9UFKNYwJFXuOEtwKka+rp99MZjpxZdV3v/hlHkMh8r87eR37eZ2xFZv0tyK21vc3i532+fft2sAQ5vQ15CamtALW3SdKx1AaVI67a12UWYL93/R7VO9UngWSOu3SutUxiR/Y0BU99VVGP2NKPpLoweMzxVo0hb5dKJytS6ziGRMTvT6S2Ig1sG38xVHo8imlS57x+6bzbqtnZ1aW4qmdLUzmr62Z1sKr/e9fhJEn3dJxb+mX1JXEXCStX3fmsqo/XhL9FbjneWUYfV8kveZpJXPfc/vgx1rcnjvG170GykT/3NF2nvc9o23ymNuF2inAWfazbvapeLGNVr+o6r3tKc6lUQdC30s9Vb0V2ENuTEQkm8OHvx48f7erqar9fzYjyHCOU+q80VFaPAKeICEGc3gKqH7/jyEGnfRqZRGpHTlfHe2RAx10xetvqxVs6T+PJdnh8fDxw5Pwp/9E4cMNc1b0CuCm9Jc5yNEZn86mA9zk6z0rcQTr5pGMUuVV0mUBPbcNl8QyKEdh6HyXn7MYw6VTq+9FP5e85g56zm9lPZXPxOqZ+Sce8LaST6dNoet+AxrFs3MPDQ/v69ev+J2JbfeJnDantAYhUx6TjybmO2netbp6LTvfaaHRfj8D2CK8kjddRAHaW2DqppQ/ymUjOYqT6Jzud9DrVY/SryK3ESW3l153M6kUy6fk7khku8Sf2cZ30/zP289hJgl7ax0gvzWMB+K8S4lMGfX2mVde29qKr6if5YB6fxT7Pz4dvJ9bWfz1fPeNvezqXxInu6NhMPbWtfFLly2fEy1Hh8N4XVty/+hJkn5VN5U18I9VnBuOk9ptph+RbPI0eDzhGTvK5Hx/ssyJArDSSMRY5rUioSOXV1dWrAaCXHCUFYPqJxGppnv90nAS3ev4sKU+S2bZjZLe1/LF2Ll/iT6BWALda16/21PN2NKxsfycwKo8PYgdGkl5bOKA6VpaMzxkym0DQOUtqYzlFRnd9KTodHR0vxwZn+LkcR/dxDHvakgQcVeYUFEs/D4CliGdl4Ef/R/uVJKfs9awIHh0Yga7sQ1rCSHLw+Pi4397d3ZXEVjZuZvlxAs5LnO6MpHZd63yre84FEI/KnOyUg2K3t7y3ur4Sn80ZrRbiNQkQOqlVuaTPPg7X2OUKbJO8OglNecz4mERqqZ/8ioP7abeR0kMHudUEgOtjj9SuIbQprRkb6PdW1/fSGtmEc9Bnjjv6TJJTBntIKInRemn7MR8T8rtcAl2tAvQgN9OrfID7rV6fMoDDILvO8Zhmk3UuCTkF8xzhBi8f+ykJ7Ya/sdwnotyeqGzOY5Kt67XzqI5+LI2Lqt5+bap/5TO87Sq9rDAP7x3JorciM1EC2TVCx+kDngZZ5JRLfXlMpFbHtXVSmxrZlxOLpJLUcnmek1vtqxz+/FkabBQN7pFTdFKQyK2URQTWv/VLZ6kZHEaN1CY/fvzY148Glc/aMgrt5JZGx2felEf6r2MVODmVc5oFIL1ypXKeo7gBcvHgUGsv5La1F2fhY5iOz8cuxy8dspyp/+8tNUqOkksTkwOg4R6R4iov/h/t837Xkyry3CO6lZ1UWzJPEgbd8/j4uL/ux48fexv37du3OFtbBeuWkFpvZ28nl5F+zTre3vHfQWbBBs85EeNWksha7zr/7y8vTIFX2gInthozzFvHKrDn5fLfTLtxTDpuSKRd+wzQc9/L01omtU5mucLCZ3Wkf629vM2ZJFc/Pg7FOlfAV+mskaSHIx31/u6l20trlM+56D/xtAij9Ii4qyK3SZKfc1vsE0ua9aV/pr/2lVmeX/IHKbii60d9lXwhbYsHzh1v9trFjyX/P/Lnsg0sl2Oe6nu15E/M1x9r1M/bl33K9u/Vp2r3Xl/M6FAitL399H9GZq9fNGPr4Kw6NpMO99WZiryw49nJUiwSXCez3PeoCJdTcLbWZ2gF9ATy0ltCfXY3LdVLTqS1F6WrDASlFxnisi6SWn7PV05Sz9j5s3Z6iUxrbb/8+P7+/sBRK3jw+PgYI01qWx90NEQc3GyP5OAqgnusUiyR35ncpr4YkVuNgdbawbditaWDaS0voadea+zTgZLMqlxK3wGqg10Z+9bysrwKDPu1ySGMnF3PCVbOevTz61K9STQFgBJweH5+3tsp7Yvk6tla2juuSOlFi9eQ2srZVpICW1XbnkIfzxEUtzYmB8mPuA77cb+/ui4RXOp8IrX+fodEYpkPxx+DyNT7BCwrYutkmHmzzkrLAWxr9fsAem3upNb9NP21r6himR3Muo1LtrHSoaWENo21nn6Orp85nq5J6Z+r/raWyS39pY639nrs9fqQ/lQ+NtlvpUnyWvnnhF2rdGd8rYvr7YjEUger9uKs7uw4Sz6d590usnzVM/K0Tew76iuJLYP2x5Bab+vU9sf41Flyy3TfCkefZClyT1IEs7XXja9rNSil1CJTFxcXe9LqUWBdQ2LrDlPlUEPKAVSEVj8tz+NLVThbK1Lry/XS4Gvt8HMBIgRV9JeSosVSHi41FmmVc0xvRdWnPvS5D7XJ9+/f2/39/d6ZCkQ8PDxEIMJyMMJeGST1u66pDBoNRjq3RhyUzcgMuVXa5yg9cMHABcmtnF4y6hq3FbnlT+BO92tfekzd4TJa9kGKCvvqCQd6TDc5ihEBqxxFcoA9h12BcJ/Fqsit7KP6x19iR+conb67u9u/CE7EVnaPq1OUli89Vr7cpnZNTnfUrmyfykaMgKzLSC8T4TlHmQEjblN7da9IrIuuoz+jL/P99NJCB3meF4Fw9U4LlietDmFZU134uIX7a91He+j+jXaqJ1yt4quqGHwmsSWxkD6rnDyWbJ8TCq+b/+/JrJ7N2MBj0hltU57noNfSoYQHuV/N0qbgp/rXA8qJ3P74cfhOGwWgdD2DO0yrCkrRB3E/EV3vH8eMql/CmYnwKj2SWb9+pj9G/op1p82Z+Uat8mjt8C3Nvjo1rZJK/tWXhHuZq32WY6mMMHrPl4/SOVYWL0Wmw6MSVorJ+5kGO8cHiP++f/9eAmQnuE5qGRXRzIb+pxkLPmvGZ8589ja9WMWX67HuEie5jH6p/g4qKKoTwYEUR4RV5FWE9tOnTwfbjx8/HrxMRuV9eHjYz8rxP/PyPkpGiGVdMvCrcXbqQd9rX5cRua2OvXdxoOvH1ackTwxk+PWt5U9oJXLLYAodrfRY6TqgZN6Mcgro0nmmZ97dafQIV+UYRo6iAnQ9QlvZtERyVWfVQe0o0poeqVCbKQCo/uRKFW69ndjOdK50pu5Y2T+jtp2VHvBdI+emsy6p/LN1SnZ2lFYKNMz6KR1LY533UJysMeAiPfd03f+T3BJIej0rPfdjtElO7Kv2oC0kuU2klkFnvr1cK9ZUHicN1dtTWS8PNI0Afm8sJb/hx9eC58qWjrbH5PmrRf6VZDb9p+x2L8+4On7ivvpa40/2WkFqklrOAu92u/37bZzkelBK+SkNJ7L0GRybPk5dqDtO7B2HsNxpn+l5wCsFvrx8jgMltD1XV1cHj/lJh/3xi9baK330YAAnyU5JatlmaX+JrCW3SzHz0vK96cujksFXp/hA0jF1bGuvwXGa9fEIcG+2lo6By275jFn1S0v10hJkAkHlRSGwpYJ6xDe1aYqAS3kU6RVx/fTp0/735cuX9vnz51fEljOzeolMa/8FwCK9VET2SSXJAOm+aoBX7TSrEG9NLGfJ7bmIG/KekOByTLsDTM5DQn3kMic6QR/7fNZHedCR08BXZJbH3Fkwb3caqb1mHEJFtlx/3JZxJqvaZ5CB9VI/qn5OaqnjDw8PB8Amvem951B7s7TeJ/zf+y2VHqhdKj2dPSdQLFla5oqQ9fy5/9evZxNJcD24k/JwcJw+seek1nXFdUaSgm/M0495PVm33hh2Us13YhDs8j0Y1RvMZS/TMsX0jelqdszr2uvbJNU1PdC8Zkyu2fb2z0Xcp1YEV35Q/oNBZ407EuXn5+cDf8v/TmqdxCp/Bpw54+v65X41zdzqf0W4JG6biDHYFvSpLFPCKKwfJ2Uq25Z0iFhG6UqX+RhgNVPreXAVVO89FpV/9fQ8D9/vtfXs8SVybBpr7j36GVsOJCqkEyI6QE+TpFay2+32n5rR8jl3ijqXCK+nz+WMUiyBP5Haf/75Z7/VzwktSS2JbRqAFQFzRdR/Km21rMkBsRzjzc3NnrSKyH7+/Ln98ccf7cuXL/tjHz9+3EeHOZNzf3+/3/e3MI6kBxCXEr63VLAKsP3bpCJvCdz6OKWT8P3Wcn8T0Er3fLkxja7Skw7oeqWva0Vy08xO9asiyTMErLc/EnfABON0gL58KX0S4Pn5eQ9qaXu/fft2MAv77du3fVtqNQuJcNU+idC2drhkivtsDw9+zLRp1V7VOPX7KnsxkmrMniMoPlZ6trFHaHrkrrXXz8rxeEqPILhH4Fo7nCnhj6BT17V2+KiT4w0H7Lpe7cKxUpFa980ktyT3rvuu87w/tQtfdun6Wy1BZn95vSsZ6cEINPd01H1NSnd2O8rrHPQ5jcnejK0CvzpO0qVt8tU8Tr9Cm6+0/b90hBNFDAa1lomtrybwWVvey7p72t4uJPStHeKERHDZzmwrivso94fKn/rNmVpNGF1fX+/124PS2sq3VsSWvrcitfyf6uDnRsLx4f7xFHp0Chw/I9PEltGP1jIYqACCFIHklh2jCJNkt9vtX1bz8PBQlsnBojtRDQy9IEn5Pj097R0DP3Xx999/738iuWm29v7+vnyu1kF6Kqu3KZeFsJxOhFlXJ7Vadvzly5f2xx9/7Antn3/+2f7888/25cuX/Qzuzc3NXjF//PixJ7Wa0SFYTuC06ouRuLJ4+yjPpFwpnXT+rRQnlfccHGYlFWnwKGlrL86Av+Q0JdUsCUkdDbuWv1NfNdZ0vedB+8HVEvxVL3WrIqEjAlbtp60kEVqC8erblf7Wcg/M6dlZ11U+Uysn//37970NZNv7M7QpMlzte53d4c78vL1mdarX5rNOuAesz1HW1oF6VdnmKg+CaekuQZzuGdka/df4FWHjagK+zIx2IX2TXbriZfGgDN/w7vaG17K8joFYDgaEE7Ft7ZD8plUbtHfKl+ReAXU+dqCXwZE4jFZXsD9nx051XU8fR/dUx2bt6yi/c9Ftn110Qqv/wtIkdWkiJPlqpZVmLbXVj37i+fnw7cksJ/2SzjtR82BV5X821gkgAABIW0lEQVRZduWj+nlASdc7uVUZ2B5OdhPn4L3SE9cnkmIGoNx/6/l4EtuEH/yxqNlZWm17PndGZnF29X9WZuzBqWXRjC0jH1SWRHpJ0nS9tuoUJzPpudTLy8v92zwrSYRWeWjgsNxcsifS+s8//7S///67/b//9/9ekVs+X0unkt7OWA0sJwZOChLA6KVBYitS+/nz5wMy+8cff7S//vrrgNje3t7uQa4I/tPT0/7zPlTqtKzJiQDbe6kkcJlI/QhwrRG295LynkLRf7X0yuzjMI3biuC21l7pMYVOQYEcLYPSuBPJVSCMqzAqB6pxzBkeJ7W951WqH/NKY30EDJO+uzP05UtcisglTQ7QHx4eDt5oLj399u3bAekVgGdwr7XDT4UwyMA69qLDqrMD5SU/H3NLAfYI5HpfpOuW2oBzkTX2bZQetzwuX87gtY7Lx1X/lQb9svSXPx0noE4znf+/vStbchtXllDv9vjc///NM2fGvdp9Hxwpp7KzCgWQ6pbazAgFKRIbQRSqsrBQZxtlxJavadtk+0RHedSx7NbTtdYs2eDyuPfDeeMc8qyzxdDH6aiYGr+ZcazvdwQuftZOZtLthcme4Zx0M7+f1g6XnSnBdaSWHT48DVl/cD7xVGYmfBhk4VFa1ZvsiNF+Ru1HnX0xYi8D6FfwvGx3oH4y25nrT+03Lrdrw8ol9N2oY0sd0up4Rnl0aVTmXHZldLq4ipEBo55eHtHB74mpNbZKPFqLR3T5ReioLU9JRONk4wrxtKPm+8jLKSaMBrlpyBitxYgtiC0IrY7Y8u7Jbk2LI3xcD2rg8vPo9Ch0Kk7xsRBhtBbTkHk9LZPa//u//9tPQ8ZOyK21g2ld7ARwm2K5Z51pNzM4VwJ5qqgaDtrxZwS3CvQRvLZH5VZ3Z+SRD5RF5bxCaiNZVaXmFJvWW8+w4jpRUgtjmMkrNo3hXc1ZUfJMDhDVx8fHA+NXN49CGV5eXvZTo/AOqiS1Ek6PlV+v7lSPZPk5RPH0fpTPnwSVdXdfjxqH+wHuF9ggZiOdneSQR5ZfR2rZqGYdqFP2K8QWeg9Ob6SLPJQY8GiwfgvefXNW840MQO3HMIoMogL9C9tDPzcY2SLZiE8mO0v0elU2e+n00v2MUJlSkqsEF2A7GvdZLtUpzEe2P93ILTuOuS3xvhfZiK3qZl0D7nRC1A9p+UHQs/rk+opIOB85Lx180hFsDsvLIHQGRsZNKst/XDm5bWiYDI6wLrGtK/1HpONH8pwp4zCxZQIbkVsNq0YECzFeNBM+JrdMAtW4iv7D0Ht5Ofz0D9KGEci7IYPMumnI2HG0OgWZ4Qx/dQJoXUZ1ykpbd1S8u7vbTzXWdbXYNOr29rZdX1/vO0MIkH7HVz3CPAWMOzr2fK8BbcAjDXrEywT0DLrRfM8FvfI6BcAyHBHbaASC42kfgTYFhcfTklmBsgzjPivgbF1t5oByhl5GvFSxuTrV0R02inWUlj/xcXd3d3AO0qtrobBetrW2l08e5cVzA7iGutWpW4DKMj9zj8y6OtL6i+pT603bW1TXLr0oXkR0s3zOFZEe4qOLk9V/1O7ZmMbP6Qhuj2xAqxzr5mcgtrAR3NQ/nq7P04Bbe0ts+b27NsH9DMKwE4o/x4Mj9DBPidZ6YCcyysLPrM53lAe6F3WBWWOZg32E1GbtPmtHkQGbyeYIsvJ9JlkFlMhyO2WCxv8hpzo1GcjIMJ8z+cUuyLxEiNsTztkRpHqZyRsTWf6PMNG71DJy+rjmZJnzRxmdnefyZjKLI3OY3W63nzUSyYY6tbhMeHYltG7pAD8r5+X0cPSfy9WzjSt2bCbTPTkdjbsUw5/7ae0tMWutvbmHayxYrND4Gs7VaGZj2AkHj8LolAeeHsSNHoahbhyFNbYgs/h2LX48GtJbV6teMRYYrk+uR/zH8yrBRXrsPeYF6/ytWvfNWp7OyCOz2Y7Q/PkPN9UJ4Gt6v6JAFZmQLSG/Gw5RJbjaFh2x5bCahmsvbPjpOlvIBc+2UAcZ+g0ommzjKO4n1CHWI7F6rBpuSmr1m5UstyC0fGSiqo453lSLR6zYyAfhRbirq6uD0SmGI7oVIptdiwiRI72alpPxSr763xlE7vpS59ZnQVT/Wbvn2UaA9hOttX37i4xMdjjraK2SWv2cBrd7hFN9i3zYpuBnQx76HEiXP8eDIzuiQLI5XzZiWX5h5GJzTMTBdV1e4D7hVV1moc85QmyzNuL+L007Sm82rXOCI7IAD+5wW+G2hms/fx6uwUX7glMl6ut0B27eKZmnJvPoJEZNs1kRrHeZFCp547KpszxySrK9jOsoP3ML3FMZYKccnoGXD7B9ws5lR9I5vPIctU8cn4Hs8/NpOR0yuagQ2h5GCGt0bYbULrXzpz/3o4SUC+DIrRJf9fSqx4qNZm5c3Hlzo9BNY2DwuWnITOpAYJnc8Zpa92mf3rRcdDqqxLle1PDTOCykjGj0B8pVp0UB6OhYUd7f39t1xff39wejt0rmUTZntGr7ULgOJoPWiV53YUew1LA9R4VbMRi4XrT+HbHln6aj75rJMIxjVng8TRmzLtw7YoXJoyK9KfSRoR7VTYXY6bOxA4rX4ympZQcUG85svGufAQNDpz3xNfYu49n1W3rcv7r6HXnurK4ymc9k2BHaqDz4XyWzLv1zJbhL+qBK3ExeXNtxfQDaLJwrjujphlFKaiE/Sm7VCcZGKINJOPqMzBEHmVJZZccxE1s3swK2Bp4RP61L3Oe+jkd4dLMo7uMcqeU8VBbde1yqx0b6itb6SwWi+BXddY6Afeb6LJ5260gtZMsRXa5nDu+uQR9g1BZyhf/QIdDR7EQCWI9HP2cP8LnaEpHjladfRyO0Wh7mDOgPdLM5jovwcBKzzcHyrHLI+/ow73C2SSSvs4R2Br30RmyA6NrafU6G1T73o8RMyS0EVz0ZriGroYHGho5fG9SPHz/azc3NXgHwKIYK3Y8fP/aKk79hy6O0uqZWt9VnTw03RPYyOTLG9aWjtSyAPQNLhVDXFnE9QsGCJOD/4+Nj+/79e/vf//7X/vvf/7a///57T3CZ3Osap2idhBPC0YY/ixGS69pwRfFWcQ7KdsQ4cKSW69ApBJVjR2i4j2A5AKllRaXGMNJURcJttDIFuVcHFZLrgPrIRmujkVpdY+u84fxfDQV1pOkz8NIMJrgaN3rWEeXVI7M9+azKapR/j+T2CPS5wDn99L67njkCNJxr/8hXR0d4ZEZlWvU8G4/Q3yzLMLR5IzV13vKOyOq44WeADeIcxmz4Il9eKsDfhedZUSyrPJMCBvPT01P7+fPn/usO2m+xLuUZFahPGNWRYYy6hmGMdLl+o/cXoScHVYfTbB5/ApF1yIiMTkNWUsvEmMkah3V9sQ4wsS2fjdrC7mTSzXmgTSI+t/vo2bgc+M/PETnakYbTPSxjrA+ZxPPyhWggDGlCHp0csp2im2+p893JLr+fCqmtyFkPFRunZwdU7PoKF1A4vVZ91uGpyJEB1drvBoqwbuQW19RARnxO2xnPTG55hAaElkdq3a5kPGIJYqs/XHffiouEJKsrRURqOR6Uq+uMOE+noPGM2OmY3wOIPUar//nnn/b333/vf7zGGCO2EaFHfpkAsJIdQVZ/meNgpPFnqKQxI6yngKzcjlhwO9eOBj+O5xSd5sPxWAnpxhSVEVt2uKgHldusa6vVeori6XNzX8WbzfCuxzCWeX2tftg9I/JOqfIzaxxn5LulHxmJzEhPdM0ZGlWjt0c2R95f9FwZgf6TUOnHsnfHzlq+jzg8UurIJo7cjhGP5Yc/peE2jWKnjSPQrR3uBo6j9hVMannvCnxZgEdsebQW/RmP8LBDH//xnOjjEEf3A3l9fftJLv3PdQh552fW9zaqs5xsVGR+VhdWjedz1b0RsgENJbRqSwM8aov35pYKtPa2vhyZRPvjNexop0xqXdqsbyMb0Dng1FHu9JO2dU6L7XynG1EGPKPuaM71yc41pMtLfdjWYGLb2u9+BmXiPiYjtccapXXyGZ1nx+haVsY1ZbOqp6dHbJ3RoILmPEx8DeDGwOmpocyjkjyaw15bnhLkRi+Z2GJUljdkYEKru6s6YhdVtCMLEFhcU8Xj0kCdsPdMyQBPl8AnjNC5PT8/70euYRQjDHaD5qnIGLHladjwGOsP5XbEgevAGbgjyIishvkonKNizTot/p+RDFU6znjN4vKoLZQwj6zsdrsDhQGwYepGP3hGgTqiZoyvah0pqYUi5GmU+ikfNs61v2Ij5uXl5c3uqLpDqo7ict2oYeBIQMWI7d3LlGgVVbI5QnBbi9fX9vrzc4V7r04+o//uuuuL1Xh2hJadWQjD8osfCB/vIK6OH5Ubp/Nd+fnIuh06brfb7R1N+IQe/5jYYuMoJbVsEKMs6pTCKNDFxcXehuGRI5SRyav2daxz1UCuGKu995xdczqhok80fi+fXnqVOOeA3mhta79nPrI9jDagZFbtzRFii3Q4XSa4kM9I3jLSpHoycgo7cqvga4446sgq8nfr893XUy4vL/fLIWCD8NRsrn8OoyPmzgFfJbWZPGayN3IvI7i9NCvlrF53mLXry8SWXyQybO2tQcBElhtyJriAkmI+4j4aPH7Pz8/7BseENxrxQCPnUVsc9fMCPAVISa3zNnFefI6Xw42e40WdvI60shJTTxFGYZnEPj09HUxnZOOY1xWDzILogtgysUddMEHgESI1clzDryi8UXwU2T03xclYo9NiZMQ2S4MNXSW4UOJuWiHSU/IW/VhpzJAsV35HlrRv0rXwziDnPg8GRGuHROHnz9/f6QWZ1Rkm/F1tJfFcT1zf/A6UdMzUibs32tYi4lnJNzPAXRh+3sx5cw7ICHp0zuiRnl69cThtZ9E71B/LD0/fd4TW7YasxrIai5AN3XgJstVa24/Ufv36dU9k8XUBfDKPlwzoaCzqhQ1Zt6Edpia31uzGV1xPOiLrnFdRnfbe7Wx7X2LYVvLt9RVL0j5FaF+n9hSD9SPi6NTjHrF1YH2jzmY+RhtHVQH7V4mu06P8TCgb+haWtdfX3zuI6yxL6FTIl+6orqSW00W+XB/Y6BLXHbllux/3WZZ7pLYnX5Fs9+TF2QX8fyQtV67oWuXemhgaseUX3VN0/KJ0/vsIyVWjVoXWkV2Nw42H57rzqKzbmCGaxsDPHeXFjYONRueh0nSY/KoAq+cXpBy7p7b2e7pxNEUKcbC2GD/eTEt3gXYjYyiTKld9zyOGqNavq3PuNKr3NN9KR5/Fz/6fA7Rzqj6DxnPtGf+zuBxGCa7z1kYERWcLKJmNjL0ZZHHV46wEF9d5owqkCZni/+rt5f7KEVt2Qqnhyz91PPKzZe9utG5GFa8rB4fpEc/KddeGON0lfcKpIHK4Zs9WfTdIh3WaS4f/u3fp+gE1HN0nfXjdOc/M0inI7vkgD6w3IU86UstTj0FuccT0Y3zeB3IEneh2MNbdjCGrqB8mDDo7RUlrj9Dy/9F3m71LvKde2OgdqxyP5r3EmD4HZLqpZ1vvdr9mNUGOWO50VLQHHQV2BFdJbY88qx7HdGa+j6Pa8TpAhXKoTc/yx6O1XFfqFIMMs06GLHPaLGvqfNIN41gm2Q7N1sBHpDazMzO5zkho1EdU+4slcjgro6MO99Ymv2Pb2uHoaq8TVIKTvUxHYqPrvWuctk7piTyp0a5l3LBxjJQ2KyAtmxrAPcOd1+LodAYltXg+rDfWHVXxzKxoYSTzOe9MyfWBjoIJRPReI+F0UOMzak89Y2stjKSVdT6niqyzq8TjY+SoiYxal57GgbEXyRqX2xG36F7UKVcJTa+fc+SWR2IY3AfhvxJfPA9PrWLZZXILYzmbWeLqKnLOzZK8TNFWjWuX/xrGrHvnnNeI8X0qcIRf26Q7j/qtyMDO0uzJaZSWcwTxLsfRp310Dw03JVLbuep7yAtk4PLy1yd9MM2YSS0TWuTb2ltHM2STZ0Ph6L6uwE48npIc2TCoSzf7RPu4UZnrvTO+HrWjioyOyNiIkd1L49RReS61hUESmcRyf6b/cS2CSw/h2Tmr11zaTsaZFGo/o0Rc7WPUi5JanoURfbkEZdUZVJwupx1xAsgrlgVqHbXW/xpIRmorMjujX1246HymHO5/9d6xsPhzPwweBXAPE71UrlwV3oyw4n5UJpcPjz66ncp04xmn6HtGtjMsnRKPFDJ7cdnA5bKC1F5eXrb7+/v9s2Gk1k2vwLOhA+DvBfJO0UrwldDq+p4KmXXkgutzpPEzCZoVmsh4H0lvRLhPDVnHVg2P/z2PcK+jdArbXcvKE/16+bs2lBnnUfuNfhwPMoiNZVr7vTxAR3IRR41yHvXRNba8Fr6nECMZdYRvFJlS1fMsjSUOh2ocfv+OJJ4yIrKp16J7ro1kz+5IrLZ1J7uuLGy4skOHdzpWMqtHGMoRqdXZDuqwxSgKPumj34Dnz26xg5jX7KmTWTei1BlR0LW85k9HxTJ7Z8QgHZW5arhemBF9UnGEuHvVMpwLMkIUOd70yGH5UzhwxMCu7IHbuo5AIr3d7vfeF07elaAivmvbSmR15qX2VSzfarOzc5efRdPCfd7kidNzpBbpcH3ix45rdsqj3L3BvYoM6/+qvI/0D5FdkP2P8JEyOE1sHXpCo8RP55kD3Ph7xFbjR1N1dKqfTqvVozY8LldktOoUSA6bkVl9Br3H00NYmcLQba0d/OcpyOrtUqHVTbJ0lBaL53VKtpJ+rS8ntMeAktylZPdPQNSxVYwKJ6t65HN9Jz2SqenqefQM2hZ7CkLT4Xx6YV0chnqCWfY4vE73ioiwjtqyzPI3P5nUujrF9SUyUsUS45rD9wz87F6FGKvhOEvmTwHOYIzuRe3AEf0qmR25x4Ysk1QQ1YjMRqTWyRzLjxqqeEbkxZ8QipbwvL6+7p1SLNPRTAre4M19Og/14/qByClRMUpnZW5JnzBSBu2TKun1ynnuujurL5VHlV21RzMZriCb6sz5axjOR0d2cWR96AiiOr7cM6EMql9dG9C2BtlnQstp8H1e0uNmqrrlkpBlxx+Up7jrPfnJ5H4k7Ihd1MN72RIjGP7cz+g93OcXzcSSG6QT1GhqjktbCZdOS3T/tRwchhERWhw1LafAXfzIGaAj0ayooUy5Q4FQZhvT6FRmJa86jcORWn1fWl9ad6eshJYYsqfyDLPQ91Xt6Bxp6rVzTaP6vyf3Wdmqxl31/UfPH7Vv7mOY0LIC5eldzoDlPo1nl0ROKO77smfWdj/TliuEs9emKnAyWjECK/k6Q03b9ikjK6MzaCNy6671HFYjPx29ae3t1Eb98ZRjt0mU03EoP8udHlk2NC/8Z5kD8Xx6enozkqX7XDDBdbOh1PmkdYKyOSNfn3Hk3F1bKv9VZOUYkbFef3bu6PXZCFNxwik5ZLLp3ns2INUjuW6WARM7/SwQz+pUOOcOdGz0vJyurs3V8mMzSjcAA5nHOdvmu93uYMOoHhyhxTEjuxnp7OnVkfNKfhGqsvbRMjn8uZ/ZexGpxQ/huPFCKFrz32tFHJ4a68hq9ovCtJavH3LEgNPrEdlex8TKF8+La0w8cc+R2ihvp/D1B0NaSa3zYjmB5To6BiJSUb0P4D18tCC+J6K2y/eieO6+M2RdfqykqvlV7vU6aP2vcjFCbjUPNjS0H4D8APDmKqHNSCJPeWSZ5GUCzvHUMx7VyFU5iGRCCWCWds/Qdqg4RHr5VuGetfJ8p4qIxEYGYWsx2Yl0X0Zw1SBlI9OR2NZiguvW0PK9SMfhqHLIcqFlZB0Lo/319XXvfOJ8cJ1Ha6ONKN2+HazHta909Rq9P33m6v/e9RlEeY4QtdmyrWmMfyR6sx2r0PrEfzdAhHauZC/q95XkIiyny9OOEQZrUiGPPG1X+whcY7lkuwFx0Q84m/3i4uJARp19ivBIw9Udh8MxI629+66Otc/ia5XzTN4r4RkVO9iFqV57TwyN2M5CCZA7stJp7VfFwMOiQoW0dKcxJWzO0HPn7qVnyjwiBiwgrAj1+V1aLNTZKDWP4iAMK1k2FLL4KvSRhzsitTq1m+vDTbM4ZiN3hvkIZg3ic4ZrtzPvKVKkkZJQ+eHjSNlHzqNyZMgITk+ZQMbVA6yGtBrmmif3jyyHug9ARGo1XffsI3UfKbCoHkbT5/AVZ0P1fVfS4bqvOjo+Gplj1B17UP3ryJWSWffTkRP3c8RSP3mjTiA3mlMFlwfGNZ6XHVAgro+Pj6FzWPfmcHtSuD07uB/gsnAZdbaXsxH0nVWujSCT8Sxsr8916R1L3s5NR0c6JZPxrO6YLLqZQWqLaluMysU2J8AEFNfdNZTFpYlfb0M4dnrxWvzr6+tw+ZzaOT0ngurSbMBMbV1Xj1FbzEjtqI1TJbhVaL+j8nvq5HbVzaMyZC8vIz8Y5VCjmBtXZPRFxNYdGWps9gwE12lzw9dOxOWnhFYVP3uV3dRGdgK4qY1aXi4X1x3qU+s1IrXOqHf3tJ4q12fx0d6iU4eTNycjrdWnrKkxNlIWd16RtRlSG8ERzAyOIPKP5SAyWCv9SqZc1Ymn77XaX3Hf6gh99ZqmXyW3mcE+2g5m4Z79XAgu4Npw1K5HCIW21R6pdborG63lczbGkZ+updXRndZaKF86vRl5uQ3WcA07lXNdQZ4jx5KbPaGkFs/DU56RPvJws12q7bKib6skNSrDDFy5qnoFGNHp56j7q+9O+2quSyen6mDitBCfySjkqGer8ftTkgsyy+nzCK+WwckrNmxz9oQOzkD/ufWxqhc5fyWlrFN50yjdRJV/SMfpwywvPl9KapciI7EuDP5rOY5FbmfSKBPbtaZK9OA6URU8DusImjZkbdRRJ+IIbWQgcBxWsi5t13BZkNlLxd6o6Pt8ajij40B6KIuWG/kr+dR1ydFPn0XLUUHP2F1beN+L5J6TMnUdq3b6LmwvzVFSNZPPsaHlHjWoVCbwv0JqnawquXUymr3DnkHqDKXs+TMj28muu6Zlculr+aLwM4jqaMbg/khE71bblgvXe9dRnCi/XvvWo5JTFy6a2uwIrhsJ4nQwusOEs7W3Mgs4WXZxIjtDCS7aFmwE/FDnkfyNohe/J7dZvCxOZJxn/f0sWa4Y4ueGnl7MSG2lLqt9QCVeVF4QWtievLY2s79bawe2r37Wy9m0asviXO1Y5MfOJZZldU7x0gL8Li4u9sv+4PTi9xKNdmeoytwaiOSld+50faY73f8o3rGxeFfkrMD8QE55ZOE5bVSWklo15pwxOFr+TKFHRq8q1Uq+7JlWoXbEFs+/2+0OhFK3decyRtORERdl1Snd0TVV5s6A5/+ZwT2KSgf8HsLzmZRo7x3NGB6Rgfae9TbSFpYYV631R2pZQVQMfkUkS5FMjsAZSO75+FqmwDhdd9T7mhffqxreM8/srkXk9txQbV/uOUfIlSOw7jxDVNfOKcv3YEyq05dlDqM+0ayjSF/xKIyeQy/yqA5vMsXPxQSWHd7Ze1izj+z15TN5rd2HH0PWzlU/j5Q7IrPunTuHUmu/7b+ebZvlHz0DD7RUbAwe1HHfr86+W+36mkhX6qxOHpnVT15yPTkHMhPa0XrS+uLzKPxou56xh6uk15WpR4KX2uej8aeJbWSsjHgCVahUWKsK0qWnCg/XVXk7Mh2R25GyuQ6Dy4WfTtGKNszg8rX2e4c3Li+PDAGO3DoHgTOUldxq+Ox/xeCtnjvMdiYzOFdlmSFSOFUjX+Vc09C89DjyriuIiNna6BHQ1n7LIeovIxvaP7h0OX0nr1r3rj57xEb770hpuT41Kq87uvL0FGSErA2Nvv9quz8l9GQzc5pk7Thrh720evWG9OGUbe2t3tIpuawrcWSnbzaSE8kIH93IjpJhNy0RIzr8bFm/ys+yJsGt6LyKPI2Ec3F6Mr8Ux9Dtp4LKc2X9E9u1HM5dz8Cjjz37NnvPPAgDeVcHLMs1k9rb29v9N6Svr6/bzc3N/lvTSnR1Txkl0xGR1R3MeWQY4ZC+Oq4yfb4Us23cxasS1YiAOkKb6Wq9FunyWRkeqZtFm0f1rlUUbzSMX2kkShKja+w9RRmrleTIqF7n/9k991+nU3GZM1KqpFb/A9GobZXc4lzDuf8KVe645s4zRESphzUU4WdUpJkBVoGSElYmrR2uOXH58fWsjJxXRNZ6HW31Oaphs/7NtdOMzFYUoxLWzHAekaeK0nFhKu+lYuBG9ah9WGTEZf+r8bI4VUPwlFAxvhjRM2btuAc1pFXXsv768ePHwfdiXXh9PuhMnuUUTWuuQvUdTyNWIosj4v348WM/xVmnVLPBHbV3beu4FrXVigw6A5fvVeRC84iwhn6dkbPPSHIr7xz/Z6FOo7XANiATRN4RWcMhLGT5+vq63d7etru7u/0PJPf6+rrd3d216+vr/femmeRyvuycgvzyd9+fnp7aw8PDvh7RD2m96PTlKqpTkzOC2bvfC1/No5f2jJ1QKbuG1/JE96ttf2rEVjN0jRrhuCC8OJ3PUWBXoZoue5OQPisRXNPwDM5HDfOMjDMR7a3/cWSYnyO7rs/PxgCua527usbzOWhHk5FW1zE5UpuRpZ7yjcJWjNiPximWKUP2nhgqC5Fs9Dr/LI8K2RhFZqy7e5nxP5I2/+f+zB1dvj1ii+PI+9N+JiJvrn6cMhslfpEs9/Jx/3vpj9zL4lTyPQVkusod+f4xiTunDR3PBjXrcBiVzoZwRpqbtoj/u93bjaaY8CINLSsbwtF6O24TmC3FaUTldXk6uXT1pyS/YqRm6Wl5q3HdM2g6FR0ARMT6WO3xHJD1463lupfbpuoJyJ3KpMtT7blquRXs5ME55JzzZ9sZOxvf3t62L1++tC9fvrSvX7/uz0Fyb25u9kSXiS3XARNazKp4fHxsj4+Pe7nh78ijTDpbQ8+z99MjszPrcGdQ7Rd64XpEukJwK8S9V0ZgVBcvGrF1L0o9QpECZXKLNao60tPa2xFHVo5KipnQqsBmhmDP+OK0ox/nrWt/IqWadVauEThvl17X56+SkZFR2Qqpdc/SU349jMSbNQJm8zsn9AjRknT5XB1IPTI2Y+D0wkZpRv+d0RURVJdPRhYjsqtpRM/QI7e99+n64qwMkaLKyG0k49H/KpGOnicrdw9RnF6+pwgur2vLVd2Dd5wZISMODZBX1uO6LwTfZ2OYpyvr2loYwiC3utlMRHL1Pet0Y15zhzCOyIIE4z4bwvr8ro4jYhv1M+68RzY5/0qbrpDPXjx3zcl+r+yzOrtavlND1G9WCYi2a7RFkFolk87um4XagXqNpyNrPkpq7+7u2tevX9tff/3Vvn371r58+dL++uuv9vXr1/0oLqYoQ+aV1MIhhSnGKAMcVCy/LPPu+9PZF1aWYM323Ut7Nq8ZMqvx9FzDzpalh6N/7oeV7Ovr4acvNIybIhF1xuwB0XhM9rghO0PQCaXrZFip8oZPrEQRRpWWm+Lg4PKNRsMzQjtqlLGwVgitlisjtFmd9v5nytE9w4YasnrNDN5Kelk+VaXgwlTadGREVtIceW41OiPDLotbDRud9+qyYsT2jN6IaGrcLI9qmIhYZuS593+kzZ47mdX/FXKrcSttKssziqv6mD9Np6SWjeBonS1GaZnc6mYzu93uzSdDuE0xqeVPefCsKGcAR4avfqs2q/PqO4nkQPufDKOypNdHyHCP4Ebl6qW7BOdiF2R9ZdWRpIQVpFany2s+ztabfYaMoPNntnDfjdZ+/fq1ffv2rf3nP/9p37592xPbL1++HKy91RFX3siN6wNOK6yrfXx8bA8PDwc/jOjyJlL6KT3ksza0vGuQ3rWIs0unci0it0vKNhJv6HM/S4fTHblFY1dPEseJEJFkXOOGzYpbFRALdUTkOE9d36ObPEXrfKKOPxIaDe9GbPmZo3sRMpKaEf6M1EYk1z3PrOAdWyiWxDkXLCVGUZoz92bQMxhH7vXuq7KJ2gW380hZVes1kxVnlFSfpZdfj4xXnmWUyLrrjjy7eu/lNVIXFbJ/alhKbt1zRiSXCaIjuZymps86igmrOn0xe8vpFZQBxjCmI8PQ1bV3PJuKy6P6nvNiwsqby8AgxpEJrn6vtkJO1iK3oxjth2fkPCNpLtyx5OycdLfr10btYadvcJ7Z75G918tP09B0eMYFO38gJ7qs4Obmpt3d3e2nH3/79m3/w4jtzc3NfjMpJbVIl9fEg8Te39+3+/v79v379/b9+/f9//v7+wNyC1IL+YZsu77onNpXhvewp9cktxUMjdiu4a1wD5hNRQZYATJxVYOT1/Q4IY+EntNlDw17ivU5cFRSy1Ay6Mh0RhyjOlAyq0R25F25MvXKpUSWz6N7Ls/oWuW8gs9MTmdxjPqIFHEv3wphjIzAynmlDFEcV65RclVF1Qjk816czCCNyGlk1ERhs/iVZ8jS1PSrZL9S7qyMSqhPFb32X5GJiNTqeZaGQ1Z3PHrr9BTrdl1/y2F41hRPSWZi60gt9Jx+7oNHbGAQ49rDw8MBqX18fLTTFUfqqyefPWR13JOBXlqR3FWcer30K/3In4iI3Pau6X2159gezpw8URlaq7Vl1x/jyxqt/d4wTu1rbByF9bOYjozRW95MCs4rlAfpYTQWJBUEFr9///23/fvvvwf/ldhC9jF7Q9faLtEHa4/0jtgL2f1Rey3DKHk9pm2++Du2M2CiyGQ2+8g6g8Pp6CyO8E7hnIWY1x0gHE9Laq0dfMQdHQN+iA9wGdyoq5LYrDPJnn9mxNbVn+YTjRRHRDsjtb1nWtKQlwrBqHJHnFM3cGdQkbMM1VGGLD9Oq6dAI2O9Z9BH5evBEZyM1EZGyRpGW8WQnG2jWfn0nbhniuqmWj733kfrrGeMjdbNORnaa5DbKF1XD5yuyp+C2wv/b+1wQ0QF61roZB0Rjd4pv3PVTZwWr8PjT3+A0D4/Px8Yu7iGsG46clQ2JytZfWXXR0hv9X1H/UvPbqheq5ClCoH6jHoYcP1k5Fhy/S/fx3W2VXe7wyUAHCbKX8sx4qhR2UN8fD4Hso14vBkcSC5/7oeXFSAeHFM6vZiJKxNZHa1105B1FgaP1mYkd5S4zuikStyqfC4t00w+72lLH5XYRkrVEdCqR1mvIx03/ZgJrftxQ4XS4+lLaOCcp1PobldiPKcjtRWjLzJS+JyNA84/8oRn6JFZF6ZHcDOyG6VdvT6aTgWzgvdZyS8wYxxXOjkOW1WakSy4+71yZecj73QJuYz6DUZE0LL8KvUwY3A7ctsrSwUuvcygquRXaVdZXZ86ue2R16gNVGStZzxH8lfJg9ub0zO8NOnl5aVdXFzsdzHFf5xjSRAb7bArmES/vr4ejNAySeW1d3xkMpsR2mgKclW+ojoaJcAz6OnkEfkZNXYrfV8l38+AinOh12er3eVs48iui+y+yLblvHnGJS8tdGtTMToLcot0drvdwcwLTDXmZ2BC/Pr6euCQ4qnFTGhBZPmoI7Q8UuuWFvBz8HuK5P4Y63CBGRnT8wr/WKMMI/bK2niXEdue0uO5/1FH6jy7rf0eLWUSi+uteaHtkVtsTc6L3SFYDCbQqtwjMo17vbpS4h4ZltpZZR2RgwpqL8wIqY3SqFyv4j2EJSIWGuZclO1sOUedT0vT1ftqSLv/EcHVNhwZARyGr2XvN7pXTSNLN7qfGZQ9UlEh/ZpOZIRmpHNUcWbpzbax0bgRoTtVjJDaqpzhPCJkmZOpUr7oHbP+cXnoFwh0p+KXl5eDTWXYKIZu5/VzmFKsI7Y8CuQ2jeLRHGfcRuTDtaf3atezGOkv9HpFfmZI/2dE1jaqfVLWd0b1nJFaXItsb04P8gbbGY4myAhwcXGxJ7YsQ1oe/Pi70Tjy1GOMtjKhxagtj8ziiPCQe+ewUgKtPMGRWa1L/p9N9c7Qk6NeWrOk9lhEd2k+I/iQqcgAE7RoGjKH7Z2zgGbfmdOGhlFZJrbs3dWF6YinpJY7HzWcq2RWzx2pzdJw624del6lyHDuEQQOMys8I4bw2ugRmCzvcyK3S1ExRNYwVjJS2/s5qOLM5LTyLp3RMEuQo3QzZGS0V/8941jv87NEfdLS9h+lXTHkHbnI4mYGwzkb2tWyV4muq9foGKUbkVr3xYDdbvfGiYxy8I/19cvLS7u6umrPz8/dnY2Z1LrRWya/vOausguye+4KwZ1B1N5n4vfCOUI0klbWT0V5/IlweiIjpi6uyshu93aXcY4zOsKYbY7Kcs4DSZCX3e7XbuW3t7dvNmhiWYZ8Ih1sQsVkl6cfK4HVqcaYfcEOLF6S4Ox8XTfPbZif7b3Wz1ZskxFbe9TWmUl3SfpL8KHEtrXa1K/outtQSq9XwJ2AKkts+42Gr96byIh2RrIqf0dklSCPGqd8XhW4SkOseH+q994Dx84rMlI+m3LODFgXbibtSt56rg4l973KyDvN8s7nGXqOjIy8Krmt1MVM+62SWiWMWfjovkvDxYvkf4RQ6/Wqgs/IbcUQP3dZztpD738l7Ci55T0h+L5ufIgjy6f73uTNzU17fn7e74iMaY6qQ5mUQp/zqK07IjwfncGra2u1jbs2e4x2tUZ7zfqCERJcvdbr89boE88Fzt6KHHwZ0Nbw4zboyG00mhjJvNso1b2nq6urvRwhn5eXl/b4+NguLi7a7e3tgSNJ17hfXf2iJs/Pz/v1teygwlKB7NM9vBmcTjdmUq3kGrKuMq6OLNVDbEc4uyKyjV16GapOpSjcqFNqaZiPILpHIba96QsOlU45UoB8z4WP4ihYcTGxVUHgMBzXHTn/7DeCESXW6xCzBjZKbtcgtaegvHokZDbsuaBnyLowM+lW84x+PC3RHRFf2yg7pVQZ6WYaWXmVyGZEVzFKtNYwWkeIXkRkK31PRZFV+7DRelor7kz4c0EkxyPy7ZxOQE9Pcxy3jIfBOpl3MH56ejrYZIZ3RHbElsmtkmROu0dmdYrisfTEjLyP2AU93T6S/6zh2svns8qfQ/asI22n5xSsLDdjsC7lz1qqDo7ILaYPwzmFGRVMPvmTPLe3t3tSi6UFvNGsfo9Wf0pkncPKLSnQpQUYxOIjnmPEIV6t5yhcj/RWdW0l3Rl568VZm9RW4x51xHa0g4ww6unFPb3O377SER40VCatPP9epyjxR90jDy7yjjqCUfIPVDs6DTdDbt39EVI7aoieCkbJbWunVf4M7tmcnDpDd9ShMhJG86gQ2mjdnfvUB5RQtJkGk92IEI286woBnDVEs/TX6HMzUs5hnEHjzj8KIwQaWKP+3gvZ8804QDM5j4hvRT873Yf7rJe57JBZJpjYPAqjOre3t/b7tcg/2kNDCSzfYyLMcVW/O/3n6sw5u7J3cAxwW1iDdI7k+x75OIw6Ez4STh+Plp3tWHXiRtOHXd4uXda1/IPsKsGFvPKneWA/Y00sTyP+8uVL+/79e7u+vm6t/SK1kHE8V/Z5Lt4ELiKyGZnloyOzOEZO8Oy/q+NZe7mnX49Naqt9x8i9Y8jou0xFjjqvKrFzCrN3jZUlKztMa3BKlYkte4YgjIh7eXnZnp+f2273ey2QKjqeGu0UW6XjGg3TS3+0wWVhMuEYJbWzhDuqn6rAZkb5DM5JkUZljchmL9xo3u4/y+8Iqa0oWTZKGeib9BcRnSUGyNoG3FKjcJS84rxiJC8luC7fXvhKWpW4xzS2jwn3fvQeI3IkuXuKjMzyNadrcWRdrPGYXEIXv74ebiaz2+32xq9+w1bLrSOuGZHVT3y4TWQcqa06u2YIC8P1QXwvkptZPZthqZwj3qy8rUEITwGRLh55Fm2T+GX9aGYDRKO12FjVnbP+xOAQ5J83a8MGT1+/fm339/ft33//3Y/O/vjxoz0+Ph6M1rbWDogt2+b83+2PEzmvmMSq7PO+O25/Ha4/JbXZe4nCLAmv8dz/Edt/Vl/P3D+WrB6N2MJLNENqM8+wKs3MGwwlCqFjQWSB1HLzaO319XV7fHxsl5eX+8XsILWaN+LyDs09A0Ex01E7Q3yNhtRLI1PuawhLBcdUZGsYIaeMrKxOBkfbciW/CpmtElpMYVLDVo1Uvu76KCXFo8+YGZ7ZtQwVElpJw8XvpV0lSpqeni9B1ShbC+dAbitEtvocKm+M3pKeUTLL+peJKOtjyCuP8mCEh9fKIp6O2GZ9gDNco1FZt4a2p+96OrzXR1TkSq+vRQxH0l3TWH1v+T5FcD/s+uQKeag4ADPd5mxrdkBBxliO+ZzJ8M+fP9vT01O7vLzcyzLvYoxRWsy6QLjHx8f9lGSehhytldcZGEpcneOKnVsq/+7btUxqmchGdaz3s36ici877/0fzbOHNcIdW7YXEdueAcdrbVXodB1u5E0GMhKLcwiW/ljpqQLkNTlowJjqoFOb3A5p6mVGOq7hR8RAlZ6rW9xznR//r5KxpV6ZGWGczeu9cK7e3rWQEdglpDYjdhGpdTLuCK0SWzebgxVTa783zIjKUHUSRfeqDoPoWk/2l6BKSLVcjij1jNG1ZalnlFXjjsQ5deh7GSW5zokEuOU8es8ZwqqTWdeyDnZTiCHLkFl2MHN5eA8M5M9GNk9HZrjpgkxWMyLL+m2provIS2QHjZCanoyugZ5hvUaafxK0HUXOj54ucu21tV/tHjsLR+k5WWabWmdFsAyrk4rz3O12e1nGSC2+Pfv9+/f9GnmEe3x8bDc3N28cXRFpVQKrpJU3fXXX+Z4jshGp7f3na+4dZ8esffTuVcnwCNYkvTPPONqHTRHbXsEcgc28x1nHnilQnnqI6+pdcj/eQZGVJStT9hZxw3dCgOeGB5lHq0cNX0dcs/MsTpR2DxUl6o7Z+QhcvF6jX0spjhCbz4SoTS41ikZJbURoWc5V2WbEFjINpQ6lk80mqdbBaLuIjNYoXEY0e2mvgah+KgQzUraOLI/kv1Q5r5neqSAit5X3FwHywnoX/5X8MomMHMtKaN0PJBgG7uvr634dHe6h/Lqh1Ovr6xtnl3NycR1UyKszTJeSWqD3DmYcMdX3PWILnJPMjJDBU4PacRG55bAaV21O/M8cWwydbcFOKbWhWZbdGnfMXsQ5b/ima2yV2PL3qAFnf0ezLdx1JbiOyPKsLj6vklr3TqLz0aNLSzFCLHv91xKSOhpu6X3FELHtPXzPk6/XslFbp0jdepxoqjGETAXQCSJ7gWEEg6DCM4zwLy8vB3lpg0a5lPRyXVUUlnZs0Xnlv0t7Btn7HxG+0TxGcU7K7BTgHEtrEKWeEwv/3Y/lPJt67Nb4sCMNXmMesb24uDiQz6gNjz5rFL9SD6NpLkl3BEpiq31X9n9tZPlV9NC5wr2bUZLLUOexnjvnMv5Hy3/wY917c3NzsKMxRmxgzAIvLy/t9va2PTw87NPU9bHuUx06cysjtzg6UqthNJ6eL4Xrhx0isqNh1Ik0i4hkrZH2mjiVcsxA25ST2czGyxwyHMe1FbW1oxmPkVPq6upqL9NqT8OBDFJ7e3u7l3dMSeYNprATMvcFPGPSPaOSTEdymdCCyHK86Jzzy/4Dei3iAKNk1rWBLJzeG5GNtUjmmsR4FGViW6mkqMPlEUy+HqE3zUmVlq7bYe8wBE4/DQCBRJpo9JjaBG+wm94M5f3y8rKP7wiuTl2ujNBERgnCaR3qvSxMhKxslXddNQK4TNV8eveOgVFHwbmi5709Vn58VDKrMq7/3dRG9AHsbQYuLi4OSG1rh55YYA1yGz1v71oljUyO1hrZWSONnuJ9b2Lp8vtMsrxWfbr2VJ1unE09Zl0L/Qvj9vb2dr++7vb2dq+3W/utfx8eHg5Gffiblzc3N3tiq+vlHLHtkVt31HDR/2OiZ3vNOJ+WlOGjcApleC/oO20tt0NGCVI0A4PXuzs5do4pR2xBHlv7RVbZDkfcf/75pz08PByQWAwi4Ro71irP7Aio2wgqIrG9UVocq9cym3iU+I30SVWOVo3Xu1e5P5vOTH+2aMQ2WkDNJBYFGyW3FVLrRmkvLi7eeJQiD7Gun8XOaaoQnbEM4XcjP0iPCe8oOVJyy9dwzvUUhXfpRvmNlK2S/kj+vbzeG5+VzAKnRmqdkexILa6x0lNSC0fVbrd7sz6G4eQW15c848z93uhKzylWyX8WvT4oi7dm3pU8eob/Z0TmEB19btV1iK9fGdBzNYgxSnt5eXmgd29ubtrd3V27vb3dH/mTPUpi8Rw8kgNdjn0wrq6u3ozE8CiNG3V2dchHdy/D0rZ+DDmuvH+102bymMFI21xSt+euw13f62xovqd6Teu6MnCkxDaypSG7bFPD3ofTib8mwoNOFxcX+8/2cLzr6+v2/Pz8Zq18JsMqv9la+tlRWf6veUaO8ug8uzZCfF0eI/eWpr8Wma3kMdI/TY3YZjuC8X2eEhiRW1U86qEZJbU8IqtkFj9e8M7eJRjDeF7XmBWRca5paKc0Q/Bcp8b/Zwj0KCJDci1ysHYaa+EzEd0lhlOvDrJRSqeYIvlxPyaxSnBZAWpZeVt/tx4nIrhroUpo9dpIXY++01HSMxO+ktYS0jnjKPxMqD5Tpb5ZR+smM6yTdV8LN21R19PqSO3d3d3BD/chyyCycEzpkoPIFoBDC8+DI56NjXn3/KN4D33QcxTzvczgb622RGmpjIwYsD1S/Vn07RpwTisXBken0/j9sr0Nnamzn3i5npv5yLMt1DEF3aoyChmEPGMGhi7/Y1LNZQNYzzt9qf3A7AZQEXnlus02ixoltRFG7OtKn5G1n6XXq/ePFa61wRHbiOBFjNqtoeVrriNlxeqmIvZGanVBuyO2brRWCa1bfK7Pz4qSCS2esecAcHXojBA9cpwobC+fpeh1rGunrfezEa61nlEVyGcit4xeexkZ/YnCOXKr59HPGbDZ5jA8eqPeWbfxmyqjNTFKaqP7mRGY1TfDycnsKM2aBOG98BnJLdDTFTNpKDJ97JYBQQ+D0DKp/fLly37ElqcxtvbbzuDNZxhqlKMMCM9GqTqTz+H9V8rZk8FTJYqzRvKaeZ0bItmO7B7WabBtI3mOHEW8T40jtbgG+cUMDeQDx5TLl+13fM5LB5mQ98vLiyXf+hyK3shqj9Bm1zQtV/d6TcPp+ci17H+W/ow8zMrQWqR3FmVimw21u2tofDx6i+t6jcluzxBkYxeeH5BaVqogte7j7WwUoyHyR53dT7cTZ8DLrF5hnhKp3huts56SUhLLcXod3toYFZjeM1bSORZ5HcVnI7cjxlHvuXsEq0pkI3LrzhVuSqJ+pF2JbfR8jvhGz633lxDakTgVkqv33DOPOi1cHxTFcWWrkoyRuqrI5cjzngNGyJp7fwroZz2qg9qRWrdho05Bxg9TkNnZrGVj+VUnsys3G8AVe2UE791eKvruGER9tD29Nyo66Bz1c0RUnQ6OnlEHVKAjleD25JntZN5o1Q0Y8RdGeNdyHFWGNc/dbrf/bBfLLpNbLh+Irup/R26RFtddlbxmxDVK14WrkM4qwXXXq219JO+lWMN5tVRPL/rcT6WgTHCz0VuekqwCzQZxNHKLaQtufYBOYeLnYKMXAvb09LQ/8q6LkVJF2ZTYtvZ2vTHynSWikXHpCHAUPyr/KHoC+ZEeo6VxgYi0nKPyVFRHBUYcE70wERGrEFoOp/nyOYxgR2h5N8SIuGZOqB7Jdc8YXVuKEVKr4SLZHC3nqDPKpX8MA72Hj8jzIzBrHFd1FGTUkVu3z0X2ZQLYBNDL7FR+fn4OHcw6kpwR+Gq/HdVZhPdsT1UHTiRrjHOSgRHH1WeD2np6Dih5ZOD/1dXVXk9GDmOWZfftWra9eXmQ6l+VWdafLPMIy3LLZdTlRhGxjWyD6qhrj7hmZNfZEXzU69n5DN6TvFbKMJJ/jwyP9lNTm0dxIbINpBhowHoP17ARk6JHdFkg3TRlFQo0Phi4utMi/3jE1hnFWh4uk9ZTZlzMECVV3kvS0rJmZY7irS08s+nNOglGUTEaTh1Z++QwvVEejd+rV+cA6qXZWt0ry7LNsqtTkVXRVkgr55OVtXd9KWZJLYeP5HmpAX8uclBp/6eOkdG13jvnER4YfVjjhrzUmGTdi6MaxGoYs7NZDVnoY/6pLoZMaz2wPdCaN0KriBwCvTgoy5pY0+B1947h1K78X5rfZ0Jm2KtuzcJV2uzLy0tr7TfB5XbLdjOTVh3JxY/ll51SbgYkriv5Rly39OD19bVLbHnwKLNjuJx6b4QAa5zomJ1Xw1XjV9LN4sxg6QDPMW31oc2jKoRW72cEF/d3u93BN684LHttMmOyN7Kj6+5aawee4Kenp/bw8NAeHx/3H4WHYmWvk2vQWl5Hxp3gjRLSSNlm+S5BTyHOxp2JVyUSlXTXIgGz4U4RmWOjSmaidn8MQ4/7DECnIUO5srKN1tc6BZDdr+BUSe1SzBj9Wdxjoir35yq3FfRIbWtvZ0XBoMQn71r7rdN5pAXgEdPoh+nGagzz5zhYF+OnOlnlmMugP35+ft6RehvFe7XxtRxMo312hdRuWA9RH8Z2qNqAajcz8dTN4UBoI1liB5br97kfgK3MA0SPj48HDiqUI+oLoNtx3T3PCKnN6jQirXxcey3tGmSzksYx8h3Fmn3FSL86vCtyde0KCqAkVoUHDRn3nbcVShTrByJiqeVCo8X6AixSb63tlSMUKZQnK9LHx8e9oLr1eRl6niOEcYSgSnAjhbSkwVYaTiQgo/lWwleIrnvmrGPRcGsaIec68uPACqPS3l0b7hGgzBut/3nNH+KyLDKhxTEarVXCWiGxI+37GO3ApVkZKZ6Ry5GRwFloe3F5ZmU5Z0fSeyDTKa7eWGdD16rBq6M8LgynpSPBiMOElr93+fLystfD9/f37f7+fq+TQWzV0RzlFzmBo+eP6uMj8RHGaIYRstyLl7XBmTyrZTl1ZEStp5tYhkFKIW+wv9V+vbi42DuJsSOx6kGni2G7s92JwSK2oXUGJMsu9w1sr3O++EWkVge4KnXnBuaqhFaPaxLaalqjmCW5vf5SdXBPJ7+XLK6yeVRVSWSjvCp02BENcZXcsieKBYI3mOARHQgG0gSp5WnIUKgPDw97BYqjS98RuioJQDwXfmY0cmR0rZdWVpbKe1+DGKwVNuo8TsFoORVU3vnIiK2mofLh8ub4ajir8sR1TYcNZCa2Tm4zIpsR3I8yiEfSdCO6ayuUXnpL+7QZRH3pnybvVQKXGSusY1mHKnFkZ3PmMOI17zB+WWZx//n5ea+DI2Ibfb4LZccxc/r2ZGJpeznVNhfJpZbXlb+q12eNaL434uR3mBmh/2iM1FMvLpM/DCBhuR+cU4Cus2WdyfEgd7pPDdc1bGa2pVl2o5kWiA+ZdnwBeaG80czPmZFbPs9GcDOC69LM8qmGmZGfavgqsv5yTftihH9kWG3zqNkOiO+j0eJ/tKZOFSt2SoPQ6bQJFhb8xygsr+OBMLqNo6LpT6OIGsFo42BFPTrim5XN5RPl745ZeavoCWeFZI3gVI2QY6P3bnuGjovnCG2lbTuyyaNBzinGO0A6IzkjtBHBrZDc9zaWKqOylbAR6Vsbs2keSw7/VNmuOCVd/81GMW/I5MJgPR50MBzG+PwOyCrSeX5+bq39/rY02qDOnuKpyGwc654XDpEzQ8lthLXay9L2vIToVNLDtaiMlfxGDPpqmjN5z8Q/JSzpM/Wcr7mRW7e/RGuHywkAHgzi0dWrq6u984vzcsT24eEhHbF1z+RsAbbp1YGlJJfD9OoNZY/CjB6zfCrysAZxPYZsLOUXM2Wa7UMXbR61pPIicgdhUQFz4MbNwof0IYBuLR6ILa+lxcgtb1yhi97ZKOZj5bmjYXs3IpXVW09hzyrTKnmcIY5LwkadQY+I99pnlbidm5LsYdS4qHRoPUKrbRv17ZYecJp6nZUp7vHaWvyvjNJqeSokt4e1SdQIqT12nhVUFNSosjoW4f0MyOo1a7/cvvW8td+yt9vtDqYmapq8ARQ7lJ+fnw8c0ADk8vLysj09Pe3zYUezI7Y6DZk3cnTI+vJM367dzmbb7to6p0oUl452Zfl9Nj16Cqi2YdaFjuAqIt2oI7c8EKUyDDnmGZFuvxrtsxypVfudSbYS3mxmaAWRsyDjP6OkNtOT2f9TQM9JXjlnzHCeHqY3j6qQ2ywtPWrDrBBb4Onp6Y0BqwKoo7XsTdbdF6FA2TvM6UfPsEYjHBlFiYyaKkHtpa1pREJXJZPV/CrXR+5FHX9kCB5rJOsU4Z5z7REL126Qh3qUXRqq4PTdqtxnhFbTjOpA86/go0nte5G/pZ5bhpPBJc/xJ8ku4Pr+Sp/t5EH7fR5RrcxW4hkUnDbiXl1d7T/rwQ4q3sgRo7NsGGMDGt1h1cln1Aa03++1tWzEewRLCOPSvKv5jTqdNlJ7PKgM95DN3FHHUmvtYDZTNJNJZz39+PGjXV9fH3xf1tnVPFjEsquzLXp6VfsjJrP6Y3KrXCKrI67r6Fpm71fiZ2n24h2L5PaI6Wwax8RoHzq8edQomXEKNlKuHEcbfiQIUJZ8xDoCrNPVNQHsIebv1+r25DzlKdqAZtY7FI3eRnUWITNqqmm4smkeLt8KeuFmiWsVUdkzRf4nKOFevY+SDTUW3TEDr5+P0o++Ce2UckRoOY7Gd78eKgbyDD6avFZwLOUWyeQpPfupwcmsC6P3ojbPusTtaeF0H6eruhL6Vo1ipAV9CwNYd1V10xiRj5NV11aqbTWTvWOT25H0Z2VC7Y4l8Xt24J+gS4+BjKwyonfpCCAAmdFBG7ZrQWDZ/lX55c/9IC4v74Mziomtzn7MUNHBzpbTo9ZTBSPktpd+Fm6W5C7FMchtZXS2WhaHkf5umtjqeS+eS6dCbCOlq8LIO6BCCPk7tgCH5w9I86YU+v1L3qhCDWd9lswodteyxjD7wjm9GWSKLxO4UaW8ZrlcuF5n86cayrNtaml70vaMNFnxuvbGikrDqKHdM9TdMZPdTJkBp0Bq1ypDL533khnX3v5kmXWo9vtZe47S5XARYcY560cd5eHpidfX1wffyeTRYHY08wwq3cRRv0ut5a0gc7gt6eOWtM2qYbwUmSFdGbWtlmXmeTbZ/oUqqcX1rD4xM4J1LBzI0YAN7oHYsvyqY4pnXSAs726ushuRWqfTRwZ7ojY7K5dVG6AnD8cmtWsT3lFUOMss4XWoxh0mttm5otqgnDHpyK2SVwge1uzAs8wfkubpEkhblS4rVhZQ5KlHTmfkuWZQHX2NjH6XThTXhasK1dqktifcWUfWu+Y8fHx9wy8sUQpuJLP33jHdCGE1Hb7P7y0ipxWF1CO0lXLPYLTNHYPUvrdSjIznEaNkI7eH6PX7a+Xh9pWIRnleXl7azc1Nu76+3hu0V1dX+8/t8QaPTh/rTCp2QOteF9X9LYAeEZiJr/3Lmu1zjXfZ0+EjMhmlW+kvR0jASN6fHWs4W9SBzLsIg4Syff3y8rKX36urq4Mf29acF39Ck2dgOPmtjtgqoe0RI2d7zOiMStseIZyzpLZaxtG4reXrs9eQr4jQrjFa3MOizaP0XkSKRgiupsfKFN+ghRHKJBSbRGE3RlWgnG420hspbDWU1VscGchRfTGqHo1KPY4Qvih+L79jCOkMtH1FxMXFi5R5lF6GzdD+hREF4mRdR25fXw+/Xcf3Na0RMqv3NV7WdtaAe/YMa5DaqtH/UdgI6zLMOEp6de5kiXVyNMrDIz1qHEfOZjaKleCqftb1tU7nZs+s16KwWg9VrNWW184309M9x1LViF/LFhghuZU0Th1rtxcltRHJxTXIHGSbB49YdlmGdY2tLjtgOdavi1Tl9qPeYWbjjtr3S+zlY9vWEd8YsYGzUdss/WOS28WbR2kYoDd1IiNfeuRv3LqpEhit5TUAPWLLhDUaldXF9RyP6yQyrEcaqqujWYwaOb10loZZmuZanV9GaJcaMsA5GeeVdpLVUy/tEeOZwRtFgNwq6dX4PVKr/6N4VWI8i6hPGG03a7ez92q3PbmrjNCek8F6TGR1U3FuchjIGv94VMUdOZxzDsPZrKT26urqIG/EhW7l2VL6DXktU5XUKpY6kIFjtMWPbN9VXTiic9co04Y6IBOuHWNwSPUpPpf5+vr6huCyXR0R29bagQwrwdVlCs5mynRBjzhFx6X1GP2fbf9LSe2o/byUb1TrsRIuI8BrY9EaW/e/QhzcVAG+runy9+7wY7LL04/5vxrEXGYlyqrMs3NOwynXyIAewVqCWR3ldfmPhJ8Js2a8Xlr6Hlwb/Agv+0fAyfBaxGY2LX4/6lHWa734fNT7WfheGh8B97ynQmorSnu0PVTJ7YZfiIjsSNvVkR2VhUj3uVFbJbnYTZWNY3yaj5GlAWNZ83U6eJboal0shbN5qvGq0DQ/QkZmjP33wCmVpYKREbIsDY6vcoswbBfzWlmsvYWMoTyQRcyGxMZRldmQ2Q7Mjtwy3H3HFUbrZQRVYtqLN3JtJM2ZdEYxU3/RqG0vrbXI7q61dl49wIYNGzZs2LBhw4YNGzZs2ECofSh2w4YNGzZs2LBhw4YNGzZsOFFsxHbDhg0bNmzYsGHDhg0bNpw1NmK7YcOGDRs2bNiwYcOGDRvOGhux3bBhw4YNGzZs2LBhw4YNZ42N2G7YsGHDhg0bNmzYsGHDhrPGRmw3bNiwYcOGDRs2bNiwYcNZYyO2GzZs2LBhw4YNGzZs2LDhrLER2w0bNmzYsGHDhg0bNmzYcNbYiO2GDRs2bNiwYcOGDRs2bDhr/D98mijF1MiWVwAAAABJRU5ErkJggg==", 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You performed a non-Cartesian CG-SENSE reconstruction using\n", + "TensorFlow MRI. The code used in this notebook works for any dataset and\n", + "trajectory. It also works for 3D imaging. Feel free to try with your own data!\n", + "\n", + "For more information about the functions used in this tutorial, check out the\n", + "[API documentation](https://mrphys.github.io/tensorflow-mri/api_docs/). For\n", + "more examples of using TensorFlow MRI, check out the\n", + "[tutorials](https://mrphys.github.io/tensorflow-mri/tutorials/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let us know!\n", + "Please tell us what you think about this tutorial and about TensorFlow MRI.\n", + "We would like to hear what you liked and how we can improve. You will find us\n", + "on [GitHub](https://github.com/mrphys/tensorflow-mri/issues/new)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright 2022 University College London. All rights reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + } + ], + "metadata": { + "interpreter": { + "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" + }, + "kernelspec": { + "display_name": "Python 3.8.2 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5217378a5b4a38692c92963cdbf3659b1b94e779 Mon Sep 17 00:00:00 2001 From: jennifersteeden Date: Tue, 4 Feb 2025 15:12:48 +0000 Subject: [PATCH 5/6] updated gradient losses --- tensorflow_mri/python/losses/iqa_losses.py | 81 ++++++++++++++++++++++ 1 file changed, 81 insertions(+) diff --git a/tensorflow_mri/python/losses/iqa_losses.py b/tensorflow_mri/python/losses/iqa_losses.py index bde0c74d..d7c5013c 100644 --- a/tensorflow_mri/python/losses/iqa_losses.py +++ b/tensorflow_mri/python/losses/iqa_losses.py @@ -414,6 +414,87 @@ def ssim_multiscale_loss(y_true, y_pred, max_val=None, rank=rank) + +@tf.keras.utils.register_keras_serializable(package="MRI") +class MeanAbsoluteGradientError(LossFunctionWrapperIQA): + def __init__(self, + method='sobel', + norm=False, + batch_dims=None, + image_dims=None, + multichannel=True, + complex_part=None, + reduction=tf.keras.losses.Reduction.AUTO, + name='mean_absolute_gradient_error'): + super().__init__(mean_absolute_gradient_error, + reduction=reduction, name=name, method=method, + norm=norm, batch_dims=batch_dims, image_dims=image_dims, + multichannel=multichannel, complex_part=complex_part) + + +@tf.keras.utils.register_keras_serializable(package="MRI") +class MeanSquaredGradientError(LossFunctionWrapperIQA): + def __init__(self, + method='sobel', + norm=False, + batch_dims=None, + image_dims=None, + multichannel=True, + complex_part=None, + reduction=tf.keras.losses.Reduction.AUTO, + name='mean_squared_gradient_error'): + super().__init__(mean_squared_gradient_error, + reduction=reduction, name=name, method=method, + norm=norm, batch_dims=batch_dims, image_dims=image_dims, + multichannel=multichannel, complex_part=complex_part) + +@tf.keras.utils.register_keras_serializable(package="MRI") +def mean_absolute_error(y_true, y_pred): + y_pred = tf.convert_to_tensor(y_pred) + y_true = tf.cast(y_true, y_pred.dtype) + return tf.math.reduce_mean(tf.math.abs(y_pred - y_true), axis=-1) + +@tf.keras.utils.register_keras_serializable(package="MRI") +def mean_squared_error(y_true, y_pred): + y_pred = tf.convert_to_tensor(y_pred) + y_true = tf.cast(y_true, y_pred.dtype) + return tf.math.reduce_mean( + tf.math.real(tf.math.squared_difference(y_pred, y_true)), axis=-1) + + +@tf.keras.utils.register_keras_serializable(package="MRI") +def mean_absolute_gradient_error(y_true, y_pred, method='sobel', + norm=False, batch_dims=None, image_dims=None): + y_pred = tf.convert_to_tensor(y_pred) + y_true = tf.cast(y_true, y_pred.dtype) + + grad_true = image_ops.image_gradients( + y_true, method=method, norm=norm, + batch_dims=batch_dims, image_dims=image_dims) + grad_pred = image_ops.image_gradients( + y_pred, method=method, norm=norm, + batch_dims=batch_dims, image_dims=image_dims) + + return mean_absolute_error(grad_true, grad_pred) + + +@tf.keras.utils.register_keras_serializable(package="MRI") +def mean_squared_gradient_error(y_true, y_pred, method='sobel', + norm=False, batch_dims=None, image_dims=None): + y_pred = tf.convert_to_tensor(y_pred) + y_true = tf.cast(y_true, y_pred.dtype) + + grad_true = image_ops.image_gradients( + y_true, method=method, norm=norm, + batch_dims=batch_dims, image_dims=image_dims) + grad_pred = image_ops. image_gradients( + y_pred, method=method, norm=norm, + batch_dims=batch_dims, image_dims=image_dims) + + return mean_squared_error(grad_true, grad_pred) + + + # For backward compatibility. @tf.keras.utils.register_keras_serializable(package="MRI") class StructuralSimilarityLoss(SSIMLoss): From df2fd2db2b493676874e1437ce9c680e71fc9bff Mon Sep 17 00:00:00 2001 From: jennifersteeden Date: Tue, 4 Feb 2025 15:32:16 +0000 Subject: [PATCH 6/6] updated guides --- tools/docs/guide/contribute.ipynb | 32 ---------- tools/docs/guide/fft.ipynb | 101 ++++++++++++++++++++++++++++++ tools/docs/guide/linalg.ipynb | 32 ---------- tools/docs/guide/optim.ipynb | 32 ---------- tools/docs/guide/recon.ipynb | 32 ---------- tools/docs/templates/index.rst | 5 +- 6 files changed, 102 insertions(+), 132 deletions(-) delete mode 100644 tools/docs/guide/contribute.ipynb create mode 100644 tools/docs/guide/fft.ipynb delete mode 100644 tools/docs/guide/linalg.ipynb delete mode 100644 tools/docs/guide/optim.ipynb delete mode 100644 tools/docs/guide/recon.ipynb diff --git a/tools/docs/guide/contribute.ipynb b/tools/docs/guide/contribute.ipynb deleted file mode 100644 index 98b41652..00000000 --- a/tools/docs/guide/contribute.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Contributing\n", - "\n", - "Coming soon..." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.10 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.8.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tools/docs/guide/fft.ipynb b/tools/docs/guide/fft.ipynb new file mode 100644 index 00000000..72099ca6 --- /dev/null +++ b/tools/docs/guide/fft.ipynb @@ -0,0 +1,101 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fast Fourier transform (FFT)\n", + "\n", + "TensorFlow MRI uses the built-in FFT ops in core TensorFlow. These are [`tf.signal.fft`](https://www.tensorflow.org/api_docs/python/tf/signal/fft), [`tf.signal.fft2d`](https://www.tensorflow.org/api_docs/python/tf/signal/fft2d) and [`tf.signal.fft3d`](https://www.tensorflow.org/api_docs/python/tf/signal/fft3d).\n", + "\n", + "## N-dimensional FFT\n", + "\n", + "For convenience, TensorFlow MRI also provides [`tfmri.signal.fft`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/signal/fft/), which can be used for N-dimensional FFT calculations and provides convenient access to commonly used functionality such as padding/cropping, normalization and shifting of the zero-frequency component within the same function call.\n", + "\n", + "## Custom FFT kernels for CPU\n", + "\n", + "Unfortunately, TensorFlow's FFT ops are [known to be slow](https://github.com/tensorflow/tensorflow/issues/6541) on CPU. As a result, the FFT can become a significant bottleneck on MRI processing pipelines, especially on iterative reconstructions where the FFT is called repeatedly.\n", + "\n", + "To address this issue, TensorFlow MRI provides a set of custom FFT kernels based on the FFTW library. These offer a significant boost in performance compared to the kernels in core TensorFlow.\n", + "\n", + "The custom FFT kernels are automatically registered to the TensorFlow framework when importing TensorFlow MRI. If you have imported TensorFlow MRI, then the standard FFT ops will use the optimized kernels automatically.\n", + "\n", + "```{tip}\n", + "You only need to `import tensorflow_mri` in order to use the custom FFT kernels. You can then access them as usual through `tf.signal.fft`, `tf.signal.fft2d` and `tf.signal.fft3d`.\n", + "```\n", + "\n", + "The only caveat is that the [FFTW license](https://www.fftw.org/doc/License-and-Copyright.html) is more restrictive than the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0) used by TensorFlow MRI. In particular, GNU GPL requires you to distribute any derivative software under equivalent terms.\n", + "\n", + "```{warning}\n", + "If you intend to use custom FFT kernels for commercial purposes, you will need to purchase a commercial FFTW license.\n", + "```\n", + "\n", + "### Disable the use of custom FFT kernels\n", + "\n", + "You can control whether custom FFT kernels are used via the `TFMRI_USE_CUSTOM_FFT` environment variable. When set to false, TensorFlow MRI will not register its custom FFT kernels, falling back to the standard FFT kernels in core TensorFlow. If the variable is unset, its value defaults to true.\n", + "\n", + "````{tip}\n", + "Set `TFMRI_USE_CUSTOM_FFT=0` to disable the custom FFT kernels.\n", + "\n", + "```python\n", + "os.environ[\"TFMRI_USE_CUSTOM_FFT\"] = \"0\"\n", + "import tensorflow_mri as tfmri\n", + "```\n", + "\n", + "```{attention}\n", + "`TFMRI_USE_CUSTOM_FFT` must be set **before** importing TensorFlow MRI. Setting or changing its value after importing the package will have no effect.\n", + "```\n", + "````\n", + "\n", + "### Customize the behavior of custom FFT kernels\n", + "\n", + "FFTW allows you to control the rigor of the planning process. The more rigorously a plan is created, the more efficient the actual FFT execution is likely to be, at the expense of a longer planning time. TensorFlow MRI lets you control the FFTW planning rigor through the `TFMRI_FFTW_PLANNING_RIGOR` environment variable. Valid values for this variable are:\n", + "\n", + "- `\"estimate\"` specifies that, instead of actual measurements of different algorithms, a simple heuristic is used to pick a (probably sub-optimal) plan quickly.\n", + "- `\"measure\"` tells FFTW to find an optimized plan by actually computing several FFTs and measuring their execution time. Depending on your machine, this can take some time (often a few seconds). This is the default planning option.\n", + "- `\"patient\"` is like `\"measure\"`, but considers a wider range of algorithms and often produces a “more optimal” plan (especially for large transforms), but at the expense of several times longer planning time (especially for large transforms).\n", + "- `\"exhaustive\"` is like `\"patient\"`, but considers an even wider range of algorithms, including many that we think are unlikely to be fast, to produce the most optimal plan but with a substantially increased planning time.\n", + "\n", + "````{tip}\n", + "Set the environment variable `TFMRI_FFTW_PLANNING_RIGOR` to control the planning rigor.\n", + "\n", + "```python\n", + "os.environ[\"TFMRI_FFTW_PLANNING_RIGOR\"] = \"estimate\"\n", + "import tensorflow_mri as tfmri\n", + "```\n", + "\n", + "```{attention}\n", + "`TFMRI_FFTW_PLANNING_RIGOR` must be set **before** importing TensorFlow MRI. Setting or changing its value after importing the package will have no effect.\n", + "```\n", + "````\n", + "\n", + "```{note}\n", + "FFTW accumulates \"wisdom\" each time the planner is called, and this wisdom is persisted across invocations of the FFT kernels (during the same process). Therefore, more rigorous planning options will result in long planning times during the first FFT invocation, but may result in faster execution during subsequent invocations. When performing a large amount of similar FFT invocations (e.g., while training a model or performing iterative reconstructions), you are more likely to benefit from more rigorous planning.\n", + "```\n", + "\n", + "```{seealso}\n", + "The FFTW [planner flags](https://www.fftw.org/doc/Planner-Flags.html) documentation page.\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.2 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.2" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/docs/guide/linalg.ipynb b/tools/docs/guide/linalg.ipynb deleted file mode 100644 index f45442d9..00000000 --- a/tools/docs/guide/linalg.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Linear algebra\n", - "\n", - "Coming soon..." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.10 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.8.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tools/docs/guide/optim.ipynb b/tools/docs/guide/optim.ipynb deleted file mode 100644 index 21363722..00000000 --- a/tools/docs/guide/optim.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimization\n", - "\n", - "Coming soon..." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.2 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.8.2" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tools/docs/guide/recon.ipynb b/tools/docs/guide/recon.ipynb deleted file mode 100644 index 5291a70b..00000000 --- a/tools/docs/guide/recon.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MR image reconstruction\n", - "\n", - "Coming soon..." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.2 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.8.2" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tools/docs/templates/index.rst b/tools/docs/templates/index.rst index 13b8384c..e75901fc 100644 --- a/tools/docs/templates/index.rst +++ b/tools/docs/templates/index.rst @@ -16,11 +16,8 @@ TensorFlow MRI |release| Guide Installation + Uniform FFT Non-uniform FFT - Linear algebra - Optimization - MRI reconstruction - Contributing FAQ