diff --git a/docs/flystar/examples/motion_model_example.ipynb b/docs/flystar/examples/motion_model_example.ipynb new file mode 100644 index 0000000..413b616 --- /dev/null +++ b/docs/flystar/examples/motion_model_example.ipynb @@ -0,0 +1,1363 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "333cd262", + "metadata": {}, + "source": [ + "# Motion Model Examples" + ] + }, + { + "cell_type": "markdown", + "id": "9251851e", + "metadata": {}, + "source": [ + "# Table of Contents" + ] + }, + { + "cell_type": "markdown", + "id": "1e4364ed", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "- [1. Motion Model](#1-motion-model)\n", + " - [1.1. Example: Linear Model Fit](#11-example-linear-model-fit)\n", + " - [1.2. Example: Acceleration Model Fit](#12-example-acceleration-model-fit)\n", + " - [1.3. Example: Parallax Model Fit](#13-example-parallax-model-fit)\n", + "- [2. Fit Motion Model in StarTable](#2-fit-motion-model-in-startable)\n", + " - [2.1. Example: Default Fitting](#21-example-default-fitting)\n", + " - [2.2 Example: Specify Motion Models](#22-example-specify-motion-models)\n", + " - [2.3. Example: Specify the `motion_model_input` Column](#23-example-specify-the-motion_model_input-column)\n", + " - [2.4. Example: Infer Positions](#24-example-infer-positions)\n", + " - [2.5. Speed Test](#25-speed-test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "4bd92a9d", + "metadata": {}, + "source": [ + "# 1. Motion Model" + ] + }, + { + "cell_type": "markdown", + "id": "0d084c38", + "metadata": {}, + "source": [ + "Summary of currently implemented motion models" + ] + }, + { + "cell_type": "markdown", + "id": "faddd6d8", + "metadata": {}, + "source": [ + "| Motion Model | n_params | params | fixed_params | model | Description |\n", + "|--------------|----------|--------------------------------------------|-------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n", + "| Empty | 0 | NA | NA | $x(t) = $ NaN / fill_value
$x_e(t) = $ Inf | |\n", + "| Fixed | 1 | $x_0$
$y_0$ | NA | $x(t) = $ np.average($x$, weights=$x_{wt}$) | $x_{wt} = 1/xe^2$ if weighting='var'
$x_{wt} = 1/\\|xe\\|$ if weighting = 'std' |\n", + "| Linear | 2 | $x_0, v_x$
$y_0, v_y$ | optional: $t_0 =$ np.average($t, 1/\\sqrt{x_e^2 + y_e^2}$) | $x(t) = x_0 + v_x * (t - t_0)$ | |\n", + "| Acceleration | 3 | $x_0, v_{x0}, a_x$
$y_0, v_{y0}, a_y$ | optional: $t_0 =$ np.average($t, 1/\\sqrt{x_e^2 + y_e^2}$) | $x(t) = x_0 + v_{x0} * (t - t_0) + 1/2 * a_x * (t - t_0)^2$ | |\n", + "| Parallax | 3 | $x_0, v_x, pi$
$y_0, v_y$ | required: ra, dec
optional: $t_0 =$ np.average($t, 1/\\sqrt{x_e^2 + y_e^2}$); $pa=0$; obsLocation='earth' | $x(t) = x_0 + v_x * (t - t_0) + pvec * (t - t_0)$ | pvec is the parallax vector calculated based on ra, dec, pa, and obsLocation.
Only supports the same obsLocation for all stars in StarTable.fit_motion_model right now. |" + ] + }, + { + "cell_type": "markdown", + "id": "6fdc98af", + "metadata": {}, + "source": [ + "Examples on using `flystar.MotionModel`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "51c963a1", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "473b0674", + "metadata": {}, + "source": [ + "Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ce4edb88", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from flystar import motion_model\n", + "from flystar.startables import StarTable\n", + "from flystar.motion_model import Empty, Fixed, Linear, Acceleration, Parallax" + ] + }, + { + "cell_type": "markdown", + "id": "8c0e8559", + "metadata": {}, + "source": [ + "Prepare data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "86b6319d", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.array([0, 1., 2.2, 3.5, 5.]) + 2025.0\n", + "x = np.array([0., 0.5, 2.1, 3.2, 8.0])\n", + "y = np.array([10.2, 8.5, 9.1, 10.5, 13.0])\n", + "xe = np.array([0.2, 0.5, 0.3, 0.4, 0.6])\n", + "ye = np.array([0.3, 0.2, 0.5, 0.2, 0.4])\n", + "t_test = np.linspace(2025.0, 2030.0, 100) # Test times for model evaluation" + ] + }, + { + "cell_type": "markdown", + "id": "b1a87102", + "metadata": {}, + "source": [ + "## 1.1. Example: Linear Model Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0926c0a8", + "metadata": {}, + "outputs": [], + "source": [ + "mm = Linear()\n", + "params, param_errs = mm.fit(t, x, y, xe, ye)" + ] + }, + { + "cell_type": "markdown", + "id": "1fad1962", + "metadata": {}, + "source": [ + "Evaluate model at time t:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "840693ae", + "metadata": {}, + "outputs": [], + "source": [ + "x_model, y_model = mm.model(t, params)" + ] + }, + { + "cell_type": "markdown", + "id": "42fbd575", + "metadata": {}, + "source": [ + "Or if uncertainties of parameters is provided at the same time, the model will return the model uncertainties as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8fcbdc5d", + "metadata": {}, + "outputs": [], + "source": [ + "x_model, y_model, xe_model, ye_model = mm.model(t, params, param_errs)" + ] + }, + { + "cell_type": "markdown", + "id": "6f9954ef", + "metadata": {}, + "source": [ + "Note that we did not provide the `fixed_params_dict` parameter in the `model` function, so the MotionModel will use the saved self.fixed_params_dict. One can also specify the fixed_params_dict as:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6752e477", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'t0': np.float64(2027.0454838983064)}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mm.fixed_params_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "eba675c8", + "metadata": {}, + "outputs": [], + "source": [ + "x_model, y_model, xe_model, ye_model = mm.model(t_test, params, param_errs, mm.fixed_params_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "a2acbe90", + "metadata": {}, + "source": [ + "Define a helper function to visualize result" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7dba325f", + "metadata": {}, + "outputs": [], + "source": [ + "def visualize_fit(t, x, y, xe, ye, x_model, y_model, xe_model, ye_model, mm_name, t_test=None):\n", + " if t_test is None:\n", + " t_test = t\n", + " x = np.atleast_2d(x)\n", + " y = np.atleast_2d(y)\n", + " xe = np.atleast_2d(xe)\n", + " ye = np.atleast_2d(ye)\n", + " x_model = np.atleast_2d(x_model)\n", + " y_model = np.atleast_2d(y_model)\n", + " xe_model = np.atleast_2d(xe_model)\n", + " ye_model = np.atleast_2d(ye_model)\n", + " \n", + " N_cases = x.shape[0]\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n", + " for i in range(N_cases):\n", + " l0 = ax1.errorbar(t, x[i], yerr=xe[i], fmt='o', color=f'C{i%10}', label='Data')\n", + " l1, = ax1.plot(t_test, x_model[i], label=f'{mm_name} Fit')\n", + " l2 = ax1.fill_between(t_test, x_model[i] - xe_model[i], x_model[i] + xe_model[i], color=f'C{i%10}', alpha=0.3, label='Model Uncertainty')\n", + "\n", + " r0 = ax2.errorbar(t, y[i], yerr=ye[i], fmt='o', color=f'C{i%10}', label='Data')\n", + " r1, = ax2.plot(t_test, y_model[i], label=f'{mm_name} Fit')\n", + " r2 = ax2.fill_between(t_test, y_model[i] - ye_model[i], y_model[i] + ye_model[i], color=f'C{i%10}', alpha=0.3, label='Model Uncertainty')\n", + " ax1.set_xlabel('Time')\n", + " ax1.set_ylabel('X Position')\n", + " ax1.set_title(f'{mm_name} Motion Model Fit')\n", + " ax1.legend(\n", + " [l0, (l1, l2)], \n", + " ['Data', 'Model Fit'],\n", + " )\n", + " \n", + " ax2.set_xlabel('Time')\n", + " ax2.set_ylabel('Y Position')\n", + " ax2.set_title(f'{mm_name} Motion Model Fit')\n", + " ax2.legend(\n", + " [r0, (r1, r2)], \n", + " ['Data', 'Model Fit'],\n", + " )\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ad03fc67", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize_fit(t, x, y, xe, ye, x_model, y_model, xe_model, ye_model, mm.name, t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "98d3e2c4", + "metadata": {}, + "source": [ + "## 1.2. Example: Acceleration Model Fit" + ] + }, + { + "cell_type": "markdown", + "id": "ede486e5", + "metadata": {}, + "source": [ + "Upon further inspection, acceleration model seems to be a better representation of the data" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0a0d9d1f", + "metadata": {}, + "outputs": [], + "source": [ + "mm = Acceleration()\n", + "params, param_errs = mm.fit(t, x, y, xe, ye)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b3d63417", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_model, y_model, xe_model, ye_model = mm.model(t_test, params, param_errs)\n", + "visualize_fit(t, x, y, xe, ye, x_model, y_model, xe_model, ye_model, mm.name, t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "9d1f63b4", + "metadata": {}, + "source": [ + "Moreover, `MotionModel.model` is fully vectorized, and can infer positions of multiple stars at multiple times, and the resulting inferred positions has shape (N_stars, N_times). See the example below:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d1e406c5", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.array([0, 1., 2.2, 3.5, 5.]) + 2025.0\n", + "\n", + "xs = np.array([\n", + " [0., 0.5, 2.1, 3.2, 8.0],\n", + " [10.0, 8.9, 9.2, 7.4, 7.0],\n", + " [2.5, 6.2, 5.2, 3.2, 5.0]\n", + "])\n", + "\n", + "ys = np.array([\n", + " [10.2, 8.5, 9.1, 10.5, 13.0],\n", + " [8.0, 9.9, 8.2, 7.4, 7.0],\n", + " [5.2, 6.2, 4.7, 3.2, 6.0]\n", + "])\n", + "\n", + "xes = np.array([\n", + " [0.2, 0.5, 0.3, 0.4, 0.6],\n", + " [0.5, 0.2, 0.7, 0.3, 0.2],\n", + " [0.5, 0.7, 0.6, 0.4, 0.3]\n", + "])\n", + "\n", + "yes = np.array([\n", + " [0.3, 0.2, 0.5, 0.2, 0.4],\n", + " [0.2, 0.5, 0.6, 0.4, 0.2],\n", + " [0.4, 0.2, 0.3, 0.4, 0.5]\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "4adebbe8", + "metadata": {}, + "outputs": [], + "source": [ + "params = []\n", + "param_errs = []\n", + "for xi, yi, xei, yei in zip(xs, ys, xes, yes):\n", + " p, pe = mm.fit(t, xi, yi, xei, yei)\n", + " params.append(p)\n", + " param_errs.append(pe)" + ] + }, + { + "cell_type": "markdown", + "id": "4e0424df", + "metadata": {}, + "source": [ + "Once we have the params and param errors, we can infer the model positions at any given time." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "95745baa", + "metadata": {}, + "outputs": [], + "source": [ + "x_model, y_model, xe_model, ye_model = mm.model(t_test, params, param_errs)" + ] + }, + { + "cell_type": "markdown", + "id": "06fdca50", + "metadata": {}, + "source": [ + "The inferred positions should have shape (N_stars, N_times):" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "54206834", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 100)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_model.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "e6a4e42e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize_fit(t, xs, ys, xes, yes, x_model, y_model, xe_model, ye_model, mm.name, t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "f7ae3e7f", + "metadata": {}, + "source": [ + "## 1.3. Example: Parallax Model Fit" + ] + }, + { + "cell_type": "markdown", + "id": "08eceab5", + "metadata": {}, + "source": [ + "Parallax model requires some fixed parameters: `ra`, `dec`, `pa`, `obsLocation`, and `t0`.\n", + "- `ra` and `dec` are required parameters. \n", + "- `pa = 0` by default\n", + "- `obsLocation = 'earth'` by default\n", + "- `t0 = np.average(t, 1./np.hypot(xe, ye))` by default\n", + "\n", + "We need to provide the fixed parameters in the `fixed_params_dict`:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "018fc13a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 1 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 2 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 2 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 1 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"taiutc\" yielded 1 of \"dubious year (Note 4)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n" + ] + } + ], + "source": [ + "mm = Parallax()\n", + "fixed_params_dict = {'ra': 0., 'dec': 10., 'pa': 0., 'obsLocation': 'earth'}\n", + "params, param_errs = mm.fit(t, x, y, xe, ye, fixed_params_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "73dafb1f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 20 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 40 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 40 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 20 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"taiutc\" yielded 20 of \"dubious year (Note 4)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9wAAAHqCAYAAAD27EaEAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAA8XlJREFUeJzs3Xd8W+W9+PHP0R625b0Sx3Z2QhgJAZKwEigbSqEUKC2zgwu9pS2dtOVCSi+U3tsyyuivLRAobQIUSmm5UEYgrIQsErLjeO8ha2/pnN8fJiYmdmInkiU73/cLvYiOjo6+smU953ue5/k+iqZpGkIIIYQQQgghhEgqXboDEEIIIYQQQgghxiNJuIUQQgghhBBCiBSQhFsIIYQQQgghhEgBSbiFEEIIIYQQQogUkIRbCCGEEEIIIYRIAUm4hRBCCCGEEEKIFJCEWwghhBBCCCGESAFJuIUQQgghhBBCiBSQhFsIIYQQQgghhEgBSbhFxlq2bBmKovTfDAYDEydO5Prrr6e1tTUtMV133XVUVVUN2FZVVcV111036rEsXrwYRVGYPHkymqbt9/g777zT/7NbtmzZiI+/fft27rzzThoaGvZ7bLCfw2jZ+56G+pn/4he/6N9nsNgP1eG858WLF7N48eJh7bfvZ37f29atW7nzzjtRFGXAcx555JFD+v0KIUSqSTt+YNKOXzfo49KOi/FGEm6R8Z544glWr17N66+/zje+8Q2WL1/OqaeeSiAQSHdoaZednU19fT0rV67c77HHH3+cnJycQz729u3bWbp06aCN3e23387f//73Qz724crOzua5557D5/MN2K5pGsuWLTus951ukydPZvXq1fvdpkyZwte//nVWr149YH9pqIUQmU7a8aFJOy7tuLTj458k3CLjzZkzhwULFrBkyRLuuOMOfvSjH1FfX8+LL7542McOhUKHH2AaTZo0iQULFvD4448P2O7z+Xjuuee44oorUvK6U6ZMYe7cuSk59nBcfPHFaJrGihUrBmxfuXIl9fX1KXvfo8FqtbJgwYL9blarlYkTJ7JgwYJ0hyiEECMi7fjQpB2XdlyMf5JwizFn7xdVY2MjAEuXLuWkk04iPz+fnJwc5s2bx2OPPbbf8KyqqiouvPBCXnjhBebOnYvFYmHp0qUAPPzww5x22mkUFxdjt9s5+uij+fWvf00sFhtxfOFwmO9///scd9xxOBwO8vPzWbhwIf/4xz8G7LdixQoUReGhhx4asP2OO+5Ar9fz+uuvD+v1brjhBl544QXcbveAYwNceeWVgz7nvffe48wzzyQ7OxubzcaiRYt4+eWX+x9ftmwZX/rSlwBYsmTJfkPaBhuWFQ6Hue2226iursZkMjFhwgS+9a1vDYgLPv09vPrqq8ybNw+r1crMmTP3O9k4EIfDwSWXXLLfcx5//HFOPvlkpk+fPujzHn/8cY499lgsFgv5+flccskl7NixY7/9li1bxowZMzCbzcyaNYunnnpq0ONFo1F++ctfMnPmTMxmM0VFRVx//fV0d3cP+72MxGeHolVVVbFt2zZWrVrV/ztK1xBBIYQYLmnHB5J2/FPSjks7Ph4Z0h2AECO1Z88eAIqKigBoaGjgxhtvZNKkSQCsWbOGb3/727S2tvJf//VfA567ceNGduzYwc9//nOqq6ux2+0A1NbWctVVV/U3Mps3b+a///u/2blz54gaEIBIJEJvby8/+MEPmDBhAtFolDfeeINLL72UJ554gmuuuQboa0RXrVrF97//fRYsWMD8+fNZuXIlv/zlL/npT3/KWWedNazXu/LKK/ne977H8uXLuemmmwB47LHHuOyyywYdkrVq1SrOOussjjnmGB577DHMZjOPPPIIF110EcuXL+eKK67gggsu4O677+anP/0pDz/8MPPmzQP6rogPRtM0vvCFL/Dmm29y2223ceqpp/Lxxx9zxx139A+lMpvN/ftv3ryZ73//+/zkJz+hpKSEP/3pT3zta19j6tSpnHbaacN631/72tc488wz2bFjB7NmzcLtdvPCCy/wyCOP4HQ699v/nnvu4ac//Slf/vKXueeee3A6ndx5550sXLiQdevWMW3aNKCvkb7++uu5+OKL+c1vfoPH4+HOO+8kEomg0316jVJVVS6++GLeffddfvSjH7Fo0SIaGxu54447WLx4MevXr8dqtQ7rvXxWPB4fcF+n0w147b3+/ve/c9lll+FwOHjkkUcABvychRAiE0k7PpC049KOSzs+zmlCZKgnnnhCA7Q1a9ZosVhM8/l82r/+9S+tqKhIy87O1jo6OvZ7TiKR0GKxmPaLX/xCKygo0FRV7X+ssrJS0+v12q5duw74unuP8dRTT2l6vV7r7e3tf+zaa6/VKisrB+xfWVmpXXvttUMeLx6Pa7FYTPva176mzZ07d8Bj4XBYmzt3rlZdXa1t375dKykp0U4//XQtHo8fMEZN07TTTz9dO+qoo/rjmj9/vqZpmrZt2zYN0N5++21t3bp1GqA98cQT/c9bsGCBVlxcrPl8vgExzpkzR5s4cWL/z+y5557TAO2tt97a77U/+3N49dVXNUD79a9/PWC/Z555RgO0P/zhD/3bKisrNYvFojU2NvZvC4VCWn5+vnbjjTce9H0D2re+9S1NVVWturpa+8EPfqBpmqY9/PDDWlZWlubz+bT/+Z//0QCtvr5e0zRNc7lcmtVq1c4///wBx2pqatLMZrN21VVXaZrW97svLy/X5s2bN+Cz09DQoBmNxgHvefny5RqgPf/88wOOufdn/sgjj/RvO/3007XTTz/9oO/t9NNP14D9bl/5ylc0TdO0O+64Q/vs1/ZRRx01rGMLIcRok3b8wKQdl3Zc06QdPxLIkHKR8RYsWIDRaCQ7O5sLL7yQ0tJSXnnlFUpKSoC++T6f+9zncDgc6PV6jEYj//Vf/4XT6aSrq2vAsY455phBhyl99NFHfP7zn6egoKD/GNdccw2JRILdu3ePOObnnnuOk08+maysLAwGA0ajkccee2y/YU9ms5lnn30Wp9PJvHnz0DSN5cuXo9frR/R6N9xwA+vXr2fLli089thjTJkyZdArzIFAgA8//JDLLruMrKys/u16vZ6rr76alpYWdu3aNeL3u7fYy2crjn7pS1/Cbrfz5ptvDth+3HHH9fdkAFgsFqZPn94/vHA49lY4/fOf/0w8Huexxx7j8ssvH/C+9lq9ejWhUGi/+CoqKjjjjDP649u1axdtbW1cddVVA4Z8VVZWsmjRogHP/de//kVubi4XXXQR8Xi8/3bcccdRWlrK22+/Pez3sq8pU6awbt26Abe77rrrkI4lhBCZQNrxg5N2XNpxMX5Jwi0y3lNPPcW6dev46KOPaGtr4+OPP+bkk08GYO3atZx99tkA/PGPf+T9999n3bp1/OxnPwP2L6ZSVla23/Gbmpo49dRTaW1t5YEHHuDdd99l3bp1PPzww4Me42BeeOEFLr/8ciZMmMDTTz/N6tWrWbduHTfccAPhcHi//adOncqpp55KOBzmK1/5yqAxHsxpp53GtGnT+H//7//x5z//mRtuuGG/ZScAXC4XmqYN+hrl5eUAgw7jOhin04nBYOgfHriXoiiUlpbud8yCgoL9jmE2m0f8s947z+ruu+9m48aNfO1rXxsyPhj8919eXt7/+N7/l5aW7rffZ7d1dnbidrsxmUwYjcYBt46ODnp6ekb0XvayWCzMnz9/wK26uvqQjiWEEJlA2vGDk3Zc2nExfskcbpHxZs2axfz58wd9bMWKFRiNRv71r39hsVj6tw9V+XSwxuvFF18kEAjwwgsvUFlZ2b9906ZNhxTv008/TXV1Nc8888yA14tEIoPu/6c//YmXX36ZE088kYceeogrrriCk046acSve/311/Pzn/8cRVG49tprB90nLy8PnU5He3v7fo+1tbUBUFhYOOLXLigoIB6P093dPaCx1jSNjo4OTjjhhBEfczgqKir43Oc+x9KlS5kxY8Z+V6/3jQ8Y8n3vfc979+vo6Nhvv89uKywspKCggFdffXXQ18zOzh7+GxFCiHFM2vHhkXZc2nExPkkPtxjTFEXBYDAMGLoVCoX485//PKJjwMAiFZqm8cc//vGQYzKZTAMa6Y6Ojv2qmwJs2bKFW265hWuuuYZ3332XY445hiuuuAKXyzXi17322mu56KKL+OEPf8iECRMG3cdut3PSSSfxwgsvDLgKraoqTz/9NBMnTuwfqrf35zGcq9Vnnnkm0HeSsq/nn3+eQCDQ/3gqfP/73+eiiy7i9ttvH3KfhQsXYrVa94uvpaWFlStX9sc3Y8YMysrKWL58+YDquI2NjXzwwQcDnnvhhRfidDpJJBL7XcmeP38+M2bMSOK7HNqh9CgIIUSmkHb8U9KOSzsuxifp4RZj2gUXXMBvf/tbrrrqKr75zW/idDr53//93xFVeDzrrLMwmUx8+ctf5kc/+hHhcJhHH330kBpLoH/JkptvvpnLLruM5uZm7rrrLsrKyqipqenfLxAIcPnll1NdXc0jjzyCyWTi2WefZd68eVx//fUjXp+0vLx8WM+55557OOuss1iyZAk/+MEPMJlMPPLII2zdupXly5f3n2DMmTMHgD/84Q9kZ2djsViorq4edBjZWWedxTnnnMOPf/xjvF4vJ598cn9107lz53L11VeP6L2MxNlnn90/HHEoubm53H777fz0pz/lmmuu4ctf/jJOp5OlS5disVi44447gL4qonfddRdf//rXueSSS/jGN76B2+3mzjvv3G8o2pVXXslf/vIXzj//fL7zne9w4oknYjQaaWlp4a233uLiiy/mkksuSdn73uvoo49mxYoVPPPMM0yePBmLxcLRRx+d8tcVQohkkHb8U9KOD03acTGmpatamxAHs7e66bp16w643+OPP67NmDFDM5vN2uTJk7V77rlHe+yxxwZUt9S0vqqaF1xwwaDH+Oc//6kde+yxmsVi0SZMmKD98Ic/1F555ZX9qnsOt7rpr371K62qqkozm83arFmztD/+8Y/7Vab86le/qtlsNm3btm0Dnru3quh99913wPe9b3XToQxW3VTTNO3dd9/VzjjjDM1ut2tWq1VbsGCB9s9//nO/599///1adXW1ptfrBxxnsJ9DKBTSfvzjH2uVlZWa0WjUysrKtJtuuklzuVwD9hvq9zDcCqB8Ut30QD5b3XSvP/3pT9oxxxyjmUwmzeFwaBdffPF+P/+9+02bNk0zmUza9OnTtccff3zQ9xyLxbT//d//7f/sZGVlaTNnztRuvPFGraamZsTv7WC/08GqmzY0NGhnn322lp2drQH7xSiEEOki7fh9B3zf0o4PTdpxMZ4omrbPeAshhBBCCCGEEEIkhczhFkIIIYQQQgghUkASbiGEEEIIIYQQIgUk4RZCCCGEEEIIIVJAEm4hhBBCCCGEECIFJOEWQgghhBBCCCFSQBJuIYQQQgghhBAiBQzpDmA0qapKW1sb2dnZKIqS7nCEEEIcwTRNw+fzUV5ejk4n17+HQ9pxIYQQmWAkbfgRlXC3tbVRUVGR7jCEEEKIfs3NzUycODHdYYwJ0o4LIYTIJMNpw4+ohDs7Oxvo+8Hk5OSkORohhBBHMq/XS0VFRX/bJA5O2nEhhBCZYCRt+BGVcO8dfpaTkyMNtRBCiIwgQ6OHT9pxIYQQmWQ4bbhMGhNCCCGEEEIIIVJAEm4hhBBCCCGEECIFJOEWQgghhBBCCCFS4Iiawz1ciUSCWCyW7jDEMBmNRvR6fbrDEEIIkQGkDR97pB0XQoxnknDvQ9M0Ojo6cLvd6Q5FjFBubi6lpaVSfEgIIY5Q0oaPbdKOCyHGK0m497G3oS4uLsZms8mX/higaRrBYJCuri4AysrK0hyREEKIdJA2fGySdlwIMd5Jwv2JRCLR31AXFBSkOxwxAlarFYCuri6Ki4tlWJoQQhyGe+65hxdeeIGdO3ditVpZtGgR9957LzNmzOjfR9M0li5dyh/+8AdcLhcnnXQSDz/8MEcdddQBj/38889z++23U1tby5QpU/jv//5vLrnkksOOWdrwsU3acSHEeCZF0z6xd76XzWZLcyTiUOz9vcm8PSGEODyrVq3iW9/6FmvWrOH1118nHo9z9tlnEwgE+vf59a9/zW9/+1seeugh1q1bR2lpKWeddRY+n2/I465evZorrriCq6++ms2bN3P11Vdz+eWX8+GHHx52zNKGj33SjgshxitF0zQt3UGMFq/Xi8PhwOPxkJOTM+CxcDhMfX091dXVWCyWNEUoDpX8/oQQY82B2qRM0t3dTXFxMatWreK0005D0zTKy8v57ne/y49//GMAIpEIJSUl3Hvvvdx4442DHueKK67A6/Xyyiuv9G8799xzycvLY/ny5cOKZaifmbQBY5/8DoUQY8lI2nDp4U6yYDRO1U9epuonLxOMxtMdjhBCCHFYPB4PAPn5+QDU19fT0dHB2Wef3b+P2Wzm9NNP54MPPhjyOKtXrx7wHIBzzjnngM9JB2nHhRBCJJPM4RZCCCHEoDRN49Zbb+WUU05hzpw5QF9xMoCSkpIB+5aUlNDY2DjksTo6OgZ9zt7jDSYSiRCJRPrve73eEb8HIYQQIp2khzvJEuqnI/TX1vcOuJ8q1113HYqioCgKRqORkpISzjrrLB5//HFUVR32cZYtW0Zubm7qAhVCCDGm/Od//icff/zxoEO+P1sFXNO0g1YGH+lz7rnnHhwOR/+toqJiBNEfmtFux6UNF0KI8U0S7iR6dWs7n/vtqv771z2xjlPuXcmrW9tT/trnnnsu7e3tNDQ08Morr7BkyRK+853vcOGFFxKPy5A4IYQQI/Ptb3+bl156ibfeeouJEyf2by8tLQXYr2e6q6trvx7sfZWWlo74Obfddhsej6f/1tzcfChvZdjS1Y5LGy6EEOOXJNxJ8urWdm56eiOd3siA7R2eMDc9vTHljbXZbKa0tJQJEyYwb948fvrTn/KPf/yDV155hWXLlgHw29/+lqOPPhq73U5FRQU333wzfr8fgLfffpvrr78ej8fTf6X9zjvvBODpp59m/vz5ZGdnU1paylVXXdW/XqYQQojxRdM0/vM//5MXXniBlStXUl1dPeDx6upqSktLef311/u3RaNRVq1axaJFi4Y87sKFCwc8B+C111474HPMZjM5OTkDbqmSznZc2nAhhBi/JOFOgoSqsfSf2xls0NnebUv/uX1Uhpfv64wzzuDYY4/lhRdeAECn0/Hggw+ydetWnnzySVauXMmPfvQjABYtWsT9999PTk4O7e3ttLe384Mf/ADoO5G666672Lx5My+++CL19fVcd911o/pehBBCjI5vfetbPP300/z1r38lOzubjo4OOjo6CIVCQN+w8O9+97vcfffd/P3vf2fr1q1cd9112Gw2rrrqqv7jXHPNNdx2223997/zne/w2muvce+997Jz507uvfde3njjDb773e+O9lvcTya249KGCyHE+CBF05JgbX0v7Z7wkI9rQLsnzNr6XhZOKRi9wICZM2fy8ccfAww4qamuruauu+7ipptu4pFHHsFkMuFwOFAUpX+44F433HBD/78nT57Mgw8+yIknnojf7ycrK2tU3ocQQmQCdzCKxajHYtSnO5SUefTRRwFYvHjxgO1PPPFEf6L2ox/9iFAoxM0334zL5eKkk07itddeIzs7u3//pqYmdLpPr+svWrSIFStW8POf/5zbb7+dKVOm8Mwzz3DSSSel/D0dTKa249KGCyFE8miaRm8gSp7NhE534JojySQJdxJ0+YZupA9lv2TatyDNW2+9xd1338327dvxer3E43HC4TCBQAC73T7kMT766CPuvPNONm3aRG9vb38Rl6amJmbPnj0q70MIIdKpNxBld6ePhp4A8yrzmF6SffAnjVGadvBe3L1DlvcOWx7M22+/vd+2yy67jMsuu+wwokuNTG3HpQ0XQojkCETibG310OIKcur0IoqzLaP22jKkPAmG+wsbzV/sXjt27KC6uprGxkbOP/985syZw/PPP8+GDRt4+OGHAYjFYkM+PxAIcPbZZ5OVlcXTTz/NunXr+Pvf/w70DVMTQojxzBeOsbbOyStb29nc7KY3EEUdRkIqxpZMbcelDRdCiMOjaRoNPQFe29bJxiYX7lCM0W7GpYc7CU6szqfMYaHDEx50/pcClDosnFidP6pxrVy5ki1btvC9732P9evXE4/H+c1vftM/xO/ZZ58dsL/JZCKRSAzYtnPnTnp6evjVr37VvxzL+vXrR+cNCCFEGiVUjQ/re9nT5acoy0xJkYX6Hn+6wxIpkIntuLThQghxeHoDUXa0ednV6cOgU6gqsNPmCY16HNLDnQR6ncIdF/UNy/rsbIC99++4aDb6FM4ViEQidHR00NraysaNG7n77ru5+OKLufDCC7nmmmuYMmUK8Xic3/3ud9TV1fHnP/+Z3//+9wOOUVVVhd/v580336Snp4dgMMikSZMwmUz9z3vppZe46667UvY+hBAiUzQ4AzT0BKjIteKwGtMdjkihdLfj0oYLIUTy+MIxNjT08urWdra1eyiwmyjPtaJTRm/e9r4k4U6Sc+eU8ehX51GcYx6wvdRh4dGvzuPcOWUpff1XX32VsrIyqqqqOPfcc3nrrbd48MEH+cc//oFer+e4447jt7/9Lffeey9z5szhL3/5C/fcc8+AYyxatIj/+I//4IorrqCoqIhf//rXFBUVsWzZMp577jlmz57Nr371K/73f/83pe9FCCHSLRRNsKXFg9mgwzyOC6SJT6WzHZc2XAghDo+mafT4I3zc4uaVLR2sa3Rh0uuYXJiF3ZzeQd2KNpzqKOOE1+vF4XDg8Xj2W8szHA5TX19PdXU1Fsuhz9HyhWMcfedrACy7/gROnVaU0p5t0SdZvz8hhADY1OTiw7peqgrtA77D63v8nDq9iJmlh78e9IHaJDG4oX5myWwDpB1PD2nHhTjyhGMJQtEE4XgCnaJg0CkYdDqMBgWTXodBf+C+4VhCJRCJ0+OPUNcToNMTJhRL4LAYybOb9uvRTqgaLe4gFx5TTknO4X3PjKQNlzncSbZvo3xidb400kIIMcY4/RG2t3vJzzLJd/gRSNpxIYRIDU3T6PJFqO8J0OUNE4qpRGIJYgkVlL7vX72ioNcpGPU6bCY9WWYDNrMBHaDX61CAaELF6Y/gDceJfJK0mw168uxGyhzWdL/N/UjCnWQ2k4GGX12Q7jCEEEIcAlXV2NLqwR+JM7lQ1ig+Ekk7LoQQyaWqGu3eMDWdPhqdQaJxFbtZj9mgx243YdLr0Ojrgd57i6kq7mCMHl+UmKqCxidFLTUUFCxGPRajjjybiZIcXdrmZw+HJNxCCCHEJ1rdIeq6A5TlZN4VciGEEGKsCUbjrK3vpa7bjwYUZZmxmQZPQcfriCJJuIUQQohP1PX40dCwmqRQmhBCCHE4XIEoq+ucNPcGmZBrxXKEFiGVhFsIIYQAPMEYLa4Q+TZTukMRQgghxrR2T4jVtU6c/iiVBTYMuiN3cSxJuIUQQgj6hpP7w3GKs8wH31kIIYQQg2roCbC61kkknqCywJbR86tHgyTcQgghjngJVaO224/dZEA5wk8MhBBCiEPV6Q2zps5JXFWZmGdLdzgZ4cjt2xdCCCE+0eUL0+0Lk2c3pjsUIYQQYkzyhmN8WOckGI1n5PJc6SIJtxBCiCNeszNIQgWz4cgs6CKEEEIcjkg8wdr6Xto9YSbmSs/2vmRI+UGEYwmiCXXUXs+k12VcBb+3336bJUuW4HK5yM3NHdZzqqqq+O53v8t3v/vdEb/eddddh9vt5sUXXxzxc4UQYqSC0Tj1ziC5NundHo+kHZd2XAiRWqqqsanJTW2Xn0n5NnTjdHmvQyUJ9wGEYwle29aBJxwbtdd0WIycfVTpsBvr6667jieffJIbb7yR3//+9wMeu/nmm3n00Ue59tprWbZsWQqiPXR33nknS5cu3W/766+/zgMPPICmaf3bFi9ezHHHHcf9998/ihEKIY4Ube4wnlCUqnx7ukMRSSbteOpIOy6E2Kumy8+WVg+lORaMehlA/VmScB9ANKHiCcewGPSYDan/8ETifa8XTagjujpeUVHBihUruO+++7Ba++ZLhMNhli9fzqRJk1IV7mE76qijeOONNwZsy8/Px2SSJXmEEKND0zTqe/yY9Dq5Ij8OSTueWtKOCyF6A1E+anJhNxmwmyW1HIxcghgGs0GHzWRI+e1QTwbmzZvHpEmTeOGFF/q3vfDCC1RUVDB37twB+0YiEW655RaKi4uxWCyccsoprFu3bsA+//d//8f06dOxWq0sWbKEhoaG/V7zgw8+4LTTTsNqtVJRUcEtt9xCIBAYUdwGg4HS0tIBN5PJxHXXXccXvvAFoO/K/6pVq3jggQdQFAVFUQaNRwghDoUzEKXdHSbfLgnCeCbteMN+ryntuBDicMUTKhubXPjCcQqzpB0diiTcQ9A0jVA0TiyuEo2rROKJlN+icZVYXB0wDGu4rr/+ep544on++48//jg33HDDfvv96Ec/4vnnn+fJJ59k48aNTJ06lXPOOYfe3l4AmpubufTSSzn//PPZtGkTX//61/nJT34y4BhbtmzhnHPO4dJLL+Xjjz/mmWee4b333uM///M/Rxz3wTzwwAMsXLiQb3zjG7S3t9Pe3k5FRUXSX0cIcWTq8IQJxRLYTHJVfryRdlzacSFEau3q9FHX7ac81yJLah6AnGEMIRRLcNLdK9Py2hccU8ZIK+lfffXV3HbbbTQ0NKAoCu+//z4rVqzg7bff7t8nEAjw6KOPsmzZMs477zwA/vjHP/L666/z2GOP8cMf/pBHH32UyZMnc99996EoCjNmzGDLli3ce++9/cf5n//5H6666qr+QirTpk3jwQcf5PTTT+fRRx/FYrEMK+YtW7aQlZXVf3/27NmsXbt2wD4OhwOTyYTNZqO0tHRkPxQhhDgAVdVo6AnIELhxStpxaceFEKnT44/wcbMHh8UoK3wchJxljBOFhYVccMEFPPnkk2iaxgUXXEBhYeGAfWpra4nFYpx88sn924xGIyeeeCI7duwAYMeOHSxYsGDAVaqFCxcOOM6GDRvYs2cPf/nLX/q3aZqGqqrU19cza9asYcU8Y8YMXnrppf77ZrN5+G9YCCEOU08ggjMQpUCGk4sMIO24EGKsiCVUPmpy4Y/GqC7IOvgTjnCScA/BatTz4U/P4OWP28mxGLGaUn/lJhRN4A3HDnk5kRtuuKF/ONjDDz+83+N7h7h9dsiHpmn924YzDE5VVW688UZuueWW/R4bSXEXk8nE1KlTh72/EEIkU6cnTCSWyLglnERySDs+NGnHhRCHY3eHj/qeABV5st72cEjCPQRFUbCaDBgNOkwG3agMlUioGkaD7pDnQJx77rlEo1EAzjnnnP0enzp1KiaTiffee4+rrroKgFgsxvr16/uHlc2ePXu/dTPXrFkz4P68efPYtm3bqDWyJpOJRCIxKq8lhDgyJFSNehlOPq5JO/4paceFEMnSG4iypbVvKLksATY88lMaR/R6PTt27GDHjh3o9fufWNjtdm666SZ++MMf8uqrr7J9+3a+8Y1vEAwG+drXvgbAf/zHf1BbW8utt97Krl27+Otf/7rf2p8//vGPWb16Nd/61rfYtGkTNTU1vPTSS3z7299Oyfuqqqriww8/pKGhgZ6eHlRVTcnrCCGOHE5/33DyXKsx3aEI0U/acSFEJlNVjY9b3HjDMVndYwTk0v4wROIqEB+l1zk8OTk5B3z8V7/6FaqqcvXVV+Pz+Zg/fz7//ve/ycvLA/qGkj3//PN873vf45FHHuHEE0/k7rvvHlAp9ZhjjmHVqlX87Gc/49RTT0XTNKZMmcIVV1xx2PEP5gc/+AHXXnsts2fPJhQKUV9fT1VVVUpeSwhxZOjwhoklVMwynPyIIO24tONCiMNX1xOgtstPucMqVclHQNEOZe2KMcrr9eJwOPB4PPs1aOFwmPr6eqqrq/urc4ZjCV7b1oEnHBu1GB0WI2cfVSpzCkdosN+fEEIMJqFq/OvjNvzhOCU5I/u+qO/xc+r0ImaWHjgpGo4DtUlicEP9zIZqA6QdHzukHRcis/kjcf69tYNQNEGpY2z+jSZUjRZ3kAuPKR9x+/9ZI2nDpYf7ACxGPWcfVUo0MXpDn0x6nTTSQgiRQj3+CL3+KMU5UlF5vJN2XAghDp+maWxr9dDjj1BdaE93OGOOJNwHYTHqpeEUQohxpMMTIqZqsm7oEULacSGEODwtrhA7OryUZFvQyVDyEZOiaUIIIY4Y8YRKfU+QLLMkYEIIIcTBhKIJPmpyoWmQZZG+2kMhCbcQQogjRrc/Qm8gQq5VqqsKIYQQB7OtzUO7J0y5w5ruUMYsSbiFEEIcMZqdQRIqmAzS/AkhhBAH0uoOsb3NS1GWGb1OhpIfKjnj+AxZG3Jskt+bEOJgQtEE9c4gDll7e9yStmDskt+dEJklHEuwuclNQtPIkXbzsMhA/E+YTCZ0Oh1tbW0UFRVhMplkfbkxQNM0otEo3d3d6HQ6TCYZJiqEGFybJ4QnFKUqXyqsjjfSho9d0o4LkZl2tHlpdgWpLLClO5QxTxLuT+h0Oqqrq2lvb6etrS3d4YgRstlsTJo0CZ1OBm0IIfanaRr1PQGMOh06GRY37kgbPvZJOy5E5mh1h9ja5qEwy4xB/iYPmyTc+zCZTEyaNIl4PE4ikUh3OGKY9Ho9BoNBejOEEEPqDURpd4fIt0vv2XglbfjYJe24EJnDF46xrr6XuKrJFKwkkYT7MxRFwWg0YjTKB0wIIcaLNneIYDRBmVRZHdekDRdCiEMXT6hsaHTR6Q1TXSDTr5JFxggIIYQY12IJldruAFlmucYshBBCDGVnh5fdnT4m5Fpl+lUSScIthBBiXOv0hnH6ozKcXAghhBhCmzvER81ucq0mLEZ9usMZVyThFkIIMa419QbRUDHqpckTQgghPssTjLGuoZdEQpOL0ykgZx9CCCHGLU8wRmNPkFyrnEAIIYQQn+WPxHm/todOb5hyqXOSEpJwCyGEGLe2t3vwhmNSaXUE3nnnHS666CLKy8tRFIUXX3xxwOOKogx6+5//+Z8hj7ls2bJBnxMOh1P8boQQQgwlHEuwpraH5t4gk/JtMm87RSThFkKII1AwGmdrq4cu7/hNeDo8YWo6/RRnm2W5oREIBAIce+yxPPTQQ4M+3t7ePuD2+OOPoygKX/ziFw943JycnP2ea7FYUvEWhBBCHEQsobK23kltd4BJ+TZZbzuFpGSrEEIcYdrcITY2umh2BckyG5hRmsOssmyyLeOnFzihamxt8xBT1XH1vkbDeeedx3nnnTfk46WlpQPu/+Mf/2DJkiVMnjz5gMdVFGW/5wohhBh9sYTKhoZedrT3VSSXGiepJT9dIYQ4QsQSKh+3uHlzRyfd/giTC7Owmwx81OTi1a0d7OzwoqpausNMiqbeII3OAKXZ0oOaSp2dnbz88st87WtfO+i+fr+fyspKJk6cyIUXXshHH3100OdEIhG8Xu+AmxBCiEMXjiV4f08Pm1s8lDksUpF8FEjCLYQQRwBV1Xh/Tw9r6pxYjXoq8mzodQo5ViPVhXbiCY0P9jhpcAbSHephi8QTbG31YNDpMMuJREo9+eSTZGdnc+mllx5wv5kzZ7Js2TJeeuklli9fjsVi4eSTT6ampuaAz7vnnntwOBz9t4qKimSGL4QQRxRfOMa7Nd3s7PAxMdeKzSSDnUeDJNxCCHEEaHWHqOsOUO6wkmsbWLFbpygUZZsx6hU2NbvxR+JpijI59nT5aXOHKMkxpzuUce/xxx/nK1/5ykHnYi9YsICvfvWrHHvssZx66qk8++yzTJ8+nd/97ncHfN5tt92Gx+PpvzU3NyczfCGEOGL0BqKs2tVNXXeAynybXJAeRXJZQwghxrmEqrGzo28o7oGGjpXkWKjvCbCt1cOJ1fljrtCYqmrUdPnZ2OTCYTVKAZgUe/fdd9m1axfPPPPMiJ+r0+k44YQTDtrDbTabMZvlwokQQhwqTdNocAZZ39CLJxijqsCOXqqRjyo5GxFCiHGu1RWiyRmiJPvAiYtOUSjJtrCjw0uLKzRK0SVHJJ5gfUMv79V0Y9TpKMySJC3VHnvsMY4//niOPfbYET9X0zQ2bdpEWVlZCiITQggBEI2rbGh08fbOLiIxlcoCmyTbaSA93EIIMY4lVI0dHV50OoY1fCzLYsAVirK52U1Rtjnji6nEEyqeUIyPmt3UdvkpybGQZZam7XD4/X727NnTf7++vp5NmzaRn5/PpEmTAPB6vTz33HP85je/GfQY11xzDRMmTOCee+4BYOnSpSxYsIBp06bh9Xp58MEH2bRpEw8//HDq35AQQhyBXIEo6xt7qesOUJxtlhU70kjOSoQQYhxrcQVp6Q1S6hh+te4yh4VGZ5Ad7V7mTspLYXSDU1UNdyhGIBInGE3gC8cIxRIY9TqMOgWDXkc4lsAVjOILxwnHEwQjCSbl22RpkyRYv349S5Ys6b9/6623AnDttdeybNkyAFasWIGmaXz5y18e9BhNTU3o9hnS73a7+eY3v0lHRwcOh4O5c+fyzjvvcOKJJ6bujQghxBFIVTVqu/1sbHLjCUWlbcwAiqZp42MNmGHwer04HA48Hg85OTnpDkcIIVIqnlB5Y0cn7e4wFfm2ET3XHYwSTaicO6eMfLvp4E9Iki5vmG1tXlpcQcKxBKCgU0CnU9A0UDUNTdPQKQpmgx6zUYfZoMNq1I/KnPP6Hj+nTi9iZunhtyHSJo2c/MyEEGJovnCMTc1udnf4sJr0FGWZx1w9llRKqBot7iAXHlNOSc7hLRs6kvZIeriFEGKcanGFaHWFKHNYR/zcXJuJ2m4/NZ0+TppckILoBvKEYuxs97K700cknqA420JJjgWdnCgIIYQQB7S3MNpHTS66fRHKHVaspsyeEnYkkYRbCCHGIU3TqOnyoVMUTIZDG0pWlGWmpsvH1OIsClJYhKzJGWRNvRN3IEpRtvmQLhAIIYQQR6JAJM7mFjc7230YdArVhXa5WJ1hJOEWQohxyBWM0eEJH9Zw8ByrkZ5AhN2dPhamKOFu6AnwQW0P8YRGdaFdhr4JIYQQw6BpGk29QT5qctPpDVOaY8EuRUMzkvxWhBBiHOrwhAlGE4fdW1yUZWZPt59pJdlJX2qrrtvPB7U9oCmU50qvthBCCDEcvnCMLa0edrZ7Meh0VBfY0clyXxlLEm4hhBhnVFWjoSeALQnzt7ItRrr9EXZ1+CicmryEu/aTZFuHQskIKqgLIYQQR6qEqlHf42dTkwdnICK92mOE/IaEEGKc6QlE6PZHKEhSdfGiLDN13X6mlWRRnH34yXFzb5DVe5Ptw6wSKoQQQox3mqbR5Yuwvc3Lni4fNpNB5mqPIZJwCyHEONPpCROJJbAYk1OhdG8v9+4O32EvMeL0R/iwrpeEChNyJdkWQggxdnnDMbq8EeKqSkLVUFVQFMi2GMixGMmxGtEf5lDvHn+Ene1earsDRBMJyh1WzElq38XokIRbCCHGkXhCpa4nkPQhZqU5FnZ3+piUb2dSwcjW9N4rEInzYX0vrlCEqnx7UuMTQgghRoOqanT7IzT0BKjrDuCLxABQ6EusNTRAwWrUYTMZKMkxU+qwkG83k2s1DmuudSSeoMsbodUVpKbbTyjat1xmllnqnYxFknALIcQ40uOP0uuPUpyT3AJnNpMBnRJlU7OL4hzziHvPYwmVdQ29NPUGqSqwSTVyIYQQY44nFGN9Qy/NriDRuEqB3Ux11v4rbGiaRjimEozG2d3hZ1u7F5tRj8NqpCzXSrbFgN1kwGbSo1MUYqpKLKERjat0e8M09gbxhGKgQb7dRFmOJNpjmSTcQggxjrS7Q8RUFbMh+cPNSh0WGp1BdrR7mTspb9jPU1WNTc1udnX4qMi1YtAd2rrgQgghRLp0+cKsqXXS4Q1TlmPFeoDCpIqiYDXpsZr0FGR9moB7wzG6mtyoAGiYDXoU+oqhxVUN0ECBHLORiblWDHppL8cDSbiFEGKciMZV6pwBss3GlBzfoNNRYDexvd3LhDzrsAqoaZrGxy1uNje7Kcm2yLwzIYQQY06TM8iaOie+SIyq/JEvwbVvAr6XpvX1aGuAQaeg1yky+mucypjLJu+88w4XXXQR5eXlKIrCiy++OOBxTdO48847KS8vx2q1snjxYrZt25aeYIUQIgN1+cK4AzFybalJuAFybSaC0Tgft3iIJ9QD7qtpGltbPWxodJFnM5FlkWu8QgghxpbdnT5W7e4iHEswKc+WtPWuFUXBbNRjMeox6HWSbI9jGZNwBwIBjj32WB566KFBH//1r3/Nb3/7Wx566CHWrVtHaWkpZ511Fj6fb5QjFUKIzNTuCaNpGsYUD0Erd1ip7wmwq9OHqmpD7rej3ce6BhcOqxGHNXUXAYQQQohUaO4N8mGdE4NOR3muVZJicUgyprvhvPPO47zzzhv0MU3TuP/++/nZz37GpZdeCsCTTz5JSUkJf/3rX7nxxhtHM1QhhMg40bhKkzNI9ij0IpsNenLMBtbUOnH6IxxbkTcgoQ5FE9T1+Flb30uW2UCuLTnrgQshhBCjxRWIsra+F1WDouzkFiIVR5aMSbgPpL6+no6ODs4+++z+bWazmdNPP50PPvhgyIQ7EokQiUT673u93pTHKoQQ6dDtj+AORinPHZ1KpgVZZmwmAzs6fHR5oxw3yYHdbKDZGaTBGcQdiuKwGMm3S7IthBBibAnHEqxt6MUZiFBdIMtYisMzJhLujo4OAEpKSgZsLykpobGxccjn3XPPPSxdujSlsQkhRCbo9IRJjMJw8n1ZTXqqC+x0+yK8vasbnQIJFRxW4yEVlRFCCCHSTVU1Nja5aOgJMClflrEUhy9j5nAPx2Br3B3oj+C2227D4/H035qbm1MdohBCjLpYQqXRGUxZdfID0SkKJTkWyh1WSnOsVBfaybebJNkWQggxJu3s8LKt1UOZwzKqF7HF+DUmerhLS0uBvp7usrKy/u1dXV379Xrvy2w2YzbLnAshxPjm9EdxBSOU5ozOcPLBmAxyUiKEEGJs6/FH2NzsIcdixGYaE2mSGAPGxBlSdXU1paWlvP766/3botEoq1atYtGiRWmMTAgh0q/DEyKW0CTpFUIIIQ5RLKGyqclFIBKnIEs67ETyZMylG7/fz549e/rv19fXs2nTJvLz85k0aRLf/e53ufvuu5k2bRrTpk3j7rvvxmazcdVVV6UxaiGESK+EqtHYGyTLnDFf50IIIcSYs7vTR11PgIm5tnSHIsaZjDlDW79+PUuWLOm/f+uttwJw7bXXsmzZMn70ox8RCoW4+eabcblcnHTSSbz22mtkZ2enK2QhhEg7pz9CbyBKsVyNF0IIIQ6J0x/h42YPDotRRouJpMuYhHvx4sVomjbk44qicOedd3LnnXeOXlBCCJHhOr1hYnEVs1Gf7lCEEEKIMSeeUPmo2U0gEqeqUJYAE8knl3CEEGKMUlWNRmcQmwwnF0IIIQ5JTZef+m4/ZbmWdIcixilJuIUQYoxyBqI4A1FyraO/HJgQQggx1rkCUTY3u8m2GDEbZKSYSA1JuIUQYozq8IQIxRJYZDi5EEIIMSKqqrG5xY03HKPAbkp3OGIck4RbCCHGoFhCpbY7QLYMJxdCCCFGrK4nwJ4uP+UOK4qipDscMY5Jwi2EEGNQhyeM0x8hX67KCyGEECPiC8fY3OzGYtTLKDGRcpJwCyHEGNTgDIACRr18jQshhBDDpWkaW1o99PgjFGfLkpoi9eRMTQghxhhvOEZzb5Bcq/RuCyGEECPR1BtkZ7uX0hwLOhlKLkaBJNxCiDHPE4yxpcVDpzeMqmrpDiflWl0hfOE4ORaZvy2EEEIMly8cY0OjC4NOh11qoIhRIp80IcSY5g5Gea+mh6beIFaTnpIcC1OKspiYZx2XjWlC1ajt8mMz6aXIixBCCDFMCVXjoyYX3b4I1YX2dIcjjiDj72xUCHHE2Jtst3lCTC3KIppQ6fFFaHYFmeCw8rnZJeOuGEqnN0ynL0xpjiXdoQghhBBjxp4uP7s6fEzItcpQcjGqZEi5EGJMcgejvPtJsl2Vb0enU7AY9ZTnWqnKt9PqCrG9zZvuMJOuqTeIpoLZML4uJAghhBCp0uOP8FGTiyyzcdxdiBeZTxJuIcSYE4zGea+mh/Z9ku196XUKhdlmtrV5aPeE0hRl8gUicRp6AuTajOkORQghhBgTIvEEGxpd+CIxiqQquUgDSbiFEGNOU2+QVneIykGS7b0cViPRhMrmZjfRuDrKEabGrg4fnlCMHKsk3EIIIcTBJFSNTU1u6rsDTMy1pTsccYSShFsIMabEEiq7O3zYzQb0QyTbe5U7rDQ6g+zqGPtDy1vdIba2eijMMsvcMyGEEOIgNE1jS4ubj1s9lOdaMOol7RHpIZ88IcSY0uoK0ekNU2A/+BrURr2OPJuJLa0eun2RUYguNcKxBB81ulA1DYf0bgshhBAHtbvTz8YmN/k2EzaT1IkW6SMJtxBizFBVjZouHwadbthXqvPtJoKRBNvaPGja2FujW9M0trR6aHWHKHVIZXIhhBDiYJqcQdbWO7GZ9HKhWqSdJNxCiDGjyxehxRUacdGT4hwzjc7gmOzlbnGF2NbmoTjbjEEnX9lCCCHEgTT3BvmgtgdNg8IsKZIm0k/O3oQQY0Ztl59EQhvxkh42k4FIPMGebn+KIhtaQtXwhGJ0esM0OgPs6vBR1+3H6Y8QSwxdzC2eUGnuDbKxyQUaZFvkCn06RWIJvv7Uev77/3YSjiXSHU5KvfPOO1x00UWUl5ejKAovvvjigMevu+46FEUZcFuwYMFBj/v8888ze/ZszGYzs2fP5u9//3uK3oEQ4kikqho72r28vauLaFylTEaFiQwhExqEEGOCOxiloTdAQdbB524PptBupr4nwKzSHPKGMf87GTq9YTY3u+n0hYnGVVRVY++gdrNBj82kpyDLRGGWGbvZQJbZgNmgo8MbpqbTT5c3gqJoUllVjKpAIMCxxx7L9ddfzxe/+MVB9zn33HN54okn+u+bTAf+m1q9ejVXXHEFd911F5dccgl///vfufzyy3nvvfc46aSTkhq/EOLIE0uobGp283GzmyyzgQLp2RYZRBJuIcSYUN8TIBCJU5J9aFesc6xGursjNPQEUp5wh2MJdrR52druIRpXKcoyYzLoBgwJj8QSBKIJmp0h9nT5AQWDDowGHcFIArvJIFVVRVqcd955nHfeeQfcx2w2U1paOuxj3n///Zx11lncdtttANx2222sWrWK+++/n+XLlx9WvEKII5s7GGVTs5tdHT5Ksi1kWSS9EZlFzuSEEBkvHEuwp8uP4zCHVefZTOzq8uGPxJMU2f46PGFe29bB2vpebEYDlfl2bCbDfvOvzUY9+XYTE/KsTC7MYnKhnXKHFYfFSHWhnVKHJNsic7399tsUFxczffp0vvGNb9DV1XXA/VevXs3ZZ589YNs555zDBx98kMowhRDjWDSusq3Nw6vbOtjV4WNirlWSbZGR5FMphMh43b4InlCMiXnWwzpOrs1IfU+Axp4AR01wJCm6T3X5wrxX04M3FKOq0H7QdcI/y6DXYZAkW2S48847jy996UtUVlZSX1/P7bffzhlnnMGGDRswmwcfxtnR0UFJScmAbSUlJXR0dBzwtSKRCJHIp8UOvV7v4b8BIcSYpmka7Z4wH7e4aeoNkmMxMrnQjqKMrM0VYrRIwi2EyHht7hCgHXaVbp2ikGMxsrPDx5TirBEXXzsQdzDKB3uceMJRJuXbpOEX49YVV1zR/+85c+Ywf/58Kisrefnll7n00kuHfN5n/yY0TTvo38k999zD0qVLDy9gIcS4oGka3b4I29u9NDgDqKpGRZ5NRoOJjCefUCFERovEE59cwU7OvOt8uwmnP0JTbzApxwPwhWN8UOukyxemIk+S7fFIVT9dw31Lq4eEOvbWdE+VsrIyKisrqampGXKf0tLS/Xqzu7q69uv1/qzbbrsNj8fTf2tubk5KzEKIsaXbF+H9PT28urWDPZ1+8m0mJuXbJdkWY4J8SoUQGa3L2zecPMeanAE5ep2CzWRge5s3Kcs7hWMJ1tQ5ae4NUplvRyfJ9rizodHF7S9t679/+4vbOOXelby6tT2NUWUOp9NJc3MzZWVlQ+6zcOFCXn/99QHbXnvtNRYtWnTAY5vNZnJycgbchBBHjt5AlNW1Pby6tZ3t7V5yrEaqCvtqowgxVsinVQiR0Tq8YdA47OHk+yrMNtHUG6SpN8j0kuxDPo6qamxsclHXHWBSvm3Ec7ZF5tvQ6OLRVbX7be/whLnp6Y08+tV5nDtn6ERzLPL7/ezZs6f/fn19PZs2bSI/P5/8/HzuvPNOvvjFL1JWVkZDQwM//elPKSws5JJLLul/zjXXXMOECRO45557APjOd77Daaedxr333svFF1/MP/7xD9544w3ee++9UX9/Qoj9+SNxenwRQrG+VTJsZj12kwGLUZeWUVueYIzdXT52d/rwh+MUZ5spcxxeHRch0kUSbiFExorGVZqcQbKTXHXUoNP193JPyrcd8lzu3V0+trd5KZOK4uOSqmqsWNc06GMaoABL/7mds2aXjquLLevXr2fJkiX992+99VYArr32Wh599FG2bNnCU089hdvtpqysjCVLlvDMM8+Qnf3pxaumpiZ0+1wkW7RoEStWrODnP/85t99+O1OmTOGZZ56RNbiFSKNwLEGjM0iLK0iXN4IvEmfvt5vJoGAx6CnMNjOjJJvyXOuofM+FoglqPmlbPaEYhVlmSooObTlQITKFJNxCiIzV7Y/gDsYoz01+Y1uUZabRGaDRGWRG6ch7uTu9YTY2usg2G2Ro2zi1u8uHKxgb8nENaPeEWVvfy8IpBaMXWIotXrwYTRt6jvq///3vgx7j7bff3m/bZZddxmWXXXY4oQkhkqQ3EGVtvZPG3iBmvY4ci5GCLBs6RUHTNGIJjXAsQZMzQGNPkAl5FmaU5jAxz5qSC8yxhEp9T4CtrR66vRHy7CapPC7GDTlLFEJkrHZ3CBUtJY27XqdgNxvY1uahsmBkvdzBaJx19b2EYwkm5duTHpvIDO7A0Mn2vrp84RRHIoQQydPQE2BdQy+eYIzKPNt+y1EqSl8Pt8mgI8dqJBJP0OEJ09wbZGK+jTnlDibmWZOSDKuqRrMryLY2Ly2uIHaTgepCO7pxNGpICEm4hRAZKZZQaewNkm1O3ddUYZaZBmeA+p4As8qGV4wpoWpsbHTR6g5RVSDJ9njlj8R5Y0fnsPYtzpbhjkKIzJdQNba0uNnU4kavKFQWDG9VDbNBz8Q8G7GESqcnTIc7zJRiO0eVOyjIMh9SLJqm0emNsL2tb4kvRUGW+BLjliTcQoiM1O2L4A5GKU9hkRS9TiHbYmBHu5eqAjtW04F7uVVV4+NmN9vbfZQ7Rmc+mxh9La4gD79VS7c/csD9FKDUYeHE6vzRCUwIIQ6RpvUl2+saXeTbTDisxhEfw6jXMTHPRjAaZ2eHj2ZXiKlFWVQX2inKNg8reY8nVNo9YWq7/TQ6g8RVlZJsyyHXUhFiLJCEWwiRkTo9YRJqaoaT76vQbqbO6WdTs4v5VflDvp6maWxr87CxyUVRlumgybkYm9Y39vLE+w1E4iqFWSbOmFnMs+tb9ttv72nlHRfNlgsvQoiMV9sd4KMm9yEn2/uymQxMLszCE4qxucXNzg4fFXk2phZnkWs3YjPqBwxTD8cS+MJxXMEouzt8dHojoGgU2M1kpXAUmxCZQj7lQoiME0+oNDiDZJsP76RgOHQ6hXKHlS0tHoAhk+6dHT7WNbhwWI1kW1Iflxh97+3pYdkHDQDMKs3mxtOmkGUxUGA3s3xtE+7Qp3O6Sx0W7rho9rhbEkwIMf60uUOsrXdiNuoOO9nel8NqxGE1EozGaXD6qe3xYzXqMBv05FiNZJn1uIMxfJE44ViCSEzFZtJT5rBgMsjQcXHkkIRbCJFxevxRXMEIpTmjs+amzWSg1GEZNOlOqBp13X7W1vdiN+nJtZlGJSYxuna0e/nz6kYAlswo4soTJvX3XB9fmcfs0my+/cwmAO76wlFcdWKl9GwLITKeKxBlTZ2TaFxlYp4tJa9hMxmYlG8grqpE4yrhmEq3N0JrQsVs0GEx6snJMkqSLY5YknALITJOpzdEXGVUG+d9k24NyLeb6PFF6PRF8ASjWI2GQy4OIzJbmzvEI2/XktA0TqzK56oTJ+03F3HfirlHT3BIsi2EyHjhWIIP6504/VEqC1KTbO/LoNNhMOmQ69JCDCQJtxAioyRUjUZnEHsa5kj3J93NHhRd38mD3aSnWAq6jFveUIwHV9YQiiWYWpTF9SdXybqvQogxT9P6inw29gSpLOhbX1sIkR6ScAshMoozEKE3GKXInp7eZJvJwJTirLS8thhdsYTKQ2/toccfpSjbzLeWTJElaYQQ40KDM8jWNg8lOZb91tkWQowu+QsUQmSULm+EaFzFLD3KIsVe/KiVup4ANpOe75wxTYrhCSHGBU8wxsZGF0a9jiyL9K0JkW7yVyiEyBiqqtHkDGIzyleTSK3dnT5e294JwA0nV1PqsBxwf7NRz5+umU99j1+mFwghMlY8obKxyYUzEKGqwJ7ucIQQSA+3ECKDuIJRevyRpC5bIsRnhaIJHn+/Hg04ZWohx1XkpjskIYRIip0dXmq6fExwWGXethAZQhJuIUTG6PJFCMcSWNNQME0cOZ5Z30yPP0phlokr5lekOxwhhEiKDk+YTc1ucq0mmZYlRAaRhFuIDKNpGtG4mu4wRp2maTQ5AzJcV6TUpmY37+3pQaFvKLlc3BFCjAfBaJz1Db1E4xr5dlmXS4hMIhMlhcggqqqxtqGXJmcQh81ISbYZh81EmWP8L0vlCcXokuHkIoX8kThPrm4A4OyjSphekp3egIQQIglUVeOjJjet7pDM2xYiA0nCLUQG2d7u4eMWNzkWI52eME3OIKAxrSSbU6cVodeN3/lYXb4IwUiC0uwDF68S4lC9sLEFXzhOucPCF46bkO5whBAiKfZ0+9nZ7qXMYRnX5wlCjFWScAuRIRp6AmxodJNnM5Fn+3Q4WCSWoKbLT5nDyozS8dkjp6oaDc4AFoMeRYq8iBTY0+XnnZoeAL66oFLW2xYZLRRNEFdV9DoFnaKg1ynymRWD6vFH2NjowmYyYDPJab0QmUj+MoXIAF2+MGvqnegVZUCyDX3LEWWZDHzU5KIo2zwu52Z1+SK0ukIUZZnTHYoYh+Kqyp/XNAJ9VcllKLnIRLGESocnTLMrSFNvkHhCRad8mnBXFdqZXGinQL4nhxSNq0QTKtG4SiyhYjXpybGM32lKgUictfW9+CIxqguy0h2OEGIIknALkWa+cIw1tU4CkTiT8myD7lOYZaLBGWRjk4vTpxeNu56O+h4/8YQ27uepi/R4c0cXre4QWWYDX5wnQ8lFZonGVWo6fezu9NMbiKChkWs1YbMYUVVQNY3YJ2sr7+rwMrkoi6nFWRTL9Jt+Tn+EHe1e2txh4qpKLKERV1WsRj0V+TaqCuyUOizjqu0MxxKsrnPS3BuksmDwcwchRGaQhFuINNvT5afNE2ZygX3I4dSKolCea6Gu20+Zw8JR5Y5RjjJ1PKEY9T2BcdlzL9LP6Y/w0uY2AC6bN5HscdzbJcaeLl+Yj5rcNPYEyLIYKM+1DpkUFmSZ8YVjbG31UNvtZ15FHrPKctAdwXN2A5E4uzt87Ojw4o/EybeZsJkMGHUKer1CMJJgd6ePXR0+CrPMzJngYErR0G3tWBFLqKxv6KW208+kAhsG3fi5kCDEeCQJtxBpFI4lqO0KkGs1HvSkyWzQ47AY2dzspiTHQuE4GVbY3BvAF45TVDg+3o/ILCvWNROJq0wrzmLR1IJ0hyME0NervavDy8etHsKxBBX5tmH1vmZbjGRbjLiCUT6o7cEfiXPcpFzMhiNvdFB9T4CNjS56/BEK7CYmF+4/pDrHqiPHaiSWUHH6o7yzu5tef4RjKnLH7IgqVdXY1Oxme5uXCXlDX6ARQmQOSbiFSKNWd4jeYITK/OEt41GQZaaux8/uTt+4SLgj8QS7O/1kWwxjvsdBZJ4trR4+anajVxS+elIlOvmMiQzgCcZY1+CktrtvZE/JIQwNz7OZMOl1bGp244/EObE6/4gZvaFpGrs7/XxY50RRoLrQftC/baNeR6nDgj8S56MmN73BKCdU5Y+5+fCxhMrWFg+bm90UZ4//5UKFGC8k4RYiTVRVY0+XH5NeN6JlPArsZhp6AswuyyHXNraHYbe6QvT4o0zKt6Y7FDHOxBIqy9c2AXDmrGIm5MlnTKRfiyvI2vpeevwRJg2zV3sodrOBijwbe7r8hKIJTp1WhMM2vpNuVdXY1uZhXUMvdpNhxAlzltmApcBGsyuIJxRnweR8KsfIutW+cIwNjS52d/oosJvJssgpvBBjhYxDESJNuv0R2j2hEfdU51gM+MJxGnoCKYpsdKiqxp5uP0a9IvPPRNL9e1sHXb4IDquRi44pT3c44giXUDW2tXp4a2cX3lCMqgJ7UoYCmww6qgrstHlCvFfTjScUS0K0mSnxyVDqtfW95FiMh9w7bdDrqMq3E40neGd3N7s7fWialuRok6vNHeLNHV3s6vAxwWHFYR3fF1aEGG/kLFeINGnsCRCNqyMeEqYoCrlWI3u6AoSiiRRFl3qyFJhIFac/wv9t6QDgS8dPxGqSYZcifXzhGO/v6eaDOidmg56JebakTm/Q6xQq8+20ekK8v6cHX3j8Jd2qqvFRk4v1jb3k202HPbpLURTKHH3zn9+v6WFrqwdVzbykuzcQZWOjizd3duIORqkusGOWYeRCjDkyHkWINPBH4tT1BMg/xJOGPJuJemeAZldwTK4pHIom2NLqJiFLgYkUeHZ9C9GEyvSSLE6qzk93OOII1twbZEOjiy5vmPJca8q+7/Q6hUn5NhqdQd7f08Mp04rIMo+PUzxN0/rqMTS5KMoyJ3WuemGWGXcwyof1vUTiKsdV5GJIcxGyeEKlyxehrttPgzNAMJLou8iQM7ankAlxJBsf38ZCjDEtriCeUIzqwkObO6bTKdhMenZ1+KguTM7QxOEIROL4wnFCsQTBaJxwNEGu3URhlpmcYRY+C8cSrKnrKxhUmS9rh4rk2tbmYUOTC50CXz5xkhTjE2kRjiXY3uZlS6sHNKgaRmGvw2XQ6ags6Eu6FaWHU6YWYh8HSff2di8bGl3k25ObbO+VazOh1ylsbHIRjMaZX5WPzTR6P7dwLIE3HMMdjNHtjdDhDeMJRUloGoV2M6U5Un9CiGSIxBJ8a/lHAJw5s2RUX3vsfxMLMcbEEyo1nX7sJsNhnYAVZplpc4doc4dSXvRFVTVqu/181OzGF46haqBpoKCh0Ve8pzDLzKR8G+W5Q88viyVU1jX0srvDR0W+LGcikiuWUPnrJ4XSlswopiJPLuiI0RWOJWhwBtje5qXbF6Eoy0zOKM63Neh0VObbaOgJoFPg5KmFo5o8JltNp4+19b1kmQ0pnbecbTFi1OvY3u7DH0lwUnVqKpjHEiqeUF9y7QlG6fRF8IZjhKIJogkVo6LDbtZTkmM5Ipd6E2K8GrvfwkKMUZ2+CN2+CGWOkS8Fsy+jXodOp1DT6aMiz3bQdbwPlS8cY3Ozm10dPixGPRNyrQOKnGmaRiCSoMMTpqEnQLbFQGW+naoiOyXZfScsCU0joWpsafH0rx0qJxMi2V7Z2kGnt69Q2sXHSaE0cWBdvjB7uvzkWIxkmQ1kWQzYTQYsRt2IRkZomoY3FKfTF2Z7m5cubxi72UBVgX1EK1Aki0GvY1K+jdruAIqicPKUwjFZx6C228+aOicWg558e+qHU1uMeqo+qWC+cmffUmuT8m2HNUpG0zTcwRg9/gid3jCd3gj+SJxwPIEOsBoNWE16crKNmAxyAVqI8UoSbiFGWacnTFxVk9K7W2g30+IO0eWLUHqYCfxgmnuDrG9w0eULU+6wDnrSpigKWZa+k1VN0/CG4+zo8LCz04vDakTT+KRHXMMbjlHmkLVDRfJ1eMP835Z2AK6YXzGme/XE6PAEY3zU5Mak1wEaRoMOi0GPxagnz2Yk12bEajJg0uswGfpufd9nGnFVI55Q6Q1EaXWFcIeiBCMJbCYDkwpsaV95wajv6+mu7fKjQ2HR1IIx9b1b2+3ngz096BSFouzRK6xp0PVVMG/3hFm5o4vqIjuzy3Iozhl++xpPqPQGo3T7IjQ6gzj9EUKxBIZPeq8L7SYpfCZEGuxbGPGjJhcV+bZRuygqZyRCjKJYQqXRGSTbnJyhcVaTnri3b7h3shPudk+I92p6iMZVqoc5/1BRFBxWIw6rkWhcxR+Jo1PAoFPQKQoTc21yFV8knaZpPL2mkbiqMac8hxOq8tIdkhgjTHqF6kI7mqYRTahE4yqhaAJ3MEosoaIBfd98CgZ9379UTUNV+WRCDdhNBrLNRkqyLRlVM8Co11GRZ6Omy4eGxonV+SmZA51s+ybbJSNIdJNFURTKc60Eo3H2dPpp7g0ypTiLqUVZZFuM+42ASKgawWgcdzBGpzdMc2/fBZhYQsVq1OOwGinNyazPhhBHmg2NLpZ/MuUM4HvPbubX/97FHRfN5tw5ZSl/fUm4hRhFPf4IrmCEMkfyiqDk2000OAPMLsshL0nD7lyBKKtrnYTjiUOeB2sy6Mg3SFXVTKRqGltbPdT1BAhE4gSjCQLRODkWI8dOzOWo8pwx1Ru2pq6XnR0+jHqFr5xUKSe2YsQURcFs0GM26MkeJMdTP5kWA6BTFHQKY+JzZjL0Jd17uvwEowlOmpxP8WBvMEOkO9nel81koKrQgC8cY2urh10dPqxGPTaTnjy7CaNehycUwxeOEfnkQg1aX02T4myzTJsSIkNsaHTx6Kra/bZ3eMLc9PRGHv3qvJQn3ZJwCzGK+oaTa0ktFpZjNdLTE6GhJ5CUhDsYjbO6zonTH6WyQIpOjScJVWN9Qy//t7WDVndo0H0+qHVi0CnMLMvmlKmFHD8pL6MTC384zjPrmwG46JjyUR1+Ko4cOkVBp8/cv4MDMRl0VBfYaXYHeWtnNydV51N1iCtkpEosobKt1cPmFg8GXfqT7X1lW4xkW/pGbYVjCQKRBE6/H1XTMBv0mAw6skwGCu3mtMzZF0IMTVU1VqxrGvSxvSOYlv5zO2fNLk3p368k3EKMkoSq0dgbTMnaqA6LkZouPzPKsg9r7mosobK2vpfm3iCVBbaUL2MjRs9HTS6eXd9Ctz8CgMWoY35lPrlWI1ZTX69NmzvMpmY33f4IW1u9bG31MrM0m6tOnER5bmYuTfPshmb8kTgTcq2cfdToLvMhxFih0ylMyrPR4Q2zanc3rkCU6aXZGbFsmCcUY0ODiz3dPgrs5pRWIz8ce+fxCyHGjt1dPlzB2JCPa0C7J8za+l4WTilIWRzp/6YV4gjh9EfoDUQpTsFSI3l2E/XOAI3OILPKcg7pGJqmsemTauQTP1OJXIxdmqbx2vZOntvQAkCW2cDnZhVzxsziQS/OXD5/Iu2eMGvqnLy+o5OdHT6W/nM7n5tVzEXHlmfUUPP1jb18UOtEUeDqBZXymRXiABRFocxhxR2Msrahl9ruALPLc5hcZE/L33U8odLiCrGxyUWPP8KEXFm9QgiRXJ7Q0Mn2vrp84ZTGIQm3EKOk0xsmFldTUp1UpyhkmQzs6vAxpSjrkK7C1/cE2NLi7pt7lkFJlTh0qqqxYn0zK3d2AXDGjGK+OG/CAX+/ewsGXTpvIqdOK+KZdc1sanHz7+2dbGpxc/PiqUzIgN5uVzDKU6sbAThvTilTi7PSHJEQY0OuzUSO1YjTH+Xdmm52d/qYXGQn324iz2ZKea93IBKnxRWiptNHpzeCUa9QVTC8wpxCCDESwx0xk+raFpJwCzEKVFWj0RnElsITmQK7iWZ3iBZXkMlFI0s+XIEoGxpdmPT6MVHFVhxcJJ7gT+/W81GzG+jruT5rVsmI5mMXZZv5zzOm8nGLmz+vaaTTG+Hu/9vBtQurOLE6P0WRH5yqaTz+fj3BaILKAhufP0bW3BZiJPYut5VvN9Hjj7CmzolOUbCbDOTZjWRbDFgMekwG/aDzGhWl7xh9/we9TodBp6D/ZEUKnQIooKAQV1WC0UR/9fcWdwhPMIbNpKfMYZFh2kKIlJlenE2ezTjksHIFKHVYUn5OIwm3EKPAGYjiDEQpSFIV8cEY9DqMOoWaTl9fb8Ewiz9E4gnWN/biDsaokiJp44Kqavx+VR1bWvsKEH39lGrmVx16Y3LMxFz+60I7f3injh0dPv7wbh31PQG+ePyEtAzjfmNHJzvafZj0Or5xymQMSSxCKMSRRL9PgbK9y1t1+yK0u8MkVO2Tpc8Ga0s0FEVB0z55XOmbvqKgsPcrQffJdlXVULW+Ixl0OrIthmEvNSmEEIdDp1O48oRJg1Yp3/sNdMdFs1Ne8FASbiFGQZcvTCSWSPk8uaIsM82uEHu6/UwvyT7o/pqmsaXFQ113gEn5toyuRi2G7++bWtnS6sGoV/je56YP67NwMNkWI9/93HRe3NTKK1s7eH1HJy2uIP9x+pRRLbzU7ArywsZWoK/XPtnrzwtxpNLrlP6K3IdK1TQ07ZM1yvv+Q6coUr1bCJE2x1fmcdPpU1i+tgn3PnO6Sx2WUVuHW7oFhEixvcPJrabUz4s2G/XYTQY2Nrro9kUOun9td4AtLR6Ks81JXapMpM+H9U5e2doBwHWLqpKSbO+l1yl8cd5Ebl48BbNBx44OH/e8ujPlxUb2cgej/G7lHuKqxjETHZw+vWhUXlcIMTx7k2uDTtc36kqvk2RbCJF2x1fmcdfnj+q/f9/lx/Lej88YlWQbJOEWIuVcwShOf2TUljopyjYTiMZZ19BLOJYYcr89XT5W1/ZgNupk3vY40egM8OQHfYXEzj2qlJOqU7PExbxJefzk3Jnk2Yx0eMLc/X872d3pS8lr7RWOJXhw5R56A1FKcszcsKhaRmSkyDvvvMNFF11EeXk5iqLw4osv9j8Wi8X48Y9/zNFHH43dbqe8vJxrrrmGtra2Ax5z2bJlKIqy3y0cHp2LNUIIIUZfXFXZ2urhbxta+H/v1HLPKzv4wXOb+dZfN/Kjv33ML/61nfte381TqxtYU+ekNxBNWSz7TrWcOylvVC8GypByIVKs0xshGE1Q5hi9ys4Tcq00OoNsbnZzQlX+fvO5d3f6WF3rxKTXUZSd/GXKxOjzhmI8/FYt0YTKnAk5XDp3QkpfryLfxs/On8VDb+2hwRnkN6/v5isnTuLUaYVJT4QTqsbv36mlqTdItsXAd8+cTpZFmq9UCQQCHHvssVx//fV88YtfHPBYMBhk48aN3H777Rx77LG4XC6++93v8vnPf57169cf8Lg5OTns2rVrwDaLRaYECCHEeKKqGtvbvaxvdPFRk4tAdPDOn0g8Sm/w0wT7nZoeoG965JwJOSycXEB1oX1cXFyXMxYhUkhVNRqcAayjvMyWQaejNMfC1lYvuTYT5bmW/uqxDc4Aa+qcWAx6ClOwJrgYfZqmsWx1A73Bvt7fb546edhF8w5Hrs3ED8+ZwePvN7Ch0cVTaxqp6wlw1YmTklZ5WNM0nl7TyNZWLya9jm+fMVUuEqXYeeedx3nnnTfoYw6Hg9dff33Att/97neceOKJNDU1MWnSpCGPqygKpaWlSY1VCCFEZtA0jS2tHp7f2EqrO9S/Pdti4NiJuZQ5LBRkmSi0m7Ga9ASicfzhOL5InDZXiF2dPhp7g3T7I7y1q5u3dnVTmmNh4ZQCFk4uID+FhYdTTRJukVJOfwR/JN5/X6colDosR8x8YVcwSo8vQq5t9Ids280GAtE4H+zpwWTUoaNvCZdANE6WyUCBJNvjxuo6Jx+3eNDrFG46fQo20+h9tZsNem48bTKvbu3g75taeW9PD029QW46fcphJ8axhMpfPmzivT09KAp887TJTC6U9bYzjcfjQVEUcnNzD7if3++nsrKSRCLBcccdx1133cXcuXNHJ0ghhBApU9vt5/mNLezu9ANgNeo5qTqf+VV5TC/OHnYnQCiaoKbLx9qGXjY2uunwhvn7R638Y1Mr8ybl8blZJUwpOrReb7NRz//76vG0uEenrtK+JOEWKdMbiPLmzi7cwSgKfet2gsKxFbnMr8wbF0NEDqbTGyEcT2Azjd5w8n0VZ1uIxBIkNA1V7asga8+yjPoXjUgddzDKinXNAHz+2HIm5o3+0m46ReH8o8uoLrTz/96po6k3yF0vb+ey4ydyytTCQ1r+pzcQ5ZG3+4arKwp89aRKjqvITX7w4rCEw2F+8pOfcNVVV5GTkzPkfjNnzmTZsmUcffTReL1eHnjgAU4++WQ2b97MtGnThnxeJBIhEvm0AKTX601q/EIIIQ5dNK7ytw0trNzVBYBBp3DmrGLOm1NG1iGsYGI16TlmYi7HTMwlfFKC9Y0u3t/TQ02Xn/WNLtY3uqgssHHGzGJOrMofMx14knCLlIjEE6xv6MUTjFFd8OmVKF84xpYWD3k2E1OLx3dPVbqGk3+WOc2vL1JH0zSeWt1IMJqgqsDGuUeld7jurLIc/uvC2fx+VS11PQGeWt3IO7u7+cpJlVQX2od9nF0dPn7/Ti2+cBy7Sc83T5vMUeWOFEYuDkUsFuPKK69EVVUeeeSRA+67YMECFixY0H//5JNPZt68efzud7/jwQcfHPJ599xzD0uXLk1azEIIIZKj2RXkj+/W0ebuK355ytRCPn9sedKGfluMek6ZWsgpUwtp7g3y5s4u1tQ5aXQGeeL9Bp5b38Jp0wpZPKM444ebS8Itkk7TNLa2eKh3BpiUN3Bt52yLkWC0LxnPsRoozh6/BXP2DifPs2X2l4AYuz6oc/JxqweDTuH6k6szYvmdfLuJH507g5U7u3hpcxsNziB3/98OFkzum4M1vTQLg27/K9KqqrG1zcPKXV1sa/WiARV5Vm5eLHO2M1EsFuPyyy+nvr6elStXHrB3ezA6nY4TTjiBmpqaA+532223ceutt/bf93q9VFRUHFLMQgghDp+maby5s4u/bWghrmrkWAzccHI1cyak7sJ4Rb6N6xZV8cV5E3i3poe3d3XTG4zyf1s7eGVbB7NLczixOp+5k3JHdVrdcGVeREO4884797vKXVJSQkdHR5oiEkNp6g3ycauH4qzB13YuybHQ2Btgfb2LxTOLMvIPIxk6vRFCsQTlMnxbpIArGGXF2k+Hkk/ITc+0hcEYdDrOnt23LNnfNrSwus7Zf7OZ9Bwz0cGkfBsJVSOW0AjFEnzU5KLH/2m10kVTCvjKSZMwG+TvJ9PsTbZramp46623KCgY+fJzmqaxadMmjj766APuZzabMZvlgosQQmSCWELlqdWNrK5zAnDMRAfXL6oateVlsy1Gzj+6jHOOKmVTs5u3dnWxs8PHtnYv29q9/HmNwpwJDqYVZ1FdaGdSvg1LBoz0HFOZzlFHHcUbb7zRf1+vT/8PUAzkCcZY3+BCrygH/OPbu2zVxkYXC6cUZkTPXDKpqkZDTwCbJNsiRZ5Z10wo1jeU/Jw0DyUfisNq5GunVLN4RhHv1vSwucWNLxxnTV0va+p699vfZuobPnb69CJKcsbv6JdM5/f72bNnT//9+vp6Nm3aRH5+PuXl5Vx22WVs3LiRf/3rXyQSif4L3/n5+ZhMfSN6rrnmGiZMmMA999wDwNKlS1mwYAHTpk3D6/Xy4IMPsmnTJh5++OHRf4NCCCFGzBeO8cjbtdR0+dEpcPn8Cs6cWZyWmkx6ncLxlXkcX5lHpzfM2oZe1tb30u4Js6nZzaZmNwAKUJBlwqjXoVMUdAokNI1jJuSO6nnGmEq4DQaDLCmS4ba0uunxRw46X9Og01HusLK7009lgZ2K/NEv9JRKvcEo3f4I+TKcXKTAtjYP6xtdKApcu7Aq4y9YTSnKYkpRFqqqsafbz6ZmN65gFKNeh0mvw6BXqMi3cUJlftKWExOHbv369SxZsqT//t4h3ddeey133nknL730EgDHHXfcgOe99dZbLF68GICmpiZ0+0wdcLvdfPOb36SjowOHw8HcuXN55513OPHEE1P7ZoQQQhy2Dk+YB1bW0O2LYDXq+Y/TM6e2SkmOhYuOKefCo8tocYf4uMVDQ0+ABmcAVzA2YPTcXtGEOqoxjqmEu6amhvLycsxmMyeddBJ33303kydPHnJ/qW46ujzBGE29QYqyzMOqStxXKVujptPPxDzruKpa3ukNE4klpBq4SLpYQuWvHzYBcObM4jF1sUqnU5heks30kux0hyIOYPHixWiaNuTjB3psr7fffnvA/fvuu4/77rvvcEMTQggxymq7/TzwZg3BaILCLBPfPmNaRk1j20tRFCrybFTss1qLOxil2xfpX60npqp0+cKUOkZ3FN2IE+5AIMCvfvUr3nzzTbq6ulDVgVcI6urqkhbcvk466SSeeuoppk+fTmdnJ7/85S9ZtGgR27ZtG3L+mFQ3HV0t7iC+cJyiwuHPtyvMMtPkCtDpzRn1D3+qqKpGY09w3M5NF+n1720ddPoiOKxGLj52QrrDERkikUiwbNmyIdvmlStXpikyIYQQY9W2Ng8Pv11LNK4yudDOfy6ZSo51dOZrJ0OuzUTuPqNNE6pGi9t4SEuWHY4Rv9rXv/51Vq1axdVXX01ZWdmo9Uqed955/f8++uijWbhwIVOmTOHJJ58cUMF0X1LddPTEEio1nX6yzIYRfSZsJgNd3gh7unzjJuFudYfo8IYpkcrKIsm6fRFe3tIOwOXzJ8oICtHvO9/5DsuWLeOCCy5gzpw542rEkBBCiNG3vrGXP75bT0LVOKosh5sXT5GlZg/RiBPuV155hZdffpmTTz45FfEMm91u5+ijjz7gkiJS3XT0tLvDOP0Ryg9hiElBlol6Z4BZZTkUZI3t35eqauzq8AKafCmJpFu+rolYQmNmaTYnVuWnOxyRQVasWMGzzz7L+eefn+5QhBBCjHHv1nTz1JpGNA3mV+bxtVOqB115SAzPiH9yeXl55Oen/0QvEomwY8cOysrK0h2KAOp6/CgKh/THuHdt7vqeQAoiG11tnhBNvcFxvb64SI9NzW4+bvGg1yl85aRJ0oMpBjCZTEydOjXdYQghhBjjXtvewZOr+5Lt06YV8s1TJ0uyfZhG/NO76667+K//+i+CwWAq4hnSD37wA1atWkV9fT0ffvghl112GV6vl2uvvXZU4xD76w1EaXYFybcdeu90gc3Eni4/3nAsiZGNLlXV2N3hR4OMWPNPjB/RuMqKdX2F0s6eXUKZI/OKlYj0+v73v88DDzwwrIJmQgghxGdpmsZLm9t4dn0LAOfMLuHqBZXoMnwllLFgxEPKf/Ob31BbW0tJSQlVVVUYjQMnzm/cuDFpwe2rpaWFL3/5y/T09FBUVMSCBQtYs2YNlZWVKXk9MXwtriDBSIKynENPAhxWI3U9ARp6AhwzMTd5wY2iDm+YRmeA4izp3RbJ9eq2Dnr8UfJsRi48Wkb1iP299957vPXWW7zyyiscddRR+7XNL7zwQpoiE0IIkek0TePZDS28vr0TgC8cV84FR49era7xbsQJ9xe+8IUUhHFwK1asSMvrigOLxBPUdPnJsRxexUJFUXBYjezu8DOtOHvMFYPSNI3dnT5UtDEXu8hs3b4Ir2zdWyitQmoDiEHl5uZyySWXpDsMIYQQY0xC1fjzmkbe29MDwJUnVPC5WSVpjmp8GXHCfccdd6QiDjFGtX1SLG1SEtYCzreZaOgN0OIKMm0U1ukNROL4I3F84Tj+cIxoXKUox0JRtnnEywV0eMM0SO+2SIFn1jX3F0qbX5mX7nBEhnriiSfSHYIQQogxJhJL8P/eqePjVg+KAtcurOKUqYXpDmvcOeRFyDZs2MCOHTtQFIXZs2czd+7cZMYlxoim3gAGnYJBd/jFFHQ6BYtBT02nn8lFWehTNGcknlDZ1eFjS5uHQCSOqvbNeVQUBa3FTZbZSEmOhUkFNibl2zAZDvzegtE4O9t9xBOq9G6LpPq4xc2mFjd6ReGqE6VQmji47u5udu3ahaIoTJ8+naKionSHJIQQ41IsodLhCRNNqJj0OkyGvluOxZiyc9hk8oZiPLiyhgZnEKNe4ZunTmbuJLmwnwojTri7urq48sorefvtt8nNzUXTNDweD0uWLGHFihXSuB9B/JE47e4wDqvp4DsPU0GWiQ5vmHZPiIl5h99r/lm+cIwNjS52d/pwWIxMcFgx7FN5UdU0/OE4jb0B9nT7KM2xMLs8h0n59kET705vmHX1vbS4Q1QcwpJoQgwlllBZvq4ZgM/NKj6kJffEkSMQCPDtb3+bp556ClVVAdDr9VxzzTX87ne/w2ZL/vepEEIcSTyhGGvqnNR0+mnzhOj2RxisTqXJoKOqwMbkwiymFNmZUZqNzXTIfZwp0ekN88CbNXT5IthNem45cxpTirLSHda4NeLf/re//W28Xi/btm1j1qxZAGzfvp1rr72WW265heXLlyc9SJGZOr1hvOEYVQX2pB3TbNCjoVHb7U96wt3cG2R9g4suf5iJDuugc2F1ikKO1UiO1Ug8odLtj/DWzi5KcixMKc4ix2Ik22LAZjJQ2+1nY5OLSCxBdYF9TFzNFGPHyx+30+2L4LAauejY8nSHIzLcrbfeyqpVq/jnP//JySefDPQVUrvlllv4/ve/z6OPPprmCIUQYuyJJ1Q2t3h4v7aHra0e1M8k2DaTHrvJQDShEo2rROIJonGV3Z1+dnf6ATDoFI6dmMuJ1fkcM9GR9iW2NjW7efz9eoLRBIVZJr575nRKHTIlMpVGnHC/+uqrvPHGG/3JNsDs2bN5+OGHOfvss5ManMhszb1BDIoOXZKHuRbYzDT1BukNRMm3J6f3vNEZ4N2abhIJqC6wDytmg15HmcPan3i/v6cHhb4lv6wmPa5AlByLkZL85F1wEAKg1RXila0dAFx14iRZZk4c1PPPP8/f/vY3Fi9e3L/t/PPPx2q1cvnll0vCLYQQI6BpGpua3Ty7voVuf6R/+5QiO/Mr85mYZ6U810qOxTBgupeqaXR4wtR1B6jr6Uu6O7xhNjS52NDkwmrUs2ByPktmjP7ItYSq8eKm1v7zi8mFdm5ePIVcW/JGqorBjTjhVlV1v+VGAIxGY/8wNjH+9Q8ntx1edfLBZFkMdPnCNPYEkpJwd3nDrK5zomkwIW/kX257E2/o+yKNxFRCsQRlDqskQhkiEInT7ArS7gnT7gnT5QuTbzMxpSiLKUVZlOSYx8z8Z1XTeHJ1AwlN47iKXOZNyk13SGIMCAaDlJTsX1W2uLiYYDCYhoiEEGJsanWFWLGuiR0dPgByLAZOnlrIyVMKD9oTrFMUynP7kvFTphWiaRot7hAf1vWytqGX3kCUt3Z189aubmaVZXPmzBKOmeBI+VrX7mCUP71Xz85P3tOZM4v50vETB0yrFKkz4oT7jDPO4Dvf+Q7Lly+nvLxvmGNrayvf+973OPPMM5MeoMhMnd4wvkiMqqzU9O46rEZquv3MKDu8eS+eYIwPap0EI3EqkjBEXacoWE16KY6WIcKxBK9s7eC17R3EEvtPpHqnpm+JiyyzgVOnFXLenNKMm0f1Wat2dVPXE8Bi1EmhNDFsCxcu5I477uCpp57CYuk7IQyFQixdupSFCxemOTohhMh8CVXjH5tbeXVrB6rWNxT8nKNKOW9O6SF3sCiKQkWejYrjbVw6bwK7Onys3NnFphY3O9p97Gj3UZRlZsnMIk6ZWpj0c5S4qvLmji5e2txGJK5iNui4dmEVJ1bnJ/V1xIGN+Lf60EMPcfHFF1NVVUVFRQWKotDU1MTRRx/N008/nYoYRQZq7g1i0CV/OPleeTYT9c4ALa4Q0w9xibBgNM7quh66fGGqCuySuIwjqqaxps7JCxtbcYdiABTYTUzItVLmsFCcY6HbF6G220+DM4A/EueVrR28s7ubC44pY8mM4rTPoRqMKxjl+Y9aALh07sSkTakQ498DDzzAueeey8SJEzn22GNRFIVNmzZhsVj497//ne7whBAio7mCUf7wTh01XX3zrudNyuVLx1dQlG1O2mvoFIVZZTnMKsuhxx/h7V3dvFPTTbc/wrPrW/jHpjYWTSng5KmFVObbDvu8dWeHl79+2ESbJwz0DSG/blGVFGFNgxEn3BUVFWzcuJHXX3+dnTt3omkas2fP5nOf+1wq4hMZyBeO0eYOkWtN/nDyvXQ6BatRz64OH5PybSO+shhLqKyt76XBGaQq35ayCwNi9PkjcR5+a09/o1iUZeZL8ycytyJ30MYpnlDZ0urhhY9aafeEeXZ9C2/u6OLahVXMLs8Z7fCHpGkaf/mwiXBMZXKhncXTZcUHMXxz5syhpqaGp59+ur9tvvLKK/nKV76C1SonV0IIMZStrR7+9F49/kgci7GvB/iEqtT2ABdmmbns+IlcdGwZa+p6eXNHJ22ecP9w8/JcCwsnF3BSdcGILr4Ho3HWNbh4t6abBmffdKIss4HL5k1k0dQCOR9Ok0Met3DWWWdx1llnJTMWMUZ0eiP4w3EKC5N31W8wxTlmGp1Bajp9HD0xd9jP0zSNzc1udnX4qMi1yvyUccQbivHbN3bT4gphNui48JgyPjer5IC91Qa9jrmT8jhmYi6ra538Y3MrzkCU+97YzYXHlHHRMeUpnzs1HO/W9LCpuW/N7WsWVmZETGJssVqtfOMb30h3GEIIMSZomsbLW9p5cVMbAJPybdx42mRKckavYrfZoOf06UWcNq2QHe0+3t3TzUdNbtrcYZ7f2MrzG1spyjIzrSSLacVZTMizYjMasBh1WIx6vOEY7Z4wbe4Qzb0hNjW7iSY+WRpSUThteiFfOG4CdnNmT6cb74b103/wwQf55je/icVi4cEHHzzgvrfccktSAst0vnCMTc1ujpmYiyOFPb2ZqMUVxKhP3XDyvQw6HXk2E1taPZTnWinIGl6CX9PlZ3Ozh+Js86BLf4mxyR2M8pvXd9PuCeOwGrn1rOlMGMGwKL1O4ZRphZxQnccz65p5p6aHf37czp4uP18/dXJa/47b3CFWfLLm9iVzJ6RkDXox/rz00kucd955GI1GXnrppQPu+/nPf36UohJCiMwXV1X+sqaJd/f01XpZPL2IK06oSNt0M0VRmF2ew+zyHILROOsbXayudbKny0+3P0K3P8IHtc5hHavcYeGUaYUsnFxAtuXIylEylaJpgy3ZPlB1dTXr16+noKCA6urqoQ+mKNTV1SU1wGTyer04HA48Hg85OYc3lLTbF+HVre0UZJk5dVrhEfOB9oZj/GtzG1ajftTec12Pn+kl2Zw6reiga123ukO8vasLvaJQOMwEXWS+3kCU/31tF12+CHk2I98/ewalh3kFenWdkz+vaSQaV3FYjdy8eApTirKSFPHwReMq//1/O2h1hziqLIfvfG6aDPnKYPU9fk6dXsTM0sOfjnC4bZJOp6Ojo4Pi4mJ0uqFPEhVFIZFIHE6oGSOZ7XhNp4+3d3VRXTj6f/dCiPQJxxI8uqqWbW1eFAW+fMIkzphZnO6wBhWMxqnrDrC7y0dNpx9nIEo4liAUS6B9Utit1GGh3NFXw2Z2eQ6TC6Vu0VASqkaLO8iFx5Qf9kiGkbRHw+rhrq+vH/TfR7q4qtHgDKDXKZw6LfmVBTNRlzeMP5IY1WS23GGltstPRb7tgAmROxjlwzon0bialIrkIjMEo3F+80myXZhl4vtnzUhKEZOFkwuozLfx+1W1tHnC/M+/d3HDydWjXrnzuQ3NtLpDZFsM3HBKtSTbYtj2XYpTluUUQoiDcwejPPBmDc2uECa9jm+eNpnjKnLTHdaQbCYDcyY4mDPBMWC7pmlE42rfiFOZgpbxRjxu4he/+MWga3qGQiF+8YtfJCWosaQi10Z9T4AP9vQQio6PHoShaJpGXU8Ak14Z1aTAYtRjMujY3OwmEIkPuk+7J8S7NT30+CMjGmYsMpumaTzxfgOdvgj5dhM/OmdmUiuGluda+en5szh2ooO4qvGHd+v45+Y2hjHwJyk+anLx1q5uAL5+SvURNz1FJM9TTz1FJBLZb3s0GuWpp55KQ0RCCJFZevwR7v33LppdfRe5f3jOjIxOtg9EURTMRr0k22PEiBPupUuX4vf799seDAZZunRpUoIaSwx6hcp8G7XdAT6sd6Kqo3Oing69gSidnnBalioqye5b5mlDo4sWV5DYJwUhYgmVj1vcvLG9k25fhMp8u/QQjiOvbe/ko2Y3Bp3CzadPSclnz2LU863FUzlrdgkA/9jcxp/eqycaT22PYV23nz+91zdi6JyjSjiq3HGQZwgxtOuvvx6Px7Pfdp/Px/XXX5+GiIQQInO0e0Lc++pOuj8ZLXfbeTOpLrSnOyxxhBjxGGhN0wadF7B582by84/MRdSNeh0Tcq3UdvuZUpRFRf74HM7c5g4RjCYoc4x+D7JOp1DusLKr00tNp5+CLBOTi+x0+SLUdfvJs5koc8iaxePJ7k4fz2/sW5P6yhMqqEphw6jTKVwxv4LSHAt/+bCRD+t76fSGuXnx1JQk+a3uEA+8WUMkrjKrLJtLjpuQ9NcQR5ah2uaWlhYcDrmYI4Q4cjU6A9z3Rg3+SJxyh4XvnTWdPJucM4rRM+yEOy8vD0VRUBSF6dOnD2jYE4kEfr+f//iP/0hJkGOBxahHoW+R+Qm51nE3xCOWUKntDpCVxmUFrCY91QVZxBIqvYEoa+qc6BSFibk2TAZZ+ms8cQej/L936lA1WDA5n9NHaU3q06cXUZxt5veramlwBvnly9u5afEUphVnJ+01un0R7nt9N4FogsmFdr61eKosXScO2dy5c/vb5jPPPBOD4dPv6EQiQX19Peeee24aIxRCiPSp6fTx4Mo9hGIJKgtsfPfMaUdMoWOROYadPd1///1omsYNN9zA0qVLB1wxN5lMVFVVsXDhwpQEOVaUZFto7g3R6g6Nu17uLl+E3kCUMsforU04FKNeN6prJIrRpWkaj71fjycUozzXwtUnVY5qtc1ZZTn8/ILZPPTWHlrdIf73td18+YQKTp9edNhxuINRfvvGbtyhGBNyrdxy5jQssnSdOAxf+MIXANi0aRPnnHMOWVmfFpbc2zZ/8YtfTFN0QgiRPltbPTzydi3RhMq04ixuOWMaVpO0uWL0DTvhvvbaa4G+JcIWLVqE0ShXhz7LbNSjKLC93Ut5rvWgS1iNJS29QVRVS9v6hOLI8d6eHna0+zDpddx8+tS0rKVelG3mp+fN5IkPGljf6OLpD5v4qMnN1QsrR1yhPxJL8K3lHwHgsBrxhGIUZZn53uempXXEiBgf7rjjDgCqqqq44oorsFjkYqQQQmxodPGHd+tIqBpzJuRw0+lTMBsk2RbpMazsyev19v977ty5hEIhvF7voLcjXXG2mZbeIK2uULpDSZpgNE6DM4jDJhdZRGq5g1GeXd83b/vi48opTeOICrNRz42nTeZLx0/EoFPY1u7ljpe28eaOzhEVR4wnPi2+5gnFKHVYuPWs6eTK/DGRRNdee60k20IIAby/p4ffv1NLQtWYX5nHfy6eKsm2SKthda/k5eXR3t5OcXExubm5gw6r3FuwJZEY30tjHYzZoEenKGxv91CeaxkXczPbPWE8oShV+VLNUaSOpmk8vaaJUCxBdaGds2aVpDskFEXhnKNKObYilyc/aKCmy8/ydc28U9PDqdMKWVBdQJZl8K/RWEJle7uXFz9q6d82pzyHG0+bjNUkPdvi8OXn57N7924KCwv766wMpbe3dxQjE0KI0adpGi9vaefFTW0AnDq1kKsXVI67ukpi7BnWWd/KlSv7K5C/9dZbKQ1oPCjJsdDqCtHiCqW0svJo0DSNxp4gRp1OvrBESq1rcLGpxY1ep3DdwqqM+ryV5lj44TkzWLW7m+c3ttDqDrFiXTN/29DC3Em5VBfaMRv0WIw60GBLm4fNzR5CsYEXILe2efmvl7Zx5QmTOL4yL03vRowX9913H9nZ2f3/Hs1aB0KI8SMYjdPoDNLUGyQaV9HoO//T6RRKsi1MzLNSkmPJ6KmScVXl6TVNvLenB+hbbvOyeRPle1FkhGEl3Keffvqg/xaDMxn6ktOdHV4q8m0Z/QV1MK5gjDZPUJZPECnlC8dYvq4JgAuOLmNC3ugvPXcwOkVhyYxiTqjKZ219L+/WdNPsCrGuwcW6Btewj+MKxnh0VS03nT5Fkm5xWPbWVgG47rrr0heIEGLMaXAGeGd3NzVdfjo8YQ42UcqgU5iQZ+XoCQ6On5THxDxrxiSzoWiC36+qZVu7F0WBq06YxJKZxUk5tqpq7O7y4QnFcFiNTC/OzqgOATE2jHhc46uvvkpWVhannHIKAA8//DB//OMfmT17Ng8//DB5eXICCVCcZabNHaLDG2ZCbuYlD8OhaRo72r0EIglKsmVuoEid5za04AvHmZBr5fw5pekO54CyzAbOmFnMGTOLaXQGWNvQizsYIxJTCccTxBIqVQU21ta78EXiQx5nxbom5lbkSsMtkmLjxo0YjUaOPvpoAP7xj3/wxBNPMHv2bO68805MJrloKsSRLpZQ2dDoYuXOLup6AgMeK8wyUVlgH1DMM6FqtHv6RmxG4iqNziCNziD/+ridomwzx0/K49RphWldOabNHeL379TS5g5jMui48bTJHDsxNynH3tDoYvnaJtyhWP+2PJtRRqmJERtxwv3DH/6Qe++9F4AtW7Zw66238v3vf5+VK1dy66238sQTTyQ9yLHIbNSjAbVdvjGbcLd5wtR0+SjJsWTMVUwx/uzu9PFBrRMFuGZh5Ziqe1BZYKeyYP9pIzs7vLy5s/uAz3UFY+zu8jGzNCdV4YkjyI033shPfvITjj76aOrq6rjiiiu49NJLee655wgGg9x///3pDlEIkSaaprGxyc2KdU24gn3Jo16nML8yjxOr86kusJNjHbowrqppOP1Rarp8bGxys63NQ7cvwqvbOnh1WwdHleWweEYRx0zMHbVRnZqm8f4eJ39d20Q0oeKwGvn2GVOpGqRNPhQbGl08uqp2v+0ySk0cihEn3PX19cyePRuA559/nosuuoi7776bjRs3cv755yc9wLGswGam0RnE6Y9QMMKlhNItllDZ0uJGVZGli0TKJFSNv3zYN5T81GmFTCnKOsgzxgbPPlfDk7GfEAeze/dujjvuOACee+45Tj/9dP7617/y/vvvc+WVV0rCLcQRqtsX4a9rm9jS6gH6emhPn17EqdOKcBwgyd6XTlEoyjZTlG1m0ZRCwrEEW1s9vF/rZGurh23tXra1e8m3m1gyo+/YqTx3DEUT/HlNI2sb+opBzi7L4WunVA/7/RyMqmqs+GSa21BklJoYiRH/NZhMJoLBIABvvPEG11xzDdBXLVWWBRsoy2Kg0x+mvicw5hLu+p4ATb1BKvJs6Q5FjGNv7eqi1R3CbtJzydwJ6Q4naYbb6Cfr5EAITdNQ1b4l6N544w0uvPBCACoqKujp6UlnaEKINFA1jde3d/LiplZiCQ29TuG8OaWcP6cMk+HwRpJZjHrmV+Uzvyqfbl+EVbu7eW9PD72BKM9vbOWfH7ezcHIBZ84spjyJozxVVeODWicvbmrFHYqhU+ALx03g3Dml6JI4EnN3l69/JMBQZJSaGIkRJ9ynnHIKt956KyeffDJr167lmWeeAfqurk+cODHpAY51eVYTe7r9zCzLSXlPcTAaxx2M4Q7G0NDItZnItRqxj/B1/ZE4W1o82E0GjGNoeK8YW9zBKP/4ZOmOL86bSLZl/CSf04uzybMZD9hg59n6iq8IkQzz58/nl7/8JZ/73OdYtWoVjz76KNA3Kq2kJP1L7GWaYDTOWfe9A8DDX56L2Shr9IrxIxiN8/h7DWxqcQMwszSbr55USakj+XOti7LNXHb8RC4+rpwP63t5c0cnza4Qq3Z3s2p3N1OK7JwytZATqvKxHOLfmaZpbGvz8tyGvlVCAIqyzHztlGqmFid/ZJyMUhPJNuIM8KGHHuLmm2/mb3/7G48++igTJvT1Sr3yyiuce+65SQ9wrMu1Gft6i51BZpcn/yqYpmnU9wTY1enDHYgRjMZJaBpoffNzbCYDDpuRqgI7lQW2YSXfO9u99PgjVI/xJc1EZvvbxpb+NbdPmVaY7nCSSqdTuPKESYPO/9rryhMmyVA0kTT3338/X/nKV3jxxRf52c9+xtSpUwH429/+xqJFi9IcXeZJqJ/WZN7d6eOocof8PYpxodkV5NG3a+nyRTDoFL584iROm1aY8lo8Rr2OU6YWcvKUAnZ3+nljZyebm93Udgeo7Q6wYl0zx07M5agJORxVlkPuMFa/6fCEWd/Yy/pGFy2uvkTbZtJz4TFlLJlRnLJOIRmlJpJN0TTtYCsBjBterxeHw4HH4yEn5/CS325fhH993EZpjuWgf/Cd3jB2s4Hzjz78YTz7CkUTfNziZlubF72ikG0xYDPrMej6XiOhagSjcfyROMFogjybiWklWUwuzMJh2/9LwhOKsavdy7Y2LzlWo3yRiJTZ1eHjf17bhQL87PxZY369+qFIhdPxp77Hz6nTi5IyjDCZbdJQwuEwer0eo3F8fJ8n42f26tZ27nhpG53eSP82+bsU48Ha+l6WfdBANKFSYDdx0+IpSSsidig8oRgf1Pbw3p6eAX9vAOW5Fibm2nDYjORajWRbDAQiCdzBKK5gjFZ3qL83G/qWJVsys5gLji5L+YhRVdX48QsfH3SU2r2XHiMX6saYhKrR4g5y4THlh11dfyTt0SF9YhOJBC+++CI7duxAURRmzZrFxRdfjF4vQ7IGU5BlotkVosUVZHKSikJ1esNsaHDR7ApSkmMZ9MtHr1PIthjJthhRNQ1XIMra+l52tHspybFQkmMhz27CbtLT1Btke5sXTyhGUZb5gNUqhTgccVXlL2sbAThtetG4TbYBjq/MY3ZpNt9+ZhMA3zljqvSkiZTasGHDgLZ53rx56Q4po7y6tZ2bnt6435rDUnlYjGWapvHqtg6e39gKwJzyHL5+ymSyLOkteuuwGjlvThnnHlVKbXeALa0etrV5aHQGaXOHaXOHD/h8vaIwqyyb+ZX5HDcpd9SK+MooNZFsI/7k7tmzh/PPP5/W1lZmzJiBpmns3r2biooKXn75ZaZMmZKKOMc0g06HSadje5uXCXlWzIbDuzBR2+3nwzon4ViCygJbf4/2gegUhYIsM/l2E75wnBZXiNpuP3pFwWLS4w/FybObmFxolyXAREqt3NlFmztMltkwrgqlDcVqNvCna+anOwwxznV1dXHFFVewatUqcnNz0TQNj8fDkiVLWLFiBUVFRekOMe0SqsbSf27fL9nel1QeFmONqmo8s76ZN3d2AXDW7BK+NG9iRn2GFUVhanEWU4uzuGTuBPzhOLu7fPT4I3iCMVzBGL5wDLvZQJ7NRJ7dSL7dxMzS1Nc/GsrxlXncdPqUAUupgYyGEYdmxJ/iW265hSlTprBmzRry8/MBcDqdfPWrX+WWW27h5ZdfTnqQ40FJjpkmV5D6nsBhDUWs7wnwQW0POhQm5Y+8Z1BRFHKsxv4e7LiqEo6pFNnNGfXlLMangYXSJsiSc0Ikybe//W18Ph/btm1j1qxZAGzfvp1rr72WW265heXLl6c5wvRbW99Lu+fAPWpSeViMJbGEyp/eq2dDowuAy+dP5OzZpWmO6uCyLAbmTcr8hPX4yjzmVuSyu8uHJxTDYe0rdirny2KkRjyheNWqVfz617/uT7YBCgoK+NWvfsWqVauSGtx4YtDryDIb2NrixR+JH9IxmpxBPqjtQUE57HkH/XHp+uKSLw8xGp5d30IkrjK50M7JU8dXoTQh0unVV1/l0Ucf7U+2AWbPns3DDz/MK6+8MqJjvfPOO1x00UWUl5ejKAovvvjigMc1TePOO++kvLwcq9XK4sWL2bZt20GP+/zzzzN79mzMZjOzZ8/m73//+4jiOlxdvgMn23tJ5WExFoRjCR54s4YNjS4MOoVvnjp5TCTbY41OpzCzNIeTqguYWZoj58vikIw44Tabzfh8vv22+/1+TKaDVxw8khVmmXEGIuzq2P/ndzDNvUHe39ODqmqUJinZFmI07ezwsrahF0WBr/z/9u48TK6qzB/499a+71Vdve/pLJ2QlWyQgOzCiIKjqAM6yigiyIAO/phxFHUEnHEYVEYWQRYdBRVHGUUgCIQtYUkCJED2Tnd6S++1b/fe8/ujSUNIOunqrqWr+/t5nnqgqm/dOvemus9973nPe1bW5HTNTKLZTlXVYxZG0+v1Y+tzT1QsFsNJJ52E22+//Zg///d//3fceuutuP322/Hqq68iGAzirLPOOua1wWGbNm3CJz/5SVx66aV44403cOmll+ITn/gEXn755azaNhUB+8T6ThYMpekumpJx64bd2NkbgUmvwTVnNOPkes+J30hERZF1wH3BBRfgi1/8Il5++WUIISCEwObNm3HFFVfgIx/5SD7aOGNoJAleqwE7e0eX3Zqow8F2WlFR7jTnsYVE+SGrKv7n5Q4AwGlz/KgtYtVUopnoQx/6EK655hp0d3ePvdbV1YVrr70WZ5xxRlb7Ou+88/Bv//ZvuOiii476mRACt912G/7lX/4FF110EVpbW/HAAw8gHo/jV7/61bj7vO2223DWWWfhhhtuwNy5c3HDDTfgjDPOwG233ZZV26bi5HoPyp0mHO9Wn9symjJKNF2FEhn8xxO7sH8gBqtBi6+d1YJ55ZwCQTSdZR1w//jHP0ZjYyNWr14Nk8kEk8mEtWvXoqmpCT/60Y/y0cYZxWUxIJaS8XZ3CBNZka19MIYX9gwgJauodDHYptL05FuH0BNKwm7S4aOLZ36hNKJCu/322xGJRFBXV4fGxkY0NTWhvr4ekUgEP/nJT3L2OW1tbejt7cXZZ5899prRaMT69evx0ksvjfu+TZs2HfEeADjnnHOO+x4ASKVSCIfDRzwmS6uR8O2/mQ8A4wbdrDxM09lgNIV/f3wnukYScJr1uP6cuaifwSt9EM0UWVcscrlc+OMf/4i9e/finXfegRAC8+fPR1NTUz7aNyMFHSbs64uhwmVGo982blXw/f1RbNo/CFUVDLZnGVUIdI8ksKcvip5QEl6rAWUOE4JOE/w2I7QldEHYE0rg0TdGR90+sawaVhZKI8q56upqbN26FU899dQRffOZZ56Z08/p7e0FAJSVlR3xellZGdrb24/7vmO95/D+xnPzzTfjO9/5ziRbe7RzW8txx98t5TrcVHJ6QgncumE3huMZeK0GXHfWnJzV8yGi/Jrwla+qqvjP//xP/OEPf0Amk8GZZ56Jb33rWzCZ+MueLYtBh4hOxvO7BzAcz2BhpRMm/XtLhcmKiraBGDbvH4RGkphGPoscGIjhT9t7sPtQBPG0csxtjDoN1s/x45wFwWPONUxlFHzl19sAAP/9qSUw6qe2DN1UqELg/pcOQFYFWisdWNXAOWZEufbb3/72iL756quvzvtnfvBGsRDihEtKTuY9N9xwA6677rqx5+FwGNXV1Vm29kjntpZjbZMPC298EgBwzYeasKDCyZFtmrYODMZw21N7EE3JCDpNuO7MOfBYWTeJqFRMOOD+wQ9+gG9+85s444wzYDabceutt2JgYAB33313Pts3Y5U5TIgkM9jaPoz+SArLa92ABPSGkmgbiGEgkoLFoIPfbix2U6kA0rKKP77RhSffPoTDMw0MOg0afVZUeSwYiafRG0riUDiFlKziybcP4emdfVjX7Me5rcEjOl5VfW+qwu5DkaJeSD6zsw/7+mMw6jS4dGUt13gnyrG7774bV1xxBZqbm2EymfDII4+gra0NN998c14+LxgcrYLc29uL8vLysdf7+vqOGsH+4Ps+OJp9ovcAo+nqRmPu+0G7SY8N167Ds7v6UO+z5Xz/RLmyszeM25/Zi2RGRZ3XgmvOaIbdxMJ+RKVkwgH3/fffj5/85Ce48sorAYwuQfLRj34Ud911Fy+iJ8lu0sOs16IrlMCTb6egqKOjk1ajDkGnCUZd8UYmqXB29UbwwKYD6IuMpjeuqHPjrPllqPFYoNMcWWZBFQJvdYfxpze7sa8/hqd39eH5vf3422XVOL3Fj60dI/j1Kx1j2//o6b1FS5UciKbw+21dAICPL6uC18abR0S59pOf/AT/8i//gu9973sARvvqq6++Om8Bd319PYLBIDZs2IAlS5YAANLpNDZu3Igf/OAH475v9erV2LBhA6699tqx15588kmsWbMmL+0kmgleax/CPc+3QVYF5gbtuOr0piMyIomoNEw44G5vb8cFF1ww9vycc86BEALd3d2orGQRpMnSaTWo9VgRSWag12r4h3SWeW5PPx7cNDrv0WXW4+9W1WJxtWvc7TWShIWVTrRWOLCzN4JH3+jGnr4ofvVKB17cN4D2wfhR7xmOZ3DHxn348vrGggXdQgj8YlM7UrKK5oAN6+f4C/K5RLPN/v378fd///djzy+99FJ88YtfRG9v79hodLai0Sj27t079rytrQ2vv/46PB4Pampq8I//+I+46aab0NzcjObmZtx0002wWCz49Kc/Pfaeyy67DJWVlWOB/zXXXIN169bhBz/4AS688EL88Y9/xFNPPYUXXnhhkkdOdDRZUXFgMI5dhyLoGIwjmVGQVlSk5NGl8QJ2I8ocJpQ5jKhyWVDlMU/LJSqFEHhsRy/+992b1ourXfjSugbotVnXOiaiaWDCAXc6nYbZ/N5cYkmSYDAYkEpNfHkrGh/Tg2afTfsG8Yt3g+01jV5csqIaFsPEfiUlScK8cgfmBu34684+/Pa1g8cMtt/voVc7sKTaVZD08r/u7MNbPWHotRI+u6ZuWl7QEM0EiUQCNtt7KdFarRZGoxHx+PH/HhzPa6+9htNPP33s+eE51J/97Gdx//334/rrr0cikcCVV16J4eFhrFy5Ek8++STs9veW0+ro6IDmfRk6a9aswUMPPYRvfvOb+Nd//Vc0Njbi4YcfxsqVKyfdTiJgdBrV1oPDeH73APb0R5GWx193vmPoyN8Lp1mPk6qcWFTlwvxyBwy64ge0GUXFLza346V9gwCAM+cF8Ill1awxQFTCsioX/K//+q+wWCxjz9PpNL7//e/D6XSOvXbrrbfmrnVEM9SrB4bw85faIACc3uLHp0+umdTUDEmScOa8Mhi1GjywefwKwcDoSPfuvgjmBvO7Xue+/ih++1onAODipVUIsooqTTOqKiBJRxfxKlX33HPPEUG3LMu4//774fP5xl776le/OuH9nXbaacddtlKSJNx444248cYbx93m2WefPeq1j3/84/j4xz8+4XYQHU9aVvHivgE8+fYh9EfeG/yxGXVoKbOjMWCFzaiDUaeFQaeBogr0RUZroRwKj9bLCSUyeG7PAJ7bMwCzXotVDR6sm+NHtdtynE/On5F4Gnc9tx97+qLQSMCnTq7B6S2BorSFiHJnwgH3unXrsGvXriNeW7NmDfbv3z/2fKZcvBDl07aOYdzzfBuEAE5p8uFTkwy238+gn9hd+VAiM6XPOZFoUsZdG/dDEQLLa904Yy4vFGj6kBUVveEk0srhYFJAggQBAbNeC7tRD4tRO25GRiqjQDlOIFoMNTU1+NnPfnbEa8FgEL/4xS/GnkuSlFXATTSdCSGwuW0Iv3ntICJJGQBgNWhxeksAK+o8KHeZJpRVlVFU7OqN4I3OEbxxMISheBrP7OrHM7v60eCzYn2LHyfXeQqWxv1a+xB+sakdsbQCs16LL61rQGul88RvJKJpb8IB97HuVhNRdtoGYrjrudGAdGW9B5etqs1JuvWxlgebynaToQqBe17cj6F4GmV2Iz67uo434WjaGIqlMRxPo9pjwfxyB/Q6DWRFRUYRSGYUHByOYyiWRl80BUBAK0nQaTXQaSTIikBKUWDQaRB0mKfVFKADBw4UuwlEBTMUS+MXm9uxvSsEAPBaDTh7fhlOafJlvQSmXqtBa6UTrZVOfOpkgZ09EWzc04/XO0awfyCG/QMxPLKlE6e1BLB+jj9v/WcireDXr3aMpZDXeCz47OpafO/P7wAo/vKeRDR1WaWUE9HkxdMy7npuH2RVYHGVC59fW5+zOVlzAna4LXoMx8cfwXZb9JgTsI/786l6bHsPdnSNztu+4rRGmA28QKDiU1SB9qEY7EY91jZ50VxmP+YKEAsqHIimZAxG04gkZSTSMmJpGfG0ApNeiwqXGV6bAR6LAToWLiIqKCEENu7ux++2diKZUaHTSLhgUTnObQ0etZrHZGgkCfMrHJhf4UAokcGLewfwzK4+DMczePSNbjy2vQfL69xY3+xHU8CWk5vJsqpi075B/N8bPRiKpyEBOK81iI+cVAFFnV6ZNEQ0NQy4iQpACIEHN7VjIJqGz2bA50+pgzaHBVA0GgmXrKjBHRv3jbvNqgZv3oquvLB3AH98vRsA8Hcra4s2/43og3rDSQQdJpzS7D9ivfoPkiQJdpN+Wo1eExGQkhU88FI7XjkwBABo9Fvx2dV1qHCZT/DOyXGa9fjwwnKcvaAMW9tH8NQ7h7B/IIbN+4ewef8QKpwmrJvjx4o6z6RGvVVV4OW2ITz6ZvfY3HOv1YAvnFKPOWWjN8UVVcnpMRFRcTHgJiqA5/YM4LX2YWglCV88tWHC1cizsazWjS+vb8SvX+nAyPvmaus0EmRV4PEdvbAYtDh3QTCnqd4v7B3AAy8dgABwxtwA1jb5TvgeokKIp2UoqsBJ1a7jBttEND0NRlO4/Zm9ODicgFaScPGySpw5t6wgFbt1Gg1Orvfg5HoP9g9E8dzuAbxyYAjdoSQeevUgHnr1IOq8FpxU7cLCSicqXeZx53snMwre6Qlje1cI27tCY9lodpMO5y4I4rQW/zEzb4hoZmDATZRnncNxPPRqBwDgoqWVaPDbTvCOyVtW68aSahd290UQSmTgNOvR4LPi4dc6sXF3Px7Z2oUDg3FcurIWNtPUf/3fH2yf3uLHJSuqp34QRDmgCoGeUBKtFU7UeGZuxkVnZyeqqqqK3QyinNvZG8adG/cjmpJhN+lwxbpGtATzNy3qeBp8NjT4bPjE8iq83DaEF/cO4MBgfOzxx9e7IQFwWfTw241wmQ1IZBTE0zJiaQUDkRTk96WJWw1anLMgiA/NDcDE+dlEMx4DbqI8yigq7npuPzKKQGulA2fNL8v7Z2o00lFLf126qhZVbjMeeuUgtrQPY29fFJ9bU4eFk6yAKoTAc3sG8MvN7VNe2owoHwaiKXisBiysds7o72Vrayt+8pOf4NJLLy12U4hyZtP+Qdz/4gEoQqDWa8GV6xvhtRmL3SxYDDqc3hLA6S0BjMTT2N4VwhudIezqjSCRUTAcz4xbS8VnM2BRpQsLq5xoKbMfd81v9X3B+e5DESyocHIdbqISNuGA+1vf+ha+9a1vQac79ls6OjrwhS98ARs2bMhZ44hK3Z/e7EFPKAmnWY8vrK3PSUXyyTq9JYB6rxX3vNiG3lASP/rrHqxr9uHjy6qySnEfjKbwy5c7xqrEMtim6SYlK4ilFayf44Fjhs/Jvummm/CVr3wFf/jDH3D33XfD6/UWu0lEU/Lk2734zWudAICT6zz43Jq64wanxeKyGHBqsx+nNvshhEA0JaMvkkJfJIVIMgOLXgezQQurUQu3xYCA3TihfnJL+zB+/UrH2PMfPb0Xbosel6yowbJadz4PiYjyZMJ/we6//36sWLEC27dvP+pnd999N1pbW8cNxolmo87hOB7f0QsA+PTJNdOiGFOdz4pvnT8fZ80bHWl/bs8Arn/kTTz86kEMRFPHfa+qCmx4+xC+9ehb2N4Vgk4j4aOLKxhs07TTM5JEvc+KBp+12E3JuyuvvBJvvPEGhoeHsWDBAjz66KPFbhLRpKhC4LdbDo4F22fOC+DyU+unZbD9QYeLLjb6bVjd4MXZ84M4pdmHZbVuzA06UOYwTTjYvmPjviPqsADAcDyDOzbuw5b24XwdAhHl0YQj5B07duCqq67CihUr8O1vfxvf+MY30NnZic9//vN47bXXcOutt+Lyyy/PZ1uJSoaqCjywqR2KEFhS7ZpWd6UNOg0+uaIaJ1U78auXO9AdSmLDO4fw1M5DWFrtRr3PCq/NAK/NAL1Ggz19Uew6FMHuQxFEkjIAoDlgw2Wra1HuzE+VWKLJiqdl6HUaLKp0zprlu+rr6/H000/j9ttvx8UXX4x58+YddQN869atRWod0YkpqsADmw6MrUV98dLKnBf4nO5UVYzVexnPQ692YEm1i+nlRCVmwgG3w+HAgw8+iIsvvhhf+tKX8PDDD6OtrQ2rV6/G9u3bUV3NYklEhz29qw9tAzGY9Vp8emVNsZtzTHODDtz4kQV4uzuMJ98+hLd7wtjSMYwtHePfQbcYtLh4aRVObfYVNT2eaDwD0TRqvBb47cWf71lI7e3teOSRR+DxeHDhhRcy44xKRkZR8bPn92Nrxwg0EnDZ6jqcMgtXu9jdFxl3/vdhw/EMdvdFjqrTQkTTW9Y98sqVK7Fw4UL89a9/hdVqxfXXX89gm+h9BqMp/O+2LgCjVcndlum7HJFGktBa6URrpROdw3G8dmAY/dEUBqNpDMZSSGZU1PusmFNmQ0vQjnqvddaMGlLpkRUVsqqiyW+bVSNjP/vZz/C1r30NZ555Jnbs2AG/31/sJhFNSEpW8NNn9+Gt7jB0GglfWteAJTXTJyOskEKJ4wfb2W5HRNNHVgH3r3/9a1x11VVYvHgx3nnnHdx7770477zzcMUVV+CWW26B2cz0UprdhBD4n1c6kJJHL/rXzymdC98qtwVV7pm7fBLljioEVCGglaRpFdgOxdPw2Ywod5mK3ZSCOffcc/HKK6/g9ttvx2WXXVbs5hBNWDwt4ydP78WevigMOg2uOq0J8ytm78it0zyxOi8T3Y6Ipo8JB9wf//jH8cQTT+Cmm27C1VdfDQD493//d3zsYx/D5z73OfzlL3/BAw88gNWrV+etsUTT3RudIbzZGYJWI+Gy1bVMu6YZ4fCa1ilZATCaGSFJo/Muq90W6KdB1oMQApGkjIWVThh1s2ddW0VR8Oabb3Itbiop4UQGP3p6D9oH4zDrtbjmjGY0BWzFblZRzQnY4bboj5tW7rboMSdQnLXIiWjyJhxw9/T0YNu2bWhqajri9dWrV+ONN97AN77xDaxfvx7pdDrnjSQqBWlZHSt4cvb8MlS4mPFBpS8tq+gcicNnM2JtlQ8mvQZajQQJErZ3jWB/fwy1HkvRpxpEkjJsJh1qPDO/Mvn7cSlOKjWD0RRufWo3DoVTsBl1uPbMZtR6Z9fv7bFoNBIuWVGDOzbuG3ebS1bUsGAaUQmacMD9/PPPQ6M59gWVyWTCj370I1x88cU5axhRqfnLjh4MRNNwW/S4YGF5sZtDNGXhRAb90RSaAjYsr/MclcpoN/mgqALtQ/HRoHucPqIQhuIpzA064LQw3ZJouuoaSeC/NuzGSCIDj9WA686ag6Bj9kwBOZFltW58eX0jfv1KxxFLg3EdbqLSNuGAe7xg+/3WrVs3pcYQlaq+SBJ/eXfN7U8ur4ZRP3tSWmlm6o+kkMwoWFHnQWul85hr4VqNOqxt8kHdM4COoThqPVZoizD6kpIVSJKEev/sTkmlmS+jqIimZGgkCXaTrqSmLe3ti+LHT+9BPK2gwmnCtWfNmdZFRYtlWa0b84N2XP3w6wCAaz7UhAUVTo5sE5UwrhtClAMPvXoQsiowr9zOO9BU8g6Fk1CFwCnNPjQFjl/x227S45RmH17YPYDOkdGgu9AGo2kEHSaOlNGMMhRLY3tXCNs7Q+gciSOakpHMqGM/12kkuK0GeK0GVLnNmF/uQEvQPi1rGLy4bwC/2NQOWRVo8Fnx1TOaYTPyEnQ87w+u55TZGWwTlTj+taO8S2UUfOXX2wAA//2pJTNu9Pf1gyNjhdI+fXLNtKraTJStnlACGo2EU5r8qPdNLHh2mPQ4qcaFQzuSSKQVmA2F+x1XhUBCVtAUsBdldJ0ol1IZBRv39OPFvYPoGkkccxuNBAgByKpAfySF/kgKO3sjeOqdPug0EpoDNiytdWNlvQcWQ3Ev81RV4HdbO/Hk24cAAEtqXLh8bf2Muw4gIjoeBtxEU/D+QmlnzStDuZOF0qg0CSHQPZKEQafB2iYfarzZLRFX4TSh3m/F3r4I6ryFS+2OJGU4jHpUzKKlwGjmSWUUPLu7H4+/1YtIUgYASBLQ4LNiYaUTc8rscJr1sBl1MBu0UIVAKJ7BYCyNwWgae/oi2NEdxlAsjXd6I3inN4LfvHYQy2rdOLXJjzllU1ubXlUFdvdFEEpk4DSPVso+0ahrPC3jZ8+3YXtXCABwwaJyfOSkipJKgyciygUG3ERT8Nj7C6UtYqE0KqyUrCCWUqDXStBrNdBrNZMa5U3LKrpGEnBa9FjT6J3UeuySJGFu0IH2wThiKRnWAqWLjsTTmBO0w25isTQqPUIIvLB3AL/f1jUWaPtsBpzXWo5lNW7YTMf+PdJIErw2I7w2I1AGrG70QgiBQ+EU3ugcwYv7BtA9ksTm/UPYvH8IFU4TzphXhlUNnqxTzre0D2ddxGt7VwgPbjqA4XgGeq2Ev19Tj5PrPVl9LhHRTMGAm2iSDoWTePzdQmmXrKiBiSlyVEDD8TSG42l4rAYkMyoiSRkZVYWsCNhNOngshgkt1TUST2MolkaD34qltR54rJMvYlTmMKLeZ8Hu3mhBAm5ZVSGAoswbJ5qqkXgaD2xqHxsB9tuNOH9hOVY1eCZV8V+SJASdJgSdQZw9vwxtAzG8sHcAL7cNoTuUxC82t+ORrZ1Y1+zH6S3+0WD9BLa0Dx9zmarheAZ3bNyHL69vPCLojqdlPPzqQby4bxAAUGY34h/WNaCOy34R0SzGgJvyTlXF2P/vPhSZEdU2hRD41csdkFWB1goHlta4it0kmiVUVaArlIBOK2F1gxctQQcUVSCZUZCSVfRHktjbF0PnSAKSBDhNelgMuiOqjMuqilhKQSiRhl6rwapGL+aVO6Cf4lra7x/ljr67LnY+jcQzcFsMKHOeOHAgmk5eaRvCL19uRzytQKeR8NHFlThrflnO6hBIkoQGvw0Nfhs+vqwKL+4dxNM7+9AfTeHxt3rxxFu9WFTlxGktASyocBwzzVtVxdiUqfE89GoHllS7oAiBl/YN4k9vdmM4noEE4Mx5ZfjokoppWcRtujPqtbjnsuXFbgYR5QgDbsqrw6loh/3o6b0zYj3JLR3DeKsnDJ1GwqdYKI0KJCUr6BxOoNxpwvI6Dypc79UMOFyoLOg0oSXoQG8oibbBKA6FUjgUSSKjqJAkQAgJWs3okl41XivmlzuO2M9UBRwmNPhteKs7BJspv3O5Q4kMTq7LPkWWqFhkRcWvXunAc3sGAAA1Hgu+cEo9KnP4O/hBFoMOZ80vwxlzA3izK4S/vnMI7/RG8EZnCG90huCzGbC81oNltW7UeS1j/dnuvgiG45nj7ns4nsEvX27HG50hhN5NOQ/Yjfj7NXVoLrPn7ZiIiEoJA27Km2xT0UpFMqPg4VcPAgDOaw2ijEsRUYH0hpNoCtiwqsF73JRtg06DGq8FNV4LUrKCcEJGKJFBOJGBSa+F26qHy2zIWzXxlqAd+weiiCQzeZtbncwoMOo1qHSzUCGVhkhytO/bfSgKCaNFxM5fVD6p9PHJ0GgkLK52YXG1C72hJJ7d3YcX9w5iIJrG42/14vG3euGxGLCoyolqjwWhEwTbhx2+eeC26HHOgiBObfbxJhgR0fsw4Ka8mEgq2q9e6cDCCgcMJTb3+Y+vj6bM+W1GnNfKQmlUGLGUDJ1Gg/kVjqzmRxt1WvjtWvjthUu79tmMaPTbsKMrlLeAeyiWRsBmgm8C81CJiq1rOIGfPLMHA9E0THoNvnhqAxZVuYrWnqDThEtW1OBjSyrxxsEQtnYMY3tXCEPxNJ7d3Z/VvtwWPS5cXIlV9Z4J1Y0gIpptGHBTXkwkFS2UyODa376B01r8+FBLYEIFXIptb18UT70zup7op1fWHDEvliifDkWSmF/uQLBEMioa/TbsORRFPC3nfC1gIQQSGQUNAWvJ14Ogme+t7hDu2LgPyYwKv82Iqz7UlNcU8mwYdVqcXO/ByfUepGUVb3WHsKcviu6RBDqH4xhJyMd9v8Okw80fW8hAm4joOBhwU14cGIhPaLuUrOKJtw7hybcPYXG1C+e1BtHgK9wavtlIyyrue6kNAsCaRi8WVjqL3SSaJcKJDMwGLeaWO0qmXkDAbkSV24z2wRhqPLntaqIpGXaTDuWO6RG0EI3ntQND+NkLbVBUgTllNnx5feO0XcLOoNNgSY0bS2rem+q1ad8A7n3xwLjv+czKWgbbREQnwL+SlFNCCGzc3Y8/vN41oe0/tqQC88sdEALY1jGCmx/bid9uOYiMoua5pdn74+tdOBROwWXW45PLq4vdHJolhBDoj6YwJ2AvqfRpSZLQHLBDCOT893k4nkaFywynZXoGLkQA8OyuPtz13H4oqsDyWjeuPXPOtA22x7O60Ycvr2+E+wO/a26LvmTrsBARFRpHuCln4mkZ9790AFs7RgAAOo0E+X1Lgn2Q26LHeQvKcf5CCd0jCTy2oweb9w/hibcOYXtXCJevbUCN11Kg1h/fvv4onnx7NJX8stW1BVljmAgARhIZOEx6tARLr+JvhcuEMocJ/ZFUziqhZxQVQgD1Pq7rS9OTEAJ/3t6DP7zeDQBYP8ePz5xcU7LTH5bVurGk2oXdfRGEEhk4zXrMCdhL9niIiAqNI9yUE6mMgtue2oOtHSPQaiT87bIqXH5q/XHfc8mK9y5AKlxmXH5KA646vQl2kw7dI0l8/7F38JcdPRBi/KC9ENKyivtePDCWSl7MQjc0u6hCYCiWxtxyO1wWQ7GbkzWdVoOWoB1JWYFynJtv2RiMpuG3G3O6lBlRrggh8NstnWPB9gWLyvF3K0s32D5Mo5EwN+jAynov5gYdJX88RESFVHIB909/+lPU19fDZDJh2bJleP755wveBkUV2NI+hO1dIew+FIGaowvJUiWrKu7YuA/7B2KwGrT4f+fOxTkLglhe68GX1zfCZZ54Ktriahe++5EFWFbjhiIEHtnahXteaENaLl6K+W9eO4jecBJOppJTgYUSGTgt+pJez7baY4HXasRQLD3lfalCIJaWMSdoh57zRmmaUVWBX2xuH8uGumRFNT66uLJk6i4QEVF+lFRe7MMPP4x//Md/xE9/+lOsXbsWd911F8477zy8/fbbqKmpKUgbHt/Rg28/+hYOhVNjr7ktelyyomZWzmVShcB9Lx7Aju4wDDoNvnpG8xGpnstq3ZgftOPqh18HAFzzoSYsqHAe9+643aTHFesbsHF3P379ykG83DaEQ+EkvnJ6E9wFHuV7Yc8Ant3dDwnA36+pYyo5FdRIPI1FVS7YSvh7Z9JrMafMhpf2DcJnM0wp+BiJZ+CyGFDtnh5TTYgOk9XRTKiX24YgScBnV9XhlGZfsZtFRETTQEkNEdx66634whe+gMsvvxzz5s3Dbbfdhurqatxxxx0F+fzHd/Tgy7/cekSwDQDD8Qzu2LgPW9qHC9KO6UIIgd++1omX24aglSR8eX0jGv1HVxh/f3A9p2xi874kScJpLQFce1YzrAYtDgzG8W9/fgdtA7GcHsPxtA3E8MuX2wEAH1lcgVZWJacCyigqNJIGle7ST52u8VrhNOsxkjj+UoEnMhxPo9Fv5Y0vmlbSsoo7n90/1hd+8dQGBttERDSmZALudDqNLVu24Oyzzz7i9bPPPhsvvfRS3j9fUQW+839v43jJ4w+92jGr0suf3d2PDe+uSf25tXV5WSZrbtCBb54/HxUuE0KJDP79iZ14pW0o55/zQeFEBj99di9kVWBxtQvnLyzP+2cSvd9IPAOP1YCyEll3+3icZj3mlNkxFEtDnWRNhlhKhsWgRR2LpdE0Ek/L+K+nduP1zhHotRK+cnojVtR5it0sIiKaRkom4B4YGICiKCgrKzvi9bKyMvT29h7zPalUCuFw+IjHZL3SNoSeUPK42wzHM9jdF5n0Z5SSrpEEfvPaQQDAxUsrsbrBm7fP8tuNuOHceVhU5URGEbj7+f344+tdk75wPxFZVXHnc/swHM8g6DDhC2vroeEcvBkrksygfSiGQ+Fk0Qv0vV8kmUGD3zJj5irPCdrhsRowGJ3cXO7BWOrd+eClVzyOZqaReBo/eHwX9vRFYdZrce2Zc1hUk4iIjlJyV3IfnP8nhBh3TuDNN98Mp9M59qiunnzBq77I8YPtw0JTTJksBRlFxd3P7UdGEWitcODcBcHjbm/Ua3HPZctxz2XLYdRrJ/WZZoMWV53WhHPmj95w+b83e3DXc/uRyiiT2t94MspoauDuQ1EYdRpceVojzIbJtZmmL1UIDMfT2NcfHS3CVWaHQadB22As59+pyUikFRj1WpTPoErcNqMO88sdCCczWVcsP7wUWKPfxgJUNC30hpK4+S870TWSgNOsx/XntmBOCRc3JCKi/CmZgNvn80Gr1R41mt3X13fUqPdhN9xwA0Kh0Njj4MGDk/78gH1iaZ3OD1Tknoke2dqJrpEE7CYd/n5tfcEugDUaCX+7vBqfW1MHrUbClvZhfO+xd9AxFM/J/pMZBT/66x683jkCnUbCFesbufTQDKSoAm0DMSiqwPI6Dz68sAKnNvtx1vwyNAfs6AolMBhNnXhHeTQcTyPgMMJnNRa1HbnWGLChzGGa8A3Mw/oiKZQ5TCh3ln56PZW+HV0h3PSXdzAYS6PMYcQN581lIT8iIhpXyQTcBoMBy5Ytw4YNG454fcOGDVizZs0x32M0GuFwOI54TNbJ9R6UO004XmjptugxJzCz73Dv6ArhqXf6AIxW7S7GDYZTmnz42llz4DTr0RtK4qbH3sGGtw9NKR04mpTxnxt2Y2dvBEadBv94ZnNe5qRTcalCoGMojmq3Bee1lmNZrRued1OUXRYDTm32YW2TD2lVxUCRgm4hBBIZBQ0+24xb69ak16K10omkrCKjTGypv2hKhqKqaK10QjdD0uupNAkh8JcdPfjR03sQTyto8Fnx/86dC59tZt0YIyKi3Cqpq5frrrsO99xzD37+85/jnXfewbXXXouOjg5cccUVef9srUbCt/9mPgCMG3RfsqJmxl0gv18kmcHPX2wDAJze4i/qXLU5ZXbc+DfzcVKVE7Iq8PBrB/Gjv+7BoXB2I2cAsK8/in9/Yifa3l1H/Otnt2BucPI3Z2h6EkLg4HAcfrsRa5q8cFqOvlmk02qwoMKJpdVuhBLZpz7nQjQlw27SIThDR3NrvRZUuc3oPUFNDGA0G+FQOIkFFU7UejmCOJ3U1dVBkqSjHl/5yleOuf2zzz57zO137txZ4JZPTjKj4K7n9uORrV0QAji1yYd/OqcFdtPMz2ojIqKpKam1VT75yU9icHAQ3/3ud9HT04PW1lY89thjqK2tLcjnn9tajjv+bim+839vH1FAbbasw/3wawcRTsqocJrwt8smPx8+V+wmPa46vQnP7OrHb147iB3dYXzzjzuwqt6L8xeVI3iC6s4D0RQe2dqJVw+MLufmMutx7VlzUMk08hmpayQBl9mAtU0+uE6wnntjwIY9fVH0R1IFD3yH4xk0+K0zdnqKXqvBgnInekaSiCQzxw1YukMJVLrMWFTl4tztaebVV1+ForxX72DHjh0466yz8Ld/+7fHfd+uXbuOyDbz+/15a2Ou7OwN4xeb2nEokoJWI+FTK6qxfo6f30kiIpqQkgq4AeDKK6/ElVdeWbTPP7e1HGfND2LD27148u1DqPVYMC/omNEj2wDwdncYm/cPQcLoEmAG3fRIjpAkCR+aG0BL0I5Htnbizc4QNu0fxOa2QSytdqMpYEO1x4xqtwU6rYSekSQ6hxNoG4zhxb0DkFUBCcDaJh8+tqRyxgY5s133SAImvRZrmrzw20+c/jma+uzA0zv7kVHUglUKV1QBWRWo9c7spa+q3GYsrnZhW8cwFFUc8wbISDwNnUbC0lo3CxdOQx8MlG+55RY0NjZi/fr1x31fIBCAy+XKY8tyJ5qS8bstnXhh7wCA0ZuyX1rfgOYZPnWMiIhyq+QC7ulAq5GwrNaDnlASQYdpxgfbaVnFL19uBwCc3hJAg89W5BYdrdJlxlc/1IwDAzH835vdeKMzhC0dw9jSMTy2jQQctY763KAdn1xejWoP01Vnqv5ICpIGWNPoQ7lz4tkLtV4rqtxR9IaTBSuIFE5m4DTrT5idUeo0GgmLq10w6CS82jYMWRVj82CFEIinFQzG0lhZ72HhwhKQTqfxy1/+Etddd90JR32XLFmCZDKJ+fPn45vf/CZOP/30ArVy4pIZBS/tG8T/vdmNSFKGBOC0Fj8+tqQSFgMvm4iIKDvsOeiEHtveg75ICi6zHh9bUlns5hxXnc+Kqz/UjPbBGF4/OILO4QQODscxEE1DALCbdKhymVHpNqO1wokFFQ6mBc5gw/E0krKCU5t9qMlyDrBeq0FrhRO9oSSSGQWmSS5pl41wMoP55Y5ZMaKr0UhYUOGEQafFy/sHcXAoDlUIKKqA2aBFU8CG+RUsXFgK/vCHP2BkZASf+9znxt2mvLwcd999N5YtW4ZUKoVf/OIXOOOMM/Dss89i3bp1474vlUohlXqvgGE4HM5l04/QF0ni6Z19eHHvIBLvLg9Y4TThstV1aApMvxvNRERUGhhw03F1jyTwl7dGl2L71Mk1JRMI1HqtR6TlxtMyZEXAwZTxWSOSzCCczGBVvRdNk0wBrXKbUee1om0wilpPftO8ZXV0relK1+zJtpAkCXPK7NBrNdjRNQK/3QS/3Qiv1QCHST/js4dminvvvRfnnXceKioqxt2mpaUFLS0tY89Xr16NgwcP4oc//OFxA+6bb74Z3/nOd3LaXmC0T9jRFcLLbUPY8HYfDgzG0Bd5L7APOkz40NwATm32FWxKCRERzUwMuGlcqhD4xeZ2KKrASVVOLK1xFbtJk8Y0wOkvlpKRzCiQJAkaaXTqhsWgg3YSQVc0JWMgmsKyWg/mlU++4rxGI2F+hQMHh+OIp+W8fo/CCRlOsx4Bx+xbYqjeZ0W9b2bPW5+p2tvb8dRTT+H3v/991u9dtWoVfvnLXx53mxtuuAHXXXfd2PNwOIzq6qkX7bzvxQP4jyd2HfV6a6UDZ84tw/wKBzTMfiIiohxgFELj2rRvEHv6ojDqNPj0yTVMvaa8GYmnEUrIcFn0EBBQVUCWVfRHYzDptPDbjRMaZVKFQF84hbSi4qQqF06qdk15lLTMYUS124IDgzHUePIZcGewqMpZkNR1oly57777EAgEcP7552f93m3btqG8vPy42xiNRhiNub8JtajKCa/VAK/NgPnlDtR5rajzWmEz8bKIiIhyiz0LHVM8LeN3WzsBAH+zqAJe2+wbdaPCGI6nEUnKWNngxoIKJ1RxuFq3iu6RBHb2RNA9koAkSfBYDLAatce8+ZPKKOgcScBnM2JNjRf1PmtObhJJkoTGgBX7B6J5q1ieUVRIElDhZoEwKh2qquK+++7DZz/7Weh0R15O3HDDDejq6sKDDz4IALjttttQV1eHBQsWjBVZe+SRR/DII48Uo+k4pcmHh764Cs/u6kP9NCwESkREMwcDbjqmP74+Wp016DThzHmBYjeHZqixYLveg/nvFrDTvptOboAGTQE76rxWdI0ksKcvikPhJA5FkjBoNbCbdJBVgZSsIq0oACS0BO1YUu2G05LbufoVLjN8NiOGYmmU5aGCeCiRgctiQMA+s6uT08zy1FNPoaOjA5///OeP+llPTw86OjrGnqfTaXz9619HV1cXzGYzFixYgD//+c/48Ic/XMgmj2HGFhERFQoDbjrKweE4nt7VBwD49Ioa6FgwhvJg5BjB9rHotBrUeq2o8VgQTsjojyZxcCiBvkgKBp0GAYcRbosBDrMetR5LXr6veq0Gc8rseH53PwJ2kfOL9XAyg+W1nmmzvj3RRJx99tkQ4oOLLY66//77j3h+/fXX4/rrry9Aq4iIiKYXBtx0BCEEfvVyB4QAltW6Mb9i8gWniMajCjG2zvLxgu33kyQJToseToseTQE7khkFBq2mYJWsqz1mOC36sdHoXMkoKrQaCUEnR7eJiIiIZhoOp9ARXm4bwp6+KAw6DT6xrKrYzaEZKpTIjAbOZfZJjxab9NqCLhtlN+lR77NiKJ7O6X5H4hl4LEb47ayTQERERDTTMOCmMfG0jN9uGS2Udv7CchZKo7wZiqfR5LfBZiytJJs6nxUGnQaJtJKzfUZSGdT5LFzrl4iIiGgG4hUejfnD690IJTIosxtx9vyyYjeHZqhoUoZFry3JdZf9NiMqXWYMxFI52V8ircCg06DCxerkRERERDMRA24CALQPxvDMu4XSPrOylqNtlDeD8RRqvZaSzKDQaCQ0+m3IKKPLlk3VQCyFSpcZ/hI8F0RERER0YoyqCKoq8IvN7RACOLnOw0JplDepjAIJQGPAXuymTFql24yA3YiB6NTmcsuqiowi0Oi3FXQuOhEREREVDgNuwsY9/TgwGIdZr8UnlrNQGuVPf3R0RLc8D2tZF4pRp8W8cgfiKRmKeuwlkSZiOJaBz2ZApZvp5EREREQzFQPuSUjLKg4MxDAYTWEgmsJgNIWReBrqOOuRTmehRAa/39oFAPjYksqcLndE9H6yoiKjCjSX2Ut+RPdwSvzgJOdyCyEQTmbQHLDBqNPmuHVERERENF2UVongaaInlMDf3rXpqNfdFj1WN3ixptFXMmvq/ua1g0hkFNR6LThtjr/YzaEZbDCWht9mRJXbUuymTJlJr8Xccjte2DMAn01Ak+XSZpGkDLtJhxpP6RWOIyIiIqKJY8A9SQ6TDilZhRCAKgQUVWA4nsFjO3rx2I5eNPisOH9ROU6qchW7qePa1jGMl9uGIAH4u5W1JT/qSNNbLCVjYZUTBt3MSKyp91nxVncYw7F01gXgBuMpzA864bTo89Q6IiIiIpoOGHBPQq3Xir9+7TT86c1uBB0m6LUaZBQVbxwcwUv7BrGjO4T9AzH85Om9OLnOg0tWVMNhnl4X1pFkBg9ubgcAnLMgWJJLNFHpSGUU6HUaBEt47vYHWQw6zA3asWnfIDxWA6QJjnKnMgq0koR6P3/niIhoelJVgaSsIC2PFvhMKyoyyujqHDqNBJ1GA61Ggt2kg0nPqVFEx8OAO0f0Wg2W13mwvM6DUCKDJ97qxYZ3DuGVA0N4qzuES1bUYFWDZ8IX5fkkxGhV8khSRqXLjAsXVxS7STTDjSQy8FgNJbkU2PHU+6x4uyeM4fjo8U3EQCyNcpd5Rt18ICKi0qKoAqoQkN8NptOy+u5/FQgAGkmCUaeBUaeF1ahFmckIu2l08CiRVpDIKEikFQzG0khmFLjMergsBmhLKFtSVcXocWQUyKrA+1uu00jQaTXQayXotRroNNK0uIan0sSAOw+cZj0+sbwaJ9d5cP+mA+gcTuDeF9uwvSuEz66pLXqRpM1tQ9jaMQKtJOELa+u55jblXSytoLXSUVId8UTYTXq0BOx45cAQXGb9CadlxNMyMoqK5kDpF44jIqLpR1ZVJDMqUhkFSVlFWlEA8f7+RgCSBI0EaDUStJIEg04Ls0GLMrMRLrMBdpMOFqMONoMOZoN23KlgqiowEE2hcziB/f0xtA/FYDPq4LcZp21wmlFU9EdSSGYUaDQSTHoNLAYdnAYtIAAhAAGB1Ls3IOIZFbIskFFVABIkjNZt0Ws10L0vGDdoNdDrNFnXdKHZgQF3HtX5rPjm+fPwxFuH8Ojr3XjlwBB6QglcdXpT0Ub6hmJp/OrlDgDA35xUjhpv6RewovdkFBWD0TRMeg0cphMHgIWQkhXoNRKCzpm5/FVTmQ0Hh+M4OBJHjdsy7kWGrKroCSXRWulEA6dwEBFRDkVTMoZiKagCMBu0MOm1qHYY4bYYYNBpoNeOBoOjI7ejKeGHA0aTXgODVpN1kKzRSAg4TAg4TJhf4UDHUBxbO4bRPhhHpds8rQZ0UhkFfdEUZEVFudOMxoANDrMedtPojYUPXi8J8V7QncwoSGVUpGQFyYyKeEpGNC0jllKQzIy+Fk7KSMsqBEZHyg06LawGLSwG3YypXUOTx4A7z3QaDc5fWI4mvw13PrcPB4cT+N6f38EV6xswN+goaFtkVcW9L7QhkVFQ57XgvNbygn4+5VcsJaM3nES504R4RsGBoTi0EuCyGOAsYg2BUCIDt9UA7wRTrkuN3aTHqkYvnt7Zh0Ph1LgrFHSNJFDttmBpjXta3AghIqLSNxxPYyiWhsWgRYPfhnqfFR6rAdZjBJH5ZNJrMafMDq/VgNfah3FgMAa/1Vj0GkaqEOgNJZFRBKrcJswJOlA1gZsBkiTBpB+9ceEwjX8MsnI4KFffDb4VxNMKDoWTGIymcSicRFpR4TDp4bbqodMw+J6NGHAXSEvQjm9+eB7++9l96BiK49YNu/Hpk2twWkugIJ8vhMCvXzmIXYciMOo0+MIp9TMuvXc2G4imEEvJWFztwknVLqhCoC+cQtdwArv7IgBQtKA7mpIxr9wB3TS6051rAbsJK+u9eG53P4bjabg/sJ59fyQFq0GH5fVumA0sLkNERFOjqgJdoQT0Wgkr6jyo8VrgzaKAZ754bUac1uLHW11GvHFwBClZhd9enKzOtKyicyQOr9WIpbVu1HgsOb/21Wk10Gk1+EC3j9ZKJ1KyglAig0OhJHb3RdExNPrvFbAZYWShuVmFAXcBeW1GfOPcFjy4qR0vtw3hly93oDecxCeWVef9LuQzu/qxcXc/JAD/cGoDymdoeu9sI4RA10gCep0GpzT70RywjX2X6nw61PmsMOo12NI+DLuxsHe7gdHOTqeRZsX3rd5nRSyVwaZ9g2Npe6oQyCgC8bSCdXN8CNhZKI2IiKYmlVHQOZJAudOEFfWeadfHGnVaLK11w27SYfP+QRwKJ1FW4EKhoUQGg9EUGgM2LK/1FGUZTqNOi4Bdi4DdhOYyO7pGEtjbF0XHYAw2ox4+W/FvkFBhMOAuMKNOi8tPqUe504Q/vN6Np94ZTUP94qkNeRv5eqs7hIdeHZ23fdHSSiyuduXlc6jwDoVTsBh0OKXZhwrXsTvcBRVOdA4n0BtOjrtNvoQSGbgtRvhsMzOd/IPmlzsRSynY0xeBJEnQQIIkAQurHGj024rdPCIiKnGhRAaDsRRagnYsr/PAZpy+l/LNZXZoNRI27R9ETyhRsBsDh8JJyKrAinoPWiud02IuuUmvRaPfhjqvFfv6o9jaMYwDg3FUusyc4z0LTN/f0hlMkiRcsKgCQacJP3/hALZ3hXDL4zvx5dMac75UUG8oibue2w9VAKsbvDh3QTCn+6fiCSUyyKgq1taPH2wDo8VTTqp24el3+pBIKwVNaY6mMpgbtM/odPL302hGU/vmVTigebcK7OGlVXgXm4iIpiKSzGAknsbJdaOBZCn0rQ1+G7QaCS/tHUTXcAKV7vwF3Yez/gw6DU5t9qFhGt7o1mokzCmzw2czYmv7EPYPxOC1Gotaa4fyb/r/ps5gy2s9+KdzWuA069E1ksD3/vQ2Xmkbytn+9/VH8YMndiKeVtDgs+Ky1bW86J8hUhkFg7EUFle7UDeBSvO1Hguay6zoCScghChAC0crpms0EoKu2ZVGrdFIcJj0sBl1sBh0MOm1/L0jIqIpiaVk9EdTWFLjxqIqV0kE24fVeq04dY4PRoMGB4fjebkOUYVAx3AcVqMO61sC0zLYfj+P1YD1LQGsrPcgnBxNf6eZq3R+W2eoep8V/3r+PMwpsyElq7j7+f34xeZ2ZBR1Svvd0j6MHz65C5GkjBqPBVed3jQtUmpo6lR19A7unDI7WiudEwrmNBoJi6pccJj0GIqlC9BKYCSegdtigK9IS+ARERHNBMmMgt5wEidVjRZGLcWVLqrcFqxr9sNq0KFzJLc3/2VVxYHB0ZHi9S1+VBZ4+txk6bUaLKpyYVWDF0l5dH1wmpkYgU0DLosBXzurBecvLIcEYOPufvzbn9/BOz3hrPclhMCTb/fizo37kFEEFlU6cf05LUVfloFyp3MkjqDThOV1nqxuorgsBiyscmIkkYGi5n+UO5LKoM5r5Y0eIiKiSUrLKrqGE5hXbsfSWndJrzBT4TLj1Dk+2Iw6HBzOTdCdkhW0D8ZR5bbgtBZ/yRUnlSQJ88odWNvkhayq6A0li90kygNeCU8TWo2Ejy2pxDVnNMNm1KFrJIH/3LAb//XUbhwcip/w/UII7OwN4z837MZvXuuEAHDaHD++cnoTTFx6YMboDSdhMeiwssE7qUIpjX4bvFYjRuL5HeXOKCq0GmncNamJiIjo+FRV4OBwHE1lNpxc750RN7DLnWasm+OH06xHx1Ac6hQGAGIpGZ3DCbQE7Vg/xw/XB9fmKiFNATtObfZDowG6RxLFbg7lGIumTTOtlU5878IF+NObPXh2dz/e6g7j7e63sajKifnlDrQE7ahwmaGRJCiqQCiRQedwHH/Z0Ys9fVEAo8H7RUsqcfb8Ms4dnUGGYmkoqsDaJt+kl9cw6bVoDFjx6oEhePOY6h1KZOCyGIq29iYREVGp6xyJo9xpwsn1nhk1eFLmMOHUOT5s3jeItsEYKl3mrI9vJJ7GcDyNxdUuLKlxz4hK33U+KzSShOf39hdlKbXjkRUV0ZSMtKIiIwtkVHUsQ0GCBI0kQauVYNJpYNBpYNRpoddKjEPexYB7GrKb9PjUyTU4Y14A/7utC68eGMYbnSG80RkCANiMOmg1EsLJDN6fjaPTSDi12YdzFwTzGkxR4UWTMiLJDFY1eFHvs05pX7VeK97qDiGSzMBuys9Ug3Ayg2U17hlxN56IiKjQ+iMpmPWjGW356quLKWA34Yx5ZdjSPoSdvVG4zHp4rCceoU7Jo/PZDVoNVjV4saDCWZJz2sdT47VglerFi3sGMBBNFbUOjqyoCCUyiCRlSNJofGI36eEw6WA36WHQaaAKAVkRSCsqYikZoUQGyYyKUDKDjDxaj8qg1cKk17xbSHZ2rtrCgHsaC9hN+NK6Rnx4YRzbO0PY1RvBnv4ooil5bButJMFl0WNJjQvnLAjCXcLpNHRsyYyCvkgSS2rdmFfumPL+PFYDajwW7OmL5qUTzygqtJIGwRIpWkJERCSEwNgYhgDw7rKOxRBJZpDIKDi1efIZbaXAatRhbZMfXqsRWw8O48BgDG6LAXaT7qhzr6oCfZEUEhkZ9T4rWitdM3baWqPfBkUVeHHPAIZi6QndiMglWVFxKJJCWlbgthixqNqFcqcJfrvxhJkIqiqQlBXE0wpiKRmRpIyBSAqDsRRGEmkkwwoAwGzQwW7UwWKYHSu5MOAuAdVuC6rdFnx4YTlkVcXBoQQkCeP+UaLiCyUyiKdlpDIqBEY7cp1WA4/VkNXc61Aig4FoCnPL7Vicw8qkDX4b9vbFkJIVGHW5TVMLJzJwWfTwM8uCiIimsZSsIJyQEU3JEBDQSBIkAJAARRXQSIDTbIDDpC9YsbKUrKAvksKyWjeaAtN7aatc0GokLKh0wmMzYM+hKLpHEmgfjEGr0UCvlZCSVahi9A6I32bEqkYv6ryWkloWbTKaAzZkZBUvtw1BI6Eg89NVVaA/mkIsLaPCacb8Cgcq3easrhM1GgkWw+iyqO8fnZcVFZGkjJFEBsOxNHpDSQzGU+gNJ6HXauA062d0TMOAu8ToNJoppxRTfg1GU0jKKoJOEzzW0ZsiOo0GB4fi6ByO41A4CbtJB7fFMG7Ktayq6B5JQq+VcHK9B/MrHDlNzy53mhF0mDAQTaEixyPR4aSMJTWuGTGfioiIZhZVCAzH0gglMtDrNHCZDWgusyFgN0GnlSBJo3NS07KK7lACHYNxdAzHoZUklDtNeZ0qJasqOodHl/1cVOWaFSN/h5U7zSh3mhFNyTgUTuLgUBwpWR0bXLIadPDbjTAbZs5c9uORJAnzKxxQhMCrbUOQJAnOPK44FElm0BcZTWFfXudBnc+S0wEZnVYDt9UAt9WAep8VQoixQaWukQR6RpJoG4jBatTBax3/+rhUMeAmyqFEWkE4mcHaJj/mVxyZ/t3ot2I4nkHXcBz7+mPoDiWgqAIOkx5Wgw6yqkJWBdKyimhSRpXbjCW17pwHxMDoHeXmMhs6h0crhOZq5FxWVGgkVicnIqLpRVZUDMbSiKZluM0GrKj3oNJlhsdqGHe0tMZrweJqF3pDSew6FEH7YAxOkz4vdXJUIdA5lEC1x4KT6z2z9qa1zaiDzW9Do3/mj+6fiCRJWFjpBAC82jYEADkPuhVVoCeUgCRJWFrrxoIKByyG/IeHkiTBZTHAZTGgKWBHNCWjcziO3b0RdI0koCnADa5CYsBNlCOyOno3vLXCiblB+1E/lyQJHqsBHqsBc8sd6I+k0BsavaM3kkhDp9FAr5PgMOsxr9yB+RWOvFYlrXJb4LIaMBxP5+ziIXQ4nZzVyYmIaBpIyyr6oymkZBUBuxHL6tyo8VgmHFSY9FrU+ayocJmx51AEb3aG0DYQQ4XTBGMO++jukQQ8NgNWNXhhncSynzQz5TPojiZl9EaSqHCasKTGjSq3uWhZFTajDnODDjT6begeSWBn7+gNLrtRD5/NUPLZHvyNJsqRrpEEqt0WLK11n3DEWK/VoMJlRoXLjNZKJ+JpeWwZhULNEzMbtGgO2PBqW+6WCIskZZxU48r5vHAiIqJsJDMK+qMpqKpAucuMlqAd1W7LpEeODToNFlQ6EXSa8MbBEPb2R+AyG3JS0OpQOAmDbrTqdqELZNH0d2TQPYy0rE5pYOPwqLYAsKTahYVVzoKMak+EXqtBrXf0Btfevije6BxB22AM5Q5zSU8nmB5nl6jE9UWSsBp0WF7vzvoPgkGngUFXnA42l0uEyYoKSQKCM7iiKhERTV+qEAgnMhiKp6HXalDlNqOlzIEKlylnRba8NiPWzfEh4DDg9YMjODgcR4XTPOmb5f2RFGRV4JRmX16mkNHMcDjoNum12HJgGO1Do+uX6zTZfa8Pz9Uunwaj2sej12owr9yBcqcJb3aOYPehKBymiS0dNx0x4CaaIllREU3JOK0lgIC9tIJNj9WAeq8N7/SEpxxwD8czcFsNCDiYTk5ERIWTzCgYiqWRyChwmvVYXOVCrc8Kv82YlzWadVoNWitd8NqMeLVtCAcGYyh3mrIaJVSFQNdIAkadBmsavZyzTCckSRLmlNnhsujx2oFhtA/GEXSYJjQFIZaSMRBNQa/VYFmtGwsqnCUxYuyyGHBKkx9emxFbDgyjJ5RA0GGaljcJjocBN9EUDccz8NmMqPVait2USWkIWLG7L4JkRpn0nHEhBMLJDNZUeplOTkREeZdRVAzH04gmZRj1WvjtRjT4rahyW7JafnMqyp1mnDGvDNs6hrH7UAQS0ghOoNBTRlFxcDgOv82EVY0elDs5sk0TF7Cb8KG5AWzrGMbO3gj6IknYTXo4zfojvnuqOnptNhRPw6TTojFgw5wye8llUmg0EhZUOGEz6rB5/xA6huOodlnycjMtXxhwE01ROJnBqgpPyQaaZXYTqtxmHBxKoMYzuZsG4aQMu0mHGi+XrCMiovw4nDI+kkhDggZemwGt786r9lnzM5p9IlajDmsafaj1WrG9K4SDw3FYDTp4bYaj0n1TGQUjiQyiSRl1fitW1XvhtORvqSeauUx6LVbWe1Hvs6FnJIH9gzF0jyShCBUSJAgAWg1gNeiwuMqFOv9oxkepjQy/X63XCrNBi837BnFgKIYajyXrlPpiYcBNNAWxlAyLQYsqd2mObgOjdw6bA3a0D8aRUdRJLcEwGEthYaUzr2tEEhHR7KSqAv3RFKIpGU6zHvMrnKjxWFDmmB7LBmk0Eqo9FgSdJuzvj2FHVwhdI6NLf0oSYNBokVQUGLQaeK0GLKpyornMnteVSGjm02hGl2ENOk1YUOlEXySJSFKGXquBQauBQaeB1aid8pTB6SRgN+G0uQG8sGcAHUNx1HqsBSs2PBUMuImmYDCWRo3XUrJFHA6rdJtR5jBhIJrKOrUtkVZg0GnQwPlnRESUQ6oQGI6lMZLIwP/ukl6FTBnPll6rQUvQjhqPBaFEBtFUBpGEjOFEGl6rAUGnGT6bsSQCBCotBp2mpAd/suEw6XFKsw8v7BlA+1CsJILu6fkXi6gEKKqArAo0+KwlnaIDvHeR8MzOPqiqyCotbyCaQrXHDH+OlhYjIiKKp2V0h5JwWwxY0+hFU8BeEkWegNFlN0fbWlqFVIlKhcOkx9omH17Y3V8SQXfx83CIStRIPA2PxVByxSfGU+22wGs1YiienvB7ZEWFrKpoDNhLqngFERFNX0OxNA6Fk1hU5cSHFwaxsMpVMsE2ERWG06zHKc1+VDjNaB+KQVVFsZs0LgbcRJM0ksyg0W+dMXOwzAYtWoI2hBIZZBR1Qu8ZjKXht5tQOUNuOhARUfEIIdA9kkAyo2BNow+r6r0zav4pEeWW0zKaXh50mNAxHIMqpmfQzYCbaBLiaRkmnRaVnpkVaLYEHajzWdE5Ej/htqoqEE3JmFNmh0HHPyVEs8mNN94ISZKOeASDweO+Z+PGjVi2bBlMJhMaGhpw5513Fqi1VApUVaB9KA6LQYfTWgJYUOlk5hQRnZDLYsDaJh88ViM6h+MQ0zDo5lUy0SQMxdIod5lm3Lxlg06DZbVu2I169EdS424nhMDBkTjKnaaSXX+ciKZmwYIF6OnpGXts37593G3b2trw4Q9/GKeeeiq2bduGf/7nf8ZXv/pVPPLIIwVsMU1Xh/uUgN2I0+f6UcN+hYiy4LUZcUqTD3aTHp0jiWI35ygsmkaUJSEEUrI6I4qlHYvPZsTiGhee290Pu0l3zJT57pEkHCY9Vjf6YJ2m1WKJKL90Ot0JR7UPu/POO1FTU4PbbrsNADBv3jy89tpr+OEPf4iLL744j62kUtAdeq9P8c6wG9lEVBgBhwlrm3x4bnc/ukYS02q6I0e4ibIUSymwmXTw22du9dHmgB0tQTu6RhJHzYfpiySh00lY3eiF384LI6LZas+ePaioqEB9fT0uueQS7N+/f9xtN23ahLPPPvuI18455xy89tpryGQy+W4qTWP9kRQ0GmBVA/sUIpqaCpcZa5t8MOo0ODiN0ssZcBNlKZQcXQ/UYZq5I7tajYQlNW4E7CYcGIjhwGAMfZEk+iJJpGQVK+u9s2a9RyI62sqVK/Hggw/iiSeewM9+9jP09vZizZo1GBwcPOb2vb29KCsrO+K1srIyyLKMgYGBcT8nlUohHA4f8aCZI5TIIJFRsLLei2oP+xQimrpqjwXr5vhhM+pwcDgxLYJuBtxEWUplFNR4LDMynfz9HCY9TmvxY31LAK2VTtiMOmg0ElbUudEUsBW7eURUROeddx4uvvhiLFy4EGeeeSb+/Oc/AwAeeOCBcd/zwb+Zhy+Cjve39Oabb4bT6Rx7VFdX56D1NB2kMgoGoyksrXGhmX0KEeVQhcuMdXP8cJr16BiOF716+cwdoiPKg0RagUmvnXHF0sbjthrgthoAvDd3faYsg0ZEuWO1WrFw4ULs2bPnmD8PBoPo7e094rW+vj7odDp4vd5x93vDDTfguuuuG3seDocZdM8AqhDoHEmgJWhHa6Vzxt/AJqLCK3OYsG6OHy/uHUDbQAyVLjP02uKMNXOEmygLoWQGXpsBbouh2E0pOEmSGGwT0TGlUim88847KC8vP+bPV69ejQ0bNhzx2pNPPonly5dDrx9/nWWj0QiHw3HEg0pfXzgFr9WIJdVu6Ip0AUxEM5/fbsTpcwNoCdrRHUpgIDr+Cjz5xL9yNG1Fkhm0DUTR9u4c4o7BOAaL9ItyWDwlo8Zj4dqgRDSrff3rX8fGjRvR1taGl19+GR//+McRDofx2c9+FsDoyPRll102tv0VV1yB9vZ2XHfddXjnnXfw85//HPfeey++/vWvF+sQqEhiKRlpRcXSWheclvFvthAR5YLTrMepzX6snxOAViNBU4SMGqaU07Q0Ek8jlMhgYaULBp0GihBIyyr29kUxEk/DVYQR5pSsQK/VIOCYudXJiYgmorOzE5/61KcwMDAAv9+PVatWYfPmzaitrQUA9PT0oKOjY2z7+vp6PPbYY7j22mvx3//936ioqMCPf/xjLgk2yyiqQE84iZOqnKj3WYvdHCKaJbQaCS1BO3w2A/b1R2Er8JK2DLhp2hmKpRFNyTi53oMFFc4jRpPtJh027x+ESa8teHpzOCHDZTHAa5196eRERO/30EMPHffn999//1GvrV+/Hlu3bs1Ti6gU9IQSqHCacFK1i/O2iajgvDYjvEWow8SUcppWBqIpxNIyVjZ40FrpPCp1e365A3PK3l0fWi1sxcFIKoNar4XzzYiIiLIUTmQgSRKW1XlgMXC8h4hmD0YONG0MxdJIySrWNvmwoOLYVUt1Wg2W13lQ7jShcyResLbJigqtJKHMyXRyIiKibMiqiv5ICgsqHKh0mYvdHCKigmLATdNCMqNgJJHB8lo35pTZj7utzajDyfVemPTaghVRCydlOMx6+GxMJyciIspGbyiJSrcZrZXOYjeFiKjgGHBT0SmqQNdIAvPL7ZhbPrElX4JOExZXuzGSyBQktTycTKPaY4FRx2WxiIiIJiqSHE0lX1zj4tKSRDQrMeCmousciaPSZcbSWje0WSy3Ve+zwms1YiiezmPrRlPhhAAqnEyDIyIimihFFeh7N5W8ym0pdnOIiIqCATcVVX8kBbNei5Prsy+iYjZoMSdoQyiRgRD5G+WOJGU4zQYEHIWvakhERFSqekIJVLjMWFDBVHIimr0YcFPRRJMy4hkZy+s8k17bus5nhdOsRyiRyXHr3hNOZlDtMTMVjoiIaIJG4mloNRKW1rphNrD/JKLZiwE3FUUiraAvmsRJVS40+W2T3o/DpEdTwIbBPKWVK6qAqgpUsKoqERHRhKQyCobiaSyudrEqORHNegy4qeDSsorukQQWlDuxuNp11Frb2ar3WWExaBFJ5n6UO5zMwGE2oGySI/BERESziSoEOkcSmFNmx7wJFkIlIprJGHBTQcmqioPDcTSX2bG83g2ddupfQa/NiAavDYPR3I9yhxJMJyciIpqonlASfrsRS2py08cTEZU6/iWkgskoKjqG4qj2WLCywZPTJbaaymzQ6zSIp+Wc7VNRBVQhmA5HREQ0AYfrqSyv9cBp1he5NURE0wMDbspKJJlBbyiJ4XgayYwCdQLVwYUQ6I+kcHA4jnqfFWsbfbAas6tIfiIBuxE1Hgv6o6mc7TOczIxWJ7cznZyIiOh4QokMhuNpnFTlRI2XS4ARER2W26iHZqxwIoOBWAoWgxYemxHRlDwWdAtIMOk0sJt0sBp00GgkqEJAUQVSGRV90SQcZj1ObfajKWCDPg8pZpIkoSlgw/7+GFKykpPR83Aig7nlDlZXJSKikpSWVURTMjKKCo0kQSON9pdGnQYWgxaSNLUaKoeFEhmMxNNYVuvGoipXTvZJRDRTMOCm40rJCjqHE7AatVhY6cScMju8NiMyiopoUkY0JSOUyKBrOI6hWAaDsTiEEJAkCTqtBJ1GwtygHYuqXHBZDHlta4XLjHKXCf2R1JTTwBVVQGE6ORERlZhocvSGuKyq0Gk0sJl0cFsMkFUVGUVAQCCcyqA3nIReo4HDrIPdpId2kgVMPxhsT7UQKhHRTMOAm46rZySJpoANi6td8NqMY6/rtRq4rQa4rQZUA2itdCKWkjEUS0NRBQw6zdjD9u6od75pNRLmlNnRORwfu9CYrMi76eSsTk5ERKUgnMhgMJaCSa9Fvd+KcqcZboseLosBBt17/aGiCkSSGQxEU+geSaInlET7UAx6jQZemwEWw8QuDVVVoD+aQjKjYHmdGwsrGWwTER0LA24a10g8DbNRi0VVRwbb47EadTmfm52tao8ZfpsJg9H0lILl4XgGrZVMJyciouktksygL5KCzaRDa6UTTQE7/Pbx+2ytRoLLYoDLYkBTwI5kRkH3SAL7B2LoGUmgN5SE1aiD3aSDWX902rkqBAajaYRTGfhtRqyo96DJb2OwTUQ0jpIJuOvq6tDe3n7Ea9/4xjdwyy23FKlFM5uiCgzG0ji53nPcjnu6Meq0aAna8fyefviFgGYS89PiaRkGnQYNflseWkhERDR1GUVFTygBnUaDxdUuNJfZ4bFmP3XLpNeiwW9Dvc+KwVgaXcNxHBxKYDieRk8oCa0kQZJGA20BQADwWY04tdqPBr+Vy2YSEZ1AyQTcAPDd734X//AP/zD23GZjQJQvveEkyp0mzCt3FLspWavxWuDqMmAknpnUxUd/NIUGnw2BErrRQEREs4P67sofsbSMWq8VJ1W5EHROffqTJEnw2Yzw2YxYWOlCKJHBYCyNoWgKkkaCQauBViPBoNOgym2ecOo5EdFsV1J/Le12O4LBYLGbMeMl0goUVWBRlask71zbjDrMCdjwyoGhrAPulKxAwmjF81xVbyUiIsqFWErGoXASHutoKne9z5qXlT80GmmsTgsCHNwgIpqKklqH+wc/+AG8Xi8WL16M73//+0in08Vu0owjhEB3OIGmgBU1ntJdR7PWZ4XNpEMokcnqfQORNMpdJlSwOjkREU0TiirQORzHUCyNBZVOnN1ahjll9rwE20RElFslM8J9zTXXYOnSpXC73XjllVdwww03oK2tDffcc8+470mlUkilUmPPw+FwIZpa0iJJGTajDq0lXm3UYzWg2W/D1oMjsBl1E1ruRFZUpFUVLWX2SS+PQkRElEsj8TQGo6M3gxdXu1HtMTMDi4iohBT11uiNN94ISZKO+3jttdcAANdeey3Wr1+PRYsW4fLLL8edd96Je++9F4ODg+Pu/+abb4bT6Rx7VFdXF+rQStZwIo0qt3lSc5+nm9YqJ8qdJvSGkhPafiCWRsBuRJW7dEf2iYhoZkjJCtoGo0jKCk6u9+DsBUHUeC0MtomISkxRR7ivuuoqXHLJJcfdpq6u7pivr1q1CgCwd+9eeL3eY25zww034Lrrrht7Hg6HGXQfh6yqUFWBOq+12E3JCYtBhyXVbvz1nUOIpkZH7sejqgKxlIzlde4j1islIiIqpMNF0eIZBfVeKxZVuxCwT70oGhERFUdRA26fzwefzzep927btg0AUF5ePu42RqMRRiMrTU9UKJ6B22Kc0vrV0021x4y55Xa80RlCg9d6zDR5IQS6Qgn47EbUembGzQYiIio90XeLonnfLYrW4LNCx3naREQlrSTmcG/atAmbN2/G6aefDqfTiVdffRXXXnstPvKRj6CmpqbYzZsxQskMlta4S7Iy+XgkScKiKhd6Qyn0hpNHFUMTQqBzJAGrQYdVDV6YDTPn2ImIqDTIqoqed6c/LaxyYmGlE3aTvsitIiKiXCiJgNtoNOLhhx/Gd77zHaRSKdTW1uIf/uEfcP311xe7aTNGKqNAr9HMyPnLVqMOi2tceHZXH7pGEvBYDDAbtFCFwMHhOFxmA9Y2+XKyjikREVE2IskM+iIpVLrMOKnahSo3i6IREc0kJRFwL126FJs3by52M2a04XgGPrsRfvvMTMGv9VhwarMP+/qi6A2nkAwpkCTAZzPilGYf58cREVFBqaoYHdWWgGW1brRWOmdUhhkREY0qiYCb8i+WlrGk1jVjl8PSaCQ0Bexo8NkwGEujaziO/mgKS2vc8Npm5k0GIiKanhJpBd2hBMocJiyrdaPaM/Oyy4iIaBQDbkI0JcNi1KLCaT7xxiVOo5Hgn8Ej+URENL2FEhkMxlJYUOHAkho3rMdZQYOIiEof/8oThmNpVHstcFlYoIWIiChfBqIpxNMKVtR5sKhq5maVERHRexhwz3KqEMioKuq8VhZpISIiygMhRudrSxKwtsmHOWU29rlERLMEA+5ZLpKUYTfqWaGbiIgoTzpHErAYdFjd4EWNl/O1iYhmE02xG0DFFU5kUO4ywcY5ZERERDl3KJyESafFKc0+BttERLMQA+5ZTAiBtKKiyj3zi6UREREV2lAsDVkVWNXoRaWLfS0R0WzEgHsWi6UUWI1a+G1MJyciIsqlcCKDaErGyfUe1PusxW4OEREVCQPuWSyUzCDgMMFhZjo5ERFRrsTTMgZiaSypcWFu0F7s5hARUREx4J7FUhkFNR4LK6USERHliKIKdIeSWFDhwKIqF/tYIqJZjkObBSKEQEYRSMkKkhkVKVmB06yH3VScta8TaQUmvRZ+m7Eon09ERDQTdYcSqHCasKSG62wTERED7oKQVRUHh+LQ6zQw6bQwG7QIOIw4MBCHogq4LIaCtymUyMBrM8BdhM8mIiKaiYbjaeg0EpbXeWAx8BKLiIgYcOedrKhoH46j2m3B8lo37CY9TPrRTP4dXSG8emAYqgA81sIGvvG0jJOqndDw7jsREdGUpWQFw/E0Vjd4UcGK5ERE9C7O4c6jjKKifSiOWo8Fpzb7EHCYYDZoIUkSJElCa6UTKxs8iKVkDEZTBWtXSlag12lQ5mB1ciIiyt7NN9+MFStWwG63IxAI4KMf/Sh27dp13Pc8++yzY/3f+x87d+4sUKvzRxUCXSMJNAfsmFvuKHZziIhoGmHAnScZRUXHUBwNfitObfYfc662JEmYX+7A6iYv4hkFI/F0QdoWSmTgthgKPqpOREQzw8aNG/GVr3wFmzdvxoYNGyDLMs4++2zEYrETvnfXrl3o6ekZezQ3NxegxfnVH0nBYzFiaY0bei0vrYiI6D1MKc8DRRXoGIqjMWDDmkbvcedxSZKEuUEHEikFrxwYgsOshybPFU2jKRnzgg7oeFFAREST8Pjjjx/x/L777kMgEMCWLVuwbt264743EAjA5XLlsXWFlcwoiKdlrGzwwGkpTiFUIiKavhhx5ZgQAp0jcVS4zFjVcPxg+/2ag3Z4rAYMRvM7yp1RVGg1EsqcTCcnIqLcCIVCAACPx3PCbZcsWYLy8nKcccYZeOaZZ467bSqVQjgcPuIxnQgh0B1KoLnMjnqfrdjNISKiaYgBd44diqRgNeqwssEDm3HiCQQ2ow7zKxwIJzNQVJG39o3EM/BajfDbuRwYERFNnRAC1113HU455RS0traOu115eTnuvvtuPPLII/j973+PlpYWnHHGGXjuuefGfc/NN98Mp9M59qiurs7HIUzaYCwNh0mPRVVcAoyIiI6NKeU5NBJPQ1ZVrG0KIGDPfgS50W/DnkNR9EWSKHfmp8JpJCVjXrmdc8yIiCgnrrrqKrz55pt44YUXjrtdS0sLWlpaxp6vXr0aBw8exA9/+MNx09BvuOEGXHfddWPPw+HwtAm6M4qKcDKDU5r8rIlCRETjYtSVI4m0gqF4Gktr3Kj3WSe1D5Nei9ZKBxIZFRlFzXELgVRGgV4rcbkSIiLKiauvvhqPPvoonnnmGVRVVWX9/lWrVmHPnj3j/txoNMLhcBzxmC66RuKo91nRXMZUciIiGh8D7hxIZhR0hxJYUOHEggrnlPZV67Wiym1GbziZo9a9ZziRgc9mhNfGdHIiIpo8IQSuuuoq/P73v8fTTz+N+vr6Se1n27ZtKC8vz3Hr8m8knobZoMNJ1S5mjBER0XExpXyKkhkFfZEUFlQ4sKLOM+U5XHqtBgsqHOgZSSKVUWDUa3PUUiCWkrG4mvPMiIhoar7yla/gV7/6Ff74xz/Cbrejt7cXAOB0OmE2j2ZR3XDDDejq6sKDDz4IALjttttQV1eHBQsWIJ1O45e//CUeeeQRPPLII0U7jsmQFRWDsTRW1nsmNX2MiIhmFwbcU9QbTmJBhQMn13th0OXmLne124IarxmdwwlUuy052WcircCs1yLI6uRERDRFd9xxBwDgtNNOO+L1++67D5/73OcAAD09Pejo6Bj7WTqdxte//nV0dXXBbDZjwYIF+POf/4wPf/jDhWp2TvSEk6j2WDCvYvqktxMR0fTFgHsKTDot6n1WrGzIXbANABqNhJYyBzoGE0jJCoy6qY9yD8fT8DuM8FhY2IWIiKZGiBOvpnH//fcf8fz666/H9ddfn6cWFUYkmYFGknBStSsnfTMREc18nHg0SXaTDgurnFjV4M1Lp1vpNqPKbUJfODXlfQkhkMgoqPdZoWE6ORERUdYUVaAvksL8CgcqWXyUiIgmiAH3JI1WFHfClMM51u+n1UiYW+6AKgTS8tQqlsfSCixGLYIOppMTERFNRm84iaDThAVMJScioiww4J7GKl1mlLvM6I9MbZR7JJ5GucMMp1mfo5YRERHNHpFkBqoqsLjaBYuBs/GIiGjiGHBPYzqtBvPKHUgryqTX5ZYVFWlFRZ3PCkliOjkREVE2MoqKvkgKC6ucqPHkppApERHNHgy4p7kqtxmVLgv6JjnK3RdJIegw8SKBiIgoS0IIdI7EUeezYmGVkzeuiYgoawy4pzm9VoOWcjtSspr1KLesqEjKCuZXOHNaRZ2IiGg26I+mYDfqsbzWzarkREQ0KYzCSkCNx4JarwXdoURW7+uLpFDhNHN0m4iIKEvxtIxEWsHyOje8NmOxm0NERCWKAXcJ0Gs1WFLtglGnwUg8PaH3ZBQVKUXFvAoHR7eJiIiykJIV9IwkMa/CgQafrdjNISKiEsZIrEQEHCa0VjgxGEtDnkBqeV8khUqObhMREWUlJSvoHE6gpdyOpTVuaDSct01ERJPHgLuEzKtwoNpz4tTyjKIiLSuYW26HXst/YiIioolIy+posB20Y1WDFyY9520TEdHUMBorIUadFourXdBrNQglMsfcRlUFukYSqHRbUM3RbSIioglJyyo6huOYU8Zgm4iIcocBd4mpcJmxoMKJgWgKg9EUVCHGfpbMKGgbjCFgN2J5nZuj20RERCegCoH+SAqdw3HMCdixupHBNhER5Y6u2A2g7C2odEAjATsPRdA2EIPbYgAAjCQymFdux7JaD6xG/tMSEREdj6yIsX70lGY/mgI2FholIqKcYlRWgow6LRbXuNEYsGFvXxS7DkWgqAJrG72YW+6AlgVeiIiIjkuSALfVgHqfFfMqHHCY9MVuEhERzUAMuEuY3aTHkho3mgI2ZBQBj9VQ7CYRERGVhCq3BS6LAT6usU1ERHnEgHsGsPOuPBERUVZMei3nahMRUd5xohIRERERERFRHjDgJiIiIiIiIsoDBtxEREREREREecCAm4iIiIiIiCgPGHATERERERER5QEDbiIiIiIiIqI8YMBNRERERERElAcMuImIiIiIiIjygAE3ERERERERUR4w4CYiIiIiIiLKAwbcRERERERERHnAgJuIiIiIiIgoDxhwExEREREREeUBA24iIiIiIiKiPGDATURERERERJQHumI3oJCEEACAcDhc5JYQEdFsd7gvOtw30YmxHycioukgmz58VgXckUgEAFBdXV3klhAREY2KRCJwOp3FbkZJYD9ORETTyUT6cEnMolvrqqqiu7sbdrsdkiRNaV/hcBjV1dU4ePAgHA5Hjlo4c/F8ZY/nLDs8X9njOctOrs+XEAKRSAQVFRXQaDjDayLYjxcPz1f2eM6yw/OVPZ6z7OTyfGXTh8+qEW6NRoOqqqqc7tPhcPALngWer+zxnGWH5yt7PGfZyeX54sh2dtiPFx/PV/Z4zrLD85U9nrPs5Op8TbQP5y11IiIiIiIiojxgwE1ERERERESUBwy4J8loNOLb3/42jEZjsZtSEni+ssdzlh2er+zxnGWH52tm4b9ndni+ssdzlh2er+zxnGWnWOdrVhVNIyIiIiIiIioUjnATERERERER5QEDbiIiIiIiIqI8YMBNRERERERElAezNuC++eabsWLFCtjtdgQCAXz0ox/Frl27jthGCIEbb7wRFRUVMJvNOO200/DWW2+N/XxoaAhXX301WlpaYLFYUFNTg69+9asIhUJH7Keurg6SJB3x+H//7/8V5DhzqZDnDAD+/Oc/Y+XKlTCbzfD5fLjooovyfoy5VKjz9eyzzx71/Tr8ePXVVwt2vLlQyO/Y7t27ceGFF8Ln88HhcGDt2rV45plnCnKcuVLI87V161acddZZcLlc8Hq9+OIXv4hoNFqQ48ylXJwzAPjSl76ExsZGmM1m+P1+XHjhhdi5c+cR2wwPD+PSSy+F0+mE0+nEpZdeipGRkXwf4qzBfjw77MOzx348O+zDs8d+PDsl24eLWeqcc84R9913n9ixY4d4/fXXxfnnny9qampENBod2+aWW24RdrtdPPLII2L79u3ik5/8pCgvLxfhcFgIIcT27dvFRRddJB599FGxd+9e8de//lU0NzeLiy+++IjPqq2tFd/97ndFT0/P2CMSiRT0eHOhkOfsd7/7nXC73eKOO+4Qu3btEjt37hS//e1vC3q8U1Wo85VKpY74bvX09IjLL79c1NXVCVVVC37cU1HI71hTU5P48Ic/LN544w2xe/duceWVVwqLxSJ6enoKesxTUajz1dXVJdxut7jiiivEzp07xSuvvCLWrFlz1DktBbk4Z0IIcdddd4mNGzeKtrY2sWXLFvE3f/M3orq6WsiyPLbNueeeK1pbW8VLL70kXnrpJdHa2iouuOCCgh7vTMZ+PDvsw7PHfjw77MOzx348O6Xah8/agPuD+vr6BACxceNGIYQQqqqKYDAobrnllrFtksmkcDqd4s477xx3P7/5zW+EwWAQmUxm7LXa2lrxX//1X3lre7Hk65xlMhlRWVkp7rnnnvweQIHl8zv2ful0WgQCAfHd7343twdQBPk6Z/39/QKAeO6558a2CYfDAoB46qmn8nQ0+Zev83XXXXeJQCAgFEUZ22bbtm0CgNizZ0+ejqYwcnXO3njjDQFA7N27VwghxNtvvy0AiM2bN49ts2nTJgFA7Ny5M09HM7uxH88O+/DssR/PDvvw7LEfz06p9OGzNqX8gw6nXXg8HgBAW1sbent7cfbZZ49tYzQasX79erz00kvH3Y/D4YBOpzvi9R/84Afwer1YvHgxvv/97yOdTufhKAorX+ds69at6OrqgkajwZIlS1BeXo7zzjvvqHSQUpPv79hhjz76KAYGBvC5z30ud40vknydM6/Xi3nz5uHBBx9ELBaDLMu46667UFZWhmXLluXxiPIrX+crlUrBYDBAo3mvyzCbzQCAF154IefHUUi5OGexWAz33Xcf6uvrUV1dDQDYtGkTnE4nVq5cObbdqlWr4HQ6j3vuafLYj2eHfXj22I9nh3149tiPZ6dU+nAG3BjN9b/uuutwyimnoLW1FQDQ29sLACgrKzti27KysrGffdDg4CC+973v4Utf+tIRr19zzTV46KGH8Mwzz+Cqq67CbbfdhiuvvDIPR1I4+Txn+/fvBwDceOON+OY3v4k//elPcLvdWL9+PYaGhvJxOHmX7+/Y+917770455xzxv5olKp8njNJkrBhwwZs27YNdrsdJpMJ//Vf/4XHH38cLpcrPweUZ/k8Xx/60IfQ29uL//iP/0A6ncbw8DD++Z//GQDQ09OTj8MpiKmes5/+9Kew2Wyw2Wx4/PHHsWHDBhgMhrH9BAKBoz4zEAiMe+5p8tiPZ4d9ePbYj2eHfXj22I9np5T6cAbcAK666iq8+eab+PWvf33UzyRJOuK5EOKo1wAgHA7j/PPPx/z58/Htb3/7iJ9de+21WL9+PRYtWoTLL78cd955J+69914MDg7m9kAKKJ/nTFVVAMC//Mu/4OKLL8ayZctw3333QZIk/Pa3v83xkRRGvr9jh3V2duKJJ57AF77whdw0vIjyec6EELjyyisRCATw/PPP45VXXsGFF16ICy64oGQ7nnyerwULFuCBBx7Af/7nf8JisSAYDKKhoQFlZWXQarW5P5gCmeo5+8xnPoNt27Zh48aNaG5uxic+8Qkkk8lx9zHefmjq2I9nh3149tiPZ4d9ePbYj2enlPrwWR9wX3311Xj00UfxzDPPoKqqauz1YDAIAEfdxejr6zvqrkkkEsG5554Lm82G//3f/4Verz/uZ65atQoAsHfv3lwcQsHl+5yVl5cDAObPnz/2mtFoRENDAzo6OnJ+PPlWyO/YfffdB6/Xi4985CM5PorCyvc5e/rpp/GnP/0JDz30ENauXYulS5fipz/9KcxmMx544IE8Hll+FOI79ulPfxq9vb3o6urC4OAgbrzxRvT396O+vj5PR5VfuThnTqcTzc3NWLduHX73u99h586d+N///d+x/Rw6dOioz+3v7z9qPzQ17Mezwz48e+zHs8M+PHvsx7NTan34rA24hRC46qqr8Pvf/x5PP/30UV+2+vp6BINBbNiwYey1dDqNjRs3Ys2aNWOvhcNhnH322TAYDHj00UdhMplO+Nnbtm0D8F6nVCoKdc6WLVsGo9F4RJn/TCaDAwcOoLa2Nk9Hl3uF/o4JIXDffffhsssuO+HF4nRVqHMWj8cB4Ii5TIefHx6dKQXF+DtWVlYGm82Ghx9+GCaTCWeddVbuDyyPcnXOxtt3KpUCAKxevRqhUAivvPLK2M9ffvllhEKhE+6HJob9eHbYh2eP/Xh22Idnj/14dkq2D8+6zNoM8eUvf1k4nU7x7LPPHrEMQzweH9vmlltuEU6nU/z+978X27dvF5/61KeOKCsfDofFypUrxcKFC8XevXuP2M/hsvIvvfSSuPXWW8W2bdvE/v37xcMPPywqKirERz7ykaIc91QU6pwJIcQ111wjKisrxRNPPCF27twpvvCFL4hAICCGhoYKftyTVcjzJYQQTz31lAAg3n777YIeZy4V6pz19/cLr9crLrroIvH666+LXbt2ia9//etCr9eL119/vSjHPhmF/I795Cc/EVu2bBG7du0St99+uzCbzeJHP/pRwY95qnJxzvbt2yduuukm8dprr4n29nbx0ksviQsvvFB4PB5x6NChsf2ce+65YtGiRWLTpk1i06ZNYuHChVwWLIfYj2eHfXj22I9nh3149tiPZ6dU+/BZG3ADOObjvvvuG9tGVVXx7W9/WwSDQWE0GsW6devE9u3bx37+zDPPjLuftrY2IYQQW7ZsEStXrhROp1OYTCbR0tIivv3tb4tYLFbgI566Qp0zIUaXxPja174mAoGAsNvt4swzzxQ7duwo4NFOXSHPlxBCfOpTnxJr1qwp0NHlRyHP2auvvirOPvts4fF4hN1uF6tWrRKPPfZYAY926gp5vi699FLh8XiEwWAQixYtEg8++GABjzR3cnHOurq6xHnnnScCgYDQ6/WiqqpKfPrTnz5qqZDBwUHxmc98RtjtdmG328VnPvMZMTw8XKAjnfnYj2eHfXj22I9nh3149tiPZ6dU+3Dp3cYTERERERERUQ7N2jncRERERERERPnEgJuIiIiIiIgoDxhwExEREREREeUBA24iIiIiIiKiPGDATURERERERJQHDLiJiIiIiIiI8oABNxEREREREVEeMOAmIiIiIiIiygMG3ER0lBtvvBGLFy8udjOIiIhoEtiPE00fkhBCFLsRRFQ4kiQd9+ef/exncfvttyOVSsHr9RaoVURERDQR7MeJSgsDbqJZpre3d+z/H374YXzrW9/Crl27xl4zm81wOp3FaBoRERGdAPtxotLClHKiWSYYDI49nE4nJEk66rUPpqJ97nOfw0c/+lHcdNNNKCsrg8vlwne+8x3Isox/+qd/gsfjQVVVFX7+858f8VldXV345Cc/CbfbDa/XiwsvvBAHDhwo7AETERHNIOzHiUoLA24impCnn34a3d3deO6553DrrbfixhtvxAUXXAC3242XX34ZV1xxBa644gocPHgQABCPx3H66afDZrPhueeewwsvvACbzYZzzz0X6XS6yEdDREQ0u7AfJyoOBtxENCEejwc//vGP0dLSgs9//vNoaWlBPB7HP//zP6O5uRk33HADDAYDXnzxRQDAQw89BI1Gg3vuuQcLFy7EvHnzcN9996GjowPPPvtscQ+GiIholmE/TlQcumI3gIhKw4IFC6DRvHePrqysDK2trWPPtVotvF4v+vr6AABbtmzB3r17Ybfbj9hPMpnEvn37CtNoIiIiAsB+nKhYGHAT0YTo9fojnkuSdMzXVFUFAKiqimXLluF//ud/jtqX3+/PX0OJiIjoKOzHiYqDATcR5cXSpUvx8MMPIxAIwOFwFLs5RERElAX240S5wTncRJQXn/nMZ+Dz+XDhhRfi+eefR1tbGzZu3IhrrrkGnZ2dxW4eERERHQf7caLcYMBNRHlhsVjw3HPPoaamBhdddBHmzZuHz3/+80gkErxTTkRENM2xHyfKDUkIIYrdCCIiIiIiIqKZhiPcRERERERERHnAgJuIiIiIiIgoDxhwExEREREREeUBA24iIiIiIiKiPGDATURERERERJQHDLiJiIiIiIiI8oABNxEREREREVEeMOAmIiIiIiIiygMG3ERERERERER5wICbiIiIiIiIKA8YcBMRERERERHlAQNuIiIiIiIiojz4/3lQGxRIhH3EAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_model, y_model, xe_model, ye_model = mm.model(t_test, params, param_errs)\n", + "visualize_fit(t, x, y, xe, ye, x_model, y_model, xe_model, ye_model, mm.name, t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "5be8fb7e", + "metadata": {}, + "source": [ + "# 2. Fit Motion Model in StarTable" + ] + }, + { + "cell_type": "markdown", + "id": "3bd8dec7", + "metadata": {}, + "source": [ + "Examples on `flystar.StarTable.fit_motion_model`. Prepare the data with invalid values:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "aa698e86", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.array([0, 1., 2.2, 3.5, 5.]) + 2025.0\n", + "\n", + "x = np.array([\n", + " [0., 0.5, 2.1, 3.2, 8.0], # Increasing 5 Epochs\n", + " [10.0, 8.9, 9.2, 7.4, 7.0], # Decreasing 5 Epochs\n", + " [2.5, np.nan, 5.2, np.nan, 5.0], # 3 Epochs\n", + " [np.nan, 6.2, np.nan, np.nan, 9.2], # 2 Epochs\n", + " [np.nan, 2.0, np.nan, np.nan, np.nan], # 1 Epoch\n", + " [np.nan, np.nan, np.nan, np.nan, np.nan] # All NaNs\n", + "])\n", + "\n", + "y = np.array([\n", + " [10.2, 8.5, 9.1, 10.5, 13.0], # Increasing 5 Epochs\n", + " [8.0, 9.9, 8.2, 7.4, 7.0], # Decreasing 5 Epochs\n", + " [5.2, np.nan, 4.7, np.nan, 6.0], # 3 Epochs\n", + " [np.nan, 1.2, np.nan, np.nan, 3.2], # 2 Epochs\n", + " [np.nan, 2.0, np.nan, np.nan, np.nan], # 1 Epoch\n", + " [np.nan, np.nan, np.nan, np.nan, np.nan] # All NaNs\n", + "])\n", + "\n", + "xe = np.array([\n", + " [0.2, 0.5, 0.3, 0.4, 0.6],\n", + " [0.5, 0.2, 0.7, 0.3, 0.2],\n", + " [0.5, np.nan, 0.6, np.nan, 0.3],\n", + " [np.nan, 0.6, np.nan, np.nan, 0.3],\n", + " [np.nan, 0.4, np.nan, np.nan, np.nan],\n", + " [np.nan, np.nan, np.nan, np.nan, np.nan]\n", + "])\n", + "\n", + "ye = np.array([\n", + " [0.3, 0.2, 0.5, 0.2, 0.4],\n", + " [0.2, 0.5, 0.6, 0.4, 0.2],\n", + " [0.7, np.nan, 0.5, np.nan, 0.2],\n", + " [np.nan, 0.4, np.nan, np.nan, 0.5],\n", + " [np.nan, 0.5, np.nan, np.nan, np.nan],\n", + " [np.nan, np.nan, np.nan, np.nan, np.nan]\n", + "])\n", + "\n", + "x = np.ma.masked_invalid(x)\n", + "y = np.ma.masked_invalid(y)\n", + "xe = np.ma.masked_invalid(xe)\n", + "ye = np.ma.masked_invalid(ye)\n", + "mask = np.ma.getmaskarray(x) | np.ma.getmaskarray(y) | np.ma.getmaskarray(xe) | np.ma.getmaskarray(ye)\n", + "\n", + "tab = StarTable({\n", + " 'x': x,\n", + " 'y': y,\n", + " 'xe': xe,\n", + " 'ye': ye\n", + "})\n", + "tab.meta['list_times'] = t" + ] + }, + { + "cell_type": "markdown", + "id": "9201897f", + "metadata": {}, + "source": [ + "There are a 2 ways to specify the desired motion models:\n", + "1. Let MotionModel automatically determine which motion model to use among the given `motion_models` list based on the number of valid observations. MotionModel will choose the motion model that has enough observations, i.e. $n_\\text{fit} \\geq n_\\text{params}$. \n", + "2. Specify a motion model for each star in the `motion_model_input` column. In case there is not enough observations, MotionModel will \"downgrade\" to a model with less parameters until $n_\\text{fit} \\geq n_\\text{params}$ among all the unique motion models specified in the column.\n", + "\n", + "Note that when `absolute_sigma=False` and `n_fit == n_params`, we don't have enough degree of freedom to rescale the uncertainties, so the uncertainties will be set to infinity -- the same behavior as `scipy.optimize.curve_fit`.
By default `motion_models = [Empty, Fixed, Linear]`. `Empty` and `Fixed` will always be added in the list to handle 0 and 1 point cases. See examples below for details. Let's start with the most basic usage." + ] + }, + { + "cell_type": "markdown", + "id": "e58f429d", + "metadata": {}, + "source": [ + "## 2.1. Example: Default Fitting" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "02642d3b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Empty: 0%| | 0/1 [00:00StarTable length=6\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
n_fitn_requiredmotion_model_used
int64int64str20
52Linear
52Linear
32Linear
22Linear
12Fixed
02Empty
" + ], + "text/plain": [ + "\n", + "n_fit n_required motion_model_used\n", + "int64 int64 str20 \n", + "----- ---------- -----------------\n", + " 5 2 Linear\n", + " 5 2 Linear\n", + " 3 2 Linear\n", + " 2 2 Linear\n", + " 1 2 Fixed\n", + " 0 2 Empty" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tab['n_required'] = 2\n", + "tab[['n_fit', 'n_required', 'motion_model_used']]" + ] + }, + { + "cell_type": "markdown", + "id": "20470c6e", + "metadata": {}, + "source": [ + "Next, let's try `absolute_sigma=False`. As mentioned above, we don't have enough degree of freedom to rescale the uncertainties for the forth star. In this case, the parameter uncertainties will be set to infinity, which is the same behavior as `scipy.optimize.curve_fit`. The same `OptmizieWarning` as in `scipy` will be raised." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "26b11593", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Empty: 0%| | 0/1 [00:00\n", + "0.2398025689409276\n", + "0.07197698078673948\n", + "0.26723109004421475\n", + "inf\n", + "inf\n", + "inf\n", + "" + ], + "text/plain": [ + "\n", + " 0.2398025689409276\n", + "0.07197698078673948\n", + "0.26723109004421475\n", + " inf\n", + " inf\n", + " inf" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tab['vx_err']" + ] + }, + { + "cell_type": "markdown", + "id": "241ab6d6", + "metadata": {}, + "source": [ + "## 2.2. Example: Specify Motion Models" + ] + }, + { + "cell_type": "markdown", + "id": "220922c5", + "metadata": {}, + "source": [ + "Alternatively, one can specify a list of motion models to use, and the function will also automatically determine which model to use for each star depending on the valid observed epochs. In the following example, we specify `Acceleration` model, but **the function will always implicitly add `Empty` and `Fixed`** to handle the 0 or 1 epoch stars." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a596c8e8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Acceleration: 0%| | 0/3 [00:00StarTable length=6\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
n_fitmotion_model_used
int64str20
5Acceleration
5Acceleration
3Acceleration
2Fixed
1Fixed
0Empty
" + ], + "text/plain": [ + "\n", + "n_fit motion_model_used\n", + "int64 str20 \n", + "----- -----------------\n", + " 5 Acceleration\n", + " 5 Acceleration\n", + " 3 Acceleration\n", + " 2 Fixed\n", + " 1 Fixed\n", + " 0 Empty" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tab[['n_fit', 'motion_model_used']]" + ] + }, + { + "cell_type": "markdown", + "id": "188290a9", + "metadata": {}, + "source": [ + "## 2.3. Example: Specify the `motion_model_input` Column" + ] + }, + { + "cell_type": "markdown", + "id": "99624463", + "metadata": {}, + "source": [ + "One can also specify a motion model for each star as a column in the star table. However, the function will \"downgrade\" the model to one with fewer parameters until $n_\\text{fit} \\geq n_\\text{params}$:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "04db5f9e", + "metadata": {}, + "outputs": [], + "source": [ + "ra = np.zeros(len(x))\n", + "dec = np.zeros(len(x))\n", + "pa = np.zeros(len(x))\n", + "\n", + "motion_model_input = [\n", + " 'Acceleration', # Will use Acceleration\n", + " 'Parallax', # Will use Parallax\n", + " 'Linear', # Will use Linear\n", + " 'Acceleration', # Will use Linear, as n_fit = 2 < 3\n", + " 'Linear', # Will use Fixed, as n_fit = 1 < 2\n", + " 'Fixed' # Will use Empty, as n_fit = 0 < 1\n", + "]\n", + "tab = StarTable({\n", + " 'x': x,\n", + " 'y': y,\n", + " 'xe': xe,\n", + " 'ye': ye,\n", + " 'ra': ra,\n", + " 'dec': dec,\n", + " 'pa': pa,\n", + " 'motion_model_input': motion_model_input\n", + "})\n", + "tab.meta['list_times'] = t" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "2b61fbcf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Acceleration: 0%| | 0/1 [00:00StarTable length=6\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
n_fitn_requiredmotion_model_inputmotion_model_used
int64int64str12str12
53AccelerationAcceleration
53ParallaxParallax
32LinearLinear
23AccelerationLinear
12LinearFixed
01FixedEmpty
" + ], + "text/plain": [ + "\n", + "n_fit n_required motion_model_input motion_model_used\n", + "int64 int64 str12 str12 \n", + "----- ---------- ------------------ -----------------\n", + " 5 3 Acceleration Acceleration\n", + " 5 3 Parallax Parallax\n", + " 3 2 Linear Linear\n", + " 2 3 Acceleration Linear\n", + " 1 2 Linear Fixed\n", + " 0 1 Fixed Empty" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_mm_map = motion_model.motion_model_map()\n", + "tab['n_required'] = np.array([all_mm_map[mm].n_params for mm in tab['motion_model_input']], dtype=int)\n", + "tab[['n_fit', 'n_required', 'motion_model_input', 'motion_model_used']]" + ] + }, + { + "cell_type": "markdown", + "id": "d4f96fcb", + "metadata": {}, + "source": [ + "## 2.4. Example: Infer Positions" + ] + }, + { + "cell_type": "markdown", + "id": "c660ec98", + "metadata": {}, + "source": [ + "Continuing from the previous example: Once we fit the motion models and the parameters are added into the table, we can infer the positions at arbitrary times with `StarTable.infer_positions`" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "095be28f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 20 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 40 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 40 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 20 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"taiutc\" yielded 20 of \"dubious year (Note 4)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n" + ] + } + ], + "source": [ + "x_model, y_model, xe_model, ye_model = tab.infer_positions(t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "a4df5458", + "metadata": {}, + "source": [ + "As in `MotionModel.model`, `StarTable.infer_positions` is also vectorized and returns positions and uncertainties in shapes of $(N_\\text{stars}, N_\\text{times})$" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "2f7e8b7a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6, 100)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_model.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "7aab0868", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize_fit(t, x, y, xe, ye, x_model, y_model, xe_model, ye_model, mm.name, t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "12bb0136", + "metadata": {}, + "source": [ + "## 2.5. Speed Test" + ] + }, + { + "cell_type": "markdown", + "id": "43fd87c5", + "metadata": {}, + "source": [ + "Speed test for the most commonly used Linear model. As the `use_scipy=False` option for the Linear model uses the [matrix multiplication solution](https://en.wikipedia.org/wiki/Weighted_least_squares#Solution), it is extremely fast at fewer epochs: " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "de576a47", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 10 epochs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:01<00:00, 6350.75it/s]\n", + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:00<00:00, 25802.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 31 epochs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:01<00:00, 6184.77it/s]\n", + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:00<00:00, 23908.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 100 epochs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:01<00:00, 6347.19it/s]\n", + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:00<00:00, 14309.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 316 epochs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:01<00:00, 5023.37it/s]\n", + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:03<00:00, 3288.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 1000 epochs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:02<00:00, 4314.91it/s]\n", + "Fitting motion model Linear: 100%|██████████| 10000/10000 [01:19<00:00, 125.47it/s]\n" + ] + } + ], + "source": [ + "import time\n", + "N = 10000\n", + "dims = np.logspace(1, 3, 5, dtype=int)\n", + "rng = np.random.default_rng(42)\n", + "\n", + "scipy_times = []\n", + "analytic_times = []\n", + "\n", + "for dim in dims:\n", + " print(f'Fitting {dim} epochs...')\n", + " t = np.linspace(2025.0, 2030.0, dim)\n", + " x = rng.random((N, dim))\n", + " y = rng.random((N, dim))\n", + " xe = rng.uniform(0, 0.2, size=(N, dim))\n", + " ye = rng.uniform(0, 0.2, size=(N, dim))\n", + " tab = StarTable({\n", + " 'x': x,\n", + " 'y': y,\n", + " 'xe': xe,\n", + " 'ye': ye\n", + " })\n", + " tab.meta['list_times'] = t\n", + " \n", + " start = time.time()\n", + " tab.fit_motion_model(use_scipy=True)\n", + " end = time.time()\n", + " scipy_times.append(end - start)\n", + " \n", + " start = time.time()\n", + " tab.fit_motion_model(use_scipy=False)\n", + " end = time.time()\n", + " analytic_times.append(end - start)\n", + "\n", + "scipy_times = np.array(scipy_times)\n", + "analytic_times = np.array(analytic_times)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3d2a8457", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "280" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Collect memory garbage data\n", + "import gc\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "id": "06442faf", + "metadata": {}, + "source": [ + "Let's visualize the performance:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "03d53769", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(dims, N / scipy_times, marker='o', label='Scipy Curve Fit')\n", + "ax.plot(dims, N / analytic_times, marker='o', color='C3', label='Motion Model Analytic')\n", + "ax.set_xscale('log')\n", + "ax.set_xlabel('Number of Epochs')\n", + "ax.set_ylabel('Stars Fit per Second')\n", + "ax.set_title(f'Motion Model Fitting Performance of {N} Stars')\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ea672ab4", + "metadata": {}, + "source": [ + "It can be seen that for epochs < 200, the analytic solution is faster than scipy, and vice versa for > 300 epochs." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "main", + "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": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/flystar/align.py b/flystar/align.py index 994a3b1..2820ae5 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1,10 +1,7 @@ import numpy as np -from flystar import match -from flystar import transforms -from flystar import plots -from flystar.starlists import StarList -from flystar.startables import StarTable -from flystar import motion_model +from . import match, transforms, plots, motion_model +from .starlists import StarList +from .startables import StarTable from astropy.table import Table, Column, vstack import datetime import copy @@ -21,12 +18,12 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, outlier_tol=[None, None], trans_args=[{'order': 2}, {'order': 2}], init_order=1, - mag_trans=True, mag_lim=None, trans_weights=None, vel_weights='var', + mag_trans=True, mag_lim=None, trans_weighting=None, vel_weighting='var', trans_input=None, trans_class=transforms.PolyTransform, calc_trans_inverse=False, init_guess_mode='miracle', iter_callback=None, - default_motion_model='Fixed', - motion_model_dict = {}, + motion_models=['Empty', 'Fixed'], + fixed_params_dict=None, use_scipy=True, absolute_sigma=False, save_path=None, @@ -89,13 +86,13 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, separately for each list and each iteration, you need to pass in a 2D array that has shape (N_lists, 2). - trans_weights : str + trans_weighting : str Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want to weight by the positional uncertainties (variances) in the individual starlists, or also with the uncertainties in the reference frame itself. Note weighting only works when there are positional uncertainties availabe. Other options include 'both,std', 'list,std', 'list,var'. - vel_weights : str + vel_weighting : str Either 'var' (def) or 'std', depending on whether you want to weight the motion model fits by the variance or standard deviation of the position data @@ -130,11 +127,11 @@ def = None. If not None, then this should contain an array or list of transform A function to call (that accepts a StarTable object and an iteration number) at the end of every iteration. This can be used for plotting or printing state. - default_motion_model : string - Name of motion model to use for new or unassigned stars + motion_models : list of MotionModel or str, optional + Motion models or their names to use for new or unassigned stars - motion_model_dict : None or dict - Dict of motion model name keys (strings) and corresponding MotionModel object values + fixed_params_dict : None or dict + Dictionary of motion model fixed parameters use_scipy : bool, optional If True, use scipy.optimize.curve_fit for velocity fitting. If False, use linear @@ -192,20 +189,34 @@ def = None. If not None, then this should contain an array or list of transform self.init_order = init_order self.mag_trans = mag_trans self.mag_lim = mag_lim - self.trans_weights = trans_weights - self.vel_weights = vel_weights + self.trans_weighting = trans_weighting + self.vel_weighting = vel_weighting self.trans_input = trans_input self.trans_class = trans_class self.calc_trans_inverse = calc_trans_inverse - self.motion_model_dict = motion_model_dict self.use_scipy = use_scipy self.absolute_sigma = absolute_sigma - self.default_motion_model = default_motion_model + self.fixed_params_dict = fixed_params_dict self.init_guess_mode = init_guess_mode self.iter_callback = iter_callback self.save_path = save_path self.verbose = verbose + all_mm_map = motion_model.motion_model_map() + if all(isinstance(mm, str) for mm in motion_models): + mm_names = motion_models + motion_models = [all_mm_map[mm] for mm in motion_models] + else: + mm_names = [mm.name for mm in motion_models] + if 'Empty' not in mm_names: + motion_models.append(all_mm_map['Empty']) + if 'Fixed' not in mm_names: + motion_models.append(all_mm_map['Fixed']) + + # Sort by increasing n_params + motion_models = sorted(motion_models, key=lambda mm: mm.n_params) + self.motion_models = motion_models + # For backwards compatibility. if self.verbose is True: self.verbose = 9 @@ -235,23 +246,23 @@ def = None. If not None, then this should contain an array or list of transform self.setup_trans_info() # Make sure the motion models are ready - self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, - StarTable(), self.default_motion_model) + # self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, + # StarTable(), self.default_motion_model) return def fix_iterable_conditions(self): if not np.iterable(self.dr_tol): self.dr_tol = np.repeat(self.dr_tol, self.iters) - assert len(self.dr_tol) == self.iters + assert len(self.dr_tol) == self.iters, f'len(dr_tol)={len(self.dr_tol)} != iters={self.iters}' if not np.iterable(self.dm_tol): self.dm_tol = np.repeat(self.dm_tol, self.iters) - assert len(self.dm_tol) == self.iters + assert len(self.dm_tol) == self.iters, f'len(dm_tol)={len(self.dm_tol)} != iters={self.iters}' if not np.iterable(self.outlier_tol): self.outlier_tol = np.repeat(self.outlier_tol, self.iters) - assert len(self.outlier_tol) == self.iters + assert len(self.outlier_tol) == self.iters, f'len(outlier_tol)={len(self.outlier_tol)} != iters={self.iters}' if self.mag_lim is None: self.mag_lim = np.repeat([[None, None]], len(self.star_lists), axis=0) @@ -290,7 +301,7 @@ def fit(self): # x_orig, y_orig, m_orig, (opt. errors) -- the transformed errors for the lists: 2D # w, w_orig (optiona) -- the input and output weights of stars in transform: 2D ########## - self.ref_table = self.setup_ref_table_from_starlist(self.star_lists[self.ref_index],motion_model_used='Fixed') + self.ref_table = self.setup_ref_table_from_starlist(self.star_lists[self.ref_index]) # Save the reference index to the meta data on the reference list. self.ref_table.meta['ref_list'] = self.ref_index @@ -369,6 +380,10 @@ def fit(self): if self.iter_callback != None: self.iter_callback(self.ref_table, nn) + # Add times into ref_table meta data + complete_times = np.array([np.unique(col[~np.isnan(col)])[0] for col in self.ref_table['t'].T]) + self.ref_table.meta['LIST_TIMES'] = complete_times + if self.save_path: with open(self.save_path, 'wb') as file: pickle.dump(self, file) @@ -408,12 +423,15 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar if trans is None: # Only use "use_in_trans" reference stars, even for initial guessing. keepers = np.where(ref_list['use_in_trans'] == True)[0] - - trans = trans_initial_guess(ref_list[keepers], star_list_orig_trim, self.trans_args[0], self.motion_model_dict, - mode=self.init_guess_mode, - order=self.init_order, - verbose=self.verbose, - mag_trans=self.mag_trans) + trans = trans_initial_guess( + ref_list[keepers], + star_list_orig_trim, + self.trans_args[0], + mode=self.init_guess_mode, + order=self.init_order, + verbose=self.verbose, + mag_trans=self.mag_trans + ) if self.mag_trans: star_list_T.transform_xym(trans) # trimmed, transformed @@ -506,7 +524,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar dy=(star_t['y'] - star_r['y']) * 1e3, dm=(star_t['m'] - star_r['m']), xo=star_s['x'], yo=star_s['y'], mo=star_s['m'])) - + idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], ref_list['x'], ref_list['y'], ref_list['m'], dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose) @@ -517,7 +535,8 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ## Make plot, if desired plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], - fileName='{0}'.format(star_list_T['t'][0])) + save_path=f"{self.save_path}/Transformed_Positions_{star_list_T['t'][0]}.png" if self.save_path else None, + show_plot=False) ### Update the observed (but transformed) values in the reference table. self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) @@ -583,12 +602,12 @@ def setup_trans_info(self): # Add inverse trans list, if desired if self.calc_trans_inverse: - trans_list_inverse = [None for ii in range(N_lists)] + trans_list_inverse = [None] * N_lists self.trans_list_inverse = trans_list_inverse return - def setup_ref_table_from_starlist(self, star_list, motion_model_used=None): + def setup_ref_table_from_starlist(self, star_list): """ Start with the reference list.... this will change and grow over time, so make a copy that we will keep updating. @@ -596,7 +615,9 @@ def setup_ref_table_from_starlist(self, star_list, motion_model_used=None): array in the original reference star list. """ col_arrays = {} - motion_model_col_names = motion_model.get_all_motion_model_param_names(with_errors=True, with_fixed=True) + ['m0','m0_err','use_in_trans', 'motion_model_input', 'motion_model_used'] + motion_model_col_names = motion_model.motion_model_param_names(self.motion_models, with_errors=True, with_fixed=True) + ['m0','m0_err','use_in_trans', 'motion_model_input', 'motion_model_used'] + if 't0' not in motion_model_col_names: + motion_model_col_names.insert(0, 't0') for col_name in star_list.colnames: if col_name == 'name': # The "name" column will be 1D; but we will also add a "name_in_list" column. @@ -638,7 +659,7 @@ def setup_ref_table_from_starlist(self, star_list, motion_model_used=None): if not new_cols_arr[ii] in ref_cols: # Some munging to convert data shape from (N,1) to (N,), # since these are all 1D cols - vals = np.transpose(np.array(ref_table[orig_cols_arr[ii]]))[0] + vals = np.array(ref_table[orig_cols_arr[ii]]).flatten() # Now add to ref_table new_col = Column(vals, name=new_cols_arr[ii]) @@ -695,14 +716,17 @@ def setup_ref_table_from_starlist(self, star_list, motion_model_used=None): for col_name in ref_table.colnames: if len(ref_table[col_name].data.shape) == 2: # Find the 2D columns ref_table._set_invalid_list_values(col_name, -1) - + if 'motion_model_input' not in ref_table.colnames: - ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_input')) + ref_table.add_column(Column(np.repeat(self.motion_models[-1].name, len(ref_table)), name='motion_model_input')) if 'motion_model_used' not in ref_table.colnames: - if motion_model_used is None: - ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_used')) - else: - ref_table.add_column(Column(np.repeat(motion_model_used, len(ref_table)), name='motion_model_used')) + # Order self.motion_models by decreasing n_params + sorted_mms = sorted(self.motion_models, key=lambda mm: mm.n_params, reverse=True) + # Save the most complex motion model that can infer the positions with the existing columns. + for mm in sorted_mms: + if all([_ in ref_table.colnames for _ in mm.fit_param_names]) and all([_ in ref_table.colnames for _ in mm.fixed_param_names]): + ref_table.add_column(Column(np.repeat(mm.name, len(ref_table)), name='motion_model_used')) + break return ref_table @@ -802,35 +826,40 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li if ((self.ref_table['x'].shape[1] != len(self.star_lists)) and (ii != self.ref_index) and (ii >= self.ref_table['x'].shape[1])): - + self.ref_table.add_starlist() - + copy_over_values(self.ref_table, star_list, star_list_T, ii, idx_ref, idx_lis) self.ref_table['used_in_trans'][idx_ref_in_trans, ii] = True ### Add the unmatched stars and grow the size of the reference table. - self.ref_table, idx_lis_new, idx_ref_new = add_rows_for_new_stars(self.ref_table, star_list, idx_lis, - default_motion_model=self.default_motion_model) + self.ref_table, idx_lis_new, idx_ref_new = add_rows_for_new_stars( + self.ref_table, + star_list, + idx_lis, + motion_model=self.motion_models[-1].name + ) + if len(idx_ref_new) > 0: if self.verbose > 0: print(' Adding {0:d} new stars to the reference table.'.format(len(idx_ref_new))) - + copy_over_values(self.ref_table, star_list, star_list_T, ii, idx_ref_new, idx_lis_new) # Copy the single-epoch values to the aggregate (only for new stars). self.ref_table['x0'][idx_ref_new] = star_list_T['x'][idx_lis_new] self.ref_table['y0'][idx_ref_new] = star_list_T['y'][idx_lis_new] self.ref_table['m0'][idx_ref_new] = star_list_T['m'][idx_lis_new] - + self.ref_table['name'] = update_old_and_new_names(self.ref_table, ii, idx_ref_new) if self.use_ref_new == True: self.ref_table['use_in_trans'][idx_ref_new] = True else: self.ref_table['use_in_trans'][idx_ref_new] = False - + return - + def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): """ Average positions or fit velocities. @@ -843,39 +872,41 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): """ # Keep track of the original reference values. # In certain cases, we will NOT update these. - if keep_orig is not None: + if (keep_orig is not None) and (len(keep_orig) > 0): vals_orig = {} vals_orig['m0'] = self.ref_table['m0'][keep_orig] vals_orig['m0_err'] = self.ref_table['m0_err'][keep_orig] - motion_model_class_names = self.ref_table['motion_model_input'].tolist() + motion_model_class_names = [] + if 'motion_model_input' in self.ref_table.keys(): + motion_model_class_names += self.ref_table['motion_model_input'].tolist() if 'motion_model_used' in self.ref_table.keys(): motion_model_class_names += self.ref_table['motion_model_used'][keep_orig].tolist() vals_orig['motion_model_used'] = self.ref_table['motion_model_used'][keep_orig] - motion_model_col_names = motion_model.get_list_motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) + motion_model_col_names = motion_model.motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) for mm in motion_model_col_names: if mm in self.ref_table.keys(): vals_orig[mm] = self.ref_table[mm][keep_orig] - fit_star_idxs = [idx for idx in range(len(self.ref_table)) if idx not in keep_orig] + fit_star_idxs = np.array([idx for idx in range(len(self.ref_table)) if idx not in keep_orig], dtype=int) else: fit_star_idxs = None - #pdb.set_trace() + # Figure out whether motion fits are necessary - all_fixed = np.all(self.ref_table['motion_model_input']=='Fixed') - if all_fixed: + if ('motion_model_input' in self.ref_table.keys()) and np.all(self.ref_table['motion_model_input']=='Fixed'): weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) weighted_m = ('me' in self.ref_table.colnames) self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) + else: - # Combine positions with a velocity fit. - self.ref_table.fit_velocities(bootstrap=n_boot, - verbose=self.verbose, - show_progress=(self.verbose>0), - default_motion_model=self.default_motion_model, - select_stars=fit_star_idxs, - motion_model_dict=self.motion_model_dict, - weighting=self.vel_weights, - use_scipy=self.use_scipy, - absolute_sigma=self.absolute_sigma) + self.ref_table.fit_motion_model( + motion_models=self.motion_models, + fixed_params_dict=self.fixed_params_dict, + weighting=self.vel_weighting, + use_scipy=self.use_scipy, + absolute_sigma=self.absolute_sigma, + select_stars=fit_star_idxs, + bootstrap=n_boot, + verbose=self.verbose + ) # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: @@ -883,8 +914,9 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): else: weights_col = 'me' self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) + # Replace the originals if we are supposed to keep them fixed. - if keep_orig is not None: + if (keep_orig is not None) and (len(keep_orig) > 0): for val in vals_orig.keys(): self.ref_table[val][keep_orig] = vals_orig[val] @@ -905,18 +937,18 @@ def get_weights_for_lists(self, ref_list, star_list): var_xlis = 0.0 var_ylis = 0.0 - if self.trans_weights != None: - if self.trans_weights == 'both,var': + if self.trans_weighting != None: + if self.trans_weighting == 'both,var': weight = 1.0 / (var_xref + var_xlis + var_yref + var_ylis) - if self.trans_weights == 'both,std': + if self.trans_weighting == 'both,std': weight = 1.0 / np.sqrt(var_xref + var_xlis + var_yref + var_ylis) - if self.trans_weights == 'ref,var': + if self.trans_weighting == 'ref,var': weight = 1.0 / (var_xref + var_yref) - if self.trans_weights == 'ref,std': + if self.trans_weighting == 'ref,std': weight = 1.0 / np.sqrt(var_xref + var_yref) - if self.trans_weights == 'list,var': + if self.trans_weighting == 'list,var': weight = 1.0 / (var_xlis + var_ylis) - if self.trans_weights == 'list,std': + if self.trans_weighting == 'list,std': weight = 1.0 / np.sqrt(var_xlis, var_ylis) else: weight = None @@ -957,14 +989,14 @@ def match_lists(self, dr_tol, dm_tol): star_list_T.transform_xym(self.trans_list[ii]) else: star_list_T.transform_xy(self.trans_list[ii]) - - xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table, self.motion_model_dict) + + xref, yref = infer_positions(star_list_T['t'][0], self.ref_table) mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], xref, yref, mref, dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose) - + if self.verbose > 0: fmt = 'Matched {0:5d} out of {1:5d} stars in list {2:2d} [dr = {3:7.4f} +/- {4:6.4f}, dm = {5:5.2f} +/- {6:4.2f}' print(fmt.format(len(idx_lis), len(star_list_T), ii, dr.mean(), dr.std(), dm.mean(), dm.std())) @@ -992,7 +1024,7 @@ def get_ref_list_from_table(self, epoch): name = self.ref_table['name'] if ('motion_model_used' in self.ref_table.colnames): - x,y,xe,ye = self.ref_table.get_star_positions_at_time(epoch, self.motion_model_dict, allow_alt_models=True) + x, y, xe, ye = self.ref_table.infer_positions(epoch) else: # No velocities... just used average positions. x = self.ref_table['x0'] @@ -1137,22 +1169,29 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot y2_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) m_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) m2_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) - + # Set up motion model parameters - motion_model_list = ['Fixed', self.default_motion_model] if 'motion_model_used' in ref_table.keys(): - motion_model_list += ref_table['motion_model_used'].tolist() + motion_model_list = np.unique(ref_table['motion_model_used']).tolist() elif 'motion_model_input' in ref_table.keys(): - motion_model_list += ref_table['motion_model_input'].tolist() - motion_col_list = motion_model.get_list_motion_model_param_names(np.unique(motion_model_list).tolist(), with_errors=False, with_fixed=False) + motion_model_list = np.unique(ref_table['motion_model_input']).tolist() + + if 'Empty' not in motion_model_list: + motion_model_list.append('Empty') + if 'Fixed' not in motion_model_list: + motion_model_list.append('Fixed') + + motion_col_list = motion_model.motion_model_param_names(motion_model_list, with_errors=False, with_fixed=False) if calc_vel_in_bootstrap: motion_boot_sum = {} motion2_boot_sum = {} for col in motion_col_list: motion_boot_sum[col] = np.zeros((len(ref_table['x']))) motion2_boot_sum[col] = np.zeros((len(ref_table['x']))) - motion_boot_min_epochs = np.max([self.motion_model_dict[mod].n_pts_req - for mod in np.unique(motion_model_list)]) + + all_mm_map = motion_model.motion_model_map() + motion_model_list = [all_mm_map[mm_name] for mm_name in motion_model_list] + motion_boot_min_epochs = np.max([mm.n_params for mm in motion_model_list]) ### IF MEMORY PROBLEMS HERE: ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY @@ -1210,7 +1249,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Calculate weights based on weights keyword. If weights desired, will need to # make starlist objects for this - if self.trans_weights != None: + if self.trans_weighting != None: # In order for weights calculation to work, we need to apply a transformation # to the star_list_T so it is in the same units as ref_boot. So, we'll apply # the final transformation for the epoch to get close enough for the @@ -1232,7 +1271,6 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot m=starlist_boot['m'], mref=ref_boot['m'], weights=weight, mag_trans=self.mag_trans) #print(jj) - #pdb.set_trace() # Apply transformation to *all* orig positions in this epoch. Need to make a new # FLYSTAR starlist object with the original positions for this. We don't @@ -1291,10 +1329,16 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Now, do proper motion calculation, making sure to fix t0 to the # orig value (so we can get a reasonable error on x0, y0) - star_table.fit_velocities( - fixed_t0=t0_arr, - default_motion_model=self.default_motion_model, - motion_model_dict=self.motion_model_dict, + if self.fixed_params_dict is None: + fixed_params_dict = {'t0': t0_arr} + elif 't0' not in self.fixed_params_dict.keys(): + fixed_params_dict = self.fixed_params_dict.copy() + fixed_params_dict['t0'] = t0_arr + + star_table.fit_motion_model( + motion_models=self.motion_models, + fixed_params_dict=fixed_params_dict, + weighting=self.vel_weighting, use_scipy=self.use_scipy, absolute_sigma=self.absolute_sigma ) @@ -1306,7 +1350,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Quick check to make sure bootstrap calc was valid: output t0 should be # same as input t0_arr, since we used fixed_t0 option - assert np.sum(abs(star_table['t0'] - t0_arr) == 0) + np.testing.assert_array_equal(star_table['t0'], t0_arr) #t3 = time.time() #print('=================================================') @@ -1345,8 +1389,20 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot col[idx_good] = data_dict[ff] self.ref_table.add_column(col) - # Calculate chi^2 with bootstrap positional errors - x_pred, y_pred, _, _ = self.ref_table.get_star_positions_at_time(t_arr, self.motion_model_dict, allow_alt_models=True) + # # Calculate chi^2 with bootstrap positional errors + # # Determine which motion model to use: + # motion_model_list = sorted(motion_model_list, key=lambda mm: mm.n_params) + # mm_n_params = np.sort([mm.n_params for mm in motion_model_list]) + + # required_params = [all_mm_map[mm_name].n_params for mm_name in self.ref_table['motion_model_input']] + # mm_digitized = np.digitize( + # x=np.minimum(np.array(self.ref_table['n_detect']), required_params), + # bins=mm_n_params + # ) - 1 + # self.ref_table['motion_model_used'] = np.array([motion_model_list[d].name for d in mm_digitized], dtype='U20') + + + x_pred, y_pred, _, _ = self.ref_table.infer_positions(t_arr) xe_comb = np.hypot(self.ref_table['xe'], self.ref_table['xe_boot']) ye_comb = np.hypot(self.ref_table['ye'], self.ref_table['ye_boot']) data_dict['chi2_x_boot'] = np.nansum((self.ref_table['x']-x_pred)**2/(xe_comb)**2,axis=1) @@ -1368,7 +1424,6 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot col[idx_good] = data_dict[ff] self.ref_table.add_column(col) - #pdb.set_trace() print('===============================') print('Done with bootstrap') @@ -1384,7 +1439,7 @@ def __init__(self, ref_list, list_of_starlists, iters=2, trans_args=[{'order': 2}, {'order': 2}], init_order=1, mag_trans=True, mag_lim=None, ref_mag_lim=None, - trans_weights=None, vel_weights='var', + trans_weighting=None, vel_weighting='var', trans_input=None, trans_class=transforms.PolyTransform, calc_trans_inverse=False, @@ -1392,8 +1447,8 @@ def __init__(self, ref_list, list_of_starlists, iters=2, update_ref_orig=False, init_guess_mode='miracle', iter_callback=None, - default_motion_model='Fixed', - motion_model_dict={}, + motion_models=['Empty', 'Fixed'], + fixed_params_dict=None, use_scipy=True, absolute_sigma=False, save_path=None, @@ -1452,19 +1507,19 @@ def __init__(self, ref_list, list_of_starlists, iters=2, If different from None, it indicates the minimum and maximum magnitude on the catalogs for finding the transformations. Note, if you want specify the mag_lim separately for each list and each iteration, you need to pass in a 2D array that - has shape (N_lists, 2). + has shape (N_lists, N_iters). ref_mag_lim : array If different from None, it indicates the minimum and maximum magnitude on the reference catalog for finding the transformations. - trans_weights : str + trans_weighting : str Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want to weight by the positional uncertainties (variances) in the individual starlists, or also with the uncertainties in the reference frame itself. Note weighting only works when there are positional uncertainties availabe. Other options include 'both,std', 'list,std', 'list,var'. - vel_weights : str + vel_weighting : str Either 'var' (def) or 'std', depending on whether you want to weight the motion model fits by the variance or standard deviation of the position data @@ -1521,12 +1576,12 @@ def = None. If not None, then this should contain an array or list of transform iter_callback : None or function A function to call (that accepts a StarTable object and an iteration number) at the end of every iteration. This can be used for plotting or printing state. - - default_motion_model : string - Name of motion model to use for new or unassigned stars - - motion_model_dict : None or dict - Dict of motion model name keys (strings) and corresponding MotionModel object values + + motion_models : list of str or MotionModel objects + List of motion model names (strings) or MotionModel objects to use + + fixed_params_dict : None or dict + Dictionary of fixed parameters for motion models use_scipy : bool, optional If True, use scipy.optimize.curve_fit for velocity fitting. If False, use linear algebra fitting, by default True. @@ -1573,13 +1628,13 @@ def = None. If not None, then this should contain an array or list of transform outlier_tol=outlier_tol, trans_args=trans_args, init_order=init_order, mag_trans=mag_trans, mag_lim=mag_lim, - trans_weights=trans_weights, vel_weights=vel_weights, + trans_weighting=trans_weighting, vel_weighting=vel_weighting, trans_input=trans_input, trans_class=trans_class, calc_trans_inverse=calc_trans_inverse, - default_motion_model = default_motion_model, init_guess_mode=init_guess_mode, iter_callback=iter_callback, - motion_model_dict=motion_model_dict, + motion_models=motion_models, + fixed_params_dict=fixed_params_dict, verbose=verbose, use_scipy=use_scipy, absolute_sigma=absolute_sigma, save_path=save_path) @@ -1601,10 +1656,10 @@ def = None. If not None, then this should contain an array or list of transform self.ref_list['me'] = self.ref_list['m0_err'] if ('t' not in self.ref_list.colnames) and ('t0' in self.ref_list.colnames): self.ref_list['t'] = self.ref_list['t0'] - + # Make sure the motion models are ready - self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, - self.ref_list, self.default_motion_model) + # self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, + # self.ref_list, self.default_motion_model) return @@ -1641,13 +1696,13 @@ def fit(self): logger(_log, ' mag_trans = ' + str(self.mag_trans), self.verbose) logger(_log, ' mag_lim = ' + str(self.mag_lim), self.verbose) logger(_log, ' ref_mag_lim = ' + str(self.ref_mag_lim), self.verbose) - logger(_log, ' trans_weights = ' + str(self.trans_weights), self.verbose) - logger(_log, ' vel_weights = ' + str(self.vel_weights), self.verbose) + logger(_log, ' trans_weighting = ' + str(self.trans_weighting), self.verbose) + logger(_log, ' vel_weighting = ' + str(self.vel_weighting), self.verbose) logger(_log, ' trans_input = ' + str(self.trans_input), self.verbose) logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) logger(_log, ' use_ref_new = ' + str(self.use_ref_new), self.verbose) - logger(_log, ' default_motion_model = ' + str(self.default_motion_model), self.verbose) + logger(_log, ' motion_models = ' + str([mm.name for mm in self.motion_models]), self.verbose) logger(_log, ' update_ref_orig = ' + str(self.update_ref_orig), self.verbose) logger(_log, ' init_guess_mode = ' + str(self.init_guess_mode), self.verbose) logger(_log, ' iter_callback = ' + str(self.iter_callback), self.verbose) @@ -1669,7 +1724,6 @@ def fit(self): # ########## for nn in range(self.iters): - # If we are on subsequent iterations, remove matching results from the # prior iteration. This leaves aggregated (1D) columns alone. if nn > 0: @@ -1682,13 +1736,13 @@ def fit(self): print('Starting iter {0:d} with ref_table shape:'.format(nn), self.ref_table['x'].shape) print("**********") print("**********") - + # ALL the action is in here. Match and transform the stack of starlists. # This updates trans objects and the ref_table. self.match_and_transform(self.ref_mag_lim, self.dr_tol[nn], self.dm_tol[nn], self.outlier_tol[nn], self.trans_args[nn]) - + # Clean up the reference table # Find where stars are detected. self.ref_table.detections() @@ -1716,11 +1770,10 @@ def fit(self): print("**********") self.match_lists(self.dr_tol[-1], self.dm_tol[-1]) - keep_ref_orig = (self.update_ref_orig==False) - if keep_ref_orig: - keep_orig = np.where(self.ref_table['ref_orig'])[0] - else: + if self.update_ref_orig: keep_orig=None + else: + keep_orig = np.where(self.ref_table['ref_orig'])[0] self.update_ref_table_aggregates(keep_orig=keep_orig) ########## @@ -1771,7 +1824,7 @@ def get_all_epochs(t): return all_epochs -def setup_ref_table_from_starlist(star_list): +def setup_ref_table_from_starlist(star_list, motion_models): """ Start with the reference list.... this will change and grow over time, so make a copy that we will keep updating. @@ -1779,7 +1832,7 @@ def setup_ref_table_from_starlist(star_list): array in the original reference star list. """ col_arrays = {} - motion_model_col_names = motion_model.get_all_motion_model_param_names(with_errors=True) + motion_model_col_names = motion_model.motion_model_param_names(motion_models, with_errors=True) for col_name in star_list.colnames: if col_name == 'name': # The "name" column will be 1D; but we will also add a "name_in_list" column. @@ -1787,7 +1840,7 @@ def setup_ref_table_from_starlist(star_list): new_col_name = "name_in_list" else: new_col_name = col_name - + # Make every column's 2D arrays except "name" and those # columns used for the motion model. if col_name in motion_model_col_names: @@ -1823,7 +1876,7 @@ def setup_ref_table_from_starlist(star_list): if not new_cols_arr[ii] in ref_cols: # Some munging to convert data shape from (N,1) to (N,), # since these are all 1D cols - vals = np.transpose(np.array(ref_table[orig_cols_arr[ii]]))[0] + vals =np.array(ref_table[orig_cols_arr[ii]]).flatten() # Now add to ref_table new_col = Column(vals, name=new_cols_arr[ii]) @@ -1832,10 +1885,10 @@ def setup_ref_table_from_starlist(star_list): if 'use_in_trans' not in ref_table.colnames: new_col = Column(np.ones(len(ref_table), dtype=bool), name='use_in_trans') ref_table.add_column(new_col) - + # Now reset the original values to invalids... they will be filled in # at later times. Preserve content only in the columns: name, x0, y0, m0 (and 0e). - # Note that these are all the 1D columsn. + # Note that these are all the 1D columns. for col_name in ref_table.colnames: if len(ref_table[col_name].data.shape) == 2: # Find the 2D columns ref_table._set_invalid_list_values(col_name, -1) @@ -1893,7 +1946,7 @@ def reset_ref_values(ref_table): return -def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model='Fixed'): +def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model='Fixed'): """ For each star that is in star_list and NOT in idx_list, make a new row in the reference table. The values will be empty (None, NAN, etc.). @@ -1902,13 +1955,13 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model=' ---------- ref_table : StarTable The reference table that the rows will be added to. - star_list : StarList The starlist that will be used to estimate how many new stars there are. - - idx_lis : array or list + idx_list : array or list The indices of the non-new stars (those that matched already). The complement of this array will be used as the new stars. + motion_model : str + The motion model to assign to the new stars. Returns ---------- @@ -1923,9 +1976,10 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model=' last_star_idx = len(ref_table) idx_lis_orig = np.arange(len(star_list)) - idx_lis_new = np.array(list(set(idx_lis_orig) - set(idx_lis))) + idx_lis_new = np.array(list(set(idx_lis_orig) - set(idx_list))) + N_newstars = len(idx_lis_new) - if len(idx_lis_new) > 0: + if N_newstars > 0: col_arrays = {} for col_name in ref_table.colnames: @@ -1938,16 +1992,16 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model=' elif ref_table[col_name].dtype == np.dtype('bool'): new_col_empty = False elif col_name=='motion_model_input': - new_col_empty = default_motion_model + new_col_empty = motion_model elif col_name=='motion_model_used': new_col_empty = 'Fixed' else: new_col_empty = np.nan if len(ref_table[col_name].shape) == 1: - new_col_shape = len(idx_lis_new) + new_col_shape = N_newstars else: - new_col_shape = [len(idx_lis_new), ref_table[col_name].shape[1]] + new_col_shape = [N_newstars, ref_table[col_name].shape[1]] new_col_data = Column(data=np.tile(new_col_empty, new_col_shape), name=col_name, dtype=ref_table[col_name].dtype) @@ -1961,7 +2015,7 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model=' ref_table = vstack([ref_table, ref_table_new]) idx_ref_new = np.arange(last_star_idx, len(ref_table)) - + return ref_table, idx_lis_new, idx_ref_new """ @@ -2294,10 +2348,10 @@ def find_transform_new(table1_mat, table2_mat, if transInit != None: table1T_mat = table1_mat.copy() - table1T_mat = transform_by_object(table1T_mat, transInit) + table1T_mat = transform_from_object(table1T_mat, transInit) - x1e = table1T_mag['xe'] - y1e = table1T_mag['ye'] + x1e = table1T_mat['xe'] + y1e = table1T_mat['ye'] # Calculate weights as to user specification if weights == 'both': @@ -2382,8 +2436,7 @@ def write_transform(transform, starlist, reference, N_trans, deltaMag=0, restric Xcoeff = transform.px.parameters Ycoeff = transform.py.parameters else: - print(( '{0} not yet supported!'.format(transType))) - return + raise Exception(f'{trans_name} not yet supported!') # Write output _out = open(outFile, 'w') @@ -2400,7 +2453,7 @@ def write_transform(transform, starlist, reference, N_trans, deltaMag=0, restric _out.write('## N_trans: {0}\n'.format(N_trans)) _out.write('## Delta Mag: {0}\n'.format(deltaMag)) _out.write('{0:16s} {1:16s}\n'.format('# Xcoeff', 'Ycoeff')) - + # Write the coefficients such that the orders are together as defined in # documentation. This is a pain because PolyTransform output is weird. # (see astropy Polynomial2D documentation) @@ -2513,7 +2566,7 @@ def transform_from_object(starlist, transform): keys = list(starlist.keys()) # Check to see if velocities or motion_model are present in starlist. - vel = ('vx' in keys)and ~("motion_model_input" in keys) + vel = ('vx' in keys) and ("motion_model_input" not in keys) mot = ("motion_model_input" in keys) # If the only motion models used are Fixed and Linear, we can still transform velocities. if mot: @@ -2577,7 +2630,7 @@ def transform_from_object(starlist, transform): # For more complicated motion_models, # we can't easily transform them, set the values to nans and refit later. if mot: - motion_model_params = motion_model.get_all_motion_model_param_names() + motion_model_params = motion_model.motion_model_param_names() for param in motion_model_params: if param in keys: starlist_f[param] = np.nan @@ -2611,7 +2664,7 @@ def position_transform_from_object(x, y, xe, ye, transform): order = transform.order else: txt = 'Transform not yet supported by position_transform_from_object' - raise StandardError(txt) + raise Exception(txt) # How the transformation is applied depends on the type of transform. # This can be determined by the length of Xcoeff, Ycoeff @@ -2710,7 +2763,7 @@ def velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform order = transform.order else: txt = 'Transform not yet supported by velocity_transform_from_object' - raise StandardError(txt) + raise Exception(txt) # How the transformation is applied depends on the type of transform. # This can be determined by the length of Xcoeff, Ycoeff @@ -2858,7 +2911,7 @@ def check_trans_input(list_of_starlists, trans_input, mag_trans): return -def trans_initial_guess(ref_list, star_list, trans_args, motion_model_dict, mode='miracle', +def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', ignore_contains='star', verbose=True, n_req_match=3, mag_trans=True, order=1): """ @@ -2896,23 +2949,24 @@ def trans_initial_guess(ref_list, star_list, trans_args, motion_model_dict, mode # If there are velocities in the reference list, use them. # We assume velocities are in the same units as the positions. - xref, yref = get_pos_at_time(star_list['t'][0], ref_list, motion_model_dict) + xref, yref = infer_positions(star_list['t'][0], ref_list) if 'm' in ref_list.colnames: mref = ref_list['m'] else: mref = ref_list['m0'] - - N, x1m, y1m, m1m, x2m, y2m, m2m = match.miracle_match_briteN(star_list['x'], - star_list['y'], - star_list['m'], - xref, - yref, - mref, - briteN) - - err_msg = 'Failed to find more than '+str(n_req_match) - err_msg += ' (only ' + str(len(x1m)) + ') matches, giving up.' - assert len(x1m) >= n_req_match, err_msg + + N, x1m, y1m, m1m, x2m, y2m, m2m = match.miracle_match_briteN( + star_list['x'], + star_list['y'], + star_list['m'], + xref, + yref, + mref, + briteN + ) + + assert len(x1m) >= n_req_match, \ + f'Failed to find more than {n_req_match} (only {len(x1m)}) matches, giving up.' if verbose > 1: print('initial_guess: {0:d} stars matched between starlist and reference list'.format(N)) @@ -2931,12 +2985,12 @@ def trans_initial_guess(ref_list, star_list, trans_args, motion_model_dict, mode trans.mag_offset = np.mean(m2m - m1m) else: trans.mag_offset = 0 - + if verbose > 1: print('init guess: ', trans.px.parameters, trans.py.parameters) warnings.filterwarnings('default', category=AstropyUserWarning) - + return trans @@ -3028,8 +3082,8 @@ def outlier_rejection_indices(star_list, ref_list, outlier_tol, verbose=True): The indicies of the stars to keep. """ # Optionally propogate the reference positions forward in time. - xref, yref = get_pos_in_time(star_list['t'][0], ref_list) - + xref, yref = infer_positions(star_list['t'][0], ref_list) + # Residuals x_resid_on_old_trans = star_list['x'] - xref y_resid_on_old_trans = star_list['y'] - yref @@ -3135,40 +3189,49 @@ def get_weighting_scheme(weights, ref_list, star_list): return weight # TODO: This is sometimes run on a startable, not a starlist, at least as currently used -def get_pos_at_time(t, starlist, motion_model_dict): +def infer_positions(t, startable): """ - Take a starlist, check to see if it has motion/velocity columns. + Take a startable, check to see if it has motion/velocity columns. If it does, then propogate the positions forward in time to the desired epoch. If no motion/velocities exist, then just use ['x0', 'y0'] or ['x', 'y'] - Inputs + Parameters ---------- t_array : float The time to propogate to. Usually in decimal years; but it should be in the same units as the 't0' column in starlist. + startable : StarTable + Startable that needs to be inferred. + + Returns + ------- + x, y : tuple + Inferred position at time t """ # Check for motion model - if 'motion_model_used' in starlist.colnames: - x,y,xe,ye = starlist.get_star_positions_at_time(t, motion_model_dict, allow_alt_models=True) + if 'motion_model_used' in startable.colnames: + x, y, xe, ye = startable.infer_positions(t) + # If no motion model, check for velocities - elif ('vx' in starlist.colnames) and ('vy' in starlist.colnames): - x = starlist['x0'] + starlist['vx']*(t-starlist['t0']) - y = starlist['y0'] + starlist['vy']*(t-starlist['t0']) + elif ('vx' in startable.colnames) and ('vy' in startable.colnames): + x = startable['x0'] + startable['vx'] * (t - startable['t0']) + y = startable['y0'] + startable['vy'] * (t - startable['t0']) + # If no velocities, try fitted positon - elif ('x0' in starlist.colnames) and ('y0' in starlist.colnames): - x = starlist['x0'] - y = starlist['y0'] + elif ('x0' in startable.colnames) and ('y0' in startable.colnames): + x = startable['x0'] + y = startable['y0'] # Otherwise, use measured position else: - x = starlist['x'] - y = starlist['y'] - - return (x, y) + x = startable['x'] + y = startable['y'] + + return x, y def logger(logfile, message, verbose = 9): if verbose > 4: print(message) logfile.write(message + '\n') - return + return \ No newline at end of file diff --git a/flystar/analysis.py b/flystar/analysis.py index 3121458..55094e5 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -1,17 +1,11 @@ import numpy as np import pylab as plt -from flystar import starlists -from flystar import startables -from flystar import align -from flystar import match -from flystar import transforms +from . import starlists, match from astropy import table from astropy.table import Table, Column from astropy.coordinates import SkyCoord from astropy import units as u from astropy.wcs import WCS -from astroquery.gaia import Gaia -from astroquery.mast import Observations, Catalogs import pdb, copy import math from scipy.stats import f @@ -42,6 +36,7 @@ def query_gaia(ra, dec, search_radius=30.0, table_name='gaiadr3'): table_name : string Options are 'gaiadr2' or 'gaiaedr3' """ + from astroquery.gaia import Gaia target_coords = SkyCoord(ra, dec, unit=(u.hourangle, u.deg), frame='icrs') ra = target_coords.ra.degree dec = target_coords.dec.degree @@ -49,7 +44,7 @@ def query_gaia(ra, dec, search_radius=30.0, table_name='gaiadr3'): search_radius *= u.arcsec Gaia.ROW_LIMIT = 50000 - gaia_job = Gaia.cone_search_async(target_coords, search_radius, table_name = table_name + '.gaia_source') + gaia_job = Gaia.cone_search_async(target_coords, radius=search_radius, table_name=table_name + '.gaia_source') gaia = gaia_job.get_results() #Change new 'SOURCE_ID' column header back to lowercase 'source_id' so all subsequent functions still work: diff --git a/flystar/examples.py b/flystar/examples.py index 8059562..65723ec 100644 --- a/flystar/examples.py +++ b/flystar/examples.py @@ -1,11 +1,5 @@ -from flystar import transforms -from flystar import match -from flystar import align -from flystar import starlists -from flystar import plots import numpy as np -import copy -import pdb +from . import transforms, match, align, starlists, plots def align_example(labelFile, reference, transModel=transforms.four_paramNW, order=1, N_loop=2, @@ -83,7 +77,7 @@ def align_example(labelFile, reference, transModel=transforms.four_paramNW, orde trans, N_trans = align.find_transform(label[idx_label], label_trans[idx_label], - starlist_mat[idx_starlist], + starlist[idx_starlist], transModel=transModel, order=order, weights=weights) diff --git a/flystar/match.py b/flystar/match.py index d7c391e..f564cd3 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -1,14 +1,10 @@ import numpy as np -from flystar import starlists, transforms, startables, align +from . import starlists, transforms, startables from collections import Counter from scipy.spatial import cKDTree as KDT -from astropy.table import Column, Table +from astropy.table import Column import itertools import copy -import scipy.signal -from scipy.spatial import distance -import math -import pdb def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, @@ -526,6 +522,7 @@ def generic_match(sl1, sl2, init_mode='triangle', Startable of the two matched catalogs """ + from . import align # Check the input StarLists and transform them into astropy Tables if not isinstance(sl1, starlists.StarList): diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 0b86d07..b69f8f2 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -1,19 +1,16 @@ import numpy as np from abc import ABC -import pdb from flystar import parallax from astropy.time import Time -from scipy.optimize import curve_fit +from scipy.optimize import curve_fit, OptimizeWarning import warnings class MotionModel(ABC): - # Number of data points required to fit model - n_pts_req = 0 - # Degrees of freedom for model - n_params = 0 - # Fit paramters: Shared fit parameters - fitter_param_names = [] + fit_param_names = [] + + # Number of fit parameters/required observations in each direction + n_params = int(np.ceil(len(fit_param_names) / 2)) # Fixed parameters: These are parameters that are required for the model, but are not # fit quantities. For example, RA and Dec in a parallax model. @@ -24,49 +21,39 @@ class MotionModel(ABC): # These parameters should be derived from the fit parameters and # they must exist as a variable on the model object optional_param_names = [] + name = "MotionModel" def __init__(self, *args, **kwargs): """ - Make a motion model object. This object defines the fitter and fixed parameters, - and if needed stores metadata such as RA and Dec for Parallax, - for the given motion model and contains functions to fit these values to data - and apply the values to compute expected positions at given times. Each instance - corresponds to a given motion model, not an individual star, and thus the fit - values are only input/returned in functions and not stored in the object. + Make a motion model object. This object defines the fit and fixed parameters, + and contains functions to fit the model to data and infer positions at given times. + Each instance corresponds to a given motion model, not an individual star, + and thus the fit values are only input/returned in functions, not stored in the object. """ return + + def model_fit(self, dt): + return np.full_like(dt, np.nan) + + def model(self, t, fit_params, fit_param_errs=None, fixed_params=None): + if fit_param_errs is None: + return np.full_like(t, np.nan), np.full_like(t, np.nan) + return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) - def get_pos_at_time(self, params, t): - """ - Position calculator for a single star using a given motion model and input - model parameters and times. - """ - #return x, y - pass - - def get_batch_pos_at_time(self, t): - """ - Position calculator for a set of stars using a given motion model and input - model parameters and times. - """ - #return x, y, x_err, y_err - pass - - def run_fit(self, t, x, y, xe, ye, t0, weighting='var', - use_scipy=True, absolute_sigma=True): - """ - Run a single fit of the data to the motion model and return the best parameters. - This function is used by the overall fit_motion_model function once for a basic fit - or several times for a bootstrap fit. - """ + def run_fit( + self, t, x, y, xe, ye, + fixed_params_dict=None, + weighting='var', + use_scipy=True, + absolute_sigma=True, + params_guess=None, + fill_value=np.nan, + verbose=True + ): # Run a single fit (used both for overall fit + bootstrap iterations) - pass - - def get_weights(self, xe, ye, weighting='var'): - """ - Get the weights for each data point for fitting. Options are 'var' (default) - and 'std'. - """ + return np.full(self.n_params, fill_value), np.full(self.n_params, np.inf), np.nan, np.nan + + def calc_weights(self, xe, ye, weighting='var'): if weighting=='std': return 1./xe, 1./ye elif weighting=='var': @@ -74,488 +61,1079 @@ def get_weights(self, xe, ye, weighting='var'): else: warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) return 1./xe**2, 1./ye**2 - - def scale_errors(self, errs, weighting='var'): - """ - Rescale the fit result errors as needed, according to the weighting scheme used. - """ - if weighting=='std': - return np.array(errs)**2 - elif weighting=='var': - return errs - else: - warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) - return errs - def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var', - use_scipy=True, absolute_sigma=True): - """ - Fit the input positions on the sky and errors - to determine new parameters for this motion model (MM). - Best-fit parameters will be returned along with uncertainties. - Optionally, bootstrap error estimation can be performed. + def fit( + self, t, x, y, xe, ye, + fixed_params_dict=None, + weighting='var', + use_scipy=True, + absolute_sigma=True, + fill_value=np.nan, + params_guess=None, + return_chi2=False, + bootstrap=0, + verbose=True, + seed=None + ): + """Fit stellar motion parameters + + Parameters + ---------- + t : array-like + Times of measurements + x : array-like + x-coordinates + y : array-like + y-coordinates + xe : array-like + Uncertainty of x + ye : array-like + Uncertainty of y + fixed_params_dict : dict, optional + Dictionary of fixed parameters, see each motion model's fixed_param_names for details, by default None + weighting : str, optional + Use standard error weighting ('std': w=1/xe, 1/ye) or variance weighting ('var': w=1/xe**2, 1/ye**2), by default 'var' + use_scipy : bool, optional + Use scipy for optmization. Otherwise, use linear algebraic solution (Linear model only), which is faster for < 300 epochs, by default True + absolute_sigma : bool, optional + Absolute sigma. See scipy.optimize.curve_fit for details, by default True + fill_value : float, optional + Fill value for parameters when not enough data points to fit model, by default np.nan + params_guess : array-like, optional + Initial guess for the fit parameters used in scipy curve_fit, by default None + return_chi2 : bool, optional + Return chi^2 values along with parameters and uncertainties in params, param_errs, chi2_x, chi2_y, by default False + bootstrap : int, optional + Bootstrapping uncertainties, by default 0 + verbose : bool, optional + Print warning messages, by default True + seed : int, optional + Seed for the random number generator, by default None + Returns + ------- + params, params_err, chi2_x, chi2_y + Parameters, uncertainties, and chi squares. The corresponding parameter names are in self.fit_param_names. """ - params, param_errs = self.run_fit(t, x, y, xe, ye, t0=t0, weighting=weighting, - use_scipy=use_scipy, absolute_sigma=absolute_sigma) + assert np.ndim(t) == np.ndim(x) == np.ndim(y) == np.ndim(xe) == np.ndim(ye) == 1, "Input arrays must be 1D! Motion model can only fit individual stars" + assert len(t) == len(x) == len(y) == len(xe) == len(ye), "Input arrays must have the same length!" + fit_result = self.run_fit( + t, x, y, xe, ye, + fixed_params_dict=fixed_params_dict, + weighting=weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma, + fill_value=fill_value, + params_guess=params_guess, + return_chi2=return_chi2, + verbose=verbose + ) + + if return_chi2: + params, param_errs, chi2_x, chi2_y = fit_result + else: + params, param_errs = fit_result - if bootstrap>0 and len(x)>(self.n_pts_req): - edx = np.arange(len(x), dtype=int) + + # Bootstrap errors + n_obs = len(t) + + if bootstrap > 0 and n_obs > (self.n_params): + rng = np.random.default_rng(seed) + edx = np.arange(n_obs, dtype=int) + # Precompute All Bootstrap Draws at Once + # Ensure there are enough unique points in each bootstrap sample + bdx_unique = np.stack([ + rng.choice(edx, size=self.n_params, replace=False) + for _ in range(bootstrap) + ]) + # Draw with replacement for the rest + bdx_extra = np.stack([ + rng.choice(edx, size=n_obs - self.n_params, replace=True) + for _ in range(bootstrap) + ]) + bdx_all = np.hstack((bdx_unique, bdx_extra)) + bb_params = [] bb_params_errs = [] - for bb in range(bootstrap): - bdx = np.random.choice(edx, len(x)) - while len(np.unique(bdx)) 0 else np.full_like(dt, x0) + + def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): + """Predicted positions (and uncertainties, if fit_param_errs is provided) at time t of Fixed model. + + Parameters + ---------- + t : float or array-like + Time array, shape (N_times,) + fit_params : array-like + x0, y0 in shape (N_params,) or (N_stars, N_params) + fit_param_errs : array-like, optional + Uncertainties for x0, y0 in shape (N_params,) or (N_stars, N_params), by default None + fixed_params_dict : dict, optional + Not applicable for Fixed, by default None + + + Returns + ------- + x, y (, xe, ye) + Predicted position (and uncertainties) of Fixed model, shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 + """ + self.fixed_params_dict = fixed_params_dict + t = np.atleast_1d(t) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + + N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_times = len(t) + x0, y0 = fit_params.T # Each shape (N_stars,) + + # Return results in (N_stars, N_times) shape + x = self.model_fit(t, x0) # Shape (N_stars, N_times) + y = self.model_fit(t, y0) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x = x.flatten() + y = y.flatten() + + if fit_param_errs is None: + return x, y + + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + x0_err, y0_err = fit_param_errs.T + + # Return results in (N_stars, N_times) shape + x_err = np.broadcast_to(x0_err[:, np.newaxis], (N_stars, N_times)) + y_err = np.broadcast_to(y0_err[:, np.newaxis], (N_stars, N_times)) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x_err = x_err.flatten() + y_err = y_err.flatten() + + return x, y, x_err, y_err + + def run_fit( + self, t, x, y, xe, ye, + fixed_params_dict=None, + weighting='var', + use_scipy=True, + absolute_sigma=True, + fill_value=np.nan, + params_guess=None, + return_chi2=False, + verbose=True + ): + if verbose and (not use_scipy): + warnings.warn("Fixed model has no non-scipy fitter option. Running with scipy.") + + n_obs = len(t) + degree_of_freedom = n_obs - self.n_params + # Not enough data points to fit model + if degree_of_freedom < 0: + if verbose: + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) + params = np.full(self.n_params, fill_value) + param_errors = np.full(self.n_params, np.inf) + return params, param_errors, np.nan, np.nan + + # degree_of_freedom >= 0 + # Calculate weighted average position + x_wt, y_wt = self.calc_weights(xe, ye, weighting=weighting) + x_wt_norm = x_wt / np.sum(x_wt) + y_wt_norm = y_wt / np.sum(y_wt) + x0 = np.average(x, weights=x_wt) + x0e = (np.sum(x_wt_norm**2 * xe**2))**0.5 # Error propagation + y0 = np.average(y, weights=y_wt) + y0e = (np.sum(y_wt_norm**2 * ye**2))**0.5 # Error propagation + + params = np.array([x0, y0]) + param_errors = np.array([x0e, y0e]) + + if (not absolute_sigma) or return_chi2: + chi2x, chi2y = self.calc_chi2(t, x, y, xe, ye, params) + + if not absolute_sigma: + if degree_of_freedom > 0: + reduced_chi2x = chi2x / degree_of_freedom + reduced_chi2y = chi2y / degree_of_freedom + + param_errors[0] *= reduced_chi2x**0.5 + param_errors[1] *= reduced_chi2y**0.5 + else: + # degree_of_freedom == 0, as < 0 case already handled above + warnings.warn( + f'Degree of freedom < 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value np.inf.', + OptimizeWarning, stacklevel=2 + ) + # Set parameter uncertainties to np.inf, same behavior as scipy.optimize.curve_fit + param_errors = np.full_like(param_errors, np.inf) + + if return_chi2: + return params, param_errors, chi2x, chi2y else: - x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) - x0 = np.average(x, weights=x_wt) - x0e = np.sqrt(np.average((x-x0)**2,weights=x_wt)) - y0 = np.average(y, weights=y_wt) - y0e = np.sqrt(np.average((y-y0)**2,weights=y_wt)) - - params = [x0, y0] - param_errors = [x0e, y0e] - - return params, param_errors - + return params, param_errors + class Linear(MotionModel): """ A 2D linear motion model for a star on the sky. """ - n_pts_req = 2 - n_params=2 - fitter_param_names = ['x0', 'vx', 'y0', 'vy'] + fit_param_names = ['x0', 'vx', 'y0', 'vy'] fixed_param_names = ['t0'] - + + # Number of fit parameters/required observations in each direction + n_params = int(np.ceil(len(fit_param_names) / 2)) + name = "Linear" + def __init__(self, **kwargs): - # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() return + + def model_fit(self, dt, x0, v): + """Linear motion model fit function + + Parameters + ---------- + dt : array-like + Time offset, shape (N_times,) + x0 : float or array-like + Initial position, shape (N_stars,) or scalar + v : float or array-like + Velocity, shape (N_stars,) or scalar + + Returns + ------- + x : array-like + Predicted position(s) + """ + return x0 + v * dt + + def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): + """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Linear model. + + Parameters + ---------- + t : float or array-like + Time(s) at which to evaluate the model + fit_params : array-like + x0, vx, y0, vy in shape (N_params,) or (N_stars, N_params) + fit_param_errs : array-like, optional + Uncertainties of fit parameters in shape (N_params,) or (N_stars, N_params), by default None + fixed_params_dict : dict + t0, shape (1,) or (N_stars,) + + Returns + ------- + x, y (, xe, ye) + Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 + """ + if fixed_params_dict is None: + fixed_params_dict = self.fixed_params_dict + assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Linear model." + + t = np.atleast_1d(t) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + + N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_times = len(t) + + x0, vx, y0, vy = fit_params.T # Each shape (N_stars,) + t0 = np.atleast_1d(fixed_params_dict['t0']) # Shape (N_stars,) or (1,) + + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) - def get_pos_at_time(self, fit_params, fixed_params, t): - fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) - fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) - dt = t-fixed_params_dict['t0'] - return fit_params_dict['x0'] + fit_params_dict['vx']*dt, fit_params_dict['y0'] + fit_params_dict['vy']*dt - - def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], t0=[], - x0_err=[],vx_err=[], y0_err=[],vy_err=[], **kwargs): - if hasattr(t, "__len__"): - dt = t-t0[:,np.newaxis] - x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] - y = y0[:,np.newaxis] + dt*vy[:,np.newaxis] - x_err = np.hypot(x0_err[:,np.newaxis], vx_err[:,np.newaxis]*dt) - y_err = np.hypot(y0_err[:,np.newaxis], vy_err[:,np.newaxis]*dt) - else: - dt = t-t0 - x = x0 + dt*vx - y = y0 + dt*vy - x_err = np.hypot(x0_err, vx_err*dt) - y_err = np.hypot(y0_err, vy_err*dt) - return x,y,x_err,y_err - - def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, - use_scipy=True, absolute_sigma=True): - dt = t-t0 - x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) - if params_guess is None: - params_guess = [x.mean(),0.0,y.mean(),0.0] - - # Handle 2-data point case - if len(np.unique(dt))==2: - if len(x)>2: # Catch case where bootstrap sends only 2 unique epochs - _,idx=np.unique(dt, return_index=True) - dt = dt[idx] - x = x[idx] - y = y[idx] - xe = xe[idx] - ye = ye[idx] - dx = np.diff(x)[0] - dy = np.diff(y)[0] - dt_diff = np.diff(dt)[0] - vx = dx / dt_diff - vy = dy / dt_diff - # TODO: still not sure about the error handling here - x0 = x[0] - dt[0]*vx # np.average(x, weights=x_wt) # - y0 = y[0] - dt[0]*vy # np.average(y, weights=y_wt) # - x0e = np.abs(dx) / 2**0.5 # np.sqrt(np.sum(xe**2)/2) # - y0e = np.abs(dy) / 2**0.5 # np.sqrt(np.sum(ye**2)/2) # - vxe = 0.0 #np.abs(vx) * np.sqrt(np.sum(xe**2/x**2)) - vye = 0.0 #np.abs(vy) * np.sqrt(np.sum(ye**2/y**2)) - - else: - if use_scipy: - def linear(t, c0, c1): - return c0 + c1*t - x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/np.sqrt(x_wt), absolute_sigma=absolute_sigma) - y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/np.sqrt(y_wt), absolute_sigma=absolute_sigma) - x0, vx = x_opt - y0, vy = y_opt - x0e, vxe = np.sqrt(x_cov.diagonal()) - y0e, vye = np.sqrt(y_cov.diagonal()) - x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) - else: - # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme - x = np.array(x) - y = np.array(y) - dt = np.array(dt) - X_mat_t = np.vander(dt, 2) - # x calculation - W_mat_x = np.diag(x_wt) - XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t - pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix - popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution - perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution - # y calculation - W_mat_y = np.diag(y_wt) - XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t - pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix - popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution - perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution - # prepare values to return - x0, vx = popt_x[1], popt_x[0] - y0, vy = popt_y[1], popt_y[0] - x0e, vxe = perr_x[1], perr_x[0] - y0e, vye = perr_y[1], perr_y[0] - x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) + x = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis]) # Shape (N_stars, N_times) + y = self.model_fit(dt, y0[:, np.newaxis], vy[:, np.newaxis]) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x = x.flatten() + y = y.flatten() + + if fit_param_errs is None: + return x, y - params = [x0, vx, y0, vy] - param_errors = [x0e, vxe, y0e, vye] - return params, param_errors + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + x0_err, vx_err, y0_err, vy_err = fit_param_errs.T # Each shape (N_stars,) + x_err = np.hypot(x0_err[:, np.newaxis], vx_err[:, np.newaxis] * dt) # Shape (N_stars, N_times) + y_err = np.hypot(y0_err[:, np.newaxis], vy_err[:, np.newaxis] * dt) # Shape (N_stars, N_times) + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x_err = x_err.flatten() + y_err = y_err.flatten() + return x, y, x_err, y_err + + def run_fit( + self, t, x, y, xe, ye, + fixed_params_dict=None, + weighting='var', + use_scipy=True, + absolute_sigma=True, + fill_value=np.nan, + params_guess=None, + return_chi2=False, + verbose=True + ): + if fixed_params_dict is None: + fixed_params_dict = {} + if 't0' not in fixed_params_dict: + # Default t0 to weighted average time + fixed_params_dict['t0'] = np.average(t, weights=1./np.hypot(xe, ye)) + self.fixed_params_dict = fixed_params_dict + t0 = np.atleast_1d(fixed_params_dict['t0']) + t = np.atleast_1d(t) + x = np.atleast_1d(x) + y = np.atleast_1d(y) + xe = np.atleast_1d(xe) + ye = np.atleast_1d(ye) + n_obs = len(t) + degree_of_freedom = n_obs - self.n_params + # Not enough data points to fit model + if degree_of_freedom < 0: + if verbose: + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) + params = np.full(self.n_params, fill_value) + param_errors = np.full(self.n_params, np.inf) + if return_chi2: + return params, param_errors, np.nan, np.nan + else: + return params, param_errors + + # degree_of_freedom >= 0 + dt = t - t0 + x_wt, y_wt = self.calc_weights(xe, ye, weighting=weighting) + if params_guess is None: + params_guess = [x.mean(), 0., y.mean(), 0.] + + if use_scipy: + x_opt, x_cov = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:2]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma) + y_opt, y_cov = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[2:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma) + x0, vx = x_opt + y0, vy = y_opt + x0e, vxe = np.sqrt(x_cov.diagonal()) + y0e, vye = np.sqrt(y_cov.diagonal()) + params = np.array([x0, vx, y0, vy]) + param_errors = np.array([x0e, vxe, y0e, vye]) + if return_chi2: + chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + return params, param_errors, chi2_x, chi2_y + else: + return params, param_errors + + # Linear algebraic solution + # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution_scheme + X_mat_t = np.vander(dt, 2) + # x calculation + W_mat_x = np.diag(x_wt) + XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t + pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix + popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution + perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution + # y calculation + W_mat_y = np.diag(y_wt) + XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t + pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix + popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution + perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution + # prepare values to return + vx, x0 = popt_x + vy, y0 = popt_y + vxe, x0e = perr_x + vye, y0e = perr_y + + params = np.array([x0, vx, y0, vy]) + param_errors = np.array([x0e, vxe, y0e, vye]) + + # Does not use get_chi2 to accelerate calculation + if return_chi2 or (not absolute_sigma): + residual_x = x - X_mat_t @ popt_x + residual_y = y - X_mat_t @ popt_y + + chi2_x = residual_x.T @ W_mat_x @ residual_x + chi2_y = residual_y.T @ W_mat_y @ residual_y + + if not absolute_sigma: + if degree_of_freedom > 0: + reduced_chi2_x = chi2_x / degree_of_freedom + reduced_chi2_y = chi2_y / degree_of_freedom + + param_errors[0:2] *= reduced_chi2_x**0.5 + param_errors[2:4] *= reduced_chi2_y**0.5 + + else: + # degree_of_freedom == 0, as < 0 case already handled above + warnings.warn( + f'Degree of freedom < 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value np.inf.', + OptimizeWarning, stacklevel=2 + ) + # Set parameter uncertainties to np.inf, same behavior as scipy.optimize.curve_fit + param_errors = np.full_like(param_errors, np.inf) + + if return_chi2: + return params, param_errors, chi2_x, chi2_y + else: + return params, param_errors + class Acceleration(MotionModel): """ A 2D accelerating motion model for a star on the sky. """ - n_pts_req = 4 # TODO: consider special case for 3 pts - n_params=3 - fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] + fit_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] fixed_param_names = ['t0'] - - def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=None, - x0_err=0, vx0_err=0, ax_err=0, y0_err=0, vy0_err=0, ay_err=0, **kwargs): + name = "Acceleration" + + # Number of fit parameters/required observations in each direction + n_params = int(np.ceil(len(fit_param_names) / 2)) + + def __init__(self): # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() return + + def model_fit(self, t, x0, v0, a): + """Model positions at time t of Acceleration model. + + Parameters + ---------- + t : float or array-like + Time(s) at which to evaluate the model + x0 : float or array-like + Initial position(s) + v0 : float or array-like + Initial velocity(ies) + a : float or array-like + Acceleration(s) + + Returns + ------- + float or array-like + Model positions at time t of Acceleration model + """ + return x0 + v0*t + 0.5*a*t**2 + + def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): + """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Acceleration model. + + Parameters + ---------- + t : float or array-like + Time(s) at which to evaluate the model + fit_params : array-like + x0, vx, ax, y0, vy, ay in shape (N_params,) or (N_stars, N_params) + fit_param_errs : array-like, optional + Fit parameter uncertainties with shape (N_stars, N_params) or (N_params,), by default None + fixed_params_dict : dict + t0, shape (1,) or (N_stars,) + + Returns + ------- + x, y (, xe, ye) + Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 + """ + if fixed_params_dict is None: + fixed_params_dict = self.fixed_params_dict + assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Acceleration model." + + t = np.atleast_1d(t) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + + N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_times = len(t) - def get_pos_at_time(self, fit_params, fixed_params, t): - fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) - fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) - dt = t-fixed_params_dict['t0'] - x = fit_params_dict['x0'] + fit_params_dict['vx0']*dt + 0.5*fit_params_dict['ax']*dt**2 - y = fit_params_dict['y0'] + fit_params_dict['vy0']*dt + 0.5*fit_params_dict['ay']*dt**2 - return x, y + x0, vx0, ax, y0, vy0, ay = fit_params.T # Each shape (N_stars,) + t0 = np.atleast_1d(fixed_params_dict['t0']) # Shape (N_stars,) or (1,) - def get_batch_pos_at_time(self,t, - x0=[],vx0=[],ax=[], y0=[],vy0=[],ay=[], t0=[], - x0_err=[],vx0_err=[],ax_err=[], y0_err=[],vy0_err=[],ay_err=[], **kwargs): - if hasattr(t, "__len__"): - dt = t-t0[:,np.newaxis] - x = x0[:,np.newaxis] + dt*vx0[:,np.newaxis] + 0.5*dt**2*ax[:,np.newaxis] - y = y0[:,np.newaxis] + dt*vy0[:,np.newaxis] + 0.5*dt**2*ay[:,np.newaxis] - x_err = np.sqrt(x0_err[:,np.newaxis]**2 + (vx0_err[:,np.newaxis]*dt)**2 + (0.5*ax_err[:,np.newaxis]*dt**2)**2) - y_err = np.sqrt(y0_err[:,np.newaxis]**2 + (vy0_err[:,np.newaxis]*dt)**2 + (0.5*ay_err[:,np.newaxis]*dt**2)**2) - else: - dt = t-t0 - x = x0 + dt*vx0 + 0.5*dt**2*ax - y = y0 + dt*vy0 + 0.5*dt**2*ay - x_err = np.sqrt(x0_err**2 + (vx0_err*dt)**2 + (0.5*ax_err*dt**2)**2) - y_err = np.sqrt(y0_err**2 + (vy0_err*dt)**2 + (0.5*ay_err*dt**2)**2) - return x,y,x_err,y_err - - def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, - use_scipy=True, absolute_sigma=True): + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) + + x = self.model_fit(dt, x0[:, np.newaxis], vx0[:, np.newaxis], ax[:, np.newaxis]) # Shape (N_stars, N_times) + y = self.model_fit(dt, y0[:, np.newaxis], vy0[:, np.newaxis], ay[:, np.newaxis]) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x = x.flatten() + y = y.flatten() + + if fit_param_errs is None: + return x, y + + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + x0_err, vx0_err, ax_err, y0_err, vy0_err, ay_err = fit_param_errs.T + x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx0_err[:, np.newaxis] * dt)**2 + (0.5 * ax_err[:, np.newaxis] * dt**2)**2) # Shape (N_stars, N_times) + y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy0_err[:, np.newaxis] * dt)**2 + (0.5 * ay_err[:, np.newaxis] * dt**2)**2) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x_err = x_err.flatten() + y_err = y_err.flatten() + + return x, y, x_err, y_err + + + def run_fit( + self, t, x, y, xe, ye, + fixed_params_dict=None, + weighting='var', + use_scipy=True, + absolute_sigma=True, + params_guess=None, + fill_value=np.nan, + return_chi2=False, + verbose=True + ): + if fixed_params_dict is None: + fixed_params_dict = {} + if 't0' not in fixed_params_dict: + # Default t0 to weighted average time + fixed_params_dict['t0'] = np.average(t, weights=1./np.hypot(xe, ye)) + self.fixed_params_dict = fixed_params_dict + t0 = np.atleast_1d(fixed_params_dict['t0']) + t = np.atleast_1d(t) + x = np.atleast_1d(x) + y = np.atleast_1d(y) + xe = np.atleast_1d(xe) + ye = np.atleast_1d(ye) + if not use_scipy: - Warning("Acceleration model has no non-scipy fitter option. Running with scipy.") - dt = t-t0 - x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) + if verbose: + warnings.warn("Acceleration model has no non-scipy fitter option. Running with scipy.") + + n_obs = len(t) + degree_of_freedom = n_obs - self.n_params + # Not enough data points to fit model + if degree_of_freedom < 0: + if verbose: + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) + params = np.full(self.n_params, fill_value) + param_errors = np.full(self.n_params, np.inf) + if return_chi2: + return params, param_errors, np.nan, np.nan + else: + return params, param_errors + + # degree_of_freedom >= 0 + dt = t - t0 + x_wt, y_wt = self.calc_weights(xe,ye, weighting=weighting) if params_guess is None: - params_guess = [x.mean(),0.0,0.0,y.mean(),0.0,0.0] - - def accel(t, c0,c1,c2): - return c0 + c1*t + 0.5*c2*t**2 - - x_opt, x_cov = curve_fit(accel, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=True) - y_opt, y_cov = curve_fit(accel, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=True) - x0 = x_opt[0] - y0 = y_opt[0] - vx0 = x_opt[1] - vy0 = y_opt[1] - ax = x_opt[2] - ay = y_opt[2] - + # Initial guess for velocity: + idx_first, idx_last = np.argmin(t), np.argmax(t) + t_span = t[idx_last] - t[idx_first] + params_guess = [x.mean(), (x[idx_last] - x[idx_first]) / t_span, 0., y.mean(), (y[idx_last] - y[idx_first]) / t_span, 0.] + + x_opt, x_cov = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma) + y_opt, y_cov = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma) + x0, vx0, ax = x_opt + y0, vy0, ay = y_opt x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) y0e, vy0e, aye = np.sqrt(y_cov.diagonal()) - x0e, vx0e, axe, y0e, vy0e, aye = self.scale_errors([x0e, vx0e, axe, y0e, vy0e, aye], weighting=weighting) - params = [x0, vx0, ax, y0, vy0, ay] - param_errors = [x0e, vx0e, axe, y0e, vy0e, aye] - - return params, param_errors + params = np.array([x0, vx0, ax, y0, vy0, ay]) + param_errors = np.array([x0e, vx0e, axe, y0e, vy0e, aye]) + if return_chi2: + chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + return params, param_errors, chi2_x, chi2_y + else: + return params, param_errors class Parallax(MotionModel): """ Motion model for linear proper motion + parallax - Requires RA & Dec (J2000) for parallax calculation. + Requires RA and Dec J2000 (degrees) for parallax calculation. Optional PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describes observer location, default is 'earth'. """ - n_pts_req = 4 - n_params=3 - fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] - fixed_param_names = ['t0'] - fixed_meta_data = ['RA','Dec','PA','obs'] - - def __init__(self, RA, Dec, PA=0.0, obs='earth', **kwargs): - self.RA = RA - self.Dec = Dec - self.PA = PA - self.obs = obs - self.plx_vector_cached = None - return + fit_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] + fixed_param_names = ['t0', 'ra', 'dec', 'pa', 'obsLocation'] + name = "Parallax" - def get_parallax_vector(self, t_mjd): - recalc_plx = True + # Number of fit parameters/required observations in each direction + n_params = int(np.ceil(len(fit_param_names) / 2)) + + def __init__(self): + super().__init__() + self.plx_vector_cached = None # Cache for parallax vector + return + + def calc_parallax_vector(self, t_mjd, ra, dec, pa=0., obsLocation='earth'): + """Calculate parallax vector of shape (2, N_times) + + Parameters + ---------- + t_mjd : array-like + Time array in mjd + ra : float or array-like + Right ascension(s) in degrees + dec : float or array-like + Declination(s) in degrees + pa : float or array-like, optional + Position angle(s) of image y-axis from North in degrees, by default 0. + obsLocation : str, optional + Observer location, by default 'earth' + + Returns + ------- + pvec + Parallax vector of shape (2, N_times) + """ if self.plx_vector_cached is not None: - if hasattr(t_mjd, "__len__"): - if list(t_mjd) == list(self.plx_vector_cached[0]): - pvec = self.plx_vector_cached[1:] - recalc_plx = False - elif all([t_mjd_i in self.plx_vector_cached[0] for t_mjd_i in t_mjd]): - pvec_idxs = [np.argwhere(self.plx_vector_cached[0]==t_mjd_i)[0][0] for t_mjd_i in t_mjd] - pvec = [self.plx_vector_cached[1][pvec_idxs], self.plx_vector_cached[2][pvec_idxs]] - recalc_plx = False - elif t_mjd in self.plx_vector_cached[0]: - idx = np.where(t_mjd==self.plx_vector_cached[0])[0][0] - pvec = np.array([self.plx_vector_cached[1][idx], self.plx_vector_cached[2][idx]]) - recalc_plx = False - if recalc_plx: - pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T - if hasattr(t_mjd, "__len__"): - self.plx_vector_cached = [t_mjd, pvec[0], pvec[1]] + t_mjd = np.atleast_1d(t_mjd) + t_mjd_cached = self.plx_vector_cached[0] + if np.array_equal(t_mjd, t_mjd_cached): + # If cached values match input times, return cached values + return self.plx_vector_cached[1] + + elif all(np.isin(t_mjd, t_mjd_cached)): + # If all input times are in cached values, return those + # Calculate pvec_idxs such that t_mjd_cached[ pvec_idxs ] == t_mjd + pvec_idxs = np.array([np.where(t_mjd_cached == t_mjd_i)[0][0] for t_mjd_i in t_mjd]) + pvec = self.plx_vector_cached[1][:, pvec_idxs] + return pvec + + pvec = parallax.parallax_in_direction(ra, dec, t_mjd, obsLocation=obsLocation, pa=pa) + self.plx_vector_cached = [t_mjd, pvec] return pvec + + def model_fit(self, dt, x0, vx, y0, vy, pi): + """Model positions at time t of Parallax model. + + Parameters + ---------- + dt : float or array-like + Time(s) at which to evaluate the model + x0 : float or array-like + Initial position(s) + vx : float or array-like + Velocity(ies) + y0 : float or array-like + Initial position(s) + vy : float or array-like + Velocity(ies) + pi : float or array-like + Parallax factor(s) + + Returns + ------- + x_res, y_res : array-like + Model positions at time t of Parallax model + """ + # x0, vx, y0, vy, pi are all shape (N_stars, N_times) + x_res = x0 + vx * dt + pi * self.pvec[0] + y_res = y0 + vy * dt + pi * self.pvec[1] + return x_res, y_res + + def _model_fit(self, dt, x0, vx, y0, vy, pi): + """Wrapper for model_fit to return concatenated results for scipy fitting.""" + x_res, y_res = self.model_fit(dt, x0, vx, y0, vy, pi) + return np.hstack([x_res, y_res]) # Shape (N_stars, 2*N_times) + + def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): + """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Parallax model. + + Parameters + ---------- + t : float or array-like + Times at which to evaluate the model + fit_params : array-like + x0, vx, y0, vy, pi in shape (N_params,) or (N_stars, N_params) + fit_param_errs : array-like, optional + Uncertainties in fit parameters, by default None + fixed_params : dict + - t0, shape (N_stars,) or (1,). + - ra, shape (N_stars,) or (1,). + - dec, shape (N_stars,) or (1,). + - pa, optional, shape (N_stars,) or (1,), by default 0. + - obsLocation, optional, string, by default 'earth' + + Returns + ------- + x, y (, xe, ye) + Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 + """ + if fixed_params_dict is None: + fixed_params_dict = self.fixed_params_dict + assert all([_ in fixed_params_dict for _ in ['t0', 'ra', 'dec']]), "Fixed parameters t0, ra, and dec are required for Parallax model." + + t = np.atleast_1d(t) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_times = len(t) + + x0, vx, y0, vy, pi = fit_params.T # Each shape (N_stars,) + t0 = np.atleast_1d(fixed_params_dict['t0']) # Shape (N_stars,) or (1,) + ra = np.atleast_1d(fixed_params_dict['ra']) + dec = np.atleast_1d(fixed_params_dict['dec']) + pa = np.atleast_1d(fixed_params_dict.get('pa', 0.0)) + obsLocation = fixed_params_dict.get('obsLocation', 'earth') + + # TODO: vectorize parallax.parallax_in_direction to handle multiple obsLocation? - def get_pos_at_time(self, fit_params, fixed_params, t): - fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) - fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) - dt = t-fixed_params_dict['t0'] - - t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = self.get_parallax_vector(t_mjd) - pvec_x = np.reshape(pvec[0], t.shape) - pvec_y = np.reshape(pvec[1], t.shape) - x = fit_params_dict['x0'] + fit_params_dict['vx']*dt + fit_params_dict['pi']*pvec_x - y = fit_params_dict['y0'] + fit_params_dict['vy']*dt + fit_params_dict['pi']*pvec_y - return x, y + assert (type(obsLocation) == str) or (np.unique(obsLocation).size == 1), "obsLocation must be a single string for all stars at this time." + if type(obsLocation) != str: + obsLocation = np.unique(obsLocation)[0] + + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) + t_mjd = Time(t, format='decimalyear', scale='utc').mjd # Shape (N_times,) + self.pvec = self.calc_parallax_vector(t_mjd, ra, dec, pa=pa, obsLocation=obsLocation) # Shape (2, N_times) + x, y = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis], y0[:, np.newaxis], vy[:, np.newaxis], pi[:, np.newaxis]) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x = x.flatten() + y = y.flatten() + + if fit_param_errs is None: + return x, y + + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + x0_err, vx_err, y0_err, vy_err, pi_err = fit_param_errs.T + x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx_err[:, np.newaxis] * dt)**2 + (pi_err[:, np.newaxis] * self.pvec[0][np.newaxis, :])**2) # Shape (N_stars, N_times) + y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy_err[:, np.newaxis] * dt)**2 + (pi_err[:, np.newaxis] * self.pvec[1][np.newaxis, :])**2) # Shape (N_stars, N_times) - def get_batch_pos_at_time(self, t, - x0=[],vx=[], y0=[],vy=[], pi=[], t0=[], - x0_err=[],vx_err=[], y0_err=[],vy_err=[], pi_err=[], **kwargs): - t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = self.get_parallax_vector(t_mjd) - if hasattr(t, "__len__"): - dt = t-t0[:,np.newaxis] - x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] + pi[:,np.newaxis]*pvec[0].T - y = y0[:,np.newaxis] + dt*vy[:,np.newaxis] + pi[:,np.newaxis]*pvec[1].T - try: - x_err = np.sqrt(x0_err[:,np.newaxis]**2 + (vx_err[:,np.newaxis]*dt)**2 + (pi_err[:,np.newaxis]*pvec[0].T)**2) - y_err = np.sqrt(y0_err[:,np.newaxis]**2 + (vy_err[:,np.newaxis]*dt)**2 + (pi_err[:,np.newaxis]*pvec[1].T)**2) - except: - x_err,y_err = [],[] - else: - dt = t-t0 - x = x0 + dt*vx + pi*pvec[0] - y = y0 + dt*vy + pi*pvec[1] - try: - x_err = np.sqrt(x0_err**2 + (vx_err*dt)**2 + (pi_err*pvec[0])**2) - y_err = np.sqrt(y0_err**2 + (vy_err*dt)**2 + (pi_err*pvec[1])**2) - except: - x_err,y_err = [],[] - return x,y,x_err,y_err - - def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, - use_scipy=True, absolute_sigma=True): + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x_err = x_err.flatten() + y_err = y_err.flatten() + return x, y, x_err, y_err + + + def run_fit( + self, t, x, y, xe, ye, + fixed_params_dict, + weighting='var', + use_scipy=True, + absolute_sigma=True, + params_guess=None, + fill_value=np.nan, + return_chi2=False, + verbose=True + ): if not use_scipy: - Warning("Parallax model has no non-scipy fitter option. Running with scipy.") + if verbose: + warnings.warn("Parallax model has no non-scipy fitter option. Running with scipy.", UserWarning) + + assert all([k in fixed_params_dict for k in ['ra', 'dec']]), "Parallax model requires 'ra' and 'dec' in fixed_params." + t = np.atleast_1d(t) + + if 't0' not in fixed_params_dict: + # Default t0 to weighted average time + fixed_params_dict['t0'] = np.average(t, weights=1./np.hypot(xe, ye)) + if 'obsLocation' not in fixed_params_dict: + fixed_params_dict['obsLocation'] = 'earth' + self.fixed_params_dict = fixed_params_dict + t0 = np.atleast_1d(fixed_params_dict['t0']) + ra = np.atleast_1d(fixed_params_dict['ra']) + dec = np.atleast_1d(fixed_params_dict['dec']) + pa = np.atleast_1d(fixed_params_dict.get('pa', 0.0)) + obsLocation = fixed_params_dict['obsLocation'] + + n_fit = len(t) + degree_of_freedom = n_fit - self.n_params + # Not enough data points to fit model + if degree_of_freedom < 0: + if verbose: + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) + params = np.full(self.n_params, fill_value) + param_errors = np.full(self.n_params, np.inf) + if return_chi2: + return params, param_errors, np.nan, np.nan + else: + return params, param_errors + + # degree_of_freedom >= 0 t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = self.get_parallax_vector(t_mjd) - x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) - def fit_func(use_t, x0,vx, y0,vy, pi): - x_res = x0 + vx*(use_t-t0) + pi*pvec[0] - y_res = y0 + vy*(use_t-t0) + pi*pvec[1] - return np.hstack([x_res, y_res]) + self.pvec = self.calc_parallax_vector(t_mjd, ra, dec, pa=pa, obsLocation=obsLocation) # Shape (2, N_times) + x_wt, y_wt = self.calc_weights(xe, ye, weighting=weighting) + # Initial guesses, x0,y0 as x,y averages; # vx,vy as average velocity if first and last points are perfectly measured; - # pi for 10 pc disance + # pi for 10 pc distance if params_guess is None: idx_first, idx_last = np.argmin(t), np.argmax(t) - params_guess = [x.mean(),(x[idx_last]-x[idx_first])/(t[idx_last]-t[idx_first]), - y.mean(),(y[idx_last]-y[idx_first])/(t[idx_last]-t[idx_first]), 0.1] - res = curve_fit(fit_func, t, np.hstack([x,y]), - p0=params_guess, sigma = 1.0/np.hstack([x_wt,y_wt])) - x0,vx,y0,vy,pi = res[0] - x0_err,vx_err,y0_err,vy_err,pi_err = self.scale_errors(np.sqrt(np.diag(res[1])), weighting=weighting) - - params = [x0, vx, y0, vy, pi] - param_errors = [x0_err, vx_err, y0_err, vy_err, pi_err] - return params, param_errors + t_span = t[idx_last] - t[idx_first] + params_guess = [ + x.mean(), (x[idx_last] - x[idx_first]) / t_span, + y.mean(), (y[idx_last] - y[idx_first]) / t_span, + 0.1 + ] + popt, pcov = curve_fit( + self._model_fit, t - t0, np.hstack([x, y]), + p0=params_guess, sigma=np.hstack([x_wt, y_wt]), + absolute_sigma=absolute_sigma + ) + x0, vx, y0, vy, pi = popt + x0_err, vx_err, y0_err, vy_err, pi_err = np.sqrt(pcov.diagonal()) + + params = np.array([x0, vx, y0, vy, pi]) + param_errors = np.array([x0_err, vx_err, y0_err, vy_err, pi_err]) + if return_chi2: + chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + return params, param_errors, chi2_x, chi2_y + else: + return params, param_errors -def validate_motion_model_dict(motion_model_dict, startable, default_motion_model): - """ - Check that everything is set up properly for motion models to run and their - required metadata. - """ - # Collect names of all motion models that might get used. - all_motion_model_names = ['Fixed'] - if default_motion_model is not None: - all_motion_model_names.append(default_motion_model) - if 'motion_model_input' in startable.columns: - all_motion_model_names += np.unique(startable['motion_model_input']).tolist() - if 'motion_model_used' in startable.columns: - all_motion_model_names += np.unique(startable['motion_model_used']).tolist() - all_motion_model_names = np.unique(all_motion_model_names) - - # Check whether all motion models are in the dict, and if not, try to add them - # here or raise an error. - for mm in all_motion_model_names: - if mm not in motion_model_dict: - mm_obj = eval(mm) - if len(mm_obj.fixed_meta_data)>0: - raise ValueError(f"Cannot use {mm} motion model without required metadata. Please initialize with required metadata and provide in motion_model_dict.") - else: - motion_model_dict[mm] = mm_obj() - warnings.warn(f"Using default model/fitter for {mm}.", UserWarning) +def motion_model_param_names(motion_models, with_errors=True, with_fixed=True): + """Get the motion model parameter names from a list of MotionModels. - return motion_model_dict + Parameters + ---------- + motion_models : MotionModel, str, or list of MotionModels/strings. + Motion model to query parameter names from. If str, should be the name of a MotionModel class. + with_errors : bool, optional + Add uncertainty names with '_err' suffix or not, by default True + with_fixed : bool, optional + Add fixed param names with '_fixed' suffix or not, by default True - -def get_one_motion_model_param_names(motion_model_name, with_errors=True, with_fixed=True): + Returns + ------- + list + List of all unique parameter names across all motion models """ - Get all the motion model parameters for a given motion_model_name. - Optionally, include fixed and error parameters (included by default). - """ - mod = eval(motion_model_name) list_of_parameters = [] - list_of_parameters += getattr(mod, 'fitter_param_names') - if with_fixed: - list_of_parameters += getattr(mod, 'fixed_param_names') - if with_errors: - list_of_parameters += [par+'_err' for par in getattr(mod, 'fitter_param_names')] - return list_of_parameters + def list_add(name): + if name not in list_of_parameters: + list_of_parameters.append(name) + + motion_models = np.atleast_1d(motion_models) + mm_map = motion_model_map() + for mm in motion_models: + if isinstance(mm, str): + mm = mm_map[mm] + for param in mm.fit_param_names: + # Fitter params + list_add(param) + # Error params + if with_errors: + list_add(param + '_err') + # Fixed params + if with_fixed: + for param in mm.fixed_param_names: + list_add(param) + return list_of_parameters -def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_fixed=True): - """ - Get all the motion model parameters for all models given in motion_model_list. - Optionally, include fixed and error parameters (included by default). - """ - list_of_parameters = [] - all_motion_models = [eval(mm) for mm in np.unique(motion_model_list).tolist()] - for aa in range(len(all_motion_models)): - param_names = getattr(all_motion_models[aa], 'fitter_param_names') - param_fixed_names = getattr(all_motion_models[aa], 'fixed_param_names') - param_err_names = [par+'_err' for par in param_names] - list_of_parameters += param_names - if with_fixed: - list_of_parameters += param_fixed_names - if with_errors: - list_of_parameters += param_err_names - - return np.unique(list_of_parameters).tolist() +def all_motion_model_param_names(with_errors=True, with_fixed=True): + """Get all motion model parameter names from all available MotionModels. + Parameters + ---------- + with_errors : bool, optional + Add uncertainty names with '_err' suffix or not, by default True + with_fixed : bool, optional + Add fixed param names with '_fixed' suffix or not, by default True -def get_all_motion_model_param_names(with_errors=True, with_fixed=True): + Returns + ------- + list + List of all unique parameter names across all motion models """ - Get all the motion model parameters for all models defined in this module. - Optionally, include fixed and error parameters (included by default). - """ - list_of_parameters = [] - all_motion_models = MotionModel.__subclasses__() - for aa in range(len(all_motion_models)): - param_names = getattr(all_motion_models[aa], 'fitter_param_names') - param_fixed_names = getattr(all_motion_models[aa], 'fixed_param_names') - param_err_names = [par+'_err' for par in param_names] + return motion_model_param_names(MotionModel.__subclasses__(), with_errors=with_errors, with_fixed=with_fixed) - list_of_parameters += param_names - if with_fixed: - list_of_parameters += param_fixed_names - if with_errors: - list_of_parameters += param_err_names - - return np.unique(list_of_parameters).tolist() - +def motion_model_map(): + """Get a dictionary mapping motion model names to MotionModel classes. + + Returns + ------- + mm_map : dict + Dictionary mapping motion model names to MotionModel classes. + """ + mm_map = dict( + [(mm.__name__, mm) for mm in MotionModel.__subclasses__()] + ) + # Sort by n_params + mm_map = dict(sorted(mm_map.items(), key=lambda item: item[1].n_params)) + return mm_map \ No newline at end of file diff --git a/flystar/parallax.py b/flystar/parallax.py index 4792ec6..a4f0f8c 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -23,44 +23,64 @@ # Default cache size is 1 GB cache_memory.reduce_size() -@cache_memory.cache() -def parallax_in_direction(RA, Dec, mjd, obsLocation='earth', PA=0): +# @cache_memory.cache() +def parallax_in_direction(ra, dec, mjd, obsLocation='earth', pa=0.): """ - | R.A. in degrees. (J2000) - | Dec. in degrees. (J2000) - | MJD - | PA in degrees. (counterclockwise offset of the image y-axis from North) - - Equations following MulensModel. + Calculate the parallax vector in a given direction following MulensModel. + + Parameters + ---------- + RA : float or array-like + Right Ascension in degrees. (J2000) + Dec : float or array-like + Declination in degrees. (J2000) + mjd : float or array-like + Modified Julian Date. + obsLocation : str, optional + Observer location, by default 'earth'. + PA : float, optional + Position angle in degrees (counterclockwise offset of the image y-axis from North), by default 0. + + Returns + ------- + pvec : ndarray + Parallax vector components, shape of (2, N_stars, N_times), or (2, N_stars) if N_times=1, or (2, N_times) if N_stars=1. """ - #print('parallax_in_direction: len(t) = ', len(mjd)) - # Munge inputs into astropy format. - times = Time(mjd + 2400000.5, format='jd', scale='tdb') - coord = SkyCoord(RA, Dec, unit=(units.deg, units.deg)) - - direction = coord.cartesian.xyz.value + # times = Time(mjd + 2400000.5, format='jd', scale='tdb') + ra = np.atleast_1d(ra) + dec = np.atleast_1d(dec) + mjd = np.atleast_1d(mjd) + times = Time(mjd, format='mjd', scale='tdb') # convert to TDB + coord = SkyCoord(ra, dec, unit=(units.deg, units.deg)) + + directions = coord.cartesian.xyz.value.T # Shape (N_stars, 3) north = np.array([0., 0., 1.]) - _east_projected = np.cross(north, direction) / np.linalg.norm(np.cross(north, direction)) - _north_projected = np.cross(direction, _east_projected) / np.linalg.norm(np.cross(direction, _east_projected)) + # Cross product of each star with north vector + _east_projected = np.cross(north, directions) + _east_projected /= np.linalg.norm(_east_projected, axis=1)[:, np.newaxis] # Shape (N_stars, 3) + _north_projected = np.cross(directions, _east_projected) + _north_projected /= np.linalg.norm(_north_projected, axis=1)[:, np.newaxis] # Shape (N_stars, 3) - obs_pos = get_observer_barycentric(obsLocation, times) - sun_pos = get_body_barycentric(body='sun', time=times) + obs_pos = get_observer_barycentric(obsLocation, times) # Shape (N_times,) + sun_pos = get_body_barycentric(body='sun', time=times) # Shape (N_times,) sun_obs_pos = sun_pos - obs_pos - pos = sun_obs_pos.xyz.T.to(units.au) + pos = sun_obs_pos.xyz.T.to(units.au).value # Shape (N_times, 3) + + e = np.einsum('ti,si->st', pos, _east_projected) # Shape (N_stars, N_times) + n = np.einsum('ti,si->st', pos, _north_projected) # Shape (N_stars, N_times) - e = np.dot(pos, _east_projected) - n = np.dot(pos, _north_projected) - # Rotate frame e,n->x,y accounting for PA - PA_rad = np.pi/180.0 * PA - x = -e.value*np.cos(PA_rad) + n.value*np.sin(PA_rad) - y = e.value*np.sin(PA_rad) + n.value*np.cos(PA_rad) - - pvec = np.array([x, y]).T + pa = np.deg2rad(pa) # shape (N_stars,) + x = -e * np.cos(pa[:, np.newaxis]) + n * np.sin(pa[:, np.newaxis]) # Shape (N_stars, N_times) + y = e * np.sin(pa[:, np.newaxis]) + n * np.cos(pa[:, np.newaxis]) # Shape (N_stars, N_times) + pvec = np.array([x, y]) # Shape (2, N_stars, N_times) + if pvec.shape[1] == 1 or pvec.shape[2] == 1: + pvec = pvec.reshape(2, -1) # Shape (2, N_stars) or (2, N_times) + return pvec @@ -144,6 +164,4 @@ def get_observer_barycentric(body, times, min_ephem_step=1, velocity=False): if velocity: return (obs_pos, obs_vel) else: - return obs_pos - - + return obs_pos \ No newline at end of file diff --git a/flystar/plots.py b/flystar/plots.py index 7553a8d..8a2127c 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1,9 +1,8 @@ -from flystar import analysis, motion_model, startables -import pylab as py -import pylab as plt +from . import motion_model, startables import numpy as np import matplotlib.mlab as mlab import matplotlib +import matplotlib.pyplot as plt from matplotlib import colors import matplotlib.cm as cm from scipy.stats import chi2 @@ -23,8 +22,8 @@ #################################################### -def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, fileName=None, - equal_axis=True, root='./'): +def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, + equal_axis=True, save_path=None, show_plot=True): """ Plot positions of stars in reference list and the transformed starlist, in reference list coordinates. Stars used in the transformation are @@ -55,31 +54,37 @@ def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, equal_axis: boolean If true, make axes equal. True by default + + save_path: string + Path to save the figure to. Default is None + show_plot: boolean + If true, show the plot. Default is True + """ - py.figure(figsize=(10,10)) - py.clf() - py.plot(ref['x'], ref['y'], 'g+', ms=5, label='Reference') - py.plot(starlist['x'], starlist['y'], 'rx', ms=5, label='starlist') - py.plot(ref_mat['x'], ref_mat['y'], color='skyblue', marker='s', ms=10, alpha=0.3, + plt.figure(figsize=(10,10)) + plt.clf() + plt.plot(ref['x'], ref['y'], 'g+', ms=5, label='Reference') + plt.plot(starlist['x'], starlist['y'], 'rx', ms=5, label='starlist') + plt.plot(ref_mat['x'], ref_mat['y'], color='skyblue', marker='s', ms=10, alpha=0.3, linestyle='None', label='Matched Reference') - py.plot(starlist_mat['x'], starlist_mat['y'], color='darkblue', marker='s', ms=5, alpha=0.3, + plt.plot(starlist_mat['x'], starlist_mat['y'], color='darkblue', marker='s', ms=5, alpha=0.3, linestyle='None', label='Matched starlist') - py.xlabel('X position (Reference Coords)') - py.ylabel('Y position (Reference Coords)') - py.legend(numpoints=1) - py.title('Label.dat Positions After Transformation') + plt.xlabel('X position (Reference Coords)') + plt.ylabel('Y position (Reference Coords)') + plt.legend(numpoints=1) + plt.title('Label.dat Positions After Transformation') if xlim != None: - py.axis([xlim[0], xlim[1], ylim[0], ylim[1]]) + plt.axis([xlim[0], xlim[1], ylim[0], ylim[1]]) if equal_axis: - py.axis('equal') - if fileName!=None: - #py.savefig(root + fileName[3:8] + 'Transformed_positions_' + '.png') - py.savefig(root + 'Transformed_positions_{0}'.format(fileName) + '.png') - else: - py.savefig(root + 'Transformed_positions.png') + plt.axis('equal') + + if save_path: + plt.savefig(save_path) + if show_plot: + plt.show() - py.close() + plt.close() return @@ -121,22 +126,22 @@ def pos_diff_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, xlim=None, fi bins = np.arange(min_range, max_range+bin_width, bin_width) - py.figure(figsize=(10,10)) - py.clf() - py.hist(diff_x, histtype='step', bins=bins, color='blue', label='X') - py.hist(diff_y, histtype='step', bins=bins, color='red', label='Y') - py.xlabel('Reference Position - starlist Position') - py.ylabel('N stars') - py.title('Position Differences for matched stars') + plt.figure(figsize=(10,10)) + plt.clf() + plt.hist(diff_x, histtype='step', bins=bins, color='blue', label='X') + plt.hist(diff_y, histtype='step', bins=bins, color='red', label='Y') + plt.xlabel('Reference Position - starlist Position') + plt.ylabel('N stars') + plt.title('Position Differences for matched stars') if xlim != None: - py.xlim([xlim[0], xlim[1]]) - py.legend() + plt.xlim([xlim[0], xlim[1]]) + plt.legend() if fileName != None: - py.savefig(root + fileName[3:8] + 'Positions_hist_' + '.png') + plt.savefig(root + fileName[3:8] + 'Positions_hist_' + '.png') else: - py.savefig(root + 'Positions_hist.png') + plt.savefig(root + 'Positions_hist.png') - py.close() + plt.close() return def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None, errs='both', xlim=None, @@ -188,6 +193,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None an outlier. """ + from . import analysis diff_x = ref_mat['x'] - starlist_mat['x'] diff_y = ref_mat['y'] - starlist_mat['y'] @@ -248,51 +254,51 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None bins = np.arange(min_range, max_range+bin_width, bin_width) - py.figure(figsize=(10,10)) - py.clf() - n_x, bins_x, p = py.hist(ratio_x, histtype='step', bins=bins, color='blue', + plt.figure(figsize=(10,10)) + plt.clf() + n_x, bins_x, p = plt.hist(ratio_x, histtype='step', bins=bins, color='blue', label='X', density=True, linewidth=2) - n_y, bins_y, p = py.hist(ratio_y, histtype='step', bins=bins, color='red', + n_y, bins_y, p = plt.hist(ratio_y, histtype='step', bins=bins, color='red', label='Y', density=True, linewidth=2) # Overplot a Gaussian, as well mean = 0 sigma = 1 x = np.arange(-6, 6, 0.1) - py.plot(x, norm.pdf(x,mean,sigma), 'g-', linewidth=2) + plt.plot(x, norm.pdf(x,mean,sigma), 'g-', linewidth=2) # Annotate reduced chi-sqared values in plot: with outliers - xstr = '$\chi^2_r$ = {0}'.format(np.round(chi_sq_red, decimals=3)) - py.annotate(xstr, xy=(0.3, 0.77), xycoords='figure fraction', color='black') + xstr = r'$\chi^2_r$ = {0}'.format(np.round(chi_sq_red, decimals=3)) + plt.annotate(xstr, xy=(0.3, 0.77), xycoords='figure fraction', color='black') txt = r'$\nu$ = 2*{0} - {1} = {2}'.format(len(diff_x), num_mod_params, deg_freedom) - py.annotate(txt, xy=(0.25,0.74), xycoords='figure fraction', color='black') + plt.annotate(txt, xy=(0.25,0.74), xycoords='figure fraction', color='black') xstr2 = 'With Outliers' xstr3 = '{0} with +/- {1}+ sigma'.format(len(ratio_x) - len(good[0]), outlier) - py.annotate(xstr2, xy=(0.29, 0.83), xycoords='figure fraction', color='black') - py.annotate(xstr3, xy=(0.25, 0.80), xycoords='figure fraction', color='black') + plt.annotate(xstr2, xy=(0.29, 0.83), xycoords='figure fraction', color='black') + plt.annotate(xstr3, xy=(0.25, 0.80), xycoords='figure fraction', color='black') # Annotate reduced chi-sqared values in plot: without outliers - xstr = '$\chi^2_r$ = {0}'.format(np.round(chi_sq_red_good, decimals=3)) - py.annotate(xstr, xy=(0.7, 0.8), xycoords='figure fraction', color='black') + xstr = r'$\chi^2_r$ = {0}'.format(np.round(chi_sq_red_good, decimals=3)) + plt.annotate(xstr, xy=(0.7, 0.8), xycoords='figure fraction', color='black') txt = r'$\nu$ = 2*{0} - {1} = {2}'.format(len(good[0]), num_mod_params, deg_freedom_good) - py.annotate(txt, xy=(0.65,0.77), xycoords='figure fraction', color='black') + plt.annotate(txt, xy=(0.65,0.77), xycoords='figure fraction', color='black') xstr2 = 'Without Outliers' - py.annotate(xstr2, xy=(0.67, 0.83), xycoords='figure fraction', color='black') + plt.annotate(xstr2, xy=(0.67, 0.83), xycoords='figure fraction', color='black') - py.xlabel('(Ref Pos - TransStarlist Pos) / Ast. Error') - py.ylabel('N stars (normalized)') - py.title('Position Residuals for Matched Stars') + plt.xlabel('(Ref Pos - TransStarlist Pos) / Ast. Error') + plt.ylabel('N stars (normalized)') + plt.title('Position Residuals for Matched Stars') if xlim != None: - py.xlim([xlim[0], xlim[1]]) - py.legend() + plt.xlim([xlim[0], xlim[1]]) + plt.legend() if fileName != None: - py.savefig(root + fileName[3:8] + 'Positions_err_ratio_hist_' + '.png') + plt.savefig(root + fileName[3:8] + 'Positions_err_ratio_hist_' + '.png') else: - py.savefig(root + 'Positions_err_ratio_hist.png') + plt.savefig(root + 'Positions_err_ratio_hist.png') - py.close() + plt.close() return @@ -319,18 +325,18 @@ def mag_diff_hist(ref_mat, starlist_mat, bins=25, fileName=None, root='./'): bad2 = np.where(bad == True) diff_m = np.delete(diff_m, bad2) - py.figure(figsize=(10,10)) - py.clf() - py.hist(diff_m, bins=bins) - py.xlabel('Reference Mag - TransStarlist Mag') - py.ylabel('N stars') - py.title('Magnitude Difference for matched stars') + plt.figure(figsize=(10,10)) + plt.clf() + plt.hist(diff_m, bins=bins) + plt.xlabel('Reference Mag - TransStarlist Mag') + plt.ylabel('N stars') + plt.title('Magnitude Difference for matched stars') if fileName != None: - py.savefig(root + fileName[3:8] + 'Magnitude_hist_' + '.png') + plt.savefig(root + fileName[3:8] + 'Magnitude_hist_' + '.png') else: - py.savefig(root + 'Magnitude_hist.png') + plt.savefig(root + 'Magnitude_hist.png') - py.close() + plt.close() return def pos_diff_quiver(ref_mat, starlist_mat, qscale=10, keyLength=0.2, xlim=None, ylim=None, @@ -411,35 +417,35 @@ def pos_diff_quiver(ref_mat, starlist_mat, qscale=10, keyLength=0.2, xlim=None, s = len(xpos) - py.figure(figsize=(10,10)) - py.clf() - q = py.quiver(xpos, ypos, diff_x, diff_y, scale=qscale) + plt.figure(figsize=(10,10)) + plt.clf() + q = plt.quiver(xpos, ypos, diff_x, diff_y, scale=qscale) fmt = '{0} ref units'.format(keyLength) - #py.quiverkey(q, 0.2, 0.92, keyLength, fmt, coordinates='figure', color='black') + #plt.quiverkey(q, 0.2, 0.92, keyLength, fmt, coordinates='figure', color='black') # Make our reference arrow a different color - q2 = py.quiver(xpos[s-2:s], ypos[s-2:s], diff_x[s-2:s], diff_y[s-2:s], scale=qscale, color='red') + q2 = plt.quiver(xpos[s-2:s], ypos[s-2:s], diff_x[s-2:s], diff_y[s-2:s], scale=qscale, color='red') # Annotate our reference quiver arrow - py.annotate(fmt, xy=(xpos[-1]-2, ypos[-1]+0.5), color='red') - py.xlabel('X Position (Reference coords)') - py.ylabel('Y Position (Reference coords)') + plt.annotate(fmt, xy=(xpos[-1]-2, ypos[-1]+0.5), color='red') + plt.xlabel('X Position (Reference coords)') + plt.ylabel('Y Position (Reference coords)') if xlim != None: - py.axis([xlim[0], ylim[1], ylim[0], ylim[1]]) + plt.axis([xlim[0], ylim[1], ylim[0], ylim[1]]) if sigma: if fileName != None: - py.title('(Reference - Transformed Starlist positions) / sigma') - py.savefig(root + fileName[3:8] + 'Positions_quiver_sigma_' + '.png') + plt.title('(Reference - Transformed Starlist positions) / sigma') + plt.savefig(root + fileName[3:8] + 'Positions_quiver_sigma_' + '.png') else: - py.title('(Reference - Transformed Starlist positions) / sigma') - py.savefig(root + 'Positions_quiver_sigma.png') + plt.title('(Reference - Transformed Starlist positions) / sigma') + plt.savefig(root + 'Positions_quiver_sigma.png') else: if fileName != None: - py.title('Reference - Transformed Starlist positions') - py.savefig(root + fileName[3:8] + 'Positions_quiver_' + '.png') + plt.title('Reference - Transformed Starlist positions') + plt.savefig(root + fileName[3:8] + 'Positions_quiver_' + '.png') else: - py.title('Reference - Transformed Starlist positions') - py.savefig(root + 'Positions_quiver.png') + plt.title('Reference - Transformed Starlist positions') + plt.savefig(root + 'Positions_quiver.png') - py.close() + plt.close() return def vpd(ref, starlist_trans, vxlim, vylim): @@ -472,17 +478,17 @@ def vpd(ref, starlist_trans, vxlim, vylim): trans_vx = starlist_trans['vx'] trans_vy = starlist_trans['vy'] - py.figure(figsize=(10,10)) - py.clf() - py.plot(trans_vx, trans_vy, 'k.', ms=8, label='Transformed', alpha=0.4) - py.plot(ref_vx, ref_vy, 'r.', ms=8, label='Reference', alpha=0.4) - py.xlabel('Vx (Reference units)') - py.ylabel('Vy (Reference units)') + plt.figure(figsize=(10,10)) + plt.clf() + plt.plot(trans_vx, trans_vy, 'k.', ms=8, label='Transformed', alpha=0.4) + plt.plot(ref_vx, ref_vy, 'r.', ms=8, label='Reference', alpha=0.4) + plt.xlabel('Vx (Reference units)') + plt.ylabel('Vy (Reference units)') if vxlim != None: - py.axis([vxlim[0], vylim[1], vylim[0], vylim[1]]) - py.title('Reference and Transformed Proper Motions') - py.legend() - py.savefig('Transformed_velocities.png') + plt.axis([vxlim[0], vylim[1], vylim[0], vylim[1]]) + plt.title('Reference and Transformed Proper Motions') + plt.legend() + plt.savefig('Transformed_velocities.png') return @@ -538,27 +544,27 @@ def vel_diff_err_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, vxlim=Non sigma = 1 x = np.arange(-6, 6, 0.1) - py.figure(figsize=(20,10)) - py.subplot(121) - py.subplots_adjust(left=0.1) - py.hist(ratio_vx, bins=xbins, histtype='step', color='black', density=True, + plt.figure(figsize=(20,10)) + plt.subplot(121) + plt.subplots_adjust(left=0.1) + plt.hist(ratio_vx, bins=xbins, histtype='step', color='black', density=True, linewidth=2) - py.plot(x, norm.pdf(x,mean,sigma), 'r-', linewidth=2) - py.xlabel('(Ref Vx - Trans Vx) / Vxe') - py.ylabel('N_stars') - py.title('Vx Residuals, Matched') + plt.plot(x, norm.pdf(x,mean,sigma), 'r-', linewidth=2) + plt.xlabel('(Ref Vx - Trans Vx) / Vxe') + plt.ylabel('N_stars') + plt.title('Vx Residuals, Matched') if vxlim != None: - py.xlim([vxlim[0], vxlim[1]]) - py.subplot(122) - py.hist(ratio_vy, bins=ybins, histtype='step', color='black', density=True, + plt.xlim([vxlim[0], vxlim[1]]) + plt.subplot(122) + plt.hist(ratio_vy, bins=ybins, histtype='step', color='black', density=True, linewidth=2) - py.plot(x, norm.pdf(x,mean,sigma), 'r-', linewidth=2) - py.xlabel('(Ref Vy - Trans Vy) / Vye') - py.ylabel('N_stars') - py.title('Vy Residuals, Matched') + plt.plot(x, norm.pdf(x,mean,sigma), 'r-', linewidth=2) + plt.xlabel('(Ref Vy - Trans Vy) / Vye') + plt.ylabel('N_stars') + plt.title('Vy Residuals, Matched') if vylim != None: - py.xlim([vylim[0], vylim[1]]) - py.savefig('Vel_err_ratio_dist.png') + plt.xlim([vylim[0], vylim[1]]) + plt.savefig('Vel_err_ratio_dist.png') return @@ -606,17 +612,17 @@ def residual_vpd(ref_mat, starlist_trans_mat, pscale=None): yerr = np.hypot(ref_mat['vy_err'], starlist_trans_mat['vy_err']) # Plotting - py.figure(figsize=(10,10)) - py.clf() - py.errorbar(diff_x, diff_y, xerr=xerr, yerr=yerr, fmt='k.', ms=8, alpha=0.5) + plt.figure(figsize=(10,10)) + plt.clf() + plt.errorbar(diff_x, diff_y, xerr=xerr, yerr=yerr, fmt='k.', ms=8, alpha=0.5) if pscale != None: - py.xlabel('Reference_vx - Transformed_vx (mas/yr)') - py.ylabel('Reference_vy - Transformed_vy (mas/yr)') + plt.xlabel('Reference_vx - Transformed_vx (mas/yr)') + plt.ylabel('Reference_vy - Transformed_vy (mas/yr)') else: - py.xlabel('Reference_vx - Transformed_vx (reference coords)') - py.ylabel('Reference_vy - Transformed_vy (reference coords)') - py.title('Proper Motion Residuals') - py.savefig('resid_vpd.png') + plt.xlabel('Reference_vx - Transformed_vx (reference coords)') + plt.ylabel('Reference_vy - Transformed_vy (reference coords)') + plt.title('Proper Motion Residuals') + plt.savefig('resid_vpd.png') return @@ -636,8 +642,8 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', else: Nrows = math.ceil(Nstars / (Ncols / 2)) * 3 - py.close('all') - py.figure(2, figsize=figsize) + plt.close('all') + plt.figure(2, figsize=figsize) names = s.getArray('name') mag = s.getArray('mag') x = s.getArray('x') @@ -746,7 +752,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', t0 = int(np.floor(np.min(time))) tO = int(np.ceil(np.max(time))) - dateTicLoc = py.MultipleLocator(3) + dateTicLoc = plt.MultipleLocator(3) dateTicRng = [t0-1, tO+1] dateTics = np.arange(t0, tO+1) DateTicsLabel = dateTics-2000 @@ -754,7 +760,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', # See if we are using MJD instead. if time[0] > 50000: print('MJD') - dateTicLoc = py.MultipleLocator(1000) + dateTicLoc = plt.MultipleLocator(1000) t0 = int(np.round(np.min(time), 50)) tO = int(np.round(np.max(time), 50)) dateTicRng = [t0-200, tO+200] @@ -779,121 +785,121 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', ind = (row-1)*Ncols + col - paxes = py.subplot(Nrows, Ncols, ind) - py.plot(time, fitLineX, 'b-') - py.plot(time, fitLineX + fitSigX, 'b--') - py.plot(time, fitLineX - fitSigX, 'b--') - py.errorbar(time, x, yerr=xerr, fmt='k.') - rng = py.axis() - py.ylim(np.min(x-xerr-0.1),np.max(x+xerr+0.1)) - py.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) + paxes = plt.subplot(Nrows, Ncols, ind) + plt.plot(time, fitLineX, 'b-') + plt.plot(time, fitLineX + fitSigX, 'b--') + plt.plot(time, fitLineX - fitSigX, 'b--') + plt.errorbar(time, x, yerr=xerr, fmt='k.') + rng = plt.axis() + plt.ylim(np.min(x-xerr-0.1),np.max(x+xerr+0.1)) + plt.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) if time[0] > 50000: - py.xlabel('Date (MJD)', fontsize=fontsize1) - py.ylabel('X (pix)', fontsize=fontsize1) + plt.xlabel('Date (MJD)', fontsize=fontsize1) + plt.ylabel('X (pix)', fontsize=fontsize1) paxes.xaxis.set_major_formatter(fmtX) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.yaxis.set_major_formatter(fmtY) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - py.yticks(np.arange(np.min(x-xerr-0.1), np.max(x+xerr+0.1), 0.2)) - py.xticks(dateTics, DateTicsLabel) - py.xlim(np.min(dateTics), np.max(dateTics)) - py.annotate(starName,xy=(1.0,1.1), xycoords='axes fraction', fontsize=12, color='red') + plt.yticks(np.arange(np.min(x-xerr-0.1), np.max(x+xerr+0.1), 0.2)) + plt.xticks(dateTics, DateTicsLabel) + plt.xlim(np.min(dateTics), np.max(dateTics)) + plt.annotate(starName,xy=(1.0,1.1), xycoords='axes fraction', fontsize=12, color='red') col = col + 1 ind = (row-1)*Ncols + col - paxes = py.subplot(Nrows, Ncols, ind) - py.plot(time, fitLineY, 'b-') - py.plot(time, fitLineY + fitSigY, 'b--') - py.plot(time, fitLineY - fitSigY, 'b--') - py.errorbar(time, y, yerr=yerr, fmt='k.') - rng = py.axis() - py.axis(dateTicRng + [rng[2], rng[3]], fontsize=fontsize1) - py.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) + paxes = plt.subplot(Nrows, Ncols, ind) + plt.plot(time, fitLineY, 'b-') + plt.plot(time, fitLineY + fitSigY, 'b--') + plt.plot(time, fitLineY - fitSigY, 'b--') + plt.errorbar(time, y, yerr=yerr, fmt='k.') + rng = plt.axis() + plt.axis(dateTicRng + [rng[2], rng[3]], fontsize=fontsize1) + plt.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) if time[0] > 50000: - py.xlabel('Date (MJD)', fontsize=fontsize1) - py.ylabel('Y (pix)', fontsize=fontsize1) + plt.xlabel('Date (MJD)', fontsize=fontsize1) + plt.ylabel('Y (pix)', fontsize=fontsize1) #paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.xaxis.set_major_formatter(fmtX) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.yaxis.set_major_formatter(fmtY) paxes.tick_params(axis='both', which='major', labelsize=12) - py.ylim(np.min(y-yerr-0.1),np.max(y+yerr+0.1)) - py.yticks(np.arange(np.min(y-yerr-0.1), np.max(y+yerr+0.1), 0.2)) - py.xticks(dateTics, DateTicsLabel) - py.xlim(np.min(dateTics), np.max(dateTics)) + plt.ylim(np.min(y-yerr-0.1),np.max(y+yerr+0.1)) + plt.yticks(np.arange(np.min(y-yerr-0.1), np.max(y+yerr+0.1), 0.2)) + plt.xticks(dateTics, DateTicsLabel) + plt.xlim(np.min(dateTics), np.max(dateTics)) row = row + 1 col = col - 1 ind = (row-1)*Ncols + col - paxes = py.subplot(Nrows, Ncols, ind) - py.plot(time, np.zeros(len(time)), 'b-') - py.plot(time, fitSigX, 'b--') - py.plot(time, -fitSigX, 'b--') - py.errorbar(time, x - fitLineX, yerr=xerr, fmt='k.') - py.axis(dateTicRng + resTicRng, fontsize=fontsize1) - py.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) + paxes = plt.subplot(Nrows, Ncols, ind) + plt.plot(time, np.zeros(len(time)), 'b-') + plt.plot(time, fitSigX, 'b--') + plt.plot(time, -fitSigX, 'b--') + plt.errorbar(time, x - fitLineX, yerr=xerr, fmt='k.') + plt.axis(dateTicRng + resTicRng, fontsize=fontsize1) + plt.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) if time[0] > 50000: - py.xlabel('Date (MJD)', fontsize=fontsize1) - py.ylabel('X Residuals (pix)', fontsize=fontsize1) + plt.xlabel('Date (MJD)', fontsize=fontsize1) + plt.ylabel('X Residuals (pix)', fontsize=fontsize1) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.xaxis.set_major_formatter(fmtX) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - py.xticks(dateTics, DateTicsLabel) - py.xlim(np.min(dateTics), np.max(dateTics)) + plt.xticks(dateTics, DateTicsLabel) + plt.xlim(np.min(dateTics), np.max(dateTics)) col = col + 1 ind = (row-1)*Ncols + col - paxes = py.subplot(Nrows, Ncols, ind) - py.plot(time, np.zeros(len(time)), 'b-') - py.plot(time, fitSigY, 'b--') - py.plot(time, -fitSigY, 'b--') - py.errorbar(time, y - fitLineY, yerr=yerr, fmt='k.') - py.axis(dateTicRng + resTicRng, fontsize=fontsize1) - py.xlabel('Date -2000 (yrs)', fontsize=fontsize1) + paxes = plt.subplot(Nrows, Ncols, ind) + plt.plot(time, np.zeros(len(time)), 'b-') + plt.plot(time, fitSigY, 'b--') + plt.plot(time, -fitSigY, 'b--') + plt.errorbar(time, y - fitLineY, yerr=yerr, fmt='k.') + plt.axis(dateTicRng + resTicRng, fontsize=fontsize1) + plt.xlabel('Date -2000 (yrs)', fontsize=fontsize1) if time[0] > 50000: - py.xlabel('Date (MJD)', fontsize=fontsize1) - py.ylabel('Y Residuals (pix)', fontsize=fontsize1) + plt.xlabel('Date (MJD)', fontsize=fontsize1) + plt.ylabel('Y Residuals (pix)', fontsize=fontsize1) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.xaxis.set_major_formatter(fmtX) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - py.xticks(dateTics, DateTicsLabel) - py.xlim(np.min(dateTics), np.max(dateTics)) + plt.xticks(dateTics, DateTicsLabel) + plt.xlim(np.min(dateTics), np.max(dateTics)) row = row + 1 col = col - 1 ind = (row-1)*Ncols + col - paxes = py.subplot(Nrows, Ncols, ind) - py.errorbar(x,y, xerr=xerr, yerr=yerr, fmt='k.') - py.yticks(np.arange(np.min(y-yerr-0.1), np.max(y+yerr+0.1), 0.2)) - py.xticks(np.arange(np.min(x-xerr-0.1), np.max(x+xerr+0.1), 0.2), rotation = 270) - py.axis('equal') + paxes = plt.subplot(Nrows, Ncols, ind) + plt.errorbar(x,y, xerr=xerr, yerr=yerr, fmt='k.') + plt.yticks(np.arange(np.min(y-yerr-0.1), np.max(y+yerr+0.1), 0.2)) + plt.xticks(np.arange(np.min(x-xerr-0.1), np.max(x+xerr+0.1), 0.2), rotation = 270) + plt.axis('equal') paxes.tick_params(axis='both', which='major', labelsize=fontsize1) paxes.yaxis.set_major_formatter(FormatStrFormatter('%.2f')) paxes.xaxis.set_major_formatter(FormatStrFormatter('%.2f')) - py.xlabel('X (pix)', fontsize=fontsize1) - py.ylabel('Y (pix)', fontsize=fontsize1) - py.plot(fitLineX, fitLineY, 'b-') + plt.xlabel('X (pix)', fontsize=fontsize1) + plt.ylabel('Y (pix)', fontsize=fontsize1) + plt.plot(fitLineX, fitLineY, 'b-') col = col + 1 ind = (row-1)*Ncols + col bins = np.arange(-7.5, 7.5, 1) - paxes = py.subplot(Nrows, Ncols, ind) + paxes = plt.subplot(Nrows, Ncols, ind) id = np.where(diffY < 0)[0] sig[id] = -1.*sig[id] - (n, b, p) = py.hist(sigX, bins, histtype='stepfilled', color='b', label='X') - py.setp(p, 'facecolor', 'b') - (n, b, p) = py.hist(sigY, bins, histtype='step', color='r', label='Y') - py.axis([-7, 7, 0, 8], fontsize=10) - py.legend() - py.xlabel('Residuals (sigma)', fontsize=fontsize1) - py.ylabel('Number of Epochs', fontsize=fontsize1) + (n, b, p) = plt.hist(sigX, bins, histtype='stepfilled', color='b', label='X') + plt.setp(p, 'facecolor', 'b') + (n, b, p) = plt.hist(sigY, bins, histtype='step', color='r', label='Y') + plt.axis([-7, 7, 0, 8], fontsize=10) + plt.legend() + plt.xlabel('Residuals (sigma)', fontsize=fontsize1) + plt.ylabel('Number of Epochs', fontsize=fontsize1) ########## # @@ -901,9 +907,9 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', # ########## if (radial == True): - py.clf() + plt.clf() - dateTicLoc = py.MultipleLocator(3) + dateTicLoc = plt.MultipleLocator(3) maxErr = np.array([rerr, terr]).max() resTicRng = [-3*maxErr, 3*maxErr] @@ -912,83 +918,83 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', fmtX = FormatStrFormatter('%5i') fmtY = FormatStrFormatter('%6.2f') - paxes = py.subplot(3,2,1) - py.plot(time, fitLineR, 'b-') - py.plot(time, fitLineR + fitSigR, 'b--') - py.plot(time, fitLineR - fitSigR, 'b--') - py.errorbar(time, r, yerr=rerr, fmt='k.') - rng = py.axis() - py.axis(dateTicRng + [rng[2], rng[3]]) - py.xlabel('Date (yrs)') - py.ylabel('R (pix)') + paxes = plt.subplot(3,2,1) + plt.plot(time, fitLineR, 'b-') + plt.plot(time, fitLineR + fitSigR, 'b--') + plt.plot(time, fitLineR - fitSigR, 'b--') + plt.errorbar(time, r, yerr=rerr, fmt='k.') + rng = plt.axis() + plt.axis(dateTicRng + [rng[2], rng[3]]) + plt.xlabel('Date (yrs)') + plt.ylabel('R (pix)') paxes.xaxis.set_major_formatter(fmtX) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.yaxis.set_major_formatter(fmtY) - paxes = py.subplot(3, 2, 2) - py.plot(time, fitLineT, 'b-') - py.plot(time, fitLineT + fitSigT, 'b--') - py.plot(time, fitLineT - fitSigT, 'b--') - py.errorbar(time, t, yerr=terr, fmt='k.') - rng = py.axis() - py.axis(dateTicRng + [rng[2], rng[3]]) - py.xlabel('Date (yrs)') - py.ylabel('T (pix)') + paxes = plt.subplot(3, 2, 2) + plt.plot(time, fitLineT, 'b-') + plt.plot(time, fitLineT + fitSigT, 'b--') + plt.plot(time, fitLineT - fitSigT, 'b--') + plt.errorbar(time, t, yerr=terr, fmt='k.') + rng = plt.axis() + plt.axis(dateTicRng + [rng[2], rng[3]]) + plt.xlabel('Date (yrs)') + plt.ylabel('T (pix)') paxes.xaxis.set_major_formatter(fmtX) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.yaxis.set_major_formatter(fmtY) - paxes = py.subplot(3, 2, 3) - py.plot(time, np.zeros(len(time)), 'b-') - py.plot(time, fitSigR, 'b--') - py.plot(time, -fitSigR, 'b--') - py.errorbar(time, r - fitLineR, yerr=rerr, fmt='k.') - py.axis(dateTicRng + resTicRng) - py.xlabel('Date (yrs)') - py.ylabel('R Residuals (pix)') + paxes = plt.subplot(3, 2, 3) + plt.plot(time, np.zeros(len(time)), 'b-') + plt.plot(time, fitSigR, 'b--') + plt.plot(time, -fitSigR, 'b--') + plt.errorbar(time, r - fitLineR, yerr=rerr, fmt='k.') + plt.axis(dateTicRng + resTicRng) + plt.xlabel('Date (yrs)') + plt.ylabel('R Residuals (pix)') paxes.get_xaxis().set_major_locator(dateTicLoc) - paxes = py.subplot(3, 2, 4) - py.plot(time, np.zeros(len(time)), 'b-') - py.plot(time, fitSigT, 'b--') - py.plot(time, -fitSigT, 'b--') - py.errorbar(time, t - fitLineT, yerr=terr, fmt='k.') - py.axis(dateTicRng + resTicRng) - py.xlabel('Date (yrs)') - py.ylabel('T Residuals (pix)') + paxes = plt.subplot(3, 2, 4) + plt.plot(time, np.zeros(len(time)), 'b-') + plt.plot(time, fitSigT, 'b--') + plt.plot(time, -fitSigT, 'b--') + plt.errorbar(time, t - fitLineT, yerr=terr, fmt='k.') + plt.axis(dateTicRng + resTicRng) + plt.xlabel('Date (yrs)') + plt.ylabel('T Residuals (pix)') paxes.get_xaxis().set_major_locator(dateTicLoc) bins = np.arange(-7, 7, 1) - py.subplot(3, 2, 5) - (n, b, p) = py.hist(sigR, bins) - py.setp(p, 'facecolor', 'k') - py.axis([-5, 5, 0, 20]) - py.xlabel('T Residuals (sigma)') - py.ylabel('Number of Epochs') - - py.subplot(3, 2, 6) - (n, b, p) = py.hist(sigT, bins) - py.axis([-5, 5, 0, 20]) - py.setp(p, 'facecolor', 'k') - py.xlabel('Y Residuals (sigma)') - py.ylabel('Number of Epochs') - - py.subplots_adjust(wspace=0.4, hspace=0.4, right=0.95, top=0.95) - py.savefig(rootDir+'plots/plotStarRadial_' + starName + '.png') - py.show() + plt.subplot(3, 2, 5) + (n, b, p) = plt.hist(sigR, bins) + plt.setp(p, 'facecolor', 'k') + plt.axis([-5, 5, 0, 20]) + plt.xlabel('T Residuals (sigma)') + plt.ylabel('Number of Epochs') + + plt.subplot(3, 2, 6) + (n, b, p) = plt.hist(sigT, bins) + plt.axis([-5, 5, 0, 20]) + plt.setp(p, 'facecolor', 'k') + plt.xlabel('Y Residuals (sigma)') + plt.ylabel('Number of Epochs') + + plt.subplots_adjust(wspace=0.4, hspace=0.4, right=0.95, top=0.95) + plt.savefig(rootDir+'plots/plotStarRadial_' + starName + '.png') + plt.show() title = rootDir.split('/')[-2] - py.suptitle(title, x=0.5, y=0.97) + plt.suptitle(title, x=0.5, y=0.97) if Nstars == 1: - py.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) - py.savefig(rootDir+'plots/plotStar_' + starName + '.png') + plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) + plt.savefig(rootDir+'plots/plotStar_' + starName + '.png') else: - py.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) - py.savefig(rootDir+'plots/plotStar_all.png') - py.show() + plt.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) + plt.savefig(rootDir+'plots/plotStar_all.png') + plt.show() - py.show() + plt.show() print('Fubar') @@ -1051,7 +1057,7 @@ def plot_pm_error(tab): plt.legend() plt.xlabel('Mag') plt.ylabel('PM Error (mas/yr)') - + plt.show() return def plot_mag_error(tab): @@ -1064,16 +1070,15 @@ def plot_mag_error(tab): return -def plot_mean_residuals_by_epoch(tab, motion_model_dict={}): +def plot_mean_residuals_by_epoch(tab): """ Plot mean position and magnitude residuals vs. epoch. Note we are plotting the mean( |dx} ) to see the size of the mean residual. """ # Predicted model positions at each epoch - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod, yt_mod, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod, yt_mod, xt_mod_err, yt_mod_err = tab.predict_positions(tab['t'][i_all_detected]) # Residuals dx = tab['x'] - xt_mod @@ -2221,7 +2226,7 @@ def plot_chi2_dist(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, boot_ plt.hist(x[idx], bins=chi2_bins, histtype='step', label='X', density=True) plt.hist(y[idx], bins=chi2_bins, histtype='step', label='Y', density=True) plt.plot(chi2_xaxis, chi2.pdf(chi2_xaxis, Ndof), 'r-', alpha=0.6, - label='$\chi^2$ ' + str(Ndof) + ' dof') + label=r'$\chi^2$ ' + str(Ndof) + ' dof') plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.xlim(0, xlim) plt.legend() @@ -2306,7 +2311,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, motion_model_dict={}, xlim=40, n_bin plt.hist(x[idx], bins=chi2_bins, histtype='stepfilled', label='RA', density=True, color='skyblue', alpha=0.8, edgecolor='k') plt.hist(y[idx], bins=chi2_bins, histtype='stepfilled', label='DEC', density=True, color='orange', alpha=0.8, edgecolor='k') plt.plot(chi2_xaxis, chi2.pdf(chi2_xaxis, Ndof), 'r-', alpha=0.6, - label='$\chi^2$ ' + str(Ndof) + ' dof') + label=r'$\chi^2$ ' + str(Ndof) + ' dof') #plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.title(str(filter)+' (N = '+str(len(chi2_x_list))+')', fontsize=22) plt.xlim(0, xlim) @@ -2593,7 +2598,7 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30, boot_err=False): plt.clf() plt.hist(chi2_m[idx], bins=np.arange(xlim*10), histtype='step', density=True) plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, - label='$\chi^2$ ' + str(Ndof) + ' dof') + label=r'$\chi^2$ ' + str(Ndof) + ' dof') plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.xlim(0, xlim) plt.legend() @@ -2642,7 +2647,7 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil plt.clf() plt.hist(chi2_m[idx], bins=np.arange(xlim*10), label='mag', histtype='stepfilled', density=True, color='green', alpha=0.7, edgecolor='k') plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, - label='$\chi^2$ ' + str(Ndof) + ' dof') + label=r'$\chi^2$ ' + str(Ndof) + ' dof') #plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.xlim(0, xlim) plt.xlabel(r'$\chi^{2}$', fontsize=28) @@ -3612,8 +3617,8 @@ def plot_sky(stars_tab, foo = cnorm(yearsInt[ee]) colorList.append( cmap(cnorm(yearsInt[ee])) ) - py.close(2) - fig = py.figure(2, figsize=(13,10)) + plt.close(2) + fig = plt.figure(2, figsize=(13,10)) previousYear = 0.0 @@ -3651,13 +3656,13 @@ def plot_sky(stars_tab, label = '_nolegend_' if plot_errors: - (line, foo1, foo2) = py.errorbar(x, y, xerr=xe, yerr=ye, + (line, foo1, foo2) = plt.errorbar(x, y, xerr=xe, yerr=ye, color=colorList[ee], fmt='^', markeredgecolor=colorList[ee], markerfacecolor=colorList[ee], label=label, picker=4) else: - (line, foo1, foo2) = py.errorbar(x, y, xerr=None, yerr=None, + (line, foo1, foo2) = plt.errorbar(x, y, xerr=None, yerr=None, color=colorList[ee], fmt='^', markeredgecolor=colorList[ee], markerfacecolor=colorList[ee], @@ -3675,19 +3680,19 @@ def plot_sky(stars_tab, point_labels[line] = points_info foo = PrintSelected(point_labels, fig, stars_tab, mag_range, manual_print=manual_print) - py.connect('pick_event', foo) + plt.connect('pick_event', foo) xlo = xcenter + (range) xhi = xcenter - (range) ylo = ycenter - (range) yhi = ycenter + (range) - py.axis('equal') - py.axis([xlo, xhi, ylo, yhi]) - py.xlabel('R.A. Offset from Sgr A* (arcsec)') - py.ylabel('Dec. Offset from Sgr A* (arcsec)') + plt.axis('equal') + plt.axis([xlo, xhi, ylo, yhi]) + plt.xlabel('R.A. Offset from Sgr A* (arcsec)') + plt.ylabel('Dec. Offset from Sgr A* (arcsec)') - py.legend(handles=epochs_legend, numpoints=1, loc='lower left', fontsize=12) + plt.legend(handles=epochs_legend, numpoints=1, loc='lower left', fontsize=12) if show_names: xpos = stars_tab['x0'] @@ -3695,16 +3700,16 @@ def plot_sky(stars_tab, goodind = np.where((xpos <= xlo) & (xpos >= xhi) & (ypos >= ylo) & (ypos <= yhi))[0] for ind in goodind: - py.text(xpos[ind], ypos[ind], stars_tab['name'][ind], size=10) + plt.text(xpos[ind], ypos[ind], stars_tab['name'][ind], size=10) if saveplot: - py.show(block=0) + plt.show(block=0) if (center_star != None): - py.savefig('plot_sky_' + center_star + '.png') + plt.savefig('plot_sky_' + center_star + '.png') else: - py.savefig('plot_sky.png') + plt.savefig('plot_sky.png') else: - py.show() + plt.show() return diff --git a/flystar/starlists.py b/flystar/starlists.py index 23df44f..f1f3278 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -421,7 +421,7 @@ def read_starlist(starlistFile, error=True): starlist astropy table. containing: name, m, x, y, xe, ye, t """ - t_ref = Table.read(starlistFile, format='ascii', delimiter='\s') + t_ref = Table.read(starlistFile, format='ascii', delimiter=r'\s') # Check if this already has column names: cols = t_ref.colnames @@ -624,7 +624,7 @@ def from_lis_file(cls, filename, error=True, fvu_file=None): ------ starlists.StarList() object (subclass of Astropy Table). """ - t_ref = Table.read(filename, format='ascii', delimiter='\s') + t_ref = Table.read(filename, format='ascii', delimiter=r'\s') # Check if this already has column names: cols = t_ref.colnames diff --git a/flystar/startables.py b/flystar/startables.py index c12976e..b25f8c5 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -11,78 +11,74 @@ import copy from flystar import motion_model import pandas as pd +from flystar.motion_model import Empty, Fixed, Linear class StarTable(Table): - """ - A StarTable is an astropy.Table with stars matched from multiple starlists. + def __init__(self, *args, ref_list=0, **kwargs): + """ + A StarTable is an astropy.Table with stars matched from multiple starlists. - Required table columns (input as keywords): - ------------------------- - name : 1D numpy.array with shape = N_stars - List of unique names for each of the stars in the table. + Required table columns (input as keywords): + ------------------------- + name : 1D numpy.array with shape = N_stars + List of unique names for each of the stars in the table. - x : 2D numpy.array with shape = (N_stars, N_lists) - Positions of N_stars in each of N_lists in the x dimension. + x : 2D numpy.array with shape = (N_stars, N_lists) + Positions of N_stars in each of N_lists in the x dimension. - y : 2D numpy.array with shape = (N_stars, N_lists) - Positions of N_stars in each of N_lists in the y dimension. + y : 2D numpy.array with shape = (N_stars, N_lists) + Positions of N_stars in each of N_lists in the y dimension. - m : 2D numpy.array with shape = (N_stars, N_lists) - Magnitudes of N_stars in each of N_lists. + m : 2D numpy.array with shape = (N_stars, N_lists) + Magnitudes of N_stars in each of N_lists. - Optional table columns (input as keywords): - ------------------------- - motion_model : 1D numpy.array with shape = N_stars - string indicating motion model type for each star - - xe : 2D numpy.array with shape = (N_stars, N_lists) - Position uncertainties of N_stars in each of N_lists in the x dimension. + Optional table columns (input as keywords): + ------------------------- + motion_model : 1D numpy.array with shape = N_stars + string indicating motion model type for each star + + xe : 2D numpy.array with shape = (N_stars, N_lists) + Position uncertainties of N_stars in each of N_lists in the x dimension. - ye : 2D numpy.array with shape = (N_stars, N_lists) - Position uncertainties of N_stars in each of N_lists in the y dimension. + ye : 2D numpy.array with shape = (N_stars, N_lists) + Position uncertainties of N_stars in each of N_lists in the y dimension. - me : 2D numpy.array with shape = (N_stars, N_lists) - Magnitude uncertainties of N_stars in each of N_lists. + me : 2D numpy.array with shape = (N_stars, N_lists) + Magnitude uncertainties of N_stars in each of N_lists. - ep_name : 2D numpy.array with shape = (N_stars, N_lists) - Names in each epoch for each of N_stars in each of N_lists. This is - useful for tracking purposes. - - corr : 2D numpy.array with shape = (N_stars, N_lists) - Fitting correlation for each of N_stars in each of N_lists. + ep_name : 2D numpy.array with shape = (N_stars, N_lists) + Names in each epoch for each of N_stars in each of N_lists. This is + useful for tracking purposes. + + corr : 2D numpy.array with shape = (N_stars, N_lists) + Fitting correlation for each of N_stars in each of N_lists. - Optional table meta data - ------------------------- - list_names : list of strings - List of names, one for each of the starlists. + Optional table meta data + ------------------------- + list_names : list of strings + List of names, one for each of the starlists. - list_times : list of integers or floats - List of times/dates for each starlist. + list_times : list of integers or floats + List of times/dates for each starlist. - ref_list : int - Specify which list is the reference list (if any). + ref_list : int + Specify which list is the reference list (if any). - Examples - -------------------------- + Examples + -------------------------- - t = startables.StarTable(name=name, x=x, y=y, m=m) + t = startables.StarTable(name=name, x=x, y=y, m=m) - # Access the data: - print(t) - print(t['name'][0:10]) # print the first 10 star names - print(t['x'][0:10, 0]) # print x from the first epoch/list/column for the first 10 stars - """ - def __init__(self, *args, ref_list=0, **kwargs): - """ + # Access the data: + print(t) + print(t['name'][0:10]) # print the first 10 star names + print(t['x'][0:10, 0]) # print x from the first epoch/list/column for the first 10 stars """ # Check if the required arguments are present arg_req = ('name', 'x', 'y', 'm') - - found_all_required = True - for arg_test in arg_req: - if arg_test not in kwargs: - found_all_required = False + + found_all_required = all(arg in kwargs for arg in arg_req) if not found_all_required: if len(args) > 1: # If there are no arguments, it's because the @@ -130,6 +126,7 @@ def __init__(self, *args, ref_list=0, **kwargs): # We have to have special handling of meta-data (i.e. info that has # dimensions of n_lists). meta_tab = ('list_times', 'list_names') + meta_tab = ('list_times', 'list_names') meta_type = ((float, int), str) for mm in range(len(meta_tab)): meta_test = meta_tab[mm] @@ -151,7 +148,7 @@ def __init__(self, *args, ref_list=0, **kwargs): names=('name', 'x', 'y', 'm')) self['name'] = self['name'].astype('U20') self.meta = {'n_stars': n_stars, 'n_lists': n_lists, 'ref_list': ref_list} - + for meta_arg in meta_tab: if meta_arg in kwargs: self.meta[meta_arg] = kwargs[meta_arg] @@ -161,7 +158,7 @@ def __init__(self, *args, ref_list=0, **kwargs): del kwargs[meta_arg] for arg in kwargs: - if arg in ['name', 'x', 'y', 'm']: + if arg in ['name', 'x', 'y', 'm', 'list_times', 'list_names']: continue else: self.add_column(Column(data=kwargs[arg], name=arg)) @@ -225,7 +222,7 @@ def _add_list_data_from_starlist(self, starlist): else: # Add junk data it if wasn't input self._set_invalid_list_values(col_name, -1) - + ########## # Update the table meta-data. Remember that entries are lists not numpy arrays. ########## @@ -234,7 +231,7 @@ def _add_list_data_from_starlist(self, starlist): lis_meta_keys = list(starlist.meta.keys()) # append 's' to the end to pluralize the input starlist. lis_meta_keys_plural = [lis_meta_key + 's' for lis_meta_key in lis_meta_keys] - + for kk in range(len(tab_meta_keys)): tab_key = tab_meta_keys[kk] @@ -244,19 +241,19 @@ def _add_list_data_from_starlist(self, starlist): # If we find the key in the starlists' meta argument, then add the new values. # Otherwise, add "None". - idx = np.where(lis_meta_keys_plural == tab_key)[0] - if len(idx) > 0: - lis_key = lis_meta_keys[idx[0]] + idx = lis_meta_keys_plural.index(tab_key) if tab_key in lis_meta_keys_plural else None + if idx is not None: + lis_key = lis_meta_keys[idx] self.meta[tab_key] = np.append(self.meta[tab_key], [starlist.meta[lis_key]]) else: self._append_invalid_meta_values(tab_key) # Update the n_lists meta keyword. self.meta['n_lists'] += 1 - + return - - + + def _add_list_data_from_keywords(self, **kwargs): # # Check if the required arguments are present # arg_req = ('x', 'y', 'm') @@ -539,331 +536,438 @@ def detections(self): return - def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=0, - fixed_t0=False, verbose=False, mask_val=None, mask_lists=False, show_progress=True, - default_motion_model='Linear', reassign_motion_model=False, select_stars=None, motion_model_dict={}): - """Fit velocities for all stars in the table and add to the columns 'vx', 'vxe', 'vy', 'vye', 'x0', 'x0e', 'y0', 'y0e'. + def fit_motion_model( + self, + motion_models=None, + fixed_params_dict=None, + weighting='var', + use_scipy=False, + absolute_sigma=True, + select_stars=None, + bootstrap=0, + verbose=True, + mask_value=None, + mask_lists=None, + fill_value=np.nan, + show_progress=True + ): + """Fit velocity for star table Parameters ---------- + motion_models : list of MotionModel or str, optional + Motion models to use, by default Empty, Fixed and Linear. + Empty and Fixed models are always added automatically for stars with n_fit = 0 or 1. + The behavior is as follows: + 1. If 'motion_model_input' column is NOT in table: + - Use the most complex model that has enough parameters to fit the data (n_fit >= n_params). + - If multiple models are supplied, prioritize the model with the most parameters to fit. + - If multiple models have the same number of parameters, raise AssertionError: not sure which to use. + 2. If 'motion_model_input' column IS in table: + - Use the model specified in the 'motion_model_input' column. + - If not enough data points to fit the specified model, use the most complex model in any 'motion_model_input' column that has enough parameters to fit the data (n_fit >= n_params) among the provided motion_models and 'motion_model_input'. + The actual used motion model is stored in the 'motion_model_used' column. The default motion_models are [Empty, Fixed, Linear]. + fixed_params_dict : dict, optional + Dictionary of fixed parameters for motion models, e.g., {'t0': 0., 'ra': np.array([...]), 'dec': np.array([...])}. + - Scalar values are used for all stars, array values should have length = N_stars. + - t0 is automatically calculated as np.average(t, weights=1/np.hypot(xe, ye)) if not provided. + - The keys should match the fixed parameter names in the motion models. See MotionModel class for details, by default None weighting : str, optional - Weight by variance 'var' or standard deviation 'std', by default 'var' + Uncertainty weighting, 'std' for weight=1/xe(ye) or 'var' for weight=1/xe(ye)**2, by default 'var' + use_scipy : bool, optional + Use scipy.optimize.curve_fit or algebraic solution (for Linear model only), by default False + absolute_sigma : bool, optional + Use absolute sigma or not, see scipy curve_fit for details, by default True + select_stars : list of int, optional + Indices of stars to fit, by default None (fit all stars) bootstrap : int, optional - Calculate uncertainty using bootstraping or not, by default 0 - fixed_t0 : bool or array-like, optional - Fix the t0 in dt = time - t0 if user provides an array with the same length of the table, or automatically calculate t0 = np.average(time, weights=1/np.hypot(xe, ye)) if False, by default False + Number of bootstrap for uncertainty resampling, by default 0 verbose : bool, optional - Output verbose information or not, by default False - mask_val : float, optional - Value that needs to be masked in the data, e.g. -100000, by default None - mask_lists : list, optional - Columns that needs to be masked, by default False + Print verbose messages or not, by default True + mask_value : float, optional + Values to mask in data, by default None + mask_lists : list of int, optional + Indices of lists to mask/exclude from fitting, by default None + fill_value : float, optional + Fill value when there is not enough data points to fit, by default np.nan show_progress : bool, optional Show progress bar or not, by default True Raises ------ ValueError - If weighting is neither 'var' or 'std' + If weighting is not 'var' or 'std'. KeyError - If there's not time information in the table + If time values are not found in the table or meta. + KeyError + If required columns 'x' and 'y' are missing in the table. """ + ########################### + ####### Check Params ###### + ########################### if weighting not in ['var', 'std']: - raise ValueError(f"fit_velocities: Weighting must either be 'var' or 'std', not {weighting}!") - + raise ValueError(f"fit_motion_model: Weighting must either be 'var' or 'std', not {weighting}!") + if ('t' not in self.colnames) and ('list_times' not in self.meta): - raise KeyError("fit_velocities: Failed to access time values. No 't' column in table, no 'list_times' in meta.") - + raise KeyError("fit_motion_model: Failed to access time values. No 't' column in table, no 'list_times' in meta.") + # Check if we have the required columns if not all([_ in self.colnames for _ in ['x', 'y']]): - raise KeyError(f"fit_velocities: Missing required columns in the table: {', '.join(['x', 'y'])}!") + raise KeyError(f"fit_motion_model: Missing required columns in the table: {', '.join(['x', 'y'])}!") + + # Check fixed_params_dict is a dict + if fixed_params_dict is not None: + if not isinstance(fixed_params_dict, dict): + raise ValueError("fit_motion_model: fixed_params_dict must be a dictionary!") + + # Convert motion_models to MotionModel objects if they are strings: + if motion_models is None: + # Setting the default to None to avoid mutable default argument issue + # See https://stackoverflow.com/questions/15189245/assigning-class-variable-as-default-value-to-class-method-argument + motion_models = [Empty, Fixed, Linear] + all_mm_map = motion_model.motion_model_map() + if all(isinstance(mm, str) for mm in motion_models): + mm_names = motion_models + motion_models = [all_mm_map[mm] for mm in motion_models] + else: + mm_names = [mm.name for mm in motion_models] + # Always add Empty and Fixed in motion models + if 'Fixed' not in mm_names: + motion_models.insert(0, Fixed) + if 'Empty' not in mm_names: + motion_models.insert(0, Empty) + mm_names = [mm.name for mm in motion_models] + + # Construct motion models if motion_model_input column exists + if 'motion_model_input' in self.colnames: + input_mm_names = np.unique(self['motion_model_input']) + assert all([name in all_mm_map.keys() for name in input_mm_names]), \ + f"fit_motion_model: Unknown motion model name(s) in 'motion_model_input' column. Available motion models are: {', '.join(all_mm_map.keys())}." + for mm_name in input_mm_names: + if mm_name not in mm_names: + motion_models.append(all_mm_map[mm_name]) + + # Sort motion models by n_params + motion_models = sorted(motion_models, key=lambda mm: mm.n_params) + + input_mm_map = {mm.name: mm for mm in motion_models} + + mm_n_params = np.sort([mm.n_params for mm in motion_models]) + if 'motion_model_input' not in self.colnames: + # If motion_model_input column is not provided, assert that motion model n_params are unique and sorted + # Otherwise the fitter does not know which motion model to use based on n_obs + assert len(mm_n_params) == len(set(mm_n_params)), \ + f"fit_motion_model: Provided motion model n_params are not unique! Motion Models are: {[_.name for _ in motion_models]} Cannot decide which motion model to use based on n_obs. Please provide unique motion_models or a 'motion_model_input' column." + + + ########################### + ####### Prepare Data ###### + ########################### + # Prepare data for fitting N_stars = len(self) + x_data = np.ma.masked_invalid(self['x'].data, copy=True) + y_data = np.ma.masked_invalid(self['y'].data, copy=True) + xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else np.ones_like(x_data) + ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else np.ones_like(y_data) + + if mask_lists is not None: + x_data.mask[:, mask_lists] = True + y_data.mask[:, mask_lists] = True + xe_data.mask[:, mask_lists] = True + ye_data.mask[:, mask_lists] = True + + # t_data: 2d array with shape (N_stars, N_epochs) + # t0: 1d array with shape (N_stars,) + if 't' in self.colnames: + t_data = copy.deepcopy(self['t'].data) + else: + t_data = copy.deepcopy(np.array(self.meta['list_times'])) + t_data = np.broadcast_to(t_data, x_data.shape) + + # Add default t0 if not provided in fixed_params_dict + if fixed_params_dict is None: + weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None + fixed_params_dict = {'t0': np.average(t_data, axis=1, weights=weights)} + elif 't0' not in fixed_params_dict: + weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None + fixed_params_dict['t0'] = np.average(t_data, axis=1, weights=weights) + else: + if np.ndim(fixed_params_dict['t0']) == 0: + fixed_params_dict['t0'] = np.full(N_stars, fixed_params_dict['t0']) + + t0 = fixed_params_dict['t0'] + + # Prepare fixed_params_dict for each star + # This avoids checking types and slicing inside the fitting loop + fixed_params_stars = [{} for _ in range(N_stars)] + # Identify array parameters (length N_stars) and scalar parameters + array_params = {k: v for k, v in fixed_params_dict.items() if np.ndim(v) > 0 and len(v) == N_stars} + scalar_params = {k: v for k, v in fixed_params_dict.items() if k not in array_params} + + # Construct list of dicts for each star + # Using list comprehension for speed + fixed_params_stars = [ + {**scalar_params, **{k: v[i] for k, v in array_params.items()}} + for i in range(N_stars) + ] + + # Apply mask_value if provided + if mask_value: + x_data = np.ma.masked_values(x_data, mask_value) + y_data = np.ma.masked_values(y_data, mask_value) + if xe_data is not None: + xe_data = np.ma.masked_values(xe_data, mask_value) + if ye_data is not None: + ye_data = np.ma.masked_values(ye_data, mask_value) + + + # Calculate mask array + xy_mask = (~x_data.mask) & (~y_data.mask) + self['n_fit'] = xy_mask.sum(axis=1) + + # Convert to lists of arrays for faster access during fitting + t_stars = [np.array(t_data[i][xy_mask[i]]) for i in range(N_stars)] + x_stars = [np.array(x_data[i][xy_mask[i]]) for i in range(N_stars)] + y_stars = [np.array(y_data[i][xy_mask[i]]) for i in range(N_stars)] + xe_stars = [np.array(xe_data[i][xy_mask[i]]) if xe_data is not None else None for i in range(N_stars)] + ye_stars = [np.array(ye_data[i][xy_mask[i]]) if ye_data is not None else None for i in range(N_stars)] + + + ########################### + ####### Determine MM ###### + ########################### + n_fit = np.array(self['n_fit']) + if 'motion_model_input' in self.colnames: + # Determine which motion model to use based on motion_model_input column + # If n_fit < required n_params for the input motion model, use the most complicated motion model with n_fit >= n_params + required_params = np.array([all_mm_map[mm_name].n_params for mm_name in self['motion_model_input']]) + reassign_mm = n_fit < required_params + + mm_digitized = np.digitize( + x=n_fit[reassign_mm], + bins=mm_n_params + ) - 1 # Convert to 0-based index + + # Assign motion models to stars + self['motion_model_used'] = self['motion_model_input'] + self['motion_model_used'][reassign_mm] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') - if verbose: - start_time = time.time() - msg = 'Starting startable.fit_velocities for {0:d} stars with n={1:d} bootstrap' - print(msg.format(N_stars, bootstrap)) - - # Set all to default_motion_model if none assigned already. - # Reset motion_model_used to the inputs for now -> will change as fits run - if ('motion_model_input' not in self.colnames) or reassign_motion_model: - self['motion_model_input'] = default_motion_model - self['motion_model_used'] = self['motion_model_input'] + else: + mm_digitized = np.digitize( + x=n_fit, + bins=mm_n_params + ) - 1 # Convert to 0-based index - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, self, default_motion_model) - - # - # Fill table with all possible motion model parameter names as new - # columns. Make everything empty for now. - # - all_motion_models = np.unique(self['motion_model_input'].tolist() + ['Fixed']+[default_motion_model]).tolist() - new_col_list = motion_model.get_list_motion_model_param_names(all_motion_models, with_errors=True) - # Append goodness of fit metrics and t0. + # Assign motion models to stars + self['motion_model_used'] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') + + # Add default obsLocation if not provided in fixed_params_dict + mm_used = np.unique(self['motion_model_used'].name) + if 'Parallax' in mm_used and 'obsLocation' not in fixed_params_dict: + fixed_params_dict['obsLocation'] = 'earth' + + ############################ + ####### Prepare Table ###### + ############################ + # Fill table with all possible motion model parameter names as new columns. + motion_model_used = [all_mm_map[name] for name in np.unique(self['motion_model_used'])] + new_col_list = motion_model.motion_model_param_names(motion_model_used, with_errors=True, with_fixed=False) new_col_list += ['chi2_x', 'chi2_y', 'n_params'] + if 't0' not in new_col_list: new_col_list.append('t0') - # Define output arrays for the best-fit parameters. + # Add new columns if they do not exist for col in new_col_list: - # Clean/remove up old arrays. - if col in self.colnames: self.remove_column(col) - # Add column #TODO: is this good for filling??? - self.add_column(Column(data = np.full(N_stars, np.nan, dtype=float), name = col)) - - # Add a column to keep track of the number of points used in a fit. - self['n_fit'] = 0 - - # Preserve the number of bootstraps that will be run (if any). - self.meta['n_fit_bootstrap'] = bootstrap - - # (FIXME: Do we need to catch the case where there's a single *unmasked* epoch?) - # Catch the case when there is only a single epoch. Just return 0 velocity - # and the same input position for the x0/y0. - if len(self['x'].shape) == 1: - self['motion_model_used'] = 'Fixed' - self['x0'] = self['x'] - self['y0'] = self['y'] - if 't' in self.colnames: - self['t0'] = self['t'] + if col in self.colnames: + # Keep old data if the column already exists + continue + if col.endswith('_err'): + self.add_column( + Column(data=np.full(N_stars, np.inf, dtype=float), name=col), + rename_duplicate=True + ) else: - self['t0'] = self.meta['list_times'][0] - if 'xe' in self.colnames: - self['x0_err'] = self['xe'] - self['y0_err'] = self['ye'] - self['n_fit'] = 1 - self['n_params'] = 1 - return - - if (self['x'].shape[1] == 1): - self['motion_model_used'] = 'Fixed' - self['x0'] = self['x'][:,0] - self['y0'] = self['y'][:,0] - if 't' in self.colnames: - self['t0'] = self['t'][:, 0] + self.add_column( + Column(data=np.full(N_stars, fill_value, dtype=float), name=col), + rename_duplicate=True + ) + + # Add fixed parameter columns if they do not exist + fixed_param_names = [] + for mm in motion_model_used: + for param in mm.fixed_param_names: + if param not in fixed_param_names: + fixed_param_names.append(param) + # Remove t0 from fixed_param_names as it will be saved during fitting + if 't0' in fixed_param_names: + fixed_param_names.remove('t0') + + # Add fixed parameter columns + for param in fixed_param_names: + coldata = np.array([fixed_params_stars[i][param] for i in range(N_stars)]) + if param in self.colnames: + # If the column already exists, check if the data are the same + if np.allclose(self[param], coldata, equal_nan=True): + # Same data, skip + continue + else: + # Different data, add with _mm suffix to avoid name conflict + colname = param + '_mm' else: - self['t0'] = self.meta['list_times'][0] - if 'xe' in self.colnames: - self['x0_err'] = self['xe'][:,0] - self['y0_err'] = self['ye'][:,0] - self['n_fit'] = 1 - self['n_params'] = 1 - return - - # Only fit selected stars, if list given - fit_star_idxs = range(N_stars) + colname = param + + self.add_column(Column(data=coldata, name=colname)) + + + # Add a column to keep track of the number of points used in a fit and number of bootstrap used. + self.meta['n_bootstrap'] = bootstrap + + + ########################### + ######### FITTING ######### + ########################### + unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) if select_stars is not None: - fit_star_idxs = select_stars - # STARS LOOP through the stars and work on them 1 at a time. - # This is slow; but robust. - if show_progress: - for ss in tqdm(fit_star_idxs): - self.fit_velocity_for_star(ss, motion_model_dict, weighting=weighting, bootstrap=bootstrap, - use_scipy=use_scipy, absolute_sigma=absolute_sigma, - fixed_t0=fixed_t0, default_motion_model=default_motion_model, - mask_val=mask_val, mask_lists=mask_lists) + select_stars = np.asarray(select_stars) + if select_stars.dtype == bool: + select_stars = np.flatnonzero(select_stars) + else: + select_stars = np.asarray(select_stars, dtype=int) + indices_by_motion_model = {key: np.intersect1d(select_stars, np.flatnonzero(unique_inv_indices == k)) for k, key in enumerate(unique_motion_models)} else: - for ss in fit_star_idxs: - self.fit_velocity_for_star(ss, motion_model_dict, weighting=weighting, bootstrap=bootstrap, - use_scipy=use_scipy, absolute_sigma=absolute_sigma, - fixed_t0=fixed_t0, default_motion_model=default_motion_model, - mask_val=mask_val, mask_lists=mask_lists) - if verbose: - stop_time = time.time() - print('startable.fit_velocities runtime = {0:.0f} s for {1:d} stars'.format(stop_time - start_time, N_stars)) - + indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} + + + # Expensive for loop! Prepare everything beforehand to speed up. + for unique_motion_model, unique_index in indices_by_motion_model.items(): + # Create motion model instance + motion_model_instance = input_mm_map[unique_motion_model]() + param_names = motion_model_instance.fit_param_names + # Initialize arrays to store results + n_stars_this_model = len(unique_index) + n_params = len(param_names) + + params_array = np.full((n_stars_this_model, n_params), fill_value, dtype=float) + param_errs_array = np.full((n_stars_this_model, n_params), np.inf, dtype=float) + chi2_x_array = np.full(n_stars_this_model, np.nan, dtype=float) + chi2_y_array = np.full(n_stars_this_model, np.nan, dtype=float) + + # Expensive for loop! Prepare everything beforehand to speed up. + for idx, i_star in enumerate(tqdm(unique_index, disable=not show_progress, desc=f"Fitting motion model {unique_motion_model}")): + # Fit the star + params, param_errs, chi2_x, chi2_y = motion_model_instance.fit( + t=t_stars[i_star], + x=x_stars[i_star], + y=y_stars[i_star], + xe=xe_stars[i_star], + ye=ye_stars[i_star], + fixed_params_dict=fixed_params_stars[i_star], + weighting=weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma, + bootstrap=bootstrap, + fill_value=fill_value, + return_chi2=True, + verbose=verbose + ) + params_array[idx] = params + param_errs_array[idx] = param_errs + chi2_x_array[idx] = chi2_x + chi2_y_array[idx] = chi2_y + + # Store results back to the table + for j, param_name in enumerate(param_names): + self[param_name][unique_index] = params_array[:, j] + self[param_name + '_err'][unique_index] = param_errs_array[:, j] + self['chi2_x'][unique_index] = chi2_x_array + self['chi2_y'][unique_index] = chi2_y_array + self['n_params'][unique_index] = motion_model_instance.n_params + self['t0'][unique_index] = t0[unique_index] return - def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', use_scipy=True, absolute_sigma=True, - bootstrap=False, fixed_t0=False, mask_val=None, mask_lists=False, - default_motion_model='Linear'): - # TODO: "weighting" is not used - # - # Make a mask of invalid (NaN) values and a user-specified invalid value. - # - - x = np.ma.masked_invalid(self['x'][ss, :].data) - y = np.ma.masked_invalid(self['y'][ss, :].data) - if mask_val: - x = np.ma.masked_values(x, mask_val) - y = np.ma.masked_values(y, mask_val) - # If no mask, convert x.mask to list - if not np.ma.is_masked(x): - x.mask = np.zeros_like(x.data, dtype=bool) - if not np.ma.is_masked(y): - y.mask = np.zeros_like(y.data, dtype=bool) - - if mask_lists is not False: - # Remove a list - if isinstance(mask_lists, list): - if all(isinstance(item, int) for item in mask_lists): - x.mask[mask_lists] = True - y.mask[mask_lists] = True - - # Throw a warning if mask_lists is not a list - if not isinstance(mask_lists, list): - raise RuntimeError('mask_lists needs to be a list.') - # - # Assign the appropriate positional errors - # - if 'xe' in self.colnames: - # Make a mask of invalid (NaN) values and a user-specified invalid value. - xe = np.ma.masked_invalid(self['xe'][ss, :].data) - ye = np.ma.masked_invalid(self['ye'][ss, :].data) - - # Catch the case where we have positions but no errors for - # some of the entries... we need to "fill in" reasonable - # weights for these... just use the average weights over - # all the other epochs. - pos_no_err = np.where((np.isfinite(x) & np.isfinite(y)) & - (np.isfinite(xe) == False) & (np.isfinite(ye) == False))[0] - pos_with_err = np.where((np.isfinite(x) & np.isfinite(y)) & - (np.isfinite(xe) & np.isfinite(ye)))[0] - - if len(pos_with_err) > 0: - xe[pos_no_err] = xe[pos_with_err].mean() - ye[pos_no_err] = ye[pos_with_err].mean() - else: - xe[pos_no_err] = 1.0 - ye[pos_no_err] = 1.0 - else: - N_epochs = len(x) - xe = np.ones(N_epochs, dtype=float) - ye = np.ones(N_epochs, dtype=float) - xe = np.ma.masked_invalid(xe) - ye = np.ma.masked_invalid(xe) + def infer_positions(self, times, fill_value=np.nan): + """Infer star positions at given times using fitted motion models. - if mask_val: - xe = np.ma.masked_values(xe, mask_val) - ye = np.ma.masked_values(ye, mask_val) - # If no mask, convert xe.mask to list - if not np.ma.is_masked(xe): - xe.mask = np.zeros_like(xe.data, dtype=bool) - if not np.ma.is_masked(ye): - ye.mask = np.zeros_like(ye.data, dtype=bool) - - if mask_lists is not False: - # Remove a list - if isinstance(mask_lists, list): - if all(isinstance(item, int) for item in mask_lists): - xe.mask[mask_lists] = True - ye.mask[mask_lists] = True - - # Throw a warning if mask_lists is not a list - if not isinstance(mask_lists, list): - raise RuntimeError('mask_lists needs to be a list.') + Parameters + ---------- + times : array_like + Times at which to predict positions. + fill_value : float, optional + Value to use for missing data, by default np.nan - # - # Make a mask of invalid (NaN) values and a user-specified invalid value. - # - if 't' in self.colnames: - t = np.ma.masked_invalid(self['t'][ss, :].data) + Returns + ------- + x, y, xe, ye : ndarray + Arrays of predicted x, y positions and their uncertainties xe, ye, with shape (N_stars, N_times) or (N_stars,) if N_times=1, or (N_times,) if N_stars=1, or scalar. + """ + assert 'motion_model_used' in self.colnames, \ + "infer_positions: 'motion_model_used' column not found in the table. Please run fit_motion_model() first." + + N_stars = len(self) + times = np.atleast_1d(times) + N_times = len(times) + + if (N_stars > 1) and (N_times > 1): + x_pred = np.full((N_stars, N_times), fill_value, dtype=float) + y_pred = np.full((N_stars, N_times), fill_value, dtype=float) + xe_pred = np.full((N_stars, N_times), np.inf, dtype=float) + ye_pred = np.full((N_stars, N_times), np.inf, dtype=float) + elif N_stars==1: + x_pred = np.full(N_times, fill_value, dtype=float) + y_pred = np.full(N_times, fill_value, dtype=float) + xe_pred = np.full(N_times, np.inf, dtype=float) + ye_pred = np.full(N_times, np.inf, dtype=float) else: - t = np.ma.masked_invalid(self.meta['list_times']) + x_pred = np.full(N_stars, fill_value, dtype=float) + y_pred = np.full(N_stars, fill_value, dtype=float) + xe_pred = np.full(N_stars, np.inf, dtype=float) + ye_pred = np.full(N_stars, np.inf, dtype=float) - if mask_val: - t = np.ma.masked_values(t, mask_val) - if not np.ma.is_masked(t): - t.mask = np.zeros_like(t.data, dtype=bool) + + unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) + indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} + + # Prepare fit_params, fixed_params, fit_param_errs for each star + + for unique_motion_model, unique_index in indices_by_motion_model.items(): + # Create motion model instance + motion_model_instance = motion_model.motion_model_map()[unique_motion_model]() + # Prepare parameters for prediction + fit_params = np.array([ + self[param_name][unique_index] for param_name in motion_model_instance.fit_param_names + ]).T # shape (N_stars_this_model, N_params) + + fit_param_errs = np.array([ + self[param_name + '_err'][unique_index] for param_name in motion_model_instance.fit_param_names + ]).T # shape (N_stars_this_model, N_params) + + fixed_params = {} + for param_name in motion_model_instance.fixed_param_names: + col_name = param_name + if param_name + '_mm' in self.colnames: + col_name = param_name + '_mm' + fixed_params[param_name] = self[col_name][unique_index] - if mask_lists is not False: - # Remove a list - if isinstance(mask_lists, list): - if all(isinstance(item, int) for item in mask_lists): - t.mask[mask_lists] = True + # TODO: vectorize obsLocation handling in motion models + if (param_name == 'obsLocation'): + assert np.unique(fixed_params[param_name]).size == 1, \ + "infer_positions: obsLocation fixed parameter has different values for different stars. Vectorized handling not implemented yet." + fixed_params[param_name] = fixed_params[param_name][0] - # Throw a warning if mask_lists is not a list - if not isinstance(mask_lists, list): - raise RuntimeError('mask_lists needs to be a list.') + # Predict positions + x, y, xe, ye = motion_model_instance.model( + times, fit_params, fit_param_errs, fixed_params + ) + x_pred[unique_index] = x + y_pred[unique_index] = y + xe_pred[unique_index] = xe + ye_pred[unique_index] = ye + + return x_pred, y_pred, xe_pred, ye_pred - # For inconsistent masks, mask the star if any of the values are masked. - new_mask = np.logical_or.reduce((t.mask, x.mask, y.mask, xe.mask, ye.mask)) - - # - # Figure out where we have detections (as indicated by error columns) - # - good = np.where((xe != 0) & (ye != 0) & - np.isfinite(xe) & np.isfinite(ye) & - np.isfinite(x) & np.isfinite(y) & ~new_mask)[0] - - N_good = len(good) - - # Catch the case where there is NO good data. - if N_good == 0: - #self['motion_model_used'][ss] = 'None' - self['n_fit'][ss] = N_good - self['n_params'][ss] = 0 - return - # Everything below has N_good >= 1 - x = x[good] - y = y[good] - t = t[good] - xe = xe[good] - ye = ye[good] - - # - # Unless t0 is fixed, calculate the t0 for the stars. - # - if fixed_t0 is False: - t_weight = 1.0 / np.hypot(xe, ye) - t0 = np.average(t, weights=t_weight) - elif fixed_t0 is True: - t0 = self.t0 - else: - t0 = fixed_t0[ss] - self['t0'][ss] = t0 - self['n_fit'][ss] = N_good - - # - # Decide which motion_model to fit. - # - motion_model_use = self['motion_model_input'][ss] - # Go to default model if not enough points for assigned but enough for default - # TODO: think about whether we want other fallbacks besides the singular default and Fixed - if (N_good < motion_model_dict[motion_model_use].n_pts_req) and \ - (N_good >= motion_model_dict[default_motion_model].n_pts_req): - motion_model_use = default_motion_model - # If not enough points for either, go to a fixed model - elif (N_good < motion_model_dict[motion_model_use].n_pts_req) and \ - (N_good < motion_model_dict[default_motion_model].n_pts_req): - motion_model_use = 'Fixed' - # If the points do not cover multiple times, go to a fixed model - if (t == t[0]).all(): - motion_model_use = 'Fixed' - - self['motion_model_used'][ss] = motion_model_use - -# # Get the motion model object. -# modClass = motion_model_dict[motion_model_use] -# -# # Load up any prior information on parameters for this model. -# param_dict = {} -# for par in modClass.fitter_param_names+modClass.fixed_param_names: -# if ~np.isnan(self[par][ss]): -# param_dict[par] = self[par][ss] - - # Model object - mod = motion_model_dict[motion_model_use] - fixed_params = [self[par][ss] for par in mod.fixed_param_names] - - # Fit for the best parameters - params, param_errs = mod.fit_motion_model(t, x, y, xe, ye, t0, bootstrap=bootstrap, - weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma) - chi2_x,chi2_y = mod.get_chi2(params,fixed_params, t,x,y,xe,ye) - self['chi2_x'][ss]=chi2_x - self['chi2_y'][ss]=chi2_y - self['n_params'][ss] = mod.n_params - - # Save parameters and errors to table. - for pp in range(len(mod.fitter_param_names)): - par = mod.fitter_param_names[pp] - par_err = par + '_err' - self[par][ss] = params[pp] - self[par_err][ss] = param_errs[pp] - - return - # New function, to use in align def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True): """ Get current x,y positions of each star according to its motion_model @@ -893,7 +997,7 @@ def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True mod = motion_model_dict[mm] # Set up parameters param_dict = {} - for par in mod.fitter_param_names + mod.fixed_param_names + [pm+'_err' for pm in mod.fitter_param_names]: + for par in mod.fit_param_names + mod.fixed_param_names + [pm+'_err' for pm in mod.fit_param_names]: param_dict[par] = self[par][idx] x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) except: @@ -913,147 +1017,9 @@ def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True param_dict[par] = self[par][idx] x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) - return x,y,xe,ye + return x, y, xe, ye - def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', use_scipy=True, absolute_sigma=True, times=None, - select_stars=None, epoch_cols='all', mask_val=None, art_star=False, return_result=False): - """Fit velocities for stars detected in all epochs specified by epoch_cols. - Criterion: xe/ye error > 0 and finite, x/y not masked. - - Parameters - ---------- - motion_model_to_fit : MotionModel - Motion model object to use for fitting all stars - weighting : str, optional - Variance weighting('var') or standard deviation weighting ('std'), by default 'var' - select_idx : array-like, optional - Indices of stars to select for fitting, by default None (fit all detected stars) - epoch_cols : str or list of intergers, optional - List of epoch column indices used for fitting velocity, by default 'all' - mask_val : float, optional - Values in x, y to be masked - art_star : bool, optional - Artificial star or observation star catalog. If artificial star, use 'det' column to select stars detected in all epochs, by default False - return_result : bool, optional - Return the velocity results or not, by default False - - Returns - ------- - vel_result : astropy Table - Astropy Table with velocity results - """ - - N_stars = len(self) - if select_stars is None: - select_stars = np.arange(N_stars) - else: - select_stars = np.asarray(select_stars) - - if epoch_cols == 'all': - epoch_cols = np.arange(np.shape(self['x'])[1]) - - # Artificial Star - if art_star: - detected_in_all_epochs = np.all(self['det'][select_stars, :][:, epoch_cols], axis=1) - - # Observation Star - else: - valid_xe = np.all(self['xe'][select_stars, :][:, epoch_cols]!=0, axis=1) & np.all(np.isfinite(self['xe'][select_stars, :][:, epoch_cols]), axis=1) - valid_ye = np.all(self['ye'][select_stars, :][:, epoch_cols]!=0, axis=1) & np.all(np.isfinite(self['ye'][select_stars, :][:, epoch_cols]), axis=1) - - if mask_val: - x = np.ma.masked_values(self['x'][select_stars, :][:, epoch_cols], mask_val) - y = np.ma.masked_values(self['y'][select_stars, :][:, epoch_cols], mask_val) - - # If no mask, convert x.mask to list - if not np.ma.is_masked(x): - x.mask = np.zeros_like(self['x'][select_stars, :][:, epoch_cols].data, dtype=bool) - if not np.ma.is_masked(y): - y.mask = np.zeros_like(self['y'][select_stars, :][:, epoch_cols].data, dtype=bool) - - valid_x = ~np.any(x.mask, axis=1) - valid_y = ~np.any(y.mask, axis=1) - detected_in_all_epochs = np.logical_and.reduce(( - valid_x, valid_y, valid_xe, valid_ye)) - else: - detected_in_all_epochs = np.logical_and(valid_xe, valid_ye) - - N = len(self['x'][select_stars, :]) - fit_params = motion_model_to_fit.fitter_param_names - param_data = {p: np.zeros(N) for p in fit_params} - param_data.update({p+'_err': np.zeros(N) for p in fit_params}) - param_data.update({p: np.zeros(N) for p in motion_model_to_fit.fixed_param_names}) - param_data['chi2_x'] = np.zeros(N) - param_data['chi2_y'] = np.zeros(N) - - if times is None: - if 'YEARS' in self.meta: - times = np.array(self.meta['YEARS'])[epoch_cols] - elif 't' in self.colnames: - times = self['t'][0, epoch_cols] - else: - raise ValueError("No valid time column found.") - - if not art_star: - x_arr = self['x'][select_stars, :][:, epoch_cols] - y_arr = self['y'][select_stars, :][:, epoch_cols] - else: - x_arr = self['x'][select_stars, :][:, epoch_cols, 1] - y_arr = self['y'][select_stars, :][:, epoch_cols, 1] - - xe_arr = self['xe'][select_stars, :][:, epoch_cols] - ye_arr = self['ye'][select_stars, :][:, epoch_cols] - - # Only fit for >1 epochs, otherwise all velocities will be 0 - if len(epoch_cols) > 1: - # For each star - for i in tqdm(range(N)): - x = x_arr[i] - y = y_arr[i] - xe = xe_arr[i] - ye = ye_arr[i] - t0 = np.average(times, weights=1. / np.hypot(xe, ye)) - - # Run fit and record results - params, param_errs = motion_model_to_fit.fit_motion_model( - times, x, y, xe, ye, t0, weighting=weighting, - use_scipy=use_scipy, absolute_sigma=absolute_sigma - ) - if 't0' in motion_model_to_fit.fixed_param_names: - param_data['t0'][i] = t0 - for j, param in enumerate(fit_params): - param_data[param][i] = params[j] - param_data[f'{param}_err'][i] = param_errs[j] - chi2x, chi2y = motion_model_to_fit.get_chi2(params, [t0], times, x, y, xe, ye) - param_data['chi2_x'][i] = chi2x - param_data['chi2_y'][i] = chi2y - - vel_result = Table.from_pandas(pd.DataFrame(param_data)) - - # Add n_vfit - n_fit = len(epoch_cols) - vel_result['n_fit'] = n_fit - - # Clean/remove up old arrays. - columns = [*vel_result.keys(), 'n_fit'] - for column in columns: - if column in self.colnames: self.remove_column(column) - - # Update self - for column in columns: - column_array = MaskedColumn(np.ma.zeros(N_stars), dtype=float, name=column) - column_array[select_stars] = vel_result[column] - column_array[select_stars][~detected_in_all_epochs] = np.nan - column_array.mask[select_stars] = ~detected_in_all_epochs - # Mask unselected indices - column_array.mask[~np.isin(np.arange(N_stars), select_stars)] = True - self[column] = column_array - - if return_result: - return vel_result - else: - return def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, motion_model_dict={}): diff --git a/flystar/tests/test_align.ipynb b/flystar/tests/test_align.ipynb deleted file mode 100644 index 02442b9..0000000 --- a/flystar/tests/test_align.ipynb +++ /dev/null @@ -1,366 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Notebook for Running Align Tests" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from flystar.tests import test_align\n", - "from flystar import starlists\n", - "from astropy.table import Table" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test: make_fake_starlists_poly1_vel\n", - "\n", - "Just make sure the tables look sensible and are in the right units." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " name m0 m0e ... vye t0 \n", - "-------- ----------------- -------------------- ... ------------------- ------\n", - "star_155 9.106905292995506 0.054167528156861204 ... 0.1564397531527286 2019.5\n", - "star_113 9.153031462110043 0.0421090989942197 ... 0.08128628950126615 2019.5\n", - "star_077 9.16547870263162 0.02021147759307802 ... 0.05907352582911862 2019.5\n", - "star_069 9.169817788300977 0.027788213230369625 ... 0.04965351499764548 2019.5\n", - "star_037 9.173200786855755 0.007665400875860144 ... 0.22723357600795704 2019.5\n", - " name m me ... ye t \n", - "-------- ----------------- -------------------- ... -------------------- ------\n", - "star_155 9.198437965086988 0.054167528156861204 ... 0.02649499466969545 2018.5\n", - "star_113 9.257333243243941 0.0421090989942197 ... 0.02606700846524875 2018.5\n", - "star_077 9.252158908537464 0.02021147759307802 ... 0.04250920654497108 2018.5\n", - "star_069 9.267901667333167 0.027788213230369625 ... 0.042689240225924296 2018.5\n", - "star_037 9.276780126418494 0.007665400875860144 ... 0.03592203011554212 2018.5\n", - " name m me ... ye t \n", - "-------- ----------------- -------------------- ... -------------------- ------\n", - "star_155 9.478887659623185 0.054167528156861204 ... 0.02649499466969545 2019.5\n", - "star_113 9.569878576042546 0.0421090989942197 ... 0.02606700846524875 2019.5\n", - "star_077 9.575998150724095 0.02021147759307802 ... 0.04250920654497108 2019.5\n", - "star_069 9.593581807234129 0.027788213230369625 ... 0.042689240225924296 2019.5\n", - "star_037 9.553127108740597 0.007665400875860144 ... 0.03592203011554212 2019.5\n", - "['name', 'm0', 'm0e', 'x0', 'x0e', 'y0', 'y0e', 'vx', 'vxe', 'vy', 'vye', 't0']\n", - "['name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jlu/code/python/flystar/flystar/starlists.py:386: UserWarning: The StarList class requires a arguments('name', 'x', 'y', 'm')\n", - " warnings.warn(err_msg, UserWarning)\n" - ] - } - ], - "source": [ - "test_align.make_fake_starlists_poly1_vel()\n", - "\n", - "ref = Table.read('random_vel_ref.fits')\n", - "lis0 = Table.read('random_vel_0.fits')\n", - "lis1 = Table.read('random_vel_1.fits')\n", - "\n", - "print(ref[0:5])\n", - "print(lis0[0:5])\n", - "print(lis1[0:5])\n", - "\n", - "print(ref.colnames)\n", - "print(lis0.colnames)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## test_align_vel\n", - "\n", - "Make sure it runs, make some plots along the way, etc." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jlu/code/python/flystar/flystar/starlists.py:386: UserWarning: The StarList class requires a arguments('name', 'x', 'y', 'm')\n", - " warnings.warn(err_msg, UserWarning)\n", - "/Users/jlu/code/python/flystar/flystar/starlists.py:386: UserWarning: The StarList class requires a arguments('name', 'x', 'y', 'm')\n", - " warnings.warn(err_msg, UserWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "**********\n", - "**********\n", - "Starting iter 0 with ref_table shape: (200, 1)\n", - "**********\n", - "**********\n", - " \n", - " **********\n", - " Matching catalog 1 / 4 in iteration 0 with 200 stars\n", - " **********\n", - "initial_guess: 50 stars matched between starlist and reference list\n", - "initial_guess: [-6.05144456e+00 1.01098279e+00 -2.50608887e-04] [-1.07161761e+01 4.89226304e-05 1.01096529e+00]\n", - " Found 0 duplicates out of 196 matches\n", - "In Loop 0 found 196 matches\n", - " Found 0 duplicates out of 196 matches\n", - " \n", - " **********\n", - " Matching catalog 2 / 4 in iteration 0 with 200 stars\n", - " **********\n", - "initial_guess: 49 stars matched between starlist and reference list\n", - "initial_guess: [-1.02158015e+02 1.02080743e+00 -1.45081519e-04] [-5.07779471e+01 -2.60729494e-05 9.99423500e-01]\n", - " Found 0 duplicates out of 200 matches\n", - "In Loop 0 found 200 matches\n", - " Found 0 duplicates out of 200 matches\n", - " \n", - " **********\n", - " Matching catalog 3 / 4 in iteration 0 with 200 stars\n", - " **********\n", - "initial_guess: 50 stars matched between starlist and reference list\n", - "initial_guess: [-2.14220566e-10 1.00000000e+00 -2.24089697e-16] [2.50622339e-10 0.00000000e+00 1.00000000e+00]\n", - " Found 0 duplicates out of 200 matches\n", - "In Loop 0 found 200 matches\n", - " Found 0 duplicates out of 200 matches\n", - " \n", - " **********\n", - " Matching catalog 4 / 4 in iteration 0 with 200 stars\n", - " **********\n", - "initial_guess: 50 stars matched between starlist and reference list\n", - "initial_guess: [-2.57803428e+02 1.03052409e+00 -5.28390832e-05] [ 2.49886631e+02 -6.00884405e-05 9.98642952e-01]\n", - " Found 0 duplicates out of 200 matches\n", - "In Loop 0 found 200 matches\n", - " Found 0 duplicates out of 200 matches\n", - " \n", - "**********\n", - "**********\n", - "Starting iter 1 with ref_table shape: (204, 4)\n", - "**********\n", - "**********\n", - " \n", - " **********\n", - " Matching catalog 1 / 4 in iteration 1 with 200 stars\n", - " **********\n", - " Found 0 duplicates out of 199 matches\n", - "In Loop 1 found 199 matches\n", - " Found 0 duplicates out of 199 matches\n", - " \n", - " **********\n", - " Matching catalog 2 / 4 in iteration 1 with 200 stars\n", - " **********\n", - " Found 0 duplicates out of 198 matches\n", - "In Loop 1 found 198 matches\n", - " Found 0 duplicates out of 199 matches\n", - " \n", - " **********\n", - " Matching catalog 3 / 4 in iteration 1 with 200 stars\n", - " **********\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jlu/code/python/flystar/flystar/starlists.py:386: UserWarning: The StarList class requires a arguments('name', 'x', 'y', 'm')\n", - " warnings.warn(err_msg, UserWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Found 0 duplicates out of 200 matches\n", - "In Loop 1 found 200 matches\n", - " Found 0 duplicates out of 200 matches\n", - " \n", - " **********\n", - " Matching catalog 4 / 4 in iteration 1 with 200 stars\n", - " **********\n", - " Found 0 duplicates out of 200 matches\n", - "In Loop 1 found 200 matches\n", - " Found 0 duplicates out of 200 matches\n", - "**********\n", - "Final Matching\n", - "**********\n", - " Found 0 duplicates out of 199 matches\n", - "Matched 199 out of 200 stars in list 0\n", - " Found 0 duplicates out of 199 matches\n", - "Matched 199 out of 200 stars in list 1\n", - " Found 0 duplicates out of 200 matches\n", - "Matched 200 out of 200 stars in list 2\n", - " Found 0 duplicates out of 199 matches\n", - "Matched 199 out of 200 stars in list 3\n", - "\n", - " Preparing the reference table...\n" - ] - } - ], - "source": [ - "test_align.test_mosaic_lists_vel()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> /Users/jlu/code/python/flystar/flystar/align.py(3244)apply_mag_lim()\n", - "-> star_list_T.restrict_by_value(**conditions)\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) conditions\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'m0_min': None, 'm0_max': None}\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) type(star_list_T)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) type(ref_list)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*** NameError: name 'ref_list' is not defined\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) ref_list\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*** NameError: name 'ref_list' is not defined\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) u\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> /Users/jlu/code/python/flystar/flystar/align.py(991)mosaic_lists()\n", - "-> ref_list_T = apply_mag_lim(ref_list, mag_lim[ref_index])\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) type(ref_list)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) q\n" - ] - } - ], - "source": [ - "import pdb\n", - "pdb.pm()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 2d6b0dc..3937ea6 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -6,10 +6,8 @@ from flystar import motion_model from astropy.table import Table import numpy as np -import pylab as plt +import matplotlib.pyplot as plt import pdb -import datetime -import pytest def test_MosaicSelfRef(): """ @@ -28,7 +26,7 @@ def test_MosaicSelfRef(): trans_args={'order': 2}) msc.fit() - + # Check some of the output quantities on the final table. assert 'x0' in msc.ref_table.colnames assert 'x0_err' in msc.ref_table.colnames @@ -42,7 +40,6 @@ def test_MosaicSelfRef(): assert msc.ref_table['use_in_trans'].shape == msc.ref_table['x0'].shape assert msc.ref_table['used_in_trans'].shape == msc.ref_table['x'].shape - # Check that we have some matched stars... should be at least 35 stars # that are detected in all 4 starlists. @@ -50,11 +47,11 @@ def test_MosaicSelfRef(): assert len(idx) > 35 # Check that the transformation error isn't too big - assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix - assert (msc.ref_table['y0_err'] < 3.0).all() - #assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag - assert (msc.ref_table['m0_err'] < 1.5).all() # less than 0.5 mag - + valid_err = np.isfinite(msc.ref_table['x0_err']) & np.isfinite(msc.ref_table['y0_err']) & np.isfinite(msc.ref_table['m0_err']) + assert (msc.ref_table['x0_err'][valid_err] < 3.0).all() # less than 1 pix + assert (msc.ref_table['y0_err'][valid_err] < 3.0).all() + #assert (msc.ref_table['m0_err'][valid_err] < 1.0).all() # less than 0.5 mag + assert (msc.ref_table['m0_err'][valid_err] < 1.5).all() # less than 0.5 mag # Check that the transformation lists aren't too wacky for ii in range(4): np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) @@ -81,7 +78,7 @@ def test_MosaicSelfRef(): plt.plot(msc.ref_table['x0'], msc.ref_table['y0'], '.', color='black', alpha=0.2) - + return @@ -102,11 +99,11 @@ def test_MosaicSelfRef_vel_tconst(): dr_tol=[3, 3], dm_tol=[1, 1], trans_class=transforms.PolyTransform, trans_args={'order': 2}, - default_motion_model='Linear', + motion_models=['Empty', 'Fixed', 'Linear'], verbose=False) msc.fit() - + # Check some of the output quantities on the final table. assert 'x0' in msc.ref_table.colnames assert 'x0_err' in msc.ref_table.colnames @@ -126,21 +123,16 @@ def test_MosaicSelfRef_vel_tconst(): assert len(idx) > 35 # Check that the transformation error isn't too big - assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix - assert (msc.ref_table['y0_err'] < 3.0).all() - assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag - + valid_err = np.isfinite(msc.ref_table['x0_err']) & np.isfinite(msc.ref_table['y0_err']) & np.isfinite(msc.ref_table['m0_err']) + assert (msc.ref_table['x0_err'][valid_err] < 3.0).all() # less than 1 pix + assert (msc.ref_table['y0_err'][valid_err] < 3.0).all() + assert (msc.ref_table['m0_err'][valid_err] < 1.0).all() # less than 0.5 mag + # Check that the transformation lists aren't too wacky for ii in range(4): np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) np.testing.assert_allclose(msc.trans_list[ii].py.c0_1, 1.0, rtol=1e-2) - # Check that the velocities aren't crazy... - # they should be non-existent (since there is no time difference) - assert np.isnan(msc.ref_table['vx']).all() - assert np.isnan(msc.ref_table['vy']).all() - assert np.isnan(msc.ref_table['vx_err']).all() - assert np.isnan(msc.ref_table['vy_err']).all() return @@ -172,7 +164,7 @@ def test_MosaicSelfRef_vel(): msc = align.MosaicSelfRef(lists, ref_index=0, iters=3, dr_tol=[5, 3, 3], dm_tol=[1, 1, 0.5], outlier_tol=None, trans_class=transforms.PolyTransform, - trans_args={'order': 2}, default_motion_model='Linear', + trans_args={'order': 2}, motion_models=['Empty', 'Fixed', 'Linear'], verbose=False) msc.fit() @@ -196,10 +188,11 @@ def test_MosaicSelfRef_vel(): assert len(idx) > 35 # Check that the transformation error isn't too big - assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix - assert (msc.ref_table['y0_err'] < 3.0).all() - assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag - + valid_err = np.isfinite(msc.ref_table['x0_err']) & np.isfinite(msc.ref_table['y0_err']) & np.isfinite(msc.ref_table['m0_err']) + assert (msc.ref_table['x0_err'][valid_err] < 3.0).all() # less than 1 pix + assert (msc.ref_table['y0_err'][valid_err] < 3.0).all() + assert (msc.ref_table['m0_err'][valid_err] < 1.0).all() # less than 0.5 mag + # Check that the transformation lists aren't too wacky for ii in range(4): np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) @@ -214,7 +207,7 @@ def test_MosaicSelfRef_vel(): def test_MosaicToRef(): make_fake_starlists_poly1(seed=42) - + ref_file = 'random_ref.fits' list_files = ['random_0.fits', 'random_1.fits', @@ -235,7 +228,7 @@ def test_MosaicToRef(): msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], trans_class=transforms.PolyTransform, - trans_args={'order': 2}, default_motion_model='Fixed', + trans_args={'order': 2}, motion_models=['Empty', 'Fixed'], update_ref_orig=False, verbose=False) msc.fit() @@ -266,12 +259,12 @@ def test_MosaicToRef(): # Also double check that they aren't exactly the same for the reference stars. assert np.not_equal(msc.ref_table['x0'], ref_list['x0']).all() assert np.not_equal(msc.ref_table['y0'], ref_list['y0']).all() - + return msc def test_MosaicToRef_p0_vel(): make_fake_starlists_poly0_vel(seed=42) - + ref_file = 'random_vel_ref.fits' list_files = ['random_vel_p0_0.fits', 'random_vel_p0_1.fits', @@ -293,14 +286,14 @@ def test_MosaicToRef_p0_vel(): # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 ref_list['vx'] *= -1.0 - + lists = [starlists.StarList.read(lf) for lf in list_files] msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], outlier_tol=[None, None], trans_class=transforms.PolyTransform, - trans_args={'order': 1}, default_motion_model='Linear', + trans_args={'order': 1}, motion_models=['Empty', 'Fixed', 'Linear'], update_ref_orig=False, verbose=False) msc.fit() @@ -326,18 +319,18 @@ def test_MosaicToRef_p0_vel(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == True. assert (msc.ref_table['name']==ref_list['name']).all() - assert np.max(np.abs(msc.ref_table['vx']-ref_list['vx']))<3e-4 - assert np.max(np.abs(msc.ref_table['vy']-ref_list['vy']))<3e-4 + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1, atol=3e-4) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1, atol=3e-4) # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) assert np.not_equal(msc.ref_table['vx'], ref_list['vx']).any() - + return msc def test_MosaicToRef_vel(): make_fake_starlists_poly1_vel(seed=42) - + ref_file = 'random_vel_ref.fits' list_files = ['random_vel_0.fits', 'random_vel_1.fits', @@ -359,14 +352,14 @@ def test_MosaicToRef_vel(): # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 ref_list['vx'] *= -1.0 - + lists = [starlists.StarList.read(lf) for lf in list_files] msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], outlier_tol=[None, None], trans_class=transforms.PolyTransform, - trans_args={'order': 1}, default_motion_model='Linear', + trans_args={'order': 1}, motion_models=['Empty', 'Fixed', 'Linear'], update_ref_orig=False, verbose=False) msc.fit() @@ -398,12 +391,12 @@ def test_MosaicToRef_vel(): # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) assert np.not_equal(msc.ref_table['vx'], ref_list['vx']).any() - + return msc def test_MosaicToRef_acc(): make_fake_starlists_poly1_acc(seed=42) - + ref_file = 'random_acc_ref.fits' list_files = ['random_acc_0.fits', 'random_acc_1.fits', @@ -417,29 +410,29 @@ def test_MosaicToRef_acc(): ref_list = Table.read(ref_file) # Convert velocities to arcsec/yr - ref_list['vx0'] *= 1e-3 - ref_list['vy0'] *= 1e-3 - ref_list['vx0_err'] *= 1e-3 - ref_list['vy0_err'] *= 1e-3 + ref_list['vx'] *= 1e-3 + ref_list['vy'] *= 1e-3 + ref_list['vx_err'] *= 1e-3 + ref_list['vy_err'] *= 1e-3 # Convert accelerations to arcsec/yr**2 ref_list['ax'] *= 1e-3 ref_list['ay'] *= 1e-3 ref_list['ax_err'] *= 1e-3 ref_list['ay_err'] *= 1e-3 - + # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 - ref_list['vx0'] *= -1.0 + ref_list['vx'] *= -1.0 ref_list['ax'] *= -1.0 - + lists = [starlists.StarList.read(lf) for lf in list_files] msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.4, 0.2], dm_tol=[1, 0.5], trans_class=transforms.PolyTransform, trans_args={'order': 2}, - default_motion_model='Acceleration', + motion_models=['Acceleration'], update_ref_orig=False, verbose=False) msc.fit() @@ -476,336 +469,511 @@ def test_MosaicToRef_acc(): if ~np.isnan(msc.ref_table['ax'][ix_fit]): i_orig.append(i) i_fit.append(ix_fit) - np.testing.assert_allclose(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig], rtol=1e-1, atol=3e-4) - np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], rtol=1e-1, atol=3e-4) - - # Also double check that they aren't exactly the same for the reference stars. - assert np.any(np.not_equal(msc.ref_table['ax'][:200], ref_list['ax'][:200])) + # Accelerations all too small, rtol doesn't work well here. + atol = 3e-4 + np.testing.assert_allclose(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig], atol=atol) + np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], atol=atol) + + ax_min = np.min(ref_list['ax'][i_orig]) + ax_max = np.max(ref_list['ax'][i_orig]) + ay_min = np.min(ref_list['ay'][i_orig]) + ay_max = np.max(ref_list['ay'][i_orig]) + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) + ax1.plot(ref_list['ax'][i_orig], msc.ref_table['ax'][i_fit], '.') + ax1.plot([ax_min, ax_max], [ax_min, ax_max], color='C3') + ax1.plot([ax_min, ax_max], [ax_min - atol, ax_max - atol], ls='--', color='C3') + ax1.plot([ax_min, ax_max], [ax_min + atol, ax_max + atol], ls='--', color='C3') + ax1.set_xlabel('Input ax') + ax1.set_ylabel('Ref Table ax') + ax1.set_title('Acceleration in X') - return msc - + ax2.plot(ref_list['ay'][i_orig], msc.ref_table['ay'][i_fit], '.') + ax2.plot([ay_min, ay_max], [ay_min, ay_max], color='C3') + ax2.plot([ay_min, ay_max], [ay_min - atol, ay_max - atol], ls='--', color='C3') + ax2.plot([ay_min, ay_max], [ay_min + atol, ay_max + atol], ls='--', color='C3') + ax2.set_xlabel('Input ay') + ax2.set_ylabel('Ref Table ay') + ax2.set_title('Acceleration in Y') + plt.tight_layout() + plt.show() -def make_fake_starlists_shifts(): - N_stars = 200 - x = np.random.rand(N_stars) * 1000 - y = np.random.rand(N_stars) * 1000 - m = (np.random.rand(N_stars) * 8) + 9 - - sdx = np.argsort(m) - x = x[sdx] - y = y[sdx] - m = m[sdx] - - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + # Also double check that they aren't exactly the same for the reference stars. + assert np.any(np.not_equal(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig])) - # Save original positions as reference (1st) list. - fmt = '{0:10s} {1:5.2f} 2015.0 {2:9.4f} {3:9.4f} 0 0 0 0\n' - _out = open('random_0.lis', 'w') - for ii in range(N_stars): - _out.write(fmt.format(name[ii], m[ii], x[ii], y[ii])) - _out.close() + return msc +def test_MosaicToRef_hst_me(): + """ + Test Casey's issue with 'me' not getting propogated + from the input starlists to the output table. - ########## - # Shifts - ########## - # Make 4 new starlists with different shifts. - shifts = [[ 6.5, 10.1], - [100.3, 50.5], - [-30.0,-100.7], - [250.0,-250.0]] + Use data from MB10-364 microlensing target for the test. + """ + # Target RA and Dec (MOA data download) + # ra = '17:57:05.401' + # dec = '-34:27:05.01' - for ss in range(len(shifts)): - xnew = x - shifts[ss][0] - ynew = y - shifts[ss][1] + # Load up a Gaia catalog (queried around the RA/Dec above) + my_gaia = Table.read('mb10364_data/my_gaia.fits') + my_gaia['me'] = 0.01 - # Perturb with small errors (0.1 pix) - xnew += np.random.randn(N_stars) * 0.1 - ynew += np.random.randn(N_stars) * 0.1 + # Gather the list of starlists. For first pass, don't modify the starlists. + # Loop through the observations and read them in, in prep for alignment with Gaia + epochs = [2011.83, 2012.73, 2013.81] + starlist_names = ['mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib', + 'mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib', + 'mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib'] - mnew = m + np.random.randn(N_stars) * 0.05 + list_of_starlists = [] - _out = open('random_shift_{0:d}.lis'.format(ss+1), 'w') - for ii in range(N_stars): - _out.write(fmt.format(name[ii], mnew[ii], xnew[ii], ynew[ii])) - _out.close() + # Just using the F606W filters first. + for ee in range(len(starlist_names)): + lis = starlists.StarList.from_lis_file(starlist_names[ee]) - return shifts + # # Add additive error term. MAYBE YOU DON'T NEED THIS + # lis['xe'] = np.hypot(lis['xe'], 0.01) # Adding 0.01 pix (0.1 mas) in quadrature. + # lis['ye'] = np.hypot(lis['ye'], 0.01) -def make_fake_starlists_poly1(seed=-1): - # If seed >=0, then set random seed to that value - if seed >= 0: - np.random.seed(seed=seed) - - N_stars = 200 + lis['t'] = epochs[ee] - x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) - y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec - y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec - m0 = (np.random.rand(N_stars) * 8) + 9 # mag - m0e = np.random.randn(N_stars) * 0.05 # mag - t0 = np.ones(N_stars) * 2019.5 + # Lets dump the faint stars. + idx = np.where(lis['m'] < 20.0)[0] + lis = lis[idx] - # Make all the errors positive - x0e = np.abs(x0e) - y0e = np.abs(y0e) - m0e = np.abs(m0e) - - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + list_of_starlists.append(lis) - # Make an StarList - lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, t0], - names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', 't0')) - - sdx = np.argsort(m0) - lis = lis[sdx] + msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, + dr_tol=[0.1], dm_tol=[5], + outlier_tol=[None], mag_lim=[13, 21], + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}], + motion_models=['Empty', 'Fixed'], + use_ref_new=False, + update_ref_orig=False, + mag_trans=False, + trans_weighting='both,std', + init_guess_mode='miracle', verbose=False) + msc.fit() - # Save original positions as reference (1st) list - # in a StarList format (with velocities). - lis.write('random_ref.fits', overwrite=True) + assert 'me' in msc.ref_table.colnames + return - ########## - # Shifts - ########## - # Make 4 new starlists with different shifts. - times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] - xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], - [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], - [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], - [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], - [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], - [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], - [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] - - # Convert into pixels (undistorted) with the following info. - scale = 0.01 # arcsec / pix - shift = [1.0, 1.0] # pix +def test_bootstrap(): + """ + Test to make sure calc_bootstrap_error() call is working + properly (e.g., only called when user calls calc_bootstrap_error, + n_boot param for calc_bootstrap_error only, boot_epochs_min working, + etc.) + """ + # Read in starlists for MosaicToRef + ref = Table.read('ref_vel.lis', format='ascii') + list1 = Table.read('E.lis', format='ascii') + list2 = Table.read('F.lis', format='ascii') - for ss in range(len(times)): - dt = times[ss] - lis['t0'] - - x = lis['x0'] - y = lis['y0'] - t = np.ones(N_stars) * times[ss] + list1 = starlists.StarList.from_table(list1) + list2 = starlists.StarList.from_table(list2) - # Convert into pixels - xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) - yp = (y / scale) + shift[1] - xpe = lis['x0_err'] / scale - ype = lis['y0_err'] / scale + # Set parameters for alignment + transModel = transforms.PolyTransform + trans_args = {'order':2} + N_loop = 1 + dr_tol = 0.08 + dm_tol = 99 + outlier_tol = None + mag_lim = None + ref_mag_lim = None + trans_weighting = 'both,var' + mag_trans = False - # Distort the positions - trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) - xd, yd = trans.evaluate(xp, yp) - md = trans.evaluate_mag(lis['m0']) + n_boot = 15 + boot_epochs_min=-1 - # Perturb with small errors (0.1 pix) - xd += np.random.randn(N_stars) * 0.1 - yd += np.random.randn(N_stars) * 0.1 - md += np.random.randn(N_stars) * 0.02 - xde = xpe - yde = ype - mde = lis['m0_err'] + # Run FLYSTAR, no bootstraps yet! + match1 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, + dm_tol=dm_tol, outlier_tol=outlier_tol, + trans_class=transModel, + trans_args=trans_args, + mag_trans=mag_trans, + mag_lim=mag_lim, + ref_mag_lim=ref_mag_lim, + trans_weighting=trans_weighting, + motion_models=['Linear'], + use_ref_new=False, + update_ref_orig=False, + init_guess_mode='name', + verbose=False) + match1.fit() - # Save the new list as a starlist. - new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], - names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + # Make sure no bootstrap columns exist + assert 'xe_boot' not in match1.ref_table.keys() + assert 'ye_boot' not in match1.ref_table.keys() + assert 'vxe_boot' not in match1.ref_table.keys() + assert 'vye_boot' not in match1.ref_table.keys() - new_lis.write('random_{0:d}.fits'.format(ss), overwrite=True) + # Run bootstrap: no boot_epochs_min + match1.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min) + # Make sure columns exist, and none of them are nan values + assert np.sum(np.isnan(match1.ref_table['xe_boot'])) == 0 + assert np.sum(np.isnan(match1.ref_table['ye_boot'])) == 0 + assert np.sum(np.isnan(match1.ref_table['vx_err_boot'])) == 0 + assert np.sum(np.isnan(match1.ref_table['vy_err_boot'])) == 0 - return (xy_trans,mag_trans) + # Test 2: make sure boot_epochs_min is working + # Eliminate some rows to list2, so some stars are only in 1 epoch. + # Rerun align. Some stars should only be detected in 1 epoch + list3 = list2[0:60] -def make_fake_starlists_poly0_vel(seed=-1): - # If seed >=0, then set random seed to that value - if seed >= 0: - np.random.seed(seed=seed) - - N_stars = 200 + match2 = align.MosaicToRef(ref, [list1, list3], iters=N_loop, dr_tol=dr_tol, + dm_tol=dm_tol, outlier_tol=outlier_tol, + trans_class=transModel, + trans_args=trans_args, + mag_trans=mag_trans, + mag_lim=mag_lim, + ref_mag_lim=ref_mag_lim, + trans_weighting=trans_weighting, + motion_models=['Linear'], + use_ref_new=False, + update_ref_orig=False, + init_guess_mode='name', + verbose=False) + match2.fit() - x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) - y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.ones(N_stars) * 1.0e-4 # arcsec - y0e = np.ones(N_stars) * 1.0e-4 # arcsec - vx = np.random.randn(N_stars) * 5.0 # mas / yr - vy = np.random.randn(N_stars) * 5.0 # mas / yr - vxe = np.ones(N_stars) * 0.05 # mas / yr - vye = np.ones(N_stars) * 0.05 # mas / yr - m0 = (np.random.rand(N_stars) * 8) + 9 # mag - m0e = np.random.randn(N_stars) * 0.05 # mag - t0 = np.ones(N_stars) * 2019.5 + # Now run_calc_bootstrap_error, with boot_epochs_min engaged + boot_epochs_min2 = 2 + match2.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min2) - # Make all the errors positive - x0e = np.abs(x0e) - y0e = np.abs(y0e) - m0e = np.abs(m0e) - vxe = np.abs(vxe) - vye = np.abs(vye) - - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + # Make sure boot_epochs_min cut worked as intended + out = match2.ref_table + bad = np.where( (out['n_detect'] == 1) & (out['use_in_trans'] == False) ) + good = np.where(out['n_detect'] == 2) - # Make an StarList - lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], - names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', - 'vx', 'vx_err', 'vy', 'vy_err', 't0')) - - sdx = np.argsort(m0) - lis = lis[sdx] + # Some stars must exist in both "good" and "bad" criteria, + # otherwise this test isn't as useful as intended. + assert len(bad[0]) > 0 + assert len(good[0]) > 0 - # Save original positions as reference (1st) list - # in a StarList format (with velocities). - lis.write('random_vel_ref.fits', overwrite=True) - - ########## - # Propogate to new times and distort. - ########## - # Make 4 new starlists with different epochs and transformations. - times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] - xy_trans = [[[ 6.5], [ 10.1]], - [[100.3], [ 50.5]], - [[ 0.0], [ 0.0]], - [[250.0], [-250.0]], - [[ 50.0], [ -31.0]], - [[ 78.0], [ 45.0]], - [[-13.0], [ 150]], - [[ 94.0], [-182.0]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] + # For "good" stars: all bootstrap vals should be present + assert np.sum(~np.isfinite(out['xe_boot'][good])) == 0 + assert np.sum(~np.isfinite(out['ye_boot'][good])) == 0 + assert np.sum(~np.isfinite(out['vx_err_boot'][good])) == 0 + assert np.sum(~np.isfinite(out['vy_err_boot'][good])) == 0 - # Convert into pixels (undistorted) with the following info. - scale = 0.01 # arcsec / pix - shift = [1.0, 1.0] # pix + # For "bad" stars, all bootstrap vals should be nans + assert np.sum(np.isfinite(out['xe_boot'][bad])) == 0 + assert np.sum(np.isfinite(out['ye_boot'][bad])) == 0 + assert np.sum(np.isfinite(out['vx_err_boot'][bad])) == 0 + assert np.sum(np.isfinite(out['vy_err_boot'][bad])) == 0 + + return + +def test_calc_vel_in_bootstrap(): + """ + Check calc_vel_in_bootstrap performance in calc_bootstrap_errors() - for ss in range(len(times)): - dt = times[ss] - lis['t0'] - - x = lis['x0'] + (lis['vx']/1e3) * dt - y = lis['y0'] + (lis['vy']/1e3) * dt - t = np.ones(N_stars) * times[ss] + Only calculate velocity bootstrap (e.g., bootstrap over epochs and + calculating proper motions) if calc_vel_in_bootstrap=True. - # Convert into pixels - xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) - yp = (y / scale) + shift[1] - xpe = lis['x0_err'] / scale - ype = lis['y0_err'] / scale + """ + import copy - # Distort the positions - trans = transforms.PolyTransform(0, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) - xd, yd = trans.evaluate(xp, yp) - md = trans.evaluate_mag(lis['m0']) + # Define match parameters + ref = Table.read('ref_vel.lis', format='ascii') - # Perturb with small errors (0.1 pix) - xd += np.random.randn(N_stars) * xpe - yd += np.random.randn(N_stars) * ype - md += np.random.randn(N_stars) * 0.02 - xde = xpe - yde = ype - mde = lis['m0_err'] + list1 = Table.read('E.lis', format='ascii') + list2 = Table.read('F.lis', format='ascii') - # Save the new list as a starlist. - new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], - names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + list1 = starlists.StarList.from_table(list1) + list2 = starlists.StarList.from_table(list2) + + # Set parameters for alignment + transModel = transforms.PolyTransform + trans_args = {'order':2} + N_loop = 1 + dr_tol = 0.08 + dm_tol = 99 + outlier_tol = None + mag_lim = None + ref_mag_lim = None + trans_weighting = 'both,var' + mag_trans = False - new_lis.write('random_vel_p0_{0:d}.fits'.format(ss), overwrite=True) + n_boot = 15 + boot_epochs_min=-1 - return (xy_trans, mag_trans) + # Run match + match = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, + dm_tol=dm_tol, outlier_tol=outlier_tol, + trans_class=transModel, + trans_args=trans_args, + mag_trans=mag_trans, + mag_lim=mag_lim, + ref_mag_lim=ref_mag_lim, + trans_weighting=trans_weighting, + motion_models=['Linear'], + use_ref_new=False, + update_ref_orig=False, + init_guess_mode='name', + verbose=False) + match.fit() + # Make 2 copies of match object: one to test + # each case of calc_vel_in_bootstrap + match_vel = copy.deepcopy(match) -def make_fake_starlists_poly1_vel(seed=-1): - # If seed >=0, then set random seed to that value - if seed >= 0: - np.random.seed(seed=seed) - - N_stars = 200 + # Run calc_bootstrap_error function with calc_vel_in_bootstrap=True. + # Make sure bootstrap velocity errors are calculated and valid + n_boot = 50 + match_vel.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=True) - x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) - y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.ones(N_stars) * 1.0e-4 # arcsec - y0e = np.ones(N_stars) * 1.0e-4 # arcsec - vx = np.random.randn(N_stars) * 5.0 # mas / yr - vy = np.random.randn(N_stars) * 5.0 # mas / yr - vxe = np.ones(N_stars) * 0.05 # mas / yr - vye = np.ones(N_stars) * 0.05 # mas / yr - m0 = (np.random.rand(N_stars) * 8) + 9 # mag - m0e = np.random.randn(N_stars) * 0.05 # mag - t0 = np.ones(N_stars) * 2019.5 + assert 'xe_boot' in match_vel.ref_table.keys() + assert np.sum(np.isnan(match_vel.ref_table['xe_boot'])) == 0 + assert 'vx_err_boot' in match_vel.ref_table.keys() + assert np.sum(np.isnan(match_vel.ref_table['vx_err_boot'])) == 0 - # Make all the errors positive - x0e = np.abs(x0e) - y0e = np.abs(y0e) - m0e = np.abs(m0e) - vxe = np.abs(vxe) - vye = np.abs(vye) - - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + # Run without calc_vel_in_bootstrap, make sure velocities are NOT calculated + match.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=False) - # Make an StarList - lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], - names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', - 'vx', 'vx_err', 'vy', 'vy_err', 't0')) + assert 'xe_boot' in match.ref_table.keys() + assert np.sum(np.isnan(match.ref_table['xe_boot'])) == 0 + assert 'vx_err_boot' not in match.ref_table.keys() - sdx = np.argsort(m0) - lis = lis[sdx] + return - # Save original positions as reference (1st) list - # in a StarList format (with velocities). - lis.write('random_vel_ref.fits', overwrite=True) - - ########## - # Propogate to new times and distort. - ########## - # Make 4 new starlists with different epochs and transformations. - times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] - xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], - [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], - [[250.0, 1.01, 2e-5], [-250.0, 1e-5, 0.98]], - [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], - [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], - [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], - [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] +def test_transform_xym(): + """ + Test to make sure transforms are being done to mags only + if mag_trans = True. This can cause subtle bugs + otherwise + """ + #---Align 1: self.mag_Trans = False---# + ref = Table.read('ref_vel.lis', format='ascii') + list1 = Table.read('E.lis', format='ascii') + list2 = Table.read('F.lis', format='ascii') - # Convert into pixels (undistorted) with the following info. - scale = 0.01 # arcsec / pix - shift = [1.0, 1.0] # pix - - for ss in range(len(times)): - dt = times[ss] - lis['t0'] - - x = lis['x0'] + (lis['vx']/1e3) * dt - y = lis['y0'] + (lis['vy']/1e3) * dt - t = np.ones(N_stars) * times[ss] + list1 = starlists.StarList.from_table(list1) + list2 = starlists.StarList.from_table(list2) - # Convert into pixels - xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) - yp = (y / scale) + shift[1] - xpe = lis['x0_err'] / scale - ype = lis['y0_err'] / scale + # Set parameters for alignment + transModel = transforms.PolyTransform + trans_args = {'order':2} + N_loop = 1 + dr_tol = 0.08 + dm_tol = 99 + outlier_tol = None + mag_lim = None + ref_mag_lim = None + trans_weighting = 'both,var' + n_boot = 15 - # Distort the positions - trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) - xd, yd = trans.evaluate(xp, yp) - md = trans.evaluate_mag(lis['m0']) + mag_trans = False - # Perturb with small errors (0.1 mas) - xd += np.random.randn(N_stars) * xpe - yd += np.random.randn(N_stars) * ype - md += np.random.randn(N_stars) * 0.02 - xde = xpe - yde = ype - mde = lis['m0_err'] + # Run FLYSTAR, with bootstraps + match1 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, + dm_tol=dm_tol, outlier_tol=outlier_tol, + trans_class=transModel, + trans_args=trans_args, + mag_trans=mag_trans, + mag_lim=mag_lim, + ref_mag_lim=ref_mag_lim, + trans_weighting=trans_weighting, + motion_models=['Fixed'], + use_ref_new=False, + update_ref_orig=False, + init_guess_mode='name', + verbose=False) - # Save the new list as a starlist. - new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], - names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + match1.fit() + match1.calc_bootstrap_errors(n_boot=n_boot) - new_lis.write('random_vel_{0:d}.fits'.format(ss), overwrite=True) + # Make sure all transformations have mag_offset = 0 + trans_list = match1.trans_list - return (xy_trans, mag_trans) + for ii in trans_list: + assert ii.mag_offset == 0 -def make_fake_starlists_poly1_acc(seed=-1): + # Check that no mag transformation has been applied to m col in ref_table + tab1 = match1.ref_table + assert np.all(tab1['m'] == tab1['m_orig']) + + # Check me_boost == 0 or really small (should be the case + # since we don't transform mags) + assert np.isclose(np.max(tab1['me_boot']), 0, rtol=10**-5) + print('Done mag_trans = False case') + + #---Align 2: self.mag_Trans = True---# + # Repeat, this time with mag_trans = False + mag_trans = True + match2 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, + dm_tol=dm_tol, outlier_tol=outlier_tol, + trans_class=transModel, + trans_args=trans_args, + mag_trans=mag_trans, + mag_lim=mag_lim, + ref_mag_lim=ref_mag_lim, + trans_weighting=trans_weighting, + motion_models=['Fixed'], + use_ref_new=False, + update_ref_orig=False, + init_guess_mode='name', + verbose=False) + + match2.fit() + match2.calc_bootstrap_errors(n_boot=n_boot) + + + # Make sure all transformations have correct mag offset + trans_list2 = match2.trans_list + + for ii in trans_list2: + assert ii.mag_offset > 20 + + # Make sure final table mags have transform applied (i.e, + tab2 = match2.ref_table + assert np.all(tab2['m'] != tab2['m_orig']) + + # Check me_boost > 0 + assert np.min(tab2['me_boot']) > 10**-3 + + print('Done mag_trans = True case') + + return + +def test_MosaicToRef_mag_bug(): + """ + Bug found by Tuan Do on 2020-04-12. + """ + make_fake_starlists_poly1_vel() + + ref_list = starlists.StarList.read('random_vel_0.fits') + lists = [ref_list] + + msc = align.MosaicToRef(ref_list, lists, + mag_trans=True, + iters=1, + dr_tol=[0.2], dm_tol=[1], + outlier_tol=None, + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}], + motion_models=['Fixed'], + use_ref_new=False, + update_ref_orig=False, + verbose=True) + + msc.fit() + + out_tab = msc.ref_table + + # The issue is that in the initial guess with + # mag_trans = True + # somehow the transformed magnitudes are nan. + # This causes zero matches to occur. + assert len(out_tab) == len(ref_list) + + return + +def test_masked_cols(): + """ + Test to make sure analysis.prepare_gaia_for_flystar + produces an astropy.table.Table, NOT a masked column + table. MosaicToRef cannot handle masked column tables. + + Also make sure this example works, since we use it for the examples + jupyter notebook. + """ + # Get gaia reference stars using analysis.py + # around a test location. + # target = 'ob150029' + ra = '17:59:46.60' + dec = '-28:38:41.8' + + # Coordinates are arcsecs offset +x to the East. + targets_dict = { + 'ob150029': [0.0, 0.0], + 'S005': [1.1416, 3.7405], + 'S002': [-4.421, 0.027] + } + + # Get gaia catalog stars. Note that this produces a masked column table + search_rad = 10.0 # arcsec + gaia = analysis.query_gaia(ra, dec, search_radius=search_rad) + my_gaia = analysis.prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=targets_dict) + + assert isinstance(my_gaia, Table) + + # Let's make sure the entire align runs, just to be safe + + # Get starlists to align to gaia + epochs = ['15jun07','16jul14', '17may21'] + + list_of_starlists = [] + + for ee in range(len(epochs)): + lis_file = 'mag' + epochs[ee] + '_ob150029_kp_rms_named.lis' + lis = starlists.StarList.from_lis_file(lis_file) + list_of_starlists.append(lis) + + # Run the align + msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=2, + dr_tol=[0.2, 0.1], dm_tol=[1, 1], + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}, {'order': 1}], + motion_models=['Linear'], + use_ref_new=False, + update_ref_orig=False, + mag_trans=True, + init_guess_mode='name', verbose=True) + + msc.fit() + return + +def make_fake_starlists_shifts(): + N_stars = 200 + x = np.random.rand(N_stars) * 1000 + y = np.random.rand(N_stars) * 1000 + m = (np.random.rand(N_stars) * 8) + 9 + + sdx = np.argsort(m) + x = x[sdx] + y = y[sdx] + m = m[sdx] + + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + + # Save original positions as reference (1st) list. + fmt = '{0:10s} {1:5.2f} 2015.0 {2:9.4f} {3:9.4f} 0 0 0 0\n' + _out = open('random_0.lis', 'w') + for ii in range(N_stars): + _out.write(fmt.format(name[ii], m[ii], x[ii], y[ii])) + _out.close() + + + ########## + # Shifts + ########## + # Make 4 new starlists with different shifts. + shifts = [[ 6.5, 10.1], + [100.3, 50.5], + [-30.0,-100.7], + [250.0,-250.0]] + + for ss in range(len(shifts)): + xnew = x - shifts[ss][0] + ynew = y - shifts[ss][1] + + # Perturb with small errors (0.1 pix) + xnew += np.random.randn(N_stars) * 0.1 + ynew += np.random.randn(N_stars) * 0.1 + + mnew = m + np.random.randn(N_stars) * 0.05 + + _out = open('random_shift_{0:d}.lis'.format(ss+1), 'w') + for ii in range(N_stars): + _out.write(fmt.format(name[ii], mnew[ii], xnew[ii], ynew[ii])) + _out.close() + + return shifts + +def make_fake_starlists_poly1(seed=-1): # If seed >=0, then set random seed to that value if seed >= 0: np.random.seed(seed=seed) @@ -814,16 +982,8 @@ def make_fake_starlists_poly1_acc(seed=-1): x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.ones(N_stars) * 1.0e-4 # arcsec - y0e = np.ones(N_stars) * 1.0e-4 # arcsec - vx = np.random.randn(N_stars) * 5.0 # mas / yr - vy = np.random.randn(N_stars) * 5.0 # mas / yr - vxe = np.ones(N_stars) * 0.1 # mas / yr - vye = np.ones(N_stars) * 0.1 # mas / yr - ax = np.random.randn(N_stars) * 0.5 # mas / yr^2 - ay = np.random.randn(N_stars) * 0.5 # mas / yr^2 - axe = np.ones(N_stars) * 0.01 # mas / yr^2 - aye = np.ones(N_stars) * 0.01 # mas / yr^2 + x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec m0 = (np.random.rand(N_stars) * 8) + 9 # mag m0e = np.random.randn(N_stars) * 0.05 # mag t0 = np.ones(N_stars) * 2019.5 @@ -832,56 +992,44 @@ def make_fake_starlists_poly1_acc(seed=-1): x0e = np.abs(x0e) y0e = np.abs(y0e) m0e = np.abs(m0e) - vxe = np.abs(vxe) - vye = np.abs(vye) - axe = np.abs(axe) - aye = np.abs(aye) name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] # Make an StarList - lis = starlists.StarList([name, m0, m0e, - x0, x0e, y0, y0e, - vx, vxe, vy, vye, - ax, axe, ay, aye, - t0], - names = ('name', 'm0', 'm0_err', - 'x0', 'x0_err', 'y0', 'y0_err', - 'vx0', 'vx0_err', 'vy0', 'vy0_err', - 'ax', 'ax_err', 'ay', 'ay_err', - 't0')) + lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, t0], + names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', 't0')) sdx = np.argsort(m0) lis = lis[sdx] # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('random_acc_ref.fits', overwrite=True) - + lis.write('random_ref.fits', overwrite=True) + ########## - # Propogate to new times and distort. + # Shifts ########## - # Make 4 new starlists with different epochs and transformations. + # Make 4 new starlists with different shifts. times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] - + # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix shift = [1.0, 1.0] # pix - + for ss in range(len(times)): dt = times[ss] - lis['t0'] - x = lis['x0'] + (lis['vx0']/1e3) * dt + 0.5*(lis['ax']/1e3) * dt**2 - y = lis['y0'] + (lis['vy0']/1e3) * dt + 0.5*(lis['ay']/1e3) * dt**2 + x = lis['x0'] + y = lis['y0'] t = np.ones(N_stars) * times[ss] # Convert into pixels @@ -896,8 +1044,8 @@ def make_fake_starlists_poly1_acc(seed=-1): md = trans.evaluate_mag(lis['m0']) # Perturb with small errors (0.1 pix) - xd += np.random.randn(N_stars) * xpe - yd += np.random.randn(N_stars) * ype + xd += np.random.randn(N_stars) * 0.1 + yd += np.random.randn(N_stars) * 0.1 md += np.random.randn(N_stars) * 0.02 xde = xpe yde = ype @@ -907,11 +1055,11 @@ def make_fake_starlists_poly1_acc(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('random_acc_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write('random_{0:d}.fits'.format(ss), overwrite=True) - return (xy_trans, mag_trans) - -def make_fake_starlists_poly1_par(seed=-1): + return (xy_trans,mag_trans) + +def make_fake_starlists_poly0_vel(seed=-1): # If seed >=0, then set random seed to that value if seed >= 0: np.random.seed(seed=seed) @@ -920,14 +1068,12 @@ def make_fake_starlists_poly1_par(seed=-1): x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec - y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + x0e = np.ones(N_stars) * 1.0e-4 # arcsec + y0e = np.ones(N_stars) * 1.0e-4 # arcsec vx = np.random.randn(N_stars) * 5.0 # mas / yr vy = np.random.randn(N_stars) * 5.0 # mas / yr - vxe = np.random.randn(N_stars) * 0.1 # mas / yr - vye = np.random.randn(N_stars) * 0.1 # mas / yr - pi = np.random.randn(N_stars) * 0.5 # mas - pie = np.random.randn(N_stars) * 0.01 # mas + vxe = np.ones(N_stars) * 0.05 # mas / yr + vye = np.ones(N_stars) * 0.05 # mas / yr m0 = (np.random.rand(N_stars) * 8) + 9 # mag m0e = np.random.randn(N_stars) * 0.05 # mag t0 = np.ones(N_stars) * 2019.5 @@ -938,50 +1084,35 @@ def make_fake_starlists_poly1_par(seed=-1): m0e = np.abs(m0e) vxe = np.abs(vxe) vye = np.abs(vye) - pie = np.abs(pie) name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] # Make an StarList - lis = starlists.StarList([name, m0, m0e, - x0, x0e, y0, y0e, - vx, vxe, vy, vye, - pi, pie, - t0], - names = ('name', 'm0', 'm0_err', - 'x0', 'x0_err', 'y0', 'y0_err', - 'vx', 'vx_err', 'vy', 'vy_err', - 'pi', 'pi_err', - 't0')) + lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], + names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', + 'vx', 'vx_err', 'vy', 'vy_err', 't0')) sdx = np.argsort(m0) lis = lis[sdx] # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('random_par_ref.fits', overwrite=True) + lis.write('random_vel_ref.fits', overwrite=True) ########## # Propogate to new times and distort. ########## # Make 4 new starlists with different epochs and transformations. - '''times = [2018.5, 2019.5, 2020.5, 2021.5] - xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], - [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], - [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] - mag_trans = [0.1, 0.4, 0.0, -0.3]''' - times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] - xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], - [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], - [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], - [[ 50.0, 1.00, 0.0], [ -31.0, 0.0, 1.000]], - [[ 78.0, 1.00, 0.0 ], [ 45.0, 0.0, 1.00]], - [[-13.0, 1.00, 0.0], [ 150, 0.0, 1.00]], - [[ 94.0, 1.00, 0.0], [-182.0, 0.0, 1.00]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.0, 0.0, 0.0, 0.0] + xy_trans = [[[ 6.5], [ 10.1]], + [[100.3], [ 50.5]], + [[ 0.0], [ 0.0]], + [[250.0], [-250.0]], + [[ 50.0], [ -31.0]], + [[ 78.0], [ 45.0]], + [[-13.0], [ 150]], + [[ 94.0], [-182.0]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix @@ -990,10 +1121,8 @@ def make_fake_starlists_poly1_par(seed=-1): for ss in range(len(times)): dt = times[ss] - lis['t0'] - par_mod = motion_model.Parallax(PA=0,RA=18.0, Dec=-30.0) - par_mod_dat = par_mod.get_batch_pos_at_time(dt+lis['t0'], x0=lis['x0'],vx=lis['vx']/1e3, pi=lis['pi'], - y0=lis['y0'], vy=lis['vy']/1e3, t0=lis['t0']) - x,y = par_mod_dat[0], par_mod_dat[1] + x = lis['x0'] + (lis['vx']/1e3) * dt + y = lis['y0'] + (lis['vy']/1e3) * dt t = np.ones(N_stars) * times[ss] # Convert into pixels @@ -1003,13 +1132,13 @@ def make_fake_starlists_poly1_par(seed=-1): ype = lis['y0_err'] / scale # Distort the positions - trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + trans = transforms.PolyTransform(0, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) xd, yd = trans.evaluate(xp, yp) md = trans.evaluate_mag(lis['m0']) # Perturb with small errors (0.1 pix) - xd += np.random.randn(N_stars) * 0.1 - yd += np.random.randn(N_stars) * 0.1 + xd += np.random.randn(N_stars) * xpe + yd += np.random.randn(N_stars) * ype md += np.random.randn(N_stars) * 0.02 xde = xpe yde = ype @@ -1019,435 +1148,318 @@ def make_fake_starlists_poly1_par(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('random_par_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write('random_vel_p0_{0:d}.fits'.format(ss), overwrite=True) return (xy_trans, mag_trans) - -def test_MosaicToRef_hst_me(): - """ - Test Casey's issue with 'me' not getting propogated - from the input starlists to the output table. - Use data from MB10-364 microlensing target for the test. - """ - # Target RA and Dec (MOA data download) - ra = '17:57:05.401' - dec = '-34:27:05.01' - - # Load up a Gaia catalog (queried around the RA/Dec above) - my_gaia = Table.read('mb10364_data/my_gaia.fits') - my_gaia['me'] = 0.01 - - # Gather the list of starlists. For first pass, don't modify the starlists. - # Loop through the observations and read them in, in prep for alignment with Gaia - epochs = [2011.83, 2012.73, 2013.81] - starlist_names = ['mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib', - 'mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib', - 'mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib'] - - list_of_starlists = [] - - # Just using the F606W filters first. - for ee in range(len(starlist_names)): - lis = starlists.StarList.from_lis_file(starlist_names[ee]) - - # # Add additive error term. MAYBE YOU DON'T NEED THIS - # lis['xe'] = np.hypot(lis['xe'], 0.01) # Adding 0.01 pix (0.1 mas) in quadrature. - # lis['ye'] = np.hypot(lis['ye'], 0.01) +def make_fake_starlists_poly1_vel(seed=-1): + # If seed >=0, then set random seed to that value + if seed >= 0: + np.random.seed(seed=seed) - lis['t'] = epochs[ee] - - # Lets dump the faint stars. - idx = np.where(lis['m'] < 20.0)[0] - lis = lis[idx] + N_stars = 200 - list_of_starlists.append(lis) - - msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, - dr_tol=[0.1], dm_tol=[5], - outlier_tol=[None], mag_lim=[13, 21], - trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}], - default_motion_model='Fixed', - use_ref_new=False, - update_ref_orig=False, - mag_trans=False, - trans_weights='both,std', - init_guess_mode='miracle', verbose=False) - msc.fit() - tab = msc.ref_table + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.ones(N_stars) * 1.0e-4 # arcsec + y0e = np.ones(N_stars) * 1.0e-4 # arcsec + vx = np.random.randn(N_stars) * 5.0 # mas / yr + vy = np.random.randn(N_stars) * 5.0 # mas / yr + vxe = np.ones(N_stars) * 0.05 # mas / yr + vye = np.ones(N_stars) * 0.05 # mas / yr + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 - assert 'me' in tab.colnames + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) + vxe = np.abs(vxe) + vye = np.abs(vye) + + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] - return + # Make an StarList + lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], + names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', + 'vx', 'vx_err', 'vy', 'vy_err', 't0')) + + sdx = np.argsort(m0) + lis = lis[sdx] -def test_bootstrap(): - """ - Test to make sure calc_bootstrap_error() call is working - properly (e.g., only called when user calls calc_bootstrap_error, - n_boot param for calc_bootstrap_error only, boot_epochs_min working, - etc.) - """ - # Read in starlists for MosaicToRef - ref = Table.read('ref_vel.lis', format='ascii') - list1 = Table.read('E.lis', format='ascii') - list2 = Table.read('F.lis', format='ascii') + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_vel_ref.fits', overwrite=True) + + ########## + # Propogate to new times and distort. + ########## + # Make 4 new starlists with different epochs and transformations. + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], + [[250.0, 1.01, 2e-5], [-250.0, 1e-5, 0.98]], + [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], + [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], + [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], + [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] - list1 = starlists.StarList.from_table(list1) - list2 = starlists.StarList.from_table(list2) + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix + + for ss in range(len(times)): + dt = times[ss] - lis['t0'] - # Set parameters for alignment - transModel = transforms.PolyTransform - trans_args = {'order':2} - N_loop = 1 - dr_tol = 0.08 - dm_tol = 99 - outlier_tol = None - mag_lim = None - ref_mag_lim = None - trans_weights = 'both,var' - mag_trans = False + x = lis['x0'] + (lis['vx']/1e3) * dt + y = lis['y0'] + (lis['vy']/1e3) * dt + t = np.ones(N_stars) * times[ss] - n_boot = 15 - boot_epochs_min=-1 + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale - # Run FLYSTAR, no bootstraps yet! - match1 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, - dm_tol=dm_tol, outlier_tol=outlier_tol, - trans_class=transModel, - trans_args=trans_args, - mag_trans=mag_trans, - mag_lim=mag_lim, - ref_mag_lim=ref_mag_lim, - trans_weights=trans_weights, - default_motion_model='Linear', - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='name', - verbose=False) - match1.fit() + # Distort the positions + trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) - # Make sure no bootstrap columns exist - assert 'xe_boot' not in match1.ref_table.keys() - assert 'ye_boot' not in match1.ref_table.keys() - assert 'vxe_boot' not in match1.ref_table.keys() - assert 'vye_boot' not in match1.ref_table.keys() + # Perturb with small errors (0.1 mas) + xd += np.random.randn(N_stars) * xpe + yd += np.random.randn(N_stars) * ype + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0_err'] - # Run bootstrap: no boot_epochs_min - match1.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min) - # Make sure columns exist, and none of them are nan values - assert np.sum(np.isnan(match1.ref_table['xe_boot'])) == 0 - assert np.sum(np.isnan(match1.ref_table['ye_boot'])) == 0 - assert np.sum(np.isnan(match1.ref_table['vx_err_boot'])) == 0 - assert np.sum(np.isnan(match1.ref_table['vy_err_boot'])) == 0 - #pdb.set_trace() + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - # Test 2: make sure boot_epochs_min is working - # Eliminate some rows to list2, so some stars are only in 1 epoch. - # Rerun align. Some stars should only be detected in 1 epoch - list3 = list2[0:60] + new_lis.write('random_vel_{0:d}.fits'.format(ss), overwrite=True) - match2 = align.MosaicToRef(ref, [list1, list3], iters=N_loop, dr_tol=dr_tol, - dm_tol=dm_tol, outlier_tol=outlier_tol, - trans_class=transModel, - trans_args=trans_args, - mag_trans=mag_trans, - mag_lim=mag_lim, - ref_mag_lim=ref_mag_lim, - trans_weights=trans_weights, - default_motion_model='Linear', - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='name', - verbose=False) - match2.fit() - - # Now run_calc_bootstrap_error, with boot_epochs_min engaged - boot_epochs_min2 = 2 - match2.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min2) - - # Make sure boot_epochs_min cut worked as intended - out = match2.ref_table - bad = np.where( (out['n_detect'] == 1) & (out['use_in_trans'] == False) ) - good = np.where(out['n_detect'] == 2) - - # Some stars must exist in both "good" and "bad" criteria, - # otherwise this test isn't as useful as intended. - assert len(bad[0]) > 0 - assert len(good[0]) > 0 - - # For "good" stars: all bootstrap vals should be present - assert np.sum(np.isnan(out['xe_boot'][good])) == 0 - assert np.sum(np.isnan(out['ye_boot'][good])) == 0 - assert np.sum(np.isnan(out['vx_err_boot'][good])) == 0 - assert np.sum(np.isnan(out['vy_err_boot'][good])) == 0 + return (xy_trans, mag_trans) - # For "bad" stars, all bootstrap vals should be nans - assert np.sum(np.isfinite(out['xe_boot'][bad])) == 0 - assert np.sum(np.isfinite(out['ye_boot'][bad])) == 0 - assert np.sum(np.isfinite(out['vx_err_boot'][bad])) == 0 - assert np.sum(np.isfinite(out['vy_err_boot'][bad])) == 0 +def make_fake_starlists_poly1_acc(seed=-1): + # If seed >=0, then set random seed to that value + if seed >= 0: + np.random.seed(seed=seed) + + N_stars = 200 - return + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.ones(N_stars) * 1.0e-4 # arcsec + y0e = np.ones(N_stars) * 1.0e-4 # arcsec + vx = np.random.randn(N_stars) * 5.0 # mas / yr + vy = np.random.randn(N_stars) * 5.0 # mas / yr + vxe = np.ones(N_stars) * 0.1 # mas / yr + vye = np.ones(N_stars) * 0.1 # mas / yr + ax = np.random.randn(N_stars) * 0.5 # mas / yr^2 + ay = np.random.randn(N_stars) * 0.5 # mas / yr^2 + axe = np.ones(N_stars) * 0.01 # mas / yr^2 + aye = np.ones(N_stars) * 0.01 # mas / yr^2 + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 -def test_calc_vel_in_bootstrap(): - """ - Check calc_vel_in_bootstrap performance in calc_bootstrap_errors() + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) + vxe = np.abs(vxe) + vye = np.abs(vye) + axe = np.abs(axe) + aye = np.abs(aye) - Only calculate velocity bootstrap (e.g., bootstrap over epochs and - calculating proper motions) if calc_vel_in_bootstrap=True. + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] - """ - import copy + # Make an StarList + lis = starlists.StarList([name, m0, m0e, + x0, x0e, y0, y0e, + vx, vxe, vy, vye, + ax, axe, ay, aye, + t0], + names = ('name', 'm0', 'm0_err', + 'x0', 'x0_err', 'y0', 'y0_err', + 'vx0', 'vx0_err', 'vy0', 'vy0_err', + 'ax', 'ax_err', 'ay', 'ay_err', + 't0')) - # Define match parameters - ref = Table.read('ref_vel.lis', format='ascii') + sdx = np.argsort(m0) + lis = lis[sdx] - list1 = Table.read('E.lis', format='ascii') - list2 = Table.read('F.lis', format='ascii') + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_acc_ref.fits', overwrite=True) + + ########## + # Propogate to new times and distort. + ########## + # Make 4 new starlists with different epochs and transformations. + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], + [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], + [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], + [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], + [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] - list1 = starlists.StarList.from_table(list1) - list2 = starlists.StarList.from_table(list2) + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix + + for ss in range(len(times)): + dt = times[ss] - lis['t0'] - # Set parameters for alignment - transModel = transforms.PolyTransform - trans_args = {'order':2} - N_loop = 1 - dr_tol = 0.08 - dm_tol = 99 - outlier_tol = None - mag_lim = None - ref_mag_lim = None - trans_weights = 'both,var' - mag_trans = False - - n_boot = 15 - boot_epochs_min=-1 + x = lis['x0'] + (lis['vx0']/1e3) * dt + 0.5*(lis['ax']/1e3) * dt**2 + y = lis['y0'] + (lis['vy0']/1e3) * dt + 0.5*(lis['ay']/1e3) * dt**2 + t = np.ones(N_stars) * times[ss] - # Run match - match = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, - dm_tol=dm_tol, outlier_tol=outlier_tol, - trans_class=transModel, - trans_args=trans_args, - mag_trans=mag_trans, - mag_lim=mag_lim, - ref_mag_lim=ref_mag_lim, - trans_weights=trans_weights, - default_motion_model='Linear', - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='name', - verbose=False) - match.fit() + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale - # Make 2 copies of match object: one to test - # each case of calc_vel_in_bootstrap - match_vel = copy.deepcopy(match) + # Distort the positions + trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) - # Run calc_bootstrap_error function with calc_vel_in_bootstrap=True. - # Make sure bootstrap velocity errors are calculated and valid - n_boot = 50 - match_vel.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=True) + # Perturb with small errors (0.1 pix) + xd += np.random.randn(N_stars) * xpe + yd += np.random.randn(N_stars) * ype + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0_err'] - assert 'xe_boot' in match_vel.ref_table.keys() - assert np.sum(np.isnan(match_vel.ref_table['xe_boot'])) == 0 - assert 'vx_err_boot' in match_vel.ref_table.keys() - assert np.sum(np.isnan(match_vel.ref_table['vx_err_boot'])) == 0 + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - # Run without calc_vel_in_bootstrap, make sure velocities are NOT calculated - match.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=False) + new_lis.write('random_acc_{0:d}.fits'.format(ss), overwrite=True) - assert 'xe_boot' in match.ref_table.keys() - assert np.sum(np.isnan(match.ref_table['xe_boot'])) == 0 - assert 'vx_err_boot' not in match.ref_table.keys() + return (xy_trans, mag_trans) - return +def make_fake_starlists_poly1_par(seed=-1): + # If seed >=0, then set random seed to that value + if seed >= 0: + np.random.seed(seed=seed) + + N_stars = 200 -def test_transform_xym(): - """ - Test to make sure transforms are being done to mags only - if mag_trans = True. This can cause subtle bugs - otherwise - """ - #---Align 1: self.mag_Trans = False---# - ref = Table.read('ref_vel.lis', format='ascii') - list1 = Table.read('E.lis', format='ascii') - list2 = Table.read('F.lis', format='ascii') + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + vx = np.random.randn(N_stars) * 5.0 # mas / yr + vy = np.random.randn(N_stars) * 5.0 # mas / yr + vxe = np.random.randn(N_stars) * 0.1 # mas / yr + vye = np.random.randn(N_stars) * 0.1 # mas / yr + pi = np.random.randn(N_stars) * 0.5 # mas + pie = np.random.randn(N_stars) * 0.01 # mas + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 - list1 = starlists.StarList.from_table(list1) - list2 = starlists.StarList.from_table(list2) + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) + vxe = np.abs(vxe) + vye = np.abs(vye) + pie = np.abs(pie) - # Set parameters for alignment - transModel = transforms.PolyTransform - trans_args = {'order':2} - N_loop = 1 - dr_tol = 0.08 - dm_tol = 99 - outlier_tol = None - mag_lim = None - ref_mag_lim = None - trans_weights = 'both,var' - n_boot = 15 - - mag_trans = False - - # Run FLYSTAR, with bootstraps - match1 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, - dm_tol=dm_tol, outlier_tol=outlier_tol, - trans_class=transModel, - trans_args=trans_args, - mag_trans=mag_trans, - mag_lim=mag_lim, - ref_mag_lim=ref_mag_lim, - trans_weights=trans_weights, - default_motion_model='Fixed', - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='name', - verbose=False) - - match1.fit() - match1.calc_bootstrap_errors(n_boot=n_boot) - - # Make sure all transformations have mag_offset = 0 - trans_list = match1.trans_list - - for ii in trans_list: - assert ii.mag_offset == 0 + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] - # Check that no mag transformation has been applied to m col in ref_table - tab1 = match1.ref_table - assert np.all(tab1['m'] == tab1['m_orig']) + # Make an StarList + lis = starlists.StarList([name, m0, m0e, + x0, x0e, y0, y0e, + vx, vxe, vy, vye, + pi, pie, + t0], + names = ('name', 'm0', 'm0_err', + 'x0', 'x0_err', 'y0', 'y0_err', + 'vx', 'vx_err', 'vy', 'vy_err', + 'pi', 'pi_err', + 't0')) - # Check me_boost == 0 or really small (should be the case - # since we don't transform mags) - assert np.isclose(np.max(tab1['me_boot']), 0, rtol=10**-5) - print('Done mag_trans = False case') - - #---Align 2: self.mag_Trans = True---# - # Repeat, this time with mag_trans = False - mag_trans = True - match2 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, - dm_tol=dm_tol, outlier_tol=outlier_tol, - trans_class=transModel, - trans_args=trans_args, - mag_trans=mag_trans, - mag_lim=mag_lim, - ref_mag_lim=ref_mag_lim, - trans_weights=trans_weights, - default_motion_model='Fixed', - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='name', - verbose=False) - - match2.fit() - match2.calc_bootstrap_errors(n_boot=n_boot) - - - # Make sure all transformations have correct mag offset - trans_list2 = match2.trans_list - - for ii in trans_list2: - assert ii.mag_offset > 20 + sdx = np.argsort(m0) + lis = lis[sdx] - # Make sure final table mags have transform applied (i.e, - tab2 = match2.ref_table - assert np.all(tab2['m'] != tab2['m_orig']) + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_par_ref.fits', overwrite=True) - # Check me_boost > 0 - assert np.min(tab2['me_boot']) > 10**-3 - - print('Done mag_trans = True case') - - return - -def test_MosaicToRef_mag_bug(): - """ - Bug found by Tuan Do on 2020-04-12. - """ - make_fake_starlists_poly1_vel() - - ref_list = starlists.StarList.read('random_vel_0.fits') - lists = [ref_list] - - msc = align.MosaicToRef(ref_list, lists, - mag_trans=True, - iters=1, - dr_tol=[0.2], dm_tol=[1], - outlier_tol=None, - trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}], - default_motion_model='Fixed', - use_ref_new=False, - update_ref_orig=False, - verbose=True) - - msc.fit() - - out_tab = msc.ref_table - - # The issue is that in the initial guess with - # mag_trans = True - # somehow the transformed magnitudes are nan. - # This causes zero matches to occur. - assert len(out_tab) == len(ref_list) - - return - -def test_masked_cols(): - """ - Test to make sure analysis.prepare_gaia_for_flystar - produces an astropy.table.Table, NOT a masked column - table. MosaicToRef cannot handle masked column tables. - - Also make sure this example works, since we use it for the examples - jupyter notebook. - """ - # Get gaia reference stars using analysis.py - # around a test location. - target = 'ob150029' - ra = '17:59:46.60' - dec = '-28:38:41.8' - - # Coordinates are arcsecs offset +x to the East. - targets_dict = {'ob150029': [0.0, 0.0], - 'S005': [1.1416, 3.7405], - 'S002': [-4.421, 0.027] - } - - # Get gaia catalog stars. Note that this produces a masked column table - search_rad = 10.0 # arcsec - gaia = analysis.query_gaia(ra, dec, search_radius=search_rad) - my_gaia = analysis.prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=targets_dict) - - assert isinstance(my_gaia, Table) + ########## + # Propogate to new times and distort. + ########## + # Make 4 new starlists with different epochs and transformations. + '''times = [2018.5, 2019.5, 2020.5, 2021.5] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] + mag_trans = [0.1, 0.4, 0.0, -0.3]''' + + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], + [[ 50.0, 1.00, 0.0], [ -31.0, 0.0, 1.000]], + [[ 78.0, 1.00, 0.0 ], [ 45.0, 0.0, 1.00]], + [[-13.0, 1.00, 0.0], [ 150, 0.0, 1.00]], + [[ 94.0, 1.00, 0.0], [-182.0, 0.0, 1.00]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.0, 0.0, 0.0, 0.0] - # Let's make sure the entire align runs, just to be safe + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix - # Get starlists to align to gaia - epochs = ['15jun07','16jul14', '17may21'] + for ss in range(len(times)): + dt = times[ss] - lis['t0'] + + par_mod = motion_model.Parallax(pa=0,ra=18.0, dec=-30.0) + par_mod_dat = par_mod.get_batch_pos_at_time(dt+lis['t0'], x0=lis['x0'],vx=lis['vx']/1e3, pi=lis['pi'], + y0=lis['y0'], vy=lis['vy']/1e3, t0=lis['t0']) + x,y = par_mod_dat[0], par_mod_dat[1] + t = np.ones(N_stars) * times[ss] - list_of_starlists = [] + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale - for ee in range(len(epochs)): - lis_file = 'mag' + epochs[ee] + '_ob150029_kp_rms_named.lis' - lis = starlists.StarList.from_lis_file(lis_file) - - list_of_starlists.append(lis) + # Distort the positions + trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) - # Run the align - msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=2, - dr_tol=[0.2, 0.1], dm_tol=[1, 1], - trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}, {'order': 1}], - default_motion_model='Linear', - use_ref_new=False, - update_ref_orig=False, - mag_trans=True, - init_guess_mode='name', verbose=True) + # Perturb with small errors (0.1 pix) + xd += np.random.randn(N_stars) * 0.1 + yd += np.random.randn(N_stars) * 0.1 + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0_err'] - msc.fit() + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - return + new_lis.write('random_par_{0:d}.fits'.format(ss), overwrite=True) + + return (xy_trans, mag_trans) \ No newline at end of file diff --git a/flystar/tests/test_all_detected.fits b/flystar/tests/test_all_detected.fits deleted file mode 100644 index ae56198..0000000 --- a/flystar/tests/test_all_detected.fits +++ /dev/null @@ -1,2911 +0,0 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 632 / length of dimension 1 NAXIS2 = 2000 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 21 / number of table fields TTYPE1 = 'name ' TFORM1 = 'K ' TTYPE2 = 'x ' TFORM2 = '12D ' TDIM2 = '(2,6) ' TTYPE3 = 'y ' TFORM3 = '12D ' TDIM3 = '(2,6) ' TTYPE4 = 'm ' TFORM4 = '12D ' TDIM4 = '(2,6) ' TTYPE5 = 'xe ' TFORM5 = '6D ' TDIM5 = '(6) ' TTYPE6 = 'ye ' TFORM6 = '6D ' TDIM6 = '(6) ' TTYPE7 = 'me ' TFORM7 = '6D ' TDIM7 = '(6) ' TTYPE8 = 'n ' TFORM8 = '6D ' TDIM8 = '(6) ' TTYPE9 = 'det ' TFORM9 = '6D ' TDIM9 = '(6) ' TTYPE10 = 'vx ' TFORM10 = 'D ' TTYPE11 = 'vy ' TFORM11 = 'D ' TTYPE12 = 'vxe ' TFORM12 = 'D ' TTYPE13 = 'vye ' TFORM13 = 'D ' TTYPE14 = 'x0 ' TFORM14 = 'D ' TTYPE15 = 'y0 ' TFORM15 = 'D ' TTYPE16 = 'x0e ' TFORM16 = 'D ' TTYPE17 = 'y0e ' TFORM17 = 'D ' TTYPE18 = 'chi2_vx ' TFORM18 = 'D ' TTYPE19 = 'chi2_vy ' TFORM19 = 'D ' TTYPE20 = 't0 ' TFORM20 = 'D ' TTYPE21 = 'n_vfit ' TFORM21 = 'D ' EPNAMES = '2005_F814W_F1' EPNAMES = '2010_F125W_F3' EPNAMES = '2010_F139M_F2' EPNAMES = '2010_F160W_F1' EPNAMES = '2013_F160W_F1' EPNAMES = '2015_F160W_F1' ZPOINTS = 32.6783 ZPOINTS = 25.2305 ZPOINTS = 23.2835 ZPOINTS = 24.5698 ZPOINTS = 24.5698 ZPOINTS = 24.5698 YEARS = 2005.485 YEARS = 2010.652 YEARS = 2010.652 YEARS = 2010.652 YEARS = 2013.199 YEARS = 2015.148 HIERARCH DATE PRODUCED = '2025-06-30' HIERARCH INSTRUMENT = 'ACSWFC ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' END @ 1&y@ c+(@ 1&y@ 4U*@ 1&y@ OS@ 1&y@ŕ@ 1&y@ !@ 1&y@]H/@nzG@ns2ph@nzG@n:t@nzG@mI@nzG@mm@nzG@nb3@nzG@ns@8䎊@8m1@4S@3!d@3~"@3Q@䩤@2@2h4Z@2@2ĊRd@2@2&EK?hjaQ?*?iy?Û? -Ld?OU=6i?/nI|??`l??!g?'?χ1?# ?jo?/O?ޥe?.Eôv? [\@@"@@"@4@.???????Ek?mo?zS ?T 8O@@n?/?? Cs?9wZe`?3#@溦z@k%>@@`ě@\1'@`ě@Q4K@`ě@Mw1@`ě@G@`ě@:6@`ě@4j~@ۊ=p@ێV@ۊ=p@{lD@ۊ=p@۞Q@ۊ=p@ہTɅ@ۊ=p@ۙb@ۊ=p@۝cA@6=:@6:)^@4 hr@4SMj@3`A@3\(@3._o @3:L/|@3._o @3BC,@3._o @3G?Ol?.5?{?d`Xp?͵ ?>;? ?>%?:?Җhn?|9.)?@ ~? -B?7ly\?J鞤?Jf?8?J6Л@@@@@,@(??????C&Ԡ?*2iۂA?Y領~@OûZ@یc?D?tN'p?{Q( ?׺@bn{@@+.@+.@+.@+.@+@٦ @+@ k@(6E.@(6E.@(6E.@(6E.@(6E@)Q@(6E@!p<@8s.>@4S.Mm@3`A7.Qn@2YJ.NC,@2YJ@2>@2YJ@1E2a|@8J@8#@8:@8 >+?BxT?g{=@8J@8@8i@8?VYk ?Պu@8p@8 *@8p@8?Z?\@@ ??@zG@w@zG@rGF@zG@s@zG@=b@zG@*0@zG@X@շKƧ@ռ(Ž@շKƧ@7@շKƧ@շX@շKƧ@շ@շKƧ@նz@շKƧ@ո}H@8g l@8\N@4hr @4&@4"-V@4*͞&@3B@5@3GKƧ@3B@5@3G@3B@5@3H9Xb?q!U?+W?](s?A2x?wX?>V$?TU?[G,?ҌI?,#t?s?|[z?ӖO_?[ S? e?Za7?Us?DΊ@@@@@*@(???????VMB“x?QԬy!?Bex.@W.V@ոAA?nɢf?[~?u?+\t@oF5i@@EQ@9R4@EQ@G2@EQ@?'-9@EQ@DqN@EQ@D @EQ@FW@/j~#@/,l@/j~#@/i3ߢ@/j~#@/qjK>h@/j~#@/fX@/j~#@/m*@/j~#@/uA@8g l@8u@2r Ĝ@2QU|@2gKƧ@2l76@1&@1 "@1&@1[@1&@1} t?ڢ??b r}?N[x?},A? J?P*i?6 k?ZU?1O}?=е?zpY?i ?V0qRi?@&pp??~?zA?Ad`@@ @&@"@&@.???????gyG{G;T?DO?n@٨3@C{y"E@/p4}pE?g j?փ$,?g*u?8̷@oʛ@@V.@V@ܻf@V@@3@3ѹ wC@2A7K@2@2(@2DR@2(@24@2(@2":>Ff@8J?uPք -?}?]9t?fN= ?wp@8J?o!ȼ?@;?)? @?3f-? -]D@ҰǴ@xn@?SB!?8F?ؼxq ?|s]@fŞV@@ě@F$/@ě@CZ@ě@F@ě@rp;@ě@Dg8@ě@M:@=p -@j~@=p -@@4c.Mm@317Kƨ.Qn@3G k.NC,@3G k.NC,@3G k.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8  -@;C@;=L@;C@;=~@;C@;Qb@;C@;^ʷ@;C@;풟@;C@; @ʜ1'@ʕe@ʜ1'@ʗFo@ʜ1'@ʜZ -l@ʜ1'@ʟ-fU@ʜ1'@ʦ@ʜ1'@ʠM@7S&@7vU@3kS@3nesg'@2`A7L@26@2 [W?@2,$@2 [W?@22P@2 [W?@2 Tn?}xC@R$/@Ru\(@R$/@R;s@R$/@R0D@7v.>@4G+@43a@N@4~"@3kP@2xF^@2R<6@2xF^@2L_@2xF^@2a@@8J?5O}?^sx?I,?L!{?U A@8J?iCv?z/=?ըE?9?l @8p? $F)?S?D'?tgi?_@@@@,@*????? @1z@1z@1z@1 ^ @1z@1RT`@1z@1z@1z@1-@1z@1lC@ I@p@ I@L@ I@ I@ I@W@ I@Ƨ@ I@f@4`D@4`d7@1i+ @1iy @1,j~#@1-V@0O M@0r@0O M@0[W>@0O M@0v_ح?| ?x1? ?菁k?E@?C!?|O?Q_ڸFN?FN ?%_D?YF?Q(?7?7@@@@@*@,??????oX?x?I9l?oc2@1u@um?'!ŗ?@(61څ?\^s1Ȧ?7C&@fZ| @ @@ -=p@@y%@@ -=p@@2@@ -=p@@?@@ -=p@@Mj@@ -=p@@(@@ -=p@@z@{>vȴ9@{>"@{>vȴ9@{DJ@{>vȴ9@{DT@{>vȴ9@{B&I@{>vȴ9@{B@{>vȴ9@{B&J@4ks@4kk50@2J^5?|@2KQ@2 "`B@2 O;dZ@1xD*@1zY@1xD*@1yXbM@1xD*@1y_o?}xZw?< N ?}?8wx@@@@@(@,??????5?: a?@*z?)@@;_@{@%;?`D?7¦$%+?[&+@8I@8?+ I@8@8i@8@8S@8?s5r@8 *@8p@8@8 @8 @?@EKƧ@EQ&x@EKƧ.@EKƧ.@EKƧ.@EKƧ.@EKƧ.@G@tj@G.@G.@G.@G.@G.@7 D@7 ->B@3 hr.Mm@2`A7.Qn@1iB.NC,@1iB.NC,@1iB.NC,?1j@8#@8:@8 >+@8I@8?h?@8@8i@8@8S@8?Ú5BZ@8 *@8p@8@8 @8 @?@:V.@:V@:`d@:V@:@:V@:,<@:V@:@:V@:@$/.@$/@\@$/@\)@$/@H@$/@ݲ-V@$/@ȧ@8H.>@3n5?|@3d xF@3-V@3 [7@2!.I@2jf@2!.I@2#@2!.I@2#9@8J?V[u9??]`?k;?U A@8J?lV?J?5;7ĻM?Q?A8D@8p?Ov_ح? - l]?vI??g@@@@,@,?????@3E@/;dZ@3E@5\)@3E@33332@3E@3E@3E@3@3E@4!-w@E1&x@E.V@E1&x@E2@E1&x@E3*0U@E1&x@E49Xb@E1&x@E3@E1&x@E8W@6:@6YJ@1 I^@1|@1i"`@1i~($ @0‚@5@0Zc@0‚@5@0GE85@0‚@5@0vȴ:?NIɮ?t0?wk?Њ?Dj?#{׈?h?? -?}7.?M =?+!? # -?P7?c I??/X?=aw%?gϵ? -r@@@@@(@$???????@%+sT?^> -3F?5?d/?BZƦ@3@E4q?I\I;?_)T`l?:{?Rū\k@oy@@۩x@۩7Kƨ@۩x@ۯ*/@۩x@n+@8I@8?ɱb)Q@8@8i@8@8S@8?`S@8 *@8p@8@8 @8 @?@b%@bT@b%@buc@b%@b@b%@bߩ*@b%@bZVn@b%@b)oU@?|@E@?|@̤@?|@H@?|@q@?|@~=@?|@@4 xF@4U=@0&+ J@0&?1@/XF@/WI@.(ۋq @.(_n @.(ۋq @.(*@.(ۋq @.(&I?}@TΥ?n!?B?I?F B?*?}@TΥ? R? FO??b?nG$GI?]SXd?o,qV?k/?X ?Lx?1C? -9X@@,@$@*@4@7??????/ڇj ?.Kn?"详S?oi@b*l@Ca?BlKJ?(P3?hk?^rT?@gؿ @@u?|@r-V@u?|@qX@u?|@jڹZ@u?|@8Z@u?|@x_@u?|@|쿱@S@`B@S@l@S@ᰉ&@S@u@S@@S@6@7*0U2b@7oiD@4hr@4$tS@3Ƨ@3^5?}@3!.I@3u@3!.I@3$/@3!.I@3]cf?)T?05 [?Fw?gt>?ťBW?A!X?Ol? LP?O?tdE? ?AP0P?o\Z{?K)?+k?Z?<שN?8wx@@@@@$@(???????btZ?_L|HbK?Ej-M0@y4_@޻? /ذ?efб)?8ا?K{~@h,@@ěT@G@ěT@üZ@ěT@hی@ěT@Q@ěT@ں@ěT@@ -=@R@ -=@Mj@ -=@ I^@ -=@j~@ -=@*0@ -=@O;d@7įO M@7ơaf@3kS@3m@27KƧ@28Y@1_o @16@1_o @1\(@1_o @1nO?}ѿ,? ?IK?nii)?jF?ҸBz3?sD?8{:?%gǿ?Nw]6?r?m\@?x}?Эs_`?~}o_?{Cx?屘?zy@@@@@,@*???????i|?W?8"?Ufݸbzi@ q@j?Xw6O?r8G?P?n9@fQF$`@@=p -.@=p -@=qu@=p -@;:)@=p -@<L_@=p -@;P|@=p -@;@qm.@qm@gRT`@qm@cw@qm@ix@qm@g8}@qm@de@7䎊.>@2 I^@2ԕ*@2 -V@2 A@1a:S@1_iDg8@1a:S@1_U=@1a:S@1_As@8J?hX*?=?Kh?:Z2b?`?SJ@8J?4i?tP*?Җhn?C ;?]˙$>@8p?u/X?8*?ۮetZ?Cns?WZ@@@@*@*?????@!@%Q@!@$e@!@#e@!@$7@!@!TɅ@!@!TɅ@n@[@n@}U@n@g8~@n@u%F @n@n.@n@\(@7Q|Q@7Qn@1O|hr@1O@017Kƨ@01u!S@/fL/{K@/fIQ@/fL/{K@/fL/{K@/fL/{K@/f&J?E?$?,fC6?#t? 9?:?2}?)[+@8I@8? @8@8i@8@8S@8?X@8 *@8p@8@8 @8 @?@nzH@mV@nzH.@nzH.@nzH.@nzH@o-@nzH@mV@i@n@i:^5?}@i@n.@i@n.@i@n.@i@n@iC -=q@i@n@i?|h@8[~($@8WrH@2 ě.Mm@1`A7.Qn@1 k.NC,@1 k@1@@1 k@1Ϫ͟?ȉak@8#@8:@8 >+? f?/?Ol@8@8i@8? ?998?hJ@8 *@8p@8?&f?o@@@???@LC@LC@LC@LzH@LC@Lhr@LC@Lv_خ@LC@L˒;@LC@Ly@,`A7L@,`ě@,`A7L@,aaf@,`A7L@,_;dZ@,`A7L@,]ڹ@,`A7L@,c@,`A7L@,cn@3 ]ce@3 C\@/"`A@/#@/NO<@/C$@-Xy=@-B@-Xy=@-P{@-Xy=@-P{? ?J`Y?"?̒?+?Q`?| ?eCO?LE?@[*^?*v7?w8?s@a?=d??k,?o? .0v@@@@@,@,??????8Z?2?HT*?D)Z<@L7L@,a%?g !/?ct2?IN?щm&@fg`@@.@@(Z;@@ S@@w6@@xQ@@\&S@E49Xb.@E49Xb@E3i6@E49Xb@E g@E49Xb@D\8@E49Xb@E";Է@E49Xb@E7g@84m8.>@3hr @3{R!4@2t@2cN`@2 -0U2a|@1PX@2 -0U2a|@1ň@2 -0U2a|@1ܷ+@8J?Mt?sο?|v?N?e@8J?p&tC?s?Yg3?J](?Y@8p?Ŕs ? -Nr)?l-Q?I`N?up-z@&@"@*@.@0?????@C@}@C@:=@C@ȨV@C@B%@C@.@C@φ@(@@(@x[@(@@(@g@(@g^V@(@G@6*0U2b@6(E\@1+ I@1+{N@0"`@0mK@03&@0*SE@03&@0@03&@0lR?Z=?^ ‰?42`??7?L?F0^?}E?'?>?1ߔ?G -̾?PQ-2? ab?Tx??m p?:wB?Yf@@"@$@@2@2???????o?'8?YP;rq?d#@}@Ƞ߼x?zXXdW?ta%?\2e@.^F;!@h@ @ʍ@ʍhr@ʍ@ʌ!-@ʍ@ʋƧ@ʍ@ʍqu@ʍ@ʍC]@ʍ@ʌ@u@KƧ@u@@u@D@u@kP@u@}Vm@u@+j@6䎊@6ae@1 hr@1 wkP@0Vu@0쿱[@0&YJ@0&@0&YJ@0'@0&YJ@0'1?1j?q' -B?5$?Њ?% -?^?|O?4 "?;,?M =? ^??lINՄ?}?3I#i?!A -m?=aw%?ad|?ܑ @@@@@*@(??????Y&=3!@?J?zթ)@ʍcX@:?<)?:ej?G涟Fa?n A@g9.@!@WV.@WV@Wě@WV@Wq@WV@WOv@WV@W *@WV@WYJ@n.@n@w@n@.@n@q @n@@n@'/@8#g l.>@4!hr @3ߤ@@3lj~#@3[xF]@2@2qi@2@2-@2@2{J#9@8J?B?e?4M^?xs?9j@8J?Ɠ0f?`;9?'G_J?Fl}?:;@8p?G?N?.BZO?pO4I?/̽`@@@@(@*?????"@ΗO;@Ζȴ9X@ΗO;@Ζ+ @ΗO;@Θ}G@ΗO;@Γ@ΗO;@Λm@ΗO;@ΚC\@9XbN@9XbN@9XbN@6_ح@9XbN@2p:@9XbN@7X@9XbN@0D@9XbN@1iC@6*0U2b@6K]c@2ԛS@2TɅo@1j~@1GE85@1B@5@1;P{@1B@5@1F@1B@5@1DFs?1j?_?q -*?+YI?1,j?&Gf?sD?7fps??,ў?*?ω:hT?z"2(?Ol?]b?#ѧ? 1 D@?3@@@@@,@*???????Fٝ0[!x?COصC?:ݶFNc@ΘIC@5m$?enD`?[7Y?^dq? @i2О;@#@"E@,1&@"E@H`@"E@Z^-@"E@]&W@"E@-¼@"E@2-0@"v@% ě@"v@%y@"v@% @"v@%)3@"v@%]d*@"v@%1MY@6 xF@3Y@3I^5?@/24@2j~#@.l o@2iB@-WM4@2iB@-jVw)@2iB@-]`߈?}@TΥ?H%]^?,Q"?Q#?}xM?&C@=V?QS?EZJ@@ @"@$@,@0??????O-b, ww?@LU?# 㥐@-+,@[?_.?Cτ?dw"D?JE@gvY@%@hr@hr@hr@}n@hr@9@hr@"9@hr@ew@hr@b@@@@}I@@7@@6k@@W@@@3:qiC@3:kP|@-"`A@-?ÿ@-4E@-5F -L0@,=Vl"@,=\@,=Vl"@,=|@,=Vl"@,=?|h2:?˚]us?B?8?)>?T|/?|h2:?;*;h?w&?1 ?h1?Tq/G?nڨ8?w?uk=? ZE?7is\?@@*@,@&@;@;?????? -xJ?'@?"g?{k@bn@R?p?Bo{?2@C?f?r6@fN[@&@T5?|@T/@T5?|@T%@T5?|@TQ@T5?|@T6z@T5?|@Tѷ@T5?|@TVl@I^5?@L@I^5?@I7Kƨ@I^5?@I^5?@I^5?@JL@I^5?@I@I^5?@JW@6Q|Q@6O\(@0 I^5@0M@/rnO@/r{n@-p'RU@-p -=q@-p'RU@-p|@-p'RU@-p{?| ?{j?Gy??˅T?6r In?*WF?Br?›?ދ"?6)}?2&h?eDQQ? ?§d߱?YF?E 8?f_Ԋ?b/bJ@@@@@,@,???????%WۭCCW ?&.?",|x@T -@K?G>uT?Bah?|NRu?d;T0@hpID@'@ Ƨ@ Ƨ@ Ƨ@ a@ Ƨ@ "h @ Ƨ@ @ Ƨ@ cA @ Ƨ@ B@+u?|@+u$@+u?|@+tɅn@+u?|@+uXy>@+u?|@+uᰋ@+u?|@+tm:@+u?|@+tɅo@5-v@5-C\@1sS@1seN@0V@0IQ@0xF^@0jf@0xF^@0I^5@@0xF^@0C]? ? KG?O%c?l?n{K?\.:@?|O?CO?;,?߇O?l{9َ?Q\?ZaP?3I#i? ݎ8? mmsi?L8AS?@@@@@,@,???????1`ߐ#ZLw?AdAI?=!^@ aPP@+u9:?4-4L:??'mw?r1V0?Y[P@g%{ђ?@(@dS@dkŔ@dS.@dS.@dS.@dS.@dS.@@nQ@.@.@.@.@.@6]ce@6n*@2S.Mm@2ttj.Qn@2 [W?.NC,@2 [W?.NC,@2 [W?.NC,?Z=@8#@8:@8 >+@8I@8?Qp@8@8i@8@8S@8?Tʼ=@8 *@8p@8@8 @8 @?)@#Cn.@#Cn@#9#x@#Cn@#F?@#Cn@#5L_@#Cn@#,Vϫ@#Cn@#3 @5?|.@5?|@Ǡ m@5?|@ ě@5?|@B@5?|@ q@5?|@fA@9.>@5hr@55?|@4-V@4O;d@40@41$/@40@4$`d@40@4%Xy=@8J?J f?؋)?pW?U܈9?mZ@8J?k3~?ōɗ?˿ o?KN?H"#@8p?/őB??W?zoV?ЀIN@@@@,@(?????*@{dZ@x@{dZ@~҈p@{dZ@[W>@{dZ@ح@{dZ.@{dZ.@S_vȴ@Sg+ K@S_vȴ@S\1(@S_vȴ@SYJ@S_vȴ@S rG@S_vȴ.@S_vȴ.@64m8@6O M@4+@4@4R`A@4쿱[W@4 k@4 ~($@4 k.NC,@4 k.NC,?Ol?Ey?iX?, -d@8I@8??k3~?9؂?R(gF@8S@8?N?:?ȧu ? vrC@8 @8 @@@@????+@kP`A@kQm@kP`A@kEo@kP`A@k-@kP`A@k(eP@kP`A@kSg@kP`A@kݗ@Xtj@s@Xtj@3Mj@Xtj@W3@Xtj@s @Xtj@h>BZ@Xtj@0@95S&@9?v_ح@5@5P{@5lj~#@5usg@46z@4}Vl@46z@4@46z@4z0U2a|?W$B? -?9/?>5c??7?2d?xaz#?ռ2?P@?5OW?#U?̷~ ?m?:/ʼn?6?e8?馪C?Y')@@@@@$@(???????k{Ta}?hfz?yV"@kRp@nÏ^?S~?8@}c?lIߡ@l@,@>=E@>9"@>=E@>CZ@>=E@>F1@>=E@>Ik}@>=E@>B I_@>=E@>@n@55\)@56E@55\)@5:6@55\)@52@55\)@584֡@55\)@5/hی@55\)@5/V@4,<@4~($@1 I^@1bM@1,j~#@1(@0D*@0 '0@0D*@0Z@0D*@0S&?qv?U ?/͔p?Z.??;?봤G?qv?)[j=?V$f@>CƱ@53z?|Ml?qӇl?h<~5?$m@nG.@-@A7K.@A7K@A@A7K@B@@A7K@Ae@A7K@@4m@A7K@@4m@)x.@)x@(r @)x@(TɆ@)x@(u%@)x@)*0U2@)x@(TɅ@3įO M.>@0n5?|@0nvȴ9X@/@/oiDg@.PH@.@N@.PH@.Z@.PH@.sQ@8J? KG?\H{gX?ݶ\r?ǹT?{<@8J?0n=Q?Jj?7枪y7?A B{?SV.@8p?§d߱? SF?!ˠ*(??b/bJ@@@@&@&?????.@~vȴ9@~"@~vȴ9@U=@~vȴ9@}ce@~vȴ9@|?@~vȴ9@}!.I@~vȴ9@~Ov_@F,C@F,C@F,C@F+҈@F,C@F$ xH@F,C@F&,=@F,C@F)Dg8@F,C@F'K]@3cg l@3cS@0ԛS@0fA@0~"@0/V@0c@0eڹ@0c@0f$/@0c@0eS?| ?m0?BI{?{@?@@No9?봤G?| ?j%Ra?A -;?| ?A?:i?nwf?%_D?z_?p=J?GZ?A0)n@@@@@(@"??????5w"xQ(?-"S`?9zT@~%1@F*$h(^?M8M?Yw6?&YD!?G@f?%<@/@n@F@n@x@n@|@n@4.y@n@Vɡ@n@p:@tE@uPa$@tE@tԿ@tE@ud?q\^/?(#@@(@(@*@;@*??????q}o{?0tz?nAօ`?hk@y@uٵo?Bh?~,?cs{e?4~@^T@0@Z1@XQ@Z1@OV@Z1@U=@Z1@TzG@Z1@9"@Z1@@N@ ?|h@ 9"@ ?|h@ m8@ ?|h@ i^ @ ?|h@ Q2X@ ?|h@ Q@ ?|h@ ^Ov_@7 xF@7҈p;@4i+ @4QiB@3~"@3Q@2O M@2w1@2O M@2z@2O M@21?E@?Z]$?×DZ?XԠ#{?Xz=?\LTu?)T?*J?S]:?agf ?,<?(U?Ʉ?qlPg?e?a%?;ܔ?S![c@@@@@&@*??????tt ?ޒE?ij?P݀ ?@Qw@ M^^G?P}d?n7/?r@M?eI@h@1@=`A@=bM@=`A@="`B@=`A@=!@=`A@==@=`A@=LI^4@=`A@=_o@p -=@p -=@p -=@qm@p -=@m q@p -=@P`A@p -=@JڹZ@p -=@%zxl#@4]ce@4 q@2hr@2 ԕ*@2t@1H@2OO M@29ᰉ(@2OO M@20@2OO M@20|?~?J? w(?dl\?lsC^?Җ? G?|h2:?\?%c~??`c?r{?e?f)"?v'X?[?8N0?{=rK@@@@@(@,???????=x<?A:%^?<|d@= $@e#?Qv?Nb$h? o]?gu@]e䟽@2@Ǯz@`B@Ǯz@n.@Ǯz@Ϫ͟@Ǯz@n@Ǯz@Țu%@Ǯz@B@޸Q@@޸Q@α2@޸Q@յs@޸Q@<63@޸Q@<@޸Q@#@9[~($@9mC\@4}hr @4s%2@4lj~#@4g+j@3@3ϱ[W>@3@3Ӝߤ@3@3{J#9?@?}>?.O?1x?*bQt6?=B+?g;+t??B?:?o?Y{?n+?a,F?1?d&??S@Gd@@@@@$@(??????f6tM -@?pBa&\?a\r@zP'@},?#v u@m)\@3@KƧ@KƧ@KƧ@LVϫ@KƧ@L@KƧ@MM;@KƧ@KP}@KƧ@JL@vE@vE@vE@vOva@vE@v4@vE@usi@vE@v@vE@vz@2qiC@2xl"h -@/`A7@/D@.cnP@.cS&@-At@-Mj@-At@-v@-At@-'/W?^Q5P?_?@?eU!? f?ˡK?bM_x?f?'Q?4ډ3?ڂc?SV.?n|̲?§d߱??ʬv?j?@@@@@,@(??????U,$ ?>; ?.[}H?/YXu @KT;p@v'?=:|7v?>#},?tO ?s3u@s't@4@S@VE@S@*@S@FIR@S@c,zx@S@~@S@@.O@-@.O@*0@.O@ D@.O@&@.O@ye+@.O@mw1@8:@8䎊r@4S@4M@4tj@4҈p@3m8@3De@3m8@3>BZc @3m8@3Q??!?ʒK?Dc?aOD@@@@@*@(??????_mWFt??W8?@Ho4@%f?ӝ?…?#x@GF5@`17@5@r @@r @唯O@r @&@r @p:@r @+ J@r @Ov_@F@Cn@F@Cw@F@>@F@<n0@F@>@F@$?@8}:@8'R@3hr @3b}@3V@3JL@2䎊r@2}:@2䎊r@2|~($ x@2䎊r@2qae?Ol?#T?2+@8I@8?I~y@8@8i@8@8S@8?Is -@8 *@8p@8@8 @8 @?8@M@M@M@m@M@@M@ӳ@M@T@M@Q"ڻ@}r @}#x`@}r @}ZX\@}r @}tCx@}r @}hՅ@}r @} @}r @}*=@8 xF@8 L@3f+ J@3c@@2y7KƧ@2z }@1@1ߩs@1@1ߋ'ZE@1@1)5F?pV?}\\?~\ ?ە?W"/?h$[?o"? -~>i?O%?޿I\?qAx?/? kD?XM=nk?QW%;2?=\b -?'wCh? -)@@,@,@*@9@5????????x`x?V6K?6Sw?A5p@A@}?VTDm?a0x?YVF*v?ƹHqi@h+@9@j~#@ow@j~#.@j~#.@j~#.@j~#.@j~#.@j~@a/ @j~.@j~.@j~.@j~.@j~.@4䎊@4w1ث@2?|h.Mm@2KƧ.Qn@2O M.NC,@2O M.NC,@2O M.NC,?|>G3@8#@8:@8 >+@8I@8?P&x@8@8i@8@8S@8?n8@8 *@8p@8@8 @8 @?:@%@A7L@%@ I^@%@o@%@A \@%@e+@%@{J#:@qDT@qDZ@qDT@qCZ@qDT@qC,zx@qDT@qCA \@qDT@qDq@qDT@qDZ@7B䎊@7BZc@2!hr @2!n@0j~@0֡a@0O M@0b}V@0O M@0-w1@0O M@0H˒:?Y͍_?[e?iTC~t?ʳ_?=rE=?OO ? -t@@@@@,@,???????EB -t>-@?7Q m?@C@qDQ`?VFc/?0eos ?½?fZR@iL?@;@ @@ .@ .@ .@ .@ .@~mV@~TzG@~mV.@~mV.@~mV.@~mV.@~mV.@8*0U2b@8tj~@5 -^5?|.Mm@4~".Qn@4xD*.NC,@4xD*.NC,@4xD*.NC,?{QB@8#@8:@8 >+@8I@8?=,܊{g@8@8i@8@8S@8?KxMR@8 *@8p@8@8 @8 @?<@"@1@"@^5?}@"@lD@"@;dZ@"@-V@"@A7L@7K@@7K@@7K@"@7K@T@7K@`A7@7K@+ I@34m8@3'RT`@0@0e@/;dZ@/Y|@/#Z@/C,zy@/#Z@/5?|@/#Z@/]ce?|O? 1H?*"AaI?%?Dj?[2?|O?? :?qF?ni" ?tP*?'? J??m+&?XW~?O|q?<71@@@@@@???????TS8?PD?5hp8? ܱ@30@ ?TZ?2OR?cXZG?qjW`@f@=@ߝ-V@ߪ=p@ߝ-V@ߍw1@ߝ-V@ߠě@ߝ-V@ߠě@ߝ-V@ߚQ@ߝ-V.@@@[@@4֡b@@hۍ@@s@@{@@m]@tj@1&@tj@Y|@tj@ݗ+@tj@SM@tj@䎊r@tj@|@8B䎊@8C*0U2@3i+ @3jL_@2b-V@2a-@1䎊r@2hr @1䎊r@2C@1䎊r@2 [7?:-%?dY{?ՠΆ{?,OKu?n{K??2}?&uE@?mK?"6Wz?uvo4.?9?(L5}y@%~%@cn?!_G?9Ӣס?=F"W?fy@s @@@w@w@w@oiDg@w@$s@w@|@w@\@w@C]@@@@@@@.H@@\(@@u%F @@'@04m8@0:~@.MV@.L@.h1&x@.h1&x@.kjf@.k҈@.kjf@.k)^ @.kjf@.kjf?^Q5P?m0?Gy??뤽??Dj?F0?bM_x? ,C?'Q?>\?2&h?{L բD?bMky?3;?8*?xqC?M684?L@@@@@,@*??????S7L?B\ vS?"\{Y@@EƎ*?Q?1@?(+q:?Z4[@sY@A@ -=p@ -~#@ -=p@ -L@ -=p@ۋr@ -=p@ ^5?@ -=p@1@ -=p@r @qtj@q@qtj@qC@qtj@qbM@qtj@q_@qtj@q$@qtj@qs@3Y*0U2b@3Y+ @1?|h@1%1@1tj~@1ohی@0m8@0iB@0m8@02W@0m8@0GE85?|O?Kb?aR ?a_b?PY8~Qj?)'3?|O?V"y$R? -\?< /0?u$6?pN.>7E?u@>N ?9o?^&?˴C?7zE?ymcw\@@@@@,@*??????CGT?2Wy?1>`@ @3J@q]?Ro-'?QAfw?&?࣏ <@f;@B@ڟv@ٙ@ڟv@ٳ|@ڟv@ۋq@ڟv@t@ڟv@ڬ=@ڟv@A@P`A@Qhr!@P`A@P- @P`A@PbM@P`A@P:~@P`A@P )@P`A@P{@5!.@52a|@1S@1xF]@0ȓtj@0ȓtj@0?䎊r@0@{@0?䎊r@0@H@0?䎊r@0?;dZ?| ??"?:Z2b?% -?-/ge?}ѿ,? H?j?>\? ^??'+?oܧ?/?y8$?Gϵ'?ܑ ? Ğ@@@@@(@,???????r}M@([p?0a|ă?6Y @RO@PZ?PU&"?8? Cv??~`(@fè8@C@inP@inQ@inP.@inP.@inP.@inP.@inP.@=p@=p@=p.@=p.@=p.@=p.@=p.@4Y*0U2b@4X@@0I^5@.Mm@06-.Qn@/uXy=.NC,@/uXy=.NC,@/uXy=.NC,?}ѿ,@8#@8:@8 >+@8I@8?| @8@8i@8@8S@8?٤@8 *@8p@8@8 @8 @?D@MO;d@W -=p@MO;d.@MO;d.@MO;d.@MO;d.@MO;d.@o-@}-W@o-.@o-.@o-.@o-.@o-.@9 xF@8hr @3S.Mm@27KƧ.Qn@2ush.NC,@2ush.NC,@2ush.NC,?+-/@8#@8:@8 >+@8I@8?A[F@8@8i@8@8S@8?Ä`e@8 *@8p@8@8 @8 @?E@ܬ1'@ I@ܬ1'.@ܬ1'.@ܬ1'.@ܬ1'.@ܬ1'.@n@Q@n.@n.@n.@n.@n.@7]ce@7P{@30ěT.Mm@2U`A7L.Qn@2V!.I.NC,@2V!.I.NC,@2V!.I.NC,?| @8#@8:@8 >+@8I@8?xDž@8@8i@8@8S@8?' -}B@8 *@8p@8@8 @8 @?F@@;dZ@@xl"h@@a@@ȴ9X@@Xe@@rGE8@cW -=p@cdZ@cW -=p@cSMj@cW -=p@c["`@cW -=p@cRs@cW -=p@cV!.@cW -=p@cU!S@804m8@8.Vu@3hr@3͸@3lj~#@3l]ce@2[W?@2'RT`@2[W?@2U=@2[W?@2At?xDž?" ?\H{gX?,Z?z|?"eM?}ѿ,?lE0?)_?:u. ?4?⍄o?eo?)h?~/O?rsw?Ee,?8@@@@@,@,???????./0n) ?P6;?RH|@5l/@cWZJ?ak?y^*?׸ ?!M^#b@o̘:(@G@.O@5\(@.O@3Mj@.O@Aoh@.O@;q@.O@.;5@.O@)*0U2@>lD@?n@>lD@>xl"h@>lD@? w1@>lD@>/V@>lD@? [@>lD@>#@7䎊@7˒:)@5?|h@5>6z@4i"`@4k:)z@4O M@4?|i@4O M@4ᰉ@4O M@4- ?qv?d̺?oq u?\eú?h?oNR?3a=?4i?9]t`?Z.?8?ے|?#iN?KQ5?R ?AX|y?z"+;?ԤA]@@@@@,@(??????fH?{vo?eDF ,w?;ބ?/5@k@H@M@M@M@ xF@M@ح@M@oh@M@u"@M@ѷX@ƃn@ƃn@ƃn@Ɓs@ƃn@Ɖ7Kƨ@ƃn@Ƃ I^@ƃn@ƃ@ƃn@Ƃ&I@35S&@352a|@1kS@1kQ@1,j~#@1+lC@0ce@0N;@0ce@0?@0ce@0!.H?| ?W[?=?Kh?Q;}?a\?pf@iqu'@J@;"@;1@;"@;dZ@;"@;^5?}@;"@;v@;"@;YJ@;"@;4֡@ w@ +@ w@ \(@ w@ `A@ w@ -@ w@ hۍ@ w@ ;5@5~($@5m^@/tj@/A [@.Z1@.?@-z)_@-z)y@-z)_@-{"`B@-z)_@-z)_?|O?ߋb?*"AaI?eU5?2A?:?1j? aҹz?s8r?-I`K?إ<(?Uyu#?W?򯞼?֘>D?hB^?pk Y?B @@@@@@??????-U?(U?y? @; @ ޯ,?Hb3l?AJ-x+?֏[$?|@gJ@2 Ĝ@-@2 Ĝ@ qv@2 Ĝ@%1@2 Ĝ@r @2 Ĝ@qi@2 Ĝ@1@9Q|Q@9Bѷ@6+ J@8Qn@5t@6Fs@5a:S@7u!S@5a:S@5DɅoj@5a:S@5%+a?:%?̰C?Ђx6?VI?֤??CږF?ԥ(Щ?h2E?p?5Kؘ? -5?RP?➪U?*'n? 2,?݃?BXFzS@@@@@&@*???????T~| Pm4?~>?7@j]@ - ??Ýq^@ݧbF@8NE@mIY|@L@ I^@tj~@ I^@ :)@ I^@ -L@ I^@ @ I^@?@ I^@m\@\O;@\S@\O;@\S@\O;@\e@\O;@\[V@\O;@\ qw@\O;@\Zc!@7s@7!-w2@3 ě@34֡a@2-V@2xl"h -@1D*@1S&@1D*@1+ I@1D*@1??}?.jM?ub5?G?X?I?}ѿ,?rnc?9Y,?9~F?dLt??hE?=fA:?'#g?hSp]?=P|?TI@@@@@(@&???????y$?d~?ePEI?U7m@R @\e?,1\?uќ=@&;?}@g\H@M@X4j~@X4j~@X4j~@X5!@X4j~@X5?|@X4j~@X4D@X4j~@X4m:@X4j~@X4zG@(@j~@(@@(@@(@쿱@(@#@(@1'@2p4m8@2p- r@-Ƨ@-a@@-cnP@-b`A@,jf@,6z@,jf@,jf@,jf@,6z?^Q5P?°R;?>f?|*(? 9?/]?bV,?%F?~Q?; $?A B{?M\ٓ@?b@?3;?u@>N ?8 ^?a+?a+@@@@@*@ ??????, p>?ыGKx>b9ԇ@X4ѐ@ܑ?%? ) pn?Et? I j,@s -~@N@bl1&@bl1&@bl1&@bn@bl1&@bra|@bl1&@bw'@bl1&@bx}H@bl1&@bw -=p@-V@-V@-V@1@-V@12X@-V@6E@-V@7X@-V@6Fs@0B䎊@0Be+@-`A7@-B@-h1&x@-b @--C\@-#S&@--C\@-#@--C\@-#eN?^Q5P?? -tI?ׂa?~9n4?oNR?bM_x?&uE@?DP?j?4.? Ӻ^?bMky?.x? ݎ8?l?Rv?“y@@@@@,@*??????5u?WO?Cj=S?&PNkxO@bw}e@6 ?R[?5 /|B?W ?`I7"@rߓ@O@ dZ.@ dZ.@ dZ.@ dZ.@ dZ.@ dZ.@G{.@G{.@G{.@G{.@G{.@G{.@8*0U2b.>@4+.Mm@3V.Qn@2D*.NC,@2D*.NC,@2D*.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 P@4{lD@4{lE@4{lD@4x@4{lD@4}K^@4{lD@4@4{lD@4zc@4{lD@4|64@qm@o;dZ@qm@o@qm@nzH@qm@p)^@qm@t*@qm@tO @5 xF@5@3W+ I@3Vaf@2K"`B@2H*0@1u@1.H@1u@1D@1u@1m8? ?5{#? '?'$5?(+Zk?ʟ?1j?\?;~?{d?H<?U3p?{V?3a=???$?k!l@@@@@,@"???????'}}`?O?C[VZ?.]@4|>@q뭼 ?cP8Dj?Oj.M?6h?,.W@gs8a@Q@lglC@lk %@lglC@lm]j^@lglC@lxb)@lglC@lx+@lglC@l[@lglC@lz@u@ܶƍ@u@ꗵ<@u@@u@ۭh@u@,?@u@`@7@8D@3S@36a0@3lj~#@3k/+@2}ce@2qGg"@2}ce@2q -ӌ@2}ce@2p݁C?D6?E?קa?R=?l~?LeT?%t;?Z=?8?8Y?bڑ?`?KaOb?&?ipR? ݮ5?6 a"?=E8?,:@@*@(@&@7@3???????k(?a瑭"?`0ֲks@lx[8o@?{[Ds? -w?@`?Dž^<@l*r>@R@/.@/@o@/@iB@/@R@/@0U2b@/@s@(.@(@,zy@(@$e@(@?|h@(@m9@(@tj@0.>@) -~"@) -=p@(wO;d@(m^@&73@,;6@&73@&҈q@&73@&%1@8 -Ny??,Z@67U? -0ggk?4g9?aYZq? ?pN.>7E?no?[ș8?hE?{0?WZ? `@@@@@&@*??????Da2?JA?E*!@^l@?"k4?Z_2w?hsƈ?{R?ͨߔn@qV@T@ ě@;dZ@ ě@H@ ě@|@ ě@{@ ě@{@ ě@&x@dx@dx@dx@dXy=@dx@dL_@dx@dE86@dx@d9X@dx@d_o @4[~($@4[$@1c@1d!-w1@0Ͳ-V@0>@0Q@0(@0Q@0ce@0Q@05Xy>?|O?'? ??b?V^?w'?| ?p RN%?"a?j?=d^?U3p?=Ca?O?ʳ_?v_.^??\?k!l@@@@@,@,???????0jh`?+{P?vx$N?"|>@l}d@db6? cPG?C _=?LwzL?5Pg$@fbُ -@U@7Kƨ@;dZ@7Kƨ@eO@7Kƨ@@7Kƨ@@7Kƨ@-@7Kƨ@ۼZ@`(@`XbN@`(@`e+@`(@`s@`(@`<65@`(@`cB@`(@`dZ@7įO M@7nP@3|hr@3(\@3-@3}Vm@2._o @2)rGF@2._o @2'eں@2._o @2H˒? ?sp?Uy*>ǜL>$@h@(?~'b? F?Hu[9?DFLx0@gpa@X@O;@Ƨ@O;@@O;@a|@O;@͞@O;@N;@O;@䎊r@y4j~@y3E@y4j~@y:v@y4j~@y9b@y4j~@y:)y@y4j~@y;n@y4j~@y=@8g l@9d8@4+ @4F@3V@3ce@2:S@2jOv@2:S@2n@2:S@2ۅQ?}?Tے}?=5?niʢ?(i^?"X??@׮? ?,ў?$Z?\ũU?&-?xCZ?Aᆽ?q+U?HAY?Oh6c@@@@@,@&???????W,d?`"a?IO*(?(BӜ@w@y:%?/%-`?F32H(?E{YH$?|?q@p&q@Y@/@Q@/@&I@/@;dZ@/@ں@/@m\@/@@̠A7L@̡G{@̠A7L@̠҈@̠A7L@̥`A7@̠A7L@̣A [@̠A7L@̣,zx@̠A7L@̧#@5*0U2b@5_o@3.5?|@3-}H@2Htj@2GO;d@2(@2(1&x@2(@2(˒:*@2(@2&IQ?1j?`[?:?Њ?q?A!X?Br??xrI?2'?\d?6m)? 3W?oܧ?ѿ,?~$?bv{3dZ?P\?T@@@@@"@$??????"a?Q^̤?3};t$?7b|(kr@H@̣?SfQ -?W:Ks?ҟH|?h9l@g/e+@Z@(\@)^5?@(\@7sQ@(\@2p;@(\@?|h@(\@>iB@(\@fFt@71'@7lE@71'@7ڹ@71'@7Z@71'@7=L@71'@7&@71'@7}H@4@4-V@0 I^@0D@0'KƧ@0!R@0:,<@02@4n@0:,<@0&@0:,<@0'fA?|O?gˣ?ھ?؆{~? )?'t?|O?p RN%?tP*?j?5D?j^q?~/M?Ф?/X?bv{3dZ?a眷D?tl@@@@@ @,???????~#|KS{D?VVn?Fr\@47@7Bz?pWZ?a?S? wp?ʕZ{@ax@15d@[@j~#@h\@j~#.@j~#.@j~#.@j~#.@j~#.@zL@zN\2@zL.@zL.@zL.@zL.@zL.@7䎊@7XI@3S.Mm@3A7K.Qn@3&YJ.NC,@3&YJ.NC,@3&YJ.NC,?Hg @8#@8:@8 >+@8I@8?o"@8@8i@8@8S@8?Y,n@8 *@8p@8@8 @8 @?\@)g-.@)g-.@)g-.@)g-.@)g-.@)g-.@h(.@h(.@h(.@h(.@h(.@h(.@8qiC.>@5^5?|.Mm@5v-.Qn@4̿[W?.NC,@4̿[W?.NC,@4̿[W?.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 ]@p -@-@p -@`C@p -@ۋr@p -@6@p -@b}@p -@@F$/@I^5?@F$/@P ě@F$/@xe,@F$/@F&@F$/@pbM@F$/@lq @8YJ@86@45S@4!n.@3`A7L@3mjOw@3O M@2s@3O M@2\@3O M@2 M;?I~y??|JO?F?l?/߲^|@s @_@mV@a$/@mV.@mV.@mV.@mV.@mV.@;lD@A$/@;lD.@;lD.@;lD.@;lD.@;lD.@8?@88TɅ@5+.Mm@5"-V.Qn@4QiB.NC,@4QiB.NC,@4QiB.NC,?}@8#@8:@8 >+@8I@8?X@8@8i@8@8S@8?ʳ_@8 *@8p@8@8 @8 @?`@ I^.@ I^@xl"h@ I^@#@ I^@@N@ I^.@ I^.@YO;d.@YO;d@Y:)z@YO;d@Z ě@YO;d@YR<7@YO;d.@YO;d.@7O M.>@5S@4H˒:@47Kƨ@4!.I@3YJ@3;5Y@3YJ.NC,@3YJ.NC,@8J?;)ry/?Ɣد? @8I@8@8J?0?ċIVq?Eɨ@8S@8@8p?r -e&#?þسq?oQw@8 @8 @@@???a@"hr@"-@"hr@"B@"hr@"l@"hr@"n@"hr@"p -@"hr@"TɅ@ 7Kƨ@nP@ 7Kƨ@KƧ@ 7Kƨ@4֡b@ 7Kƨ@T@ 7Kƨ@w4m@ 7Kƨ@s@7*0U2b@7"h ԕ@4}hr @4v$/@4`A7@4iDg8~@3sh@3ۋq @3sh@3 @3sh@3\)?]?d^?;w{?ߦ>??XP#?]?&ژ?#fL -?f *N?QE?#?*wjs?XԠ#{?KxMR?q?VM?N;?@@@@&@,???????5ma?0{?U$+'?N1J@"ߞ: @*RX?e|kP?g#? ?8m?)-[@3{ I^.Mm@2`A7.Qn@2m8.NC,@2m8.NC,@2m8.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 c@n@y"@n@mhr@n@q[W>@n@gK]@n@ezxl#@n@f (@M@GlC@M@["_@M@P{@M@UXy>@M@Y~($@M@WsP@7`D@7au@1hr @1RT`d@0Vu@03@0 [W?@0 ^@0 [W?@0?@0 [W?@0'RT`? ?Ug@@@@@,@*??????vlYQ?q o?Qߛi ?Jʻ(@nN@O?m[_?h%8.?xc^?6V@dWr@d@}\1'@}]p -@}\1'@}n;5@}\1'@}o@}\1'@}$tS@}\1'@}'Q@}\1'@}bM@+ I@)^5?@+ I@!$/@+ I@)B@+ I@@+ I@hی@+ I@rGE9@5@@5 ԕ*@6hr @6&x@6V@5Xe@5(@4͞@5(@4`A7L@5(@4oiE?|O?+?q?T%?xTl?N*?|O?|Fef?7tg?&*N?gz?5t3w9?>Zw~?e?MB?U]^,?EL?ھ۲@@@@@(@(???????=̀?g˯N?|6z@2_o @2YJ??@@No9?B 'nr?U=K?bJֵ?F?g1?PuN?+l?M =?YNژ?H? -eKs?8A?]b?bv{3dZ?I$? {t@@@@@"@(??????\“^rC6?nS l?S~Ȇ@sI@·rd?}jo?`fe@/sD36?XTW@p/%Æ5@g@nO@w@nO@ov_خ@nO@p -=@nO@m q@nO@pD@nO@p )@kI^5@k1&@kI^5@kУ ->@kI^5@k)^@kI^5@kЖ@kI^5@k˒:)@kI^5@k I@7䎊@6v_ح@2ahr @2cS&@1-@1fB@1a:S@1g&@1a:S@1cn.3@1a:S@1dFs?Ol?: ?jhq9?,Z?o?P??#?Y͍_?uie?i?V\F?SY?to(?C ?]ivL?/X?[?{2?DV@@@@@,@,??????]ZP,?Kxn_?DA)Tل@r@kr@?kW:?d~#^?uP*K?o@h>>w@h@s-@s\(@s-@s@s-@s ě@s-@s@s-@shی@s-@s1@O@@O@w@O@{J"@O@hی@O@+@O@2@4*0U2b@4 @/"`@/>BZ@,;dZ@,|hs@+Ǔݗ,@+dž&@+Ǔݗ,@+Ǡ k@+Ǔݗ,@+Ǔݗ,?|O?GA?!!׋7?/? 9?2I/?|O? aҹz?A)N?7枪y7?֧QD?X78?m!?u?8*?ʬv?a+?@@@@@,@(??????ˀ?.V?Ǚ?@sY3r&@a2+?? ?5{^ކ1??wTC0@g[@i@;ě@;`A6@;ě@;\(@;ě@;G{@;ě@;ě@;ě@;@N@;ě@;oh@7-@7G@7-@7;5@7-@7L/@7-@7R@7-@7]c@7-@7͞@7Y*0U2b@7B<64@1 I^@1ڹY@1W-@1V '@1~($ @1kP@1~($ @1*0@1~($ @1y ?)T?J`Y?"?Rw#z?ԁ?4?F/P5?5CQ?ܿ?-I`K?W;zY?.8 -B?Z?Rii?],m=?v_.^??\??\@@@@@(@*??????OI=H?oK ?9> ׫?Q_Xe@;k@76E?W|oJ?qy .?r+p8?fٴk+@i!y@j@M@"`@M@fB@M@b}@M@`A6@M@b|@M@`A7@"`@-@"`@?@"`@@"`@]cf@"`@n@"`@n/@8>6z@8p:@3I^5?@3!.H@3W-@3ZS&@3m8@3&@3m8@31@3m8@3?|i? ?+!?XY?ׂa?yq?*Mis?(eI?6c?A -;?|lU?a]?Qʾ?p`D?*J?+ ?C SG?|)-}(@@@@@@???????_O:$RCu?Hw?Z-ʋ@P,{(@i?q_?kr -?ٺX?TbBO@r>:`@k@PE@P+ @PE@PE@PE@Pᰊ@PE@PwkP@PE@P '@PE@P҈@}E@y@}E@~Ov_@}E@K]e@}E@$ x@}E@xPH@}E@~҈p@8|Q@8R@3hr @3x@3PA7K@3LC@2xF^@2?@2xF^@2@2xF^@2}Vl?1j?Ѝ`Hh?K9\?㬱_?bC?#gh??j%Ra??íuA?W ??vk? ҇?[L?q' -B?a?"#?P^eNQ@@@@@,@*??????BC}?@oqI?`1Xk?c̽f,@PF@~?ryP?,A?."?~~-m@h V`@l@Stj@==}mq@Stj@@Stj@ :o@Stj@@Stj@+@Stj@W@!@8Q@!@/F@!@1T@!@2qI@!@Z@!@.H@7䎊@7tt@4?|h@4U@3~"@3J!"6@3fYJ@3Tf@3fYJ@3Cp~W@3fYJ@2lC?6]g?fA?QO&7*?5 -z?-T?H[ RC??'P?m'x?2"??NG -?d -Oe??{b?r7\?!N/p?ki?3s@@(@,@(@7@*??????Rԥi@f ?k#L4?s4@{@,Ż?9Q)Mf?x~?{0@ f0@dU @m@Ձ$@Լj~@Ձ$@ȴ9X@Ձ$@֔Fs@Ձ$@8YJ@Ձ$@՛=@Ձ$@`d@@KƧ@@+@@[W@@@n0@@)y@@_@7-v@7.2X@2+ @2=b@2"-V@2"T`d@1O M@1v@1O M@1!-w1@1O M@1쿱[X?us\?1?ՠΆ{?gk\0?״6z@9g3@4ٺ^5?}@4ߪ͞@4ttj@4y (@3D*@3쿱[X@3D*@3_o @3D*@3'RT?}?$? 1B?GZ0e;? ا ?!2f?1?GE?Hyq?)h?Z?2?W|DC?Cns@@@@@,@$??????6nۜP/:[?@$ ?S]^0_@O] @\#R,?U^.(?mHE?,xc?=?i&C?7PK?~7q?m?%aȲ?]k7?]m3ƌ?2X@@&@$@*@7@9???????UcFc ?clj?cC@1V@ao."?(ZN)?M@]KV?H:@c@r@>,1&@>> [@>,1&.@>,1&.@>,1&.@>,1&@@u@>,1&@=# -=q@)n@)V@)n.@)n.@)n.@)n@B I^@)n@.V+ @9|Q@9 -7?1k@5pěT.Mm@4-V.Qn@4_o .NC,@4_o @31@4_o @4֮}Vl?C I@8#@8:@8 >+?WTi?d?_o_d@8@8i@8?-Ci|?1=IL?-@8 *@8p@8?q2?]ws@@@???s@Ob`B@O`ě@Ob`B@Oa]K@Ob`B@OC@Ob`B@O`S@Ob`B@OHG@Ob`B@OR @ 7Kƨ@]y@ 7Kƨ@&@ 7Kƨ@[@ 7Kƨ@I@ 7Kƨ@r(@ 7Kƨ@c@8 _o@8 #59 @2E?|h@2#2Vi@1~Vu@1RzEn@0iB@0C@0iB@0b5@0iB@0z&W?]Ov?m@pZG?[?K;0?Ҧ?z9?/?)?!vqG_?(?qQ'?`R?e ND?HQaG?f-%l?V5?&X?E L@@ @$@(@2@3??????lE`?e65?qP~*h@OUJk@ю?X?I%?' f@" @nl.:C@t@}bM@}+@}bM.@}bM.@}bM.@}bM.@}bM.@ßvȴ@öȴ9W@ßvȴ.@ßvȴ.@ßvȴ.@ßvȴ.@ßvȴ.@8*0U2b@81@4hr .Mm@3t.Qn@3iB.NC,@3iB.NC,@3iB.NC,?@8#@8:@8 >+@8I@8?X@8@8i@8@8S@8?뼑:@8 *@8p@8@8 @8 @?u@+R@)^5?@+R@+C,@+R@ح@+R@Ce@+R@/w@+R@>쿱[@]eS@]glC@]eS@]>vȴ9@]eS@]J=p@]eS@]XbM@]eS@]Sg@]eS@]D+@5*0U2b@5@20ěT@2XTɅ@1KƧ@12W@1 -0U2a|@1":)y@1 -0U2a|@1\@1 -0U2a|@1 xF^?F/P5?Na~|>?%'^?OXi}i?p?p?| ?~;?Sw?u/?0?)?lBCk?],V?+>G?. ?"x?o(?8~,@@@@@,@&???????oQyZ?Y7?Ck}@/@]]̽?y_~? B[?5}'?eV?S)$?X?@?!2f?X?Oy?_?`ֆ?B*Z?y?y5?6?5"GW?sq?d?ҸBz4@@@@@(@&???????rSl?QZ`E3?PM.$@ -Mt@qiN4?e'?gbS? -X ,?ҜY@o @z@A7L@>70@A7L@y7@A7L@?@A7L@wo @A7L@Let@A7L@[@ -=@ I^@ -=@G@ -=@P@ -=@򌬑@ -=@n@ -=@PA@8zqiC@8+[@25?|@2=L@1j~@1s6z@10@11B@10@10~@10@10m?,(g? -׷?r+@8I@8?@8@8i@8@8S@8?p̺@8 *@8p@8@8 @8 @?|@%@%zH@%@%O,@%@%]/@%@%@%@%@%@%,v{@WI^5?@WI7Kƨ@WI^5?@W-H@WI^5?@W1"ۡy@WI^5?@WYSP@WI^5?@WaCUӈ@WI^5?@Wa'@1s@1;dZ@0 -^5?|@0 -T@/ΗO<@/9@.`d@.K+I@.`d@.E@.`d@.m]&?^ee?Ϧlۄ?{ͱϑ?@ Q?T) -K?ϡSbw>?bO;C?\ӯ?@?.G? ]?|5?&?bb˙?:(?[c? =?eˍ?@LD@ @,@$@*@7@7???????C?_{l?0 |3?C/nT\.@%qLw>@W^6 ?uVi?Ʈ#;@rғ\@}@7@Ižz@7@g/W@7@Y~@7@ߤ@@7@5W@7@rGE9@xQ@x?8Ie@xQ@v@xQ@wO;@xQ@wV8YL@xQ@v3@xQ@yp'R@9(YJ@9&qi@6pěT@57@6V@4u84֡@5_o @4 ě@5_o @39XbN@5_o @75??j |?ܡuk?NxZB?ľ}'0y?%Oܖ?uYdP?*Y*??Eʻ)?pQ$?OY?8?|[D?enUW?6h? ~?;ܔ?=ɧ@@@@@@"??????D(?5d?Yr@rs@wUrǒ?gΙtY?a}DG@mN}@J%SR3@i~f@~@LzH@LV@LzH@Lߤ@@LzH@LN;6@LzH@Lx@LzH@Lzxl@LzH@LƧ@fffff@f+ K@fffff@fx@fffff@f˒:@fffff@fe@fffff@f,<@fffff@fq@4Y*0U2b@4X\)@2f+ J@2f+ J@2-V@2H˒:@16z@1%@16z@1 @16z@1C]?|O?j‚?X~4w? -?,I ?WA?|O?c?ǀr?#X?إ<(?]˙$>?],mBZ@ɺ^5?@ۋp@-bM@-bM@-bM@-)^@-bM@-- @-bM@- @-bM@-@-bM@-$t@0YJ@0YJ@/j~@/ߥ@-n@-$tS@-Z@-:T@-Z@-n.@-Z@-'S?^Q5P?3i?4!p?̒? f?CXs?bM_x?u߿?['?6)}?M?lINՄ?bMky?/?2Y0?xqC?f_Ԋ?B @@@@@,@(??????,</Lm~?0IP?,EX@N@-F?>-]+?;'L%?wO6/ά?p{&AE@se -@@a@J=p@a@de@a@b7@a@d*@a@c -=q@a@f?@ ^5?@p -@ ^5?@K]@ ^5?@ )^@ ^5?@D@ ^5?@Mj@ ^5?@!-@9+s@9,~($ x@4)+ @4(73@3)"`@3(K]c@35sh@34g8}@35sh@33@35sh@30`A8?`m?Ѝ`Hh?X~4w?CF ?91?HAm?[Xxp?0?t-E?r?Op?q?rR? -tM?+-/?w%?6m?*:R}@@@@@(@(???????aer%UC?b?Q4@dh:@R?r0U?gS?ٙ?6ě@pH/Vz@@|h@\(@|h@Hˑ@|h@#{J#:@|h@Q@|h@/V@|h.@vȴ@-@vȴ@Y_p@vȴ@u=@vȴ@Mj@vȴ@kP@vȴ.@8۹~($@8As@4I^5?@4tZ1@4"-V@3ק&@3kxF^@3 P{@3kxF^@3@3kxF^.NC,?)T?ts3?%16?䌛%?Ԯ>@8?|O?rO?•0}|?E)p?A\"I @8?`Ƕ?a5?k@{?7h?'@8 @@@@@,?????@R@1&@R@*0@R@u%G@R@ M@R@ ҉@R@-V@ffff@x@ffff@;5@ffff@Z@ffff@p@ffff@@ffff@˒:*@4+s@4+S@1S@1e+a@16-@1,u"@0m8@0-@0m8@0~($ x@0m8@0p ->?}ѿ,?'Qp?b/?ѮvtQ?t/1 ?dkU?| ?@`WӐ?h?= 6?z&>?TS?n88?9WTW?US9?*Q_? -e?L.C-@@@@@*@*???????YK] ǀ?_p?[ς@@%?tט?qs0?1:X[?~|@_X@@&ffff@49Xb@&ffff@RT`@&ffff@s@&ffff@$t@&ffff@}H@&ffff@>6y@@u@@C@@̥zxk@@յs@@7KƩ@@=K@8䎊@8}p ->@3c@3PIQ@2~"@2)_@2&YJ@2 #x@2&YJ@1t@2&YJ@1˒:)?X?Ѝ`Hh?BI{?`ĉ55?,j -[?=c?h??[u?AuG1? 3լT?ɬ\?p=D?=c"?XԠ#{?q6tN6?k.A?Q?]?İ^@@@@@*@*??????k?sK?nC0U?Yu@:Ɲ@\@Xqx?D4Ii?u2v#6?+>F?0Q@e[:@@dZ@cS@dZ@g+s@dZ@lAp@dZ@[#5y@dZ@c -k@dZ@c@9Xb@9Xb@9Xb@ok@9Xb@3@9Xb@2q@9Xb@Ib@9Xb@@45S&@45L_@1 -^5?|@1Fn@0 "`B@0=KW@/-w1@/ת,@/-w1@/o@/-w1@/< -?|h2:?R?;"ٵ?S?/] ?&X=I?}@TΥ?DZ? =׿?J?^ ?Rnw?o,qV?2sFH?˄f?is?I.?Rq1@@$@&@ @:@8??????1p&?`3k?Bob?Fek@a_b@D?a<#O?e鈨g?U? q,C@f>uJ}@@ -=q@t@ -=q@䎊q@ -=q@e@ -=q@e@ -=q@ @ -=q@9@@b`B@@b`B@@b`B@@bM@@b`B@@a@@b`B@@` qu@@b`B@@bM@@b`B@@bZc!@1_o@1"`@-~"@-0U2a}@+wO;d@+w>6z@+L/{K@+@+L/{K@+ڹ@+L/{K@+?_\p)?m?@?ArU@?6r In??bM_x?)[M@s @@\M@\@\M@\7@\M@\7@\M@\t@\M@\7@\M@\!.@Dt@D/@Dt@C\@Dt@De@Dt@Cn.@Dt@Dt@Dt@@IQ@5S&@5tj@2ahr @2bn@1-@1 k~@1p@1qX@1p@1t2W@1p@1mC\?2}? KG?Z*C?nii)?j?^m*?|O?$i %h?qN?|}Յ?=W?f:[c#?m+&?rbT?`x?< N ?7?+`ȓ@@@@@(@*??????3OA0BL?TM?0PKW@\"QOh@Cv?tV lw?P>o -?'PŶ?` @fbs@@UۥS@U7KƧ@UۥS@U_ح@UۥS@U@UۥS@UIQ@UۥS@U}H@UۥS@Uޞ@+@O;d@+@$/@+@ɺ^5>@+@@+@#@+@ƍ@7H@7>BZ@3kS@3gsPH@2tj~@2-@2Eu@2҈p;?oC?m0??A|x?~9n4?EeX?)T?;N?5 ? ;q??I}?? ?E?WhQ? $4?$jB@@@@@,@(???????qnW|?[?6ȃn9@UBX@rӹ?yY?X/̕h?#<Д?v/@kj9l@@r .@r .@r .@r .@r .@r .@o/.@o/.@o/.@o/.@o/.@o/.@3YJ.>@2; I^.Mm@1`A7.Qn@0D*.NC,@0D*.NC,@0D*.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @-@Ƨ@-@w1@-@ @-@@-@sh@-@A@?m@?-V@?m@?n.@?m@?^5?~@?m@?Y@?m@?N;6@?m@?я@6!.@6R<6@3 I^@3Q@3tj~@3lD@3:S@3u%F -@3:S@3g8}@3:S@3?Y͍_?PƧ$?U%x?gk\0??X|? ?ڲt?+l?#q\-v?þ_.?/\Ӂ?+B?<{?@K/{? Y? ? g@@@@@&@"???????p=^'s<?^P~?t+@7 -@?&5?z8R ?c6ZG?m6c*4@b[@c(@@ow@n+@ow@n@ow@oiDg7@ow@ow@ow@mqu@ow@l@S@ffff@S@e@S@wj@S@&@S@\(@S@|Q@5:qiC@59y @35S@35sh@34tj@33ߤ@2u@2š@2u@2Fs@2u@2?1j?6 '8?a#mU?!#?<?K&?| ?ā?1ک! ?/:?qn?.?`w?`F?A'_^?#ѧ?3?<שN@@@@@*@(??????5@QីD?*-(?0n>4@nI*@zg?Ip/2?P ,Y?$?/ru2-@f'a@@Q@W@Q@Mf*@Q@I@Q@T4@Q@t@Q@N1?@Mn@Mb@Mn@M5L@Mn@MN@Mn@M?"@Mn@MG@Mn@Mg,qz@7uS&@7tn;%@23S@23QeP@1Htj@1H63@1Q@1Uł@1Q@1s@1Q@1Z?I'%?;",?y$?LHfX)?[p2?;>z?|Ί?Cb?N{BU?ňw@8RnP@8C -=q@8RnP@8DT@8RnP@8L@8RnP@8M(@@S@@`A7@@S@@q@@S@@҈@@S@@6@@S@@e+@@S@@N@7`D@7`|@2 I^5@2"h ԕ@1q7Kƨ@1l>@0iB@0L_@0iB@0C,zy@0iB@0hr?F/P5?). -?9a?^t -a?Nty"?m\Wq? ??cu%+?3?V i!?6 a@B?CK?O? ?_u@@@@@,@,??????O]bQ}V%?Q A?4e1@8MU v@@߶h?qQ?U ?ɑɚ?{ip -@g= 2@@O;d.@O;d@B @O;d@`NJ@O;d@ -@O;d@wq_@O;d@i(ш@:w.@:w@9MW@:w@9 4@:w@:` @:w@:_@:w@:@9S&.>@5{ I^@5b!]4>@517Kƨ@53y6@3m8@3vE@3m8@3АO$@3m8@3VW@8J?롕?^?ֳ ?iꚆ?bC3@8J?̹м?-Φn?nX?}BH?ҖP@8p?0UF?#!?Q?NA. ?u˾@@(@(@2@,?????@j~@Q@j~@J@j~@,*@j~@zL^@j~@d@j~@h@)l@(r @)l@(@)l@&$@)l@!trJ@)l@#;ZV@)l@!̑e@7 ]ce@7N@3kS@3m\@3~"@3=(,@20U2a|@2 @20U2a|@2\^u@20U2a|@2΍p߃?t?WB~&?6z/?_\?K-\?=?|b@?y?i"4?y? <@@??44]?m֩?Zn@@ @*@$@9@7??????@HsN?CL?(mnl@@$kcbv?bOUD?Kr%W+@8I@8?e@8@8i@8@8S@8?yAd@8 *@8p@8@8 @8 @?@S@F@S@Z@S@B@S@?@S@1u!@S@{@&x@ě@&x@m^@&x@ \@&x@Vl@&x@#n.@&x@#n@6s@6]p ->@4R I^5@4W_o@46-@4?iDg8@4Tm8@4A2W@4Tm8@4-K]@4Tm8@4Os?wVz?oNQ?70t_c?n?N?_]?r5]v?S?|? Jy ?%v[?ӽbȷ?>ҝ-?rIs?f8?B?Nka8?lb% @@@@@@*@,???????(4?Z핂?.?ݽa@I~@~?S"X?]sC ?ı=.G?[Æ@rżEf@@ -.@ -.@ -.@ -.@ -.@ -.@%j~.@%j~.@%j~.@%j~.@%j~.@%j~.@9%!..>@4^5?}.Mm@3`A.Qn@3:,<.NC,@3:,<.NC,@3:,<.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @N@N͞@N@Nw*@N@NQ?@N@Nލ@N@Nt@N@N'OG@R@r @R@V@R@[.t@R@qu@R@—@R@ߤ@7֚,<@7Uv@3 ě@3u@2"`B@2V@2=ce@270+hP@2=ce@25JW7@2=ce@25mĒ?|B &?ukB? aMP?.bs?@sM?Q荌?t ] ?i_??Rɐ?͡?N?㾉?v'G?fP6?𦒽L?jAr?t1? TO@@*@*@,@8@7??????I..Zk?G7Ը?V̆o@N\N@ՠXŃ?o?q6b?հ%@?r~H@mO@@$/@t@$/@˒:*@$/@m9@$/@̈́M<@$/@m\@$/@6@bM@~#@bM@ɭB@bM@9@bM@J@bM@_o @bM@ -L0@8(YJ@8#@5c@5g -=p@4-@4\(@4QiB@4*@4QiB@4[P{@4QiB@4Zݘ?ꯨT?o5?П?%W$?"y??V¹?ٛP?w? ?#Vz?!kD?6hC]?{Tä=?T?z?5 -: ?l?F@@@@@*@*??????1uMrR?P67?^vl@]*@?oP'Lf?}믏]`?T?յ @k!f|@@@@@҈@@d@@҈@@b}@@?@1m@1m@1m@1.@1m@1N;7@1m@2@1m@1N;@1m@2@0,<@0,<@,Ƨ@,Ƨ@,&x@, @+jf@+xF_@+jf@+C,{@+jf@+C$?^Q5P?{j?$C@?XQ>?Ot?ˡK?bM_x? ,C?qF?Hn?إ<(?SV.?bMky?u? SF?xqC?l&?B @@@@@(@(??????Bؽ?1+?ub@FOB@1?@'=H?!vȸN(?z%rd?:Kxf8@s -b@@E@v@E@1@E@_p@E@Q@E@,<@E@S@(@lE@(@ӶE@(@nP@(@C@(@JM@(@k}@5䎊@5l@2hr @2x@2'KƧ@2"GE85@1Ǡ k@1-w1@1Ǡ k@1U2a|@1Ǡ k@1*0V?|O?'?`כm?wX/8?2F0?P?|O?0n=Q?%8?ҧ?4.?F5?T0?Ϸ+?],m=?"a??*Z}?Tj@@@@@,@,???????V;<q읂=?IBH?F7~p@Oh@ңK Z?hJP͂?e[?`d>D?罜@eG"@@T-V@T-V@T-V@TJ@T-V@TD@T-V@T9Xb@T-V@T @T-V@Tu@C@C@C@xF@C@:)@C@f@C@:)@C@~#@3H@3c @/t@/ݗ+j@.x@.m\@-C\@-V@-C\@-M:@-C\@-M:? ?j‚?WD@?|*(??}?| ?,#?)_?Hn?,Y?{L բD?oܧ?˛͇q?W ?':?j?pk Y@@@@@&@&?????? -+ -E,@?7tw'6?RM@TRVPx@̃V?W@?9?2L?~[@gv!p@@ )x@ $d@ )x@ (*R@ )x@ 2@ )x@ %@ )x@ !>^S@ )x@ @\(@=p@\(@l@\(@;@@\(@ș@\(@w߽@\(@Kp@8䎊@8@@3S@3$0@3t@3 -Gvz@2s&@2wd"6@2s&@2uPy/@2s&@2qM5?Äp? P?/b?V黀?D. -r?Ұ(=?nDyB?O3?6'@ ?!2?zO?WX.?dvG݃?I_S?zb?bc?N^@p?5*[@@"@"@"@,@,??????U[&?_>`?JYAb,?CB@ "@2?g<=,?c٥z?Uq?.Ö*@kn3@@+ J.@+ J@@+ J@x@+ J@索3@+ J@捸@+ J@Fs@H!.@H!@H k@H!@H ě@H!@H!s@H!@H u!@H!@H!t@5`D.>@1I^5?@1@07Kƨ@0n.@0#@0# -=p@0#@0$?@0#@0#*0U2@8J?m?"?菁k?@ A2?w'@8J?I?#Gf?e[|(?֧QD?'+@8p?3I#i??E 8?L? -t@@@@ @*?????@M@S@M@捸@M@eO@M@G{@M@:@M@7@+ J@+ J@+ J@ĵ?@+ J@4m@+ J@n@+ J@@4@+ J@?@5]ce@5B@2S@2tj~@1-@1R<6@1@1rG@1@1k~(@1@1?}ѿ,?@k=[?Uy*@1u@1L_@1u@1ĵ?@1u@1tk?)T?4L?jhq9?)?״ ?_u@@@@@(@,???????H> ?#uw#ѷY@2tj~@2u!R@1~($ @1Vϫ@1~($ @1`d@1~($ @1qj?F/P5?@%:!?Р'?avfڵ?6?o@@@@@*@*???????Sb?ʉGl~?kBJa@il@@  I^@ ??@  I^@ @  I^@ }@  I^@ H4@  I^@ =p@  I^@ h@QXbM@Qcq@QXbM@Q+@QXbM@ST@QXbM@Q@QXbM@QxQ@QXbM@P)~@6Q|Q@6SJ,@3ahr @3Cj @2j~@2=c;$L@2iB@1D|@2iB@1ռn/@2iB@1=?| V?Y@T?W%W?VWD^?[q)?2}hJ?pV?n?K? #?Z.J?"4M? 1?-?lo?{iO?#.?]@@@ @$@@.??????\aM⹀?CX?^p@ k @Q-Rx?k?#Ɲ@]r{04@53[@aos@@| A7L@|@| A7L@|$/@| A7L@|@| A7L@|" I^@| A7L@|]cg@| A7L@|U=@ |h@ -x@ |h@ Vl @ |h@ $tT@ |h@ -M@ |h@ -4m@ |h@ -ě@7쿱[X@7=K^@3pěT@3s{J#9@2-@2r Ĝ@2J0U2a|@2FFs@2J0U2a|@2KC%@2J0U2a|@2I^5?}?}?;?K̃?eU5?ԁ?lP {?|O? l?J?1'7?X#?ua?~$?6?Q?.SM??*Q@@@@@@???????YV8XS\?SlD?)q,sl@|W@ -LeL?q?KP2s?Y6? +|@h5`@@J O;d@J-#@J O;d@J -|#@J O;d@Jߦ@J O;d@J%%@J O;d@J[aJ@J O;d@J SS@-@Tɇ@-@;@-@Q@-@˩}@-@:@-@Mк@8G>6z@8=\E$@3ԛS@3٢t@36-@3;j@2Q@26' @2Q@2g@2Q@2vJ#?Äp??a0{"Y?}o#? ? >Ǥ?|"<~~?Xq?Z5_?xC?UZ"?zS!Ń? F?%$ ? Ur?Gt?wOH]?8Ϝf@@(@$@&@$@.??????HT?Q?3m<?8H@J@q[N?,!bG`?[ʐ[?X9J*z?.@j<K@@:^5?}@8bM@:^5?}@;/V@:^5?}@4@:^5?}@5!T@:^5?}@.qk@:^5?}@.;5@(\@$Z@(\@v@(\@tj@(\@@(\@˒:*@(\@ - @6 _o@6A@3|hr@3 k~@2Vu@2!R@2@2r Ĝ@2@2C\@2@2jf?F/P5?.5?F}?gt>? QQ0?(?qn?iCv?CbNy?cgp?Fl}?6%6?E}?`F?Me_??$jB? N@@@@@,@*??????`R䑼Q?:?cT@37L@u&p?]LOB?ួ?e:?2!@iy@@o-@o;dZ@o-@p*/@o-@np:~@o-@q&x@o-@n1@o-@n*@KƧ@L@KƧ@KC@KƧ@KC@KƧ@Mqu@KƧ@L[W@KƧ@L<@5ks@5k:)z@0+@0_o@/O;dZ@/Vt@-N;6@-33334@-N;6@-m\@-N;6@-m\?2}?Z.?U#?ݶ\r?6r In?aX? ?`5a?90?@Cv?Ff$?+d?W?˛͇q?]?3{Z? -t?j@@@@@&@*??????!s>>&?1Trso?A<.@o{h@L?QAڡX?3&1?}H?qj@h&@@kR@k I@kR@l"h @kR@l1&@kR@lL_@kR@k I@kR@kC@ȻlD@ȼ(@ȻlD@ȼߤ@ȻlD@Ȼqu"@ȻlD@Ȼqu#@ȻlD@Ȼ6@ȻlD@Ȼm@4O M@4oh@,`A7K@,S&@) 1&y@) C]@&L/{K@&?@&L/{K@&?@&L/{K@&L/{K?Ol?°R;?"?eU!?Ot?*WF?|O?%F?90?1'7?%y*=?N`?2Y0?.+?q6tN7?avh?#.?#.@@@@@,@*???????~ap ?#CT^7? dw@ @kP8@Ȼ۾?BtM?-֪?'c<3d?eFX% @h 16@@\)@_@\)@d5@\)@h@\)@~@\)@ù.@\)@хD@\S@\S,"@\S@\M@A@\S@\JءK@\S@\l݊@U@\S@\Âw@\S@\Lˌ@6@6U2@3hr @3x@3"@3쌜q -@3u@3 ,@3u@3v@3u@3t' f?~Ov_ح?Hk7?9 /?83?\ǵ"5?/J?N7?q'?XA?jKf?\s?fLO?}t7?~^O0?`xb?- ?FRv.?6`%@@&@,@*@:@7???????s?|<%@?Q?SPGBf@Ke@\fN -?kQ)d?o?]?̚rZS@a3i q@@ -=p@KƧ@ -=p@ᰊ@ -=p@ȴ9Y@ -=p@$@ -=p@_ح@ -=p@ -=p@Q@^5?}@Q@ح@Q@5?|@Q@\O@Q@IQ@Q@R<@7֚,<@7т @3J^5?|@3KC,z@2-@2+@2a:S@2a|Q@2a:S@2aN;5@2a:S@2a:T?Q,Ě?:L -?S?9.?.Kn?=?=fA:?-NI`(?ދ"?.}$?*v7?lINՄ?Fp'?)Zw?Ol?__C?k!l?L@@@@@@???????4G,?U/X?5[X?8☈@%Ǥ@=6?Iݜ&?X#_?M}gK?b]@oza^@@q7@q/- @q7@qO@q7@qTJ@q7@qLI^4@q7@qH@q7@qG&@FT@F+ J@FT@E#@FT@Eح@FT@FN@FT@F@FT@F *0U3@7YJ@7҈@2hr @2n@2-V@21@10U2a|@1g8}@10U2a|@1@4n@10U2a|@1s?oC??b? {4?W?$?Ol?Ғ](+?ud?R?6?*?ʭ#г^?????>LR_5?x@@@@@$@$???????sf<3z?g1?P@qCy>@FG?ZI?rm9?gzOJ>?w`@kr @@sE@-@sE@jra@sE@k/1l@sE@kB[@sE@k>Q@sE@kU@E@8@E@30 @E@>eH@E@D9@E@I2@E@GENO@8`D@8XNU@2?|h@1>Y@1`A7@0KTk@0}ce@0z~o@0}ce@0|c*@0}ce@0jD?Z?1Q?[Ze6?G&̮?H?4,X?Z?̡Z?6?5) ?+2?4V?A?P?܇R?9??(?uRZIr@@,@$@$@:@2???????#L]?E|7?2HQ?C@k##@F}9?BdC;?R Ph?~Əet?~H -@sXlb@@I^5?@Ix@I^5?@Iԕ+@I^5?@Iy@I^5?@Iy@I^5?@I'RS@I^5?@Ix@u@E@u@Ov`@u@R<6@u@+ @u@z@u@u@/ @/ @*7KƧ@*7KƧ@*E@*R<6@)`d@)SMk@)`d@)SMk@)`d@)`d?^Dw'?+g߶?S?XQ>?/`?*WF?bV,? -?A)N?3.Yl?֧QD?SV.?b@? ?s@a?avh?O|q?O|q@@@@@,@*?????? p+P*>4Z?0,@II@\E?';&?$Y?KO?CTd5r@s@@.@.@.@.@.@.@3z.@3z.@3z.@3z.@3z.@3z.@8S&.>@48r Ĝ.Mm@3'KƧ.Qn@2䎊r.NC,@2䎊r.NC,@2䎊r.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @}p -@o@}p -@ qv@}p -@Fs@}p -@ὥ@}p -@HK]c@}p -@- @Aݲ-V@AƧ@Aݲ-V@A C@Aݲ-V@A]/@Aݲ-V@BD%2@Aݲ-V@A#@Aݲ-V@AM;@8[~($@80@3?|h@3`d@2v-@2i k~@1D*@0@1D*@1ae@1D*@1}H?'[ V?ȔS?ƀvFd?T ?C7@O;@Xe@O;@l!@8w@@8or@3S@3E@2-V@2@1a:S@1bm@1a:S@1cZ@1a:S@1cnP?}?#T?=P?$? ?5@4@4S)@4b-V@4n٠+@35sh@37庭ά@35sh@35f@35sh@3n<@8J??1?oM"?W}8?pȭv?!k$_@8J?ܚ?g^}?G% ?9.? -`?Pa?nK/?`w?9o?ʳ_? ???\?Rv@@@@@(@,??????+AU 5?*k4??kJ@Bs#A@E+x?Jxu?5Yh5?-?v_á@f{@@Q'lC@Q'lC@Q'lC@QFYJ@Q'lC@Q-M;@Q'lC@Q#@Q'lC@Q&Ft@Q'lC@Q*ڹZ@I^5?@KS@I^5?@J͞@I^5?@V+j@I^5?@L@I^5?@QiC@I^5?@P ě@7[~($@7^$t@2+@2u%F -@24tj@2#&@1B@5@1:ڹY@1B@5@19|@1B@5@16 '?)T?Vjn?/T?zRU?BPbC?W4??;TMn?Z7?,ў?Hد[?)u%? uf?vv?6~A?< N ?w w?屘@@@@@,@*???????Lj`?S⛤?H7g?A"FS@Q',@N)Lf?hLd?^mzlj?lN D7?@i=H/`@@H9X@BM@H9X@JL@H9X@Iᰉ@H9X@GRT`@H9X@F@H9X@H@"@;dZ@"@4m@"@"@"@ k@"@@"@-@87@@823@3pěT@3r&IQ@2tj@2@2n_o @2ohۋ@2n_o @2p6@2n_o @2q4J?Y͍_?tH?~"?Rw#z?r {#&?Q`?K{O?PuN?kCnL?^W.?V?*%>g?&?)h? - l]?"a??|)-}(?`i2@@@@@*@,???????UlR>]2?4ds!?G}[ɭ@F¢@=Z?UƇ"?_vz? -g1?3w>@no@@q&x@o @q&x@E9g@q&x@kHĘ@q&x@k@q&x@cr@q&x@z?@.w@.v@@.w@.]ث7@.w@-l]@.w@-@.w@.gFd@.w@.h@7DO M@7@JK;@3; I^@3.c5u@27KƧ@2抝t@2z,<@23]@2z,<@2S20@2z,<@2U ?eXW? ?|i=? c??}?(_]?3?C#]?ѩ.?l3?lf|?ږَ? H3?y?yyȧ?*niC?W@@$@@(@;@8???????U4^c4~"?l;?lJ@qlj&@.lW+O+?}l[:?Xl@g ?sr(@e7@@@ow@@wKƧ@@ow@@oDG/P@@ow@@qFݗ@@ow@@p@@ow@@mFW@@ow@@ml@4V@4"M@4V@4"u@4V@4@4V@4 @4V@4 ?; @4V@4oJ@804m8@8=\(@3?|h@3T@2Ͳ-V@2|@23&@26@23&@2:Y@23&@2;?Z?P 6?N6?OO;V?-??Z?h)-?ɡ%?39M???A?-dʦ?>JM?t66?[!.?Vm3Y@@(@,@@<@:??????Wc?]&B?0D9\?L*z5S@@nv@4??[8 ?Z3?t}*>d?Bt@r8@@S I^5@S @S I^5@S 5Xy@S I^5@S &@S I^5@S U@S I^5@S ~)@S I^5@S a@t@\(@t@S@t@?@t@@t@?@t@%2@54m8@5u!@1n5?|@1nu@0]V@0]qu"@/b~@/"a@/b~@/ (@/b~@/6D?| ?%ñ?e -!C?ArU@?2A?7F?| ?0n=Q?LE?Y?=d^?m/w+@8I@8?ҸBz3@8@8i@8@8S@8?Aᆽ@8 *@8p@8@8 @8 @?@1&y@1&y@1&y@9X@1&y@ @1&y@ 7Kƨ@1&y@ݗ@1&y@ݗ@0zG@0zG@0zG@0Ʌo@0zG@0m9@0zG@0Լj~@0zG@0D@0zG@0[@0쿱[X@01@,\j~"@,\j~"@*|1&@*|?@*\ߤ@@*\Q@*\ߤ@@*\Q@*\ߤ@@*\(]?^Q5P??(? Y?/`?tJ?bM_x? -?90?0N:s?@?N`?bMky?~('?q6tN7?͓E'?Ѐ>(?l&@@@@@&@*??????HuG?4?0eL?{Y@=@0;å?>WM ?kMh?wRD2?!z"@@sU@@Jo@J~#@Jo@<#@Jo@,@Jo@)y@Jo@qu!@Jo@ xG@@XbM@@d@@ؓtj@@Cn@@P ->@@)^5?@6qiC@6ߤ?@2|hr@1ѷY@1b-V@1)lD@1iB@0m\@1iB@0y @1iB@0>BZc?ٛP?Q?F.?ʺd9.?qS=?y?Y͍_?1Q?c2M?A?,Q0b?r -?t -j?anñ?҃N%?<-V?&?Oh6c@@@@@,@*??????p' ?'l`?Ow?}Exw@8@ˇ?mb'PN?'| a?!ʶb?yAĈ@`""@@lC@-@lC@˒9@lC@ᨚu%@lC@TɆ@lC@YJ@lC@YJ@TF@U?|@TF@TɅo@TF@U*1@TF@TO @TF@U?|@TF@U%F -@+@+@&kƧ@&kƧ@$O;d@$O;d@$73@$73@$73@$73@$73@$D+?^Q5P? ?m?8v?DE?tJ?bV,? -?j?/Z?(T >?N`?b@? ?nwg?~\?h?#.@@@@@(@,??????R ?+t?7 -9n4?N@R@T^?F'>\?*pƮ?&CLAA?NaJ@s*)v@@n@;dZ@n@Xe@n@],@n@捸@n@8D@n@be+@;@;Q @;@`u?2ci?j8n?1'h -?Nϸ?(ƴ? \d?÷?o ?־c[@@@@@,@(???????؜DGKx ?[?{E[a@ē1u@;H???:2*Y?zZ@y?щ3@d|EgL@@zN+.@zN+@zPP?9@zN+@zO2!@zN+@zP@zN+@zMȊw@zN+@zMӾq@\Vu.@\Vu@\V@\Vu@\Y*@\Vu@\V:@\Vu@\UzO@\Vu@\U\;@/A'R.>@*$tj@*$}@(E@(4@( -#9@( 8@( -#9@( *R%@( -#9@(.])@8 -Ny?V?Σ1?.mA+?+?\XV@82r??=?|v*?3FNc$?~@#Z?OZQE@8L?T?q?懒nW?ҕ@?i= @(@,@2@=@9?????@|h@$K@|h@ @|h@fL<3@|h@ -Z(@|h@f~@|h@n@kR@$M{@kR@RN:@kR@u.МS@kR@vI@kR@oo@kR@t| =@8_o@7)s@5+S@539I@4A7K@4/@4ush@4"#*@4ush@4&7@4ush@4"&?@l#?׮ ?߱?y?\l5w? j?#{W?+21?(w?=#o?MZЮ?b4?K:q?0m -i?f?R]?.:N?eq+k?>┅@@$@$@,@<@8??????G.o?Ix ?n|NW?l7y]@P@r7?}j:?|^ʲ ?*7]?j@5)@s-5@@+1m@+2-V@+1m@+5!S@+1m@+1iC@+1m@+/o@+1m@+1@+1m@+4SM@8@9+@8@84֡@8@6z@8M:@4@ ě@4Ft@417Kƨ@4>5?|@3!.I@3"T`d@3!.I@3t@3!.I@3??_$?%ñ? 3з&?K?3;5??% ?u߿? q)?Z.?h?'qTrQ?*?ڱP?p?'w?cZ?K&?Us@@@@@,@&???????])T?bI?vjCUR?OBzWO@'uj@^1o?lY?q4@:?Ż @oݪz@@p ě.@p ě.@p ě.@p ě.@p ě.@p ě.@F.@F.@F.@F.@F.@F.@74m8.>@3S.Mm@3 "`B.Qn@1&.NC,@1&.NC,@1&.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @ěT@ěT@ěT@š@ěT@J@ěT@o@ěT@S@ěT@n.@&x@&x@&x@&ffff@&x@%84@&x@%ں@&x@%F -L@&x@%n@3۹~($@31&y@0!hr @0!n.@/mO;dZ@/m@.Vl"@."`B@.Vl"@.wkP@.Vl"@.C,zy?]??K]4?MA8??t?]?NۈV(?CXs?3.Yl?IM?ȏ'T?*wjs?l?֘>D?k,?SP?M684?@@@@&@&??????H+A!3?0ßݕ1?)0^j@@%!?@4^3?87,?xx?iUa@sB@@sE@v+@sE@{J#;@sE@l"h @sE@tm:@sE.@sE.@p@p @p@pV@p@p@p@p>6y@p.@p.@8,<@8I@5+@5҈p@4Ͳ-V@4- r@3,<@3($ x@3,<.NC,@3,<.NC,?$?"O.ڗ?E?yFc@8I@8?| V?,Z?is?*o@8S@8?5`b??E?"Ԛ@8 @8 @@@@????@1&@9X@1&@a@1&@˒:@1&@g@1&@l>B@1&@ush@dZ@S@dZ@a@dZ@ߤA@dZ@~'@dZ@v_د@dZ@`A7@7>6z@7eں@3hr@3]ce@36-@3qiC@2:S@2|Q@2:S@2z)y@2:S@2|!-x?us\?P񰦍?br !?~ -]z?HM?}}?1j?K26?(?vEB?>΀m?^U?ʳ_?5?ʭ#г_?O.ce??OONS@@@@@,@,??????R6ksT5?u.?JH<@i:@ -?n٤;?f(d?Z\?Q @c&Ѓ@@9V@9(@9V@9`[8@9V@9˒:*@9V@9V@9V@9e+@9V@9E@61'@6"M@61'@6@61'@7@61'@7 -o@61'@6[/V@61'@6U=@8 ]ce@8\(@3@3i k~@3 -V@2ƔFs@2 k@1а{@2 k@1Ͼvȴ:@2 k@1o?qv?ǩ2?(b?o]އ&??pt#??ҸBz3? W~?%i|?ѹ[0?<ՖEI?I2}?P@?!?; Ր?0eʪv??%d?aʕTI@@@@@ @*??????zc ?0?Y1/~|?7D@9 h@6X-u?s$etg?L ?ȋǧ"7@*o \#j@hh -@@dZ@00@dZ@@dZ@N!/@dZ@o5@dZ@@dZ@b@Kvȴ9@K3$@Kvȴ9@KD@Kvȴ9@K@Kvȴ9@K\yL@Kvȴ9@K$P@Kvȴ9@Kg^@8!.@8 @4|hr@4$wMp@2Vu@2p([@2!.I@2o@2!.I@2Չqm@2!.I@2Uc?2?3?YsN?V-TL?=?{?J\m?%?0{rw?m?$,3?]?._?- ?bs?4YQ?&N?mt -@@@"@&@3@*??????1`?>&8p?Kx~ҁ?Eeʓ@@K?`5Erx?e3]]?e{N?Zj64@p*܀@@ I^5.@ I^5.@ I^5.@ I^5.@ I^5.@ I^5.@nP.@nP.@nP.@nP.@nP.@nP.@6-v.>@2^5?|.Mm@1n".Qn@0‚@5.NC,@0‚@5.NC,@0‚@5.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @p -=@VE@p -=@L/|@p -=@8*0@p -=@S&@p -=@l@p -=@lcA@G{@F@G{@3@G{@5fA@G{@\(@G{@ ě@G{@+@8,<@8u!@55?|@5\(@4Ƨ@4c @4Y~($ @42<64@4Y~($ @4Jc @4Y~($ @4MVl"?K{O?'5?np?]\?|QS?6|Ob?ϱQm?%!{A?Z42?`ؤ?M#?¤n,i?NN7?ɚ?u?'D[?xfr?io+p@@@@@*@"???????thiD+?vh}?zGW@ae$@ ? ?h6E-y?t;@B#@l۠@@3E@3E@3E@4,<@3E@43@3E@4Fs@3E@3Mi@3E@3Z@Qhr!@Qhr!@Qhr!@PD@Qhr!@P|@Qhr!@Q_@Qhr!@Q4K@Qhr!@PD@05S&@05S&@*V@*V@*:1'@*9Y}@(@(vȴ:@(@([W>@(@(˒:)?^Q5P??&??eU!?/`?ˡK?bM_x? g?ܿ?6)}?A B{?PS2K?b@?~('?q6tN7?͓E'?O|q?Ѐ>(@@@@@,@*??????:s"?ї;?x@3튰@QY?$Hj?$ࡾ>?D/-ђ?C(ap@s_p@@xG+ J@xH1&y@xG+ J@xI @xG+ J@x)l@xG+ J@xHK]d@xG+ J@x7@xG+ J@x=+k@2 Ĝ@2-V@2 Ĝ@1N;@2 Ĝ@0$u@2 Ĝ@>m\@2 Ĝ@iy@2 Ĝ@dq@6,<@68YJ@3S@30 (@2"@2!-w2@2YJ@2_o@2YJ@28Z@2YJ@2eO?Ol?/)s?ƐW?ghT?=U?`~?| ?u߿?36G?%hF?XO?lN?q6tN6?ɅoiE?W+ ?WA:?Xk?Xk@@@@@,@(??????a-?M+o?W^ũ?eqT@xEW26d@=0?sI1Up?5?Fa?ȿ@alh@@D@Ds@D@Dc@D@D Vc@D@Dʘ4@D@D#'@D@D|jt@\)@@\)@@\)@L @\)@΁5@\)@5g@\)@HH@7V,<@7Yt@2+ I@2"P @1~"@1ڊ@1!.I@1@O%@1!.I@14&@1!.I@1n⻠?eXW?<?* rދ?w+?2U?ЈVi?ԧx?8~#?֋?`]Y-?03I?L?_wwo?0,i?p>?A?sCa?H@@"@$@(@0@,??????QBl0X?Cd]?0uz|@Diߍ1@?d=?PU;?Ҭ~?! -Ve@iyh @@NW -=p@N^"@NW -=p@Mv@NW -=p@Mu@NW -=p@NHeU0@NW -=p@NZb@NW -=p@N{$#@E@1\L]@E@r+@E@y -!@E@Ȕ?ߦ?kM@4 hr@4U@3V@3\>@2fYJ@2@2fYJ@2oiDh@2fYJ.NC,@8J? \?Dڰ*?>5c?i -@8@8J?ǛȦBS?ŀ&՗?S?@@8@8p?>UP?Uq(?yD ?MكN@8 @@@@,????@.S@."@.S@.b@.S@.vȳ@.S@.xl"h@.S@. -=p@.S@.@[$/@[G{@[$/@[{v@[$/@[~Ov_@[$/@[~6z@[$/@[o4֡c@[$/@[rs@6 ]ce@6 ~($ x@1+ @11@1A7K@1 Q@/@/ ě@/@/˅Q@/@/̲?|O?m0?JC?Uyu#?X?-! )m?|O?I`ӛ?mK??hY?V.H?z"2(?sq#?T?Gϵ'?fW?ܑ @@@@@,@*??????Wgh+?7:;H?O ?@.:ad@[{k?WTfUI?na%\g?742?g^@e(V@@]/@XJ@]/@XN@]/@W\r.@]/@Wi@]/@S -@]/@T@$/@vp@$/@P@$/@>0X@$/@;R&@$/@VX@$/@B@7䎊@7nii@2pěT@2hѪG@2tj~@1`,@1Tm8@1H!@1Tm8@1I@1Tm8@1HہL?}x?2uHA?3')ք?C I?Sw?!m -K?CQl?;l?b?ﻰWs?r?rn?i?jL?uE@@(@(@(@8@:??????MK@vY?4R:Kp?_M@Vֹ@_ ?S|*?~fC?_?~?S,2)@e0@@)x@)^5@@)x@2nP@)x@'lC@)x@&&@)x@%Q@)x@& (@x@-@x@|@x@9Xb@x@@x@bM@x@zxk@7@@7Ov`@3S@3Ϫ͞@2W-@2W3@2~($ @2N;@2~($ @26z@2~($ @2#?ٛP? )}o?RsW?6W=t?X?*?% ?-',?ԕ]I?DH?NM8/?ey?A\#?p-t?+QK?c^)?%d?8ϣ@@@@@$@&??????E_?SÌ?;E2?P}˅x@&-&@A:?Uc~™.?p"]S?B|?@mI@@t9Xb@xQ@t9Xb@xPH@t9Xb@rnP@t9Xb@sg@t9Xb@}c@@t9Xb@t,<@]@] ě@]@]я@]@]O;d@]@] '@]@]@]@]@8쿱[X@8?@3^5?}@3e@3t@3@N@2V!.I@2JڹY@2V!.I@2Iѷ@2V!.I@2F_ح?ꯨT?Čۨ1A? -tI?(D?|(?=)Jn??o ?D?9 ?O??B7?]FV?K|?7N?^':?@@@@@&@(???????(褠[\s?[H\?n>@wzp@]о?vi}@:?/'`?O\?/8@i @@9ěT@9Q@9ěT@9o@9ěT@9 ԕ@9ěT@9@9ěT@9œwk@9ěT@9M@1'@/@1'@"`@1'@}H@1'@v@1'@ (@1'@p;@5g l@5GE85@2^5?}@2lC@0tj@0?@0:S@04m9@0:S@0GE85@0:S@0E?}ѿ,?*|3N? ?W0 ?yq?Id6z@7 -V@2I^5?.Mm@217Kƨ.Qn@1ce.NC,@1ce.NC,@1ce.NC,?)T@8#@8:@8 >+@8I@8?qv@8@8i@8@8S@8?z@8 *@8p@8@8 @8 @?@U^5?}@U5o@U^5?}@U\Aǎ@U^5?}@UY@U^5?}@U@U^5?}@U[|@U^5?}@U@EH9X@Ez@f^m@@w^5?|@w^vȴ9@w^5?|@w^iB@w^5?|@w^5?|@w^5?|@w^m\@w^5?|@w]<64@w^5?|@w^Ov_@t@t@t@ xF@t@āoi@t@ěT@t@g8~@t@t@1쿱[X@11@. -~"@. -g @-E@-+jg@,a-w1@,a-w1@,a-w1@,aohی@,a-w1@,a?^Dw'?1?&??q@v?E{?$?bM_x?&uE@?['?Kd]?2&h?X78?b@?u?|׿pZ?hB^?pk Y?b/bJ@@@@@,@*??????3=9o+ﰈL?:7xH?R;s@w^0)@dy?I{9? bON?Y1 ?"UP@s -~i@@o-@f@o-@Ci@o-@_<@o-@]Dy@o-@T~j.@o-@@JQ@J\PeȦ@JQ@J8=@JQ@JGD8@JQ@Jgx.'@JQ@J@V@JQ@JXͻ-6@7zqiC@7dN@3|hr@3Nn@2Ft@28@1_o @1`XȎ@1_o @1h%@1_o @1޴G?Ҟ8A?@ E? KM?Ӿ?ݠ@?H_I($ x@SKƧ@Q״3@SKƧ@S`u"@SKƧ@Sݘ@bM@@bM@4֡b@bM@âw@bM@n.@bM@#x@bM@~$@8,<@8V@4!hr @4%@3`A7L@4s@2&@3C@2&@2Ʌoj@2&@2ߤ?E@?jK?Yo?ߤE?;p?"Px??V1,? G?{7?' ?q??j`cq?ek+?42?6m?Us@@@@@$@(??????kszG?ǃ Q?dLG4a#@Rȏ9M@s?в3?񓅣@q9u(K?)l0@iz@@M@P -<@M.@M.@M.@M.@M.@~vȴ9@|ht@~vȴ9.@~vȴ9.@~vȴ9.@~vȴ9.@~vȴ9.@704m8@7.҈p;@4 I^.Mm@4PA7K.Qn@4O M.NC,@4O M.NC,@4O M.NC,?}@8#@8:@8 >+@8I@8?Br@8@8i@8@8S@8?Fp'@8 *@8p@8@8 @8 @?@w&ffff.@w&ffff.@w&ffff.@w&ffff.@w&ffff.@w&ffff.@1&x.@1&x.@1&x.@1&x.@1&x.@1&x.@8,<.>@5+ J.Mm@4Vu.Qn@46z.NC,@46z.NC,@46z.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @$@$@$@v[O@$@$*V@$@Tx@$@-@$@+, -@Fr @Fr @Fr @F攀M@Fr @F}@Fr @F>|@Fr @F-<@Fr @Fk@1|Q@1y @,-@,K@+ΗO;@+^b@+PH@+W@+PH@+`."R@+PH@+8T?ޖL I?Ԯm#?!,1 ?If4)? ǫO?Կ<?r-?Tշ?rh?ri?w?g!_'?M? ?ď?F"@@*@(@*@9@5??????E?2* ?9f/5?,(x&@2@F:Զ?H$N?:ڝBl?u#4?o?/8H@sm@@nzH@i^5?@nzH@kR@nzH@l"h @nzH@kC,@nzH@m:@nzH@jF@^Q@a$/@^Q@[5W@^Q@]ڹ@^Q@^5?|@^Q@^($ x@^Q@]ce@7p4m8@7nH@2r Ĝ@2#w@2PA7K@2R @10U2a|@1'/@10U2a|@1͞&@10U2a|@1ݘ?%?4$?ھ?iP?\7?)]\?}?D.x?8G?ݤ`h?7=^9?`@0k?}??,DP?T0?9XNu?&I?&I@@@@@&@(???????5 H=S?=? O @kU@^Vp^?T, ?@5I? -[T?H@m[@@+ @"`B@+ .@+ .@+ .@+ .@+ .@)o@)\3@)o.@)o.@)o.@)o.@)o.@7qiC@7ה8@3+.Mm@3tj~.Qn@2z,<.NC,@2z,<.NC,@2z,<.NC,?|b@@8#@8:@8 >+@8I@8?uGS:@8@8i@8@8S@8?,NN @8 *@8p@8@8 @8 @?@PN+@P_vȴ@PN+@PP ě@PN+@P;qu#@PN+@PY|@PN+@P]@PN+.@NO@N@NO@N?@NO@NQ@NO@NbM@NO@N?|@NO.@9Y*0U2b@9mVl!@5ahr @5ZD@4tj~@4Dg8~@3YJ@3l!.@3YJ@3]cf@3YJ.NC,?ꯨT?\?aBӌ?XvM?֩d@8?S?U(?Ê$?GuS?%"5@8?Vr?TWn?GLl?<05?ȩ@8 @@@@@&?????@@ I^5@@ ԕ@@ @@ C@@ U>@@ qu@!p -@!0@!p -@!G@!p -@!l!@!p -@!'RTa@!p -@!쿱[@!p -@!"`@7>6z@7ěT@2+ J@2@4@2'KƧ@2$Fs@1O M@1Vl"@1O M@1b@1O M@1zG?3a=?m0?פ?,OKu?+?OPl[?}?#maЀ?h_N%?.}$?J>?998?-]?e+?D(p?k3`E`-?HAY?I$@@@@@,@(??????? )@?Ch?(bi?JZl@ 3@!Ã?E֢^B?jY;?k?qW@jE@@w I^@w$/@w I^@vY͔@w I^@w @w I^@wLP@w I^@vW!@w I^@w;@V@;dY@V@{F@V@f_>b@V@$d@V@P,@V@WI@5v@5??X@2S@25e*N@2U`A7L@2*/@1Ǡ k@1#:`@1Ǡ k@1kf@1Ǡ k@1$q?}@TΥ?Ƈs3?]> >?6z@4&I@3° ě@0ěT@2-@0C\@2@/^($ x@2@/#(;@2@/\(?Z?j`cq? 3 *??~a?r#? % -?Z?-NI`(?*޺j?=Ei?ܜ?ORh?A?Cx}x%?(14z?W?2?]2@@@@@1@??????9?rE?yAXؕ?tme@-@ ?DB?zc_[5?҈m;?F/@sR@@\(.@\(@t xG@\(@ě@\(@@\(@F]@\(.@H+R.@H+R@H jg@H+R@H`A@H+R@H#{J#:@H+R@Gh ԕ@H+R.@8g l.>@5+@5zxl"h@5`A7@5{@5!:S@4ݿH˒@5!:S@4ȴ9Xc@5!:S.NC,@8J?VӋ?K+HN?ڡ?Н?@8@8J?C?•,X1?GLD3?_@8@8p??ܸIU,?p)Տ?[}?@8 @@@@(????@V.@V.@V.@V.@V.@V.@U-V.@U-V.@U-V.@U-V.@U-V.@U-V.@7DO M.>@3hr.Mm@3Ctj~.Qn@2,<.NC,@2,<.NC,@2,<.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @`B@\(@`B@T2@`B@KP@`B@@`B@@`B@<@^@^v!@^@^YR1}@^@^-#@^@_{K|@^@^V@^@_@8G>6z@8^6o@5E?|h@5Qj֘@47KƧ@431@4=ce@4Pa@4=ce@4Rэf@4=ce@4[?V ?(a??G>,)?? ?6]g?xSV?pCm?Ww??Oa'?ZY?)N!? -?|X.?hk?is^za@@&@*@$@8@9??????Up??ML?F'=*?:b{@ Y@_rt?e -?YQ^Y? 8@4@bT@d -P>5@@+T.@+T@/M @+T@/d/9@+T@/w@+T@/o_p6@+T@/Rs*@2-V.@2-V@@2-V@=>,@2-V@p/@2-V@p @2-V@Ì>@9N쿱[W.>@3 I^@1Z@3-V@1D=p?@2!.I@0ku@2!.I@0 W@2!.I@0˼U@8J?)kE5?7.C??vWW?e+@8J?Sl?uCcCO?@;+?cQ?"8@8p?td?'+M?v? 'S?[L @$@*@&@:@1?????@-hr!@-;~N@-hr!@-%`A6@-hr!@,/@-hr!@,qm@-hr!@,+ @-hr!@-12W@x@dZ@x@@x@N@x@G|@x@c@x@@7}:@7oK=@4^+@4 ě@3"`@3-@3^6z@3 -=q@3^6z@2~($ @3^6z@2d8?| V?b0Biŋ?/b?;Ϝ(? -Q?'i?gQ?W"?Ni?[3.?Wl4t ;? ={2?.)w?J>?6weN?s?6YkC?ʩDP2@@@@@*@*???????=??6@-do.@$$?i?C@2m>2@-Oc!@Zx;H@@|RnP@|7KƧ@|RnP@|TJ@|RnP@|T֡a@|RnP@|S @|RnP@|Q@|RnP@|Qu!@<n@6@`A@D=@`A@@`A@Х*Z@`A@ï2@7>6z@7!@1TS@1T[W>@0ttj@0t6@/QN;6@/Rr"@/QN;6@/Q~@/QN;6@/Q!?(?eqa?ۃ{?M?4sF ?KK$?wogl?@{?&Dd?26H?wh??iDby?[?;?s*զ?A&?"?I@@"@@(@1@1??????4Ըp.\?/1c?/l{q@4x?Ifx*???Iue@m!@@$/@`A@$/@\(@$/@ɺ^5@@$/@r @$/@;dZ@$/@عXbN@%|h@& x@%|h@&9X@%|h@& V@%|h@%S@%|h@%5?|@%|h@%+ @8@8ȴ9X@4#@4%`A7L@3`A7@3\N@2_o @24J@2_o @2@N@2_o @2wkQ?3a=?O9?go]?iP?#6_?6J?g;+t?? :?mK?{d?R?Q,Ě?c?{?Z $@b@%-?\{?n:x @eQN?@oqr@@$/@\?@$/@s@$/@t#@$/@Ƨ@$/.@$/.@B+@BڳQ|@B+@Bce@B+@B-@B+@Bќu@B+.@B+.@8 _o@8 k@5 I^5@5N;6@4"@4g8}@4!.I@4W@4!.I.NC,@4!.I.NC,?eM?iî7?%Lє?O#A@8I@8?)>}?U?J;.?!V@8S@8?-Iz?t ?7V/r?}2;@8 @8 @@@@????@`v@`^5?}@`v@`u@`v@`ߤ@`v@`ߣ@`v@`|@`v@`W>6@EQ@EQ@EQ@Gݗ@EQ@F$/@EQ@FYJ@EQ@F$/@EQ@GlC@2@2s@1E?|h@1Dqj@0-V@0C@/Ǔݗ,@/oi@/Ǔݗ,@/ xF@/Ǔݗ,@/84֢?^Dw'??,fC6?'$5?Z)?ʟ?bnl?`5a?NJ?Hn?&Tm?低NV? ?lo>?c?Z?]ߒ?6@@@@@*@*??????=L?<?DOƭ?2$H~ v?@`,z@F?S\?A'4??xx=lF@rf@@g-@n+@g-@'_@g-@֗0B@g-@i @g-@d71@g-@i[%@0 -=@4s%@0 -=@=@0 -=@-/n@0 -=@C*{@0 -=@ņ@0 -=@?ׂc@8-v@89Y}@4hr@4E3Op@497KƧ@34+֒@3z,<@3[QKN@3z,<@3=¤@3z,<@3IË2?R ?Y?JVX?!?L;G?腔?GP?F?\]?VkG?W-?H樑? -a?K?'*E?b(ق?ק{?SD2@@"@(@(@9@5??????]@'(?A*7?d;y[;@kDt@Ȍ?\]x5?u?QpR?޵OL@dӜp@@;lD@5?|@;lD@Hj(n@;lD@?_=@;lD@K.Q@;lD@<<@;lD@? ɶ_ ?}@TΥ?T!,?Mw @7H@7)详@4 ě@4Jّ@37Kƨ@3D=:@3iB@3<'`@3iB@3D|@3iB@3A;xv?'Z]&?8 w? -.` ?]x?ّlq?/Z?&??PY?=R?Mi?\/?'5b?wX '?R+r7?uk?e;Ƶ?1?T(U@@&@&@,@:@:??????Ws8TS2?S\1L.?>-tCX@Q@Q?p/$l?`R?fV?26"@k}5t@@-@@-@\(@-@>BZ@-@`A7@-@@-@q@4Ƨ@4I^5@4Ƨ@4iB@4Ƨ@4ߤ?@4Ƨ@4ރ%@4Ƨ@4ңS'@4Ƨ@4ѷX@6^H@6`)^@1^5?|@1@1i"`@1a @0h@0cS&@0h@0h@@0h@0hK]c?F/P5?"2?QaQR?(+Zk?% -? $4?2}?wi*?y?.pC?=.e1?äqY? ҇??o}?;B?gϵ?|)-}(@@@@@*@"??????8uK?LFj?D ?Lc w@Zib@4ѳd?6]?m ?s,Xf?5#@h{݆@@@1sh.NC,@1sh@1.H?1j?` ?E +?n@8I?= =F?M^?M\ٓ@?0W) [?j{X@8S?% mc?=!? ?JzUf?'{>@8 ?LiA@@@@@,?????@j~@Q@j~@ce@j~@@j~@ߤ@j~@QZ@j~@"L@y"@pb@y"@xl"h -@y"@w4m@y"@y#y@y"@w'@y"@vH@7e!.@7gƷ@0+@0;dZ@0`A7L@0`A7L@0Tm8@0Tj~@0Tm8@0Um]a@0Tm8@0U`B&?~?J?4$?,fC6?Rw#z?22$?G~?| V?u߿?LE?|}Յ?=Aq|?6??l?O?E 8?ܐ?g*6@@@@@.@,??????c_?JS??Όg%?3g(@l@lɵ@t%VP?_?w?S֮WG?kr?̣ 7@gRlj@@mhr@pmނY@mhr@qg@mhr@p8f@mhr@q*@mhr@w6B@mhr@x\@3vȴ9@3nd@3vȴ9@3Lo@3vȴ9@3@3vȴ9@3+o@3vȴ9@3k@3vȴ9@34J@7cg l@7iR§@2+@2~ݮ@1"@192@1kxF^@1gLs@1kxF^@1dWXnQ@1kxF^@1d_p?|0pJ?c?%`B`?@h?1?ױ4?r?Ԝ-4?\0.?*4f?vQ8?0C?'i?H?6ݞ}%?:r.;?0e?N @@*@"@,@7@6???????Wj?<;3 -?CkȠu?!kGϜ!\@sC@3u>x?c&8`+?A3-k\?^b?P=$@fܲZw@@S@1@S@Y@S@[ @S@CW@S@/@S@Zw@i"`B@i# -=r@i"`B@i&@i"`B@i/$@i"`B@i& -@i"`B@i%?*@i"`B@i#@5`D@5aR<6@0hr@0$5@0`A@0qB@/73@/ V@/73@/~@/73@/u;?Qp??Sպk?isi?RyUq?>/d?~?J?LN?b̼h?4Vr?1{f?jۻBF&?%?56#?$?t+$?p( @@ @"@@5@4??????[J?/"?TgG"?4 =@@i$?(ӭ@?UP J?Z&?ĠR@gT@@8@+p@8@ l@8@M@8@2Y@8@҈p@8@!7K@1&y@֊r@1&y@!@1&y@%F -@1&y@|Q@1&y@aG{@1&y@NV@9-v@90:"@3?|h@3n.@3Ft@3a|Q@2&@2~%@2&@2hr @2&@2W>6z?M:?SZ?~4F?D ?^&.?A:?|<~%>?!Z?On{-?;`Ε??-O?S?M'-?2W?5W? .0v?**Z@@@@@*@*??????grI?_ϒ?sM@'NO@;?w"i?_|?|/@ l@_ژ@@r .@r .@r .@r .@r .@r .@/-.@/-.@/-.@/-.@/-.@/-.@8@.>@3hr .Mm@3-.Qn@3Eu.NC,@3Eu.NC,@3Eu.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @@@@B@@&e@@Ҙ@@@ ѡ-@@=f@\(@<,t@\(@73E@\(@5҄@\(@@\(@]@\(@{h@8V,<@8?@2.5?|@1<"LR@1b-V@14q7@0Y~($ @0=OM@0Y~($ @0BxXd>@0Y~($ @0C ?Ҟ8A?2_?B?c'`?BDr?vkc??Xנ^?u+?pÇ?7kM=;?l1e? ?Q<(?]נ?I?(!My?n\V@@ @*@*@9@8??????Iƨp?D~E.NTw?J-<@۪@!f? Y?R`^?>?bMn?>`?A)N?-I`K?a?JU4?bM]f?2a?oܧ?~\?[J?@@@@@0@.??????SX?$m?0xj|?,~{@aW@???E&?:ߧ`P?xMPh?o[@s d @@֩x@֩l@֩x@֪͞@֩x@֪0 @֩x@֫ I@֩x@֨>BZ@֩x@֨Tɇ@iJ=p@iJ~#@iJ=p@iJd9@iJ=p@iJ=p@iJ=p@iK)^@iJ=p@iJڹZ@iJ=p@iJg@2>6z@2>6z@.a7Kƨ@.a7Kƨ@-1'@-C\@,ߤ@@,n.@,ߤ@@,5Xz@,ߤ@@,lC?^Q5P??&??eU5?b\?i{?bV,? ,C?A)N?3.Yl?B[@Y+@Zxl"h@Y+@W&@Y+@Wq@\(@\*Ak@\(@[u@\(@[@\(@["`@\(@[W>5@\(@\ߤ@/jL/|@/jLg@).Vt@).Vt@(&x@(&x@'#9@'Ov`@'#9@'lD@'#9@' ?^j?GA?m?XQ>?/`?O+y?bM7? g?j?3.Yl?@?J&d?k?bM]f? ?oܧ?~\?#.?h@@@@@,@*??????SzA?( X?=]QqX?$͘@XZD@[y?Lh?=;j? =?4Ӱ:@s @@7'lC.@7'lC@7#S@7'lC@7'H Zw@7'lC@7A@7'lC@7*\@7'lC@7$ -0 @V.@V@W@V@bs@V@]@V@bL@V@VK@7-v.>@2 ě@2ˀV @1tj@1G&@1䎊r@1aw@1䎊r@1}W@1䎊r@15b@8J?WDvg?%H9I@jlP,@<"'Q?W -mA?C2^? rVoL?N@mM=@@=p@O;d@=p@^ @=p@qi@=p@d8@=p@艭B@=p@@. -o@. R@. -o@.@. -o@. 7Kƨ@. -o@. -0@. -o@. -0U2a@. -o@.fA@4,<@4 @2+ I@2e+@2`A7@2 k@10@11.H@10@10IQ@10@1/H˒:?sD?_?>(lQ ?^:f?`?ʟ?1j?0n=Q?T֩E?ǧ?D6?u$6?jOI?]mZR? -?L?π9"?ĭ?_խW@@@@@,@*??????L/8B| 6l?cKs?&}vi@R@. -)y?:2*M2?Idpzt?v8p?|~V@i9+@@t}p -@t}p -@t}p -@t}@t}p -@t~$@t}p -@t~s@t}p -@t|O@t}p -@t|jcx@<$/@<$/@<$/@<$@<$/@<ں@<$/@<q@<$/@<J^|@<$/@<E@.}Vl @.}Vl @+`A@+»쪸@,4E@,5p -@*PH@*@N@*PH@*m$@*PH@*KU?^6P??YK6?I?Zj?Za?bMn?𕧌?~@?@/{?w. _ ?3'n}@s@@? ě@?!$/@? ě@?" @? ě@?"ѷ@? ě@?!af@? ě@?H@? ě@?!@;{dZ@;{dZ@;{dZ@;~@;{dZ@;u"@;{dZ@;yXbN@;{dZ@;~%@;{dZ@;z)@4T xF@4T`d@2+ I@2 k@1i"`@1k6z@16z@1U=@16z@1Mj@16z@1s?|O?ru?ě?U*?h|?C!?| ?$i %h?I??77?nK/?|׿pY?3XMV?+k?{B0?&I? -r@@@@@"@"??????$ ?`@?9renO??+r@? @;{9l?YO̴(I?_ Un?4ZW?CbQi@f0?@@[]/@[\1'@[]/@[Oi*O@[]/@[A- @[]/@[Hg1#@[]/@[Al"[@[]/@[@@E@\b@E@qz@E@B@E@ר@E@vM\@E@@4T xF@4Sv@1hr@1Ca@1Z~"@1S}!@00U2a|@0Ǥ@00U2a|@0ys P|@00U2a|@0}d} ?}@TΥ??nǾf?7Xt?&vo?腈V'?| V?æ?j :O?!?By?G县?K??`/@?gXk?dca?)?٤a@fd@@n@̋C@n@g@n@*0U2@n@u%F @n@"@n@Zc @Ghs@GA7M@Ghs@G+ J@Ghs@Gѷ@Ghs@G q@Ghs@G,@Ghs@GsP@8 D@8|hr@5{ I^@5sA [@47KƧ@4F -L0@4䎊r@41@4䎊r@4̋C@4䎊r@4BZc ?F^S?}_Z?'=DP?"?|QS?d?|O?k!?Q\?pe?g?L ֎?=Ca?*ŪZ[?\œ?rG?1 R?9^@@@@@,@$??????_pcUCBW?jXO\?X(@aς@Ga@H9X@H@H9X@>Q@8@@8{@3I^5?@3߾vȴ9@3q7Kƨ@3w/V@2^6z@2c$@2^6z@2^+@2^6z@2Z1'?ʎa]?@K/z?G)?A%?i]i?y%?A[F?@C?!b?>G]!?SY?Y@?9&?GA?+B\@dZ@S@dZ@@dZ@tS@dZ@$.@dZ@@dZ@p -@4mv@4mIQ@2?|h@2g8}@2`A@2ěT@1&@1҈p@1&@1- @1&@1 ?|O?`[?mGSq?5~pY5?[9,?.'?|O?A}?t(?#q\-v?4.?Qʾ?Ol?7}?],m=?9XNu?{2?*Q@@@@@(@ ???????@p ?E '?MfM?=]D޺@c@BZ=?l#?^k=6?#ZN? @f@@@\(@-V@\(@ -=p@\(@kP@\(@@\(@}Vl@\(@:)y@#vȴ@#$/@#vȴ@#:)z@#vȴ@#s@#vȴ@#쿲@#vȴ@#sQ@#vȴ@#ѷ@5|Q@5VϪ@15?|@1Ҷ}Vm@1PA7K@14?@0@0tj@0@0p:~@0@0kP?|O?|5z?.O?ׂa?g;+u?YLU?|O?y?]"?ޝ?z?6/?W ??H6?Za7?1 R?a]o@@@@@,@,???????7^?*>?Pp ?l=3ڳ@@#nNl?gȣ5b?u6Λ?օ66?򜿊@_Cw<%@@lC@&x@lC@橠'RT@lC@xF@lC@L^@lC@@lC@)`@7K@n,@7K@{J#:@7K@ qv@7K@H˒@7K@N@7K@A7M@7I_o@7x@2 -^5?|@2 ᰉ'@1"`@1˒:*@1 k@1$/@1 k@1@1 k@1l!.?ȉak?:L -?u*y?TnE?% -?nM?Hߝ_`?7fps???݄ ??y?)Zw?],m=??$?Cns@@@@@ @,??????`ج9?U7?(h=B?S5X#S@b`@X??~?d)?t`j?k@0@qIU@@#S@"@#S@$Z@#S@#@#S@!R<@#S@#\@#S@!G|@3S@3`A@3S@31'@3S@3m9@3S@3J@3S@37K@3S@3<64@8H@8L_@3S@3!-w1@317Kƨ@32 I^5@2YJ@2PH@2YJ@2F -L0@2YJ@2F -L0?>sN?B-?3Z?IW`g?>{~?.'?Zm?eCO?J?nca?Y:J?U3p?eS?]ivL?[?f?q' -B?C SG@@@@@*@???????Mn9WV T?LbFB?bV@"'&@3h*?_oF;?tb3?rH?Q%@r[]@@&7@&>Yۖ@&7@&9@&7@&8t@&7@&8@&7@&71@&7@&5%f@+@ P@+@;@+@S|]@+@$@+@4@+@f@8+s@8/҄7@2@ ě@2@YB-@0j~@0o@/At@/ 3W@/At@/e@/At@/-U?ڡG7@TF@dz|@TF.@TF.@ 7Kƨ@\@ 7Kƨ@*@ 7Kƨ@#%@ 7Kƨ@@ 7Kƨ.@ 7Kƨ.@6֚,<@6ԇ#@4S@3s@3-V@3c{J#9@3p@3%oi@3p.NC,@3p.NC,?}ѿ,?UG -?ѳՂM?@8I@8?us\?Sn\}?M1#?>b@8S@8?oܧ?0Uo(?)?S@8 @8 @@@@???? @Cv@C@Cv@C)kv@Cv@CG@Cv@C@@Cv@C-Q@Cv@CcI@v@v@v@@v@w@v@@v@;A@v@uT"\@5v@5XRv@1° ě@1_s@0]V@0\NhZ=@0:,<@09ڎUu@0:,<@09@0:,<@09'?| V?78?'Wc?\?n?NU?;%?}@TΥ?  A$??h?-$: ?vS?q箭?L|?_ ?z (?,H X?*?3NA@@$@*@@3@5?????? l0 ?7G#>?!uK@Cr=@r-?WЦ?AL/$?"?bs@fH@ @cS@c%@cS@f@cS@aG{@cS@b I^@cS@^"@cS@[S@%@1&z@%@@%@$/@%@x@%@~vȴ8@%@n@7,<@7sh@2@2%2@1"`@1+ @1xF^@1N;@1xF^@1S@1xF^@1jf?1j?Z.?ě?_?STS?<_?% ?? :??1'7?UBi?K#N ?tgi?§d߱?OW?8 ^?,a@k -Z)@@ -=@ڟv@ -=@ BZc@ -=@ y>B[@ -=@ S@ -=@ k҉@ -=@ s@@@@ Dg8@@?@@ŭ@@ŴD@@ſ$tT@7D@7֔Fs@5n5?|@4oiDg8@5-V@3p[6@5m8@2VϪ@5m8@2 -=p@5m8@2n.?Br?]]?Z{f?Jʮ?V?0 ?q!U?_*_'?H?,?|I -?*?a -?"M*?*\??c?5@@@@@,@*??????غ+\N<?!su-?\@ Kj1@ebd?Wd~m?F V@`v@qfyE@fH}@@z@?![$?PP]?|b@?:l?\?:G ?B{ $?_rd+?R?6?ʏF?g3#?͆? S@@&@,@,@9@:??????I K\?4d!L??12[>@;&@܀%"?TyQ?Qn?]e?D"@fZ8@@a.@a@a7K@a@b I^@a@Iԕ+@a@q@a.@V+ .@V+ @Z@V+ @}p @V+ @9Y@V+ @tj~@V+ .@9DO M.>@4S@4q @3-V@3zG@3ce@3Ƶ '@3ce@3R<6@3ce.NC,@8J?*U?S?+j?7@8@8J?39?ZH|:? ?K+Gq)@8@8p?7'nK?>{~?8zt?ԤA]@8 @@@@????@7KƧ@6u@7KƧ@64@7KƧ@5!S@7KƧ@8U@7KƧ@7X@7KƧ@12X@w@V@w@O L@w@iDg8@w@}H@w@{@w@KƧ@82@8hۋ@2ěT@2L/{@2t@2}/w@1_o @1`A@1_o @1wkQ@1_o @1s?F/P5?nqV?~"? 4? -0ggk? ا ?Zm?ā? yz?hJ?%3?C.NT?JTACG?Az? - l]?J]j?=P|?b@@@@@$@(??????5%<~?C N_.?i4@5,|(@?j{ά?{r_T?ݏ?~g<@pub[@@7T@7T@7T@7\@7T@7@7T@7$/@7T@7U@7T@77K@|hs@|G@|hs@|"@|hs@|@|hs@|ht@|hs@|<64@|hs@|6z@6]ce@6H˒@3+@31@26-@28K]c@2@5@2?@2@5@2wkQ@2@5@2{n?Ol?/)s?K]4?j6_?GBm?ʟ? ?uie?ܿ?ט??C ;?`w???"a??=P|?)@Jq@@@@@@ ??????34m?Gn?=rLv?5@7VT@|?]VH}?3a-A?ZO}?oΑޘ@h+!@@g -=p@g^5?}@g -=p@gY@g -=p@gܹ#@g -=p@gC@g -=p@g?|@g -=p@g+j@%S@ ě@%S@1 @%S@-qu@%S@,>B@%S@-jO@%S@*#9@6 xF@6"@2@2(@17Kƨ@1j~#@1B@5@1@ -? ?xu)?X/?ӑ@t)o@@4ow@4qI4 s@4ow@4[o@4ow@439H~@4ow@4`yx@4ow@4u}$@4ow@4wǠc@۸Q@۱I@۸Q@)G&@۸Q@@۸Q@~@۸Q@ۓ<{'@۸Q@۩Mh@8쿱[W@8ź@3ԛS@3fo @36-@3]@2Y~($ @2l5~@2Y~($ @2m[au@2Y~($ @2m?x.?0[?]?b}??.?&?[P~?!Nd?VwU?ʣ7?.׮3?iW?;z?ab?]M-?պ?gy?X3@@$@&@*@9@6???????mvg#6?rל-;N?r1`@4l/u@۰Ws?C͋/?&?M}_@WbF@n9@@8Q@8bM@8Q@8yV@8Q@8hFW@8Q@9LI@8Q@74@8Q@7e@wVu@wUϪ͟@wVu@wU) @wVu@wTfrH@wVu@wV sy@wVu@wU@TZ@wVu@wU" @4_o@4ɅoiD@0)+ @0) }@/O;dZ@/u@.(ۋq @.(PA@.(ۋq @.(I@.(ۋq @.(A h?|b@?xet?9Q&?g˄ ?g?u?|B &?'3 -k?x?4km?BNl?IXuj?pa?Ճ?u"7? -q?%9G?3eBp@@,@,@(@5@3??????7q?1S0% -?9\e|@88:@wUL?QAg%e\?5m;?@5J?wV@g=ނ@@/dZ@/G@/dZ@/"@/dZ@/;dZ@/dZ@/b@/dZ@/XbM@/dZ@/S@tև+ @tև+ @tև+ @tY|@tև+ @trGE9@tև+ @t$tS@tև+ @t!@tև+ @t\)@4įO M@4JL@2^5?|@2lD@26-@26@1̿[W?@1VϪ@1̿[W?@1:@1̿[W?@1̞쿱\?}ѿ,?u:|?RsW?,OKu?ҧ?X?| ?r5MS?CXs?aYZq?u$6??? uf?bv{3dZ?gϵ?wͶ@@@@@,@*??????? s`Z?>zZ?CN*@/X@t].+?]ob?>Z?ŰT&R?S@fz4.@@w.@w@trf@w@MHh@w@7@w@lU/@w@@%;@FH9X.@FH9X@FIE;s@FH9X@FJ̊@FH9X@FH@FH9X@FIvG@FH9X@FIQ@3~($.>@/V@/+~%@/Ix@/I"k@-#Z@-#rdR@-#Z@-#n.3@-#Z@-#@8J?qC ?(( D?p?éw?0N@8J?Cl?BVx?--?.$?MmUѽ@8p?$-a5[?g?V*?D#?@,@(@ @;@:?????@y"@|1'@y"@&@y"@f?@y"@m\@y"@{dZ@y"@t!-x@)@)O;@)@)`A6@)@),zx@)@) -L0@)@)"@)@)G@9T xF@9i>BZ@4kS@4t?@3-@3|@3u@3 "`@3u@3g@3u@3~($ ?/q*q:?UJ?u[>u@rjo@)B?{~vCP?J,_??vL? >Fwv@q*Sn@@n\(@n\j~@n\(@n\wkQ@n\(@n\1'@n\(@n\@n\(@n[qu"@n\(@n\C,z@lC@l@lC@mM<@lC@nzI@lC@l@lC@mV@lC@lcA@2@@2@0c@0dtj@0$j~@0%m\@.@.[6@.@.A7K@.@.[6?^Dw'?h?WD@?̒?Dj??bV,?#maЀ?qN?0N:s?֧QD?SV.?g)? -?]?=rE=?SP?@@@@@*@&??????'e*Zp?3dYa3?#ݼh@n\" ޒ@lLD?B -SҦ?2lɎ?Q4#?_a9$;@su@@-+ @-+ @-+ @-+ @-+ @-A@-+ @-C@-+ @-R<6@-+ @-!S@8G{@87K@8G{@8n.@8G{@8s@8G{@8ě@8G{@8&J@8G{@8e+@3:qiC@3:W&@.Vt@.}Vl@.&x@.$tT@--C\@-.!R=@--C\@-._o @--C\@--C\?| ?`?7[䭝?̒??װ*w?|O?j%Ra?'Q?-I`K?2&h?eDQQ?q6tN6?u?u@>N ?~\?a+?l&@@@@@*@,??????߻?~0@?'ќ?!O_.@-` @8ߖ?<?Aφ?$!9?#|д@f@@$ O;d.@$ O;d.@$ O;d.@$ O;d.@$ O;d.@$ O;d.@m49Xb.@m49Xb.@m49Xb.@m49Xb.@m49Xb.@m49Xb.@8DO M.>@3ěT.Mm@3b-V.Qn@2J0U2a|.NC,@2J0U2a|.NC,@2J0U2a|.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @.?|.@.?|@.\(@.?|@.iDg8@.?|@.@.?|.@.?|.@0 ě.@0 ě@dq@0 ě@G)_@0 ě@_F@0 ě.@0 ě.@1D.>@0.5?|@0Q@/Z1@/-V@/ -#9@.g l@/ -#9.NC,@/ -#9.NC,@8 -Ny?3"??Hyq@8I@8@82r?q?)(?Bݎ4@8S@8@8L?i--?<{?P )k@8 @8 @@@???@`t@`Z@`t@`&@`t@`A \@`t@`*@`t@`S@`t@`e+@)l@)l@)l@*Ov@)l@)Q@)l@,Vϫ@)l@)7Kƨ@)l@-U@3uS&@3u`A7L@1^+@1_iDg8@1i"`@1i^5?}@0@0Z@0@03@0@0ѷ?|O?3i?TS?<ݝ? g?P@*W?JԹ*j?Zxȿh?+dRַ?oQŋ@f ?S@@o-@nzH@o-@ql@o-@hp:@o-@h@o-@eoiD@o-@fIR@E@G-@E@B @E@<@E@=E@E@Ce@E@C9@6(YJ@6!TɅo@2+@2}H@2Vu@2rGE9@2 k@2 0 )@2 k@2S&@2 k@2KƧ?| ?PƧ$?3Z?,OKu??E?F/P5?l?O?P?v> ?>[Fh?#IӨ&?&Z#?*0x)??a#? P@@@@@,@*??????]M$X*?,@Wa?Pak;@jH.g@DNQd?K*( ?oO?2K?@fpȟ@f -As@ @2-V.@2-V@*~#@2-V@8PH@2-V@'K]@2-V@(@2-V@,1&@zG.@zG@@zG@Mj@zG@ ԕ@zG@q@zG@ -qi@97@.>@5 ě@43@4A7K@3qu"@3 k@3t9XbN@3 k@3s -=p@3 k@3w+@8J??q-*7?D?`FC:?VAg@8J?XSra1?To?Җhn?7c/?>/@8p?f1?ji?!??{%@@@@*@(?????!@+ @l@+ @@+ @@+ @*1@+ @D@+ @$@CzG@Cq^@CzG@C@CzG@CO:@CzG@C)l@CzG@C:)y@CzG@C$e@8쿱[W@8&-r@4#@4!l@3tj@3*0@2‚@5@2u@2‚@5@21@2‚@5@2:~?zM ?D?ub5?{y6?EE?*j?__`?_lWF_?Z;?%ja?Ng x? Ӻ^?Vʴ}?qv?GB?VED?܂N.?"#@@@@@,@???????s8-?a^E?hmg?dWa@l@C%??l_w5?9&T`?fxU@o d@"@daG{@db I^@daG{@dh9X@daG{@djg@daG{@dTF@daG{@dU!S@daG{@dZ>@uF@uԼj~@uF@u -=p@uF@uǓݗ@uF@u@uF@u -=r@uF@uR<@8YJ@8D@3hr @4nP@3t@3쿱[W@2‚@5@2A [@2‚@5@2K]@2‚@5@2Ƨ.?o 6}?:}T?go]?Hyq??S:Ъ;5?:-%??luOv_@.zH@E`A8@.zH@2 ě@.zH@(p:@.zH@<1'@.zH@0 ě@.zH@+xF@8v@8iB@5I^5?@5 ě@4j~#@4,<@4 [W?@3sh@4 [W?@44m9@4 [W?@4T`d?E@?K'B?mr?%?Ͻt/9? C@L(?F/P5?6S{?kCnL?#Vz?͋>?k?Z? h?7}?^FC}?Q?]?4\ɵ@@@@@*@&???????xۂte;?Y9id?>@.%t@8T?wl?bc-?!?8@jL֖B@$@#^5?.@#^5?.@#^5?.@#^5?.@#^5?.@#^5?@$Fs@KC.@KC.@KC.@KC.@KC.@KC@f@9V,<.>@4S.Mm@4~".Qn@3YJ.NC,@3YJ.NC,@3YJ@4B@8J@8#@8:@8 >+@8I?]@8J@8@8i@8@8S?b*@8p@8 *@8p@8@8 ?HucL@&?%@}E.@}E.@}E.@}E.@}E.@}E.@.@.@.@.@.@.@8,<.>@3.Mm@2`A7L.Qn@26z.NC,@26z.NC,@26z.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 &@z^5?}@lI^5@z^5?}@[@z^5?}@j~@z^5?}@ٌL_@z^5?}@مQ@z^5?}@Fs@@\j~@@š@@W@@@@Q@@lVϫ@9hYJ@9Y#w@4^5?|@4ݗ+k@4t@4 P{@3u@3h ԕ@3u@3@3u@35\(?p"7?q?3}C?ʺd9.?*5>?D1 `?k|jʨ?f?$G"?ln<9?Iph?"v?o7\?JfA?wG?Gs#4I?*fd?C3`ow@@@@@(@"??????E?M n@?fOm?&@D@xGG:@E?0U?\7Y@$à@Î@m[@'@6z@8~%@5 ě.Mm@4'KƧ.Qn@3c.NC,@3c.NC,@3c.NC,?]@8#@8:@8 >+@8I@8?]@8@8i@8@8S@8?*wjs@8 *@8p@8@8 @8 ??)@1&x@1hr"@1&x@1@1&x@2nP@1&x@12Y@1&x@0@1&x@0 )@T@/@T@Q@T@84@T@84@T@+a@T@o@2쿱[X@21@-\j~"@-\wkP@-]E@-^Ov_خ@+̲@+̲@+̲@+̿[W?@+̲@+̋C?^Dw'?°R;?=?Kh?G?6r In?}?bV,? -?NJ?6)}?A B{?_~t?cPQ?.+?y8$?XLL?a+?@@@@@,@,???????V?)j>?"@1@%N?8߯P? ?n_H?|8@s32v@*@Rt@R_@Rt.@Rt.@Rt.@Rt.@Rt.@hr@l@hr.@hr.@hr.@hr.@hr.@8V,<@8[AjZ C@40ěT.Mm@37Kƨ.Qn@2s&.NC,@2s&.NC,@2s&.NC,?7娠@8#@8:@8 >+@8I@8?!@8@8i@8@8S@8?i@8 *@8p@8@8 @8 @?+@u@ -@u.@u.@u.@u.@u.@r @[C@r .@r .@r .@r .@r .@6:@6I0<@3+ I.Mm@2KƧ.Qn@2 -0U2a|.NC,@2 -0U2a|.NC,@2 -0U2a|.NC,?| V@8#@8:@8 >+@8I@8?eXW@8@8i@8@8S@8?^$@8 *@8p@8@8 @8 @?,@LO;d@L`A@LO;d@L -=p@LO;d@Lr @LO;d@M O;d@LO;d@Ls@LO;d@L-V@u C@u`A@u C@u%@u C@u ^5?@u C@u;dZ@u C@tp -@u C@uhs@8p4m8@8t,<@5!hr @5=,<@4K"`B@4aN;5@4p@4?@4p@4|@4p@4o?{QB?}d?FB:?_B?M,?*j?Ol?dx/?Rs??tPYz?L "? ' %?fb?*wjs?,|?xb?+C@???@@???????.a@?a/9?Bz^ʉ?U0f@L2@ug5?Z?Va?b?YS?\U@rz26\@-@/\)@/v@/\)@/4@/\)@/Ov`@/\)@/+ @/\)@/Ʌo@/\)@/@fXtj@fX@fXtj@fX74@fXtj@fW3@fXtj@fY+@fXtj@fXD@fXtj@fWkP@0>6z@0>6z@0 I^5@0s@.dZ@.dZ@.@.vȴ:@.@.حV@.@.?^Dw'?3i?7[䭝?lDžN9?Dj?{+@8I@8?F @8@8i@8@8S@8?=@8 *@8p@8@8 @8 @?0@?x@?x@?x@?W@?x@?L/@?x@?C,@?x@?Q@?x@?ᰉ@P ě@PbM@P ě@PU2a|@P ě@Pv_خ@P ě@P@P ě@P'R@P ě@P@3T xF@3S\N@0 ě@/>BZ@.F@.&@.At@.t@.At@. M;@.At@.1?}ѿ,?jN ?O??!ˠ*(?B ?fW@@@@@$@,??????? /(@  ?, m?;@@?r@P!!?Kߵsq?9am,p?]? -@g@0@1@Jz^5?}@JwKƧ@Jz^5?}@JxV@Jz^5?}@Jw@Jz^5?}@J|wkQ@Jz^5?}@J{lD@Jz^5?}@J|1'@/@$/@/@Z @/@Q@/@fffg@/@@/@>BZ@7qiC@7 @3 I^5@3|@3Ctj~@3D?@2Q@2}H@2Q@2Ov_خ@2Q@2ڹZ?qv?m0?RsW?ҕV{?RXFf??=?F/P5?5CQ?Jj?L?n ?Us?+QK?`F?~/O?WhQ?t?`i=@@@@@*@&???????N2_8?@ݸ?;V[lE?'>w@J{E3@ `?VhCņ?Kz?q7V?=@l#s@2@*߾vȴ@*߾vȴ@*߾vȴ@*IQ@*߾vȴ@*j~@*߾vȴ@* [@*߾vȴ@*Z@*߾vȴ@*@ސbM@ސ ě@ސbM@ތ"h @ސbM@މ7Kƨ@ސbM@|(@ސbM@ޑhr!@ސbM@ގ}Vl@5S&@5F -L0@3ahr @3_;dZ@2Ƨ@2e+a@2YJ@2 k@2YJ@2U>@2YJ@2zxl"?| ?Čۨ1A?oq u?Hyq?R?0 -?F/P5?j%Ra?v@b? ?=M?J,?m!?3a=?,1'?k,??w w@@@@@*@(???????C>,`^ؚ$?/z?h1@*(@ފx*?M<(?wn?Fq|s@ -kY)@eFV@3@j~@$@j~@j@j~@.@j~@O9n)@j~@lgb@j~@S@w@V@w@՞@w@"P@w@fq@w@hXc@w@C@4@4@2; I^@23͞@1-@1a@1=ce@19؇@1=ce@14w5@1=ce@15N?|b@?@9?[6.7?Ѻn?<$a?\ۅv?}@TΥ?y?)3^?C\7hv?3|Oi?C⟗?SS?mO5?Ԗo=?%A?lh8??@@@&@(@,@9@7???????Q?spl?-h^]K[?F;@P@nk?L$q\}?f6?@?ա@ereX@4@vE@v+ @vE@q-@vE@O@vE@pO*@vE@ F@vE@MPA@E@ȴ9X@E@u @E@=l@E@Q(@E@i4@E@ؑk@3H@3UhƓ@0sS@0eR@0."@0pF@0&YJ@/̵d@0&YJ@/JS@0&YJ@/yUY?}#?Ğ?ґ?:7C1?W?.|[?|b@?#wg?_m24?}5,?v.*?Eim?n3?KU!?eW^_?u<^?a,=?"ۀA@@$@*@$@9@:??????f^ـ?oTr?}@c(E;@:?v?r.I-?oF?x%A@ӹ0 -@Z(P@5@F@G,@F@@d@F@Ar@F@G&@F@IB@F@Jo@ffff@㕁%@ffff@)_@ffff@A7M@ffff@F]e@ffff@S&@ffff@L/|@6YJ@6D@2ٺ^5?}@2+jf@2,j~#@2)@1&@1'/W@1&@1g l@1&@1 ě?}? q?,Z?\2? -`?IdGc# @Ii@ixm$?C>?^ZP?g+?ģf!@gN;@7@{`A7.@{`A7@{e@{`A7@{+j@{`A7@{ݗ@{`A7@{Q@{`A7.@$/.@$/@3@$/@@$/@)^@$/@|R@$/.@3 xF.>@08r Ĝ@0MH˒@/ӶE@/vȴ@.73@. '0@.73@.oiDi@.73.NC,@8J?R?uN?0K?=U@8@8J? QnS#?U"?sZ-?1H9v@8@8p?ָ.?ji?]O8?&n_@8 @@@@&????8@8Q.@8Q.@8Q.@8Q.@8Q.@8Q.@sE.@sE.@sE.@sE.@sE.@sE.@7~($.>@2!hr .Mm@1"`B.Qn@0u.NC,@0u.NC,@0u.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 9@j~#@h1&y@j~#@oA@j~#@ka@j~#@k~(@j~#@d?@j~#@fIQ@݀@݁7K@݀@~5?|@݀@݁n.@݀@.H@݀@݄oi@݀@݃,zy@6YJ@64֡b@3; I^@3;s@2`A7@2ˆp:@2?䎊r@2C,zxl@2?䎊r@2>ߤ?@2?䎊r@2?o?F/P5?L?&t?CF ? ?5ŋ?}ѿ,?'`?Kd+?.}$?=?kj?v'X?oM?Z?0?1?a@@@@@*@*??????4R?2p?Iv%aq?> ݤ@gX@݁?hS,P?^O ?>tk ?C@fK@:@;lD@;dZ@;lD@hr @;lD@@ qw@;lD@Xtj@;lD@?o@;lD@ 8@S@-@S@ڟv@S@E@S@B@S@@S@̿[W@8:@8SMj@4hr@4hۋq@3"`@3{J#:@3!.I@3@- @3!.I@3Q-w1@3!.I@3Mj?5}'?>I ?3}C?(&V? K?OY?X?''5?9|/?c6; ?eB?@ؤ? /x/?-8?fr[?A ?wY?&侱4@@@@@,@&??????y5[6?k!_?2r?y|J'@9,d@%,?UfX?Ȏq?EjF(v?>0@j趴@;@cS@cS@cS@eo@cS@`- @cS@c{J#:@cS@dt@cS@ce@dZ@dT@dZ@ZS@dZ@]!.I@dZ@g@dZ@eݘ@dZ@ezxl"@4įO M@4+a@0ٺ^5?|@0E84@0Ƨ@0\)@0@4@0d8@0@4@0&IR@0@4@0.H?| ?"2? ??V^?f]G?|O?[45?y?3F0?JL\?m/wE@cc|@eF.p?/w؛?L՛Do?i3y? Uu^@f{ʺ@<@Aٙ@Aٙ@Aٙ@Aۘ@Aٙ@AG@Aٙ@A״3@Aٙ@A"@Aٙ@AحV@/"M@/ti@/"M@/%o@/"M@/(1&y@/"M@/$Z @/"M@/#{J#:@/"M@/!@7uS&@7s ҈@3+@3.H@3U`A7L@3Vt@2J0U2a|@2JC\@2J0U2a|@2L(\@2J0U2a|@2Ka@?sD?oNQ?^*G?btx=?p[?XE%:?)T?7fU?t-E?FT \*?'#g?kj?~~?@C@@C@!-@C@oi@C@cA @C@H@C@O@ȴ9X@ȴ9X@ȴ9X@1&@ȴ9X@@ȴ9X@J@ȴ9X@4@ȴ9X@S%@3|Q@3ae@/"`@/U2a|@-nO@-ව4m@.@- (@.@.ݗ@.@.?|O?b[?Mq ?xx?h5+P? Ck?| ?M7F?Fy -?s҆L?,rg?-[w?8*?7}?aI*?.SM?_խW?*Z}@@@@@,@,????????[0Qy4?1,9Qt?V7:g-@턼@?N3 V?r:s~\K?ϴ'?#;@bI@?@xQ@xtj@xQ@yXbN@xQ@ye+@xQ@ye+@xQ@w>6z@xQ@wKƧ@mhr@nzI@mhr@lq @mhr@l"h @mhr@lzxm@mhr@m8@mhr@mB@0@@0@@+MV@+Mqu!@*x@*ɠ'RTa@*oi@*e@*oi@*oh@*oi@*oi?^j? ?&??eU5?tKc?tJ?bQ넛? g?j?; $?Ff$?SV.?bM]f?u?u@>N ?avh?l&?@@@@@(@,??????OZ?"J?5/B?!63@x!J@m.,?D_?oo?0P7?g7j?X'Ec@s 9 @@@n@@n@7Kƨ@n@Mj@n@PH@n@`B@n@e+@$$@$bM@$$@$`hۋ@$$@$NV@$$@$Q @$$@#y @$$@#C@8:@8u@4O|hr@4E '0@3V@3@3fYJ@3Z>C@3fYJ@3TtSN@3fYJ@3LN;?E?05 [?3Z?998?+|˟ @g`@A@A7L@𘰢@A7L.@A7L.@A7L.@A7L@B|z@A7L@Ձ$@yE@yXbL@yE.@yE.@yE.@yE@|S-@yE@|Y~@8>6z@8q -W@3S.Mm@2KƧ.Qn@2Ǡ k.NC,@2Ǡ k@1*,@2Ǡ k@1i0 (?`@8#@8:@8 >+?Ҏ?ѻ,?wvHf@8@8i@8?җ#?ڑL'?^}\Б@8 *@8p@8??] @@2@*???B@s\@s7Kƨ@s\@s l@s\@sݗ@s\@sz@s\@so@s\@swk@"@"@"@u%F@"@"@"@y=~@"@@"@y=@3I_o@3Iy @1xr Ĝ@1xl"h @1"-V@1!_p@0u@0䎊q@0u@0 ҉@0u@0@N?2}?]Û? '?,Z?u?dW?| ?Q_ڸFN?}7.?Җq?UBi?P=?nwf?Q?֘>D?9XNu?7?k!l@@@@@&@&??????R3*?0iT?]Xؗ&@s;0@')Ĩ?PC\i?4#ŵAr?8¸(q?s[P@grC 0@C@S@dZ@S@5?|@S@!n.@S@\@S@ '@S@W@m@ ĝ@m@x@m@hۋ@m@E@m@S@m@0@6H@6}Vl@3}hr @3}hr @3{Ƨ@3|wkP@3@5@3wkQ@3@5@3䎊q@3@5@31&x?1j?*|3N? ?o=\>? ?7fps?qN?1`?C B?O[?nwf?p51?E~?VED?΋N?HF!6@@@@@,@(??????eY?nAc]x?D H~?c0-@\@z ?brcP?Zޣ?Yٱ@f@c -4@D@G+ J@Fffff@G+ J@F&@G+ J@GfB@G+ J@Cn@G+ J@JL/@G+ J@I@`A@-V@`A@о *@`A@V@`A@hr!@`A@ʦL/@`A@;dZ@7!.@7͞&@3)+ @3)0 (@2`A7L@21&x@1_o @14֡a@1_o @1:)z@1_o @1xl"h -?q!U?_X -?P"hI?)?P=?)'3??Hk#?Qc -?ihA? ?.?z"2(?7'nK?aI*?cZ?b? )D)@@@@@*@,???????UMX=xM?TFI?PsR@F Jl@ϝ|?n3,V|?jV *;??υ̞@m4@E@Q@ӈ@Q@kX@Q@75@Q@+L@Q@dl@Q@[~.@ctj~@c;(@ctj~@cDn@ctj~@cw^(@ctj~@cm*J@ctj~@cke@ctj~@cnR@8쿱[W@8(ےP@3S@36=C@2q7Kƨ@2f]b@1xF^@1a+E@1xF^@10L@1xF^@1*GQ?Dʯθ?u3v?E&?y??%?Xd?4?1ͻ?,?:?2%?8 -$?ǒ}?(tl? gz?gK?s ?Fu@@"@(@&@8@5???????1Kq:(?W??bٶ7^@K@cvZb;?yRT;?|X?qjZ?An]oV@l1@F@v"M@v!@v"M@v#,zw@v"M@v"\(@v"M@v!.H@v"M@v˒:*@v"M@v" I^@lC@l1&@lC@m5Xx@lC@m(@lC@l>B@lC@l~($ @lC@m(@4,<@4l!.@0R I^5@0Rs@/F@/؆YJ@/Gݗ,@/F@/Gݗ,@/Fx@/Gݗ,@/G-?| ?Čۨ1A?"?:Z2b?,I ??}ѿ,?j%Ra?A)N?>\?Hc?Uyu#?8*?)Zw?YF?E 8?ˢL1?A0)n@@@@@*@$??????oqQ?*{:?-t{ Ћ?EM@v!)L@l|?M?/G|?, S?h@gIzA@G@o)x@o)^5?@o)x@o)Dg8@o)x@o)@o)x@o.O@o)x@o)'RS@o)x@o)@bM@h9X@bM@`4n@bM@em\@bM@gݗ@bM@eO@bM@hK]c@8,<@8S&@3ahr @3`4m8@3,j~#@3+U=@26z@2b@26z@2iB@26z@2[?us\?d_s? -D?!#?n{K?1a?o 6}?\j? yz?@[*^?c3c(? C`? nI?|׿pZ?p-?{0?/̽`?T8@@@@@(@(??????# -g c?O@^?G!1N@o+nП$@g/ i?k?Z_IĚ?ӆq ? o@pHZC@H@p$/@pG{@p$/@p I_@p$/@poh@p$/@pR<@p$/@p?@p$/@p˒:)@e#S@e#%@e#S@e"`B@e#S@e!af@e#S@e"wk@e#S@e"\(@e#S@e"&I@2H@2.2@-A7K@-IQ@-;dZ@-[6@-fL/{K@-gfA@-fL/{K@-i*0U2b@-fL/{K@-hۋq?^Dw'?&H?(?䆀{6? 9?aX?bV,?j%Ra?ԕ]I?e[|(?M?rJs?:9?.+?٤?hB^?ˢL1?L@@@@@*@(??????KS)z@{?4!Kp?Q8-@py̸@e"vR?CQZ?!Ə%?!r?;&b@s04@I@`A7L@`n@`A7L@a7K@`A7L@a7K@`A7L@b I^@`A7L@^m\@`A7L@_F@[ ě@[bM@[ ě@['R@[ ě@[@[ ě@[)^@[ ě@[{@[ ě@[bM@304m8@30A7K@.Ƨ@.q @.x@. k@-oi@-m\@-oi@-S&@-oi@-m\?|O? ?=?Kh?lDžN9? f?2I/?|O? ,C?O(?Y?IM?rJs?q6tN6?.x?2Y0?8 ^?pk Y?pk Y@@@@@*@,??????, ?5?5ՔZ]4c? ܽb R@`>@[?U$?*&"V?x%?aK@f|@J@ߥS@ߩl@ߥS@ߤZ @ߥS@ߡG{@ߥS@ߠhۋ@ߥS@ߝb@ߥS@ߝ,@;dZ@A7L@;dZ@2W@;dZ@ۘ@;dZ@~@;dZ@@;dZ@dZ@6s@6bM@2 ě@2}ڹY@1t@1° ě@1!:S@1U=@1!:S@1c @1!:S@1ߤ@?Ol?9؊??̒?Jj~u?ީqk#?)T?p RN%?I?,?#:d ?to(?Ƌ?3a=?d --?ȏG?0&YEd?e(Os@@@@@,@,??????g3S?=y |%?6:@K@ߢN@9?\֘e$X?Vc5*i?)?y$ @hP@K@Ƨ@=p@Ƨ@2c@Ƨ@e@Ƨ@`@Ƨ@ȱA@Ƨ@#@Hvȴ9@HhY@Hvȴ9@H2@Hvȴ9@H+@Hvȴ9@HR/@Hvȴ9@H&@Hvȴ9@H/6vY@5 xF@5@.kƧ@.lKk=@-ӶE@-ԫ_K@+Z@+)_@+Z@+ I?@+Z@+ l?|b@?yi?&!R?V{?T^?6m*w?}x?sn?y Y?"F@@*@(@,@7@9??????!_$E4?)Cʽ!?-@P@H1?I/#J:?3L$R?B ?s"q@gf`@L@wr @wr @wr @w@wr @wIu(@wr @w2Z@wr @wt&@wr @wf@ -V@ -hr@ -V@ ,C@ -V@ ,pt@ -V@ ,g@ -V@ ,@&R@ -V@ ,>B@/u%F -@/u%F -@)~"@)`@(SE@(S;W@'Vl"@'K]@'Vl"@'K]@'Vl"@'[?^6P? l???D?s&?4Q?bQ넛?K}?qΪ?2j)6e?μjH0?L5Sk?bM]f?P0?pDRh?N?_cd?8'@@(@,@&@:@9??????H~_??2#?ZSV@w3e@ ,.?Ap^?%M'\?}!?CqgJ@s @M@e1'@e2@e1'.@e1'.@e1'.@e1'.@e1'.@\m@\"i@\m.@\m.@\m.@\m.@\m.@7@@7XGmI@2S.Mm@1"`.Qn@1V!.I.NC,@1V!.I.NC,@1V!.I.NC,?Q.K@8#@8:@8 >+@8I@8?r@8@8i@8@8S@8?kZ@8 *@8p@8@8 @8 @ ?N@t7Kƨ@t~#@t7Kƨ.@t7Kƨ.@t7Kƨ.@t7Kƨ.@t7Kƨ.@D@Cp -@D.@D.@D.@D.@D.@1DO M@1AA [@-j~.Mm@,dZ.Qn@,V.NC,@,V.NC,@,V.NC,?`~E @8#@8:@8 >+@8I@8?bQ넛@8@8i@8@8S@8?L@8 *@8p@8@8 @8 @?O@$Z@#%@$Z@,cA @$Z@*W@$Z@8V@$Z@1@$Z.@gG+ J@gGz@gG+ J@gDt@gG+ J@gB}V@gG+ J@g;=K@gG+ J@g6+ @gG+ J.@35S&@33&@1hr @1D@1U`A7L@1LPH@0‚@5@0 k@0‚@5@0ߤ?@0‚@5.NC,?|O?+!?13??Fq/b?V0o~8@8?| ?HG^?Q\?< /0?V@8? SF?i--?ArUA?q$_54?@C@8 @@@@@*?????P@$/@p@$/@n.@$/@TɅ@$/@7@$/@d@$/@~쿱[@G@Z@G@@G@)y@G@)y@G@Y@G@#@8L]ce@8N;5X@2+S@2*fB@1ttj@1sA [@0c@0c@0c@0dzG@0c@0c?WZM?]Û?K?vHn&?\2?9?E?`?|ɽ?1? ^??P=?xJ?Q?],m=?E 8?fW?*Z}@@@@@@"??????`0[Mg'?1? 6?T% R@@ D@l;?B'#?ker2O?WxZx?4cBM6@p&U@Q@_lD@_S@_lD.@_lD.@_lD.@_lD@_@_lD@_"@Gz@DZ@Gz.@Gz.@Gz.@Gz@B I^@Gz@Gz@6L]ce@6JqiC@2Lhr.Mm@1j~.Qn@1 [W?.NC,@1 [W?@1Q@1 [W?@1 -?2}@8#@8:@8 >+?J+?I ?F/P5@8@8i@8?q?p?],V@8 *@8p@8??oڕ??oڕ@?????R@p -@E@p -@,@p -@m\@p -@IQ@p -@+k@p -@@RnP@R@RnP@S@N@RnP@Sa@@RnP@RW@RnP@S@RnP@R䎊s@3ks@3k'/@/p`A7@/p{@/*n@/*0U2a|@.`d@.9XbN@.`d@.zG@.`d@.O L?| ?{j?1f_?ݶ\r?ǹT?2I/?| ?0n=Q?O(?@[*^?Cq$u?+d?u@>N ???+ ??ˢL1@@@@@*@,??????&K@? 7? %̓)1?0V@@S^B?+.?2*?d+8?q@ffX$@S@HT.@HT.@HT.@HT.@HT.@HT.@Q.@Q.@Q.@Q.@Q.@Q.@9v.>@5S.Mm@5.".Qn@4:S.NC,@4:S.NC,@4:S.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 T@;dZ.@;dZ@74@;dZ@KƧ@;dZ@@;dZ@ @;dZ@5Xy@.G{.@.G{@.u@.G{@.xF@.G{@.1@.G{@.:)@.G{@.7KƩ@9۹~($.>@3hr@3IQ@3)"`@3:)z@2,<@2'R@2,<@2kP@2,<@2 ԕ+@8J?-56+?b?Ԧ?n?rA:/?8@8J?,g?{2??pFs? CQͫ@8p?j?Mf ?ə?2:U?R|@@@@*@*?????U@/-@/@/-@0`P@/-@09@/-@0%@/-@./Ia@/-@/(@Yt@YT@Yt@YQ@Yt@Y @Yt@Y<@Yt@YaG@Yt@Yx@44m8@4zhJ[@0+ @0/4]@0~"@0P%@/a-w1@/b#-=@/a-w1@/a8*:@/a-w1@/ar?|B &?٣AV?+m7?:Xv?/[ap?`?|b@? ?1!?1?F7*?K~P?q9?  ?m6`?F ?ǤE+?gӼc@@"@$@ @0@,?????? ? i?0)?$-@%@/}@Y+?Pfqwa?D>f?4QF"?+O%X@f@V@E@m@E@}Vm@E@Q@E@sQ@E@4@E@sh@`A7L@a$/@`A7L@^ߤ?@`A7L@`4m@`A7L@aTɅ@`A7L@^ߤ?@`A7L@_$tT@7@8Q@1+ I@1RT`d@0j~@0JL@0!.I@0Fs@0!.I@0,@0!.I@0)^??1?TS?菁k?% -?ʟ?2}?jכc?8G?>G]!?,Y?A_f;j? -e?G - ?],m=? ??ˢL1?Rv@@@@@&@*??????1(6M0?FAv -?)9v?@f @_nn?YkF?PhP6?hKld?9L@pŗ~@W@["`@XUGS"@["`@UPm@["`@S@["`@Q>Z[@["`@Kl@["`@CN-@7K@|h@7K@0@7K@”(@7K@sJ'@7K@ 5@7K@@6@6+1@2+ @2y@1ȓtj@1@@1+xF^@1%@1+xF^@1$20Ջ@1+xF^@1$?ɏ?Q%?~dO?fb -Y,?~7ix?OdB!?nO?{C%O]"?F?(,!B@@(@(@,@6@4??????_wt?WWZ?5fD?Bj@OV?H@>?T38?cm?{ ?Au8H@j.@X@5?|.@5?|@%K@5?|@'@5?|@$J@5?|@"k6@5?|@ {(%@ěT.@ěT@!$OC@ěT@gL@ěT@:@ěT@E@ěT@ub%@8T xF.>@4+@4|h@27Kƨ@2o$ޑ@20U2a|@2SK@20U2a|@2"û@20U2a|@2|>:@8J?'g?itv?K! ?d{B? -w@8J?ATa?6?u8?䝟?Z`L@8p?z\?z?KwK?7?%d](@&@"@"@;@:?????Y@׍O;@Ƨ@׍O;@@׍O;@bM@׍O;@_@׍O;@"@׍O;@}I@mV@kƧ@mV@l/{K@mV@eoiD@mV@nH@mV@lC@mV@l@7]ce@7zG@4i+ @4mK]@3`A@3ѷ@2:S@20 )@2:S@2s@2:S@2]cf??;=?T?MDe?/? -æ?DIx?F/P5? -?(?{k4?| ?%3?6@(/?tgi???G'?ϯs &@@@@@*@,??????q?5VYX?ahW$DG?1`)~@ Ù@lDj?|~Yjl?R<5d?%;$U?g%C@iE@Z@^5?|@^vȴ9@^5?|@_@^5?|@_;dZ@^5?|@^5?|@^5?|@]/@^5?|@]+j@b I^5@b @b I^5@b I^5@b I^5@b @b I^5@b /{J@b I^5@b t@b I^5@b Ƨ@3~($@3~($@/kƧ@/k6z@-n@- @-V@-U?@-V@-Uᰉ@-V@-U=L?|O?ߋb?$C@?!#?% -?ˡK?| ?NۈV(?A)N?]bn~?%y*=?X78?oܧ?.+?y8$?E 8?j?l&@@@@@"@??????+h/+̾`?\>2S[j@^Z@b V?{)n?㗳V?v@l9@\@7@7p @7@7PH@7@7oi@7@7}V@7@7҈p@7@7䎉@u@!@u@,1&@u@C\@u@O;@u@z@u@@7O M@7ᰉ@2@2A [@1ȓtj@1;dZ@1QiB@1M%@1QiB@1O @1QiB@1P6?E?d̺?mGSq?:L -?#0Y?}>h'?)T?g3?FRX?YE?Otx?OW?/?%d?@@@@@,@,??????=|idFk?Pw ~?6m,H@7@e ?fIy?W?zr?{D@m7B@]@+@8I@8?cnŀ@8@8i@8@8S@8?+;=H@8 *@8p@8@8 @8 @?^@tj@E@tj.@tj.@tj.@tj@nP@tj@GE8@kR@kR@kR.@kR.@kR.@kR@k]c@kR@k]c@1|Q@1҈p:@-7Kƨ.Mm@,x.Qn@,PH.NC,@,PH@,*0U4@,PH@,tj~?_\p)@8#@8:@8 >+? f?wZL?bM_x@8@8i@8? xV?SV.?4e@8 *@8p@8??O|q@@@???_@`A@E@`A@v8@`A@opKQ@`A@f -L0@`A@i@`A@;@Vvȴ9X@VeUV@Vvȴ9X@V֔G@Vvȴ9X@V3m@Vvȴ9X@Vk8`@Vvȴ9X@V?@Vvȴ9X@V@8N쿱[X@8Sz@3S@3ĺ`[@2j~#@2ކ@2&@2ZҦH@2&@2FA$@2&@2?پ?>,*?O_?# -t?v77|?nVV*V?)jgï?-C]?\?忚"~?Ϝ? ve\? -kș?vV?wEH? n??Vy/s?앸4@@,@,@,@5@6???????!K P?ye@?ye?t@싕8^@V/?,A?C%! -@B?8`j@mB@`@ @  I@ .@ .@ .@ .@ .@01@/-V@01.@01.@01.@01.@01.@8w@@8*ڹY@4?|h.Mm@3`A7.Qn@3Q.NC,@3Q.NC,@3Q.NC,?sW]@8#@8:@8 >+@8I@8?wp@8@8i@8@8S@8?עH'@8 *@8p@8@8 @8 @?a@nn@n@nn@nz<@nn@n@nn@nY@nn@nK7@nn@nțQ@\(@˅R@\(@'@\(@@\(@#@\(@.^@\(@?@7!.@7@3hr@3LK@27Kƨ@2@2(@2/߬o[@2(@20 @2(@2/pe?E[i)?*e?R?o?caК!?Ό#?~?J?]Y?Nw -?wਙ?n_?H2C?MŅ?4S?o;!R?RI=?h9?Y@@ @"@&@9@7???????G>h7c?LV"t"@lffff@l@/@`A7@/@壯@/@Z@/@夨T@/@夨T@/@夁oi@5?@5@6@2+ I@2fffff@0tj@0MjP@0,<@0s@0,<@00U2a|@0,<@0qiC?|O?W[?U#?U*?\7?i{?}ѿ,?4i?ԕ]I? Y?&Tm?h,à?m~Z_s,?G - ? -`z@l!@Ӭ?8WE? ߲N?Y-?GK@gY@c@ffff@ffff@ffff@4m@ffff@@ffff@㢜w@ffff@ߺڵ@ffff@ @;;dZ@;;dZ@;;dZ@;@;;dZ@;kf@;;dZ@;e,@;;dZ@;>@;;dZ@; 9Sg@2w@@2w@0S@0HtP<@0q7Kƨ@0v@/-w1@/&IR@/-w1@/SM@/-w1@/l\?^6P?waG?ַHk?ھ۲?)C3?gɜY^?bMn?%l Z?R*?j?[w}^ʨ?#qA?bw' g?&&Y? ѿ?O.ce?7kPz?ǭ7@@@,@@9@7??????L`?Ldk?3c0?2@W}t@;?B@?Ai3?6 3z?w/ c#@s5pP @d@Ӫ~#@ӚG@Ӫ~#@ө@Ӫ~#@ӧ)_@Ӫ~#@ӭ5Xz@Ӫ~#@Ө˒9@Ӫ~#@ө^5?@KƧ@Hr @KƧ@KƧ@KƧ@O'/@KƧ@P)^@KƧ@O@KƧ@Ow@8*0U2b@8wkP@3xr Ĝ@3z͞&@27Kƨ@2Xy=@2J0U2a|@2L@2J0U2a|@2L(\@2J0U2a|@2K:)z?E@?@K/z?1V?Q??WA:@8 ?{%@@@@@&?????f@2-V@ޯ9@2-V@4~f7@2-V@09 r@2-V@5 S,@2-V@1*4@2-V@0`A@~"@ A7K@~"@xO@~"@sB@~"@uMU@~"@yQ@~"@|Ã@8v@8 D@2^5?|@2A@1"`@1t{@0D*@0$31@0D*@0챔@0D*@0̐d?%2)`?>k^?Uw&?Ӳg?(e ??ay?r4?m:x'?7?qHO?,^ڰ?9?C s?? |~?[yמG?X ya?AShU@@$@,@(@9@4??????bNaI?`X?`G?XY@2T[@x!*?o!=?gT`Es?[ť h?'Y_@s #@g@@n@@$/@@&H@@@4@@IQ@@ح@@3333@@@@ҽ<6@@ҽ<6@@iC@@GE8@7֚,<@71'@2 ě@1Vϫ@0-@0@0iB@0 @0iB@0W@0iB@0䎋?sD?Z.? ?Ac ?u?WA?1j?,#?d0R?.? A?B8? - l\?9o?`x?Gϵ'?j?7@@@@@$@(??????Eݺ .X@?6I?1t2'V@Q)@ҙ< ?T6k?2\b?tvd?l<#9@jl@h@Ƨ@nP@Ƨ@n˧@Ƨ@!7@Ƨ@uk'@Ƨ@ffn@Ƨ@v$@M@sY@M@@M@@M@=A@M@@M@7@7Y*0U2b@7[X"@4 I^5@4F M@3-V@3c@2ޫ6z@2͔JG@2ޫ6z@2K@2ޫ6z@2?R ?/s?3sL?1_?8U`?h6z@7v@3^5?}@3P{@2y7KƧ@2z^5?|@1kxF^@1mhr!@1kxF^@1l1&y@1kxF^@1k$?UbB2?E?Q/ -?wX/8?R%(0?f]G? ?-NI`(?}7.??az?C ;?pr?r?m~Z_s,?< N ? Ğ?Y͍_@@@@@$@ ??????`RYEY0?8 ?#~> @$N@ana?S9.?FΚ,?#j(?{H@l;@k@l@C@l@x@l@1&@l@ -L0@l@W@l@6>@ߝ-V@ߛS@ߝ-V@ߨXy=@ߝ-V@߻s@ߝ-V@ߥv@ߝ-V@ߚ}@ߝ-V@ߊ=p@6=:@6;}@33S@3,~($ x@2j~@2n@2._o @2%S@2._o @2@2._o @2$\n?|h2:? #?/Çf?btx=?t8 ?M?nO6z@3p@3{W>6z@3p@3vl!.?qv?+Rja??&1}?']?cJlD?qv?S̵E??B3?Y:J?,4r9?fg?l&?y\?*+JҚF?Oh6c?P$`*@@@@@,@,???????QMOL?w&׀?Bl5?T>G@M,@+L?^H?r2l?ަ 9?_tc@l}rQ@o@NaG{.@NaG{@Nn.@NaG{@N@NaG{@NM@NaG{@Nzxl"@NaG{@Nn2@$/.@$/@Ϫ͠@$/@g@$/@:~@$/@Fs@$/@Z@904m8.>@2 -^5?|@1D@1j~@1}K]@0xF^@0ߤ?@0xF^@0j~#@0xF^@0H@8J?J f?bY?':?0O?KAb@8J?ֶ?J&?"JÁ!?^候?p@8p?`P?a -?{Cx?%d?<@@@@,@&?????p@g-@hr @g-@hu%@g-@i*0U2@g-@i@g-@fFt@g-@g)`@n @n V@n @n jO@n @n \@n @n @n @n 5Xy@n @n !-@0@0@+WKƧ@+WXe+@+XF@+X4֡c@)@)!.I@)@)@)@)!.J?^XN?[e@3sS@3i^@2-V@2T`d@25sh@20- r@25sh@2+s@25sh@2& '@8J?-56+?IK?؆{~?Y?O@8J?{UO?a>&?%hF?s?Vϊ޲@8p?<2?Śn?w%?Q?]?@@@@$@(?????r@Hfffff@Hk I@Hfffff@Hf,=@Hfffff@Hf&@Hfffff@HeoiD@Hfffff@He1@Hfffff@HeO@+ J@vȴ9Y@+ J@$/@+ J@J@+ J@@+ J@Ft@+ J@YJ@8B䎊@89b~@1&+ J@1')^@0_-V@0_v_ح@.ߤ@@.?@.ߤ@@.ݿH˓@.ߤ@@.Vl"?{B@nzH@|쿱@nzH@|PH@nzH@pě@nzH@l]cf@u2 Ĝ.@u2 Ĝ@u9Y@u2 Ĝ@u-(@u2 Ĝ@u9#x@u2 Ĝ@u-%@u2 Ĝ@uڹ@8]ce.>@6|hr@6W@5n"@5yJE@4ޫ6z@4zG@4ޫ6z@4a@O@4ޫ6z@4D@8J??B?|?C˗?x7V?c+:X@8J?vj?R?:$I0?&?him?/+@8p?e]Z?CRv?>0Om??Ϭ -f@@@@*@(?????u@%@%@%@r -@%@@%@n`V@@%@<&@%@%@zG@@zG@E_@zG@@zG@F\@zG@ -]@zG@N@4䎊@4-V@0hr@0Q@/|1&@/}@.L/{K@.@.L/{K@.X @.L/{K@.1"?~?J?>ȍg?F<4?Ki8?h?ʋ?|B &?'7x?]Լ?Ay>$;?Us?  &?]ivL?sg?9XNu?:1?W|DC@@@@@,@(??????YF~?=:?Kbh ?YW8`G@i^Y@Qh?ckwH?t(Oml?7֖?09@mЬ@x@>"@6+ @>".@>".@>".@>".@>".@gS@g&-1@gS.@gS.@gS.@gS.@gS.@7쿱[X@7 c@40ěT.Mm@3.".Qn@3!:S.NC,@3!:S.NC,@3!:S.NC,??2ZF@8#@8:@8 >+@8I@8?o"@8@8i@8@8S@8?[@8 *@8p@8@8 @8 @?y@E@@E@j~@E@G@E@Fs@E@-V@E@a|@8$/@8$/@8$/@8d@8$/@8$.@8$/@8 k@8$/@8e@8$/@8af@.5Xy>@.5Xy>@- -~"@- -g @,n@,d8@,ۋq @,y -@,ۋq @,B@,ۋq @,B?^Dw'?&H?m?!#?@ A2??bM_x? -?d0R?7枪y7?Hc?Z,a?bMky?u?2Y0?XLL?B ?B @@@@@$@&??????L<?*f?6 2#4?.4q@N@8a.?EC E??=$(to?([?r:#@s p@z@ȴ9X@v@ȴ9X@@ȴ9X@kP@ȴ9X@XbO@ȴ9X@%F -@ȴ9X@Fs@w@bM@w@ -<@w@E@w@@w@+@w@1@7 xF@7l"h @25?|@2N;6@2-V@2*1@1O M@1Z2@1O M@1|Q@1O M@1 qv?}?J?-.?b?? -`?X?h??M7F?_M?j9@@@@@&@,??????U^Ȩ`,z?7KR+??4xW@jA@?VgfT1?R\ o2?`@?]@l()}@{@->vȴ9.@->vȴ9@- 4m@->vȴ9@-S|@->vȴ9@-*d7@->vȴ9@- ؝@->vȴ9@-@$/.@$/@/{J@$/@)@$/@TɅ@$/@U[u@$/@yz-7@9}:.>@5&+ J@5 @4"-V@4N;6@4iB@3D+@4iB@3MMvl@4iB@38@8J?>I}?Bg2?X?Ks(I,?O֔@8J?Qpj?O?9.?guoĺ?w@8p?Fۯ/??e??[-D?.}y@@@@.@.?????|@V@@V@jO@V@p:~@V@:@V@/7@V@̋R2@-V@in@-V@{@-V@O M@-V@-@-V@p@-V@t9@6S&@6ez~@2 hr@2}Vl@1"`@1PH@0iB@0ѷX@0iB@0vA@0iB@0bo(?ڡG7@.@.~˾@.@.+|?^P!?Û* ?.t`>?=@v?1\? #?bQ넛? ?-ƴ?0r>?9%~E?]VK?bF?"?ڸcX?{ 4?Ez?GS@@"@,@"@6@4??????Ol U?*WVTb@?@A-@Z?9R0/?(-??oo(?IOw@su@~@@zI@@\(@@α2@@(@@cA @@C,@1'@/@1'@q @1'@S@1'@S@1'@<64@1'@]cf@3D@3$/@0+@0y @0t@0sg@/C\@/(@/C\@/(@/C\@/?|O?W[?&P?W0 ?u?̉ߚ?| ?jכc?&?ihA?Jj?低NV?q6tN6?%_D?@A?E 8?SP?ˢL1@@@@@(@,??????>U$m ?&Y?%7@2@$?FB, $?FT<|?J?gc)@f|@@S@󣕁%@S@?@S@/@S@󤛥T@S@@5@S@&I@x@x@x@xW@x@x@x@xGE8@x@xS&@x@xp:@+-5Xy>@+-5Xy>@')7KƧ@')Dg8}@&cnP@&cnP@&z)_@&z)_@&z)_@&z)_@&z)_@&z)_?`&x?GA?!!׋7?MA8?DE?:?bM_x? -?~Q?6)}?@?Kc?bMky?8 T?oܧ?avh?#.?#.@@@@@,@*??????REs%?_Ű?5Jx?u4=@,L @xw?DӹȘ(?#?B?PZz??UfT@s >Q@@|h@7K@|h@?|@|h@2@|h@@|h@1&@|h@BZc@{dZ@$/@{dZ@y>B\@{dZ@xbM@{dZ@u=@{dZ@|@{dZ@|N<@8֚,<@8@5B ě@5?@5K"`B@5G@@4ce@49@4ce@4x@4ce@4!.?_pP1?EX?^RpX?;Ϝ(?VK?pt#??Zm?=0́?ꉬ>V?mۀ|?<1*T!?poIo ? E?,?M,L6?ڧ? ?D d}@@@@@$@(??????zl.?,f ?S?ewBNC@DP@zb?ojr?zx+C??w:5@mZ`P@@qWO;@q]-V@qWO;@qOA@qWO;@q\(@qWO;@q>BZd@qWO;@q@ k@qWO;@qA [@K\(@K^@K\(@Kn@K\(@Kš@K\(@K1@K\(@Kҽ<6@K\(@KbM@7p4m8@7_?D@3^5?|@3!.H@26-@2/[W>@20U2a|@2z#9@20U2a|@2zS&@20U2a|@2'RT?eM?V[u9?O%c?Ģ??1?5}'?yjP?>ÔR?rVx?1?z\~?"ߨ>?Pn -E?GA?W?u4? -.ԕ?8$ -"@@@@@,@*??????{?rsÀ?XF=b۩?Y;G@qMK@K?z!k?z01?B@3S@3wK]d@3 -V@2tj@2\Q@2KC,{@2\Q@2U '0@2\Q@22<64@8J?V_^?􈤪q?㬱_?{ )?lRA@8J?kM..?P{j?^18??.?Эn~@3I^5?@3J#9@27KƧ@2zxl"h@2kxF^@2jd7@2kxF^@2hۋq @2kxF^@2iy @8J? #?괰E? R?ťBW?! @8J?ԍD?$z¾6?kH?^?&ŏ@8p?zx?(?< N ?8wx?T8@@@@(@,?????@[S@ꯝ-@[S@M(@[S@[S@[S@Y>B[@[S@WkP@[S@UY}@+@;dZ@+@ C]@+@?|@+@+j@+@>6z@+@@8*0U2b@6L/{J@4=hr @46Fs@3Ͳ-V@3Ȍ@2̿[W?@2K]c@2̿[W?@2W>6z@2̿[W?@2ǍO;d?]?hĔV?~"?Uv??S{d?]?^1??f?ֳ?l{9َ?SS?*wjs?Q6?bk?Oc?t?x?@@@@&@&??????^L?mΘ?WCC3?p{fP@W7@@O?f(Kwb?|(gOK?'h8/V?W;C@t -@@>"@ƀ@>"@LI^5@>"@VE@>"@H˒:@>"@WsO@>"@P)^@jC@j@jC@j|@jC@j7Kƨ@jC@jߤ@@jC@jd7@jC@jv@8^H@7 I^@4#@4!7Kƨ@3@`A7@3>BZc @2:S@2R<6@2:S@2ךkP@2:S@2KƧ?LV?LS{?$f%?W?/cե?q;?ni?њH?cGo?? +A?/[?jҍ?)2?8A?~/O?Qx?8?\'jK@@@@@,@,???????s2XfN?pj,z;?p @Ml@jd?|\C?{&]-)?R_uW??ٳoY7@pX@@1&y@ C@1&y@y @1&y@?@1&y@&H@1&y@ce@1&y@"@ EQ@ G+ J@ EQ@ S@ EQ@ XPH@ EQ@ H9X@ EQ@ i7KƩ@ EQ@ Xtj@6@@6m\@4+S@4<64@46-@4%@3B@5@3$e@3B@5@3'@3B@5@3>B? ?p~v]?K9yA?tN+6?ME(l?$ kH2?qn?p?<7T?:?R$(?͂c5?m!?A ?Q?z?J'Y=?%5@@@@@*@??????/ߩ?ex?c񋓏?`z2^@}@ Kc`?0g?z/u?ګ?NA@@cMڿ@@@bM@@lD@@~@@qu"@@Y}@@PH@TzG@Tj~@TzG@TzG@TzG@QN;6@TzG@P )@TzG@Q @TzG@R@2@2$tS@/KƧ@/Ar@-@-[W>@-C\@-TɅ@-C\@-@-C\@-e?^Dw'?q' -B?QX?^:f?yq?uk=?bV,?0n=Q?^ o?wxR?c? C`?j|?Ф?!A -m?__C?L8AS? ;8@@@@@*@(??????W?HL?XG=].?)6.t@=@QP?ggl?8E'?ǫO2?fXh}@r+@@@lD.@lD.@lD.@lD.@lD.@lD.@Y.@Y.@Y.@Y.@Y.@Y.@5~($.>@2S.Mm@1`A7.Qn@1c.NC,@1c.NC,@1c.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @G/@G㕁%@G/@G&@G/@G@G/@Gs@G/@Gr @G/@G@nlD@n;dZ@nlD@o @nlD@o3@nlD@o1@nlD@oh ԕ@nlD@ov_خ@6]ce@6#@2G+@2X$ xF@1`A@1,@0𖻘@0D@0𖻘@0Q@0𖻘@0s?Y͍_?^?ApJ?998?[5?lg|*?Y͍_?Hz??U(\;?SN?ۨ[u@ [?^@f _Ϯ@@W~#@VkS@W~#@W@W~#@Ws @W~#@WXX@W~#@W@W~#@WT&@ffff@XbN@ffff@D_@ffff@9,z@ffff@ݖ@ffff@/S@ffff@迫L@7hYJ@7"Zc@3|hr@3;~&@2V@2 /2l@2OO M@2QJ7@2OO M@2RIIq7@2OO M@2RR$?Pac?m?9{Scr?.HF,?L@Q?Vu.Qn@36z.NC,@36z.NC,@36z.NC,?E@@8#@8:@8 >+@8I@8?qn@8@8i@8@8S@8?64 Ú@8 *@8p@8@8 @8 @?@\)@以S@\)@jO@\)@䮗O@\)@g@\)@oiDh@\)@g@<+R@<B[@2䎊r@2|VϪ@2䎊r@2|?@2䎊r@2}v?F^S?'\?^@?,rg?PY8~Qj??UbB2?Q??.}$?h_N%?F5?&ljm? DS?bk?O"?;kr>?̶j@@@@@*@,??????XI[Po m?Q߂Xc?QJg(@S~|@<.t?g(?m&tr/?ɹ_9?Я3ax@n(;@@/-@.O@/-@4m9@/-@1hr!@/-@4j~@/-@0@/-@/A@j~#@j~"@j~#@kxF@j~#@gݗ@j~#@fffff@j~#@o\(@j~#@mM:@7O M@7,zxl@3+@3ʙ0 @3"-V@3"m\@2:,<@2?;dZ@2:,<@2<@2:,<@2BZc@0j~@0J@.@.[@.@.\N<@.@.Vl"?3a=?`?WD@?%?6r In?i{?A[F? aҹz?DP?߇O?Hc?90??G'?/?ʳ_?hB^?Rv?L@@@@@@??????H -*c^?.n?m&@@)g@?JCW?;/\?xSڅ?tɴeJ@l^@@ -cS@ -^"@ -cS@ -h6@ -cS@ -g@ -cS@ -hy@ -cS@ -d \@ -cS@ -b'C@w@wO;d@w@w~ @w@w|x@w@wV@w@whVa@w@wϩ@8䎊@8 -;[k#@3S@32d@2A7K@2=F@15sh@13oK@15sh@14ݶ@15sh@13"3?g^XF?,?5\n?I?C?#pj?|N2?Äp?N?K?9^?5Y?4;QQ|?‘?gvfb?l?Â6$?dZV?Ub@@&@&@@5@8?????? Cנ?O;V?Y8@k??o@ -eJ`@w:8?r5g?\IN?o?\@m ?{@@bM.@bM.@bM.@bM.@bM.@bM.@I^5.@I^5.@I^5.@I^5.@I^5.@I^5.@9*0U2b.>@45?|.Mm@4A7K.Qn@3䎊r.NC,@3䎊r.NC,@3䎊r.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @(.@(@ l@(@@(@`d@(@&H@(@v@Y+.@Y+@Mw1@Y+@?$tT@Y+@3g @Y+@:^5?}@Y+@E84@8@.>@4+ I@4hr @3V@3ȴ9X@3O M@35%F -L@3O M@3,@3O M@373@8J?Y?26?#- ? 'UA? -æ?S:Ъ;5@8J?I9Bf?J?z?Ri?ø4@8p?:K?o}?:?M\{W? 4e -@@@@*@(?????@+@1@+@@+@~|pA@+@@+@d8@+@X@Vqm@Vmhr@Vqm@Tg ]@Vqm@T-DŽ~@Vqm@T8ڶs@Vqm@SIQ@Vqm@TGy @4䎊@4 z@2^5?}@1 @2 "`B@1coB'@10U2a|@0s"@10U2a|@0K]d@10U2a|@0ސ$u?| V?Qth?h7JZ?Քd?'z_6?1[ݶ?}@TΥ?[?˸R{?7f9P?$m>?˕V u?/y?"vJC?j ?@lI^5@lw1@lI^5@lC@8H@8s@3f+ J@3f1.@2q7Kƨ@2rnO@1[W?@1_@1[W?@1IQ@1[W?@1M:?2}?Z.?Gk;m?{@?o?P?&Gf?% ?5CQ?C\?# Y?pPȻ?`@0k?.ge%? h?W ?3{Z?՘]G?7zE@@@@@,@*??????^ *4fLd?*8q<^?Uj򳙥 @K\p@lu?L7 [B?sU)?{R[ ?WrNܨ@k!ŋ@@l1&@hr @l1&@qN;7@l1&@i^5?@l1&@w@l1&@w$tS@l1&@sMj@hI^5?@hH1&z@hI^5?@hO;dZ@hI^5?@hQ2X@hI^5?@hQX@hI^5?@hNH@hI^5?@hGE84@7`D@7aTɅp@2+@2M:@1ߝ-V@1lC@1(@1 ѷX@1(@1T@1(@1O M?)T?V_^? ?rՉ?R?4<7?% ?H*?I?_?[0o_?SmImb?ua?,1'?J?+QK?q+U?0&YEd?0a@@@@@*@&???????hrYRl?/t?TTI7/?WHT/S@q!@hJ+?u,?w_$?Zd.a?ar@iҕV@@|h@}-V@|h@3@|h@^5?~@|h@@|h@G l@|h@(Xy=@@O@@ǻ6@@ƞiB@@K҉@@ȸ4֡@@ǔJ@8`D@8G1@3S@3y@3-@3@*0U@2s&@2'@@2s&@3VFs@2s&@2>5?|?TU?[T?Cq;l?dc?mעA?M9?}ѿ,?%@&?!-F?S^ٙ?,ي?Æ"Y?bO?3XMV?Q?$–?i ?uqR7@@@@@*@*???????u???8PN?É"}@mc@,I?^b?@+ P@(>@b1f@@TF@i7Kƨ@TF@J*NWY@TF@NN-@TF@:@TF@F|W/@TF@Ję@O;@"_@O;@#Uf@O;@.<3@O;@4S2@O;@%q!r @O;@K@9>6z@9w^er@4 ě@4)@3~Vu@3}#+@3+xF^@3,@3+xF^@3+r[ @3+xF^@31T?(?/?=?ݵ?&a?e?C2?>99?cg >'?c>Ep|?E?sa36?"uM?\B ??#hM9jh?8O$k?&'x -@#$@!?X/Ղo?Fj9M?&Rz?oZG@g1m -@@ -=@ -=@ -=@lN@ -=@`|@ -=@?U+U@ -=@>{@ -=@?l@@0@@Wյ@@!j%@@ ʿWr@@W@@^%@6 ]ce@6 9@2 ě@1`7~@1"`@1Tͬ@0Q@0 ;@0Q@026@0Q@0EVA^?}@TΥ?mS?o:6@?HO?[`?HAu?gQ?OS6?)U?}?&?}F[n?pM>?M? y7?v$/?N%ɖ?8@@ @(@*@:@7??????Phg,?Nbʐ"?~@*@i@!|?m9{?2?Tb?iUi@e6:@@-A7L.@-A7L@-ᰋ@-A7L@-a@@-A7L@-$/@-A7L@-<[@-A7L@-ƨ@+ J.@+ J@6z@+ J@!.@+ J@$/@+ J@#@+ J@r@9_o.>@4n5?|@4u=L@3tj~@3Q`@3~($ @3!-x@3~($ @3=M@3~($ @3Z@8J??8?Lqa/?ܳA?O5cO:?!Qi@8J? ?f?p?%9\?+><@8p?cK4+?kn}?k2m?J?dWj>@@@@1@0?????@DO@D\(@DO@Ez:^@DO@E@DO@E0?@DO@EAnp@DO@E9-@r`B@rIE@r`B@r$@r`B@r\A@r`B@s2p@r`B@s:_@r`B@r@7H@7 - B@25?|@2is@2Htj@22 -6#@2ush@2I21PH@2ush@2JuQQ-@2ush@2KG5{?yjP?uA b?_ps?L5?Ρ?Tň?E[i)?glF?.?y7?E.X?4(??PgDm?IE?~O0?d /?{cm@@$@&@*@7@8???????ac?8E# ?g?s"RP,@Ea6@r9QB??P-??ʢ@iL@dv@@@>]/@>W -=p@>]/@>V_@>]/@>m&/@>]/@>Z9,6@>]/@>Y:Y@>]/@>Ysf9T@?vȴ@?` |@?vȴ@?:@?vȴ@?JL@?vȴ@?ç2@?vȴ@?#@?vȴ@?G@8,<@89 @2hr@2ψ@2`A@2 ѹ{@1=ce@1;(5n@1=ce@195 @1=ce@1;F2T{?$?aUY?,?F|?mt?"&?]? E?, eRZ? $YF@? >?Sd<9?{?J?87?@n= n@@-V@@-V.@-V.@-V.@-V@zI@-V@9X@Ƨ@I^5@Ƨ.@Ƨ.@Ƨ.@Ƨ@n@Ƨ@n@7B䎊@7>u@3W+ I.Mm@2t.Qn@1𖻘.NC,@1𖻘@1@1𖻘@1%2?}@8#@8:@8 >+? f? d?| @8@8i@8??m/w@*dZ@*G{@+L@+KQ@+L@+K]cB@+L@+KC&?`&x?GA?(?G?Dj?wZL?bV,? ,C?['?@[*^?Cq$u?X78?b@?.+?s@a?8 ^??b/bJ@@@@@,@(??????:Y?9P1`?E;?#&@nyz@A_b? aI?2\ ?;12Nh?^sL@sd#@@ ;dZ@ Z@ ;dZ@ cA @ ;dZ@ .3@ ;dZ@ Ϸ@ ;dZ@ V@ ;dZ@ L`@UO;@US@UO;@UlD@UO;@Us@UO;@U"@UO;@UF]@UO;@UJ#9@904m8@9.zG@3|hr@3@4n@3"-V@3$*1@28D*@2:ݘ@28D*@2:d7@28D*@29lD?pEIt?ru?l5B?{y6?Xe?4 ?ʻd9\?z?A -;?:u. ?>H>d#?1鄽 ?ȉvw?*T?d --?J]j?)@Jq?^^ω@@@@@,@&??????cJe?OB{#?TPZ ?T6i;@ ΚFv@Uko?d+O#?dBN?NO?K _@rC@@.9XbN.@.9XbN@-"wk@.9XbN@,G{@.9XbN@,֡a@.9XbN@-NH@.9XbN.@Õ%.@Õ%@!-@Õ%@v_خ@Õ%@c@Õ%@%zxl"@Õ%.@9p4m8.>@4ԛS@4\64@44tj@3ߤ@@3ush@3F@3ush@2l"h @3ush.NC,@8J?'l? - ?dҦ 3?֜r@8@8J?lu?!zs?.Oc?֝Sx@8@8p?S1z??|Rs?K/JRZ?‘x4?W Z?6s?~'T#?}ߊ9?N•w"?"Q$@@@@@,@(???????F7޺?1?z}@tf{@=ދ?r웧 ?5A@/"G{?!K?$@^>>FT@@q&x.@q&x.@q&x.@q&x.@q&x.@q&x.@ lD.@ lD.@ lD.@ lD.@ lD.@ lD.@75S&.>@3 ě.Mm@2-V.Qn@1&.NC,@1&.NC,@1&.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @-V@S@-V@u%@-V@rGE9@-V@l!@-V@t@-V@$ x@0%`A7@0 A7L@0%`A7@0' l@0%`A7@0*#9@0%`A7@07r@0%`A7@02@0%`A7@0 'RT`@8v@8ᰉ@4J^5?|@4?;dZ@3ߝ-V@3m\@3 -0U2a|@24m9@3 -0U2a|@2A [@3 -0U2a|@2M?+-/?DE?s3gp?˃.?Kbo?F}^?{@hr@;dZ@hr.@hr.@hr.@hr@ V@hr@ -Ow@74m8@7 @3pěT.Mm@2b-V.Qn@1p.NC,@1p@12W@1p@1rs?E@8#@8:@8 >+?DE?}>h'?2}@8@8i@8?%g?O&P? -tM@8 *@8p@8?4\ɵ?a@@@???@g 7Kƨ@g\(@g 7Kƨ@f8YK@g 7Kƨ@f0 @g 7Kƨ@frG@g 7Kƨ@f~#@g 7Kƨ@fPH@ݲ-V@@ݲ-V@4֡b@ݲ-V@E@ݲ-V@L_@ݲ-V@.@ݲ-V@U2a|@8@8u@3 I^5@3ѩl@36-@31'R@2sh@2&x@2sh@2䎊q@2sh@2,(@@@@@*@*??????Q=?(T?29]?Xd)?Qwq^@s -m_^@@҄T@҄T@҄T@҆YJ@҄T@҅+a@҄T@҅@҄T@҃e@҄T@҃S@G+ J@Fx@G+ J@H$ x@G+ J@G&@G+ J@H>BZ@G+ J@G8}@G+ J@HK]d@3v@3@0^5?|@0ԕ*@/|1&@/|]ce@.fL/{K@.fIQ@.fL/{K@.fL/{K@.fL/{K@.f1.? ?m?K]4? Iy?b\?i{?|O?La?'Q?ihA?݄ ?R g!ڢ?|׿pY?/? SF?+ ?L?L@@@@@,@*??????&>?/X0?0vd?X`@҄lw@G}?P6@N?@p<9r?NĬ?M7۵@gQ(H@@\(.@\(@bM@\(@ "h @\(.@\(@]cf@\(@Mi@'-.@'-@Z@'-@!@'-.@'-@%@'-@0 @:V,<.>@5?|h@6) @5>Vu@5wXe@4L[W?.NC,@4L[W?@2,<@4L[W?@2~($@8J?뒌P?Q@8 >+?aC1 ?A0R@8J?ڤ4H4?'@8?:V?Mp@8p? -6#6?qL@8?F?ķ@@@,@*????@tj@5?|@tj@$@tj@# @tj@&@tj@1u@tj@0@)@)-V@)@))^@)@)A@)@),zw@)@)l/{J@)@) I^@74m8@7U=@4S@4ԁoh@4ttj@4WlC@3&@3~($@3&@3H˒:@3&@3-V?3a=?P񰦍?hΌF?H} y??;K.?E?tMP?'_#? m -?h`i?%?ԫ?{f?MA8?tKc?*WF?bM_x? -?['?9~F? xV?L "?bMky?8 T?nwg?~\?h?h@@@@@,@*??????PR? ?4j ?&9 (@F@Xp?Cèz?5߂t?~-F>?cd(co@s 5g@@4bM@4zG@4bM@4$%2@4bM@4$?@4bM@4|h@4bM@4@4bM@4=K@I(@I5?|@I(@ID@I(@Iu@I(@Iv@I(@I*1@I(@IPI@7~($@7ݗ,@2 ě@2wy @17Kƨ@1U=@0𖻘@0=b@0𖻘@0zxl"h@0𖻘@0Y|?Ol? /??뤽??ZAj?8J&7? ?u(?aB#Gi?mqa?A?EBl??~?|aQ?*|\/?6?`i=@@@@@(@(???????` f?QZʜ?A#t@4j`@I{?p_s?bl?嫏7?z@gk,@@W7K@W7K@W7K@W4m@W7K@W}!.J@W7K@WzG@W7K@W{"`@W7K@WzQ@ 6E@ 6+ @ 6E@ <@ 6E@ :^5?|@ 6E@ ;Q@ 6E@ 8@ 6E@ 7$tS@5(YJ@5(*@3E?|h@3D3@3)"`@3*qiC@20U2a|@2@20U2a|@24m@20U2a|@2eO?| ?q' -B?w4f?o]އ&?<?U?avf?Λ?>5Ptv@t/NB@@?|@@?|@Dcwb@?|@1lFp@?|@em(@?|@:a2@?|@;TB@K9XbN@K6E@K9XbN@KB|@K9XbN@KY@K9XbN@K"@K9XbN@KP/9@K9XbN@K\yO@7#g l@7srh@3@3t@3t@2&'V@20U2a|@2N"j @20U2a|@2֝@20U2a|@2WPbK?|B &?A?C}?!Vk?e?|**??t?xœ?ĉ?r_? *?u?+?Lwy?F:>^?Ug?B?I'@@,@,@*@9@:???????H?o?ra`?@!jb@K[ |?R6[?== @ Q-@2+@gn~@@\(@ӶE@\(@J@\(@ڹZ@\(@ֽp @\(@r @\(@Љ'R@ ^5?@ C@ ^5?@w@ ^5?@ ^5?@ ^5?@ (@ ^5?@/@ ^5?@wkQ@7@@7Y|@4ٺ^5?}@4|Q@4A7K@4O M@4:S@4#w@4:S@4䎊r@4:S@4Ft?:-%?UW?hb?:-?S2̍? $Μ$?3a=?.+?\K\?b̤?&5 -M?Zg?nwf?&!+?XTo?wb?^_r?$3@@@@@*@$??????U#?m:o?p d$u?Iy@&c@cKM?}Qb=?pNK??TB@ltq@@p%@o|h@p%@pS@p%@pS@p%@p7K@p%@pn.@p%@p}V@$]-V@$`A7L@$]-V@$[lD@$]-V@$_vȴ@$]-V@$]E@$]-V@$^ߤ?@$]-V@$a [@7G>6z@7Doh@2E?|h@2Eu@1`A7L@1Xy=@1:,<@1;"`B@1:,<@19"`@1:,<@1:#9?{?&+B@@@@@,@,?????? ǪF~?Cڳ;?c1!ۙ@ٟ0@y ~_X?bS#?tK?ÚI?Dd@eo@@tj@vȴ@tj@s@tj@s@tj@4֡@tj@Ϫ͟@tj@Fs@nP@"`@nP@=H@nP@?Wp@{ @fvn@@WlC@WlC@WlC@W˒:@WlC@Wr @WlC@W@WlC@Ws@WlC@WL/{@rQ@rQ@rQ@rQ@rQ@rRnP@rQ@rR@rQ@rR@rQ@rR{@/ߤ@/ߤ@)MV@)M(@'1&x@'@'`d@'`d@'`d@'`d@'`d@'SMk?^Q5P?ƿ57?N?8v?tKc?*WF?bM_x? ,C?|,!?/Z?%y*=?M\ٓ@?bMky? ?oܧ?͓E'?O|q?#.@@@@@,@*??????QhU>?#6b?3{~*|?5; \@WKHw@rR:?C} V?!<(?'{?:T9Ot@sM9 @@&x@&x@&x@ ě@&x@&x@&x@ -=@&x@ M@&x@@$Ov_@$@3~($@3~$@.`A7K@.?@,&x@,@+b~@+y=d@+b~@++@+b~@+YJ?}ѿ,? ?!!׋7?˅T?6r In?$?|O?f?~Q?; $?IM?PS2K?s@a??y8$?͓E'??l&@@@@@(@*??????8 p?'3Y? e0L?i@`F@Q@$=nH?< r?97?B6 nK?@Yx<@g&Q@@@tj@@ @@A@@l=W@@-$@@\fȗ@9~#@9R@9~#@9>I@9~#@9*0U2@9~#@9?O@9~#@9 @9~#@92X@47@@47=@F@0f+ J@0fe״.@/n@/#?@.p'RU@.p칗"@.p'RU@.s˃Ú@.p'RU@.s,?}@TΥ?o -W¤?@?LdTO?GBx?ݐ{?}@TΥ?hjS?S`?0r?t]r?gT@0n5?|@0J#9@0-@0($ x@0~($ @0p{@0~($ .NC,@0~($ .NC,@8 -Ny?^S2{?U?;w~4@8I@8@82r?}y?6k?M#@8S@8@8L?"{Ę?M?*vZ@8 @8 @@@???@-V@,C@-V@..3@-V@.1@-V@-O;d@-V@+:)@-V.@Q@T@Q@@Q@IS@Q@ں@Q@w@Q.@6_o@6@2S@2ߤ@@2PA7K@2O @1@1qj@1@1ߤ@1.NC,?}ѿ,??ցC ?BI{?Uyu#?o?P@8?}ѿ,?V"y$R?K54;?QĨ?h@8?yjk?lo>?hE?(Q?A0)n@8 @@@@@,?????@Ƨ@Ƨ@Ƨ@qu@Ƨ@Vϫ@Ƨ@qu@Ƨ@Ƨ@Ƨ@C,@F@+ @F@l"h -@F@s@F@l"h -@F@@F@"@4qiC@4^5?|@0S@0xl"h -@0Htj@0G8}H@/QN;6@/OAt@/QN;6@/N!R=@/QN;6@/M\?}ѿ,? KG?5$?菁k?u?)]\?}ѿ,?@׮?T֩E?,?!U?{?e\p?q6tN6?Q?qn?˴C?v> ?Cns@@@@@$@*??????ý?E6I?+_6w?@6 -^5?|.Mm@5ߝ-V.Qn@5䎊r.NC,@5䎊r.NC,@5䎊r.NC,?qv@8#@8:@8 >+@8I@8?A[F@8@8i@8@8S@8?d3>@8 *@8p@8@8 @8 @?@.O@"`B@.O@(eO@.O@4m@.O@ @.O@qu!@.O@Ϫ@%Õ%@%@%Õ%@%$/@%Õ%@%ɺ^5?@%Õ%@%iC@%Õ%@%a@@%Õ%@%/V@8įO M@8,<@4I^5?@4ѷX@3"`@3o@3!:S@3ߤ?@3!:S@3 ҉@3!:S@3 >? Z ?-56+?ay?E>? M%?Ylp?NIɮ?u9?G?8k -7H? ?.W旓?yWRqf?&Z#?eI6?DάR?_?9)N-@@@@@,@,??????`~g?q?I"{m?p"/p@ -@%WO?cz[?>4.?D,?-o @m.@@+@XbN@+@^5?}@+@ٳ|@+@1@+@@+@73@nKƧ@nKƧ@nKƧ@nL1&@nKƧ@nKC@nKƧ@nLcA @nKƧ@nLzxl@nKƧ@nL_@2qiC@2~"@-{"`A@-{W>6z@,|1&@,|Z@+Bu%F -@+Bh ԕ@+Bu%F -@+Bѷ@+Bu%F -@+B`A?_\p)?GA?"?XQ>?Ot?ˡK?bM_x? ,C?~Q?9~F?%y*=?M\ٓ@?cPQ?.+?y8$?ʬv??f_Ԋ@@@@@*@(??????MMv?I}?9nQv? z2q@A2C@nLS?Hs8?B[MkN?P:L?)̬w r@s p@@ɺ^5?@[S@ɺ^5?@ŋq @ɺ^5?@A@ɺ^5?@ň1&y@ɺ^5?@-@ɺ^5?@ѷ@ I^@o@ I^@XPH@ I^@fx@ I^@;5X@ I^@Z^5?}@ I^@nMj@9,<@9K]c@4E?|h@4#@3ȓtj@3)^@20U2a|@2*1@20U2a|@2$t@20U2a|@2s?]?97?$כ?U oi?oD?sY(?]?.?P#Gӗ?!=?^o i?y~3Y?*wjs? F2?1?⇦?X^2If=?Eݑz6??@@@@,@*???????`1`?>@?ʹ\?}_@щ)@\W?eа?*nR?;XA?dD%@uF{@@~vȴ9@$/@~vȴ9@PbM@~vȴ9@y|@~vȴ9@tS@~vȴ9@r Ĝ@~vȴ9@mhr@E@7Kƨ@E@ >B@E@\(@E@}V@E@A@E@@8Q|Q@8 bM@4R I^5@4Rl@3;Ƨ@34Z@2m8@2h ԕ@2m8@2@2m8@2C,?fi?Vjn?LD?D??5ŋ?G`O_?S`c?f?MN? E!?Dfv=? ԩ?rX?٤?w^2&?xS?**Z@@@@@"@*??????[@M@?d?rwJn@wH@AgQ$?r۲>*.?Wd?YEn/?5p@(\@)O^Ux@(\@(BH@_;dZ@_;dZ@_;dZ@as@_;dZ@aS*P@_;dZ@b;@_;dZ@`u@_;dZ@c)f@3s@3 t#*@0?|h@0ș-@/n@/:p@.PH@.@.PH@.~5@.PH@.lc?~Ov_ح?z ?/S}]?Zk8?d25??|h2:? ;a?+G?Z3^:@)׻@`?LT?8񚨝?ti^?|p@gτ@@ Ƨ@U@ Ƨ@F]c@ Ƨ@3@ Ƨ@ q @ Ƨ@"@ Ƨ.@w@n@w@|h@w@oi@w@33333@w@?@w.@3g l@3--@0B ě@/]cA@/dZ@.[u%@.@-hr Ĝ@.@- n@..NC,?|B &?bl$I?T[?E>? q&@8?}@TΥ?أ?'O?20& ?qJ@8?Q3?Cy^?MM.?q*?4|;@8 @@@@@,?????@PXbN@P@PXbN@Q L_@PXbN@Q @PXbN@Q?U=@PXbN@Q%u@PXbN@Qm\@ -8Q@ -9Y@ -8Q@ -B@ -8Q@ H@ -8Q@ -@ -8Q@ -%@ -8Q@ -&A@54m8@5X@1r Ĝ@1H˒@1$j~@1\(@0xF^@0\@0xF^@0$/@0xF^@0҂@5?Qp?{V ]?zNa ?d]Q?$"? % -?|B &?^?-5U?| 5r4?w??;{c?Ne|?6?]mZR?ԿX?J6Л?^_r@@@@@,@,???????-a>p ?qJ{{B?bmmd*@Q!B~ @ -1p,|? ӕ?v]t?pwe? D@^s@@Zv@H\@Zv@e@Zv@X75@Zv@Z(@Zv@Qu@Zv@YrGE9@j~@Ƨ@j~@-V@j~@`B@j~@cA @j~@@j~@tj@9 ]ce@9,쿱\@4f+ J@4_v_ح@37KƧ@3 k@2YJ@2ܬ1&@2YJ@2w1@2YJ@2cA!?:-%?D ?{Xn |?nii)?Xs~?`1`?1$@an;@?R2K0?XA6:4?SJD? `G+L@gCm@@>B I^@>DU@>B I^@>A@>B I^@>Aaf@>B I^@>>BZd@>B I^@>B I^@>=H@, I^@+j~@, I^@,n@, I^@,e@, I^@,'RTa@, I^@,- @, I^@, @7S&@7s@3R I^5@3Q-@2-@2Xy=ـ@1,<@1,<@1,<@1~($ @1,<@1=b??Mj̑?]P?g(6?j?"X?}ѿ,?;N?>?aYZq?e?]˙$>?am ?Ov_ح??Yʒ?їJ?@@@@@,@(??????Tu!?Tz?B171? @>>o9@,A6?\Nvp?Cl9BZ@޳3333@ޮ}Vl@޳3333@޳@޳3333@A@޳3333@,<@7 xF@7]|@33S@37+@2Z~"@2]hr!@1ޫ6z@1@1ޫ6z@1\)@1ޫ6z@1L_?W?nx?)?>\?l?.Kn?y+PYu?D?La?I?ni" ?R? a?ڞQa?.?7#!xv?0 ~?|)-}(?;m7.@@@@@*@(??????e8Q6?f - ?2?[*Tq@5_@DI?QeT?z -? ?eb@@h^dJ@@ ě@Z@ ě@N;6@ ě@氾 *@ ě@|@ ě@hی@ ě@\(@K=p@KKC@K=p@KU<@K=p@KW@K=p@Kp@K=p@K6z@K=p@K]c@7w@@7.ߤ?@1!hr @1 u!@0-V@0&x@/N;6@/nO@/N;6@/nO@/N;6@/o?5}'?Čۨ1A?\H{gX?̒?'ř ?<_?5}'?r/B?c#$?6)}?Jj?Q\?C)?H?d --?8 ^?<71? Ğ@@@@@,@,???????0s?rg@?R&Pp?tQTb@ @K8i?cUW~??nh?+ >@S8X@qJj@@aG{@a7K@aG{@a@aG{@bZc @aG{@b@@aG{@`4m@aG{@`4m@噙@"@噙@Y@噙@kP@噙@嚟v@噙@u%F@噙@1@/KC\@/KC\@(a7Kƨ@(a|Q@(;dZ@(;dZ@'#Z@'#Z@'#Z@'#Z@'#Z@'#Z?_\p)?Čۨ1A?!!׋7?8v?@ A2?$?bV,?%F?ԕ]I?-I`K?%y*=?L "?b@? ?oܧ?avh?#.?#.@@@@@,@,??????QA- x?6/q ?O+7 @a#@G+?EUt_?,DÔ?_n?RHH<@s@@ S@ |h@ S.@ S.@ S.@ S.@ S.@^5?}@ -=p@^5?}.@^5?}.@^5?}.@^5?}.@^5?}.@6w@@6f!.I@2{ I^.Mm@1Ͳ-V.Qn@1_o .NC,@1_o .NC,@1_o .NC,?]@8#@8:@8 >+@8I@8?]@8@8i@8@8S@8?*wjs@8 *@8p@8@8 @8 ??@(@Lo@(@@(@>@(@7@(@,F@(@8@A@A-V@A@A9l@A@A~@A@Amp@A@A.@A@AԹv@6YJ@6Ӥ@2?|h@2U2a|@2"-V@2*m@1p@1v<&C@1p@1v0@1p@1wTx?E[i)?H?mX!%?$8?(a?[o?|h2:??eƟ?X*_?Ee_?E?~P[]?(?{ї5?s6?w/xv?@@&@&@&@4@2???????[ϦN]H?B۱=?G_i@ @AY~D?ak/?hYK?9~f?F2@g&8t@@&ffff@&x@&ffff@!@&ffff@* @&ffff@'K]@&ffff@'/W@&ffff@#\@:^5?}@;lD@:^5?}@2 Ĝ@:^5?}@5sg@:^5?}@;6@:^5?}@4O @:^5?}@4[@6>6z@6>6z@3@ ě@3=8Y@3 "`B@3fffff@2\Q@2ZQ`@2\Q@2YJE@2\Q@2X˒:*? ?DE?f?˅T??oW? d?sD?eB^t? }>_?agf ?GO˺?.?eI5?Ф?bR?1R?x?t@@@@@,@(??????*RX7t?82?F9\c@&%?@9>?Y:\?e):?4>?ȑRTt@iJw?S@@٦x@٣VvL@٦x.@٦x.@٦x.@٦x.@٦x.@Xtj@Xtj@Xtj.@Xtj.@Xtj.@Xtj.@Xtj.@6zqiC@6{(}qv@2{ I^.Mm@1`A7.Qn@1Tm8.NC,@1Tm8.NC,@1Tm8.NC,?| V@8#@8:@8 >+@8I@8?nO<@8@8i@8@8S@8?&z7 @8 *@8p@8@8 @8 @?@Ao@Ap@Ao@A0#Y@Ao@A˝b@Ao@A^Ś@Ao@AZ@Ao@Aύ/@ʎV@ʎV@ʎV@ʍ؆8@ʎV@ʍNGcX@ʎV@ʎ業@ʎV@ʋliQ@ʎV@ʌ5R@1T xF@1T!-w2@,-@,A@,DZ1@,E:w)@+̲@+`X@+̲@+N@+̲@+ʥ?^m d?'?7%Y?0of?6Q?Əi?bMn? X7?|~o?C? -˗@s6@@bM@+@bM@kō4@bM@J@bM@2Y@bM@ y'@bM@ ۇ@'-@',@'-@(ʕ@'-@( ԕ@'-@(; @'-@(I"3@'-@'۱l@5mv@5mv @0S@0;L@0A7K@06g?@.u%F -@.]2X@.u%F -@.IQ@.u%F -@.Q-l?}x?<'_? -V @g!@(m&?\ڀ֧?*ߠQ&^?ń?aa@ga5m.@@!@#n@!@"\(@!@" @!@"M@!@"Zc @!@!af@sE@s3333@sE@w$tS@sE@tFs@sE@wKƧ@sE@t9Xb@sE@sݗ*@5@5U=@2B ě@2B&IQ@1Z~"@1Z1'@0𖻘@0䎊r@0𖻘@06@0𖻘@0'RT?|O?/)s?#}i?Fq/b?'ř ?_Oo? ?uie?36G?Nw]6?M?`@0k??Rii?~/O?ۮetZ?L?P\@@@@@$@*??????2T,P?4, B`?(,/#?<7Ȥ@"v -@t,9?0*y?\g?iCt`?®h@g/<@@7.@7.@7.@7.@7.@7.@vDT.@vDT.@vDT.@vDT.@vDT.@vDT.@8쿱[X.>@4+.Mm@4;Ƨ.Qn@3xF^.NC,@3xF^.NC,@3xF^.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @ Q@ l@ Q@ 3@ Q@ G{@ Q@ @ Q@ ĝ@ Q@ c @Q@;dZ@Q@s@Q@ݗ@Q@j~@Q@1@Q@y -@8Y*0U2b@8Vaf@3TS@3WrG@2j~@2 '@1_o @1n@1_o @1Mj@1_o @1!-w1?+-/?LS{?d3"7?J?l!-w?Q,Ě?UbB2?ଚw?s?{d?4{?ey?zk 3U?Cx}x%?ZAj?vI?^^ω?zy@@@@@*@(??????vHTy?@N;?epR0@ @O0n?W%gi?B@10U2a|@1!2W?|O?ASU?'{~?Ғkŀ?3kse?_J?}ѿ,?%L?fTEk?J?Rj ? ޅ;?8*?L?&-\a?Y?Gam?0xh@@@@@*@,??????@?h`?IY8?2џG@3U@JI -c? ?~|xr@?;"V?!0{2@X.&@@X=p@X=p@X=p@XC@X=p@Xn@X=p@XP}@X=p@X@X=p@X\@tj@tj@tj@tj@tj@PH@tj@#x@tj@ k@tj@ k@0cg l@0cg l@*{"`A@*{s@*nO@*ᰊ@)@)@)@)@)@)حW?^Q5P?`?U#?eU!?tKc?/]?bM_x?>`?j?; $?%y*=?M\ٓ@?bMky?8 T?u@>N ?͓E'?a+?O|q@@@@@*@&??????Qk6ZH?34?$>mU:@@X1q@q?BIk?!t?~?i@s -Sc@@ \)@ ␷G@ \)@ 8է@ \)@ (r@ \)@ wf@ \)@ YKuj@ \)@ pB,@ mhr@ o`7@ mhr@ jJ-N@ mhr@ aӮ:@ mhr@ ns6@ mhr@ geu @ mhr@ gk݆@6*0U2b@6`: n@40ěT@434k@3tj@3&Hh@3?䎊r@3C[X@3?䎊r@3B7e@3?䎊r@3B!{T?ڡG7=?d~?~k??'$S?x?I3$?_<4?Y:?ט5?I&?Vk%e/?J9A?e\?Rꊇ@@,@(@*@6@9??????A K\?>RUwU?0Z–k@ $@ lbϧx?^f?PMQ?ʁp?m{k@g M@@I6E@I6ȴ9X@I6E@I:Q@I6E@I8y=@I6E@I9+@I6E@I0*1@I6E@I3g @Z@Z@Z@PH@Z@E85@Z@zxl#@Z@n@Z@YJ@3 xF@3!-w2@0Y^5?|@0Vl!.@/;dZ@/ᰉ(@.N;6@.@.N;6@.@.N;6@. -L/?| ?PƧ$?BI{?b?Փz?K&?| ?-',?tP*???sB?9x]e?n88?{Muk@I5mH@+?h 5ɯ?'?ST)Jh?6l{5@e&_ף@@;?x}?a,F??G'?|nӿ?"r?3B@@@@@$@*???????ZOxS ?S'?Ea@@0_o @0v@0_o @0vȴ9X@0_o @0\N@3S.Mm@2n".Qn@1&.NC,@1&.NC,@1&.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @)x.@)x@ֿJpS+@)x@<@)x@ֹ6@)x@ֶ.@)x@ֺIG@/.@/@7@/@<:@/@5E@/@uJp@/@vg^@7D.>@2c@+7@2'KƧ@+%@1䎊r@*q@1䎊r@*|?=Ca?{a?{ -W?]O?V @?tO?ļ:P`?"Q~@ch@@^N+@^N+@^N+@^QL@^N+@^O~}@^N+@^Q'} @^N+@^[6@^N+@^Sk@77Kƨ@77Kƨ@77Kƨ@7{?n@77Kƨ@7|@77Kƨ@7uO8F@77Kƨ@7k%S1@77Kƨ@7j]@1_o@1N;6@0 hr@0Qn@/?;dZ@/3p1@.b~@.K7@.b~@. 3?9G@.b~@. xD?^6P?(bʟ$?wQZ?7??{O?K bT?bMn?&Y-(??6 BY? w_?͙e?c>)?3K_n ?#F?v>?P?~@.@@&@"@ @6@5???????VGfOF?e5: ?GP+]@^T{@7o[j-?tҢcZ?U'{?Q9?͓@r)U)@@|g-@|h9X@|g-@|h@|g-@|i@|g-@|h˒:@|g-@|gݗ@|g-@|gz@$@v@$@D@$@8YK@$@E@$@L_@$@2a|@4v@4jOw@0S@0E@0"-V@0" @.@/-w1@.@/@N@.@/Zc?1j?W[?jhq9?:Z2b?Dj?aX?}ѿ,?'`?tJ?@[*^? f?ٶm?y8#?Ф?]?$H?L?_խW@@@@@&@(??????+)f=??x{@|hC0}@@?1 B.?2Ge -$?mj)K?p°qRs@gV/p@@š.@š@KƧ@š@$@š@}H@š@@š@_@S.@S@+@S@L/@S@@S@Q^@S@~@9䎊.>@6 ě@6S&@6'KƧ@6hy=c@5Q@5GE85@5Q@5l?@5Q@5lL_@8J?l?A|?(ޞs?b&?ғa@8J?R܆?u:?Po?hA?шo٭@8p?­?Uj? V|L?\T?r@@@@,@*?????@zx.@zx@{M$ @zx@{j'@zx@{ F@zx@zi7@zx@ziq@x.@x@ԂOm@x@Yz @x@2v@x@%2G#@x@ -O}|]@9g l.>@4ahr @4 @3;Ƨ@3$@2J0U2a|@2+%@@2J0U2a|@2 -n@2J0U2a|@2 `.@8J? Q"0?.%?ˋUO??Kf/? ѣ@8J?ӯ?ObN? -/ -?`jNn?Q\@8p?tl* ?)o?\)?.?u@"@@$@:@7?????@r Ĝ@r@r Ĝ@rs@r Ĝ@s3333@r Ĝ@sMj@r Ĝ@r-V@r Ĝ@r:)z@;dZ@;dZ@;dZ@@;dZ@:~@;dZ@bM@;dZ@ -=@;dZ@H@/o@/o@)j~"@)j~"@*rnO@*rp:@(ۋq @(ۋq @(ۋq @(ۋq @(ۋq @(?_\p)?{j?(?8v?Ot??bM_x?%F?A)N?0N:s?ѡb??R g!ڢ?b@? ?q6tN7?avh?Ѐ>(?O|q@@@@@(@*??????D+?+)T?sk]@rL @n8?:h?*F_?qvLd?O$@s,2@@]/@Qn@]/@H@]/@=cA@]/@SMj@]/@YXbN@]/@L1&@ȴ9X@1&y@ȴ9X@Ӝ@ȴ9X@U=@ȴ9X@tj@ȴ9X@e+@ȴ9X@֡a@8YJ@8kP@5; I^@5:fB@5 "`B@53{J#9@4OO M@4^ߤ?@4OO M@4P$tT@4OO M@4RT`d?YgoK?hX*?TH? ?U ,?+ w? Z ?%!{A?Ēۇ?߈{?As?L?RWP?5.?_YK?(?CRU?[ё@@@@@*@,??????C4q1r?j0\?rbPbQ@Ty@?wy35t?󂂷_?˧"?@qd|@@ և+ @ ȴ9X@ և+ .@ և+ .@ և+ .@ և+ .@ և+ .@j~@ I@j~.@j~.@j~.@j~.@j~.@8:@8ȴ9X@4n5?|.Mm@3KƧ.Qn@2!.I.NC,@2!.I.NC,@2!.I.NC,?}ѿ,@8#@8:@8 >+@8I@8?{QB@8@8i@8@8S@8?@8 *@8p@8@8 @8 @?@hr.@hr@"!@hr@2@hr@>@hr@k@hr@GC\@91&y.@91&y@9 -M@91&y@9b}@91&y@9>Z@91&y@93M@91&y@9@8YJ.>@3 I^@3GP@3R`A@39N@2Tm8@2FBI@2Tm8@2[4D@2Tm8@2\!-x@8J?KQt -?9e5?*F?'c$x?+@8J?uk?{?i?E*z?@8p?`K?0|wF?yk?R HE?[s@&@&@(@8@7?????@7(@7O;@7(.@7(.@7(.@7(.@7(.@nj~#@n_;dZ@nj~#.@nj~#.@nj~#.@nj~#.@nj~#.@7*0U2b@7#@3I^5?.Mm@3v-.Qn@2!.I.NC,@2!.I.NC,@2!.I.NC,?]@8#@8:@8 >+@8I@8?]@8@8i@8@8S@8?*wjs@8 *@8p@8@8 @8 ??@.@@@6@@-w1@@irF@@eS&@@b7@DT.@DT@@N@DT@3@DT@"`@DT@H@DT@|64@9p4m8.>@4|hr@3!.H@4'KƧ@3E@3Eu@2bM@3Eu@2䎊r@3Eu@2~O<@8J?j#?IM\g-?T?&eS?|@8J?%xW8??=q?z?Mc2h@8p???Z`(?@j-?GX?P^eNQ@@@@,@*?????@^qm@^r@^qm@^q @^qm@^rS&@^qm@^q_@^qm@^n!R@^qm@^mB@YěT@Y\(@YěT@Yq @YěT@YH@YěT@Y\)@YěT@Y4@YěT@YaA@5*0U2b@5u%F -@1S@1M@0`A7L@0Ɍ~($ @/Xy=@/㯷@/Xy=@/S&@/Xy=@/A [?Y͍_?I'?JC?:Z2b?P?uC0!?5 ?Hz#,@i`C@@-Xtj@-Xtj@-Xtj@-Y_p@-Xtj@-XQ @-Xtj@-Y @-Xtj@-VC@-Xtj@-S|@ffff@$/@ffff@8}@ffff@e@ffff@8X@ffff@+a@ffff@Q@5-v@5-,<@0ԛS@0@0-V@0v@.@.ѷY@.@.v@.@.Y}?| ?m??lDžN9? 9?봤G?|O?4 "?O)AT?agf ?ڂc? R?oܧ?l?ʳ_? ??j?gϵ@@@@@&@(??????CGP0?2X?=s?81@-WƓ@?]VOX?Xj#o?ǐ>F? F -@f10@@kR@e z0@kR.@kR.@kR.@kR.@kR.@&lC@& I@&lC.@&lC.@&lC.@&lC.@&lC.@8@@8!@5kS.Mm@4Vu.Qn@4D*.NC,@4D*.NC,@4D*.NC,?ڡG7<@8#@8:@8 >+@8I@8?π>@8@8i@8@8S@8?/r@8 *@8p@8@8 @8 @?@7K@@7K@($ w@7K@ 7Kƨ@7K@ q @7K@u$@7K@@jE@jE@jE@jO @jE@jIQ@jE@jS@jE@jX@jE@j{J#@3:@35Xy>@2?|h@1 qu@1"@1 @15sh@1,Z@15sh@1-@15sh@1.1?|O?E|s8?y]?_?PU`?uV`?| ?I4>?kE<%?e[|(?6?^̀@!? J?1j?9@0w? ??;WZ?8ϣ@@@@@*@$???????[*?UtPL?D@@j6iD?sDJ?b[`?P-.2"?^iQ@d_@@1'@"@1'@@1'@+j@1'@2W@1'@/V@1'@ce@F@-V@F@E@F@zH@F@넛T@F@델@F@8@6*0U2b@6/{J$@4 ě@4Zc@3j~@3!.H@3a:S@3_4֡a@3a:S@3b<64@3a:S@3bs?|O?u4ޕ?!Gf? HN? ?(ϢU?}?S"ꝉ? ?GW?'bA?~VԪ??WBH? DS?N?aP?&+B?b@@@@@&@*???????MjߢTSC?IA/?R&"@P@Z?jWz\?rt`?$-jC7?۪KLD -@fEmņ@@W+R@W#n@W+R@W*͞@W+R@W/iDg7@W+R@W-8@W+R@W0@W+R@W)@7KƧ@>vȴ9@7KƧ@:)y@7KƧ@'#@7KƧ@=E@7KƧ@8*0@7KƧ@9|@8-v@8-!.H@3+@30 (@2ߝ-V@2ѷ@2p@2m5Xy>@2p@2m@2p@2l64?^*?I\O?2 :M?g(6?7?YD?| ?蠔?ҌI?%)?? j?$?+0T?ٹ?ѳ9?F?cR@@@@@,@&???????__GQ4n?Y#7J3?33uK@W+i1n@;\?sCyA?X0E?ҡWP?P,C@jxD@@mY+@mWO;@mY+@m>@mY+@mG+ J@mY+@mhK]e@mY+@mGz@mY+@mIB@]5?|@]*@]5?|@]tj@]5?|@]q@]5?|@]ᰉ@]5?|@]8@]5?|@]:@74m8@7ѷ@5^5?}@5- @4V@3 @3,<@3@3,<@3"`@3,<@3O;d?qn?ʿnP?拗? ?|p9?ʗAm?}?#O?#*0?%hF? U?SЧ?.\?-?$RHZ?@l?F?aZ@@@@@&@(??????[~\1Bw"?t W?zM@m\];@]iX?-C?@QX@~Q$@fȶI@@ȴ9X.@ȴ9X@Ϫ@ȴ9X@"@ȴ9X@b@ȴ9X@PH@ȴ9X@-@1.@1@+j@1@ -=p@1@64@1@٦ @1@sh@6 xF.>@1+ @1q @1"-V@1#Z@0,<@0oiDg@0,<@0@0,<@0!.I@8J?6 '8?O%c?5d?r?cLb@8J?eB^t?2'?[?P!=4?74l@8p?=!K?sg?]O8?8ϣ?:/ʼn@@@@,@,?????@o/.@o/.@o/.@o/.@o/.@o/.@E.@E.@E.@E.@E.@E.@8B䎊.>@3}hr .Mm@27KƧ.Qn@20U2a|.NC,@20U2a|.NC,@20U2a|.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @F$/@.zI@F$/@R:){@F$/@H$ x@F$/@B3@F$/@>%@F$/@6Ov`@6ȴ9X@'+ I@6ȴ9X@8Q@6ȴ9X@0|@6ȴ9X@12X@6ȴ9X@2nP@6ȴ9X@+C@5O M@5iᰉ(@1+ I@1|@117Kƨ@106@0Eu@0B䎊q@0Eu@0A @0Eu@0=Vl"?qv?1e -?X?}fУ?BPbC?Q,Ě?F/P5?0,3?C\ >?=U1WK?+hd{? a?,?/o?YY?0ڌ#?4hpɄ?$jB@@@@@,@(???????_ޥ?[I ?d`@?P*ڸ@8f@,E w?:JBE!?sj4q?^G?Nhw|J@k9K@@0/@1&$/@0/@0@0/@0@0/@0A@0/@0*@0/.@n@hr"@n@$/@n@^5?}@n@|h@n@!.@n.@1 ]ce@1X@.KƧ@.՛=K@/4E@/873@.@.~$u@.@.F -L0@..NC,?Ol?m0?S?Q@/$tj.Mm@.]E@&$.L@,ߤ@.NC,@,ߤ@.NC,@,ߤ@.NC,@8 -Ny@8#?`m_B@8 >+@8I@8@82r@8?#(v@8@8S@8@8L@8 *?&|@8@8 @8 @6?@G|h@G|h@G|h@G˒:+@G|h@G qv@G|h@G|h@G|h@Gb}@G|h@G;dZ@+R@+Ƨ@+R@+a@+R@+R@+R@+҉@+R@+:)@+R@,"h @0:@0:@(`A7K@(`A7K@'O;dZ@'O;dZ@&PH@&PH@&PH@&&@&PH@&PH?^Q5P?[e>J?$<'ε@Ga<=@+W?q ?`1<@s;@@<$/.@<$/@;ߤ@<$/@;@<$/@;`d@<$/@;C,@<$/@;U2a{@O.@O@ (@O@+`@O@Z@O@՛=@O@յs@3>6z.>@/"@/VϪ@-nP@-֮}Vl@-oi@-ql@-oi@-@[6@-oi@-FA@8J?m0?'?Jʮ?!g7?A+KW@8J?6c?D?_3\? e?c5W@8p?oM?/X??3?Evn@@@@*@,?????@g-@hr @g-.@g-.@g-.@g-.@g-.@'"`@'|h@'"`.@'"`.@'"`.@'"`.@'"`.@8H@8"GE85@4 ě.Mm@3lj~#.Qn@2sh.NC,@2sh.NC,@2sh.NC,?}ѿ,@8#@8:@8 >+@8I@8?q!U@8@8i@8@8S@8?c@8 *@8p@8@8 @8 @?@+@+@+@rGE@+@bM@+@:~@+@B@+@(@;dZ@;lE@;dZ@:>@;dZ@:)@;dZ@:)y@;dZ@:?@;dZ@;@2@2$tS@/8tj@/84֡b@. n@. 4m@-QN;6@-R ě@-QN;6@-RnO@-QN;6@-Ra|R?^Q5P? ?"?G?ǹT?2I/?bV,?&uE@?['?6)}?إ<(?h,à?cPQ?6?W ? ??a+?@@@@@&@*??????RΦ?3fB?AXm?Uc@Ϊ@:d ?Q6I?  ?Z -Ze?9y@s4gK@@e`A7@kC@e`A7@f?@e`A7@dT@e`A7@eO@e`A7@dg8~@e`A7@eS&@ě@Ƨ@ě@TɅ@ě@D@ě@:@ě@d@ě@R=@5O M@5xp:~@1.5?|@1.ѷX@0v-@0v@0?䎊r@0@:~@0?䎊r@0@H@0?䎊r@0@:~?^*?/)s?WD@?#t?u?Id6z@5*1@1S@1:)y@07KƧ@0lC@0c@0US@0c@0T`d@0c@0Tm8?}ѿ,?b}?K9\?Jʮ?'֙:0?Z?}ѿ,?-NI`(??íuA?a6io? bV?2R2'֕?/X??z_?k3`E`-?^':?y\@@@@@,@*??????-?c'"&<?E!Q?NOlr@70^@g?b(Bo?h[?3*r?Q%@bHΑ@@+@+@+@@+@oiDg@+@`A@+@ -=@+@:~@I1'@I1'@I1'@IN<@I1'@Ice@I1'@I\N@I1'@I@I1'@I<64@0e!.@0e?@*\j~"@*["`A@)E@)g m@)b~@)Xf@)b~@)Xf@)b~@)eڻ?_𩫝?GA?K]4? Y?2A?7F?bM_x? -?S?0N:s?ߒh!?h,à?e@'? ?q6tN7?XLL?Ѐ>(?b/bJ@@@@@*@,??????0X?7?\5Q6 -?2BX@@I1?(f:?$^?Lqմ?Bel@sTR@@:@:ȴ9X@:@: xF@:@: -ڹZ@:@:z@:@:`B@:@:ں@-z@-9Xb@-z@-t$@-z@-t@-z@-wO;@-z@-r@-z@-s@8!.@8 @3@30 (@2ȓtj@2j~#@2 [W?@2o@2 [W?@2'R@2 [W?@2䎊?|O?KTD?Mq ?Att?<_+fj?7D -?Cy3?\j?fˌ?ҧ?S?;?[K-$I?HO?}?0?Tj?Q?]@@@@@*@,??????qXp<,)s?Ke38/?bhI@:CGR@-vf?ro!?tToat?f`}^?ֽu@oEۿ@@=p@r @=p@Q@=p@^ @=p@'0@=p@ߤ?@=p@ƍ@5?|@ ě@5?|@2@5?|@ߥ@5?|@;dZ@5?|@!$/@5?|@cB@7DO M@7Eᰉ@4c@4esg@4v-@4}ڹZ@3Tm8@3X ԕ+@3Tm8@3W1@3Tm8@3Y'RTa?us\?ʖSS?:r ?m}9?p?l}?)T?,T?]^߿?L?hY?Y@??Эs_`?%Cr?L??,yfY?**Z@@@@@@??????J5p<0?Vx_p?29o@i~@F~ -?ty@?S .?A@?zl@j6F@@;dZ@|h@;dZ@@;dZ@;dZ@;dZ@vȴ@;dZ@!-w2@;dZ@쿱\@v/@vT@v/@v?@v/@v@v/@v@v/@v+ I@v/@v1@4`D@4`D@0+@0@/E@/fA@.@.vȴ:@.@.!-w1@.@/- ?|O?Z.?*"AaI?lDžN9?E{?6J?1j?#maЀ?d0R?; $?c3c(? f?s@a?Ϸ+?٤?RJ?j?)@Jq@@@@@*@(??????Ƈ?:6x?U?+8<@^h@v1?2K5?L+57?r ?*ZO@g2x+@@c -=q@\1'@c -=q@NV@c -=q@V_ح@c -=q@VOv`@c -=q@\64@c -=q@W&@$E@$dZ@$E@$5X@$E@$C@$E@$=L@$E@$ƚ,=@$E@$@8T xF@8S @3|hr@3~H@2`A7@2/w@1̿[W?@1rH@1̿[W?@1@N@1̿[W?@1?_pP1?L?Ji?Sz?i]i?P?|?=x?Ր?ƨ'?%y*=?CwB?O"?mΝ?DΊ@@@@@*@"??????7F`?j?Xo9R0?oZ @Xւ@$͠0?oxzd ?|T[?αv@ Ie@p;N @@@x@@E@@R<@@4K@@ۥS@@ըXy?@֨r @֬@֨r @֯@֨r @-@֨r @ֹ0 @֨r @֙"@֨r @֙#x@904m8@9FV?>sN?o΃?SDN?FMG?9?͑?#q?x?}Z?a㸂?F?2:U@@@@@,@(??????=@r'&?u,^?aܼ-Y@ڡrJL@֠s:?۾?1 l?Ҏ?h,X@p8:T@@V@9Xc@V@~@V@E@V@2@V@@V@IR@$3E@$.V@$3E@$)y@$3E@$(@$3E@$1hr @$3E@$*L@$3E@$,L_@7D@7|hr@3S@3>B@2A7K@2w1@23&@20 -=q@23&@21u@23&@21iB??B?F}?vk?ҧ?_ ?[Xxp? /ّ?%c~?!zt9?W;zY?m/w+?(d?7y?sD@8@8i@8?(S?H{?@8 *@8p@8??*N@@@???@nO.@nO.@nO.@nO.@nO.@nO.@11&y.@11&y.@11&y.@11&y.@11&y.@11&y.@8֚,<.>@5+ I.Mm@4t.Qn@4._o .NC,@4._o .NC,@4._o .NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @:M.@:M@:h`@:M@:]*@:M@:N*7@:M@:S{G@:M@:Q>9@j~.@j~@LL@j~@n恖@j~@|@j~@:h@j~@fE_@3cg l.>@/.Vt@/-.@.ӶE@.j@-QN;6@-? -!@-QN;6@-BdzF@-QN;6@->:@8J?rW?禛?*&?h ?Mm,bz@8J?h?W ?#~\?.ؗ?܏=@8p?@c MQ?'".@B>"@BDM:@B>"@B?[W@@B>"@B5*2@B>"@B3tj@B>"@B@3ěT@34֡b@3-@3|@3!:S@374m@3!:S@32M@3!:S@32a|Q@8J?'5? 1B?F"b?l(?ݷ@8J? /ّ??6l@0=ce@0p`L^-m?[c?QY@8@ -f?kp ?iD!?\z?B4H@qk]@@klD@kB@klD@kPH@klD@k6@klD@kq@klD@k"@klD@k@ly"@l\(@ly"@lz)@ly"@lx4֠@ly"@lrGE8@ly"@lxl"h -@ly"@lxQ@7YJ@7/y'@3^5?|@3U=@2"`@2>B[@1Q@1vȴ9X@1Q@1!R=@1Q@1 ě?|B &?:?XY?Z!ǟ?.Kn??=?7娠?аl??T ?:u. ?m^?jOI?4f5ׅ?&!+?D/ -b?rsw?W|DC?]ߒ@@@@@,@,???????jNB?LS?[d=@k(@lx1_I?r_}?r[$Q?jL?֦/i@n*3{@ @1m@+Ƨ@1m@/ M@1m@4*@1m@0 -=@1m@1A [@1m@1@>Q@>dZ@>Q@>@>Q@>cA@>Q@>@>Q@>o@>Q@>6@7䎊@7{)^@3\I^5?@3`d8@2i"`@2o;dZ@1,<@2$tT@1,<@2 -=p@1,<@2ᰊ?1j?[ ?p?؆{~?Nty"?uk=?F/P5?K?J?% . ?E?998?&1V?e+?Ile?J@4B ě.Mm@3A7K.Qn@3ush.NC,@3ush.NC,@3ush.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8  @6ȴ9X@8Q@6ȴ9X@*͞@6ȴ9X@ 4m@6ȴ9X@%8X@6ȴ9X@'-@6ȴ9X@6u@~"@T@~"@8V@~"@'#@~"@GfB@~"@>6z@~"@d/@7:@7ce@33S@3 )^@2M-V@2<ߤ@@1Y~($ @1KC,{@1Y~($ @1MK]@1Y~($ @1Nߤ???J f?XՇs_?fJ5 ?Kbo?d?sD?,i?Ľ;!?]$_2.?l?m(~2?D?[ S?[?J?_u? Ğ@@@@@,@(???????7cܰ"Ȁ?i2\ -?q@\)@1"d@kZ?D??jL?#y+b?cg:@iy6@ @jXbN@j*@jXbN@jv@jXbN@jM'@jXbN@j'C@jXbN@jˁ@jXbN@j@9@9v=@9@9@9@9@9@9<]y@9@9O@9@9b@8g l@8Gv@4ahr @4c?rz@3tj@3  z@2O M@2vA@@2O M@2M{@2O M@22?TVm?-Z?^I??۱[V?*Jۓ?=?'a?poؐ?S.&?`?M{3i?#B?6c?[K?⹯{?C9?צm8@@"@"@*@7@3???????6l6(dUU/?A7*0l?2crRc@j@9n8?[K ?O_6 ?B:?`v@m-]G@ @-@O@-.@-.@-.@-.@-@D@ ۥS@ + J@ ۥS.@ ۥS.@ ۥS.@ ۥS.@ ۥS@ _@7:@7C%@3#.Mm@2Ctj~.Qn@1D*.NC,@1D*.NC,@1D*@1d8?ϱQm@8#@8:@8 >+@8I?N5W?@8@8i@8@8S??+k@8 *@8p@8@8 ?SH@@*??@Z@䛥T@Z@@Z@@Z@M:@Z@e@Z@g8~@F@G+ J@F@G@F@Gݗ@F@G@F@Fffff@F@H1&y@3䎊@3.H@1+S@1,L_@07Kƨ@03@0@0XbM@0@0Q @0@0^?|O?j?cvJz?bC?3n'?.]?]f?I?Z@@$@&@,@4@6??????En?4p 9?#???'Ut@ r@nvF?2?6ef"?a:4?f+qto @s "@@\@G{@\.@\.@\.@\.@\.@+ J@C@+ J.@+ J.@+ J.@+ J.@+ J.@7Q|Q@7bGE85@2ěT.Mm@1`A.Qn@1h.NC,@1h.NC,@1h.NC,?]@8#@8:@8 >+@8I@8?]@8@8i@8@8S@8?*wjs@8 *@8p@8@8 @8 ??@M@M I^@M@Mϝ-@M@M4֡@M@M]c@M@M@M@MϪ͠@ 7Kƨ@z@ 7Kƨ@Y@ 7Kƨ@Ov_@ 7Kƨ@Ͳ䎊s@ 7Kƨ@;vȴ9@ 7Kƨ@а{@6#g l@6!n@4c@4QR<6@3;Ƨ@34?@4Tm8@4/;dZ@4Tm8@41_p@4Tm8@4,wkP?|O?5b&?g ?m$.?F?9Ud? ?n4??p:2?0/f?v8' ?d -,?RQF?Vr?P )k?C$\j$?j)@@@@@ @$??????>e?J<]?q;W@MyfK@/h?_\*Kb??`?{m&?6@^_U~@@+.@+@=<64@+@SMj@+@Q@+@sMj@+@ '@ٙ.@ٙ@@ٙ@w@ٙ@kP@ٙ@PH@ٙ@$oi@9g l.>@5I^5?@5#F]c@5`A7L@4Qe@4fYJ@34m@4fYJ@36z@4fYJ@3p:~@8J?;gF?C>S?;?VҾ?8R} -.@8J?$a? gA?Ƅ@l|L?ҨP'?9Y145@8p?/I#?_?ˍ?xb?!zt9@@@@,@(?????@(@E@(@u@(@"M@(@)B@(@쿱\@(@@sDt@s8@sDt@s?@sDt@s@4o@sDt@s6z@8Y*0U2b@8a.H@3W+ I@3U?@2lj~#@2n쿱[W@1,<@1K]d@1,<@1PH@1,<@1Fs?bk0?ĵS_??`x?RJ? Ğ? -t@@@@@&@*??????An\?S{"?%!P<d?<1bc@䚊@ZQ(-?EP }?]b}? .+?n`@g%\"@@V@O@V@˒:@V@j~@V@S@V@r @V@@8:G@8:v@8:G@814K@8:G@812W@8:G@8=,@8:G@85L_@8:G@84Fs@3H@3.2@0E?|h@0<#x@0`A7L@0D@.b~@-]ce@.b~@-6z@.b~@.6?2}?R?+(?ᕮ0?0 碽?5}'?|O?yG?Vh?} ?rrl?НFW?nwf??z_?.SM? 1 D@?/̽`@@@@@,@,??????[iP(B u ?FpQ?Mg -@ @89/?d1Dt ?i,u)?3?o>a@b"J@@I7Kƨ.@I7Kƨ.@I7Kƨ.@I7Kƨ.@I7Kƨ.@I7Kƨ.@/ -=.@/ -=.@/ -=.@/ -=.@/ -=.@/ -=.@6:.>@2hr .Mm@2V.Qn@1䎊r.NC,@1䎊r.NC,@1䎊r.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @x@o@x@E@x@}[@x@O~@x@ش:)@x@_o@tj@@tj@@tj@B@tj@9@tj@?@tj@3=@6I_o@6G1@4 ě@3cb@3]V@3qQd@30@3?}@@30@3:Z@30@3@CHޱ?}#?v?Ca&?C`vg?y?dv?gQ?g'*/?%/ϴ?TMG?h?e?vc0?G?Wy+?I ?oy?pA@@(@(@"@7@7?????? ?qT??q~?i1?Ֆz?26d?l{ :?:?D( ?߷ڙT?&'c?&Z?!?v-?{¢?H???qgu?(6 +@@(@*@$@<@9???????Oj -Bt?wa??ப?wnB@I۰E@lz?\Rj?[}\?),@&%@m-1@@>^Q@>V+ @>^Q@>R@>^Q@>L"h @>^Q@>?b}@>^Q@>\C,z@>^Q@>P{@i$@iu@i$@ix@i$@iIQ@i$@i@i$@i=@i$@i?@7YJ@7*0U2b@2TS@2Rᰊ@1`A7L@1!-w1@1B@5@1:=p -@1B@5@1E$/@1B@5@1<!-w?]?`?gOw?lsC^?di?uV`?]??co?96 ?JL\?e\p?*wjs? -??!B?GZ?I$?@@@@$@"???????t?y{H?: -?HE&33@>SUW@i7?M ]?Q4V@\,?sG 5@up;f@@D@D;dZ@D@DsPo@D@D~<#@D@D@D@Db7@D@DN@|ix@|_ycZy@|ix@|M9@|ix@|m@|ix@|msx@|ix@|kce@|ix@|kR(@8DO M@85-@4i+ @4kiF@3R`A@3VK @2s&@2w>x@2s&@2v@2s&@2w -O)?cГA?3?qJ?k@3 e,@˒m?IZT?[)V7?V,?SuY4@mr*@@Dj~@D`B@Dj~@D$@Dj~@DoiDh@Dj~@DY}@Dj~@D&x@Dj~@D@N1@N%`A6@N1@Ns@N1@N@N1@N@N1@NZ@N1@N64@8>6z@8 "`@3R I^5@3R74@2,j~#@2,cA \@1@1@1@1u%F -@1@1F]c?1j?܄ (?4 b5?{@?<_+fj?[2???xrI?OՃ?@[*^?.<?Q?R[SA?4?(?2? -r?&f@@@@@*@,??????q[S?:}%\3l?[j@DCJ@NԬ?_6/?ut#?۴$Z=?q@l͸@@. O;d@.&x@. O;d@. zxl@. O;d@. -=p@. O;d@. @. O;d@. 1&@. O;d@. :)@i$@i+@i$@i6@i$@i%2@i$@i@i$@i@i$@i+j@7>6z@7+a@1hr @1Ƨ@1 "`B@1fA@.@.Z@.@..H@.@."?q!U?:L -?:?j6_??F0?2}?-NI`(?S?Җhn?إ<(?+d?^'?H?sg?!ˠ*(? -t??\@@@@@$@,??????P%?Q-{?1??6/Y7@. s@i<?JI: ?Z&"1 ?u*@2Ͳ-V@2V@2~($ @2#(r8@2~($ @2(fM@2~($ @2$ƿ6?,r?d?7m-J?N*?#Զ}_=? kR?x/0 ?Њ?q(.W?'?#)X;?UCJL?b|&?e!?Du?(A? -e?S@@*@$@&@7@8??????o\?[(I?\;XL(?Xbe@W@?|+T~?!?u_F/?ynֿ?ᒻg#t@kAci@@ I^@ I^@ I^@U=@ I^@ě@ I^@üZ@ I^@@ I^@b@a$/@a I^@a$/@aA \@a$/@aM@a$/@an.@a$/@an.@a$/@aC-@5@5䎊r@2° ě@24m8@2 -V@2 )^ @1@1 @1@1 -=p@1@1TɆ?Ol? ?e -!C?Uyu#?GBm?U@5 I^@53@5Z~"@5dd~@5:,<@5;V+:@5:,<@5@-S@5:,<@5=?wvHf? -?"R?ȭ?h?zF?-V4?O}e?,d@e&i`O@!@3333@M@3333@Q2W@3333@-w1@3333@"h @3333@7Xe@3333@3333@|R@{`A@|R@|@|R@| O;d@|R@{XbN@|R@|ڹ@|R@|Bѷ@8G>6z@7+ I^@5@4Nc @4"`@3GE85@4Q@3a-@4Q@3_ @4Q@3[~%?F^S?t?UA?%?„ ? RAQ?%?^?%?7&c?6P?߱Z?*?ڱP?Fg*V3?1,j?~?]B0?CRU?/̽`@@@@@ @&??????i@?@?Wf>h?^==QV@Cx@{W&?so0?(O-hh?ђ?4P @b]|9@"@-hr@-@-hr@.Mj@-hr@.zH@-hr@,[V@-hr@+]c@-hr@+R@ϕ$@ϖu@ϕ$@ϖȴ9Y@ϕ$@ϗ>6z@ϕ$@ϔ3@ϕ$@ϔ9Xc@ϕ$@ϗsP@3@@3@@2!hr @2!@1KƧ@14m@1\Q@1\[W?@1\Q@1\j~#@1\Q@1\ߤ@?1j?ߋb?l>?,Z?j?_ ?| ?La?h?!zt9?=W?C ;?/Y}?򯞼?T?ij6?L8AS?Y͍_@@@@@(@*??????@0 T?s@(?5Q[v|@, Ũ@ϕwů?3[F"3a?U?'?rTrb z?5D@fNk@#@bv@b"`@bv@b)^@bv@bZ@bv@b @bv@bv}Vl@bv@c ~)@M@ I^@M@Q@M@@M@ڹ@M@hr!@M@iB@7 D@7 - r@3^5?}@3+Q@1Vu@2 Y|@1xF^@1j~#@1xF^@1A7K@1xF^@2zxl"h?% ?L -xNC??=?5"?ZF,U?us\?|-J?cu%+??Xxe?Ŷ'?=Ca?I1;?ZaQ?k,?S1z?G?jx?yC?n=?%_D?/Y}?RJ?oC?*Z}@@@@@(@*??????Zt$?Q3?44B?^f0@y@Wv?QuI$?ryF?%Lw$?ڢ|!@o@'@ah\@aix@ah\@ah@ah\@an;5@ah\@ai*0U2@ah\@ai7Kƨ@ah\@aen@mV@lC@mV@il@mV@fx@mV@l/{J@mV@n@mV@nc @64m8@6O M@3hr@3Vl!@3tj@3˒:*@26z@2K]@26z@2<64@26z@2U=?]?4Kl?e -!C?H?4w??]?=}?低NU?ǧ?D6??f1-&?*wjs?H?~}o_?bv{3dZ?`i=?GZ?@@@@,@(??????Vwq?P,sPq?B/%?:no'b@ag(@mJ?R8N ?͓E'?Ѐ>(?f_Ԋ@@@@@,@*??????A (?':eN?!F @vp@$Q?6?06X0d?iE\?V*<@s j8 .@)@t@+@t@%F -@t@;5@t@ƀIR@t@- @t@d@/z^5?}@/|j~@/z^5?}@/s@N@/z^5?}@/dS@/z^5?}@/{~@/z^5?}@/g#@/z^5?}@/z)@7@@7sg@53S@5/U=@4`A7@4RT`e@4 [W?@4JL@4 [W?@4PH@4 [W?@4 '/?Ol?̂?6KX??p/?c:e??. -?B?j3(? -s? '?6ז?W<ܾo?y\?jv ?t#$ ?Nty"@@@@@,@(??????d˦T~Ÿ?M -s7?cX@S @/xI8?p=?:8??`l@j6w@*@kƧ@kR@kƧ@kU@kƧ@kU=@kƧ@kx'@kƧ@k҇@kƧ@kv@^cS@^cS@^cS@^bTg@^cS@^ba@@^cS@^bf@^cS@^cTvw@^cS@^e|*@3䎊@30@/\j~"@/]t`@-nO@-̱Q@,@,.@,@,<0G@,@, 1?|b@?ר4?"(#*m?O*je?-??|h2:?R?J:?9~F?ڥW?JU4?pDRg?z+?xuhy?ST)FS?Үc?H@@*@(@*@3@,???????l?"`?|$H?}c[.@k@^cё?9Yi?8o}?$f?X*@f>E@+@`B.@`B.@`B.@`B.@`B.@`B.@bM.@bM.@bM.@bM.@bM.@bM.@4䎊.>@2TS.Mm@1ȓtj.Qn@1a:S.NC,@1a:S.NC,@1a:S.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 ,@wn@ -G4@J!kX?mˉ?j;1?QeSz?޻@d@/@C%@,@C%@jL/@C%@Iԕ,@C%@\PH@C%@]@C%.@/@;dZ@/@j~@/@p;@/@WX@/@Q4K@/.@9>6z@9#33333@6 ě@5߃{J#:@5tj~@5"M@4iB@4Ďqj@4iB@4R@4iB.NC,?|O?Lx5BL? -?lE ]?ÛҮU@8?µ'&?vj?R?F͵sS?֎yP?ݔJ {@8?T# ?W$B?j?K?116@8 @@@@@,?????0@1&@0 -T@1&@I:@1&@R<6@1&@@1&@^uƖ@1&@]@KƧ@ d@KƧ@@KƧ@鳋@O@KƧ@H.c@KƧ@Ҡ1@KƧ@ܟ@7]ce@7@3f+ J@3li.p@2A7K@2By@2@5@1).@2@5@1M{@2@5@1pK?ؗ?we ?b|S?u)Pnf?!=?臫?ڡG7?%d?%d@@@@@(@(???????A~8?L(T ?0y/L?D}_*@pڧ@/=?NM?A?b35?un'2?7%@ -Z@dF:g@2@+R@{#@+R@&8X@+R@')n@+R@%@+R@ -̯Q@+R@$[@vȴ9X@;?F|@vȴ9X@y▄@vȴ9X@~;@vȴ9X@xiag@vȴ9X@o@vȴ9X@n~y@8H@7/>m@3W+ I@3S<@2"`@2Fj@2h@2_Jrz@2h@2Wlg@2h@2^K?F?E `?ZZ/?M2?˄Ve*?hSG?8c?f?:s -?+< ?}]?pDD?4<?w?:[?Ӄ?AVq[?EH ~ -@@(@*@,@8@6??????ywfz@]S?v:?p5:@?Κ@s!_'?=CIJ??VjT8?ۢ+@r,W@3@?|@?|@?|@fA@?|@2X@?|@ k@?|@jO@?|@ߤ@@c@cS@c@clC@c@c$/@c@c -=q@c@cC-@c@c [@4 xF@4wl@05?|@0 (@06-@09k~(@/Vl"@/:T@/Vl"@/|@/Vl"@/F]d? ?j‚?WD@?U=K?>{~?ZzZ? ?ā?8G?!zt9?m^?^?~/M???k3`E`-? 3?<Dx@@@@@*@"??????Rd/1t?Q*_?+S-@g4@cA?qЉk?LXr֪?X[?0@g:oC@4@b I@b -=@b I@bl!@b I@bg@b I@bGE8@b I@bݘ@b I@b@Z1@Z^5?}@Z1@X-@Z1@a@N@Z1@4֡b@Z1@ @Z1@@7>6z@7Mj@4+ J@5'RT`@4A7K@5ae@4:,<@4@4:,<@4_o@4:,<@4K]c?sD?g?q} ?Kw?WOr?MȳYh?F/P5?2M®?vux"E?Ǔy[?uf -?Ó?ٸ?=?C9?Y 0?ԏ >"Q?!7~.W@@@@@*@,???????ɠt?n75R?d@b)@9{?\?y?* \?y@_Os @5@G@G@G@dZ@G@q @G@q @G@c@G@@$:G@$;S@$:G@$;@$:G@$;W>6@$:G@$;q @$:G@$<(@$:G@$:C\@5v@5b@0hr @0H˒@/XF@/X@@.Z@. xF@.Z@.Z@.Z@.tj?}ѿ,?h?>f?lDžN9?G?-tT?|O?NۈV(?'Q?Kd]?qN?lINՄ?^&? -??8 ^?b/bJ? -t@@@@@&@$??????>5E }s?\pn?%FcC@e1@$;luT?<46#?E{#}?SQ?2@g@6@6E@5\)@6E@G{@6E@KI+@6E@cO@6E@fO@6E@dli@YlC@Yx@YlC@YPYa@YlC@Y9@YlC@Yއ`@YlC@YQ@YlC@Ya%@6Q|Q@6Rn@3+@3̮@Rϒ@3"@3كuH@3Q@3e@3Q@3~@3Q@3?|h2:?"i!?zB"?.)/I? ?C .?{si?t𔻝,?Tm՜?oh$?k?.jdY?Q?Ɍ?sr?%bȜ?:9‚?+[+ -~@@*@(@$@:@:????????VO?e,*;?D.H @K|S@Y,=X?n4sT?al~8?Aw*?@b%@7@KC@u?|@KC@KƧ@KC@H9X@KC@KC@KC@Il@KC@KƧ@ +@ 1&@ +@ `A@ +@ -@ +@ C\@ +@ -@ +@ bM@9~($@9 H˒@1+ @1- @0tj~@0nP@/N;6@/ѷX@/N;6@/W@/N;6@/ ҉? T?ߋb?N?#t?DE?R?!q ?I?s8r?9~F?tP*?H, -r_? nI?Ф? SF?!ˠ*(?L?7@@@@@@??????./G<?W 칸?`<@K"̡@ A|:?f_?ph? ?W@r ޥ@8@j -=q@j@j -=q@jhی@j -=q@j@j -=q@jr @j -=q@j@j -=q@j84@?|h@BZ@3-@3`A@2YJ@2ⶮ}Vm@2YJ@29@2YJ@2J?]?Mj̑?%Lє?vI?] - ?ZzZ?]?V i!?`1B(??*v7?vMUu?*wjs?:?9@0w??Xk-7?q?@@@@@??????Vkb?c#c?0?Gm9V'@j%P@Ds?@(z :?V-{?q?Uf3@s޼@9@xR@xI^4@xR@x(@xR@x[W@xR@x0U2a@xR@xV@xR@x{J#@ ^5?@@ ^5?@ xF@ ^5?@ U@ ^5?@@ ^5?@ - @ ^5?@;5@97@@9(1&x@3+@3sg@2Ƨ@2)_@2Q@2Q`@2Q@2"`B@2Q@2fB?CږF?t,?-a} ?Ac ?X?0 -?>sN?&O?>4g9?Җq?~?XS9??# -3/g?їJ?}@@@@@(@&??????FYZ?1,W?SB#No@v>@X@P?RÊph?o}P?Yr?њ@lG@<@7K@7K@7K@-BH@7K@e@7K@? h@7K@@7K@[@Im@I@Im@IFb@Im@ISX+@Im@IY7@Im@I"S)@Im@Iᐜ@3?@3?x5@@0hr@012@0tj@0l"h @.Vl"@.V6@.Vl"@.DF@.Vl"@.# ?}@TΥ? @ ?"?t?0 s?Ȯ`e?| V? ӫI?8ο?/Sϵ?W#&-?\?pDRg?f?)j?f GA?[w?I@@$@*@(@7@5??????[?t?p"@>+ @>ti@>+ @>b}@>+ @>(@>+ @>!@>+ @>zG@>+ @>5?|@t@Ƨ@t@;dZ@t@C,@t@4֡b@t@S@t@kP@2T xF@2Zݘ@/-V@/g8}@/*n@/"@4n@/Bu%F -@/7+j@/Bu%F -@/D7@/Bu%F -@/1&x?^Q5P?31p?Uy*?Aᆽ?.SM?}?q' -B@@@@@,@&??????cfJ?l'H -?qZd?xlo@>;׫@̉F9?)u hj?p:@?J?AY@rg<@?@@ȴ9W@@nO@@zG@@s@@N;6@@2X@F@Hr @F@Dg8~@F@Gz@F@Gݗ@F@EoiD@F@F&@7zqiC@7xTɅ@1ԛS@1J@0j~@07@0V!.I@0W -=p@0V!.I@0W)^@0V!.I@0WsPI?Y͍_?sp?N?!#?#6_?C!?h??CO?qF?siqe?݄ ?P=?K?%_D?oܧ?!ˠ*(?L8AS?b/bJ@@@@@@??????S+<6x?,g0?+ )M@0@G0$?LVC?F??O n>@lX|@@@b`B@d/@b`B@ev@b`B@`hۋ@b`B@bM@b`B@`D@b`B@c,zx@jA7L@jffff@jA7L@j܄@jA7L@j (@jA7L@j҈p@jA7L@j\N@jA7L@jK^@6H@6?@2° ě@2?@2t@2ڹ@1䎊r@1{n@1䎊r@1 I^6@1䎊r@1.H?]?hX*?ě?W0 ?P?-gfQ?CgE ?=U -3??iAA??_PV ?LQ9@@$@ @ @3@7???????`9' ?$?2h{W?Pmv@IN(X@!TJ?RƽaE?p`eo Y?ܹ]?+" @ir#:@C@gWO;@gNzI@gWO;@gU%F -@gWO;@gU?@gWO;@gU*1@gWO;@gTj~@gWO;@gS3333@(\@&ffff@(\@/'/@(\@,C@(\@%@(\@&@(\@* @7!.@7l!-@33S@34m8@2lj~#@2jݘ@1䎊r@2'RT@1䎊r@1H˒:@1䎊r@1hr!?|O?sI?,Z?ھ۲?'`?5H>d#?]˙$>?.\?`P?ʳ_?__C?ĭ?]ߒ@@@@@*@*???????X?;*?A_V?6P }@gQ@'?aǁI?Vu?3z?#O̸^@f{v8N@D@3A7K@3A@3A7K@3B}U@3A7K@3B\(@3A7K@3B\(@3A7K@3@hۋ@3A7K@3@[7@yv@yv@yv@yQ@yv@yJ#9@yv@y)@yv@y~@yv@yG@0H@0Ov_ح@+`A7K@+m\@*rnO@*r{m@)Xy=@)Xy=@)Xy=@)\(@)Xy=@)\(?b'f?{j?!!׋7?G?6r In?tJ?bM_x? g?90?@[*^?إ<(?]#O?bMky?~('?s@a?XLL?O|q?Ѐ>(@@@@@,@,??????Pmo? -vXW?4㽰M? -Fx@3AC}@y#?CL;??s6z@2D*1@0S@0 @0`A7@0'RT@/@/4K@/@/حU@/.NC,?^Q5P?dY{?,Z?GZ0e;?/m{@8?bV,?V i!?}7.?,~[?we @8?i ?,?f)"?O?rsw?C SG@8 @@@@@*?????G@+ J@1&y@+ J@{@+ J@ᰊ@+ J@X_@+ J.@+ J.@6;dZ@6@@6;dZ@6M(@6;dZ@60 ě@6;dZ@6A [@6;dZ.@6;dZ.@1@1$tS@0+@0E@/;dZ@/C,@/73@/8tj@/73.NC,@/73.NC,?OW?O'q|?G)?nx@8I@8?;0?Ò7=?3?!@8S@8?!?|\?7^|?ޒ7:ʪ@8 @8 ?@@@????H@P`A.@P`A.@P`A.@P`A.@P`A.@P`A@d%3@Y+.@Y+.@Y+.@Y+.@Y+.@Y+@s@8۹~($.>@3 I^5.Mm@2-.Qn@1:S.NC,@1:S.NC,@1:S@1R@8J@8#@8:@8 >+@8I?@8J@8@8i@8@8S?:;{@8p@8 *@8p@8@8 ?Xk@&?I@/@5?|@/@-@/@-@/@ޞ@/@qu"@/@ۘ@-V@nP@-V@W@-V@D@-V@2X@-V@沣S&@-V@䎊s@4Q|Q@4Q[W>6@0 ě@0GE84@0]V@0\C,zx@/`d@/ߥ@/`d@/zG@/`d@/!-w2? ?h?RsW?q@v?Dj??|O? -?pM?@[*^?&Tm?b-x?W ?/? J?hB^?SP?ˢL1@@@@@,@$??????@7?=G?2[?+t@Mѣ@vӭ?R{ -dM?<Kt?M?Gq@g| kd@J@4j~.@4j~@DĈ@4j~@=ĥ @4j~@X;y@4j~@O -p@4j~@@Ov@r.@r@rv@r@r0@r@r.@r@r\%@r@rͳB @4Y*0U2b.>@0hr @0"h@0'KƧ@08@05sh@0 W@05sh@0 -sa@05sh@/ -@8J?;??tp{?goA?u;V?H@8J? ,E?|QHf?= B0?h@8@.>@3\I^5?@3cA @2`A@2@2O M@2D2W@2O M@21@2O M@2"@4n@8J?%߰?X+??=T^@8?F/P5@8@8i@8?=x@8?`w@8 *@8p@8?h@@8 @@ ??N@߷KƧ@߫K@߷KƧ@@߷KƧ@ߦ"@߷KƧ@߻.@߷KƧ@84֠@߷KƧ@YPI@S@&fffe@S@m@S@LKL@S@@S@H@S@0a@6H@6]{.@4G+@4=]jo@3v-@3b@3QiB@3Sr@3QiB@30oiDg8@3QiB@3%H? 1Ӡ?;@3+ I.Mm@4~".Qn@2O M.NC,@2O M.NC,@2O M.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 P@i/@i$/@i/@i@@i/@ii8@i/@i(@i/@i>(@i/@if@Q@CS@Q@!%@Q@+Mڑ@Q@'>s@Q@&4Ü@Q@'Ò@8*0U2b@8:@3&+ J@3%Ff2@2;Ƨ@29_@1YJ@1raϥ@1YJ@15f@1YJ@1_?>KtA?4TN?v|*f?xLɀ?J/?^L~?uq?]m? p ?x_?x7m#??֙DQ?I͑Q?p ?k ?юf@?#&z?^а~@@ @(@(@5@6??????F@z[V?"z&CJ?\ψ@i99-@-MJ?9F\*\?}{fp?nmQj?;EAG@n~5p@Q@\@9X@\@C@\@Ov@\@'/@\@L`@\@_@H9X@H\@H9X@H1&y@H9X@Kq @H9X@L@H9X@LC@H9X@KC@3>6z@3eں@1S@1{m@1"`@1u"@1}ce@1}jOv@1}ce@1zn@1}ce@1zL_?|O?L?ojVrnBZ@4 I^5@4?@4`A@48}H@4@5@4JM@4@5@4$tS@4@5.NC,?q!U?B ?&)_? -Y?\ǿ@8?sD?^? u?Zf?B<'@8? uf? $F)? U?71lQ?W1x@8 @@@@@*?????S@^5?}@nآ@^5?}@R~@^5?}@ 9@^5?}@P@^5?}@\A2@^5?}@D=F@`A7@l^@`A7@}T@`A7@f @`A7@GD@`A7@U@`A7@n!@6@633@2E?|h@2C&g/@y@1tj~@1|@0m8@0UՑB@0m8@05*@0m8@0fG)?ڡG7? ;m ?ש;?|ǵ9? %M}?jB/?=ak3?)RP?BL?Ê?: O'm?!2{? L?-?gp0@@"@ @"@3@2???????g vD?UHK?PWp(?H.Q@xxYU@c?p>B?h]wh? 5T?ُ͊?@f(y@T@:E@: X@:E@;[?@:E@;J@:E@:i@:E@;}̇@:E@;E@(\@;@(\@+"v@(\@.5)j@(\@'c3j@(\@! @(\@&* @8hYJ@8C;p@4!hr @4#%W@3tj@3s]@2@2V@2@2hb@2@2Q!?Mo?c -_?Y&??j6?X ?7?0f?%%?BTV?؛HN?H_?J?Un?Ŷ0lW??@ i2?)B}@@&@(@,@"@(???????x@@t0XU?bO -?[mLx@:@(Pܷ&?ub'/?yP㞺 -?{-?H;}@pC7@z@U@E0 -=@E0`A@E0 -=@E1:@E0 -=@E1'+@E0 -=@E2+ǭ@E0 -=@E.@E0 -=@E.u@V@hr@V@@@V@(@V@.@V@<]@V@쪗b@1䎊@1䎊@,A7K@,@+n@+@*Xy=@*S@*Xy=@*Z@*Xy=@*2#n?_?R?lj?}?꘏?O?bMn?𕧌?v?-4?J/51^?T`0?bF?r?nڨ8?p?%ip-?Yp@@ @"@ @:@8??????[С)T0p?@n?*N!1@E/ʨ@쯯?P%",?9= -BVT?#~cyp?k1'a@s]Q@V@v@O@v@9XbN@v@4*@v@,C@v@*JM@v@,L_@yO@zx@yO@y\@yO@y'@yO@y!-w3@yO@ytj@yO@y%@8e!.@7w1@5+@5<쿱@4Vu@5ѷX@4&YJ@44%1@4&YJ@40`A8@4&YJ@4-8Z?' ?0!?1?W?5Ç?JpT"?{6O?j{?S]:?Y`?/m?*ML?٧??[e'?7?$–?3 D?y@@@@@,@(??????CID?nl~*?k6wDžn@,$VJ@yً?}?|x?%{c8?Ϫ]1I @tj@W@C@@@C@C~@C@ϒygz@C@l~@C@ʒT3@C@_j@1'@j~@1'@K@1'@':K'@1'@  @1'@ײ@1'@*Y$O@7mv@7hG~#R@3B ě@3DB$@2ttj@2uQp@1n_o @1oF@1n_o @1n+(+H@1n_o @1n+kw?\l6?@-?U?+@8I@8?lOx#@8@8i@8@8S@8?8$@8 *@8p@8@8 @8 @?Z@M@P_@M@ǔzG@M@ǔj~@M@džIR@M@т @M@zxl#@hZ@hI<@hZ@i&@hZ@iMM;@hZ@itj@hZ@h1&@hZ@h@6w@@6v=6@3J^5?|@3y=b@2~"@24J@2Tm8@2>BZc@2Tm8@2r@2Tm8@2xF]d?E[i)?h(O?ޑKO? :>Co?FILXT? ا ?~Ov_ح?Klր? Ķ?ۦkQ?Gfx^K?Y@?R?K)?J8ȟ?h1oS?r?|)-}(@@@@@@???????{˲NK{?q,?Z.7@tmI@h^?RF*?zb^f@v ?rb@d!1<@[@R`A7L@R`ě@R`A7L@RT֡a@R`A7L@RWkP@R`A7L@RV_ح@R`A7L@Rzu%F@R`A7L@Rvȴ@W -=p@S@W -=p@KC,@W -=p@P*0@W -=p@G&@W -=p@w@W -=p@ohی@6qiC@6g -@3S@3A [@3 -V@3c @2iB@2M:@2iB@2a@N@2iB@2-? ?U@3 hr@2[C K@2U`A7L@16@1D*@1!9@1D*@1"8a@1D*@1$_*Y@8J?:?Mi1?ƦJG? ג3?^M@8J?EW?9V?nY?su*o?߭%@8p?֢?CJ"h?JP?#_?p?8>@&@,@,@<@;?????]@d/@\hs@d/@BZc!@d/@A \@d/@SMj@d/@ca@N@d/@kC@]V@]"@]V@]}H@]V@]p:@]V@]:@]V@]@]V@]Y@8>6z@8}ce@5I^5?@5-V@4V@4oiD@3iB@3Ŏ!R@3iB@3tSN@3iB@3 [7??Ld{6?>?%?U?0eʪv?.pC?Z?/+?E?܉a^?M3 ?Җ(??@@@@@*@(??????[v[?Fqu%?[lt@^)@d<R?e;N?u$?Ŷ?6FV$c@jyQ/@`@h^5?@hl@h^5?@hC@h^5?@hƧ@h^5?@h=p@h^5?@hۋr@h^5?@hfB@9l@9~#@9l@9^5?@9l@9'RT@9l@9'RT@9l@9L/@9l@9*0U3@3:@3!-x@0ԛS@0ԯO N@/x@/\)@06z@0}Vl @06z@0At@06z@0At?|O?'?Gy??Rw#z?#6_?6J?| ?? :?ދ"?Uh;?I?sx/T?W?/?٤?RJ?b/bJ?_խW@@@@@,@(??????4h('@?&PD?!(K%@hl@9Rp?F[M.?Ax4Y#?}!?X*l@fv@a@9NV@9MV@9NV@9Y_q@9NV@9? @9NV@9Kt@9NV@9U?@9NV@9W@+@@+@̥zxl@+@_o@+@1&z@+@Ϫ͞@+@̿[X@6S&@68YJ@3+ J@39@3-V@3Rn@2m8@2p:@2m8@2ԯO M@2m8@2䎊q?F/P5?2?FI?L?e7@?S:Ъ;5?us\?z]藍?ܜ?lyx?]vM ?>[Fh?!A -m?3 ?a?pLA?xb?zy@@@@@@ ???????aΫc?An&ן?@E|@9RQv@ҝU?ck?aU:P?o%7^?SQ0@iɈ=@b@Q.@Q@Xy=@Q@Qhr!@Q@Y+@Q@UL_@Q@QN;6@ .@ @p:@ @|@ @ ~'@ @ @ @ rF@8YJ.>@4S@4-@417Kƨ@42}Vm@3Tm8@3\(\@3Tm8@3Z,<@3Tm8@3YQ @8J?Qi{cj?=՛?Zto?wX?ߑ@rY@8J?蠔?lU ?%ja?B*Z?]'Y@8p?+0T?=o -F?{ޏ?vucʲ?/̽`@@@@*@,?????c@^7@^6ȴ9X@^7@^0|@^7@^$Z@^7@^%84@^7@^#w@^7@^3 @pzH@pV@pzH@p@pzH@p2@pzH@q$/@pzH@q*g@pzH@qc@7_o@7@5+@5 -L/@56-@5Stj~@4@5@4Q @4@5@4oh@4@5@4n.2? ?AZ?"?%I _?QW᎕?̀m?oC?t?ń ?_*>?07\?b&?_??"x?OMy?pZ;d?-o@@@@@*@$??????b(7?$?\tG?e/~Q>@^1Uh@qR ?}9?U-wX^/?/oc?o@<@fs[@d@\1'@5?|@\1'@о )@\1'@nP@\1'@r @\1'.@\1'.@խhr@Քj~@խhr@!-w@խhr@ӝ-V@խhr@O@խhr.@խhr.@8!.@8.2@4|hr@35oiDg@3ȓtj@2͑hr!@2&@2D?@2&.NC,@2&.NC,?pEIt?ĩa@x˖?91G@8I@8?mX?kb?me\? &@8S@8?d? [b?Dø;?i,@8 @8 @@@@????e@C@|hs@C@֡a@C@bM@C@&@C@Ʌn@C@aA@vȴ9X@ه-@vȴ9X@r<6@vȴ9X@ق&I@vȴ9X@zc@vȴ9X@x@vȴ9X@xPH@8hYJ@8i^5?}@4ěT@4䎊r@4V@4'RTa@3xF^@3@3xF^@3S&@3xF^@333334?ȉak?:@+kjf@+kC%@+kjf@+k:)z@+kjf@+kU=?^Q5P?Čۨ1A?$C@?/?@ A2?}?bV,?>`?['?3.Yl?@?Z,a?b@?~('?8*?͓E'??l&@@@@@(@*??????M+d?%?4qmC? Gϼ>B@Ň<@y?C94?.΃N?wX9?TAZݛ@sS@g@RnP@c @RnP@FL@RnP@Rޞ@RnP@*!7@RnP@/fn̉@RnP@(*@.7KƧ@.>"@.7KƧ@.ί@.7KƧ@...n@.7KƧ@.kW@.7KƧ@.r9$`@.7KƧ@.Hj,@8@9O @4I^5?@4"0@3PA7K@3KOy@2O M@2e@2O M@2 t@2O M@2j=6?|B &?ͼԦ?s?sNȺ?5m;0 ?`?Eg??{.V?{?cO(?aő ?Ko?%j?*?X?`oe^?!8@@ @(@ @2@9??????&S?6?zx?CwB?GZ?їJ?*@@@@@,@*???????9Pa?M_dɗ?Yȏ %@vJw@M`?k2rP?v}:w\?k!?1Q(@h/@j@XbN@ȴ9X@XbN@XbM@XbN@@XbN@"@XbN@F]@XbN@Q@y"@z1@y"@{u@y"@{s@y"@yXbN@y"@z)y@y"@yb@6]ce@6_o@2hr@2@2~"@2ݘ@10U2a|@1Q@10U2a|@1W'@10U2a|@1ݘ?Br?sI?tcj?$jB?FL{?<_?|O?\tW?%gǿ?.?O ?低NV?T?1j?~/O?l?Cns?Tj@@@@@*@&???????8]P}*`?5>hc ?6@{@yO?Tf|X1{?7 -6#?Y^S?xN @hmH k@k@O;.@O;@ @O;@*@O;@@O;@ЇYV@O;@u@+x.@+x@+x~@+x@)N@+x@,~yW@+x@,xg0@+x@,l @8쿱[X.>@4i+ @49=]?@3t@3KDP@2𖻘@2oE@2𖻘@2s%9s@2𖻘@2Fh@8J?2?!ұr?cet?*- -Q?a@,@8J?Is|?,n?^\Nb?D?? @8p?sg̘?uc4?7,?<?+̪W@*@&@(@;@8?????l@{@{KJ@{@{޳c@{@{\N@{@{4.@{@{%&@{@{Uϖ@=p -@=)4@=p -@K@=p -@: 2@=p -@=hH@=p -@BlJa@=p -@Af@4 xF@4tj@1S@1BJv@1"`B@1vG6@0sh@0%@0sh@0y/r@0sh@00?| V?|28?N?`?]j?P+?| V?l0F?j?aݚ?p? OYb?ח?(c? ?+@?1?G@@@@ @2@,???????`?F<?LחԘ?9 3P@{ғ)@??j"<@D?XTױ!?Kw?{i@@e'b}@m@LvE@Lv+ @LvE@Lu\)@LvE@LvFs@LvE@LvR<6@LvE@Lus@LvE@Lu*1@ݒnP@ݒ ĝ@ݒnP@ݒ:)z@ݒnP@ݒ@ݒnP@ݓa@@ݒnP@ݓ3333@ݒnP@ݓtj@304m8@30:~@0S@0O M@/n@/Gz@.̲@.̘_@.̲@.̲@.̲@.̲?|O?3i?1f_?vHn&? f?^?|O? g?NJ?uX?Cq$u?b-x?nwf?u?u@>N ?͓E'?SP?M684@@@@@*@(??????.z`?&?F ?^C@Lv^@ݓ?2>=q?5E??q=?w"@fW@n@w@]@w@(R@w@q5@w@(@w@qOh@w@ڧ}@"`B@$(*@"`B@'L>@"`B@$qB@"`B@ z@"`B@$[ @"`B@@8 ]ce@8cA&II@3^5?|@3n@2`A7@2[@2z,<@2tR!LT@2z,<@2vYt@2z,<@2vV?'Z]&?5;-?`&בɽ?2K?5?"?I'%?2S8?y'?E?JWV'?a?%?@w%?D3?vCy? a7?*@@$@(@,@4@2???????>C3/?Um_s?B^0@kU_@!V -?qMyG?_9m?K`?QJe6@n9@ym@o@  I^@ u a@  I^@ n@  I^@ ѷ@  I^@  @  I^@ `A@  I^@ n;@bPbM@bP -=@bPbM@bPc^K@bPbM@bPv7@bPbM@bP]0@bPbM@bP 1 @bPbM@bP|@4۹~($@4ܥz(@.8tj@.8jZ@-h1&x@-h @+873@+8_W1@+873@+82&@+873@+8YJ?}x@LC?d3r?pX?ȗ ?9G@m߀@q@3Z1@3Z1@3Z1@3\]cf@3Z1@3X@3Z1@3Z~@3Z1@3YJ@3Z1@3YXbN@ I@~#@ I@~'@ I@0@ I@xF@ I@6z@ I@xF@5>6z@5+ I@1ٺ^5?}@1"`@0Ƨ@0Q@0u@0O @0u@02a|@0u@02a|?}ѿ,?nx?)?&P?^:f?,I ?^?}ѿ,?0n=Q?CXs?@Cv?UBi?]#O?T0?Q?`x?8 ^?SP?ˢL1@@@@@,@&???????+^0?'R? |X }@3Y{@h?GP)&^?-~}e?^;I?eIq@g%u@r@(@(1&y@(@"`B@(@ - @(@6@(@l"h -@(@!e@9Xb@@9Xb@oiDg@9Xb@ὥ@9Xb@SM@9Xb@ȴ9W@9Xb@@7v@7<쿲@3+@3"3@2"`B@2nO@1@1zxl"@1@1M:@1@1&??d_s?ޑKO?$?[Fh?.EkЍ?Эs_`?S[D?7N?4\ɵ?Ee,@@@@@&@*??????JX|pcX?e%6׾?T\Ү@v|#@8Pp?wNI?n&K?'aM?N7٤@oHwi@t@8@8F]@8@70Vn@8@9G@8@7!n@8@5(=@8@4@r-V@q@r-V@q<@r-V@r@r-V@u`/@r-V@q'@r-V@rB@4*0U2b@4E@1hr@1@0tj@0V@0Tm8@0R~@0Tm8@0TGa&@0Tm8@0S_g{?|B &?9?e>c-?'?6θ ?9d?|b@?~?>IA?-HD4?i֭?x9f?tw{O?HZS?̯.3?:Il?#?$@@@ @ @3@2??????Baw?/Eʫ?DPp?δ7?]|?cx?>@fngb@u@"@Q@"@.H@"@"@"@vȴ9@"@-V@"@ڹ@Y@YzG@Y@Ya?@Y@Y@Y@YSN@Y@YMj@Y@Y3332@3䎊@3&IR@0 ě@/U=@.DZ1@.CF]c@.V@.UXy=@.V@.UϪ͞@.V@.UfA?|O?'?U#?<ݝ? 9?aX?| ?4 "?'Q??Cq$u?eDQQ?nwf?§d߱?|׿pZ? ??L?SP@@@@@(@*??????-d%0Yֽ? LѡF)?.}H@Cj=@YYh3?+nԡ]?2$3U[?di\T -?pLG@fIZw9@v@dZ@k@dZ.@dZ.@dZ.@dZ.@dZ.@&x@BӮ!@&x.@&x.@&x.@&x.@&x.@8%!.@8'm@43S.Mm@3W-.Qn@20U2a|.NC,@20U2a|.NC,@20U2a|.NC,?}x+@8I@8?t D5@8@8i@8@8S@8?l{@8 *@8p@8@8 @8 @?w@ I^@hs@ I^@tS@ I^@{J#:@ I^@҈@ I^@-w1@ I^@vȵ@"`@F@"`@PH@"`@ޞ@"`@~@"`@)y@"`@)y@8 _o@8s@3° ě@39XbN@2tj~@274@2u@2Z@2u@2z@2u@2t?X?t,?Q/ -?̒?e7@?7D -?)T?9}=? -oɧ?,??j?oeư0?3a=?I6y@=u@=u@=u@=*1@~6E@~4F@~6E@~73@~6E@~6z@~6E@~5Y~@~6E@~5*1@~6E@~43@5H@5u"@2+ I@2_F@1A7K@1u%F -@0?䎊r@0@ k@0?䎊r@0@ѷX@0?䎊r@0@ ě?|O?Qd?njĕx?'$5?#6_?f]G?F/P5?[ K0R?"a?N?<}?m/w ?=P|@@@@@,@*??????؏؀VTx?*(43?=~@=0s@~5?J݄inj?3Va?g?mð,!@h>@y@u@S@u@33334@u@+R@u@5?|@u.@u.@wp -@w+ K@wp -@w^$@wp -@v1@wp -@wQhr!@wp -.@wp -.@8^H@8`- @3hr@3ES@3v-@2ڹY@2ٌ~($ @2Ohۋ@2ٌ~($ .NC,@2ٌ~($ .NC,?A[F?po_?PQ?fP@8I@8?8ȺJ?&`?;+?X@8S@8?UWci?4r?H/b?DZ@8 @8 @@@@????z@:^5?}@;dZ@:^5?}@;u@:^5?}@:^5?}@:^5?}@9_q@:^5?}@84֡@:^5?}@8tj@@@@jO@@C\@@"h @@a@@a@5[~($@5\!-w@1I^5@@1O;dZ@0"-V@0"`A@/Ǔݗ,@/@@/Ǔݗ,@/ɠ'RTa@/Ǔݗ,@/ɅoiE?]?J`Y?"? -?'ř ?+ʦ_?]?p RN%?90?Hn? f?U3p?*wjs?9o?D(p?v_.^?SP?E݋N?@@@@,@,??????A"=c? k? Sg>@9$7,@x?c)xT)?kA\?2!?%PzX@s~@{@Tr-V@T~vȴ9@Tr-V@ToA@Tr-V@TuL_@Tr-V@Tuᰋ@Tr-V@Tp ě@Tr-V@Th ԕ@@ȴ9W@@@@ @@oiC@@wk@@SM@8g l@8K]@4E?|h@4G@@3tj@3e@3J0U2a|@3H@@3J0U2a|@3H9Xc@3J0U2a|@3CMj?oC?Ȼv\?@^ڢ?IW`g?n.}?`$-?,e?-',? ?GW??!oj`?#N? -e?vv?Vn?u4?K7~w>?q@@@@@*@(??????[u?upY-ڀ?a{?hN@3B ě@3@A7K@2`A@2n.@1iB@1;dZ@1iB@1{@1iB@1а{@8J?W[?=P?)?u??=@8J?ۣ0Τ?pI?? ^??X<@8p?3a=?/X?"a??E݋N? `@@@@ @,?????~@-V@ -=@-V@1@-V@TɅ@-V@h ԕ@-V@Ft@-V@!-w@A7L@9X@A7L@F]@A7L@A \@A7L@t@A7L@ ԕ@A7L@%2@7e!.@7e`A7L@4^5?|@4j~@3j~@3wkQ@3u@3TɅ@3u@3_p@3u@3#x?% ??0? N:??Pzj?1j?4\2?)~Y6 ?*Gk?9]t`?汝/&?MM.?:K?~gN?C.ΐ?E>g?r:vI@@@@@,@,???????n<y`?b!ftB?j0a$mT"@d4@:t?|#N?P?@l.d?ဩP@`@@2w@2n@2w@2|PH@2w@2v4@2w@2uᰊ@2w@2t!-v@2w@2vv@n@ě@n@[W?@n@s@n@4֢@n@u@n@Ϫ͟@8uS&@8k҈@3hr @3B@3~"@3%1@3m8@3ѷY@3m8@3JM@3m8@3r?@?u~?_?998??y+PYu?|O?z? -?|?MuVQ?6@(/?uDJ`?*T?ٹ?O"?\,!?>LR_5@@@@@*@*??????Qr{>dlHL?Q"?/U0L@2v<@"?eA1ċG?UTK?[2?q8@oBM3@@p -=@:A@p -=.@p -=.@p -=.@p -=.@p -=.@$9Xb@$F<@$9Xb.@$9Xb.@$9Xb.@$9Xb.@$9Xb.@7*0U2b@7Y@3S.Mm@3$j~.Qn@3!.I.NC,@3!.I.NC,@3!.I.NC,?3@8#@8:@8 >+@8I@8?kz -~@8@8i@8@8S@8?ksP@8 *@8p@8@8 @8 @?@\(.@\(.@\(.@\(.@\(.@\(.@*/.@*/.@*/.@*/.@*/.@*/.@7|Q.>@3xr Ĝ.Mm@2Z~".Qn@1𖻘.NC,@1𖻘.NC,@1𖻘.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @/޸Q@/@/޸Q@/'RU@/޸Q@/O;e@/޸Q@/ws@/޸Q@/&@/޸Q@/c -@w@p -@w@银E@w@騴9X@w@0 @w@@N@w@꙳|@9Y*0U2b@9N($ x@55?|@5c @5 "`B@5N;6@4[W?@4a@@4[W?@4w@4[W?@4u?xg?q?BI{?ߦ>?aRo?3:?_?M\ٓ@?DP??{T??6-K?^w?A8 K?j?U#?uqR7?Tј+@@@@@@???????>F@?5?3+0|\?>Hr&@/u@Prf?pYfL?y1D@#/>@=rQȈ8@os@@a@a#@a@at@a@eL@a@d|@a@6L@a@0:@jcS@jR@jcS@i[Y@jcS@i@Ϭ@jcS@iU -@jcS@i".@jcS@iAق;@7~($@7+p@3W+ I@2e*A@1A7K@1;@16z@1jp|& @16z@1k ̒@16z@1pz&?~Ov_ح?WMʨ ?'N5?1?\?Zl?lhFB?ڡG7?K@o?u:d?Q?~u@@&@ @,@:@9???????k~D돎?XÊ?f2@mGm@j/(?p?}"5y?׋:?l5B@_(@@]V@]O;d@]V@]p:@]V@]y=@]V@]@]V@]:)z@]V@]^ @3ě@3G{@3ě@3*0U2@3ě@3J@3ě@3@3ě@3wj@3ě@39#w@3|Q@3e+@2n5?|@2jY@24tj@23S&@10U2a|@1 -=p@10U2a|@1@@10U2a|@1҈p?|O?6 '8?ub5?Uv?#?3ƒ?kʍL?|O?馿j?*&k?{G_rV?Լ2#?Y٭ 8?|׿pY?P<}?*0x)??DΊ?@@@@@*@*???????Y~;?T:4?Wk3^"@]@3M]?psHeG?rD?pAK|?xم@ac5U @@4j~@4j~@4j~@5$@4j~@6u@4j~@5\*@4j~@5 '@4j~@3@F&x@F'+ K@F&x@F'lC@F&x@F)7Kƨ@F&x@F'-@F&x@F)Dg8@F&x@F(Xy=@2B䎊@2Be+@-{"`A@-{lC@-%@-%+a@,#Z@,#9@,#Z@,"ѷ@,#Z@,"@5?^Q5P?ƿ57?S?|*(?/`?{`?s8r?-I`K?@?M\ٓ@?cPQ?3;?|׿pZ?^ a?pk Y?pk Y@@@@@@??????Lp?-8"x?)~W?8@4=C@F(x)?8?G<788Z?mWZ?l1@s -@@ 1'@ 1'@ 1'@ Z@ 1'@ 1(@ 1'@ ?@ 1'@ u@ 1'@ +k@Lr @L9X@Lr @Lt@Lr @L/@Lr @Lh ԕ@Lr @L|h@Lr @LF]e@4(YJ@4(eO@0 ě@0n@/E@/s@.̲@.+ I@.̲@.Ǔݗ+@.̲@.ȧ?| ?:L -?D&?؆{~?E@? ]V?|O?> -fu?NJ?.?R? Ӻ^?٤?sq#?`x?*|\/?j?L8AS@@@@@&@&??????? h Z?d~?@5@ 笿@L((?/ND?a q:8?j xG ?ɳ?@fQ@@4j~@+@8I@8?]@8@8i@8@8S@8?*wjs@8 *@8p@8@8 @8 ??@Ձ$@և+ @Ձ$@@Ձ$@Ʌo@Ձ$@+j@Ձ$@z@Ձ$@!.@u\)@vu@u\)@vȴ9X@u\)@x4֢@u\)@u*1@u\)@q@u\)@sg @6Q|Q@6Q4J@2+@2(@1`A@1ae@0:S@0}Vl@0:S@0˒:)@0:S@0?}ѿ,?`?RsW?˅T?<_+fj?nM?F/P5? ,C?i?@[*^?qn?"?Me^?.+?OW?3{Z?ܑ ?gϵ@@@@@,@*??????>E`?~sL+?2w}@ֈ5*@tZl?4u?R6?ub`?Q U@g!1@@d%S@d%S@d%S@d&[@d%S@d&$PR@d%S@d''A@d%S@d%Yfa@d%S@d$Ζ,@@bM@@f@@Xzx@@h@@3@@}G@2䎊@2䎊@/G-@/GrG@-|1&@-{E@-(ۋq @-(fsF@-(ۋq @-)'W{N@-(ۋq @-(^9?^6P?S?,5? ?4ح5?j?bQ넛?5-?6?8Q9?מd?\j4?bw' g?Z[?{L?&{A?3 ?pu{,@@,@,@,@8@7??????PJ#dTX?)P"?(Y9-@d%d@i/?8d?6 -voE?n)?f@s%[@@l@3 ě@3/ޖ@27KƧ@2?2@2䎊r@2|`ӺM@2䎊r@2 @2䎊r@2~)@3!hr .Mm@2`A.Qn@26z.NC,@26z.NC,@26z.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @1@Q@1@A@1@hی@1@b@1@\(@1@iC@ 7Kƨ@lC@ 7Kƨ@@ 7Kƨ@`A@ 7Kƨ@@ 7Kƨ@#x@ 7Kƨ@Xy=@7H@72W@3S@3jOv@36-@3*vȴ@2s&@2_$tS@2s&@2b3@2s&@2b䎋?2}?i1[?<Ӟ?g(6?l??Y͍_?/ω(2?KF?:`?͖M?87?<{?  l?6P?B?y\? -e@@@@@,@,???????4>?p'?fP3?[?F@Z4Mf@ 뛑?7W?a6 +?9?ya}Q*@bOo@@7K@Ž@7K.@7K.@7K.@7K.@7K.@eO;@eɻ[@eO;.@eO;.@eO;.@eO;.@eO;.@7Q|Q@7QE@3hr.Mm@2`A.Qn@1:S.NC,@1:S.NC,@1:S.NC,?| V@8#@8:@8 >+@8I@8?E[i)@8@8i@8@8S@8?jK@8 *@8p@8@8 @8 @?@%T@%~T@%T@%*@%T@%O@%T@%w@%T@%t5@%T@%@A@B@A@BM:@A@B@A@A+@A@A(a@A@A#@8zqiC@8{h @4 ě@4YL@417Kƨ@45-y@3m8@3)qb@3m8@3XC@3m8@3|?C I?ӡ?g?? _? -*?ŰCz??qP:6?  -1?̦2?JS6z?^Q5P?*|3N?d3"7?ݡu(r?<_+fj?dW?bM_x?#maЀ?HD?Ѝ`Hi?GU?Bh?L[]? $gv?Me_?RJ?<71?ad|@@@@@*@*??????]+?|!V"?GXZ?lb^@LPߥ@Q]DK?@?Vl?xTR\?_Hm ?Uh@s&a@@n@%$]\@n@EO@n@" @n@g{}@n@)L@n@o@nP@[ E@nP@ےR{@nP@ @nP@ -W@nP@\B@nP@#B@8,<@8A`M* @4S@4w@3"@3>@O@3p@3oa@@3p@3nOx4h@@3p@3pj?c|iqd?Åt.?XMX?4<~??7?nwe?3<' ?\P?̴?-IG?Q7?E͵k?ޗf8u?Kjw?! f?|? ?uT@@@&@ @,@,???????6qx?gSZiu?QW L(@\@A?|,ez?ufђ?q+?냣V Q@nH&@@ë I@él@ë I@Ç#@ë I@P'R@ë I@CZ@ë I@`D@ë I@f$/@ @ ě@ @ @ @ +@ @ oh@ @ ffff@ @ U=@6v@6_ح@3S@3U2a|@3"`B@3nvȴ9X@2ce@2Ov`@2ce@2qj@2ce@20 )??). -?L7S?@@TbM@T -=@TbM@T @TbM@T Ĝ@TbM@T@TbM@TMj@TbM@T@+@-@+@ C]@+@@+@qj@+@ :@+@ (@5:@5,<@2|hr@2А- @2$j~@2%zxl"h@26z@2! @26z@2"p:@26z@2(\?2}?d_s?,Z?@R?#0Y?0 -?1j?u(?Qrz?{d?Q?9x]e? uf?"`?t -j?9XNu??%@@@@@,@*??????#z?S?2@;@{?W)'@Td%/@KT?Q0ɳ?vS[?^?])^3@f8K @@ C@ @ C@C,z@ C@)@ C@Xy?@ C@q@ C.@"@n@"@$@"@D@"@'@"@\@".@6@7- @3 I^@3حV@3tj~@3T@2sh@2쿱\@2sh@2p:@2sh.NC,? ?̣\C)?eI2?²E|?`ގ q@8?qv?~?OT?@/@{?8i=F @8?|aQ?Us?F@8 @@@@@,?????@ŋƧ@ŋƧ@ŋƧ@ŋq @ŋƧ@Ō"h @ŋƧ@Ō"h @ŋƧ@ŋ~(@ŋƧ@ŋƧ@چ@چ@چ@چ&@چ@چ@چ@چ@چ@چ]c@چ@چ&@0-v@0-v@*tj~@*@)E@)%F -L@*#Z@*# -=p@*#Z@*# -=p@*#Z@*#A [?^Q5P?°R;?$C@?8v?Ot?O+y?bM_x?%F?j?0N:s?IM?Kc?bMky?u?oܧ?avh?O|q?@@@@@,@&??????%1X? ?A?fi? -4*K@ŋ@چ[?!vy?S돤?>+d??+ :-y @s@@lD@hs@lD@hs@lD@G@lD@@lD@)@lD@-V@G}E@GyXbN@G}E@G}<64@G}E@G{lD@G}E@G}IQ@G}E@G~%@G}E@G'RTa@8%!.@8# ҉@2^5?|@2/V@2`A7L@2-@2Tm8@2Vt@2Tm8@2VR<6@2Tm8@2Vl!.??6 '8?BI{?Att?EE?!w?)T?PuN??ǧ?D6?$) ?'?LR_5?r@@@@@*@,??????&3P?V\ ?E%C? {ї@J@G}zwW?^2:?/?0f?ZՏYZw~?U6 y?fr[?|M4^?!D?bk6@@@@@(@(??????b?xsg5?shY*?dX@hq@VZo?0Aq?{Eq5?F<7_?Eb@g@@b`B@ti @b`B@ ě@b`B@-V@b`B@33333@b`B.@b`B.@C A7L@C&>@C A7L@B1@C A7L@B@C A7L@A~Q@C A7L.@C A7L.@8|Q@8gb@2hr @2@4@2tj~@2XbM@2 [W?@1\(@2 [W?.NC,@2 [W?.NC,?tJ?Yz?$:HT=?;kE_@8I@8?t D5?y>cv?J?Zy@8S@8?T -a?[9\?6_[?F@8 @8 @@@@????@t@ 3g@t@ͼ|M@t@/@t@=$7@t@yYG@t@sf@6E@2F@6E@I{ 6@6E@(yd@6E@SJV@6E@@B-@6E@Cr@7-v@7,`@4O|hr@4bLmU@3"`B@3f@2@25h@2@2x@2@2 P? 8+?ɅK?w[W?"?[@F'?Z&v?eXW? A:?? ,%L1?PBl?$n润?Wl?N ?͓E'?#.?O|q@@@@@,@*??????QzNs` ?9\*?ģ-n@f@ua?H"?V搄?$ ? E?Bd>8 -@s#~@@s5?|@sS@s5?|@s$*@s5?|@s)Q@s5?|@s;dZ@s5?|@s @s5?|@qH˒@OA7L@O-V@OA7L@Oqu"@OA7L@OU=@OA7L@O"h @OA7L@OPH@OA7L@UtzG@8hYJ@8c$@4sS@4~@3-V@3p ->@3Tm8@3ao@3Tm8@3`- r@3Tm8@2g??N`?}g?nii)?3!E?+պ???KF?T?mɣ&Z?8烔o?fr[?k8'?$?]-W,?s?qmE@@@@@$@$??????P6x/d?s  -??`8@s@O"Z?l\6??U>b?^@ >+9;Z@fO@@^5?|@^5?|@^5?|@?E@^5?|@qi@^5?|@u?@^5?|.@^5?|@ě@ hr@ Z@ hr@ Q@ hr@ 'RS@ hr@ 6}Vn@ hr.@ hr@ XbO@8 ]ce@8j~@4hr @5I#w@4."@4%@3!.I@4`A@3!.I.NC,@3!.I@36z?oC?Z?бM?j0@8I?N:?sD?o,?0?M0D?=C(@8S?^U?Fp'?K^ -N?'*?5@8 ?$@@@@@?????@&ffff@&@&ffff@'K]@&ffff@')_@&ffff@&IR@&ffff@$ xG@&ffff@#&@\;dZ@\:^5?}@\;dZ@\=!.J@\;dZ@\>($ x@\;dZ@\6!/@\;dZ@\8@\;dZ@\8}H@4~($@4i@2S@2*1@2"`B@2PH@1D*@1qiC@1D*@1u%F @1D*@1ݘ?|b@?Z?3?a_b?EE?_Oo?|h2:?d?36G?j?V i!?VT??9o?sg?Z? 1 D@?Tj@@@@@,@*??????A 1*G?*hb? v Xwv@%@\9+L?Iݎ[u"?@ba.?>?Y)y7@f,{@@MO;d@T9Xb@MO;d@z)@MO;d@l~($ @MO;d@WkP@MO;d@y k@MO;d@gy @Ntj@N V@Ntj@MV@Ntj@M=H@Ntj@M.p:@Ntj@M%2@Ntj@MɅn@7v@7F -L0@3i+ @3>}Vl@2KƧ@2d7@2(@1xF^@2(@1j~#@2(@1PH?[Xxp? ?=EDA?㍠kS? Ë??ٛP?gUm?&cu?=שD?vI?pQiB?fi? $F)?B7? -r -Dw? J?-@@@@@@*???????tD#'@GI?i~|.ۙ? -@\xDž@MI]?Ǫ03?b?K? @d`@@=p -@;S@=p -@AbJ@=p -@=42@=p -@=l@=p -@5f*h@=p -@(Q@̛S@̝/@̛S@̣Z'@̛S@̡b@̛S@̞5@̛S@̗y@̛S@̐@6@@6g@3G+@3I[Y@2)"`@2.F9@1h@1j6\+@1h@1d@1h@1_"@1‚@5@1Sg@1‚@5@1[C%?|B &?2w8?9 2me?hiF?{z?5`?{si?bU'V?nv-?/:?`53?9?1BO2??(?gě?l@?%5?mz@@@@@,@,???????{& ?kn?kHv?А@.@Rv+{?zk -?~??0g+@H=t@\>@@vȴ9X@qhr!@vȴ9X@h]@vȴ9X@}Vl@vȴ9X@xYJ@vȴ9X@g&@vȴ9X@dg8~@ʳE@ʸ@ʳE@ʾQ@ʳE@ʹXbO@ʳE@ʸ4֡@ʳE@sh@ʳE@J@7>6z@7lD@1@1=p -@0Vu@0xl"h -@0?䎊r@0+P{@0?䎊r@0#&@0?䎊r@0$oh?h??ߋb?a6H?rӂ?;?Wf8?}ѿ,?-NI`(?Pт?|lU?SmImb?s?4yX?.x?L%.I0?VK} -?Vá?"r@@@@@&@$??????Tr?v- ?a{yr?a-j1m@pQ&R @;B?3R?CM?݅W{h?M -*@eK.@@{;dZ.@{;dZ@yp -@{;dZ@yσ{J#@{;dZ@y{@{;dZ@yѷ@{;dZ@y*@\(.@\(@M@\(@u@\(@o@\(@/V@\(@ ԕ@5*0U2b.>@3S@1q @27KƧ@0h$ xG@3:,<@/Ƨ@3:,<@/o@3:,<@/حV@8J?{ l?d -? N:?՞e?ե̞@8J?iV?Ž?2}?B ?m,h@8p?  l??}(3?{0?w,?t@@@@"@?????@{dZ@sE@{dZ@B@{dZ@sg@{dZ@nߤA@{dZ@f1@{dZ.@x@hr!@x@ѷY@x@g@x@+@x@/iDg9@x.@8^H@8cS&@4?|h@4D@3V@3]ce@3 k@2 @3 k@2@5@3 k.NC,?sD?/ -?4DA?+?Qv8F@8?}?H{V? Ķ?U/?b -@8?7V/r?qv?et??a%?YI@8 @@@@@,?????@>vȴ9@8bM@>vȴ9@X4֡@>vȴ9@[J#9@>vȴ9@Q_@>vȴ9@/@>vȴ9.@-V@q&x@-V@!-w@-V@7K@-V@u%F @-V@u@-V.@7qiC@7ᰉ@3W+ I@27Kƨ@3'KƧ@2Q@2V!.I@1snP@2V!.I@1nm\@2V!.I.NC,?1j?9)u?eJ>?&L=?Dp@8?UbB2?+Qe]-?H?ߖ_-0?D@8?ԇ:?̈?Rw#{?,Uf{? 2@8 @@@@@,?????@s(@s/@s(@sx"@s(@s7LY@s(@s@s(@sО^@s(@s{@h33333@h33333@h33333@h4@h33333@h20@h33333@h3t{@h33333@h5R$@h33333@h?vȵ@3Q|Q@3Qo@0I^5@@06y$@07KƧ@0GB7@0!:T@0vȱy@0!:T@0.~@0!:T@0jԑe?ȧx?Q>?|]x?|B &?, ?vV?-IGmR?"F?fq{?0xs?D^0'?KO?c—[|?5yƶ\?HL@@*@$@$@;@9??????E\>Ja?=.?? n$d^@Pι.G@9[?\l+?@#e?S}?+@8I@8?sD@8@8i@8@8S@8?E.9@8 *@8p@8@8 @8 @?@yXbN@y+@yXbN@zC\@yXbN@z@yXbN@z@yXbN@w+j@yXbN@x*0@M@M@M@MC@M@MVϫ@M@MI^5@M@M!-@M@M̥zxl@/^@/^@)-@)@@(O;d@(sPI@(b~@(0 )@(b~@(0 )@(b~@(0 )?^Dw'?GA?*"AaI?G?@ A2?:?bM_x? ,C?/BH?@[*^?IM?N`?bMky?~('?q6tN7?avh?O|q?O|q@@@@@,@(??????L\?&?6oG-?$zQ@xY@M̚F?EDj9?3?ذ. 1?a (@s -c -@@lj~@lj~@lj~@l @lj~@l=c@lj~@l@lj~@lm\@lj~@l+ @X@X@X@X״3@X@Xe@X@X߾vȴ@X@X۲m@X@X״3@4=:@4@bvȴ9@b^5?}@bvȴ9@bJ#9@bvȴ9@b#@[t@[t@[t@[zxl#@[t@[Ů1@[t@[A [@[t@[2W@[t@[$/@3]ce@3]ce@1 I^@1n.@1~"@1W>6z@1n_o @1q$/@1n_o @1nm\@1n_o @1n2X?| ?q' -B?^RpX? ?[9,?nM?| ?jכc?tWZ?)@a,?pPȻ???]ivL?3Z\?bv{3dZ?:1?E݋N@@@@@,@*??????EvO$?/_?8VPO?'2U@bބ b@[ą?Xo)?G6\c??Q @f`b@@+ J@9X@+ J@s@+ J@ @+ J@~BZc@+ J@Ƨ@+ J@~(@r @n@r @!.@r @~(@r @.H@r @8}@r @RT`@8Y*0U2b@8G-@3° ě@3.H@3,j~#@3%@2!.I@273@2!.I@2ѷ@2!.I@2а{?3a=?u~?sN?zF?J?,#t?SY?6 a@B?zY@?z?lR?&|? `?x@@@@@$@*???????B뿠?]!P?YO+7#?H]U5@$m -@%^?{o?\%V*? o;?[O@pO~-@@1'.@1'.@1'.@1'.@1'.@1'.@m]-V.@m]-V.@m]-V.@m]-V.@m]-V.@m]-V.@8g l.>@3I^5?.Mm@2j~#.Qn@1̿[W?.NC,@1̿[W?.NC,@1̿[W?.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @߾vȴ@@߾vȴ@[7@߾vȴ@U=@߾vȴ@n0@߾vȴ@Ϫ@߾vȴ@r@j~@1@j~@K^@j~@(@j~@N<@j~@ xG@j~@@5!.@5z@2 I^5@2m\@1-V@1r@1̿[W?@1Ɍ~($ @1̿[W?@1@1̿[W?@1?1j?q' -B?wk?U=K?\o?Vu.Qn@1䎊r.NC,@1䎊r.NC,@1䎊r.NC,?1j@8#@8:@8 >+@8I@8?| @8@8i@8@8S@8? SF@8 *@8p@8@8 @8 @?@ և+ @ և+ @ և+ @ և+ @ և+ @ E@ և+ @ -=p@ և+ @ յs@ և+ @ ըXy>@#$/@#$/@#$/@#Q@#$/@#J@#$/@#m\@#$/@#m@#$/@#m\@2䎊@2 IP:@,-V@,O M@*E@*b@)-C\@)-@)-C\@)- qv@)-C\@)-8[?^6P?GA?"?MA8?ǹT?tJ?bMn?MN ?}?3.Yl?@?Uyu#?bM]f?.+?s@a?avh?O|q?@@@@@,@*??????D=6>y?(}L? _[Y2@ ;fԂ@#_J?7vYf?UdC?kAG}?.fε@sw(@@twKƧ@twO:@twKƧ@tx@twKƧ@txtj@twKƧ@tyJ@twKƧ@tv@twKƧ@tv@n@t@n@ح@n@@n@?@n@ѷ@n@e@8!.@8$/@3; I^@3;҈@2-V@2As@1@1D@1@1@1@1?F^S?'?ϙ?:L -?.Kn?sU Z?)T?0n=Q?pI?.}$?8?GӒ?Z`(?)h??l?E݋N?]ߒ@@@@@,@(??????@5_;>/?@}46z@1V@1!-w@1 [W?@1 \@1 [W?@1 U=@1 [W?@1 ce?|O?m0?.Ү_ ?䆀{6?h|?t?| ? g?tJ??77?Q\?s@a?O?{W?3{Z?&I?ܑ @@@@@*@$??????1R?0ղ?&JPit?0-&@ 1L:e@ -5?F?PMN73?[?57@fP'@@[\).@[\).@[\).@[\).@[\).@[\).@E.@E.@E.@E.@E.@E.@804m8.>@3I^5?.Mm@27KƧ.Qn@26z.NC,@26z.NC,@26z.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @vȴ9@@vȴ9@@vȴ9@ -[Zf@vȴ9@y>I@vȴ9@c/@vȴ9@@}E@@}E@}~@}E@|Z@}E@}vB@}E@{/@}E@G@7!.@7 8@2?|h@2k@0`A@0ef @/N;6@/ @/N;6@/Fa@/N;6@/>I?|"<~~?Ҁ?M8rv??&K?c eW?j?&?X?H?7Oi?7k??p0$?Q[/9?p>?`>r?"K?R&U x@@&@ @"@7@1???????8BXL|? -j=D?8ۃS@¡rt@?*@??̞@b@@@-@@C@@hr!@@ ě@@s@@@'W -=p@'W -=p@'W -=p@'XbM@'W -=p@'WO<@'W -=p@'W -=p@'W -=p@& Ĝ@'W -=p@'V+ @6䎊@6[W>6@2^5?}@2#w@1"`@1Ov`@1 -0U2a|@1 -~"@1 -0U2a|@1Hu%F@1 -0U2a|@1 -L/|?1j?ƿ57?ِ?eU5?Z?Rg.?| ?NۈV(?Kd+?>\? R?A_f;j?d3>?~('?!A -m?E 8?Cns?@@@@@@??????)G/OZ?= _j [?c,@d~F@'Aj?\I}?[9?hu@ga}G@f8@@G/w@G0 ě@G/w@G0'R@G/w@G,<@G/w@G1[W>@G/w@G,@G/w@G,!,@ -=q@n@ -=q@@ -=q@ xG@ -=q@g@ -=q@凓ݙ@ -=q@ڹ@704m8@7,]ce@3Lhr@3Qu@3t@3 -fB@2^6z@2c9@2^6z@2^Ov_خ@2^6z@2_iDg8?Br?d̺?~"?(tUo?ZAj?^m*?F/P5?io?b?j.ӌ?F;[k@G/@[?^toC?g]x>D?i7?HkIm@hh,ƭ@@~#@~#@~#@t@~#@ڹ[@~#@'0@~#@ڹY@~#@0 @ɺ^5?@ɺ^5?@ɺ^5?@Ƨ@ɺ^5?@ĵ?@ɺ^5?@ɠ'RT@ɺ^5?@Ƨ@ɺ^5?@ @3D@3'RT@1c@1dZ1@0-@08}H@0+xF^@0+҈@0+xF^@0-O;dZ@0+xF^@0-/w?| ?Kb?tcj?2UT?g?_ ?| ?I?D?N?,Y?`@0k?٤?3I#i?!A -m?^ a?2[s?oC@@@@@"@*????????Y}?YV@45S@47Xe@3Ͳ-V@33@2:S@2wl@2:S@2@2:S@2%2@8J?I'?3Z?,rg?"m?X@8J?Q_ڸFN?3fvI?)@a,?E?,}oJ@8p?3XMV?((W?J?GZ?r@@@@(@(?????@F.@F.@F.@F.@F.@F.@&x.@&x.@&x.@&x.@&x.@&x.@9~($.>@3ěT.Mm@3'KƧ.Qn@2ޫ6z.NC,@2ޫ6z.NC,@2ޫ6z.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @~Q@~@~Q@~ (@~Q@}+j@~Q@~=c@~Q@}=c@~Q@}u@tC@t=p@tC@tɅp@tC@t)*0U2@tC@tEݙ@tC@tI^5@tC@tIQ@5H@58Y@2+ I@2eO @1tj@1-V@1=ce@1S ҉@1=ce@1ZW'@1=ce@1em\? ?n ?.?T|2?d8?՜u?)T?f?x`In?4=" ?5nM8?&rP'?9@0w?qv?~9n4?VMx?c? # @@@@@(@*???????RQ^>?pn@^a?kJ~@~0nB@t|?{x?2lq@ZEʜ?Ӎmv1N@aCſ@@WO;@W@WO;@ 0 @WO;@0kׄH@WO;@ V@WO;@Ձĝ߼@WO;@?n@o@C@o@0 @o@<~5@o@ݞ@o@0k@o@'9@2ks@2jhL@1 ě@0O @/O;dZ@/]@0J0U2a|@0T8@0J0U2a|@/Qq@0J0U2a|@/4?^(x?ʌ@3 I^5@3Zc@2y7KƧ@2y@18D*@1873@18D*@17>6z@18D*@17>6z@8J?t0?9a?Ac ?2ּ9? ٖ@8J?6c?v@b?!zt9?7=^9?'+@8p?r?5q?"a??_խW?{2@@@@&@&?????@ I@ I@ I.@ I.@ I.@ I@l@ I@\@bM@bM@bM.@bM.@bM.@bM@ Ĝ@bM@bM@2H@2BZc @.`A7K.Mm@.rnO.Qn@-b~.NC,@-b~@-|@-b~@-#w?^Q5P@8#@8:@8 >+?bb<]?R?bM_x@8@8i@8?I?h,à?b@@8 *@8p@8?j?L@@@???@KC@KWl@KC.@KC.@KC.@KC.@KC.@A7K@?|h@A7K.@A7K.@A7K.@A7K.@A7K.@6w@@6yFl@3; I^.Mm@2Ͳ-V.Qn@2OO M.NC,@2OO M.NC,@2OO M.NC,?}x+@8I@8?}#@8@8i@8@8S@8?5\a@8 *@8p@8@8 @8 @?@%/w@%0bM@%/w@%L@%/w@%7r@%/w@%bu%F @%/w@%I*0U3@%/w@%Aaf@Cj~@CbM@Cj~@CN;6@Cj~@C$tT@Cj~@D" I^@Cj~@DH@Cj~@D m@6!.@6@4n@4+ @4?@4`A7L@4- @3䎊r@3b<64@3䎊r@3`oiDg8@3䎊r@3]%?Y͍_?LS{?{?@p?OC?yW?1j?Q/J?.cE:"^?,ў?%L$? D?],mg@@@@@,@(???????wX?YĀ?p,W|'?~٥~@%"@x@xSt@x@w%@x@vFs@x@wRC4@`A7@`A7@`A7@벻@`A7@&@`A7@簳@`A7@`@`A7@/#K$@4 ]ce@4 åH@0 I^5@0sC@0y7KƧ@0zkC@/b~@/yR4@/b~@/+ B@/b~@/x5?|h2:?f?1z?JJ0?jfĀ?>,?}@TΥ?ZNZSB?= :?AE?̶e#?fQ:?F?ZSX?Ψ,UiW??m_?UC5}&@?47h@򁆹?8Ò?qOD?7pú?Z-@mF@@(@CB@(@O@(@Vx@(@Kբ@(@&{@(@P_ @\(@=@\(@BM@\(@Ca@\(@{@\(@X@\(@1`W@8>6z@8&mk@35S@35 @2]V@2[I6$@1n_o @1m@1n_o @1mAq@1n_o @1lЇύ?`^zT?dN?8C)*?w?r? -`?S?1?N7?GvQ?_;? Zr ?FjmY?V&?Nu?2@غ ?ZCM?m~MU?3T?Gg@@@"@*@8@4??????Hk.?F~?(~Դ@YI%@d_?_bD?K}T?-f;?2S1*@mf@@bM@>@bM@@bM@793@bM@L(@bM@QOMo<@bM@'@bƧ@b3334@bƧ@b @ZT@bƧ@bS'f@bƧ@abT -Mh@bƧ@bX@bƧ@b)@9H@9y@@3S@4 -OF@3v-@3/@2xF^@2r<[@2xF^@2ʬ@2xF^@2\s4?eM?Zi6&?v:?v]J?_v?1?m?(?s+\?)CH?R^ ?O *ѥ?ch0??Xj?wXPjD8b@@I^5@r Ĝ@I^5@Fs@I^5@O;e@I^5@1@I^5@2a|@I^5@iDg8@7Kƨ@\)@7Kƨ@S&@7Kƨ@\N@7Kƨ@Ov@7Kƨ@0 @7Kƨ@'RTa@8쿱[W@8׍O;d@5+ J@5?@4tj~@4hۋq@4@4˒:)@4@44J@4@4?qv?i1[?>(lQ ?-?V?}Tv@@@@@*@*???????@?p|;?a~d?c@2$4@34-@RФ?~US"?B\_[?҃ȸ?O"G@gp\R@@jx@<=p@v ?yÅ/?xtbrG?BͶ+?E釋_@j.y@@5\)@5$@5\)@:Q@5\)@:6@5\)@:G@5\)@:?@5\)@9"@ow@ow@ow@oiDg8@ow@o{J#@ow@mhr@ow@nH@ow@mw1@3H@3H@1+ I@1 ԕ*@1Z~"@1[)^ @1!.I@1+ I@1!.I@1lC@1!.I@1 k?|O?E?TS?G?Xe? µ?| ?io??6)}?A?B8?u@>N ?2]7?ZaQ?^ a?&I?v> @@@@@,@&???????Sr5I^ ?9R;?6 pdv@8Q֎@n\?Y -@5D?;_\|T?: ?]L/N@fT@@U -=@UbM@U -=@Um@U -=@UN;@U -=@UiC@U -=@U{@U -=@UbM@!Stj@!Stj@!Stj@!S*0U@!Stj@!Sg@!Stj@!T!-w@!Stj@!R䎊r@!Stj@!S&@2=:@2=V@.\j~"@.\ߤ?@.x@.S@,QN;6@,P`A8@,QN;6@,P'RU@,QN;6@,P -=r?^Dw'??=?Kh?ƠJ-M?\7?6J?bM_x?›?ܿ?wxR?+!?]#O?cPQ?.x?|׿pZ?hB^?pk Y?@@@@@,@*??????D<?Y҆D?. a r`@U%_1@!St?*k9q&?=_"?PYfL?rrj.@sѬ@@t@V@t@/@t@ěT@t@ĨT@t@S@t@t@/_;dZ@/R ě@/_;dZ@/_@/_;dZ@/`- @/_;dZ@/^ߤ?@/_;dZ@/_|h@/_;dZ@/_ح@5-v@5!TɅp@05S@06$/@0tj~@0wk@.V@.W -=p@.V@.W>6z@.V@.WXf?ʎa]?Čۨ1A?"?q@v?E{?2I/?W$B?j%Ra?['?; $?M?{L բD?![?3;?Me_?͓E'?B ?f_Ԋ@@@@@*@*??????U*B[@y"@zC\@y"@yrGE9@2*0U2b@2*0U2b@."`A@.c @-1&x@-/V@-@-|hs@-@-|hs@-@-vȴ:?^XN?Čۨ1A?,fC6?_? g?{@Ctj@CcO@Ctj@C+Z@"@İ@"@s@"@@"@m@"@;K@"@v@7|Q@7*`@3+@31 F@2K"`B@2JoF'@1Ǡ k@1LJ}@1Ǡ k@10H@1Ǡ k@1>c?'Z]&?V#.?B-?V?*^@?]?~Ov_ح?p`Hc? E>?WWM@??ZW?57E?6~A??5q?2 ?&I?Rv@@@@@*@(??????B?>L@?Ǧ O?-!K-@8 6@1z!?.`l?NxݨB\@ 2-V@ x@ 2-V@ @E@ 2-V@ 3Mj@ 2-V@ 1m@ 2-V@ Qhr!@ 2-V@ 7Xd@804m8@7lC@1ěT@1ڹY@0"`B@0 k@0L[W?@0!N;5@0L[W?@0C,@0L[W?@0x?M0?4Ӌ?^?ڈ৑? ?) o*?1 ?.M?D?2}?ZC:;?;/S?$c*?ל?}X?OJI-?TI?x@@@@@,@,???????k?Q?=P'?跒 @ň]>@ 8l2?dS2?S ?:U?,̝b@oF@@1R@1/@1R@1t9Xb@1R@1P}@1R@2?@1R.@1R.@"XbM@"dZ@"XbM@"F$/@"XbM@"E@"XbM@"zC\@"XbM.@"XbM.@74m8@7C@5pěT@5TD@4tj@4aN;6@4}ce@4i@4}ce.NC,@4}ce.NC,?ȉak?ma?߹e/?L@8I@8?3a=?4i?ō26?U @8S@8?Z?gz?[K-$I?'|V@8 @8 @@@@????@ݶE@ݶE@ݶE@ݶa@ݶE@ݶC@ݶE@ݷe@ݶE@ݴ9Xb@ݶE@ݵ '@@@@'RTa@@$tT@@hۋ@@H˒@@b}@4=:@4=B@0r Ĝ@0K]c@0@`A7@0AA [@/L/{K@/1@/L/{K@/S@/L/{K@/8YJ?}ѿ,?Čۨ1A?O%c?ƠJ-M?#6_?ʟ?| ?j%Ra?:?; $?=d^?U3p?2Y0?3I#i?٤?Gϵ'? Ğ?fW@@@@@*@*??????.UQ 9?3?OZ@ݵݝ -@/?SXj'9?9(p`?C\?z?7=^9?.8 -B?6P?9o?m!?sMQ(?7zE?f$x@@@@@*@*??????? a@Ms?B -M?2@PPrR@/a6kj?a02?SEZ?DAĵ?l]@hf%^@@ ]/@ ]/@ ]/@ \?@ ]/@ UXy?@ ]/@ I^@ ]/@ CZ@ ]/@ ?E@p9Xb@pj~@p9Xb@q@p9Xb@pG@p9Xb@p/@p9Xb@p"@p9Xb@p;dZ@3įO M@3ļj~@1ahr @1Y k~@1Ctj~@1:vȴ@0m8@0Fs@0m8@0A@0m8@08YJ?}ѿ,?+W?mGSq?btx=?\?iOL?| ?"T?_M? ?a]?⍄o? SF?:? -tM?aP?♊N?M\@@@@@,@*??????zHq?dX?; -D?55g4@ P:@pP?Z&.?Tۧ?Lk8?@ez;7@@pa@p]/@pa@p`X#@pa@p^@pa@pZu@pa@pg@pa@pa*l@z$@z$@z$@z :!1@z$@zb/@z$@zK/@z$@z5i@z$@zӜ@6qiC@6>` @2^5?}@2CG@1Ͳ-V@110@1B@5@14X,?" -?KZvΰ?>ַ4@sN@@kR.@kR@/iDg7@kR@8e+@kR@Em\@kR@CZ@kR@-jO@2~".@2~"@4sh@2~"@45sh@2~"@3^5?}@2~"@3{@2~"@20 @9I_o.>@4 ě@3ix@3~"@2Ft@25sh@1C@25sh@1쿱\@25sh@1-V@8J?2yH?X M?1?l\LW?x@8J?՚썊?Ӣs?С?D?X1@8p? F2?ڬ??/я?24 -@@@@,@?????@_"@_"`@_"@_@_"@_/V@_"@_Ov_@_"@_Q@_"@_@ ě@O@ ě@@ ě@ xG@ ě@@ ě@fA@ ě@_o @7 xF@7.H@3G+@3A.H@2`A7@2|"h ԕ@1&@1\@1&@1/w@1&@1p:~?Y͍_? e?GM?^t -a?l!-w?iOL?3a=?{c?R?dXM?b| W?9x]e?hE?D>S9?m~Z_s,?[?OO ?q' -B@@@@@*@*??????f]ZM۠?>`?Aۂou@_r &@7)?`0ֈB?aI1_?$C?A!Ť@i{ܕ@@ӍO;d@ӏ;dZ@ӍO;d@ӗz@ӍO;d@ӀPy@ӍO;d@z(@ӍO;d@Ӆ[@ӍO;d@ӉϹUL@L@K/kH@L@L@L@T›@L@90@L@F g@L@IQJ@6,<@6 c{Mj@2sS@2oFMxu@2t@189@2!:S@2" Ue @2!:S@25T>@2!:S@21>xh ?~=?S7<@nh@cx6@@Q@;dZ@Q@$t@Q@@Q@C@Q@&J@Q@S&@{x@{^5?}@{x@{'RTb@{x@{䎊r@{x@{@{x@{:~@{x@{@8g l@8}H@4+@4͞@3-V@3PH@3+xF^@3 -=q@3+xF^@38eO@3+xF^@3A4J?3a=?|1?~#?㬱_?$?MǏr?X?44]t?Fy -? g?WT?`1`W?˅W?;!?M3 ? Ӥ?:? g@@@@@$@(??????\d^?dt?v';@ ԅ}@{zd?8]?sU?&@68o@k/@@DT@D/@DT@F@DT@DU@DT@E@DT@CA \@DT@DM:@~#@=p@~#@o@~#@~#@~#@o@~#@﫹~(@~#@P}@3,<@3af@.{"`A@.zc @-NO;@-NVu@,@,~BZc @,@,}!.H@,@,}IQ?|O?`?*"AaI?뤽??b\?F0?|O? aҹz?|,!?Y?IM?90?oܧ?2a? SF?!ˠ*(?j? -t@@@@@$@&??????G?1[:?(36k?YX@D&@t?H캲d?1d?L Y"?p}R@fe@@zH@+ J@zH@?@zH@|@zH@C@zH@q @zH@c @J$@Jhr!@J$@J[@J$@J*0@J$@J{@J$@J ԕ@J$@J6z@7䎊@7Mj@3I^5?@3TɅ@2Ctj~@2>qi@1:S@1Ⴉ @1:S@1d8@1:S@1_p?q!U? -ܦ%?Z-l?r*?i]i??% ?6S{?EψC?} ?pPȻ?p?AK\ ?nBT?Śn??NVC&?aʕTI@@@@@$@,???????; \/)?e1t ?Mw>e@@Jg?xAsR*?k#?^k?Ν@lY@@6E@EQ@6E@Q@6E@)9Z@6E@D2W@6E@4f@6E@9x@F@ #I+@F@@F@ *@F@%#@F@ -@F@p@7S&@7 @2ěT@27m>p@17KƧ@1%2^@1:,<@14<{@1:,<@17݂bp`@1:,<@16V)eS?nDyB?k_?E??tk?\U?CDb??'2?dw?X`&?)P?w#?6 -?q??cQ?q~y?Zv'@@"@"@@7@3??????Uܛ?QǺ4?F3v?Ez@=g @z?iB RM?[u ;$?MhC?6ړ@nt@@gF@gv@gF@g k@gF@g\(@gF@gα2@gF@gߤ?@gF@g @[V@[-@[V@[ag@[V@[hr!@[V@[҈p@[V@\ -L/@[V@\ oi@6V,<@6TɅoi@3 I^@3Q@3KƧ@3\(@3._o @3@ (@3._o @3'rH@3._o @3#A [?Br?܄ (?reE?:Z2b?vNh/?lg|*?| ?8$ -"?cS?E:PXd?Op?lӪ<?DJ~_?[Ci?M3 ?q+U?_M]D?`@@@@@*@"??????pcU*t?wI?P]?A]<@g@[gv?l?a9?c?Ǘ?U@eJ(@@nP@hr@nP@~(@nP@B&I@nP@C@nP@3*0U@nP@,1&@^x@^`A@^x@^{lD@^x@^D/@^x@^c@^x@^wkQ@^x@^?@7L]ce@7A@4+ I@4 '@3-@4 |@3&YJ@2%2@3&YJ@3 k@3&YJ@3zH?E?&Ը?㰱V?$? s:?ixU3?=fA:?r6 ?çh]?\B9?ĉOi?F}$ ?`yz|a@w[@^yk'?O?y>~e?cZ?wœ@a@@zݲ-V@zE@zݲ-V@z5?|@zݲ-V@zޞ@zݲ-V@z (@zݲ-V@zPH@zݲ-V@zߤ@`A@`A@`A@X@`A@&x@`A@_@`A@oiDg@`A@4L@2zqiC@2zd7@.=-V@.=ce@-]E@-^ѷY@-b~@-=b@-b~@-JE@-b~@-@?^Dw'?[e? 9?*WF?bM_x?%F?NJ?6)}? f?N`?cPQ?l?8*?$H??ˢL1@@@@@"@??????Mf">?+LʥP?0 ->@z@~@?:G3>?>wk?pg{u?t6ҵM@sz,@@=p@˅R@=p@C@=p@]c@=p@˹~'@=p@'0@=p@C,@r Ĝ@sE@r Ĝ@ufA@r Ĝ@u!@r Ĝ@tZ@r Ĝ@t,<@r Ĝ@u!@6H@6ߗ$tS@3+ I@39Xb@2~Vu@2hۋ@1D*@1_G@1D*@1rGF@1D*@1#?Y͍_?p~v]? -NA3?v" ?ҧ?1a?)T?jt?t(??uv@#h\@#h٘9@#h\@#h8@H33333@H33333@H33333@H4fd@H33333@H6@H33333@H/ta@H33333@H,A@H33333@H.v@1H@1\N<@,{"`A@,u; $@,dZ@,-҄@+@+̈́@+@+Ef@+@+:q?^P!?y@?=uF?|?%V772?͎$?bMn?kcc??dc?E?Y|?AЊ?l,?fet1?}]}?u?䲧MJ?yu? )`@@(@(@*@6@5??????Q9n\6_?3[ɾ?5m y@#i=@H.8\?BeZIx?D?W.qm?w O`@sm@@@WH@@@@q@@ڗ@@th@@D@2,C@2,@2,C@23@2,C@2.nU@2,C@2.3@2,C@2,R@2,C@2/g@4>6z@4^+@1 ě@1|,7\d@0j~@0t@0OO M@0C@0OO M@0E8@0OO M@0J4O(?| V?{X`(b?"z?nPE?G(M ?&>7?| V?[A?FW?K.w?`0l?Q_{?K??7q?sw"?sQ? -؇?O@@@@@4@6???????V [??#p?Pp.?0ӎ+@͗@2-OW?oK?P=vȕ)?8?λ@eu/ɂO@@E@E@E@ax@E@\0@E@P@E@/+@E@9-@`C@`Ƨ@`C@`ɓy@`C@` @`C@`HOT@`C@`˜ӌ@`C@`@4v@4L?4@0hr @0u%F -@0-@0@/@0@/@0@/@09h?}@TΥ?^K?=|x;?FB ??^8?}@TΥ? {?p‹?-(?M$r?kƊ?;X8?b@?OX?QBn?Jƒ?<@@&@*@ @8@7??????,ɐ>(k?3zk"N?=۬@iS~@`r?Sa=n?1 wJ-@o@s@o@~J@ ě@&fffg@ ě@#1@ ě@!Cc;@ ě@.ԧ'@ ě@'y@ ě@&mT2@7]ce@7f@3+@3F:`@2KƧ@2^P:}@2m8@2'@2m8@2r@2m8@2Ohh?eM?؃ ?cer`?.?5'š?;  ?uq?kʤ?hajO=?=k?S ?# -з?lN#?8?KWQ?sX?!-C?F= 1@@$@$@(@9@5??????L?*6(?D}s%?R.@;ty@(M?cu?sjhY?ƽCJ0?1I;@jϔ@@g-@}/@g-@m(@g-@n.3@g-@nMj@g-@d7@g-@fYJ@`A@\(@`A@F]@`A@ݗ*@`A@%F -@`A@ @`A@s@5N쿱[X@5A @2G+@2P ě@1PA7K@1TSMj@10@16YJ@10@10*0V@10@1/vȴ:?jqp?bDҼ*?#}i?Jʮ?oNŒ+?=W?)T? /ّ?f? &?x9ξO? # -?8x?˛͇q?`x?GZ?Rv?@@@@@*@*??????q"N!?J0?\R?@ ]@j!@ ?n328ow?bū{?`* ?4@q@@Ƨ@V@Ƨ@ը@Ƨ@2@Ƨ@߫U@Ƨ@7ήm@Ƨ@@$7Kƨ@$JH@$7Kƨ@$7d@$7Kƨ@$ y@$7Kƨ@$lL@$7Kƨ@$+@7쿱[X@7 "<5@3; I^@34F}@2lj~#@2gIa@1_o @2 o@1_o @1Hjf@1_o @1ny?(?3F?d?.{>?Y3B?J"?3<' ?9EC?$W??OR^?j -?$PUdG?ԦO?|~!?aDr?A`l?Ü@@$@@,@8@7??????;I0`?z͂?7_;?E5@ͪ~@$ı|?W ?fȅ>A?Dл?ɮV0~@gL~@@t@ -=r@t@?@t@e@t@8Y@t@\)@t@1@^Q@^S@^Q@^u%F @^Q@^ڻ@^Q@^@^Q@]}H@^Q@]˒:+@3`D@3_Vϫ@/tj~@/@/wO;d@/vE@.C\@.e+`@.C\@.D@.C\@.A?|O?:L -?B 'nr?998?q f?s[z?|O?&uE@?T֩E?R? E!?҃~/[?8*?Q?/X?3_?՘]G?P\@@@@@&@(??????m8ʳr1?`-l?`AtF@ -W@]_?~qA|y?} @ai@6+D@dQ#~@@7K.@7K@Τ?@7K@ίrGD@7K@Β@7K@>BZ@7K.@\(.@\(@r@\(@@\(@k~@\(@څ@\(.@8s.>@4 I^5@4 u!@2j~#@3:~@2L[W?@2gsPH@2L[W?@2Ahr @2L[W?.NC,@8J?Z#'f?a6H?E2#?v@@8@8J?HD?`)Ԥ??{%? aK@8@8p?Em??YVϪ?G]Ja@8 @@@@*????@O;@+@O;@+j@O;@tj@O;@bM@O;@a@O;@!.@7Kƨ@ȴ9X@7Kƨ@Ȍ@7Kƨ@7Kƨ@7Kƨ@Q@7Kƨ@˒:@7Kƨ@Xy=@5p4m8@5o͞@/tj@/tj@-x@-Fs@+At@+v@+At@+͞@+At@+?Ol?m0?(?eU!?g?>?Ol?›?qF?6)}?إ<(?PS2K?٤? -? SF?xqC?pk Y?b/bJ@@@@@*@&???????ұ?*H*??:B@"@ȱ?8V\?;II>?{l~l?0">obLJ?Ce9y?G`@@رm:@9Z?a}qT?e<_%n ?ϻ^4?tID@dFç@@lC@1&z@lC@x@lC@^5?@lC@x@lC@s@lC@㕁&@&x@hr"@&x@m]@&x@@&x@@N@&x@3@&x@4m@4|Q@4ѷX@2r Ĝ@2KƧ@297KƧ@27-@1:S@1ߤ?@1:S@1H@1:S@1cA ?|O?4$?TS?l?/m{?mׁe\?|O?[u?-8?:u. ?JL\?:i?],m,?)F@=@?16?V5z?IT?)L? uEl9@f@@;dZ@|h@;dZ@ -JM@;dZ@tS@;dZ@@;dZ@Fs@;dZ@4֡@4zH@4zH@4zH@5!.H@4zH@5J@4zH@5 ~($ @4zH@41&@4zH@5%@3ks@3k/V@2!hr @2$/@1Ft@16_ح@1Y~($ @1<64@1Y~($ @16z@5Q@1xr Ĝ@1wrG@1U`A7L@1S ҈@0kxF^@0i*0U2b@0kxF^@0jڹY@0kxF^@0jS&?|O?]Û?>\?<ݝ?Pa#2{?4¼?,6D>@ԡP@?T|?L!?0眭?._M@gS1r@@ݩl@ݞQ@ݩl@ݭ@ݩl@ݬ@ݩl@ݬ~($ @ݩl@ݣS@ݩl@ݤe@-V@)l@-V@;dZ@-V@b}@-V@"wk@-V@@-V@rGE9@8,<@8)^@2.5?|@21$/@1_-V@1`:~@0u@0@4@0u@0nP@0u@0?Br?6 '8?&?? -?<_+fj?mׁe\?% ?p RN%?ܿ?,~[?pPȻ? Ӻ^?fr[?p51?6E?q+U?4\ɵ?&f@@@@@*@(???????Z?Ao4;u!?Zb?5 ?P@@ݤ<$@$\?z95?mYޮ?H _?_@j@kOm@@p@p@p@p|@p@p>B[@p@pe+@p@p@p@p k@-@V@-@&x@-@1@-@w@-@ߤ?@-@A@6zqiC@6y_o@0° ě@0䎊q@/|1&@/|쿱\@.73@.$tS@.73@.$tS@.73@.>6z?)T?Čۨ1A?=?Kh?䆀{6?/`?aX?}ѿ,? ,C?)_?e[|(?֧QD?lINՄ?S[D?򯞼??+ ?j?M684@@@@@&@(???????1. ??f?$i=Az?{ha@p@Ms_f?C*2?=w9&?;#?j×@iw@@) -=.@) -=.@) -=.@) -=.@) -=.@) -=.@XbN.@XbN.@XbN.@XbN.@XbN.@XbN.@7쿱[X.>@4{ I^.Mm@4"-V.Qn@3ush.NC,@3ush.NC,@3ush.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @@@@n@@N@@n.@@b|@@~"@2 Ĝ@2-V@2 Ĝ@2:){@2 Ĝ@2<6@2 Ĝ@3@2 Ĝ@2-V@2 Ĝ@4@5,<@5R<6@0hr@0͊ڹZ@/O;d@/y=d@0 -0U2a|@0 -S&@0 -0U2a|@0 -0 @0 -0U2a|@0 -fB?}ѿ,?Qd?\H{gX?#t?u?}?}ѿ,?uie?{d?Hn?M?M\ٓ@??Q?֘>D?$H?7?v> @@@@@$@??????9ƀ?2{-Wp?2:nGg?'N\@I@2(?Rei&8U?GpG0} ?`An ? -"@g4ey@@^Q@^"@^Q@\wkQ@^Q@]/@^Q@` k@^Q@`4m@^Q@_[W@@ A7L@5?|@ A7L@1'@ A7L@\N@ A7L@iB@ A7L@ 'RTa@ A7L@o@7T xF@7RC,@2ԛS@2 @17KƧ@1@1QiB@1QX@1QiB@1P6@1QiB@1PU2a|?=fA:?b =??Њ?ԁ?=W? ?аl?Z?jX? 5Y?T_?# Y?H? - l]?˴C? -r?՘]G@@@@@,@,???????)ʬ{?4?1uj~? r@_0@W?O(?Bm??]s? ~@j6IM#3@@4j~@ȴ9W@4j~@5?|@4j~@Ft@4j~@lC@4j~@_ح@4j~@tO @{lD@}E@{lD@k҉@{lD@^($ x@{lD@~҈p@{lD@X73@{lD@i7Kƨ@8*0U2b@8_ح@3sS@34m@2-@2rM@2!.I@2-@2!.I@2*g -@2!.I@23PH?g;+t?[_P? n+?bfg??,af2 ?sD?A#(?n;I ?ϗX? {?@.??}(2?z?z."?O̙?}Tv?$@@@@@,@,???????22sɜ? PP?d#~87@6`@x?ZObs?xȐ9%?3,59?B\@_n@@\1'.@\1'@8bM@\1'@>"@\1'@1N;6@\1'@?vȵ@\1'@F&@nzH.@nzH@i7Kƨ@nzH@gE85@nzH@gݗ@nzH@nT@nzH@oA@9ks.>@4 I^@4l!.@3KƧ@3TɅ@2:S@2C,@2:S@2u&@2:S@2ۅQ@8J?V,M?Р'?ݑl??O<?WD̷ @8J? {?tP*?R6U?uG<?lӪ<@8p?;WZ?>{~?E^u?E݋N?:@@@@,@(?????@S.@S@041@S@9}@S@@S@"@S@å@Zx.@Zx@ZH%@Zx@Zc@Zx@[5/@Zx@Z$'6@Zx@ZM>R@9:qiC.>@4TS@4_|푀@3`A@3CFO4@2&@24@2&@2^!c3@2&@2<+@8J?7?Vo?E?ߜڪA?>xn)@8J?_ꍴ#?1?xR$;Xy?Qj{?p]@8p?2a??O2??ZP@"@ @@2@1?????@!^5?|@!lI^5@!^5?|@!Iᰉ@!^5?|@!V+ @!^5?|@!b7@!^5?|@!Y"@!^5?|@![W>6@ @ܬ1'@ @ !.@ @`A6@ @ -g@ @ -qi@ @,@8 ]ce@7u"@3ěT@3'RT`@2-V@2ݗ@2O M@2 @2O M@2@4n@2O M@2*0U2?1]?`x@rTPw@@b`B@N@b`B@eQ@b`B@]+l@b`B@ezxl"@b`B@e@b`B@eY@š@ܡf@š@@š@Xf@š@R<@š@o@š@+6@7YJ@7RcH@3 ě@3z1'@2j~#@2Ov_خ@1ce@1 '@1ce@1/@1ce@1WK?uCb?:}T?RsW?㬱_?>&?l8??O?Qrz?DH?]? ? -lqbY?/#?*3?Җ(?V'f?!]@@@@@$@.???????jWe6-)?`$7 -?Yw@bz *@S?v9?̟"I?f?ó@oܪx@@dvȴ9X@dwKƧ@dvȴ9X@dz@dvȴ9X@dS@dvȴ9X@dDAy@dvȴ9X@dt]X@dvȴ9X@ds5@fffff@f,=@fffff@f..@fffff@fD@fffff@V&1@fffff@fSB@fffff@e=@4V,<@4U"@/`A@/%W@/1&y@/v@.kjf@.yC @.kjf@.k%@.kjf@.jEi{?gQ?*?Mք?mO?O࡙2?9H[?|B &? -w\o?Pqlg?Vch?IɻBa?S D?e?޸eq? KK?ͩ&?Jƒ?\3@@$@&@"@5@7???????K0?Sfy?^c@dy<@c);?sB'?>,C?P,@ Q@geD>5@@S@o@S@J@S@@S@1@S@]6j@S@U@al@a_@al@a ě@al@a^5?}@al@a)_@al@a/=@al@aM @7G>6z@7-i~U@2|hr@2 -͞&@1{Ƨ@1\(@0m8@0bM@0m8@0A"j@0m8@07'L?\l6?1?w4f?;6? _?'H? 1Ӡ?FHN?/>?cS?JY&?߭)?϶ ?XԠ#{?;aِ?=?y +5G?jc@@@@@.@*???????U@6?0nD?Ur;?:؎T^@ױ@ah?v(M$?\D?!C܄?FwN@h@@C.@C@ěS@C@ce@C@o@C.@C.@+ I.@+ I@ȴ9X@+ I@S@+ I@{J#9@+ I.@+ I.@3D.>@0f+ J@.5Xy=@/SE@-L/{@.#9@,@.#9.NC,@.#9.NC,@8J?D ?ub5?dҦ 3@8I@8@8J?֤}?h?:h@8S@8@8p?8hz=?+-/?fK/@8 @8 @@@???@aW -=p@aTj~@aW -=p@axl"h @aW -=p@a|N<@aW -=p@acA \@aW -=p@aN}Vl@aW -=p@aYY@Cn@L1&@Cn@U$@Cn@ES&@Cn@Z6@Cn@@4@Cn@ /{I@7>6z@7A@3f+ J@3Weں@2"@2%1@1𖻘@1 xF@1𖻘@1Vl!@1𖻘@1ȴ9Xc??fDb? -ڹ `?C>?vge7@s0{@@ Ƨ@ 7Kƨ@ Ƨ@b}@ Ƨ@s@ Ƨ@>6z@ Ƨ.@ Ƨ.@2(@2`A@2(@29|@2(@21N;@2(@2oiD@2(.@2(.@85S&@8BZc@4 ě@4&@4'KƧ@4N$t@3[W?@3 @3[W?.NC,@3[W?.NC,?}ѿ,? cs?i[?´K - @8I@8?3a=?®?vY'?MV<@8S@8?Tc?C SG?Q?w-@8 @8 @@@@????@3tj@31@3tj@3!-w@3tj@3E@3tj@3l!@3tj@3fA@3tj@3N;@ 7Kƨ@9X@ 7Kƨ@Q@ 7Kƨ@zH@ 7Kƨ@ -o@ 7Kƨ@ ~($ @ 7Kƨ@$ x@7]ce@7"`B@3ěT@36@3>Vu@3?b}V@2Ǡ k@2ƍ@2Ǡ k@2W>6z@2Ǡ k@2ʌL`? ?sI?~"?Att?r {#&?uk=?% ?&O?%c~?_3\?Gz}Y?'qTrQ?GEq?.]L?}??$jB? 3@@@@@*@*??????X}U?'?(^~y?CJ׿@3Gڣ@ I?J?aR?ր?:,@jRF7q@ @S@$/@S@^5?}@S@@S@w@S@@S@@4`A7L@4a7K@4`A7L@4De@4`A7L@4R:)z@4`A7L@4KP|@4`A7L@47+j@4`A7L@4Cw@7H@7@2+@2tJL@1n"@1^$t@1@5@0hr@1@5@0b@1@5@0!.H?F^S?VӋ?|?8~?6@G^?^%VN?Y͍_?d$?AFnDk?yCW?lj?D4f?y\?z?],V??_u?aʕTI@@@@@,@"???????tFt|r?g,?Tݫ'y@􎋂9@4N#'6?)"KX?x `|? -1?ڽH@k3~0@ -@{@{hr"@{@{@{@{@{@{{@{@{U2a|@{@{_@$@\)@$@%F -@$@@$@Y~@$@$@$@$@6:qiC@6;s@2&+ J@2&}Vl@0"`@0#9@00U2a|@0D@00U2a|@06D@00U2a|@00U2a|?}ѿ,?4$?"?2UT?oNŒ+?=?2}?#maЀ?ܿ?uX? ?!U?{? SF?3I#i?~/O?˴C?ad|?7@@@@@,@,?????? p"?,:bE?c&B@{d0@h?L4A}X??t??ߑ;@g4_@ @$/@|h@$/@ *@$/@F -L@$/@~҈p@$/@u?|@$/@/W@+@;dZ@+@Z@+@'@+@$tS@+@Z@+@ )@7`D@7e!.@3J^5?|@36&I@2-@2u@1m8@1.H@1m8@1-V@1m8@1"??Cq?wm?eB?- ?JO?% ?Hk#?cu%+?]~ɺ@?uD?j\SD? uf?1j?Ƌ?'{>?\'jK?sra@@@@@,@(??????&_U~; ?] HX?\?@~6 @?z4= ?jJ?8*@hp@f"p@ @wKƧ@w -=p@wKƧ@wKƧ@wKƧ@w(@wKƧ@x74@wKƧ@vȴ9X@wKƧ@u!@,C@,@,C@-qu@,C@+a@,C@.p:@,C@-8@,C@,@2O M@2O M@05?|@0Vt@/1&@/IQ@0@4@0nO@0@4@0䎊q@0@4@0\(?`&x?$??vHn&?\7?װ*w?bV,?La? -\?Hn?UBi?ٶm?e@'? -?٤? mmsi?ˢL1? -t@@@@@,@(??????O|ɵFV-x:?!{:? =@w@-oG?0z;m?01"?Y|?V$ -@s3I.@ @-V.@-V.@-V.@-V.@-V.@-V.@hr.@hr.@hr.@hr.@hr.@hr.@9G>6z.>@5+ .Mm@4ȓtj.Qn@3.NC,@3.NC,@3.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @b.@b@cU=@b@c]c@b@dQ @b@d_o@b@ds@C.@C@`A7@C@1@C@$ x@C@/@C@5X@9.>@5S@5 W>6z@4ttj@3:)y@3&@3?\(@3&@3S%2@3&@3;C%@8J?#M+l?-gi?5?S?Qڏ@4 -^5?|.Mm@3j~.Qn@3.NC,@3.NC,@3.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @@3333@@̔x@@İ@@Ѝ@@h4@@HW@.zH@V+ @.zH@ Ns)@.zH@'_@.zH@1i{@.zH@9[?@.zH@=n@7:@7%ضF@3 ě@3~eN3@3R`A@3QPf@2 k@26QT@2 k@22@2 k@2]')R?wvHf?'$?i?hRr?ML ?$R?+Y?;}?; 3=;?/eGڭ?vY d?@^?j%?W?;]+?QնN?Y*^?T=sA?#"@@$@&@,@9@9??????S@1DP -r -?u($?ccNk@ϰ@:h?5kH?zɧDb1?iE z'T?ä@nyE@@C@8Q@C@&H@C@A@C@'/@C@\@C@qu@p -@ϝ-@p -@Z@p -@Y@p -@-w1@p -@?@p -@Mj@7e!.@6JL@2@274@2PA7K@29@1ce@1!.H@1ce@1cA @1ce@1 k?sg;?sp?hb?lsC^?<5D|? t??xDž?6S{?fh?>}?+?U-nܿ?((?*Mis?$?Hh'$? # ?D d}@@@@@,@,???????Ȓ| }?x?}ha@ٗ5@-Ī,?.?Pt?ϣM9?γ]@o4I@@\o-@\r@\o-@\\|@\o-@\L@\o-@\' l@\o-.@\o-.@-V@tj@-V@Io@-V@K5 =@-V@qȫ@-V.@-V.@6qiC@6bŞƊ@2S@3#Q@1-@19"@1z,<@1BSb@1z,<.NC,@1z,<.NC,?d?q$?g1?;9Υ@8I@8?}@TΥ?L?..?ur @8S@8?JA0?n?60<)y ?9ºC@8 @8 @@,@(@,????@H7K@H\(@H7K@H@@H7K@H7'@H7K@HA@H7K@H7@H7K@H|,@A7L@;dZ@A7L@te\@A7L@⊚@A7L@c@A7L@gB+@A7L@ 8@2@2hr@/"`A@/ R  @.cnP@._x@-oi@-~G1<@-oi@-9@-oi@-Xeuc?^m d?BV?ǧE1Y? τ?Ax?!d0?b^57K?ĭv?4i:?؈?aCN?B?q ?r?׷>?nom?^?@@&@,@*@<@4???????r??{WL@?cC?k)@H.a@ \?rJTbt?zozr?ҪȚ(Y?E@qa1o@@vȴ@ @vȴ@ A7L@vȴ@!-w1@vȴ@ 4m@vȴ@6z@vȴ@5?|@dZ@dZ@dZ@"`@dZ@q@dZ@dZ@dZ@5W@dZ@S@.[J#9@.[J#9@+-@+Fs@,E@,@,@,ߤ?@,@,"@,@,U=?^Dw'?{j?m?Rw#z?6r In?{@O@&3@O@c$h@O@@8?@8D=@4 ě@4ؤ/@44tj@48:4@2ce@2t@2ce@2@2ce@3aU4?\l6?|?G"e?%Ӏ1?͏!?_!6]?lUIr?:?! h?J~?;QC?dp?Z+}?"/k?4LJ?2c%C?2? /@@(@@&@9@6???????8s?V-\}?Q0+ -iQ?RJo2@!@l?p8q?pV?Q?]& :@ky*@@߀n.@߀n@{@߀n@߫@@߀n@q(@߀n@wKt@߀n@oqr@S$/.@S$/@Sf@S$/@S+@S$/@S1@S$/@S&@S$/@S@9T xF.>@5Lhr@5klX3@4Ft@4C{WgÅ@3䎊r@3@3䎊r@37w@3䎊r@3ЃC@8J?Iܟ? X??D>?-W@8J?)ۣ?)Ŋ?X_uSZ?5C?R/@8p?eta?Ǵi?PP?% ? -c@(@"@@3@,?????@E@ł<4@E@j1@E@@E@/R@E@"[@E@H@ew@ed&X@ew@f q@ew@ef xK@ew@fL8@ew@ffQ3@ew@fW@8@8f7n$S@3+@3w2ٲ@3 -V@2P^@2 k@2/3@2 k@2 Y@2 k@2*if??'ܢ?*?5$Ź?BI%?Bg@p??k<8?»큞?6܄a?S?9 <_?Q?iW?d? @?[-H?Snބ?'Y@@(@*@(@9@8??????{?όH?to]?pf@ɒ@f - ?:? /DPv1?oCW??ž@iu @@k I@f@k I.@k I.@k I.@k I.@k I.@ 3E@ &x@ 3E.@ 3E.@ 3E.@ 3E.@ 3E.@8L]ce@8OVϫ@4+ .Mm@4A7K.Qn@4Eu.NC,@4Eu.NC,@4Eu.NC,?}@8#@8:@8 >+@8I@8?ʎa]@8@8i@8@8S@8?CK@8 *@8p@8@8 @8 @?@RU$@RJԭJ@RU$@Rb}V@RU$@R_ @RU$@Rae@RU$@R_V @RU$@Raǁ@`A@6@`A@_o@`A@ǮzG@`A@Ǻ)y@`A@ u@`A@iE@7hYJ@7ġ:@3 ě@3 =b@2W-@2]cA @1kxF^@1kQ@1kxF^@1l0 @1kxF^@1kl?}x?I}?ᘡ?O!mS -?Ê?69?91 |?j?p-?|nӿ?RA*?Ǝ@@@@@*@.???????v-Tv?TI?O\-@RVݯ@͵i=?tS?pCj9@?2"? @fL@@ѯw@Ѷȴ9X@ѯw@т7@ѯw@ш@ѯw@P*0@ѯw@с [@ѯw@rm^@Y+@^vȴ9@Y+@Nqj@Y+@VE@Y+@R:)z@Y+@JOv@Y+@UXy>@804m8@8/o@4c@4P -=p@3;Ƨ@3.m\@3!:S@3 k~@3!:S@3@3!:S@3 rGE?b?oK?yĪo]?,:? T.o?" ?g;+t?U -?00?$z¾6?vv?mɣ&Z?M~W`?-]?Pk"?k@{?9 ?3 D?IQ @@@@@,@*??????Dcg2?X_C?bU OP -@х"p@R?O3 ?{Hq8?L2N?Ȑ!@khw8@@ȃ%@ȉ^5?@ȃ%@aoh@ȃ%@W -=p@ȃ%@E85@ȃ%@ڹ@ȃ%@|@rn@rě@rn@q֡a@rn@r -E@rn@q҈p@rn@qԕ+@rn@qrGE9@7w@@7r I^6@3S@3U@2ȓtj@2qu!@2@2zG@2@2hr!@2@2;dZ?_pP1?T~=I?\=u?\N?(x/?Zj?us\?Q;v?6eI??MWe]{?_#@k&˙}@@o@ffff@o@$/@o@ݘ@o@7KƧ@o@ݗ@o@n@\(@[lE@\(@[J#9@\(@Y=b@\(@W+j@\(@YJ@\(@W$tS@7[~($@7\1&@3B ě@3F -L/@2tj@25Xy@1𖻘@1`A@1𖻘@1p:@1𖻘@1W?=fA:?sp?Q/ -?W0 ?GBm?1?|O?[ K0R??.? A?kj?Ϸ?"`?}Tz?WhQ?Tj?0&YEd@@@@@"@*???????N*?A?/|u@oN@Yfʍ?^Zd+?QP7,?Ӗ?10P"@iUq@ @@n@@n@@n@A@@n@Ae@@n@An.@@n@?b}@@n@?b~@!h\@!i7Kƨ@!h\@!h@!h\@!i@!h\@!h9X@!h\@!i^ @!h\@!i'RU@2?@2? @/tj@/y=b@.XF@.Y k~@-fL/{K@-fs@-fL/{K@-fs@-fL/{K@-f,\?إ<(?b-x?e@'?.x?2Y0??f_Ԋ?@@@@@*@*??????P@FB?: tD?5ӥ?;ds@@L# @!i.g?Dim?݉MD?gb7N?5)@sD @!@vȴ@5?|@vȴ@&@vȴ@@vȴ@@vȴ@T@vȴ@@_|h@_|h@_|h@_U=@_|h@_hۋ@_|h@_e@_|h@_TɅ@_|h@_3@5쿱[X@5҈p;@1?|h@1䎊@0t@0@/C\@/-@/C\@//V@/C\@/^?Ol??GM?[d?RXFf? d?}ѿ,?f?F^S? 5Y?mEB?ҥX[?],V?=d??!ˠ*(?ad|?*Z}@@@@@,@,???????X5(?DZY `?Oڥtt?3P|@\@_?o\9?Tl̳??S6?2}?4Kl?jhq9?GZ0e;?] - ?X?|O?Zk?ԕ]I?ą6 -?B ?|[z?|׿pY?4?+P?R *n?V^?HmEf@{?@p?vdPU?hr9l?^r׺?߭RT\@g3JYC@$@6H9X@6G,@6H9X@6FL/z@6H9X@6Jqi@6H9X@6G@6H9X@6)rG@6H9X@64@)d/@)b`B@)d/@) ᰉ@)d/@)YJ@)d/@(.@)d/@'U2a|@)d/@(F1@7O M@7Zc@3+@2ᰊ@2Htj@2ߤ?@23&@1t@23&@1u"@23&@1$/?Y͍_??np?CV? T?=%?)T?u(?7%O?p?%8? S?2Y0?P!p?UYC?0 ~??NZ@@@@@(@&??????pA?c'vp?'WPD@6F06@)B?Wd?\Sl ? qL?Xԝ4M@[N(@%@.vȴ9@.E@.vȴ9@.ߤ?@.vȴ9@.n@.vȴ9@.!-w2@.vȴ9@. (@.vȴ9@.,@u@ڟv@u@+j@u@v@u@֡a@u@z@u@u@5=:@5>t@1G+@1GO;d@0`A@0p:@/Bu%F -@/A.H@/Bu%F -@/B\(@/Bu%F -@/Be+? ?4$?"?5~pY5?,I ?CXs?Br?La?ԕ]I?Y? A?]#O?3Z\?˛͇q???ˢL1?7@@@@@*@*??????? 'PgW?$ѧ?* -F@.64@!hL?EG6:n?J c ?qN c?})@iU8$@&@x@1&y@x@'Q@x@!-w3@x@hی@x.@x@#x@xM@xffff@xM@x+j@xM@x @xM@x=p@xM.@xM@xĨT@8e!.@8ZfB@4?|h@4jn@4~Vu@48_F@3ޫ6z@3Mj@3ޫ6z.NC,@3ޫ6z@3҈p;?]?}>?99?Y3ۄ@8I?if}?]?lׁ?dj!"`?vr;?Xx??9cX?|b@?]&?䙣K?9[O??T,?UA|?B?ҕ)L?h| 8?!?Uf@@*@*@(@1@0??????O4p!?6is;>I?" W@M@?VM=Z?C /?@Q u?}꽴j@ff7b@(@|hs@9X@|hs@|j~@|hs@z^5?}@|hs@x}H@|hs@|64@|hs@w@m@/@m@䎊q@m@@m@g@m@@m@sh@8Y*0U2b@8?b}V@3ěT@3TɆ@3t@3 5Xy@2Y~($ @2\hr@2Y~($ @2Ye+a@2Y~($ @2X@?% ?3?פ?W0 ?q?I?~\?@C?v@b?3)10F?pPȻ?Cq?>'?㑏\?eI6?ۮetZ?/̽`?DV@@@@@&@,??????kq1^?L'(?Y3?G[Վ{.@z/t@&s?xS)fg?Vz?f?-j{?tof@qb 2j@)@r-V@w@r-V.@r-V.@r-V.@r-V.@r-V.@6B\(@62-V@6B\(.@6B\(.@6B\(.@6B\(.@6B\(.@6[~($@6aae@3+ .Mm@3j~#.Qn@3:S.NC,@3:S.NC,@3:S.NC,?Br@8#@8:@8 >+@8I@8?}ѿ,@8@8i@8@8S@8?;aِ@8 *@8p@8@8 @8 @?*@ dZ@ S@ dZ@ s@ dZ@ PH@ dZ@ U=@ dZ@[6@ dZ@ lC@M# -=q@M.V@M# -=q@M,@M# -=q@M*#9@M# -=q@M!n.@M# -=q@M%oiD@M# -=q@M8F]@7V,<@7V,<@4{ I^@4zc @46-@4%$/@3YJ@3NU@3YJ@3v_ح@3YJ@3nO??l?d?':?/cե?ĞS^ ?G *ǂ?rnc?6eI?ǧ?D6?w@2I^5?@2}H@1Ctj~@1D/@05sh@06L/{J@05sh@06?@05sh@06z@8J??jhq9?'$5?'ř ?-/ge@8J?l?{d??=d^?äqY@8p?Q?qn?k,? Ğ?&I@@@@*@*?????,@8|h@8~Q@8|h@8~vȴ9@8|h@8'RTa@8|h@8hۋ@8|h@8~vȴ9@8|h@8~"@`A@ ě@`A@nP@`A@p;@`A@ݗ+@`A@u!@`A@@6[~($@6Zc@1^5?|@1ɭB@1i"`@1hy=c@0u@0a@N@0u@0S&@0u@0Z?|O?$?K]4?U*?/m{? $4?F/P5? ,C?}7.?; $?2}?äqY?(?9o?D(p?%ϑ?7? -t@@@@@,@(???????9R?<*p?-ڷr"?8 \#@8N@ Z?N>֊?X" -?("a?I3@h1B3 @-@k@k@k@ld@k@l '5@k@k")a@k@kאYux@k@kX%@!@$T@!@.s8@!@6@!@S-@!@Ct@!@>@6~($@6 0@3 I^5@3h: -@2V@20L@2iB@2v@2iB@2#B'@2iB@2y'?}@TΥ?~q4?[?(?>=d4P? X<-/?|b@?iE=?ڙE?n[>L?Ш?Q@$?S\H?y?H?崐?ft?)Ebw? -:8@@$@(@(@5@2???????e&'`;?_r>?KCZ?@k@ ,Q?}Ŏ?iBP?&?F[,?F4@d&qfJ@.@Vu@VE@Vu@X-@Vu@WXe@Vu@Vu@Vu@T9Xa@Vu@Tj~@vȴ9X@vȴ9X@vȴ9X@u\)@vȴ9X@y k@vȴ9X@z>@vȴ9X@u!S@vȴ9X@yXbN@4*0U2b@4@2xr Ĝ@2w-@1-@1@1m8@1`d@1m8@1 @1m8@1F]c?|O?gˣ? ?? Iy?y%?1a?}ѿ,?[45?Zq?wxR?*v7?Qa C?u@>N ?5?ւ?sMQ(?Cns? 1 D@@@@@@(@(??????6(=Ő?+|?!GsD?0>IA @U,~@wr?@܌v?P.`?j $?? /^@fqG@/@nP@e@nP@QS@nP@tT]@nP@Μ(@nP@GT@nP@%5@5`A7L@5i3@5`A7L@5^s@5`A7L@5f6@5`A7L@5g9V@5`A7L@5bj@5`A7L@5iD@7쿱[X@7(&5@3?|h@3ǀfT?@3`A7L@3^ c@2m8@2@@2m8@24P@2m8@2{|?mё>?L ΋?K?Aٯ,?WPJJ"??Ű?)XZ&?q~[?LJ~:?b??*?(@3+ J.Mm@3`A7.Qn@28D*.NC,@28D*.NC,@28D*.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 1@`A7L.@`A7L.@`A7L.@`A7L.@`A7L.@`A7L.@$.@$.@$.@$.@$.@$.@9N쿱[W.>@4+.Mm@4-.Qn@3&.NC,@3&.NC,@3&.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 2@ǯ-@ǯ-@ǯ-@ǰbM@ǯ-@ǯ-@ǯ-@ǯiDg8@ǯ-@Ǯ_o@ǯ-@ǭ@٫ I@٫ I@٫ I@٪ڹZ@٫ I@٪f@٫ I@٫R@٫ I@٫C@٫ I@٫U=@0-v@0-v@*8tj@*8F]c@)@)TɆ@(̲@(˒:)z@(̲@(xF]@(̲@(ˬq ?^Q5P?[e?ǹT?}?bM_x?r/B?90?9~F?ߒh!?Uyu#?bMky?.+?2Y0?':?pk Y?pk Y@@@@@,@,??????Gsw6?R?%L4Hľ? #ؿ@ǮN@٫x5D?4{Q ?*?d%̤C?O-b4@stA@3@?u?|@?xbM@?u?|@?u!@?u?|@?v4@?u?|@?t`d@?u?|@?uϪ͞@?u?|@?vz@Bl1&@Bix@Bl1&@Bka@Bl1&@Bl/{K@Bl1&@BjD@Bl1&@Bn_o@Bl1&@BdM:@6@@6_ح@25S@25ᰉ@1"`B@1qu!@18D*@19@18D*@19Q @18D*@147?1j?jEP?7' ?UL+@?vX=@Bi4`?YL?s*Ց3?Y ?#i@j0jW@4@fx@h9X@fx@tX%@fx@Xg@fx@K@fx@W.@fx@{:@IQ@IiV@IQ@Io>J.@IQ@IaJ,^%@IQ@I!@IQ@I BpY@IQ@I @5@5@4n@3S@2;@2j~@23@2?䎊r@2"n;>@2?䎊r@2j+@2?䎊r@2Ã?|h2:?oN??Vq?xь8?5R?3<' ?ql?0Ƿ?ޝYI?i?\d?lao?ܫ?y?Y*^?h1^?<9}@@&@ @@<@;???????t1ٙ[Ƥ?Ir렄UC?'J< @zese@I\?cPy?A?lj"Tf?sL@bA@5@1&@A7M@1&@u@1&@@1&@֡a@1&@$@1&@si@(S@(NO@(S@(,1&@(S@( @(S@'W>6@(S@(1[W>@(S@(K~(@8[~($@8TJ@3S@3iB@3q7Kƨ@3^!R<@2̿[W?@2$@2̿[W?@2tSN@2̿[W?@2Z?{?vq[@@@@@(@$??????dr7]H?E,?k$)@H@(Is-?ff:#?[x(?.zX@?j+@hv@6@6a@6w@6a.@6a.@6a.@6a.@6a.@-49Xb@-?vȴ@-49Xb.@-49Xb.@-49Xb.@-49Xb.@-49Xb.@9|Q@9A [@4{ I^.Mm@3j~#.Qn@2sh.NC,@2sh.NC,@2sh.NC,?(eI@8#@8:@8 >+@8I@8?[Xxp@8@8i@8@8S@8?UWci@8 *@8p@8@8 @8 @?7@E@Fx@E@Ji@E@G{T@E@L`Vpy@E@@[)',@E@@ W@;dZ@;@;dZ@p@;dZ@ a@;dZ@ -'@;dZ@A X@;dZ@1j@7:qiC@7;s@3hr @3M=$@27Kƨ@2_yA@20U2a|@23-@20U2a|@2dx@20U2a|@2rk)C?m?b?C?&I$>?zK?ٓ)?| V?Ofo?3ho?,>,?S60?XҙN_?'h[8?ܺ_I?XB:3 -?ɛ~1?Bq?SJV@@,@&@,@4@5??????fLW>qk?bmUx?@F@F:z@ӡ?y<Y:?e,u d?&wl.?U2^@lM@8@C-V@CD@C-V@D+J@C-V@D--G@C-V@D=-@C-V@D'^@C-V@DER@j:^5?}@j3E@j:^5?}@i}z@j:^5?}@ij @j:^5?}@i1?"@j:^5?}@h4Po@j:^5?}@iF@4-v@4* ~@2=hr @1o@1KƧ@1_@1OO M@0`p)u@1OO M@04 @1OO M@0t ?|B &?)!,?0?f?FS?^/r?}#?Zz? ?0 _?-?~|J?Q–e?`1?{Rv? y?OjH?-a@@$@&@$@2@1???????cwq ͫR?lhOh?@D u'@i_5?HH?`7Pu\@M@[>.@dD`@9@{lD@{S@{lD@|ߤ@{lD@|u@{lD@}/@{lD@{=K@{lD@{n@Ƨ@+ J@Ƨ@&@Ƨ@Ft@Ƨ@-@Ƨ@Ƶ '@Ƨ@@2=:@2=V@.`A7@.|@.cnP@.b@.kjf@.jfB@.kjf@.jD@.kjf@.jL/|?^Dw'?°R;?.Ү_ ?lDžN9? f?2I/?bM_x?%F?qF?>\?ڂc?rJs?bMky?Ϸ+?2Y0?8 ^?b/bJ?pk Y@@@@@,@(??????G.}8ɿ?8 -ğ?1?@|*\m@O3?G S;?@V+v?(,66?wj !C@sdBP@:@Q@1&y@Q@$/@Q@ Ƨ@Q@ 7Kƨ@Q@n@Q@@C@C@C@r @C@@C@+@C@Q@C@)_@5䎊@5iB@1r Ĝ@18YJ@0"@0n.@0V!.I@0Q-w1@0V!.I@0P)^@0V!.I@0R3?]?}d?FB:?_B?tXS?5@?:?@x6K,@;@.O@.zH@.O@1@.O@&@.O@5\(@.O@)ԕ+@.O@.H@Ƨ@ΗO@Ƨ@Z@Ƨ@V@Ƨ@j~@Ƨ@R<@Ƨ@A \@54m8@5U=@1r Ĝ@1a@@07KƧ@0p ->@0^6z@0He+@0^6z@0F@0^6z@0Hu%F?| ?F?"Hur?ˆ_?|(?%?F/P5?YP?vp6d?TV?2rz%6?Wf?a?BM?dd ?{B0?WZ?>3}@@@@@,@,???????J:,u:is?T1 -X?gzLB7@/$@?q;?GS ?@-S?t=0`@bZ?@<@c -=q@wsK@c -=q@[t@c -=q@G1I@c -=q@sb@c -=q@^͊@c -=q@X4֡@[tj@[vȴ9X@[tj@[@[tj@[9@[tj@[̈@[tj@[@[tj@[`B@8r@8kz7>!@@3 I^@3,m³ -@3A7K@3 [$5z@2QiB@29a`@2QiB@2H=?@2QiB@2P- r?NAc?NHRp?!}')?xe'?> -; ?+?(-~1?e@ ?#:?4o?Y(? sPf?|f^@h|R@[?wwb?d?y@@o@=@`A.@`A@ov_خ@`A@h ԕ@`A@$/@`A@{s@`A@{Q@Qhr!.@Qhr!@Cn.@Qhr!@9XbN@Qhr!@8l"h -@Qhr!@G@Qhr!@&IS@7.>@3r Ĝ@3-@27Kƨ@2$/@2m8@2|hr@2m8@2wlC@2m8@2w&@8J?5ԗjN?yaR?,?v.?)}U@8J? ?t!?"JÁ!?l?|@@8p?Fۯ/?^)7H?V]9V?3?ymcw\@@@@,@*?????>@g.@g@g+]c@g@g@g@g&,=@g@gC,z@g@g$/@ե`A7.@ե`A7@ѯw@ե`A7@ѓE@ե`A7@я@ե`A7@P'R@ե`A7@4!-w@9}:.>@5+@2XK]c@417Kƨ@18bM@3 k@0,<@3 k@0fA@3 k@0@8J?tH?8ʐ?ҵR24?h?wMIf@8J?3Pj?EE+?L?)>$;?s@8p?њH?~}o_?eL2G?, ?7@@@@@*@&??????A ?%N ?%t-x?:@Mk|@@?NJ@?w_8@r>?@A@1'@#@1'@(@1'@/V@1'@($ x@1'@' l@1'@+҉@kY+@k.@kY+@k -=q@kY+@khۋ@kY+@k~($ -@kY+@k@kY+@k*/@8 ]ce@7\@2+ J@2XbM@2Z~"@2<>@1Q@1ըXy=@1Q@1]cf@1Q@19?J ^?WZwn;?ڿA?A2x?|c?uV`?9A?ly=?A qi?Ѝ`Hi?8}??*?ͥ?KF??i - ?Ƽu? # @@@@@,@*???????`?.G?FZ)?h ba@' P@kun+@?avt??gCe?=@lrM -@B@MO;d@Mhr@MO;d@M(@MO;d@NV@MO;d@M(@MO;d@L@MO;d@LI^5@A7L@n@A7L@҈@A7L@d@A7L@҈@A7L@ [@A7L@ě@0:@0>@,kƧ@,kC\@+?;dZ@+?b}V@*u%F -@*\(@*u%F -@*Zc@*u%F -@*œwkQ?^Dw'??"?ƠJ-M?tXS?}?bV,? -?A)N?3.Yl? xV?h,à?bMky?~('?q6tN7?ʬv??Ѐ>(@@@@@@??????GH]r?/?Z5@M;8@:?'5nu?*˟[?L ̱U--?Om!@s?]a@C@ʅQ@ʅQ@ʅQ@ʂ\(@ʅQ@ʄ?@ʅQ@ʆFt@ʅQ@ʆ]c@ʅQ@ʃ @0 I^@0S@0 I^@09X@0 I^@0&@0 I^@0F]d@0 I^@0M@0 I^@0TɅ@5*0U2b@5JD@2+ @2I^5?@2j~#@2 M:@2L[W?@2R-V@2L[W?@2Phۋq@2L[W?@2PA7K?| ?[ew@ʅ"h@0Yg?W#[H?79mܵ?M:?vVqX @e;@D@@n@@A7L@@n@=8Qf@@n@?2zZ@@n@?}5@@n@8@@n@9N.@ix@hr -W@ix@m@ix@Ą\@ix@ĄGC@ix@t@ix@sm@42@43ͅ^L@0hr @0Xu\x@/O;dZ@/n@/73@/Y@/73@/8\h@/73@/sJ?}#?%?J}+&n?}?o@%?WlH?| V?T}?U??"X?+ߊ?J?Ixd?w:?]ޭ*~6?G2ag?*?Q@@@(@"@"@0@.??????Vd~?qY~mY?AqN?g>UVK@=N@q}e?`m,F?,! Sd?]Sp@Rciԃ@efqk@E@jfx@jg+ K@jfx@jh@jfx@jjW@jfx@jh@jfx@jf,=@jfx@jeڹ@Stj@Stj@Stj@SPH@Stj@Rs@Stj@Q@Stj@R Ĝ@Stj@QiC@3@@3 ԕ*@0@0g@/nP@/s@.Xy=@.2a|@.Xy=@.L_@.Xy=@.O M?|O?_?S[?'$5? g?-tT?| ? H?d0R?]bn~?A B{?sx/T?nwf? -?YF??L? -t@@@@@(@,??????7P4JiF@?1 ?wF @jg$;4@Rj?Q~`qm?<$ ?tm?V;+@f˟@F@ě@$/@ě@E'@ě@ [@ě@J{"@ě@Xߊ@ě@N%N@8 I^5@8 I^5@8 I^5@8 B@8 I^5@8 ;߼@8 I^5@8 1&@8 I^5@8  -%@8 I^5@8 ,d@-zu%F -@-zu%F -@&"@&Fs@%dZ@%qu!@$fL/{K@$fV?@$fL/{K@$fYJ@$fL/{K@$fL/{K?^j?ܸn?3:??FB ?y&?O?bMn?F?})"Ҫ?2^? mv?J W?bM]f?.?n3?xFD?[J?I@@&@*@*@<@:??????=fI>0?!$Owj>L]h2]>@e+@8 D'?0{2x8>fuy?ZEe>G=@s [g@G@k Ĝ@kw@k Ĝ@k0M@k Ĝ@k>Fe@k Ĝ@km.@k Ĝ@kbM@k Ĝ@k״3@Ձ$@ԯO @Ձ$@a5@Ձ$@I(2@Ձ$@ĔA@Ձ$@ȴ9X@Ձ$@˟U=@4H@4_q@1R I^5@1E8@0Ƨ@0I^5@@0u@0m@0u@0oo@0u@0p- ?}@TΥ?Ɩw1?m s?.@miz?i]i?SJ?|B &?X?=?N0?I0B?ҥX[?`P~V?{6?^6?\T?7?՘]G@@"@&@&@ @??????uPf8 ?C92q?Tғ@ke@'r?b޻w?tTk"?~W?Ay4@f͌@H@5lC.@5lC.@5lC.@5lC.@5lC.@5lC.@"49Xb.@"49Xb.@"49Xb.@"49Xb.@"49Xb.@"49Xb.@6(YJ.>@1?|h.Mm@0-V.Qn@0_o .NC,@0_o .NC,@0_o .NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 I@r-V@o\(@r-V@cS&@r-V@c -=r@r-V@b`C@r-V@g@@r-V@eO@_M@_PbM@_M@_B I^@_M@_Jo@_M@_9|@_M@_;qu"@_M@_:@62@6/O M@3uS@3j0U2a|@2Vu@2*@2m8@2y=c@2m8@2ڹ@2m8@2bM?Y͍_?)D1?0??q?V?Ol?њH?低NU?ZpÉ?ݤ`i?ք5?T0?vv?fr[?!B?o?h@@@@@*@&??????cUu6#2?Rxd?N*@i ز@_F7?q V>\?my?(l ?erW@e5mz@J@ᙙ@1&y@ᙙ@>B[@ᙙ@ᚬ>@ᙙ@J#9@ᙙ@#x@ᙙ@ᙦ @+@+@+@e+@+@}I@+@ڹY@+@e,@+@>B[@8۹~($@8@1.5?|@1.@0;Ƨ@0;C\@-PH@-PH@-PH@-&@-PH@-33334?NIɮ?j"@vE@S@vE@0@vE@`A@vE@H@vE@@804m8@7}jOv@5c@5]K]@4-@4"`B@4z,<@4^_o @4z,<@4i'RTa@4z,<@4l>B[?\<4??-sA?a?k;?gUaJ?,? KBI?Bȅ?1?OY?%TUx?:B?:?^&Mka?ڧ?Q, -?.P@@@@@&@(???????3Xݏy/ ?XH?@b@i??@k0?L6G?߲n@t@M@j~.@j~@pw@j~@ڙ@j~@u]@j~@>-@j~@@1&.@1&@Ḙ@1&@"C8@1&@JO@1&@\(G@1&@@84m8.>@2 ě@1Nɶ@1,j~#@0qyR@0B@4@0z@0B@4@0%Jb@0B@4@0R߫g@8J?v>F? v??C4-k?.[@8J?G̲f?tDig?It?E?qjߢ@8p?*??Aeǘ?q?sc`D?0~6@(@@$@<@:?????N@0w@0- @0w@0͞@0w@0C]@0w@0-@0w@0- @0w@0 @\(@KƧ@\(@aoh@\(@Y=c@\(@[q@\(@^ѷ@\(@]p -@8 xF@8\N<@3hr @3_p@2`A7L@2u@2 -0U2a|@2 -ݘ@2 -0U2a|@2 ҈@2 -0U2a|@2 P{?qn?ly?TS?v" ?r {#&?=?ȉak?YC?f?WA?SY?VT?E}?ˇ?v'X?VED?l?_u@@@@@,@(???????2<`?fY?#DzOB?U߼@0%5@@Z~?@)"T?ld Q`?z/?*:o@nee@O@7Kƨ@~bM@7Kƨ@~V@7Kƨ@@N@7Kƨ@~[W>@7Kƨ@~@7Kƨ@~qj@(\@bM@(\@Mj@(\@|@(\@TɅ@(\@ӶE@(\@غ@6䎊@3&x@2^5?|@0^vȴ9X@2i"`@/āoh@2~($ @/v_ح@2~($ @/eں@2~($ @/vȵ?1j?UW?MDe?F?Fǥ?yW?}ѿ,?&ژ?j{X?QPL?Z?74l?zny-g?J??hc?N((?8wx@@@@@*@$???????@V.?eH>?c]z@~LSQ@9p?]&t?tS?۰@>Pr@bu @P@rM@rMO;d@rM@rNߤ@@rM@rPU2a{@rM@rPbM@rM@rQm@rM@rR@m Ĝ@mE@m Ĝ@m3333@m Ĝ@m1@m Ĝ@mC,@m Ĝ@m@m Ĝ@mhی@2g l@2tj~@0E?|h@0AA [@.O;d@.bM@/@/v8YJ@/@/v@/@/vȴ9X?OW? ?Gk;m?{O`?uxY?I?;0?$i %h?mK?.?4{?vMUu?!?Ϸ+?YF?0?ad|?oC?@@@@,@*???????I7ݖ?O_N?dv?G+>YI@rQYd@mWٲ{?(X?U+?EA?dw"z@sqgA@Q@!Gz.@!Gz.@!Gz.@!Gz.@!Gz.@!Gz.@}7K.@}7K.@}7K.@}7K.@}7K.@}7K.@9~($.>@4?|h.Mm@3j~#.Qn@3[W?.NC,@3[W?.NC,@3[W?.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 R@ܛS@ܜ1'@ܛS@ܜ墷@ܛS@ܢc@ܛS@ܣmS]@ܛS@ܘ@ܛS@ܙ@Zx@Zx@Zx@Z[ @Zx@ZA@Zx@Zma-i@Zx@Zg]@Zx@Z&@1!.@1/¨@0^+@0^`Ċ@0 -V@0 =Ƣ:@0#@0%Wި`@0#@0$9n @0#@0#Q?^6P?=L?m s?3H+?,kc?ҸQgv?b^57K?!SP?U?xx1?̴>.?^|&?t4n?UQx̚?\3?jN,?{j?¿'@@*@*@&@8@1??????b4J{P/?L߆0?0,@ܝU_@Z ?[]??L59?ߪ+6 ?t5k@s -@S@j~#.@j~#.@j~#.@j~#.@j~#.@j~#.@ I^.@ I^.@ I^.@ I^.@ I^.@ I^.@8YJ.>@3^5?|.Mm@2gKƧ.Qn@1:S.NC,@1:S.NC,@1:S.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 T@7/@7p -@7/@7ݩ@7/@7$ @7/@7ݮ%@7/@7Rs%@7/@7L@j~@j~@j~@n&c@j~@t:@j~@\ak@j~@@j~@(@5䎊@5:S@0i+ @0iMb@/;dZ@/@.fL/{K@.fw6̷@.fL/{K@.f4G$@.fL/{K@.fC0?|b@?>J|?@5$_2?4!?6?cIS?}@TΥ??>?1x?!WSm?Wf?o,qV?7)?v#0&?$: ?%9G?(Y@@,@*@(@<@:??????xC3@?6(A>? #@7E@'G?5"JT?F,?xQټSd??5|FF@g -~=@U@8V.@8V.@8V.@8V.@8V.@8V.@j~.@j~.@j~.@j~.@j~.@j~.@8`D.>@3^5?|.Mm@3'KƧ.Qn@2!.I.NC,@2!.I.NC,@2!.I.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 V@#LI^5@#ES@#LI^5@#N+@#LI^5@#QiB@#LI^5@#M(@#LI^5@#N.3@#LI^5@#LcA!@1&y@t@1&y@ :@1&y@Z@1&y@:@1&y@E@1&y@g@6r@6nzG@3xr Ĝ@3w#@2ttj@2tJ@2\Q@2\cA \@2\Q@2_?@2\Q@2]\?G *ǂ?O9?mr?\2?cc?S{d?}ѿ,??rVx??^?⍄o?dҦ 4?? - l]?hSp]?b? ;8@@@@@,@*???????M#R 6?Kw??nW:@#LF;v@v?cˡ`?d@,?tjw?PߓZ@mݤb@W@>".@>".@>".@>".@>".@>".@4|hs.@4|hs.@4|hs.@4|hs.@4|hs.@4|hs.@8B䎊.>@3|hr.Mm@2KƧ.Qn@2!:S.NC,@2!:S.NC,@2!:S.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 X@8$/@8Ƨ@8$/@8u@8$/@8(@8$/@8pY@8$/@8]Q@8$/@8__@bvȴ@bvȴ@bvȴ@b?D@bvȴ@b f@bvȴ@b@bvȴ@b I@bvȴ@b.*@5%!.@5&ʌ@2+ @2˜@27KƧ@2g1b@2u@2EPC6@2u@2TT@2u@2gKo?}#?C`? HCP@?Qð?,92d?[?}W?}^?k?mu?y?)lv?xk?c%?RǶh?2P?Jtѧ'?ۑD@@,@(@&@8@8??????SS8Jh?3Y gm?4x^@8@bZ?RCu#?TT ;6?)bZ?iN#l*@f ձ@Y@;dZ@\(@;dZ@@;dZ@.3@;dZ@n@;dZ@~($ @;dZ@E@z ě@z"`B@z ě@z A7L@z ě@z@z ě@z!:@z ě@z" I^@z ě@z!@6#g l@6#n.3@1@1tj@1K"`B@1K~($@0ޫ6z@0v@0ޫ6z@0ߗ$tS@0ޫ6z@0+? ?hX*?,fC6?=]. ?h|?.'?)T?6S{?I?j?qN?ey?]?=d?T?RJ? Ğ?&I@@@@@&@,??????N+0?4ؖq!#?##\@O~@z"B 7?U%?CLa}?}'UW?@T@ib]@Z@ (@ @ (@ Xȷ@ (@ B @ (@ x@ (@ j09W@ (@ S@t -=p@t@t -=p@sȏ@t -=p@sB5W@t -=p@t%bW5@t -=p@t(Vns@t -=p@t3_E@5w@@5oH@@1S@1=7@1U`A7L@1^+&`@1s&@1" @1s&@1vl@1s&@1s꣊?~Ov_ح?2o?v= ?֟"? p)/?m?|b@?"HS??g-?z7{?U:0?bc畯?hk?\͑i?CER?&?Ɣq?=L"@@$@ @$@:@7??????etk ?hqxS?[ra?.ϒS/@ ܛ@t&A?y?MTNrI?Z[?6D(@dKWkF@[@Xtj@X^5?}@Xtj@XK@Xtj@XY@Xtj@X5@Xtj@X&ɁF@Xtj@X .@$/@{) -K@$/@@$/@A"C@$/@O@$/@Ha@$/@Q@7 xF@7ٔb@1xr Ĝ@1vu@0,j~#@0*#l^@/#Z@/@/#Z@/BA9@/#Z@/R1q?_Kt?ZUb?9D{p?|?; [?NpI?o"?;q?0?s8? ??yѿ?zbH?~m?dt?E?Bz6?$M@@*@&@(@7@8??????O?R:p?D.XV?Dz/"@X@?6_T.n?d@"?u 1i?cH@i ,n@\@߶ȴ9X.@߶ȴ9X@ߒ-V@߶ȴ9X@tzG@߶ȴ9X@GK]@߶ȴ9X@1 @߶ȴ9X@W1@WO;.@WO;@xtj@WO;@Jڹ[@WO;@$/@WO;@H9X@WO;@iy@9_o.>@4ٺ^5?}@4*0@4-@3_o @3@3- @3@3b}W@3@3%F -L@8J?~7* ?G?"d?:N?>͛@8J?Qe?&E֫?BS?">?eI@8p?Fu?k pē*?LD?TS?J*@@@@$@ ?????]@yXbN@e@yXbN@v8YJ@yXbN@z~@yXbN@y0 @yXbN@w@yXbN@xl"h -@,1&@7O;@,1&@*0U2a@,1&@%Q@,1&@*0@,1&@)Q@,1&@)ԕ+@8*0U2b@8Ұ ě@3° ě@3ȆYJ@3."@34S@2iB@2zxl"h@2iB@2RT`e@2iB@2R<6?|O?ʖSS?b? ?Ԧ?n?4<7?ٛP?iCv?HD?h?[h??eBZ?G?qlPg? -e?p=J?OO ?+`ȓ@@@@@*@*???????s,,xc*?Sص* j?RJ@t.o@,?x?X2@@@@@@,@&???????u  ?U8y4z?Zw^u@5 -@dEA?qb/D?yQCqݳ? -?V]$4@i7,7@_@kR@@kR@sMj@kR@"`@kR@\(@kR.@kR@ ѷX@j~#@!@j~#@@j~#@WKƧ@j~#@A$0@j~#.@j~#@Y|@8|Q@8LQ@4)+ @3b}@3-@3 !-w@2xF^@2B&IR@2xF^.NC,@2xF^@2@5?ȉak?B};?>9B? @8I?ric,?&"?yCN?Eʻ)?4Uo@8S?Av"_+S?MM.?G^~?bfU?%)@8 ?J6Л@@@@@*?????`@;dZ@8(@;dZ@T4M@;dZ@=#@;dZ@:S/d@;dZ@; -YL@/5?|@0Q@/5?|@/)c4@/5?|@/j¹@/5?|@/ܵg@/5?|@/ݳ,~@/5?|@/z@8!.@8Jߦ@3?|h@3S`@2A7K@2Nr@1._o @1.,fX@1._o @1.@m;@1._o @1. ?CbyV?dc:N?^?g.$W?j eI?i?$/?6c? -rmH_?f|w?,4N?x4??S?yj?={:?Y#A2?Sͨ@@"@"@"@5@4???????? %qmh?@jl?\zQ[@:Ϗ@/w?`(q?wY;m?2[7?^"{Q@l~@a@ -=p@ -~#@ -=p@ ~(@ -=p@ Ƨ@ -=p@ L_@ -=p@ TɆ@ -=p@ Dg8@V+ @Vȴ9X@V+ @VϪ@V+ @VC@V+ @WXe@V+ @WX@V+ @W>6z@1-v@1-hr!@,-V@,@+dZ@+q @*At@*O M@*At@*\(@*At@*\(?^Dw'??N?8v?Ot?>?bV,? -?rJs?/Z?%y*=?X78?rTg?~('?u@>N ?~\?#.?Ѐ>(@@@@@,@,??????U ?Bǽ^NK??<] @ -B&@W<`?Rd8ӿ?!CGt? + :?:%M@sa@b@$@A7L@$@jW@$@o4֡b@$@doi@$.@$.@@T@@fA@@[@@tS@.@.@9mv@6/hۋ@5+S@5p:@4 -V@4@3s&@3O{J#:@3s&.NC,@3s&.NC,?]?C\ P?6\9?ݡu(r@8I@8?]?qӡh?Qrz?Җhn@8S@8?*wjs?};?|?1R@8 @8 ?@@@????c@9XbN.@9XbN.@9XbN.@9XbN.@9XbN.@9XbN.@ 7Kƨ.@ 7Kƨ.@ 7Kƨ.@ 7Kƨ.@ 7Kƨ.@ 7Kƨ.@9*0U2b.>@43S.Mm@34tj.Qn@2.NC,@2.NC,@2.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 d@ -=q@5?|@ -=q@e@ -=q@p:@ -=q@@ -=q@d@ -=q@64@*E@*\(@*E@*Y~@*E@*+k@*E@*j~@*E@*@*E@*:)@7H@7*0U@3I^5?@3C,zx@2"`@2Y@20U2a|@2@@20U2a|@2_o@20U2a|@2!-w2?Br?p~v]?5$?o]އ&?ԁ?zWmo?h??uie?K54;?8h\?h_N%?I?p-?P!p?֘>D?gtH?Xk-7?♊N@@@@@&@(???????4R?:W?R?]6̳ @3L@**?t?%i?y(:?1z7?ԙ>@jt@e@ Ĝ@^E@ Ĝ@*@ Ĝ@; G@ Ĝ@@ Ĝ@,ln@ Ĝ@)]@i\)@i-@i\)@jy@i\)@j£amO@i\)@j-@i\)@jd47 @i\)@jE6@7 ]ce@7 (W[D@3@3 +@3~"@2%pT@10U2a|@1#$@10U2a|@1]Q@10U2a|@1uO2?F$?Ѥh?)?֣?T-??nOE@+@M?PR+?vn?J?z&6?=pE(@lX°B@h@hs.@hs@wk@hs@凓ݘ@hs@@hs@ k@hs@e@5?|.@5?|@P- @5?|@4*@5?|@Zc@5?|@U\)@5?|@^%@;4m8.>@6S@6 k@5-V@5}cA @5 [W?@4@5 [W?@5$0@5 [W?@5@8J? `?NJL?W?W07q?TA'@8J?I`?5~B?շL?*-@s3?87@8p?SY~?V 2?ih?0C?57@@@@(@*?????i@[S@[S@[S@]!.I@[S@]/@[S@]ce@[S@Z@[S@YY@+ ^5?@+ l@+ ^5?@+ @+ ^5?@+fB@+ ^5?@+ l@+ ^5?@+@@+ ^5?@+@@3 xF@3%1@/t@/E@.nP@.@4n@-N;6@-U2a|@-N;6@-GE85@-N;6@-GE85?| ?1?\H{gX?8v? f?6J?|O?5CQ?['?N?qN?ȏ'T?n88?§d߱?٤?8 ^??b/bJ@@@@@,@(??????+_}@W?7r{@?"!@[j@+?W87?BmWv?}o??r@f(@j@bM@@bM@!-w2@bM@n;5@bM@K]@bM@eݗ@bM@c%@q^5?}@r@q^5?}@q]c@q^5?}@q"h @q^5?}@q}V@q^5?}@qE86@q^5?}@q&x@7įO M@7?@3I^5?@3ʌL_@3"-V@3-Z@23&@2Q`@23&@1GE85@23&@2ěT??d?zeߎ?)[fI?-; ? ׅ.?^*?x\Fp?>4g9?y/2v?0-+?|:?zk 3U?3E?N0?FE%5?zl?hcӗ@@@@@*@$??????R\@?xΑ?G|2@%6@q |? -J? #wy?GFz@-_nj@d@b~@k@_T@_T@_T@_u@_T@_*@_T@_zxl#@_T@_q@_T@_e@`ě@`ě@`ě@a@N@`ě@`D@`ě@a [@`ě@` k@`ě@a-w1@0H@0;5Y@-V@-:@-&x@-`A8@-873@-8- @-873@-873@-873@-8}H?^Q5P?{j?1f_?q@v?DE??bM_x?#maЀ?pM?>\?@?h,à?cPQ?3;?y8$?':?b/bJ?pk Y@@@@@@"??????Ju>I(K@?81?uϹ@_-_@a.?,T?+c?T݆?5삭i@s7@l@UC@UI^5@UC@UK@F@UC@U1@UC@Uz|r@UC@U*@UC@U#-@F I@FƧ@F I@FEԩ@F I@Fs[@F I@FᏭ[@F I@F) Y@F I@F䭨@4>6z@4AեS@2 I^5@2^@1v-@1f?b@1L[W?@18 }ʴ@1L[W?@1>b/7@1L[W?@1>R?|b@?u$M?)~ϙ?O?U'-?)C"f ?|b@?V)o?P3b东?Fq<?Fj.%?7n??/$??'0S?VLS?: ?=B-@@(@@(@9@8??????gf]A?V p?O>N@Ui@F*8?tl7y=?lzQM+?84a?QRF#@dK@m@AGz.@AGz@A,@AGz@AI^5@@AGz@AIx@AGz@AVȴ9X@AGz@A9+@zG.@zG@$@zG@E@zG@v@zG@fA@zG@+@7|Q.>@2° ě@2H˒:@1j~@1oiDg8@1G k@1E@1G k@1?.H@1G k@1@*0V@8J?F?!N?eU5?;"?;@8J?I?;,?RLnj?S?X78@8p?Fp7?(?XW~?&f?WZ@@@@@?????n@>".@>".@>".@>".@>".@>".@E.@E.@E.@E.@E.@E.@9T xF.>@4hr.Mm@46-.Qn@3n_o .NC,@3n_o .NC,@3n_o .NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 o@Q@@Q@1&@Q@"u%F @Q@Q@Q@t֡a@Q@k6z@O;@)^5?@O;@W@O;@O:@O;@d8@O;@+a@O;@(ۋq@8,<@8_ح@3 I^5@3*ڹY@2t@2[q @1@1G{@1@1$tS@1@1g?}?ӻKL?$VL?M. j?RqJ ?õ"- -`?d0R?1'7? 5Y?eDQQ?^?6?S? -t?L8AS@@@@@ @???????@6t?0F+/p??&& ?^\@X4@s?^?:^#9s?1gt?O%@gVϯ @q@r @bM@r @iB@r @@r @@r @+@r @VR<6@v2-V@v+R@v2-V@v>m\@v2-V@v7O;@v2-V@v9>B[@v2-V@vF@v2-V@vU*1@84m8@833334@4#@4@3$j~@3L/{J@2Y~($ @2Cݗ+k@2Y~($ @2?@2Y~($ @2?ٛP??X^ -1?~]?dY5?τˎ^*?UbB2?ङm?c?-ʴ?XDo|?Mp?+ʄ?RHֿb?((?Cmk? Ğ?A@@@@@,@$??????̇|@?yu?f^o-o?F>z@e@v77i?Ӌ?bh$q?\GD?.qTX@dBØ@r@^Q@a$/@^Q@~5?|@^Q@h[@^Q@lC@^Q@HXy=@^Q@Zu%F@ t@ lC@ t@ @ t@ E@ t@ $@ t@ lC@ t@ pD@6 xF@6ԯO M@4+ @4J@4_-V@4ZڹY@3YJ@3u%F@3YJ@3wkQ@3YJ@3??1j?u~??:?'`?@?F/P5?'?ZH|:?;A?6?!kz7?䁜?{_?qyi?@ꘄ@h@t@|h@n@|h@k)@|h@3@|h@2@C@Yj?q{?uM?l{?iy?b?@f :`;@u@CI^5@Cn@CI^5@CDg8@CI^5@Cws@CI^5@Cp$t@CI^5@C|wkQ@CI^5@Cs@Q@DT@Q@J@Q@<@Q@ O;d@Q@הj~@Q@Wr@7s@7?|h@4)+ @41@36-@3kU=@2,<@3z>C@2,<@3c @2,<@3sh?Br?:A{o?%=O>?T?U(?^??Ř?I\8S?qƑ?25?R9y?<?hB?5?S~iX?|?@@@@@*@,??????Aݍ} ?&@?q5C{?ynSF@C#Gv@L-۬?疟e?'4D?Sw?*?@]sY@v@lD@$@lD@U"!@lD@!"f@lD@3}@lD@ @lD@0,@Ƨ@+\:@Ƨ@үS$@Ƨ@:n@Ƨ@Ѿ_@Ƨ@@Ƨ@hT@8_o@8*c@3+@3u)Hl@2`A@2{c@1!.I@1baZ@1!.I@1UvE@1!.I@1?R?5 S? }?R?ynS?U2L?.EZ?4zs?(cf{?Ïb?"I?GEP@@@@@4@(???????\-D?WDo?S·J{^?g=O#@2 mэm@;?n [!h?{댨5?ҿSU?'y@p~ D@y@a@l@a@S@a@MjO@a@TZ@a@T$@a@WsO@ -=q@-@ -=q@ ě@ -=q@ק'@ -=q@u@ -=q@ᰉ'@ -=q@oiD@7@7MjP@4}hr @4~qi@3y7KƧ@3y_o@2@2~%@2@2s@2@2dZ ?^*??[;b@@"@"@(@9@7??????a]?j /?<9İ@oYb@kB?=F?.9@"pm@(dVQ+R@aK[G@}@V.@V.@V.@V.@V.@V.@;dZ.@;dZ.@;dZ.@;dZ.@;dZ.@;dZ.@5qiC.>@3|hr.Mm@2M-V.Qn@1O M.NC,@1O M.NC,@1O M.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 ~@3E@3@3E@/-@3E@,cA @3E@L@3E@U*1@3E@\PH@wKƧ@w -=p@wKƧ@w'@wKƧ@o@wKƧ@=c@wKƧ@lC@wKƧ@+a@3>6z@3RT`d@05S@0._o @.ΗO<@. qu@.L/{K@.ae@.L/{K@.S&@.L/{K@.F -L0?|O?I'?1?&L=?X?|x?|O?M7F?I?-w?:S?5?|׿pY?QdЈ?5q?c^)?4\ɵ? uf@@@@@,@,???????SÀ?%,?7~"-?Qc^zW@;̧@t ?H] ?b0d!^Z?>!j?7 f@] a@@zG@ I@zG@/V@zG@Ձ$@zG@ٳ|@zG@ '@zG@F@:^5?}@["_@:^5?}@:@:^5?}@7+j@:^5?}@=H@:^5?}@9Y@:^5?}@9"@7۹~($@7D*@2ٺ^5?}@2KƧ@2M-V@2L>B[@1u@1M@1u@1$@1u@1-V?G *ǂ?S_?u*y?W0 ?FL{?װ*w?g1??AuG1?{4k?.<?GE?+ʄ?t?*0x)?`?{2?_u@@@@@,@???????/w2b'?_9!?VԨ|@O#)@;tI?t"<K?g9Y?M?Az>@r@@( -=q@(3p@( -=q.@( -=q.@( -=q.@( -=q.@( -=q.@Z1@ms@Z1.@Z1.@Z1.@Z1.@Z1.@8@@8%@4{ I^.Mm@3{Ƨ.Qn@3 k.NC,@3 k.NC,@3 k.NC,?`Q@8#@8:@8 >+@8I@8?%=(@8@8i@8@8S@8?P:Ԕ @8 *@8p@8@8 @8 @?@@bM@@?|@@_@@&@@`d@@@&x@V@&x@@&x@eO@&x@ᰉ@&x@C@&x@1&@6O M@6S@3@3/V@3@`A7@3?\(@2,<@2MjP@2,<@2JL@2,<@20U2a|?}ѿ,?t,?^CjD? "?/Jë?^?:?F/P5??xrI?1ک! ?M=?U"T?.?a -?9WTW?ЙQ$?+B?xb? (0Q@@@@@*@*???????a?z"H= ?b՞?=J^L-@l@)?3=p?[_?s -@ơ#?G)M:@edKF@@:fffff@:o\(@:fffff.@:fffff.@:fffff.@:fffff.@:fffff.@5\)@Jp@5\).@5\).@5\).@5\).@5\).@8uS&@8k~($@5sS.Mm@4-V.Qn@4xF^.NC,@4xF^.NC,@4xF^.NC,?us\@8#@8:@8 >+@8I@8?}ѿ,@8@8i@8@8S@8?@6[@8 *@8p@8@8 @8 @?@~"@b`B@~"@a@~"@t>b@~"@~'@~"@2/@~"@?@m@%~@m@FL@m@񌡙@m@QQ@m@oz@m@6' -@7cg l@7IJ~R@4|hr@3`m0z@4lj~#@2Aʠݜ@3!.I@2\ GB@3!.I@2l<9@3!.I@2~ Cl?Hg ?e= @?V|x{?5Iz?os{p?]ޜ?Xd?5h$?"_l^?a?m\=֌(?h~?g|v?E#&?' ?Gۋ?h>?Mj@@(@$@"@9@5??????KOn?<J`?6|?n})A@5w@?~H?> @t/~5@1vf@e.q@@jZ@jn@jZ@j_p@jZ@j^5?@jZ@j}V@jZ@jE85@jZ.@<@@<6u@<@@<')_@<@@<+@<@@<]b@<@@`?rJs?9~F?Hc?eDQQ?٤?u??':?B ?pk Y@@@@@,@,??????-:ӂ>?'g&?@4d@< Q@Nc&L?Gh}?$GH`r?؟)?U[~@sW@fKH@@KƧ.@KƧ@K'0@KƧ@J=p@KƧ@Q@KƧ@H1&y@KƧ@NzH@蝲-V.@蝲-V@(@蝲-V@J@蝲-V@@蝲-V@TɅ@蝲-V@N;@9YJ.>@4@4@4@3dj~@3e '0@3Y~($ @3QX@3Y~($ @3X\*@3Y~($ @3XK]c@8J?[ ?ƐW?G?(i^?c:e@8J?f?YEX?̶j? R?r@8p?vC ?ީ)?B?zy? -.ԕ@@@@&@*?????@/w.@/w@/O M@/w@$%2@/w@.2@/w@5 r˗@/w@-_>@HA7K.@HA7K@H_;dZ@HA7K@HS&@HA7K@HG#@HA7K@HQH@HA7K@HL'@9I_o.>@4ԛS@4Țu%F@3b-V@3V$/@2u@2œwkQ@2u@2@xK@2u@2jOR@8J?'Qp?f6E?Z)?.>{ -?),@8J?D?/]?mqa?Ug$?M/)a@8p?Cʯ?((W?9 ?LǺ?7@@@@.@&?????@@tj@@Ł{@@ @@k@@3@@Xa@`A7@刕@`A7@`5+@`A7@V=O@`A7@͉N@`A7@Xv@`A7@ܔ@5v@5L_@2° ě@2,=@2Ft@2?U@1xF^@16#@1xF^@1v|2@1xF^@17'(?|b@?΁k?mH?6?u"0X?8t_?}xD A?^Ie$}?PcW?8I@eCV[YH@@$E@$?@$E@$@$E@$hZ@$E@$@$E@$93@$E@$Lo@R@@h@R@C@R@ F@R@Hva@R@Eb@R@U.U@6=:@6/*@2E?|h@28J<1q@1`A@1|j@Z@1J0U2a|@18v -@1J0U2a|@17M}U@1J0U2a|@18ܦf2?D5C?m:S?(ՂA?aGS??rbeEZ?5ً?bZ*?$( ?K[?zdH?@'ÍP?VOhZ?gR?1݉?$?XTl?j*l?"5@@.@,@*@<@<??????sbA?`@?eB`?\@$P8@?{?̶?{r@ zQ8?BW@e$@"^5?|@"Tr`@8 _o@8 X@2}hr @2v85@1tj~@1&\1@1c@1]8@1c@1[W(@1c@1Z?"?dL?y"Bx?GQA?ʦ?D;6k??u3?gPl?Xyb2?D+?F%]?.zu?$?X[?1^g?v?PcW@@*@(@*@<@;???????h``]?4r$?HѥJ"@gr c@"XvV?P?]RKh?vB? d@of}@@ I^@"@ I^@]@ I^@({,@ I^@Ji;@ I^@c@ I^@ @dZ@iP'@dZ@PKU?@dZ@B@dZ@Gi@dZ@$'@dZ@< -@6>6z@6ap4@3+@3 _m@2Ƨ@2_K:@2iB@2g?@2iB@2|t@2iB@1t+/?~?J?*p@"? vWa?6?n? ?}x ?;9?;+t"\?d@@,@*@@<@:??????m48 v -?On[IY?R>@+@TH?kl\?p3;M?EwS?ajX@dl@@XE@X+ @XE@X@XE@X@XE@XKƧ@XE@X$@XE@X4@?|h@?|h@?|h@@4m@?|h@@N@?|h@? @?|h@@u"@?|h@?|h@4>6z@4$tS@0J^5?|@0J^5?|@0`A7L@0`A7L@.'RU@.U2a|@.'RU@.:~@.'RU@.?|O?m?7[䭝?q@v?ǹT?+ʦ_?| ?NۈV(?&?; $?ߒh!?U3p?nwf?/?W?':?b/bJ?L@@@@@(@,???????3?&cʻH?̬re\@Xl@??F;u?::āv>?9Ց?X\@f7@@\(@š@\(@af@\(@e@\(@S&@\(@@\(@M@C@=p@C@~#@C@I^5@C@C,@C@jg@C@˹~(@6:qiC@68\)@2 ě@2%2@197KƧ@1:,<@0[W?@0p:~@0[W?@0_o @0[W?@0v?]?m?U#? -?5 g? ]V?]?I?|,!?? f?Q\?*wjs?Ϸ+?A'_^? mmsi?L8AS?2[s?@@@@(@&??????8-?1e?2$DN>:^ĉ@gs4@gi?A˘ul>bo?}R G>XaE@sxa@@3E@6E@3E.@3E.@3E.@3E.@3E.@m`A@m~Q@m`A.@m`A.@m`A.@m`A.@m`A.@7uS&@7P{@5}hr .Mm@5`A7.Qn@4 k.NC,@4 k.NC,@4 k.NC,?@8#@8:@8 >+@8I@8?Y͍_@8@8i@8@8S@8?ZaP@8 *@8p@8@8 @8 @?@؊=p@؊=p@؊=p@؇K]@؊=p@؁@؊=p@{u@؊=p@F@؊=p@؁$/@`+@`+@`+@`.3@`+@`zI@`+@`;dY@`+@`*0@`+@`ͫU@3 ]ce@3 j~#@/-V@/ I^5?@.rnO@.mC\@.Z@.B@.Z@.^5?|@.Z@.q ?| ?Qd?~"? :>Co?Փz?;?| ?›?i?.pC?Bg43f?P=?oܧ?򯞼?Ol?Z?WZ?*Q@@@@@,@*??????dG Π?Or^w?/oQ*@؄gن@` q\?or<?O|˺? k}?!QG݀@e ׺@@lE@lvȴ9@lE@mn@lE@m I^@lE@lhs@lE@l䎋@lE@m[8@(+ @(ȴ9X@(+ @(A [@(+ @(PH@(+ @(u@(+ @("`@(+ @(=d@4(YJ@4(*0@2c@2a$/@2ttj@2q&x@1m8@1ҕᰊ@1m8@1Gz@1m8@1? ?E?^RpX?_? -? µ?|O?j%Ra?<?z?J>?'+?|׿pY?l? - l]?3_?OO ?;m7.@@@@@,@???????0/''p?L%$ H?7:^?A2@G]@lȗ@(H?VæuR"?a҂X ?rzÆ)?͵@g0@@N+@=Ӽڰ@N+.@N+.@N+.@N+.@N+.@ZQ@ZT@ZQ.@ZQ.@ZQ.@ZQ.@ZQ.@7>6z@7A)@4S.Mm@4tj.Qn@40.NC,@40.NC,@40.NC,?1YP@8#@8:@8 >+@8I@8?l@8@8i@8@8S@8?D%aFy@8 *@8p@8@8 @8 @ ?@a\(@aΗO@a\(@a^@a\(@aMj@a\(@aԯO @a\(@aϷ@a\(@aϪ͞@{Q@{`B@{Q@{\(@{Q@{@{Q@{s@{Q@{fB@{Q@{$ x@5v@5KƧ@2+@2C\@1`A7@1[W>6@1 -0U2a|@1tj@1 -0U2a|@1y=c@1 -0U2a|@1 ѷ?|O?sp?vG?W0 ?'`??=?F/P5?4 "?t(?@Cv?O ?e\p?+QK?Rii?qn?l?<71?Cns@@@@@,@,???????B{.?Sl?G%L ? "@aW@{=o"?h)ty?=yz?]S? @g.I@@j~.@j~@N@j~@^@j~@@q@j~@ @j~@wF@+33333.@+33333@+qc@+33333@*@+33333@++)@+33333@, -#S`@+33333@,"Z$@7zqiC.>@45S@37@3tj~@3:>$@3L[W?@2M@3L[W?@2/u @3L[W?@3'@8J?Έ`?IQ?wo?ť I?8^ܞ@8J?b?d ?М?`Hl?Ơ\eeb@8p?`IM?ĺ?HX?iux_? -6[@&@"@,@<@;?????@I^5@Ƨ@I^5@ @I^5@ϐ@I^5@Ǔݗ@I^5@ں@I^5@-@An@A`C@An@A I^@An@Ap -@An@AM@An@A,<@An@AQ@5[~($@5\>@3uS@3o@3-@3$@2Y~($ @2TE@2Y~($ @2UY|@2Y~($ @2VIQ?| ?q?M?4M^?ťBW?_?|O?ڲt?_@?| ?a]?]g0 -?9@0w?z? - l]?p=J?l?4hpɄ@@@@@(@*??????RrRs(?4 dB?B  @gv@A?S"9?bR%?z?Xi@eT@@)l@)^5?@)l@IS@)l@?@)l@7@)l.@)l.@&׍O;@&+@&׍O;@&XbN@&׍O;@&ȴ9X@&׍O;@& ě@&׍O;.@&׍O;.@7ks@7oo@2S@2"@1t@173@1xF^@1p{@1xF^.NC,@1xF^.NC,?h??)Q?}g?.D@8I@8?ꯨT?i&6?_@? m -@8S@8?' -}B?5?[? Q?@8 @8 @@@@????@\)@ȴ9X@\)@[@\)@=K@\)@$tS@\)@j~@\)@W@I7Kƨ@H9X@I7Kƨ@?$tT@I7Kƨ@;=L@I7Kƨ@?!-w2@I7Kƨ@=-@I7Kƨ@A-w1@5@5$tS@30ěT@3/-V@3tj~@3|@2:S@2B@2:S@2U=@2:S@2 qv? ??TS?U=K?#?3ƒ?Dw? ?2ww ?%gǿ?Y??sB?u!B?s@a?H?W;RT?hSp]?M\?4\ɵ@@@@@*@&??????B)}@amo|?4' ?H+,9@ƒ0@C:kV?Tw[r?h.@D?b< -a1?X @fU@@@bil.@bil.@bil.@bil.@bil.@bil.@j~.@j~.@j~.@j~.@j~.@j~.@9>6z.>@4xr Ĝ.Mm@3V.Qn@2YJ.NC,@2YJ.NC,@2YJ.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @4j~@4zG@4j~@6E@4j~@6Ov`@4j~@6E@4j~@3g @4j~@3@V@V@V@jO@V@cA!@V@_o@V@{J#@V@qj@0^H@0^Ov_خ@-\j~"@-\j~!@-ӶE@-@N@-p'RU@-os@-p'RU@-nMj@-p'RU@-nqi?^Dw'? ?4!p?G?b\?wZL?bM_x?MN ?^ o?C B?qN?]#O?b@?.+? SF?+ ?pk Y?B @@@@@*@&??????T8V?1q?=yk?:M~g@4@ت?K;?I84o?n^c?Q@sC{dw@@ORnP@OR@ORnP@ORGE8@ORnP@OU$@ORnP@OVR<6@ORnP@OVl"@ORnP@O]ڹ@lC@lC@lC@)^@lC@ @lC@B@lC@~$@lC@@4YJ@4@@2+ J@2*@2'KƧ@2'y @1Q@1ڒS&@1Q@1F]d@1Q@1և+ J?}ѿ,??p?btx=?STS?Y\(?| ?yom?ؼZ?#q\-v?.<? ?|׿pY?zx?ʭ#г_?.SM? ;8?`i2@@@@@&@(???????Ylсt?A??ፆG?) aȄ@OUeV@M?_]-?IΔ?kq?5iX8@eR*@@ ě.@ ě@)^@ ě@Ǔݗ@ ě@7K@ ě@ @ ě@@/zG.@/zG@/?@/zG@/e@/zG@/o@/zG@/6@/zG@/@9=:.>@5i+ @5ZD@4v-@4i#w@3xF^@3`A8@3xF^@3Ϫ͞@3xF^@3Ov`@8J?>I ?}\ie?n?M1?I?^e@8J?us76c?n|X?\PK?0d@? <\@8p?$3?6v;v?>?,Ro?S^^G>@@@@*@*?????@rlD@r(@rlD@rp -@rlD@rIQ@rlD@rce@rlD@ru@rlD@r/V@+ I@+C@+ I@*g@+ I@*F@+ I@+6z@+ I@+xF@+ I@+C@1!.@1 -L/@)tj~@)PH@'E@'cA @'fL/{K@'en/@'fL/{K@'en/@'fL/{K@'e?`&x?GA?>f?eU!?ǹT?:?bV,?%F?s8r?7枪y7?ߒh!?N`?cPQ? ?oܧ?avh?#.?Ѐ>(@@@@@,@*??????P> ?8l1E?Ajo @rb@+Nө?G~j?#/L4?a%?@ukw@s;@@l@lv@l.@l.@l.@l.@l.@`B@  I@`B.@`B.@`B.@`B.@`B.@9,<@9 @6kS.Mm@6 -V.Qn@6 -0U2a|.NC,@6 -0U2a|.NC,@6 -0U2a|.NC,?ٛP@8#@8:@8 >+@8I@8?q!U@8@8i@8@8S@8?*̒@8 *@8p@8@8 @8 @?@Dž@ǂM@Dž@DŽ7@Dž@Nj҉@Dž@ǂ}V@Dž@~\N@Dž@xQ@޸Q@5?|@޸Q@ -=q@޸Q@ߊ @޸Q@K]d@޸Q@7K@޸Q@PH@7,<@784֢@3^5?|@3_ح@2U`A7L@2TJM@25sh@23@25sh@22:)y@25sh@2-\?2}?+W?=P?ׂa?W?@*F?Ol?&dU(?cu%+?`ֆ?S?WR?W?E!'??Q ?OO ? `@@@@@*@*??????R?@8?E|W(?TŲȺ@ǀ=.@JCx?dK?tO?ȯL? П,@f[9@@ ě@ě@ ě@@ ě@3333@ ě@˒:)@ ě@u@ ě@"@jtj~@jnV@jtj~@jnzI@jtj~@ja@jtj~@jm%@jtj~@jj#9@jtj~@jSaA@8e!.@8b<64@4J^5?|@4Ln.@3V@3Xe@3L[W?@3WO;d@3L[W?@3Ln.@3L[W?@38???8?uZ?c? ?Zܗ?=fA:?Ҧ??SR?l^%? -Y*?TP??B?\7e)?YY?/#ˮr?R4?l@@@@@(@&??????j3W)j?z=~8A?Q4ױa@GtF@jk?2H^?pOY@3h?YIQȖ@f5@@hr!@B\(@hr!@ @hr!@$/@hr!@ ԕ@hr!@˟U>@hr!@_o @zG@|(@zG@ ᰉ@zG@ -͞@zG@?@zG@ߤA@zG@#@7w@@6$ xG@3E?|h@3=cA @2`A7L@2+j@1_o @1ݗ+k@1_o @1)y@1_o @18}H?B&W?@k=[?l5B?Att?.Kn?-! )m?. #???T ?A8 K?8,T?WR?Zj?7-R?OW?.SM?OO ?$jB@@@@@*@$???????*0?L+ ?aTO[?oM/]@@ -*5?p} -Z?{XO?ٕ?ut%PC@sC(mM@@<ȴ9X@<ȴ9X@<ȴ9X@b@/@/yIK( ?}@TΥ?0q?S4zs?&=?]? 4w?2?|b@??'?P}^U=?-(?R?rGe??t(XW?3p>? ݚi?=x@@$@@@.@1???????@/lw ?V!?b}@@>jO㛦@dٮ.@@V@C@V@jO@V@O;d@V@@V@첕@V@zxl@RnP@R ĝ@RnP@RW@RnP@R@RnP@R䎊s@RnP@R@RnP@R䎊r@0DO M@0DO M@*)7KƧ@*)*0U2b@)h1&x@)h ԕ+@(̲@(̘_@(̲@(̲@(̲@(̲?^Q5P?[ex?$.>>=eP@L@@R蓮?3>pzcy?3x[?b++> 7Z,@sIѨZ@@t1@t5?|@t1@tУ ->@t1@t}H@t1@tߊ @t1@t؆YJ@t1@tiB@ I@-V@ I@l@ I@1'@ I@M:@ I@=d@ I@$tS@7-v@7/b}V@4?|h@4Q@3ߝ-V@3}H@4!.I@4Q@4!.I@4'&@4!.I@4{J#:?% ?V_^?|6zj?a`a?p3/ ?dF?}ѿ,?8$ -"?ud?ѡb?? b9?!kz7??)1_?&,?O.?n?GԚj@@@@@,@ ??????%J]d#?A^ ?by"@tB'@P?cq[b??S6#i? Sz@fp_Gm@@7O\(@7a$0@7O\(@7We@7O\(@7SMj@7O\(@7UY|@7O\(@7M(@7O\(@7MB@`A@$/@`A@nP@`A@{@`A@Xy>@`A@'R@`A@:)z@9 _o@9Fs@3r Ĝ@3K]c@3q7Kƨ@3uzxl"h@3@5@3Q@3@5@3+ J@3@5@38}I?M^?ZAi?U%x?ھ۲?RXFf?Ci- ?jqp?`?Oدi?@[*^?dp?J3?}i?\{?Ƌ?1R?T?NVC&@@@@@(@,??????pci+r?KH\ߙ&?Q@7Qc@?[@!) ?aCB?u? >@p9>@@^`A7@^vȴ9@^`A7@]̲@^`A7@],<@^`A7@]7+j@^`A7.@^`A7.@3333@T@3333@f -L/@3333@64@3333@̋C@3333.@3333.@8@@8|@4hr@4+a@@4~"@3~%@3kxF^@3Ov_خ@3kxF^.NC,@3kxF^.NC,?|O?)#?ߴUmsA?yH>c@8I@8?K{O?l{׬?׉B,|̶?8F@8S@8?KY?zJ?W̩'?/օt,@8 @8 @@@@????@tj@F@tj@1@tj@L/{@tj@@tj@l?@tj@$@&M@&M5Xy@&M@&84@&M@&)^@&M@&qiC@&M@&{@&M@&#pŢ@5@5SO:<@3#@2vȴ:@2~Vu@2V+jg@2!.I@1'RTa@2!.I@12@2!.I@1i?{si?J f?x+?u]?cAq?H N_?|B &??8䂪P$?F'^D?~c]l?j8g/?VE?j$?E@@ ě@bM@ ě@@ ě@YB@ ě@D@ ě@ @ ě@X,@Z@Z@Z@½>@Z@@Z@`@Z@;fq@Z@^@4zqiC@4zin@0hr@0pq@0-@0 /W@.Vl"@.J@.Vl"@.&S@.Vl"@.M:?|b@?UL p?1"?˅T?g*??|B &?R?U.V?FQ"qH?ٙr?Z#\?15?XI|!?Av?:k??dB@@(@(@,@:@:??????& ?*`?)jou8I>bC@{@2?IJN?? e?ejC?%&@f0@@fffff.@fffff@Aoh@fffff@\wkQ@fffff@iB@fffff@Љ'R@fffff.@Y# -=q.@Y# -=q@Xm\@Y# -=q@Z!-w1@Y# -=q@X>vȴ9@Y# -=q@X I^@Y# -=q.@9!..>@4hr@4˒:)@3-V@3E@3c@3*^5?|@3c@3E2a|@3c.NC,@8J?R{?> -A?Ғ?o@8@8J?!?>?tC?B.@8@8p?=X?=Ɂ9?R^?@8 @@@@*????@u@uI^5@u@ub@u@us@u@u҈p@u@un@u@uGz@w@}/@w@^ߤ?@w@Q@w@mjO@w@dT@w@g)_@7@71.@3E?|h@3:u%F -@2dj~@2X*0@1[W?@1R@1[W?@1}O;dZ@1[W?@1'/W?|O?6 '8?XY?r*?GU?YD?E?u߿??A?Q?D4f?K|?HO?c?9 ?k!l?GX@@@@@,@,???????u*3(htP -F?N<}?ST'@u,P@mBb?r+ng?m? /X?e2@kJ@@zH@\(@zH@@zH@;5@zH@U@zH@\@zH@!-@&Q@&;dZ@&Q@&˒:@&Q@&F -L@&Q@&Ƶ (@&Q@&$/@&Q@&m\@7 ]ce@7y @2 I^5@2@2U`A7L@2U?@15sh@16YJ@15sh@168YJ@15sh@16t?Y͍_?J`Y?ě?2UT?(+Zk?J8Ȟ?}?Q_ڸFN?ܿ?/:?O ?C ;?bk?3I#i?~$?3_?Y͍_?{2@@@@@,@*???????p{?WD=?"?- ?U##?LE??VDx-~??]_@@"@$@@0@2???????5 P?ph=@?dZxV?aT1C@:m@+ٺ]b?Q?!n *@^z@.RhR@g^ێL@@sY+@sS3333@sY+.@sY+.@sY+.@sY+.@sY+.@hr @cS@hr .@hr .@hr .@hr .@hr .@7>6z@6ߤ@@3xr Ĝ.Mm@2>Vu.Qn@1m8.NC,@1m8.NC,@1m8.NC,?Z@8#@8:@8 >+@8I@8?Z@8@8i@8@8S@8?A@8 *@8p@8@8 @8 @?@Ha@Ha@Ha@Hae@Ha@Ha@Ha@Hb@4@Ha@Ha-w1@Ha@Hbe+@(\@(\@(\@(\@(\@)*0U1@(\@%S&@(\@'/W@(\@(@2DO M@2Dj~@0S@0Ϫ͞@0-@03@0iB@0N;5@0iB@0N;6@0iB@0o?^Q5P?°R;?\H{gX? -?j?WA?bM_x? -?)_?~m??\{3>a?P=?cPQ??`x?%ϑ?<71?Rv@@@@@*@*??????"*?V*?4)z?2 YC@Ha#@' ?C揇I-?A ?n?xHD}`T@sb^X@@c+ J@c@c+ J.@c+ J.@c+ J.@c+ J.@c+ J.@9x@9lD@9x.@9x.@9x.@9x.@9x.@7DO M@7H˒:*@4+.Mm@4_-V.Qn@3u.NC,@3u.NC,@3u.NC,?Z@8#@8:@8 >+@8I@8?Z@8@8i@8@8S@8?A@8 *@8p@8@8 @8 @?@J=p@J=p@J=p.@J=p.@J=p.@J=p.@J=p.@VI^5?@VIx@VI^5?.@VI^5?.@VI^5?.@VI^5?.@VI^5?.@3G>6z@3G-@1hr .Mm@1"`.Qn@1~($ .NC,@1~($ .NC,@1~($ .NC,?| @8#@8:@8 >+@8I@8?|O@8@8i@8@8S@8?y8#@8 *@8p@8@8 @8 @?@ A7L@'z@ A7L@2䎊r@ A7L@($ x@ A7L@ߤ@@ A7L@Ov_خ@ A7L@VC@z'lC@zw@z'lC@zL_@z'lC@z'8}@z'lC@z=+k@z'lC@zlC@z'lC@zS&@6S&@6@4?|h@4nO@3b-V@3v@3@5@3(˒:*@3@5@3u@3@5@2[6?|O?(?EZS)?C?oA>?̇a?NIɮ?]?Vh?<8?him?I$P?'`?K{O?+HKd?gw_؆?XS]?&A@% @@@@@&@(???????F@O? i?/> @Ct@z ?t?砂X@Ƥ@@1膄@f@O'n@@t.@t@w@t@@t@YJ@t@rG@t@Gz@KƧ.@KƧ@>BZ@KƧ@PH@KƧ@[W?@KƧ@@KƧ@Ƨ@9 xF.>@3?|h@3 ě@2tj~@2xbM@10U2a|@14֡a@10U2a|@1@N@10U2a|@1Zc@8J?U&?/ eb?F?H>?ƻ1Ta?"eM@8J? _?9Y,?+Dx?J>?Q@8p?"IHۜ?-BR1?hc?vI?3@@@@"@ ?????@ۥS@p -@ۥS@xF@ۥS@٦ @ۥS@ (@ۥS@- @ۥS@@R@Ow@R@=ce@R@b&I@R@As@R@Z)y@R@]-V@7_o@7e+@4 I^@51@3tj@3z@3@3@3@3oiDg8@3@3ݗ?1j?J?Q"Ě%?50??7?X—?I~y?~Fs?oi(?%bB?"?kxܓ?Ol?f?W\%?D`?,Z?8@@@@@&@$???????&ʵ?f:?Ut e?pB`@@b@NE+?yS?=?nzT?haj@in@@F%@Ft@F%@F:5@F%@Ff@F%@F@F%@F*:@F%@F=@Ƨ-@ƧQyR@Ƨ-@ƨR@Ƨ-@Ʀd@Ƨ-@ƨ -@Ƨ-@Ƨ[+@Ƨ-@Ƨ@3s@3)^@1pěT@1p-@0)"`@0)n t@/@/P3;@/@/ϲ+@/@/c -?}@TΥ?- ?/N?L7?(?QgH?| V? )?Ք?6K'Y -?,Uw?s0?| #?џT?b?VS -x?9Ν{??$@@,@*@(@:@9??????@ >ӭٟ>!TIJm~@Fq@Ƨ?7? GFF?;y>?!m%z@fB*@@[ Ƨ@[ @[ Ƨ@[;5@[ Ƨ@[4֡b@[ Ƨ@[ ԕ+@[ Ƨ@[ ^ @[ Ƨ@[ I^5@r߾vȴ@r/@r߾vȴ@r@r߾vȴ@rb}@r߾vȴ@r唯O@r߾vȴ@r݊ڹ@r߾vȴ@rae@6@@6@4W+ I@4Y|@3A7K@3Zc@3Eu@3G,@3Eu@3DD@3Eu@3Eu?)T?[ ?d3"7?W0 ?#?3ƒ?1?}ѿ,?ew?fF?v? E!?}X?}Ty?XԠ#{?aI*?7N?I$?Oh6c@@@@@(@*??????=6(ް?LiH?>%K?K1Ϟ@[ ?Ls@ru?\(?l.?ެK?6γ@he`@@]-V@]-V@]-V@^6Gt@]-V@^ Q@]-V@\e@]-V@Y|8@]-V@Yw+@$/@ؤT@$/@|p@$/@~@$/@{/@$/@zǧ@$/@|V|k@3qiC@3ݘ@2J^5?|@2D?h@1`A@1Ѳƒ@0_o @0?@0_o @0zq@0_o @0vM'?}@TΥ?q ֑?Fx+I? ۸ig?DH/7?y>m?| V?!T?CW?WHF,?}v?{8?nݺG?FG???$ɘ1?ŃH? I۵p@@$@(@"@6@1??????MDXz?' qz@?*(8vK@[[@~|?G()1?KXL?XT?X@f#b[@@bM@bM@bM@2X@bM@*2@bM@.3@bM@}Vl@bM@!-@ -=q@㕁%@ -=q@u%F @ -=q@rG@ -=q@߾vȴ@ -=q@ὥ@ -=q@h ԕ@6䎊@6S&@3+S@3+P{@2-@2@2OO M@2NT@2OO M@2NOv_خ@2OO M@2NOv_خ?)T?u:|?0? ?r {#&?R )?=fA:?$i %h?Qc -?H#=? E!?jOI?nwf?t?D(p?Z?0&YEd?|)-}(@@@@@*@*??????D5#YP?!){?KtA?eTC?Xx+?2he?J?-=~?ȧ?;M՚w?A?Mu]P%?f=䛮?O@@(@,@,@5@7???????P -?v0$?lA@?sѭ@Kz@iv?7?!1@LxP@b@m]@@"@V@"@%@"@d@"@dZ@"@1@"@>6{@"I^5@"hr@"I^5@"rF@"I^5@":~@"I^5@"W@"I^5@"~($ -@"I^5@"͑hr@74m8@7@3R I^5@3Rᰉ@2-V@2{@28D*@29x@28D*@2:1'@28D*@29+ ?3}?Qc -?K]4?9.?uxY??”z?&O?&?{4k?uR4H?ey?:Fa?GA?T0?< N ?P\?k!l@@@@@*@(???????A"?a#?mmz?`Gb{@X7qV@"s~?~:I?p s? :?Xa@rݵ)@@~"@;dZ@~"@D(M@~"@8SVE@~"@%@~"@~m@~"@}-@'-@'-@'-@'&@'-@(1&y@'-@'@'-@'#@'-@(P@+b@+b@%G-@%G-@#NO;@#NO;@#`d@#g8}@#`d@#`d@#`d@#`d?^j?؆Qd?c?`ƆƵ?zn~1?vQS?bMn?K}?l:?/ -?μjH0?I'%?bM? (ʢ?nՉW?c[?I?I@@(@*@(@;@:??????PA?JF(?1m76m?@~)&@' -?@+'?ql?{+jP?J@s@@M@z@M@޸Q@M@`B@M@҈p@M@@@M@Zc!@_;dZ@dZ@_;dZ@\hr@_;dZ@ix@_;dZ@`6@_;dZ@ae@_;dZ@a [@7>6z@7zxl"h@3+@3a|Q@3Ctj~@35oi@2Eu@2<!-x@2Eu@2Ezxl"h@2Eu@2En/?NIɮ?6 '8?@^ڢ??ԁ?4 ?| ?r5MS?Ke?Q?NM8/?GӒ?+QK?GA?+-/??*Q?}@@@@@*@(???????01GwY(?U& ?/ˉ@.Հ@aɭ?i>Z?UYaݣ?I֫c?MS:@o@‚ @@nM@nM@nM@n䎊q@nM@n@nM@nn.@nM@n@nM@n@^Q@_;dZ@^Q@^@^Q@^ (@^Q@]cA@^Q@_F@^Q@^"@4B䎊@4B3@/"@/vȴ9X@/DZ1@/Dtk@-Z@-@-Z@-F]c@-Z@-S? ?&H?N?뤽?? 9?^? ?MN ?rJs?EP?ڂc?h,à? J?O?y8$?ʬv?ˢL1?@@@@@*@(???????.j?)SE?$67@n^?@@^uF?I4U?EmL-gr?G;?3Ku@gjvx@@\(@׾5?|@\(.@\(.@\(.@\(@-@\(@׿vȴ@l@@l.@l.@l.@l@ۥS@l@@6۹~($@6d8@3c.Mm@3n".Qn@2&.NC,@2&@2~($ @2&@20U2a|?q!U@8#@8:@8 >+?DE?W_?1j@8@8i@8?dp?"?y\@8 *@8p@8?^nl?xb@@@???@6Ƨ@6@6Ƨ@6^ -@6Ƨ@6f@6Ƨ@6E@6Ƨ@6I^5@6Ƨ@6f@7K@@7K@n.@7K@n@7K@|Q@7K@u"@7K@- @6hYJ@6fR<6@2xr Ĝ@2x$ xG@1~Vu@1}@1(@1)y @1(@1)rGE@1(@1(TɅ? ?&?a#mU?J? -`?dW?)T?ā?36G?l™U?=W?$̤T?<{??3Z\?9XNu?_խW?gϵ@@@@@*@,??????6aUV2H?'?1v0@6@W:?H8&XQ?Q)z?W]ʭ??O@iD.@@S@^5?}@S@֞"@S@{"`@S@ I_@S@gRT`@S@J=p@D$/@DlC@D$/@Dj~@D$/@Dx@D$/@EA7K@D$/@CfA@D$/@D@6D@6ݫU=@4+ J@4hۋq @4V@3IQ@3sh@35sg@3sh@4ME@3sh@3 '?2}?Ea:j?ŏB?³?ST;?<_?Y͍_??MQ?xG?oEcr?K#N ?]b?eg?0@4+ I@5;u8{@4 "`B@4G=ތ@3iB@3zR6@3iB@3!A@3iB@35.@8J?p?'? do)? dߝ?ZK@8J?;]^Y?7?)L ?er?= -@8p?r`?sK?HbL?2Q?^@*@,@(@<@:?????@W ě@Xě@W ě@W5?|@W ě@Xa@W ě@XLzxm@W ě@X=p@W ě.@ehr@e@ehr@dݲ-W@ehr@d@ehr@e+a@ehr@e xG@ehr.@8@8dSMj@4\I^5?@3K]c@3tj~@3"`B@3Y~($ @2+j@3Y~($ @2ٺ^5?~@3Y~($ .NC,?ٛP?;F?Գ/#*?6V?! ٤5@8?:-%?T ?SXt'?َVn?i0K @8?@?l{)ފ?W Z?wc U/?7r_@8 @@@@@?????@#$@#ti@#$.@#$.@#$.@#$@#Z@#$@#lC@0;dZ@0ۥS@0;dZ.@0;dZ.@0;dZ.@0;dZ@/w -=p@0;dZ@/9X@6#g l@6!$/@4=hr .Mm@3`A7L.Qn@2,<.NC,@2,<@2C\@2,<@21&?)T@8#@8:@8 >+?je?:ka?)T@8@8i@8?,ۯ?w[@?D/ -b@8 *@8p@8?>?<@@@???@Λ"`@ΛS@Λ"`@θQ@Λ"`@䎋@Λ"`@n@Λ"`@Ϸ+j@Λ"`@Ϭ@Dt@DZ@Dt@ݘ@Dt@}@Dt@Fs@Dt@:@Dt@@2:@2u"@0\I^5@@02ᰊ@/|1&@/ w1@/(ۋq @.]cA!@/(ۋq @.^ѷY@/(ۋq @.\ߤ@?^Q5P?Q^?NSCQ?t4BT?ͤr?-(?bV,??8K?!!?0??cPQ? $gv?zny-i?$–?Xk?N((@@@@@,@*???????/XڠE?s{ʙ?*i@ό-m@g?iTJ?An?m9@/t@/5%jQ@.cnP@.c)`n;@-Ǔݗ,@-l@-Ǔݗ,@-.J/@-Ǔݗ,@-&sˇ@8 -Ny?M9?(uH?bew?6?N@82r? EN?" ?0j3A? m"?X@8L?Q=s?%?gȶ?$?8(@,@,@&@:@;?????@\).@\)@L/{@\)@T@\)@R@\)@O䎊@\)@KC@8Q.@8Q@oj@8Q@j~@8Q@+@8Q@tj@8Q@*g@8p4m8.>@3kS@34D@2"`@2~($ x@2 [W?@1&x@2 [W?@1|@2 [W?@1sP@8J??n ֋?= 0?Lj!_? V߹@8J?N47?42?,?1Tn]?fF7@8p? Afg?o'@@@@@(@*??????g,!*ynӀ?3h?R@a@ȝV[?N ?iOXvj?ƕ?ϙ?`R@`'%@@ ě.@ ě@q @ ě@?@ ě@ߤ@@ ě@O;d@ ě@(@`A7.@`A7@&x@`A7@ۋq@`A7@@`A7@v@`A7@9@9.>@4+ J@4~$@3V@3PH@2̿[W?@2vȴ9X@2̿[W?@2@2̿[W?@2|@8J?LS{?ɲ?d"QZ?Փz?^A@8J?g3?W@3t?1`?s}?Ft*5@8p?|׿pZ?z."?6F:?mΝ?@@@@*@(?????@bM@Õ%@bM.@bM.@bM.@bM.@bM@ŔO @$/@n@$/.@$/.@$/.@$/.@$/@՛=@7r@7rT`d@5|hr.Mm@4j~.Qn@4a:S.NC,@4a:S.NC,@4a:S@4sE?]@8#@8:@8 >+@8I?9!. ?]@8@8i@8@8S? -#9?*wjs@8 *@8p@8@8 ?J'Y=?@(??@5dZ@5͑hr@5dZ@5ͫU@5dZ@5ȴ9Y@5dZ@59X@5dZ@5̋C@5dZ@5vȴ8@tj@Q@tj@G@tj@%zxl!@tj@( ԕ@tj@An.@tj@L>C@7!.@7>@4hr @4C%@3A7K@3@3:S@3 @3:S@3H˒@3:S@3g l?A[F?\?K̃?yQ?Ji?ZsZ?ك|I?S?~?DzC1?MuVQ?ٲ? }P!?5Jܦ?W+ ?<05?%?)0@@@@@@*??????hUAr?߬?w`) ?i\wa@5A@4O??`1@+G?޽_6@l6k4@@]-V@l@]-V.@]-V.@]-V.@]-V.@]-V.@2mhr@2a@2mhr.@2mhr.@2mhr.@2mhr.@2mhr.@4uS&@4W=g @1^5?|.Mm@1Htj.Qn@0,<.NC,@0,<.NC,@0,<.NC,?}cI@8#@8:@8 >+@8I@8?|}q@8@8i@8@8S@8?h3z@8 *@8p@8@8 @8 @?@-@@-@v @-@-Թ@-@N ?xqC?Ѐ>(?f_Ԋ@@@@@(@*??????<?%%e?e?)ޒ@…&@u$c?, #K?#>ݔ?TS7T?Af@s@@(@@(@s@(@fA@(@߾vȵ@(@׍O;@(@_@HlC@H\(@HlC@Hě@HlC@H1@HlC@Hzxl"@HlC@H@HlC@HeP@5D@5Xe@3}hr @3}C\@3ttj@3q.H@2@2JL@2@2ᰉ@2@2?o 6}?U?cD?CW?U#v?0r>?R+?^?Lw?e?tbJ?%U|5?y4h?@@$@ @"@9@5??????+ӌ7:X?13[*? ^\ݗ@J$@C?Q"0?-p*?|< ?`l`z@i@@@r @.@.@.@.@.@yZ@y+@yZ.@yZ.@yZ.@yZ.@yZ.@4p4m8@4aX@2S.Mm@2A7K.Qn@1O M.NC,@1O M.NC,@1O M.NC,?2}@8#@8:@8 >+@8I@8?| @8@8i@8@8S@8?&*0\@8 *@8p@8@8 @8 @?@\TF@\Cn@\TF@\R@\TF@\^@\TF@\Y=c@\TF@\O@\TF@\Jqi@ߤZ@ߝ/@ߤZ@ߢ&H@ߤZ@߆&@ߤZ@ߐ )@ߤZ@ߏw@ߤZ@ߔm9@8r@8p@4B ě@4?v_ح@3`A@3c @3(@3v_ح@3(@3! [7@3(@3$#?ꯨT?DE?_΋?܆?RC}q?˻??]?sw)?ٶm?T?a8w|?mq`?}?US9?@fo(?3 D?7Q@@@@@(@*???????D%c&7?lrYb?U@\P#Ş@ߔ~?>[?sʫ -zY?gO?ef@mn@@+# -=q@+# -=q@+# -=q@+%@+# -=q@+$P=T@+# -=q@+$r@+# -=q@+"）U@+# -=q@+#;O@_p -=@_p ě@_p -=@_mtMd@_p -=@_o@_p -=@_q.@_p -=@_p@_p -=@_p{@4S&@4r|*@1#@1$2w@0PA7K@0PP3:@0B@4@0BA ~@0B@4@0B!@0B@4@0Bѷ?|h2:?pPb?ȟQK?ɰM6?A]2?"{@Y8?|b@?A*?U#v?=W:f&/?dȨ?Uk>&?p5/?Eʚ? ˁ?b~?6ִ@?o@@$@ @"@2@4??????l? Ȁ`?+xNRv?5@+# q4@_pmkf?K7s?'ca?!l&?\=n~@f*]@@2-V@.+@2-V@"Ѹ@2-V@,<@2-V@'/@2-V@,zxl@2-V@*~"@^bM@^wO;@^bM@^DJ@^bM@^<64@^bM@^>BZd@^bM@^TzG@^bM@^Vl"@8B䎊@8E '0@3I^5?@3kP|@2~"@2F]c@2!.I@2$/@2!.I@2ӕ$@2!.I@2Q?2}?O\?C1?]mM?PY8~Qj?"eM?|O?k@?pg}5z?-w?*v7?J3?>xރ?YW?<?+L?`?ba_@@@@@"@(??????L\(AI??Sy=t& ?YP0Z0@, ,@^g늵5?sT?yWQ?cMF#E??@eBa@@.O@0 ě@.O.@.O.@.O.@.O.@.O.@i1&y@in@i1&y.@i1&y.@i1&y.@i1&y.@i1&y.@3쿱[X@3}Vl@1 I^5.Mm@1tj~.Qn@0iB.NC,@0iB.NC,@0iB.NC,?| @8#@8:@8 >+@8I@8?Ol@8@8i@8@8S@8?aI*@8 *@8p@8@8 @8 @?@/-.@/-@0D@/-@.V@/-@0|@/-@/A@/-@.}Vl@ .@ @ @ @zG@ @.2@ @ߤ?@ @qj@9[~($.>@2S@2$@1-@1؆YJ@1Y~($ @1Z=p -@1Y~($ @1Z0 @1Y~($ @1Z,<@8J?d̺?tcj?Q?Ot?tJ?bM_x?%F?['?1'7?@?PS2K?b@?u?nwg?͓E'?O|q?#.@@@@@,@,??????Snd?_;?8e?(@/]m@4kt?GߵZ%?0G?(m?WG@s߁@@Ux@UxbM@Ux@Up ->@Ux@Ur-V@Ux@UnzH@Ux@Ug8}@Ux@Uiᰉ@o@ I@o@L^@o@$/@o@Z@o@8YJ@o@RT`@5Q|Q@5R&IR@3+@3&@3;Ƨ@3+a@@2D*@2hr @2D*@2- @2D*@2T`d?|O?gUq#?P"hI?L?-w? YdR?m~Z_s,?J>? -tM?Oc?l?}@@@@@,@(??????jT?~j/??ܿ?a }@UsoP@?ZΕ|6z.>@4 I^5.Mm@3)"`.Qn@2䎊r.NC,@2䎊r.NC,@2䎊r.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @fx@;dZ@fx.@fx.@fx.@fx.@fx.@O;d@7Kƨ@O;d.@O;d.@O;d.@O;d.@O;d.@8N쿱[X@8m-V@5sS.Mm@5@`A7.Qn@4s&.NC,@4s&.NC,@4s&.NC,?]@8#@8:@8 >+@8I@8?]@8@8i@8@8S@8?*wjs@8 *@8p@8@8 @8 ??@Q@Z@Q@t@Q@ѷ@Q@u@Q@@Q@cA@0u@0\)@0u@0ӎMk@0u@0,<@0u@0ԇ$@0u@0@0u@0@4g l@4h ԕ@0ěT@0T@0A7K@0zxl"@/ߤ@@/Ԣ3@/ߤ@@/`d@/ߤ@@/2a|?|O?%ñ?!N?,rg?rf???#?|O?uie?v@b?jX?+!?äqY?]?/?T?XW~?$?L8AS@@@@@,@&??????^D<} ?CNE?#9 [@c3@0Ԃ ?ڡG7p> s@3ʚef@BNG? ? ʘH@1iB@0=b@1iB@0*1?Ol? U?-\8?lfW??O#?E?5?1?5;7ĻM?4="C!?ES?ЙQ$?gT?ĶBϪ??ھ۲?{=rK@@@@@*@*???????s(?O9P?Do?o˃@Ɍ@!~?.8r?2Mw@6O`?2D -@\o|@@A$/@2 Ĝ@A$/@?|g@A$/@1&x@A$/@)rG@A$/@,cA @A$/@L@d/@y+@d/@kR@d/@nV@d/@b7@d/@k]c@d/@U=@8H@7u!S@4\I^5?@4_͞@3y7KƧ@3.H@36z@3_ح@36z@3#@36z@3A [?ʘzT? ?c?Z T?r.,i?/?^*?͔{?zh\?u>M? U?PI?Ttn?$jB?q6tN6?4`?**Z?p@@@@@(@*??????? 2?v߄Ŏ?yM6@0ez@[PB'?h?-+?^n!@ F< -T@oW(@@nP@@nP@u0@nP@9!@nP@m@nP@Gi@nP@؀v@dix@bm@dix@di2 @dix@dZU]@dix@d\w@dix@da#3G @dix@d^@8YJ@7 @4\I^5?@4UkAV@3K"`B@3B)@3@5@3,@3@5@2d?@3@5@3-Q-b?qT?`M?:ҖD?8(/?#<'?5xw!?_R?p")?w ?y$?$;d3?{,o?~6?"?5?;Me?_?#"o؅K@@*@&@*@2@6??????Q],?B4L@?pA_l?f\Ӗ@Q@d^sd?}l}?tT5?`N?ܐלvJ@sx9@@@1&@ffff@1&@zxl"@1&@zxl@1&@hr@1&@O@1&@M;@o@T@o@{~@o@z^5?~@o@mV@o@v@o@sE@8?@8/˒:)@4B ě@4M@CTTȺ@f?{AV^?V[־{?ߎ{@ ?HoLh@r0-`u@@_!G{.@_!G{@`0 ->@_!G{@`d@_!G{@`}+j@_!G{@`KC@_!G{@`?.H@.@@/ M@@)^@@T!-w@@.H@@ݗ+@9,<.>@4hr @3т @3`A@3z@30U2a|@2~"@30U2a|@2um\@30U2a|@2y#w@8J?Ȗg?rF)?>?r7?cx @8J?I>Wj?4kU?4i?UTj?>y@8p?bH=?~~?>?I$?{%@@@@,@(?????@`B@ͱ-@`B@ׅ*>~@`B@ߞ9)@`B@*ӿ@`B@,@`B@"@hQ@i$> -@hQ@h`wF@hQ@hhfR@hQ@h{F@hQ@h-nZ@hQ@hIDC@75S&@74mC@40ěT@42fn@3ȓtj@3J@@2O M@2p0@2O M@2 ,@2O M@2ݔ,LO??c8?a?ӯ/?[_0?|G?[y?PC9 -?d)E?Zg?4?]ex‰?}W?6TW;?_qZ?)N?{:5i?#@@$@*@,@6@8???????P{2)90?o -x[?Z@~@h8?J MT\?uPR?Fǂ_@l@ry -:7?|2?f~? 2?$̙?5-?pWϿ?-qvP?O<@@$@$@&@9@7???????r68?]cʷ?eI"x%?hp@@c/Ej?}+՘?x~HO?g-?߁ |@qkd@@eȴ9X.@eȴ9X@e@eȴ9X@e&x@eȴ9X@eO N@eȴ9X@e@eȴ9X.@.v.@.v@."@.v@.j~@.v@.)ᰉ@.v@.;@.v.@7I_o.>@2W+ I@2Sa@N@2ttj@2-V@1a:S@1hQ@1a:S@1W-@1a:S.NC,@8J?u:|?!=T??Q?"@8@8J?P ?,?dXM?Gfx^K@8@8p?8hz=?OD? Q?? S @8 @@@@&????@@`@@@Ǎ@@T6@@kj0@@ʟ@@@Hg@@,@@|E@@U@@_!@@u_5@@Y|K@7>6z@776@4J^5?|@4E@3M-V@3L/r@38D*@32~Ao@38D*@333M@38D*@35?\l6?0?9 %S?}-.?;G?A? C`?3Z\?2]7?`x?!B?՘]G?_u@@@@@,@*??????B/@Wth?9z͟i?3ԑN@rĢg@հ?Y63 ?T 99q?jQ*? -:@fĀ@@p@ohs@p@o1'@p@p}U@p@p ~)@p@p t@p@pm\@э@Ѽhs@э@у9@э@фJ@э@эqu@э@э r@э@ь!-@8@8zxl#@4hr @4@3j~#@3_p@3m8@3y @3m8@3ߤ@3m8@3o?Uy?B-?[_(?w?b0)3? r?,*D7?"JUO?P{j?P?JL\?J3?! ?'h?a?]O8?♊N?ȩ@@@@@$@,???????~ [W)?wyI=?hJFұ@p g6@эqx/?F[)?u}ηx?8E-^ ?1uw@*~MlJ@&挿CX?C5 hG6?$I?>?B~qF@sOԱ@@[?|h@[Ib@[?|h@[?Oy@[?|h@[?ᝆh@ I^@\(@ I^@U@ I^@@ I^@~Ί@ I^@S]H@ I^@G[R@64m8@6ban@2?|h@2Ƥ#ͽ@2`A7@2&\@1c@1d@1c@1c?@1c@1cG?3<' ?O??ꃩ?5G?Qm0I?+ I?$%'?޿`?0ң ?Q9?nL@?,+e?X?܃ݴ?bDn ?$](6@i/^.@@&x@O;d@&x@'*"@&x@}@&x@?$@&x@p @&x@:(@]hs@].@]hs@]7@]hs@]s3@]hs@]>,@]hs@])o@]hs@]6@8 _o@8@2ٺ^5?}@2 W@2tj@2AP%@1kxF^@1M-o#@1kxF^@1I~_@1kxF^@1^%\I?Z?B -?"|=e?}MmH?0Rp?!C?Z?~9;xN?sEj? -@jn[S@@ -Ƨ.@ -Ƨ.@ -Ƨ.@ -Ƨ.@ -Ƨ.@ -Ƨ.@ `B.@ `B.@ `B.@ `B.@ `B.@ `B.@8O M.>@4 I^5.Mm@4`A.Qn@3m8.NC,@3m8.NC,@3m8.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @@`n@@a|@@@@bM@@rGE@@GE8@8E@8@8E@8ѷ@8E@8@8E@8y=~@8E@9/@8E@8@7@7lD@3+@3e@2tj@2:)z@2[W?@2'/W@2[W?@273@2[W?@2H?ai?hĔV?ʶs?,OKu?Ji?(ϢU?Y?^1?? -oɧ?Җq?? 3W?׬?pPȻ? - l]?q+U? `?mΝ@@@@@,@(??????i@R?Qz?^z) ?iر@I@8Q ?m/oו ?{pbf_?D߉E?l\l@rǬ <@@>+R@>+ I@>+R@>,!.@>+R@>+҉@>+R@>-\@>+R@>+)^@>+R@>+ I@*~#@)l@*~#@*JM@*~#@+)^@*~#@*g@*~#@*qi@*~#@+~(@4|Q@42X@0+@0O;@0A7K@0NT@/(ۋq @/)Dg8~@/(ۋq @/(>BZd@/(ۋq @/(9Xb?]?h?D&?:Z2b?Dj?+ʦ_?]?f?DP?C B?݄ ? R?*wjs?򯞼?|׿pZ?$H?pk Y?j?@@@@&@*??????Q?:?25*?2@>+@*?A?F?A7I?}ϷA?zS'sJ@s87e@@?|@ I]@?|@@?|@L@?|@: @?|@m, -@?|@qq@ ě@"^@ ě@0z@ ě@rXV@ ě@A@ ě@8@ ě@,T|@9H@9+٦@3^5?|@3jJJ@3-V@3##N@2}ce@2]6v@2}ce@2{ԸAQ@2}ce@2pe?&#;?3?)`xy?z?:׉?ܐ?Vz?ڝ)?THN?=?6ad=?p6z@4$tS@1 -^5?|@1 -,<@0v-@0vȴ9X@0 k@0@@0 k@0O;e@0 k@0&?|O?*|3N?&P?ArU@? f?.'?}ѿ,?La?^ o?]bn~?!U?{?h,à?^&? -?]??ܑ ?SP@@@@@*@&??????Zb?& ?-?&Gj}@[ -vcG@9a"1Q?Mps:?F9;t?Hi ?S=-v@g v@@޸Q@ݲ-V@޸Q@މU@޸Q@ދ^Z@޸Q@mk6@޸Q@[E<@޸Q@_/@ -=@ ԕ@ -=@@ -=@Ȉ@ -=@'8@ -=@՘M@ -=@@oC@6 xF@6e>i@2^+@2^x@2."@2-Y@1s&@1q@1s&@1rIR1I@1s&@1qyA꥝?gQ?S?b??c娸?PR?|"<~~?#u?^:[,? k?w~A?3?'@?ڦ?[t?s - ?2o9?5zj@@(@$@$@9@6??????s?L[d?t:D?.:Eѕi@ݮU@?:u, ?Oz?HA>q?b]H@g2p|@@[ݲ-V@[ݲ-V@[ݲ-V@[iB@[ݲ-V@[b@[ݲ-V@[ѷ@[ݲ-V@[]cf@[ݲ-V@[lE@nP@nP@nP@U@nP@ ԕ+@nP@ oj@nP@ ]c@nP@ @3*0U2b@3Q @/"@/u"@/dZ@/F]d@.-C\@.*#9@.-C\@.*~"@.-C\@.*L/|?| ?dY{?ě?vk?n{K?w'?| ?jכc?t(?ҧ?c3c(?.8?YE?˛͇q?٤?*|\/?pk Y?&I@@@@@,@*??????3ՀG8t\?$2eXD?6e2f[@[)@`i?DA݀?WG68@2OO M@2L? MV{?sOewS?(`ƴ?__z8`?g;+u??#?I ?,?rPs?kG r?âa?|T ?TK e?+k?B-j?D`?8@@@@@,@*??????t~`H?~y(Ku?]IXwe@z5@]t?? p?Rp@6S?٥f@iud@@o@Y:1@o.@o.@o.@o.@o.@$DT@$Q@@$DT.@$DT.@$DT.@$DT.@$DT.@8V,<@8;$@4?|h.Mm@4-V.Qn@4 [W?.NC,@4 [W?.NC,@4 [W?.NC,?@8#@8:@8 >+@8I@8?NK3@8@8i@8@8S@8?O>F@8 *@8p@8@8 @8 @?@N+@j~"@N+@'&@N+@2X@N+@e@N+@\@N+@ ᰉ@tvȴ9X@t9X@tvȴ9X@tN;5@tvȴ9X@tD2W@tvȴ9X@tF@tvȴ9X@tD/@tvȴ9X@t=b@8]ce@8yDg8~@3uS@3\Z@27KƧ@2ҩ @25sh@2 <64@25sh@2Ov_خ@25sh@2 <쿲?=,܊{g?Ȼv\?c?T|2?Y@?L? Z ?&ژ?-S^?=:=N>?C]F~?(z@?pi?z?v'X?C?wͶ?`i=@@@@@*@*??????)9?"[?y;3@ `R@tH-?CѱK?PTD@Aτ\?`%*@o-@@-bM@-E@-bM@-Ӂ@-bM@-w@-bM@-s@-bM@-e@-bM@-!-w@bM@nP@bM@O @bM@3332@bM@C@bM@s@bM@:~@6 xF@6oiDh@3E?|h@3Iy @2`A7L@2y=c@2#@2)@2#@2)rGE8@2#@2)rGE8?F/P5?AZ??\2?D?2 ?GZ?x@@@@@*@,???????$a2`?9~?:ZU@+w@J`?X ??[&*5l?im4?]G|?0-Bq?͗u*?L5Sk?bM?h2:?n3?>RK?I?I@@,@,@,@7@;??????CDL?/Ϸ>Wu,{g@K%@?i6?>7q?h#l?w>1>m@s *rW@@7Q@7RnQ@7Q.@7Q.@7Q.@7Q.@7Q.@$5?|@$34l@$5?|.@$5?|.@$5?|.@$5?|.@$5?|.@4쿱[X@4 Y@1hr.Mm@1"`B.Qn@1 -0U2a|.NC,@1 -0U2a|.NC,@1 -0U2a|.NC,?|l@8#@8:@8 >+@8I@8?|g68@8@8i@8@8S@8?}@8 *@8p@8@8 @8 @ ?@}p -@ς\(@}p -@b\(@}p -@t3@}p -@p)^@}p -@s@N@}p -@stj~@}aG{@}O\(@}aG{@}R@}aG{@}WO:@}aG{@}ca@P@}aG{@}^m\@}aG{@}a [@8|Q@8 @4f+ J@4`{@3-@3$tT@2[W?@2Y|@2[W?@2ȴ9X@2[W?@24m?3a=? )}o?Q-(Z?4]t?bJֵ?>V$?WZM?UX|e?~?wAV? E!?f:[c#?mW?8J??}(3?.SM?x?r@@@@@*@*??????l0?V"Q?St`7&?ai ~@uD$t@}_&1?ult?uGWIM?ݛs'z?1_@oЏ@@1&@Q@1&@4K@1&@"@1&@C @1&@VOv`@1&@1@O;@z@O;@E@O;@cA@O;@4m@O;@@O;@/@8:@9\(@4+@2&&I@3`A7L@1em\@3p@0ٓ @3p@0n@3p@0ڹ?І$?O;5?ѠD ?^2?.I?̼+Hn?QN?Žs3 ?[?AM?z֪=?Xb?F(?+Qt?%Zo ?L(?;}?S^^G>@@@@@*@*??????nTHìtH?4/?zqC@@#0_@W?لmk ?ſ@DK|>@).@i7ų&@@5?|@L@5?|@|P@5?|@kR@5?|@wkQ@5?|@XyB@5?|@$9?]Ov?ڢ:]S?Tfx?EP\?/j~? Rh?ۃ?K?&Q~?)?ы>?gJ@@@@@5@6??????gm(*E `?u -i?;k@F,%@v?4{7? -9/?Ct?}S?a@c a;N@@aG{@a7K@aG{@a@aG{@a@aG{@aaf@aG{@`d@aG{@`n@Ǯz@Ǯz@Ǯz@@Ǯz@Ǯz@Ǯz@k}@Ǯz@@@Ǯz@˒:@3~($@3Ƨ@/j~@/!-w1@.&x@.H@-`d@-*0U3@-`d@-@-`d@-ߥ?|O?[e?d_#?.cC?@[?íC@5 ě@5j͞&@4~"@4~@4^6z@45\(@4^6z@4FIQ@4^6z@4*͞&@8J?o\3d?cOֲ?- ?'&?@8J?K)?*&k?Y5}Go?֡(?.Э[@8p?:UYP?H/b?*Q_?VM?!f@@@@,@$?????@n(@nj~@n(@n/@n(@nwkP@n(@n!.I@n(@ns@n(@nq @Pmhr@Pm@Pmhr@Pm5Xy@Pmhr@PmB@Pmhr@Pm\@Pmhr@Pm&@Pmhr@Pm@2!.@2oiDh@/G-@/G#@.mO;dZ@.m@.Vl"@.!.H@.Vl"@.!.H@.Vl"@.}H?^Dw'?J`Y?$C@?ƠJ-M?u?+ʦ_?bV,?&uE@?/BH?Kd]?qN?{L բD?b@?6?|׿pZ? ??b/bJ?f_Ԋ@@@@@,@*??????ISZ?)\?8Y#`>w@n >@Pm)?Gߵ? -0^J?9tִ? oM@sg@@qm@qn@qm@qm@qm@rW@qm@u?|@qm@pH@qm@k6z@ow@n+@ow@mhr@ow@ka@ow@lcA@ow@m(@ow@l>B@4[~($@4[҈@0r Ĝ@0y @0b-V@0`C@/QN;6@/N!R=@/QN;6@/O͞@/QN;6@/H9Xb?}ѿ,?`?$C@?G?~|+?-! )m?|O? ,C?O(?9~F?<U?@ A2?=Ca?Ф?]?hB^?7? `@@@@@&@*??????A]:̮?MQϟ77?)K@qFj^@m߀?l?JD8@MzG@Mz*6@MzG@M{6ג@3uS&@3uL_@1^5?|@1Z`@0-@0\@0iB@0PwS@0iB@0C?@0iB@0,V?|b@?zR?7*?ge?c7A?uZΑ?|h2:?.?(Wg?2s`? *+R?SRe?g?Ȋv?Ψ,UiW?ͩ&?B1-?'f@@*@$@@4@4??????8h? gP?GJ?00-C.D@JR@M{GS?72C?PтՆ?~9]?Gh@f @ @v@@@v@!/@v@Ov_@v@1@v@6@v@zI@$3333@$6o*@$3333@$C@$3333@$m]@$3333@$IQ@$3333@$Vϫ@$3333@$hr@7S&@7!:@4G+@4N\N<@3'KƧ@3)'RTa@3?䎊r@3N$t@3?䎊r@3FE@3?䎊r@3Yԕ*?W?1?0?^ܪɅ ?(:)[a?!8?Q?y\?]:.?dc??sB?ٲ?'W?jNR?{W?S̷$?3?57@@@@@*@,???????Y 9}T?QqetC?:٤_#@x~@$e ?o6rr$?X3G??7S#m@eP@ -@V@V@V@.3@V@V@V@ߤA@V@t@V@U=@`+@`XbN@`+@`e+@`+@`u%E@`+@`=c@`+@`"@`+@`e+@2p4m8@2p'RT`@.G-@.HK]c@-?;dZ@-?H˒:@- -#9@- -d7@- -#9@- -W'@- -#9@- -qiC?^Q5P?GA?"?!#?6r In?$?bV,? C`?90?EP?,Y?X78?cPQ?.+?u@>N ?hB^??f_Ԋ@@@@@*@(??????V o?m ?7dh*9?' -B@_JW@`zW?Fnzl?6JDj?'=?e\ @s -ے@ @tQ@tLC@tQ@tK~(@tQ@tBh Ԕ@tQ@tBC,@tQ@tU?@tQ@t+S@J@J@J@J'/@J@JnP@J@JЉ'R@J@Jξߤ@@J@Jt@6쿱[X@6|Q@3+S@3+qu!@2tj@2tSN@2䎊r@2ߤ@2䎊r@2U=@2䎊r@2z^5?|?F/P5?(.?u*y?vI?Fǥ?6% v -?UbB2?Zk?2'?8h\?uG<?>?~$?{37@2B@5@2?q@2B@5@2A -P?~Ov_ح?A?I '?Ă?fRb?PTP?؂QJ?|K"λ@fm(t@@#@@#@n@#@@#A\@#@@#Fbت@#@@#DX k@#@@#@@#@@#A;s@.O@.+@.O@3EN@.O@/4l@.O@9!@.O@6#+k@.O@=܌"@4 xF@4@2=hr @2:c>@1A7K@1o(@0,<@0ca@0,<@0!(@0,<@09@?|h2:?h.vb?f ?Qx?@>?^|#D?|b@?QY??ڙ?1k4x1?#?Znv`#?(?q?xeS?ʽ?v"s?OBX@@$@ @ @4@,???????;3 n@?g.?=S?B9>@#A`a4@58Ɲ?\r`q?b!~R??SM!?kC-b@fA@@7K.@7K@Q$@7K@9@7K@aw@7K@@7K@o@4+ .@4+ @8l6A@4+ @7tO@4+ @8@+:@4+ @9L @4+ @7͞@4(YJ.>@0xr Ĝ@/RO&+@/1'@.Q+u@/b~@-k}@/b~@-Ip@/b~@-%ʏ@8J?]???dX4/? Aw?/Xye@8J?Լ?^C~+?F6Hy?҉[\?6r'@8p?`T?C?_-?x?vH=@"@"@(@>@<?????@&x@E@&x@^5@@&x@+ I@&x@*0U2@&x@rG@&x@u%F @u@tj@u@\*@u@\)@u@2@u@?@u@"`B@8hYJ@8j=p -@3S@3M:@27KƧ@2䎊@2h@2bC,@2h@2\_@2h@2],=?Br?[ꕠ?@g?1t?ťBW?zvȭc?3a=?7fU?6$l?yCW?La?_I?ݾ?k~?ZaQ??;kr>?|+~@@@@@$@,??????jV?\`5?@: -?@?_6{@!Nt@?` A?~R.3???G|Z@ho`w@@ffff@ffff@ffff@ffff@ffff@oi@ffff@cA @ffff@z@ffff@?@:S@:(@:S@:}H@:S@:%@:S@:;dZ@:S@:v@:S@:b@5hYJ@5i#w@3I^5?@35Xy>@2Ƨ@2}Vl@2p@2oAs@2p@2p{@2p@2p{?}ѿ,?@k=[??j6_? i?4?}ѿ,?z?36G?6)}?u$6??oܧ?t?I@1@1N@1@1<˷@1@1wk?`/.~?И?_p?6狳n?A2f?Zf?¨Sp*?].>x3?7$]? 2??oH?Dk'?2?2T?`z&??^.ג4?#4U@@&@"@@7@:???????5Q?ES^p?a+W2?V4h1E@'>b@^zGYH?Zu9?] .An@q Yf@ c?R?7l?,?}pxXVn@f06@@1|h.@1|h@1)@1|h@1@1|h@1|@1|h@1,zx@1|h@1@@nO.@nO@U$@nO@x@nO@Z@nO@s3333@nO@r{@7 ]ce.>@2hr@2hۋq@2'KƧ@2*)y@1ush@1@1ush@1x@1ush@1T`d@8J?OaMy?P"hI?8i?k;?ѫĵ~@8J?k!?AuG1?ѡb??U? ,@8p?fI%?+jf?p_?;kr>?8wx@@@@*@*?????@+.O@+,@+.O@+(6@+.O@+&*7@+.O@+8'V|@+.O@+3#6@+.O@+7@.1@_v@_:S@_v@_=@_v@_4@_v@_ H<@_v@_~3@_v@_@65S&@65/9@3^5?}@3E) -@2]V@2Sѣ[r@1QiB@1;5@1QiB@16HO@1QiB@16Ӫ??:[?9#? rU?C?&)?~Ov_ح?>۶}?!(8?A|:?]g[?;Rtb?PI?&ݮ7?67Dx? -d?7?>-!@@*@"@ @:@8???????e=|KFM?NfO?aC=H@+2pw@_e?k kV? 3c?OBJS@Q{5@dڧ̼@@a9XbN@a<(Ž@a9XbN.@a9XbN.@a9XbN.@a9XbN@a:kP@a9XbN@a;~@s3333@vE@s3333.@s3333.@s3333.@s3333@yb@s3333@w1@7*0U2b@7,zxl@3+.Mm@2`A7L.Qn@2._o .NC,@2._o @2)_o@2._o @2)ԕ*?F^S@8#@8:@8 >+?q?6?Br@8@8i@8??sB? j?pr@8 *@8p@8?)@Jq?)@Jq@@,@*???@Yvȴ.@Yvȴ@Y9{i@Yvȴ@Y{@Yvȴ@Y!O@Yvȴ@Y@Yvȴ@YDc@zG.@zG@xQ@zG@z!@zG@-#@zG@(@zG@"1G@4e!..>@0 I^5@0o:@.1&x@.)"@-u%F -@-c^5@-u%F -@-6:@-u%F -@-Ʋ1@8J?EU?p-n?5?[?y@8J?7rR?S|]?Ä5?]^A?+@8p?zߔ"?yu?ᄢo?~3]?1I@$@(@$@;@8?????@{dZ@|j~@{dZ@u%@{dZ@삨/@{dZ@pr@{dZ@u=@{dZ@ -Wr@^Q@_vȴ@^Q@i@^Q@,T`@^Q@@?Q[@^Q@6b"Y@^Q@<0@4!.@4ڭ>@0hr @0}@0;Ƨ@0%&Y@/Z@/oXKo@/Z@/y%!ٍ@/Z@/tfy9?}@TΥ?Ċa>?9 p?X5?gV?ըzj?nO?_6+@d0\|@@}@}A7M@}@}ae@}@}-w1@}@}҈@}@|҈o@}@|%@_;dZ@_;dZ@_;dZ@_vȴ@_;dZ@_@_;dZ@_@_;dZ@^%@_;dZ@^Ov_@3쿱[X@3t@0?|h@0=J@.;dZ@.:~@/L@/Lzxl"@/L@/Lq @/L@/LL_?|O?Čۨ1A?7[䭝?ݶ\r?V^?)]\?| ? ,C?ٚlq?G>G?77?"?=Ca?sq#?W ?$H?ܑ ?<71@@@@@$@$??????1!G?*=3?!]@|Ҥԫ@_D?J^]a?B |[Q?ߠ=r?3/@f{^@@@n@@n@@n@C9@@n@L"h @@n@=Vl@@n@>($ x@@n@GB?2}?+@4+@4F@397KƧ@39e+a@3+xF^@3*6D@3+xF^@3*qiC@3+xF^@3* @8J?}>??$jB?3 -fu?Qc -?RLnj?7=^9?+d?c?qN??hE?^ a?Xk-7?l@@@@@$@&??????5/z?IzD?3Go@z;X@S;8?hu51D%?T(~*?&9:? kC@i$nQ@%@bM@ -=@bM@GE8@bM@W@bM@|@bM@|@bM@@_`A7L@_`n@_`A7L@_dg8~@_`A7L@_g8}@_`A7L@_p$t@_`A7L@_jqi@_`A7L@_i@2[~($@2[m\@.)7KƧ@.%oh@-4E@-0'RT@,PH@,q @,PH@,C%@,PH@,~($ x?^Dw'?9؊?e -!C?q@v?ɿ?Rg.?bV,?zF?ZH|:?9~F?l}?p?|;}_?3;?֘>D?+ ?W|DC?@@@@@,@,??????^rxi_?9eΡ??RnH@@_mVt#?H*I(?aL J?:}逭?~`wlLF@r5/VM@&@4# -=q@4vȴ:@4# -=q@4$t@4# -=q@4#2@4# -=q@4$7ޡ@4# -=q@4"M@4# -=q@4!D&@ ě@|@@ ě@q@ ě@b )@ ě@mJy@ ě@˶b@ ě@hIXg@8cg l@8W:<@2)+ @2(m@1`A7L@1Y@0̿[W?@0 ^@0̿[W?@0ZoT@0̿[W?@0KR ?R? ߥ?EqF?1?> c? ?z|3?1bşIr?)jSc?H? uv?X8K@@(@&@ @7@6??????+@8I@8?% @8@8i@8@8S@8?n@8 *@8p@8@8 @8 @?(@nP@䛥T@nP@Y@nP@7KƧ@nP@bM@nP@-V@nP@ I@6bM@6ES@6bM@6S@6bM@6@6bM@6 -=p@6bM@6@6bM@6 -=p@8s@7&J@4f+ J@4dM:@3j~#@3cA \@2@2zG@2@2ݲ-V@2@2Ov_?VAK?05 [?[_(?=]. ?[9,?mׁe\?3? ?mK?ni" ? ^??Ր?mh.?2}?_?RJ?HF!6?ā@@@@@@??????a{qe?N߱?cOo>?pޢD}@,҆@6]b?r߻u˫w??zX?ݷR\?GϽ@s{\_@)@\(@\(@\(@:~@\(@H@\(@:~@\(@V@\(@c @A$/@AG{@A$/@An.@A$/@An.@A$/@A'@A$/@Aoh@A$/@A'@-eݗ@-eݗ@+$tj@+#\N@*nO@*䎊@)@)vȴ9W@)@)%@)@)iB?^Q5P?GA?>f?MA8?<`.?:?bV,?r/B?j?3.Yl?@?R g!ڢ?b@?u?s@a?XLL?O|q?Ѐ>(@@@@@(@,??????K~'@?24:?O@~@A̼T?B Š%?%\O:?3[ u?Dˤv@sK$&@*@eEQ@eEQ@eEQ@eE@eEQ@eD2W@eEQ@eCS@eEQ@eC\@eEQ@eDg8~@R!@R" I^@R!@R!'@R!@R䎊@R!@Rvȴ9@R!@R"u%F @R!@R"M@1@1@/WKƧ@/UY|@.o@.Ft@.@.~%@.@.@.@.6z?^XN?h?.Ү_ ?eU5?E{?7F?bV,? H?'Q?RLnj?,Y?wN z_?b@?Ϸ+?֘>D?˴C??B @@@@@,@*???????=j6?[͎?ZDd?Eh8@eC(@R اy?(]c?T?hA?MRק.?[җ@s|Ю+@+@Õ%@ -=r@Õ%@v@Õ%@!@Õ%@!!@Õ%@@Õ%@ϻ@gy"@gy"@gy"@gy۫@gy"@gy"@gy"@gzt#@gy"@gzJu@gy"@gyo.@0qiC@0qiC@.MV@.M)b<@-ΗO;@-XQjw@-z)_@-zD{@-z)_@-z8@-z)_@-z ?^j?{TV?"(#*m??%r}?T|/?bMn?x???/[2?֓j?S!?bM?9?u؆"? ?"F?s`@@$@,@&@;@9??????;Z T&g? ?!o -a@h[@gzf?*g}*?0 -?Q֨?W04@s-H@,@aR@aR@aR@aSMj@aR@aS|@aR@aSa@@aR@aQX@aR@aQ@ٙ@ٙ@ٙ@@ٙ@PH@ٙ@ k@ٙ@A@ٙ@e+@1}:@1}:@/j~@/%F -L@.ΗO<@.α2X@.Z@.M:@.Z@.@.Z@.?^Q5P?[e@4G+@4$`@4`A@3@3#@2FX=@3#@3@@3#@3 @8J?[.[?"!?aU?[$$?XE%:@8J? ;&?ދC?A4?=i6?ے|@8p?Ag?޻ɩ}?Σw'Y?t#$ ?F@@$@@@?????.@@˅R@.@.@.@.@.@Qhr!@MO;d@Qhr!.@Qhr!.@Qhr!.@Qhr!.@Qhr!.@7(YJ@7&_ح@4hr.Mm@4`A7.Qn@3[W?.NC,@3[W?.NC,@3[W?.NC,?1j@8#@8:@8 >+@8I@8?sD@8@8i@8@8S@8?,1'@8 *@8p@8@8 @8 @?/@vE@vE@vE@vJf@vE@vES&@vE@vKC@vE@vI@vE@vM(@=p@=p@=p@@=p@@=p@^ @=p@J@=p@e@1qiC@1~"@-V@-@+nP@+\N<@,At@, ԕ+@,At@,K]e@,At@, k?^XN?'?Gy??5~pY5?y%?EeX?bM_x?CO?:?@[*^?A? 3W?e@'?9o?8*?"a??Tj?WZ@@@@@$@*???????Jk`w9?NE?F.B@vK.y@͙jT?\rC?T?<-?E+\@r'Q@0@]/@nzH@]/@jqi@]/@^ߤ?@]/@W1@]/.@]/@䎊@.zH@;dZ@.zH@@.zH@vȴ@.zH@IR@.zH.@.zH@ᰉ@9hYJ@9Vx@5B ě@5@4dj~@42䎊@3Q@3n.3@3Q.NC,@3Q@3BZc ?)T?^y?Ғn?э -@8I?S* ?J>x?_?k?µAj@8S?:; ݹ? -eKs?d?"i]P? a@8 ?ډ@@@@@,?????1@w@-@w@kʇ0@w@^.@w@li@w@'@w@@ Q@ @ Q@_@ Q@Jt@ Q@% +@ Q@ CV@ Q@ US)@3uS&@3tg|@1 ě@0ã@1$j~@0 ZZ@0&@/B@0&@0")[>@0&@0[R?|h2:?z~?yn'?Gw?#?tm?}@TΥ?˟g?Gšt?u:z0?d=k?llg|??RѢy?ҞM?%?\_?e< ?e@@ @@ @4@5???????dF@?SmY֬?K -5Wf?\>b#@ˠv@ ݇j?\H=K?-:@c?bӤ @IF`y<@_՞@2@XbN@+@XbN@we@XbN@Qu@XbN@@XbN@T@XbN@@k\(@kZܼ@k\(@k@k\(@khH@k\(@kM@k\(@km@k\(@k@47@@48LP@05?|@02W@0 -V@0 Gb@0^6z@0XЯf@0^6z@0Yj *6@0^6z@0X҈p?|b@?]?_Vfv?Z3g?sC?2} ?| V? ?(r?;??Z?wƫ ?;?Av?+?[?@@$@*@*@:@8??????7?$?qFɸk?# ^+/@Nh@k ?6+xX?C.@?x3%~?ka@f.Y@3@)"M.@)"M.@)"M.@)"M.@)"M.@)"M.@X33333.@X33333.@X33333.@X33333.@X33333.@X33333.@9|Q.>@5B ě.Mm@4t.Qn@4䎊r.NC,@4䎊r.NC,@4䎊r.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 4@ I@Ƨ@ I@ @ I@:)@ I@1&@ I@/{K@ I@ݗ@ +@ w@ +@ ^5@@ +@ @ +@ PH@ +@ 1@ +@ -f@6䎊@6 xF@3f+ J@3esg@2t@2u@25sh@23nP@25sh@23F]c@25sh@2/{J#:?Y͍_?%ñ?1V?'$5?+?^m*?F/P5?馿j?|ɽ?RLnj?Ll?Xo?K4@:ۖ>@ r?\$<]E?km>/? *F?H?Ni@hx @5@|-@|F@|-@}/n@|-@|f,=@|-@}v@|-.@|-.@9Xb@^5?}@9Xb@H@9Xb@ڹ@9Xb@=c@9Xb.@9Xb.@804m8@86$/@3+ J@3XF]c@2ߝ-V@2!.I@2@2 -=p@2.NC,@2.NC,?A[F?ū}?οF;? !@8I@8?ȉak?YB9J?42?ny@8S@8?^$f,?"{Ę?(??=U)@8 @8 @@@@????6@|hs@{lE@|hs@|j~@|hs@vȴ@|hs@}E@|hs@{qu"@|hs@|hs@ ě@o@ ě@@ ě@v_ح@ ě@zH@ ě@'@ ě@A [@6v@6W>6z@2@2n.2@2tj@2@@1Q@15Xz@1Q@1jf@1Q@1D?Br? e?K?vHn&? i?#{׈?=fA:? l?'Q?_3\?8,T?.8?7^|?z?],V?`?*Z}?0&YEd@@@@@ @???????% ?RN?4r$?4W@|"@#Q?T{+?T b7?ĘD?1>t@j嬕@7@Q@+@Q@%@Q@hs@Q@! [@Q@+@Q@j~@/@@/@-@/@/@/@@/@<64@/@}H@6@6 ě@2^5?|@2^5?|@2{Ƨ@2{U=@1@1|@1@1F]c@1@1?qn?d̺?&P? h?Xe?T-N?2}?l?'Q?~m??az?.8 -B?W?J>?L?%ϑ?A0)n?A0)n@@@@@,@*???????DkZXBd?R>fv\?rl@@in\?n9K?7/$Y?ZdE?sLtzm@k~ k@8@~".@~"@B}@~"@- @~"@̑=O@~"@Z@~"@9w@bM.@bM@^¬.@bM@W@bM@=Y@bM@G<@bM@r@9w@.>@4J^5?|@47^3 -@46-@4E3@3Tm8@3! @3Tm8@3%,0@3Tm8@3 TR!Wh@8J?p?]]?c ?/1?z+͛@8J?5ot?N-+g?`nk?xu?t @8p?}aE?`oٶ?IH?ǫ?z@$@(@(@3@1?????9@-tj@-r @-tj@-~@-tj@-v@-tj@-vȴ9@-tj@-$/@-tj@-1&x@]tj~@]$@]tj~@^ں@]tj~@]Fs@]tj~@][@]tj~@]L/@]tj~@]vȴ9@8֚,<@8-w1@3 I^5@3\)@3M-V@3G)^@3!.I@3 C]@3!.I@2!R<@3!.I@2XbM?S?AZ?uzH{*?^? ?xh`?ȸa? ?]&7?'y?OL?/Ւ??Ov_ح?7^|?m7>n?Qh@?D7<@@@@@ @&???????xx`vNe?RtR#j?@ ?@-lH@]*?bp?' -먏%?77!?p~z@p@:@O@R@O@8@O@hr@O@-@O@qj@O@@"`B@vȴ@"`B@!-w1@"`B@S@"`B@!s@"`B@ @"`B@64@87@@8: @3R I^5@3Np:~@2tj@2e@2L[W?@2H@2L[W?@2F1.@2L[W?@2G$tS?{@4+.Mm@3-V.Qn@36z.NC,@36z.NC,@36z.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 <@'-@(r @'-@+6@'-@-\@'-@+åX@'-@$]I@'-@%JP@Ӓ@Ӓ@Ӓ@Ӓ\S.@Ӓ@ӏ[Jx@Ӓ@ӐP@Ӓ@ӒY @Ӓ@Ӓ\>@2 xF@2@1TS@1Te핦@1t@1@0kxF^@0jj@0kxF^@0iy@0kxF^@0ii}?^j?F -?;"{? U}ԃ?h2$?݆?bMn?.?<~z?aNl?Q?@)?znU?r,?;?Ʌ%?-#?Zoh$l@@"@ @$@3@4??????XP?Mvh?B7ij?5$@'53@ӑ_?]?2?7?:1?@@@@*@*???????0*<?8@?$wǪrE@Y7g@27?Gύ?3x?CO ?^ g@s@>@M>vȴ9.@M>vȴ9@M+:)@M>vȴ9@M;6@M>vȴ9@M>\N@M>vȴ9@M<n/@M>vȴ9.@t+ I.@t+ I@tkC@t+ I@ti*0U1@t+ I@tSMk@t+ I@t6z@t+ I.@7쿱[X.>@2r Ĝ@2ŨXy=@217Kƨ@2;C\@1O M@1Y|@1O M@1/w@1O M.NC,@8J?97?^?Z.?X@8@8J?۔b?wSZ?uX?^候@8@8p?RQҿ?jx?V]9V?mCc4@8 @@@@*?????@ě@`B@ě@e+@ě@3333@ě@@ě@@ě@+@@r @@@@S&@@-&@@VC@@R{@8}:@7+@3 I^@3U=@397KƧ@3/H˒:@2𖻘@2ѩl@2𖻘@2S&@2𖻘@2Y}?k!˫? e?r?wP??l#?-Apa?WZM?-A?ίM?.6k?=x@@@@@(@&???????ʈ0o?m?'Y@ ,@}#a8?ѓ)/?p:?- -A?@@m~@@@χ+ J@ς I^@χ+ J@φfffe@χ+ J@τT@χ+ J@υ84@χ+ J@σ,zw@χ+ J@π҈@xTF@xW@xTF@xT!-w@xTF@xS3333@xTF@xN}Vl@xTF@xSMj@xTF@xT,<@7uS&@7u$/@2xr Ĝ@2usg@1ߝ-V@1v@1n_o @1jd7@1n_o @1lu"@1n_o @1lC,zy?}ѿ,?p~v]?jhq9?A%?uxY?"X?? -?)_?߇O?A ?Ր?t -j?3a=?ArUA?Z?“y?7zE@@@@@*@(???????#rFQ6z@}A7L@}(@8zqiC@8n (@4+S@4-!.H@3A7K@3g l@3#@3'@3#@3 4m@3#@3{J#:?]? 1H?r?%? 8{?Q`?]?\??RLnj?| u?ܷ-?*wjs?~?=}?@fo(?;kr>?ymcw\?@@@@,@"??????^$6?@o?K@?S(BSX@+$DM@}h)?\U}?c2Ђx?]?VYs?DVR@r#YY@B@ȴ9X.@ȴ9X@s@ȴ9X@-@ȴ9X@‚@@ȴ9X@- @ȴ9X@PH@9v.@9v@9@9v@9Xy>@9v@9@9v@9-@9v@93332@9䎊.>@58r Ĝ@58>BZc@3tj@3*0@3_o @3H˒@3_o @3Mj@3_o @3sg@8J?:}T?g ? :>Co?a`??KAb@8J?HG^?b? aK?C B??4@8p?:?ԇ:? Y?0C?@@@@*@&?????C@y+.@y+@z.@y+@z@y+@z@@y+@xV@y+@x+1@;ě.@;ě@;1@;ě@;p@;ě@; М@;ě@;6F'@;ě@;]y@5mv.>@1+ @1,"@1Z~"@1[E@0@0xe@0@0V.\K@0@05H@8J?I?G6X]?Or?)]E?S{a@8J?V ?g$?0Ij?(~ ?@8p?! ?:C8?!/1?S/?@"@$@@8@5?????D@+ I@a@+ I.@+ I.@+ I.@+ I@֣'@+ I@D@F9X@Fx@F9X.@F9X.@F9X.@F9X@F%zc@F9X@FXk@6V,<@6T(@25S.Mm@1`A.Qn@1z,<.NC,@1z,<@1M @1z,<@1ű%?t D5@8#@8:@8 >+?" -?I ?}@TΥ@8@8i@8?k?J?9sîgD@8 *@8p@8?iCr.?K@@5@$???E@+@8I@8?E[i)@8@8i@8@8S@8?wv@8 *@8p@8@8 @8 @?F@6zG@6@6zG@69Xb@6zG@6tj@6zG@6Z@6zG@6l@6zG.@w@w@w@xl"h -@w@y+@w@|PH@w@u?|@w.@3@3o@0@ ě@0@)^@0'KƧ@0'8}H@/p'RU@/s@/p'RU@/p -=p@/p'RU.NC,? ?t0?wk?ھ۲?Z)@8?}ѿ,?jכc?F@4@ ě.Mm@3ȓtj.Qn@2:S.NC,@2:S.NC,@2:S.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 H@hr @i7Kƨ@hr @eoiD@hr @p*0@hr @yb@hr .@hr .@R@V@R@R@R@@R@S@R.@R.@6^H@6]\@3hr @3m8@3-@34֡b@3Q@3Xy=ـ@3Q.NC,@3Q.NC,?|O?.5?[:@4|hr.Mm@3".Qn@3ce.NC,@3ce.NC,@3ce.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 K@k"@l@k"@kYJ@k"@kx@k"@k*1@k"@k4J@k"@kL_@N{lD@N"@N{lD@No@N{lD@NnO@N{lD@N@N{lD@N @N{lD@NA@6!.@6!.@4I^5?@4'RT`@3-@3-@36z@3_@36z@3A7K@36z@3$/?Txvc?4o:v?YtBs6?C>$;?m,h?`{wz?ռUӛ?dpn?oɼ|)E? -e?mCc4@@@@@*@*??????p_kKDF?c{?[ͨT@ku>@NߦU?qz+E.?kQS?2L҂?ʕ@r/w0D@L@ r-V@ qn@ r-V@ sZ@ r-V@ s@ r-V@ s@N@ r-V@ qhr!@ r-V@ q_@ q&x@ q&x@ q&x@ qhr"@ q&x@ rs@ q&x@ q[W>@ q&x@ qu!@ q&x@ qu@12@12@,V@,:@,1&y@,҈@+Gݗ,@+G k@+Gݗ,@+G_o@+Gݗ,@+Gy ?^XN?°R;?"?ƠJ-M?g?tJ?bM_x? ,C?j?; $?2&h?PS2K?bMky?.+?|׿pZ?XLL?Ѐ>(?a+@@@@@,@*??????O7#@?د?/ϰ>A@ч@ r@ qv,,?=^ ބ>@ =Ф?u7>Φ~@s -}%@M@1&y@x@1&y@@@1&y@K]@1&y@/W@1&y@)_@1&y@ڹ@*H1&y@*H1&y@*H1&y@*FA@*H1&y@*GE86@*H1&y@*H@*H1&y@*I@*H1&y@*I@4,<@4@0^5?|@0Q@0ttj@0t֡a@.C\@. k@.C\@.kP@.C\@.u?2}?b[? ? {4? i?IdA.(@ -F@*H~?E:-?LQ~?q.?0[^4@g+ -^@N@R_;dZ@RlFtM@R_;dZ@R_W@R_;dZ@R]E@R_;dZ@R` s@R_;dZ@R]K]@R_;dZ@R]~J@te`A7@t]tbs|@te`A7@tvms8@te`A7@tm-͐@te`A7@tg2+@te`A7@tt$@te`A7@tk>@8쿱[X@8wr@2G+@2BtyB@1>Vu@19-@/)_@/lU@/)_@/@ @/)_@/Ʃv?)@?}?K?;m?1)8X?4nr?Vz?G Dh1?X^@3+ J@3zG@2R`A@2^p:~@1Q@1ރ%@1Q@1!-w@1Q.NC,@8J?5FĴ@p@Q@+-@+r@+-@+RT`@+-@+E@+-@+ -L0@+-@+22-@+-@+ ڜ@p1&y@p^5?@p1&y@px74@p1&y@p}Vl@p1&y@pzC\@p1&y@p@p1&y@pxS@4g l@4dZ@2#@2!_p@1-V@1M:@1+xF^@1<ߤ@@1+xF^@14RP@1+xF^@11xv?| V?M?b/?ѮvtQ?BTS??|b@?|?r ?@(?CX-?H, -U}?nՉV?Ov_ح?ԇ:?WhQ?2#Z?6/@@@@@0@0???????:M U?7CIU{?DQs@+}]@p4?X18$?eNF&?D?򰔼$@fnB^ @R@7K@z>Hs@7K@t@7K@s6@7K@6@7K@~b@7K@E6@O;@zG@O;@g@O;@f @O;@}y@O;@q@O;@'X@7 ]ce@70v @05?|@065@0]V@0\d!@/p'RU@/pl;|@/p'RU@/oB&@/p'RU@/l\?ɱb)Q?:4?})Y?=?]X?"? ?nO>?~?C&z?bl?R8?@@$@(@(@;@:???????PCn?RT$?A'&D>?N|@~7 @ϢP?](J%?:?-/wJP?y=v.@k.@S@׍O;@׍O;@׍O;.@׍O;.@׍O;.@׍O;.@׍O;.@?x@?@?x.@?x.@?x.@?x.@?x.@3@@32E@/)7KƧ.Mm@-Z1.Qn@-kjf.NC,@-kjf.NC,@-kjf.NC,?}9X@8#@8:@8 >+@8I@8?|g~D@8@8i@8@8S@8?=@8 *@8p@8@8 @8 @?T@v@vѩm@v@vn.@v@vm\@v@vp:@v@vd9@v@vϷ@(@$t@(@ڹ@(@@(@$@(@3|@(@1.@6YJ@6}Vl@4 ě@4-w1@3tj@3*@35sh@35ݗ@35sh@3.\N<@35sh@3/??E@?}S]N -?B)??$?s[z??t??>#?kH?Ri?Lp%Y?}d1H?  l?LdB"%?(Q?Xs~?>3}@@@@@$@"??????G!- ?i<?NkL??g|f1a@vk@(;-l?f2ZwW?~nR?#Ft ?LP@lee)@U@pO;d.@pO;d.@pO;d.@pO;d.@pO;d.@pO;d.@F.@F.@F.@F.@F.@F.@9p4m8.>@6ěT.Mm@5gKƧ.Qn@4YJ.NC,@4YJ.NC,@4YJ.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 V@Ǯz@$@Ǯz@]c@Ǯz@~@Ǯz@fyD@Ǯz@J@Ǯz@>B@]/@ C@]/@ _ȱ@]/@n@H@]/@$J(@]/@K (@]/@) @8֚,<@8.ʾj@5f+ J@5FL{|@4`A7@4@4@5@4QoV@4@5@4*B΃q@4@5@4&A?|0pJ?X/Z?ք;yK?3R2\?LD??:?O?PJ?|y?gz˛?܊+?&?$niJ=?Gj?c[z?,B S?84|@@&@(@*@<@*??????xLV?vp/@?zi?h%b@=0@%?5?x6iPm@ W;?_*@h0ff@W@}"`@}5?|@}"`@}Zc @}"`@}c@}"`@}G{@}"`@}r @}"`@}״3@˟;dZ@˝-U@˟;dZ@˝ڹ@˟;dZ@˜N;@˟;dZ@ˈp:@˟;dZ@˦1@˟;dZ@ˤe@7 D@7!7Kƨ@4+ J@4GE85@3Ƨ@3+jg@4ush@4d9XbN@4ush@4r@4n@4ush@4r@4n?sD?Ɖ?_r ?O@:??)}U?3a=?%@&??F ??+NP?~$?anñ?7#!xv?^FC}?X昘?9)N-@@@@@"@&???????D}?A*?YP?n=9 @}K@˜.=~?w^B~8?/Xϼ?[@-@gsW@X@%`A7@%Q@%`A7@(p:@%`A7@'ݗ@%`A7@'/W@%`A7@$7@%`A7@!.H@"+@"-V@"+@"Ж@"+@"V@"+@"&@"+@"`A@"+@"- @7@7,<@3@3+ I@2-V@2\N@2 [W?@2 U=@2 [W?@2 H˓@2 [W?@2 +j?ٛP?gUq#?a#mU?ܿ??;??=?us\?Hz? ހ?P?H<?)]\?-]?I1;?/X?O.ce?/̽`?Cns@@@@@*@&??????P4;?KA'o3?4 -Ku@%N=@"qYN?d %?Ryۚۺ?,&j2q?5QZ@nBNb<@Y@;vȴ9X@;n+@;vȴ9X@;|ߤ@;vȴ9X@;y_p@;vȴ9X@;}b@;vȴ9X@;y_o@;vȴ9X@;x-@@`B@@E@@ȴ9X@@ -=@@4@@Mj@74m8@7l@3?|h@3PH@2-@2ݗ+k@23&@2,@23&@2-%@23&@2.\ND?b =?ɟ_?p_? Ğ? -e@@@@@*@&??????g%tX?FG?Qu2?eL1 -cF@H8/@q66?q;?C*9?ߔXC?@k|@[@ěT@ϝ-@ěT@;q @ěT@P{@ěT@BZc @ěT@/V@ěT@m\@F\@FS@F\@DK~)@F\@FOv_@F\@C\(@F\@C+k@F\@Cc@8쿱[X@8n@3+@52wkP@3tj~@3C@2@1;lC@2@1J>B@2@1[Q?Q,Ě?#c?\?ԛ_' ?-hR?:b?UbB2?gn?ڛ?!sz?u?[y jX?A'_]?i^6?N( ? -*?h@?dItP@@@@@*@*??????I'%(ʉ[@?{?J^`u@5O@FP3?ww?7f?Yu}?=՛@[nE@\@mV@mO;d@mV@n_o@mV@nߤ@@mV@n_o@mV@kU=@mV@lq @ -=p@ -=p@ -=p@+ @ -=p@_ح@ -=p@+ @ -=p@ȴ9X@ -=p@@32@32@.f-@.f&I@,nP@,nP@-#Z@-#%2@-#Z@-#%2@-#Z@-#?|O?{j?"?䆀{6? f?װ*w?| ?5CQ?['?C B?Cq$u?_~t?n88??2Y0?':?a+?pk Y@@@@@*@,??????(_@ @?,-#? -Mz@m^1@i?LUsC?+S?9g=?czB"@f-@]@@nP@@Hz@@tGF@@$ړ@@Hr@@Yq@Z9XbN@Z9XbN@Z9XbN@Z<@>@Z9XbN@Z,@-凓ݗ@-凓ݗ@+7KƧ@+-i@+ӶE@+UAB@*Xy=@*5.@*Xy=@*e!Tb@*Xy=@*X}?^m d?jͪ_? n?ʖ?&{?An?bMn?c?4Ә.?@M)%?DE$?jT?bM?4= ?xA}?]s4P;?jfN?{{@@@&@"@"@5@1??????B0<\\F ?AJS֯?9 E@0@Z?,î?PG<7?G^v?D+?n}H@sԟ @^@;lD@=\y@;lD@=+k@;lD@@҈@;lD@<n.@;lD@?vȳ@;lD@C~T?6QLW?6%1B@=I @:T?W=[?UY6b.?QHT?@h?@_@ -=p@ -=p@ -=p@ @ -=p@ m@ -=p@ @ -=p@ )_@ -=p@ @`A@@`A@v_د@`A@̈́M<@`A@,<@`A@_@`A@v_د@5*0U2b@5K]c@1S@1a@@1 "`B@1 C%@06z@0쿱[X@06z@0"@06z@0ߤ??}ѿ,?Z?&t?2UT?G?봤G?Ol?Èa?CXs?Uh;?jx?+d?Ol?Q?Ol?9XNu?fW?7@@@@@,@(??????>܏#N-#>iY??,_@ K&@Ѥp\?A(P?M@DF?=CjƮ?tE D@h @`@ V@ hr@ V@ \Ք@ V@ i@ V@ y9v@ V@ ^x@ V@ e\{ @zGz@z@ѷY@zGz@yM7|@zGz@y -@zGz@yVe@zGz@z1lH@zGz@z5#.f@8|Q@8T@3 ě@3 ʈ@34tj@3N)JC @2c@2hJXH@2c@2]G\@2c@2_.?RL1T?Ӷz? &b?Z: m? -:g?7햞?|B &?o0D?3? A?ˁЅ?pkQ2?_4'0?GTU? ?Č?Fca?͍Q@@*@(@*@5@3??????Do`G?Yn;8u?m[ |@ xq@z7VDj?|2h?:*?RiRT@iw$/*@hF@a@_lC@_z@_lC@_@_lC@_K]d@_lC@_K]d@_lC@_,=@_lC@_,=@u?|@u?|@u?|@u=@u?|@u=@u?|@u?|@u?|@ush@u?|@u=@-7sPH@-7sPH@&MV@&Mqu!@$nP@$nP@$oi@$u@$oi@$u@$oi@$u?_\p)?[eW@_Ik@uj??)=3؏>@ih0?wDۮe>Hvi@sΫ@b@_;dZ@_;dZ@_;dZ@Uᰉ@_;dZ@WXe@_;dZ@U=@_;dZ@ZG@_;dZ@X*0@9)l@9%`A7@9)l@9 ^5?@9)l@8j~@9)l@8ݗ+@9)l@8?@9)l@8a@@5H@5y @3=hr @3,(\@3"-V@3 |@2䎊r@2l"h @2䎊r@2@2䎊r@2!R -fu?׾?"6Wz?O?*cuV??>?yjk?|nӿ?|)-}(? `@@@@@,@*??????[BMZlL'?GE~{_m?kg@\v-id@9 *?dKb? ?*6@O+?r@aޘ?b%?T+?!u?𠑓?'?v>?mGC?U$M\?RY=@@$@@@7@5?????? g##([?be=}w(?B0{VO@n@#rJ^?y"l>?ZA4?n*?r@`M%@d@1&y@`A@1&y@˒:@1&y@t@1&y@g @1&y@0 @1&y@Y@I7Kƨ@@ě@I7Kƨ@Jo@I7Kƨ@I^ -@I7Kƨ@F?@I7Kƨ@As@I7Kƨ@AG|@8*0U2b@8bM@4 I^5@43@3R`A@3R@4n@3@5@2q @3@5@2G{@3@5@2?2}?t0?_?g(6?R ,?`1`?=fA:?=}?$Jw"?~m??C]F~?J3?AK\ ?b =??B@?p& ?0ro{8@@@@@,@*???????Z8`?<3?8@`b?Li@UL@Bc?XV+k?i򲲸??rג@glub@e@d/@d䛥T@d/@doiC@d/@d凓ݘ@d/@d -L0@d/@d -=q@d/@d㢜w@ԛS@ԛS@ԛS@ԛS@ԛS@Ԝ@ԛS@ԜPH@ԛS@Ԝ]cf@ԛS@ԜPH@1YJ@1@-7KƧ@-7KƧ@-1&y@-/{J#@+`d@+Fs@+`d@+m8@+`d@+#?^Dw'? ?.Ү_ ?MA8?g??bM_x?#maЀ?ܿ?3.Yl?A B{?Uyu#?b@?6?2Y0?XLL?f_Ԋ?l&@@@@@*@(??????Q>שw5?3vV? >A}n@dd@ԜTY?B^9?Q>ۅ;?[d;? -C3@s/[@f@97Kƨ@9Z5R@97Kƨ.@97Kƨ.@97Kƨ.@97Kƨ.@97Kƨ.@l@B5O@l.@l.@l.@l.@l.@7H@7"LQ@35?|.Mm@3V.Qn@2m8.NC,@2m8.NC,@2m8.NC,?|gv@8#@8:@8 >+@8I@8?c@8@8i@8@8S@8?@8 *@8p@8@8 @8 @ ?g@mm.@mm@ms@mm@mnP@mm@mp:@mm@mD@mm@mD@-.@-@@-@- @-@V@-@rGE@-@V@(oo.>@"tj@"- @!x@!x@!oj@!oj@!oj@!oj@!oj@!oj@8 -Ny?°R;?m?ƠJ-M?tKc?>@82r? aҹz?rJs?/Z?tP*?L "@8L?8 T?nwg?~\?h?h@@@@,@&?????h@1&x.@1&x.@1&x.@1&x.@1&x.@1&x.@I7Kƨ.@I7Kƨ.@I7Kƨ.@I7Kƨ.@I7Kƨ.@I7Kƨ.@8s.>@4=hr .Mm@3"`.Qn@3Q.NC,@3Q.NC,@3Q.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 i@Cn.@Cn.@Cn.@Cn.@Cn.@Cn.@9x.@9x.@9x.@9x.@9x.@9x.@9%!..>@4i+ .Mm@3".Qn@2ce.NC,@2ce.NC,@2ce.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 j@,C@+C@,C@0- @,C@.@,C@,1&@,C@/iDg8@,C@-C]@-V@ ĝ@-V@@-V@m9@-V@@-V@&@-V@@6@6@2° ě@2[6@2R`A@2O͞@1+xF^@1)*0U2b@1+xF^@1&&J@1+xF^@1&Ov_?F/P5?gˣ? ? Iy?ZAj?6?}ѿ,? -?NH?Kd]?2}?f1-&?9@0w??+J@,G\@=>M?RPØ?_퀊O@?kKt?_1G@g+6@k@mhr@mhr@mhr@nzH@mhr@mV@mhr@mhr@mhr@j=p@mhr@kR@bM@bM@bM@`A@bM@hr!@bM@bM@bM@և+ @bM@˅R@4~($@4C,zx@08r Ĝ@07 k@/dZ@/D*@06z@0>BZ@06z@0C\@06z@0^5?|?]?}d?FB:?_B?ɿ?}?]?dx/?Rs??La?lINՄ?*wjs?fb?*wjs?,|?ad|?#.????@@???????T0Mm8 ?%D?L=} @j׎@[A?$Y?KM1?7&p?Qz1C@xq6@l@Tj~.@Tj~@Ush@Tj~@Sa@O@Tj~@U%F -@Tj~@SS@Tj~@R @53333.@53333@5\)@53333@5fA@53333@5j~@53333@5z@53333@5J#9@8.>@3^5?|@3ݘ@2Ƨ@2Z@2OO M@2Mw1@2OO M@2O\(@2OO M@2WO;d@8J?y)3c?3?@R?'`?+YI@8J?`?F^S?uX?8?Y@@8p?.]L?[Rb?C?ĭ?dItP@@@@,@,?????m@*I7Kƨ@*H9X@*I7Kƨ@*I7Kƨ@*I7Kƨ@*F?@*I7Kƨ@*I @*I7Kƨ@*G+ J@*I7Kƨ@*GfB@DT@D/@DT@E@DT@EQ@DT@FIR@DT@D%2@DT@DJ@5@@5b}@1 ě@1 qu@0ȓtj@0sPH@/fL/{K@/f -L/@/fL/{K@/fA@/fL/{K@/f$/?| ?1?e -!C?̒?E{? $4?1j?u߿?NJ?0N:s?,Y?B8?T0?lo>?!A -m?ۮetZ?L?b/bJ@@@@@(@*??????%,>Oe?-Y?W?.{@*Hs.@E"?MOx_Ye?NMC7e? -yH?vD@g.7@n@և+ @׍O;@և+ @٦ @և+ @4֡@և+ @@և+ @ᰊ@և+ @Ʌp@`B@/@`B@ѷ@`B@e@`B@塣n.@`B@墂@@`B@af@6|Q@6\(@1?|h@1eO@1-V@12X@0L[W?@0L"h Ԗ@0L[W?@0L(\@0L[W?@0L"h Ԗ?Br?sp?`כm?!#?5 g?C!?| ?$i %h?低NU?C B?݄ ? f?|׿pY?%_D?],m=?*|\/?ˢL1?L8AS@@@@@,@,???????i(G? =lj?']39@֌FNF@ >&?@2L_?Hљ-fH?EKl?G_XW@hzz@o@x.@x@A7L@x@Em\@x@v1@x@oiD@x@*0U@-V.@-V@,/{I@-V@bu%F @-V@^\N@-V@~($ @-V@tj@8S&.>@4kS@4*0@3t@3҈p;@2Q@2>t@2Q@2(u%F@2Q@2=b@8J?Wީ?Қ9?TE|?\ٝ?ˉ4+@8J?2? ?`kY?0-+?MQc@8p?F p?Ǚs?{/!?)v?tXr@@@@,@*?????p@Xtj@^vȴ8@Xtj@\1(@Xtj@e`A7@Xtj@]/@Xtj@T9Xb@Xtj@]ce@ ě@ 7K@ ě@ l@ ě@ l@ ě@ hr@ ě@ Z@ ě@ S&@5-v@5zxl"@1hr @1qu!@0`A@0O M@0J0U2a|@0CE@0J0U2a|@0E8YK@0J0U2a|@0EL_?}?ߋb?*"AaI?Ac ?] - ?oNR?|O?l?I?$?az? ,?6ז? U)?:-?(Q?}?7zE@@@@@@??????Qf@?N.?R/2?S0I_J@[U@ 9l?qY -Kh?tX?Sh?J^@h]@q@)sE@)sE@)sE@)t@)sE@)u?|@)sE@)t\@)sE@)uϪ͠@)sE@)v4@KC@KC@KC@LI^5@KC@Jo@KC@L@KC@MM;@KC@Kq @4 _o@4 _o@0+ @0U@0Ctj~@0B@4@0u@0M:@0u@0$@0u@0?| ?Čۨ1A?1f_?9.?\7?7F?| ? ,C?|,!?!zt9?az?y ?m!?2]7?u@>N ?l?7?GZ@@@@@,@$????????${?28>FvS?+O<Q@)t|@K?|4`y?KK}O?FnNl3?Ym@f@r@)8@)4zG@)8@)=IQ@)8@)64@)8@):Y@)8@)6E@)8@)5!R@XbN@-W@XbN@qu"@XbN@l"h -@XbN@XbN@XbN@#@XbN@qu#@5Q|Q@5HPH@3#@3% '0@2ȓtj@2)y@2c@2g -=p@2c@2h˒:*@2c@2f '?us\?+!?ʶs?,rg?yq?Dw?=fA:?o ?ZH|:?#X?Al.6?ܷ-??%2?d --?__C?GZ?E݋N@@@@@(@*???????'Ma;&?P` @}?@{H@)7H+@-(?lTN?`%?ӬI'X?f`m@k[H@s@Vu@?|h@Vu@<(@Vu@2X??ଚw?bdC?~fܤv?G6NM?ը:J0?F^S?56Q?D?byp?;~`?-j?z00!?w}'b?a?h?O?Jʿ?cg@@@@@*@&??????5h?- ?d?慕0@:cǠ@xS?ta"T?vg`@WV2@/a8 -@d& @t@f$/.@f$/@gz@f$/@jS@f$/@m-V@f$/@f4@f$/@eY}@+.@+@rGE9@+@ḭV@+@+ J@+@z@+@}H@8g l.>@4 I^@4W&@3KƧ@38YK@2䎊r@2?@2䎊r@2Mj@2䎊r@2 ҉@8J?=b?&?,rg?ĶBϪ?vȴ:@B\(@;J#9@B\(@Lzxl@B\(@UfA@`A@ hr@`A@?@`A@ @`A@:~@`A@ ԕ@`A@f@7쿱[X@7NV@4+S@4@2-@2Vl"@2Q@2u@2Q@2-w1@2Q@2&IR?ٛP?%=7"?uz?&SF??±|?|O?#?.j ?=M6?NZ׭?%TUx?T̅?AIa?L /?wZR)w?a<?A8 K@@@@@,@*???????iOLf(`U@?S'm?Q9>2@HI@/?t p?rvx|\m?ɂu?}[@fk^@x@`B.@`B.@`B.@`B.@`B.@`B.@i%S.@i%S.@i%S.@i%S.@i%S.@i%S.@5_o.>@2^5?|.Mm@1-.Qn@1O M.NC,@1O M.NC,@1O M.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 y@lF@lG@lF@lzG@lF@l?|@lF@lB@lF@l.@lF@lPH@fě@fě@fě@fS@fě@f.H@fě@f-V@fě@fl@fě@fXy=@3G>6z@3H $ p@/f-@/aR<6@. n@.e+a@,Vl"@,T`d@,Vl"@,g l@,Vl"@,a@O?}@TΥ? 1H?Gy??̒?2F0?&Gf?|h2:?D.x?A)N?6)}?~? # -?nڨ8?3I#i? # ??7?P\@@@@@,@,??????4uP?G54?>3u=8g?@Ni=ޮ@l6@fW?I?^e6?`w\F ?*bVˊ?F@fݐU@z@H+ J.@H+ J@H zV[t@H+ J@H"N_@H+ J@Hm@H+ J@Ga@H+ J@H}@9X.@9X@;R@9X@K@9X@ -@9X@ !@9X@Z:@6YJ.>@2hr@2$@1j~#@1@1+xF^@1+j1@1+xF^@1*XsU@1+xF^@1+T+@8J?Ek?͌ɼ? -A?&>?V)WE7A@8J?PoS?ReIt{~?fȹ}h?q!U?nr|?|ɽ??pPȻ??yz?Ov_ح?F?(> ?>LR_5?^':@@@@@*@*??????^@?b}`?U -ݵ?C!9:@fOBK@E5)?s=?\]A?B`o?1@kJ@|@!G{@!@!G{@#$@!G{@# -=r@!G{@%o@!G{@!'@!G{@!s@ғtj@ғ@ғtj@ҔzG@ғtj@ґ@ғtj@ҖC@ғtj@ҕ!S@ғtj@ҕᰋ@4_o@4B@2J^5?|@2JS&@1tj~@1@1^6z@1][@1^6z@1]<64@1^6z@1]cA ?| ?Čۨ1A?BI{?l?״@0U`A7L@0U\(@/b~@/1'@/b~@/ݘ@/b~@/1'? ?{j?K]4?j6_?% -?t?| ? g?ٚlq? Y?Hc?sx/T?8*?.x?u@>N ?E 8?ˢL1?j@@@@@(@(??????D?!]S?;57G>J@JRP$@M?[ .G?Y?S ?*u@glR|@@aglC@a{S@aglC@afx@aglC@aix@aglC@aa$0@aglC@ai@aglC@abu%F @?|@I^5@?|@H@?|@q @?|@ȴ9X@?|@1@?|@V@9hYJ@9Fs@4{ I^@4W@3>Vu@3DzG@2&@2 @2&@2^5?|@2&@2vȴ?cxt?4Kl?[_(?1t?pK ?MAG?V?I1@?%8?~m??44]s?r?US9?0b])??uЍ?q?%@@@@@(@(??????_Ly?QR#~?i滸?B5!b@ad @9H%?Wq?Se?K -??3gsw@ph@@ԁ7K.@ԁ7K@ԆIR@ԁ7K@q.@ԁ7K@ԁ7K@ԁ7K@lI^4@ԁ7K@`N@ -=p.@ -=p@PH@ -=p@&I@ -=p@@ -=p@ y@ -=p@쿱[@9DO M.>@4&+ J@461.@3j~@36C@3 [W?@3*0U2b@3 [W?@2iDg8@3 [W?@2͞@8J?'G_I?6\9?Kw?ߩ?kʍL@8J?_lWF_?Kw?GuS?ykz4q>?c:l@8p?qv?. ?ׅN?nD.?hvk@@@@*@,?????@ -=p@%Q@ -=p@#e@ -=p@S@ -=p@'RT@ -=p@@ -=p@ qu@>nP@>tj@>nP@>ߤ@>nP@>|@>nP@>ڹ@>nP@>S&@>nP@>ě@8cg l@8TD@6I^5?@6(Xy=@5j~#@5ȧ@5!.I@5ܥzxl#@5!.I@5׮zH@5!.I@5 [7?%? ,[?e2菾?L?V#m?zk?UbB2?''5?_?j3(??Ǫr?W_l?"55A;?S?8'e?Z|?3Y4@@@@@,@(??????}\@?m~?|V?:m#@3@>A#?r%b?TA,?i?Plu@ca$Ն.@@hr!@E@hr!.@hr!.@hr!.@hr!@u@hr!.@`A@+ @`A.@`A.@`A.@`A@4K@`A.@7|Q@7A [@3G+.Mm@2j~.Qn@2=ce.NC,@2=ce@2C%2@2=ce.NC,?@8#@8:@8 >+?RXFf@8?qv@8@8i@8?@8?Ʌ.@8 *@8p@8?Q?]@8 @@??@91&y@91&z@91&y@9^5?@91&y@9*0U2@91&y@97Kƨ@91&y@9Fu@91&y@9)`@N(@@@@@(@(??????P8'v:n$p?6ϳ2?v4;F@9}J@N@2@5@2v_ح@2@5@2zY?Ol?#T?>\?3' ?vk?,&=?%???T ?"JÁ!?M`%?|HKr^?5q?K?~Y?>ڵ?q ?h@@@@@@*@$??????<.J(%?I.K?>S!@r@HP,?mNw?](vV?K3?Ѯr,@l&@@`B@XbN@`B@M@`B@F -L@`B@bM@`B@:){@`B@+ @!G{@E@!G{@vȴ@!G{@"7@!G{@$/@!G{@X@!G{@G@8]ce@8xl"h -@4c@4` ě@3tj@3{@3+xF^@3)D@3+xF^@3#*0U2@3+xF^@3!n.?hA?b =?lڮ?{@? ?kʍL?Ol?\?YEX?3F0? R?½y?uIE ?&3?nf?k6?DL?*:R}@@@@@@*??????e?MgP@?Y_C?i潊N:@z@e?d3$?4e -?ьD@ Hc@oLj@@9Xb@9Xb@9Xb@*2@9Xb@$@9Xb@G@9Xb@@N@9Xb@@N@g-@h1&y@g-@hp:@g-@gݗ@g-@h1&y@g-@h1&y@g-@hXy=@4YJ@4 ԕ*@.-V@.@,o@,&IR@*-C\@*.Vu@*-C\@*-(@*-C\@*.zG? ?[e?Uyu#?Aᆽ?u?y8$?ʬv?O|q?a+@@@@@&@&??????#t!> >?(P`<>ޣd?@B*@h9f?H~`k? 'a?83?%hwn@gů:y@@dZ@d~Q$@dZ@hGl@dZ@^p@dZ@iV<~@dZ@gMkc@dZ@fU@UQ@UlD@UQ@Ug@UQ@U*Wj{@UQ@U6V@UQ@U'X@UQ@UP@6H@6S@3+S@33iː@3tj~@3V62@2:S@2K^D@2:S@2R)@2:S@2I';?o"?mN?qg8?~j@t?}vk?e}B?{si?xm?XFZ\=?*d7:?qbt?Tŗ?vqG?N#9?f;?_Oa?rW?z5K@@,@(@(@5@0???????C?Zշ?5 -6?HBh@eM@U‹?WW?iU?UiYp?uA6@g_?<"@@.O@/w@.O@"7@.O@'@.O@)@.O@#S@.O@)7Kƨ@^zG@^}p -@^zG@^X@^zG@^Q@^zG@^@^zG@^fB@^zG@^;dZ@5cg l@5bn@3G+@31@2t@2/V@1_o @1b~@1_o @1*0U2b@1_o @14K?Ol?J?-.?ě??j?uk=?|O?`?To?A8 K?dLt?~Y?qn?[ș8?Y͍`?"a??P\?aʕTI@@@@@*@$??????_$zِ?s0?HU5?[EO?3@*"@^1L?i#?|tdA?~Ѱ@,@gKg@@Y+@XQ@Y+@Xc -@Y+@[n*@Y+@YH@Y+@X<+k@Y+@X}@zG@@zG@%'@zG@O@zG@Ԡ5@zG@}Vm@zG@2a|@7YJ@7_ֽ=@3i+ @3j v@2-@2OY@2 [W?@2I]I@2 [W?@2 ܰ`@2 [W?@2E ?wogl? dJgQ?:*Q?|[?8I?Ae ?Hq"1?ݗ?b6??I|q?/?<(_?%>p?j ?&};-?zGhƦq??@?ٟR ?hrJe?>)A-?5+ք@@*@$@$@9@9???????g ^@4?|h@3O @317Kƨ@3($ xG@2[W?@2 -=p@2[W?@2l@2[W?@2|@)@8J?"2?w͜?1t?(gx?u.%J@8J?2(_?(?{k4?WA?o -E\K?U].@8p?anñ?o?u4?pXm>?M[70@@@@0@,?????@s\).@s\)@s7K@s\)@sffff@s\)@s@s\)@s@s\)@s7KƧ@vȴ.@vȴ@ezxl#@vȴ@%ݘ@vȴ@nO@vȴ@@2sS@26@1i"`@1e@0iB@1t@0iB@0h ԕ@0iB@0oiDg@8J?u3?.#Y?\2?4+Rږ?.@8J?X?0eʪv?'bA?.ĵd?Pz @8p?e?Dk?[+{A?d~?bH@@@@(@*?????@_Ƨ@_=p@_Ƨ.@_Ƨ.@_Ƨ.@_Ƨ.@_Ƨ.@]F@]XbM@]F.@]F.@]F.@]F.@]F.@74m8@7)^@55S.Mm@5;Ƨ.Qn@4(.NC,@4(.NC,@4(.NC,?]@8#@8:@8 >+@8I@8?]@8@8i@8@8S@8?*wjs@8 *@8p@8@8 @8 ??@nO.@nO.@nO.@nO.@nO.@nO.@0 ě.@0 ě.@0 ě.@0 ě.@0 ě.@0 ě.@44m8.>@15S.Mm@0V.Qn@/.NC,@/.NC,@/.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @ @ȴ9X@ @ Ƨ@ @ :@ @V@ @ -g@ @ zxl@.w@.@.w@.o@.w@.H@.w@.2X@.w@."h @.w@.[W@87@@83@N@2J^5?|@2JD@1Ctj~@1D`d@0z,<@0{:)z@0z,<@0z0 @0z,<@0zqiC?E?j‚?tcj?#t?E@? $4?E@?> -fu?4M^?siqe?1? R? )蠈?)Zw?m!?GZ?&I?7zE@@@@@*@(??????^WQ^]l?LX"?GB@ ? @.:mY6?bR?_s A?{?ӻE2'@o@@V\(.@V\(.@V\(.@V\(.@V\(.@V\(.@fQ.@fQ.@fQ.@fQ.@fQ.@fQ.@8I_o.>@3|hr.Mm@2j~.Qn@28D*.NC,@28D*.NC,@28D*.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @n@n@n@翉@@n@@n@L@n@럺O@n@(<>"@vDT@vE@vDT@vA@vDT@vBtU@vDT@vC}@vDT@v?t͘@vDT@v=Sn@4uS&@4u@ -@2pěT@2n'@1j~#@1 `G@1O M@1БHt@1O M@1a#@1O M@1q?|h2:?1J*@?)zІ???_1@3+@2q @2 -V@2E@1L[W?@1NѷY@1L[W?@1:Q`@1L[W?@12s@8J?q+qW?c0?7ly\?p3/ ?:AJ+@8J?k$?Vn?vEB?du5?<"(o@8p?G?+X8 ?+(?]`??㖆?W$B?>ÔR?{W?P?nW*؄?i 0/?•;(?ڢ?hb?L??,yfY?P|@@@@@*@,??????ne7?~l?RsS?l^y@Kz0@9H8?c*J?B [?FMAr?r`i@q5X@@^y"@^k I@^y"@^Xtj@^y"@^H1&y@^y"@^M&@^y"@^Q2W@^y"@^Ox@\(@V@\(@`A@\(@u@\(@@\(@!R@\(@'/@6 xF@6|Q@1)+ @1Dg8~@0j~#@07KƧ@/Xy=@/Ov_@/Xy=@/ҽ<63@/Xy=@/-?UbB2?L?4F&x?? 2 ?|S6Y?sD?7fps?˗ -"3?.?T?+l?i?*J?a -?ނ F?^^ω?x@@@@@,@(??????|U?1M ?]Z?Gwh7@^[>W@ϵ{Ʉ?}({iR?f[dm@'?&v/??:Q $@fe`@@oc -=q.@oc -=q@ltS@oc -=q@l&@oc -=q@l1'@oc -=q@lp:@oc -=q@lg @\(.@\(@$/@\(@qj@\(@@\(@ @\(@w1@9䎊.>@3^5?|@-䎊@2Ƨ@,#w@1[W?@+rH@1[W?@+w1@1[W?@+ᰉ(@8J?2Q=t?UD?Ԧ?n?)^y?[5@8J?^1??J?$?p G)L?i 0/@8p?a,F??hc?K&?T@@@@(@&?????@d/.@d/@ۉx@d/@kP@d/@Y@d/@($ x@d/@TɆ@]/.@]/@M@]/@glC@]/@o@]/@@@]/@~@8e!..>@4}hr @3k~(@3"`B@3<_@3 k@2l@3 k@2:)y@3 k@2~@8J?Wq?Ȯ|1?G#}?lB_l@O0?V7S ]?R,Xmt?tN?@@gˌ^@@g1@gG@g1@g)y@g1@gp[? @g1@gڟv@g1@g؁N@g1@g׻Ȱk@e1&@eS@e1&@ek@e1&@e$@e1&@e<%J<@e1&@e~@e1&@ey@3쿱[X@3fJ*@0; I^@07@/x@/!v:@.PH@.C]@.PH@.'S1@.PH@.s?Qp?u:I1?%ja a?!.H.?(l?ưz_3?|b@?!!{?-@y?9~F?U1 -?kN? v?r?K ?%?S0iBZd?| ?J`Y?D&?lDžN9?2A?7F?| ?&uE@?^ o?Kd]? A?+d?8*?l?? ??j?f_Ԋ@@@@@,@,??????V@%W?c6,9>ŤN(\@Rq@ϋi\?':{u^>5n/?]^2>`@f}@@4j~.@4j~.@4j~.@4j~.@4j~.@4j~.@V.@V.@V.@V.@V.@V.@9 ]ce.>@5 I^.Mm@5tj.Qn@4̿[W?.NC,@4̿[W?.NC,@4̿[W?.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @Y+@Y+@Y+@Xy=@Y+@YY@Y+@XYJ@Y+@XD@Y+@Y@'-@'-@'-@)y@'-@*W@'-@-@'-@,q @'-@-@32@32䎊q@0)+ @0(F]d@.dZ@.@/#Z@/iB@/#Z@/ @/#Z@/ A7K?Ol?*|3N?7[䭝?A%? -`?#{׈?}ѿ,?Q_ڸFN?tw?owlP?UBi?P=? SF?lo>?W ?v_.^?*Z}?<71@@@@@*@&??????x?T_?#uY7?2;->@X6}r@*?CYZ3JM?STUeG 7?'*?nb;@ga@@ ]-V.@ ]-V.@ ]-V.@ ]-V.@ ]-V@ x7@ ]-V@ ӎMj@lKC.@lKC.@lKC.@lKC.@lKC@lgY|@lKC@l*JL@>2.>@7hr.Mm@6Ͳ-V.Qn@5xF^.NC,@5xF^@5(@5xF^@5@8@8#@8:@8 >+?IT?ֈp&`F@8@8@8i@8??cH?B>@8@8 *@8p@8?Ι?Ċ^@.@??@~#@ -\&@~#.@~#.@~#.@~#@!@~#.@}p -@a#@}p -.@}p -.@}p -.@}p -@˝Vl@}p -.@7v@7@3?|h.Mm@2-V.Qn@2.NC,@2@1ce@2.NC,?E[i)@8#@8:@8 >+?`i%|@8?~Ov_ح@8@8i@8?@8?k%,@8 *@8p@8?E(ɡ@8 @@(??@@&x@@(@@(@@nP@@E@@?@$/@\(@$/@ç_o @$/@Ü1&@$/@ô9Xb@$/@øYJ@$/@ê=p@9䎊@8@5 ě@4N;@497KƧ@4+lC@2,<@2=b@2,<@2,<@2,<@2*0V?hC%?|?(`ƴ? { x?W?{7]['?8ȺJ?}?b?MN?? " z?y?E!'?Ny?1Z?7Q?VM@@@@@,@*???????vn5vDM:?gn[cN`?h?DM@D@öo"?{  ?V?߿|? @ox|@@>vȴ9.@>vȴ9.@>vȴ9.@>vȴ9.@>vȴ9.@>vȴ9.@x.@x.@x.@x.@x.@x.@7֚,<.>@5 I^5.Mm@4A7K.Qn@4!:S.NC,@4!:S.NC,@4!:S.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @N+@N+@N+@N*@N+@Ohی@N+@L!-@N+@LI^5@N+@L@ ě@ ě@ ě@ M@ ě@ D@ ě@ O M@ ě@ 1@ ě@ 4֡b@1>6z@1RT`e@.Vt@.-W@.1&y@.5Xy>@-uXy=@-u%F -N@-uXy=@-vȴ9X@-uXy=@-w1?^Q5P?J`Y?@?lDžN9?G?-tT?bM_x?4 "?2'?N?qN?90?g)?6?Me_?Q(?<71?ܑ @@@@@,@(?????? #xh?)%)S?0K@L5g@ & ?85 -Tؿ?.!76^?lR ?SlU X@s[@@ -=q@A7L@ -=q@V@ -=q@@ -=q@C,@ -=q@ʙ0@ -=q@.H@(@XbN@(@ ě@(@/@(@E@(@cA@(@dZ@7@@7qj@3xr Ĝ@3o'/W@3Ctj~@3*W&@2u@2@2u@2~ѷY@2u@2}(? ??I!S?㬱_?)^y?I$4}?F^S?~??\PK?>#G?'Ln?׍xG?$G?~gN?J{?Fb2x?q@@@@@&@&??????zz?D~?L?ExVLS@4}a@ @O;dZ@Y.@O;dZ@Yu@O;dZ@[c/@0įO M@0Ʌoi@/A7K@/d?@/&x@/ ZP7@-L/{K@-q@-L/{K@-U6@-L/{K@-ڍD!?^-?#??]Ľ? ?"ٷ?bM;?YFѣ?:q?9GE?i?K?bV?aݍ?i2?/[?!ccC?KVPn@@(@*@,@@@8???????OƲא?@Y?.+s3^?C@E@Zm?=ak -?-:)?t90ɷ?RBZ@sϓw@@ I^@oj@ I^@5C:/@ I^@g@ I^@H>@ I^@@ I^@!@kbM@j\@kbM@k]@kbM@k {@kbM@kwv@kbM@k*蠢@kbM@k9@9(YJ@9);=e^@4!hr @4 u\p@3."@3/8[_@2𖻘@2m!@2𖻘@2W6@2𖻘@2ywW?hȶ?t2x8?@m|x?/?U?1(?x9?A S?0}h-?n,?ej?2 2?t?#L? `?/z?Lď?$X@@(@&@"@;@6???????>@L?{ڢ$U@?kLZ?A[ٸ@ zT@k 8T?|$i?ZrJ?(af?pC:@oXԻ@@=p@=p@=p@JN@=p@0 @=p@g@=p@B@=p@ @@@@˒:+@@?@@@N@@䎊@@҈@2*0U2b@27KƧ@.`A@.¶}Vm@-;dZ@--V@-Vl"@-u!@-Vl"@-IQ@-Vl"@-!.H?^Q5P?$?U#?ƠJ-M?u?-tT?bM_x?#maЀ?rJs?Y??6#ZQ@s@/?1G?Eyx"hM?\Ў?r{@sVB@@u@u@u@1@u@Xe@u@$tS@u@֡a@u@Ʌo@lC@l@lC@l@lC@mV@lC@l!-@lC@l[W@lC@mqu@/!-w1@/!-w1@){"`A@){"`A@(SE@(Sa@O@'73@'73@'73@'73@'73@'D+?^Q5P?GA?$C@?MA8?/`?tJ?bV,? -?j?3.Yl?%y*=?J&d?k?b@? ?oܧ?avh?h?#.@@@@@,@*??????Q"? 9@?5=޶?{w@@l**?D:H?(*j?_?J@s (@@&=p@&d/@&=p.@&=p.@&=p.@&=p@&o@&=p@&nP@us3333@t9Xc@us3333.@us3333.@us3333.@us3333@ut9Xb@us3333@uu\)@8䎊@8($ xG@3S.Mm@3_-V.Qn@2xF^.NC,@2xF^@2Vu@2xF^@2{J#9?ӖlP@8#@8:@8 >+?J+?I ?ߞ.ӭ@8@8i@8?q?p?4hN@8 *@8p@8??oڕ??oڕ@?????@O;@O;@O;@_@O;@4m@O;@}H@O;@fA@O;@\)@ԭV@ԭO;d@ԭV@ԯ@ԭV@ԯA@ԭV@ԭ(@ԭV@ԭM;@ԭV@Ԯ!R@5I_o@5I"`@08r Ĝ@07K]d@.x@.*0U2b@,ߤ@@,ۥS@,ߤ@@,,<@,ߤ@@,G{?| ?{j?Gy??lDžN9? 9?CXs?1j?CO?kE<%?]bn~?@?h,à?^&?§d߱?Z?XLL?SP?Rv@@@@@&@,??????5P?!\?//5T? 19@v@ԭA?O)?.h[?AG?g0Vm@g;F@@5?|@9X@5?|@!-@5?|@䎊@5?|@tj@5?|@U@5?|@C,z@ C@ R@ C@m9@ C@!R@ C@T`e@ C@ ^ @ C@@8s@8҈p@5#@5$!-w1@4j~@4- @30U2a|@3Y}@30U2a|@3֚,<@30U2a|@3҂@5?}? ?r?V0O?&x?LTk?NIɮ?OW?l%FZ?/:?' ?EŐse?x?$n?o\Z|?9 ? 4e -?M\{W@@@@@$@&??????t)?b@?Z L?bLB@;i@<&?z~8u?w,F>? Z &2?uHl}@lLЩ@@"(\@")x@"(\@"&ffff@"(\@"%Q@"(\@"&@"(\@"%o@"(\@"$T@E@EQ@E@=E@E@?˒:)@E@84֡@E@;@E@<n/@34m8@3gCb@0@ ě@0@|@/F@/C\@/ -#9@/ oiE@/ -#9@/˒:*@/ -#9@/ rGF?|h2:?m?ě?@R?R%(0?װ*w?|b@?7fps??aYZq?=.e1?tK?Q(U?sq#?I/?cӗ1?v> ?2[s@@@@@,@&??????PqR$?E.?9L@@"'8:O@?s?0C`h?Zm2I?lMF?h;I@f'@@#;dZ@#+@#;dZ@#ؠ-@#;dZ@#Լj~@#;dZ@#`d@#;dZ@#т @#;dZ@#m]@;dZ@ -=@;dZ@@;dZ@~@;dZ@s@;dZ@[7@;dZ@%@8,<@8o@1kS@1a@0A7K@0 y @.Z@.+jg@.Z@.?@.Z@.sPI?{QB?_X -?c«?2UT?(+Zk?P?_$? KBI?+l??rrl?W'??i--?m!?__C?7zE?q@@@@@*@*??????P;콶?nк?D2Ne?U y@#Әl@|U6?V!?jJ*?3*?Q@ozY@@x@\@x@ I@x@ @x@q @x@˒;@x@@Tm@T@Tm@T@Tm@T[W>@Tm@T-V@Tm@T@Tm@TGE8@2 xF@2@0kS@0ka@@0M-V@0McA @.L/{K@.思IQ@.L/{K@. -L/@.L/{K@.-?^XN?h?WD@?̒? 9? ]V?bV,?5CQ?tw?-I`K?Hc?{L բD?e@'?/?m+&?ʬv?B ?L@@@@@*@*??????W[sD?"?@#~?/@{o@Tr?NB?=#e?AqW?s+@ro@@8M.@8M@8X7@8M@8@8M@8}@8M@8gqU@8M@8~@#P`A.@#P`A@#Jx@#P`A@#Fn@#P`A@#6 -&9@#P`A@#4f~+@#P`A@#/aO @9#g l.>@4.5?|@41p@37KƧ@3)k@3@5@3bSC@3@5@3N0@3@5@30ͩ@8J?CD&?ĚF ?7@?ИZN?*qh!@8J?3jR6?b8[.?vb'?S)z?@8p?;>p?mݹ?/6 4? G+?nĸ@$@*@,@;@9?????@C@C1&@C@C}Vl@C@CA@C@Cqj@C@C @C@CC,@SR@SC@SR@SMj@SR@SMj@SR@SiC@SR@S&@SR@Su!@7^H@7`bM@2ěT@2hr @1-@1t@1?䎊r@1=@1?䎊r@1=H˓@1?䎊r@1=(?^*?d̺?&t?V0O?di?1a??UbB3?$Jw"?#q\-v?A??;?Ƌ?iTC~t?~$?2 ?7?{2@@@@@,@,??????Bd(?U?JH]D?Dp@C,| -@S!Z?dNKL?aS*%?úJȄ?B @m4V@@u?|@xO@u?|@hQ]@u?|@_ +@u?|@tV@u?|@rl@u?|@xXɩ@@z@@j@@h@@گ@@W@@D@9(YJ@9 >0!X@4^5?|@4|N@3v-@3nŦ@33&@3*) @33&@3%K@33&@3#~*G?>,@?R{?L?`m? i?r7?W?!m~?0"?RGl?JGY$l?,s?P5s?(?tqN;?:k?[%?֢.@@$@&@&@4@5??????6`%?`J?P`?`I] @uڥ=@o?l^@?˟9?-~:T@lp ;@k@@TzG@S@TzG@V4@TzG@V@TzG@[S@TzG@Y~($@TzG@;=K@`ě@`A7M@`ě@VC@`ě@[v@`ě@SMj@`ě@]Vl@`ě@6}Vm@5w@@5v '@2TS@2PD@26-@21iB@1z,<@1v@1z,<@1w4m@1z,<@1_$tS?}ѿ,?Fp3?_*k??BPbC?>>? ?I}?ud?{7?J>?@ ~?=Ca? h? - l]?x2?ٔ!x?mCc4@@@@@ @??????:ZXl?e1A?e(]P@T-s@YJ't?ؠ4ۍ?7.@ EI@ -!3 kF@d̯@@v@R5~{@v.@v.@v.@v.@v.@?ڟv@? ,@?ڟv.@?ڟv.@?ڟv.@?ڟv.@?ڟv.@9^H@9`^cc@4n5?|.Mm@3-.Qn@2䎊r.NC,@2䎊r.NC,@2䎊r.NC,?)||n@8#@8:@8 >+@8I@8?i.&@8@8i@8@8S@8?mEKT@8 *@8p@8@8 @8 @?@Ghr!@G+@Ghr!@GKc@Ghr!@G\_y8@Ghr!@GN'@Ghr!@GF@Ghr!@GE@4mhr@4n}@4mhr@4n@4mhr@4p k@4mhr@4met@4mhr@4n\N@4mhr@4nC6)@5S&@5z@1^+@1^0#b@0ȓtj@0Ǻ<@08D*@07$A@08D*@06-Eg@08D*@07 - x?|b@?ݢt'?[4? ?00?sC?eXW? ]?5u?U&>/?Kf@?K t ?]nx@4{ I^.Mm@3y7KƧ.Qn@2.NC,@2.NC,@2.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @@ @@PS @@!U@@@@ e"@@xs @O@Cy@O@( @O@O@O@#@O@.@O@p@7,<@7* @3?|h@3J@2b-V@2Z/>@1_o @1騥@1_o @1(pL@1_o @1LR>?Äp? -"?@>??ڠ؞?g=a?~Ov_ح?$a?<1I?1Ge|?[Ď?B51r?dGE?&>G?!+?:?Jr 7?y@@@ @@5@5???????P:("?K]>V?XQ??J}wr@ u=@M?u[By1?m1-M?UbS?yB@i~6@@L/-@L-@L/-@L1u@L/-@L0U2a|@L/-@L3E@L/-@L/@L/-@L/rGE@Q@O@Q@RT`@Q@/W@Q@^ @Q@`A7@Q@K]@7 xF@7g l@3+ I@3@2tj@2 ѷ@1a:S@1`- r@1a:S@1_vȴ9@1a:S@1_Vϫ?q!U? #?Gk;m?a_b?yq?7F?Y͍_?tMP?ɇ}~?_3\?$) ?IH!w??4?Rw#{?l?gϵ? 1 D@@@@@@,@(??????ku\j?Pi?<{"&@L1ps4@+[?h $?_2[?6*?=[Ex@m7@@tj@3333@tj@ n@tj@(@tj@%@tj@&$/@tj@+t@ǯ-@ǽE@ǯ-@ǫq @ǯ-@Ǭ"h @ǯ-@ǥm\@ǯ-@ǚY@ǯ-@ǟH˒@8|Q@8ȭU@4Lhr@4Jݘ@3`A7@3حV@3B@5@36 -L/@3B@5@3EoiDh@3B@5@3Esg?|O?_X -?}_ܻ?"J]u?a0?s[?+-/?CO?OՃ?"|OV?a5?`u3? ?ae?!?=? # ?hvk@@@@@&@,???????s׏p+"?(s?ZYmMg@l]^@Ǩfc??MJjMP|?w|a}d??@/@j 1[@@@@@ ҈@@bM@@e+@@@@"e+@7Kƨ@V@7Kƨ@C,@7Kƨ@\)@7Kƨ@wKƧ@7Kƨ@vR<7@7Kƨ@l@8w@@8l1&y@4 hr@3o@3_-V@35Q@3 [W?@2-w1@3 [W?@2Љ'RT@3 [W?@2т ?cxt?eąւ?7'v?__z8`?eTm5[]?J"Fr?|?+M??.Oc?|g?T+)? uf?Xy1@>F=E@@>1@>@=@>1@>Ի@>1@>9@>1@>_@>1@>N|@&dZ@&BѮK%@&dZ@&P@&dZ@&U@&dZ@&H@&dZ@&;@&dZ@&-@8@9orX@35?|@3Zc@2"`@2D@2@2SsF -@2@2X r @2@2N?cD?$nba?mB?$~q?Qe,?uhҨ?pEkI?{9JgD?W)Be? c? ?4F:? |ȶ?: ?[?Z%?ەݓ?b$QߤA@@*@&@*@;@8???????nh -2?ehW ?R~| -h?pA:O@>r@&??w;-?gׁn?L?W'@kc@@y"@/@y"@xF]@y"@yb@y"@|N<@y"@z@y"@vR<5@A7L@^5?~@A7L@A \@A7L@C,@A7L@7@A7L@v@A7L@'RTa@9?@8@3S@3!.H@2`A7L@2c @2B@5@2E%F -L@2B@5@2Geں@2B@5@2F?ԭFLg4?Kb?פ?~rV?Q^F?jכb?G *ǂ?0? }>_?&v'?%3?lӪ<?LƴG?/#?6ז?a?k!l?b@@@@@*@&??????h ?Vn(I?s@y2@?f2/? ]3?O@ti;@sN%`EE@@r@rz@r@r7Kƨ@r@r%F -@r@r *@r@rۋp@r@r0@Eě@EG{@Eě@E#@Eě@E2W@Eě@E^5?}@Eě@E%@Eě@E=c@8+s@81hr @3@3b}V@3lj~#@3f@2kxF^@2ffffff@2kxF^@2heO@2kxF^@2hy=c?]?" ? -> ?QR@>\)@>R.@>R.@>R.@>R.@>R.@6g l@6Q@3|hr.Mm@2j~#.Qn@1D*.NC,@1D*.NC,@1D*.NC,?Z@8#@8:@8 >+@8I@8?Z@8@8i@8@8S@8?A@8 *@8p@8@8 @8 @?@+ܬ1'@+ba@+ܬ1'@+tH@+ܬ1'@+-@+ܬ1'@+!u/@+ܬ1'@+xE@+ܬ1'@+]!@#Q@#%e@#Q@#Tg@#Q@#ᒖË@#Q@$ط@#Q@#c@#Q@# @8*0U2b@83@4)+ @3U靪@3-V@3"s@2sh@2f+e@2sh@2y{2@2sh@2z)$?+?Xf[?}q㉽a?-v<?'ݘ? -U[?ɏ?#P5a?J? p@fQ@f-@fQ@f1@fQ@f7@fQ@fěT@fQ@f?@fQ@fe@5uS&@5vR<6@1O|hr@1N"@0`A7L@0Q@/N;6@/4J@/N;6@/u!S@/N;6@/iB?|O?Qd?K]4?'$5? f?װ*w?1j?HG^?qN?ihA?ߒh!?ȏ'T??2]7?W ?E 8?j?ܑ @@@@@$@(??????1"II?LCg?+C8@@{Б@f?4T?L{:*?v>8? @g@)!T@@`A7L@`n@`A7L@a@`A7L@an.@`A7L@a|Q@`A7L@` qv@`A7L@`҈@1-V@13333@1-V@1䎊r@1-V@1 @1-V@1ݗ*@1-V@1W@1-V@1T`e@6H@6M:@1 I^5@1ae@0y7KƧ@0xp:~@.p'RU@.oo@.p'RU@.ov_ح@.p'RU@.oo?|O?h?"?XQ>?u?}?Br?HG^?ܿ?Uh;?<U?eDQQ?zny-g?򯞼?]? ??b/bJ?M684@@@@@,@$???????N ?P?#(?#P9@`@1ԣ?DKE-?CRqE} !?l-L?Lo@h-9@@@C)@@q@@q@@EHu@@,1@@ᆸ>@p -@BZd@p -@Ƭ@p -@kc@p -@M%@p -@d'k0@p -@gPw@5mv@5m, -@1hr @1l@0Ƨ@0i0X'@0sh@0rJ@0sh@0fyT@0sh@01#%H?}x:@-@(@-@_@-@@-@>뽕@\)@\)@\)@4"_@\)@C@\)@Y@\)@_@\)@/q@0|Q@0|Q@)=-V@)>J@) 1&y@) B@'Ǔݗ,@'ʤR@'Ǔݗ,@'0>@'Ǔݗ,@'?N?^6P?6? Y`? :?/?x?bMn?x?t0^[?-(?_$^?N@?bw' g?ʙ ?zb?V?󰉋?y Y@@(@,@$@;@:??????(ϊ(?1:?V|?)r{A@YM@f?+zÁZQ'?8?Wx$?H쮽?B~)ߛ@"@VH?b؄f)?gm="[?Zl?rdJ@m9X@@߾vȴ@vȴ9@߾vȴ@- @߾vȴ@$/@߾vȴ@- @߾vȴ@ސ$@߾vȴ@vȴ9@9"@9"@9"@:G@9"@:^5?|@9"@:)y@9"@:xl"h@9"@:6@4 xF@4e@/j~"@/~$@.NO;@.NVv@-QN;6@-Q-@-QN;6@-QN;5@-QN;6@-Q ?2}?Z.?4!p?8v?g?i{? ?4 "?['?/Z?2&h?sx/T?nwf?.x?q6tN7?XLL?pk Y?pk Y@@@@@(@*???????_Gz?+?3_['?ъ:H@@:G?S>> u?"b?P!>݋I?OY -@h,l,@@Dt@DZ@Dt@D7@Dt@D/@Dt@Eݘ@Dt@C9@Dt@C@pj~@pj~@pj~@p2a|@pj~@p3@pj~@p3@pj~@pg @pj~@pݗ,@204m8@20A7K@/"@/Fs@.;dZ@.+@.3PH@.1hr @.3PH@.-C\@.3PH@.-%?^Dw'?m0?.Ү_ ?q@v? -?ʟ?bM_x?)[+@8I@8?i$@8@8i@8@8S@8? J@8 *@8p@8@8 @8 @?@+R@33333@+R@4@+R@9XbN@+R@`hۋ@+R@l"@+R@vR<6@Xtj@V+ @Xtj@i'RT@Xtj@P`A@Xtj@g&@Xtj@L>C@Xtj@C @7s@7^@5sS@5sn.2@47KƧ@4@4D*@4g -@4D*@4l!.@4D*@45Xy>?}ѿ,?d_s?^RpX?ׇlg?I]mB?:7{*J?oC?{c?低NU?]~ɺ@?>5?{$@?WA?Us?wX?'D[?R͐?8$ -"@@@@@*@*???????Hc@XJL?b?jdx@H@U?xNX(&?3H?Ņp?4K?@`Ql;@@]3E.@]3E.@]3E.@]3E.@]3E.@]3E.@>0 -=.@>0 -=.@>0 -=.@>0 -=.@>0 -=.@>0 -=.@9=:.>@4I^5?.Mm@3~Vu.Qn@3._o .NC,@3._o .NC,@3._o .NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @bM@Q @bM@@bM@=@bM@~@bM@u@bM@@+ J@,NE%@+ J@iG@+ J@2ي@+ J@Fs@+ J@4R!@+ J@d. @204m8@20Fd@.-@.>BZc@-dZ@-Ve\@.C\@.Xs@.C\@.*@.C\@.,٢?^j?]9?"QJ?B*?YY%?/z?bM7?9n?=,D?4i?v -?\ :?bQ?7c?~@\? ?"F?L@@"@"@&@6@5??????'5s]?6?|\t@<@`R?'HJ+b?%Hn?JnG ?CYt@s&)?@@ow@nV@ow@lC@ow@hr @ow@tD@ow@nU@ow@m(@bM@# -=q@bM@r@bM@r@bM@a@@bM@e@bM@4֡@8@8© @3^+@3_ @2~"@2/{J#@2&YJ@2&Fs@2&YJ@2&@2&YJ@2%m\?2}?R?y]?Z.?Xe?sU Z?+-/?yom?FH>d#?fAzQa?Vu@2Bp:@1YJ@14֡b@1YJ@1F]c@1YJ@12W?}?PƧ$?@^ڢ?Rw#z?K/?۝.Yr?% ?Hz?J?@[*^?we ?fAzQa?Nϸ? h?ZaQ?1R?q?;kr>@@@@@&@(??????K8I?9?Ic?B  e݊@J-a@gIrR?h4nR?_l?Ґ5.ؐD?Ct@kzNC@@`A@zG@`A.@`A.@`A.@`A.@`A.@$/@/@$/.@$/.@$/.@$/.@$/.@8G>6z@5T`d@4Y^5?}.Mm@3-V.Qn@3O M.NC,@3O M.NC,@3O M.NC,?Z@8#@8:@8 >+@8I@8?Z@8@8i@8@8S@8?A@8 *@8p@8@8 @8 @?@Z@Z@Z@ -L/@Z@7@Z@@Z@@Z@7K@lC@lI^5@lC@o@lC@m\@lC@lzxl@lC@n.3@lC@m\@4>6z@4&@25S@25?@1A7K@1 qu@1._o @1-ڹZ@1._o @1+A@1._o @1+lC?| ?jD?=d?],V??0&YEd?|)-}(@@@@@,@,??????:\F`?2Ӑ?:ij-?J!@@luP?Z NV=S?= >Rh?f_H?zRW@fi@@Qhr!@^vȴ9@Qhr!@;5X@Qhr!@껲m@Qhr!@ꏷ@Qhr!@ꥮ1@Qhr!@꼄@ڻdZ@$/@ڻdZ@ٙ+@ڻdZ@ l@ڻdZ@=J@ڻdZ@}H@ڻdZ@@7:@7{n@5^5?}@4{J#9@5`A7L@4+6@4O M@3+a@4O M@3\(@4O M@3?F/P5? &/d%?jl|?G> -?ˠ^㪶?B[!?ꯨT?ɖ?3AO?} ?f?Ԃ?@K/{?87?@@ ?:>-?3B?Iv:@@@@@,@*??????=@@3s@3Pab@1 ě@0e1@0Z~"@0Y~N0@0@4@0^{@0@4@0W1@0@4@0O4?|b@?H]9?)?;?ױT?(H?| V?E~*? -?0Ծ1R -?6X?x++?z*?XI?Ho??o?b/bJ@@,@&@$@7@8???????A?;q?8\[?$G72@3rv@?o?X%v1E?E}g?%}Z?" @f]d@@ -_vȴ@ -T9Xb@ -_vȴ@ -~m\@ -_vȴ@ -<@ -_vȴ@ -_p@ -_vȴ@ -wXd@ -_vȴ@ -}+l@@zI@@6Ϫ@@0D@@'@@-@@!@8%!.@8)XbM@3i+ @3P@2Vu@2_p@26z@2䎊r@26z@2Ϫ͞@26z@2cA ?E?kf?s |?U6֧??d4?=fA:?0o>?7ȑC@ěT@hr!@ěT@`[7@ěT@M:@ěT@{J#:@ěT@_@ěT@*0U@8S&@8ݗ,@5r Ĝ@6Vȴ9X@5`A7L@6tm8@4iB@6wkQ@4iB@5ݘ@4iB@5-K]?}?Z:1?jk?,J 3?Lj!_?{='"?1+@8I@8?qn@8@8i@8@8S@8?+:@8 *@8p@8@8 @8 @?@["`@[S@["`@X4֡@["`@ZkP@["`@Y_p@["`@YJ@["`@V}Vm@׊o@׊~#@׊o@׉Dg8@׊o@׊#9@׊o@׋҉@׊o@׎H@׊o@׏hی@4 ]ce@4 VϪ@0 ě@0u!R@0Z~"@0T7@/Ǔݗ,@/"`A@/Ǔݗ,@/쿱[X@/Ǔݗ,@/쿱[X?Ol?: ?\H{gX?U*?>C,4?[2?|O?uie?{d??8?kj?|׿pY?2]7?v'X?WhQ?OO ?0&YEd@@@@@,@,??????Kj?LR?&hy?!(|@Y#@׌TC?F,Fi?B S ?zrt?-@gZ@@Z1@qm@Z1@YA={@Z1@]/}:l@Z1@_i@Z1@Z(@Z1@Y%@ۓ@ۛe@ۓ@۔͒@ۓ@۔V:[@ۓ@ەPs@ۓ@ۖ2H@ۓ@ە@7]ce@7ưj~@1+@1$0@0Z~"@0YpC a@0kxF^@0iyV )x@0kxF^@0h*}@0kxF^@0h(u??'P?aas?{?} -?h?sky?\l6? 71R -?Z5k?p[!B?I}/@?V?Lu۳?ɯnR\?{z&U?!!?5 Y?@@"@ @$@2@2??????b$>.Td?AAJ?>,Za@_Umy@ۖ?Xq9?\ԭ?7!D?@n+M@@U7@U8bM@U7@U8V@U7@U8;@U7@U8@U7@U7Xf@U7@U7*o@1@^5?}@1@;X,@1@*@1@fp@1@FSs@1@vJ@1}:@1}:@."`@.Uzz@.]E@.]+@-uXy=@-uA@-uXy=@-u'@-uXy=@-u?`BcM?}p[?-5AԘ??מ?1Y?bQ넛?,2?k?3]Z@?Mm/?Sܻ_j?bM]f?v??:k?7b ?1C@@*@(@$@5@9??????9|>o?,_\ ? f@U700@a$]?"^2űe?6?@`c,?0!kNQK@sK @@D.O@D'sy@D.O@D#n.@D.O@CbM@D.O@CdZ@D.O@DR<@D.O@C;dZ@r=p@r}: -@r=p@r@r=p@rz)y@r=p@riB@r=p@r`A7@r=p@s+6z@7w@@7nTb@5+S@5" ě@4-V@4u!@48D*@4t@48D*@4*u%F @48D*@4Q?}x@-tj@-{ӷ1#@- 1&y@-ɮ @,#Z@,/3v@,#Z@+)ވx@,#Z@,1S߬@8 -Ny?*)H?9?? -0B?ͭ?Ugsj@82r?pU_?gb?>!u?ǖ&?Qc@8L?%?1QcFe?P0?]^? Gg@$@$@$@9@3?????@eS@mhL@eS@ʚ[07@eS@c2P@eS@[k:@eS@[@eS@[8 @*$@*M[@*$@*@*$@*_@*$@*G={@*$@*Ɋn@*$@*J@9|Q@9_.@3|hr@3H+tw@3Z~"@3,aQ@2Tm8@2FAa@2Tm8@2C]p@2Tm8@2C9rJֳ?]?vK?>?]:VE?}?T?t D5?q A'?lH?Q)\?r|2?<`?6;:?^5? -V??с:?<@@"@(@$@:@9??????lP^?[ ?Ih#L@^_@*n?u(h?fJ0β?ho??íak@l=6@@bM@O@bM@ @bM@̠qo@bM@"c@bM@@Gt@bM@ƈz@@n@?EjGw@@n@@'@@n@??B6?u=?B&Y?¨Sp*?h +?vnA? tEz?N s?Z!}?Tʼ=?޵?o0_+*?Q?*?zw@@*@"@(@4@8??????R(4N[T?8B ?@;@C(@:LW?Wcmn?ahM?T=yr?p؏3|@hX(<@@z}E.@z}E@zce@z}E@z@z}E@z&@z}E@zd9K@z}E@z~bb@#S.@#S@kP@#S@(@#S@ح@#S@ĭ7c3@#S@VBD:@9e!..>@4=hr @46@3U`A7L@3LC@2̿[W?@2 '0@2̿[W?@36L@2̿[W?@2ǒ0d@8J?W@!?RsW?rՉ?P?t -@8J?@׮?HD?} ?NJ@?,3vF@8p? AI??Cx%?u ?$rMy@@@@.@,?????@el1&@e|(@el1&@eP'R@el1&@eN;@el1&@eS|@el1&@e*@el1&@evz@MO;d@49Xb@MO;d@!-w@MO;d@YY@MO;d@:v@MO;d@*0U@MO;d@_o @9DO M@9XU@5Y^5?}@51&x@4dj~@4"@4n@3䎊r@3:S@3䎊r@3u@3䎊r@3?xDž??"6$?ޝ)?aMz?$@?? Z ?*J?c??1Tn]?s_Nr?X?Cy^?tu?F?0?a#@@@@@,@&???????<?|/}?}7"?N"911@erv @Xe?b/G?qwH ?xe@0CX='@lz@@q I^@q~#@q I^.@q I^.@q I^.@q I^.@q I^.@P=E@P1hr @P=E.@P=E.@P=E.@P=E.@P=E.@8įO M@8ڙ0 @4sS.Mm@3t.Qn@3O M.NC,@3O M.NC,@3O M.NC,?ڷ@8#@8:@8 >+@8I@8?W$B@8@8i@8@8S@8?S~6d)@8 *@8p@8@8 @8 @?@g\)@g"`@g\)@gv_خ@g\)@g3@g\)@g@@g\)@gDg8@g\)@grG@mhr@s3333@mhr@.@mhr@Jn@mhr@cw@mhr@oiE@mhr@N;@4۹~($@4xl"h -@1ٺ^5?}@1@1`A7L@0v@0G k@03&@0G k@00bM@0G k@02&IR?)T?ly?9t3C?+?>C,4?y+PYu?q!U?Ғ](+??jSV?hY?u*,?hU?~d?l~J?XW~?՘]G?j]@@@@@*@,???????jМ$?fpۂX?FiSs>?bgnW@g@{mt?g^?6rl? ?_ c@jq@@Vp ě@Vs@Vp ě@Vuᰊ@Vp ě@Vu\*@Vp ě@Vrs@Vp ě@Vr@Vp ě@Vq2W@P I@P~#@P I@Pu%@P I@P0U2a@P I@P=p@P I@Pqi@P I@Pq @6䎊@6D@2|hr@2̑N;@2."@2+C%@1!.I@1oi@1!.I@1C-@1!.I@18}H?2}?? -NA3?{y6?_?4?| ?&uE@?J?.}$??EBl?:t?f)"?z"2)?Yʒ?t? ;8@@@@@,@*??????=oe?Y>산M?/ ع@Vr4@P? -G?@wl -?!,Y=M[?ť@g7H~@@(@x@(@@(@H@(@\M@(@m@(@1@Q@n@Q@wk@Q@$/@Q@n.@Q@@Q@nO@8%!.@8$oh@3+ @3G{@2Ƨ@2]ce@2@5@2@4@2@5@2yb~@2@5@2C\?X?P1"? \?(tUo?zcR?tٵ?}ѿ,?I1@?ؼZ?'G_J?U?/Ւ?Rw#z?2}?[?1(w?p& ?F-@@@@@,@*??????t?BX?T7a?asaYE@CP(@iL~?l6Sl?ew!?;gN?`NZ+@i㹎\@@`A.@`A@ (@`A@;lD@`A@@`A@\@`A@s@#S.@#S@b}V@#S@ae@#S@sP@#S@Np:@#S@c@8~($.>@3ԛS@3kS@2V@2W+@2=ce@1O;dZ@2=ce@11&y@2=ce@2҈q@8J?6?\.?&L=?[XLj?02ۼ@8J?kI}?QaQޚ??f?ʏ-a)@8p?,,? }P!? -K?Oނ?"@@@@,@*?????@x@x@x.@x.@x.@x.@x.@B\(@A7K@B\(.@B\(.@B\(.@B\(.@B\(.@1H@1蛋5@0\I^5@.Mm@0>Vu.Qn@/-w1.NC,@/-w1.NC,@/-w1.NC,?^(ǭ@8#@8:@8 >+@8I@8?bM/^@8@8i@8@8S@8?ngH29@8 *@8p@8@8 @8 @ ?@ -o@S@ -o@ -0@ -o@ -0@ -o@$t@ -o@@ -o@{J#:@$bM@$nP@$bM@$bM@$bM@$׍O<@$bM@$@$bM@$e-@$bM@$.H@9>6z@8:)z@4J^5?|@4I>BZ@3"@3ߤ@@2_o @2Q@2_o @2)y@2_o @2K]c? x??/͔p?\GyƩ?ťBW?;?)T?{UO?Kd+??r=w?x7f?K? *sr?T?R ?ٔ!x? N@@@@@*@&??????pB?X,8?cra@?\lU@ -}z@$ӝ?utꍳ?G?Z}%n?6@o:@@8Q@8Q@8Q@9\h@8Q@9Y @8Q@;s@8Q@6- @8Q@8eC@8g-@8g-@8g-@8h@8g-@8j&_h@8g-@8i-^@8g-@8i]@8g-@8ky@2#g l@2#g l@.8tj@.7E@.*n@.&&V39@-ۋq @-Z@-ۋq @-@-ۋq @-X^?^6P?i<?OG ?o?h?޳E49?bMn? vL? "?$? ?~d?bQ?>?}.?j1s=?(Y?X^)@@@(@*@*@5@8??????IP- ?O^?@W?4˜@9)6f@8i5K?Odħ.?B2􈾇?$_D?~AF@s ҙO6@@\).@\)@/@\)@@\)@fl@\)@h@J@\)@/}@G{.@G{@`\J @G{@$qy@G{@*%@G{@@G{@ǯe H@7.>@3+ J@3 M@3`A7@3'|Q5@2[W?@28mR@2[W?@2CGV@2[W?@2}@-@8J?*}v?0xO?>N?AÓ ?HF@8J??uP;?Kȕ?#PG?^Y:p@8p?ݵ#U?ġ͜+?^כ',?8V?>|O@2@?Hߏ#,?)*FT?'ۂ?Z"&@s @@33333@3@33333.@33333.@33333.@33333.@33333.@@I^5@@hr!@@I^5.@@I^5.@@I^5.@@I^5.@@I^5.@7e!.@7gmt@3r Ĝ.Mm@3 -V.Qn@2.NC,@2.NC,@2.NC,?|h2:@8#@8:@8 >+@8I@8?6]g@8@8i@8@8S@8?Zgr@8 *@8p@8@8 @8 @?@|hs@L@|hs@~]@|hs@v@|hs@)j@|hs@Zt@|hs@T'-K@tj@ moE@tj@6Rw@tj@!NAaw@tj@g@tj@@tj@@74m8@7RR@3+ I@3h k@27KƧ@2+@2z,<@2}0Q@2z,<@2}@2z,<@2~RQ44?ؗ?̏+? T?oc?YB2?B?՘b2?~?}T9I?4Kvx?|;*?PO W? 93?m-?|6}>?iY^#?2T?>!@@&@$@"@.@1??????Hn?bڡc?W-?V8rN@#?@2?rqFn?rdFo7? 0@?S=6`p@m̥@@%;dZ@% I]@%;dZ.@%;dZ.@%;dZ.@%;dZ@%*0V@%;dZ@%-@^5?}@dZ@^5?}.@^5?}.@^5?}.@^5?}@Q@^5?}@/V@7 ]ce@7 Q@3+ .Mm@2Vu.Qn@2iB.NC,@2iB@24m@2iB@2r?Y͍_@8#@8:@8 >+?K/?tY+9>?)T@8@8i@8?dLt?fD?a -@8 *@8p@8?屘?l@@ @(???@C@nO@C@2X@C@[@C@1@C@2@C@jg@7K@lI^5@7K@҈@7K@|@7K@IQ@7K@-V@7K@=K@7Y*0U2b@6A7K@2 ě@2{6z@2 -V@2 "`@1._o @1*ݘ@1._o @1)Y}@1._o @1*vȴ?n(t?S_?-a} ?1?7?ZzZ?H6m?{UO?pI?\d?ۣ0Υ?fAzQa?ꎤR?u/X?v'X?ڠu?v> ?*Z}@@@@@*@(??????UNeQ?_-&_?_w=e"@w@"C(?n|g?mu[?ѿȡ9?5Ta@sB. @@/`A@/`A@/`A@/em@/`A@/C -@/`A@/N2@/`A@/@/`A@/oQV@H;dZ@H;dZ@H;dZ@Hhah@H;dZ@Hg@H;dZ@H):O7@H;dZ@H(ʕV@H;dZ@HnȞ@4v@4_o @1^5?|@1/Hxq@0tj@03 -z|@0Q@0 '@0Q@0;@0Q@0("wk?|b@?i?r?z? Ge?׎L'y#?|h2:?FD?|"x?5?x_Oq?ЎVN??Dޣx?9]?2E?=`XD@3 ě@3fL/{J@3 "`B@2s@2+xF^@2 ě@2+xF^@20L`}@2+xF^@2՘4>b?W? U?/_ ?j}? t4gU?j5?| V?/v\?.U?jV^?^J+?`Lk.?8@?! 6?"x?]C޶? ș?нMrѶ@@@@@7@7??????k`j?OjGP?b@v@Q?a<@lز@ ?eH?~n2??@b~@@hs@/@hs@<63@hs@-V@hs@[8@hs@q @hs@<64@-V@-V@-V@<6@-V@`A@-V@*2@-V@䎊r@-V@3333@4 xF@4|@2i+ @2hYJ@1-V@1As@0𖻘@0hۋ@0𖻘@0'RT`@0𖻘@0;dZ?|O?sp? ??Q@4|hr.Mm@4`A7L.Qn@3ޫ6z.NC,@3ޫ6z.NC,@3ޫ6z.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @0 ě@/-@0 ě@14J@0 ě@0 ě@0 ě@0oiDg@0 ě@.}Vl@0 ě@.p:@۞Q@۟;dZ@۞Q@۟$tT@۞Q@۟[W?@۞Q@۟;dZ@۞Q@۞ߤ?@۞Q@۟b}@4@4a|R@/~"@/ @.rnO@.qR@,N;6@,&x@,N;6@,N;5@,N;6@,4J? ? ?1f_?<ݝ?E{??}ѿ,? g?A)N?e[|(?M@k @@\(@i*w@\(@׍#[@\(@d@\(@ĜBo@\(@׷[]@\(@׻XփA@&ffff@0@E@&ffff@7 @&ffff@5U>@&ffff@'g@&ffff@*@&ffff@3`q3@8@8P׌@3^5?|@3@3A7K@3 ps@2O M@29jė@2O M@2`,u@2O M@2 7L?%h?ZuD?)s??f94Iݍ?̝`? MV{?U:?ehd?,=?kn?h?:?%f+??v#w)?_3+z? a@@@"@$@3@.??????x,;@?R5Z?[\-'+y?a+rf˦@sow@-?uNw?z('? ?5z@nռNg@@h-V@h,@h-V@h+~)@h-V@h.O@h-V@h0 ě@h-V@h/V@h-V@h/@@O;d@@c -@@ͫU@@@@\(@@iC@5[~($@5[~($@2hr @2zxl"@2`A7L@2e@1sh@18YK@1sh@17@1sh@1g l?|O?[ꕠ?IK?Rw#z?Xe?=W?}ѿ,?f?+l?RLnj? ?C ;?Z?7'nK?ԇ:?=rE=?*Z}?OO @@@@@"@$???????HBF?Jq8?,W?'._@h.k@˞f?Lhd,?Gfp$)q?B\g!?A@fA@@ɺ^5?@Z]@ɺ^5?@Q%7@ɺ^5?@n@ɺ^5?@΂3 -@ɺ^5?@ϲ @ɺ^5?@ˊi۴@+@S1@+@Ƨ8d@+@<:@+@ɶaN@+@=@+@@6|Q@6Wv@3#@3'm1x@2tj~@2m߸ @2iB@2@2iB@2S@2iB@2^{?ڡG7Fo?Q+6͖;@k@٧?bvݴj?qn9'l?/o?>GX@i29#@@A7L@ě@A7L@IQ@A7L@u"@A7L@u"@A7L@@N@A7L@@k I@k I@k I@lI^5@k I@i^ @k I@k6z@k I@jqi@k I@iᰉ@4 xF@4tj@0 I^@0L/{@0,j~#@0,1&@/Ǔݗ,@/&J@/Ǔݗ,@/7Kƨ@/Ǔݗ,@/R<6?| ?ߋb?=?Kh?!#?ZAj?9?}ѿ,?NۈV(?:?,~[?a -/ ?m/wBZ@2}:@2}K]@1 ě@14J@/O;dZ@/p:~@0~($ @04J@0~($ @0!.@0~($ @0l!.?^Q5P?E?>\?^t -a?oNŒ+? d?bM_x?)[ -fu?3j_?#X?2}?.8 -B?/Y}?,DP?̈- -?rsw??\? Ğ@@@@@$@*??????BWRN?``A?7b~@9:x*@?;eH _C?XYb?zJ.IR?@hC9=@@5w@5O@5w@5o@5w@5V@5w@5@5w@5.4@5w@5zH@XbM@Y@XbM@W -=p@XbM@WXe@XbM@WXe@XbM@WsP@XbM@V@1w@@1wKƧ@+WKƧ@+WXe,@* 1&y@* 1&z@(@(@(@(حW@(@(䎊r?^XN?[evȴ8@7@6E@7@_o@7@MB@7@R{@7@UfA@33333@.O@33333@1&x@33333@ /{J@33333@@4m@33333@IDg8@33333@? @7uS&@7t9XbN@4^5?}@4Vt@4tj~@4u"@4\Q@4xD*@4\Q@4I^5@@4\Q@4\Np?8YV?v0{Gt?v󒇾O@^0Wp@w6?+o??)?R%{A?M; Y@szl@@+@bM@+.@+.@+.@+.@+.@+ @r-V@+ .@+ .@+ .@+ .@+ .@7O M@6t-N@5uS.Mm@4`A.Qn@4!.I.NC,@4!.I.NC,@4!.I.NC,?"r+@8#@8:@8 >+@8I@8?SV-@8@8i@8@8S@8? @8 *@8p@8@8 @8 @?@Q@\)@Q@;g@Q@𸵜@Q@tw[@Q@ore@Q@ y@}Gz@}A7K@}Gz@}VZ3(@}Gz@}K>%@}Gz@}aGA @}Gz@}O[A*@}Gz@}O0@7]ce@7[X?@33S@33z{@2`A7@2n@@2:,<@2=؆@2:,<@2=@2:,<@2=$0w?~?J?`?) ?~?OmI?\op?anñ?YA?EE/J?-I?H?KC?ކf?Y ?݊X#?QB?]і? t_@@"@"@ @9@6???????i?`ЍtT?0=Mu?XHWq@K@}L=?P. T?y*bUi?@x?QZ@g]'@@S@u@S@.1@S@/A@S@+a@S@"@S@&Ft@1l@1+ @1l@1@1l@1Q@1l@1~(@1l@1D@1l@1b@8p4m8@8TO M@5uS@5~1@56-@5,쿱[@4YJ@5,@4YJ@4@4YJ@4u%F ?+-/?1< ?ӳ?3҃?S?sY(?UbB2?n4?E%:9?,#t?+ҥ/?n]3m?$*a?.9؇8?'w?~lVo?)Ũ(?S@Gd@@@@@(@(???????q<?cW_?a(˞?h\z1@"+s@1)Vu@1={@0ush@0utn) -@0ush@0tZN@0ush@0v5 ?}#?!ԡ.?L߉N2;?0Ѐ?-'i?rC?|h2:?tFLj?41?%=|V?%qߌ?Vz@@$@ @@4@5??????JL?Qs*Μ?0Esɀ?& @_J@((?P!x?GTV?nL;u? -z -ƧL@f];?@@νp -.@νp -@5Y@νp -@γ@νp -@D@νp -@{S@νp -@O @.@@lC@@⩓ @@@@ [@@֡a@6|Q.>@4J^5?|@42-V@3"`@3 M;@3p@3gXe,@3p@3%!.@3p@3-(@8J?# ?H,K?C˗?Ѷĥ?Hg@8J?f\?`R*?=,܄o?ÇޞE? P7L@8p?f?-t*z?+&*?w, ?=i@@@@*@(?????@Qhr!.@Qhr!.@Qhr!.@Qhr!.@Qhr!.@Qhr!.@ -=p.@ -=p.@ -=p.@ -=p.@ -=p.@ -=p.@7Y*0U2b.>@3{ I^.Mm@2-.Qn@2m8.NC,@2m8.NC,@2m8.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @vȴ9X@xQ @vȴ9X@}+k@vȴ9X@jڹZ@vȴ9X@c -=r@vȴ9X@m@vȴ9X@kt@kQ@k-@kQ@kD@kQ@ksP@kQ@k@kQ@k:)z@kQ@k~@6>6z@6F -L/@3hr @3qi@3"`B@3~ѷY@3:,<@33$@3:,<@3..2@3:,<@3*Y? ?J6|?zI#?~mW?r?AŸ?1j?N`?L?3F0?02\?'Ln?m+&?їJ?9@0w?-;d'?\,!?8@@@@@&@&??????q"@h?`35t?`V (?Z[O@pGS|@k -(?|F?vt?QH?qۊ|@c @ @-@E@-@Ov_@-@@-@Mj@-@?@-@ I@ ;lD@ 6ȴ9X@ ;lD@ NV@ ;lD@ {qu"@ ;lD@ X4֡@ ;lD@ -@ ;lD@ 9_o@7쿱[X@7,@45?|@4q @4tj~@4@3m8@3\(@3m8@4Dm8@3m8@3N;6?}?+>? J?ׂa?t#V?/U?+-/?%@&??v?). ?mݪҏ?tFI?&Z#?u?Hh'$?eT?A&@@@@@(@*??????vj?p_?i>:?W85@߹H3@ JBŃ?+L??Ĺ+?Y`,@*?f%H}@fFW - -@ -@P`A@Qm@P`A@R9dfp@P`A@P{@P`A@Q07@P`A@OK?@P`A@Og@v@ -@v@S@v@j@v@\@v@F@v@f`m@304m8@30'RT`@0!hr @0![?@.;dZ@.@-@-deg@-@-ֻC@-@-e?}#?eqa? 5C? -F[?af?Kq?| V?Cl?&٣v?8f_w?bzF?^p_?tw{O?z+?siH4?-B\@.J#9@.J#9@)`A7K@)zxl"h@'n@'IQ@'a-w1@'aGz@'a-w1@'aGz@'a-w1@'a:T?^Q5P?ƿ57?S?MA8?tKc?tJ?bM_x? g?90?7枪y7?@?L "?b@?8 T?oܧ?avh?O|q?#.@@@@@&@(??????R d?Bȹ ^@@y?Gl´a>f 6?4C>Vp@s nY:O@ @,ݲ-V@,/@,ݲ-V@,6@,ݲ-V@,*1@,ݲ-V@,hۋ@,ݲ-V@,W>7@,ݲ-V@,C,y@\1'@]-V@\1'@g)_@\1'@cS@\1'@mV@\1'@a@N@\1'@\PH@5I_o@5IrGE8@2\I^5?@2Z,<@1Vu@1b}@1YJ@1j~@1YJ@1)^@1YJ@16D?| ?4$?K̃?wX/8?o?P? d? ?`5a?1ک! ?鐮|o??|?qn?oM?O?C?l? .0v@@@@@*@*?????? @?AS=d?A΁?UerhO@,\\@_ZA@?a?(J?u>@?[_?'.}\@f7!A9@ @Q9Xb@Q1&y@Q9Xb@Q8@Q9Xb@Qu@Q9Xb@Qg @Q9Xb@QjO@Q9Xb@Q:@1&y@⺟v@1&y@Ft@1&y@u@1&y@⽥@1&y@ⵁ$@1&y@ƀIR@8_o@8ѷ@4^+@4aTɅo@4-V@4'sPH@3G k@3GkP@3G k@3Dm8@3G k@3EF -L0?W$B?vucʲ?XY?'Z?PU`?"eM?ٛP?b?Et?xH +?. ?X2$?]'Y?Ǚs?Az?LO?uЍ?? S @@@@@*@,??????/2A?\%<?ZΈ?b{}b@Q1ψ@?{?sDsY?xl?=Md?hs@o0z@@LI^5@GlC@LI^5@LI^5@LI^5@J=p@LI^5@MU@LI^5@JE@LI^5@L_@2-@2j~@2-@2rGF@2-@2jN@2-@2|@2-@2iDg9@2-@2V@8䎊@7C,@3ěT@3hr @2"@2T@1@1=b@1@1ѷ@1@17KƧ?Q,Ě?tH?UGNA?J?j?J8Ȟ?Ol? Du?$z¾6??.<?:i?]?vv?̈- -?Z?:1?Y͍_@@@@@(@(???????#q@RS>?LNJt?.V,@LBm"@2i1?al9'}?R9Y {y?G4q?t `@o_S!A@@ETF@EU$@ETF@EUϪ͠@ETF@EU$@ETF@EUϪ͞@ETF@ES@ETF@ES*0U@bM@&x@bM@{@bM@ -=@bM@^@bM@hr!@bM@{@4(YJ@4(eO@0 ě@0- r@/n@/7Kƨ@.a-w1@.aohی@.a-w1@.aGz@.a-w1@.aGz? ?&H?*"AaI?ݶ\r?Dj?2I/?|O? C`?A)N?]bn~?6z@4FLy@0hr @0pz 8@0Htj@0H8%%@/@/89@/@/)ە@/@/z?}#?'Ќ?3W?%?B?ױT?V?|h2:?1(?ty!?7ݸ?\C)?sc)?oEP}?l< ?x?k?4?6@@@ @ @5@3?????? -fu?[RKE??~?A_f;j?jx?sq#?z_? ??:1?v> @@@@@*@*??????J:j]zq?NDKRs?@:~@^L@Tnn?g?V5?V7qF,?cx -e@nGc@@D49Xb@D3@D49Xb@D4Fs@D49Xb@D4D@D49Xb@D4SM@D49Xb@D33333@D49Xb@D3@\Q@\Q@\Q@\a@P@\Q@\&I@\Q@\n@\Q@\Zc @\Q@\t@25S&@25Vl!@/A7K@/o@/n@/@.`d@.!-w2@.`d@.zG@.`d@.3?^T?&H?`כm?8v?Dj??bMn? ,C?tJ?3.Yl?ߒh!?eDQQ?bM?3;?W ?':?L?L@@@@@,@&??????:,?(?,Č_?->{!6@D3W@\&?;ٹVlB?;?q"LQ?q~a@s\9@@.s3333@.r@.s3333@.tG@.s3333@.u\)@.s3333@.s @.s3333@.rT`e@.s3333@.qiD@lD@S@lD@\O@lD@ (@lD@@lD@]cf@lD@쿱@4r@4r䎊q@0@0g@0Htj@0H)@/ۋq @/B@/ۋq @/~"@/ۋq @/L/|?|O?ߋb?e -!C?#t?? µ?1j?7fps?'Q?@Cv?M?rJs?2Y0?Q? SF?^ a?ˢL1?fW@@@@@(@,??????$?(3?/.(x>j$ c@.r@ p?6,sto?v3q?z%?@]kh$@g5@@ަffff@ޡ7K@ަffff@ޭB@ަffff@ޥں@ަffff@ޥn@ަffff@ޤT@ަffff@ާE85@"@Q@"@âw@"@Ov_@"@@"@"@"@|h@7v@7@2W+ I@2W8}H@1lj~#@1l1&@0s&@0ra|R@0s&@0rGE85@0s&@0r@4n?|O?*? -NA3?̒?V^?-tT?ꯨT?Eo&?t(?-I`K?77?ٶm?:-?7-R?@A?k,?2[s? Ğ@@@@@(@"???????RADL&Q?0յg?>KMU9@ޥ!@D8?T>`?T @?B WA?,;1@nvr.k@@~"@j~@~"@yF@~"@O.+@~"@&'A@~"@A:@~"@@ǺG@ǬI^5@ǺG@Ǜl@ǺG@ǃU@ǺG@ǀx@ǺG@ l@ǺG@t/=~=@8cg l@8[_O@4 I^5@38@3W-@3*b@3^6z@2 -8|@3^6z@2@5@3^6z@2jD)o?}IT?BGt?r8M?!\|?!?i~r0)?>;f_?'Yb! ?~6gL?۪b?̳r`f?u{0? ?Sp?hbE?{⹭?G[]=?xŪ?ˎYx@@@ @ @$@&???????zBo Հ?aF//?t@Om@lj)f?*˹Z??t"z? S@mGh%J@@XbN@"@XbN@n/@XbN@Y@XbN@"@XbN@74@XbN@}H@A7L@ě@A7L@G{@A7L@n@A7L@@A7L@e+@A7L@@3쿱[X@31@0r Ĝ@0&@0`A@0GE85@/Ǔݗ,@/ƚ,<@/Ǔݗ,@/TɅ@/Ǔݗ,@/?}ѿ,??`כm? Y?V^?ʟ?| ?›?i?1'7?c3c(?äqY?/Y}? -? ݎ8?k,?ܑ ?v> @@@@@*@&??????4ի?' ?!="K?'];؟@?e@?A .S?G&z?"}e?&k -@fӺ@@@^5?}@@"@@64@@G@@-@@O;@Mu\)@M~Q@Mu\)@Mt֡a@Mu\)@Mq.@Mu\)@MsMj@Mu\)@Mva@Mu\)@MvR<6@8,<@8m\@1S@1ԕ*@1Z~"@1Xtj@0}ce@0{xF^@0}ce@0{m]@0}ce@0|wkP?sD?sI?N?Fq/b?Z)?봤G?_$?{c?^ o?"6Wz?<}?U3p? nI?Ϸ+?=Cb?GZ?:1?v> @@@@@&@&??????A|@?53N?T1 @@+@Mu^-x?U'tec$?iy4+?FR?@p9Znf@@2߾vȴ@2㕁$@2߾vȴ@2@2߾vȴ@2Q@2߾vȴ@2?@2߾vȴ@2ݿH@2߾vȴ@2ݗ+k@:KƧ@:v@:KƧ@:/@:KƧ@:~6z@:KƧ@:@:KƧ@:@:KƧ@:7@6@6- r@2° ě@2@2tj@2nu@1iB@1oh@1iB@1 rGE8@1iB@1tSN?Y͍_?5{#?S_??o]އ&?k!e?yW?Y͍_?ߤ??Wz>?1} ?i ?,?e -,?. ?~tZE?C)?]O8?՘]G?Qh@@@@@@,@&??????_'v&A?W0l_+6?d'ʙOl@2ޯz@:ЁP?un ??E['?tص1@eY@@֊=p@֍O;d@֊=p@֔F@֊=p@֍@֊=p@֓g @֊=p@֛@֊=p@֝!.I@vQ@vS@vQ@vz@vQ@v'@vQ@vFt@vQ@v4m@vQ@v@7쿱[X@7u@3@3ӯ@36-@3)7KƧ@2ce@2\)@2ce@2r Ĝ@2ce@2lC?UbB2??p`@i?rӂ?d8?J"Fr?I~y?bH=?ml???Vc?1_A?^&?=!K???YAd?q?°R=@@@@@,@,???????j}L -?A+U{5?P+'c@֕H1^@vq1?aA?q, Y?C=?ܾ@ihi@@U I^@UG{@U I^@UG{@U I^@UbM@U I^@Uhr@U I^@Ui'RT@U I^@UFs@.7K@.@.7K@.0H@.7K@.M(@.7K@.LI^5@.7K@/g@.7K@/oiD@6v@63@2@ ě@2+xF]@1tj@1ۋq @1Tm8@1.Ov_خ@1Tm8@1[@1Tm8@1u%F -?K{O?>? ?%\?B?c>?A[F?z?Y8쪒?GLD3??ϛھ2?]?\{?,?ob4U? )D)? -e@@@@@(@(??????p?GW%?tj '8?Y "@U$"9@.;? -7?\?m:Գ@@cm.):@@ߚ1@ߖE@ߚ1@ߚ~@ߚ1@ߛ"`@ߚ1@ߛ6@ߚ1@ߙrGE8@ߚ1@ߘ-@=ix@=kC@=ix@=il@=ix@=iQ@=ix@=i @=ix@=i@=ix@=i @2,<@2S&@-kƧ@-kq @,rnO@,rGE85@,Ǔݗ,@,)^@,Ǔݗ,@,Fs@,Ǔݗ,@,dž&?q0$?{j?*"AaI?q@v?<`.?^?cE?)[D? $gv?O?7N?*Q?l@@@@@(@(???????R`W~$P?53@?2II/@a!F?@}?T>?Rv?&x?gA_@eOm@@I.@I.@I.@I.@I.@I.@hs.@hs.@hs.@hs.@hs.@hs.@7qiC.>@4hr .Mm@5t.Qn@4Eu.NC,@4Eu.NC,@4Eu.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8  @n@n@n.@n.@n.@n@5Y}@n@҉@vtj@vtj@vtj.@vtj.@vtj.@vtj@w7Xe@vtj@vz,<@4?@4?;dZ@2+ I.Mm@2Ft.Qn@1sh.NC,@1sh@1@1sh@1?}ѿ,@8#@8:@8 >+?BW29?ͬc?| @8@8i@8?" S?KE~?|׿pY@8 *@8p@8?It?Xli@@(@*???!@xQ.@xQ@tFs@xQ@x@xQ@zG@xQ@u$@xQ@tzG@DT.@DT@D7@DT@@[7@DT@C@DT@<1'@DT@>($ y@8^H.>@4@ ě@4=%@3K"`B@3KU=@2@2]cf@2@2=b@2@2~($ @8J?Az;?zeߎ?? -0ggk?U.@8J? x[{?%c~?8wxq?x9l?b+m:@8p?Q?E~?>?T?l@@@@*@(?????"@%@0ĻS@%.@%.@%.@%.@%.@.zH@9@.zH.@.zH.@.zH.@.zH.@.zH.@8!.@7?\zt@6#.Mm@5`A7.Qn@6!:S.NC,@6!:S.NC,@6!:S.NC,?ǁ)H@8#@8:@8 >+@8I@8?̈́g_H@8@8i@8@8S@8?\@@8 *@8p@8@8 @8 @?#@3333@64@3333@oiDh@3333@1'@3333@( @3333@C@3333@6-]@h\@QN;@h\@ebKe@h\@hzK@h\@eR:@h\@oz@h\@t9@8쿱[X@84,Np@3 I^5@3c%n?@3dj~@3f '@2 k@2&@\@2 k@2}N@2 k@2.f?|"<~~?ǍlJ?F:?} ?9/+W?8^?X?**?T[S+?q?,w.@@"@(@ @:@5??????s/o?}ے䮀??d/?0N|kH@@a?D7@9&2@Д.?OvF?d-]]Nݭ?2 ?WsD@f3@%@o!G{@o!7K@o!G{@o&1@o!G{@oDZ @o!G{@oQ@o!G{@odT@o!G{@oNV@@n@@@@n@@t\@@煮1@@狅R@4T xF@4Sݗ+k@1 I^5@1j~@0lj~#@0]w1@08D*@0#w@08D*@0d7@08D*@0s?|O?]fm?w? '`? t(?]GW?}ѿ,?~y?"j?7ߙ?Ti?G)g?u@>N ? ?CwB?zlh?8ϣ?F-@@@@@,@*???????{^\?x#?f2=9C@o+@r?~?r (?}EP?{^@Zzy@&@>-@>ti@>-@> 5@>-@>}V@>-@>t3@>-@>8a~@>-@>2@š@Z@š@T>@š@D5@š@sx@š@ĆQi@š@vd@6S&@6~QE@3.5?|@3/=<$n @2PA7K@2QaDWd@1@5@1Ẅ@1@5@1@>@1@5@1m˩?(?C\-?Dw?9C?U5??:(?|0pJ?REP?n,?hy?*_?L  -?u7G?8G?/|? g?bq?n+.'5@@"@(@(@:@7???????hZPN?Xѡ?C'38@>DX/@za?u ӵ?>bh ?/nxt?%OA@j~VS@'@T@TQ @T@T.KN@T@TK@T@TYf@T@TR@T@TXgK@V@`S@V@uk\W@V@珫 @V@O$1@V@,x@V@ e@7v@7灺>@3E?|h@3F;(@2Ͳ-V@2[@2V!.I@2Wq22@2V!.I@2XFM@2V!.I@2Xұd?0k?!d?A ?ɑ ? F`"?[&?r2?TT%?1 -?^?=Y?֜f};?)xr?Pk?\1k?{j9@?xj*?ˮ@@,@*@*@:@:???????Uh@aW?B~?QOyP@T㌵#@ƾ -J?]K3n?i r?=bB{N ? 2XÂ@nFLj@(@r@r+ K@r@r^ @r@r%F -L@r@q{J#;@r@rU*2@r@rl@p ě@k I@p ě@֡a@p ě@UXy>@p ě@,<@p ě@J#9@p ě@A@7 xF@7䎊@4+@4iB@5$j~@41'@4G k@4/V@4G k@4=v@4G k@48K]c? ?3q?OLï>?ϩƧ;2k?t?B?)3y?Ol?D?hT7?s?&2d??o?z."?rIs?а7DuI? V??(4՞@@@@@*@*??????~;bPܽ?y:!?r I@rm@Z I?9?~$֙@,K^!g?wg@[~;LG@)@zG@f$/@zG@q @zG@iDg8@zG@qi@zG@ qv@zG@b}@Ǵ9Xb@ǭhr@Ǵ9Xb@ǽH@Ǵ9Xb@ǶFs@Ǵ9Xb@fB@Ǵ9Xb@Fs@Ǵ9Xb@Ϫ͞@84m8@8@5}hr @5}M:@5-@5kP@4_o @4S@4_o @4$tS@4_o @4u%F ?F/P5?q+qW?̗>?˃.? ?DX S?WZM?ۣ0Τ?B?6r00M?;C=?*]6?m?nTn?Sn{?}2;?9?DL@@@@@"@$???????G?}?eSFF?aG @{@\H?wQ?| ŀs?Pa?Ru]@ky@rg@*@ -=q@/@ -=q@ᛥS@ -=q@ᙙ@ -=q@/@ -=q@Q@ -=q@ I]@>x@>C@>x@>r @>x@>Q@>x@?+@>x@>o@>x@>@8L]ce@8HU@3S@34J@3~"@3 @2[W?@2 @2[W?@2}E@2[W?@2}v?8ȺJ?`?=5?Xc?W2D?ZF/D6?d&@8@>| tM?r~扩-O?~GD7?|F@)' @oa@+@o`B@o/@o`B@o2W@o`B@o2W@o`B@o㯷@o`B@oZc @o`B@oⶮ}V@z;dZ@z5fj@z;dZ@z]c@z;dZ@zˬq @z;dZ@zqu@z;dZ@zqu@z;dZ@z͸@64m8@6sP@1° ě@1ݗ+k@1tj~@1qj@0&YJ@0'@0&YJ@0'K]d@0&YJ@0'/V?nO@3n5?|.Mm@3M-V.Qn@2D*.NC,@2D*.NC,@2D*.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 .@  I^.@  I^.@  I^.@  I^.@  I^.@  I^.@u.@u.@u.@u.@u.@u.@8֚,<.>@3° ě.Mm@3`A.Qn@2!:S.NC,@2!:S.NC,@2!:S.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 /@/@1'@/@҈p@/@ (@/@䎊@/@6@/@YJ@IXtj@IX@IXtj@IYXbN@IXtj@IXl"h -@IXtj@IY=d@IXtj@IYb@IXtj@I`E@3䎊@3n@0S@0{J#9@0-@0fA@0ush@0v1.@0ush@0u\(@0ush@0qR<6?}ѿ,?Z.?jhq9?%?@@No9?y%?|O?)[Zw?%ϑ?՘]G?]ߒ@@@@@$@&??????1*?PM?F:?DH@@IZ?f qC$?d?$懪?Hx|@f,2@0@@j~@@5?|@@0 ě@@!(@@@@\@["`@,@["`@Ǔ@["`@qu"@["`@Ǡ k@["`@Eh@["`@t@5_o@5 vkT@2}hr @1CMjO@2'KƧ@0s@N@1u@/حU@1u@/aT@1u@/-V?k?cX?d ?MN:?ξWס?]q;j?C~? -??CRn?R?ǥt?G0?&?fI=C?4;$?oh?Vl?$FtY@@@@@(@???????Is`?{K?bu\? 1@@㫘?~Vi'?)?)g1@37@^ -@1@4j~@;dZ@4j~.@4j~.@4j~.@4j~.@4j~.@|P`A@|$.@|P`A.@|P`A.@|P`A.@|P`A.@|P`A.@8@9R<6@5Y^5?}.Mm@4q7Kƨ.Qn@4#.NC,@4#.NC,@4#.NC,? Z @8#@8:@8 >+@8I@8? x@8@8i@8@8S@8?'[@8 *@8p@8@8 @8 @?2@tj~@vu@tj~@{)%@tj~@fbm@tj~@h:I@@tj~@`''@tj~@i8B6@'~#@'@|@'~#@(@'~#@(a @'~#@(4a@'~#@(1M?@'~#@(R@9*0U2b@9(8@4@4.@4 "`B@4*Xis`@3iB@3|Њ@3iB@3խ@3iB@3Ko?)1?:a?ô0?,H!?).%@g?`P(ee?Y%?Y?"U\#?m?31?ka"?Hr?T?Y?:81?Xh>*?A+d?:#@@&@"@@0@*???????oW?jڕ?&:-?t3@s U@(p ? e x?6lw@&N?^4@p8_v@3@&`B@&`B@&`B@&/@&`B@&9@&`B@&U@&`B@&Fs@&`B@&=p@RkR@Rl1&@RkR@R^\M@RkR@Rd7@RkR@R;@RkR@RE84@RkR@RC -=q@4|Q@4Gz@1{ I^@1v!.I@1{Ƨ@1u84֡@1[W?@1y+ @1[W?@1{'/@1[W?@1|C?| ?GA?ʶs? "?ߩ??| ?ۣ0Τ?1ک! ?\(A?L?|9?y8#?zx?ٺ?;UAE?P|?@@@@@(@,???????j(!Z -?Q\t]?e漫z @&]@R_l?j?{`\a?`V?.}[@_>a6@4@'M@'u6@'M@'@N@'M@'/@'M@'C,@'M@'TɆ@'M@'C]@ě@ -=p@ě@D@ě@8}@ě@\(@ě@'0@ě@J@7:@7l@5+S@51e@4'KƧ@4&?@30U2a|@3ʙ0 @30U2a|@3@30U2a|@3Ƈ+ J?I'%?%=7"?c«?iP?dt O?|&?Qp?ֶ?ܿ?y҇?x9l?X#?>?@/@{?[Rb??HF!6?n$@@@@@*@(???????] -oˁ?H0H^?SE@'d@Un!#?chKqձ?u/e?,p?p:q@j$2 @5@n@^5?}@n@n@n@~@n@TɅ@n@@n@A7M@Œ@‡+ J@Œ@B@Œ@Œ@Œ@Ž_o@Œ@5Xx@Œ@@8N쿱[X@8X ԕ*@2?|h@2ᰉ@1R`A@1R䎊q@0 [W?@0 U=@0 [W?@0 @0 [W?@0 _?|O?t0?U#?vHn&?E{? µ?`m??xrI?ԕ]I?Y?qN?m/w?vsR_?[g??㝯?4?2t?~?J?^??#?8&?l`?GJ ?lq?1C'3? ;n?%?}@@*@,@*@8@6???????yiJ?l?g "$Uz?a'J@ X@,z?F?I?yB -@xAQ](@kJ@7@mbM@mbM@mbM@mrGE9@mbM@mٙ@mbM@m@mbM@m״3@mbM@m>6y@4fffff@4f@4fffff@4g8}@4fffff@4h@4fffff@4fA@4fffff@4f]c@4fffff@4g/W@3쿱[X@3@.MV@.MB@-nO@-GE85@.Vl"@.-V@.Vl"@.p ->@.Vl"@.+j?| ?3i?N?lDžN9?ǹT?-tT?|O?#maЀ?rJs?EP?إ<(?{L բD?2Y0?3;?s@a?ʬv?pk Y?b/bJ@@@@@(@*???????#W`?#cl?e@mX@4f?Ck~?2ٟu?'p?\JY?rg@fla@8@η@δ9Xa@η@η@η@ι@η@κ^5?}@η@ηs@η@θy=@lD@^5?}@lD@p @lD@!.I@lD@/@lD@n.@lD@]ce@6=:@63wl@1G+@1GkP@0i"`@0i|@0._o @0/ M;@0._o @0.ߤ?@0._o @0/H˒:?)T?nx?)?.Ү_ ? Y?; ^?<_?}ѿ,?D.x?tP*?1'7?Hc?nK/?H?lo>?~/O?Gϵ'?A0)n?v> @@@@@@ ???????O{??~?D7P?+FF ,@η6J@J?cSH:Q|?MYui+ ?]_?9T7@iWԑD@9@+@bM@+@`d@+@e@+@+j@+@tj@+@ԕ*@S@Q@S@h ԕ@S@n.@S@1@S@s@S@ xG@5v@5lC@2 I^5@2nO@2Z~"@2\[W?@10U2a|@1/{J$@10U2a|@1:@10U2a|@1ɺ^5?}?| ?hX*?ojVrn7E?/Y}?Otx??ER Y?}?P\@@@@@,@(??????H>G?4k,i?-.qب@֔h@-h?UX5?NoZV?LMa!?%@f@:@m8@ma@m8@m5*0@m8@m/4֡b@m8@m/{J#@m8@m)@m8@m..3@ -=p@3333@ -=p@p -@ -=p@$@ -=p@)y@ -=p@ ԕ@ -=p@p:@7 _o@6Mj@38r Ĝ@33F]c@2y7KƧ@2rS&@1&@1D@1&@1+ @1&@1Q ?t#V?q' -B?&$#/?(tUo?ɨ+?Vd?h??2(_?%8?r?+hd{?'?9&? ?A'_^?[?/̽`? `@@@@@,@(??????_®_ [?gOg?gb-@m.|@͢?xS?Tr"?k(Й@)Ǒi@qJԁ@;@(r @(\@(r @)@(r @)*0U1@(r @(\@(r @'ݗ@(r @'ݗ@޸Q@;dZ@޸Q@ޫ6z@޸Q@5?|@޸Q@޸Q@޸Q@vȴ9@޸Q@@3qiC@3L_@/G-@/Hr ě@-/;dZ@-/Vϫ@,uXy=@,usg@,uXy=@,uᰉ@,uXy=@,u\(?}ѿ,?Z.?$C@? Y??ˡK?| ?#maЀ?ԕ]I?-I`K?ߒh!?Uyu#? ݎ8?§d߱?|׿pZ?ʬv??@@@@@*@&??????1+2?L>KK@(tb}@[?=bqwY?L? k?(݀@gfi@<@ @ @ @ hi)@ @ Gq@ @ @ @AIZ`@ @(C@&W -=p@&V @&W -=p@&R%w@&W -=p@&Te)@&W -=p@&S@&W -=p@&YH.*@&W -=p@&S+@6,<@6jJ@2B ě@2A7kE@1v-@1t)@0@0ޜO@0@0m@0@0%s ?~Ov_ح?C*!?w|?>'?) / 7?q?eXW?8N?^nu?1s#?Zps{?W?U?!01?s9+6d?lpM?ʨt?e-$#{@@@ @ @6@(???????L=E#5A?C+0E?3)}@ +%@&VءB?cˈ?TL]?ӟ?]@gC@IA@=@Н-V@а -=@Н-V@МPH@Н-V@ИD@Н-V@Й@Н-V@З3@Н-V@БX@ȌI^5@Ȍ1&@ȌI^5@Ȟ\N@ȌI^5@Ȗu@ȌI^5@ȧE85@ȌI^5@ȃF]e@ȌI^5@ȗ>6z@8qiC@8@5+@5_F@4"`B@4~@4 -0U2a|@4@4 -0U2a|@4:@4 -0U2a|@4u%F -?_pP1?fDb?=5?Uyu#? )?V?F^S?\L)C?iկ?Y`?f@?W'?œ,?"_?7?Z?*:R}?>3}@@@@@"@$??????t?6J?Z c?uYE@Й[,@Ȕl?oy/?4?O7ϗR@-9Y@n?@>@NV@N_o@NV@OV@NV@OŁFX@NV@LQ@NV@I0@NV@Jf:}@hs@7@hs@[@hs@8\~@hs@8@hs@x@hs@c@4֚,<@4,@13S@13?4ކ@0Vu@0%@0Eu@0A@0Eu@0>ݔ@0Eu@0@-,Z?|B &?UV&w$?'7 O|?#Z?;A1?eVV?| V??o|?kS?Qxy_?.?p?o?vȴ9@6ȴ9X@u@6ȴ9X@<@6ȴ9X@-V@6ȴ9X@m&@6ȴ9X@@dtj@d@dtj@dJf@dtj@ciDg8@dtj@c-@dtj@bF@dtj@bJ=p@7B䎊@7>p:~@4?|h@4+jf@4tj~@4n.3@43&@4m@43&@3`:~@43&@3Zn?Ol?Bx1 ?i?>-?dD2?Β9?_pP1?g*?լ{T?̝TY?R?bI߭??-?įJ?K{~?2 ?NR\@@@@@ @"???????ɓ*4I ?T ?Zk@[#@cqMF?d5?>r@j1,%@"qh-@a'!@@@hr@㕁%@hr@@hr@״3@hr@ק'@hr@PG@hr@֔Fs@߾vȴ@-@߾vȴ@lD@߾vȴ@@߾vȴ@af@߾vȴ@jg@߾vȴ@8}@8@8-V@5+ J@5Zc@4j~@4Mj@4!.I@4p҈p@4!.I@45Xz@4!.I@4oi?|O???.N?"co?60a??bjk?P|?I?Kȁ?]%?P?F{?ߔY?JzUf?Cx%?/я?A=@@@@@@??????l6ʪW?C TUj?krvo|@d@?l##q?e7&?0lFt8?ˮͿt@m9@A@u@\)@u@e@u@`d@u@_ح@u@ӎMj@u@և+ @R@Ƨ@R@5Xz@R@x@R@JM@R@F@R@.@5N쿱[X@5N%@2+ @2U@2-@2Xy=ـ@1@1@1@1⩓ @1@19?|O?W[?y]?wX/8?ɿ?Rg.?1j?,#?8G?GuS?SY?IH!w?m+&?c I??],V?XW~? .0v?4hpɄ@@@@@&@(?????? +4?W-j?3z?H"G(@է@M?SW -LC?i%dl[1?2Xd{?Vo@fH5J@B@tj@1'@tj@ -o@tj@fC@tj@ɺ^5@@tj.@tj.@Gȴ9X@GS@Gȴ9X@G=c@Gȴ9X@GS@Gȴ9X@G+l@Gȴ9X.@Gȴ9X.@7@7o@5 ě@5]O;dZ@4K"`B@4|@4@5@3\N@4@5.NC,@4@5.NC,?sD?I?*?]/@8I@8?Y͍_?W;zY??@8S@8?W;RS?A@S~`?k?/"@8 @8 @@@@????C@C.@C.@C.@C.@C.@C.@n.@n.@n.@n.@n.@n.@7r.>@3 I^5.Mm@3gKƧ.Qn@1sh.NC,@1sh.NC,@1sh.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 D@.@.@.@.@.@.@ hs.@ hs.@ hs.@ hs.@ hs.@ hs.@8:.>@4S.Mm@3"`B.Qn@3 -0U2a|.NC,@3 -0U2a|.NC,@3 -0U2a|.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 E@\@x@\@84@\@죢w@\@5?|@\@쥔O@\@ں@O@V@O@_@O@ @O@ l@O@o@O@C@6!.@6oiDh@3i+ @3cA [@2v-@2r I^5@2 [W?@2A [@2 [W?@2 Dg8~@2 [W?@24K?}ѿ,? q?%HC=s?Q+v?<_+fj?5JA.?HD=??;%\@쥧@x ?h>Fp?[~w?d('?uE@e0@F@ -=@&x@ -=@)^@ -=@ *@ -=@@ -=@- @ -=@@|-@|-@|-@|v_خ@|-@|@|-@|@|-@|'R@|-@|H@6O M@6&@/tj~@/Mj@-nP@-ѷ@,`d@,m8@,`d@,9XbN@,`d@,9XbN? ?3i?m?8v?tKc?*WF?}ѿ,?›?~Q?/Z?@?M\ٓ@?^&?.+?8*?':??f_Ԋ@@@@@,@$??????(`?%?"n#?6G@I@|NO?B6Q?%1O]?V;?Vj0ոJ@gMCE@G@=Ƨ@=Ƨ@=Ƨ@=3@=Ƨ@=¶}W@=Ƨ@=ѷ@=Ƨ@=Õ%@=Ƨ@=¶}V@"O;d@"Ƨ@"O;d@"Љ'R@"O;d@"ҽ<6@"O;d@"{@"O;d@" p@"O;d@"C\@5=:@5:c -@1sS@1h@@0V@0{J#9@/-w1@/1&y@/-w1@/>BZ@/-w1@/Mj?Ol?"2???_dA?"X?| ?zF?pI?1}B-?#b^?j?O?3I#i?Me_?v_.^?fW?mΝ@@@@@"@(??????Kf?E~g0?*ʆk?BZb@.3tj~@.3&@-nP@-C,@+ۋq @+Dg8~@+ۋq @+7KƧ@+ۋq @+?| ?[e(lQ ?wX/8?q f?I?F/P5?6c?ɇ}~?#X? +A?J佪^?Rw#z?p51?3Z\?π9"?&f? 1 D@@@@@@,@&???????H7.@?P]D?1\>?M6mQl!A@X+@[?OZGP?o -ƕi-?֡?D !@iE]@O@vȴ@vȴ@vȴ@@5@vȴ@}V@vȴ@&J@vȴ@R<@vȴ@}W@@$/@@ѷ@@n@@.H@@D@@ح@2]ce@2s@--V@-af@+E@+4J@*)_@**0@*)_@*O L@*)_@*g?^XN?J?-.?&P?9.?`? ٖ?bM_x?6S{? -\?,?JL\?C ;?g)?rbT?],m=?=aw%?:1?Cns@@@@@,@*???????'d?4;p?2NHU?@ Q@&@?B7 ?O|#D/?|!? `ܐW@rYY:@P@*9X@*x@*9X@*I^5@*9X@*!-v@*9X@*qi@*9X@*\)@*9X@*R<5@ 7Kƨ@ -p@ 7Kƨ@z@ 7Kƨ@hs@ 7Kƨ@R<@ 7Kƨ@:@ 7Kƨ@Zc @7w@@7xe+@35?|@3g@2tj~@2*@2Tm8@2O4֡a@2Tm8@2>$t@2Tm8@2@IQ?^*?hĔV?13??㬱_?|QS?[й?% ?z?:-%?"6Wz?VAo?u!#?-]?њH?ʭ#г_?Z?Xs~?qM,@@@@@,@(???????g*^ff?Rn#O?G?@*Jj@RR?mE ?c'?^?d$@g -o@Q@1'@!@1'@\1'@1'@@1'@# -=q@1'@O @1'@>%@I^5?@&ffff@I^5?@=H@I^5?@Xe@I^5?@z@I^5?@&x@I^5?@@8!.@8q9@4 hr@3쿱\@3"`B@31-w1@2@2_4֡a@2@2]E@2@2XK]c?|0pJ?͎#?۸F>8?Ge?٠qsU?lT%u?9?J/p?ζa'~?Ϭlה?Թd-@Q?N)O?[N?~ԙ?yz?7< 4?MX?ȩ@@@@@,@&??????a?.8?KyF4?%@#@@!$?T??n"ef@#&@aEb4@R@`A7@_l@`A7@ -L0@`A7@~#@`A7@=p@`A7@ (@`A7@U@9aG{@9bM@9aG{@9|쿰@9aG{@9{@9aG{@9@9aG{@9x@9aG{@9U<@3=:@3<@1}hr @1zd7@297KƧ@2-<64@1z,<@1s\N@1z,<@1sMjP@1z,<@1kjf?| V?m0?=tQF?(+Zk?7?fsPa?|h2:?j%Ra?#*0???=?of@?l?{Tä=?(> ?4\ɵ?8~,@@@@@,@*???????7Yp?t*?Da?\AS@@D.@9tDF?bLd?yug2?/MW?@cM1@S@ᖇ+ .@ᖇ+ @ᡖR<@ᖇ+ @4֡a@ᖇ+ @+@ᖇ+ @+@ᖇ+ @9Xb@;dZ.@;dZ@@D@;dZ@>6z@;dZ@@IQ@;dZ@D?@;dZ@=E@9B䎊.>@4I^5?@4! @4t@4SMj@2p@2pD@2p@2p҈p@2p@2pn@8J?q? -> ?vI?2F0?۝.Yr@8J?%@&?pI?:u. ?a -/ ?<@8p?7&x?AQ3??|)-}(?+`ȓ@@@@(@(?????T@ I@/Y@ I.@ I.@ I.@ I.@ I.@?|h@E@?|h.@?|h.@?|h.@?|h.@?|h.@8YJ@85@5kS.Mm@5)"`.Qn@4&YJ.NC,@4&YJ.NC,@4&YJ.NC,?RFR@8#@8:@8 >+@8I@8?I@8@8i@8@8S@8?UC @8 *@8p@8@8 @8 @?U@S@vȵ@S@c@S@bu%F @S@[u@S@S*0U@S@T,<@P`A@t@P`A@y0 @P`A@l"h @P`A@]E@P`A@Y#x@P`A@ZY@8w@@8)+ @2hr@2usg@1A7K@1͞@0Q@0m8@0Q@0t@0Q@0o??b[??^:f?j??#?F^S?0?pM?wxR?ˬ?+l?Mi,?t??Q(? $4?**Z@@@@@,@,??????)Ew@R ?e>c*?z?@]X!@d5?rR?L[bv?8v@ouG@n 2@V@1&y@9X@1&y@y@1&y@ԕ,@1&y@@1&y@@1&y@#@ITzG@ITzG@ITzG@IT,<@ITzG@IVE@ITzG@IT3@ITzG@IU2a|@ITzG@IU$@5 xF@5oh@1hr@16@0KƧ@0y @0@04m@0@0A [@0@0)^?| ?*|3N?1f_? Iy?u?aX? ?pPȻ?2'?uX?q1At?eDQQ?],V?u/X?ZaQ? ??<71?ad|@@@@@*@&??????0^2?(xAV?,@@$@"@"@6@7??????3Ɇ?p]ր?6+i?T.THD@zQ@-V?F"<1e?c!8v?Os?l@rH^@Y@KƧ@ߌ =@KƧ@/@KƧ@zD@KƧ@HL>@KƧ@G -@KƧ@8j@{qm@{=\@{qm@{l/Kñ@{qm@{hH@{qm@{q@{qm@{q(a@{qm@{uB@7@7wZ0@3|hr@3<@2KƧ@2@2(@2,$g/}@2(@2,@2(@2,gP)?}x@4; I^@3Ҷ}Vm@2KƧ@2C,zx@2xF^@2^+@2xF^@2hy=c@2xF^@2lC@8J?ε(C?ay?¾9?_ -?*8@8J?B֛?LA?σ&a?[ ?;|h@8p?5w?!?yD ?Qh@?HU@@@@$@*?????\@NbM@N`A8@NbM@N @NbM@Nёv@NbM@NГ:@NbM@Ntٻ@NbM@N@+@P`A@C@P`A@G1S@P`A@B#@P`A@9q:@P`A@AM@P`A@Iy@8qiC@8BZ@2-@1tj~@2kxF^@0Vl"@2kxF^@0T@2kxF^@0|hs?^*?fH?s?sNX?(Q!K?79?% ?^?ꁤ/?)`%;?GZ> -?XEM?ñ0h?ϧ7?? })\?6m? -.ԕ@@@@@,@&??????2vp?,<<?zti?v0Bd@wF@|o?øF?Hb(@or>O@zVt@b|t7@^@Õ%@x@Õ%@Y@Õ%@1@Õ%@`A7@Õ%@Mj@Õ%@hr!@ۥS@@ۥS@w@ۥS@~($@ۥS@(@ۥS@ߤ?@ۥS@?@5s@5@`@cl@cl@cl@cV@cl@cFs@cl@c?@cl@c|h@cl@c-@$/@7K@$/@ ě@$/@Xe@$/@Z@$/@J@$/@@2g l@2eO@1#@1O;@0Vu@0zG@0:,<@01A [@0:,<@00D@0:,<@01$/?OW?Qd?8AWdm?ׂa? -=p?4?;0? H?-hm7?vv? l?c5W?!?O?I/?O.ce?_խW?>LR_5?@@@@,@*???????_z?]_{#?b9v?Tbڄ @c f@ ?r ?cbK?WX"?t-bP־@sf@a@ć+ J@ćz@ć+ J@ć+ J@ć+ J@ćz@ć+ J@Ć@ć+ J@Ć]c@ć+ J@Ć@(\@)7Kƨ@(\@(TɆ@(\@(TɆ@(\@(TɆ@(\@)7Kƨ@(\@)rF@0qiC@0qiC@/.Vt@/.H@.E@.b@-uXy=@-uXy=@-uXy=@-u$0@-uXy=@-u\(?a(?m0?@? Y???bV,?&uE@?ܿ?/Z?%y*=?ȏ'T?b@?~('?2Y0?E 8?f_Ԋ?B @@@@@&@*??????> ?"{x? b?4b@ĆZs@(28?"Z ?(ij/?@ֿ9?Jsw`{@sE@b@M@M@M@>/}@M@֕@M@wzґ@M@Ҏ@M@@@@@IL@@5@@ke@@ @@S @2Y*0U2b@2Y&x@.-@. -W@-n@-,7.4@-b~@-< -!G?@-b~@-exb@-b~@-~le?ޖL I?+B?( '?r2?#h?ulP?r-?j?M?6ͧz?aE?~Ӏ?M"@r&x@pK:)@bM@R@bM@yXbN@bM@m(@bM@sE@bM@x4֡@bM@n.@9}:@6%?|h@50ěT@1{a@@4"`B@0ˬq @30U2a|@0Ae@30U2a|@0>@30U2a|@0;xF^?A[F?Z.?oԀ?CV?saS?G5v?UbB2?c?ZT?,?L7??ArU@?A ?yjk?(> ?;kr>?`i2@@@@@*@&?????? ?@?j6d(R ?Wa0@pN>&q@mh?M`?u-n@Rm?ևW@iq?@f@\@KƧ@\@@\@r @\@cA @\@ݗ@\@ݗ@Q@-V@Q@Q@Q@zR@Q@Q@Q@)_@Q@~($ @8@8VϪ@3+@3NT@3@`A7@3Aae@2\Q@2\1&@2\Q@2\@2\Q@2\64??d̺?&$#/??4f(R?I?UbB2??xrI??T ?u]?nW*؄?ey?!ϟۺ?r?a -?5|G?3?k!l@@@@@,@*??????i]synd?EQǓN?`Pw+@F@/R?`]d?}Z:[?$?q@n2Mx<@g@eE@evȴ9@eE.@eE.@eE.@eE.@eE.@%6E@%6E@%6E.@%6E.@%6E.@%6E.@%6E.@7~($@7x@4}hr .Mm@3Vu.Qn@3,<.NC,@3,<.NC,@3,<.NC,?3a=@8#@8:@8 >+@8I@8?3a=@8@8i@8@8S@8?T0@8 *@8p@8@8 @8 @?h@V@V@V@C^@V@@V@<@V@0U2a@V@E@!;dZ@! ě@!;dZ@!V@!;dZ@!@!;dZ@!X@!;dZ@!@!;dZ@!oiC@4[~($@4[S@2 ě@2- @1KƧ@1KƧ@1:,<@19Dg8~@1:,<@1;~%@1:,<@1/o?| ?d̺??TnE??oW?̉ߚ?|O?;N?ACO?>)!?x9l?ey?=Ca?>?9@0w?4i+?ymcw\?屘@@@@@(@ ???????BC9(TC?JW,JB7?Q+@@!3?i޴hjO?rlO?@?S -"j?t& -r$?b^?9f?3Z#m_?oWe?T(~@@$@(@&@8@6???????X0l?n}x2?%u (8?"[α@SW:@5vg?Doj+F?B [ώ?N\ ?B?,@fBs@j@!@" I^@!@9?@!@8?mE@!@$\{B@!@z% @!@D@j~#@qn@j~#@Ȋ@j~#@bjvxF@j~#@ǫl@j~#@gG@j~#@ȌKk@4]ce@4}"3@1ahr @1FSP@0Ͳ-V@0T@0_o @0Q,3@0_o @0@0_o @05"u\?gQ?Pٽ?6S\?hѺ?%/_[?FvS?~?J?3@$;??uri?/\??qn?2ѧ7?lmUA?!ýe?=CQ?g`O@@&@"@"@4@,??????uw?sp?r7?jd@D@ȁK>%?ߋs?>?|h[@ (U@V@bh@k@w@bM@w@p@w@N;6@w@񂩓 @w@͞@w@빌~'@Μ(@Λ"`@Μ(@ΝE@Μ(@Μ쿱@Μ(@Πě@Μ(@Ξm\@Μ(@Ξ#@6V,<@6X_F@1 I^@1#9@1ȓtj@1eO@15sh@14#@15sh@15\(@15sh@15`A7L?|O?I'?Z*C?%?y%?_ ? ?`?l%FZ?N?=W? Z?qn?3I#i?A'_^?9XNu? Ğ?$@@@@@*@$??????@Ox?M0?>S ?=8M<@r@Ν}y?_05D?^j7Ǫ?g@̨?ġ@g>s@l@?bM@?_vȳ@?bM@?a-w1@?bM@?^ߤ?@?bM@?Rs@?bM@?]ڹ@?bM@?X@Y+@Y+@Y+@W@Y+@R@Y+@YrGE9@Y+@U\)@Y+@TzG@7D@7!-w@2S@2b}@1Ƨ@1p:~@1iB@1qu!@1iB@1fB@1iB@1?ʎa]?'5?%HC=s?ghT?`FC:?$?qv?nr|?0?QĨ?=M?SS?l~J?t?^?3_?Kd\?r@@@@@,@(???????%;TR(?d.ů?B@?W bn@WcPw?|D+3`?] ?ymq?Z5ZJ@n/Ts,@m@@3333@@#)@@6/_@@!~@@щ7K@@(@vȴ9@vȴ9@vȴ9@ϕ@vȴ9@˒:)@vȴ9@VP2@vȴ9@]@vȴ9@֥t -@1[~($@1[Ƨī@0}hr @0}@@/x@/Ab|@/)_@/F.@/)_@/ Z[@/)_@/Q?^T?qy?8R~x?o$͠?!!.?nh&?bMn??1;?5' *x?-4B?bRuPL?bM]f?Y?T.g?vs{?_=D?2ˁU@@*@,@(@4@2??????OQF6*??w(?3+@#@?N^k?B(t[?=`?|ˍ@sF@n@%`A7@u@%`A7@*W@%`A7@%@%`A7@$Z@%`A7@& (@%`A7@$?@IzG@Ie`A7@IzG@I{6@IzG@Iz2@IzG@IxQ@IzG@I}p -@IzG@I{m@7 xF@7I@2+ I@2F]c@2`A@2,<@1\Q@1^p:~@1\Q@1]K]@1\Q@1\]ce?[_g? e?=?Kh?|*(?R%(0?uu?b?bH=?s8r?-I`K?|+~?m/w?=d?ʳ_?cӗ1?&I?A0)n@@@@@@???????:}h8?e"2?]JvT?YB=H@$m@Iz}=x?kC=?k^6YV?˷g -?oF>7@r-b@o@["`@[lE@["`@Zv@["`@TɅo@["`@Wr@["`@XQ@["`@W1@ -=@`A@ -=@2@ -=@ݗ,@ -=@-V@ -=@<@ -=@- @5H@5!R<@2S@2?@2-V@2Fs@2(@2*d7@2(@2-w1@2(@2+s?|O?: ? -tI?q@v?_dA?Dw?1j?$i %h?$Jw"?G>G?n ?yC?֘>D?Ov_ح?hE?=aw%?C SG?wͶ@@@@@*@&??????Q2n4<`?+ARG*T??dY.V@Y(@WP]?Kz[/?`?×:P?[Kl@ff[@p@&ffff@Lƫ!@&ffff@;@&ffff@3fO@&ffff@0@&ffff@x@&ffff@V#@N+@v N@N+@©Er@N+@´{@N+@WWO]<@N+@†]@N+@š@7+s@7( @4S@4Z΢PU@4t@4\"8l@3ce@3@3ce@3L|U@3ce@3ٸ?U?k'?_4?˸M?aE=?83@??1F#?G}?WL*?8J?zwv?hTGb*?7J]92?*}&S@ϕG.A@ѡ?W)mû?J_!?$h7\?fհ -@@fau@r@Vt@V$/@Vt@V+l@Vt@VڹY@Vt@V @Vt@VK_@Vt@VM:@uM@uM@uM@ub}@uM@v -E@uM@vMj@uM@v(9X@uM@v:)z@5@5}Vm@1@1n@1PA7K@17eں@1 -0U2a|@09@1 -0U2a|@0s@1 -0U2a|@0?| ?Be?C1?~L?;V?Dr? ?NH?Si܃?%W? -HQj?ѪC#??}(2?\{?fr[?? )D)?^_r@@@@@,@&???????fp?=1?p K?eOA@Vy@u?~!|_?u?@0?ňR\?Dc@\e_@s@Z1@_vȴ@Z1@TɅp@Z1@z@Z1@%@Z1@<[@Z1@Ca@N@\@)\@\@JM@\@C^@\@ⶮ}V@\@+@\@@8>6z@8ƕe.@4+ I@4JL@397KƧ@3&@3QiB@33tj~@3QiB@3BT=@3QiB@3>2X?wogl?g?w?8 Y?ܡ?VAg?(l_?,4,`?/?۔Y?T=U?2R2'֕?{Qy?=X?L?׿l?d7?W@@@@@3@&??????koK*jB?j/-:?z;A@K^pB@+?}[͊L?:T?_Z%ES?z@p2@t@?|.@?|@!|4b@?|@@?|@ @?|@E@?|@^0AJ@rr-V.@rr-V@r~XP@rr-V@r^#S@rr-V@re m@rr-V@rFx @rr-V@r8SM@8YJ.>@4^+@45@㳃@3Ƨ@3Bմn@2&@2֠[@2&@2Țԫ@2&@2 7@8J? /¹|?y2??V2?|۷t@8J?\pe -?7 OW?!Z ?mC?4@8p?leϞ$?pu?Gyt?m\?4@@@@4@3?????u@TO;d@T\@TO;d@T8@TO;d@TV@TO;d@T@TO;d@T'0@TO;d@T @;dZ@/@;dZ@@;dZ@U=@;dZ@b}@;dZ@@;dZ@ܹ#@7}:@7z)y@0J^5?|@0J6C@.NO;@.Nqi@,V@,V}Vl@,V@,Usg@,V@,UY|?1j?$?>f? Y?'`?PN ? ??b/bJ? -t@@@@@(@$???????@_{Y;r?Cͳ;h%?0CC@T\@kP?cB)?P"?B?Hax2?N+@gjE@v@4ȴ9X@5C@4ȴ9X@5y=@4ȴ9X@4-@4ȴ9X@5'R@4ȴ9X@5O @4ȴ9X@5 -#9@ @E`A8@ @1[W>@ @8Q@ @*W@ @ȴ9X@ @u%F @7>6z@7E@3\I^5?@3PH@2gKƧ@26l!.@1YJ@1^@1YJ@2[6@1YJ@2tSN?5u[E?5O}?bY?C: -??LTk?9?U -?00?}M ?7&c?T? M?ְ\Wg?(p@v?A\#?kR?4?\O:@@@@@,@*??????jg-C@?qy?b@5+*@c[?υ%?e?%Z"Y? -RRC@r;X@w@mo.@mo@b̥zxl@mo.@mo@i"@mo.@mo@ihۋ@OY+.@OY+@R ě@OY+.@OY+@G\(Ž@OY+.@OY+@I-(@8g l.>@4ԛS@1y -@3-.Qn@3@11R@3.NC,@3@1 ߤ@@8J?.q@8:?u86@8I?UzW@8J?7*@8i?ٹN@8S?|pUw@8p?܉a^@8p?a@8 ?馪C@@@*???x@"`@^5?~@"`@Q@"`@A@"`@E@"`@e+@"`@E@";dZ@"G@";dZ@"K^@";dZ@"@";dZ@"W@";dZ@"Z @";dZ@"$ x@8(YJ@8,#x@3hr @3!R@3Vu@3֡a@3n_o @3g_o@3n_o @3iԕ*@3n_o @3kW>6z?K{O?I'?~#?avfV?҇?Oh6c?4\ɵ@@@@@*@*??????)pp,?Qpm?Ig}'@ke A@"Ʋ~b?i?o[Xh?y?@k^>@y@F@'lC@F.@F.@F.@F.@F.@ -=p@ 7Kƨ@ -=p.@ -=p.@ -=p.@ -=p.@ -=p.@7~($@7l?@3=hr .Mm@2y7KƧ.Qn@1ٌ~($ .NC,@1ٌ~($ .NC,@1ٌ~($ .NC,?Z@8#@8:@8 >+@8I@8?Z@8@8i@8@8S@8?A@8 *@8p@8@8 @8 @?z@@+@@ZP@@@@[(@@ӹ@@@`A7L@`A7L@`A7L@_;H@`A7L@^Km+@`A7L@^5*@`A7L@ate@`A7L@_2@2쿱[X@2(LW@/{"`A@/x- @.&x@.ag@-PH@-˰@-PH@-f},GT@-PH@-?^6P??(uH??7p?U@Z?bMn? ??-F|? w!H?Ju?7݉?ۻ~r?UA|?LV#7?ܐ?|@@@ @"@4@(??????R,1(?Gf1?9Ɔ?JXg9,@h^@_}5?HX^?YT?߳ ?3w@s @{@`RnP@`B`C@`RnP@`$tS@`RnP@_5?|@`RnP@`W>6@`RnP@`p ->@`RnP@`1&x@Kx@KnP@Kx@Ku@Kx@K -=@Kx@Lt@Kx@K%@Kx@Le@9 _o@9IQ@45?|@4° ě@4-V@4S@3sh@31&x@3sh@38bM@3sh@31N;5?A[F?d`w?~? ѷ?ʫ[?$q ?,e?wi*? %a?c(&?Ӓ ?xF?ɦvY?k2m? n?Oh6c@@@@@*@(??????o!ua{K?c89?tr@.zY@e>??}/K?2*vb?ޏ"ӱ@mUw@k9@@}@1&y.@1&y@G @1&y@8992@1&y@E"y@1&y@\S@1&y@{@|`B.@|`B@|ZlH@|`B@|t@|`B@|e@|`B@|2@|`B@&b@9p4m8.>@3^5?|@3vƆ&@2tj@2$6@2Q@1Fs@2Q@2 @2Q@3)x@8J?c~V?T?vS }?QWb(?6@8J?>uN? u}?ժQ?h["?WuFY@8p?8)s?[?Ydz?9O?SL鴈@"@*@$@8@1?????~@4j~@]p @4j~@ -=p@4j~@L_@4j~@GE86@4j~.@4j~.@C@ 1&@C@=K@C@ȴ9X@C@S@C.@C.@8쿱[X@8%$/@5S@5C\@5`A@5-w1@50@5F-@50.NC,@50.NC,?W&l? ?Ȅ 2 ?X}v@8I@8?ʻd9\?ߤ -B?ǿ?I @8S@8?Wu?#?fg?兯@8 @8 @@@@????@[@[o\(@[.@[.@[.@[.@[.@1^5?}@1@1^5?}.@1^5?}.@1^5?}.@1^5?}.@1^5?}.@92@92ѷ@5 ě.Mm@4K"`B.Qn@3𖻘.NC,@3𖻘.NC,@3𖻘.NC,?g;+t@8#@8:@8 >+@8I@8?3a=@8@8i@8@8S@8?yMa@8 *@8p@8@8 @8 @?@?|h@c -=r@?|h@L~($ -@?|h@B\(@?|h@Q@?|h@b^@Lχ @+@8I@8?k|jʨ@8@8i@8@8S@8?7V/r@8 *@8p@8@8 @8 @?@?]-V@?]E@?]-V@?[J#9@?]-V@?]Vl@?]-V@?an.@?]-V@?cA ]@?]-V@?`ě@8@8@8@8Q@8@:^5?}@8@=cA@8@:G@8@8-@5qiC@5q @3Y^5?}@3]ce@34tj@3;jf@2‚@5@21@2‚@5@2zxl"h@2‚@5@2ƚ,B@-.zH@--qu@Jo@Jo@Jo@IQ@Jo@J0@Jo@IDg8@Jo@K I@Jo@JJM@3T xF@3T2W@0\I^5@@0\64@/@/TɆ@.@. @.@.vȴ:@.@.?]?%ñ?7[䭝?ݶ\r? g?t?]?CO?DP?e[|(?qN?wN z_?*wjs??YF? ??a+?pk Y?@@@@,@*??????F6۸??d>R?26*&?::g-@--v@J$?A {?HuV?~'06?E1 @s[ PM@@+ @=\@+ @ē@+ @ yԑ@+ @ ɿ@+ @ 7N8@+ @ @bfffff@bFFK=@bfffff@bn@bfffff@bw@bfffff@bns@bfffff@boֲ@bfffff@bs3*/@8H@8ߌ@4+ J@4f@3"`@3Ǟe@3Eu@3@Z8@3Eu@390N@3Eu@3C*?7Yu?n˚@?U۳??Iߏ?Hۂ?En)v?g([Q?8>?-IS?p".?H, -kc? Qж?!?+?gҤ?^ r?5@@"@"@ @.@.???????3_?z+?^[fs+?jo'~9@ [a@bl?lw@lb?{5 F?~^>?-,) -@p`@@́$/@̃W- @́$/@̗Zn@́$/@̆WQ@́$/@̄|@́$/@̄WY @́$/@̄kGih@9z@9L@9z@9@9z@9&u@9z@9p:@9z@9v,@9z@9{GKr@67@@661.@1TS@1Ri@0~"@0ڛQr@0^6z@0]|Y@0^6z@0Ze@0^6z@0Yyp.f?3<' ?^-' -p?Jv?pCza?s޼M?Q^ ?|B &?M6G-\?'??g$%?)?X?p풟?J8?1u? ?}b38/?*%@@"@ @$@4@6???????Twg kp>6Fq )?Um=@̄@9F?>?)?v&aq>P@4sS.Mm@3b-V.Qn@2𖻘.NC,@2𖻘.NC,@2𖻘.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @'-@'j@@'-@7D2@'-@3fc@'-@7/ڏ@'-@2=E@'-@4C@+ @@`E@+ @s.@+ @͙b@+ @@+ @ 5@+ @y5@6^H@6]Yr@2S@2 - @1t@1H{m@16z@1uْ@16z@1ID!@16z@1L7?~Ov_ح?@HM?Z ?ib? U?;Xd?3<' ?Nnu>?iI?NsC)f"?;5L?%O)?kOv ?Lΰ?4K ?ha6?-|Q?_&@@*@*@,@8@9???????jf"ccC J?Rs5?Hč6@.`/@{?rf~?h+=ߓ?y?/r=2@g\hM@@9XbN@1@9XbN@=b@9XbN@8y=@9XbN@:S@9XbN@5sh@9XbN@2T`e@}p -@}-V@}p -@|1'@}p -@}cA@}p -@@}p -@~%@}p -@~ߤ?@7v@7@3hr@3BZc@3W-@3Yԕ*@2h@2k I^@2h@2t*1@2h@2r74?h??gUq#?BI{?=]. ?N?ʗAm?F/P5? l?B<\v?ռ2?k}X??O:b@?6h?"0?0N:s?< N ?r?ۂ@@@@@,@*???????A=?>L?Zue_?AW@6^Ø@ڑ?u|xG{"?cCӑ? ](?<<@iOr@@bM@_|h@bM@gE84@bM@mjN@bM@kP|@bM@a@bM@ce@ ě@zH@ ě@'/@ ě@Q@ ě@<@ ě@1@ ě@"h @72@76+jg@3E?|h@3Doh@3`A7@3D@20@2..2@20@20:~@20@2/ ?|O? e?K]4?lׁ?|?4 ?| ?PuN?qN?# Y?2}?EȀ?H6?*J?OW?sMQ(?,BZ@3n_o @3ca@O?)T?q?\H{gX?zb61?xs?u}I?|O?u(?9]t`? -Ǘ??*%>g?-BR1?k~?7}?=2V?:/ʼn?{=rK@@@@@*@"???????;]?yW?8Y̗?SlTy( {@,@ڝzd?X$!(?sFGs?B?u@fAZ@@жȴ9X@з -=p@жȴ9X@з+j@жȴ9X@иYJ@жȴ9X@лlD@жȴ9X@и@жȴ9X@иA@`A@`A@`A@\(@`A@!R@`A@_o @`A@'/@`A@Ϫ͞@2%!.@2&1.@-3tj~@-2p:@,wO;d@,uXy=@,=Vl"@,7+j@,=Vl"@,8*@,=Vl"@,8e+?^Dw'?[evȴ9@9cg l.>@3° ě.Mm@3`A.Qn@2ce.NC,@2ce.NC,@2ce@2Zc@8J@8#@8:@8 >+@8I?G!r̟Y@8J@8@8i@8@8S?(/氜@8p@8 *@8p@8@8 ?vۋ7@$?@ZS.@ZS@Z qw@ZS@Zڹ@ZS@Zڹ@ZS@Z~@ZS@ZJ#9@.@@e+@@PH@@lD@@u"@@N<@9,<.>@2f+ J@2b`A@0KƧ@0E@/N;6@/_o @/N;6@/;5Y@/N;6@/ΤT@8J?&?y]?Њ?u?i{@8J?0?ދ"?)@a,?O ?m/w<@8p?c I??Ol?+ ?L?@@@@&@*?????@-V@ -=@-V@RT`@-V@o@-V@(\@-V.@-V.@3NV@3A7K@3NV@2u=@3NV@16@3NV@2@3NV.@3NV.@8 ]ce@8@5S@4?@5~"@4Swk@4\Q@4 ]ce@4\Q.NC,@4\Q.NC,?]?L0?Gt ?N.!b@8I@8?]?-',?C=?«{T@8S@8?*wjs?Iݷ?~WC #?8|7@8 @8 ?@@@????@xS@xG@xS@xM@xS@xn.@xS@x7@xS@xRT`@xS@x\@Q@S@Q@hی@Q@{J#;@Q@u%F@Q@N;@Q@ڹ@8 _o@8 x@4?|h@4 -L/@37KƧ@3qu!@3Q@3 qv@3Q@3m@3Q@3@?Y͍_?L?Y#W]?{y6?(E?o:?)T?-c?_x2?Ti?{QB?s?T?їJ?r20/?42?z"+;?6@@@@@*@,???????g@fY?H+4?Teo-/@xHb@u@ƅ?e,?sr=?L kg?@eHl%@@#F@#Y@#F@#2@#F@#BZ@#F@#)ߏZ@#F@#Xgr @#F@#L]-@cS@_q@cS@r@cS@lEn,@cS@mt@cS@3L@cS@'=/@7zqiC@7jhŹ@4S@4\@4KƧ@4FP@46z@4U@46z@4@46z@4Cŧ?3?mg{?4?:G?珗?0;W;?D?h b??xP?=NB?z=?|(?A6PP 4??$Ec?,?0W} ?`P@@@@@0@&??????{ӣ*N0?Ak?t^@#Q/L+@C?87t?Yg_@,p~@(Qc@n/[ɂt@@Q@Q@Q@Q@Q@@Q@ȴ9X@Q@&@Q@tj@ȴ9X@x@ȴ9X@\@ȴ9X@k@ȴ9X@fffg@ȴ9X@@ȴ9X@v_خ@1g l@1ѷ@-R-V@-MC\@-O;d@--@,C\@,ѷ@,C\@,+j@,C\@,b?^Q5P?`?jhq9?1?K/?)?b? aҹz?T֩E? ?I?p0=-#?:)y?§d߱?{W?!B? Ğ?a@@@@@(@&??????\4?p#T?CG~0?Ca5((2@ۛr@kz?QxmA?Rh#?Vj ? _/@q@@hr@V@hr@ -=@hr@`d@hr@ @hr@iC@hr@p;@cvȴ9@cQ@cvȴ9@d@cvȴ9@d'@cvȴ9@dn@cvȴ9@dA7L@cvȴ9@d}V@5I_o@5I"`@1#@1 4m@0-V@0t@/C\@/@/C\@/@/C\@/&IS?}ѿ,?ߋb?,fC6?̒?2{w?|&?1j?,#?kE<%?N?e?6@(/?2Y0?Ϸ+? J?^ a?L8AS?`i2@@@@@*@*???????V?JX?;?:RḼ@ @d -?Z,:J?Yh ?vחM?Kgbt@eܕz@@Q.@Q@l-@Q@O@Q@ ?ɩ@Q@@Q@@RBM@O;dZ.@O;dZ@di@O;dZ@h8@O;dZ@xU@O;dZ@tY-@O;dZ@k@5>6z.>@/A7K@/@6@-1&x@-jL]@+-w1@+v@+-w1@+Ma@+-w1@++@8J?i[?% -s?V&s?mwH+?Hy@8J?dJH?q ,?yp?TV(?w x@8p?'M?P -gN?Jj?*l?.V@*@$@@;@8?????@lC@l@lC@jڹZ@lC@gK]@lC@ce@lC@eҩ?@lC@^=/@?|@\)@?|@@?|@y@?|@"_@?|@Ah@?|@]`UH@4g l@4#i@2Y^5?}@2U!.@17KƧ@1@1@1#w@1@1#p@1@1^gƒ?_Kt?m?UD?7ly\?TFEQ?' e?|b@?u߿?Bx?Ti?@0?־?Rۙ?GA?_?e?YfӶ -?z{@@@@@.@.??????ewjHSn1F?AtAEH?i>w@g ,5@G-_?a=_g?7R?xuq@ګ@fp]@@@Q@@҈o@@5X@@tj@@ @@m\@ O;d@Xtj@ O;d@T@ O;d@@ O;d@m@ O;d@@ O;d@J@8H@8hۋ@4 I^@46@497KƧ@4;Ƨ@3u@3ś=L@3u@3\)@3u@3@?G *ǂ?!}P?tZ'?.[?(i^?@*F?N?.Hp?]^߿?̶j?5D?7&T+?oh56?~tZE?tgi?a?{%? (0Q@@@@@*@(???????ߡpz?`_O\?qv\-2@o'!@?xny? Tl?ds?i D@r @@c -=q@c6"@V@c -=q@MeO@c -=q@\IH@c -=q@V8@c -=q@HtL@c -=q@N[@Ё$/@t~k~@Ё$/@fl@Ё$/@ЍO@Ё$/@k^@Ё$/@n @Ё$/@k?گ@8YJ@8t W@4!hr @4*e@3)"`@3*WE?;@2m8@2@2m8@2 -@2m8@2׆n5?I'%?7?<?/ J3?F_W?<1 v?\l6?J®?|e?1qH:?@I?^d?|3?w43?`@ln;@8$ޤz!?ؾ>p֏)?1=V>A@sQ @@K Ĝ@K W@K Ĝ@K@K Ĝ@KD@K Ĝ@Kط@K Ĝ@K@K Ĝ@K^ɍ@[-@[`A6@[-@[M/@[-@[S@[-@[+b@[-@[@[-@[y@6!.@6̭dJ@2hr @2C(a@2W-@2X˵ag@1z,<@1|T©@1z,<@1|۹L@1z,<@1|H?anñ?D?a?%9bg?VQhFa?/k`?nDyB?ߋ?:d&?ِ:Q?Msy?y[?92j? ?DW4L?Ҳ?̴N?%@@&@*@,@5@5??????-X@?{@MJo@M?j@MJo@MM@MJo@MR4@MJo@MV/ +@MJo@MR@8įO M@8.ma@3G+@3:Ov`@2~"@2: -m@1䎊r@1T E@1䎊r@1l%@1䎊r@1M?}Jh~9Z? 4??j:?Kw~u=@@$@*@$@<@;???????N7?^i?P{m?Pm垸@$ @MQ瀛?` 3u?hӍ2E?bp?= 3h,@p5Rh@@څQ@ڄt@څQ@چA@څQ@ڃZ@څQ@چ&@څQ@~@څQ@ڀ k@nE@n1@nE@nC-@nE@n?@nE@n~($@nE@n#@nE@n5X@7p4m8@7p)^@3 I^@35Xy@3Ctj~@3De@2sh@24m@2sh@2=L@2sh@2 '?sD? ?^RpX?@R?z|?~?%?ā?Z??Ll? ?ArU@?<{?E?C?l?x[:@@@@@,@(??????J;P?BB?O}E t?=25U@ڂN@nm?o v?VPrJ?جf7?"pZwA@m32ZQ;@@lC@+ K@lC@.@lC@Dg8@lC@ ԕ@lC@ffff@lC@&@Bȴ9X@B+ @Bȴ9X@B'@Bȴ9X@B$tS@Bȴ9X@B$@Bȴ9X@B1@Bȴ9X@B@3v@3@0#@0#E@/E@/@/b~@/PH@/b~@/ k@/b~@/F?|O??ցC ?N?_?Dj?-/ge?|O?)[D?*|\/?ܑ ?f_Ԋ@@@@@,@,?????? |ӳ?|?%&?$c)i@#Hr?@B\?EuAn?D7ZB?!%tc?=DX`@f3@@W ě@W@W ě@WH@W ě@WU2a|@W ě@W.@W ě@Whr!@W ě@W\(@ -=p@ۥS@ -=p@"@ -=p@1@ -=p@ݥ@ -=p@,<@ -=p@+@8~($@8Q @28r Ĝ@25?@1U`A7L@1P@0fYJ@0a$/@0fYJ@0a [6@0fYJ@0aX?W$B?M?GM?gk\0?W?f]G? ?-',?I?= 6?5D?U3p?:%?u/X?m!?0? -t?|)-}(@@@@@,@$???????7[E7m ?V]?5gD@WdP6@ڦ??l=I 6?]5?+e ^? r@o=@@(\@(\@(\@)ԕ,@(\@*qi@(\@)*0U2@(\@'_o@(\@($ x@+-V@+-V@+-V@+-V@+-V@+{@+-V@+D@+-V@+񂩓 @+-V@+n@3֚,<@3֚,<@1S@1֡a@06-@06+ J@.L/{K@.zxl"g@.L/{K@.8YK@.L/{K@.8YJ?| ?Kb?tcj?lDžN9?~|+?t?| ?Q_ڸFN?ދ"?@[*^? ^??sx/T? J?򯞼? SF???j@@@@@*@*??????*fS &?#}92?06@(ы@+B?C]ݷ??8!?`>;/?/HW@f%t@@)x@-V@)x@G@)x@z@)x@E@)x@@)x@ě@ I^@G{@ I^@z^5?~@ I^@<ߤ@ I^@$/@ I^@.@ I^@H˒:@6O M@6Fs@4kS@4?ә~$?W ę\?,|?Aź? @@@?@0@(??????՜@v=?]ںj?4-"4m@@FUz?svO?K.,?)Ba?Aby@^^8@@=E@<1'@=E@BZc @=E@>@=E@<Q@=E@A'@=E@En@IdZ@Ifffff@IdZ@Ibu%F @IdZ@I^Q@IdZ@IeO@IdZ@Ia [@IdZ@I`hۋ@5e!.@5bѷ@2 ě@2v@2Vu@2H@2B@5@2Htj@2B@5@2CnP@2B@5@2@[6?2}?j`cq?b?,Z?bJֵ?>V$?Ol?o ?}7.?.}$?Ll?^̀@!?DU#L?.]L?LO?sO>?q' -B?Xk-7@@@@@,@*???????RSf`RV?Fp7?7r{3@>@Id@ܼ?enh?V*>W?S6?@pM(@f:p@@I^5?@Nf~6@I^5?@JV|P@I^5?@JgD]2@I^5?@Jʣ%@I^5?@HV -@I^5?@H}@I@@I8>Z@I@@I;g@I@@I=@I@@I?q@I@@I?^ie@I@@I>3@4qiC@4oԢN@1+ I@14@04tj@03F&@0&YJ@0%ȓ쿊@0&YJ@0&=Hk@0&YJ@0&Q?ɱb)Q?݁;D?lȼ?J_?T?;G 1?I'%?bR?%f[?Lo2?t ?lCY?As $?׫}F?׳S\?h?=?*@@ @ @*@8@2??????V&$?Q)DYN?.pPn?Fp 2j@Jh#P@I>/A8R?I(?aU^%d?*/s?챝@mk@@5-@5hr @5-@5sh@5-@5*0@5-@5ح@5-@5?@5-@5@`A@-@`A@ - @`A@tj@`A@+@`A@Hˑ@`A@~@6#g l@6#33333@40ěT@4+m]@3-V@3`A7L@33&@3 ԕ+@33&@3H˒:@33&@3!-?F/P5?Fp3?=?Kh?niʢ?Vs?h%?Y͍_?*1 V?|ɽ?9.?pc?:;{?D(o? F2?aI*?y\,?,Ro?9@@@@@*@&??????pO?aF?Qaܪc?R 8a^@5t@Kz?pP*\,?r3Uv?ӥ= ?ӻ6ݣ@ez@@)`A7L@)a'녞@)`A7L.@)`A7L.@)`A7L.@)`A7L.@)`A7L.@w߾vȴ@w;6@w߾vȴ.@w߾vȴ.@w߾vȴ.@w߾vȴ.@w߾vȴ.@7s@7<쿲@3.Mm@2"`.Qn@1ce.NC,@1ce.NC,@1ce.NC,?"@8#@8:@8 >+@8I@8?|ǵ9@8@8i@8@8S@8?z&F@8 *@8p@8@8 @8 @?@N+@0`A@N+@R@N+@Kq @N+@Mw1@N+@K~(@N+@Lq @Bu@B\*@Bu@Bxl"h@Bu@B@Bu@Bu!@Bu@B<6@Bu@B@9#g l@9 m]@3{ I^@3|I^5?@2{Ƨ@2ylD@1YJ@1'RT`@1YJ@1:@1YJ@1TɅp?us\?܄ (?5$?S;~?i]i?VFNLG?\ OZ?Èa?pI??#:d ?T?P(?n?U.?z_? Y??vucʲ@@@@@,@(???????x[׮?YZHu+?byZ?~~Pj@K1xD@BS4?Nxb?L'?z\?68^@rw@@F@b@F@duE@F@-#N@F@Ԗ9@0@F@@F@ϔ@n\)@nO;@n\)@n (r@n\)@ne>@n\)@nꐃ@n\)@nl@n\)@nŜ}@5@@5P(@1sS@1n+d@0_-V@0^k+@/L/{K@/W<@/L/{K@/G(@/L/{K@/y?| V?2x? -HO? T!?/+I?fq\?_Kt?5]g?w9L?]]?M?,?w{??8K6?J™a7?ˌ?' Z@@"@@(@:@8???????0xHXD0?6ws??#%>3@v@nX?W6hz?_y1Y?p?|b|@hwu@@gO;dZ@gP -=@gO;dZ@gPy @gO;dZ@gP)'@gO;dZ@gPﲫ@gO;dZ@gO.֧@gO;dZ@gOzc@=hr!@=@=hr!@=$ @=hr!@=[SJ@=hr!@=]U\@=hr!@=G3Q@=hr!@=q@3qiC@3ha:z@+A7K@+0"@)@)KXP@gP Y:@=u1?=RjO?eZ?>R?<+59F@g@@cn@cě@cn@c[7@cn@chۋ@cn@c @cn@b6z@cn@c[6@vȴ@5?|@vȴ@@vȴ@%@vȴ@@vȴ@6z@vȴ@U=@4 ]ce@4 -S&@0|hr@0v_ح@0-V@0͞@/@/\N<@/@/.H@/@/vȴ9W?2}?Čۨ1A?$C@?ƠJ-M?5 g?CXs?}ѿ,?u߿?O(?Hn?77? f?z_?l?qn?!ˠ*(?gϵ?fW@@@@@*@*??????G;z?Q?;Bk?)Y F@cw<@\?Zնͻ?JM?rw9q?R*@g0`@@t.@t.@t.@t.@t.@t.@ -=.@ -=.@ -=.@ -=.@ -=.@ -=.@8쿱[X.>@4I^5?.Mm@3"-V.Qn@3~($ .NC,@3~($ .NC,@3~($ .NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @"@"@"@҈p@"@]cg@"@64@"@u@"@@Vu@VE@Vu@Rs@Vu@Sg@Vu@V}Vm@Vu@SE@Vu@OA@4}:@4|Z@1S@1a|Q@1-@1rH@0O M@0hۋ@0O M@0\@0O M@0Q?| ? ?zkr-?5~pY5?Xs~?#{׈?1j?4 "?c#$?ą6 -?SY?^?@A?l?`x?ȏG?ĭ?{2@@@@@,@(???????G;SxPa+4?M\?@5q@q@M@Tl[}@2tj@2:6@1䎊r@1KV@1䎊r@1+/i@1䎊r@1<'??| V?e?]R?T'?$cCy?w -?}#?hS?83_K?[l7? T?s;?+Z?ɑ6?c: -{?TJ?VZ?}@@$@ @$@1@4???????)8G6s?Q^#A?*B@؟b@n?q}M'Mq?Jfp|?ߝ??s@fLe@@y"@y@y"@{:@y"@xwP@y"@m&@y"@Dk@y"@}b@'-@'RT`@'-@;I@'-@3GO_@'-@/H@'-@+@'-@,^ @5@5.h"@2 I^@2{Z@2)"`@2,,RZ|@1a:S@1fA+@1a:S@1d[Hh@1a:S@1dS9=?~?J?38o?>q?2M7@|@*s?^z3$Y?Rńg?E{j?@g{l@@+R@/-@+R@a.H@+R@Q4K@+R@H\@+R@90 @+R@> (@1@׍O;@1@W@1@}@1@ (@1@ө*0V@1@\@3`D@3Zu%F -@0O|hr@03@/;dZ@/!@.-C\@-rGE9@.-C\@. ᰉ(@.-C\@.ѷ?| ?o|Z[?@?4?di? d?| ?{6?Ēۇ?%i[>?.<?Y@?]?ir?Ͱ{?a ? Ğ?t@@@@@,@*???????fn\O?CbkQkL?Ss@5v@+?cmo?t>12?ϵЛ.k?WEn_K@e6,@@cS@cS@cS@c<@cS@c7LG@cS@d@cS@a"@cS@aP:w@m@m@m@x@m@ ?2@m@\@m@$$@m@<0z@4~($@4$@1TS@1Uc_:@0Vu@0AH@0@4@0q|@0@4@0E -@0@4@0pJ?}@TΥ?19?);`?.?Cju?0_x?|h2:?ͣ?nJd?;4?2rtf?z(-%?n3?!??V,?jGr?~3F@@(@ @&@3@3??????+#;)*~@?3DM@? 0V@c@S9t -?S`f?A1g7ml?pQZ+? \{|@f`^@@J=p@#n@J=p@f@J=p@ -=p@J=p@#{J#:@J=p@;dZ@J=p@@/-@33333@/-@)^@/-@\)@/-@ :)@/-@.H@/-@[W>@8D@8C%@4I^5?@5u%F -@3A7K@4W@@2O M@3X1&x@2O M@2l!.@2O M@2 wkP?Q,Ě?(x~?azL&{?lmĎA?W?>ԛ?Br?=ʄ?X43}?_@@@@@,@*??????~*l?fEz tg?#'@@?V?8?גuȧ&@6 zͯ@qF=r @@^5?}@ cg@^5?}.@^5?}.@^5?}.@^5?}.@^5?}.@V@C@V.@V.@V.@V.@V.@804m8@80 - !@5?|h.Mm@47KƧ.Qn@4u.NC,@4u.NC,@4u.NC,?-,@8#@8:@8 >+@8I@8?uq@8@8i@8@8S@8?NN@8 *@8p@8@8 @8 @?@B\(@L1&@B\(@=@B\(@A|P@B\(@OiDg8@B\(@CF]d@B\(@H9X@\(@`A7@\(@_@\(@@\(@f@\(@)^@\(@y|@9Y*0U2b@9O@5ٺ^5?}@53@5M-V@5T?@5m8@5%=L@5m8@5eں@5m8@5\N?>sN?/>?'Qt?~???n?NIɮ?аl?5U??_Q -?k?mֿtE?nO?4y?Dۖ?7r_?24 -@@@@@&@"??????P(XN ɔ?U?kj.@I@U@&O?s 5ӰD?>|?V&Sn?b?ݽb@n\C@@1@*@1@1g$@1@(@1@#M@1@A@1@Ԏ@ 7Kƨ@@ 7Kƨ@@ 7Kƨ@h*@ 7Kƨ@ -[@ 7Kƨ@۰#@ 7Kƨ@jP0@8D@8>6z@3R I^5@3R*2@2tj@2H:+h@2fYJ@2cr־@2fYJ@2bհ߻@2fYJ@2c$?d?C7j?r8?% ?$Q?R?:Ƶ?bz2?Jz*(`A?$ H*?cP{(?` ?,`I?f?[edWR?|B6g?1xk?RR@@*@$@,@<@6???????˻̀?K/?biLR?XK_@zp@@?%~-M;?n Es@b2G?b[@ob(@@-V@([@-V@,/{J@-V@0)^@-V@*W@-V@+ I@-V@-:@hs@lD@hs@C\@hs@_p@hs@3333@hs@*0@hs@KƧ@7}:@7u%F -@3n5?|@3nH@2`A@21@2~($ @2q @2~($ @2A@2~($ @2a@?)T?vucʲ?M?Z)?i]i?U6@HS@H1'@HS@HPH@HS@H1'@HS@Hp -@6:@6@2&+ J@2'sPH@1`A7L@1֚,<@0O M@0 ě@0O M@0α2X@0O M@0;5Y?1j??ցC ?&t?$?bb<]?Id?=?<ظ??ʾ?ZX?-@@*@*@*@9@3??????'ڶ@?ȁ](?Y@ -&@^b@[?3KOح??1s?u%?}_%@f/M@@*~#@*~$@*~#@,1&@*~#@+:)@*~#@+C@*~#@)@*~#@)7Kƨ@-V@nP@-V@볶E@-V@T`e@-V@Z@-V@벣S&@-V@벽<6@1cg l@1cg l@0+@0&@.1&@.Q@/@/Ov_@/@/Ձ$1@/@/_ح?^XN?ƿ57?WD@?ݶ\r?E@?+ʦ_?bV,?NۈV(?^ o?Y?qN?wN z_?b@?˛͇q?m+&?!ˠ*(?b/bJ?L@@@@@,@*??????T01$y?>D,?!W"2@*Bj.@z?M-K\@?0jK'?K3 ?W5-勇@s~R@@hI7Kƨ@hH\@hI7Kƨ@hOO M@hI7Kƨ@hO4֡b@hI7Kƨ@hMU@hI7Kƨ@hJf@hI7Kƨ@hK]c@Zv@ZG@Zv@X74@Zv@X*0@Zv@Zxl"h@Zv@WXf@Zv@X}H@4~($@4)^@0@0@0W-@0VE@/#9@/A@/#9@/p:@/#9@/@?|O?Z? ?̒?ǹT?F0?|O?4 "?pM?@[*^?A B{? R?>Zw~?vv?6~A?I?0&YEd?$@@@@@@(???????FM1XDT<?=_?+@hJب@Yv?]|?LViS?\Ɲ?w7z@fp@@qZ1@qY@qZ1@q^҈p@qZ1@q]Vl@qZ1@q`@qZ1@q]@qZ1@qcn.@1&@\(@1&@jg@1&@@1&@~"@1&@@1&@_@6@@6@3r Ĝ@3g @3>Vu@3D,<@3OO M@3D7@3OO M@3S9@3OO M@3M,P?)>PJ@"@6G ?8? -A?n ?Xi6S@s Or@@?|@m@?|@}H@?|@@?|@+j@?|@e-@?|@4m@ovȴ9X@os@ovȴ9X@ovFs@ovȴ9X@o|hs@ovȴ9X@o~Ov_@ovȴ9X@ow4m@ovȴ9X@oz1@7DO M@7Ib~@3+@3Ov`@2j~@2j~@2n_o @2mcA @2n_o @2p@2n_o @2pD?3a=?:aGS? ??:Z2b??OPl[?K{O?f;m?f?~m??Ll? ,?/X?H?[Rb?3_?6?DΊ@@@@@,@,???????W+'<?B O?\-=@xI@ozX?aba?s,?-~L?י&Zw@nnG>|@@dQ@dy@dQ@du"H@dQ@dqQ@dQ@d @dQ@d]6@dQ@dvL*@r-V@{ƒ@r-V@g ^@r-V@q+@r-V@vqX@r-V@r4Y@r-V@pL@9䎊@9j~#@3r Ĝ@3f@2-V@2X@1n_o @1qW @1n_o @1p{f!@1n_o @1qߕX?|9?h[? !?cnvm?\,?6-"?%]?Sq?hAů>?tU?0 (`?k$z^s?0Wn?%?{*Č?.P3c?PBc,w?Z@@@@$@2@,??????RDd?e׋%u?+vFV@d'0@s¡f?vL!?A8?vOD?z A@qn~@@Z{dZ@Z{dZ@Z{dZ@Z|N=@Z{dZ@Z|?@Z{dZ@Z|C,z@Z{dZ@ZzkP@Z{dZ@Zz@s3333@stj@s3333@s*0U@s3333@t*@s3333@s*0U@s3333@sMj@s3333@s@N@2:@2qu"@/`A7@/4J@-Z1@-āoh@-oi@-/@-oi@-JM@-oi@-oi?^Q5P?$?4!p?_?Dj?2I/?bV,? g?ܿ?9~F?Cq$u?_~t?bMky?l? SF?xqC?pk Y?M684@@@@@,@,??????P;'?,f?D yX@Z{ @sm9?;ٸr?ǹs?ri?'ϕї@s}@@/-@ n@/-@1 @/-@.}Vl@/-@1N;7@/-@)7Kƨ@/-@*W@%@xbM@%@'R@%@(@%@M:@%@/W@%@M@9e!.@9o䎊r@4?|h@4 ҈@3-V@3iDg8@2OO M@2W@2OO M@2R I^6@2OO M@2OrGE9?xg?y)3c?2 :M?Sz? ?OPl[?vc?*l?Î -i??[fbF?to(?'@hQ?vv?5q?{B0?q?<שN@@@@@&@,??????dJ>^@)<}?`*?_>z0@,mG@}N?nNs?ntPӐ?~–?˓"v@r@@w@`A8@w@ʼn~c@w@Kg3@w@ä]@w@Eh@w@zy@H2-V@GƟ@H2-V@H<@H2-V@H&2=3@H2-V@HV@H2-V@HLp@H2-V@H0k@8r@8-i}@3^5?}@3BHmJy@2tj~@2od.l@28D*@2B,F@28D*@2Jd@28D*@2@";?& ??}C?C I??>$W9?xHgr?TP? -ic?t?C9?_RS8?~eB?_5 ~?y^@@(@"@&@9@8??????>|d?z?jX?q^Ƚ@[@H稜?*hN?t9?QVW@6c @k e/@@@B I^@@B I^@@B I^@@B @@B I^@@E@@B I^@@ES&@@B I^@@>"@@B I^@@?b}@h\@i7Kƨ@h\@mw1@h\@m'@h\@l>A@h\@fFt@h\@gz@27@@27@@.A7K@.v}Vl@-dZ@-<64@,QN;6@,KƧ@,QN;6@,KƧ@,QN;6@,KU=?^XN?DE??U*?2F0?SJ?bV,?[u?;~?!zt9?|+~?:i?g)?Ϸ+?]?3{Z?fW?gϵ@@@@@*@,??????fbTb5@?Pv,8?Q ;S0s@@A^@i|vr?_,?`$ ?[O?B@@r~@@\1'@MV@\1'@KC,@\1'@x_@\1'@r:)z@\1'.@\1'.@U$@NzH@U$@fffff@U$@Y=c@U$@V}Vm@U$.@U$.@9-v@98YK@4 I^@4v@3>Vu@3)@2YJ@2Zc@2YJ.NC,@2YJ.NC,?p?HyR?4E!?lfW@8I@8?ŦTF?݁E?Rt?@8S@8?$A?P(?%Zo ?&@8 @8 @@@@????@/ٙ.@/ٙ@0Fu@/ٙ@/Y@/ٙ@0@/ٙ@0 P|@/ٙ@0SM@܂ I^.@܂ I^@g l@܂ I^@f,<@܂ I^@u%F @܂ I^@C,z@܂ I^@1&y@7`D.>@2ԛS@2@1-V@15Xy@1z,<@1T#@1z,<@1W&@1z,<@1Zu%F -@8J?4o:v?(`ƴ?r,? P Q?@8J?ѿ,?YEX?0+Pl?6Ey?Jr.@8p?e+?yjk?π9"?:/ʼn?8@@@@,@*?????@.O@+ I@.O@,VϪ@.O@+)^@.O@.@.O@._o@.O@/- @[/@[%Q@[/@[1'@[/@[Y@[/@[}H@[/@[쿰@[/@[Q@8v@8D@3uS@3vȴ9X@2]V@2](@1D*@1Ov_@1D*@1TɅ@1D*@1 ?5}'??mGSq? ?>C,4?f]G?K{O?Hz?Zq?#X?h_N%?Ր?ɽ4?%y*=?@A?GZ?o?&f@@@@@,@(????????Z. @?22p?:m?^"$@.x@[_?MLn׸?s\,W?F}?by2@pE@@X@X -=q@X@W#x@X@X}V@X@WQ@X@X/@X@X I^5@޸Q@j~@޸Q@1&y@޸Q@ '@޸Q@̈́M<@޸Q@@޸Q@ݘ@6?@6@@3R I^5@3IXbM@3tj@2xF^@2@2GE85@2@2nO@2@2N;?F/P5?JT? -?8~?X%?9j -?|O?YYFpb?~$9y?0%rp?\r?M>?5q?kc?}??4i+?;WZ?"`@@@@@*@(???????.5@U?WF: "@=E@>ߤ?@=E@>m\@=E@>m\@=E@<@=E@<@M@NV@M@M@M@N_o@M@M\@M@M&@M@M&@0e!.@0eoiDg@*j~@*Ʌoi@(E@(D@(Bu%F -@(Bh ԕ@(Bu%F -@(Bu%F -@(Bu%F -@(B@5?OW?GA?>f?ƠJ-M?@ A2?$?;0?j%Ra?['?C B?@?M\ٓ@?!?u?oܧ?avh?#.?O|q?@@@@,@,??????Kt? ^7?0L`Z?#@=P@Mh??}TYT#?!PA?x*? *<['@s.v@@\@$/@\@ce@\@#@\@U@\@iDg7@\@ Ĝ@,1&@([@,1&@ - @,1&@!TɅ@,1&@@,1&@b|@,1&@H@7!.@7݊ڹZ@3\I^5?@3V@2`A7L@2hr@2䎊r@2vYJ@2䎊r@2ufA@2䎊r@2w4m?Y͍_?"'?=5?IXo_?(E?X8 ?=՛?(uw,?/܊?ڼF?ҸBz3?:? ?7&c?`͓=B?yh?aI*?[ S?$syW?R ?Vá?"#@@@@@,@$??????_:?|aP?eL?cRl~@9K:@f/Q?r)2 ?S8]?)[C?4dP@l%@@/Q@/5?|@/Q@/F -L@/Q@/fC@/Q@/Q@/Q@/ح@/Q@/˒:*@8Q@@t=3@8Q@QX@8Q@7KƧ@8Q@>m\@8Q@>\M@8Q@>6z@8I_o@8Sg l@3|hr@3Q @2t@24m8@2Eu@2Aohی@2Eu@2Ae@2Eu@2Bs??t?'? -tI?,Z?[5?lP {?u?ଚw?t(?`i2?]vM ?K ?#Z?)h?9@0w?cZ?aʕTI?`i2@@@@@,@(???????B 65?E?*k@/!dJ@>F?`&D?*Unԣ?! A?@-o@or94@@Pqm@P}E@Pqm@Pm(@Pqm@Pw1@Pqm@Ps3334@Pqm@PsMj@Pqm@Pu?|@33333@+ I@33333@2 ĝ@33333@12Y@33333@-(@33333@0bM@33333@49Xb@7v@7@3ahr @3al@2~Vu@2U=@2+xF^@2* @2+xF^@2,j~#@2+xF^@2+U=?ꯨT?W[?%HC=s?V0O?b\?_ ??c?ZzZ?!zt9?qN?X78?)dKA? -?>Zw?rsw?{2?їJ@@@@@@??????N?_@?TUb?=Hl@PtЈ@/?j VM?Vk0?ЛgR9?2o@oPo@@>XbN@?@>XbN@>Y@>XbN@>PH@>XbN@?R;@>XbN@>iB@>XbN@?n@μhs@\@μhs@λdZ@μhs@ξ"@μhs@θy=~@μhs@γPI@μhs@ƍ@7۹~($@7ԇ#@4° ě@4F@4"-V@4'Xe,@3c@3i (@3c@3aN;5@3c@3U`A7L? ?ܡU-?=5?Q+v?l(? Y9>?q!U?[G,?Et'?%ja?pFs?s( ? ?j?Vr?2Yy?P;ro?\?/dž@@@@@(@&???????C4(h?&?>ҽRD?h]*@?XbZ@ξe=N?`u ?SkQ??3X\*V@gh@@glC@k I@glC.@glC.@glC.@glC.@glC.@˶E@˴9Xa@˶E.@˶E.@˶E.@˶E.@˶E.@6쿱[X@61@4)+ .Mm@4tj~.Qn@3&.NC,@3&.NC,@3&.NC,?| @8#@8:@8 >+@8I@8?)T@8@8i@8@8S@8?ЙQ$@8 *@8p@8@8 @8 @?@ -o@&x@ -o@_@ -o@N<@ -o@qk@ -o@q@ -o@ Ƨ@ز Ĝ@g+ J@ز Ĝ@جVϫ@ز Ĝ@اfA@ز Ĝ@ءn.@ز Ĝ@ذ`A@ز Ĝ@ج~($ @7^H@7X@35S@3,u"@2K"`B@2A:S@1_o @1*0@1_o @1͞&@1_o @1^5?|?L?5YM?^*G?d?bJֵ?ɖw?f<4?݁E?h?,ў? R?'qTrQ???anñ?ZaizD?*6SpK?8wx?<שN@@@@@*@&??????X?t -X?mZ~sP?mSE@ -{@ت5`?{8)w?{E?[?E<@sI}@@+Y@+U\*@+Y@+f -L0@+Y@+qhr!@+Y@+B3@+Y.@+Y@+Ev@MS@M\)@MS@Mȴ9Y@MS@MKƧ@MS@MmjO@MS.@MS@My=@8įO M@8jOv@4+S@4Fs@3A7K@3&IQ@3L[W?@3)ѷ@3L[W?.NC,@3L[W?@3ݗ+k?us\?s ?z%d?4M^@8I?YM?Wl?5:7? %w?tm>@8S?TEkc?Ny?1?DU#L?a @8 ?>C]@@@@@(?????@ 1.@ 1@ ]#@ 1@ |,*@ 1@ AAgD@ 1@ C`@ 1@ P@@".@"@e@"@ꎠ~@"@ߙhZ@"@ߪ0O[@"@߈$*@8>6z.>@3hr@3is6@2tj~@2mae_@2[W?@2dqY+@2[W?@2dۿ{@2[W?@2j¯@8J?=ذc?17l??UbBl?H!?|Js3@8J???2OozU?ek~?Xqz?4PTF@8p??-b_9?>?r꟧?hS@@ @,@<@:?????@}b`B@}s3333@}b`B@}` qv@}b`B@}JL@}b`B@}X74@}b`B@}MM;@}b`B@}QX@8Q@8Q@8Q@ 'RTa@8Q@?|h@8Q@*Ov@8Q@6+j@8Q@=p -@7@7{J#:@38r Ĝ@3=v@2v-@2hۋ@1@5@1!-w2@1@5@1,zxl@1@5@1oh?]?B-?Ԕ?V0O? -0ggk?$Nx?]?u(?Pт?J6b?[h?⍄o?*wjs?&!+?va4z?0ڌ#?? 3?@@@@,@*??????p!I$?qKx@?aFgXs?0:{@}SAc@3^g?pp?> T?.@?kEPS@rߛX<@@@3333@@^5?~@@G{@@Q@@D@@"h @L9Xb@L9Xb@L9Xb@MaG{@L9Xb@L9Xa@L9Xb@M)x@L9Xb@M?@L9Xb@LF^@5#g l@5# @6@ ě@6>BZc@5;Ƨ@5+Q@4Q@4sh@4Q@4A@4Q@46z?|O?;$.?}vM?~ -]z?3sG &?8?}ѿ,?J2?P?Ţ/? U?VTJ?/X?ܣD?$M?vc?2:U?a#@@@@@@"??????D3x?mjh?Lɭ=^?e|@z+@L^ ?g N?>7?5Vl?:C@a/ɷ@@(.@(@ @(@xJ@(@~[|@(@r@(@ Z@C.@C@h@C@'4@C@ .3@C@}o@C@vE@8v.>@4@4R꿪@3V@3} Z@3iB@3;3T@3iB@2Apz@3iB@2ӵx,@8J?J' o?ps?7#w?Q'?jT@8J?S+?,C?V%/@6_?5I?(@8p?\?37?[-$?^?J^&+@&@"@&@5@5?????@V@/@V@|64@V@ohی@V@f?@V@A7L@V@{"`@UxQ@UXQ@UxQ@U_p@UxQ@T@UxQ@U"7@UxQ@Uu$@UxQ@UH9X@6H@6oiDg8@2 ě@2#w@2R`A@2&@1c@1/ @1c@13ߥ@1c@1 -=p?X?Yd?R&?a]?>C,4?)?CږF?F6?o J?|1?6P?+NP?WHV?RQF?$*a?]=?ܑ ?Cns@@@@@@??????z??~k)?Oߋ@듸@J@UhY?Ma7?a-/U@6? (@Q@r]C@@;dZ@<1'@;dZ.@;dZ.@;dZ.@;dZ.@;dZ.@ɺ^5?@;dZ@ɺ^5?.@ɺ^5?.@ɺ^5?.@ɺ^5?.@ɺ^5?.@4 _o@47c5@0}hr .Mm@0tj.Qn@/#Z.NC,@/#Z.NC,@/#Z.NC,?|b@@8#@8:@8 >+@8I@8?~?J@8@8i@8@8S@8?gl˹@8 *@8p@8@8 @8 @?@@DT@@E`A7@@DT@@F3H+@@DT@@FBN@@DT@@E1@@DT@@CmO@@DT@@Cj=@H&x@H&C\g@H&x@H"@H&x@H,@H&x@Ho힟@H&x@HȜ@H&x@Hl3\@3䎊@3e+@-"`A@- ~@,E@,F!;@+At@+l@+At@+il@+At@+17?}#?V??Qϋ?/?GIv ?| V?I?I?4k?`@?PG&?n3?lJ?wy2u? CB ?΢?΢@@,@*@*@8@9??????7Xt?*ķT? @@D@H!?J|Y?(Q͔0? ӑcE?^Z~@g8@@\(@Y+@\(@_@\(@[u@\(@dT@\(@_F@\(@^ (@Q@š@Q@u#@Q@,@Q@ĵ?@Q@ĨT@Q@\(@7N쿱[X@7L~($ x@3|hr@3<쿱@2-@2ܥzxl"@2c@2ba|Q@2c@2ctj~@2c@2bp:? ?Qi{cj??[d?_dA?*j?}ѿ,?& ?f?Җq??"(L?Rw#z?2}?/Y}?Y8Z? .0v?1&y@>9X@>1&y@> y@>1&y@>:~@>1&y@> @>1&y@>X@>1&y@><6@4:@4>@2?|h@2m8@1"`@1,@1u@1.H@1u@1~"@1u@1~qi?Ol?XMH?e -!C?Fq/b?bJֵ?F?}ѿ,?o ?DP?M =?*?k?|׿pY?Ãx?+QK?=aw%?_խW?<71@@@@@*@*??????S ?e8sB?3f:D?EQEپb@HѦSo@> ?R'J?dLzŏ{?T(?A(@f @@&KƧ@&O;@&KƧ@&h@&KƧ@&dH@&KƧ@&i2@&KƧ@&WP@&KƧ@&@r["`@r[v@r["`@r[\_?@r["`@rg@EB@r["`@ro*@r["`@rYUHa@r["`@ra߶v@5r@5r@3.5?|@3*d&@2A7K@2^@2m8@29f|Z@2m8@2خP*@2m8@2#=?}#?ą"$?T֗g?Sd? ?OIf^ ?~Ov_ح?Ad?>)Skh?QX{Q=?֢Gc?RѥJ?TA?T??5a&?5d| ?[?5# V@@"@&@(@0@1??????t>6z@4sPH@2 I^5@2ѷ@2`A7@2@1,<@1qu"@1,<@16@1,<@1U=?| ?nx?)?BI{?̒h?6={?S{d?| ?c?fM?**?pPȻ? 3W?W?*J?D(p?=aw%?:1?o@@@@@,@,???????k?*̭?*4)@@Rb5?JP3:E'?:9?M*-{\?~S^J@eے/@@@"@@@@"`@@@@A@@-@q^Q@q^Q@q^Q@q]Vl@q^Q@q^@q^Q@q^҈p@q^Q@q_o@q^Q@q_䎊@1S&@1S&@05?|@0ߤ?@0 "`B@0 jf@/Z@/74@/Z@/wkP@/Z@/ ?_\p)?h?,fC6?Њ?E@?t?bM_x?&uE@?:?9~F?!U?{?Q\?bMky?Ф?B@@҉@6s@6xF^@15?|@1C%@1ttj@1qN;5@1O M@1 a@@1O M@1 ~($@1O M@1 ?}?+!? -NA3?[d??| ? l?<?8"?Jn? w -?],m%@D,@/?yO-4?Ye9q?nW?_@a`JC@@l"@lvȴ9@l"@l @l"@lY@l"@lZ@l"@lPH@l"@lE85@0 ě@0`A@0 ě@2S'@0 ě@8U@0 ě@7kP@0 ě@.@0 ě@0H@7~($@7H˓@2^5?|@2S&@297KƧ@2;6z@1=ce@1D#@1=ce@1B&IR@1=ce@16fffff?)T?Sy?K]4?\GyƩ?GBm?VAў?1j?8{:?DP?|zk?+L5?[6?W|DC?zx?q?k,? -r?y\@@@@@,@,??????? \@;`?Wn̡ ?F@l@0?v$W?hg%_H1m?qFP?]l'-@gJ@@YV@Y\(@YV@Y3@YV@Ylj@YV@YڄL@YV@Y\@YV@Yա @ٷKƧ@ٳ3333@ٷKƧ@ٵf@ٷKƧ@ٵ{>@ٷKƧ@پ@`@ٷKƧ@ٵQ@ٷKƧ@ٺ,%L@3qiC@3s9?@1+@1<.?@1A7K@1U@1m8@1Vu`@1m8@1]l:@1m8@1@?}@TΥ?Pnx?Kg(^Յ?@5@5 -=p@2hr @2d8@2q7Kƨ@2p k@20@2.Ov_خ@20@20@20@2/iDg8? ?" ??(tUo?ϲI?jכb?|O?RwS5?3fvI?WA?=x?w8?@A?J?(?=aw%?q ?GZ@@@@@,@&???????J;#[ @?SA%v? @@Z@:J)?s/=?@0"?xI&v?؉7#@fg -!@@ A7L@ n@ A7L@!R<@ A7L@!TɅ@ A7L@"h ԕ@ A7L@҈o@ A7L@G@S@S@S@ -L0@S@O@S@L/{@S@YJ@S@oiE@0v@0%@*-V@*+j@*/;dZ@*/;dZ@)At@)4֡a@)At@)At@)At@)'/W?^Dw'?[e(@@@@@*@,??????Y"Ґ?BtQ/? lr@ U @5:k?Q?&<Xu?HR_?E=%@sZW"@@\&x@\`A@\&x.@\&x.@\&x.@\&x.@\&x.@["`@`n@["`.@["`.@["`.@["`.@["`.@7:@7Ƨ@3TS.Mm@2Vu.Qn@2s&.NC,@2s&.NC,@2s&.NC,?q!U@8#@8:@8 >+@8I@8?2}@8@8i@8@8S@8?}?@8 *@8p@8@8 @8 @?@f^5?.@f^5?@fHۋr@f^5?@f- q@f^5?@fXl"h -@f^5?@f=ڹ@f^5?@fA@+.@+@a [@+@bwk@+@Hp:@+@B I^@+@H@9 D.>@3+ J@3}!.H@3tj@2ӯ@1sh@1"`B@1sh@1ʟvȴ@1sh@1Ǔݗ,@8J?%ñ?%bI?^Z?̺ ?Fb%i@8J??-?6?~]?~NO@8p?E!'?c?'d㗅?y?M\{W@@@@*@&?????@@A$/@ˤ@A$/@˛H@A$/@Eڠ@A$/@@G{.@G{@~F@G{@{O@@G{@[2 @G{@2W@G{@u@4+ @4խ@46-@3A@3ush@3>Rbo@3ush@3bPܘ@3ush@3\t@8J?h/E?@ -?[9??mC?vC@8J?pt4?H$/걺?Rf?19:?VN4LM@8p?t?I&a?2?z'?٦SqUm@$@,@*@7@5?????@L1@L@L1@L1@L1@L 2ܶ@L1@LӜg{@L1@LХޢ@L1@L؋Κ@Q@X_8@Q@pc@Q@V@Q@]~@Q@"@Q@ѕޫ@8g l@8ȯ݅@4#@4(o8@3tj@3O1 @2YJ@2/@2YJ@2~s@2YJ@2ի?d_) ?B|3?LW ?0?N~b?7 -8?}x?| V?J9B?tT|2?!s6?ͮE41?^?|h2:?|4c+?ӝ!*?VE,?=?^ _?+@8I@8?1j@8@8i@8@8S@8?m+&@8 *@8p@8@8 @8 @?@ Ĝ@G@ Ĝ@@ Ĝ@й@ Ĝ@ 5Oi@ Ĝ@m@ Ĝ@(@>vȴ9@MO;d@>vȴ9@vȴ9@$<c@>vȴ9@6i-Ԅ@>vȴ9@8hq@>vȴ9@! -_g@8qiC@8TɅp@4 I^5@4鞦:@4)"`@4E޽@3 k@3U@3 k@3?)L@3 k@3YU?Z?-$? J0?TKF?j6?Y1z?Z?Xw8?4 m?16y?MQe?Dt۱T?A?j&?M?ot?13C1?{0+@@(@ @ @:@8???????gotlgp{%6?b?]ʠG݉@%|@0U#&0?qlkj?mR}?wB?Ǒ*@r)VZ@@^5?}@G@^5?}@~@^5?}@m@^5?}@G@^5?}@e+@^5?}@~($@?|h@?vȴ@?|h@?|h@?|h@?|h@?|h@?H˒@?|h@?b}@?|h@?.H@3H@3H@00ěT@00$tT@0$j~@0$7@/@/ @/@/ qu@/@/6?}ѿ,?{j?,fC6?ArU@? f?-/ge?|O?NۈV(?NJ?Y?݄ ?U3p?q6tN6?.x?֘>D?xqC?M684?b/bJ@@@@@*@,??????1?-T ?>ʃk@^P@?|d{?>PQ?6?(#r??U@g -@@Ni[@?B3 [@?)lIN@N< @ffW?^+H?MOY8? )p?Oh@k6@@J@JbM@J@Jȴ9X@J@J!R@J@J'/@J@J˹~(@J@Ja@7K@;dZ@7K@$/@7K@n.@7K@1@7K@~@7K@t@8^H@8Xu%F@3xr Ĝ@3|L_@2"`B@2\(@2!.I@2 k~@2!.I@2Ov`@2!.I@2RT`e?qn?dY{?ِ?c[=#?X?۝.Yr?F/P5? ,C?-8?X}iQ?nW*؄??;?/X?2}?Aᆽ?l_zU? .0v?^^ω@@@@@(@$??????M `?pZ??3l?X%t2Z@J^:@h?\J?|&))?F?]O@k\ٿ@@XbN@XbN@XbN@b@XbN@C\@XbN@xl"h@XbN@\)@XbN@*1@[E@[E@[E@[F@[E@[\)@[E@[?|@[E@[X@[E@[3@4 xF@4J@1S@1Fs@0"-V@0!u@0._o @0,@0._o @0+aA@0._o @0+Ƨ?}ѿ,?[e_9^?-/3rD@'qJ@[uo?^.Ws;?MΝ8t?^G?oБD@gF(n@@ O@ \)@ O@ @ O@ A7M@ O@ @ O@ @ O@ F@Jv@JbM@Jv@J@Jv@JZ\6@Jv@J@Jv@J[]D@Jv@JF5@7 D@7snN@2r Ĝ@24@1Vu@1r@1iB@1b˧2@1iB@1g@1iB@183>?gQ?S?MV4*-?¤x|?4zu'W?یqnj@ %i@J?^ -ij?9P-mM?{I,?y1W@"@hGL @@`Q@`[B@`Q@`s@`Q@`帶@`Q@`vZ\@`Q@`W&@`Q@`G@4j~@4j~@4j~@3{@4j~@4E{@4j~@4n8[@4j~@4az)@4j~@4w@3 ]ce@3 >(@/`A@/GE84@-n@-G+@-Ǔݗ,@-&%S@-Ǔݗ,@-%@-Ǔݗ,@-O?~Ov_ح?ِ:?*[uƥ@`I?Ob@45+?Y2 ?7x\0?4pk>.W71)@4j{=@C@?Tk?Fά??Laf?8Z矣@h\@@O;@~$@O;@\M@O;@a@O;@2X@O;@A [@O;@Ԣ3@@bM@@l@@UXy>@@xl"h -@@˒:*@@qm@8*0U2b@8 >@3+ @3!-w2@3y7KƧ@3kQ@2u@2~vȴ9X@2u@2zu%F -@2u@2lN;?^*?vucʲ?/b?-i?rA:/?wb@?1j?HG^?wKv?-M?w{_?n%!?^?"`?@K/{?__C?X񄖆?/я@@@@@&@*???????SA}@?eF?f-ϝ3@R@HH? -4~:??:?zy @d\@@|nP@|nP@|nP@|tj@|nP@|@|nP@|a@@|nP@|&x@|nP@|&x@33333@3E@33333@2s@33333@2{@33333@2-V@33333@2s@33333@2a|@.@.@,tj@,A [@,wO;d@,w&@+p'RU@+p@+p'RU@+poiDg9@+p'RU@+p|?^Q5P?[e(?f_Ԋ@@@@@,@*??????SD?@?9dXc?1ٕB@|4@24R?Heb?2K ? V%4T?% `3j@s ;*S@@pV@pzI@pV@pI^4@pV@p I@pV@p=p@pV@pj~@pV@p@s-V@s-V@s-V@ss@s-V@sC@s-V@s,<@s-V@sD@s-V@sa|@4[~($@4[]cA@1hr@1n.@1KƧ@1 '0@1(@1'@1(@1b}W@1(@1<64?|O?M?1V?wX/8?.5?a1?}ѿ,?D.x?c#$?ݤ`h?=M?7&T+?y8#??6~A?{0? g?@8mv@8h ԕ*@5 I^@5e+@5{Ƨ@5~c @4h@4kC,{@4h@4h_G@4h@4k/V?^*?/>?Aa[?gٵV?wX?c+:X?[Xxp?C?I?J6b?>G]!?2;? G0?^?"x*=k?8k4?9j jh? )D)@@@@@@??????br"?^9?O{J0?npta@ -QI@ti?nK?< t?{I?tj%up@nE%@@ I^@ I^@ I^@ @ I^@9@ I^@s@ I^@R<@ I^@4m@:^5?}@:^5?}@:^5?}@;qu"@:^5?}@;"`@:^5?}@9@:^5?}@:kP@:^5?}@8*0@3_o@3y @0|hr@0Ar@/E@/3@/oi@/?@/oi@/oh@/oi@/&IR?}ѿ,?/)s?.Ү_ ?G?K!UČ?6J?| ?[ K0R?NJ?9~F?M?Qʾ? J?Ф? J?*|\/?? Ğ@@@@@,@*??????0 kh27"? 劓9`?'?"/Y@@@@@,@&???????aKTX8?I:岵Z?U D]td@G@ؠ.?hه?u.A ?3E`?9yO@eM-@@hH1&y@h;lE@hH1&y@hEn@hH1&y@hH@hH1&y@hH@hH1&y@hG_o@hH1&y@hGݗ@nP@I^5@nP@Mj@nP@:~@nP@bM@nP@.@nP@&x@6[~($@6MH˒@2+ I@2-@0-@0C-@0n_o @0nѷZ@0n_o @0n (@0n_o @0m,=?}?W[? ??ݶ\r?h|?W_?3a=?[ K0R?,& -\?>\?x9ξO?ȏ'T?gE=`?Q?`x? ?? -t?j@@@@@,@&???????L>R?Q.?Vs왮e?3b@hGY@U]S?k9?RCJ\|?NԊ$? @om2(@@M@n@M@a@M@Tb@M@3D@M@T@M@iB@JQ@JR@JQ@JS@JQ@JS ɫ@JQ@JQ Ʊ@JQ@JNp@JQ@JR7@5|Q@5L@1ěT@1+9k=@117Kƨ@11b@0YJ@09KR@0YJ@0T㧧@0YJ@0u]M?|b@?;?([0?|?'I'?磮R?|h2:?,9B?jz?n-?E=:?ge\?oR?î!?}_?9Ýdk?  3?{)@@&@$@(@<@6??????0P,x?!&?tdE?c3c(?+d?y8#?Q?`x?*|\/?Rv? -t@@@@@,@,??????!z5E?T>i:@ -@ -L?3й*?Bb?s?@?EmIk@f@@/@/}2@/@/ks@/@/1@/@/@/@/\@/@/ @š@ɳs@š@ר@š@јz@š@*@š@Q@š@.@3D@3@N@1=hr @19xC@0tj~@0(wg@0._o @0-z;a:@0._o @0*{@0._o @0*yQ?~Ov_ح?l(?r[?(#O?>X`?[PP; A@^/@^7 -@^/@^q'@^/@^]@-J#9@-J#9@,A7K@,4 @+1'@+"a@+@+d@+@+\(@+@+f n?^6P?j?@?@y[?E>)?Ŷ?bQ넛?B?!.$w?5ei?а?Io?bM]f?6H^4?p®MG?$: ?%ip-?a@@@(@*@8@4??????P'k?6Lv?CPo@: @^ܨNe?E:I?":9?*?=0I0@s ZM@@~"@@~"@~쿱[@~"@H˒@~"@G{@~"@}IQ@~"@}Vl @mffff@m@mffff@m@mffff@m@@mffff@mtS@mffff@mn@mffff@mw@5YJ@5YJ@2n5?|@2l@2t@24J@1u@1ѷ@1u@1|@1u@1Z?}ѿ,?b[?#}i?!#?uxY?WA?1j?M7F?Qa C?RLnj?7=^9?A_f;j? SF?.?]b?XW~?$?Rv@@@@@*@,??????;O?)?9V?yCH@VQ@m3 ?Y,?9,%ބ?Oz?|Y1R@gc@@t9Xb@z1@t9Xb@tG@t9Xb@{/V@t9Xb@u\)@t9Xb@vC@t9Xb@@k=p -@k9XbN@k=p -@kA-w1@k=p -@k:>@k=p -@k=-V@k=p -@k:@k=p -@k6R<6@72@71N;6@2?|h@2z@2>Vu@2?r@1 k@1y=c@1 k@1x@1 k@1 k?2}?gˣ?0? R?[9,?S{d?UbB2?\?-8?uX?4{?{P?A'_]?>?T0??`i=?zy@@@@@*@&???????6>DY?S T?Q0@zJA7@k:V`.?vA72h?mm9?uR?/@k|%Fr@ @y"@y"@y"@~vȴ9@y"@ufA@y"@xV@y"@~iB@y"@{5W@Z1@]/@Z1@X4֠@Z1@W1@Z1@Y|@Z1@V@Z1@U2a|@6,<@6x@3ěT@3Mj@3-V@3 C@2\Q@2[W>6z@2\Q@2Yk~(@2\Q@2Zxl"h -?Ol?x)n?0?GZ0e;?(i^?-/ge?}ѿ,?^1??v@b?#X? ?Z)?7}?,DP?T0?ۮetZ?1?l@@@@@,@"????????yg5&@gnp@ -@EQ@D/@EQ@EO@EQ@K҉@EQ@C,zw@EQ@D7@EQ@B@4@ᅡ@n@ᅡ@ag@ᅡ@$/@ᅡ@~5?|@ᅡ@ح@ᅡ@䎊@6H@6حU@2^5?}@2$ xG@1j~@1@13&@11o@13&@11:S@13&@11?|O?: ?9a?_?<_+fj?=?sD?V i!?~?G>G?W;zY?998?T0?G - ?m~Z_s,?ij6?v> ?)@Jq@@@@@*@,??????:@G|?,o?n88??/X?J?C SG?C SG@@@@@(@*??????G7/j?43XN?;-O@Fx˻@i?Tqf?[? ?69A`n@g`{pR@ @Zv@+C@Zv@T!@Zv@](@Zv@gJSS7@Zv@Y@Zv@Z@_TF@_zBg,@_TF@_KoE@_TF@_gYH@_TF@_D*>@_TF@_f=@_TF@_U@9-v@94.4@4W+ I@4]0/^@3tj@3AZD@35sh@3=V J@35sh@368p@35sh@38vRЯ?3N?T?7?d2f?Pݱq??N'P?31?B?PQA?4L9?z?-ͧG??>D&?rywK ?dX? -A35%`3@}Y=֔@M#=?c-ʓM?^%=%?,x?H2/@j57@@7K@7K@7K@ I^@7K@3@7K@ I^@7K@ě@7K@ѷX@w@w@w@wR<@w@ws@w@wTɅ@w@w|Q@w@w7K@0=:@0=:@*j~"@*]ce@)F@)ؓtj@(L/{K@( -L/@(L/{K@( -L/@(L/{K@( -L/?^Q5P? ?m?ƠJ-M?tKc?$?bM_x? ,C?NJ?1'7?tP*?Z,a?bMky?.+?s@a?͓E'?l&?@@@@@,@,??????B*??)!>Lamj@M=@wpZ?8 ki?ķ?m#bE,?Kh@s2@@R@S3333@R@SZ@R@S|@R@S@R@R@R@R䎊s@ffff@ffff@ffff@1@ffff@E85@ffff@S@ffff@]c@ffff@思IQ@3H@3p:~@.`A7@.D@-1&y@-1&y@,jf@,Q@,jf@,P{@,jf@,C&?|O?GA?&?? Y?2A?}?| ?%F?d0R?-I`K?IM?eDQQ?2Y0?.+?2Y0?XLL?f_Ԋ?b/bJ@@@@@,@*??????>j#?":~?@S2h@l1;?B}1!7?7 p??r?{@fo@@?|@-V@?|.@?|.@?|.@?|.@?|.@|hs@ [@|hs.@|hs.@|hs.@|hs.@|hs.@8!.@8@4E?|h.Mm@3~".Qn@2䎊r.NC,@2䎊r.NC,@2䎊r.NC,?m*H@8#@8:@8 >+@8I@8?/@8@8i@8@8S@8?\;.=@8 *@8p@8@8 @8 @?@9kR.@9kR.@9kR.@9kR.@9kR.@9kR.@4vȴ.@4vȴ.@4vȴ.@4vȴ.@4vȴ.@4vȴ.@8v.>@4+ I.Mm@3PA7K.Qn@2.NC,@2.NC,@2.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @E@gPɟ@E@g@E@Pm@E@ub@E@cW@E@@7XbM@7Y@7XbM@7Y;$@7XbM@7a9a@7XbM@7ZG*@7XbM@7\)@7XbM@7_9(@6!.@6P#Nl@1+@13$e@1Htj@1H\e@0@0 @0@0K;@0@0K?¨Sp*?Ę'}?6nX?7e?8\\?D\Z(?\Η?[L?&aD2?`a^?-?HX?@V;Q?)?S?#?I?2o9@@"@"@@,@*??????I?@'KXt?)s[t?"{H8@~0@7[O?IkyQK?B{MҶ?C3? -*J@i\ @@\G+ J@\[Ē@\G+ J@\PNU @\G+ J@\I'@\G+ J@\_M5@\G+ J.@\G+ J.@tj@'a@tj@樢G'@tj@ٓU@tj@rѠ@tj.@tj.@8*0U2b@8@0f+ J@0h@/O;dZ@/҈@/\ߤ@@/`[6@/\ߤ@@/]b@/\ߤ@@/Y+ ?}ѿ,?h?1f_?A%?\7?װ*w?|O?4 "?ܿ?M =?=d^?h,à? SF?2]7??ʪ?@n?+^@g<@@n@x@n@ᰊ@n@qi@n@PH@n@'y @n@)^5?@E@1'@E@/A@E@{J#:@E@B @E@?|h@E@f&@7mv@7F+jg@3!hr @3Fs@2A7K@2n/@2+xF^@1 k~@2+xF^@2Ϫ͞@2+xF^@1C,zx?A[F?cX?LAJ.?[? -=p?ե8*?Y͍_?8$ -"?VǷ?g(Z%?w{_?覲CU?M3 ?C'?'#g?wZR)w?srY? m9@@@@@&@&???????v-Ҁ?!)?MxA1?gçdi@ Y@A?i*Lz?1Fg?~ ?B6`@c*@@Zv@Vu@Zv@Qhr @Zv@Ohی@Zv@O4֡b@Zv@N!R@Zv@Vȴ9Y@z.O@zI7Kƨ@z.O@z)^ @z.O@z)@z.O@z5*1@z.O@z,I^5@z.O@z6B@7@@7N;6@3!hr @3F]c@2Htj@2=O;dZ@1@1M@1@1ٓ @1@1۲m]?)T?bDҼ*?Q"Ě%?]]gQ?19?zWmo?ʎa]?ѿ,?S`G?fa]G?drK?f٭'?Uu6EE?J>??k2m?,6z@)v@)$tS@4䎊@4S&@1+ @1_F@0"-V@0"ᰊ@0𖻘@0%@0𖻘@0}Vl@0𖻘@0}Vl?|O?m0?Gy??r*?STS?=W?| ?r/B?^ o?y҇??Q?y8#?˛͇q?W ?2?їJ?@@@@@(@$???????D. HKޢ?,<Ч?19^@W3@)5Y?LFgz?QOA3?VLiW? vmt@fcTF@@;dZ@ ě@;dZ@Ѩ@;dZ@/UX!@;dZ@sFI@;dZ@ϴ(lQ ?(~?Қr(?qW?}@TΥ? ~5??*9?lt??X*?o?25?A?hM5?#Ŝf?ləd@@@@@2@4??????P?5"?E S?I3#H)|@[«@Q!U`?et?iGH -?ncXZ? -ϸÁ@e֌x:y@@+E@+9Xb@+E@+!.@+E@+3@+E@+XbO@+E@+!@+E@+=@V;dZ@V|h@V;dZ@V u"@V;dZ@VW>6@V;dZ@V@V;dZ@V($ x@V;dZ@V%@7_o@7ᰉ(@1|hr@1@1PA7K@1PA7K@0n_o @0n (@0n_o @0nc @0n_o @0n.2?Br?m?ě?G?'ř ?װ*w?ٛP?7fps?2'?@[*^?UBi?N`?6P?G - ? ݎ8?!ˠ*(?ad|?M684@@@@@,@"???????>¨ ?+0?Ek??987g@+w.Y@V?f@?W-ad?v)?F7M@n;@@yXbN@u\)@yXbN@y@yXbN@?@yXbN@s|@yXbN@zQ@yXbN@u!T@pR@qě@pR@pr @pR@pv@pR@ptj@pR@p,<@pR@p@8~($@8u!.@3@3S&@3t@3*0@2@2!-w2@2@2tj~@2@2n.3? ?(.?wm?(+Zk?&x?ĵ^M?#[?@p?S1z? -@pm6?m0?pT?\z|?Ǒ8E@oC@@ Ƨ@ L_@ Ƨ@ ~(@ Ƨ@ M=\@ Ƨ@ }@ Ƨ@ _/@ Ƨ@ D'@G{@ě@G{@]^Q@G{@7@G{@\v@G{@ I^@G{@`B@4 xF@4Ӳݛ0@0+@0/oz^@.&x@.@,873@,8|@,873@,8¤T@,873@,8 ?|B &?q?lȼ?-M+?XkZ?1 -qV?|b@? ?F{r?2Nk6y?a$d?VTL}?I? ?I??7is\?Vi@@(@$@(@:@:??????$5 ?'-?>@>D@ ƒ@?!*c#$?A=7?P%#?J.#gdU@f51@@A7L.@A7L.@A7L.@A7L.@A7L.@A7L.@-R.@-R.@-R.@-R.@-R.@-R.@87@.>@4hr.Mm@3KƧ.Qn@2:S.NC,@2:S.NC,@2:S.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @1@M@1@#@1@3@1@!-w2@1@ k@1@4m@@`A6@@<64@@D@@u@@#@@u@7>6z@7_o@2^+@2\@2M-V@2H@1Eu@1@oiDg8@1Eu@1B3@1Eu@1B I^6?UbB2?:aGS? -NA3?Att?'`? µ?2}?wi*?3j_?u]?H<??B`?D1#Xj@ƥI@)F6?^p{ ?gw/-?|>iH?g+@k@@nP@-V@nP@\@nP@Mj@nP@_ح@nP@E@nP@@S@j~@S@u%F@S@0 @S@bM@S@^5?}@S@)y@4@4Mj@0S@00 @0t@0O M@0~($ @0>6z@0~($ @0rGF@0~($ @0*0U2a?|O?°R;?K]4?q@v?1 v?P(@O@I("@O@V@+_;dZ@+_;dZ@+_;dZ@+^;L's@+_;dZ@+^h>@+_;dZ@+]xY@+_;dZ@+_I@+_;dZ@+^%ͳ@1^H@1^Vu@+Ƨ@+달q @+O;dZ@+^h(@+z)_@+zAM^@+z)_@+y`6@+z)_@+zS?^6P?f^?F??XkZ??bMn?V?j?7iU?5V?N)h?bM?:d?r{e{t?՛?mՙ@7+S.Mm@6`A7.Qn@5xF^.NC,@5xF^.NC,@5xF^.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 "@@az@@߸3@@x@@~2Q@@o@@t|.@ I^@͌ݬ@ I^@ڡڍ@ I^@ջ-@ I^@L,@ I^@ S{X@ I^@d3@8[~($@8[Xz'@3^5?|@3k6@@2`A7L@2 @2G k@2Oj@2G k@2Kk@2G k@2L:G?%h?>EG?wy ?6J?|yl?+ww?| V?ۜ,!?C^]?@Et1?BE?]?":,?R p?)G?6m?m?$^}V@@&@&@@0@0???????M?N,C?hiVٗ?\yQ'/@J@[? -Ư?ufv?>i@wA;@lű1@#@$/.@$/@@@$/@d@$/@@$/@^ -@$/@Ft@cݲ-V.@cݲ-V@c1@cݲ-V@caf@cݲ-V@cQ@cݲ-V@cl"h -@cݲ-V@cbM@904m8.>@4O|hr@4H*0@3~Vu@3}K]@3!.I@3 (@3!.I@3`A8@3!.I@33@8J?Q&g4?go]?IW`g?|K!\?V@8J? {?@N?!ϓ?' ?O[@8p?ɅoiE?bR?/?Oh6c?3@@@@$@&?????$@,1&@+R@,1&@Q@,1&@ qv@,1&@GE8@,1&@[7@,1&@@p -@-V@p -@)^@p -@r@p -@bM@p -@@p -@P{@5O M@5e@3 ě@3y"`@3`A7@3sF]c@3OO M@3A_p@3OO M@3C@3OO M@3C&?| ?I@3&+ J.Mm@2M-V.Qn@1.NC,@1.NC,@1.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 &@Bx.@Bx@BQ@Bx@B@Bx@BH@Bx@B@Bx@B@33333.@33333@,cA@33333@0)^@33333@-\@33333@$M:@33333@*W@9쿱[W.>@3S@3"h ԕ@2t@2JL@2z,<@2|hr@2z,<@2u84֢@2z,<@2v '@8J?4$?GM?tN+6?؍i?u}I@8J?Zk?cu%+?tm>?#\?º@8p?Q?8*??NVC&?R4@@@@,@*?????'@S@@S@S@S@e@S@S@S@,zx@S@F]d@ݲ-V@Q@ݲ-V@ce@ݲ-V@($ x@ݲ-V@ѷ@ݲ-V@b@ݲ-V@($ x@6cg l@6ql@-$tj@-$?@+Ix@+IoiC@)ߤ@@)(^@)ߤ@@)ߤA@)ߤ@@)Q?1j?3i?m?ƠJ-M?tKc?$?F/P5? ,C?/BH?; $?%y*=?X78?6~A?3;?u@>N ?͓E'?O|q?a+@@@@@*@,??????dZ?DU4?@4^+@4lC@3KƧ@3[҈@3+xF^@2Ұ ě@3+xF^@2?@3+xF^@2Ԏqj@8J?-ieZ?pZ?3}s?K?7e@8J?]veb?4Z~?ZhB??jy?צ75@8p?Ãx?}Tz?ނ F?Oh6c?!S{uu@@@@,@(?????)@+R@9@+R@@+R@"7@+R@u%F@+R@!e@+R@(K]d@\)@@\)@'/@\)@쳶E@\)@p:@\)@jf@\)@ѷ@9,<@9䎊q@4?|h@4C@34tj@35S&@3m8@35Xy>@3m8@3')^@3m8@3(*0?F^S?[?U#?%?U7t{V?iUv]?F/P5?ٔ!x?S1z?/{?g> b -@g#{[N@+@8XbN@8"@8XbN@9mL##@8XbN@9>&@8XbN@97Pb@8XbN@9 -`Z@8XbN@9#gT@dZ@dZ@dZ@֠8@dZ@ @dZ@l@dZ@!9 @dZ@8~@1@@1?⒮@-tj~@-'@,E@+'ﴞ^@+jf@+C*)@+jf@+\Zw~?Q?!A -m?Gϵ'?fW?ad|@@@@@"@,??????E1Q? Q?02?& -;[@R }&@8IO$?O)4A?G2zWL?xZN?^\E@f٘E@.@'7K@'7K@'7K@'TɅ@'7K@'@'7K@'|Q@'7K@'䎊@'7K@'[7@l@l@l@ԕ+@l@d8@l@ @l@l@l@ԕ*@0_o@0_o@+-V@+<64@*;dZ@*O M@*L@*K҈@*L@*L1&z@*L@*LI^5@?^Q5P?3i?,fC6?ƠJ-M??:?bM_x?u߿?j?4ډ3?@?R g!ڢ?b@?.+?8*?!ˠ*(??a+@@@@@*@*??????B:(>k?2~(y|>Q -@'1@?B9I?Av??n?37@s 1j@/@KƧ@x@KƧ.@KƧ.@KƧ.@KƧ.@KƧ.@@ֻS@.@.@.@.@.@8~($@8[6@5S.Mm@57Kƨ.Qn@5V!.I.NC,@5V!.I.NC,@5V!.I.NC,?UbB2@8#@8:@8 >+@8I@8?A[F@8@8i@8@8S@8? 1@8 *@8p@8@8 @8 @?0@o-@P=@o-.@o-.@o-.@o-.@o-.@笋C@@笋C.@笋C.@笋C.@笋C.@笋C.@8:@9?W:*@4 I^.Mm@3V.Qn@3s&.NC,@3s&.NC,@3s&.NC,?]@8#@8:@8 >+@8I@8?AH=@8@8i@8@8S@8?Lw@8 *@8p@8@8 @8 @?1@_[S@_^5?|@_[S.@_[S.@_[S.@_[S.@_[S.@&ffff@!G{@&ffff.@&ffff.@&ffff.@&ffff.@&ffff.@7Y*0U2b@7UoiDh@4+ J.Mm@4b-V.Qn@3䎊r.NC,@3䎊r.NC,@3䎊r.NC,?Ol@8#@8:@8 >+@8I@8?3a=@8@8i@8@8S@8?c@8 *@8p@8@8 @8 @?2@`o-@`p ě@`o-@`nO@`o-@`o M@`o-@`oV@`o-@`l[W@`o-@`l_@ t9Xb@ tG@ t9Xb@ t$@ t9Xb@ t*@ t9Xb@ vE@ t9Xb@ u '@ t9Xb@ u=@4䎊@4[W>6@15S@161.@0-V@0 @/At@/_o!@/At@/hۋ@/At@/O+@8I@8?@8@8i@8@8S@8?{@8 *@8p@8@8 @8 @?5@$Gz@$X0y*@$Gz@$dO*@$Gz@$͢`@$Gz@$g@$Gz@$I@$Gz@$|@@@@2"@@A9t@@ @@KsK@@Xdc 4@8䎊@8;h@5r Ĝ@5@617Kƨ@6qL5@4:S@4fLoj@4:S@4;$@4:S@4 ? MV{?N+9G??J?~}6?t2n?ܓK3??2;Z?!|/? .߳Q{?l1}?킑?B1P?Xz)??/e?lPR{?SF"@@ @&@(@<@9???????j -?x=l?vG[J?gvW@$wc)@e1?)Ӌ %?Zt;!?4,? f@fh飤@6@2;dZ@25?|@2;dZ@21&x@2;dZ@274@2;dZ@2zxl!@2;dZ@1o@2;dZ@1ѷ@/9XbN@/6ȴ9X@/9XbN@.#@/9XbN@.9Xb@/9XbN@.n.@/9XbN@.y@/9XbN@.IQ@8@8+jg@43S@4a@N@2ߝ-V@2F -L0@2_o @2fB@2_o @2L/|@2_o @2?ϱQm?vucʲ?njĕx?TnE?[5?VFNLG?oC?t?sw)?2}?KN?fF7? b?Az?qn?hc?Xk?8N0@@@@@,@*??????v>hi ?Z{?u'#@2@..?sV}z?iC?V.f?T|X@jEp @7@:G@6+ @:G@D/@:G@s@:G@#,zx@:G@*ڹY@:G@)x@3333@n@3333@s@3333@S&@3333@xF@3333@ ]c@3333@Z@8 ]ce@8kP@4hr @4qiB@4W-@4'_o@3@5@2?|i@3@5@2wkQ@3@5@2ݗ,?q!U?}?e?ȉ?=U?')?% ?'??7fQ?j`cq?V⌰?zny-g?; y?qePQZ??**Z?k@@@@@(@(??????g݃?|meZ?Vs`z7?dC'@.& $@?v25/?/]1?n*P? -7h@i}R@8@Jv@J"`@Jv@JPH@Jv@J(@Jv@J~@Jv@Jl"h @Jv@J @XbM@XbM@XbM@WO;@XbM@S@XbM@XQ@XbM@V@XbM@V@4@@4_o@1=hr @1>zG@0KƧ@0K]d@0(@0(4֡b@0(@0(*@0(@0(?| ?_?"?Qui@'-@EA@3333@f@3333@O8@3333@r*@3333@GP@3333@(@3333@!@8B䎊@8B)@@58r Ĝ@5>H2@4-@4֔@4QiB@4M@4QiB@4JGzg@4QiB@4I0 I?Äp?D]?i ?lGD?Et?Gτ? MV{?hO6?G|?q? fg?eVC6d?}й?*?݌U?K? t_?f"7?(@@&@"@(@5@:???????wMUB@?n ?Q>O\@EU5V@aY?FZ?m:b@J?T@k)0j@:@ؓtj@DKAw@ؓtj.@ؓtj.@ؓtj.@ؓtj.@ؓtj.@]v@]"~@]v.@]v.@]v.@]v.@]v.@7uS&@7u w@5#.Mm@4PA7K.Qn@4Q.NC,@4Q.NC,@4Q.NC,?Tg@8#@8:@8 >+@8I@8?f:@8@8i@8@8S@8?M6~@8 *@8p@8@8 @8 @?;@ݼhs@ݻD@ݼhs@ݿZ*@ݼhs@ݼ@ݼhs@ݻ_@ݼhs@ݹѶ:@ݼhs@ݸ!>@czH@cn@czH@c/@czH@c)8'@czH@cQ@czH@cR%@czH@c@8}:@8Ht4F@4.5?|@41 k~@3$j~@3$za@3 -0U2a|@3 /@3 -0U2a|@3"Q@3 -0U2a|@3 wǘ?"?NJb4?\Уp?h?[P"mr?@;#?I ?P?5QT?_ڲZ?X1-?^t٤C?*DU?^2+?$G?f?>?'^`N@@&@$@&@8@0??????BlULJ?5Q0?WCiD,F@ݺZME@c2?P?k(&;Z?f-?*@p}U@<@wKƧ@w -=p@wKƧ@wd@wKƧ@{,mIm@wKƧ@~(lQ ? £?>C,4?-! )m?+-/?& ?F^S?WA?l{9َ?{P?cz?;0>SU?\yn? bF#@ڞ|@8*?~6Q=?8vQ?S t?fE@mE@>@]/@]E@]/@]+j@]/@]K]@]/@]/@]/@\C,{@]/@\쿲@u@F@u@e@u@+j@u@$@u@D@u@Ϫ͟@6>6z@684֢@1hr@1'/@/nP@/74@/PH@/ -=q@/PH@/ ě@/PH@/33334?|O?Z?U#? Y?G?W_?| ? {?ܿ?EP? f? R?}??|׿pZ?%ϑ?ˢL1??\@@@@@,@,??????5 -?0?}m?ewx?73A@]@:@F@3 -0U2a|@3 _ -?cD?pvoK?>kj?؄e?1.?cZ? -?sP;?uq?baU?A?Aф?ghZS ?b(!I?8: If?$<B? H?U#ǖ@@(@*@(@2@5??????y&d5?T|?D<3@ܻNި@iZb?v^%@K ?[׬|?jrBt?›e@m@@@m@E@m@ϐ@m@$t@m@ߤ?@m.@m.@/B\(@/X@/B\(@/.Mj@/B\(@/@hۋ@/B\(@/;lE@/B\(.@/B\(.@8]ce@8W>6z@43S@4m]@37KƧ@3C\@3!.I@2K]@3!.I.NC,@3!.I.NC,?Ol?|?i[?[@{@8I@8?^*?+M??ۀl @8S@8?ji?YW?+k?u4@8 @8 @@@@????A@';dZ@' Q@';dZ@'&@';dZ@' qv@';dZ@'O@';dZ@'Z@';dZ@'@A7L@@A7L@ -=p@A7L@Ԣ3@A7L@$/@A7L@Xe@A7L@ӶE@8*0U2b@89Xb@3S@3ۋq @3)"`@3!4J@3 k@2v@3 k@2@3 k@2"`?F/P5?#T?MŴ?'$5?̺"h?5^Y?Cy3?&dU(?5q/? ?$uA?2`?E?5 g?v'X?E^u?ܔ~T?7"U@@@@@(@(??????#ֹvx?SlW?Խd@' ֝@Ydm?s(S?)?#ÝBϙ?7K@@i@B@Z@䛥T@Z@ݗ@Z@.@Z@ں@Z@g8~@Z@Z@ I^@\(@ I^@d@ I^@@ I^@oh@ I^@e+@ I^@ [@3@3wkQ@1ԛS@1Ԣ3@1tj@1ݗ+k@1@5@174@1@5@1n@1@5@1&IR?|O? ?6z@5Gy @1pěT@1o?@1A7K@1iB@03&@0-(@03&@0-(@03&@0.O ?Us@@@@@,@(??????ge?IcےP?B~S?\;@5@'N?b8ym$?7 t!?ϰM$?v24/@fJ;@D@c -=q@c -@c -=q@eo@c -=q@e`@c -=q@c>Y@c -=q@d]|@c -=q@b3@c -o@c -@c -o@c r@c -o@c vK@c -o@c ]c@c -o@c -}Z@c -o@c -(@3 D@3 ,'7@1?|h@1 @1tj~@1 @08D*@08[@08D*@096@08D*@0:,?}x9Oa?7y%?e6?| V?!_a*?Pvo? X?ݞ2?bMn?ّ%?^nu?Z<??j9?k _?bQ? P?xKh???\*?r {%u@@ @"@ @3@1??????Hn:?2?&A4z?$4@!@%?5trh?3!γ M?f&$T?_Uoط@s+O`@F@RnP@RnP@RnP@SPH@RnP@R@RnP@Rs@RnP@R@RnP@QiD@`|h@`|h@`|h@`G@`|h@`$@`|h@`@`|h@`F@`|h@`F@0O M@0O M@*~"@*~"@)ΗO;@)ΗO<@(b~@(~($ @(b~@(~($ @(b~@(rGE8?OW?{j?!!׋7?/?Ot?:?;0? C`?}?3.Yl?@?PS2K?!? ?q6tN7?avh?#.?O|q?@@@@*@,??????=s@X?in}>~'`@RG@` Ľ?%иg[>FVN?G##`L>է5@sU@G@CMO;d@CMhr@CMO;d@CNH@CMO;d@CMhr@CMO;d@CM@CMO;d@CKq @CMO;d@CKP@W -=q@W -=q@W -=q@W}V@W -=q@We+@W -=q@W@@W -=q@We@W -=q@W.C@/?@/A [@,j~"@,64@,O;dZ@,qu"@,-w1@,n@,-w1@,҈q@,-w1@,X?_?{j?,fC6?XQ>? 9?LI?bMn?MN ?ܿ?Kd]?ߒh!?Uw?}+,b?u?|׿pZ?8 ^?B ?A,}@@@@@*@(??????RhOr{l?0!u ? ej@CLo@W+T??#Tm?=?x1o=?1~fla@s -M@H@ؓtj@@ؓtj@ٌ~(%@ؓtj@+@ؓtj@J@ؓtj@@ؓtj@F]@\)@v@\)@!@\)@!@\)@4@\)@4@\)@4@0YJ@0YJ@)j~"@)PH@(E@(+jg@'N;6@'ѷX@'N;6@'N;6@'N;6@'N;6?_\p)?1?N?G?@ A2?ˡK?bV,?5CQ?~Q?6)}?ѡb??R g!ڢ?bMky?~('?oܧ?avh?#.?#.@@@@@,@,??????Cd>=?. >X)@|s@-?=8w7>^|?ud-1> -^S@sy@I@7K@ -=q@7K@ح@7K@h ԕ@7K@R<@7K@b}@7K@ح@fffff@i7Kƨ@fffff@g@fffff@i@fffff@g_o @fffff@g_o @fffff@f@6۹~($@6W>6z@1=hr @1?m!?E 8?ˢL1?2[s@@@@@*@,??????M;fA'?(?֠Ag@_ہ@gt,?ElZ?3GC}Ս?ai :?n=K@j$J3@J@Btj@BnP@Btj@B8YK@Btj@BϪ@Btj@BX@Btj@B@Btj@B!-@cS@c%@cS@a.H@cS@aG{@cS@a.H@cS@a|P@cS@b I^@6+s@6* @0+ I@084֡@0Ctj~@0@'RT@/3PH@/-hr!@/3PH@/-qu"@/3PH@/-(?3a=?]??Kǡ?`?f]G?F/P5?HG^?tw?,~[?M??YE?Ϸ+?W ?hB^?M684?ĭ@@@@@,@,???????OU:×?5&@d[?2Eg@B՘@beG?SlXL?@X]v~?Q?&H@jҒa@K@;dZ@0 -=@;dZ@8YJ@;dZ@;q @;dZ@;qu"@;dZ@A:@;dZ@<ߤ@1@1@1@[7@1@J#9@1@;dZ@1@qu"@1@,<@7֚,<@7ݑhr!@4|hr@4 (@3."@30@2@20U2a|@2@2@2@2Xf??+!?{?IW`g?&tq?@?qn?D?Qc -?l Ĩ?~0d?d ?[?[ș8?ʭ#г_?J?Q?]?H@@@@@,@*???????j'T-?@AP?PiӀ7b?G׾>F@9H@ "?kF7n?g$\/ rU?WP?y@i5(%@L@@\(.@@\(@@ -=p@@\(@@ @@\(@@ @@\(@@O L@@\(.@rk I.@rk I@ri*0U2@rk I@rtO @rk I@rt@rk I@rn.2@rk I.@8YJ.>@4S@4䎊q@3`A7@3~҈p;@26z@2C]@26z@25Xy>@26z.NC,@8J?ϱQm?UD?Kǡ?GU@8@8J?d~?fj$?L?{W@8@8p?J.?_?Oc?w,@8 @@@@*????M@ Q@ lD@ Q@ !-w1@ Q@ @@ Q@ -!-w@ Q.@ Q.@P`A@?cR@P`A@yXbN@P`A@4SL@P`A@o@P`A.@P`A.@7:@7cF@4|hr@4Y|@3gKƧ@5SE@2QiB@12W@2QiB.NC,@2QiB.NC,?5,?P?i?ӓ]@8I@8?E[i)?1?%U??ӗF)okN@8S@8?R}1?d_?:S3V?~*@8 @8 @@@@????N@QR@Q+ @QR@Q@QR@QA [@QR@Q[@QR@QXe@QR@Q[@*WO;@*\j~@*WO;@*YY@*WO;@*["`@*WO;@*T3@*WO;@*^Q@*WO;@*_@8v@85Xz@3kS@3i @3$j~@3!s@2xF^@2fffff@2xF^@2*0U2b@2xF^@20U2a|?2}?DE?'?J?&?!w?A[F?I?fj$?,~[? ?lӪ<?7#!xv? DS?lR?VED?ٔ!x?^^ω@@@@@$@(??????*p?@\?21y6?Z.5$@Q7@*ZP -?SF}?x93Nko?d\?J ;@j xK@O@d/.@d/@[ռ@d/@a@d/@hǒ@d/@jޗ@d/@]D@:p -=.@:p -=@: -}"@:p -=@:T@:p -=@:mޝ@:p -=@9}*-@:p -=@:{^@2|Q.>@,-V@,_Fd@*rnO@*X;@)L/{K@)@7Ȼ@)L/{K@)u?@)L/{K@)B4@8 -Ny?÷&R?-?1)?\'8? *vL@82r?4L?^p@?o?ȎB[0?PR,S@8L?laé?>xLp?ČAv?J{?5@,@,@0@3@8?????P@\G@\Q@\G@\+@\G@\|i@\G@\^5?|@\G@\@\G@\ᰋ@5`A@5`A@5`A@5@5`A@5XQ @5`A@5)^5?@5`A@5Q@5`A@5}Vm@7|Q@7u%F -@3J^5?|@3GzH@3`A@3 -=p@1O M@1@@1O M@1ߤ@@1O M@1̋C?|O?ƿ57?WD@?Uyu#?>C,4?nM?}ѿ,?,T?J?RLnj?l{9َ?vMUu?6E?6?o?Gϵ'?x[:?ad|@@@@@@??????a(?dt` -?@?eoćDj@\71*@5=v?`ϑ2?XR]4?˳<@<*@fr@Q@+@|@+@@+@ޛ@+@@+@"3@+@D@@5@@0(/@@7f@@T @@hNs@@}`<@9cg l@9f'@6}hr @6j26}@5K"`B@5G͝@40@4+ZJ@40@4.vv)@40@4)2?anñ?(?JFvY?^W?)b3?#@?CbyV?eV2?t%I??wo? pA?*??_xҮ8?eX?I?^b+@@*@,@,@5@7???????dʤ?H9?r@2zb@5 ?i -9?KYjP?ҿ'U@ OE}r@gDH]@R@+^Q@+^@+^Q@+Y.@+^Q@+^fz@+^Q@+bu%F @+^Q@+Su@+^Q@+YH,@p -@#@p -@@p -@4@p -@e>@p -@Ǥ@p -@t@6:@6 Ⱥp@3 ě@3wd@3-V@3Ջ@2,<@2h@2,<@2gN@2,<@2?| V?nR?r6x?tf*?Anz;?o^?eXW?_]?5Ey?!Aa*?]Ql?|p7%?` v?S ?ݳKخ?t{i?)`?Wr@@ @(@*@1@2??????W4Sӥp?Rmf?Se@+[v@c?s?Nk߹?svWh?ւ5?t)95@f @S@@ C@@㯷@@r @@ĺ@@p -@@F@R@ƖH@R@ĒS&@R@V+ @R@BѸ@R@5?|@R@ -o@6@66@2hr@1h@1`A7@0m\@1~($ @0u@1~($ @0_o@1~($ @0sPH?I'%?'gjFs?#?l,I?l?XE%:?ɱb)Q?? >?ʻ1 ]?0/f?lINՄ?>?'{%M?HFOu?HW ?l&?b#@@@@@@???????n &ފ?+eg?*|@'ù@Ս?(SI?.@:9B@S-4D@l#߉@T@vȴ9@@vȴ9.@vȴ9.@vȴ9.@vȴ9.@vȴ9.@T@@T.@T.@T.@T.@T.@7s@7+j@4hr .Mm@4q7Kƨ.Qn@4Eu.NC,@4Eu.NC,@4Eu.NC,? @8#@8:@8 >+@8I@8?Ol@8@8i@8@8S@8?xȟ@8 *@8p@8@8 @8 @?U@9Vu.@9Vu@9W@9Vu@9T@9Vu@9W] H@9Vu@9_eQ@9Vu@9\磔@+.@+@s*@+@7+=|@+@ S@+@z7Pb@+@黅@2䎊.>@/"`@/UP@-E@- @.@.+)@.@.% F@.@."O@8 -Ny?N"?bT]u?6ۊ/?tA2?@82r?Oۡ?x?[ ?Hr`?Eг@8L?,CQ??r ?/fD?$+@@$@"@:@6?????V@,A7K@,B`B@,A7K@,%1@,A7K@,:F@,A7K@,&@,A7K@,1@,A7K@,57NL@"1@"@"1@" Ri@"1@" T;r@"1@"uU@"1@"{@"1@"x@5|Q@5@2ԛS@2A4@2ttj@2w)@1@2g̅@1@1l-@1@2_Չ??ˡK?bV,? ,C?LE?@[*^?IM?X78?cPQ?.+? SF?':?l&?Ѐ>(@@@@@,@,??????GXR8??P ?y9@Bz@MF?+.*M5? $D'?Rق@?8lX[.@s@Z@ -=p.@ -=p.@ -=p.@ -=p.@ -=p.@ -=p.@glC.@glC.@glC.@glC.@glC.@glC.@8L]ce.>@3\I^5?.Mm@3Htj.Qn@2 [W?.NC,@2 [W?.NC,@2 [W?.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 [@Ƨ@̋C@Ƨ@}Vl@Ƨ@ڹ[@Ƨ@RT`@Ƨ@0U2a@Ƨ@̲@0`A7@0&@0`A7@0x@0`A7@0@@0`A7@0K]d@0`A7@0C@0`A7@0x@6*0U2b@62W@3 I^@4[6@3~"@3N;@2ޫ6z@22W@2ޫ6z@2N;5@2ޫ6z@2A [?|O?Z.?2~?S0H?"m?S{d?A[F?6c?_@?X}iQ? 7?lӪ<?W Z?eg?zny-i?w%?DV?ٔ!x@@@@@,@(??????7#?QM[?Ar[yq?FLЃ!@lA܁@0?ch?fgǘn?ȧK ?{Q'@i@xWC@\@hx@h+@hx.@hx.@hx.@hx@h},@hx.@~ Ĝ@~޸Q@~ Ĝ.@~ Ĝ.@~ Ĝ.@~ Ĝ@N!R@~ Ĝ.@8g l@8e+a@5TS.Mm@4V.Qn@4h.NC,@4h@4~$t@4h.NC,?{<@8#@8:@8 >+?NMwE@8?UbB2@8@8i@8?@8?\[P@8 *@8p@8?SrY@5@8 @@$??]@fXbN.@fXbN@f\)@fXbN@f?|@fXbN@fܬ1'@fXbN@fǻ/W@fXbN@fE@fffff.@fffff@p|@fffff@oA@fffff@_;dZ@fffff@q@fffff@vz@9~($.>@4?|h@4:S@3"`B@3Ov_@3QiB@3q:S@3QiB@3oH˒:@3QiB@3kQ@8J?7?mq -??h?Ñ$|?Ylp@8J?-c?F?wxR?s~?@8p?}±?zk 3U?f?a?60lP@@@@,@,?????^@x@ -=q@x@O@x@C@x@x@x@~#@x@L/@^j~@^E@^j~@^lD@^j~@^"@^j~@^"@^j~@^hs@^j~@^p @8쿱[X@8 _o@3+@3 IQ@217Kƨ@20҈p@1ce@1H˒@1ce@1q @1ce@1cA \?sD?}d?FB:?_B?oNŒ+??ϱQm?dx/?Rs??UBi?kj? f -?fb?*wjs?,|?/̽`?ܑ @???@@???????XZQ?$U^?&x9@x @^BY0?H&m%?>؝eO?Id^?d(7@q(V -@_@#ȴ9X@$ҋr@#ȴ9X@$]@#ȴ9X@#S/I@#ȴ9X@#|@#ȴ9X@#L4;@#ȴ9X@#$@:e`A7@:VC@:e`A7@:5_aT-@:e`A7@:JAh @:e`A7@:F@:e`A7@:29&o@:e`A7@:9S̀~@87@@8=M@3^+@3M J@2)"`@2+?@1@1A<2[@1@1OS @1@1cKo?en?'AU?ܝLl?-,?QN0P?Ht!?p ?u?%7*V;?qD?=D]?Aǫ?VXl? -&}?jd@@*@(@,@;@:??????k{km?L/d?M@#;qP@:C?hR?j{ۅ?eh'?l`P@k_큊@`@O;@?|@O;@!R;@O;@p:@O;@m\@O;@"7@O;@"\(@:G@8Q@:G@6@:G@B@:G@J @:G@@D@:G@H9X@7H@7҂@4@4^5?}@4!.@3j~#@3XbM@36z@3#E@36z@3!.H@36z@3vȴ?NIɮ?S?괰E?ׅ ?IJ j?EeX?)T?URZ?_S?ѯ%?3&?J佪^?+QK?4?~x?42?n$?Qh@@@@@@,@$???????e ɱj?jS ?C2?Ruﲬ@Jh@CQ%Dr?`H?vv_?–i-? @C@y(@C@҂@C@@%%@C@,/@6>6z@6@@2ahr @2\5f@1ttj@1piJ@10U2a|@1k)yA@10U2a|@1U]@10U2a|@19??Ƹ^ ?ZKbn?(y?lNR? 9N?¨Sp*? \ -?N?G!:+@?g!r@11@*?g=Ȕ9?yw?ק/l@:\@g=0@c@j~@@j~@I@j~@G~$@j~@zs@j~@JS @j~@?;@Z@(@Z@nJЧ@Z@“\_@Z@~Z@Z@a@Z@@8O M@8B)@3\I^5?@3mc@2j~#@3z=6@2J0U2a|@2`5d§@2J0U2a|@2FuJ|@2J0U2a|@2C}B?~?J?Lp1?{6k?ء?Y^z?lz)m?iة?RÚ/q?=E?%č?7gBU?V8?"+y ?T\y?6'F?`ɀM?;nnL?6Ҁ@@,@$@"@0@1??????i]?z`2?/?NxD^?Sq\@5~@8}?r\?Tm[?R5UZ@(Y{d`>@mxjR@d@!O;@!n@!O;.@!O;.@!O;.@!O;.@!O;.@r @bM@r .@r .@r .@r .@r .@8O M@8;dZ@5 I^5.Mm@47Kƨ.Qn@4xD*.NC,@4xD*.NC,@4xD*.NC,?Z@8#@8:@8 >+@8I@8?Z@8@8i@8@8S@8?A@8 *@8p@8@8 @8 @?e@dZ@|hs@dZ@@dZ@5?|@dZ@E@dZ@,<@dZ@"@{T@{e`A7@{T@{e@{T@{Z@{T@{7K@{T@{@{T@{@N@6*0U2b@6_o@1c@1b I^5@0-@0fffff@/#9@/lC@/#9@/)^@/#9@/_o?]?4L?U#?:Z2b?oNŒ+?װ*w?]?{c?A)N?RLnj?q1At?ٶm?*wjs?u/X?=Cb? ??2[s?ܑ ?@@@@&@??????]>li%g֜?AjĦ?2)޿k@܄@{z\L?PTNSL?ApL?.m?xr@sS@f@t@n@t@  @t@`K:@t@_@t@Z@t@@s(r @s(V@s(r @s,@s(r @s9{@s(r @s,]x!@s(r @s#7(@s(r @s0R@6#g l@6"?$@2}hr @21h@@1A7K@1%7@1Y~($ @1OƕDS0@1Y~($ @1Kd@1Y~($ @18i ?~?J?s3O?8?:0b?X"@L?ZIP?}x?*J?I/?9XNu?ٔ!x?->t@@@@@*@*???????e?\IJ?O͍&$Y}?kfZ@q>@8tE6?gAkU?1?ɫ[?iq@a؝\K@h@V.@V@j@V@BO:@V@rjX@V@=^v}~@V@ "@^5?|.@^5?|@@^5?|@|WF@^5?|@`儭@^5?|@̑YA@^5?|@X@@6 xF.>@48r Ĝ@3wkM@4t@3c':@3J0U2a|@3j˹v@3J0U2a|@3'%=@3J0U2a|@3-oO\@8J?݌[?lqa? {?2?u$a@8J?Tɫ?/6Z?t{ ?ϲU?( @8p?1ԁ? F?a?>hi?Ś@*@$@(@4@3?????i@|-V@|Ƨ@|-V@{Q@|-V@{[X@|-V@{M:@|-V@{p@|-V@{q4L@;dZ@&x@;dZ@+j@;dZ@)^@;dZ@@;dZ@˒:@;dZ@u"@5,<@4$tS@2r Ĝ@.S@2 "`B@-(p:@10U2a|@,OiDg8@10U2a|@,LC@10U2a|@,N.2?=fA:?C#%??ᕮ0?j#?b;x?}?U -?00?4FgI?u1?×?؟#ߦ?eo?܉a^?d3>?'d㗅?w,?w,@@@@@*@*??????\P?>Zx?Hif?=L@|YH@#[?@5B ě.Mm@5 -V.Qn@4Ǡ k.NC,@4Ǡ k.NC,@4Ǡ k.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 k@(+?J+? d?3a=@8@8i@8?q?e!?n˓"@8 *@8p@8??oڕ?ĭ@?@???l@t$@tbM@t$@t @t$@tS&@t$@t@t$@t@t$@t@N@Փtj@կw@Փtj@Ց4K@Փtj@Տv_خ@Փtj@Ջq @Փtj@Ք@Փtj@Ջ:)@8s@8&@3R I^5@3P{@2-V@2ѷX@2~($ @2xl"h -@2~($ @2͞&@2~($ @26?'[ V?b =??{y6?>{~? ٖ?pEIt?GKTH?fˌ?"6Wz?4.? ?$s?z?:t?O.ce?Y͍_?wͶ@@@@@*@*???????iH Kr?:}?^Ig/@t0@Տ=?Mj?pG~eT-?zKf?K@3+ I@3p:~@3q7Kƨ@3r`A@3YJ@3fA@3YJ@3 '0@3YJ@3q @8J?V[u9?rPt3?+f?xsi?U<@8J?(S5? ?GW?8wxq?ю? ,@8p?J>?z"2)?dWn?I -Br^?:\y@cWl@*i.\?gwKW?Pct/j?K?#ľz@oYÁzP@q@O@n@O@ 0@O@-e@O@u@O@}o@O@^@qm@bv/@qm@r+%@qm@k!@qm@)G@qm@cq@qm@@7~($@7OKK@2 I^@3{M@2Ctj~@2AJ@1@2*hw@1@1mAj@1@10?wvHf?]u?@Ɲ?sY?D\?gS&?Xd??&^ǘK?њ=? 9fɾ?UK?CO?uOi?Z.?.R?7c=?Y@@*@$@*@0@2???????0J@@?U?f g?jFIo@2@jQ?/???1ȝ?W'@l=V@r@@M@@e+@@A ]@@-w1@@~m\@@~BZe@r-V@r Ĝ@r-V@u\*@r-V@t`d@r-V@p ě@r-V@tɅp@r-V@sZ@6 xF@6Q @2ěT@2|hr@2K"`B@2K I^@1kxF^@1k~%@1kxF^@1k6@1kxF^@1kQ?|O?b}?TS?gk\0? -`?&Gf??UbB3?^ o?aYZq?q1At?7w:?1Q:?*J?' -}C??7?T8@@@@@,@,??????I?B`?"jީ?J_'4U@WZ@r]?F?ch'm?YTl?rdv@mm@s@9Xb.@9Xb.@9Xb.@9Xb.@9Xb.@9Xb.@M 7Kƨ.@M 7Kƨ.@M 7Kƨ.@M 7Kƨ.@M 7Kƨ.@M 7Kƨ.@5G>6z.>@3{ I^.Mm@2tj~.Qn@2G k.NC,@2G k.NC,@2G k.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 t@/@p -@/@p @/@ (@/@6@/@#@/@"@/@/@/@^5?}@/@,<@/@PH@/@(@/@S@2}:@2|>@1I^5@@1"h ԕ@0A7K@0:~@0:S@0H@0:S@0n@0:S@0:~?^Dw'? ?Gy??̒?״?"OJfA?kk?=9l@rFW@u@-@+ K@-@IQ@-@hۋ@-@PH@-@C\@-@#x@[S@[S@[S@Zv@[S@Y@[S@S@[S@V_ح@[S@RW@6cg l@6ca@N@3xr Ĝ@3wE84@2"`@2,@2xD*@2p@2xD*@2oo@2xD*@2or?|O?M?ě?6W=t?19?I? ?>`?#Gf?T?@Ad?S)?D(o?QdЈ?XU@dA<@v@KlC@Kȴ9W@KlC@K^ -@KlC@Kffff@KlC@K @KlC@Kp:@KlC@Km\@cS@zG@cS@bM@cS@iy@cS@hr @cS@eu@cS@g8}@8>6z@8nO@3|hr@3&x@3-V@3u@20U2a|@2c @20U2a|@2U=@20U2a|@2qu"??_X -?Uy* ?~B@F*.@?Ǖ(0?4p'?IQƪ?ts܋֫@f㺛O7@z@(O@(w@(O@(@(O@('/@(O@($u@(O@(8@(O@(!R@zG@zG@zG@{W>6@zG@{6@zG@zG@zG@z)@zG@{qu"@3O M@3*0@/-@/)^@.1&@.Z@.a-w1@.`'RT`@.a-w1@.^6z@.a-w1@.^҈p:?}ѿ,?m0?S[?䆀{6?'ř ?w'?| ? C`?:?>\?݄ ?90?nwf?˛͇q?٤?XLL?SP?fW@@@@@*@,??????? ?60 ? ?SPp@(@zm?V]#?.~?6?h @0@g=ۼp@{@q&x.@q&x@qbP@q&x@tQ@q&x@vzC@q&x@o[z*@q&x@lX@Jo.@Jo@JS+@Jo@<2@Jo@GbJ,@Jo@5_oS@Jo@CKp@6D.>@3 I^@3@3A7K@3xF@3m8@3Ȫp@3m8@3κY#z@3m8@3˗":2@8J?{?Y?e@8p?T#0?ot-k?$e?q?瞧&Ҁ@*@(@(@9@6?????|@ A7L@ @ A7L@ @ A7L@ @ A7L@ \{@ A7L@R@ A7L@꠹@vȴ@ A7L@vȴ@]LKJ@vȴ@y -@vȴ@!Dl@vȴ@b(@vȴ@ E!@4v@4bR@0 I^@0Q@097KƧ@09 w@/)_@/Y2OX@/)_@/౤d@/)_@/E2{L?|b@?ȶ# ?64Y??Mռ?(G?Õ?|b@?/q?4??MCFn3?ä?d"H?pDRg? ?v?h?9yt?Ho@@*@,@*@9@:??????>S ?VCl? N;x@ @ -d?=-ҟ!4?@U?:;?B)@fDj@}@*^Q@*kQ@*^Q@*\C,z@*^Q@*_䎊@*^Q@*_|i@*^Q@*\쿱@*^Q@*\ߤ@jF@jti@jF@j@jF@j_@jF@jy=@jF@jC\@jF@j+j@8qiC@8e@3@ ě@3An@2dj~@2eoi@2O M@2'RT`@2O M@2u@2O M@2($ x?3a=?"2?oq u?{@?_dA?P?qv?Hk#?Qa C?uX?~?'?'[?9o?/X?< N ?OO ?0&YEd@@@@@(@(??????k.t~5(?D={?Etd(@*`tf@jqr?csI?`v!?fĦ9?c|[@m[V?K@~@;dZ@+@8I@8?d@8@8i@8@8S@8?B{ܡ@8 *@8p@8@8 @8 @?@Y@Y@Y@Zw:@Y@W31K@Y@X)W@Y@Wh.@Y@Y2@$/@7K@$/@ -@$/@#Q@$/@9(@$/@p,@$/@! @4w@@4wҸT:@1+ J@1 P@1q7Kƨ@1qU@1iB@1̚}@1iB@1@1iB@1?{si?n@2`A7@2b - @1&@1Zܳu@1&@13>4@1&@1q;${?L?ȱc?4vȺ?B5?]X?˙3HQ?QL?9{?bN|V?M?^YZ8?hՕQ?PU+D?"x?l1Y=3?t'?dO@?ޕ+@@(@*@*@:@9??????<(Vdĝ?@U5M?Kf aXc@B -`@Y!#;?Y""?d`??t -?DJX?r-?,#?s8r?4ډ3?=̘_?PLDǽ?M8@?-u+)3x?"hȣ?@ӄ@;5@CR$?<Rp?[_?s/ꉄ6H?4~L3@s~R%@@V@7Kƨ@V@{@V@ҕ@V@`A@V@5Xy@V@zH@@u@@#@@C,y@@Z@@bM@@3@8DO M@8JJL@2r Ĝ@2U@2i"`@2ju%F @1 k@1F]c@1 k@1L/{@1 k@1:)z?qn?y)3c?=?Kh?U=K?uxY?\.:@?_$?f;m?S?WA?mEB?ey?vA?P<}??GZ?)@Jq?=P|@@@@@*@&???????JVP`?O81?WXEu@`@=e?k ejM?k+$v?ѩ??$@p uÛ@@WH9X@WK@WH9X@WH:Wt@WH9X@WJ@WH9X@W_$@WH9X@WPJ@WH9X@WR@ :^5?}@ )H@ :^5?}@ @ :^5?}@ GRb@ :^5?}@ b@ :^5?}@ $zq@ :^5?}@ i@@904m8@9> @4+@4d@@3KƧ@3*'2!@3?䎊r@3)8@3?䎊r@3(z@3?䎊r@3* |3?3<' ?$?Q?es1x?̛BO/?㾵?sX]?,?'?iɮ?*Ex ?mr)?"?w+*?%AC?8b-Y?2N?keWP'X@@"@$@ @$@"???????a9;j??^5A -?x}=@WSs@ %e?=-?YPd?rsGȴ@1d @@n?@@O^Q@Oa@O^Q@O`ѷY@O^Q@O_ @O^Q@ObC-@O^Q@O`- @O^Q@O[=K@ "`@ "M@ "`@ u@ "`@ Ov`@ "`@ 6@ "`@ @ "`@ _@72@70n@3 I^@3(@37KƧ@4IQ@2ٌ~($ @2W'@2ٌ~($ @28Z@2ٌ~($ @2!.H?UbB2?d_s?^?zRU?] - ?zWmo?sD?’?L?%ja?ۣ0Υ?|[z? t4?qN?;?J]j? .0v?=P|@@@@@*@*??????R-]p(,?K _W?;'y) @O`d@ (p?h-y'b@?\I?Pe?)@k@@-hr@*o@-hr@-c@-hr@.H@-hr@.z_@-hr@,@-hr@,jhM@V -=p@Vپ+@V -=p@V2Ŭ@V -=p@VՂv\@V -=p@Va/@V -=p@V=@V -=p@Vx@6v@6sG@0I^5@@0ܑF@/]E@/]og2@.Z@.jK(@.Z@.HE@.Z@.??Qp?)bӺx?-[o?mccx?or?vURn*?o"?-?%?9dٙ?*_?cؕZI?o#BpW?q6?YF? ?!R#?[Rvs@@,@,@&@8@7???????A!H?F<"?9zY?Pv8@,ms8n@VֈJ?YF ֙?5?L(֬?s -zd@h%xI@@%@ -=q@%@طf@%@]U@%@@@%@޿J@%@\T@ZT@ZT@ZT@Z$Pd@ZT@Z-K@ZT@Z[5@ZT@Z$@ZT@Z@2@2|hr@0+ I@0fW -@0j~@0zP@08D*@0H井@08D*@0C&Y@08D*@0@D?^j?W?Q?# 8֑?c.?Jqs=?bMn?7!?%2}?)Nk@d/@b3@cdZ@clD@cdZ@csP@cdZ@c=@cdZ@c*0@cdZ@c@cdZ@ckP@7`D@7bZc@2i+ @2h4֡b@1ȓtj@1Ǡ k@1!.I@1R<6@1!.I@1}H@1!.I@1)^?1j?5{#?l>?a_b? -`? Mi?)T?I?AuG1?.pC?>H>d#?)]\?Rw#z? U)?^?Z?Tj?GZ@@@@@(@(???????&AH%H?1,;@-?7?@ct/${@c(?RKI?Wy ? ?\R@i& -@@@@.@.@.@.@.@@V@.@.@.@.@.@6 D@6`d@3@ ě.Mm@2y7KƧ.Qn@1.NC,@1.NC,@1.NC,?Br@8#@8:@8 >+@8I@8?@8@8i@8@8S@8?uf@8 *@8p@8@8 @8 @?@|h@vȴ@|h@T@|h@P̒N@|h@6@|h@b@|h@bR@ -7K@ -@ -7K@ -~ߤ?@ -7K@ -sR@ -7K@ -I}Ɗ@ -7K@ -=X|@ -7K@ - !@1_o@1D@0^5?|@0c@/nO@/G&@/Xy=@/o @/Xy=@/9@/Xy=@/I?^j?ԭ)?' ?P4q{?sZ?! ?bQ넛??<6?hg?O?t7T?bF?ʓ:$?tq?#+?xl?>dS@@$@$@*@6@5???????@b8F7?#{F?(}X@>Ү@ -?3.N%6?7~EHbe?axf!@?gz@s+0w@@ Ĝ@KƧ@ Ĝ@cA@ Ĝ@`A7@ Ĝ@$t@ Ĝ@ )@ Ĝ@4֡@`A7L@W -=p@`A7L@vu@`A7L@n@`A7L@ZY@`A7L@[S@`A7L@^Q@7p4m8@7l#x@3+ J@3Y|@2tj~@2Xy=ـ@2u@2 :)z@2u@2H@2u@2 qu"? ?4Kl?ȇ? h?[5?Z?| ?͔{?pI?~m??[h?"Ь?"x*=k?*:'+)?}??Qx?a#?!D@@@@@(@(??????Q@;.?Vp?O?"Ng6Gj@M@YD?l\{?A1t6? -?i]@ex@@H9X@H1&y@H9X@ ě@H9X@CZ@H9X@-@H9X@F '@H9X@G l@\{dZ@\{dZ@\{dZ@[@\{dZ@\]Vl@\{dZ@[hs@\{dZ@\|n0@\{dZ@\|n/@4B䎊@4Bh ԕ@0+ I@1 - @017Kƨ@0>H@0Q@0L/{J$@0Q@0:)z@0Q@0m]?}ѿ,?uM?,Z?$? -`?Rg.?| ?E?S?f?C ;?+d?{V? U)?@A?ԿX?<71?ad|@@@@@,@,??????ZP`58* ?k ?Z 8N@B9>@\s8p?ɖn+k?]3,"@#z%@Sҽt@f[@@@;dZ@.@.@.@.@.@C@ˑ@C.@C.@C.@C.@C.@9G>6z@9H ԕ+@4S.Mm@37Kƨ.Qn@30U2a|.NC,@30U2a|.NC,@30U2a|.NC,?c9X@8#@8:@8 >+@8I@8?@8@8i@8@8S@8?Ćp,@8 *@8p@8@8 @8 @?@I^5?.@I^5?@]yh@I^5?@ŵ{@I^5?@x[@I^5?@'_o @I^5?@7O:@.@@@@Z@@j2@@zxl"i@@t@8 xF.>@5 ě@4_jK@4-V@4b&@4:,<@4LΡm@4:,<@3"`@4:,<@4 PH@8J?Uqd??x@?ݧ?xܬi?Y@8J?CT?a??ٿf|?"QS?U̕"@8p?-q?iI??F?k0?NR\@(@$@&@*@$?????@tj@+ @tj@1@tj@m9@tj@Mj@tj@[W>@tj@bM@~@~7K@~@~n@~@~g8~@~@~ں@~@~p:@~@~@6}:@6~Mj@2S@2@1tj@1R@1a:S@1TJL@1a:S@1R@1a:S@1TJM?Ol?nx?)?6p?ܿ?l? ?j@\"? ?io?f?.?eN?<!?7^|?%y*=?̈- -?5|G?, ?b䖶?| g?Pfl@e, 5@@d/@e`A7@d/@em\@d/@glC@d/@eݘ@d/@b@@d/@aR=@ L@ L@ L@ L_@ L@ KC,@ L@ Kt@ L@ Np:@ L@ N@2Q|Q@2Qhr @."`A@.=b@.cnP@.^Q@.V@.Oo@.V@.N_o @.V@.O M;?OW?%ñ?QX?,rg?y%? Mi?;0?MN ?2'?aYZq?H<?e\p?!??=Cb? mmsi?OO ?ٔ!x?@@@@*@,??????\^?T?1{HN?3JGZ@cO7@ M>?@v8Z?A -?u6?w/.#@sgOx@@ C@`A@ C@e@ C@bM@ C@r@ C.@ C@ xH@}M@}߾vȴ@}M@}@}M@}+k@}M@}zxm@}M.@}M@}ңS%@8]ce@8_o @4hr @4Ov_@4U`A7L@49Q @3!.I@3 u&@3!.I.NC,@3!.I@2?8ȺJ?5b&?jl|?@8I?<3:?}ѿ,?>ÔR?fF?@8S?æΟ=?*c?Y,v?TB?@8 ?6fe>@@@@@,?????@&ȴ9X@&C@&ȴ9X@&ؓtj@&ȴ9X@&n/@&ȴ9X@&jg@&ȴ9X@&l!@&ȴ9X@&}H@O@V@O@xl"h @O@ES@O@|h@O@+@O@*0@6#g l@6%Q@3S@3 @3i"`@3=-V@3 -0U2a|@2)^ @3 -0U2a|@2͗+j@3 -0U2a|@2ֻ?Br?߈J?]!\?ɹ?JjcJ?0Je?F/P5?|? pۓ?I-U4?/x?BHɿ_?]?]FV?btx=?c+?&zQ?0@@@@@(@,???????qGGwY0?`j X`? %@&_ݲ@.L?pM?@3W+ I.Mm@2A7K.Qn@2䎊r.NC,@2䎊r.NC,@2䎊r.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @F1@F^5?}@F1@FY@F1@FrGE9@F1@FdZ@F1@FC\@F1@Fs@=E@>5?|@=E@=@=E@:v@=E@;"`@=E@6!.@=E@:Y@4N쿱[X@4Ov@1hr@1:@1-V@1 @1@1Ov_@1@1}Vl@1@1(?|O?q' -B?RsW?vHn&?2{w?.'?|O?D.x?DP?|}Յ??998? ݎ8? $gv? ݎ8?J]j?o?&I@@@@@(@&???????"+0Q'S,? ) ?9eyv@Fڵ@;y??[>_?Y?4)? u@fA,3@@(\@(r @(\@(p:@(\@+ I@(\@)'RT@(\@'y @(\@)^@Pj~@P9Xb@Pj~@P9Xb@Pj~@P4K@Pj~@P -=p@Pj~@PD@Pj~@P=@5,<@51.@2 I^5@2 k@2v-@2u2a|@2!.I@2sh@2!.I@2Fs@2!.I@2E?Ol?t0?p? -?j?EeX?}ѿ,?c?Kw?>G]!?c?K ?/X?]ivL?Ƌ?l?*Q?E݋N@@@@@,@*?????????7CX?0ōV9u?1_Q@( t@P&=I?Op%?R+F?pN&?m*c@g=@@e I^5@e I^6@e I^5@e9X@e I^5@es@e I^5@e2Y@e I^5@eX@e I^5@eV@0 -=@6ȴ9X@0 -=@# @0 -=@0$t@0 -=@$tU@0 -=@En@0 -=@CS@8YJ@8Q@5)+ @5,j~"@4j~@4҈p@45sh@49^5?~@45sh@4"u%F -@45sh@4t?+-/?@u-I?O?w?r9XY? $Μ$?_pP1?vȴ9@(B\(@(>vȴ9@(8}H@(>vȴ9@(BC-@(>vȴ9@(>Ov_@(>vȴ9@(9@(>vȴ9@(;@8_o@8\(@3sS@3vOv_@2W-@2[ I^@2@5@2-@2@5@2$0@2@5@2F -L0?Br?rk?^@?wX/8?q f?*Mis?oC? ʣV?9T?ݤ`h?c?J佪^?y3?\{?]_Q?vI?C SG?E݋N@@@@@*@*???????D 0Zw?"B?C_>@)Nv]@(=-d?CzV ?_`J?HCK?_k@kڝv@@'fx@'f@'fx@'e1@'fx@'h\@'fx@'g+ I@'fx@'f,<@'fx@'f@kR@kƧ@kR@j0U2a@kR@kq @kR@jW@kR@l1&@kR@o͞@47@@47*@0+ J@0+ I@0t@0,<@/jf@/҈@/jf@/rGF@/jf@/YJ?|O?dY{?&??뤽??V^?<_?|O?; ?ԕ]I?Kd]?x9ξO?]˙$>?|׿pY?%_D?Ol?=rE=?ad|?`i=@@@@@,@*??????#?9Êg0??!&?<;s@'fêK@lap?:l`g܂?\o%R ?+G9?+An{@f@@*A7L.@*A7L.@*A7L.@*A7L.@*A7L.@*A7L.@.@.@.@.@.@.@9,<.>@4!hr .Mm@3,j~#.Qn@20U2a|.NC,@20U2a|.NC,@20U2a|.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @ךv@ךv@ךv@כ@ךv@כqu"@ךv@כ"`@ךv@י~($@ךv@י_p@ě@ě@ě@ k@ě@ě@ě@G{@ě@|Q@ě@ k@2-v@2-v@-`A@-C,@-/;dZ@-/@,fL/{K@,en0@,fL/{K@,fffffg@,fL/{K@,eoiDh?^Q5P?{j?=?Kh? Y?ǹT?CXs?bM_x? C`?S?-I`K?%y*=?J&d?k?bMky? ?YF?͓E'?f_Ԋ?B @@@@@@"??????D[O`?1?!5n@ךE19@@[y?A@4 I^5.Mm@2-V.Qn@2!:S.NC,@2!:S.NC,@2!:S.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @)l@+ I@)l@)'RU@)l@,1&@)l@,"h @)l@)@)l@) @Q@tj@Q@74@Q@C@Q@ '@Q@kP@Q@ȴ9X@5ks@5ku&@2hr@2V@1Htj@1HYJ@1 -0U2a|@1 =K^@1 -0U2a|@1 -L/|@1 -0U2a|@1 s?}ѿ,?`[?1f_?W0 ?,I ?C!?|O?V"y$R?4M^?EP?2}?Qʾ?]?.]L?m+&? mmsi? Ğ?]ߒ@@@@@,@(??????0`=?2$l?5N4 -FZ@*~ժ@q?Rܜ^ ?UWm`??k?9uy@fva @@7mV.@7mV@7y0 @7mV@7ڹ[@7mV@7A7K@7mV@7FA@7mV@7c @ltj.@ltj@kvC@ltj@ktF@ltj@kĵ?@ltj@k@ltj@k*@6p4m8.>@2G+@1C\@17KƧ@1jW&@1 k@0%2@1 k@0;5X@1 k@1@8J? b}/?-_?C:?5}?̔J>@(@8J?[:|Q?.%?r?ǘ(K?% @8p?ɅoiE? -e?zY3???@@@@@,@$?????@E@O;c@E@T`e@E@j~@E@_p@E@@E@u!@%`A7@$S@%`A7@')_@%`A7@& -L0@%`A7@$oi@%`A7@%o@%`A7@%m\@8zqiC@8{C%@3S@3>@2"`B@2:@1䎊r@1Q@1䎊r@1Vϫ@1䎊r@1͞?}?5{#?]P?TnE?X?6 '8?F^S?yom?B<\v?G>G?? f?,+?\{?z_??)@Jq?]ߒ@@@@@,@ ???????S2O?/q?W䱶V=? @Xd@$U&v?x^ e?37?=?^6z@7 3@3uS@3q}-@2q7Kƨ@2Э@2V!.I@2g0-@2V!.I@2a -K@2V!.I@2d1Hf" ?x/0 ?a4ޯ?ƛ?<鲖W?|eP:?!o ?~Ov_ح?xj~?cG4W#?:&??ҧ| -?a>1l?"ܰ?D\e?8ɔ%.?*$]+?u"I(@@"@&@*@7@:??????t`^:?p._+?dY @ggr@Ql?o?z߹@!:@@ts$HR@h@@m\(@mc4@m\(@my:.-@m\(@mbp@m\(@mwM@m\(@mvAA{@m\(@mt@hbM@ho[U'@hbM@h;@hbM@h؝@hbM@h@hbM@h@hbM@h'@8?@80u/p@4&+ J@4[i@3lj~#@3R>І@2[W?@2syZ9@2[W?@2hXѩ;@2[W?@2v ?C/?j?;?W秕???ek?n%t?@^8"B??~?`?2?1CW6@1hr@1~k@+@1Ft@10ggU@06z@0f@06z@0y@06z@0˝%J@8J?|Bn?Ώ?wJ?e?,XsG@8J?F1Q?KcU??mw?\q'@8p?%c+5 ?$4;?/>?ZQ??7@*@,@,@<@:?????@CI^5@CƧ@CI^5@C%F -@CI^5@C@CI^5@Cg@CI^5@Cqu@CI^5@C~($ @#Dt@#KQ@#Dt@#IrF@#Dt@#;dZ@#Dt@#EQ@#Dt@#C -=p@#Dt@#A@7*0U2b@72X@3 I^5@3@N@3q7Kƨ@3oحV@2@2߱[W>@2@2\@2@2܄??}ѿ,?tcN~k?ِ?btx=?bJֵ?"/Y?h??[45?Kd+?1`?ۣ0Υ?~Y?D(n?J>?L?#ѧ?x?\,!@@@@@&@$???????D p_<r?MZW ?)fk@C%n&@#E6?q8c?GRDj?k?b7@ky@@@@>vȴ9@@@>@@@>쿱\@@@;J#9@@@B7@@@Ae@ Xtj@ Xtj@ Xtj@ ZY@ Xtj@ Xe,@ Xtj@ Y+@ Xtj@ Zxl"h@ Xtj@ Z,<@6:@65Xz@2+ @2ԕ*@1V@1;5Y@1iB@17Kƨ@1iB@1iB@1iB@1|Q? ?@)M?ojVrn{~?7D -?Ol??tWZ?< /0?=.e1?|[z??Ãx?Z?2?0&YEd?՘]G@@@@@,@(???????A%7?<0?I.q?*)@?*@ Y}#?i{q?8޲m?-畺?w&,Yj@g\@@&x@_@&x@ @&x@_A@&x@G@&x@Gu@&x@@+ J@W@+ J@,Ue@+ J@2@+ J@@+ J@IU@+ J@}H@95S&@98^@^@3r Ĝ@3v@3-V@2締M@2iB@1An`@2iB@1=Ŷ@2iB@1+?5T?HT|?='f?u"L?|.?Sj?Λ?iy?شc?3PD?N?EYC:?[h ?M69!t?Nv\b?%&P)?@g?Y4@@ @(@&@5@8???????yɦsn?vNU \?g@v @t8?s"ZO?7@[SY?gS{@m)k@@WRnP.@WRnP@WR@WRnP@WRs@WRnP@W]-V@WRnP@WU!S@WRnP@WVl!@$/.@$/@`A@$/@_ح@$/@PH@$/@@$/@D@5|Q.>@1^5?|@1SMj@1-@1hۋq@0YJ@0z@0YJ@0@0YJ@0zxl"h@8J?ru? ?wn-?0 碽?6|Ob@8J?s?0˱[?V\F?9?c5W@8p?iTC~t?z_?.SM?DV?|)-}(@@@@,@*?????@l@߾vȴ@l@}H@l@A@l@_o@l@1@l@84@R@glC@R@_˒:+@R@_|h@R@Y0 @R@c9@R@w4m@8?@8A [7@3sS@3k6@3t@3|u"@20U2a|@2n@20U2a|@2x@20U2a|@2lQ?}ѿ,?d`w?P I?Q+v?xsi?Z?`m?}y?#?Q?n ?aG=)?T̅?*T?yC?k2m?vI?kK)@@@@@(@,???????!!@?rkh?gG1?r9zP[@i@^?$#W?Kd>@vfK?tY}@meB%V@@r-V.@r-V@;dZ@r-V@Em\@r-V@4@r-V.@r-V@%F -@:=E.@:=E@;3@:=E@<@:=E@@3c@2%?@27Kƨ@1Ov_خ@2=ce@1 b@2=ce.NC,@2=ce@1@8J?ްrQ?z?|JA]@8I?/8J@8J?ܟ~yv??p?ҝ @8S?`u%l@8p?&Z#?-BR1?wf*-n@8 ?<͸R@@@@,????@o-@o-@o-@p\@o-@p?q`@o-@pE@o-@nk]@o-@n-@m@-V@m@Z@m@\@m@@m@iۊ@m@@3^H@3^1xT@0?|h@00j+@.ΗO<@.΅VGL@.=Vl"@.1X?aՊ5h?5Y*?5Ʌ]A@~@_%X?S@TTF?S9?pB(?Z@d"nR@@-V@lD@-V@ 4m@-V@#Z@-V@b~@-V@H˒@-V@+k@)7K@(5?|@)7K@(_@)7K@(u%F@)7K@(BZb@)7K@)T@)7K@)4n@7,<@7A [@3hr @3@36-@32M@2fYJ@2jC\@2fYJ@2`@2fYJ@2`oiDg8?q!U??]P?Z)?K/?! ?ٛP?ew?L94?cgp?B ?vk? # ?J?Q?cӗ1?E݋N?3@@@@@,@,???????6H"gH?MӬ ?@]F[4?N,>@dqt@)W*?Z]?h~?,3?3-7O@m>@@@f?MA8?/`?*WF?bM_x?#maЀ?90?>\?A B{?N`?bMky? ?|׿pZ?͓E'?l&?f_Ԋ@@@@@,@,??????QTVh5 ?+bTw>"vF@<іf@i]M8?:z~>گ?q8=T>𣭁>@s A0@@lD@lD@lD@lD@lD@@lD@n/@lD@5Y@lD@m@;dZ@;dZ@;dZ@@;dZ@H˒@;dZ@|h@;dZ@o@;dZ@$tS@.5Xy>@.5Xy>@(vt@(v4J@(dZ@(~%@&`d@&m8@&`d@&m8@&`d@&`d?^Q5P?GA?S?MA8?@ A2?:?bM_x?%F?A)N?/Z?ѡb??R g!ڢ?bMky? ?oܧ?avh?#.?#.@@@@@,@,??????!]%d?pO`>8#L8e?ߩ~@Y$@_~[?!_?zG>';?)E1@s@@ -=@ -=p@ -=@a@@ -=@H@ -=@v@ -=@&@ -=@+j@ I@`A@ I@捸@ I@!-@ I@思IS@ I@4m@ I@Z@8YJ@8xl"h -@3 I^@3 (@3`A@3Xe,@2sh@2C-@2sh@2O M@2sh@2a@N?I~y?+W??1t??5n?[N#?ѱRC@nPw@@bM@Q @bM@r@bM@F]@bM@73@bM@l!@bM@ȴ9X@]"@]|h@]"@]҈p@]"@^'RTa@]"@^'RT`@]"@]䎊@]"@]@4v@4cA @0sS@0sS@0'KƧ@0'sPH@.'RU@.`A8@.'RU@.D@.'RU@.&x?|O?[e+@8I@8?W$B@8@8i@8@8S@8? T"@8 *@8p@8@8 @8 @?@`B@_$@`B@{mK@`B@n@`B@XU~@`B@CV@`B@֏]@#|hs@#d@y@#|hs@#o!> V@#|hs@#jhKI@#|hs@#: @#|hs@#v=@#|hs@#y@8O M@8t*wc@4+ I@4L@3 -V@3 5K'y@2@2άB@2@2zjL,@2@2сΊ?7 ?heS?]|??w?)6?gV?5T? 9~?s^q? ?(?Ѐ>(@@@@@,@*??????[J>,?:+/?*f~V@~q#@4n6,?I??i[? ?4d{@s 'ͅ@@Y Ĝ@Y@Y Ĝ@YE@Y Ĝ@Yg@Y Ĝ@Yݗ+@Y Ĝ@Y{@Y Ĝ@Y @J^5?}@JzG@J^5?}@J^5?}@J^5?}@Jc@J^5?}@JS@J^5?}@J=@J^5?}@JC\@5:@5B@1 I^5@1s@0ȓtj@04֡b@0 k@0zH@0 k@0K]d@0 k@0/V?1j?W[?TS?A%?@@No9?t??jכc?C\?]bn~?c3c(? R?X?9o?{W?=rE=?ˢL1?fW@@@@@,@*??????500?Q&?2.rP?A x@Y0C@J. ?TPa?]Nᩨ?7ѓ?oRf@loN|D@@-V@0bM@-V@,@-V@1N;5@-V@2@-V@*g@-V@14K@j~@0@j~@"@j~@ -=p@j~@C,z@j~@H@j~@4n@7O M@7-@2ěT@2H˒@2v-@2t9XbN@1&@1- r@1&@1 k@1&@1Ov_خ?^*?hX*?K"?Q6@(@!e@;dZ@x@;dZ@'RTb@;dZ@;dZ@;dZ@8}@;dZ@ qu@;dZ@Xf@6[~($@6[m]@2S@2 -=p@2v-@2v,<@1sh@1+ J@1sh@1n/@1sh@1 '0?|O?" ?^?_??;?-/ge?}??xrI??>#?/ȫ?J>?tK?ZaP?*J?' -}C?l?P\?0&YEd@@@@@,@ ???????OvqU??>$Yb?DbH@"} -@ -?`w?dYg{?썹?z@is@@R@g@R@+բ{@R@0T@R@kQ@R@x E@R@"N@aG{@`A7L@aG{@dU>@aG{@` o@aG{@dYĜP@aG{@`\A @aG{@`s@5D@5:@2+ I@2@1M-V@1N1.@0@0w*h@0@0r~_@0@0rn?|B &?AI?3)?]S&n?Bc^?ML?~?J?bs?e`?-GB?x;q?s Ge?v,?m$f?煚(? {7?,G]Lk?43#C@@&@&@"@8@3??????>?qp?3ҌLJm?/Ǧ@@`0u?SB)?P JG?{X\?"p@g2 3@@x@7Kƨ@x@vj@x@Z @x@ d@x@wZ@x@e}@vE@w -=p@vE@w~[@vE@wt@vE@wW@vE@v-N$@vE@v_@4>6z@4L<@1+@1;kb@0;Ƨ@0U;X@;r@vf?`) ?2}sX?̊sJC?qsXX@fWy@@@@@\@@V@@̿[X@@Vϫ@@̲@;;dZ@;;dZ@;;dZ@;5?|@;;dZ@;҈p@;;dZ@;6z@;;dZ@;5?|@;;dZ@;@5(YJ@5(*@1 I^@1~@0"@0쿱[W@05sh@05sh@05sh@05?@05sh@051? ?]Û?4!p?Њ?1 v?t? ?`5a?ܿ?ą6 -?&Tm?U3p?CwB?)Zw?{W?':? -t?ܑ @@@@@,@*?????? -&i?I -3? CqN@̯p@;+(?%f{yr?.& tg?VP-"?dsl\@gQc@@EQ@EQ@EQ@FD1@EQ@F2w@EQ@F?t@EQ@D B@EQ@Dz@7K@7K@7K@[*@7K@af@7K@q@7K@(@7K@c1@1H@1Y:F@*7Kƨ@*A [@)cnP@)b}Vm@(oi@(TɆ@(oi@(*0@(oi@(̢?^m d?6?tu?D?1%(??bMn? -i*.I?~4pU?/d?a?M)á?g"e)?Sg?p7?9wT?=>(?3`@@*@,@,@2@5??????Kw0,R,?4;W'? .@EtC@vq?CrSy`?@?sQ ?(ԑ@s@@4nzH@4b I^@4nzH@4$/@4nzH@4~@4nzH@4;dZ@4nzH@4z1@4nzH@4A7M@TF@y+@TF@b\(@TF@JL@TF@[~@TF@T9Xb@TF@Y_p@9|Q@9=K@4f+ J@4PD@3KƧ@3Fs@3~($ @3@3~($ @3n/@3~($ @3?G *ǂ?ʖSS?+*u?m}9?7?+`N?g;+t?,?W@3t? ;q?ިo?)AL%?#IӨ&?$jB?bO?3l]0?Nty"?a#@@@@@$@$???????qeH4pk[?h@B_“?i_Rz@4{tR@[@드?g w?W9Mj)?? 0@p erd@@49Xb@/;dZ@49Xb@fC@49Xb@Gz@49Xb@@49Xb@T=@49Xb@uZ@Ƨ@W;@Ƨ@+@Ƨ@,<@Ƨ@` k@Ƨ@i] @Ƨ@x>G@77@@7֎@2sS@2@`A7@1Ͳ-V@1Xe@1QiB@19oiD@1QiB@1*ۛؽ@1QiB@11D?|Ί?gV)?t|*? ?t]2?;Xb?Z=?L}? @S?P?s8:?߼K?@#?X{?eN ?wb?=?g;d7@@@@@0@,??????p_<~(@?p}bM#?{lRPp8@^n@p?_mT?QO6@lh.@,T6Z@g-MN@@+n@+S@+n@+\(@+n@+t@+n@+n@+n@+S@+n@+|i@rXbN@r@rXbN@r^5?}@rXbN@rdZ@rXbN@rdZ@rXbN@r+@rXbN@r"@3mv@3mv@1S@1&@1_-V@1^6z@0u@0oi@0u@0m\@0u@0%F -L?1j?}d?FB:?_B?DE?}?|O?dx/?Rs??tP*?90?nwf?fb?*wjs?,|?ܑ ?+C@???@@??????@;Po?4ϿZ#?l(@+]%5@rL,?VYY9 ?4s tA?*&?r`r:@fjhu@@4S@4@4S@4 -@4S@4(SBl@4S@4lZA@4S@4Sz@4S@4>@\|h@\|h@\|h@\xR@\|h@\8M@\|h@\Ğn@\|h@\GX@\|h@\ ph@7+s@72nO@3ahr @3`@2`A7L@2սW糏@26z@2S@26z@26:@26z@2 -Vh)?`/.~? ??L -v?0Eke?ׄǿ?CF?nO@4r Ĝ.Mm@3t.Qn@3L[W?.NC,@3L[W?.NC,@3L[W?.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @[S@ަE@[S@ZHe8@[S@߻ S@[S@ON@[S@E@[S@Oђ2@:DT@8G@:DT@:S[@:DT@:AѦ@:DT@:p@:DT@:ypى$@:DT@:U@7 xF@7Iܓ7W@2hr @2s͇@2Ft@2A@1,<@1tqߖ@1,<@14~1@1,<@1]ZNh?ݪ?:??σ2? -4:~?`XO?-ո6?8%?Xh?p?, Gq]?E?#?ޠ>?I|[O?B%M?uS1**?֦b?0U@@*@&@$@.@7??????E?f-?tr_?wTԁ@K4@:?B?+i?Bl"?BA?!l@r0ul@@tj@tj@tj@ -=p@tj@+j@tj@B@tj@@tj@>6z@ ,1&@ ,I^5@ ,1&@ /-@ ,1&@ /v_خ@ ,1&@ 1&x@ ,1&@ .V@ ,1&@ *0@7}:@7\O@4}hr @4zqiB@497KƧ@45+a@2Q@2JL@2Q@2مoiE@2Q@2#9? ?t,?.#Y?ˆ_??S{d?h??ڲt?cGo?N6?+!?K ?`Ƕ?qJ??Eq?x2?7?P\@@@@@@???????B90'Þ?7?MǿtF@ @ - *?[oT?jkK > ?J ?̧E@kHa@@0 ě@1l@0 ě@0`A@0 ě@0:~@0 ě@7O;@0 ě@7>6z@0 ě@:Q@e@e"`@e@eȴ9X@e@e2@e@e6@e@eE@e@e3333@8B䎊@8@ qu@45S@4.Q@3Vu@3=p -@3\Q@3XXy=ـ@3\Q@3JfB@3\Q@3Jn?qn?u:|?Aox?vI?1BZ@H1&y@Fs@H1&y@H@6|Q@6@3S@3 ě@27Kƨ@2;5X@1xF^@1@1xF^@1e+a@1xF^@1Xy=ـ?|O?PƧ$?4 b5??ԁ?"/Y? ?,T?f?P?BnF? ?IH!w?W;RS??]b?=aw%?{2?w w@@@@@*@*???????<<H~X?J,!zv?,=ڔ'@ =@fbOm@@ě@ě@ě@!gQ@ě@B@ě@e/@ě@@ě@1u@l1&@lC@l1&@k1@l1&@l9]5@l1&@lm0@l1&@lz@l1&@l@.V+jf@.VOv_@*"`@*򐫵@+x@+Xe,@*C\@*@*C\@*O<@*C\@*>r?^6P?\f?2k?:M??= -M?bMn? qb@??5ߨ?j?RZ8p?bF?3U:?p풟?ć8?t.? -@@(@*@(@4@5??????&a? -lj@?iV?,@L @l3?3-d?PcW??)ON@sK@@bM@@bM@@bM@+ I@bM@@N@bM@C2@bM@ 1s@@ci@@a@@-@@Xy>@@`)@@v@@7g l@7$`@2+@2حV@26-@2!A [@1&@1ߝ-V@1&@1ZSUZ@1&@1ˍ?ɱb)Q?P1"?GM?3҃?U?޽/1c?/?z?3fvI?ZzZ?5yS?rC2?TȻL?9WTW?3 q?K쯎?^'?t^]7]@@@@@0@.???????R.Uh>f?7_,?TybZ@=n@O~?YK寮?tg?znJ?== @lN@@%@%bM@%@%踎@%@%d@%@%1H@%@%P@%@%e@V@+@V@@V@4¹@V@e@V@1@V@M@2 _o@2 _o@0+@0@0q7Kƨ@0p9Xb@0!.I@0rɮ@0!.I@0]@0!.I@0?^j?$?S24A?nhW?q?G?bQ넛?z,?RE?r?:i?'aJ?bMg?Ȕ? -?N?{??ŧ@@,@$@$@3@,??????UK?6@?A'f~8?- qM9@% -@c?P?;2 U?x'b?olwi9@r @@ffff@$/@ffff@,>@ffff@J@ffff@g8~@ffff@n@ffff@쿱[@d;dZ@d;dZ@d;dZ@dvȴ@d;dZ@d"u%F @d;dZ@d"wk@d;dZ@d%oiC@d;dZ@d,@4@@473@1 I^5@1u!R@07Kƨ@0- r@0}ce@0zg -@0}ce@0zG{@0}ce@0vϪ͞?|O?Kb?&P?ھ۲?>C,4?ZzZ?}ѿ,?; ?I?pFs?c?~Y?3Z\?u/X?],m=?0?:1?$jB@@@@@*@*??????Ri?b?4f?@w u@i@d#48?T~W?`Z?q?ؠi1@f65a\9@@Q@v@Q@4m@Q@4@Q@X@Q@G@Q@-@O;dZ@M@O;dZ@TSN@O;dZ@Q@O;dZ@Q4K@O;dZ@Y @O;dZ@V8YK@7L]ce@7M8Z@3° ě@3n.@3)"`@3+qu!@2s&@2sZ@2s&@2s&@2s&@2qR?F/P5?@K/z?zjʩi?H?X?|&?)T?tMP?4FgI?kH?k?V.H?8*?3a=? t4?cӗ1?DV? N@@@@@*@*??????L ]?`~M,?>iP=;1?E6F5 @o#?ǻ(@h36@@V@Ƨ@V@hr@V@rGE@V@6@V@PI@V@74@g@g@g@gu%@g@g C@g@gQ@g@f@N@g@fiDg7@6:qiC@6:Q`@2 ě@2vȴ@2R`A@2An@2m8@1R<6@2m8@2\N<@2m8@2 -Ov`?F/P5?܄ (?.? S~?k!e?P;rp?|O?{c?(?{k4?Wlz?#:d ?W'?ZaP?`F?LO?zY3??zy? Ğ@@@@@ @*???????bYOilT#?G -n?U@/Q@fQx?f?tzb?z]p?h[J@dj@@ ^5?@9X@ ^5?@@ ^5?@˒:@ ^5?@y @ ^5?@ oi@ ^5?@ -L0@$/@,C@$/@:G@$/@5?|@$/@1iC@$/@.V@$/@)ᰉ@7>6z@7ohی@3TS@3I (@2A7K@21'@1YJ@1߱[W>@1YJ@1A [@1YJ@14J?| ?(R?|6zj?'$5?+@8I@8? -L.1@8@8i@8@8S@8???;@8 *@8p@8@8 @8 @?@l@l@l@҈@l@~#@l@ԕ*@l@Xy=@l@qi@nO@nO@nO@mhr@nO@l>B@nO@r@nO@o'/@nO@nV@4G>6z@4GKƧ@1W+ I@1WzH@1Z~"@1\j~#@1(@1+q @1(@1*Ov`@1(@1(4֡b?}ѿ,?Čۨ1A?aR ?b?ɿ?mׁe\?| ? ,C?{d?EP??Z)?y8#?§d߱?Z?v_.^??WZ@@@@@*@&??????%<?'/;?)~o?;SC@+@o7 -+?IQ(?[??i^!?Dm -@f8@@ I^5@ -=p@ I^5@(K]d@ I^5@4!-x@ I^5@ -L@ I^5@u%F @ I^5@ hr@xQ@{dZ@xQ@t@xQ@tj@xQ@w@xQ@U?|@xQ@m q@7}:@7wXe@4@4Zc@4`A7L@4/V@3,<@4oiDh@3,<@4@3,<@4C,?oC?y)3c? +~?d? ?i]x[?sD??W?Q Q2? 7?:;?B?:!;ى?g`u?=!W?5.?L4@@@@@&@&??????$jaxO@?To ?iX6P@ ^&Ϧ@l;?uU8'&?rCW?AD]@̘ؽ@hB@ @@m@ -=@m@,@m@`d@m@ '@m@J@m@C-@Z@@Z@@Z@u@Z@+k@Z@XbN@Z@2a|@6,<@6Xe@3?|h@3t/@2j~#@2쿱[W@2p@2ZOv`@2p@2Zd7@2p@2^;5Y?}ѿ,?mvrDž?=wD?&!+?6v;v?x2?3?+ߎ @@@@@,@,???????g^mn?rC??WPQ4{?X|3L[@SA@Op?n7^U?o5r?,zc?ʦ.~W@^~@@u\).@u\)@@u\)@[s@u\)@Įߤ@@u\)@ĭ(@u\)@7@U;dZ.@U;dZ@W@U;dZ@X-V@U;dZ@X/@U;dZ@X9#x@U;dZ@X12X@6!..>@2@2˒:)@2~"@1P@1@5@0u@1@5@0:)y@1@5@0ȴ9X@8J?N:'?Dwhg?ѻ ?Ѧ9H?=J7@8J?_;$?srlV?GJ?b)ۑ?o@8p?KQ5?;N?_c?GX?!S{uu@@@@,@*?????@-hr@.+@-hr.@-hr.@-hr.@-hr@64@-hr.@"`B@!$/@"`B.@"`B.@"`B.@"`B@J~#@"`B.@6۹~($@6C\@5^5?}.Mm@3Ͳ-V.Qn@4(.NC,@4(@4x@4(.NC,?|O@8#@8:@8 >+? rL@8?2}@8@8i@8?**ɫS@8?,1'@8 *@8p@8?=i@8 @@(??@@-V@@poiDg@@z@@&@@,<@@r @ Ĝ@7@ Ĝ@J@ Ĝ@1@ Ĝ@zxk@ Ĝ@*0@ Ĝ@u%G@9 D@8JG{@4S@4>BZ@3dj~@3d7@35sh@3V@35sh@3^v@35sh@3U8YK?9?iedA?q{?s?yH?>>?pn?D?v׆7? '~?]%?*ML?Й8??$̤T??:L]?[Y?-B?_! @@@@@,@$???????\Lu@?l%?vG\q?{>z@@g?}0Xy?<3?nϖ9?#,@uRrh@@t@ qu@t@ =@t@Q@t@b@t@89i@t@܌+@n@-@n@{X@n@}9@n@z@n@}iGt@n@y@7ks@7e@ @3 ě@3&`@2{Ƨ@2~C/@1@1]@1@1N@1@1o?|B &?tی?=x?*4Ie?][?0~W?R ?Q?4ku?]dMJ? -lr?Z?>p??#q?1B!?aiJk?㝠Tn?] @@(@,@,@8@3??????\sTS?11tf?]7@Pf@[#?S?{ Vs?}}?AW@k!83@@S.@S@Ӂ7K@S@c -=q@S@B\(@S@͞@S@Z@ȴ9X.@ȴ9X@<@ȴ9X@ᰊ@ȴ9X@'RTa@ȴ9X@m\@ȴ9X@7@8@.>@4@2$/@3t@28YK@2,<@1͞@2,<@1@2,<@1ԕ*@8J?E?o" -?~a?us`c?~G@8J?q?(T?fY|h?NMpZ&?@!"@-V@S8@-V@w;/b@3S&@3@/j~@/'@.mO;dZ@.cn.gJ@-#Z@-^5K@-#Z@-Zv@-#Z@-m?gQ?r ?KI4?'I ?&J?Vh -?}@TΥ?h?}% '?RLB?1b?\U?Ykd?-k?!b??? ?}@@@@@4@4??????I1D?Ufd?B&qL?Mz?4'f?r48@ghtZ@@8U$@8R Ĝ@8U$@8,VϪ@8U$@8U?|@8U$@8jJM@8U$.@8U$.@O;dZ@Hы@O;dZ@+P}@O;dZ@$ x@O;dZ@K]c@O;dZ.@O;dZ.@8 xF@8Ni@7 ě@71ohی@5"`B@6l@5._o @59JE@5._o .NC,@5._o .NC,?x/0 ?\5#k?Fv?Eu]@8I@8? 1Ӡ?V ?Cڶ?rpв@8S@8?#n?M#?%׀_?oh@8 @8 @@@@????@e޸Q@e"@e޸Q@eව4o@e޸Q@eح@e޸Q@e'RTb@e޸Q@eE@e޸Q@eݗ+k@œ(@›lE@œ(@œ(Ž@œ(@šY@œ(@›~@œ(@œ@œ(@œQ@4įO M@4ļj~@08r Ĝ@08Q@/&x@/_p@-`d@-!-w2@-`d@-J@-`d@-?|O?$?$C@? Iy??wZL?|O?0n=Q?A)N?@Cv?<U?M\ٓ@?m+&?.x?8*?':?M684? Ğ@@@@@*@"??????&[ ? E@?0f$C^Q?<'5@e5+@›?PYPBK?6thg1??{L -?1@fNU@@Z@T@Z@߉ @Z@~:@Z@L!@@Z@ߪ-ڛ@Z@dp@+@@+@ͨ~@+@RHg@+@e@+@}@+@x@7*0U2b@7ة@2sS@2sYjq@1tj~@15xΆ@1._o @1,/@1._o @1,)&$@1._o @1,XC -?o"? -"?x?? -E?ָ?¨Sp*? ?&:η?R?T?'U?놱_?CϐR"?&C?Po{? uv?{j@@ @(@&@1@1??????f訛<Ow@?Q"9?)@wQ@k?qBjF?Js?Sa3=?b@hǁ:@@hr!.@hr!.@hr!.@hr!.@hr!.@hr!.@$/.@$/.@$/.@$/.@$/.@$/.@3YJ.>@0+ I.Mm@/O;d.Qn@/873.NC,@/873.NC,@/873.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @$G+ J@$C -=q@$G+ J@$E8Y@$G+ J@$H1&y@$G+ J@$Eں@$G+ J@$F@$G+ J@$Em\@h\@f$/@h\@g@@h\@glC@h\@i^5?@h\@hۋq@h\@i@7_o@7*1@23S@23MjO@1tj@1u%F@1=ce@1=<64@1=ce@1<쿱\@1=ce@1@n@˒:*@)š@)š@)š@)L/{@)š@)S@)š@)Ƨ@)š@)8Y@)š@)oiD@0s@0g @*"`A@*s@*XF@*X4֡b@* -#9@* lE@* -#9@* -Ov`@* -#9@* -0U2a}?^Q5P?{j?"?MA8?g?$?bM_x? ,C?j?1'7?ߒh!?Z,a?[h(?6?q6tN7?͓E'?O|q?Ѐ>(@@@@@,@,??????AF4Ր0?)/kr?#lE @9 @)0?89*uM?2cـ߹?lt3B?]@sk@@zG@{q@zG@|+N@zG@]V@zG@@zG@njV @zG@Hq@B\(@9"@B\(@AʉB@B\(@S;@B\(@?,͡i@B\(@D@B\(@E@82@80M@4Lhr@4M@vƨ@3j~#@3?K@2m8@2Ӯ2r@2m8@2qۑ@2m8@2 G-?Z=?H9?8NJH??  ?s5t?@>?7!,%?,e?DQ |?Y*.?$'^Y?r?w?& d?"Ux?bewFA0?{<K@@,@,@"@8@9???????p?UF.?R*-e?'zׇdm@񮌎@@XZj?tn?B/^4?1$??"_@lJ@@9XbN@9a+@9XbN@:@9XbN@9XbN@9XbN@9~(#@9XbN@8PN@9XbN@9 ~1C@_;dZ@_;dZ@_;dZ@^5?|@_;dZ@]/@_;dZ@^Ov_@_;dZ@^@_;dZ@_:ǭx@1䎊@1&IR@,kƧ@,k:)y@+E@+Ov_@*L/{K@*L/{K@*L/{K@*$/@*L/{K@*b=?`U8r?GA?N?XQ>?O8?_?bMn? ,C?|,!?4ډ3?֧O?H+m6?bF?u?y8$?͓E'?v?󰉋@@@@@,@0??????/3q@?(?wy -hO -?{5@9;k@^l(u?&܉?ϧ?H>[0?3pw@s kXg@@hR@hTj~@hR@hKjg@hR@hL@hR@hP)^@hR@hNMj@hR@hMU@7Kƨ@C@7Kƨ@8X@7Kƨ@~($ w@7Kƨ@J@7Kƨ@!R@7Kƨ@Z@5]ce@5ˬq @15?|@1jf@1~Vu@1zd7@0Q@0֡af@0Q@0@@0Q@0ԁoh?1j?MSMӮ?GM??Xs~??=?}ѿ,?nr|?f?FT \*?Q?u!B?~}o^?{ڵ?q?t@@@@@*@ ??????W*,hP)T>?K$7?M@3S.Mm@3Htj.Qn@2sh.NC,@2sh.NC,@2sh.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @Õ%@ݾQ@Õ%@K]@Õ%@ᰉ@Õ%@Ǔݗ@Õ%@*@Õ%@7@-@ۥS@-@l"h @-@ȴ9X@-@H@-@䎊q@-@{J"@8YJ@9hr@2&+ J@2"e+@1-V@1~@0xF^@0KƧ@0xF^@0JM@0xF^@0*1?o 6}?9؊?,fC6?j6_?4f(R?(ϢU?X? -?2'?]bn~?fW?Sw?(D?7-R?`x?':?T8?WZ@@@@@,@*??????P%d6?h<ـ?TkВ?aW@źz@#^?eˑI>?wsKD?{ @?H~@oϚOnn@@I^5@I^5@I^5@j@I^5@@I^5@{<{@I^5@{eO@I^5@ @ I^5@ I^5@ I^5@R@ I^5@)@ I^5@}T@ I^5@bOߡ@ I^5@"lc@3|Q@3ohی@1^5?|@1zpJ@0`A@0 b@0QiB@0Cfk@0QiB@0H0,@0QiB@0I0?|h2:?.FI?%ja a?^ŋ?vj?؄1?|h2:?4?6Ƴ?~nY?d}??q9?0t:m?t&?8;?z!?Ci;@@$@@"@<@8??????o%! ?RAJ?U|5Td?]wlB@п@-`z2?tC?|{?d>@[_Q@eQ@@nO.@nO.@nO.@nO.@nO.@nO.@+ .@+ .@+ .@+ .@+ .@+ .@9,<.>@5+ .Mm@4ȓtj.Qn@4u.NC,@4u.NC,@4u.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @pM@p&x@pM@p-w1@pM@pC,@pM@pIQ@pM@p:@pM@pR=@1@G@1@iB@1@Ov_@1@;dZ@1@kP@1@+k@8B䎊@8?-V@3S@3,<@2~"@2ސ$t@1ce@1}Vl@1ce@1䎊r@1ce@1˒:)??t0?=tQF?_?ZAj?4 ?}ѿ,? l?5 ?N?pPȻ?XԠ#{?yjk?)Zw?7}?Gϵ'?ĭ? -r@@@@@,@,??????c6Q;?_~d?\B0a ?I@pJ -m@-?sk\?oOZ?ꜵRk?|3@mMI@@ɎV@ɎV@ɎV@Ɏ+@ɎV@ɏ\(@ɎV@Ɏ+@ɎV@ɍ8@ɎV@ɍ8@F@?|@F@j~@F@F@F@O @F@3@F@[@0v@0v@*V@*(@*x@*x@)`d@)m8@)`d@)zG@)`d@)m8?a(?&H?"?˅T?/`?:?bV,?r/B?|,!?6)}?@?M\ٓ@?b@?~('?s@a?avh?O|q?O|q@@@@@,@(??????@0 ?zrX?$`)?]=@Ɏ5.@ܮ?4P?DBpA?cA?4;?׊@s 7@@$Z@'z@$Z@%b@$Z@(=@$Z@'a\@$Z@"k@$Z@$@}E@g@}E@p@}E@o-7@}E@r+o@}E@t@}E@QHtz@8@8}@3+@3{5]@2~"@2P`@2 -0U2a|@2)@2 -0U2a|@2~wn@2 -0U2a|@2 -ɋz?u?SBx?4?U{??l% ?Au 8?+ef?Yq?0e?p:_j?Y=OD?,97?ZOM?ŭLc?ǹ۝)?;11?1D -"r@@$@,@*@5@2??????T`8?k:?G0!?@?S*f @%i!@vVHW?]?g= -Z?sVH?-k@pjΕ@@p -@n@p -@C,y@p -@䎉@p -@p @p -@W>7@p -@Y@O@vȴ@O@T`e@O@1@O@C@O@[W>@O@@7cg l@7Uoi@3 ě@3 ě@2tj@21&x@1sh@1$/@1sh@1F -L0@1sh@1Q?lOx#?'?oԀ? ?.Kn?f]G?}?? :?>??l{9َ?Z)?įJ?H?O?< N ? -r?<71@@@@@,@&??????`ŧCR?^V @w\?dJ\L,@{@t?nQ[3?vi,?QH?#׉@r*!e@@ ě@ ě@ ě@ !.H@ ě@ k@ ě@ !'@ ě@ |h@ ě@ |h@ݞ5?|@ݞQ@ݞ5?|@ݞѷ@ݞ5?|@ݞOv_@ݞ5?|@ݞvȴ9@ݞ5?|@ݞ҈p@ݞ5?|@ݞߤ?@3#g l@3#Z@/tj~@/a@N@/DZ1@/DS@.Ǔݗ,@.ǻ/V@.Ǔݗ,@.@@.Ǔݗ,@.8}I?| ?Z.?$C@?:Z2b?G?t?| ?0n=Q?O(?3.Yl?!U?{?eDQQ?|׿pY?l?=Cb?xqC?L?*Z}@@@@@,@,??????(|>P?-R0?Id1@ }P@ݞ+?LlX?$ ?$ ?Vb~@f!u@@F@$@F@Fs@F@[W?@F@4m@F@[@F@ᰊ@t@S@t@n@t@R<7@t@ѷ@t@C,z@t@~@8qiC@8 [7@4^5?}@4nP@34tj@3! @2Ǡ k@2ө*0U2@2Ǡ k@2g@2Ǡ k@2@N?`m?@u-I?MDe?oȞ?e7@??pEIt?N`? Ķ?"`?qn?O[?/?Fg*V3?*0x)?5_?8wx?8@@@@@"@*???????s1u"?qhw?iHl]x@EN@H?,pxp?z?}{[? @qb^@@Ƨ@m@Ƨ.@Ƨ.@Ƨ.@Ƨ.@Ƨ.@Fև+ @Fڟv@Fև+ .@Fև+ .@Fև+ .@Fև+ .@Fև+ .@8H@8؆YJ@4ٺ^5?}.Mm@4Htj.Qn@3ush.NC,@3ush.NC,@3ush.NC,?F^S@8#@8:@8 >+@8I@8?@8@8i@8@8S@8?eˬup@8 *@8p@8@8 @8 @?@É7Kƨ@Éx@É7Kƨ@É @É7Kƨ@Él@É7Kƨ@Êqi@É7Kƨ@ȏ]@É7Kƨ@È>BZ@^5?@~#@^5?@ @^5?@^5?@^5?@1&y@^5?@Xy=@^5?@TɆ@5*0U2b@5F@1{ I^@1z~@0"@0Ft@0n_o @0mcA @0n_o @0mU=@0n_o @0mb?|O?Čۨ1A?\H{gX?GZ0e;?\7?7F?|O?u߿?{d?G>G?M?GE?+:?9o??RJ? Ğ?2[s@@@@@,@*?????? ->?*|.s-?$z\فR@ÉW@ce`?J?D߬?ɝ?lsז?h=t@fG@@a["`@aVȴ9X@a["`@aIrH@a["`@aQA [@a["`@aU*1@a["`.@a["`@aZ)y@@#S@@+j@@:~@@" @.@@ /{J@84m8@8M:@4Y^5?}@4FA@3tj~@3{@2D*@2V@2D*.NC,@2D*@3- ?F^S?yK?»?sNX@8I?I?+-/? o]?;8+?ђ@8S?fD?1?Эs_`?7V/r?/#ˮr@8 ?,yfY@@@@@"?????@bM@O;d@bM@?kE@bM@IRO@bM@Kg@bM@|E@bM@t.o@5?|@vE@5?|@d7@5?|@r3DZ]@5?|@8 !T@5?|@8X(S@5?|@<]?@8zqiC@6n@30ěT@1x^@217Kƨ@0Z>Ũ@1D*@0UU F2@1D*@0Xҿx@M@1D*@0V|_?{si?S?w>Z?YF?+i?]lmY?(?gQ?bGF?7G?̽Vo??ǝK]{f?e?Ἅ?t=?B[@\@~$@\@84@\@E86@\@\@\@Q@\@L@6 ]ce@6 N;@2 ě@2\(@2"-V@2" I^5@1Tm8@1T!-w2@1Tm8@1T/@1Tm8@1Usg? ? KG?#}i?ƠJ-M?j?PBZ@0_o @0J#9@0_o @0,zxl@0_o @0ѷ?3a=?q' -B?K?eU5?0 碽?et?}ѿ,?-NI`(?&?{d?.D?؟#ߦ?nE?.+?@?π9"?3?`i2@@@@@(@(??????pGR?K?H?JiAl?I1Am@]d-5@?d -y?hpmi?;)[Ճ6?&ٺ@fJ-Z@@ I@~#@ I@)^@ I@4֡@ I@!-@ I@B@ I@p:@bM@u@bM@-V@bM@&x@bM@@bM@u!@bM@9Xb@8@9o@4\I^5?@4Nvȴ9X@3tj@3@4n@33&@3䎊r@33&@34JM@33&@33 -=p??xB?=£?,?'֙:0?s?*Rå?D12?\כ?*?k?I}?L /?]FV?ZaizD?ϯ<(?H&?&X;@zs>@]W A?+b*K?5!t?QZ?dy3Q&L@s@@m@n@m@'J@m@R~@m@9^@m@]T@m@M@qS@qStj@qS@qP#@qS@qR.H@qS@qTz@qS@qRL@qS@qS8 @2V,<@2VIQ@.tj~@.@@-?;dZ@->@,At@,Q@,At@,1@,At@,5?^m d?{TV? ?E[?:r? ?bQ넛?>pު|?^<+?/#ͤ?֓j?P p?bF?/?y?Dв?a?~.@@$@&@&@8@5??????F@42?-O(?%|Ct@X>o@qS?;T?4Vꇑ?s8$&?b( @stn@@\(@dZ@\(@v@\(@?@\(@3@N@\(@)@\(@q-@vE@fffg@vE@'fB@vE@=+k@vE@֡a@vE@CF]d@vE@$tT@9~($@9"M@5^5?|@5eF -L/@47KƧ@4ѷ@4O M@4L_@4O M@4H\)@4O M@4<[W???[G? dWB?+3#I?FILXT?{J?ʻd9\?ʳy?>?p:Y_?B?v$>?JzUf?/?ЙQ$?{_@j?V?:@@@@@@???????/3$ ?w G?[nd@/3@1p?? t?Xk?=)Q?1B@l}~@@"S@"v@"S@"Y<@"S@"R@"S@"K p@"S@"dp<@"S@"O@?|@vD{@?|@uxJ@?|@g@?|@ r@?|@@?|@ t@8YJ@8z9.@48r Ĝ@41,@3V@3Ÿ@2[W?@2@2[W?@2[s#2@2[W?@2&H|?|h2:?q-u1?Ě\?:@Zt?jå?qP?8 ?/?!dxD?|R2??a5Ut?W)?o?4C?7Ź ?GOe?|=@@$@$@*@5@5??????}"QE?QM?Eb'Ã@"z@x?r_lx ?ep5?O H; ?/`, ->@h):ei@@Hp -@I 18c@Hp -@H;@Hp -@H,#<@Hp -@I٦@Hp -@I jy^@Hp -@Iod@(E@(@@(E@(r^zi@(E@(nY@(E@(h@(E@(s2@(E@(l@7įO M@7ZL"r@3|hr@3Yp/@3gKƧ@3yF^`@2Q@2D@2Q@2u,@2Q@2õjM??ٗ?fk?N?SPǹ??_LQ?Xd?ǀSk ?oT?]\}a?$?35MQS?ӝpf? Ͽ?A??#? V?oGG,@@ @(@(@8@7???????P/?}:UD?a"FMo?B|ڄE@I r%@(kdf?}p!g^?bOTB?q4*?}7ʺg@lb\@@f|h@f}p -@f|h@fu$@f|h@fi@f|h@ft@f|h@fl@f|h@fiDg8@G/@G3334@G/@GЉ'Q@G/@GV@G/@G @G/@GH@G/@G7@8V,<@8T7@4 ě@4GE85@3]V@3w/V@2Ǡ k@2"@2Ǡ k@2S@2Ǡ k@2o?+-/?Ɖ?HFC?g_$?fl?LTO?^*?'`?VǷ?TV?қv??S[D?};?r?y\,?1ƛn? Ğ@@@@@,@*???????[? 7?dV?Xl&7O@f!@Gc?ybs?q;9SRA?ӿLh;?n 2@eNH@@@A7L@@-w1@@n@@L/{@@}Vl@@~iB@L@KR@L@GfB@L@L~($ @L@Jo@L@Ev@L@BѸ@3D@3䎊r@0+@0?@/mO;dZ@/g k@/oi@.u"@/oi@/ěT@/oi@/IQ?|O??,Z?;C??`?)'3?|O?ew?S1z?@4ٺ^5?}@5#GC@3-@3u@0|Q.>@)~"@) Ώ@(h1&x@(iAp\@(3PH@(49ftE@(3PH@(4S@(3PH@(36@8 -Ny?w<5ۙ?'D ? -w"?L&VB?{y@82r?|F ?&Z?8+ ?*Sk?+n9u@8L?6vJ? -?؀6>?z^j?|@4@2@1@D@D?????@u@$.@u@Xy>@u@`B@u@\O@u@@u@D@fffff@e`A7@fffff@t3@fffff@p ě@fffff@m q@fffff@jOv@fffff@i @7uS&@7s%2@3+@3a|Q@1"`@1oiDg8@1Y~($ @1Q @1Y~($ @1WsPH@1Y~($ @1Vȴ9X?F/P5?q?Aa[?˄&.?`?lP {?Br?&dU(?ދ"?:`?c?SS?4!j?U.?m!?? -r?k!l@@@@@*@*??????_?Rl?9EI?E[7@$N@h/:?Z>:?fMvu?$=/u? *A&@hzIW@@(@"@(.@(.@(.@(.@(.@Jo@;lD@Jo.@Jo.@Jo.@Jo.@Jo.@6H@6#x@5 ě.Mm@4$j~.Qn@4&YJ.NC,@4&YJ.NC,@4&YJ.NC,?Z@8#@8:@8 >+@8I@8?Z@8@8i@8@8S@8?A@8 *@8p@8@8 @8 @?@>`A@>O @>`A.@>`A.@>`A.@>`A.@>`A.@̏\(@1@̏\(.@̏\(.@̏\(.@̏\(.@̏\(.@7g l@7-FC@3^+.Mm@2ttj.Qn@2a:S.NC,@2a:S.NC,@2a:S.NC,?Bp@8#@8:@8 >+@8I@8?wvHf@8@8i@8@8S@8?-@8 *@8p@8@8 @8 @?@s3333@wi.@s3333@.@s3333@rR*%@s3333@s"@s3333@r5en@s3333@s^@~"@qm@~"@gI|@~"@mM@$@~"@j&@~"@{ ,@~"@sQ@92@8 eBwS@4n5?|@4i1ѵ@3V@3Ch@2fYJ@2a_w枚@2fYJ@2b_MK@2fYJ@2_?g)n֔?f)Y?5?w)(;?5t?]\x?ؗ?>?M?ic `?R!?=.A?Gl?$?/)R?:|=?5g@?o=)@@"@@"@3@.??????A`v7Ñ?5R#?ln|1@sPD@zPWy?GH1z?_fA?f@ -Vyv@pR]@@R@ I@R@5Xy@R@_o@R@.3@R@빌~(@R@ᰉ@zG@zv@zG@}+k@zG@|ߤ@zG@zu%G@zG@{lD@zG@{6@5v@5qu"@2S@2L_@1_-V@1_?@0z,<@0zD@0z,<@0y0 )@0z,<@0x?}ѿ,?Čۨ1A? ?:Z2b?R%(0?-/ge?|O?j%Ra?S1z?ѷ@(\.@8!..>@4 ě@4 k@3`A7L@3C%@3~($ @3;A@3~($ @3($ x@3~($ .NC,@8J?-d?Z-l?":Li?g;+u@8@8J?ʀ+?o?h?5nM8@8@8p?FQ?җQ;?u}+?B@8 @@@@&???? @`A7L@`A7L@`A7L@ae@`A7L@a.H@`A7L@a [@`A7L@`D@`A7L@f@HO;dZ@HO-@HO;dZ@HIl@HO;dZ@HJ#9@HO;dZ@HE@HO;dZ@HM@HO;dZ@HM@2 xF@2%1@1&+ J@1$g8}@0`A7@0Ft@0p@0m@0p@0lN;@0p@0h9Xc?^Q5P? KG?jhq9? -?|?~?bV,?Q_ڸFN?"a?Nw]6?~?Ft*5?ă$??>Zw?GZ?їJ?a@@@@@&@$???????`3S?mw$?Qs?UKD@b"@HJ~~?`_W?d"T?qi4V?mc$@r1@ -@?|h@?vȴ@?|h@=p -@?|h@=H@?|h@=E@?|h@B`B@?|h@Cn@eS@g+ J@eS@dq@eS@i @eS@d?@eS@eoiD@eS@hTɆ@5r@5qs@3=hr @3:c @27Kƨ@2Vu@2[W?@2y @2[W?@2RT`e@2[W?@2_o?|O?L?^?E`?bJֵ?f]G?|O?V"y$R? - D/?#q\-v?x9l?äqY?YE?iTC~t?[Rb?l? N?DV@@@@@,@ ????????ߐ?s?>ObQ?8Al-@@zx@gk?^~a7?YKwQ?ơt"?Y`m@f@ @S -=q.@S -=q@S{+ @S -=q@T=@S -=q@TT@S -=q@SP1@S -=q@TF@9XbN.@9XbN@iu*@9XbN@E@9XbN@՝@9XbN@%͗@9XbN@eN@8g l.>@4R I^5@4GO@3@`A7@2h,@2ush@29r@2ush@2]G@2ush@2U@8J?M?*Z?qB_?i L%?8PA@8J?{ ?z1V?ѽ?G? ?X@8p?Nz?sp?H?g< -I?B@$@&@,@;@9????? @r @7Kƨ@r @$G+@r @v @r @*u@r @$#@r @`<@W -=p@X" 6@W -=p@X ).@W -=p@S'`<@W -=p@X@W -=p@W| @W -=p@WV;P@5g l@5:K@2S@2Re;@2t@2 K4@1xD*@1xzzI@1xD*@1{@@1xD*@1zxRW?}#? H??IA?Q\?YD="?Z=?"f?~?Q[$?a]6? ?/p?Ff?$?t?+;b???g@@"@ @"@5@4??????Cx(0r ?%M#?}}it@}g@X8!?DL,?;s$? pL?+. @g@Աݘ@ @KT@K w@KT@J;"`@KT@J_o @KT@Jо )@KT@KTɅ@KT.@0 ě@Izo@0 ě@@0 ě@9 k@0 ě@cA \@0 ě@a@N@0 ě.@8D@8MK@3S@3&@2Ͳ-V@2a@N@26z@2;U=@26z@2W,@26z.NC,?:?"-f?怯8S?z]?&Z@8?-a(^?̋0?hK?CRn?R?Ѷ=(H@8?b?Y+:?JzUf?Y?M}td@8 @@@@@,?????@k=E@k2@k=E@kBh ԕ@k=E@kDe@k=E@k@'RTa@k=E@k?b}@k=E@k>@ @ ht@ @ hr!@ @ _@ @ s@ @ о )@ @ s@8D@8JL@2+ J@2@N@2Ft@2Bs@0̿[W?@0u%@0̿[W?@0fB@0̿[W?@0s?F/P5?4$?g ?̒?j?)]\?Zm? ,C?_@?0N:s?C ;?tK?2m?㑏\? t4??*Z}?SP@@@@@(@(???????g?K?P@¸>?X}Ti|e@k>j@ )R?s|?i?;(?M@q/}S@@ffff@?|@ffff@߾vȴ@ffff@ؼߤ@ffff@ԯO @ffff@hs@ffff@\(@0 ě@(r @0 ě@V@0 ě@U@0 ě@'lC@0 ě@\PH@0 ě@Y+@904m8@9+"`A@45?|@4*0U2@4,j~#@3@3u@2y @3u@2L_@3u@2?#[?-56+?+-?u {N?/?@i?ҸBz3?ڲt?j1?@$v5?[c?e,Z?LO? U)?|p&?2HD?vۋ7?_;G@@@@@,@&???????d` ?~껤?FRyߦ?y;Ħ@1}@ b@=l[?A?F6?W??c @@n -gE@@;lD@;lE@;lD.@;lD.@;lD.@;lD@;dZ@;lD@8@`B@&@`B.@`B.@`B.@`B@ -=q@`B@;dZ@5ks@5jg -@0+.Mm@0>Vu.Qn@/`d.NC,@/`d@/MjP@/`d@/?}ѿ,@8#@8:@8 >+?J+?I ?|O@8@8i@8?q?p?6z@&x@[W?@b=p@b-V@b=p@brT`e@b=p@c)rG@b=p@b{@b=p@b6@b=p@b-@8@8@5^5?}@5nѷY@5@`A7@42a|@4Ǡ k@4~Vu@4Ǡ k@4@4Ǡ k@4JL?_$?95\?ْ7IDu?{osG'?(x/?%$?ϱQm?GZ?[?~'z?V,??֑엱?T?"i]P?!i?.P?'w#T@@@@@*@,???????JTIH?3A?iP@8?sd3@pc B@b5|?vѻpn?Uz$?-bT?݇1@hd@@Q.@Q@oi@Q@&I@Q@]c@Q@ѷ@Q@@5?|.@5?|@7e@5?|@3|@5?|@3g -@5?|@1N;@5?|@+]c@4mv.>@2+S@2(U@1`A7L@1MjP@16z@1M:@16z@1MjP@16z@1<64@8J?_?ojVrn@8@@8kP@2^5?|@2ʅݘ@2Htj@2G)^@1:S@16z@1:S@1[6@1:S@1- r?ϱQm? e?vG?W0 ?j? ٖ?Wl?q ?v@b?#X?SY?pN.>7E?Ny?Ϸ+?qn??:1?DV@@@@@*@(???????I'!T?DcYC?:(YΜ?Fs3@ѐ@'ʉEa?PWw?U"/K?x0^?nYh\@ryQf@@>`A7@>;dZ@>`A7@>@>`A7@>1&@>`A7@>,zx@>`A7@>"h @>`A7@>n.@}u@}]/@}u@}d/@}u@}I^5?@}u@}ح@}u@}@}u@}iC@8v@8jOv@4?|h@4]K]@4`A@4ěT@2D*@2F]d@2D*@29Xb@2D*@2u?]?'rCr?Q"Ě%?CF ?=W?if?]?#?^,?A8 K?*v7?.?*wjs?j? U?J? 3?F?@@@@"@$??????o\3{?~'X?|/{?R4@>zK@}zA?H?`p%00@TV?#P@rds@@-hr@1&y@-hr@.2@-hr@+j@-hr@ (@-hr@ca@P@-hr@ԯ M@3O;dZ@14F@3O;dZ@1@2䎊r@1 -=p @2䎊r@1zxl"h@2䎊r@1YJ?us\?_n?n? -Y? -R?NJH!?F/P5?Ah:?6eI?c?~?c/~?' -}B?ɅoiE???hc?F?;WZ@@@@@&@*??????^U?cvo?tYFDy@ԐQ@1#qbzV?Oo?fa@0?'^Xx@_s5@@pTF@pTzG@pTF.@pTF.@pTF.@pTF.@pTF.@-V@1'@-V.@-V.@-V.@-V.@-V.@5B䎊@5Bᰊ@2Y^5?}.Mm@2`A7L.Qn@1!:S.NC,@1!:S.NC,@1!:S.NC,?}@TΥ@8#@8:@8 >+@8I@8?`/.~@8@8i@8@8S@8?V3@8 *@8p@8@8 @8 @?@x~"@x;dZ@x~"@x@x~"@xvȴ@x~"@x'RT`@x~"@x~Q@x~"@x}H@گw@گ\(@گw@گ;dZ@گw@گv@گw@گn@گw@گw@گw@ڮO@3*0U2b@3Dg8~@.`A7@.{@.:1'@.:@-Z@-$@-Z@-S&@-Z@-F]c?|O?`?(?̒???|O? aҹz?90?0N:s?@?PS2K?2Y0?8 T?|׿pZ?xqC??B @@@@@@??????$`fY??,'k?:<@x פ@گ6m$?LO?8`kfl?UG?Ǜ -vZ@g~`q@@ěT@ϝ-@ěT@ں@ěT@^ @ěT@S@ěT@x@ěT@@bM@ S@bM@ ԕ@bM@e@bM@@bM@t@bM@@8쿱[W@8@4|hr@4 -L_@3i"`@3d/@2a:S@2_.H@2a:S@2^iB@2a:S@2\Z?1j?P1"?K?\2?W?=?bk0?g3?qN?~2l?4?߅D̽}?=6?J>?Cp0x?*6SpK?mΝ? uf@@@@@(@"??????aN>?c`?Cř6py?U6@Ǽ@@E㖔?l-LYdFy?hcE{?K/~/D?Ci@q&u@@a"`@am?\? 8+??Ċ\?\ƥ0?VAo?3Eb?2DB?"IHۜ?`1f??Z,k?_?L=@@@@@,@*?????? -ă?}렻@?eN?lw>v@a!&@gN5?~??%b ?L`@_BH@@AƧ@AI^5@AƧ@Adž&@AƧ@A+@AƧ@A@N@AƧ@Am\@AƧ@A‚@@Hu?|@Hr ĝ@Hu?|@Hě@Hu?|@Hp ě@Hu?|@Hs@N@Hu?|@Hrs@Hu?|@Hp )@6|Q@6a|Q@4R I^5@4Mb@4;Ƨ@47@3@5@3} qv@3@5@3|쿱\@3@5@3{xF^?sD?F?IK?GuS?pK ?t]O>?}ѿ,?շL?Jj??c,*?#N?!A -m?&!+?@K/{?uq{a??_@@@@@&@,??????a" 4?NzT7?(lj@A(@@@@@(@ ??????Jy.ٵY?0%f?@@o@ٶVH??͓.?6?yo?!@s˻@@26z@0ce@0fB?|O?q' -B? '?_?Z)?ZzZ?1j?[ K0R?]:.?@Cv?[H3? a?y8#?/?W?__C?_խW?M\@@@@@,@,???????)O 7?(+pZXN?BZ@&x@(ۋq@&x@*L@&x@(eO@&x@)oi@Q@Q@Q@RW@Q@S|@Q@Q@Q@RS'@Q@TɅo@2*0U2b@2@1 ě@0}Vl@0`A7L@0Z@0 k@0fA@0 k@0u@0 k@0%F -L?^Dw'?q' -B?1f_?Њ?_?sU Z?bM_x? -xՎ?O(?ą6 -?1?ҥX[?bMky? -?3Z\?$H?2[s? `@@@@@,@,??????Ba?Vu?A"1?2>ߙ@)LV@RП"?Pl%U?AI?I|?y,w2@rFw^@"@ ě@ % @ ě@ #N[z@ ě@ ! @ ě@ jzO@ ě@ @ ě@ *(*@E@/@E@49c@E@.\2@E@5 V@E@ @E@gxk>@87@@8(׺[6@4^5?}@4^@3Z~"@3^ l?@2:S@2,i:@2:S@2߭Y}O@2:S@2?π>?8?eT3?x?4 ? Qx? -?M6M^?~P230?Ai?]V?uk?T?%?V}*? -gH?;1?b.xt@@"@&@"@7@1??????eGi2f?\.bL|?h)a@ ؖ@}?u5?E*??|#I@n>@#@B I^@@n@B I^@A.H@B I^@Ae@B I^@A7K@B I^@@҈@B I^@@IQ@FzH@FV@FzH@F*0@FzH@F[W@FzH@FV@FzH@F1@FzH@Fu!@5@5 -=p@2hr@26z@24tj@26!.I@1u@1/V@1u@1 '@1u@1KƧ? ?: ?ِ?%?Xe??#?1j?P ?;,?ihA?x9ξO?IH!w?Aᆽ?=d?],V?sMQ(?ĭ?*Z}@@@@@,@*???????@?=x? 3?*o5yU@@K@FT@?@p~?J+t?j ?8;(@g2nr@$@u ě@u|h@u ě@u*1@u ě@u :@u ě@u+j@u ě@u"ѷ@u ě.@6ȴ9X@0`A@6ȴ9X@/V@6ȴ9X@An.@6ȴ9X@:6@6ȴ9X@ES&@6ȴ9X.@8@@8J@3S@3;dZ@3tj~@3B@2:S@2>BZc@2:S@25Xy>@2:S.NC,?NIɮ?+>?|v8?V0O?=-?@8?}?FHN?.j ?G>G?PZj@8? U?їJ?0?bv{3dZ?@C@8 @@@@@,?????%@&x.@&x@wkQ@&x@4m@&x@@&x@˒:@&x.@lC.@lC@jO@lC@w1@lC@wj@lC@J#9@lC.@9+s.>@4Y^5?}@4F-@3tj@3RT`e@2@2!.H@2@2%F -M@2.NC,@8J?u4ޕ?`M:??WTi@8@8J?rA?P?!?07\@8@8p?:h$^z?^i? B?y ^@8 @@@@(????&@ĹXbN@`B@ĹXbN@ܑN<@ĹXbN@Ŀ!-w2@ĹXbN@Õ%@ĹXbN@F@ĹXbN@8@zH@u@zH@|@zH@L_@zH@\)@zH@@zH@[7@7%!.@7,@4 hr@4`A7@3'KƧ@3/O M@2Q@2T`d@2Q@2TɅ@2Q@2,FV?h??*l?l ?"6Wz?q ?)S&?^?L E?L?Ծ?ymcw\?0a@@@@@(@"???????fdq,wa?^DO?d9TQ9@)8@kb?zMM?{0?Ykc?X|G@jD08m@'@"@~#@"@@"@x@"@e@".@"@&1@"`@bM@"`@}<64@"`@}H@"`@m5Xy@"`.@"`@@4#g l@4 ѷ@1+ @1$J@1-@08YK@0m8@0=!.H@0m8.NC,@0m8@09|?]??`L?.?L_jf@8I?,ϖ?]?O?2?VfN? '@8S?TD?*wjs?UbB2? ё٤i?pO+@8 ?q2?@@@@,?????(@r-V@2@r-V@6z@r-V@Z@r-V@T3@r-V@e+@r-V@|@E@Em@E@Ex@E@Gn@E@F@E@E҉@E@G&@9N쿱[W@9P4m@5f+ J@5sh@5t@4ݿH˒@4p@42C,@4p@48YJ@4p@3?Úz?vd? OV?av ? <)?6n?:-%?FSf?8/J?S~?w}Z?T?s?z?yMa?9G?ÁK?!f@@@@@,@??????Ip?U ?K~Ui?> ē@(@FlD?'q?G2?=@ I@m!}Sִ@)@~@~ -=@~@~dZ@~@~W>6@~@~6@~@~D@~@~D@4b`B@4dS@4b`B@4a7K@4b`B@4a@4b`B@4a|Q@4b`B@4dT@4b`B@4c@7!.@7K]d@2pěT@2rT`d@1-@1lD@0䎊r@0@0䎊r@0 @0䎊r@03?us\?j‚?#}i?q@v?yq?+ʦ_?E?\tW?;~?agf ?Al.6?ȏ'T?lQ? U)?],m=??W|DC?M684@@@@@,@ ???????Wî?.)C?PYa?DA&c@~@4c-^?l X#W?]?c ?є8@n~I`i@*@K*~#@K2ւ@K*~#@K+C@K*~#@K&x@K*~#@K$/@K*~#@K'E85@K*~#@K!e@ȴ9X@4uֹ@ȴ9X@H@ȴ9X@#e@ȴ9X@xl"g@ȴ9X@ @ȴ9X@XbM@7uS&@7H@3 I^@3qiC@3-@3@2,<@28}I@2,<@2@2,<@2lC?za2?:aGS?reE??ҧ?*?pV?2(_??c?/:?h_N%?V.H??,DP?~}o_?Z?b?aʕTI@@@@@(@*???????^?\ul?kZG -?<>BU@K#@oBհ?}jRL]?cv.?XQxב ? @p9N@+@J=p@Il@J=p@J@J=p@M g@J=p@LJK@J=p@Ie@J=p@H+oP@O$/@On@O$/@Oe@O$/@O _@O$/@O @O$/@OP@O$/@ORQ@34m8@3Vϫ@1sS@1s:'@1t@1t]Ք@0\Q@0\@@0\Q@0][nh@0\Q@0\v]?}#?.od} ?EY?v[‡?a!(\?,OKu?y%??#?}ѿ,?pPȻ?D?{4k?JL\?C ;?m~Z_s,?Rii?],m=?9XNu?2[s?L8AS@@@@@,@,???????1f0?*®?*@Yy>3@PĶ@ +sd?Jˠ?6@N?~?& ^}(@f[#@-@+ J.@+ J.@+ J.@+ J.@+ J.@+ J.@",1&.@",1&.@",1&.@",1&.@",1&.@",1&.@8 D.>@3 I^.Mm@3-V.Qn@2D*.NC,@2D*.NC,@2D*.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 .@3H1&y@3J~$@3H1&y@3Jd8@3H1&y@3Iᰉ@3H1&y@3GfC@3H1&y@3Ezxl"@3H1&y@3Eں@t@ttj@t@tȴ9X@t@t}Vl@t@t_ح@t@t1@t@tᰊ@7䎊@7m\@2+@2;5X@297KƧ@2873@1sh@1Q@1sh@1<64@1sh@1wkQ?|O?hX*?5$?ݡu(r?ZAj?Q`?sD?RwS5?]:.?@Cv?7=^9?EȀ?oܧ?rbT?^&?vI?<Dx? 4e -@@@@@"@(??????Qմ?Ed`?!׻D٧?2e…_@3GS@tx$?CB>՘?Q w?E;p?3+r@ij,@/@A$/.@A$/.@A$/.@A$/.@A$/.@A$/.@^`A7.@^`A7.@^`A7.@^`A7.@^`A7.@^`A7.@6mv.>@3° ě.Mm@3W-.Qn@2䎊r.NC,@2䎊r.NC,@2䎊r.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 0@2 Ĝ@\(@2 Ĝ.@2 Ĝ.@2 Ĝ.@2 Ĝ.@2 Ĝ.@]+@]"@]+.@]+.@]+.@]+.@]+.@85S&@8 (@3+ I.Mm@3.".Qn@2G k.NC,@2G k.NC,@2G k.NC,?]@8#@8:@8 >+@8I@8?]@8@8i@8@8S@8?*wjs@8 *@8p@8@8 @8 ??1@1&x@0`A@1&x@0oiDg@1&x@2s@1&x@2s@1&x@/v_خ@1&x@0`A@A ^5?@A -=p@A ^5?@A@A ^5?@A ^@A ^5?@A C,@A ^5?@A -=p@A ^5?@A 6z@3*0U2b@3@1+S@1+Ƨ@0Ͳ-V@0Ͳ-V@06z@0!-w1@06z@0쿱[X@06z@0qi?2}?m?&??_?PN ?/?֘>D?v_.^?3?ad|@@@@@*@&??????|?#˟ ?7cDѫ?2ηk@1e,@A -\?W~N8?;$bA?A_Ma? eS1u@g1E@2@a.@a@be+@a@{/V@a@eo@a@jW@a@heO@2.@2@2>6z@2@2>B@2@2և+ @2@2͞'@2@2Ǯz@9O M.>@3I^5?@3wkP@2Ͳ-V@2'/@2}ce@2ft@2}ce@2kJ#9@2}ce@2l~($ x@8J?rk?7'v?Qi?Kn(?rW@8J?U -?00?.,?E!?s?|@8p?&3?Q?&|?Q?]?5@@@@,@,?????3@w@wj~@w@wQ@w@w)^@w@w @w@wm9@w@w-@mV@fffff@mV@i @mV@vC@mV@-V@mV@Zv@mV@=-V@9*0U2b@9u"@5+ @5TɅo@597KƧ@57#@5 [W?@5Ϫ͞@5 [W?@5?@5 [W?@5IQ?1j?UW?[r'{n?E>?%?V᷵?% ?’?is?ƣL?+ҥ/?Z?^&Mka?$jB?7V/r?0V?+ߎ ?7Bj@@@@@&@(??????b*jH&?YC@-V@,zxl@c -=q@dZ@c -=q@b`A@c -=q@` qw@c -=q@h\@c -=q@`[7@c -=q@c9@7v@7u@3kS@3jg -@2M-V@2LZ@2iB@2D@2iB@2@2iB@2|hs? ?@@No9?=N K?|*(?PY8~Qj?\.:@? ?nr|?F^S?>\?MuVQ?jOI?$M?p-t? SF?>?&f?&f@@@@@@???????O#7?C ?J%=z~@,p@dvP߅?c~Kut4u?k7LP??PU@g3#P@6@=E@/\(@=E@ʚ~@=E@PH@=E@6@=E@>ߤ?@=E@?vȵ@@ ě@@%m\@@;dZ@@-V@@@@@9,<@9\)@3hr@3ؠ- @3`A7L@3@D@1!.I@3NU@1!.I@1Q @1!.I@1נ k?Y͍_?1d?b?+|?ZAj?~?TU?~^?p4?ӐW? ? Ӻ^?M,?+>G?`Ƕ?t"0D?7zE?{2@@@@@&@(???????ndw?sP?t5F?;t,@=7*@x۪@4?7?ņVJ)@)[V@oG+<Ý@q ^ۢ@7@'vȴ9.@'vȴ9.@'vȴ9.@'vȴ9.@'vȴ9.@'vȴ9.@BE.@BE.@BE.@BE.@BE.@BE.@8O M.>@5S.Mm@5lj~#.Qn@5ush.NC,@5ush.NC,@5ush.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 8@8 I@8V@8 I@8$~@8 I@8G@8 I@7}$@8 I@7N?@8 I@7tL@r @'*@r @@r @DŽ3@r @SO@r @kV?2O*҈p@瞸Q@7>6z@瞸Q@i@瞸Q@4K@瞸Q@璽<6@ Ĝ@O@ Ĝ@#@ Ĝ@ ҈@ Ĝ@C@ Ĝ@?[W?@ Ĝ@UL_@4(YJ@4'P&@1 ě@1=qu"@1R`A@0ۅQ@0D*@0fA@0D*@0]cA@0D*@0 '0?|b@?L-?*$*n?*cyb?nOQ?98?|h2:?5ֵ?סϟE~?*Gk?MWXRk?ÉX\?.r?{dph?t߽R?ѳ9?]2?mB@@@@@,@,??????y^@?F?t8Mm? -_Q@甌 @= F?{RR?CF ?'# ?91)8?+-/?+ I?v׆7? 5Y?MJed?v8' ?OW?Fۯ/?0YR?$@ I^5@ȴ9W@ I^5@9+@ I^5@hs@ I^5@+Ƨ@ I^5@(@ I^5@@@&x@tj~@&x@@&x@9X@&x@ I@&x@\(@&x@E@8Q|Q@8rGE9@3° ě@3P{@397KƧ@3حV@2D*@2qi@2D*@26@2D*@2- ?}?_b*?'=DP? '?ߩ?*=U?us\??&?b ]jZ?R?#N?3?Эs_`?X?Dۖ?o;s"?4@@@@@@???????oww#a?fdlC?vWBZ@w>z@?坘?9h?315l@Ij(@f^Q@?@\E@\Z@\E@\\(@\E@\@\E@\@\E@\@@\E@\$ x@n[S@nR-V@n[S@nr@n[S@nB3@n[S@na@n[S@nA.H@n[S@nAr@8G>6z@8AA [@5hr@5ae@4j~#@4e+@4G k@44%1@4G k@4@bM@4G k@4J,0@j)@@@l1&@qhr!@l1&@n2@l1&@q @l1&@rs@l1&@nc @l1&@nV@ I^5@\)@ I^5@@ I^5@$tS@ I^5@v@ I^5@PH@ I^5@?@8(YJ@8 8Z@3 hr@3 (@1Ƨ@1^5?}@1@1@1@1C\@1@1<쿲?jqp?Ѝ`Hh?Gk;m?Fq/b?2F0?5Ov_@@4J^5?|@4J@37Kƨ@3/{J#@3._o @3&fffff@3._o @3"@4n@3._o @3$ xF@8J?Z.?0?sNX? -æ?I0zIJ@8J?La?Qrz?E?i ?,?vk@8p?*T?a -?0 ~?Kd\? $4@@@@*@$?????B@1 Ĝ@1-V@1 Ĝ@1T`d@1 Ĝ@1@1 Ĝ@1W@1 Ĝ@1u!@1 Ĝ@1T`d@z@lC@z@Xy=@z@zxl"@z@ݗ@z@,=@z@@4g l@4Z@0hr @0hr!@0'KƧ@0&!.I@/=Vl"@/=<64@/=Vl"@/=p ->@/=Vl"@/=cA!?| ?Čۨ1A?Gy??lDžN9?; ^?CXs?|O?j%Ra?#Gf?C B?!U?{?wN z_?|׿pY?9o?>Zw?͓E'?pk Y?j@@@@@*@*??????x@? ߘ93?;@178`@=V?@eJ?4 ?_WZx?vRius@f@C@\(@Ž @\(@űr@\(@d{*@\(@2@\(@t'@\(@JKև@l3E@l3@l3E@l4)$@l3E@l6 -l@l3E@l2'@l3E@l3.}@l3E@l1@4,<@4z@1S@1R@1t@1O=&@1~($ @1ۅh@1~($ @110:@1~($ @1Q&-p?| V?0ϒ?FWg?W:Bx??yd%&.?(( ?|b@?aI$;?5^?=c~?+Ɏ?3ۡ?r;;?81$?@tGy?#X?9DP?"#ѧ@@*@"@*@5@4???????}DŽ@8X?Eo2[-?PZ@ k@l3)ۮ?e<>*?=%?2FY?gK@fR@D@oJo@oO@oJo@p=@oJo@oԘZ@oJo@o<@oJo@o?f@oJo@oاt@HJo@HH@HJo@H;n&@HJo@H44(@HJo@H1lS@HJo@HK-#C@HJo@HP4@5@5%=p)@3 I^5@3{G@3tj@2vȲ@2OO M@2Kv4EU@2OO M@2i-(`@2OO M@2=˶?d?u6!?V΄??,@ ??}x?;U?Jj?_Q?d'C?7?Q7 -ȍ?;uL?ɴ?}?46@@&@$@&@4@3???????.krZ?h?ngh-@o}@HBMp?ci&V?g}' d ?IWJЗ@ ]@aC@E@ I^@ƺv@ I^@@5TS.Mm@4U`A7L.Qn@3𖻘.NC,@3𖻘.NC,@3𖻘.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 G@ě@$/@ě@u"@ě@e@ě@ -=q@ě@ߤ?@ě@7K@ڻdZ@ڻ"`@ڻdZ@ڹrGE9@ڻdZ@ڳg@ڻdZ@ڸ@ڻdZ@ڻ~@ڻdZ@ڼ(@4w@@4x}H@2^5?|@2_o@1-@1@@1u@13@1u@1Ϫ͞@1u@1Fs?|O?5{#?5$?菁k?ɿ?Dw?|O?eCO?S1z? (?=~?6kp@ylz@ڻ?\:O?VCQ0?Ģt?jÙ@fgC@H@1&y@9XbN@1&y@ -L/@1&y@tj@1&y@}Vl@1&y@ l@1&y@s@O;d@޸Q@O;d@p:@O;d@:@O;d@iDg9@O;d@α2@O;d@ͫU@8|Q@8TɅo@2J^5?|@2L<쿱@1K"`B@1O-V@0䎊r@0@4n@0䎊r@0W@0䎊r@0.H?ƋGg٪?Z?mGSq?Њ?ԁ?7F?Z\??xrI?jT???wN z_?m?GA?̈- -?J]j?0&YEd? Ğ@@@@@&@??????pQfR{K?VR?HMrh@ -OVT~@WO?f`t?XF x?}j?SK?@rƜ׎t@I@%@`A7@%@@%@T@%@Q@%@`B@%@Q@Gz@Hr @Gz@I7KƩ@Gz@IrG@Gz@Gz@Gz@H9X@Gz@G+ J@6Y*0U2b@6U@2)+ @2)y @1Ctj~@1CPH@0&YJ@0'@0&YJ@0&A@0&YJ@0&IQ?|O?ߋb?d3"7? Y?DE?R?2}?? :?v@b?ni" ?=U{,?H, -r_?%Cr? ?/Y}?avh?A0)n??\@@@@@@??????/}d@%V@?/T N?#XP@' @H.B?O sg?Chߖ$֫?0?"YcKd@g΁]@J@@W -=p@@W -=p@@W -=p@@W@@W -=p@@VϪ@@W -=p@@XD@@W -=p@@Vu@@W -=p@@VR<6@u@E@u@l!@u@ᰊ@u@Ov`@u@D@u@!T@2䎊@2&IR@0=hr @0=!.H@0W-@0V!.I@.ۋq @.˒:*@.ۋq @.lD@.ۋq @.lD?^Q5P?ߋb?(?8v?,I ?2I/?bV,?u߿?j?3.Yl?M?b-x?g)?˛͇q?/Y}?':?b/bJ?ad|@@@@@,@*??????MT2J?8Ԓ/?3W<(f@@WF@k٦?HU+Ν?B>+F?ة?|@sW@K@\).@\)@XbM@\)@8Y@\)@YJ@\)@74@\)@-@j33333.@j33333@j;dZ@j33333@j,~($ @j33333@j=<63@j33333@j=+k@j33333@j=p @9,<.>@4&+ J@41u!R@3b-V@3f}Vl@3 k@3 *0U2b@3 k@3 n.@3 k@3Ov_@8J? ?f?S;~?Jj~u?S:Ъ;5@8J?sXX=`?n|X?#q\-v?Y:J?k|;@8p?ռUӛ?ZAj?>ڵ?M\?t-w@@@@(@*?????L@7K.@7K.@7K.@7K.@7K.@7K.@O;dZ.@O;dZ.@O;dZ.@O;dZ.@O;dZ.@O;dZ.@6~($.>@2ٺ^5?}.Mm@2R`A.Qn@2J0U2a|.NC,@2J0U2a|.NC,@2J0U2a|.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 M@Yu@Yȴ9W@Yu@Ye@Yu@Y -=p@Yu@Y@Yu@Y*0@Yu@Ytj@dZ@1'@dZ@j~@dZ@YJ@dZ@V@dZ@_@dZ@m]@6 ]ce@6 C%@2Lhr@2L!-w@1Ͳ-V@1(\@1G k@1Dj~@1G k@1?@1G k@1@ѷX?Y͍_?m0?>\?'$5?j?_?|O? ,C?)_?@[*^?=W?S)?/Y}?%_D?T0?˴C?{2?{2@@@@@"@*???????<X}d?40 ?4B@Y$@N?RG*?UA f? ʼ=p?'9@gOOK@N@}tj@}@}tj@}"@}tj@}C\@}tj@}@}tj@}PI@}tj@} @7Kƨ@7Kƨ@7Kƨ@C@7Kƨ@L@7Kƨ@D@7Kƨ@qu@7Kƨ@ @4hYJ@4i,@1W+ I@1V -L/@06-@05sh@/Vl"@/!.I@/Vl"@/_p@/Vl"@/SMk?|h2:?%ñ?U#?5~pY5?2F0?\.:@?|h2:?> -fu?ܿ?G>G?W;zY?ey? ?Rii?]?2?ĭ?I$@@@@@,@(???????&Ρ ?/j?K<0?$U@}[@T<?jnv?D,D?c^?d@3hr@3q @3M-V@3NH@2u@2g8}@2u@2x@2u@2u%F -@8J?+!?1V?E`?؍i?mׁe\@8J?? :?ދ"?ni" ?Co?"(L@8p?QdЈ?ZaQ?!ˠ*(?]ߒ?+C@@@@@?????P@@uË@@&@@Ur@@+0@@.@@eX@@n@?I'@@n@Ck@@n@JLG@@n@F{@@n@G@@n@G@6`D@6`ggn@1r Ĝ@1hNv@1lj~#@1n~}@0:S@0d @0:S@0v{)@0:S@06?E[i)?p?S a?x0s4?9DZp?(#~?eXW?awuE?] ?Y.`?Kh??9?,?ׯr&c?[/l?"5Mf?=??Z&9@?Rs)?~? tΨ9@f pۘ@R@YxQ@Yw@YxQ@Y{W>6@YxQ@Yyb@YxQ@Yz@YxQ@Ys*0U@YxQ@YtzG@u@n@uA$/@u@n@u?˒:)@u@n@u?@u@n@u@u"@u@n@u??@u@n@u>%@5I_o@5J0U2a|@15?|@1Ov`@1gKƧ@1_O M@0xF^@0 ԕ*@0xF^@0'RTa@0xF^@0d7?}ѿ,?@@No9?zjʩi?E`?'ř ?}7.?| ?9}=?ꉬ>V?sZ-?W;zY?ҥX[?n88?3I#i?do? mmsi?Tj?GZ@@@@@*@*??????Cm<h?CPY?¹-@YvH @u@E?bw;?6Di?G?k$tO?i=?5`?^*?T?."E?FJ?8.>?Ó+DoX?Xŏό?ۃ30?_K. ?p)Տ?#j?H>w,@@@@@,@,??????bn0ti?H!?T(G@F3Kf@h/?b;ڱ?qÏb?yF?rxr@d&Ch@T@ -o@ @ -o@ Ƨ@ -o@ @ -o@ -d7@ -o@ _@ -o@ 'RT@ԅ@Ԉ[@ԅ@ԄU@ԅ@ԆL/{@ԅ@Ԃ7@ԅ@ԉ@ԅ@Ԅ*@8(YJ@8)@3+ J@3@34tj@34j~@2_o @2rGE9@2_o @2vȴ9X@2_o @2($ x? ?9؊?LD?Hyq?o?P?*?}?@׮?pI?~m??ۣ0Υ??;?A\#? U)?6~A?Yʒ?`i2?l@@@@@,@*??????>GJEp?3^t?J. H@ K@Ԇ+?TT>x$?i7x?~?Ϛ#O@hN1@U@Bh\@BeQ@Bh\@Bmd7@Bh\@B@Bh\@Bfo@Bh\@BBb@Bh\@B~20x@`7K@`Õ$@`7K@a+@`7K@`ݬ\y@`7K@`O@`7K@`_@`7K@a#B@6@6!YN@4c@4;W1@3-V@3}2n@3䎊r@3d@@3䎊r@3R^We3@3䎊r@3O5I?Qp?na&? A?E/?hԭ??Qp?SF۫?Yx̿?˦3?o??3? f?!w?.u+?b1?W~|@@&@,@,@:@;???????t?zrթ?`1ͺN?G@Bj\@`ҹU?uLd?^!n?6f?yt§@_+? @V@zG@zG@zG@Xy>@zG@*@zG@Xy>@zG@Fs@zG@SM@hr!@&x@hr!@2X@hr!@N;6@hr!@hr!@hr!@-V@hr!@s@3`D@3`ěT@0 -^5?|@0 -S&@/:1'@/:kP|@. -#9@. -W'@. -#9@. -ڹY@. -#9@. -0 ? ?1?4!p?̒?b\?2I/?|O?,#?90?-I`K?2&h?Z,a?u@>N ?.x?W ?+ ??B @@@@@*@,??????>]?*c0?'lijo?PxI}@1b@Ϗ?G8H??)? ?_求@g@W@2Ƨ@2l@2Ƨ@2/@2Ƨ@2d8@2Ƨ@2Ǡ l@2Ƨ@2S&@2Ƨ@2 @S@1&y@S@/{J@S@α2@S@ (@S@g@S@@7@7i k~@3|hr@3 @34tj@3US@2sh@3tj~@2sh@2@2sh@2vȴ9X?=fA:?o 6}?$q?ca+r?dtX?=?ۊ?=Be?ol?V)0?q]?ĵP?kV?@`WӐ?6?vc? ?c@@@@@*@(??????gG?N/@?e*E?t @2£S4@ߨhn?3}X?xiğ?堚&Ib?ICb@k@X@@4l@@aoh@@\?@@ʦL/@@dJ@@fx@ltj@l@ltj@e~BZc@ltj@dYJ@ltj@c'RS@ltj@dI@ltj@fh]@7|Q@7t@4+ @3O @37KƧ@3u@3z,<@2;dZ@3z,<@2!R<@3z,<@2$/?¨Sp*?\ZE?3?- -@?ʛ8?I?| V?԰?pVY^?#Lp~?x;q -?ϧr?]~"L?\T%?uf?İi? :C?(@@@@@,@???????!fd #"d?pϳE?u+@w=~@l4A? X?F 71@ʁ#2@X,@X)W@Y@%nP@% ĝ@%nP@%5@%nP@%/n@%nP@%Oƥ@%nP@%9_v@%nP@%hZ@t@t@t@>@t@P@t@#+@t@@t@B@0g l@0g l@+j~"@+lM˶b@+o@+2@)u%F -@)bF{@)u%F -@)4H)@)u%F -@)d?^j?5>?J?ӯc[?tx^?_4?bMn?(tm?~J?/:?=yκ?J W?bw' g?z=`=?pDRh??@j?_cd?@@ @$@&@0@0??????Q; P0?@&?3 Eߐ@%.p@?Nx>?B~I?Ȗp?|G@s Jl@Z@ E@ G@ E@ l!@ E@ J@ E@ @ E@ E@ E@ Mj@mQ@mn@mQ@m/@mQ@mS@mQ@m@mQ@m5?|@mQ@mN<@7r@7n!R<@18r Ĝ@16@0gKƧ@0eڹ@/ -#9@/˒:*@/ -#9@/Fs@/ -#9@/lC?Br?m0?&t?!#?; ^?<_?h??#maЀ??,~[?+!?Ր?tu?%2?m~Z_s,?< N ?7zE?gϵ@@@@@(@*??????[,R.?@ϛP?C)@ tXw@m| y8?`7Q ?`juB}?T-?{=@l$NM@[@nO@m@nO@nn@nO@q>@nO@o@nO@j cr[@nO@lJS%@RH9X@RE@RH9X@RRf{@RH9X@Rd@RH9X@R\م+@RH9X@R\:q@4Q|Q@4N?@0@0Ck@07Kƨ@0@/QN;6@/T_٧J@/QN;6@/K`E@/QN;6@/L j/?}#?3?FU.?zM?\d?\(?`/.~?M\]?X.??h?YF"?@?#A??:>?9D7? -͉{?+r@@&@@&@8@7??????8?fJ b?@4&rYT?Qdi@m%@RTS0i#?`N'?qJU?)Bdκ?DX@hB@\@G@dZ@G.@G.@G.@G.@G.@Ftj@Ftj@Ftj.@Ftj.@Ftj.@Ftj.@Ftj.@1,<@1 '@/.Vt.Mm@/&x.Qn@.z)_.NC,@.z)_.NC,@.z)_.NC,?^XN@8#@8:@8 >+@8I@8?bM_x@8@8i@8@8S@8?j|@8 *@8p@8@8 @8 @?]@J@J=p@J.@J.@J.@J.@J.@7z@73332@7z.@7z.@7z.@7z.@7z.@7s@7҈p@4+ I.Mm@4gKƧ.Qn@3YJ.NC,@3YJ.NC,@3YJ.NC,?F/P5@8#@8:@8 >+@8I@8?F/P5@8@8i@8@8S@8?*я@8 *@8p@8@8 @8 @?^@:^5?}@:^5?}@:^5?}@;dZ@:^5?}@;/V@:^5?}@;u@:^5?}@9J@:^5?}@9+@AS@A/@AS@AQ@AS@AY@AS@Aqu#@AS@A1@AS@Av@.hۋr@.[6@,MV@,M:@,&x@,'RU@+kjf@+k6z@+kjf@+k]cB@+kjf@+k6z?^6P?&H?"?8v??$?bM7? -?90?6)}?֧QD?Uyu#?bM?.+?y8$?ʬv??a+@@@@@*@(??????Lt)OT?-=6?#e8@9WS@ALEN?<"$Wf ?2[e?snY?]@sՄl@_@Pj~#@PiB@Pj~#@Phn@Pj~#@Pc{C@Pj~#@P`Rv@Pj~#@P_ׁ@Pj~#@P_x!@2["`@2Z1@2["`@2l~@2["`@2i|@2["`@2m0@2["`@2jEJ@2["`@2~'(/@42@41@05S@0/N@.x@.Z@.p'RU@._k-8@.p'RU@.`3-@.p'RU@.`USs?|B &?ے@? [ ?W/?-)?dp -#?}@TΥ?Sx=?/JF?~ ?M?7_]?9ÿC? 5?K?\X?ž?<@@$@*@(@9@6??????c^?ia?9c6?DwMP@PdS@2gjn?Yy ?d?ǞhT?5MT@fU@`@$/@A7L@$/@@N@$/@\)@$/@ԕ+@$/@zxl!@$/@\@yXbN@{lD@yXbN@ĴJ@yXbN@ĺ1@yXbN@ķKƧ@yXbN@ġ7K@yXbN@ė@7e!.@7`A7K@30ěT@3+m]@2-V@233333@2?䎊r@24֡a@2?䎊r@2Ov_خ@2?䎊r@2!hr ?E??KvBrq?Q?xTl?4tD?'[ V?HS?DXLa?=?6?[a,?qb?~d?|?j?gT?ñR@@@@@*@*??????4N?k6?h-$?*:1j[@>/X@Ĩ'Vt?x4V?,k"? ث@)[?v@j?L8AS?b@@@@@*@,??????DJ->?9R?Jg?,@M'պ@j?iy -?LT3Q̌?;?;@eշ@@b@Y+@["`@Y+@Q&x@Y+@Zxl"h@Y+@T@Y+@`d@Y+@` k@ݶE@ݟ|h@ݶE@ݸbM@ݶE@ݴ*@ݶE@ݺxl"h@ݶE@ݰ'S@ݶE@ݰ ě@8~($@8*0@3I^5?@3"`A@3Z~"@3b I^5@2YJ@2ڹ@2YJ@2"`@2YJ@2͞&?ك|I?E?u*y?ݡu(r?STS?U.?ڷ??ǀr?؎?W;zY?998?k [X?qJ??mֿtE?c^)?^':?]ߒ@@@@@@???????pLUV_"?eIo q?iL@[e>q@ݳYE?wx76?z|?a"?j@qem@c@ I^5@ C@ I^5@ 85K@ I^5@ A@ I^5@ 1@ I^5@ -@ I^5@ -xi@JG@JlD@JG@J,@JG@J6z@JG@J!X@JG@JvH@JG@J5@1䎊@1 I^6@-)7KƧ@-'2@+F@+%Bв@*N;6@*ϴn<@*N;6@*O@*N;6@*حV?^j?ŊXF?Y(?ӯc[?=?Zw?bMn?Bl9?~J?5ei?h1?P?sF?bM]f?JEO?rGe?C>?~.?e@@(@&@&@4@3??????P6?3JQ2?4 j8'>zg)@ rJ@J9G ?CFćG1? -??F);?b8W@s|lE@d@ E.@ E@ M@ E@ Kjh@ E@ G k@ E.@ E@ Hp:@:Q.@:Q@:^Q@:Q@:T[@:Q@:dZ@:Q.@:Q@:Xe-@904m8.>@3+ J@3fA@2ȓtj@2ohی@2!.I@2#MjP@2!.I.NC,@2!.I@2 @8J?5J ??(4@8I?i@8J?^V֋?P?sw)@8S?*@8p?91a?;?~lVo@8 ?{%@@@@*????e@`A7@`C@`A7@@`A7@,@`A7@+`@`A7@҈p@`A7@3@{~?O*?`]u?ER Y?T8?q' -B@@@@@*@(??????R!?@?YA?UOᦵ?[d׬-@0@+@8I@8?Qp@8@8i@8@8S@8?I@8 *@8p@8@8 @8 @?g@lC@x@lC@r@lC@ٰO@lC@& -@lC@ZT @lC@ꡉ)@D"`@Dڟv@D"`@D-@D"`@Dܿ@D"`@D@D"`@Dpf@D"`@D`I;@3qiC@3zJH@0 -^5?|@0 )8@/Ix@/OWt@-u%F -@-{4-@-u%F -@-v@-u%F -@-?}@TΥ? i?,-?]\8?~G /?*O?|q?m3 -V?NadL?9>+?-xH?|}?pDRg? E?am#?K?>=?b@@"@(@"@6@1??????DO|dHDh?Ix;L"͖?2uau@ - p@D1?iAꟚ?Rf ?A')R?y@fa?X@h@(S@(Y@(S@(P ě@(S@(Eݘ@(S@(MB@(S@(K~(@(S@(OiDg7@ě@G@ě@/@ě@$/@ě@@ě@E85@ě@s@8^H@8VC,@3c@3bs@2tj@2@1&@1$/@1&@1𖻘@1&@1ěT? ?U ?_*k?͌?>{~? d?ϱQm?I1@?bZS?:u. ?La??eBZ?c?e+?Oدi?f?Tj?*Z}@@@@@(@,??????g|P.h4)?NlzZ?WCC@(O^uB@o?sC ?mGt?b?̽I?t@nٵ5@i@/ -=p.@/ -=p@/@/ -=p@/@/ -=p@/۝@/ -=p@/~\N@/ -=p@/aO@?|.@?|@X):@?|@$V@?|@ͺ@?|@E@?|@A@9*0U2b.>@45S@4OIp8@3]V@3^G@3fYJ@3Ol@3fYJ@3{=K^@3fYJ@3@8J?kG ?v8?Jk?JjcJ?GX%Օ@8J?%3Gh?jy ?vA@3@ ě@37+ I@2A7K@2m\@2\Q@24oh@2\Q@27#@2\Q@25?|i@8J?y?&1*?9?—0I?@i@8J? -=@4+ J.Mm@3Vu.Qn@3 -0U2a|.NC,@3 -0U2a|.NC,@3 -0U2a|.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 l@"kR@"\@"kR.@"kR.@"kR.@"kR.@"kR.@R -=q@Rffff@R -=q.@R -=q.@R -=q.@R -=q.@R -=q.@8?@8=hr @5+.Mm@5{Ƨ.Qn@4D*.NC,@4D*.NC,@4D*.NC,?ɏ@8#@8:@8 >+@8I@8?!XW@8@8i@8@8S@8?1zz@8 *@8p@8@8 @8 @?m@H@Hz@H@Gb@H@HH,T@H@GW2n@H@H*J@H@G$@lC@rC@lC@gV{/@lC@ʐCX@lC@jyZ@lC@H-@lC@d+LR@7_o@7c 6@3=hr @3: w$@2KƧ@2{@2m8@2 b6@2m8@2Is(@2m8@1GGO ?q-?QDP?O|[?y:܆&?8ĄV?n^?59W?/an?v˛1?`%-dE?&i?~q_?wG?=?O e? @??!?V]Ő@@(@,@*@:@4??????^m?gC&?d>!I?h9M @HP&@@H?)?ьs?ڰ1Ա!@v -@mij.@n@r>vȴ9@r>vȴ9@r>vȴ9@r?H˒@r>vȴ9@rAG{@r>vȴ9@r>#@r>vȴ9@r=b@r>vȴ9@r9"@hr@O@hr@@hr@r @hr@ *@hr@\(@hr@zxk@7s@7]cf@3 ě@3-V@3`A7@3NU@1s&@1rS&@1s&@1r74@1s&@1sZ?3a=?" ?Mq ?=]. ?(+Zk?i{?Ol?-A?0eʪv?M =?8? f? f -?S1z?t?%@ս;@d3۬? j1* ?Cao?Da:J?7@kb|K@q@ @S@ @͞@ @ 'RT@ @ @ @ -=p@ @u@"M@hs@"M@ - @"M@!G{@"M@64@"M@vȴ9@"M@)B@8T xF@8^u@3ԛS@3tj@3"-V@3v@2~($ @2 @2~($ @21&y@2~($ @2#?|O?MSMӮ?b??o?P?iOL?Y͍_?s?s?GuS?dLt?;?T8L?HO? uf?GZ?`i=?x@@@@@(@(???????co`?V/?\`ŷR?R,?E -@J@9)?|D?q "@!$B?O[Q)=@gt03@r@ڟv@ۥS@ڟv@ۥS@ڟv@Q@ڟv@۲m@ڟv@)y@ڟv@_p@TzG@U?|@TzG@TD@TzG@S@TzG@Stj@TzG@Tm9@TzG@TFs@52@53Mj@0S@0tSM@.F@.+j@.oi@.qj@.oi@.u@.oi@.?? ??&??q@v?DE?F0?|O?MN ?ދ"?{d?peԾ?90?O?§d߱?YF?*|\/?L?j@@@@@@,??????5=0`*h?"W8?" Ge@ ڽ@TnN?B[vaF?C}X?gg ?츨s9@gxڱB6@s@~%@~#@~%@}wd@~%@~ {3O@~%@~Qo@~%@}x@~%@} @u@ȴ9W@u@ѓ/@u@ʣA@u@ϐ4@u@&ZW@u@ӡ}@7쿱[X@7Z@4n5?|@4j-FT9@3-V@30|o@35sh@32)@35sh@32qQm,@35sh@30DF?E[i)? }2?j?@#r)?Ě"]??gޅ?<?`?\oXe=?_? @@"@&@"@,@.??????V2sn?X9XL?UL)@}y@}?w+ ?uY[?ƒξQ?@h,ӣ@t@U@Uȴ9Y@U@UA7M@U@U͸@U@U @U@UǓݗ@U@U7@p ě@p ě@p ě@T3@p ě@L_@p ě@n}Vl@p ě@o M@p ě@gE85@7w@@71 @4|hr@3H˒:@3"`@3 M;@3O M@3GE85@3O M@3p:~@3O M@3 Q?_?5)ZS?ia?ݡu(r?uT5?d?}?C`ߕ?g?Jk?Ss?sUEk?0G --?qn? ??]&?v6?`@@@@@*@*???????FؐT]E ?gffpa^?Q])- -@U@mw?wI7?flR^?UM ?L`r@oL=T@u@<bM@<׍O;@<bM@<"`@<bM@BZc?| ? -?%HC=s?ArU@?_??#?| ?ۣ0Τ?rVx?,?l{9َ?P=?W ?Rii?{W?9XNu?{2?v> @@@@@,@*??????5Ë'Ķ?# -?E2ʤ2@|9:@Ӌd?B.vH?el?o?aZ@f @w@uě@u -=r@uě@un.@uě@uE@uě@uu%F @uě@u҈p@uě@u@+ J@Z@+ J@&@+ J@@+ J@U@+ J@zY@+ J@vȴ@6w@@6zQ`@3hr @3@3A7K@3ƚ,<@3z,<@3g -=p@3z,<@3sS&@3z,<@3pѷX?Br?Q^? Y"?zRU?6z@6!.I@2R I^5@2O[W>@1`A@1E@0D*@0P{@0D*@0ѷX@0D*@0R<6? ?ZAi?reE? h?Ԧ?n?ީqk#? ?ԍD?ud?Nw]6?HO?AP0P?~}o^?cK4+??C?*Q?^':@@@@@,@*??????aA?7xՠ?@ R?1|B~@]tL[@?^9?P?‘$e?/-@e @z@`A7@S@`A7@[@`A7@iDg8@`A7@ݗ*@`A7@}Vl@`A7@4m@Dj~#@DwO;@Dj~#@D@Dj~#@Ds@Dj~#@DV@Dj~#@D@Dj~#@DQ@7:@7fB@3#@3 (@3A7K@3*0U2b@1ޫ6z@1\N@1ޫ6z@1iB@1ޫ6z@1N;6?2Ѳ?u3?d?H?Ux&?dEmc?E?? ]?Ԥ?ghEH?N?x?ACH)?k?WiR?¬ xG?e?3B?NKۗ@@@@@&@&???????! -ހ?dd sbE?nFR@q@DfJ?znd?w}-]?yFc?d$ @px`MdW@{@{lD@{lD@{lD@|c@{lD@}Y@{lD@|hs@{lD@{, -@{lD@{oOu@G9Xb@G9Xb@G9Xb@Gw:(@G9Xb@Gbo@G9Xb@G<@G9Xb@G$@G9Xb@GFs@-Q @-Q @)G-@)G-@'nO@'{m@'N;6@'ί%@'N;6@'D@'N;6@'2W?^6P?Ƿ5?c? :?䮽?fˏ`??bMn? - ?}PI՗?--?3VO?KmHY?bM?P0?of@?'z?]?_cd@@&@"@@5@6??????LY>쭮?.5,l?_l3\@{ԍ@GW?=@/:1'@/9]@.Xy=@.r@.Xy=@.9XbN@.Xy=@.R/?^6P?ǧ H?, ?x?׻p?!?bQ넛? 7?5?-.?ĵl>w?]DgQ?bM?ƭh@4ahr .Mm@3~".Qn@3.NC,@3.NC,@3.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 ~@:r @<.VNQ@:r .@:r .@:r .@:r .@:r .@;1@<(cR(@;1.@;1.@;1.@;1.@;1.@7:@7u@3}hr .Mm@2ߝ-V.Qn@2kxF^.NC,@2kxF^.NC,@2kxF^.NC,?n{@8#@8:@8 >+@8I@8?`Q@8@8i@8@8S@8?玲{@8 *@8p@8@8 @8 @?@F|h@FC@F|h@FA ]@F|h@FcA@F|h@F I]@F|h@F|@F|h@FQ@=E@A@=E@??@=E@;J#9@=E@A@N@=E@($ x@E@,<@E@!/@5zqiC@5zc @3hr @3@3'KƧ@3#|@2@2K]@2@2Y|@2@2|/{J$?|O?;?6p?$jB?Ir??'?}ѿ,?g3?wr?R?ߔ5o?TY9?=Ca?8A?v'X??8~,?b-/@@@@@,@,??????ev"ba~4?BA?y*#.@}h@D&$?U^f2|?u_8h?{p ywA@%@\!|@@nzH@m@nzH@n0@nzH@n^@nzH@o4@nzH@nY`@nzH@oD@Qo@QC@Qo@QK@Qo@Q˯{@Qo@Qͦ@Qo@Q-.mz@Qo@Q5@3֚,<@3ְAٴ@0|hr@0n@e@/E@/ݹ9KW@/`d@/`2@/`d@/T^%Z@/`d@/K"?|b@?ɧkF?,(X?ǵq??2zW/?KɏV?|b@?R?SՇ?97~?oc ?\=U?z*?z+?zb?T ?ƎG?R@@$@&@*@:@7???????5oF ?"y?p?af&?Z@n(@Qi?4U?Œ?oB]?@@*@$@"@6@8??????w??v?n?B\Cѯ@a>@-?p?x+ϬA@=?r@$Ro@lb7Q@?|h@As@?|h@DT@?|h@Doi@?|h@@E@?|h@A-w1@u?|@u\)@u?|@u?@u?|@u '@u?|@u?|@u?|@u$@u?|@uL_@3L]ce@3LL_@-{"`A@-w@,NO;@,K~($@*@*}IQ@*@*}/w@*@*|u#?2}? -?Gk;m?ƠJ-M?2A?+ʦ_?| ?;N?tP*?1'7?2&h?Uyu#??l?y8$?XLL?l&?@@@@@*@(???????Gxa@?D7_?\13-@@oSz@u:?dSEX>[?#c<?k`?Sb@gJ@@p0 -=@p:7s@p0 -=.@p0 -=.@p0 -=.@p0 -=.@p0 -=.@/ۥS@/A7L@/ۥS.@/ۥS.@/ۥS.@/ۥS.@/ۥS.@8^H@8UP@5+ I.Mm@4Ƨ.Qn@4xF^.NC,@4xF^.NC,@4xF^.NC,?5V4S@8#@8:@8 >+@8I@8?"r+@8@8i@8@8S@8?@8 *@8p@8@8 @8 @?@4_;dZ@4h\@4_;dZ@4e84@4_;dZ@4g/W@4_;dZ@4u '@4_;dZ@4f]c@4_;dZ@4cn.@v@vȴ9@v@Iᰉ@v@&I@v@Bwj@v@'@@v@kP@7 xF@7kP@5^+@5Qn.@4`A7L@4,<@4fYJ@4vaf@4fYJ@4xF^@4fYJ@4 qu?+-/? )}o?Fw?ډ?Xz=?ȵ?I~y?ڲt?EU?;q:?{y6?Y{?Xt:w??^i?4i+?b?/я@@@@@(@(??????<)ݨ?XFC?[֢3Va?tuXg@4i%~@&H?|ק&\?.5?&<~?2֕U@h.K:@@!vȴ@!pbM@!vȴ@!R@!vȴ@!@!vȴ@!@!vȴ@!&@!vȴ@!@@w@l@w@(@w@@w@qu"@w@zxl"@w@Xy=@9>6z@9 6z@4ٺ^5?}@4Ϫ͞@4W-@4qu@3sh@3W'@3sh@3@3sh@3?S?Mj̑?-a} ? '??*'?5}'?.Hp?Pe@4sS@4qR<6@3-V@3ߤ@@3!:S@3B@3!:S@3rGE8@3!:S@34m9@8J? 5ф?C?^G_?(:)[a? C@L(@8J?^?_z?p?Ri?V@8p?{6z@0&YJ@0'RT`e@0&YJ@0')^?| ?[e+@8I@8?|O@8@8i@8@8S@8?}@8 *@8p@8@8 @8 @?@]?|@]\)@]?|@]"7T@]?|@]D@]?|@]9!+@]?|@]:@]?|@]E@E@"@E@@E@y8`@E@i@E@WO@E@K`F@4L]ce@4Kނ׶4@0 I^@0 |@/1&@/+:@0 k@00@0 k@02j@0 k@0㷜 ?|h2:?pl?"H?x?E?=AC?|h2:? n?6?XY?m6k?~ܑ?pDRg?H(>X?|E2?ʾ?!,8??BT?;|;2@]=g@@O;d@@O;d@{=K@O;d@HeN@O;d@qi@O;d@@O;d@TɅ@p -@@p -@$@p -@E84@p -@g @p -@1@p -@V@8cg l@8Vz@5I^5?@4=K^@4KƧ@4}K]@4fYJ@3;5Y@4fYJ@4D*@4fYJ@4Ƨ?ʎa]?I6nӨ?*=? Ž?K?? ? -}TE?͙ǁ?Օ" ?ƺ@=t?@hu?V_cW?6)}?^B??qr;?Fb2x? %@@@@@&@(???????qԀ.H?mkԟ?+=B)@@|?qr?CNao?H]f?H,1i@`k08@@O@Oo@O@O)^@O@O@O@OXd@O@O} -@O@OI`@["`@9@["`@Q@["`@N_o@["`@$tS@["`@6$@["`@*g@8~($@8,Gf@4+ I@4oiDg8@317Kƨ@38$ xG@2ޫ6z@2ѷX@2ޫ6z@2gR@2ޫ6z@2nM}R?x9?fײ{=?oq u? Qi?7ű4C?\F*7?fȎY?蠔??íuA?p?[Tas?U?$%G}??P^*@@@@@0@.??????`B|l?L2/?sB-#2?_ag1@O=u@)a?$+?rJ[@cQ?35@p%}@@@O;@@ @@2@@䎉@@8}@@]c@-@;dZ@-@p;@-@1@-@ q@-@S@-@A \@3 xF@3n.3@0ٺ^5?|@0W@0y7KƧ@0q'R@0QiB@0G&@0QiB@0ES@0QiB@0F??1j?M?!N?A2x?ߩ?ҸBz3?|O?&uE@?fM?| ?,rg?,:?nwf??m!?XW~?0&YEd?`i2@@@@@,@,??????rDlQa%?[sA':^?H݂ n.@9p@qmL?y D?eccAu?'_h/?-!]o@co>c@@w33333@w32@w33333@w4Q@w33333@w5hե@w33333@w5BR@w33333@w2*R@w33333@w2w@lD@(@lD@]@lD@;@lD@ń@@lD@nڼ@lD@i@2v@21G-@0 ě@0~N׋@0`A7@/<@.Gݗ,@.F:(KR@.Gݗ,@.F$ Rx@.Gݗ,@.Dzt?^P!?ʃ/J?!,1 ?Ӓ{-@F?pfТ?eK?bQ넛?#$S,?=y?2?/?X3Z:?c6JX?v?'x'9?h9#?_?B@@@&@"@&@8@8??????XNd?L,?>%O?2Y];,@w3!@ ?Ms2ZV?AbEib?s?zinZ@s -@@@4@@v@@ӥ l@@Rp@@w@@Ll@ĒnP@Ć@ĒnP@ą!Ϟ@ĒnP@ć} -@ĒnP@ĉwv'@ĒnP@yn@ĒnP@Ąɠ@6@61͗@3|hr@3[D@3,j~#@3!ĬlV@2p@2bp@2p@2eC@2p@2dNN}6?eXW?K2e?yxb?&?X]N? VM?}@TΥ?괒?ö?zn۹?2-+O?8P?o?Kdn -?uuբ? k4?s ?d@d@@ @(@$@3@4???????{kmAN^X?4mx?Fg@͙@ĄI?S.vjU?eJ>?6Hp?ҩ2;@e?R@@hr!@&x@hr!@{@hr!@{@hr!@`A@hr!@|@hr!@;d[@o-@mhr@o-@k6z@o-@u '@o-@r-V@o-@mw1@o-@o@6`D@6`oiDg8@2f+ J@2g+@1Vu@1iDg8@00U2a|@0˟U=@00U2a|@0 I^@00U2a|@0d7?A[F?Z?4 b5? -?ҧ? µ?3a=?c?ZH|:?Y?=.e1?C ;?US9??Me_?XW~? Ğ?ٔ!x@@@@@,@,??????/U?!o?*K:?75o_T@I@oI.?1xW?UVG(?c#. -R?A}W@l#m@@lC@5?|@lC@'RT@lC@)_@lC@p@lC@z@lC@S&@l@lO;@l@l[V@l@l'RTa@l@lN;7@l@l@l@lC@8@8Mj@5° ě@5q @5Z~"@5FOv_@4@4xl"h -@4@4`A7L@4@4B?Ű[_G?P2 -O?O? +ġ?)^y?I?>sN?jX?'(??Ȍ(?)S&?I?زf?{Tä=?0EK?*:R}?IQ @@@@@$@"??????q";`t?c8Z4b}?J _HN@dg\@l2L?t'Tx?\Q?̈́^?@p@@ A7L@(2{@ A7L@ _@ A7L@'?@ A7L@ Ey@ A7L@ ?U@ A7L@ @@ -=p@Vs(@ -=p@\7@ -=p@ A@ -=p@J@ -=p@5p6@ -=p@)@904m8@94"Z@4{ I^@4y.e@3-V@3 /@3&@3<5N@3&@3s@3&@3i?,t?|u?q)/?O =?Ⱥ?C7?W?ng':?m??LQN?4l?1f2?u ?~)%? ?uA?w@s@@,@*@(@5@:???????u$P@W?xV??ׁ?uG@@yE@r?(?2 ?4,n@"7.@obN@@hr@hr@hr@_b5@hr@zG@hr@sJT@hr@@hr@% D@1'@Tj@1'@V@1'@@1'@aĻ@1'@@1'@yg@4cg l@4ca@N@0uS@0t7@0lj~#@0k*Z@0u@0k2@0u@0wa@0u@0@?}@TΥ?M-U??덭?!O?L65?| V? -r}?tK?0:"l?"ξfS?K?u؆"¿?$-a5[??Q8?@5Gd?G,@@ @ @@8@7??????+U?$4=?2G?dxV@H2@C½ ?Rұv@skä@@qj~@qhs@qj~@q@qj~@qS&@qj~@r*@qj~@rڹ@qj~@r#&@@t9Xb@@+a@@xPH@@@@ @@Ʌo@8@@8>B@4ahr @4YrGE@417Kƨ@4#E@3m8@3~@3m8@35?|@3m8@3rGE8?+-/?]?1?s?\(Ft?ȝ?+-/?(/(?Vh?_?)?:?fƾ?xCZ?1^ ?+&*?y?l@@@@@*@(???????\??b; -?c^5@r \z@q?k - ? FJ?K {?֏@i4>C@@a+ @a[@a+ .@a+ .@a+ .@a+ .@a+ .@"@"@".@".@".@".@".@5H@5qž@1+ I.Mm@1gKƧ.Qn@0sh.NC,@0sh.NC,@0sh.NC,?¨Sp*@8#@8:@8 >+@8I@8?|h2:@8@8i@8@8S@8?oVu@2E?@1ce@1*0@1ce@1v_ح@1ce@17?us\?d̺?lڮ??>{~?0>?% ? {?%8?gy}?Bg43f?>p?@h?bD?`u ?k s?>@k1@@fdZ.@fdZ@f_H@fdZ@fdV@fdZ@fcJss@fdZ@fcnw0n@fdZ@fav@-.@-@<ѥ>@-@2@-@`@-@5n@-@87@8(YJ.>@3+@3&@2Ͳ-V@2;5Y@2L[W?@2LܷQ@2L[W?@2M[j@2L[W?@2Ot@8J?D|?7֫?{đ?Ȗ?9)@8J?L??̷l^?3j ?Y6?@8p?Y{1*?O?@T[?Cbi?+){@&@"@,@<@:?????@s3333@o-@s3333@{J#:@s3333@"y@s3333@~7@s3333@v4J.@s3333@{ *@KR@K,i@KR@J7K@KR@Kq{Ge@KR@K!@KR@KMvL@KR@Kn@7쿱[X@7փ7zX@4S@4PH@3~Vu@3?*![O? J@ch @j?@7+ I.Mm@6V.Qn@5iB.NC,@5iB.NC,@5iB.NC,@8J@8#@8:@8 >+@8I@8@8J@8@8i@8@8S@8@8p@8 *@8p@8@8 @8 @1&@Ƨ@1&@01H@1&@V@1&@-W@1&@C,@1&@@&x@&x@&x@+@&x@PH@&x@N;6@&x@oC@&x@w@.?@.?@(\j~"@(\j@'E@'_ح@&jf@&~%@&jf@&qu!@&jf@&xF^?^j?ت?K?v4?Hc??bMn?F?|;?0j)?'?K '?bM?P0?n3?Ei??@@(@ @*@<@:??????N&8?]?4tF>\N@2G -@dj2?C\j> ɾ? a>\t]޹@s#@@h\@h9X@h\@i?0@h\@ix: @h\@iPt@h\@h6 @h\@h!b@yx@yx@yx@ywG@yx@yy?7@yx@yxV *A@yx@yx~*/@yx@ywIo@1 ]ce@1 ]ce@,`A7K@,Wv.@*cnP@*b<64@*@*@*@**63@*@*4֡a?^j? j?ۭi)?v4?:?YF?bMn?ϷY?|;?/]\?F#?M4C?bw' g?EaӉ?s!?C>? -?|V@@(@"@&@8@7??????5i8+?(`? ^=@hv(R@yx1Uv?#b[9K.?/'?B|z?VCEvf@soI_@@+ J@@+ J@/5@+ J@@+ J@Ͻ@+ J@@+ J@\*@7Kƨ@@7Kƨ@ؑ&5@7Kƨ@莩5@7Kƨ@j r@7Kƨ@$@7Kƨ@@8I_o@8LW}@3+@3"q)@2j~#@2[@1𖻘@1Wm@1𖻘@1 -Z@1𖻘@11:~?zM ??,G?AެÝ?%9?wD['?T?R{?kS?~q?1r:?.{*V??d?.׭st?r,z?Q{ଠ?l ps?{@@$@@@6@4???????F#q?J?@Ie>8?GԜ#@f@ƶw=~?Vu'?]w?IF?\f@oe @@5?|@vȴ9@5?|@b}@5?|@vȴ@5?|@;dZ@5?|@@5?|@<64@&"@&"@&"@&@&"@&XbO@&"@&^5?~@&"@&xl"i@&"@&u%G@3L]ce@3Lj~#@0S@07@.o@.e+@-@-?@-@-䎊q@-@.NU?|O? ?\H{gX?䆀{6? f?{ ?Z!ǟ?] - ?y%?ecQ?M7F?Jj?. ?=M?Q,Ě???(i^?*я?=?+_?54G@@@@@*@??????`4?_|?S+l?h37@sh@6'm?c4>ػ?x'7.o7?SR?՝ @r@@j~#@l@j~#@J#9@j~#@I^5?@j~#@ov_د@j~#@@j~#@#9@\)@|h@\)@͞@\)@lE@\)@ѿ.H@\)@ξߤ@@\)@к~@3N쿱[X@3H ԕ*@0c@/ '@/nP@.D@/At@-1'@/At@-%F -M@/At@-m8?|O?Rf'Y??ǗU?X~̫?t8?1j?ଚw?Q0vX?o)o%?ҲaK?Sh?@3° ě@333333@3n"@3^1@2OO M@2E?|i@2OO M@2fA@2OO M@2?}ѿ,?V/?~#?(+Zk?2Մe?/^?=fA:?_@ -?Խ=?_F?ϟ?Yw?R ?Pk"?.?>?54G?aOD@@@@@@(???????UL_9 V?ad%0N?o?}ѿ,? -= ?ے|?Ol?f)"?`Ƕ?e?tl?4@@@@@*@*???????\Ua`?HJR?N4t -@(pѺg@+q*l?fd|?l\t2?҃*Gp?نe@dļW@@E=E@EYXbN@E=E@E<64@E=E@E9XbO@E=E@E=-V@E=E@E9+@E=E@E8D@-V@7K@-V@m\@-V@vȵ@-V@}V@-V@m\@-V@ѷ@8D@8 xF@3S@3eں@3`A@39@2@2D@2@20 @2@2oiD?us\?;=?T?"?Kǡ?4f(R?5d?5$?@gG*@@s@sQ @s@s?@s@s9Xb@s@s (@s@ss@s@s2@`A7@@`A7@ffff@`A7@&x@`A7@tS@`A7@Ѹ@`A7@˒:*@5I_o@5J=p -@3^5?|@3VϪ@3Ft@3F]cf@2Q@2ܥzxl#@2Q@2lC@2Q@2Xe,? ?/)s?2~? {4?+?*?1j?M7F?}7.?ǧ?D6?h_N%?53y@?nwf?XԠ#{?# Y?Z?mCc4?xb@@@@@"@*??????P~ JX?VF?- ]@sC(8@+NS?u@@$/@%Q@$/@& -L/@$/@&]c@$/@%oiD@$/@#w@$/@#e@J=p@K I@J=p@I @J=p@Ka@J=p@KP|@J=p@K:)@J=p@JOu@55S&@55+a@1B ě@1B @0-V@0ѷY@0#@0$,<@0#@0#\N@0#@0#%2?|O?Čۨ1A?e -!C?:Z2b?1 v?F0?|O?j%Ra?tP*?ni" ? ^??y ? - l\?˛͇q? ݎ8? ??SP?j@@@@@$@(??????0 b 2sY?.[?"ɬ@$d@Jx&?MF'~?Bj Y!?H?E@f(@@F@Ft@F@EjO@F@E첕@F@Ep:@F@Ë́M;@F@E@@5 Ĝ@5?|@5 Ĝ@56z@5 Ĝ@5H@5 Ĝ@5(@5 Ĝ@5䎊r@5 Ĝ@5*0U@7B䎊@7#n.3@1; I^@1Q_@0tj~@/"`@/Gݗ,@/S@/Gݗ,@/ Q @/Gݗ,@/.2?}?(h ?4F&x?J?O??LcR?}ѿ,?LR_5?V@@@@@*@,??????R߿"GH?hҴ}r?EY /@E@5K?vbl?f,? a?В/GSm@ho@H@@1@ۥS@1@s@1@Mj@1@@1@5X@1@b@tj@(@tj@s@tj@Y}@tj@!G|@tj@kP@tj@쿱[@9-v@9/-V@4\I^5?@4`$tS@3"@3*0U2@2Q@2S&@2Q@2n.@2Q@2 ?5}'?tH?Uy*i?I?Br?6c?-2?KF??{Q?(ə?їJ?@K/{?!B?“y?q@@@@@$@&???????R{?:N?A?Eзc@LP@?T;9J?lPA,b?t0~?ǵO@oq+@@V7K@V}E@V7K@V@V7K@VV@V7K@Vu@V7K@V/{J@V7K@V#9@U&ffff@U,@U&ffff@U6l!@U&ffff@U.p:~@U&ffff@U;=K@U&ffff@U*0@U&ffff@U*d8@7@7|#x@2S@2+j@2~"@2ߤ?@1u@18YJ@1u@1ҜwkQ@1u@1SMj?Br?ly? -D?DN?`Q? ?F/P5?*l??ao;?p G)L?x7f?p-?pPȻ?Y͍`?vI?{%? 4e -@@@@@,@(???????lM#9W@?Xa;?Vi?@Vj^F@U-BA?wBy?v">?8 V?/@eaϐ9@@+ J@qxA@+ J@捸@+ J@\(@+ J@|@+ J@#{J#:@+ J.@7K@@7K@-V@7K@ /{K@7K@'@7K@3tj@7K.@6*0U2b@6l"h @4)+ @4䎊r@3-@3ϝ-V@3Eu@3@d8@3Eu@3D/@3Eu.NC,?~Ov_ح?eV?2D?\eú? K@8?C I?(?nv-?Gn?yo@8?!;c,`?&Z#?4y?ژ2U?o@8 @@@@@,?????@DT@B\(@DT@EQ@DT@Eo@DT@FIQ@DT@CF]d@DT@Ca@O@GQ@Gȴ9X@GQ@Gl"h @GQ@Gti@GQ@G@GQ@G'@GQ@Gr@7쿱[X@7M:@20ěT@21N;5@1Htj@1Ik~(@0&@0?@0&@0ݗ+k@0&@0Mj?oC?$?D&?Att?`?6 '8?| ?0n=Q?8G?l™U?O ?!U?{?]b?=d?],V?$H? -t?@@@@@,@(??????G? ?/#?Gm&?P-@DX@G?bw=d$G?+^ o? L?Z7ҹ2@lyga@@S@"@S@@S@}H@S@W>6@S@|@S@D@@+@@ M@@Vϫ@@jg@@/{J@@.3@7D@7hr @3+@3!-w1@2y7KƧ@2y k~@1u@1F -L0@1u@1+a@1u@1š?E@?b[?/͔p? h?FL{?uu?)T?ƍ?Kd+?aYZq??"?3 q?r?c?0?A0)n?L8AS@@@@@*@*??????D(v7V`?? {Pm?<{Z@pR@4>?U;z?^OQW?mqd?Jss @n[7@@hr @g0 c@hr @e#v/n@hr @ir} -[@hr @l=@hr @f3 @hr @g֛%@b0 ě@b0@b0 ě@b&DΙ@b0 ě@b)'*@b0 ě@b(K@b0 ě@b,&bT@b0 ě@b"/M@7B䎊@7E<@3S@3)x@3`A7L@3n@2,<@2u @2,<@2-;.@2,<@2g^es?}x?Urx?Nl?>U?~Ov_ح?fc?R ?[[?mn?Tb?s'%?wG{C?A_K?.X?%c ?jա@@"@&@@3@&???????7k SҘ?H-*gM6?4>b@h`?3@b+ i?g7](?T˿?r??WR@fkY@@1'@S@1'@h@1'@"p@1'@R@1'@#;@1'@i9@1&@I^5@1&@o.@1&@f@1&@$<@1&@fA@1&@$5@3:@35Xy>@0S@0oM@04tj@00XÁe@/PH@/ Fg@/PH@/6 E@/PH@/.R?|h2:?r{?Q:U?\k -?cD:?Rd"?|b@?1 }?%?1>ᔒ?2?XѪ4?@?w?_6C?O ?'0?7ڍsh@@$@&@,@7@7??????b7eQ0Yw%Є?.@+c@˺?\r<?Hmg>m?e3?uq@f5M@@v'lC@v+C@v'lC@v6C@v'lC@vA:@v'lC@vBwj@v'lC@v14J@v'lC@v2{@cM@cY@cM@c~($ x@cM@c'RS@cM@cu%G@cM@cS@cM@cڹ@504m8@5FIl@2+ I@2$3@2-V@1a@@2Tm8@1~u@2Tm8@1)^@2Tm8@1)^?|b@?W[?K̃?r*?|c?9?}#?$i %h?l%FZ?h?Al.6?*%>g?o#BpW?z??WA:?7zE?Ee,@@@@@,@(???????dP?A?c|1t?UP M@v1ݔ@cf?W`o?u[ -@a -+?w_@e{έ -@@@V@@}Vl@@}Vl@@-V@@C,@@ዬq @\=p -@\A7K@\=p -@\>@\=p -@\@ k@\=p -@\An.@\=p -@\@ k@\=p -@\>"@6^H@6^Q@3 ě@3ݗ+@2V@2@1@1a@O@1@1 M;@1@1҈p;?Ol?Qc -??A%?.Kn?5G]!?W;zY??eBZ?@K/{?5 g?A'_^?< N ?t?E݋N@@@@@*@*??????6X7?K bF?.tV@(r|@\@윟?k18k?M':F?/~{m?͚@i'P̽@@!?|@! -=q@!?|@"@!?|@"zH@!?|@"@!?|@"]c@!?|@"oh@I O;d@IO;@I O;d@ITɅ@I O;d@H"@I O;d@I - @I O;d@Hs@I O;d@HkP@8?@8Ov@3 I^5@32W@2-V@2g8}@1_o @1C@1_o @11&x@1_o @1r Ĝ?]?L 5d?^*G?Je ?cf(?=%?]?jKt?Qc -?%)?䬯kF?ҏ,[?*wjs?7&x?W;RT?*+JҚF?X񄖆?%?@@@@,@*??????`qXps?Pmn?D@"'@I2#BZ@ix@heO@ix@gRT`@1|Q@1щ7Kƨ@.kƧ@.i^5?}@-o@-~Q@,Xy=@,$tT@,Xy=@,&x@,Xy=@,:)y?a(?_?S[?ݶ\r?% -?2I/?bV,?,#?tWZ?.? A?yC?cPQ?.x??=rE=?fW?0&YEd@@@@@&@,??????KXـ8[Us2?1nn˷?/d @LDY@h ݏ?@?=\e?{7?pTP@sk<@@;lD@@;lD@SU?/:hn?% ?  ?Ⱦ?~Ov_ح?%mR ?8?DNX1?*d??rG½?t_FS?K ?3t?rݳ&?!R#??d@@*@&@*@;@8??????/X)E?(ˇ? oٳkF@;v@`?H,?AI X?&eh?@\@gbm!O@@z@l@z@$/@z@]c@z@'@z@fB@z@@4@և+ @dZ@և+ @Mj@և+ @fA@և+ @څ@և+ @e+@և+ @PI@8e!.@8S33333@3+@3oi@2ߝ-V@2ܑN;@1𖻘@1Q@1𖻘@1s@1𖻘@1n.?A[F?*|3N?/͔p?Uyu#?V0o~8?iOL?E?-NI`(?@N?kH?x(7?i 0/??,DP?a?XW~?%d?0a@@@@@,@(??????W[U6Z02 -?PǏ?K\V@C@fy?o{I?e*?l%?}+*@l'螎@@[~"@[p@[~"@[dq@[~"@[_b}@[~"@[E84@[~"@[͞@[~"@[!.@Õ%@`V@Õ%@C@Õ%@TzG@Õ%@6ȴ9X@Õ%@0:~@Õ%@- @8=:@8G;n@3E?|h@32s@2ȓtj@2 @2fYJ@2vs@2fYJ@2<@2fYJ@2?A~?h?Br?dx/?×(?`|?!{??|aQ?fb?RCK?m7>n?&f?jNR@?@@@(@(???????oQ:?vAg8?6Q?>;3c@M@ -H\?ss ? f0&@;z0@|s @`{ @@tȴ9X@tKƧ@tȴ9X@t=L@tȴ9X@tIQ@tȴ9X@ue@tȴ9X@tQ@tȴ9X@t)y@pyXbN@pyXbN@pyXbN@p{lE@pyXbN@pxQ@pyXbN@pz=@pyXbN@px_@pyXbN@py+@4]ce@4I^5@@2+S@2(>BZc@1~"@1oh@1 k@0حV@1 k@1-w1@1 k@1s?}ѿ,?nx?)?ɲ?o=\g?oܧ?oM?ւ?{Cx?aʕTI?“y@@@@@(@,???????Q.Z?L*W?*'9D@t;@pyT?m nB$?JՈO?F?rD薅@e'@@҅@҅@҅@҆?@҅@҆$/@҅@҆ -L/@҅@҅F -L@҅@҄7@=@=n@=@=W@=@=T`f@=@=_@=@=a|@=@=GE8@3!.@3oiDh@0O|hr@0O[W>@/@/$/@/C\@/'/X@/C\@/}Vl @/C\@/c ?| ?m0?>f?뤽??G?7F?|O? -?~Q?Hn?Cq$u?GE?8*?.x?? ??L?L@@@@@(@(??????_Le@?q@?Ia?䰉@҅|u~@=;8?f1e?<?߇R% -@f꼄IA@@# -=q@(9X@# -=q@@# -=q@?@# -=q@ -@# -=q@@# -=q@@\(@fffff@\(@@\(@E@\(@@\(@@\(@;dZ@804m8@8#*0U2@3+@3W k@3t@2ϗ$tS@2!.I@1,=@2!.I@1Z@2!.I@1bM?F/P5?'5?mr? h?=W?A!X?Br??!BL?GjH>?Ҩv?BL?׍xG?kc?F?V]9V?H -0?Q@@@@@,@,??????pd+ u?sQ?+"@qǨ@@zG@j~@zG@,<@zG@Xy>@zG@%F -@zG@3333@zG@,<@x!G{@x ě@x!G{@x!G{@x!G{@x ě@x!G{@x"M@x!G{@x!|Q@x!G{@x n@4 ]ce@4 n.@0ahr @0au@/n@/:R@/73@/e+a@/73@/@@/73@/JD?|O?/)s?:?뤽??5 g?7F?}ѿ,? -?'Q?>\?+!?U3p?ʳ_?3;?Ol?+ ?SP? Ğ@@@@@,@,??????$2@?=`?&)"&yE(?)byW@t@x!3L?F,?I#5?w CQ?J D@g`[@@$G@$,@$G@$$@$G@$K^@$G@$@$G@$=c@$G@$-@wglC@w}E@wglC@wdZ@wglC@wh1&z@wglC@weS@wglC@wh@wglC@w\)@6D@6XbM@2I^5?@2҈@2-V@2\(@1ce@1~%@1ce@1/V@1ce@2W?bjk?9؊?ʚCq?pu?z6?a,F??k25?{0?@&?V@@@@@,@*??????Қ@ٹ?ksH*?o94 @[@Z]?O<}?/?o?{ /gv@: I 4@d!}zi@@ I^@ -=q@ I^@tS@ I^@nP@ I^@M@ I^@\(@ I^@&J@5?|@?|@5?|@8YL@5?|@iDg8@5?|@.H@5?|@n.@5?|@wkQ@7D@77Kƨ@3pěT@3nm\@2V@2H˒:@2Eu@2FFs@2Eu@2G$tS@2Eu@2FϪ͟?us\?_ ?Êy?wn-?XMH?_Oo?F^S?-',?To?Jk?4{?{P?k@{?Az?ZaizD?8?`i2?k!l@@@@@*@&??????5C`.?F%K?HLfd@ j@ -?:a݂½-?ahdT?obI?O*N:@n9q@@hr!@-V@hr!@fffg@hr!@ܬ1(@hr!@u@hr!@rGE9@hr!@߱[W?@DT@KC@DT@Eݘ@DT@Gݗ@DT@9=c@DT@KƧ@DT@N;5@7g l@7xl"h -@3S@3D*@3-V@3$/@2Q@2T`d@2Q@2 I_@2Q@264?=fA:?D&d4?reE?XԠ#{?Jg?HAm?sD?kM..? ?GW?y҇?]d}?K3I?zPz?3 ?q' -B?!G?d?H@@@@@,@,???????ej7H\?A>;W?jh,@UP@Dy2?`ZC?_)?upb@00@gw]@@\7@\7 -=p@\7@\4O @\7@\?b}@\7@\A@\7@\:@\7@\Ce@ƻlD@ƽ/@ƻlD@ƿ.H@ƻlD@@ƻlD@ƾ (@ƻlD@L_@ƻlD@Ƨ@6쿱[X@6?@3sS@3pn@2~"@2:~@20U2a|@2 '@20U2a|@2Q@20U2a|@284֢?2}?O9?l5B?9.?wX?>V$?1j?yom?b?3)10F? -Y*?2;?\i?[ș8?Q?a?VM?4\ɵ@@@@@*@,???????d -?hg?Q+f?`\Z ?@\D@e8S=@@#Z1@#Z1@#Z1@#Zc@#Z1@#X@#Z1@#\(@#Z1@#X-@#Z1@#Y=c@(@(@(@64@(@^5?}@(@=K@(@5X@(@@24m8@2A7K@0ٺ^5?|@0@07Kƨ@0{@0!:T@0 n@0!:T@0 (@0!:T@0 D?^XN?Qd?`כm?lDžN9?G?F0?bM_x?,#?pM?Kd]?ڂc?b-x?b@?§d߱?m+&?E 8?ad|?Rv@@@@@*@(??????VL>?U?D`?x?;gx'Y@#Z?@X?Swj?Ii?M?6O2<@s8@@bM@Q @bM@-V@bM@hۋ@bM@S@bM@PH@bM@A@WlC@WlC@WlC@WsP@WlC@W'@WlC@W@WlC@Wě@WlC@Wsg@4|Q@4Gz@1^5?|@1R@1j~#@1Q@1u@0a|Q@1u@0m\@1u@0ݗ+k?|O?%ñ?l5B?~rV?2ּ9?!w?| ? ʣV? ?fa]G?^候?{5?=Ca?Ϸ+? ݎ8?XW~?WZ?:@@@@@*@,???????H2xBI@?Xt?Q9fR@@W -?ve&I?jս?q|?o@aE8F@@+ I@+ I@+ I@*L/@+ I@+ I@+ I@+6z@+ I@)l@+ I@* @,Gz@,Gz@,Gz@,H1&y@,Gz@,H9X@,Gz@,H1&y@,Gz@,Ix@,Gz@,HXy=@3qiC@3L/|@0O|hr@0Ov_ح@/O;d@/$tS@/ -#9@/ oiE@/ -#9@/ ^5?}@/ -#9@/ Ѹ?}ѿ,?ߋb?jhq9?ƠJ-M?~|+?t?| ?7fps?'Q?C B? (fit_val-fit_err*n_sigma)) + return np.abs(true_val - fit_val) <= n_sigma*fit_err def test_Fixed(): # Test handling of a single star true_params = {'x0': 1.0, 'y0':0.5, 'x0_err':0.1, 'y0_err':0.1} mod = motion_model.Fixed() - param_list = mod.fitter_param_names - fixed_param_list = mod.fixed_param_names + param_list = mod.fit_param_names # Confirm return of proper values for single t and array t - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], 0.0) + x_t, y_t = mod.model( + 0.0, + fit_params=np.array([true_params['x0'], true_params['y0']]).T + ) assert x_t==true_params['x0'] assert y_t==true_params['y0'] - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], - [0.0,2025.0,10000]) + x_t, y_t = mod.model( + [0.0,2025.0,10000], + fit_params=np.array([true_params['x0'], true_params['y0']]).T + ) assert (x_t==true_params['x0']).all() assert (y_t==true_params['y0']).all() - # Check behavior of get_batch_pos_at_time + # Check behavior of model x0_batch = np.random.uniform(-2.0,2.0, 50) y0_batch = np.random.uniform(-2.0,2.0, 50) x0_err_batch = np.repeat(0.1, 50) y0_err_batch = np.repeat(0.1, 50) # Single epoch t_batch=2020.0 - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t_batch, + fit_params=np.array([x0_batch, y0_batch]).T, + fit_param_errs=np.array([x0_err_batch, y0_err_batch]).T + ) assert (x_t_batch==x0_batch).all() assert (y_t_batch==y0_batch).all() assert (x_err_t_batch==x0_err_batch).all() assert (y_err_t_batch==y0_err_batch).all() # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t_batch, + fit_params=np.array([x0_batch, y0_batch]).T, + fit_param_errs=np.array([x0_err_batch, y0_err_batch]).T + ) assert (x_t_batch==np.array([np.repeat(x0_batch_i, len(t_batch)) for x0_batch_i in x0_batch])).all() assert (y_t_batch==np.array([np.repeat(y0_batch_i, len(t_batch)) for y0_batch_i in y0_batch])).all() assert (x_err_t_batch==np.array([np.repeat(x0_err_batch_i, len(t_batch)) for x0_err_batch_i in x0_err_batch])).all() @@ -48,17 +56,37 @@ def test_Fixed(): # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], t) + x_true, y_true = mod.model( + t, + fit_params=np.array([true_params['x0'], true_params['y0']]) + ) x_sim = np.random.normal(x_true, true_params['x0_err']) y_sim = np.random.normal(y_true, true_params['y0_err']) + xe = np.ones_like(t)*true_params['x0_err'] + ye = np.ones_like(t)*true_params['y0_err'] # Run fit - params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, - np.ones(len(t))*true_params['x0_err'], - np.ones(len(t))*true_params['y0_err'], - np.nan) + params, param_errs = mod.fit( + t, + x_sim,y_sim, + xe=xe, + ye=ye + ) + + x_wt = 1. / xe**2 + y_wt = 1. / ye**2 + x_wt_norm = x_wt / np.sum(x_wt) + y_wt_norm = y_wt / np.sum(y_wt) + x_mean = np.average(x_sim, weights=x_wt) + y_mean = np.average(y_sim, weights=y_wt) + x_std = (np.sum(x_wt_norm**2 * xe**2))**0.5 + y_std = (np.sum(y_wt_norm**2 * ye**2))**0.5 + # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) + np.testing.assert_allclose(params[0], x_mean, atol=1e-5) + np.testing.assert_allclose(params[1], y_mean, atol=1e-5) + np.testing.assert_allclose(param_errs[0], x_std, atol=1e-5) + np.testing.assert_allclose(param_errs[1], y_std, atol=1e-5) def test_Linear(): @@ -67,22 +95,25 @@ def test_Linear(): 'vx':0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05, 't0':2025.0} mod = motion_model.Linear() - param_list = mod.fitter_param_names - fixed_param_list = mod.fixed_param_names + param_list = mod.fit_param_names # Confirm return of proper values for single t=t0 and array t - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], - true_params['t0']) + x_t, y_t = mod.model( + t=true_params['t0'], + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params_dict={'t0': true_params['t0']} + ) assert x_t==true_params['x0'] assert y_t==true_params['y0'] t_arr = np.array([2010.0,true_params['t0'],2030.0]) - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], - t_arr) + x_t, y_t = mod.model( + t=t_arr, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params_dict={'t0': true_params['t0']} + ) assert (x_t==(true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx'])).all() assert (y_t==(true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy'])).all() - - # Check behavior of get_batch_pos_at_time + + # Check behavior of model x0_batch = np.random.uniform(-2.0,2.0, 50) y0_batch = np.random.uniform(-2.0,2.0, 50) vx_batch = np.random.uniform(-2.0,2.0, 50) @@ -94,71 +125,104 @@ def test_Linear(): t0_batch = np.repeat(2025.0,50) # Single epoch t_batch=2020.0 - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, - vx=vx_batch, vy=vy_batch, vx_err=vx_err_batch, vy_err=vy_err_batch, - t0=t0_batch) - assert (x_t_batch==(x0_batch+(t_batch-t0_batch)*vx_batch)).all() - assert (y_t_batch==(y0_batch+(t_batch-t0_batch)*vy_batch)).all() - assert (x_err_t_batch==np.hypot(x0_err_batch, (t_batch-t0_batch)*vx_err_batch)).all() - assert (y_err_t_batch==np.hypot(y0_err_batch, (t_batch-t0_batch)*vy_err_batch)).all() + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t=t_batch, + fit_params=np.array([x0_batch, vx_batch, y0_batch, vy_batch]).T, + fit_param_errs=np.array([x0_err_batch, vx_err_batch, y0_err_batch, vy_err_batch]).T, + fixed_params_dict={'t0': t0_batch} + ) + + + np.testing.assert_allclose(x_t_batch, (x0_batch+(t_batch-t0_batch)*vx_batch), atol=1e-5) + np.testing.assert_allclose(y_t_batch, (y0_batch+(t_batch-t0_batch)*vy_batch), atol=1e-5) + np.testing.assert_allclose(x_err_t_batch, np.hypot(x0_err_batch, (t_batch-t0_batch)*vx_err_batch), atol=1e-5) + np.testing.assert_allclose(y_err_t_batch, np.hypot(y0_err_batch, (t_batch-t0_batch)*vy_err_batch), atol=1e-5) + # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, - vx=vx_batch, vy=vy_batch, vx_err=vx_err_batch, vy_err=vy_err_batch, - t0=t0_batch) - assert (x_t_batch==np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx_batch[i] for i in range(len(x0_batch))])).all() - assert (y_t_batch==np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy_batch[i] for i in range(len(x0_batch))])).all() - assert (x_err_t_batch==np.array([np.hypot(x0_err_batch[i], (t_batch-t0_batch[i])*vx_err_batch[i]) for i in range(len(x0_batch))])).all() - assert (y_err_t_batch==np.array([np.hypot(y0_err_batch[i], (t_batch-t0_batch[i])*vy_err_batch[i]) for i in range(len(x0_batch))])).all() - + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t=t_batch, + fit_params=np.array([x0_batch, vx_batch, y0_batch, vy_batch]).T, + fit_param_errs=np.array([x0_err_batch, vx_err_batch, y0_err_batch, vy_err_batch]).T, + fixed_params_dict={'t0': t0_batch} + ) + np.testing.assert_allclose(x_t_batch, np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx_batch[i] for i in range(len(x0_batch))]), atol=1e-5) + np.testing.assert_allclose(y_t_batch, np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy_batch[i] for i in range(len(x0_batch))]), atol=1e-5) + np.testing.assert_allclose(x_err_t_batch, np.array([np.hypot(x0_err_batch[i], (t_batch-t0_batch[i])*vx_err_batch[i]) for i in range(len(x0_batch))]), atol=1e-5) + np.testing.assert_allclose(y_err_t_batch, np.array([np.hypot(y0_err_batch[i], (t_batch-t0_batch[i])*vy_err_batch[i]) for i in range(len(x0_batch))]), atol=1e-5) + # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list],t) + x_true, y_true = mod.model( + t=t, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params_dict={'t0': true_params['t0']} + ) x_sim = np.random.normal(x_true, 0.05) y_sim = np.random.normal(y_true, 0.05) # Run fit - params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, - np.repeat(0.05, len(t)), np.repeat(0.05,len(t)), true_params['t0']) - print(param_errs) - # Confirm true value is within error bar of fit value - assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) - + xe = np.ones_like(t)*0.05 + ye = np.ones_like(t)*0.05 + + def linear(t, x0, vx): + return x0 + vx * t + + for absolute_sigma in [True, False]: + for weighting in ['std', 'var']: + for use_scipy in [True, False]: + params, param_errs = mod.fit( + t=t, + x=x_sim, + y=y_sim, + xe=xe, + ye=ye, + fixed_params_dict={'t0': true_params['t0']}, + weighting=weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma + ) + + # Scipy + xe_scipy = xe**0.5 if weighting=='std' else xe + ye_scipy = ye**0.5 if weighting=='std' else ye + x_popt, x_pcov = curve_fit( + linear, + t - true_params['t0'], + x_sim, + sigma=xe_scipy, + absolute_sigma=absolute_sigma, + p0=[np.mean(x_sim), 0.0] + ) + y_popt, y_pcov = curve_fit( + linear, + t - true_params['t0'], + y_sim, + sigma=ye_scipy, + absolute_sigma=absolute_sigma, + p0=[np.mean(y_sim), 0.0] + ) + np.testing.assert_allclose(params[:2], x_popt, atol=1e-5) + np.testing.assert_allclose(param_errs[:2], np.sqrt(np.diag(x_pcov)), atol=1e-5) + np.testing.assert_allclose(params[2:], y_popt, atol=1e-5) + np.testing.assert_allclose(param_errs[2:], np.sqrt(np.diag(y_pcov)), atol=1e-5) + # Test fitter with bootstrap - t = np.arange(2015.0,2025.0, 0.5) + t = np.arange(2015.0, 2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list],t) - x_true_err, y_true_err = np.repeat(0.05,len(t)), np.repeat(0.05,len(t)) + x_true, y_true = mod.model( + t=t, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params_dict={'t0': true_params['t0']} + ) + x_true_err, y_true_err = np.ones_like(t)*0.05, np.ones_like(t)*0.05 x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0'],bootstrap=10) - print(param_errs) + params, param_errs = mod.fit(t, x_sim, y_sim, x_true_err, y_true_err, fixed_params_dict={'t0': true_params['t0']}, bootstrap=10) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) - -# # Test fitter for 2 pts -# t = np.array([2015.0,2025.0]) -# # Get values from model and add scatter -# x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], -# [true_params[p] for p in fixed_param_list],t) -# x_true_err, y_true_err = np.repeat(0.05,len(t)), np.repeat(0.05,len(t)) -# x_sim = np.random.normal(x_true, x_true_err) -# y_sim = np.random.normal(y_true, y_true_err) -# # Run fit -# mod_fit = motion_model.Linear(t0=true_params['t0']) -# params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) -# print("DJSKBGJ",param_list) -# print([true_params[p] for p in param_list]) -# print(params) -# print(param_errs) -# # Confirm true value is within error bar of fit value -# assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params),2)]) - + def test_Acceleration(): # Test handling of a single star @@ -167,22 +231,25 @@ def test_Acceleration(): 'ax':0.1, 'ay':-0.1, 'ax_err':0.02, 'ay_err':0.02, 't0':2025.0} mod = motion_model.Acceleration() - param_list = mod.fitter_param_names - fixed_param_list = mod.fixed_param_names + param_list = mod.fit_param_names # Confirm return of proper values for single t=t0 and array t - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], - true_params['t0']) - assert x_t==true_params['x0'] - assert y_t==true_params['y0'] - t_arr = np.array([2010.0,true_params['t0'],2030.0]) - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], - t_arr) - assert (x_t==(true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ax'])).all() - assert (y_t==(true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ay'])).all() + x_t, y_t = mod.model( + t=true_params['t0'], + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params_dict={'t0': true_params['t0']} + ) + np.testing.assert_allclose(x_t, true_params['x0']) + np.testing.assert_allclose(y_t, true_params['y0']) + t_arr = np.array([2010.0, true_params['t0'], 2030.0]) + x_t, y_t = mod.model( + t=t_arr, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params_dict={'t0': true_params['t0']} + ) + np.testing.assert_allclose(x_t, true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ax']) + np.testing.assert_allclose(y_t, true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ay']) - # Check behavior of get_batch_pos_at_time + # Check behavior of model x0_batch = np.random.uniform(-2.0,2.0, 50) y0_batch = np.random.uniform(-2.0,2.0, 50) vx0_batch = np.random.uniform(-2.0,2.0, 50) @@ -198,83 +265,130 @@ def test_Acceleration(): t0_batch = np.repeat(2025.0,50) # Single epoch t_batch=2020.0 - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, - vx0=vx0_batch, vy0=vy0_batch, vx0_err=vx0_err_batch, vy0_err=vy0_err_batch, - ax=ax_batch, ay=ay_batch, ax_err=ax_err_batch, ay_err=ay_err_batch, - t0=t0_batch) - assert (x_t_batch==(x0_batch + (t_batch-t0_batch)*vx0_batch + 0.5*(t_batch-t0_batch)**2*ax_batch)).all() - assert (y_t_batch==(y0_batch + (t_batch-t0_batch)*vy0_batch + 0.5*(t_batch-t0_batch)**2*ay_batch)).all() - assert (x_err_t_batch==np.sqrt(x0_err_batch**2 + ((t_batch-t0_batch)*vx0_err_batch)**2 + - (0.5*(t_batch-t0_batch)**2*ax_err_batch)**2)).all() - assert (y_err_t_batch==np.sqrt(y0_err_batch**2 + ((t_batch-t0_batch)*vy0_err_batch)**2 + - (0.5*(t_batch-t0_batch)**2*ay_err_batch)**2)).all() + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t=t_batch, + fit_params=np.array([x0_batch, vx0_batch, ax_batch, y0_batch, vy0_batch, ay_batch]).T, + fit_param_errs=np.array([x0_err_batch, vx0_err_batch, ax_err_batch, y0_err_batch, vy0_err_batch, ay_err_batch]).T, + fixed_params_dict={'t0': t0_batch} + ) + np.testing.assert_allclose(x_t_batch, x0_batch + (t_batch-t0_batch)*vx0_batch + 0.5*(t_batch-t0_batch)**2*ax_batch) + np.testing.assert_allclose(y_t_batch, y0_batch + (t_batch-t0_batch)*vy0_batch + 0.5*(t_batch-t0_batch)**2*ay_batch) + np.testing.assert_allclose(x_err_t_batch, np.sqrt(x0_err_batch**2 + ((t_batch-t0_batch)*vx0_err_batch)**2 + + (0.5*(t_batch-t0_batch)**2*ax_err_batch)**2)) + np.testing.assert_allclose(y_err_t_batch, np.sqrt(y0_err_batch**2 + ((t_batch-t0_batch)*vy0_err_batch)**2 + + (0.5*(t_batch-t0_batch)**2*ay_err_batch)**2)) + # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, - vx0=vx0_batch, vy0=vy0_batch, vx0_err=vx0_err_batch, vy0_err=vy0_err_batch, - ax=ax_batch, ay=ay_batch, ax_err=ax_err_batch, ay_err=ay_err_batch, - t0=t0_batch) - assert (x_t_batch==np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ax_batch[i] for i in range(len(x0_batch))])).all() - assert (y_t_batch==np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ay_batch[i] for i in range(len(x0_batch))])).all() - assert (x_err_t_batch==np.array([np.sqrt(x0_err_batch[i]**2 + ((t_batch-t0_batch[i])*vx0_err_batch[i])**2 + (0.5*(t_batch-t0_batch[i])**2*ax_err_batch[i])**2) for i in range(len(x0_batch))])).all() - assert (y_err_t_batch==np.array([np.sqrt(y0_err_batch[i]**2 + ((t_batch-t0_batch[i])*vy0_err_batch[i])**2 + (0.5*(t_batch-t0_batch[i])**2*ay_err_batch[i])**2) for i in range(len(x0_batch))])).all() - + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t=t_batch, + fit_params=np.array([x0_batch, vx0_batch, ax_batch, y0_batch, vy0_batch, ay_batch]).T, + fit_param_errs=np.array([x0_err_batch, vx0_err_batch, ax_err_batch, y0_err_batch, vy0_err_batch, ay_err_batch]).T, + fixed_params_dict={'t0': t0_batch} + ) + np.testing.assert_allclose(x_t_batch, np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ax_batch[i] for i in range(len(x0_batch))])) + np.testing.assert_allclose(y_t_batch, np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ay_batch[i] for i in range(len(x0_batch))])) + np.testing.assert_allclose(x_err_t_batch, np.array([np.sqrt(x0_err_batch[i]**2 + ((t_batch-t0_batch[i])*vx0_err_batch[i])**2 + (0.5*(t_batch-t0_batch[i])**2*ax_err_batch[i])**2) for i in range(len(x0_batch))])) + np.testing.assert_allclose(y_err_t_batch, np.array([np.sqrt(y0_err_batch[i]**2 + ((t_batch-t0_batch[i])*vy0_err_batch[i])**2 + (0.5*(t_batch-t0_batch[i])**2*ay_err_batch[i])**2) for i in range(len(x0_batch))])) + # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list],t) - x_true_err = np.sqrt(true_params['x0_err']**2 + ((t-true_params['t0'])*true_params['vx0_err'])**2 + - (0.5*(t-true_params['t0'])**2*true_params['ax_err'])**2) - y_true_err = np.sqrt(true_params['y0_err']**2 + ((t-true_params['t0'])*true_params['vy0_err'])**2 + - (0.5*(t-true_params['t0'])**2*true_params['ay_err'])**2) + x_true, y_true = mod.model( + t=t, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params_dict={'t0': true_params['t0']} + ) + x_true_err = np.sqrt(true_params['x0_err']**2 + ((t - true_params['t0']) * true_params['vx0_err'])**2 + + (0.5*(t - true_params['t0'])**2 * true_params['ax_err'])**2) + y_true_err = np.sqrt(true_params['y0_err']**2 + ((t - true_params['t0']) * true_params['vy0_err'])**2 + + (0.5*(t - true_params['t0'])**2 * true_params['ay_err'])**2) x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - mod_fit = motion_model.Acceleration(t0=true_params['t0']) - params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) + mod_fit = motion_model.Acceleration() + params, param_errs = mod_fit.fit( + t=t, + x=x_sim, + y=y_sim, + xe=x_true_err, + ye=y_true_err, + fixed_params_dict={'t0': true_params['t0']} + ) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) - + #@pytest.mark.skip(reason="not written") def test_Parallax(): # Test handling of a single star true_params = {'x0': 1.0, 'y0':-0.5, 'x0_err':0.1, 'y0_err':0.1, 'vx':-0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05, - 'pi':0.5, 'RA':17.76, 'Dec':-28.933, 'PA':0, - 't0':2020.0} - mod = motion_model.Parallax(**{'RA':17.76, 'Dec':-28.933, 'PA':0}) - param_list = mod.fitter_param_names - fixed_param_list = mod.fixed_param_names - print(param_list) + 'pi':0.5, 'ra':17.76, 'dec':-28.933, 'pa':0, + 't0':2020.0, 'obsLocation': 'earth'} + mod = motion_model.Parallax() + param_list = mod.fit_param_names + fixed_params_dict = { + 't0': true_params['t0'], + 'ra': true_params['ra'], + 'dec': true_params['dec'], + 'pa': true_params['pa'], + 'obsLocation': true_params['obsLocation'] + } # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list],t) - x_true_err, y_true_err = np.repeat(0.1,len(t)), np.repeat(0.1,len(t)) + x_true, y_true = mod.model( + t=t, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params_dict=fixed_params_dict + ) + x_true_err, y_true_err = np.ones_like(t)*true_params['x0_err'], np.ones_like(t)*true_params['y0_err'] x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) + params, param_errs = mod.fit(t, x_sim,y_sim, x_true_err, y_true_err, fixed_params_dict=fixed_params_dict) + + x_model, y_model = mod.model( + t=t, + fit_params=params, + fixed_params_dict=fixed_params_dict + ) + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) + ax1.plot(t, x_true, 'k-', label='True x') + ax1.errorbar(t, x_sim, yerr=x_true_err, fmt='ro', label='Sim x') + ax1.plot(t, x_model, 'r-', label='Model x') + ax1.set_xlabel('t') + ax1.set_ylabel('x') + ax1.legend() + ax2.plot(t, y_true, 'k-', label='True x') + ax2.errorbar(t, y_sim, yerr=x_true_err, fmt='ro', label='Sim x') + ax2.plot(t, y_model, 'r-', label='Model x') + ax2.set_xlabel('t') # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) - def test_Parallax_PA(): # Set PA=0 model x0, y0 = 2.0, -1.0 vx, vy = 0.2, 0.5 - RA, Dec = 17.76, -28.933 + ra, dec = 17.76, -28.933 pi = 0.5 - mod_pa0 = motion_model.Parallax(RA=RA,Dec=Dec, PA=0) + mod_pa0 = motion_model.Parallax() # Set PA=90 model with equivalent parameters in that frame - mod_pa90 = motion_model.Parallax(RA=RA,Dec=Dec,t0=2020.0, PA=90) - t_set = np.arange(2018,2024,0.01) - dat_pa0 = mod_pa0.get_pos_at_time([x0,vx,y0,vy,pi],[2020.0],t_set) - dat_pa90 = mod_pa90.get_pos_at_time([y0,vy,-x0,-vx,pi],[2020.0],t_set) - assert (np.abs(dat_pa0[0]-(-dat_pa90[1]))<1e-10).all() - assert (np.abs(dat_pa0[1]-(dat_pa90[0]))<1e-10).all() + mod_pa90 = motion_model.Parallax() + + t_set = np.arange(2018, 2024, 0.01) + t0 = 2020.0 + dat_pa0 = mod_pa0.model( + t = t_set, + fit_params = np.array([x0, vx, y0, vy, pi]).T, + fixed_params_dict = {'t0': t0, 'ra': ra, 'dec': dec, 'pa': 0} + ) + dat_pa90 = mod_pa90.model( + t = t_set, + fit_params = np.array([y0, vy, -x0, -vx, pi]).T, + fixed_params_dict = {'t0': t0, 'ra': ra, 'dec': dec, 'pa': 90} + ) + np.testing.assert_allclose(dat_pa0[0], -dat_pa90[1], atol=1e-10) + np.testing.assert_allclose(dat_pa0[1], dat_pa90[0], atol=1e-10) \ No newline at end of file diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 9962c05..4475970 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -3,7 +3,6 @@ from flystar import motion_model from flystar.startables import StarTable from flystar.starlists import StarList -from flystar import motion_model import numpy as np import pytest import os @@ -41,9 +40,14 @@ def test_StarTable_init1(): starlist_names = np.array(['file1', 'file2', 'file3', 'file4', 'file5', 'file6', 'file7', 'file8']) # Generate the startable - startable = StarTable(name=name_in, x=x_in, y=y_in, m=m_in, xe=xe_in, ye=ye_in, me=me_in, - ref_list=1, - list_times=starlist_times, list_names=starlist_names) + startable = StarTable( + name=name_in, + x=x_in, y=y_in, m=m_in, + xe=xe_in, ye=ye_in, me=me_in, + ref_list=1, + list_times=starlist_times, + list_names=starlist_names + ) # Now put in some assertions to make sure all our startable columns # have the right dimensions. @@ -57,7 +61,7 @@ def test_StarTable_init1(): assert len(startable['name']) == N_stars assert startable.meta['list_times'][0] == starlist_times[0] assert type(startable) == StarTable - + return def test_StarTable_init2(): @@ -77,7 +81,6 @@ def test_StarTable_init2(): assert len(tab) == len(list1) - return def test_combine_lists(): @@ -102,7 +105,7 @@ def test_combine_lists(): t.combine_lists('x', mask_val=-100000) assert t['x0'][0] == x_avg_0 assert t['x0'][-1] == pytest.approx(2108.855, 0.001) - + # Test 4: weighted average of x. x_wgt_0 = 1.0 / t['xe'][0, :]**2 x_avg_0 = np.average(t['x'][0, :], weights=x_wgt_0) @@ -170,78 +173,79 @@ def test_add_starlist(): t.add_starlist(x=x_new, y=y_new, m=m_new, xe=xe_new, ye=ye_new, me=me_new, meta={'list_times': t_new}) - assert len(t) == len(t_orig) + np.testing.assert_equal(len(t), len(t_orig)) expected_shape = np.array(t_orig['x'].shape) expected_shape[1] += 1 - - assert len(t['x'].shape) == len(expected_shape) - assert t['x'].shape[0] == expected_shape[0] + + np.testing.assert_equal(len(t['x'].shape), len(expected_shape)) + np.testing.assert_equal(t['x'].shape[0], expected_shape[0]) assert t['x'].shape[1] == expected_shape[1] - assert len(t['y'].shape) == len(expected_shape) - assert t['y'].shape[0] == expected_shape[0] + np.testing.assert_equal(len(t['y'].shape), len(expected_shape)) + np.testing.assert_equal(t['y'].shape[0], expected_shape[0]) assert t['y'].shape[1] == expected_shape[1] - assert len(t['m'].shape) == len(expected_shape) - assert t['m'].shape[0] == expected_shape[0] + np.testing.assert_equal(len(t['m'].shape), len(expected_shape)) + np.testing.assert_equal(t['m'].shape[0], expected_shape[0]) assert t['m'].shape[1] == expected_shape[1] - assert len(t['xe'].shape) == len(expected_shape) - assert t['xe'].shape[0] == expected_shape[0] - assert t['xe'].shape[1] == expected_shape[1] - - assert len(t['ye'].shape) == len(expected_shape) - assert t['ye'].shape[0] == expected_shape[0] - assert t['ye'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['xe'].shape), len(expected_shape)) + np.testing.assert_equal(t['xe'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['xe'].shape[1], expected_shape[1]) - assert len(t['me'].shape) == len(expected_shape) - assert t['me'].shape[0] == expected_shape[0] - assert t['me'].shape[1] == expected_shape[1] - - assert len(t['name']) == len(t_orig['name']) - assert len(t.meta['list_times']) == expected_shape[1] - assert t.meta['n_lists'] == 9 + np.testing.assert_equal(len(t['ye'].shape), len(expected_shape)) + np.testing.assert_equal(t['ye'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['ye'].shape[1], expected_shape[1]) + np.testing.assert_equal(len(t['me'].shape), len(expected_shape)) + np.testing.assert_equal(t['me'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['me'].shape[1], expected_shape[1]) + np.testing.assert_equal(len(t['name']), len(t_orig['name'])) + np.testing.assert_equal(len(t.meta['list_times']), expected_shape[1]) + np.testing.assert_equal(t.meta['n_lists'], 9) # Test 2: Add as starlist rather than with keywords. - starlist = StarList(name=t_orig['name'], x=x_new, y=y_new, m=m_new, - xe=xe_new, ye=ye_new, me=me_new, list_time=2001.0, list_name='A.lis') + starlist = StarList( + name=t_orig['name'], + x=x_new, y=y_new, m=m_new, + xe=xe_new, ye=ye_new, me=me_new, + list_time=2001.0, list_name='A.lis' + ) t = make_star_table() t.add_starlist(starlist=starlist) - assert len(t) == len(t_orig) + np.testing.assert_equal(len(t), len(t_orig)) expected_shape = np.array(t_orig['x'].shape) expected_shape[1] += 1 - - assert len(t['x'].shape) == len(expected_shape) - assert t['x'].shape[0] == expected_shape[0] - assert t['x'].shape[1] == expected_shape[1] - assert len(t['y'].shape) == len(expected_shape) - assert t['y'].shape[0] == expected_shape[0] - assert t['y'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['x'].shape), len(expected_shape)) + np.testing.assert_equal(t['x'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['x'].shape[1], expected_shape[1]) - assert len(t['m'].shape) == len(expected_shape) - assert t['m'].shape[0] == expected_shape[0] - assert t['m'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['y'].shape), len(expected_shape)) + np.testing.assert_equal(t['y'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['y'].shape[1], expected_shape[1]) - assert len(t['xe'].shape) == len(expected_shape) - assert t['xe'].shape[0] == expected_shape[0] - assert t['xe'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['m'].shape), len(expected_shape)) + np.testing.assert_equal(t['m'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['m'].shape[1], expected_shape[1]) - assert len(t['ye'].shape) == len(expected_shape) - assert t['ye'].shape[0] == expected_shape[0] - assert t['ye'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['xe'].shape), len(expected_shape)) + np.testing.assert_equal(t['xe'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['xe'].shape[1], expected_shape[1]) + np.testing.assert_equal(len(t['ye'].shape), len(expected_shape)) + np.testing.assert_equal(t['ye'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['ye'].shape[1], expected_shape[1]) - assert len(t['me'].shape) == len(expected_shape) - assert t['me'].shape[0] == expected_shape[0] - assert t['me'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['me'].shape), len(expected_shape)) + np.testing.assert_equal(t['me'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['me'].shape[1], expected_shape[1]) - assert len(t['name']) == len(t_orig['name']) - assert len(t.meta['list_times']) == expected_shape[1] - assert t.meta['n_lists'] == 9 + np.testing.assert_equal(len(t['name']), len(t_orig['name'])) + np.testing.assert_equal(len(t.meta['list_times']), expected_shape[1]) + np.testing.assert_equal(t.meta['n_lists'], 9) return @@ -257,7 +261,7 @@ def test_get_starlist(): assert t['x'][0,2] == t_list['x'][0] assert type(t_list) == StarList assert len(t_list['x'].shape) == 1 - + return @@ -289,11 +293,11 @@ def test_combine_1col(): t.combine_lists('x', weights_col='xe') - assert t['x0'][0] == t['x'][0] + np.testing.assert_equal(t['x0'][0], t['x'][0]) return -def test_fit_velocities(): +def test_fit_motion_models(): tab = make_star_table() tt = make_tiny_star_table() @@ -305,36 +309,28 @@ def test_fit_velocities(): tab = table.vstack((tab1, tab2, tab3)) tab.meta = tab1.meta - tab.fit_velocities(verbose=True) + tab.fit_motion_model(verbose=True, mask_value=-100000.) # Test creation of new variables - assert len(tab['vx']) == len(tab) - assert len(tab['vy']) == len(tab) - assert len(tab['vx_err']) == len(tab) - assert len(tab['vy_err']) == len(tab) - assert len(tab['n_fit']) == len(tab) - assert tab.meta['n_fit_bootstrap'] == 0 + np.testing.assert_equal(len(tab['vx']), len(tab)) + np.testing.assert_equal(len(tab['vy']), len(tab)) + np.testing.assert_equal(len(tab['vx_err']), len(tab)) + np.testing.assert_equal(len(tab['vy_err']), len(tab)) + np.testing.assert_equal(len(tab['n_fit']), len(tab)) + np.testing.assert_equal(tab.meta['n_bootstrap'], 0) # Test no-fit for stars with N<2 epochs. n_epochs = (tab['x'] >= 0).sum(axis=1) idx = np.where(n_epochs < 2)[0] - assert (tab['vx'][idx] == 0).all() - assert (tab['vx_err'][idx] == 0).all() - assert (tab['n_fit'][idx] == 2).all() + np.testing.assert_equal((tab['vx'][idx] == 0).all(), True) + np.testing.assert_equal((tab['vx_err'][idx] == 0).all(), True) + np.testing.assert_equal((tab['n_fit'][idx] == 2).all(), True) # Test that the velocity errors were calculated. - assert (tab['vx_err'][0:100] > 0).all() - assert (tab['x0_err'][0:100] > 0).all() - assert (tab['vy_err'][0:100] > 0).all() - assert (tab['y0_err'][0:100] > 0).all() - assert np.isfinite(tab['x0']).all() - assert np.isfinite(tab['vx']).all() - assert np.isfinite(tab['y0']).all() - assert np.isfinite(tab['vy']).all() - assert np.isfinite(tab['x0_err']).all() - assert np.isfinite(tab['vx_err']).all() - assert np.isfinite(tab['y0_err']).all() - assert np.isfinite(tab['vy_err']).all() + np.testing.assert_equal((~(tab['vx_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['x0_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['vy_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['y0_err'][0:100] < 0)).all(), True) ########## # Test running a second time. We should get the same results. @@ -343,118 +339,69 @@ def test_fit_velocities(): x0_orig = tab['x0'] vxe_orig = tab['vx_err'] x0e_orig = tab['x0_err'] - tab.fit_velocities(verbose=False) + tab.fit_motion_model(verbose=False, mask_value=-100000.) - assert (vx_orig == tab['vx']).all() - assert (x0_orig == tab['x0']).all() - assert (vxe_orig == tab['vx_err']).all() - assert (x0e_orig == tab['x0_err']).all() + np.testing.assert_allclose(tab['vx'], vx_orig) + np.testing.assert_allclose(tab['x0'], x0_orig) + np.testing.assert_allclose(tab['vx_err'], vxe_orig) + np.testing.assert_allclose(tab['x0_err'], x0e_orig) ########## # Test fixed_t0 functionality ########## fixed_t0 = tab['t0'] + np.random.normal(size=len(tab)) - tab.fit_velocities(fixed_t0=fixed_t0) - - assert(np.sum(abs(tab['t0'] - fixed_t0)) == 0) + tab.fit_motion_model(verbose=False, mask_value=-100000., fixed_params_dict={'t0': fixed_t0}) + np.testing.assert_allclose(tab['t0'], fixed_t0) ########## # Test bootstrap ########## tab_b = table.vstack((tab1, tab2, tab3)) tab_b.meta = tab1.meta - tab_b.fit_velocities(verbose=True, bootstrap=50) - - assert tab_b.meta['n_fit_bootstrap'] == 50 - assert tab_b['x0_err'][0] > tab['x0_err'][0] - assert tab_b['vx_err'][0] > tab['vx_err'][0] - assert tab_b['y0_err'][0] > tab['y0_err'][0] - assert tab_b['vy_err'][0] > tab['vy_err'][0] + tab_b.fit_motion_model(verbose=True, bootstrap=50) + + np.testing.assert_equal(tab_b.meta['n_bootstrap'], 50) + np.testing.assert_array_less(tab['x0_err'][0], tab_b['x0_err'][0]) + np.testing.assert_array_less(tab['vx_err'][0], tab_b['vx_err'][0]) + np.testing.assert_array_less(tab['y0_err'][0], tab_b['y0_err'][0]) + np.testing.assert_array_less(tab['vy_err'][0], tab_b['vy_err'][0]) ########## # Test what happens with no velocity errors ########## tab.remove_columns(['xe', 'ye', 'x0', 'y0', 'x0_err', 'y0_err', 'vx', 'vy', 'vx_err', 'vy_err', 'n_fit']) - tab.fit_velocities(verbose=False) - - assert len(tab['vx']) == len(tab) - assert len(tab['vy']) == len(tab) - assert len(tab['vx_err']) == len(tab) - assert len(tab['vy_err']) == len(tab) - assert len(tab['n_fit']) == len(tab) - assert (tab['vx_err'][0:100] > 0).all() - assert (tab['x0_err'][0:100] > 0).all() - assert (tab['vy_err'][0:100] > 0).all() - assert (tab['y0_err'][0:100] > 0).all() + tab.fit_motion_model(verbose=False) + + np.testing.assert_equal(len(tab['vx']), len(tab)) + np.testing.assert_equal(len(tab['vy']), len(tab)) + np.testing.assert_equal(len(tab['vx_err']), len(tab)) + np.testing.assert_equal(len(tab['vy_err']), len(tab)) + np.testing.assert_equal(len(tab['n_fit']), len(tab)) + np.testing.assert_equal((~(tab['vx_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['x0_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['vy_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['y0_err'][0:100] < 0)).all(), True) ######### # Test mask_list ######### # Test 5a: Masked print("Testing Masked List") - tt.fit_velocities(bootstrap=0, verbose=False, mask_lists=[1]) - assert np.arange(2.25, 48, 5) == pytest.approx(tt['x0'].data) - assert np.arange(2.25, 48, 5) == pytest.approx(tt['y0'].data) - assert np.full(10, 0.05) == pytest.approx(tt['x0_err'].data) - assert np.full(10, 0.05) == pytest.approx(tt['y0_err'].data) - assert np.ones(10) == pytest.approx(tt['vx'].data) - assert np.ones(10) == pytest.approx(tt['vy'].data) - assert np.full(10, 0.03380617) == pytest.approx(tt['vx_err'].data) - assert np.full(10, 0.03380617) == pytest.approx(tt['vy_err'].data) - assert 2017.25 * np.ones(10) == pytest.approx(tt['t0'].data) - - # Test 5b: Things that should break the code. - with pytest.raises(RuntimeError): - tt.fit_velocities(bootstrap=0, verbose=False, mask_lists=np.arange(2)) - with pytest.raises(RuntimeError): - tt.fit_velocities(bootstrap=0, verbose=False, mask_lists=True) + tt.fit_motion_model(verbose=False, mask_lists=[1]) + np.testing.assert_allclose(np.arange(2.25, 48, 5), tt['x0'].data) + np.testing.assert_allclose(np.arange(2.25, 48, 5), tt['y0'].data) + np.testing.assert_allclose(np.full(10, 0.05), tt['x0_err'].data) + np.testing.assert_allclose(np.full(10, 0.05), tt['y0_err'].data) + np.testing.assert_allclose(np.ones(10), tt['vx'].data) + np.testing.assert_allclose(np.ones(10), tt['vy'].data) + np.testing.assert_allclose(np.full(10, 0.03380617), tt['vx_err'].data) + np.testing.assert_allclose(np.full(10, 0.03380617), tt['vy_err'].data) + np.testing.assert_allclose(2017.25 * np.ones(10), tt['t0'].data) return -def test_fit_velocities_1epoch(): - ########## - # Test: only 1 epoch - ########## - tab = make_star_table_1epoch() - - # We don't need the entire table... lets just - # pull a small subset for faster testing. - tab1 = tab[0:100] - tab2 = tab[10000:10100] - tab3 = tab[-100:] - tab_1 = table.vstack((tab1, tab2, tab3)) - tab_1.meta = tab1.meta - - tab_1.fit_velocities(verbose=False) - - assert 'n_fit' in tab_1.colnames - assert 't0' in tab_1.colnames - assert 'x0' in tab_1.colnames - assert 'y0' in tab_1.colnames - assert 'vx' in tab_1.colnames - assert 'vy' in tab_1.colnames - assert 'x0_err' in tab_1.colnames - assert 'y0_err' in tab_1.colnames - assert 'vx_err' in tab_1.colnames - assert 'vy_err' in tab_1.colnames - - assert (tab_1['x0'] == tab_1['x'][:,0]).all() - assert (tab_1['y0'] == tab_1['y'][:,0]).all() - assert (tab_1['x0_err'] == tab_1['xe'][:,0]).all() - assert (tab_1['y0_err'] == tab_1['ye'][:,0]).all() - - assert(np.isnan(tab_1['vx'])).all() - assert(np.isnan(tab_1['vy'])).all() - assert(np.isnan(tab_1['vx_err'])).all() - assert(np.isnan(tab_1['vy_err'])).all() - - assert(tab_1['t0'] == 2001.0).all() - assert(tab_1['n_fit'] == 1).all() - - return -def test_fit_velocities_2epoch(): - +def test_fit_motion_model_2epoch(): ########## # Test: only 2 epoch2 ########## @@ -468,69 +415,26 @@ def test_fit_velocities_2epoch(): tab_2 = table.vstack((tab1, tab2, tab3)) tab_2.meta=tab1.meta - tab_2.fit_velocities(verbose=False) + tab_2.fit_motion_model(verbose=False, mask_value=-100000.) - assert 'n_fit' in tab_2.colnames - assert 't0' in tab_2.colnames - assert 'x0' in tab_2.colnames - assert 'y0' in tab_2.colnames - assert 'vx' in tab_2.colnames - assert 'vy' in tab_2.colnames - assert 'x0_err' in tab_2.colnames - assert 'y0_err' in tab_2.colnames - assert 'vx_err' in tab_2.colnames - assert 'vy_err' in tab_2.colnames + assert all([_ in tab_2.colnames for _ in ['n_fit', 't0', 'x0', 'y0', 'vx', 'vy', 'x0_err', 'y0_err', 'vx_err', 'vy_err']]) # 2 detections print(tab1.meta) np.testing.assert_almost_equal(tab_2['x0'][0], tab_2['x'][0,0], 1) - assert tab_2['n_fit'][0] == 2 - - # 1 detection - assert tab_2['x0'][100] == tab_2['x'][100, 0] - assert tab_2['n_fit'][100] == 1 - - # 0 detections - assert np.isnan(tab_2['x0'][-1]) - assert tab_2['n_fit'][-1] == 0 - - return - -def test_fit_velocities_all_detected(): - """ - Test the fit_velocities function when all stars are detected in all epochs. - """ - tab = StarTable.read(test_dir + '/test_all_detected.fits') - tab_orig = tab.copy() - # tab = tab[:1] - - epochs = ['2005_F814W', '2010_F160W', '2013_F160W', '2015_F160W'] - epoch_cols = [['_'.join(_.split('_')[:2]) for _ in tab.meta['EPNAMES']].index(epoch) for epoch in epochs] - - mm = motion_model.Linear() - tab.fit_velocities_all_detected( - weighting='var', - use_scipy=False, absolute_sigma=False, - motion_model_to_fit=mm, - epoch_cols=epoch_cols, - art_star=True - ) + np.testing.assert_equal(tab_2['n_fit'][0], 2) - # Check that the output table has the expected columns - for col in ['n_fit', 't0', 'x0', 'y0', 'vx', 'vy', 'x0_err', 'y0_err', 'vx_err', 'vy_err']: - assert col in tab.colnames + # 1 detection + np.testing.assert_equal(tab_2['x0'][100], tab_2['x'][100, 0]) + np.testing.assert_equal(tab_2['n_fit'][100], 1) - # Check that the fitted values match the original values - np.testing.assert_almost_equal(tab['x0'], tab_orig['x0']) - np.testing.assert_almost_equal(tab['y0'], tab_orig['y0']) - np.testing.assert_almost_equal(tab['t0'], tab_orig['t0']) - np.testing.assert_almost_equal(tab['vx'], tab_orig['vx']) - np.testing.assert_almost_equal(tab['vy'], tab_orig['vy']) - np.testing.assert_almost_equal(tab['vxe'], tab_orig['vxe']) - np.testing.assert_almost_equal(tab['vye'], tab_orig['vye']) + # 0 detections + np.testing.assert_equal(np.isnan(tab_2['x0'][-1]), True) + np.testing.assert_equal(tab_2['n_fit'][-1], 0) return + def make_star_table(): # User input cat_file = test_dir + '/test_catalog.fits' @@ -554,9 +458,15 @@ def make_star_table(): starlist_names = np.array(['file1', 'file2', 'file3', 'file4', 'file5', 'file6', 'file7', 'file8']) # Generate the startable - startable = StarTable(name=name_in, x=x_in, y=y_in, m=m_in, xe=xe_in, ye=ye_in, me=me_in, n=n_in, - ref_list=1, - list_times=starlist_times, list_names=starlist_names) + startable = StarTable( + name=name_in, + x=x_in, y=y_in, m=m_in, + xe=xe_in, ye=ye_in, me=me_in, + n=n_in, + ref_list=1 + ) + startable.meta['list_times'] = starlist_times + startable.meta['list_names'] = starlist_names return startable diff --git a/flystar/transforms.py b/flystar/transforms.py index 6cc865a..968ccfa 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -5,8 +5,9 @@ from astropy.table import Table import collections import re -import pdb -from flystar import motion_model +import copy +import datetime +from . import motion_model class Transform2D(object): ''' @@ -126,7 +127,7 @@ def evaluate_starlist(self, star_list): complex_motion_model=False # Cannot transform more complex motion models - set values to nan if complex_motion_model: - motion_params = motion_model.get_list_motion_model_param_names(new_list['motion_model_input'], with_errors=True, with_fixed=False) + motion_params = motion_model.motion_model_param_names(new_list['motion_model_input'], with_errors=True, with_fixed=False) for param in motion_params: if param in new_list.colnames: new_list[param] = np.nan @@ -220,7 +221,7 @@ def evaluate(self, x, y): yn = self.py[0] + self.py[1]*x + self.py[2]*y return xn, yn - def evaluate_error(self, x, y): + def evaluate_error(self, x, y, xe, ye): """ Transform positional uncertainties. @@ -245,7 +246,7 @@ def evaluate_error(self, x, y): """ xe_new = np.hypot(self.px[1] * xe, self.px[2] * ye) - xe_new = np.hpyot(self.px[1] * xe, self.px[2] * ye) + ye_new = np.hpyot(self.px[1] * xe, self.px[2] * ye) return xe_new, ye_new @@ -666,7 +667,7 @@ def from_file(cls, trans_file): return trans_obj - def to_file(self, trans_file): + def to_file(self, transform, outFile): """ Given a transformation object, write out the coefficients in a text file (readable by java align). Outfile name is specified by user. @@ -677,9 +678,9 @@ def to_file(self, trans_file): Parameters: ---------- - trans_file : str - The name of the output file to save the coefficients and meta data to. - This file can be read back in with + transform : PolyTransform + The transformation object containing the coefficients and meta data to save. + This object can be recreated with trans_obj = PolyTransfrom.from_file(trans_file). @@ -695,7 +696,7 @@ def to_file(self, trans_file): # Write output _out = open(outFile, 'w') - + # Write the header. DO NOT CHANGE, HARDCODED IN JAVA ALIGN _out.write('## Date: {0}\n'.format(datetime.date.today()) ) _out.write('## File: {0}, Reference: {1}\n'.format(starlist, reference) )