From 00afd9c60053fb808b3dacd687e7fb0876207a89 Mon Sep 17 00:00:00 2001 From: Cara Giovanetti Date: Wed, 11 Feb 2026 18:58:49 -0800 Subject: [PATCH 1/3] updated to kwargs --- abcmb/__init__.py | 1 - abcmb/main.py | 11 ++++++----- abcmb/model_specs.py | 5 +++++ pytests/accuracy_test.py | 41 +++++++++++++++++++--------------------- 4 files changed, 30 insertions(+), 28 deletions(-) diff --git a/abcmb/__init__.py b/abcmb/__init__.py index f11f79f..63d24b1 100644 --- a/abcmb/__init__.py +++ b/abcmb/__init__.py @@ -4,5 +4,4 @@ A fully differentiable Boltzmann solver for the CMB. """ -__version__ = "0.1.1.1" __author__ = 'Zilu Zhou, Cara Giovanetti, and Hongwan Liu' \ No newline at end of file diff --git a/abcmb/main.py b/abcmb/main.py index d7d0d91..5ac4fe7 100644 --- a/abcmb/main.py +++ b/abcmb/main.py @@ -81,8 +81,8 @@ class Model(eqx.Module): # In the init, all species that are present within the model should be set to True. # All couplings present between species should be set to true. def __init__(self, - input_specs = {}, - user_species=None + user_species=None, + **kwargs ): """ Initialize Model instance. @@ -92,14 +92,15 @@ def __init__(self, Parameters: ----------- - input_specs : dict - A dictionary containing run options (expected to be static) user_species : tuple A tuple of user-defined fluids to be included in the cosmology + **kwargs : dict + Configuration options passed as keyword arguments. + Any unknown keys will be preserved for custom species extensibility. """ # Fill in all user defined and missing specs parameters - specs = model_specs.load_specs(input_specs) + specs = model_specs.load_specs(kwargs) self.specs = specs # Populate all species diff --git a/abcmb/model_specs.py b/abcmb/model_specs.py index 9b1707d..3a9cfea 100644 --- a/abcmb/model_specs.py +++ b/abcmb/model_specs.py @@ -83,6 +83,11 @@ def load_specs(input_specs): specs["scale_dop"] = input_specs.get("scale_dop", 1) specs["scale_pol"] = input_specs.get("scale_pol", 1) + # Preserve any unknown keys for custom species extensibility + for key, value in input_specs.items(): + if key not in specs: + specs[key] = value + return specs def populate_species(user_species, specs): diff --git a/pytests/accuracy_test.py b/pytests/accuracy_test.py index 3c970f8..da491d4 100644 --- a/pytests/accuracy_test.py +++ b/pytests/accuracy_test.py @@ -45,17 +45,6 @@ def test_accuracy_checker(h = 0.6762): "exp_reion" : 1.5 } - specs = { - "output_Cl" : True, - "l_max" : ellmax, - "lensing" : True, - "output_Pk" : True, - "output_k_max" : 0.5, - "l_max_g" : 12, - "l_max_pol_g" : 10, - "l_max_ur" : 17, - "l_max_ncdm" : 17 - } if params["N_nu_massive"] > 0: user_species = ( species.MassiveNeutrino, @@ -65,17 +54,25 @@ def test_accuracy_checker(h = 0.6762): model = Model( user_species=user_species, - input_specs=specs - ) + output_Cl=True, + l_max=ellmax, + lensing=True, + output_Pk=True, + output_k_max=0.5, + l_max_g=12, + l_max_pol_g=10, + l_max_ur=17, + l_max_ncdm=17 + ) full_params = model.add_derived_parameters(params) # CLASS CLASS_params = { - "output": "mPk, tCl, pCl, lCl" if specs["lensing"] else "mPk, tCl, pCl", + "output": "mPk, tCl, pCl, lCl" if model.specs["lensing"] else "mPk, tCl, pCl", #"temperature_contributions" : "tsw", "l_max_scalars" : ellmax, - "P_k_max_1/Mpc" : specs["output_k_max"], - "lensing" : "yes" if specs["lensing"] else "no", + "P_k_max_1/Mpc" : model.specs["output_k_max"], + "lensing" : "yes" if model.specs["lensing"] else "no", "accurate_lensing" : 1, "H0": full_params["h"]*100, "omega_b": full_params["omega_b"], @@ -92,11 +89,11 @@ def test_accuracy_checker(h = 0.6762): "helium_fullreio_redshift" : params["z_reion_He"], "helium_fullreio_width" : params["Delta_z_reion_He"], "reionization_exponent" : params["exp_reion"], - "l_max_g": specs["l_max_g"], - "l_max_pol_g": specs["l_max_pol_g"], - "l_max_ur": specs["l_max_ur"], - "l_max_ncdm":specs["l_max_ncdm"] - } + "l_max_g": model.specs["l_max_g"], + "l_max_pol_g": model.specs["l_max_pol_g"], + "l_max_ur": model.specs["l_max_ur"], + "l_max_ncdm":model.specs["l_max_ncdm"] + } CLASS_Model = Class() CLASS_Model.set(CLASS_params) @@ -104,7 +101,7 @@ def test_accuracy_checker(h = 0.6762): CLASS_Model.set({"m_ncdm": full_params["m_nu_massive"], "T_ncdm": full_params["T_nu_massive"]}) CLASS_Model.compute() - if specs["lensing"]: + if model.specs["lensing"]: cl = CLASS_Model.lensed_cl(ellmax) else: cl = CLASS_Model.raw_cl(ellmax) From 73490ceb11cb14c05689d3fc2c773b0b0f7347b2 Mon Sep 17 00:00:00 2001 From: Cara Giovanetti Date: Wed, 11 Feb 2026 19:37:41 -0800 Subject: [PATCH 2/3] partial update of example notebooks --- example_notebooks/ABCMB_Fluids.ipynb | 88 ++++++++----------------- example_notebooks/ABCMB_basics.ipynb | 55 ++++++++++------ example_notebooks/ABCMB_with_LINX.ipynb | 46 +++++++------ 3 files changed, 86 insertions(+), 103 deletions(-) diff --git a/example_notebooks/ABCMB_Fluids.ipynb b/example_notebooks/ABCMB_Fluids.ipynb index 3037d6a..0022772 100644 --- a/example_notebooks/ABCMB_Fluids.ipynb +++ b/example_notebooks/ABCMB_Fluids.ipynb @@ -40,34 +40,7 @@ "execution_count": 2, "id": "81ea175b-9122-40bd-a054-4ace224a81fc", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR:2026-02-11 14:49:01,512:jax._src.xla_bridge:477: Jax plugin configuration error: Exception when calling jax_plugins.xla_cuda13.initialize()\n", - "Traceback (most recent call last):\n", - " File \"/ext3/miniforge3/lib/python3.12/site-packages/jax/_src/xla_bridge.py\", line 475, in discover_pjrt_plugins\n", - " plugin_module.initialize()\n", - " File \"/ext3/miniforge3/lib/python3.12/site-packages/jax_plugins/xla_cuda13/__init__.py\", line 328, in initialize\n", - " _check_cuda_versions(raise_on_first_error=True)\n", - " File \"/ext3/miniforge3/lib/python3.12/site-packages/jax_plugins/xla_cuda13/__init__.py\", line 285, in _check_cuda_versions\n", - " local_device_count = cuda_versions.cuda_device_count()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: jaxlib/cuda/versions_helpers.cc:113: operation cuInit(0) failed: Unknown CUDA error 303; cuGetErrorName failed. This probably means that JAX was unable to load the CUDA libraries.\n", - "ERROR:2026-02-11 14:49:02,019:jax._src.xla_bridge:477: Jax plugin configuration error: Exception when calling jax_plugins.xla_cuda12.initialize()\n", - "Traceback (most recent call last):\n", - " File \"/ext3/miniforge3/lib/python3.12/site-packages/jax/_src/xla_bridge.py\", line 475, in discover_pjrt_plugins\n", - " plugin_module.initialize()\n", - " File \"/ext3/miniforge3/lib/python3.12/site-packages/jax_plugins/xla_cuda12/__init__.py\", line 328, in initialize\n", - " _check_cuda_versions(raise_on_first_error=True)\n", - " File \"/ext3/miniforge3/lib/python3.12/site-packages/jax_plugins/xla_cuda12/__init__.py\", line 285, in _check_cuda_versions\n", - " local_device_count = cuda_versions.cuda_device_count()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: jaxlib/cuda/versions_helpers.cc:113: operation cuInit(0) failed: Unknown CUDA error 303; cuGetErrorName failed. This probably means that JAX was unable to load the CUDA libraries.\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "sys.path.append('..')\n", @@ -102,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "b94bb915-8efe-4658-b8e9-c2e5ebc2a733", "metadata": {}, "outputs": [ @@ -126,7 +99,7 @@ ], "source": [ "LCDM = Model()\n", - "output_LCDM, aux_LCDM = LCDM.run_cosmology({})" + "output_LCDM, aux_LCDM = LCDM.run_cosmology()" ] }, { @@ -321,9 +294,7 @@ { "cell_type": "markdown", "id": "b1278b55", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "# Defining new fluids: The Fluid Module" ] @@ -341,7 +312,9 @@ { "cell_type": "markdown", "id": "06e48447-3777-4dde-8ea6-8a00c5a00160", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, "source": [ "## The Module" ] @@ -434,7 +407,9 @@ { "cell_type": "markdown", "id": "57bc5cd4-b80f-4a58-bedd-fddbf3565c0c", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, "source": [ "## The Equinox Fields\n", "\n", @@ -454,7 +429,9 @@ { "cell_type": "markdown", "id": "325925db", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, "source": [ "## The Methods\n", "\n", @@ -499,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "edae6b41-71cf-4943-a66c-9f7518a40957", "metadata": {}, "outputs": [], @@ -543,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "15443a54-3b61-4d5c-b750-67c902d86135", "metadata": {}, "outputs": [], @@ -594,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "1504c7f0-67e8-4b16-bcf5-162bea81d77b", "metadata": {}, "outputs": [ @@ -603,15 +580,11 @@ "output_type": "stream", "text": [ "w0wa:\n", - "{'DynamicalDarkEnergy': 0, 'ColdDarkMatter': 1, 'Baryon': 2, 'Photon': 3, 'MasslessNeutrino': 4}\n" + "{'ColdDarkMatter': 0, 'Photon': 1, 'DynamicalDarkEnergy': 2, 'Baryon': 3, 'MasslessNeutrino': 4}\n" ] } ], "source": [ - "specs = {\n", - " \"use_LCDM_species\" : False # First, ask ABCMB not to use default species\n", - "}\n", - "\n", "# Since we're not using the default species set, we need to add all the other fluids too.\n", "user_species = {\n", " species.Baryon,\n", @@ -621,7 +594,7 @@ " DynamicalDarkEnergy\n", "}\n", "\n", - "w0wa = Model(specs, user_species=user_species)\n", + "w0wa = Model(user_species=user_species,use_LCDM_species=False) # ask ABCMB not to use default species\n", "print(\"w0wa:\")\n", "print(w0wa.species_dict)" ] @@ -954,11 +927,7 @@ " SIDR,\n", ")\n", "\n", - "specs = {\n", - " \"max_steps_PE\" : 4096,\n", - "}\n", - "\n", - "SIDRmodel = Model(specs, user_species)\n", + "SIDRmodel = Model(user_species,max_steps_PE=4096)\n", "print(\"SIDR model:\")\n", "print(SIDRmodel.species_dict)" ] @@ -968,7 +937,7 @@ "id": "abb08ccd-49e6-4d0d-be9c-62ef35d57ad1", "metadata": {}, "source": [ - "Here we used an extra `specs` parameters `max_steps_PE`. This parameter tells the adaptive step size solver what is the maximum number of steps it should expect to take, in order to solve the linear perturbations equations. This number is 2048 by default, but adding new physics scenarios such as the SIDR can make these equations more difficult. With this method we allow our solver to take more steps to solve, though this may increase runtime. " + "Here we used an extra keyword argument `max_steps_PE`. This parameter tells the adaptive step size solver what is the maximum number of steps it should expect to take, in order to solve the linear perturbations equations. This number is 2048 by default, but adding new physics scenarios such as the SIDR can make these equations more difficult. With this method we allow our solver to take more steps to solve, though this may increase runtime. " ] }, { @@ -1238,10 +1207,7 @@ "outputs": [], "source": [ "lmax = 2500\n", - "specs = {\n", - " \"use_LCDM_species\" : False,\n", - " \"max_steps_PE\" : 4096,\n", - "}\n", + "\n", "user_species = (\n", " species.DarkEnergy, \n", " IDM, \n", @@ -1251,7 +1217,7 @@ " species.MasslessNeutrino\n", ")\n", "\n", - "NADMmodel = Model(specs, user_species)" + "NADMmodel = Model(user_species,use_LCDM_species=False, max_steps_PE = 4096)" ] }, { @@ -1418,9 +1384,7 @@ { "cell_type": "markdown", "id": "37f91f5c-c8f4-4def-a795-8edc7cf3a8f5", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "# Cheat Sheet: Define your own species!" ] @@ -1463,9 +1427,9 @@ ], "metadata": { "kernelspec": { - "display_name": "default", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "my_env" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1477,7 +1441,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/example_notebooks/ABCMB_basics.ipynb b/example_notebooks/ABCMB_basics.ipynb index 0ce5a09..1e50f58 100644 --- a/example_notebooks/ABCMB_basics.ipynb +++ b/example_notebooks/ABCMB_basics.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "80e9a0dc-c54f-4a0d-888c-9b0191b4b3f2", "metadata": {}, "outputs": [], @@ -41,7 +41,7 @@ "source": [ "ABCMB is object-oriented, with all calculations running through an instance of `abcmb.main.Model`. The default ABCMB model is $\\Lambda$CDM cosmology in flat space and without neutrino masses. The five fluids present are cold dark matter, baryons, photons, massless neutrinos, and the cosmological constant. \n", "\n", - "To start, we initialize an instance of `Model` with all default options. Once initialized, our instance of `Model` can be used for calculations. It expects a dictionary of parameters when called; for now we'll specify an empty dictionary to run with the default options." + "To start, we initialize an instance of `Model` with all default options. Once initialized, our instance of `Model` can be used for calculations, either with `run_cosmology` or just by calling the initialized model." ] }, { @@ -72,8 +72,8 @@ ], "source": [ "model = Model()\n", - "params = {}\n", - "output, aux = model.run_cosmology(params)" + "output, aux = model.run_cosmology()\n", + "# equivalent: output, aux = model()" ] }, { @@ -145,20 +145,36 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "8382673d-5e71-4c49-b66a-2d08471044d9", "metadata": {}, "outputs": [], "source": [ - "specs = {\n", - " \"output_Cl\" : True, # Will return TT, TE, EE spectra.\n", - " \"output_Pk\" : False, # Turn off P(k) computation for now.\n", - " \"l_min\" : 2,\n", - " \"l_max\" : 2100, # Will output at all integer l's between l_min and l_max (both inclusive).\n", - " \"lensing\" : True # Include lensing. This is False by default, so be sure to turn it on.\n", - "}\n", + "# output_Cl = True to return TT, TE, EE spectra.\n", + "# output_Pk = False to turn off P(k) computation for now.\n", + "# l_min and l_max define range of output modes ell for computation (inclusive)\n", + "# lensing = True includes lensing (False by default)\n", + "model = Model(output_Cl=True,output_Pk=False,l_min=2,l_max=2100, lensing=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fae9789-ec9c-48f0-8407-547abfac01b7", + "metadata": {}, + "outputs": [], + "source": [ + "# equivalent way to specify specs:\n", + "\n", + "# specs = {\n", + "# \"output_Cl\" : True, # Will return TT, TE, EE spectra.\n", + "# \"output_Pk\" : False, # Turn off P(k) computation for now.\n", + "# \"l_min\" : 2,\n", + "# \"l_max\" : 2100, # Will output at all integer l's between l_min and l_max (both inclusive).\n", + "# \"lensing\" : True # Include lensing. This is False by default, so be sure to turn it on.\n", + "# }\n", "\n", - "model = Model(specs)" + "# model = Model(**specs)" ] }, { @@ -171,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "926d894f-59f7-4705-be04-64515a42cd6e", "metadata": {}, "outputs": [ @@ -179,8 +195,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "You did not specify either N_nu_massless or Neff, and did not ask LINX to compute these quantities.\n", - "N_nu_massless will be set to 3-N_nu_massive=3.\n", "\n", " /\\ \n", " / \\ \n", @@ -196,8 +210,7 @@ } ], "source": [ - "# let's keep using the empty params dict for now\n", - "output, aux = model.run_cosmology(params)" + "output, aux = model.run_cosmology()" ] }, { @@ -212,13 +225,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "9b540a55-8098-4869-a430-b3b2eb391f2f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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blBJjHfIMJ1EGM05ERKSgoA2cOjo6MGXKFLz44ot+XV9WVoZrr70Wc+fOxf79+/GTn/wEjzzyCD744AOFV0pidijNNrxsE8CMExERKcus9QKUsnDhQixcuNDv61euXIns7Gy88MILAIAJEyZgz549+O1vf4ubbrpJoVUS0JNxyhjiUStnS5dGEjDjRERE8gvajNNgbd++HQsWLOh129VXX409e/bA6XT2eR+73Y7W1tZeHzR4YmO4HBknsbmcgRMRESmBgZNPdXU1UlNTe92WmpoKl8uF+vr6Pu+zYsUK2Gw26SMrK0uNpQYd8Wy5dBkCJzH4qmHgRERECmDgdBaDoXdjsiAIfd4ueuKJJ9DS0iJ9VFRUKL7GYFTt60dKl6FUlxJjBQC02V3odHAIJhERyStoe5wGKy0tDdXV1b1uq62thdlsRmJiYp/3sVqtsFqtaiwvqFXJWKqLtpoRaTGh0+FGbasduUn8FiciIvkw4+RTWFiIdevW9brtX//6F2bMmIGwsDCNVhX8PB4BNa3yleoMBoOUdRIfl4iISC5BGzi1t7ejqKgIRUVFALzjBoqKilBeXg7AW2a7/fbbpesfeOABnDp1CsuXL0dxcTFef/11vPbaa3jssce0WH7IqO+ww+kWYDT0lNmGKyXWG4DVttlleTwiIiJR0NYx9uzZgyuuuEL67+XLlwMA7rjjDqxatQpVVVVSEAUAeXl5+PTTT/H9738ff/jDH5CRkYHf//73HEWgsGpp+GU4zCZ54vhUX+DEjBMREcktaAOnefPmSc3dfVm1atV5t11++eXYt2+fgquic4kTvuXobxKJmas6ZpyIiEhmQVuqo8DQs6NOvsApNZY9TkREpAwGTqSpKqkxfPijCETiQcE1rcw4ERGRvBg4kaaqZRx+KUrxZZxq25hxIiIieTFwIk1VKdLj5NtVx4wTERHJjIETaaqq1dvjlBEnf48Tp4cTEZHcGDiRZjweATUt3qxQmow9TuL0cIBZJyIikhcDJ9JMY6cDDrcHBhmHXwKcHk5ERMph4ESaEbNBiVEWhMk0/FLE6eFERKQEBk6kGXHXW3KMfP1NImaciIhICQycSDNiNkjOMp1IPHaF08OJiEhODJxIM3UKBk7iY7JUR0REcmLgRJqp9ZXRxIGVckqK9j5mfTsDJyIikg8DJ9JMT6lO/h6nJB70S0RECmDgRJpRsscpKdoCAKhvd8j+2EREFLoYOJFmxF11SpTqkn2lusYOO9weQfbHJyKi0MTAiTQhCII0x0mJUl1ClAUGA+ARgKZOZp2IiEgeDJxIE63dLthdHgBAsgKlOrPJiPhIsVzHPiciIpIHAyfSRJ2vTBcbbkZ4mEmR55D6nNqYcSIiInkwcCJNSGW6WPnLdCJxJEFdO6eHExGRPBg4kSaU3FEnkmY5MeNEREQyYeBEmpB21KkROLHHiYiIZMLAiTRRo0apLsbb41THwImIiGTCwIk0oWqpjkMwiYhIJgycSBPiOXVKjCIQiY9dz2NXiIhIJmatF0ChqU7Bc+pEyexxCipOtwer91eisqkLN0zNwKjkaK2XREQhiIETaUIq1Slw3IpILNU1dDjg8QgwGg2KPRcpy+n24O5Vu7G5pB4A8KdNJ/D6HRdjzugkjVdGRKGGpTpSXafDhXa7C4CyPU6JvgGYbo+A5i6nYs9Dynt5Uyk2l9QjIsyEgkwbup0ePPLefrTwdSUilTFwItWJwy8jwkyItiqX9AwzGREXGQaA5bpA1tLlxEvrjwMAfnljPv72QCFGJUehvt2BVzaVarw6Igo1DJxIdWeX6QwGZctnPUMwGTgFqjX7K9HhcGNsajS+OTUT4WEmPLZgHADgnZ2nYHe5NV4hEYUSBk6kOjWGX4rE8+o4yylw/WVXOQDgtlk5Up/a/ImpSLeFo6nTibUHq7VcHhGFGAZOpDrpnDoFd9SJOMspsJ2s78CR6jaYjQZ8c2qmdLvZZMTNM7IAAB8VndFqeUQUghg4kerEUp2SM5xEPHYlsH1RXAMAmJmXAJuvX010XUE6AGDz8Xp0+DYbEBEpjYETqU4q1Sk4ikDEIZiBbf3RWgDANyaknve5sanRyEmMhMPlwaZjdWovjYhCFAMnUp0awy9FYo8TM06Bx+HyYM/JJgDAZWPPn9dkMBhw5fgUAN6sExGRGhg4kep6epzULNWxxynQHDzTArvLg/jIsH6nhM8Z5Q2odpQ2qLk0IgphDJxIdWqW6tjjFLh2lzUCAGbkJvQ7tmJmbgIMBqC0rkM6/5CISEkMnEhVDpcHTZ3eac+qlOpiegInQRAUfz6Sz+6T3sBpZm5Cv9fYIsMwMT0WALDDF2gRESmJgROpSpynFGYyIP6cXVJKSIzy9jg53QLauPMqYAiCgP3lzQCA6bnxA147e2QigJ4MFRGRkhg4karEckpKTLjiU8MBIPysY10a2OcUMGpa7WjocMBkNEgZpf5MyYoDAHxd2aLCyogo1DFwIlWpOcNJlODLOjWwzylgHK7yBkGjkqMQHmYa8NopI2wAgOIzrXC4PIqvjYhCGwMnUpV0Tp2KgVOibyRBQwczToHiUGUrAFww2wQA2QmRsEWEweH24Gh1m9JLI6IQx8CJVFXXqt6OOlGilHFi4BQoDlf5AqeMCwdOBoMBk31Zp68rm5VcFhERAydSV62Kwy9FiVHeIK2xg6W6QHHojDdwmpRh8+t6KXCqYJ8TESmLgROpSotSXYI0PZwZp0DQ2u1EeWMnAP9KdQBQkBkHgA3iRKS8oA6cXnrpJeTl5SE8PBzTp0/H5s2b+712w4YNMBgM530cOXJExRUHvxoNS3WN7HEKCCU17QCAtNhwxPteuwuZ5Cvpnahth9PNBnEiUk7QBk7vv/8+Hn30Ufz0pz/F/v37MXfuXCxcuBDl5eUD3u/o0aOoqqqSPsaMGaPSikODJqU6qTmcpbpAcKLOGziNSony+z6ZcRGIspjgcHtwqqFDqaUREQVv4PT888/jnnvuwb333osJEybghRdeQFZWFv74xz8OeL+UlBSkpaVJHybTwFuhyX9ujyCNBFB1V52vx4nN4YGhtM4b+PR3Pl1fjEYDxqTGAACOcGcdESkoKAMnh8OBvXv3YsGCBb1uX7BgAbZt2zbgfadNm4b09HRcddVVWL9+/YDX2u12tLa29vqg/jW02+ERAKMBSIzWYI4TS3UBQco4DSJwAoDxad7AiSMJiEhJQRk41dfXw+12IzU1tdftqampqK6u7vM+6enpePnll/HBBx/gww8/xLhx43DVVVdh06ZN/T7PihUrYLPZpI+srCxZv45gI5bpEqOtMBmVnxouEg/6bexwwOPheXV6N9TAaRwDJyJSgVnrBSjp3CM9BEHo95iPcePGYdy4cdJ/FxYWoqKiAr/97W9x2WWX9XmfJ554AsuXL5f+u7W1lcHTAGrbxONW1Ms2AT0ZJ7dHQGu3E3GR/jUck/qcbg/KG7w76kYm+9/jBADjfKW6ozUMnIhIOUGZcUpKSoLJZDovu1RbW3teFmogs2fPRklJSb+ft1qtiI2N7fVB/attVb+/CQAsZiNiwr3vETiSQN/KGzvh8giItJiQFju4DQRixqm8sROdDh7oTETKCMrAyWKxYPr06Vi3bl2v29etW4c5c+b4/Tj79+9Henq63MsLWVrsqBOdXa4j/TpR6y3TjUyOgnGQ5dzEaCsSoywQhJ4GcyIiuQVtqW758uVYunQpZsyYgcLCQrz88ssoLy/HAw88AMBbZqusrMRbb70FAHjhhReQm5uLSZMmweFw4M9//jM++OADfPDBB1p+GUFFKtWpOMNJlBBlQVl9Bw/61bkTQ9hRd7aRyVFo6HDgRF078jP9mzpORDQYQRs43XLLLWhoaMAzzzyDqqoq5Ofn49NPP0VOTg4AoKqqqtdMJ4fDgcceewyVlZWIiIjApEmT8Mknn+Daa6/V6ksIOlqV6oCzzqtjxknXSn2N4SOThhg4JUVj98kmZpyISDFBGzgBwEMPPYSHHnqoz8+tWrWq13//6Ec/wo9+9CMVVhW6xFJdsgalOmkIJnucdO2U76iV3KTIId1fbCgvrWfgRETKCMoeJ9KnOrHHSYNSHQ/6DQwVvsApK2GogZM3UyX2ShERyY2BE6lCEISewEmDUp04kqCepTrdsrvcqPadZZg95MDJm3Eqq+/gzC4iUgQDJ1JFc6cTDt/hq8la9DhJpTpmnPSqsqkLggBEhJmknrTByk6IhNloQJezJwgjIpITAydShdjfFBcZBqtZ/fP/ekp1zDjpVUVTFwBv8NPfoNoLCTMZkZ3ozVaxQZyIlMDAiVSh1dRwEZvD9a9c6m+KGNbjiDvySuvZ50RE8mPgRKroGUWg/o46oGccQVOnA272vujS6WE2hotGiTvrmHEiIgUwcCJV1GrYGA4A8b7AySMAzZ3MOulRRZMvcIofXuCUm+QNnE42MHAiIvkxcCJV9EwN1ybjFGYywhYRBoB9TnolluqGuqNOlOO7v3hYMBGRnBg4kSq0nBouEvucAuWg36YOB3625gC+vXI7XttSFvTb6ysavc3hwy3Vic3hFU2dLMsSkeyCenI46YeW59SJkqKsKK3rCIiMU6fDhe++uhPFVa0AgF0nG9HYYccPrx6v8cqU0dLlREuXE8Dwm8PTbREIMxngdAuoaunCiGGW/oiIzsaME6mip8dJm1Id0DMEsyEApoe/tP4EiqtakRhlwf2Xj/TetuEEDla2aLwyZYgTw5OiLYi0DO/9nMlokIIlsfxHRCQXBk6kOEEQWKobhOZOB17dUgoA+NWSAjyxcAIWTcmAIAD/92WJxqtTxnCPWjlXNvuciEghDJxIcW12F7qcbgDalurEkQR6P6/u73tPo9vpwYT0WCyYmAoAePjK0QCAL4prUROEE7Erm739TZlxwyvTiXJ8fU6nmHEiIpkxcCLFidmmGKt52GWY4UiM9gZteh+C+dc9FQCA/5idLU3QHpsag4tz4+H2CPhwX6WWy1PEmWZvMJghU+DEjBMRKYWBEylObAxP1jDbBJzd46TfwKmsvgPHatphNhpw/eSMXp9bPDUTAPBFcY0WS1NUVYs345Ruk6cHLifRO8vpVCNnORGRvBg4keLqNB5+KQqEg36/OOwNimaPTJTmTomumpACANhX3oR6HX8NQ3GmxRtcp9vkzTidauiEIHAkARHJh4ETKU7syUnVaPilKBAO+l3nC5y+4QuSzpZui8CkjFgIArC5pE7tpSmqytfjlBEnz/eIGDi1dbukMQdERHJg4ESK08OOOqAn49TU6YTL7dF0LX3psLuwt7wJAHDVhNQ+r5kzKhEAsKusSbV1Kc3h8qDOl0GTK+MUYTFJ32+n2OdERDJi4ESK08MMJwCIj7TA12uNpk79ZSH2lzfD7RGQGRfR77b8i3MTAAC7TzaquTRF1bR2QxAAi8ko7XyUA3fWEZESGDiR4vQwNRzwDkaMj9TvEMxdvmDo4tz4fq8RA6fjte26LjkORpWvvynNFg6j0SDb42YneBvEy3nYLxHJiIETKa6nVKdtxgno2VnXqMORBLvLfIFTXkK/18RHWTAmJdp7fZBkneTeUScS+5zEM/CIiOTAwIkUJ5XqNM44AT1DMOt1lq1xuT3YX+HtW5qZ23/gBAAXZXszUgdOB8fxK3LPcBKNiPc+njhck4hIDgycSFGdDhfa7S4A2jeHA/odSVBS245upwcxVjNGJUcPeG1+ZiwA4NCZ4AiclMo4ZfoCp9NN7HEiIvkwcCJFiWW6iDAToq3aTQ0X6XUkgXh478SM2Av2+UzMsHnvc6ZV8XWpQcw4pSuYcfJ4OMuJiOTBwIkUJc5wSom1SseHaEmvB/2KgVNBpu2C105Ij4HR4B0sWhsE59aJGacMmTNOabHhMBkNcLoFqVxMRDRcDJxIUeIfrFQdNIYD+j3oV8we5fsROEVazBjpK+cdCoKsU5XMU8NFZpMRab6hq5XNLNcRkTwYOJGixMBJ63PqRHo86NftEXBYCpxi/brPpAzvdYerAjtw6na6pbKpXFPDzzZC6nNigzgRyYOBEylKmuGkg8ZwQJ8H/ZbWtaPL6UakxYS8pIEbw0VjU2MAACU1bUouTXHVvmxTeJjxvLP55DAi3juSgIETEcmFgRMpSk8znAAgSYe76sSs0fi0GJj8HAApznI6Xteu2LrUIJVyY8MV6YHLZMaJiGTGwIkUJWacUvVSqvPtqmvtdsHh0sd5dSU13uBnXFqM3/cZLQZOte0BvWNMOgBaocB6BEcSEJHMGDiRovSWcbJFhElZHb2MJDjmK7eNSfE/cMpOiITFZES30xPQAx7P3nWpBGkkATNORCQTBk6kKD1NDQcAo9Eg9TnV66RcV1LrzTiJfUv+MJuMyEvynsV2vDZwy3V1Z5XqlDAiztvjVNncBUEI3MwcEekHAydSTLfTjZYuJwD9NIcDPSMJ9NAg3u1045TvENqxqf41hotGp/aU6wKVlHFS6PsjzRYOowGwuzyo00mgTESBjYETKUbMJljMyuyYGqokaSSB9n9IT9S1wyN4S4jJgwweRicHQ+CkbMbJYu6Z5cQGcSKSgyKB08cff4w1a9agpaXnLK3u7m50dwf+lGPy39mjCPQwNVzUc16d9hmn41KZLnrQ/0ajg2BnXU2bsj1OQM/OOvY5EZEcFAmcHn/8cezfvx/PPPMMnn76aVRXV8PlcuGee+5R4ulIp8SJ0GkKZROGStxZV6+D6eFSY/gg+ptEYo+TWOoLRHUqbB7gLCcikpMip66+//77eP7551FcXIyMjAzMnz8f8+fPh8Oh/Tt8Uo843DBN5jPIhktPGadjvlEEY1MG198EANmJ3oCgvt2BdrtLF4coD0aH3YU2uwuAsuMqOJKAiOSkyG/agoICvPHGGwCAsrIyVFRUoKGhATt37lTi6UinqnWacdLTEMwTvjLb6EGMIhDFhochIcqCxg4Hyhs6MTHDv+Na9ELccRlpMSka9GXGcQgmEcln2L+tSkpK8Prrr+Pxxx9HXFzceZ/Py8tDXl4eAGDx4sXDfToKINWtOs04+Up1Wu+qc7k9KG/wZkFGJkcN6TGyEyLR2OHAqYaOwAucxOGXCk0NF4mlukCedyUSBAFfHqnF3lNNSLOFY/HUTF1tvNDC6aZOHK9tR2xEGPIzbLCYueeJlDXswOnZZ59FU1NTn0FTd3c3SktLMXHiRACA0chv6FDCUt3ATjd1weURYD1r59dg5SRGoqiiGacaA68MVSMeAK3wqIqzS3WCIOhqo8JgdDnceOS9/Vh3uEa67ff/LsGrd1yMqVlx2i1MI8dr2/HzfxzEthMN0m2JURYsu3I07ijMhdHP44uIBmvYkczGjRvxyCOP9Pm58PBwPPDAA/jVr3413KehACRmnNJ1FjiJ4wjq2+2aDkUs8zV15yVFDfmXfE5i4DaIn51xUlJ6nPfxu50ezbOMQyUIApb/tQjrDtfAYjLiW9NHYFRyFOrbHfiPV3eiNIB3Vg7FhqO1uOHFLdh2ogEmowHjUmOQEGVBQ4cDT398GPe+tQcdvv45IrkNO3CqrKzEqFGj+v38/fffj48++mi4TxPy6trsqGoJnFKDxyNIww3TbBEar6Y3MeNkd3nQ4XBrto6yOm+wk5s4tDIdAOQkeMtQpxoCL+MkHfCrcMbJajZJAzYDdSTBB/sq8dnBalhMRrx1z0z89uYp+GjZpZiRE492uwvL3t0Pl1sfZy8qbc/JRtz/9l50OtwoHJmIjT+ch8+/fxl2/eQq/GLxJFjNRnx5pBb3vrkH3U7tfr4peA07cEpISEBVVVW/n585cyaOHz8+3KcJabWt3Zj3v+ux4PlNUvlL7xo7HXC6BRgM+poaDgCRFjMiwkwAtG0QPylmnIbY3wR4S3VAYAZONSplnICzZjkFYJ9Ta7cTKz4tBgA8On8MZo9MBABEWc146baLEBcZhsNVrfjLrnItl6mKpg4Hlr27H3aXB9+YkIK37pkp9bCZTUYsLczFX/5zNqKtZmwvbcCT/zik8YopGA07cLrsssuwatWq/p/AaITdrv3upUC29UQ9OhxutNld+OfXZ7Rejl/EAC8p2oowk/5628SsU72GfU5l9T2luqESS3VnWrpgdwXWu2ulD/g9m7izLhAzTm9vP4WGDgdGJkfh3ktH9vpcSmw4ls8fCwB4bt2xoC9P/WzNQVS3dmNkUhR+/51pff5uuSg7Hiv/YzqMBuD9PRX4254KDVZKwWzYf9Eee+wxvPLKK3j55Zf7/Pz27dsxcuTIPj+ntJdeegl5eXkIDw/H9OnTsXnz5gGv37hxI6ZPn47w8HCMHDkSK1euVGmlA6tt7Qk8/3WoZoAr9UOvowhEiTo4dkWOwCkp2oJIiwmCEHjb7WtVGH4pCtSMU7fTjTe2ngQALLtidJ87xr47Mxt5SVFo7nTivd3BGyRsO16PTw5UwWQ04PffmYZIS/97my4dk4QfLBgHAHjmn4elIJ1IDsMOnKZPn44//vGPeOihhzB//nysWbMG5eXlaGxsxD/+8Q88/vjjuO222+RY66C8//77ePTRR/HTn/4U+/fvx9y5c7Fw4UKUl/edzi4rK8O1116LuXPnYv/+/fjJT36CRx55BB988IHKKz/f/ZePwvrH5gEA9pxqRE1rN7Ydr8eKz4qx+A9bMftX/8Z3Xt6BD/edhsejjxPgq3Q6ikCUpPFBv91Ot/RHfDiBk8FgQLavz6k8wMp1Uo+TChmnEQE6y+mTr6tQ325HZlwEFk3J6PMas8mI++Z635y+trkUziDsdXJ7BDzzz8MAgP+YlY38TNsF7/PA5aMwJSsObd0u/PwfB5VeIoUQWWoo9957LzZs2ICWlhYsWbIEeXl5SE5Oxo033oiCggJ8//vfl+NpBuX555/HPffcg3vvvRcTJkzACy+8gKysLPzxj3/s8/qVK1ciOzsbL7zwAiZMmIB7770Xd999N37729+qvPK+5SVFIT8zFh4BmPWrf+O7r+7EnzaW4quKZlS3dmN7aQOW//UrfOeVHWjpcmq9XNToPuOk7RDM8sZOCAIQYzUj0RfEDdWIAMymtNtdaPeVlVLY49Svv+89DQD4zsysAUveSy7KRGKUBWdaurHxaJ1ay1PN2oPVOFLdhthwMx79xli/7mMyGvDskgKYjQZ8fqgGG48F378LaUO25pNLL70Uu3btwuHDh/H222/jT3/6E7Zt24a1a9ciLEzdAW0OhwN79+7FggULet2+YMECbNu2rc/7bN++/bzrr776auzZswdOp/aBCAAsu2IMxBE0iVEWLJmWiee/PQUfPjQHP5g/FlEWE3aWNeKeVbs1f9dZpdMZTqJEaSSBNhknqUyXHDXsuUJS/04ABQXiKIIohaeGizLjfEMwA+jYlYrGTmwvbYDBANx40YgBrw0PM+HGaZkAgL8GWU+PIAj440bvBqO7LslD/CDeaExIj8Udc3IBAM9+dgRunWTkKbDJ/htr/PjxmDRpEtxu7RpV6+vr4Xa7kZqa2uv21NRUVFdX93mf6urqPq93uVyor69Henr6efex2+29Gt9bW1tlWH3/rslPw6ePzEVjhwOz8hJgPusd6EXZ8bhqQipufXk79pxqwu/WHcOPrhmv6HoGIo0i0GvGSeNSnRg4DWcUgUjKpgRQGUos06mRbQJ6/o1au11o63YiJlz/07bX7K8EABSOTJSC44F8++IsvLqlDF8eqUVdm13xwaJq2VxSj4OVrYgIM0lB0GAsu2I0/rqnAsVVrVizvxI3TR84CCW6EEW2O2k5VPBs576Tv9DU4L6u7+t20YoVK2Cz2aSPrKysYa74wiakx+KS0Um9gibRxIxYPHvTZADAyo0nUFylbCA3EHHmlN6GX4qSNG4OPylDY7hIyqYEUMap3vfvnhytzh/3aKtZOpokUP6d1h7yvsn75tRMv64fmxqDqVlxcHkEfPxVYOy+9ccbW8sAALdcnIWEIZS146Ms+N4VowEAz687pnk2ngLfoAKnnTt34tFHH8XFF1+MrKwsjBkzBosXL8bKlSvR0tIiXaf1kQZJSUkwmUznZZdqa2vPyyqJ0tLS+rzebDYjMTGxz/s88cQTaGlpkT4qKrRPkV9bkI5rC9LgEYD/+eSwZkGsXo9bEWl97IocO+pEgZhxqvdlnJJihtffNRiBNJLgTHMXDp1phcEAXDUhxe/7iQ3kaw/2nVkPNGeau6TepKFkm0R3zslFcowVlc1dWO3L5BENld+B07XXXos33ngD8+fPx0cffYSysjIUFRXhmWeegcPhwE033YQ1a9YA0D7jZLFYMH36dKxbt67X7evWrcOcOXP6vE9hYeF51//rX//CjBkz+u3RslqtiI2N7fWhB08snACLyYitxxuwq6xR9edv63ZKE7l1GzhJB/1qk3ESd3dl+XbEDYcYENS0dcPhCox302JvWZJKGScgsBrE/13sHTsyPTte6sfzxzX5aQCA3acaUdsW+Fvw/773tHdDTF7CsN5khIeZcN9c72Hzf9xwgr1ONCx+B07vvvsuVq5cieuuuw7p6ekwm82IiorClClT8Mgjj+CLL77AvHnzAGifcQKA5cuX49VXX8Xrr7+O4uJifP/730d5eTkeeOABAN5s0e233y5d/8ADD+DUqVNYvnw5iouL8frrr+O1117DY489ptWXMGRZCZG4eYa3jv+nTaWqP7+YbYoNNw84a0VLSb6MU2OHQ/Vfog6XRyplZiUM/ziaxCgLLGYjBAEBM1leLNWpGjgFUMZpXXEtAOAbE/vOkPcnMy4CU0bYIAiBM/OtPx6PgPd9c6lunTn8NojbZuUgLjIMZfUd+ORA/6ddEF2I34FTXFycLNeo5ZZbbsELL7yAZ555BlOnTsWmTZvw6aefIicnBwBQVVXVa6ZTXl4ePv30U2zYsAFTp07FL37xC/z+97/HTTfdpNWXMCz3zh0JgwH48kgtjteqewBotc5nOAGQduZ4BKC5U91y3ZnmLngEIDzMKEuPj9FoCLiddVoETuLYhtM6/zfqsLuw40QDAOAbEwYXOAHANfnejSyBXq7bdqIBlc1diAk3Y2H++ZtzBivKasY9l3izTn/48rjmlRE5CYKAk/Ud2HC0Fv8oqsT6I7UorWsPqq9RT2RJB+zduxfTp0+X46Fk9dBDD+Ghhx7q83N9HRNz+eWXY9++fQqvSh15SVG4anwKviiuxd/2VuCJhRNUe25xFIEaZ5ANVZjJiLjIMDR3OtHQ4RhUOWS4Knxb4rPiI2XLzmbGRaCsviNgAqc6qVTHHqdz7TrZCIfbgxHxERg1hHMMr8lPw6/XHsHOsga0212qjHtQgni81KIpGQj3nS05XLfPycWfNpXiaE0bNpXU4/KxybI8rlZqWrvx9vZTWL2/ss+f/ZQYKxZPzcBts3KQK0M/JXnJsqvuxhtvlONhSGY3z/Cmtz/YW6nqThLxD5P4Dl+vxJEE9SrvrKtolK+/SRQoQYGopzmcPU7n2u7LNl0yKmlIgXVeUhRyEiPhdAvSYwUap9sj7Sq8vmD42SaRLSJMamN4fUuZbI+rNqfbgz9uOIHL/3c9Xlx/HJXNXbCYjBifFoPCkYmYlBELi9mI2jY7Xtlchque34ifrj6AurbAPjfW7nLj9/8uQWu3trMV/X4r8u1vf7vP2wVBQGOj+g3IdGFXjk9BUrQF9e12bDxaN+h+iaES/zD5M3tGS4nRVpyo61B9Z11Pxkm+f5+eoED/Ax4FQegp1UWp3+NU12ZHt9MtWxZDbmKwUziq7928/rh8bDLe2n4KG47WYr5KP/dy2naiAc2dTiRFWzAzL0HWx75rTh5WbTuJjcfqcLy2DaNTYmR9fKU1tNvx4Dv7pI0/07LjcO+lI3HF+ORePaXdTjc2l9Tj7R2nsOlYHd7ZWY5PDlTh6Rsm4YYpGbroRR6ME3XtePjd/Thc1YoTde34f7dO02wtfgdOX3zxBd5++21ER0f3ul0QBGzatEn2hdHwhZmMWDw1E69tKcPHX59RLXA64wucMnQeOCVpdOxKRaMvcFIi46TzbArgPW7F7tv9p+Y4goQoC8LDjOh2elDV0i3LKAi5tXQ6cfCMd7TLcAKneeO8gdPGY3UXnF+nR59+7W3eviY/rc+ZdcORnRiJBRNT8fmhGry25SRWLCmQ9fGVVFbfgaWv7cTppi5EW814ctFEfGv6iD5f3/AwE+ZPTMX8ianYVdaIpz8+hENnWvFf7xVh/ZFarFgyGREWfb55ONf6o7V45N39aLO7EB8ZhkWT+z63US1+f0fOmzcP0dHRuPzyy3t9zJs3D9OmaRf50cCuLfBuT/53cS3sLnWmuQdMxkkaSaByxskXOI2IlzFwCqBZTuIogkiLSdVdlwaDQfclzR1lDRAEYFRy1LB6BGePTITFZMTppi6U+maGBQqn24PPD3vLdNcVKPMH8m5fk/iH+06jUaPTAwbrVEMHvvPyDpxu6kJuYiTWfG8Obp6R5VdQPDMvAWu+dwm+/42xMBkNWFN0Bjf9cZv0u0ivBEHAa1vKcM+q3Wizu3BxbjzWPnqZakmA/vgdOH344Ye4/PLL+/zc2rVrZVsQyWtaVjxSY61ot7uwpaRe8efzeARUNXubwzP13uMUrc2xKxW+P9rZCmSczjR3634nTYMGO+pEmfHilHV9/sHYUTr8Mh0ARFrMUolrQ4Ad+rujVCzTWWUv04lm5iUgPzMWdpcHf9lVfuE7aKyqpQvfeXkHqlu7MSYlGn9/cM6gS4xhJiP+6xtj8M69s5AYZcHhqlbc+NJWHDjdcuE7a8Dh8uCJDw/gF/88DI8AfHvGCLxz72xdbDpS5MiVs1155ZXnHZLb3Nys9NOSj9FowDWTvFmnz1TYnlzXbofD7YHRoN9z6kSJGhy70mF3Se9w5ZjhJBJ/mTjcHt2/g+4ZRaBemU6k94zT3lNNAICLc4cfMFw2NgkAsO248m+Y5PRv3wyr+RNTYDIqU2I0GAxS1unNbSd1PTi20+HCvW/uwZmWboxKjsK7980e1puO2SMT8c9HLsXE9FjUtztwy8vbpensetHY4cB/vLYT7+2ugNEA/Oy6Cfj1TZNhMSsesvhF8VXs3r0bkyZNwoQJE3DTTTfhiSeekAZlkjrEuS7rDtfApfDuOrFMlxYbLntvgtySotQ/dkVsDI+LDJP1oFmL2Sj9Mq3S+RDMOg2mhov0PMup2+nG4TPe8yUvyo4f9uMVjvQGTrvKGgNmUrYgCPj3Ee/gzivG+X/UzFBcPzkDKTFW1LbZ8alOB2J6PAKWv/8VDp1pRWKUBW/ePVOWw5vTbRF4//7ZuHR0Ejodbtyzajf+vve0DCsevmM1bfjmH7ZiV1kjoq1mvHbHxb65hPrp0xvyXzZ/ywGjR4/GsWPHsHv3bvzwhz/E2LFj8ZOf/GSoT0tDcHFuPGwRYWjpcuKr082KPpf4Tl7vZTrgrIyTihma8oaeGU5yS7N5v56aVn0HTlqMIhDpOeN0sLIFLo+A5BirLKM8JmbEIibcjDa7SwrI9O5EXTsqGr1b6y8ZnaToc1nMRiyd7R2I/PrWMl2WuF/bUoa1h6phMRnxp6XTZe2LjAkPw+t3XoxvTs2AyyPgsb99hT+s13Yw6JdHarDkpW0ob+xEdkIkVj80B1eMVzaAHoohB07+Drxsb2/H4cOHER4ejtmzZ+Ouu+7qd7QBKcNsMuLSMd5fQhsV7ncIlMZwoKfHSc05ThXSGXXy//ukxXofU+8ZJy2mhov0PMtpX7m3TDctK06Wd9cmowGzfD1C20sDo1z35RFvmW72qEREqTC487uzsmExG/H16RapTKoXX59uxm8+PwIA+PmiiZghQ/n2XBazEc9/eyruv3wkAOB/Pz+Kn//jkOoZSkEQ8MqmUtzz5h60212Y5WtmH5Oqz1ERimecGhsbsXz5cowdOxb5+fn49re/jaeffnqoT0tDNM83IVfpWnYgZZzEGUJt3S7VdhwqMYpAFDAZJ1/glKxhj1N1S7fuylf7y5sBABflDL9MJ5o90ttkvqM0MGbtif1NV45TZ6J3YrQVN07NBODNOulFu92FR/6yH063gIX5abhtVrZiz2U0GvDEwgl4ctFEGAzA2ztO4cE/70W3U53fid1ON37496/xy0+LIQjArRdn4e17ZiEhSv3fD/4aVEj/1ltvAfAGTU1NTdJ/A+h1YO7Ztm7divHjxwMA2tracOjQIRw+fHio66UhEo8W+LqyBQ3tdsWOGAmUGU4AEBthhtlogMsjoLHDgXSb8ms+3aRcqU5cv/4zTtr1OKXGhkuveU1rt26+TwVB6JVxkosYOO0ua4TL7dF132FLlxN7fFmfK8ert9387kvz8P6eCqw9WI3TTZ2ylsOG6n/+eRgnGzqRYQvHs0smq9Lfc9cleUiNDcej7xfhX4drcNurO/Hq7TOkcz2VUN3Sjfv/vBdfVTTDaAB+et1E3H1Jrq76mfoyqJ8iQRCkTNPZ/9tf9qmrqwsxMT2ptpiYGMyePRuzZs0a6nppiFJiwzEhPRaCAGxWcCxBIJXqDAZDz0gClRrElThuRSTurAuUjJOa5wOKTEaDdPi0nsp1VS3dqGm1w2Q0oGCETbbHnZAei1hfn9Mhnfc5bTpWB7dHwOiUaGQnqhe8jEuLwaWjk+ARgLe2n1LtefuzpaQe7+2uAAD87papsEXKt4nkQq4tSMfbd89EbLgZe0814VsrlZv1tPdUIxa9uAVfVTTDFhGGt+6ehXsuzdN90AQMMnC64447pI/ExETcfvvt0n+fTRAEfPDBBxg7diyuvfZaTJ48GTt37pQ+v3TpUnlWT4Myb5zy5bpAOadOJA7BVKPPSRAElDfKf9yKSBz/oPuMU5t24wgAfTaIi9mmCekxsg4FNRkNmJknluv0fW6dOG/qSg2age++NBcA8Jdd5eiwu1R/flGH3YUff/g1AOD2whzMGjm8eV5DMWtkIv7+4Byk28Jxoq4DN7y4Bet9vWdy8Hi8/Uy3vrwDdW12jEuNwcfLLpX6cAOBYj1Ov/jFL7Bv3z589dVXeP3113H33Xfj3Xff9eu+pIy5vl0q207UK/IatHQ50eb7paOXEsiFqJlxauhwoMvphsGgTA+YmEmp0XHg1OVwo8Ph7Z3QYlcdoM8G8a8qmgEAU2Us04lmj/Q2Fes5cBIEAVt986YuG6NOf9PZ5o1NQV5SFNq6Xfhgn3bb8v/386M43dSFzLgI/Oia8ZqtY2xqDD58aA7yM2PR1OnEXat2Y8WnxcPue6pu6cYdb+zCLz8thtMt4NqCNHz40BxVM4xyGHLgtHfv3gE/73K5kJzs/QGYMWMGNm3ahD/96U945plnAiIVF4wuyomHxWRETasdZQocwyCmdJOiLaoepTEcYp+NGhkn8d8nLTYcVrP8Z0SJgVOb3YV2Dd81D0T8d7aYjYhRYddUX0b4gvrTOso4Ha7yltEKMuUr04lm+TJOe081waOzhnhRWX0Hqlu7YTEZMV3G5nh/GY0G3HVJLgDgja0nNfl32nOyEW9uPwkAWLGkANEa/XyI0m0R+ODBObij0Duy4U+bSrHw/20e0kBVu8uNlRtP4MrnNmBzST3Cw4z41Y0F+MN3L1Jl96Tchhw4GY0D3zUlJQVff/219N+JiYlYt24diouLe91O6gkPM2FadhwAYLsC7z5P+WYUyXmUiNKSVBxJII0iUKj5NNpqloKRap1mnXp21Fk1ewOlt4yTIAjSnKUJ6bGyP763/GdCa7cLJbXtsj++HLad8P4+uignTrODZ2+6aARiws0oq+/A+qPylab80e1040cffA1BAL41fQQuG6t+1q0vVrMJTy/Ox5+WTkdKjBVl9R347qs7sfS1ndhZ2nDBykVzpwOvbi7FZb9Zj2c/O4JOhxsXZcfhnw9fiu/Oyg7YJIpiod7bb78Nk6n3D4DFYsFf/vIXLFu2TKmnpQsoHJWInWWN2H6iAbfNypH1sU81erNYOYn6O3W+Pykx3ixNbZt6GacRCsxwEqXawtFW247qlm6MTolW7HmGqmdHnXZbjTPjfOfVNenjvLqaVjuaOp0wGQ0Yq8DcGrPJiKlZcdh2ogF7TjViXJr+ZuNsO+HNYswZpV2fS5TVjO/MzMbLm0rx2pYyXDVBvZ19//dlCUrrOpAcY8V/XzdRtef119WT0lA4KhG//fwo3tlZjs0l9dhcUo+shAhcOS4F+Zk2pNm8mfSWLieO17Zj98lGbC6pg9PtDa7SYsPxgwVjcdNFI2BU6CgdtQwqcPr444/hdrtxxRVXwGbzppS7u73vbMPDe84lMxgMyMzM7PdxLrnkkqGslWRQODIRL6AEO0obIQiCrBF/eQBmnMTjC2pb1QuclMo4AUC6LRzHa9tRrdOddVoOvxSdnXGS+2dgKIp9ZbqRSVEID1Mm2zIjN8EbOJ1skv0N03B5PAK2+zJOl4xWvxn6bLcX5uC1LWXYdqIBRRXNivScnevwmVb8aWMpAOAXi/NV3UU3GLHhYXhmcT7umzsSL204gX8UVaKisQtvXmAn4oT0WNw5JwffnJapSIuCFgYVOD3++OO4+eabsXnzZsTGxuL+++9HdHQ07r//frzzzjtKrZFkNDU7DlazEfXtdhyvbZd1MqtYqssJoEa/FF/gVKdKqU654ZcicSRBdYs+ylDn6tlRp13glO7rBet2eg9E1mIswtnE/iYlynSiGb6+oT2n9DcIs7i6FU2dTkRZTJg8Ik7TtYyIj8Q3p2big32n8X//LsFrd16s6PO5PQJ+/OHXcHm8gy6vyU9T9PnkkJUQiRVLCvDz6ydiw9Fa7D3VhMNVrWjscKDb6YYt0oLMuHBclB2PuWOSdZnhHK5BBU7vvfcefve736G4uBgZGRmYP38+5s+fD4dD36exUw+r2YTpOfHYdqIB20sbZA6cxFJd4AROPRkn5TM04gwnJTNyYlCg+4xTjHaluvAwE5JjrKhrs6OyuSskAqdp2XEwGrzfg7Wt3UiJDb/wnVQiZptm5iUgTAcDOr93xSis3n8a/z5Si4OVLchXoGFf9MbWMnx9ugUx4WY8fcMkxZ5HCREWExYWpGNhQbrWS1HdoL5LJ0+ejDfeeAM7duzAc889hz/84Q+YO3cuRo0apdT6SAGFvtkg4i8sOdhdblT5/lhnJwRej1Nrt0vRIwZcbo80VV2Jc+pEPRknvQZO2k0NP5ueZjmJpbqJGcoFTjHhYRiX5n38PTo7k01sDNeyv+lsI5OjccOUDADe3iOlVDR24rl/HQMA/PTaCboKZmlgfgdOV199NV599VXU1np3G+Tl5eGyyy7D4sWLccMNN2DZsmV48803AXBOk94VjuoZiCfXttuKxi4IAhBpMWna+DtYsRFmWMzeH4M6BRvEq1q64fIIsJiMSI1R7hek3jNOdTrocQL0s7Ouy+HGSd9okAnpypY0xHLd7pP6Kdc53R7s9O3wFX8v6cGyK0fDYAA+P1Qj7XiUkyAI+Omag+hyujErLwG3XJwl+3OQcvwOnFavXo2uri4sXrwYeXl5mDZtGiZOnIhRo0bh5ZdfxtKlS6UJ4lo3W9LAJo+IQ0SYCU2dThyrbZPlMct9O+qyEyID6vU3GAxI9v0RV3JnndjflBkfoeiOEv1nnMTjVrQNrvUyy+loTRs8gneXYYqCATUAzMj1Bk57dZRx+vp0CzocbsRFhmGigqXKwRqdEoPrJ3uzTis+K5Y9GfC3Paex6VgdLGYjViwpCKjfmTSIHqfIyEg8/PDDePjhh+F0OlFfX4/w8HDEx6s/rIyGx2I2YkZuPDaX1GP7iQaMTxv+L6xAbAwXpcRaUdncpWjG6XSjOkfRiBmn+nYHHC6PlE3TC7E5PJkZJwA9ZTol+5tEM3K9E8QPnWlFp8OliyG14jDFwpGJutui/sMF4/D5wWpsLqnHhmN1uGKcPEfBlDd04umPDwEAls8fi5HJ+hsbQgMb1G/Vjz/+GGvWrEFnZyfS09MRHx8Pu90ujSSgwCGemi7XMQw9gVPg9DeJxD/idW3KfR+LGSelRzUkRFlg8TXY6u2wX4fLg9Zu70RzzUt1OulxEstAamRbMuMikG4Lh9sjoMh3xIvWpP6m0frobzpbdmIk7pjjHd3wq0+K4XJ7hv2YLrcH3/9rETocbszMTcB9c0cO+zFJfYMKnB5//HHs378fzzzzDJ5++mlUV1fD6XTinnvuUWp9pBAxcNpZ1ihLn9OJOu9E4pFJgRc4pcQqX6qTDvdVOHAyGAw9OwVVGOo5GA0d3vWYjQbYIrSdVROKGScA0nEme05qX67rdrqx13e48Rwd9TedbdkVYxAXGYaS2na8sfXksB/vD+tPYO+pJkRbzXju21Ng0lmWjfwzqMDp/fffR3l5ObZu3YqvvvoK8+fPx89//nOOIwhAk0fYEGkxobnTiaM1w+9zKq3z9jiN0uG06gsRe0uULNWpMfxSJAaCSmbQhqK+zft7IjHaonlZRsw4tXQ5NTvXz+MRcKTa+7OnVuDUM89J+8Bp76kmOFwepMZadfuGyxYZhh/7Dtv97b+ODuuMz/VHa/HCv7276J5ZPEnxN1GknEEVuQsKCvDGG28AAMrKylBRUYGGhgbs3Lmz13XcVad/YSYjZuQmYNOxOuwobRjWL+5Oh0t65z46AOv1amRopHPqFBxFIBKHetaoMA19MPQwNVwUEx6G2HAzWrtdqGzq0mRI3+mmLrTbXbCYjBiZrE7gIPY57T/VBLdH0DTjIR6zcsmoJF03R99ycRb++XUVthyvxw//9hXe+8/ZMA9y3tSphg7811/2QxCA78zMxpKLRii0WlLDkDtHxXEEN954I371q1/1+pzHM/xaMClv9kjvL9HhznMSs00JURbERwXOKAKRND1cocCp2+mWHluN42jEnXW1Oss46WUUgSjTl/2rbNbmzLrDVS0AgDGp0aoNfhyfFoMoiwltdheOVsuzo3aoth7X3xiCvhgMBqxYUoAoiwl7TjXh2c+ODOr+tW3duOP1XWjtdmFadhyeukF/Z9HR4Mjy02o06mvnDvlHrj4nsb8pELNNwNkZJ2UCjdO+xvAYq1mV3p4UFc/fGww9ZZwA7RvED1d5Axc1t+GbTUZcpIPjV1q7nfj6dDMAfTaGnysrIRLPfXsKAODVLWV4d2e5X/dr7HDg9td24WRDJ0bER2Dlf0wPmvPaQhkjnhBWkOntc2rpckq9FkNxvNYbOI1K0WefwoWIPU717Q64ZRoIejaxMXyESjOuxK9Hb83hYo+TlsetnE0cDXFaowZxtRvDRTNyvJlmLRvEd5c1wiMAuYmRUgCrd9fkp+PhK0cDAH6y+gDe2n5ywLaUsvoOLHlpK45UtyEp2oo/3zNLygZTYGPgFMLCTEZc7Ot5GM5YAjHjNCpAM06J0RYYDN4DN5s65d/oIJ5Rl6XwDCdRsgq7BIdCzDhpPcNJpHXGSavA6eJccWeddhmnnjKd/rNNZ1s+fyzunJMLAPj5Pw5h+V+/Om/sR7fTjTe2luH632/GyYZOZMZF4C/3zUKuThvgafC0n4BGmpo9MhEbj9Vhe2kD7r40b0iP0ZNxCszAKcxkREKkBQ0dDtS22mUvJVWoNIpAlKLiwcWDoZep4SItRxK0dDmlqeVqT8yemh0Hk9GAMy3dqGzu0iTjIzWGj9Z3f9O5DAYDnlw0Eamx4fjfz49g9f5KfHKgCpeOTkJ2QiQaOxzYerweDR3eN2AzcxPw4m3TFJ8KT+pi4BTixAbxXb4+p8FuE7e73FJz+NhU9XcmySU5xoqGDofUwCwncfilWhknsRzQ0OGA0+3RxYnzAHucznbEl23KjIuALVLdmVaRFjPyM2Lx1ekW7DnZiMypmao+f327XWoNEA8cDyQGgwEPzhuFmXkJ+NWnxdh7qglfHqntdU2GLRwPXjEat83M1nz0BsmPgVOIy8+0IcrX51Rc3YpJGbZB3b+kph0ujwBbRBgybIH7rio5xooj1W2KZGnKfaW6bJWOo0mItMBsNMDlEVDfbke6TR89JA3tvh4nvQROvkC2ts0Ou8utatNuT5lOmzcb03MSfIFTExarHDiJbQHj02KQqJPvhaGYnhOPvz9QiOKqNuwobUBdux0x4WZMzozDrJEJunnDQvJj4BTiwkxGXJyXgA1H67CjtHHQgdPhqp4jI/Q8i+VClGqoFgQBpxvVOW5FZDQakBRtRXVrN2pb9RE4udweNHbqK3BKjLIgPMyIbqcHVc3dqvagFFepO/jyXBfnxuP1rWXYrUGfk3TMSoD1N/XFYDBgYkYsJmbo54BiUh5DYhrWuXXSWVsB/osjWaFZTs2dTrT5JlOPUGFquEiNY2QGo7HTAUEAjAbvvC89MBgMyIjTps+puFqbxnDRdF+D+NGaNrR0OVV9bvFgX70es0J0IQycqGeeU2nDoLfjH9ZoZ5DcUmPFadvylurEUQQpMVaEh6lXCkpReDbVYImjCBKiLLo6n0uLPieX2yP1+KjdGC5KiQlHbmIkBAHYX67eWILK5i6cbOiEyWjALF9/JVGgYeBEyM+IRbTVe/yE2HvhD0EQpOu1+gMgl3Rff1a1zIGT2BiuVplOlOJrENfLsSt6awwXaTHLqay+Aw6XB1EWk+rfF2cTj19Rc56TmG0qyLQhJlzbg56JhoqBE8FsMkqzXbb4frH543RTF9q6XQgzGTA6QEcRiNJ8fUDVLcpknNQ+0LPnGBmdZJx0GjhpkXESs7Tj0mI03XEl/syr2ee01ff75dIAmBZO1B8GTgQAmDcuBQDO21Y7kH2+FP/4tFhYzIH9rSRmnGrb7HC55TtrURp+qXrg5Pt6dJdx0kd/k6hnlpN659Vp3Rgumu6bIF5U0QyHS/nzRQVBwFaxMTzA5jcRnS2w/9qRbK4c7w2c9p5qQkunf82i+8ubAXi35Qa6pGgrTEYD3B4B9e3yTQ+Xhl+qNMNJ1NPjpJfASV876kSZceJBv+pnnLTeUDEqOQrxkWGwuzw4dKZF8ecrqW1HXZsd4WFGXJQd+L8zKHQxcCIA3ozImJRouD0CNpXU+XWfvae8GadgCJxMRgNSfcFGVYt8f0TLVR5FIOrZVaeTUp0vgEuK0Vng5Atoq5q7FTmnsC9aHbVyLoPBoGqfk1imuzg3QdWNEkRyY+BEEjHrtN6Pcl2nwyW9c74oCAInAEgTG8Rl6nNyuT0406zu8EuROD28rs2uWkAwEHEie6JORhGIUmO8mUaXR1AlyKxvt6OuzQ6DwTsAUmtq9jmJgdMl7G+iAMfAiSRX+AKnDcfqLvjHtqi8GW6PgLTY8ICeGH42cVBklUyBU1VLN1weARaTEakqn1WVGOU9uNgjAA0d2pfrpKnhOss4mU1GpPmCTDUaxMVsU25iFCIt2s8fFjNOu056j1xSisvtwY5Sb3B2SRAMvqTQFpSBU1NTE5YuXQqbzQabzYalS5eiubl5wPvceeedMBgMvT5mz56tzoJ1YnpOPGwRYWjscGBn2cDDMDf6ynlzRiUG9MTws8k9kkAcRTAiPkL13VNmkxGJUeJhv9oHTmJzeLLOepwAdQ/71fqolXNNzrQh2mpGc6dTGsqphK9Ot6Dd7kJcZJjmvV1EwxWUgdN3v/tdFBUVYe3atVi7di2KioqwdOnSC97vmmuuQVVVlfTx6aefqrBa/QgzGXFtQRoA4KOiMwNeu+mYN+1++bhkxdelFrFUJ1fGSWwMH6HRrJ4UhaahD5bHI0inxeutORzomeUkvl5Kkibt62TumdlkxMw8b9Zp+4nBnxzgr7OnhetpACrRUARd4FRcXIy1a9fi1VdfRWFhIQoLC/HKK6/gn//8J44ePTrgfa1WK9LS0qSPhITQm2x7wxTvgZ+fHqiC3eXu85qa1m4UV7XCYAiueSzp0iwneTIPPY3h2pwVp5cG8eYup1T6TdTZOALAWzYDgFMNygdOehlFcDbx6JNtCgZOW6TAKXh+X1DoCrrAafv27bDZbJg1a5Z02+zZs2Gz2bBt27YB77thwwakpKRg7NixuO+++1BbO3CTtN1uR2tra6+PQDczLwGpsVa0drv6bRJfd7gGgHf6byCfbn4u+TNOvhlOKp5Rdzaxr0rr6eFimS4uMkyXJ8bn+Br3lQ6c7C43TtS1A9BX4FQ4qufIJaeMM8xEnQ6XNPMtmN5oUejS32+xYaqurkZKSsp5t6ekpKC6urrf+y1cuBDvvPMOvvzySzz33HPYvXs3rrzyStjt/f/RWbFihdRHZbPZkJWVJcvXoCWT0YAlF40AAKzadrLPa/5RVAkAuH5yulrLUoXY41TT2i1Lo6xWowhEesk4SaMIdBpkixmnkw0dij5PSU07XB4Btogw6XtNDyakxSIuMgwdDjcOVMo/z2n7iQY43QJGxEdIQSpRIAuYwOmpp546r3n73I89e/YAQJ/NyoIgDNjEfMstt+C6665Dfn4+Fi1ahM8++wzHjh3DJ5980u99nnjiCbS0tEgfFRUVw/9CdWDp7ByYjAbsKG2UejJEJ+s7sPtkEwyGnrJesEiOscJoAJzunp6c4ajQ6LgVkTQEU+OMU51Op4aLxMCpts2OTodLsec5fNa5jnraUGE0GlDoO+hbiT6n9Ue9mesrxqXo6usmGqqACZyWLVuG4uLiAT/y8/ORlpaGmpqa8+5fV1eH1NRUv58vPT0dOTk5KCkp6fcaq9WK2NjYXh/BICMuAtfke5vEn193rNfn/rSpFAAwb2yyVNoKFmEmI5J9wcZwZzl12F1S8KVV4JQc03OMjJb0OjVcZIsMQ1yk98BZJct1ehl82ZeePif/z6r0hyAIWH/EuwP3ivHBs5GEQpv2g0T8lJSUhKSkC9fHCwsL0dLSgl27dmHmzJkAgJ07d6KlpQVz5szx+/kaGhpQUVGB9PTgKkf56/vfGIu1B6vxRXEN/nWoGgsmpaGkpg1/3+vNqj10xWiNV6iMNFsEalrtONPShYIRtiE/Tlm9t+wTHxkGW4Q2p8CLpTqtd9Xp9YDfs+UkRqG5sxmnGjoVC2z0NorgbIW+pu09J5vQ7XTLNtn7eG07Kpu7YDEbUTiS/U0UHAIm4+SvCRMm4JprrsF9992HHTt2YMeOHbjvvvtw/fXXY9y4cdJ148ePx+rVqwEA7e3teOyxx7B9+3acPHkSGzZswKJFi5CUlIQbb7xRqy9FU6NTonH3JbkAgOV//QorN57AvW/tgdMt4MrxKbg4Nzh3HGbGyTMMUeyXyUuKGvaahqrnvLpuCIJ208N7epz0WaoDgFypQVyZPidBEHS5o040KjkKKTFW2F0eWY9fEct0hSMTEWHhMSsUHIIucAKAd955BwUFBViwYAEWLFiAyZMn4+233+51zdGjR9HS4m2ENJlMOHDgABYvXoyxY8fijjvuwNixY7F9+3bExOjv3aFafnTNeBSOTES73YVnPzuCUw2dGBEfgRVLCrRemmLEHXCnhxs4+TJOuRoGTmLZ0ekW0Oznwc1K0PMMJ1GO1CCuTKmuqqUbLV1OmI0GjEmNVuQ5hsNgMGCebybbl34cueQvqUwXRPPeiAKmVDcYCQkJ+POf/zzgNWe/A4+IiMDnn3+u9LICTpjJiFV3X4xXN5dh24l6jEqOxrIrRiMlNrh6m84mDUNsGt4f0LJ67/3zErULnKxmE+Iiw9Dc6URtmx3xGp0TFwilOqUzTuImi9Ep0bCa9Zl5uXJ8Cv665zTWH63FzxdNHPbjtXQ5pTPw5o07f6czUaAKysCJ5GM1m/C9K0bje0Ha03Quccr3sDNODdpnnABvuc4bOHVjnEaHykqlOp2dU3e2HIWHYOq5MVx06ZhkhJkMKKvvQGldO0YmDy8z9u/iGrg8AsamRmv+c0Akp6As1RENVZYv43S6sXNYfUFiqU7LHicASBF31mk0kkAQhLN21em/x+lMSxe6nX1PzB+Os0cR6FW01SwdvyJHue6zg965edfkh+YGGwpeDJyIzjLC1+PUZnehtWtoM31au51SX4/W77R7GsS1CZxau11w+KZR67lUlxBlQYzVDEEATg+zTNuXQ75S3SSdH3B7ha+kJjZ1D1WH3YVNx7z9TQt9o02IggUDJ6KzhIeZpD/wQ+1zErNNSdFWRFu1rYYnazw9XOxvirGaZdvirgSDwYCcJG/QLPanyaW12ylNkZ+o88DpqgneWXe7yhrR1j30DQXrj9bC7vIgNzES4zUqERMphYET0TmyfIfyDjXzUCaV6bQ/XiJF4yGYgdDfJBIniJfVt8v6uMW+bFNmXATiIvVbrgS8peVRyVFwugV8UXz+IGF/fXagp0zHaeEUbBg4EZ1DLNeJh/QO1klfxiJXwx11IrFUV6dRj1Mg9DeJRqd4m6GP18obOIllOj03hp/t+skZAICPv6oa0v1bupxY5wu6gu08SyKAgRPRecSRBEPNOOllRx3QewimFgJhFIFIqcBJbAzXe3+TaNEUb7Cz6VgdmjsHf2bjJ19XweHyYFxqTMB8zUSDwcCJ6BziEMyKIY4kKPWV6kbqIXCK1bhUF6CBk5yT1sWMk977m0SjU2IwIT0WLo+Atb6dcYPxwb7TAICbpmeyTEdBiYET0TmkIZiNg884CYKA4zXeozX0MCFazDh1Otxotw9tl+BwBFLglJcUBaPBuxNQrvP9HC4Pjtd6vx8CKfsiZp3+vvf0oO5XWteOvaeaYDQA35yaqcTSiDTHwInoHGJv0qnGTng8g8s8nGnpRofDDbPRIA1V1FKU1Ywo3xlhta3ql+vqpOZw/fc4Wc0mZPsGoMpVrjtW0wanW4AtIgyZcRGyPKYabrpoBExGA/acasKR6la/7/fW9lMAvJPCg/mEAQptDJyIzpEZHwGLyQiHy4MzLYMr15X4sk15SVEIM+njx0vLcl1du/7PqTvbKN+07ON18gROZw++DKSyVWpsOBZM9I4meGdHuV/3ae124m97KgAAd/kOCCcKRvr4zU6kIyajATm+SdKldYM7u0zMVOihTCdK1nAIpjSOIEACJ7kbxA8HyODLvvzH7BwA3p6lhvYLf++8t6scHQ43xqRE49LRSUovj0gzDJyI+iAelSLOZPJXSY0vcErRz9A/aWedyqU673Er3j+4yQESOI1SKHAKlMbws80ZlYj8zFh0Otx4eXPpgNd22F3400bvNffNHRlQ2TWiwWLgRNSHvOQhBk61+mkMF4lDMOVqePZXu90Fu8t33EoA9DgB8mac3B4BB8+0AAAmZdiG/XhqMxgMePSqsQCAt7adGjDwfmVzKRo6HMhNjMSNF7EpnIIbAyeiPoijBEoHETgJgqDPjFOsNqU6cfhlpMWESIu2R8/4SwycatvsaOka+pEjAHCirh2dDjciLSbpcQPNVRNSMDUrDl1ON576+FCf1xyvbcdL608AAH6wYJxuevuIlMLvcKI+5CV5/9AN5viNmlY72uwumIwG5OrguBWRVkMwA2kUgSg2PAwZNm+G7mh127Aeq6iiGQBQkGmDyRiYpSuDwYBf3pgPk9GATw9U492dvRvFuxxu/Nd7++Fwe3D52GROCqeQwMCJqA9ij9Pppi7YXW6/7nPUt6MuJzESVrN+DrSVzqtT+diVnsbwwCjTicR+pMO+MttQfeULnKZmxQ1zRdqalGHDo1eNAQD89z8O4u0dp+D2CKhq6cKdb+zCoTOtSIiy4FdLCtjbRCEhMPLnRCpLirYgJtyMtm4Xyuo7MD7tws29h3x/aCfq7Ewy7Up1gZdxArxnyn1RXIviquFlnL463QwAmDwibviL0tiyK0ejvLETf9t7Gv+95iBWfFqMbqcbHgGIsZrx8tLpATWnimg4mHEi6oPBYMD4NG+f0hE//4AeqvTuoMrP1FcjsFiqa+lyotvpX/ZMDtIMp5jACpzEwFecwTQU3U639H0zJUtf3w9DYTAY8JtvTcbPrpuA+MgwdDq8QdPM3AT8/cE5mJGboPUSiVTDjBNRP8anxWL3ySYUV7fim7jwTiEx45Svsx1UtogwWMzegZ51bXZkJajTfxXIGSfAW3p1uT0wD6HZ+XBVK1weAUnRlqDJxBgMBtw7dySWFuagvKETsRFhSOV0cApBzDgR9WN8uv8Zp9ZuJ042eM+209uwQ4PBIM1RUrNcJ/Y4JQdYj1N2QiSiLCY4XJ5Bj6MQif1Nk0fEBV3fj9VswpjUGAZNFLIYOBH1Q8w8+HNWlzjoMDMuAvFR+gsUxD6nOhV31gVqxsloNEiv/VDLdfvKmwEAU4Kgv4mIemPgRNSPcanejFNNqx2NHY4Brz2k86M1UjQ4dqU+QHucgJ6gWQyIB0MQBOwuawQAXJwbL+u6iEh7DJyI+hFlNUtn1h25QOZhf3kTAP01hou0GEkQqBknwDt7CejZGTcYp5u6UN3aDbPRgGnZDJyIgg0DJ6IBTPCNITg0QOZBEATsOekNnGboNMOg9hDMTocLnQ7vDr5Am+MEANOy4wAAX59ugcvtGdR9d/myTfmZNkRY9DPPi4jkwcCJaABTfX9A955q6veayuaeDINehx2qPcupvs1bprOajYi2Bt7m3VHJ0YixmtHpcONYzeDOrdt90hs4zczjFn2iYMTAiWgA03O8GaS95U0QBKHPa8SgalJGrG7PZFO7VFd3VpkuEHeVGY0GKWjeX9F/0NyXXSfF/iYGTkTBiIET0QAKMm0IMxlQ12ZHRWNXn9eIpRk9DwFMVrk5XOpvCsDGcNE0X/Zwv2+HnD/q2uworfOOMJiRo8+yLRENDwMnogGEh5mkhu+95Y3nfV4QBGw8VgcAKByZqOraBkMs1TV02AfdszMUYuAUaDOcziZmnPYNUKY91+YS7/dCfmasLsdSENHwMXAiugCx5LKlpOG8z5XWd+B0UxcsJiPmjNZv4JQYZYXRAAgC0HCB0QpyEHucAnFHnWh6dgKMBu9rXN3iX1O9GERfNiZZyaURkYYYOBFdwLxx3j+CG47WwuPp3ef0ZXEtAG8jsF77mwDAZDRIQYwafU6BPIpAZIsMQ4FvgOXW4/UXvN7jEbC5xHvd5WMZOBEFKwZORBdwcW4CYqxmNHQ4UHTOXJ9/fFUJAFgwKVWDlQ1Oz8465UcS9AROgV2uutSXRfQncDp4pgWNHQ5EW824iP1NREGLgRPRBYSZjJg3PgUAsHpfpXR7SU0bDla2wmw04PrJGVotz2/SzjoVGsSDoTkcAC4ZlQQA2HK8vt9dlaJ1h2u89xmdiLAhHAxMRIGBP91Efrj14iwAwOr9lWi3uwAAr28tAwBcMT4FCQHQCCwNwVSlVBf4PU4AcFFOPKxmI2rb7DhS3f9hz4Ig4JMDVQCAhfnpai2PiDTAwInID4UjEzEyKQrtdhf+78sSHKxswd/2nAYA/OdlIzVenX/EwKmuXflSXW1rd6/nDFThYSbM9TV6f+YLjPpytKYNpXUdsJiNuGpCilrLIyINMHAi8oPRaMCPF44HAPxpYymWvLQNLo+AayalBcygw+RYdYZgdthd6PAdt5Lie85Adt3kNADAJweq+i3XiSXcy8cmIyY8TLW1EZH6GDgR+WnBpDR874pRAACH24PJI2z41ZICjVflvxSVhmCKjx9pMQXkcSvn+saEVFjMRpyo60Bx1fnlum6nG3/dUwEAuGVGltrLIyKVBf5vNSIV/fDq8bhhSiYaOuy4ODchoJqApVKd0oFTkJTpRDHhYZg/IRWfHKjC2ztOYcU5wfI/v65CU6cTmXERuGI8y3REwS5wfusT6cS4tBjMGZUUUEET0FM2q2uzX3CH2HCI59SJu/iCwe2FOQCA1ftP9xrn4HB58Pt/lwAA/mN2DkzGwDuXj4gGJ7B+8xPRkCX7drg53B40dzoVex6xhyo5NjgyToB3wOnUrDh0Oz349WdHpdtXbjyB8sZOJEVbccecHA1XSERqYamOKERYzEbER4ahqdOJ2ja7YmepiT1OwVKqAwCDwYAnF03EjS9twwf7TiM7IRJxkWH4f75s00+uHa/ryfFEJB9mnIhCSM8QTOVGEoiPHUylOgCYlh2PR78xBgDwuy+O4cmPDsHtEXDLjCzcOC1T49URkVr4FokohKTEWnG0pk3RkQR1QZhxEv3XVWOQGhuON7edhMsj4NszRuDeS0fCYGBvE1GoYOBEFEKSVRhJIPU4BWHgZDAY8J2Z2fjOzGytl0JEGgnKUt0vf/lLzJkzB5GRkYiLi/PrPoIg4KmnnkJGRgYiIiIwb948HDp0SNmFEqlM1VJdEDWHExGJgjJwcjgcuPnmm/Hggw/6fZ/f/OY3eP755/Hiiy9i9+7dSEtLw/z589HW1v/5VESBRukhmA6XB02+HXvB1uNERAQEaeD09NNP4/vf/z4KCvyb6iwIAl544QX89Kc/xZIlS5Cfn48333wTnZ2dePfddxVeLZF6xCxQnUI9TuIMpzCTAfGRPHqEiIJPUAZOg1VWVobq6mosWLBAus1qteLyyy/Htm3b+r2f3W5Ha2trrw8iPVO6VCdODU+OtrJhmoiCEgMnANXV1QCA1NTUXrenpqZKn+vLihUrYLPZpI+sLJ5TRfqmdKlOfNzkIDjcl4ioLwETOD311FMwGAwDfuzZs2dYz3HuO2RBEAZ81/zEE0+gpaVF+qioqBjW8xMpTSzVdTrcaLe7ZH/8YBx+SUR0toAZR7Bs2TLceuutA16Tm5s7pMdOS0sD4M08paenS7fX1tael4U6m9VqhdXKPxAUOCItZkRbzWi3u1Db2o3o5GhZHz+YZzgREQEBFDglJSUhKSlJkcfOy8tDWloa1q1bh2nTpgHw7szbuHEjfv3rXyvynERaSYmxegOnNjtGyh44BefUcCIiUcCU6gajvLwcRUVFKC8vh9vtRlFREYqKitDe3i5dM378eKxevRqAt0T36KOP4le/+hVWr16NgwcP4s4770RkZCS++93vavVlEClCLNfVtMrfIC4Ov+QMJyIKVgGTcRqMn//853jzzTel/xazSOvXr8e8efMAAEePHkVLS4t0zY9+9CN0dXXhoYceQlNTE2bNmoV//etfiImJUXXtREpLt0UAAKpbFAicWKojoiAXlIHTqlWrsGrVqgGvEQSh138bDAY89dRTeOqpp5RbGJEOpNm8ZbQqRQInluqIKLgFZamOiPqXIQVOXbI+rtsjoL7dAYClOiIKXgyciEJMmkKluoZ2O9weASajAUnRDJyIKDgxcCIKMem+jNMZmQMnsfSXEmOFycip4UQUnBg4EYUYscepvt0Oh8sj2+OKgVMqp4YTURBj4EQUYhKjLLCYjBAEec+sq/b1TIkZLSKiYMTAiSjEGAwGKeskZ59TtW+GUxoDJyIKYgyciEJQmgJ9TmLGKY2lOiIKYgyciEJQupRxkm8kgdjjxIwTEQUzBk5EIUicHi7nEEzxCBfxsYmIghEDJ6IQJGacqprlCZwEQejJOLFUR0RBjIETUQiSjl2R6aDf5k4n7L7RBpwaTkTBjIETUQiSu8ep2heAJUZZEB5mkuUxiYj0iIETUQgSM061bXY43cMfglnN4ZdEFCIYOBGFoKQoK8JMBt8QTPuwH0/sb+LwSyIKdgyciEKQ0WiQskNVzcMv14mlOo4iIKJgx8CJKERlxnnHBlTKEThx+CURhQgGTkQhakR8JADgdNPwAyfpgF9mnIgoyDFwIgpRI+K9GafTTZ3DfqxKX/AlPiYRUbBi4EQUonoCp+FlnARBkMp9I+Iih70uIiI9Y+BEFKLkKtXVtztgd3lgMLA5nIiCHwMnohCVleBrDm/qgscjDPlxxFJfWmw4LGb+SiGi4MbfckQhKi02HCajAQ63Z1iznMQynbhLj4gomDFwIgpRZpNRGlg5nAZxNoYTUShh4EQUwuRoEBfvm8nAiYhCAAMnohDW0yA+jIyTVKrjjjoiCn4MnIhCmBwZJ5bqiCiUMHAiCmHDHUkgCIKUrWKpjohCAQMnohA23OnhLV1OdDjcALirjohCAwMnohCWleDNOFU2d8Hl9gz6/mKmKinaivAwk6xrIyLSIwZORCEs3Te00ukWcKa5e9D3P9XgzVRlJzDbREShgYETUQgzGg3I8WWdSuvbB33/kw0dAIDcpChZ10VEpFcMnIhCXJ4v6DlZ3zHo+4r3yU1k4EREoYGBE1GIkwKnhsE3iIulupxEznAiotDAwIkoxIllttKhZJx8pbo8luqIKEQwcCIKcUMt1XXYXdLhwDkJDJyIKDQwcCIKcWLgdLqpEw6X/yMJxDJdfGQYbJFhiqyNiEhvGDgRhbiUGCsiLSZ4BKBiEIMwT3FHHRGFIAZORCHOYDBIu+LK6vwv15U1cEcdEYUeBk5EJJXrygbR53S8tr3XfYmIQgEDJyLC6JRoAMCxmja/71NS4w2cxqZGK7ImIiI9YuBERBiXFgMAOOpn4OTxCFLGaUxqjGLrIiLSGwZORCQFTsdq2uDxCBe8vrK5C11ONywmo3RkCxFRKGDgRETITYyCxWxEt9OD8sYL76wrqfVmpkYmR8Fs4q8RIgod/I1HRDAZDRjj63Pyp1x3zNffJPZGERGFiqAMnH75y19izpw5iIyMRFxcnF/3ufPOO2EwGHp9zJ49W9mFEumI1OdUfeHA6UhVKwBgLPubiCjEBGXg5HA4cPPNN+PBBx8c1P2uueYaVFVVSR+ffvqpQisk0p9xqf4HTgcqWwAABZk2RddERKQ3Zq0XoISnn34aALBq1apB3c9qtSItLU2BFRHp34T0WADAoTMtA17XbndJBwLnM3AiohATlBmnodqwYQNSUlIwduxY3HfffaitrdV6SUSqmTzCGwSdbOhEc6ej3+sOn2mFIABpseFIjrGqtTwiIl1g4OSzcOFCvPPOO/jyyy/x3HPPYffu3bjyyitht9v7vY/dbkdra2uvD6JAFRdpkaaAF1U093udWKZjtomIQlHABE5PPfXUec3b537s2bNnyI9/yy234LrrrkN+fj4WLVqEzz77DMeOHcMnn3zS731WrFgBm80mfWRlZQ35+Yn0YIov6/RVRf/lugOnmwGwv4mIQlPA9DgtW7YMt95664DX5ObmyvZ86enpyMnJQUlJSb/XPPHEE1i+fLn0362trQyeKKBNyYrDmqIzKKpo6vPzgiBgV1kjAOCinDgVV0ZEpA8BEzglJSUhKSlJtedraGhARUUF0tPT+73GarXCamWPBwWPi7LjAQB7TjXB5facN9zydFMXzrR0w2w0YHpOvBZLJCLSVMCU6gajvLwcRUVFKC8vh9vtRlFREYqKitDe3i5dM378eKxevRoA0N7ejsceewzbt2/HyZMnsWHDBixatAhJSUm48cYbtfoyiFSXn2lDbLgZbd0ufF15frlupy/bVDDChkhLwLzvIiKSTVD+5vv5z3+ON998U/rvadOmAQDWr1+PefPmAQCOHj2KlhbvHwaTyYQDBw7grbfeQnNzM9LT03HFFVfg/fffR0wMB/xR6DAZDbhkdBI+O1iNzcfqpQyUaOOxOgDA7JGJWiyPiEhzQRk4rVq16oIznASh5yDTiIgIfP755wqviigwXDrGFziV1OG/vjFGut3h8mDDEe+Ijm9MSNVqeUREmgrKUh0RDd3lY5MBAHvLm1DV0iXdvqO0AW12F5KirZiWFafR6oiItMXAiYh6GREfiZl5CRAEYPX+Sun2v+6pAABck58Ko9Gg1fKIiDTFwImIzvOti0YAAP68/RTsLjfKGzrx+aFqAMCtF2druTQiIk0FZY8TEQ3PDVMz8Py6YzjT0o1f/PMwSus64HQLmDsmiRPDiSikMeNEROcJDzPhv6+fCAD4845ybDvRgIgwE3523USNV0ZEpC1mnIioT9dNTkdTZz7+sP44oqxmPH3DJIxL43gOIgptBuHsffk0LK2trbDZbGhpaUFsbKzWyyEiIiI/DObvN0t1RERERH5i4ERERETkJwZORERERH5i4ERERETkJwZORERERH5i4ERERETkJwZORERERH5i4ERERETkJwZORERERH5i4ERERETkJwZORERERH5i4ERERETkJwZORERERH5i4ERERETkJ7PWCwgmgiAAAFpbWzVeCREREflL/Lst/h0fCAMnGbW1tQEAsrKyNF4JERERDVZbWxtsNtuA1xgEf8Ir8ovH48GZM2cQExMDg8Eg2+O2trYiKysLFRUViI2Nle1xSRl8vQIHX6vAwtcrcATaayUIAtra2pCRkQGjceAuJmacZGQ0GjFixAjFHj82NjYgvgHJi69X4OBrFVj4egWOQHqtLpRpErE5nIiIiMhPDJyIiIiI/MTAKQBYrVY8+eSTsFqtWi+F/MDXK3DwtQosfL0CRzC/VmwOJyIiIvITM05EREREfmLgREREROQnBk5EREREfmLgFABeeukl5OXlITw8HNOnT8fmzZu1XlJIeeqpp2AwGHp9pKWlSZ8XBAFPPfUUMjIyEBERgXnz5uHQoUO9HsNut+Phhx9GUlISoqKicMMNN+D06dNqfylBadOmTVi0aBEyMjJgMBiwZs2aXp+X6/VpamrC0qVLYbPZYLPZsHTpUjQ3Nyv81QWXC71Wd95553k/a7Nnz+51DV8rdaxYsQIXX3wxYmJikJKSgm9+85s4evRor2tC9WeLgZPOvf/++3j00Ufx05/+FPv378fcuXOxcOFClJeXa720kDJp0iRUVVVJHwcOHJA+95vf/AbPP/88XnzxRezevRtpaWmYP3++dAQPADz66KNYvXo13nvvPWzZsgXt7e24/vrr4Xa7tfhygkpHRwemTJmCF198sc/Py/X6fPe730VRURHWrl2LtWvXoqioCEuXLlX86wsmF3qtAOCaa67p9bP26aef9vo8Xyt1bNy4Ed/73vewY8cOrFu3Di6XCwsWLEBHR4d0Tcj+bAmkazNnzhQeeOCBXreNHz9e+PGPf6zRikLPk08+KUyZMqXPz3k8HiEtLU149tlnpdu6u7sFm80mrFy5UhAEQWhubhbCwsKE9957T7qmsrJSMBqNwtq1axVde6gBIKxevVr6b7len8OHDwsAhB07dkjXbN++XQAgHDlyROGvKjid+1oJgiDccccdwuLFi/u9D18r7dTW1goAhI0bNwqCENo/W8w46ZjD4cDevXuxYMGCXrcvWLAA27Zt02hVoamkpAQZGRnIy8vDrbfeitLSUgBAWVkZqqure71GVqsVl19+ufQa7d27F06ns9c1GRkZyM/P5+uoMLlen+3bt8Nms2HWrFnSNbNnz4bNZuNrKLMNGzYgJSUFY8eOxX333Yfa2lrpc3yttNPS0gIASEhIABDaP1sMnHSsvr4ebrcbqampvW5PTU1FdXW1RqsKPbNmzcJbb72Fzz//HK+88gqqq6sxZ84cNDQ0SK/DQK9RdXU1LBYL4uPj+72GlCHX61NdXY2UlJTzHj8lJYWvoYwWLlyId955B19++SWee+457N69G1deeSXsdjsAvlZaEQQBy5cvx6WXXor8/HwAof2zxUN+A4DBYOj134IgnHcbKWfhwoXS/y8oKEBhYSFGjRqFN998U2pcHcprxNdRPXK8Pn1dz9dQXrfccov0//Pz8zFjxgzk5OTgk08+wZIlS/q9H18rZS1btgxff/01tmzZct7nQvFnixknHUtKSoLJZDov6q6trT0vyif1REVFoaCgACUlJdLuuoFeo7S0NDgcDjQ1NfV7DSlDrtcnLS0NNTU15z1+XV0dX0MFpaenIycnByUlJQD4Wmnh4YcfxkcffYT169djxIgR0u2h/LPFwEnHLBYLpk+fjnXr1vW6fd26dZgzZ45GqyK73Y7i4mKkp6cjLy8PaWlpvV4jh8OBjRs3Sq/R9OnTERYW1uuaqqoqHDx4kK+jwuR6fQoLC9HS0oJdu3ZJ1+zcuRMtLS18DRXU0NCAiooKpKenA+BrpSZBELBs2TJ8+OGH+PLLL5GXl9fr8yH9s6VJSzr57b333hPCwsKE1157TTh8+LDw6KOPClFRUcLJkye1XlrI+MEPfiBs2LBBKC0tFXbs2CFcf/31QkxMjPQaPPvss4LNZhM+/PBD4cCBA8J3vvMdIT09XWhtbZUe44EHHhBGjBghfPHFF8K+ffuEK6+8UpgyZYrgcrm0+rKCRltbm7B//35h//79AgDh+eefF/bv3y+cOnVKEAT5Xp9rrrlGmDx5srB9+3Zh+/btQkFBgXD99der/vUGsoFeq7a2NuEHP/iBsG3bNqGsrExYv369UFhYKGRmZvK10sCDDz4o2Gw2YcOGDUJVVZX00dnZKV0Tqj9bDJwCwB/+8AchJydHsFgswkUXXSRtByV13HLLLUJ6eroQFhYmZGRkCEuWLBEOHTokfd7j8QhPPvmkkJaWJlitVuGyyy4TDhw40Osxurq6hGXLlgkJCQlCRESEcP311wvl5eVqfylBaf369QKA8z7uuOMOQRDke30aGhqE2267TYiJiRFiYmKE2267TWhqalLpqwwOA71WnZ2dwoIFC4Tk5GQhLCxMyM7OFu64447zXge+Vuro63UCILzxxhvSNaH6s2UQBEFQO8tFREREFIjY40RERETkJwZORERERH5i4ERERETkJwZORERERH5i4ERERETkJwZORERERH5i4ERERETkJwZORERERH5i4ERERETkJwZORER+2rJlC8aNG4drr71W66UQkUYYOBER+emRRx7BI488gpMnT2q9FCLSCAMnIiI/HD16FDU1NUhLS0NBQYHWyyEijTBwIiLyw9q1azF//nysXbuWpTqiEMbAiYjID5s3b8ZFF12ELVu24Oabb9Z6OUSkEbPWCyAiCgQHDx5EQkICbr31VkRGRmq9HCLSiEEQBEHrRRAR6V10dDTGjBmDTZs2ISYmRuvlEJFGWKojIvKDIAh4/PHHGTQRhTgGTkREF/DRRx+hs7MTubm5+Oqrr/Dll19qvSQi0gh7nIiIBmC327Fq1Sq89tpruPvuu5GdnY1Vq1ZpvSwi0gh7nIiIiIj8xFIdERERkZ8YOBERERH5iYETERERkZ8YOBERERH5iYETERERkZ8YOBERERH5iYETERERkZ8YOBERERH5iYETERERkZ8YOBERERH5iYETERERkZ8YOBERERH56f8D2qn7AQ7BBJEAAAAASUVORK5CYII=", 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", 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" ] diff --git a/example_notebooks/ABCMB_with_LINX.ipynb b/example_notebooks/ABCMB_with_LINX.ipynb index 3473ca8..fbebb2c 100644 --- a/example_notebooks/ABCMB_with_LINX.ipynb +++ b/example_notebooks/ABCMB_with_LINX.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "80e9a0dc-c54f-4a0d-888c-9b0191b4b3f2", "metadata": {}, "outputs": [], @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "id": "4a4dc262-8332-4b0b-bbda-e45786b0dc97", "metadata": {}, "outputs": [], @@ -70,7 +70,7 @@ " \"linx_reaction_net\" : 'key_PArthENoPE' # Can select between 'key_PArthENoPE' and \"key_PRIMAT_2023\" (default) networks\n", "}\n", "\n", - "model_LINX = Model(specs_LINX)" + "model_LINX = Model(**specs_LINX)" ] }, { @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "8f054803-7bc7-45a9-99a4-18919d47166a", "metadata": {}, "outputs": [], @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "73a01be0-d3bd-4bc4-a3b6-cc21479d727c", "metadata": {}, "outputs": [ @@ -106,9 +106,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "You have specified a value for N_nu_massless and/or Neff, but LINX instead expects a \n", - "parameter 'Delta_Neff_init' which will be used to compute Neff. Refer to LINX \n", - "docs or https://arxiv.org/abs/2408.14538 for more information.\n" + "You have specified a value for N_nu_massless and/or Neff and/or T_nu_massless, \n", + "but LINX instead expects a parameter 'Delta_Neff_init' which will be used to \n", + "compute Neff. Refer to LINX docs or https://arxiv.org/abs/2408.14538 for more info.\n", + "\n" ] }, { @@ -117,14 +118,14 @@ "output_type": "error", "traceback": [ "An exception has occurred, use %tb to see the full traceback.\n", - "\u001b[0;31mSystemExit\u001b[0m\n" + "\u001b[31mSystemExit\u001b[39m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/ext3/miniconda3/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3513: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", + "/opt/anaconda3/envs/linx312/lib/python3.12/site-packages/IPython/core/interactiveshell.py:3680: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", " warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n" ] } @@ -157,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "id": "b7021cd7-2e27-4697-9049-0311e2b9c217", "metadata": {}, "outputs": [], @@ -180,19 +181,24 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "a9289396-b860-4a48-84ea-51c455cb7e28", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Array(3.12600995, dtype=float64)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "`\\ /´ |||| |||| ||||| |||| |||| ||||\n", + " /\\_______/\\ |||| |||| ||||||| |||| |||| ||||\n", + " ) __` ´__ ( |||| |||| |||| |||| |||| |||||||\n", + "/ `-|_|-´ \\ |||| |||| |||| |||| ||| ||||||| \n", + "/ (_x_) \\ |||||||||| |||| |||| ||||||| |||| ||||\n", + " ) `-´ ( |||||||||| |||| |||| |||||| |||| ||||\n", + " \n", + "Compiling thermodynamics model...\n", + "Compiling abundance model...\n" + ] } ], "source": [ From 87617bb00270e2d9d11ac582352c5a74020a49ef Mon Sep 17 00:00:00 2001 From: Cara Giovanetti Date: Wed, 11 Feb 2026 19:52:37 -0800 Subject: [PATCH 3/3] small notebook update --- example_notebooks/ABCMB_with_LINX.ipynb | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/example_notebooks/ABCMB_with_LINX.ipynb b/example_notebooks/ABCMB_with_LINX.ipynb index fbebb2c..6082243 100644 --- a/example_notebooks/ABCMB_with_LINX.ipynb +++ b/example_notebooks/ABCMB_with_LINX.ipynb @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "a9289396-b860-4a48-84ea-51c455cb7e28", "metadata": {}, "outputs": [ @@ -199,6 +199,16 @@ "Compiling thermodynamics model...\n", "Compiling abundance model...\n" ] + }, + { + "data": { + "text/plain": [ + "Array(3.12600995, dtype=float64)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [