diff --git a/README.md b/README.md index c2998a4..f776461 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,7 @@ conda activate fdiff ```shell pip install -e . ``` + 4. If you intend to train models, make sure that wandb is correctly configured on your machine by following [this guide](https://docs.wandb.ai/quickstart). 5. Some of the datasets are automatically downloaded by our scripts via kaggle API. Make sure to create a kaggle token as explained [here](https://towardsdatascience.com/downloading-datasets-from-kaggle-for-your-ml-project-b9120d405ea4). diff --git a/cmd/conf/datamodule/ecg.yaml b/cmd/conf/datamodule/ecg.yaml index fced0cc..305dbc7 100644 --- a/cmd/conf/datamodule/ecg.yaml +++ b/cmd/conf/datamodule/ecg.yaml @@ -2,4 +2,5 @@ _target_: fdiff.dataloaders.datamodules.ECGDatamodule data_dir: ${hydra:runtime.cwd}/data random_seed: ${random_seed} fourier_transform: ${fourier_transform} +standardize: ${standardize} batch_size: 64 diff --git a/cmd/conf/datamodule/synthetic.yaml b/cmd/conf/datamodule/synthetic.yaml new file mode 100644 index 0000000..05cbb97 --- /dev/null +++ b/cmd/conf/datamodule/synthetic.yaml @@ -0,0 +1,10 @@ +_target_: fdiff.dataloaders.datamodules.SyntheticDatamodule +data_dir: ${hydra:runtime.cwd}/data +random_seed: ${random_seed} +fourier_transform: ${fourier_transform} +standardize: ${standardize} +batch_size: 64 +max_len: 100 +num_samples: 1000 + + diff --git a/cmd/conf/score_model/default.yaml b/cmd/conf/score_model/default.yaml index 485f956..53c41c2 100644 --- a/cmd/conf/score_model/default.yaml +++ b/cmd/conf/score_model/default.yaml @@ -4,6 +4,8 @@ d_model: 72 num_layers: 10 n_head: 12 lr_max: 1.0e-3 +fourier_noise_scaling: False +likelihood_weighting: False defaults: - - noise_scheduler: ddpm + - noise_scheduler: vpsde diff --git a/cmd/conf/score_model/noise_scheduler/customddpm.yaml b/cmd/conf/score_model/noise_scheduler/customddpm.yaml new file mode 100644 index 0000000..92c0081 --- /dev/null +++ b/cmd/conf/score_model/noise_scheduler/customddpm.yaml @@ -0,0 +1,2 @@ +_target_: fdiff.schedulers.ddpm.CustomDDPMScheduler +num_train_timesteps: 1000 diff --git a/cmd/conf/score_model/noise_scheduler/vesde.yaml b/cmd/conf/score_model/noise_scheduler/vesde.yaml new file mode 100644 index 0000000..6112750 --- /dev/null +++ b/cmd/conf/score_model/noise_scheduler/vesde.yaml @@ -0,0 +1,5 @@ +_target_: fdiff.schedulers.sde.VEScheduler +eps: 1e-5 +sigma_min: 0.01 +sigma_max: 2 +fourier_noise_scaling: ${score_model.fourier_noise_scaling} diff --git a/cmd/conf/score_model/noise_scheduler/vpsde.yaml b/cmd/conf/score_model/noise_scheduler/vpsde.yaml new file mode 100644 index 0000000..00c4baf --- /dev/null +++ b/cmd/conf/score_model/noise_scheduler/vpsde.yaml @@ -0,0 +1,5 @@ +_target_: fdiff.schedulers.sde.VPScheduler +eps: 1e-5 +beta_min: 0.1 +beta_max: 20 +fourier_noise_scaling: ${score_model.fourier_noise_scaling} diff --git a/cmd/conf/train.yaml b/cmd/conf/train.yaml index 4c1ac8d..1aa1ccc 100644 --- a/cmd/conf/train.yaml +++ b/cmd/conf/train.yaml @@ -1,5 +1,7 @@ random_seed: 42 fourier_transform: false +standardize: true + defaults: - _self_ - score_model: default diff --git a/cmd/sample.py b/cmd/sample.py index 7c0264f..1217ae1 100644 --- a/cmd/sample.py +++ b/cmd/sample.py @@ -42,7 +42,6 @@ def __init__(self, cfg: DictConfig) -> None: self.fourier_transform: bool = self.datamodule.fourier_transform self.datamodule.prepare_data() self.datamodule.setup() - # Get number of steps and samples self.num_samples: int = cfg.num_samples self.num_diffusion_steps: int = cfg.num_diffusion_steps @@ -67,10 +66,16 @@ def __init__(self, cfg: DictConfig) -> None: def sample(self) -> None: # Sample from score model + X = self.sampler.sample( num_samples=self.num_samples, num_diffusion_steps=self.num_diffusion_steps ) + # Map to the original scale if the input was standardized + if self.datamodule.standardize: + feature_mean, feature_std = self.datamodule.feature_mean_and_std + X = X * feature_std + feature_mean + # If sampling in frequency domain, bring back the sample to time domain if self.fourier_transform: X = idft(X) diff --git a/cmd/train.py b/cmd/train.py index ccf4607..cc05267 100644 --- a/cmd/train.py +++ b/cmd/train.py @@ -51,6 +51,9 @@ def __init__(self, cfg: DictConfig) -> None: self.score_model = self.score_model(**training_params) def train(self) -> None: + assert not ( + self.score_model.scale_noise and not self.datamodule.fourier_transform + ), "You cannot use noise scaling without the Fourier transform." self.trainer.fit(model=self.score_model, datamodule=self.datamodule) diff --git a/notebooks/viz.ipynb b/notebooks/viz.ipynb new file mode 100644 index 0000000..9bb43cb --- /dev/null +++ b/notebooks/viz.ipynb @@ -0,0 +1,556 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "from pathlib import Path\n", + "import torch\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "model_id = \"ub0lv98f\"" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "samples = torch.load(Path.cwd() / f'../lightning_logs/{model_id}/samples.pt')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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3/b58PPHJbgDA1PQUhJkNCo+osYggA/54RW8AwKs/HOHsCBFRgGEYUbHM/QV48MOdsDslTBiSiMeu7a/0kJo1bXQqIoIMOF5UiTV78pQeDhERdSGGEZWyOpz486e7PUHklduG+FWtyPlCTXrcV3uI3us/HeXOGiKiAOK/707ULj8eKERJlR3x4Wa/DyKyaem9EGrS41BBOTIPFCg9HCIi6iL+/w5FbfLJ9hwAwORhPYQIIgAQEWzA1FHuZmyv/3SUXVmJiAKEGO9S5JNCSw3WHz4LAPh9WpLCo/HN9MtTEWTQYU9uGX48wK6sRESBgGFEhT7PyoVLAtJSItFbwfNn2iI61ISp6e7ZkX9+d4g7a4iIAgDDiMpIkuRZorlVsFkR2awr+yLc7K4d+XJXrtLDISKiTsYwojK/nDiHY2crYTZoccPFCUoPp00igg2YeVUfAO6urFaHU+ERERFRZ2IYURG704VnvtwHAJg8LMkvG5x56970VMSGmXC6pBofbM1WejhERNSJGEZU5O2NJ3CooBxRIUY8mXGB0sNplyCjDrNrm7T987uDOFpYrvCIiIioszCMqER2cRVe/fEwAGDe+IGIDDEqPKL2u31EMsb0i0GN3YWHP9qFGjuXa4iI1IhhRAVKq2x46KOdqLG7MKp3NCYP66H0kDqEVqvBy7cNQXSIEQfyLPjfbw8qPSQiIuoEDCOCKyyvwZSlW/Hb6TJEBhvwj8mDodFolB5Wh4kNM+OlW4cAAN75+SSOFlYoPCIiIupoDCMCK6+x4/alW3GooByxYSb8+0+jkBoTovSwOtzVA2JxzYBYAMDHv7KYlYhIbRhGBPbG2mM4XlSJxAgzPp2Zjn5xYUoPqdPceWlPAMCnO0+zdoSISGUYRgSVc64KyzedAAA8P2kQekYHKzyiznXVBbFIjDCjtMqOb/fmKz0cIiLqQAwjglrw34OwOV0Y3Tca1wyMVXo4nU6n1WDKCPfsyIe/cKmGiEhNGEYEtO3kOXyzJw9aDfC3Gy5UVcFqS6aMSIZOq8GvJ8/hUD77jhARqQXDiGDKquyY8+9dANxvzgMTwpUdUBeKjzBjbG0h6y1LNuO1H4+g0upQeFRE/uGzHacx7Z1fsfV4sdJDIfIZw4hAXC4Jj3+yCznnqpEcFYSnfjdQ6SF1uadvuBCDeoSjwurAwszDmPLWFp7sSwGvwFKDeav3YN2hs7j9ra146rPfYKmxKz0sIq8xjAhk6Ybj+OFAIYx6LZbclYaIYHHPnmmrntHB+OrBy/HaHUMRbtZjb64F3+zJU3pYRIr6109HUGN3Ibq28/LH23Lw9Oq9Co+KyHsMI4JYf/gs/vmduwPpcxMvwqAeEQqPSDlarQYThyRi+uW9AQCL1x6Fi7MjFKCyi6vw8a85AIA37hqGZydcCAAotFiVHBaRTxhGBHD8bAUe+nAnXBJw2/Ak3D4iWekh+YVp6b0QatLjYH45fjpYqPRwiBTxyg+H4XBJuKJ/d1zWOxrdw8wAAJfEgE7iYBjxcznnqjDjve0or3EgLSUS/3PToIDZPdOaiGAD7h6VAgB4fe1RSHzxpQCzM7sEq3flAoDnpG5t7csDwwiJhGHETx07W4FHP87CVS+tw/GzlUiIMOPNP6TBpNcpPTS/Mv3yVJgNWuzKKcW6Q2eVHg5Rl6mwOvDYql2QJODmoT0wOMm9dCt/WOHKJYmEYcQPbT5WhEmv/4wvd52B0yVhTL8YvD/9UnQPMyk9NL8TE2rC3Ze5Z0ee/nIvqmzc6kuB4fmv9+FUcRV6dAvCsxMv8lzOmRESkV7pAVBD/92Th0c/3gWb04WRvaLw9wkXBnSxqjdmX9sfa/bk43RJNRZ+fxh/u/FCpYdE1CleyTyMrceLYXO6kJVdCo0GePm2IYgIqttZp9NyZoTEw5kRP5K5vwAPfrgTNqcLv7soHu9NH8kg4oUQkx4v3DwIALD85xP4hU2fSIWOFJTj1R+P4JcT55CVXQoAmHllH1zWO7rB9bS1yzSsoSKRcGbETxzMt2D2x1lwScCtaUlYcMvFnk841LqrL4jFpEsS8eWuM5jy1lYkRQbhhosT8Ph1F8CoZ+Ym8a3Z4z4gcnhKJGaM6Y1Qkx7pfaIbXU/DZRoSEMOIHygsr8GMd7ej0ubEqN7R+MfkwQwibfDMhItQXuPAhsNncbqkGkvXH0e42YAHr+6r9NCI2u2/e93N/aaMSMbvBsU3ez15ZsTl6pJhEXUIhhGFnC6pwo8HCpG5vwBbjxfD4ZKQEh2MxXcNg0HHT/JtERVixPJpI1BpdeDDX7Lx/9YcwGs/HsGEixPRMzpY6eER+WTz0SIcKazA3Zel4NS5KhzML4deq8F1F8a1eDtPGOHMCAmEYaSL5Jyrwnf78nGkoAK7T5fi4Hmnzg6ID8Prdw5FZG07Z2q7EJMeM8ak4qeDhdhyvBh//2ov3pk2gv1ZSBjVNif+9P4OlFsdqLA6PEsvo/pEo1twy68R3E1DImIY6QJnSqsx/tWNKK93wqxWAwxPicK1F8bi2oFx6N09VMERqo9Go8ELNw/CuEUbse7QWSzbdAL3jk7l8hcJ4fv9+Z7Xi5e/P4TY2q6q4wcntHpb9hkhETGMdDJJkvD06r0otzrQNzYUNwxOQP+4MIzqE40ozoJ0qj7dQ/HAVX3w6o9H8MI3B/D5zlw8N+kijOgVpfTQiFr06Y7TAIDIYANKquzIt9RAqwEyWlmiAepv7WUaIXG0qThh8eLFSE1NhdlsRlpaGjZu3Nji9VeuXIkhQ4YgODgYCQkJuPfee1FcHBjbL//zWx5+PFgIo06LJXcNw2PX9ccNFycwiHSRR67ph3njByLMrMf+PAvueGsrtp08p/SwiJqVV1aNTUeLAAAf3n8ZeseEAAAuTY1GdGjrjQ/lyT9mERKJz2Fk1apVmD17NubNm4esrCyMGTMG48aNQ3Z2dpPX37RpE6ZOnYrp06dj3759+OSTT7Bt2zbMmDGj3YP3V9/8loc/vb8dj63ahWe+2gcAePDqvugXF6bwyAKPTqvB/Vf0xronrsK1A+PgcEmYtXInCiw1Sg+NqElfZOVCkoCRvaIwMCEcb00djvGD4/Hk7y7w6vYaFrCSgDSSj51xLr30UgwbNgxLlizxXDZw4EDcdNNNmD9/fqPrv/TSS1iyZAmOHTvmuexf//oXXnzxReTk5Hh1nxaLBRERESgrK0N4eLgvw+1yJZU2jP7fn1Blc3ou6x8Xiv88PIb9LhRWZXPg5jc241BBOYb17IY/XtEHdqcLAxPC0TeWNTuknKXrj+GDX07hyv7dsfFIEU4VV+HFWy7GbW04oTsruwQ3L96M5KggbPzz2E4YLZH3vH3/9qlmxGazYceOHXjqqacaXJ6RkYHNmzc3eZv09HTMmzcPa9aswbhx41BYWIhPP/0UN9xwQ7P3Y7VaYbVaGzwYUbzz8wlU2ZzoFxuKW4cnweZwYeKQHgwifiDYqMfSu9Mw4fVN2Jldipkf7PB87fqL4jBjTG8kRwYjzKxHiInlVNQ1ThVX4qXvD8HulPDBVvcMs9mgxbjBzfcSaQn7jJCIfHrFLSoqgtPpRFxcwyKquLg45OfnN3mb9PR0rFy5ElOmTEFNTQ0cDgcmTpyIf/3rX83ez/z58/Hcc8/5MjS/UF5jx4rNJwEAj13X36vKd+pavWJC8PbU4Xj1xyOwOVxwShJ25ZTiu30F+G5fged6Q5IiMH1Mb4wfFA89+75QJ3rxO3cQSUuJREp0MDYcPos7L01BmNnQ+o2bwD4jJKI2ffw7v1+DJEnN9nDYv38/HnnkEfz973/H9ddfj7y8PDz55JOYOXMmli1b1uRt5s6dizlz5nj+bbFYkJzs+3RlV3t/6ylYahzo0z0Ev7uobZ9qqPNd2jsaH9Y7z+NIQTneWHsU6w+fhaXGAadLwu7TZXjkoyy8HB2MJXel4cJE/14eJDFlZZfgm9/yoNEAL9w0CAMT2v9zpq3NzgwjJBKfwkhMTAx0Ol2jWZDCwsJGsyWy+fPnY/To0XjyyScBABdffDFCQkIwZswYvPDCC0hIaDx7YDKZYDK1XjXuT6ptTizbeAKAu1hVy34WwugXF4ZFtw8F4A7WZyus+PCXbLy/5RROFVfh1jc347U7huKaga1vqyTyliRJmP/fgwCAW4YldUgQAerPjHTItyPqEj6FEaPRiLS0NGRmZuLmm2/2XJ6ZmYlJkyY1eZuqqiro9Q3vRqfTAVDXqZI/HSxEcaUNPboFYeKQRKWHQ22k0WgQG2bG7Gv74970VDywcgc2HyvGjPe2I71PNC5O6oboECPOVdrglCT86Yo+3KZNPnO5JMxbvRe/njgHk16LxzP6d9j35qm9JCKfl2nmzJmDu+++G8OHD8eoUaPw1ltvITs7GzNnzgTgXmLJzc3Fe++9BwCYMGEC7r//fixZssSzTDN79myMHDkSiYnqedPeeOQsAOD6i1hjoBYRwQa8e99IPPPVPnz4SzZ+PlqMn4827I9zuqQab9w5TKERkoicLgl//vQ3fLbzNDQa4B83D0ZCRFCHff+6dvAd9i2JOp3PYWTKlCkoLi7G888/j7y8PAwaNAhr1qxBSkoKACAvL69Bz5Fp06ahvLwcr7/+Oh5//HF069YNY8eOxf/+7/923KNQmCRJ2HDYHUau6B+j8GioIxl0Wvzj5sGYlt4LO06V4LfTpaiwOhFu1uOjX7PxzW95uGtkEdL78nmnxlwuCZuOFiHEpEfvmBBsOV6M1348goP55dBpNVh42xBMuqRHh96nXL/nZBohgfjcZ0QJ/t5n5GhhBa5duB5GvRa7/56BIKNO6SFRF/j7l3vx3pZT6B8Xim8eGcPTlqmR97acxN+/3Nfo8lCTHv/8/cUY1wk77o6frcDYl9cjzKzHnmev7/DvT+QLb9+/+erZAeRZkZG9ohhEAsic6/ojMtiAwwUVeLd2SzeRTJIkz89FRJB7m26YSY9HxvbFpr9c3SlBBKhfM9Ip356oU7CzUweQ60W4RBNYugUb8effDcDcz/dg/n8PIsiow12Xpig9LPIT206W4NjZSgQbddj0l6uh1Whg0Gk7vQEiD8ojETGMtJPV4cTW4+6D18b0667waKirTRmejB2nSvDpjtOY98VeHMizYFBiBIx6La7s392rg81InT761V07N3FIYpsbmLWFxlPAyjBC4mAYaacdJ0tQbXeie5gJA+J5EF6g0Wo1+OfvL0ZyZDBe+eGwp503AIzoFYl//2lUsw0BSb1Kq2z4Zk8eAOCOkT279L7ZZ4RExDDSTutrl2jG9Ivhm06A0mg0ePTafrggPhSrs87A7nRh45EibDtZgl9OnMNl9bq9UmD4fGcubA4XLkwIx8VJEV163+wzQiJiAWs77couBQCM4htOwPvdoAS8eXcalk0bgVuHJwEA3lh7VOFRkRL+vd19Ivkdl/bs8g8pcp8Rbu0lkTCMtNOxsxUAgP5xXKKhOjOv7AOdVoONR4qwO6dU6eFQFzpZVImD+eXQazWYeHHXN3bUcJmGBMQw0g6lVTYUVdgAAH1iQxUeDfmT5KhgTLrE/UbE2ZHAkrnfffrzZb2jERHcdYWrMl29c7G4VEOiYBhpB3lWJCHCjFATy2+ooVlX9YVGA3y/vwBHCyuUHg51ke/3uw8Sve5CZQ5WrH9GJ2dHSBQMI+0gv8H05awINaFvbCiuGeB+Q1r5yymFR0NdoajCiu2nSgAoF0bq16hwey+JgmGkHeQw0qc7wwg17e5R7iZon+44jSqbQ+HRUGf78UABJAkY3CMCid067vA7XzScGWEYITEwjLTDsbOVAFgvQs0b0zcGKdHBKK9x4KtdZ5QeDnWy7/e560UyFJoVAeq29gKAy6XYMIh8wjDSDp5lGs6MUDO0Wg3+UNsi/r0tp1hQqGKVVgc2Hi0CAGRcFK/YOLRcpiEBMYy0UY3diZySKgCsGaGW/T4tCSa9FvvzLNhRW09A6vPryXOwOVxIjgpC/zjlXhM0XKYhATGMtNHxs5WQJCDcrEdMqFHp4ZAfiwwxYsIQ9zbfae9sw3tbTrIhlQqdqF22HZQYoWg35vpbe/ljRqJgGGkjeVtv39hQtoGnVv35+gtwSXI3VFgd+PuX+3D3sl9gdTiVHhZ1oJPF7jCSEh2i6DjqL9NwWZBEwTDSRtzWS76IDTfjswfS8fykixBi1GHzsWK88J8DSg+LOtDJYveybWpMsKLjYJ8REhHDSBsdPcswQr7RaTWYOqoXXr9rGDQa4P2tp/Dlrlylh0Ud5GSRe2akl8IzI+wzQiJiGGmjY+wxQm109QWxePjqvgCAv3z2G25/awvufedXrM5iMBGVzeHC6dqC9l4xyoYRoG52xMWpERIEe5i3gdMl4XjtpyDOjFBbPHptf+w6XYYNh89i6/FzAIBNR4swJLkbUv3gzYx8c7qkCi4JCDLoEBtmUno40Go0cEkSl2lIGJwZaYN8Sw1sDhcMOg2SIpVdHyYx6bQaLL9nON67byT+dcdQjEyNgt0p4fmv9yk9NGqDU7X1IinRwX5R0K7Vyif3Mo2QGBhG2qCo3AoAiAk1NdhGR+QLvU6LK/p3x4QhiZg/eTAMOg3WHjqLHw8UKD008tGJ2plSf5nV8izTMIyQILhM0wbFle4wEs3+ItRB+nQPxX2Xp2Lp+uN4evVebDlWDLNBh7hwE5KjgnFhYjhiw8wAgAJLDWZ/vAthZj1e/P3F6Bbcvp/D7OIqbDhyFrcNT4ZRz88nbSFv6/WHehGgbnsvswiJgmGkDYoqbACA6BDl14ZJPR4e2w+rs3JxpqwGb2860eBrRp0WD17dFzdcHI97V2xDzrlqAMCRxZvxzrQRbX4TLLTU4LalW5BvqcHpkmo8NW5Aux9HIJK39faK9o9lWzmMcGaERMEw0gbFtWEkJpRhhDpOqEmPd+8bif/uyUeNw4lqmxNnSmtwoqgCx85W4pUfDuOVHw4DcNcmOJwSThRVYuLrm3B5vxhcEBeOSZckeh1MauxO3P/+DuRbagAAb288jpuGJmJAfHinPUa18pdtvTK5bIWdfkkUDCNtUFQh14xwmYY61oD48EZhQJIkfP1bHp7/eh+KKmwYEB+G96aPBADc/+527D5dhjV78rFmTz6WbTqOD++/DIN6RKDK5sB/fstDldUBnVaD7mEmjOgVhagQIw7ml+OVzMPYnVOKiCADLkwIx5bjxZj7+R58NjPdUwBJrfO3bb1A/ZkRhQdC5CWGkTYormDNCHUdjUaDiUMScUW/GGw4UoSrLuiOcLMBAPDpA+nYerwYB/PK8dXuM9iTW4a7l/2COdf1x5vrjyO3tLrR94sOMaK40j27p9dqsOQPw9A7JhTXLlyPrOxS/PWLPbiyf3dcmBiOnlH+sTvEn8nbeoON/rGtF6grYGU7eBIFw0gbyC/krBmhrtQt2IiJtQfuyQw6Lcb0644x/brj9pHJuHvZr9iVU4qnv3RvEe7RLQhDe3aDwynhZHElDuaXo7jSBqNeiyv6xeCe9F5I7xMDAHg8oz+e+3o/Pt6Wg4+35QBA7WxKJKZfnoq0lKhGY3K5JBw9W4Gs7BKU1zgweVgSokICK6TXbesN8ZvgptNyZoTEwjDSBnIBa4yffAoiAoAwswHv3jcSU5f/in25ZZg+JhWPXtMPwca6X/NzlTYcP1uBgQnhCDE1/PWflt4LkcFGbD5WhAN55TiUX46z5Vas2ZOPb/fm46Gx/fDI2L7QaTU4VVyFj7fl4NMdOZ7fBwB47ccjePTa/pg6KgUGnXtnjsPpwo5TJRiYGO6Z0VGTE556Ef8oXgXqWsKzgJVEwTDSBp5lmgD7BEj+LyLIgM8fSEelzdHkG39UiBFRIY1nOAD3G9hNQ3vgpqE9ALgLXPfklmHl1lNYvesMXvvxCJZtPA6rwwVHvY/cQQYdLk6KQFm1HQfzy/E//9mPf2/LwT9vvRhRIUbM/ngXtp8qQbBRh1uGJSE2zIQfDhTgcEEFXrhpEG5JS4IkSXgl8zA2Hi3Ci7dcjH5xYZ3zP6gTnPKzbb0A+4yQeBhGfORySZ5lGu6mIX+k02o6ZAbCbNBhRK8ojOgVhasHxOJvq/eivMYBwL1b44p+3XHnpT0xdkAsDDotnC4Jn2zPwYvfHcKhgnLcvHgzggw6VFgd0GiAKpsT72891eA+nvx0N8wGHbadPIcVm08CAO5dsQ1fzBqN7oLMPMrbelOi/GdmhH1GSDQMIz4qq7Z7tssF2to4Ba5Jl/TANQPjkFtSjfAgPSKCDA2WfwB3CLp9ZE9cd2Ecnv16P77efQYVVgcuSe6G124fitMlVfhoWw6sdifGDojFzuwS/Hv7aTz44U7P94gJNeF0STX++P52fHT/ZTAbdF39UH0m76RJ9sMwwq29JAqGER/J3VcjggzsVkkBJdSkxwXxrS+fRIea8K87huKWYT1wuqQaU0Ykw6DTomd0MNL7xniud+vwZNTYXfhq9xkAwILJgzEiNQqTF29GVnYppi7/FS/fOsSv3uTPJ0mSZ8dSj25BCo+mjobLNCQYhhEfebqvclsvUYuuuiC2xa/rtBq8fNsQDOoRjj7dQ3HNwDgAwJt/SMN9K7bh1xPncP2iDZg7fiD+cGnPBjtVyqrteO7rfdhwuAhL7x7W5E6frnCu0oYauwsAkNDNrMgYmsLdNCQafrT3kaf7Krf1ErWbQafFH6/o4wkiADCqTzS+nT0GI1OjUGVz4unVe/HG2qOer28+WoRxizbg8525KKqwYtEPR5QYOgB4ZkViw0ww6f1nSamuZoRphMTAmREfFbHhGVGnS4kOwcf3X4Y31h7Fy5mH8dL3h+F0AcfOVniWdZKjgpBbUo2NR4pwtLACfWNDu3ycuSW1SzSR/rNEA9RfplF2HETe4syIj4o9reA5M0LUmbRaDR6+ph9mX9sPAPDKD4fx1e4z0GiAP1zWE98+eoVnRuX9LScVGaM/1osAPCiPxMMw4qOiStaMEHWlR6/ph5lX9gEApKVE4uuHLscLNw1GiEmPe0b1AgB8uuM0ymvsXT620346M+LpM8KpERIEl2l8VHcuDWdGiLqCRqPBU+MG4P4xqYgKMTYoZB3dNxp9uofg2NlKfL4zF/ek9+rSsckzI0l+OzOi8ECIvMSZER/VFbByZoSoK0WHmhqd/aLRaDwB5IOtp7q8YNN/a0a4TENiYRjxkVzAynNpiPzDzUN7wKTX4khhBfbmWrr0vutqRvyrF0rtsUAMIyQMhhEfyTMjPJeGyD+EmQ3IuCgeAPDZztNddr8VVgfKqt11Kv42M8J28CQahhEf1NidKLe6z+ZgzQiR/5hce7jf17vPwO50dcl9yks0EUEGhJr8q/yOyzQkGoYRH5yr3Ulj1GkRbvavFx+iQDamXwxiQo0orrRhw+GzXXKfuaXuM2n8bVsvUP/UXmXHQeQthhEfFNdrBX9+IR0RKUev02LiEPfsyOdZuV1yn/5avArwoDwSD8OID9h9lch/TR7mDiOZ+ws8tRydydNjxI9nRtgOnkTBMOIDTxjhuTREfueixHD0iw2FzeHCukOFnX5/p+UeI344M6JhnxESDMOID4rZfZXIb2k0Glx1QXcAwOajxZ1+f/IyjT+GER0LWEkwDCM+kAtYua2XyD+l940BAPx8rKjT78tfe4wAgJZ9RkgwDCM+KKtyr0N3C2YYIfJHI3tFQa/V4HRJNbKLqzrtfmrsTpwtdy/b+nMBK7MIiYJhxAel1e6ZkfAgg8IjIaKmhJj0uCS5GwBgcyfOjhRYagAAZoMWkcH+93qg4W4aEgzDiA/kCv0IhhEiv1W3VNN5dSN1S7aNz8vxB3V9RhhGSAwMIz4oq3Z3X2UYIfJfo/tEAwC2HCvqtK2tpZ4lW/98LeAyDYmGYcQHFs6MEPm9oT0jEWTQoajChkMF5Z1yHyVV7pmRSD+tH9NyNw0JhmHEB1ymIfJ/Rr0WI1KjAAA/d9IW35LamZFIP91Zx3bwJBqGES85nC5UWLlMQyQCeanm1xOdE0ZKPTMj/vlawJkREg3DiJcsNQ7Pf/OQPCL/NiAhHABwsqhztvfKBaz+us1f7jPCdvAkijaFkcWLFyM1NRVmsxlpaWnYuHFji9e3Wq2YN28eUlJSYDKZ0KdPHyxfvrxNA1aKvEQTZtJDr2OGI/JnPaPcjciyz1V1yhuyXMDqrzMj3NpLovH5I/6qVaswe/ZsLF68GKNHj8bSpUsxbtw47N+/Hz179mzyNrfddhsKCgqwbNky9O3bF4WFhXA4HE1e11/J07LsMULk/3p0C4JGA1TbnSiqsKF7WMeeJyVOAavCAyHyks9hZOHChZg+fTpmzJgBAFi0aBG+++47LFmyBPPnz290/W+//Rbr16/H8ePHERXlLirr1atX+0atABavEonDqNciMSIIuaXVyD5X1QlhxN+39rr/Zs0IicKn9QabzYYdO3YgIyOjweUZGRnYvHlzk7f56quvMHz4cLz44ovo0aMH+vfvjyeeeALV1dXN3o/VaoXFYmnwR2kMI0RiSY5yt2nPOdfxdSOlfj4zomOfERKMTzMjRUVFcDqdiIuLa3B5XFwc8vPzm7zN8ePHsWnTJpjNZnzxxRcoKirCrFmzcO7cuWbrRubPn4/nnnvOl6F1OvYYIRJLcmQwtuIcsjshjMjLNFF+urVXw900JJg2VWKe3/5YkqRmWyK7XC5oNBqsXLkSI0eOxPjx47Fw4UKsWLGi2dmRuXPnoqyszPMnJyenLcPsUJwZIRJL/SLWjlRtc6LG7gIgwjKNsuMg8pZPMyMxMTHQ6XSNZkEKCwsbzZbIEhIS0KNHD0RERHguGzhwICRJwunTp9GvX79GtzGZTDCZOnaNt708YcRPX3yIqKGe0e4w0tHLNPKsiF6rQajJP7f5s88IicanmRGj0Yi0tDRkZmY2uDwzMxPp6elN3mb06NE4c+YMKioqPJcdPnwYWq0WSUlJbRiyMjgzQiSW5KjODSPdgo1+eUgeUNdnxMWpERKEz8s0c+bMwdtvv43ly5fjwIEDeOyxx5CdnY2ZM2cCcC+xTJ061XP9O++8E9HR0bj33nuxf/9+bNiwAU8++STuu+8+BAUFddwj6WRyXwGGESIxyMs0eZYaWB3ODvu+/t5jBKhfM6LwQIi85PMc45QpU1BcXIznn38eeXl5GDRoENasWYOUlBQAQF5eHrKzsz3XDw0NRWZmJh5++GEMHz4c0dHRuO222/DCCy903KPoApwZIRJLdIgRwUYdqmxO5JZUo3f30A75vv7eYwSo203DZRoSRZsWPGfNmoVZs2Y1+bUVK1Y0umzAgAGNlnZEwzBCJBaNRoOeUcE4mF+O7HNVHRhG/LvHCFBXwMp28CQK9jX3Erf2EomnM+pGSiv9e1svwGUaEg/DiJc4M0IkHrluJKek+SaLvqqbGfHfMMLdNCQahhEv2J0uVNrcBXAMI0Ti8PQaKe64mZG6mhH/fS2Ql2mcDCMkCIYRL8hLNAAPyiMSidwSviMbn4lQwKrVsh08iYVhxAvyEk2YSQ+d1j/7ChBRYz3r1Yx0VDGnCAWscvsT9hkhUTCMeKGU3VeJhJQU6Q4j5VaHpz9Ie3kOyfPjAlYdC1hJMAwjXmDxKpGYzAYdwszuDgal1R0TRkoqBVimYQErCYZhxAvc1kskLpNeBwAd0oXV4XTBUuMAIEYBK/uMkCgYRrzAmREicZn07pc5a+1Ju+1RVm92xZ9fD+Q+I9xNQ6JgGPFCGc+lIRKWyVAbRhztDyPyTppwsx56nf++fGpZM0KC8d/fJj/CmREicXXkMo28k8afi1cBLtOQeBhGvCCHEfYYIRJPRy7TyMWr/tx9FajrM+Jq/0Mm6hIMI17gzAiRuDxhpAOWaeTtwf5cvApwNw2Jh2HEC/KWQH9uckRETTMZOnKZpvaQPH+fGZGbnjGLkCAYRrzArb1E4urImRERDskD6mZGWDNComAY8QKXaYjEVVcz0v6ZkUJLDQAgJsy/w4iGB+WRYBhGvOApYDUzjBCJRt5NU9MBMyN5Ze4wkhgR1O7v1Zm4tZdEwzDSCofThSqb+xMVd9MQicfTZ6QDdtPklVUDABIizO3+Xp2prmaEaYTEwDDSigqrw/Pf8hkXRCQOcwf1GZEkyTMzkuDnMyPy6eKsGSFRMIy0orz2HAqzQQuDH3dcJKKmdVQH1nOVNs/3iIswtXtcnUluB88+IyQKvru2wlLjrhcJY70IkZDqdtO0b2ZEnhWJCTV56lD8FfuMkGgYRlohz4xwiYZITJ528O2sGfEUr3bz73oRgDUjJB6GkVbUhRHOjBCJqKP6jMjFq/HhIoQR7qYhsTCMtKK8Rt7Wy5kRIhHV1Yy0b5nmTKk8M+LfxatAXZ8RzoyQKBhGWsFlGiKx1Z3a276ZkXxBtvUCdbtpODNComAYaYU8MxJm4jINkYg66tTeM/K2XgFmRtgOnkTDMNIKzowQia3jdtOIMzPCZRoSDcNIKyy1YYTdV4nEVHdqb9tnRlwuCfmehmf+H0a07DNCgmEYaUVdnxHOjBCJqCN20xRX2mB3StBogDiBdtPwoDwSBcNIK7i1l0hsHbFMIy/RxIaZhOjELPcZYc0IicL/f6sUVs6ZESKhdUTTM3lbb7yfn0kj07DPCAmGYaQVLGAlEltHnE0jz4wkClAvAtTf2ss0QmJgGGlFXdMzLtMQiahjlmnEOK1XVtcOXtlxEHmLYaQVnBkhElv9pmdtraEQ6VwagH1GSDwMIy1wOF2osrk/TbGAlUhM8jKNJAF2ZxvDSGntuTSCLNOwzwiJhmGkBRVWh+e/OTNCJCZ5mQYAatq4VCPeMk3t1l72GSFBMIy0QF6iMRu0QmznI6LGjPV+d9uyo0aSJBRY5N00YsyMcJmGRMN32BbUNTzjEg2RqDQaTbuKWGvsLjhqK0EjBOnErK19ZecyDYmCYaQFLF4lUof2dGEtt7o/lGg0QHBta3l/p2WfERIMw0gL2H2VSB0859O0YZmm0uqeTQk16qGV98z6ubowwjRCYmAYaUFdjxHOjBCJzGxo+zJNRe2HkhCTOK8Dde3glR0HkbcYRlrAZRoidajfa8RX8jJNqECvAxrPbhqmERIDw0gLPOfSmLhMQySy9tSMeJZpBJwZ4TINiYJhpAWcGSFSB08YsbdhmUaeGREqjMhbexUeCJGXGEZaYGEBK5EqtGeZRq4ZESmM8KA8Eg3DSAs8yzScGSESWntO7q2Ql2kEeh1gO3gSDcNIC7hMQ6QO7Wl6JvIyDetXSRQMIy0oZwdWIlXwLNO0oc+IiMs0bAdPomEYaYE8M8I+I0Ria89uGhGXaeTdNNzaS6JgGGkBO7ASqYOpPU3PapdpRGp6puEyDQmGYaQFLGAlUod27aax1n4oESiMsM8IiYZhpBkOpwuVNvenKIYRIrHV9Rlp+zKNSDMj8tZeZhESBcNIM+RPQwCXaYhEVzcz0pazaUTeTcM0QmJgGGmGXC9iNmhh1PN/E5HI5JqRmjbNjIi3xZ99Rkg0fJdthoXbeolUoz19RioFXKbxzIz4nr2IFMEw0gw2PCNSj7YWsLpckmdmhMs0RJ2nTWFk8eLFSE1NhdlsRlpaGjZu3OjV7X7++Wfo9XpccsklbbnbLsVtvUTq0dY+I5W2+rVjIoUR998MIyQKn8PIqlWrMHv2bMybNw9ZWVkYM2YMxo0bh+zs7BZvV1ZWhqlTp+Kaa65p82C7krytlw3PiMTn6TPi46m98hKNTqvxBBoRaLXsM0Ji8fm3a+HChZg+fTpmzJiBgQMHYtGiRUhOTsaSJUtavN2f/vQn3HnnnRg1alSbB9uVuExDpB5tXaapfy6N3EhMBNp6Y2VLeBKBT2HEZrNhx44dyMjIaHB5RkYGNm/e3Ozt3nnnHRw7dgzPPPOMV/djtVphsVga/OlqnoZnJi7TEImurcs0nlbwAtWLAHXLNABnR0gMPoWRoqIiOJ1OxMXFNbg8Li4O+fn5Td7myJEjeOqpp7By5Uro9d79Qs+fPx8RERGeP8nJyb4Ms0NwZoRIPdq6m0bEQ/IANJjFYd0IiaBNi6DnT1dKktTkFKbT6cSdd96J5557Dv379/f6+8+dOxdlZWWePzk5OW0ZZrtYWMBKpBpmQ9tO7fUs0wj2oaT+zAgPyyMR+PQbFhMTA51O12gWpLCwsNFsCQCUl5dj+/btyMrKwkMPPQQAcLlckCQJer0e33//PcaOHdvodiaTCSaTyZehdTieS0OkHnUH5QXKMk39mhEFB0LkJZ9mRoxGI9LS0pCZmdng8szMTKSnpze6fnh4OPbs2YNdu3Z5/sycORMXXHABdu3ahUsvvbR9o+9EXKYhUo+2toMXsRU80DCMcJmGRODzb9icOXNw9913Y/jw4Rg1ahTeeustZGdnY+bMmQDcSyy5ubl47733oNVqMWjQoAa3j42NhdlsbnS5vylnB1Yi1Wh7AauYNSPaeh8zGUZIBD7/hk2ZMgXFxcV4/vnnkZeXh0GDBmHNmjVISUkBAOTl5bXac0QE8swI+4wQiU8OIzaHq9kat6Z4lmkEex1oODOi4ECIvNSm37BZs2Zh1qxZTX5txYoVLd722WefxbPPPtuWu+1S7MBKpB6m2gJWwD07Yq7375bIBawinUsDsM8IiUecloJdjAWsROpRv3uqL0s18tbeMOHCSN1/czcNiYBhpAlOl4RKm3t6lmGESHx6rcbzBu1LEWuFgCf2Auf3GVFwIEReYhhpgvxpCOAyDZEaaDSauh01PvQaEbXPCFA3O8JlGhIBw0gTLLVLNCa9FkaBDscioua1pdeIvJtGtGUawH24H8CZERID32mbwOJVIvVpS0v4SkGXaYC6pRpu7SURMIw0QS5e5bZeIvWQl2lqfFimKRf0bBqgbpmGYYREwDDSBHZfJVKftsyMyDUjIr4WyNt7mUVIBAwjTSi3svsqkdr4WjPicLo8sygiLtPIYYRbe0kEDCNN4MwIkfr4uptGrhcBgBCTd03S/ImGyzQkEIaRJjCMEKmPr8s08gypUa/1BBmRaDXcTUPiYBhpgqWayzREauPrYXnyzIiIxatA3dZe9hkhETCMNMHiOSSPYYRILTzLNF6GEU/DM0HDSN1uGmXHQeQNhpEm8FwaIvXxFLDavVymEXhbL8A+IyQWhpEmsGaESH0CbZlGnhnhbhoSAcNIE+pmRrhMQ6QWbV6mEfRDCfuMkEgYRppQ7qkZEfNFiIga83k3jeDLNFou05BAGEaawLNpiNTHbPCtz4hF8OVabe2rO8MIiYBhpAksYCVSH3NtAWuN1wWstWdUBYn5oYR9RkgkDCPncbokVNrcL1YMI0TqIc+MeB9GBJ8Z0bDPCImDYeQ8FbUvQACXaYjUxGTw7dRe0QvZNewzQgJhGDmPpfYFyKTXwqjn/x4itTDX/j5XezkzYqkWu5CdB+WRSPhuex4WrxKpk8/LNFaxa8fkPiNcpiERMIycx1O0JugLEBE1zRNGvOwzUi74sRAsYCWRMIycR/SiNSJqmrmN7eBFnSVlnxESCcPIeeqmZsV8ASKipgX5sEwjSZLwW/zZZ4REwjByHs6MEKmT2YfdNDV2F+xO95u4qK8FbAdPImEYOQ/DCJE6eZqeedEOXp4V0WqAEKOYrwUa7qYhgTCMnMcieG8BImqafFCeN8s0lnrn0mjlbSmC0Xr6jDCMkP9jGDkPZ0aI1Kn+Mk1r211Fb3gGcDcNiYVh5DyiV9ATUdPkZRoAsLayvVcNH0p0bAdPAmEYOY/oFfRE1DR5ZgRofanG4uk3JO6HEraDJ5EwjJynrtERwwiRmhh0WuhqCyla21HjeR0IEvd1gH1GSCQMI+cpqxb72HAiap63vUZUUTPCPiMkEIaR85RW2QAAkcFGhUdCRB3N2+29aqgZ4cwIiYRhpB5JklBa5f5ExDBCpD5123vVX8Aq9xlxeXcUD5GiGEbqKbc64Kit9uoWLO70LBE1TZ4Zqba1UsBaLX4BK/uMkEgYRuoprXS/AAUZdA0q74lIHepO7m1tN434W/x1bAdPAmEYqafEUy8i7gsQETVPDiOtndyrhi3+GtaMkEAYRuqRw0g31osQqZKngDUAaka07DNCAmEYqcdTvBrCmREiNTJ7eT5NuVX8Lf7ybhonZ0ZIAAwj9XhmRoI4M0KkRmajd2HEUi1+80O5zwjbwZMIGEbqKamdGeFOGiJ18syMtHA2jSRJqLCKX8Bat7WXYYT8H8NIPWx4RqRudTUjzc+MVNmccNa+gYtcM6Ljqb0kEIaRejgzQqRu8m6a6hbCiFy8qtNqPO3jRcQ+IyQShpF6ODNCpG7yzIi1hd005Z4Te/WepQ4RadlnhATCMFKPp88Id9MQqZI3u2ksKjgkD2CfERILw0g9JZXyMg1nRojUyOzFqb0WFfQYAeqWabi1l0TAMFIPl2mI1M2bpmdqaHgGcJmGxMIwUsvmcKGy9vAstoMnUidvzqapqxkR+3VA7jPCrb0kAoaRWvKsiFYj/osQETXNm2WachUckgfUzYwwi5AIGEZqydt6I4IM0GrFraAnoubVbe1tfpnGUi3+IXlA/TDCNEL+j2GkVgnrRYhUr25rb+szIyK3ggfqCljZDp5EwDBSq9RzYq/YU7NE1DzvlmnUtbWXu2lIBAwjteRlGs6MEKlXXZ+R1nfThAeJPjPCmhESB8NILc+JvQwjRKrl2drb4m4atRSwuv9mzQiJgGGkVqlnZkTsFyAiap53Tc/UUcCq07LPCImDYaSWp+FZCGdGiNSqLoy4mi3slA/RCzaKHUY87eC5TkMCaFMYWbx4MVJTU2E2m5GWloaNGzc2e93PP/8c1113Hbp3747w8HCMGjUK3333XZsH3Fl4Yi+R+snLNABgdTRdN1JV2/xQ5BN7gfrLNMqOg8gbPoeRVatWYfbs2Zg3bx6ysrIwZswYjBs3DtnZ2U1ef8OGDbjuuuuwZs0a7NixA1dffTUmTJiArKysdg++I7EVPJH6mesFjOaWamps8syI6GGEfUZIHD6HkYULF2L69OmYMWMGBg4ciEWLFiE5ORlLlixp8vqLFi3Cn//8Z4wYMQL9+vXDP/7xD/Tr1w9ff/11uwffkTgzQqR+Bp3WU0vR3I4aeZkmSPgw4v6bYYRE4FMYsdls2LFjBzIyMhpcnpGRgc2bN3v1PVwuF8rLyxEVFdXsdaxWKywWS4M/nY0zI0SBwayXD8trPDNid7rgqF3XMAu+TKPhzAgJxKcwUlRUBKfTibi4uAaXx8XFIT8/36vv8fLLL6OyshK33XZbs9eZP38+IiIiPH+Sk5N9GabPJEmqt5uGYYRIzVo6LK+6XkARv2aEfUZIHG0qYJUTt0ySpEaXNeWjjz7Cs88+i1WrViE2NrbZ682dOxdlZWWePzk5OW0ZptfKrQ7PpyEu0xCpW/0dNeeT60V0Wg0MOrHPqNLVvrqzHTyJwKe9azExMdDpdI1mQQoLCxvNlpxv1apVmD59Oj755BNce+21LV7XZDLBZDL5MrR2Ka10z4oEGXTCT80SUcs8jc+aWKbx1IsYdF59wPJndVt7FR4IkRd8mhkxGo1IS0tDZmZmg8szMzORnp7e7O0++ugjTJs2DR9++CFuuOGGto20E5XwXBqigNFS4zM5jKjhQwl305BIfO7qM2fOHNx9990YPnw4Ro0ahbfeegvZ2dmYOXMmAPcSS25uLt577z0A7iAydepUvPrqq7jssss8sypBQUGIiIjowIfSdnLHxYgghhEitWtpmcbTY8Qofj9IeTcND8ojEfgcRqZMmYLi4mI8//zzyMvLw6BBg7BmzRqkpKQAAPLy8hr0HFm6dCkcDgcefPBBPPjgg57L77nnHqxYsaL9j6AD1J1FIXbHRSJqXUvLNDUqaXgG1M2MMIuQCNr07jtr1izMmjWrya+dHzDWrVvXlrvoUhUqORiLiFpXd3JvCzUjgreCBwAN+4yQQMSfi+wA8jJNqEn8FyAiapk3NSNBBvFfGrm1l0Qi/m9cB+AyDVHgMMnLNE2cTVOtomUaudMsZ0ZIBAwjqB9GuExDpHYtzYzUqKQVPFBXwMo+IyQChhEAFVb3Mg1nRojUL6iF3TRq2trLPiMkEoYRcJmGKJC02PTM5n7nVsMyjVwzwq29JAKGETCMEAWSlnbTVNndrwXqCCPuv7lMQyJgGAFQXrubJszEmhEitWuxZsSmppoR7qYhcTCMwH1QHsCZEaJAULdM03zNiCrCCHfTkEAYRlC3TBPKMEKkeiZ5ZsTRVJ8RNdWMuP/mzAiJgGEEdcs04dzaS6R6LTY9U1Gfkbp28Ewj5P8CPozYnS7PdC2XaYjUz6xvfplGTX1G2A6eRBLwYUQ+lwZgO3iiQCAHjZbawauhz4hnay/XaUgAAR9G5HqRIIMOel3A/+8gUj05aFibaAdfpcJlGmYREkHAv/vKh+RxiYYoMMh9RuT6kPrUtEzDPiMkkoAPI2x4RhRYPFt7m9pNo6aZES1nRkgcAR9GKqw8JI8okLS4m0ZVMyPsM0LiCPgwUs5lGqKAEmysOyjv/OJOTxhRw8wI+4yQQBhGuExDFFDqz4LW303ndEmwOdTU9Ew+tZdphPwfwwjPpSEKKEa91lM3IhewAw2XbdSwTMM+IyQShhGeS0MUcORuy2XVdWGkul4YMenFf2nk1l4Sifi/ce3Ec2mIAk94kDuM1J8Zqb+TRiNPKwhMp2U7eBIHw0gNd9MQBZrw2g8f5fVqRtS0kwbgMg2JhWGEu2mIAo5nZqS66ZkRNeAyDYkk4MOIXE0fzjBCFDDkmVBLEzMjcnGr6NhnhESijt+6dvDUjHA3DVHAkD98WJooYA02quODiafPCKdGSAAMI1ymIQo4TRWw1qhsmUbDZRoSCMMIt/YSBRx5a29TBaxmlRSwalnASgIJ6DDickk8m4YoAIW1sEwTpJKakbqtvQoPhMgL6vita6NKm8Pzi8qZEaLA0VqfETVgASuJJKDDiDxFa9BpVNFxkYi8U1fAWm+ZxsY+I0RKCeh34PpLNGrouEhE3pFnRsqtjZdpzCqbGXG6FB4IkRcCOoxwJw1RYJILWBvMjNjVuUzDdvAkgoAOIxZPjxGGEaJAUtcO3u7pw1Hj6TOiljDi/pvLNCSCgA4jdefSMIwQBRJ5mcYluQvZgbqaEdUs02jZZ4TEEdBhpIKH5BEFJJNeC6PO/fInfyhR20F53E1DIgnoMMKaEaLApNFoEB5Uu6Om9nWg2u6u9FRPzYj7b2YREkGAh5HamRHWjBAFnLDzilira5dr1BNGODNC4gjwMCLPjHCZhijQnH9YntrawcvdCpwsGiEBBHYY4bk0RAHr/C6sau3AyokREkFAhxGtRoMgg44zI0QB6PzD8mpUVzPCZRoSR0BPCbx06xC8dOsQNgUiCkCeAtbzlmlU02ek9qMmwwiJIKBnRmRsBU8UeDwFrOct06imz4iGfUZIHAwjRBSQ6h+WJ0mSavuMAGwJT/6PYYSIAlL9w/LkehFATTUjdf/NHTXk7xhGiCgg1T8sT54VAdSzTFN/+ZlZhPwdwwgRBaT6HVjlMGLUa6HTqqOGrP7DYBEr+TuGESIKSHUdWO2q6zECoEGoYhYhf8cwQkQBybNMU+NAjV19YUTbYJmGaYT8G8MIEQUkeZmmvMaOytpuzGrZSQPUtYMHGEbI/zGMEFFAkmdG7E4JPx0sBAD0jApWckgdSssCVhIIwwgRBaRgo85TV/Hhr9kAgMnDeig5pA7VIIwwjZCfYxghooCk0Wg8h2SW1zgQZtbj+oviFR5Vx+FuGhIJwwgRBazweodkThySqJoeIwD7jJBYGEaIKGDJRawAcOvwZAVH0jnkZSi2gyd/xzBCRAFLnhnpFxuKIUkRCo+m48lLNZwZIX/HMEJEAatHtyAAwJQRyao8vVvjObmXaYT8W5vCyOLFi5Gamgqz2Yy0tDRs3LixxeuvX78eaWlpMJvN6N27N9588802DZaIqCM9cf0FePX2S3Df6FSlh9Ip5JkRHpRH/s7nMLJq1SrMnj0b8+bNQ1ZWFsaMGYNx48YhOzu7yeufOHEC48ePx5gxY5CVlYW//vWveOSRR/DZZ5+1e/BERO0RF27GpEt6QKuS82jOJ2/v5cQI+Tufw8jChQsxffp0zJgxAwMHDsSiRYuQnJyMJUuWNHn9N998Ez179sSiRYswcOBAzJgxA/fddx9eeumldg+eiIiap+UyDQlC3/pV6thsNuzYsQNPPfVUg8szMjKwefPmJm+zZcsWZGRkNLjs+uuvx7Jly2C322EwGBrdxmq1wmq1ev5tsVh8GSYREaFumSYrpwTZ56pgdbhgdThhtbtgdbhQY3ei0upAhdWBcqsDFTUO2BwumAxamPRamA06mPRamPQ6mA31/jboEG7WIyrEBJ1Wg/1nyrA/rxzRIUak9YpEYkQQjhZW4PjZClTZnXA4XbA7JThcLgBAQkQQkqOC0S3IAK1GA7vLhYKyGhRYrIgNN+HipAjEh5uRV1aD0yVVOF1SjdMl1XC6JMSFmxARbMS5ChsKymtg1GkRH2FGsFGHnHNVyDlXjTCzHslRwQg26nC6pBp5ZdVwOCW4JAkGnRahZj3CTHqEmvUINRk8/9ZpNSipsqGk0g69zt2HRgP3+UUVVgcigw2ICzcDAM5V2lBpdaBbsBExoUa4JPfRAuU1DlhqHKi2OTA4qRuuGxgHs0GLfWcs2HGqBA6XBK3GvdNJq9Eg1KTHmH4xiA414djZCiz470FkZZcisZsZyZHBCA8yIMSoQ2SIET2jghEZbMTu06XYcaoEVTYHws0GBBl1qKhxoLzGgVCzHgkRZkSHmmDQaqDVamCpseNchQ1OSUKIUY8gow622p8FvVaLMLP7/8W1A+PQPy5MkZ9Vn8JIUVERnE4n4uLiGlweFxeH/Pz8Jm+Tn5/f5PUdDgeKioqQkJDQ6Dbz58/Hc88958vQiIjoPPLW3sdW7VZ4JIErxKhDmNmAfEtNs9fRaTW4JLkbdueUwlFb31NUYcVvp8u6apgA3AXdQoQR2flV55IktViJ3tT1m7pcNnfuXMyZM8fzb4vFguRk9fUAICLqTHeP6oUvsk7DqHPPapgMWvd/e2Y8aj8Vm/QIMbn/Num1tTMoLljtTs9/19idqKn9d7XdCUu1Hecqbaixu3BBfBguSgxHgcWKHafOoajChj6xoegXG4pwswEGnQZ6nQZ6rRaSJCG3tAY556pQbnXA5ZKg1WoQH25C9zATTpdUY8/pMpRbHegeZkKPbkFIigxCj8ggGLRaFJbXoLTKjuhQI7qHmWF3upBfVoMKqwPJkcFIjgpCRY0D2eeqUG13IikyGIndzDDrdYAGsDtdqKid6Siv/Vv+t83pQlSwEZEhBjicEiptDjhdEiKCDAg26lFSZUNBbaiIDjEhxKRDSZUdxRVW6LQahJkNCDPrEW42QKsBfjpUiJxz1ai0ORFk0GFUn2iEmfVwuiRIkruw+HRpFfbmumdNAOCaAbG4/4reKKu2I7ekGuU1DlTZHDhbbsWpc1UoqrBiYHw4RqZGISbMhLJqO2psToSZ9QgzG1BWbUdeWTVKqmxwOCU4XRLCgwyICjFCr9Wg0upAtd0JY+2Ml8Pp8syK9Y4JVexn1acwEhMTA51O12gWpLCwsNHshyw+Pr7J6+v1ekRHRzd5G5PJBJPJ5MvQiIjoPHOu64851/VXehg+c7kk2F0umPRid8R9VpKwK6cUlVYnhveKbLbD78miSmw4chb9YsMwqk/T74tq51MYMRqNSEtLQ2ZmJm6++WbP5ZmZmZg0aVKTtxk1ahS+/vrrBpd9//33GD58eJP1IkREFNi0Wg1MWrGDCOCe/R/aM7LV6/WKCUGvmJAuGJH/8nk3zZw5c/D2229j+fLlOHDgAB577DFkZ2dj5syZANxLLFOnTvVcf+bMmTh16hTmzJmDAwcOYPny5Vi2bBmeeOKJjnsUREREJCyfa0amTJmC4uJiPP/888jLy8OgQYOwZs0apKSkAADy8vIa9BxJTU3FmjVr8Nhjj+GNN95AYmIiXnvtNdxyyy0d9yiIiIhIWBpJgBOULBYLIiIiUFZWhvDwcKWHQ0RERF7w9v2bZ9MQERGRohhGiIiISFEMI0RERKQohhEiIiJSFMMIERERKYphhIiIiBTFMEJERESKYhghIiIiRTGMEBERkaIYRoiIiEhRPp9NowS5Y73FYlF4JEREROQt+X27tZNnhAgj5eXlAIDk5GSFR0JERES+Ki8vR0RERLNfF+KgPJfLhTNnziAsLAwajabDvq/FYkFycjJycnIC5gA+Pmb1P+ZAe7xA4D3mQHu8AB+zqI9ZkiSUl5cjMTERWm3zlSFCzIxotVokJSV12vcPDw8X9oluKz5m9Qu0xwsE3mMOtMcL8DGLqKUZERkLWImIiEhRDCNERESkqIAOIyaTCc888wxMJpPSQ+kyfMzqF2iPFwi8xxxojxfgY1Y7IQpYiYiISL0CemaEiIiIlMcwQkRERIpiGCEiIiJFMYwQERGRohhGiIiISFEBHUYWL16M1NRUmM1mpKWlYePGjUoPqUPMnz8fI0aMQFhYGGJjY3HTTTfh0KFDDa4zbdo0aDSaBn8uu+wyhUbcfs8++2yjxxMfH+/5uiRJePbZZ5GYmIigoCBcddVV2Ldvn4Ijbp9evXo1erwajQYPPvggAHU8vxs2bMCECROQmJgIjUaD1atXN/i6N8+p1WrFww8/jJiYGISEhGDixIk4ffp0Fz4K37T0mO12O/7yl79g8ODBCAkJQWJiIqZOnYozZ840+B5XXXVVo+f+9ttv7+JH4p3WnmNvfo7V9BwDaPL3WqPR4J///KfnOiI9x94K2DCyatUqzJ49G/PmzUNWVhbGjBmDcePGITs7W+mhtdv69evx4IMPYuvWrcjMzITD4UBGRgYqKysbXO93v/sd8vLyPH/WrFmj0Ig7xkUXXdTg8ezZs8fztRdffBELFy7E66+/jm3btiE+Ph7XXXed5xBG0Wzbtq3BY83MzAQA3HrrrZ7riP78VlZWYsiQIXj99deb/Lo3z+ns2bPxxRdf4OOPP8amTZtQUVGBG2+8EU6ns6sehk9aesxVVVXYuXMnnn76aezcuROff/45Dh8+jIkTJza67v3339/guV+6dGlXDN9nrT3HQOs/x2p6jgE0eKx5eXlYvnw5NBoNbrnllgbXE+U59poUoEaOHCnNnDmzwWUDBgyQnnrqKYVG1HkKCwslANL69es9l91zzz3SpEmTlBtUB3vmmWekIUOGNPk1l8slxcfHSwsWLPBcVlNTI0VEREhvvvlmF42wcz366KNSnz59JJfLJUmS+p5fANIXX3zh+bc3z2lpaalkMBikjz/+2HOd3NxcSavVSt9++22Xjb2tzn/MTfn1118lANKpU6c8l1155ZXSo48+2rmD6wRNPd7Wfo4D4TmeNGmSNHbs2AaXifoctyQgZ0ZsNht27NiBjIyMBpdnZGRg8+bNCo2q85SVlQEAoqKiGly+bt06xMbGon///rj//vtRWFioxPA6zJEjR5CYmIjU1FTcfvvtOH78OADgxIkTyM/Pb/B8m0wmXHnllap4vm02Gz744APcd999DU61VtvzW583z+mOHTtgt9sbXCcxMRGDBg1SxfMOuH+3NRoNunXr1uDylStXIiYmBhdddBGeeOIJYWcAgZZ/jtX+HBcUFOCbb77B9OnTG31NTc8xIMipvR2tqKgITqcTcXFxDS6Pi4tDfn6+QqPqHJIkYc6cObj88ssxaNAgz+Xjxo3DrbfeipSUFJw4cQJPP/00xo4dix07dgjZevjSSy/Fe++9h/79+6OgoAAvvPAC0tPTsW/fPs9z2tTzferUKSWG26FWr16N0tJSTJs2zXOZ2p7f83nznObn58NoNCIyMrLRddTwe15TU4OnnnoKd955Z4MTXe+66y6kpqYiPj4ee/fuxdy5c7F7927PUp5IWvs5Vvtz/O677yIsLAyTJ09ucLmanmNZQIYRWf1PkYD7jfv8y0T30EMP4bfffsOmTZsaXD5lyhTPfw8aNAjDhw9HSkoKvvnmm0Y/+CIYN26c578HDx6MUaNGoU+fPnj33Xc9BW9qfb6XLVuGcePGITEx0XOZ2p7f5rTlOVXD826323H77bfD5XJh8eLFDb52//33e/570KBB6NevH4YPH46dO3di2LBhXT3Udmnrz7EanmMAWL58Oe666y6YzeYGl6vpOZYF5DJNTEwMdDpdo+RcWFjY6JOWyB5++GF89dVXWLt2LZKSklq8bkJCAlJSUnDkyJEuGl3nCgkJweDBg3HkyBHPrho1Pt+nTp3CDz/8gBkzZrR4PbU9v948p/Hx8bDZbCgpKWn2OiKy2+247bbbcOLECWRmZjaYFWnKsGHDYDAYVPHcn/9zrNbnGAA2btyIQ4cOtfq7DajjOQ7IMGI0GpGWltZoSiszMxPp6ekKjarjSJKEhx56CJ9//jl++uknpKamtnqb4uJi5OTkICEhoQtG2PmsVisOHDiAhIQEz3Rm/efbZrNh/fr1wj/f77zzDmJjY3HDDTe0eD21Pb/ePKdpaWkwGAwNrpOXl4e9e/cK+7zLQeTIkSP44YcfEB0d3ept9u3bB7vdrorn/vyfYzU+x7Jly5YhLS0NQ4YMafW6qniOFSyeVdTHH38sGQwGadmyZdL+/ful2bNnSyEhIdLJkyeVHlq7PfDAA1JERIS0bt06KS8vz/OnqqpKkiRJKi8vlx5//HFp8+bN0okTJ6S1a9dKo0aNknr06CFZLBaFR982jz/+uLRu3Trp+PHj0tatW6Ubb7xRCgsL8zyfCxYskCIiIqTPP/9c2rNnj3THHXdICQkJwj5eSZIkp9Mp9ezZU/rLX/7S4HK1PL/l5eVSVlaWlJWVJQGQFi5cKGVlZXl2jnjznM6cOVNKSkqSfvjhB2nnzp3S2LFjpSFDhkgOh0Oph9Wilh6z3W6XJk6cKCUlJUm7du1q8LtttVolSZKko0ePSs8995y0bds26cSJE9I333wjDRgwQBo6dKhfPuaWHq+3P8dqeo5lZWVlUnBwsLRkyZJGtxftOfZWwIYRSZKkN954Q0pJSZGMRqM0bNiwBltfRQagyT/vvPOOJEmSVFVVJWVkZEjdu3eXDAaD1LNnT+mee+6RsrOzlR14O0yZMkVKSEiQDAaDlJiYKE2ePFnat2+f5+sul0t65plnpPj4eMlkMklXXHGFtGfPHgVH3H7fffedBEA6dOhQg8vV8vyuXbu2yZ/je+65R5Ik757T6upq6aGHHpKioqKkoKAg6cYbb/Tr/w8tPeYTJ040+7u9du1aSZIkKTs7W7riiiukqKgoyWg0Sn369JEeeeQRqbi4WNkH1oyWHq+3P8dqeo5lS5culYKCgqTS0tJGtxftOfaWRpIkqVOnXoiIiIhaEJA1I0REROQ/GEaIiIhIUQwjREREpCiGESIiIlIUwwgREREpimGEiIiIFMUwQkRERIpiGCEiIiJFMYwQERGRohhGiIiISFEMI0RERKSo/w8u+Pc/JJTyYQAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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GWMBKRL4wGHFgZoSo/cxumRGdhsM0RNQ2BiMOXJuGqP3kzIjerWaEwzRE5AuDEQe1Y5zGwnU0iILmPJsGcM6M8HVFRN4xGHFQMiOCb5pEwWpVwKqsT8OVe4nIOwYjDmo2PSNqN6N7AStn0xCRHxiMOGjYDp6o3dwLWJWmZxymISIfGIw4qOTMCN80iYKmTO1VakbU9u3MjBCRDwxGHJgZIWo/95oRdmAlIn8EFYysWrUK2dnZMBgMyMnJwdatW33u/9prr2H06NGIjo5Geno6br31VlRXVwd1wJ1F7jPCqb1EwTO5t4PXcG0aImpbwMHI+vXrsXjxYixbtgzFxcWYMmUKCgoKUFpa6nH/bdu2Yfbs2Zg3bx7279+Pt956C7t27cL8+fPbffAdibNpiNrPWwdWZkaIyJeAg5EVK1Zg3rx5mD9/PoYNG4aVK1ciMzMTq1ev9rj/zp070a9fPyxatAjZ2dmYPHkyfvWrX2H37t3tPviOpGbNCFG7eZvay3bwRORLQMGIyWRCUVER8vPzXbbn5+dj+/btHu8zceJEnDx5Ehs3boQQAmfOnMG///1vXHvttV6fx2g0ora21uWrs7FmhKj9WhewcmovEbUtoGCkqqoKVqsVqampLttTU1NRUVHh8T4TJ07Ea6+9hlmzZkGn0yEtLQ09evTA3/72N6/Ps3z5ciQkJChfmZmZgRxmUNhnhKj9WhWwcm0aIvJDUAWskiS5fC+EaLVNduDAASxatAgPP/wwioqK8NFHH6GkpAQLFizw+vhLly5FTU2N8lVWVhbMYQZEzcwIUbt5m03DzAgR+aIJZOfk5GSo1epWWZDKyspW2RLZ8uXLMWnSJPzmN78BAIwaNQoxMTGYMmUKnnjiCaSnp7e6j16vh16vD+TQ2o0L5RG1n7dhGmZGiMiXgDIjOp0OOTk5KCwsdNleWFiIiRMnerxPY2MjVCrXp1Gr7Y2QxAU0c4VTe4naz/vaNAxGiMi7gIdplixZgpdeeglr167FwYMHce+996K0tFQZdlm6dClmz56t7D9jxgxs2LABq1evxrFjx/DFF19g0aJFGD9+PDIyMjruTNqJmRGi9vNWM8LZNETkS0DDNAAwa9YsVFdX4/HHH0d5eTlGjBiBjRs3IisrCwBQXl7u0nNk7ty5qKurw7PPPov77rsPPXr0wBVXXIE//vGPHXcWHYA1I0TtY7MJ5fUjD9NolT4jfF0RkXcBByMAsHDhQixcuNDjbevWrWu17e6778bdd98dzFN1GY2amRGi9nAeimm9aq81JMdEROGBa9M4qCQ5M8J0MlEwPAUjemZGiMgPDEYcWDNC1D7O03e1joJwLdemISI/MBhxYM0IUfvIAYdWLUHleD3pHDPnGIwQkS8MRhzkmhEbgxGioLiv2AvYAxOAU3uJyDcGIw5ynxFmRoiCY3Zbsdf538yMEJEvDEYcWDNC1D5Gi/dghB1YicgXBiMOLbNpGIwQBcPkKTPCDqxE5AcGIw7sM0LUPi0FrB4yIxymISIfGIw4tMym4ZsmUTA8F7AyM0JEbWMw4iDXjDAWIQqOHIzoPdSMcG0aIvKFwYgDMyNE7eNxNo2aBaxE1DYGIw4ax9Re1owQBcdjASun9hKRHxiMOLADK1H7GD3UjMj/tgkG+kTkHYMRBzkYsXJBL6KgeJpNo3XKkjA7QkTeMBhx0DAzQtQuJk9Nz9QMRoiobQxGHNTswErULp4KWOW1aQBO7yUi7xiMOCjt4AWDEaJgeJraK0kSu7ASUZsYjDg4Z0YEAxKigCmzadSubyvswkpEbWEw4iBP7QU4VEMUDE81I0DLUA0zI0TkDYMRB6dYhEWsREEwephNA7DXCBG1jcGIAzMjRO3jqekZwPVpiKhtDEYc5JoRgJkRomCYvQzTMDNCRG1hMOKgcQpGbAxGiALmtYCV69MQURsYjDioVBIkRzzCzAhR4DxN7QWYGSGitjEYcaJh4zOioHmbTaP0GWEwQkReMBhx0rJYHt80iQIlD9O4z6ZhASsRtYXBiBO1xMwIUbCMLGAloiAxGHGi5mJ5REEzeylg1SoFrHxdEZFnDEacaBxvmpxNQxQ4bzUjeiUzYu3yYyKi8MBgxAkzI0TB81rAqmFmhIh8YzDihLNpiIInF6i6T+3l2jRE1BYGI06YGSEKnqmNtWmMLGAlIi8YjDhRK5kRvmkSBcr7qr3swEpEvjEYcaJkRji2TRQwr+3gObWXiNrAYMQJa0aIgud1Ng0zI0TUBgYjTtQq+4/DKhiMEAVCCNGSGfEyTMPMCBF5w2DEiYYFrERBsdgE5Bi+VdMzTu0lojYwGHGiFLDyTZMoIM5ZD/fMiIZrPhFRGxiMOOHUXqLguAQjai/BCIN8IvKCwYgTNQtYiYIiF6eqpJZlFWRqx/fMjBCRNwxGnDCdTBQcbyv2AoCWmREiagODESdyZsTG2TREAfHWYwRoyZRw+JOIvGEw4oRj20TB8dZjBGDGkYjaxmDEidJnhJ/giAKiBCMeMyP2YIRTe4nIGwYjTthnhCg43hqeAexsTERtYzDihLNpiIJj9jlM46gZYTt4IvKCwYgT9hkhCo7RV2ZEzdcVEfnGYMRJSzqZn+CIAuGzZkTJjDAYISLPGIw4aRmmCfGBEIUZORjR+ipgZZBPRF4wGHEiv2kyM0IUGH+m9rIWi4i8YTDihDUjRMGR28HrPdaMcJiGiHxjMOJELfETHFEw/Jnay6ZnROQNgxEnctMzZkaIAuNP0zNmRojIGwYjTlpqRvimSRQIXwvlybNpzKwMJyIvGIw4YdMzouD4nE3D1xURtYHBiBO+aRIFx2fNiDK1l68rIvKMwYgTNQvtiILiqx28nC1hkE9E3gQVjKxatQrZ2dkwGAzIycnB1q1bfe5vNBqxbNkyZGVlQa/XY8CAAVi7dm1QB9yZmBkhCo6cGdF7GKZxHv4Ugq8tImpNE+gd1q9fj8WLF2PVqlWYNGkSXnjhBRQUFODAgQPo27evx/vcdNNNOHPmDNasWYOBAweisrISFoul3Qff0VQqVv0TBcNX0zOtqmWb2Sqg00hddlxEFB4CDkZWrFiBefPmYf78+QCAlStX4uOPP8bq1auxfPnyVvt/9NFH2Lx5M44dO4bExEQAQL9+/dp31J2EmRGi4PgKRtTqluCDry0i8iSgYRqTyYSioiLk5+e7bM/Pz8f27ds93uf9999Hbm4unnrqKfTu3RuDBw/G/fffj6amJq/PYzQaUVtb6/LVFeQ+I1amkokCIq/a62s2DcD1aYjIs4AyI1VVVbBarUhNTXXZnpqaioqKCo/3OXbsGLZt2waDwYB33nkHVVVVWLhwIc6ePeu1bmT58uV47LHHAjm0DqFhO3iioPgcpnEKUKwcAiUiD4IqYJUk1zFfIUSrbTKbzQZJkvDaa69h/PjxmDZtGlasWIF169Z5zY4sXboUNTU1yldZWVkwhxkwpdCOb5hEAZEbmnnqwOqUGGFmhIg8CigzkpycDLVa3SoLUllZ2SpbIktPT0fv3r2RkJCgbBs2bBiEEDh58iQGDRrU6j56vR56vT6QQ+sQzIwQBcdXZkSSJGjVEsxWwZoRIvIooMyITqdDTk4OCgsLXbYXFhZi4sSJHu8zadIknD59GvX19cq2w4cPQ6VSoU+fPkEccudRKQWs/PRGFIhmsxUAYNCqPd6u5kw1IvIh4GGaJUuW4KWXXsLatWtx8OBB3HvvvSgtLcWCBQsA2IdYZs+erex/yy23ICkpCbfeeisOHDiALVu24De/+Q1uu+02REVFddyZdABmRoiC02S2B/BRXoIRLdenISIfAp7aO2vWLFRXV+Pxxx9HeXk5RowYgY0bNyIrKwsAUF5ejtLSUmX/2NhYFBYW4u6770Zubi6SkpJw00034Yknnui4s+ggXJuGKDhtZUa4CCUR+RJwMAIACxcuxMKFCz3etm7dulbbhg4d2mpo50Ikry7KN0yiwMjBiLfMiFrJjPC1RUStcW0aJ8yMEAWnSQ5GdJ7fUrTMjBCRDwxGnLBmhCg4cmZEr/FdwMqpvUTkCYMRJ2p+eiMKmM0m0CwXsOq8FLA6+o9wNg0RecJgxIlaYmaEKFBGS0u2w2sBq5J1ZGaEiFpjMOJEwz4jRAGTh2gAwOCh6RnAPiNE5BuDEScsYCUKnFy8qlOroPHQDh5oGabha4uIPGEw4oS9EIgCpxSvar2/nSgFrGx6RkQeMBhxIvdCYM0Ikf+a2ugxArRM7eVri4g8YTDiRMNhGqKAKQ3PvMykAVoaCjIYISJPGIw4UXE2DVHA5Gm9Bi89RoCWIVALh2mIyAMGI05YM0IUuCaTY10an5kRBvpE5B2DESct0w/56Y3IX80WRzDiZVov4FSPxam9ROQBgxEn8qc3fngj8p+cGfFVM9JSwMpAn4haYzDiRM0ukUQBa2vFXgBK/xFmRojIEwYjTuSKf9aMEPlPKWD1FYww0CciHxiMOFGzyI4oYHKfEf+CEb62iKg1BiNO5GBECPtKpETUtmYlGPH+dtIytZevKyJqjcGIEzkYAfgJjshf/nRgVZqecaYaEXnAYMSJxikYsQkGI0T+8K+AlcM0ROQdgxEnzIwQBS6wAla+roioNQYjTpwzI1aObRP5xa8OrJzaS0Q+MBhx4poZ4dg2kT/86cDKqb1E5AuDESeSJEGOR9hrhMg//nRglQtYzcyMEJEHDEbccKlzosAEUsBqZWaEiDxgMOJGHqphZoTIPwEVsDIzQkQeMBhxo2EwQhQQvzqwqplxJCLvGIy4UbMfAlFA/OrAygJWIvKBwYgbZkaIAuNXB1ZHkM8CViLyhMGIGzU/wREFRClg9TGbRssVsYnIBwYjbtQSMyNE/rJYbUq2w6DxHozIQb6Za9MQkQcMRtywZoTIf82WluDCZ58RNYN8IvKOwYgbuc+IjW+aRG2SG54BgN5nB1a2gyci7xiMuFFzQS8ivznPpJEkyet+Lav2cpiGiFpjMOKGs2mI/OdP91UA0HL4k4h8YDDihoV2RP7zp/sqAKi5Ng0R+cBgxI3cKZKZEaK2+dNjBAC0Kq5NQ0TeMRhxo2VmhMhvcjCibzMzwrVpiMg7BiNu2CmSyH8tNSO+30q4Ng0R+cJgxI1WLY9tMzNC1BZ/uq8CTgWsfF0RkQeaUB/AhUYORphOtjvbYMKOo9X4vrIeNU1mzLo4E0PS4kJ9WHSBUKb2+ui+CjgVhjMzQkQeMBhxI3+CM/ETHKw2gRtWb0dJVYOy7d9FZXjltvEY27dnCI+MLhRy0zNDm5kRFoYTkXccpnGjjG0zGMHWIz+gpKoBMTo1fprTB6Mze6C22YJfrvkKu46fDfXh0QWgSZ7a629mhK8rIvKAwYgbHQvtFG98VQYAuDE3E3++cTRenz8Bef2TUG+0YO7ar7D/dE2Ij5BCraVmxPdbCVftJSJfGIy4kTuwdvdhmh/qjPj04BkAwM/G9wUAxOg1ePnWi5HXPwkNJivmrduN8pqmUB4mhZi/HViVdvCsxSIiDxiMuNFqWMAKAG/vOQmLTWBs3x4uBasGrRrP/zIHA1NiUVHbjNvW7UaD0RLCI6VQalmbpo1gRClg7d5BPhF5xmDEDZueAUIIrN9lH6K5+eLMVrcnRGnx8tyLkRyrw8HyWvzrq9KuPkS6QDT5G4w4hj+F4IrYRNQagxE3GnX3XUPj3eJTmPb0Vox69BOlcHX6qAyP+2YmRmPBpQMAAFuPVHXlYdIFpMnvtWlaVvRldoSI3HFqr5vu2vTsbIMJy975Fg2OqZqSBNwxdQBi9N5/RSYPSgYAfFVyFiaLDToNY9vuJtBVewEWsRJRawxG3HTXTpEvbD6KBpMVF6XH45mfjUGfntFtftodkhqH5FgdqupNKC49hwn9k7roaOlC4e9sGo2q5fbumHUkIt/4UdaNnBkxdaM3zMq6Zryy4zgA4P5rBmNgSlybgQgASJKEvAH27MgXR6sB2OtNhGj7Z/fC5qN4c3dZ8AdNFwR/O7BqnIZpulugT0RtYzDiRtMNMyOrPj+KZrMNYzJ74PIhKQHdd/JAezbki++rIITAkje/xpDffoQ7Xt2ND74p9zjcdfhMHZZ/+B0efPsb1DWbO+QcKDSUAtY2OrCqVBLkeITDNETkjsGIG103qxk5fb4Jr39pnw1zf/4QSJLUxj1cTXRkRvaWncebu8vwTvEpmKw2fHLgDO58fQ9mr/lK+fQs2338HADAJoCvy9g4LZwp7eDbyIwALUM1XJ+GiNwxGHGj6WYLev3ts+9hstowITsRkwYGXvORmRiNvonRsNoEHnpnHwDgl5dk4deXDUCMTo0dx6pxzxvFLpmmohPnPP6bwk+zYzZNW6v2Ai1ZR2s3GgIlIv8wGHHT0vQs8jMjJ6ob8JajbuO+ILIiskkD7dkRq01gQK8Y/Hb6MDzwo6H4+5xc6NQqfLz/DH777j5l/6ITLevaFJUyGAln/s6mAdj4jIi8YzDiRl5DoztU/D/9vyOw2ASmDu6F8dmJQT+Oc0blyRtGQe9I2U8ckIxnfjYWKgl4Y1cZikvPoareiOPVjcr+xSfOsQlWGGvpwNr2W0nLIpS83kTkilN73Wg1kduBVQiBFYWHcbC8DllJ0Xi3+BQA4L6rB7frca8alorpo9IxJrMHLu7nGtT8aEQaZo7OwLt7T+PtPScxZVAvAMCAXjEor2lGndGCw5V1GJoWrxzjoTN1qG+2ILdf6wBJCIHKOiNS4vRBZ3KoYwghlALWQDIjFmZGiMgNgxE3SpFdBAYjB8vr8LfPvnfZdvVFqRid2aNdj2vQqvHsLeO83v7TnEy8u/c03t97Wvn5js9OwonqBmw/Wo2iE+cwOCUOL28/jvW7SnH4TD0A4LlbxuHaUekuj/X3rcfwh43f4amfjsJNua1b1VPXMVltkJNa+kCCEWZGiMhNUMM0q1atQnZ2NgwGA3JycrB161a/7vfFF19Ao9FgzJgxwTxtl9BG8Oqi7399GgAwuk8CfnFJX1w7Kh0PT7+o0583b0ASMhIMqG224HXHOjY5WT2Rm9UTgL2I9cWtx/D7/x5QAhEAeOT9/ahpbJn622iyYNWmowCAj/dVdPpxk29y8SrgZ2ZEHqbhsBwRuQk4GFm/fj0WL16MZcuWobi4GFOmTEFBQQFKS30vllZTU4PZs2fjyiuvDPpgu0KktoMXQuA/jmDkjqkD8MSPR+K5W8YhMzG6059brZLwk3G9AQAmi/3nmpvVE+McwcimQz9gxSeHAQD3XjUYu397FQb0ikFVvRHLPzyoPM6bu8pw3hGc7Ck951dzNeo8cr2IWiW5tHv3pjv28CEi/wQcjKxYsQLz5s3D/PnzMWzYMKxcuRKZmZlYvXq1z/v96le/wi233IK8vLygD7YrROpCeXtKz+PU+SbE6NS4clhgjc06wg3j+ij/TorRISspGmP72oORsw0mmKw2XDUsFYuuHIjkWD2evGEUAHvh6+bDP8BiteGlbSXKY5xrNKOkqqFrT4JctHRfVflVv9NSMxJZry0iar+AghGTyYSioiLk5+e7bM/Pz8f27du93u/ll1/G0aNH8cgjj/j1PEajEbW1tS5fXUX+hBdpmRE5K5I/PM2vVu8drX+vWOQ4MiHjsnpCkiQkRGkxODUWgD1AWX79SOWP2sX9EnHLhL4AgNvW7cKCfxbh5LkmJMboMKK3vdiVPUpCq9Ekr0vj3++TXC/EYISI3AUUjFRVVcFqtSI1NdVle2pqKioqPI/hHzlyBA8++CBee+01aDT+1csuX74cCQkJyldmZtcVKmojcFzbYrXhv9+UAwBmjs4I2XHcc+UgpMTpccv4vsq2H4/tDYNWhad+Ogq94vQu+//22mGYMToDVpvApwcrAdgbqk1ydH3dU3q+y46dWmswWgDA58rOzjhMQ0TeBFXA6p6SFUJ4TNNarVbccssteOyxxzB4sP/TR5cuXYqamhrlq6ys6xZUi8SakZ3HzqKq3oge0VqlQVkoTB3cC18tuwqXD20ZJlp42UDse/QaXDkstdX+0ToN/vazsXj2lrHoEa1FSpwes/OylOGdPcyMhFS9HIzo/AxGOExDRF4ENLU3OTkZarW6VRaksrKyVbYEAOrq6rB7924UFxfjrrvuAgDYbDYIIaDRaPDJJ5/giiuuaHU/vV4PvV7fantXULpERlAw8laRPZibNjIdOs2F1+dOrtPxZvqoDORflAajxYo4gxbjsnoAAA5X1qG22Yx4g7YLjpLcNRjtwzSxfmdG2PSMiDwL6C+TTqdDTk4OCgsLXbYXFhZi4sSJrfaPj4/Ht99+i7179ypfCxYswJAhQ7B3715MmDChfUffCXSayHrDrK434sNv7cHjzy7u28beFy6dRoU4R9CREmdA38RoCAHsvYCGakwWG6rqjaE+jC7TMkzjb80Im54RkWcBNz1bsmQJfvnLXyI3Nxd5eXl48cUXUVpaigULFgCwD7GcOnUKr776KlQqFUaMGOFy/5SUFBgMhlbbLxTyG6YpQjIj/y46CZPVhlF9EjCyT0KoD6fDjOvbA6VnG1F04hzG9u2BqnoTspNjQnpMSzd8i/e/PoXX5l/Srvb64aI+4JqRyAr0iajjBByMzJo1C9XV1Xj88cdRXl6OESNGYOPGjcjKygIAlJeXt9lz5EKmjaA3TJtN4F+OJmPORaORICerJ97dexrrth/H6s1HYbLY8PfZubj6otbDhV1BCIFNhyphtgqs/PQwXr/9kpAcR1eSMyN+D9MwM0JEXgRVQLBw4UIcP34cRqMRRUVFmDp1qnLbunXrsGnTJq/3ffTRR7F3795gnrZLRFIB6/aj1The3Yg4vQYzQjiLpjPkZNkzDzVNZqWR2upN3/u6S6eqrDOiusEEwP5z39MNViNucEztjWYBKxG104VXzRhiyvRDmwjrDp/7T9dgReEhAMBPxvX2O5UeLi7KiMfvpl+ERVcOwuu3T4BWLWFP6XkUhygIOFDu2gtn1effo6bJjIff24cl6/cqDcIiSUtmxL+akUjKOhJRx4qsv1AdQOs0s8NsFdBpwmtl2Op6I+58fQ92HjsLANCpVfjFJVkhPqrOMW9ytvLvGaMzsGHPKbz8xXFl6m9XOugIRkb3ScC3p2rw6cFKXPPXLaiobQZgX5/nxghb2C/QPiNqZkaIyAtmRtw4r7ERjmPbD7+3HzuPnYVGJWH6qHS8tSAPg1PjQn1Yne62SfbAZOO35Sivaery5z9w2h6MXDMiDdNG2lcarqhtht4xO+sfO090+TF1tsALWNn0jIg8Y2bEjUtmxCIAXQgPJkCf7K/AB9+WQ62S8M7CSRE1e6YtI3onYHx2Ir4qOYvFb+xF3oAkZCVFY2TvBPRPjoVK1bkZLjkzMiw9HgUj0nH0hwaM7dsDv750AK78y2Z8c7IGe8vOY0xmj049jq7UYAq2gJWZESJyxWDEjcbpj5Y5jDIjtc1m/O69fQCAO6b271aBiGz+5Gx8VXIWXzq+ZHEGDZZcPRi3Tsr2ce/gNZmsyqJ9w9PjkRJvwIf3TFFunz4qHRuKT+EfO05EVDBS72h6xqm9RNReHKZxI0kty6GH05vmik8O40ytEdnJMbjnykGhPpyQuPqiVPx9di7uu3owfjY+E7lZPWHQqlDXbMFj/zmAFZ8c6pSi5ENn6mAT9sX+3NfXAYBf5Nlrdv7zzWmcdcy4iQSBNj3TcmovEXnBzIgHGpUKZqs1bKb3Gi1WvF10EgDwyIyLQrIq74VAkiRcfVGqS68Ri9WGF7Ycw58+PoRnPvsexWXnEaVVw6BV487LB2JIWvvraeQhmosy4j2u0TQ2swdG9I7HvlO1mPnsNozvl4gZYzJw+ZCUVvuGk0D7jKi5ai8RecHMiAdyZiRcgpFNh35AndGCtHgDpg7qFerDuaBo1CrceflAPH7dcADA1iNV+OTAGbz/9WnMenEH9p+uCfqx5em6zvUinkiShLsuHwi1SsLJc03YUHwKd7y6Gz/UtbSObzJZYQ2zP9KBFrBqWcBKRF4wGPGgpfFZePxx+M/XpwHYaxM6u1AzXM3O64d/L8jDsmnD8MSPR2B0Zg+cbzTjlr9/iX2nAg9IXtp6DBc9/BH++NF3yv0v8hKMAMCPRqSj6LdX4eVbL8bg1FiYrQLvFp8CABz9oR4T/vApfv3PouBOLgSEEEFkRuQgPzxeV0TUdRiMeKAJo8xIg9GCTw+eAQDMHBNZXVY7Wm6/RNw+tT9+cUkW/jFvPMb27YGaJjPueHW30sXV2Z8/PoTb1u3y2LDs30UnYRPA6k1HscexWJ+3zIisR7QOlw9JwdyJ9kLaN3eXQQiBv/3vCGqbLfjfd5VKtuFC12y2QU7kBFrAGm4ZICLqfAxGPAinlvCfHjyDZrNNmcZK/ok3aPHqbeORGq/H6ZpmvO/ILsmKTpzDs59/j8++q3SZmQMAFTXN+K6iDpLUssqzTq1C/17+LdQ3fXQ6DFoVjlTW4729p5XnttoEdh8/28a9LwzOQVO0nzVKLGAlIm8YjHigtK0Og09w8hDNzNEZHosnybs4g1bJUvx9yzFlpo0QAn/88DtlP/dhnC1HfgAAjOqdgDd/lYcBvWJw08V9XHrU+BJv0GLaCHtjtP/79zdw/jVzD3wuVI2OHiMxOrXfQ4PqMJylRkRdg8GIB0oBq4fUfWeqrG3GzGe34bnPPS/4duyHemz/vgpNJitMFhte2X4cmw/b/zDOjLCF8LrKLRP6IkanxqEzddhypAoA8Nl3lfjKKUPRKhhx/MynDu6FMZk98L/7LsMTPx4Z0PPKreFNjuzbLRPsqyp/eaw6uBPpYnJmJDqANY+0nE1DRF5waq8HGsebprmL3zRf2XEc35yswaGKOvwyLwvxBq1yW73Rgh8/9wVqmy3QqiUkRGlRVW/vWTF1cC8M6gYt3ztDQpQWN4/vizXbSvD8pqNIjNbhjx/ZsyLj+vbAntLz2Oc048ZqE9j2vT1ouXRw8DOXJmQnom9iNErPNmLywGT8+tIBeP3LUnxzsgaNJovfK+GGSoOj4Zm/xauAcwErh2mIyBUzIx5oNXKnyM590/zgm3JsP1qlPNe/Hb1CjBYb/vt1ucu+hQcqUNtsgSTZZyNU1ZuQEqfH7388Ai/Nzu3U44x0t07qB7VKwo5j1Zjx7DYcPlOPhCgtnr55LACg7GwTahrNAIBvTp7H+UYz4gyadnVTVakkPPCjoRiWHo+Hpg1Dn55RyEgwwGIT2HPifAecVecKtOEZ0JJxZAErEbm7sD9+hYi2Cz7BHSyvxZ2v74FWLeHDe6ag9GwjztS29J14c3eZkroHgPf32mtD7r5iEK4f2xsnzjZifL9EROm6Z4OzjtSnZzRuGd8X/9h5AokxOgxOjcU9Vw5GZmI0MhOjUHa2CftO12DSwGRsOWwPHicNSFZmhwTr2lHpuHZUuvL9hP5JeKf4FL4sqcbkQcmt9t92pApL3tyL+Cgt+iVF49pR6fjJ2D7tOoZgKT1GAsjgaMJsyjwRdR0GIx50RZ+RD/dVKM+xdMO36BFtX5HvujEZ+OCbcuwtO4/vK+swMCUO5xpM2OqoZ5g5OgP9kmPQL9m/mRvkn8evG44HC4a2mqY6sneCPRg55QhGHMWrlw7p+OZyE7IT7cHIMc9FrP/ceQKVdUZU1hnxfWU9Nh36AVdflBbQUElHCbTHCNAyTGPlbBoicsNhGg8C6TPyx4++w4hHPg648PCT/RXKv3cdP4fCA/ZeIXdePhCXOdqEv7XbPmzz4b4KWGwCF6XHY2BKbEDPQ/6RJMljv4zhGfbp0vtO16LsbCOKS88BsNfpdLQJ/ZMAAHvLzrfqbWK1CWVI7/fXDVeGdHaFaPZNoN1XAacOrBymISI3DEY80Pm5uug/dhzH6k1HUW+0YNm7+7wGL1X1Rvz+vwfwxH8PwGoTOFHdgO8q6qBWSVh0xUBlv7F9e2BwahxuzLWn3t/ecwrnGkzK9N0ZnDHT5UY4erfsO1WDVZu+h00Akwcmo3ePqA5/rn5J0UiJ08NktWGHW3C771QNapstiDNo8LPxfZVgSA5QulpDgCv2Ai1r07CAlYjccZjGAzkzYvLxprnl8A949D8HANg/8X1fWY9Xth/H/Cn9lX1sNoG1X5Tg6U+PoM7xSTIzMRpGi/2N/JL+ibjnqsHYfPgHfH2yBj+fYF/d9YqhKUiO1aOq3ohJf/wMTY5PydOd6guoa4zIsHdVLalqwMlzjQCAe67qnFWRJUnCtJHpWLf9ONZuK3FZSE+ewZPXPwkatQp5A5Lwxq4ybD8amqnADSZ5mCaAAlYVC1iJyDNmRjzQqD3Ppmk2W7F2WwluemEH5r78Faw2gevH9cbvrxsBAHj60yMui5+9suM4nvjgIOqMFqQnGAAAf/r4kDJr5prhaVCrJLx62wS8PPdi3DCuNwB7zcqLs3MwPCMejSYrhLBPM81MjO70cydXSbF6ZDiundkqMGlgEi7ul9hpzzdvcjZUkn1BvwOna5XtXziCEbmwNW+AfUjnQHktzjWYOu14vAlmmIYFrETkDYMRD3Re3jTXbCvB4/89gK9KzsImgKuGpWL59SNxU24mRvVJQJ3RguUbDwKwr8L63OdHAQCLrxqErf93OcZk9kC90YLDZ+oBAPkXpQEAEqK1uHxoiksH1XF9e+K/d0/G32fn4vqxvfHozOGdft7k2XCnNvv3XDm4U58rMzEaBSPtGbCXth4DYP9d2n3cXqsycYA9GEmJM2BQSiyEAL4s6frsSDAFrBpmRojICwYjHshvmma3qv+ys/Y0/bWj0rHtgcvx0pxc6DX2dtiPzhwOSQI2FJ/CB9+U4587T6Cq3ojMxCjceflAaNQqLL9+pDKjYHRmD6Q5PnF7I0kSrr4oFStmjcGoPj06/kTJL3I/kbz+SRif3XlZEdmvptqH+t7/+jROn2/C7hNnYbLakBZvwACn9W8mOrIjoRiqCaZmJJwWoCSirsVgxAO56ZnZ4voJTu54OmlAMvr0dB0yGde3JxZeNgAAsHTDN3h+sz0rcvflg5SpwsPS43Hn5faC1ZtyQ9MfggJ366R+WDZtGJ7+2Zgueb5RfXrgkv6JsNgElry5F298VQYAmDQw2SV7lufIkuwISTAS+DCNWsXZNETkGQtYPfC2umh1g70eJClW5/F+i68ajG3fV+PrsvMAgL6J0fiJow5Edu9VgzDr4kylDoEufNE6DW6f2r/tHTvQPVcOxq7jX2KnU8+RyYOSXPa5pH8iJAk4UlmPyrpmpMR13e9UUAWsjqCcwzRE5I6ZEQ+8NT2rdmRGkmI8ByNatQrP3DwGMY6uqHddMbDVSq6SJKF3jyiusEs+5Q1Iwkf3TMH143pDrZIQZ9Bg8kDX3iY9onUY7pjt8+mBSmX77/97AD9auQWVtc2tHvdMbTOe/PA7l0LrYCgL5QXSgZVr0xCRFwxGPGip+nfLjNTLmRG91/tmJcXgH/Mn4PfXDcdPx3EohoI3KDUOK24agx1Lr0DhvZeiV1zr3zt5tean/3cYjSYLvvi+Cmu2leC7ijr83VEA6+zR9/fj+c1H8eePDynbvi47jz9sPKgEGP4IroCVmREi8ozBiAc6uVOkUzDSbLaiwWQv2vM2TCMb17cnfpnXDyoVsx/UfilxBq/FzrPz+qFPzyicqTXi2c++x8Pv7VNu+9dXZahpMivf/1BnVDr9fnygAmarDUII3P/W13hxyzE89/n3fh9T+wpYGYwQkSsGIx7ImRGT05tmtaOXg06tQlwI1gIh8sSgVWNpwTAAwKpNR3H0hwYkxegwoFcM6o0WvP5lqbLvhj0nleLR841mbD9ajeKy8zhSaZ9q/trOE35lR4QQSs1IIKv2arzUYhERMRjxQOMhM9IyRKNjvQddUKaNTMPF/Xoq3y+dNgy/vsw+a2vtFyUwWqwQQmD9LvusnNR4+3DPB9+cxpuObQBQ22xx+d4buREfEOAwjVzAyswIEblhMOKBzkPNiFK82sYQDVFXkyQJD08fDoNWhckDk3H92N6YOToDafEG/FBnxOpNR7H9aDWOVTUgRqfG8utHAgA+3n9GWffoujH22pM120padR52J9eLqCQgSht4ZsS9fw8REccbPGh502z5BFflyIwkxngvXiUKlZF9EvDlQ1chWmdvwqdTSZg/JRtPfHAQKz89ovxOzxyTgUsHt6x9BADZyTF48vpR2HqkCqfON+HDfRU+F2VUWsHrNAFlCeWMIwtYicgdMyMetDQ9c8qMOGpGkr1M6yUKtYQorctU8tsmZeOhaUORGKNTakVuvrgv1CoJ00amKfvdmNsHUTo1ZufZF2r8a+FhJfvhSTDFq0DLbBqzVUAIBiRE1ILBiAdax5umc6dI55oRonCgUkm4Y+oAbP2/y/HYzOF46oZRGO1obX+tY/0blQTc4JiCPndiP6TG63GsqgEPv7ff6+O2LJLn/xAN0JJxBAAmR4jIGYdpPNBqWjdnaqkZ4TANhZcYvQZzJvZz2TY+OxH35w9GarwBqfH2acM9onV45uax+Nnfd+LtPSeRNyAJP81p3Sun0RR4jxGgZZgGsL+21KrAghkiilzMjHjQkk5uPUzjrfsqUTiRJAl3XTEIN+Zmumyf0D8J915lX5n4d+/uQ0VN6y6u9UGsSwO4Frs2Onr2EBEBDEY8ksfdLS59RuzDNMnMjFCEW3j5QIzsnYAmsxUf769odXvQNSNqlbJUQq1TMzYiIgYjHmg9LHUuD9MkMjNCEU6tkjB9lL2m5NODZ1rdrqzYqwt8mCUhSgsAqG1mMEJELRiMeOC+UJ4Qgn1GqFu5clgKAODLY2eVYZlT55vwzcnzSov5QDMjABAvByNN/q+DQ0SRjwWsHmjcMiN1RgtMjn8nsc8IdQMDesUiKykaJ6obse3ID8jJSkTByi2obW4JIgItYAWAeIM9GKnhMA0ROWFmxAOlZsQx/1DOisTo1IgKIjVNFG4kScKVQ1MBAP87WIm/fnrYJRABgL5J0QE/bnyUPYDhMA0ROWNmxAM5GDE5mp619BhhVoS6jyuHpWDtFyX4aF+FsjDeG3dcgjiDBhU1zZg8KDngx2wZpmEwQkQtGIx44L66qDKtl/Ui1I1c3C8RcXoN6hw1I9cMT8Ul/ZMAAMMzEoJ6THmYhpkRInLGYRoPdBrXAlaleJX1ItSN6DQqTB3SC4A9QH+wYFi7H1POjLBmhIicMRjxQFkoz+o2TMNpvdTN3HxxJiTJ3nskOzmm3Y8Xb3DUjHA2DRE54TCNB+5NzzhMQ93VlEG9cPDxH8Gg7ZjCbfYZISJPmBnxoKXPiD0zUsUCVurGOioQAVjASkSeMRjxQO7AarEJl4ZnycyMELUL+4wQkScMRjzQqFt+LGarUNalYQErUfu09BlpXTPSZLJ6XJiPiCIfa0Y80DkFIxabDWdZM0LUIZSpvU6ZkbKzjXhl+3G8ubsMDSYrXpqTi8uHpITqEIkoBJgZ8UBuBw8ARnNLMMJF8ojaJyHaHowYLTY0m62orG3G1X/djJe2laC22QKrTeC37+xDo4mzbYi6EwYjHshTewGgusEIR1d49IxmMELUHrE6DSTHy6uu2YJDZ+rQbLYhOVaPF36Zg949onDqfBOe/t+R0B4oEXUpBiMeSJKkFLGeqbXXi8TpNUozNCIKjkolIc6xwF5NkxnljhqR4RnxuGZ4Gh6/bjgAYM1Wexv6I2fq0Gy2hux4iahrsGbEC41KBbPViso6+5tlIutFiDpEfJQWtc0W1DablYLV9AQDAODKYam4ZngqPt5/Bgv+WQTAPjz68tyLMTqzR6gOmYg6GT/qe+GeGeEQDVHHSHDqNSJnRtIcwQgA/P66Ebh0cC/07xWDOL0GZxtMmPPyVzhUUQejxYrPD1Wi6MS5kBw7EXUOZka8kBufnal1ZEZYvErUIVoWy7OgoqYJQEtmBABS4g145bbxAIB6owU/f+lLfF12Hje/uANWm1CmBU8ZlIylBcNwUUZ8F58BEXU0Zka8kIORyjp7ZoTBCFHHkHuN1LhkRqI87hur1+CVWy/G0LQ4nGs0o7bZgl5xemjVErYeqcK1f9uK7d9XddmxE1HnCCoYWbVqFbKzs2EwGJCTk4OtW7d63XfDhg24+uqr0atXL8THxyMvLw8ff/xx0AfcVeTpvZXMjBB1KOdeIxWO11davMHr/j2idXht/gT85poheH3+BOxceiX+t+QyjOydACGAvSfPd8VhE1EnCjgYWb9+PRYvXoxly5ahuLgYU6ZMQUFBAUpLSz3uv2XLFlx99dXYuHEjioqKcPnll2PGjBkoLi5u98F3Jp0yTMOaEaKOJNeMVNY243yjvfmZc82IJ0mxetx5+UBMHJgMtUpC36Ro5PbrCcA+RZiIwlvANSMrVqzAvHnzMH/+fADAypUr8fHHH2P16tVYvnx5q/1Xrlzp8v0f/vAHvPfee/jPf/6DsWPHenwOo9EIo9GofF9bWxvoYbabRilgtX9yS2JmhKhDyIvlHT5TDwCI1qkRbwi8fC3OQzdXIgpPAWVGTCYTioqKkJ+f77I9Pz8f27dv9+sxbDYb6urqkJiY6HWf5cuXIyEhQfnKzMwM5DA7hEZl/9EYLfaVe3syGCHqEHLgcehMHQB7VkSSJF938fk4zIwQhb+AgpGqqipYrVakpqa6bE9NTUVFRYVfj/GXv/wFDQ0NuOmmm7zus3TpUtTU1ChfZWVlgRxmh9C6NThLjNF2+TEQRSI5MyIvs5DexhCN18dxZEbqmpkZIQp3QU3tdf8UI4Tw65PNv/71Lzz66KN47733kJLifSEsvV4PvT60K+RqVa7nk8gVe4k6hFwzIkuL9zyTpi1xzIwQRYyAgpHk5GSo1epWWZDKyspW2RJ369evx7x58/DWW2/hqquuCvxIu5hW7ZYZYQErUYeIdwtGgs2MxCmZEQYjROEuoGEanU6HnJwcFBYWumwvLCzExIkTvd7vX//6F+bOnYvXX38d1157bXBH2sWcV+5VqyTlUxgRtY88vCJrayaNN/JrspbDNERhL+C/sEuWLMEvf/lL5ObmIi8vDy+++CJKS0uxYMECAPZ6j1OnTuHVV18FYA9EZs+ejaeffhqXXHKJklWJiopCQkJCB55Kx9I5ZUZ6RuugUgVeYEdErclNz2S+eoz4wmEaosgRcDAya9YsVFdX4/HHH0d5eTlGjBiBjRs3IisrCwBQXl7u0nPkhRdegMViwZ133ok777xT2T5nzhysW7eu/WfQSZwzIyxeJeo4rWpG2jlMU2+0wGoTUPMDA1HYCmrsYeHChVi4cKHH29wDjE2bNgXzFCGndcuMEFHHiNKqoVFJsNgEgPbUjLS8fdUbLUiI0kIIgXqjRQlUiCg8cG0aL5yDkaRYBiNEHUWSJKWIVadWBb3UgkGrhs4xBV+e3nv/W98g54lPUVrd2DEHS0RdgsGIF1qnYRpmRog6ltywLNiGZ+6PI9eN7D5xFiaLDd9VdH3XZiIKHoMRLzROmREukkfUseTMSLD1IjL3lvBn6+2N1JrM1nY9LhF1LQYjXjg3PWMwQtSx5CLWYOtFZM4zaowWK+qM9gxJk4nBCFE4YTDihZaZEaJOI/caaW9mRGkJbzQr7eUBoJHBCFFYYTDihYazaYg6zcg+9h5DOX17tutxnDMj1fUtwQiHaYjCC9uKeqFTc5iGqLP8amp/3JjTB0mx7VvzySUYccqMcJiGKLwwM+IFC1iJOo8kSe0ORADXAtazDUZlO4dpiMILgxEv2PSM6MLXsj4Nh2mIwhmDES/kPiNRWjWidOoQHw0ReaIUsDab3YZpuF4NUThhMOKFnBnhEA3Rhcu1gJXDNEThigWsXsgL5TEYIbpwxTllRpy7JnOYhii8MBjxIkZn/9GkxLW/yI6IOke8U82IcNrO2TRE4YXBiBdXDkvBHVP7Y8aojFAfChF54ZwZMVlsynZmRojCC4MRL+IMWjw0bVioD4OIfIiPaqkZaZBaAhBmRojCC4MRIgpbcmbEvWCVBaxE4YWzaYgobMmzadxxmIYovDAYIaKwpVWrYNC2vI3Ji21zmIYovDAYIaKwJg/VAEBavH0VYJPVBovV5u0uRHSBYTBCRGEt3mmopk9itPJvDtUQhQ8GI0QU1pwzI+kJBg7VEIUhBiNEFNaci1iTYvSI0trXkuKMGqLwwWCEiMJavFNmJClWhyhH92QO0xCFDwYjRBTWXDMjOkTrmBkhCjcMRogorMVHtWRGEmN0yjANa0aIwgeDESIKa3F6p8xIrB5RjswIh2mIwgeDESIKa+7DNC0FrJZQHRIRBYjBCBGFtTi3Ala5ZqSZmRGisMFghIjCmpwZ0alViNVrlGEaFrAShQ8GI0QU1uQC1qRYHSRJYp8RojDkeclLIqIwMSazB64YmoLJA5MBgMM0RGGIwQgRhTWDVo21cy9WvpebnjEzQhQ+OExDRBGFwzRE4YfBCBFFFA7TEIUfBiNEFFFaZtOwzwhRuGAwQkQRhcM0ROGHwQgRRRQO0xCFHwYjRBRR2PSMKPwwGCGiiMJVe4nCD4MRIooo0Y4+I1y1lyh8MBghoogSpbO/rXGYhih8MBghoogSxcwIUdhhMEJEESXaUTNisthgtYkQHw0R+YPBCBFFFHk2DcDGZ0ThgsEIEUUUvUYFSbL/m0M1ROGBwQgRRRRJkpShGk7vJQoPDEaIKOKw8RlReGEwQkQRRw5GOExDFB4YjBBRxInWOqb3MjNCFBYYjBBRxDFwmIYorDAYIaKIoxSwcpiGKCwwGCGiiBMt14ywzwhRWGAwQkQRh8M0ROGFwQgRRRwO0xCFFwYjRBRxlKm9zIwQhQUGI0QUcRiMEIWXoIKRVatWITs7GwaDATk5Odi6davP/Tdv3oycnBwYDAb0798fzz//fFAHS0TkD7nPSCOHaYjCQsDByPr167F48WIsW7YMxcXFmDJlCgoKClBaWupx/5KSEkybNg1TpkxBcXExHnroISxatAhvv/12uw+eiMiTKJ39rY2ZEaLwIAkhRCB3mDBhAsaNG4fVq1cr24YNG4Yf//jHWL58eav9H3jgAbz//vs4ePCgsm3BggX4+uuvsWPHDr+es7a2FgkJCaipqUF8fHwgh0tE3dA/dp7A797dh4EpsXho2lCMyewJq03AahOI0qoRpVPDahOoazbDaLEhKVaHaJ2m1eMIIVDbbMHp800432iG2WoDAPRNjEZmYjTUKglCCBgtNtQbLWg0WmHQqpAQrYVeo/b4eEaLDQ1GCxqMVjSYLNBpVOgZrUNClBZqldTpP5vOYrRYoVWpoArhOVhtAk1mK6K1apfjsFhtEABUkgSVZF9M8ULUbLZi/+lanGswoX+vGGQlxYT17wTg/9/v1q8+H0wmE4qKivDggw+6bM/Pz8f27ds93mfHjh3Iz8932XbNNddgzZo1MJvN0Gq1re5jNBphNBpdToaIyF/9kqIBAN9X1uO2dbv9uk+sXoOEKC2idGqoJQm1zWacbzR7nZGj06hg0KjQYLLCamv9mU6jkqCSJMD+HyQJsFgFLB72heP2hCgtekbroFFJELAHLzKtWgWtWgWrTaDZYoXJYoNOo4Jeo4ZBq4JBo4ZWo4LNEXRZhYDNJmATwvFYgID9H/L3FptAg9GCeqMFVpuASgLUKgmSJEEtSY7HV0GvtT+PXv5eo4aAPbCqa7ag7GwjKuuMiNGpMTgtDukJBtQbrahtMqO22YzaJguMltY/x3iDFhk9DEiI0uKHehN+qG2GcPxshQBqmsyoazZDkiRoVBJi9Br0itWjR7QWRosNTSZ7QNdosqLBaIHRYg8WDVoVshJjEK1X49S5JlTWGVs9t1olIc5gv+YJUVrEG7QwaNVoMtsDRfmxASi3W20CRosVRosNRosNZqsNapUEtcp+fGqVChIAm7D/3K02ASHs39uvsYRonRpxBg3UKglmq4DZan+cZrMNJ6obYLa2XHOdWoWkWHugqpIk1BstaDBaYLHZr63V6TmsjueM1WuQHKtHnEHjuOYCPaN1SIkzQEDg9Pkm/FBnhMlqU34fLY5//+nG0fjRiDSPv5+dLaBgpKqqClarFampqS7bU1NTUVFR4fE+FRUVHve3WCyoqqpCenp6q/ssX74cjz32WCCHRkSkmDKoF9bfcQk2fluOTw6cQXlNMwD7HyDnwEElARq1CiZHZqPe6LlJWmKMDj2jtdCqVbAJgRPVjTBabDA5/vjJorRqGC1W2Bx/6B1//j2K0qoRo1crf9CFAM432gOgcNVgsqK49DyK/dy/rtmCU+eb2t7R8UfXaDHhbIOpzd2bzTYcOlPncx+rTfj98z55zo9j7CDJsTr0ijOgpKoezWYbymuald9ff9Q1W1DXHFyzP08BY1cJKBiRuae4hBA+016e9ve0XbZ06VIsWbJE+b62thaZmZnBHCoRdVMT+idhQv8kPDpzOCw2AY3jE7/ZakOj0Qq1WkKMY9ZNvdGCyjoj6pstaDBZYLMB8VEaxBu0SI03KLNzZFab/ROm0WJDrF6DGL0a0Tr7p12bTaDOaEGjyaJkI4Swf3rVqlUu+8rMVhvON5pxrtGEcw0mWIWABPvt8tuk1SZgstogAYjWaaBVSzA5PqE3m61otthgttg/qatU9syGWmV/n7VnZiQlQyM/pkqyZwdi9BpoVCoIIWdU7J/mTVYbjGabSzbA6HgulQToNWpE69To0zMKvXtE4WyDCd9V1KGq3oh4gxbxUVrEGzSIj9JCr1G5vOcLIXCu0YzymibUNJnRK1aPlHgDNCpJ+aMYb9AizqCFJNl/RvVGC36oM+JcoxkGjQoxeg2idWrl/9E6DQxaFX6oM+J4dSOaTBb07hGN9B4GaNWO87MJR7BoDwJrm8yocWRwmkw2x+PYH1O+7jVNZtQ2maFVt2SGdBoVNGrJ8Zj2x5MfW+0YDlI5sibyz90m7L9r9uBTKNkurVqCVq1C38Ro9OkZBUmSlN+xc40m1DSZYbUJxBm0iNGroVGp7BkZSYJKZQ+yVZIESQJqmyyorjei3mixD1UJoLrBhDO1zZAkICMhCinxehi0amgdj6NVS9CoVegVp+/ol6HfAgpGkpOToVarW2VBKisrW2U/ZGlpaR7312g0SEpK8ngfvV4PvT50PxQiihySZH+zlWnVKiREu9buxzn+6PlLrZKQmRjt8TaVSlJS//7SOv4QhPKPQUdIitVjUGpcgPfqGdDeQ/0YRchK0iArKabN/dITAnrqLiX/jnn7PfMmJQ4YmBLbSUfVeQKaTaPT6ZCTk4PCwkKX7YWFhZg4caLH++Tl5bXa/5NPPkFubq7HehEiIiLqXgKe2rtkyRK89NJLWLt2LQ4ePIh7770XpaWlWLBgAQD7EMvs2bOV/RcsWIATJ05gyZIlOHjwINauXYs1a9bg/vvv77izICIiorAVcM3IrFmzUF1djccffxzl5eUYMWIENm7ciKysLABAeXm5S8+R7OxsbNy4Effeey+ee+45ZGRk4JlnnsENN9zQcWdBREREYSvgPiOhwD4jRERE4cffv99cm4aIiIhCisEIERERhRSDESIiIgopBiNEREQUUgxGiIiIKKQYjBAREVFIMRghIiKikGIwQkRERCHFYISIiIhCKuB28KEgN4mtra0N8ZEQERGRv+S/2201ew+LYKSurg4AkJmZGeIjISIiokDV1dUhISHB6+1hsTaNzWbD6dOnERcXB0mSOuxxa2trkZmZibKysm6z5g3POfLPubudL9D9zrm7nS/Acw7XcxZCoK6uDhkZGVCpvFeGhEVmRKVSoU+fPp32+PHx8WF7oYPFc4583e18ge53zt3tfAGeczjylRGRsYCViIiIQorBCBEREYVUtw5G9Ho9HnnkEej1+lAfSpfhOUe+7na+QPc75+52vgDPOdKFRQErERERRa5unRkhIiKi0GMwQkRERCHFYISIiIhCisEIERERhRSDESIiIgqpbh2MrFq1CtnZ2TAYDMjJycHWrVtDfUgdYvny5bj44osRFxeHlJQU/PjHP8ahQ4dc9pk7dy4kSXL5uuSSS0J0xO336KOPtjqftLQ05XYhBB599FFkZGQgKioKl112Gfbv3x/CI26ffv36tTpfSZJw5513AoiM67tlyxbMmDEDGRkZkCQJ7777rsvt/lxTo9GIu+++G8nJyYiJicHMmTNx8uTJLjyLwPg6Z7PZjAceeAAjR45ETEwMMjIyMHv2bJw+fdrlMS677LJW1/7mm2/u4jPxT1vX2J/f40i6xgA8vq4lScKf/vQnZZ9wusb+6rbByPr167F48WIsW7YMxcXFmDJlCgoKClBaWhrqQ2u3zZs3484778TOnTtRWFgIi8WC/Px8NDQ0uOz3ox/9COXl5crXxo0bQ3TEHWP48OEu5/Ptt98qtz311FNYsWIFnn32WezatQtpaWm4+uqrlUUYw82uXbtczrWwsBAAcOONNyr7hPv1bWhowOjRo/Hss896vN2fa7p48WK88847eOONN7Bt2zbU19dj+vTpsFqtXXUaAfF1zo2NjdizZw9+97vfYc+ePdiwYQMOHz6MmTNnttr39ttvd7n2L7zwQlccfsDausZA27/HkXSNAbica3l5OdauXQtJknDDDTe47Bcu19hvopsaP368WLBggcu2oUOHigcffDBER9R5KisrBQCxefNmZducOXPEddddF7qD6mCPPPKIGD16tMfbbDabSEtLE08++aSyrbm5WSQkJIjnn3++i46wc91zzz1iwIABwmazCSEi7/oCEO+8847yvT/X9Pz580Kr1Yo33nhD2efUqVNCpVKJjz76qMuOPVju5+zJV199JQCIEydOKNsuvfRScc8993TuwXUCT+fb1u9xd7jG1113nbjiiitctoXrNfalW2ZGTCYTioqKkJ+f77I9Pz8f27dvD9FRdZ6amhoAQGJiosv2TZs2ISUlBYMHD8btt9+OysrKUBxehzly5AgyMjKQnZ2Nm2++GceOHQMAlJSUoKKiwuV66/V6XHrppRFxvU0mE/75z3/itttuc1nVOtKurzN/rmlRURHMZrPLPhkZGRgxYkREXHfA/tqWJAk9evRw2f7aa68hOTkZw4cPx/333x+2GUDA9+9xpF/jM2fO4IMPPsC8efNa3RZJ1xgIk1V7O1pVVRWsVitSU1NdtqempqKioiJER9U5hBBYsmQJJk+ejBEjRijbCwoKcOONNyIrKwslJSX43e9+hyuuuAJFRUVh2Xp4woQJePXVVzF48GCcOXMGTzzxBCZOnIj9+/cr19TT9T5x4kQoDrdDvfvuuzh//jzmzp2rbIu06+vOn2taUVEBnU6Hnj17ttonEl7nzc3NePDBB3HLLbe4rOj685//HNnZ2UhLS8O+ffuwdOlSfP3118pQXjhp6/c40q/xK6+8gri4OFx//fUu2yPpGsu6ZTAic/4UCdj/cLtvC3d33XUXvvnmG2zbts1l+6xZs5R/jxgxArm5ucjKysIHH3zQ6hc/HBQUFCj/HjlyJPLy8jBgwAC88sorSsFbpF7vNWvWoKCgABkZGcq2SLu+3gRzTSPhupvNZtx8882w2WxYtWqVy22333678u8RI0Zg0KBByM3NxZ49ezBu3LiuPtR2Cfb3OBKuMQCsXbsWP//5z2EwGFy2R9I1lnXLYZrk5GSo1epWkXNlZWWrT1rh7O6778b777+Pzz//HH369PG5b3p6OrKysnDkyJEuOrrOFRMTg5EjR+LIkSPKrJpIvN4nTpzAp59+ivnz5/vcL9Kurz/XNC0tDSaTCefOnfO6Tzgym8246aabUFJSgsLCQpesiCfjxo2DVquNiGvv/nscqdcYALZu3YpDhw61+doGIuMad8tgRKfTIScnp1VKq7CwEBMnTgzRUXUcIQTuuusubNiwAZ999hmys7PbvE91dTXKysqQnp7eBUfY+YxGIw4ePIj09HQlnel8vU0mEzZv3hz21/vll19GSkoKrr32Wp/7Rdr19eea5uTkQKvVuuxTXl6Offv2he11lwORI0eO4NNPP0VSUlKb99m/fz/MZnNEXHv33+NIvMayNWvWICcnB6NHj25z34i4xiEsng2pN954Q2i1WrFmzRpx4MABsXjxYhETEyOOHz8e6kNrt1//+tciISFBbNq0SZSXlytfjY2NQggh6urqxH333Se2b98uSkpKxOeffy7y8vJE7969RW1tbYiPPjj33Xef2LRpkzh27JjYuXOnmD59uoiLi1Ou55NPPikSEhLEhg0bxLfffit+9rOfifT09LA9XyGEsFqtom/fvuKBBx5w2R4p17eurk4UFxeL4uJiAUCsWLFCFBcXKzNH/LmmCxYsEH369BGffvqp2LNnj7jiiivE6NGjhcViCdVp+eTrnM1ms5g5c6bo06eP2Lt3r8tr22g0CiGE+P7778Vjjz0mdu3aJUpKSsQHH3wghg4dKsaOHXtBnrOv8/X39ziSrrGspqZGREdHi9WrV7e6f7hdY39122BECCGee+45kZWVJXQ6nRg3bpzL1NdwBsDj18svvyyEEKKxsVHk5+eLXr16Ca1WK/r27SvmzJkjSktLQ3vg7TBr1iyRnp4utFqtyMjIENdff73Yv3+/crvNZhOPPPKISEtLE3q9XkydOlV8++23ITzi9vv4448FAHHo0CGX7ZFyfT///HOPv8dz5swRQvh3TZuamsRdd90lEhMTRVRUlJg+ffoF/XPwdc4lJSVeX9uff/65EEKI0tJSMXXqVJGYmCh0Op0YMGCAWLRokaiurg7tiXnh63z9/T2OpGsse+GFF0RUVJQ4f/58q/uH2zX2lySEEJ2aeiEiIiLyoVvWjBAREdGFg8EIERERhRSDESIiIgopBiNEREQUUgxGiIiIKKQYjBAREVFIMRghIiKikGIwQkRERCHFYISIiIhCisEIERERhRSDESIiIgqp/wePuhNmD1eDdAAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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7+vTpg7lz5yIzMxNjx47Fww8/jF27dnX44omITK1URkSxKagQUWDzKoyYTCbk5eUhJyfH5facnBxs3brV7WOys7Nx6tQprF27FqIo4uzZs/jss88wadKkVj+P0WhEdXW1yxsRkTvy1l6Na88IwPNpiJTCqzBSXl4Oq9WK5ORkl9uTk5NRWlrq9jHZ2dn48MMPMWXKFOh0OqSkpCA2NhZ/+9vfWv08ixYtQkxMjPyWnp7uzWUSUQhpMWdE3fRtzWhmEyuREvjUwCoIgsufRVFscZvk4MGDmDt3Lv74xz8iLy8P33zzDQoLCzF79uxWP/6CBQtQVVUlvxUXF/tymUQUApqfTaNSCfLOGlZGiJRB482dExMToVarW1RBysrKWlRLJIsWLcKYMWPwu9/9DgBwySWXICIiAuPGjcPLL7+M1NTUFo/R6/XQ6/XeXBoRhShp14wUQAB7E6vZaoHRzDBCpAReVUZ0Oh2ysrKQm5vrcntubi6ys7PdPqa+vh4qleunUTvOjmCnOxF1VPNlGsBpey8rI0SK4PUyzfz58/Huu+9i5cqVOHToEJ544gkUFRXJyy4LFizAtGnT5PtPnjwZX3zxBd5++20UFBTgxx9/xNy5c3H55ZcjLS2t854JEYUkt2FEzVkjREri1TINAEyZMgUVFRVYuHAhSkpKMHToUKxduxYZGRkAgJKSEpeZIzNmzEBNTQ3efPNNPPnkk4iNjcWECRPwl7/8pfOeBRGFrOZzRgBAr+UUViIl8TqMAMCcOXMwZ84ct+9btWpVi9see+wxPPbYY758KiKiNpmtLXtGpGDC82mIlIFn0xCRojXNGXFXGWEYIVIChhEiUrTmW3sB9owQKQ3DCBEpmlQZcekZcZxPw8oIkTIwjBCRoknVD41zzwhP7iVSFIYRIlK0NueMMIwQKQLDCBEpmtutvRpu7SVSEoYRIlI0VkaIlI9hhIgUzeRmzggbWImUhWGEiBTNIm3t1bRcpmFlhEgZGEaISNHcb+1lzwiRkjCMEJGisWeESPkYRohI0aTA4e5sGqmfhIgCG8MIESmau3Hw8tk0ZoYRIiVgGCEiRXO7TCOd2svKCJEiMIwQkWLZbCIsNqky4rS1V+vY2svKCJEiMIwQkWKZbU1hw3lrr5Y9I0SKwjBCRIol9YsArlt7pSqJhWGESBEYRohIscxOW3e16paVEYtTWCGiwMUwQkSKJS3TqARArWrqGeEyDZGyMIwQkWK529YLABppmcbGMEKkBAwjRKRY0jKNrlkY0XGZhkhRGEaISLHkGSOaZpURx5INl2mIlIFhhIgUy2RtOQoeADSsjBApCsMIESmW1DOiUblfpjGzMkKkCAwjRKRYUtjQNV+mcVRKzKyMECkCwwgRKZbZzYm99j+zMkKkJAwjRKRYJjeH5Nn/zAmsRErCMEJEimVpZc6IXBmxcZmGSAkYRohIseSekVaGnnGZhkgZGEaISLHkZRpNs54Rx+4aUQSsrI4QBTyGESJSrNbGwTsPQWN1hCjwMYwQkWKZW2lg1TgdmscwQhT4GEaISLHMrUxgdQ4nnDVCFPgYRohIsUwW95URtUqAVBzh9l6iwMcwQkSK1VrPiPNt3N5LFPgYRohIsVrrGXG+TZrSSkSBi2GEiBSrac6I0OJ90qwRi41hhCjQMYwQkWJ5skxjsnCZhijQMYwQkWLJyzQaN2FExcoIkVIwjBCRYrXZM6Lhyb1ESsEwQkSK1WbPiEo6n4bLNESBjmGEiBRL6gdpq2fEwjBCFPAYRohIsaTKiKatrb1cpiEKeAwjAESRvzkRKZEnW3sZRogCX0iHkfmr92Dg7/+Lj34q8velEJEPPBp6xmUaooAX0mEEsJ9tUdto8fdlEJEPTG3OGeHWXiKlCOkwEmXQAABqGEaIFMnS1pwReegZwwhRoAvpMBLpCCO1RoYRIiVqe2uvYzcND8ojCnihHUb0WgCsjBApVVvLNDqNY5mGDaxEAS+kw0iUXBkx+/lKiMgX0om87sKIVBkxsYGVKOAxjICVESKlams3jXxqLysjRAEvpMNIpJ49I0RK1hRGWvaM6Dj0jEgxQjqMRBnsPSPc2kukTOY2ekaahp5xmYYo0IV0GJEqI9UMI0SKZPJo6BkrI0SBLqTDCBtYiZRN3tqrablMIx+Ux629RAGPYQRAo9nG356IFKit3TRSHwmHnhEFvpAOIxGOZRqAfSNESmS2tdEzIg89YxghCnQ+hZFly5YhMzMTBoMBWVlZ2Lx5c5v3NxqNeP7555GRkQG9Xo9+/fph5cqVPl1wZ9KqVTBo7X8F3FFDpCyiKLZzUJ60tZfLNESBTtP+XVytXr0a8+bNw7JlyzBmzBi88847mDhxIg4ePIjevXu7fczdd9+Ns2fPYsWKFejfvz/KyspgsQTGD/9IvRaNZiNnjRApjNUmQnTkDF0bDawmLsESBTyvw8iSJUswc+ZMzJo1CwCwdOlSrFu3Dm+//TYWLVrU4v7ffPMNNm7ciIKCAsTHxwMA+vTp07Gr7kTRBg3Ka42sjBApjPOWXa2bBlaN1MDKyghRwPNqmcZkMiEvLw85OTkut+fk5GDr1q1uH/Pvf/8bo0aNwuLFi9GzZ08MHDgQTz31FBoaGlr9PEajEdXV1S5vXSVSnsLKHTVESuJc8XB7No08Z4SVEaJA51VlpLy8HFarFcnJyS63Jycno7S01O1jCgoKsGXLFhgMBnz55ZcoLy/HnDlzcP78+Vb7RhYtWoSXXnrJm0vzGaewEimTc8jQqFqvjHDoGVHg86mBVRBcv/BFUWxxm8Rms0EQBHz44Ye4/PLLcdNNN2HJkiVYtWpVq9WRBQsWoKqqSn4rLi725TI9wvNpiJTJeRS8u+8/HHpGpBxeVUYSExOhVqtbVEHKyspaVEskqamp6NmzJ2JiYuTbBg8eDFEUcerUKQwYMKDFY/R6PfR6vTeX5rNIvX0kPMMIkbKYLa1v67Xf7thNw629RAHPq8qITqdDVlYWcnNzXW7Pzc1Fdna228eMGTMGZ86cQW1trXzb0aNHoVKp0KtXLx8uuXNxCiuRMrU1Ct75di7TEAU+r5dp5s+fj3fffRcrV67EoUOH8MQTT6CoqAizZ88GYF9imTZtmnz/e++9FwkJCbj//vtx8OBBbNq0Cb/73e/wwAMPICwsrPOeiY/kMMLKCJGiSBWP1sKI1EfCZRqiwOf11t4pU6agoqICCxcuRElJCYYOHYq1a9ciIyMDAFBSUoKioiL5/pGRkcjNzcVjjz2GUaNGISEhAXfffTdefvnlznsWHSA1sNawgZVIUaRlGmnXTHNabu0lUgyvwwgAzJkzB3PmzHH7vlWrVrW4bdCgQS2WdgJFJBtYiRRJXqbRtLdMw8oIUaAL6bNpACDKYG9g5TINkbK0NQoeADScM0KkGAwjnDNCpEjthRE2sBIpR8iHEU5gJVImKYy03jMiHZTHyghRoAv5MNK0tZeVESIlMTkaWDXtVUZsrIwQBbqQDyPybhr2jBApivMEVne07BkhUoyQDyNRjgmsRosNJgu/aREpRbsNrCpu7SVSipAPI1LPCMClGiIlaeoZaWWZxrHl18TKCFHAC/kwolYJCNepAXB7L5GSSLtkWt1No2IDK5FShHwYAZynsHJHDZFSmD0cemYTASubWIkCGsMIOIWVSInaa2DVON3OJlaiwMYwAk5hJVIiaZmm1Z4Rp9strIwQBTSGEXAKK5ESSbvf2pvACgBm7pQjCmgMI3CeNcKeESKlkJZeNK0s06hVAgTHu8w2hhGiQMYwgqYprDWsjBApRntbewGeT0OkFAwjaGpgZc8IkXK0t7UX4PZeIqVgGAF7RoiUyNTOBFagadsvd9MQBTaGETTtpuHWXiLlkJpStRr3PSNA00h4LtMQBTaGEXDOCJESSdt12+oZ0fGwPCJFYBhB026aWk5gJVIMo8UKoO1lGg0bWIkUgWEETrtpWBkhUgyj2V7tMGjbCiNsYCVSAoYRNIURNrASKYfR0TOi16hbvY+OlREiRWAYARDhWKapYxghUoxGs32ZxpPKCIeeEQU2hhE4T2BlGCFSCk8qI/LQM46DJwpoDCNoCiNGi41d90QKITWw6tuojGgdW3t5UB5RYGMYQdMyDcClGiKlaDR7UBnRcGsvkRIwjMBeytU7JjWyiZVIGeTKiKaNnhEOPSNSBIYRB+6oIVKWRnlrb1s9I9zaS6QEDCMO3FFDpCyeVEaaTu1lGCEKZAwjDhE67qghUgpRFJt207S5tZfLNERKwDDiIJ1PU2e0+vlKiKg9JqsNoiNfeLJMw8oIUWBjGHHg+TREymF0mhvS5jINt/YSKQLDiENTGGFlhCjQSefSCELbp/ZKW3tNHHpGFNAYRhykBtZa9owQBTxpFLxeo4IgCK3eTyNXRhhGiAIZw4iDtLW3zsQwQhToPBkFDwA6xxKOhQ2sRAGNYcRB2k3DOSNEgc+TQ/IAQKNyLNOwgZUooDGMOEi7abhMQxT4PK2MSFt7WRkhCmwMIw6Revs3NQ49Iwp8ngw8AwAdt/YSKQLDiEOkXgsAqGEYIQp4Rg9GwQMcekakFAwjDhGsjBAphqeVEY6DJ1IGhhGHpjkjDCNEgU7qGWmvMiIflMetvUQBjWHEoWkcPMMIUaBznjPSFi2XaYgUgWHEgQflESmHJ4fkAU1be7lMQxTYGEYcpKFnRouN37iIApzcwNrO1l4tt/YSKQLDiIM0Dh7gUg1RoJOXadqpjEhhhEPPiAIbw4iDVq2S15/ZxEoU2DwfeuZoYGUYIQpoDCNOuKOGSBk8rYzo2MBKpAgMI064o4ZIGbytjLAPjCiwMYw44Y4aImWQhp61d1Ce3MBqY2WEKJAxjDhpqoxY/XwlRNSWRrNnlREtKyNEisAw4kTqGeEyDVFg83QcvEbFrb1ESsAw4kTa3svD8ogCm+fj4Lm1l0gJGEacsDJCpAyej4Pn1l4iJWAYcRLpOLmXW3uJAlvTbhqeTUMUDBhGnETqtQAYRogCndTA2t4yDbf2EikDw4iTCKkywq29RAHN0wbWpqFnDCNEgYxhxEkUh54RKYLR48qI/VucTQRsnDVCFLAYRpxwN413Gs1W7DtVBVHkN3nqXnJlpJ2hZ9IyDQCYbayOEAUqhhEn3E3jnZfXHMTkN7fguS/3MZBQtzJ6OPRMWqYB2MRKFMh8CiPLli1DZmYmDAYDsrKysHnzZo8e9+OPP0Kj0WDEiBG+fNoux4PyPGey2PCvPWcAAB//VIyX/nOQgYS6TdOckfaGnjVVRri9lyhweR1GVq9ejXnz5uH5559Hfn4+xo0bh4kTJ6KoqKjNx1VVVWHatGm49tprfb7YrsaD8jy35ZdzqGm0IFxn/8101dYT+NsPv/j5qigUWG2iPMSsvcqIWiVAcOQRDj4jClxeh5ElS5Zg5syZmDVrFgYPHoylS5ciPT0db7/9dpuPe/jhh3Hvvfdi9OjRPl9sV+NBeZ77+ucSAMDdo9Kx8NaLAQDv/VjI6gh1OZOlKVS0t5tGEARoORKeKOB5FUZMJhPy8vKQk5PjcntOTg62bt3a6uPee+89HD9+HC+88IJHn8doNKK6utrlrTtIyzRGi41bAdtgtFiRe+AsAGDSJam4e1Q61CoBF+rNOFtt9PPVUbCTpq8C7YcRANA57mO08GuaKFB5FUbKy8thtVqRnJzscntycjJKS0vdPubYsWN49tln8eGHH0Kj0Xj0eRYtWoSYmBj5LT093ZvL9Jm0mwbgUk1bNh8tR43RguRoPbJ6x8GgVaN/j0gAwMGSKj9fHQU7KVRoVIK8dbct0lJivYlf00SByqcGVkEQXP4simKL2wDAarXi3nvvxUsvvYSBAwd6/PEXLFiAqqoq+a24uNiXy/SaTqOSf4tiE2vr1u6zL9HcNCwVKkeD4ODUKADAoZIav10XhQZpW297M0Yk0i8Z9SZrO/ckIn/xrFThkJiYCLVa3aIKUlZW1qJaAgA1NTXYtWsX8vPz8eijjwIAbDYbRFGERqPBt99+iwkTJrR4nF6vh16v9+bSOk2UXoMKi4lhpBUVtUZ8e9C+RHPzJany7UPSovHVnjM4eKZ7ltQodDWaPTuXRiJVRljtJApcXlVGdDodsrKykJub63J7bm4usrOzW9w/Ojoa+/btw549e+S32bNn46KLLsKePXtwxRVXdOzqu0AEZ420ymYTMf/Tvag1WjAwORIj0+Pk9w1OjQYAHCxhGKGu5ekoeInUmM7KCFHg8qoyAgDz58/Hb37zG4waNQqjR4/G3//+dxQVFWH27NkA7Essp0+fxj/+8Q+oVCoMHTrU5fFJSUkwGAwtbg8UEfKsEX7jAoC/fncMu4su4I5Le6L4fD02Hj0HvUaFN+4ZKS/RAE1h5ERFHeqMFpf+G6LO1DRjxLNlmnA9KyNEgc7rnxhTpkxBRUUFFi5ciJKSEgwdOhRr165FRkYGAKCkpKTdmSOBLMwxRMm5Yz9Unayow+vfHQUAbDx6Tr594a0XY1BKtMt9EyP1SIrSo6zGiMOlNcjKiANRV5C+NnWsjBAFDZ8aWOfMmYMTJ07AaDQiLy8P48ePl9+3atUqbNiwodXHvvjii9izZ48vn7ZbSL9tMYwAH+2wh8rMxAj0iLL38NwxsifuHuV+d9OQNC7VUNeTR8F7WhmReka4m4YoYLGW3oy0Di19wwtVRosVn+6y72J67qbBuGpgDxwprcGQtGi3O6cA+1LNhiPncIhhhLqQvEzjaWXEsWTYwMoIUcDiQXnNyJURS2h/4/rvvlJcqDcjNcaAay7qAZ1GhWG9YqBWuQ8iADBEamI9U40Gkz3MFJ+v765LphAhVS09rYyEybtpQvtrmiiQsTLSjBRGQr0y8uGOkwCAey7v7dFgKaBpmeZwaTVu/ttmHD9Xh2iDBst/k4Xsfolddq0UWryujHDoGVHAY2WkGQMbWHH0bA12nrgAtUrAlMs8n37bJyECBq0KjWYbjp+rg0oAqhstmLbiJ3nJh6ijvK2MhDsaWOu4TEMUsBhGmpFOAQ3lZZpcx1Czay7qgeRog8ePU6sEeRfNDRcnY+uz1+KW4Wmw2EQ898U+lNU0dsn1UmiRKiMezxlxbO2t59ZeooDFZZpm9HJlJHSXaX78pRwAMH5gD68f+8bUkSgor8OojDgIgoC/Th2BvacqcbKiHr+U1SIpyvNwQ+RO0zh4TyewSpURhhGiQMXKSDMGR2XEGKKVkUazFbtOXgAAn/o8EiL1uKxPvLzjRhAE9EmIAAAUVbCZlTquaRy8p2fTSD0jofk1TaQEDCPNNM0ZCc3KSN7JCzBZbEiO1qNfj4hO+ZgZCeEAgCLurKFO4O04eLkywmUaooDFMNKM9A0uVBtYpSWaMf0SW50n4q3e8fYwcpJhhDqBt+PgOYGVKPAxjDQT6pWRH49XAACy+3feVtx0RxjhzBHqDPJuGk8rI1ymIQp4DCPNSE1xodgzUt1oxr5TlQCAMf0TOu3jcpmGOpPXB+VxzghRwGMYaSaUh57tKDgPmwj0TYxAakxYp33c9Dh7GKmsN6OqwdxpH5dCk9HbyohjmcZsFWGyhN7XNZESMIw0I/eMhGBlROoXye7EqghgPxskMVIHgEs11HHynBGPt/Y2VVBYHSEKTAwjzYTyqb07T5wHAIzu2/mj2+UmVm7vpQ6SqpYGD7f2atUq6By/ZHAKK1FgYhhppqlnJLTKuY1mK46U1gAARvSO7fSPL4UR9o1QR0lVS08rI4DT+TTc3ksUkBhGmpHHwYdYZeRIaQ0sNhEJETqkxXT+lNTe0uCz83Wd/rEptBi9HHoG8HwaokDHcfDNGEJ0HPzPp6sAAEN7xnTafBFnrIz47mRFHTYdK8fJ8jqcrzNhzjX90D8pyt+X5TfejoMHeD4NUaBjGGkmVCsj+0/Zw8iwnjFd8vHZM+Ibi9WGu5ZvQ1mNUb7twJlq/PuxMV5VBoKJt+PgAVZGiAIdl2makbf2WmwQRdHPV9N99jkqI8N6dU0YkWaNnKlsgNkaWlWnjth54gLKaoyI0mswc2wmEiJ0OHK2Bm+tP+7vS/MbXyojnDVCFNgYRppx/gYXKk2sjWYrjp61N692VWWkR6Qeeo0KNtEeSMgzuQfPAgByLk7BH24egoW3DgUALFv/Cw6cqfLnpflNRyojnMJKFJgYRppx/gYXKoPPDjs1r6Z2QfMqAKhUApdqvCSKInIPlQIArh+SDACYdEkqJg5NgcUm4tnP98FmC53qHWD/O/H2oDygqWeEh+URBSaGkWa0agEqR/9mqAw+c16i6YrmVYkURt5a/wvu/d/teHXdkS77XMHg6NlaFJ9vgE6jwrgBTbNfFt46FFF6DfadrsJ3h8768Qq7n8UmQspfeg/HwQOsjBAFOoaRZgRBCLnBZ9J5NF21RCPJcGzv3VF4HluPV+DN9b/gl7KaLv2cSpZ70F4VGds/ERH6pl7zHlF6/GZ0BgB7sAul3ibnr0mvKiOOnpE69owQBSSGETecm1hDwb7T1QDs23q70vTsDNw+sidmjc3EiPRYAMA/d53q0s+pZFK/iLRE42zm2EwYtCrsPVWFLY4x/qHgnGNXkV6j8iqMhDvCXL0xNH7BIFIahhE35PNpQqAycr7OhGNd3LwqyUiIwOtTRuD3Nw/Bb6/uBwD4fPdp7q5x42x1I/aeqoIgANcOTmrx/oRIPe65vDcA4M0ffunuy/ObgyX24DwoNdqrJUVWRogCG8OIG03LNMH7Q7Kq3ow/fLUfY175ARabiB5R+i5rXnVnwqAkJEbqUF5rxIYj57rt8yrFRsffyfBesUiKcv+6PDS+L7RqATsKzyPv5IXuvDy/OXjGHkaGpEZ79ThWRogCG8OIG6FQGVnw5c/4YPtJNJitGJIajdfuGt6lzavNadUq3HFpLwDAp7uKu+3zKsWuk/ZDC7P7tX6CcmpMGCYNSwUAfOvoLwl2UmVkSJp3YYSVEaLAxjDiRrD3jBSfr8c3++0/vFZMH4U1c8di/MAe3X4dd2XZw8gPh8twyPFDhuykSkdWRlyb9xs7wP66/VR4vsuvKRD4XBlxhJEG7qYhCkgMI24Ee2Vk1dYTsInAuAGJuHZwcrdWRJwNSI7CyN6xsNpETPzrZkz+2xasP1Lml2sJJJX1Jhw/Zz9QcGTvtsPIFZnxAIB9p6qCfrrouRojymqMEARgUIp3Z/NwHDxRYGMYcSOYt/bWNJqxeqd9WeSBsZl+vhpgyd0jcPVFPaBWCdh3ugoLPt8XUltV3ckvqgQA9E2MQHyErs379ooLQ1qMARabiN0nK7v+4vxIWqLJTIxw2ersCfmgvCAPbERKxTDihnxybxAu0/xz1ynUGi3o2yMCVw3o/qWZ5jITI7Dq/suxfcG1MGhVKK1uxJGzoT17RFqiubSdJRrAPhfnir72vpIdhRVdel3+5usSDeBUGWEDK1FAYhhxQ+4ZCbLKiCiKWLX1BADggTGZUKn8szzjTo8oPUY7fqgG2u6as9WNeO7LffL5PV1td5Fn/SISaalmR5D3jfjavAoAEfIEVlZGiAIRw4gbUs9IsDWwFpTXoeh8PfQaFe64tKe/L6eFqy+yz9PYEGB9I39eewgf7SjCn9ce6vLPZbHasKe4EgBwaTv9IpLLHWFkT3FlUC4tSg46Dgb0qTIiL9NYQ+48HyIlYBhxI1h7RvY4ehGG9oyRy9aB5CrHjp5dJy6gNkAONDt1oR5f/1wCANj6S4Xbg9bWHy7DkdLOqZocLq1BvcmKKL0GA5IiPXpMZmIEekTpYbI0BZlgU2+yoKDc3tTbkcoIADQE2dc1UTBgGHEjaMOI4weVNIo90PRJjECfhHBYbCJ+DJAR5+/9eAJWx2/SJqsNm466LiH9fKoS96/aiQdW7eyUxtt8xxLNyIw4j5fRBEGQqyPBusX3cGkNRBFIjNS3OgSuLQatCtKmMc4aIQo8DCNuGIJ0mUYKIyN7x/r1OtoiVUc2Nvuh/8b3x/Dkp3tRcK62266lqsGMT34qAgAM7Wn/bTy32Sm53znOjzld2YBfyjp+bXLzqpev0ZWOMLK9IDibWKXm1Yt9qIoA9sAmVUc4a4Qo8DCMuKEPwspIo9kqDxYL1MoI0NQ3svHIObnScLi0Gktyj+Lz3adww9JNWPTfQzB1Q1D8cMdJ1JmsGJQShd9PGgLAPqDN4nSWznqnZtutxzsWBERRlJtQR2XEe/XY0f0SAdiXuNwtJSndcUcIHZjs2dKVO2HSFFbuqCEKOAwjbjQNPQueysiBM1Ww2EQkRurRMzbM35fTqiv7JkCnUeF0ZQMOOH4b/miHvToRZdDAbBXxzsYC/H3T8S69DrPVhvcdO48eHNcXozLiEBuuRWW9Wa5elNU0Yt/pKvkxW493bGnpWFktSqoaodeoMKqPZ82rkn49ItA7Phwmq01e4vqlrAY5r2/EXcu34qX/HMCWY4Gx9OWL4vP1AOyHLfpKGgnPHTVEgYdhxI1g7BmRBmmNSI/128RVT4Tp1Lh+cDIA4H/WHUG9yYIvd58GACy771IsmDgIAJB7qGt33Px3fynOVhvRI0qPycPToFGrMGGQvWqT61iakQ6zizLYy//bjlfI/SW+kPpRruybIP8b9JQgCPL1SVNsl60/jqNna7HzxAW89+MJTFu5AyccTaBKc7LCHkZ6x4f7/DE4hZUocDGMuBGMZ9MooV9E8rsbLoJWLWDj0XN4+rOfUWO0ICMhHGP6JeLWEfYtyftOVaKq3txl1yBVRe67ojd0jkpZzhB7SFp3sBRmq02ehzJtdAai9BpUN1rk3gZfSH0yvp4TdI0jjPxwuAyV9Sas2WffBfS7Gy7CxWnRsInAJzuVdyihKIooOt/xMCJPYQ3CZSwipWMYcSMYz6ZxrowEuj6JEZiR3QcA5G21917eGyqVgJQYA/onRcImAtsKOr7s4G7mxL5TVcg7eQFatYB7r+gt3z5uQA9E6TUoPt+AZz77GZuO2cPDdYOTcUVfe4+Hr0s1DSar3C9y1cBEnz7GFZnxCNOqcbbaiD+tOQSjxYZBKVGYc3U/zL12AADgs7zibum36UznaowwWmxQCUDPON+XGFkZIQpcDCNuyMs0Cvum3ZpzNUacrmyAIACX9Irx9+V45NEJA+RzWXRqFX7lOOEXAMb2t/+w3uxjD8TafSW4/E/fYcDza9H3ubV47ON8l/dLU2onDUt12UYaodfgr/eMgFol4Iv806hptCA+QodLesXKDaS+NrFuL6yAyWJDz9gw9OvhW5OmQavG2AH26/hn3ikAwD2X95aXcJKi9CivNeG7ZjuCAp1UFUmLDYNW7fu3LJ5PQxS4GEbckM6mCZZx8NISTf8ekYgyaP17MR6KCdPi6RsuAgDcPrInEiL18vukMOLLLJLC8jo8+elelNUYYbbaqyJf/3xG3oFSXmvEf/aeAQDMGNPyIMEJg5Lx59uHyn++aqD9kL8x/e2j7HeeOC9XHkRRxDOf/Ywp72xrdzvpJnmJJrFDPT1S3whgr/Dd5ljW0qpVuHtUOgDgY8d2ZaXojH4RgOfTEAUyhhE3lNwz0mi2YknuUew60TT86l977A2gnp51EiimXt4buU+Mx8LbLna5/Yq+8VCrBJyoqJd3WXjCbLVh3uo9aDBbkd0vAdsWTEBKtAGiCHnnzg+HymCy2jC0Z3SrS1pTLuuN524ahMRIvbyMMzApCgkROtSbrNjsWL5Zd+AsVu8qxo7C89jSTnCSwshVPvaLSK65qCmMTLokFTHhTeFzymXpEAR7RamowvO/N38rknfSdCyMcDcNUeBiGHFDyT0jS787hje+P4aZ7+9CWU0jDpVUy30X0x19GEoyIDkKeo3rzpIogxYjHUFhw5EyvLu5ALPe34mfT1W2+bHe/OEX7C2uRLRBg1fvGo7UmDAMcyxbSY+Vtu2Oa+dE44fG98Ou31+Hy/rYe0VUKgGTh6cBAJ7/cj/OVjfiT2sPyveXAoqzg2eqMfuDPFy3ZCOOn6uDWiUgu79v/SKSlBgDrsi0h7Vpo/u4vC89Plx+Xh/+dLJDn6c7SYEzvYOVkUjHrqfKLmx8JiLfBN4BJQFAqVt7j5+rxYotBQDs00P/8NV+SBPKJ12SisE+HDAWqMb0T8Sukxfwwr8PQOpB3Xj0HJ65cZDbE4nziy7gzfW/AABevn0Y0hyzVi7pGYPcg2ex3zEvJE86MdfDQ+qcPX3jRdh09BwKyusw6Y0tKK81Qq0SYLWJbmd8vPH9MXxzoFT+803DUhHdCctof//NKJTXGd32nvzmygxsOnoOn/xUjMevHYBwnQY2m4hztUYkR3s/Zr07nOyEnTQAkBFvn1FyokKZ25uJghkrI24YNFIYUc4yjSiKeOk/B2G2irikVww0KgHrDpzFtwfPQiUAT1w3wN+X2KnGORo1bSKQEKHDuAGJMFtFvLzmEG56YzPe3nAcpysbAAB1RgueWL0HVpuIW0ek4RZHBQNAU2XkdBUq603ySPdLfVjSCtdp8MY9I6FVCyivNQIA/njzEKhVAgrK6+TrkRw9WyPfZ8sz1+CNqSO8/pzuxIRrW22CnTAoCRkJ4ahqMOPz3adhs4l46IM8XPHn7/GnNQdb7C6yWG1Yd6AUlfWmTrk2X8jLNPG+DzwDgL497I8vOMcwQhRoGEbckBtYLdZOOfysO+QePItNR89Bp1bhjakjMeea/vL7bh3RE/2Tovx4dZ3v0t5xmHN1P/z26n744amr8Y8HLsf/u20o9BoVDpfW4C/fHMa4v/yAxz7Ox9Of/4wTFfVIizFg4a1DXT7OsJ72MFJwrk6e89E3MULeyeOtoT1j8MyNgxzXGIvfXJmB4Y7As8VpqcZoscq/oU+6JBW94sK7ZRidWiXI26bf+7EQ7209Ie+u+d/NhZj9f3kuPRUv/ecgHv4gD79avq1L57q0psFkxbkae7DraGUkM9EeRs5UNSiu6kkU7BhG3JB6FGwi5B0Xge5/N9uXZ2aNy0SfxAg8ek1/DO8Vgyi9Bo9fG1xVEcDeo/H0jYPwzI2DEBOmhSAI+M2VGdi+4Fr8+fZhuCIzHjYR+M/eM1jzcwkEAXj17uGICXNdBklwGo//wTZ7H4UvVRFnM8dmYvVDV+K9GZdDpRIw1tGn4bwVubC8DjbRPr01KUrf2ofqEneNSkeUXoOCc3V4eY29r+W2EWnQaVT49uBZ3PfuDlQ1mLH1eDk+2G7/O/mlrBYPfbALRkv3/hAvvmCvikQbNC7NuL6Ij9AhJkwLUeRSDVGgYRhxQ69t+mtp7OZvvr5oNFvl7btTLrNv39RpVPh09mhsXTABfRI7Vt5WkrgIHe69ojdWPzwaa+aOxc2XpEKtEvD4tQOQ3c99c6hUHdnlaF4d1cEwIggCruibIP/wlJaUfvylXF4GOXbWvhw0ICmy28fzR+o18r8TUQSuHZSE16eMwEezrkBsuBb5RZX49bs78Ozn+wAA1w1OQpRegx2F5/H0Zz93yjXUNJrxw+GzbofOOZO29XbkTBqJIAhydYRLNUSBhWHEDb1GBenngxLKuXuKK2G2ikiK0ruUsvUatWLminSFi9Ni8Oa9l+LYyxMx77qBrd5vWLNBcJ29BXpEeiwi9RpcqDfLW4il3pQBflo+m57dB2FaNZKj9fjLry6BIAgY1SceH826EvEROuw7XYWi8/alrdenjMDbv86CRiXgX3vOyMG3I17PPYYHVu3C8nYOPOyMMfDOmvpGajvl4xFR52AYcUMQBHl7r1EBTaw7HWPEL8uMD+hD8Pyl+c6a5pyn0kYbND5PQG2NVq3ClX3tQ9E2/2LvG5HDSHLnfi5PpceH47snr8J/Hx+PRKeBckPSovHJQ1ciMVIPlQC8cucliDJoMXZAIm4ZYW/8/ce2Ex3+/LtO2v/Nfri9qEV15FyNEbM/yMPKLYUociyndHRbr6SvVBlR6IGBRMGKYaQVUt9Id6+R++Inx4Czyx0zL8g7Q9OawsilGXHthhdfjHecN7PhsD2MHCuz76Tpl+SfMAIAPWPD3DbqDkyOwg9PXYXvn7za5dA+aW7J13tLUOHYLeQLq03EkVL78z9d2YAfm53n8/aG4/jmQCkWfn0QH/9kP9ivowPPJH0dQZPLNESBhWGkFdKOmkDf3mux2rDb0etwGcOIT+IidEiPtzex+jJfxBPSZNRdJ8/jXI0RhY7fzAf4MYy0JdqglfsrJCPSY3FJrxiYrDas3uX76b8nKupcphuvdjpJuNZowT8dH1sQAJPVfr/OWqZp6hmpVcxOOaJQwDDSCqUMPjtUUoM6kxVRBg0uSgmu7bvd6e6sdCRE6DDpktQu+fjp8eG4KDkKNhH4YNsJmK0iwnVqpMX4fgqtP/zmygwA9uUVq9Pyyo6CCrzwr/0efb1IVRGpKvPtgbM4X2efY/LF7lOoMVrQNzECHz94JRIidDBoVRjUSf+2MxMjIAhAdaNF/pxE5H8MI60waJRxPo20RDMqIw7qLlheCBWPXTsAeX+4Xi7jd4UJg+3VkX84tsv2T4rskiWhrjR5eBriwrU4XdmA3IP2+SSNZise+Sgf7287KR890JbDJfYm3usHJ2NYT3ul5ct8+wA26cTk6dl9cGXfBGx8+hps+t01LgcldoRB2xQAA6lvZMWWQkx4dQNOXVDOmUFEnYlhpBV6bfefT3Ouxoir/mc9LvvTd7h92Y9YtPZQu5/fuXmVAtu1jhN1pbNR+gfoEk1bDFo17rncfjjg4nWHYbLY8PFPRfLEWakxty2HHJWRQalR8hbjV9cdwUMf5KHgXB0i9RrcmdULgH0bclInj6mXdtQUBlDfyAfbTqCgvA47nQ64JAolDCOt8MdI+K3Hy3Gyoh7naozIL6rEO5sKsPS7Y63eXxRFeVcCm1cD38jecYhzGtylxDACALOv7ofESB0KztXhnY3HsXxj0/bc4x5smT1caq+MDEqJxu0jeyIrIw4NZqs8CfZXWb0Qqe+6Y7OkHTXHywNje29FrREnHPNUlLB7j6gr+BRGli1bhszMTBgMBmRlZWHz5s2t3veLL77A9ddfjx49eiA6OhqjR4/GunXrfL7g7tKVlRFRFHG+zoQ9xZW44LRufeqC/eySCYOS8PtJgwEAf990HPmOw9uc7SiowF3Lt6G81gSdRtViVgYFHrVKkBtZAf/NGOmoaINWHnn/Wu5RnK02ykuE7uZ31DSa8cH2kzhXY0RNoxnF5+3/zgelRCFCr8Fns0fjowevwI0Xp2BEeiwevqpvl15/oA0+c57bEujLwkRdxeswsnr1asybNw/PP/888vPzMW7cOEycOBFFRUVu779p0yZcf/31WLt2LfLy8nDNNddg8uTJyM/P7/DFdyWpgbWzvzlsOnoOo17+Dpf+v1zc9taPuPudbfL7pPXiYT1jMGtcX9w2Ig02EXjqn3tdQtH/bT+JKX/fjl0nL0CvUeGFyUPkrcgU2KS+ESBwd9J44s5Le2Fk71j5zw+NtweIovP1MFubvmYazVbMfH8X/vDVfsz/dI98OGBKtAFxjgZWQRCQ3S8Ry3+Tha8eGYPULm7qlfqCCgOkZyS/qFL+/0BvmCfqKl6HkSVLlmDmzJmYNWsWBg8ejKVLlyI9PR1vv/222/svXboUTz/9NC677DIMGDAAf/7znzFgwAD85z//afVzGI1GVFdXu7x1N2noWWd/c/hoRxEqnKohx8pq5QPIpMpIrzj7N+MXb7kYPaL0OH6uDm98b1+uMVls8v/fMbInNv7uGtx3RUanXiN1nfEDeyAhQodecWGdNsjLH1QqAS/dcjE0KgG94sLw+LUDYNCqYLaKKHZMTbXaRMz/dA9+cvQ1bT5Wjvd+PAHA3i/iL9JslxPldX45/K+5/OKmyicrIxSqvAojJpMJeXl5yMnJcbk9JycHW7du9ehj2Gw21NTUID6+9R6HRYsWISYmRn5LT0/35jI7hby1txOHnomiiDzHkssnD10pH5BW6Jgy2RRG7D+kYsN1ePk2+ymzK7YUoqSqAWv2nUFZjRFJUXq8cuclSInp3OY+6lrRBi3+O28c/v3oWMXvfrqkVyy+fWI8vpwzBgatGn0TXQeKLV53GGv3lUKrFjDaMYFW2m0zKCXaPxcN+7C3QSlRsNhErN3f/u6frmS1idhbXCX/WQlDFom6gldhpLy8HFarFcnJyS63Jycno7S01KOP8dprr6Gurg533313q/dZsGABqqqq5LfiYt8HLPmqK4aeFZ9vwLkaI7RqASPSY+UD7E6U18FmE3G6WWUEAHKGJOOyPnEwWmz463fHsGJLIQBg2ugM6DTsP1aipCiD28mnStS3RyR6OEK1tEvl+LlaNJqtchXk1buG469TR8hfUwAw2I+VEQC4bWRPAMCX+ae7/XNvOVaOJz/dixPldfilrBa1Rov8vkAfskjUVXz6adb8/BNRFD06E+Xjjz/Giy++iNWrVyMpKanV++n1ekRHR7u8dTdDF4yDl3a+DO0Z4/hN0rHFsLwO52qNMFltUKsEpDpVOwRBwLMT7c2Cn+wsxv7T1dBrVLiXSzMUYPo5jVr/qfA8TBYbUqINuGV4GpKiDbh/TKZ8X39WRgDgluFpEATgp8Lz3TbbQxRF/O+mAkxbuQOf7z6FuZ/ky98TJKyMUKjyKowkJiZCrVa3qIKUlZW1qJY0t3r1asycOROffvoprrvuOu+vtJtJu2k6c6tdXrMj6uXKSEWdvESTEm2ARu36smRlxOO6wU1/v3dc2jNofrOm4CGfiFtei83H7GfwjB+YKP+iMnt8P6REG9ArLky+r7+kxYbhCsdsnn/vPdPln89mE/HM5z/jT2sPwSbad1b9fKoKS749CgCKOpiTqCt4FUZ0Oh2ysrKQm5vrcntubi6ys7NbfdzHH3+MGTNm4KOPPsKkSZN8u9Ju1jRnpPN+U5HCiHREfZ+EpmUa6bcz5yUaZ0/feBGkFgPn3zCJAoVUGTl+rg6bjtoPvxs3oOmgvZhwLb6dPx7r5o2HVu3/JcbbpaWa3ae7/Jyapd8fw6e7TkGtEvDi5CFYeOvFACA3s0vfExrZwEohyuvvCPPnz8e7776LlStX4tChQ3jiiSdQVFSE2bNnA7D3e0ybNk2+/8cff4xp06bhtddew5VXXonS0lKUlpaiqqqqtU8REDr7bJqqBjOOOLY1ZmXYfyPLdFqmad682tzA5CisnHEZ/nfaKAxMVuZ8Cgpu0r/n83UmHDlbA0EAxvRPdLlPtEGLiC4caOaNG4emQqdR4VhZLQ6cadqxt+TbI5j8ty0uM4Ak/91XgruWb8W/9njea7Lm5xJ5B9yiO4ZhxphM3HNZb7lCCgBXOhp8jdzaSyHK6+8KU6ZMQUVFBRYuXIiSkhIMHToUa9euRUaGvYehpKTEZebIO++8A4vFgkceeQSPPPKIfPv06dOxatWqjj+DLiI123XWVrs9xZUQRftR6FLDn3QsenWjBT+fqgTQemUEAK6+qPU+GyJ/i9BrkBpjQElVIwD7vJxAXk6MCdPi2kFJ+O/+UqzZV4KhPWNQa7Rg+aYCmCw2fHuwFFMus4++F0UR/7u5AH9eexgAsPPEBWw6Wo6Ft17sNlwVnKvF57tP4UR5Pb4/bJ8sO3NsJu4eZd8ZqFIJ+PMdw3DHsq0YkByJnrH2r3tWRihU+fQrypw5czBnzhy372seMDZs2ODLp/A7vaMyUm/qnN9U8hxnTjgfUW8/tMuAM1WN2Hq8AgDQs40wQhTo+vWIlMPIeKclmkB107BU/Hd/Kb7ZX4qnb7gIG4+cg8kRCHYUnJfDyF++OSKPvc/ul4DtBRX4fPcpHCqpxhdzsuVKKgCUVTfi9mVbUdXQNMPkqoE9sMDRiC4ZmByFTU9fg3CdWh6Fz8oIhSr/L9wGqLhw+290lQ2dMxRJmi+S1SfO5XapibWm0b69r63KCFGgc25MHTcgsY17BoZrBiVBp1GhsLwOR87WYN2Bpub8HYXnIYoiyqob5SDy/E2D8eGsK/DJQ6OREKHDwZJq/OWbw/JjRFHEs1/sQ1WDGQOSIvH7SYPx3v2XYcX0US0a0wEgPkIHg1YtT1Dm0DMKVQwjrYiPsB9o5m7d2FsVtUannTSuw96kMCJJb6VnhEgJpO3qETo1RvaOa+fe/hep18gVnH/vOYP1h8vk952ubMCpCw1Yd9BetRjZOxYPju8LQRBweWY8Xr1rOADgvR9PYNNR++6hf+46hR8Ol0GnVuGt+y7FrHF9cc1FSW6DiDODH04JJwokDCOtiHVURjojjPzth1/QaLZhaM9oDEx2PY8kM6EpjKgEcKIqKdq4gT2g06hwZ1YvxQzlmzg0BYB9ynGN0YKkKD2Gp8cCALYXVGDdfnu15MaLU1wed82gJEwbbe+Ve/yTfNyx7Ef84V/7AQBP5gz0qtFcqoyYWBmhEBUYbe0BKN4RRmqMFpgsNp+/sZ4or8P/bT8JAHhu4uAWw+GcKyOpMWEBseWRyFf9ekRi/4s3KGrU/XWDk6FRCfISyfVDkhFl0GJvcSW+PXgW2wrs/Vw3NAsjAPDcTYOx9XgFfimrxQXHgXdj+idg1jjvTh7u7IZ5IqVhGGlFdJgWKgGwiUBlvQlJ0b5VLP7n2yOw2ERcfVEPZPdvuYae6RRG2LxKwUApFRFJTLgW2f0T5aWWnItTYBNFLN94HLmOJZpBKVEtllQBexP6BzMvxw+HyxAfrkNyjAHDe8V6Hcb0XTDXiEhJGEZaoVYJiA3X4XydCed9DCP7TlVhzc8lEATII92b6x0fLoceNq8S+cfEoSnYdPQcogwajO6bAKPFCrVKgNVmH4bmrioiSY0J6/DJ2fIEVlZGKEQxjLQhLlxrDyM+9o1Ig5FuviSt1bM4dBoVesaFofh8Q6sDz4ioa906Ig0/FZ5Hdr8E6DQq6DQqDE2Lxt5T9uGMNw5tPYx0hs4eskikNMqqp3YzaWDThTrftvducpzP0bzxrbmLHI1ufd2UgYmo64XrNHh9ygjc5RhKBgBXOKaiZiSEY1BK1049liojFpsIi5XVEQo9rIy0QZo1cqHe+8rImcoGHD1bC5UAjHXTK+Ls95OGYEz/REwc1rW/fRGR5+4elY7vD53Fb6/u79Gp5B0hHcwJACarrd2twETBhmGkDU2VEe/DiHRq6fD0WMSEa9u8b5/ECNyfyMPviAJJ/6RIfP/k1d3yuaQGVgBoNNsQHrhT9Im6BON3G+IcYeS8D5UR6dRSJYzEJiL/UqsEaNX26ovRwr4RCj0MI22I93HwmcVqkysjV13EMEJE7TNII+HN7Bmh0MMw0oamyoh3Dax7T1WhutGCmDAthveK7YIrI6JgI/WNNLIyQiGIYaQNvp5PIw1PGts/UVGTKInIf/SsjFAIYxhpg3Q+jbdzRrb8Yu8XuWogl2iIyDN6HpZHIYxhpA3xPmztFUURR0trAAAjesd2xWURURCSKyOcwkohiGGkDVLPSL3J6vFvK+W1JtQYLRAE+6h3IiJPcCQ8hTKGkTZEGzRyz4en1ZETFXUAgJ6xYfKIZyKi9hi4TEMhjGGkDYIgyFNYPe0bKTxnDyOZHO1ORF7gMg2FMoaRdjTtqPFse29hBcMIEXlPqoxw6BmFIoaRdnh7Po1UGemTwDBCRJ6TKiON3NpLIYhhpB3y+TRe9oxk9mAYISLPNTWwsjJCoYdhpB3yFFYPekZsNhGF5Y4wwsoIEXlBanhnZYRCEcNIO7w5n6a0uhFGiw0alYBecWFdfWlEFERYGaFQxjDSDm/Op5GqIr3jw6FR86+WiDwnTWDlOHgKRfyJ2Y64cM/Pp5GXaLiThoi8JJ/ay8oIhSCGkXZ40zMihZE+DCNE5CVWRiiUMYy0w5vzaU4wjBCRj6QGVg49o1DEMNKOeKfKiCiKbd5Xqoz0ZRghIi9JDawcB0+hiGGkHdIyjdFiQ0Mb3yQsVhuKztcDYGWEiLzHcfAUyhhG2hGhU0Pn2BlTUdv6Us2pCw2w2EToNSqkRhu66/KIKEjwoDwKZQwj7RAEAYmR9upIRRtNrNLk1YyEcKgcJ/0SEXmKlREKZQwjHugRpQcAnKsxtnqf4gsNAOwzRoiIvMWhZxTKGEY8kBjZfhg55egX6RXHMEJE3tNzHDyFMIYRD3hWGbGHkXRWRojIB6yMUChjGPGAFEbKa9sII+ftyzTpPJOGiHzAOSMUyhhGPMDKCBF1Nc4ZoVDGMOKBHlLPSCuVkZpGMyodB+nxtF4i8oU8Dt5ia3fAIlGwYRjxQHuVkVOOnTSx4VpEGbTddl1EFDykZRpRBExWLtVQaGEY8UB7YaTYsZMmnTtpiMhH0jINwL4RCj0MIx6QtvY2mK2oM1pavF+aMZIezyUaIvKNTq2C4JiXyJN7KdQwjHggQq9BuM5eQnVXHWFlhIg6ShAENrFSyGIY8ZC8VOOmifWUYydNL+6kIaIO4Eh4ClUMIx7q0cYUVqmBlTNGiKgjDFoOPqPQxDDioeZNrP/acxo/HD4LURTlZRqOgieijpAqIxwJT6FG4+8LUArnMFJ8vh6Pf7IHapWAz2aPRp3J/lsMZ4wQUUdwJDyFKlZGPCQt05TXGpF38gIAwGoT8dyX+wEASVF6eU4AEZEv5JHwrIxQiGEY8VCiU2VkT3GlfPuhkmoAHANPRB3HygiFKi7TeMh5JHx5nQkAEGXQoKbRPneEzatE1FHOI+GJQgkrIx6SekZOX2jAwTNVAIA/3z5Mfj8rI0TUUQa5gZWVEQotDCMeksJIRZ0JZquI+Agdbr4kFTlDkgEAl/SK9ePVEVEwYGWEQhWXaTyUEKlz+fPI9FgIgoA37hmJA2eqcGnvOD9dGREFC6kywgZWCjWsjHhIr1EjNrzpRN4R6bEA7N3vWRnxEKRDJYiIfCRVRrhMQ6GGYcQLUhMrAIzoHeu/CyGioMRx8BSqGEa8kOgURtgjQkSdjZURClUMI16Qmlj79YhATJi2nXsTEXmHlREKVQwjXkiOtoeREelsViWizsehZxSqfAojy5YtQ2ZmJgwGA7KysrB58+Y2779x40ZkZWXBYDCgb9++WL58uU8X629TLkvHzZek4sHxmf6+FCIKQtI4eB6UR6HG6zCyevVqzJs3D88//zzy8/Mxbtw4TJw4EUVFRW7vX1hYiJtuugnjxo1Dfn4+nnvuOcydOxeff/55hy++u/VPisKb916KQSnR/r4UIgpCrIxQqPJ6zsiSJUswc+ZMzJo1CwCwdOlSrFu3Dm+//TYWLVrU4v7Lly9H7969sXTpUgDA4MGDsWvXLrz66qu48847O3b1RERBRD4ojz0jMptNhCAg6Mcn2GwiCsprkXfyAirqTBiUEoWL02KQGKmHWmV/7iaLDTWNZqhVArRqFQQBsImAKIryfzVqFQwaFTTq9msNoiiizmRFVYMZVfVm9IwL81s/pFdhxGQyIS8vD88++6zL7Tk5Odi6davbx2zbtg05OTkut91www1YsWIFzGYztNqWT9xoNMJoNMp/rq6u9uYyiYgUSaqMbDtegZzXN0KvUaPRbIXJaoNWrUKYVg2rTUSdyQKj2YZwvRpReg2iDFpE6jXQa1WoN1lRb7KfmaUSBKhVAjQqAYIgwGixyTt19BoVVIKA6kb7DyKT1R6ARNF+LYIAJETq0Ss2DHERrf+AqjdaceRsDX4pq0V0mBaDU6ORGm3A+XoTymuN9rcaE4wWK8K0aoTp1DBo1S7/rxYEXKg3oaLOBKtNhADAKoqoN1lhstggCPbr1alV0GnU9v93/DnSoEFcuBZatQpnKhtwurIReo0KseFaqFUCymuMOF9vP09Mq1JBoxagVqmgVQvQqAVoVCpE6jWIDdciXKdGvcmKWqMF9Ub7f6W/l85i0Kqaxv5brDCabTBabGgw25+rOxE6+/3rTJ5XzNQqAQbH35PFKqLRYoVKEBBlsD/PWqMF1Q1mWGyi/Jjlv74UNw5N7cCz851XYaS8vBxWqxXJyckutycnJ6O0tNTtY0pLS93e32KxoLy8HKmpLZ/4okWL8NJLL3lzaUREijcoJQo6tQpGiw1Hz9b6+3Jw6kID9jqdUt6eczVGnKs51+r760xWr36gSkTR3kdj76WxePSY05UNLW5rRGBXnAxaFYb3ikVStAGHS6px/FwtbKJ3IURiD63N/75FGGuNLe6rVQuICdOik3OXV3waB9+8XCaKYpslNHf3d3e7ZMGCBZg/f7785+rqaqSnp/tyqUREijEgOQo7nrsWpysbUFlvhslqhUFrrwSYLCIazBYIgoAovQZ6jRr1JgtqGi2oNVpQY7TAaLYiXKdBuE4NQQAsVhFWUYTNZv+vXqOGQauCAAEmqxUWq4iYMC1iwrTQO5aIAMiVibJqI05XNqC6wYzWvsVr1Sr0T4rEgKRIXKg341BJNSpqTUiI1CExUo8eUfb/SlWeBsdbo6np/602EXHhOsRH6KDTqGATRagEAeGOyonNJsJoscFktcFksVcSTI63mkYzKhvMMJqtSI0NQ8/YMFhsIi7Um2CxikiM1CEhQm//+7CJsFhtMFtFWG0izDYbLFYRtUYzzteZ0WC2IkKnRoRegwidBhF6NfQadavP3Vs2UYTJYkODyQoI9q3ceo1Kfo1TYgzQOi2vSM9POh0+LlyHKIPG/nEcyUGAAEGwV8Gk17zRbIXRYoPRYv+vVq2CQWuvkNQ0WlBvsiDSoJFf+zCt2u/LYF6FkcTERKjV6hZVkLKyshbVD0lKSorb+2s0GiQkJLh9jF6vh16vd/s+IqJgFhehQ1yErv07BqisDI4+6Cw6jQoJkXokRLr+PFRBaLUnRKsGwnRqt+8LZF7tptHpdMjKykJubq7L7bm5ucjOznb7mNGjR7e4/7fffotRo0a57RchIiKi0OL11t758+fj3XffxcqVK3Ho0CE88cQTKCoqwuzZswHYl1imTZsm33/27Nk4efIk5s+fj0OHDmHlypVYsWIFnnrqqc57FkRERKRYXveMTJkyBRUVFVi4cCFKSkowdOhQrF27FhkZGQCAkpISl5kjmZmZWLt2LZ544gm89dZbSEtLwxtvvMFtvURERAQAEESpmzSAVVdXIyYmBlVVVYiO5sAxIiIiJfD05zfPpiEiIiK/YhghIiIiv2IYISIiIr9iGCEiIiK/YhghIiIiv2IYISIiIr9iGCEiIiK/YhghIiIiv2IYISIiIr/yehy8P0hDYqurq/18JUREROQp6ed2e8PeFRFGampqAADp6el+vhIiIiLyVk1NDWJiYlp9vyLOprHZbDhz5gyioqIgCEKnfdzq6mqkp6ejuLg4ZM684XMO/uccas8XCL3nHGrPF+BzVupzFkURNTU1SEtLg0rVemeIIiojKpUKvXr16rKPHx0drdgX2ld8zsEv1J4vEHrPOdSeL8DnrERtVUQkbGAlIiIiv2IYISIiIr8K6TCi1+vxwgsvQK/X+/tSug2fc/ALtecLhN5zDrXnC/A5BztFNLASERFR8ArpyggRERH5H8MIERER+RXDCBEREfkVwwgRERH5FcMIERER+VVIh5Fly5YhMzMTBoMBWVlZ2Lx5s78vqVMsWrQIl112GaKiopCUlITbbrsNR44ccbnPjBkzIAiCy9uVV17ppyvuuBdffLHF80lJSZHfL4oiXnzxRaSlpSEsLAxXX301Dhw44Mcr7pg+ffq0eL6CIOCRRx4BEByv76ZNmzB58mSkpaVBEAR89dVXLu/35DU1Go147LHHkJiYiIiICNxyyy04depUNz4L77T1nM1mM5555hkMGzYMERERSEtLw7Rp03DmzBmXj3H11Ve3eO2nTp3azc/EM+29xp78Ow6m1xiA269rQRDwP//zP/J9lPQaeypkw8jq1asxb948PP/888jPz8e4ceMwceJEFBUV+fvSOmzjxo145JFHsH37duTm5sJisSAnJwd1dXUu97vxxhtRUlIiv61du9ZPV9w5Lr74Ypfns2/fPvl9ixcvxpIlS/Dmm29i586dSElJwfXXXy8fwqg0O3fudHmuubm5AIC77rpLvo/SX9+6ujoMHz4cb775ptv3e/Kazps3D19++SU++eQTbNmyBbW1tbj55pthtVq762l4pa3nXF9fj927d+MPf/gDdu/ejS+++AJHjx7FLbfc0uK+Dz74oMtr/84773TH5XutvdcYaP/fcTC9xgBcnmtJSQlWrlwJQRBw5513utxPKa+xx8QQdfnll4uzZ892uW3QoEHis88+66cr6jplZWUiAHHjxo3ybdOnTxdvvfVW/11UJ3vhhRfE4cOHu32fzWYTU1JSxFdeeUW+rbGxUYyJiRGXL1/eTVfYtR5//HGxX79+os1mE0Ux+F5fAOKXX34p/9mT17SyslLUarXiJ598It/n9OnTokqlEr/55ptuu3ZfNX/O7vz0008iAPHkyZPybVdddZX4+OOPd+3FdQF3z7e9f8eh8Brfeuut4oQJE1xuU+pr3JaQrIyYTCbk5eUhJyfH5facnBxs3brVT1fVdaqqqgAA8fHxLrdv2LABSUlJGDhwIB588EGUlZX54/I6zbFjx5CWlobMzExMnToVBQUFAIDCwkKUlpa6vN56vR5XXXVVULzeJpMJ//d//4cHHnjA5VTrYHt9nXnymubl5cFsNrvcJy0tDUOHDg2K1x2wf20LgoDY2FiX2z/88EMkJibi4osvxlNPPaXYCiDQ9r/jYH+Nz549izVr1mDmzJkt3hdMrzGgkFN7O1t5eTmsViuSk5Ndbk9OTkZpaamfrqpriKKI+fPnY+zYsRg6dKh8+8SJE3HXXXchIyMDhYWF+MMf/oAJEyYgLy9PkaOHr7jiCvzjH//AwIEDcfbsWbz88svIzs7GgQMH5NfU3et98uRJf1xup/rqq69QWVmJGTNmyLcF2+vbnCevaWlpKXQ6HeLi4lrcJxi+zhsbG/Hss8/i3nvvdTnR9b777kNmZiZSUlKwf/9+LFiwAHv37pWX8pSkvX/Hwf4av//++4iKisIdd9zhcnswvcaSkAwjEuffIgH7D+7mtyndo48+ip9//hlbtmxxuX3KlCny/w8dOhSjRo1CRkYG1qxZ0+IfvhJMnDhR/v9hw4Zh9OjR6NevH95//3254S1YX+8VK1Zg4sSJSEtLk28Ltte3Nb68psHwupvNZkydOhU2mw3Lli1zed+DDz4o///QoUMxYMAAjBo1Crt378all17a3ZfaIb7+Ow6G1xgAVq5cifvuuw8Gg8Hl9mB6jSUhuUyTmJgItVrdIjmXlZW1+E1LyR577DH8+9//xvr169GrV68275uamoqMjAwcO3asm66ua0VERGDYsGE4duyYvKsmGF/vkydP4rvvvsOsWbPavF+wvb6evKYpKSkwmUy4cOFCq/dRIrPZjLvvvhuFhYXIzc11qYq4c+mll0Kr1QbFa9/833GwvsYAsHnzZhw5cqTdr20gOF7jkAwjOp0OWVlZLUpaubm5yM7O9tNVdR5RFPHoo4/iiy++wA8//IDMzMx2H1NRUYHi4mKkpqZ2wxV2PaPRiEOHDiE1NVUuZzq/3iaTCRs3blT86/3ee+8hKSkJkyZNavN+wfb6evKaZmVlQavVutynpKQE+/fvV+zrLgWRY8eO4bvvvkNCQkK7jzlw4ADMZnNQvPbN/x0H42ssWbFiBbKysjB8+PB27xsUr7Efm2f96pNPPhG1Wq24YsUK8eDBg+K8efPEiIgI8cSJE/6+tA777W9/K8bExIgbNmwQS0pK5Lf6+npRFEWxpqZGfPLJJ8WtW7eKhYWF4vr168XRo0eLPXv2FKurq/189b558sknxQ0bNogFBQXi9u3bxZtvvlmMioqSX89XXnlFjImJEb/44gtx37594j333COmpqYq9vmKoiharVaxd+/e4jPPPONye7C8vjU1NWJ+fr6Yn58vAhCXLFki5ufnyztHPHlNZ8+eLfbq1Uv87rvvxN27d4sTJkwQhw8fLlosFn89rTa19ZzNZrN4yy23iL169RL37Nnj8rVtNBpFURTFX375RXzppZfEnTt3ioWFheKaNWvEQYMGiSNHjgzI59zW8/X033EwvcaSqqoqMTw8XHz77bdbPF5pr7GnQjaMiKIovvXWW2JGRoao0+nESy+91GXrq5IBcPv23nvviaIoivX19WJOTo7Yo0cPUavVir179xanT58uFhUV+ffCO2DKlCliamqqqNVqxbS0NPGOO+4QDxw4IL/fZrOJL7zwgpiSkiLq9Xpx/Pjx4r59+/x4xR23bt06EYB45MgRl9uD5fVdv36923/H06dPF0XRs9e0oaFBfPTRR8X4+HgxLCxMvPnmmwP676Gt51xYWNjq1/b69etFURTFoqIicfz48WJ8fLyo0+nEfv36iXPnzhUrKir8+8Ra0dbz9fTfcTC9xpJ33nlHDAsLEysrK1s8XmmvsacEURTFLi29EBEREbUhJHtGiIiIKHAwjBAREZFfMYwQERGRXzGMEBERkV8xjBAREZFfMYwQERGRXzGMEBERkV8xjBAREZFfMYwQERGRXzGMEBERkV8xjBAREZFf/X/dv4BQ//voegAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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gZYSCsfmswKp05QFcj4aISEtRhZGlS5eiqKgIZrMZxcXF2LhxY6fnr169GmPHjkVycjLy8vJwyy23oL6+PqoGdxed7P6rYBghXzZP6DDoZOhkCZ5xzrBz2wAiIs1EHEbWrFmDefPm4dFHH8XOnTsxceJETJo0CRUVFUHP37RpE6ZPn45Zs2Zh7969ePPNN7F161bMnj075sZryTOzl2GE/Ch70xj0MiRJgsETWlkZISLSTsRh5JlnnsGsWbMwe/ZsjBo1CkuWLEFBQQGWLVsW9PwtW7Zg6NChuOeee1BUVITzzz8fd9xxB7Zt2xZz47XEyggFo44Z0bnvD2U9GoYRIiLtRBRGbDYbtm/fjtLSUr/jpaWl2Lx5c9DHlJSU4MiRI1i7di2EEKipqcHf/vY3XH755SF/jtVqhcVi8fvqbp73Gq7ASn7UMSN6dwhRxo3YOJuGiEgzEYWRuro6OJ1O5OTk+B3PyclBdXV10MeUlJRg9erVmDp1KoxGI3Jzc9GvXz/88Y9/DPlzFi9ejIyMDPWroKAgkmZGRamMuBhGyIfv1F7APasGABwcM0JEpJmoBrBKkuT3ZyFEh2OKffv24Z577sFvf/tbbN++He+//z7Ky8sxZ86ckM+/YMECNDY2ql+VlZXRNDMirIxQML4DWAFAzzEjRESa00dyclZWFnQ6XYcqSG1tbYdqiWLx4sWYMGECHnjgAQDAj3/8Y6SkpGDixIl44oknkJeX1+ExJpMJJpMpkqbFTHmT4a695Mt3nRHAO2aEi54REWknosqI0WhEcXExysrK/I6XlZWhpKQk6GNaW1shy/4/RqdzLx4lTqI3fu7aS8F4l4OX/X7lfUJEpJ2Iu2nmz5+Pl19+GStXrsT+/ftx7733oqKiQu12WbBgAaZPn66eP2XKFLz11ltYtmwZDh06hM8++wz33HMPzjnnHOTn52v3SmKkfOLlmBHy5Z1N4z+AVZnyS0REsYuomwYApk6divr6ejz++OOoqqrC6NGjsXbtWhQWFgIAqqqq/NYcmTlzJpqamvD888/jvvvuQ79+/XDJJZfgf//3f7V7FRpgZYSCsQV00yiVETvvEyIizUQcRgBg7ty5mDt3btDvrVq1qsOxu+++G3fffXc0P6rHcNdeCqZjN42yzggrI0REWuHeNB4ywwgFETi1V69URjibhohIMwwjHnru2ktBKKFDXYFVVrrzWBkhItIKw4iHumsvP/GSj8CpveqYEXbTEBFphmHEQ8fKCAVhDTFmhN00RETaYRjx0EkcM0IdeceMeKb26rgCKxGR1hhGPHQcwEpBqGFEHzCbhmNGiIg0wzDiwTBCwdgd7vvBFLA3DbtpiIi0wzDiwTBCwdgCKiPcm4aISHsMIx46mSuwUkeBi54Z1TEjDCNERFphGPFQwgh37SVf3r1pAisjvE+IiLTCMOKh4940FIR3nRFlozxl115WRoiItMIw4sFdeymY0HvT8D4hItIKw4iHd9defuIlL6U7JnBvGhvHjBARaYZhxEPZc4RZhBRCCDV0BC4Hz8oIEZF2GEY8ZG6ARgF8B6mq3TS8T4iINMcw4uHdtTfODaGThu9aIsaAbhrOpiEi0g7DiIesLnrGT7zk5htGlIGrBi56RkSkOYYRD7UywvcY8lDGi8iStyKi3CccM0JEpB2GEQ/vrr1MI+QWOK0X8C4Lz8oIEZF2GEY8uDcNBVLGhRh9w4i66BnvEyIirTCMeDCMUCB7wLRegBvlERF1B4YRDzWMcG8a8gjWTeOdTcMwQkSkFYYRDzWMcGAieSgDWA2efWkAn3VGeJ8QEWmGYcSDlREKZA82gFWpjLA7j4hIMwwjHhwzQoGCDWDVqxvlsZuGiEgrDCMeeoYRCmBzOgH4D2Dl3jRERNpjGPHw7trLNxlyszn8d+wFvKGVA1iJiLTDMOKh96wf4WIYIQ8lcChLwAM+s2m4OB4RkWYYRjw8WYSVEVJ51xnRqceM7KYhItIcw4iHUhnhmBFSKOuMGP0qI0o3De8TIiKtMIx4KJURTu0lhbebxncAqzK2iN00RERaYRjxUCojQnDcCLnZlKm9vsvBe+4TZQ0SIiKKHcOIh7JrL8DqCLkFrYzouegZEZHWGEY8dD7jAjhuhIDge9N4l4NnZYSISCsMIx5+lRGGEYLPbJogU3td7M4jItIMw4iHshw8wOm95GZTp/Z2XA4e4FojRERaYRjx8A0j/MRLQKhuGu/vOb2XiEgbDCMePlmElREC0PnUXoDjRoiItMIw4iFJklodcXE2DQGwOzpO7fWtoLEyQkSkDYYRH8obDSsjBPgOYPX+M5EkiQufERFpjGHEhzKjhmNGCACsQTbKA7wLn3F/GiIibTCM+NCzMkI+lFVWDXr/fybKjBobx4wQEWmCYcSH7AkjXGeEgODdNL5/ZmWEiEgbDCM+9Awj5MMeZG8awHfnXlZGiIi0wDDig5UR8hVsnRHAZ8wI7xMiIk0wjPhgZYR82YKsM+L+M/enISLSEsOID9kzm4a79hLgM2akQzeN+88cwEpEpA2GER/KWAAn148g+K7A6j+118ABrEREmmIY8aGsM8IPvAR4x4wEzqbhomdERNpiGPHhXYGVbzLknU3TcQCr5Pd9IiKKDcOID3VvGmYRQugBrMqYEU7tJSLSBsOID1ZGyJd3AGvgmBFlNg0rI0REWmAY8cFde8mXEjaUdUUUBlZGiIg0xTDiQ62M8BMvwRs29KE2yuN6NEREmmAY8aHu2svKCCH03jRc9IyISFsMIz503LWXPJwuAeU20IcYwGpjBY2ISBMMIz50XA6ePHzHg3RY9ExmZYSISEtRhZGlS5eiqKgIZrMZxcXF2LhxY6fnW61WPProoygsLITJZMKwYcOwcuXKqBrcnRhGSOFbHeu4Nw3HjBARaUkf6QPWrFmDefPmYenSpZgwYQKWL1+OSZMmYd++fRgyZEjQx1x//fWoqanBihUrMHz4cNTW1sLhcMTceK0xjJDC4VcZCeymURY9Y2WEiEgLEYeRZ555BrNmzcLs2bMBAEuWLMEHH3yAZcuWYfHixR3Of//997FhwwYcOnQImZmZAIChQ4d2+jOsViusVqv6Z4vFEmkzo8Jde0mhLHgmSd6QquDeNERE2oqom8Zms2H79u0oLS31O15aWorNmzcHfcy7776LcePG4amnnsKgQYMwYsQI3H///Whrawv5cxYvXoyMjAz1q6CgIJJmRo279pJCCRoGueM/Ee9y8KyMEBFpIaLKSF1dHZxOJ3JycvyO5+TkoLq6OuhjDh06hE2bNsFsNuPtt99GXV0d5s6dixMnToQcN7JgwQLMnz9f/bPFYumRQOLdtZdhJNGF2rEX8F0OnvcJEZEWIu6mAQBJ8v8PWgjR4ZjC5XJBkiSsXr0aGRkZANxdPb/4xS/wwgsvICkpqcNjTCYTTCZTNE2LiVoZYRhJeErQCJzWCwBG7tpLRKSpiLppsrKyoNPpOlRBamtrO1RLFHl5eRg0aJAaRABg1KhREELgyJEjUTS5+3DMCCmUoBE4eBVgZYSISGsRhRGj0Yji4mKUlZX5HS8rK0NJSUnQx0yYMAHHjh1Dc3OzeuzAgQOQZRmDBw+OosndR2YYIQ+7wzNmJGg3DdcZISLSUsTrjMyfPx8vv/wyVq5cif379+Pee+9FRUUF5syZA8A93mP69Onq+dOmTcOAAQNwyy23YN++ffj000/xwAMP4NZbbw3aRRNPeq7ASh52V/B9aQDvoFYOYCUi0kbEY0amTp2K+vp6PP7446iqqsLo0aOxdu1aFBYWAgCqqqpQUVGhnp+amoqysjLcfffdGDduHAYMGIDrr78eTzzxhHavQiPqrr0MIwnP7gjdTaNUS+y8T4iINBHVANa5c+di7ty5Qb+3atWqDsdGjhzZoWvnZMS9aUih3ANBp/aq64ywMkJEpAXuTeODu/aSQln0zKAP0k2jjhnhfUJEpAWGER86mXuOkJsSNPRBFz3zjBnhfUJEpAmGER/K8ACOGSFHp4ueecaMONhNQ0SkBYYRH6yMkELtpgm66JlynzCMEBFpgWHEBxc9I4WjkxVYuegZEZG2GEZ8cNEzUihriBg7W/SMlREiIk0wjPhQKyOcTZPwlMGpwQawKtN9OZuGiEgbDCM+lHVGnHyTSXjqAFZ9sG4a931i4zojRESaYBjxoWNlhDyUbhqDHGydEVZGiIi0xDDiQ1n0jGNGyK4OYO1s0TNWRoiItMAw4kPHAazkYe9kai8XPSMi0hbDiA+GEVIoXTCdbZTHyggRkTYYRnwwjJDC7upsBVauM0JEpCWGER/ctZcUdkfoRc+UgGJnZYSISBMMIz6UMMJde0lZ0KzT2TQMrUREmmAY8aHMpuGbDHU+gNXbncdNFYmIYscw4kOZxsk3GLKHsTcN4B1bQkRE0WMY8SFL3HOE3LyVkdDrjABc+IyISAsMIz6U8juzCHU+tVfucB4REUWPYcSHLLMyQm5KZSTYCqx6n0Gt7KYhIoodw4gP7669cW4IxV1nA1glSVLvFVZGiIhixzDiQ1ZnSfDTbqJTZlQFGzMCeCsmXGuEiCh2DCM+vFM249wQijubI3RlBAAMyv40vFmIiGLGMOLDu2sv32ASnVIZUTbFC2TQc+EzIiKtMIz44N40pFA2wTPqQ3TTyOymISLSCsOID4YRUticXVRGlCXhOYCViChmDCM+1DDCvWkSnqOTqb2+x1kZISKKHcOIDzWM8NNuwlNChjHEAFZvNw3vFSKiWDGM+GBlhBSd7U0D+O7cy8oIEVGsGEZ8cMwIKZSQEWqdEY4ZISLSDsOIDz3DCHnYO9mbBuCYESIiLTGM+PDu2sswkujUvWnkEJURddEz3itERLFiGPGhTON0MYwkvM72pgG8lRGOGSEiih3DiA9lSQlWRsjRRTeNcpyVESKi2DGM+FArI5xNk9CEEF1ulKccd3DMCBFRzBhGfLAyQoB/tSPU1F4luNp5rxARxYxhxIfyBiMEx40kMt9xIKEqI+psGgcrI0REsWIY8aHs2gtw4bNEZnd4r32oMSNGLnpGRKQZhhEfOp9PwVxrJHHZfQJGqKm93nVGeJ8QEcWKYcSHX2WEYSRheaf1SpCkUGGEK7ASEWmFYcSHTmY3DXkDhjKGKBiDzHVGiIi0wjDiwy+M8BNvwlJXXw0xeNX9Pfc/HRun9hIRxYxhxIfv8ABWRhKXMg7EGGLwKsCN8oiItMQw4kOSJO7cS2FVRrjoGRGRdhhGAjCMUFf70gBc9IyISEsMIwGUGTUMI4nLuxR8J2GEi54REWmGYSSAnpWRhOc7tTcUtZuG9wkRUcwYRgLIMt9kEp09nKm96q69rIwQEcWKYSSAUhnhzr2JyxFGZYSLnhERaYdhJIBaGeGbTMIKZwArFz0jItIOw0gAVkZI7aYJa9Ez3idERLFiGAkgSxwzkujCqoxwnREiIs0wjARQPg1zNk3iUrroOg8jHDNCRKQVhpEAXGeE7J5xIHq5k24az/fsHDNCRBQzhpEAXIGVlIXMDHpWRoiIegLDSACGEVJXYO2sMqKswMoxI0REMYsqjCxduhRFRUUwm80oLi7Gxo0bw3rcZ599Br1ejzPOOCOaH9sjdJyymfBskexNwzBCRBSziMPImjVrMG/ePDz66KPYuXMnJk6ciEmTJqGioqLTxzU2NmL69On4yU9+EnVje4J3ZU1WRhKVQ53aG/qfh1HPWVdERFqJOIw888wzmDVrFmbPno1Ro0ZhyZIlKCgowLJlyzp93B133IFp06Zh/PjxUTe2Jxi5zHfCC2sFVpljRoiItBJRGLHZbNi+fTtKS0v9jpeWlmLz5s0hH/fKK6/gu+++w8KFC8P6OVarFRaLxe+rpxg9gxZt3I01YdnCmNrLMSNERNqJKIzU1dXB6XQiJyfH73hOTg6qq6uDPubgwYN4+OGHsXr1auj1+rB+zuLFi5GRkaF+FRQURNLMmCifhm18k0lYSmWksxVYuVEeEZF2ohrAKkn+/0kLITocAwCn04lp06bhsccew4gRI8J+/gULFqCxsVH9qqysjKaZUWFlhJSAYex0ACv3MCIi0kp4pQqPrKws6HS6DlWQ2traDtUSAGhqasK2bduwc+dO3HXXXQAAl8sFIQT0ej3WrVuHSy65pMPjTCYTTCZTJE3TDD/xkt0zKFUZFxKMep9w1hURUcwiqowYjUYUFxejrKzM73hZWRlKSko6nJ+eno49e/Zg165d6tecOXNw2mmnYdeuXTj33HNja303YGWE1AGs+q67aVgZISKKXUSVEQCYP38+br75ZowbNw7jx4/HSy+9hIqKCsyZMweAu4vl6NGjePXVVyHLMkaPHu33+IEDB8JsNnc4frLgbBpSpnUbOqmMKONJHC4RspuSiIjCE3EYmTp1Kurr6/H444+jqqoKo0ePxtq1a1FYWAgAqKqq6nLNkZOZWhnhJ96EZQ9nAKtPULE7hbruCBERRS7iMAIAc+fOxdy5c4N+b9WqVZ0+dtGiRVi0aFE0P7ZHKOV3dtMkLns4K7D6BBWHywUjd1YgIooa/wcNoFRG2E2TuBzqOiNdjxkBuFovEVGsGEYCsDJC4exN4xtUHAyuREQxYRgJYGJlJOGFszeNJEk+myqyMkJEFAuGkQDqCqysjCQsZcdmg9z5oFRl4TPeK0REsWEYCaBM7eVy8IkrnL1pAO+9wsoIEVFsGEYCGLjoWcILZ28a3+9zzAgRUWwYRgJw0TMKZ28awDumhLNpiIhiwzASwLvoGcNIogpnACvgHVPi4P40REQxYRgJoG6A5uCn3USlbH7XdTcNq2hERFpgGAmglOatfINJWEoQ7aqbRpl5xW4aIqLYMIwEUAaw2jmANWE5wqyMcOdeIiJtMIwE4NReUmZSdTW1Vwkrdo4ZISKKCcNIAGX3VY4DSFzKuiG+O/MGo5dZRSMi0gLDSACjTgeA64wkMnWjPH1X3TRcDp6ISAsMIwEMrIwkNCGE2kWn76IyYuBsGiIiTTCMBFBn07AykpCcPlUOQ5hTezmAlYgoNgwjAfhpN7H5TtPtagArFz0jItIGw0gAE/emSWi+M2PC3ZvGxsoIEVFMGEYCKJ+GXcK/ZE+JwXdmTJezadRuGgZXIqJYMIwEUPamAVgdSUTKzBidLEGWO6+MGDlmhIhIEwwjAXzHCXDhs8RjV2fSdB5EfM/homdERLFhGAngO4OClZHEowxg7WpfGoCzaYiItMIwEkCSJPWNiDNqEo8y/qOrwauA70Z5vE+IiGLBMBKEMm6EbzKJR+ma62paL+CzHDwrI0REMWEYCUL5xMtumsSjLgUfRhhRVuvlbBoiotgwjAShVEY4gDXxKAuYhdVN46mMcG8aIqLYMIwEoXwqZmUk8dgc4VdG9BwzQkSkCYaRILxjRviJN9GolZEwpvZy6wAiIm0wjARhZGUkYSnBwnfxu1CUwMKpvUREsWEYCYKzaRKXUg2LqDLCMSNERDFhGAlCeZOxsjKScNpsTgBAklHX5bnKrCvOpiEiig3DSBBczCpxtdgcAIAUo77Lc/U6ji0iItICw0gQRr37UzHHjCSeFqsnjJjCCCMyQysRkRYYRoIwsjKSsJqt7m6aFFM43TTKOiO8T4iIYsEwEgQXPUtcrRFURgzspiEi0gTDSBBc9CxxRTZmhANYiYi0wDAShLrOCN9kEo63myacyognjHBqLxFRTBhGgjAo64w4+CaTaJQBrKlhjBlRdu1lBY2IKDYMI0F4KyPOOLeEelpEs2lYGSEi0gTDSBDcmyZxRTJmRAmtHDNCRBQbhpEguDdN4mqJYMwIFz0jItIGw0gQBg5gTVjNajdNOGNGlG4a3idERLFgGAlCXWeElZGE06oOYOU6I0REPYVhJAjuTZOYXC6BFs9GeclhjBnhfUJEpA2GkSBMrIwkpFa7d/ZUJJURBysjREQxYRgJwlt+ZxhJJEoXjSwBZkPX/zS8U3t5nxARxYJhJAhlzIiVlZGEog5eNeohSVKX5yuLntmdAkKwOkJEFC2GkSBYGUlMkUzrBbxjRgAufEZEFAuGkSC46FliimRaL+ANrQDHjRARxYJhJAguepaYWm3hT+sFvGNGAMDOcSNERFFjGAnCWxnhG0wiUSoj4UzrBQCDzMoIEZEWGEaCMLAykpAiHTMiyxI8i7AyuBIRxYBhJAhlYCKXg08sLerqq+GNGQF896fhvUJEFC2GkSC4HHxiUnfsDbMyAvju3MtuGiKiaDGMBGHkp92E1GKNPIxw4TMiotgxjATBykhialbGjIQ5gBXwX/iMiIiiE1UYWbp0KYqKimA2m1FcXIyNGzeGPPett97CpZdeiuzsbKSnp2P8+PH44IMPom5wT+BurImp1RbZOiMAN8sjItJCxGFkzZo1mDdvHh599FHs3LkTEydOxKRJk1BRURH0/E8//RSXXnop1q5di+3bt+Piiy/GlClTsHPnzpgb313UyojTxWW+E0gs3TQMrkRE0Ys4jDzzzDOYNWsWZs+ejVGjRmHJkiUoKCjAsmXLgp6/ZMkSPPjggzj77LNx6qmn4n/+539w6qmn4p///GfMje8uvitr8k0mcTRHEUa8O/eyMkJEFK2IwojNZsP27dtRWlrqd7y0tBSbN28O6zlcLheampqQmZkZ8hyr1QqLxeL31ZNMeu9fC6f3Jg5lnZFIpvYqC59xbxoiouhFFEbq6urgdDqRk5PjdzwnJwfV1dVhPcfTTz+NlpYWXH/99SHPWbx4MTIyMtSvgoKCSJoZM7/KCAexJgx1am8kA1g5ZoSIKGZRDWAN3F5dCBHWluuvv/46Fi1ahDVr1mDgwIEhz1uwYAEaGxvVr8rKymiaGTWdLEEnc+GzRBPdmBEOdiYiilX4/+sCyMrKgk6n61AFqa2t7VAtCbRmzRrMmjULb775Jn760592eq7JZILJZIqkaZoz6CQ4XYLTexNIpMvBA4DBE1o5ZoSIKHoRVUaMRiOKi4tRVlbmd7ysrAwlJSUhH/f6669j5syZ+Mtf/oLLL788upb2MHXnXr7JJAQhhM8KrJFM7fVURjhmhIgoahFVRgBg/vz5uPnmmzFu3DiMHz8eL730EioqKjBnzhwA7i6Wo0eP4tVXXwXgDiLTp0/Hs88+i/POO0+tqiQlJSEjI0PDl6It7tybWNrsTiizuFOjWYGV9wkRUdQiDiNTp05FfX09Hn/8cVRVVWH06NFYu3YtCgsLAQBVVVV+a44sX74cDocDd955J+688071+IwZM7Bq1arYX0E3MXLn3oSiTOuVJCDJEHllhHvTEBFFL+IwAgBz587F3Llzg34vMGCsX78+mh8RdwZWRhJKi89S8OEMxlboOdCZiChm3JsmBKUyYmVlJCF4Z9KEXxUBuOgZEZEWGEZC4P40iUUNIxGsMQJ496bhomdERNFjGAmBO/cmFu9MmsjCCNcZISKKHcNICEYdx4wkkmZ1jZFIu2k4m4aIKFYMIyGwMpJYWj3dNJFM6wUAvczQSkQUK4aREJRPvJwlkRiUqb3JEY4ZUfem4ZgRIqKoMYyEwMpIYolmKXjA253HbhoiougxjIRg4JiRhKIMYE2NcMyId9deVkaIiKLFMBICB7Amlmh27AW8Y0YcLt4nRETRYhgJgd00iSXWdUbsDlZGiIiixTASgkHdtZdvMomgOcoxI95dexlaiYiixTASAisjicXSbgcApJqjW/SMG+UREUWPYSQEDmBNLN/XtQAACvonRfQ473LwvE+IiKLFMBICKyOJo6ndjtomKwDglOzUiB6rDGC1ccwIEVHUGEZCMKpTNhlG+rrv61oBAFmpRmQkGSJ6rJ6VESKimDGMhMDKSOI4VNcMADglK7KqCOC76BkrI0RE0WIYCcE7m4ZhpK/77rh7vMgp2SkRP1bPChoRUcwYRkJgZSRxHDruqYxEE0bURc9YGSEiihbDSAjK2IEdFT+g1tIe59Zor7HNjn/vqUKbzRnvpsTdIaUyEkU3jYGVESKimDGMhPCTkTk4LScNdc023PX6zj61EVp9sxW/WLYZv1q9A9cv/xzVjX0vbIXL5RIor4ulm0aZAs7KCBFRtBhGQkgy6rDsl2ch1aTHl+Un8LsPvol3kzTR2GrHzSu+xMFad9fEnqONuPL5Tdhd2RDfhsVJtaUdbXYn9LKEgszkiB+vrjPSh8IqEVFPYxjpxCnZqXjqFz8GACz/9JD6Cbq3sjlcuGXVl9hXZUFWqhF/vvUcnJaThtomK65f/jne3X0s3k3scUoXzZAByeqg5Ugoj+GYESKi6DGMdGHymDxMPDULALB2T1WcWxOb5z46iB0VDchIMuC12efiwhHZ+NuvxuOSkQNhdbhwz+s78cy6byBE4ryxxjKtFwD0MseMEBHFimEkDFf8OA8A8K//9N4wsqPiByxd/y0AYPG1YzAyNx0AkGY24E/Tx+H2C04BADz38bdYt68mbu3saUplZFgU40UAINWzsV5jqz2hQhwRkZYYRsJQ+qNc6GQJ+6ssvbKrptXmwH1/3Q2XAK4+Ix+Tx+T5fV8nS3hk8ijMOr8IAPD6lxXxaGZcfBfDtF7A3b2jkyU0WR3qkvJERBQZhpEw9E8xYsLw3ttVs/ST71Be14LcdDMeu3J0yPNuPq8QAPDpgeOoamzrqebFlTqtN8I9aRQmvQ6FnoGvB2uaNWsXEVEiYRgJ0+VjcgEA7/WyrppaSzte3nQIALDoytORkRx675WhWSk4tygTLgH8bduRnmpi3LTbnTjmCV2nZEVXGQGA4QPdQebb2iZN2kVElGgYRsKkdNXs62VdNc9+dBDtdhfOGtIPl52e0+X5U88uAAD8dXslXH18hsi+KguEAPolG5CZYoz6eU7NcYcRZbo0ERFFhmEkTL5dNf/YdTTOrQlPeV0L3thaCQB46GcjIUlSl4+ZNDoPaSY9Kk+0YUt5fXc3Ma4+2u8eqDtheFZYfzehKJURhhEiougwjETg52cNAgC8+vlhtNoccW5N51wugf9+bz+cLoGLT8vGuacMCOtxSUYdrjwjHwDwZh/vqinzzBoq/VHXFaPOnDowDQDwLcMIEVFUGEYicPmYPAzJTMaJFhv+8sXJO+PE5RJ4+K3/4MP9NdDJEh782ciIHn+tJ3R9uL+mz66fcbi+BQdqmqGXJVw0YmBMzzUsOxWSBJxosaG+mTNqiIgixTASAb1OxtyLhgEA/rTxENrtJ98mcy6XwCNv78Fftx2BLAFLpp6BUXnpET3HGQX9MSDFiKZ2B7Z+f6KbWhoZIQScGo5hUaoi5xRldjqoNxxJRh0G9UsCwOoIEVE0GEYidO1Zg5GXYUaNxYq/bT/5ujH+sfso3thaCVkC/jD1DEwZmx/xc+hkCRePdFcLPtpfq3UTI1bb1I4r/rgJlzy9XrPBw8rCbpfG2EWjOJXjRoiIosYwEiGjXsYdntVKX9zwnaaf1mPlcgm88Ml3AIB5Px2Bq84YFPVz/XSUEkZq4rqyaH2zFTf96QvsPWbB4fpW/PLlL3C0IbY1UE602LDNU/HRLIzkcNwIEVG0GEaicMM5Q9A/2YAjP7SpMzJOBuv21eDb2makmfWYOWFoTM818dRsGHUyvq9vxXfH4zOV+USLTd1hOCfdhFOyUnC0oQ2/fPkLvLPzKL462gibI7wxLQ2tNtz3192Y9OxGTPvTFrgEMCovHYP7R75TbzDDs5W1RhhGiIgipY93A3ojs0GH688uwPINh/B/Ww6j9PTceDcJQgh175kZ44ci3RzbOIgUkx7nDRuATw8cx4f7a9Tpqz3lu+PNuHXVVhyub0VWqgl/ue08JBl0uO7Fz1Fe14J5a3YBAHLTzXjwZ6fh6jMGQZaDT8/96mgj5ry2HUd+8K+oTB6t3XUbrq41woXPiIgixTASpV+eW4iXPj2EjQfrcOh4c9TLiWtl/YHj+M+RRpgNMm6JsSqi+Omogfj0wHF8tL8Gcy4cpslzdkUIgbJ9Nbj/zd2wtDswuH8SVt1yNoZ5/n7X3HEe/vTpIeyvbsLXVRZUW9ox/6+7sWz9d8hJN0OSgP7JRgzqnwSXENhV0YAdFT/A7hQYkpmMh342Eg6XCy4hMGl0XhetCZ8S1mosVlja7Ugy6GDQsfBIRBQOhpEoFWQm4+LTBuLjr2vx2pYK/HbKj+LSjn/uPoa/fFGhLlB24zlDMCDVpMlzXzJyIH77j73YfvgHHG+yIjtNm+cNZd3eajz38UF8ddQCADhrSD+8NH0csnxez+D+yXjsKvf+Ou12J1Z+Vo4XPv4WB2ubOx08+tNRA/H0dWfEPHMmlHSzATnpJtRYrDjr8TI4XAIXjMjGE1eNxpAB2nQFERH1VZLoBfueWywWZGRkoLGxEenpkU1T7U6ffFOLW17ZijSzHl888hMkG3s2273yWTke++c+9c/nD8/CczeeGdPS5oGuWfoZdlY04K6Lh+P+y07T7Hl9CSHwh7IDeO5jdzdTkkGH6eMLce+lI2A26Lp8/PEmKz4/VA+XS8AlBOqarTj6QxvsLoGxgzNw5pD+OHVgakyrrIZj/l934a0d/qvzmg0yHpk8CtPHD+3Wn01EdDIK9/2bYSQGLpfARb9fj4oTrfjVRcPwUISLi8Xio/01uO3VbXAJYGbJUMw6vwgFmdp/An//q2rMeW070s16fPbwJUiLcSxKICEEnnz/ayzf4N7Mb/b5RZh78XBNA1VPcboEyutakGzUoandgcf+uRebv6uHJAEb7r+YFRIiSjjhvn+zUzsGsiyp1YJl67/rkT1r6puteP3LCtz9+k64BDB1XAEWTvlRtwQRwL1U+inZKbC0OzRfddbmcOGRt/eoQeS3V/wI/3XFj3plEAHc67MMH5iK/H5JOC03Datnn4vTctIghHtALhERBccwEqMrx+bjjgvd64488Lf/YFdlQ7f8HCEEHnhzN87+7w+x4K09aLU5MWH4ADxxzehu7X6QZUkdvPrypnLNVp2ttbTjxj9twetfVkKSgCeuHo1bzy/S5LlPFpIkodBTDTnyQ2ucW0NEdPJiGNHAg5eNxE9GDoTN4cKsVVvxbTdM71y7pxpvbj8ClwBGD0rH/aUj8NLN43pkxsbVZwxCXoYZx5useHtn7NWfxjY7rlm6GdsP/4B0sx4rZ56NX55XqEFLTz7KOiaVP8S2UBsRUV/GMKIBnSxhyQ1nYPSgdNS32DDtT1/ge42WLQcAu9OF36/7BgBwz09Oxb/unoi7LjkVKaaeGTBr1MuYPdFd/VmuwaqzL274Dkcb2lCQmYR/3HU+Lj4tto3qTmaD+7v3rGFlhIgoNIYRjaSZDfi/W8/FyNw01DZZMe1PWzTbR+Wv2ypRXteCASlG3O5Zir6n3XhOAfolG/B9fSv+/VVV1M9T1diGlZvKAQC/veJ0FGWlaNXEk5IylidwwTUiIvJiGNFQ/xQj/m/WuRiWnYJjje247sXN+OpoY0zP2WZz4tkPDwIA7rpkOFJ7qBoSKNmox8ySoQDcg3WjnYT1h7IDsDpcOHtof3X/m75MqYxUnmBlhIgoFIYRjWWnmfDG7ePxo7x01DXbcONLW/DpgeNRPZcQAo//ay9qm6wY3D8J084donFrIzNj/FAkG3XYe8yCTw/WRfz4AzVN6k7HD08a1e3rfpwMlDDyQ6sdzVZHnFtDRHRyYhjpBtlpJrxxx3k4pygTTVYHpq/8Eo//c1/EM1Fe3HAIr39ZCVkC/t9Vo2HSd70AWHfqn2LEjee4A9EfPzoIh9O9Sd23tc24/LmNWPDWf0KOJ2m3OzHvjV1wCeBnp+eiuLB/j7U7ntLMBvTzrPp6lF01RERBMYx0k3SzAa/eeg5u8lQzVn5Wjqtf+AwNrbaQjxFC4ItD9fi/LYfx3+/tw/++/zUA9/obF488Obo0Zk8sglEvY9vhH3DXX3bi29om3PTyFuw9ZsHrX1Zi0bt7O3ThCCHwm3e+wr4qCwakGLHoytPj1Pr4YFcNEVHnGEa6kdmgw39fMwYrZ45DVqoRX1c3Yc5r22FzuCCEwObv6vB1tUU9/w9lBzD1pS34zTtf4U8b3YM8b5kwFDMnnDzrb+RlJOGFaWfBqJPx/t5qXLZkI2osVgzqlwRJAv5vy2Es+fAgLO12AECNpR3Pf/wt3tx+BLIE/PHGM5GbYY7zq+hZg/txrREios5wo7wecMnIHLw2+1z8Ytnn2HLoBO5+fQeON1mxo6IBkuQei5Fu1qt7s1x8WjZyM8z4UX4Gpp0T33EiwVz6oxysmDkOt726De12F07JSsGaO8bjvf8cw6J/7sOzHx3Esx8dRL9kAxpa7erj7r/sNJQMz4pjy+OjIFOZ3stuGiKiYBhGesjI3HT8cdqZmLVqKz7YWwMAMOpk2JwurNr8vXreI5NH4vYLhsWpleGbeGo21tw+Hmu/qsKtE4qQnWbCzAlFsDldWLGpHDUWKxpa7ZAl4PT8DFz+4zzcPjE+05LjzbvwGSsjRETBMIz0oItPG4j/uWYMfr/uG1x2ei5+/ZNT8U1NE/7rna9wuL4VD1x2Wq8IIoqxBf0wtqCf37HbLxiG2y8YBku7HRX1rSjITEZGkrab6/U23oXPWBkhIgqGYaSH3XDOENzg0/UyMN2MdfdegFqLtds2u4uHdLMBowdlxLsZJwWlMsIwQkQUHAewngRMel2fCiLkT6mMNLbZ1YG9RETkxTBC1M1STHpkphgBAEdOsDpCRBSIYYSoB3DDPCKi0KIKI0uXLkVRURHMZjOKi4uxcePGTs/fsGEDiouLYTabccopp+DFF1+MqrFEvVWBOqOGlREiokARD2Bds2YN5s2bh6VLl2LChAlYvnw5Jk2ahH379mHIkI5rYpSXl2Py5Mm47bbb8Nprr+Gzzz7D3LlzkZ2djZ///OeavAiik51SGXnp0++QZtKjcEAy3t19DAdqmnDRaQMx9ewCZKWa4txKIqL4kESE26+ee+65OOuss7Bs2TL12KhRo3D11Vdj8eLFHc5/6KGH8O6772L//v3qsTlz5mD37t34/PPPw/qZFosFGRkZaGxsRHp6eiTNJTopfF1twa2vbMWxxvag3zfoJJw5pD+GZadiSGYy0pP0SDXpkZ1qwsB0M/onG6DXydDJEtrtTrRanWixOdBqc8DmEOiXbED/ZCNkCbA6XDAZZAxMc69063IJ/OdoI060WDG4fzLy+yUhxajz26iwsc2O3ZUN2HO0EVa7E0a9jGSjHjnpZmQkGfB1tQU7KxvQZnMiv58ZuelmpJr0SDbq4RIC7XYn2uwutNudaHc4kZ1qwtABKTDqZRyoacKhuhYIIaCXZaSa9RiYZkJWqgkGnQRZkmB1uNBidaCxzY7aJivqmq3ITjXhtNw0ZCQZcKyhDbVNVuhkCSa9DJNeB5NBhixJaGi1ob7FhhOerx9abfihxY6mdjuGDUzF+FMGIK9fEo780IrqxnY4XQKSJEECIEnu9X7y+iVhcL8kZCQbYNLLkCQJTe12WNoc7l/b7bDaXXAJQJbc+zRlpRqRYtJDL0vQyzL0Ou+vBp0EnSxDL7tf34kWG6ot7aixtKPW0o6GNjty080oyEyGUS+j1eqAzemCUSfDbHC/NrPe/1eTXgezQYbNIfBDqw3NVgfSzQZkphiRbNRBr5MgQYLN4YLV4YTV86skSeifbES6WY9WmxONbe5B1OlmA4x6GcebrKixtEOS3HspmQ0y7E4BlxDon2xEXoYZKQG7hTtd7u/rZQkOl0CN57VlJBkwJNN93ZXzmq0ONFsdcLkEkow6mPQyXC7A7nLBbNCp96LV4URdsw3tdidcLgGnEO6f4wJcwv1nnSQh2ahDskmPZINOfT7lXm6zOVHb1A6dLCHN5F5SoL7FigbPa9ZJEnSy+0svS5A9v/ZLNvotQdBmc8LhcqnPq9wrQgAC7vYIAUAAAu7f63TuexMAmtodaG53wGzQIT1JD7Neh672BXW6BL493oz/VDbieLMVeRlm5GaYYTboIEsSZAmQJQmSBOg895XV7kKT1X1vKsd1kvt1pZr0GDIgGelmA5wugfpmK5qsDggh4BLe16D8KoR7gcZ+ycbOGxqhcN+/I6qM2Gw2bN++HQ8//LDf8dLSUmzevDnoYz7//HOUlpb6HbvsssuwYsUK2O12GAwd16CwWq2wWq1+L4aoNxuZm46P778Ir205jKXrv4Pd4cLPRufi9Px0vL3rGHZXNuDL8hP4svyEZj+zIDMJo/MzsLOiAdUW/xAkS0CyUQ8JgNXpgs3h0uznnkx2VjRgZ0VDvJvR65n0Mgw6GbIEtDs6v190soT+yQa0WJ1oC2NzUKNeRpJBp4akSKn3suQOAdFKM+mRnmTAiRZbWO3uLTKSDGixOuAIsYmprz/eeCamjM3vgVZ1FFEYqaurg9PpRE5Ojt/xnJwcVFdXB31MdXV10PMdDgfq6uqQl5fX4TGLFy/GY489FknTiE56ZoMOsyeeglnnF8El3P9pA8DMCUU4UNOEfccs+O54M442tKG53YGmdgeON1tR09iOJqv/f7IpyqdDow56WUJjmx0nWmwQAMx6HawOJypPtKHSM3sn1aRHQWYyjjW0obHNDpcAmgOes3BAMs4o6Id0swE2h/sTV63FihMtNhRlpeCswv7on2xEVWMbaiztaLE60WpzQJYkmI06mPU6JBllGHU61FjaUV7XAqvDiRE5aRg+MBVGnQy7S8DSZkdtUzvqm21wuNyffk16GakmPdLM7mpMZooRVY3t+Ka6CS02B/IzkpCbYYYQwvOJ312FcbrcVaHMFBMGpBjRP8WIzBQD+iW7qwV7jjTi80P1aGy1oyAzGYP6JUGvk9RPuMrzHW1ow5Ef2tDcbofV4a6ApJn1SDcbkG52v0kplRiXS+BEiw11zVa02txtsLsEHE6X+/dOFxwuAYdTwOFywekCMlMMyEk3e75MyEgyoKqxHZUnWuFwCaSY9O4VmT3VjHZ76F8NOnelI8Wkh6XdjoZWO9rtTjg81QyTXobRp3rkcAo0tNrQYnPCbJCRbnZ/AGxqd8DqcCIr1aTuF9Xc7kC73Qmdp2JV3+yuwCh/56EYdBIGppnVn1PX7L8hqFHvCTL2js9h8wk37sqQrFYvJElSqxmyDDicAq02J9psTtg8u4YH3stmgwyXgPqcqSY9+iUbIEsSnJ77zeGp7Dg816rV5kST1dHh31ksko06WB2ukDuZB5Ni1GHM4Azk90tCjaUd1Y3tsDldcLngV9FQfjXq3JVGs0GGEN6KldMl0NjmQF2zVQ15suSe2adUVdz//XgrLrIEtbITD1EteiYF1JuEEB2OdXV+sOOKBQsWYP78+eqfLRYLCgoKomkq0UnH/R+s/7EROWkYkZMW8jEun/9AjToZstzx347vv6tmqwM7Dv+Ar441YmRuGiYMz4JJrwMAtNrcZfMWq/vTn1EvI9WoR0Zy31spd2RuOq4bx/87APcblU6O7P9uALC022Fps8PhdHeVmA06JBl00EmS2pXRL8kAWZYghECNJ8CmmvRINeuRYtKp954S/vSesNFmd+JEiw2tNnfXXr9kQ5ftUTicLrTa3cHEHQpdyE4zI92shyS5u6tcnvZ2pdXmwLGGdlja7RiQYsSAVBP0nr8r4dMVI3neuAH37yUob+qAwyVgc7oghDsA6Tx/H212Z4cQFmp0RL9kY4drFIumdjuONrQhI8mA7FQT9LqTdwJtRGEkKysLOp2uQxWktra2Q/VDkZubG/R8vV6PAQMGBH2MyWSCycTBfEQKWZZg7OI/Kd//xFNNelwwIhsXjMjucF6y0T3WA6GzD/VBwd7kwnnjd1eHwguqkiQh1zPWIdT3fcOBei9GQa+Tka6TQ7bNGMGn/GSjHsMHpkbVDm970CH4SJLkeY0xPXXU0swGjMztHR8yIopJRqMRxcXFKCsr8zteVlaGkpKSoI8ZP358h/PXrVuHcePGBR0vQkRERIkl4prN/Pnz8fLLL2PlypXYv38/7r33XlRUVGDOnDkA3F0s06dPV8+fM2cODh8+jPnz52P//v1YuXIlVqxYgfvvv1+7V0FERES9VsT1salTp6K+vh6PP/44qqqqMHr0aKxduxaFhYUAgKqqKlRUVKjnFxUVYe3atbj33nvxwgsvID8/H8899xzXGCEiIiIAUawzEg9cZ4SIiKj3Cff9++QdWktEREQJgWGEiIiI4ophhIiIiOKKYYSIiIjiimGEiIiI4ophhIiIiOKKYYSIiIjiimGEiIiI4ophhIiIiOIquu0Se5iySKzFYolzS4iIiChcyvt2V4u994ow0tTUBAAoKCiIc0uIiIgoUk1NTcjIyAj5/V6xN43L5cKxY8eQlpYGSZI0e16LxYKCggJUVlYmzJ43fM19/zUn2usFEu81J9rrBfiae+trFkKgqakJ+fn5kOXQI0N6RWVElmUMHjy4254/PT29117oaPE1932J9nqBxHvNifZ6Ab7m3qizioiCA1iJiIgorhhGiIiIKK4SOoyYTCYsXLgQJpMp3k3pMXzNfV+ivV4g8V5zor1egK+5r+sVA1iJiIio70royggRERHFH8MIERERxRXDCBEREcUVwwgRERHFFcMIERERxVVCh5GlS5eiqKgIZrMZxcXF2LhxY7ybpInFixfj7LPPRlpaGgYOHIirr74a33zzjd85M2fOhCRJfl/nnXdenFocu0WLFnV4Pbm5uer3hRBYtGgR8vPzkZSUhIsuugh79+6NY4tjM3To0A6vV5Ik3HnnnQD6xvX99NNPMWXKFOTn50OSJLzzzjt+3w/nmlqtVtx9993IyspCSkoKrrzyShw5cqQHX0VkOnvNdrsdDz30EMaMGYOUlBTk5+dj+vTpOHbsmN9zXHTRRR2u/Q033NDDryQ8XV3jcO7jvnSNAQT9dy1JEn73u9+p5/SmaxyuhA0ja9aswbx58/Doo49i586dmDhxIiZNmoSKiop4Ny1mGzZswJ133oktW7agrKwMDocDpaWlaGlp8TvvZz/7GaqqqtSvtWvXxqnF2jj99NP9Xs+ePXvU7z311FN45pln8Pzzz2Pr1q3Izc3FpZdeqm7C2Nts3brV77WWlZUBAK677jr1nN5+fVtaWjB27Fg8//zzQb8fzjWdN28e3n77bbzxxhvYtGkTmpubccUVV8DpdPbUy4hIZ6+5tbUVO3bswG9+8xvs2LEDb731Fg4cOIArr7yyw7m33Xab37Vfvnx5TzQ/Yl1dY6Dr+7gvXWMAfq+1qqoKK1euhCRJ+PnPf+53Xm+5xmETCeqcc84Rc+bM8Ts2cuRI8fDDD8epRd2ntrZWABAbNmxQj82YMUNcddVV8WuUxhYuXCjGjh0b9Hsul0vk5uaKJ598Uj3W3t4uMjIyxIsvvthDLexev/71r8WwYcOEy+USQvS96wtAvP322+qfw7mmDQ0NwmAwiDfeeEM95+jRo0KWZfH+++/3WNujFfiag/nyyy8FAHH48GH12IUXXih+/etfd2/jukGw19vVfZwI1/iqq64Sl1xyid+x3nqNO5OQlRGbzYbt27ejtLTU73hpaSk2b94cp1Z1n8bGRgBAZmam3/H169dj4MCBGDFiBG677TbU1tbGo3maOXjwIPLz81FUVIQbbrgBhw4dAgCUl5ejurra73qbTCZceOGFfeJ622w2vPbaa7j11lv9drXua9fXVzjXdPv27bDb7X7n5OfnY/To0X3iugPuf9uSJKFfv35+x1evXo2srCycfvrpuP/++3ttBRDo/D7u69e4pqYG7733HmbNmtXhe33pGgO9ZNderdXV1cHpdCInJ8fveE5ODqqrq+PUqu4hhMD8+fNx/vnnY/To0erxSZMm4brrrkNhYSHKy8vxm9/8Bpdccgm2b9/eK5cePvfcc/Hqq69ixIgRqKmpwRNPPIGSkhLs3btXvabBrvfhw4fj0VxNvfPOO2hoaMDMmTPVY33t+gYK55pWV1fDaDSif//+Hc7pC//O29vb8fDDD2PatGl+O7redNNNKCoqQm5uLr766issWLAAu3fvVrvyepOu7uO+fo3//Oc/Iy0tDddee63f8b50jRUJGUYUvp8iAfcbd+Cx3u6uu+7Cf/7zH2zatMnv+NSpU9Xfjx49GuPGjUNhYSHee++9Djd+bzBp0iT192PGjMH48eMxbNgw/PnPf1YHvPXV671ixQpMmjQJ+fn56rG+dn1Dieaa9oXrbrfbccMNN8DlcmHp0qV+37vtttvU348ePRqnnnoqxo0bhx07duCss87q6abGJNr7uC9cYwBYuXIlbrrpJpjNZr/jfekaKxKymyYrKws6na5Dcq6tre3wSas3u/vuu/Huu+/ik08+weDBgzs9Ny8vD4WFhTh48GAPta57paSkYMyYMTh48KA6q6YvXu/Dhw/jww8/xOzZszs9r69d33CuaW5uLmw2G3744YeQ5/RGdrsd119/PcrLy1FWVuZXFQnmrLPOgsFg6BPXPvA+7qvXGAA2btyIb775pst/20DfuMYJGUaMRiOKi4s7lLTKyspQUlISp1ZpRwiBu+66C2+99RY+/vhjFBUVdfmY+vp6VFZWIi8vrwda2P2sViv279+PvLw8tZzpe71tNhs2bNjQ66/3K6+8goEDB+Lyyy/v9Ly+dn3DuabFxcUwGAx+51RVVeGrr77qtdddCSIHDx7Ehx9+iAEDBnT5mL1798Jut/eJax94H/fFa6xYsWIFiouLMXbs2C7P7RPXOI6DZ+PqjTfeEAaDQaxYsULs27dPzJs3T6SkpIjvv/8+3k2L2a9+9SuRkZEh1q9fL6qqqtSv1tZWIYQQTU1N4r777hObN28W5eXl4pNPPhHjx48XgwYNEhaLJc6tj859990n1q9fLw4dOiS2bNkirrjiCpGWlqZezyeffFJkZGSIt956S+zZs0fceOONIi8vr9e+XiGEcDqdYsiQIeKhhx7yO95Xrm9TU5PYuXOn2LlzpwAgnnnmGbFz50515kg413TOnDli8ODB4sMPPxQ7duwQl1xyiRg7dqxwOBzxelmd6uw12+12ceWVV4rBgweLXbt2+f3btlqtQgghvv32W/HYY4+JrVu3ivLycvHee++JkSNHijPPPPOkfM2dvd5w7+O+dI0VjY2NIjk5WSxbtqzD43vbNQ5XwoYRIYR44YUXRGFhoTAajeKss87ym/ramwEI+vXKK68IIYRobW0VpaWlIjs7WxgMBjFkyBAxY8YMUVFREd+Gx2Dq1KkiLy9PGAwGkZ+fL6699lqxd+9e9fsul0ssXLhQ5ObmCpPJJC644AKxZ8+eOLY4dh988IEAIL755hu/433l+n7yySdB7+MZM2YIIcK7pm1tbeKuu+4SmZmZIikpSVxxxRUn9d9DZ6+5vLw85L/tTz75RAghREVFhbjgggtEZmamMBqNYtiwYeKee+4R9fX18X1hIXT2esO9j/vSNVYsX75cJCUliYaGhg6P723XOFySEEJ0a+mFiIiIqBMJOWaEiIiITh4MI0RERBRXDCNEREQUVwwjREREFFcMI0RERBRXDCNEREQUVwwjREREFFcMI0RERBRXDCNEREQUVwwjREREFFcMI0RERBRX/x9Hp7vaEX+kLwAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", 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Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/home/jonathan/anaconda3/envs/fdiff/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_plot = 20\n", + "for i, sample in enumerate(samples[:n_plot]):\n", + " sns.lineplot(sample.numpy().flatten())\n", + " plt.title(f\"Sample {i}\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "fdiff", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyproject.toml b/pyproject.toml index 808622f..79e85b7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -90,3 +90,6 @@ exclude = [ '^file1\.py$', # TOML literal string (single-quotes, no escaping necessary) ] ignore_missing_imports = true + +[tool.isort] +profile = "black" diff --git a/src/fdiff/dataloaders/datamodules.py b/src/fdiff/dataloaders/datamodules.py index f36c926..05af4f4 100644 --- a/src/fdiff/dataloaders/datamodules.py +++ b/src/fdiff/dataloaders/datamodules.py @@ -4,6 +4,7 @@ from pathlib import Path from typing import Any, Optional +import numpy as np import pandas as pd import pytorch_lightning as pl import torch @@ -19,12 +20,28 @@ def __init__( X: torch.Tensor, y: Optional[torch.Tensor] = None, fourier_transform: bool = False, + standardize: bool = False, + X_ref: Optional[torch.Tensor] = None, ) -> None: + """Dataset for diffusion models. + + Args: + X (torch.Tensor): Time series that are fed to the model. + y (Optional[torch.Tensor], optional): Potential labels. Defaults to None. + fourier_transform (bool, optional): Performs a Fourier transform on the time series. Defaults to False. + standardize (bool, optional): Standardize each feature in the dataset. Defaults to False. + X_ref (Optional[torch.Tensor], optional): Features used to compute the mean and std. Defaults to None. + """ super().__init__() if fourier_transform: X = dft(X).detach() self.X = X self.y = y + self.standardize = standardize + if X_ref is None: + X_ref = X + self.feature_mean = X_ref.mean(dim=0) + self.feature_std = X_ref.std(dim=0) def __len__(self) -> int: return len(self.X) @@ -32,6 +49,8 @@ def __len__(self) -> int: def __getitem__(self, index: int) -> dict[str, torch.Tensor]: data = {} data["X"] = self.X[index] + if self.standardize: + data["X"] = (data["X"] - self.feature_mean) / self.feature_std if self.y is not None: data["y"] = self.y[index] return data @@ -44,6 +63,7 @@ def __init__( random_seed: int = 42, batch_size: int = 32, fourier_transform: bool = False, + standardize: bool = False, ) -> None: super().__init__() # Cast data_dir to Path type @@ -53,6 +73,7 @@ def __init__( self.random_seed = random_seed self.batch_size = batch_size self.fourier_transform = fourier_transform + self.standardize = standardize self.X_train = torch.Tensor() self.y_train: Optional[torch.Tensor] = None self.X_test = torch.Tensor() @@ -72,7 +93,10 @@ def download_data(self) -> None: def train_dataloader(self) -> DataLoader: train_set = DiffusionDataset( - X=self.X_train, y=self.y_train, fourier_transform=self.fourier_transform + X=self.X_train, + y=self.y_train, + fourier_transform=self.fourier_transform, + standardize=self.standardize, ) return DataLoader( train_set, @@ -94,7 +118,11 @@ def test_dataloader(self) -> DataLoader: def val_dataloader(self) -> DataLoader: test_set = DiffusionDataset( - X=self.X_test, y=self.y_test, fourier_transform=self.fourier_transform + X=self.X_test, + y=self.y_test, + fourier_transform=self.fourier_transform, + standardize=self.standardize, + X_ref=self.X_train, ) return DataLoader( test_set, @@ -115,6 +143,16 @@ def dataset_parameters(self) -> dict[str, Any]: "num_training_steps": len(self.train_dataloader()), } + @property + def feature_mean_and_std(self) -> tuple[torch.Tensor, torch.Tensor]: + train_set = DiffusionDataset( + X=self.X_train, + y=self.y_train, + fourier_transform=self.fourier_transform, + standardize=self.standardize, + ) + return train_set.feature_mean, train_set.feature_std + class ECGDatamodule(Datamodule): def __init__( @@ -123,12 +161,14 @@ def __init__( random_seed: int = 42, batch_size: int = 32, fourier_transform: bool = False, + standardize: bool = False, ) -> None: super().__init__( data_dir=data_dir, random_seed=random_seed, batch_size=batch_size, fourier_transform=fourier_transform, + standardize=standardize, ) def setup(self, stage: str = "fit") -> None: @@ -161,3 +201,66 @@ def download_data(self) -> None: @property def dataset_name(self) -> str: return "ecg" + + +class SyntheticDatamodule(Datamodule): + def __init__( + self, + data_dir: Path | str = Path.cwd() / "data", + random_seed: int = 42, + batch_size: int = 32, + fourier_transform: bool = False, + standardize: bool = False, + max_len: int = 100, + num_samples: int = 1000, + ) -> None: + super().__init__( + data_dir=data_dir, + random_seed=random_seed, + batch_size=batch_size, + fourier_transform=fourier_transform, + standardize=standardize, + ) + self.max_len = max_len + self.num_samples = num_samples + + def setup(self, stage: str = "fit") -> None: + # Read CSV; extract features and labels + path_train = self.data_dir / "train.csv" + path_test = self.data_dir / "test.csv" + + # Read data + df_train = pd.read_csv(path_train, header=None) + X_train = df_train.values + + df_test = pd.read_csv(path_test, header=None) + X_test = df_test.values + + # Convert to tensor + self.X_train = torch.tensor(X_train, dtype=torch.float32).unsqueeze( + 2 + ) # Add a channel dimension + self.y_train = None + self.X_test = torch.tensor(X_test, dtype=torch.float32).unsqueeze(2) + self.y_test = None + + def download_data(self) -> None: + # Generate data, same DGP as in Fourier flows + + n_generated = 2 * self.num_samples # For train + test + phase = np.random.normal(size=(n_generated)).reshape(-1, 1) + frequency = np.random.beta(a=2, b=2, size=(n_generated)).reshape(-1, 1) + timesteps = np.arange(self.max_len) + X = np.sin(timesteps * frequency + phase) + X_train = X[: self.num_samples] + X_test = X[self.num_samples :] + + # Save data + df_train = pd.DataFrame(X_train) + df_test = pd.DataFrame(X_test) + df_train.to_csv(self.data_dir / "train.csv", index=False, header=False) + df_test.to_csv(self.data_dir / "test.csv", index=False, header=False) + + @property + def dataset_name(self) -> str: + return "synthetic" diff --git a/src/fdiff/models/score_models.py b/src/fdiff/models/score_models.py index 415a2be..7226a73 100644 --- a/src/fdiff/models/score_models.py +++ b/src/fdiff/models/score_models.py @@ -1,14 +1,21 @@ +from typing import Callable + import pytorch_lightning as pl import torch import torch.nn as nn -import torch.nn.functional as F import torch.optim as optim from diffusers import DDPMScheduler from diffusers.optimization import get_cosine_schedule_with_warmup from pytorch_lightning.utilities.types import OptimizerLRScheduler -from fdiff.models.transformer import PositionalEncoding, TimeEncoding +from fdiff.models.transformer import ( + GaussianFourierProjection, + PositionalEncoding, + TimeEncoding, +) +from fdiff.schedulers.sde import SDE from fdiff.utils.dataclasses import DiffusableBatch +from fdiff.utils.losses import get_ddpm_loss, get_sde_loss_fn class ScoreModule(pl.LightningModule): @@ -16,28 +23,34 @@ def __init__( self, n_channels: int, max_len: int, - noise_scheduler: DDPMScheduler, + noise_scheduler: DDPMScheduler | SDE, + fourier_noise_scaling: bool = True, d_model: int = 60, num_layers: int = 3, n_head: int = 12, num_training_steps: int = 1000, lr_max: float = 1e-3, + likelihood_weighting: bool = False, ) -> None: super().__init__() # Hyperparameters self.max_len = max_len self.n_channels = n_channels - assert hasattr(noise_scheduler, "config") - self.max_time = noise_scheduler.config.num_train_timesteps - assert isinstance(self.max_time, int) + self.noise_scheduler = noise_scheduler self.num_warmup_steps = num_training_steps // 10 self.num_training_steps = num_training_steps self.lr_max = lr_max + self.d_model = d_model + self.scale_noise = fourier_noise_scaling + + # Loss function + self.likelihood_weighting = likelihood_weighting + self.training_loss_fn, self.validation_loss_fn = self.set_loss_fn() # Model components self.pos_encoder = PositionalEncoding(d_model=d_model, max_len=self.max_len) - self.time_encoder = TimeEncoding(d_model=d_model, max_time=self.max_time) + self.time_encoder = self.set_time_encoder() self.embedder = nn.Linear(in_features=n_channels, out_features=d_model) self.unembedder = nn.Linear(in_features=d_model, out_features=n_channels) transformer_layer = nn.TransformerEncoderLayer( @@ -82,31 +95,8 @@ def forward(self, batch: DiffusableBatch) -> torch.Tensor: def training_step( self, batch: DiffusableBatch, batch_idx: int, dataloader_idx: int = 0 ) -> torch.Tensor: - # Get X and timesteps - X = batch.X - timesteps = batch.timesteps - - # If no timesteps are provided, sample them randomly - if timesteps is None: - timesteps = torch.randint( - low=0, - high=self.max_time, - size=(len(batch),), - dtype=torch.long, - device=batch.device, - ) + loss = self.training_loss_fn(self, batch) - # Sample noise from distribution and add it to X - noise = torch.randn_like(X, device=batch.device) - assert hasattr(self.noise_scheduler, "add_noise") - X_noisy = self.noise_scheduler.add_noise( - original_samples=X, noise=noise, timesteps=timesteps - ) - noisy_batch = DiffusableBatch(X=X_noisy, y=batch.y, timesteps=timesteps) - - # Predict noise from score model - noise_pred = self.forward(noisy_batch) - loss = F.mse_loss(noise_pred, noise) self.log_dict( {"train/loss": loss}, prog_bar=True, @@ -119,31 +109,7 @@ def training_step( def validation_step( self, batch: DiffusableBatch, batch_idx: int, dataloader_idx: int = 0 ) -> None: - # Get X and timesteps - X = batch.X - timesteps = batch.timesteps - - # If no timesteps are provided, sample them randomly - if timesteps is None: - timesteps = torch.randint( - low=0, - high=self.max_time, - size=(len(batch),), - dtype=torch.long, - device=batch.device, - ) - - # Sample noise from distribution and add it to X - noise = torch.randn_like(X, device=batch.device) - assert hasattr(self.noise_scheduler, "add_noise") - X_noisy = self.noise_scheduler.add_noise( - original_samples=X, noise=noise, timesteps=timesteps - ) - noisy_batch = DiffusableBatch(X=X_noisy, y=batch.y, timesteps=timesteps) - - # Predict noise from score model - noise_pred = self.forward(noisy_batch) - loss = F.mse_loss(noise_pred, noise) + loss = self.validation_loss_fn(self, batch) self.log_dict( {"val/loss": loss}, prog_bar=True, @@ -161,3 +127,55 @@ def configure_optimizers(self) -> OptimizerLRScheduler: ) lr_scheduler_config = {"scheduler": lr_scheduler, "interval": "step"} return {"optimizer": optimizer, "lr_scheduler": lr_scheduler_config} + + def set_loss_fn( + self, + ) -> tuple[ + Callable[[nn.Module, DiffusableBatch], torch.Tensor], + Callable[[nn.Module, DiffusableBatch], torch.Tensor], + ]: + # Depending on the scheduler, get the right loss function + + if isinstance(self.noise_scheduler, DDPMScheduler): + assert hasattr(self.noise_scheduler, "config") + scheduler_config = self.noise_scheduler.config + self.max_time = scheduler_config.num_train_timesteps + + training_loss_fn = get_ddpm_loss( + scheduler=self.noise_scheduler, train=True, max_time=self.max_time + ) + validation_loss_fn = get_ddpm_loss( + scheduler=self.noise_scheduler, train=False, max_time=self.max_time + ) + return training_loss_fn, validation_loss_fn + + elif isinstance(self.noise_scheduler, SDE): + training_loss_fn = get_sde_loss_fn( + scheduler=self.noise_scheduler, + train=True, + likelihood_weighting=self.likelihood_weighting, + ) + validation_loss_fn = get_sde_loss_fn( + scheduler=self.noise_scheduler, + train=False, + likelihood_weighting=self.likelihood_weighting, + ) + + return training_loss_fn, validation_loss_fn + + else: + raise NotImplementedError( + f"Scheduler {self.noise_scheduler} not implemented yet, cannot set loss function." + ) + + def set_time_encoder(self) -> TimeEncoding | GaussianFourierProjection: + if isinstance(self.noise_scheduler, DDPMScheduler): + return TimeEncoding(d_model=self.d_model, max_time=self.max_time) + + elif isinstance(self.noise_scheduler, SDE): + return GaussianFourierProjection(d_model=self.d_model) + + else: + raise NotImplementedError( + f"Scheduler {self.noise_scheduler} not implemented yet, cannot set time encoder." + ) diff --git a/src/fdiff/models/transformer.py b/src/fdiff/models/transformer.py index 2942d3e..45d8f87 100644 --- a/src/fdiff/models/transformer.py +++ b/src/fdiff/models/transformer.py @@ -1,5 +1,6 @@ import math +import numpy as np import torch import torch.nn as nn @@ -51,3 +52,33 @@ def forward(self, x: torch.Tensor, timesteps: torch.LongTensor) -> torch.Tensor: t_emb = t_emb.unsqueeze(1) # (batch_size, 1, d_emb) assert isinstance(t_emb, torch.Tensor) return x + t_emb + + +class GaussianFourierProjection(nn.Module): + """Gaussian random features for encoding time steps. + Courtesy of https://colab.research.google.com/drive/120kYYBOVa1i0TD85RjlEkFjaWDxSFUx3?usp=sharing#scrollTo=YyQtV7155Nht + """ + + def __init__(self, d_model: int, scale: float = 30.0): + super().__init__() + # Randomly sample weights during initialization. These weights are fixed + # during optimization and are not trainable. + self.d_model = d_model + self.W = nn.Parameter( + torch.randn((d_model + 1) // 2) * scale, requires_grad=False + ) + + self.dense = nn.Linear(d_model, d_model) + + def forward(self, x: torch.Tensor, timesteps: torch.Tensor) -> torch.Tensor: + time_proj = timesteps[:, None] * self.W[None, :] * 2 * np.pi + embeddings = torch.cat([torch.sin(time_proj), torch.cos(time_proj)], dim=-1) + + # Slice to get exactly d_model + t_emb = embeddings[:, : self.d_model] # (batch_size, d_model) + + t_emb = t_emb.unsqueeze(1) + + projected_emb: torch.Tensor = self.dense(t_emb) + + return x + projected_emb diff --git a/src/fdiff/sampling/metrics.py b/src/fdiff/sampling/metrics.py index 3c27a95..f202eba 100644 --- a/src/fdiff/sampling/metrics.py +++ b/src/fdiff/sampling/metrics.py @@ -1,10 +1,10 @@ from abc import ABC, abstractmethod, abstractproperty from functools import partial +from pathlib import Path from typing import Optional import numpy as np import torch - from fdiff.utils.fourier import dft from fdiff.utils.tensors import check_flat_array from fdiff.utils.wasserstein import WassersteinDistances @@ -157,6 +157,16 @@ def __call__(self, other_samples: np.ndarray | torch.Tensor) -> dict[str, float] "marginal_wasserstein_max": float(np.max(distances)), } + def save(self, other_samples: np.ndarray | torch.Tensor, path: str | Path) -> None: + # Save the distances array for post-processing + wd = WassersteinDistances( + original_data=self.original_samples, + other_data=check_flat_array(other_samples), + seed=self.random_seed, + ) + distances = wd.marginal_distances() + np.save(path, distances) + @property def baseline_metrics(self) -> dict[str, float]: # Compute the Wasserstein distance between 2 folds of the original samples diff --git a/src/fdiff/sampling/sampler.py b/src/fdiff/sampling/sampler.py index 52908e3..52dc38e 100644 --- a/src/fdiff/sampling/sampler.py +++ b/src/fdiff/sampling/sampler.py @@ -1,9 +1,12 @@ from typing import Optional import torch +from diffusers import DDPMScheduler from tqdm import tqdm from fdiff.models.score_models import ScoreModule +from fdiff.schedulers.ddpm import CustomDDPMScheduler +from fdiff.schedulers.sde import SDE from fdiff.utils.dataclasses import DiffusableBatch @@ -15,6 +18,11 @@ def __init__( ) -> None: self.score_model = score_model self.noise_scheduler = score_model.noise_scheduler + + # Disable clipping for the noise scheduler + if isinstance(self.noise_scheduler, (DDPMScheduler, CustomDDPMScheduler)): + self.noise_scheduler.config.clip_sample = False + self.sample_batch_size = sample_batch_size self.n_channels = score_model.n_channels self.max_len = score_model.max_len @@ -30,7 +38,6 @@ def reverse_diffusion_step(self, batch: DiffusableBatch) -> torch.Tensor: # Predict score for the current batch score = self.score_model(batch) - # Apply a step of reverse diffusion output = self.noise_scheduler.step( model_output=score, timestep=timesteps[0].item(), sample=X @@ -76,11 +83,7 @@ def sample( self.sample_batch_size, ) # Sample from noise distribution - X = torch.randn( - (batch_size, self.max_len, self.n_channels), - device=self.score_model.device, - requires_grad=False, - ) + X = self.sample_prior(batch_size) # Perform the diffusion step by step for t in tqdm( @@ -94,16 +97,38 @@ def sample( timesteps = torch.full( (batch_size,), t, - dtype=torch.long, + dtype=torch.long + if isinstance(t.item(), int) + else torch.float32, device=self.score_model.device, requires_grad=False, ) # Create diffusable batch batch = DiffusableBatch(X=X, y=None, timesteps=timesteps) # Return denoised X + X = self.reverse_diffusion_step(batch) # Add the samples to the list all_samples.append(X.cpu()) return torch.cat(all_samples, dim=0) + + def sample_prior(self, batch_size: int) -> torch.Tensor: + # depending on the scheduler, the prior distribution might be different + if isinstance(self.noise_scheduler, DDPMScheduler): + X = torch.randn( + (batch_size, self.max_len, self.n_channels), + device=self.score_model.device, + requires_grad=False, + ) + + elif isinstance(self.noise_scheduler, SDE): + X = self.noise_scheduler.prior_sampling( + (batch_size, self.max_len, self.n_channels) + ).to(device=self.score_model.device) + + else: + raise NotImplementedError("Scheduler not recognized.") + + return X diff --git a/src/fdiff/schedulers/ddpm.py b/src/fdiff/schedulers/ddpm.py new file mode 100644 index 0000000..4f441ab --- /dev/null +++ b/src/fdiff/schedulers/ddpm.py @@ -0,0 +1,596 @@ +# mypy: ignore-errors + +# Copyright 2023 UC Berkeley Team and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim + +import math +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.schedulers.scheduling_utils import ( + KarrasDiffusionSchedulers, + SchedulerMixin, +) +from diffusers.utils import BaseOutput +from diffusers.utils.torch_utils import randn_tensor + + +@dataclass +class DDPMSchedulerOutput(BaseOutput): + """ + Output class for the scheduler's `step` function output. + + Args: + prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the + denoising loop. + pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + The predicted denoised sample `(x_{0})` based on the model output from the current timestep. + `pred_original_sample` can be used to preview progress or for guidance. + """ + + prev_sample: torch.FloatTensor + pred_original_sample: Optional[torch.FloatTensor] = None + + +def betas_for_alpha_bar( + num_diffusion_timesteps, + max_beta=0.999, + alpha_transform_type="cosine", +): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of + (1-beta) over time from t = [0,1]. + + Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up + to that part of the diffusion process. + + + Args: + num_diffusion_timesteps (`int`): the number of betas to produce. + max_beta (`float`): the maximum beta to use; use values lower than 1 to + prevent singularities. + alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. + Choose from `cosine` or `exp` + + Returns: + betas (`np.ndarray`): the betas used by the scheduler to step the model outputs + """ + if alpha_transform_type == "cosine": + + def alpha_bar_fn(t): + return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2 + + elif alpha_transform_type == "exp": + + def alpha_bar_fn(t): + return math.exp(t * -12.0) + + else: + raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}") + + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta)) + return torch.tensor(betas, dtype=torch.float32) + + +class CustomDDPMScheduler(SchedulerMixin, ConfigMixin): + """ + `DDPMScheduler` explores the connections between denoising score matching and Langevin dynamics sampling. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + beta_start (`float`, defaults to 0.0001): + The starting `beta` value of inference. + beta_end (`float`, defaults to 0.02): + The final `beta` value. + beta_schedule (`str`, defaults to `"linear"`): + The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from + `linear`, `scaled_linear`, or `squaredcos_cap_v2`. + variance_type (`str`, defaults to `"fixed_small"`): + Clip the variance when adding noise to the denoised sample. Choose from `fixed_small`, `fixed_small_log`, + `fixed_large`, `fixed_large_log`, `learned` or `learned_range`. + clip_sample (`bool`, defaults to `True`): + Clip the predicted sample for numerical stability. + clip_sample_range (`float`, defaults to 1.0): + The maximum magnitude for sample clipping. Valid only when `clip_sample=True`. + prediction_type (`str`, defaults to `epsilon`, *optional*): + Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), + `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen + Video](https://imagen.research.google/video/paper.pdf) paper). + thresholding (`bool`, defaults to `False`): + Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such + as Stable Diffusion. + dynamic_thresholding_ratio (`float`, defaults to 0.995): + The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. + sample_max_value (`float`, defaults to 1.0): + The threshold value for dynamic thresholding. Valid only when `thresholding=True`. + timestep_spacing (`str`, defaults to `"leading"`): + The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. + steps_offset (`int`, defaults to 0): + An offset added to the inference steps. You can use a combination of `offset=1` and + `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable + Diffusion. + """ + + _compatibles = [e.name for e in KarrasDiffusionSchedulers] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + beta_start: float = 0.0001, + beta_end: float = 0.02, + beta_schedule: str = "linear", + trained_betas: Optional[Union[np.ndarray, List[float]]] = None, + variance_type: str = "fixed_small", + clip_sample: bool = True, + prediction_type: str = "epsilon", + thresholding: bool = False, + dynamic_thresholding_ratio: float = 0.995, + clip_sample_range: float = 1.0, + sample_max_value: float = 1.0, + timestep_spacing: str = "leading", + steps_offset: int = 0, + ): + if trained_betas is not None: + self.betas = torch.tensor(trained_betas, dtype=torch.float32) + elif beta_schedule == "linear": + self.betas = torch.linspace( + beta_start, beta_end, num_train_timesteps, dtype=torch.float32 + ) + elif beta_schedule == "scaled_linear": + # this schedule is very specific to the latent diffusion model. + self.betas = ( + torch.linspace( + beta_start**0.5, + beta_end**0.5, + num_train_timesteps, + dtype=torch.float32, + ) + ** 2 + ) + elif beta_schedule == "squaredcos_cap_v2": + # Glide cosine schedule + self.betas = betas_for_alpha_bar(num_train_timesteps) + elif beta_schedule == "sigmoid": + # GeoDiff sigmoid schedule + betas = torch.linspace(-6, 6, num_train_timesteps) + self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start + else: + raise NotImplementedError( + f"{beta_schedule} does is not implemented for {self.__class__}" + ) + + self.alphas = 1.0 - self.betas + self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) + self.one = torch.tensor(1.0) + + # standard deviation of the initial noise distribution + self.init_noise_sigma = 1.0 + + # setable values + self.custom_timesteps = False + self.num_inference_steps = None + self.timesteps = torch.from_numpy( + np.arange(0, num_train_timesteps)[::-1].copy() + ) + + self.variance_type = variance_type + + def scale_model_input( + self, sample: torch.FloatTensor, timestep: Optional[int] = None + ) -> torch.FloatTensor: + """ + Ensures interchangeability with schedulers that need to scale the denoising model input depending on the + current timestep. + + Args: + sample (`torch.FloatTensor`): + The input sample. + timestep (`int`, *optional*): + The current timestep in the diffusion chain. + + Returns: + `torch.FloatTensor`: + A scaled input sample. + """ + return sample + + def set_timesteps( + self, + num_inference_steps: Optional[int] = None, + device: Union[str, torch.device] = None, + timesteps: Optional[List[int]] = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, + `timesteps` must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default + timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed, + `num_inference_steps` must be `None`. + + """ + if num_inference_steps is not None and timesteps is not None: + raise ValueError( + "Can only pass one of `num_inference_steps` or `custom_timesteps`." + ) + + if timesteps is not None: + for i in range(1, len(timesteps)): + if timesteps[i] >= timesteps[i - 1]: + raise ValueError("`custom_timesteps` must be in descending order.") + + if timesteps[0] >= self.config.num_train_timesteps: + raise ValueError( + f"`timesteps` must start before `self.config.train_timesteps`:" + f" {self.config.num_train_timesteps}." + ) + + timesteps = np.array(timesteps, dtype=np.int64) + self.custom_timesteps = True + else: + if num_inference_steps > self.config.num_train_timesteps: + raise ValueError( + f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" + f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" + f" maximal {self.config.num_train_timesteps} timesteps." + ) + + self.num_inference_steps = num_inference_steps + self.custom_timesteps = False + + # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891 + if self.config.timestep_spacing == "linspace": + timesteps = ( + np.linspace( + 0, self.config.num_train_timesteps - 1, num_inference_steps + ) + .round()[::-1] + .copy() + .astype(np.int64) + ) + elif self.config.timestep_spacing == "leading": + step_ratio = self.config.num_train_timesteps // self.num_inference_steps + # creates integer timesteps by multiplying by ratio + # casting to int to avoid issues when num_inference_step is power of 3 + timesteps = ( + (np.arange(0, num_inference_steps) * step_ratio) + .round()[::-1] + .copy() + .astype(np.int64) + ) + timesteps += self.config.steps_offset + elif self.config.timestep_spacing == "trailing": + step_ratio = self.config.num_train_timesteps / self.num_inference_steps + # creates integer timesteps by multiplying by ratio + # casting to int to avoid issues when num_inference_step is power of 3 + timesteps = np.round( + np.arange(self.config.num_train_timesteps, 0, -step_ratio) + ).astype(np.int64) + timesteps -= 1 + else: + raise ValueError( + f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." + ) + + self.timesteps = torch.from_numpy(timesteps).to(device) + + def _get_variance(self, t, predicted_variance=None, variance_type=None): + prev_t = self.previous_timestep(t) + + alpha_prod_t = self.alphas_cumprod[t] + alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one + current_beta_t = 1 - alpha_prod_t / alpha_prod_t_prev + + # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf) + # and sample from it to get previous sample + # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample + variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * current_beta_t + + # we always take the log of variance, so clamp it to ensure it's not 0 + variance = torch.clamp(variance, min=1e-20) + + if variance_type is None: + variance_type = self.config.variance_type + + # hacks - were probably added for training stability + if variance_type == "fixed_small": + variance = variance + # for rl-diffuser https://arxiv.org/abs/2205.09991 + elif variance_type == "fixed_small_log": + variance = torch.log(variance) + variance = torch.exp(0.5 * variance) + elif variance_type == "fixed_large": + variance = current_beta_t + elif variance_type == "fixed_large_log": + # Glide max_log + variance = torch.log(current_beta_t) + elif variance_type == "learned": + return predicted_variance + elif variance_type == "learned_range": + min_log = torch.log(variance) + max_log = torch.log(current_beta_t) + frac = (predicted_variance + 1) / 2 + variance = frac * max_log + (1 - frac) * min_log + + return variance + + def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: + """ + "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the + prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by + s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing + pixels from saturation at each step. We find that dynamic thresholding results in significantly better + photorealism as well as better image-text alignment, especially when using very large guidance weights." + + https://arxiv.org/abs/2205.11487 + """ + dtype = sample.dtype + batch_size, channels, *remaining_dims = sample.shape + + if dtype not in (torch.float32, torch.float64): + sample = ( + sample.float() + ) # upcast for quantile calculation, and clamp not implemented for cpu half + + # Flatten sample for doing quantile calculation along each image + sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) + + abs_sample = sample.abs() # "a certain percentile absolute pixel value" + + s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) + s = torch.clamp( + s, min=1, max=self.config.sample_max_value + ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] + s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 + sample = ( + torch.clamp(sample, -s, s) / s + ) # "we threshold xt0 to the range [-s, s] and then divide by s" + + sample = sample.reshape(batch_size, channels, *remaining_dims) + sample = sample.to(dtype) + + return sample + + def step( + self, + model_output: torch.FloatTensor, + timestep: int, + sample: torch.FloatTensor, + generator=None, + return_dict: bool = True, + ) -> Union[DDPMSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + generator (`torch.Generator`, *optional*): + A random number generator. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`. + + Returns: + [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] is returned, otherwise a + tuple is returned where the first element is the sample tensor. + + """ + t = timestep + + prev_t = self.previous_timestep(t) + + if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in [ + "learned", + "learned_range", + ]: + model_output, predicted_variance = torch.split( + model_output, sample.shape[1], dim=1 + ) + else: + predicted_variance = None + + # 1. compute alphas, betas + alpha_prod_t = self.alphas_cumprod[t] + alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one + beta_prod_t = 1 - alpha_prod_t + beta_prod_t_prev = 1 - alpha_prod_t_prev + current_alpha_t = alpha_prod_t / alpha_prod_t_prev + current_beta_t = 1 - current_alpha_t + + # 2. compute predicted original sample from predicted noise also called + # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf + if self.config.prediction_type == "epsilon": + pred_original_sample = ( + sample - beta_prod_t ** (0.5) * model_output + ) / alpha_prod_t ** (0.5) + elif self.config.prediction_type == "sample": + pred_original_sample = model_output + elif self.config.prediction_type == "v_prediction": + pred_original_sample = (alpha_prod_t**0.5) * sample - ( + beta_prod_t**0.5 + ) * model_output + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or" + " `v_prediction` for the DDPMScheduler." + ) + + # 3. Clip or threshold "predicted x_0" + if self.config.thresholding: + pred_original_sample = self._threshold_sample(pred_original_sample) + elif self.config.clip_sample: + pred_original_sample = pred_original_sample.clamp( + -self.config.clip_sample_range, self.config.clip_sample_range + ) + + # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t + # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf + pred_original_sample_coeff = ( + alpha_prod_t_prev ** (0.5) * current_beta_t + ) / beta_prod_t + current_sample_coeff = current_alpha_t ** (0.5) * beta_prod_t_prev / beta_prod_t + + # 5. Compute predicted previous sample µ_t + # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf + pred_prev_sample = ( + pred_original_sample_coeff * pred_original_sample + + current_sample_coeff * sample + ) + + # 6. Add noise + variance = 0 + if t > 0: + device = model_output.device + variance_noise = randn_tensor( + model_output.shape, + generator=generator, + device=device, + dtype=model_output.dtype, + ) + if self.variance_type == "fixed_small_log": + variance = ( + self._get_variance(t, predicted_variance=predicted_variance) + * variance_noise + ) + elif self.variance_type == "learned_range": + variance = self._get_variance(t, predicted_variance=predicted_variance) + variance = torch.exp(0.5 * variance) * variance_noise + else: + variance = ( + self._get_variance(t, predicted_variance=predicted_variance) ** 0.5 + ) * variance_noise + + else: + variance = torch.zeros_like( + model_output, device=model_output.device, dtype=model_output.dtype + ) + #! Multiply by the conditioning matrix G! + max_len = pred_prev_sample.shape[1] + G = ( + 1 + / (math.sqrt(2 * max_len)) + * torch.eye(max_len, device=pred_prev_sample.device) + ) + # Double the variance for the first component + G[0, 0] *= math.sqrt(2) + + pred_prev_sample = pred_prev_sample + torch.matmul(G, variance) + + if not return_dict: + return (pred_prev_sample,) + + return DDPMSchedulerOutput( + prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample + ) + + def add_noise( + self, + original_samples: torch.FloatTensor, + noise: torch.FloatTensor, + timesteps: torch.IntTensor, + ) -> torch.FloatTensor: + # Make sure alphas_cumprod and timestep have same device and dtype as original_samples + alphas_cumprod = self.alphas_cumprod.to( + device=original_samples.device, dtype=original_samples.dtype + ) + timesteps = timesteps.to(original_samples.device) + + sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 + sqrt_alpha_prod = sqrt_alpha_prod.flatten() + while len(sqrt_alpha_prod.shape) < len(original_samples.shape): + sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) + + sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() + while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) + + noisy_samples = ( + sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise + ) + return noisy_samples + + def get_velocity( + self, + sample: torch.FloatTensor, + noise: torch.FloatTensor, + timesteps: torch.IntTensor, + ) -> torch.FloatTensor: + # Make sure alphas_cumprod and timestep have same device and dtype as sample + alphas_cumprod = self.alphas_cumprod.to( + device=sample.device, dtype=sample.dtype + ) + timesteps = timesteps.to(sample.device) + + sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 + sqrt_alpha_prod = sqrt_alpha_prod.flatten() + while len(sqrt_alpha_prod.shape) < len(sample.shape): + sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) + + sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() + while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape): + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) + + velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample + return velocity + + def __len__(self): + return self.config.num_train_timesteps + + def previous_timestep(self, timestep): + if self.custom_timesteps: + index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0] + if index == self.timesteps.shape[0] - 1: + prev_t = torch.tensor(-1) + else: + prev_t = self.timesteps[index + 1] + else: + num_inference_steps = ( + self.num_inference_steps + if self.num_inference_steps + else self.config.num_train_timesteps + ) + prev_t = timestep - self.config.num_train_timesteps // num_inference_steps + + return prev_t diff --git a/src/fdiff/schedulers/sde.py b/src/fdiff/schedulers/sde.py new file mode 100644 index 0000000..3a1bdf1 --- /dev/null +++ b/src/fdiff/schedulers/sde.py @@ -0,0 +1,246 @@ +"""Abstract SDE classes, Reverse SDE, and VE/VP SDEs. Adapted from https://github.com/yang-song/score_sde.""" +import abc +import math +from collections import namedtuple +from typing import Optional + +import torch + +SamplingOutput = namedtuple("SamplingOutput", ["prev_sample"]) + + +class SDE(abc.ABC): + """SDE abstract class. Functions are designed for a mini-batch of inputs.""" + + def __init__(self, fourier_noise_scaling: bool = False, eps: float = 1e-5): + """Construct an SDE. + Args: + N: number of discretization time steps. + """ + super().__init__() + self.noise_scaling = fourier_noise_scaling + self.eps = eps + self.G: Optional[torch.Tensor] = None + + @property + def T(self) -> float: + """End time of the SDE.""" + return 1.0 + + @abc.abstractmethod + def marginal_prob( + self, x: torch.Tensor, t: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor]: + """Parameters to determine the marginal distribution of the SDE, $p_t(x)$.""" + + @abc.abstractmethod + def step( + self, model_output: torch.Tensor, timestep: float, sample: torch.Tensor + ) -> SamplingOutput: + ... + + def set_noise_scaling(self, max_len: int) -> None: + """Finish the initialization of the scheduler by setting G (scaling diagonal) + + Args: + max_len (int): number of time steps of the time series + """ + + G = torch.ones(max_len) + if self.noise_scaling: + G = 1 / (math.sqrt(2)) * G + # Double the variance for the first component + G[0] *= math.sqrt(2) + # Double the variance for the middle component if max_len is even + if max_len % 2 == 0: + G[max_len // 2] *= math.sqrt(2) + + self.G = G # Tensor of size (max_len) + self.G_matrix = torch.diag(G) # Tensor of size (max_len, max_len) + assert G.shape[0] == max_len + + def set_timesteps(self, num_diffusion_steps: int) -> None: + self.timesteps = torch.linspace(1.0, self.eps, num_diffusion_steps) + self.step_size = self.timesteps[0] - self.timesteps[1] + + def add_noise( + self, + original_samples: torch.Tensor, + noise: torch.Tensor, + timesteps: torch.Tensor, + ) -> torch.Tensor: + x0 = original_samples + mean, _ = self.marginal_prob(x0, timesteps) + + # Note that the std is not used here because the noise has been scaled prior to calling the function + sample = mean + noise + return sample + + def prior_sampling(self, shape: tuple[int, ...]) -> torch.Tensor: + # Reshape the G matrix to be (1, max_len, max_len) + scaling_matrix = self.G_matrix.view( + -1, self.G_matrix.shape[0], self.G_matrix.shape[1] + ) + + z = torch.randn(*shape) + # Return G@z where z \sim N(0,I) + return torch.matmul(scaling_matrix, z) + + +class VEScheduler(SDE): + def __init__( + self, + sigma_min: float = 0.01, + sigma_max: float = 50.0, + fourier_noise_scaling: bool = False, + eps: float = 1e-5, + ): + """Construct a Variance Exploding SDE. + Args: + sigma_min: smallest sigma. + sigma_max: largest sigma. + N: number of discretization steps + """ + super().__init__(fourier_noise_scaling=fourier_noise_scaling, eps=eps) + self.sigma_min = sigma_min + self.sigma_max = sigma_max + + def marginal_prob( + self, x: torch.Tensor, t: torch.Tensor + ) -> tuple[ + torch.Tensor, torch.Tensor + ]: # perturbation kernel P(X(t)|X(0)) parameters + if self.G is None: + self.set_noise_scaling(x.shape[1]) + assert self.G is not None + + sigma_min = torch.tensor(self.sigma_min).type_as(t) + sigma_max = torch.tensor(self.sigma_max).type_as(t) + std = (sigma_min * (sigma_max / sigma_min) ** t).view(-1, 1) * self.G.to( + x.device + ) + mean = x + return mean, std + + def prior_sampling(self, shape: tuple[int, ...]) -> torch.Tensor: + # In the case of VESDE, the prior is scaled by the maximum noise std + return self.sigma_max * super().prior_sampling(shape) + + def step( + self, model_output: torch.Tensor, timestep: float, sample: torch.Tensor + ) -> SamplingOutput: + """Single denoising step, used for sampling. + + Args: + model_output (torch.Tensor): output of the score model + timestep (torch.Tensor): timestep + sample (torch.Tensor): current sample to be denoised + + Returns: + SamplingOutput: _description_ + """ + + sqrt_derivative = ( + self.sigma_min + * math.sqrt(2 * math.log(self.sigma_max / self.sigma_min)) + * (self.sigma_max / self.sigma_min) ** (timestep) + ) + + diffusion = torch.diag_embed(sqrt_derivative * self.G).to(device=sample.device) + + # Compute drift for the reverse: f(x,t) - G(x,t)G(x,t)^{T}*score + drift = -( + torch.matmul(diffusion * diffusion, model_output) + ) # Notice that the drift of the forward is 0 + + # Sample noise + z = torch.randn_like(sample) + assert self.step_size > 0 + x = ( + sample + - drift * self.step_size # - sign because of reverse time + + torch.sqrt(self.step_size) * torch.matmul(diffusion, z) + ) + output = SamplingOutput(prev_sample=x) + return output + + +class VPScheduler(SDE): + def __init__( + self, + beta_min: float = 0.1, + beta_max: float = 20.0, + fourier_noise_scaling: bool = False, + eps: float = 1e-5, + ): + """Construct a Variance Preserving SDE. + Args: + beta_min: value of beta(0) + beta_max: value of beta(1) + N: number of discretization steps + G: tensor of size max_len + """ + super().__init__(fourier_noise_scaling=fourier_noise_scaling, eps=eps) + self.beta_0 = beta_min + self.beta_1 = beta_max + + def marginal_prob( + self, x: torch.Tensor, t: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor]: + # first check if G has been init. + if self.G is None: + self.set_noise_scaling(x.shape[1]) + assert self.G is not None + + # Compute -1/2*\int_0^t \beta(s) ds + log_mean_coeff = ( + -0.25 * t**2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + ) + + mean = ( + torch.exp(log_mean_coeff[(...,) + (None,) * len(x.shape[1:])]) * x + ) # mean: (batch_size, max_len, n_channels) + + std = torch.sqrt( + (1.0 - torch.exp(2.0 * log_mean_coeff.view(-1, 1))) + ) * self.G.to( + x.device + ) # std: (batch_size, max_len) + + return mean, std + + def get_beta(self, timestep: float) -> float: + return self.beta_0 + timestep * (self.beta_1 - self.beta_0) + + def step( + self, model_output: torch.Tensor, timestep: float, sample: torch.Tensor + ) -> SamplingOutput: + """Single denoising step, used for sampling. + + Args: + model_output (torch.Tensor): output of the score model + timestep (torch.Tensor): timestep + sample (torch.Tensor): current sample to be denoised + + Returns: + SamplingOutput: _description_ + """ + beta = self.get_beta(timestep) + assert self.G is not None + diffusion = torch.diag_embed(math.sqrt(beta) * self.G).to(device=sample.device) + + # Compute drift + drift = -0.5 * beta * sample - ( + torch.matmul(diffusion * diffusion, model_output) + ) + + # Sample noise + z = torch.randn_like(sample) + assert self.step_size > 0 + x = ( + sample + - drift * self.step_size + + torch.sqrt(self.step_size) * torch.matmul(diffusion, z) + ) + output = SamplingOutput(prev_sample=x) + return output diff --git a/src/fdiff/utils/losses.py b/src/fdiff/utils/losses.py new file mode 100644 index 0000000..53549dd --- /dev/null +++ b/src/fdiff/utils/losses.py @@ -0,0 +1,168 @@ +from typing import Callable + +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers import DDPMScheduler + +from fdiff.schedulers.sde import SDE +from fdiff.utils.dataclasses import DiffusableBatch + + +# Courtesy of https://github.com/yang-song/score_sde_pytorch/blob/main/losses.py +def get_sde_loss_fn( + scheduler: SDE, + train: bool, + reduce_mean: bool = True, + likelihood_weighting: bool = False, +) -> Callable[[nn.Module, DiffusableBatch], torch.Tensor]: + """Create a loss function for training with arbirary SDEs. + + Args: + sde: An `sde_lib.SDE` object that represents the forward SDE. + train: `True` for training loss and `False` for evaluation loss. + reduce_mean: If `True`, average the loss across data dimensions. Otherwise sum the loss across data dimensions. + continuous: `True` indicates that the model is defined to take continuous time steps. Otherwise it requires + ad-hoc interpolation to take continuous time steps. + likelihood_weighting: If `True`, weight the mixture of score matching losses + according to https://arxiv.org/abs/2101.09258; otherwise use the weighting recommended in our paper. + eps: A `float` number. The smallest time step to sample from. + + Returns: + A loss function. + """ + reduce_op = ( + torch.mean + if reduce_mean + else lambda *args, **kwargs: 0.5 * torch.sum(*args, **kwargs) + ) + + def loss_fn(model: nn.Module, batch: DiffusableBatch) -> torch.Tensor: + """Compute the loss function. + + Args: + model: A score model. + batch: A mini-batch of training data. + + Returns: + loss: A scalar that represents the average loss value across the mini-batch. + """ + if train: + model.train() + else: + model.eval() + + X = batch.X + y = batch.y + timesteps = batch.timesteps + + # Sample a time step uniformly from [eps, T] + if timesteps is None: + timesteps = ( + torch.rand(X.shape[0], device=X.device) * (scheduler.T - scheduler.eps) + + scheduler.eps + ) + + # Sample the gaussian noise + z = torch.randn_like(X) # (batch_size, max_len, n_channels) + + _, std = scheduler.marginal_prob(X, timesteps) # (batch_size, max_len) + var = std**2 # (batch_size, max_len) + + std_matrix = torch.diag_embed(std) # (batch_size, max_len, max_len) + inverse_std_matrix = torch.diag_embed(1 / std) # (batch_size, max_len, max_len) + + # compute Sigma^{1/2}z to be used for forward sampling: noise is x(t) + noise = torch.matmul(std_matrix, z) # (batch_size, max_len, n_channels) + + # compute Sigma^{-1/2}z to be used for the loss: target_noise is grad log p(x(t)|x(0)) + target_noise = torch.matmul( + inverse_std_matrix, z + ) # (batch_size, max_len, n_channels) + + # Do the perturbation + X_noisy = scheduler.add_noise( + original_samples=X, noise=noise, timesteps=timesteps + ) + + noisy_batch = DiffusableBatch(X=X_noisy, y=y, timesteps=timesteps) + + # Compute the score function + score = model(noisy_batch) + + if not likelihood_weighting: + # lambda(t) = E[||\grad log p(x(t)|x(0))||^2] + + # Compute 1/tr(\Sigma^{-1}) + weighting_factor = 1.0 / torch.sum(1.0 / var, dim=1) # (batch_size,) + assert weighting_factor.shape == (X.shape[0],) + + # 1/tr(\Sigma^{-1}) * ||s + \Sigma^{-1/2}z||^2 + losses = weighting_factor.view(-1, 1, 1) * torch.square( + score + target_noise + ) + + # No relative minus size because: + # log(p(x(t)|x(0))) = -1/2 * (x(t) -mean)^{T} Cov^{-1} (x(t) - mean) + C + # grad log(p(x(t)|x(0))) = (-1) * Cov^{-1} (x(t) - mean) + + # Reduction + losses = reduce_op(losses.reshape(losses.shape[0], -1), dim=-1) # type: ignore + + else: + # Compute the Mahalanobis distance, cf. https://arxiv.org/pdf/2111.13606.pdf + https://www.iro.umontreal.ca/~vincentp/Publications/smdae_techreport.pdf + + # 1) s - \grad log p(x) + difference = score + target_noise # (batch_size, max_len, n_channels) + + # 2) Sigma(s - \grad log p(x)) + scaled_difference = torch.matmul(std_matrix, difference) + + # 3) Compute the loss + losses = torch.square(scaled_difference) + losses = reduce_op(losses.reshape(losses.shape[0], -1), dim=-1) # type: ignore + + loss = torch.mean(losses) + return loss + + return loss_fn + + +def get_ddpm_loss( + scheduler: DDPMScheduler, train: bool, max_time: int +) -> Callable[[nn.Module, DiffusableBatch], torch.Tensor]: + def loss_fn(model: nn.Module, batch: DiffusableBatch) -> torch.Tensor: + if train: + model.train() + else: + model.eval() + + X = batch.X + timesteps = batch.timesteps + + # If no timesteps are provided, sample them randomly + if timesteps is None: + timesteps = torch.randint( + low=0, + high=max_time, + size=(len(batch),), + dtype=torch.long, + device=batch.device, + ) + + noise = torch.randn_like(X, device=batch.device) + + assert hasattr(scheduler, "add_noise") + + # Add the noise to obtain x(t) given x(0) + X_noisy = scheduler.add_noise( + original_samples=X, noise=noise, timesteps=timesteps + ) + noisy_batch = DiffusableBatch(X=X_noisy, y=batch.y, timesteps=timesteps) + + # Predict noise from score model + noise_pred = model(noisy_batch) + loss = F.mse_loss(noise_pred, noise) + return loss + + return loss_fn diff --git a/tests/test_datamodules.py b/tests/test_datamodules.py index fdb612c..7344f67 100644 --- a/tests/test_datamodules.py +++ b/tests/test_datamodules.py @@ -22,12 +22,14 @@ def __init__( max_len: int = max_len, n_channels: int = n_channels, fourier_transform: bool = False, + standardize: bool = False, ) -> None: super().__init__( data_dir=data_dir, random_seed=random_seed, batch_size=batch_size, fourier_transform=fourier_transform, + standardize=standardize, ) self.max_len = max_len self.n_channels = n_channels @@ -78,3 +80,39 @@ def test_fourier_transform() -> None: X_tilde = datamodule_fourier.train_dataloader().dataset.X assert torch.allclose(X, idft(X_tilde), atol=1e-5) + + +def test_standardization() -> None: + # Default datamodule + datamodule = DummyDatamodule(standardize=True) + datamodule.prepare_data() + datamodule.setup() + + train_dataset = datamodule.train_dataloader().dataset + + X_0 = train_dataset.X[0] + X_0_standardized = train_dataset[0]["X"] + X_0_unscaled = ( + X_0_standardized * train_dataset.feature_std + train_dataset.feature_mean + ) + + # Assert that X_train and X_unscaled are close + assert X_0.shape == X_0_unscaled.shape + + assert torch.allclose(X_0, X_0_unscaled, atol=1e-5) + + val_dataset = datamodule.val_dataloader().dataset + + X_0 = val_dataset.X[0] + X_0_standardized = val_dataset[0]["X"] + X_0_unscaled = X_0_standardized * val_dataset.feature_std + val_dataset.feature_mean + + # Assert that X_train and X_unscaled are close + assert X_0.shape == X_0_unscaled.shape + + assert torch.allclose(X_0, X_0_unscaled, atol=1e-5) + + assert torch.allclose( + val_dataset.feature_mean, train_dataset.feature_mean, atol=1e-5 + ) + assert torch.allclose(val_dataset.feature_std, train_dataset.feature_std, atol=1e-5) diff --git a/tests/test_sampling.py b/tests/test_sampling.py index c8793fa..ed0534e 100644 --- a/tests/test_sampling.py +++ b/tests/test_sampling.py @@ -1,7 +1,9 @@ +import pytest from diffusers import DDPMScheduler from fdiff.models.score_models import ScoreModule from fdiff.sampling.sampler import DiffusionSampler +from fdiff.schedulers.sde import SDE, VEScheduler, VPScheduler n_channels = 3 max_len = 50 @@ -10,16 +12,26 @@ num_samples = 48 -def test_sampler(): - # Create a score model - score_model = ScoreModule( - n_channels=n_channels, - max_len=max_len, - noise_scheduler=DDPMScheduler( +@pytest.mark.parametrize( + "noise_scheduler", + [ + DDPMScheduler( num_train_timesteps=10, ), + VPScheduler(), + VEScheduler(), + ], +) +def test_sampler(noise_scheduler: DDPMScheduler | SDE) -> None: + # Create a score model + score_model = ScoreModule( + n_channels=n_channels, max_len=max_len, noise_scheduler=noise_scheduler ) + # In case of SDEs, set the noise scaling + if isinstance(noise_scheduler, SDE): + noise_scheduler.set_noise_scaling(max_len=max_len) + # Create a sampler sampler = DiffusionSampler(score_model=score_model, sample_batch_size=batch_size) diff --git a/tests/test_schedulers.py b/tests/test_schedulers.py new file mode 100644 index 0000000..3f58d78 --- /dev/null +++ b/tests/test_schedulers.py @@ -0,0 +1,134 @@ +from copy import deepcopy + +import pytest +import pytorch_lightning as pl +import torch + +from fdiff.models.score_models import ScoreModule +from fdiff.sampling.sampler import DiffusionSampler +from fdiff.schedulers.sde import SDE, VEScheduler, VPScheduler +from fdiff.utils.dataclasses import DiffusableBatch + +from .test_datamodules import DummyDatamodule + +n_head = 4 +d_model = 8 +n_channels = 3 +max_len = 20 +num_layers = 2 +num_diffusion_steps = 10 +low = 0 +high = 10 +num_samples = 48 +beta_min = 0.01 +beta_max = 20 +batch_size = 50 + + +@pytest.mark.parametrize( + "scheduler_type", + [ + VEScheduler, + VPScheduler, + ], +) +def test_forward(scheduler_type: SDE) -> None: + # Create the SDE + scheduler: SDE = scheduler_type() + + # Create a dummy time series + x = torch.randn(size=(batch_size, max_len, n_channels), device="cpu") + noise = torch.randn(size=(batch_size, max_len, n_channels), device="cpu") + timesteps = torch.rand(size=(batch_size,), device="cpu") + x_noisy = scheduler.add_noise(original_samples=x, noise=noise, timesteps=timesteps) + + assert x_noisy.shape == x.shape + + +@pytest.mark.parametrize( + "scheduler_type", + [ + VEScheduler, + VPScheduler, + ], +) +def test_backward(scheduler_type: SDE) -> None: + t = 0.5 + + scheduler: SDE = scheduler_type() + scheduler.set_noise_scaling(max_len=max_len) + scheduler.set_timesteps(num_diffusion_steps=1000) + + noise = torch.randn(size=(batch_size, max_len, n_channels), device="cpu") + model_output = torch.randn(size=(batch_size, max_len, n_channels), device="cpu") + + scheduler_output = scheduler.step(model_output, timestep=t, sample=noise) + assert scheduler_output.prev_sample.shape == noise.shape + + +@pytest.mark.parametrize( + "scheduler_type", + [ + VEScheduler, + VPScheduler, + ], +) +def test_training(scheduler_type: SDE) -> None: + torch.manual_seed(42) + noise_scheduler = scheduler_type() + score_model = instantiate_score_model(noise_scheduler) + + # Check that the forward call produces tensor of the right shape + X = torch.randn((batch_size, max_len, n_channels)) + timesteps = torch.rand(size=(batch_size,)) + batch = DiffusableBatch(X=X, timesteps=timesteps) + score = score_model(batch) + assert isinstance(score, torch.Tensor) + assert score.size() == X.size() + + # Check that the training updates the parameters + trainer = instantiate_trainer() + datamodule = DummyDatamodule( + n_channels=n_channels, max_len=max_len, batch_size=batch_size + ) + params_before = deepcopy(score_model.state_dict()) + params_before = {k: v for k, v in params_before.items() if v.requires_grad} + + trainer.fit(model=score_model, datamodule=datamodule) + params_after = deepcopy(score_model.state_dict()) + params_after = {k: v for k, v in params_after.items() if v.requires_grad} + + # only look at the params which require grad + + for param_name in params_before: + assert not torch.allclose( + params_before[param_name], params_after[param_name] + ), f"Parameter {param_name} did not change during training" + + # Create a sampler + sampler = DiffusionSampler(score_model=score_model, sample_batch_size=batch_size) + + # Sample from the sampler + samples = sampler.sample( + num_samples=num_samples, num_diffusion_steps=num_diffusion_steps + ) + + # Check the shape of the samples + assert samples.shape == (num_samples, max_len, n_channels) + + +def instantiate_score_model(scheduler: SDE) -> ScoreModule: + score_model = ScoreModule( + n_channels=n_channels, + max_len=max_len, + noise_scheduler=scheduler, + d_model=d_model, + n_head=n_head, + num_layers=num_layers, + num_training_steps=10, + ) + return score_model + + +def instantiate_trainer() -> pl.Trainer: + return pl.Trainer(max_epochs=1, accelerator="cpu") diff --git a/tests/test_transformer.py b/tests/test_transformer.py index 00337b2..7dc53da 100644 --- a/tests/test_transformer.py +++ b/tests/test_transformer.py @@ -1,6 +1,12 @@ +import numpy as np +import pytest import torch -from fdiff.models.transformer import PositionalEncoding, TimeEncoding +from fdiff.models.transformer import ( + GaussianFourierProjection, + PositionalEncoding, + TimeEncoding, +) max_len = 20 # maximum time series length max_time = 100 # maximum diffusion time step @@ -9,7 +15,7 @@ EPS = 1e-5 # tolerance for floating point errors -def test_positional_encoding(): +def test_positional_encoding() -> None: torch.manual_seed(42) pos_encoder = PositionalEncoding(d_model=d_model, max_len=max_len) @@ -30,10 +36,16 @@ def test_positional_encoding(): ) -def test_time_encoding(): +@pytest.mark.parametrize( + "time_encoder", + [ + TimeEncoding(d_model=d_model, max_time=max_time), + GaussianFourierProjection(d_model=d_model), + ], +) +def test_time_encoding(time_encoder: TimeEncoding | GaussianFourierProjection) -> None: torch.manual_seed(42) - time_encoder = TimeEncoding(d_model=d_model, max_time=max_time) X = torch.randn((batch_size, max_len, d_model)) timesteps = torch.randint(low=0, high=max_len, size=(batch_size,)) @@ -48,6 +60,23 @@ def test_time_encoding(): # Check that each batch element is assigned the right encoding vector for b in range(batch_size): for l in range(max_len): - assert torch.allclose( - (enc_out - X)[b, l, :], time_encoder.embedding(timesteps[b]), atol=EPS - ) + if isinstance(time_encoder, TimeEncoding): + ground_truth = time_encoder.embedding(timesteps[b]) + else: + + def fourier_embedding(t: torch.Tensor) -> torch.Tensor: + time_proj = t[:, None] * time_encoder.W[None, :] * 2 * np.pi + embeddings = torch.cat( + [torch.sin(time_proj), torch.cos(time_proj)], dim=-1 + ) + + # Slice to get exactly d_model + t_emb = embeddings[:, : time_encoder.d_model] + + projected_emb: torch.Tensor = time_encoder.dense(t_emb) + + return projected_emb + + ground_truth = fourier_embedding(timesteps)[b] # (d_model) + + assert torch.allclose((enc_out - X)[b, l, :], ground_truth, atol=EPS) diff --git a/tests/test_utils.py b/tests/test_utils.py index 65522c4..c47d81d 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -38,14 +38,14 @@ def test_dft() -> None: x_even = torch.randn(batch_size, max_len, n_channels) x_odd = torch.randn(batch_size, max_len + 1, n_channels) - # Compute the DFT - x_even_tilde = dft(x_even) - x_odd_tilde = dft(x_odd) - - # Compute the inverse DFT - x_even_hat = idft(x_even_tilde) - x_odd_hat = idft(x_odd_tilde) + # Check that IDFT of DFT is identity + x_even_hat = idft(dft(x_even)) + x_odd_hat = idft(dft(x_odd)) + assert torch.allclose(x_even, x_even_hat, atol=1e-5) + assert torch.allclose(x_odd, x_odd_hat, atol=1e-5) - # Check that the inverse DFT is the original time series + # Check that DFT of IDFT is identity + x_even_hat = dft(idft(x_even)) + x_odd_hat = dft(idft(x_odd)) assert torch.allclose(x_even, x_even_hat, atol=1e-5) assert torch.allclose(x_odd, x_odd_hat, atol=1e-5)