From ef0a1ee2a76120086b27cb55fcc74c3a21b0b411 Mon Sep 17 00:00:00 2001 From: Eric Kenji Lee Date: Mon, 20 Mar 2023 23:38:42 -0400 Subject: [PATCH] Updated to require Python3.8.0 --- WaveMAP_Example.ipynb | 955 +-- WaveMAP_Figures_Data.ipynb | 15226 ++++++++++++++--------------------- poetry.lock | 451 +- pyproject.toml | 4 +- requirements.txt | 395 +- 5 files changed, 6794 insertions(+), 10237 deletions(-) diff --git a/WaveMAP_Example.ipynb b/WaveMAP_Example.ipynb index 673f950..2fe1edb 100644 --- a/WaveMAP_Example.ipynb +++ b/WaveMAP_Example.ipynb @@ -1,487 +1,500 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vZ4iROLKzvz8" - }, - "source": [ - "## Import all packages and install packages not included in Colab\n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "id": "_IdxQha2qWBP", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "from sklearn import datasets\n", - "from PIL import Image\n", - "from io import BytesIO\n", - "import base64\n", - "\n", - "from bokeh.plotting import figure, show, output_notebook, output_file\n", - "from bokeh.models import HoverTool, ColumnDataSource, CategoricalColorMapper, ContinuousColorMapper\n", - "from bokeh.palettes import Turbo256\n", - "from bokeh.transform import linear_cmap\n", - "from bokeh.transform import factor_cmap\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7kVw0RhLqSky", - "outputId": "3cd04cdb-97a5-44c7-fcbb-387908e4bb10", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting umap-learn==0.5.0\n", - "\u001b[?25l Downloading https://files.pythonhosted.org/packages/f1/53/3b1598971f4cfe30841ed3390a09820ce830143fd3374fe4f7707175d219/umap-learn-0.5.0.tar.gz (81kB)\n", - "\r\u001b[K |████ | 10kB 11.3MB/s eta 0:00:01\r\u001b[K |████████ | 20kB 5.8MB/s eta 0:00:01\r\u001b[K |████████████ | 30kB 2.9MB/s eta 0:00:01\r\u001b[K |████████████████ | 40kB 2.6MB/s eta 0:00:01\r\u001b[K |████████████████████ | 51kB 3.1MB/s eta 0:00:01\r\u001b[K |████████████████████████ | 61kB 3.6MB/s eta 0:00:01\r\u001b[K |████████████████████████████ | 71kB 4.1MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 81kB 3.1MB/s \n", - "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from umap-learn==0.5.0) (1.19.5)\n", - "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from umap-learn==0.5.0) (0.22.2.post1)\n", - "Requirement already satisfied: scipy>=1.0 in 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sha256=db93e78cb06033b33ea530a85d808da3a3a19b3543bae6c900f695064d7a3611\n", - " Stored in directory: /root/.cache/pip/wheels/c6/64/c4/6ff874f1bfedf37c36d1799b6f3da78c5bbcde007fbda096aa\n", - "Successfully built umap-learn\n", - "Installing collected packages: umap-learn\n", - " Found existing installation: umap-learn 0.5.1\n", - " Uninstalling umap-learn-0.5.1:\n", - " Successfully uninstalled umap-learn-0.5.1\n", - "Successfully installed umap-learn-0.5.0\n", - "Collecting networkx==2.4\n", - "\u001b[?25l Downloading https://files.pythonhosted.org/packages/41/8f/dd6a8e85946def36e4f2c69c84219af0fa5e832b018c970e92f2ad337e45/networkx-2.4-py3-none-any.whl (1.6MB)\n", - "\u001b[K |████████████████████████████████| 1.6MB 4.0MB/s \n", - "\u001b[?25hRequirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx==2.4) (4.4.2)\n", - "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is 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"\n", - "!pip install networkx==2.4\n", - "import networkx as nx\n", - "\n", - "!pip install python-igraph==0.8.2\n", - "import igraph as ig" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jqA7vl9Uz3z-" - }, - "source": [ - "## ECG needs to be defined and added manually to iGraph" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "alie_AJHp1ym", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "def community_ecg(self, weights=None, ens_size=16, min_weight=0.05):\n", - " W = [0]*self.ecount()\n", - " ## Ensemble of level-1 Louvain \n", - " for i in range(ens_size):\n", - " p = np.random.permutation(self.vcount()).tolist()\n", - " g = self.permute_vertices(p)\n", - " l = g.community_multilevel(weights=weights, return_levels=True)[0].membership\n", - " b = [l[p[x.tuple[0]]]==l[p[x.tuple[1]]] for x in self.es]\n", - " W = [W[i]+b[i] for i in range(len(W))]\n", - " W = [min_weight + (1-min_weight)*W[i]/ens_size for i in range(len(W))]\n", - " ## Force min_weight outside 2-core\n", - " core = self.shell_index()\n", - " ecore = [min(core[x.tuple[0]],core[x.tuple[1]]) for x in self.es]\n", - " w = [W[i] if ecore[i]>1 else min_weight for i in range(len(ecore))]\n", - " part = self.community_multilevel(weights=w)\n", - " part.W = w\n", - " part.CSI = 1-2*np.sum([min(1-i,i) for i in w])/len(w)\n", - " return part\n", - "\n", - "ig.Graph.community_ecg = community_ecg" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d4xmDNue0CUd" - }, - "source": [ - "## Loading Fashion MNIST data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "imFWrVmXrfbg", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "fmnist = datasets.fetch_openml('Fashion-MNIST')\n", - "np.random.shuffle(fmnist.data)\n", - "fmnist_subset = fmnist.data[:15000,:]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kY__FJxk0E66" - }, - "source": [ - "## Compute UMAP step of WaveMAP\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "KRYvqF20psn3", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "reducer = umap.UMAP()\n", - "mapper = reducer.fit(fmnist_subset)\n", - "embedding = reducer.transform(fmnist_subset)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YATfxi590b1t" - }, - "source": [ - "## Calculate ECG of UMAP high-dimensional graph" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "fSAnVqGIp1Sp", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - "umap_igraph = ig.Graph(len(G), list(zip(*list(zip(*nx.to_edgelist(G)))[:2])))\n", - "\n", - "umap_ECG = umap_igraph.community_ecg(ens_size=10,min_weight=0.5)" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vZ4iROLKzvz8" + }, + "source": [ + "## Import all packages and install packages not included in Colab\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "_IdxQha2qWBP", + "tags": [], + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn import datasets\n", + "from PIL import Image\n", + "from io import BytesIO\n", + "import base64\n", + "\n", + "from bokeh.plotting import figure, show, output_notebook, output_file\n", + "from bokeh.models import HoverTool, ColumnDataSource, CategoricalColorMapper, ContinuousColorMapper\n", + "from bokeh.palettes import Turbo256\n", + "from bokeh.transform import linear_cmap\n", + "from bokeh.transform import factor_cmap\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "7kVw0RhLqSky", + "outputId": "3cd04cdb-97a5-44c7-fcbb-387908e4bb10", + "tags": [], + "vscode": { + "languageId": "python" + } + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "eJuaVC8e0fwD" - }, - "source": [ - "## Plot WaveMAP i.e. UMAP with ECG clusters" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: umap-learn==0.5.3 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (0.5.3)\n", + "Requirement already satisfied: scikit-learn>=0.22 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (from umap-learn==0.5.3) (1.2.1)\n", + "Requirement already satisfied: numba>=0.49 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (from umap-learn==0.5.3) (0.56.4)\n", + 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(from numba>=0.49->umap-learn==0.5.3) (6.0.0)\n", + "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (from numba>=0.49->umap-learn==0.5.3) (0.39.1)\n", + "Requirement already satisfied: joblib>=0.11 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (from pynndescent>=0.5->umap-learn==0.5.3) (1.2.0)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (from scikit-learn>=0.22->umap-learn==0.5.3) (3.1.0)\n", + "Requirement already satisfied: zipp>=0.5 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (from importlib-metadata->numba>=0.49->umap-learn==0.5.3) (3.14.0)\n" + ] }, { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 700 - }, - "id": "6aiRxRiJpkXO", - "outputId": "57f7a187-610f-4ac8-975e-f4bdfec7c3ed", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 7, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "umap_df['color'] = umap_ECG.membership\n", - "\n", - "ecg_colormap = [sns.color_palette(\"husl\", len(set(umap_ECG.membership)))[i] for i in umap_ECG.membership]\n", - "\n", - "f, arr = plt.subplots(1,figsize=[15,12])\n", - "\n", - "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", - " marker='o',c=ecg_colormap, s=10, edgecolor='w',\n", - " linewidth=0.25)\n", - "\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "\n", - "arr.set_xticks([])\n", - "arr.set_yticks([])" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "-9C8rbqIf-DF" - }, - "source": [ - "## Here we construct an interactive WaveMAP plot with Bokeh" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: networkx==2.4 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (2.4)\n", + "Requirement already satisfied: decorator>=4.3.0 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (from networkx==2.4) (5.1.1)\n", + "Requirement already satisfied: python-igraph==0.8.2 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (0.8.2)\n", + "Requirement already satisfied: texttable>=1.6.2 in /Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages (from python-igraph==0.8.2) (1.6.7)\n" + ] + } + ], + "source": [ + "!pip install umap-learn==0.5.3\n", + "from umap import umap_ as umap\n", + "\n", + "!pip install networkx==2.4\n", + "import networkx as nx\n", + "\n", + "!pip install python-igraph==0.8.2\n", + "import igraph as ig" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jqA7vl9Uz3z-" + }, + "source": [ + "## ECG needs to be defined and added manually to iGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "alie_AJHp1ym", + "tags": [], + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "def community_ecg(self, weights=None, ens_size=16, min_weight=0.05):\n", + " W = [0]*self.ecount()\n", + " ## Ensemble of level-1 Louvain \n", + " for i in range(ens_size):\n", + " p = np.random.permutation(self.vcount()).tolist()\n", + " g = self.permute_vertices(p)\n", + " l = g.community_multilevel(weights=weights, return_levels=True)[0].membership\n", + " b = [l[p[x.tuple[0]]]==l[p[x.tuple[1]]] for x in self.es]\n", + " W = [W[i]+b[i] for i in range(len(W))]\n", + " W = [min_weight + (1-min_weight)*W[i]/ens_size for i in range(len(W))]\n", + " ## Force min_weight outside 2-core\n", + " core = self.shell_index()\n", + " ecore = [min(core[x.tuple[0]],core[x.tuple[1]]) for x in self.es]\n", + " w = [W[i] if ecore[i]>1 else min_weight for i in range(len(ecore))]\n", + " part = self.community_multilevel(weights=w)\n", + " part.W = w\n", + " part.CSI = 1-2*np.sum([min(1-i,i) for i in w])/len(w)\n", + " return part\n", + "\n", + "ig.Graph.community_ecg = community_ecg" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d4xmDNue0CUd" + }, + "source": [ + "## Loading Fashion MNIST data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "imFWrVmXrfbg", + "tags": [], + "vscode": { + "languageId": "python" + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "ZNAZHUrP153g", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "def embeddable_image(data,color=None):\n", - " fig, ax = plt.subplots()\n", - " fig.set_figheight(0.5)\n", - " fig.set_figwidth(0.5)\n", - " ax.imshow(data.reshape(28,28))\n", - " ax.axis('off')\n", - " fig.canvas.draw()\n", - " img_data = np.array(fig.canvas.renderer.buffer_rgba())\n", - " image = Image.fromarray(img_data, mode='RGBA')\n", - " buffer = BytesIO()\n", - " image.save(buffer, format='png')\n", - " for_encoding = buffer.getvalue()\n", - " plt.close()\n", - " \n", - " return 'data:image/png;base64,' + base64.b64encode(for_encoding).decode()\n", - "\n", - "\n", - "def rgb2hex(rgb):\n", - " rgb = tuple([int(x*256) for x in rgb])\n", - " return '#%02x%02x%02x' % rgb" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kenjilee/opt/anaconda3/envs/WaveMAP_Paper_Test_2/lib/python3.8/site-packages/sklearn/datasets/_openml.py:932: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n", + " warn(\n" + ] + } + ], + "source": [ + "fmnist = datasets.fetch_openml('Fashion-MNIST', as_frame=False)\n", + "np.random.shuffle(fmnist.data)\n", + "fmnist_subset = fmnist.data[:150,:]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kY__FJxk0E66" + }, + "source": [ + "## Compute UMAP step of WaveMAP\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KRYvqF20psn3", + "tags": [], + "vscode": { + "languageId": "python" + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KgwO0RWowmhz", - "outputId": "01dfebfc-0541-4c4d-d15f-b5f0fdfe6b3b", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/bokeh/models/mappers.py:147: UserWarning: Palette length does not match number of factors. ['17'] will be assigned to `nan_color` gray\n", - " warnings.warn(\"Palette length does not match number of factors. %s will be assigned to `nan_color` %s\" % (extra_factors, self.nan_color))\n" - ] - } - ], - "source": [ - "umap_df['data'] = list(fmnist_subset)\n", - "umap_df['image'] = list(map(embeddable_image, umap_df.data))\n", - "datasource = ColumnDataSource(umap_df)\n", - "\n", - "plot_figure = figure(\n", - " title='WaveMAP of dataset',\n", - " plot_width=900,\n", - " plot_height=900,\n", - " tools=('pan, wheel_zoom, reset')\n", - ")\n", - "\n", - "plot_figure.add_tools(HoverTool(tooltips=\"\"\"\n", - "
\n", - "
\n", - " \n", - "
\n", - "
\n", - " Index:\n", - " @index\n", - "
\n", - "
\n", - "\"\"\"))\n", - "\n", - " \n", - "n_clusts = len(umap_df['color'].unique())\n", - "color_map = factor_cmap(field_name='color', \n", - " #palette=[rgb2hex(x) for x in list(sns.color_palette('husl',n_clusts))], \n", - " palette = all_palettes['Category20c'][17],\n", - " factors=[str(x) for x in umap_df['color'].unique()] )\n", - "\n", - "plot_figure.circle(\n", - " 'x',\n", - " 'y',\n", - " source=datasource,\n", - " line_alpha=0.6,\n", - " fill_alpha=0.6,\n", - " size=4,\n", - " color = color_map\n", - ")\n", - "\n", - "output_file('WaveMAP_on_data.html')\n", - "show(plot_figure)" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #273: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], + "source": [ + "reducer = umap.UMAP()\n", + "mapper = reducer.fit(fmnist_subset)\n", + "embedding = reducer.transform(fmnist_subset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YATfxi590b1t" + }, + "source": [ + "## Calculate ECG of UMAP high-dimensional graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fSAnVqGIp1Sp", + "tags": [], + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", + "umap_igraph = ig.Graph(len(G), list(zip(*list(zip(*nx.to_edgelist(G)))[:2])))\n", + "\n", + "umap_ECG = umap_igraph.community_ecg(ens_size=10,min_weight=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eJuaVC8e0fwD" + }, + "source": [ + "## Plot WaveMAP i.e. UMAP with ECG clusters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 700 }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "id": "DMF-c3KSQKNp", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "umap_df['data'] = list(fmnist_subset)\n", - "umap_df['image'] = list(map(embeddable_image, umap_df.data))\n", - "datasource = ColumnDataSource(umap_df)\n", - "\n", - "plot_figure = figure(\n", - " title='WaveMAP of dataset',\n", - " plot_width=900,\n", - " plot_height=900,\n", - " tools=('pan, wheel_zoom, reset')\n", - ")\n", - "\n", - "plot_figure.add_tools(HoverTool(tooltips=\"\"\"\n", - "
\n", - "
\n", - " \n", - "
\n", - "
\n", - " Index:\n", - " @index\n", - "
\n", - "
\n", - "\"\"\"))\n", - "\n", - "color_ixs = np.round(np.linspace(0, len(Turbo256) - 1, len(umap_df['color'].unique()))).astype(int)\n", - "colors = [Turbo256[i] for i in color_ixs]\n", - "colormap = {i: colors[i] for i in umap_df['color'].unique()}\n", - "color_list = [colormap[x] for x in umap_df['color']]\n", - "umap_df['color_hex'] = color_list\n", - "\n", - "plot_figure.circle(\n", - " 'x',\n", - " 'y',\n", - " source=datasource,\n", - " line_alpha=0.6,\n", - " fill_alpha=0.6,\n", - " size=4,\n", - " color = 'color_hex'\n", - ")\n", - "\n", - "output_file('WaveMAP_on_data.html')\n", - "show(plot_figure)" - ] + "id": "6aiRxRiJpkXO", + "outputId": "57f7a187-610f-4ac8-975e-f4bdfec7c3ed", + "tags": [], + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", + "umap_df['color'] = umap_ECG.membership\n", + "\n", + "ecg_colormap = [sns.color_palette(\"husl\", len(set(umap_ECG.membership)))[i] for i in umap_ECG.membership]\n", + "\n", + "f, arr = plt.subplots(1,figsize=[15,12])\n", + "\n", + "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", + " marker='o',c=ecg_colormap, s=10, edgecolor='w',\n", + " linewidth=0.25)\n", + "\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['left'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['bottom'].set_visible(False)\n", + "\n", + "arr.set_xticks([])\n", + "arr.set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-9C8rbqIf-DF" + }, + "source": [ + "## Here we construct an interactive WaveMAP plot with Bokeh" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZNAZHUrP153g", + "tags": [], + "vscode": { + "languageId": "python" } - ], - "metadata": { + }, + "outputs": [], + "source": [ + "def embeddable_image(data,color=None):\n", + " fig, ax = plt.subplots()\n", + " fig.set_figheight(0.5)\n", + " fig.set_figwidth(0.5)\n", + " ax.imshow(data.reshape(28,28))\n", + " ax.axis('off')\n", + " fig.canvas.draw()\n", + " img_data = np.array(fig.canvas.renderer.buffer_rgba())\n", + " image = Image.fromarray(img_data, mode='RGBA')\n", + " buffer = BytesIO()\n", + " image.save(buffer, format='png')\n", + " for_encoding = buffer.getvalue()\n", + " plt.close()\n", + " \n", + " return 'data:image/png;base64,' + base64.b64encode(for_encoding).decode()\n", + "\n", + "\n", + "def rgb2hex(rgb):\n", + " rgb = tuple([int(x*256) for x in rgb])\n", + " return '#%02x%02x%02x' % rgb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "colab": { - "authorship_tag": "ABX9TyN8x2dFdp0oDOnIIRKXAgKm", - "include_colab_link": true, - "name": "WaveMAP_Example.ipynb", - "provenance": [] + "base_uri": "https://localhost:8080/" }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "id": "KgwO0RWowmhz", + "outputId": "01dfebfc-0541-4c4d-d15f-b5f0fdfe6b3b", + "tags": [], + "vscode": { + "languageId": "python" + } + }, + "outputs": [], + "source": [ + "umap_df['data'] = list(fmnist_subset)\n", + "umap_df['image'] = list(map(embeddable_image, umap_df.data))\n", + "datasource = ColumnDataSource(umap_df)\n", + "\n", + "plot_figure = figure(\n", + " title='WaveMAP of dataset',\n", + " plot_width=900,\n", + " plot_height=900,\n", + " tools=('pan, wheel_zoom, reset')\n", + ")\n", + "\n", + "plot_figure.add_tools(HoverTool(tooltips=\"\"\"\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Index:\n", + " @index\n", + "
\n", + "
\n", + "\"\"\"))\n", + "\n", + " \n", + "n_clusts = len(umap_df['color'].unique())\n", + "color_map = factor_cmap(field_name='color', \n", + " #palette=[rgb2hex(x) for x in list(sns.color_palette('husl',n_clusts))], \n", + " palette = all_palettes['Category20c'][17],\n", + " factors=[str(x) for x in umap_df['color'].unique()] )\n", + "\n", + "plot_figure.circle(\n", + " 'x',\n", + " 'y',\n", + " source=datasource,\n", + " line_alpha=0.6,\n", + " fill_alpha=0.6,\n", + " size=4,\n", + " color = color_map\n", + ")\n", + "\n", + "output_file('WaveMAP_on_data.html')\n", + "show(plot_figure)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DMF-c3KSQKNp", + "tags": [], + "vscode": { + "languageId": "python" } + }, + "outputs": [], + "source": [ + "umap_df['data'] = list(fmnist_subset)\n", + "umap_df['image'] = list(map(embeddable_image, umap_df.data))\n", + "datasource = ColumnDataSource(umap_df)\n", + "\n", + "plot_figure = figure(\n", + " title='WaveMAP of dataset',\n", + " plot_width=900,\n", + " plot_height=900,\n", + " tools=('pan, wheel_zoom, reset')\n", + ")\n", + "\n", + "plot_figure.add_tools(HoverTool(tooltips=\"\"\"\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " Index:\n", + " @index\n", + "
\n", + "
\n", + "\"\"\"))\n", + "\n", + "color_ixs = np.round(np.linspace(0, len(Turbo256) - 1, len(umap_df['color'].unique()))).astype(int)\n", + "colors = [Turbo256[i] for i in color_ixs]\n", + "colormap = {i: colors[i] for i in umap_df['color'].unique()}\n", + "color_list = [colormap[x] for x in umap_df['color']]\n", + "umap_df['color_hex'] = color_list\n", + "\n", + "plot_figure.circle(\n", + " 'x',\n", + " 'y',\n", + " source=datasource,\n", + " line_alpha=0.6,\n", + " fill_alpha=0.6,\n", + " size=4,\n", + " color = 'color_hex'\n", + ")\n", + "\n", + "output_file('WaveMAP_on_data.html')\n", + "show(plot_figure)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyN8x2dFdp0oDOnIIRKXAgKm", + "include_colab_link": true, + "name": "WaveMAP_Example.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "WaveMAP_Test_2", + "language": "python", + "name": "wavemap_test_2" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/WaveMAP_Figures_Data.ipynb b/WaveMAP_Figures_Data.ipynb index 9182cd6..0d4e58e 100644 --- a/WaveMAP_Figures_Data.ipynb +++ b/WaveMAP_Figures_Data.ipynb @@ -1,9245 +1,5999 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "-JFFk_4DZokr" - }, - "source": [ - "# This notebook will allow the user to regenerate all figures and supplementary figures in the [WaveMAP manuscript](https://www.biorxiv.org/content/10.1101/2021.02.07.430135v1) except for Fig. S9 (which was a composite of previous figures) and diagrams/schematics that were drawn by hand. Please direct questions to the corresponding author, Chandramouli Chandrasekaran at cchandr1@bu.edu." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PFVwe-_fLdRb" - }, - "source": [ - "# Step 1: Download various data files by cloning the paper's Git repo.\n", - "\n", - "---\n", - "*Note to reviewers: PLEASE DO NOT STAR OR FORK THE REPO WHICH IS DEANONYMIZING. The Github repository helps clone important files necessary for analysis.*\n", - "\n", - "This git repository has various data files which we clone below to allow you to run the google colab. There is no need to go to the original git repository the process of running this code leads to the files being downloaded and attached to this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "z16uiX0wj2ni", - "outputId": "06ba12cb-7696-4056-8667-f2531a485cd5", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cloning into 'WaveMAP_Paper'...\n", - "remote: Enumerating objects: 300, done.\u001b[K\n", - "remote: Counting objects: 100% (300/300), done.\u001b[K\n", - "remote: Compressing objects: 100% (255/255), done.\u001b[K\n", - "remote: Total 300 (delta 120), reused 172 (delta 42), pack-reused 0\u001b[K\n", - "Receiving objects: 100% (300/300), 4.38 MiB | 13.86 MiB/s, done.\n", - "Resolving deltas: 100% (120/120), done.\n" - ] - } - ], - "source": [ - "!git clone https://github.com/EricKenjiLee/WaveMAP_Paper.git" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0m-cBuzkMyYN" - }, - "source": [ - "## Step 1a: Importing packages\n", - "\n", - "Here are imported standard packages." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "KbP0Q7Z3biq_", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "# Importing packages \n", - "# --- Importing matplotlib, seaborn, etc.\n", - "\n", - "import os\n", - "import random\n", - "\n", - "import matplotlib as mpl\n", - "from matplotlib import pyplot as plt\n", - "from matplotlib.lines import Line2D\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib.gridspec import GridSpec\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import pandas as pd\n", - "import scipy\n", - "from scipy import io\n", - "import pickle as pkl\n", - "import h5py\n", - "import xml.etree.ElementTree as ET" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VaM9ETbvM38B" - }, - "source": [ - "These non-standard packages are pinned for compatibility reasons. Google Colab's default versions for import change frequenetly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SvvK4nfZmFtn", - "outputId": "7dcc8e64-eec3-41fc-9550-ca85a4c1bf5e", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting networkx==2.4\n", - "\u001b[?25l Downloading https://files.pythonhosted.org/packages/41/8f/dd6a8e85946def36e4f2c69c84219af0fa5e832b018c970e92f2ad337e45/networkx-2.4-py3-none-any.whl (1.6MB)\n", - "\u001b[K |████████████████████████████████| 1.6MB 8.4MB/s \n", - "\u001b[?25hRequirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx==2.4) (4.4.2)\n", - "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n", - "Installing collected packages: networkx\n", - 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"import igraph as ig" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Sj0vY7akb92a" - }, - "source": [ - "#Step 2: Colormap selection for clusterings\n", - "\n", - "---\n", - "\n", - "The clusterings get clustered in a random order so although these colors reflect those used in the manuscript, they may need to be permuted to match what is found in our figures." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 233 - }, - "id": "R8Nkf7EOb1jE", - "outputId": "bae30630-c725-41a7-e983-6dee2a065761", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": "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", - 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "CUSTOM_PAL_SORT_3 = ['#5e60ce','#00c49a','#ffca3a','#D81159','#fe7f2d','#7bdff2','#0496ff','#efa6c9','#ced4da']\n", - "GMM_PAL = ['#d62424','#12db41','#f0c905','#248cd6']\n", - "\n", - "# In RGB form\n", - "coherence_colors = [[0.609, 0.283,\t0.724],\n", - "[0.259,\t0.314, 0.635],\n", - "[0.251,\t0.412, 0.698],\n", - "[0.176,\t0.631, 0.859],\n", - "[0.369,\t0.749, 0.549],\n", - "[0.898,\t0.654, 0.169],\n", - "[0.898,\t0.41, 0.165]]\n", - "sns.palplot(CUSTOM_PAL_SORT_3)\n", - "sns.palplot(GMM_PAL)\n", - "sns.palplot(coherence_colors)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "A4kW4JO8d4hh" - }, - "source": [ - "# Step 3: Setting of parameters\n", - "\n", - "---\n", - "\n", - "This sets various global parameters like sampling rate, U-probe depths, and random seed state. Setting the random seed at the level of the Python kernel, Numpy, and random packages is essential to produce the same qualitative WaveMAP projection across instances. Note that qualitatively, the results be the same no matter the seed because it affects the projection step of the algorithm and note the graph construction (the latter is what is used in clustering)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LAC7l7TNb8N1", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "#These are the depths that the V-probe channels are located at\n", - "DEPTHS = [0.15,0.3,0.45,0.60,0.75,0.9,1.05,1.20,1.35,1.50,1.65,1.80,1.95,2.1,2.25,2.4]\n", - "\n", - "#This converts time points to real time. There are 48 samples per waveform colleted at 30 kilosamples\n", - "SAMP_RATE_TO_TIME = 1/(48/30000) \n", - "\n", - "#Setting of random seed across Python kernel and packages to ensure reproducibility \n", - "RAND_STATE=42\n", - "np.random.seed(RAND_STATE)\n", - "os.environ['PYTHONHASHSEED'] = str(RAND_STATE)\n", - "random.seed(RAND_STATE)\n", - "\n", - "#UMAP Parameters\n", - "#The number of neighbors considered when constructing the high-d graph. \n", - "#Made more global-information preserving by increasing it from 15 to 20.\n", - "N_NEIGHBORS = 20 \n", - "\n", - "#The minimum distance between points in the projected space.\n", - "#Used for visualization but doesn't affect clustering.\n", - "MIN_DIST = 0.1\n", - "\n", - "#Louvain Clustering Parameters\n", - "RESOLUTION = 1.5\n", - "\n", - "# BLUE COLOR\n", - "BlueCol = '\\033[94m'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "W0-CCJCAeLOx" - }, - "source": [ - "# Step 4: Loading of processed waveform data\n", - "\n", - "---\n", - "\n", - "This cell loads varous files including the 250 Hz high-pass 4th order Butterworth-filtered waveforms, the GMM features, BIC values used for selecting number of GMM clusters, and other waveform info." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b7A2Qlu4exDE", - "outputId": "ca3deee3-d6b5-4286-acee-9341622e09d2", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[94mLoading data\n", - "/content\n", - "/content/WaveMAP_Paper/data/full_data.npy\n", - "/content/WaveMAP_Paper/data/waveformsClassified_250hz_refiltered.mat\n", - "/content/WaveMAP_Paper/data/gmm_features.mat\n", - "/content/WaveMAP_Paper/data/BIC_list.mat\n", - "/content/WaveMAP_Paper/data/8_class_GMM.mat\n", - "/content/WaveMAP_Paper/data/filt_full_df.pkl\n", - "/content/WaveMAP_Paper/data/full_data_df.pkl\n" - ] - } - ], - "source": [ - "print(BlueCol + 'Loading data')\n", - "\n", - "rel_path = os.getcwd()\n", - "fullDataPath = os.path.join(rel_path,'WaveMAP_Paper/data/full_data.npy');\n", - "GMMclasslabelpath = os.path.join(rel_path,\n", - "'WaveMAP_Paper/data/waveformsClassified_250hz_refiltered.mat')\n", - "GMMfeaturepath = os.path.join(rel_path,\n", - "'WaveMAP_Paper/data/gmm_features.mat')\n", - "BICpath = os.path.join(rel_path,\n", - "'WaveMAP_Paper/data/BIC_list.mat')\n", - "eightclassGMMpath = os.path.join(rel_path,'WaveMAP_Paper/data/8_class_GMM.mat');\n", - "filtfulldfPath = os.path.join(rel_path,'WaveMAP_Paper/data/filt_full_df.pkl');\n", - "\n", - "print(rel_path)\n", - "print(fullDataPath);\n", - "print(GMMclasslabelpath)\n", - "print(GMMfeaturepath)\n", - "print(BICpath)\n", - "print(eightclassGMMpath)\n", - "print(filtfulldfPath)\n", - "\n", - "full_data = np.load(fullDataPath)\n", - "allDataDFPath = os.path.join(rel_path,'WaveMAP_Paper/data/full_data_df.pkl');\n", - "\n", - "GMM_class_labels = scipy.io.loadmat(GMMclasslabelpath)['classifies'].T\n", - "gmm_features_data = scipy.io.loadmat(GMMfeaturepath)['features']\n", - "\n", - "GMM_class_labels = GMM_class_labels[~np.isnan(GMM_class_labels)]\n", - "GMM_class_df = pd.DataFrame(GMM_class_labels,columns=['Class'])\n", - "gmm_feat_data_nonan = gmm_features_data[~np.isnan(gmm_features_data)].reshape(len(GMM_class_df),3)\n", - "\n", - "BIC_list = scipy.io.loadmat(BICpath)['BIC_list'][0]\n", - "\n", - "eight_GMM_classes = scipy.io.loadmat(eightclassGMMpath)['classifies']\n", - "\n", - "all_data_df = pkl.load(open(allDataDFPath,'rb'))\n", - "filt_full_df = pkl.load(open(filtfulldfPath,\"rb\"))\n", - "\n", - "print(allDataDFPath)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BF50mJmCz5cQ" - }, - "source": [ - "# Step 5: Loading of processed firing rate data.\n", - "\n", - "---\n", - "\n", - "Raw data is too large to be hosted and processed given Colab I/O speeds and file size storage limits; therefore, processed data is used here. Please contact the corresponding author for access to raw data (cchandr1@bu.edu)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4yCjav9sZsR7", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "def read_pkl(pkl_file_loc):\n", - " return pkl.load(open(pkl_file_loc,'rb'))\n", - "\n", - "FR_trace_loc = 'WaveMAP_Paper/data/FR_traces'\n", - "FR_traces = os.listdir(FR_trace_loc)\n", - "\n", - "UMAP_FR_traces = [x for x in FR_traces if not x.startswith('GMM')]\n", - "GMM_FR_traces = [x for x in FR_traces if x.startswith('GMM')]\n", - "\n", - "UMAP_traces_df = pd.DataFrame(columns = ['clust','PREF','NONPREF','PREF_UPPER_BOUND','PREF_LOWER_BOUND',\n", - " 'NONPREF_UPPER_BOUND','NONPREF_LOWER_BOUND'])\n", - "GMM_traces_df = pd.DataFrame(columns = ['clust','PREF','NONPREF','PREF_UPPER_BOUND','PREF_LOWER_BOUND',\n", - " 'NONPREF_UPPER_BOUND','NONPREF_LOWER_BOUND'])\n", - "\n", - "for i in range(0,8):\n", - " traces = sorted([x for x in UMAP_FR_traces if str(i) in x],key=len)\n", - " trace_arr = []\n", - " \n", - " trace_arr.append(i)\n", - " for trace in traces:\n", - " if 'pref_'+str(i)+'.pkl' == trace:\n", - " trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n", - " elif 'nonpref_'+str(i)+'.pkl' == trace:\n", - " trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n", - " elif 'pref_bounds_'+str(i)+'.pkl' == trace:\n", - " upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n", - " trace_arr.append(upper_bound)\n", - " trace_arr.append(lower_bound)\n", - " elif 'nonpref_bounds_'+str(i)+'.pkl' == trace:\n", - " upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n", - " trace_arr.append(upper_bound)\n", - " trace_arr.append(lower_bound)\n", - " \n", - " trace_series = pd.Series(trace_arr,index=UMAP_traces_df.columns)\n", - " UMAP_traces_df = UMAP_traces_df.append(trace_series,ignore_index=True)\n", - "\n", - "for i in range(1,5):\n", - " traces = sorted([x for x in GMM_FR_traces if str(i) in x],key=len)\n", - " trace_arr = []\n", - " \n", - " trace_arr.append(i)\n", - " for trace in traces:\n", - " if 'GMM_pref_'+str(i)+'.pkl' == trace:\n", - " trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n", - " elif 'GMM_nonpref_'+str(i)+'.pkl' == trace:\n", - " trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n", - " elif 'GMM_pref_bounds_'+str(i)+'.pkl' == trace:\n", - " upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n", - " trace_arr.append(upper_bound)\n", - " trace_arr.append(lower_bound)\n", - " elif 'GMM_nonpref_bounds_'+str(i)+'.pkl' == trace:\n", - " upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n", - " trace_arr.append(upper_bound)\n", - " trace_arr.append(lower_bound)\n", - " \n", - " trace_series = pd.Series(trace_arr,index=GMM_traces_df.columns)\n", - " GMM_traces_df = GMM_traces_df.append(trace_series,ignore_index=True) " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Obep1YIAZnAx" - }, - "source": [ - "# Step 6: Loading of decision-related functional activity" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WAd35wPqP1Es" - }, - "source": [ - "Processed data calculated from behavioral trials is loaded here. Raw data is not provided for the same reasons as in the previous step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "jxLKNL3XKJ0-", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "FR_stats_loc = 'WaveMAP_Paper/data/FR_stats'\n", - "decision_dynamics_loc = os.path.join(rel_path,'WaveMAP_Paper/data/decisionDynamics.mat');\n", - "\n", - "\n", - "baseline_FR_df = pkl.load(open(os.path.join(FR_stats_loc,'baseline_FR_df.pkl'),'rb'))\n", - "max_FR_df = pkl.load(open(os.path.join(FR_stats_loc,'max_FR_df.pkl'),'rb'))\n", - "GMM_baseline_FR_df = pkl.load(open(os.path.join(FR_stats_loc,'GMM_baseline_FR_df.pkl'),'rb'))\n", - "GMM_max_FR_df = pkl.load(open(os.path.join(FR_stats_loc,'GMM_max_FR_df.pkl'),'rb'))\n", - "\n", - "dynamic_range_FR = np.subtract(max_FR_df['max_FR'],baseline_FR_df['baseline_FR'])\n", - "dynamic_range_FR_df = pd.DataFrame({'dynamic_range_FR': dynamic_range_FR, \n", - " 'dbscan_color': baseline_FR_df['dbscan_color']})\n", - "\n", - "GMM_dynamic_range_FR = np.subtract(GMM_max_FR_df['max_FR'],GMM_baseline_FR_df['baseline_FR'])\n", - "GMM_dynamic_range_FR_df = pd.DataFrame({'dynamic_range_FR': dynamic_range_FR, \n", - " 'dbscan_color': baseline_FR_df['dbscan_color']}) \n", - "\n", - "with h5py.File(decision_dynamics_loc,\"r\") as f:\n", - " diffV_list = []\n", - " for i in range(8):\n", - " diffV_list.append(np.array(f[f['forKenjiDat']['diffV'][i][0]]))\n", - "\n", - " coherences = []\n", - " for i in range(7):\n", - " coherences.append(f['metaData']['coherences'][0][i])\n", - "\n", - " dec_dyn_data = []\n", - " dec_dyn_data_err = []\n", - " \n", - " for i in range(8):\n", - " dec_dyn_data.append(np.array(f[f['forKenjiDat']['timeSlope'][i][0]]))\n", - " dec_dyn_data_err.append(np.array(f[f['forKenjiDat']['timeSlopeE'][i][0]]))\n", - " \n", - " for i,x in enumerate(dec_dyn_data):\n", - " dec_dyn_data[i] = [val for sublist in x for val in sublist]\n", - " \n", - " for i,x in enumerate(dec_dyn_data_err):\n", - " dec_dyn_data_err[i] = [val for sublist in x for val in sublist]\n", - "\n", - " dec_dyn_slope = []\n", - " \n", - " for i in range(8):\n", - " dec_dyn_slope.append(np.array(f[f['forKenjiDat']['slopeAsfCoh'][i][0]]))\n", - " \n", - " for i,x in enumerate(dec_dyn_slope):\n", - " dec_dyn_slope[i] = [val for sublist in x for val in sublist]\n", - "\n", - "discrim_data_path = 'WaveMAP_Paper/data/discrimination_times.pkl'\n", - "discrim_file = pkl.load(open(discrim_data_path,'rb'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "9-LNNSLOZ-wT", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "masterArray = scipy.io.loadmat(os.path.join(rel_path,'WaveMAP_Paper/data/masterArray.mat'))['masterArray']\n", - "\n", - "uprobeMask = scipy.io.loadmat(os.path.join(rel_path,'WaveMAP_Paper/data/uprobeMask.mat'))['uprobeMask']\n", - "uprobeMask = [i[0] for i in uprobeMask]\n", - "\n", - "ch_depth = scipy.io.loadmat(os.path.join(rel_path,'WaveMAP_Paper/data/allChansUprobe.mat'))['allChansUprobe']\n", - "ch_depth = [i[0] for i in ch_depth]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UgFaghmrlqyo" - }, - "source": [ - "# Figure 1E Bottom (Plots all the waveforms used in the analysis). \n", - "\n", - "The normalized and filtered average single-unit waveforms are plotted on top of each other here with transparency." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "45Htregu1CV8" - }, - "source": [ - "### We plot all normalized single unit waveforms together" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 276 - }, - "id": "DDwk8jADeLez", - "outputId": "40741102-c378-49ce-8b3a-dcae56d83b53", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[94mPlotting: 625 Waveforms\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "# This plots all the normalized single unit waveforms together. The total waveform length is 48 points at 30000 samples/s\n", - "# 14, 28, 42 are the 0.5 ms, 1.0 ms, and 1.5 ms\n", - "\n", - "# Generate subplots\n", - "f, arr = plt.subplots(1,figsize=[4.5,3.4])\n", - "\n", - "print(BlueCol + \"Plotting: \" + str(full_data.shape[0]) + \" Waveforms\")\n", - "for i in range(0,full_data.shape[0]):\n", - " arr.plot(full_data[i].T, c = 'k', alpha = 0.03,linewidth=2.);\n", - " \n", - "arr.tick_params(direction='out',colors='k', axis='both')\n", - " \n", - "# Set various x and y axes and labels etc.\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "\n", - "arr.spines['left'].set_bounds(-1,1)\n", - "arr.spines['bottom'].set_bounds(0,48)\n", - "\n", - "arr.set_xlabel('Time (ms)', fontsize=24);\n", - "arr.set_xticks([0,14,28,42,48])\n", - "arr.set_xticklabels(['0','0.5','1.0','1.5',''],fontsize=24)\n", - "\n", - "arr.set_ylabel('Amplitude (a.u.)', fontsize=24)\n", - "arr.set_yticks([-1.0,0.0,1.0]);\n", - "arr.set_yticklabels([-1.0,0.0,1.0], fontsize=24);\n", - "\n", - "arr.set_title('Figure 1E', fontsize=18)\n", - "\n", - "# Plot the data\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6amI4WSZeKkm" - }, - "source": [ - "# Figure 3: WaveMAP on of waveforms using UMAP and Louvain Clustering." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mY0jVoHj0XfK" - }, - "source": [ - "## Figure 3A: Computation of WaveMAP clusters" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FjVdH1Ferdv0" - }, - "source": [ - "### First we construct the high-dimensional graph with UMAP\n", - "\n", - "---\n", - "\n", - "This function computes the UMAP graph construction and projection. Note the important parameters are N_NEIGHBORS and RAND_STATE. N_NEIGHBORS determines the balance between weighing global vs. local information and has been set higher than defaults (more globally-attentive). Random seed is set for qualitative reasons but produces no quantitative differences (see Figure S2) as this is used in the stochastic gradient descent in the force-directed layout procedure of the projection step in UMAP---we generate clusterings before this step." - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": { - "id": "ZvUkLtpSrbnF", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE)\n", - "mapper = reducer.fit(full_data)\n", - "embedding = reducer.transform(full_data)\n", - "\n", - "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "umap_df['waveform'] = list(full_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xKKPxxUVrlem" - }, - "source": [ - "### Next we apply Louvain clustering to the high-dimensional UMAP graph\n", - "\n", - "---\n", - "\n", - "This is the graph clustering that operates on the high-dimensional graph found by UMAP and occurs before the projection is computed." - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": { - "id": "TuXXBsLbeKxP", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - "clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n", - "clustering_solution = list(clustering.values())\n", - "umap_df['color'] = clustering_solution\n", - "\n", - "cluster_colors = [CUSTOM_PAL_SORT_3[i] for i in clustering_solution]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9CpooetSruMn" - }, - "source": [ - "### This yields our UMAP graph colored by Louvain cluster\n", - "\n", - "---\n", - "\n", - "Shown is a plot of the UMAP projected space with clusters colored according to high-dimensional graph clusters found in the previous step by Louvain clustering." - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 355 - }, - "id": "edO6Ch3peIcI", - "outputId": "255e779f-3095-43a6-a7d6-1e3ba4a0769e", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", - "f.tight_layout()\n", - "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", - " marker='o', c=cluster_colors, s=32, edgecolor='w',\n", - " linewidth=0.5)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xticks([]);\n", - "arr.set_yticks([]);\n", - "arr.set_xlim(-4,12)\n", - "arr.set_ylim(0,12)\n", - "\n", - "arr.arrow(-3,0.8,0,1.5, width=0.05, shape=\"full\", ec=\"none\", fc=\"black\")\n", - "arr.arrow(-3,0.8,1.2,0, width=0.05, shape=\"full\", ec=\"none\", fc=\"black\")\n", - "\n", - "arr.text(-3,0.3,\"UMAP 1\", va=\"center\")\n", - "arr.text(-3.5,1.0,\"UMAP 2\",rotation=90, ha=\"left\", va=\"bottom\")\n", - "\n", - "N_CLUST = len(set(clustering_solution))" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "id": "v-MwbFeHrCk5", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "# Defines a nice function that plots all the waveforms in long column.\n", - "def plot_group(label_ix, labels, groups_df, colors, mean_only=False, detailed=False):\n", - " group_ixs = [i for i,x in enumerate(labels) if x == label_ix-1]\n", - " group_waveforms = groups_df.iloc[group_ixs]['waveform'].tolist()\n", - " \n", - " f, arr = plt.subplots()\n", - " f.set_figheight(1.8*0.65)\n", - " f.set_figwidth(3.0*0.65)\n", - " if not mean_only:\n", - " for i,_ in enumerate(group_waveforms):\n", - " plt.plot(group_waveforms[i],c=colors[label_ix-1],alpha=0.3,linewidth=1.5)\n", - " \n", - " if not mean_only:\n", - " plt.plot(np.mean(group_waveforms,axis=0),c='k',linestyle='-')\n", - " else:\n", - " plt.plot(np.mean(group_waveforms,axis=0),c=colors[label_ix-1],linestyle='-')\n", - "\n", - " arr.spines['right'].set_visible(False)\n", - " arr.spines['top'].set_visible(False)\n", - "\n", - " if detailed:\n", - " \n", - " avg_peak = np.mean([np.argmax(x) for x in group_waveforms[14:]])\n", - " arr.axvline(avg_peak,color='k',zorder=0)\n", - " \n", - " arr.set_ylim([-1.3,1.3])\n", - " arr.set_yticks([])\n", - " arr.set_xticks([0,7,14,21,28,35,42,48])\n", - " arr.tick_params(axis='both', which='major', labelsize=12)\n", - " arr.set_xticklabels([0,'',0.5,'',1.0,'',1.5,''])\n", - " arr.spines['left'].set_visible(False)\n", - " arr.grid(False)\n", - " arr.set_xlim([0,48])\n", - "\n", - " if not detailed:\n", - " arr.set(xticks=[],yticks=[])\n", - "\n", - " if not mean_only:\n", - " x,y = 2.1,0.7\n", - " ellipse = mpl.patches.Ellipse((x,y), width=9.0, height=0.72, facecolor='w',\n", - " edgecolor='k',linewidth=1.5)\n", - " label = arr.annotate(str(label_ix), xy=(x-0.25, y-0.15),fontsize=12, color = 'k', ha=\"center\")\n", - " arr.add_patch(ellipse)\n", - "\n", - " if i != -1:\n", - " x, y = 23,-0.7\n", - " n_waveforms = plt.text(x, y, \n", - " 'n = '+str(len(group_waveforms))+\n", - " ' ('+str(round(len(group_waveforms)/len(groups_df)*100,2))+'%)'\n", - " , fontsize=10)\n", - " \n", - " return f, arr" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IWfn2ph106IF" - }, - "source": [ - "### Lastly we plot the waveforms for each cluster together along with their average waveform" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 665 - }, - "id": "3P802ziWshvW", - "outputId": "49392275-ebe4-48f7-f3de-48b3c74c25ec", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "BSclass = [1, 6, 7]\n", - "NSclass = [2, 3, 4, 5, 8]\n", - "for i in BSclass:\n", - " plot_group(i,clustering_solution,umap_df,CUSTOM_PAL_SORT_3)\n", - "\n", - "for i in NSclass:\n", - " plot_group(i,clustering_solution,umap_df,CUSTOM_PAL_SORT_3)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dMp537GB04g9" - }, - "source": [ - "## Figure 3B: Optimizing number of Louvain clusters\n", - "\n", - "This goes through resolution parameter from 0 to 10 in steps of 0.5 as a function of number of clusters and \"modularity\" (this is the cost function which Louvain optimizes; see Supplementary Methods). Note that this section can take very long to run; you can run the next cell which loads a saved version of the output variable on a sample run." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RFAnsidu15wr" - }, - "source": [ - "### We calculate the modularity score and number of Louvain clusters across a range of resolution parameters while randomly permuting the waveform order. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 443 - }, - "id": "IF8dr-bZ2PoI", - "outputId": "badbb3c8-bfb5-4539-a41d-fa02c800aa2e", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[94m0.0\n", - "\n", - "\u001b[94m0.5\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m random_state=random.randint(1,100000))\n\u001b[1;32m 16\u001b[0m \u001b[0mrand_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermutation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfull_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfull_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfrac\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mmapper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreducer_rand_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrand_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0membedding_rand_test\u001b[0m \u001b[0;34m=\u001b[0m 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- "modularity_dict = {}\n", - "n_clusts_dict = {}\n", - "\n", - "subsets=[80]\n", - "\n", - "for res in resolution_list:\n", - " print(\"\\n\" + BlueCol + str(res))\n", - " for frac in subsets:\n", - " rand_list = []\n", - " n_clusts = []\n", - " for i in list(range(1,25)):\n", - " reducer_rand_test = umap.UMAP(n_neighbors = N_NEIGHBORS, \n", - " min_dist=MIN_DIST, \n", - " random_state=random.randint(1,100000))\n", - " rand_data = np.random.permutation(full_data)[0:(int(len(full_data)*frac)),:]\n", - " mapper = reducer_rand_test.fit(rand_data)\n", - " embedding_rand_test = reducer_rand_test.transform(rand_data)\n", - "\n", - " umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n", - " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - " clustering = cylouvain.best_partition(G, resolution = res)\n", - " modularity = cylouvain.modularity(clustering, G)\n", - " clustering_solution = list(clustering.values())\n", - " rand_list.append(modularity)\n", - " n_clusts.append(len(set(clustering_solution)))\n", - " modularity_dict.update({str(res): rand_list})\n", - " n_clusts_dict.update({str(res): n_clusts})" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "o5Ge1CrX38I8" - }, - "source": [ - "### And plot both on the same axis\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 205 - }, - "id": "dE4TxUfY2YFN", - "outputId": "863164c4-db90-4e9d-922f-d351bb28275c", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "resolution_list = np.linspace(0,10,21)\n", - "\n", - "if 'n_clusts_dict' not in list(locals().keys()):\n", - " n_clusts_dict = pkl.load(open('WaveMAP_Paper/data/n_clusts_dict.pkl','rb'))\n", - "\n", - "if 'modularity_dict' not in list(locals().keys()):\n", - " modularity_dict = pkl.load(open('WaveMAP_Paper/data/modularity_dict.pkl','rb'))\n", - "\n", - "avg_n_clusts = []\n", - "for k in list(n_clusts_dict.keys()):\n", - " avg_n_clusts.append(np.mean(n_clusts_dict[k]))\n", - " \n", - "std_n_clusts = []\n", - "for k in list(n_clusts_dict.keys()):\n", - " std_n_clusts.append(np.std(n_clusts_dict[k]))\n", - " \n", - "std_modularity = []\n", - "for k in list(modularity_dict.keys()):\n", - " std_modularity.append(np.std(modularity_dict[k]))\n", - " \n", - "avg_modularity = []\n", - "for k in list(modularity_dict.keys()):\n", - " avg_modularity.append(np.mean(modularity_dict[k]))\n", - "\n", - "f, ax1 = plt.subplots(figsize=[3,2.5])\n", - "\n", - "ax1.errorbar(resolution_list,avg_modularity,yerr=std_modularity,\n", - " c = '#5c95ff', marker='o', fillstyle='full', markerfacecolor='w', \n", - " linewidth=1, markeredgewidth=1)\n", - "ax1.set_ylabel('Modularity Score')\n", - "ax1.set_xlabel('Resolution Parameter',fontsize=12)\n", - "ax1.set_xlim([0,8])\n", - "ax1.set_xticks([0,2,4,6,8])\n", - "ax1.yaxis.label.set_color('#5c95ff')\n", - "ax1.tick_params(axis='y',colors='#5c95ff')\n", - "ax1.set_ylim(0,1.0)\n", - "ax1.set_yticks([0,0.2,0.4,0.6,0.8,1.0])\n", - "# ax1.set_yticklabels([0.0,'',0.2,'',0.4,'',0.6,'',0.8,'',1.0],fontsize=12)\n", - "ax1.spines['top'].set_visible(False)\n", - "ax1.spines['right'].set_color('#f87575')\n", - "ax1.spines['left'].set_color('#5c95ff')\n", - "\n", - "ax2 = ax1.twinx()\n", - "ax2.errorbar(resolution_list[1:],avg_n_clusts[1:],yerr=std_n_clusts[1:],\n", - " c = '#f87575', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n", - "ax2.set_ylabel('Number of Clusters',fontsize=12,c='#f87575')\n", - "# ax2.spines['left'].set_color('b')\n", - "ax2.tick_params(axis='y',colors='#f87575')\n", - "ax2.set_ylim([0,18])\n", - "ax2.set_yticks([0,4,8,12,16]);\n", - "ax2.spines['top'].set_visible(False)\n", - "ax2.spines['right'].set_color('#f87575')\n", - "ax2.spines['left'].set_color('#5c95ff')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8lx6iIIZ1NPA" - }, - "source": [ - "## Figure 3C: Classifier performance on clustering" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Kl0JCjku1VR-" - }, - "source": [ - "### First we create a 70/30 test-train split of the data" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "id": "91XymkZ9smsf", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "testSize = 0.3;\n", - "\n", - "UMAP_X = np.stack(umap_df['waveform'].to_numpy().tolist(), axis=0)\n", - "UMAP_y = umap_df['color'].to_numpy()\n", - "\n", - "unclassified_ixs = [ix for ix,clust in enumerate(UMAP_y) if clust == -1]\n", - "\n", - "UMAP_X = np.delete(UMAP_X,unclassified_ixs,axis=0)\n", - "UMAP_y = np.delete(UMAP_y,unclassified_ixs,axis=0)\n", - "\n", - "UMAP_X_train, UMAP_X_test, UMAP_y_train, UMAP_y_test = train_test_split(UMAP_X, UMAP_y, test_size=testSize, random_state=RAND_STATE)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zmkb1S7U1cdU" - }, - "source": [ - "### Next we use XGBoost to train a hyperparameter optimized random forest classifier on the WaveMAP clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "21efwyEus9jt", - "outputId": "8b3d9ce9-4d0f-4b88-e612-fa298cb70724", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 2.6s\n", - "[Parallel(n_jobs=-1)]: Done 3 out of 5 | elapsed: 3.2s remaining: 2.1s\n", - "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 3.5s remaining: 0.0s\n", - "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 3.5s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[22, 0, 0, 0, 0, 1, 0, 0],\n", - " [ 0, 26, 3, 0, 1, 0, 0, 0],\n", - " [ 0, 0, 28, 1, 0, 0, 0, 0],\n", - " [ 0, 0, 1, 18, 0, 0, 0, 0],\n", - " [ 0, 0, 1, 0, 28, 0, 0, 0],\n", - " [ 1, 0, 0, 0, 2, 24, 1, 0],\n", - " [ 0, 0, 1, 2, 0, 0, 7, 0],\n", - " [ 0, 0, 0, 0, 1, 0, 0, 19]])" - ] - }, - "execution_count": 47, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "numCV = 5\n", - "\n", - "UMAP_model = xgb.XGBClassifier(objective='multi:softmax')\n", - "UMAP_param_dist = {\"max_depth\": [4],\n", - " \"min_child_weight\" : [2.5],\n", - " \"n_estimators\": [100],\n", - " \"learning_rate\": [0.3],\n", - " \"seed\": [RAND_STATE]}\n", - "UMAP_grid_search = GridSearchCV(UMAP_model, param_grid=UMAP_param_dist, \n", - " cv = numCV,\n", - " verbose=10, n_jobs=-1)\n", - "UMAP_grid_search.fit(UMAP_X_train, UMAP_y_train)\n", - "\n", - "confusion_matrix(UMAP_y_test,UMAP_grid_search.predict(UMAP_X_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "V2le2oqP1rS7" - }, - "source": [ - "### Lastly we plot a confusion matrix for the test accuracy of the classifier" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 225 - }, - "id": "Wb7rtQkztL9Q", - "outputId": "20bf9c46-dd97-456b-eb7f-965eab9d3293", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "confusion_mat_counts = confusion_matrix(UMAP_y_test,UMAP_grid_search.predict(UMAP_X_test))\n", - "\n", - "conf_mat_row_list = []\n", - "\n", - "for row in confusion_mat_counts:\n", - " row_sum = np.sum(row)\n", - " \n", - " row_percent = []\n", - " \n", - " for val in row:\n", - " row_percent.append(val/row_sum)\n", - " \n", - " conf_mat_row_list.append(row_percent)\n", - "\n", - "conf_mat = np.array(conf_mat_row_list)\n", - "\n", - "colormap = mpl.cm.YlGnBu\n", - "colormap.set_under('white')\n", - "\n", - "eps = np.spacing(0.0)\n", - "f, arr = plt.subplots(1,figsize=[4,3])\n", - "mappable = arr.imshow(conf_mat,cmap=colormap,vmin=eps,vmax=1.)\n", - "color_bar = f.colorbar(mappable, ax=arr, extend='min')\n", - "color_bar.set_label('P (Predicted | True)',fontsize=12,labelpad=15,fontname=\"Arial\")\n", - "color_bar.ax.tick_params(size=3,labelsize=12)\n", - "\n", - "#Specify label behavior of the main diagonal\n", - "for i in range(0,N_CLUST):\n", - " if int(conf_mat[i,i]*100) == 100:\n", - " arr.text(i-0.38,i+0.17,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n", - " else:\n", - " arr.text(i-0.34,i+0.16,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n", - " \n", - "#Specify label behavior of the off-diagonals\n", - "for i in range(0,N_CLUST):\n", - " for j in range(0,N_CLUST):\n", - " if conf_mat[i,j] < 0.1 and conf_mat[i,j] != 0:\n", - " arr.text(j-0.2,i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n", - " elif conf_mat[i,j] >= 0.1 and conf_mat[i,j] < 0.5 and conf_mat[i,j] != 0:\n", - " arr.text(j-0.4, i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n", - "\n", - "arr.set_xticks(range(0,N_CLUST))\n", - "arr.set_xticklabels(range(1,N_CLUST+1),fontsize=12);\n", - "arr.set_yticks(range(0,N_CLUST))\n", - "arr.set_yticklabels(range(1,N_CLUST+1),fontsize=12);\n", - "arr.set_xlabel('Predicted Class',fontsize=12);\n", - "arr.set_ylabel('True Class',fontsize=12);\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "smNYVaGpCfJo" - }, - "source": [ - "# Figure 4: Evaluates the solutions obtained from GMM clustering and shows that it is much poorer than the ones obtained by WAVEMAP\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4KjFpBbooqNr" - }, - "source": [ - "## Figure 4B: GMM clustering in a 3-D feature space" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1_LxFk8-pSHP" - }, - "source": [ - "### We first set up a pandas dataframe with the specified feature values" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "id": "yrOHmzfUgDo-", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "# reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - "# random_state=RAND_STATE)\n", - "# mapper = reducer.fit(full_data)\n", - "# embedding = reducer.transform(full_data)\n", - "\n", - "# umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "# umap_df['waveform'] = list(full_data)\n", - "\n", - "UMAP_and_GMM = pd.concat([umap_df,pd.DataFrame(gmm_feat_data_nonan,columns=['troughToPeak','prePostHyper','FWHM1'])],axis=1)\n", - "UMAP_and_GMM['dbscan_hex'] = cluster_colors\n", - "UMAP_and_GMM['gmm_labels'] = GMM_class_labels\n", - "\n", - "UMAP_and_GMM['troughToPeak_abs'] = UMAP_and_GMM['troughToPeak'].divide(SAMP_RATE_TO_TIME)\n", - "UMAP_and_GMM['FWHM1_abs'] = UMAP_and_GMM['FWHM1'].divide(SAMP_RATE_TO_TIME)\n", - "UMAP_and_GMM['color'] = umap_df['color']" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MT5A7R4VpePj" - }, - "source": [ - "### and then plot the points in this space by cluster identity (clustering done in MATLAB)." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 211 - }, - "id": "h8U6KoclndYf", - "outputId": "81b04de6-7801-4cfa-9a67-c2857bbabcf9", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 0.6)" - ] - }, - "execution_count": 58, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=[3.8,3])\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "\n", - "for i in [int(x) for x in np.unique(UMAP_and_GMM['gmm_labels'])]:\n", - " to_plot_df = UMAP_and_GMM[UMAP_and_GMM['gmm_labels'] == i]\n", - " x = to_plot_df['troughToPeak_abs']\n", - " y = to_plot_df['prePostHyper']\n", - " z = to_plot_df['FWHM1_abs']\n", - " ax.scatter(x,y,z,c=GMM_PAL[i-1],marker='o',alpha=0.75,s=20,linewidth=0.75,edgecolor='w',depthshade=True)\n", - " \n", - " ax.plot(x, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='y', zs=1.6)\n", - " ax.plot(y, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='x', zs=1.4)\n", - " ax.plot(x, y, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='z', zs=0.)\n", - "\n", - "ax.tick_params(pad=-1)\n", - "\n", - "ax.set_xlabel('Trough to peak ($\\mu$s)',fontsize=12,labelpad=5)\n", - "ax.set_ylabel('Peak ratio',fontsize=12,labelpad=5)\n", - "ax.set_zlabel('AP width ($\\mu$s)',fontsize=12,labelpad=0)\n", - "ax.view_init(elev=20, azim=220)\n", - "\n", - "ax.set_xticks([0,0.7,1.4])\n", - "ax.set_xticklabels(['',0.7,1.4],fontsize=12)\n", - "ax.set_yticks([0,0.5,1,1.5])\n", - "ax.set_yticklabels([0,0.5,1.0,1.5],fontsize=12)\n", - "ax.set_zticks([0,0.3,0.6])\n", - "ax.set_zticklabels([0,0.3,0.6],fontsize=12)\n", - "\n", - "ax.set_xlim([0,1.4])\n", - "ax.set_ylim([0,1.6])\n", - "ax.set_zlim([0.,0.6])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZuyPbtAmplYJ" - }, - "source": [ - "## Figure 4C: Optimal GMM cluster number" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ff6Ck19grZeI" - }, - "source": [ - "### We use Bayesian information criterion to calculate the 'elbow' in a BIC vs. cluster number" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 181 - }, - "id": "zQnMYPFdpl3s", - "outputId": "f0e93b7b-8cfd-47db-81c9-9cb17310292f", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f, arr = plt.subplots()\n", - "f.set_size_inches(3., 2.)\n", - "\n", - "arr.plot(BIC_list,c='k',\n", - " marker='o',fillstyle='full',markerfacecolor='w',linewidth=1,markeredgewidth=1)\n", - "\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xlabel('# of Clusters', fontsize=12)\n", - "arr.set_xticks([0,2,4,6,8,9])\n", - "arr.set_xticklabels([1,3,5,7,9,''],fontsize=12)\n", - "arr.set_ylabel('BIC', fontsize=12)\n", - "arr.set_yticks([12500,13000,13500,14000,14500])\n", - "arr.set_yticklabels(['1.25','1.3','1.35','1.4','1.45'],fontsize=12)\n", - "arr.spines['left'].set_bounds(12500,14500)\n", - "arr.spines['bottom'].set_bounds(0,9)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "w6guLtZu3hEm" - }, - "source": [ - "## Figure 4D: Classifier trained on GMM classes" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PrXIt6zT-4Uf" - }, - "source": [ - "### Now we train a random forest classifier on the GMM classes" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "id": "qU-00XSv3hoY", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "X = np.stack(UMAP_and_GMM['waveform'].to_numpy().tolist(), axis=0)\n", - "y = UMAP_and_GMM['gmm_labels'].to_numpy()\n", - "\n", - "unclassified_ixs = [ix for ix,clust in enumerate(y) if clust == -1]\n", - "\n", - "X = np.delete(X,unclassified_ixs,axis=0)\n", - "y = np.delete(y,unclassified_ixs,axis=0)\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=RAND_STATE)\n", - "\n", - "data_dmatrix = xgb.DMatrix(data=X,label=y)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0eUK4fub3jDL" - }, - "source": [ - "### and show a confusion matrix of the five-fold cross-validated test accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TC4VHCk_3jlq", - "outputId": "07ea738c-adf1-4755-b26d-b9f3105082c9", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 1.2s\n", - "[Parallel(n_jobs=-1)]: Done 3 out of 5 | elapsed: 2.5s remaining: 1.7s\n", - "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 3.3s remaining: 0.0s\n", - "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 3.3s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[65, 4, 1, 0],\n", - " [10, 57, 0, 1],\n", - " [ 5, 0, 28, 1],\n", - " [ 0, 2, 5, 9]])" - ] - }, - "execution_count": 61, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "model = xgb.XGBClassifier(objective='multi:softmax')\n", - "param_dist = {\"max_depth\": [10],\n", - " \"min_child_weight\" : [2.5],\n", - " \"n_estimators\": [110],\n", - " \"learning_rate\": [0.05],\n", - " \"seed\": [RAND_STATE]}\n", - "grid_search = GridSearchCV(model, param_grid=param_dist, \n", - " cv = 5, \n", - " verbose=10, n_jobs=-1)\n", - "grid_search.fit(X_train, y_train)\n", - "\n", - "confusion_matrix(y_test,grid_search.predict(X_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 225 - }, - "id": "9RfN6ebs_Kpy", - "outputId": "e2390c8e-34f9-4649-c507-654ea22f5dd3", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "confusion_mat_counts = confusion_matrix(y_test,grid_search.predict(X_test))\n", - "\n", - "conf_mat_row_list = []\n", - "\n", - "for row in confusion_mat_counts:\n", - " row_sum = np.sum(row)\n", - " row_percent = []\n", - " \n", - " for val in row:\n", - " row_percent.append(val/row_sum)\n", - " \n", - " conf_mat_row_list.append(row_percent)\n", - "\n", - "conf_mat = np.array(conf_mat_row_list)\n", - "colormap = mpl.cm.YlGnBu\n", - "colormap.set_under('white')\n", - "\n", - "f, arr = plt.subplots()\n", - "f.set_size_inches(3, 3)\n", - "plt.tight_layout()\n", - "mappable = arr.imshow(conf_mat,cmap=colormap,vmin=0.,vmax=1.)\n", - "color_bar = f.colorbar(mappable, ax=arr)\n", - "color_bar.set_label('P (Predicted | True)',fontsize=12,labelpad=15)\n", - "color_bar.ax.tick_params(size=3,labelsize=12)\n", - "arr.set_xticks([0,1,2,3])\n", - "arr.set_xticklabels([1,2,3,4],fontsize=12);\n", - "arr.set_yticks([0,1,2,3])\n", - "arr.set_yticklabels([1,2,3,4],fontsize=12);\n", - "arr.set_xlabel('Predicted Class',fontsize=12)\n", - "arr.set_ylabel('True Class',fontsize=12)\n", - "\n", - "for i in range(0,4):\n", - " if int(conf_mat[i,i]*100) == 100:\n", - " arr.text(i-0.35,i+0.15,int(conf_mat[i,i]*100),fontsize=12,c='white')\n", - " else:\n", - " arr.text(i-0.25,i+0.15,int(conf_mat[i,i]*100),fontsize=12,c='white')\n", - " \n", - "for i in range(0,4):\n", - " for j in range(0,4):\n", - " if conf_mat[i,j] < 0.1 and conf_mat[i,j] != 0:\n", - " arr.text(j-0.15,i+0.15,int(conf_mat[i,j]*100),fontsize=12,c='k')\n", - " elif conf_mat[i,j] >= 0.1 and conf_mat[i,j] <= 0.5:\n", - " arr.text(j-0.2,i+0.15,int(conf_mat[i,j]*100),fontsize=12,c='k')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kzkrWPTjmVio" - }, - "source": [ - "## Figure 4E: GMM labels in UMAP space" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-FuCclNKpJHH" - }, - "source": [ - "### We take the GMM cluster labels as shown previously and display these in UMAP space" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 297 - }, - "id": "tYd7v_4xCfZ3", - "outputId": "18472590-9243-4915-a621-c868b25c3cc3", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "GMM_class_labels = GMM_class_labels[~np.isnan(GMM_class_labels)]\n", - "GMM_class_df = pd.DataFrame(GMM_class_labels,columns=['Class'])\n", - "\n", - "full_data_df = pd.DataFrame({'Waveform': full_data.tolist()})\n", - "data_classified_df = pd.concat([umap_df,full_data_df,GMM_class_df],axis=1)\n", - "\n", - "f, arr = plt.subplots()\n", - "\n", - "class_labels = [x for x in np.unique(data_classified_df['Class']) if str(x) != 'nan']\n", - "\n", - "for ix in np.unique(class_labels):\n", - " filt_df = data_classified_df[data_classified_df['Class']==ix]\n", - " arr.scatter(filt_df['x'],filt_df['y'], s=30,marker='o', linewidth=0.25, \n", - " edgecolors='white', alpha=1, c=GMM_PAL[int(ix-1)])\n", - "\n", - "ns = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[0], \n", - " color='w', label='Narrow-Spiking (NS)')\n", - "bs = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[1], \n", - " color='w', label='Broad-Spiking (BS)')\n", - "nst = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[2], \n", - " color='w', label='Narrow-Spiking Triphasic (NST)')\n", - "bst = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[3], \n", - " color='w', label='Broad-Spiking Triphasic (BST)')\n", - "\n", - "arr.legend(handles=[ns,bs,nst,bst],fontsize=12)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xticks([]);\n", - "arr.set_yticks([]);\n", - "plt.tight_layout()\n", - "f.set_size_inches(6, 4)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0CPLlCn2Kj1N" - }, - "source": [ - "### Here we also show all the single units per GMM cluster with the averaged waveform in black" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "id": "kVwRMJ5JKj8I", - "outputId": "ceedc3c7-2964-45ad-a470-0b354d6029ef", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "for i in np.unique(GMM_class_labels):\n", - " f, arr = plt.subplots()\n", - " f.set_size_inches(2, 1.75)\n", - " GMM_cluster = data_classified_df[data_classified_df['Class']==i]\n", - " \n", - " for _,row in GMM_cluster.iterrows():\n", - " plt.plot(row['Waveform'],alpha=.3,linewidth=.6,c=GMM_PAL[int(i-1)])\n", - " \n", - " plt.plot(np.nanmean(GMM_cluster['Waveform'].tolist(),axis=0),c='k',linewidth=1.)\n", - "\n", - " arr.spines['right'].set_visible(False)\n", - " arr.spines['top'].set_visible(False)\n", - " arr.set_ylim([-1.4,1.1])\n", - " arr.set_xticks([0,14,28,42])\n", - " arr.set_xticklabels(['0','0.5','1.0','1.5'])\n", - " arr.set_xlabel('Time (ms)',fontsize=12)\n", - " arr.set_xlim([0,42])\n", - " arr.set_yticks([])\n", - " arr.tick_params(axis='both', which='major', labelsize=12)\n", - " \n", - " arr.spines['left'].set_visible(False)\n", - " \n", - " x, y = 23,-0.8\n", - "\n", - " n_waveforms = plt.text(x, y, 'n = '+str(len(GMM_cluster))\n", - " , fontsize=12)\n", - " plt.tight_layout()\n", - " plt.margins(0,0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "njq5UwqpAzsE" - }, - "source": [ - "# Figure 5: Interpretable Machine Learning on WaveMAP" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4ZTxMJZLA_su" - }, - "source": [ - "## Figure 5A: Inverse mapping of WaveMAP" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7ZOMRpSNBFA6" - }, - "source": [ - "### We use UMAP's inverse transform function to produce waveforms found on a grid of test points tiling the manifold\n", - "\n", - "---\n", - "\n", - "Note that this *is* sensitive to the stochasticity in projection and likely will look incorrectly aligned to the grid. This is because the projection of the high-dimensional graph uses a force directed layout algorithm using stochastic gradient descent. Although the stochastic gradient descent can be made deterministic through setting a seed, the seed will vary at the level of the CPU OS itself and cannot be set from within Colab. Since Colab spins up a new instance every session, this can look different day-to-day if you let your session expire. You can change the location of the corners of the grid in the `corners` variable to try to produce a good fit." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 411 - }, - "id": "HStjBK5Ptill", - "outputId": "f60732d8-6f56-42d4-e842-74b2e90eac8b", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "def find_nearest_color(embedding, test_coord, threshold_dist=0.5):\n", - " x_array, y_array = embedding[:,0], embedding[:,1]\n", - " \n", - " # Take coordinates of test point to calculate an array of each point's distance to test then return index\n", - " # where the minimum value is found\n", - " dist_array = np.array(np.abs(x_array-test_coord[0])+np.abs(y_array-test_coord[1]))\n", - " idx = dist_array.argmin()\n", - " \n", - " if dist_array[idx] <= threshold_dist:\n", - " return cluster_colors[idx]\n", - " \n", - " else:\n", - " return (0.8,0.8,0.8)\n", - "\n", - "\n", - "corners = np.array([\n", - " [6.7, 10.2], # top-left\n", - " [9.6, 10.5], # top-right\n", - " [-3.3, 2.], # bottom-left\n", - " [8, 2], # bottom-right\n", - "])\n", - "\n", - "test_pts = np.array([\n", - " (corners[0]*(1-x) + corners[1]*x)*(1-y) +\n", - " (corners[2]*(1-x) + corners[3]*x)*y\n", - " for y in np.linspace(0, 1, 10)\n", - " for x in np.linspace(0, 1, 10)\n", - "])\n", - "\n", - "inv_transformed_points = reducer.inverse_transform(test_pts)\n", - "\n", - "# Set up the grid\n", - "fig = plt.figure(figsize=(4.5,7))\n", - "gs = GridSpec(20, 10, fig)\n", - "gs.update(wspace=0.05, hspace=0.05)\n", - "scatter_ax = fig.add_subplot(gs[:10, :10])\n", - "waveform_axes = np.zeros((10, 10), dtype=object)\n", - "for i in range(10):\n", - " for j in range(10):\n", - " waveform_axes[i, j] = fig.add_subplot(gs[10+ i,j])\n", - "\n", - "scatter_ax.scatter(reducer.embedding_[:, 0], reducer.embedding_[:, 1],\n", - " c=cluster_colors, s=30,linewidth=0.25,edgecolor='white',zorder=1)\n", - "scatter_ax.scatter(test_pts[:, 0], test_pts[:, 1], marker='x', \n", - " c='k',\n", - " s=30, zorder=2, alpha=1)\n", - "\n", - "# Plot each of the generated waveforms\n", - "for i in range(10):\n", - " for j in range(10):\n", - " waveform_axes[i, j].plot(inv_transformed_points[i*10 + j], \n", - " c = find_nearest_color(reducer.embedding_,\n", - " test_pts[i*10 + j]),\n", - " linewidth=1.0)\n", - " \n", - " waveform_axes[i, j].set(xticks=[], yticks=[])\n", - " waveform_axes[i, j].spines['right'].set_visible(False)\n", - " waveform_axes[i, j].spines['top'].set_visible(False)\n", - " waveform_axes[i, j].spines['left'].set_visible(False)\n", - " waveform_axes[i, j].spines['bottom'].set_visible(False)\n", - " \n", - "scatter_ax.set(xticks=[], yticks=[])\n", - "scatter_ax.spines['right'].set_visible(False)\n", - "scatter_ax.spines['top'].set_visible(False)\n", - "scatter_ax.spines['left'].set_visible(False)\n", - "scatter_ax.spines['bottom'].set_visible(False)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G6Eh0SoWlIfb" - }, - "source": [ - "## Figure 5B: SHAP Values" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "syORYQJDlU1F" - }, - "source": [ - "### First we plot the SHAP values at the top-10 time points broken down by their \"informativeness\" per waveform cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 275 - }, - "id": "KSEsNopQvgCy", - "outputId": "f2a7cf17-d920-4910-a4d0-5b45e30b7584", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "n_bars = 10\n", - "X = np.stack(UMAP_and_GMM['waveform'].to_numpy().tolist(), axis=0)\n", - "y = UMAP_and_GMM['gmm_labels'].to_numpy()\n", - "\n", - "unclassified_ixs = [ix for ix,clust in enumerate(y) if clust == -1]\n", - "\n", - "X = np.delete(X,unclassified_ixs,axis=0)\n", - "y = np.delete(y,unclassified_ixs,axis=0)\n", - "\n", - "UMAP_model = xgb.XGBClassifier(UMAP_grid_search.best_params_)\n", - "UMAP_model.fit(UMAP_X_train,UMAP_y_train)\n", - "explainer = shap.TreeExplainer(UMAP_model)\n", - " \n", - "shap_values = explainer.shap_values(X)\n", - "\n", - "clust_colors = []\n", - "SHAP_REORDERING = [2,5,7,4,1,0,3,6] #Need to do this so the UMAP colormap aligns with the SHAP color order\n", - "for i in SHAP_REORDERING:\n", - " clust_colors.append(UMAP_and_GMM[UMAP_and_GMM['color']==i]['dbscan_hex'].iloc[0])\n", - "\n", - "umap_cmap = mpl.colors.ListedColormap(clust_colors, name='umap_cmap')\n", - "\n", - "fig = plt.figure();\n", - "\n", - "shap.summary_plot(shap_values[:], X, [str(np.round(x*(1/30000)*1000,2)) for x in pd.DataFrame(X).columns.tolist()],\n", - " plot_type='bar',show=False,color=umap_cmap,\n", - " max_display = n_bars)\n", - "\n", - "ax = fig.gca();\n", - "ax.set_xlabel('Mean Abs. SHAP Value',size=12,fontname='Arial')\n", - "ax.set_ylabel('Time Point (ms)',size=12,fontname='Arial')\n", - "ax.get_legend().remove()\n", - "ax.set_xlim([0,3.5])\n", - "ax.set_xticks([0.0,3.5])\n", - "ax.set_xticklabels([0,3.5],fontsize=12,fontname='Arial')\n", - "fig.set_size_inches(4,3.5);\n", - "\n", - "ytick_labels = [round(np.float(i.get_text())/(1000/30000)) for i in ax.get_yticklabels()][::-1]\n", - "bar_heights = []\n", - "\n", - "for j in range(n_bars):\n", - "\n", - " bar_height = ax.patches[j].get_width()\n", - " bar_heights.append(bar_height)\n", - "\n", - "bar_heights = bar_heights[::-1]\n", - "percent_total_height = [x/sum(bar_heights) for x in bar_heights]\n", - "for k,label in enumerate(ytick_labels):\n", - " arr.axvline(label,color='k',alpha=percent_total_height[k])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7gzYoWFJletv" - }, - "source": [ - "### And show where these time points are located with their relative importance encoded by opacity" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 149 - }, - "id": "DU36L7jw2hnz", - "outputId": "70ef447d-8706-453a-e658-664bf95f87d1", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f, arr = plt.subplots(1,figsize=[2,1.5])\n", - "\n", - "for i in range(0,full_data.shape[0]):\n", - " arr.plot(full_data[i].T, c = 'k', alpha = 0.025,linewidth=1.5);\n", - " \n", - "arr.tick_params(direction='out',colors='k', axis='both')\n", - " \n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "\n", - "arr.set_xlabel('Time (ms)', fontsize=12,fontname='Arial');\n", - "arr.set_xticks([0,14,28,42])\n", - "arr.set_xticklabels(['0','0.5','1.0','1.5'],fontsize=12,fontname='Arial')\n", - "\n", - "arr.set_yticks([]);\n", - "\n", - "for i,t in enumerate(ytick_labels):\n", - " arr.axvline(t,alpha=percent_total_height[i]*20,\n", - " color='goldenrod',lw=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mZdlpLuFljEd" - }, - "source": [ - "## Figure 5C: SHAP values broken down by WaveMAP cluster" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jK57eQSCpe0H" - }, - "source": [ - "### We plot each WaveMAP class with their top-three individually informative time points in a SHAP-sense. We also use bar plots to show the relative importance of these three locations which is also encoded as opacity in the lines." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "t_gAGTHCRD1E", - "outputId": "b7abf611-b4aa-43ef-aeca-dc34adf076c5", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
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" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "n_bars = 3\n", - "for i in range(len(shap_values)):\n", - " fig = plt.figure()\n", - " \n", - " clust_color = UMAP_and_GMM[UMAP_and_GMM['color']==i]['dbscan_hex'].iloc[0]\n", - " shap.summary_plot(shap_values[i], X, \n", - " [str(np.round(x*(1000/30000),2)) for x in pd.DataFrame(X).columns.tolist()],\n", - " plot_type=\"bar\", \n", - " max_display = n_bars, color = clust_color,show=False)\n", - " ytick_labels = [np.float(i.get_text())*0.05 for i in ax.get_yticklabels()][::-1]\n", - " \n", - " ax = fig.gca()\n", - " fig.set_size_inches(0.6,1.0)\n", - " ax.set_xlabel('',fontsize=12)\n", - " ax.set_ylabel('',fontsize=12)\n", - " ax.set_xlim(0,1)\n", - "\n", - " f, arr = plot_group(i+1,clustering_solution,UMAP_and_GMM,CUSTOM_PAL_SORT_3,mean_only=True)\n", - " ytick_labels = [round(np.float(i.get_text())/(1000/30000)) for i in ax.get_yticklabels()][::-1]\n", - " bar_heights = []\n", - " arr.spines['left'].set_visible(False)\n", - " arr.spines['right'].set_visible(False)\n", - " arr.spines['bottom'].set_visible(False)\n", - "\n", - " for j in range(n_bars):\n", - "\n", - " bar_height = ax.patches[j].get_width()\n", - " bar_heights.append(bar_height)\n", - "\n", - " bar_heights = bar_heights[::-1]\n", - " percent_total_height = [x/sum(bar_heights) for x in bar_heights]\n", - " for k,label in enumerate(ytick_labels):\n", - " arr.axvline(label,color='k',alpha=percent_total_height[k])\n", - " f.set_size_inches([1.5,1.0])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TNQT2bTVpCY8" - }, - "source": [ - "# Figure 6: Physiological properties" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9TWRyR1FBOoF" - }, - "source": [ - "## Figure 6A,B: Average FR traces aligned to stimulus" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0BoC8n5Yk9r0" - }, - "source": [ - "### We next plot the averaged FR traces for each WaveMAP cluster aligned to stimulus onset" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 673 - }, - "id": "m8NciEo7pCvw", - "outputId": "c18629a9-dc02-49be-e3a7-14ba7092e4c9", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "BS_ORDERING = [5,6,0]\n", - "NS_ORDERING = [7,1,2,3,4]\n", - "\n", - "f, arr = plt.subplots(3,figsize=[2,3.33])\n", - "\n", - "time = np.arange(-0.1,0.5,0.001)\n", - "\n", - "for i,ix in enumerate(BS_ORDERING):\n", - " PREF = UMAP_traces_df.iloc[ix]['PREF']\n", - " NONPREF = UMAP_traces_df.iloc[ix]['NONPREF']\n", - " PREF_UPPER = UMAP_traces_df.iloc[ix]['PREF_UPPER_BOUND']\n", - " PREF_LOWER = UMAP_traces_df.iloc[ix]['PREF_LOWER_BOUND']\n", - " NONPREF_UPPER = UMAP_traces_df.iloc[ix]['NONPREF_UPPER_BOUND']\n", - " NONPREF_LOWER = UMAP_traces_df.iloc[ix]['NONPREF_LOWER_BOUND']\n", - " arr[i].plot(time,PREF,color=CUSTOM_PAL_SORT_3[ix])\n", - " arr[i].plot(time,NONPREF,'--',color=CUSTOM_PAL_SORT_3[ix])\n", - " arr[i].fill_between(time,PREF_UPPER,PREF_LOWER,\n", - " color='gray',alpha=0.2)\n", - " arr[i].fill_between(time,NONPREF_UPPER,NONPREF_LOWER,\n", - " color='gray',alpha=0.2)\n", - " arr[i].set_ylim(5,30)\n", - " arr[i].set_xticks([-0.1,0.1,0.3,0.5])\n", - " arr[i].set_xlim(-0.1,0.5)\n", - " arr[i].set_yticks([5,30])\n", - " arr[i].spines['left'].set_position(('axes', -0.05))\n", - " arr[i].spines['top'].set_visible(False)\n", - " arr[i].spines['right'].set_visible(False)\n", - " arr[i].axvline(0,ymin=0.,ymax=30,linestyle='dashed',color='k')\n", - " f.tight_layout()\n", - "\n", - "f, arr = plt.subplots(5,figsize=[2,6])\n", - "\n", - "time = np.arange(-0.1,0.5,0.001)\n", - "\n", - "for i,ix in enumerate(NS_ORDERING):\n", - " PREF = UMAP_traces_df.iloc[ix]['PREF']\n", - " NONPREF = UMAP_traces_df.iloc[ix]['NONPREF']\n", - " PREF_UPPER = UMAP_traces_df.iloc[ix]['PREF_UPPER_BOUND']\n", - " PREF_LOWER = UMAP_traces_df.iloc[ix]['PREF_LOWER_BOUND']\n", - " NONPREF_UPPER = UMAP_traces_df.iloc[ix]['NONPREF_UPPER_BOUND']\n", - " NONPREF_LOWER = UMAP_traces_df.iloc[ix]['NONPREF_LOWER_BOUND']\n", - " arr[i].plot(time,PREF,color=CUSTOM_PAL_SORT_3[ix])\n", - " arr[i].plot(time,NONPREF,'--',color=CUSTOM_PAL_SORT_3[ix])\n", - " arr[i].fill_between(time,PREF_UPPER,PREF_LOWER,\n", - " color='gray',alpha=0.2)\n", - " arr[i].fill_between(time,NONPREF_UPPER,NONPREF_LOWER,\n", - " color='gray',alpha=0.2)\n", - " arr[i].set_ylim(5,30)\n", - " arr[i].set_xticks([-0.1,0.1,0.3,0.5])\n", - " arr[i].set_xlim(-0.1,0.5)\n", - " arr[i].set_yticks([5,30])\n", - " arr[i].spines['left'].set_position(('axes', -0.05))\n", - " arr[i].spines['top'].set_visible(False)\n", - " arr[i].spines['right'].set_visible(False)\n", - " arr[i].axvline(0,ymin=0.,ymax=30,linestyle='dashed',color='k')\n", - " f.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sPazuw5flmIT" - }, - "source": [ - "## Figure 6C: Baseline WaveMAP cluster FRs" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JieOh-3Rq5Ra" - }, - "source": [ - "### From these stimulus-aligned FR traces, we also calculated the baseline FR which is the FR averaged over the pre-stimulus period" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - }, - "id": "hz1xZMyOt7fD", - "outputId": "649e1237-4e28-4f14-fb50-4f4cf27e79f7", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "def bootstrap_median(data,iter_=5000):\n", - " median_list = []\n", - " \n", - " for i in range(iter_):\n", - " median_list.append(np.median(np.random.choice(data,len(data))))\n", - " \n", - " return np.mean(median_list), np.std(median_list)\n", - "\n", - "def get_baseline_FR_stats(df,clust_ix,conf=0.95,UMAP_clusts=True):\n", - " baseline_FR = baseline_FR_df[baseline_FR_df['dbscan_color']==str(clust_ix)]['baseline_FR'].tolist()\n", - " \n", - " if not UMAP_clusts:\n", - " GMM_baseline_FR = GMM_baseline_FR_df[GMM_baseline_FR_df['GMM_class']==str(clust_ix)]['baseline_FR'].tolist()\n", - " \n", - " n = len(baseline_FR)\n", - " m, se = np.median(baseline_FR), scipy.stats.sem(baseline_FR)\n", - " h = se * scipy.stats.t.ppf((1 + conf) / 2., n-1)\n", - " return m, se, m-h, m+h\n", - "\n", - "def get_baseline_FR(df,clust_ix,UMAP_clusts=True):\n", - " baseline_FR = baseline_FR_df[baseline_FR_df['dbscan_color']==str(clust_ix)]['baseline_FR'].tolist()\n", - " \n", - " if not UMAP_clusts:\n", - " GMM_baseline_FR = GMM_baseline_FR_df[GMM_baseline_FR_df['GMM_class']==str(clust_ix)]['baseline_FR'].tolist()\n", - " \n", - " return baseline_FR\n", - "\n", - "f, arr = plt.subplots(1)\n", - "f.set_size_inches(3,2.5)\n", - "\n", - "for i,clust_ix in enumerate([5,6,0]):\n", - " start_ix = 0 \n", - " \n", - " median, med_se = bootstrap_median(get_baseline_FR(baseline_FR_df,clust_ix))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", - " yerr=med_se)\n", - " \n", - "for i,clust_ix in enumerate([7,1,2,3,4]):\n", - " start_ix = 4\n", - " \n", - " median, med_se = bootstrap_median(get_baseline_FR(baseline_FR_df,clust_ix))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", - " yerr=med_se)\n", - "\n", - "arr.set_ylabel('Baseline FR (spikes/s)')\n", - "arr.set_xticks([1,6]);\n", - "arr.set_xticklabels(['Broad-Spiking','Narrow-Spiking'],fontsize=12,fontname='Arial')\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.set_ylim(0,15);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oFXG_47PT3y9" - }, - "source": [ - "## Figure 6D: Max WaveMAP cluster FRs" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "k2jcxJ7krDQK" - }, - "source": [ - "### And next calculate the max FR as the highest FR reached over the course of the entire post-stimulus trial." - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - }, - "id": "jsS17aRFUEQs", - "outputId": "60a1ac10-bccb-4d14-d176-958478cba3cd", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f, arr = plt.subplots(1)\n", - "\n", - "f.set_size_inches(3,2.5)\n", - " \n", - "def get_max_FR_stats(df,clust_ix,conf=0.95,UMAP_clusts=True):\n", - " max_FR = max_FR_df[max_FR_df['dbscan_color']==str(clust_ix)]['max_FR'].tolist()\n", - " \n", - " if not UMAP_clusts:\n", - " max_FR = max_FR_df[max_FR_df['GMM_class']==str(clust_ix)]['max_FR'].tolist()\n", - " \n", - " n = len(max_FR)\n", - " m, se = np.median(max_FR), scipy.stats.sem(max_FR)\n", - " h = se * scipy.stats.t.ppf((1 + conf) / 2., n-1)\n", - " return m, se, m-h, m+h\n", - "\n", - "def get_max_FR(df,clust_ix, UMAP_clusts=True):\n", - " max_FR = max_FR_df[max_FR_df['dbscan_color']==str(clust_ix)]['max_FR'].tolist()\n", - " \n", - " if not UMAP_clusts:\n", - " max_FR = GMM_max_FR_df[GMM_max_FR_df['GMM_class']==str(clust_ix)]['max_FR'].tolist()\n", - " \n", - " return max_FR\n", - "\n", - "for i,clust_ix in enumerate([5,6,0]):\n", - " start_ix = 0 \n", - " \n", - " median, med_se = bootstrap_median(get_max_FR(max_FR_df,clust_ix))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", - " yerr=med_se)\n", - " \n", - "for i,clust_ix in enumerate([7,1,2,3,4]):\n", - " start_ix = 4\n", - " \n", - " median, med_se = bootstrap_median(get_max_FR(max_FR_df,clust_ix))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", - " yerr=med_se)\n", - "\n", - "arr.set_ylabel('Max FR (spikes/s)')\n", - "arr.set_xticks([1,6]);\n", - "arr.set_xticklabels(['Broad-Spiking','Narrow-Spiking'],fontsize=12,fontname='Arial')\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.set_ylim(0,40);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xKOQstlZUbuh" - }, - "source": [ - "## Figure 6E: Firing rate range for WaveMAP clusters" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E6CovcATrKa8" - }, - "source": [ - "### Taking the difference between the max FR and baseline FR, we have the FR Range." - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - }, - "id": "YltyClzbU0rR", - "outputId": "51120210-e320-4615-fcfc-4e0815701162", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "def get_dynamic_range_stats(clust_ix,max_FR_df,baseline_FR_df,conf=0.95,UMAP_clusts=True):\n", - " if UMAP_clusts:\n", - " max_FR = max_FR_df[max_FR_df['dbscan_color']==str(clust_ix)]['max_FR'].tolist()\n", - " baseline_FR = baseline_FR_df[baseline_FR_df['dbscan_color']==str(clust_ix)]['baseline_FR'].tolist()\n", - " \n", - " if not UMAP_clusts:\n", - " max_FR = max_FR_df[max_FR_df['GMM_class']==str(clust_ix)]['max_FR'].tolist()\n", - " baseline_FR = baseline_FR_df[baseline_FR_df['GMM_class']==str(clust_ix)]['baseline_FR'].tolist()\n", - " \n", - " dynamic_range_FR = np.subtract(max_FR,baseline_FR)\n", - " \n", - " n = len(dynamic_range_FR)\n", - " m, se = np.median(dynamic_range_FR), scipy.stats.sem(dynamic_range_FR)\n", - " h = se * scipy.stats.t.ppf((1 + conf) / 2., n-1)\n", - " \n", - " return m, se, m-h, m+h\n", - "\n", - "def get_dynamic_range(clust_ix,max_FR_df,baseline_FR_df,UMAP_clusts=True):\n", - " if UMAP_clusts:\n", - " max_FR = max_FR_df[max_FR_df['dbscan_color']==str(clust_ix)]['max_FR'].tolist()\n", - " baseline_FR = baseline_FR_df[baseline_FR_df['dbscan_color']==str(clust_ix)]['baseline_FR'].tolist()\n", - " \n", - " if not UMAP_clusts:\n", - " max_FR = GMM_max_FR_df[GMM_max_FR_df['GMM_class']==str(clust_ix)]['max_FR'].tolist()\n", - " baseline_FR = GMM_baseline_FR_df[GMM_baseline_FR_df['GMM_class']==str(clust_ix)]['baseline_FR'].tolist()\n", - " \n", - " return np.subtract(max_FR,baseline_FR)\n", - "\n", - "\n", - "f, arr = plt.subplots(1)\n", - "\n", - "f.set_size_inches(3,2.5)\n", - "\n", - "for i,clust_ix in enumerate([5,6,0]):\n", - " start_ix = 0 \n", - " \n", - " median, med_se = bootstrap_median(get_dynamic_range(clust_ix,max_FR_df,baseline_FR_df))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", - " yerr=med_se)\n", - " \n", - "for i,clust_ix in enumerate([7,1,2,3,4]):\n", - " start_ix = 4\n", - " \n", - " median, med_se = bootstrap_median(get_dynamic_range(clust_ix,max_FR_df,baseline_FR_df))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", - " yerr=med_se)\n", - "\n", - "arr.set_ylabel('FR Range (spikes/s)')\n", - "arr.set_xticks([1,6]);\n", - "arr.set_xticklabels(['Broad-Spiking','Narrow-Spiking'],fontsize=12,fontname='Arial')\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.set_ylim(0,25);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2T8r9cVvsL9D" - }, - "source": [ - "# Figure 7: Functional properties of WaveMAP clusters" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RLXBKjDdrUjE" - }, - "source": [ - "### Next we show the change in firing rate as a function of time against various stimulus coherences. We show this for an example BS and NS cluster." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RKgU_7JdVhEQ" - }, - "source": [ - "## Figure 7A: Average FR change across coherence for WaveMAP cluster 6\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 213 - }, - "id": "7oZM8CpHVvrk", - "outputId": "32718b95-ffc3-41af-da62-31691863f367", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "def hex_to_rgb(hex_code):\n", - " h = hex_code.lstrip('#')\n", - " rgb_256 = tuple(int(h[i:i+2], 16) for i in (0, 2, 4))\n", - " rgb = tuple(x/256 for x in rgb_256)\n", - " return rgb\n", - "\n", - "def fr_by_coherence(ix,CLUST_IX):\n", - "\n", - " f, arr = plt.subplots(1,figsize=[3,2.5])\n", - " for i in range(diffV_list[ix].shape[1]):\n", - " arr.plot(np.linspace(-0.1,0.4,499),diffV_list[ix][:,i],c=coherence_colors[i],linewidth=3)\n", - " arr.spines['top'].set_visible(False)\n", - " arr.spines['right'].set_visible(False)\n", - " arr.spines['left'].set_position(['axes',-0.05])\n", - " arr.set_xlim(0.,0.4)\n", - "\n", - " arr.set_ylim(0,15)\n", - " arr.set_yticks([0,5,10,15])\n", - " arr.set_xlabel('Time Relative to \\nStimulus Onset (ms)')\n", - " arr.set_ylabel('Average Firing Rate \\n(spikes/s)')\n", - "\n", - " for i,slope in enumerate(dec_dyn_data[ix]):\n", - " interval = [0.175,0.325] #Interval that Chand did regression (don't change)\n", - " x = [interval[0],interval[1]]\n", - " y = [diffV_list[ix][274,i],diffV_list[ix][424,i]]\n", - "\n", - " arr.plot(x,y,color='k',linestyle='dashed',alpha=0.5,linewidth=3,zorder=10)\n", - " \n", - "fr_by_coherence(2,4)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "c6Sg0GPBbB-Z" - }, - "source": [ - "## Figure 7B: Average FR change across coherence for WaveMAP cluster 1" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 213 - }, - "id": "qV-qJlDHbAwt", - "outputId": "43517fe5-d347-4945-a8a4-2c4642af479b", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "fr_by_coherence(7,4)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "foV7Y1LabiYO" - }, - "source": [ - "## Figure 7C: FR rate of rise per coherence for BS WaveMAP clusters" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E-3JeUJVr4ja" - }, - "source": [ - "### We next show the FR rate of rise (how quickly it increases after stimulus presentation) versus coherences" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 209 - }, - "id": "Y4yc6UIDboV7", - "outputId": "eb4be807-2c09-4848-be02-662e91ecea70", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f, arr = plt.subplots(1,figsize=[3,2.5])\n", - "\n", - "for i,clust_ix in enumerate([5,6,0]):\n", - " arr.errorbar(range(7),dec_dyn_data[i],yerr=dec_dyn_data_err[i], marker='o', fillstyle='full', markerfacecolor='w',\n", - " c=hex_to_rgb(UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'].tolist()[0]),clip_on=False)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['left'].set_position(['axes',-0.05])\n", - "arr.spines['bottom'].set_position(['axes',-0.05])\n", - "arr.set_xticks(np.arange(len(coherences)))\n", - "arr.set_xlim(0,len(coherences)-1)\n", - "arr.set_xticklabels([int(x) for x in np.round(coherences,0)])\n", - "arr.set_ylim(0,70)\n", - "arr.set_yticks([0,35,70])\n", - "arr.set_ylabel('FR Rate of Rise \\n(spikes/s/s)')\n", - "arr.set_xlabel('Coherence (%)')\n", - "arr.invert_xaxis()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "elIWdg6_b5yR" - }, - "source": [ - "## Figure 7D: FR rate of rise per coherence for NS WaveMAP clusters" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KJz0yfR0sEgz" - }, - "source": [ - "### and similarly we show this for NS clusters as well" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 209 - }, - "id": "Z2kwP3tfb400", - "outputId": "f9194796-7cd1-4934-bcc5-33196a182543", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f, arr = plt.subplots(1,figsize=[3,2.5])\n", - "offset=3\n", - "\n", - "for i,clust_ix in enumerate([7,1,2,3,4]):\n", - " arr.errorbar(range(7),dec_dyn_data[i+offset],yerr=dec_dyn_data_err[i+offset], marker='o', fillstyle='full', markerfacecolor='w',\n", - " c=hex_to_rgb(UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'].tolist()[0]),clip_on=False)\n", - " \n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['left'].set_position(['axes',-0.05])\n", - "arr.spines['bottom'].set_position(['axes',-0.05])\n", - "arr.set_xticks(np.arange(len(coherences)))\n", - "arr.set_xlim(0,len(coherences)-1)\n", - "arr.set_xticklabels([int(x) for x in np.round(coherences,0)])\n", - "arr.set_ylim(0,70)\n", - "arr.set_yticks([0,35,70])\n", - "arr.set_ylabel('FR Rate of Rise \\n(spikes/s/s)')\n", - "arr.set_xlabel('Coherence (%)')\n", - "arr.invert_xaxis()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JQeBa4iKclat" - }, - "source": [ - "## Figure 7E: Coherence slope per WaveMAP cluster" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RjtsY6l6sKHG" - }, - "source": [ - "### Now we take a slope of this FR rate of rise vs. coherence and pplot these differences. " - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - }, - "id": "PIi4yNlzclyz", - "outputId": "f04aa6fe-7d83-43e0-84be-2f9412a6f8f8", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "RT_bins = [12]\n", - "\n", - "offset=3\n", - "\n", - "f, arr = plt.subplots(1)\n", - "\n", - "f.set_size_inches(3,2.5)\n", - "\n", - "arr.set_ylabel('Coherence Slope \\n(spikes/s/s/coherence)',fontsize=12,fontname='Arial');\n", - "# arr.set_ylim(ymin=150,ymax=300)\n", - "\n", - "arr.set_ylim(ymin=-0.0,ymax=0.4)\n", - "arr.set_yticks([0, 0.2, 0.4]);\n", - "# arr.set_yticklabels([150,200,250,300],fontsize=12,fontname='Arial')\n", - "arr.set_xticks([1,6]);\n", - "arr.set_xticklabels(['Broad-Spiking','Narrow-Spiking'],fontsize=12,fontname='Arial')\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['top'].set_visible(False)\n", - "\n", - "# discrim_GMM_dict = {}\n", - "\n", - "# for i,label in enumerate(GMM_class_df['Class'].to_numpy()):\n", - "# discrim_GMM_dict[label] = get_discrim(label,GMM_class_df['Class'].to_numpy(), vmIndex_df)\n", - " \n", - "for i,clust_ix in enumerate([5,6,0]):\n", - " \n", - " median, med_se = bootstrap_median(dec_dyn_slope[i])\n", - " \n", - " start_ix = 0\n", - " arr.bar(start_ix+i, median, \n", - " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", - " yerr=med_se)\n", - " \n", - "for i,clust_ix in enumerate([7,1,2,3,4]):\n", - " start_ix = 4\n", - " \n", - " median, med_se = bootstrap_median(dec_dyn_slope[i+offset])\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", - " yerr=med_se)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-LlF0DHXfOoB" - }, - "source": [ - "## Figure 7F: Discrimination Time " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rqcPrca7s3nW" - }, - "source": [ - "### We now calculate the discrimination time which is the earliest time point at which we can predict the reach direction by observing a given neuron. We do this for all WaveMAP clusters." - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 314 - }, - "id": "7ZAvkjWUgLNY", - "outputId": "becce98f-e2a6-423f-8c93-5b9f295d028f", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "def get_all_discrim(cluster_ix):\n", - " RT_bin = 12\n", - " clust_data = list(all_data_df[all_data_df.cluster_ix == cluster_ix]['disc'])\n", - " discrim_times = [x for x in clust_data if not np.isnan(x)]\n", - " return discrim_times\n", - "\n", - "RT_bins = [12]\n", - "\n", - "f, arr = plt.subplots(1)\n", - "\n", - "f.set_size_inches(3,2.5)\n", - "\n", - "arr.set_ylabel('Discrimination Time (ms)',fontsize=12,fontname='Arial');\n", - "# arr.set_ylim(ymin=150,ymax=300)\n", - "\n", - "arr.set_ylim(ymin=150,ymax=300)\n", - "arr.set_yticks([150, 200, 250, 300]);\n", - "arr.set_yticklabels([150,200,250,300],fontsize=12,fontname='Arial')\n", - "arr.set_xticks([1,6]);\n", - "arr.set_xticklabels(['Broad-Spiking','Narrow-Spiking'],fontsize=12,fontname='Arial')\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['top'].set_visible(False)\n", - "\n", - "# discrim_GMM_dict = {}\n", - "\n", - "# for i,label in enumerate(GMM_class_df['Class'].to_numpy()):\n", - "# discrim_GMM_dict[label] = get_discrim(label,GMM_class_df['Class'].to_numpy(), vmIndex_df)\n", - " \n", - "for i,clust_ix in enumerate([5,6,0]):\n", - " start_ix = 0\n", - " \n", - " median, med_se = bootstrap_median(get_all_discrim(clust_ix))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", - " yerr=med_se)\n", - " arr.annotate(str(clust_ix+1), xy=(start_ix+i, 0.003),fontsize=12, fontname='Arial',color = 'white', ha=\"center\")\n", - " \n", - "for i,clust_ix in enumerate([7,1,2,3,4]):\n", - " start_ix = 4\n", - " \n", - " median, med_se = bootstrap_median(get_all_discrim(clust_ix))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", - " yerr=med_se)\n", - " arr.annotate(str(clust_ix+1), xy=(start_ix+i, 0.003),fontsize=12, fontname='Arial',color = 'white', ha=\"center\")\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sFj_-b_jZYSk" - }, - "source": [ - "# Figure 8: Laminar distributions" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "87V8W5vOlxWW" - }, - "source": [ - "### First we plot the laminar distributions of each WaveMAP cluster. Each bar corresponds to one of 16 channels along the U-probe located from the pial surface to 2.4 mm into cortex (the bottom of Layer VI in PMd)." - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "ze2O18N-Y0gO", - "outputId": "24d31be2-d831-45e4-dbe6-e41179429ab5", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n" - ] - }, - { - "data": { - "image/png": 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", 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XAjMltY+lH0dYtt0d2E9YIv5G9P8zwC9Syv8/QrcxMZqbgboB282svI50L5vZ0uggfJzYj2BmvzSzzWZ2yMwWAH8C4i9aXW9ms6O8c4HjCF3FSh4zs3cszBxeSJgdDOGAWRrVe8jMfg2UcXjmcEPYTDAjhDdcPGhmr5pZhZnNJRyEZ8bSzzSzjWa2A7gT+G4d5de4n1LoHP1NXUu1zswei/IvIKwbmmZm+83sBcL6nt6x9IvN7PWodVlMWB4+L5b/jJTyP4vVnQjNzUAfA91rmHofJ77kdy/wxXR9SRNj3aBdwFcIZ8Aqee3worH21cVHZVfGnUSY+r8rVvbZBAM2lC8BO2Ll/yCl/BMI0/wr2Rj7vD4lrjpq3E8p7Ir+pq7m/TD2+XMAM0sNa19L+trSVta3iwRpbgb6HeGs26ChVEknERarlQDdzKwz8HsgG0scNxIeTNI5trUzs7saqLUAGAn8T6z8O1PKb2tmT8WynRD7fCKhBYMMFwhG3cB3Cd3CxqQf4TkWidGsDGRhgduPgQckjZLUVlJLSRdIuieNItoRDqaPACRdTmiBssETwEhJ50uqXKQ2TFK91u8rLHbrR3gmQxGHrwtmA9dKGqpAO0kXSYq3ClMk9VJ4ZvnNhG4RhDN9t2gQpqEsJeXlaUmisOBvMPDrJOtpVgYCMLOfEx73dAvBCBsJLcqzaeT9A/BzQkv2IfBV4JUs6dpIGEr/55iuG0n/NxovaTdh9eVzhO7q4MoLfjMrA64mXOjvJKwKnZRSxpOE59m9R2gx7ojyvk0w5HtR96+url11PARckjKgkiQjgZdiAx6J4AvqHCAMYwNXmdlvEqzjScJz4uo8WWWhrleBK83s90nW40/lcRoNM6vx0cgJ1DW0Meppdl04x8km3oVznAzwFshxMsAN5DgZ4AZynAxwAzlOBriBHCcD/h+9ikKV9yJB2wAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "ORDERING = [0, 1, 2, 3, 4, 5, 6, 7]\n", - "TIME_LENGTH = 1600\n", - "\n", - "for i,ix in enumerate(range(0,8)):\n", - " f, arr = plt.subplots(1, figsize = [3,2])\n", - " clust_color = UMAP_and_GMM[UMAP_and_GMM['color']==ix]['dbscan_hex'].iloc[0]\n", - "\n", - " g = sns.countplot(filt_full_df[filt_full_df['cluster_ix']==ix]['depth'].tolist(),\n", - " ax=arr, color=clust_color, order=DEPTHS)\n", - " sns.despine(ax=g)\n", - " \n", - " g.set_ylabel('Counts')\n", - "\n", - " g.set_xlim([-1,17])\n", - " g.set_xticks([3,7,11,15])\n", - " g.set_xticklabels(['0.6','1.2','1.8','2.4'],fontsize=12)\n", - " g.set_yticks([0,15])\n", - " g.set_yticklabels([0,15],fontsize=12)\n", - " arr.set_xlabel('Channel Depth (mm)',fontsize=12)\n", - " x,y = 15,13\n", - " ellipse = mpl.patches.Ellipse((x,y), width=2.0, height=3.1, facecolor='w',\n", - " edgecolor='k',linewidth=1.5)\n", - " label = arr.annotate(str(ORDERING[ix]+1), xy=(x-0.07, y-0.8),fontsize=12, color = 'k', ha=\"center\")\n", - " arr.add_patch(ellipse)\n", - " plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SC3LqWpPl5MX" - }, - "source": [ - "### and compare that to the distributions of GMM clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 630 - }, - "id": "LO6x9DdblE5p", - "outputId": "17ec0d4b-7c34-48ac-dda4-6c53e90723c0", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "for i,ix in enumerate(range(1,5)):\n", - " f, arr = plt.subplots(1, figsize = [3,2])\n", - " clust_color = GMM_PAL[i]\n", - "\n", - " g = sns.countplot(filt_full_df[filt_full_df['gmm_ix']==ix]['depth'].tolist(),\n", - " ax=arr, color=clust_color, order=DEPTHS)\n", - " sns.despine(ax=g)\n", - " \n", - " g.set_ylabel('Counts')\n", - "\n", - " g.set_xlim([-1,17])\n", - " g.set_xticks([3,7,11,15])\n", - " g.set_xticklabels(['0.6','1.2','1.8','2.4'],fontsize=12)\n", - " g.set_yticks([0,25])\n", - " g.set_yticklabels([0,25],fontsize=12)\n", - " arr.set_xlabel('Channel Depth (mm)',fontsize=12)\n", - " plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "R6zD1Vqb6raq" - }, - "source": [ - "# Figure 9: Laminar distribution of inhibitory subtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WkbPoDMztZv6" - }, - "source": [ - "### We first calculate the laminar distribution of the CB-, CR-, and PV-positive inhibitory cell types. The following code contains functions to read stereological hand counts done in FIJI (ImageJ)." - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 265 - }, - "id": "DUhkzznC6r0v", - "outputId": "68346912-dfc2-42b2-965d-58aca06f1a6e", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "import xml.etree.ElementTree as ET\n", - "xml_files_path = 'WaveMAP_Paper/data/annotations/'\n", - "\n", - "SHRINKAGE_FACTOR_DICT = {'AM289': 1.2, \n", - " 'AM292': 1.3,\n", - " 'AM294': 1.1,\n", - " 'AM295': 1.1,\n", - " 'AM296': 1.3,\n", - " 'AM299': 1.2}\n", - "\n", - "def plot_stain_density(norm_anatomical_dict,stain, ylim = None, color = 'blue'):\n", - " nonan_norm_anat = [x for x in norm_anatomical_dict[stain] if not np.isnan(x).any()]\n", - " nonan_norm_anat_zip = list(zip(*nonan_norm_anat))\n", - "\n", - " nonan_norm_anat_mean = [np.mean(x) for x in nonan_norm_anat_zip]\n", - " nonan_norm_anat_err = [np.std(x)/len(x) for x in nonan_norm_anat_zip]\n", - " \n", - " f, arr = plt.subplots(1, figsize=[3.5,2])\n", - " arr.set_ylim(0,0.3)\n", - " arr.set_xticks([0,4,8,12,16])\n", - " arr.set_xticklabels([0.0,0.6,1.2,1.8,2.4])\n", - " arr.spines['top'].set_visible(False)\n", - " arr.spines['right'].set_visible(False)\n", - " arr.bar(range(16),nonan_norm_anat_mean,yerr=nonan_norm_anat_err,color = color)\n", - " arr.set_ylabel('Normalized Counts')\n", - " arr.set_xlabel('Depth from Surface (mm)')\n", - " arr.spines['bottom'].set_bounds(0.0,16)\n", - " return \n", - "\n", - "def point_to_line_dist(point_coords,line_coord_pair,shrinkage=None):\n", - " SCALE_FACTOR = 3.7313 #pixels/um\n", - " \n", - " [line_x1, line_x2], [line_y1, line_y2] = line_coord_pair\n", - " p1, p2 = point_coords\n", - " \n", - " distance = np.abs((line_y2-line_y1)*p1 - (line_x2-line_x1)*p2 + line_x2*line_y1 - line_y2*line_x1)/np.sqrt(np.power(line_y2-line_y1,2)+(np.power(line_x2-line_x1,2)))\n", - " \n", - " if shrinkage:\n", - " distance = distance*shrinkage\n", - " \n", - " return distance/SCALE_FACTOR\n", - "\n", - "def parse_annotation(xml_files_path):\n", - " marker_dict = {}\n", - " n_skip = 2 #empty items to skip past \n", - " for f in os.listdir(xml_files_path):\n", - " if f.endswith('.xml'):\n", - " marker_list = []\n", - " file = os.path.join(xml_files_path,f)\n", - " tree = ET.parse(file)\n", - " root = tree.getroot()\n", - "\n", - " for mark_ix in range(len(root[1][2])-2): \n", - " x = int(root[1][2][mark_ix+n_skip][0].text)\n", - " y = int(root[1][2][mark_ix+n_skip][1].text)\n", - " marker_list.append([x,y])\n", - " else:\n", - " continue\n", - " marker_dict[f] = marker_list\n", - "\n", - " return marker_dict\n", - "\n", - "def calc_densities(xml_files_path,shrinkage_factor=None):\n", - " marker_dict = parse_annotation(xml_files_path)\n", - " marker_dist_dict = {}\n", - " for f in list(marker_dict.keys()):\n", - " specimen_info_ix = f.find('AM')\n", - " if f.endswith('.xml'):\n", - " csv_file = f[specimen_info_ix:-4]+'.csv'\n", - " if os.path.exists(os.path.join(xml_files_path,csv_file)):\n", - " xml_files = list(parse_annotation(xml_files_path))\n", - " f_df = pd.read_csv(os.path.join(xml_files_path,xml_files[specimen_info_ix:-4]+'.csv'))\n", - " line_coords = [f_df['X'].tolist(), f_df['Y'].tolist()]\n", - " dist_list = []\n", - " for mark in marker_dict[f]:\n", - " for specimen in list(SHRINKAGE_FACTOR_DICT.keys()):\n", - " print(f)\n", - " if specimen in f:\n", - " shrinkage = SHRINKAGE_FACTOR_DICT[specimen]\n", - " dist = point_to_line_dist(mark,line_coords,shrinkage=shrinkage)\n", - " if shrinkage_factor:\n", - " dist = dist*shrinkage_factor\n", - " dist_list.append(dist)\n", - "\n", - " marker_dist_dict[f] = dist_list\n", - " else:\n", - " marker_dist_dict[f] = []\n", - " return marker_dist_dict\n", - "\n", - "def calc_densities(xml_files_path,shrinkage_factor=None):\n", - " \n", - " marker_dict = parse_annotation(xml_files_path)\n", - " marker_dist_dict = {}\n", - " for f in list(marker_dict.keys()):\n", - " specimen_info_ix = f.find('AM')\n", - " csv_file_suffix = f[specimen_info_ix:-4]+'.csv'\n", - "\n", - " csv_file = [s for s in os.listdir(xml_files_path) if csv_file_suffix in s][0]\n", - " f_df = pd.read_csv(os.path.join(xml_files_path,csv_file))\n", - "\n", - " line_coords = [f_df['X'].tolist(), f_df['Y'].tolist()]\n", - " dist_list = []\n", - " for mark in marker_dict[f]:\n", - " for specimen in list(SHRINKAGE_FACTOR_DICT.keys()):\n", - " if specimen in f:\n", - " shrinkage = SHRINKAGE_FACTOR_DICT[specimen]\n", - " else:\n", - " shrinkage = 1.0\n", - " dist_list.append(point_to_line_dist(mark,line_coords,shrinkage))\n", - " \n", - " marker_dist_dict[f] = dist_list\n", - " \n", - " return marker_dist_dict\n", - "\n", - "FILES_TO_SKIP = ['AM299 CB dPMC40x__2Merge','AM299 CB dPMC40x_1Merge']#these have sideways tissue that hasn't been converted to work yet.\n", - "def process_counts(xml_files_path):\n", - " marker_dist_dict = calc_densities(xml_files_path)\n", - " specimen_df = pd.DataFrame(columns=['Specimen_ID','PV','CR','CB','MAP2'])\n", - " density_dict = calc_densities(xml_files_path)\n", - " \n", - " specimen_list = []\n", - " for k in list(marker_dist_dict.keys()): \n", - " specimen_info_ix = k.find('AM') \n", - " specimen = k[specimen_info_ix:specimen_info_ix+5]\n", - " specimen_list.append(specimen)\n", - " \n", - " specimen_set = set(specimen_list)\n", - " for i,s in enumerate(specimen_set):\n", - " specimen_df.at[i,'Specimen_ID'] = s\n", - " \n", - " specimen_df = specimen_df.set_index('Specimen_ID')\n", - " \n", - " for k in list(marker_dist_dict.keys()):\n", - " if k not in FILES_TO_SKIP:\n", - " specimen_data = []\n", - "\n", - " for s in specimen_set:\n", - " if s in k:\n", - " specimen_data.append(k)\n", - "\n", - " for s in specimen_data:\n", - " specimen_info_ix = k.find('AM') \n", - " specimen = k[specimen_info_ix:specimen_info_ix+5]\n", - " if 'C1.xml' in s:\n", - " if np.isnan(specimen_df.at[specimen,'PV']).any():\n", - " specimen_df.at[specimen,'PV'] = density_dict[s]\n", - " else:\n", - " specimen_df.at[specimen,'PV'] = specimen_df.at[specimen,'PV']+density_dict[s]\n", - " elif 'C2.xml' in s:\n", - " if np.isnan(specimen_df.at[specimen,'CR']).any():\n", - " specimen_df.at[specimen,'CR'] = density_dict[s]\n", - " else:\n", - " specimen_df.at[specimen,'CR'] = specimen_df.at[specimen,'CR']+density_dict[s]\n", - " elif 'CB' in s:\n", - " if np.isnan(specimen_df.at[specimen,'CB']).any():\n", - " specimen_df.at[specimen,'CB'] = density_dict[s]\n", - " else:\n", - " specimen_df.at[specimen,'CB'] = specimen_df.at[specimen,'CB']+density_dict[s]\n", - " elif 'MAP2' in s:\n", - " if np.isnan(specimen_df.at[specimen,'MAP2']).any():\n", - " specimen_df.at[specimen,'MAP2'] = density_dict[s]\n", - " else:\n", - " specimen_df.at[specimen,'MAP2'] = specimen_df.at[specimen,'MAP2']+density_dict[s] \n", - " return specimen_df\n", - "\n", - "norm_anatomical_dict = {}\n", - "IHC_STAINS = ['PV','CR','CB','MAP2']\n", - "SPECIMENS_TO_SKIP = ['test']\n", - "anat_data = process_counts(xml_files_path)\n", - "\n", - "for stain in IHC_STAINS:\n", - " norm_anatomical_dict[stain] = []\n", - " \n", - "for s in list(anat_data.index):\n", - " for stain in IHC_STAINS:\n", - " counts, bins, bars = plt.hist(anat_data.at[s,stain],bins=16,range=[0,2400]);\n", - " if not np.isnan(anat_data.at[s,stain]).any():\n", - " n_points = len(anat_data.at[s,stain])\n", - " norm_counts = [x/n_points for x in counts]\n", - " norm_anatomical_dict[stain].append(norm_counts)\n", - " else:\n", - " continue" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yHmNMav9Gcak" - }, - "source": [ - "## Figure 8C,D,E: Laminar distribution of each inhibitory subtype" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VmzKfnigt1r4" - }, - "source": [ - "### Here we plot the distributions for the three types with 16 bins to match Figure 8." - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 175 - }, - "id": "Nc3jy_boEp2P", - "outputId": "64bc0c23-4b98-4b3b-f9be-a812a18d9bc6", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_stain_density(norm_anatomical_dict,'PV',color = hex_to_rgb('c4539f'))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nYHyb-yQ4QMi" - }, - "source": [ - "# Figure S2: Stability analysis of WaveMAP" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-Qn4UMb5oQpd" - }, - "source": [ - "## Figure S2A: WaveMAP across random seeds and resolutions" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M-FiTFXw0lsg" - }, - "source": [ - "### Here we apply WaveMAP across three random seeds and four resolution parameter values. \n", - "\n", - "---\n", - "\n", - "This demonstrates that the WaveMAP method is stable across random seeds (up to a nonlinear perturbation) and hierarchical across resolution parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 553 - }, - "id": "X2Lqp5iGoY0a", - "outputId": "e8fcadb5-1fb6-4335-e710-85b7e866ddf4", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[94m1.0\n", - "\u001b[94m1.5\n", - "\u001b[94m2.0\n", - "\u001b[94m5.0\n", - "\u001b[94m10.0\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "n_plots = 15\n", - "n_rows = 3\n", - "n_cols = 5\n", - "resolutions = [1.0, 1.5, 2.0, 5.0, 10.0]\n", - "f,arr = plt.subplots(nrows=n_rows, ncols=n_cols,figsize=[15,8])\n", - "\n", - "for i,res in enumerate(resolutions):\n", - " print(BlueCol + str(res));\n", - " for j in range(n_rows):\n", - " resolution = res\n", - " my_umap = umap.UMAP(n_neighbors=N_NEIGHBORS\n", - " ,min_dist=MIN_DIST,random_state=random.randint(0,10000), metric='euclidean')\n", - " my_umap.fit(full_data)\n", - " embedding = my_umap.transform(full_data)\n", - "\n", - "\n", - " G = nx.from_scipy_sparse_matrix(my_umap.graph_)\n", - " clustering = cylouvain.best_partition(G, resolution = resolution)\n", - " clustering_solution = list(clustering.values())\n", - "\n", - " umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "\n", - " umap_df['dbscan_color'] = clustering_solution\n", - " husl_colors = [sns.color_palette('husl',len(set(clustering_solution)))[i] for i in clustering_solution]\n", - "\n", - " arr[j,i].scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", - " marker='o',c=husl_colors, s=30, edgecolor='w',\n", - " linewidth=0.25)\n", - "\n", - " arr[j,i].spines['top'].set_visible(False)\n", - " arr[j,i].spines['left'].set_visible(False)\n", - " arr[j,i].spines['right'].set_visible(False)\n", - " arr[j,i].spines['bottom'].set_visible(False)\n", - "\n", - " arr[j,i].set_xticks([])\n", - " arr[j,i].set_yticks([])\n", - "\n", - "plt.subplots_adjust(wspace=0, hspace=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "brChZFr744Nm" - }, - "source": [ - "## Figure S2B: Random seed and random subsetting to check for robustness" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iTTpUoaG5Ll5" - }, - "source": [ - "### We generate 100 random subsets over different random seeds across various percentages of the full dataset and apply WaveMAP to each\n", - "---\n", - "**WARNING: THIS CAN TAKE 45 MINS IN COLAB**; the final results of one run are cached as a data file but if you choose to run this cell, those results will be used for graphing" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 382 - }, - "id": "HHBe9HF32iBR", - "outputId": "a9743583-2c7a-4e2b-b8b7-81eacdfe1f55", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m random_state=random.randint(1,100000))\n\u001b[1;32m 12\u001b[0m \u001b[0mrand_data\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "subsets = [0.1,0.2,0.3,0.4,\n", - " 0.5,0.6,0.7,0.8,\n", - " 0.9,1.0]\n", - "\n", - "clust_rand_dict = {}\n", - "for frac in subsets:\n", - " rand_list = []\n", - " for i in list(range(1,100)):\n", - " reducer_rand_test = umap.UMAP(n_neighbors = N_NEIGHBORS, \n", - " min_dist=MIN_DIST, \n", - " random_state=random.randint(1,100000))\n", - " rand_data = np.random.permutation(full_data)[0:(int(len(full_data)*frac)),:]\n", - " mapper = reducer_rand_test.fit(rand_data)\n", - " embedding_rand_test = reducer_rand_test.transform(rand_data)\n", - "\n", - " umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n", - " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - " clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n", - " clustering_solution = list(clustering.values())\n", - " rand_list.append(len(set(clustering_solution)))\n", - "\n", - " clust_rand_dict.update({str(frac): rand_list})\n", - "\n", - "subset_avg_rand_list = []\n", - "subset_std_rand_list = []\n", - "\n", - "for k,v in clust_rand_dict.items():\n", - " subset_avg_rand_list.append(np.average(v))\n", - " subset_std_rand_list.append(np.std(v))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qj--ua1oo_Kd" - }, - "source": [ - "### We then plot the mean and standard deviation of the number of clusters per percentage of the full dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 189 - }, - "id": "xPGkKtwLhhue", - "outputId": "70961630-dd2c-4677-bbd2-aa9cb67a67e1", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "subsets = [0.1,0.2,0.3,0.4,\n", - " 0.5,0.6,0.7,0.8,\n", - " 0.9]\n", - "\n", - "if 'subset_avg_rand_list' not in list(locals().keys()):\n", - " subset_avg_rand_list = pkl.load(open('WaveMAP_Paper/data/subset_avg_rand_list.pkl','rb'))\n", - "\n", - "if 'subset_std_rand_list' not in list(locals().keys()):\n", - " subset_std_rand_list = pkl.load(open('WaveMAP_Paper/data/subset_std_rand_list.pkl','rb'))\n", - "\n", - "f, arr = plt.subplots(1,figsize=[3,2.5])\n", - "arr.errorbar(np.array(subsets,dtype=np.float),subset_avg_rand_list[:-1],yerr=subset_std_rand_list[:-1],c = 'k', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xlabel('% of Full Dataset', fontsize=12,fontname=\"Arial\")\n", - "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n", - "arr.set_xticklabels(['','20','40','60','80',''],fontsize=12,fontname=\"Arial\")\n", - "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\")\n", - "arr.set_yticks([0,2,4,6,8,10])\n", - "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\")\n", - "arr.spines['left'].set_bounds(0,10)\n", - "arr.spines['bottom'].set_bounds(0.1,1)\n", - "arr.axhline(np.max(subset_avg_rand_list),color='k',linestyle='dashed')\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AcVEOq4Xduv3" - }, - "source": [ - "## We also generate adjusted mutual information scores that we can superimpose on this plot of Louvain communities." - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": { - "id": "k4nsd-cQdqy8", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "from sklearn.metrics import adjusted_mutual_info_score\n", - "from scipy.stats import sem\n", - "import community" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": { - "id": "BIAVgT7SfV9T", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "mutual_info_stability = []\n", - "data_fractions = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]\n", - "ITERATIONS = 100\n", - "\n", - "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE)\n", - "mapper = reducer.fit(full_data)\n", - "embedding = reducer.transform(full_data)\n", - "\n", - "CV_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "CV_df['waveform'] = list(full_data)\n", - "G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - "clustering = community.best_partition(G, resolution = RESOLUTION, \n", - " random_state = RAND_STATE)\n", - "clustering_solution = list(clustering.values())\n", - "CV_df['truth_ix'] = clustering_solution" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": { - "id": "SvMd6RrbgPMM", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "def cluster_scoring(CV_df,scoring_func,sample_frac = 0.8):\n", - "\n", - " sample_df = CV_df.sample(frac=sample_frac).sort_index()\n", - " sample_data = np.stack(sample_df['waveform'].to_numpy())\n", - "\n", - " samp_reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE) #n_epochs = 5000, learning_rate = 0.25, negative_sampling_rate = 15\n", - " samp_mapper = samp_reducer.fit(sample_data)\n", - " samp_embedding = samp_reducer.transform(sample_data)\n", - "\n", - " samp_G = nx.from_scipy_sparse_matrix(samp_mapper.graph_)\n", - " samp_clustering = community.best_partition(samp_G, resolution = RESOLUTION, \n", - " random_state=RAND_STATE)\n", - " samp_clustering_solution = list(samp_clustering.values())\n", - " sample_df['sample_ix'] = samp_clustering_solution\n", - "\n", - " sample_ixs = sample_df['sample_ix'].tolist()\n", - " truth_ixs = sample_df['truth_ix'].tolist()\n", - "\n", - " info = scoring_func(sample_ixs,truth_ixs)\n", - "\n", - " return info" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uoIUAJKBg0xL" - }, - "source": [ - "## Warning: this next cell generates AMI values for data subsamples and can take very long to run. Run the next cell to load a cached version of the results. If you abort this cell, be sure to delete the empty `mutual_info_stability` variable with the command `del mutual_info_stability`." - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 654 - }, - "id": "5RpUBfM3f0Bl", - "outputId": "1e89bd62-70d3-4364-948e-388843dcfbb5", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m_ctypes/callbacks.c\u001b[0m in \u001b[0;36m'calling callback function'\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/llvmlite/binding/executionengine.py\u001b[0m in \u001b[0;36m_raw_object_cache_notify\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0mffi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLLVMPY_SetObjectCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_object_cache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m_raw_object_cache_notify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 172\u001b[0m \"\"\"\n\u001b[1;32m 173\u001b[0m \u001b[0mLow\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlevel\u001b[0m \u001b[0mnotify\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mITERATIONS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mami\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcluster_scoring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCV_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0madjusted_mutual_info_score\u001b[0m\u001b[0;34m,\u001b[0m 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self._fit_embed_data(\n\u001b[0;32m-> 2555\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# JH why raw data?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2556\u001b[0m )\n\u001b[1;32m 2557\u001b[0m \u001b[0;31m# Assign any points that are fully disconnected from our manifold(s) to have embedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/umap/umap_.py\u001b[0m in \u001b[0;36m_fit_embed_data\u001b[0;34m(self, X, n_epochs, init, random_state)\u001b[0m\n\u001b[1;32m 2600\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_metric\u001b[0m \u001b[0;32min\u001b[0m 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output_metric, output_metric_kwds, euclidean_output, parallel, verbose)\u001b[0m\n\u001b[1;32m 1147\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1148\u001b[0m \u001b[0mdensmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdensmap\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m \u001b[0mdensmap_kwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdensmap_kwds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1150\u001b[0m )\n\u001b[1;32m 1151\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/umap/layouts.py\u001b[0m in \u001b[0;36moptimize_layout_euclidean\u001b[0;34m(head_embedding, tail_embedding, head, tail, n_epochs, n_vertices, epochs_per_sample, a, b, rng_state, gamma, initial_alpha, negative_sample_rate, parallel, verbose, densmap, densmap_kwds)\u001b[0m\n\u001b[1;32m 369\u001b[0m \u001b[0mdens_R\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 370\u001b[0m \u001b[0mdens_mu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 371\u001b[0;31m \u001b[0mdens_mu_tot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 372\u001b[0m )\n\u001b[1;32m 373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "for frac in data_fractions:\n", - " ami_scores = []\n", - " i = 0\n", - " while i < ITERATIONS:\n", - " ami = cluster_scoring(CV_df,adjusted_mutual_info_score, sample_frac=frac)\n", - " ami_scores.append(ami)\n", - " i+=1\n", - " mutual_info_stability.append(ami_scores)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "s_vJuu5cEj9C", - "outputId": "0ca2d483-2be7-423c-905d-5f66bb2ccf03", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "9" - ] - }, - "execution_count": 103, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "len(subsets)" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 211 - }, - "id": "o7NtK__BhA7a", - "outputId": "ed206dc0-8982-4dc7-958a-f6506bafc859", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "if 'mutual_info_stability' not in list(locals().keys()):\n", - " mutual_info_stability = pkl.load(open('WaveMAP_Paper/data/mutual_info_stability.pkl','rb'))\n", - "\n", - "f,arr = plt.subplots(1,figsize=[3.8,2.8])\n", - "arr.errorbar(np.array(subsets,dtype=np.float),[np.mean(i)*10 for i in mutual_info_stability[:-1]],\n", - " yerr=[np.std(i)*10 for i in mutual_info_stability[:-1]],\n", - " marker='o',markerfacecolor='w',color='#ef476f',linewidth=1.5,\n", - " markeredgewidth=1)\n", - "\n", - "arr.spines['top'].set_visible(False)\n", - "arr.set_xlabel('% of Full Dataset',fontsize=12)\n", - "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\",color='#ef476f')\n", - "arr.set_ylim([0,10])\n", - "arr.set_yticks([0,2,4,6,8,10])\n", - "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\",color='#ef476f')\n", - "arr.spines['left'].set_bounds(0,10)\n", - "arr.spines['bottom'].set_bounds(0.0,1)\n", - "arr.spines['right'].set_color('#0ead69')\n", - "arr.spines['left'].set_color('#ef476f')\n", - "arr.tick_params(axis='y',colors='#ef476f',width=1.5)\n", - "arr.tick_params(axis='x',width=1.5)\n", - "arr.set_xticklabels(['','20','40','60','80',''],fontsize=12,\n", - " fontname='Arial')\n", - "\n", - "arr.spines['right'].set_linewidth(1.5)\n", - "arr.spines['left'].set_linewidth(1.5)\n", - "arr.spines['bottom'].set_linewidth(1.5)\n", - "\n", - "\n", - "# arr.axhline(np.max(subset_avg_rand_list),color='k',linestyle='dashed')\n", - "\n", - "plt.tight_layout()\n", - "arr.errorbar(np.array(subsets,dtype=np.float),\n", - " subset_avg_rand_list[:-1],yerr=subset_std_rand_list[:-1],\n", - " c = '#0ead69', marker='o', fillstyle='full', \n", - " markerfacecolor='w', linewidth=1.5, markeredgewidth=1)\n", - "\n", - "arr2 = arr.twinx()\n", - "arr2.spines['top'].set_visible(False)\n", - "arr2.spines['right'].set_visible(False)\n", - "arr2.set_xlim([-0.1,1.1])\n", - "arr2.set_xticks([0.,0.2,0.4,0.6,0.8,1.0])\n", - "arr2.set_xlabel('% of Full Dataset',fontsize=12,fontname='Arial')\n", - "arr2.set_ylim([0.0,1.1])\n", - "arr2.set_yticks([0.0,0.2,0.4,0.6,0.8,1.0,1.1])\n", - "arr2.set_yticklabels(['0.0','0.2','0.4','0.6','0.8','1.0',''],fontsize=12,\n", - " fontname='Arial',color='#0ead69')\n", - "arr2.yaxis.set_label_coords(1.17,0.5)\n", - "arr2.set_ylabel('Adj. Mutual \\nInfo. Score',fontsize=12,color='#0ead69')\n", - "arr2.spines['bottom'].set_bounds([0.0,1.0])\n", - "arr2.spines['right'].set_bounds([0.0,1.0])\n", - "arr2.spines['left'].set_visible(False)\n", - "arr2.tick_params(axis='y',colors='#0ead69',width=1.5)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rCChwvdapAvW" - }, - "source": [ - "## Figure S2C: Ensemble clustering on graphs" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_157Y1P8uH8n" - }, - "source": [ - "### We use ECG (ensemble clustering on graphs) as an alternate clustering method to validate that the cluster we find with Louvain are robust to the method. ECG is an ensembled version of Louvain." - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 265 - }, - "id": "ZAHhI8N04OBw", - "outputId": "821600fa-d4e5-4e7b-a95f-5a2443908c84", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "[]" - ], - "text/plain": [ - "[]" - ] - }, - "execution_count": 106, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "# add ECG to the choice of community algorithms\n", - "def community_ecg(self, weights=None, ens_size=16, min_weight=0.05):\n", - " W = [0]*self.ecount()\n", - " ## Ensemble of level-1 Louvain \n", - " for i in range(ens_size):\n", - " p = np.random.permutation(self.vcount()).tolist()\n", - " g = self.permute_vertices(p)\n", - " l = g.community_multilevel(weights=weights, return_levels=True)[0].membership\n", - " b = [l[p[x.tuple[0]]]==l[p[x.tuple[1]]] for x in self.es]\n", - " W = [W[i]+b[i] for i in range(len(W))]\n", - " W = [min_weight + (1-min_weight)*W[i]/ens_size for i in range(len(W))]\n", - " ## Force min_weight outside 2-core\n", - " core = self.shell_index()\n", - " ecore = [min(core[x.tuple[0]],core[x.tuple[1]]) for x in self.es]\n", - " w = [W[i] if ecore[i]>1 else min_weight for i in range(len(ecore))]\n", - " part = self.community_multilevel(weights=w)\n", - " part.W = w\n", - " part.CSI = 1-2*np.sum([min(1-i,i) for i in w])/len(w)\n", - " return part\n", - "\n", - "ig.Graph.community_ecg = community_ecg\n", - "\n", - "def readGraph(fn, directed=False):\n", - " g = ig.Graph.Read_Ncol(fn+'.edgelist',directed=directed)\n", - " c = np.loadtxt(fn+'.community',dtype='uint8')\n", - " node_base = min([int(x['name']) for x in g.vs]) ## graphs have 1-based or 0-based nodes \n", - " comm_base = min(c) ## same for communities\n", - " comm = [c[int(x['name'])-node_base]-comm_base for x in g.vs]\n", - " g.vs['community'] = comm\n", - " g.vs['shape'] = 'circle'\n", - " pal = ig.RainbowPalette(n=max(comm)+1)\n", - " g.vs['color'] = [pal.get(int(i)) for i in comm]\n", - " g.vs['size'] = 10\n", - " g.es['width'] = 1\n", - " return g\n", - "\n", - "\n", - "rand_data = full_data[0:(int(len(full_data))),:]\n", - "my_umap = umap.UMAP(n_neighbors=20\n", - " ,min_dist=0.1, metric='euclidean',random_state=random.randint(0,10000))\n", - "my_umap.fit(rand_data)\n", - "my_umap_embedding = my_umap.transform(rand_data)\n", - "\n", - "G = nx.from_scipy_sparse_matrix(my_umap.graph_)\n", - "umap_igraph = ig.Graph(len(G), list(zip(*list(zip(*nx.to_edgelist(G)))[:2])))\n", - "\n", - "umap_ECG = umap_igraph.community_ecg(ens_size=10,min_weight=0.5)\n", - "\n", - "umap_df = pd.DataFrame(my_umap_embedding, columns=('x', 'y'))\n", - "\n", - "umap_df['dbscan_color'] = umap_ECG.membership\n", - "ecg_colormap = [sns.color_palette(\"husl\", len(set(umap_ECG.membership)))[i] for i in umap_ECG.membership]\n", - "\n", - "f, arr = plt.subplots(1,figsize=[5,4])\n", - "\n", - "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", - " marker='o',c=ecg_colormap, s=30, edgecolor='w',\n", - " linewidth=0.25)\n", - "\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "\n", - "arr.set_xticks([])\n", - "arr.set_yticks([])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "F6__-Zvbh95h" - }, - "source": [ - "# Figure S3: Comparison of WaveMAP against DBSCAN on t-SNE and GMM on PCA" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FOIS5vHhioNZ" - }, - "source": [ - "## First we generate a plot of DBSCAN applied to the 2-dim. embedded t-SNE space" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "eG-TirkLiF6n", - "outputId": "21e6e76c-84b9-4b1a-b920-2cdfb604955b", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting openTSNE\n", - "\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/af/9b59afb6f642eb8d4beaa8b38249526b88796a58329d2a3a4d14cba3e37b/openTSNE-0.6.0-cp37-cp37m-manylinux2010_x86_64.whl (2.3MB)\n", - "\u001b[K |████████████████████████████████| 2.3MB 8.2MB/s \n", - "\u001b[?25hRequirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from openTSNE) (1.19.5)\n", - "Requirement already satisfied: scikit-learn>=0.20 in /usr/local/lib/python3.7/dist-packages (from openTSNE) (0.22.2.post1)\n", - "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from openTSNE) (1.4.1)\n", - "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20->openTSNE) (1.0.1)\n", - "Installing collected packages: openTSNE\n", - "Successfully installed openTSNE-0.6.0\n" - ] - } - ], - "source": [ - "!pip install openTSNE\n", - "from openTSNE import TSNE\n", - "\n", - "from sklearn.cluster import DBSCAN\n", - "from sklearn.mixture import GaussianMixture\n", - "import cycler\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": { - "id": "WaRCxoG9iZaz", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "tsne = TSNE(\n", - " perplexity=30,\n", - " metric=\"euclidean\",\n", - " n_jobs=-1,\n", - ")\n", - "\n", - "tsne_embed = tsne.fit(full_data)\n", - "tsne_df = pd.DataFrame(tsne_embed, columns=('x', 'y'))\n", - "clustering = DBSCAN(eps=3, min_samples=15).fit(tsne_embed)\n", - "tsne_df['cluster'] = clustering.labels_\n", - "\n", - "tsne_colormap = [sns.color_palette(\"husl\", len(set(clustering.labels_)))[i] for i in clustering.labels_]" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 355 - }, - "id": "N-90HUGHimLS", - "outputId": "619dd8bc-772e-446f-a279-85a7c921f5b3", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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c+nFCCDGfrpNR5drn+5pde5xSKAB4/wOPvj6N642Pcem0IhZT5HLgXUe71tSiGGpsZsDf14/7vTaM+khZKABQIRPrkcWQHru9YCnsX1iBqi+vS2xVGVR/vHy/f+pWs7SFMFRnUPNwiEWfj5DZESqbCfCdykDhDmsp9yWE+FCQGYM54Hmarm6fU6c8IlHF4haT6uqpM5jWMDJSOfJ4rqY6Y7B2tUFDg0VXl08oDEtaTeKxaz/X6VEX3VeAiEnoN9aiz4ygHR+jJYbRFJvwOWZrnPDv3oDuK6DiNqouMuH9/sRWC6tGUTjtE2owiCwp3+qnlEJNsJsxVG9gZRRu//j3I32PfVlrCoQQ4lolJZHnwLm24JbA+f/aRFzx5KcjVF2iwc2x4y4vvlQofZxMKJ58PEIqaXDqjMtzz49/LhqFpx6PkE4vnHUG/mAR3T4KPqimKEZm6lLEfscoxS8dQ5/IQszE/t9WYG6qvqLp+tlS6PDJ7nVxun2SN1lElpmY4emdV/6sR+59D5QittogvGjhfI+EEGKMlES+mg4ecspub2dHNL19/iWDQWuLyWOfDHP4iEsmY7ByhUVqbPvckSPlZYBzOejt06SvsNzvbPF78xT/5ij6eBYA1Rgl9BtrMOonaD0IaM/H/UF7EAoARj2cLx7F+P1NqElmCq6mcKNBuPHySw8Xznmc++N8addDfwQWfSFCuEnCgRDi2iDB4AoNDHgMDQdX8Jm0gWUprAkK0BiXmPX3PM3IiCaVMvjojnDFa0Qila9pLqCxxj89UgoFALojh390aPJgkPPwDw+WH3Q1esiBprk807mVO+aXbYX081A440swEEJcMyQYXIH2Do/nXshTLIJSsOOeEOvXWaxba3HgkFtaSFhXq6iZYo1BLuezZ5/LO+8GMw1bNlvctNUmHh9/zto1Fu8dcCmObU5obDCoqVlAawxylY2N9IAzwQMDKmZhbq3B/X7b+MGwgcpc2w2C1ETj/wK4NSKEENMlwWCGCgXNq68XKY5dHWoNP/5JkUXNJo0NJk89EaGn18e2FXW1BslJKuoBdHX7vPXO+CC6e4/LoiaTlRdU1quvM3nqySi9fT6WRfCaiYUTDIxF8WDLoTd2D0WBsTY16eOVoTDvbsAfLOC/04uqi2D//HJU3QQlAueI9jTeiMaIqFnrPxBZYWLEKTU/MlOKyOKF830SQohLkWAwQ66rGRgo3y/o+1AoBgNjXa1JXe30po8HBivXePb0+qxcUX6sptqYcOZBa83gkMb3IJlU2NNo6TvbVGuc0G+vx/1RB7g+1o4mjMVTb9o36iKEfmEF+pHFEDYxkrPTCXE6ij0+Az92yJ/wCDUbZO6zZ2W6P9xo0PL5KIVzPqig4mGoXoKBEOLaIcFghmIxxQ0b7FI1QoDFLQZVVVM8aRLVmcqBvGGCwSSf9/G84L2VCp7jOJrDR1x+8noRx4G1a0w+cluotGjxalGGwlyRwlieBM20dxYo20RNM0DNFt/RDL5eJNRgohQYUcXo+x5mUmHNwixMqMEg1CBhQAhxbZJgMENKKTZusAjZcOh9l5tvtOnu9vnmS0XWr7NYtnT6dQbq60zuvSfE628W0Rpuvdmm8YKBRWvN2XM+P3mtQHZEs3WTzfr1Fom4QU+vzw9fHl/tdviIR1Ojx+aN8zMwKaWm2ASzMHjDGrvapOe58UZKdq0iutyUyoRCiA89CQZXIJk0uOnGEKtXW7z4rTzdPcEo09Ze5M6P2Ny0bXoL6cJhxeaNNsuXmmgd3A44PyMAwbbEb7yQL1U6fOMtZ2zGwiCbrbwNcfqMx+aNV29a/lqjoprRw15ZlQ6nR+MOamidv/MSQoiFQOY7Z8HIiC6FgvN273UZGfEZGfXp6vYYHr50/eJk0iCVMlBKkR3xGRjwcV3N4JBfUf740BEXrTWpZOXl+bKlsjVuKlbUxJig9pIhWUoIIWTGYDbYtkKp8p49yYRiNK/51ksFBgY1sSg8+ECExgaDrm6f3l6fVErRUG8Qu+CWg+9rTp72+OGPi4yMaNasNtm4ofLb1NJsolTQfvnjD4T48U+CHRKbN1osXXxtBoNiUdPd4zOcDQJPXa0xZwsp03fbZPd5MNYrKbzEINQoOVkIIaQk8ixwXc2efQ6vvREsRDTNoC3y2zuLnDk7fqkfjcCDD4R57oXx0sabN1nc9ZFQqaBRT6/PV57O4V8wQ7DjHhvXhdfedPD9YGHigw+EyVzQtnc46+N7kEgozGuwpr/Wmr37XV7+yfh6iQc+GmL92rm5jNe+ptDmU2zzMaKK8CID+xL9LIQQ4joiJZHnkmUpNt1g09JsMprTVFUZ2Lamrc2ntsZgxfJg9fup0x5d3eX3BPbuc9m4waZ2rOtfNuuXhQKA/Qc8nvhUmCVLLFxHk0opotHyQWw+axp4nqaj0+eDYy7RqGL5UpPay9xpMDikee2NYtmxV14t0rrInLIGxEwpQxFpMYm0VJ6n9jV4Psq+NmdehBDiSkgwmCWhkKKxcXwgcRzNTTda5Auw810HX8Pa1Rax2AQhbWzWxnGCKXTDoCwcLFtqEgopaqZo4pPP+5jm/NQw6Oj0eObZ8VmQ3XscPvNEtGxG41J8Hzyv/JjrUBGS5pp/ZgTnR+3QmcPc3oi5IY2Ky+IDIcSHh8ydzhHbVjQ0mOzd5+L5wdh/6IiL9sG84H99/TqTeEJx/KTLcy/keeW1Ig9/PExdXTDAL1tqsH6tVbZL4UK5nM97Bx2e/nqeF76V51ybxyVuD826wxc3eMpDb+/ljeippGLD+vIr9K1bLJITLK6cK353nsL/exD/zW7841mcv/0A7+DAVXt/IYRYCGTGYA6NjlYO0P0DPp96NMy5Np+ajEFjk0FPj88L3xy/4j5ztsATj0WCxklK09Hlc+qMR2ODQX2dURYSzpzz+cGPgin4/n5NW3uen/lMtHRr4moIz0KDJ8tS3LQ1RFOjx7lzHotbLVpaDIyr2GdAd+VgpDzkeC93Ym6pltsKQogPDQkGcyg9QZvl5iaTlkUWLYvGj+3eW95syPdhcMgnmTT5xosFevuCgGEY8OSnIzRdcMvi8OGLBjIPBgaDtQ1Xy+qVFvv3uxTGlgg01BvU1k7//UdzPseOe+zZ61CdUdx6S4jamnkYiKOVvw6qMQqmgXY8/BMj+Pv6oDqMuT6N0Thx50ghhLiWSTCYQ/V1BvdtD/HaG0U8H265yaapqXLAnChAhCOK3j6/FAogCAzvHXDKgkF9vcHxk+U356MTXMHPpfo6k888Mdbgybz8Bk+nTgXbMyEo5nTqdDDrkclc3TtdRmMUc0cj3o86ggMJC3N7I7qvgN+ew/nTw6XHerVhQr+9HqP66jV9EkKIq0GCwRwKhRQbb7BZumTiiobnLW41uPsOG8eFvj6foqOprzXo7qm8T+9fdHdi9SqL94+69PUHn9i2xaKm+uovQKypNkgmFAODPtmsxrR8YtFLD+yep9l/oHzWo+gEsx5TBQPP0/g+s7rYUsUs7E+2Yt5UC3kXMmH89wbw3+sPOkdeQPcU0O15kGAghLjOSDC4Cqbabue6mnNtPm+85eC60NxksGN7iETCwNeQSSv6B8ZvJWzcUL5Cvjpj8OnHIgwOaiwLMmmD0Cy1EL4cuZzPm2877NsfDPKNDUGthYlmQy5kGMHMSlt7eQiKTDHr0dXtsWuPQ2+fZvNGi+VLzbIiUVdCRS3M5UkAvAP9uF8/hVqaQKUnKG9tXXv1IoQQ4lIkGMyzvn6f7/1gfP9+W7vP4SMud9xukEoaPPqJCG0dHvl8EBrq6yoHwETcIBG/mmcNo6M+HZ0+3d0+dXUGhkEpFAB0dPqcPOmxZbMx4fP6+31q6wwa601u2GBx7ITH8HAQgG6+yZqwvTTA4KDPcy/kyeWCj3/woyIf3RHihvWzf9vBPzcKgD6ZxfrssmDmwB0LaWtSqCZZYyCEuP5IMJhnIyMTNEE663OrC7YN6bRB+jLqAVwprYMp+qmqJ3qeZs8+t6zl9L13V15Rd/d4wPgMh+tq3tnlsHvPeIC4954QmzfafObTEQaHNLY9+ayH72tGRjXxmCKXG/9/27vfZc0qa9ZrOBjNsdK/ne+cw3qkFRW3UMkQqjWGkZpekywhhLiWSDC4CnI5n6FhTchWVFWp0ha8YlGjdVDX4MImSatXmvNSqKi7x2Pf/rEp+k02S1oNIpHKUDI8rHl3V/lOCsfVFf0iViwv//EaHNLs2Vu+nuCNnxZZsSyobphMTn5uAwM+e/Y5nDjl0dRosnqV4o2fBudQUx0UhZptxtIE1sMtuN89B1kHCj7mR6pRCSl4JIS4fkkwmGN9/T7f+V6Brm4f04Ttd4dYt8bCshRd3T4//kmRu+4MsWevw3BWs2GdxepVV//bMjjo89zzeUbHpujb2gs8+ECYtasrR1ylqKiyffCwy6MPh3nzbYdCUXPzjTbNTbOz5dBxNK+8VuDESX/sXF1aFhksX2rS3umxbYs9J/0hVMLGeqgF89ba4OOaCOoa7EMhhBCXQ4LBHNJas2+/U+qP4Hnwwx8Xaag3qK8zGRr2GR7WvP5mkdWrLGJRRTisSc2wN0BXt8eJkx5aw7IlJvX1RsUuiJHRYNdAJKKoSo2/z8CgXwoF5+3Z67ByuVlq8HReMqm47Wab1386Pmuwfq3FkiUmTU0mvtZEJ5hpqEoptt9tUywGWy+VgnhckbjE1sZsVpdCwXlnz/k88akIyWSo7OuYbcpUqHpZSyCE+PCQYDCHXBfOtVVuOTxfEfF8AHAcOHAwmGJ/6GNhAIaHfXwdtG+eTvW/7h6PZ57N44yN1Tt3OXzm8Qj1deNX7V3dHt/6doHBIU04DA/eH2bZ0uBHYKJ7+pn0xFP0hqHYeINFfYPBQL8mk1HU1wVtoMNhmKxpl+tBZ5fPwUNB3QXbhk89cuntfnYoCBAXrseIxRTpqkuHCiGEEJdH/qrOIdtWrF1dnr0sC1JjV7jhMHzkNhvbDrbt3bjVorHR4NARh//1dI5/+HKON35aJDty6b4DnV1+KRRAEEou3AJYdILuhYNDweBaKMC3v1dgYDB4TCZjsGXT+LmGw3DTjZPfS49EDJa0WmzeZLO41Zpye+F5/X3joQCCQLR7j4PnTd3bIRE3uH9HCGvs9EwTHrgvJKFACCHmgMwYzLHVq01yeYv977mkUortd4XJpBU9PR7PfD1PLKbYssnGNGHFMoOREc13vz++fXHnLpeaGpN1a6YeBM0LZhWUgpu22SgFu/Y4NDUG2xk7OsoDRrEI+byGKoiEFbfdGmL1KouiowmHYM8+l4EBn00bbVpbTCJTdHecDsetPDacPb8LYurnLlls8rNPRRkZ0cTjikxa7vULIcRckGAwx5IJg4/cFmLzJhvborTKv7Pbp+hAcVDzzrvBpb6hbOLxygHv1GmXdWum/lY1NhgkEopsVrNlk8WxEy59Y+WUlYLHH4uwYoXJocPjV+yJuCJxwftFwormJpOBQZ+vPpMjnw+Onzlb4OEHw6xaeWU/Lum0IplUpXoFANu22NPagaGUojqjqM5c0SkIIYS4BAkGV4FhKJKJ8sHPnGDdQNEBq1g5rd7SbNLf79PTF+xsqKsxKqopZjIGTzwWprsn2AK5+4JtgVoHCwnvvtPG9+GDYx4NdQb33B3C9eC9gw7ZYU1rq0lDvUH/gF8KBeft3e+wfJl5Rav/U0mDxz4R4chRl55enw3rLFoWSddCIYRYSCQYzJOGBoNEXJEdW1AXjQb39Y+f8Lhpm82efUGJ5PVrTerrDJ7++vgVfH2t4hMPRyp2L6TTJuk0nD4dhAKlYMVyk9oag3A4KM18/44wd9yuCYcUrqv5xot5unuCc/jpOw6f+mSY0ARX8OlJFiJerpoag4/UTF0YKJfzGRzSWGZQ4OniXRFCCCHmjgSDeZJJGzz+qeAK3ylqsiOat3c6OE5QDfG+e0M01JmkUorde52yK/iuHk1Pjz/ptsbqaoP6WsXatTZHP3D54JhDVZWiod6kuckklQwG2q5uvxQKztu52+HjD4TZsM7iwKEgYEQisOkGe8IGULNtYMDjez8s0tbuoxTcfKPNtq32Fa9vEEIIMT0SDOZRJm2SScPQkFGr3N8AACAASURBVM/Xnx/fajg4pElXGVSP9QvI5StvL7gTLOQ7L5EwePihMD9+xaG943xRIM03XwraGZ+/DTHROG8aEA4r7vyIzfp1FsWiJpNWpNNzP+XvepoPjnul3RRaw9s7HZYsNlnULLcchBDiapBgsACkUgaPfTJMe4dPsQBNFzVLWrncYvcet1RuOBwKpuSnYijF2XNe2bHRHIyM6lLp4eqMorXF4MzZYCA2DLjpxhCmqYhGFYsuo67PwIDP4LAmFg1mQy53+r+n1+PoUZfO7soQNJqbejujEEKI2SPBYIEIZg+Cq+KREZ8TJzz6BzV1dYq6WoMnPx3hyPsu4bBi1Qpz0u6D54UjwaB/YcXAaBRi0fEBOxYzePD+MB2dPvm8pqbGoCqlaGv3KBZ10MDpEm2TAdo6PL7xfJ6iE8xC7NgeYsM6a1qFmc5/vS9+q8DIaFBK+eSp8UBjGJBOyW0EIYS4WiQYLDCOo/np2w77D4zfK3jgvhDr111e74GQrbjz9hC5XJGOTp9kUvGxj4ZLxZX6BzxOnvLp6/NYvsxi1UoL39e8/ub4e0ci8PijEerqJn/fQkHzk9eKFMdug2gNL79SZFGzSXVmegP60LAuFV7q7fW5aZvN4SMu0SjcdUf4krMjQgghZo8EgwVmcEiXhQKA13/qsGSJSTx2eQNkTY3JY58MMzoa7HiIx4Pnj4z4vPSdQmnh4f4DHg99LEwiocreO5+Hve853Le9sufCeY6jGRwoL5zk+UHnyOkKh1Spw+T7H3gkEj6bNllsWGuVzlkIIcTVIX91rwFaAzO8zV50YHDYp7vHZ3g4GMAHBnXFboR33nWCKogX6e8PKhNOJh5X3LChvHRydSYoZDRd6bRix/ZQaTGk62oWt5gSCoQQYh7IjMECU5VS3LDB4r0LrtzvuN2e0SA5MODz/Dfz9A8EA35NjeKRhyITlh+OhIMBOhKhbGtkUK558kFeKcWmjRbhEBw64tLcZLBls31ZsxuGoVizxqKhwSSf0ySTiqpprG0QQggx+5TWU16KynLweTAy4tPR6dM/4FNfZ9DYYE7Y/fBSDh52+N4PimXHPv5AmKVLTF55tcDBsfLIhgGffjRCyyKTrm6Pffsd+gd00CCpxZy0QVKhoOntCxYuVlUZxOPB2obpLjoUQggxbyb9Qy0zBgtQPG6wYvmVXzHn8pXHCkU9VqcgxNrVPrk8ZDKK2rEFfvV1Jvfda4w1Npp8gC86mp27irzzbjCzYVlBuGhumt0r/eyIj/YhkVBXpcCSEEJ82EkwuI4tajJQilL9A8OAhvpg4I7FDBYvnngQV0pdstvhwIBfCgUQFFx6860ijzwcmVZTpPHnBb0dLn6O42iOn3B55TUH19HcfJPNhnUWsctcgCmEEOLySDC4jtXXGTz5qQj7DjgoFZQ1vrBw0pU4X6XxQtmsxvP0tIKB72va2n3e3lmkWISbbrRZ3GqW+jR09/h8+3vjt0Fef9MhkzZYuUKCgRBCzCUJBtcxw1A0N5s0T1FO2PM0nV0+R466hEOKlStM6qeoW3BeukqRSavSwkaArVvsUlvpS+nu8Xn2+Xxpx8M3Xyrw6UfDLG4NfiQHByuXt5w+47FyhfzICiHEXJK/sh9ynV0+zzybL91u2LPP4aknopesrBiPG3zyoQiHjjh0dPpsWGdPemtiIhNtgzxx0isFg0Si8jmNjTJbIIQQc03+0n7IHT8x3oMBoFgMqg9ORyqlWL3K4tabQyxqNohFp//jFImM/1upYAai9oIKh3W1JrffapdaPa9dY9K6SBopCSHEXJMZgw+5ibYiXmrhIQSLA/e95/Dq68Fig0Rc8egnw9TVTm/wrq01WLnCJJfTLF9m0dvrUyxq+gc8Mulgi+RN2+yxUs1BfYfLWdQohBBiZmTG4ENu6RKT6AVX77U1QdOmS+kf8EuhACA7otm128Hzplf6IhE3+Oi9ITbdYPHq60UOHg52IHzrOwVGRoIZC9NUVGcMamsMCQVCCHGVyIzBh1xtjclnnojS2+tjGMGVfCp56WCQn6BGQnePj+tOb8YBgoF/157yvhA9PZr+AU08Pr3XEEIIMbskGAgyaYNM+vImj6pSleWTb9hgEw5P/8peqaC508VMmccSQoh5I3+CxYxUVRl8+pEIy5eZpNOKe+4MsXLF9KYKCgXNcDaYobjlplBpgSHA6pUmmWm2axZCCDH7pFeCuCKuq3FdPWH9AsfR9PX5DGc1qZSiptqgq9vnlVeL9Pf7rFtrsW2LRb4AfX2aSATqao0JG0Y5jqav32d0VFOVMqi+xHZKIYQQU5r0CkyCgZgxz9N4HhM2eNJa894Blx++HFQvVAoe+liIV98oMjQ0/rhbbrS5/TZ7yj4IrqfZt9/lJ68Fr2VZ8KlHIiyaonCTEEKIKU36R1cuuwQAA4M+HZ1eaUfApXR1eXz3BwWeeTbPewcdRkfLnzc4qHnltfGSxlrDK686LFtSvqzl8FGXfH7q/DkwoHntjfHXcl149Y0ihYLkViGEmG2y+FBw/KTLd75XoFiEVFLxiYfCU5ZFHhj0efaFfGnh4Q9+VOT++0JsWDeeM10vGMAvlMvpiroJzY0G9iVaSjtOZZXEoSGN62nCk4deIYQQMyAzBh9yA4N+KRQADA1rfvJakWJx8qvxwUG/Yrvivv0ujjP+nFRSsWxp+Y/X5k0WDXUKayyOptOKm260saZo7wzBDoiamvLHbNloEYtKKBBCiNkmMwYfcvm8LoWC8zq7fAoFPeHaAWDCLYm1NeWtmkMhxfa7wrQ0u5w45bF6lcWypSaJuOLnnjIoFCGZUBMuNLxYLGbw0MciHDjo0Nbus26NxcoV5pTrEoQQQsyMLD78kBsZ8fnq1/IMD49/q9etNblvexjLmnjgLRY1O3c5vL0zqHwYiyk+9cj0yyHPlNbBYsfJzkvMnNYa3VMAx0dlQqioXDMIcZ2TXQlicl3dHj95rUhnl8/KFSY3brEpOhAJK9JphWFU/vwEfQ18CoWgAVIqJXelrlXa8fB29uJ89QQUfIwNaazHl2A2x+b71IQQc0eCgZhasagpFDRFx+cbLxYZHtaYJtx3b4g1qyzMS6wDENcu/3SWwn/aX/bbbu5oxNrRhFEbmfyJQohrmWxXFFMLhRTRqOLNt9zSbQXPC3YcDAxIPrye6QGn4hJAn8zinczOzwkJIeaVBANRUnQ0nV3l+wJ9H3KXqDMgrm2qJgQX3S4ylifxjwzO0xkJIeaTBANREo0o1q0pX3QWjQZbDwFyeZ+z5zyOn3Dp759eISSx8KnGGPavroJ0EBDMW2rRGsz16fk+NSHEPJClx7PM7y8AYGQmaBu4wCmluGG9he9rDh72qK1R3HF7iFTKIJfz+cnrRQ4d9gAIh+DxxyLU10tZ4mudMhXm5mpUVQj/VBbv8CDW6hTm6tR8n5oQYh7I4sNZokdd3Hd6cL9xGgDr0VasW+pQsWsve2mtGR0N6hjYdjBbcK7N45lny6sarV9rcv99YakncB3ReRftaoyEPd+nIoSYW5P+4b72Rq0Fyj+Vxf2nE6WP3a+exKiPXpPTsUop4vHyn5kLqxqeNzQctE/2PUgmjSveuZAd8Tl7zufMWZeWZpPWFpNEQu52XU0qYkmRaSE+5CQYXIJ2fPyzI+iuPCppoxbHJ7ya8s+OTnBs5JoMBhPJZAxiMcXo6AWFkNZY/NM/5ykUYMsmi21bbRLTqGQ4Ec/T7N7r8O6uoMHCgYMeG2+wuOfOkBQ0EkKIq0iCwSV4hwZw/vxI6aaKeW8j9qcWo0Ll99aNhmjFc43Ga7tAjOsGuxROnXZJJg0++VCY9z9w6e/XrFtrcuCgSy4XPHbXHpeGBpM1q2YWDLJZze495V2X3jvgsm2zTSYjwWCuae0zNNqJoQySsYb5Ph0hxDySYHAR7fjonjw4PiRt3OdOl6208F7uwLqjHtUSL3ueWhbH+mgT7o/aAbB2NKGWlj/mWtPW7vHs84XSx9UZxeOPRYjHDQ4fcTl9pnxnwrlzHmtWzexHShlgW1C4oG+DaYIhdxLm3Ei+j33Hvs5bB/8Oywxxz5bfYk3r/YTsazvYCiFmRoLBBXTOxX2lE/eFM+Br7N9YAwXvogcBXuX9diMZQj22GPOOegBUXQRlXbujmu9rdu12yo719Wt6en3icYNksvIqflHzzHcopJIGd90Z4gc/Gk8Gd34kRColswVz7Vz3bl7f/+cAuF6e7779B2SSS2ip2zLPZyaEmA8SDC7gt42WdhWomjD+B8OYt9XjvnS29BhjfRW6NkTniINtQHV0fL2BsgxU0/VzlWXZlYPy+b4JdbUG2+8O8fqbRTwvaKm8aNGVBaHVqyyqMwZDQ5pkUlFXa8iOh6vA8x3u3PibAPja5e1DX2Iwe06CgRAfUhIMLjQ4foWsGiLoY8PoqIn16cXoc6Oo6jADW6t54fggXznSSzJk8tvbGri9KYFtXruzAxMxDMW2LTYnTnh4Y3cMFjUbVFcHA3UopNi80WLZEhOtIZlUV7wrIWQrmptMmpuu9OzFdPUOneTl3f+DkXwPAPFILbeu/2VZZyDEh5gEgwuoukhQC9IH/8wI1u31uN9rwz84gKqNoN8f5N0NSf7mQPBHNOe6/OvXzvE3DyxlbXXl4sNrXVOjwVNPRujp9QmHFPX1BvFYEIB8X2MYiqoquaK/lrX17C2FAoCRfA+2GaU+s2Yez0oIMZ+ur8vcK6Sao4R+cy2qMQI+qKVxzPubYGw2IPSra3i1s7yxjAbask7li82Q62uGCi7+1IWnrgqlFPV1JuvX2qxYbpFMGGSzPvv2O3ztuTw/fatI/4CURr6WeV7lz65l2kRCyXk4GyHEQiAzBhdQpoG5IYNakgDXR1WFMDfXYN3bBLaBkbTZ6jr8+Ex5OKiLzs5/48nBAl853Mue7lF2tKZ4ZEWa5kRoVl57Nmit2fueyzs7g8Gkrd3n9DmPRx4KE4lIxrwWNdduxDYjOF5Q1dI2IzTXbp7nsxJCzCcJBhMoK2Bkgqoe73twR3OCdzpHePVcFttQ/NqmOpanr7wvwmDB5Q9+eo4jY70W/uFQL4MFj9/aVs+pYYfD/XmipmJdTZRF8xQWsiOavfvKrzDb2nyGhjWRyLyckrhC9Zk1fPa+v+V4+2sALG+6Q24jCPEhJ8HgMjUlQvzftzXTlnUImYrmRAjLuPL77J2jbikUnPftk4N8fFkVX3j5NMWxLZJLkjb/Y/timuKXDgcF1+dstkjR1yyK26TCV/bttkyIxxTF4vhtDtMEe6wy4fl1B+La0lC9lobqtfN9GkKIBUKCwQzEbJOVmdntKhi3DWKWwag7fs/+poYYL58dLoUCgFPDDscHCpcMBsNFj68c7uUfD/Xia9hSF+X3b22+otmGaNRg+z1hnn8xjz92mnfdESIShQ+Ouezd71BVpdh0g019nXRdFEKIa5HcGF4gmuM2//qWJs7XRIpZBr+ysRbXr1yEOMGhCh8M5PnSwd7SY/d053j93PCMz2901Ker2yNdBT/32QiPPBzms09G2LDOoq3N55vfLnDmrM97Bzy+/o08/QPepV90jD9QxDsyiPf+IP7w7C3kFEIIcflkxmCBUEpxT0uSL31sOX15l4aYRUsyjO8rvnGsn/MTCQ0xi+qIyZ/v7SJuG9zRnGBFuvIGf1++cmB+rzfPZ2Zwbt09Hi99p0D/gCYagQcfCLN8WfCjo7Vm7/7ywbxQgP4BTWYa/aP87jzFvz6CPh00oTLWpLB/cSVG5srXbYjr12C2jYHsGUJ2nJrUMkL2tV1+XIiFRILBAmIZimVVYZZVjQ+K62oi/OV9S9ndNUo8ZLA8FeYLPz5Nbuz2wlcO9/GXH13CklT5QNqSsDFVefXm7S1Tb0HryTmcHCyggWWpMLUxm6KjefX1Iv0DwQvl8vDSdwv87FMGVamgMmE6bVT0TQiHprfWwP9gqBQKsBT6hjzZg8/gFXqIrrqbcNMNKGvh7MwQ86+7/yjPvPIbjOZ7Abh57ee4bf2vEA4l5vnMhLg+SDBY4AwV7ERYVxMUUPqT3Z2lUAAwVPQ4PlCoCAYr0hH+292t/MneLgYLHr+wroZt9ZOXa27PFvk3r5/jUH+wbW1VOsx/vKOFFBbtHeWDfqEAuZymKhV8vHGDxdGjLrngqaxbY1I9zY6Ium+8N4L6WJjeI/8Gb7gDgOG3vkz9z/45kSU3T+u15oo72I7TdxojFMOqWYoZkT3+80Vrn11Hny6FAoB3Dn+JVS07aK7dOI9nJsT1Q4LBNSYWqlwWMtGuCMtQ3NqUYH1NBMfTZT0dJnKwL1cKBQBHBwrs783x0ZYUy5aavH90/NZEPK5IxMffs67W5Kkno/T3+4RCiuqMIhqd3vIVY00KXgz+7SW6SqEgoBl+95/nNRgUu4/R/fTn8YY7AUhse5Kqu38dM1o1b+c0leHRHnKFPsKhBFXx5vk+nVnn+S49gx9UHC84M18/I4QoJ4sPrzF3NydJ2uPftuWpECunuB+fDFmXDAUAAxd3kQT68y6WpVizyaBliUIpqKlVrLld0+eXrytIVxksW2qxqNmcdigAMJbECX1hHcbaKlS08uswwjOfHtZukWL3MQrtB/Fyg5f9fK+QJbvnuVIoAMjuegan5/iMz2kutfXs45Xd/52Xfvr77DryT5zu3IleABU0Z5Nlhti66smyY5FQikxi8TydkRDXH5kxWEBcTzPqeiRD5qRdBVdmIvzl/Us5NpDHNgxWZcLTqmlwKeuqo2VrEgwFm2qD2xf/cLyb4YTHTXcmOJ7N88V9Q/y7RDOLU1e+QFDZJub6NMbqFN5oHeEPtlI4szv4nBUhsfWJGb2ul8+S3flVBl/7K9Ae4dZtVD/8b7EzrdN6vvZdcu+/gtNdeXXq5xfe1enQSAffeev/oW/4JAA9g8dwvDxViSaq4ovm9+Rm2bKmO3jw1j9g9/tfJZNczM1rP0c62TLfpyXEdUOCwQJxYqwc8t7uUe5bHJRDnmzAX5oKs3QWBuULrc5E+ON7F/P0kT584LNrqlkz1hiqKWHz/UND/LRzpPT4qDW7dQqUZWCl6qh55D/gdB7Bd0axa1cQql81o9dzuz9g8NU/L31cOLOL3PsvY9/6C9N7/kAb/d//r6Ru+xyFU++Mn2cojl298K5OB0fOlULBeUfP/pBNKz694IKB77uM5HuxzCjRcOqynx8Np7lh2SdZ0/JRDNPGNOTPmBCzSX6jFoCBvMu/feMsxwaDhXhfOtjLSNHnC9saZqWq4nRYhmJLfZxNdTG0BvOC971/SYoXjw/QP3a74e5FCVZUzc12QivVgJW68pa/3khvxbHCmd0wzWCA9tHFUQrn9lF196+TO/oKRryG1O2/iF2z9IrPb7ZFwxlMw8a74BZPVXwR0dA09oxOYWD4DD1DxzGURW16JakrbMc8NNLBrve/wt5jz1IVb+a+G3+Xlrptk86QTcW2r7+OpkIsBBIMFoCOUacUCs771skBfm5dDQ3xS68PmE2GUnDR3+gV6Qh/df9STg0ViJgGy6rCZCIL+0fHyrSAMkGPr52Irb1/2s83q5pIbHuC7Lv/TOH0LiKLbySyejvhRZvm4nSvWCbZyvatv8OPdv0XtPYJWXHu2vR5wqEEJ9rfoLPvEDVVy2mu2UQ8WgNAdrSLbL6HWDhNaoKFir2Dx/nnH/9aqS1zQ2Ydj97530jFm2Z8nodPf4edR74MQM/gBzz7yuf5hY/9E9WpJTN+TSHE7FrYf90XoKLnU/A0yVD5VPpw0ePEYIGhokdrMsSSVJi863N6uMCo49OcCFEfm3iQj9sGEVORv2AbYmsyRNReOH0HFiVC89a8aSbsuhXUfeZ/MvCjP8Ib7Sd1688TWXbLtJ9vWGFSt/8y4datFNsOEG7ZQrh1C0otzPW6pmGzcfmjNNdsIpvvoiq+iNqq5ex+/2l+uOu/lB63ZeVTbN/yL+kZPMbzr/8Ow6MdRENpPnHHf2ZJQ/n/z4n2N0qhAKCz/xBd/UdmHAyKziiHTn277Jjj5RkcaZNgIMQCIsHgMhzszfG373XTNuLw5OpqtrckyUQsRooeX9zfzTNH+4GgnPGf7ljM621Z/ua9HjTQFLf5w7tbWF5VWaVwUSLEv7qliX//VhuuHwSF397WSCok356ZUoZFdPlthJr+CjwHM1ELgJftxRk4g2FHsTKLMUKTT0dbyTqsdQ8QX/fA1TrtGSk6o3QNHGYge45krIFFNZuJhFMMj3by2v4/K3vs3mPPsGnFY3x/539geDTYGporDvCtN36Pn//Yl0nFGkuPHS0MVLzX+fbMM2GZYVrqbqJ74GjpmFIG8UjNjF9TCDH7ZOSZptNDBb7w49OlJkd/uLODsKl4aFmaU8PFUigAGHV93h8o8MX3xq+22kccnj82wL/c2lBxP9VQih2tKVamw/TnPRpjNouSV3513pdz2dU9wjsdI2yui3FjQ5yGSWYtrlcX1htwek/S/ezv4vYEOw1St/8Syds+d80XLDpy5vt89+0/KH18x8Zf55Z1vwioCWY4FL7v0dl/uOzoaKGPfGGwLBisWHQX7xz+e7QOfuZtK0pt1coZn6dhmGxZ9QRtPXvp7D+IZUa478bfpTq1dMavKYSYfRIMpulc1inrfAjw/LEB7l+cqjgOwa2Fix3qzeP6GtucuCDR8qoITFI3Z7jo4WtN1TRbJ3u+5mtH+/j7g8EivBePD/Lwsip+56ZGwubCnA6fayMHv1sKBQBDb/4d0ZV3YrZsmcezujLDo128suePyo69eeCvWdP6ANWpJdy1+fN8/51/X/rcjat/lnSyleXNd3K87dXS8UxyKYloXdnrNFav56kdf82BEy8SsuKsW/px6tIzDwYANallPLH9Txkaace2YmSSLQv29owQH1YSDKbpwjUFpoKn1lTTmgixq2uUhphNU8yifdQtPWZFVbiijfKjK9LYlzkoO57PO52j/PneLgqezy/fUMddzQniocrtgh8M5Dk5WCBmGzTFbb5ypK/s8y+dGOTn1tawdI52FCwk2vdQRvn/UbFtf8XjvNHK6fJridYenle86Jhfuspf2/oAmUQrPYPHSCdaaKxeTySU5J4tv4VphDje9irNtZu5Z/O/oG/4NO29B8gkF1OdWoJp2LTUbaWlbuusnnM0XIVtRWnr2ccbB/6CaKiKG5Y9QkP1ull9HyHEzKhLVEa7vsqmXYGRosdXDvfydwd7+ZUbavnOyUHOZYOtYWszEX735ka+e2qIDwbyPLoiwy0NMc5kHb64//yahAw7WlOXvZr/QE+O//0HJ8u+EX90Tyu3NpVXBDzYm+M3f3SKwtgCxpvqYyxJhfj6B+MDX8hQ/OPHl9GavH6DgTvURe7oK4we/A7hpbcS3/Bx7OqgqNHIge/Q+8Lvjz/YDNH4S18mVLdins52cgPZc7T17CWb62ZR7WYaqtdjmcHtpa7+I5zp2gnKYGnDrRw99zKv7fuT0nM3rXiCHVv/Tyxr6u+z4+bJFfoxjBBvHfgiuz94GoCQneAz9/4FjdXr5+zrO9O1i6d/9Kuc/xNjWzF+7v5/oLZq+Zy9pxCizKSr22XGYJriIZOfX1/DPS1J9vfkSqEA4HB/nvYRhy9sbcDXOtjyB6wPW/zHO1so+v6ECwldX+N4PlF78mJBp4YLFelsZ+dIWTDQWvP19/tKoQBgZ9coj67MlAWD/2NTHc2zUCVxodK+x/Cuf2b4zb8DoHB2D4Uz71L36f+GEUkSWXYrmQd/j+G3/hEjUUf6nt/Erl14A9FIrodvvfF7tPedn+FQPLH9T1naeBtd/e/zTz/8FRw36EgZthM8dd8XqUkt51z3bhprNrCoduslQwGAbUWwrSbaevaXQgFA0cmy8/A/8vHb/t2cFQ96/8wPuPC6w3FH6R08JsFAiAVAgsFliFomq6ujvNkxUvG5YSeYujUuWlgYsQwiE7SkODaQ5+kjfbw/UODR5WnubkmSDpsc6cvxWluWuG1wW1OCuglmGC6ueqiBwWLlOoeopfjLjy6hfcShPmqxKh0pK1x0PfDdAt5QJ8oKg1Jkd3617POFUztxB9sIRdZgxjIktz5ObO19KDOEEZq82+R86hs6dUEoANC8fejvaKnbxtnuXaVQAFBwsnT2HWLj8kdZ1bJ9Ru9XdCt/ngey5/B9b86CwcXrGSBY3CiEmH8SDGZgW30MQ4E/dsFjG4rV6elPz3eOOvz2K2fozgVrEv7w3Q5crVlfHeHXfniq1K/gHw/18cf3LuaTy6t48XjQBOiWxhjbGuJlr2coxZOrM7zRni0da4xZLKsK+ihsrL2CL3YBcwfbGfzJXzBy4CWMSIrMA79LqHUbheOvlx6jrDAacLM9WGNbFs3olVUDnHMTZDeFiQIMVTm7NNGxy1GdXEI8UltWs+DGNT+DPY1Zh5lasehu3n3/K6X2yYtqt1KXXj1n7yeEmD4JBpep6PpELYP/elcLbVmHI/05Hl2RYVWmsj7BZNqyxVIoOO/Vs8N0jzpccDeAoaLHmeECX9jawKdXZvA0tCTtCW9LbK6L8Sf3Lubls8M0xW1ub07MSnOlhWz08A8Zee+bAPi5AXqf/z3qPvundJ98C/zg/7fqrl+j79v/AW+og/S9XyC2egdGeGHOFJxXk1pGS92NnO1+Fwj2+t+6/pcwzRAt9duIhKrIF4OgGIvU0FRzw2W9vu+7tPXuZ//x57GMEDf8/+ydZ4Ab5bm2r5lR79JK2/t63QuuGBvcsE0J5ZAQCDXt5IN0EgIJSTjpOek5hBRCQhIghDQghA4xNrZxN+69bO+70qr3me+H1lprV1tccMG6flmv3hmNZK3ee573ee6n+jpuWvQIO48+R5f3ENNqbhxkdnS6cVpruPXyP9Djq0MS1ThtY1AUONKymlDUg9NaQ75tHJJ0YZXX5shxLpBLGR2mIAAAIABJREFUPjwBQvEkzxzu5bc7O5GVVMvj715aSqVFiyeSQFYU8kbR4vigJ8xHX8tMKHxflYUSk4ZHd3VnzP3BpaUsKB2+zl5RFHyxJAaVeMJVD+cripyk86n/R7R5e8a48wM/Q2UrIeFtRZDUeNc8Qqx1d/r5/Nt+h658xpm+3BPGG2yjw72HUMRNgX0i+Y5xSGLqu9XjPUq7ex+CIFDgmEjeCfoAtHbv4ukVH0Pps4tWSVpuXfon8u3jUBT5tJYPJpIxguFu1Co9Bp19yHnhqJfXN3+HQ81vAikxdMNlD1HsnIJOM7pGS8FwD529B4jGAzjMlbhstSfVgyFHjguEXPLh6aDeF+M3OzrTj4/6YrxwpJcZLj0/faeDhAwfn+JkSZklo7wxKSu0BGJEkgpFRjXlZi13T3Xxm51dAJg1IldU2AAFs1pM5ytUmjXUjrBF0R6M88JRD683+JiYp+POCU5qbKOPXpyvCKKEvnbhAGEgIFkK0eSPQZM/ht7VmaIAUiZHpyoM4r0txFp2IYc8aEqmoCkcj3Ca9+KtxiKsQ1gP51mryRtFkp432EaX5wBJOY7TNoY8SxUAjR2b0qIAIJGM0tl7kHz7uNMqCryBFtbv+T176l/Eaixm+eyvUZY/O+ti7fE3pkUBpEou1+95lCLHZMZVLKfEOQ1/qAMAkz5/0DnC0V5WvPPDvqRGkEQNNy1+hBLX+etRkSPH2SInDE6A3mhi0Niu7jBHvVHa+zwMfri5nUKDOl01EEnIvFzXyy+2dRKTFabnG3hgdhE3jrUzu9CIO5ygLRTna283A/CRSU5sWgmDSqTWrqNomP4ESVnhnwfdab+ClkCcfT0RfnN5xagiF+cr0fb9BHe9gDqvGsPEKwjtfR1RZ8Z+xVfQOKvS89SuwWY8KuvJNwACSPg76X7mXuKdfba+gkT+bb9FV3Z6a/1PFV+wjefW3EN3n/2wVm3m5iW/I98+Fr12cI6F5jQn/imKwu66f7O77nkAegNNPLv6Hu4comGSrAw2BEvKcQLhTjbt/SOVRfNYu/NXKCjMn3w3kyqvQXdcy+YeX31aFKSOjbFp3+Nc55iU247IkeMEuTDizqeJEqMG7QDXwiVlZva5M/3jd3aH0/+u80b5ydYOYn2Zits6Q7ze6EWvkhjv0CMIAj/d2oE/LuOPyzy8vZO2UJyFZRaKR2ha5IkkeKnOmzHWHIinRcp7kXh3HZ1/uYvAlr/iee37JINu8m9/lMKPP41x4hUIqv7PTFs6DePU6wEBBBHLZXejspUQ97aiyEmSoV6SgcHtmYd//aP9ogBASeLf/FdG2JI743R6DqZFAUA07udI6yoAyvJnZXRTdNnGkm8/veZC0XgwY6EGSCQj6f4MA3GYKyhxZoqrCeVXUte+jtL8GazY+gOicT+xeICV235CmzszEiQf1276GJGYF5nB1To5cuQYnlzE4ASosGp5aFE5v97RQUcowY21DsY5dPgG2B9XWfsXp57I4EV6S3uQD09wIokCkeTgH66e8OgWdr1apMKiyRAiGknArH7v6r14Tx1KtL/6ItqwmaCliLxrvjForsrswr7sPsxzbgVEYu176HjiI8ixMOZZH0JRkoT3vYFl/n9jGL8MST+6vezzAVkZ/B2KxFKfm8NSwc2LH6XbdwRREHFaxmA2FpzW19eo9JQXzKXHV5ceEwUVRl32EhmDzs5Vc79Nc9c7dLr3YzEWcbB5BSgK4Yhn0Px2916qiualH9vNFdjNlXj89emx2eM/jFp675p55cjxbpETBifIVJeBny0sJ5pUsOtUeCIJbqy188whDwqwvMLCVGd/1nuRUY0kkFFtsLTcmvYTqLJqMapFgnH5uOdHt0AZ1RJfmJ7Pdze1c8QbRRLg/lmF51V75BNFUA8OeUuWoRc1UaNH4xpDtHkH7he/mR73b3wC64JPIoe9eF79PpIpH0PtZSO+vtpZjTq/lkRvK0oiCoqCefaHzrkkN6d1DHqNjXAsZXAlCBLjypamn7eairGaioc6/JQRRYnptTfR6dlHS/d2tGoTy2d/HbulfMhjbKYSbKYS3HlT+ffb99HtPYxa0uGyjxs012nNdKs0G/K54bKfc7R1DW5/I2PLllDsnHra31eOHBcCF3xVgj+W5KAnQmcoTrFJTa1Nh2GAE6E3mkAjCkM6FEYSMi2BGLICJSZ1xvFJWWFLR5CfbW2nJ5Lkxlo7H6i14zquy+FBT4RX6714IgmurbYx2alHM4rqgkOeCOvaAoTjSWYWGHHpVZSYtFmbNL1XSPi76Xnpm0Tr1gMgGuzkXf+/KIkIGteYrDkECW8bkfpNuF/+dsa4Yfwy4t1HiHcfxTTzZhzL7x98bNCNKGkQdamcESURJ9qyg2jLTkS9FU3BuHcl+XAkIjE/LV3bOdK6mnzbWCoLL8FmLs2Y09V7iKOta4nEfNSWLqbQMRFxwHV6/E0cal5Bc+c7jK+4goqCuRj1/W2QewPNNHVuweNvoqLwYoryJqNR9QtfWU7S6dlPa88udBoLxc6p2Ez91xGJ+fCHOlCrDNhMJaN+f6GIG1+oHa3ahFZtYsuBp9iy/0n0WhuLp9+LKErotQ6c1hr02iE6j+XIkWM4hlwoLmhhkJAVnt7fk64OAHhgdhHX1qSSs3ojCVY2+/jLfjdOvYq7prqY5jSc1N1hbyRBNKng1KtOi/tgnTfCXf9pINAXaVCJ8MjllUzMGz6JTFEUDnoi7OoOo1OJTHXqKbecX+HWRKCHeNcR5KifRG8LvrWPosTDqOzluG76Rbo3AkCkeSfdz3wRy8V34N/8NKaZN4GcRJDUCDoLvSt+hhIP43jfNzBNvS59XDLkIbT3dXwbn0A02LEv/hza8plEGjbT9dfPcOxPQ1s+E+cNP0QyDF2K926w++gLvLqpf/uk1DWd6y/9adbEwqEIR708v/ZemrveSY8tmPZ55kz4MJBanP+19l5au3ekn7/+0p9SW7o4/bilawd/e/MT6a2LPEs1H1j0y4z2zaeDRDKON9hMKOLm2bc+SzyZyuuZXvshLp3yKbQa0whnyJEjxwCGXIjeu5vRo6AtGOOx3Zm+Ab/c3kFHKJXItLkjyI+3dNASiLOjK8w9K5uo80ZP6rVsOhUFRvVpsyQ+3BtNiwKAhAwb2gNDzg/Gk3QE4+xzh7lrRQM/e6eD729q4/OrGmnxx4Y87lxEZcpDXzUH4hG8Kx9CiadyLBKexnQHRUVOEus8RKx1F7rqSwg3bMa29F58b/8e75pH6F31MPGuI2gKxqEbcxm6itkZrxGp34znjR+R9LUTb99H598+S9zdiO/txzheL0cbtxLvqT9Tbx1IleZt2Pv7jLHmrm14/E0ndB5voCVDFABs2f8EgVBKKLv9DRmiAGD9nt8TjfdbKO84/M+MfIYe31F6vEdP6DpGg0pSYzWWsOXAn9OiAOBA4+t09R5if8NrHGlZky5pzJEjx8lzQecYyAokBkRMorKC3Df2yoCM/5is0ByIU32afAK6w3F8MZk8nYRVe2L/FeosAkM/xPbDPneYh97pwB9LbTnEjkt46AglOOyNUmI+//IS5OhgISRHA8jxKKG9r+J5/YcoiSiawgmYZtyEf/PTqbyAPgJb/4brtt+iyR+LpMs0kQrtf33AiRPIgW6UcyCIJgoqtOrBd8jHDJBGi1ptYN7ku4nG/eyrf4VQ1I1e50DqS9jLZrWslnSII3odZH5Gspygs/cg3d4j6DVW8u3jMRvyT+haj53HH8ysarhk0id45q1Pp8VCiXM6187/QdZeDDly5BgdF3TEoMig5r9qMkOvd07II7/PA2CSc3BY3qY9NV/6Y+zoCvGJN+q5/ZWjfObNBg73RmgNxFjV5OM/jV7qR4hM1Np1lJn6FwKrRmJOoXHQvM5QnC+vbmZnd5hwUskQBec7mtKpcHyNuqRGUzKVhLse98vfSYuAWPs+Ym17ENQDBZ2CIEiDRAGAtjhL4pqkxjrv4xwfgdOWz0TtGFyXfyr4Q500dmymtXsnkZhv8LVpTFw29bMZZkQXjbkJu7k/sc/jb6KubR2t3buIxrI1SWpm7c5fsW73b9lX/zJzJnwYu6mSxRfdi77PH8BurmDscQmLgiAyb/JdGc2Opo35AKLQL2rzLNXkDUgMbO7ewVNvfJhXN36D59bcwxtbvk8oS6XBSGjUBmaNvz392GWrpaV7R0YEoaV7Gz3eIyd87hw5cvRzQUcMNCqRj0x0Ms1lYG9PmGkuA9NchnS4f2m5hVVNPo54YwjAHRPyqLKe+n58ZyjOg2+30N1XynjEG+OdjiDPHPbQ5E9tY5jVIr9cUjFkD4YSk4afLyrnkCdCQlEYY9NRkSVXoDOUSL9OezBOlVWLVhLSLZoLDSrGnEADqHMJTcE4Cm77HYHdLyEgYJzyPjQFY4kcXc/Au9ZY2x5Ms28lWr8xPaZy1qC2l5IN/dhFBA+sIN66O+WBcMlHUbtqEFVa8u94jFjrLiRzPtriKUhGx2l7Tz2+ep5b/Xl6A6ltgUmV17Lwos9j0GW+RlnBTG5b9gQefyMGnQOXtRaNOpUU2O7exz9WpiIBAHMmfIy5Ez+Wfl5RFPbUvcih5hUAhKIeVu94mJuX/I6ivEn9n4HWypLp9zGp6lrC0V6clmpc9sxGR0V5U7h16R/7kg+tFDsnZ+QXJJJRNu75fYbT4tHW1Xj8DcNaJA9FddFlXDf/x+w68i9qShbS0LFx0Jyk/N718ciR40xwQQsDAKdBzbIKK8sqBmc2WzUS90wvxB9PUmhUU2HRoFf1RwziSYWDvRHqvFHsWolxdh1Ow8jh3N5oMr1YHyOcVNKiAFJtnNe3BYZtzlRs0mSYIAXjSSQBdMddo1UrolcJhBOphfLP+3r40sxCIkkZg0piklN33pY3CoKItmQK2pIpGeMqazFIGkj2507oJyxDVzmHvOu+R2DnC2hLpmCceCWSKXtdvdpRTv4HHyLR24yg0qKylyOqUwJKVzoNXem0d+U9HWpekRYFAHvqX2Bi1fuo0GU2NZJENYWOiRQ6JmaMJ5MJNu37U1oUAGza9wfGli1Jz40nIhxpeSvjOFlJEEsEB1UtmAwuTIbMsHwo4sbtb0RAwGGuoDBvEoXHCYrjURSFeHJw9Cub0+Fo0GktjC27nLFllwMpT4aUlXLq+23SF4zKLjpHjhxDc0EKg2Z/lG1dYdyRBDPzDYyz6weV+LnDCX72TjtvNqV+YAsNKn62sJxKa/+iu60ryBffakq3X15UauIrs4uwjJAv4NBJFBpUGQ6FAx0VIVUGORqCsSTr2wM8sbcHq0bi45OdTHUZEAUBEYVPTs3nt7u6CMZldCqBGquW8SNUL5zPqPIqyL/5F7hf/xFJbxumGTdinHgFKpMT1aQrMU66clTnkQw2JMOZbdHcnSUMHs2ynTAUSTmGx98w+Bzx/nwMtUpHTckCOnsPpMdEQYVZP/K+vzfYysvrH6SlexsAVYXzWT7n65gN2b0k1Codl037LKve+Qkdnn0AFNgnYDefnu2XYuc0bl7yOw63rMKkz6eqaB5W47vnz5Ajx4XABScMOkNx7l/dTH1fJr4owMOLy5men7k/f8QbSYsCgPZQglXNPj5iTd09heNJHtvdlRYFAKuaA9w2PsakEYSBU6/mu/NL+d9NrRzxxpjuMjDVacCikdIuiioRLi0ZvqviMXZ2h/mfda3pxztWNfLYsipq7Trebgvx1L4ebqy1oxYF3JEkofjJ3a2dqyiJOHF3A3LIg2QrQm0rRVcxm4Lbf4cSjyKZ8s64z8DJMqH8SvY3vJp+rJK02M2Voz5eozYwY+yHeG1Tv2eDUefEbuov4RQEgUlV19Ltq+NQ038waO0sn/0gjgFdGhVFxuNvJBTtxWIowGIsoqVrR1oUANS1v01bz27cvgba3XtwmCsoypuCyeDCH+pkf+Nr7Kl7gQL7BOZO+m8iMR9l+TMw6bNHao6n19/EoZa3aOnaxtiypVQUzsGoy8uYo5I0lOXPoCz/3O+YmSPH+cL58Wt5GqnzRtOiAFKVCf846OYiV6Y/wfFOhMdo8vUfpwCxLOvrwNw+fyzJ9q4Qq5r8jLVrmV9sotSsZWKenl8uqcAfk7HrJIxqiV8tKWdjW5BwQuaSYhPjHaOrfnirOfOOMiFDgz9KrV3Hkd4IXeEEj+/t7wkwzfXeiRYocoLgnpdxv/I9UJKIOguumx5GWzIZSW+D8+ytlrpmcO28H7J5/xMYdHnMnfixQS5/I1FTvJCrLv422w//A5e1luljb8YyoFOjzVTKVRd/i8umfBq1Sjfojl9RFA41r+TlDQ+SSEYwaO3csOAh/OHBvQ58wVbW7PwVSTn19zGp6jqWTL+fXUf/xbrdjwDQ7T1Mc/d2brn8sVGJgkjMx2ubv0NT5xYADresZOG0e5g94c70HH+og1g8hMngylqlcQxvsJWWru34Qm2UOqdT6JiISvXe70CaI8fJcsEJg2zeRCpxcHFGuVmDThKIHLfSL6vsz0MwqCU+OimPr6xtSY9Nc+opG1D2t6bZz3c3tQHwSj283uDjJwvKsOtUWLUqjs9lrLHpTqplcraESGtf2+eFpRZeONpfdikJEJcVWgOxEZs0nQ8k3E24X/tf6NuzliM+PG/+HNdNv0DSDq7SOBtEYj6au7ZxpOUt8u3jqSqcN8il8BhajYlx5cuoLr4UUZCQpBP/PzLo7EyquoZx5cuRRNWgVsqReIBeXyNJJY7dVJ41CdAbbObVjd8k0ZfxH4p6WPHOj1g648usAcyGQqbWvB9FkbGZysizVKW3JvbU/ZuZY29l55FnM88ZaMIbaBldtCDQkhYFx9i8/3EmVF6NUeegrm09r278BqGom/KCOSyd+UDWro2hiDtj6wPg/Qt+QXXxpSNeQ44cFyoXnDCotmqZ6NCxt68jokqEG2vtg9wMq206frmkgr8dcNMTifOhcXlMHVC+OLvQyMOLy3mnM0SJSc1FLgN2Xf9H6osleHJfZve+fe4IzYFYxrxTZV6xmZfrvBzqTSV5va/Kypg+gTHVqedrc4p4+oAbg0pkeYWFP+7p5tpqG3dOHPkH+lxHjgYgmdlZL+FuQImF4BwRBoeaV/Lapm+lH4/GpVB9Gtogq7KIimC4h7d2PMTe+heBVN3/VXO/lWFjDCnL5Vgis8yxp/cweq2N9y98mECoixVbf9AXJRCYN/kuYolQX+KkgCSqsJvKCYQ708eLgmrUDoUqUYMgSBnVDDqtDUlU4/E38u+370uLlsaOTWw98BRLZt43yMvB7WvIEAUAG/Y+Rln+zNPyGefI8V7kghMGTr2a78wv5YA7jD+eZKxNN2Tm/8Q8Pf8ztxhZUVBnMQ/SqyRmFhiZWZB9AVIJAmbN4OM0p8n98BhlZg0/W1hGkz+GRhIoN2sx9UUMTBqJPJ1EtVVLOCHzi+0dJGR4uzXAbePzTpsT49lCZS1CZS8j4enP5DdO+y8kU94wR505QlEPG/Y+ljHW3LWN3kDzCdkXny46ew+kRQGk6v4bOzYPEgZmQyF2c0VGIuP4iisx6vPRqI08/c7H01sHoLBp35+YPf4O1u/5HVNrbsBsLGLBtM/xz7c+1Zf4KLB4xpewm/q9FqLxAGpJjygO9gaxmcuYP/ku1u76NZBqArV4+r3otVa6eg+lRcEx6tvXEY0HMGgzox/Z7MtTxk3n9/c+R453kwtOGECq42GRUU1CVvBGE8RlJWtVAIAkCkgn+SNiUEvcNTWfe1Y1pnMPPjDGTum74DKYp1eTp89eKpmnV/Nmky8jUXJhifm8FwUAksmJ8wM/xbfhCWKtuzBOvgbj5KsHhc/PFqKgPimXQllOZl0wT5VwtHfQWI9vsIWxUefguvk/Yt3uR2nt3sG48mXMGHsLKklNKBLBH2rLmJ9IRnDZxnHDZf9HoWMSGpWeIudkbl/+FL5gK3qtDbu5AklS4wu2s7f+ZfY3vkqpawbTa28mz1qVcT6VpGF67Ycoy59FINKN3VSWzrUw6V2oJV2GsVF18QJ06sHJug5LJRWFl9DQvr5vROCSSZ9AncsxyJFjSC7YJkot/hh/O+jmzSYfk/P0fGKKi2KjmkZ/jLisUGrSYDsN4f6krHDEG6XJH8WmTZkJnaj98akSTyqsa/Pzo83teGNJrq608tHJLoqMJ2ahe6rIvVGQRETz6X9dJZlAjoWQ9BYURSbhaUaJBZEsRWe85HAg9W0beGb1Z9Nh8Rm1t3DptM+gyRLK7g00s7/hNera3mZs2VJqS5dgMZ6+hkTtPXt46j8fRlH6k2tvXPgrKosuyTo/nowSiwXQa62IoopkMo4sJ3h792/YcuDP6XnFzmm8f8FD6DTDtwyX5QSrd/wi41iXbRwfXPTrdK7DsWqIQLgbsyEfm6ks485fURQaOzbx6qZv4Q+1U1OyiEUX3YPdXE48EUUSpQw/Bn+og/aePQQjPeTbx1NgH48kndnvfo4c5yC57orHE0/K/HhLOy8e1wuh3KzhjgkOvrcplXU9pW8b4XzsITAUXaE40aRCvl6FRnXm7qjlQJzkhi4Sr7Yg6CRUN1YiTbIhqAdfgxyPgpxE1BqynGlklESc4P7X8bz6fZR4BHXhePKu/S4aZ9XIB79LJOU43b2H8fgb0evs5NvGZt1GiMaDvLz+axxpXZ0eu2jMzSyefi+SdHrEZFKO09SxlbW7f0M8HuSSSZ+gqmj+iHv/spygpXsHm/b+iaQSZ96ku2jt2cWBxtcoL5jDlOr/ypr8NxBfqIM/vXzjoPyF25f/OW3AdKR1DS+8fT+JZBS1ysANl/2c8oLZg84VCHcTT4Qw6V0kkjGOtq1l28GnsZsrmTX+dgrs40/gk8mR44IjJwyOpyMY55aXj2RUHAD892Qnvz+u2+IXZxRw49jTZ3d7oZJ4p4f4owf7BwTQfHkKUmX/YqQoMtHmHXjX/g454sMy9yPoqy9BPMEEwljnIdr/cFu6SgHANOOD2Jfdh/AuhOZPJ93eI/zplQ9mjImCio+97zlsppLT+lrReBBFSY54h3+Mtp7d/OU/Hz0uGVDgtmWP47TWopI0o25FHon5+Nubd9F1nLmSStJx5xVP47BU4Au28+Trt2ZseViNxdy67AmMuqH/FvfVv8xLG76efqzTWLht2ZPYzWVDHpMjxwVOru3y8RjVItUD+gMYVCJxOVMoHPBkJjjlODmSuwY0zFFA6cr8bOOdh+n8y91E6zcSb99Hz7++TLQ5s+XvqF4r6M4QBQDRxnfSrZnPZVSSFrUqM1Ji0NlRS8P3sojFw8jyiZlWadXGYUVBINSFx9+UtjPu6j2UUSEACg3tG1GrtKMWBZBasC+feX+6IkAQRJbO+ko6+TGWCA7Kg/AG24gnQkOeM5mMse3wPzPGIjFfhrV0jhw5Rs8FmXxo0kh8cUYh961uIppUmFdk5JpqKz/ekmnesmCUzoM5hkeqNiOv78oYE6yZe7zxnnoY0PwmdOBN9DXzRjy/HI8Q6zhAwtOEylqMoNZnCAHDxCsQtaMrkzubWI0lXD7zfl7d+E0glYm/dNbXMA5R9x8IdXGo+U121T1PoWMSM8Z+6ITNkAYiywnq2tfz+qbvEIq6GV9+JfOnfBKdZnAvEdNJtE6GVLnmHcv/gi/UhlHrwG6pSidamvQFlDinZ5QY1pZdjlE3dGmtKKpwWqpp7d6eMT6c6VGOHDmG5oIUBpAqRXx0WSW7u8P89YCbJ/e5uWdGIS8c8XDUF+POiU4uyj+5fe4cmYgTrYhT7cg7PSCC6qpSxNLMz1bUD757VbtG1wwnfGg1Pc8/kDqP0Ynjfd/A9/ZjJHqbMU67HuPkq0/9TZwBBEFgXNlyXNZaAuEuzMZC8ixD50bsbXiZ1TseAqDTs5+G9o0pZ8EBTY9OBLe/gefX3ovcJ9L2NbyMw1LOpMrrKHPNoqkrZTpUYJ9AqevkbYgdloqsOQk6jYkr5jzIlgN/pqF9AzUli5hee/OwVQSCIHJR7U0cbllFKOoGYOa423EM89kdQ4kmkI8ESGzsQnTpkGbkIRbn/u5zXNhckDkGx9jYFuALb/WHGyUBfru0ghKT5oxXDrzXUcIJlO4ISCJCvg5hQPJjMujG8+b/Edr9EgAq1xhc7/8xakd5ttP1Hxfoof1Pd5D0d6THBLWegg8/gag1IBnzEM7zDPRAuJtwtBejLi+duR+KuPnzG3fgC2aWDd5y+R8ocV100q9V17aOZ976TMZYvn08t1z+GPF4mB5/PYqSxGGpSjsY+kOduH31iKKKPGvVIC+BkyEpx4nGA+jUllGXbXoDLXgCTWjVRhzmqlGZKSV3e4j9cn//gFWN9r7JiM5cOWOO9zxD7gFe0Kvf262BjMdJBZoDcSbmZd4xhOJJeqNJzBoRs+aC/shOGkGvQigb+odaMjqwL/sS5hkfREnGUDsqsrZEVhSZpK8DBBGVpQBFkVESmW19lXgY5AQqy+kr8ztbtHTv5KV1D+ALtWE3V3DNJd+nwDEBtUqP3VSeIQwEQURziuFzkz5/kONgVdF8VJIOtUqPQZ+ZAOjxN/Lc6i/i9h/tm3spV8x5EJP+5KMWkPJ5OFGBYTWVYD2BJE1FVkisyBRWeOMo7WHICYMcFzAXXPJhayDG2y1+3ukIUpalnv5Yj4Fj1HmjfHVtMze+eITPrWxkv/vcT2I7X5F0FrQlU9CVz8wqCpIhD/6NT9L2uw/S9tjN+Lc9i6gxYLn0ExnzDBOvRGXP3ovgfCIQ7k6LAgCPv4HXNn+XSMyHWqXn0imfzhACC6elavlPBYelgmvm/W86p6CmeAFTqq8fMsGwvn19WhQA1LWtpdOzP+vcbMTiQerb1rNq28/ZeeRfeAMtIx90uhAAW5Zy5CxupTlyXEhcULe/R71RPr+ykZ5Iav/0vpmFjLVrOehJ3XEuL7dQe1wTo1A8yS+2dbAtQ3lpAAAgAElEQVSpI5URfcAT5cG3W3hkacWQLoM53j2iLbvoXfmL1IM4eF79HmpHBcZJV6GylhBt3IKmYDza8umImvN/nzgc7U2LgmN0evYRifnQaSwUOSdzx/I/4w20otdacViqsvZHOBEkUc24sqXk28biD7XT1rOHA41vMKZ0MXkD2jJDqtnRQFIWyCmSyfiwZkJHW9fy4voH0o9LXTO5/tIfnxG7aEEQUC0qJPZOD0RThk/iNDtCUa6HQo4LmwtKGLzV7EuLAoCfvtPOr5eUoyigEgXKLVrMx0UMvNEkm9ozjVhagnF6IsmcMDgLxFp2DhpLeJrQVczEUHsZhtrLzsJVnTj+UCfxRBizIX/YRj5GnWNQv4IS53T0mv5F024uP+UoQTY6PQd5Yd396ce76/7NTYt/i3lAJUJN8QK2HudiKIlq8izV+EOdHG5Zxd76lynNn8GUqutwDBAWkZif9Xt+lzHW3LU1ZQR1hvpISBUmtF+Zkto+0EoIJQbE95CpWY4cJ8MFJQw6gpld+GQFeiJJFpdlr+c2aSQm5+nZ1dO/fZCnU2HTnttGOe9V1IWDnewka9FZuJKTQ5aT1LWv47VN3yYU6WFMyWIWXXQPtiFMeAw6B9dc8n1e2/xdOj37KHFOZ+msBzKS6pLJOL5QO6KowmIoTIf8E8k43mAzKCmDIJVqeC8EWU4QjLjRqA2IgsTm/U9kPO/xN+DxNwwSBkV5k3n/gl+wef+TaNVGZo+/E6d1DBv2/p51u38LQFvPThraN3Ljwl9mtHgWBBFVFmEkCmf2Z0ksMkDR+R9hypHjdHFBCYOlFVb+fbTfBtmgEqm0DP2DadZI3DurkG+sa6HBH8OpV/HtS4rJN+SiBWcDbelFmGbeTOCdf4AgYpn3cTRZxEI2lGQCRU4gqk89qSwRdCMHuhH1VlSWglEf5/Y38O+1XyIppwTq4ZaVOCyVXDb100M2fSpwTOCmxb8hEvOh19gyREEg3M2W/U/yzqGnUYlaFl50DxMqriKRjLL14F/YvO9xFGQuGnMTF0/8WLqKYCDeQCtbDz7FnroXcVprWHTRF7Aai2l3786Yp8pitKRW6aguvpSKgjkgiEiiikCoi22H/pYxr9OzD1+wNUMYaNVGLpvyqb4+EqlQ/sTKa7BZTn8EJEeOHKPnvClX7AzFafTH0EkCFQNC/qMlkpDZ1R3i2UMe7DoV19XYGO8YeT/RE0nQE0lg1Ui4cqLgrCInoiR7W1NVCbbiUZUiRlv34NvwOElfB+bZt6KrmYekOznzqljHQbr/9QAJdz2iMQ/ndd9DVznYxz8b9e0b+OeqT2WMOa013LL0jydlxrOv4VVeWv/VjLEPXf4Y8UR4UMnh9fN/Qm3ZEgDiiQiKoqBR61EUmTU7f8WmfX9Mz9VrbNyw4CH+vvL/kehzPpxYeQ1Lpn8JnXZkC+VIzMffV96dkYQoiio+fMXfBnVRTCbjdPUepMdfj0FrI982HqP+3GiZfT4id0WQj/pR/HHEajNihREhS8v4HO8uPZEgjQEvalGi3GTDohk+YneWOLfKFX3RJLt7wuzqDjHGpmWqy4BrmD37Om+U+1Y30dq3FXBNtZVPTc0/4e6HOpXI7EITY+261LaCAOFEEr1qeJFh16mwn4ZOi2cDRVZQmtuRjzaBTodYXYqYf/72fxBVWsQTaIgU766j8+m7UWKpBNKef38V5/t/jGHckmGPS0b8JL2tCJIGlb0UQVKTjAbxrPgpCXc9AHKwh+7nH6DwI39GZR25NNKkz0cS1emIAaTaBWtUJxfGburcOmjMH+okGvMNGu/yHqK65DJauraxbvejxJMR5k78OIWOSexveDVjbjjWS1KOcduyJ3H769FpLListaMSBZCyPV48/V6eeeszaWGx6KIvYDMPrhSRJDWFeZMozJs0qnPnGBq5N0rstwdQmvvsowXQfGES0tjR/b/lOD00BXp5YNPrHPSm+u5cVTaWz0+eh0N3/mxXnfHVzhtJ8FK9l19u70yPva/KyhdnFgy5QK9o9KVFAcCLR71cWWllxkks1g2+VPlhnS8GwO3jHdwx0XlSEYjzAbmxjfiv/gLJvja7DguaT34IMe/stiI+U8TdjWlRcIzAjn8NKwzinmY8r32fSN1GEFXYFnwS44wbUSKBQf0b5JAHOeyBUQgDh7mC6+b/OJVjEHVTU7KIqdX/NeQ2wkiUumaw88gzGWNmvQujbvAdd6FjEl29h/jHqk+nPQqeX3svH1z0G4ryJmdUP0iiGr3WjtNajcs25qSv7Y7lT9EbbMWoc5BnqUYSc9G2dxOlPdwvCgAUSKxoRRxjRhBH388ix6mxvqMxLQoAXmk6yNXl47g4Jwyy0xOOs6rZz+N7uzPGX67zcss4B9W27IvzEe/gZkb+2Ik1jTnG6w2+tCgA+PN+N/NLzExznT//aSMh9/QiH2pE6fEgFOQhlBWh1PeVlbl9KG3dcIEIAzHLloE6f+ywx4QPvZUSBQBygt5VD6Mtm446fyz66vmED61Kz5UshYhZPBeyXosoUVOygDuu+MuoqhJGoix/FjPG3sK2Q39HJWlYMPVz5NvHoSgKy2Z/nTU7foEsJ5k35W6K8qZwtHXNgEZI0NC+kXlT7qbDs5/eQBMqSccVc/4HxylWOgiCQJ61mjzr0LbWvmArHe79xJMRdBoLOo2NAvu4YcsbcwxDto3fE2hwleP0cNTvGTTmjZ1fDfnOqDA41Bul0RdDPUC9CgJDGqjs7g4xzWXgreb+2midJFBuPrk9m709gw2KfNGTExnnIrI/SPzJF1Aa++8AVdcuItHaCbG+qMsF9FuhdtVgmv4BAttSd9aSuRDjpKuGPSba+M6gsWSgG23pNGyLP4siJ4gceRt14TgcV34NlenEXP4GZvaPlmCkh8aOTRxqfpMS53RqihewYNo9TK+9GUGQsBqL039H02reT3XRpSiKgtmQjyAIWUsAbeZy8ixVfOjyx/CH2tGqzdjNZcNGMRLJKIIgIYkn//PR62/imdWfS5diWgxFTK6+nngiSEXhxSd93gsZociAUGFEaegrsRZBdXlRLlpwhllYVMWzdXvSj1WCSIXp/LoRO6PCIBhPsqLJx421Dh7d1d9t75ZxDkqMg+8S2oNxvrymmbF2HXdPdbG62Y9dJ/HhCU6qrCcnDK6usrLxOG8CjSRQksUB8byly50hCgCS2/YjjqtE3nUIId+OUHRqdrVnCzkaItq6k9De11A5yjHULkI9Qr6BpLdiXfQ5jFOuRZGTqOxlqEzDJ7cZxl+eERVAEBFNTkKH1iCotNiv+AqCIiPqzIi6M7N/qygKu4++wJqdKYOng00rONq6lmvn/3BIH4OBAiTfNo6xpZdzsHkFAIWOyZTnzwLApHcOWbVwjEjMT0P7BrYceAqLsYjZ4++g0DHxpN5Pm3tPhj+DL9SGLMc50rIGjdpILBHCZizFaio+qfNfiIg2DZpPjEWuD6AEEoiVJsQy49m+rAuOqY5C/nf2cv508B3MGi2fGD+bMdbzK6H2jAqDCouWQFxmQ1uAz1yUTyAuM9am5SKXAY1q8B1KdziBJ5pkY3uQXd1hpucbSMoy+VlExFBEEkkCcRmbVoVKFJhTaOTLswp5+oAbl17FJ6a4KDGq2dEVZF1rEKdexZxCIxXDlDEewxdNoMC51XBJzHKnp1Ejzp6MdNF4hLIiRMfgFrrnKnLETzLoRtSZibXupuufX0g/F9j+HAW3Pjpi4p+kMyHrzAT3vUFsw+MYJ1+NrnIOkj7756CtvBjrwk/j3/gkot6Gbcnn8Kz4P+KtKYMlbdUl5L3vm6dVFCiKQjDSg0rSocvS/CcY6WLLAG+Bho4NeIMt6DSjK9k0GVwsm/11Zo2/A1lOYreUZ81HGIqmzi28sO7LQMqboK51Lbcvf3KQcdFoiCdCg8b0WjuxeIin3rgTAIPWwY2Lfk2+ffitnxwpFEVByNOiyvV5OKsY1RouLx3D3IJyJFFEJ51D68MoOaNXXGPV8qvF5Tx9wM361gC3jM9jer5+yKRDh07CrBbxx2VCCZm3WwNcWmzCMspEwcO9ER7Z2cm+nghLyix8aJyDErOG68fYWVxmRiMJ6FQSWzuCfG5lY3qLrsio4ldLKig0ZndAiyRS4uaRnV0kFYX/N8XF/GITBvXZT2AU8h2IU2qRdx1KDYgCqivmIdUObnF7rhPrOoL7le8Ra9mBYfL7Us2TjiPZ20LcXT+iMEgGuul69n4S3UcAiBxejePq/8E07fqs81WmPAxTr0M/ZgGi3kpg5wtpUQAQrVtPvGM/gmoqUpZ20SeKP9TFrqPPsf3Q37EYi1g0/YuUOC/K2F4TRTVajYVwrLd/TFAhiRrCUS9dvQcJRnqwm8px2WqH3KfXa63otVNP6jp3Hnku43EsEcTtbxi1MFAUmQ7PAZo7t2I3l6OStOmqBVFUYTYUsnLbT9LzQ1E3Ww88xRVzHkQ8hW2L9zqyHKe3Yxf1u58GQaRy8i3Y8ifnPrMzhC8Wod7fS0xOUG6yka9PCXuj+vx10Dyj3xxBEJjkNPBNhx5FUVCPUF9bbNLw3fklfHtDGz2RBOPsWu6emo8uS3RhID3hOF9d20xzILWv/sxhD3FZ5t6ZhaglEctxd/nPHHJn5O20BRPU+WJDCoMD7ghffbvfI/4b61t5eHE5MwvOfthOMOhRfWAZysVTIRhGKMxDKB69CU82ZHcv+ENgNp5StEGJJlHCSQSLesR9TzkepvfNh4i1pKoAEp4mJP3gfTphBEc/gLi3DdPkq1GSMQSVjsDO5/FtfALDuMWD7voVOUn46Nu4X/4ucrAH06xbkCP+QeeMdR3Gs+LnWBd+Cn3N/FMyTjrcspJ1ux8BUovhP1d9mjuWP5VR82/Q2lky40s8t+aetBnQvCl3YdYXsGHv79m8//G+mQLvX/AQ1cWXDvuakZiPYLgbrcYy4hbCMZzWGura1mY81qpMNHVuxWIsxmoc3oWy3b2Xv674OEk5jknvYvH0L9Hde5iEHKOqaH7WY7q8h0gm4++pRS4RDxMNdSGpdOiMJ5dvcjy+rv1seOku6PtedNSv5JLr/4jNlSsBfbdxR8L83661vNqcuhGrMFn5ydyrqTCfeuvxs8lZ+WtTiQLHZ8C5Iwl8sSQOnYQnkmS/O4KiwHiHjtmFJh5bXok/lsSlV2Us6MPRGUqkRcEx/tPo56OTXRQYMoWFQT1YaKiGyeY93Ds4w3RrR5ApTj2ac8BMRLSYYOKptd89RvJQA/HHn4dQBIx61B+9Aan6xDsXJhsCJJ5tQG4No7rEhbSwADFv6MVUDnmJNGxOP4617MK+/H4i9ZtQEqnPXz92Ceq84XMM5HiU8IEV+Dc+mRoQROyXf5Fw3QbIclcddzfQ/ez9kEx9dwJb/0betd8mtPvF9BxBawI5QcJdT89z95N/2+/Qlc8Y9WdxPIlklD11Lw4Yi+ANtgwyA6oovJjbl/2Z3kAzRn0eLmstvlDrAPtihVXbf05R3hT02uwirtt7hNc2fZu2nl2YDQVcNfe7lOfPHPFaJ1Vdw76GVwmEO8izVDGp8lr+seqTyEoCg9bO+xc+PGzOwcHG/6Q9HALhLt7Y8j1uXPQbKvuSDXsDLeg1toyoyEVjPoha/d5pahT0NXNg40O017+JRp/HlAUPkl86D0E8+Wijp2NHWhQAKEoSb9fenDA4Axz2dadFAUBDwMva9gYsGi3dkRBWjS4dQTifOOsyfFd3iO9saKU5EOeLM/L5094e3JFUlYBFI/Hry8uptuqy2hAnkgodoThqSRj0vEUjYVCJhBL9fzBjbFpMapGjvRFWt/hxh5MsLrdwY62D/zT6iSVTcYMpTj1V1qHDQAVZchw0kshRb3RUTornC7I3QPwvL6VEAUAwTOKvLyN89jZE8+ijI3JPlNgv94E/1cAq8XorSALCdWVDVqOIegu6illEjq7rG1Hwb3uW/NsfJeFtQ9SYUOfXIhmGz/ZNeJrwb+xv8oMiE9j+LPZrvoWYZcFJ+rvSouDY/NDBVbhu/iWB7c8h6ixoXDV41z6anhLr2HfSwkASNZS6pg+yHzZk2fuXRDUFjvEUOPpzCpKBOAPr1GLxILKcIBvxRIS1O39FW88uAPyhDv699kvcccVTWI3DJ/o5rTXcuvQPePyNSJKGv6+8C1lJvU4o6mHtzl9x/aU/GbIEM9vil0j0VwnZTCV8cPFv2LTvcXp8R5leexM1JQuHvabzCUVRaD7wPO31bwKQiAXYueobzL3295jtQ5d1DkSWEyiKkt4u0ugHG5aphxCFOU4v/ng047EkCBQaTNy9+nnqAh7ytHq+M3sZs1yZN1OKouCLRTCoNKils78FPZCzKgy6QnG+9nYL3eEEKhG8MTktCgB8sSQ7usJUWwffWXaF4vx5fw/PHvJgUkt8cWYBC0vM6STGYpOaBy8u4lsbWokkFfJ0Kj4/vQBPJMFnVzbi6StRfOawh4cXl/P7pZW0h+KYNSJFBjXOYZwYxzt0LC4zs7IpFWKeW2SkO5ygPRh/TwkDwhHwBjKGlO5eiEThBISB4o6mRcExkpu6kZYUIQxRESJqDNiW3ENP2Ee8bTeStQTHFV9BWzQJbdHo74SUZIyBC6ccDaAyZ99ekcyuVCThOHGgspeiq7oYffUlhI5uoPtvn844RmU5+cx5QRCYUvNfNHRspKv3IKKoYtFFXyDPMjp3R5uplIqCuTR0bEiPXTzxY0PaCkdiXho7Ng8aC0XcIwoDAIuxCIuxiJau7YPER7f3MLF4aEhhMLbscrYdfJp4MtJ37WU4bbUZc/Lt47jq4m+TkKNo1Wd/a+50koiH6GxYjSCqqJn2URAgmYgQCXZislaMGDVQFIXezt3U73qKWMRD1dTbcRTNwl4wFbOjFr87dedqyZuALT8XLTgTVJjsaESJmJxaT+YXVvCPo7upC6S8DHqiYb666XWeWPxBCg0pT5W2kJ8X6vfxSvNBJtsL+Mi4GdRYzq2qhbMqDDzRJN3h1I+LgICcxaAjmW0Q2NAW4B8HUx++N5bkG+tbeWx5JRP6FmZBELis1MzjV1TRG0uSr1dTYFSzptmfFgWQWjJePNrLxye72N4ZYq87zLXVduYWiUPaIDv1apaUmRlrSwmWfe4wzx72cHXVqat0JRgGtQpBc/ZLKAWLCaGiCKWhv/xRGFuJcAKiAEAwqkASINn/fymUGxF0w2+7aFw1FHzoYZKBHkSdBWmEMsPjSQbdJENuRL0NXfW84yIPYJn70SFLFtWOCpw3/Bj3K99BDvagH7sY07R+d0KNsxrD+GWE9r8BgGHCFWiKT+1HOM9SxQcX/QZvsAW1yoDdVDYqk594Ikq39wjTxnyAyqJ59HiPUFOygFLX0NELlaRj8Ywv0eM9yr6GVwhGujFo7SdUnQBgMRZjNhTiD7WnxyZXXY9B58AXbCMaD2I2FKDT9BtMFTomcsvSP9Hh2YtK0lHomITNVDLo3JKkQjoPM7lHQqU24Cq/DJcyj9YjrxDyNQNQv/tp5l7zO+wFwyeFBjyH2fjSXch9CZs9bVuYc/WvcZZczOwrf4HfcxQBAZO9Gp3x/CxJPt+osTj49aXX8/jBrfRGI9xaM43PrnshY05vLEJvNEKhwUxSkfn7kV08dXg7AC1BH3s9HTy64P3knUPOiGf1r8+hlXDpVXSFE8RlBZ0kYFKLBOKp8L9BJQ7pSPh2a2DQWHswnhYGAKIgUGbRcnxTW7U0OHRt1kj8aEsbmztSJVTbu8LcN6uQG8YMnUAyMc/A1o4uYjKoBPjK7MJhtx9GQvYFkLfuJbl+O4LLgerKSxHLRrbZfTcRDDrUN19F4pU1yIcbEcdXoVo+H0E3fMKfIssIx5VNCgU61LfXEH/qCCRSJVXq95UijKKKQ9RZTrgsMNq2l57nv0bC04jKXobj6m+gr11ItGUnhrGL0JZlXzgVRSHWtpfQwVWY534YbcE41PljMyoPVJZ87Fd/HfPcO0EQUNnLkLTZ9xATiSjBaA9atQmdZvj3YNDZMzoPjobmri0889ZnAdCqzdQUL6TEOW3I3AKPv4mV237C0dY16DU25kz8CHWt65g35W4sIyQODsRsyOeGy/6PjXt/T7t7H5OqrmVS5bUcaXmLVzd9i0jMS4lzOlfMeTCjaiHfPvaCLT8UBIGy8dfT1bQuLQoAFDlB04F/jUIY1KdFwTHa6lbgLLkYnTE/I5FRkZPIipxzkTxNRJMJPNEwJrUGk7r/908QBKbmFfKDOVeSVGQUYH5BBava6tJzCvQmnPrUOuaOhHixcX/GuZuCPtpD/pwwOIbToOb780v49oZWmgJxNrcH+fa8Et7pCKKRROYWGam1Z09Qm1NoYnVLpjjIN6TejqIoQ+5d11i1THXq2dmd2ts0qkVmFhj5x6FMG8u/H3CztNyMWZP9I9JJAmNsurQfQrlZg+4UEg/lHQdIvLAqdf3dvcQaWtF84c6z3tNALHSivu0alFAEwahHUA/9lVHCUeTDjSTffgccNlTzLkIsLUCQRKQ5TsQqE0oogeDQINrenW5jyVAvPS9+k4SnEUjlGLhf+Q4Ftz+GecaNwx4b7zpE51/uQkn0//jm3/II0oDuiZLWhFQ0vLGPx9/E27t+w8Gm/5BnrWHZrAcodp5cmWA8EcEbaAFBwGYqRSVpiCcirN/zWHpONO5nb8OLTK25AYNu8J6zoijsOvo8R1vXAKlGSWt2/JJblz1OoWPCSV1Xvn0sV138HWLJEHqNDbevnhfWfYWknLIcb+nextYDf2HJzPtyfRL6MFrKCJgGi7DR9MtQaQZH6gxZGlN5u/dTv/uvhHxNVEy6CVfJJajPkBHXe5EGfy+P7NvIW611TLC7uG/qZYy3Z1aTqCUJNakbnU9NmktMTrKuo5FxVhcPTF+IU5f6vzOoNFSabezo6Y+0aUUpQ2ycC5z1eN0kp4FHllbSHU7w/BEP961uQq8SEQTojSYY79D3VTFkMq/YyFU9Vl6t96JXiXxuej5FBg0rm3w8f9hDpVXLtdU2amyZwsJlUPOtS4pZ3xakN5pEANqCsUHnLzSqUWczC+pjS0eQn2xN1dW3BOLcs6qJPyyvpNp24mVrSiRKcsPOzMFQBKWn95zoaSBo1KPa2pCPNhL/47Fa9wZiO/ajuedORJcdQRIQCt/9/As57E37FRwj4W5ADvciGYe/I49312eIAoDQwTdH3Vb5GMlkgs37n2B/Y6prYVfvAZ5bcw93LP8zllHs4x9PINTFut2/ZefR5xAEkVnjbmf2+DtRq/SIWRaToQRxLBFKi4JjyEqCcLQ36/zRolJpUfWVjAYj3WlRcIyGjo3E4qEhoxgXIhZHLUZrFUFv6q5SFNWUjr1uxOPMeWPJr1hIZ8NbAOjNpRRULMiYE/Q2senlTxGPegHwdGxn+uU/oKh62Wl+F6cf2RNF3ukhua0HcYINaUYeoqv/91QJxpHrAshNQcQSA0KlCdHy7noFxJIJfrtvIytaUr8pu9wd3L/xVR5b+AFc+uxbqpVmO9+ZtZSeSAiHzoBF0/8ejGoN90yezz3rX8QbiyIJIg9MX0Sp6dwSbmddGECqrXFbMM6zh1M/Use2Et5o8HHnRGfWioRCo4b7Zxdy58Q81KJAkVHNm01+HlyX8hfY1BFiRaOfR5dWUGTK/PLYdSpequtlT08qCeoil57ra2w8fyT1+nqVwH9Pdg7rl/BKnTfjcUxWaA7ET0oYoFYhlBagtPXbRCOkQvnnE8l12zMHwlGUzh5wnbmaXtFgR104nnh7f7hO7apFNI7calrUDv5DV9lOvDQzHOvlcPPKzLFoL/5QJzqNldaeXTR1bsFhrqIsf8awYfyW7u3sPPoskCpD27z/ccoLZlNVNI+5k/6bf676NMeSKysK5mK3ZDey0qgMjClZSLe3v7QqZSp06nX0xzAbClCr9MSPqzSoLVmMVn3+lWu9m+jNRcy64uf0du0hEfNjc03G4hw34nE6g5Oplz2If8rtyMkYJlslelPmdmPAczQtCo7RsPefFFQuPqe9IJSkQvLNNhJvpPKZ5P0+5MM+NB+vRdCpUGSFxNpOEs81po9RXVGMcG0Zwih8bU4WdzTMmrb6jLH2cIDuSHBIYXCgt4vH9m/hiM/NdRUTuKp8bEbJ4iRHAX9adCPtoQB2rZ4ykxXpJDusvlucM98Us0ZEJwlEjktQG2PTYcriMXAMrSSmrYvjSZm/H3BnPN8TSdDojw0SBhpJ5IYxdvb0pL6E27vCWDUiDy8uJ5KQKTVrRrREnuTUs+G4ngsANu3JlZ0IkoRq4Sxihxqg15/au752EUL+uZWpOiJOB1CXOaY7s+5fkt5C3tXfoOeV7xFv2426aBJ5V31tSPvj41Hnj0M35lIih1MmPpKtDH1NduOd4dBqzBQ7p3G4pV8cqFV6jLo8jrSu4aX1X02PVxZewvsu+f6Qd9Rdxy3kx/D1JfyVumZy69I/0uHZj1GXR6FjEgZtdhEmCAKTq6/DE2jiQOPrGHV5qf1/c+UJv7+hsJnKuOGyn/Papu/iC7UyrmwZ08bciHgKNfrvVYzWMozWspEnDkCjt5OnH/x/HIt4aT3yCnJycJmq0VaJIJzb/wdKb5TEyvaMMXlXL0pPFKFEheKJknipOeP5xH/akOblIxS8e5FIs1rLZHsBW3ta02NWjRabJvtNW1vIzz3rXqQnmhLHv9q7AUGAO8dm5jWVGK2UGM/dKNo5IwxKTBoenFvMtze0Eu0rL/zs9PxR2wwLgkCxWc2uAd0Ts5kXAcwrMvHVOUX886D7/7N33oFxXXW+/9w2van3ZkuW3Lsd1/Tq9ISQEFh6TUIL7Ftgy3vssiwLPFgIZKlheRASSgglnXTHjuPeuy2r9zKaPvfe8/648khjjSw7kWwp8fcvzZk7954puud7fuX7pcxr470zc6FSTGkAACAASURBVKg9i1bDK8t9vNQY5Gh/Agl438ycN23sBCAX52P79N3Q3Q9OB1Je1mnz+ecT5kAYcbQR41A9clUpck0FcsCLunwuiW37IGx9B/KimcgFZ6aqN56wFcyg4M4HMKJ9KE7/GRcvqt5ccq773yS7jyOMBFpOFarv7FUjNcXO6rmfpDfUQHf/URw2H9cs/yp2m48Ne36Udmx920b6Qo2jEoPinJF1CdleKyqgKhrFufPOuHYh4CnlmmX/wuq5n0RTnHhc41u5LkkS5QXLeM+VvyCpR3A789CUyZU7nSjoySjB7oOE+o7jdBfiz61DUjSiAy3Iig2XrwR5Aussetq2s2/DN8mvuJiiaVfReuxZAGyOLCpm3jZqiul00JMRYmFLodHpeWvqqWNCk5G8GqI3kTZG6v4tjXSFPQemkW7NxufmreLvNz1DSySI32bnX5dcSZE78z2lJRxMkYKT+FP9ftYUVhLRkxS5vGRPoiLD0SAJkbkdcBCnfXK8YQpB00CC/oRBvkujIEMK4XQ42BPjvhdPpFIRN00L8KkF+XhP460QSRrYZBk1Q7fCcOim4FBvjIM9Mfx2hVk5TjQZmsNJHIpEmdc2qufD2wEiErU0DACjtRPj0adTz8nL5qLdegWSTcPs6kN0doPNhlyYi+R+G+k6nCUisT4Gou04NC9+TzGxxACPPP+RtHA+wHuu+AUFWbMytujF4kH2nXiC13b/N4qssXbBp6kpvQK7NvlvLu8kNB95mp0vfiX1uHL2XSDJ1O/5NbKsMWPpPZTX3YZqm5jvbff6f6dxv2Utnl++hqyC+TjchWQVzMPlG9kSOhbCwSYObPou7fUvYnNmM3fNP5FXtnJC0xH6rh6SDx5MrTrqe6ahrs5HkiWEEOgvtKL/bsiRU11XinpdCdI5UJvtjkXojIXxa/ZRSQHA4f4u3vfC7zCHLZ1rB0nBlq5mpnmz+Pqyq6nyjZ3aPAcYddGbVMRgPHCkL8bRvjgSkONUqQ7Yz9j9MGGY9MUNvJqM85RIxY6OMPe92JBqxZ+T4+Drq0vJOY0Q0tsFZl8Q/bG/Ye45AoA8uxrJ48TYZKnnIYHt7z90XqIDUw3HWl7lsVc+y8l/rdryq1EVOyBYWvd35PqnZ3zdQKQDSZLwOC/0p082xCLdbHj8vcTCHUODkkzNoo9zeOuDqaEVNz40Zkvim0XDgcfZ8+q/po0tW/ff5BaPLJyNhtoxzSQOd0HGdkYhBIe2/JCjO36eGpNkldW3Pow3K/PvczwgDBPRErXSBwENqciFNCw9KyI65okQZmsUucCBXOFB8py7+69uGhwb6KUlHCTP4abKl41L1TCFoDMaQpMVPJqNvzUdpTkSRJYkOqNhpvuz+ebOocLf26pm84X5ayZDXcGoxGBSxqp1U3C8P05LOEmOQ2Ga335GKYWkYfJMfT+/PjBUa/DJeXncPTMHeYxQWuNAnP/Z280rzQPMynZw78ICqgcLCXVT8OsDPcP1edjTHaM+mJgwYiAME9HcjtnQiuRyIFUUp1oXRTiK2dppmSTlZSMV5o5pSjTqdU7RHMh4zPHmFCkAMPceQV231vpZCUBRMts9X8AIlBcs5z1XPER38DgIQVPnNvYe/zMAbd17efdlPx7RbtgfaqG99wCGGSfXX0NeoPp8TP0CMiASbCIcbBopQS1MTr3lxCKdTBTySpaTX34xHQ0vgyQzbd778WWn60UYeoL2hpfZ99p/kIyHKKu7hekLPpAqYDRNnXB/A7FQOzZnNi5fBcXVVyHLKpKkkIiPNBMbT0iKjFTmhrLMRX2SS0WZGUCZeX46td7oaOL+15/EGNxM3z9vNZcXTeexE3v59eEd+GwO/mnhpYT1BP9zaBsJ02BBdhEFp3gl7OppI67ruCax++KkJAZb28N84ZXG1EJ834J83jUjO2Pb4nC0RpI8ckoB4s/3dnFFhY+iUZwSAeK6yU93d/Jcg/XDf6M9wlfWN/HgFZVkO1QkwMygwHj6YMtbg9nQQvIHv+GkHKRUnI/2kduQnHaSz23AfGWrdaCioH3iDpTpZ1fIZPYPYO47irFlH3JNOcriWch5mcNbZmfviDERS1iEQDdQr12DNAnaKqcCVMVGce48PK4CfvHUu0gkh7Q4uoPHCIbb0ohBMNzKH1/5LF1Bi5hpqos7L/tpml/CBZwfxMLtbH3uCyTj/ZTV3py2wy6adjU9bcO6dCQZlzdzSD8W6SIe7kRzBHB5z05o6iSc3iLmX/pVIsEmZFnF5StDOcV5NNxfz9HtD6Enwgih07D/d3izq6mYZel7dDS8yvbn/wFh6siKnYVXfIPdr3yVRNS6pwby57Hoiv/A4Z7geoNJhFAizqFgF22RAQaSCWYG8tnTa7Wpf3/PRso9AX56YAsA0WiIA/2dPLB3SJ58R08r1f5silxeeuNRbq2azaxAPh2xMKWKijpJN1STjhj0x3W+u609bXf+4K4OVhR7qByjU0DCUjs0hq3YiiSNWaPSG9d5qSldLKkxlKQjkiTboaLIEnfV5fB6WziVW6nNclD5FpQOTwchBMarWxmuES1aOhDt3eB2DJECAMNAf3o98kduR7KfWfRCmALj9Z0Yz1gywcbxJsyD9dg+clvGmgC5qgTj1LHppciFuRDwIpfkjxl1uAAQuo6IxpFcTmyqi2xvBW09e1PPq4oD2ymtfZ19h1OkACCpRzjY+NwFYjAJEAm1E8ifQ1fT6wS7D1G37DNEQ21kFS7AlzOD1qPP0te+C83hZ9bKv8ebPTIM39+5n21/+yLRUCua3c+Cy/6dvNKLRr/mQCvC1K00gJp+/9FsHvy5mX8X4f5GOhrW4/IVUzz9Snrbd9HR8ArtJ16iYtbtREOt7H7lXxGDkQ/N7qW7+Y0UKQDo69hFsPvwO4YY6KbJY/X7eGDvxtTYJ2ctpzUyQHc8QsI0aI+kR1Hixql3SjgS7GFNQQXT/Dn8/OBWHj6yE5us8JWFl3Blac2kJAeTjhjEDUFPPD0sp5sQN8xRXjGEIreN98/K4ad7ulJjH52bO2YRo0dTqMmys697yE7Za5PxDytanJ/n4r+vqGBXZ4Rsh8r8PNdpjZbeMkaLRiQzuOYNhMHQgTMkBgMhjFe3pY+daEH09IPdBvGE1RkxGKGRy4vQ3ncD+tOvgSKhXrcWeXr5pO2amIww27vQ//Y65tFG5Dk12NYu5tJFX+Sxl+8lngwhSypXLf1Hsk5RssvkkjhcJ+CdDjOUtMSznOf2t9jbvouDm75HNNRCYdXl6MkIBzZ/n7LaWyiadiWSJFG96COU1t6ErGjYBx0Q9WSUcH89iVg/Dnc+Bzf/gGjIapt2eUsY6D4ECDyBqjSNAl2P0Xr0WfZv/DaGHqW07haqF3zojLoF4tEedrz4Ffo7LRLaXv8itcs+TXbhQlz+itS8husfqJqbRGyk+JXxDvrttYSD/Gj/prSx3xzZyXXltTx8ZCfL88vSxIsANFlBlWT0YTbY15bVcFnxdL667QU6otYGNGEa/Nv2F6kL5E2WQsQ0TLo7e65T5c7abH6ye2hxX5Tvosit0TyQ4I22MPXBOCuLPczJdeIeVnugyhK31WQxO8dJUyhBuddGXbZzzFYdj03h/kWF/K/1TXRFddyazL9cVJymf6ApEnNzXczNnfhqcEmSUNYswtx9aCiVUJSHVJADsoxUnI9oGSp0Ui5bjuQ68+p/SVWRfB5EZIgIoSggTJK/fxZxrBF5Xi3KivnIOQEkuw1l4UzkGZUgSVNOeOl8Q4SjJB9+EtFo9Wmb67ehhyIU33kt77vq1wQj7bgcWWR5y0dI4+YGqnE5cojEugGQJIWZFdec8/cw2SBCSfRt3RjPtoBHQ7ulHLnG96Zrbc4Gob4TvPHUPRhJy1ulfs/DTJ//QeyObEpqrkvdbyRJTlu4DT3GiX2/5eAb3wNAtXmoXXovXS2b8OfOIrtoIQc2fx+EicOdz5Krv4cvx3KfDPUcYfcr/yd1rsb9vyeQO4uyupvGnG8k2JQiBSfRdPBxAvnzqN/7KJrNjS+3jpzi5XS3WAthuP8E0xd+mJYjT3Fyl6JqbjwTWHw42aALE91M35BGDZ0yt497Z6/gsuJpyJLEmsJKXm2rxyYrBGwO/u+K6/jWrlcJJRN8cf4a+hNxnm46zPL8Mvb2dtAz2M6YNE36E0P34HAywbGBHvriMUrcPqq8WW+qzXQ8MCm7ErqjSTa2hvlbQz+L891cWubFpSl88ZVG9vcMfZD/trKYy8rHTySiI5KkM6oTsCuUeMY/TSCiccymNkR7N1JOALmsEMmTmWikig/rmy2Pgopi5FxL2MTs6MHcfRizqc1asKvLzooYABiHT5D88e9gMBKj3nQZxvEmxK5DqWPk5fPQbrsCSZ10/HFMCFNAJAoO23mfv9nSQeJbv0gflCRsX/7oGXlhdPUfo751I/FkkKqi1RRmz5zUKnbnAvrWLpI/Gdb2qUjYvzQXuXTirZo7Gzew+en70sb8ubOYe/G/4A1UjWqfHOw+xPo/3g3DdpM5xcuQZIWcosUc3PwDht9yi6vXMW/tPyMrKq3HnmP78/+Qdr7CqitYdMU3xpxvf9cBXvvj3SPm6/AU0F7/Iprdx6qbf4UpDBr2/Y7Opg0UVlxKae3NxMLtNB78EzZHgJKa60ZNVbydYApBSzhIzNBZ31bPD/cNRQ0+VreUD9UtSStmDycTtESC2GSVErcPVZbpi0fpiIb4zDCxI4ei8olZy/jubiuFm2N38dAlt1Ho8hLXdR4+soMH978BgE1W+P6qG1iY++Yt3c8AU6srIcepcf20ANdPG7pp7uyMpJECgF/u62Z5kSctavBWkO/SMsovjxeM7fvRf/9s6rFy2XLUa1ZlXLgkRUYqL0IuH1mMJOdnI1++/C3NRZ5eju3z70d09yP53AhFQfzphbRjzC17EVetRMqaXDreY8Hs7sN4bTvmrkNI00pRL1tu1UOcLzjs4HLAsAiNlJeFZD8z8pnrn0auf9pEzW5KwtjafcqAQHTE4BwQA5tjJJkLFMzDmzXttGZIeiKcRgoAoqEWyme+C0OPceo+bKD7IKYRQ1Y8ONwjnVZzS1ec0XzdvnIqZ99F/d7fACBJKiUzrufA698BIBkPEo92k1Uwj5nLP0vNoo+h2txIkozbX0pO8eIzus7bARE9yVONB/mv3RuIGzpXllTz3ZXreKnlOMvySlmUWzyiw82t2ajxp99fAnYnWzqb08SOYoZOMBHn0sIqYqbOJ2dfRKHLsiVvDPfzo/2bU8cmTIMH9rzO91Zdj/s8dC9MSmKQCWoGbuPSZMbQJTrvMFsimCdCSFkC/YmX054zXnoDZekcK0VwjiHJElJRHhRZffFmV+9QfcHJY/Ky0syTRDyBeaIFY+8R5Lxs5Noq5HPog3AmELqO/txGzDcsjQXR00+ypQPbJ949anRmoiFn+9Huuo7kL/9s1Yg47Kh3XHPe5vN2gFzhwdyW3oHEOeppdweqqLvocxzc9D2EMHD7KymfeduYDokuXwlOTzHR0JC8bsWsd1M+8zbC/Q3U73k4Lc9fWnczqs0qRvXm1DB37T+zf+P/TdUY5J0hMVBtLqoXfZSCykuIR3swjQRHtv8E00wCoNl92J3WPUiSFTS796w+j7cTjgV7+MaOV1KPn20+woLcIr688JKzP1mGtSmUTHD79DnMyS7EqQ79XqN6Mk0UCaAjFiJu6BeIwelQ7rNzTaWPp+uDACgSfHhOHo5JrDZoNoeJf3svRAzUq3NG9jcK3nLPo0gmER09iHAMKduXSjecLaScANqd15D81RNgGGC3od5+VVqXgnnoRMo90QSk4jy0j70L2Td5THJEMIy5bV/6WEsnom/gvC7E8szp2O7/gFUo6vci515o73wrUBZmY2zrRpwIgwTqFcXIJedGZVPVnFTMvIPckoswkhFc3mLsLmvHaOgxett3Ur/3t9gcfspn3kYgbzbAYN3Ad6jf91v6O/dSVnsLBZWXoKh23P5yll77fXrbd9N8+AmKp19N0bQrhq6pOiirvYmc4mUIM4nDXTiiK+F0sDn85BQvASDUV4/DXUgk2ITDU8j8i7/6ptQRpxKCiRg7ult5ueU4MwK5rCqooNQzMg3dHh2p1bCpo4nbp80962vO8OeS63DRFbNqUVyqRrHbyy8Obec7K9alHVvi9lPty+FIcCgSdlf1vPMmnzwpawxGQ09U53BfjP6EQbnXRnXAMaa2wflE8sVW9EfrAZBybSjL4xgvrk89L69ZjHb9xW+6ul/oOsbru9D/+Dfrm3La0T5+B0p5EWbfAKKhFdHTj1RWaNUzjGGdLAwT0dmDGAgjZaWTDJFIkvjhI4iG1rTXaJ+6E6W6/E3NfyyYoYgVwXA5oD8EsThk+09LREQ4SuLBRxAtw8Rk7DZs97//TZOmC5icMAeSiM4YkiYh5TvTVPLOF7pbtrDpiY+nHiuqg5U3/TKtVVGYBoaRQNUsIhMZaOHw1h/TfOQJ7M4c5qz+Mvllq0atVRgPJBMh4pEuVJsHh+vtr1j61xMH+Oq2oVTpnKwCvr3iWrLs6Qvvvt4OPvDS79PGvrLwEm6qnPWmrrujq5XNnU0YwsSt2vh/h7dTF8jjWxddh01J/35PDPTyVOMhdna3sq68jpUF5RNNDKZWjcFoyHaqLHdOnt3pmBiWThRdCcwGH+rdNyO6u5CLcpEqit9Sy5/o7EV//IUh+haNo//1FaT3XY/+p+cxdw4VEmofuBll3ozM5zEMiCXAabdy8Zny8ZKE5LSPYIrSBEVsjONN6I8+jejoQa6rQq4pR//Ly0g5frQP3YpclFkaWHI7UW+7kuRP/mARCVVBu/PaCwJMb0PIXg28k0uSvO3482mPDT1GqPdoGjGQZAVVHoputBx5mubDfwEgHulk29++OOHyw5rNg2abQvfSt4BgIsYvDqW3Z+/pbacpFBxBDKb7svna0iv51s71hPQ415bNoD8R43B/14g6gqRhoCmnv//5bHY2dzSyv6+TuGkgI/Ge6vkjSAFAhTeLT8xajinEmEq9E40pRQwmAieCcfZ0RdFNwewcJ9VZ49eKp8zwodtliFsMwTwaRbtlGvLizAv0WSMWh1PaaURXD6KrN40UAOhPv4pcU47kTH9/Zkc3+stbEYfqkedUo6xamHFnLWkqyhUrMA83pK4pz69FGkUt8a3A7Okn+fM/plwazQPHQVORKosR9S3oL2xCe/c1o3YbKFWlSJ9/P/QFweu2aiXO8z/aBbwz4PCMLBJUTmN4ZSRjtNWnkwlh6kRDbRNKDN5JaI2EcGdIu2Ra1O2KytqiKkLJBB3RMK+1n+DPJw7wp/p9/HjtreQ4XDQM9PHXhgNs7Wrm6tIZXFJcRf4oG9ad3a3UZeWzorCCqJ7Eb3NkVNEdjvNNCuAdTgwaBuLc+0ID3TFLRMalyjx4eQU140QO5DI39i/MwTgcBFlCmeFDLh7H0FB2ACnHj+geKlhSVsxHysRiTTGinkFEYyR/+wzimOVzbry8BdEXQrvr2oxpB7mqBNtn32cpMLocSCX5E+OeGAylSEFq+kcbURbPwqhvQTS0QSIJp2lDlHMDcIZ5fDMUQZKk1Hsxe/ox9x/DPNKAMrsaaUbFiPSFSOqIrl7QDaRsP8IwEG1d1uPCXOTsyeu1fgETh/zytZzY+yixsCWbm1O8FF/O6BsBWbWTX7aaYNeBoUFJxuHKn+ipjgrTSBIONpKM9+P0FE+85fIEIpiI8Y0dL7GufAaH+jtTqri3V82h3J35f7QnHuXbu9aTMIdUDBvDQTqjYVRZ5qvbXmBXj6VJsrunnc5YmE/MWpbRFKk3HuWRo7uQkdAUhbih88+LLhtz3kIImsNBwnqCQqcHv/3cutS+o4nB4d54ihQARHSTTW2hcSMGYJEDeRRTkLd8br8H7UO3or+8GdHQhrJ0DvLCmUh2DXneDMxhmgTqdWtGaB2IvoEUKTgJc9cBxPVrM4beJVlGKi2A0gm+UXhc4LBZ6Y1ByAtnIhXkol6z2jKNOkvdhkwQ0TjmnsPoT68HRUG9fi3S9DL0v7yEufMgAObOgyhXrkC6elVK9llEY+ivbsV4dgOYAuX6izH3H0McbbROnOXF9rE7kM9Dt8kFnF94s6q46PqfEuo7jqxoeLKmnTaHL0kSJTOup7/rAJ2N61FtXuas/jKeQOU5ma8QZlo3hWkkaT7yJHvW/zvC1LG78lhy9X/hz609J/MZb4STSfb3ddGfiPOpWRcRN3VsssLSvBJ29bSxv7eTKl8Wc7IKyHVa92mPZmOGPzfliQDg1ez4bHbawgMpUnASvz+2m9ur5lDgsjYPndEwB/o6aI+GmJ1VgIyEiSBuWNeu8We+LzSE+tjY3kA4GSff6eGbO18laujU+vP46pIrqPKduxqpdzQxyISJNEaaEKgqyszpMKsaqSQfOWC1Gqk3X45YNAvR04dUVoScaTF32MDtTNudS1l+GKNIcaIh52ahvf8mkr9+AkIRpMWzkXxu9D88Z31BAS9SXtaodQZnCrOxleRvnkw9Tv7iT2j33JkiBSdhvLQZ5aJ51mcDmK1dGE+/lnpeEmKIFAD0DmDuP3qBGEwhhPtPEB1ow+bMwu2vPKuK/1Ph8hXj8p25MI3bV8bCy79ONNSGojpweSdU1AawjJs6G9bTfORJcoqWUDT9ajyBCsLBBva8+u8IYW2Y4pFODm/7EQsv+/oIU6apgByHk8uLp/Fs8xG+P+h5MM2bxTRvFp9//anUcbdUzuJz81bjUFS8mp0vzl/DF15/is5YGLdq46tLrqDY7eOE2YdNVtKiCflOD47BKG0oGef7ezbwdJMlvlXhCfCti67lmabD2BSVWypnMcM/kih2REN88fWnOD7QyydnLedr21/GGNS8ONjfyW+O7uTv569BncCC1OF4RxODmoCdbIdCT8z6kp2qxPKiqVOQY7Z3kfjho1YLHJbugPbRdyHnBiyCEBjqRxaJJGZjKyIYRsoJIOXnIGf50d59Lclf/gl0A2wa6ruvQfaOT4RDhKNWuF2WrTy/48xvLEptFdLn/w6icUgkSXzvV0NFln0D6C9vQXvXVSPSJkKIM64nME+JlgCIeBJUyzUyBYfdkow+iYF0w61T6zwAzL7QiLELmJzoadvB5qfvsySOJZm5a/6R0prrJ7Qr4FSomgtv1rkRsRJC0HTwcQ5teRCAntatdDZtYMnV3yUZC6ZIwUkEuw+iJ8NTkhjYFJWPz1qOU9V4qfU4C3IK+VDtEu5//cm04x6v38cd0+YyfXA3PzMrn4cuuY2OaJiA3UHpYNqhxO3jc/NWpbQONFnm/nmrU6H+xlB/ihQAnAj18YO9G/npxbfi1kb//E4M9HF8wHKx1U0zRQpOYltXCxE9ic92gRhMOMp9dh64rILdnVESpmBernNc0wgTDfNwQ4oUgNWlIJrbIDeAGQxZngguB8IwMDbtQv/jYJGToqB97HaUmgrkWdOw3f8BRDCEFPAijVNLn9ndT/LRpxBHGgBLXlm9bk1G0mG2d2PsOIBo7kBZOtsyaHI5kAM+CIBxqH5E46xoarPEggYXbLOxDX3DDgiGUFYvQp5WOqayoJyfPcI1Ek1FXXcx+kkVSAm0265IqzGQcrNAlobcLxXFirIkkqljlHk1Y35GF3D+kYwPsH/jt1K+BwiTva/9B1kFC/AEKs7v5CYI8UgX9Xt+kzbW17GbyEALTm8Rdmcu8eiQV01pzQ3YHFO31bfM4+d/LVjLx2Yuw2uzE0rGMU8JDUtInLqfyHd6UkWFumlyJNjNiYFeqrxZ/GztrfQnYxS7fFR6h7V1Z7h+Ugj0MULRwx0WBQKXqhHRh+4nV5ZU4z0NsRhvvKOJAUClzz6mnfOkxbAcfAqahv7SZvSXNiN5XKg3XgIBL/qfXxw6xjDQH38B+Z47kVxOS3lxnMPe5uETKVIAYG7ahVhQC7VV6cf1DZD82WNWZAEw9xxGu3sdyuLZqWOk7MAISWHlovmpCITZ1kXih4+kVBvN/cfQPnEHyozK085RqipNq8WQL5qPXJCbkqIW/QNWdOWU9k2pMA/tI7ej//kFRDgGDhvax+/AeGM3RKIoqxYhl42sTr+AyQdDjxIZaE4bM42EZTf8NiUGsmLH5spLc0+UJBVFdeD0FLLkmv/i8NYfEew+RGntjZTV3jTlu3pUWSFvsIbAoah8avZF/Nu2oXviHdPnUuIavWB4Z3cr9772l9RO/obyOj43bxWeUxbrMrefi4uqeLn1eGrsEzOX4bedfsNZ5c1KmTH98fhe7pu9gt8d201zOMi6ijpurKg7p9/BlBI4ah5I0BhK4FJlqvx2vOcorDJZYTa0kvj+r1NGSHjdqNetRX90KHeGLKN96t0kH0jfIeByoH34NqSAF/lNeCEIIRCtnVaHgt2OVJKH7B9KXSQefwHzlS1pr9Hesw5lyey0MeNIA8kfPpI2JhXnY7v3rrTUg9nYhv70ekRHD8rKBciLZiH7LTZv7DpI8hd/SjuHvGQOtvdcN/b7iMYR3b2WTkPumfsXwGCqxDDGTfnR7A8BIu1zvICJhTANDmx+gOO7fpka82RNo3TGjZTOuCGjL8JURHSglYHeowhh4s2qITrQxBtP34sYtPWuW/5ZKmffiaxY9UWGHkNPRrA53p6tvuFkggN9nRwN9lDq9lEXyBtVTCiqJ/nMhr+yoztd3O0XF9/GrOyRtVvtkQF297TTGO5jbnYhs7LycZ1BzUpXNMyh/i4GknGqvFnkOz3EDJ1ch2uiagumvsDR4d4Yn36xgf6EFfy9tTrAx+flv6PJgVRaiO3euzF2H7SKEBfNGgqBn4RpQjCCVF6UplqoLJyJ/shTYBhoH739rAvlzBMt1oI+mIuXZ01DvfM65EHZYaWuMp0YSBJS/kjNg0xtkZLfk57TB+SyQrT3IT8yGAAAIABJREFU3wRJfWSLZIbFXMo5s3ZByWlHKn1zu3vJ7Rz9P+ssIGIJjJ0H0J94BYRAvXYNysK6EZoTF3B2ME0DeYwbqiQrlM98F5Ik0dnwGt7sany5dRx84wEC+XPILlw4LnPp7zpIZ9MGECZ5ZavOqUthJNjElmc+S6jP2sU6vMUsufI7LLry20QH0wf+7NoUKQBLsVFRz/3vL5yMEzeMCZcCdms2FueVsDhvbCloQwhCyZHR2aQYWVsEUODyUuA6e3Kf63SnOiPON6YEMTCF4A+He1KkAOCxI31cXeFnbt7oP6DemE7CFOQ41EktnfxmIckSUkURcsWQA6M8rRRz/7H04/wetLvXYWzZi3m4AXl6KYRjiE7LhMbce+SsiIEwTIyXN6cV6Jn7jiHau8BjySPLlSVod69Df3Yj2DXU69YiFY/szZbyspBXLsDcsMMa0FSrPfAURUgRjSHauhGRGFJeADl/aL5SUR7yrOmY+45aAz4Pyvyp015lNraiP/p06rH++2eRcgIotZXnb1JTGPFID51Nr9F48E9k5c+lZMaNeLOqRj3e4c4h0t+EO1DJQO8xWo5a34Vp6qO+5mwQ7D7M63/9SKqO4eiOn3PRjT/Hn3NufqN9nftSpADAl1XNoS0/oKNhyCyobvlnmTbvfedkPpkghGBndyvf27ORjmiIO6vncW3ZDHIc53+h9Gg2Pli7iK9sfi41NiuQR9koOghvB0wJYmCYgoaBkYwtrGdmbIYp2NIe5ltb2+iK6txWk8UdM7In1FL5bCCEQDS0om/aBYkkyooFyBXF4yIvLM+rRdpzBHGixRJVunqV1ffvtCNfuwajusHqQhjWomh29p7tO4BIfOTwsO9DcthRFs9GrpsGijxqR4LkdKBduwZz4UyIRJHyskdYJItYAv1vr2O8aHmVY7dZNQQVVluX7POg3nkNoq3biijkZyNPEQlks6cfYgnk+bVWrcNgak80tsEFYvCm0Hr8OfZt+E8Aetu2037iFZZf/6MRegLxWB+YOnZXLqW1N7Llmc+knvNkTcfjH58ag77OPUPFjQwaLbXtOGfEYPi1AQL5s1MdCSdxdMdDFE+/Gof7/AgrHR/o5b7X/kJ8sA3we3s24tXsb9qjYLyxoqCC/1pxPa+0HafKm30ufAzOK6YEMdAUmdtqstnROVQk5LcplHky523qg3G+8EojxmCFxMMHeihy27itZnJU1oqWDqtYLmntSMztB9Duew9K5Vt3OJPzsrB95DZEd58lI5yblbb7ljzOtOp5AGXB2YU1JUVBuWQJ5uETQ2N5WUgFVqrAHAiDYSD5vUhupxVhqG/GPHDcIii1VciFuZg9/YjmDuvYwlzk6WUZryc6eoZIAUA8gfH868jvuzH13mSPG6rP/+5CxBOgaUhjRKiEaWLuPUry0acgEkOqKEa9bo2VToDUZ3kBZ4dErI/63Q+njYX76y0nwZQDYpzOpg0c2PRdDD1G9cKPUlB5KcvX/YjOxg1kFy1CT4Tp69iNN7sGt/+tmYTJysj7lKKcu4JnX24dsmLHNCwyL4SEJKlpbYmq5kKSz99y0BIOpkjBSTzRcJDry+tQ5NPbWZ8LeDQbKwrLWVE4MYZxkw1TghgALCt0868rS/jD4R7KvTZuq8mmxGv9w5ntUcz6ECRN5EoPrZKZIgUn8dyJfm6eHkCZBCkF0dqZIgXWgMA8WD8uxAAGc9+jSBXLRXlon7oT46XNiHAE9eKlyJWZBVVEPIHZ0gHd/RDwIhfnI7msvKM8vRztk+/G2H0YOcePPHMakseFsecI+uPPIyIx1MuWIS+fh+juI/nAw0Ptfd430D52O/rDT1qfBYDDju3eu5AzpBtEYmS0SHT3W/bQY5hQmd39lu6AxzWh7opmTz/G1n2YOw8gz6hEWTEf+TQ+EqKzh+Qv/2y9B0CcaMHM8SMV51vmWhUTL3LzdoSs2HB4C4kMDNeokFCH+RUEuw+x7bkvpB7vfe3rONz5FFSsxe7K440nP0UsbKnb2V25LF/3o7ekRBjIm4PdlU880gGAzZlDVsH8N32+08FsiyLao+BQkIpdyF4NX04tF13/E5qPPIlpJskrW4kQOke2/XjwVRIzV9yPavMSGWhFUR3Yned2E5WVQfJ3fk7RpCAF70RMGWLgtSlcXu7j4hIvsjxkNGF2xkj81z5Ez+DiYZPJ+YfZI16/rMg9KUgBALYMxXKeNxeWMvsHrLSA34Psts4h4glEZy/oOuRmpQoCT0KpKEZ+7w2AyOyrMAhj50GrQHEQ6rq1KJcsQ1JkJJuGUlOBUjMUbjUb20g+9Fiql0V/8lW0ojyMg8eHSAHAQBjR0jlECgBicYw9hzMSAyk3ANk+6AkOvYeLl4wpmGQcayL588esNkeHHe0DN43ZwvhmIHQd/fnXMTfutK7b0onR3Yd2yVKMTbvBMCyiUFGCpAzKKgfDKVKQOk9DG9oHb7ZaJM+z+uRUhaq5qF1yD5ue+CSmYbW31iz+GK5haYFwsGHE67pb3qCgYi19nXtSpACsnv/e9p1viRh4AhUsv/5HDHQfAgTe7BlvSiNhrGJK40SIxHf2waBgm7wsB9sdVUgejUD+bAL5Q/dFt7eE3OLlxMLtuHylaM4s9m38Jk0H/oTTU8icNV8hp3hJmlzyRKLKl8U9sy/iwb2bMBFU+3JYV35u64S6omFaowN4NTulbn+atsA7DVOGGJyEqqQv7qIpPEQKABImZUdD/P2SQr67vZ2EIVic7+LK8rNvyZsoSKUFSGWFVh4ZINuPXHP2ISrjaAPJX/0V+kNIBTlo770BKeAl+dzGVEeAVFGEdvf1ltFPezeibwDJ50EqyB7VnRAsfYE07QNAf/o15LkzMnYXAFb64pRIjXGwPnMLYCaSFs1QtwDIAR+2j9yOsWkXorkd5aL5yHWjF5OBlc7QH3lySPsgFif58JNIn3vfuLcDimAY843daWNqVSnJHz6aWvzN7fux3Xd3KhIg+b0jRJHkuTVIBbljpiEu4PTIKpjHqlt/RSTYhM0RwBuYhjpMtc/hHClJ682qBsDUR/4GDT02Yuxs4fGX4xlMSQjTIJkIo9nOLPUVGWih7fjztNe/RH7FWoqmXYHLmx5dFKZAf6ktRQoAzDe6MdcUotSkk0w9GSHYc4i+zr24fWU43Hk0HfoLjfv/MHi9JrY88xlW3fLwaYs2xxMu1cad0+exuqCCiJ6kxO0n23HujIOO9nfzxU1P0RQOoskyX1pwCVeX1aCdQ/XLyYQpRwwAhCEQXTGIGwiXanVjDluQ7D0JblhVwOJ8FzFDUOTW8EyitkY524/2oVssNz7DfFNufGZfkOQv/5JSPhTt3ST/+DfUq1amtQmKE62Ye48gFeWR/MnvLc0DSUK7e51lTDRaj7Jppqc7AEwjo/xvChn6+eX8bOTKYoz121MCRFJRniWqNNynQZaQT9NJIBfmIt90GcI0EW1d6Ou3IcIxlPm1yOVFIws3YwlEV1/6WDBkkY/x1gmwaUhZvqHrKQoiFk+PCJgC82gj8klikJeF9uFb0R99GtEbRF5QazljXiAF4wJvoApvIPOi5s2uoXzm7TTs/wMgyC25CFnRMIwkgfw5KKojRQZkxU5WwYJxm1eor56Gfb+ju3UrxdOvobj6GpwZrJpPwjDiHN76I5oP/xWA3vYdBLsPMm/tP6e3EwoBPRmIdWKEticdDa+y44Uvpx4XT78G1Zb+v2saCWLhtnNGDMCyPJ4+isHQRCJpGPzPoW00ha2IZNI0+dr2l5gZyBu3+cT0JJ2xCC5VI2cKFC1OCWJgdkYxGyNgCORiJ8bxEPojx0EXSNO9qLeUoz82GB7UZOQF2SiyRNkkVjSU/d43tUCZPf2IE63WjnuYHDKAqG9BDERGvqaxHXGwfkgISQiSv3sWW0VxRhdFACngQ7n8Ioyn1w/Nefk8pNMQGLkoD3XdWsut0DCRZ01DnjkdOceP7dN3Y7Z0ghCIgTD6E6+g/d2NiIZWRDSOPKcauaxo1HOn3mNXr1W4ORgJMNdvQ7vnLpRppenz97mR66owDwy1aUkVRUjjJEY0HLLHhXr7VSR/+ofBFk6RWUdhWOpDkiSUmgqkz7wXEkkrijNGvcQFjA8MPUqor54ZSz4BQH/nfna9/FUC+XPw5czgoht+SseJVxHCpKBi7Wltk88GiWgfO174CsFuy2L5YM9hErFeapfdhzxK4V8s1E7zkXRd/9ajz1Kz6ONp6QhJkVEuLcI8OJRuw6chFaTvuuOxPg5t/mHaWMvRp5l/6ddo2P/7ofNJCvbTuEK+nRAxkmlOigCGMOlLvPVIEUBjqI/v79nIy63HKXR5+ceFl7Ikr2RSC0dN+juR2Rkl8d39iO5BNuxSUa8pBn2wrevoANR40T5cgxhIotR4kUrPf3X6RMAMhkj+8s+IhlaU1Ysgywe9QzcCubYSOT8bZDltZ6/MqU5zEQSs3fupEYFhkGTJyosX5mIcPoFcWWx5GJwm9y057SgXL0WeUwO6gZTtR3Jai6FclIeIxUl+f6hiPHmoHu0z70UtP/NCO9HWlSaNjBAYOw6MJAZ2G+rNl6M/twHzwHHkmnLUq1eniifHG3J1Bbb734/oHUDyuRGhCFJBjqUMCdZnkUFhcrwMqy7gzCFJMv1d++hpHYqsyYodBvPp/tyZ+HNnjvt1o+G2FCk4icYDf6RyzntwejJbmcuKHZvdlyZfrNm9Gd0flVo/0j216Os7kAucKMvzkHPTf++SIKPendNTSHbhInratqFqbmav/jIef+VZv8epCJ9m57qyWn58YHNqzG+zU+QauYnQTYN9vR0803QYt2rjipLpzAgMubw2h4M0hfvx2+xUeLLQZIXfHt3NS4MSya2RAb7w+pP8v0vvoNw7eVuqJz8xaAgPkQKAiA79SXCrEB5s9zs8gO2GslRh19sVor07pV5obN6D9q6r0F94A9HSgTSjEvXGS60Q9cffZbW9RWIoly9Hml6KvHg25us7U+eS66qQAqePWMheN8ybgTLvzHdMkqpYaYJM8z/RcsqAFeVgFGIgonEwzfQOiwx1EaMt9nJ+Ntq7r0GEo5YnxATuyCVZQirIhQJrl6Vv2oVcW4m0eJb1PmNxREsnzDw3DnoXMDocnkJql97Hvg3fSI3VLPo4Ls/EdoKomgvNHiAZ7+dk7tPpLUFRR8+lOz0FzF71Jba/8CUQJiAxe9U/4PSMjK5JTgVlbjbK3NG7YWzOADOW3MOOF76UGiuuvg5vdg2Lr/q/RENtqJoTp3dy72jHE5IkcUnxNELJBE81HqLCG+Azc1ZSnEHAaF9vJx9/9XGMQb2R3x3bw88uvpVpvmwO9HVy32t/oX8w0nDPrItYV17Lq231aeeIGjqdsfAFYvCWkCGlLQwBw4oQ5aW5b3tScCqUi5dgbN2LVJSLsqAOqazQqmaXZcs18RN3WPULbidC11EvWYKZE8DYdRC5rgpl6ZyzskEeD0iBkTtmKUM6RRgm5tEGi9zE4iiXX4QypwbJ5bDqE0oLEE2DoT+X47TERVLVjNcYC0LXIZ4Ep2NsTYJEErOhFbO+GTk7gFRVjFyST+L3zw3VGUgStk/ffdbzuIDxhyRJlNRchzdrOpGBZpyeIvy5dRNqs6wnI8TCnSl1wVDfMdqOP8/slV/E5jh9YXRBxcWsuuVXxELtONz5eAJvjVzml69m+fU/Jth1AKe3hEDe7FQhpGZ/5/l01A/08tkNf8Wj2biytBqBIDtD+yTAK631KVIAENYTHA12U+r289DBLSlSAPDDfa+zPL+UZfllPF6/LzVulxVyJ3mdwaQnBnKpC1yqFSkAUCWU+VmYB/oQCRP1kkKUBZNDuGiiIRXkIJUWWKH0UARzvxWeMsFaeO5/f0p2OOU82NJhRRUa21DWLEL94C3Ifs952Q1IFUVIMyoQhyxhJKm2Eqli5M5HtHaS/PHvUi2O+iNPIX3YiTK7GjnLh/bBWxAtHVa6ojD3rH0exoLZ1oX+/OuI483IC+ssZcrT1FaYB+tJPvRHAIzB96XdfQPaPXdhbNhutSuuXGh9dxcwKaDZPOQULyaHxefkeh0nXmHHi19JPS6dcROrbnk4TTwpMtBKsPsARjJmeTbkWNbdsqLhz6kdN6VEVXORU7SYnKLxfe/9iRgnBvowhUmZJzAliuwA3uhopCMWpiMW5tiApQK7NK+UIvdIwuazDW2mXKpGTNexySoxQ6d+IL3YWQAhPcHd1fNpGOhjW3cLWTYn/7T4Uso8kzdaAFOBGBS5sN8/C2NfHyQF8uwAcrkb2xfmQNJE8tveMZXcss+D9v6bEJ296E++mv6kEIj+EAzTATD7B0j87LFUHYL+2N9QIlHkK1eey2mnIGf5sb3vRkRHD0gg5WVnFGISnT3pugeAsecIyuzqwfP4rPqKM4AwTUQwhGSznVF9gRmKkPyfP6VqA4znNyHiCbSbLsuo+SBicfRn1qePHayHrl6UymKUUcSjLuCdg3i0h4ObH0gbazr0Zyrn3JUi6NFQO9ue+yLB7v2AZWK0/PqfEMibHJLAY6EzGuabO1/lpVbLp2VOVgH/uvQKSqaAn0CmIsOokcxwJKwqLGdLRxPLC8rojkfItbso8/jw2ezcWjWbb+8auhfkOlyUuv0Uurx8c8W1dEbDuFSNwjdhsHSuMemJAYBc4kYuSS/SkjzvTAEYOScAOQHMxjaMprZhT8gjawZ6+tOKEwGMjTtRVixAmoCiNxFPYNa3YOzYb5kAzZ0xYjcvuZ1IVWMoPGYgC3LJMMLT1Wvl64VAKs4bVWHQ7AtivLoNY+NOpBw/6s2XI08rPX20pLc/RQpS59m8F3HZ8oypEGtyGdJY7wyuegFnCDHCiU8wvMc61HskRQrA0k5oPvzXKUMMDvV3pkgBwJ7edrZ2tkwJYrCioJyHDm5NpQgcikqNL3MUcrovhxsq69IMla7qq+b6sjqcisZn56zkicaDzPDn8p7q+SkS4NXseLXJ2yV3KqYEMbiAkVAWz0J09mBu3QdeN9q7rkLKP+XH7HSM6FCQCnIzCw6NA8yjjVbL3iCMjTuw3XM3cvbZiUvJxfnIqxZivrYdAKmyxDJjAszOHhL//Sj0DlgH+zzYPvnujOkEc/uBlMeCaO4g+ZPfY/v83438nIbD4RghPCTlZ4/6mUkOO+o1q0n+7A+p+7w8twYp752R3rqAsWF3ZjNj8SfZ9cr/To0VV1+HyzfUSWOaI/UG9GR0xNhkRV985K67Kdx/HmZy9pgZyOdHa27hryf241Q1rimbQbU/c6tmMBHjx/s3p40923SE6b4cHty3iSybk/vnr+bSoio05eyW16ie5FB/F03hfvIdHmoDufhs58d6/W1JDAwhaBgI0RWNked0UO71pCSU3y6Qs/1o77oacdUq0FRk/8jWGik3C/XWK9D/8JwlgOJyoK1bOyFyu8IwMF7ekj7YO4Do6LbkjM8CkseFdv3FiOXzwNCRcrJSktHiROsQKQAIhjCPN48gBiKewNi6L22MRBLRE4TTEAMpJ4B2x9VWe6dhgtOOesvlSM7R/0HlmnK0++5GNHcg+T1WIajr3Km2XcDkR0HVpSzzFNDXvht3ViVZ+XPT/Bu8WdNxuAuIhQeLaiWZstobz9Nszx5VvmxkJMxhUZDl+ZlN0SYbVFlmXk4h83JGF5o6CSvOI0Z9vjcR5WvbXmTuFYUUnZIyiBs6rZEBFEmi2O1DOUVu+qWWY/zL1udTjz9at5QP1C46L+qLb0tisLG1nS+9vpWkaWKTZb6xcikrCs+Pneh4wgxFIBQBtxPZ60bSVMtHYBRIqoKydA5yZTEiEkPK8k2cHbEkQSa/hzfZIijZbRmL9UQm7YVkhnygpiFNL7WKFIfNcawUiiRLyPPrsBXnW1oEAe+Y5kuSplkGWONkgnUBUxuhvhOE+o6hKHa82TU43HloNg9ZhQux2QOEg41EB1pRNTfqYDeAy1fCsmt/QFfzJuKxXvLLVuHPG38thYnCDH8OD6y+gZ8d2EJM1/lg3WJmZY285/bGIhwN9hDWE1R4s6j0Zv7fihs6CcPAa5tc4Xe/zcEnZi7ny5ufTY2tKChnX+/QfUaTFZRTNqId0TAPHdzCH4/vQ5VlPjZzKbdUzcKrWRuOzmiY7+7ekPaanx/cypWl1aN+RhOJtx0xaI9E+drWnSQHw+cJ0+Tft+zkocvXkHuaXd9kh9nYRvLXf0V09CDlBlDfewNK+dhKgZKmpjoVJhKSLKOsXYy590gqDC/PqR73jgG5oig91K+pyFWlI46TZAl15UKSx5oRze2gKqi3XYmUP/RPJoRABMOW9sKwugZJkZEK3xmqbxdgQU9Grd/wWdghJ+Mhwv0nAIHLV47N4aO/+xBvPPFxknGrtieneCnzL/k3HO5cupo2sPXZ+zmZc6pb/lkq59yVUj30ZFXhOYcSxOMJVVZYklfKnOxChBA41ZFRyb54lP/c+SrPtxwFwK1q/HD1Tcw8hUDs7WnnJwc20xoZ4M7p87i0eBqBUdoHR0PC0NnT28GzTYfJsbu4tHga1eMkb7yysIIHV9/E3t52Slw+dGHyT1v+lnr+03NWku9Mj+Bu72rmD8f3WnMzDR7Y+zqzsgpYkmdtJkxhkjDTNz2mMDFG1KacG0wZYmAIQVzXcWmnD4NHdZ3eeLpNb1csRlQfXeVvskOEoyQffcqq5gdEVx/6r/+KdO97JpVynlJRjPSZ91rztNuQSvLOyjVS6Dqis9cSJBqMbpg9/UOtiUV5yMX52O69C2PvURACZfZ0pJLMxEcuyMH28XcheoPWfHItnQewVCTNN3ajv7wFyetGvfky5Ory1POpOQmBaGrHPHzCIiHVFchFF0jDVEQ01EF3yya6W7aSW7qcnOIlKKqTrqaNHN35P9jsAaoXfYSsgnkjXAUT8SDxSCeazYvDnU8s0sn+jd+h9dgzAOSWrmTumq/QeuSpFCkA6G7ZzEDPYSRZZu9r/8nwgsNDm39AfvnaN+W0OFnhOE1e/fhAb4oUAIT1JL87tpt/WnRZqiD4xEAv97z2ZyK6Rfy/vuNl7IrKdWfptLinp51Prv9T6tP+7bHd/OziWykfhzZBl6qxOK+ExYOLejAR44FVN9AeDVHq9lOboT7hVMllgI5oKPV3vtPDB2uX8MDejamx68vrKHadH/O/KUEM6oMD/OFoPTu6erimvJQryoopGCWHm+90sKIgn43tQ6Gdi4sLyXNO3ZyviEStKvzhY529lgHRJCIGYEkfU5Q39oGnQBgmxrZ96L99xmpVdDvRPngL+u+fQ7QNvneXA9un7kQuLUQuHTsfCFa9QiZyYu4/lmr5FOHoYGHi+5GK8hDhqOUUqamIpE7ygYcHPRAAtxPbve/JGAkR8YRFQlQFKTvwjmmjnQow9DhHtv+ExgOPAdB8+C9Uzf8AOYUL2f78P6SO62ndwsqbf5nmjxDqPcbOV/4P/R17sLtymX/JVzH1RIoUAHQ1baCnbQfx2CnGXVhGSKaRHFQ8HIJpJjGNxIjjpxp000A9gzx4phbAjmgYU4hU6L0pHEyRgpN4vH4fV5dWo5xFrv2JxkNplQD9iRjHgj3jQgxOhc/mYNkY9RQLc4p59Gi6A2vxsBoESZK4saKOCm+ArZ3NzMrKZ1FuccbIy7nApCcGvbE4/7hpG0f6LRZ+ePc++hMJPja7lphh4FbVtPYzl6bx+QWzeeyYm41tnawqKuCWaRU4TnXfm0KQ3K50m2ZAKsw9q934RMLsH8A83IB5pMEyBqopRz5LsyLR1Yv+u+eG9AvCUfSnXkXK9g0Rg0gMY99R5LeYGhFCYG47pTDRMBFdfZiaRvKRpxDHGsHvQVk8e4gUDM5LtHTAKcTA7OlD//NLmLsOgU1DvfFSlCWzJ6TQ8wLOHtFQK40HH08b62raSCLSlTZmmklCffUpYmAYcQ5t/RH9HXsAiEe62P78V6hZ/LER14gEGyitvYnmQ39OjWl2P96saTjceVTNex9Htv049Vxe+Rqc3qmrc1E/0MtfThxgZ3crN1TUsbqw8rSiRpWeLHLsLrrjQ0Zvd0yfizIsSufLUFMwMyvvrEgBQFaGan7bWXYJjCcW5BTx0bql/PLQNmyKymfmrGRGID2yELA7ubioiouLzn86adITg7ZIJEUKwGoP92gqP9y9n43tHawpKuTGqnJKPUM75zKvh3vnzeZDM5O4NW3KdyRILgfau68h+dunEQ1tSCUFaO++ZlIQA6Hr6M+/jrneai0039iNcvESpHUXj7RCPh2isXSbYkB09CCfKnccy2Ate5aQJAmpuhwON6SP+9yYe49YpACsroRMKagMugXm7iMWKQBIJNF//yxyaQHSGdSBXMDEQ5ZVFMWWslMGMJJRPFkj5YVt9qHe+2QsSHdLerdNMt6L2z9yh5hduIhAbh3Lr/8JTQf/jMOVS9H0q1PqhuUzb8PlLaX9xIvkFC0hv2JNSop4qqEnFuFLbzzD0aCV3tzV08an56zgvTULR31NsdvHA6tu4JmmwzSE+rixYiYLctL/P6Z5s/ngjEU8dGib9RqXl5sqzl7L4aqyGh6r30soaUVk5mUXUuPLRgiBbppoGcTKJhLZDhcfqlvMuvJaFEmmIINB02TCpCcGLk3DJsskBosJVxUVsLmji80dFtM/HjxCYyjMvyxdmBYVUCQJr21i+vXPB+TifGwfu8PKv7sdk6YdTvQGMTfsSBsz1m9DWbUQaYxq/jRk+ZGK88BmQ3T1QiiCsmQ25tFhi7csI889vaGTMAWiqwfRH7JaB3OzM4b0lQUzMQ8cRxxvBllGXbcWqTAX8+VhPcqhiOWz4LAPEZJsX8aahuH2zqm59A3ABWIwKeD0FlO77NPs2/CfqbHqBR8kUDCf5sNPEOq1ct8lNevwZtekjtHsfvLKVtIyzPpYs/tx+cpZfNV3ObTp+fEbAAAgAElEQVT1QYSZZMaSe1BtHhr2PwbCpHLuXSMkjB2uXEpnrKN0xroJfrcTj+ZwMEUKTuKRo7u5rqyW7NNEDab7c/jUaYoA3ZqN99cu5tL/z955x8dxl/n/PTM727t671ax3Ht3eu8h9CNH4AIh4RJqfhAIEAjkCBAggUBIOY6jBY4SIJCeOO69V9myel9pe535/bHySuuVZCmWbdnZd/7Ia2dnZ4tXO595vs/z+eRX4I+FKTTZUhr5xsM0WybPrLyFo54+dJKGKlsGvmiY/92zk919HVxbXMOK3FIyDWdPmEmCSP4INstTkSkvDApMRj47u57vbNuFCtQ7HTy5Nzm69I3Wdu6qr6HIcu5VmBqKofYEIaYiZOkRDJP3EQtG/RmLDX7HiCLIcjzG+QSyZmQ3wDFRkZbMRtl7BHHhDISiXMTiXKR5dcT2NUAwhFg/7ZS9BcqhwdyCSBRkDfLtNyKNkGgoZjnQfvRm1L4B0GriXgkaCWlODcrhpnj/BhB9bSPyx2+F3n6QRITC3BHHF8WZVSgHh4kDIR61nGZqIAgihVXXYc2oJujtxGDJw+KsQiMbWHjVE/jcTYiSFrOtBFk39OMtabRUzvkYkVA/3c3rMFqLmLnyQUzWAkzWApx5c1BVhZCvi/V/vYNoJN5QJslGllz3dFKvwoWEUSOn+Bbk6M3oJqFcb9TI1Dgm3qd0MqVWB6XW+N9qb9DP/Rv/lchC2N3XiXd6mA9PG73CcSqOuvs45nFhkDRU2TLJOosi40wz5YWBRhS5sqSQWqcdVzBEhl7Pbw8fpT88dCLK0OswjBDH+07pCQTZ2dPH3j4XMzOdzMpw4hhHEqHijRD9Rwux1ztABXGOE/k9pYjOdz6LqwZDIEkTjgxWvf74lbckIWQ5zliSouC0obl2VdxEaRDN9ReNGTp0MmpMiVsXv7YxvuHAMcQZVUg1ZQgO27h7CmJdfUR++2JcFABEokR++yLCff+GOELEtGAyJI0pKm3dqL1upOVzEYx6YjsPorlsCWJx3iltnMXainji5dvb40ZSt16GkDu5o5pp3hnh4AAe1xEiIQ9mWzHO3NlJ9+tNWehNo5+IzPYS5lzyCCF/LxqtCZ1hyIJb1sYvRjqPvZYQBQCxiB9Xx/YUYRD0deHpa0BFxeIox2AeXxPtVKPIbOOu6Yt4fO8GAHSShnvql2CS41XaqBLj0EAvx7392LV6qm2ZY1YSznSJv83nToiCE/zu6G6uKR67wjEaB/q7+OSav+AbbJRckFXAN+ZfSob+whAHU14YAOgkiWn2oRPNl+bP4svDDIy+NG/WpHkUhGMx/ufgEX535Bh6SUInSXjCEQrNJsqtFmwnWeOqEQW1zY/SEwRZRG0PJCaSlO19KLMciIsn3iynev3E9hwhtmYLgsOGdNkSpJLxNSopvf1EfvNiYq1cXDQTzdUrzshooyAISPPrEPKyoN+N4LBO2DdBdXuIvZW8jqvsPozaNxA/7niOEY3G/Qrc3uQ7PD4In7rzW2nvIfz4r4eWDLRyfAJinEsBot2CcO0qpBXzQCNNuPkyzZkhEvJwcMsTNO+PW3VLGj2LrvkZ9uz6CR1HIxvR2EY/gQhi6k+pICY3nvrdbWx9+TN4+g4DYLKVMv+Kx0bsV5jqaCUNt5bXMy+zAFcoQIHZRumwjv9tPW18eu3fEhWFa4tr+MzMZZhHyAs4PtjEuK2njauKprE6v4ysd7B8MBYjVTjyje+8wvGXxv0JUQCwubuVBndfWhicS5bl5fDLS1bSHQySbdBP6hJChz/AHxoaAbhzejX/c7CBvlD8ZHF5YT73za5Pqh7E9vUTefJgQgxIl+QheCKoLfHO2xP/nyjKwUaiv/9n/BjtPSgNzQj3fRhxLJ//E4891DjUQAcoG3ehzq6G6jPT7SrodEjlqSZD40aS4sFJA8NO6rIGJlAFUvvcKFv3Ik4rQRmMdQYQa8rifQKnQGluT25sDEdQGtuShIHS40Lt6AVZinsqnHTyFyQpvXwwxfAONCZEAcTDiRp2Psfsi7+NJJ3exIinr4G+ju2AijWjGqOtBP9A/Lun1dtxnFSZ6O/ekxAFAL6BRlydO08pDEKBPgRBQqufWt8to0bLdGeqO6kvEuZn+zYlnYT/1nSAW8vrU9wQB0IBHtzyKvv64+Ple1yd9IX8fLx24aQ2jReZbdxdv5gf7Yn7BOglDZ+aPlThmCiuUGqORTiWmndxvnJeCgNJECizWSgbxw/+RBEFAY0gkG8yctTtSYgCgJda2rihvIR5g8JAcYeJ/r5xuGcJsTc60FxdSLTFD1YZsXbic7NqTCG6YWfyxlAYtad/TJ//EygdPakbvZMTyKL6I8QOuYlt6kEsNCHNdSLmnt50hGg1I998KZHn/pz4LDXXX4QwEftmUUA53ITmksVgNaM2tiGU5KFZvXBcoVGCNEJPxLBtSns34Z/8NtF7IFQUIX/oWsQz8B1MM3nERggiCno7UZUonIYw8LgaWP/CHUTD8dwOjWxmwVU/JuBpQ0XFmlmDxZ4sxCNhT8pxwkFXyrbEfSEPnY2vcXjbz5EkHTWL/pPMgsVImqllE3wyiqrii6ZW6aJKqotfm9+TEAUneP7oHm4qm/6Omg5HQytpuLmsnrmZBfSHAuSbrKdlNXxz2XReaxtKk8zUGcgymPjloW1s6W7lssJKlmSXkGk495Nj74TzUhicSfJMRj4+vZo32zroGWE0zj98fC2movpPGmeLqSg2DUfuLGKz6MUs97HALVFmHf8JRJBExIJsYg3NydvH2Xgo1ZSjrNk27IHCpKX9xXb3E3n2CADKtj5im7rR3jcd0XZ6EyBibTnaez+C6upHsFniPg0TMAgSnDY0Vywj+rc3EfIyEUryEGorkuKax3x8UW7cLMrji28wGRArhqogsZ0HE6IAQG1ojptOpYXBlMZkK8FgzifgbUtsK5vxITTy6U319HftSYgCgGjEi7v3ECV1t476GHtWHYKoiYsSQBAknLmjN7/1d+1m91vfSNze+tJnWHL9szhyZp7Waz/TWLQ6Plo9nwe2DPUdzXLmUmROrXgYNDKyKCYs7AFyDOYxHRRPxh0OsrWnjTfajlJjz2JFbimFIzyXUSOPmN8wFt5ImGZvPxElRpHZhkNnTLyfny6/gZdaDpOtN3FRQTnf3bGGrb3x79mGrmY+UbuQf6+eN3bM+xQlLQxOQhIEbigvodZhpycYZH3HkJo1yxpKhi1bCHYtmsvzif5l6AQuzrBzqErmE29vSeR7ZxzS8bNVSymcwJKHtHgWsT2HoS/u4SBdvAhhnLkDYlkB8gevIfryetDJaK5eOapt8ERQg1GiL7Ulb+sIonYG4DSFgaDRIBTlQFFqaXJcjxdFpCWz4id4lxsy7IgF4z+WmJ2B9lPvR23pBFVFKMhGHJaXoLp9qQ8aKbwpzZTCYM5h/pU/pK3hn3h6DlFYcwMZefNP+7gCqT/2w08AQV83Az37Cfq7sTgrsWXUYM2oZvG1T3F83x9AjVFcdyvWzJpRn6O3dVPKNt9A05QXBhDPE/j+kqtZ39FEmdXJkpwiHCPkHWTrjXxn4RUccfexu6+DTV0tfGbm8lPGDXsjIVp8bmRBYGdfJ9/Z8SYALzYf4tXWBh5dfNWE8xVOxhUK8NO9G/nz8bgZWr0jh2/Mv5RCsw3dSbbIB1zdCVFwgt807OTakppJrXycLdLCYAQsssy87Ex8kQjfXbqA3x85Rp7RwC0VZRQPFwaCgLQsGyFTR2ynC6nSgjjLycvHDydEAUBvMESD2zshYSDmZqK9+wOoPf3xpMFs57hK4gCCXoc0bzpiTTmIIoIhXnpUVRXVNQCRGILDgjBRnwdRQLDJqK0nPZ92arhKCgY9UtU7950Xs52Q7RzxPmluLcrw5R29DiEnnZlwPmBxlFM9/65JPaY9ux6t3k540AJZ1llx5MwC4g2P+zd8n/ajQwl886/4IdnFy3HkzBz3iX24n8IJdMbzY9Klwd3Lt7e9iV6jYWN3M9NsmRSYkq/i+0MBnju0jd8c2YkKXFFQyTOrbkEnafBFQphGaFQE6PR7+N7ut3mj7RjXldSwtTv5hLyrr4MWn/u0hcHhgZ6EKIB4/8OGriZuNc9I2VcnSUiCmBR65NAa0J2DyOTJIC0MxsAky6zIz2VpbjaiIIxYEhKtWsQFWWgWDHXP61tSvwzSO6gmiXYr2N+5IcbwUTw1EiG24yDRP70CoTDirBo0165EdI5/HV/QSmiuLiR8yA3RuPCRVuUg5E4xb4UzgFiSh/zJ9xLbtAvBbIoLr0lOjkwzucSiQbz9TYQDvRgs+ZMaVmR2lLPo2qdwde5CAOw5M7EMuij63M1JogDg4Jaf4sidnRhvHA8Z+fPILl5BV1M806O47j1YM0avMEwVeoN+HtzyCt0hHwyuxn5j66s8tfImHMNGAw/29/DrI3GxbZF1zMzM4/9t+ifNPjdzMvK5f/ZKyqypQn1HbztvtMU9QwLRCOaTGggFQDthH5VU+sPBlG2HB3pH3LfQbONT0xclmhslQeAzM5djO01xcq44L4RBKBajyePDEwmTbzSSazq7DR3SBL9kFxfm8fyRYwQGu1TLLGYqbGfH8UoZCMdL+6KAkGtANMcbrNSOXqK/GXJvU3YcQCnJQ1y1YELHFyss6L40M/4cJg1ivhFBf158jU4LQZaRqkpOqyKR5uwQjQTo795Dd/M6JI2OWCRI0/4/sOCqx1M8DCBeSZvoOnDQ10XHsVdpOvAnDKZcTPaSoeOMEJWrKhFQ1RGONDoGcy6zVj+E392MIGowWotOuzfibOCOhGjzJzdaNvkGcEfCScKgJzi0PHd9SS3PHNhC72C3//beNn66byPfWHBZSr/BMfdQw+aa9kbuqV/CD3avTVRp3185C70ks7O3nUy9MaVSMV6KTDYkQUiq/q4cJcdAFiVuLp3OLGcevSE/BSYrZZaRq4/nA1P+Fz0Ui/Hno8f54c69KECmXscPli+iyj61RneGU+Ow89TFyznc70YviVTbbeSdBTGjdAcJP3UItSn+ByfOdCB/oBzRro3b8568/94GmKAwEAQBId8I+ednt22aC5+e1g1se/lzidv27Bnkll3M/vXfZ+HVTyDr4g2jfk8bHcdeo+v4m+SUXkxu6WoMltF9KwLeTvo6tuHrP45GNnF465MAhHydbPrH3Sy/6VeYHWUYrcVkFS2ju3lt4rHT5t2ZeN6JIOss2LImnhVwLsnUGaizZydNG8x05uIcvHruDwVo93soMFkptzg46nFh1MgJUXCCDV3NDIQC6I3Jn9vszDwYjCUJKTF+17CbHy+7jv5QEIfOgCgIfOSN5/FFI1hkHY8uvoo5mRMPq6qyZfKjpdfx5L5NeKMhbp82j1nOZEOqZm8/rT43Nq2eEouDGRnnp2HVyUx5YdDk8SZEAUBPMMSzBw7ztQVz0I7hkuUJR4goCs5xOP7FFGXUpYJ3SqXNSuVZqhKcQDk4kBAFAMouF+oKH9i1CA5rvMY27KJFnJG6hpkmzflMJOzl8Nankrb1d+0mu3gF3c1riUUDyDoLsWiQQ5ufoK0h7hXS17ENr+sIdUu/MOI4YDTi5+Dmx2k78g+yi1cQCiTnBCixIH5PG2ZHGVq9lenL7sfrOkrI34vJVow1szrlmBcqFq2er8y9iMf2rGNbTyvzMwv4zxnLsGh1HPe4+MqWVzjQ340sitxVt5htPa2oqopDa8AVHhIH87MKRmxCrHfk8uC8S3hq/yY0osjd05cww5GLTqOhK+DlI6//IWE+5ImE+Oa213hq5S049ROrtmhEkQXZhUx3ZBNTFSwnvZZ9ri7uXvvXRFDTp+oW896Kmegn0YX3XDHl34E7HOHkwlzDgIcOfyCpEfAEUUVhW3cvj+/ax0A4woerK7isKB+bLvWP3RuJsLWrh+ePNJJnMnBrRRnVjqlbiTgVanfqmpjqi/+BCLkZyP92A5E/vgz+AOLiWYj1aWGQ5sJCEMRRjIsEimtvRWeMN4wGvB20NfwraY+WQy9QPuv2EQ2H/J7WRJCSz92MPWsGA917k/bRGeOlYyUWobtpLXvXPQKoSLKRhVc9nmhOfDdQYcvgkYVX4o4EsWn1GDQyqqry1+MHONAfj1GPKAo/3LOOp1feTL7RwoLsQh7c8godAS/T7dncVbcYbySMqsbD9E5g1mq5priapTlFiAhJ6/jeSDgp1hmg2efGFw3jJL7fUXcfb3c00hXwcXFBBfWO7DEjmY0jmCCFYlF+vn9TQhQA/GTfBpbkFKfEKZ+PTHlhkGvU49BpcQ0L6VmWl01XIDiiMGgY8HDf2xsT60KP7tiDU6/j4sLUUtL27l6+uH7Qircb3mjt4OmLl4943PMBsc4O/xrWoSuCmBcv+QsaDdKsaoSSfIjF4smDF4CyTfPuIBoNMtC1h66mtzFaCsgsXJSIMx6ORjZSNe8TbP7nPZwoj2UWLcNoLcSRMwtBiPcLiZIOjdZENDzktinrrIiakSd1BEFCECRUNYavv5HCaddhdlTgdTUgiBpqF92LadDQyOduZt+GRxPPH4v42b/hByy46vEJNR+e7xhlOemEHlFibOtpTdnPEwmRYcglw2DimVW34ImEkESRPzbs4R8th6i2ZXL39CUpwUonPAWGk6k3McORy25XR2Lb8pwSMgd7G1p9bu5e+1d6gnHx8Puju3li2fUsyJ6Yc2soFqXFN5C0TQW8Ixg7nY9IX/va18a6f8w7zwYWWSbfZMQXiSIIAteWFOGLRpnutJM9QvTwrt4+XmlJHl8REEYUBj/cuZdm71DpPawoLMrJOm+FgWCRESssqH1BhFwD8ocrEEstSUZBgl4XT2mchK7dNGnOFj2tG9n84qfo79pFd/Pb9LZuIqd0NRptqje93pxLdvFy7Nn1FNXcRFH19ThPmgiQdRb0phw6G98Y3CIwc9XXRh0l1MgmFCWCqzMeMd7Xto3ZF32TkrrbKK1/P1mFSxJLEH53C80H/i/p8YoSpaj6RjTyu7c3RxJFwrEY6zqHotR1osSNZXXoJA0GjYxR1mKRdTx3aCu/adhFKBal1e9mTUcjlxZUpGQtNHn62dnXTnfQh16Ssev0zMrIxR0O0R8KcGlBJXfWLUrEK+/qbedPjfuSjhGKRbm0sHJC70UnaVBUlfVdQx42uQYzM5y5mDXalEmJKcrXR7tjyl8yCoJAicWMQZKY7nTwSksblxcVUDrKydsxwqx//SjWusUWE+s6krcZz+OraEEnIc1wIFbH+wkE+fycoU2TZjixWJhjO3+ZtM3bfwxvfyN6U6pxlyTJOLJn4MhOnTcfTm7ZJZjtZQR9XejNuZgdqfHciWNqtJTN+DCZ+QsJ+rswWouwZtag0aSugRutBZjsZfj6h2K4i2tvRWdIj7euzi+jL+Tnj8f2kK03c2NZHV/e9BKZBiMPzb+cIrON/lCAl5oPJz2uJ+inM+AlZ1gj4sH+bu56+694IvGZyEvyy/nC7FWUWhw8MPciPOEQNq0+KbFxpPRGu27sceveoI89ri6OuvuotWcx3ZmNRdZzWWElkiDyt6YDFJps1Dtz+MbWV7m9eh531Jy+ida5ZMpXDACceh31Tgc5JiNXlxSyuiAP8yjmPCZZRhRgV0+8OWhWhoMPV1dhHWH/TIOeN1rbE2OFVxYXcFVJEXrNOz+hKqrKkQEPO3p6cQVDGDXSWW9GESRxZO//NGnOI9RAFLXNj+qP0t27Ft/A8aT7C6tvxDjGFMGpEEUNelMWZnspemMm4inMaDSyHqO1EGtGNQZzLuIIiYrx/Qxk5MdPDKoSo3zWRyiougZZaybk78Pde5BQoBdJY0QaZeliJBQlllgKGS9qJIbqiYAkTInfBJOsZW5mAfMy8mnze/hdwy7ckRDdQT85BjOzMvIQBYGdve00DyvV60SJD1TOSjIt+sWBLezsbU/cPuZxsTynhDyTFY0oYpS1KaPmelHDPlcXHYH4EpJB0vDZmSvIMoycihiKRnly/yYe272OLd2tvNh8iByDmemOHIwaLRFFocHTS6vPzd+bDhJTVToDHq4srEKvOb2QrrPA+VsxOEGuaXz+BVatzEdrqrissICwEqPAZEqJSj5Bpc3KUxctp8nrwyBJlFnNo+47Xnb19HHPmg0J7++by0v4VH0tJu2U/5KkSTNlUAbCRP58HGV9DwhQetdtdLWsTeQMZBUtw2w/M2mhEG9OHOg5QCwSwOKswOKsmtDUksVRzvQlnycWCye8B3zuFna89qVE02LBtOuoWXDPKd0MPX0NNB/8MwM9ByiuuYnMwiXoDA4UJYrXdYyAtw29MQuTozypgqF0BIj8rRll/wBirQ352iLE3HPvgyAKAl1BH/9qSa4K7HN1AvHAo09NX0I4FmNzTys6ScNX516UlH+gqiodvtQR7EAsmrJtOJkGEw8tuIzDAz34o1EqrE7KRzBROkGr380fjyY3mf5s3yZW5ZWRbTBj0WpZ23E8yeug1p6NYeqLgjE5b4TBRNBpNFSO0zGwwGyiwDw5GdrBaJSf7zuYFAjyf0ePc11pMbUTcBhMk+bdjtrsi4sCABWMv9Ww5KO/wKe0IOssWDOmoTNMTjDYyQR93Wx/9X76u3YD8UbFxdc+hT17+oSOI4gSGnHoRNzTvD5pkqH10AvkV1xJ1hjCIODrZPO//pOgN35l7OrYxowVX6Wo5gZ6Wjay9aX7UNV4xbN++ZcpqrkRQRBRA1Eiv25AORQ/eSpbeokMhNHeVYNgOPc/+4UmW4qF8KWFlbR4B/hz437ebD/KouwiPlG3CIdOj16SicRiSJp4BUAQBG6tqGdd11C/gkNroMR86t/ZbIN53PkFAiAIkKkzEohG8UXDcS+XwayMYpOd+2ev4pEdbxFVFXINZv69et6YUw7nA+f3q59iRBWVgVBqV2pImfycbkVVafX68ESi5Br1OPUXvi1xmncPqvukgKreCKZdWdhvuuqMP7fX1ZAQBQBKLETTgT+OKAz87hZCQRd6YxYG89jmNp7+hpRtJ7IWRsM/0JwQBSdo3PNrMgsXs2ftwwlRALBv/ffIyJ+PyVaMOhBJiILE+zjsQR2ITAlhUGFz8viy63h87wZcoQD/Nm0OM515PLZ7LS+3xtNbj3v7OeZxcUlBOU/t30ytPZtP1C1KjAPOzcjnsSXX8OfGfRSabFxdPG3EVMUTtPvdbOlu48hADwuzC5mZkYflpGbGgVCQva5O9vd3UWnNpNaexTfmX8q2njZMshaDJJOpMyaWHmRJ4priamY6c/FEQuQZLWSdh6FJJ3PuvyEXEGatzIeqK/n65u2JbVVWC0WmyalInCCqKLzW0s7DW3cSjMUoNZv41pL5Z812OU2aM42Qa0g15KqZPI+RaCSAgIAkpwpqZQQhH434U7Z1t2xg+6tfJBr2ojU4mXfZ98YMSMopXknTvucTtwVBwmwb22JbklN/OwyWAkAlPILJUiw6GE5gkBCcWtS+oQsVwakFw9RoSJYEkXlZBTy+7FrCioJDZ6DFO8Arg6LgBJu7W5iTmUdvKMDbncdp8PTx9KqbydSbMMpaluaWsDR36DMMRCPIooTmpN4CdzjEIzveSkxE/KZhFw/MvYjrS2oT+yiqyl+P7+fHe+N5BwLwzfmX8ZUtr6AMfhHzjRZ+tPS6pGNrRGnETIfzmXPfjXKWicQm/+p9OMvzcvju0gVcUVzAPTNq+daS+WQYJvdqvtnr4xubtxMcfC+NXh+/PHCE8Bl+b2nSnC3EIhPaT9UgFBoRcvTId1Qhlk7cUvhkotEgncffYuPfP8Gmf95DT+tGFCW5OmFxlKO3DB9vFiipfU/SPgFvJzvf+ErCByEc6GPXmofGrADYc2Yy++JvY3FU4sidzcKrn8CSMbbJmNleQvms2xO3JdlI5Zw70BkzKam7LWnfzMIlCUtn0aZFvr0SjIPXfkYN8u2ViKcZjz7ZmGRdIo7ZoJFTSvw2rR5/dOjfp93vocOf2lvQF/Tzl8Z93Lnmz3xr++sc7u9Jur/FN5A0Jgnw9IEtuIaZIXUGPPziwObE7Rp7Fi+3HkmIAoA2vyepKfJC5V1TMWj3+Xm1pY3XW9tZlpvDFcUFk9ZbMByLNp7IuCL/zHlmu0IhoicFsuzq7cMfjY5pE50mzfmCIItI9Q7EcguoIJgm56dqoGsvW1+6L3F704t3s/T6Z7Fn1ye2GSx5LL7mZ3Q3ryPo6ySraBm2rORlhEjYnXLF7nMdJRr2odWPvM4ta83kV1xOVtFSBEEcl6eBRjZSMft2ckpWEQ65MVmLEimRpfUfwGgtoK3hJbIKlpBXeXmSV4M0zYbuSzNQ3REEq4yYOfoFihqKoXrjywyC8dycFjL0Rh6YexGfW/8PQkoMjSDyH7ULeObAlsQ+OkmDdYQqz1vtjTy84w0ADvR3s6GziWdW3UKeKV5F1Qw2jmbojCzKLsIVDtAd8CINm/IQEBCHNZhGFQXNCJMq0iRa509V3hXCIByL8ez+Q/y1MW5GsbevnwOufr48bxaBmIJdpz2tEcWzTa7RgFWWcUeGlPTlxQUjjmSmSXOuCERD6CXtaWWQTPZJqrd9c/IGVcHjOpoQBgFPO90t6+nr2E5W0TJKyy4a0X9Ab8zGmlGNu/dgYlt28Uq0hnhJOeDrwtN7kGjYh9lRhsU5LfE5TNT9UNZaRlyiMJhzKKm7jeKaWxBGGbUUM/UwhiAAUDr8RP54HGVPP0KpGfn9ZUjF52adfEFWIb+6+Da6Aj4y9EaisRgMNvrpRCllOgHAFwnz/LHdSdt6QwFa/O6EMCgy2/jirJV0BLy83tZAhs7EPfVLk7IYcgxmPlm3mEd3xWOuD7t7+XjtAt5qP0Z4cHmp1p415hTDhcK7Qhh0B4L8/XhL0ra32jtZ0tbBYzv2sCAnk0/NqKPMOr5SZVRR6AkEkVJQCYYAACAASURBVCWJDL2OmKrS4vHSGwqRZTBQdAYqEcPJN5n4/vJF/GDHHho9Xq4uKeSG0mLavD66gyEy9DoKzaYk9ZsmzdmizdfF31vf5vX2zSzLns31xaspMuWc65cFgNFSkLJNq49PN0QjfvZv/CEdx14GoO3IP6ia90kq53w0xT9Aq7cxa/VDHNz8OH0d28kuXkHlnI+hkQ2EAn3sevNr9LZuBECUtCy65mdj9h8A+Aaa8PQdBkHE6pyG0Zr6WkdiNFEwHtRQjMgf4qIAQD3mJfKLwwifm45oPbsXGqqq0uoboC8UIM9oSQiA51bfSkfAg11roNBsTfyuuUJ+ooP9CWUWB4cHesnUG/GEQ4SUGOZhHhEGjRZZlPjvQ9sAaPIOsH9jF7+86D2UWuL//oIgcHXRNMqsDo57XBSYbNTasnhm1S0cdvdg0miptmeOe6LhfOZdIQx0koRDp6UnGEpsM8sa2nx+QorC2+1d+CMxvrtsASZ57PnT7kCQ3x85yu8OH8Ou0/L5OTOQRZEvrttMSFEwaTQ8umwhc7LOrMtZfYaDx1YsJhCN4tTr2NnTx+fXbcIfjaGTJL69eB5L86bGj3Gadw+hWJgnD/6Bv7fGr7oOuhs57D7Ow3PvwSiPPUPfHexjV99hGjzNzHBMo95RiWWSLYSduXOxZ8+iv2snEL/Ktw0mH8ZjmF9O2v/ozuconHYtINDfuRO/tw179gxsmXVYnBXMufhhImEPss6WsET2uo4mRAGAEgvTuOc32LPrRzUo8riOsvHvdyaWJ4zWIhZc+eMRA53eKUp3EGVfP0qbP+6QWmFB9UVR9ib3RahdQRiIwFkWBhu7mvnixn8SiEUxabQ8uvgq5mUVYNXqkEURh86AIAhEYjHWdzXx/V1r8UXD3D5tDh+umkO9I5fjXhdOnZESs50Sy9A4a0yJ8bemA0nPF4xFOebuQxIE8owWNKKEWatjQVYhC7KGshPsesMFEYw0Ed4VwiDToOf+uTP54votxFQVEfhQdSV/ahhyUtvW00tvMIRJlokpCoIgjHjFva69k/85GB876goE+eK6zdw9o47QoHeBLxrlka07efKiZdhHSHQ8QSAa5WD/AC1ePzkGPdV2G9YJmitZtDIWrUxfMMg3t+zAH42Xu0KxGN/csoNnL1lJzgh5EmnSnCm6gi5ebH07aduaru10BHspl0cPqglEgzxx4Pe80PxmYtuXZnyMW0ovmdTXZ7QWMO/yR/ENHEcQREy2UrT6+JWpKMqIopzUjKjRmlFVhX1r/4uu5jWJ7XMve5Tc0ouQZAPSSYJHUVJNdqJhL6qqMloRr7d1U1LPgt/djLv3wKQJA8UTIfzMYdRj8WbJ2JudyO8rQ6yxIpSYURuHwqSwymA+u6eGroCXr299NWFQ5IuGeWjbazyy8Eqe2LeBRo+Lm0rruKakhp6Aj89veDHREvjDPev5ytyLeHzv+kTJf1FWIYtyioD4hZ4kStQ5stnRmzz62ewb4IEtL/PhqjncVj4Dp/7dm2UxnHeFMACYk+nkmYuX0xMMYdNq+fneA3QGhrK/i80m9JLImrYOfn/kGJl6PbdVlqUYE73emvzFUgD/4Jf5xHRVk9eHPxIdUxi81tLOQ1t2JG7fUVvF7bXTkEcIN4rEYjR7ffiiUfKNxpQpB380Rrs/kLStLxTGHx3bBSxNmonQ4utkj+sI3miA6fYKamylKf0DBklLht5Od9CV2GaTzRik0f8WAFr9XUmiAODJg8+zMncuWfrJNTLSGZzoDKnrxEZrPlXzP8nBTT9KbKtb8jnCgb4kUQBwaMuTZOTNR9alLj+a7WWYrMX43ENd8KUzPjim5XI0GkjZFotFRtjznaF2BRKiIPGcb3YgdgeQbysl8sxh1J4QWGW0H6tCdIz97zXZxOOSkz+DNr+H9V1NbBgMKvrp/k3IokS2wYx60uPXdTRRZLbR4I6Lq43dLTR7+7E5cwnHYiiqwo0ltbzd3kiTbwABuLlsOhu7WogoCs8c3Mp0RzYr8s6cm+b5xAUvDCKKwvbuXn6x7yBRReHfa6dRabPw0dppHB5w4wqFydDr+Mr82TS4vXx+3VBz0hut7TxzyYqk3oMFOZls6OxOeo4cg5676muIKCqSKODUaskcY0SxOxDkx7uSE76eO3CEy4sKKDmpzyEYjfJCYzOP7dxLTFUpMZv49pL5lA/zLMjU61iZn8NbbZ2JbbMznWSlTY/STBLt/h7+c9MjNA6a7WhFmaeWfpV6R3IqXabewQMzP85nN3+PqBpDEkS+NPNj5BmzRjpsAmHwP3XYT74kigmHubOBKMoU196CI2cmQV8XRmshFkclHteRlH0FQWC0y3+DOYd5VzxGT+sGAp52sktWYM8a2zUxs2ARh7c+mbB81sgmrBnTTv9NnXi9mhGWMHRSXAzEFLSfrwd3BIwShBWUYx7I1CNazo61b7bBxOyMXHb0DqXaLc0pZvtJV/gvNB3gczNXpDy+zOJgU3dyH5ksSuzsbee5Q9twh0J8eNpsfrzsejoCHtzhIL86soOdw57v0EBPWhgMcl6EKJ0O+/v6uevNdXQGgnQHQ7zc3MbcrEzmZWdyWVEBVxQX8L6qcsptVn6+9wBH3UMzslFVZUF2JqXDTtYZOh3tPj9NXh86SeIzs6dTZrXwyLZdbOzsZktXD81eHyvzc7GMko/gi0T43ZFjSdbJADeVl+DQJyv1IwNu7l+/hRN7DoQjCAIsys1OLHVoRJEahw1VVekLhbmoII9PzagbMZY6TZp3wo6+g/z22L8St2OqglaUWZ4zJ2XfAlMOl+QvZGXOXP6t4jpmOauTxsJGwiTp8ceC7OkfOgn/v/qPMiujevLexDiQJC0Gcx4WZyV6UzaiqEHSGPANNCelJc5Y8RWsY3gQaPV27Nn1ZBUuxmjJHzVw6QQ6YwZZhcuQ9TYyChZSvfAebJMoDNCJKJ4oavNgzLwI8uX5xN7sRLMyFzHHAAaJ2NZeIk8eIramC2WfC7HaimA+8+JAK2mY4czFH43QHwpyeWEVH6yaxU/3bkyqDizJKeaa4mr0kszO3nZUYEVuKVcWVfGnxiG76csKKpnhzOXONX+m0dNPV9DLy61HWJpTzILsQroCXp49uC3pNXygclZSX8K7gPM/ROmd0jDgTik7rW3vpMJqIcdoSFqDLzandpua5OSPqMBs4msL59DuD6ATRfLNJv569DhVdhu7e/pQgEaPl4YBN3kjhD5FYjG8kQhfnjeTdR3dvHi8GQW4rrSI/BGmGfpD4ZTXv7u3n3AshmFYamOR2cy9s+q5ozaCRSunOH+lSXM6xEZaN1dHNtSSBJEKSxEVlvGvj+s0Oj5adSNLsmbRFuimwlJIjW1qXL3JOgvTl36OgqqrCfo6sWZUY8usmdTnEAQRe/b0CecxjPv4Bg3yTUUoM+yobQEwSCg9IeT3lyHkxH8D1Y4A0d82Jh6jtgaIrulEvqUUQTzzlZtSi4MvzV6NOxLCpo1Pe31m1nK+v2stMVUhz2Dmw1VzsOsMfGTaHC7KL8cTCaIRJKxaHc+uvoVD/T1k6E3U2LPY0duWcvH1ettRFucUU2PP4tP1S/j5/s0oqsrt0+Yyw3nmvGfONy54YXDyFTjET+67+1zUOGzkGodO3pcU5vPCsSa6gkEALi7Io8KaajNskmUqbTIRRWFHdy9vtXdikCTum13P748co9nrI6qcfDoHfzTKn4428pPdB4ipKnUOOw8tmosCzM7IQD+COVG+yYhRIyUaCwGuKSlMEgUn0IjiiO83zYWL0hGIl32jKmK5GbHgzIzK2rQWCozZtPq7ANAIEqtz4tHC3UEXsqDBPsJ6+0Rw6mwsy5l92q/1TKA3ZZNryh73/kF/D9GwF1HSocTC6E1ZYxoaxWIRlFgYWXvmRp1FixZxTgaxXD9qo5fY3n6U/f1glpHqbPF45pNQDnsgqoD27Pi8yJJEhhT/nDTAjSV1zM0owBsJkWeyJEYFJVFkf383D29/nZiqYtJo+eHSa7mxbEhYWbWpv4UlFvvgfXo+UDmbi/PLUYA8o+WUVa13E4Kqpp7AhjHmnecD3YEA39y8g41dcYvM6U47szMz+N9DDeQZDXx/+aKkHoJ2n5/jHi8CYNNpydDryRqlX2B3bx93vr42UeaXBIFPzajlVwcb+NnqpajAps5uOvwBluZmI4siH39jbdIx7qidxsenj10u3d3r4vFd+2j1+bilopRrS4tHfU1p3j0onQFC39sbXxsG0InoPlePWDT5J5fnj71Eb9gNqkowFiJT70BAQC9pefLgH7DKJj4z/UMszJqBfIqy+XgJxSL4owFsWjPiefSj3de+jZ1vfJWAtx1bVj35FZfT3byOuqVfSLgWDmegez8NO5/FN9BEyfT3klu6OuGtcCaIHRwg/IPkHift56YjmGVCD++C8NBVtua9pcgX5Z2x1zJRQtEoA5Eg3kiYf3v9+cQUAkC9I4cfLbsW82AwUm/Qz/d3v83LLfHlqVKzg0cXX0WxJZ10O8ioZaALvmKQZTDw8bpq5mRlkmcysLmrh/89FB83bPcHeLm5lf+YPlQWzDMZaRjw8NVN2/BHoxSajHxj0Vz6Q2G0kkS51YJz8Kp85+DSwQliqoqiqvxk1RJ0ksSn12yg0RPvBP7VoQa+tiB1PXZbdy+KqiaNRrZ6fbR4fVi0WkotZmZkOPj+8oUEYzGcOt1pOcmluXBQWnxDogAgpBDb139GhEGpOZ/vbHgWnSgjizLeqJ+H597DA9seR0GlLzzAvZu+yy9XfItae3wJIBKL0hHsQRIk8gyZE/reHnE389Sh/2NP/xGuzF/KLaWXkn+KBsapgN/TztaXP08kFPcGGOjeg6yzoKoxGnf/L3XLPo8oDq3Z+waa2fTiXURCbgD2rPkmoqihcNp1Ix5/MlCOpmYNKM0+5Ivy0H66lsjzx1H7QmhW5iDNTvZjUToDxHa5UNr8aOY6ESutZy2tsdHj4qn9m1nX2cTd0xcniQKAJm8//mgkIQwy9Ea+MHMl7y2fQViJUWK2XxDJh2eDC14YAFQ7bERVlYYBN38btEU+wTF38ghPq9fHg4OiAOCG8hK+uG4L3YPLC4tzsnhg/mwyDXoyRijb5xmNlFotbOrsToiCE4RiMWRRTFr3uqqkMEkUHHQN8Ok1GxgIx1PR7qidxgerKzDJ8inNl9K8yxipnjd2BfAdU2Mr44v1t/PEgd+hqAr31n2QNn93UsCMgkqzr4NaexndwT5+eeRv/L7xJbSizH/WfZCrC5dh1Jy6IbY32M/nt/yAJl+8I/25hr8SVMLcV/ehEb3rpxLhQE9CFJygv3MXRTU30X70FapCn0galfR7WhKi4ATH9/2BvIrLkU4x4vlOEbJSq42iM/5cUqUV8d5a1LCCYJWTxJwyECb8s0OobfHgofD6buSPVaGZf+bNf/yRCD/Y9TbrB0cXDw30UGK2c9w79FnfUFJLxkk+BDadnpm6qVPxOF84f+pzp4EsSczOymBudmbKOv7VJcmmKwPhCL5BUZCl19PlDyREAcCGzm4OD8T/kGdmOKkaNjZY77RTN+h7oBmhWWd/n4sfLF/ENJsVp07LJ+trWJY7tG4ZVRT+5+CRhCgAeHr/IY6fJDDSpIF4AiHWYWJRKyJNPzNlUovWxG1lV/C7Vf/F71f/Fx8qv4YCYzZ31dzGJ6tv45K8hQBk6uLPv6VnP78+9iJRNYY/FuTbu5/mkPv4WE+RoCPQmxAFJ/h7yxr6QlM/1U5ryEwJUbLnzMTde5CsoqUpvgeyNrUvw+KsTKoqTDZiuQVx5tBShTjPiVAyVGUSDBpEW2rGhdoVSIiCE0T/2YoamFy/FG8kxEA4mLStL+RPiAKAF44f4KayOq4rrqHU4uCTdQt5T8WMdJ/AJPGuqBicoMxq4YlVS/jfgw10B4K8f1o5czOTS2U5Bj15RgPt/gAWrYwrFE45TmBQOBSYTXx/+UIa3fGehFKrJeFfUGaxsCgnk42d8d4GWRS5urSYmZlOnli1hFBMIUOfvCwQisVSqgwAvkjaqChNKmKOAd19dShHPahRFanCglh4ZnM6co3xq0NXyM223v08fzxuIbwgczrfnvtpqmzFAGzt3Zfy2HZ/L7PHkT9jlo3oJS3B2NDfXrm5AKNm6vfVGC15zL30u+x880ECnrh9clbhErqb11E240MpJ3ydMYvi2ltp2v+HwduZOHNmEY34Jxy2NF5Epw7tRypQu+MW8UKOfszlADWmIEjiiF4IglkD0uQsbUaUGFu6W/nJ3g0EYhE+Wj2flbllmLVazLKWYrONJm9cHEZVhV8d3sF/r74VnSSP2GiY5p1zwTcfjkRUUYipKrpRIooP9Q/w4137ONDXz32z6/nGMIdCgyTx9MUrKLedugO7KxDgUP8AvkiUMouFSrv1lMFGfz56nO9s25W47dTpePri5SOOPqZJc7boDPQSioXJMWSgk7Ss79rF3Ru/nbTPg7M+wfXFqwB4sWUtD2x/POn+Z5Z9jVnOU/sSKKrCy23reXDHk0SUKHathe8v+CwZOjtOne28EAhBfy/RsAdR0qLEwuiMWSNOHHhcR9m79hGyChejKDFiUT/H9/6O5Tf/GpMttVHxbKL0h1B2uoht6kGcZkFclEX0pTaUdYMGb5KA9t46pKrUya3RiKkK+13dvN52FL2kYVVeWSKHYG9fJx99849JJ53Hll7D0pz457Crt50vbPwnfaEAVlnHtxdewYLs0W2205ySd2/z4UhoRHHMNz7NbuORJQvwRCKYNBKPLV/E80eO4dDruKW8dFyiACDbYCDbMDGToVX5uYiCwP8dbaTCauF9VeVpUZDmnBFRorzVsZWHdz/NQNjLlQVLuavmvSOW9Y94hiyA52fW8cGyq/ld47/QSVo+XfsBqqyjn+g6Ar20+DqxyEZKTHlckreYadYS+sIetKKGR3f/N3sGGliePYd7at9PpXXywoXOBHpjBhhPHaQm62wEvO0c3DwkokyOcmStbYxHnXlURSW2povo3+NugkqDB2G/G+3HqlAXZKH6Ioi5BoT8if02Hejv5uNv/YmYGu+z+vWRHTy96hbKrU6afP0pV6IbO5sTwmBmRh7Prb6VnqAfp85Avmn8giTNxHhXCoPxYJQ1GAfNjRbnZrMoJytR9u/w+ekLhXDqdUk+CF3+AB2BADatlkKzCekdTA849DquLyvm8qICNKKQNipKc0455mnl/q0/TDQZvti6liprCfMyalMsjBdk1rO77zCNvjYydHY+Unk97ym77JRTCYfdTdyz8Tt0B10ICNxT+z5uK72CMkshOn83H17zZfrD8U76t7u2x4VGzfsoNJ//hjR6YwazL/om21+9H63eji27npKaW9Eazu1IneoOE32jI3lboxd8UaTady5a1nc0JUQBgC8a4fBAD+VWJw5t6kVUhTV57SnXaCHXeHp+GWlOTVoYjJMTP2rbu3v5f+u30B8O49Bp+c6SBczKdHLINcBn122iOxBEJ4o8MH82FxflvyNxAKDXTO3u6zTvDjqDvUmTBwBvdGzmtpLL+N6Cz/Kj/b8hGAvx8aqb0Ykabl/71cR+t5Rcyr11Hxyz9B9Vovyq4e+J0CUVlR/t/w0LM+uptZfTE3QlRMEJtvbuY7+78YIQBgCOnJnMu/wHNO17HlfHdgzGbGSdBYPl3HXTC7KEYJdRfcP6myQBtBO/UFFVFX80jEGjxSSnJshqBydNptkyuaGklr8c3w/A/MwC5mellwrOBWlhMAF6AkG+vnk7/YNTA65QmIc2b+cnq5bwxJ59dAfinbQhReGbW3YwzWGjxJKem00zNegOuhARyNCP/2o0W+9EREgSBytz5mGQ9azKncdsZzUxVUEWJP597YNJj/3j8Ve4peRSqsdYKw/FIiNOK7gjcU9/h86GWWPAOyx9sNZWzva+g1ySt/C8Mj4ajVDAxa43H8TTdxiAQ1t/SiTsoXrhPafMWDhTCCYN8m1lhH+8H6Lxf3vNLSUI2RPr72jxDfBC437ebG9kaW4xlxVU4tQa6AvH/z1LzHam2eP+FE69kXtnLOOW8nqiikKR2YZNO/X7SS5E0sJgAngjETpOijdu8fnpD0c43J88ixxSFNzh1ImGNGnONu6wl3+2ruVnh/6IVpS5t+4DrMqZj15z6k7ucnMBD8/7NN/e9TTuiI/L85dwRf6SxP22wc55V8hNWEm11I2NkqdwApNs4Iai1Xx3738PHVM2U2CMj/EWmXJ4aM7dPLTz5/SFB5hmLWFZ9iz0ku6CEAUAAW97QhScoPnAnyit/wAGc845elUgVlnRfWkmak8QwapFyDMgSOP/zIOxKD/Zu4FXWuOGckc9fRx19/HzlTdxxN2LJIhU2TKSegVMspYa+9Q3srrQSQuDCZCh1zHdYWeva8hUY25mBrlGA5cXF/Dbw0Ppa1kGfVJAE5wIUIqmQ47SnFV2uQ7zyJ7nEre/tO1xnl76NWaPI7lQlmQuy19Mvb2SsBImx5CJXkotBzt0Vv5j2i08uOOniW0rcuZSZBr7xNbs7aAn2M9HKq7jzc6tFBiz+FjVLRQOe9yynNk8uuA+9rgaaPS24o34uTR/8Tje+fmBRjYhSnqU2NDsvtFahEY+t+mogijEmwsn2GB4gu6Aj1cHRcEJ1nU2cW+9wsUFFZPxEtOcIdLCYAJYtFoeWDCbn+zez5auHhbkZPHJ+hqsWi23VZYhIPCvpham2W3cVV+bNJHQ6Pbwy4NH2NLVw8r8XN5XVU7hCGmKadJMNtt7D6Rsa/K1n1IYNHpaafC0oJO0VFmLKTHnj7n/qpx5/HjR/Wzp2UuFpYi5GTVY5LG/4xt7dvNsw1/I1DlYkFlHd9CFJ+pL2kcSRGY5qyk1FxCKhcnU28+7akE0GiLgbiYWC2G0FKLVDzXwaY0Z1C39HHvf/jaqGkOSjdQt/TyybnK77hVXCFxhMGsQsvRn3FpdL2nI1JvoDg79e9q0egyatIPrVCctDMaJLxKhNxjCppV5aNE8BsJhbDptwkkx32Ti7pl1fHBaBWZZg34w/TCmqhxyDbC1q4cyq4W+YIg/NDTSFwzx1YVzRkxUTJNmMqkaYY0/Sz+209ChgeP8x/qH8Ayu9c+wV/Jf8+8j2zD64yxaE0uzZ7E0e9a4X9txb7zzvSfk4sXWeMDYjcUXj7iv7QwZ/pxpIiEPR3f9koadz4Gq4Midy8xVD2KyFuIbaGbP2w8TDvQxbf4nMVqL0RuzCIcGGOg5gNlehnSKJZ9QoI+QvwdZZ0eJBRBEGYM5L+nEHzvuJfzTg9AfBp2IfEcVmpnjcJs6DbIMJr48ZzWf3/giEUVBI4h8ec5qctJTBVOetDAYB8c9Xr63fTebunooMBl5YP5s5mSlzihLgpBwPjzBtq4e7n17I7FBI6mby0uY7rTzems7dwfryE97FKQ5gwSjIfIMmXyh/nY8ER9/aHyFqwqXUWMrG/NxL7etT4gCgN39RzjsbkoRBkc9LRz1tGCQ9FRZS8g2TCwVcEXOHH597B+J2xpBoswydmViKhP0dREKuNAZnOhN8bVyd99hGnY8k9jH1bGNruNrKJvxftqPvkxv2yYAju76Hyrn3MGO1/4fqhoDBGau/joFlVePenU/0HOA7a/eT1bhYsIhDx1HX0bS6KlZ/BnyK65AIxtQA1Eiv2+MiwKAkELkmSOIX56JOEJuwmSyKKeIX130XrqCXrL0JorN6WTD84G0MDgFoViMZ/YdYtNgbHOrz88X12/muUtWkG8au0zqCUf48a59CVEAcWfDhxbN47jXiyk9kpjmDBJRorzQ/Bbf2RM/KeklLd+b/1nmOGvQaVL7BIbjCqcm8J3cXLi//xh3rn8I3+DEwPyMOr41924yJxAZXO+o5HvzP8uzR/6CSWPgjmk3jWmENJXp69jB9lfvJ+TvRm/KZe6lj2DPricc6E3Zt79rN6r6PnpaNya25VdcwdFd/zMoCgBU9q19BGfOLIzW1LG9SMjNnjXfIhxwIetsHN/3PADRiI89ax7C4qjAkTMDNRBDbU5eniEYA18UznCfnySIlFkdlFnPXIx0msnn/FqoOwe4wxE2dHanbOsNhk752IgSw3XSZIICNHm9PH/kGAf6p34oTJrzlxZfJ9/b98vE7WAszI8P/JbQCNMDJ3N14XKEYY6pdq2FckvyyemvzW8kRAHAlt59HPEkp5eeCqNGz+q8+Ty55AF+sPBzzMuoPS+DcIL+Hna+/gAhf/y3IujrYOdbXycc6MdoLUIQki8CcssuQhAE8isuT2yT9TbCAVfSftGIj1h05N+aSMjDQM8+jLZC3CdNNQAEBpdpBKuMODe5wilk6sA5tjhM8+7l/PsLPMvYtDLzs1ODlrIMpy7BOfV6PjQtufu2xmGjxevDFQrzlQ3baPP5Rnl0mjSnRyAaJKIkB3B1BHpo83fxyyMv8Hzjyxz1tIz42Bn2Sp5a+lXeU3IZd067lSeXPECJOdlwZyRb5JOfb7wYNDp0I0w7nC9EQgMEvMmJkD7XUSJhNxZnJfOv/BEWZxV6UzZ1S7+AM28+ANnFK6mY/VEkjQFPbwMF065NOkZW0XL0oxg5aQ0OMvIX4R9oxuqsSrnfaIk/TtCIyNcUIC3LAqOEWGdDe2c1ovX8/bzTnFnelSFKE6XR7eHhrTvZ3evizunVGDQaOgMBluRkU5/hwKAZfUXGFQyxpauHl5tbyTcZMckyz+w/lPhgn7l4RSKq2RsOc2jATZc/SL7JSJXdOuax06QZi/6Qh7s2PMxBd2Ni279X3sCajm0c8cav7J1aG79Y9iAl5jy8ET8t/k4EBIqMORiHjcupqsq+/qO80PwmISXCjcWrUVSVj637emKfDJ2NZ5Z9PWnU8EIkEvbhdTUQDvZjshZhdpQRDvaz8e+fxNN3KLGfI3cO8y9/DFkXC+wMQwAACLZJREFUb5oMh9yoShTdSX0aqhIj6O8ZDFwK0XH0FdqPvUpW0VLyK6/CNMIywgk8rgb2rfseJlsRsWiQtiMvIskGahd/lryKy9EMc51UowqqJ4Jg1CDo0suYaUYPUUoLg3HiCUfoCgT41pad7BvmY/DIkvmsKki1Lu0NBmnyeFFUKLWa+f/t3WtsW/UZx/Hv8S3xcWLn6sZp0qaXJE0g7dq0BTpuA21MMHUvGNqgkxBCaGisUiXQJrQxARPbAIE2BEwDBhuwjbGJyxioYgLGqMqoKBDSNiR1mzQtCWniXNzEdnzdi2YnMaaD0iatw+/zKnHOcY6lSPmd//85z+N1OrnnvV081z09aKbSXcijF51LpdtNKpPhz137uL+9w/r5T9au4ht1i2b3g8m81nOkj2d7X+WdUAeX1ZyH11XELe8+kHXMna1bWFlaz6/3/JGtfdsBuKzmPDY3XUnlVL1A51gP12z7qbUNYTdsPLLhVuLpBP/sexN/YRkXVq1l2Wk+3OhEJZMx9r/3e4LvPgyAzV7IWZc9SOmCVYRDe9mz/S6GB96jPLCO5nNupLjs8z2vn04lsdk/201BIj5BYnIUh9NDYvIIhs2BeQrbKUve0HTFE1XscrJ/LJwVCgAe69jLOn8FpnP62dz+iQi37niHttDR/cIzSkv42dlruKphGcOTk2zrG6ChxMeP1qykcqrXQd/4BA/t7sx67/va9rDeX4nfPLWNTiR/1RVXs6V5E/F0ggK7i4e7nsk5xm7YaB8NWqEA4MVDb3Bx4CwuqGoFjgaDmbUJqUyanaE9XFP/TdZWNM/+BzlNREYPEHz3Eev7dCpG19u/ofVr9+Itr6f1kl+RmAzjKvThcH7+J44+aygAcLo81khn13G0uxY5FgWD42Cz5QYs0+nA9rEuhnuGR61QALB7ZJT20AiXLKrh9vWtjExO4nE68Lqm9/hSmQzJdDrrfSZTqawnGkQ+D8MwrP378xes4fHgC0SmuuwtdFfS6F3My31v5pzXFzlsfe3+hEFI+dpX4EQkk1E+vpAaHf+IdCoOTjPrn7RIvlIwOA6Li4q4eGGAVz48WmRkNwyubWrIaVI0MplbRTwUjRFJJDCdTgIOk3gqRXf4COlMhmqPScB0c2X9UjpGxwiOhQnHE3y3cblWC+SkavTV8di5t9EfGaLA7qTGXEC1x8+Zpctzjp3Z6+AM31Lqi2vZO/XUQbW7ktbypjm77tOF6a3B41vCxNh0+/MlLZt0py7zimoMjtNQNEbXaJixeJy64iLqS7w5cw92h0a47rVt/O/+3wBuW7+GxzuDfLW2mq8srOKlAx/yRGeQVCbDpYtr2NSwjO39A7w1MMhafyXNZT6W+7yUFWq6mJxcnWM9/Lbzb3SFD7Cx9gI21l6I11XEvz/ayYOdT1PkMPnBim+zurwJ94yuewPREMHwQdKZNMuKa6j2+E/hpzh1xke6ObT3H4we3kVt40Yqas7JKSgUyQMqPpxLyXSatqFhnuwMkibDRQureTrYzb7w0aYxP25dxR0727LO2dzSxEO7O5mc2k64qn4p329p0rAlOak+ioa4+o1bGJqc3ur6XsPlXNdwOYZhsC98kJ2hDrrCPXzZv5rVZSsoKVAL20+SyaQx8rDngsiUYwYD/VXPAofNRqu/grs2rOPGVS0cjkZpLivBbbdjAAPRaM45+8PjlBVO3539dV8Pg9FYznEiJ6I/MpgVCgCeP/g6I/EwwbGD3LP7Ce7c9RjP9r7GTW/fy9a+o/MLxhMRhmKjfMqNxBeKQoHMV6oxmEWDsRhPdgV56cAhAh6TG1qa+MMHQUpcuY1F6rxFbO2dbjZTUuDCpdUCOcm8Tg8Ow07SarsLDd5FpNMZdoR28dZQe9bxj+59jjNKlnNn+6Mcjg3znSVfZ2PtBSTSKQZiIUpcxdR6qvKyW6GIfDIFg1mSyWR4secQf+85Wqx1cHyC+9s7uHpFPQ0+H7etW8197XuIp9Jc29RAlem2nkCwAT9c3UL5Z+iuKHI8FhUFuLnlWn7R/juSmRQVBSVc33gFofgokWQMGwbpGTuIpsPN872v0TFVbPfAB3+hyGHyVPdWDkz0U2BzcvvqG7g4sH7Wx/iKyNxQjcEsGY8nuO5f2+gOj2e9fveGdZwbWIBhGISiMVKZDH7TTSyZZH/4CIPRGAGPyRJvMU6tGMgsSKST9I73E05MEHBXUGVW0B8Z5I73H6HKXcGzva8CYGDw8zWbueP9hxmfMRPhS2WNFNhc1uqC217An87/JYuO0bpXRE5LanA019xOB2srK7KCgd0wqPaY1p3VzBWBQoeD5jJNIJPZ57Q5cjoUBsxKrlp6KS/0vs7mpitJplOsLK3H6/RkhQKApUU17Jix5RBNTWaNaBaR/KZgMEvshsG3ltfRMTLKruFRTIeDm9esZHHxF68pjOSHDf5VLPYEGImH8ReW4neXE0nG2NK8ifs7niKZSbHCu4S1FU080/uKdV6NuYAF7vL/884ikk+0lTDLwvE4A5EopsORtVogciLaR4IEw73YDRuNvjoafXWz9rsSqQS9EwNEUzEWmn4iyRhPdW/l5b43ObN0Odc3XEG9TzM9RPKM+hiIzBdtw51s2XE34anl+4C7gnvX3USDb/GcXUMynWIsfoQip5nX45JFvsDUx0BkvvjP4PtWKADojw7ROdYzp9fgsNkpLyxRKBCZhxQMRPJMPJXMeS01oy+BiMiJUDAQyTNnV67EZZse8+11eqj3zt02gojMb6oxEMkziVSStpEutg+2UWhzclblSlaVNZzqyxKR/KLiQxEREbGo+FBEREQ+nYKBiIiIWBQMRERExKJgICIiIhYFAxEREbEoGIiIiIhFwUBEREQsCgYiIiJiUTAQERERi4KBiIiIWBQMRERExOL4lJ8fs5eyiIiIzD9aMRARERGLgoGIiIhYFAxERETEomAgIiIiFgUDERERsSgYiIiIiOW/+YUxDCThUooAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", - "f.tight_layout()\n", - "\n", - "arr.scatter(tsne_df['x'].tolist(), tsne_df['y'].tolist(), \n", - " marker='o', c=tsne_colormap, s=32, edgecolor='w',\n", - " linewidth=0.5)\n", - "\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xticks([]);\n", - "arr.set_yticks([]);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gLl_Wxeri0iu" - }, - "source": [ - "## Next we apply a GMM to the 3-D PC-space (which explains 94% of variance) and generate AMI estimates. We also show that the BIC score at the \"elbow\" of the number of clusters yields four clusters." - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": { - "id": "eSYbOzwxjslZ", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "data_fracs = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]\n", - "PC_reduce = PCA(n_components=3)#3\n", - "PCs = PC_reduce.fit_transform(full_data)\n", - "ground_truth_gmm = GaussianMixture(n_components=4)\n", - "ground_truth_PCA_classes = ground_truth_gmm.fit_predict(PCs)\n", - "GMM_on_PCA_df = pd.DataFrame(PCs, columns=('x', 'y', 'z'))\n", - "GMM_on_PCA_df['waveform'] = list(full_data)\n", - "GMM_on_PCA_df['truth_ix'] = ground_truth_PCA_classes\n", - "\n", - "n = len(data_fracs)\n", - "for k,frac in enumerate(data_fracs):\n", - " BIC_aggregates = {}\n", - " for n_clusts in range(1,11): #11\n", - "\n", - " BIC_scores = []\n", - "\n", - " for _ in range(1,100): #25\n", - " PC_reduce = PCA(n_components=3)#3\n", - "\n", - " sample_df = GMM_on_PCA_df.sample(frac=frac).sort_index()\n", - " random_rows = np.vstack(sample_df['waveform'].to_numpy())\n", - "\n", - " PCs = PC_reduce.fit_transform(random_rows)\n", - " \n", - " gmm_PCA = GaussianMixture(n_components=n_clusts)\n", - " gmm_PCA.fit(PCs)\n", - " BIC_scores.append(gmm_PCA.bic(PCs))\n", - "\n", - " BIC_aggregates[n_clusts] = BIC_scores\n", - "\n", - " BIC_means = [np.mean(BIC_aggregates[i]) for i in list(BIC_aggregates.keys())]\n", - " BIC_stds = [np.std(BIC_aggregates[i]) for i in list(BIC_aggregates.keys())]\n", - "\n", - "AMI_aggregates = {}\n", - "\n", - "for k,frac in enumerate(data_fracs):\n", - "\n", - " AMI_scores = []\n", - "\n", - " for _ in range(1,100): #25\n", - " PC_reduce = PCA(n_components=3)#3\n", - "\n", - " sample_df = GMM_on_PCA_df.sample(frac=frac).sort_index()\n", - " random_rows = np.vstack(sample_df['waveform'].to_numpy())\n", - "\n", - " PCs = PC_reduce.fit_transform(random_rows)\n", - " \n", - " gmm_PCA = GaussianMixture(n_components=4)\n", - " gmm_PCA.fit(PCs)\n", - " PCA_classes = gmm_PCA.fit_predict(PCs)\n", - " truth_classes = sample_df['truth_ix'].tolist()\n", - " AMI_scores.append(adjusted_mutual_info_score(PCA_classes,truth_classes))\n", - "\n", - " AMI_aggregates[k] = AMI_scores\n", - "\n", - "AMI_means = [np.mean(AMI_aggregates[i]) for i in list(AMI_aggregates.keys())]\n", - "AMI_sems = [sem(AMI_aggregates[i]) for i in list(AMI_aggregates.keys())]" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": { - "id": "urtx08STMM42", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "if 'BIC_means' not in list(locals().keys()):\n", - " BIC_means = pkl.load(open('WaveMAP_Paper/data/BIC_means.pkl','rb'))\n", - "\n", - "if 'BIC_stds' not in list(locals().keys()):\n", - " BIC_stds = pkl.load(open('WaveMAP_Paper/data/BIC_stds.pkl','rb'))" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 117 - }, - "id": "CjOmFMLLjGDY", - "outputId": "fc8d88f3-7453-44a7-8cb3-513110df2213", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f,arr = plt.subplots(1,figsize=[1.5,1.5])\n", - "arr.errorbar(range(1,11),BIC_means,yerr=BIC_stds,marker='.')\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['bottom'].set_bounds(1,10)\n", - "arr.set_xticks([1,2,3,4,5,6,7,8,9,10])\n", - "arr.set_xticklabels(['','2','','4','','6','','8','','10'])\n", - "arr.set_xlabel('# of Clusters')\n", - "arr.set_yticks([])\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 247 - }, - "id": "t6vQtScTlnuJ", - "outputId": "c73b4a2c-15b7-4a01-dd3f-85d83476b6f8", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "PCs = PC_reduce.fit_transform(full_data)\n", - "\n", - "gmm_PCA = GaussianMixture(n_components=4)\n", - "gmm_PCA.fit(PCs)\n", - "\n", - "PCA_classes = gmm_PCA.fit_predict(PCs)\n", - "cluster_colors_PCA = [sns.color_palette(\"husl\", len(set(PCA_classes)))[i] for i in PCA_classes]\n", - "\n", - "if PCs.shape[1] == 2:\n", - " f, ax = plt.subplots(1);\n", - " ax.scatter(PCs[:,0], PCs[:,1], cmap=plt.cm.nipy_spectral,\n", - " edgecolor='w',c=cluster_colors_PCA,s=25);\n", - "elif PCs.shape[1] == 3:\n", - " fig = plt.figure(1, figsize=(4, 3));\n", - " ax = Axes3D(fig, elev=18, azim=54);\n", - " ax.scatter(PCs[:, 0], PCs[:, 1], PCs[:, 2], cmap=plt.cm.nipy_spectral,\n", - " edgecolor='w',c=cluster_colors_PCA,s=25);\n", - "\n", - " ax.w_xaxis.set_ticklabels([])\n", - " ax.w_yaxis.set_ticklabels([])\n", - " ax.w_zaxis.set_ticklabels([])\n", - "\n", - " ax.w_xaxis.set_label_text('PC 1', fontsize=12)\n", - " ax.w_yaxis.set_label_text('PC 2', fontsize=12)\n", - " ax.w_zaxis.set_label_text('PC 3', fontsize=12)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UdIGVLyhtlyE" - }, - "source": [ - "## Next we apply the same bootstrapping procedure to find AMI estimates for DBSCAN on t-SNE. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "AZ4WgHgwmLjo", - "outputId": "0bd5accb-e1d3-4d97-ce7f-a67b4d9764a6", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "27\n", - "28\n", - "29\n", - "30\n", - "31\n", - "32\n", - "33\n", - "34\n", - "35\n", - "36\n", - "37\n", - "38\n", - "39\n", - "40\n", - "41\n", - "42\n", - "43\n", - "44\n", - "45\n", - "46\n", - "47\n", - "48\n", - "49\n", - "50\n", - "51\n", - "52\n", - "53\n", - "54\n", - "55\n", - "56\n", - "57\n", - "58\n", - "59\n", - "60\n", - "61\n", - "62\n", - "63\n", - "64\n", - "65\n", - "66\n", - 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tsne.fit(full_data)\n", - "tsne_df = pd.DataFrame(tsne_embed, columns=('x', 'y'))\n", - "clustering = DBSCAN(eps=3, min_samples=15).fit(tsne_embed)\n", - "tsne_df['truth_ix'] = clustering.labels_\n", - "tsne_df['waveform'] = list(full_data)\n", - "\n", - "tSNE_AMI_aggregates = {}\n", - "\n", - "for k,frac in enumerate(data_fracs):\n", - " tSNE_AMI_scores = []\n", - "\n", - " for _ in range(1,100):\n", - " tsne = TSNE(\n", - " perplexity=30,\n", - " metric=\"euclidean\",\n", - " n_jobs=-1)\n", - " \n", - " sample_df = tsne_df.sample(frac=frac).sort_index()\n", - " random_rows = np.vstack(sample_df['waveform'].to_numpy())\n", - " tsne_embed = tsne.fit(random_rows)\n", - " samp_clustering = DBSCAN(eps=3, min_samples=15).fit(tsne_embed)\n", - " truth_classes = sample_df['truth_ix'].tolist()\n", - " tSNE_AMI_scores.append(adjusted_mutual_info_score(samp_clustering.labels_,truth_classes))\n", - "\n", - " tSNE_AMI_aggregates[k] = tSNE_AMI_scores\n", - "\n", - "tSNE_AMI_means = [np.mean(tSNE_AMI_aggregates[i]) for i in list(tSNE_AMI_aggregates.keys())]\n", - "tSNE_AMI_stds = [np.std(tSNE_AMI_aggregates[i]) for i in list(tSNE_AMI_aggregates.keys())]\n", - "tSNE_AMI_sems = [sem(tSNE_AMI_aggregates[i]) for i in list(tSNE_AMI_aggregates.keys())]" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": { - "id": "6rWHMmzY7RGA", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "if 'AMI_aggregates' not in list(locals().keys()):\n", - " AMI_aggregates = pkl.load(open('WaveMAP_Paper/data/AMI_aggregates.pkl','rb'))\n", - "\n", - "if 'AMI_means' not in list(locals().keys()):\n", - " AMI_means = pkl.load(open('WaveMAP_Paper/data/AMI_means.pkl','rb'))\n", - "\n", - "if 'AMI_sems' not in list(locals().keys()):\n", - " AMI_sems = pkl.load(open('WaveMAP_Paper/data/AMI_sems.pkl','rb'))\n", - "\n", - "\n", - "if 'tSNE_AMI_aggregates' not in list(locals().keys()):\n", - " tSNE_AMI_aggregates = pkl.load(open('WaveMAP_Paper/data/tSNE_AMI_aggregates.pkl','rb'))\n", - "\n", - "if 'tSNE_AMI_means' not in list(locals().keys()):\n", - " tSNE_AMI_means = pkl.load(open('WaveMAP_Paper/data/tSNE_AMI_means.pkl','rb'))\n", - "\n", - "if 'tSNE_AMI_sems' not in list(locals().keys()):\n", - " tSNE_AMI_sems = pkl.load(open('WaveMAP_Paper/data/tSNE_AMI_sems.pkl','rb'))" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": { - "id": "Whho_D2tNGZ-", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "\n", - "if 'tSNE_AMI_aggregates' not in list(locals().keys()):\n", - " tSNE_AMI_aggregates = pkl.load(open('WaveMAP_Paper/data/tSNE_AMI_aggregates.pkl','rb'))\n", - "\n", - "tSNE_AMI_sems = [sem(tSNE_AMI_aggregates[i]) for i in list(tSNE_AMI_aggregates.keys())]" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 242 - }, - "id": "hUJhSlU69oou", - "outputId": "b397f8bf-aed0-415c-ab6e-d76a8bbc41c3", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 132, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f,arr = plt.subplots(1,figsize=[4,3])\n", - "wavemap = arr.errorbar(data_fractions[:-1],[np.mean(i) for i in mutual_info_stability[:-1]],\n", - " [sem(i) for i in mutual_info_stability[:-1]],\n", - " marker='o',markerfacecolor='w',color='dodgerblue',label='WaveMAP')\n", - "\n", - "tsne = arr.errorbar(data_fractions[:-1],tSNE_AMI_means[:-1],tSNE_AMI_sems[:-1],\n", - " marker='o',markerfacecolor='w',color='green',label='DBSCAN on t-SNE')\n", - "\n", - "gmm = arr.errorbar(data_fractions[:-1],AMI_means[:-1],\n", - " AMI_sems[:-1],\n", - " marker='o',markerfacecolor='w',color='red',label='GMM on PCA')\n", - "\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xlim([0.0,1.1])\n", - "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n", - "arr.set_xticklabels(['','20','40','60','80','100'])\n", - "arr.set_xlabel('% of Full Dataset',fontsize=12)\n", - "arr.set_ylim([-.1,1.1])\n", - "arr.set_yticks([0.0,0.2,0.4,0.6,0.8,1.0])\n", - "arr.set_ylabel('Adj. Mutual \\nInfo. Score',fontsize=12)\n", - "arr.spines['bottom'].set_bounds([0.1,1.0])\n", - "arr.spines['left'].set_bounds([0.0,1.0])\n", - "plt.tight_layout()\n", - "arr.legend(loc=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vlTABhrlRkkv" - }, - "source": [ - "## Fig S3C: Comparing individual AMI scores\n", - "We show a strip plot of the AMI scores for 100 different subsamples at 40% of the full dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 200 - }, - "id": "KogFPY7iJmEx", - "outputId": "f082cf38-4293-415e-afd5-8ed47eb445ee", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "data1 = pd.DataFrame(mutual_info_stability[3],columns=['AMI'])\n", - "data1['DimRed'] = 'WaveMAP'\n", - "\n", - "data2 = pd.DataFrame(AMI_aggregates[3],columns=['AMI'])\n", - "data2['DimRed'] = 'GMMOnPCA'\n", - "\n", - "data3 = pd.DataFrame(tSNE_AMI_aggregates[3],columns=['AMI'])\n", - "data3['DimRed'] = 'DBSCANontSNE'\n", - "\n", - "data = pd.concat([data1,data3,data2])\n", - "\n", - "f, arr = plt.subplots(1,figsize=[1.25,3])\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['left'].set_bounds([0.25,1.0])\n", - "plot = sns.stripplot(x='DimRed',y='AMI',hue='DimRed',data=data,ax=arr,alpha=0.1)\n", - "plot.set(xticklabels='',xlabel='',ylabel='Adj. Mutual \\nInfo. Score');\n", - "arr.tick_params(bottom=False)\n", - "arr.set_ylim([0.25,1.1])\n", - "arr.set_yticks([0.25,0.5,0.75,1.0])\n", - "plot.get_legend().remove()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_c5uG5-ZRvzx" - }, - "source": [ - "## And now we show a strip plot of the AMI scores at 100 random subsamples 90% of the full dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 200 - }, - "id": "pvstrQKXR3BA", - "outputId": "452a15bf-b8af-46ed-9eaa-30383139430e", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "data1 = pd.DataFrame(mutual_info_stability[8],columns=['AMI'])\n", - "data1['DimRed'] = 'WaveMAP'\n", - "\n", - "data2 = pd.DataFrame(AMI_aggregates[8],columns=['AMI'])\n", - "data2['DimRed'] = 'GMMOnPCA'\n", - "\n", - "data3 = pd.DataFrame(tSNE_AMI_aggregates[8],columns=['AMI'])\n", - "data3['DimRed'] = 'DBSCANontSNE'\n", - "\n", - "data = pd.concat([data1,data3,data2])\n", - "\n", - "f, arr = plt.subplots(1,figsize=[1.25,3])\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['left'].set_bounds([0.25,1.0])\n", - "plot = sns.stripplot(x='DimRed',y='AMI',hue='DimRed',data=data,ax=arr,alpha=0.1)\n", - "plot.set(xticklabels='',xlabel='',ylabel='Adj. Mutual \\nInfo. Score');\n", - "arr.tick_params(bottom=False)\n", - "arr.set_ylim([0.25,1.1])\n", - "arr.set_yticks([0.25,0.5,0.75,1.0])\n", - "plot.get_legend().remove()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XGtdfFYAxtox" - }, - "source": [ - "# Figure S4: Comparing normalizations\n", - "This figure will demonstrate the other normalizations (or without normalization)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Fo8L6GExybbp" - }, - "source": [ - "## Waveforms with -1 to +1 normalization (used in the paper)" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 274 - }, - "id": "weMX4PEKybGd", - "outputId": "13d8cad9-9305-496e-c254-b2a3e3af6e7f", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[94mPlotting: 625 Waveforms\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f, arr = plt.subplots(1,figsize=[4.5,3.4])\n", - "\n", - "print(BlueCol + \"Plotting: \" + str(full_data.shape[0]) + \" Waveforms\")\n", - "for i in range(0,full_data.shape[0]):\n", - " arr.plot(full_data[i].T, c = 'k', alpha = 0.03,linewidth=2.);\n", - " \n", - "arr.tick_params(direction='out',colors='k', axis='both')\n", - " \n", - "# Set various x and y axes and labels etc.\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "\n", - "arr.spines['left'].set_bounds(-1,1)\n", - "arr.spines['bottom'].set_bounds(0,48)\n", - "\n", - "arr.set_xlabel('Time (ms)', fontsize=24);\n", - "arr.set_xticks([0,14,28,42,48])\n", - "arr.set_xticklabels(['0','0.5','1.0','1.5',''],fontsize=24)\n", - "\n", - "arr.set_ylabel('Amplitude (a.u.)', fontsize=24)\n", - "arr.set_yticks([-1.0,0.0,1.0]);\n", - "arr.set_yticklabels([-1.0,0.0,1.0], fontsize=24);\n", - "\n", - "# Plot the data\n", - "arr.set_title('-1 to +1 Normalization',fontsize=24)\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UFG-GER5iaRT" - }, - "source": [ - "# Fig. S4A: Louvain communities vs. proportion of the full dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 189 - }, - "id": "PaEBc6L9ytr9", - "outputId": "844607a3-4914-4376-e8fd-eb4a103eec63", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "if 'subset_avg_rand_list' not in list(locals().keys()):\n", - " subset_avg_rand_list = pkl.load(open('WaveMAP_Paper/data/subset_avg_rand_list.pkl','rb'))\n", - "\n", - "if 'subset_std_rand_list' not in list(locals().keys()):\n", - " subset_std_rand_list = pkl.load(open('WaveMAP_Paper/data/subset_std_rand_list.pkl','rb'))\n", - "\n", - "subsets = [0.1,0.2,0.3,0.4,\n", - " 0.5,0.6,0.7,0.8,\n", - " 0.9,1.0]\n", - "\n", - "f, arr = plt.subplots(1,figsize=[3,2.5])\n", - "arr.errorbar(np.array(subsets,dtype=np.float),subset_avg_rand_list,yerr=subset_std_rand_list,c = 'k', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xlabel('% of Full Dataset', fontsize=12,fontname=\"Arial\")\n", - "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n", - "arr.set_xticklabels(['','20','40','60','80','100'],fontsize=12,fontname=\"Arial\")\n", - "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\")\n", - "arr.set_yticks([0,2,4,6,8,10])\n", - "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\")\n", - "arr.spines['left'].set_bounds(0,10)\n", - "arr.spines['bottom'].set_bounds(0.1,1)\n", - "arr.axhline(np.max(subset_avg_rand_list),color='k',linestyle='dashed')\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZwskuO1RyDKd" - }, - "source": [ - "# Figure S4B: WaveMAP plot with -1 to +1 normalized average waveforms" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 355 - }, - "id": "8qIwMFeuiJxk", - "outputId": "e78a1394-a75d-4079-9dd7-ba770b2257dd", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE)\n", - "mapper = reducer.fit(full_data)\n", - "embedding = reducer.transform(full_data)\n", - "\n", - "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "\n", - "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", - "f.tight_layout()\n", - "\n", - "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", - " marker='o', c=cluster_colors, s=32, edgecolor='w',\n", - " linewidth=0.5)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xticks([]);\n", - "arr.set_yticks([]);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v-bIgDT3i1JN" - }, - "source": [ - "## Here we now show the waveforms with trough normalization (unbounded peak height)" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 274 - }, - "id": "mvwWmXwWiWV8", - "outputId": "f4e89dee-4bae-4c68-e4bb-8f4e97598ac1", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[94mPlotting: 625 Waveforms\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "trough_normalizedWaveforms = np.load('WaveMAP_Paper/data/trough_normalizedWaveforms.npy')\n", - "\n", - "f, arr = plt.subplots(1,figsize=[4.5,3.4])\n", - "\n", - "print(BlueCol + \"Plotting: \" + str(trough_normalizedWaveforms.shape[0]) + \" Waveforms\")\n", - "for i in range(0,trough_normalizedWaveforms.shape[0]):\n", - " arr.plot(trough_normalizedWaveforms[i].T, c = 'k', alpha = 0.03,linewidth=2.);\n", - " \n", - "arr.tick_params(direction='out',colors='k', axis='both')\n", - " \n", - "# Set various x and y axes and labels etc.\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "\n", - "arr.spines['left'].set_bounds(-1,1)\n", - "arr.spines['bottom'].set_bounds(0,48)\n", - "\n", - "arr.set_xlabel('Time (ms)', fontsize=24);\n", - "arr.set_xticks([0,14,28,42,48])\n", - "arr.set_xticklabels(['0','0.5','1.0','1.5',''],fontsize=24)\n", - "\n", - "arr.set_ylabel('Amplitude (a.u.)', fontsize=24)\n", - "arr.set_yticks([-1.0,0.0,1.0]);\n", - "arr.set_yticklabels([-1.0,0.0,1.0], fontsize=24);\n", - "arr.set_title('Trough Normalization',fontsize=24)\n", - "# Plot the data\n", - "plt.tight_layout()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d_cTfiPWiyA_" - }, - "source": [ - "# Fig. S4C: : Cluster number vs. data subset proportion on trough normalized waveforms\n", - "\n", - "---\n", - "\n", - "**THIS CELL CAN TAKE 40 MIN**; skip it and run the next cell to read cached values of the plot." - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 897 - }, - "id": "UTnDsA7_iSM0", - "outputId": "f34e05e2-2486-4338-c835-03a1fdd4a9d8", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Exception ignored in: .remove at 0x7fd20bbe4950>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.7/weakref.py\", line 109, in remove\n", - " def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n", - "KeyboardInterrupt\n", - "Exception ignored in: \n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.7/weakref.py\", line 572, in __call__\n", - " return info.func(*info.args, **(info.kwargs or {}))\n", - " File \"/usr/local/lib/python3.7/dist-packages/numba/core/dispatcher.py\", line 261, in finalizer\n", - " for cres in overloads.values():\n", - " File \"/usr/local/lib/python3.7/dist-packages/numba/core/types/abstract.py\", line 118, in __hash__\n", - " def __hash__(self):\n", - "KeyboardInterrupt\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m_ctypes/callbacks.c\u001b[0m in \u001b[0;36m'calling callback function'\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/llvmlite/binding/executionengine.py\u001b[0m in \u001b[0;36m_raw_object_cache_notify\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0mffi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLLVMPY_SetObjectCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_object_cache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m \u001b[0;32mdef\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numba/core/ir.py\u001b[0m in \u001b[0;36m_rec_list_vars\u001b[0;34m(self, val)\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0mA\u001b[0m \u001b[0mrecursive\u001b[0m \u001b[0mhelper\u001b[0m \u001b[0mused\u001b[0m \u001b[0mto\u001b[0m \u001b[0mimplement\u001b[0m \u001b[0mlist_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msubclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \"\"\"\n\u001b[0;32m--> 317\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 318\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mInst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "subsets = [0.1,0.2,0.3,0.4,\n", - " 0.5,0.6,0.7,0.8,\n", - " 0.9,1.0]\n", - "\n", - "renorm_clust_rand_dict = {}\n", - "for frac in subsets:\n", - " print(frac)\n", - " rand_list = []\n", - " for i in list(range(1,100)):\n", - " reducer_rand_test = umap.UMAP(n_neighbors = N_NEIGHBORS, \n", - " min_dist=MIN_DIST, \n", - " random_state=random.randint(1,100000))\n", - " rand_data = np.random.permutation(trough_normalizedWaveforms)[0:(int(len(full_data)*frac)),:]\n", - " mapper = reducer_rand_test.fit(rand_data)\n", - " embedding_rand_test = reducer_rand_test.transform(rand_data)\n", - "\n", - " umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n", - " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - " clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n", - " clustering_solution = list(clustering.values())\n", - " rand_list.append(len(set(clustering_solution)))\n", - "\n", - " renorm_clust_rand_dict.update({str(frac): rand_list})\n", - "\n", - "renorm_subset_avg_rand_list = []\n", - "renorm_subset_std_rand_list = []\n", - "\n", - "for k,v in renorm_clust_rand_dict.items():\n", - " renorm_subset_avg_rand_list.append(np.average(v))\n", - " renorm_subset_std_rand_list.append(np.std(v))" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 189 - }, - "id": "JuNArZYdip66", - "outputId": "e76ed852-0c71-4589-a1d6-0b301346e288", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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r9/jjj1uXLl28zVzDpVreI30D9MGtSgl6E179Xa3vkWqDoqIiDj30UEaNGsWyZcvo06cPEyZM4JJLLqntqXlqgbCC9AfgFUnTcMmPD8M50d0WZTBJbYEDk+ssy2es2uSZZ57hueeeY+nSpbRv397bzHnCCZKZ/V9Jq3EOfcfiEitfamavhmkfeLA+CvRM7RoXrqvesHr1aq6++mqef/552rdvX3UDT6MgtImQmS3ExbMDQFJzSW9YuNjdfwbuxGn+6m3koNLSUoYOHcptt93GiSeeWNvT8dQhcrG1awKcEvLclsB/mlm91guPGTOGXr16ce2119b2VDx1jFwEKQp/Av5N0r1mIV5c1UHmzJnDggULWLJkSQXTH2/q44kc/GRfQ6kFsNMypHVJOfco4CWcG3g50yIzOzyrCaQf4wPgaTO7rLJzo76QXbVqFSeffDILFixIG/nU0yCJL9KqpPHZtk3haZyz3Vyq7xnpYZxzX6zs2rWLiy66iHHjxnkh8mSkKmHoXsXxjIEfU+gFHG9m34U8PxJBcMgtwGLgyDj7Hj16NEcddRTXXFMnXJ08dZWob3CzKbikzmdUU99tgY+BQ3HJz57McN4VuEhDRT169Mj4OjvZbXz27Nl2xBFH2JYtWzKe72mwxG+0miuS5gC/xG3vvkoR5Jz8kST9B7DBzO6TNA440nJ4Rkpkevj444855ZRTePnllzn++ONzmaKnflIt2Shy5aOgxIqk/sAZQKx3eklJCUOHDmX8+PFeiDyhqBFBMrM7qqnr03DWEusClfSBQFNJfczsH7Lt9Prrr+foo4/mqquuimWSnoZPRjcKSfcnff5prgNJOkzSryRdmlxy7HYqcATQPyhTcJkEfxa1o4KCAvr164ck5s6dy1lnnVWlq7jHk6CyFekK9kcImod7qM8KSTfjDFyXUV79bbj0MFlhZjtx2dYT42wHdplZpODe6dzG8/PzycvL89bcnlBkVDZIegMXangZcCMubUoFzOz3VQ4iFQOnmlmdcORLVTb069ePSZMmMXjw4H11hYWFjBo1ylt1N15iUzZciFuVDgs6TfdOKazK72tcNvQ6yfLlyxk4cGC5uoEDB7J8+fJampGnvpFRkMxsI0GuWEnNzGxkDuNcD0yVNBG3yiWPsy6HfmMh4TaevCJ5t3FPFMJm7BspqYOk30i6Ofg3SvjQA4CzgHdwK1OifJKxRQ0yduxY8vPzKSwspKysjMLCQvLz8xk7dmxtT81TXwjz1hYXPWgzzgSnAJd/aDNwcsj2nwP/RKCeTi5R3yDHUdLFbPCpVjwpxG/ZIOlt4E9mNjupbhjwr2ZWpYebpK+ArlZH/JHCWDZ4Gj3VEo7r+8CclLqnCW8g+gBwk/yLGU8DJawg/T/g4pS6iwiflfw6nEHpdknrkkvI9h5PnSasidD1wHOSrsNFEeoJHIUzRA1DpUakdQnv7erJhtDW35I6AL/AJT/eALxgZpurcW7VRhwhiz0Nnuqx/jazb3DRUSMjqTlwKzCc/YI4A5d5fHc2fXo8dYmacqP4A/BD4Cr2B5i8DWe/9y81NAePp9qoKUG6CDjOzL4Ovq+UtBQXAtkLkqfeE1ZrlyuZ9pteHe5pEIQWJEmH5TDOXGC+pJ9JOkbS2TjXjLk59JmYVwtJ0yStlbRN0t8k/TzXfj2eKERZkd4DCFTgUfk34BVcyKwlwCRc+OMwGdGrohnwGTAIaIdTasyR1DOGvj2eUFQqSJKWSJoq6Wr2B7sfF7ZzSadIus/MdpvZ783sSDNrZWZH4fIrZe0OnsDMdpjZODP71My+M7PncMawPgidp8aoakW6EHgZp2VrFSgIWkgaLKldiP5vAd7IcKwQiN28WtLBOJOm2IOteDyZqPSFrKQjzWxV8HkzcBywEliAi9yzJ1hdMrX/HOiRzlg1yCG7zsy65nYJ5fpsDrwIrDazK1OOXYFzVKRHjx4nrF27Nq5hPQ2TWI1WZ0r6QtKruIwSHXAxEc43l4XvpCrat8X5IqWjOdAmymQrQ1IT3Eve3UCFdBFmNtXMBpjZgM6dO8c1rMcDVCFIZnYSzsX8X3Fu5Q8BbSRNlvTPuFDElbEC59CXjrOC4zkTWJVPAw4GLjCzsjj69XjCUqXWzsz2mNl7wG5zScV2AK/hjFbTBkRJ4k/Ao5LOD1YMJDWRdD4udNa/5zL5JCYDxwBDzKzeJjLz1F+iWDYkLBDMzJ4CnqqqgZnNknQI8DhOSVGMS+1SCtxuZgVRJ5xK8H7ryqDPL5Ncnq40s5m59u/xhCFy7G9JHQID1iht2uLc1Q/CRRR6y8y+jTRwjHjrb08Iqjf2d1QhCtp8i0s05vE0SGrK1s7jadB4QfJ4YsALkscTA16QPJ4Y8ILk8cSAFySPJwa8IHk8MeAFyeOJAS9IHk8MeEHyeGLAC5LHEwNekDyeGPCC5PHEQIMQJEkdJT0raUcQ3+7S2p6Tp3FRUyGLq5uHcbEaDgb6A89L+ruZ+UhCnhqh3q9IkloDFwC3mdl2M1sE/Dcu84XHUyM0hBXp+7iwYB8n1f0dF3l1H8nhuHAx7/ql60zSh8CuiHPoBBRHbJMLDXm8mr62lmaW9l6IQkMQpAOBVLf1raSE+jKzqcDUEP3tMrMBUSYgqShqm1xoyOPVxrXF0U+939oB23Hx85JpC2yrhbl4GikNQZA+BppJSo74ehw+ZLGnBqn3gmRmO4C/AOMltZZ0CnAuLupqNoTZ/sXRJhca8nj18toih+Oqi0jqCDwGnIkL93WTmc2q3Vl5GhMNQpA8ntqm3m/tPJ66gBckjycGvCBFoKp8tZJOl7RC0k5JhTnm3U0e9yhJuyQ9mVR3aTCPHZLmBc+JcYx1saTlQb+rJf0kqI/12iT1lPSCpG8kfSnpoSBnFpL6B9kidwb/9s+i/2slFUkqlTQ95VjGawn+jx+T9G0wr9GhBjQzX0IWoDUu9WdP3I/QL3Hvq3ri3shvBS7C5ZK6H/hrTOO+DLwJPBl87xuMeyruhfQsYHYM45wJrAV+FFxft6DEfm3AC8D0oL9DgA+A63D5tNbikja0COrWAgdE7P984DxcppLpSfWVXgtwT/C37oDLcPIlcHaV49X2zVnfC/A+ztbvCmBxUn1roAQ4Osf+LwbmBAKcEKS7gVlJ5xyBM9ptk+NYi4H8NPWxXxuwHDgn6fv9wKO4vFmfEyjCgmPrwtzMGca5K0WQKr0WYANwVtLxO8P8SPmtXQ6k5Kvti7PxA/a931od1Gfbf1tgPJC6vUgdazVOkL6fw1hNgQFAZ0mrJK0Ptlt5acbL+dqAicDFklpJ6gb8HJdStS/wvgV3ccD7OY6VTMZrkdQB6JJ8PPhc5dhekLIkyFc7E3jczFbgtlhbU06rYPMXkTuBaWa2PqW+OsY6GJeO9ELgJzh3lOOBW6tpvDdwN+i3wHqgCJhXTWMlU1n/ByZ9jzS2F6QsyJCvNlabv+AB+wxc1sNUqsO+MJHpcJKZfWFmxbiMiufEPV7w91uAs0hpjXtu6YDLAFndtpOV9b896Xuksb0gRaSSfLUf4Wz8Eue1xj27ZGvzdxpOibFO0pe4PL4XSFqaZqzDcQ/mH1fsJhzm8l6tx+UK3lcd/Bv3tXUEegAPmVmpmX0N/CdOaD8CjlVS6kXg2BzGSiXjtQR/gy+SjxPWbrOmHsobSsHlvv0rcGBKfWfcNuACnDboPnLQbAGtcNqsRHkAeDoYJ7El+gnuF/1J4tHajQfeBb6HWyHexG0vY722YKw1wE04V572wLM47WNCa/d/cD8O15Kd1q5ZMNd7cLuHlkFdpdcC3Au8Hlz/0YFgea1dnAU4DPcrvQu3DUiUXwfHz8Blai/BJazuGePY4wi0dsH3S3HarB3AfwEdYxijOfAIsAWn9n0Q5/gW+7XhnsFeA77BOfLNAQ4Ojh0PLAnGWgocn+Xfy1LKuKquJRDex4Ifqq+A0WHG87Z2Hk8M+GckjycGvCB5PDHgBcnjiQEvSB5PDHhB8nhiwAuSxxMDXpDqKJLuklQcWDXE1ee4hE9T4A9kCR8gT254QcoBSRMDx7S3JB2aVH+ppAdz6LcHMAboY2aHpDl+mqTvJG1PKvOzHS/DHD6VVBI4MG6RtFjSVYGdXJj2NSKodeUHwQtSlkj6IXACznxnEc7cBUntgBtwVtPZ0gP42sw2VnLOBjM7MKkMyWG8TAwxszY4i457gRtxdoaeFLwgZU8vYJGZlQKvAocH9ROA+80sNYxyOSS1k/SEpE2By/itkppIOgP4H6BrsNJMDzuhYKVan1L3adBn1pjZVjP7b2AY8FtJ/YK+fyHpvcAt+zNJ45KavRH8uyW4jpMlHSFpoaSvg23rTEntk+Z6o6TPg1VwpaTTg/omkm4KXN+/ljRH+13rK4yTy7Vmixek7PkI+Eng+HY68JGkAUBvCxdTbxLQDieAg4DfACPN7BWck1tixRlRLbPPAjN7B2ch/pOgagdu3u2BXwBXSzovOHZq8G/74DreAoQzIu2Kc+PujrOJQ1JvnIHqicEq+DPg06CPUTi38UFB229wqXwyjVPjeEHKEjP7EHgGZwneA/gDzsjzOknXSXoj9Rc3QeCNejFws5ltM7NPgT8SLRVN1+DZJVGG5npNIdmAc4PAzF4zsw/M7Dszex8oICULSDJmtsrM/sec68QmnL9T4vy9OIPRPpKam9mn5jx/Aa4CxprZ+mAHMA64sLafi5LxgpQDZvYnMzvOzIYBQ3HbjCa4uACn4+IS3JSmaSecpfXapLq1uEAjYdlgZu2TypysLiI63YDNAJJOCqLwbJK0FXfDd8rUUNLBkmYH27dvce4fncAJGXA9Tkg2Bud1DZoeBjyb+NHA/V334nzC6gRekGIgiN1wBc6fpx8u5kAZzrfn2DRNioEy3A2SoAcu6Ecu7MD5MSXm1RTnfxMLkk7ECdKioGoWLqlbdzNrh/PVSjjkpXMruDuo/4GZtQUuSzofM5tlZgPZ765yX3DoM+DnKT8cLc3s8wzj1DhekOLh33G+LjuBT4ATJR2I83Jdk3qyme3F+d9MkNRGLq7aaNwvdC58DLQMlADNcZrDFjn2iaS2kn4JzMb5RH0QHGoDbDazXYEWMzl37ybgO/YrYRLnbwe2ygU8uSFpjN6SfiqpBc7fqyRoD05AJwR/JyR1lnRuJePUOF6QckTST3EPus/Cvgfy53G/ooNxauN0jMKtIGtwv/CzcA5lWWNmW4FrgD/jVrcdOOVAtsyXtA13LWNxPxgjk45fg8sCsg34Pe7HITGXnTgN5v8GW7IfAXcA/4DzUH0eF7MhQQvc36oY51T4PeDm4Nh/4Fa+l4Ox/gqcVMk4NY537PN4YsCvSB5PDHhB8nhiwAuSxxMDXpA8nhjwguTxxIAXJI8nBrwgeTwx4AXJ44mB/w80CFDdbMijPAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "subsets = [0.1,0.2,0.3,0.4,\n", - " 0.5,0.6,0.7,0.8,\n", - " 0.9,1.0]\n", - "\n", - "if 'renorm_subset_avg_rand_list' not in list(locals().keys()):\n", - " renorm_subset_avg_rand_list = pkl.load(open('WaveMAP_Paper/data/renorm_subset_avg_rand_list.pkl','rb'))\n", - "\n", - "if 'renorm_subset_std_rand_list' not in list(locals().keys()):\n", - " renorm_subset_std_rand_list = pkl.load(open('WaveMAP_Paper/data/renorm_subset_std_rand_list.pkl','rb'))\n", - "\n", - "f, arr = plt.subplots(1,figsize=[3,2.5])\n", - "arr.errorbar(np.array(subsets,dtype=np.float),renorm_subset_avg_rand_list,yerr=renorm_subset_std_rand_list,c = 'k', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xlabel('% of Full Dataset', fontsize=12,fontname=\"Arial\")\n", - "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n", - "arr.set_xticklabels(['','20','40','60','80','100'],fontsize=12,fontname=\"Arial\")\n", - "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\")\n", - "arr.set_yticks([0,2,4,6,8,10])\n", - "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\")\n", - "arr.spines['left'].set_bounds(0,10)\n", - "arr.spines['bottom'].set_bounds(0.1,1)\n", - "arr.axhline(np.max(renorm_subset_avg_rand_list),color='k',linestyle='dashed')\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "O9z7VXnMjHgZ" - }, - "source": [ - "# Figure S4D: WaveMAP plot with trough normalization\n" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 355 - }, - "id": "Va3fIMICjASq", - "outputId": "76b4321d-e611-40f9-889f-1e2b0a009892", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE)\n", - "mapper = reducer.fit(trough_normalizedWaveforms)\n", - "embedding = reducer.transform(trough_normalizedWaveforms)\n", - "\n", - "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "\n", - "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", - "f.tight_layout()\n", - "\n", - "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", - " marker='o', c=cluster_colors, s=32, edgecolor='w',\n", - " linewidth=0.5)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xticks([]);\n", - "arr.set_yticks([]);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CreqDDpajVSu" - }, - "source": [ - "# Fig. S4E: Unnormalized WaveMAP" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2KyilzLKj8iZ" - }, - "source": [ - "### and now generate our WaveMAP plot" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "metadata": { - "id": "t2D0ETLbj8FL", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "cleanedWaveforms = scipy.io.loadmat(os.path.join(rel_path,'WaveMAP_Paper/data/cleanedWaveforms.mat'))['cleanedWaveforms']\n", - "\n", - "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE)\n", - "mapper = reducer.fit(cleanedWaveforms)\n", - "embedding = reducer.transform(cleanedWaveforms)\n", - "\n", - "umap_df_unnormalized = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "umap_df_unnormalized['cleaned_waveform'] = list(cleanedWaveforms)\n", - "\n", - "spike_amplitudes = [np.log(max(x)-min(x)) for x in umap_df_unnormalized.cleaned_waveform.tolist()]\n", - "umap_df_unnormalized['log_amp'] = spike_amplitudes\n", - "\n", - "G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - "clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n", - "clustering_solution = list(clustering.values())\n", - "umap_df_unnormalized['color'] = clustering_solution\n", - "\n", - "cluster_colors = [sns.color_palette(\"husl\", len(set(clustering_solution)))[i] for i in clustering_solution]" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 372 - }, - "id": "RVLMjAi5kOqP", - "outputId": "82d01582-113d-4801-d48b-8372a538da9c", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(1.05, 7.75, 'UMAP 2')" - ] - }, - "execution_count": 143, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", - "f.tight_layout()\n", - "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", - " marker='o', c=cluster_colors, s=32, edgecolor='w',\n", - " linewidth=0.5)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xticks([]);\n", - "arr.set_yticks([]);\n", - "\n", - "arr.set_xlim(0,11)\n", - "arr.set_ylim(7,15)\n", - "\n", - "arr.arrow(1.3,7.5,0,1.5, width=0.05, shape=\"full\", ec=\"none\", fc=\"black\")\n", - "arr.arrow(1.3,7.5,1.3,0, width=0.05, shape=\"full\", ec=\"none\", fc=\"black\")\n", - "\n", - "arr.text(1.6,7.25,\"UMAP 1\", va=\"center\")\n", - "arr.text(1.05,7.75,\"UMAP 2\",rotation=90, ha=\"left\", va=\"bottom\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fIYpnpNbkPzn" - }, - "source": [ - "# Fig. S4F: WaveMAP without normalization focuses on capturing amplitude" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 390 - }, - "id": "FLiIgslKkYuy", - "outputId": "9901bbc4-391a-4ce6-8d37-b6cb910e2988", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(1.05, 7.75, 'UMAP 2')" - ] - }, - "execution_count": 144, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", - "f.tight_layout()\n", - "scat = arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", - " marker='o', c=umap_df['log_amp'], s=32, edgecolor='w',\n", - " linewidth=0.5)\n", - "\n", - "cax = f.add_axes([0.97,0.18,0.015,0.5])\n", - "cbar = f.colorbar(scat, cax=cax, label='Log(Amplitude) (A.U.)',orientation='vertical')\n", - "\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xticks([]);\n", - "arr.set_yticks([]);\n", - "\n", - "arr.set_xlim(0,11)\n", - "arr.set_ylim(7,15)\n", - "\n", - "arr.arrow(1.3,7.5,0,1.5, width=0.05, shape=\"full\", ec=\"none\", fc=\"black\")\n", - "arr.arrow(1.3,7.5,1.3,0, width=0.05, shape=\"full\", ec=\"none\", fc=\"black\")\n", - "\n", - "arr.text(1.6,7.25,\"UMAP 1\", va=\"center\")\n", - "arr.text(1.05,7.75,\"UMAP 2\",rotation=90, ha=\"left\", va=\"bottom\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FnjTowV6kimo" - }, - "source": [ - "# Figure S5: PCA of waveforms\n", - "\n", - "\n", - "---\n", - "\n", - "First we conduct PCA on the normalized waveforms and generate a scree plot of explained variance" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 286 - }, - "id": "W7XrQHqykxk0", - "outputId": "cbae6609-5e1a-417c-d67d-ae10212a11e0", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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rK2P9+vUNHnf99dezdetWRowYsa1sypQpvPfeeyxdupRTTjmFfv368cknn+QkbhHZOZGnCnf3Knf/rruXh9t33V3/9NvNJBIJqqurdyirrq6mTZs2GY+55557mDRpEk8//TR77bW9C6tPnz4UFxezzz77cOWVV7Lvvvsyd+7cnMUuIk0XeTAdgJkdBHQgJbm4+6vZDEoKV/fu3dmyZQtLliyhW7duAFRVVdW7jVTnoYce4uabb2bOnDl06tSpwXObGe6e9ZhFZOdFHUz3ZYKxEl8ELGW3E6wrIbuBkpIShgwZwrXXXssDDzzAggULmDFjBi+8UP+u45QpU7jqqquYNWsWhx566A77li1bxvLly+nVqxdbt27l7rvvZs2aNfTp06feeUQkPk2Z4G850Bc4lGDhobrt0AaOkxZo3LhxbNiwgQ4dOjB06FDGjx9PeXk5c+fOJZFIbKt39dVX89FHH9GrV69tYyEuueQSANavX8+oUaPYb7/96NixI8888wx/+tOfaNeuXVwfS0TSsCjNezOrBb7s7otzH9LO69mzp8+fPz/tvvKH7shzNLm38IIfxR2CiLQMqXeItonakngdODA7sYiIyK4iasf1VcBYM7uaIGFsTt7p7muzHZjkjlpVIhJV1CTx1/C/zxJ0VNcx1HEtItJiRU0Sp+Q0ChERKUhRV6Z7LteBiIhI4dmZwXSHAK2Ty9093TTiIiKyi4s6mO4gYCpwEkEfRF1fRB31SYiItEBRH4G9A/icYH3rTwkG1X0L+BdwRm5CExGRuEW93XQy0N/d/21mDqx293lmthG4EfhLziIUEZHYRG1JFANrwtdrCSb5A3gT6JHtoEREpDBETRL/JpjcD2ABcImZdSZYme79XAQmIiLxi3q76U62T8txA/AMMBTYCAzPQVwiIlIAoo6TmJL0+lUz60LQsljm7msyHSciIru2Jo2TqOPunwJaaEhEpIXLmCTM7C7gSnevDV9n5O7/L+uRiYhI7BrquD4a2DN83SN8n247KurFzKytmU03s1ozW2pm38lQ76dm9oaZrTezd83sp1GvISIi2ZOxJeHupyS9/mqWrncvsAk4ADgGeNrMqtx9YUo9A84DXgMOA541s+Xu/l9ZikNERCJo9BFYM9vTzD4ws/Qr3UdkZiXAWcA17l7j7s8DTwLDUuu6+1h3f9Xdt7j7ImAGoMWPRUTyrNEk4e6bCRYZanyd04Z1B7akLIFaBTSYfMzMCKYBSW1t1O0faWbzzWz+6tWrmxmiiIgkizqY7m7gSjPbqaehQgmgOqVsHdCmkeOuJ4jz4XQ73X2Cu/d0957t27dvRngiIpIq6o9+X4L5m943szeA2uSd7j4wwjlqgNKUslJgfaYDzOz7BH0Tfd19Y8RYRUQkS6ImiTXAtGZeazFQZGbd3H1JWFZB5ttIFwA/A05y9xXNvLaIiOyEqCOuRzT3QuF4i8eBG8zsIoKnmwYBJ6TWNbNzgZuAU9z9neZeW0REdk7UPolsuZRgRtkPgUeBUe6+0Mz6mllNUr1fAu2Al82sJtzuy3OsIiK7vcgd0WY2gmBSv3TLlx4a5RzuvhY4M035XIKO7br3XaPGJSIiuROpJRGOeL4NeAXoAjwBvAG0BR7KVXAiIhKvqLebvgeMdPcrCcZM3BM+0XQb0DlXwYmISLyiJolOwD/D1xvY/ijrowSjqEVEpAWKmiQ+APYPXy8FvhK+Ppzmj8QWEZECFTVJ/B2oGzD3IHC7mc0C/gA8novAREQkfg0+3WRmp7n7X4GRhAnF3e8zs48JJtybBtyf8yhFRCQWjT0C+6yZvUfQengYWAng7n8gaEWIiEgL1tjtpnKC20k/AJaa2dNmNtjMWuU+NBERiVuDScLd/+XuPyF4uun/EnRS/5Fgor9bzOyIPMQoIiIxidRxHS7+87i7f5NgXMRdwBDgTTObk8sARUQkPk2eu8ndVwLjCBLFJ2jFOBGRFqtJiwiZ2WnABQTzL31GMJjugRzEJSIiBaDRJGFmhwAjgPMJbjU9R/BI7GPu/llOoxMRkVg1Nk7ir8BXCab2ngg86O5v5SEuEREpAI21JGoJOqifdvfP8xCPiIgUkAaThLsPylcgIiJSePK9Mp2IiOxClCRERCQjJQkREclISUJERDJSkhARkYyUJEREJCMlCRERyUhJQqQZ1q5dy+DBgykpKaFz585MnTo1bT13Z8yYMbRr14527doxZswY3IPl4efOnUsikdhhMzOmTZuWz48iklaTJvgTkR2NHj2a1q1bs2rVKhYsWED//v2pqKigvLx8h3oTJkzgiSeeoKqqCjPj9NNPp2vXrlxyySX07duXmpqabXVnz57NgAEDOOOMM/L9cUTqUUtCZCfV1tYybdo0brzxRhKJBCeeeCIDBw5k8uTJ9epOnDiRyy+/nE6dOtGxY0cuv/xyHnnkkbTnnThxImeffTYlJSU5/gQijVOSENlJixcvpqioiO7du28rq6ioYOHChfXqLly4kIqKikbr1dbW8thjjzF8+PDcBC3SREoSIjuppqaG0tLSHcrKyspYv3592rplZWU71KupqdnWL1Hn8ccfZ//99+fkk0/OTdAiTaQkIbKTEokE1dXVO5RVV1fTpk2bRutWV1dv66BONnHiRM4777x65SJxUZIQ2Undu3dny5YtLFmyZFtZVVVVvU5rgPLycqqqqhqst3z5cmbPns15552Xu6BFmkhJQmQnlZSUMGTIEK699lpqa2uZN28eM2bMYNiwYfXqnnfeedx+++28//77rFy5kttuu43zzz9/hzqTJ0/mhBNO4LDDDsvTJxBpnJKESDOMGzeODRs20KFDB4YOHcr48eMpLy/fNvahzsUXX8yAAQM4+uijOeqoo+jfvz8XX3zxDueaNGmSOqyl4Fhqx9murGfPnj5//vy0+8ofuiPP0eTewgt+tFPH6bsQkRQZO8HUkhARkYw04lp2a2pViTRMLQkREclISUJEsiIbkx0uXryYQYMG0b59e9q2bUu/fv1YtGhRPj+GpFCSEJGsSJ7scMqUKYwaNSrt1CPJkx2+9tprzJw5k/vvvx+ATz75hIEDB7Jo0SJWrVpF7969GTRoUL4/iiRRkhCRZsvWZIe9e/fmwgsvpG3btuy5555cdtllLFq0iI8++ijPn0jqKEmISLPlYrJDgDlz5nDggQfSrl277AedQ9m49QYwcuRIjjjiCPbYY4+MswbnmpKEiDRbLiY7XLFiBaNHj+b222/PTdA5lI1bbxAk0HHjxnHsscfmM/wdKEmISLNle7LD1atXU1lZyaWXXsrQoUNzF3gOZHOdkdGjR3Pqqaey99575/ET7EhJQkSaLZuTHX788cdUVlYycOBAfv7zn+c28BzI1a23uChJiEizZWuyw+rqavr160efPn24+eab8/wpsiMXt97ipCQhIlmRjckOp0+fzssvv8zDDz9MIpHYti1btiyuj9VkuVhnJE6alkNEsqJt27Y88cQT9cr79u1LTU3NtvdmxtixYxk7dmy9usOHD9/lZ8JNvvXWrVs3oPFbb717926wXpzUkhARyaJsrjOyadMmPvvsM9ydzZs389lnn7F169Y8fholCRGRrMvWOiOVlZUUFxfzwgsvMHLkSIqLi5kzZ05eP4tuN4kIoBlxsykbt94AZs+enasQI1NLQkREMlKSEBGRjHS7SUQkhW69bZfXloSZtTWz6WZWa2ZLzew7GeqZmd1iZh+F2y1WSA8Oi4jsJvLdkrgX2AQcABwDPG1mVe6eOg59JHAmUAE48BfgXeC+PMYqIrLby1tLwsxKgLOAa9y9xt2fB54E6j88DMOB29x9hbu/D9wGnJ+vWEVEJGD5miPEzL4MzHP3fZLKfgKc7O4DUuquAyrd/aXwfU9glrvXG9duZiMJWh4ARwCFsNbh/sCauIMoEPouAvoettN3sV2hfBdr3P2MdDvyebspAVSnlK0D6k9oEtRdl1IvYWbmKVnN3ScAE7IZaHOZ2Xx37xl3HIVA30VA38N2+i622xW+i3x2XNcApSllpUD9qRHr1y0FalIThIiI5FY+k8RioMjMuiWVVQDpJk9fGO5rrJ6IiORQ3pKEu9cCjwM3mFmJmfUBBgH1l2uCScCPzayjmR0EXA48kq9Ys6Cgbn/FTN9FQN/Ddvoutiv47yJvHdcQjJMAHgJOBz4CfubuU82sL/And0+E9Qy4BbgoPPQBYIxuN4mI5Fdek4SIiOxaNHeTiIhkpCQhIiIZKUlkiZl938zmm9lGM3sk7njiZGZ7mdmD4fxc681sgZl9Pe644mJmvzez/5hZtZktNrOLGj+q5TKzbmb2mZn9Pu5Y4mRms8PvoSbcCmEgcD1KEtmzEvglQcf87q4IWA6cDJQBVwN/NLMuMcYUp18DXdy9FBgI/NLMjos5pjjdC7wcdxAF4vvungi3I+IOJh0liSxx98fd/QmCp7Z2a+5e6+7Xu/t77r7V3Z8imKBxt/xhdPeF7r6x7m24HRZjSLExs3OAT4C/xR2LRKMkITlnZgcA3dmNB0Sa2Tgz+xT4N/Af4H9iDinvzKwUuAH4cdyxFJBfm9kaM5tnZl+NO5h0lCQkp8xsT2AKMNHd/x13PHFx90sJ5inrSzCodGPDR7RINwIPuvuKuAMpEGOAQ4GOBIPqZppZwbUwlSQkZ8xsD4IR9ZuA78ccTuzc/fNwivxOwKi448knMzsGOA34bdyxFAp3f8nd17v7RnefCMwDvhF3XKm0fKnkRDhq/kGCBaa+4e6bYw6pkBSx+/VJfBXoAiwLF5lMAK3M7Eh3PzbGuAqJAwW3AqdaElliZkVmtjfQiuAv/95mtjsn4fHAl4AB7r4h7mDiYmYdzOwcM0uYWSsz6wcMZffruJ1AkBiPCbf7gKeBfnEGFRcz29fM+tX9TpjZucBJwDNxx5Zqd/4Ry7argeuS3n8X+AVwfSzRxMjMOgMXE9x3/yBpefKL3X1KbIHFwwluLd1H8I+ypcCP3P3JWKPKM3f/FPi07r2Z1QCfufvq+KKK1Z4Ej8x/Efic4IGGM919caxRpaG5m0REJCPdbhIRkYyUJEREJCMlCRERyUhJQkREMlKSEBGRjJQkREQkIyUJERHJSElCYmNmj5iZh9tmM3vHzH5jZiUp9YaY2d/N7BMzqzWz183sV2bWIaVeazNbHS7gUhYxBjOzi8zsH+ECSdVm9qqZXRHOWiqhcJGce+KOQ/JLSULi9lfgCwSzYV4NXAr8pm6nmf0K+G9gAfBN4Ejgh0BX6k+SdybBuhX/AL4T8fqTgbsJpu4+FegBXAOcAgzZmQ8k0qK4uzZtsWzAI8BTKWW/A/4Tvu5NMK3FjzMcv2/K+z8DPwCGAfMjXP/b4fmHNHR+gn9MXUOw2t5G4HVgUFK9LuF5zgGeAzYA/0uQcI4CXgBqgeeBrknHXQ+8AVwELAuPewLYP6lO1GufBfyFYOqLN4HTUz7LkQRzJa0HPgQeBQ5M/bMgSMDvAx8DDwP7JO33lK0LwfQSdxGszLgxjPPmuP9uacveFnsA2nbfLUOSuAtYE76+E6gB9oxwrs7hj1R7oCQ8rqKRY2YAiyKc+zKgmqB10p1g4ZzPgWPC/XU/1IsIpnr+IjCLYJGlWQStknJgPjAz6bzXh3HOBr4M9AmPeXInrv1vYADQDZhIsEJiIqzzBWANcAvBpIs9gJnAS8AeSX8W6wiS9JeASoIV5K4M95cRJLuHgAPDrRVweZgYTgIOAU4ARsT9d0tb9rbYA9C2+26pSYKg5bAG+EP4/n+Aqojnuj7lXJOAexo55k1gRoRzvw9cm1I2G/h9+Lruh/ripP3fJKWVApwP1KTE/DlwSFLZieFx3Zpx7Y5h2Ynh+xuAv6WcY7+wTu+kP4vlQKukOr8D/ppy3XtSznMXwYy2FvffJ2252dQnIXE7I+xo/oygL2EOwS0jiDi3fri40QiC/oU6k4Fzw+nbMx4a4dylwEEEC8Ike57gFk6y15Jerwr/+3pKWYmZ7ZNU9r67L0t6/xKwFfhSM669MvxvXcf+ccBJ4fdcE87Aujzcl7yuxZvu/nnKeXZ4OCCNRwim/l5sZveaWf/wz0NaCE0VLnGbA4wENgMrfcfFiRYDfY1QeQ4AAAI/SURBVM2stbtvauAclQS3OqaYWfJU5K0I7tVnmp58McGtlZ2VOoXy5jT70pVl40c047Xd3cPp2euuswdBf8RP0pxnVdLr1IWhnEZidfdXzawLwboQpxLc6qoys9PdfWvDH0F2Bcr4ErdP3f0td1/q9Vevm0rQv5B26VMz2zd8eSHButHHpGy/C/dlMhXoZmZpn2Iys33dvZrgX9R9UnafSHC7qrk6mtnBSe97E/x/+a8sXvtVgj6RpeF3nbytb8J5NhEk3h14sATnY+4+CugPfA04vAnnlQKmloQULHd/yczGAreaWSdgGrCC4PHXC4G3zGwcMBD4lru/kXy8mT0I/MPMDnP3t9Nc4o/AYIIWyE0Eq4J9SNC6+AHBo7ePALcCN5jZEuAVggWl+gLZWHZzAzDRzH4MFBOu2ObuS8L92bj2vcD3gD+Y2S3AaoJHjr8NXN6ERPEe0DtsOdQAa4EfAf8heER5M0EHezXBn5O0AEoSUtDcfYyZzQdGEySGIoKxEDOAcQSPu24kePw11T8J7r1fCFyV5txuZkMJfkAvBMYQ9Ae8TfCI6LSw6l1AG2AswZrdi4Cz3L0qCx/xPeC/CJ422h94luCR2DrNvra7rzSzPsCvCRLh3gSP3D5L8N1F9RuC20lvEiS0rgSP1P6U4KkqJ3j09+serEQnLYBWphOJiZldD5zt7kfFHYtIJuqTEBGRjJQkREQkI91uEhGRjNSSEBGRjJQkREQkIyUJERHJSElCREQyUpIQEZGM/j/OOhLEFEt4EQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "pca = PCA()\n", - "pca.fit(full_data)\n", - "\n", - "f, arr = plt.subplots(1)\n", - "arr.bar(np.arange(full_data.shape[1]),pca.explained_variance_ratio_,color='#2a9d8f')\n", - "arr.set_xlabel('PCA Components',fontsize=14)\n", - "arr.set_ylabel('Variance Explained',fontsize=14)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xlim([-0.5,4.5])\n", - "arr.set_xticklabels(['','1','2','3','4','5'],fontsize=12)\n", - "arr.set_ylim([0,0.7])\n", - "arr.set_yticks([0.0,0.2,0.4,0.6,0.7])\n", - "arr.set_yticklabels(['0.0','0.2','0.4','0.6',''],fontsize=12);\n", - "xlocs = [0,1,2,3,4]\n", - "for i, v in enumerate(pca.explained_variance_ratio_[:5]):\n", - " num = np.round(v,2)\n", - " arr.text(xlocs[i] - 0.18, v + 0.01, str(num),fontsize=12)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I4G5sm3wky7l" - }, - "source": [ - "### Next we do WaveMAP on the 3-PC dim. embedded data which captures 94% of the variance and show the associated waveforms." - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 271 - }, - "id": "2jGOBvOLk7Rl", - "outputId": "3cd0f675-3b36-4bf9-92f5-68f30c016852", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[94mPlotting: 625 Waveforms\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "PC_reduce = PCA(n_components=3)\n", - "PCs = PC_reduce.fit_transform(full_data)\n", - "data_approx = PC_reduce.inverse_transform(PCs)\n", - "\n", - "# Generate subplots\n", - "f, arr = plt.subplots(1,figsize=[4.5,3.4])\n", - "\n", - "print(BlueCol + \"Plotting: \" + str(data_approx.shape[0]) + \" Waveforms\")\n", - "for i in range(0,data_approx.shape[0]):\n", - " arr.plot(data_approx[i].T, c = 'k', alpha = 0.03,linewidth=2.);\n", - " \n", - "arr.tick_params(direction='out',colors='k', axis='both')\n", - " \n", - "# Set various x and y axes and labels etc.\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "\n", - "# Plot the data\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 355 - }, - "id": "ef9SOQuZlFrY", - "outputId": "39df19c8-5d8e-4ca2-c6d6-463ee7849553", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE)\n", - "mapper = reducer.fit(data_approx)\n", - "embedding = reducer.transform(data_approx)\n", - "\n", - "umap_approx_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "\n", - "G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - "clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n", - "clustering_solution = list(clustering.values())\n", - "umap_approx_df['color'] = clustering_solution\n", - "\n", - "cluster_colors = [sns.color_palette(\"husl\", len(set(clustering_solution)))[i] for i in clustering_solution]\n", - "\n", - "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", - "f.tight_layout()\n", - "\n", - "arr.scatter(umap_approx_df['x'].tolist(), umap_approx_df['y'].tolist(), \n", - " marker='o', c=cluster_colors, s=32, edgecolor='w',\n", - " linewidth=0.5)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xticks([]);\n", - "arr.set_yticks([]);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Gsxw-y5c77H8" - }, - "source": [ - "# Figure S6: Comparison of UMAP and GMM in the specified feature space." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_ZbeKAN37_qU" - }, - "source": [ - "## Figure S6A: WaveMAP labels in the 3-D feature space" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cPlT0of0ue6K" - }, - "source": [ - "### To demonstrate that WaveMAP locates structure that traditional methods cannot parse, we show the WaveMAP cluster labels in the 3 feature space used by traditional methods." - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 211 - }, - "id": "NGadrhJo77Qz", - "outputId": "59f106a4-00b4-4378-ac98-33aabde44516", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 148, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=[3.8,3])\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "ax.set_xlim([0,1.4])\n", - "ax.set_ylim([0,1.6])\n", - "ax.set_zlim([0.,0.6])\n", - "ax.view_init(elev=20, azim=220)\n", - "ax.set_xticks([0,0.7,1.4])\n", - "ax.set_xticklabels(['',0.7,1.4],fontsize=12)\n", - "ax.set_yticks([0,0.5,1,1.5])\n", - "ax.set_yticklabels([0,0.5,1.0,1.5],fontsize=12)\n", - "ax.set_zticks([0,0.3,0.6])\n", - "ax.set_zticklabels([0,0.3,0.6],fontsize=12)\n", - "ax.tick_params(pad=-1)\n", - "\n", - "for i in [int(x) for x in np.unique(UMAP_and_GMM['gmm_labels'])]:\n", - " to_plot_df = UMAP_and_GMM[UMAP_and_GMM['gmm_labels'] == i]\n", - " x = to_plot_df['troughToPeak_abs']\n", - " y = to_plot_df['prePostHyper']\n", - " z = to_plot_df['FWHM1_abs']\n", - " ax.scatter(x,y,z,c=GMM_PAL[i-1],marker='o',alpha=0.75,s=20,linewidth=0.75,edgecolor='w',depthshade=True)\n", - " \n", - " ax.plot(x, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='y', zs=1.5)\n", - " ax.plot(y, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='x', zs=1.4)\n", - " ax.plot(x, y, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='z', zs=0)\n", - "\n", - "ax.tick_params(pad=-1)\n", - "\n", - "ax.set_xlabel('Trough to peak ($\\mu$s)',fontsize=12,labelpad=5)\n", - "ax.set_ylabel('Peak ratio',fontsize=12,labelpad=5)\n", - "ax.set_zlabel('AP width ($\\mu$s)',fontsize=12,labelpad=0)\n", - "ax.view_init(elev=20, azim=220)\n", - "\n", - "ax.scatter(UMAP_and_GMM['troughToPeak_abs'],UMAP_and_GMM['prePostHyper'],UMAP_and_GMM['FWHM1_abs'],\n", - " c=UMAP_and_GMM['dbscan_hex'],marker='o',alpha=0.75,s=20,linewidth=0.25,edgecolor='w',depthshade=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 194 - }, - "id": "_Ab0SJSoWj4Y", - "outputId": "06e6e57f-b6b8-4a83-d7d1-0f5b5466a14c", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=[3.8,3])\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "ax.set_xlim([0,1.4])\n", - "ax.set_ylim([0,1.6])\n", - "ax.set_zlim([0.,0.6])\n", - "ax.view_init(elev=20, azim=220)\n", - "ax.set_xticks([0,0.7,1.4])\n", - "ax.set_xticklabels(['',0.7,1.4],fontsize=12)\n", - "ax.set_yticks([0,0.5,1,1.5])\n", - "ax.set_yticklabels([0,0.5,1.0,1.5],fontsize=12)\n", - "ax.set_zticks([0,0.3,0.6])\n", - "ax.set_zticklabels([0,0.3,0.6],fontsize=12)\n", - "ax.tick_params(pad=-1)\n", - "\n", - "ax.tick_params(pad=-1)\n", - "\n", - "ax.set_xlabel('Trough to peak (ms)',fontsize=12,labelpad=5)\n", - "ax.set_ylabel('Peak ratio',fontsize=12,labelpad=5)\n", - "ax.set_zlabel('AP width (ms)',fontsize=12,labelpad=0)\n", - "ax.view_init(elev=20, azim=250)\n", - "\n", - "ax.scatter(UMAP_and_GMM['troughToPeak_abs'],UMAP_and_GMM['prePostHyper'],UMAP_and_GMM['FWHM1_abs'],\n", - " c=UMAP_and_GMM['dbscan_hex'],marker='o',alpha=0.75,s=20,linewidth=0.25,edgecolor='w',depthshade=True)\n", - "\n", - "# plt.savefig('view3.pdf',format='pdf')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "irB9MlJI9PoD" - }, - "source": [ - "## Figure S6B: GMM with eight clusters" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qPIGMvwjvJKB" - }, - "source": [ - "### To verify that the quality of our clustering with WaveMAP was not just due to using more clusters, we train an eight cluster GMM model" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": { - "id": "OqH3qfnC9cFt", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "eight_GMM_classes = eight_GMM_classes[0,:].tolist()\n", - "eight_GMM_classes = [x for x in eight_GMM_classes if not np.isnan(x)]\n", - "\n", - "classifies_pal = sns.color_palette(\"husl\", 8)" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 353 - }, - "id": "VsngCAgj9OG0", - "outputId": "87179ca2-3fd2-4936-c1e6-e2aa4b26a716", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=[3.8,3])\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "ax.set_xlim([0,1.4])\n", - "ax.set_ylim([0,1.6])\n", - "ax.set_zlim([0.,0.6])\n", - "ax.view_init(elev=20, azim=220)\n", - "ax.set_xticks([0,0.7,1.4])\n", - "ax.set_xticklabels(['',0.7,1.4],fontsize=12)\n", - "ax.set_yticks([0,0.5,1,1.5])\n", - "ax.set_yticklabels([0,0.5,1.0,1.5],fontsize=12)\n", - "ax.set_zticks([0,0.3,0.6])\n", - "ax.set_zticklabels([0,0.3,0.6],fontsize=12)\n", - "ax.tick_params(pad=-1)\n", - "\n", - "classifies_df = UMAP_and_GMM\n", - "classifies_df['eight_gmm_classes'] = eight_GMM_classes\n", - "\n", - "for i in range(1,9):\n", - " to_plot_df = classifies_df[classifies_df['eight_gmm_classes'] == i]\n", - " x = to_plot_df['troughToPeak_abs']\n", - " y = to_plot_df['prePostHyper']\n", - " z = to_plot_df['FWHM1_abs']\n", - " ax.scatter(x,y,z,c=classifies_pal[i-1],marker='o',alpha=0.75,s=20,linewidth=0.75,edgecolor='w',depthshade=True)\n", - " \n", - " ax.plot(x, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='y', zs=1.5)\n", - " ax.plot(y, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='x', zs=1.4)\n", - " ax.plot(x, y, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='z', zs=0)\n", - "\n", - "ax.tick_params(pad=-1)\n", - "\n", - "ax.set_xlabel('Trough to peak ($\\mu$s)',fontsize=12,labelpad=5)\n", - "ax.set_ylabel('Peak ratio',fontsize=12,labelpad=5)\n", - "ax.set_zlabel('AP width ($\\mu$s)',fontsize=12,labelpad=0)\n", - "ax.view_init(elev=20, azim=220)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LhElsHhpBMFX" - }, - "source": [ - "### We train a random forest classifier on the eight GMM cluster data with the same hyperparameters as the four cluster dataset and show it performs poorly" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "X5mI5yYbBMfA", - "outputId": "b891c509-e54e-4c2b-8c0f-d225cb16f944", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 2.6s\n", - "[Parallel(n_jobs=-1)]: Done 3 out of 5 | elapsed: 3.6s remaining: 2.4s\n", - "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 4.1s remaining: 0.0s\n", - "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 4.1s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[ 3, 1, 1, 0, 0, 0, 1, 0],\n", - " [ 0, 12, 2, 1, 2, 0, 0, 2],\n", - " [ 0, 2, 31, 4, 2, 0, 0, 0],\n", - " [ 0, 1, 5, 11, 7, 0, 0, 0],\n", - " [ 0, 0, 0, 2, 49, 0, 0, 2],\n", - " [ 0, 0, 0, 0, 2, 4, 4, 2],\n", - " [ 0, 1, 0, 0, 0, 4, 20, 1],\n", - " [ 0, 0, 0, 1, 3, 2, 0, 3]])" - ] - }, - "execution_count": 152, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "eight_classifies_nonan = [x for x in eight_GMM_classes if ~np.isnan(x)]\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(full_data[~np.isnan(full_data).any(axis=1)], \n", - " eight_classifies_nonan, test_size=.3, random_state=RAND_STATE)\n", - "\n", - "model = xgb.XGBClassifier(objective='multi:softmax')\n", - "param_dist = {\"max_depth\": [4],\n", - " \"min_child_weight\" : [2.5],\n", - " \"n_estimators\": [100],\n", - " \"learning_rate\": [0.3],\n", - " \"seed\": [RAND_STATE]}\n", - "UMAP_grid_search = GridSearchCV(model, param_grid=param_dist, \n", - " cv = 5, \n", - " verbose=10, n_jobs=-1)\n", - "UMAP_grid_search.fit(X_train, y_train)\n", - "\n", - "confusion_matrix(y_test,UMAP_grid_search.predict(X_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YQyMXRZlvu5N" - }, - "source": [ - "## Figure S6C: Confusion matrix for a random forest classifier on the 8 cluster GMM" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SbEcU68b_hki" - }, - "source": [ - "### and show the performance as a confusion matrix of the 5-fold CV test accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 225 - }, - "id": "-P5Sco-R_iEW", - "outputId": "794c2e2b-06e4-4737-c713-2ee2a4f25c05", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "confusion_mat_counts_eight_GMM = confusion_matrix(y_test,UMAP_grid_search.predict(X_test))\n", - "\n", - "conf_mat_row_list = []\n", - "\n", - "for row in confusion_mat_counts_eight_GMM:\n", - " row_sum = np.sum(row)\n", - " \n", - " row_percent = []\n", - " \n", - " for val in row:\n", - " row_percent.append(val/row_sum)\n", - " \n", - " conf_mat_row_list.append(row_percent)\n", - "\n", - "conf_mat = np.array(conf_mat_row_list)\n", - "\n", - "colormap = mpl.cm.YlGnBu\n", - "colormap.set_under('white')\n", - "\n", - "eps = np.spacing(0.0)\n", - "f, arr = plt.subplots(1,figsize=[4,3])\n", - "mappable = arr.imshow(conf_mat,cmap=colormap,vmin=eps,vmax=1.)\n", - "color_bar = f.colorbar(mappable, ax=arr, extend='min')\n", - "color_bar.set_label('P (Predicted | True)',fontsize=12,labelpad=15,fontname=\"Arial\")\n", - "color_bar.ax.tick_params(size=3,labelsize=12)\n", - "\n", - "n_classes = len(set(eight_classifies_nonan))\n", - "\n", - "#Specify label behavior of the main diagonal\n", - "for i in range(0,n_classes):\n", - " if int(conf_mat[i,i]*100) == 100:\n", - " arr.text(i-0.38,i+0.17,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n", - " else:\n", - " arr.text(i-0.34,i+0.16,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n", - " \n", - "#Specify label behavior of the off-diagonals\n", - "for i in range(0,n_classes):\n", - " for j in range(0,n_classes):\n", - " if conf_mat[i,j] < 0.1 and conf_mat[i,j] != 0:\n", - " arr.text(j-0.2,i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n", - " elif conf_mat[i,j] >= 0.1 and conf_mat[i,j] < 0.4 and conf_mat[i,j] != 0:\n", - " arr.text(j-0.4, i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n", - "\n", - "\n", - "arr.set_xticks(range(0,n_classes))\n", - "arr.set_xticklabels(range(1,n_classes+1),fontsize=12);\n", - "arr.set_yticks(range(0,n_classes))\n", - "arr.set_yticklabels(range(1,n_classes+1),fontsize=12);\n", - "arr.set_xlabel('Predicted Class',fontsize=12);\n", - "arr.set_ylabel('True Class',fontsize=12);\n", - "\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "C2vBzAGUvoHI" - }, - "source": [ - "## Figure S6D: Eight GMM cluster waveforms" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AATotmmr2HYj" - }, - "source": [ - "### Here we plot each of the eight GMM clusters and show that although they seem sensible to the eye, they represent a representation that is difficult to learn (as shown in the previous panel)" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 961 - }, - "id": "LCGchIEv2Hyx", - "outputId": "a9f1a986-a248-46f8-f265-23fc1cf9ad97", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "for i in range(1,9):\n", - " f, arr = plt.subplots()\n", - " f.set_size_inches(2, 1.75)\n", - " GMM_cluster = classifies_df[classifies_df['eight_gmm_classes']==i]\n", - " \n", - " for _,row in GMM_cluster.iterrows():\n", - " plt.plot(row['waveform'],alpha=.3,linewidth=.6,c=classifies_pal[int(i-1)])\n", - " \n", - " plt.plot(np.nanmean(GMM_cluster['waveform'].tolist(),axis=0),c='k',linewidth=1.)\n", - "\n", - " arr.spines['right'].set_visible(False)\n", - " arr.spines['top'].set_visible(False)\n", - " arr.set_ylim([-1.4,1.1])\n", - " arr.set_xticks([0,14,28,42,48])\n", - " arr.set_xticklabels(['0','0.5','1.0','1.5',''])\n", - " arr.set_xlabel('Time (ms)',fontsize=12)\n", - " arr.set_xlim([0,48])\n", - " arr.set_yticks([])\n", - " arr.tick_params(axis='both', which='major', labelsize=12)\n", - " \n", - " arr.spines['left'].set_visible(False)\n", - " \n", - " x, y = 23,-0.8\n", - "\n", - " n_waveforms = plt.text(x, y, 'n = '+str(len(GMM_cluster))\n", - " , fontsize=12)\n", - " plt.tight_layout()\n", - " plt.margins(0,0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bkB-hROxqA1P" - }, - "source": [ - "# Figure S6E: Classification accuracy across various cluster numbers for WaveMAP and GMM\n", - "\n", - "\n", - "---\n", - "The proceeding code can take very long to run. Execute only the plotting cell to load cached data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8CuaemdVqKhp", - "outputId": "e9e67fb0-7504-4d7f-c2a3-932e6ca69a7e", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[94m5\n", - "\n", - "\u001b[94m10\n", - "\n", - "\u001b[94m20\n", - "\n", - "\u001b[94m50\n", - "\n", - "\u001b[94m250\n", - "\n", - "\u001b[94m500\n", - "\n", - "\u001b[94m625\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "n_neighbors is larger than the dataset size; truncating to X.shape[0] - 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[94m0.0\n", - "\n", - "\u001b[94m0.5\n", - "\n", - "\u001b[94m1.0\n", - "\n", - "\u001b[94m1.5\n", - "\n", - "\u001b[94m2.0\n", - "\n", - "\u001b[94m2.5\n", - "\n", - "\u001b[94m3.0\n", - "\n", - "\u001b[94m3.5\n", - "\n", - "\u001b[94m4.0\n", - "\n", - "\u001b[94m4.5\n", - "\n", - "\u001b[94m5.0\n", - "\n", - "\u001b[94m5.5\n", - "\n", - "\u001b[94m6.0\n", - "\n", - "\u001b[94m6.5\n", - "\n", - "\u001b[94m7.0\n", - "\n", - "\u001b[94m7.5\n", - "\n", - "\u001b[94m8.0\n", - "\n", - "\u001b[94m8.5\n", - "\n", - "\u001b[94m9.0\n", - "\n", - "\u001b[94m9.5\n", - "\n", - "\u001b[94m10.0\n" - ] - } - ], - "source": [ - "resolution_list = np.linspace(0,10,21)\n", - "modularity_dict = {}\n", - "n_clusts_dict = {}\n", - "\n", - "n_neighbors_list = [5,10,20,50,250,500,625]\n", - "n_neighbors_modularity_dict = {}\n", - "n_clusts_neighbors_dict = {}\n", - "\n", - "subsets=[100]\n", - "\n", - "for neigh in n_neighbors_list:\n", - " print(\"\\n\" + BlueCol + str(neigh))\n", - " for frac in subsets:\n", - " rand_list = []\n", - " n_clusts = []\n", - " for i in list(range(1,2)):\n", - " reducer_rand_test = umap.UMAP(n_neighbors = neigh, \n", - " min_dist=MIN_DIST, \n", - " random_state=random.randint(1,100000))\n", - " rand_data = np.random.permutation(full_data)[0:(int(len(full_data)*frac)),:]\n", - " mapper = reducer_rand_test.fit(rand_data)\n", - " embedding_rand_test = reducer_rand_test.transform(rand_data)\n", - "\n", - " umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n", - " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - " clustering = cylouvain.best_partition(G, resolution = 1.5)\n", - " modularity = cylouvain.modularity(clustering, G)\n", - " clustering_solution = list(clustering.values())\n", - " rand_list.append(modularity)\n", - " n_clusts.append(len(set(clustering_solution)))\n", - " n_neighbors_modularity_dict.update({str(neigh): rand_list})\n", - " n_clusts_neighbors_dict.update({str(neigh): n_clusts})\n", - "\n", - "for res in resolution_list:\n", - " print(\"\\n\" + BlueCol + str(res))\n", - " for frac in subsets:\n", - " rand_list = []\n", - " n_clusts = []\n", - " for i in list(range(1,2)):\n", - " reducer_rand_test = umap.UMAP(n_neighbors = N_NEIGHBORS, \n", - " min_dist=MIN_DIST, \n", - " random_state=random.randint(1,100000))\n", - " rand_data = np.random.permutation(full_data)[0:(int(len(full_data)*frac)),:]\n", - " mapper = reducer_rand_test.fit(rand_data)\n", - " embedding_rand_test = reducer_rand_test.transform(rand_data)\n", - "\n", - " umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n", - " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - " clustering = cylouvain.best_partition(G, resolution = res)\n", - " modularity = cylouvain.modularity(clustering, G)\n", - " clustering_solution = list(clustering.values())\n", - " rand_list.append(modularity)\n", - " n_clusts.append(len(set(clustering_solution)))\n", - " modularity_dict.update({str(res): rand_list})\n", - " n_clusts_dict.update({str(res): n_clusts})" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": { - "id": "rr3H0RFvqVuL", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "resolutions_to_use = {0.5: 17, 1.0: 13, 1.5: 8, 2.5: 6, 3.0: 5, 5.5: 4, 6.5: 3, 9.0: 2}\n", - "n_neighbors_to_use = {5: 15, 10: 13, 20: 9, 50: 7, 250: 6, 625: 5}\n", - "\n", - "joint_clusts_to_use = [2,3,4,5,6,7,8,9,13,15,17]\n", - "\n", - "gmm_data = np.array(list(zip(UMAP_and_GMM['troughToPeak_abs'].tolist(),\n", - " UMAP_and_GMM['FWHM1_abs'].tolist(),\n", - " UMAP_and_GMM['prePostHyper'].tolist())))" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": { - "id": "8uTVOYbXriJw", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "def train_classifier(data_X,data_y,test_size = 0.3):\n", - " accuracies = []\n", - " skf = StratifiedKFold(n_splits=5)\n", - "\n", - " unclassified_ixs = [ix for ix,clust in enumerate(data_y) if clust == -1]\n", - "\n", - " data_X = np.delete(data_X,unclassified_ixs,axis=0)\n", - " data_y = np.delete(data_y,unclassified_ixs,axis=0)\n", - "\n", - " for train_index, test_index in skf.split(data_X, data_y):\n", - " data_X_train, data_X_test = data_X[train_index], data_X[test_index]\n", - " data_y_train, data_y_test = data_y[train_index], data_y[test_index]\n", - "\n", - " model = xgb.XGBClassifier()\n", - " param_dist = {\n", - " \"max_depth\": [10], #10\n", - " \"min_child_weight\" : [2.5], #2.5\n", - " \"n_estimators\": [110], #110\n", - " \"learning_rate\": [0.05], #0.05\n", - " \"seed\": [RAND_STATE]}\n", - " grid_search = GridSearchCV(model, param_grid=param_dist, \n", - " cv = 5, \n", - " verbose=0, n_jobs=-1)\n", - " \n", - " fit_model = grid_search.fit(data_X_train,data_y_train)\n", - " \n", - " accuracies.append(accuracy_score(data_y_test,fit_model.predict(data_X_test)))\n", - "\n", - " return accuracies" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 365 - }, - "id": "Ud5iceTBqZuV", - "outputId": "bf3ee1f2-3311-4dd4-a7da-a0d21b257726", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mgmm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGaussianMixture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mgmm_labels\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "gmm_scores = []\n", - "\n", - "for i in list(joint_clusts_to_use):\n", - " gmm = GaussianMixture(n_components=i)\n", - " gmm_labels = gmm.fit_predict(gmm_data)\n", - " gmm_score = train_classifier(UMAP_and_GMM['waveform'].tolist(),gmm_labels)\n", - " gmm_scores.append(gmm_score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NDizdJB2qcfA", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "wavemap_scores = []\n", - "wavemap_n_neigbors_scores = []\n", - "\n", - "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE)\n", - "mapper = reducer.fit(full_data)\n", - "embedding = reducer.transform(full_data)\n", - "\n", - "\n", - "for i in list(resolutions_to_use.keys()):\n", - " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - " clustering = cylouvain.best_partition(G, resolution = i)\n", - " clustering_solution = list(clustering.values())\n", - " wavemap_score = train_classifier(UMAP_and_GMM['waveform'].tolist(),clustering_solution)\n", - " wavemap_scores.append(wavemap_score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "QHNLGPjJqemh", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "wavemap_n_neigbors_scores = []\n", - "\n", - "for i in list(n_neighbors_to_use.keys()):\n", - "\n", - " reducer = umap.UMAP(n_neighbors = i, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE)\n", - " mapper = reducer.fit(full_data)\n", - " embedding = reducer.transform(full_data)\n", - "\n", - " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - " clustering = cylouvain.best_partition(G, resolution = 1.5)\n", - " clustering_solution = list(clustering.values())\n", - " wavemap_n_neighbors_score = train_classifier(UMAP_and_GMM['waveform'].tolist(),clustering_solution)\n", - " wavemap_n_neigbors_scores.append(wavemap_n_neighbors_score)" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 309 - }, - "id": "lLuuxC6pqicr", - "outputId": "9a13be6d-cccc-47d4-93b4-ec7b22e3afa1", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Classifier Accuracy')" - ] - }, - "execution_count": 160, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "if 'wavemap_scores' not in list(locals().keys()):\n", - " wavemap_scores = pkl.load(open('WaveMAP_Paper/data/wavemap_scores.pkl','rb'))\n", - "\n", - "if 'wavemap_n_neigbors_scores' not in list(locals().keys()):\n", - " wavemap_n_neigbors_scores = pkl.load(open('WaveMAP_Paper/data/wavemap_n_neigbors_scores.pkl','rb'))\n", - "\n", - "if 'gmm_scores' not in list(locals().keys()):\n", - " gmm_scores = pkl.load(open('WaveMAP_Paper/data/gmm_scores.pkl','rb'))\n", - "\n", - "\n", - "wavemap_means = [np.mean(x) for x in wavemap_scores]\n", - "wavemap_stds = [np.std(x) for x in wavemap_scores]\n", - "gmm_means = [np.mean(x) for x in gmm_scores]\n", - "gmm_stds = [np.std(x) for x in gmm_scores]\n", - "\n", - "n_neighbors_means = [np.mean(x) for x in wavemap_n_neigbors_scores]\n", - "n_neighbors_stds = [np.std(x) for x in wavemap_n_neigbors_scores]\n", - "\n", - "f, arr = plt.subplots(1)\n", - "wavemap_n_neighbors = arr.errorbar(list(n_neighbors_to_use.values()),\n", - " n_neighbors_means,yerr=n_neighbors_stds,\n", - " marker='o',label='WaveMAP ($\\Delta$ n_neighbors)')\n", - "wavemap = arr.errorbar(list(resolutions_to_use.values()),wavemap_means,\n", - " yerr=wavemap_stds,marker='o',label='WaveMAP ($\\Delta$ resolution)')\n", - "gmm_on_feat = arr.errorbar(joint_clusts_to_use,gmm_means,yerr=gmm_stds,marker='o',\n", - " label='GMM on Features ($\\Delta$ n_components)')\n", - "\n", - "arr.legend(loc=3)\n", - "arr.set_ylim([0.5,1.])\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['bottom'].set_bounds(2,17)\n", - "arr.set_xticklabels(['',2,4,6,8,10,12,14,16],fontsize=12)\n", - "arr.set_yticklabels([0.5,0.6,0.7,0.8,0.9,1.0],fontsize=12)\n", - "arr.set_xlabel('Number of Clusters',fontsize=16)\n", - "arr.set_ylabel('Classifier Accuracy',fontsize=16)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wIHRl6qSLCIo" - }, - "source": [ - "# Figure S7: WaveMAP implicitly captures specified features" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YQr7cn4fv-4o" - }, - "source": [ - "## Figure S7B,C,D: Here we show in WaveMAP space each waveform's feature values across the three specified features used." - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": { - "id": "SGnerw78LDM6", - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "gmm_feat_df = pd.DataFrame(gmm_feat_data_nonan,\n", - " columns=['trough_to_peak','peak_ratio','trough_fwhm'])\n", - "\n", - "GMM_class_df = pd.DataFrame(GMM_class_labels,columns=['Class'])\n", - "full_data_df = pd.DataFrame({'Waveform': full_data.tolist()})\n", - "data_classified_df = pd.concat([umap_df,full_data_df,GMM_class_df,gmm_feat_df],axis=1)\n", - "data_classified_df.loc[data_classified_df['Class']==1,'color'] = GMM_PAL[0]\n", - "data_classified_df.loc[data_classified_df['Class']==2,'color'] = GMM_PAL[1]\n", - "data_classified_df.loc[data_classified_df['Class']==3,'color'] = GMM_PAL[2]\n", - "data_classified_df.loc[data_classified_df['Class']==4,'color'] = GMM_PAL[3]\n", - "\n", - "data_classified_df['trough_to_peak_abs'] = data_classified_df['trough_to_peak'].divide(SAMP_RATE_TO_TIME)\n", - "data_classified_df['trough_fwhm_abs'] = data_classified_df['trough_fwhm'].divide(SAMP_RATE_TO_TIME)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_rivsSqeLDUY" - }, - "source": [ - "### We plot the specified feature values of each waveform in UMAP space" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 917 - }, - "id": "6GO3iwuJLCPA", - "outputId": "3b37de14-029a-4dc5-befe-124ccc11ed13", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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TyySSuTN/nWdBPD6BUkFXKFBIB8t20FpTLu+SSOR6FQv9CE4pGYKTSoXStk88eMY9fESY3xWJGDpukoncEslk/ineZQ9juAyPjNaa0vEWRzu3aNVKxOIZZpffJjO5+NDjAr/NxtqHqCBKPtQK5bcRKtSR6pSlACMNWDxxtgZFa83O5sdUyjvdqepxcZ2p2ZtMz94Y2r9a2edw/zZeu0EsnmZ67rUzuwk7noqUj3Y71mtHHB3cw/MapFJT5Kau8mDjw67Rsux417i02zUOD78mnZ7FcRO0W7WTLz40pQvzsIYNkrSd7nc1sO8IRJ+3trj8zTOXujGGy/BI+H6TjbVfUdnb6E4xvFaNWuWApevfYWr+5tAxntdgf/8rGo0w4VExHMvSSiGssJYNFSAiD2x4xzN9OzTqBSqlHRC661VorTja/4pMdoFYPN3dt3y8ze7277vToEa9yObar1hefY9kath4aa0JgjZCSCzLGTsGpQL2976kXNpCa43jJpmff4tkamrsMcfFTfa2P+uW/LSaYWuy0PDrSLrmpHFRVKv7zMy/yc6D3/VN6SJvSmmQfR+x7u+2EX4+rpsi0MOF10LIE55X77VFlIV/2mfwpBhZG8Mjsbn5W0p79/Abw/lV0nJ467v/DCl7mlBKBayvD6oMdJ7kXX0oe8RzsxMHO4HWmonMIrMLbz5VYFdrTelgnYOj22ih+zd0850sJ8ZEfoWpqVcpFO5zXNygc5mrIIDIQ4snJli9/sOB8zfqRfZ2P8P3W2itSSbzzC28g227Q2PZ2vwtterBibiQxerV7xOLZ4CwLvC4sEG1sotSilar0vWGRmWo27Fk6BmdQAiLmZkbJOITHBc2aLdrWJY78N76bYGUYdlOMjXF/OI7+H6Drc3fDuWBjVqdDFNcLISQ5CZXmJ5+dWg8j8jYL9p4XIaHEgQezWaJoD0mp0pr2s0K8b74ULW6N5Qh3Vmt6sgGh3+TjLpxRr1G4eBuqCix9PYTvQ+tAu787t/R1k2cdGbQKyHMAteBj0ZTKe9QLu8Bqlu/ByAtgZYS7XtDxeFeux7d3L33Xa8X2Nr8DSur3xt4Pc9rDhmt8G0GFI7usbD0LkHgsX73/cgIqoHPZ1xZTVi8PpzNDmBZLrYTZ3qu5x1btkPh8B5hTAoEgrnFd5DSwo2lcaN0FmuEMRwYS4eO0Yr07vNTj5Bn9gQYw2U4Fd9rcnhwB4haXQXD0z2lfIqFdeYTE4CmXitQre6Pz9KWUVyLwVUr0NFv0RJ8569ao/x2eFMf3mNm4Y0B7+5RKezeoXa8R2JhcbhMpZN4GUTvEwGo4fgP4c2NZWNbg17U8fGDEe9Z47XrtJpl4ole/Z/n1aNp1vBn1GpXw/MV1rtGq/M59J93FIHfRjrD8SQpbRIj4oTTszeZmLxCvXqItGxS6dmRn62UNtMzNzk8uD3yPUZ7kUzmWVx6Fymdc23AYQyXYSRKBezufBY2UohaXTmpLK3jI07eNNKNUanu4j/wwnhWJCwXThdGXbyRBzNU4yZJJKdQ2qfZKHX/3q/gCRoVeE9muHa+Rin/1BtKOrEBL1D0eVudf2sAoZmaGZwChWVKow2K5zeI0zNcrpseY9hFt8C5Wt7relrK9wb3lxI5KpEzmorbsUQ30dNxEszPvzX2fTtOgonJKyO39ZObXCEWy3Bc3MAPWqTSs2SzC9TrRQK/RSKRGzDO54kxXIYhfL/NvTs/QwWtMDZr2aGKZiyOk5nAq0ZGRWtkLE4sHZaRNOpH3XNoFYSey1ASoo6a0Yy4iYQgkcgxNX2d+1//LFTNVMHACpeQFtaIeNGj0LmR/WYDO5EcWh3rxGY6/+4/ZnC8Ek3AyRBMMjlJo3400iOJxwZLbWzbJTuxTLm0ObC/lJKpaHrVeZ+B3x74DJTvh55vLD6sNU/4fQFMT79KIpHDcRKnfSyPRSI5SSI52O06m50/s/M/KsZwGYZYu/sLgr4iZ60C8D2sWAI3lcFJptG+B/JkID1swNC5EZXfim6i8OayLBflNzlNP6CTRzW/+DZrd97v6j2F+umCbG7p1ONPY3r5DarHO7SKR1ixeBSPkWHTCNlbzez0HhTRathII6thd+dTQDORWwYgO7FEsbBOELTpeWySdHpupHjf3PybuG4iOsYjkZxkdu717r6TU9eoVQ97wXilaB4coNo9PSt3IkdssmdIhGWH70krWq0q2ezCE31WFx2zqmgYoNksc//2347cJp1Y34pVdGP2bQ9bvevhIHK0r7RcZmZucnR0N8w5OmEQpLS5/sqPu15OrXrE9oPfoGX/8jqkUtMsLL772DEUrTUPbr3P0fZXIAROKoPlumSnVkilp6g3ijSb5W5ZjJD22Olu4HuooI3jxHnlxp90/+77LY4O71CrHiClxURuhdzkyhPHe3a2PqWw/zUAjf19VPOErpkQJGbncdKZsBFGn8xMLrfC9PQrT/S6FwSzqmh4NPZ2Phu7TQUeQkikZfXygE54XDDsofSWzBWJZI7ruZ+wt/t5FMDX3TjS4uK7AwW4ldI2+mQZClCrHVGvF0idku80CiEEK2/8EbMr71ApbGM7MSZmVrs1kzlWqdeO2N76OJKX9vsKwwffjw58BKKb9tAZo23HmJt/67HGdRqz869TOLiD9v1hoxUOBq9cIjE9e+K9SjKZuTMbx0XDGC5DF6UCWqfoYAkhcJ0kV66+x4ONX+P7je62UzOohYUKApTXwrbjYWHwwts0m2Ua9QLSckinZ7uJikHgsbP1MbXqYTde04/Wilr14LENV4d4Kkc8NToTX1rOwHtRgYe0eitknX6EWgVRq68RKqJniGU5uPE0zXJhqPymg0BGBr+3Gjs1dZ1YLD2074uCMVyGAYS0xt4g0nKZX3oHy3LITixSLNx/qDAdRPW7lk2zVKRZOyaVnQEgHs8SH6EPtbv9+66O1zieZFXxUYjFMth2DM8LDbhWAUEQdKfIKvBDb6svvhfWWZ6fXoHlJnAzORp7eyPVHybnrnPl6g+p1w+7Sa/Popv088SoQxi6CCGQUmLHksOpCraLtG2q1X0AJiaWotq6Xi6WVgq/Ucdv1KN41zD7W5+fOgbfb0UlQuN1noQQZCdOr4+EMMh+fLzJ7u7nHB3dw/PG6973n3vpyrejFl4WluUgpET5HkE7lD+WltVdzTuZLnEeJBIT2G6c1OKVE9+LwHbiLN58D8uyyWTmyWYXXnijBcbjMvRRqx1Bp/9ePNUNtvcHqEulLeLxLOn0LAuL77K19bvQK2nVqe/vDJwvMTWDnUjSm8Jo2s3qqWMIS4SiYHxkME6WsExOXsV1T++07PttNjd/E8aqonhcqbTF4uI3xjaC6OC6Sa5e/yNq1QNKpS18r0m7XUVbcshEpTPz5zpVhHDa12iUiOfyWK5L42Af5bXJzV7lyhs/wo0/WtfpFwljuF5yPK9JvX6EEJJqZZ/Aa0YlGxJhdbwJ3RdgDygU1kmnZ7uZ4kqp0Gid8JAaRwekFpajUhqFDgLiqRytVhUVeMTi2aEpn+uG+VVB0Gtrpbw2dPKppCCTncX3mlSK2yAE2cmlodyuQuF+lJbQIXwP+/tfsbLy3sM/l3aNne1PorKl3qKDFv3GSxBPTI45w9nhuimuXPk2xeI6TSfBxPQqk5MrI6fZLwvGcL3EHB3d5/h4A60VXqM2UPsmpI0dD42ICno3L0CrVRkQpvNrY5qHao1Xq+JmsrRKBRCSll/lwfovQyOgYWb2dSZySwC0W1UOD+6gOtNMHZXXCHoqDmi2135LtbAdqXCGAfOVmz8iN9VTSA29x2E8r0kQtLGs05NY9/a+HGqjFdYHBoiO/IwQeM+oqYfjJM5EgO9FwRiul5RGo9Q1Wn6zPlSwq5VP4LWiadqJWJNW7O581g1Ih80/x9TOtVs0C4dYsTh2KhN6X8oPlVO0ZnfnU3Z3Pg0XBfpyvjq1g+HLqejPGqUUldIWoQfVG/PG7fdJ/+F/gR3V6Ulp9drDn+BhgXTPa1Cvje5yQzTCcDXRwo29fNO0i4AJzr+kVCo73Tq4cZrv6kSpST+NRpFMZg4hJHYiNRTMB0AI4rkpkrOLxCbyPYWBSMNJdzscd8YwvGKGEN1W7gCq3Rrej9ApKx315J2z2cURBiosKXqYYN/e3henbu8gpSSTefblLgZjuF5alFIDHs4opLRPEYETZNJzJBJ57FgCJz0xYLyEtLCTaaQ7uMLVWYV7lDSKgeOikhwhRqdBhB5czwDncsskk/lI6zzUhnLdJHNzp0+3lApoNkqneGVhGkQimefKynvnlpZxnnhe89K3fTNTxZeQWu2IWnWfjoyMHUvgt5snvKuwG4vlxiiXtiFqJ9a3Gct2WFx8h3a7RnOmSqtapHK4CUBmeolidRvoFTB3fn/SKrF4cpL84ir3v/zpsJcoIDO51PunEMzPv0W7XafVquI4cWKxzCOvAAppoaN29kLayMiAW5ZDPn+NiUdIx7hoNBsltjZ+i9cO5Ztj8QxLK9/GvYSJqsZwvWR4XpPdnd8PejxChMarWY/+aWHZDrPzoXdSqex19++/8Tc2fs2VK9/BdVNhekJ2jpnF8JhiYQ1RCbo2SiNAWuEa5UhvSxP2LR02LL3au2XceBorFo+UWHtywRMzV7uqoYPHSur1ozBDXzokEhO4sTSp5BS2Ex/aX0qLeGKCZuMYy3LQCLrdiQhzw46O7mJZDun0zLiP+ZnhtxuUDtZDhdiZFZwxMbdQlPAXAwsOzUaJtbu/4NXX//TSeY7GcL1kVMrbQ4mdnelbJrcYaWJNkpu8grRsWo0yXq2M36oDIpS2SWailAWP9bUPWFn97kBeVbV6wNHhnROvrBFaMzl1jWbjmEa9MCDr3NlnwHgJ0ZOVIRTqk3IXN53FjsXxmw0QYMeTKBkMSej4fpMHGx8SBG0C3w+z95ulriHKT71CfkQz1bm5N9l88FFXwXW49lJRKKw9d8NV2L3L+uc/Dcen4cFXv2D5xveYWRlWiC0VH4CQSNsJVVKjlVutfKrlPbK5y+VBGsP1kuH7PcmVfoQQZCYWmJjoTbc8r8mdL/6/bvcY0AStBjoIiGXD/CWtFQf7X7G0/C2UCtBanVIKpJmYWCKfv8qdr38aTcc6AfrePh3j1e/phOPpeIQCaTtYcSDqUuO1myjlD8TkioV1At/DbzWwYom+c4XHFI7ukkrmicUzaK2p1Q6oVg+w7RhXVt6jVjvkcMgAd8bSGKE19uzw2g3WPw+nzP2f3ubXvyQztUw8lUOpgMOdWxT37yFTCaTr0EnuDZOGGyil8LzGyNe4yBjD9ZKRTOWpVHYHAtkdTkr7FvbuDOUyQbjaqAK/m8LQqBfY2vqYeid3amzgPfTSpAxTLIQIW73rodc4EU87ge953YYV4e6htHOltEMu38vlqtcLBF5rpMJDeJiiXN5hyk2yvvYhQdApCRKUjjeZnXsTy3JGeIbhjV8ubw8Y+mdJaf9+1CfxxLi0orh7l/nr3+LeZ39NvXpILDfFUBMSaSFtF62CS9lL0qwqvmSkUtPEYumBVTMhJOnM/FAZTaNeGJMOISIp5SiVQevIaOm+nxFHiTAzXkrZy/oeY+QE41UXRKdRRPcnDKIf7H0VpVloDg/vRF6RQjpjVkY1KB2wtfU7gqDRN/ZQDHF//0smJq4MTa077ceKR/dHn/cZoLUabu4abkCrgGppl0b1KGxZ5rhDn2XHa00kciTOqD/ks8QYrpcMISRLS99iavpVYvEs8USO2bk3RmZlx5OTY9ICdJieoALCS2iwucWoomMhJNMzr3XPNzv3ZphPNcY4nexq03tlRntoaALloZRPo1GkFPUqtJwYjDGCGoVrJ2g1S4N/j3LMlO+hAi9UhFCR9rsKCPwWaEUwJsH1WZCd7sXmhG0j4/HwJxYnls5RLx+E+vqnFIBLy+HKte89t+nu02Cmii8hQkpyuSvkcqc3SJiafZWjvdvoYNArEpaNFelkuW6alnci8z4KFttOPExQtWPk89cGGp3GYmmuXvsBR0f3I89laNKDZbloHQzEy0Z1Gep7YaS0KJW2w2M6CaxjhA2DVot6rXBiGwOpFp1FhsA/aaQEqdTzC87HEhkWrn+b3exdW/cAACAASURBVI2PByW0BWzf/4iZ5beiGGKADnywBluWCSEihY/LtZrYwRguw1gcN8Erb/wpW+sfUa8chKqak4vMLLyBUkGU/6PZWP9wyOwIaZGbDIuBx2FZLrOzr2FJm6PDO30pFzJa8bvKcWmTcmmrmzCpRsSbOmQys5xs+TUuvSLw2njVEvb0NWgRxdQEesiLihqS9Z23k9Tablb4+sv/EPYqnLlBZuJ89d2LO3fZ/OqXtBsVUhOzzL3yB+Eq4YnptlIBrUYlHKdjoQIfy7J7DUGExHESI1dULwtGc97wSPS63gwbgu2tT6if6G5jWS6rV3/Q9cweRqtVoVLeBSCTnScW6+Vk1WqH7O5+gdZBqIkVDC8YgGD1lR+SSE5Sqeyxv38rTGcY1TRVK7xaBeX7vPrOn7O99btuR0d1SkZ5Rx4nlZ6hVjkYLEoXFjPzrzN5TsbgYP1z1j/7W1Tfe5eOg5vNjlzB1VqTys6gY32a+VGAPjd5lampawghUSoIvzsVdq0eXynxXDCa84an47Q4yMLiOxQL65RKWygVkE5PMzX96iMbLQiVR2MzwwmkAMlkHillV4k0GGG43FiKeLQqmk7PUKns0miUIg9tsHmHDsI8pmR+jkQihxvL0G5VT30C9x7wmlp5n5PPa60DDvduk5u8cuZqqForNr54f8BoASjPo12voX0fISRWPIa07UjFQqNskP3fm1ZoDbXKLtPTr1CvF9l68FH3fWmtmZ17g9wpXvJFwRguw1MTTu2ukZ+6dm7nX1r6A3Z2Pg2bUzigvEZUt6hxYxmWr36na1yFkCwsfIN6vUCtdojvt6hFjTm0CkAI3MwEi0vfBMJ4W7tdG9lFGxixSDDaxGkdEPjtkRn5T4PfaoydIqtGszsi1W5jJRNYsVhYqjSi5yKEjWuVCth68NFQusv+3pckEpMjqxAuEsZwGS4FrptiZeW7eF4dpRSuk6DdriKlM1JaRghBKjXVbaihVEClvEO9XsB1k0zkrnQljjOZOarVgzB1wnZCVYyIfk/rUZDnMNWy3PgjB2SCeqPbxEMrNdJ4WZZL6XhzZI6e1opSaZPZ+BtPO+xzxRguw6WhU2Z0dHg3KpxOMDV9/ZE0scIeh8vd5q39xGJh0456LRQ7tJw4KvCxLRfPqw/HkKREaE4sAkgmJlfOZZVOSgu/4iNTp0/ZO+ggQNg2Xq2Cm8kO5exlJ5Y42L899vjLoBxhDJfh0tBoHLO1+duuwWi1PHa2f89U/hXqtSNq1QOEECRT00zP3Xyk9lyNepHNBx/RKTXSWpGdWGJ27jWkdHiw8SuajeO+FcXQAAokxaN73fNorWl7tXMrA6puHpNcjmOnetPQsa/TKQj3PLSv0FZosKQMV2ubrcrIyokOlrxQAfqRmARUw6Xh8OD2YE6XUvhek92tTygfbxL4LXyvSfl4k/tf/x1Hh3dPPZ9SQVRM7Ud1lqGYYaW8je+1EEJwZeU7zM2/STKZJ5WeYXHpXWZmX6dc3owkpaMfGRrBcmnrXN57emaB+vYxrYMy7UIVr1wfWdQgbRfLieGkMsQm82D1YnaOm2IyvxrpjY03rs5DGpFcBIzHZbg0tFphhyCtwwYa45RbIUwiPTz4mmRyinqjQKtZxo1lyOWWu7GtWvVgZNlMf5xHCMlE7goTfcm6jcYxqpPgOnBcQOl4c+R09Gl56x//Ez74X/9nWsU60rUQlkT5ithkmrChuMBNZHn9B/85wnZYu/ezobG1mhXKpR1s28Vrj++2NCr2ddEwHpfh0tAxOGEJzqDR0loTtNsEXruvI5Fia+u3FAtr1GqHHBfXWV/7gFaritY6jOWMCXqfTD04yVh/5ZzKZ7Jz83zrv/pvadcDvJpHu9KicVCjXa6iGh65mVd490//G+KpHFsbvx5dLqUDisW1aPV3zDg1HB183SuYv6AYj8twacjnr7G/f2soATVot/AqfZ2GBDiZLI4bG5xaRnlM21uf0KoU8IM2djwxNG0SwiKdmRs7jnh8IkrFGDSenfjXeTH7yg2+/Zd/yf2P/qabHiGkhWW5XP3mHyOECHtANqtgjfBJtKbVKOM6KTKZeSqVneF90KDhYO8Wq9d/eG7v5WkxhstwachOLBIon93N39NxlbRSeJXy4I4avHIJJzk6F8nz6vhRV2vltQfUE4SwuvGscQghWFr+Fg8e/Dq8z7VCCEkqPU02e76CfPM3v0U8k2fryw9p1ytMLr7C0pvv4cRTqMCn2Sh3vb6RCwUajosbLCx9A2+tTrNRilLXulK1QG9aflExJT+GS8f+zpcUDu+GrdUadfx6bcReglguj5MaXlnUWtOuHPf2lBJpu2Qnl8hOLJLOzD3SymAQeFTKuwRBm2RqakjP7FmhtWZ/94tI5VSgVYBSKmzqG72NsLtb6E2lMrPML7yFtGLcvf3X4aLEibvYdVNcu/GTZ/1WTmJKfgwvDlOzNygW1sLu2GqcaKEmHssQIID+6WLUGbt/TxVK2MzMvo4bSz7yOCzLITd5usLGs+Bw/ytKxQdRf8vwb6Gcs44cqbAECCGQtk2zUeT+vZ8DYTKq124iRM9yCSGZnr357N/IY2CC84ZLRyfJUwiJsMc/e6cX3iCZzA20KJNCdpuC9CMQZ16q8yzQWlMsrA8lyXZUIDoiiwDSduh0/+54lIFqExr2UB3DslxmF946d6WLp8V4XIbLR9TirNtr0bbR/mDAXjgO7VqFhcV38bwG7XYN102ifJ971cH2ZkJa5KZW2d35PQDZiSVSqelLIbCntTolLSSMcWnd+awG6W4TYa7XtRs/wXGGFysuIibGZbiUrN19n1azjN9uoXwP7fsoL1ppcxx0EBBU67iJNG/84C+wnV5j2nr1iN3NT2nWi9hOHDeRoe3Xu9nkQlhks/PML37jicamtabROOa4uE4QeCST0+QmrzyWWsbjcO/2T8c2vOgYNWFZJ7T3Q52xjpKEVpq5hTeZnLp6LmN8QsZnnRjDZbiMNBslNtZ+iQoCgnajK60c3oSKVrEIKkwSjWcnSc0uIaRASptsbpnc5DJCSJrNMhtrfz9iqmWxsvpd4omJxxqX1prdnU+pVvejzIJI6UtIVle/90h1lY9LtbLP1sZHQ38XlhX2BojiW8KyGXW7dhrfTs28yszca2c+vqfABOcNLxbxxATXXvkxxcJaWMKiJYdrn6ECH9XuC75rjYg5tNvV7hSo1SxTLe8wOXU96ng0SogvoFY7IBbPUqsdUansIoUkk10gmZwcO65OizMgjCV12oFpxc72p6xe+97ZfQgR6cwsiVSeRkeGWohoRVFg2W7okeqTbeD6iKqCLlPTDGO4DJcWx00wOx/Kr/jtJnu3PhoyQtKNDSWZaq2o145oNI7HPtOFkHhei/t3f06rUYqmXILi0Rr5mVeYnR3tmZTLO4wzEK1WGaX8sEnIGTOZX6Xdrg0VTwtpsXz1W+xufYIXtE6JXwlS6ekzH9d5YVYVDS8EthsnlZvlpCWy4/HxnYRUMNLb6nTQLpe2adYKfcHvsH/j0f4d2u1RuWM9D2s85xP4TmfmSSbzUUY/gEQIyfziO6TS08TT+VOD7rn86qUIyncwhsvwwnD9m3+GE0uigih/SQj0uJI8rVEnPCMhwi5BUkhAE3jN0ccGHpXK7sht4zLnRTR96xQwKxVQrR5Qre6fSVGzEIKFpW8yO/8WjpsOUx8sl1JpC89rEI9PgBj29LTWSGExOz/cnu4iYwyX4YUhlszyjT/5r6kdxNi/e4yVzoQyxmNQrdZAMXIsPkG/RzTSG+sc6482NsnUFI4zOolVCEmtdkitdsS9uz9jb/czdnc+597dn1EujzaEj4PWmkLhXl+/R02zWWZr63dkMgvRyurJukzJ6vUfXro2ZcZwGV4opLR46yd/xt2frbP+yw2Ur2jXat0Gs52foB02de0v2A49n/4M8vFTp2xuvGc1vs4xTD/Y2f4ErYOuBpjWiv29L8amNDwqtdohakQlgVKKVqvMysp3yOaWsOw40nLJZBd59eY/fCTBxYuGMVyGF465V6/yJ//dP2fjw3V+/i9+SnW/TNBqErSjn1aj21pMdRrMCtktvO5g2e7I87uxDK47+mZvNjsF36OMnh4pNwOht1R5Sq/L9xsjlU21DvC8JkoFxOMTzM6+xvVXfsLi0rsXrR3ZI2PyuM4YrTWtVgVL2jjuo9e9Gc6edqPJb/79vyKWt4ilxkwZbRs3kQ4bwmrVzcrvoIKAwOubUmqNHUuSnphjdvYNnKhMqJO/Vasd9uoD0YRB8jDfYG7uLRqNIqXSVp8UdM/ATU6uMj1z44nfb612xN7el8Mri0ISi2Vo1AtRSlf4motL3yCZnBp1qouCSUB9FlTKu2xv/DZy/zWxeJYrV7+L4yae99BeWrx2g3u3forvDdcnAtjJDJYT5joF7XYYoHec7kKkCny8Wqj1FXgefrWG8n2k45CcniU1Oc/C4jeoN4452L81GBfrSN60FOn8AtXGQTiVG8ipEghpIaXF0vK3nkphQmvN5uZvaLfrA+e3LBffG+2NJZJ55ufevKh1msZwnTetZoV7t/926OKwLJeV6z8gkTz9gvT9NseHa1TLe7ixFPnZVx47a9swnp2Njykc3Bn4m3TjWLEErdIR2h/sWxjLTpKeWKBW3KXdLBO027SPS4MnFYLk7BzJyRksJ0GreUIXjFB5orG/R2JuPvp3qGt/knR2kcXFbzx1SoJSPoXCfSqVA0CTTs/SbtWo1w/HHmPbMa5e++GZN7I9A4zhOm92Nj+heLTG6I8jTO67cu37yBPFrseFDQ72boXTEXS4WqU1QkiuXP8usXi2K1mSzS0Rf4gBNIzH91sU9u8SKJ9mu4zSAc1ygWCEWgRAcnKedu0YpXyaheJQITeEZTUT125gOwl8fzi4rpXCb7XCfDL06IJoIZievsHU9CtP+xZHsrX1MfXaeMMlhMXc/JtkTlF9fU6Ykp/zJlwRGmfDNbXqAXdv/TXXbvy465YXj9bY3/ki9NKi8hBp2yivjd+qs3Hn77ESYZxM+T77O7eYnn2V+SvvPps39YJh2zFmF98EwilcpbLP2v5/HLt/s36M68Rpt6ojjRaEPQzbtQqB2xpQUu1uVwppyahB65jrI4qLnhfZ7AKNenFM4F6jtY/XHm28LyoXzje8rKTSM31Zy6PxvDpbD34LhBfM4d5XIwKpYTGskDbCcWnsbVPfeUBzf5ugXudg93avJs3wxAghyWbnOW3CICybZHo61PMaIQsTnQiNCj1mFfQadahQ5LC+u43fqId/P6W8KB5/tLCA1opGvRgF2sfnmfWTTs9GWfUSrQJalWPqh7s0jvZpV4/xmzVq1cNHPt9FwHhcZ8RkfpXC4d3Q8+p2mRnOCWrWi/h+CyltgqA98lxCSqTj0DzY6WmBA0GzjvI9ikcbJFL56DUUlfIurWYFN5Ymm10Yf5MZhpCW0208cRLLiWFbMeavfJMH9Q8GG3IAiFAeuivK12ogpIWwLIJWm+bBHjoIkK4baV9Bt6K5/zRRl22tFfV6ERV4xBO57oplh3q9wM7WJ5HGfXiuhcV3SaZOXxkUQrCw+A0qpR3uf/7XXdVYDSi/jRVLUC3tsv3gY5ZWvvVoH9xzxhiuM0JaNtdv/gMO977mcP9runK5EaGYW+iRaRUgLBfLcscaL79eGzi+e57Ax2+Fbr3nNbn/9d8R+G20DpDSZs/6jJXr38e249hj8pBeFny/TfHwPvXaIY6bJD99fWDBQ2uNsBwYYbhkLCzMdmIpJmeuUyht0Iwd0Swcdr+X+GSeWH4Kr95rLKFVgFYBtu2ABjuRIjkzFymSCrQQKBVEK4uCVGqa2fk38f0WW5u/DbeFZyKXu8LU9KsIIQgCj+3N33U99M6lsb31MVev/9FDv2shBJXC5sgmskGrgXRdysebzC2+1W0Dd5ExhusMsSw3fPrpr8bvY7vYToJ67Qg1svdd+Dc7lY7E8YYNWyyeodkocf/rnw1MNZUOEMLiwfovQQhcN8X8wjcuZWb00+J7Te59/XeoIJJ0qRUoH2+ztPptMtlwha9RL9I8OsTN5dDK61oDO5HCiicIGg1y06tIaZFITCBEaKx0ECCs8CEUjPh+QJDNX+HqjX/A2u2/HSjyFkJgWXYUEH+LdHoGrTVr938x9BA7Pn5AIjFJKj1NtbLH6GmtplrZJTe58tDPpFzY6ibeDp1FBQjLwWvXL4XhMnOKM+Zg79YpWwULy3+A5zXYXP9o4OnZKUXpIC0Ld3Kye4P0M5G/wvq9D9A6iPTUwx/bjfWWtLWm3aqyufErgoc0N30ROdi/HXminRtVo3XAzuYn3c/5we/fp3VUoLbxACc1gTsxRWxiCjueRPs+q6/+EMt2w2z46HMVUcOJ8JRh441Qz75nnKS0mJ69wd76xwTKH5PioFHKw/MaHB3eHel5a604Pn4AhB2FRuuGKYIxU92TnBZCEEKitcJ1z17o8DwwHtcZorUO+9qNQghiySzbWx+H+3Y6z3QytkcfhJVI4ld7sZX8wo1QZlh1jFZ0U0gLEEM3iVIB1cruuTYqvYjUyqM9FBWEK2hSWBTWvwqNT7tN8bPPcCcmkI6D1orlN79PKjNDrXbIzvbvB4yGEIJ0apZ0eoZUZpbC4V0KB3cJAo9Uapq5pbdx3CTFvTtI10UnUyONRqN+zN7O552zjgze+34TrRXJVJ7CkRyp1PqwGBeE+V2BGK1NLx0Xy4kxkVsaW+Z00TCG6wwIAo/i0RpHB3fQoyRKhMByY4N5Pp2LNJJfGRV8EEIg3RjIWli8O7XE6pv/oJsvNvCUt6yxT3ZvTNb4i4y0HBhZtBxqrTcqRaRlEXRiSlrTPg57LUrXZXrhtbBf4d6tIWMRSuIE3U4407M3R7bz0irs+xi0W1huDCFlV146npykXNqKvv6oacUIy9Vu17h/7+csX/kOqfQsterBgDZ+MjV16opku1HhYPNLauUDtO/jpCfwquVuE1jpxEhMTmE7CeaXLk+ajTFcT4nnNbh/++/w/bBAt3MR9iMte+iSFEL0mnSegrQtErPzWNLm2s2wzXoidUI6WAx7Wv3E4y9f0mp+5hV2N38/lG7SKpb52b/4n3ASKYSrsZPDRcYTMytYlkMQePh+a+T5m43jkX/vJzu1TOlwg8bBLu7EJG46A4QPqUajEH5v0b4948WJB5km8FvsbH/Cyur3qFb3KJe2QBM1r50f+92Xjza585t/G+nxK4Rtk125hptMo3wvLDWKpr0ju15fYIzhekK01uztfE7h4C79dWGhEdFRsmH4dyHlWBVO6Fy0kl5hbnguISXp1CzJ9Ay5/Eq3kj/e1Y2K+uU9pMK/UtkjFeUjvSxM5JZpNUoUj9YQQqJUQLtU4ejjLwHwGjVEK/xe7ETvNpCWzZV3fhT+Lq2RD6Jwv4erKizf/AG10j5aCpxUGiFtiHKpRk1ju6+l1dCDzmvX8P0mmcw8mcz8Q19ba829T/4a1R/fjJpmQDg97Me9ZPW0xnA9IQd7tyge3mcoJ0cIQIDsPTG7T9IThM0MYliWw8TkCqApHt1HBT7p7Bwzc6+NLH4VQjA1c4Ojw68HLsaRCEm9fsjR0V2mp59ceeCyIYRgbvFtpmZv0GyU+PTf/Etq+/sD+2ilCOo+biaB8n0S2TzX//Afkc7PR+cIm2NUyjsnYlySydzqQ8fgxtO8+f1/xtra36PpXQ8PrZIb850+ToJos1ZE+YMBfyedCWOjfaKBHaOcnXj+HbkfB2O4ngCtNYWDuyemIYPTtd7volt7ePLiT6dnWVz+g4Fz56evdV+j1argB+2wR6DfIpHKd4On03M3AU3h6F54oVsODMXXeh1mjo8fkEzkSSRP1x5/0bDtGOnMLLWDg5Hbg3aL9/7ifwzjPSNWcGdmbqICn1rtoPsdTuSuPPJiR71R7DrHQoie/tcYtFYQBEMdui3LGausOgop7SFP0Z3IdVdH+14QHQRYl0wB1RiuJ6CjXvmo5PKrpDOz7O990V26zmQXmZt/c+T+tVqBna2P8dpNglY9zL6WFlorZpfeZnbxTYQQzMy/zvTsTXy/xfbO7/G8JuhwGqI7hjQylloptjZ/h+MmuLLy3qUVkHtSEtlJGqXhUql4NjdU+N6PlBYLi+/QbFQ42rpFq3qMsuqonIflnL4CFwQeBwe3gf4H2cO9LRX4SMsODZ6QCATzC+OVI9rNGu1mhXgq1/XQY8ks8eQEjWoR0FE8yxmupQwHd2nSIDoYw/UECGFhO3H8R5HaFZJYPEs6M0s6M0cQtJHSHqvx3W432Lj7fjc2IW0HmUiC1gStJvvbn5NI5shE0sFCShw3QT5/lf39WyCs3tR0YGqh0VrRbtU42LvF/OI7T/chXDJe/fF/wuf/7v8MG6RGSNvh1T/684ce26wd88Xf/R9hz8bAp2DZbH31S976yV8SS2bHHlet7o+Y3g2X/PTTUY+IxbJIx8W2Y0xNXcdxhmNQKvBZ++w/UjrcQEgLrQJmlt9k6eb3EULwyrf+U7768F8T+B7SdUZOQcPaWOfSiV4aWZsnpHy8zdbGb/qmi6NX9oRtR+UeFjEnTX7mOunM3Nin54O1X1MuhkmHlhvvPtU7gdug3SRmp7j2+h93j1HKD+MoI5btO+kZnVIU5YdP89fe+sdP+xFcOva//pw7P//3NEoFEhOTvPKjP2fu5tsPPe7WL/4vygcPGLzUBbn569z83n828hilAtbu/wKl1cB3rbUaLW0TbsRvNTq/4iZSCMtBSpulpW/i9hmX4sEaD26/H5UPabTvhxn90mbpxneZXQnfl1aK8tEDmrUSpdbesMelNclknuUr337o5/AcMLI2Z002t4hlOezvfUm7VcO2Y7Rb1cGdZC9BVCmfev2IxkaR/NQ1ZhdGTxPr1VA3SUiJdUImRQiB5cbxvTa+36LROEZK69TMeCEkgd/Gq1UImo3uUvvXH/9brr31D6POLy8HszfeYvbGW491jNZ6hNEC0JT218YeV6nshsXbJzxrISRIThgvgfLbXW+w08yj0arjJLPY8QTb25+wuvo9hBAcR0ZLd4yiEOCEU38d+Oyvf9I1XEJKJmZWwdnkeGdn4JrsOC3yEhbQGMP1FKQyM1zLhB1dGo1jHqz/qqc0MCq3Smu8eoXdyu8oH22ysPJNUtnZgV06XY6lNf6rsdwY6+u/DFN9dJijQ9TE9KShU0rjNxuh0YrGAFCvFdi4/T7X3/qHT/EJvBwIOVpL67T0klB2JgClIfpOO9+NEBJh9Y7VOhSQ7EriBH53mu/VK1ixOEHg0WpViMez7Kz/bkQGvQDbRgcBvjece+a1m+ggKu6WVijHo1QYmLcuR7Z8P5fP1F5QYrEMED7hhJQjp4JBuxldlJp6eZ97X/wN5eLWwD4T+XBZ+rQZvEKFra2UF01Vw9wfrXoXP4Q3iBSyZ7T60Yrq8Q6BP1qdwhAihCC/eHPISAlpMXXltZE5XgC2nSCKrnfPMw7LcrGkE+neNwficECYLCroLgh5rVpncEjb7pUTReGEZGZ64Phq9YBSZRvQaKVQvofy2lFrNk3bqw11OLroGMN1RkhpMTP7GqdMy1HtQSOhVcD2vV8P/k2rsOOxP75wVgU+frMWelJee+Dm6UxBOukWV6//KOrMPIxGU6nsj9xm6LH6jT8mnskjLQdh2UjLxnJj1HWFW5/+v+xsftztRt1pM1avHhL43vh4VoTWmmx2AddNdh9qJ+nEN+PxcCHAiYXqFW5mAjuRwklncVKZ7v7Lr31/4PyHh3cAjeWOyAmUkmbzmI31D8+ko/azwkwVz5DJ/Crtdo3j4vqJLQKvUhp5TLtVQ6mgu8rYbB5jubEwOdJrYQ3EoES4stUnpaIDnyAIsGJxOm2wJiYWSaVmicezCCGYmFqhsNef4d9je+Mjmo0S84th7KdW3KO4fQ/LdphaeQM3cbmWyc8D243z9h//cypHW9SOdzkq3Ee4dmRQAo4LG7TbdVav/4D9nS84Lj7oLtoo30MFAXak79Wh97DRxGJp3NkbVMt7Q4ZOSIG0Xaanb3SvkezMKuXSZi++BWBZ2PEkuewK0o3xYOMjWq0yUtqhpyZEGH6IJcLEVK0HZgaB8igdbzGZf3hi7UXAGK4zQmvF4cEdyuUdpOV0hQO7mfRjphRhWUnPI3LcFM1mqev+q8DrXpxChEveA+UakRyOCnws2wEE+fz17kUe+G2cWKhOcPKmsGJx0JrC/m2ktKhsrHFw/zNUlF29/vHfcvOH/4T88suTcT8OIQTZ6WUa7WNkzaX/IaC1ol49pFo54Li4MZwCEa0kCqtXF9iJTUpp4bpJ3HSKmfnXOdj5Mkx0jYrB8/Ovkp9+hXi851G1WqWhqacQAisWQ6cdNh981B1DVy5H90Y8nLcWXp+Hh1/TbFWYm3tjbLrORcEYrjNib/dLqtWelEpPJyu8wOxUJvK6+mJQ0mJq4bWBizA/dY1qZXfg4g+bZ7SwY/GBJEIxlESqwdfc+vW/JpmeYnL+VR58/QFK+aEqZyJN0G5GahXOQOnH7t3fUt/c6eaP6cBHA1+9/2/4zj/9H7BHTDNeRpqNwe+wgxCSanmXcaGCTlEzhNN55bVAWiQSk93kz7nld8jPvkq9eoBluaSysyMXAMbpbwkhaNXLUV/I08vAejl+fV4bUK2E3bQXFh6eJvI8MTGuMyAI2lSreyeetIMXt5NIR3GIzuqfxeTsK8yvDEqJxONZlpa/HSUcCvxGndZxgaBRCwOxHaPVt1LV+QFBq16i3axwfLjG/c/+Bt9rdj0tO5HATqaw4/EBowXQPi6N1F7XWvHFz//VYLHuS0w8OaJshjDuWK8WhtQoOmgVELQaBK16aLQAGWnG9+O4CSbyK6Qn5seuWqYzsyP/3tGSD6ewKmw+O4bo8TqyJrJa2X2sypDnwIP6zQAAIABJREFUgTFcZ4DnNUc/4U6Iz7npCdLzK1x/+894872/YPn6eyMvzlR6mmuv/ITVq98PtefDEzDwNB+RbiGEwEn2yzRr6Lt4vVp17NqBHpdHrDWtRoXDzS+HNqkgoF2vdm+Y8C0rjrfvc3DvC9r16tAxl5389PXhxQ4dto+rVw7GrjJ2d+wjlpjAOiXtZRzTc6+PTGHwqpUw5un7tMrFU5aJCLW/xnllWl/41WYzVTwDHCc58oLVWhFzMmg0QeCTSOSYmro2snzjJEIIauX9XnF2FBfhIS3QhGUhbGeoMzN0utAM5hCFryWZWFilUP1ypGclbEFh5zazq+9039e9D/6Gjd+8H/YNtG2u/+AfMXXtBh//3/9bdzoKcOWbP+Lad/7koe/3suA4Ca7d+DG7W59Sqx0isNCBR8coBe0mlhuj51mLcJM4qSJikZ+69oRjiHP95h9z787fEfgtQHRXmKmWkfFYGPtUwalt1cahAeuC686bkp8z4mD/NqXS5gkFCIuV1feeuIC1uH+PB3c+6BoTK5bAzWSjBMYRBbNRSZDfauBXehLSwrK6GfNCSFKTi3hBPfKUNOnsPItXvsWdX/4VRxuDmvl2JonlOkjHYXr1beaW32Hztx+y9qu/Har7ExYIezBnSVgWb//ZX5JfeTED/F67we1P/+1wQL7jlfk+Sgc4iTTCsroJxtOzN7tKIE9Ks1lmc+PXtMol6ns72Klw5VLGwxVmaTs4qexjqYForXHdFFev/eCpxnZGmJKf82Z65ga2E6NY3Aj74sUnmJ658VRV99n88sBiZNBq0NYKJ5lB0BOz6+T5aKVCbwcir8sHywoLbCOkcLjy6vdwnDheux4mP0ZSOTd/+E/4pFGgVQ1XrWTM6T6xhetyfLRO+XiL/Y8+HZkkSQDOCcUEHQRsff7hC2u4xlY4aDVQK+rVK2HBfSLLjXf+vGvAnoZ4PMvy8nf4+K/+FwDs1KAnr3wPr1bGTqTCmKbW4crmKbpeQkiWlv5g7PaLgvG4Ljjlwib3v/xpV49ca4WdymI5Dl6rgRNPRgmrflc4TmtN0Kj3FXiLUNOr2Yy8LEF2apmVN36Mc0L5slE54qtf/z/hylcUrJeui5VM0mkjv//BpyPTO7TWOOnhNvRohz/8L/97UlMPb+pwGdm48wGV0vZguECHAfuTRkIIyRt/+E/PpAVY4Pv84l/+70jrCMuWWMn4/9/em8fXdVWH/t91zp11NU+WRzmW58RxnMRJCJnIACUTUxgLBCi08IC20Pd4lNKG17zS32v7HrRlKIU2QEJaKJCUQkJIiDOPzmgnnm15kCxruNIddKdz9v79cY6kO0mWZNnWTc7387mW7zn77Omcu87ea6+9Fv66KEYgiJgluzdEqG1YSCjaTCLRM2meS5ddML4LZB4w6YjLU87PY3LZJLHhgwTrmzDDEcL1raw85zpqmxeSH01iGIYzNcyMFnm7FBFCTW2Or3t3NGaNjhYo0TXxocPs3vqLMt1cuLaZDZd/iIaWLoZ29NH72B56H9/N0K4eRhMZEDADlfe2peJ5ul+JFR3TShM/GuPev/pr0vFJIiBVOYs6zyccbUHEdKzrxXC911Qe2cyVC+37v3Ebx3bvwTBcI9LRDNljQ2SHYmhbub7mHfVAIFCDRjMS6558AUFMAoHqiMHpCa55irItuvc/QSY9jIjgC4ZQoug9/ALt7eum9mNvK5LdB0gf7cNKjWLnKqwQaU0+lyYRK3/7ZkZGePWue0gfHUYMMA1F5tBRXvzJk3z/K/cQWLCU0pehbSu6dw5zeNcI2YzltkGhbEWyL42VzbLrwS0n2CvzE9Pn54zVl9O1/mqWrriI1RuupaW9fH8jCDV1bXPixDGTSLLzkaeI9SRQBRvAtVLYyVFGDx0BS+ELhvEFwyhRZDIjU5q1tLaunpE+7HTi6bjmKfF4b5GZgYPGVnly2SSRaAvpzPBEyKuJJGAYjhmF1mRzOXzRGgxf+Wqk1opcOg4sKjq+5+EHsPN5gnUBDJ+BuG/05SsasLXmB//311x68UpqwmlCEZPRRJ6Du+Mkh3OIQN/eOPUtIdLDWcRW4+YC/bt2O20bjKEsm/q25qr5oUyHYKiWoGvh3rboTEYTA6QSjstoQfD5Qyzpmhuld2JgCNPnIzmYZrgnSUNHFNM/tttC07x0Ff7WevK51Hj5MOaJIofhm/DzBtDUvIIG1zllNeAJrnlKPpuqaMyolSKfG6Vt0Zkc3PsoUOJ5QAAt+GtrycfjjivgXA7DV26CISKEo01lx4cO7MfwUSS0wNk3d8aKBnbtjjMyotj/Un+ZrktriB1KEe8ZxecT6hrcH4hhEKit458+9SWidVnal9WiERat3czaS6+ZMsryfMTKZVC27egYKwhfwzA5Y92VjCYHSadiBII1rlHp3Ajq+vZWbNd//e7Hj9C8tI7W5fUggj/QQud5V9LtPh+FjOs77TRimvh8IZZ3XVp1rrw9wTVPCYbrnb2JunTTreMKuibaQk1dO6kK3h3EMDAjEUdwASqbg0i4JI1JONpMpK7cCrumpZV0or9IaBXS1hLE39yEr7cPK1s8DTVNwTSd64KhglGeCE/e9wwbr1xIKBLG9DmCamDfszy4azurLr6exevLg6rON3LpFLse+0/ixw4CQrCmnlVvuI7a1kUV00eizUSizqJE4thRYgf3EYjW0rZy7ZQ+145HIBLmnOuu5oVf3o+VzTHYHWewO44/FOR9f/v7aG27+s3J89C2ItrQWnVCCzzBNW+prW1nwB8kn0tTuP8xEIiOh1xvaVvFaGqoaPO0jDmIyxfYWJk+2pdsJJ9PMtzfjRgGTQtWsfCMcyuOAFZefhX9+16pHCRUg6Eg13eYqz52Ew/dfjfphDMtNU0hEnJ93puC6QdbKVKZPC/sH+LMNR0Ew+a40AKc6Y2k+fn//r+sf9PlvOn33jt3nTjHaK3Z9ps7SCdi44r3TGKIbQ/cyaYbfp9gpPJqnNaKF++6k75XtwHui8XnY/OHP0lt6/FjJE7GGz/0Lmpbmnj25/eQjifoWLWCSz/6HpqXLKygZphog4Ngmj6a26rTTMUTXPMUMQyaW7ro69nmbqoVautbWbj4nHFhEvDXoXMKbeqyHf+5kUI3OkLbsjPxBUJMx2lJw6IlhFtXYiX2lS1Iaw39faN01IXY9+Bv+PT3/5b+g73c/70f07t9F6m0RSqTZ9exGMlclqzKM5qzaKuJsmpta8UFBa014TqTZ+++jw3XXErL0vmpa4n3HyY7Gne9PWjXoyionMXOh+7irDd/oOKU98iLWzm2Y1uR7Zudy/Lcv9/Gpf/tC7OePooIG6+9ko3XlnuxFcNgweKN9B56fnzUPmbJb/oCRGvbaG5bOa1dHPMR85ZbbjnRPE44g/mO1grLymJM4tn0ZBAf7ilyUAeOlXYo0kgwGEUpxb1f/Vu6H3+Z6IIG/JEAytZYmRzZgWOofB7D8GH6A6x6w9sJ15XrsqaiuXM5j955D7VNAZRyInNbOcWuFwZZ3BbB5+q/alta6Dz3bDZecwldl5zHd755J92DI2Qtm5xtk81bBE0/OVuxr3uIxx4/QEdHHc3NE4a5Sml6dw+THXUU9ovXzc9RQLzvILEje1GWjbaKp/DZ1Aj57ChNS8rrvv2XPyU9HCs7rqw8C9ZuIBA5OT7PgqFaaus7EAz8gRBNLSvoWHIOza1dROvaq2GK+JXJTngjrinQWhOLdROLHWRsutbQsISmpuUnVYBppTh65KUy/ZbWNj0HnyPb38/Q/j4Gu7tRluKVf38CXziA6TfJJdKc9e4r6LpgE4FglGjjglkpvutamrj605/kF//nW/j9CjtvkUlaNDUHCbirV1Y2Ryo28YOM1EbJ2RNTlDOXN7HjYAzLdvoubymwFHfe+Ryf/vhF2IMZcqk8hs/ASuYwTANfcP76P482dzh7AO0K0zCtObrreTo3XYEvWOwCyLLyYAj5jIVW4A8a404AlTV7rxvxgcMc3vYY6cQgwWgDDYvPINLYRl39won4isEo7QtnFiCkGqiupZyTgFI2yWQ/icRRLKs4yMDIyGFisW60tp2wUm5E6MOHtnK4+1kG+/fO+S76TGqYV5796YQDuBJsO0c2nSA9nMQMTNw+K50jG0+jNQxu30/zwlXUNi88odW65ZvO4tN3/ANv/MBN1EVDtLeHxoUWgC8YpGV55/j3+qYGVp21CsM0uGD9AlrqK8fqU0rz1JZ9ZOM5tK2xszatrSFqwgar33j+rOt7sonUt9CwcMWk5w3DRyY5PP5da83+p+7HtoYJ1AaIttUQrAsweGQUK6cw/QFq22an4xo+up8dD/+EeP9B8pkUyYEjHH7xUY7seYY9Ox4gGe+bVb7VwutacKXTw+zf/xh9fa9y7NhOurufdEdXDrEK3iy1VmSycVLJYwz272b/nofJTycwbAXy+TTDsUOMDB/BtvPOg/7qb7Gt7OTKVdvGTo3SsKiGMy5byrKLF2MGJlbvRMDOza4+lTBMkzOvuYK2FcvwFVjMm34/9Qs76Fi3tij9F7/+ZdoXtLChqwXLVkXGkWPYtiaTLR5pGIZQX+8jMThcln4+sfqSG4k0tlY8p5RFMFo//v3Ynpc5+PxjAJg+A8MUgjV+mpfVEutNc/bb3j/rF8uBFx4oNybVmvSxPrS2OXJw67z3qXUivG4Fl1I2vb0vuaOpiRHV0NB+Mpk4StmTjnrG0Fph2zn6+3bOuPzBgX3s3/swx/peoe/odvbu/i2D/bvHR3Aqly3bmqGVItPvGjQagmEahOuDLNncMZ5GDGHBmq4Z12cqxDC46vN/yPrfeTPRlhZqWppZ9zvXcPWf/HHZD6990QK+duff4Q8EWNJeO74dpRCfKSxuKV+BMwR+8Y0fzmnd5xrDMFl9yY0YvmL9kOHz075yI/7ghLL78POPuZF0JhBDCNUFMAMBbDU798haazKJcp0ZgMpOzBpGU4Ozyr8aeN3quNLpWEUbF60VAwN7ybkWxxUpuXDMOnq6ZDIj9Pe+Qj476qzyhMIYhkl/3y7GlvGU5fiaNwLOZlxByAwMjNtmjSGGEKwNEIj6sXM2Lcvq6brk6hnVZzr4AgE23HAtG2649rhpo80tiEBrQ4SuxY3sOTyM5eqFDBE6GmtY0Fg+jdQa9r20a87rPtdEmxdw5jXvZ++T95Ia6sMMhFi0bjNLN15SlC6fGa2cgdaYfoNcapLzx2HMZU2lSFBiFoy+p3QlWN28bgXXVMPobHZMODhRcwpxtkwUPzAzDSxwaPfjZIYHxr9bo6ME6howQxGs5IRgUvkcKp9DDB8dyzaxb9++yeuM0Hs0i7Q2k04ragErn2fXs9vIpbOs3LSemoZTs+vfHw6zbPOFHHzmKa46dwlnLKjjle4hYrEszZEInc11KA1mwe/KtjW9/WnCtfPGM8GU1C9Yyqa3faKyrZtL07JV9Gx7hrJnSEE+k6e164xZlT2aihFqaWO0r7d4I7cIwQIPHOGama0kVxOvW8EViTQyWdCDif+PWR6Peeg2sPPpEpfMBg1NS6ddbip+jNRI6QhNk4vHiAQj1LYuJTFwaNzJn2H4CEUaaGpfwb5JvAooBXtfGSKfs9n+6PPsfGob13/uZn72/76Psm20u1fwxs9+kMve/dZp1/VE2HDjO/GHwux77GFWLBRWLGzgiaePIQiJpMWhwykWdUQwTaePe/tH6RuyueEz15+S+p0oWmue+Nmveej2u0nG4rR1LuLaT3+QrvPOQtk2fTteBR0B/Gjt7OHUSo8P1hef2cgzP/oayy+8isUbLphR2UMDewkv6EBrRfrYRICWYFMTgYYGREwWLTtv3kfqORFe1/64hocPMzi4d1wBL2JgGH6UckZUWmuyw0NkR2KIGLQsWYstljONdJ5EorVtdCzeOC1XJVprdr98D6PxfvzhqOviF6ys45omWNdITUM7LU1dHNz+FJlkHDsfIm7VsHDZEtJHHiMdnxipiQi2pdj22BH2Pl+y9UeEdK54ZOgPBvjjf76VJWtm96afDVpr7Hye3Vse4La//D5K6aIpjGE6oy0lBpfc9BZu/OyHqmLj9YM/+Dlbbr+bfGZCp+QPBnjvn3+Sfb/5L6xsxnEkqDV17S0EG3xkUykMKR3pC/k0hOvbWHPVm2ntOv62p4MHniRnZcZ3Sah8Hgwnco9Gs2LlFfh889esZAZM+iC8rgUXQDabJB7vRSmbaLQFy8oxOLgXpWwShw5gpVPjOi0xfTS0dbJ47UXk86MEg3UEgtM3Hkwk+ji4+zHHzXGBMavW2lHiioE/UMuLt20h0T9APpPFshWWUtz/6hFueOt66hASR5OgoaY1zJHeNEe7y/1caSCTL1UMG1z89qt4zxc+MfsOOwG23HE3v/rWvxW7t0ZYf/lmbvrCJ4jUVYcvKCtvcet1HyeXLg9bH436Wb2qvuiYGQhw5g1v4+grj1TUe9mWItGTxPT72fTe32Xx2eUeSLVWJOJ9JAd6UCjSKlm2W0JrTShYx+Ilm06whfOG6nDdrJVi6OBuRvoOEaypo63rLPyhk7slIRiM0to6Ye2slCIe7yEVO1YktMCJNTjct5+OFZuore+olN2UjIz04AuF0bZd7JddBAwT8QXYe9/zjPT2oSwLAfymgSmweVkLvsE0ibwaf1Wk+tNERY+5kz8uWinSidkphOeCy95/A8mRJI/++FdufTQX3Hglb/ujj2CY1bPAnYqNTGquksmUG5TauRwHHn8UM1S57w1X2Wfn87x0109ZtGFj0fORy42ya+uvGNm/1w2+oRG/n8Y1a/CFI+POIgWhY+FZc9DC+c9pEVxWPkMq1Q9iEI06jtVsK88Ld/0rqdgxVD6H4fOz74nfsPHGj1DbthCtNYlEH8PDB7HtPJFIE01Ny/H7TzxQqVI2qWQ/lpUlHGlk0aJz2H3s3sruiZXNUO8eauorx7abGg1iIEYFKSOCsm2ObN2Bsop/FIZh0FYfcY6XXGqaQkN9gNjwhOmGYZrk7fLFh0A4yMYrL5xFvecGEeG6T32Aqz/yTkaODVLX0kSopvr2ytU01E3qOSPaVNno1rYsItF6ssmRsnMqXxBCbnSU3GiKYE0UrRWxgQP0HdnOyP69oNT47dfZLMM7dtJyzjmAUFPTXBURqOeKUyq4stkkgwN7SCYn/Dj1sY2OhRsZ2r2D1OBR8An+xlqCDbX4akLs3fEbmuOrCdU1EU/0jk8zEomjpFIDLF26+YT8d2ezCQ51P+3aco15YTDJZMofsDHSU5ybirq6DtKpoYormspWPH7H40VxEEup5AlYgKbWMEPDWUd/JMK6izexaO0K7vnuj7GyjmFrIBxi+Vmr2HDp6bdMD4ZDtC2r7AamGvAF/Fz4jit5/Cf3YeUmRlhmwOT8911EfOurWKMTui/D52PppvNoOmMxO+7/WdGqtFaa9HDBlNMQfMEgWmu69zzGaGqIdF9/xedCWXla61ZQ37bk5DR0HnNKBJfWmoGBPU74LqXGfXIr5QQT6DnyPLEdOwgtaMBXE8IIBosCng4N7COiR8uUtkpZDA7up719zazrdeTwc2WGprZt4QuHyI1UmIOJ4Jvl9DUabSMe6SFVsh1D2YrR2Ci7n9jHogVhakL+oqmIUorhdI7munDZpN8wBanxsXdgmJpImPOvuZibv/p5AFaeu57H77qfTHKUc656A2dfvhnDfH28kU8259x4LqlMH9vu3UY2laWuvY7z330+7Svb0P0DxHYcBhy9Yk1LKyuveBO+YBDT52fv4/eRHOhD5W0yw1msrPMi0xqWnbsZ0+cnlTjGSN8hsrERZAo1spWdu10S1cQpEVyZzAgjI0eK/EaBY/+kbGfZnxD4akKIzzcutMQ0CdY1loWLL2Rk+BAqn2HBorNnvBqVz49i5csVrGiN4fMRbmklPTgwoZw3DAINjdTN0MWtnc+x57F76Nv5IkrZBNqWEGpvIFIfdoTnKz088oMn0EoTS+TpWNZBcmgYK5PFUhrLVjy8q4cr1y+hPhIomi6Kz0e/1czmq9u58p3XcN7lE0vrnetX0rl+fnpaqHZEDM68Zj1nXrO+yJbLHlsQEbByCqVsst1HGOzeT/uqNbScsYbY4QH2Pf5jRGwCoQndnrKFukWO46Ejr2xlYOt20JqWTevJJRLloy6tqWtdfEraO984JYIrkThaJrTGEMOHVhaBhijOhnlXaBkGoYYWnF9p5TeOs9ysGB46RDDUQFNLZ9F5y8rS3/sqifhRxDBobOqkubVrfJuKM+2sIOxcQWWGQtQsXITK5wBBfD5Mw6SxdWaBPLf9+t8YOXJgfG9ZuucAv/j7feNFKXvMHAOWru3knf/nKxx+4WV6du5h5+4DbN/bw5vfdw43vOc6Dj3yKPuefBplK5ZsPIuLPvQ+Ig0NM6qPx4lTV7+Q2NABQJcttCQP9WOYQjZju95gNQ9+/R+56ev/zxlNDQ5iu6YqVlYhjiUDwaifw88/RM+2x0gP9yM+57eQPNiDGQ5iZ7PjwksMg8VnXYw/VFmn9lrnFOq4pl72CjY1kYsNOXomrfGHo0XXaNtCG2bZErDKZx0l5uDeIsGlbIv9ux/CymedfGwYOLaL9GiMJcudUUkgEMUwfdhWBd/u7ltURMbtrUBY1HnBjPwYjY4MFgktANM0WHtuKzueGxgv2zCFYCTMO/70jzAMg6WbzmbpprMpVaUvW3UGb/zYh6ZdvsfsmNB3TgilIy8+x/Z7fkG8bxDTF2DJZRuo6WwCBdoVYD0PvYSVydF3bJTug0nWr2rAFzDRtmLPIw+x+oqraF7eiS8YxHL3FWoFkaYg0aYg2aSzB1ED5DX4ITMQwwyFCLU2oJVNTWMbS9ZfRGPHqbPHm2+cEsEVDjUwwuGK55wpoR9fSMib8fFNqRPCYgwNykLhG18StnLpcV2QXbKZdTh2CNvKUyT8tCKVHCCTjhMKO6HJ21pX09v7UkGasRGeY+VcaGsVqWkkWlvZM8BkpIcHnf1jJfVb2lVP27LFHD6QZ6R/iJXnb+DyD1xPXUvjjPL3mFtixwb42pf/licefBrbtjnvkvP5zJc/w9Fnn+DAE4+Td80d8ljs/q8nCTZGaF7VgZW1iB/oQ+XyJFMWBw6l2HjFUpZ0NTqGwjmb5KDzG1i04Sxq21oZOXoUlbcQQ4g2BYsDk7h/ta0Rn2BnMqQOHaV+0TI2XPWBU90t845TIrj8geMPZw3TR6C+gdzwMDqXwxEeFaZxysK2FarId5ZQW1fs1yg9OlQxSg7i6NxC4TrS6WH6+rZPnNNgpUdRVt7dyBoA0wQ0djbLkvUXzFiPVtPU5rj4La2GabLynLN5yx9eM6P8PE4eOx/4DX/2+b9hKJ4Zd8fzzENP8/HHb+ami1cSCphl9z8bG2X/U3voPZKisSGIlVMc7U9z/jWdtC+pG7dP84V8ZHJH2fLNW/CHgqz9nQvpeXkfh559AX9kigWToomKwbJNl85xq6uTUyK4gsFaBHFcx0DlH79W+MMRwtEmVCaDXcnWyaU4arOBafppbV9dXqYYZf60AAL+CMnEMXqOPF98Xhx/V1opMAzsfBadttCW5Vq7V37A+g728vITzxOORjj3igsIFUTUCdU20Lx8DYP7dxRNFw3Tx+KzTp9NlUcxPS+/yP0/+jEjyWxxgFWtsSybvT3DrO9srnit5BWLWiOOO/2ASWNziPYldUVBQcYwAgap/mFe+dU9KA1t65sQrbHT5Yarzujf2dYlhtCwuIuWM2a3gv5a45QILhGhY+EGeo68gNYKkRIBUCA8LDsDfiZViSnbRmWzNC9cTSY9glY2lpXhwN7HaGzupLG5E8MwqW9aymD/7hJrBsHvDxOKNLJ/75bKQi1aR3rwWLFltAi+aC3JeF/ZiuIdf/cv3Pej/0LEMRT93lf+kf/+zVtYe96Z42nWXvlOurc+RM+2p7HzORoWLWfFxW8hGK2bZg96nGx2PXg/A0NJdAXHh5bSDCUrrD7jCBfTNe8ZIxD2VzReFhGsvOLY3jjadlQRw4eSRNvDNC6pxUoWm+VopenZH0djEow2ceUfVsc+zlPBKVPO10RbWbb8YvqObieTGZnQHU3hXqZwmVlrjbacaDe+SA2JeC9KKbQbTMK28xzr3c5A/y6aW7tobllBx5Jz6T08MaoSMWjrWIeyc1iTuVwWCDe3kR9NovJ5xDQxA0HnoStx7bztyRf4zZ2/JF8SW/DvPvOXfPvh2/H5HSW+YZos3/wmlm9+04z7zePUkEnEaaoLOXqmEuHlM4Sm2hDoCSX8GJW2WuVT+YqW9Uopjr7cj7KKL0r2pYk0hfGZBtpWGD4fytYMDYClall3xUWc/863VtW2qJPNKbWcDwQiLF5yHt0HniCfL9m35YZOclYV3dGO1o6aXClQNrgeGERkwvq8ZKOesvIMDx9EaZtE7AjFy9Wa3sMvsnjp+e7Ib5K3lwjassknk2ilMAMB/LV1+MziHff3/uAucpls2eXKVrzy9MtsuPg1s9n1NU/bytWkh4dpiAYZHMmgCp4pv8+ka0EDSjneLLTW2MqJI2kGDKxs8VYsZSlGDsdpWNowHo1Na00umcPOVn5RjxxK0LaqAZU3OOv699PSuQrDN6+2Es8rTrkIFxE6l7/BEU7jXhf8iOGb+JgBRzlu+hwdlvjG4/FJwV/nU94EZdvER3qwVaUIKpqBvh3Y2WwFf/IaO58jPdhPdjiGtixQCjuTITPQz2hikKHDexjo3oGVy9C7c0/FNtr5PFZ+9tFbPE49a65+C/5QmHdevpo1yxrxmYIhQmtdmGvPXT5uhpPLKTLaT/TMjdz17H5ik7j3HtwVo2PVhYSiDWilsdN5Mv3JSf0d2LZCDJPFGy+krWudJ7SOw2nrHUeXoBDTD0iRQNJaI4YfrW0Mw8QfCJEejU17fi8Tr7kEeHM2AAATxElEQVTiMrVGa5t8PkM+MYJIA0YggGv3gDWaQtk2ViJRlqfK5ti75ZfjW2aUUixcEKLvWNnMAtu2Wbf59bFL/7VCpLGJKz//BXY9eD/+0FNcfUGefYfjbH25n2d3DrC4tYaAz2QgnuOPv/NVlq5ezgu7e/j3ex9i1YI63rBqIUppTPeFuvzSyzjjvMuAyxg6uJfn/+M2fBWU9WPUtkVYfcXbWLDm7FPX6CrmtPnj2rPjAWyVwwyEKwqkMUPUaG078aGDKGVPmq4oBL1h4AuGETGwcxOjKmXb4/owEFQui5UedfximaYT38612LdzObBdB21uGdn+WFlLbcvmp7e9zNBIFqU1gjMwXN/VwRd//s+z6RaPecDI0AifeetHOKM1zHA8y7HBNEqDP+Dny7f9NavOnljZ2/PKbl54+kVUKkVgZJhQJMR577iWlqXFW3HsXI5nfvRdup/fTjZRPF00Awbnvvt61rzpd05J+6qI+eePq6llOceOvjLpeRGhrX0NNTVtjAweGNd3FStGi229xDQx/UFAqIm2kcskHXuuIqEFoDECAXw4dlvOCqI4ClWtMf1+tE9jBPxYo2lUBT0WOEr3yy7vZOdzRxlM5PCZQkdLHRe/pzrcD3tUpr6pnr/56Tf57le/zdFHtxKIhHjjdZfz0S9+Ep+/+CfTtW4lXdOIvG0GAizacD4D+/ZjSJrcqIVGCNb4CEeDLFy38WQ15zXJaRtxKaXY9cqvMHxBx0aqRCBFIk10dJxFLHbQda+s0fkKIbvyE9FwTJ8fjUYr5Y60HJ/tVjZTIrjGEAxbyIwOj/uXLxzUOSuZNtmhYaxJHPClYprurb2I4fjTWnr2mVzzuT/A9HQUHiXYlsVDf/83jMYGxyNYm34/bavXcf4HPnKaazcvmX+um7XW7N55H0rZmP5QmZK9s/Mi4vEehoa6i4pQSqHzrmW9CFZm1PEIafrw15TYRWmFsvPO5tSK1RSsTJp8KonOW4hhYAQDRa5ftNbkYiPkhsrdIxs+P2sufxdW1iR+rJ/mpUtoXDS7yMQerw/ymQx7HnqAIy89h+n307n5YjovvPiEIo6/hpl/ggugt+clEvEe117LGDd3qK1dwIKO9ezb92jFbTta2Sgrj1aKfDJObiROuL0Df020bOSmlYWVSaMr6MiUZZHuL3fS5ouEMVwbLK0UyQNHyjTwhs9PQ8dy1r7pJs8o0MPj5DD/dFwAbe1ryWWTZLNJ54DWBEO1tC9Y626ankQmugIun4yTOtIDItRFasoEiOPL3YeYJlrZZQatFX0cAVY6g9+d6lmZDGIIWmkM00e0dRGBUISW5etpXrraE1oeHqeB0yq4TNPP0s6LyGRGyGWTBIJRQqF61z5LFcQ1LEbbFvl0iuxQzJkmHmeYLSJYyQRG0NGnjRm5qmzlbRxo7WyM1hormULbE8Kt68K3EGmYmYcIDw+PueW0T6xFhHC4gfqGxYTDDQX2XAaNjZ1lui+tNXYuR9vCddgZR/Bo28Z249iVptXKxs5ksLNZ8vE42aFB7FE3es8UsRDziSTZ2DAqZznqNNNHbdsST2h5eMwDTrvgmoqGhiW0tHRhFmy1Cfpq6Ox6I20d6wiEJ5TxiYMHnNVEd+o35lcrn06RHR4qyteJ7qzxRSr7jtdaEwjU4PfXojIWhulnQdfZrLvy3XPfSA8PjxlT1QFh+/a/RPcLvx13FyOmj1BzCzVN7fjCEVKxHjBN8vG46355Al9NFAzBSo1ip9PjdhDBcB1rLno7gZATnLRQL+bh4XFKmZ/K+ROlrfMssslhendvdQJvKJuQL8ryNZdjmD5SiX6GBw+QDg6TTQ47xqZorOQo6b5ugi1NBOpqMYNBgv4oi1deSE1De3mwVg8Pj3lFVY+4xrByGdKJGMFILYFw5TDutp3HtvPse+o+Yod2o2xnO4+YPsK1jZz91o9gmFUtxz08XmvMTzuu04FWir49L3J013Mo26Klcx0L123G5599UFkPD4+Tgie4PDw8qo5JBde8XlX08PDwqIQnuDw8PKoOT3B5eHhUHZ7g8vDwqDo8weXh4VF1zIXhkmeh6eHhcUrxRlweHh5Vhye4PDw8qg5PcHl4eFQdnuDy8PCoOjzB5eHhUXV4gsvDw6Pq8ASXh4dH1eEJLg8Pj6rDE1weHh5Vhye4PDw8qg5PcHl4eFQdnuDy8PCoOqbcZH3ppZt1LDYycaA04GrBv+UOnHXZVz3FucrHyo/rCvUoTlAp9HXxF13x3PGvKz1X3uRJ6lWpb/TUpyf1iH3c7p6sHyt8nezLZHWbykm31pVPT9WtkzVxkn483nVuNSY7M+n56Xgv15N0xAk8PsX5TNWmkv9M0tMTScrKqvQbmuz646etVH5xHUtbR4XvuvJvqOB8Kj/6a631WyqdnVJwxWIj/Pzn33bzURMPlJ4Iuqrd/6N00fnCh09r5TzYY8dU8fnivLUT1FWPPWgTx8fzUgXnxj7jWZVcqzVaFZaly8say7+0TFWatvgXrQuvUQX1KDw2XiaV046Vpzh+WZOkdbuwuKzxtLqwKLfOY31V8P+C/CfPq6hI9/5T0FfF5ejCeisqlFWSVlXItyivgu+q/PxYnqXfS8ss/j+oontRfr7welV2vvD49NIWnlMlfQCgSoSscp/Z4u8U5KXHhUlpvmN5T+Spi+o11nZdmFfBOU2FvMavmfj/+LGyssqPF5ZTKe1Y3k8debqFSfCmih4eHlWHJ7g8PDyqDk9weXh4VB2e4PLw8Kg6PMHl4eFRdXiCy8PDo+rwBJeHh0fVIZMZ+wGIyL3ApLYUVUoLMHC6KzHHeG2qDrw2zYyByQxQpxRcr0VE5Fmt9Xmnux5zidem6sBr09zhTRU9PDyqDk9weXh4VB2vR8H1ndNdgZOA16bqwGvTHPG603F5eHhUP6/HEZeHh0eVU3WCS0SWiMh/iMiIiMRF5GcisnQa150nIt8RkR0iMioiB0XkDhFZXiHtARHRFT5vm09tcq+tVE8tIhtL0hki8kW3bRkReVFE3nky2uOWN9v7dMsUbcqUpD3V92mxiPyDiDzhPkNaRDqnee20+19EPu4+p1kR2SkifzCX7Sgpa1ZtEpFVIvJ1EXlJRJIi0isi/ykiZ1dIu2WS+/RHs633lP645hsiEgF+C2SBD+O4EboVeFBENmitU1Nc/l5gPfD3wHZgEfBl4FkR2ai1PlSS/tfALSXHdp5wI0o4wTaNcRvwTyXHdpV8/0vgT4AvAVtx+uMnInKd1vpXs29BOSfYpu8C95Ycq3GP/WeF9KfkPrl0Ae/G6b9HgGtmcO20+l9EPo5zL78K3A9cCXxTRERr/a05aUUxs23TNcAVwPeB54AG4H8AT4rIG7XWW0vSvwT8fsmxA7Os85gDs+r4AH8I2EBXwbHlgAV87jjXtlY4tgzHPd7/Kjl+ALh9vrfJTauBW4+Tpg1HiHyl5PgDwEvzrU0V8vug285rT9d9csszCv7/e26dOqdx3bT6H2cgcQz4fkm6f8Ex8vTPoza14OrIC47VAzHgByXHtwCPzmW9q22qeAPwpNZ6z9gBrfV+4DHgxqku1Fr3VzjWDfTjjL5OF7Nu0wx4MxAAbi85fjtwVqXp8gky1236MNCHM7o6bWhd6Hd1Rky3/y8CWiuk+yHQDLxxluVPymzbpLUe0K5UKjg2gjPSP+m/p2oTXOuBbRWObwfWzTQzEVmL8zZ8tcLp6905f1ZEnjxZehPmpk2fdOs5KiK/FZFLKpSRBfaUHN/u/p1x3x2HObtPIrIEZ0pyh9baqpDkVN2nE2G6/b/e/VvadyfrPs0pItIEnEnl39M5rr4z7+rFPnYiZVWb4GrCGYqWMgQ0ziQjEfEB38YZcX2v5PQvgM/gvCk/AGSAn4vI7860wtPgRNt0O/Ap4CrgEzhv5t+KyOUlZQyXviHdMsbOzyVzdp+A38V5Tr9f4dypvE8nwnT7f+xvad+drPs01/wDIMDXSo4/DPwRzkj8XcBu4Lsi8mezLaiqlPNzzD8Cb8DRmxQ9KFrrzxR+F5GfA0/iKExLh/GnFa31Bwu+PiIid+O8sW/lJEwtTgMfAp7XWr9UeqKa7tNrHRH5IvB+4GOFKgIArfWflyS/271XXxKRr2mtkzMtr9pGXDEqv7Ene8NXRET+Gmd08lGt9X3HS6+1toGfAItFpGO65UyTOWnTGFrrBPBL4PySMhpERCqUARNv9Lliru7TZmANlUdbZZzk+3QiTLf/x/qmtO9O1n2aE1xzjb8C/kxr/S/TvOxOIAScNZsyq01wbWdCD1DIOuCV6WQgIl8CvgB8Vmv9w1nUYa63GpxwmyahsJ7bgSCwokIZnGA5lZirNn0YyAM/mkUd5tOWkOn2/5guq7TvTtZ9OmFE5IPAN4G/01r/71lkMbv7dDKWjU/WB2eebAFnFBzrxHm4Pz+N6z/rdtSfzrBcH46dS/d8a1OF/OqAg8DDBcfagBzwFyVp7wdeno9twlmFGwTung/3qUJZMzWHOG7/A34cneu/lqT7rtsXgfnSJjf92937/J1ZlHU3MArUzKquJ/sGz3HH1uCszLyMs6x+A/AisA+IFqRb5nbonxccey+OzdY9wIUln3UF6d4H/BuObuUK97pH3Bv63nnWpj8B/hlHt3A5zgjlZfdHcklJOX+No7z+nJv2W25/XDef2lRw7h1un79jkjJO6X0qKPdd7udbblmfdL9fVpDGAr43m/4H/sA9fqub7n+53//bfGoTcKnbnq04uuLC39M5BekuwVFdfAzHmPYdOEJLA1+YdZ1PVmecxE5eCvwUiAMJ4C5K3hA4b3cN3FJw7Db3WKXPloJ0F+JYfffhjBCGcd6Mb56HbboexzZqwK3rII51+eYKZZjAnwHdOEvzLwHvmm9tKjh3N1OMMk7HfXLLnc4zpIHbZtv/OBbmu9x0u4FPzbc24exWmOy6AwXpunAGC0fc9iSBx4H3nUidPe8QHh4eVUe1Kec9PDw8PMHl4eFRfXiCy8PDo+rwBJeHh0fV4QkuDw+PqsMTXB4eHlWHJ7g8PDyqDk9weXh4VB2e4PLw8Kg6PMHl4eFRdXiCy8PDo+rwBJeHh0fV4QmuKkVEbi4Jrplwg4x+2vWnfzLK3CIij87ius6SutoiclScgLxLZlmXm0Xko5Mcn3agVo/q5PXsc/61wk3AYRwHgjfhBCxoA0r9fM8HvorjdieA45bmL4C1InKB1jo/w7xuxnl+S10F/xInzFfviVXVYz7jCa7q5wU9EZzgPhHpwgnIOh8F1z6t9ZPu/x8WET+Ow7xzcYJcnDDaiZ9ZFkPT47WFN1V87fEMUCcibQAicraI/KeIxEQkLSKPlcZdFJHzReQ/ROSwm2aniPyViISPV5iIfFlEcrMMCfac+3dpQX5dIvJDEdnv1mWfiHxLRBoL0mwBLgMuLph+bnHPlU0VRcQvIreKyAG3rgfc7/5Z1NljHuCNuF57LAdsICkim3DcGT8PfBzHx/cfAPeLyBu01lvda5YCL+B4iU3gBGv4c+AMHJfIZYiIAXwD+CBwvdZ6NlGmO92/ewuOLQQO4fitj7l1+FPgVzhTQHDiSN6O41X0991j8SnK+T7wbpxINI/iuBr+kpv3+2dRb4/Tzcl0Cet9Tqq73Ztx3OSuxnkBNeL8iG3gLjfNAzhRhQMF15nusbsmyVfc/H4Xx9d5c8G5LTg//BCOW+Z+4Pxp1LXTresn3LwjwJtwdHP/cZxrfTjxITXFvsy3AI9O0S+d7vczqeAeGseNsgY2nO576X1m/vGmitXPDhyf60M4YaLuAD7qTvMuw4kzqETE5642Co5v9kvHMhCROhH5/0RkL45f8DzwQzftypLyaoFfA5uAi7XWz8ygrv/k5p3CEap9OAJyHBEJiMifisgOEUm76R9xT6+eQVljjLWzNEDs2PfLZpGnx2nGmypWP2/HGbkkcMJyZQBEZBHO6OrL7qcMETG01gr4V+AqnOnhCziCZTPOVDBUctlSnKnkd7TWu2ZY11txgmBEcFZAP40jbAvNGr4KfAYnus3jbrsWAz+rUJfpMBZMtXSV8WjJeY8qwhNc1c82XRLy3GUYZ6r3DeAHlS7UWisRCeGEELtFa/31sXMiMlmE4e1unj8UkbTW+vMzqGu31vpZ9/8Pi0gt8BER+bbW+mn3+HuBH2itby2oS3QGZZQyFv15AcW6tAUl5z2qCE9wvUbRWqdE5BHgbOA5d2RViSDOyKzUjurmKfK+U0Rs4A531PbHs6zm/8RRmv8FcK17LFKhLh+pcG0WZ9p6PB52/74XKIy0/AH375bpVNRjfuEJrtc2n8P54f5aRL6HM11qwdFPmVrr/6m1HhGRJ4HPi0gvTozGjwKLpspYa/1jV3jdKSKm1vqzM62c1vqoiHwD+BMROVc7q5z3Ah8WkZdxgsq+A2cVsJRXgE+JyHtwRlIJrfXOCmVsE5E7gVtcHd/jOKuTXwbu1Fq/PNN6e8wDTvfqgPeZ3YeJ1bOu46RbixPx+RjOKOUwjvX6WwvSdOIE7Uy46f4RZwSkgcsL0m2hZCUPeJub7zfAidNZoQ6dbl6/V+FcC44pw90F3/8NxxQihrPYcL57/c0F1y3AMZFIUBC8lJJVRfdYAEe/1o0zmut2v/tP9330PrP7eAFhPTw8qg7PHMLDw6Pq8ASXh4dH1eEJLg8Pj6rDE1weHh5Vhye4PDw8qg5PcHl4eFQdnuDy8PCoOjzB5eHhUXV4gsvDw6Pq+P8B1hwomSFvIG0AAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "def feature_scatter(feature_name,cmap='mako',save=False):\n", - " cmap = sns.color_palette(cmap, as_cmap=True)\n", - "\n", - " fig, ax = plt.subplots()\n", - " fig.set_size_inches(5, 4)\n", - " scat = ax.scatter(data_classified_df['x'],data_classified_df['y'],c=data_classified_df[feature_name],cmap=cmap)\n", - " cax = fig.add_axes([0.1, 0.05, 0.8, 0.03])\n", - " cbar = fig.colorbar(scat, cax=cax, orientation='horizontal')\n", - " \n", - " \n", - " if feature_name == 'trough_to_peak_abs':\n", - " feature_label = 'Absolute Trough to Peak'\n", - " \n", - " elif feature_name == 'trough_fwhm_abs':\n", - " feature_label = 'Absolute Trough FWHM'\n", - " \n", - " elif feature_name == 'peak_ratio':\n", - " feature_label = 'Peak Ratio'\n", - " \n", - " cbar.set_label(feature_label,labelpad=10,fontsize=16)\n", - " cbar.ax.tick_params(labelsize=16)\n", - " ax.spines['left'].set_visible(False)\n", - " ax.spines['right'].set_visible(False)\n", - " ax.spines['bottom'].set_visible(False)\n", - " ax.spines['top'].set_visible(False)\n", - " ax.set_xticks([]);\n", - " ax.set_yticks([]);\n", - " \n", - " if save:\n", - " plt.savefig('Feature_'+feature_name+'.pdf',format='pdf') \n", - " \n", - " return None\n", - "\n", - "feature_scatter('trough_fwhm_abs',cmap='crest',save=True)\n", - "feature_scatter('trough_to_peak_abs',cmap='flare',save=True)\n", - "feature_scatter('peak_ratio',cmap=\"ch:start=.2,rot=.5\",save=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Yem2TivfEdGZ" - }, - "source": [ - "# Figure S4: Effect of normalizations on WaveMAP structure" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 189 - }, - "id": "NSfmYpMGRmOi", - "outputId": "3dfed7a0-51f1-41d2-949d-9443f5a4fd8f", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "subsets = [0.1,0.2,0.3,0.4,\n", - " 0.5,0.6,0.7,0.8,\n", - " 0.9,1.0]\n", - "\n", - "if 'renorm_subset_avg_rand_list' not in list(locals().keys()):\n", - " renorm_subset_avg_rand_list = pkl.load(open('WaveMAP_Paper/data/renorm_subset_avg_rand_list.pkl','rb'))\n", - "\n", - "if 'renorm_subset_std_rand_list' not in list(locals().keys()):\n", - " renorm_subset_std_rand_list = pkl.load(open('WaveMAP_Paper/data/renorm_subset_std_rand_list.pkl','rb'))\n", - "\n", - "f, arr = plt.subplots(1,figsize=[3,2.5])\n", - "arr.errorbar(np.array(subsets,dtype=np.float),renorm_subset_avg_rand_list,yerr=renorm_subset_std_rand_list,c = 'k', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xlabel('% of Full Dataset', fontsize=12,fontname=\"Arial\")\n", - "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n", - "arr.set_xticklabels(['','20','40','60','80','100'],fontsize=12,fontname=\"Arial\")\n", - "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\")\n", - "arr.set_yticks([0,2,4,6,8,10])\n", - "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\")\n", - "arr.spines['left'].set_bounds(0,10)\n", - "arr.spines['bottom'].set_bounds(0.1,1)\n", - "arr.axhline(np.max(renorm_subset_avg_rand_list),color='k',linestyle='dashed')\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4fN3gPPKws1w" - }, - "source": [ - "### We show the WaveMAP clustering and projection of this normalization (same as in paper)" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 355 - }, - "id": "77d8DuFUFhwD", - "outputId": "5c097366-d72f-4078-aeb2-bd16da0fb3c8", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE)\n", - "mapper = reducer.fit(full_data)\n", - "embedding = reducer.transform(full_data)\n", - "\n", - "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "\n", - "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", - "f.tight_layout()\n", - "\n", - "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", - " marker='o', c=cluster_colors, s=32, edgecolor='w',\n", - " linewidth=0.5)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xticks([]);\n", - "arr.set_yticks([]);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zXpz0x46J9kz" - }, - "source": [ - "## Figure S4C: Number of clusters across random seed and subsets with trough normalization" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kyTA8ffqwycs" - }, - "source": [ - "### We compare the -1 to +1 normalization by using the other commonly used trough normalization (entire waveform aligned and mean-centered and divided by the negative amplitude of the trough). Here we show all the waveforms and note that the heights of the waveforms no longer \"cap\" at +1." - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 274 - }, - "id": "UoK6IB1AN43b", - "outputId": "6ec1ee6d-eabb-42b7-e560-915e62d2bf0a", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[94mPlotting: 625 Waveforms\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "trough_normalizedWaveforms = np.load('WaveMAP_Paper/data/trough_normalizedWaveforms.npy')\n", - "\n", - "f, arr = plt.subplots(1,figsize=[4.5,3.4])\n", - "\n", - "print(BlueCol + \"Plotting: \" + str(trough_normalizedWaveforms.shape[0]) + \" Waveforms\")\n", - "for i in range(0,trough_normalizedWaveforms.shape[0]):\n", - " arr.plot(trough_normalizedWaveforms[i].T, c = 'k', alpha = 0.03,linewidth=2.);\n", - " \n", - "arr.tick_params(direction='out',colors='k', axis='both')\n", - " \n", - "# Set various x and y axes and labels etc.\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "\n", - "arr.spines['left'].set_bounds(-1,1)\n", - "arr.spines['bottom'].set_bounds(0,48)\n", - "\n", - "arr.set_xlabel('Time (ms)', fontsize=24);\n", - "arr.set_xticks([0,14,28,42,48])\n", - "arr.set_xticklabels(['0','0.5','1.0','1.5',''],fontsize=24)\n", - "\n", - "arr.set_ylabel('Amplitude (a.u.)', fontsize=24)\n", - "arr.set_yticks([-1.0,0.0,1.0]);\n", - "arr.set_yticklabels([-1.0,0.0,1.0], fontsize=24);\n", - "arr.set_title('Trough Normalization',fontsize=24)\n", - "# Plot the data\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OHmSivAgKDwM" - }, - "source": [ - "## Figure S4C: Cluster number vs. data subset proportion on trough normalized waveforms\n", - "\n", - "---\n", - "\n", - "**THIS CELL CAN TAKE 40 MIN**; skip it and run the next cell to read cached values of the plot." - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 400 - }, - "id": "yVu2sdyfKQ4Q", - "outputId": "4d80cb2f-344e-4948-a871-da76c1682ee0", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m random_state=random.randint(1,100000))\n\u001b[1;32m 13\u001b[0m \u001b[0mrand_data\u001b[0m \u001b[0;34m=\u001b[0m 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- " random_state=random.randint(1,100000))\n", - " rand_data = np.random.permutation(trough_normalizedWaveforms)[0:(int(len(full_data)*frac)),:]\n", - " mapper = reducer_rand_test.fit(rand_data)\n", - " embedding_rand_test = reducer_rand_test.transform(rand_data)\n", - "\n", - " umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n", - " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", - " clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n", - " clustering_solution = list(clustering.values())\n", - " rand_list.append(len(set(clustering_solution)))\n", - "\n", - " renorm_clust_rand_dict.update({str(frac): rand_list})\n", - "\n", - "renorm_subset_avg_rand_list = []\n", - "renorm_subset_std_rand_list = []\n", - "\n", - "for k,v in renorm_clust_rand_dict.items():\n", - " renorm_subset_avg_rand_list.append(np.average(v))\n", - " renorm_subset_std_rand_list.append(np.std(v))" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 189 - }, - "id": "GGRMNgd6Yutg", - "outputId": "ee551f98-f85a-41bd-c127-050857f79f79", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "subsets = [0.1,0.2,0.3,0.4,\n", - " 0.5,0.6,0.7,0.8,\n", - " 0.9,1.0]\n", - "\n", - "if 'renorm_subset_avg_rand_list' not in list(locals().keys()):\n", - " renorm_subset_avg_rand_list = pkl.load(open('WaveMAP_Paper/data/renorm_subset_avg_rand_list.pkl','rb'))\n", - "\n", - "if 'renorm_subset_std_rand_list' not in list(locals().keys()):\n", - " renorm_subset_std_rand_list = pkl.load(open('WaveMAP_Paper/data/renorm_subset_std_rand_list.pkl','rb'))\n", - "\n", - "f, arr = plt.subplots(1,figsize=[3,2.5])\n", - "arr.errorbar(np.array(subsets,dtype=np.float),renorm_subset_avg_rand_list,yerr=renorm_subset_std_rand_list,c = 'k', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xlabel('% of Full Dataset', fontsize=12,fontname=\"Arial\")\n", - "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n", - "arr.set_xticklabels(['','20','40','60','80','100'],fontsize=12,fontname=\"Arial\")\n", - "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\")\n", - "arr.set_yticks([0,2,4,6,8,10])\n", - "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\")\n", - "arr.spines['left'].set_bounds(0,10)\n", - "arr.spines['bottom'].set_bounds(0.1,1)\n", - "arr.axhline(np.max(renorm_subset_avg_rand_list),color='k',linestyle='dashed')\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "J2yCZEenyhf3" - }, - "source": [ - "## Figure S4D: WaveMAP plot with trough normalization\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VJHReVG8yj90" - }, - "source": [ - "### We next apply WaveMAP to waveforms with the trough normalization. Note the similarities between the two types or normalization." - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 355 - }, - "id": "_UCnKNbffXkT", - "outputId": "10233d0d-f8df-4110-8547-69869c9f6158", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", - " random_state=RAND_STATE)\n", - "mapper = reducer.fit(trough_normalizedWaveforms)\n", - "embedding = reducer.transform(trough_normalizedWaveforms)\n", - "\n", - "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", - "\n", - "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", - "f.tight_layout()\n", - "\n", - "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", - " marker='o', c=cluster_colors, s=32, edgecolor='w',\n", - " linewidth=0.5)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.spines['bottom'].set_visible(False)\n", - "arr.spines['left'].set_visible(False)\n", - "arr.spines['right'].set_visible(False)\n", - "arr.set_xticks([]);\n", - "arr.set_yticks([]);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rjqcjprTwl_x" - }, - "source": [ - "# Figure S8: GMM Clusters lack the physiological and functional diversity of WaveMAP classes" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "13v0A7WEwoUb" - }, - "source": [ - "## Figure S8A: FR traces for GMM clusters" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r-5MWzIPwsui" - }, - "source": [ - "### As in Figure 6A,B, we plot the stim-aligned trial-averaged FR traces but this time for GMM clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 369 - }, - "id": "tIG6qHgVi_8i", - "outputId": "070c55da-1ace-45ff-843e-0ac0e7568bd5", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f, arr = plt.subplots(4,figsize=[2,5])\n", - "\n", - "time = np.arange(-0.1,0.5,0.001)\n", - "\n", - "for i,ix in enumerate([0,1,2,3]):\n", - " PREF = GMM_traces_df.iloc[ix]['PREF']\n", - " NONPREF = GMM_traces_df.iloc[ix]['NONPREF']\n", - " PREF_UPPER = GMM_traces_df.iloc[ix]['PREF_UPPER_BOUND']\n", - " PREF_LOWER = GMM_traces_df.iloc[ix]['PREF_LOWER_BOUND']\n", - " NONPREF_UPPER = GMM_traces_df.iloc[ix]['NONPREF_UPPER_BOUND']\n", - " NONPREF_LOWER = GMM_traces_df.iloc[ix]['NONPREF_LOWER_BOUND']\n", - " arr[i].plot(time,PREF,color=GMM_PAL[ix])\n", - " arr[i].plot(time,NONPREF,'--',color=GMM_PAL[ix])\n", - " arr[i].fill_between(time,PREF_UPPER,PREF_LOWER,\n", - " color='gray',alpha=0.2)\n", - " arr[i].fill_between(time,NONPREF_UPPER,NONPREF_LOWER,\n", - " color='gray',alpha=0.2)\n", - " arr[i].set_ylim(5,30)\n", - " arr[i].set_xticks([-0.1,0.1,0.3,0.5])\n", - " arr[i].set_xlim(-0.1,0.5)\n", - " arr[i].set_yticks([5,30])\n", - " arr[i].spines['left'].set_position(('axes', -0.05))\n", - " arr[i].spines['top'].set_visible(False)\n", - " arr[i].spines['right'].set_visible(False)\n", - " arr[i].axvline(0,ymin=0.,ymax=30,linestyle='dashed',color='k')\n", - " f.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - }, - "id": "icazrrtefKj9", - "outputId": "3b5fd0a1-c829-44ea-fbcd-4a75179d8e1c", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f, arr = plt.subplots(1)\n", - "f.set_size_inches(3,2.5)\n", - "\n", - "for i,clust_ix in enumerate([1,2,3,4]):\n", - " start_ix = 0 \n", - " \n", - " median, med_se = bootstrap_median(get_baseline_FR(baseline_FR_df,clust_ix,UMAP_clusts=False))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=GMM_PAL[clust_ix-1],\n", - " yerr=med_se)\n", - " \n", - "arr.set_ylabel('Baseline FR (spikes/s)')\n", - "arr.set_xticks([1.5]);\n", - "arr.set_xticklabels(['GMM Clusters'],fontsize=12,fontname='Arial')\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.set_ylim(0,15);" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - }, - "id": "NNLI4S6jta6H", - "outputId": "db4b786a-9aa6-42ce-b364-56bb5d6b4943", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "f, arr = plt.subplots(1)\n", - "f.set_size_inches(3,2.5)\n", - "\n", - "for i,clust_ix in enumerate([1,2,3,4]):\n", - " start_ix = 0 \n", - " \n", - " median, med_se = bootstrap_median(get_max_FR(max_FR_df,clust_ix,UMAP_clusts=False))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=GMM_PAL[clust_ix-1],\n", - " yerr=med_se)\n", - " \n", - "arr.set_ylabel('Max FR (spikes/s)')\n", - "arr.set_xticks([1.5]);\n", - "arr.set_xticklabels(['GMM Clusters'],fontsize=12,fontname='Arial')\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.set_ylim(0,40);" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PFVwe-_fLdRb" + }, + "source": [ + "# Step 1: Download various data files by cloning the paper's Git repo.\n", + "\n", + "---\n", + "*Note to reviewers: PLEASE DO NOT STAR OR FORK THE REPO WHICH IS DEANONYMIZING. The Github repository helps clone important files necessary for analysis.*\n", + "\n", + "This git repository has various data files Which we clone below to allow you to run the google colab. There is no need to go to the original git repository the process of running this code leads to the files being downloaded and attached to this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "z16uiX0wj2ni", + "outputId": "edb7653a-4037-43bb-df05-d02ff8a62db7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fatal: destination path 'WaveMAP_Paper' already exists and is not an empty directory.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/EricKenjiLee/WaveMAP_Paper.git" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0m-cBuzkMyYN" + }, + "source": [ + "## Step 1a: Importing packages\n", + "\n", + "Here are imported standard packages." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "KbP0Q7Z3biq_" + }, + "outputs": [], + "source": [ + "# Importing packages \n", + "# --- Importing matplotlib, seaborn, etc.\n", + "\n", + "import os\n", + "import random\n", + "\n", + "import matplotlib as mpl\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.lines import Line2D\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib.gridspec import GridSpec\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import pandas as pd\n", + "import scipy\n", + "from scipy import io\n", + "import pickle as pkl\n", + "import h5py\n", + "import xml.etree.ElementTree as ET" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VaM9ETbvM38B" + }, + "source": [ + "These non-standard packages are pinned for compatibility reasons. Google Colab's default versions for import change frequenetly." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SvvK4nfZmFtn", + "outputId": "ef809541-bf39-4545-f31a-5a5da153c7c1" + }, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'cylouvain'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m--------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# !pip install cylouvain==0.2.2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mcylouvain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# !pip install shap==0.35\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cylouvain'" + ] + } + ], + "source": [ + "# !pip install networkx==2.4\n", + "# import networkx as nx\n", + "\n", + "# !pip install scikit-learn==0.22.2.post1\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "# !pip install xgboost\n", + "# import xgboost as xgb\n", + "\n", + "# !pip install umap-learn==0.5.0\n", + "from umap import umap_ as umap\n", + "\n", + "# !pip install cylouvain==0.2.2\n", + "import cylouvain\n", + "\n", + "# !pip install shap==0.35\n", + "import shap\n", + "\n", + "# !pip install python-igraph==0.8.2\n", + "import igraph as ig" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Sj0vY7akb92a" + }, + "source": [ + "#Step 2: Colormap selection for clusterings\n", + "\n", + "---\n", + "\n", + "The clusterings get clustered in a random order so although these colors reflect those used in the manuscript, they may need to be permuted to match what is found in our figures." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 233 + }, + "id": "R8Nkf7EOb1jE", + "outputId": "1bef0f02-3c63-4dd6-a033-2f9e0b22b191" + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "CUSTOM_PAL_SORT_3 = ['#5e60ce','#00c49a','#ffca3a','#D81159','#fe7f2d','#7bdff2','#0496ff','#efa6c9','#ced4da']\n", + "GMM_PAL = ['#d62424','#12db41','#f0c905','#248cd6']\n", + "\n", + "# In RGB form\n", + "coherence_colors = [[0.609, 0.283,\t0.724],\n", + "[0.259,\t0.314, 0.635],\n", + "[0.251,\t0.412, 0.698],\n", + "[0.176,\t0.631, 0.859],\n", + "[0.369,\t0.749, 0.549],\n", + "[0.898,\t0.654, 0.169],\n", + "[0.898,\t0.41, 0.165]]\n", + "sns.palplot(CUSTOM_PAL_SORT_3)\n", + "sns.palplot(GMM_PAL)\n", + "sns.palplot(coherence_colors)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A4kW4JO8d4hh" + }, + "source": [ + "# Step 3: Setting of parameters\n", + "\n", + "---\n", + "\n", + "This sets various global parameters like sampling rate, U-probe depths, and random seed state. Setting the random seed at the level of the Python kernel, Numpy, and random packages is essential to produce the same qualitative WaveMAP projection across instances. Note that qualitatively, the results be the same no matter the seed because it affects the projection step of the algorithm and note the graph construction (the latter is what is used in clustering)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "LAC7l7TNb8N1" + }, + "outputs": [], + "source": [ + "#These are the depths that the V-probe channels are located at\n", + "DEPTHS = [0.15,0.3,0.45,0.60,0.75,0.9,1.05,1.20,1.35,1.50,1.65,1.80,1.95,2.1,2.25,2.4]\n", + "\n", + "#This converts time points to real time. There are 48 samples per waveform colleted at 30 kilosamples\n", + "SAMP_RATE_TO_TIME = 1/(48/30000) \n", + "\n", + "#Setting of random seed across Python kernel and packages to ensure reproducibility \n", + "RAND_STATE=42\n", + "np.random.seed(RAND_STATE)\n", + "os.environ['PYTHONHASHSEED'] = str(RAND_STATE)\n", + "random.seed(RAND_STATE)\n", + "\n", + "#UMAP Parameters\n", + "#The number of neighbors considered when constructing the high-d graph. \n", + "#Made more global-information preserving by increasing it from 15 to 20.\n", + "N_NEIGHBORS = 20 \n", + "\n", + "#The minimum distance between points in the projected space.\n", + "#Used for visualization but doesn't affect clustering.\n", + "MIN_DIST = 0.1\n", + "\n", + "#Louvain Clustering Parameters\n", + "RESOLUTION = 1.5\n", + "\n", + "# BLUE COLOR\n", + "BlueCol = '\\033[94m'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W0-CCJCAeLOx" + }, + "source": [ + "# Step 4: Loading of processed waveform data\n", + "\n", + "---\n", + "\n", + "This cell loads varous files including the 250 Hz high-pass 4th order Butterworth-filtered waveforms, the GMM features, BIC values used for selecting number of GMM clusters, and other waveform info." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b7A2Qlu4exDE", + "outputId": "80b5e856-0fd9-4165-cac2-6ca70b0fcd96" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94mLoading data\n", + "/Users/kenjilee/Documents/GitRepos/waveformAnalysis/notebooks/exploration\n", + "/Users/kenjilee/Documents/GitRepos/waveformAnalysis/notebooks/exploration/WaveMAP_Paper/data/full_data.npy\n", + "/Users/kenjilee/Documents/GitRepos/waveformAnalysis/notebooks/exploration/WaveMAP_Paper/data/waveformsClassified_250hz_refiltered.mat\n", + "/Users/kenjilee/Documents/GitRepos/waveformAnalysis/notebooks/exploration/WaveMAP_Paper/data/gmm_features.mat\n", + "/Users/kenjilee/Documents/GitRepos/waveformAnalysis/notebooks/exploration/WaveMAP_Paper/data/BIC_list.mat\n", + "/Users/kenjilee/Documents/GitRepos/waveformAnalysis/notebooks/exploration/WaveMAP_Paper/data/8_class_GMM.mat\n", + "/Users/kenjilee/Documents/GitRepos/waveformAnalysis/notebooks/exploration/WaveMAP_Paper/data/filt_full_df.pkl\n", + "/Users/kenjilee/Documents/GitRepos/waveformAnalysis/notebooks/exploration/WaveMAP_Paper/data/full_data_df.pkl\n" + ] + } + ], + "source": [ + "print(BlueCol + 'Loading data')\n", + "\n", + "rel_path = os.getcwd()\n", + "fullDataPath = os.path.join(rel_path,'WaveMAP_Paper/data/full_data.npy');\n", + "GMMclasslabelpath = os.path.join(rel_path,\n", + "'WaveMAP_Paper/data/waveformsClassified_250hz_refiltered.mat')\n", + "GMMfeaturepath = os.path.join(rel_path,\n", + "'WaveMAP_Paper/data/gmm_features.mat')\n", + "BICpath = os.path.join(rel_path,\n", + "'WaveMAP_Paper/data/BIC_list.mat')\n", + "eightclassGMMpath = os.path.join(rel_path,'WaveMAP_Paper/data/8_class_GMM.mat');\n", + "filtfulldfPath = os.path.join(rel_path,'WaveMAP_Paper/data/filt_full_df.pkl');\n", + "\n", + "print(rel_path)\n", + "print(fullDataPath);\n", + "print(GMMclasslabelpath)\n", + "print(GMMfeaturepath)\n", + "print(BICpath)\n", + "print(eightclassGMMpath)\n", + "print(filtfulldfPath)\n", + "\n", + "full_data = np.load(fullDataPath)\n", + "allDataDFPath = os.path.join(rel_path,'WaveMAP_Paper/data/full_data_df.pkl');\n", + "\n", + "GMM_class_labels = scipy.io.loadmat(GMMclasslabelpath)['classifies'].T\n", + "gmm_features_data = scipy.io.loadmat(GMMfeaturepath)['features']\n", + "\n", + "GMM_class_labels = GMM_class_labels[~np.isnan(GMM_class_labels)]\n", + "GMM_class_df = pd.DataFrame(GMM_class_labels,columns=['Class'])\n", + "gmm_feat_data_nonan = gmm_features_data[~np.isnan(gmm_features_data)].reshape(len(GMM_class_df),3)\n", + "\n", + "BIC_list = scipy.io.loadmat(BICpath)['BIC_list'][0]\n", + "\n", + "eight_GMM_classes = scipy.io.loadmat(eightclassGMMpath)['classifies']\n", + "\n", + "all_data_df = pkl.load(open(allDataDFPath,'rb'))\n", + "filt_full_df = pkl.load(open(filtfulldfPath,\"rb\"))\n", + "\n", + "print(allDataDFPath)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BF50mJmCz5cQ" + }, + "source": [ + "# Step 5: Loading of processed firing rate data.\n", + "\n", + "---\n", + "\n", + "Raw data is too large to be hosted and processed given Colab I/O speeds and file size storage limits; therefore, processed data is used here. Please contact the corresponding author for access to raw data (cchandr1@bu.edu)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "4yCjav9sZsR7" + }, + "outputs": [], + "source": [ + "def read_pkl(pkl_file_loc):\n", + " return pkl.load(open(pkl_file_loc,'rb'))\n", + "\n", + "FR_trace_loc = 'WaveMAP_Paper/data/FR_traces'\n", + "FR_traces = os.listdir(FR_trace_loc)\n", + "\n", + "UMAP_FR_traces = [x for x in FR_traces if not x.startswith('GMM')]\n", + "GMM_FR_traces = [x for x in FR_traces if x.startswith('GMM')]\n", + "\n", + "UMAP_traces_df = pd.DataFrame(columns = ['clust','PREF','NONPREF','PREF_UPPER_BOUND','PREF_LOWER_BOUND',\n", + " 'NONPREF_UPPER_BOUND','NONPREF_LOWER_BOUND'])\n", + "GMM_traces_df = pd.DataFrame(columns = ['clust','PREF','NONPREF','PREF_UPPER_BOUND','PREF_LOWER_BOUND',\n", + " 'NONPREF_UPPER_BOUND','NONPREF_LOWER_BOUND'])\n", + "\n", + "for i in range(0,8):\n", + " traces = sorted([x for x in UMAP_FR_traces if str(i) in x],key=len)\n", + " trace_arr = []\n", + " \n", + " trace_arr.append(i)\n", + " for trace in traces:\n", + " if 'pref_'+str(i)+'.pkl' == trace:\n", + " trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n", + " elif 'nonpref_'+str(i)+'.pkl' == trace:\n", + " trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n", + " elif 'pref_bounds_'+str(i)+'.pkl' == trace:\n", + " upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n", + " trace_arr.append(upper_bound)\n", + " trace_arr.append(lower_bound)\n", + " elif 'nonpref_bounds_'+str(i)+'.pkl' == trace:\n", + " upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n", + " trace_arr.append(upper_bound)\n", + " trace_arr.append(lower_bound)\n", + " \n", + " trace_series = pd.Series(trace_arr,index=UMAP_traces_df.columns)\n", + " UMAP_traces_df = UMAP_traces_df.append(trace_series,ignore_index=True)\n", + "\n", + "for i in range(1,5):\n", + " traces = sorted([x for x in GMM_FR_traces if str(i) in x],key=len)\n", + " trace_arr = []\n", + " \n", + " trace_arr.append(i)\n", + " for trace in traces:\n", + " if 'GMM_pref_'+str(i)+'.pkl' == trace:\n", + " trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n", + " elif 'GMM_nonpref_'+str(i)+'.pkl' == trace:\n", + " trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n", + " elif 'GMM_pref_bounds_'+str(i)+'.pkl' == trace:\n", + " upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n", + " trace_arr.append(upper_bound)\n", + " trace_arr.append(lower_bound)\n", + " elif 'GMM_nonpref_bounds_'+str(i)+'.pkl' == trace:\n", + " upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n", + " trace_arr.append(upper_bound)\n", + " trace_arr.append(lower_bound)\n", + " \n", + " trace_series = pd.Series(trace_arr,index=GMM_traces_df.columns)\n", + " GMM_traces_df = GMM_traces_df.append(trace_series,ignore_index=True) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Obep1YIAZnAx" + }, + "source": [ + "# Step 6: Loading of decision-related functional activity" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WAd35wPqP1Es" + }, + "source": [ + "Processed data calculated from behavioral trials is loaded here. Raw data is not provided for the same reasons as in the previous step." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "jxLKNL3XKJ0-" + }, + "outputs": [], + "source": [ + "FR_stats_loc = 'WaveMAP_Paper/data/FR_stats'\n", + "decision_dynamics_loc = os.path.join(rel_path,'WaveMAP_Paper/data/decisionDynamics.mat');\n", + "\n", + "\n", + "baseline_FR_df = pkl.load(open(os.path.join(FR_stats_loc,'baseline_FR_df.pkl'),'rb'))\n", + "max_FR_df = pkl.load(open(os.path.join(FR_stats_loc,'max_FR_df.pkl'),'rb'))\n", + "GMM_baseline_FR_df = pkl.load(open(os.path.join(FR_stats_loc,'GMM_baseline_FR_df.pkl'),'rb'))\n", + "GMM_max_FR_df = pkl.load(open(os.path.join(FR_stats_loc,'GMM_max_FR_df.pkl'),'rb'))\n", + "\n", + "dynamic_range_FR = np.subtract(max_FR_df['max_FR'],baseline_FR_df['baseline_FR'])\n", + "dynamic_range_FR_df = pd.DataFrame({'dynamic_range_FR': dynamic_range_FR, \n", + " 'dbscan_color': baseline_FR_df['dbscan_color']})\n", + "\n", + "GMM_dynamic_range_FR = np.subtract(GMM_max_FR_df['max_FR'],GMM_baseline_FR_df['baseline_FR'])\n", + "GMM_dynamic_range_FR_df = pd.DataFrame({'dynamic_range_FR': dynamic_range_FR, \n", + " 'dbscan_color': baseline_FR_df['dbscan_color']}) \n", + "\n", + "with h5py.File(decision_dynamics_loc,\"r\") as f:\n", + " diffV_list = []\n", + " for i in range(8):\n", + " diffV_list.append(np.array(f[f['forKenjiDat']['diffV'][i][0]]))\n", + "\n", + " coherences = []\n", + " for i in range(7):\n", + " coherences.append(f['metaData']['coherences'][0][i])\n", + "\n", + " dec_dyn_data = []\n", + " dec_dyn_data_err = []\n", + " \n", + " for i in range(8):\n", + " dec_dyn_data.append(np.array(f[f['forKenjiDat']['timeSlope'][i][0]]))\n", + " dec_dyn_data_err.append(np.array(f[f['forKenjiDat']['timeSlopeE'][i][0]]))\n", + " \n", + " for i,x in enumerate(dec_dyn_data):\n", + " dec_dyn_data[i] = [val for sublist in x for val in sublist]\n", + " \n", + " for i,x in enumerate(dec_dyn_data_err):\n", + " dec_dyn_data_err[i] = [val for sublist in x for val in sublist]\n", + "\n", + " dec_dyn_slope = []\n", + " \n", + " for i in range(8):\n", + " dec_dyn_slope.append(np.array(f[f['forKenjiDat']['slopeAsfCoh'][i][0]]))\n", + " \n", + " for i,x in enumerate(dec_dyn_slope):\n", + " dec_dyn_slope[i] = [val for sublist in x for val in sublist]\n", + "\n", + "discrim_data_path = 'WaveMAP_Paper/data/discrimination_times.pkl'\n", + "discrim_file = pkl.load(open(discrim_data_path,'rb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "9-LNNSLOZ-wT" + }, + "outputs": [], + "source": [ + "masterArray = scipy.io.loadmat(os.path.join(rel_path,'WaveMAP_Paper/data/masterArray.mat'))['masterArray']\n", + "\n", + "uprobeMask = scipy.io.loadmat(os.path.join(rel_path,'WaveMAP_Paper/data/uprobeMask.mat'))['uprobeMask']\n", + "uprobeMask = [i[0] for i in uprobeMask]\n", + "\n", + "ch_depth = scipy.io.loadmat(os.path.join(rel_path,'WaveMAP_Paper/data/allChansUprobe.mat'))['allChansUprobe']\n", + "ch_depth = [i[0] for i in ch_depth]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UgFaghmrlqyo" + }, + "source": [ + "# Figure 1E Bottom (Plots all the waveforms used in the analysis). \n", + "\n", + "The normalized and filtered average single-unit waveforms are plotted on top of each other here with transparency." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "45Htregu1CV8" + }, + "source": [ + "### We plot all normalized single unit waveforms together" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 276 + }, + "id": "DDwk8jADeLez", + "outputId": "88583a91-996e-40bc-c774-7446ceff17b1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94mPlotting: 625 Waveforms\n" + ] + }, + { + "data": { + "image/png": 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g0+kgkUgYFZv5t+12G9VqFefPn8fKygoKhQLa7Tby+bzJzw2FQgiHwyZtrVqtwu/3o9VqGamOBNtqtYwUSPvf3NwcqtUqotGo2U4kEhlJFJSweKw8tzzmwXVbrVbfenSu7IeIbJV2bm6uj/BIrDb5cZ8nCZMSXhxAQVXtskl1AInBFVX1C71QjsfvY3wOhwQ+bINShE1Q+90+MybsjA47oDgcDiOVSkFE0Gq14Pf7kclkUC6XcfXqVXQ6nb4HNxqNIhqNYn19vS/8JB6PY319HaVSyTguWJWlVquZcJVms9n33m63jURJ22AymTSODtoO5+bmTOhMLBYz8YDjVFEbtsRmr8/4xFmRjk2kJP5R5HdUbJj7xaSEdx3Aioj4GMQLLw7unIisqqpJvO/Z8yLYdmI4HCOMUmmntW2+mBfLfTWbTUN48/PzRoWmF5VBxoVCAevr6/D5fIjH40in04hGo1BVhEIh5PN546U9c+ZMnwc2kfDmZzou/H4/arWa+T/gOTwALxCaREwyaLfbiEQixsbYarXM/ihBRqPRPseOna3C77bzgZ5iOkj4OkiQ/Obm5vqIjyR8EqS+Sc/ovfC6kq0CuL+37JO9Zc8G8OvWus8EEABw3z7H6HDAGEd205DwbNWONjTAs1mVy2WjrlK6uXLlilE5mTubz+dRrVaxsLCARCKBbDaLYDCISqVi7Gvr6+u4evUqMpkMbr75ZsRiMcRiXn3XarVqjoexfrVaDeFwuI/Y4vG4IR7a/TY3NxEIBJBMJvt6cJTLZZRKJUSjUdRqNSQSCSQSCQQCgbETB4nXdigcpgfVlvxswrOlvmmo2IeBSQnv/QD+Fbyk+t/qLXsbvLaMr+013/4neLa+l8FzcLxnKiN1ODDMUrqzt28TXr1eRzAYRLFYRLlcRjAYxJkzZ5DP51EqlbC1tYVUKoXNzU2sr6+jXC6bQGjAIyNmU1A9azQa2NraQqFQQKvVwqVLl4y6KiLGE5xOp5FIJBAKhfqIkDY5FiQNhUJGEmw2mygUCmg0Gob0OB4WOmAwczweN6r74IvB1j6fD8Fg0JyXZrNpVM3DBFVtnlOOl1L3UQrZ2Q0mJbw/hheE+wz0CE9V/1BE/gRe28LXWusKgLsA/AwcjhV2Ijy7cspebvRB4z1VOp/PZ4oEJJNJRCIR1Go1I9EVCgUEAgEUi0WICILBIOr1Ou6++26k02kAnpQYDAbN+JLJJPL5PDY3NxEOh5FMJiEixvNKexwJzXZoUO1st9smMDoWi6HdbpvwmXq9jlKphEgkglQqZfZNSbBcLmNra8s4NxjWYjsmRMTEItoSND2pO0mIBwVb6qOn/rjVJpy0a9nn4AUXD+I7ATwfnjf2HLwWhu8H8F9VNbffQTocLEZ5aIn9Eh7QnxlAaSwUCqFcLpvlzWYTa2truHHjhom1Y2BwIpFAPB43JJjP5w3ZUMpbWlpCtVqFqqJarRpHRzKZNLa/cDiMTqdjPLh0kjBtrVarGc9vNBpFoVAw3ttQKGSIKZ/P922bJasqlQoikYghTdrBbCKjSmwHE5MQacNkHUD7uhxW9gSlUUp6dv7zUSe+qVhF1WuI/T96L4djjlEe2mlunwQAbDfhZlEA2u+2trZw+fJlXLt2zTgFFhYWTBvHQqFgJDYSEbcdDodNUYFgMIhqtYqtrS2zr/Pnz5uHk0HI0WjUSC1UXe1lrVbLqLns5haLxZBMJk0AdD6fRygUMipuIBAw4TSMLyTJMZRlMGSF5EGi9vl8aDabCIVCI+2q9uugsigomfIcDhLfYavjw3ASqqU4TBm2SssHnfYoLrfX28v2GYPHfQAwJZ+o8t13333Y3NxEpVIxXkzmxQLoy3qg04EhJ51OB9euXTPqK8e+ubmJbreLWCyGRCKBRqOBYrGIUChkiKdYLJqCA81mE4lEoi9ol2WqqtWqGVswGDQ2wEajYaQgnqtSqWSILxwOw+fzIRKJmHPLc0G1MRgMGg8yYwEB9MX6DdoDB8EYv1n3+B1FfEwbPEopbI7wHB4Am/CY8UAD9bS2T8KjPahcLmNjYwOlUgmhUAi5XA6XL182RQSY3sWHKhQKmdAQ2uhINIuLiyaej02BaA9ketn999+PbreLSCSCdDptYvwY9MyCBfl8HvF4HAsLniWHAdGhUAidTgelUsmEx7A6CwBDAH6/30iM+XweiUQCfr8f2WwWwHY1aZK3fY4oJdm9gRuNhnGC2OvahDzo8eWEMutMCpv4KG3TSXTQITajMHIUIvJyAL+iqvlp7UxEngogrqp/Oq1tOkwXNtkNqlqMhZuWhEfPJTMjmPtKFbVQKKBer5uxRCIRqKrxxrLgQKFQQLlcRrlchoiYdxIDySmdTqNaraJWq+HatWvY2trC2bNnEQgEcO7cub7+GMzdZRZGsVg0ZMZsDAY1U3Vut9vIZDKm/DzVUdrhWq2W2U61WjXrkgzouBhWIIDVX1jbzw6HsW159qQ0uJ2DSiOjVE1nEB0cR8H5Mk7WfDmAe0TkVSJyaa87EJGgiHy7iHwYniPja/e6LYfJQMP3JGlgVEVs6W4W5d3tfVCqYu5qNBpFuVxGLpdDs9k0lVMoOWxubqJQKBhnBktARSIR+P1+5PN5lMtlVKtVY1ynvc4mzVwuZ8rD53I55PN5rK+vo9FoGEKj5EWyqlQqqNfrKBQKJsvi4sWLmJ+fBwBD2iTLer2OSqWCUCiEVCoFv9+P69evY21tDdeuXevLrGAOL9XjwcmFam673UalUjFSFH8nuZBc7U5vgyX0mYZXr9dNn5BpIxAI9DVdstPlDgvj5MwXwCO9lwD4KRH5GIB3AvgwgE+pan3UH3sE+Sh44SvPwnbq2TsBvHUK43bYBXiD2TFhO4EEYz8ozFO1QzSAvUl4gzanbreLZrNpSjaxtt3a2hpKpZLxVNoqnM/nQ7lcNp8pPXB7JNJCoYBOp4OFhQUjnbEpOo+nUqngK1/5CiqVClKpFMLhMKLRqPEG05YHwKio9LrSo0x1u1qtYnNzExsbG8hkMubcAzBe6Gg0imKxiBs3bqDRaKBSqSCbzRo7I+13tgpoOzL8fr8JhWE4DG2CwM6xk7Tp2Q4GLrdV0ml5WmnLpO2V9s7DwkjCU9U3icjvwIu7+zF4nboe3fu5IyJ3wat3lwPQhFcXLwOvKCh7Swi8WnnvA/CzqvqJ6R+CwzDQG8lZnQ/4Tp4zW9oAYG5OSlcM1gX2R3j2/ur1OvL5vFEVGWjMUk6UXAAgGo32dSyrVquGFBmsa+fLrq+vI5fLIZPJ4KabbkK320Uq5TWcy+VyyOVyRv28cOEClpeXTUwgAFMuivm2PK+0rTFu0K7rxzp7DHvhua/VakZq7HQ6uH79upG0MpmMSUejR5YGf9smxwmJgdYsY2WTtP2yvba2NMjQGDu0hDZC2kJpQ7S9v3ZmjE2I48iRUitJj+02DwNjLYk9Ke51IvJ6eNLa8wA8BV6LwlvH/RXAPwP4AwC/par3j1nXYcrgTQxs24Wo4nS73T7vIYmNSfgkSaohzG+1A2b3o/4M7qPVaqFer6NcLpsqJKx/V6lUkEwmjdGeSfoMQSkWi4hEIsZLy8optIuxNBTT1RqNBs6ePYuzZ88imUyi0+lga2sL+XzexNmFw2GsrKyYc8VzmUqljETUarUQi8UwPz9vvK8kRfbeoATIGn/1et2Ey7C0fCAQwNbWltlmJpMxRMdUOh4fySYYDJrKLY1Gw9gyW62WkcyGVT3h/wdzenmPUComAdtlqSjZcht2Tu1gCIxtT7RDZbgdkv9hkd6uXCfqFQp4D4D3iDf6r4Yn7a3AC0QOAdiCJ/F9EcDfu4DjwwNJzFYFGcvFh4NSBsnL9poC2yEfXMbYONves1cJzw6upcRSLBbRarWwurqKjY0NVCoVo7byYaRkxXSxeDyOxcVFo64yvo0FCCjNzc3NoVwuo1ar4fr16/D5fDh37hxuvvlmiAiKxSKazSbuueceMzEw1i8SiWB+ft44TexzWC6X4ff7EQ6H0W63EYvFUKlUUK1WjeMkkUiY0JN2u41cLmcyOkiKXBaLxbCwsGAKjdrxbDw2dl8j+Q1eO3qQqbayqovtqeV/7GsyKKHZzgyb/GyCs80eNH3wf4NkZ+/LdvJw8h1WJmsWmNhXrN5d/uney+GIwXY6DFbrYEVfW3Lhbwz8tT2xtoqmvXxPu3rJXmdojo+SENPHWM6pXC73eWzpMWX5JGZF0CvKdKdSqWSCee3STSwsUCwWjUSWy+Vw8eJF46W9ceMGKpUK7r77bpTLZczPz2NxcdFUbEkkEiY0h/sCPGIgwVCiajQafWp4MplEs9k0x0xnBdVw/i+RSCCXy2F5eRnJZBLxeNyom9x+o9EwKjvPP72gHBPQL5UxM4TvtqRnh8XwfrElOBIdr/9gULrt/SWp0kE0SH6816i285zYv7E8/6xwNIJjHKYCSh4AhsY92QZrGuJpVOaDxRg0PhC2NAbA/N8OWZl0jJQM6CVkxzGOY3NzE/l83pAXjfTsb0HJRVWxsbEBEUG9XjdeXj7k9O4Wi0Vjt1NVU9WkWq3i3LlzSCaTJri3VqvhypUrhtR5nhhOQnWTqhlJibZGBgvTZsViCNFotM8JROcAS1LVajXjBd7Y2EA8HkcmkzHkx9xe2t+ofvLYKVnyWtG5ZJMZJzKbrOjQIllRmmSFadteZ6umtq2UxGiHMtmNk2zy47YAmH3YecQ8l7OCI7wTBDuMZJRHlg8LDeO8uWj3IpkQgx5ZW53ZC2zCo0rLoFqSyZUrV0xoCH8HYEiJElSns93FjEQIbJM9vb/0htLZQRsYPcELCwumYEC5XEaj0TBFRMPhMO69916jrtrhKoBHhHboBWvlxWIx1Ot15HI502Njfn4eiUQCmUzGlKGiM4OEQm8uMzhI9AxricViaLVaJk2NDhZbAqMU1e12TdYJCyXQ6WNL7PQCM93Nlg4JW4qj04Q2WNpQKekOBj1zIradJ1xGEwJVfBL6rNRbR3gnBHYIxDhPrO3143dg+4bmzUrViVLBoLNhcNkk42TQMR+eXC5nxl8oFEzeLB94Ww0HYKRR5qlScmV4iO3pZBgHidF+qMLhsInhs1VW2gBZh49VjNnPNhqNGqmQkiXPqy2R0t7J8VHFVlVEIhEzPkp5jPfjthqNBvL5vJGqqfJFo1Fjy7O96LwWkUgEkUgEyWSyL8uB15pETlK0i5tycrEbGw16eFkCnxMnCZDFHRh/ZzvC+F+bZGlvBbarXdumjVnAEd4JAaW7nWZHWzKz1yP52BVvOQPT6D4NwiO4farRfGgYKsLSSyJi6st1u12j3tXrdZMFQSM+SzfRYUDVttFomIY/dmknlpJitkY8Hjdl3Le2tlAsFlEoFJBKpRCPx5FMJlEsFk25d2ZwkMRDoZDxPgaDQcTjcWMDLBQKZntMn+M6yWQS8/PzpngCA67p4aVqSALlNYnH4wBgipRSbe52u8YjOpjfyutF1Z/HYedJ2+o9nSbUBij50YFkq9ecTChZ2w2Q7PuG44vH48bDTXKs1WrGGTQL1dYR3gmBbTAeB8asUWKypSZKUvbMzBg51noD9lc8gORAG1632+3rE3vvvfcagrMfLD58NmjzK5fLxvPJdSi18AFkVzPWzeODR+9wJBJBpVJBOp1GLBYzxQioCrM0FFU3qqIkGZaJp3c3kUjgzJkzpvoxCaTdbqNYLGJjY8M81AxViUaj5rhs+x7VUYbd8HxR+i2Xy4hEIkb9pVTr9/tNPjHDW2jzoyTJCYfERvWUExIdIpwoKH3ajg0SE+8rqqd0HFHF5nHZzjA7tjEWi5nxlMtlc62mCUd4JwC27c62u3Bmt5PY7a5dvJFtbyG9r9VqFd1u15RSV1WT+D4o4e0WtmrDB4r9aGln29jYMOOmemPviw8TbVG0d9lVeflgkmgodTGBn+okvYQ+n8/k51arVfM7KxtTYlxaWjIk3el0EI1G0el0kM1mjc2K4TAkJAYEU/Wm+svMDNrvWAyBjpFsNmtq/jGGb2try5Spj8fjZhxMNeN54IuqbSwWg4gY2yFzeKnCcgLidbFDYXjPsC1mIBBAKpUyZhNbauR2GI7D+4mqLwOvOaGGw+G+cltzc3MmjIexmZRipwVHeCcAg8b6wbxFSjtUA0mAfGCoSlD6m5ubQ71ex9ramnkoOPPa8VKTSni2x482Hz5w9GQynYwSJyUvAMYjSM8ox0JJyC6LTjWJqqaImKwHSq6UHOk15bEyL5fnQ8QrSNBut03VFACm6EG1WkUqlTLVW2yVnDa6dDptyIOTCW15lJS4vbm5OeRyOaMSMs5wbm4OpVIJqVSqj/CpQtvnlQRpq62hUAgLCwvIZrOmxBULLNCGa5snKL3atkoGa9vqKiclO9uF55DOGV5P205XqVRM5g4nZNpHmQ/NfUwLjvCOOQYTxDmj2hKRrU7ZdheiUqmY4FuqFVTR6P1MpVKGHDiLc9+7BcdEBwWdEnxA19bWjDrL9dl5jORm3/wsALq0tIRarYZSqYRut4tEImEM4iQCegXtwFc6MpjDy33yN9bXoxrG8JnFxUVDFNVqFRsbG30ODTvsp1QqYWNjAxsbG0in0yYNjOeONiyqx5SGABi1kCWheOyMt8tkMn2qIgBTHIEFDnjtK5UK/H4/1tbWjJ1yaWnJ9PIg8QJ4QLBzJBIx6jQDwu2qzzzHvAdpM2VpKI6RkidtnzQ1UA1nPUGOhy00HeE5GNjSHW0zdvwTDcp2qhKlO0p9uVzOqFx2kU2SRLFYhKoadQ9AXzjEJLDtd5SCGNdG6YpEQBsU1/f5fMaQXyqV+pwmVOWpqjMmj7FilJz48FBiIxmyKdCglMIwFrsAZ7lcxtLSkjkXPp8PW1tbRiKhVMJQG+bENptNU0GZUtXc3Jxx2vDY6Jih6sdx2QUEKCWm02mzHebiUgpOp9PGJEEzgZ3Gd+PGDSQSCePxpZ3Qzsml9BiNRhGJRLCwsICFhQWjjlPdpZpux9hxwuC1pf2TZgZKfjSjMGWPxEupkG01p4E9E56InAHwZAA3AYiq6iumNSiH3cFWQ0helIL4gADeA8l4LBIVyZABuLTf2LY8SoWsXnLu3Dljx7J7OuwWtg2PN7xteGf6F50nDC0BtuvB2RIajfEM7qVtjyq4z+czJeCZe8r8W1ZcsWMO7YmC5xZA3zrMCsnlciamLpFImO1Vq1XTLIheX9vzeeXKFeOcoB2L6/K82gHMDC4GtgstUHrkOaPTgzaxdDptpE3aAIvFImq1mvnOcJxcLodisWj+b3ttOUHw/mEcH5uOR6NRM8HYjoxwOGxsmZxEaT6h6ktplk4YFoqwawPy3iPJTwMTE56IhAG8AcC/G/j/K6x10gC+AiAJ4JKqut60M4Bd2oeNo6li2CotvZl24ChVUs74lHYozdgvVUWlUjFExH0C/TmZO8FOUyNpMe+UuamUGLhtPvi84SnlUKIJhUIoFosmMZ8PFcMeUqmUefDq9bqptUey4T6oZnF/TPi3yc5Gq9UyOb2ZTAaRSASZTKbvHBcKBUPYJD2eBzoz6HRIJBKmQAE9s7Sx0URBUPLrdDpmwqJa6Pf7ce3aNdNFjWSazWYNWbK0PWv78ZyyYANVZLt7G80FJD86TijBUmXld/4PgFH9qWlQMuZExLg826vL60/S4ySxX0xEeCIyB6/U09cDqAL4W3h9avuiBFU1LyJvBPCTAL4DwC9PY7AO27AdDiQi3mg2oXCm5I3Km65UKsHv9xpbb25uQkSQTCbNg9Dtds2Nz1l5Y2MDly55tWBtaW23GCQ8SnO0C9lJ7sB2iSs7pozSGSW4wR6wdDSw2U8+nzfETzuTHVtGQqL0yJQw2sns9Lph6HQ62NjYMKlrtkoNbBdhoPREKYbkYKt4JACeg1QqZaQtW4qy08Zoo6NqTjNBqVQy5enpraXKC8BMBpRYKeXb15TeXxIQSZie6/X1dZP+R0nQlgb5G4shcCKyYzztkBueN3pmOUnRvjkNTCrh/SA8NfYLAP61qt4rItcALA1Z9x3wCO+ZcIQ3VdgGeKoFjBUD0Be9zvVtJwNjyFgaqVarIRaLmRuWTgJKipR8CoWCKW5pe/YmGTdfNGzTq8hy7oPbs8Mn4vF434NDGxMlJ3pFGUdWKBQAwJCAXfXDDruw7ZX0JBK7PT6WpKfUSdsXw0NsOyOP345P437t4OJCoYBsNmti+zihkfRIFvQU2+FJNHNwuyRBltGiQ4HX2U4rpP3SNn/wfPF6UNXmtbGlPNp8mfXBY7DNFPbEwEmNpa4YvkJpvFqtYn5+firpZpMS3vfBq3X3o6p67w7rfgpAB8DtexnYaUOtVjOzvh1LNwx2JWPO+nYIAW+2QCBgbiJb1WKw7pUrV0xUO9DflpFSnh1DVa1WcePGDeNpozQ5CUgsNGoziJYqN8ma4EPFB55SH+1JjKejk4OSDL/TKM8mP1RZqXLRhkRVeBICHwa7lwXj+Zg9wJg4hoRUKhXjDKBKbZNPp9NBLpcz0hhDVGhD4zlkiAqlRNsTbpeQ4gS5vr5uHAjxeLyvAg7vKZKVLX2SSDkx2uvWarW+6st2njADonltmLJGFZzXPBKJYGtrC9euXTOEznhK5lbvF5MS3u3wSOyDO62oqh0RyQOYn3xYpw+0kQB4QIyTHfvGm8wOCeFDwhsWgCE7EhcdDLYnksZs+6ZrtVrI5/MoFAqGFPL5vDE+02Y1mCS+G3B9kht7udI+yPi7QfDBsMuPcwysksx4OT5U7PnAkJNMJoN4PG5CRmyCY5c0nk9b6iPZDhvXTsdKqcuu2kzJlhkWDGq2sxx4/TmBsYkQPewkDTsVjuXp2QAc2Jb0SB6cUKhW0inFIGaC9xDjLhmOQmmQDg5KaLR1MmyJNj561XkdeD8Gg0Ekk8m+svt28HWxWMTm5qYJheG5PAzCCwOoq9d4ezeIARjZ+8JhG5xN+dDbDxhVOFva4WcSI6UmYLsc+2CjF7t+Gm02fr/f2Js2Nzexvr7el2WRzWbNuouLi4b0bIP/bvJpbUnQDkkpFosmLMMOliZoc+LDn0wmsby8bCQneqbT6bSRkHmsJDYGMNMBwPNFibHZbCKbzZrUMNuuxWtBycn2fu/meOkwYrgQpWuSF+12lGZ4Huk4ob3L7/cjmUz2lZViE/CtrS1Eo1EsLS31BSDbkjs1AVZbsQPRmTo4mH5ml32iqs3GSMFg0GSFxGIx43GnREf12S4PxWboiUTCSISUxqnCxuNxY+PN5XJGYiwUCqZR0n4wKeFdA3BBRBZUdWPciiLyaHgEeddeB3eawFgjPhRUJeyEbJIXJSTaqjgT00NGFY/xUSQn3rDXrl1DPp83/+cDct9995km1FT7WKacsVKqahLtOUPvh/BYBsm2rw2eFx4LU4/OnTtnshlIDIwlAzxbns/nMwRNYq5UKsYxsbCwYMib0k8mkzHe1VwuZ6QLBjBT0rSzU3YCCwXYWS8ATHwfg21tCZbqqR0PyLHSjsn16SkNh8O4fv26SUnjMdlOJ5IvCYglrDjxVCoVY6vj/6laU7qiCYJSp50SF4vFTDoez439md7ljY0NI+GSvJPJJJLJpJFsKdVREmdlnP1WUZmU8D4I4AfghaT8wqiVRMQH4NXw7H3v3+vgdgMRWYbXWe2ZAM4CKAD4GIBfVtW/PmrbHQV62mxPF6Pm7bphvHko6dm5kFQzSZhUHZhqxW5ZzFe11YQbN25gfX0dAEzCO8lBREz4CKt60DYIwEgPuwEllkajga2tLRMqMUxl5ANBA3k0GsXFixf7JLv5+XljwwJg1EeSB5sCMWWNdiubRHnueJ4BYH5+3nQno/eYaqkt7Q2WdBoGNsaxU8AoGdvOC0p6dtYMpXvuZ3193ZAdSYwTTzAYxPz8PJaXlzE/P98XmE0JkmOgapxKpZBKpfqa9/A82JkXvD6sUVgul9HteoUfcrlc3/g5+VKjsOMdbbswQ1Dm5uZw48YNI+HRxsnahN1u11zHgya8XwTw/QB+WkS+oEMaaovIQ+HF6T0VQAPAr+xrhGMgIg8H8AEA2d6iIrweG88E8AwReamqvvaobHcc1tbWjMRiV/0Atgt6ctbl7MzvtNPwpmUogR3bVKlUjOTCVCM+bACwsbFhouHpGet0OshkvAZ0fNjOnz/fJy0BuwtNsR8+zvocA2uwDcJOLPf7/Zifn0c2m8Xa2lpfGA5tUKpqnCm0ZVF6YgcxSmlcnxIGHTpU0Wjw73a7JhSG6jeJgfY2kr9dWWQYKLWRwJipwuNjtgclMBL+4GRCswcdHwwxUlVcv34dX/7yl5FOp5HNZk36GbA9MdFhwWO1Q2poRxy0X5LMKSEuLCwY7y+vJSdtSsGMI+T47YZD7XbbxONRyvf7/chkMiZYnNI1J+V4PN6Xy7wXTER4qvo5EXkRgF8F8C4RuQe9lowi8ocAbgPwEK4O4D+o6uV9jXAERCQC4E/hkdI/Avi+3viSAH4GwE8AeI2IfFJV//Kwt7sT8vm8eYgZYwVs9weYm5vDxsaGMbIPq0nGh4e2GAbPshYbg3vX19extrZmJAn2kCiXy0ilUiZZ3y4JRNuMnSHBh2A3jgubHKkS8UEZ9X+mhNExMT8/35efaUtaJCk7DpGVhlnuiWMIBAKmQxolSEqNtPHZhS/T6TRKpZIJ2WH6E0mH4UAkcJLgKJAQ7fpy3BaAvvQ6BlnTM26H0lA6pX0N2HZU3Lhxw8TDsbQXiZuqJW2InCToMaZDgfehPVnZlV4GzSh2sLpd8YYB1gCMlGwHiNPm3O12TTl+tststVom1zsWi+H8+fP7isnbSxOf/yYi98GT3C5ZP3279fkyvNCV9+x5ZDvjBQAuACgD+BZVvdIbXxHAi0XkFgDfBuA1ACYhplltdyS63S42NzeN6E511jZA84HjzE/phb/Z9iTmPrKQJNWCSqWCra0t3HPPPcjn8+bmKxaLRj1kf1hmIpCQGIlPyYk3KSuV7AQ76Nj2PFK1HAZKeCJiVDFKFFQx4/E4EomEkQIYNEwpgyoupQvGoNkxh1RbRbyyRtweibFQKBgvOMM4KLWQlHi+KGEDeIDzaRDDnFM2AXKc9GBz8qPkZZMGpVSSNHOgQ6GQKVfPiYKTBSVI28bH42aKWjQaNWPiMQ86Wjhp2JKg7RW2J2OaJ2xJ2fZIt9teTxOqr5xweZ5XV1dx663jOsSOx55yaVX13SLyHnhByI+H167RB+A6gH8A8NeqOpkff3J8T+/97SSlAbweHjHdISK3quoXDnm7I+Hz+fDpT38ajUbDqGk0xNte0GAwiFQqhatXr5oHnrY2PnB2jBRnUnpsWXF3Y2PDEBd/CwQCuHbtmiEUBs9+8YtfNDZA9npIJpPGwwfsXsKzb/qd1Fk+AIziz2azxjNr25hotLfTrUjGN910kzF+b21tQVWxtrZmJCsApt8tQyko4TCsp91um+Vcn1IqTRA8Nk5QdCZQgrXP1U7nyCbAwbQ2XnMAD1jPnvBsMqRKT4mfv9sxdZT0eO/wRbJLJpPG7mcXNeAxU03mu23/Y/wc90H1mvcq7bmMFmAc440bN4wXlxNfLpc7HMIDTK/aD/ReBwoRSQB4RO/rX4xY7SPwHA0pePbEHYlpVtvdDe6//36jJlJistVZznb2jcmZebAKCkmOKBaLprUhK6PYoS0MlbDTnmi/Yn4lb3Z2E+OsTpLZDfiQMItgMHnfBlUqeoeTyaTZBjE/P28a8JCAOp1OXwDsYA4uQ1NImlTzWG598Lhpa0omk4YAmBXCTmGsGsIH3SZeFj2wqzrvN7h5J/B62HGFDM8ZB9u2aS8DYNL67HJbvPfsc01C473EmD9K6vY9xnvYdqwwi4OtOWmXDQQCSCaTJntmrziu5aEeCoBxEJ8btoKqdkXki/Aaht92yNsdC1U1/VB589BWRdIjbKcGjdC04/E3qim8meiltJvjcL+8yQCYQpXFYtEYpjc2Now6nU6nUSgUTAkhO25up9AUO+iYwaj0nA4DVUi/32+6ddkqIHtBLC8vY2NjwyTzR6NRtFotpFIpcw4ymYwJQyHhUsK0MyL8fn+fF5xqG21g9ORmMhmT8M9joQpqN+/hOyVZhvjYPTqOEoZNXLbtbSfCHIQdV0hV25Yoge0MEPv+5f44udO5xDS1O++8c8/HeFwJb8X6fHXMevxtZcw6B7HdsVBV0wiaF94OCeBsSHKgZGerJPzOZZTCmNbEuD7amWw7VrfrNcdhnF4ul0MoFMLZs2exurqKy5cvG1Uyl8thfn7eeEI5/t0QHsmGDz8dEsNAqYDqJgBTHJLexfn5eRPSwp4XLIHU6XhFS1XVSHGUAO0EdZ/PZwiVoRIsLc59sPgnnSC0NdkTjV06n+eXUh8rhHAfDGymHcvOTeUkxXN2nDEse2O/ePOb34w3velNe/7/SMITka/seav9UFW9ZUrbImLW53HRn9Xe+24L409tuyLyfADPB4Dz58+P3Skjyek84DI7vs6+YSjd2WRoEx5VBjo0aEymHYreYKqCNOonk0mTaRGLxbCxsYEHP/jBALa7mrXbXulwO08XGP9w2r+ReO3ad4Pg+BkiQamN24lGo+Y3qj708lGNTKfTRjrmJEAVmTFoNNLzvDBlK5vNIhAIIJ1O96Uz0cHDclTVahWFQsFMBqwxxzHQUUSvLaXiUCiERCLRpw7TpkpbqcNsME7Cu7jDfxXb6t+432YxTc2mS+8Ut6uqbwTwRgB45CMfOfYcMEmcEhNVVRIY7XK2BMeHn3Y4OhZIhFT/GOLg9/tN3izDTWhfoWQCbBuTS6US1tfXkU6n+zI9AJg6b/xO6W3MuTDHZXtoR4Wk2LY3hthwnCR7u5Yc82VZ8p3J6XalXUpdzD0luXFsVNtZOHNlZcUcO69Rs9k0BVDtar8st8VKwpxESF4kPnqlmfLHop/MaWVOKv9jBznTLminCjpMjnGE95QRy2+BF4AcBvAHAP4GwBV4ZLECr1bec+Dl0P4EvEKg00bZ+hwBUBqxHgN2yiN+P6jtjoWI4OrVcRr0/jGoOrI6y2COLj239GhGIhGzzC7DZDfIBnavftHYXygURtqw7PQtBliXy+U+RwbHZeeFMkr/zJkzfU4EOx81nU4bNZW2oWaziVQqZQzwKysriEQifdVHfD6f8VZSEmNSPCU7erxLpRIKhUJfOiAJjFWXKRlynJR67fg1m/i4T5oFbGIcLCaxE2y12V7GScaODOC9wTp93NdxxUjCU9W/GVwmImfh1blbh1cP78tD/voWEfl5AP8bXnrZI4ass1/Y7LAK4Isj1lvtvV875O0eSZDICD7wzOFstVq4//77jeTBTADbAG8/iKNg/8awF0o1g7BJmJ85Vj58dlFMOw+Xtd6YX2yvA8DUZSNpJJNJ43ywg29p/7Q9rnZsHAOTWdzSbklI0mNRU9YbJMl0Oh0sLS0Zc0OhUMDW1paR+GgrZFwdXyR1Er5NoLZKb5d4ssNYeH0ZSsLcVF47mkRsB5ld5omTgy1x8poA6JsYSL7831FyzkzqtHg5vBSrbx9BdgAAVf2KiPw7AB/q/ecFex/iUHwB22rz7RhCTOLl8zLr4/OHvN2xGGfsPyiwoggDle2IegbS8jPVRIZtsJbbKNgqrf2gDws4tlOrGCTLskMMUWGQrK0usq8EwyboQWXll1gs1kfm2WzWOB5isRgWFxdNiXaSJ8dux4vZUhbtp3Zz7m6322cGsCVajoUmCTpQ7GKs+Xze2AkZFE3bKSVc/t/OWGBoEdV8vuzcVU4YBKVkEpLtDLNtwpyYBic2kpxNaDxf7XbbEOZOE+Ik2G3O9ihMSnhPB1BW1Q/vtKKqflhEygD+9Z5GNn7bJRH5OIBHAfgmAH88ZLXHwIuVA4BdJfvParvHDfQWM+CThJfJZEyWA5ta2ylRozy1dmyXnU42SsLjA8fMABINM0wYosKIfQYpr66uQlVNcQASYjQaNTnBPp8P2WzWSCuxWAxnzpzBmTNnDJmwzDvVZabzhUKhvlJPAAwp2NVObAmIJM+eEpRwSba0T9LbzKIKa2tryOVyWFtbM53HeEwEnTccSzqdNuox7YR2mhdVYZItJTteI0qv/E5pzVaBeex2CBQJ2HZg2fF3dnC2LSEOkhe/06QxrBrNs5/97J1v4DGYlPAWAexKPhXvzvf3/jMLvB0eMX2PiLxCVQfVyxf33j+hqqNU04Pc7kgcFSO0nYBPzyMlCUoizI202ysO5lsOwn4QqtUq8vm8SRgfBNVQpi/xwaRURWcDA4A5noWFBZORQQKp1Wqm+KcdzkK1Nx6PY3FxESsrK33BwqxWQ6nSjpmjg8cOXiax2k4lOli4Pp0ljMmjjc4u4cV9Ly8v4/bbbzfFWDc3N3HlyhVsbm6iUCiYoGjbiUHnFh0xLKhAkqQEaOe62nY6StXcHq+rnbNNdRhA3yRnZ20AMAHyXGcwxs7+P88pzz/vJXq3eY5SqRQuXbqEZz3rWfu6x/daD+8ZqvpnO6z7zfAM//fsZWC7wP8C8CJ4ea/vFZHvU9XP97IlXobt3N6X2n8SkYsA7u59fZ6qvmUa2z0JKBaLSKfTpuAnw1UoVZDkeJPzZrSDSAdnbVvaoQrHWnbDYNvvqEq2Wi3zQNLWRk8vA4cZS0epiuqd3eM1HA6bPq7JZBLpdBrLy8smrQnYzizgZ0o5tlOADz5VRDvwmOtR0gL6H247WJznhqqvrW5SMmJq1dmzZw3RXblyxfTBpZPEtucxBIlElUgkoKpGQqeDhNeYaLfbpso1z+NgVg9BkrIlXC4bRm72OaBZgueS73Y6Gs0W3EcymcQTnvAE3H77/jpGTEp4fwzgPwP4TRH5TlX922EricgTAPwmPHvYH+1rhCOgqjUReRY8tfIOAJ8TkSK82Dhfb98v1Qkrmsxqu8cFbHANbNfoIygpkKwYS8cHd5ikymUkB4ZljCK8QZWWkhVtSmwGw0IGi4uLOHfunLH3zc3NmaIJzMNlXnA2mzXSTyKRwOrqqql+bO/DBqUfu0w7j4sPKUsgkUCoPtrvtkSlvWBoSrC0m/L88jvDamwHTiqVQjQaNWX4aetj+podr2g7FahW0onDrBC7FiH/x2tsT1QkT0rg9vq2esp1+d3OtrBts/yN55jnuVarmYop3W7XtBe47bbb8MQnPhE333zzmLt3Z0xKeK8A8Cx4oSn/R0Q+Ai8shd7NVQBPAvA4eIb/fwHw8/sa4Rio6qdE5GHoL9S5Ca9Q5xt0j4U6Z7XdUeDNbJMAbwY+WIyT441oz7CEfXPz5qSEYldQJjiD2qqlPePTkUGwnh5nfRKeLeENwn44GKw7LqXMdlbYdj67flq5XDaNYRYWFkwRTKpbDBNZWlpCLBbD3NycKQHPdVdWVvrqvgHo81COGpudr0xpathx2/1ZBycE25Bvx1RSTea7PcGQSOiNZRcvSofr6+sm1o/2O0qYgyl0rMBMCZ4SIkOTaEOjvY5qN7AdI2nff9yW7ZCxY0jtiYTboWbA/h6silIsFrG0tIREIoHLly+jUqlgcXERT3ziE/GEJzzB1O7bKyath1cUkScC+G0A3wiP2B47sBqfwPcD+AH1yirNDKq6BuCFvddu1r8HuwgwnnS7+wFnfHoQ7VLf/H0w6Nh+WOyZ0q5KYSd681UqlUwAMmdaO6/UJkTerDb4gDHTgqqgvQ+bhG0Jr9FomHCWUdkEtg3PJncSiN/vN/Yr9p7lb6FQqK+0EPMvGUwMeEUJVldXzXeS3TDpbhRI+MxDHlR3BzGo0g2eU67DrA4She1xtZ0Pg2YCVcWFCxeMlLexsWE8vpSEa7UaCoWCkUrZ8Y3B1qy4QgmR196WtG3vNYufUvXkMdn37OD9yW2Q8DudTl/f3rW1tb7zxxTARz7ykXjSk55kHEv7wV7q4a0BeFpPbf038NQ+OibWAXwSwDt1F55ch22wzLYdD0XSs43KdhlwAH0kaAeh2jMs7TK2ykDViTM4PZODqma1Wu37TmcA4Km/jMWjdDRIeLb0Q3XWrpY8CPt47HLtfNA4xlAohFQqZaQDhtBsbW2ZY2ZGRSaTgaqaRjd2Lw4e7zAS2g3svOedYE8KvF6Dy2xQIiWZD4b+2JKm7bi4dOmSydzI5/PI5XLI5XLY2trCxsaGkQBZdt520th17Tg+Wz1n4QP72OlN53e7IRFDUyg1MqbRnsDn5uaMem5nxvj9ftx666140pOehIc85CF7vkY29lMe6u8A/N2+R+AAEcHFixcf0KqRJMXvAMzNb5MAt2FLR/SykSBYPoo3ox1MzBuJDVgGDdk22G1KRIzRnKQEPNBxYT/EfADH5YpSiuCDRrKmWk+CpQprtwxk6ha9sel02uTU+nw+LCwsIJPJGELmcVJimzUGJb3B4waGx7rZL64zKC0NI9BEIoGlpSUAMPZASn7Xr1/H1taW8ZbzPyQ2Sma8r2yV2i5zNRhnx4maExawTYz2evb3ZrNpyM4m91tuuQV33HEHHvGIR+y7lwVxXKulnDhcuHDBNNXhDQOgb5YFYOK37Bgu2opIdswCiMVifcnurCvHqsb1et10zWIgbTQaRak0KqMOJgtAVY2KOhiLR9jqrPZCDYrF4sggZTuDwl6HqqndwyORSJjqw3SG5PN5ADDhKTTwd7tdLCwsIJvN9hHObm13B4FJCJckZEs844iP5MMqxp1OB7fddhs2NzdN+S8WMuBEyEKc3DYnhXA4bDrs0YvO62+bW2yzBK+R7ZzgbzRv2D132bIxnU7j0Y9+tLFXTgOHf6UdoKp4/OMfj3LZS83ljUyiGAwe5c1FQzHTkJjnSbtKKpXCgx70IBO/Rcnvxo0bxj52/fp1Q0ScdccRHrAdNsA4MZvwbBXWjl1jqMjW1tbYGnhc336Q6NHkuWJLRqp7LBhJqWR+fh7z8/OmTNTCwgLm5+f7iM1WAw9Cups1Rh3DIPHZUhbtmazpZ3t7i8WisbfavUzoyKDDg8vtCdoOKwL6C5LaPVjo8GLT7mg0imw2i4WFBaTTaayuruLmm2+e6oQ00ZZE5Df3sA9V1R/cw/9ODUQED3/4w01gqK3W2jM5Qwna7ba5CRin5vP5TFVd3myVSgWRSASXLl1CpVJBLpczcXS0ozGAl43ARQQbG2NbDpuZmXFhdq7oKAmPUhhVl1FgJWWqoSwPRYIFtrtrcZ+0K3Y6HUSjUSwvLyOVSsHn85madra3GTha0t0sMU6NBmD6ytq5xZQI2+22kf6YUmhnatjB6LQL2qE4QL+qbjsx6DmPRqOmoGwwGEQmk0EqlUI6ncbCwoJJp5sWJr3ad2J8WSigvxyU9L47wtsB8XjcBItSTWUoAQmDNyDVQtro2Je11Wrh2rVrJhWs0+ngvvvuQ7VaxfLyspl9K5WKSc8CYIp/0pO6E0iWlNroxCABUQUaJuGNkx7tEBHaG6m22i0q/X6/eRAombDqyfz8PFKplCkrlc1mTbFPgranwWDa0wpmg3CyAWA0hoWFBaNlMEOEEyWvC+8bTmj0+tN+bMcH2hNiPB43bSQ5OdE+nUqlEI/Hpz4hTbq1n9vh9xS8tKx/BWALwP8AMOtmPsceqoqzZ88a6Y7eQ8Y82SEZVCOpcqh6DWXOnDljsg2uX79uIu+vXLmC9fV1k0Naq9WMbQ+AKZlE0lhcXMT6+jpyudzYMdN4TUcBPbWjQjQ6nY4Jkt3pXHCMtEPaOZ1UYyktMAWpVCohmUxiYWEBi4uLEBGk02lT6t3Gfj2zJxWUqgcdVQyZoa3Ybp7OyTmZTJowF6q8dmiNHQRtZ3HQbMFsF6YPUuqbNiaNw9uJ8AAAvVi9d8ErDfWMPYzrVEFEsLi4aNQEqgasrkEvKJPYV1dXEY/HTfvAfD6Pq1evml4Oc3NzKBaLxnlxzz33GGcDQwji8bhRQ9jxnWEKkUhkR8KjDYaqjk1ugwGoAEw6E+2Uw8DcVdogWeGEKhJjxpgkTwnPzkKgVzISiSCTyTxAQqAECjjCGwbGcNLOZttk7WBqW+JjUQQ6NZgOx+2NUqvtwGqSLMONpuWVHcRMDBiq+iER+WEAvw8vL/UNs9jPSYPtpWKpcNq7GCvHaHg2luH6jLWiGky14+abbzYpOs1mE7FYzKi8du5nJpMxjo1oNGrU03GgHY+EZ3tqBz20zWbTjGEU6LljbB2ba/v9Xq8Kel0ZLsHc31arhfn5eaTTaZNZkc1mH2C3A/qlu2nahk4aaD9ltovtOed9Go1GzcRH+zO9u5TG6VwaPNd0egxWY2Hu86wwS4vtuwC04Nn9HOFNAJZmop2E6iZJhfYV3hhnzpxBIBDA5uYmtra2TPkjVhJhTw2qyOl02sT8FQoF0zuURMnKwTt5a3mDsx/DoMoCbBMe1d9hJX8ISmyhUAjxeNzkmrbbbWPPSSQSJk2OHe9J/uxFkclkkEwmH/CQ2TbCk+6smBZs4rMLktrkF4/HkUqlDPkxtc8uR0UzBMOOSKQMXOYkZwfVzwIzu+qq2haRJoAHzWofJx2Mgifp0bZHDxpj0+zqvCQXxp8x53RpaQlXr1418VisqLG1tWU6kDHbIhwO74rwmBdr99O1cysBGPtbo9EwRTBHgfYcxthRGqONJ51Om8bZzWYTiUTClItKJBImuyKTyQxVV+0xOeluMtDzTzutndVjh0tRpaVUT8cFCc4OSaJnlvcwMPuJaGZb7yXfxwGMNwY5jAXDLwCY8j9UQzudjrFz2aV5crmckRKZhB4IBBCNRk1wczKZRC6Xw9LSkom4j0ajaDQapuvXbgiK5Z5sQ7TtuGC6E/vijgP3HY/HTQXhVquFRCJh7EcA+jqUsT9FIBAw8Xej7D9Outs/7Owf3oOD+bH2xMKAeLsCM7CdfcE8XDsPfJaYyZUXka8D8BZ4ISkfmsU+ThNYB46zJkEiYaObeDyOer1uJKNIJGIqxzLeamtryxBjMpk0jgCm9diveDy+o1eVhGeHx9ggEReLxR0Jj0HFlE4ZV0d1iAHPdi4tO3/F4/GxcVsnLdD4KMAmPwB9xDcYf0dnmB0/eRhS9qSBxx/YYZUwgHPwyikJvP6tP7unkTn0gbMlY6Ls4gBUR5mHSElNVc0MSvtcuVzG+vo6ABibF5vRdDod0+IwHA6bYpvjAoXZs4GOi2FVbZmzy1CYcQgGgyZDwq6fFgwG+9T5bDaLxcVFc26Wl5eRTCZHel45CTjpbnaYJCf5sEwKk179J0+w7scA/Jiq/tOE+3AYAXpgge22iwzlsNOw7NaAoVDIBCqvrq6aRG12IAuHw4jFYtja2gIAUzMNABKJxI7hKQw1GeaMsKu4FIvFHW2CdjHNVqtlyrGziADj7hKJhEkdKxQKWFxcxJkzZ0aqsrZ90YWinG5MSnjP2+H3NoA8gM+o6uU9jchhLJhzyKrBlHrsLAQ7no2SYLVaRSqVwsrKiglIrlar8Pl8RjJisHMsFkMsFjNq6rjYuXq93tdEHECfykLCo51wHFi7jo4T20NIT7KqYn5+HsvLyyYFamFhYWiAMeECjR2ISQOP3zqrgTjsHky4Zt+Ger1uJL9QKGSCl+0KKCwQkMlkMD8/j2aziVwuZ9TVQCBg7HXJZNI0i2ahzFFg20VKlVRrGShMFXxzc3OsakwPH0mLuZb8D0uhz8/PI5PJIBKJoFgsYnFxEdlsdmRUvgs0drDhrLfHFCJi8kwZtgJsx/AxN5I9XSn1VatVk4WQyWRMUCilp3K5bDy68/PzOwaB1ut1U7zRLkXEkBp6cguFwlgJj7FaAPrK09se2WAwiMXFRdOUhilI48p+u0BjBxsTEZ6IdEXkygTr3y0iLpd2RqCkR3JgMUfWvWOcHiU9O5yFCdoM2mVhTGZOMEE/Ho/vmNPIogDDPKHdbtc4NsaBUfkA+mIF7eDVTCbT58VlGaFx0p0LRXGwsRcJb9Jp0k2rMwTDSvhAszIFw0pSqZQpoUQpj3m5DPsg8THXlnmRkUgEiURiaIqWDbZvZGkhG/Qi0ykyCnaeJr8zmZzByAwuZrtF9nodpaq6QGOHQcxapQ0BGF7e1mFqoHrL0ths8kzJLZ1Om4oizF2kKkwVOB6PY3l52eTrMimfSfzjUKlUsLGxYdRPgvazjY0NbG5ujt0GY7TYVYu9NijFkuzYkIfltGy73yCcdOcwiJkRnogsA1gCML6apMNUQOcE80+Zzzg3N4czZ84Yu1exWDTeV+apkkioMgIw6qTd8WsUSqUSNjY2+jqY8b3T6aBUKpnYv1FgTiUAU5mZndQYW5hIJLC4uGjq2zEDYxjs/qgu0NiBGDv1iciT8MDYu7iI/My4vwFIA3h677PrXnZAYBwbnQ8MO/H7/Thz5ozpWEabXiAQQKlUMiWjUqkUMplMX/Va2gPtgp6DYEVc2unskJR2u40bN27sWDSAQdOsFMPKKbZjgnmyzLRgJeRhcKEoDsOwk6z/FAAvR38V41hv2ThQx9jCzkVDHaYIkl6n00G5XDYOAObHMpFfRBAKhZDP51Eul5HJZIyqyGKPVIvT6bT5PgzVahWbm5vI5/MP6G/QbreRz+fHpqiRuOiooHc2EokglUphcXER0WgU586dM13NBttV2rCdJ47wHGzsRHj/BMCOvfsBAHUA7xjzny6AIoDPAXiXqo433jhMHXNzcyZwmJ3J2OyHDW+YohUIBFAoFJBIJJBMJg3hsdAAU7zS6TSuX78+cp/sVWEXDmCM4Pr6+tjg5bm5OSOZ1ut1kzHCNouU7BKJhJHc6J0eBme7cxiFsXeEqr4bwLv5XUR+AEBBVXfKuHA4ZLC+Hctx0wtKMqGkFAqFTNn4c+fOGWeAXYiU5dbHoVAomJaNNuExd3eUOgxse5qZUUG7YTKZNGEz586dA7Dd2IjlxAfh+lU4jMOk1tynAPiOWQzEYfpgmAlBErCLMc7PzyMYDCKXy6FUKmFpackENDO+jRWWx6FUKiGXy5mYOcAjn/X19bExeHbMHaXDcDhsgp7T6TSWlpZMFWYAps/FMMJztjuHcZiI8FT1b1T1H2Y1GIfpglVG7OKN7FZG8guFQqafxvr6urGbsWoJSSUWi40NQM7lcqZjFTMuut0url+/jrW1tbHjZOkr1sNjlZb5+XnTmAfYLkfEatCDcGlkDjvB+etPMNhXgKRFtZY1yZrNppHyIpEI8vk86vW66Q1B1ZYByvPz8yP3Va1Wsba2ZhwXdFjs1AHN7kLW7XaNVBeJRLC4uGhaLvIYqMruJN25QGOHYRhpwxORr/Q+3qWqTxtYNglUVW/Zy+Ac9o/B+DYWEiiVSvD7/cYbms1mUS6Xsbm5aaooM1SEZdXHSU1s9J3P5038XbPZxJUrV8ZmWZDAGDNIT/Hi4qIpFGBXc2YVlUHCc2lkDrvBuDvjYu+9PmTZJBjf+sphpqAqy9g7EUEymTQVVShZ0QtbLBZNIPLGxoaR8EiMo8DgYmZzAJ6au7a2NlbCIzl1Oh2k02njNMlms6ZdH8mNJOykO4e9YhzhPaX3Xh2yzOGYgORA6Yxl1FlogHXwEokEYrEYNjY2UCqVTOwe1dl6vW4yG+xGLDaovjJ97fr169jY2BhZFsouXc9ipIlEAtlsFgsLC4hEIibVjWlkdLbYsFsIOunOYRxG3h2q+je7WeZwtGFXLrELc7KSMEmPtea2trZQq9WMNDU3N2eKgqbTaUSjURQKhaH72trawo0bN1CtVo3nd1wOrU2eoVDItFdcXl42trtOp2McGQyrGZTw7PLtTrpzGAfntDgFIEFQpaVzgLF2dGyw0Q/gBRJT+mImRDKZRCKRGLmfXC6He++9F+vr6+h0Orh8+fJY+x3VbcArHjA/P29UWdu5Qq+tfSyE3QPXeWYddoIjvFMAuzadbTOjmsgmPoFAAIlEwjTksSWqVCpl+l+MwubmJtbX17G5uYl2u43Lly/jxo0bY8fFgqHZbBbZbBZnzpzpawpOsrMrIdtw0p3DJBjnpX3StHaiqn87rW05TA4SHm1lbNoNwBQPBWAqm7AxN4OIm80mgsGgqZvHtLRBVKtVXL16FfV6HfV6HVevXt2xNaPf70c0GsXq6iqy2ayJ92M3+nA4PLJvqZ1V4Wx3DrvBuLvkg5iOh1V32I/DAYDeWEp5JIpAIGAkKQCG1Gq1monTo9MhEolgaWkJyWRypKp65coV3HfffThz5gzuu+++sWMi2S4vL+PMmTOmEU88HjchNKFQaKR0R/ufU2UddotxRHQZLqTkxMAmPKaXtVotU52EPWxTqRSWl5dNbiy7ntVqNROfZ7d1HMSNGzdw1113YXFxcSzhUWJLJpM4f/480um0yakNhUImn5bxd4MqK3N2nXTnMAnGeWkvHuA4JoKIPBjAEwE8EsCjADwcQBDAR1X1sVPaxzcCeCGAxwBIArgC4L0AXq2qo8uGHFEw/IQGftbDI3GwkGY8HkcsFsP8/Lypi9dut1Gv100hgZWVlZFkVqlUcM899yCbzY5NKSPprqysIJlMGrsdPcGRSMSQ7SCpsc4egJEFQB0chuG4To2vB/CsWW1cRP4/AK/sfe0CKAO4GcCPAXiuiDxVVT87q/3PAiQO5rnS+9loNEwcG2vSJRIJLC0t4erVqwgGg6YNZKVSMaQUDodHxtfde++9iEQiI+P1ABgHydLSEpaWlhAMBnHmzBkj2bHvBr21NmxV1lUzdpgEx/Vu6QD4ZwC/DY+EfmdaGxaRb8Y22f0igLSqpgA8DF59wEUA7xaR4W3ujzBsby0Ri8X64vMAmPaN8/Pz6HQ6xqYGeHa0VCo1Nq/2vvvuw9133z12LIFAwKSPsdkQ83fpVKFkZ9voKKXS/ujgMAn2JeGJyMMB3AGPBABgHcAnVfXT+x3YDvguVTXNgUTkZ6e47Vf33v9EVV/Mhar6ORH5FnhEezOA5wP4tSnud+Zgq0ZKc8B2tkO9Xjf2PVXF0tISrl+/jnA4jHa7bZL82edidXUVV69eHbqftbW1B3QvG0QqlcJNN92E1dVVhEIhI+VRlR0m3TlV1mG/2JOEJyLfLSJfBPCPAH4DwGt7r98A8I8i8s8i8pzpDbMfNtlNEyJyO4Cv6X39hSH7vR/A7/W+fs8sxjBLMKzDDtYFYJp2217cWCyGbDZrGubYebRURcdhXHXkUCiElZUVI+GxI1k6nTZOFBYqtVXWZrNpjsOpsg57wcR3jYj8CoC3AXgwvN4VawA+AeCTAK71lj0EwNt76x4nMFe4AOCjI9b5i977o0UkPvshTReU7Ow4OtrzbNWRNrVEIoFgMNhXAVlEkMlkTJ26SZFOp3HLLbdgZWUFIoKlpaW+frQAHkB2jAl0qqzDfjAR4YnIcwH8KDxSeyuAr1LVs6r6aFV9lKqeg0eEv9Vb50dE5LunPegZ4rbe+z+r6qia5J/vvQuAW2c/pOmC4R2DUh47l9nl0WmrowTI9LNOp4NUKoVsNrunMSwtLWFlZcWUcWdTHvbJpfpMsIMa4FRZh/1hUgnvR+HF5v1XVX2eqt41uIKqfllVfxCeJ1UA/Mj+h3lgWOm9DzdOPfC3lZFrHWHQLmZ7UekgYHUSqpQXLlxAMpmEqhr7WrvdRjKZ3FGtHYZwOIzz589jfn4esVjM2O5YyZhkRycJm3sPFjN1cNgLJr17vhpemMardrHuq3vrfs1OKx4hMFF0dBPV/nJZI1VaEXm+iHxcRD4+rifrYYC2PLtoJgAjxfn9fjQaDbTbbSwsLOD8+fOmXLwd1sLGOpNgYWEBD37wg01VZfaXJdmxoABT2hzZOUwTk3ppWwCaqjq8PpAFVS2ISAHbPWqPAzjWcS7GXWWfqOobAbxxkv8cJFgUtN1u99nuKF2pKlqtFjqdDlZXV02tu2AwiFarhWazabIydupZYePChQvIZrMIh8NYXl5GIBAw4SisZMzy8CTWYDDoCgM4TAWTTpmfApASkcWdVuytk4bnyd01ROT/isjakNeLd/73vsHmqdEx69jlQkY3Wz3iGCXl+Xy+PieFiCAWi+GWW25BNptFIBBAs9k0rR/Pnj27632mUilcuHABiUQCqVQKiUTCSHp+v98EQNNBwbE4snOYFiYlvF/q/ed1u1j3tfAkpl+acB+LAM4MeR2ER5T2udUx69i/XZvhWGYO2vLa7fYDqp/QhheLxQyx3XzzzUgmk6YvRrvdNtWJd0I0GsXy8jJWV1eRSCRw7tw5BINBJJNJ+P1+U4yUGSFUoR3ZOUwTE6m0qvoeEXkRgNeLyAqA1wD4e1VtA4CIzAF4HICXAHgqgBep6p9NuI+Lk6w/ZdAD+1AR8Y3w1NKTq/CCkI8t/H4/Op2OUSEZkGwH+9IrKyK45ZZbUCwW8elPfxoigmKxaFLEfD7fyNp3wWAQy8vLuHDhApaWlrC6uopkMmnUWIa62GWhHBxmgYnuLKtrWQvA03qvlohs9JYvAGDcQBXAi3oEOYij2sns//TeU/CKEgyLxXta7/2jqjq6w/QxQTAYNITHd1vas9XMTCaDRCKBm266CdeuXUOtVjMpYuxZO4z0FhYWjIS4srJi4u9os7OrGjuJzmGWmHQqvThkWRDDVcAY+u1dNo6cER8AVPXzIvIpeJ7l/wLg39i/i8gqgOf2vr7tgIc3M9BWZhOf/Rtr5rVaLZw5c8ZIZI1GA41GA7VazUhrg4QXj8dx8eJFPPjBD8alS5ewvLxs2i0yBIXtIB3ZOcwakxLekeha1kvct5sr0MkwJyK2Qak16FHu5d2+HABUddgT9lIAfwbgO0TkFwD8vKqWROQ2eEUKEgC+AuBN0ziWowQS3yBYhr3ZbGJhYcHUydvc3EStVjMS4NzcHEqlEu6//37z37Nnz+L8+fO49dZbce7cOVMkgP1nh9W6c3CYFSa14R2VrmXPhZfNMYhHwCtgQPwNgCdPsmFVfZ+IvAzAz8OT8v6ziFTg1cQDgA0Az1LVxqSDPq6gysmetpcuXcK1a9dMQ+9EIoFkMolkMolms4lms4lSqYRUKoWLFy/ijjvuwOrqKs6cOWPq3dHu5wp4Ohwk3J02BKr6ShH5CIAXwSsASqnu2BYA3S/sTIxQKIRz585hbm4O+XwerVYL4XAYq6urpmjo5uYmFhYWcMcdd+Dmm2/G8vKy8fDaebKO7BwOErJTGR+HqeDEnORWq4VarYZ6vY5qtYq77roL//RP/2QyI3K5HK5du4YbN27g/PnzeNzjHofbbrsNqVQKwWAQ2WzWFCoYpUI7OEwBQ20ke5peRSQD4BnwimJmsO2ZHQbt5dY6nADQgcHXysoKarUaCoUC8vm8ybWNx+NYWVnBxYsXTcByJBIxBT0d2TkcBiYmPBH5CQCvABDmoh3+ogAc4Z0gMF7O5/Mhm82i1WphcXER+Xwem5ubCAaDqNVqePjDH45bbrnF9LJlRRZHdg6HhYlUWhH5IWznh94FL27tOryS6yOhqj+31wGeEJwYlXYQtVoNxWIRrVYL9XodjUYD5XIZ3W4X586de4D6Olj6ycFhRhgqiE1KeJ+Bl2nwPwH8iDoD4G5xos8T08L4uVqtotPpmDp69suRncMBYSqEV4Nnr0ur6rFNnD8EnGjCI1iIgFVWGLTMQgUu1s7hADEVwrsPQExVR7eschiGU0F4NuwuaA4Oh4ChN9+k1VI+AK881IX9j8fhJMORncNRxKQS3oMAfAzA3wL49jF9Hxz6ceokPAeHQ8b+VVoAEJHHwmtV2IDXt+Kz2KEunKpenmgnJw+O8BwcDhZTI7wsgFcCeAF29yCrqp72/CFHeA4OB4v9Z1qIyFl4CfmXxm10Nzt2cHBwOGhMKnm9CsDN8Jpv/ySAvwJww9nydsSuSF9EPgugPuOxHCUswKs+c1pwmo73oI41rKoP2+3KkxLe0+CpZ9+pqh+e8L8OO6Ouqo887EEcFETk4+54TyYO6lhF5OOTrD9pWEoSQMWRnYODw3HEpIT3LwACvWY9Dg4ODscKkxLemwGEMNDrwWFqeOPOq5wouOM9uTioY51oP3sJS/k9AN8M4D+p6u9O9GcHBweHQ8SkmRa/2fv4bHj2vPvh9XIdF3jsCoA6ODgcCUxKeF14XtpJYutUVf2TDszBwcFh2pjU+XDaC3lOHSKyDOAlAJ4J4CyAArx85V9W1b8+zLHtBtMev4jcieEd6WxUVDU+6bb3AxFJwGtT+igAj+y9Z3s/P1RVv7DP7R+p+2BWx3vY13fSNo2O8KYIEXk4vAo0vJGK8AI2nwngGSLyUlV97WGNbyfMePwtAFsjfqvscZv7wTcAeNcsNnxE74OZHW8Ph3J9J/XSTgwRSe681umDiEQA/Cm8m/wfATxMVVPwmiL9IjyzwWtE5GmHN8rROIDx/72qLo943TKVg5gcNwC8D56m8/xpbPCI3wdTP14Lh3N97Q5U03rBu0hPh1dVpTKLfRz3F7yetwqgBODskN/f1fv9E4c91oMcP4A7e//74GEf48C4/APfL/bGqQBuPWrn8Qgf76Fe36lKeCJyu4j8Ajzv7Z8BeA62u5s59ON7eu9vV9UrQ35/fe/9DhG59YDGNAmO+/gngqqObVS1DxzJ8zjD4z1U7JvwRCQrIj/ay2n7NICfALACoA3gL+CVkXKw0DMIP6L39S9GrPYReIZrAHjqzAc1AY77+I8K3Hk8eOyJ8ERkTkSeLSJ/AuAqgF8GcEfv5/cC+H4AS6r6r1X1zdMY6AnDQ7Ed2vO5YSuoV4Hmi72vtx3EoCbAQYz/dhH5nIjURKQkIp8VkTeIyKWd/3pscNzvg/3gUK7vRIQnIo8UkV+DF2j8hwC+FZ6n90PWat+nqr+rqoVh23AA4EnAxNUx6/G3lTHrHAYOYvwL8AihCs8scjs8e9fnROTf7mF7RxHH/T7YDw7l+u5IeCKyKiI/KSKfA/BRAP8JnkfpswB+CsBFVX3yrAZ4QhGzPtfGrFftvR9ozNkuMMvxXwXwcgAPg1frLNv7/zPgZfVEAPy2iDxpgm0eVRz3+2AvONTrOzYOT0T+HF48jg+e6H0Znuf1bar62VkM6JTguFeBntn4VfUvAfzlwLIGgPeJyIcBfBzAgwC8FsDjZzWOA8Jxvw8mxmFf350kvKf11nk7gCep6kVVfYkju33DbmIeGbNedMj6RwGHMv6emeTVva+PFZHFaWz3EHHc74Op4iCu725teN8G4AUi8nQRcXmx+4dtr1kdsx5/G9sV7hBwmOP/aO9d4MWGHWcc9/tgFpjp9d2J8J4N4N0AgvDihf4MwDUR+TUROe7qxGHiC9juZHb7sBVExAfgIb2vnz+IQU2Awxy/rQYe925wx/0+mAVmen3HEp6qvltVvx2ed+iF8FJfFuA5Lj4kIl8RkVeKyNCL5TAcqlqCZ6sAgG8asdpjAKR6n49UEYFDHv+jrc/3TnG7B47jfh/MCLO9vntIDbkNwC8AuAKgC6DTe/Hzww4jZeS4vbCdUlQEsDLk9z/q/f7xwx7rQY0fvXJlY35PwotJUwAfPeTjv4jpppYd6ftgGsd7FK7vfk6AD16+7O/Dc5t3LdL7JICfhldG5tAu0lF+wTNS39O7uJ8AcFtveaI3ofDmetphj3Wa4x94cO4c8ttHAPwggPPW8mDvXvtM738dAE89hGNesF5fZx3HYwd+8+3meI/6fTDt4z0K13daJyYJr5rC3w0QXwfA5w/6Qh2XF4Cvgde7kzdIoXfOtHcOf+qwxzjt8e/igVDrVettv2ktq8ALbj+M49Vdvi7u5niP+n0w7eM9Ctd3KsUDVLWoqm9U1ScAeDC8ht2X4RkgHzL2z6cYqvopeAGYvwrgK/AaJG3Ccw59kx7hWnjATMZ/HcCPAXgHPNWmCs9+VYVn63odPAnod6ZyAEcEx/0+mACHfn0nbuIz0cZFngLg+1X1eTPbiYODg8MuMVPCc3BwcDhKmHnFYwcHB4ejAkd4Dg4OpwaO8BwcHE4NHOE5ODicGjjCc3BwODVwhOfg4HBq4AjPwcHh1MARnoODw6mBIzyHA4OIfFBEVETuPOyxTBsi8qresR3prCIReVNvnM847LEcBhzhOewavQdlL68PHvbYZwkRWYZX5ukeAEc9z/c18AoTvLpXXPRUYWwTHweHAVwfsXweQABAHdtNo21s9d4vw0saP2ktPF8Or+/E61S1fdiDGQdV/YqI/B6A7wXwXABvO+QhHShcLq3DvtGT4L4ewFtV9c7DHc3BQkQy8HpTKLzm80e+0Y6IfD2ADwL4pKo+4pCHc6A4dSKtg8OU8b3wGkm/9ziQXQ8fglex/A4R+dpDHsuBwhGew4FhlNNCRJ7cW35P7/v/IyJ/JSJbIpIXkfeLyOOs9VM9J8GXRKQmIveJyOtEZFyrQ4jIE0Tk90XkfhFpiMhmbz/PFZG99oilk+IdI/Z5p23H7O3r70WkKCLrIvIuEXmotf5Kr0nWPSJSF5G7ROSnRnULFJElEXm9iHxWRCq9/9zX28crROTC4H9UtQvgDwfGfzpwGJVU3etkveCpRwrgLbtc786B5U/uLb8HwH/EdsXsAvqr4z4BwCK2S4GXATSsdd47Zt+vQ3+13SK2qworvAbzvgmPe8H6/wP6UfTWubP3+wetMbR6++d/NwF8FbziufdZ42tb6/z6kG1fwLY6rb31t3rnj8v+w4hxfWfv938+7PvnIF9OwnM4SlgE8AZ4nsSsqqYAXALwD/DUxl8C8D/hOUieCK/vQwLAD8F72J8hIt88uFEReSGAnwSwDo9QM6qaBBAD8F3w+r1+N4D/d8Lx/qve+zVV3aln7NcC+HF43txUb/8Ph+fEmQfwWgC/C4/wvrb3exJebxgA+GERedjANl8Or6PgXQCeBCCoqvPw+mR8NYBXAlgbMR52S7v1BDQ03z0Om3Hd6/i/MD0JTwH81pD/nce21NIE8KAh6/xG7/ffHFieBlCCJ1U9esS4Htvb/hY80tjtcb+qt88/H7POndaxvXzI70+0ft8CkB6yzl/3fv+ZgeWf7y1/zh6vGyXoZxz2PXRQLyfhORw1vGZwgapeBvAvva/vVNW7hvyPPVsHpaDvABAH8Heq+rFhO1TVj8DrJZEBMInXcqX3vrGLdZvwJNRBfBheOA8A/A9VzQ9ZZ9SxFQfGMSk29/n/YwdHeA5HCXVsE9sgbvTePzvid8YIZgaWP773/hgRWRv1gidFAsBNE4x3ofee28W696jXeLsP6jkQSJiTHtv7eu+vE5FfF5Gn7OS4GQDHvTB2rRMEF3jscJRwXXu61hB0eu+jbGX8PTCwnNJLpPfaCdFdrEOEeu/NXaw7zsa312N7HTyJ9Fvh2Sb/I4C2iPxfAO8C8KYREiNByXISkjzWcBKew0kH7/E3qKrs4vWWCbbNDJL0VEe8S6hqQ1WfBeBx8Jp2fwSeTY7fvyQiXzNmE5QYN8esc6LgCM/hpIPq4G0z2DZV0UFV80Chqh9R1f9XVR/XG8tz4aXxLQJ485i/cty7sUGeCDjCczjp+Ife+9eLSHbK2/5i7/3SlLe7Z6hqRVV/H8Dze4seISKxwfVEJAqPEAHgCwc1vsOGIzyHk453AqjAi+N7/bgVe3mxk+DDvfeHiUh4D2PbF0QkOObnGlcDMGy9OwD44YXsfGrKQzuycITncKKhqpsAXtL7+jwReYcdwCsi4V7K2a9jm8B2i0/DCw2ZgxdYfND4rIi8WkQeRfITD48G8Gu9df6vqg7zIj+q9/73qtoZ8vuJhCM8hxMPVf01AC+DZ9D/TgCf6eWdbsGT/j4Ez8M5kbeyRxR/1Pt6GAU1l+CR+ccAVEVkE16q3UfhZXFswMtCGQaO9w9mPcijBEd4DqcCqvpKAF8D4I3wYv0EXmrZNQD/G8APA3jMHjb9G73350xhmJPiWfACtT8ML6c2Di9E5tPwUtVuV9VPD/5JRJbgZbeUMaLowUmFq4fn4LBPiMhnAdwO4FGq+vGd1j9siMiPAvhVAG9U1Rcc9ngOEk7Cc3DYP17ee3/xoY5iF+iVmXohPNX31Yc8nAOHIzwHh31CVf8Int3sO0Xkqw57PDvg3wK4BcD/VNV7D3swBw2XWubgMB38MDyb2iqALx3yWMZBAfwcgF8/7IEcBpwNz8HB4dTAqbQODg6nBo7wHBwcTg0c4Tk4OJwaOMJzcHA4NXCE5+DgcGrgCM/BweHU4P8HMJvuUjatm9QAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# This plots all the normalized single unit waveforms together. The total waveform length is 48 points at 30000 samples/s\n", + "# 14, 28, 42 are the 0.5 ms, 1.0 ms, and 1.5 ms\n", + "\n", + "# Generate subplots\n", + "f, arr = plt.subplots(1,figsize=[4.5,3.4])\n", + "\n", + "print(BlueCol + \"Plotting: \" + str(full_data.shape[0]) + \" Waveforms\")\n", + "for i in range(0,full_data.shape[0]):\n", + " arr.plot(full_data[i].T, c = 'k', alpha = 0.03,linewidth=2.);\n", + " \n", + "arr.tick_params(direction='out',colors='k', axis='both')\n", + " \n", + "# Set various x and y axes and labels etc.\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "\n", + "arr.spines['left'].set_bounds(-1,1)\n", + "arr.spines['bottom'].set_bounds(0,48)\n", + "\n", + "arr.set_xlabel('Time (ms)', fontsize=24);\n", + "arr.set_xticks([0,14,28,42,48])\n", + "arr.set_xticklabels(['0','0.5','1.0','1.5',''],fontsize=24)\n", + "\n", + "arr.set_ylabel('Amplitude (a.u.)', fontsize=24)\n", + "arr.set_yticks([-1.0,0.0,1.0]);\n", + "arr.set_yticklabels([-1.0,0.0,1.0], fontsize=24);\n", + "\n", + "arr.set_title('Figure 1E', fontsize=18)\n", + "\n", + "# Plot the data\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6amI4WSZeKkm" + }, + "source": [ + "# Figure 2: WaveMAP on of waveforms using UMAP and Louvain Clustering." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mY0jVoHj0XfK" + }, + "source": [ + "## Figure 2A: Computation of WaveMAP clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FjVdH1Ferdv0" + }, + "source": [ + "### First we construct the high-dimensional graph with UMAP\n", + "\n", + "---\n", + "\n", + "This function computes the UMAP graph construction and projection. Note the important parameters are N_NEIGHBORS and RAND_STATE. N_NEIGHBORS determines the balance between weighing global vs. local information and has been set higher than defaults (more globally-attentive). Random seed is set for qualitative reasons but produces no quantitative differences (see Figure S2) as this is used in the stochastic gradient descent in the force-directed layout procedure of the projection step in UMAP---we generate clusterings before this step." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "ZvUkLtpSrbnF" + }, + "outputs": [], + "source": [ + "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", + " random_state=RAND_STATE)\n", + "mapper = reducer.fit(full_data)\n", + "embedding = reducer.transform(full_data)\n", + "\n", + "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", + "umap_df['waveform'] = list(full_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(umap_df.x,umap_df.y)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy.random import normal\n", + "\n", + "noise = np.zeros(full_data.shape)\n", + "for i,row in enumerate(noise):\n", + " noise[i,:] = np.random.normal(0,0.11,full_data.shape[1])\n", + "\n", + " \n", + "noisy_data = full_data+noise" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.3266666666666667" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "0.349/0.15" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.3490391240013654" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(np.std(full_data,axis=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f,arr = plt.subplots(1)\n", + "arr.plot(noisy_data.T,c='k',alpha=0.05);\n", + "f.tight_layout()\n", + "arr.spines['left'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['bottom'].set_visible(False)\n", + "# plt.savefig('waveforms_02.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "reducer_noise = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", + " random_state=RAND_STATE)\n", + "mapper_noise = reducer.fit(noisy_data)\n", + "embedding_noise = reducer.transform(noisy_data)\n", + "\n", + "umap_df_noise = pd.DataFrame(embedding_noise, columns=('x', 'y'))\n", + "umap_df_noise['waveform'] = list(noisy_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f,arr = plt.subplots(1)\n", + "\n", + "f.tight_layout()\n", + "arr.spines['left'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['bottom'].set_visible(False)\n", + "arr.scatter(umap_df_noise.x,umap_df_noise.y,s=15,c='k')\n", + "# plt.savefig('waveforms_scatter_02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xKKPxxUVrlem" + }, + "source": [ + "### Next we apply Louvain clustering to the high-dimensional UMAP graph\n", + "\n", + "---\n", + "\n", + "This is the graph clustering that operates on the high-dimensional graph found by UMAP and occurs before the projection is computed." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "TuXXBsLbeKxP" + }, + "outputs": [], + "source": [ + "G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", + "clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n", + "clustering_solution = list(clustering.values())\n", + "umap_df['color'] = clustering_solution\n", + "\n", + "cluster_colors = [CUSTOM_PAL_SORT_3[i] for i in clustering_solution]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9CpooetSruMn" + }, + "source": [ + "### This yields our UMAP graph colored by Louvain cluster\n", + "\n", + "---\n", + "\n", + "Shown is a plot of the UMAP projected space with clusters colored according to high-dimensional graph clusters found in the previous step by Louvain clustering." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 355 + }, + "id": "edO6Ch3peIcI", + "outputId": "55d0721b-a733-4c79-b82d-c093c7184271" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", + "f.tight_layout()\n", + "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", + " marker='o', c=cluster_colors, s=32, edgecolor='w',\n", + " linewidth=0.5)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['bottom'].set_visible(False)\n", + "arr.spines['left'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.set_xticks([]);\n", + "arr.set_yticks([]);\n", + "arr.set_xlim(-4,12)\n", + "arr.set_ylim(0,12)\n", + "\n", + "arr.arrow(-3,0.8,0,1.5, width=0.05, shape=\"full\", ec=\"none\", fc=\"black\")\n", + "arr.arrow(-3,0.8,1.2,0, width=0.05, shape=\"full\", ec=\"none\", fc=\"black\")\n", + "\n", + "arr.text(-3,0.3,\"UMAP 1\", va=\"center\")\n", + "arr.text(-3.5,1.0,\"UMAP 2\",rotation=90, ha=\"left\", va=\"bottom\")\n", + "\n", + "N_CLUST = len(set(clustering_solution))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "v-MwbFeHrCk5" + }, + "outputs": [], + "source": [ + "# Defines a nice function that plots all the waveforms in long column.\n", + "def plot_group(label_ix, labels, groups_df, colors, mean_only=False, detailed=False):\n", + " group_ixs = [i for i,x in enumerate(labels) if x == label_ix-1]\n", + " group_waveforms = groups_df.iloc[group_ixs]['waveform'].tolist()\n", + " \n", + " f, arr = plt.subplots()\n", + " f.set_figheight(1.8*0.65)\n", + " f.set_figwidth(3.0*0.65)\n", + " if not mean_only:\n", + " for i,_ in enumerate(group_waveforms):\n", + " plt.plot(group_waveforms[i],c=colors[label_ix-1],alpha=0.3,linewidth=1.5)\n", + " \n", + " if not mean_only:\n", + " plt.plot(np.mean(group_waveforms,axis=0),c='k',linestyle='-')\n", + " else:\n", + " plt.plot(np.mean(group_waveforms,axis=0),c=colors[label_ix-1],linestyle='-')\n", + "\n", + " arr.spines['right'].set_visible(False)\n", + " arr.spines['top'].set_visible(False)\n", + "\n", + " if detailed:\n", + " \n", + " avg_peak = np.mean([np.argmax(x) for x in group_waveforms[14:]])\n", + " arr.axvline(avg_peak,color='k',zorder=0)\n", + " \n", + " arr.set_ylim([-1.3,1.3])\n", + " arr.set_yticks([])\n", + " arr.set_xticks([0,7,14,21,28,35,42,48])\n", + " arr.tick_params(axis='both', which='major', labelsize=12)\n", + " arr.set_xticklabels([0,'',0.5,'',1.0,'',1.5,''])\n", + " arr.spines['left'].set_visible(False)\n", + " arr.grid(False)\n", + " arr.set_xlim([0,48])\n", + "\n", + " if not detailed:\n", + " arr.set(xticks=[],yticks=[])\n", + "\n", + " if not mean_only:\n", + " x,y = 2.1,0.7\n", + " ellipse = mpl.patches.Ellipse((x,y), width=9.0, height=0.72, facecolor='w',\n", + " edgecolor='k',linewidth=1.5)\n", + " label = arr.annotate(str(label_ix), xy=(x-0.25, y-0.15),fontsize=12, color = 'k', ha=\"center\")\n", + " arr.add_patch(ellipse)\n", + "\n", + " if i != -1:\n", + " x, y = 23,-0.7\n", + " n_waveforms = plt.text(x, y, \n", + " 'n = '+str(len(group_waveforms))+\n", + " ' ('+str(round(len(group_waveforms)/len(groups_df)*100,2))+'%)'\n", + " , fontsize=10)\n", + " \n", + " return f, arr" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IWfn2ph106IF" + }, + "source": [ + "### Lastly we plot the waveforms for each cluster together along with their average waveform" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 665 + }, + "id": "3P802ziWshvW", + "outputId": "de9680c0-2e5b-4455-b109-cc5ccd4b157a" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "BSclass = [1, 6, 7]\n", + "NSclass = [2, 3, 4, 5, 8]\n", + "for i in BSclass:\n", + " plot_group(i,clustering_solution,umap_df,CUSTOM_PAL_SORT_3)\n", + "\n", + "for i in NSclass:\n", + " plot_group(i,clustering_solution,umap_df,CUSTOM_PAL_SORT_3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dMp537GB04g9" + }, + "source": [ + "## Figure 2B: Optimizing number of Louvain clusters\n", + "\n", + "This goes through resolution parameter from 0 to 10 in steps of 0.5 as a function of number of clusters and \"modularity\" (this is the cost function which Louvain optimizes; see Supplementary Methods)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RFAnsidu15wr" + }, + "source": [ + "### We calculate the modularity score and number of Louvain clusters across a range of resolution parameters while randomly permuting the waveform order. \n", + "\n", + "---\n", + "\n", + "**THIS CELL CAN TAKE 20 MIN**; skip it and run the next cell to read cached values of the plot.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IF8dr-bZ2PoI", + "outputId": "c677dffd-fdcc-4f1f-a0f7-6b517ef6b4d8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[94m0.0\n", + "\n", + "\u001b[94m0.5\n", + "\n", + "\u001b[94m1.0\n", + "\n", + "\u001b[94m1.5\n", + "\n", + "\u001b[94m2.0\n", + "\n", + "\u001b[94m2.5\n", + "\n", + "\u001b[94m3.0\n", + "\n", + "\u001b[94m3.5\n", + "\n", + "\u001b[94m4.0\n", + "\n", + "\u001b[94m4.5\n" + ] + } + ], + "source": [ + "resolution_list = np.linspace(0,10,21)\n", + "modularity_dict = {}\n", + "n_clusts_dict = {}\n", + "\n", + "subsets=[100]\n", + "\n", + "for res in resolution_list:\n", + " print(\"\\n\" + BlueCol + str(res))\n", + " for frac in subsets:\n", + " rand_list = []\n", + " n_clusts = []\n", + " for i in list(range(1,25)):\n", + " reducer_rand_test = umap.UMAP(n_neighbors = N_NEIGHBORS, \n", + " min_dist=MIN_DIST, \n", + " random_state=random.randint(1,100000))\n", + " rand_data = np.random.permutation(full_data)[0:(int(len(full_data)*frac)),:]\n", + " mapper = reducer_rand_test.fit(rand_data)\n", + " embedding_rand_test = reducer_rand_test.transform(rand_data)\n", + "\n", + " umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n", + " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", + " clustering = cylouvain.best_partition(G, resolution = res)\n", + " modularity = cylouvain.modularity(clustering, G)\n", + " clustering_solution = list(clustering.values())\n", + " rand_list.append(modularity)\n", + " n_clusts.append(len(set(clustering_solution)))\n", + " modularity_dict.update({str(res): rand_list})\n", + " n_clusts_dict.update({str(res): n_clusts})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o5Ge1CrX38I8" + }, + "source": [ + "### And plot both on the same axis\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 205 + }, + "id": "dE4TxUfY2YFN", + "outputId": "f8c39074-c65d-4305-8d4b-533e4b81bade" + }, + "outputs": [ + { + "data": { + "image/png": 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6d9fvi5bD/5QwloKXovsQYKBr258Dv0fv7lvAs65tA7xiOc6PU+4b7ad9y3F8L8mzjfwXo48lQIfY3hMdLWQiOgXXgelvKy1+1/zBKVNomjWL6unTW6z5q6ZVlBNjRVLIJuKq9TA8TUeR7ZhxaD844yDYuFnvNWzcogeHUtojZEjR7cdobjn6bD/bOZZCx8v0RTblb46G8U7NOQa4Lxrma2BeKILjp/Fcufq8OiehI5Ao4I1ouFWE37yoa/Cp/N66vnn2bJ3Jx7IIHnWUWe+XGYmOItvOfLYZQuos8IX3sgd7SUc5HB9ajtPuCXayNRgPRdjei6U/Fb3RlyBnrms/ufpCEcaipzz7R8Psjk5DVDAJ676+Pjb8QHcA3S69VH/ee2/YsKEtjzcUkWzKt+9Yrej3vQhn/V2/51pKvLNK7xu8swoiz0LPbtDgpm//n3kbzhYH17ZrXNuekOG7Ca5t5+XVl035rwAWA58AT0TDvA0QinAw/o76/OTqOw+4NRqmFrZuLhZMg6ttzHNZ96UjeMABxF59FdWYV8YjQ5mw79htJsfXnJx5WZGuozhpEuw4EH77sLZFKGNstCVfOs5CR/TxTcZpfzTMk6EII4DeCeX0WAz8yEfb6XL1pbqZ7AIQirAAvTS4Mhrm36kNhSKcj3e8GMzSXfk95kuHbLcdgVGjiC9eTHC//QprxFARJJYSoci2DuOg3eD1T3QEqAPGwQ/3gSrfq+cO40fA4Rm+uxFtJvxHv41lXUdEwzSnKD7RMPXRMJv8PiAHVcBY9O7nKegEHv3SyHFbNMze0TB7Z1X+fKz7kggedph+P+ggYi+9hIoXmBDQUHJOKNjERvseXH2SNji68lF4apm2AwD9vvDD9Pct/FB/70p1gUOPb4ZbjpOa8g4Arzw1EW5Wipml10+uvs/RS4qmaJgVwAfozqAg/O70p5Lw6JORI8GyiL/3XqEiGEpMPhtz6TqKvj3gkqNg5CB4+nU4/UC4+3/0+yOvtu4AEnYDpx8IVvGN3utd294x3Reube8E+A7eCf4s/ArFT66+x9Ej/t2hCAPRy4CCTYcLHfkTiIge/V94geD48blvMFQ0mToKEVi+FsJpfBBunatDpvWp0a8nkhK1ZNgvbE/mAH8mfZDOPwJP5dOYH5fex9DnkE9Hw/ieD/vM1fcMcEQowjvoCEG/9I4TC6KuAYb0KfRuTeBb36J5zhziq1YRGJ7XLMrQiciU/GXjZm1B+s1mnWPhqzSJWorI5cBC17bfQMfUXANsj06m2wfIa7PKz8gfQe8k3hyK8AhwdzSMLz9YH7n6FPBz79Vmauth3PZta0OCQYL77UfsxRcJnFwxVs2GdiZbIJOjvr2tLF2ilmJhOc4Xrm3vhU5weySwHfA18C/gRstx8jJV8mPbPw9t2NMXPUWfF4rwGTpZ58wkQ6CSU+iaP5XgpEk0XnstasMGpG/f3DcYOh1+w6Il19u5A+TyFPxytjnaFYyvNX8ownbADPRaYxna5fAA4AzyzAxaTNq65k8gPXoQmDiR2MKFVB15ZNsbNFQcfkyLU+v9plkb6FcKftb8/0RnBIkCx0bDrPG+eigUoUxsn3TQybo2nPOnEtx/f5oiEYJTpiDdurVPo4aKIp09QLZ67tKmvHbbS42fkf92b+2+lVAEKxrGjYZpw6lq+7Jpi44X162dzi8CgwYRGDGC+JIlBPfdt30aNRjKCD/n/FenKVvY3oK0lfaa8icTPPBAY/RjaJPhUHvh2vYrSZ9/3x5tZvPnH4q2GKoJRZjINlfCPmwNt1A+tNdmXzIyejRUVxN//32Cu+3Wvo0bKoZSe/R57OLadnfLcbagd/uvamuD2SbJ04Az0ZZ5NyaVbwR+09YHtzfFGPlFRI/+L75olN9QamYDH7i2/QlQ49r2C+kqtUvc/miYe4F7QxF+GA3zaL6SdjR1DdC/CPORwJ570vz008RXryb+1lsVn9zDUJlYjnOWa9sHACOB79IOWbOyTftnRMPMBEaGIq2NcKLhFrOBklNXr49j2hupqtJGPy+9RHzxYqP8hpJhOc5LwEuubXdLF7s/X7JN+xOTaB/hMEtPbQNMSOvy0A706EH8zTcBE9/fUHosx7nLte1DgNPR+3KrgGhS0k5fZJv2/92LxvNNNJxfSOBSUFfffmf8ycSWLSP2/PNUn3GGie9vKAtc2z4X7eBzB/AqsBPwgGvbv7Mc53a/7WQ9FY+GiYUinEKe8cBLQTE2/MDE9zeUJTZwuOU4byQKXNt+CHgUbXbvCz8mMQtCEW5BpxCqTxSWU+jueFx7WRVj5Dfx/Q3tiWvblwDnogPWvonOyLslz2a2A95JKXsfyGvXy4+Rz7eB3dEJAG/wXtfn85Bi880W6GEVJ+xSIr5/MmrFCmTQoPZ/mKFT49r2cOCnwN5eeu4ghSXEeQm40bXtHl67PYHr0Fl7fOPHq69dsoMUk7r64hzzQYb4/g8+iPTqhYrFkGD5BXozlDVV6HP6JrSxXCEhQ3+MnolvcG17PXrEfxntdZuXIDkJRTgaPfpvDQ0cDftPBVxsirXeh9bx/WXIEIJHHUX89ddpfuwxqqZPR/zkAzd0BbJm6bUcZ5Vr29cDK4HNwNyU9Ny+sBxnDXCQa9s7AMOA1ZbjfJ63sLkqhCL8Dd1DHYreXZwOLMr3QcWktgimvckEJ04kOHEizXPnbj3nVxMm0HTbbcTmzjVZfgwJsmbpdW27Pzp8/SigDnjEte0ZluPMLORhnsLnrfQJ/Kz594uGOR2ojYa5CtgXL+R2uVAMu/50JBv4iGVRfdZZxN94g9jLeS21DF2Xw4AVluN8aTlOEzoUV8nixPuZ9m/23htCEYahwwb5CpblJ12XV++HwCzgu9Fw/jECahtg5MB872o70qsX1eeeS2MkAr17E9xjj44XwlBJrAQmext1m9GZsEoWE8PPyP+kF0v/OmApOoPPA7lu8pOuy6vXG50U9FX/Yreko0b+dMiAAVSfdRbNjz1G/ONtgYeb5+a9lDN0cizHeRU9yC1FH/MFgNuy3pSCa9sB17anuLbd5ggzfnb7ExlAHg1FeBLoHg3jJ6nd1nRdAKHI1nRdqeeTfwSuJc9UQ8kUc8PPD4Hhw6k+9VSaZs4keOCBxJcuRa1dS/zNN40psKEFluP8Hp2Wu9D7465tz7Ycp3dbZcnm2PODLN8RDfNYjrZzpusKRdgL2DEa5qlQJLPy50rXVcqRP0Fg7FgC3/oWsZdeovqUU4wpsKGYvODa9mTLcV7JXTUz2Ub+Y7N8pyCn8mclFCGAjhNwZq660TC34U2PzrkNlfxdLA6bXJ1AodSojz6i+pRT8jIFTj5BMBh88inwtGvbs9ED7FadsBzniox3pZDNseesNomXO11Xb2AC8HwoAsBQ4IlQhOPy2fTb0AC9u2dP4NlRZDMFjn/5JTJw4FabgNiyZcTmzzfLA0Mh1KCzXYHWq4Lwc86ftifxYeSTNV2Xt2+wdY8+FOF54Bf57vaXw5Q/QcIUWLyRH7QpMD170nT77dDcTGDUKFR1NWrFCqpPPNEsDwx5YzlOWwdmwN9RX33S5+7AMcC7uW7yma6rzdQ2lHazL5m0psCzZlF17LEEJ05E1dYSX7GC5qeeynt5YDAk49r2rsCJwBDLcS5ybXscYFmO81+/bfjZ7b8h+ToU4Xq0QuckV7qulPJD/LSZSm0R7frzJZ0pcNW0aVvLpX9/gv370/zQQ8ZT0FAwrm2fiE6j9yh6Nn0Rehl9DdqQyBeFrJR70IZ1RntTVw99y2TkB90BdLv0UoKHHabf04zkmTwFGVCEOGSGzsgfgMMsx/kxOsEtwBvAnvk04mfN/ybbdhODwCDv4WVBbT2M6bgsqb7JtoOfdnnw8MOweTOxhQtNkhBDLgYDiem9SnpX6aunx8+a/5ikz83A2miY5nweUkyKFbW3mKRdHhx1FIGddqLp7rtR69YRPOYY4y5syMQSdN7M+5LKTiZPh7tsRj6JOejGlK/6eEY+6/N5ULEotXVfoaTzFASovvBCmmbOJH7PPVSfeipSs82AwdgEGDx+Csx1bfscoKdr28+gne3y+s+Rbc2/BO10sAT4EvgA+ND7vKQQiYtBOR31FUKqMktNDdVnn40MGEBTJIJav62Pjc2b19HiGcoQy3HeA3ZF+85cDtwN7GE5zof5tJNR+aNhRkXDjAbmobPzDoyG2Q69DCgLr5WmGDQ0Qu8ysO5rTyQYpPqEEwhOnkzjrbfS/OyzNN6gD10ab7iB2LJlJZbQUGosx2kAFgDPAy9ajrMp3zb8rPknR8Ocl7iIhnk6FMHJ90HFYEMD9K2BQCcNpBPcf3/Uhg3EFi7USwBjEGQAXNveCbgfmAzUAv29RJ4zLMf51G87fpR/dSjC5UAi2shpFBZ3rN2p1PV+PsTffZfqU09tbRD0+OOtlH+ryfC6dcjgwcZkuPNyL3rpfaTlOPWubfdCe8feC/7tZfwo/yloF8R/etcvkGegwGJR6et9P2TzF2i86SYCo0bp6/p6Yv/5TyvrQkg/QzAdRUXzHeAILxoQluNscm37V+hAO77xY+G3HrjYC7qhomHyXlsUi64w8mfyF5AhQ6g64QTiK1YQX7yY+IoVVJ9xhi+T4diyZTQ/84zvjsJQdryCjpexIKlsb2BhPo34MfLZA32eOMC7/go4IxrmrXweVAzKybS3WGT0F5g2jcCIEQRGjIBDDsH91a/SzxDWrsX94x+R/v23vmKvv071SScZ1+MKwrXtZMO6j4A5rm0/hXbp3RH4HvCPfNr0M+3/O/DzaJjnAEIRDkH71pcs8GCCugbYbVippSguufwFEmSbIVSfcw6qtla/6upgw4aMSwnV2Ih02xYhqhDXY9NRFIXUNLSJeBqDARe9LO9OHvhR/p4JxQeIhnk+FKEsJttdYdoPmQ2CWtTJMkOQfv2Qfv3AU/j4smXpXY9rami86ipk2DDteqwU8f/+17frsYlRUDzay403GT/K/3Eowu+AqHc9A/g4S/0Oo66h82/4JZPVXyB5huBt4qWbIUCWjuL44wnsvjtq5Uq9l/Dyy1Sfdlqr5UHTI4/ozqRXL6RXL+jeXScxMfsIHYYXAXgM0Cu53HIc33Hk/Sj/2cBVbJtmvOiVlZy6LjLy+yUxQ/BTDzJ3FDJmDIExY4jNm5d2eUBdHc1z5qA2bYKNG3Wm1Koqqk8/3cQo6ABc2z4duAVoZFtofdCOPTv5bcfPbn8t2pa4rGhs1q9eVqklqUz8dBQZ9xEGD6bbhRduK2tspPF3vzMxCnzi2nYQbTq/ynKcY3LVT4MD/NBynGfbIkc2x56skXaiYY5ry4PbSl099O0BJk1e8ci2j5CMdOuWOYRZt240P/EEwQMOQEy8ggQXo6Nh9Snw/ka0WW+byDby74s+RngAnVCjrNSstgH6dfJjvlLj96QBMncUwSOPhLo6Gm++mcDYsQQPOgj11Ve+DYw6mzGSl1zzaOBPwM8LbOZ36BTdV1mO81WhsmRT/qHA4WhrvlOBp4AHomHe9tt4rnRdoQg/B85Fxwn4Ejg7GsaXbXJXsO4rB/ycNCTqQeaOIjh1KrFFi2i6804IBn3lNijEGKkCjhlvAmx02K1C+QAdUCfs2naiTABlOY7vIBDZQnfHgH8D/w5FsNCdwPOhCFdFw9ySq+GkdF2HoxN2vBaK8EQ03CJjzzJg72iYhlCEC9BrmR/5EbyrHPOVC34UKltHId27U3XQQcRfe02fKqSeIESjxObPh6oq/aquRq1Zk/a0IZPVYhnYI2RN0e3a9jHAOstxlri2fUgbnhNFG949RMsNv7zIuuHnKf3RaMUfCdzMNhv/XORM15VsP4A2WZzhV/Bip+U2FE42hcrkq8CWLVRdcAE0NUEsBk1NNN1xR0arxcZbb0W2357A9tujNm0itnhxOdgjZE3RDewPHOfa9vfQBjl9XNueaTmO7//3HtsBV1iOk1fYrlSybfjdh06qMQe4qgBz3pzpulI4B3jab+N19bCj2T+qOLKdIASGDvVdt+rII1FffEF81Srib71F9YwZ6e0R+vfXNgl9+iCBQEn9GizHuQy4DMAb+X9RgOKDDt6RGsYrb7KN/DPQMfsvBn7qZS/DDJ4AAAxrSURBVNUBb20RDRe8U9mKUIQZaMeEgzN83ypXXznF6zf4x+8JQq66gZ13hp13Jgi4r72W2R7hySdRtbXQ0AB9+oDrpu0oKsweYR/gIte2fwusTf7CcpyD/DaSbc3f1gRYudJ1ARCKcBjwW+DgaBg3gyytcvWZDb/KJC9LRJ91s9ojXHSRvm5qgg0baLzuusx+DZs3t4iZWMyTBstxnqfw47rbvVeb8GPhVyhZ03UBhCJMRDsOHRkNk5c1iNnwq1z8WiL6retnNiHV1TBwYGZ7hB49aPzTn5CddiIwfjwoRWzBAl/Lg0Qngc5pUXQsx7m3PdopmvL7TNd1Hdo2+RFvWbHSj/HQ5kaIK+jRLVdNQ1egXfwajjuOwPjxxD/8kPg77+h9hHTmyikRlJL3EJqfeqpD/l7XtjOa11uOc5ffdoo58udM1xUN+08tlEydZ+BjrPsMCQrya0hnjzBhAsEJE3CXLMm4PHAvvxx690Z69UJ9/XWLMGsdRCjleiiwMzq4R3kof7Ew631DW/BjuJQ1PkI4rJ2aNm2i6W9/a9VJFBvLcQ5NLfNmA7vl004ZZLXPH7PeN7QHflKqxZcvR8VixJcv1+bKU6YgNTUEBg3S8RPT5F0sEfegj8t9U5kjfxfz4zd0PH73EZL3EDoK17ZTB+0e6KP5unzaqUzlrzdOPYbi4+ukIWUPoYNopnVSzlWwLb+GHypS+WvrYeSgUkthMGgSnYRr2w0d9MjUTYb6Qrz7KlP5jXWfoQuTT1aebFSk8td1gZDdBkMqrm0/R+vpfjLKcpypfturSOU3u/2GLsrMDOXD0aH28hoSK1L5AwI1xrrP0MWwHOfO5GvXtrdDewmeh/bt/0O6+zJRccqvFPTrlbuewdBZcW27D/BL4CLgSWAvy3E+yredylN+zHrf0DVxbbsG+BlwKdoj8ADLcXyH1Uul4pQfZdb7hi7LJ2irXAcd+nuIa9tDkitYjjPfb2MVp/wKY91n6LJsRqvABRm+V8Bov41VnvKbkd/QRbEcZ2R7tleRjj1mzW8wtJ2KU36lzLTfYGgPKk/5MdN+g6E9qDjlB+PRZzC0B0Xd8PORrstCxx7/DvA18KNomE9ytWtVt7+sBkNH4Np2C52wHOeaHLcUjaKN/Enpuo4CxgOnhCKMT6l2DlAbDTMG+AtwrZ+2f/0gLPywPaU1GIqPl5q7hU64tp2qEx1GMaf9W9N1RcM0wtZ0XckcDyTCEM8CpoYi2bMBd6uC0w+ER141HYCh4tgHWG45zseW42TSiQ6jmMqfLl3X8Ex1omGagQ3oPGRZGT8czj0UZi9pJ0kNho7Bj050GBVh5JOcruuZ6/el5yX/BcBthmtPaeio6Cn5UoUOt1TuGDnbiQN22KHHPG12m6BFlt5yo5jK7yddV6LO56EIVUBf9MZfC5LTdcmFryxWSmXLhFoWiIiRsx2pBDlFZHGOLL2+Uth1FCVN1wU8AZwBLASmA/Oj4ayRSgyGSuY1YKxr29l0osMo2prfW8Mn0nW9CzycSNcVimxNyXUnsF0ownLg58CviyWPwVBqLMdppRNtccltK6JUZQ20InK+Uqps11EJjJztSyXIWQkyJlNxym8wGNqHijTvNRgMbaeilF9EjhSR90VkuYiU3f6AiOwoIs+JyDsi8raIXFxqmbIhIkERWSYiT5ZalkyISD8RmSUi74nIuyKyb6llSoeIXOL9m78lIg+ISPdSy5SLilF+EWllGikiJTONzEAzcKlSajwwGbiwDGVM5mL0xlM587/Av5VSuwJ7UobyikgidPbeSqkJaLv9k0srVW4qRvnxTCOVUh8rpUpuGpkOpdQapdRS7/NG9H/UkllwZUNEdgCOBu4otSyZEJG+wEHoUyGUUo1KqbySUXYgVUCNiFSh4+evLrE8Oakk5S8r08hciMhIYCLwamklychNgA3ESy1IFkYBXwJ3e8uTO0Sk7KI5KKVWAdcDK4E1wAal1NzSSpWbSlL+ikFEegGPAj9TSn1TanlSEZFjgHVKqXL3jqgC9gL+qpSaCNRThrYgItIfPQsdBQwDeorIjNJKlZtKUv6yMo3MhIhUoxX/fqXUY6WWJwP7A8eJyCfo5dMUEcmUCqqUfA58rpRKzJ5moTuDcuMwYIVS6kulVBPwGLBfiWXKSSUp/2vAWBEZJSLd0BsqT5RYphaIiKDXp+8qpW4stTyZUEpdppTaQSk1Ev07zldKld1IpZT6AvhMRMZ5RVOBd0ooUiZWApNFpIf3f2AqZbgxmUpFePUBKKWaRSRhGhkE7lJKlcw0MgP7AyHgTRF53Sv7jVJqTgllqnR+AtzvdfgfA2eVWJ5WKKVeFZFZwFL0ic8yPEe0csZY+BkMXZRKmvYbDIZ2xCi/wdBFMcpvMHRRjPIbDF0Uo/wGQxfFKH8HIyIjRUR5NuCF3H+aiJS96aih/Onyyi8in4jIZhHZJCJfiMg9nnluyUnXUSil7ldKHVGEZx0iInHvd9jouU6X3Zl6KiJyZZlaJ5Y9XV75PY5VSvUCvo12xrmsxPKUitXe79AH+BVwe74uyYXOaEpFpcnbnhjlT8IzJ30G3QkAICKTReRlEakTkTdE5JCk784UkY+9kXKFiJzmlQdE5HIR+VRE1onIfZ57aiu8mcdhSdfJI9kL3nudNyLv6z3zpaT6+4nIayKywXvfL+m750XkjyKywJNxrogM9PE7KKXU40AtMF5Ejva86r4Rkc9E5MqkZyRmJ+eIyEpgvlf+iDeT2iAiL4jI7kn33CMiERF52vu7FojIUBG5SURqvcAdE5PqDxORR0XkS+93/qlXfiTwG+BHXjtveOV9ReROEVkjIqtE5GrR8SAS/2YLROQvIvI1sPVv6WoY5U9CtI/7UcBy73o48BRwNTAA+AXwqIgM8lxLbwaOUkr1RjtyJEx6z/RehwKjgV7ALQWIdJD33k8p1UsptTBF3gGefDejMx3dCDwlIslZj05Fm8QOBrp5f0NWvM7rBKAf8Cbam+507/po4AIR+X7KbQcDuwHTvOungbHec5cC96fUPwm4HBgIuOjw7Uu961ne34KIBIB/AW+gXbinAj8TkWlKqX8DfwYe8n6fPb2270Gb2Y5Bz+SOAM5NevYktKnwEOBPuX6PTotSqku/gE+ATcBGQAH/h1Y20FPfaEr9Z9C5BnoCdcAPgZqUOv8HhJOuxwFNaF+Kkd5zqpKef1hS3SuBmd7nFnW9sjOBl7zPIWBRyrMXAmd6n58HLk/6LoyOipPudzgE7dtfB6xHd2QnZ6h7E/CXFBlHZ/mN+3l1+nrX9wC3J33/E7QzVOJ6D6DO+zwJWJnS3mXA3am/l3c9BN2Z1CSVnQI8l/T7rcwka1d6ddn1TgrfV0rNE5GDgX+gR586YARwoogcm1S3Gv0fqV5EfoQeSe8UkQXoEF7voX26P02651O04g9pZ7lTn5N4VnKQky+SPjegZyGZWK2U2iG1UEQmAdcAE9CzBwt4JKXaZ0n1g+gR9URgENsChgxE52MEWJt07+Y01wk5RwDDRCQ5gk8QeDHD3zAC/W+0RjvYAXqGmxwI5rPUm7oiZtqfhFLqP+hR6Xqv6DP0yN8v6dVTKXWNV/8ZpdThwPbAe8Dt3n2r0f8JE+yEnoYm/wdPUI8O+5RgaLJIOUROfU7iWe0d5+AfaPfpHZVSfYG/QatsysmynooObnEYOgXbSK88awbmDHyG9pVP/jforZT6XprnJuq7wMCk+n2UUrsn1THebBjlT8dNwOEisicwEzhWRKaJjnTb3TsS20FEhojI8d7a30UvHRIj3APAJaJjD/Ri27o0XaLJ14GTRaRaRPZGpy1L8KXX5ugMss4BdhGRU0WkypuJjAfaOxpvb2C9UmqLiOxD7hRTvdG/ydfoju3PbXj2ImCjiPxKRGq8f4cJIvJd7/u1wEhvbwCl1BpgLnCDiPTx9i929mZ1hiSM8qeglPoSuA+4Qin1GXoE+w1aET8Dfon+3QLoFGOr0Wvkg4ELvGbuAqLo3foVwBb0ujYdvwN2Ru+sX4UeZROyNKCnzwu804bJKbJ+DRwDXIpWNBs4Rin1VeG/QFrCwB9EZCNwBfBwjvr3oZcfq9DBN14p9MFKqRj6b/w2+rf8Ch10NHF6klh+fC0iS73Pp6OXJ++gf9dZ6NmZIQnjz28wdFHMyG8wdFGM8hsMXRSj/AZDF8Uov8HQRTHKbzB0UYzyGwxdFKP8BkMXxSi/wdBFMcpvMHRR/j9fw8zvDsni8AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "resolution_list = np.linspace(0,10,21)\n", + "\n", + "if 'n_clusts_dict' not in list(locals().keys()):\n", + " n_clusts_dict = pkl.load(open('WaveMAP_Paper/data/n_clusts_dict.pkl','rb'))\n", + "\n", + "if 'modularity_dict' not in list(locals().keys()):\n", + " modularity_dict = pkl.load(open('WaveMAP_Paper/data/modularity_dict.pkl','rb'))\n", + "\n", + "avg_n_clusts = []\n", + "for k in list(n_clusts_dict.keys()):\n", + " avg_n_clusts.append(np.mean(n_clusts_dict[k]))\n", + " \n", + "std_n_clusts = []\n", + "for k in list(n_clusts_dict.keys()):\n", + " std_n_clusts.append(np.std(n_clusts_dict[k]))\n", + " \n", + "std_modularity = []\n", + "for k in list(modularity_dict.keys()):\n", + " std_modularity.append(np.std(modularity_dict[k]))\n", + " \n", + "avg_modularity = []\n", + "for k in list(modularity_dict.keys()):\n", + " avg_modularity.append(np.mean(modularity_dict[k]))\n", + "\n", + "f, ax1 = plt.subplots(figsize=[3,2.5])\n", + "\n", + "ax1.errorbar(resolution_list,avg_modularity,yerr=std_modularity,\n", + " c = '#5c95ff', marker='o', fillstyle='full', markerfacecolor='w', \n", + " linewidth=1, markeredgewidth=1)\n", + "ax1.set_ylabel('Modularity Score')\n", + "ax1.set_xlabel('Resolution Parameter',fontsize=12)\n", + "ax1.set_xlim([0,8])\n", + "ax1.set_xticks([0,2,4,6,8])\n", + "ax1.yaxis.label.set_color('#5c95ff')\n", + "ax1.tick_params(axis='y',colors='#5c95ff')\n", + "ax1.set_ylim(0,1.0)\n", + "ax1.set_yticks([0,0.2,0.4,0.6,0.8,1.0])\n", + "# ax1.set_yticklabels([0.0,'',0.2,'',0.4,'',0.6,'',0.8,'',1.0],fontsize=12)\n", + "ax1.spines['top'].set_visible(False)\n", + "ax1.spines['right'].set_color('#f87575')\n", + "ax1.spines['left'].set_color('#5c95ff')\n", + "\n", + "ax2 = ax1.twinx()\n", + "ax2.errorbar(resolution_list[1:],avg_n_clusts[1:],yerr=std_n_clusts[1:],\n", + " c = '#f87575', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n", + "ax2.set_ylabel('Number of Clusters',fontsize=12,c='#f87575')\n", + "# ax2.spines['left'].set_color('b')\n", + "ax2.tick_params(axis='y',colors='#f87575')\n", + "ax2.set_ylim([0,18])\n", + "ax2.set_yticks([0,4,8,12,16]);\n", + "ax2.spines['top'].set_visible(False)\n", + "ax2.spines['right'].set_color('#f87575')\n", + "ax2.spines['left'].set_color('#5c95ff')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8lx6iIIZ1NPA" + }, + "source": [ + "## Figure 2C: Classifier performance on clustering" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kl0JCjku1VR-" + }, + "source": [ + "### First we create a 70/30 test-train split of the data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "91XymkZ9smsf" + }, + "outputs": [], + "source": [ + "testSize = 0.3;\n", + "\n", + "UMAP_X = np.stack(umap_df['waveform'].to_numpy().tolist(), axis=0)\n", + "UMAP_y = umap_df['color'].to_numpy()\n", + "\n", + "unclassified_ixs = [ix for ix,clust in enumerate(UMAP_y) if clust == -1]\n", + "\n", + "UMAP_X = np.delete(UMAP_X,unclassified_ixs,axis=0)\n", + "UMAP_y = np.delete(UMAP_y,unclassified_ixs,axis=0)\n", + "\n", + "UMAP_X_train, UMAP_X_test, UMAP_y_train, UMAP_y_test = train_test_split(UMAP_X, UMAP_y, test_size=testSize, random_state=RAND_STATE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zmkb1S7U1cdU" + }, + "source": [ + "### Next we use XGBoost to train a hyperparameter optimized random forest classifier on the WaveMAP clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "21efwyEus9jt", + "outputId": "086f7a98-ec64-4133-9f1b-e03e3dfac307" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 2.3s\n", + "[Parallel(n_jobs=-1)]: Done 3 out of 5 | elapsed: 2.8s remaining: 1.9s\n", + "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 3.2s remaining: 0.0s\n", + "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 3.2s finished\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[22, 0, 0, 0, 0, 1, 0, 0],\n", + " [ 0, 26, 3, 0, 1, 0, 0, 0],\n", + " [ 0, 0, 28, 1, 0, 0, 0, 0],\n", + " [ 0, 0, 1, 18, 0, 0, 0, 0],\n", + " [ 0, 0, 1, 0, 28, 0, 0, 0],\n", + " [ 1, 0, 0, 0, 2, 24, 1, 0],\n", + " [ 0, 0, 1, 2, 0, 0, 7, 0],\n", + " [ 0, 0, 0, 0, 1, 0, 0, 19]])" + ] + }, + "execution_count": 18, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "numCV = 5\n", + "\n", + "UMAP_model = xgb.XGBClassifier()\n", + "UMAP_param_dist = {\"max_depth\": [4],\n", + " \"min_child_weight\" : [2.5],\n", + " \"n_estimators\": [100],\n", + " \"learning_rate\": [0.3],\n", + " \"seed\": [RAND_STATE]}\n", + "UMAP_grid_search = GridSearchCV(UMAP_model, param_grid=UMAP_param_dist, \n", + " cv = numCV, \n", + " verbose=10, n_jobs=-1)\n", + "UMAP_grid_search.fit(UMAP_X_train, UMAP_y_train)\n", + "\n", + "confusion_matrix(UMAP_y_test,UMAP_grid_search.predict(UMAP_X_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V2le2oqP1rS7" + }, + "source": [ + "### Lastly we plot a confusion matrix for the test accuracy of the classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 259 + }, + "id": "Wb7rtQkztL9Q", + "outputId": "fc9fda81-b9a8-4e71-b201-65c868ec8704" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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V1R0iUhr4C/AkJouuSbA3VFWPo/J8BlBspgiqVDbrac6okUK3zk2ZOMnIN7Zr1ZA1a7cfCyTBYOX+ig4qoIkJvrf4CRwRc9RtiyNdROoC5wNLVDXTUWAOx5EkiskYB8DYNwdSKbk8OTkeHh4ynv3ODEqPrhcxMcRuipX7K0pIAAWw6HoSBndGypDbwPECRhnZA/R09nXASLwXiIhUxXRppgAZzrW9na1YcGWvV33uH/ToSdIHrrFyf0WIgNmx8RE5VPXjSNlyFThUdYyITHBe55U7W4SRVHdlAtMteQfTPdoI/FVVJwXnrsUSQ+IjPkQFt7MqqUCGqh4SkUSgH5CLKXVQIKqajin/aLHEJ84Yh79jpxpuB0enAPWc1y9hBFUHA77b5xZLscNPFTeXC8DcFp12zm0mIt+LyCGn+PQDEX2UCOB2jKM+8LPz+mbM1OwhTF0I10VcLJZ4RQQSE/0fc4F30ekmwFQRWa6qJ9RWEZEqwHTM52oiRun8DMJERIIdVd+gqn4XhLkNHB6gpIjUB/ar6iZHEKR8AdcVS7avDnup/zFqnDM2YrZ2/c9KBxYmfgNEAYHDq+h0I6fo0TwRySs6/bd8pz+IKTqdVzYkE1gVqs9ePBfk+aMBv/+cbgPHN5jiLZWBT5x952IETi2WU4IEP9mxzt7eItLD2fVuvvqx/opO+xr3awH8KiILgLrAj8A9gb793aCql4VzfX7cBo7bgVuAbI4PiFYBhkTSGYulqCICCX66Kk7kGK+qQ/2cEUzR6TOAZkBH4FfgH8B4oFWQLhcqbqdjM3GWgXvtm1MYDlksRZUwVpwHU3Q6A/hSVZcY2/IcsEtEKqrq/iDcLVRc63GIyNWYplUVvHp1qtqvEPyyWIoUEmDyxMXYaDBFp3/hxPT3Ipmv5Wo6VkSeBUY659+AqX3ZGQg+AcNiiVMSEsTnVlDkUNXDQF7R6XIi0gpTdNrXOqjRwLUi0kRESgBPA/OKUmsD3Lc4bgM6quoKEblVVQeLyHjgqUL0LW5pUKc7FSqUIzExgaSkROb/OCao6wf2a0PfG1ogIoz7bCEjP/ieR+/tTN8bW7Brz2EAXho2lVnfR2Kw3eKKAGMcLqdjXRWdVtXZIvIEMBVTKGke4HfNR6xwGziSVXWF8zpLREqo6mIRCXo1qIjUwwz6TFTVm4O9Pl6YPutNqlRJDvq6hvWq0/eGFnS6YThZ2R4mvHcnM7/7DYB3xszlzffnRNhTixsESAgjVcVt0Wln39vA28F76Z9AFerz3Tui1erXish5zmKVFcDdIrIX2Ovyem/eBEJLFz0FqF+nGst+2UjG0WwAFiz5g66drMZoUcCvVnF84K3vWwfTi/gAkzdWEzNr+r5bY24Dx1OYNRwAjwMfYaJkUAVZRaQXZlwkb466WCIidLvifkSEAXdcy4A7Tvqi8cuq1dt48q9XkpJclqNHs+nQ5lx+XrGZvfsOM6BPa27sfhE/r9jMM698zf4DwYkfW0JHJLwWR6zxrlAvIouAzt6rVkXkY0zgeNaNPbfTsdO8Xv9ICB96ETkNI/7THrMuxN95cScdmJ9v544kLa0qO3fuoWuX+2nQoBZ/aeNuVeeadTsZ8d5sJo66iyMZWaz4fSue3FxGj5/Pv96aiSo8/sAVPP+3a3jgiU8KNmiJGAl+9Dgk/rLczgHW5tu3Hmjo1oDfWRURqe1mC8LZF4BRqrol0Emq+q6qNlfV5qmpkavkHk3S0qoCULVqJa6+pi1LlvwW1PUfTfyRy3sMo9vNb7Bv/xHWbkgnffchcnMVVWXcZwtpdn58BtW4xWlx+NriL24wFxgjIvVEpIyTSjIK+MGtgUDTsX8Aa5yf/rY1fq/2QkSaYMR7XFfDjlcOH87g4MHDx17P+s9izjsvmPgKVSqZsbK0Gsl07dSYzycvo1rq8fVDV3VozO9rtkXOaUuBCGaMw9cWf3GD/s7PlZhK9b9iHsN1EpbfroqqRrJ0QjvgLGCTUxekPJAoIueqarMI3ifm7Nyxh57XPwZATo6Hnr060alLy6BsjH79ViollyU7x8Ojz33OgYNHeeXpHjRqeDoKbN66h4ee+awQvLcEIszp2CKDM8PTy0lUTQXSVTW3gMtOIOAYh4iUAep4TcV6H2sE/KGqR13c512OJ8eB0fM4i2IkVpzH2bXTWPzTh2HZ6Nbn9ZP2DXr0Ix9nWqKFGRyNe83RY4hIQ8xizmqqeq9T9qSUqv7i5vqCWhWPAgP8HLsVeMTNTVT1iKpuz9swa/ePOspgFktc4G+MI95aHCJyA2Y8Iw2j5gcm4W6YWxsFzar0xGTp+WIY8B/MoGdQqOqQYK+xWGJJXiE3n8ei60okeB7ooKrLRSRPfHw5Jn/GFQUFjjRV9am5oapbRSTN7Y0slngnzovVe1MVk0wHx1eTKkEk1BXUVTksImf6OiAiNYEjvo5ZLMUNUx1BfW7RrNweIZZxcinIXpgKja4oqMUxDXjZx03AdFGmur1RcSJRSkbM1q7/RW6GOrnOXRGzBbBv7TsRtRfPCJAkvr+Qxc/+Isz9wEwRGYApRD0Do1LWya2BggLHU8BCEVmOSQveBtQArsUIkVwa4FqLpfggkFRMJlVU9XdnVqUrpoLBZmCKo4fqioCBQ1W3i0gz4CGgCyZfZTcwGRimqqEkuVkscUfeAjB/x+IJERmhqvdjdIS99/+fqv7VjY0Cu2equldVn1LVlqpa3/n5tA0allONxAT1ucXbdCzHV47mx9eQhE9cSwdaLKcyAcc4outKyIjIbc7LJK/XedQGdrm1ZQOHxeICCTDGEUc6HXktipKc2LpQYAdGk8MVNnBEkKNHM2nf7nYyM7PIyfFwXY/LeXZIbFfV39W/Pf16tkIQxn46j7fHzOb9EbdT7+xqAFQ8rSz7DxyhdbeXYupnPBBHAcInebVVRORFVQ1L9jNqgUNE5mCKzeQ4u7aqaoNo3T8alCpVkpmzRlK+fFmys7Np12YAXbq04pIWsVHwOqf+6fTr2YrLr32FrGwPn4++j+nf/cpt9x8Xg3rx8R4cOGgFgQpCMGs2fJEQf9Ox34tIfe8CUU6uSk1V/Y8bA25VzkuJyEsisk5E9jv7OonIvUE6fK+qlne2YhU0wCh/lS9fFoDs7Byys3OQGI6c1a9TnWU/byDjaDYeTy7zF6+hW+cTBYW6X3UhE6csjZGH8UPeGIevzc1fOJii0875JUVklYgE1K8JkTc5uabLQWe/K9wuehsONAL6cHxZ6kqKYXZruHg8Hpo360Va9Q5c3uESLr7k/Jj5smr1n7S8qC4pyeUoU7oEHds24owaKceOX3pRXdJ3HWTdhp0x8zFeEIGkBN9bCEWn+wBvi8h5Ac5/BCisJNCqqppf0GUbUN2tAbeB41rgJlVdCOSCyVXBZNcFw1AR2SUi80Wkna8TRGSgiCwVkaXp6fGXPJuYmMjSnz5h/abpLF2ykhUr/oiZL6vXbue1kTP48oP7+Xz0/fy6ajMez3HZhR7dLuLzyVY32i0JfraC4oZX0emnVfWQqs4D8opO+zr/bOBmwF9JyXBZJyLt8+1rh5EPdIXbwJFFvvEQEUnFLAZzy2OYKZ80jD7HZBGpk/+k4iAdCJCcXIG27Zozc8aCmPox7rMFtLtmKFf2fpV9+4/wx3rTukhMTKBb56Z8MdV2U9wQKFfFq+j0UmcbmO9yf0Wn/bU4XgeewJSDLAyGAF+IyKsiMkhEXgU+B55xa8Bt4PgM+MCJhIhIDeANThTnCYiq/qiqB1U101Fcng9c6fb6eCA9fS/79pmuY0bGUb6dtYgGDc6KqU9VKpu6xmfUSKFb56ZMnGTymNq1asiatdv5c7stxucGM8bhe3MCx/i8L7x8leohiKLTInItkKiqX0b+KQyq+jUmL6UccJXzs7Oz3xVuZ1WeAP6O0SYsi9Ea/TfwXDAO50OJn7Uzrti2LZ0Btz6Lx+MhN1e5/oaOXNXVVX2bQmPsmwOplFyenBwPDw8Zz35nBqVH14uYaLsp7hGzctTnoYJnVVwVnXa6NP8gCl+oqrqYILJh8+O2PEIWMBgY7HRRdqmq6zkoEUkGLsGoK+dgBILaAA8E7XERpnHj+ixZNj7WbpzAlb1e9bl/0KMf+Nxv8U2glaMumu1ui07Xw0hq/uDMxpUEKorIdqCFqm4I3nODiDypqi85r5/3d56quuquuAocPsogVMibZlTVdS5MlABexNRt8AC/A93z9fksliJLoCQ3N0WnRSSv6PTtQBNM0en82eUrAG/9m0sxQwLNCH+G5Qyv1z41doLBbVflD07uWuSFXz/az14nGm3Ri4JzzWIpOghQwt+Sc3cmCiw6rao5wPZj9xTZA+Q6Or1hoap3e712XQbBH267Kif8bkSkOqZUnOsCLhZLXCP+V45Guui017E5nNhSCBm3xdNc9iBCW3Lu6HT8FdN3+zgUGxZLPCFACT9NiziRDvTuNXhHwPzvC+xBQHjP3AAzw2KxFHsCVXKLh7lBVU1Q1USn93A7ZilFQ6C08/Nj/JdCOQm3g6M/cGJUKotZvOJ3dDYSKEfIyv0pIrZKJkSuYFyJBHcFpKPN1v/1iai9epe+HTFbK+ddEjFbkfxbukbwn+TmXhy8qPACUE9V8xaYrRGROzE9iDFuDLjtqryX7/1hYLnX1JLFUqwJNDgahwpgCZhp31Ve+2rhspsCLgKHiCQC7YGBqpoZpIMWS7HAjHGEvI6jqDEcmC0iozFCxWdi5ARdS+4XGDhU1SMinXCS2yyWUxGRAGLFcdbiUNV/isivmNqxTTGZsbep6nS3Ntx2VYYDz4nIs6qaHbyrFkt8Y5Lc/B+LN5wg4TpQ5KegavW9VXU8cB8mV/9BEUnHa6BUVWuGevNw8Xg8tLy4L6efnspXk1+LlRtxwer/beKWm4Yce79h/Z889ext3PPAja5t9O/ZmBu7nYMCq9fu5rGXviMry8PgOy/misvqkJurfPzlSsZ+9mtQvsXD39GMcfhTAIuuL+EiIqUwmbC9gcqqWtHpVdRX1Tfc2CioxTESGI/RBggbEemFWThWE7NCrr+zACYkXh8xnoYNz+LAgcORcK9YU79BTRYuex8wH9R6tXrQrbv7BLxqVcrR74bzueKmT8jM8vDaCx3p2qEuIlCjank69x6PKlRKKRO0b3Hxd5Ri1eIYjpG36AN84+xb6ex3FTgKGtcRAFWd629z66mIdMRk2N6KSSduA7hapeaLLVt28M20edw64KTFeJYCmDN7GbVrn07NWq4FnwBISkygdKkkEhOFMqWT2LnrML2vPY833l9KXsrjnr3BSUjEy99RgERRn1scloAMW5iroBZHoohcRoCgqqqzXd7rOeB5VV3kvN/q8jqfPDz4VYa+8gAHDxbhb6kiysRPZ3N9z8uDumbHrsOMGv8zc7/sS2ZmDvMWb2be4i0Me64jV3WoS8c2Z7Nn31FeGD6PjVv2u7YbL3/HYjbGEbYwV0EtjlLAqABb/vUdPnGmdJsDqSLyh4hsEZE3ROSkdq23dOCudN/F4qZO+Z7Uqik0u/AcN7e3eJGVlc3UKfO59vrLgrrutAolubz12bS//kNaXT2WMmVKcHXnepQskUhmlofrBnzOhEm/MfQJ93bj6e8oYpac+9ribYyDCAhzFRQ4DqtqbVU928/mKnEGI9BaArgeaI1JK26KKWp9At7SgVVSU/IfBmDhguVMnfw99Wt3pe9NTzDnuyX07xtWmYhThpnTF9GkaT2qVasU1HWXNj+DLX8eYM++o+R4cpk5Zx3Nzq/O9vRDzJxjepwz566nYV33duPp7yj4Vjh3q3JexHgCoy/6K5CMEeb6kyCEuaK1diWv4/u6qm5T1V3AMEJUOnrx5ftYt+kbVq+bwriPX6bdZRcxZtyLEXO2OPPZp99yQ88OQV+3bcchmpxXjdKlTAu3ZfMzWLthL7O+X0+LZqZrfHHT01m/2X03JZ7+jnlJbvHe4nBa/08Bf1PV8pgv9QqqOtgR7HJFQWMcEfmVqOpepz6E9yhS3I0oxTuHD2fw3ayljHjr4aCvXf7bTqZ/t46vxlyPx6P8tjqdT7/+jVKlkhg2pAP9ezXmSEY2Tw6dE3nHiwhxWHjpJJwFnYMwgqnp7kgAABPYSURBVMV5WjlBEzBwqOpJYqphMBq4T0SmA9kYKcIp4Rpt2645bds1D9fMKUG5cmXYtCP0X/mIUUsYMepEndKs7CzueHhauK4V+b+jBJiOjacWh8NY4C7grVANRLN27AtAFUwG3lFgAmALllrihsT4CxD+uBjzJf4oJlfFe0Gnq8U9UQsczlL1Qc5mscQVgTRH4zCe/NvZQsZWq7dYXOJvJiGeAoeINMSUZfg1HFmMOMwItliij2lxqM8NF4OmbotOi8gjIrJCRA6KyHoReSRizyDSHzMF+w6wQkRuCNWWDRwWixv8yAYmiOsWh9ui0wL0A1KALsC9To5XJHgMuF5VqwK9gMdDNVSkuypC2djIxBXA/qyZEbP155HIaSOdk9wtYrYA1ixoHTFbZz84KWK21g+L/v+EyVXxfyzgtceLTjdS1UPAPBHJKzr9N+9zVfUfXm//JyJfA60IYlVnAE73KvP4FWGMc9gWh8XiEhHfm4smR7BFp537iWBWWuev+BYqxzx1KjGG/Pkv0i0Oi6UoUcDgaG8R6eHsejdf4WnXRafzMcS57ehg/AxAORHZ5PW+Yr73rvV1bOCwWFzgoqsyXlWH+rncVdHpE2yK3IsZ62gdQa3f9hGyYwOHxeIWf0vOXQyOui06beyJ3IYZ+2ijqltC8dUXwejnFIQNHGFyz8BXmTHtR1JTk1n4X9M63bvnALf2eZlNG3dQs1Y1xnz8JMkpBa/e37Ylncfv/j92pe9DBG68pTN977qafXsP8tBt/2Drpp2k1azKsNGPUTHZZ9XAuKB2ajle73d8efmZlcsyfPr/+GLpZt7o25y0SmXYuieDe8Yu5UBGEZG4zRvP8HMsEEEUnUZE+gAvA5e5LcfoBhG5HxgZqPXiSAreqaojCrIXlcFRETmUb/OIyOvRuHdhc1PfTkycfOLK+eH/nEDb9k356bfRtG3flOH//NSVraSkRB598TamLHqTT2b+k4/fm8Yfv2/iveETadHmAqYvG0mLNhfw3vCJhfEoUWNd+mGuenUuV706l27D5nI0y8PMX7dxd/t6zF+TTvuhs5m/Jp27L68ba1ePkddV8bW5nI4dBJTBFJ0ej1fRaRE55HXei0BlYInX5+WdCDxCdeAPERkpIjeJyIUiUt/52VtERmLS66u6MRaVwOFU4i7vpPFWx6TZfxaNexc2rVqfT0q+1sS0yQvpfbNJXe99cwemTlroylZq9Uqce0EdAMpVKEvt+mewc9tuZn+zmO69Tfe0e+/2fDvtxwg+QWxpVS+VjbuPsHVvBh0bVefzJZsB+HzJZjo1qhFj704knHUcqrpHVburajlVramqHzv7f3A+F3nnna2qJbw/M6p6V7i+q+oTGA2cNZhSj98AK4BpwG3A70BTVXUliBKLrkoPTNQttpXud+7cS/UalQGoVr0SO3f6VjILxNZNO1j1yzoaX9iA3Tv3kVrdCORUqZbC7p37IupvLOnaNI3J/zXd+CoVSpF+0LSk0w9mUqVCqVi6dgIBB0fjZM25o4PzL2cLi1is47gFGOvMI5+Et3RgenpIUgFFChFBgvzPOnwogwf6vcLjQ2+n/Gkn1vU29iLpYewokSh0OK8a037e5vO4n3+RmOF3yfkpSFQDh4jUAtoCH/g7x1s6MDU1NXrORZCqVVPYvs3ovm7ftpvU1GTX12Zn5/DXW16h6w1t6djNjJ1VrppM+vY9AKRv30OlIOwVZdo1rMbKrfvZdci0MnYdzCTVaWWkVijF7kOuBakKHSlgO9WIdoujLzBPVddH+b5R5YquLRj/4SwAxn84iyu7tXR1nary9H2vU7v+GfS/53i5gMu6XMxX442Y/FfjZ9P+iosj73QM6NYsjUk/HRe7n7VyOz0uOhOAHhedyX9WbI+VaycTIFflVCTagaMfAVob8ciAvkPp1HYwa1Zv4dzafRg7ejqDH+nJd7N+otm5tzLn258Y/Ii7amk/LVrFpE+/48fvf+Ha1g9wbesHmDtzKXcM7sGCOT/T5cI7WTh3ObcPvr6Qn6rwKVMykb/UT2XGr8e7KW9/u4a/1E9l9uPtaVU/lbdnh5z1HXECzqqcgsEjaoOjInIppuBLsZhNyWPUON8JhpNm/D1oWxe2PJff9vpOBhv9ddEU8Q2VjCwPzZ4+sXTpviPZ3PyOuxmoWCBxntolImUxQsWNgJ+AoaGuSo3mb+IW4AtV9bvM1mIpyogk+N7iZ5TjTaAbZur1esKYXYmmdOCd0bqXxRJ5JECLI24CRxegmapucxZgfo8pKB80dsm5xeISU5LE55Go+hEG5VR1G4CqbhaRiqEasoHDYnGBBGhxxE3YMIl23rWg8793XQvaBg6LxSX+xzLiJnTsBN73er8733sFXJV1tYEjBCqW7BRBWxEzRaZnceSMAaUSI7deZP2wqyNmq3L9ByJiZ/+mzUGcLf67KnEyH6uqZ0XKlg0cFotLikFXJWLYwGGxuEIQ4n5wNGLYwGGxuMAIE8f9GEfEsIHDYnGJvxbHqRc2bOA4pahfpzsVKpQlMTGBpKREFvxYPNKGBvZrS78bWyICYycsZOQHc3n0vi70u7Elu/YYca0Xh01l1tzfwriLIBL3C8AiRjRzVc4C3gJaApnAROCvqpoTLR8sMGPWW1SpUjzS8gEa1qtBvxtb0vH6V8nK9vDZqLuY+Z3RAH579BzefP+7iN3LjnEcJ5q5Km9h5pFrYMRa22Ir11vCpH6daixbvpGMo9l4PLnMX/wHXTs1LoQ7WTUOb6IZOM4GJqjqUVXdDkyngEpWlsgiAl2vuJ+WF/fjvX9/GWt3IsLva7bRonltUpLLUqZ0CTq2PZe0GikA3H5za76f9BgjXu5NxdPKhHUfU3Q6wefmRuEtiKLTIiJ/F5HdzvZ3CVZCLgpEc4zj/4BeIjIHU1D3CuDp/CeJyEBgIEDNmq6KSllcMnvuu6SlVWXnzj1c1eU+GjQ4i9ZtmsbarbBYvXYHI/79LRPfH8SRjExWrNqKx5PL6I/n8683Z6AKT/z1Sl74W3fuf2J8GHcKezrWu+h0E2CqiCxX1fy1VQYC3TF1VxT4D7AeU2G+yBDNFsf3mBbGAWALsBRT+PYEioN0YFElLc0o31etWomrr2nH0iWRKkkaWz6auIjLr/sX3fq8zr4DGazdkE767oPk5iqqytgJC2nWuFbY9wk1rd6r6PTTqnpIVecBeUWn83ML8KqqblHVrcCrQP+wnY8w0aqrkoDpmnwBlAOqYFodwavdWELi8OEMDh48fOz1t//5kfPOqxNjryJDlUqmukBajRS6dmrMxMnLqJZ6vOLiVR0bs2qNb0Fkt2Rl5ZCd5UFIPGnbvXs/wNEAlwdTdPo851hB58WUaHVVKgE1gTccxaFMERmNKT7zaJR8OKXZsWMPPa83v+qcHA89e3WmUxd3WqhFnTFv3Eal5HJk53h49LmJHDiYwd+f6UGjhmmowqatu3nomQlh3eO663oxduwibr/99hP2L1myhIoVqwNc7XSzIbyi0+WdY97nlRcR8VcZIBZItHwRkXXAuxjVofKYCtwZqupzkAigefPmunTp0qj4VxwoyklukSRySW6fkHN0h6sBChFJueCCC/YsXryYkiWPZyZ27dqVqVOnXqiqPwW4tikwX1XLeu17CGinqt3ynbsf6Kiqi533FwJzVLXgGqJRJJpjHNdhFIjSgT+AbGBwFO9vsYSMqu695pprGDt27LF9S5YsITExkUBBw+FY0Wmvff6KTq90jhV0XkyJWuBQ1Z9VtZ2qpqhqFVW9UVV3ROv+Fku4PP/885XeeOMNsrJMvZfnnnuOSZMmXVjQdap6GDO+97yIlBORVpii0+N8nD4WeFBE0kTkdOAhYEykniFSxLdss8USRbxbHUG0NvJwW3R6JDAZ+BVT23Wqs69IYXNVLJYgeP755ytdcMEFe2rUqMH06dMLbG3koap7MOsz8u//ATPml/deMRMGRXrSwLY4LJYgUNW93bt3p1SpUsG0NoodUZtVCQURSQc2FnBaFWBXBG8bSXvWVtG2VUtV7SrDECjSgcMNIrJUVZsXRXvWVvGwZTkZ21WxWCxBYwOHxWIJmuIQON4t+JSY2bO2ioctSz7ifozDYrFEn+LQ4rBYLFHGBg6LxRI0NnBYLJagidvAISL3ishSEckUkTFh2iolIqMcLciDIvKziFwRhr0PRWSbiBwQkdUicnvBVwW0V09EjorIh2HamePYOeRs/wvTXi8RWeXoaK4VkdYh2DiUb/OIyOth+HSWiEwTkb0isl1E3hARm1oRYeI2cAB/YoSA3i/oRBckAZsxyusVgaeACU5Jh1AYCpylqqcBVwMvOroKofImsCSM6725V1XLO1uDUI2ISEeMgtutGEGaNsC6YO14+VIeqA5kAJ+F6hdWTT8qxG3gUNUvVPUrYHcEbB1W1SGqukFVc1V1CkYgNqQPu6qudJTOwAjOKhCSTp+I9AL2Ad+Gcn0h8hzwvKoucn5nWx2NzHDogfnQ/xCGDaumHwXiNnAUJiJSDaMTGbKAioi8JSJHgN+BbcC0EGycBjwPPBiqHz4YKiK7RGS+iLQLxYCIJALNgVQR+UNEtjhdgvBqEBih3rFhSuTlqemXFZE0jJr+9DD9suTDBo58iEgJ4CPgA1X9PVQ7qjoI04RvjRFxyQx8hU9eAEap6pZQ/cjHY0BtIA2zQGqyiITSEqoGlACuxzxfE6ApposXEiJSC9OtCLcupSs1fUt42MDhhaPGPg5T/+LecO2pqseRwj8DuDtIX5oAHYDh4frh5c+PqnpQVTNV9QNgPnBlCKYynJ+vq+o2Vd0FDAvRVh59gXmquj5UA1ZNP3rYwOHgVMsahfk27aGq2RE0n0TwYxztgLOATSKyHXgY6CEikdSAUEKoYaiqezHf5t5dinCXIPcj/NbGCWr6qrobI4odTkCz+CBuA4eIJIlIaSARSBSR0mFOu70NnAN0U9WMgk4O4FdVZ5qyvIgkikhnoDfBD26+iwk2TZztHYyMXOcQ/UoWkc55vycR6YOZCQm1/z8auM953hSM8PSUEH27FNN9Cmc2Baflsx6423nGZMy4yS/h2LX4QFXjcgOGcHzGIm8bEqKtWs71R4FDXlufEGylAnMxMyEHMNqRd0ToeT8M4/pUzJTuQce3RRgZ/lDtlcBMfe4DtgMjgNIh2hoJjIvQ/0UTYA6wFyPkMwGoFov/0eK82SQ3i8USNHHbVbFYLLHDBg6LxRI0NnBYLJagsYHDYrEEjQ0cFoslaGzgsFgsQWMDRwwQkTEi8qLzunW4uhhB3FdFpG6I1/YXkXmR9skSn9jA4QcR2SAiGY64zA7nw16+4CuDQ1V/UBe6GNH44DorS793xIzSRWSuiFxdmPe0xCc2cASmmxqBmWaYNPKTsj+Li7qUiFyPWfI9FpOUVw14BugWS78sRRMbOFygRqDmG6ARHGvy3yMia4A1zr6ujuTgPhFZICKN864XkaYi8pPzTf4pUNrrWDsR2eL1/kwR+cL5xt/t6Fycg8lVaem0gPY555YSkX+JyCanVfSOtyaGiDwiRsLwTxG5zd/zOQl+w4AXVPU9Vd2vRpxnrqre4eea10Rksxh5xGXesoEicrEYWccDjl/DnP2lxcgq7nZ+T0sc7RNLnGEDhwtE5ExMhuV/vXZ3By4BzhWRphgJwzuBypjci0nOB7skRg9iHCZ78zOM0pWv+yRiEsU2YjJj04BPVHUVcBewUI3MXrJzySsYwaEmQF3n/GccW10wGbUdgXqYFH1/NADOBCa6+40AJu+lifNMHwOfOUmHAK8Br6mRTqyDyRcBk3BW0blXZeeZQk4otMQOGzgC85Xz7T4Pk7j2stexoaq6R00m7UBgpBq9C48arYtMoIWzlQD+T1WzVXUi/vVDLwZOBx5RI2d4VI2ex0k4rYSBwGDHj4OOf72cU24ERqvqClU9jEmS80dl5+e2AOecgKp+qKq7VTVHVV8FSmECEEA2UFdEqqjqIVVd5LW/MlDX+T0tU9UDbu9pKTrYwBGY7qqarKq1VHWQnphuv9nrdS3gIaf5vc8JNmdigsDpwFY9MZtwo5/7nQlsVNUcF76lAmWBZV73nO7sx7mvt4/+7gnHdVtruLgvACLysBiF8/3OvStihHMABmBaQr873ZGuzv5xwAzgE6f79A9Hcc0SZ9jAETregWAz8JITZPK2sqo6HvMtnua0EPKo6cfmZqCmnwHX/GnMuzDN/PO87lnRGczFue+ZLu4J8D/n3j67UPlxxjMexbRqUpyu034cUSBVXaOqvYGqGPWtiSJSzmlxPaeq5wKXAl0xAj6WOMMGjsjwb+AuEblEDOVE5CoRqQAsBHKA+0WkhIhch+mS+GIx5gP/imOjtIi0co7tAM5wxkxQ1VznvsNFpCqAiKSJEQ4CM67QX0TOFZGywLP+nHdaQw8CT4vIrSJymogkiMhfRMRX8eYKzjOlA0ki8gxwWt5BEblZRFIdH/c5u3NF5DIROd8ZyzmA6brk+vPLUnSxgSMCqOpS4A7gDYyAzB9Af+dYFnCd834P0BOjienLjgcz/VkX2ISR5+vpHJ6NUV3fLiK7nH2POfdaJCIHgFk44wyq+g1G8Xu2c87sAp5honOv2zA1a3Zg6tZ87eP0GZhu0WpMF+goJ3aLugArReQQZqC0l9PNq44ZgD0ArMKMG40L5JelaGKFfCwWS9DYFofFYgkaGzgsFkvQ2MBhsViCxgYOi8USNDZwWCyWoLGBw2KxBI0NHBaLJWhs4LBYLEHz/x95HDOawz3aAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "confusion_mat_counts = confusion_matrix(UMAP_y_test,UMAP_grid_search.predict(UMAP_X_test))\n", + "\n", + "conf_mat_row_list = []\n", + "\n", + "for row in confusion_mat_counts:\n", + " row_sum = np.sum(row)\n", + " \n", + " row_percent = []\n", + " \n", + " for val in row:\n", + " row_percent.append(val/row_sum)\n", + " \n", + " conf_mat_row_list.append(row_percent)\n", + "\n", + "conf_mat = np.array(conf_mat_row_list)\n", + "\n", + "colormap = mpl.cm.YlGnBu\n", + "colormap.set_under('white')\n", + "\n", + "eps = np.spacing(0.0)\n", + "f, arr = plt.subplots(1,figsize=[4,3])\n", + "mappable = arr.imshow(conf_mat,cmap=colormap,vmin=eps,vmax=1.)\n", + "color_bar = f.colorbar(mappable, ax=arr, extend='min')\n", + "color_bar.set_label('P (Predicted | True)',fontsize=12,labelpad=15,fontname=\"Arial\")\n", + "color_bar.ax.tick_params(size=3,labelsize=12)\n", + "\n", + "#Specify label behavior of the main diagonal\n", + "for i in range(0,N_CLUST):\n", + " if int(conf_mat[i,i]*100) == 100:\n", + " arr.text(i-0.38,i+0.17,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n", + " else:\n", + " arr.text(i-0.34,i+0.16,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n", + " \n", + "#Specify label behavior of the off-diagonals\n", + "for i in range(0,N_CLUST):\n", + " for j in range(0,N_CLUST):\n", + " if conf_mat[i,j] < 0.1 and conf_mat[i,j] != 0:\n", + " arr.text(j-0.2,i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n", + " elif conf_mat[i,j] >= 0.1 and conf_mat[i,j] < 0.5 and conf_mat[i,j] != 0:\n", + " arr.text(j-0.4, i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n", + "\n", + "arr.set_xticks(range(0,N_CLUST))\n", + "arr.set_xticklabels(range(1,N_CLUST+1),fontsize=12);\n", + "arr.set_yticks(range(0,N_CLUST))\n", + "arr.set_yticklabels(range(1,N_CLUST+1),fontsize=12);\n", + "arr.set_xlabel('Predicted Class',fontsize=12);\n", + "arr.set_ylabel('True Class',fontsize=12);\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "smNYVaGpCfJo" + }, + "source": [ + "# Figure 3: Evaluates the solutions obtained from GMM clustering and shows that it is much poorer than the ones obtained by WAVEMAP\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4KjFpBbooqNr" + }, + "source": [ + "## Figure 3B: GMM clustering in a 3-D feature space" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1_LxFk8-pSHP" + }, + "source": [ + "### We first set up a pandas dataframe with the specified feature values" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "yrOHmzfUgDo-" + }, + "outputs": [], + "source": [ + "UMAP_and_GMM = pd.concat([umap_df,pd.DataFrame(gmm_feat_data_nonan,columns=['troughToPeak','prePostHyper','FWHM1'])],axis=1)\n", + "UMAP_and_GMM['dbscan_hex'] = cluster_colors\n", + "UMAP_and_GMM['gmm_labels'] = GMM_class_labels\n", + "\n", + "UMAP_and_GMM['troughToPeak_abs'] = UMAP_and_GMM['troughToPeak'].divide(SAMP_RATE_TO_TIME)\n", + "UMAP_and_GMM['FWHM1_abs'] = UMAP_and_GMM['FWHM1'].divide(SAMP_RATE_TO_TIME)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MT5A7R4VpePj" + }, + "source": [ + "### and then plot the points in this space by cluster identity (clustering done in MATLAB)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 211 + }, + "id": "h8U6KoclndYf", + "outputId": "78c908b1-5219-457f-a485-12b2ff57e758" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 0.6)" + ] + }, + "execution_count": 21, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[3.8,3])\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "for i in [int(x) for x in np.unique(UMAP_and_GMM['gmm_labels'])]:\n", + " to_plot_df = UMAP_and_GMM[UMAP_and_GMM['gmm_labels'] == i]\n", + " x = to_plot_df['troughToPeak_abs']\n", + " y = to_plot_df['prePostHyper']\n", + " z = to_plot_df['FWHM1_abs']\n", + " ax.scatter(x,y,z,c=GMM_PAL[i-1],marker='o',alpha=0.75,s=20,linewidth=0.75,edgecolor='w',depthshade=True)\n", + " \n", + " ax.plot(x, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='y', zs=1.6)\n", + " ax.plot(y, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='x', zs=1.4)\n", + " ax.plot(x, y, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='z', zs=0.)\n", + "\n", + "ax.tick_params(pad=-1)\n", + "\n", + "ax.set_xlabel('Trough to peak ($\\mu$s)',fontsize=12,labelpad=5)\n", + "ax.set_ylabel('Peak ratio',fontsize=12,labelpad=5)\n", + "ax.set_zlabel('AP width ($\\mu$s)',fontsize=12,labelpad=0)\n", + "ax.view_init(elev=20, azim=220)\n", + "\n", + "ax.set_xticks([0,0.7,1.4])\n", + "ax.set_xticklabels(['',0.7,1.4],fontsize=12)\n", + "ax.set_yticks([0,0.5,1,1.5])\n", + "ax.set_yticklabels([0,0.5,1.0,1.5],fontsize=12)\n", + "ax.set_zticks([0,0.3,0.6])\n", + "ax.set_zticklabels([0,0.3,0.6],fontsize=12)\n", + "\n", + "ax.set_xlim([0,1.4])\n", + "ax.set_ylim([0,1.6])\n", + "ax.set_zlim([0.,0.6])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZuyPbtAmplYJ" + }, + "source": [ + "## Figure 3C: Optimal GMM cluster number" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ff6Ck19grZeI" + }, + "source": [ + "### We use Bayesian information criterion to calculate the 'elbow' in a BIC vs. cluster number" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 181 + }, + "id": "zQnMYPFdpl3s", + "outputId": "62e8ed64-97b8-48ff-b799-47ac9de69f58" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "f, arr = plt.subplots()\n", + "f.set_size_inches(3., 2.)\n", + "\n", + "arr.plot(BIC_list,c='k',\n", + " marker='o',fillstyle='full',markerfacecolor='w',linewidth=1,markeredgewidth=1)\n", + "\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.set_xlabel('# of Clusters', fontsize=12)\n", + "arr.set_xticks([0,2,4,6,8,9])\n", + "arr.set_xticklabels([1,3,5,7,9,''],fontsize=12)\n", + "arr.set_ylabel('BIC', fontsize=12)\n", + "arr.set_yticks([12500,13000,13500,14000,14500])\n", + "arr.set_yticklabels(['1.25','1.3','1.35','1.4','1.45'],fontsize=12)\n", + "arr.spines['left'].set_bounds(12500,14500)\n", + "arr.spines['bottom'].set_bounds(0,9)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w6guLtZu3hEm" + }, + "source": [ + "## Figure 3D: Classifier trained on GMM classes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PrXIt6zT-4Uf" + }, + "source": [ + "### Now we train a random forest classifier on the GMM classes" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "qU-00XSv3hoY" + }, + "outputs": [], + "source": [ + "X = np.stack(UMAP_and_GMM['waveform'].to_numpy().tolist(), axis=0)\n", + "y = UMAP_and_GMM['gmm_labels'].to_numpy()\n", + "\n", + "unclassified_ixs = [ix for ix,clust in enumerate(y) if clust == -1]\n", + "\n", + "X = np.delete(X,unclassified_ixs,axis=0)\n", + "y = np.delete(y,unclassified_ixs,axis=0)\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=RAND_STATE)\n", + "\n", + "data_dmatrix = xgb.DMatrix(data=X,label=y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eUK4fub3jDL" + }, + "source": [ + "### and show a confusion matrix of the five-fold cross-validated test accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TC4VHCk_3jlq", + "outputId": "746a1345-4365-493c-dd0b-3a4cd94da793" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 1.2s\n", + "[Parallel(n_jobs=-1)]: Done 3 out of 5 | elapsed: 2.5s remaining: 1.6s\n", + "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 3.3s remaining: 0.0s\n", + "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 3.3s finished\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[65, 4, 1, 0],\n", + " [10, 57, 0, 1],\n", + " [ 5, 0, 28, 1],\n", + " [ 0, 2, 5, 9]])" + ] + }, + "execution_count": 24, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "model = xgb.XGBClassifier()\n", + "param_dist = {\"max_depth\": [10],\n", + " \"min_child_weight\" : [2.5],\n", + " \"n_estimators\": [110],\n", + " \"learning_rate\": [0.05],\n", + " \"seed\": [RAND_STATE]}\n", + "grid_search = GridSearchCV(model, param_grid=param_dist, \n", + " cv = 5, \n", + " verbose=10, n_jobs=-1)\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "confusion_matrix(y_test,grid_search.predict(X_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "id": "9RfN6ebs_Kpy", + "outputId": "d21ca253-929a-4ac7-cc42-36d04dec6117" + }, + "outputs": [ + { + "data": { + "image/png": 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muSdH0qV9b7q0783c3xZzea8HefzhN9Mmo3GDWrRu2YAX357Cxvwilq/cwJvv/cjhB7RJWD4vL4c2rSo/wF/UkJKnFEwH8iR1jMnrQoLeJTCZkueuq+TMaNgJtYuBY81siqSLzOw6Sa/iwvKGQm624zncTOQJZpZf9uaWTkFBIQUFhTExqTeSl5fL8FF3UpBfuKnc8YfdwMD7+nDUcfukTfaylRuYM3cl556yG8+9Npm6dWpweved+XHGUvbatTm5uWLytEXk5IjeZ+zOttvUYdIPC9MmvypSUFAQ9/+xgby8XPLyMuRCQJBTgR1qZrZG0gjgTkmX4GbLT2XzachYngfelPR3nPLfBowzsxXlbkAGCPtJNzazKcHrjZJqmNnXkg4vg6wngV2BY8xsXZlaGYLH7h/OI4OGbbp+c9gYru93Njfeek6Jcrm5uTRqXI969eukVf5VAz7k1qu70fecvSgsMr6c+Dv3PPEFO+3QmP5/Oog2rRpQUFDE9F+X0veW0SxcsjZ1pdWYyogRHmLMnYorgSHAQlzv9AozmyrpUOA9M6sPYGYfS+qHm2SuC4wDSl0TD0uyyJ6xhI3yGSo+t6RvgQuCB/0Y57htGXCXmbULcX9bYCZuySDW295lMcsJW9Blnw42etwjKduXSQ47cX6lyvcOEh25OZ2Txtauu2Mn63jnM0nrmHzBYVU6Prek3jGXO+F6zC/gPKHuAPQGhpjZ7WHqC2u5++PWuAFuwU2I1cf90qXEzGbhj4d6Mkx131oeG9lT0pdA99gNNJJewfUs0qfcZvafmNdfAR3CNtjjyQbFs+URYlfcTs5YfgU6ha2gVOWWtGOYCsxsRlhhHk8mqe6WO46xwFBJtwG/AW1wm8Y+DVtBMsv9M25wn+wjMyA3rDCPJ2MIcqJ1KuxC3HmMqTg9zQdGAKH9mZeq3GYWrU6OJ/JEyXKb2VLg7GBvSDNgkZkVlaWOpAosqY6k3Ut5b/fAYaLHU+lELw4gSOoE3ArcFkT+3EXSnmHvT/XIfwH6lPLeRcBNYQV5PJmmgjvUqhSS/oAbX7fGeT4Ct8899NpwKuX+I85PeSIeAc4p5T2PJ6sIyMlNnqoZd+I2fF2O86cA7pRal7AVpFoKa21mCc9sm9nvklqHFeTxZBRRofPcVZDmuD3ssHnXmlGGfeypLPcaSQlPPwTnV6O9h9JTrYhStxznSjw+rNDZuGg/oUhluf8DDEogBJwv81FhBVVXKnv7Z4fjQi9rZoxpo6vsjs0SpMHNUlXiGuADSX1wwQHfx/lAOC5sBamUuz/whaRJuDW2eUAr4HTcWddEJ2Y8nqwjohJwxGFm04LZ8pNw3ofnACMDF1ChSKrcZjZf0j7ADUAP3P7yJcC7wCNmtqy8jfd40okEeTlV8lh1uZD0dzO7Bue7MDb/MTO7NkwdKTsyZrbMzPqbWTcz2zn4e5tXbE9VI0fJUzXjwlLyQ4f3zdDJeY8nuwjIU/W33JIuDl7mxbwuZkfKEFnXK7cnMlRD65yIYstck5JW2oAFuDPdofDK7YkEEuRFQLmLfZdLutvMQrsxS0S0Fg88WzWSJU3VjE+CCD+bCPaWHxu2grDeT2tJukfSDEkrgrzjJGXFg6nHkwo35k6eqhlPsKVb5VVBfijCWu5Hgd2B89i8/W0qcEVYQR5PJhFGjpKnakZzM5sXlzcPaBm2grBj7tOBDoH71yLwe8s9VY9qaJ2TMUPSUWb2cUzeEThXS6EIq9wb48tKaobb0OLxVDqqnmvZyRgIjJD0HM6X2k64Y9ahPbGE7Za/DrwgqT2ApFbA42yO+OnxVCrC7VBLlqoTZvZv3D7yesCJwd/uQX4owip3P1x34HugMfATMJcyxBOW9JKkeZJWSpoeRHVIG0OeGkWPQ66n3TY9ubbv3xKWeeTeYWxX71Q++fi7dIquErRuUZ9n7z6OCW+ezxfDzuH2q7qRmyPatW7IUwOP4evh5zL+jfN5flB32m+f2ZBWGzZs5NJLBrJT++PZptFB7LvPWYx+b1xGZYL7MidL1Q0z+9rMLjezE4O/35Tl/rCujTfiAo1fF3THF1uYaAYluRfoY2Ybgg3xYyRNNLMJZawnIS1bNeHPN5/F2P9OZP26jVu8P3PGPEaO+JwWLbdJh7gqxx1/Oogly9fR7exXaVi/Ji/c14PzTtmV735YyEdfzubmhz9hzdp8rj5/b5664xi690lfrLR4CgoKadOmBR/971l22KEV7/1nHOec/RcmTnqddu0yM01TbLkrVIfUBBfy6jjcTrBbzOyVJOVr4hwoNDCz7Ssk3NV3q5ndE7y+s7RyZjYgTH1hl8J2LE44Vy/tY65DYWZTg1DAsPnQ+U5h70/FCad24/iTD2SbJokirkK/657m1rt6UaNmjXSJrFJs37IB/xn7KxvzC1m8bB2fjP+Njm23YfKPi3l99HRWrNpIQaHx/Igp7NSmMY0b1MpYW+rVq8OA26+gXbvW5OTkcOJJh9GufWu+nVDh+PSlk2Jfecjx+BO4+aUWuJWhJyUlis9dzE3Aogq3fTOxPxBtkqRQhJ1QS+TmuPhnMrQDG0n/xG2IrwNMxJ0XzzjvjviMmrVqcHSPrnDd09kQmXWGvjWFk47Yka8mz6NR/Vocvl8bHn1hy07Rfnu0YuGStSxftSFBLZlhwYIl/DR9Frt1Tttv+RZUdG+5pHpAT2D34FjlOEnv4LaA/jVB+fbA+cD1QPI4RiExsytiXoeeOCuNsN3yEhZeUktcSJMyeRIwsysl/QnohpvW3+IbJqkv0BegdZtmZak+IatXreW+gS8y7N3Q0wPVkm8mz+fsEzrx3du9yMvN4c0PpvPhZ7NKlGnZtC4Dr+7GoKe/ylq78vPz6XVBPy7odTKdOrXPqKwKzpbvDBSY2fSYvElAacEu/4Gbi0pbUMt0BwIp1zyDmc0HrsWNo8t6b6GZjcN1QbbYBGNmg82sq5l13bZpxWNYP3zPMHqecwRt2raocF1VFQmGDOrB++NmsucpL9C150s0ql+Lv1yy36YyTRrVZui9PXj53R8YOSY7QWKKioq4sFd/atbI4+//2ML4pZWQO9SaShofpL5xVdQHVsblrcANQ0vKkk4Hcs3srTQ/xs+4yerivz+Vch2Kikwi7oILX1pe8kjjmLs0Ph0zmeeeHEmX9r3p0r43c39bzOW9HuTxhzM3oZRtGjeoResW9Xnx3//Hxvwilq/awBsf/MQR+7vhWcP6NRl6bw8++mI2T746KSttMjMuvWQgCxYuYfgbD1OjRubnOkLsLV9cbDjMbHDc7atx3oViaUjcFtCg+/4Azg1SWjGzHDPLDXrKl+CWmjsBtYO/r1C6q/EtCNUtTxA3uC7QGed+Ncz9zYGjcO5i1gHH4Nwip801ckFBYVyw943k5eUyfNSdFOQXbip3/GE3MPC+Phx13D7pEl3pLFu5gdnzVnLeybvy7OvfU7dODc44tgPTZiylft0aPD+oBxOmLuDBIeOz1qarrryHadN+5f0PnqZOnczHrkjDJpbpuDPUHc2s2Dp2wW2zjqUj0A74NPC2WhNoJGk+cKCZzaxQKzZzF9AxJpb9T5IuC9o5NEwFYSfUno27XgNMivkQUmG4LvhTuN7CLOBaM3sn5P0peez+4TwyaPOemjeHjeH6fmdz460lfz9yc3Np1Lge9erXSZfoKsFVd3xE/ysOpO9Ze1JYZHz53VzueepLjj24LV06NaNju8accVzHTeV7XPIm8xatyUhbZs2ayzOD36BWrZpsv93Rm/L/+WR/zj3vxIzIFFahCbVga/UI4M5gD8ZewKls6SdwCiVnrA/Cbejah/TOnOfgfkRilxjaUoYJ7JTKLSkXZ3X7xixllQkzW0TpExNp4cZbz9lCkRPx9Q9pmdiscvwwYynn3bTl4sNbH/7MWx/+nNW2tG27HfmF2d8olIbtp1fi4l8vxG2tvsLMpko6FHjPzOqbWQEwv/gGSUuBomAeKp08Cnws6Xmcc8Q2uJWmR8NWkFK5zaxQ0nFAmYKQeTzZRECNCip3EHzvtAT5n+Im3BLdM4aS69NpwcwelPQ98Adgb9yJsIvNbHTYOsJ2yx8F7pB0u5nll72pHk/mqYbHOpMSKHJoZY4nVZTP4n7un3C7cVZJmiNpdnEqr2CPJ50418bJU3UiHQ5SUlnup4FXcTtxPJ4qi4DcaB35fBQX4fM84L0gb2qQ/3iYClIptwDMbGw5G+jxZIV0HBypYlTYQUoq5c6VdCQl95SXIM5ThMdTOUTPWUOFHaSkUu5auCNwpX1shnOU7vFUKumYLa9iFDtIuQ42OUh5jDI4SEml3GvMzCuvp1oQsdnyfsD9OAcpdXF7yp+hDA5SfFACTyQQUKOazYiXRrBxrD/wVzMrt4OUVB9HtDo6nshSvLc8CoEAzawQt1suP7heVA7PRylD+CZ2a5IlauTUplXdTpXZBJasn1ap8m9/ovK9R+/78orKbkJKnOWOVLf8X8DlwD/LW4HvlnsiQ0R65cXsD/xJ0l9we8s3/XKZ2WFhKvDK7YkEbp27sluRVp6hgu6bvHJ7IoFk5EZktjzwDrwK+L4Mx6q3wCu3JxJExXJLuhBnsZfhnECcb2avl6euCHwcHo8jIrPlNwNnmllz4GzglvJW5JXbEwkkN1ueLFUTtosJGfQ2sEN5K/Ldck9kiIil2tTHMDOTVO7H8srtiQSiWnW9k1Evzk9Co3i/CWYWypp75fZEgghtYjkqXRV55fZEhihY7nT6TojIMMWztaMU0UbyqoHiS7pGUtIIjYH7pVABEbzl9kQGVQMFTkFL4GdJ/wHGAj/iNrM0wMUyOwI4HrfvPCVZV25JHXFnVN8ws0j5Znv2yVG8+tJH/DBlFmecdRiPP/NnAMZ/9SP33vkykyb+Qm5uDgcfujuDHr6Ulq2apL0NT934AFO/+I4Na9fTqFkTTrzkTI44qwcFG/N58ob7+XXKTyz+fSG3vHg/ux6wZ1plP3vsnuzZrCGFRW7su3DtBk59x0U52aZWDf6y304c2roJZsanc5fRb1z6DuVEYULNzPpJegTnn7wPsAfQGLehZTIuKm4/MwvljaUyLPcTwDeVIDfjtGzVhBtuPouP/zuR9es2bspfvnw1vS7uzlHH7k1uXi5/ve5prrns7wx/Z2Da23DSZX+kz6BrqVGzJnN/mcO9F9xM2912os3O7ei4b2eO630aj/95UNrlFnPv1z/z1s9b+ud/5PDdmLpkFT1GfMX6giI6NK5ImLnERGGMaWaLgYeCVCGyqtySzgaWA58DHbIpOxucdFo3AL779mfm/r75x/WY7vuWKNfn8hM55bh+GWnD9h3bbnotCQQLZ8+j/e4d6XHh6QDk5GRXDbq12oYW9WrR58NJBEadacvSH8ooYp5YKkzW/pclNcQFDrw+Rbm+xWFWFy1alp3GZZkvxk2l067l3niUkqEDH+eSPU/j5h6X0rhZE7ocvl/qm9LENXu3Y8wfujG0exe6tmgEwJ5NGzBr5VruPmgXxp7VjZeP35t9mzdKq1zhxtzJUso6pCaS3pK0RtIsSeeWUu4mSVMkrZL0q6Sb0vowaSKblvsu4Dkz+01JPukgtOpggK5dd4/cT/HU72fy0L2v8eLwzFhugAsHXk2v267gp4k/MO3r78mrmfnwuQB/m/grvyxfS35RET3aNefvR3bmrJHf0rxeLQ7argkDv5jOgM+nc0zbpjx2ZGdOfvtrlm8oSI9wpcVv+RM4r6MtcIEAR0maZGbxkT4F9MKNg3cCPpA0x8xCOy/MBlmx3JL2woXtDR3ELIrM+GUefzz1DgY9dAndDumcUVk5ubns0nV3ls5fzMevjsqorGK+X7yKtQWF5BcZ785YwHcLV3Jo6yZsKCji99Xreevn+RSYMXrmIhas2cBezTJgvZOkpPe6uNs9gdvMbLWZjQPeAS6IL2tmD5jZt2ZWYGY/Av8GDk7fk6SHbHXLj8CFI50dxDG+Eegp6dssya905sxaSM8TBnDDLWdx1rlHZk1uUUEhC2fPy5q8WAzXHf5p+RriXYAZ6e2UFc+WpzgV1rR4yCepb1wVOwMFZjY9Jm8SLg596XJdN/RQtozjXb7nkOpKGiTpHUkDU2x642YAAAp/SURBVK17JyNbyj0Y133ZK0hPAaOA7lmSnxUKCgpZv34jhYVFFBYWsX79RgoKCpn3+xJOP/42+lx+AhddenzG5K9cspwvR45h/Zp1FBUWMvnTCXwxagy7ddsLgPyNG9m4wc3iF+Tns3HDxi2Urrw0qJHLQa22oWaOyBWc0L45+7ZoxGe/L+Oj2YtpWDOPk3dsQY7gmB2a0qJuLb5blF7fbCEs92Iz6xqkwXG31wdWxuWtwK0xJ2MgTo+er0DTY3kCOBmYBpxJBWbNszLmNrO1wNria0mrgfVB3O7I8PB9w3nwns3DrtdfHcNNt56NJGb+Op8H7xlW4v1Zi19LbwMEH706iqG3P05RURFNW7fgvH6Xsc/RBwJwc/dLWfz7QgAevLi/a/PHQ2m2fYsKi87LyeGqvdrRvlEdCg1mrljLtWOmMmvVOgCuGTOVW/fvSL/9O/DryrX8eczU9I23SUussNVAw7i8hrhNJIlluqB8vYBDyxu7PgE9gH3MbJ6kfwCf4AJxlhml65c7E3TturuNHz+iUttQ2d5P/zOnZqXKB3joy/SvSZeVyb0On2BmXUt7f4+9O9pbYx9JWkfHRqeUWkcw5l4GdC52bSTpX8BcM/trgvIX41Z/DjOzGeGfJDmSVppZw5jrpWZWrt1OfvupJxpU0NtKEHBvBHCnpEtww8dTgYO2ECWdBwwCjkynYgfkxcXni78OHZ/PK7cnEoi0TCBdCQwBFuIC7l1hZlMlHQq8Z2b1g3J3A9sC38Qs675kZpdXvAksDNpQzJK469Dx+bxyeyJDRfeWm9lS4LQE+Z/iJtyKr9tXTFLSNrRLV11euT2RIQKnwtKKV25PJEjDbHnk8MrtiQxet0vildsTEQz5U2El8MrtiQSq4FJYFPHK7YkMXrdL4pXbExn8hFpJqrRyT5gwdbG0y6wKVNEUWJyu9vg2VKr8tsnfDnOwc+uiSiu3mTWryP2Sxifbj5wNfBuyI98d+czNpIhqR5VWbo+nbHjLHYtXbk9EEIqE/9P0EXXljj+QXxn4NmRJfgUCYkaSSCt3Am8bvg0RlS9vubcg0srt2bqQH3OXwCu3JzLIz5aXIJL9GElXBx4uN0gaWgnya0l6LnBsv0rSd5Iy5xmx9Ha8JGmepJWSpgceRrKOpI6S1kt6KYNSQqSti6ha7rk4bxndgTqVID8PmAMcDswGTgCGS9rDzGZmsR33An3MbIOkTsAYSRPNbEIW2wBZig8nvOWOJZKW28xGmNnbOBc1lSF/jZkNNLOZZlZkZiOBX4F9U92b5nZMjfHKaUHaKZttiIkP91HGZaX4t7URSeWuakhqgXN6nxbH9WWU/U9Ja3F+sOfhwsBmS3ao+HBpkoWUkzRtbWx9T5xlJNUAXgZeMLOs+0k2sytxjvUPBUYA6fKvHYZN8eGyIUzkJE1bG1vfE2cROXPxIi643NWV1Q4zKwxiX20PXJENmdmPD+ctdzxRnVCrdIIYUs/hIkaeYGb5ldwkcP/f2RpzH8Hm+HDgvIfmStrNzPbJjMitb1ydjEgqt6Q83LPl4r5QtXFB3tIXvyY1TwK7AseY2bosygVAUnPgKGAksA5nRc8JUjYYDMSGtL0Rp+wZ6zn42fKSRLWv0h/3hf4rcH7wun+2hEtqC1yGi1oxX9LqIJ2XrTbgZsavAH7Dhcl5CLjWzN7JinCztWY2vzjhYnFlMD5cqrnyrc+qV+lYYR5PWLp27WxffZM8sGJezh5J441Fjahabs9WSEUtt6Qmkt6StCbYXXhuKeUk6X5JS4J0v1T1QiJEcszt2RpJyxbTJ3ArGy1wQ6pRkiaZWfz+hL64sENdcMOfD3GblJ6qaAPSibfcnsgg5SZNye9VPaAncJuZrQ6WDt8BLkhQvDfwsJn9Zma/Aw8DF6b3aSqOt9yeSDBhwtT3c9SpaYpitSWND14PjjtnvjNuRWV6TN4k3PmAeDoH78WW61zWNmcar9yeSGBmPSpYRX1gZVzeCtzuvkRlV8SVqy9JVoVmqH233ONxrAYaxuU1BFaFKNsQWF2VFBu8cqcNSUMl3R28PlTSj1mSa5I6lPPeCyWNS3ebqinTgTxJHWPyupD4sM/U4L1U5SqVrUq5Jc2UtC7YULIgUMj6qe8sG2b2qZntEqI9GVcuSd0lfRI4jVgkaaykUzIpszpiZmtwB2vulFRP0sHAqbizAfH8C7heUmtJ2wE3AEOz1tiQbFXKHXCymdUH9gG6kmDnWrB9tdoj6UzgddyXcXvcEs8A4OTKbFcV5kqcc4+FwKvAFWY2NeiJrY4p9zTwLvA9MAUYFeRVLcxsq0nATNxe7+LrB4GRwWsDrgJ+An4N8k4CvsM5G/gc2DPm3r2Bb3Fjstdw+6jvDt47AvgtpmwbnFVYhHMg8Thu3/l6oBA3hlselK2F2yo6G1iAWzutE1PXTbhz2XOBi4N2d0jwrArquCnJ53EhMC7m+m84DzIrgQnAoTHv7Q+MD95bADwS5NcGXgqeaznO40qLyv6/9sm2SssNgKQ2OPdHE2OyTwMOAHaTtDcwBLdHfFvcL/M7gX+0msDbuC5bE5x17FmKnFzc4Y1ZuIMTrYFhZvYDcDnwhZnVN7PGwS334ZZl9gI6BOUHBHX1wB3AOBboiDsMUhq74H5U3gj3iQBOMfcKnukV4PXg0A04xf+bmTXEnSwbHuT3BhoFsrYNninrB2U8CajsX5dsJpzlXo2zMLOAfxJYRZwFPCqm7JPAXXH3/4hb9zwMZzkV897nJLDcQDecxc5L0J4LKWk5BawBdorJ68bmnsQQ4L6Y93amdMt9cPBe7SSfRwn5Cd5fBnQJXn8C3AE0jStzMXG9Gp+qRtoaLfdpZtbYzNqa2ZVW8jjmnJjXbYEbJC0vTjjrtF2Qfrfg2x1QWjTSNsAsC3fctBlQF5gQI3N0kE8gN7aNySKgFvuPaxVCLgCSbpT0g6QVgexGuAidAH1wPybTJH0j6aQg/0XgfWCYpLmSHgi8z3gqma1RuZMRq6xzgHuCH4LiVNfMXsWNeVvHHRbYoZQ65wA7lDJJF78uuhjXpe0cI7ORuQlAArltQsgE18uYQynDhXgkHQr8BTgL2MbcMGEFwYZtM/vJzM4BmgP3A29Iqmdm+WZ2h5ntBhyEm6foFUamJ7N45S6dZ4DLJR0QnAKqJ+lESQ2AL4AC4BpJNSSdgZtwSsTXOKW8L6ijdrDMAm5iavtgDI+ZFQVyHw2cLRAst3QPyg8HLpS0m6S6wO2lNT7oVVwP3CbpIkkNJeVIOkRSovA+DYJnWoRb7x1AzEYNSedLaha0cXmQXSTpSEl7BHMLK4F8oKi0dnmyh1fuUjCz8cCluJntZcDPBIcDzGwjcEZwvRT4I242PFE9hbilpw642evfgvIAH+M2P8yXVByc/uZA1peSVgL/xU2OYWbvAY8F9/0c/E32DG8Esi7GzREswPlz/3eC4u/jhgDTcd399ZQcAvQApgZLQn8Dzg6GNC1xk3YrgR+AsSReG/ZkGe+sweOJKN5yezwRxSu3xxNRvHJ7PBHFK7fHE1G8cns8EcUrt8cTUbxyezwRxSu3xxNRvHJ7PBHl/wHgvlIpAuK4wAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "confusion_mat_counts = confusion_matrix(y_test,grid_search.predict(X_test))\n", + "\n", + "conf_mat_row_list = []\n", + "\n", + "for row in confusion_mat_counts:\n", + " row_sum = np.sum(row)\n", + " row_percent = []\n", + " \n", + " for val in row:\n", + " row_percent.append(val/row_sum)\n", + " \n", + " conf_mat_row_list.append(row_percent)\n", + "\n", + "conf_mat = np.array(conf_mat_row_list)\n", + "colormap = mpl.cm.YlGnBu\n", + "colormap.set_under('white')\n", + "\n", + "f, arr = plt.subplots()\n", + "f.set_size_inches(3, 3)\n", + "plt.tight_layout()\n", + "mappable = arr.imshow(conf_mat,cmap=colormap,vmin=0.,vmax=1.)\n", + "color_bar = f.colorbar(mappable, ax=arr)\n", + "color_bar.set_label('P (Predicted | True)',fontsize=12,labelpad=15)\n", + "color_bar.ax.tick_params(size=3,labelsize=12)\n", + "arr.set_xticks([0,1,2,3])\n", + "arr.set_xticklabels([1,2,3,4],fontsize=12);\n", + "arr.set_yticks([0,1,2,3])\n", + "arr.set_yticklabels([1,2,3,4],fontsize=12);\n", + "arr.set_xlabel('Predicted Class',fontsize=12)\n", + "arr.set_ylabel('True Class',fontsize=12)\n", + "\n", + "for i in range(0,4):\n", + " if int(conf_mat[i,i]*100) == 100:\n", + " arr.text(i-0.35,i+0.15,int(conf_mat[i,i]*100),fontsize=12,c='white')\n", + " else:\n", + " arr.text(i-0.25,i+0.15,int(conf_mat[i,i]*100),fontsize=12,c='white')\n", + " \n", + "for i in range(0,4):\n", + " for j in range(0,4):\n", + " if conf_mat[i,j] < 0.1 and conf_mat[i,j] != 0:\n", + " arr.text(j-0.15,i+0.15,int(conf_mat[i,j]*100),fontsize=12,c='k')\n", + " elif conf_mat[i,j] >= 0.1 and conf_mat[i,j] <= 0.5:\n", + " arr.text(j-0.2,i+0.15,int(conf_mat[i,j]*100),fontsize=12,c='k')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kzkrWPTjmVio" + }, + "source": [ + "## Figure 3E: GMM labels in UMAP space" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-FuCclNKpJHH" + }, + "source": [ + "### We take the GMM cluster labels as shown previously and display these in UMAP space" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "tYd7v_4xCfZ3", + "outputId": "ed4d1d03-b430-450f-af1b-94d418428621" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "GMM_class_labels = GMM_class_labels[~np.isnan(GMM_class_labels)]\n", + "GMM_class_df = pd.DataFrame(GMM_class_labels,columns=['Class'])\n", + "\n", + "full_data_df = pd.DataFrame({'Waveform': full_data.tolist()})\n", + "data_classified_df = pd.concat([umap_df,full_data_df,GMM_class_df],axis=1)\n", + "\n", + "f, arr = plt.subplots()\n", + "\n", + "class_labels = [x for x in np.unique(data_classified_df['Class']) if str(x) != 'nan']\n", + "\n", + "for ix in np.unique(class_labels):\n", + " filt_df = data_classified_df[data_classified_df['Class']==ix]\n", + " arr.scatter(filt_df['x'],filt_df['y'], s=30,marker='o', linewidth=0.25, \n", + " edgecolors='white', alpha=1, c=GMM_PAL[int(ix-1)])\n", + "\n", + "ns = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[0], \n", + " color='w', label='Narrow-Spiking (NS)')\n", + "bs = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[1], \n", + " color='w', label='Broad-Spiking (BS)')\n", + "nst = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[2], \n", + " color='w', label='Narrow-Spiking Triphasic (NST)')\n", + "bst = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[3], \n", + " color='w', label='Broad-Spiking Triphasic (BST)')\n", + "\n", + "arr.legend(handles=[ns,bs,nst,bst],fontsize=12)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['bottom'].set_visible(False)\n", + "arr.spines['left'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.set_xticks([]);\n", + "arr.set_yticks([]);\n", + "plt.tight_layout()\n", + "f.set_size_inches(6, 4)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0CPLlCn2Kj1N" + }, + "source": [ + "### Here we also show all the single units per GMM cluster with the averaged waveform in black" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "kVwRMJ5JKj8I", + "outputId": "7f375b67-27ba-4119-afb4-9761de1e76ae" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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yBxgOxnFwcfBwcdErozrlnok3rTJVKcDfn8Pfm8Pfn8fvzhMcyOMfKqDy/vkTSK83rGq02OnmQJLxcxSVQ4/bR7fTx4g/higYDVJ0Gm0c9obI+Fn2uof4WOKdXJ/ceDbnwD+NfY+n8i8w6I4w5E8wxAgK8FEoJyBz07NEPzQX/bdaAIhgU0scF58CDg4OrdQTNcJssNeRIks+KHFZZAUfq7npjOZiQFksY0GaEX8CV3l4ysdVLvmgxKA7wkvOAXYVDzDup+j1hwnwSFXC6DYmFiYFHFxcQoSoIUoRp9LVLUeOAyDAx6vMgz3+31SBYmzJL89fI/Wh7BMUA4d0kCs7cKUQQBcNE4NsUGDEn8BQOqkgg0eAqzwyQY6Mn8dXHiMqRZYCISx0dD43fCfbvf28OXIla8NLZ3wCaT9HNshxxBugSa9nu9+DhuBUmoPFrx/CbA2jvbcZgCg2zTQwRgoHBx/FUq2DuBGhK7SYQX+ciIT4rcRGfiN2+RmLA0ATjQa9ZnKdjas80kGOdJClVo/TZjWxKryYPcUD7Csdps8dIa8KZIMi46Tw8KkhRoAiQ55BxrEwCGFNNnxVxR8K4OFXhg99BEHNwkqNGZ39l0buIYSJgU4Rl2yl4RUQEKAmFXy0Xp/KN3m4GJgUKPJsbhfFwGV1aNGMZ3sfcQcZ9McpBA5+UO4rGJUFg35PnuI9R0j86BJEBKPSW0EUReWgUCzTO6jTkywzO+lx+1lrL+G98d/A1M2zEsfJMMWgXk9SryehEkgNVEAuWmTEHefn+Wd4rvAS+5xDRJTFRJAjq/IYGLRQh4tXGajz0fAxMBEEHx8PH6kEygSLYNK/nB0zugLjZKb8XZ3i/cnwUHgVV/m4s5VD7hAfTb6TNmt6df1R9rmH6XMGiWgWu7zuSt4BSilyn99N5LYO9DkhAOqpoUbiHFIDeHgskXnUGzWstBaw0+3hvbE3c1Pyjewo7afLWDIjO84UTTTiEiFuR/i4fRPpZI5txb3scw5xpDTE1tJLdDu9TJAlhEU9SXx8Sjho6ISxsXQDQ3RUAI44eEG5ga7PwsraGQkk8/FtoBQE5cZnOUQIEtKRkIYkDaTOQmuw0BdE0OdHkGbrtNPjFAG71UEeyW7mA3Vvm9EJHHYH6XeHaTDq2EnP5PfOfwyicj6hW9tRQCM1RCXMoBohS55GammxmlhhdbLd2c+f1t/CVZG17HYOsqAy+ffVIKFF2RBZyzKrk83GDi6PrsISk//KPsGm/IuMBmlQ5e65h0+KHLZvEJMwMS1CQmLYVsWPnMHam5czI4HYN7eBJuX6QwMRKQulWBn+TrsEoy7+zgzOTwbx9+dBE8xLkxhX1GLd0IRWY74i30pfg7tS/8EttTdMe76lr3wGvBEyFAi5uUmHqpyAwj/2EP+b5Si9XLUY6BRViRR5LCyWGR0st+axy+3hLxp+l9XhxWSCPMCs9KzOlgajhutjV3PEHeSZ4g5uiG3gD2rey5PFF3gkt4VRL4WrXDJBlnE/y5AaZ8AfAxSGp2NgYPHKaz1TZiQQ69qGGWSsY2JQOJKi9Mw47mNjFP6+G+u6RkIfmYu+8JVrQ3b7PWwu7OSyyMpplTHhZxjz0rjKp18NT35f+kE/WkcY/fJy4zBBuawUGQIUy5jLknAH+9wjfKHh46wKLQKgx+1lhbVg2ud4Pmg3m2k3mzno9LOttI85RhN/Unczg/44zxd3M+6n8AhI+VlG/HGyfoGCKuL6Hr6aXjd5KmYkkFUynxI+tqZjaxaBKvciiqo02VAKUBQoVvrzQsfcpdTNT9Lz7iOkR9IE9w3Sf/PztH1pPflrT+wul/B4JL+ZRXY7dXrytPbscQ4x4k0QqIDxykitKvoUvnqA+J2rATDRUSjSZAlQ1BClK7qUg+4gn6n/8KQ4hrwx6rQExiy45XNBh9XKXLOZPm+Yg24/EQnx+vC68mdvgPlmG8vNTmJamB3OAXY73Rxyh8663BkJ5FONH2DAG2EkSBGXMHPNFuIS5SXnAJuLO9hXOkRIs1iqdfKks40kUXpUH47nU2fUYjdYFP4wgXZ1Lb2feJr6TyzGv6XxhDJ+lX6Gd8WvmZZAdjg9jE4GmsrVVPFbvZhdSYzVCaA8tzNHsTLiabLU6GDQH+OTte9jTbjcEA1UQJ83Qpe9eCaX47yjiUa72Uyb0ciAP8qwN84Su4NV9iL6vCF2ON30ekPEtAiXh9bwx7VnH62ecSTVUz57nUNsKezCUQ51eoJOs410kGNLYSePZ7fSHwyzzl5Kt9fLXGnlSed55tJEvzaGFmgUKWEc8tj7kUewP9WJfWPzZAFRQny56X/z7sS1pzXmi8P/wgPpRxlUozh4qLzPxLX/Q/zedRiLoxgIRqUeriWGoLPaWsCHat/BW+NXTuZzwO0r7w0yDVFeaKSDHAPeKEVVIiphklqUtJ/j+dJudjk9/HnDx87vNpiG6Cy35/PuxBtZbM+jELj0eH3U60k+VPt2fqfuHaw0F9AfjGGLwb7gIEuNDoaYYK5qxhKDAiWYZ9H5t1dQ+Nv9qPyxAawcRR7NbzmtHQW/xJifYkKlJ0PSpR8NYLwuibE4Wg7gYeDhYGDQLA2ENZu3xV/PW2JXTObjKJdMkL8oxQHlXs8Sax5r7MW0GPXlFXqUWGp3clP8jWed/xl3lKNamI3hdXSFF6MpYcRLsaW4kzWhxVwZW8Nio51V9hKiEmaeXQ5xDzBCmzSgo+HgUXtJO+alNRT+5eAJee8s9uCrqUc9e7xe0n6eIuWp/cpXFL9xmNBH5k6m8XDRMLjSXEUfw7w9uoH317zlhF5St3OEBZW1uxc7US1Mu9nMSnshq+yFLLfmn3WeZxVJERFW2gu4IrKGoWCcWkky6I2yyOogYoQRYI7ZhFLCexNvxlU+vcEw9STLk1yUQ+2fLaN0Xx/+ocJkvj1eLyk/O2XZu0uH6C714lZCcu4jI0jSwFh/zBMIOvNo4WAwyBy9kf/VcOsJkdpMkEdEO+3Y0sXKWexKdCyPWbCDFqOeG2NX0+sPMhakWV7ZBqEUOFiYjPrjWJrBWnsxcT1c2WBFY4IssdZaoh+eR/5v90/mN06WXm/qFvh+5xAHg/7Jz8W7y97jqHdQQIwwId0mpEx+p+YdRPTQCXn0uL0XhPf45je/yfr160kkErS3t/PpT38azzvWRR0bG+Omm24iGo3S0dHBv//7v59w/Fe+8hXmz59PIpHgkksu4Yknnpg122Zt462IFuZdsWsIYfJw7im67CX8VvItOOKQVQVGvQxNVj2Nei2tej1hbDLkKFKg8SPL8DaNEwwcW0PyfGH3lOUdcvrJUvY63gtpgv4S1luP9Yii2ISwqdGjLAnP48b4iSPGw944NVp81sdbzoR8Ps+Xv/xlRkZG2LRpE7/4xS+44447Jn//xCc+gWVZDA4O8u1vf5vbbruNHTt2ALBp0yY+85nP8IMf/IBUKsVHP/pRbrrpJnx/ZhOTTsWs7symica1sctYay3h5/mnKaoSf5B8HxEJ80JxF02qljwlGo0aOrRWNHSKOERCEay3NlL68cBkXk/ntp+ynIyfY4fTPfm5eM9h7FvnIMax09ExmGe0MM9o4y2RK0nq8cnfAhXQ6w0z12jmdHR2dnLHHXewZs0akskk73vf+ygWz35J4/HcdtttbNy4EcuymDNnDrfccgtPPvkkALlcjvvvv5/bb7+dWCzGhg0beMc73sG9994LwIEDB1i5ciXr169HRLj11lsZGRlhaOjsYyBwjvZJXRteyg3RjTxR2IqLywcS15PUYzztbKdFb+CIN8TKUCdhbARhlBSRm+ZQ+tEAR7vdzxZ3cqou+O7SQbq9cvUSjDi4j41h/2br5O8JwtSTZEO0i7ge4YromhOOP+D202G2TDuk/73vfY+HH36Ynp4etm3bxj333HPSdE888QQ1NTWnfE3X9T/22GOsXFmOJu/ZswfDMFiy5NjgYVdX16QHuf766/F9n02bNuH7PnfffTdr166lpaVlWmWdjnPmXxfbc9nor2O3c5ikEaXDaqWoXHzfZzzI4KFoooZeXIq41KxvJuVsx9+ewVid4DD9jPsZ6ozEK/J+ofASacqN2NJ3+7Cua0RLlOMdGmBg8sboJRSVw4ZIF616/eSxxcChoIos0Kff9vjkJz9JW1sbAG9/+9vZunXrSdNt2LCBiYmJaed7Mu6++262bNnC17/+dQCy2SyJxInXIJlMksmUR9bj8Tjvec972LBhA0opampqeOihh2ZtL7RzutPy1eEuYlqIFq2OBUY7eVXA0HQus1byeP45LomsQENDR8hLCfumFko/KlczJXwOuf0nzffn+WeB8pLH0n192Lcc+7MtTNZZS1lozyWhx1hozzuhNb/PPcxCc+4r8pyK4+/GSCRCNjt1D+tMeeCBB/jsZz/LQw89RENDedwrFouRTqdPSJdOp4nHy1XmXXfdxTe+8Q127NiB4zh861vf4sYbb6Svr29WbDqnAtFFZ4HVji4ab4pfTkTCBApiZhRLTPpKw5WNV8oLmq13teD85xDKKY/Lbi/se0WeQRDwbKnsXt1HRtHabIwV8cmTWaTPY6ndwYA/zKWhlbQbx+aXDHljxLQwIe3sFzWfjMcff5xYLHbK1+OPP37KYx9++GE+/vGP8+CDD7J69erJ75csWYLneezdu3fyuxdeeGGyCtq6dSs33ngjS5YsQdM0rrvuOlpbW3nqqadm5ZzO+V7tLUY9dXoNBVXkA4kbEE3IqyIrzPns8g/SQC2gEcLAmBtGXxTBfXQUgMdyz70iv8PuIKOU76jit3uxf/uY92iglqVWB7ZYrLWXUWckJmMcrvLo90boMFpfkedssXHjRrLZ7ClfGzeefO7tI488wi233ML999/PZZdddsJv0WiUd7/73XzhC18gl8vx5JNP8uMf/5gPfvCDAFx66aX85Cc/obu7G6UUP/vZz9izZw+rVs3OqoFz3sdr1GvY6fcwx2iinxEuD69kzEuz2d+OjUGePBpCgMLCmKxmrDc38svSFlzfxdSPzWv4zsRDAPj7cvi7s1jXlT1EBIsOvZWEHqNWT7DU7qDVODY9YY9zkEXW3Fndp3S2uP3220mlUtxwww2T323cuJGHHiqf61e/+lU+8pGP0NTURH19PXfeeeekB7n11lvZv38/11xzDePj47S3t/O1r32NZcuWzYpt52VdzM5SN0usDob8MV4o7qUUOGT8PPdlHma3c5AipXJ3F5tMJkfq9U+RfORKtFqTbzT/BRsj66jVyw21rn3v5whD5D73ElqLTfiP5qMBDVLDpeZKXhddzlWhNdSZSRZZ5bbGoDdGSTnMM2enZX+RceE/s67JqGPQH6PNaKTVqKdWT1BvJnlbZCMtWgMgCBoePnrcwHxDPc5/lfvxE0GWMT/Ni6V9/CK1iSMMEQyXcB4enmycRggzR2tmZWQhl4RXoukaC83KnqFBjmF/fFoxjyqv5LwIpE5LMOaXJ/SsDS2loIo063UsCs1lbXgJMUKYaJTwsDCx3tVC6YFyb+bhiSfoNFpZbS/iX9I/BMpzPqwbm9DqLEw06rUk68LLuCrcRSrIsMJagIiUN/F3elludV6QVcvFwHkRiCYaUQkzWhHJm6KXMeiPUa8neHP0cuq0GiKUn57gE2BuqCU4UsQ/kOen7ibuHP8B947/J4+UNqPyPqXv9BH6nXL1ESLEOmMp8602+v0RNoS7iGghXOWxy+lhhT3/jDbOr1LmvD1xar7ZxmF3EE/5mGJwZXg1WVVgkT2P6yJXolW2YfLwCRk21o1Nk17kX8d/wD+Pfh+A0g/7MS5JondGMNHoMFposRuZb7ZxRXgNST1Oys+yo7KhXWW/8ipnyHkTiIiwwJrDPvcwALV6ggXmHAa8EdZHV7DaWESoMvtLCLDf1YLz40GUE9DLKHs5jH+4QOGrBwn/7jyAyt5dbeVoqdlIm15Pt9NLvz/CGnsRES10SnuqTI/z+sy6hBbFQGfAK8c55potXBu5lEVWO29JXkGDlFfOF/HRV8TQV8TJ/N42VNYjyHhkf3cb4d/vwFibxMZgmdXJG6LrmWs24yqXXe4BavU4y6zOWZkLUeVV2B+kPJI6xJifLqvJWaAAAAa6SURBVD/ZoPLkpmcKO1BewKdHv8IEqfKmT15A/ot78HZkkaSB3hkh8heLERGuNrpYFu7kmsh6DM3gqnDXyZ6VUuVi3qs9GxQoqfLyy3xQZEthJ99L/ZTnS7sZPrqMQSmK/3QAb3eO2JdXIIZGM3XcELuajZF1dJitdIWWVHspp+biFcjLyQYFfp7dxHOFXdyb+Qlp8q9Is4A5XB5dyQ2xjbQZDSyz55+zsZXXCK+dPcpiWphro5egaUI+KPJA7ldkyZcfwwHMp43Xx9ayLrScJqOOBXZ7VRznmAvKgxzPAaeP76R/Sp87xJ7CAeJEeWvt1dTqcRZb81hlL6zGN6bHa6eKORmOcjnsDDDoj7PAbCOpxycbtlWmxWtbIFXOmgt/sK7KxUtVIFWmZEZVjIhsB2Z3zv/Z0QCMvNpGvIwLzaaQUuqMp5fNtJtbVEpdcqaFzTYisuVCsgcuPJtE5PQr4aegWsVUmZKqQKpMyUwF8q/nxIoz50KzBy48m87KnpnGQar8mlGtYqpMSVUgVaakKpAqUzItgYhInYj8SERyInJQRH77XBs207JF5Isi4opI9rjXrO+KKyJ/KCJbRKQkIvecJu2fiMiAiKRF5G4RmfVRxunaIyIfFhH/ZdfnmtPlP10P8s+AAzQDtwB3isj0tkM+e2ZS9neVUrHjXt2nSHc29AF/Ddw9VSIReSvwGeBNQAewAPjLV8ueCv/zsuvzq9MdcFqBiEgUeA/weaVUVin1BPAfwAenYdBZ8WqWfSqUUj9USj0AjJ4m6YeAu5RSO5RS48DtwIdfRXvOiOl4kCWAp5Tac9x3LwDnw4PMtOy3i8iYiOwQkdvOvXlTspKyrUd5AWgWkfpTpD8frBORERHZIyKfFzn9Bm3TGYuJAemXfZcC4idJO9vMpOzvUQ4KDQKXA/eLyIRS6jvn1sRTEoPjHvV97H2cc3S3n4bHgFXAQcri/S7gAX8z1UHT8SBZ4OX7QCXgNE8Xmh2mXbZSaqdSqk8p5SulngL+EfjN82DjqXi57Uffn4/r9gqUUt1KqR6lVKCUehH4K6ZxfaYjkD2AISLH73TfBew4M1NnxNmUfexJ6q8OOyjbepQuYFAp9Wp4j5MxretzWoEopXLAD4G/EpGoiFwNvBO496xNnMWyReSdIlIrZS4DPgn8eLZtEhFDREJQfo6xiIROUZf/G/BREVkhIjXAnwP3vFr2iMj1ItJceb8M+DzTuT5KqdO+gDrgASAHHAJ+ezrHzcbrVGUDG4Hscem+Q7luzwIvAZ88R/Z8kWPPbjz6+iIwr1L2vOPS/inlNlEa+AZgv1r2AHdUbMkB3ZSrGPN0+VcH66pMSTXUXmVKqgKpMiVVgVSZkqpAqkxJVSBVpqQqkCpTclEJpDIId815KmtFZZ7FrEZjReR+Ebl+NvM8l1xQcRAROf4xChGgBBx9dNLvKaW+fR5tuR/4vlLqvlnO9zLgTqXU+tnM91xxQQnkeETkAPAxpdTPX4WyWymPpbQppWZ9qamI7AVuVkqd1aq388HFVsUcEJHfqLz/ooh8X0S+JSIZEXlRRJaIyGdFZEhEDovIW447Nikid4lIv4j0ishfi5xyB5o3A88dL45K2X8mItsq0x/vEpFmEXmoUv7PRcrbNFbGQ74lIqMiMiEim4+Og1T4FfC2Wb9A54CLSiAn4e2UB+5qgeeBn1I+pzmUxxq+dlzaeyjPf1gErAPeAnzsFPmuBk72VMX3UBbPkkrZDwH/B2islPvJSroPAUlgLlAP/D5QOC6fXZw40nvBcrEL5HGl1E+VUh7wfcp/1JeUUi5wH9ApIjWVu/cG4FNKqZxSagj4B+D9p8i3hpPP2/iKUmpQKdULPA5sUko9X/E0P6IsPACXsjAWVeanPKuUOn7iU6ZSxgXPBbWJ3RkweNz7AjCi1OQju4/esTGgDTCB/uM6JRpw+BT5jnPyWWsvL+/ln2OV9/dS9h73VYb6vwV8riJcKnmf3cPtzhMXuweZLocp94galFI1lVdCKXWqua3bKFcjZ4RSylVK/aVSagVwFXAjcOtxSZZz4nzVC5ZfC4EopfqB/wb+XkQSIqKJyEIRecMpDvkZ8LrKRJwZIyJvFJHVlUZwmnKVExyX5A2U2y8XPL8WAqlwK2ABOylXIT8ATvoAO6XUIPAI5dlrZ0JLJf805Qbpo1RmwYnIpZQnOj1zhnmfVy7YOMirjYisAL4JXKZm8SJVAnB3KaX+a7byPJdUBVJlSn6dqpgqZ0BVIFWmpCqQKlNSFUiVKakKpMqUVAVSZUqqAqkyJf8fQIH51bfCGbcAAAAASUVORK5CYII=\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in np.unique(GMM_class_labels):\n", + " f, arr = plt.subplots()\n", + " f.set_size_inches(2, 1.75)\n", + " GMM_cluster = data_classified_df[data_classified_df['Class']==i]\n", + " \n", + " for _,row in GMM_cluster.iterrows():\n", + " plt.plot(row['Waveform'],alpha=.3,linewidth=.6,c=GMM_PAL[int(i-1)])\n", + " \n", + " plt.plot(np.nanmean(GMM_cluster['Waveform'].tolist(),axis=0),c='k',linewidth=1.)\n", + "\n", + " arr.spines['right'].set_visible(False)\n", + " arr.spines['top'].set_visible(False)\n", + " arr.set_ylim([-1.4,1.1])\n", + " arr.set_xticks([0,14,28,42])\n", + " arr.set_xticklabels(['0','0.5','1.0','1.5'])\n", + " arr.set_xlabel('Time (ms)',fontsize=12)\n", + " arr.set_xlim([0,42])\n", + " arr.set_yticks([])\n", + " arr.tick_params(axis='both', which='major', labelsize=12)\n", + " \n", + " arr.spines['left'].set_visible(False)\n", + " \n", + " x, y = 23,-0.8\n", + "\n", + " n_waveforms = plt.text(x, y, 'n = '+str(len(GMM_cluster))\n", + " , fontsize=12)\n", + " plt.tight_layout()\n", + " plt.margins(0,0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "njq5UwqpAzsE" + }, + "source": [ + "# Figure 4: Interpretable Machine Learning on WaveMAP" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4ZTxMJZLA_su" + }, + "source": [ + "## Figure 4A: Inverse mapping of WaveMAP" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7ZOMRpSNBFA6" + }, + "source": [ + "### We use UMAP's inverse transform function to produce waveforms found on a grid of test points tiling the manifold\n", + "\n", + "---\n", + "\n", + "Note that this *is* sensitive to the stochasticity in projection and likely will look incorrectly aligned to the grid. This is because the projection of the high-dimensional graph uses a force directed layout algorithm using stochastic gradient descent. Although the stochastic gradient descent can be made deterministic through setting a seed, the seed will vary at the level of the CPU OS itself and cannot be set from within Colab. Since Colab spins up a new instance every session, this can look different day-to-day if you let your session expire. You can change the location of the corners of the grid in the `corners` variable to try to produce a good fit." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 411 + }, + "id": "HStjBK5Ptill", + "outputId": "f48bbada-9f3c-42cd-8248-8118070c2f09" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "def find_nearest_color(embedding, test_coord, threshold_dist=0.3):\n", + " x_array, y_array = embedding[:,0], embedding[:,1]\n", + " \n", + " # Take coordinates of test point to calculate an array of each point's distance to test then return index\n", + " # where the minimum value is found\n", + " dist_array = np.array(np.abs(x_array-test_coord[0])+np.abs(y_array-test_coord[1]))\n", + " idx = dist_array.argmin()\n", + " \n", + " if dist_array[idx] <= threshold_dist:\n", + " return cluster_colors[idx]\n", + " \n", + " else:\n", + " return (0.8,0.8,0.8)\n", + "\n", + "\n", + "corners = np.array([\n", + " [6.7, 10.2], # top-left\n", + " [9.6, 10.5], # top-right\n", + " [-3.3, 2.], # bottom-left\n", + " [8, 2], # bottom-right\n", + "])\n", + "\n", + "test_pts = np.array([\n", + " (corners[0]*(1-x) + corners[1]*x)*(1-y) +\n", + " (corners[2]*(1-x) + corners[3]*x)*y\n", + " for y in np.linspace(0, 1, 10)\n", + " for x in np.linspace(0, 1, 10)\n", + "])\n", + "\n", + "inv_transformed_points = reducer.inverse_transform(test_pts)\n", + "\n", + "# Set up the grid\n", + "fig = plt.figure(figsize=(4.5,7))\n", + "gs = GridSpec(20, 10, fig)\n", + "gs.update(wspace=0.05, hspace=0.05)\n", + "scatter_ax = fig.add_subplot(gs[:10, :10])\n", + "waveform_axes = np.zeros((10, 10), dtype=object)\n", + "for i in range(10):\n", + " for j in range(10):\n", + " waveform_axes[i, j] = fig.add_subplot(gs[10+ i,j])\n", + "\n", + "scatter_ax.scatter(reducer.embedding_[:, 0], reducer.embedding_[:, 1],\n", + " c=cluster_colors, s=30,linewidth=0.25,edgecolor='white',zorder=1)\n", + "scatter_ax.scatter(test_pts[:, 0], test_pts[:, 1], marker='x', \n", + " c='k',\n", + " s=30, zorder=2, alpha=1)\n", + "\n", + "# Plot each of the generated waveforms\n", + "for i in range(10):\n", + " for j in range(10):\n", + " waveform_axes[i, j].plot(inv_transformed_points[i*10 + j], \n", + " c = find_nearest_color(reducer.embedding_,\n", + " test_pts[i*10 + j]),\n", + " linewidth=1.0)\n", + " \n", + " waveform_axes[i, j].set(xticks=[], yticks=[])\n", + " waveform_axes[i, j].spines['right'].set_visible(False)\n", + " waveform_axes[i, j].spines['top'].set_visible(False)\n", + " waveform_axes[i, j].spines['left'].set_visible(False)\n", + " waveform_axes[i, j].spines['bottom'].set_visible(False)\n", + " \n", + "scatter_ax.set(xticks=[], yticks=[])\n", + "scatter_ax.spines['right'].set_visible(False)\n", + "scatter_ax.spines['top'].set_visible(False)\n", + "scatter_ax.spines['left'].set_visible(False)\n", + "scatter_ax.spines['bottom'].set_visible(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G6Eh0SoWlIfb" + }, + "source": [ + "## Figure 4B: SHAP Values" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "syORYQJDlU1F" + }, + "source": [ + "### First we plot the SHAP values at the top-10 time points broken down by their \"informativeness\" per waveform cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 275 + }, + "id": "KSEsNopQvgCy", + "outputId": "cae35f6c-0568-4b1f-88d3-1c7a7febcee1" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "n_bars = 10\n", + "X = np.stack(UMAP_and_GMM['waveform'].to_numpy().tolist(), axis=0)\n", + "y = UMAP_and_GMM['gmm_labels'].to_numpy()\n", + "\n", + "unclassified_ixs = [ix for ix,clust in enumerate(y) if clust == -1]\n", + "\n", + "X = np.delete(X,unclassified_ixs,axis=0)\n", + "y = np.delete(y,unclassified_ixs,axis=0)\n", + "\n", + "UMAP_model = xgb.XGBClassifier(UMAP_grid_search.best_params_)\n", + "UMAP_model.fit(UMAP_X_train,UMAP_y_train)\n", + "explainer = shap.TreeExplainer(UMAP_model)\n", + " \n", + "shap_values = explainer.shap_values(X)\n", + "\n", + "clust_colors = []\n", + "SHAP_REORDERING = [2,5,7,4,1,0,3,6] #Need to do this so the UMAP colormap aligns with the SHAP color order\n", + "for i in SHAP_REORDERING:\n", + " clust_colors.append(UMAP_and_GMM[UMAP_and_GMM['color']==i]['dbscan_hex'].iloc[0])\n", + "\n", + "umap_cmap = mpl.colors.ListedColormap(clust_colors, name='umap_cmap')\n", + "\n", + "fig = plt.figure();\n", + "\n", + "shap.summary_plot(shap_values[:], X, [str(np.round(x*(1/30000)*1000,2)) for x in pd.DataFrame(X).columns.tolist()],\n", + " plot_type='bar',show=False,color=umap_cmap,\n", + " max_display = n_bars)\n", + "\n", + "ax = fig.gca();\n", + "ax.set_xlabel('Mean Abs. SHAP Value',size=12,fontname='Arial')\n", + "ax.set_ylabel('Time Point (ms)',size=12,fontname='Arial')\n", + "ax.get_legend().remove()\n", + "ax.set_xlim([0,3.5])\n", + "ax.set_xticks([0.0,3.5])\n", + "ax.set_xticklabels([0,3.5],fontsize=12,fontname='Arial')\n", + "fig.set_size_inches(4,3.5);\n", + "\n", + "ytick_labels = [round(np.float(i.get_text())/(1000/30000)) for i in ax.get_yticklabels()][::-1]\n", + "bar_heights = []\n", + "\n", + "for j in range(n_bars):\n", + "\n", + " bar_height = ax.patches[j].get_width()\n", + " bar_heights.append(bar_height)\n", + "\n", + "bar_heights = bar_heights[::-1]\n", + "percent_total_height = [x/sum(bar_heights) for x in bar_heights]\n", + "for k,label in enumerate(ytick_labels):\n", + " arr.axvline(label,color='k',alpha=percent_total_height[k])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7gzYoWFJletv" + }, + "source": [ + "### And show where these time points are located with their relative importance encoded by opacity" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 149 + }, + "id": "DU36L7jw2hnz", + "outputId": "3d5bf86f-53f5-4104-f6c9-e3a1c2eff146" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "f, arr = plt.subplots(1,figsize=[2,1.5])\n", + "\n", + "for i in range(0,full_data.shape[0]):\n", + " arr.plot(full_data[i].T, c = 'k', alpha = 0.025,linewidth=1.5);\n", + " \n", + "arr.tick_params(direction='out',colors='k', axis='both')\n", + " \n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['left'].set_visible(False)\n", + "\n", + "arr.set_xlabel('Time (ms)', fontsize=12,fontname='Arial');\n", + "arr.set_xticks([0,14,28,42])\n", + "arr.set_xticklabels(['0','0.5','1.0','1.5'],fontsize=12,fontname='Arial')\n", + "\n", + "arr.set_yticks([]);\n", + "\n", + "for i,t in enumerate(ytick_labels):\n", + " arr.axvline(t,alpha=percent_total_height[i]*20,\n", + " color='goldenrod',lw=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mZdlpLuFljEd" + }, + "source": [ + "## Figure 4C: SHAP values broken down by WaveMAP cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jK57eQSCpe0H" + }, + "source": [ + "### We plot each WaveMAP class with their top-three individually informative time points in a SHAP-sense. We also use bar plots to show the relative importance of these three locations which is also encoded as opacity in the lines." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "t_gAGTHCRD1E", + "outputId": "b43cfbdd-d59c-40ce-d1c9-ecb8c6abc7a0" + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
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" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "n_bars = 3\n", + "for i in range(len(shap_values)):\n", + " fig = plt.figure()\n", + " \n", + " clust_color = UMAP_and_GMM[UMAP_and_GMM['color']==i]['dbscan_hex'].iloc[0]\n", + " shap.summary_plot(shap_values[i], X, \n", + " [str(np.round(x*(1000/30000),2)) for x in pd.DataFrame(X).columns.tolist()],\n", + " plot_type=\"bar\", \n", + " max_display = n_bars, color = clust_color,show=False)\n", + " ytick_labels = [np.float(i.get_text())*0.05 for i in ax.get_yticklabels()][::-1]\n", + " \n", + " ax = fig.gca()\n", + " fig.set_size_inches(0.6,1.0)\n", + " ax.set_xlabel('',fontsize=12)\n", + " ax.set_ylabel('',fontsize=12)\n", + " ax.set_xlim(0,1)\n", + "\n", + " f, arr = plot_group(i+1,clustering_solution,UMAP_and_GMM,CUSTOM_PAL_SORT_3,mean_only=True)\n", + " ytick_labels = [round(np.float(i.get_text())/(1000/30000)) for i in ax.get_yticklabels()][::-1]\n", + " bar_heights = []\n", + " arr.spines['left'].set_visible(False)\n", + " arr.spines['right'].set_visible(False)\n", + " arr.spines['bottom'].set_visible(False)\n", + "\n", + " for j in range(n_bars):\n", + "\n", + " bar_height = ax.patches[j].get_width()\n", + " bar_heights.append(bar_height)\n", + "\n", + " bar_heights = bar_heights[::-1]\n", + " percent_total_height = [x/sum(bar_heights) for x in bar_heights]\n", + " for k,label in enumerate(ytick_labels):\n", + " arr.axvline(label,color='k',alpha=percent_total_height[k])\n", + " f.set_size_inches([1.5,1.0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TNQT2bTVpCY8" + }, + "source": [ + "# Figure 5" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9TWRyR1FBOoF" + }, + "source": [ + "## Figure 5A,B: Average FR traces aligned to stimulus" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0BoC8n5Yk9r0" + }, + "source": [ + "### We next plot the averaged FR traces for each WaveMAP cluster aligned to stimulus onset" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 673 + }, + "id": "m8NciEo7pCvw", + "outputId": "dd603aaa-a801-412d-8584-ab723de07026" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "BS_ORDERING = [5,6,0]\n", + "NS_ORDERING = [7,1,2,3,4]\n", + "\n", + "f, arr = plt.subplots(3,figsize=[2,3.33])\n", + "\n", + "time = np.arange(-0.1,0.5,0.001)\n", + "\n", + "for i,ix in enumerate(BS_ORDERING):\n", + " PREF = UMAP_traces_df.iloc[ix]['PREF']\n", + " NONPREF = UMAP_traces_df.iloc[ix]['NONPREF']\n", + " PREF_UPPER = UMAP_traces_df.iloc[ix]['PREF_UPPER_BOUND']\n", + " PREF_LOWER = UMAP_traces_df.iloc[ix]['PREF_LOWER_BOUND']\n", + " NONPREF_UPPER = UMAP_traces_df.iloc[ix]['NONPREF_UPPER_BOUND']\n", + " NONPREF_LOWER = UMAP_traces_df.iloc[ix]['NONPREF_LOWER_BOUND']\n", + " arr[i].plot(time,PREF,color=CUSTOM_PAL_SORT_3[ix])\n", + " arr[i].plot(time,NONPREF,'--',color=CUSTOM_PAL_SORT_3[ix])\n", + " arr[i].fill_between(time,PREF_UPPER,PREF_LOWER,\n", + " color='gray',alpha=0.2)\n", + " arr[i].fill_between(time,NONPREF_UPPER,NONPREF_LOWER,\n", + " color='gray',alpha=0.2)\n", + " arr[i].set_ylim(5,30)\n", + " arr[i].set_xticks([-0.1,0.1,0.3,0.5])\n", + " arr[i].set_xlim(-0.1,0.5)\n", + " arr[i].set_yticks([5,30])\n", + " arr[i].spines['left'].set_position(('axes', -0.05))\n", + " arr[i].spines['top'].set_visible(False)\n", + " arr[i].spines['right'].set_visible(False)\n", + " arr[i].axvline(0,ymin=0.,ymax=30,linestyle='dashed',color='k')\n", + " f.tight_layout()\n", + "\n", + "f, arr = plt.subplots(5,figsize=[2,6])\n", + "\n", + "time = np.arange(-0.1,0.5,0.001)\n", + "\n", + "for i,ix in enumerate(NS_ORDERING):\n", + " PREF = UMAP_traces_df.iloc[ix]['PREF']\n", + " NONPREF = UMAP_traces_df.iloc[ix]['NONPREF']\n", + " PREF_UPPER = UMAP_traces_df.iloc[ix]['PREF_UPPER_BOUND']\n", + " PREF_LOWER = UMAP_traces_df.iloc[ix]['PREF_LOWER_BOUND']\n", + " NONPREF_UPPER = UMAP_traces_df.iloc[ix]['NONPREF_UPPER_BOUND']\n", + " NONPREF_LOWER = UMAP_traces_df.iloc[ix]['NONPREF_LOWER_BOUND']\n", + " arr[i].plot(time,PREF,color=CUSTOM_PAL_SORT_3[ix])\n", + " arr[i].plot(time,NONPREF,'--',color=CUSTOM_PAL_SORT_3[ix])\n", + " arr[i].fill_between(time,PREF_UPPER,PREF_LOWER,\n", + " color='gray',alpha=0.2)\n", + " arr[i].fill_between(time,NONPREF_UPPER,NONPREF_LOWER,\n", + " color='gray',alpha=0.2)\n", + " arr[i].set_ylim(5,30)\n", + " arr[i].set_xticks([-0.1,0.1,0.3,0.5])\n", + " arr[i].set_xlim(-0.1,0.5)\n", + " arr[i].set_yticks([5,30])\n", + " arr[i].spines['left'].set_position(('axes', -0.05))\n", + " arr[i].spines['top'].set_visible(False)\n", + " arr[i].spines['right'].set_visible(False)\n", + " arr[i].axvline(0,ymin=0.,ymax=30,linestyle='dashed',color='k')\n", + " f.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sPazuw5flmIT" + }, + "source": [ + "## Figure 5C: Baseline WaveMAP cluster FRs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JieOh-3Rq5Ra" + }, + "source": [ + "### From these stimulus-aligned FR traces, we also calculated the baseline FR which is the FR averaged over the pre-stimulus period" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "id": "hz1xZMyOt7fD", + "outputId": "e4722657-82fa-48f6-a788-0be27f86844a" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "def bootstrap_median(data,iter_=5000):\n", + " median_list = []\n", + " \n", + " for i in range(iter_):\n", + " median_list.append(np.median(np.random.choice(data,len(data))))\n", + " \n", + " return np.mean(median_list), np.std(median_list)\n", + "\n", + "def get_baseline_FR_stats(df,clust_ix,conf=0.95,UMAP_clusts=True):\n", + " baseline_FR = baseline_FR_df[baseline_FR_df['dbscan_color']==str(clust_ix)]['baseline_FR'].tolist()\n", + " \n", + " if not UMAP_clusts:\n", + " GMM_baseline_FR = GMM_baseline_FR_df[GMM_baseline_FR_df['GMM_class']==str(clust_ix)]['baseline_FR'].tolist()\n", + " \n", + " n = len(baseline_FR)\n", + " m, se = np.median(baseline_FR), scipy.stats.sem(baseline_FR)\n", + " h = se * scipy.stats.t.ppf((1 + conf) / 2., n-1)\n", + " return m, se, m-h, m+h\n", + "\n", + "def get_baseline_FR(df,clust_ix,UMAP_clusts=True):\n", + " baseline_FR = baseline_FR_df[baseline_FR_df['dbscan_color']==str(clust_ix)]['baseline_FR'].tolist()\n", + " \n", + " if not UMAP_clusts:\n", + " GMM_baseline_FR = GMM_baseline_FR_df[GMM_baseline_FR_df['GMM_class']==str(clust_ix)]['baseline_FR'].tolist()\n", + " \n", + " return baseline_FR\n", + "\n", + "f, arr = plt.subplots(1)\n", + "f.set_size_inches(3,2.5)\n", + "\n", + "for i,clust_ix in enumerate([5,6,0]):\n", + " start_ix = 0 \n", + " \n", + " median, med_se = bootstrap_median(get_baseline_FR(baseline_FR_df,clust_ix))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", + " yerr=med_se)\n", + " \n", + "for i,clust_ix in enumerate([7,1,2,3,4]):\n", + " start_ix = 4\n", + " \n", + " median, med_se = bootstrap_median(get_baseline_FR(baseline_FR_df,clust_ix))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", + " yerr=med_se)\n", + "\n", + "arr.set_ylabel('Baseline FR (spikes/s)')\n", + "arr.set_xticks([1,6]);\n", + "arr.set_xticklabels(['Broad-Spiking','Narrow-Spiking'],fontsize=12,fontname='Arial')\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.set_ylim(0,15);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oFXG_47PT3y9" + }, + "source": [ + "## Figure 5D: Max WaveMAP cluster FRs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "k2jcxJ7krDQK" + }, + "source": [ + "### And next calculate the max FR as the highest FR reached over the course of the entire post-stimulus trial." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "id": "jsS17aRFUEQs", + "outputId": "2dc9b1cf-f44b-4cc5-a835-a76a41d393b6" + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "f, arr = plt.subplots(1)\n", + "\n", + "f.set_size_inches(3,2.5)\n", + " \n", + "def get_max_FR_stats(df,clust_ix,conf=0.95,UMAP_clusts=True):\n", + " max_FR = max_FR_df[max_FR_df['dbscan_color']==str(clust_ix)]['max_FR'].tolist()\n", + " \n", + " if not UMAP_clusts:\n", + " max_FR = max_FR_df[max_FR_df['GMM_class']==str(clust_ix)]['max_FR'].tolist()\n", + " \n", + " n = len(max_FR)\n", + " m, se = np.median(max_FR), scipy.stats.sem(max_FR)\n", + " h = se * scipy.stats.t.ppf((1 + conf) / 2., n-1)\n", + " return m, se, m-h, m+h\n", + "\n", + "def get_max_FR(df,clust_ix, UMAP_clusts=True):\n", + " max_FR = max_FR_df[max_FR_df['dbscan_color']==str(clust_ix)]['max_FR'].tolist()\n", + " \n", + " if not UMAP_clusts:\n", + " max_FR = GMM_max_FR_df[GMM_max_FR_df['GMM_class']==str(clust_ix)]['max_FR'].tolist()\n", + " \n", + " return max_FR\n", + "\n", + "for i,clust_ix in enumerate([5,6,0]):\n", + " start_ix = 0 \n", + " \n", + " median, med_se = bootstrap_median(get_max_FR(max_FR_df,clust_ix))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", + " yerr=med_se)\n", + " \n", + "for i,clust_ix in enumerate([7,1,2,3,4]):\n", + " start_ix = 4\n", + " \n", + " median, med_se = bootstrap_median(get_max_FR(max_FR_df,clust_ix))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", + " yerr=med_se)\n", + "\n", + "arr.set_ylabel('Max FR (spikes/s)')\n", + "arr.set_xticks([1,6]);\n", + "arr.set_xticklabels(['Broad-Spiking','Narrow-Spiking'],fontsize=12,fontname='Arial')\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.set_ylim(0,40);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xKOQstlZUbuh" + }, + "source": [ + "## Figure 5E: Firing rate range for WaveMAP clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E6CovcATrKa8" + }, + "source": [ + "### Taking the difference between the max FR and baseline FR, we have the FR Range." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "id": "YltyClzbU0rR", + "outputId": "87724540-c234-47a6-d6ab-e2bfd28f87ea" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "def get_dynamic_range_stats(clust_ix,max_FR_df,baseline_FR_df,conf=0.95,UMAP_clusts=True):\n", + " if UMAP_clusts:\n", + " max_FR = max_FR_df[max_FR_df['dbscan_color']==str(clust_ix)]['max_FR'].tolist()\n", + " baseline_FR = baseline_FR_df[baseline_FR_df['dbscan_color']==str(clust_ix)]['baseline_FR'].tolist()\n", + " \n", + " if not UMAP_clusts:\n", + " max_FR = max_FR_df[max_FR_df['GMM_class']==str(clust_ix)]['max_FR'].tolist()\n", + " baseline_FR = baseline_FR_df[baseline_FR_df['GMM_class']==str(clust_ix)]['baseline_FR'].tolist()\n", + " \n", + " dynamic_range_FR = np.subtract(max_FR,baseline_FR)\n", + " \n", + " n = len(dynamic_range_FR)\n", + " m, se = np.median(dynamic_range_FR), scipy.stats.sem(dynamic_range_FR)\n", + " h = se * scipy.stats.t.ppf((1 + conf) / 2., n-1)\n", + " \n", + " return m, se, m-h, m+h\n", + "\n", + "def get_dynamic_range(clust_ix,max_FR_df,baseline_FR_df,UMAP_clusts=True):\n", + " if UMAP_clusts:\n", + " max_FR = max_FR_df[max_FR_df['dbscan_color']==str(clust_ix)]['max_FR'].tolist()\n", + " baseline_FR = baseline_FR_df[baseline_FR_df['dbscan_color']==str(clust_ix)]['baseline_FR'].tolist()\n", + " \n", + " if not UMAP_clusts:\n", + " max_FR = GMM_max_FR_df[GMM_max_FR_df['GMM_class']==str(clust_ix)]['max_FR'].tolist()\n", + " baseline_FR = GMM_baseline_FR_df[GMM_baseline_FR_df['GMM_class']==str(clust_ix)]['baseline_FR'].tolist()\n", + " \n", + " return np.subtract(max_FR,baseline_FR)\n", + "\n", + "\n", + "f, arr = plt.subplots(1)\n", + "\n", + "f.set_size_inches(3,2.5)\n", + "\n", + "for i,clust_ix in enumerate([5,6,0]):\n", + " start_ix = 0 \n", + " \n", + " median, med_se = bootstrap_median(get_dynamic_range(clust_ix,max_FR_df,baseline_FR_df))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", + " yerr=med_se)\n", + " \n", + "for i,clust_ix in enumerate([7,1,2,3,4]):\n", + " start_ix = 4\n", + " \n", + " median, med_se = bootstrap_median(get_dynamic_range(clust_ix,max_FR_df,baseline_FR_df))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", + " yerr=med_se)\n", + "\n", + "arr.set_ylabel('FR Range (spikes/s)')\n", + "arr.set_xticks([1,6]);\n", + "arr.set_xticklabels(['Broad-Spiking','Narrow-Spiking'],fontsize=12,fontname='Arial')\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.set_ylim(0,25);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2T8r9cVvsL9D" + }, + "source": [ + "# Figure 6: Functional properties of WaveMAP clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RLXBKjDdrUjE" + }, + "source": [ + "### Next we show the change in firing rate as a function of time against various stimulus coherences. We show this for an example BS and NS cluster." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RKgU_7JdVhEQ" + }, + "source": [ + "## Figure 6A: Average FR change across coherence for WaveMAP cluster 6\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 213 + }, + "id": "7oZM8CpHVvrk", + "outputId": "e48a716e-070e-48dd-cd14-d4708acd249b" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "def hex_to_rgb(hex_code):\n", + " h = hex_code.lstrip('#')\n", + " rgb_256 = tuple(int(h[i:i+2], 16) for i in (0, 2, 4))\n", + " rgb = tuple(x/256 for x in rgb_256)\n", + " return rgb\n", + "\n", + "def fr_by_coherence(ix,CLUST_IX):\n", + "\n", + " f, arr = plt.subplots(1,figsize=[3,2.5])\n", + " for i in range(diffV_list[ix].shape[1]):\n", + " arr.plot(np.linspace(-0.1,0.4,499),diffV_list[ix][:,i],c=coherence_colors[i],linewidth=3)\n", + " arr.spines['top'].set_visible(False)\n", + " arr.spines['right'].set_visible(False)\n", + " arr.spines['left'].set_position(['axes',-0.05])\n", + " arr.set_xlim(0.,0.4)\n", + "\n", + " arr.set_ylim(0,15)\n", + " arr.set_yticks([0,5,10,15])\n", + " arr.set_xlabel('Time Relative to \\nStimulus Onset (ms)')\n", + " arr.set_ylabel('Average Firing Rate \\n(spikes/s)')\n", + "\n", + " for i,slope in enumerate(dec_dyn_data[ix]):\n", + " interval = [0.175,0.325] #Interval that Chand did regression (don't change)\n", + " x = [interval[0],interval[1]]\n", + " y = [diffV_list[ix][274,i],diffV_list[ix][424,i]]\n", + "\n", + " arr.plot(x,y,color='k',linestyle='dashed',alpha=0.5,linewidth=3,zorder=10)\n", + " \n", + "fr_by_coherence(2,4)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c6Sg0GPBbB-Z" + }, + "source": [ + "## Figure 6B: Average FR change across coherence for WaveMAP cluster 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 213 + }, + "id": "qV-qJlDHbAwt", + "outputId": "5ef1b5a3-6cf8-4f10-f0a1-ec342527e3f8" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "fr_by_coherence(7,4)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "foV7Y1LabiYO" + }, + "source": [ + "## Figure 6C: FR rate of rise per coherence for BS WaveMAP clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E-3JeUJVr4ja" + }, + "source": [ + "### We next show the FR rate of rise (how quickly it increases after stimulus presentation) versus coherences" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209 + }, + "id": "Y4yc6UIDboV7", + "outputId": "89731aec-c434-4faf-d8cc-6738613dc7d8" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "f, arr = plt.subplots(1,figsize=[3,2.5])\n", + "\n", + "for i,clust_ix in enumerate([5,6,0]):\n", + " arr.errorbar(range(7),dec_dyn_data[i],yerr=dec_dyn_data_err[i], marker='o', fillstyle='full', markerfacecolor='w',\n", + " c=hex_to_rgb(UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'].tolist()[0]),clip_on=False)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['left'].set_position(['axes',-0.05])\n", + "arr.spines['bottom'].set_position(['axes',-0.05])\n", + "arr.set_xticks(np.arange(len(coherences)))\n", + "arr.set_xlim(0,len(coherences)-1)\n", + "arr.set_xticklabels([int(x) for x in np.round(coherences,0)])\n", + "arr.set_ylim(0,70)\n", + "arr.set_yticks([0,35,70])\n", + "arr.set_ylabel('FR Rate of Rise \\n(spikes/s/s)')\n", + "arr.set_xlabel('Coherence (%)')\n", + "arr.invert_xaxis()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "elIWdg6_b5yR" + }, + "source": [ + "## Figure 6D: FR rate of rise per coherence for NS WaveMAP clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KJz0yfR0sEgz" + }, + "source": [ + "### and similarly we show this for NS clusters as well" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209 + }, + "id": "Z2kwP3tfb400", + "outputId": "d587d9bc-1d53-44a5-cb75-e32b9ddcd799" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "f, arr = plt.subplots(1,figsize=[3,2.5])\n", + "offset=3\n", + "\n", + "for i,clust_ix in enumerate([7,1,2,3,4]):\n", + " arr.errorbar(range(7),dec_dyn_data[i+offset],yerr=dec_dyn_data_err[i+offset], marker='o', fillstyle='full', markerfacecolor='w',\n", + " c=hex_to_rgb(UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'].tolist()[0]),clip_on=False)\n", + " \n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['left'].set_position(['axes',-0.05])\n", + "arr.spines['bottom'].set_position(['axes',-0.05])\n", + "arr.set_xticks(np.arange(len(coherences)))\n", + "arr.set_xlim(0,len(coherences)-1)\n", + "arr.set_xticklabels([int(x) for x in np.round(coherences,0)])\n", + "arr.set_ylim(0,70)\n", + "arr.set_yticks([0,35,70])\n", + "arr.set_ylabel('FR Rate of Rise \\n(spikes/s/s)')\n", + "arr.set_xlabel('Coherence (%)')\n", + "arr.invert_xaxis()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JQeBa4iKclat" + }, + "source": [ + "## Figure 6E: Coherence slope per WaveMAP cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RjtsY6l6sKHG" + }, + "source": [ + "### Now we take a slope of this FR rate of rise vs. coherence and pplot these differences. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "id": "PIi4yNlzclyz", + "outputId": "3b179829-99ee-408a-a2a9-97e5ea017a7e" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "RT_bins = [12]\n", + "\n", + "offset=3\n", + "\n", + "f, arr = plt.subplots(1)\n", + "\n", + "f.set_size_inches(3,2.5)\n", + "\n", + "arr.set_ylabel('Coherence Slope \\n(spikes/s/s/coherence)',fontsize=12,fontname='Arial');\n", + "# arr.set_ylim(ymin=150,ymax=300)\n", + "\n", + "arr.set_ylim(ymin=-0.0,ymax=0.4)\n", + "arr.set_yticks([0, 0.2, 0.4]);\n", + "# arr.set_yticklabels([150,200,250,300],fontsize=12,fontname='Arial')\n", + "arr.set_xticks([1,6]);\n", + "arr.set_xticklabels(['Broad-Spiking','Narrow-Spiking'],fontsize=12,fontname='Arial')\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['top'].set_visible(False)\n", + "\n", + "# discrim_GMM_dict = {}\n", + "\n", + "# for i,label in enumerate(GMM_class_df['Class'].to_numpy()):\n", + "# discrim_GMM_dict[label] = get_discrim(label,GMM_class_df['Class'].to_numpy(), vmIndex_df)\n", + " \n", + "for i,clust_ix in enumerate([5,6,0]):\n", + " \n", + " median, med_se = bootstrap_median(dec_dyn_slope[i])\n", + " \n", + " start_ix = 0\n", + " arr.bar(start_ix+i, median, \n", + " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", + " yerr=med_se)\n", + " \n", + "for i,clust_ix in enumerate([7,1,2,3,4]):\n", + " start_ix = 4\n", + " \n", + " median, med_se = bootstrap_median(dec_dyn_slope[i+offset])\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", + " yerr=med_se)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-LlF0DHXfOoB" + }, + "source": [ + "## Figure 6F: Discrimination Time " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rqcPrca7s3nW" + }, + "source": [ + "### We now calculate the discrimination time which is the earliest time point at which we can predict the reach direction by observing a given neuron. We do this for all WaveMAP clusters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 314 + }, + "id": "7ZAvkjWUgLNY", + "outputId": "645951d2-e3df-46bc-92a9-1e6c58a806a9" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "def get_all_discrim(cluster_ix):\n", + " RT_bin = 12\n", + " clust_data = list(all_data_df[all_data_df.cluster_ix == cluster_ix]['disc'])\n", + " discrim_times = [x for x in clust_data if not np.isnan(x)]\n", + " return discrim_times\n", + "\n", + "RT_bins = [12]\n", + "\n", + "f, arr = plt.subplots(1)\n", + "\n", + "f.set_size_inches(3,2.5)\n", + "\n", + "arr.set_ylabel('Discrimination Time (ms)',fontsize=12,fontname='Arial');\n", + "# arr.set_ylim(ymin=150,ymax=300)\n", + "\n", + "arr.set_ylim(ymin=150,ymax=300)\n", + "arr.set_yticks([150, 200, 250, 300]);\n", + "arr.set_yticklabels([150,200,250,300],fontsize=12,fontname='Arial')\n", + "arr.set_xticks([1,6]);\n", + "arr.set_xticklabels(['Broad-Spiking','Narrow-Spiking'],fontsize=12,fontname='Arial')\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['top'].set_visible(False)\n", + "\n", + "# discrim_GMM_dict = {}\n", + "\n", + "# for i,label in enumerate(GMM_class_df['Class'].to_numpy()):\n", + "# discrim_GMM_dict[label] = get_discrim(label,GMM_class_df['Class'].to_numpy(), vmIndex_df)\n", + " \n", + "for i,clust_ix in enumerate([5,6,0]):\n", + " start_ix = 0\n", + " \n", + " median, med_se = bootstrap_median(get_all_discrim(clust_ix))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", + " yerr=med_se)\n", + " arr.annotate(str(clust_ix+1), xy=(start_ix+i, 0.003),fontsize=12, fontname='Arial',color = 'white', ha=\"center\")\n", + " \n", + "for i,clust_ix in enumerate([7,1,2,3,4]):\n", + " start_ix = 4\n", + " \n", + " median, med_se = bootstrap_median(get_all_discrim(clust_ix))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=UMAP_and_GMM[UMAP_and_GMM['color']==clust_ix]['dbscan_hex'],\n", + " yerr=med_se)\n", + " arr.annotate(str(clust_ix+1), xy=(start_ix+i, 0.003),fontsize=12, fontname='Arial',color = 'white', ha=\"center\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sFj_-b_jZYSk" + }, + "source": [ + "# Figure 7: Laminar distributions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "87V8W5vOlxWW" + }, + "source": [ + "### First we plot the laminar distributions of each WaveMAP cluster. Each bar corresponds to one of 16 channels along the U-probe located from the pial surface to 2.4 mm into cortex (the bottom of Layer VI in PMd)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ze2O18N-Y0gO", + "outputId": "ba14c3d5-b30d-4dc9-b328-56d4d4dad80e" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "ORDERING = [0, 1, 2, 3, 4, 5, 6, 7]\n", + "TIME_LENGTH = 1600\n", + "\n", + "for i,ix in enumerate(range(0,8)):\n", + " f, arr = plt.subplots(1, figsize = [3,2])\n", + " clust_color = UMAP_and_GMM[UMAP_and_GMM['color']==ix]['dbscan_hex'].iloc[0]\n", + "\n", + " g = sns.countplot(filt_full_df[filt_full_df['cluster_ix']==ix]['depth'].tolist(),\n", + " ax=arr, color=clust_color, order=DEPTHS)\n", + " sns.despine(ax=g)\n", + " \n", + " g.set_ylabel('Counts')\n", + "\n", + " g.set_xlim([-1,17])\n", + " g.set_xticks([3,7,11,15])\n", + " g.set_xticklabels(['0.6','1.2','1.8','2.4'],fontsize=12)\n", + " g.set_yticks([0,15])\n", + " g.set_yticklabels([0,15],fontsize=12)\n", + " arr.set_xlabel('Channel Depth (mm)',fontsize=12)\n", + " x,y = 15,13\n", + " ellipse = mpl.patches.Ellipse((x,y), width=2.0, height=3.1, facecolor='w',\n", + " edgecolor='k',linewidth=1.5)\n", + " label = arr.annotate(str(ORDERING[ix]+1), xy=(x-0.07, y-0.8),fontsize=12, color = 'k', ha=\"center\")\n", + " arr.add_patch(ellipse)\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SC3LqWpPl5MX" + }, + "source": [ + "### and compare that to the distributions of GMM clusters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 649 + }, + "id": "LO6x9DdblE5p", + "outputId": "b6bc0394-8ba6-4007-a243-f2849ccc3771" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "for i,ix in enumerate(range(1,5)):\n", + " f, arr = plt.subplots(1, figsize = [3,2])\n", + " clust_color = GMM_PAL[i]\n", + "\n", + " g = sns.countplot(filt_full_df[filt_full_df['gmm_ix']==ix]['depth'].tolist(),\n", + " ax=arr, color=clust_color, order=DEPTHS)\n", + " sns.despine(ax=g)\n", + " \n", + " g.set_ylabel('Counts')\n", + "\n", + " g.set_xlim([-1,17])\n", + " g.set_xticks([3,7,11,15])\n", + " g.set_xticklabels(['0.6','1.2','1.8','2.4'],fontsize=12)\n", + " g.set_yticks([0,25])\n", + " g.set_yticklabels([0,25],fontsize=12)\n", + " arr.set_xlabel('Channel Depth (mm)',fontsize=12)\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R6zD1Vqb6raq" + }, + "source": [ + "# Figure 8: Laminar distribution of inhibitory subtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WkbPoDMztZv6" + }, + "source": [ + "### We first calculate the laminar distribution of the CB-, CR-, and PV-positive inhibitory cell types. The following code contains functions to read stereological hand counts done in FIJI (ImageJ)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "DUhkzznC6r0v", + "outputId": "a974ee0e-7381-4780-b480-b4b418012654" + }, + "outputs": [ + { + "data": { + "image/png": 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fw43dkMeWw8MhnMB9T7IWuAj4Eg3u+yP6DxPe/ys5BFrxuqq6mLk7sr47yaWjb9bcsWEz1/m21l/gduB8YANwALh1acuZrCSvBB4A3ltVPxp9r4V9P0//J77/V3IINHFriqra3z0fAh5k7nDv4OFhnu75UDf7ifqZHGt/T5jPoaoOVtWLVfUScAdz+x9OwL4neRlz/wB+qqo+0zU3s+/n6//x2P8rOQRO+FtTJHlFklMPTwOXAzuZ6+fhqx6uBz7bTT8EvLW7cuK1wA9HDqVXsmPt7+eBy5Os6g6fL+/aVpwjzulcw9z+h7m+X5fklCTnAuuAL7NCvxdJAtwJ7Kqqj4281cS+X6j/x2X/L/VZ8Z5n1K9i7iz6HuCDS13PBPp3HnNn978OPHG4j8CrgUeAp4F/B07v2sPcH/PZA3wDmF7qPozR53uZO+z9GXPjme8cp7/AO5g7WbYbePtS96tH3/+p69uO7su8ZmT+D3Z9fwq4cqR9xX0vgNcxN9SzA9jePa5qaN8v1P+J739vGyFJDVvJw0GSpJ4MAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhhkCktSw/wWePrb1OW6EaQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "import xml.etree.ElementTree as ET\n", + "xml_files_path = 'WaveMAP_Paper/data/annotations/'\n", + "\n", + "SHRINKAGE_FACTOR_DICT = {'AM289': 1.2, \n", + " 'AM292': 1.3,\n", + " 'AM294': 1.1,\n", + " 'AM295': 1.1,\n", + " 'AM296': 1.3,\n", + " 'AM299': 1.2}\n", + "\n", + "def plot_stain_density(norm_anatomical_dict,stain, ylim = None, color = 'blue'):\n", + " nonan_norm_anat = [x for x in norm_anatomical_dict[stain] if not np.isnan(x).any()]\n", + " nonan_norm_anat_zip = list(zip(*nonan_norm_anat))\n", + "\n", + " nonan_norm_anat_mean = [np.mean(x) for x in nonan_norm_anat_zip]\n", + " nonan_norm_anat_err = [np.std(x)/len(x) for x in nonan_norm_anat_zip]\n", + " \n", + " f, arr = plt.subplots(1, figsize=[3.5,2])\n", + " arr.set_ylim(0,0.3)\n", + " arr.set_xticks([0,4,8,12,16])\n", + " arr.set_xticklabels([0.0,0.6,1.2,1.8,2.4])\n", + " arr.spines['top'].set_visible(False)\n", + " arr.spines['right'].set_visible(False)\n", + " arr.bar(range(16),nonan_norm_anat_mean,yerr=nonan_norm_anat_err,color = color)\n", + " arr.set_ylabel('Normalized Counts')\n", + " arr.set_xlabel('Depth from Surface (mm)')\n", + " arr.spines['bottom'].set_bounds(0.0,16)\n", + " return \n", + "\n", + "def point_to_line_dist(point_coords,line_coord_pair,shrinkage=None):\n", + " SCALE_FACTOR = 3.7313 #pixels/um\n", + " \n", + " [line_x1, line_x2], [line_y1, line_y2] = line_coord_pair\n", + " p1, p2 = point_coords\n", + " \n", + " distance = np.abs((line_y2-line_y1)*p1 - (line_x2-line_x1)*p2 + line_x2*line_y1 - line_y2*line_x1)/np.sqrt(np.power(line_y2-line_y1,2)+(np.power(line_x2-line_x1,2)))\n", + " \n", + " if shrinkage:\n", + " distance = distance*shrinkage\n", + " \n", + " return distance/SCALE_FACTOR\n", + "\n", + "def parse_annotation(xml_files_path):\n", + " marker_dict = {}\n", + " n_skip = 2 #empty items to skip past \n", + " for f in os.listdir(xml_files_path):\n", + " if f.endswith('.xml'):\n", + " marker_list = []\n", + " file = os.path.join(xml_files_path,f)\n", + " tree = ET.parse(file)\n", + " root = tree.getroot()\n", + "\n", + " for mark_ix in range(len(root[1][2])-2): \n", + " x = int(root[1][2][mark_ix+n_skip][0].text)\n", + " y = int(root[1][2][mark_ix+n_skip][1].text)\n", + " marker_list.append([x,y])\n", + " else:\n", + " continue\n", + " marker_dict[f] = marker_list\n", + "\n", + " return marker_dict\n", + "\n", + "def calc_densities(xml_files_path,shrinkage_factor=None):\n", + " marker_dict = parse_annotation(xml_files_path)\n", + " marker_dist_dict = {}\n", + " for f in list(marker_dict.keys()):\n", + " specimen_info_ix = f.find('AM')\n", + " if f.endswith('.xml'):\n", + " csv_file = f[specimen_info_ix:-4]+'.csv'\n", + " if os.path.exists(os.path.join(xml_files_path,csv_file)):\n", + " xml_files = list(parse_annotation(xml_files_path))\n", + " f_df = pd.read_csv(os.path.join(xml_files_path,xml_files[specimen_info_ix:-4]+'.csv'))\n", + " line_coords = [f_df['X'].tolist(), f_df['Y'].tolist()]\n", + " dist_list = []\n", + " for mark in marker_dict[f]:\n", + " for specimen in list(SHRINKAGE_FACTOR_DICT.keys()):\n", + " print(f)\n", + " if specimen in f:\n", + " shrinkage = SHRINKAGE_FACTOR_DICT[specimen]\n", + " dist = point_to_line_dist(mark,line_coords,shrinkage=shrinkage)\n", + " if shrinkage_factor:\n", + " dist = dist*shrinkage_factor\n", + " dist_list.append(dist)\n", + "\n", + " marker_dist_dict[f] = dist_list\n", + " else:\n", + " marker_dist_dict[f] = []\n", + " return marker_dist_dict\n", + "\n", + "def calc_densities(xml_files_path,shrinkage_factor=None):\n", + " \n", + " marker_dict = parse_annotation(xml_files_path)\n", + " marker_dist_dict = {}\n", + " for f in list(marker_dict.keys()):\n", + " specimen_info_ix = f.find('AM')\n", + " csv_file_suffix = f[specimen_info_ix:-4]+'.csv'\n", + "\n", + " csv_file = [s for s in os.listdir(xml_files_path) if csv_file_suffix in s][0]\n", + " f_df = pd.read_csv(os.path.join(xml_files_path,csv_file))\n", + "\n", + " line_coords = [f_df['X'].tolist(), f_df['Y'].tolist()]\n", + " dist_list = []\n", + " for mark in marker_dict[f]:\n", + " for specimen in list(SHRINKAGE_FACTOR_DICT.keys()):\n", + " if specimen in f:\n", + " shrinkage = SHRINKAGE_FACTOR_DICT[specimen]\n", + " else:\n", + " shrinkage = 1.0\n", + " dist_list.append(point_to_line_dist(mark,line_coords,shrinkage))\n", + " \n", + " marker_dist_dict[f] = dist_list\n", + " \n", + " return marker_dist_dict\n", + "\n", + "FILES_TO_SKIP = ['AM299 CB dPMC40x__2Merge','AM299 CB dPMC40x_1Merge']#these have sideways tissue that hasn't been converted to work yet.\n", + "def process_counts(xml_files_path):\n", + " marker_dist_dict = calc_densities(xml_files_path)\n", + " specimen_df = pd.DataFrame(columns=['Specimen_ID','PV','CR','CB','MAP2'])\n", + " density_dict = calc_densities(xml_files_path)\n", + " \n", + " specimen_list = []\n", + " for k in list(marker_dist_dict.keys()): \n", + " specimen_info_ix = k.find('AM') \n", + " specimen = k[specimen_info_ix:specimen_info_ix+5]\n", + " specimen_list.append(specimen)\n", + " \n", + " specimen_set = set(specimen_list)\n", + " for i,s in enumerate(specimen_set):\n", + " specimen_df.at[i,'Specimen_ID'] = s\n", + " \n", + " specimen_df = specimen_df.set_index('Specimen_ID')\n", + " \n", + " for k in list(marker_dist_dict.keys()):\n", + " if k not in FILES_TO_SKIP:\n", + " specimen_data = []\n", + "\n", + " for s in specimen_set:\n", + " if s in k:\n", + " specimen_data.append(k)\n", + "\n", + " for s in specimen_data:\n", + " specimen_info_ix = k.find('AM') \n", + " specimen = k[specimen_info_ix:specimen_info_ix+5]\n", + " if 'C1.xml' in s:\n", + " if np.isnan(specimen_df.at[specimen,'PV']).any():\n", + " specimen_df.at[specimen,'PV'] = density_dict[s]\n", + " else:\n", + " specimen_df.at[specimen,'PV'] = specimen_df.at[specimen,'PV']+density_dict[s]\n", + " elif 'C2.xml' in s:\n", + " if np.isnan(specimen_df.at[specimen,'CR']).any():\n", + " specimen_df.at[specimen,'CR'] = density_dict[s]\n", + " else:\n", + " specimen_df.at[specimen,'CR'] = specimen_df.at[specimen,'CR']+density_dict[s]\n", + " elif 'CB' in s:\n", + " if np.isnan(specimen_df.at[specimen,'CB']).any():\n", + " specimen_df.at[specimen,'CB'] = density_dict[s]\n", + " else:\n", + " specimen_df.at[specimen,'CB'] = specimen_df.at[specimen,'CB']+density_dict[s]\n", + " elif 'MAP2' in s:\n", + " if np.isnan(specimen_df.at[specimen,'MAP2']).any():\n", + " specimen_df.at[specimen,'MAP2'] = density_dict[s]\n", + " else:\n", + " specimen_df.at[specimen,'MAP2'] = specimen_df.at[specimen,'MAP2']+density_dict[s] \n", + " return specimen_df\n", + "\n", + "norm_anatomical_dict = {}\n", + "IHC_STAINS = ['PV','CR','CB','MAP2']\n", + "SPECIMENS_TO_SKIP = ['test']\n", + "anat_data = process_counts(xml_files_path)\n", + "\n", + "for stain in IHC_STAINS:\n", + " norm_anatomical_dict[stain] = []\n", + " \n", + "for s in list(anat_data.index):\n", + " for stain in IHC_STAINS:\n", + " counts, bins, bars = plt.hist(anat_data.at[s,stain],bins=16,range=[0,2400]);\n", + " if not np.isnan(anat_data.at[s,stain]).any():\n", + " n_points = len(anat_data.at[s,stain])\n", + " norm_counts = [x/n_points for x in counts]\n", + " norm_anatomical_dict[stain].append(norm_counts)\n", + " else:\n", + " continue" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yHmNMav9Gcak" + }, + "source": [ + "## Figure 8C,D,E: Laminar distribution of each inhibitory subtype" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VmzKfnigt1r4" + }, + "source": [ + "### Here we plot the distributions for the three types with 16 bins to match Figure 8." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "id": "Nc3jy_boEp2P", + "outputId": "2f50e1da-be05-4315-ad6c-8bebb7ba3d55" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_stain_density(norm_anatomical_dict,'PV',color = hex_to_rgb('c4539f'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nYHyb-yQ4QMi" + }, + "source": [ + "# Figure S2: Stability analysis of WaveMAP" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Qn4UMb5oQpd" + }, + "source": [ + "## Figure S2A: WaveMAP across random seeds and resolutions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M-FiTFXw0lsg" + }, + "source": [ + "### Here we apply WaveMAP across three random seeds and four resolution parameter values. \n", + "\n", + "---\n", + "\n", + "This demonstrates that the WaveMAP method is stable across random seeds (up to a nonlinear perturbation) and hierarchical across resolution parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 534 + }, + "id": "X2Lqp5iGoY0a", + "outputId": "6eeeb690-150d-4402-94cb-10cb450f6772" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94m1.0\n", + "\u001b[94m1.5\n", + "\u001b[94m5.0\n", + "\u001b[94m10.0\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "n_plots = 12\n", + "n_rows = 3\n", + "n_cols = 4\n", + "resolutions = [1.0, 1.5, 5.0, 10.0]\n", + "f,arr = plt.subplots(nrows=n_rows, ncols=n_cols,figsize=[12,8])\n", + "\n", + "for i,res in enumerate(resolutions):\n", + " print(BlueCol + str(res));\n", + " for j in range(n_rows):\n", + " resolution = res\n", + " my_umap = umap.UMAP(n_neighbors=N_NEIGHBORS\n", + " ,min_dist=MIN_DIST,random_state=random.randint(0,10000), metric='euclidean')\n", + " my_umap.fit(full_data)\n", + " embedding = my_umap.transform(full_data)\n", + "\n", + "\n", + " G = nx.from_scipy_sparse_matrix(my_umap.graph_)\n", + " clustering = cylouvain.best_partition(G, resolution = resolution)\n", + " clustering_solution = list(clustering.values())\n", + "\n", + " umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", + "\n", + " umap_df['dbscan_color'] = clustering_solution\n", + " husl_colors = [sns.color_palette('husl',len(set(clustering_solution)))[i] for i in clustering_solution]\n", + "\n", + " arr[j,i].scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", + " marker='o',c=husl_colors, s=30, edgecolor='w',\n", + " linewidth=0.25)\n", + "\n", + " arr[j,i].spines['top'].set_visible(False)\n", + " arr[j,i].spines['left'].set_visible(False)\n", + " arr[j,i].spines['right'].set_visible(False)\n", + " arr[j,i].spines['bottom'].set_visible(False)\n", + "\n", + " arr[j,i].set_xticks([])\n", + " arr[j,i].set_yticks([])\n", + "\n", + "plt.subplots_adjust(wspace=0, hspace=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "brChZFr744Nm" + }, + "source": [ + "## Figure S2B: Random seed and random subsetting to check for robustness" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iTTpUoaG5Ll5" + }, + "source": [ + "### We generate 100 random subsets over different random seeds across various percentages of the full dataset and apply WaveMAP to each\n", + "---\n", + "**WARNING: THIS CAN TAKE 45 MINS IN COLAB**; the final results of one run are cached as a data file but if you choose to run this cell, those results will be used for graphing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 709 + }, + "id": "HHBe9HF32iBR", + "outputId": "dbaf8f56-fc07-453c-e3ea-1172fed16db8" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Exception ignored in: .remove at 0x7f3968c505f0>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.7/weakref.py\", line 109, in remove\n", + " def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n", + "KeyboardInterrupt\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m_ctypes/callbacks.c\u001b[0m in \u001b[0;36m'calling callback function'\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/llvmlite/binding/executionengine.py\u001b[0m in \u001b[0;36m_raw_object_cache_notify\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0mffi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLLVMPY_SetObjectCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_object_cache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m_raw_object_cache_notify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m 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160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mTerminator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mInstruction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 163\u001b[0m super(Terminator, self).__init__(parent, types.VoidType(), opname,\n\u001b[1;32m 164\u001b[0m operands)\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "subsets = [0.1,0.2,0.3,0.4,\n", + " 0.5,0.6,0.7,0.8,\n", + " 0.9,1.0]\n", + "\n", + "clust_rand_dict = {}\n", + "for frac in subsets:\n", + " rand_list = []\n", + " for i in list(range(1,100)):\n", + " reducer_rand_test = umap.UMAP(n_neighbors = N_NEIGHBORS, \n", + " min_dist=MIN_DIST, \n", + " random_state=random.randint(1,100000))\n", + " rand_data = np.random.permutation(full_data)[0:(int(len(full_data)*frac)),:]\n", + " mapper = reducer_rand_test.fit(rand_data)\n", + " embedding_rand_test = reducer_rand_test.transform(rand_data)\n", + "\n", + " umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n", + " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", + " clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n", + " clustering_solution = list(clustering.values())\n", + " rand_list.append(len(set(clustering_solution)))\n", + "\n", + " clust_rand_dict.update({str(frac): rand_list})\n", + "\n", + "subset_avg_rand_list = []\n", + "subset_std_rand_list = []\n", + "\n", + "for k,v in clust_rand_dict.items():\n", + " subset_avg_rand_list.append(np.average(v))\n", + " subset_std_rand_list.append(np.std(v))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qj--ua1oo_Kd" + }, + "source": [ + "### We then plot the mean and standard deviation of the number of clusters per percentage of the full dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 189 + }, + "id": "xPGkKtwLhhue", + "outputId": "b6c0256e-86a9-461c-cfd9-51752971c5bf" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "subsets = [0.1,0.2,0.3,0.4,\n", + " 0.5,0.6,0.7,0.8,\n", + " 0.9,1.0]\n", + "\n", + "if 'subset_avg_rand_list' not in list(locals().keys()):\n", + " subset_avg_rand_list = pkl.load(open('WaveMAP_Paper/data/subset_avg_rand_list.pkl','rb'))\n", + "\n", + "if 'subset_std_rand_list' not in list(locals().keys()):\n", + " subset_std_rand_list = pkl.load(open('WaveMAP_Paper/data/subset_std_rand_list.pkl','rb'))\n", + "\n", + "f, arr = plt.subplots(1,figsize=[3,2.5])\n", + "arr.errorbar(np.array(subsets,dtype=np.float),subset_avg_rand_list,yerr=subset_std_rand_list,c = 'k', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.set_xlabel('% of Full Dataset', fontsize=12,fontname=\"Arial\")\n", + "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n", + "arr.set_xticklabels(['','20','40','60','80','100'],fontsize=12,fontname=\"Arial\")\n", + "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\")\n", + "arr.set_yticks([0,2,4,6,8,10])\n", + "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\")\n", + "arr.spines['left'].set_bounds(0,10)\n", + "arr.spines['bottom'].set_bounds(0.1,1)\n", + "arr.axhline(np.max(subset_avg_rand_list),color='k',linestyle='dashed')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rCChwvdapAvW" + }, + "source": [ + "## Figure S2C: Ensemble clustering on graphs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_157Y1P8uH8n" + }, + "source": [ + "### We use ECG (ensemble clustering on graphs) as an alternate clustering method to validate that the cluster we find with Louvain are robust to the method. ECG is an ensembled version of Louvain." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 }, + "id": "ZAHhI8N04OBw", + "outputId": "0750432b-4a4e-4582-b921-d2d0c5f2013b" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 183, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - }, - "id": "fxNTfdaNwZFz", - "outputId": "1c2fb31d-dad0-4d58-e02d-97743c5a8ade", - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } + "data": { + "text/html": [ + "[]" ], - "source": [ - "f, arr = plt.subplots(1)\n", - "\n", - "f.set_size_inches(3,2.5)\n", - "\n", - "for i,clust_ix in enumerate([1,2,3,4]):\n", - " start_ix = 0 \n", - " \n", - " median, med_se = bootstrap_median(get_dynamic_range(clust_ix,max_FR_df,baseline_FR_df,UMAP_clusts=False))\n", - " \n", - " arr.bar(start_ix+i, median, \n", - " color=GMM_PAL[clust_ix-1],\n", - " yerr=med_se)\n", - "\n", - "arr.set_ylabel('FR Range (spikes/s)')\n", - "arr.set_xticks([1.5]);\n", - "arr.set_xticklabels(['GMM Clusters'],fontsize=12,fontname='Arial')\n", - "arr.spines['right'].set_visible(False)\n", - "arr.spines['top'].set_visible(False)\n", - "arr.set_ylim(0,25);" - ] + "text/plain": [ + "[]" + ] + }, + "execution_count": 29, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "# add ECG to the choice of community algorithms\n", + "def community_ecg(self, weights=None, ens_size=16, min_weight=0.05):\n", + " W = [0]*self.ecount()\n", + " ## Ensemble of level-1 Louvain \n", + " for i in range(ens_size):\n", + " p = np.random.permutation(self.vcount()).tolist()\n", + " g = self.permute_vertices(p)\n", + " l = g.community_multilevel(weights=weights, return_levels=True)[0].membership\n", + " b = [l[p[x.tuple[0]]]==l[p[x.tuple[1]]] for x in self.es]\n", + " W = [W[i]+b[i] for i in range(len(W))]\n", + " W = [min_weight + (1-min_weight)*W[i]/ens_size for i in range(len(W))]\n", + " ## Force min_weight outside 2-core\n", + " core = self.shell_index()\n", + " ecore = [min(core[x.tuple[0]],core[x.tuple[1]]) for x in self.es]\n", + " w = [W[i] if ecore[i]>1 else min_weight for i in range(len(ecore))]\n", + " part = self.community_multilevel(weights=w)\n", + " part.W = w\n", + " part.CSI = 1-2*np.sum([min(1-i,i) for i in w])/len(w)\n", + " return part\n", + "\n", + "ig.Graph.community_ecg = community_ecg\n", + "\n", + "def readGraph(fn, directed=False):\n", + " g = ig.Graph.Read_Ncol(fn+'.edgelist',directed=directed)\n", + " c = np.loadtxt(fn+'.community',dtype='uint8')\n", + " node_base = min([int(x['name']) for x in g.vs]) ## graphs have 1-based or 0-based nodes \n", + " comm_base = min(c) ## same for communities\n", + " comm = [c[int(x['name'])-node_base]-comm_base for x in g.vs]\n", + " g.vs['community'] = comm\n", + " g.vs['shape'] = 'circle'\n", + " pal = ig.RainbowPalette(n=max(comm)+1)\n", + " g.vs['color'] = [pal.get(int(i)) for i in comm]\n", + " g.vs['size'] = 10\n", + " g.es['width'] = 1\n", + " return g\n", + "\n", + "\n", + "rand_data = full_data[0:(int(len(full_data))),:]\n", + "my_umap = umap.UMAP(n_neighbors=20\n", + " ,min_dist=0.1, metric='euclidean',random_state=random.randint(0,10000))\n", + "my_umap.fit(rand_data)\n", + "my_umap_embedding = my_umap.transform(rand_data)\n", + "\n", + "G = nx.from_scipy_sparse_matrix(my_umap.graph_)\n", + "umap_igraph = ig.Graph(len(G), list(zip(*list(zip(*nx.to_edgelist(G)))[:2])))\n", + "\n", + "umap_ECG = umap_igraph.community_ecg(ens_size=10,min_weight=0.5)\n", + "\n", + "umap_df = pd.DataFrame(my_umap_embedding, columns=('x', 'y'))\n", + "\n", + "umap_df['dbscan_color'] = umap_ECG.membership\n", + "ecg_colormap = [sns.color_palette(\"husl\", len(set(umap_ECG.membership)))[i] for i in umap_ECG.membership]\n", + "\n", + "f, arr = plt.subplots(1,figsize=[5,4])\n", + "\n", + "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", + " marker='o',c=ecg_colormap, s=30, edgecolor='w',\n", + " linewidth=0.25)\n", + "\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['left'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['bottom'].set_visible(False)\n", + "\n", + "arr.set_xticks([])\n", + "arr.set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Gsxw-y5c77H8" + }, + "source": [ + "# Figure S3: Comparison of UMAP and GMM in the specified feature space." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_ZbeKAN37_qU" + }, + "source": [ + "## Figure S3A: WaveMAP labels in the 3-D feature space" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cPlT0of0ue6K" + }, + "source": [ + "### To demonstrate that WaveMAP locates structure that traditional methods cannot parse, we show the WaveMAP cluster labels in the 3 feature space used by traditional methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 211 + }, + "id": "NGadrhJo77Qz", + "outputId": "de7d55f0-c515-4888-9ae9-5c14d937d1fe" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[3.8,3])\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.set_xlim([0,1.4])\n", + "ax.set_ylim([0,1.6])\n", + "ax.set_zlim([0.,0.6])\n", + "ax.view_init(elev=20, azim=220)\n", + "ax.set_xticks([0,0.7,1.4])\n", + "ax.set_xticklabels(['',0.7,1.4],fontsize=12)\n", + "ax.set_yticks([0,0.5,1,1.5])\n", + "ax.set_yticklabels([0,0.5,1.0,1.5],fontsize=12)\n", + "ax.set_zticks([0,0.3,0.6])\n", + "ax.set_zticklabels([0,0.3,0.6],fontsize=12)\n", + "ax.tick_params(pad=-1)\n", + "\n", + "for i in [int(x) for x in np.unique(UMAP_and_GMM['gmm_labels'])]:\n", + " to_plot_df = UMAP_and_GMM[UMAP_and_GMM['gmm_labels'] == i]\n", + " x = to_plot_df['troughToPeak_abs']\n", + " y = to_plot_df['prePostHyper']\n", + " z = to_plot_df['FWHM1_abs']\n", + " ax.scatter(x,y,z,c=GMM_PAL[i-1],marker='o',alpha=0.75,s=20,linewidth=0.75,edgecolor='w',depthshade=True)\n", + " \n", + " ax.plot(x, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='y', zs=1.5)\n", + " ax.plot(y, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='x', zs=1.4)\n", + " ax.plot(x, y, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='z', zs=0)\n", + "\n", + "ax.tick_params(pad=-1)\n", + "\n", + "ax.set_xlabel('Trough to peak ($\\mu$s)',fontsize=12,labelpad=5)\n", + "ax.set_ylabel('Peak ratio',fontsize=12,labelpad=5)\n", + "ax.set_zlabel('AP width ($\\mu$s)',fontsize=12,labelpad=0)\n", + "ax.view_init(elev=20, azim=220)\n", + "\n", + "ax.scatter(UMAP_and_GMM['troughToPeak_abs'],UMAP_and_GMM['prePostHyper'],UMAP_and_GMM['FWHM1_abs'],\n", + " c=UMAP_and_GMM['dbscan_hex'],marker='o',alpha=0.75,s=20,linewidth=0.25,edgecolor='w',depthshade=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "irB9MlJI9PoD" + }, + "source": [ + "## Figure S3B: GMM with eight clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qPIGMvwjvJKB" + }, + "source": [ + "### To verify that the quality of our clustering with WaveMAP was not just due to using more clusters, we train an eight cluster GMM model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OqH3qfnC9cFt" + }, + "outputs": [], + "source": [ + "eight_GMM_classes = eight_GMM_classes[0,:].tolist()\n", + "eight_GMM_classes = [x for x in eight_GMM_classes if not np.isnan(x)]\n", + "\n", + "classifies_pal = sns.color_palette(\"husl\", 8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350 + }, + "id": "VsngCAgj9OG0", + "outputId": "40e1d276-61f6-4064-857f-025b3d73c8d4" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" } - ], - "metadata": { + ], + "source": [ + "fig = plt.figure(figsize=[3.8,3])\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.set_xlim([0,1.4])\n", + "ax.set_ylim([0,1.6])\n", + "ax.set_zlim([0.,0.6])\n", + "ax.view_init(elev=20, azim=220)\n", + "ax.set_xticks([0,0.7,1.4])\n", + "ax.set_xticklabels(['',0.7,1.4],fontsize=12)\n", + "ax.set_yticks([0,0.5,1,1.5])\n", + "ax.set_yticklabels([0,0.5,1.0,1.5],fontsize=12)\n", + "ax.set_zticks([0,0.3,0.6])\n", + "ax.set_zticklabels([0,0.3,0.6],fontsize=12)\n", + "ax.tick_params(pad=-1)\n", + "\n", + "classifies_df = UMAP_and_GMM\n", + "classifies_df['eight_gmm_classes'] = eight_GMM_classes\n", + "\n", + "for i in range(1,9):\n", + " to_plot_df = classifies_df[classifies_df['eight_gmm_classes'] == i]\n", + " x = to_plot_df['troughToPeak_abs']\n", + " y = to_plot_df['prePostHyper']\n", + " z = to_plot_df['FWHM1_abs']\n", + " ax.scatter(x,y,z,c=classifies_pal[i-1],marker='o',alpha=0.75,s=20,linewidth=0.75,edgecolor='w',depthshade=True)\n", + " \n", + " ax.plot(x, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='y', zs=1.5)\n", + " ax.plot(y, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='x', zs=1.4)\n", + " ax.plot(x, y, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='z', zs=0)\n", + "\n", + "ax.tick_params(pad=-1)\n", + "\n", + "ax.set_xlabel('Trough to peak ($\\mu$s)',fontsize=12,labelpad=5)\n", + "ax.set_ylabel('Peak ratio',fontsize=12,labelpad=5)\n", + "ax.set_zlabel('AP width ($\\mu$s)',fontsize=12,labelpad=0)\n", + "ax.view_init(elev=20, azim=220)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LhElsHhpBMFX" + }, + "source": [ + "### We train a random forest classifier on the eight GMM cluster data with the same hyperparameters as the four cluster dataset and show it performs poorly" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "colab": { - "collapsed_sections": [], - "name": "WaveMAP_Figures_Data.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "base_uri": "https://localhost:8080/" + }, + "id": "X5mI5yYbBMfA", + "outputId": "ef81ed2f-b9ef-4dc0-c9c8-c03959efe9ec" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 0.9s\n", + "[Parallel(n_jobs=-1)]: Done 3 out of 5 | elapsed: 1.8s remaining: 1.2s\n", + "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 2.3s remaining: 0.0s\n", + "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 2.3s finished\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[ 3, 1, 1, 0, 0, 0, 1, 0],\n", + " [ 0, 12, 2, 1, 2, 0, 0, 2],\n", + " [ 0, 2, 31, 4, 2, 0, 0, 0],\n", + " [ 0, 1, 5, 11, 7, 0, 0, 0],\n", + " [ 0, 0, 0, 2, 49, 0, 0, 2],\n", + " [ 0, 0, 0, 0, 2, 4, 4, 2],\n", + " [ 0, 1, 0, 0, 0, 4, 20, 1],\n", + " [ 0, 0, 0, 1, 3, 2, 0, 3]])" + ] + }, + "execution_count": 55, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "eight_classifies_nonan = [x for x in eight_GMM_classes if ~np.isnan(x)]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(full_data[~np.isnan(full_data).any(axis=1)], \n", + " eight_classifies_nonan, test_size=.3, random_state=RAND_STATE)\n", + "\n", + "model = xgb.XGBClassifier()\n", + "param_dist = {\"max_depth\": [4],\n", + " \"min_child_weight\" : [2.5],\n", + " \"n_estimators\": [100],\n", + " \"learning_rate\": [0.3],\n", + " \"seed\": [RAND_STATE]}\n", + "UMAP_grid_search = GridSearchCV(model, param_grid=param_dist, \n", + " cv = 5, \n", + " verbose=10, n_jobs=-1)\n", + "UMAP_grid_search.fit(X_train, y_train)\n", + "\n", + "confusion_matrix(y_test,UMAP_grid_search.predict(X_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YQyMXRZlvu5N" + }, + "source": [ + "## Figure S3C: Confusion matrix for a random forest classifier on the 8 cluster GMM" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SbEcU68b_hki" + }, + "source": [ + "### and show the performance as a confusion matrix of the 5-fold CV test accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "id": "-P5Sco-R_iEW", + "outputId": "fcefd3a7-bcdf-4a4e-ac96-e66da649ea8b" + }, + "outputs": [ + { + "data": { + "image/png": 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kMzM7iIXAiAgSwa+bF/dYrPH3Mxs7jiOd4xd/hYXbMY4xQCfn9bMYibEDwMvAeW4vpqrDRORWjIpzV8yNFDzmNeA1MHocbm1Hii+JvlC/VF3a1+LXv1LZve8IAGs2pzHw0U8AqH9Kebqm1A50uk+qVavItq27qVGzCtu27iY5uULINnLx4h6LLQHHOGLGc2Sp6ldeGHLbVUlW1e0iUgo4B3gAE0XXNtQLqmq2qi7CjOYODfX8wsArib5u5zZg9sK1ee8rVygFmP+rW/5xGjM+Cqwm5otLu53JjGmfAjBj2qdc1t2dAlhBrAxhZKiAxsf53mLHcXhWUbctjp0i0hg4FViuqumOAnMkFUmgiIxxRCLRl0vpkgmc3aYmD768JK+se+cG9L2sGQAfL93AzM/+DGjDlxTeiLuuYeB1Y5n65nzq1K3GpOkPhHh3Bi/usXjjKJr72RUj3BT8EHe4dRyPY5SRs4HcPAwXYCTegyIi1TBdmjnAYefc3s52wmnQsBbLvnOtYeKTw+lZnN7vnWPKJs/5jclzfnNt4/Wp9/ks/2D+UxHVDby5x2JNwOjY2PAcqjrdK1uuHIeqThKRd53XubLfSzGS6q5MYLolr2K6R+uBO1T1g9Cqa7GcQGLDP0QFt7MqycBhVT0gIvFAfyAHk+ogKKq6E5P+0WKJTZwxDn/7ihtuB0fnAE2c109gBFVHAOMKo1IWS9HDTxY3lwvA3Caddo5tLyILROSAk3z6dk9vxQPcjnE0BXLXSvfFTM0ewOSFcJ3ExWKJVUQgPt7/PhfkTzrdFpgrIj/6SI9QFfgI872aiVE6D30e/7g6iiv18nysU1W/C8LcOo5soISINAX2qeoGRxAkKch5RYY4D8NyZk2q7JmtGxeVDn6QS65rmOqZLYCe9T01F/P4dRBBHEeISafvxCSdzk0bkg64H2H3z2MhHv8mMMXfTrffpnnAu5i0dG87ZS0xAqcWS7Egzk90rFPaW0SucopeK5A/1l/SaV/jfmcCP4vI10Bj4BtMQqaIhG5V9W+RnF8Qt47jBmAAkMnRAdGqwKNeVsZiKaqIQJyfrorjOWao6pN+jggl6XRtoD1wIfAz8AwwAzg7xCoXKm6nY9NxloHnK/uyMCpksRRVIlhxHkrS6cPA+6q63NiWx4BdIlJBVfeFUN1CxXXHX0R6YJpWVcnXq1PV/oVQL4ulSCEBJk9cjI2GknT6J44Nf49avFYouJqOFZFHgInO8Vdjcl9eDHib5dhiKcLExYnPLZjnUNWDwHuYrPNlReRs4O/4Xgf1JnCFiLQVkUTgIWBRUWptgPsWxyDgQlX9RUSuV9URIjIDeLAQ6xZVsrOzOeuMfpxySjVmffCc6/O2bdrJ/cOeY/eOvYhArwEX0/fmHsyftYhXnp7Bmj82MePTZ2nVrklwY/kQ4KnT25Oans7TP5kfpmsb1ufMalXJUfhk8xbmbdri9/xdW3byz5Hj2LtrDyLChb0vofv1PVn76xpeffBFjhw6TLVa1Rnx3N2UKeddsqqTlgBjHC6nY10lnVbVz0XkfmAuJlHSIsDvmo8ThVvHUVFVf3FeZ4hIoqouE5GQV4OKSBPMoM9MVe0b6vmFxQvPz6B58wakpR0M6bz4hHhGPT6Ilm0acXD/Ia45707O6tqWJi3qMWHKfYy+8+Ww6nNZnVpsPniI0gnmv7VrzepUKVmSEUtXoED5xMSA58clxDPwgRto1Loxhw8cYmT322h7Tntevu+fDLjvBlqfeSqfvvsxs16byXUjbW8zGALERRCq4jbptFP2CvBK6LX0T6AM9QWu7Wq9h9uVo3+JSCvn9S/AUBHpB+xxeX5+XgKWh3FeobFp03bmfbiY6wcd97kGJblGZVq2MUG+ZcuVoUHT2mzfupuGzerQoEl463YqlyxB+yqV+Wzrtryyi2rVZOa69XmffFqmf9FjgMrVKtOodWMASieVoXbjuuzetostazfT6ozWALQ9px1LPlocVh2LI3Hie4sR/g287mxfAg2BhZgM9QuABsAXbo25bXE8iFnDAXAf8BbGSw5zeyEAEbkWMy6SO0ddJBh15ziefOq2PL2KcNm8YTurflrDaR2aRWRnYJNGTPtrLaXzLVWsXro0naol0zG5KmmZmbz5x59sO3zElb0dm7az9te/aNq2OXWa1GPZJ0s446JOLP5wIbu27oqorsUFkchaHCea/BnqRWQpcHH+VasiMh3TlXrEjT1XLQ5V/VBVFzivv1HVxqpaQ1Xfc1txESmPEf+5M8hxQ0RkhYis2Llzp1vzYTN3zkKSq1WmfYcWEdk5dOAwIwY8xT1jbyCpfPhjBu2rVGZfRiZr9x84pjxR4sjMyeG+Fd/z2ZatDG3hzjkdPniYp4c+waCHhlCmXBmGP3MH86bOZWT32zhy8DAJiTZDhlvi4sXnJrEX5dYC+KtA2VqguVsDfv9rRMSVPJSqrnF5rceB11V1UyDdzGhLBy75+kfmzl7A/HmLOXIkg7S0Awzs/xCTpjzu2kZmZhYjBjzF5b26cEH3TsFPCECzCuVJqVqFdlUqUyIujtIJ8dzashm709P5ZuduAJbt3M0wF44jKzOLZ4Y+wbl/78pZl5j1Q7Ub1eHRqU8AsHnNJlZ8XqR6jUWXAC2O2PMbfAVMEpGHgE1AHcxizoVuDQT6ufkTM5gS6LEo4G89XR4i0hYj3lPkBC7HjB3OmLHDAfjqyxVMGD8tJKehqjxy2ws0bFqbAbeEPkZSkBlr1jFjzToAWlasQPe6tXnh19+5rlF9WleqwBdbj9CyYgW2HDoctF4v3fMctRvX4e83XJlXvnfXXipWrUhOTg4zX3ybi/tcFnGdiwOC//GM2PMbDMToBa/E+IBMzHSx63wrfh2HqnqZOqErUB/Y4LQ2koB4EWmpqu09vE7U+f6b35j9zhc0aVmPXuea6OfbHupHZnomY+95jT279zHs2tE0b92Qif8NNc7oKLPWb+S2ls25vE5tjmRnM3HVHwGP/23Fr3z5/ufUa1afEZcZx9j3rgFsWbeZeVPmAHDmJWdz/tUXhl2n4kaE07FFBmeG51onUDUZ2KmqOaHYCNjBFZHSQKN8U7H597UG/lRVNyN0r3E0OA6Mnkd9iohYcS5duqbQpWtKSOe0P7MlP6f6FjI7v1tkmp6/7t3Hr3vNup9DWdk89ZOvhYa+aXl6K95fe1z2CTpwOt2vj7xlVNwwg6MnT5NDRJpjFnNWV9XhTtqTkqr6k5vzg7Uq7gb85Ru8HrjLzUVU9ZCqbsvdMGv3jzjKYBZLTBAX53uLtRaHiFyNGc+ohVHzAxNwN96tjWBD6tdgovR8MR74BDPoGRKq+mio51gsJ5LcRG4+90W3Kl4wGrhAVX8UkVzx8R8x8TOuCOY4aqmqT80NVd0sIrXcXshiiXViPFl9fqphgung6GpSJYSAumBdlYMiUsfXDhGpCxzytc9iOdkw2RHU5xbNzO0e8S3Hp4K8FpOh0RXBWhwfAmN9XARMF2Wu2wudaErEd/DMVutK3tma1vU7z2zVP/Vjz2wB9Fx5qaf2vCIjx5tnpiH87gmQIL5/kMVPeRHmNuBjERmMSUQ9H6NSdpFbA8Ecx4PAEhH5ETPPuxWoCVyBESKJbLWTxRIrCCScJJMqqrrKmVXphslgsBGY4+ihuiKg41DVbSLSHhgJXIKJV9kNzAbGq2o4QW4WS8xxMi0AE5HnVfU2jI5w/vLnVPUONzaCds9UdY+qPqiqZ6lqU+fvQ9ZpWIob8XHqc4u16VjMylFf+BqS8ImNcLJYXBBwjCO6VQkbERnkvEzI9zqXhoDrUGnrOCwWF0iAMY4Y0uTIbVGU4NjWhQLbMZkMXGEdRxEnOzubszr245RTkpk1+58hnz+471n06ZWCCLw1cwX/nrqEh0ZezIVdm5ORmc36jamMePA90va70/Yo6kT6vAIRQw7CJ7m5VURkjKpGJPsZtSloEflSRI44+TAPiMjv0bp2LGMkDeuHdW6zxtXo0yuFy699lQuufIkLuzSnft3KLFjyF3/r+QIXXPkia9bv4tYbQ80OWHSJ5HkFQvC9hiMhTomLvenYBU5WxjxEpJmIuI54dKtyXlJEnhCRNSKyzym7SESGh1ZfhqtqkrNFJpNVDDCShou4fnB4QWlNGibz/U+bOHwkk+zsHJasWMtlF7Tkq6//JDvbBEN+++NGalav4GW1TxiRPq9A5I5x+NrcNERCSTrtHF9CRH4TkU3e3MExvMTxOV32O+WucNvimAC0BvpwdFnqSopYdOvJxqgR43jyqduJ86sgE5hVf+6gY4d6VKpQmtKlEjmvc1NOqXGsk+h9ZQc+Xxg4RD9WiPR5BUIEEuJ8b2Ekne4DvJJPx9cXdwGFFQRaTVW3FijbCtRwa8DtE74CuE5VlwA5YGJVMNF1ofCkiOwSkcUi0tXXAdGWDiyqzJ2zgORqlSKSNPxzzU5efn0hM/41kLcmDmDlqq1k5xxtVt82pAtZWTm8N+dHL6p8QvHieQUjzs8WzG/kSzr9kKoeUNVFQG7SaV/HNwD6Av5SSkbKGhE5r0BZV4x8oCvcDo5mFDxWRJIxi8Hccg/wq2PrWmC2iLRV1WO0D6MtHVhU8Slp2O9BJk0dE5KdGe99y4z3vgXg3tsvZOt2o+/xj57tuKBLM64Z/KbndT8RePW8/JEbq+JvH94lnQZ4Abgfkw6yMHgUeE9EXsdojzbCyGS4VgBz2+L4DzDZ8YSISE3gRY4V5wmII3K8X1XTHcXlxYDVrfPDmLG3smbDPP5YM4ep08fS9W+nh/UlqFK5LAC1albgsgta8v7cn+h6ThOGDerMwOHTOHwkcJqFWMGr5+UPM8bhe3McxwxVTXG21wqc7jrptIhcAcSr6vueVb4Aqvo/TFxKWeBy5+/FTrkr3LY47geexiRSKgOsBv4FhK+FF1zP1OIB/36uN5UqliEzK5v7x8wmbf8RnnigGyUTE3jn3+YH5tsfN3LvaN8qZhYHMStHfe4KPqviKum006V5hij8oKrqMkKIhi2I22z1GcAIYITTRdmlqq67ESJSETgDo66chREIOhe4PeQaF0PCkTTM5Yr+/z6u7OxLJ0RapSJNJM/LH4FWjrpotrtNOt0EI6m50NHmLQFUEJFtwJmqui70mhtE5AFVfcJ5Pdrfcar6sBt7rhyHj1QJ5XJTHLhMj5AIjMHkbcgGVgE9C/T5LJYiS6AgNzdJp0UkN+n0DUBbTNLpgtHlv2BSFeTSCTMk0J7IZ1jypxX0qbETCm67Kr5SJeS636DpERxt0dNDq5rFUnQQINHfknN3JoImnVbVLCAv76eIpAI5jk5vRKjq0HyvXQ+C+sNtV+WYZyMiNTCp4lwncLFYYhpR/7MqHiedzrfvS45tKYSN1wnWwopVcXQ67sD03aaHY8NiiSUESPTTtIgR6cD8vYb8HrDg+6A9CIjsnpthZlgslpOe3DEOn9nqY2BuUFXjVDXe6T3cgFlK0Rwo5fydjv9UKMfhdnB0Icd6pTJAK4zMuiUCSsR5l8huyTfehjW8+tt8T+15xaV10j2xk5mT4f5g8b8ALM69OHhR4XGgiarmLjBbLSI3YXoQk9wYcNtVKTindxD4Md/UksVyUhNocDQGFcDiMNO+v+Urq4fLbgq4cBwiEg+cBwxRVW9cvcUSY5gxjrDXcRQ1JgCfi8ibGKHiOhg5QdcLfII6DlXNFpGLcILbLJbiiEgAseIYa3Go6v+JyM+Y3LHtMJGxg1T1I7c23HZVJgCPicgjqnpyBDdYLCFggtz874s1HCfh2lEUJFi2+t6qOgO4FROrf6eI7CTfQKmq1g334pbC466hz/P5RyuoklyBj5e9AMAtA55hzeotAKTtO0j5CmWZ9/VzQW1lpmcwvv9dlCGB7KxselzRk3/cMZjbb76F3b9vJF7iaNS4MWNeGseytLVkaXZAW+P6jyIrI5OcrGzaXdSZ7rf2Y+qD41m/cjWoUq1+bfo/MZJSZUtH7R6DYcY4/HRVYsxziEhJ4GGgN1BFVSs4vYqmqvqiGxvBWhwTgRkYbYCIEZFrMQvH6mJWyA10FsBYPKZXn/MZcNPl3Dnk6Jfmpcl3570ec98blKvgbjY9oUQit7/xFDeKQFkAABjDSURBVElJSaRnpPNs35F0veg8hj5yN+kllcycbGY+PZHXX32N3sMH88Nu/7IOCSUSueONpylVtjTZmVk823ckrc5Node9N1E6yUTyznx6Il9N/4CLb7zGrx2v7zEoclK1OCZgtHT6APOcspVOuSeOQwBU9aswK3jUkNEzfBoT4LYMkxHOUkiccU4rNq7f7nOfqjL3/UVMn+Mu7FxEKFW2NFmaTXZWFjlZ2cRJPGXKleHAkX2oKhlHMoh3obyVawsgOyuL7KwsBMlzGrm23AwceHmPQesNxJ88KSCvABo7MTR5wlyhJJEP5jjiReRvBHCqqvq5y2s9BoxW1aXO+80uz7N4zLLFv1K1WkUaND7F9Tk52dk82etWUjdtZ+iwoVRv2YBvdv7BlPvH8cfi72nVsiXDXriFL3f/5trWzg1b6HJddxq0aQ7AlPvH8cvC5dRsVJded98Y9v1BePcYiJNsjCNiYa5gPxElgdcDbMfHbPvAmdJNAZJF5E8R2SQiL4rIcZ1YKx1Y+HwwcwE9eoWmbB4XH88D77/M459NZs5Xn5C6ZguVSibRf+xIRn8+GWqW5+133qZR+eCylbm2xn4xjXU//87m1esA6D92JE99+RY1GtZlxbwF4dxaHuHcYyBEzJJzX1usjXHggTBXMMdxUFUbqmoDP5urwBmMQGsi0AvojAkrbodJan0MqvparpJScnKy2/uwuCQrK5v5Hyyh21XnhHV+mfJJNOl4Gh98OJu6ZasCxhGkXNaF/70/i4blqodkq2nHNvy6cEVeWa6t7z9ZFFb9IPJ79IXgW+Hcrcp5EeN+jL7oz0BFjDDXFkIQ5orW2pXcpa0vqOpWVd0FjMdKB0adRV/8SMOmtalZq6rrc/an7iXnYDol4hLIOJLOqiXf0751G379YxXlE8ugqvz0+VJaNG/BnoyDQW0dSjNJ0TOOpPPb199RvUFtdqw3MyG5tmo0CF8yIpx7DEZukFustzic1v+DwL2qmoT5US+nqiMcwS5XuBocjRRV3ePkh8g/ihRzI0qxxK3XP8vShb+wZ3caZzYbxIj7e3PNgAuZPXMhPa7uHJKtfTtTefmBCZSJK0FOTg69rrqC5p07cN2lV1Ai3XyMrU9tzYjRo/h2119BbU2+bxyak01OjtLhknNp3aUj4/qN4siBQ6gqtZs1pPcjwVP2eHmPbojBxEvH4SzoHIYRLM7VygkZCUEBMCIcubJLMeKomRh5+C9V9SF/56SkpOiKFSv87bYUYP0Bb3VD520s6ak9r/AqyK37uXfy03d/uvpxbN6msb75ie+UkvcPGsuXc79uEIm0XzQRkfHAn6r6crg2opk79nGgKiYC7wjwLvBEFK9vsUREfAx1SYLQEbhVRO7GxKrkX9DpakQ5ao7DWao+zNkslpgikOZoDPqTfzlb2Nhs9RaLS/zNJMSS4xCR5pi0DD9HIosRgxHBFkv0MS0O9bnhYtDUbdJpEblLRH4Rkf0islZE7vLsHkQGYqZgXwV+EZGrw7VlHYfF4gY/soFx4rrF4TbptAD9gUrAJcBwJ8bLC+4BeqlqNUwa1vvCNVRsuioZOd95ZstLuT8vqZfUw1N73et6N0tz7uADntkaNL25J3YS40q4PtbEqvjfF/Dco0mnW6vqAWCRiOQmnb43/7Gq+ky+t7+LyP+AswlhVWcATsmX5nEWEYxz2BaHxeISEd+biyaHv6TTvloc+a4ngllpXTDjW7jk1dTJxBj297/YtDgslkgJMjgaKFu966TTBXjUueybodQzAGVFZEO+9xUKvHetr2Mdh8XiAhddlRmq+qSf010lnT7GpshwzFhHZw+1fs/zyI51HBaLW/wtOXcxOOo26bSxJzIIM/Zxrqp6lvPCC12dXKzjcMjOzuasjv045ZRkZs32vbS4uDJy6PN8Om8FVZMr8NlyI9E37okZTJ/0MVWqVgDgnkf7cv7F7jLEN6hZjudvPxq5WqdaEs/95yfaN61Kg5rmh7l82UTSDmbS/d55/sz4pNA+x9zxDD/7AhFC0mlEpA8wFvib23SMbhCR24CJgVovjqTgTar6fDB7UXEcIlJwSL008LKq3hqN67vhhedn0Lx5fdLSAkd3Fkeu7nM+A2+6nDtuPFa788bhPbj59itCtrd26/48hxAnwtev9OTj5RuZNO/3vGPu69uO/YdC18UurM8xklkVh6BJp53jxgBVgOVy1FNNU9Wbw6x6LjWAP0XkQ+Ar4HdMV6kcZvC2KyaWbIobY1GZVXEycSc5D6cGJsz+P9G4ths2bdrOvA8Xcf3g43ICW4Azz2lFxUo+8yJHTKdTq7Nh+wG27Dp0TPnlZ9VlztfrQ7JV2J9jJOs4VDVVVXuqallVrauq053yhfmcBo7OTWL+74wHTgNVvR+jgbMak+pxHvAL8CEwCFgFtFPV4zRyfHEiuipXYbxukREpHjViHE8+dTv799vWRihMmvghM6d/QZv2jXlo7KCwnEu3s+oxu4CDOL15Mrv2HmHdNr9jhz4pzM8xYIsjRtacOzo4zzpbRJyIdRwDgCnqJ54/2tKBc+csILlaJdp3aFHo1zqZ6H/DpSz++VU+XvIc1apX4vH73wjZRmJ8HOd3qMWHS4+ZEaT72fWPcybBiMbn6HfJeTEkqo5DROoBXYDJ/o6JtnTgkq9/ZO7sBTRt2I1+193Pl18sZ2A/V621Yk1y9YrEx8cTFxfHdddfxA8rQo+X6tK2JivX7WH3viN5ZfFxwsWn12buktAcR2F/jhJkK25Eu8XRD1ikqv4Tb0SZMWNvZc2GefyxZg5Tp4+l699OZ9JUbyT1T2a2b0vNe/3R7KU0axl6Xq7uZ9dn9uJjHcTZp9bgry1pbEs97Ocs3xT65xggVqU4Eu0xjv7AU1G+piVCbhn4LEsW/kLq7jRSmg5i5AO9WbLwF1b+tBYRqFOvGk89H5rMSumS8Zx9ag0e+NeyY8q7dTp+zKMocDKMcXhJ1ByHiHTCZI8qMrMpBenSNYUuXd2tRShOvDRp1HFlvQdcGJHNw+nZpNz43+PK735lqY+jQ6OwPkeJ8dAuESmDESpuDXwHPBnuqtRoPokBwHuqGtpQucVSRBCJ873FzijHS0B3zNRrLyKYXYmmdOBN0bqWxeI9EqDFETOO4xKgvapuFZEXgAWYhPIhY5ecWywuMSlJfO6Jaj0ioKyqbgVQ1Y0iUiFcQ9ZxWCwukAAtjphxGybQLn8u6ILvXeeCto7DYnGJ/7GMmHEdOzDxMrnsLvBeAVdpXYuN4yiqcn970z/0zNbba/w1pcPjrGrejZ3/9bZPbd6wePW3+Z7Y2XUkK4SjxX9XJUbmY1W1vle2io3jsFgi5SToqniGdRwWiysEIeYHRz3DOg6LxQVGmDjmxzg8wzoOi8Ul/locxc9tWMdRpBg+5Dnmz1tG1eSKLPnOJBKf9d+FPD1mOr+v2shniybQrkMTV7Yy0zMY138UWRmZ5GRl0+6iznS/tR9THxzP+pWrQZVq9WvT/4mRlCpbOqCtbZt38uCwCaTu3AsCV/W/hD439WD8I2+wYP4yEkskUrt+DR574XbKVygcwR9fZKZnML7/XZQhgeysbHpc0ZN/3DGY22++hd2/byRe4mjUuDFjXhrHsrS1ZGl2BFcTRGJ+AZhnRDNWpT7wMnAWkA7MBO5Q1VCGtk9qeve7gBuHduPmwePzylq0qseUdx5gxC0vhmQroUQid7zxNKXKliY7M4tn+46k1bkp9Lr3JkonlQVg5tMT+Wr6B1x84zUBbcXHxzNy9CBatGnMwf2H6H3+CM7s2pYzu7bltocGkJAQz3OPTeKN52ZyxyMDQ77vcEkokcjtbzxFUlIS6RnpPNt3JF0vOo+hj9xNekklMyebmU9P5PVXX6P38MH8sDuyoGw7xnGUaMaqvIyZR66JEWvtgs1cfwxnd25NpUrHptpo1rwuTZrWDtmWiOS1JLKzssjOykKQPKehqmQcyXA1lZhcozIt2jQGoGy5MjRsWocdW3fT6W/tSUgwX6bTUpqxfcuukOsZCbn3mKXZZGdlkZOVTZzEU6ZcWTJzsvPuMT7Oi39zq8aRn2g6jgbAu6p6RFW3AR8RJJOVJTJysrN54oph3H3OtbTo1J4GbUzqxCn3j+Oec3uzfe1G/tYntLSRmzdsZ9XPf3Fqh2bHlM966xPOOb+DZ3V3S+493nduH67u1pPqLRuw48g+ptw/joe69kO27WfYLbfwS2pkofom6XScz83/oGm+890nnRYReVpEdjvb0+LmAlEmmo7jOeBaESkjIrUwisofFTwo2tKBJzNx8fE88P7LjP1iGut+/p3Nq9cB0H/sSJ768i1qNKzLinkLXNs7dOAwowY+yV1P3EhSuTJ55f8a/w7xCfFcdnVXj+8gOLn3+Phnk5nz1SekrtlCpZJJ9B87ktGfT4aa5Xn7nbdpVL5GhFcy07G+NpetDrdJp4cAPTF5V07DRLMWuQDRaDqOBZgWRhqwCViBSXx7DNGWDiwOlCmfRNOObfh14Yq8srj4eFIu68L3nyxyZSMzM4uR1z/JZb26cn63o+lA/jfjUxZ+vJyxr4509ctbWJQpn0STjqfxwYezqVu2KnD0Hv/3/iwalqse8TXCDavPl3T6IVU9oKqLgNyk0wUZAIxT1U2quhkYBwyMuPIeExXHIWY4+iPgPaAsUBWoBDwdjesXR/an7uVQmklnk3Eknd++/o7qDWqzY/0WwIxx/PT5Umo0qBPUlqry2O3P06BpHfoNO5p6YPFn3zL5hfd4btpDlC5TqnBuJAD7U/eSczCdEnEJZBxJZ9WS72nfug2//rGK8oll8u6xRfMW7MmITPk8IyOLzIxsny2O3bv3ARwJcHooSadbOfuCHXdCidasSmWgLvCioziULiJvYpLP3B2lOhR5Bvd7msULf2b3rjRaNerPvQ/2oVLlctxz56vs2rmPa654lFNPa8h/5zwe1Na+nalMvm8cmpNNTo7S4ZJzad2lI+P6jeLIgUOoKrWbNaT3I8OD2vrhm1+Z8+4XNGlZn390vQ2AWx/ozzP3v0ZGeiY393oIgNM6NOPBcbdE9hBCYN/OVF5+YAJl4kqQk5NDr6uuoHnnDlx36RWUSDfq461Pbc2I0aP4dtdfEV3ryiuvZcqUpdxwww3HlC9fvpwKFWoA9BCRIU5xJEmnk5x9+Y9LEhHxlxngRCDRqouIrAFew6gOJWEycB9WVb/RTykpKbpixQp/u08KinaQW+iZ1PzRpko3z2x5FeT25NW3sv6XP1z1r0SkUps2bVKXLVtGiRIl8sq7devG3LlzO6jqdwHObQcsVtUy+cpGAl1VtXuBY/cBF6rqMud9B+BLVQ2W2T6qRHOM40qMAtFO4E8gExgRxetbLGGjqnv+/ve/M2XK0QyJy5cvJz4+nkBOwyEv6XS+Mn9Jp1c6+4Idd0KJmuNQ1R9UtauqVlLVqqr6D1XdHq3rWyyRMnr06MovvvgiGRkZADz22GN88MEHQeegVfUgZnxvtIiUFZGzMUmnp/o4fApwp4jUEpFTgJHAJK/uwStiW7bZYoki+VsdIbQ2chmGSba+A5hBvqTTBZKyTwRmAz9jcrvOdcqKFDZWxWIJgdGjR1du06ZNas2aNfnoo49cr3hT1VTM+oyC5QsxY3657xUzYVCkJw1si8NiCQFV3dOzZ09KliwZSmvjpCNqsyrhICI7gWBrhasCXgZJeGnP2iratuqpql1lGAZF2nG4QURWqKpnabu8tGdtnRy2LMdjuyoWiyVkrOOwWCwhczI4jteCH3LC7FlbJ4ctSwFifozDYrFEn5OhxWGxWKKMdRwWiyVkrOOwWCwhE7OOQ0SGOxKD6SIyKUJbJUXkdUcLcr+I/CAil0Zgb5qIbBWRNBH5Q0RuCH5WQHtNROSIiEyL0M6Xjp0DzvZ7hPauFZHfHB3Nv0Skcxg2DhTYskXkhQjqVF9EPhSRPSKyTUReFBEbWuExMes4gC0YIaA3gh3oggRgI0Z5vQLwIPCuk9IhHJ4E6qtqeaAHMMbRVQiXl4DlEZyfn+GqmuRszYIf7hsRuRCj4HY9RpDmXGBNqHby1SUJqAEcBv4Tbr2wavpRIWYdh6q+p6qzgN0e2Dqoqo+q6jpVzVHVOcBaIKwvu6qudJTOANTZGoVjS0SuBfYCn4VzfiHyGDBaVZc6z2yzo5EZCVdhvvQLI7Bh1fSjQMw6jsJERKpjdCLDFlARkZdF5BCwCtgKhCz1JSLlgdHAneHWwwdPisguEVksIl3DMSAi8UAKkCwif4rIJqdLEDglXHAGAFMilMhzpaZviQzrOAogIonAW8BkVV0Vrh1VHYZpwnfGiLikBz7DJ48Dr6vqpnDrUYB7gIZALcwCqdkiEk5LqDqQCPTC3F9boB2mixcWIlIP062YHK4NB1dq+pbIsI4jH44a+1RM/ovgKr5BUNVsRwq/NjA0xLq0BS4AJkRaj3z1+UZV96tquqpOBhYDl4Vh6rDz9wVV3aqqu4DxYdrKpR+wSFXDztNo1fSjh3UcDk62rNcxv6ZXqap3Sr1m8DXUX/auQH1gg4hsA0YBV4mIlxoQShg5DFV1D+bXPH+XItIlyP2JvLVxjJq+qu7GiGJH4tAsPohZxyEiCSJSCogH4kWkVITTbq8ALYDuqno42MEB6lXNmaZMEpF4EbkY6E3og5uvYZxNW2d7FSMjd3GY9aooIhfnPicR6YOZCQm3//8mcKtzv5UwwtNzwqxbJ0z3KZLZFJyWz1pgqHOPFTHjJj9FYtfiA1WNyQ14lKMzFrnbo2HaquecfwQ4kG/rE4atZOArzExIGkY78kaP7ndaBOcnY6Z09zt1W4qR4Q/XXiJm6nMvsA14HigVpq2JwFSP/i/aAl8CezBCPu8C1U/E/+jJvNkgN4vFEjIx21WxWCwnDus4LBZLyFjHYbFYQsY6DovFEjLWcVgslpCxjsNisYSMdRwnABGZJCJjnNedI9XFCOG6KiKNwzx3oIgs8rpOltjEOg4/iMg6ETnsiMtsd77sScHPDA1VXagudDGi8cV1VpYucMSMdorIVyLSozCvaYlNrOMITHc1AjPtMWHkx0V/nizqUiLSC7PkewomKK868DDQ/UTWy1I0sY7DBWoEauYBrSGvyX+LiKwGVjtl3RzJwb0i8rWInJZ7voi0E5HvnF/yd4BS+fZ1FZFN+d7XEZH3nF/83Y7ORQtMrMpZTgtor3NsSRF5VkQ2OK2iV/NrYojIXWIkDLeIyCB/9+cE+I0HHlfVf6vqPjXiPF+p6o1+zvmniGwUI4/4bX7ZQBHpKEbWMc2p13invJQYWcXdznNa7mifWGIM6zhcICJ1MBGW3+cr7gmcAbQUkXYYCcObgCqY2IsPnC92CYwexFRM9OZ/MEpXvq4TjwkUW4+JjK0FvK2qvwE3A0vUyOxVdE55CiM41BZo7Bz/sGPrEkxE7YVAE0yIvj+aAXWAme6eCGDiXto69zQd+I8TdAjwT+CfaqQTG2HiRcAEnFVwrlXFuaewAwotJw7rOAIzy/l1X4QJXBubb9+TqpqqJpJ2CDBRjd5Fthqti3TgTGdLBJ5T1UxVnYl//dCOwCnAXWrkDI+o0fM4DqeVMAQY4dRjv1O/a51D/gG8qaq/qOpBTJCcP6o4f7cGOOYYVHWaqu5W1SxVHQeUxDgggEygsYhUVdUDqro0X3kVoLHznL5V1TS317QUHazjCExPVa2oqvVUdZgeG26/Md/resBIp/m913E2dTBO4BRgsx4bTbjez/XqAOtVNctF3ZKBMsC3+a75kVOOc938dfR3TTiq21rTxXUBEJFRYhTO9znXroARzgEYjGkJrXK6I92c8qnAfOBtp/v0jKO4ZokxrOMIn/yOYCPwhONkcrcyqjoD8ytey2kh5FLXj82NQF0/A64Fw5h3YZr5rfJds4IzmItz3Tourgnwu3Ntn12ogjjjGXdjWjWVnK7TPhxRIFVdraq9gWoY9a2ZIlLWaXE9pqotgU5AN4yAjyXGsI7DG/4F3CwiZ4ihrIhcLiLlgCVAFnCbiCSKyJWYLokvlmG+8E85NkqJyNnOvu1AbWfMBFXNca47QUSqAYhILTHCQWDGFQaKSEsRKQM84q/yTmvoTuAhEbleRMqLSJyInCMivpI3l3PuaSeQICIPA+Vzd4pIXxFJduq41ynOEZG/icipzlhOGqbrkuOvXpaii3UcHqCqK4AbgRcxAjJ/AgOdfRnAlc77VOAajCamLzvZmOnPxsAGjDzfNc7uzzGq69tEZJdTdo9zraUikgZ8ijPOoKrzMIrfnzvHfB7kHmY61xqEyVmzHZO35n8+Dp+P6Rb9gekCHeHYbtElwEoROYAZKL3W6ebVwAzApgG/YcaNpgaql6VoYoV8LBZLyNgWh8ViCRnrOCwWS8hYx2GxWELGOg6LxRIy1nFYLJaQsY7DYrGEjHUcFoslZKzjsFgsIfP/fFjrrHiG5lIAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "confusion_mat_counts_eight_GMM = confusion_matrix(y_test,UMAP_grid_search.predict(X_test))\n", + "\n", + "conf_mat_row_list = []\n", + "\n", + "for row in confusion_mat_counts_eight_GMM:\n", + " row_sum = np.sum(row)\n", + " \n", + " row_percent = []\n", + " \n", + " for val in row:\n", + " row_percent.append(val/row_sum)\n", + " \n", + " conf_mat_row_list.append(row_percent)\n", + "\n", + "conf_mat = np.array(conf_mat_row_list)\n", + "\n", + "colormap = mpl.cm.YlGnBu\n", + "colormap.set_under('white')\n", + "\n", + "eps = np.spacing(0.0)\n", + "f, arr = plt.subplots(1,figsize=[4,3])\n", + "mappable = arr.imshow(conf_mat,cmap=colormap,vmin=eps,vmax=1.)\n", + "color_bar = f.colorbar(mappable, ax=arr, extend='min')\n", + "color_bar.set_label('P (Predicted | True)',fontsize=12,labelpad=15,fontname=\"Arial\")\n", + "color_bar.ax.tick_params(size=3,labelsize=12)\n", + "\n", + "n_classes = len(set(eight_classifies_nonan))\n", + "\n", + "#Specify label behavior of the main diagonal\n", + "for i in range(0,n_classes):\n", + " if int(conf_mat[i,i]*100) == 100:\n", + " arr.text(i-0.38,i+0.17,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n", + " else:\n", + " arr.text(i-0.34,i+0.16,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n", + " \n", + "#Specify label behavior of the off-diagonals\n", + "for i in range(0,n_classes):\n", + " for j in range(0,n_classes):\n", + " if conf_mat[i,j] < 0.1 and conf_mat[i,j] != 0:\n", + " arr.text(j-0.2,i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n", + " elif conf_mat[i,j] >= 0.1 and conf_mat[i,j] < 0.4 and conf_mat[i,j] != 0:\n", + " arr.text(j-0.4, i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n", + "\n", + "\n", + "arr.set_xticks(range(0,n_classes))\n", + "arr.set_xticklabels(range(1,n_classes+1),fontsize=12);\n", + "arr.set_yticks(range(0,n_classes))\n", + "arr.set_yticklabels(range(1,n_classes+1),fontsize=12);\n", + "arr.set_xlabel('Predicted Class',fontsize=12);\n", + "arr.set_ylabel('True Class',fontsize=12);\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C2vBzAGUvoHI" + }, + "source": [ + "## Figure S3D: Eight GMM cluster waveforms" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AATotmmr2HYj" + }, + "source": [ + "### Here we plot each of the eight GMM clusters and show that although they seem sensible to the eye, they represent a representation that is difficult to learn (as shown in the previous panel)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 961 + }, + "id": "LCGchIEv2Hyx", + "outputId": "f95d49f2-b203-4a94-a8c7-6390c523a30b" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(1,9):\n", + " f, arr = plt.subplots()\n", + " f.set_size_inches(2, 1.75)\n", + " GMM_cluster = classifies_df[classifies_df['eight_gmm_classes']==i]\n", + " \n", + " for _,row in GMM_cluster.iterrows():\n", + " plt.plot(row['waveform'],alpha=.3,linewidth=.6,c=classifies_pal[int(i-1)])\n", + " \n", + " plt.plot(np.nanmean(GMM_cluster['waveform'].tolist(),axis=0),c='k',linewidth=1.)\n", + "\n", + " arr.spines['right'].set_visible(False)\n", + " arr.spines['top'].set_visible(False)\n", + " arr.set_ylim([-1.4,1.1])\n", + " arr.set_xticks([0,14,28,42,48])\n", + " arr.set_xticklabels(['0','0.5','1.0','1.5',''])\n", + " arr.set_xlabel('Time (ms)',fontsize=12)\n", + " arr.set_xlim([0,48])\n", + " arr.set_yticks([])\n", + " arr.tick_params(axis='both', which='major', labelsize=12)\n", + " \n", + " arr.spines['left'].set_visible(False)\n", + " \n", + " x, y = 23,-0.8\n", + "\n", + " n_waveforms = plt.text(x, y, 'n = '+str(len(GMM_cluster))\n", + " , fontsize=12)\n", + " plt.tight_layout()\n", + " plt.margins(0,0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wIHRl6qSLCIo" + }, + "source": [ + "# Figure S4: WaveMAP implicitly captures specified features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YQr7cn4fv-4o" + }, + "source": [ + "## Figure S4B,C,D: Here we show in WaveMAP space each waveform's feature values across the three specified features used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SGnerw78LDM6" + }, + "outputs": [], + "source": [ + "gmm_feat_df = pd.DataFrame(gmm_feat_data_nonan,\n", + " columns=['trough_to_peak','peak_ratio','trough_fwhm'])\n", + "\n", + "GMM_class_df = pd.DataFrame(GMM_class_labels,columns=['Class'])\n", + "full_data_df = pd.DataFrame({'Waveform': full_data.tolist()})\n", + "data_classified_df = pd.concat([umap_df,full_data_df,GMM_class_df,gmm_feat_df],axis=1)\n", + "data_classified_df.loc[data_classified_df['Class']==1,'color'] = GMM_PAL[0]\n", + "data_classified_df.loc[data_classified_df['Class']==2,'color'] = GMM_PAL[1]\n", + "data_classified_df.loc[data_classified_df['Class']==3,'color'] = GMM_PAL[2]\n", + "data_classified_df.loc[data_classified_df['Class']==4,'color'] = GMM_PAL[3]\n", + "\n", + "data_classified_df['trough_to_peak_abs'] = data_classified_df['trough_to_peak'].divide(SAMP_RATE_TO_TIME)\n", + "data_classified_df['trough_fwhm_abs'] = data_classified_df['trough_fwhm'].divide(SAMP_RATE_TO_TIME)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_rivsSqeLDUY" + }, + "source": [ + "### We plot the specified feature values of each waveform in UMAP space" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 917 + }, + "id": "6GO3iwuJLCPA", + "outputId": "e39bb6a2-a746-43ec-da76-225b8e7c5ef8" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "def feature_scatter(feature_name,cmap='mako',save=False):\n", + " cmap = sns.color_palette(cmap, as_cmap=True)\n", + "\n", + " fig, ax = plt.subplots()\n", + " fig.set_size_inches(5, 4)\n", + " scat = ax.scatter(data_classified_df['x'],data_classified_df['y'],c=data_classified_df[feature_name],cmap=cmap)\n", + " cax = fig.add_axes([0.1, 0.05, 0.8, 0.03])\n", + " cbar = fig.colorbar(scat, cax=cax, orientation='horizontal')\n", + " \n", + " \n", + " if feature_name == 'trough_to_peak_abs':\n", + " feature_label = 'Absolute Trough to Peak'\n", + " \n", + " elif feature_name == 'trough_fwhm_abs':\n", + " feature_label = 'Absolute Trough FWHM'\n", + " \n", + " elif feature_name == 'peak_ratio':\n", + " feature_label = 'Peak Ratio'\n", + " \n", + " cbar.set_label(feature_label,labelpad=10,fontsize=16)\n", + " cbar.ax.tick_params(labelsize=16)\n", + " ax.spines['left'].set_visible(False)\n", + " ax.spines['right'].set_visible(False)\n", + " ax.spines['bottom'].set_visible(False)\n", + " ax.spines['top'].set_visible(False)\n", + " ax.set_xticks([]);\n", + " ax.set_yticks([]);\n", + " \n", + " if save:\n", + " plt.savefig('Feature_'+feature_name+'.pdf',format='pdf') \n", + " \n", + " return None\n", + "\n", + "feature_scatter('trough_fwhm_abs',cmap='crest',save=True)\n", + "feature_scatter('trough_to_peak_abs',cmap='flare',save=True)\n", + "feature_scatter('peak_ratio',cmap=\"ch:start=.2,rot=.5\",save=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Yem2TivfEdGZ" + }, + "source": [ + "# Figure S5: Effect of normalizations on WaveMAP structure" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MK57BCasEhqx" + }, + "source": [ + "## Figure S5A: Number of clusters across random seed and subsets with -1 to +1 normalization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oz3MNtcYwTR0" + }, + "source": [ + "### We show the waveforms after a -1 to +1 normalization (this is used in the paper)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 274 + }, + "id": "24xDxxg0PpaW", + "outputId": "ea47edc7-9e5a-4ee8-b152-52ad2cbc9f9f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94mPlotting: 625 Waveforms\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "f, arr = plt.subplots(1,figsize=[4.5,3.4])\n", + "\n", + "print(BlueCol + \"Plotting: \" + str(full_data.shape[0]) + \" Waveforms\")\n", + "for i in range(0,full_data.shape[0]):\n", + " arr.plot(full_data[i].T, c = 'k', alpha = 0.03,linewidth=2.);\n", + " \n", + "arr.tick_params(direction='out',colors='k', axis='both')\n", + " \n", + "# Set various x and y axes and labels etc.\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "\n", + "arr.spines['left'].set_bounds(-1,1)\n", + "arr.spines['bottom'].set_bounds(0,48)\n", + "\n", + "arr.set_xlabel('Time (ms)', fontsize=24);\n", + "arr.set_xticks([0,14,28,42,48])\n", + "arr.set_xticklabels(['0','0.5','1.0','1.5',''],fontsize=24)\n", + "\n", + "arr.set_ylabel('Amplitude (a.u.)', fontsize=24)\n", + "arr.set_yticks([-1.0,0.0,1.0]);\n", + "arr.set_yticklabels([-1.0,0.0,1.0], fontsize=24);\n", + "\n", + "# Plot the data\n", + "arr.set_title('-1 to +1 Normalization',fontsize=24)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dmfqQmaowa-O" + }, + "source": [ + "## Figure S5A: Cluster number vs. data subset proportion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tsLGUduJwjIg" + }, + "source": [ + "### Just as in Figure S2B, we show the number of clusters vs. data subset proportion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 189 + }, + "id": "ediq-GkSEsnN", + "outputId": "96c6cd71-fe3b-43df-8016-cf29bcd658e6" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "if 'subset_avg_rand_list' not in list(locals().keys()):\n", + " subset_avg_rand_list = pkl.load(open('WaveMAP_Paper/data/subset_avg_rand_list.pkl','rb'))\n", + "\n", + "if 'subset_std_rand_list' not in list(locals().keys()):\n", + " subset_std_rand_list = pkl.load(open('WaveMAP_Paper/data/subset_std_rand_list.pkl','rb'))\n", + "\n", + "f, arr = plt.subplots(1,figsize=[3,2.5])\n", + "arr.errorbar(np.array(subsets,dtype=np.float),subset_avg_rand_list,yerr=subset_std_rand_list,c = 'k', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.set_xlabel('% of Full Dataset', fontsize=12,fontname=\"Arial\")\n", + "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n", + "arr.set_xticklabels(['','20','40','60','80','100'],fontsize=12,fontname=\"Arial\")\n", + "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\")\n", + "arr.set_yticks([0,2,4,6,8,10])\n", + "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\")\n", + "arr.spines['left'].set_bounds(0,10)\n", + "arr.spines['bottom'].set_bounds(0.1,1)\n", + "arr.axhline(np.max(subset_avg_rand_list),color='k',linestyle='dashed')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZCP0LifbFebf" + }, + "source": [ + "## Figure S5B: WaveMAP plot with -1 to +1 normalized average waveforms" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4fN3gPPKws1w" + }, + "source": [ + "### and next show the WaveMAP clustering and projection of this normalization (same as in paper)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 355 + }, + "id": "77d8DuFUFhwD", + "outputId": "a9cb45ef-ff3d-41ae-f2ed-3787f3c120cd" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", + " random_state=RAND_STATE)\n", + "mapper = reducer.fit(full_data)\n", + "embedding = reducer.transform(full_data)\n", + "\n", + "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", + "\n", + "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", + "f.tight_layout()\n", + "\n", + "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", + " marker='o', c=cluster_colors, s=32, edgecolor='w',\n", + " linewidth=0.5)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['bottom'].set_visible(False)\n", + "arr.spines['left'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.set_xticks([]);\n", + "arr.set_yticks([]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zXpz0x46J9kz" + }, + "source": [ + "## Figure S5C: Number of clusters across random seed and subsets with trough normalization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kyTA8ffqwycs" + }, + "source": [ + "### We compare the -1 to +1 normalization by using the other commonly used trough normalization (entire waveform aligned and mean-centered and divided by the negative amplitude of the trough). Here we show all the waveforms and note that the heights of the waveforms no longer \"cap\" at +1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 274 + }, + "id": "UoK6IB1AN43b", + "outputId": "e20ad9e9-2ab5-4e84-8240-11e6a54135b1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94mPlotting: 625 Waveforms\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "trough_normalizedWaveforms = np.load('WaveMAP_Paper/data/trough_normalizedWaveforms.npy')\n", + "\n", + "f, arr = plt.subplots(1,figsize=[4.5,3.4])\n", + "\n", + "print(BlueCol + \"Plotting: \" + str(trough_normalizedWaveforms.shape[0]) + \" Waveforms\")\n", + "for i in range(0,trough_normalizedWaveforms.shape[0]):\n", + " arr.plot(trough_normalizedWaveforms[i].T, c = 'k', alpha = 0.03,linewidth=2.);\n", + " \n", + "arr.tick_params(direction='out',colors='k', axis='both')\n", + " \n", + "# Set various x and y axes and labels etc.\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "\n", + "arr.spines['left'].set_bounds(-1,1)\n", + "arr.spines['bottom'].set_bounds(0,48)\n", + "\n", + "arr.set_xlabel('Time (ms)', fontsize=24);\n", + "arr.set_xticks([0,14,28,42,48])\n", + "arr.set_xticklabels(['0','0.5','1.0','1.5',''],fontsize=24)\n", + "\n", + "arr.set_ylabel('Amplitude (a.u.)', fontsize=24)\n", + "arr.set_yticks([-1.0,0.0,1.0]);\n", + "arr.set_yticklabels([-1.0,0.0,1.0], fontsize=24);\n", + "arr.set_title('Trough Normalization',fontsize=24)\n", + "# Plot the data\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OHmSivAgKDwM" + }, + "source": [ + "## Figure S5C: Cluster number vs. data subset proportion on trough normalized waveforms\n", + "\n", + "---\n", + "\n", + "**THIS CELL CAN TAKE 40 MIN**; skip it and run the next cell to read cached values of the plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yVu2sdyfKQ4Q", + "outputId": "df7cf806-18c0-426b-ec31-1657519332d9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1\n", + "0.2\n", + "0.3\n", + "0.4\n", + "0.5\n", + "0.6\n", + "0.7\n", + "0.8\n", + "0.9\n", + "1.0\n" + ] + } + ], + "source": [ + "subsets = [0.1,0.2,0.3,0.4,\n", + " 0.5,0.6,0.7,0.8,\n", + " 0.9,1.0]\n", + "\n", + "renorm_clust_rand_dict = {}\n", + "for frac in subsets:\n", + " print(frac)\n", + " rand_list = []\n", + " for i in list(range(1,100)):\n", + " reducer_rand_test = umap.UMAP(n_neighbors = N_NEIGHBORS, \n", + " min_dist=MIN_DIST, \n", + " random_state=random.randint(1,100000))\n", + " rand_data = np.random.permutation(trough_normalizedWaveforms)[0:(int(len(full_data)*frac)),:]\n", + " mapper = reducer_rand_test.fit(rand_data)\n", + " embedding_rand_test = reducer_rand_test.transform(rand_data)\n", + "\n", + " umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n", + " G = nx.from_scipy_sparse_matrix(mapper.graph_)\n", + " clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n", + " clustering_solution = list(clustering.values())\n", + " rand_list.append(len(set(clustering_solution)))\n", + "\n", + " renorm_clust_rand_dict.update({str(frac): rand_list})\n", + "\n", + "renorm_subset_avg_rand_list = []\n", + "renorm_subset_std_rand_list = []\n", + "\n", + "for k,v in renorm_clust_rand_dict.items():\n", + " renorm_subset_avg_rand_list.append(np.average(v))\n", + " renorm_subset_std_rand_list.append(np.std(v))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 189 + }, + "id": "GGRMNgd6Yutg", + "outputId": "9b7ff898-84a8-4b99-a9bd-b3d8360260ac" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "subsets = [0.1,0.2,0.3,0.4,\n", + " 0.5,0.6,0.7,0.8,\n", + " 0.9,1.0]\n", + "\n", + "if 'renorm_subset_avg_rand_list' not in list(locals().keys()):\n", + " renorm_subset_avg_rand_list = pkl.load(open('WaveMAP_Paper/data/renorm_subset_avg_rand_list.pkl','rb'))\n", + "\n", + "if 'renorm_subset_std_rand_list' not in list(locals().keys()):\n", + " renorm_subset_std_rand_list = pkl.load(open('WaveMAP_Paper/data/renorm_subset_std_rand_list.pkl','rb'))\n", + "\n", + "f, arr = plt.subplots(1,figsize=[3,2.5])\n", + "arr.errorbar(np.array(subsets,dtype=np.float),renorm_subset_avg_rand_list,yerr=renorm_subset_std_rand_list,c = 'k', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.set_xlabel('% of Full Dataset', fontsize=12,fontname=\"Arial\")\n", + "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n", + "arr.set_xticklabels(['','20','40','60','80','100'],fontsize=12,fontname=\"Arial\")\n", + "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\")\n", + "arr.set_yticks([0,2,4,6,8,10])\n", + "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\")\n", + "arr.spines['left'].set_bounds(0,10)\n", + "arr.spines['bottom'].set_bounds(0.1,1)\n", + "arr.axhline(np.max(renorm_subset_avg_rand_list),color='k',linestyle='dashed')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J2yCZEenyhf3" + }, + "source": [ + "## Figure S5D: WaveMAP plot with trough normalization\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VJHReVG8yj90" + }, + "source": [ + "### We next apply WaveMAP to waveforms with the trough normalization. Note the similarities between the two types or normalization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 355 + }, + "id": "_UCnKNbffXkT", + "outputId": "8fb7cfdb-a5fa-407e-cb39-cc00c3ba5d59" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n", + " random_state=RAND_STATE)\n", + "mapper = reducer.fit(trough_normalizedWaveforms)\n", + "embedding = reducer.transform(trough_normalizedWaveforms)\n", + "\n", + "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n", + "\n", + "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n", + "f.tight_layout()\n", + "\n", + "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n", + " marker='o', c=cluster_colors, s=32, edgecolor='w',\n", + " linewidth=0.5)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.spines['bottom'].set_visible(False)\n", + "arr.spines['left'].set_visible(False)\n", + "arr.spines['right'].set_visible(False)\n", + "arr.set_xticks([]);\n", + "arr.set_yticks([]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rjqcjprTwl_x" + }, + "source": [ + "# Figure S6: GMM Clusters lack the physiological and functional diversity of WaveMAP classes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "13v0A7WEwoUb" + }, + "source": [ + "## Figure S6A: FR traces for GMM clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r-5MWzIPwsui" + }, + "source": [ + "### As in Figure 5A,B, we plot the stim-aligned trial-averaged FR traces but this time for GMM clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 369 + }, + "id": "tIG6qHgVi_8i", + "outputId": "aa9b7f7c-aba6-4c59-e74d-a40c2f41db71" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "f, arr = plt.subplots(4,figsize=[2,5])\n", + "\n", + "time = np.arange(-0.1,0.5,0.001)\n", + "\n", + "for i,ix in enumerate([0,1,2,3]):\n", + " PREF = GMM_traces_df.iloc[ix]['PREF']\n", + " NONPREF = GMM_traces_df.iloc[ix]['NONPREF']\n", + " PREF_UPPER = GMM_traces_df.iloc[ix]['PREF_UPPER_BOUND']\n", + " PREF_LOWER = GMM_traces_df.iloc[ix]['PREF_LOWER_BOUND']\n", + " NONPREF_UPPER = GMM_traces_df.iloc[ix]['NONPREF_UPPER_BOUND']\n", + " NONPREF_LOWER = GMM_traces_df.iloc[ix]['NONPREF_LOWER_BOUND']\n", + " arr[i].plot(time,PREF,color=GMM_PAL[ix])\n", + " arr[i].plot(time,NONPREF,'--',color=GMM_PAL[ix])\n", + " arr[i].fill_between(time,PREF_UPPER,PREF_LOWER,\n", + " color='gray',alpha=0.2)\n", + " arr[i].fill_between(time,NONPREF_UPPER,NONPREF_LOWER,\n", + " color='gray',alpha=0.2)\n", + " arr[i].set_ylim(5,30)\n", + " arr[i].set_xticks([-0.1,0.1,0.3,0.5])\n", + " arr[i].set_xlim(-0.1,0.5)\n", + " arr[i].set_yticks([5,30])\n", + " arr[i].spines['left'].set_position(('axes', -0.05))\n", + " arr[i].spines['top'].set_visible(False)\n", + " arr[i].spines['right'].set_visible(False)\n", + " arr[i].axvline(0,ymin=0.,ymax=30,linestyle='dashed',color='k')\n", + " f.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "id": "icazrrtefKj9", + "outputId": "4ca4c369-6367-4cb6-f7fd-a380b7680a73" + }, + "outputs": [ + { + "data": { + "image/png": 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A1zW9ZiH0EbXyjvZvZUVC6Kvq7pIUQmiMCMIQChZBGELBIghDKFgEYQgFiyAMoWARhCEULIIwhIIVEoSSJkp6RNISSedVWL+1pGvS+rtSLpoQ2lLLg1BSf+C7wDHAPsDxkvYpK3Ya8A/bewEXA//d2lqG0DpFnAnHA0tsP277eeBnZL33S00GrkrT1wJHpLQYIbSdIoJwFLCsZH45W+YdfblMGpR0PTC0JbULocXy5Jjp0SRNBaam2Y2SHimyPlX18BO5tsw4Mgx4soCqVNHD//3+vcsic2xXzEhRRBCuAHYpmR+dllUqs1zSVmS9+ytmW7M9HZjehHr2aZLm2+5WasuQTxGXo/cAYyTtIWkgMIVsJOBSs4CT0/RxwO8i21poVy0/E9rukHQmcDPQH7jS9oOSvgrMtz2LLC/pjyQtAZ4iC9QQ2pLiBBMqkTQ1XeqHJosgDKFg8dpaCAWLIAw1SVoq6d1F16OdRRAWSNKU9G7ss5LWpOnTO98OkjRDkiVNLtvu4rT8lDR/Spq/uKzc5LR8Ro06bCfpfyQ9IWmjpMfSfMVRsOr8nedL+nGj9tduIggLIukc4DvAt4CdgBHAJ8gGzBlYUvRR4KSS7bYiG8L8sbJdPgZ8KK3vdHLavlodBgK3AmPJhkTfDjiI7Jns+Hp+VzOU/aa2E0FYAEmdI1udbvta2xuc+Yvtj9h+rqT4b4BDJe2Q5icCfwVWle12FXA/cHQ6xo7AwWz5DLbUSWSjYb3f9iLbL9leY/traVzI8nrPkPT1kvkJkpaXzJ8raYWkDamXzBFp3JLPAx9OZ9qFnf8Gkq6QtDJt8/X0cn/nmf2P6Yy/Djhf0l6Sbpe0XtKTkq7p6t+5t4ggLMZBwNbA9TnK/jOV63xWehIws0rZmbxy1pyStnuuSlnIRl2e0zkga3dI2hs4E3i77cFkfwyW2p4DXABcY/v1afRlgBlAB7AX2ZDpRwEfLdnlO4DHya4Q/pNsxOhbgB3I3rK6tLt17ikiCIsxDHgyvZwOgKQ/SXpa0mZJ7ywrPxM4SdIQ4DCqDx9+HTAhnWlrBWunocDKun7Bll4k+8Oyj6QBtpfaLr9kBkDSCGAS2VDpz9peQ9ZlrfSljP+zfantDtubgReA3YCRtv+ZhthrCxGExVgHDCu917F9sO0had2r/ruk/+GGA18Abkj/U24hLZ8NfBEYavuPOeqxc92/4tXHXgKcDZwPrJH0M0kjqxTfDRgArEx/eJ4GLgfeUFJmWdk2nyV7i/tuSQ9KOrUR9e4JIgiL8Weyy8TyfpS1/Bg4h67PbjNTuTytkb8Fjpa0bc46PAtsUzK/U+lK2z+xfShZkJlXOmOXvxGyjOz3D7M9JH22sz22dHdl+15l+2O2RwIfB74naa+c9e7RIggLYPtp4Ctk/yMdJ2mwpH6S9gOqBcQlwJHAHV3s/vZULs8904/IAuKXkt6U6jBU0uclTapQ/j5gkqQdJe1EduYDsntCSe+StDXZfexmXhnZazWwu6R+6fevJLu/uzA9IuknaU9Jh1WrqKQPShqdZv9BFqQvVSvfm0QQFsT2N4FPk11mrU6fy4FzgT9VKP+U7Vu76k2SWllvtf1Ujjo8R9Y48zAwF3gGuJvsnvWuCpv8CFgILCULotIWyq2Bb5D1QVxFdmn5ubTuF+l7naQFafokskcxi8iC6lpqXxq/HbhL0kayFt+zbD/e1W/sDeLd0RAKFmfCEAoWQRhCwSIIQyhYBGEIBYsgDKFgEYQhFCyCMISCRRCGULAIwhAK9v9s7xuKHQU6TQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" } + ], + "source": [ + "f, arr = plt.subplots(1)\n", + "f.set_size_inches(3,2.5)\n", + "\n", + "for i,clust_ix in enumerate([1,2,3,4]):\n", + " start_ix = 0 \n", + " \n", + " median, med_se = bootstrap_median(get_baseline_FR(baseline_FR_df,clust_ix,UMAP_clusts=False))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=GMM_PAL[clust_ix-1],\n", + " yerr=med_se)\n", + " \n", + "arr.set_ylabel('Baseline FR (spikes/s)')\n", + "arr.set_xticks([1.5]);\n", + "arr.set_xticklabels(['GMM Clusters'],fontsize=12,fontname='Arial')\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.set_ylim(0,15);" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 403 + }, + "id": "NNLI4S6jta6H", + "outputId": "cce4be8e-6893-48e4-ef09-2d6589b71a1d" + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mstart_ix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mmedian\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmed_se\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbootstrap_median\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_max_FR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_FR_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclust_ix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mUMAP_clusts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m arr.bar(start_ix+i, median, \n", + "\u001b[0;31mNameError\u001b[0m: name 'get_max_FR' is not defined" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "f, arr = plt.subplots(1)\n", + "f.set_size_inches(3,2.5)\n", + "\n", + "for i,clust_ix in enumerate([1,2,3,4]):\n", + " start_ix = 0 \n", + " \n", + " median, med_se = bootstrap_median(get_max_FR(max_FR_df,clust_ix,UMAP_clusts=False))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=GMM_PAL[clust_ix-1],\n", + " yerr=med_se)\n", + " \n", + "arr.set_ylabel('Max FR (spikes/s)')\n", + "arr.set_xticks([1.5]);\n", + "arr.set_xticklabels(['GMM Clusters'],fontsize=12,fontname='Arial')\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.set_ylim(0,40);" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 403 + }, + "id": "fxNTfdaNwZFz", + "outputId": "fc9edbe0-235d-4007-e3fc-06338c19a3f2" + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mstart_ix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mmedian\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmed_se\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbootstrap_median\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_dynamic_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclust_ix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_FR_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbaseline_FR_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mUMAP_clusts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m arr.bar(start_ix+i, median, \n", + "\u001b[0;31mNameError\u001b[0m: name 'get_dynamic_range' is not defined" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "f, arr = plt.subplots(1)\n", + "\n", + "f.set_size_inches(3,2.5)\n", + "\n", + "for i,clust_ix in enumerate([1,2,3,4]):\n", + " start_ix = 0 \n", + " \n", + " median, med_se = bootstrap_median(get_dynamic_range(clust_ix,max_FR_df,baseline_FR_df,UMAP_clusts=False))\n", + " \n", + " arr.bar(start_ix+i, median, \n", + " color=GMM_PAL[clust_ix-1],\n", + " yerr=med_se)\n", + "\n", + "arr.set_ylabel('FR Range (spikes/s)')\n", + "arr.set_xticks([1.5]);\n", + "arr.set_xticklabels(['GMM Clusters'],fontsize=12,fontname='Arial')\n", + "arr.spines['right'].set_visible(False)\n", + "arr.spines['top'].set_visible(False)\n", + "arr.set_ylim(0,25);" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "XQUIAJhiNnAw" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "include_colab_link": true, + "name": "WaveMAP_Figures_Data.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/poetry.lock b/poetry.lock index dac025b..be1faa0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,3 +1,19 @@ +[[package]] +name = "aiofiles" +version = "22.1.0" +description = "File support for asyncio." +category = "main" +optional = false +python-versions = ">=3.7,<4.0" + +[[package]] +name = "aiosqlite" +version = "0.18.0" +description = "asyncio bridge to the standard sqlite3 module" +category = "main" +optional = false +python-versions = ">=3.7" + [[package]] name = "anyio" version = "3.6.2" @@ -81,17 +97,19 @@ test = ["astroid", "pytest"] [[package]] name = "attrs" -version = "22.1.0" +version = "22.2.0" description = "Classes Without Boilerplate" category = "main" optional = false -python-versions = ">=3.5" +python-versions = ">=3.6" [package.extras] -dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] -docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] -tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"] +cov = ["attrs", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] +dev = ["attrs"] +docs = ["furo", "sphinx", "myst-parser", "zope.interface", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] +tests = ["attrs", "zope.interface"] +tests-no-zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] +tests_no_zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] [[package]] name = "babel" @@ -114,7 +132,7 @@ python-versions = "*" [[package]] name = "beautifulsoup4" -version = "4.11.1" +version = "4.11.2" description = "Screen-scraping library" category = "main" optional = false @@ -129,7 +147,7 @@ lxml = ["lxml"] [[package]] name = "bleach" -version = "5.0.1" +version = "6.0.0" description = "An easy safelist-based HTML-sanitizing tool." category = "main" optional = false @@ -141,7 +159,6 @@ webencodings = "*" [package.extras] css = ["tinycss2 (>=1.1.0,<1.2)"] -dev = ["build (==0.8.0)", "flake8 (==4.0.1)", "hashin (==0.17.0)", "pip-tools (==6.6.2)", "pytest (==7.1.2)", "Sphinx (==4.3.2)", "tox (==3.25.0)", "twine (==4.0.1)", "wheel (==0.37.1)", "black (==22.3.0)", "mypy (==0.961)"] [[package]] name = "bokeh" @@ -183,18 +200,15 @@ pycparser = "*" [[package]] name = "charset-normalizer" -version = "2.1.1" +version = "3.0.1" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." category = "main" optional = false -python-versions = ">=3.6.0" - -[package.extras] -unicode_backport = ["unicodedata2"] +python-versions = "*" [[package]] name = "cloudpickle" -version = "2.2.0" +version = "2.2.1" description = "Extended pickling support for Python objects" category = "main" optional = false @@ -224,21 +238,21 @@ test = ["pytest"] [[package]] name = "contourpy" -version = "1.0.6" +version = "1.0.7" description = "Python library for calculating contours of 2D quadrilateral grids" category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" [package.dependencies] numpy = ">=1.16" [package.extras] -bokeh = ["bokeh", "selenium"] -docs = ["docutils (<0.18)", "sphinx (<=5.2.0)", "sphinx-rtd-theme"] -test = ["pytest", "matplotlib", "pillow", "flake8", "isort"] -test-minimal = ["pytest"] -test-no-codebase = ["pytest", "matplotlib", "pillow"] +bokeh = ["bokeh", "chromedriver", "selenium"] +docs = ["furo", "sphinx-copybutton"] +mypy = ["contourpy", "docutils-stubs", "mypy (==0.991)", "types-pillow"] +test = ["matplotlib", "pillow", "pytest"] +test-no-images = ["pytest"] [[package]] name = "cycler" @@ -263,7 +277,7 @@ scipy = "*" [[package]] name = "debugpy" -version = "1.6.4" +version = "1.6.6" description = "An implementation of the Debug Adapter Protocol for Python" category = "main" optional = false @@ -285,14 +299,6 @@ category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" -[[package]] -name = "entrypoints" -version = "0.4" -description = "Discover and load entry points from installed packages." -category = "main" -optional = false -python-versions = ">=3.6" - [[package]] name = "executing" version = "1.2.0" @@ -355,7 +361,7 @@ python-versions = ">=3.5" [[package]] name = "importlib-metadata" -version = "5.1.0" +version = "6.0.0" description = "Read metadata from Python packages" category = "main" optional = false @@ -365,13 +371,13 @@ python-versions = ">=3.7" zipp = ">=0.5" [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] perf = ["ipython"] testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8", "importlib-resources (>=1.3)"] [[package]] name = "importlib-resources" -version = "5.10.1" +version = "5.12.0" description = "Read resources from Python packages" category = "main" optional = false @@ -381,12 +387,12 @@ python-versions = ">=3.7" zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8"] [[package]] name = "ipykernel" -version = "6.19.2" +version = "6.21.2" description = "IPython Kernel for Jupyter" category = "main" optional = false @@ -395,27 +401,28 @@ python-versions = ">=3.8" [package.dependencies] appnope = {version = "*", markers = "platform_system == \"Darwin\""} comm = ">=0.1.1" -debugpy = ">=1.0" +debugpy = ">=1.6.5" ipython = ">=7.23.1" jupyter-client = ">=6.1.12" +jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" matplotlib-inline = ">=0.1" nest-asyncio = "*" packaging = "*" psutil = "*" -pyzmq = ">=17" +pyzmq = ">=20" tornado = ">=6.1" traitlets = ">=5.4.0" [package.extras] cov = ["coverage", "curio", "matplotlib", "pytest-cov", "trio"] -docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt"] -lint = ["black (>=22.6.0)", "mdformat (>0.7)", "ruff (>=0.0.156)"] +docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"] +pyqt5 = ["pyqt5"] +pyside6 = ["pyside6"] test = ["flaky", "ipyparallel", "pre-commit", "pytest-asyncio", "pytest-cov", "pytest-timeout", "pytest (>=7.0)"] -typing = ["mypy (>=0.990)"] [[package]] name = "ipython" -version = "8.7.0" +version = "8.10.0" description = "IPython: Productive Interactive Computing" category = "main" optional = false @@ -430,13 +437,13 @@ jedi = ">=0.16" matplotlib-inline = "*" pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""} pickleshare = "*" -prompt-toolkit = ">=3.0.11,<3.1.0" +prompt-toolkit = ">=3.0.30,<3.1.0" pygments = ">=2.4.0" stack-data = "*" traitlets = ">=5" [package.extras] -all = ["black", "ipykernel", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "docrepr", "matplotlib", "stack-data", "pytest (<7)", "typing-extensions", "pytest (<7.1)", "pytest-asyncio", "testpath", "nbconvert", "nbformat", "ipywidgets", "notebook", "ipyparallel", "qtconsole", "curio", "matplotlib (!=3.2.0)", "numpy (>=1.20)", "pandas", "trio"] +all = ["black", "ipykernel", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "docrepr", "matplotlib", "stack-data", "pytest (<7)", "typing-extensions", "pytest (<7.1)", "pytest-asyncio", "testpath", "nbconvert", "nbformat", "ipywidgets", "notebook", "ipyparallel", "qtconsole", "curio", "matplotlib (!=3.2.0)", "numpy (>=1.21)", "pandas", "trio"] black = ["black"] doc = ["ipykernel", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "docrepr", "matplotlib", "stack-data", "pytest (<7)", "typing-extensions", "pytest (<7.1)", "pytest-asyncio", "testpath"] kernel = ["ipykernel"] @@ -446,7 +453,7 @@ notebook = ["ipywidgets", "notebook"] parallel = ["ipyparallel"] qtconsole = ["qtconsole"] test = ["pytest (<7.1)", "pytest-asyncio", "testpath"] -test_extra = ["pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.20)", "pandas", "trio"] +test_extra = ["pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "trio"] [[package]] name = "ipython-genutils" @@ -507,7 +514,7 @@ python-versions = ">=3.7" [[package]] name = "json5" -version = "0.9.10" +version = "0.9.11" description = "A Python implementation of the JSON5 data format." category = "main" optional = false @@ -552,28 +559,27 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339- [[package]] name = "jupyter-client" -version = "7.4.8" +version = "8.0.3" description = "Jupyter protocol implementation and client libraries" category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" [package.dependencies] -entrypoints = "*" -jupyter-core = ">=4.9.2" -nest-asyncio = ">=1.5.4" +importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""} +jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" python-dateutil = ">=2.8.2" pyzmq = ">=23.0" tornado = ">=6.2" -traitlets = "*" +traitlets = ">=5.3" [package.extras] -doc = ["ipykernel", "myst-parser", "sphinx-rtd-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt"] -test = ["codecov", "coverage", "ipykernel (>=6.12)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"] +docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinx (>=4)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] +test = ["codecov", "coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] [[package]] name = "jupyter-core" -version = "5.1.0" +version = "5.2.0" description = "Jupyter core package. A base package on which Jupyter projects rely." category = "main" optional = false @@ -585,37 +591,40 @@ pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_ traitlets = ">=5.3" [package.extras] -docs = ["myst-parser", "sphinxcontrib-github-alt", "traitlets"] +docs = ["myst-parser", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"] test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] [[package]] name = "jupyter-events" -version = "0.5.0" +version = "0.6.3" description = "Jupyter Event System library" category = "main" optional = false python-versions = ">=3.7" [package.dependencies] -jsonschema = {version = ">=4.3.0", extras = ["format-nongpl"]} -python-json-logger = "*" -pyyaml = "*" -traitlets = "*" +jsonschema = {version = ">=3.2.0", extras = ["format-nongpl"]} +python-json-logger = ">=2.0.4" +pyyaml = ">=5.3" +rfc3339-validator = "*" +rfc3986-validator = ">=0.1.1" +traitlets = ">=5.3" [package.extras] cli = ["click", "rich"] -test = ["click", "coverage", "pre-commit", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "pytest-cov", "pytest (>=6.1.0)", "rich"] +docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"] +test = ["click", "coverage", "pre-commit", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "pytest-cov", "pytest (>=7.0)", "rich"] [[package]] name = "jupyter-server" -version = "2.0.1" +version = "2.3.0" description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." category = "main" optional = false python-versions = ">=3.8" [package.dependencies] -anyio = ">=3.1.0,<4" +anyio = ">=3.1.0" argon2-cffi = "*" jinja2 = "*" jupyter-client = ">=7.4.4" @@ -635,14 +644,28 @@ traitlets = ">=5.6.0" websocket-client = "*" [package.extras] -docs = ["docutils (<0.20)", "ipykernel", "jinja2", "jupyter-client", "jupyter-server", "mistune (<1.0.0)", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxemoji", "tornado"] -lint = ["black (>=22.6.0)", "mdformat (>0.7)", "ruff (>=0.0.156)"] +docs = ["docutils (<0.20)", "ipykernel", "jinja2", "jupyter-client", "jupyter-server", "mistune (<1.0.0)", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"] test = ["ipykernel", "pre-commit", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "pytest (>=7.0)", "requests"] -typing = ["mypy (>=0.990)"] + +[[package]] +name = "jupyter-server-fileid" +version = "0.8.0" +description = "" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +jupyter-events = ">=0.5.0" +jupyter-server = ">=1.15,<3" + +[package.extras] +cli = ["click"] +test = ["jupyter-server[test] (>=1.15,<3)", "pytest", "pytest-cov"] [[package]] name = "jupyter-server-terminals" -version = "0.4.2" +version = "0.4.4" description = "A Jupyter Server Extension Providing Terminals." category = "main" optional = false @@ -653,12 +676,43 @@ pywinpty = {version = ">=2.0.3", markers = "os_name == \"nt\""} terminado = ">=0.8.3" [package.extras] -docs = ["jinja2", "jupyter-server", "mistune (<2.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxemoji", "tornado"] -test = ["coverage", "jupyter-server (>=2.0.0rc8)", "pytest-cov", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout", "pytest (>=7.0)"] +docs = ["jinja2", "jupyter-server", "mistune (<3.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"] +test = ["coverage", "jupyter-server (>=2.0.0)", "pytest-cov", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout", "pytest (>=7.0)"] + +[[package]] +name = "jupyter-server-ydoc" +version = "0.6.1" +description = "A Jupyter Server Extension Providing Y Documents." +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +jupyter-server-fileid = ">=0.6.0,<1" +jupyter-ydoc = ">=0.2.0,<0.4.0" +ypy-websocket = ">=0.8.2,<0.9.0" + +[package.extras] +test = ["coverage", "jupyter-server[test] (>=2.0.0a0)", "pytest-cov", "pytest-timeout", "pytest-tornasync", "pytest (>=7.0)"] + +[[package]] +name = "jupyter-ydoc" +version = "0.2.2" +description = "Document structures for collaborative editing using Ypy" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""} +y-py = ">=0.5.3,<0.6.0" + +[package.extras] +test = ["pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)", "ypy-websocket (>=0.3.1,<0.4.0)"] [[package]] name = "jupyterlab" -version = "3.5.1" +version = "3.6.1" description = "JupyterLab computational environment" category = "main" optional = false @@ -669,16 +723,17 @@ ipython = "*" jinja2 = ">=2.1" jupyter-core = "*" jupyter-server = ">=1.16.0,<3" -jupyterlab-server = ">=2.10,<3.0" +jupyter-server-ydoc = ">=0.6.0,<0.7.0" +jupyter-ydoc = ">=0.2.2,<0.3.0" +jupyterlab-server = ">=2.19,<3.0" nbclassic = "*" notebook = "<7" packaging = "*" -tomli = "*" +tomli = {version = "*", markers = "python_version < \"3.11\""} tornado = ">=6.1.0" [package.extras] -test = ["check-manifest", "coverage", "jupyterlab-server", "pre-commit", "pytest (>=6.0)", "pytest-cov", "pytest-console-scripts", "pytest-check-links (>=0.5)", "requests", "requests-cache", "virtualenv"] -ui-tests = ["build"] +test = ["check-manifest", "coverage", "jupyterlab-server", "pre-commit", "pytest (>=6.0)", "pytest-cov", "pytest-console-scripts", "pytest-check-links (>=0.5)", "pytest-jupyter (>=0.5.3)", "requests", "requests-cache", "virtualenv"] [[package]] name = "jupyterlab-pygments" @@ -690,7 +745,7 @@ python-versions = ">=3.7" [[package]] name = "jupyterlab-server" -version = "2.16.5" +version = "2.19.0" description = "A set of server components for JupyterLab and JupyterLab like applications." category = "main" optional = false @@ -701,17 +756,15 @@ babel = ">=2.10" importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""} jinja2 = ">=3.0.3" json5 = ">=0.9.0" -jsonschema = ">=3.0.1" +jsonschema = ">=4.17.3" jupyter-server = ">=1.21,<3" packaging = ">=21.3" requests = ">=2.28" [package.extras] docs = ["autodoc-traits", "docutils (<0.20)", "jinja2 (<3.2.0)", "mistune (<3)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi"] -lint = ["black[jupyter] (>=22.6.0)", "mdformat-gfm (>=0.3.5)", "mdformat (>0.7)", "ruff (>=0.0.156)"] -openapi = ["openapi-core (>=0.14.2)", "ruamel-yaml"] -test = ["codecov", "ipykernel", "openapi-core (>=0.14.2,<0.15.0)", "openapi-spec-validator (<0.6)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6)", "pytest-timeout", "pytest (>=7.0)", "requests-mock", "ruamel-yaml", "strict-rfc3339"] -typing = ["mypy (>=0.990)"] +openapi = ["openapi-core (>=0.16.1)", "ruamel-yaml"] +test = ["codecov", "ipykernel", "jupyterlab-server", "openapi-spec-validator (>=0.5.1)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "pytest (>=7.0)", "requests-mock", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"] [[package]] name = "kiwisolver" @@ -731,7 +784,7 @@ python-versions = ">=3.7" [[package]] name = "markupsafe" -version = "2.1.1" +version = "2.1.2" description = "Safely add untrusted strings to HTML/XML markup." category = "main" optional = false @@ -739,7 +792,7 @@ python-versions = ">=3.7" [[package]] name = "matplotlib" -version = "3.6.2" +version = "3.7.0" description = "Python plotting package" category = "main" optional = false @@ -749,11 +802,12 @@ python-versions = ">=3.8" contourpy = ">=1.0.1" cycler = ">=0.10" fonttools = ">=4.22.0" +importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""} kiwisolver = ">=1.0.1" -numpy = ">=1.19" +numpy = ">=1.20" packaging = ">=20.0" pillow = ">=6.2.0" -pyparsing = ">=2.2.1" +pyparsing = ">=2.3.1" python-dateutil = ">=2.7" setuptools_scm = ">=7" @@ -770,7 +824,7 @@ traitlets = "*" [[package]] name = "mistune" -version = "2.0.4" +version = "2.0.5" description = "A sane Markdown parser with useful plugins and renderers" category = "main" optional = false @@ -778,8 +832,8 @@ python-versions = "*" [[package]] name = "nbclassic" -version = "0.4.8" -description = "A web-based notebook environment for interactive computing" +version = "0.5.2" +description = "Jupyter Notebook as a Jupyter Server extension." category = "main" optional = false python-versions = ">=3.7" @@ -806,7 +860,7 @@ traitlets = ">=4.2.1" [package.extras] docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "myst-parser"] json-logging = ["json-logging"] -test = ["pytest", "coverage", "requests", "testpath", "nbval", "pytest-playwright", "pytest-cov", "pytest-tornasync", "requests-unixsocket"] +test = ["pytest", "coverage", "requests", "testpath", "nbval", "pytest-playwright", "pytest-cov", "pytest-jupyter", "pytest-tornasync", "requests-unixsocket"] [[package]] name = "nbclient" @@ -829,7 +883,7 @@ test = ["ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest-asy [[package]] name = "nbconvert" -version = "7.2.6" +version = "7.2.9" description = "Converting Jupyter Notebooks" category = "main" optional = false @@ -855,16 +909,16 @@ traitlets = ">=5.0" [package.extras] all = ["nbconvert"] -docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)"] +docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"] qtpdf = ["nbconvert"] qtpng = ["pyqtwebengine (>=5.15)"] serve = ["tornado (>=6.1)"] -test = ["ipykernel", "ipywidgets (>=7)", "pre-commit", "pyppeteer (>=1,<1.1)", "pytest", "pytest-dependency"] +test = ["ipykernel", "ipywidgets (>=7)", "pre-commit", "pytest", "pytest-dependency"] webpdf = ["pyppeteer (>=1,<1.1)"] [[package]] name = "nbformat" -version = "5.7.0" +version = "5.7.3" description = "The Jupyter Notebook format" category = "main" optional = false @@ -877,7 +931,8 @@ jupyter-core = "*" traitlets = ">=5.1" [package.extras] -test = ["check-manifest", "pep440", "pre-commit", "pytest", "testpath"] +docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] +test = ["pep440", "pre-commit", "pytest", "testpath"] [[package]] name = "nest-asyncio" @@ -970,7 +1025,7 @@ python-versions = ">=3.8" [[package]] name = "packaging" -version = "22.0" +version = "23.0" description = "Core utilities for Python packages" category = "main" optional = false @@ -978,18 +1033,14 @@ python-versions = ">=3.7" [[package]] name = "pandas" -version = "1.5.2" +version = "1.5.3" description = "Powerful data structures for data analysis, time series, and statistics" category = "main" optional = false python-versions = ">=3.8" [package.dependencies] -numpy = [ - {version = ">=1.20.3", markers = "python_version < \"3.10\""}, - {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, - {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, -] +numpy = {version = ">=1.20.3", markers = "python_version < \"3.10\""} python-dateutil = ">=2.8.1" pytz = ">=2020.1" @@ -1037,14 +1088,14 @@ python-versions = "*" [[package]] name = "pillow" -version = "9.3.0" +version = "9.4.0" description = "Python Imaging Library (Fork)" category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] +docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"] [[package]] @@ -1057,19 +1108,19 @@ python-versions = ">=3.6" [[package]] name = "platformdirs" -version = "2.6.0" +version = "3.0.0" description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo (>=2022.9.29)", "proselint (>=0.13)", "sphinx-autodoc-typehints (>=1.19.4)", "sphinx (>=5.3)"] -test = ["appdirs (==1.4.4)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest (>=7.2)"] +docs = ["furo (>=2022.12.7)", "proselint (>=0.13)", "sphinx-autodoc-typehints (>=1.22,!=1.23.4)", "sphinx (>=6.1.3)"] +test = ["appdirs (==1.4.4)", "covdefaults (>=2.2.2)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest (>=7.2.1)"] [[package]] name = "prometheus-client" -version = "0.15.0" +version = "0.16.0" description = "Python client for the Prometheus monitoring system." category = "main" optional = false @@ -1080,11 +1131,11 @@ twisted = ["twisted"] [[package]] name = "prompt-toolkit" -version = "3.0.36" +version = "3.0.37" description = "Library for building powerful interactive command lines in Python" category = "main" optional = false -python-versions = ">=3.6.2" +python-versions = ">=3.7.0" [package.dependencies] wcwidth = "*" @@ -1119,14 +1170,6 @@ python-versions = "*" [package.extras] tests = ["pytest"] -[[package]] -name = "py" -version = "1.11.0" -description = "library with cross-python path, ini-parsing, io, code, log facilities" -category = "main" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" - [[package]] name = "pycparser" version = "2.21" @@ -1137,7 +1180,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "pygments" -version = "2.13.0" +version = "2.14.0" description = "Pygments is a syntax highlighting package written in Python." category = "main" optional = false @@ -1174,7 +1217,7 @@ diagrams = ["railroad-diagrams", "jinja2"] [[package]] name = "pyrsistent" -version = "0.19.2" +version = "0.19.3" description = "Persistent/Functional/Immutable data structures" category = "main" optional = false @@ -1193,15 +1236,15 @@ six = ">=1.5" [[package]] name = "python-json-logger" -version = "2.0.4" +version = "2.0.7" description = "A python library adding a json log formatter" category = "main" optional = false -python-versions = ">=3.5" +python-versions = ">=3.6" [[package]] name = "pytz" -version = "2022.6" +version = "2022.7.1" description = "World timezone definitions, modern and historical" category = "main" optional = false @@ -1217,7 +1260,7 @@ python-versions = "*" [[package]] name = "pywinpty" -version = "2.0.9" +version = "2.0.10" description = "Pseudo terminal support for Windows from Python." category = "main" optional = false @@ -1233,7 +1276,7 @@ python-versions = ">=3.6" [[package]] name = "pyzmq" -version = "24.0.1" +version = "25.0.0" description = "Python bindings for 0MQ" category = "main" optional = false @@ -1241,11 +1284,10 @@ python-versions = ">=3.6" [package.dependencies] cffi = {version = "*", markers = "implementation_name == \"pypy\""} -py = {version = "*", markers = "implementation_name == \"pypy\""} [[package]] name = "requests" -version = "2.28.1" +version = "2.28.2" description = "Python HTTP for Humans." category = "main" optional = false @@ -1253,7 +1295,7 @@ python-versions = ">=3.7, <4" [package.dependencies] certifi = ">=2017.4.17" -charset-normalizer = ">=2,<3" +charset-normalizer = ">=2,<4" idna = ">=2.5,<4" urllib3 = ">=1.21.1,<1.27" @@ -1282,7 +1324,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" [[package]] name = "scikit-learn" -version = "1.2.0" +version = "1.2.1" description = "A set of python modules for machine learning and data mining" category = "main" optional = false @@ -1302,19 +1344,19 @@ tests = ["matplotlib (>=3.1.3)", "scikit-image (>=0.16.2)", "pandas (>=1.0.5)", [[package]] name = "scipy" -version = "1.9.3" +version = "1.10.1" description = "Fundamental algorithms for scientific computing in Python" category = "main" optional = false -python-versions = ">=3.8" +python-versions = "<3.12,>=3.8" [package.dependencies] -numpy = ">=1.18.5,<1.26.0" +numpy = ">=1.19.5,<1.27.0" [package.extras] -test = ["pytest", "pytest-cov", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack"] -doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-panels (>=0.5.2)", "matplotlib (>2)", "numpydoc", "sphinx-tabs"] -dev = ["mypy", "typing-extensions", "pycodestyle", "flake8"] +test = ["pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack", "pooch"] +doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-design (>=0.2.0)", "matplotlib (>2)", "numpydoc"] +dev = ["mypy", "typing-extensions", "pycodestyle", "flake8", "rich-click", "click", "doit (>=0.36.0)", "pydevtool"] [[package]] name = "send2trash" @@ -1331,7 +1373,7 @@ win32 = ["pywin32"] [[package]] name = "setuptools-scm" -version = "7.0.5" +version = "7.1.0" description = "the blessed package to manage your versions by scm tags" category = "main" optional = false @@ -1339,7 +1381,7 @@ python-versions = ">=3.7" [package.dependencies] packaging = ">=20.0" -tomli = ">=1.0.0" +tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""} typing-extensions = "*" [package.extras] @@ -1398,11 +1440,11 @@ python-versions = ">=3.7" [[package]] name = "soupsieve" -version = "2.3.2.post1" +version = "2.4" description = "A modern CSS selector implementation for Beautiful Soup." category = "main" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" [[package]] name = "stack-data" @@ -1495,7 +1537,7 @@ telegram = ["requests"] [[package]] name = "traitlets" -version = "5.7.0" +version = "5.9.0" description = "Traitlets Python configuration system" category = "main" optional = false @@ -1503,13 +1545,11 @@ python-versions = ">=3.7" [package.extras] docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] -lint = ["black (>=22.6.0)", "mdformat (>0.7)", "ruff (>=0.0.156)"] -test = ["pre-commit", "pytest"] -typing = ["mypy (>=0.990)"] +test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"] [[package]] name = "typing-extensions" -version = "4.4.0" +version = "4.5.0" description = "Backported and Experimental Type Hints for Python 3.7+" category = "main" optional = false @@ -1548,7 +1588,7 @@ dev = ["mypy", "flake8 (<4.0.0)", "flake8-annotations", "flake8-bugbear", "flake [[package]] name = "urllib3" -version = "1.26.13" +version = "1.26.14" description = "HTTP 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scikit-learn = "^1.2.0" diff --git a/requirements.txt b/requirements.txt index eb7933d..116e047 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,346 +1,129 @@ +aiofiles==22.1.0; python_version >= "3.7" and python_version < "4.0" +aiosqlite==0.18.0; python_version >= "3.7" anyio==3.6.2; python_full_version >= "3.6.2" and python_version >= "3.8" -appnope==0.1.3; platform_system == "Darwin" and python_version >= "3.8" and sys_platform == "darwin" \ - --hash=sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e \ - --hash=sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24 -argon2-cffi-bindings==21.2.0; python_version >= "3.8" \ - --hash=sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3 \ - --hash=sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367 \ - --hash=sha256:9524464572e12979364b7d600abf96181d3541da11e23ddf565a32e70bd4dc0d \ - --hash=sha256:b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae \ - --hash=sha256:58ed19212051f49a523abb1dbe954337dc82d947fb6e5a0da60f7c8471a8476c \ - --hash=sha256:bd46088725ef7f58b5a1ef7ca06647ebaf0eb4baff7d1d0d177c6cc8744abd86 \ - --hash=sha256:8cd69c07dd875537a824deec19f978e0f2078fdda07fd5c42ac29668dda5f40f \ - --hash=sha256:f1152ac548bd5b8bcecfb0b0371f082037e47128653df2e8ba6e914d384f3c3e \ - --hash=sha256:603ca0aba86b1349b147cab91ae970c63118a0f30444d4bc80355937c950c082 \ - --hash=sha256:b2ef1c30440dbbcba7a5dc3e319408b59676e2e039e2ae11a8775ecf482b192f \ - --hash=sha256:e415e3f62c8d124ee16018e491a009937f8cf7ebf5eb430ffc5de21b900dad93 \ - --hash=sha256:3e385d1c39c520c08b53d63300c3ecc28622f076f4c2b0e6d7e796e9f6502194 \ - --hash=sha256:2c3e3cc67fdb7d82c4718f19b4e7a87123caf8a93fde7e23cf66ac0337d3cb3f \ - --hash=sha256:6a22ad9800121b71099d0fb0a65323810a15f2e292f2ba450810a7316e128ee5 \ - --hash=sha256:f9f8b450ed0547e3d473fdc8612083fd08dd2120d6ac8f73828df9b7d45bb351 \ - --hash=sha256:93f9bf70084f97245ba10ee36575f0c3f1e7d7724d67d8e5b08e61787c320ed7 \ - --hash=sha256:3b9ef65804859d335dc6b31582cad2c5166f0c3e7975f324d9ffaa34ee7e6583 \ - --hash=sha256:d4966ef5848d820776f5f562a7d45fdd70c2f330c961d0d745b784034bd9f48d \ - --hash=sha256:20ef543a89dee4db46a1a6e206cd015360e5a75822f76df533845c3cbaf72670 \ - --hash=sha256:ed2937d286e2ad0cc79a7087d3c272832865f779430e0cc2b4f3718d3159b0cb \ - --hash=sha256:5e00316dabdaea0b2dd82d141cc66889ced0cdcbfa599e8b471cf22c620c329a -argon2-cffi==21.3.0; python_version >= "3.8" \ - --hash=sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b \ - --hash=sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80 +appnope==0.1.3; platform_system == "Darwin" and python_version >= "3.8" and sys_platform == "darwin" +argon2-cffi-bindings==21.2.0; python_version >= "3.8" +argon2-cffi==21.3.0; python_version >= "3.8" arrow==1.2.3; python_version >= "3.7" asttokens==2.2.1; python_version >= "3.8" -attrs==22.1.0; python_version >= "3.7" +attrs==22.2.0; python_version >= "3.7" babel==2.11.0; python_version >= "3.7" -backcall==0.2.0; python_version >= "3.8" \ - --hash=sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255 \ - --hash=sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e -beautifulsoup4==4.11.1; python_full_version >= "3.6.0" and python_version >= "3.8" \ - --hash=sha256:58d5c3d29f5a36ffeb94f02f0d786cd53014cf9b3b3951d42e0080d8a9498d30 \ - --hash=sha256:ad9aa55b65ef2808eb405f46cf74df7fcb7044d5cbc26487f96eb2ef2e436693 -bleach==5.0.1; python_version >= "3.8" \ - --hash=sha256:085f7f33c15bd408dd9b17a4ad77c577db66d76203e5984b1bd59baeee948b2a \ - --hash=sha256:0d03255c47eb9bd2f26aa9bb7f2107732e7e8fe195ca2f64709fcf3b0a4a085c +backcall==0.2.0; python_version >= "3.8" +beautifulsoup4==4.11.2; python_full_version >= "3.6.0" and python_version >= "3.8" +bleach==6.0.0; python_version >= "3.8" bokeh==3.0.3; python_version >= "3.8" certifi==2022.12.7; python_version >= "3.7" and python_version < "4" -cffi==1.15.1; implementation_name == "pypy" and python_version >= "3.8" \ - --hash=sha256:a66d3508133af6e8548451b25058d5812812ec3798c886bf38ed24a98216fab2 \ - --hash=sha256:470c103ae716238bbe698d67ad020e1db9d9dba34fa5a899b5e21577e6d52ed2 \ - --hash=sha256:9ad5db27f9cabae298d151c85cf2bad1d359a1b9c686a275df03385758e2f914 \ - --hash=sha256:b3bbeb01c2b273cca1e1e0c5df57f12dce9a4dd331b4fa1635b8bec26350bde3 \ - --hash=sha256:e00b098126fd45523dd056d2efba6c5a63b71ffe9f2bbe1a4fe1716e1d0c331e \ - --hash=sha256:d61f4695e6c866a23a21acab0509af1cdfd2c013cf256bbf5b6b5e2695827162 \ - --hash=sha256:ed9cb427ba5504c1dc15ede7d516b84757c3e3d7868ccc85121d9310d27eed0b \ - --hash=sha256:39d39875251ca8f612b6f33e6b1195af86d1b3e60086068be9cc053aa4376e21 \ - --hash=sha256:285d29981935eb726a4399badae8f0ffdff4f5050eaa6d0cfc3f64b857b77185 \ - --hash=sha256:3eb6971dcff08619f8d91607cfc726518b6fa2a9eba42856be181c6d0d9515fd \ - 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--hash=sha256:a5c84c68147988265e60416b57fc83425a78058853509c1b0629c180094904a5 \ - --hash=sha256:3b926aa83d1edb5aa5b427b4053dc420ec295a08e40911296b9eb1b6170f6cca \ - --hash=sha256:87c450779d0914f2861b8526e035c5e6da0a3199d8f1add1a665e1cbc6fc6d02 \ - --hash=sha256:4f2c9f67e9821cad2e5f480bc8d83b8742896f1242dba247911072d4fa94c192 \ - --hash=sha256:8b7ee99e510d7b66cdb6c593f21c043c248537a32e0bedf02e01e9553a172314 \ - --hash=sha256:00a9ed42e88df81ffae7a8ab6d9356b371399b91dbdf0c3cb1e84c03a13aceb5 \ - --hash=sha256:54a2db7b78338edd780e7ef7f9f6c442500fb0d41a5a4ea24fff1c929d5af585 \ - --hash=sha256:fcd131dd944808b5bdb38e6f5b53013c5aa4f334c5cad0c72742f6eba4b73db0 \ - --hash=sha256:7473e861101c9e72452f9bf8acb984947aa1661a7704553a9f6e4baa5ba64415 \ - --hash=sha256:6c9a799e985904922a4d207a94eae35c78ebae90e128f0c4e521ce339396be9d \ - --hash=sha256:3bcde07039e586f91b45c88f8583ea7cf7a0770df3a1649627bf598332cb6984 \ - --hash=sha256:33ab79603146aace82c2427da5ca6e58f2b3f2fb5da893ceac0c42218a40be35 \ - --hash=sha256:5d598b938678ebf3c67377cdd45e09d431369c3b1a5b331058c338e201f12b27 \ - --hash=sha256:db0fbb9c62743ce59a9ff687eb5f4afbe77e5e8403d6697f7446e5f609976f76 \ - --hash=sha256:98d85c6a2bef81588d9227dde12db8a7f47f639f4a17c9ae08e773aa9c697bf3 \ - --hash=sha256:40f4774f5a9d4f5e344f31a32b5096977b5d48560c5592e2f3d2c4374bd543ee \ - --hash=sha256:70df4e3b545a17496c9b3f41f5115e69a4f2e77e94e1d2a8e1070bc0c38c8a3c \ - --hash=sha256:d400bfb9a37b1351253cb402671cea7e89bdecc294e8016a707f6d1d8ac934f9 -charset-normalizer==2.1.1; python_version >= "3.7" and python_version < "4" and python_full_version >= "3.6.0" -cloudpickle==2.2.0; python_version >= "3.6" +cffi==1.15.1; implementation_name == "pypy" and python_version >= "3.8" +charset-normalizer==3.0.1; python_version >= "3.7" and python_version < "4" +cloudpickle==2.2.1; python_version >= "3.6" colorama==0.4.6; python_version >= "3.8" and python_full_version < "3.0.0" and platform_system == "Windows" and sys_platform == "win32" or python_full_version >= "3.7.0" and platform_system == "Windows" and sys_platform == "win32" and python_version >= "3.8" comm==0.1.2; python_version >= "3.8" -contourpy==1.0.6; python_version >= "3.8" -cycler==0.11.0; python_version >= "3.8" \ - --hash=sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3 \ - --hash=sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f -cylouvain==0.2.2 \ - --hash=sha256:714ee713b605d6d9cb2a26a9a842c5d4c0a8a8f79e8f0f9357dab179ad4ee55b -debugpy==1.6.4; python_version >= "3.8" -decorator==5.1.1; python_version >= "3.8" \ - --hash=sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186 \ - --hash=sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330 -defusedxml==0.7.1; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.8" \ - --hash=sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61 \ - --hash=sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69 -entrypoints==0.4; python_version >= "3.8" \ - --hash=sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f \ - --hash=sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4 +contourpy==1.0.7; python_version >= "3.8" +cycler==0.11.0; python_version >= "3.8" +cylouvain==0.2.2 +debugpy==1.6.6; python_version >= "3.8" +decorator==5.1.1; python_version >= "3.8" +defusedxml==0.7.1; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.8" executing==1.2.0; python_version >= "3.8" fastjsonschema==2.16.2; python_version >= "3.8" fonttools==4.38.0; python_version >= "3.8" fqdn==1.5.1; python_version >= "3.7" and python_version < "4" idna==3.4; python_full_version >= "3.6.2" and python_version >= "3.8" and python_version < "4" -importlib-metadata==5.1.0; python_version < "3.9" and python_version >= "3.8" -importlib-resources==5.10.1; python_version < "3.9" and python_version >= "3.7" -ipykernel==6.19.2; python_version >= "3.8" -ipython-genutils==0.2.0; python_version >= "3.7" \ - --hash=sha256:72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8 \ - --hash=sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8 -ipython==8.7.0; python_version >= "3.8" +importlib-metadata==6.0.0; python_version < "3.9" and python_version >= "3.8" +importlib-resources==5.12.0; python_version < "3.9" and python_version >= "3.8" +ipykernel==6.21.2; python_version >= "3.8" +ipython-genutils==0.2.0; python_version >= "3.7" +ipython==8.10.0; python_version >= "3.8" isoduration==20.11.0; python_version >= "3.7" jedi==0.18.2; python_version >= "3.8" -jinja2==3.1.2; python_version >= "3.8" \ - --hash=sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61 \ - --hash=sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852 +jinja2==3.1.2; python_version 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--hash=sha256:0b94faa5251c0f23782c03f7b7eedda91d80144059645f452c4bc80fab875976 \ - --hash=sha256:f5d5f7b45f98d155b9c0ba6554fa9770c6b26d5793a3e77a1030fb56910ebeec +six==1.16.0; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.8" +slicer==0.0.7; python_version >= "3.6" sniffio==1.3.0; python_full_version >= "3.6.2" and python_version >= "3.8" -soupsieve==2.3.2.post1; python_full_version >= "3.6.0" and python_version >= "3.8" \ - --hash=sha256:3b2503d3c7084a42b1ebd08116e5f81aadfaea95863628c80a3b774a11b7c759 \ - --hash=sha256:fc53893b3da2c33de295667a0e19f078c14bf86544af307354de5fcf12a3f30d +soupsieve==2.4; python_full_version >= "3.6.0" and python_version >= "3.8" stack-data==0.6.2; python_version >= "3.8" terminado==0.17.1; python_version >= "3.8" -threadpoolctl==3.1.0; python_version >= "3.8" \ - --hash=sha256:8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b \ - --hash=sha256:a335baacfaa4400ae1f0d8e3a58d6674d2f8828e3716bb2802c44955ad391380 +threadpoolctl==3.1.0; python_version >= "3.8" tinycss2==1.2.1; python_version >= "3.8" -tomli==2.0.1; python_version >= "3.8" \ - --hash=sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc \ - --hash=sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f +tomli==2.0.1; python_version < "3.11" and python_version >= "3.8" tornado==6.2; python_version >= "3.8" tqdm==4.64.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" -traitlets==5.7.0; python_full_version >= "3.7.0" and python_version >= "3.8" -typing-extensions==4.4.0; python_version >= "3.8" -umap-learn==0.5.3 \ - --hash=sha256:dbd57cb181c2b66d238acb5635697526bf24c798082daed0cf9b87f6a3a6c0c7 +traitlets==5.9.0; python_full_version >= "3.7.0" and python_version >= "3.8" +typing-extensions==4.5.0; python_version >= "3.8" +umap-learn==0.5.3 uri-template==1.2.0; python_version >= "3.7" -urllib3==1.26.13; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4" or python_version >= "3.7" and python_version < "4" and python_full_version >= "3.6.0" -wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.8" \ - --hash=sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784 \ - --hash=sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83 +urllib3==1.26.14; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4" or python_version >= "3.7" and python_version < "4" and python_full_version >= "3.6.0" +wcwidth==0.2.6; python_full_version >= "3.7.0" and python_version >= "3.8" webcolors==1.12; python_version >= "3.7" -webencodings==0.5.1; python_version >= "3.8" \ - --hash=sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 \ - --hash=sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923 -websocket-client==1.4.2; python_version >= "3.8" -xgboost==1.7.2; python_version >= "3.8" -xyzservices==2022.9.0; python_version >= "3.8" -zipp==3.11.0; python_version < "3.9" and python_version >= "3.7" +webencodings==0.5.1; python_version >= "3.8" +websocket-client==1.5.1; python_version >= "3.8" +xgboost==1.7.4; python_version >= "3.8" +xyzservices==2023.2.0; python_version >= "3.8" +y-py==0.5.9; python_version >= "3.7" +ypy-websocket==0.8.2; python_version >= "3.7" +zipp==3.14.0; python_version < "3.9" and python_version >= "3.8"