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@michaelchughes
Created September 24, 2019 21:56
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Simple visualization of ventilator usage and vital signs in the ICU
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import unicode_literals\n",
"\n",
"import numpy as np\n",
"import sys\n",
"import os\n",
"import pandas as pd\n",
"pd.set_option('display.width', 200);\n",
"\n",
"import time\n",
"import glob\n",
"import itertools\n",
"from collections import OrderedDict"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Important things at the top"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_colnames = [\n",
" 'meanbp',\n",
" 'temp',\n",
" 'hr',\n",
" 'spo2',\n",
" 'fio2',\n",
" 'spontaneousrr',\n",
" 'urine']"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"T = 13"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"D = len(x_colnames)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_TD = np.random.randn(T,D)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_colnames = ['vasopressor', 'ventilator']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"C = len(y_colnames)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_TC = np.asarray(np.random.randn(T,C) > 0, np.int32)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PREP MATPLOTLIB"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import seaborn as sns\n",
"sns.set_style('ticks')\n",
"sns.set_context(\"notebook\", font_scale=1.0)\n",
"\n",
"from matplotlib.colors import ListedColormap\n",
"import matplotlib.gridspec as gridspec"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SETUP SEQ PLOTTING FUNC"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def make_vitals_cmap():\n",
" hex_color_list = [\n",
" '#ffce00',\n",
" '#e31a1c', '#ff7f00', '#6a3d9a',\n",
" '#a6cee3', '#1f78b4', '#33a02c']\n",
" my_list_cmap = ListedColormap(hex_color_list)\n",
" my_palette = sns.color_palette(hex_color_list)\n",
" return my_palette"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAABrCAYAAAB37ojBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAA/5JREFUeJzt3LFqW2cYx+FPlq1A1JRCkgOlhq4F7556A4VOXrx47WA6\nG9rNt1BQO/QO1MF34NIL8FKMR7eLh2CRBIJNE0cnp0O3YkfoOPHh+P8869EnvbwI9ONIaNA0TVMA\nAAKsdD0AAMBdET4AQAzhAwDEED4AQAzhAwDEED4AQAzhAwDEED4AQAzhAwDEED4AQAzhAwDEED4A\nQAzhAwDEED4AQIzVRQ84Pz8vs9ns2ms7OztlPp+Xqqo++GAAAP83m83KaDQqR0dHrc4vDJ/pdFom\nk8mN1weDppSrv1q9eKr62cK1c43ho3nXI/TS89dPux6hdx48ftj1CL308uJN1yP0zsroRdcj9E7z\ntpSmaVqfHzQLTr/vjs/u7m5ZmZ+VP37+u/UAic6+Xe96hF5a/+6s6xF66fvff+16hN75+qdvuh6h\nl3787c+uR+idJ1/90PUIvXPxy1X54pP1cnh42Or8wlsPVVXd+FXW2tpaKbeoLgCAu+THzQBADOED\nAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQ\nPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBA\nDOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOED\nAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQ\nPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBA\nDOEDAMQQPgBADOEDAMQQPgBADOEDAMQQPgBAjEHTNM37HnB+fl5ms9m117a3t8u7+qp8/uTtRxnu\nvqqfrXY9Qi8NH827HqGXnr9+2vUIvfPg8cOuR+illxdvuh6hd1ZGL7oeoXeaV6WMVkfl+Pi41fmF\nn8DT6bRMJpMbrw8GK6UeflmGw2GrAdLUdV0uP7ss4/HYzpZQ13V5dWlvy6rruqwN7W0ZdV2XS++1\npdV1Xd6tzu1tCf+91z61syXN/pmV0WjU+vyt7vicnp6Wvb29cnBwUDY2NloPkeTk5KRsbW3Z2ZLs\nrR17W56dtWNvy7Ozbiy841NVVamq6i5mAQD4qPy4GQCIIXwAgBjCBwCIIXwAgBjCBwCIMdzf39+/\nzROMx+OyublZxuPxBxrp/rOzduytHXtbnp21Y2/Ls7O7t/B/fAAA7gtfdQEAMYQPABBD+AAAMYQP\nABBD+AAAMYQPABDjX84GyrZ+dWgsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10464c810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.palplot(make_vitals_cmap())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_icu_seq(\n",
" x_TD=None,\n",
" y_TC=None,\n",
" scale=5.0,\n",
" x_colnames=None,\n",
" y_colnames=None,\n",
" x_cols_in_first_plot=5,\n",
" x_cols_in_second_plot=5,\n",
" z_score_max_val=3,\n",
" z_score_ticks=[-2, 0, 2],\n",
" text_buffer=1.0):\n",
" '''\n",
"\n",
" Args\n",
" ----\n",
"\n",
" x_TD : 2D array, n_timesteps x n_feature_dims\n",
" Holds time series values\n",
" y_T : 1D array, n_timesteps\n",
" '''\n",
" if isinstance(x_cols_in_first_plot, int):\n",
" x_cols_in_first_plot = np.arange(x_cols_in_first_plot)\n",
" if isinstance(x_cols_in_second_plot, int):\n",
" x_cols_in_second_plot = np.arange(x_cols_in_second_plot)\n",
" \n",
" T = x_TD.shape[0]\n",
" timesteps_T = np.arange(T)\n",
" \n",
" height_ratios = [2, len(x_cols_in_first_plot), len(x_cols_in_second_plot)]\n",
" sns.set_style(\"darkgrid\")\n",
" fig_h = plt.figure(figsize=(\n",
" text_buffer + T/scale, 2.0 * sum(height_ratios)/scale))\n",
" \n",
" my_palette = itertools.cycle(make_vitals_cmap())\n",
" axes = [None for _ in range(3)]\n",
" gs = gridspec.GridSpec(\n",
" len(height_ratios), 1,\n",
" height_ratios=height_ratios,\n",
" )\n",
" axes[0] = plt.subplot(gs[0])\n",
" axes[1] = plt.subplot(gs[1])\n",
" axes[2] = plt.subplot(gs[2])\n",
" xticks = np.arange(0, T, 1)\n",
" \n",
" C = y_TC.shape[1]\n",
" ax = axes[0]\n",
" for col in np.arange(C):\n",
" sns.heatmap(\n",
" y_TC[:,col:(col+1)].T,\n",
" vmin=0, vmax=1,\n",
" ax=ax,\n",
" cbar=False,\n",
" yticklabels=[y_colnames[col]],\n",
" cmap=ListedColormap(['#d9d9d9', '#525252']))\n",
" ax.invert_yaxis()\n",
" ax.yaxis.tick_right()\n",
" ax.yaxis.set_ticklabels(ax.yaxis.get_ticklabels(), rotation=0)\n",
" ax.set_xlim([0, T])\n",
" ax.set_xticks(0.5 + xticks)\n",
" ax.set_xticklabels(['' for _ in xticks])\n",
"\n",
" ## PLOT FIRST SET OF FEATURES\n",
" ax = axes[1]\n",
" for col in x_cols_in_first_plot:\n",
" ax.plot(x_TD[:, col], 's-',\n",
" label=x_colnames[col],\n",
" color=next(my_palette),\n",
" markersize=4)\n",
" ax.legend(bbox_to_anchor=(1., 1.))\n",
" ax.set_ylim([-z_score_max_val, z_score_max_val])\n",
" ax.set_yticks(z_score_ticks)\n",
" ax.set_xlim([0-0.5, T-0.5])\n",
" ax.set_xticks(xticks)\n",
" ax.set_xticklabels(['' for _ in xticks])\n",
" \n",
" ax = axes[2]\n",
" for col in x_cols_in_second_plot:\n",
" ax.plot(x_TD[:, col], 's-',\n",
" label=x_colnames[col],\n",
" color=next(my_palette),\n",
" markersize=4)\n",
" ax.legend(bbox_to_anchor=(1., 1.))\n",
" ax.set_ylim([-z_score_max_val, z_score_max_val])\n",
" ax.set_yticks(z_score_ticks)\n",
" ax.set_xlim([0-0.5, T-0.5])\n",
" ax.set_xticks(xticks)\n",
" ax.set_xticklabels(\n",
" ['%d' % x if a % 10 == 0 else '' for a, x in enumerate(xticks)])\n",
"\n",
" return axes"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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0PI0qBw8IHssQDN61GLx/IPszUfQJodW3jhzYnc/BPQXAmZ9Ro75GFVYlIiKC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\nz3KyY/W/3Dow7/1hebH5h0NkZQd79BrSEkPzSqhtWuMaOxot+jjqpt9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fzPzd7GCer8fn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\nz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wdRd+8iYNguAh/wgmYGKpgr8USD0XQO6oF4bbFnQAj2Lu9j+W0kUsoZ/BdqnZv9Mzcn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\nF8/ZigEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a1a64d850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axes = plot_icu_seq(\n",
" x_TD, y_TC,\n",
" x_colnames=x_colnames,\n",
" y_colnames=y_colnames)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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