KarrLab/wc_sim

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examples/Read and plot wc_simulate dataframe.ipynb

Summary

Maintainability
Test Coverage
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_file = '/Users/arthur_at_sinai/gitOnMyLaptopLocal/wc_sim/wc_sim/multialgorithm/dataframe_file.h5'\n",
    "store = pandas.HDFStore(dataframe_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = store.get('dataframe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196d8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = predictions.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "store.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}