extract bokeh data

extract_bokeh_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "recreational-interest",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import base64\n",
    "import struct\n",
    "\n",
    "import mydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "western-polyester",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['c45bf17d-1e65-45ee-a52d-b20e1b4ec0ef'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_json = json.loads(mydata.docs_json)\n",
    "docs_json.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "competitive-commonwealth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['roots', 'title', 'version'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_json['c45bf17d-1e65-45ee-a52d-b20e1b4ec0ef'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "informed-atlanta",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['references', 'root_ids'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_json['c45bf17d-1e65-45ee-a52d-b20e1b4ec0ef']['roots'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "atmospheric-schedule",
   "metadata": {},
   "outputs": [],
   "source": [
    "references = docs_json['c45bf17d-1e65-45ee-a52d-b20e1b4ec0ef']['roots']['references']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "seasonal-variety",
   "metadata": {},
   "outputs": [],
   "source": [
    "xdata = references[19]['attributes']['data']['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "violent-trade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'__ndarray__': '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',\n",
       " 'dtype': 'float64',\n",
       " 'shape': [365]}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "stone-string",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = struct.unpack('365d', base64.b64decode(xdata['__ndarray__']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "entertaining-board",
   "metadata": {},
   "outputs": [],
   "source": [
    "ydata = references[19]['attributes']['data']['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "orange-cornell",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = struct.unpack('365d', base64.b64decode(ydata['__ndarray__']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aquatic-welsh",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 158.42948285908477,\n",
       " 259.54553967476846,\n",
       " 266.05310041328664)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "continuing-newfoundland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15905091488>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eastern-trace",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

EXTRACT_BOKEH_DATA.md

mydata.py

docs_json = 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requirements.txt

matplotlib