visualizations/matplotlib/matplotlib-interactive-histogram-chart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e074ff3e-20e9-4231-990c-5d3590bfe83f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import matplotlib.pyplot as plt\n",
    "import mercury as mr\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e88b25a9-f89e-4740-a2ef-498d3562d5b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/mercury+json": "{\n    \"widget\": \"Select\",\n    \"value\": \"blue\",\n    \"choices\": [\n        \"blue\",\n        \"green\",\n        \"red\"\n    ],\n    \"label\": \"\",\n    \"model_id\": \"a75b19137394478cbea905c740a5268d\",\n    \"code_uid\": \"Select.0.40.16.2-rand6fd23686\",\n    \"url_key\": \"\",\n    \"disabled\": false,\n    \"hidden\": false\n}",
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a75b19137394478cbea905c740a5268d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "mercury.Select"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mercury widget \n",
    "selection = mr.Select(value=\"blue\", choices=[\"blue\",\"green\",\"red\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "405e1d21-8285-419e-936c-571ad89d9ca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create data \n",
    "x = [random.gauss(0,1) for _ in range(1000)]\n",
    "\n",
    "# plot \n",
    "plt.hist(x, color=selection.value)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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