{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vectorbt as vbt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from numba import njit\n",
    "import itertools\n",
    "import ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 100)\n"
     ]
    }
   ],
   "source": [
    "big_df = pd.DataFrame(np.random.uniform(size=(100, 100)).astype(float))\n",
    "big_df.columns = list(map(str, big_df.columns))\n",
    "print(big_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indicator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'domain': {'x': [0, 1], 'y': [0, 1]},\n",
       "              'gauge': {'axis': {'range': […"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gauge = vbt.plotting.Gauge(value=0, value_range=(-1, 1))\n",
    "gauge.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gauge.update(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172 ms ± 1.65 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "468 µs ± 862 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.plotting.Gauge(value=0)\n",
    "\n",
    "big_gauge = vbt.plotting.Gauge(value=0)\n",
    "%timeit big_gauge.update(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipywidgets.Widget.close_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'name': 'a',\n",
       "              'showlegend': True,\n",
       "              'type': 'bar',\n",
       "     …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar = pd.DataFrame([[1, 2], [3, 4], [5, 6]], columns=['a', 'b']).vbt.barplot(return_fig=False)\n",
    "bar.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar.update([[7, 8], [9, 10], [11, 12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'name': 'a',\n",
       "              'showlegend': True,\n",
       "              'type': 'bar',\n",
       "     …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar1 = pd.DataFrame([[1, 2], [3, 4], [5, 6]], columns=['a', 'b']).vbt.barplot(return_fig=False)\n",
    "bar2 = pd.DataFrame([[7, 8], [9, 10], [11, 12]], columns=['c', 'd']).vbt.barplot(return_fig=False, fig=bar1.fig)\n",
    "bar2.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar1.update([[7, 8], [9, 10], [11, 12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar2.update([[1, 2], [3, 4], [5, 6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350 ms ± 36.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "4.05 ms ± 17.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.DataFrame(big_df).vbt.barplot()\n",
    "\n",
    "big_bar = pd.DataFrame(big_df).vbt.barplot(return_fig=False)\n",
    "%timeit big_bar.update(big_df.values * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipywidgets.Widget.close_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'name': 'a',\n",
       "              'showlegend': True,\n",
       "              'type': 'scatter',\n",
       " …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter = pd.DataFrame([[1, 2], [3, 4], [5, 6]], columns=['a', 'b']).vbt.plot(return_fig=False)\n",
    "scatter.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "scatter.update([[6, 5], [4, 3], [2, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'name': 'a',\n",
       "              'showlegend': True,\n",
       "              'type': 'scatter',\n",
       " …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter1 = pd.DataFrame([[1, 2], [3, 4], [5, 6]], columns=['a', 'b']).vbt.plot(return_fig=False)\n",
    "scatter2 = pd.DataFrame([[7, 8], [9, 10], [11, 12]], columns=['c', 'd']).vbt.plot(return_fig=False, fig=scatter1.fig)\n",
    "scatter2.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scatter1.update([[7, 8], [9, 10], [11, 12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scatter2.update([[1, 2], [3, 4], [5, 6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.34 ms ± 48.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.DataFrame(big_df).vbt.plot()\n",
    "\n",
    "big_scatter = pd.DataFrame(big_df).vbt.plot(return_fig=False)\n",
    "%timeit big_scatter.update(big_df.values * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipywidgets.Widget.close_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'name': 'a',\n",
       "              'opacity': 0.75,\n",
       "              'showlegend': True,\n",
       "   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = pd.DataFrame([[1, 2], [3, 4], [2, 1]], columns=['a', 'b']).vbt.histplot(return_fig=False)\n",
    "hist.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist.update([[4, 9], [4, 5], [3, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'name': 'a',\n",
       "              'opacity': 0.75,\n",
       "              'showlegend': True,\n",
       "   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame([[1, 2], [3, 4], [2, 1]], columns=['a', 'b']).vbt.histplot(horizontal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'name': 'a',\n",
       "              'opacity': 0.75,\n",
       "              'showlegend': True,\n",
       "   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist1 = pd.DataFrame([[1, 2], [3, 4], [2, 1]], columns=['a', 'b']).vbt.histplot(return_fig=False)\n",
    "hist2 = pd.DataFrame([[4, 9], [4, 5], [3, 0]], columns=['c', 'd']).vbt.histplot(return_fig=False, fig=hist1.fig)\n",
    "hist2.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist1.update([[4, 9], [4, 5], [3, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist2.update([[1, 2], [3, 4], [2, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "331 ms ± 24.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "4.32 ms ± 43.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.DataFrame(big_df).vbt.histplot()\n",
    "\n",
    "big_hist = pd.DataFrame(big_df).vbt.histplot(return_fig=False)\n",
    "%timeit big_hist.update(big_df.values * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipywidgets.Widget.close_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'name': 'a',\n",
       "              'showlegend': True,\n",
       "              'type': 'box',\n",
       "     …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = pd.DataFrame([[1, 2], [3, 4], [2, 1]], columns=['a', 'b']).vbt.boxplot(return_fig=False)\n",
    "box.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "box.update([[4, 9], [4, 5], [3, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'name': 'a',\n",
       "              'showlegend': True,\n",
       "              'type': 'box',\n",
       "     …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame([[1, 2], [3, 4], [2, 1]], columns=['a', 'b']).vbt.boxplot(horizontal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'name': 'a',\n",
       "              'showlegend': True,\n",
       "              'type': 'box',\n",
       "     …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box1 = pd.DataFrame([[1, 2], [3, 4], [2, 1]], columns=['a', 'b']).vbt.boxplot(return_fig=False)\n",
    "box2 = pd.DataFrame([[4, 9], [4, 5], [3, 0]], columns=['c', 'd']).vbt.boxplot(return_fig=False, fig=box1.fig)\n",
    "box2.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "box1.update([[4, 9], [4, 5], [3, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "box2.update([[1, 2], [3, 4], [2, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "329 ms ± 25.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "4.29 ms ± 36.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.DataFrame(big_df).vbt.boxplot()\n",
    "\n",
    "big_box = pd.DataFrame(big_df).vbt.boxplot(return_fig=False)\n",
    "%timeit big_box.update(big_df.values * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipywidgets.Widget.close_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'colorscale': [[0.0, '#0d0887'], [0.1111111111111111, '#46039f'],\n",
       "               …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap = pd.DataFrame(\n",
    "    [[1, 2], [3, 4], [5, 6]], \n",
    "    columns=['a', 'b'], \n",
    "    index=['x', 'y', 'z']\n",
    ").vbt.heatmap(return_fig=False)\n",
    "heatmap.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "heatmap.update([[6, 5], [4, 3], [2, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'colorscale': [[0.0, '#0d0887'], [0.1111111111111111, '#46039f'],\n",
       "               …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    [[1, 2], [3, 4], [5, 6]], \n",
    "    columns=['a', 'b'], \n",
    "    index=['x', 'y', 'z']\n",
    ").vbt.ts_heatmap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'colorscale': [[0.0, '#0d0887'], [0.1111111111111111, '#46039f'],\n",
       "               …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap1 = pd.DataFrame([[1, 2], [3, 4], [5, 6]], columns=['a', 'b'], index=['x', 'y', 'z']).vbt.heatmap(\n",
    "    return_fig=False, trace_kwargs=dict(showscale=False))\n",
    "heatmap2 = pd.DataFrame([[6, 5], [4, 3], [2, 1]], columns=['c', 'd'], index=['x2', 'y2', 'z2']).vbt.heatmap(\n",
    "    return_fig=False, fig=heatmap1.fig)\n",
    "heatmap2.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "heatmap1.update([[6, 5], [4, 3], [2, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "heatmap2.update([[1, 2], [3, 4], [5, 6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'colorscale': [[0.0, '#0d0887'], [0.1111111111111111, '#46039f'],\n",
       "               …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap_sr = pd.Series(\n",
    "    [1, 2, 3, 6, 5, 4], \n",
    "    index=vbt.base.index_fns.stack_indexes([\n",
    "        pd.Index(['i1', 'i2', 'i3', 'i1', 'i2', 'i3'], name='first'),\n",
    "        pd.Index(['i4', 'i5', 'i6', 'i4', 'i5', 'i6'], name='second'),\n",
    "        pd.Index(['i7', 'i7', 'i7', 'i8', 'i8', 'i8'], name='third')\n",
    "    ])\n",
    ")\n",
    "heatmap_sr.vbt.heatmap(x_level=0, y_level=1, symmetric=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'colorscale': [[0.0, '#0d0887'], [0.1111111111111111, '#46039f'],\n",
       "               …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap_sr.vbt.heatmap(x_level=0, y_level=1, symmetric=True, slider_level=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "427 ms ± 53.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "112 µs ± 7.16 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.DataFrame(big_df).vbt.heatmap()\n",
    "\n",
    "big_heatmap = pd.DataFrame(big_df).vbt.heatmap(return_fig=False)\n",
    "%timeit big_heatmap.update(big_df.values * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipywidgets.Widget.close_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'colorscale': [[0.0, '#0d0887'], [0.1111111111111111, '#46039f'],\n",
       "               …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y, z, g = np.mgrid[0:15, 15:25, 25:30, :2]\n",
    "volume_sr = pd.Series(\n",
    "    np.random.randint(1, 10, size=x.flatten().shape), \n",
    "    index=vbt.base.index_fns.stack_indexes([\n",
    "        pd.Index(x.flatten(), name='first'),\n",
    "        pd.Index(y.flatten(), name='second'),\n",
    "        pd.Index(z.flatten(), name='third'),\n",
    "        pd.Index(g.flatten(), name='fourth')\n",
    "    ])\n",
    ")\n",
    "volume = volume_sr.vbt.volume(x_level='first', y_level='second', z_level='third', return_fig=False)\n",
    "volume.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "volume.update(np.random.randint(1, 10, size=x.flatten().shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'colorscale': [[0.0, '#0d0887'], [0.1111111111111111, '#46039f'],\n",
       "               …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "volume_sr.vbt.volume(x_level='first', y_level='second', z_level='third', slider_level='fourth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.33 s ± 96.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "822 µs ± 478 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "x, y, z = np.mgrid[:50, :50, :50]\n",
    "big_volume_sr = pd.Series(\n",
    "    np.random.randint(1, 10, size=x.flatten().shape), \n",
    "    index=vbt.base.index_fns.stack_indexes(\n",
    "        pd.Index(x.flatten(), name='i1'),\n",
    "        pd.Index(y.flatten(), name='i2'),\n",
    "        pd.Index(z.flatten(), name='i3')\n",
    "    )\n",
    ")\n",
    "%timeit big_volume_sr.vbt.volume()\n",
    "\n",
    "big_volume = big_volume_sr.vbt.volume(return_fig=False)\n",
    "%timeit big_volume.update(big_volume_sr.values * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipywidgets.Widget.close_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.3"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}