{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import ipywidgets as widgets\n",
    "from bqplot import pyplot as plt\n",
    "from bqplot import topo_load\n",
    "from bqplot.interacts import panzoom\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initializing data to be plotted\n",
    "np.random.seed(0)\n",
    "size = 100\n",
    "y_data = np.cumsum(np.random.randn(size) * 100.0)\n",
    "y_data_2 = np.cumsum(np.random.randn(size))\n",
    "y_data_3 = np.cumsum(np.random.randn(size) * 100.0)\n",
    "\n",
    "x = np.linspace(0.0, 10.0, size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_data = pd.DataFrame(\n",
    "    np.cumsum(np.random.randn(150, 2).dot([[0.5, 0.8], [0.8, 1.0]]), axis=0) + 100,\n",
    "    columns=[\"Security 1\", \"Security 2\"],\n",
    "    index=pd.date_range(start=\"01-01-2007\", periods=150),\n",
    ")\n",
    "\n",
    "symbol = \"Security 1\"\n",
    "dates_all = price_data.index.values\n",
    "final_prices = price_data[symbol].values.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_data.index.names = [\"date\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(x, y_data)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = plt.ylabel(\"Stock Price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the title for the current figure\n",
    "plt.title(\"Brownian Increments\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(\"Security 1\", data=price_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(title=\"Scatter Plot with colors\")\n",
    "plt.scatter(y_data_2, y_data_3, color=y_data, stroke=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Horizontal and Vertical Lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## adding a horizontal line at y=0\n",
    "plt.hline(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## adding a vertical line at x=4 with stroke_width and colors being passed.\n",
    "plt.vline(4.0, stroke_width=2, colors=[\"orangered\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.scatter(\n",
    "    \"Security 1\",\n",
    "    \"Security 2\",\n",
    "    color=\"date\",\n",
    "    data=price_data.reset_index(),\n",
    "    stroke=\"black\",\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(y_data, colors=[\"OrangeRed\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(\"Security 1\", data=price_data, colors=[\"MediumSeaGreen\"])\n",
    "plt.xlabel(\"Hello\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "bar_x = [\n",
    "    \"A\",\n",
    "    \"B\",\n",
    "    \"C\",\n",
    "    \"D\",\n",
    "    \"E\",\n",
    "    \"F\",\n",
    "    \"G\",\n",
    "    \"H\",\n",
    "    \"I\",\n",
    "    \"J\",\n",
    "    \"K\",\n",
    "    \"L\",\n",
    "    \"M\",\n",
    "    \"N\",\n",
    "    \"P\",\n",
    "    \"Q\",\n",
    "    \"R\",\n",
    "    \"S\",\n",
    "    \"T\",\n",
    "    \"U\",\n",
    "]\n",
    "plt.bar(bar_x, y_data_3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.bar(\"date\", \"Security 2\", data=price_data.reset_index()[:10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "d = abs(y_data_2[:5])\n",
    "plt.pie(d)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.pie(\"Security 2\", color=\"Security 1\", data=price_data[:4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OHLC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = np.arange(\n",
    "    dt.datetime(2014, 1, 2), dt.datetime(2014, 1, 30), dt.timedelta(days=1)\n",
    ")\n",
    "\n",
    "prices = np.array(\n",
    "    [\n",
    "        [187.21, 187.4, 185.2, 185.53],\n",
    "        [185.83, 187.35, 185.3, 186.64],\n",
    "        [187.15, 187.355, 185.3, 186.0],\n",
    "        [186.39, 190.35, 186.38, 189.71],\n",
    "        [189.33, 189.4175, 187.26, 187.97],\n",
    "        [189.02, 189.5, 186.55, 187.38],\n",
    "        [188.31, 188.57, 186.28, 187.26],\n",
    "        [186.26, 186.95, 183.86, 184.16],\n",
    "        [185.06, 186.428, 183.8818, 185.92],\n",
    "        [185.82, 188.65, 185.49, 187.74],\n",
    "        [187.53, 188.99, 186.8, 188.76],\n",
    "        [188.04, 190.81, 187.86, 190.09],\n",
    "        [190.23, 190.39, 186.79, 188.43],\n",
    "        [181.28, 183.5, 179.67, 182.25],\n",
    "        [181.43, 183.72, 180.71, 182.73],\n",
    "        [181.25, 182.8141, 179.64, 179.64],\n",
    "        [179.605, 179.65, 177.66, 177.9],\n",
    "        [178.05, 178.45, 176.16, 176.85],\n",
    "        [175.98, 178.53, 175.89, 176.4],\n",
    "        [177.17, 177.86, 176.36, 177.36],\n",
    "    ]\n",
    ")\n",
    "\n",
    "plt.figure()\n",
    "plt.ohlc(dates, prices)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.boxplot(np.arange(10), np.random.randn(10, 100))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.geo(map_data=\"WorldMap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(padding_y=0)\n",
    "plt.heatmap(x * x[:, np.newaxis])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridHeatMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(padding_y=0)\n",
    "plt.gridheatmap(x[:10] * x[:10, np.newaxis])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(dates_all, final_prices)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Editing existing axes properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## adding grid lines and changing the side of the axis in the figure above\n",
    "_ = plt.axes(\n",
    "    options={\n",
    "        \"x\": {\"grid_lines\": \"solid\"},\n",
    "        \"y\": {\"side\": \"right\", \"grid_lines\": \"dashed\"},\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Marks on the same Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(x, y_data_3, colors=[\"orangered\"])\n",
    "plt.scatter(x, y_data, stroke=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using marker strings in Line Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mark_x = np.arange(10)\n",
    "plt.figure(title=\"Using Marker Strings\")\n",
    "plt.plot(\n",
    "    mark_x, 3 * mark_x + 5, \"g-.s\"\n",
    ")  # color=green, line_style=dash_dotted, marker=square\n",
    "plt.plot(mark_x ** 2, \"m:d\")  # color=magenta, line_style=None, marker=diamond\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Partially changing the scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(x, y_data)\n",
    "\n",
    "## preserving the x scale and changing the y scale\n",
    "plt.scales(scales={\"x\": plt.Keep})\n",
    "plt.plot(\n",
    "    x,\n",
    "    y_data_2,\n",
    "    colors=[\"orange\"],\n",
    "    axes_options={\"y\": {\"side\": \"right\", \"color\": \"orange\", \"grid_lines\": \"none\"}},\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a label to the chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "line = plt.plot(dates_all, final_prices)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## adds the label to the figure created above\n",
    "_ = plt.label(\n",
    "    [\"Pie Day\"],\n",
    "    x=[np.datetime64(\"2007-03-14\")],\n",
    "    y=[final_prices.mean()],\n",
    "    scales=line.scales,\n",
    "    colors=[\"orangered\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing context figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1)\n",
    "plt.plot(x, y_data_3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(2)\n",
    "plt.plot(x[:20], y_data_3[:20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-editing first figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## adds the new line to the first figure\n",
    "fig = plt.figure(1, title=\"New title\")\n",
    "plt.plot(x, y_data, colors=[\"orangered\"])\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing the properties of the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "marks = plt.current_figure().marks\n",
    "marks[0].get_state()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Showing a second view of the first figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clearing the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Clearing the figure above\n",
    "plt.clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting a figure and all its views."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactions in Pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brush Selector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "plt.scatter(y_data_2, y_data_3, colors=[\"orange\"], stroke=\"black\")\n",
    "\n",
    "label = widgets.Label()\n",
    "\n",
    "\n",
    "def callback(name, value):\n",
    "    label.value = str(value)\n",
    "\n",
    "\n",
    "## click and drag on the figure to see the selector\n",
    "plt.brush_selector(callback)\n",
    "\n",
    "widgets.VBox([fig, label])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fast Interval Selector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "n = 100\n",
    "plt.plot(np.arange(n), y_data_3)\n",
    "label = widgets.Label()\n",
    "\n",
    "\n",
    "def callback(name, value):\n",
    "    label.value = str(value)\n",
    "\n",
    "\n",
    "## click on the figure to activate the selector\n",
    "plt.int_selector(callback)\n",
    "\n",
    "widgets.VBox([fig, label])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brush Interval Selector with call back on brushing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# click and drag on chart to make a selection\n",
    "def callback(name, value):\n",
    "    label.value = \"Brushing: \" + str(value)\n",
    "\n",
    "\n",
    "_ = plt.brush_int_selector(callback, \"brushing\")"
   ]
  }
 ],
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