examples/Advanced Plotting/Advanced Plotting.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "import numpy as np\n",
    "import bqplot.pyplot as plt\n",
    "from bqplot import LogScale, LinearScale, OrdinalScale, ColorScale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple marks in a single figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(title=\"Multiple Marks\")\n",
    "\n",
    "# create multiple marks. they'll be added to the same figure\n",
    "bar_chart = plt.bar([1, 2, 3, 4, 5], [2, 9, 10, 40, 40])\n",
    "line_chart = plt.plot([1, 2, 3, 4, 5], [10, 5, 30, 60, 20], \"rs-\")\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample Histogram with mid-points set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(title=\"Histogram\")\n",
    "hist = plt.hist(sample=np.random.randn(100), bins=10, colors=[\"orange\"])\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_axis = plt.axes()[\"sample\"]\n",
    "\n",
    "# Set the tick values to be the mid points of the bins\n",
    "x_axis.tick_values = hist.midpoints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line Chart Log Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = np.arange(\"2005-02\", \"2005-03\", dtype=\"datetime64[D]\")\n",
    "size = len(dates)\n",
    "prices = 100 + 5 * np.cumsum(np.random.randn(size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "plt.scales(scales={\"y\": LogScale()})\n",
    "axes_options = {\n",
    "    \"x\": dict(label=\"Date\", grid_lines=\"dashed\", num_ticks=4),\n",
    "    \"y\": dict(label=\"Log Price\", tick_format=\"0.1f\"),\n",
    "}\n",
    "logline = plt.plot(dates, prices, \"m\", axes_options=axes_options)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting min and max along an axis for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "line = plt.plot(np.arange(100), np.random.randn(100).cumsum(), \"r\")\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## changing the min/max od x scale\n",
    "line.scales[\"x\"].min = -10\n",
    "line.scales[\"x\"].max = 110"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marks which do not affect the domain along an axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "x = np.arange(50)\n",
    "y = np.cumsum(np.random.randn(50) * 100.0)\n",
    "line1 = plt.plot(x, y, \"b\")\n",
    "line2 = plt.plot(x, y * 2, \"r\", preserve_domain={\"y\": True})\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "line2.preserve_domain = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preserve domain for color scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(50)\n",
    "y = np.cumsum(np.random.randn(50) * 100.0)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.scales(scales={\"color\": ColorScale(colors=[\"red\", \"white\", \"green\"], mid=0.0)})\n",
    "scat1 = plt.scatter(x, y, color=y)\n",
    "scat2 = plt.scatter(\n",
    "    x, y * 2, color=y * 2, preserve_domain={\"color\": True}, marker=\"cross\"\n",
    ")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reversing a scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(50)\n",
    "y = np.cumsum(np.random.randn(50) * 100.0)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.scales(scales={\"x\": LinearScale(reverse=True)})\n",
    "line = plt.plot(x, y, \"b\")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixing the domain of an ordinal scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(10)\n",
    "y = np.cumsum(np.random.randn(3, 10), axis=1)\n",
    "\n",
    "fig = plt.figure()\n",
    "axes_options = {\n",
    "    \"x\": dict(set_ticks=True, grid_lines=\"none\"),\n",
    "    \"y\": dict(grid_lines=\"none\"),\n",
    "}\n",
    "plt.scales(scales={\"x\": OrdinalScale(domain=list(range(20)))})\n",
    "bar_chart = plt.bar(\n",
    "    x,\n",
    "    y=y,\n",
    "    colors=[\"hotpink\", \"orange\", \"green\"],\n",
    "    labels=[\"One\", \"Two\", \"Three\"],\n",
    "    axes_options=axes_options,\n",
    "    display_legend=True,\n",
    ")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying clip to marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(100)\n",
    "y1, y2 = np.random.randn(2, 100).cumsum(axis=1)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.scales(scales={\"x\": LinearScale(min=10, max=90)})\n",
    "line1 = plt.plot(x, y1, \"r\", labels=[\"Clipped Line\"], display_legend=True)\n",
    "line2 = plt.plot(\n",
    "    x, y2, \"g\", apply_clip=False, labels=[\"Non clipped line\"], display_legend=True\n",
    ")\n",
    "fig"
   ]
  }
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