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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "(howto-visualization)=\n",
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37ffb69-f376-4389-a5f5-10155e8d7b6f",
   "metadata": {},
   "source": [
    "(howto-visualization-cyclic-colormaps)=\n",
    "## Using cyclic colormaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46088a17-5798-4a02-84a7-759e032288e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "from quantify_core.visualization.mpl_plotting import set_cyclic_colormap\n",
    "\n",
    "zvals = xr.DataArray(np.random.rand(6, 10) * 360)\n",
    "zvals.attrs[\"units\"] = \"deg\"\n",
    "zvals.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "462363f7-bcee-45d7-af09-ca5604df7596",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "color_plot = zvals.plot(ax=ax)\n",
    "set_cyclic_colormap(color_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c132f43f-d5d5-453b-a9cf-f6d83232f911",
   "metadata": {},
   "outputs": [],
   "source": [
    "zvals_shifted = zvals - 180\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "color_plot = zvals_shifted.plot(ax=ax)\n",
    "ax.set_title(\"Shifted cyclic colormap\")\n",
    "set_cyclic_colormap(color_plot, shifted=zvals_shifted.min() < 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ad6a50f-495a-46a4-b105-ee20b64b81e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "color_plot = (zvals / 2).plot(ax=ax)\n",
    "ax.set_title(\"Overwrite clim\")\n",
    "set_cyclic_colormap(color_plot, clim=(0, 180), unit=\"deg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94f0cb57-081e-4ccf-9fea-427406af67d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "zvals_rad = zvals / 180 * np.pi\n",
    "zvals_rad.attrs[\"units\"] = \"rad\"\n",
    "color_plot = zvals_rad.plot(ax=ax)\n",
    "ax.set_title(\"Radians\")\n",
    "set_cyclic_colormap(color_plot, unit=zvals_rad.units)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e579a6e-5755-4c00-aa5a-e1a3b5e5adee",
   "metadata": {},
   "source": [
    "(howto-visualization-custom-colormaps)=\n",
    "## Creating custom colormaps\n",
    "\n",
    "In this example we use this function to create a custom colormap using several\n",
    "base colors for which we adjust the saturation and transparency (alpha,\n",
    "only visible when exporting the image)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ad4cd9c-85e4-4474-ac71-a16dd9e0771a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import colorsys\n",
    "\n",
    "import matplotlib.colors as mplc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from quantify_core.visualization.color_utilities import set_hlsa\n",
    "\n",
    "color_cycle = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\"]\n",
    "all_colors = []\n",
    "for col in color_cycle:\n",
    "    hls = colorsys.rgb_to_hls(*mplc.to_rgb(mplc.to_rgb(col)))\n",
    "    sat_vals = (np.linspace(0.0, 1.0, 20) ** 2) * hls[2]\n",
    "    alpha_vals = np.linspace(0.4, 1.0, 20)\n",
    "\n",
    "    colors = [\n",
    "        list(set_hlsa(col, s=s)) for s, a in zip(sat_vals, alpha_vals)\n",
    "    ]\n",
    "    all_colors += colors\n",
    "\n",
    "cmap = mplc.ListedColormap(all_colors)\n",
    "\n",
    "np.random.seed(19680801)\n",
    "data = np.random.randn(30, 30)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 3), constrained_layout=True)\n",
    "\n",
    "psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-4, vmax=4)\n",
    "fig.colorbar(psm, ax=ax)\n",
    "plt.show()"
   ]
  }
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