{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plots: VectorYXQuiver\n",
        "========================\n",
        "\n",
        "This example illustrates how to plot and customize vector fields in PyAutoLens figures and subplots.\n",
        "\n",
        "__Start Here Notebook__\n",
        "\n",
        "If any code in this script is unclear, refer to the `plot/start_here.ipynb` notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "%matplotlib inline\n",
        "from pyprojroot import here\n",
        "workspace_path = str(here())\n",
        "%cd $workspace_path\n",
        "print(f\"Working Directory has been set to `{workspace_path}`\")\n",
        "\n",
        "from os import path\n",
        "import autolens as al\n",
        "import autolens.plot as aplt"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "First, lets load an example Hubble Space Telescope image of a real strong lens as an `Array2D`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "dataset_path = path.join(\"dataset\", \"slacs\", \"slacs1430+4105\")\n",
        "data_path = path.join(dataset_path, \"data.fits\")\n",
        "data = al.Array2D.from_fits(file_path=data_path, hdu=0, pixel_scales=0.03)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We need a `VectorField` to plot over the image. We make a simple example of a vector field below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "vectors = al.VectorYX2DIrregular(\n",
        "    values=[(1.0, 2.0), (2.0, 1.0)], grid=[(-1.0, 0.0), (-2.0, 0.0)]\n",
        ")"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To plot the vector field manually, we can pass it into a` Visuals2D` object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "visuals = aplt.Visuals2D(vectors=vectors)\n",
        "\n",
        "array_plotter = aplt.Array2DPlotter(array=data, visuals_2d=visuals)\n",
        "array_plotter.figure_2d()"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can customize the appearance of the vectors using the `VectorYXQuiver` matplotlib wrapper object which wraps \n",
        "the following method(s):\n",
        "\n",
        " https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.quiver.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "quiver = aplt.VectorYXQuiver(\n",
        "    headlength=1,\n",
        "    pivot=\"tail\",\n",
        "    color=\"w\",\n",
        "    linewidth=10,\n",
        "    units=\"width\",\n",
        "    angles=\"uv\",\n",
        "    scale=None,\n",
        "    width=0.5,\n",
        "    headwidth=3,\n",
        "    alpha=0.5,\n",
        ")\n",
        "\n",
        "mat_plot = aplt.MatPlot2D(vector_yx_quiver=quiver)\n",
        "\n",
        "array_plotter = aplt.Array2DPlotter(\n",
        "    array=data, mat_plot_2d=mat_plot, visuals_2d=visuals\n",
        ")\n",
        "array_plotter.figure_2d()"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Finish."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [],
      "outputs": [],
      "execution_count": null
    }
  ],
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    "anaconda-cloud": {},
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      "display_name": "Python 3",
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      "codemirror_mode": {
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      "file_extension": ".py",
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