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      "cell_type": "markdown",
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      "source": [
        "Plots: PositionsScatter\n",
        "=======================\n",
        "\n",
        "This example illustrates how to customize the positions of plotted data.\n",
        "\n",
        "Although a positions is a 2D array of values, it is actually plotted as a `Grid2D` of (y,x) coordinates corresponding to the\n",
        "centre of every pixel at the edge of the positions. The positions is therefore plotted using the `GridScatter` object described\n",
        "in `autolens_workspace.plot.mat_wramat_plot.wrap.base`.\n",
        "\n",
        "The `PositionsScatter` object serves the purpose is allowing us to uniquely customize the appearance of any positions\n",
        "on a plot."
      ]
    },
    {
      "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 will also need a positions to plot on the figure, we'll set these up as a `Grid2DIrregular` of 2D (y,x) coordinates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "positions = al.Grid2DIrregular(values=[(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)])"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To plot the positions manually, we can pass it into a` Visuals2D` object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "visuals = aplt.Visuals2D(positions=positions)\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": [
        "The appearance of the positions is customized using a `Scatter` object.\n",
        "\n",
        "To plot the positions this object wraps the following matplotlib method:\n",
        "\n",
        " https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.scatter.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "positions_scatter = aplt.PositionsScatter(marker=\"o\", c=\"r\", s=50)\n",
        "\n",
        "mat_plot = aplt.MatPlot2D(positions_scatter=positions_scatter)\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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