{
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      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plots: MassProfileCentreScatter\n",
        "===============================\n",
        "\n",
        "This example illustrates how to customize the mass profile centres plotted over data.\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",
        "import autolens as al\n",
        "import autolens.plot as aplt"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This means the centre of every `MassProfile` of every `Galaxy` in a plot are plotted on the figure. \n",
        "A `Tracer` object is a good example of an object with many `MassProfiles`, so lets make one with three."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "lens_galaxy_0 = al.Galaxy(\n",
        "    redshift=0.25,\n",
        "    mass=al.mp.Isothermal(centre=(0.0, 1.0), einstein_radius=0.8, ell_comps=(0.2, 0.2)),\n",
        ")\n",
        "\n",
        "lens_galaxy_1 = al.Galaxy(\n",
        "    redshift=0.5,\n",
        "    mass=al.mp.Isothermal(\n",
        "        centre=(0.0, -1.0), einstein_radius=0.8, ell_comps=(0.2, 0.2)\n",
        "    ),\n",
        ")\n",
        "\n",
        "source_galaxy = al.Galaxy(\n",
        "    redshift=1.0,\n",
        "    bulge=al.lp.SersicSph(\n",
        "        centre=(0.1, 0.1), intensity=0.3, effective_radius=1.0, sersic_index=2.5\n",
        "    ),\n",
        ")\n",
        "\n",
        "tracer = al.Tracer(galaxies=[lens_galaxy_0, lens_galaxy_1, source_galaxy])"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We also need the `Grid2D` that we can use to make plots of the `Tracer`'s properties."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "grid = al.Grid2D.uniform(shape_native=(100, 100), pixel_scales=0.05)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The mass profile centres are an internal property of the `Tracer`, so we can plot them via an `Include2D` object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "include = aplt.Include2D(\n",
        "    light_profile_centres=False,\n",
        "    mass_profile_centres=True,\n",
        "    tangential_critical_curves=False,\n",
        "    radial_critical_curves=False,\n",
        "    tangential_caustics=False,\n",
        "    radial_caustics=False,\n",
        ")\n",
        "tracer_plotter = aplt.TracerPlotter(tracer=tracer, grid=grid, include_2d=include)\n",
        "tracer_plotter.figures_2d(image=True)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The appearance of the mass profile centres are customized using a `MassProfileCentresScatter` object.\n",
        "\n",
        "To plot the mass profile centres 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": [
        "mass_profile_centres_scatter = aplt.MassProfileCentresScatter(marker=\"o\", c=\"r\", s=150)\n",
        "mat_plot = aplt.MatPlot2D(mass_profile_centres_scatter=mass_profile_centres_scatter)\n",
        "tracer_plotter = aplt.TracerPlotter(\n",
        "    tracer=tracer, grid=grid, include_2d=include, mat_plot_2d=mat_plot\n",
        ")\n",
        "tracer_plotter.figures_2d(image=True)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "By specifying two colors to the `MassProfileCentresScatter` object the mass profile centres of each plane\n",
        "are plotted in different colors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "mass_profile_centres_scatter = aplt.MassProfileCentresScatter(c=[\"r\", \"w\"], s=150)\n",
        "\n",
        "mat_plot = aplt.MatPlot2D(mass_profile_centres_scatter=mass_profile_centres_scatter)\n",
        "\n",
        "tracer_plotter = aplt.TracerPlotter(\n",
        "    tracer=tracer, grid=grid, include_2d=include, mat_plot_2d=mat_plot\n",
        ")\n",
        "tracer_plotter.figures_2d(image=True)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To plot the mass profile centres manually, we can pass them into a` Visuals2D` object. This is useful for plotting \n",
        "the centres on figures where they are not an internal property, like an `Array2D`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "mass_profile_centres = tracer.extract_attribute(\n",
        "    cls=al.mp.MassProfile, attr_name=\"centre\"\n",
        ")\n",
        "visuals = aplt.Visuals2D(mass_profile_centres=mass_profile_centres)\n",
        "image = tracer.image_2d_from(grid=grid)\n",
        "\n",
        "array_plotter = aplt.Array2DPlotter(\n",
        "    array=image, 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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