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      "source": [
        "Plots: UltraNestPlotter\n",
        "=======================\n",
        "\n",
        "This example illustrates how to plot visualization summarizing the results of a ultranest non-linear search using\n",
        "a `UltraNestPlotter`.\n",
        "\n",
        "Installation\n",
        "------------\n",
        "\n",
        "Because UltraNest is an optional library, you will likely have to install it manually via the command:\n",
        "\n",
        "`pip install ultranest`\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",
        "\n",
        "import autofit as af\n",
        "import autolens as al\n",
        "import autolens.plot as aplt"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "First, lets create a result via ultranest by repeating the simple model-fit that is performed in \n",
        "the `modeling/start_here.py` example."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "dataset_name = \"simple__no_lens_light\"\n",
        "\n",
        "search = af.UltraNest(path_prefix=\"plot\", name=\"UltraNestPlotter\")\n",
        "\n",
        "dataset_path = path.join(\"dataset\", \"imaging\", dataset_name)\n",
        "\n",
        "dataset = al.Imaging.from_fits(\n",
        "    data_path=path.join(dataset_path, \"data.fits\"),\n",
        "    psf_path=path.join(dataset_path, \"psf.fits\"),\n",
        "    noise_map_path=path.join(dataset_path, \"noise_map.fits\"),\n",
        "    pixel_scales=0.1,\n",
        ")\n",
        "\n",
        "mask = al.Mask2D.circular(\n",
        "    shape_native=dataset.shape_native, pixel_scales=dataset.pixel_scales, radius=3.0\n",
        ")\n",
        "\n",
        "dataset = dataset.apply_mask(mask=mask)\n",
        "\n",
        "# Lens:\n",
        "\n",
        "mass = af.Model(al.mp.Isothermal)\n",
        "shear = af.Model(al.mp.ExternalShear)\n",
        "\n",
        "lens = af.Model(al.Galaxy, redshift=0.5, mass=mass, shear=shear)\n",
        "\n",
        "# Source:\n",
        "\n",
        "bulge = af.Model(al.lp.Sersic)\n",
        "\n",
        "source = af.Model(al.Galaxy, redshift=1.0, bulge=bulge)\n",
        "\n",
        "# Overall Lens Model:\n",
        "\n",
        "model = af.Collection(galaxies=af.Collection(lens=lens, source=source))\n",
        "\n",
        "analysis = al.AnalysisImaging(dataset=dataset)\n",
        "\n",
        "result = search.fit(model=model, analysis=analysis)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We now pass the samples to a `UltraNestPlotter` which will allow us to use ultranest's in-built plotting libraries to \n",
        "make figures.\n",
        "\n",
        "The ultranest readthedocs describes fully all of the methods used below \n",
        "\n",
        " - https://johannesbuchner.github.io/UltraNest/readme.html\n",
        " - https://johannesbuchner.github.io/UltraNest/ultranest.html#module-ultranest.plot\n",
        " \n",
        "In all the examples below, we use the `kwargs` of this function to pass in any of the input parameters that are \n",
        "described in the API docs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "ultranest_plotter = aplt.UltraNestPlotter(samples=result.samples)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `cornerplot` method produces a triangle of 1D and 2D PDF's of every parameter in the model fit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "ultranest_plotter.cornerplot()"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `runplot` method shows how the estimates of the log evidence and other quantities progress as a function of\n",
        "iteration number during the ultranest model-fit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "ultranest_plotter.runplot(\n",
        "    span=None,\n",
        "    logplot=False,\n",
        "    kde=True,\n",
        "    nkde=1000,\n",
        "    color=\"blue\",\n",
        "    plot_kwargs=None,\n",
        "    label_kwargs=None,\n",
        "    lnz_error=True,\n",
        "    lnz_truth=None,\n",
        "    truth_color=\"red\",\n",
        "    truth_kwargs=None,\n",
        "    max_x_ticks=8,\n",
        "    max_y_ticks=3,\n",
        "    use_math_text=True,\n",
        "    mark_final_live=True,\n",
        "    fig=None,\n",
        ")"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `traceplot` method shows how the live points of each parameter converged alongside their PDF."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "ultranest_plotter.traceplot(\n",
        "    span=None,\n",
        "    quantiles=[0.025, 0.5, 0.975],\n",
        "    smooth=0.02,\n",
        "    post_color=\"blue\",\n",
        "    post_kwargs=None,\n",
        "    kde=True,\n",
        "    nkde=1000,\n",
        "    trace_cmap=\"plasma\",\n",
        "    trace_color=None,\n",
        "    trace_kwargs=None,\n",
        "    connect=False,\n",
        "    connect_highlight=10,\n",
        "    connect_color=\"red\",\n",
        "    connect_kwargs=None,\n",
        "    max_n_ticks=5,\n",
        "    use_math_text=False,\n",
        "    labels=None,\n",
        "    label_kwargs=None,\n",
        "    show_titles=False,\n",
        "    title_fmt=\".2f\",\n",
        "    title_kwargs=None,\n",
        "    truths=None,\n",
        "    truth_color=\"red\",\n",
        "    truth_kwargs=None,\n",
        "    verbose=False,\n",
        "    fig=None,\n",
        ")"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Finish."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [],
      "outputs": [],
      "execution_count": null
    }
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