.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_gallery/gridplot/multigraphic_gridplot.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download__gallery_gridplot_multigraphic_gridplot.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr__gallery_gridplot_multigraphic_gridplot.py:


Multi-Graphic GridPlot
======================

Example showing a Figure with multiple subplots and multiple graphic types.

.. GENERATED FROM PYTHON SOURCE LINES 7-118



.. image-sg:: /_gallery/gridplot/images/sphx_glr_multigraphic_gridplot_001.webp
   :alt: multigraphic gridplot
   :srcset: /_gallery/gridplot/images/sphx_glr_multigraphic_gridplot_001.webp
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Imageio: 'coins.png' was not found on your computer; downloading it now.
    Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/images/coins.png (77 kB)
    Downloading: 8192/78467 bytes (10.4%)78467/78467 bytes (100.0%)
      Done
    File saved as /home/runner/.imageio/images/coins.png.
    /home/runner/work/fastplotlib/fastplotlib/fastplotlib/graphics/_features/_base.py:18: UserWarning: casting float64 array to float32
      warn(f"casting {array.dtype} array to float32")







|

.. code-block:: Python


    # test_example = false

    import fastplotlib as fpl
    import numpy as np
    import imageio.v3 as iio
    from itertools import product

    # define figure
    figure = fpl.Figure(
        shape=(2, 2),
        names=[["image-overlay", "circles"], ["line-stack", "scatter"]],
        size=(700, 560)
    )

    img = iio.imread("imageio:coffee.png")

    # add image to subplot
    figure["image-overlay"].add_image(data=img)

    # generate overlay

    # empty array for overlay, shape is [nrows, ncols, RGBA]
    overlay = np.zeros(shape=(*img.shape[:2], 4), dtype=np.float32)

    # set the blue values of some pixels with an alpha > 1
    overlay[img[:, :, -1] > 200] = np.array([0.0, 0.0, 1.0, 0.6]).astype(np.float32)

    # add overlay to image
    figure["image-overlay"].add_image(data=overlay)

    # generate some circles
    def make_circle(center, radius: float, n_points: int = 75) -> np.ndarray:
        theta = np.linspace(0, 2 * np.pi, n_points)
        xs = radius * np.sin(theta)
        ys = radius * np.cos(theta)

        return np.column_stack([xs, ys]) + center


    spatial_dims = (50, 50)

    # this makes 16 circles, so we can create 16 cmap values, so it will use these values to set the
    # color of the line based by using the cmap as a LUT with the corresponding cmap_transform
    circles = list()
    for center in product(range(0, spatial_dims[0], 15), range(0, spatial_dims[1], 15)):
        circles.append(make_circle(center, 5, n_points=75))

    # things like class labels, cluster labels, etc.
    cmap_transform = [
        0, 1, 1, 2,
        0, 0, 1, 1,
        2, 2, 8, 3,
        1, 9, 1, 5
    ]

    # add an image to overlay the circles on
    img2 = iio.imread("imageio:coins.png")[10::5, 5::5]

    figure["circles"].add_image(data=img2, cmap="gray")

    # add the circles to the figure
    figure["circles"].add_line_collection(
        circles,
        cmap="tab10",
        cmap_transform=cmap_transform,
        thickness=3,
        alpha=0.5,
        name="circles-graphic"
    )

    # move the circles graphic so that it is centered over the image
    figure["circles"]["circles-graphic"].offset = np.array([7, 7, 2])

    # generate some sine data
    # linspace, create 100 evenly spaced x values from -10 to 10
    xs = np.linspace(-10, 10, 100)
    # sine wave
    ys = np.sin(xs)
    sine = np.dstack([xs, ys])[0]

    # make 10 identical waves
    sine_waves = 10 * [sine]

    # add the line stack to the figure
    figure["line-stack"].add_line_stack(data=sine_waves, cmap="Wistia", separation=1)

    figure["line-stack"].auto_scale(maintain_aspect=True)

    # generate some scatter data
    # create a gaussian cloud of 500 points
    n_points = 500

    mean = [0, 0]  # mean of the Gaussian distribution
    covariance = [[1, 0], [0, 1]]  # covariance matrix

    gaussian_cloud = np.random.multivariate_normal(mean, covariance, n_points)
    gaussian_cloud2 = np.random.multivariate_normal(mean, covariance, n_points)

    # add the scatter graphics to the figure
    figure["scatter"].add_scatter(data=gaussian_cloud, sizes=2, cmap="jet")
    figure["scatter"].add_scatter(data=gaussian_cloud2, colors="r", sizes=2)

    figure.show()

    # NOTE: `if __name__ == "__main__"` is NOT how to use fastplotlib interactively
    # please see our docs for using fastplotlib interactively in ipython and jupyter
    if __name__ == "__main__":
        print(__doc__)
        fpl.run()



.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 3.335 seconds)


.. _sphx_glr_download__gallery_gridplot_multigraphic_gridplot.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: multigraphic_gridplot.ipynb <multigraphic_gridplot.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: multigraphic_gridplot.py <multigraphic_gridplot.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: multigraphic_gridplot.zip <multigraphic_gridplot.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_