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

.. only:: html

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

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

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

.. _sphx_glr__gallery_guis_imgui_legend_iris.py:


ImGUI Legend with Markers
=========================

The iris dataset, with the species of each sample shown by color and the k-means cluster it was
assigned to shown by marker shape. A feature that is not the same for every datapoint needs a
label for each of its values, so the legend has an entry per species and an entry per cluster.

.. GENERATED FROM PYTHON SOURCE LINES 9-67



.. image-sg:: /_gallery/guis/images/sphx_glr_imgui_legend_iris_001.webp
   :alt: imgui legend iris
   :srcset: /_gallery/guis/images/sphx_glr_imgui_legend_iris_001.webp
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/runner/work/fastplotlib/fastplotlib/fastplotlib/graphics/features/_base.py:19: UserWarning: casting float64 array to float32
      warn(f"casting {array.dtype} array to float32")







|

.. code-block:: Python


    # test_example = true

    import numpy as np
    import fastplotlib as fpl
    from fastplotlib.ui import Legend
    from sklearn.cluster import KMeans
    from sklearn import datasets

    figure = fpl.Figure(size=(700, 560))

    iris = datasets.load_iris()

    # one marker shape per predicted cluster
    kmeans = KMeans(n_clusters=3, n_init=10, random_state=0).fit(iris["data"])
    markers = np.asarray(["circle", "square", "diamond"])[kmeans.labels_]

    scatter = figure[0, 0].add_scatter(
        data=iris["data"][:, :2],  # sepal length and width
        sizes=12,
        cmap="tab10",
        cmap_transform=iris["target"],  # species
        markers=markers,
        alpha=0.5,
    )
    # qualitative colormap, span its full range so that species k always gets color k
    scatter.cmap_range = (0, scatter.cmap.num_colors)

    # the species entries are colored by the colormap, the cluster entries show the marker shapes
    legend = Legend(
        [
            scatter.create_legend_item(
                label="iris",
                cmap_transform_labels={
                    i: str(name) for i, name in enumerate(iris["target_names"])
                },
                markers_labels={
                    "circle": "cluster 0",
                    "square": "cluster 1",
                    "diamond": "cluster 2",
                },
            )
        ]
    )

    # a floating legend is drawn over the plot and can be dragged around, it reserves no canvas space
    figure.add_imgui_window(
        legend, location="floating", rect=(0.8, 0.1, 0, 0), title="legend"
    )

    figure.show(maintain_aspect=False)


    # NOTE: fpl.loop.run() should not be used for interactive sessions
    # See the "JupyterLab and IPython" section in the user guide
    if __name__ == "__main__":
        print(__doc__)
        fpl.loop.run()


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

   **Total running time of the script:** (0 minutes 0.130 seconds)


.. _sphx_glr_download__gallery_guis_imgui_legend_iris.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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