image-match/python/py/Lib/site-packages/matplotlib_inline/backend_inline.py

"""A matplotlib backend for publishing figures via display_data"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the BSD 3-Clause License.

import matplotlib
from IPython.core.getipython import get_ipython
from IPython.core.interactiveshell import InteractiveShell
from IPython.core.pylabtools import select_figure_formats
from IPython.display import display
from matplotlib import colors
from matplotlib._pylab_helpers import Gcf
from matplotlib.backends import backend_agg
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure

from .config import InlineBackend


def new_figure_manager(num, *args, FigureClass=Figure, **kwargs):
    """
    Return a new figure manager for a new figure instance.

    This function is part of the API expected by Matplotlib backends.
    """
    return new_figure_manager_given_figure(num, FigureClass(*args, **kwargs))


def new_figure_manager_given_figure(num, figure):
    """
    Return a new figure manager for a given figure instance.

    This function is part of the API expected by Matplotlib backends.
    """
    manager = backend_agg.new_figure_manager_given_figure(num, figure)

    # Hack: matplotlib FigureManager objects in interacive backends (at least
    # in some of them) monkeypatch the figure object and add a .show() method
    # to it.  This applies the same monkeypatch in order to support user code
    # that might expect `.show()` to be part of the official API of figure
    # objects.  For further reference:
    # https://github.com/ipython/ipython/issues/1612
    # https://github.com/matplotlib/matplotlib/issues/835

    if not hasattr(figure, "show"):
        # Queue up `figure` for display
        figure.show = lambda *a: display(
            figure, metadata=_fetch_figure_metadata(figure)
        )

    # If matplotlib was manually set to non-interactive mode, this function
    # should be a no-op (otherwise we'll generate duplicate plots, since a user
    # who set ioff() manually expects to make separate draw/show calls).
    if not matplotlib.is_interactive():
        return manager

    # ensure current figure will be drawn, and each subsequent call
    # of draw_if_interactive() moves the active figure to ensure it is
    # drawn last
    try:
        show._to_draw.remove(figure)
    except ValueError:
        # ensure it only appears in the draw list once
        pass
    # Queue up the figure for drawing in next show() call
    show._to_draw.append(figure)
    show._draw_called = True

    return manager


def show(close=None, block=None):
    """Show all figures as SVG/PNG payloads sent to the IPython clients.

    Parameters
    ----------
    close : bool, optional
        If true, a ``plt.close('all')`` call is automatically issued after
        sending all the figures. If this is set, the figures will entirely
        removed from the internal list of figures.
    block : Not used.
        The `block` parameter is a Matplotlib experimental parameter.
        We accept it in the function signature for compatibility with other
        backends.
    """
    if close is None:
        close = InlineBackend.instance().close_figures
    try:
        for figure_manager in Gcf.get_all_fig_managers():
            display(
                figure_manager.canvas.figure,
                metadata=_fetch_figure_metadata(figure_manager.canvas.figure),
            )
    finally:
        show._to_draw = []
        # only call close('all') if any to close
        # close triggers gc.collect, which can be slow
        if close and Gcf.get_all_fig_managers():
            matplotlib.pyplot.close("all")


# This flag will be reset by draw_if_interactive when called
show._draw_called = False  # type: ignore[attr-defined]
# list of figures to draw when flush_figures is called
show._to_draw = []  # type: ignore[attr-defined]


def flush_figures():
    """Send all figures that changed

    This is meant to be called automatically and will call show() if, during
    prior code execution, there had been any calls to draw_if_interactive.

    This function is meant to be used as a post_execute callback in IPython,
    so user-caused errors are handled with showtraceback() instead of being
    allowed to raise.  If this function is not called from within IPython,
    then these exceptions will raise.
    """
    if not show._draw_called:
        return

    try:
        if InlineBackend.instance().close_figures:
            # ignore the tracking, just draw and close all figures
            try:
                return show(True)
            except Exception as e:
                # safely show traceback if in IPython, else raise
                ip = get_ipython()
                if ip is None:
                    raise e
                else:
                    ip.showtraceback()
                    return

        # exclude any figures that were closed:
        active = set([fm.canvas.figure for fm in Gcf.get_all_fig_managers()])
        for fig in [fig for fig in show._to_draw if fig in active]:
            try:
                display(fig, metadata=_fetch_figure_metadata(fig))
            except Exception as e:
                # safely show traceback if in IPython, else raise
                ip = get_ipython()
                if ip is None:
                    raise e
                else:
                    ip.showtraceback()
                    return
    finally:
        # clear flags for next round
        show._to_draw = []
        show._draw_called = False


# Changes to matplotlib in version 1.2 requires a mpl backend to supply a default
# figurecanvas. This is set here to a Agg canvas
# See https://github.com/matplotlib/matplotlib/pull/1125
FigureCanvas = FigureCanvasAgg


def configure_inline_support(shell, backend):
    """Configure an IPython shell object for matplotlib use.

    Parameters
    ----------
    shell : InteractiveShell instance

    backend : matplotlib backend
    """
    # If using our svg payload backend, register the post-execution
    # function that will pick up the results for display.  This can only be
    # done with access to the real shell object.

    cfg = InlineBackend.instance(parent=shell)
    cfg.shell = shell
    if cfg not in shell.configurables:
        shell.configurables.append(cfg)

    if backend in ("inline", "module://matplotlib_inline.backend_inline"):
        shell.events.register("post_execute", flush_figures)

        # Save rcParams that will be overwrittern
        shell._saved_rcParams = {}
        for k in cfg.rc:
            shell._saved_rcParams[k] = matplotlib.rcParams[k]
        # load inline_rc
        matplotlib.rcParams.update(cfg.rc)
        new_backend_name = "inline"
    else:
        try:
            shell.events.unregister("post_execute", flush_figures)
        except ValueError:
            pass
        if hasattr(shell, "_saved_rcParams"):
            matplotlib.rcParams.update(shell._saved_rcParams)
            del shell._saved_rcParams
        new_backend_name = "other"

    # only enable the formats once -> don't change the enabled formats (which the user may
    # has changed) when getting another "%matplotlib inline" call.
    # See https://github.com/ipython/ipykernel/issues/29
    cur_backend = getattr(configure_inline_support, "current_backend", "unset")
    if new_backend_name != cur_backend:
        # Setup the default figure format
        select_figure_formats(shell, cfg.figure_formats, **cfg.print_figure_kwargs)
        configure_inline_support.current_backend = new_backend_name


def _enable_matplotlib_integration():
    """Enable extra IPython matplotlib integration when we are loaded as the matplotlib backend."""
    ip = get_ipython()

    import matplotlib

    if matplotlib.__version_info__ >= (3, 10):
        backend = matplotlib.get_backend(auto_select=False)
    else:
        backend = matplotlib.rcParams._get("backend")

    if ip and backend in ("inline", "module://matplotlib_inline.backend_inline"):
        from IPython.core.pylabtools import activate_matplotlib

        try:
            activate_matplotlib(backend)
            configure_inline_support(ip, backend)
        except (ImportError, AttributeError):
            # bugs may cause a circular import on Python 2
            def configure_once(*args):
                activate_matplotlib(backend)
                configure_inline_support(ip, backend)
                ip.events.unregister("post_run_cell", configure_once)

            ip.events.register("post_run_cell", configure_once)


_enable_matplotlib_integration()


def _fetch_figure_metadata(fig):
    """Get some metadata to help with displaying a figure."""
    # determine if a background is needed for legibility
    if _is_transparent(fig.get_facecolor()):
        # the background is transparent
        ticksLight = _is_light(
            [
                label.get_color()
                for axes in fig.axes
                for axis in (axes.xaxis, axes.yaxis)
                for label in axis.get_ticklabels()
            ]
        )
        if ticksLight.size and (ticksLight == ticksLight[0]).all():
            # there are one or more tick labels, all with the same lightness
            return {"needs_background": "dark" if ticksLight[0] else "light"}

    return None


def _is_light(color):
    """Determines if a color (or each of a sequence of colors) is light (as
    opposed to dark). Based on ITU BT.601 luminance formula (see
    https://stackoverflow.com/a/596241)."""
    rgbaArr = colors.to_rgba_array(color)
    return rgbaArr[:, :3].dot((0.299, 0.587, 0.114)) > 0.5


def _is_transparent(color):
    """Determine transparency from alpha."""
    rgba = colors.to_rgba(color)
    return rgba[3] < 0.5


def set_matplotlib_formats(*formats, **kwargs):
    """Select figure formats for the inline backend. Optionally pass quality for JPEG.

    For example, this enables PNG and JPEG output with a JPEG quality of 90%::

        In [1]: set_matplotlib_formats('png', 'jpeg',
                                       pil_kwargs={'quality': 90})

    To set this in your notebook by `%config` magic::

        In [1]: %config InlineBackend.figure_formats = {'png', 'jpeg'}
                %config InlineBackend.print_figure_kwargs = \\
                                        {'pil_kwargs': {'quality' : 90}}

    To set this in your config files use the following::

        c.InlineBackend.figure_formats = {'png', 'jpeg'}
        c.InlineBackend.print_figure_kwargs.update({
                                        'pil_kwargs': {'quality' : 90}
                                    })

    Parameters
    ----------
    *formats : strs
        One or more figure formats to enable: 'png', 'retina', 'jpeg', 'svg', 'pdf'.
    **kwargs
        Keyword args will be relayed to ``figure.canvas.print_figure``.

    In addition, see the docstrings of `plt.savefig()`,
    `matplotlib.figure.Figure.savefig()`, `PIL.Image.Image.save()` and
    :ref:`Pillow Image file formats <handbook/image-file-formats>`.
    """
    # build kwargs, starting with InlineBackend config
    cfg = InlineBackend.instance()
    kw = {}
    kw.update(cfg.print_figure_kwargs)
    kw.update(**kwargs)
    shell = InteractiveShell.instance()
    select_figure_formats(shell, formats, **kw)


def set_matplotlib_close(close=True):
    """Set whether the inline backend closes all figures automatically or not.

    By default, the inline backend used in the IPython Notebook will close all
    matplotlib figures automatically after each cell is run. This means that
    plots in different cells won't interfere. Sometimes, you may want to make
    a plot in one cell and then refine it in later cells. This can be accomplished
    by::

        In [1]: set_matplotlib_close(False)

    To set this in your config files use the following::

        c.InlineBackend.close_figures = False

    Parameters
    ----------
    close : bool
        Should all matplotlib figures be automatically closed after each cell is
        run?
    """
    cfg = InlineBackend.instance()
    cfg.close_figures = close