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	"""
	The image module supports basic image loading, rescaling and display
	operations.
	"""

	import math
	import os
	import logging
	from pathlib import Path
	import warnings

	import numpy as np
	import PIL.Image
	import PIL.PngImagePlugin

	import matplotlib as mpl
	from matplotlib import _api, cbook
	# For clarity, names from _image are given explicitly in this module
	from matplotlib import _image
	# For user convenience, the names from _image are also imported into
	# the image namespace
	from matplotlib._image import * # noqa: F401, F403
	import matplotlib.artist as martist
	import matplotlib.colorizer as mcolorizer
	from matplotlib.backend_bases import FigureCanvasBase
	import matplotlib.colors as mcolors
	from matplotlib.transforms import (
	Affine2D, BboxBase, Bbox, BboxTransform, BboxTransformTo,
	IdentityTransform, TransformedBbox)

	_log = logging.getLogger(__name__)

	# map interpolation strings to module constants
	_interpd_ = {
	'auto': _image.NEAREST, # this will use nearest or Hanning...
	'none': _image.NEAREST, # fall back to nearest when not supported
	'nearest': _image.NEAREST,
	'bilinear': _image.BILINEAR,
	'bicubic': _image.BICUBIC,
	'spline16': _image.SPLINE16,
	'spline36': _image.SPLINE36,
	'hanning': _image.HANNING,
	'hamming': _image.HAMMING,
	'hermite': _image.HERMITE,
	'kaiser': _image.KAISER,
	'quadric': _image.QUADRIC,
	'catrom': _image.CATROM,
	'gaussian': _image.GAUSSIAN,
	'bessel': _image.BESSEL,
	'mitchell': _image.MITCHELL,
	'sinc': _image.SINC,
	'lanczos': _image.LANCZOS,
	'blackman': _image.BLACKMAN,
	'antialiased': _image.NEAREST, # this will use nearest or Hanning...
	}

	interpolations_names = set(_interpd_)


	def composite_images(images, renderer, magnification=1.0):
	"""
	Composite a number of RGBA images into one. The images are
	composited in the order in which they appear in the images list.

	Parameters
	----------
	images : list of Images
	Each must have a `make_image` method. For each image,
	`can_composite` should return `True`, though this is not
	enforced by this function. Each image must have a purely
	affine transformation with no shear.

	renderer : `.RendererBase`

	magnification : float, default: 1
	The additional magnification to apply for the renderer in use.

	Returns
	-------
	image : (M, N, 4) `numpy.uint8` array
	The composited RGBA image.
	offset_x, offset_y : float
	The (left, bottom) offset where the composited image should be placed
	in the output figure.
	"""
	if len(images) == 0:
	return np.empty((0, 0, 4), dtype=np.uint8), 0, 0

	parts = []
	bboxes = []
	for image in images:
	data, x, y, trans = image.make_image(renderer, magnification)
	if data is not None:
	x *= magnification
	y *= magnification
	parts.append((data, x, y, image._get_scalar_alpha()))
	bboxes.append(
	Bbox([[x, y], [x + data.shape[1], y + data.shape[0]]]))

	if len(parts) == 0:
	return np.empty((0, 0, 4), dtype=np.uint8), 0, 0

	bbox = Bbox.union(bboxes)

	output = np.zeros(
	(int(bbox.height), int(bbox.width), 4), dtype=np.uint8)

	for data, x, y, alpha in parts:
	trans = Affine2D().translate(x - bbox.x0, y - bbox.y0)
	_image.resample(data, output, trans, _image.NEAREST,
	resample=False, alpha=alpha)

	return output, bbox.x0 / magnification, bbox.y0 / magnification


	def _draw_list_compositing_images(
	renderer, parent, artists, suppress_composite=None):
	"""
	Draw a sorted list of artists, compositing images into a single
	image where possible.

	For internal Matplotlib use only: It is here to reduce duplication
	between `Figure.draw` and `Axes.draw`, but otherwise should not be
	generally useful.
	"""
	has_images = any(isinstance(x, _ImageBase) for x in artists)

	# override the renderer default if suppressComposite is not None
	not_composite = (suppress_composite if suppress_composite is not None
	else renderer.option_image_nocomposite())

	if not_composite or not has_images:
	for a in artists:
	a.draw(renderer)
	else:
	# Composite any adjacent images together
	image_group = []
	mag = renderer.get_image_magnification()

	def flush_images():
	if len(image_group) == 1:
	image_group[0].draw(renderer)
	elif len(image_group) > 1:
	data, l, b = composite_images(image_group, renderer, mag)
	if data.size != 0:
	gc = renderer.new_gc()
	gc.set_clip_rectangle(parent.bbox)
	gc.set_clip_path(parent.get_clip_path())
	renderer.draw_image(gc, round(l), round(b), data)
	gc.restore()
	del image_group[:]

	for a in artists:
	if (isinstance(a, _ImageBase) and a.can_composite() and
	a.get_clip_on() and not a.get_clip_path()):
	image_group.append(a)
	else:
	flush_images()
	a.draw(renderer)
	flush_images()


	def _resample(
	image_obj, data, out_shape, transform, *, resample=None, alpha=1):
	"""
	Convenience wrapper around `._image.resample` to resample data to
	out_shape (with a third dimension if data is RGBA) that takes care of
	allocating the output array and fetching the relevant properties from the
	Image object image_obj.
	"""
	# AGG can only handle coordinates smaller than 24-bit signed integers,
	# so raise errors if the input data is larger than _image.resample can
	# handle.
	msg = ('Data with more than {n} cannot be accurately displayed. '
	'Downsampling to less than {n} before displaying. '
	'To remove this warning, manually downsample your data.')
	if data.shape[1] > 2**23:
	warnings.warn(msg.format(n='2**23 columns'))
	step = int(np.ceil(data.shape[1] / 2**23))
	data = data[:, ::step]
	transform = Affine2D().scale(step, 1) + transform
	if data.shape[0] > 2**24:
	warnings.warn(msg.format(n='2**24 rows'))
	step = int(np.ceil(data.shape[0] / 2**24))
	data = data[::step, :]
	transform = Affine2D().scale(1, step) + transform
	# decide if we need to apply anti-aliasing if the data is upsampled:
	# compare the number of displayed pixels to the number of
	# the data pixels.
	interpolation = image_obj.get_interpolation()
	if interpolation in ['antialiased', 'auto']:
	# don't antialias if upsampling by an integer number or
	# if zooming in more than a factor of 3
	pos = np.array([[0, 0], [data.shape[1], data.shape[0]]])
	disp = transform.transform(pos)
	dispx = np.abs(np.diff(disp[:, 0]))
	dispy = np.abs(np.diff(disp[:, 1]))
	if ((dispx > 3 * data.shape[1] or
	dispx == data.shape[1] or
	dispx == 2 * data.shape[1]) and
	(dispy > 3 * data.shape[0] or
	dispy == data.shape[0] or
	dispy == 2 * data.shape[0])):
	interpolation = 'nearest'
	else:
	interpolation = 'hanning'
	out = np.zeros(out_shape + data.shape[2:], data.dtype) # 2D->2D, 3D->3D.
	if resample is None:
	resample = image_obj.get_resample()
	_image.resample(data, out, transform,
	_interpd_[interpolation],
	resample,
	alpha,
	image_obj.get_filternorm(),
	image_obj.get_filterrad())
	return out


	def _rgb_to_rgba(A):
	"""
	Convert an RGB image to RGBA, as required by the image resample C++
	extension.
	"""
	rgba = np.zeros((A.shape[0], A.shape[1], 4), dtype=A.dtype)
	rgba[:, :, :3] = A
	if rgba.dtype == np.uint8:
	rgba[:, :, 3] = 255
	else:
	rgba[:, :, 3] = 1.0
	return rgba


	class _ImageBase(mcolorizer.ColorizingArtist):
	"""
	Base class for images.

	interpolation and cmap default to their rc settings

	cmap is a colors.Colormap instance
	norm is a colors.Normalize instance to map luminance to 0-1

	extent is data axes (left, right, bottom, top) for making image plots
	registered with data plots. Default is to label the pixel
	centers with the zero-based row and column indices.

	Additional kwargs are matplotlib.artist properties
	"""
	zorder = 0

	def __init__(self, ax,
	cmap=None,
	norm=None,
	colorizer=None,
	interpolation=None,
	origin=None,
	filternorm=True,
	filterrad=4.0,
	resample=False,
	*,
	interpolation_stage=None,
	**kwargs
	):
	super().__init__(self._get_colorizer(cmap, norm, colorizer))
	if origin is None:
	origin = mpl.rcParams['image.origin']
	_api.check_in_list(["upper", "lower"], origin=origin)
	self.origin = origin
	self.set_filternorm(filternorm)
	self.set_filterrad(filterrad)
	self.set_interpolation(interpolation)
	self.set_interpolation_stage(interpolation_stage)
	self.set_resample(resample)
	self.axes = ax

	self._imcache = None

	self._internal_update(kwargs)

	def __str__(self):
	try:
	shape = self.get_shape()
	return f"{type(self).__name__}(shape={shape!r})"
	except RuntimeError:
	return type(self).__name__

	def __getstate__(self):
	# Save some space on the pickle by not saving the cache.
	return {**super().__getstate__(), "_imcache": None}

	def get_size(self):
	"""Return the size of the image as tuple (numrows, numcols)."""
	return self.get_shape()[:2]

	def get_shape(self):
	"""
	Return the shape of the image as tuple (numrows, numcols, channels).
	"""
	if self._A is None:
	raise RuntimeError('You must first set the image array')

	return self._A.shape

	def set_alpha(self, alpha):
	"""
	Set the alpha value used for blending - not supported on all backends.

	Parameters
	----------
	alpha : float or 2D array-like or None
	"""
	martist.Artist._set_alpha_for_array(self, alpha)
	if np.ndim(alpha) not in (0, 2):
	raise TypeError('alpha must be a float, two-dimensional '
	'array, or None')
	self._imcache = None

	def _get_scalar_alpha(self):
	"""
	Get a scalar alpha value to be applied to the artist as a whole.

	If the alpha value is a matrix, the method returns 1.0 because pixels
	have individual alpha values (see `~._ImageBase._make_image` for
	details). If the alpha value is a scalar, the method returns said value
	to be applied to the artist as a whole because pixels do not have
	individual alpha values.
	"""
	return 1.0 if self._alpha is None or np.ndim(self._alpha) > 0 \
	else self._alpha

	def changed(self):
	"""
	Call this whenever the mappable is changed so observers can update.
	"""
	self._imcache = None
	super().changed()

	def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
	unsampled=False, round_to_pixel_border=True):
	"""
	Normalize, rescale, and colormap the image A from the given in_bbox
	(in data space), to the given out_bbox (in pixel space) clipped to
	the given clip_bbox (also in pixel space), and magnified by the
	magnification factor.

	Parameters
	----------
	A : ndarray

	- a (M, N) array interpreted as scalar (greyscale) image,
	with one of the dtypes `~numpy.float32`, `~numpy.float64`,
	`~numpy.float128`, `~numpy.uint16` or `~numpy.uint8`.
	- (M, N, 4) RGBA image with a dtype of `~numpy.float32`,
	`~numpy.float64`, `~numpy.float128`, or `~numpy.uint8`.

	in_bbox : `~matplotlib.transforms.Bbox`

	out_bbox : `~matplotlib.transforms.Bbox`

	clip_bbox : `~matplotlib.transforms.Bbox`

	magnification : float, default: 1

	unsampled : bool, default: False
	If True, the image will not be scaled, but an appropriate
	affine transformation will be returned instead.

	round_to_pixel_border : bool, default: True
	If True, the output image size will be rounded to the nearest pixel
	boundary. This makes the images align correctly with the Axes.
	It should not be used if exact scaling is needed, such as for
	`.FigureImage`.

	Returns
	-------
	image : (M, N, 4) `numpy.uint8` array
	The RGBA image, resampled unless unsampled is True.
	x, y : float
	The upper left corner where the image should be drawn, in pixel
	space.
	trans : `~matplotlib.transforms.Affine2D`
	The affine transformation from image to pixel space.
	"""
	if A is None:
	raise RuntimeError('You must first set the image '
	'array or the image attribute')
	if A.size == 0:
	raise RuntimeError("_make_image must get a non-empty image. "
	"Your Artist's draw method must filter before "
	"this method is called.")

	clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)

	if clipped_bbox is None:
	return None, 0, 0, None

	out_width_base = clipped_bbox.width * magnification
	out_height_base = clipped_bbox.height * magnification

	if out_width_base == 0 or out_height_base == 0:
	return None, 0, 0, None

	if self.origin == 'upper':
	# Flip the input image using a transform. This avoids the
	# problem with flipping the array, which results in a copy
	# when it is converted to contiguous in the C wrapper
	t0 = Affine2D().translate(0, -A.shape[0]).scale(1, -1)
	else:
	t0 = IdentityTransform()

	t0 += (
	Affine2D()
	.scale(
	in_bbox.width / A.shape[1],
	in_bbox.height / A.shape[0])
	.translate(in_bbox.x0, in_bbox.y0)
	+ self.get_transform())

	t = (t0
	+ (Affine2D()
	.translate(-clipped_bbox.x0, -clipped_bbox.y0)
	.scale(magnification)))

	# So that the image is aligned with the edge of the Axes, we want to
	# round up the output width to the next integer. This also means
	# scaling the transform slightly to account for the extra subpixel.
	if ((not unsampled) and t.is_affine and round_to_pixel_border and
	(out_width_base % 1.0 != 0.0 or out_height_base % 1.0 != 0.0)):
	out_width = math.ceil(out_width_base)
	out_height = math.ceil(out_height_base)
	extra_width = (out_width - out_width_base) / out_width_base
	extra_height = (out_height - out_height_base) / out_height_base
	t += Affine2D().scale(1.0 + extra_width, 1.0 + extra_height)
	else:
	out_width = int(out_width_base)
	out_height = int(out_height_base)
	out_shape = (out_height, out_width)

	if not unsampled:
	if not (A.ndim == 2 or A.ndim == 3 and A.shape[-1] in (3, 4)):
	raise ValueError(f"Invalid shape {A.shape} for image data")

	# if antialiased, this needs to change as window sizes
	# change:
	interpolation_stage = self._interpolation_stage
	if interpolation_stage in ['antialiased', 'auto']:
	pos = np.array([[0, 0], [A.shape[1], A.shape[0]]])
	disp = t.transform(pos)
	dispx = np.abs(np.diff(disp[:, 0])) / A.shape[1]
	dispy = np.abs(np.diff(disp[:, 1])) / A.shape[0]
	if (dispx < 3) or (dispy < 3):
	interpolation_stage = 'rgba'
	else:
	interpolation_stage = 'data'

	if A.ndim == 2 and interpolation_stage == 'data':
	# if we are a 2D array, then we are running through the
	# norm + colormap transformation. However, in general the
	# input data is not going to match the size on the screen so we
	# have to resample to the correct number of pixels

	if A.dtype.kind == 'f': # Float dtype: scale to same dtype.
	scaled_dtype = np.dtype("f8" if A.dtype.itemsize > 4 else "f4")
	if scaled_dtype.itemsize < A.dtype.itemsize:
	_api.warn_external(f"Casting input data from {A.dtype}"
	f" to {scaled_dtype} for imshow.")
	else: # Int dtype, likely.
	# TODO slice input array first
	# Scale to appropriately sized float: use float32 if the
	# dynamic range is small, to limit the memory footprint.
	da = A.max().astype("f8") - A.min().astype("f8")
	scaled_dtype = "f8" if da > 1e8 else "f4"

	# resample the input data to the correct resolution and shape
	A_resampled = _resample(self, A.astype(scaled_dtype), out_shape, t)

	# if using NoNorm, cast back to the original datatype
	if isinstance(self.norm, mcolors.NoNorm):
	A_resampled = A_resampled.astype(A.dtype)

	# Compute out_mask (what screen pixels include "bad" data
	# pixels) and out_alpha (to what extent screen pixels are
	# covered by data pixels: 0 outside the data extent, 1 inside
	# (even for bad data), and intermediate values at the edges).
	mask = (np.where(A.mask, np.float32(np.nan), np.float32(1))
	if A.mask.shape == A.shape # nontrivial mask
	else np.ones_like(A, np.float32))
	# we always have to interpolate the mask to account for
	# non-affine transformations
	out_alpha = _resample(self, mask, out_shape, t, resample=True)
	del mask # Make sure we don't use mask anymore!
	out_mask = np.isnan(out_alpha)
	out_alpha[out_mask] = 1
	# Apply the pixel-by-pixel alpha values if present
	alpha = self.get_alpha()
	if alpha is not None and np.ndim(alpha) > 0:
	out_alpha *= _resample(self, alpha, out_shape, t, resample=True)
	# mask and run through the norm
	resampled_masked = np.ma.masked_array(A_resampled, out_mask)
	output = self.norm(resampled_masked)
	else:
	if A.ndim == 2: # interpolation_stage = 'rgba'
	self.norm.autoscale_None(A)
	A = self.to_rgba(A)
	alpha = self.get_alpha()
	if alpha is None: # alpha parameter not specified
	if A.shape[2] == 3: # image has no alpha channel
	output_alpha = 255 if A.dtype == np.uint8 else 1.0
	else:
	output_alpha = _resample( # resample alpha channel
	self, A[..., 3], out_shape, t)
	output = _resample( # resample rgb channels
	self, _rgb_to_rgba(A[..., :3]), out_shape, t)
	elif np.ndim(alpha) > 0: # Array alpha
	# user-specified array alpha overrides the existing alpha channel
	output_alpha = _resample(self, alpha, out_shape, t)
	output = _resample(
	self, _rgb_to_rgba(A[..., :3]), out_shape, t)
	else: # Scalar alpha
	if A.shape[2] == 3: # broadcast scalar alpha
	output_alpha = (255 * alpha) if A.dtype == np.uint8 else alpha
	else: # or apply scalar alpha to existing alpha channel
	output_alpha = _resample(self, A[..., 3], out_shape, t) * alpha
	output = _resample(
	self, _rgb_to_rgba(A[..., :3]), out_shape, t)
	output[..., 3] = output_alpha # recombine rgb and alpha

	# output is now either a 2D array of normed (int or float) data
	# or an RGBA array of re-sampled input
	output = self.to_rgba(output, bytes=True, norm=False)
	# output is now a correctly sized RGBA array of uint8

	# Apply alpha after if the input was greyscale without a mask
	if A.ndim == 2:
	alpha = self._get_scalar_alpha()
	alpha_channel = output[:, :, 3]
	alpha_channel[:] = ( # Assignment will cast to uint8.
	alpha_channel.astype(np.float32) * out_alpha * alpha)

	else:
	if self._imcache is None:
	self._imcache = self.to_rgba(A, bytes=True, norm=(A.ndim == 2))
	output = self._imcache

	# Subset the input image to only the part that will be displayed.
	subset = TransformedBbox(clip_bbox, t0.inverted()).frozen()
	output = output[
	int(max(subset.ymin, 0)):
	int(min(subset.ymax + 1, output.shape[0])),
	int(max(subset.xmin, 0)):
	int(min(subset.xmax + 1, output.shape[1]))]

	t = Affine2D().translate(
	int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t

	return output, clipped_bbox.x0, clipped_bbox.y0, t

	def make_image(self, renderer, magnification=1.0, unsampled=False):
	"""
	Normalize, rescale, and colormap this image's data for rendering using
	renderer, with the given magnification.

	If unsampled is True, the image will not be scaled, but an
	appropriate affine transformation will be returned instead.

	Returns
	-------
	image : (M, N, 4) `numpy.uint8` array
	The RGBA image, resampled unless unsampled is True.
	x, y : float
	The upper left corner where the image should be drawn, in pixel
	space.
	trans : `~matplotlib.transforms.Affine2D`
	The affine transformation from image to pixel space.
	"""
	raise NotImplementedError('The make_image method must be overridden')

	def _check_unsampled_image(self):
	"""
	Return whether the image is better to be drawn unsampled.

	The derived class needs to override it.
	"""
	return False

	@martist.allow_rasterization
	def draw(self, renderer):
	# if not visible, declare victory and return
	if not self.get_visible():
	self.stale = False
	return
	# for empty images, there is nothing to draw!
	if self.get_array().size == 0:
	self.stale = False
	return
	# actually render the image.
	gc = renderer.new_gc()
	self._set_gc_clip(gc)
	gc.set_alpha(self._get_scalar_alpha())
	gc.set_url(self.get_url())
	gc.set_gid(self.get_gid())
	if (renderer.option_scale_image() # Renderer supports transform kwarg.
	and self._check_unsampled_image()
	and self.get_transform().is_affine):
	im, l, b, trans = self.make_image(renderer, unsampled=True)
	if im is not None:
	trans = Affine2D().scale(im.shape[1], im.shape[0]) + trans
	renderer.draw_image(gc, l, b, im, trans)
	else:
	im, l, b, trans = self.make_image(
	renderer, renderer.get_image_magnification())
	if im is not None:
	renderer.draw_image(gc, l, b, im)
	gc.restore()
	self.stale = False

	def contains(self, mouseevent):
	"""Test whether the mouse event occurred within the image."""
	if (self._different_canvas(mouseevent)
	# This doesn't work for figimage.
	or not self.axes.contains(mouseevent)[0]):
	return False, {}
	# TODO: make sure this is consistent with patch and patch
	# collection on nonlinear transformed coordinates.
	# TODO: consider returning image coordinates (shouldn't
	# be too difficult given that the image is rectilinear
	trans = self.get_transform().inverted()
	x, y = trans.transform([mouseevent.x, mouseevent.y])
	xmin, xmax, ymin, ymax = self.get_extent()
	# This checks xmin <= x <= xmax or xmax <= x <= xmin.
	inside = (x is not None and (x - xmin) * (x - xmax) <= 0
	and y is not None and (y - ymin) * (y - ymax) <= 0)
	return inside, {}

	def write_png(self, fname):
	"""Write the image to png file fname."""
	im = self.to_rgba(self._A[::-1] if self.origin == 'lower' else self._A,
	bytes=True, norm=True)
	PIL.Image.fromarray(im).save(fname, format="png")

	@staticmethod
	def _normalize_image_array(A):
	"""
	Check validity of image-like input A and normalize it to a format suitable for
	Image subclasses.
	"""
	A = cbook.safe_masked_invalid(A, copy=True)
	if A.dtype != np.uint8 and not np.can_cast(A.dtype, float, "same_kind"):
	raise TypeError(f"Image data of dtype {A.dtype} cannot be "
	f"converted to float")
	if A.ndim == 3 and A.shape[-1] == 1:
	A = A.squeeze(-1) # If just (M, N, 1), assume scalar and apply colormap.
	if not (A.ndim == 2 or A.ndim == 3 and A.shape[-1] in [3, 4]):
	raise TypeError(f"Invalid shape {A.shape} for image data")
	if A.ndim == 3:
	# If the input data has values outside the valid range (after
	# normalisation), we issue a warning and then clip X to the bounds
	# - otherwise casting wraps extreme values, hiding outliers and
	# making reliable interpretation impossible.
	high = 255 if np.issubdtype(A.dtype, np.integer) else 1
	if A.min() < 0 or high < A.max():
	_log.warning(
	'Clipping input data to the valid range for imshow with '
	'RGB data ([0..1] for floats or [0..255] for integers). '
	'Got range [%s..%s].',
	A.min(), A.max()
	)
	A = np.clip(A, 0, high)
	# Cast unsupported integer types to uint8
	if A.dtype != np.uint8 and np.issubdtype(A.dtype, np.integer):
	A = A.astype(np.uint8)
	return A

	def set_data(self, A):
	"""
	Set the image array.

	Note that this function does not update the normalization used.

	Parameters
	----------
	A : array-like or `PIL.Image.Image`
	"""
	if isinstance(A, PIL.Image.Image):
	A = pil_to_array(A) # Needed e.g. to apply png palette.
	self._A = self._normalize_image_array(A)
	self._imcache = None
	self.stale = True

	def set_array(self, A):
	"""
	Retained for backwards compatibility - use set_data instead.

	Parameters
	----------
	A : array-like
	"""
	# This also needs to be here to override the inherited
	# cm.ScalarMappable.set_array method so it is not invoked by mistake.
	self.set_data(A)

	def get_interpolation(self):
	"""
	Return the interpolation method the image uses when resizing.

	One of 'auto', 'antialiased', 'nearest', 'bilinear', 'bicubic',
	'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser',
	'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos',
	or 'none'.
	"""
	return self._interpolation

	def set_interpolation(self, s):
	"""
	Set the interpolation method the image uses when resizing.

	If None, use :rc:`image.interpolation`. If 'none', the image is
	shown as is without interpolating. 'none' is only supported in
	agg, ps and pdf backends and will fall back to 'nearest' mode
	for other backends.

	Parameters
	----------
	s : {'auto', 'nearest', 'bilinear', 'bicubic', 'spline16', \
	'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', \
	'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos', 'none'} or None
	"""
	s = mpl._val_or_rc(s, 'image.interpolation').lower()
	_api.check_in_list(interpolations_names, interpolation=s)
	self._interpolation = s
	self.stale = True

	def get_interpolation_stage(self):
	"""
	Return when interpolation happens during the transform to RGBA.

	One of 'data', 'rgba', 'auto'.
	"""
	return self._interpolation_stage

	def set_interpolation_stage(self, s):
	"""
	Set when interpolation happens during the transform to RGBA.

	Parameters
	----------
	s : {'data', 'rgba', 'auto'} or None
	Whether to apply up/downsampling interpolation in data or RGBA
	space. If None, use :rc:`image.interpolation_stage`.
	If 'auto' we will check upsampling rate and if less
	than 3 then use 'rgba', otherwise use 'data'.
	"""
	s = mpl._val_or_rc(s, 'image.interpolation_stage')
	_api.check_in_list(['data', 'rgba', 'auto'], s=s)
	self._interpolation_stage = s
	self.stale = True

	def can_composite(self):
	"""Return whether the image can be composited with its neighbors."""
	trans = self.get_transform()
	return (
	self._interpolation != 'none' and
	trans.is_affine and
	trans.is_separable)

	def set_resample(self, v):
	"""
	Set whether image resampling is used.

	Parameters
	----------
	v : bool or None
	If None, use :rc:`image.resample`.
	"""
	v = mpl._val_or_rc(v, 'image.resample')
	self._resample = v
	self.stale = True

	def get_resample(self):
	"""Return whether image resampling is used."""
	return self._resample

	def set_filternorm(self, filternorm):
	"""
	Set whether the resize filter normalizes the weights.

	See help for `~.Axes.imshow`.

	Parameters
	----------
	filternorm : bool
	"""
	self._filternorm = bool(filternorm)
	self.stale = True

	def get_filternorm(self):
	"""Return whether the resize filter normalizes the weights."""
	return self._filternorm

	def set_filterrad(self, filterrad):
	"""
	Set the resize filter radius only applicable to some
	interpolation schemes -- see help for imshow

	Parameters
	----------
	filterrad : positive float
	"""
	r = float(filterrad)
	if r <= 0:
	raise ValueError("The filter radius must be a positive number")
	self._filterrad = r
	self.stale = True

	def get_filterrad(self):
	"""Return the filterrad setting."""
	return self._filterrad


	class AxesImage(_ImageBase):
	"""
	An image with pixels on a regular grid, attached to an Axes.

	Parameters
	----------
	ax : `~matplotlib.axes.Axes`
	The Axes the image will belong to.
	cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`
	The Colormap instance or registered colormap name used to map scalar
	data to colors.
	norm : str or `~matplotlib.colors.Normalize`
	Maps luminance to 0-1.
	interpolation : str, default: :rc:`image.interpolation`
	Supported values are 'none', 'auto', 'nearest', 'bilinear',
	'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite',
	'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell',
	'sinc', 'lanczos', 'blackman'.
	interpolation_stage : {'data', 'rgba'}, default: 'data'
	If 'data', interpolation
	is carried out on the data provided by the user. If 'rgba', the
	interpolation is carried out after the colormapping has been
	applied (visual interpolation).
	origin : {'upper', 'lower'}, default: :rc:`image.origin`
	Place the [0, 0] index of the array in the upper left or lower left
	corner of the Axes. The convention 'upper' is typically used for
	matrices and images.
	extent : tuple, optional
	The data axes (left, right, bottom, top) for making image plots
	registered with data plots. Default is to label the pixel
	centers with the zero-based row and column indices.
	filternorm : bool, default: True
	A parameter for the antigrain image resize filter
	(see the antigrain documentation).
	If filternorm is set, the filter normalizes integer values and corrects
	the rounding errors. It doesn't do anything with the source floating
	point values, it corrects only integers according to the rule of 1.0
	which means that any sum of pixel weights must be equal to 1.0. So,
	the filter function must produce a graph of the proper shape.
	filterrad : float > 0, default: 4
	The filter radius for filters that have a radius parameter, i.e. when
	interpolation is one of: 'sinc', 'lanczos' or 'blackman'.
	resample : bool, default: False
	When True, use a full resampling method. When False, only resample when
	the output image is larger than the input image.
	**kwargs : `~matplotlib.artist.Artist` properties
	"""

	def __init__(self, ax,
	*,
	cmap=None,
	norm=None,
	colorizer=None,
	interpolation=None,
	origin=None,
	extent=None,
	filternorm=True,
	filterrad=4.0,
	resample=False,
	interpolation_stage=None,
	**kwargs
	):

	self._extent = extent

	super().__init__(
	ax,
	cmap=cmap,
	norm=norm,
	colorizer=colorizer,
	interpolation=interpolation,
	origin=origin,
	filternorm=filternorm,
	filterrad=filterrad,
	resample=resample,
	interpolation_stage=interpolation_stage,
	**kwargs
	)

	def get_window_extent(self, renderer=None):
	x0, x1, y0, y1 = self._extent
	bbox = Bbox.from_extents([x0, y0, x1, y1])
	return bbox.transformed(self.get_transform())

	def make_image(self, renderer, magnification=1.0, unsampled=False):
	# docstring inherited
	trans = self.get_transform()
	# image is created in the canvas coordinate.
	x1, x2, y1, y2 = self.get_extent()
	bbox = Bbox(np.array([[x1, y1], [x2, y2]]))
	transformed_bbox = TransformedBbox(bbox, trans)
	clip = ((self.get_clip_box() or self.axes.bbox) if self.get_clip_on()
	else self.get_figure(root=True).bbox)
	return self._make_image(self._A, bbox, transformed_bbox, clip,
	magnification, unsampled=unsampled)

	def _check_unsampled_image(self):
	"""Return whether the image would be better drawn unsampled."""
	return self.get_interpolation() == "none"

	def set_extent(self, extent, **kwargs):
	"""
	Set the image extent.

	Parameters
	----------
	extent : 4-tuple of float
	The position and size of the image as tuple
	``(left, right, bottom, top)`` in data coordinates.
	**kwargs
	Other parameters from which unit info (i.e., the xunits,
	yunits, zunits (for 3D Axes), runits and thetaunits (for
	polar Axes) entries are applied, if present.

	Notes
	-----
	This updates `.Axes.dataLim`, and, if autoscaling, sets `.Axes.viewLim`
	to tightly fit the image, regardless of `~.Axes.dataLim`. Autoscaling
	state is not changed, so a subsequent call to `.Axes.autoscale_view`
	will redo the autoscaling in accord with `~.Axes.dataLim`.
	"""
	(xmin, xmax), (ymin, ymax) = self.axes._process_unit_info(
	[("x", [extent[0], extent[1]]),
	("y", [extent[2], extent[3]])],
	kwargs)
	if kwargs:
	raise _api.kwarg_error("set_extent", kwargs)
	xmin = self.axes._validate_converted_limits(
	xmin, self.convert_xunits)
	xmax = self.axes._validate_converted_limits(
	xmax, self.convert_xunits)
	ymin = self.axes._validate_converted_limits(
	ymin, self.convert_yunits)
	ymax = self.axes._validate_converted_limits(
	ymax, self.convert_yunits)
	extent = [xmin, xmax, ymin, ymax]

	self._extent = extent
	corners = (xmin, ymin), (xmax, ymax)
	self.axes.update_datalim(corners)
	self.sticky_edges.x[:] = [xmin, xmax]
	self.sticky_edges.y[:] = [ymin, ymax]
	if self.axes.get_autoscalex_on():
	self.axes.set_xlim((xmin, xmax), auto=None)
	if self.axes.get_autoscaley_on():
	self.axes.set_ylim((ymin, ymax), auto=None)
	self.stale = True

	def get_extent(self):
	"""Return the image extent as tuple (left, right, bottom, top)."""
	if self._extent is not None:
	return self._extent
	else:
	sz = self.get_size()
	numrows, numcols = sz
	if self.origin == 'upper':
	return (-0.5, numcols-0.5, numrows-0.5, -0.5)
	else:
	return (-0.5, numcols-0.5, -0.5, numrows-0.5)

	def get_cursor_data(self, event):
	"""
	Return the image value at the event position or None if the event is
	outside the image.

	See Also
	--------
	matplotlib.artist.Artist.get_cursor_data
	"""
	xmin, xmax, ymin, ymax = self.get_extent()
	if self.origin == 'upper':
	ymin, ymax = ymax, ymin
	arr = self.get_array()
	data_extent = Bbox([[xmin, ymin], [xmax, ymax]])
	array_extent = Bbox([[0, 0], [arr.shape[1], arr.shape[0]]])
	trans = self.get_transform().inverted()
	trans += BboxTransform(boxin=data_extent, boxout=array_extent)
	point = trans.transform([event.x, event.y])
	if any(np.isnan(point)):
	return None
	j, i = point.astype(int)
	# Clip the coordinates at array bounds
	if not (0 <= i < arr.shape[0]) or not (0 <= j < arr.shape[1]):
	return None
	else:
	return arr[i, j]


	class NonUniformImage(AxesImage):
	"""
	An image with pixels on a rectilinear grid.

	In contrast to `.AxesImage`, where pixels are on a regular grid,
	NonUniformImage allows rows and columns with individual heights / widths.

	See also :doc:`/gallery/images_contours_and_fields/image_nonuniform`.
	"""

	def __init__(self, ax, , interpolation='nearest', *kwargs):
	"""
	Parameters
	----------
	ax : `~matplotlib.axes.Axes`
	The Axes the image will belong to.
	interpolation : {'nearest', 'bilinear'}, default: 'nearest'
	The interpolation scheme used in the resampling.
	**kwargs
	All other keyword arguments are identical to those of `.AxesImage`.
	"""
	super().__init__(ax, **kwargs)
	self.set_interpolation(interpolation)

	def _check_unsampled_image(self):
	"""Return False. Do not use unsampled image."""
	return False

	def make_image(self, renderer, magnification=1.0, unsampled=False):
	# docstring inherited
	if self._A is None:
	raise RuntimeError('You must first set the image array')
	if unsampled:
	raise ValueError('unsampled not supported on NonUniformImage')
	A = self._A
	if A.ndim == 2:
	if A.dtype != np.uint8:
	A = self.to_rgba(A, bytes=True)
	else:
	A = np.repeat(A[:, :, np.newaxis], 4, 2)
	A[:, :, 3] = 255
	else:
	if A.dtype != np.uint8:
	A = (255*A).astype(np.uint8)
	if A.shape[2] == 3:
	B = np.zeros(tuple([*A.shape[0:2], 4]), np.uint8)
	B[:, :, 0:3] = A
	B[:, :, 3] = 255
	A = B
	l, b, r, t = self.axes.bbox.extents
	width = int(((round(r) + 0.5) - (round(l) - 0.5)) * magnification)
	height = int(((round(t) + 0.5) - (round(b) - 0.5)) * magnification)

	invertedTransform = self.axes.transData.inverted()
	x_pix = invertedTransform.transform(
	[(x, b) for x in np.linspace(l, r, width)])[:, 0]
	y_pix = invertedTransform.transform(
	[(l, y) for y in np.linspace(b, t, height)])[:, 1]

	if self._interpolation == "nearest":
	x_mid = (self._Ax[:-1] + self._Ax[1:]) / 2
	y_mid = (self._Ay[:-1] + self._Ay[1:]) / 2
	x_int = x_mid.searchsorted(x_pix)
	y_int = y_mid.searchsorted(y_pix)
	# The following is equal to `A[y_int[:, None], x_int[None, :]]`,
	# but many times faster. Both casting to uint32 (to have an
	# effectively 1D array) and manual index flattening matter.
	im = (
	np.ascontiguousarray(A).view(np.uint32).ravel()[
	np.add.outer(y_int * A.shape[1], x_int)]
	.view(np.uint8).reshape((height, width, 4)))
	else: # self._interpolation == "bilinear"
	# Use np.interp to compute x_int/x_float has similar speed.
	x_int = np.clip(
	self._Ax.searchsorted(x_pix) - 1, 0, len(self._Ax) - 2)
	y_int = np.clip(
	self._Ay.searchsorted(y_pix) - 1, 0, len(self._Ay) - 2)
	idx_int = np.add.outer(y_int * A.shape[1], x_int)
	x_frac = np.clip(
	np.divide(x_pix - self._Ax[x_int], np.diff(self._Ax)[x_int],
	dtype=np.float32), # Downcasting helps with speed.
	0, 1)
	y_frac = np.clip(
	np.divide(y_pix - self._Ay[y_int], np.diff(self._Ay)[y_int],
	dtype=np.float32),
	0, 1)
	f00 = np.outer(1 - y_frac, 1 - x_frac)
	f10 = np.outer(y_frac, 1 - x_frac)
	f01 = np.outer(1 - y_frac, x_frac)
	f11 = np.outer(y_frac, x_frac)
	im = np.empty((height, width, 4), np.uint8)
	for chan in range(4):
	ac = A[:, :, chan].reshape(-1) # reshape(-1) avoids a copy.
	# Shifting the buffer start (`ac[offset:]`) avoids an array
	# addition (`ac[idx_int + offset]`).
	buf = f00 * ac[idx_int]
	buf += f10 * ac[A.shape[1]:][idx_int]
	buf += f01 * ac[1:][idx_int]
	buf += f11 * ac[A.shape[1] + 1:][idx_int]
	im[:, :, chan] = buf # Implicitly casts to uint8.
	return im, l, b, IdentityTransform()

	def set_data(self, x, y, A):
	"""
	Set the grid for the pixel centers, and the pixel values.

	Parameters
	----------
	x, y : 1D array-like
	Monotonic arrays of shapes (N,) and (M,), respectively, specifying
	pixel centers.
	A : array-like
	(M, N) `~numpy.ndarray` or masked array of values to be
	colormapped, or (M, N, 3) RGB array, or (M, N, 4) RGBA array.
	"""
	A = self._normalize_image_array(A)
	x = np.array(x, np.float32)
	y = np.array(y, np.float32)
	if not (x.ndim == y.ndim == 1 and A.shape[:2] == y.shape + x.shape):
	raise TypeError("Axes don't match array shape")
	self._A = A
	self._Ax = x
	self._Ay = y
	self._imcache = None
	self.stale = True

	def set_array(self, *args):
	raise NotImplementedError('Method not supported')

	def set_interpolation(self, s):
	"""
	Parameters
	----------
	s : {'nearest', 'bilinear'} or None
	If None, use :rc:`image.interpolation`.
	"""
	if s is not None and s not in ('nearest', 'bilinear'):
	raise NotImplementedError('Only nearest neighbor and '
	'bilinear interpolations are supported')
	super().set_interpolation(s)

	def get_extent(self):
	if self._A is None:
	raise RuntimeError('Must set data first')
	return self._Ax[0], self._Ax[-1], self._Ay[0], self._Ay[-1]

	def set_filternorm(self, filternorm):
	pass

	def set_filterrad(self, filterrad):
	pass

	def set_norm(self, norm):
	if self._A is not None:
	raise RuntimeError('Cannot change colors after loading data')
	super().set_norm(norm)

	def set_cmap(self, cmap):
	if self._A is not None:
	raise RuntimeError('Cannot change colors after loading data')
	super().set_cmap(cmap)

	def get_cursor_data(self, event):
	# docstring inherited
	x, y = event.xdata, event.ydata
	if (x < self._Ax[0] or x > self._Ax[-1] or
	y < self._Ay[0] or y > self._Ay[-1]):
	return None
	j = np.searchsorted(self._Ax, x) - 1
	i = np.searchsorted(self._Ay, y) - 1
	return self._A[i, j]


	class PcolorImage(AxesImage):
	"""
	Make a pcolor-style plot with an irregular rectangular grid.

	This uses a variation of the original irregular image code,
	and it is used by pcolorfast for the corresponding grid type.
	"""

	def __init__(self, ax,
	x=None,
	y=None,
	A=None,
	*,
	cmap=None,
	norm=None,
	colorizer=None,
	**kwargs
	):
	"""
	Parameters
	----------
	ax : `~matplotlib.axes.Axes`
	The Axes the image will belong to.
	x, y : 1D array-like, optional
	Monotonic arrays of length N+1 and M+1, respectively, specifying
	rectangle boundaries. If not given, will default to
	``range(N + 1)`` and ``range(M + 1)``, respectively.
	A : array-like
	The data to be color-coded. The interpretation depends on the
	shape:

	- (M, N) `~numpy.ndarray` or masked array: values to be colormapped
	- (M, N, 3): RGB array
	- (M, N, 4): RGBA array

	cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`
	The Colormap instance or registered colormap name used to map
	scalar data to colors.
	norm : str or `~matplotlib.colors.Normalize`
	Maps luminance to 0-1.
	**kwargs : `~matplotlib.artist.Artist` properties
	"""
	super().__init__(ax, norm=norm, cmap=cmap, colorizer=colorizer)
	self._internal_update(kwargs)
	if A is not None:
	self.set_data(x, y, A)

	def make_image(self, renderer, magnification=1.0, unsampled=False):
	# docstring inherited
	if self._A is None:
	raise RuntimeError('You must first set the image array')
	if unsampled:
	raise ValueError('unsampled not supported on PColorImage')

	if self._imcache is None:
	A = self.to_rgba(self._A, bytes=True)
	self._imcache = np.pad(A, [(1, 1), (1, 1), (0, 0)], "constant")
	padded_A = self._imcache
	bg = mcolors.to_rgba(self.axes.patch.get_facecolor(), 0)
	bg = (np.array(bg) * 255).astype(np.uint8)
	if (padded_A[0, 0] != bg).all():
	padded_A[[0, -1], :] = padded_A[:, [0, -1]] = bg

	l, b, r, t = self.axes.bbox.extents
	width = (round(r) + 0.5) - (round(l) - 0.5)
	height = (round(t) + 0.5) - (round(b) - 0.5)
	width = round(width * magnification)
	height = round(height * magnification)
	vl = self.axes.viewLim

	x_pix = np.linspace(vl.x0, vl.x1, width)
	y_pix = np.linspace(vl.y0, vl.y1, height)
	x_int = self._Ax.searchsorted(x_pix)
	y_int = self._Ay.searchsorted(y_pix)
	im = ( # See comment in NonUniformImage.make_image re: performance.
	padded_A.view(np.uint32).ravel()[
	np.add.outer(y_int * padded_A.shape[1], x_int)]
	.view(np.uint8).reshape((height, width, 4)))
	return im, l, b, IdentityTransform()

	def _check_unsampled_image(self):
	return False

	def set_data(self, x, y, A):
	"""
	Set the grid for the rectangle boundaries, and the data values.

	Parameters
	----------
	x, y : 1D array-like, optional
	Monotonic arrays of length N+1 and M+1, respectively, specifying
	rectangle boundaries. If not given, will default to
	``range(N + 1)`` and ``range(M + 1)``, respectively.
	A : array-like
	The data to be color-coded. The interpretation depends on the
	shape:

	- (M, N) `~numpy.ndarray` or masked array: values to be colormapped
	- (M, N, 3): RGB array
	- (M, N, 4): RGBA array
	"""
	A = self._normalize_image_array(A)
	x = np.arange(0., A.shape[1] + 1) if x is None else np.array(x, float).ravel()
	y = np.arange(0., A.shape[0] + 1) if y is None else np.array(y, float).ravel()
	if A.shape[:2] != (y.size - 1, x.size - 1):
	raise ValueError(
	"Axes don't match array shape. Got %s, expected %s." %
	(A.shape[:2], (y.size - 1, x.size - 1)))
	# For efficient cursor readout, ensure x and y are increasing.
	if x[-1] < x[0]:
	x = x[::-1]
	A = A[:, ::-1]
	if y[-1] < y[0]:
	y = y[::-1]
	A = A[::-1]
	self._A = A
	self._Ax = x
	self._Ay = y
	self._imcache = None
	self.stale = True

	def set_array(self, *args):
	raise NotImplementedError('Method not supported')

	def get_cursor_data(self, event):
	# docstring inherited
	x, y = event.xdata, event.ydata
	if (x < self._Ax[0] or x > self._Ax[-1] or
	y < self._Ay[0] or y > self._Ay[-1]):
	return None
	j = np.searchsorted(self._Ax, x) - 1
	i = np.searchsorted(self._Ay, y) - 1
	return self._A[i, j]


	class FigureImage(_ImageBase):
	"""An image attached to a figure."""

	zorder = 0

	_interpolation = 'nearest'

	def __init__(self, fig,
	*,
	cmap=None,
	norm=None,
	colorizer=None,
	offsetx=0,
	offsety=0,
	origin=None,
	**kwargs
	):
	"""
	cmap is a colors.Colormap instance
	norm is a colors.Normalize instance to map luminance to 0-1

	kwargs are an optional list of Artist keyword args
	"""
	super().__init__(
	None,
	norm=norm,
	cmap=cmap,
	colorizer=colorizer,
	origin=origin
	)
	self.set_figure(fig)
	self.ox = offsetx
	self.oy = offsety
	self._internal_update(kwargs)
	self.magnification = 1.0

	def get_extent(self):
	"""Return the image extent as tuple (left, right, bottom, top)."""
	numrows, numcols = self.get_size()
	return (-0.5 + self.ox, numcols-0.5 + self.ox,
	-0.5 + self.oy, numrows-0.5 + self.oy)

	def make_image(self, renderer, magnification=1.0, unsampled=False):
	# docstring inherited
	fig = self.get_figure(root=True)
	fac = renderer.dpi/fig.dpi
	# fac here is to account for pdf, eps, svg backends where
	# figure.dpi is set to 72. This means we need to scale the
	# image (using magnification) and offset it appropriately.
	bbox = Bbox([[self.ox/fac, self.oy/fac],
	[(self.ox/fac + self._A.shape[1]),
	(self.oy/fac + self._A.shape[0])]])
	width, height = fig.get_size_inches()
	width *= renderer.dpi
	height *= renderer.dpi
	clip = Bbox([[0, 0], [width, height]])
	return self._make_image(
	self._A, bbox, bbox, clip, magnification=magnification / fac,
	unsampled=unsampled, round_to_pixel_border=False)

	def set_data(self, A):
	"""Set the image array."""
	super().set_data(A)
	self.stale = True


	class BboxImage(_ImageBase):
	"""The Image class whose size is determined by the given bbox."""

	def __init__(self, bbox,
	*,
	cmap=None,
	norm=None,
	colorizer=None,
	interpolation=None,
	origin=None,
	filternorm=True,
	filterrad=4.0,
	resample=False,
	**kwargs
	):
	"""
	cmap is a colors.Colormap instance
	norm is a colors.Normalize instance to map luminance to 0-1

	kwargs are an optional list of Artist keyword args
	"""
	super().__init__(
	None,
	cmap=cmap,
	norm=norm,
	colorizer=colorizer,
	interpolation=interpolation,
	origin=origin,
	filternorm=filternorm,
	filterrad=filterrad,
	resample=resample,
	**kwargs
	)
	self.bbox = bbox

	def get_window_extent(self, renderer=None):
	if renderer is None:
	renderer = self.get_figure()._get_renderer()

	if isinstance(self.bbox, BboxBase):
	return self.bbox
	elif callable(self.bbox):
	return self.bbox(renderer)
	else:
	raise ValueError("Unknown type of bbox")

	def contains(self, mouseevent):
	"""Test whether the mouse event occurred within the image."""
	if self._different_canvas(mouseevent) or not self.get_visible():
	return False, {}
	x, y = mouseevent.x, mouseevent.y
	inside = self.get_window_extent().contains(x, y)
	return inside, {}

	def make_image(self, renderer, magnification=1.0, unsampled=False):
	# docstring inherited
	width, height = renderer.get_canvas_width_height()
	bbox_in = self.get_window_extent(renderer).frozen()
	bbox_in._points /= [width, height]
	bbox_out = self.get_window_extent(renderer)
	clip = Bbox([[0, 0], [width, height]])
	self._transform = BboxTransformTo(clip)
	return self._make_image(
	self._A,
	bbox_in, bbox_out, clip, magnification, unsampled=unsampled)


	def imread(fname, format=None):
	"""
	Read an image from a file into an array.

	.. note::

	This function exists for historical reasons. It is recommended to
	use `PIL.Image.open` instead for loading images.

	Parameters
	----------
	fname : str or file-like
	The image file to read: a filename, a URL or a file-like object opened
	in read-binary mode.

	Passing a URL is deprecated. Please open the URL
	for reading and pass the result to Pillow, e.g. with
	``np.array(PIL.Image.open(urllib.request.urlopen(url)))``.
	format : str, optional
	The image file format assumed for reading the data. The image is
	loaded as a PNG file if format is set to "png", if fname is a path
	or opened file with a ".png" extension, or if it is a URL. In all
	other cases, format is ignored and the format is auto-detected by
	`PIL.Image.open`.

	Returns
	-------
	`numpy.array`
	The image data. The returned array has shape

	- (M, N) for grayscale images.
	- (M, N, 3) for RGB images.
	- (M, N, 4) for RGBA images.

	PNG images are returned as float arrays (0-1). All other formats are
	returned as int arrays, with a bit depth determined by the file's
	contents.
	"""
	# hide imports to speed initial import on systems with slow linkers
	from urllib import parse

	if format is None:
	if isinstance(fname, str):
	parsed = parse.urlparse(fname)
	# If the string is a URL (Windows paths appear as if they have a
	# length-1 scheme), assume png.
	if len(parsed.scheme) > 1:
	ext = 'png'
	else:
	ext = Path(fname).suffix.lower()[1:]
	elif hasattr(fname, 'geturl'): # Returned by urlopen().
	# We could try to parse the url's path and use the extension, but
	# returning png is consistent with the block above. Note that this
	# if clause has to come before checking for fname.name as
	# urlopen("file:///...") also has a name attribute (with the fixed
	# value "<urllib response>").
	ext = 'png'
	elif hasattr(fname, 'name'):
	ext = Path(fname.name).suffix.lower()[1:]
	else:
	ext = 'png'
	else:
	ext = format
	img_open = (
	PIL.PngImagePlugin.PngImageFile if ext == 'png' else PIL.Image.open)
	if isinstance(fname, str) and len(parse.urlparse(fname).scheme) > 1:
	# Pillow doesn't handle URLs directly.
	raise ValueError(
	"Please open the URL for reading and pass the "
	"result to Pillow, e.g. with "
	"``np.array(PIL.Image.open(urllib.request.urlopen(url)))``."
	)
	with img_open(fname) as image:
	return (_pil_png_to_float_array(image)
	if isinstance(image, PIL.PngImagePlugin.PngImageFile) else
	pil_to_array(image))


	def imsave(fname, arr, vmin=None, vmax=None, cmap=None, format=None,
	origin=None, dpi=100, *, metadata=None, pil_kwargs=None):
	"""
	Colormap and save an array as an image file.

	RGB(A) images are passed through. Single channel images will be
	colormapped according to cmap and norm.

	.. note::

	If you want to save a single channel image as gray scale please use an
	image I/O library (such as pillow, tifffile, or imageio) directly.

	Parameters
	----------
	fname : str or path-like or file-like
	A path or a file-like object to store the image in.
	If format is not set, then the output format is inferred from the
	extension of fname, if any, and from :rc:`savefig.format` otherwise.
	If format is set, it determines the output format.
	arr : array-like
	The image data. Accepts NumPy arrays or sequences
	(e.g., lists or tuples). The shape can be one of
	MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA).
	vmin, vmax : float, optional
	vmin and vmax set the color scaling for the image by fixing the
	values that map to the colormap color limits. If either vmin
	or vmax is None, that limit is determined from the arr
	min/max value.
	cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`
	A Colormap instance or registered colormap name. The colormap
	maps scalar data to colors. It is ignored for RGB(A) data.
	format : str, optional
	The file format, e.g. 'png', 'pdf', 'svg', ... The behavior when this
	is unset is documented under fname.
	origin : {'upper', 'lower'}, default: :rc:`image.origin`
	Indicates whether the ``(0, 0)`` index of the array is in the upper
	left or lower left corner of the Axes.
	dpi : float
	The DPI to store in the metadata of the file. This does not affect the
	resolution of the output image. Depending on file format, this may be
	rounded to the nearest integer.
	metadata : dict, optional
	Metadata in the image file. The supported keys depend on the output
	format, see the documentation of the respective backends for more
	information.
	Currently only supported for "png", "pdf", "ps", "eps", and "svg".
	pil_kwargs : dict, optional
	Keyword arguments passed to `PIL.Image.Image.save`. If the 'pnginfo'
	key is present, it completely overrides metadata, including the
	default 'Software' key.
	"""
	from matplotlib.figure import Figure

	# Normalizing input (e.g., list or tuples) to NumPy array if needed
	arr = np.asanyarray(arr)

	if isinstance(fname, os.PathLike):
	fname = os.fspath(fname)
	if format is None:
	format = (Path(fname).suffix[1:] if isinstance(fname, str)
	else mpl.rcParams["savefig.format"]).lower()
	if format in ["pdf", "ps", "eps", "svg"]:
	# Vector formats that are not handled by PIL.
	if pil_kwargs is not None:
	raise ValueError(
	f"Cannot use 'pil_kwargs' when saving to {format}")
	fig = Figure(dpi=dpi, frameon=False)
	fig.figimage(arr, cmap=cmap, vmin=vmin, vmax=vmax, origin=origin,
	resize=True)
	fig.savefig(fname, dpi=dpi, format=format, transparent=True,
	metadata=metadata)
	else:
	# Don't bother creating an image; this avoids rounding errors on the
	# size when dividing and then multiplying by dpi.
	if origin is None:
	origin = mpl.rcParams["image.origin"]
	else:
	_api.check_in_list(('upper', 'lower'), origin=origin)
	if origin == "lower":
	arr = arr[::-1]
	if (isinstance(arr, memoryview) and arr.format == "B"
	and arr.ndim == 3 and arr.shape[-1] == 4):
	# Such an ``arr`` would also be handled fine by sm.to_rgba below
	# (after casting with asarray), but it is useful to special-case it
	# because that's what backend_agg passes, and can be in fact used
	# as is, saving a few operations.
	rgba = arr
	else:
	sm = mcolorizer.Colorizer(cmap=cmap)
	sm.set_clim(vmin, vmax)
	rgba = sm.to_rgba(arr, bytes=True)
	if pil_kwargs is None:
	pil_kwargs = {}
	else:
	# we modify this below, so make a copy (don't modify caller's dict)
	pil_kwargs = pil_kwargs.copy()
	pil_shape = (rgba.shape[1], rgba.shape[0])
	rgba = np.require(rgba, requirements='C')
	image = PIL.Image.frombuffer(
	"RGBA", pil_shape, rgba, "raw", "RGBA", 0, 1)
	if format == "png":
	# Only use the metadata kwarg if pnginfo is not set, because the
	# semantics of duplicate keys in pnginfo is unclear.
	if "pnginfo" in pil_kwargs:
	if metadata:
	_api.warn_external("'metadata' is overridden by the "
	"'pnginfo' entry in 'pil_kwargs'.")
	else:
	metadata = {
	"Software": (f"Matplotlib version{mpl.__version__}, "
	f"https://matplotlib.org/"),
	**(metadata if metadata is not None else {}),
	}
	pil_kwargs["pnginfo"] = pnginfo = PIL.PngImagePlugin.PngInfo()
	for k, v in metadata.items():
	if v is not None:
	pnginfo.add_text(k, v)
	elif metadata is not None:
	raise ValueError(f"metadata not supported for format {format!r}")
	if format in ["jpg", "jpeg"]:
	format = "jpeg" # Pillow doesn't recognize "jpg".
	facecolor = mpl.rcParams["savefig.facecolor"]
	if cbook._str_equal(facecolor, "auto"):
	facecolor = mpl.rcParams["figure.facecolor"]
	color = tuple(int(x * 255) for x in mcolors.to_rgb(facecolor))
	background = PIL.Image.new("RGB", pil_shape, color)
	background.paste(image, image)
	image = background
	pil_kwargs.setdefault("format", format)
	pil_kwargs.setdefault("dpi", (dpi, dpi))
	image.save(fname, **pil_kwargs)


	def pil_to_array(pilImage):
	"""
	Load a `PIL image`_ and return it as a numpy int array.

	.. _PIL image: https://pillow.readthedocs.io/en/latest/reference/Image.html

	Returns
	-------
	numpy.array

	The array shape depends on the image type:

	- (M, N) for grayscale images.
	- (M, N, 3) for RGB images.
	- (M, N, 4) for RGBA images.
	"""
	if pilImage.mode in ['RGBA', 'RGBX', 'RGB', 'L']:
	# return MxNx4 RGBA, MxNx3 RBA, or MxN luminance array
	return np.asarray(pilImage)
	elif pilImage.mode.startswith('I;16'):
	# return MxN luminance array of uint16
	raw = pilImage.tobytes('raw', pilImage.mode)
	if pilImage.mode.endswith('B'):
	x = np.frombuffer(raw, '>u2')
	else:
	x = np.frombuffer(raw, '<u2')
	return x.reshape(pilImage.size[::-1]).astype('=u2')
	else: # try to convert to an rgba image
	try:
	pilImage = pilImage.convert('RGBA')
	except ValueError as err:
	raise RuntimeError('Unknown image mode') from err
	return np.asarray(pilImage) # return MxNx4 RGBA array


	def _pil_png_to_float_array(pil_png):
	"""Convert a PIL `PNGImageFile` to a 0-1 float array."""
	# Unlike pil_to_array this converts to 0-1 float32s for backcompat with the
	# old libpng-based loader.
	# The supported rawmodes are from PIL.PngImagePlugin._MODES. When
	# mode == "RGB(A)", the 16-bit raw data has already been coarsened to 8-bit
	# by Pillow.
	mode = pil_png.mode
	rawmode = pil_png.png.im_rawmode
	if rawmode == "1": # Grayscale.
	return np.asarray(pil_png, np.float32)
	if rawmode == "L;2": # Grayscale.
	return np.divide(pil_png, 2**2 - 1, dtype=np.float32)
	if rawmode == "L;4": # Grayscale.
	return np.divide(pil_png, 2**4 - 1, dtype=np.float32)
	if rawmode == "L": # Grayscale.
	return np.divide(pil_png, 2**8 - 1, dtype=np.float32)
	if rawmode == "I;16B": # Grayscale.
	return np.divide(pil_png, 2**16 - 1, dtype=np.float32)
	if mode == "RGB": # RGB.
	return np.divide(pil_png, 2**8 - 1, dtype=np.float32)
	if mode == "P": # Palette.
	return np.divide(pil_png.convert("RGBA"), 2**8 - 1, dtype=np.float32)
	if mode == "LA": # Grayscale + alpha.
	return np.divide(pil_png.convert("RGBA"), 2**8 - 1, dtype=np.float32)
	if mode == "RGBA": # RGBA.
	return np.divide(pil_png, 2**8 - 1, dtype=np.float32)
	raise ValueError(f"Unknown PIL rawmode: {rawmode}")


	def thumbnail(infile, thumbfile, scale=0.1, interpolation='bilinear',
	preview=False):
	"""
	Make a thumbnail of image in infile with output filename thumbfile.

	See :doc:`/gallery/misc/image_thumbnail_sgskip`.

	Parameters
	----------
	infile : str or file-like
	The image file. Matplotlib relies on Pillow_ for image reading, and
	thus supports a wide range of file formats, including PNG, JPG, TIFF
	and others.

	.. _Pillow: https://python-pillow.github.io

	thumbfile : str or file-like
	The thumbnail filename.

	scale : float, default: 0.1
	The scale factor for the thumbnail.

	interpolation : str, default: 'bilinear'
	The interpolation scheme used in the resampling. See the
	interpolation parameter of `~.Axes.imshow` for possible values.

	preview : bool, default: False
	If True, the default backend (presumably a user interface
	backend) will be used which will cause a figure to be raised if
	`~matplotlib.pyplot.show` is called. If it is False, the figure is
	created using `.FigureCanvasBase` and the drawing backend is selected
	as `.Figure.savefig` would normally do.

	Returns
	-------
	`.Figure`
	The figure instance containing the thumbnail.
	"""

	im = imread(infile)
	rows, cols, depth = im.shape

	# This doesn't really matter (it cancels in the end) but the API needs it.
	dpi = 100

	height = rows / dpi * scale
	width = cols / dpi * scale

	if preview:
	# Let the UI backend do everything.
	import matplotlib.pyplot as plt
	fig = plt.figure(figsize=(width, height), dpi=dpi)
	else:
	from matplotlib.figure import Figure
	fig = Figure(figsize=(width, height), dpi=dpi)
	FigureCanvasBase(fig)

	ax = fig.add_axes([0, 0, 1, 1], aspect='auto',
	frameon=False, xticks=[], yticks=[])
	ax.imshow(im, aspect='auto', resample=True, interpolation=interpolation)
	fig.savefig(thumbfile, dpi=dpi)
	return fig

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