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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>matplotlib.image — Matplotlib 3.0.3 documentation</title>

type="text/css" />

type="text/css" />

title="Search within Matplotlib 3.0.3 documentation"

href="../../_static/opensearch.xml"/>

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<body>

<div id="unreleased-message"> You are reading an old version of the documentation (v3.0.3). For the latest version see <a href="/stable/">https://matplotlib.org/stable/</a> </div>

<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px">Version 3.0.3</div>

<div id="forkongithub"><a href="https://github.com/matplotlib/matplotlib">Fork me on GitHub</a></div>

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</div>

<h1>Source code for matplotlib.image</h1><div class="highlight"><pre>

"""

The image module supports basic image loading, rescaling and display

operations.

"""

from io import BytesIO

from math import ceil

import os

import logging

import urllib.parse

import urllib.request

import warnings

import numpy as np

from matplotlib import rcParams

import matplotlib.artist as martist

from matplotlib.artist import allow_rasterization

from matplotlib.backend_bases import FigureCanvasBase

import matplotlib.colors as mcolors

import matplotlib.cm as cm

import matplotlib.cbook as cbook

# For clarity, names from _image are given explicitly in this module:

import matplotlib._image as _image

import matplotlib._png as _png

# For user convenience, the names from _image are also imported into

# the image namespace:

from matplotlib._image import *

from matplotlib.transforms import (Affine2D, BboxBase, Bbox, BboxTransform,

IdentityTransform, TransformedBbox)

_log = logging.getLogger(__name__)

# map interpolation strings to module constants

_interpd_ = {

'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,

}

interpolations_names = set(_interpd_)

<div class="viewcode-block" id="composite_images"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.composite_images">[docs]</a>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 instance

magnification : float

The additional magnification to apply for the renderer in use.

Returns

-------

tuple : image, offset_x, offset_y

Returns the tuple:

- image: A numpy array of the same type as the input images.

- offset_x, offset_y: The offset of the image (left, bottom)

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_alpha() or 1.0))

bboxes.append(

Bbox([[x, y], [x + data.shape[1], y + data.shape[0]]]))

if len(parts) == 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</div>

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, np.round(l), np.round(b), data)

gc.restore()

del image_group[:]

for a in artists:

if isinstance(a, _ImageBase) and a.can_composite():

image_group.append(a)

else:

flush_images()

a.draw(renderer)

flush_images()

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(martist.Artist, cm.ScalarMappable):

zorder = 0

def __str__(self):

return "AxesImage(%g,%g;%gx%g)" % tuple(self.axes.bbox.bounds)

def __init__(self, ax,

cmap=None,

norm=None,

interpolation=None,

origin=None,

filternorm=True,

filterrad=4.0,

resample=False,

**kwargs

):

"""

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

"""

martist.Artist.__init__(self)

cm.ScalarMappable.__init__(self, norm, cmap)

self._mouseover = True

if origin is None:

origin = rcParams['image.origin']

self.origin = origin

self.set_filternorm(filternorm)

self.set_filterrad(filterrad)

self.set_interpolation(interpolation)

self.set_resample(resample)

self.axes = ax

self._imcache = None

self.update(kwargs)

def __getstate__(self):

state = super().__getstate__()

# We can't pickle the C Image cached object.

state['_imcache'] = None

return state

def get_size(self):

"""Get the numrows, numcols of the input image"""

if self._A is None:

raise RuntimeError('You must first set the image array')

return self._A.shape[:2]

def set_alpha(self, alpha):

"""

Set the alpha value used for blending - not supported on all backends.

Parameters

----------

alpha : float

"""

martist.Artist.set_alpha(self, alpha)

self._imcache = None

def changed(self):

"""

Call this whenever the mappable is changed so observers can

update state

"""

self._imcache = None

self._rgbacache = None

cm.ScalarMappable.changed(self)

def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,

unsampled=False, round_to_pixel_border=True):

"""

Normalize, rescale and color 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.

`A` may be a greyscale image (MxN) with a dtype of `float32`,

`float64`, `float128`, `uint16` or `uint8`, or an RGBA image (MxNx4)

with a dtype of `float32`, `float64`, `float128`, or `uint8`.

If `unsampled` is True, the image will not be scaled, but an

appropriate affine transformation will be returned instead.

If `round_to_pixel_border` is 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

in cases where you want exact scaling, however, such as

FigureImage.

Returns the resulting (image, x, y, trans), where (x, y) is

the upper left corner of the result in pixel space, and

`trans` is the affine transformation from the 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:

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, 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 just slightly to account for the

# extra subpixel.

if (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 = int(ceil(out_width_base))

out_height = int(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)

if not unsampled:

if A.ndim not in (2, 3):

raise ValueError("Invalid dimensions, got {}".format(A.shape))

if A.ndim == 2:

# 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

# need to

# TODO slice input array first

inp_dtype = A.dtype

a_min = A.min()

a_max = A.max()

# figure out the type we should scale to. For floats,

# leave as is. For integers cast to an appropriate-sized

# float. Small integers get smaller floats in an attempt

# to keep the memory footprint reasonable.

if a_min is np.ma.masked:

# all masked, so values don't matter

a_min, a_max = np.int32(0), np.int32(1)

if inp_dtype.kind == 'f':

scaled_dtype = A.dtype

# Cast to float64

if A.dtype not in (np.float32, np.float16):

if A.dtype != np.float64:

warnings.warn(

"Casting input data from '{0}' to 'float64'"

"for imshow".format(A.dtype))

scaled_dtype = np.float64

else:

# probably an integer of some type.

da = a_max.astype(np.float64) - a_min.astype(np.float64)

if da > 1e8:

# give more breathing room if a big dynamic range

scaled_dtype = np.float64

else:

scaled_dtype = np.float32

# scale the input data to [.1, .9]. The Agg

# interpolators clip to [0, 1] internally, use a

# smaller input scale to identify which of the

# interpolated points need to be should be flagged as

# over / under.

# This may introduce numeric instabilities in very broadly

# scaled data

A_scaled = np.empty(A.shape, dtype=scaled_dtype)

A_scaled[:] = A

# clip scaled data around norm if necessary.

# This is necessary for big numbers at the edge of

# float64's ability to represent changes. Applying

# a norm first would be good, but ruins the interpolation

# of over numbers.

self.norm.autoscale_None(A)

dv = (np.float64(self.norm.vmax) -

np.float64(self.norm.vmin))

vmid = self.norm.vmin + dv / 2

fact = 1e7 if scaled_dtype == np.float64 else 1e4

newmin = vmid - dv * fact

if newmin < a_min:

newmin = None

else:

a_min = np.float64(newmin)

newmax = vmid + dv * fact

if newmax > a_max:

newmax = None

else:

a_max = np.float64(newmax)

if newmax is not None or newmin is not None:

A_scaled = np.clip(A_scaled, newmin, newmax)

A_scaled -= a_min

# a_min and a_max might be ndarray subclasses so use

# item to avoid errors

a_min = a_min.astype(scaled_dtype).item()

a_max = a_max.astype(scaled_dtype).item()

if a_min != a_max:

A_scaled /= ((a_max - a_min) / 0.8)

A_scaled += 0.1

A_resampled = np.zeros((out_height, out_width),

dtype=A_scaled.dtype)

# resample the input data to the correct resolution and shape

_image.resample(A_scaled, A_resampled,

t,

_interpd_[self.get_interpolation()],

self.get_resample(), 1.0,

self.get_filternorm(),

self.get_filterrad())

# we are done with A_scaled now, remove from namespace

# to be sure!

del A_scaled

# un-scale the resampled data to approximately the

# original range things that interpolated to above /

# below the original min/max will still be above /

# below, but possibly clipped in the case of higher order

# interpolation + drastically changing data.

A_resampled -= 0.1

if a_min != a_max:

A_resampled *= ((a_max - a_min) / 0.8)

A_resampled += a_min

# if using NoNorm, cast back to the original datatype

if isinstance(self.norm, mcolors.NoNorm):

A_resampled = A_resampled.astype(A.dtype)

mask = np.empty(A.shape, dtype=np.float32)

if A.mask.shape == A.shape:

# this is the case of a nontrivial mask

mask[:] = np.where(A.mask, np.float32(np.nan),

np.float32(1))

else:

mask[:] = 1

# we always have to interpolate the mask to account for

# non-affine transformations

out_mask = np.zeros((out_height, out_width),

dtype=mask.dtype)

_image.resample(mask, out_mask,

t,

_interpd_[self.get_interpolation()],

True, 1,

self.get_filternorm(),

self.get_filterrad())

# we are done with the mask, delete from namespace to be sure!

del mask

# Agg updates the out_mask in place. If the pixel has

# no image data it will not be updated (and still be 0

# as we initialized it), if input data that would go

# into that output pixel than it will be `nan`, if all

# the input data for a pixel is good it will be 1, and

# if there is _some_ good data in that output pixel it

# will be between [0, 1] (such as a rotated image).

out_alpha = np.array(out_mask)

out_mask = np.isnan(out_mask)

out_alpha[out_mask] = 1

# mask and run through the norm

output = self.norm(np.ma.masked_array(A_resampled, out_mask))

else:

# Always convert to RGBA, even if only RGB input

if A.shape[2] == 3:

A = _rgb_to_rgba(A)

elif A.shape[2] != 4:

raise ValueError("Invalid dimensions, got %s" % (A.shape,))

output = np.zeros((out_height, out_width, 4), dtype=A.dtype)

alpha = self.get_alpha()

if alpha is None:

alpha = 1.0

_image.resample(

A, output, t, _interpd_[self.get_interpolation()],

self.get_resample(), alpha,

self.get_filternorm(), self.get_filterrad())

# at this point output is either a 2D array of normed data

# (of int or float)

# 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_alpha()

if alpha is None:

alpha = 1

alpha_channel = output[:, :, 3]

alpha_channel[:] = np.asarray(

np.asarray(alpha_channel, np.float32) * out_alpha * alpha,

np.uint8)

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.frozen().inverted()).frozen()

output = output[

int(min(subset.ymax + 1, output.shape[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):

raise RuntimeError('The make_image method must be overridden.')

def _draw_unsampled_image(self, renderer, gc):

"""

draw unsampled image. The renderer should support a draw_image method

with scale parameter.

"""

im, l, b, trans = self.make_image(renderer, unsampled=True)

if im is None:

return

trans = Affine2D().scale(im.shape[1], im.shape[0]) + trans

renderer.draw_image(gc, l, b, im, trans)

def _check_unsampled_image(self, renderer):

"""

return True if the image is better to be drawn unsampled.

The derived class needs to override it.

"""

return False

@allow_rasterization

def draw(self, renderer, *args, **kwargs):

# 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_alpha())

gc.set_url(self.get_url())

gc.set_gid(self.get_gid())

if (self._check_unsampled_image(renderer) and

self.get_transform().is_affine):

self._draw_unsampled_image(renderer, gc)

else:

renderer, renderer.get_image_magnification())

if im is not None:

gc.restore()

self.stale = False

def contains(self, mouseevent):

"""

Test whether the mouse event occurred within the image.

"""

if callable(self._contains):

return self._contains(self, mouseevent)

# 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

x, y = mouseevent.xdata, mouseevent.ydata

xmin, xmax, ymin, ymax = self.get_extent()

if xmin > xmax:

xmin, xmax = xmax, xmin

if ymin > ymax:

ymin, ymax = ymax, ymin

if x is not None and y is not None:

inside = (xmin <= x <= xmax) and (ymin <= y <= ymax)

else:

inside = False

return inside, {}

def write_png(self, fname):

"""Write the image to png file with fname"""

im = self.to_rgba(self._A[::-1] if self.origin == 'lower' else self._A,

bytes=True, norm=True)

_png.write_png(im, fname)

def set_data(self, A):

"""

Set the image array.

Note that this function does *not* update the normalization used.

Parameters

----------

A : array-like

"""

self._A = cbook.safe_masked_invalid(A, copy=True)

if (self._A.dtype != np.uint8 and

not np.can_cast(self._A.dtype, float, "same_kind")):

raise TypeError("Image data cannot be converted to float")

if not (self._A.ndim == 2

or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):

raise TypeError("Invalid dimensions for image data")

if self._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(self._A.dtype, np.integer) else 1

if self._A.min() < 0 or high < self._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).'

)

self._A = np.clip(self._A, 0, high)

# Cast unsupported integer types to uint8

if self._A.dtype != np.uint8 and np.issubdtype(self._A.dtype,

np.integer):

self._A = self._A.astype(np.uint8)

self._imcache = None

self._rgbacache = 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 '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 a value from rc setting. 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 : {'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', \

'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', \

'bessel', 'mitchell', 'sinc', 'lanczos', 'none'}

"""

if s is None:

s = rcParams['image.interpolation']

s = s.lower()

if s not in _interpd_:

raise ValueError('Illegal interpolation string')

self._interpolation = s

self.stale = True

def can_composite(self):

"""

Returns `True` if 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 or not image resampling is used.

Parameters

----------

v : bool

"""

if v is None:

v = rcParams['image.resample']

self._resample = v

self.stale = True

def get_resample(self):

"""Return the image resample boolean."""

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

<div class="viewcode-block" id="AxesImage"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.AxesImage">[docs]</a>class AxesImage(_ImageBase):

def __str__(self):

def __init__(self, ax,

cmap=None,

norm=None,

interpolation=None,

origin=None,

extent=None,

filternorm=1,

filterrad=4.0,

resample=False,

**kwargs

):

"""

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

"""

self._extent = extent

super().__init__(

ax,

cmap=cmap,

norm=norm,

interpolation=interpolation,

origin=origin,

filternorm=filternorm,

filterrad=filterrad,

resample=resample,

**kwargs

)

<div class="viewcode-block" id="AxesImage.get_window_extent"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.AxesImage.get_window_extent">[docs]</a> 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.axes.transData)</div>

<div class="viewcode-block" id="AxesImage.make_image"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.AxesImage.make_image">[docs]</a> def make_image(self, renderer, magnification=1.0, unsampled=False):

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)

return self._make_image(

self._A, bbox, transformed_bbox, self.axes.bbox, magnification,

unsampled=unsampled)</div>

"""

Return whether the image would be better drawn unsampled.

"""

return (self.get_interpolation() == "none"

and renderer.option_scale_image())

<div class="viewcode-block" id="AxesImage.set_extent"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.AxesImage.set_extent">[docs]</a> def set_extent(self, extent):

"""

extent is data axes (left, right, bottom, top) for making image plots

This updates ax.dataLim, and, if autoscaling, sets viewLim

to tightly fit the image, regardless of dataLim. Autoscaling

state is not changed, so following this with ax.autoscale_view

will redo the autoscaling in accord with dataLim.

"""

self._extent = xmin, xmax, ymin, ymax = 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._autoscaleXon:

self.axes.set_xlim((xmin, xmax), auto=None)

if self.axes._autoscaleYon:

self.axes.set_ylim((ymin, ymax), auto=None)

self.stale = True</div>

<div class="viewcode-block" id="AxesImage.get_extent"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.AxesImage.get_extent">[docs]</a> def get_extent(self):

"""Get the image extent: 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)</div>

<div class="viewcode-block" id="AxesImage.get_cursor_data"><a class="viewcode-back" href="../../api/image_api.html#matplotlib.image.AxesImage.get_cursor_data">[docs]</a> def get_cursor_data(self, event):

"""Get the cursor data for a given event"""

if self.origin == 'upper':

ymin, ymax = ymax, ymin

arr = self.get_array()

data_extent = Bbox([[ymin, xmin], [ymax, xmax]])

array_extent = Bbox([[0, 0], arr.shape[:2]])

trans = BboxTransform(boxin=data_extent, boxout=array_extent)

y, x = event.ydata, event.xdata

point = trans.transform_point([y, x])

if any(np.isnan(point)):

return None

i, j = 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:

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