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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 3.3.3 documentation</title>

title="Search within Matplotlib 3.3.3 documentation"

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

</head><body>

<div id="unreleased-message"> You are reading an old version of the documentation (v3.3.3). For the latest version see <a href="/stable/">https://matplotlib.org/stable/</a> </div>

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<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px">Version 3.3.3</div>

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

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

"""

The image module supports basic image loading, rescaling and display

operations.

"""

import math

import os

import logging

from numbers import Number

from pathlib import Path

import numpy as np

import PIL.PngImagePlugin

import matplotlib as mpl

import matplotlib.artist as martist

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

# 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_ = {

'antialiased': _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,

}

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`

magnification : float, default: 1

The additional magnification to apply for the renderer in use.

Returns

-------

image : uint8 3d 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:

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, 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()):

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*.

"""

# 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 == 'antialiased':

# 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(martist.Artist, cm.ScalarMappable):

"""

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,

interpolation=None,

origin=None,

filternorm=True,

filterrad=4.0,

resample=False,

**kwargs

):

martist.Artist.__init__(self)

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

if origin is None:

origin = mpl.rcParams['image.origin']

cbook._check_in_list(["upper", "lower"], origin=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):

"""Return the size of the image as tuple (numrows, numcols)."""

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

"""

if alpha is not None and not isinstance(alpha, Number):

alpha = np.asarray(alpha)

if alpha.ndim != 2:

raise TypeError('alpha must be a float, two-dimensional '

'array, or None')

self._alpha = alpha

self.pchanged()

self.stale = True

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

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 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.

*A* may be a greyscale image (M, N) with a dtype of float32, float64,

float128, uint16 or uint8, or an (M, N, 4) RGBA image 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 if exact scaling is

needed, such as for `FigureImage`.

Returns

-------

image : (M, N, 4) 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 : 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:

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 (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 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

# 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:

cbook._warn_external(

f"Casting input data from '{A.dtype}' to "

f"'float64' for imshow")

scaled_dtype = np.float64

else:

# probably an integer of some type.

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

# give more breathing room if a big dynamic range

scaled_dtype = np.float64 if da > 1e8 else 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

# Always copy, and don't allow array subtypes.

A_scaled = np.array(A, dtype=scaled_dtype)

# 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:

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

# used to rescale the raw data to [offset, 1-offset]

# so that the resampling code will run cleanly. Using

# dyadic numbers here could reduce the error, but

# would not full eliminate it and breaks a number of

# tests (due to the slightly different error bouncing

# some pixels across a boundary in the (very

# quantized) color mapping step).

offset = .1

frac = .8

# we need to run the vmin/vmax through the same rescaling

# that we run the raw data through because there are small

# errors in the round-trip due to float precision. If we

# do not run the vmin/vmax through the same pipeline we can

# have values close or equal to the boundaries end up on the

# wrong side.

vmin, vmax = self.norm.vmin, self.norm.vmax

if vmin is np.ma.masked:

vmin, vmax = a_min, a_max

vrange = np.array([vmin, vmax], dtype=scaled_dtype)

A_scaled -= a_min

vrange -= 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) / frac)

vrange /= ((a_max - a_min) / frac)

A_scaled += offset

vrange += offset

# resample the input data to the correct resolution and shape

A_resampled = _resample(self, A_scaled, out_shape, t)

# 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 -= offset

vrange -= offset

if a_min != a_max:

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

vrange *= ((a_max - a_min) / frac)

A_resampled += a_min

vrange += 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.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)

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

del mask

# Agg updates out_alpha 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_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)

# we have re-set the vmin/vmax to account for small errors

# that may have moved input values in/out of range

s_vmin, s_vmax = vrange

if s_vmin < 0:

s_vmin = max(s_vmin, np.finfo(scaled_dtype).eps)

with cbook._setattr_cm(self.norm,

vmin=s_vmin,

vmax=s_vmax,

):

output = self.norm(resampled_masked)

else:

if A.shape[2] == 3:

A = _rgb_to_rgba(A)

alpha = self._get_scalar_alpha()

output_alpha = _resample( # resample alpha channel

self, A[..., 3], out_shape, t, alpha=alpha)

output = _resample( # resample rgb channels

self, _rgb_to_rgba(A[..., :3]), out_shape, t, alpha=alpha)

output[..., 3] = output_alpha # recombine rgb and alpha

# 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_scalar_alpha()

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.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):

"""

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) 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 : 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, *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_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:

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."""

inside, info = self._default_contains(mouseevent)

if inside is not None:

return inside, info

# 1) This doesn't work for figimage; but figimage also needs a fix

# below (as the check cannot use x/ydata and extents).

# 2) As long as the check below uses x/ydata, we need to test axes

# identity instead of `self.axes.contains(event)` because even if

# axes overlap, x/ydata is only valid for event.inaxes anyways.

if self.axes is not mouseevent.inaxes:

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

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 *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")

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 = 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 of dtype {} cannot be converted to "

"float".format(self._A.dtype))

if self._A.ndim == 3 and self._A.shape[-1] == 1:

# If just one dimension assume scalar and apply colormap

self._A = self._A[:, :, 0]

if not (self._A.ndim == 2

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

raise TypeError("Invalid shape {} for image data"

.format(self._A.shape))

# 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 '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 : {'antialiased', 'nearest', 'bilinear', 'bicubic', 'spline16', \

'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', \

'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos', 'none'} or None

"""

if s is None:

s = mpl.rcParams['image.interpolation']

s = s.lower()

cbook._check_in_list(_interpd_, interpolation=s)

self._interpolation = 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`.

"""

if v is None:

v = mpl.rcParams['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

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

"""

An image attached to an Axes.

Parameters

----------

ax : `~.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 : `~matplotlib.colors.Normalize`

Maps luminance to 0-1.

interpolation : str, default: :rc:`image.interpolation`

Supported values are 'none', 'antialiased', 'nearest', 'bilinear',

'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite',

'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell',

'sinc', 'lanczos'.

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 : `.Artist` properties

"""

def __str__(self):

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

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