"""

The image module supports basic image loading, rescaling and display

operations.

"""

from __future__ import division, print_function

import os, warnings

import math

import numpy as np

from numpy import ma

from matplotlib import rcParams

import matplotlib.artist as martist

from matplotlib.artist import allow_rasterization

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 BboxBase, Bbox, IdentityTransform

import matplotlib.transforms as mtransforms

class _AxesImageBase(martist.Artist, cm.ScalarMappable):

zorder = 0

# 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,

}

# reverse interp dict

_interpdr = dict([ (v,k) for k,v in _interpd.iteritems()])

interpnames = _interpd.keys()

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=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

"""

martist.Artist.__init__(self)

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

if origin is None: origin = rcParams['image.origin']

self.origin = origin

self.set_filternorm(filternorm)

self.set_filterrad(filterrad)

self._filterrad = filterrad

self.set_interpolation(interpolation)

self.set_resample(resample)

self.axes = ax

self._imcache = None

# this is an experimental attribute, if True, unsampled image

# will be drawn using the affine transform that are

# appropriately skewed so that the given position

# corresponds to the actual position in the coordinate. -JJL

self._image_skew_coordinate = None

self.update(kwargs)

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

ACCEPTS: 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, magnification=1.0):

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

def _get_unsampled_image(self, A, image_extents, viewlim):

"""

convert numpy array A with given extents ([x1, x2, y1, y2] in

data coordinate) into the Image, given the viewlim (should be a

bbox instance). Image will be clipped if the extents is

significantly larger than the viewlim.

"""

xmin, xmax, ymin, ymax = image_extents

dxintv = xmax-xmin

dyintv = ymax-ymin

# the viewport scale factor

if viewlim.width == 0.0 and dxintv == 0.0:

sx = 1.0

else:

sx = dxintv/viewlim.width

if viewlim.height == 0.0 and dyintv == 0.0:

sy = 1.0

else:

sy = dyintv/viewlim.height

numrows, numcols = A.shape[:2]

if sx > 2:

x0 = (viewlim.x0-xmin)/dxintv * numcols

ix0 = max(0, int(x0 - self._filterrad))

x1 = (viewlim.x1-xmin)/dxintv * numcols

ix1 = min(numcols, int(x1 + self._filterrad))

xslice = slice(ix0, ix1)

xmin_old = xmin

xmin = xmin_old + ix0*dxintv/numcols

xmax = xmin_old + ix1*dxintv/numcols

dxintv = xmax - xmin

sx = dxintv/viewlim.width

else:

xslice = slice(0, numcols)

if sy > 2:

y0 = (viewlim.y0-ymin)/dyintv * numrows

iy0 = max(0, int(y0 - self._filterrad))

y1 = (viewlim.y1-ymin)/dyintv * numrows

iy1 = min(numrows, int(y1 + self._filterrad))

if self.origin == 'upper':

yslice = slice(numrows-iy1, numrows-iy0)

else:

yslice = slice(iy0, iy1)

ymin_old = ymin

ymin = ymin_old + iy0*dyintv/numrows

ymax = ymin_old + iy1*dyintv/numrows

dyintv = ymax - ymin

sy = dyintv/viewlim.height

else:

yslice = slice(0, numrows)

if xslice != self._oldxslice or yslice != self._oldyslice:

self._imcache = None

self._oldxslice = xslice

self._oldyslice = yslice

if self._imcache is None:

if self._A.dtype == np.uint8 and self._A.ndim == 3:

im = _image.frombyte(self._A[yslice,xslice,:], 0)

im.is_grayscale = False

else:

if self._rgbacache is None:

x = self.to_rgba(self._A, bytes=False)

# Avoid side effects: to_rgba can return its argument

# unchanged.

if np.may_share_memory(x, self._A):

x = x.copy()

# premultiply the colors

x[...,0:3] *= x[...,3:4]

x = (x * 255).astype(np.uint8)

self._rgbacache = x

else:

x = self._rgbacache

im = _image.frombyte(x[yslice,xslice,:], 0)

if self._A.ndim == 2:

im.is_grayscale = self.cmap.is_gray()

else:

im.is_grayscale = False

self._imcache = im

if self.origin=='upper':

im.flipud_in()

else:

im = self._imcache

return im, xmin, ymin, dxintv, dyintv, sx, sy

@staticmethod

def _get_rotate_and_skew_transform(x1, y1, x2, y2, x3, y3):

"""

Retuen a transform that does

(x1, y1) -> (x1, y1)

(x2, y2) -> (x2, y2)

(x2, y1) -> (x3, y3)

It was intended to derive a skew transform that preserve the

lower-left corner (x1, y1) and top-right corner(x2,y2), but

change the the lower-right-corner(x2, y1) to a new position

(x3, y3).

"""

tr1 = mtransforms.Affine2D()

tr1.translate(-x1, -y1)

x2a, y2a = tr1.transform_point((x2, y2))

x3a, y3a = tr1.transform_point((x3, y3))

inv_mat = 1./(x2a*y3a-y2a*x3a) * np.mat([[y3a, -y2a],[-x3a, x2a]])

a, b = (inv_mat * np.mat([[x2a], [x2a]])).flat

c, d = (inv_mat * np.mat([[y2a], [0]])).flat

tr2 = mtransforms.Affine2D.from_values(a, c, b, d, 0, 0)

tr = (tr1 + tr2 + mtransforms.Affine2D().translate(x1, y1)).inverted().get_affine()

return tr

def _draw_unsampled_image(self, renderer, gc):

"""

draw unsampled image. The renderer should support a draw_image method

with scale parameter.

"""

trans = self.get_transform() #axes.transData

# convert the coordinates to the intermediate coordinate (ic).

# The transformation from the ic to the canvas is a pure

# affine transform.

# A straight-forward way is to use the non-affine part of the

# original transform for conversion to the ic.

# firs, convert the image extent to the ic

x_llc, x_trc, y_llc, y_trc = self.get_extent()

xy = trans.transform(np.array([(x_llc, y_llc),

(x_trc, y_trc)]))

_xx1, _yy1 = xy[0]

_xx2, _yy2 = xy[1]

extent_in_ic = _xx1, _xx2, _yy1, _yy2

# define trans_ic_to_canvas : unless _image_skew_coordinate is

# set, it is simply a affine part of the original transform.

if self._image_skew_coordinate:

# skew the image when required.

x_lrc, y_lrc = self._image_skew_coordinate

xy2 = trans.transform(np.array([(x_lrc, y_lrc)]))

_xx3, _yy3 = xy2[0]

tr_rotate_skew = self._get_rotate_and_skew_transform(_xx1, _yy1,

_xx2, _yy2,

_xx3, _yy3)

trans_ic_to_canvas = tr_rotate_skew

else:

trans_ic_to_canvas = IdentityTransform()

# Now, viewLim in the ic. It can be rotated and can be

# skewed. Make it big enough.

x1, y1, x2, y2 = self.axes.bbox.extents

trans_canvas_to_ic = trans_ic_to_canvas.inverted()

xy_ = trans_canvas_to_ic.transform(np.array([(x1, y1),

(x2, y1),

(x2, y2),

(x1, y2)]))

x1_, x2_ = min(xy_[:,0]), max(xy_[:,0])

y1_, y2_ = min(xy_[:,1]), max(xy_[:,1])

viewLim_in_ic = Bbox.from_extents(x1_, y1_, x2_, y2_)

# get the image, sliced if necessary. This is done in the ic.

im, xmin, ymin, dxintv, dyintv, sx, sy = \

self._get_unsampled_image(self._A, extent_in_ic, viewLim_in_ic)

if im is None: return # I'm not if this check is required. -JJL

fc = self.axes.patch.get_facecolor()

bg = mcolors.colorConverter.to_rgba(fc, 0)

im.set_bg(*bg)

# image input dimensions

im.reset_matrix()

numrows, numcols = im.get_size()

im.resize(numcols, numrows) # just to create im.bufOut that

# is required by backends. There

# may be better solution -JJL

im._url = self.get_url()

im._gid = self.get_gid()

renderer.draw_image(gc, xmin, ymin, im, dxintv, dyintv,

trans_ic_to_canvas)

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 self.get_visible(): return

if (self.axes.get_xscale() != 'linear' or

self.axes.get_yscale() != 'linear'):

warnings.warn("Images are not supported on non-linear axes.")

l, b, widthDisplay, heightDisplay = self.axes.bbox.bounds

gc = renderer.new_gc()

gc.set_clip_rectangle(self.axes.bbox.frozen())

gc.set_clip_path(self.get_clip_path())

gc.set_alpha(self.get_alpha())

if self._check_unsampled_image(renderer):

self._draw_unsampled_image(renderer, gc)

else:

if self._image_skew_coordinate is not None:

warnings.warn("Image will not be shown correctly with this backend.")

im = self.make_image(renderer.get_image_magnification())

if im is None:

return

im._url = self.get_url()

im._gid = self.get_gid()

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

gc.restore()

def contains(self, mouseevent):

"""

Test whether the mouse event occured 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

#print x, y, xmin, xmax, ymin, ymax

if x is not None and y is not None:

inside = x>=xmin and x<=xmax and y>=ymin and y<=ymax

else:

inside = False

return inside,{}

def write_png(self, fname, noscale=False):

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

im = self.make_image()

if im is None:

return

if noscale:

numrows, numcols = im.get_size()

im.reset_matrix()

im.set_interpolation(0)

im.resize(numcols, numrows)

im.flipud_out()

rows, cols, buffer = im.as_rgba_str()

_png.write_png(buffer, cols, rows, fname)

def set_data(self, A):

"""

Set the image array

ACCEPTS: numpy/PIL Image A

"""

# check if data is PIL Image without importing Image

if hasattr(A,'getpixel'):

self._A = pil_to_array(A)

else:

self._A = cbook.safe_masked_invalid(A)

if self._A.dtype != np.uint8 and not np.can_cast(self._A.dtype, np.float):

raise TypeError("Image data can not convert to float")

if (self._A.ndim not in (2, 3) or

(self._A.ndim == 3 and self._A.shape[-1] not in (3, 4))):

raise TypeError("Invalid dimensions for image data")

self._imcache =None

self._rgbacache = None

self._oldxslice = None

self._oldyslice = None

def set_array(self, A):

"""

Retained for backwards compatibility - use set_data instead

ACCEPTS: numpy array A or PIL Image"""

# 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.

ACCEPTS: ['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 self._interpd:

raise ValueError('Illegal interpolation string')

self._interpolation = s

def set_resample(self, v):

"""

Set whether or not image resampling is used

ACCEPTS: True|False

"""

if v is None: v = rcParams['image.resample']

self._resample = v

def get_resample(self):

"""Return the image resample boolean"""

return self._resample

def set_filternorm(self, filternorm):

"""

Set whether the resize filter norms the weights -- see

help for imshow

ACCEPTS: 0 or 1

"""

if filternorm:

self._filternorm = 1

else:

self._filternorm = 0

def get_filternorm(self):

"""Return the filternorm setting"""

return self._filternorm

def set_filterrad(self, filterrad):

"""

Set the resize filter radius only applicable to some

interpolation schemes -- see help for imshow

ACCEPTS: positive float

"""

r = float(filterrad)

assert(r>0)

self._filterrad = r

def get_filterrad(self):

"""return the filterrad setting"""

return self._filterrad

class AxesImage(_AxesImageBase):

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,

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

_AxesImageBase.__init__(self, ax,

cmap = cmap,

norm = norm,

interpolation=interpolation,

origin=origin,

filternorm=filternorm,

filterrad=filterrad,

resample = resample,

**kwargs

)

def make_image(self, magnification=1.0):

if self._A is None:

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

# image is created in the canvas coordinate.

x1, x2, y1, y2 = self.get_extent()

trans = self.get_transform()

xy = trans.transform(np.array([(x1, y1),

(x2, y2),

]))

_x1, _y1 = xy[0]

_x2, _y2 = xy[1]

transformed_viewLim = mtransforms.TransformedBbox(self.axes.viewLim,

trans)

im, xmin, ymin, dxintv, dyintv, sx, sy = \

self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],

transformed_viewLim)

fc = self.axes.patch.get_facecolor()

bg = mcolors.colorConverter.to_rgba(fc, 0)

im.set_bg( *bg)

# image input dimensions

im.reset_matrix()

numrows, numcols = im.get_size()

if numrows < 1 or numcols < 1: # out of range

return None

im.set_interpolation(self._interpd[self._interpolation])

im.set_resample(self._resample)

# the viewport translation

if dxintv == 0.0:

tx = 0.0

else:

tx = (xmin-transformed_viewLim.x0)/dxintv * numcols

if dyintv == 0.0:

ty = 0.0

else:

ty = (ymin-transformed_viewLim.y0)/dyintv * numrows

im.apply_translation(tx, ty)

l, b, r, t = self.axes.bbox.extents

widthDisplay = (round(r*magnification) + 0.5) - (round(l*magnification) - 0.5)

heightDisplay = (round(t*magnification) + 0.5) - (round(b*magnification) - 0.5)

# resize viewport to display

rx = widthDisplay / numcols

ry = heightDisplay / numrows

im.apply_scaling(rx*sx, ry*sy)

im.resize(int(widthDisplay+0.5), int(heightDisplay+0.5),

norm=self._filternorm, radius=self._filterrad)

return im

def _check_unsampled_image(self, renderer):

"""

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

"""

if self.get_interpolation() == "none":

if renderer.option_scale_image():

return True

else:

warnings.warn("The backend (%s) does not support interpolation='none'. The image will be interpolated with 'nearest` mode." % renderer.__class__)

return False

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 = extent

xmin, xmax, ymin, ymax = extent

corners = (xmin, ymin), (xmax, ymax)

self.axes.update_datalim(corners)

if self.axes._autoscaleXon:

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

if self.axes._autoscaleYon:

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

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()

#print 'sz', sz

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)

class NonUniformImage(AxesImage):

def __init__(self, ax, **kwargs):

"""

kwargs are identical to those for AxesImage, except

that 'interpolation' defaults to 'nearest', and 'bilinear'

is the only alternative.

"""

interp = kwargs.pop('interpolation', 'nearest')

AxesImage.__init__(self, ax,

**kwargs)

self.set_interpolation(interp)

def _check_unsampled_image(self, renderer):

"""

return False. Do not use unsampled image.

"""

return False

def make_image(self, magnification=1.0):

if self._A is None:

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

A = self._A

if len(A.shape) == 2:

if A.dtype != np.uint8:

A = self.to_rgba(A, bytes=True)

self.is_grayscale = self.cmap.is_gray()

else:

A = np.repeat(A[:,:,np.newaxis], 4, 2)

A[:,:,3] = 255

self.is_grayscale = True

else:

if A.dtype != np.uint8:

A = (255*A).astype(np.uint8)

if A.shape[2] == 3:

B = np.zeros(tuple(list(A.shape[0:2]) + [4]), np.uint8)

B[:,:,0:3] = A

B[:,:,3] = 255

A = B

self.is_grayscale = False

x0, y0, v_width, v_height = self.axes.viewLim.bounds

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 *= magnification

height *= magnification

im = _image.pcolor(self._Ax, self._Ay, A,

height, width,

(x0, x0+v_width, y0, y0+v_height),

self._interpd[self._interpolation])

fc = self.axes.patch.get_facecolor()

bg = mcolors.colorConverter.to_rgba(fc, 0)

im.set_bg(*bg)

im.is_grayscale = self.is_grayscale

return im

def set_data(self, x, y, A):

"""

Set the grid for the pixel centers, and the pixel values.

*x* and *y* are 1-D ndarrays of lengths N and M, respectively,

specifying pixel centers

*A* is an (M,N) ndarray or masked array of values to be

colormapped, or a (M,N,3) RGB array, or a (M,N,4) RGBA

array.

"""

x = np.asarray(x,np.float32)

y = np.asarray(y,np.float32)

A = cbook.safe_masked_invalid(A)

if len(x.shape) != 1 or len(y.shape) != 1\

or A.shape[0:2] != (y.shape[0], x.shape[0]):

raise TypeError("Axes don't match array shape")

if len(A.shape) not in [2, 3]:

raise TypeError("Can only plot 2D or 3D data")

if len(A.shape) == 3 and A.shape[2] not in [1, 3, 4]:

raise TypeError("3D arrays must have three (RGB) or four (RGBA) color components")

if len(A.shape) == 3 and A.shape[2] == 1:

A.shape = A.shape[0:2]

self._A = A

self._Ax = x

self._Ay = y

self._imcache = None

# I am adding this in accor with _AxesImageBase.set_data --

# examples/pylab_examples/image_nonuniform.py was breaking on

# the call to _get_unsampled_image when the oldxslice attr was

# accessed - JDH 3/3/2010

self._oldxslice = None

self._oldyslice = None

def set_array(self, *args):

raise NotImplementedError('Method not supported')

def set_interpolation(self, s):

if s != None and not s in ('nearest','bilinear'):

raise NotImplementedError('Only nearest neighbor and bilinear interpolations are supported')

AxesImage.set_interpolation(self, 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, s):

pass

def set_filterrad(self, s):

pass

def set_norm(self, norm):

if self._A is not None:

raise RuntimeError('Cannot change colors after loading data')

cm.ScalarMappable.set_norm(self, norm)

def set_cmap(self, cmap):

if self._A is not None:

raise RuntimeError('Cannot change colors after loading data')

cm.ScalarMappable.set_cmap(self, cmap)

class PcolorImage(martist.Artist, cm.ScalarMappable):

"""

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,

**kwargs

):

"""

cmap defaults to its rc setting

cmap is a colors.Colormap instance

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

Additional kwargs are matplotlib.artist properties

"""

martist.Artist.__init__(self)

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

self.axes = ax

self._rgbacache = None

# There is little point in caching the image itself because

# it needs to be remade if the bbox or viewlim change,

# so caching does help with zoom/pan/resize.

self.update(kwargs)

self.set_data(x, y, A)

def make_image(self, magnification=1.0):

if self._A is None:

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

fc = self.axes.patch.get_facecolor()

bg = mcolors.colorConverter.to_rgba(fc, 0)

bg = (np.array(bg)*255).astype(np.uint8)

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 = width * magnification

height = height * magnification

if self._rgbacache is None:

A = self.to_rgba(self._A, bytes=True)

self._rgbacache = A

if self._A.ndim == 2:

self.is_grayscale = self.cmap.is_gray()

else:

A = self._rgbacache

vl = self.axes.viewLim

im = _image.pcolor2(self._Ax, self._Ay, A,

height,

width,

(vl.x0, vl.x1, vl.y0, vl.y1),

bg)

im.is_grayscale = self.is_grayscale

return im

def changed(self):

self._rgbacache = None

cm.ScalarMappable.changed(self)

@allow_rasterization

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

if not self.get_visible():

return

im = self.make_image(renderer.get_image_magnification())

gc = renderer.new_gc()

gc.set_clip_rectangle(self.axes.bbox.frozen())

gc.set_clip_path(self.get_clip_path())

gc.set_alpha(self.get_alpha())

renderer.draw_image(gc,

round(self.axes.bbox.xmin),

round(self.axes.bbox.ymin),

im)

gc.restore()

def set_data(self, x, y, A):

A = cbook.safe_masked_invalid(A)

if x is None:

x = np.arange(0, A.shape[1]+1, dtype=np.float64)

else:

x = np.asarray(x, np.float64).ravel()

if y is None:

y = np.arange(0, A.shape[0]+1, dtype=np.float64)

else:

y = np.asarray(y, np.float64).ravel()

if A.shape[:2] != (y.size-1, x.size-1):

print(A.shape)

print(y.size)

print(x.size)

raise ValueError("Axes don't match array shape")

if A.ndim not in [2, 3]:

raise ValueError("A must be 2D or 3D")

if A.ndim == 3 and A.shape[2] == 1:

A.shape = A.shape[:2]

self.is_grayscale = False

if A.ndim == 3:

if A.shape[2] in [3, 4]:

if (A[:,:,0] == A[:,:,1]).all() and (A[:,:,0] == A[:,:,2]).all():

self.is_grayscale = True

else:

raise ValueError("3D arrays must have RGB or RGBA as last dim")

self._A = A

self._Ax = x

self._Ay = y

self._rgbacache = None

def set_array(self, *args):

raise NotImplementedError('Method not supported')

def set_alpha(self, alpha):

"""

Set the alpha value used for blending - not supported on

all backends

ACCEPTS: float

"""

martist.Artist.set_alpha(self, alpha)

self.update_dict['array'] = True

class FigureImage(martist.Artist, cm.ScalarMappable):

zorder = 0

def __init__(self, fig,

cmap = None,

norm = 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

"""

martist.Artist.__init__(self)

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

if origin is None: origin = rcParams['image.origin']

self.origin = origin

self.figure = fig

self.ox = offsetx

self.oy = offsety

self.update(kwargs)

self.magnification = 1.0

def contains(self, mouseevent):

"""Test whether the mouse event occured within the image."""

if callable(self._contains): return self._contains(self,mouseevent)

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

xdata, ydata = mouseevent.x, mouseevent.y

#print xdata, ydata, xmin, xmax, ymin, ymax

if xdata is not None and ydata is not None:

inside = xdata>=xmin and xdata<=xmax and ydata>=ymin and ydata<=ymax

else:

inside = False

return inside,{}

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 get_extent(self):

"""Get the image extent: 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 set_data(self, A):

"""Set the image array."""

cm.ScalarMappable.set_array(self, cbook.safe_masked_invalid(A))

def set_array(self, A):

"""Deprecated; use set_data for consistency with other image types."""

self.set_data(A)

def make_image(self, magnification=1.0):

if self._A is None:

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

x = self.to_rgba(self._A, bytes=True)

self.magnification = magnification

# if magnification is not one, we need to resize

ismag = magnification!=1

#if ismag: raise RuntimeError

if ismag:

isoutput = 0

else:

isoutput = 1

im = _image.frombyte(x, isoutput)

fc = self.figure.get_facecolor()

im.set_bg( *mcolors.colorConverter.to_rgba(fc, 0) )

im.is_grayscale = (self.cmap.name == "gray" and

len(self._A.shape) == 2)

if ismag:

numrows, numcols = self.get_size()

numrows *= magnification

numcols *= magnification

im.set_interpolation(_image.NEAREST)