Module: viewer.utils

skimage.viewer.utils.figimage(image[, …])	Return figure and axes with figure tightly surrounding image.
skimage.viewer.utils.init_qtapp()	Initialize QAppliction.
skimage.viewer.utils.new_plot([parent, …])	Return new figure and axes.
skimage.viewer.utils.start_qtapp([app])	Start Qt mainloop
skimage.viewer.utils.update_axes_image(…)	Update the image displayed by an image plot.
skimage.viewer.utils.ClearColormap(rgb[, …])	Color map that varies linearly from alpha = 0 to 1
skimage.viewer.utils.FigureCanvas(figure, …)	Canvas for displaying images.
skimage.viewer.utils.LinearColormap(name, …)	LinearSegmentedColormap in which color varies smoothly.
skimage.viewer.utils.RequiredAttr([init_val])	A class attribute that must be set before use.
skimage.viewer.utils.canvas
skimage.viewer.utils.core
skimage.viewer.utils.dialogs

figimage

skimage.viewer.utils.figimage(image, scale=1, dpi=None, **kwargs) [source]

Return figure and axes with figure tightly surrounding image.

Unlike pyplot.figimage, this actually plots onto an axes object, which fills the figure. Plotting the image onto an axes allows for subsequent overlays of axes artists.

Parameters

imagearray

image to plot

scalefloat

If scale is 1, the figure and axes have the same dimension as the image. Smaller values of scale will shrink the figure.

dpiint

Dots per inch for figure. If None, use the default rcParam.

init_qtapp

skimage.viewer.utils.init_qtapp() [source]

Initialize QAppliction.

The QApplication needs to be initialized before creating any QWidgets

new_plot

skimage.viewer.utils.new_plot(parent=None, subplot_kw=None, **fig_kw) [source]

Return new figure and axes.

Parameters

parentQtWidget

Qt widget that displays the plot objects. If None, you must manually call canvas.setParent and pass the parent widget.

subplot_kwdict

Keyword arguments passed matplotlib.figure.Figure.add_subplot.

fig_kwdict

Keyword arguments passed matplotlib.figure.Figure.

start_qtapp

skimage.viewer.utils.start_qtapp(app=None) [source]

Start Qt mainloop

update_axes_image

skimage.viewer.utils.update_axes_image(image_axes, image) [source]

Update the image displayed by an image plot.

This sets the image plot’s array and updates its shape appropriately

Parameters

image_axesmatplotlib.image.AxesImage

Image axes to update.

imagearray

Image array.

ClearColormap

class skimage.viewer.utils.ClearColormap(rgb, max_alpha=1, name='clear_color') [source]

Bases: skimage.viewer.utils.core.LinearColormap

Color map that varies linearly from alpha = 0 to 1

__init__(rgb, max_alpha=1, name='clear_color') [source]

Create color map from linear mapping segments

segmentdata argument is a dictionary with a red, green and blue entries. Each entry should be a list of x, y0, y1 tuples, forming rows in a table. Entries for alpha are optional.

Example: suppose you want red to increase from 0 to 1 over the bottom half, green to do the same over the middle half, and blue over the top half. Then you would use:

cdict = {'red':   [(0.0,  0.0, 0.0),
                   (0.5,  1.0, 1.0),
                   (1.0,  1.0, 1.0)],

         'green': [(0.0,  0.0, 0.0),
                   (0.25, 0.0, 0.0),
                   (0.75, 1.0, 1.0),
                   (1.0,  1.0, 1.0)],

         'blue':  [(0.0,  0.0, 0.0),
                   (0.5,  0.0, 0.0),
                   (1.0,  1.0, 1.0)]}

Each row in the table for a given color is a sequence of x, y0, y1 tuples. In each sequence, x must increase monotonically from 0 to 1. For any input value z falling between x[i] and x[i+1], the output value of a given color will be linearly interpolated between y1[i] and y0[i+1]:

row i:   x  y0  y1
               /
              /
row i+1: x  y0  y1

Hence y0 in the first row and y1 in the last row are never used.

See also

LinearSegmentedColormap.from_list

Static method; factory function for generating a smoothly-varying LinearSegmentedColormap.

makeMappingArray

For information about making a mapping array.

FigureCanvas

class skimage.viewer.utils.FigureCanvas(figure, **kwargs) [source]

Bases: object

Canvas for displaying images.

__init__(figure, **kwargs) [source]

Initialize self. See help(type(self)) for accurate signature.

resizeEvent(event) [source]

LinearColormap

class skimage.viewer.utils.LinearColormap(name, segmented_data, **kwargs) [source]

Bases: matplotlib.colors.LinearSegmentedColormap

LinearSegmentedColormap in which color varies smoothly.

This class is a simplification of LinearSegmentedColormap, which doesn’t support jumps in color intensities.

Parameters

namestr

Name of colormap.

segmented_datadict

Dictionary of ‘red’, ‘green’, ‘blue’, and (optionally) ‘alpha’ values. Each color key contains a list of x, y tuples. x must increase monotonically from 0 to 1 and corresponds to input values for a mappable object (e.g. an image). y corresponds to the color intensity.

__init__(name, segmented_data, **kwargs) [source]

Create color map from linear mapping segments

segmentdata argument is a dictionary with a red, green and blue entries. Each entry should be a list of x, y0, y1 tuples, forming rows in a table. Entries for alpha are optional.

Example: suppose you want red to increase from 0 to 1 over the bottom half, green to do the same over the middle half, and blue over the top half. Then you would use:

cdict = {'red':   [(0.0,  0.0, 0.0),
                   (0.5,  1.0, 1.0),
                   (1.0,  1.0, 1.0)],

         'green': [(0.0,  0.0, 0.0),
                   (0.25, 0.0, 0.0),
                   (0.75, 1.0, 1.0),
                   (1.0,  1.0, 1.0)],

         'blue':  [(0.0,  0.0, 0.0),
                   (0.5,  0.0, 0.0),
                   (1.0,  1.0, 1.0)]}

Each row in the table for a given color is a sequence of x, y0, y1 tuples. In each sequence, x must increase monotonically from 0 to 1. For any input value z falling between x[i] and x[i+1], the output value of a given color will be linearly interpolated between y1[i] and y0[i+1]:

row i:   x  y0  y1
               /
              /
row i+1: x  y0  y1

Hence y0 in the first row and y1 in the last row are never used.

See also

LinearSegmentedColormap.from_list

Static method; factory function for generating a smoothly-varying LinearSegmentedColormap.

makeMappingArray

For information about making a mapping array.

RequiredAttr

class skimage.viewer.utils.RequiredAttr(init_val=None) [source]

Bases: object

A class attribute that must be set before use.

__init__(init_val=None) [source]

Initialize self. See help(type(self)) for accurate signature.

instances = {(<skimage.viewer.utils.core.RequiredAttr object>, None): 'Widget is not attached to a Plugin.', (<skimage.viewer.utils.core.RequiredAttr object>, None): 'Plugin is not attached to ImageViewer'}