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'}