neulab/docprompting-conala huggingface dataset:
__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.
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.
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]
__init__(figure, **kwargs) [source]
 
Initialize self. See help(type(self)) for accurate signature.
skimage.viewer.utils.init_qtapp() [source]
 
Initialize QAppliction. The QApplication needs to be initialized before creating any QWidgets
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.
__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.
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.
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'}
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'}
__init__(init_val=None) [source]
 
Initialize self. See help(type(self)) for accurate signature.
skimage.viewer.utils.start_qtapp(app=None) [source]
 
Start Qt mainloop
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.
class skimage.viewer.viewers.CollectionViewer(image_collection, update_on='move', **kwargs) [source]
 
Bases: skimage.viewer.viewers.core.ImageViewer Viewer for displaying image collections. Select the displayed frame of the image collection using the slider or with the following keyboard shortcuts:  left/right arrows

Previous/next image in collection.  number keys, 0–9

0% to 90% of collection. For example, “5” goes to the image in the middle (i.e. 50%) of the collection.  home/end keys

First/last image in collection.    Parameters 
 
image_collectionlist of images 

List of images to be displayed.  
update_on{‘move’ | ‘release’} 

Control whether image is updated on slide or release of the image slider. Using ‘on_release’ will give smoother behavior when displaying large images or when writing a plugin/subclass that requires heavy computation.      
__init__(image_collection, update_on='move', **kwargs) [source]
 
Initialize self. See help(type(self)) for accurate signature. 
  
keyPressEvent(event) [source]

  
update_index(name, index) [source]
 
Select image on display using index into image collection.
class skimage.viewer.viewers.ImageViewer(image, useblit=True) [source]
 
Bases: object Viewer for displaying images. This viewer is a simple container object that holds a Matplotlib axes for showing images. ImageViewer doesn’t subclass the Matplotlib axes (or figure) because of the high probability of name collisions. Subclasses and plugins will likely extend the update_image method to add custom overlays or filter the displayed image.  Parameters 
 
imagearray 

Image being viewed.     Examples >>> from skimage import data
>>> image = data.coins()
>>> viewer = ImageViewer(image) 
>>> viewer.show()               
  Attributes 
 
canvas, fig, axMatplotlib canvas, figure, and axes 

Matplotlib canvas, figure, and axes used to display image.  
imagearray 

Image being viewed. Setting this value will update the displayed frame.  
original_imagearray 

Plugins typically operate on (but don’t change) the original image.  
pluginslist 

List of attached plugins.      
__init__(image, useblit=True) [source]
 
Initialize self. See help(type(self)) for accurate signature. 
  
add_tool(tool) [source]

  
closeEvent(event) [source]

  
connect_event(event, callback) [source]
 
Connect callback function to matplotlib event and return id. 
  
disconnect_event(callback_id) [source]
 
Disconnect callback by its id (returned by connect_event). 
  
dock_areas = {'bottom': None, 'left': None, 'right': None, 'top': None} 
  
property image 
  
open_file(filename=None) [source]
 
Open image file and display in viewer. 
  
original_image_changed = None 
  
redraw() [source]

  
remove_tool(tool) [source]

  
reset_image() [source]

  
save_to_file(filename=None) [source]
 
Save current image to file. The current behavior is not ideal: It saves the image displayed on screen, so all images will be converted to RGB, and the image size is not preserved (resizing the viewer window will alter the size of the saved image). 
  
show(main_window=True) [source]
 
Show ImageViewer and attached plugins. This behaves much like matplotlib.pyplot.show and QWidget.show. 
  
update_image(image) [source]
 
Update displayed image. This method can be overridden or extended in subclasses and plugins to react to image changes.
class skimage.viewer.widgets.BaseWidget(name, ptype=None, callback=None) [source]
 
Bases: object  
__init__(name, ptype=None, callback=None) [source]
 
Initialize self. See help(type(self)) for accurate signature. 
  
plugin = 'Widget is not attached to a Plugin.' 
  
property val
__init__(name, ptype=None, callback=None) [source]
 
Initialize self. See help(type(self)) for accurate signature.
class skimage.viewer.widgets.Button(name, callback) [source]
 
Bases: skimage.viewer.widgets.core.BaseWidget Button which calls callback upon click.  Parameters 
 
namestr 

Name of button.  
callbackcallable f() 

Function to call when button is clicked.      
__init__(name, callback) [source]
 
Initialize self. See help(type(self)) for accurate signature.
__init__(name, callback) [source]
 
Initialize self. See help(type(self)) for accurate signature.
class skimage.viewer.widgets.CheckBox(name, value=False, alignment='center', ptype='kwarg', callback=None) [source]
 
Bases: skimage.viewer.widgets.core.BaseWidget CheckBox widget  Parameters 
 
namestr 

Name of CheckBox parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addition, this name is displayed as the name of the CheckBox.  value: {False, True}, optional

Initial state of the CheckBox.  alignment: {‘center’,’left’,’right’}, optional

Checkbox alignment  
ptype{‘arg’ | ‘kwarg’ | ‘plugin’}, optional 

Parameter type  
callbackcallable f(widget_name, value), optional 

Callback function called in response to checkbox changes. Note: This function is typically set (overridden) when the widget is added to a plugin.      
__init__(name, value=False, alignment='center', ptype='kwarg', callback=None) [source]
 
Initialize self. See help(type(self)) for accurate signature. 
  
property val
__init__(name, value=False, alignment='center', ptype='kwarg', callback=None) [source]
 
Initialize self. See help(type(self)) for accurate signature.
class skimage.viewer.widgets.ComboBox(name, items, ptype='kwarg', callback=None) [source]
 
Bases: skimage.viewer.widgets.core.BaseWidget ComboBox widget for selecting among a list of choices.  Parameters 
 
namestr 

Name of ComboBox parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addition, this name is displayed as the name of the ComboBox.  items: list of str

Allowed parameter values.  
ptype{‘arg’ | ‘kwarg’ | ‘plugin’}, optional 

Parameter type.  
callbackcallable f(widget_name, value), optional 

Callback function called in response to combobox changes. Note: This function is typically set (overridden) when the widget is added to a plugin.      
__init__(name, items, ptype='kwarg', callback=None) [source]
 
Initialize self. See help(type(self)) for accurate signature. 
  
property index 
  
property val
__init__(name, items, ptype='kwarg', callback=None) [source]
 
Initialize self. See help(type(self)) for accurate signature.
class skimage.viewer.widgets.OKCancelButtons(button_width=80) [source]
 
Bases: skimage.viewer.widgets.core.BaseWidget Buttons that close the parent plugin. OK will replace the original image with the current (filtered) image. Cancel will just close the plugin.  
__init__(button_width=80) [source]
 
Initialize self. See help(type(self)) for accurate signature. 
  
close_plugin() [source]

  
update_original_image() [source]
__init__(button_width=80) [source]
 
Initialize self. See help(type(self)) for accurate signature.
class skimage.viewer.widgets.SaveButtons(name='Save to:', default_format='png') [source]
 
Bases: skimage.viewer.widgets.core.BaseWidget Buttons to save image to io.stack or to a file.  
__init__(name='Save to:', default_format='png') [source]
 
Initialize self. See help(type(self)) for accurate signature. 
  
save_to_file(filename=None) [source]

  
save_to_stack() [source]
__init__(name='Save to:', default_format='png') [source]
 
Initialize self. See help(type(self)) for accurate signature.
class skimage.viewer.widgets.Slider(name, low=0.0, high=1.0, value=None, value_type='float', ptype='kwarg', callback=None, max_edit_width=60, orientation='horizontal', update_on='release') [source]
 
Bases: skimage.viewer.widgets.core.BaseWidget Slider widget for adjusting numeric parameters.  Parameters 
 
namestr 

Name of slider parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addition, this name is displayed as the name of the slider.  
low, highfloat 

Range of slider values.  
valuefloat 

Default slider value. If None, use midpoint between low and high.  
value_type{‘float’ | ‘int’}, optional 

Numeric type of slider value.  
ptype{‘kwarg’ | ‘arg’ | ‘plugin’}, optional 

Parameter type.  
callbackcallable f(widget_name, value), optional 

Callback function called in response to slider changes. Note: This function is typically set (overridden) when the widget is added to a plugin.  
orientation{‘horizontal’ | ‘vertical’}, optional 

Slider orientation.  
update_on{‘release’ | ‘move’}, optional 

Control when callback function is called: on slider move or release.      
__init__(name, low=0.0, high=1.0, value=None, value_type='float', ptype='kwarg', callback=None, max_edit_width=60, orientation='horizontal', update_on='release') [source]
 
Initialize self. See help(type(self)) for accurate signature. 
  
property val
__init__(name, low=0.0, high=1.0, value=None, value_type='float', ptype='kwarg', callback=None, max_edit_width=60, orientation='horizontal', update_on='release') [source]
 
Initialize self. See help(type(self)) for accurate signature.
class skimage.viewer.widgets.Text(name=None, text='') [source]
 
Bases: skimage.viewer.widgets.core.BaseWidget  
__init__(name=None, text='') [source]
 
Initialize self. See help(type(self)) for accurate signature. 
  
property text
__init__(name=None, text='') [source]
 
Initialize self. See help(type(self)) for accurate signature.
class abc.ABC  
A helper class that has ABCMeta as its metaclass. With this class, an abstract base class can be created by simply deriving from ABC avoiding sometimes confusing metaclass usage, for example: from abc import ABC

class MyABC(ABC):
    pass
 Note that the type of ABC is still ABCMeta, therefore inheriting from ABC requires the usual precautions regarding metaclass usage, as multiple inheritance may lead to metaclass conflicts. One may also define an abstract base class by passing the metaclass keyword and using ABCMeta directly, for example: from abc import ABCMeta

class MyABC(metaclass=ABCMeta):
    pass
  New in version 3.4.
register(subclass)  
Register subclass as a “virtual subclass” of this ABC. For example: from abc import ABC

class MyABC(ABC):
    pass

MyABC.register(tuple)

assert issubclass(tuple, MyABC)
assert isinstance((), MyABC)
  Changed in version 3.3: Returns the registered subclass, to allow usage as a class decorator.   Changed in version 3.4: To detect calls to register(), you can use the get_cache_token() function.
__subclasshook__(subclass)  
(Must be defined as a class method.) Check whether subclass is considered a subclass of this ABC. This means that you can customize the behavior of issubclass further without the need to call register() on every class you want to consider a subclass of the ABC. (This class method is called from the __subclasscheck__() method of the ABC.) This method should return True, False or NotImplemented. If it returns True, the subclass is considered a subclass of this ABC. If it returns False, the subclass is not considered a subclass of this ABC, even if it would normally be one. If it returns NotImplemented, the subclass check is continued with the usual mechanism.
@abc.abstractclassmethod  
 New in version 3.2.   Deprecated since version 3.3: It is now possible to use classmethod with abstractmethod(), making this decorator redundant.  A subclass of the built-in classmethod(), indicating an abstract classmethod. Otherwise it is similar to abstractmethod(). This special case is deprecated, as the classmethod() decorator is now correctly identified as abstract when applied to an abstract method: class C(ABC):
    @classmethod
    @abstractmethod
    def my_abstract_classmethod(cls, ...):
        ...
@abc.abstractmethod  
A decorator indicating abstract methods. Using this decorator requires that the class’s metaclass is ABCMeta or is derived from it. A class that has a metaclass derived from ABCMeta cannot be instantiated unless all of its abstract methods and properties are overridden. The abstract methods can be called using any of the normal ‘super’ call mechanisms. abstractmethod() may be used to declare abstract methods for properties and descriptors. Dynamically adding abstract methods to a class, or attempting to modify the abstraction status of a method or class once it is created, are not supported. The abstractmethod() only affects subclasses derived using regular inheritance; “virtual subclasses” registered with the ABC’s register() method are not affected. When abstractmethod() is applied in combination with other method descriptors, it should be applied as the innermost decorator, as shown in the following usage examples: class C(ABC):
    @abstractmethod
    def my_abstract_method(self, ...):
        ...
    @classmethod
    @abstractmethod
    def my_abstract_classmethod(cls, ...):
        ...
    @staticmethod
    @abstractmethod
    def my_abstract_staticmethod(...):
        ...

    @property
    @abstractmethod
    def my_abstract_property(self):
        ...
    @my_abstract_property.setter
    @abstractmethod
    def my_abstract_property(self, val):
        ...

    @abstractmethod
    def _get_x(self):
        ...
    @abstractmethod
    def _set_x(self, val):
        ...
    x = property(_get_x, _set_x)
 In order to correctly interoperate with the abstract base class machinery, the descriptor must identify itself as abstract using __isabstractmethod__. In general, this attribute should be True if any of the methods used to compose the descriptor are abstract. For example, Python’s built-in property does the equivalent of: class Descriptor:
    ...
    @property
    def __isabstractmethod__(self):
        return any(getattr(f, '__isabstractmethod__', False) for
                   f in (self._fget, self._fset, self._fdel))
  Note Unlike Java abstract methods, these abstract methods may have an implementation. This implementation can be called via the super() mechanism from the class that overrides it. This could be useful as an end-point for a super-call in a framework that uses cooperative multiple-inheritance.
@abc.abstractproperty  
 Deprecated since version 3.3: It is now possible to use property, property.getter(), property.setter() and property.deleter() with abstractmethod(), making this decorator redundant.  A subclass of the built-in property(), indicating an abstract property. This special case is deprecated, as the property() decorator is now correctly identified as abstract when applied to an abstract method: class C(ABC):
    @property
    @abstractmethod
    def my_abstract_property(self):
        ...
 The above example defines a read-only property; you can also define a read-write abstract property by appropriately marking one or more of the underlying methods as abstract: class C(ABC):
    @property
    def x(self):
        ...

    @x.setter
    @abstractmethod
    def x(self, val):
        ...
 If only some components are abstract, only those components need to be updated to create a concrete property in a subclass: class D(C):
    @C.x.setter
    def x(self, val):
        ...
@abc.abstractstaticmethod  
 New in version 3.2.   Deprecated since version 3.3: It is now possible to use staticmethod with abstractmethod(), making this decorator redundant.  A subclass of the built-in staticmethod(), indicating an abstract staticmethod. Otherwise it is similar to abstractmethod(). This special case is deprecated, as the staticmethod() decorator is now correctly identified as abstract when applied to an abstract method: class C(ABC):
    @staticmethod
    @abstractmethod
    def my_abstract_staticmethod(...):
        ...
abc.get_cache_token()  
Returns the current abstract base class cache token. The token is an opaque object (that supports equality testing) identifying the current version of the abstract base class cache for virtual subclasses. The token changes with every call to ABCMeta.register() on any ABC.  New in version 3.4.
abs(x)  
Return the absolute value of a number. The argument may be an integer, a floating point number, or an object implementing __abs__(). If the argument is a complex number, its magnitude is returned.
aifc.aifc()  
Create an AIFF-C file. The default is that an AIFF-C file is created, unless the name of the file ends in '.aiff' in which case the default is an AIFF file.
aifc.aiff()  
Create an AIFF file. The default is that an AIFF-C file is created, unless the name of the file ends in '.aiff' in which case the default is an AIFF file.
aifc.close()  
Close the AIFF file. After calling this method, the object can no longer be used.
aifc.getcompname()  
Return a bytes array convertible to a human-readable description of the type of compression used in the audio file. For AIFF files, the returned value is b'not compressed'.