neulab/docprompting-conala huggingface dataset:
set(*, UVC=<UNSET>, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, antialiased=<UNSET>, array=<UNSET>, capstyle=<UNSET>, clim=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, cmap=<UNSET>, color=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, gid=<UNSET>, hatch=<UNSET>, in_layout=<UNSET>, joinstyle=<UNSET>, label=<UNSET>, linestyle=<UNSET>, linewidth=<UNSET>, norm=<UNSET>, offset_transform=<UNSET>, offsets=<UNSET>, path_effects=<UNSET>, paths=<UNSET>, picker=<UNSET>, pickradius=<UNSET>, rasterized=<UNSET>, sizes=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, transform=<UNSET>, url=<UNSET>, urls=<UNSET>, verts=<UNSET>, verts_and_codes=<UNSET>, visible=<UNSET>, zorder=<UNSET>)[source]
 
Set multiple properties at once. Supported properties are   
Property Description   
UVC unknown  
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array  
alpha array-like or scalar or None  
animated bool  
antialiased or aa or antialiaseds bool or list of bools  
array array-like or None  
capstyle CapStyle or {'butt', 'projecting', 'round'}  
clim (vmin: float, vmax: float)  
clip_box Bbox  
clip_on bool  
clip_path Patch or (Path, Transform) or None  
cmap Colormap or str or None  
color color or list of rgba tuples  
edgecolor or ec or edgecolors color or list of colors or 'face'  
facecolor or facecolors or fc color or list of colors  
figure Figure  
gid str  
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}  
in_layout bool  
joinstyle JoinStyle or {'miter', 'round', 'bevel'}  
label object  
linestyle or dashes or linestyles or ls str or tuple or list thereof  
linewidth or linewidths or lw float or list of floats  
norm Normalize or None  
offset_transform Transform  
offsets sequence of pairs of floats  
path_effects AbstractPathEffect  
paths list of array-like  
picker None or bool or float or callable  
pickradius float  
rasterized bool  
sizes ndarray or None  
sketch_params (scale: float, length: float, randomness: float)  
snap bool or None  
transform Transform  
url str  
urls list of str or None  
verts list of array-like  
verts_and_codes unknown  
visible bool  
zorder float
set_offsets(xy)[source]
 
Set the offsets for the barb polygons. This saves the offsets passed in and masks them as appropriate for the existing U/V data.  Parameters 
 
xysequence of pairs of floats
set(*, UVC=<UNSET>, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, antialiased=<UNSET>, array=<UNSET>, capstyle=<UNSET>, clim=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, cmap=<UNSET>, color=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, gid=<UNSET>, hatch=<UNSET>, in_layout=<UNSET>, joinstyle=<UNSET>, label=<UNSET>, linestyle=<UNSET>, linewidth=<UNSET>, norm=<UNSET>, offset_transform=<UNSET>, offsets=<UNSET>, path_effects=<UNSET>, paths=<UNSET>, picker=<UNSET>, pickradius=<UNSET>, rasterized=<UNSET>, sizes=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, transform=<UNSET>, url=<UNSET>, urls=<UNSET>, verts=<UNSET>, verts_and_codes=<UNSET>, visible=<UNSET>, zorder=<UNSET>)[source]
 
Set multiple properties at once. Supported properties are   
Property Description   
UVC unknown  
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array  
alpha array-like or scalar or None  
animated bool  
antialiased or aa or antialiaseds bool or list of bools  
array array-like or None  
capstyle CapStyle or {'butt', 'projecting', 'round'}  
clim (vmin: float, vmax: float)  
clip_box Bbox  
clip_on bool  
clip_path Patch or (Path, Transform) or None  
cmap Colormap or str or None  
color color or list of rgba tuples  
edgecolor or ec or edgecolors color or list of colors or 'face'  
facecolor or facecolors or fc color or list of colors  
figure Figure  
gid str  
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}  
in_layout bool  
joinstyle JoinStyle or {'miter', 'round', 'bevel'}  
label object  
linestyle or dashes or linestyles or ls str or tuple or list thereof  
linewidth or linewidths or lw float or list of floats  
norm Normalize or None  
offset_transform Transform  
offsets (N, 2) or (2,) array-like  
path_effects AbstractPathEffect  
paths list of array-like  
picker None or bool or float or callable  
pickradius float  
rasterized bool  
sizes ndarray or None  
sketch_params (scale: float, length: float, randomness: float)  
snap bool or None  
transform Transform  
url str  
urls list of str or None  
verts list of array-like  
verts_and_codes unknown  
visible bool  
zorder float
halign={'E': 'left', 'N': 'center', 'S': 'center', 'W': 'right'}
pivot={'E': 'tip', 'N': 'middle', 'S': 'middle', 'W': 'tail'}
set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, gid=<UNSET>, in_layout=<UNSET>, label=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, rasterized=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, zorder=<UNSET>)[source]
 
Set multiple properties at once. Supported properties are   
Property Description   
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array  
alpha scalar or None  
animated bool  
clip_box Bbox  
clip_on bool  
clip_path Patch or (Path, Transform) or None  
figure unknown  
gid str  
in_layout bool  
label object  
path_effects AbstractPathEffect  
picker None or bool or float or callable  
rasterized bool  
sketch_params (scale: float, length: float, randomness: float)  
snap bool or None  
transform Transform  
url str  
visible bool  
zorder float
valign={'E': 'center', 'N': 'bottom', 'S': 'top', 'W': 'center'}
matplotlib.rc(group, **kwargs)[source]
 
Set the current rcParams. group is the grouping for the rc, e.g., for lines.linewidth the group is lines, for axes.facecolor, the group is axes, and so on. Group may also be a list or tuple of group names, e.g., (xtick, ytick). kwargs is a dictionary attribute name/value pairs, e.g.,: rc('lines', linewidth=2, color='r')
 sets the current rcParams and is equivalent to: rcParams['lines.linewidth'] = 2
rcParams['lines.color'] = 'r'
 The following aliases are available to save typing for interactive users:   
Alias Property   
'lw' 'linewidth'  
'ls' 'linestyle'  
'c' 'color'  
'fc' 'facecolor'  
'ec' 'edgecolor'  
'mew' 'markeredgewidth'  
'aa' 'antialiased'   Thus you could abbreviate the above call as: rc('lines', lw=2, c='r')
 Note you can use python's kwargs dictionary facility to store dictionaries of default parameters. e.g., you can customize the font rc as follows: font = {'family' : 'monospace',
        'weight' : 'bold',
        'size'   : 'larger'}
rc('font', **font)  # pass in the font dict as kwargs
 This enables you to easily switch between several configurations. Use matplotlib.style.use('default') or rcdefaults() to restore the default rcParams after changes. Notes Similar functionality is available by using the normal dict interface, i.e. rcParams.update({"lines.linewidth": 2, ...}) (but rcParams.update does not support abbreviations or grouping).
matplotlib.rc_context(rc=None, fname=None)
 
Return a context manager for temporarily changing rcParams.  Parameters 
 
rcdict


The rcParams to temporarily set.  
fnamestr or path-like


A file with Matplotlib rc settings. If both fname and rc are given, settings from rc take precedence.      See also  The matplotlibrc file
  Examples Passing explicit values via a dict: with mpl.rc_context({'interactive': False}):
    fig, ax = plt.subplots()
    ax.plot(range(3), range(3))
    fig.savefig('example.png')
    plt.close(fig)
 Loading settings from a file: with mpl.rc_context(fname='print.rc'):
    plt.plot(x, y)  # uses 'print.rc'
matplotlib.rc_file(fname, *, use_default_template=True)[source]
 
Update rcParams from file. Style-blacklisted rcParams (defined in matplotlib.style.core.STYLE_BLACKLIST) are not updated.  Parameters 
 
fnamestr or path-like


A file with Matplotlib rc settings.  
use_default_templatebool


If True, initialize with default parameters before updating with those in the given file. If False, the current configuration persists and only the parameters specified in the file are updated.
matplotlib.rc_file_defaults()[source]
 
Restore the rcParams from the original rc file loaded by Matplotlib. Style-blacklisted rcParams (defined in matplotlib.style.core.STYLE_BLACKLIST) are not updated.
matplotlib.rc_params(fail_on_error=False)[source]
 
Construct a RcParams instance from the default Matplotlib rc file.
matplotlib.rc_params_from_file(fname, fail_on_error=False, use_default_template=True)[source]
 
Construct a RcParams from file fname.  Parameters 
 
fnamestr or path-like


A file with Matplotlib rc settings.  
fail_on_errorbool


If True, raise an error when the parser fails to convert a parameter.  
use_default_templatebool


If True, initialize with default parameters before updating with those in the given file. If False, the configuration class only contains the parameters specified in the file. (Useful for updating dicts.)
matplotlib.rcdefaults()[source]
 
Restore the rcParams from Matplotlib's internal default style. Style-blacklisted rcParams (defined in matplotlib.style.core.STYLE_BLACKLIST) are not updated.  See also  matplotlib.rc_file_defaults

Restore the rcParams from the rc file originally loaded by Matplotlib.  matplotlib.style.use

Use a specific style file. Call style.use('default') to restore the default style.
matplotlib.rcParams[source]
 
An instance of RcParams for handling default Matplotlib values.
find_all(pattern)[source]
 
Return the subset of this RcParams dictionary whose keys match, using re.search(), the given pattern.  Note Changes to the returned dictionary are not propagated to the parent RcParams dictionary.
matplotlib.rcsetup.cycler(*args, **kwargs)[source]
 
Create a Cycler object much like cycler.cycler(), but includes input validation. Call signatures: cycler(cycler)
cycler(label=values[, label2=values2[, ...]])
cycler(label, values)
 Form 1 copies a given Cycler object. Form 2 creates a Cycler which cycles over one or more properties simultaneously. If multiple properties are given, their value lists must have the same length. Form 3 creates a Cycler for a single property. This form exists for compatibility with the original cycler. Its use is discouraged in favor of the kwarg form, i.e. cycler(label=values).  Parameters 
 
cyclerCycler


Copy constructor for Cycler.  
labelstr


The property key. Must be a valid Artist property. For example, 'color' or 'linestyle'. Aliases are allowed, such as 'c' for 'color' and 'lw' for 'linewidth'.  
valuesiterable


Finite-length iterable of the property values. These values are validated and will raise a ValueError if invalid.    Returns 
 Cycler

A new Cycler for the given properties.     Examples Creating a cycler for a single property: >>> c = cycler(color=['red', 'green', 'blue'])
 Creating a cycler for simultaneously cycling over multiple properties (e.g. red circle, green plus, blue cross): >>> c = cycler(color=['red', 'green', 'blue'],
...            marker=['o', '+', 'x'])
matplotlib.rcsetup.validate_bool(b)[source]
 
Convert b to bool or raise.
matplotlib.rcsetup.validate_color(s)[source]
 
Return a valid color arg.
matplotlib.rcsetup.validate_color_or_inherit(s)[source]
 
Return a valid color arg.
matplotlib.rcsetup.validate_colorlist(s)[source]
 
return a list of colorspecs
matplotlib.rcsetup.validate_cycler(s)[source]
 
Return a Cycler object from a string repr or the object itself.
matplotlib.rcsetup.validate_dashlist(s)[source]
 
return a list of floats
matplotlib.rcsetup.validate_dpi(s)[source]
 
Confirm s is string 'figure' or convert s to float or raise.
matplotlib.rcsetup.validate_floatlist(s)[source]
 
return a list of floats
matplotlib.rcsetup.validate_fonttype(s)[source]
 
Confirm that this is a Postscript or PDF font type that we know how to convert to.
matplotlib.rcsetup.validate_hatch(s)[source]
 
Validate a hatch pattern. A hatch pattern string can have any sequence of the following characters: \ / | - + * . x o O.
matplotlib.rcsetup.validate_hatchlist(s)[source]
 
Validate a hatch pattern. A hatch pattern string can have any sequence of the following characters: \ / | - + * . x o O.
matplotlib.rcsetup.validate_markevery(s)[source]
 
Validate the markevery property of a Line2D object.  Parameters 
 
sNone, int, (int, int), slice, float, (float, float), or list[int]

  Returns 
 None, int, (int, int), slice, float, (float, float), or list[int]
matplotlib.rcsetup.validate_markeverylist(s)[source]
 
Validate the markevery property of a Line2D object.  Parameters 
 
sNone, int, (int, int), slice, float, (float, float), or list[int]

  Returns 
 None, int, (int, int), slice, float, (float, float), or list[int]
matplotlib.rcsetup.validate_stringlist(s)[source]
 
return a list of strings
classmatplotlib.rcsetup.ValidateInStrings(key, valid, ignorecase=False, *, _deprecated_since=None)[source]
 
Bases: object valid is a list of legal strings.
finish()[source]
 
Adjust the axes and return a list of information about the Sankey subdiagram(s). Return value is a list of subdiagrams represented with the following fields:   
Field Description   
patch Sankey outline (an instance of PathPatch)  
flows values of the flows (positive for input, negative for output)  
angles list of angles of the arrows [deg/90] For example, if the diagram has not been rotated, an input to the top side will have an angle of 3 (DOWN), and an output from the top side will have an angle of 1 (UP). If a flow has been skipped (because its magnitude is less than tolerance), then its angle will be None.  
tips array in which each row is an [x, y] pair indicating the positions of the tips (or "dips") of the flow paths If the magnitude of a flow is less the tolerance for the instance of Sankey, the flow is skipped and its tip will be at the center of the diagram.  
text Text instance for the label of the diagram  
texts list of Text instances for the labels of flows    See also  Sankey.add
classmatplotlib.scale.FuncScale(axis, functions)[source]
 
Bases: matplotlib.scale.ScaleBase Provide an arbitrary scale with user-supplied function for the axis.  Parameters 
 
axisAxis


The axis for the scale.  
functions(callable, callable)


two-tuple of the forward and inverse functions for the scale. The forward function must be monotonic. Both functions must have the signature: def forward(values: array-like) -> array-like
       get_transform()[source]
 
Return the FuncTransform associated with this scale. 
   name='function'

   set_default_locators_and_formatters(axis)[source]
 
Set the locators and formatters of axis to instances suitable for this scale.
set_default_locators_and_formatters(axis)[source]
 
Set the locators and formatters of axis to instances suitable for this scale.
classmatplotlib.scale.FuncScaleLog(axis, functions, base=10)[source]
 
Bases: matplotlib.scale.LogScale Provide an arbitrary scale with user-supplied function for the axis and then put on a logarithmic axes.  Parameters 
 
axismatplotlib.axis.Axis


The axis for the scale.  
functions(callable, callable)


two-tuple of the forward and inverse functions for the scale. The forward function must be monotonic. Both functions must have the signature: def forward(values: array-like) -> array-like
  
basefloat, default: 10


Logarithmic base of the scale.       propertybase

   get_transform()[source]
 
Return the Transform associated with this scale. 
   name='functionlog'
classmatplotlib.scale.FuncTransform(forward, inverse)[source]
 
Bases: matplotlib.transforms.Transform A simple transform that takes and arbitrary function for the forward and inverse transform.  Parameters 
 
forwardcallable


The forward function for the transform. This function must have an inverse and, for best behavior, be monotonic. It must have the signature: def forward(values: array-like) -> array-like
  
inversecallable


The inverse of the forward function. Signature as forward.       has_inverse=True
 
True if this transform has a corresponding inverse transform. 
   input_dims=1
 
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. 
   inverted()[source]
 
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. 
   is_separable=True
 
True if this transform is separable in the x- and y- dimensions. 
   output_dims=1
 
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. 
   transform_non_affine(values)[source]
 
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.  Parameters 
 
valuesarray


The input values as NumPy array of length input_dims or shape (N x input_dims).    Returns 
 array

The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
input_dims=1
 
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
is_separable=True
 
True if this transform is separable in the x- and y- dimensions.
output_dims=1
 
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
transform_non_affine(values)[source]
 
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.  Parameters 
 
valuesarray


The input values as NumPy array of length input_dims or shape (N x input_dims).    Returns 
 array

The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
matplotlib.scale.get_scale_names()[source]
 
Return the names of the available scales.
classmatplotlib.scale.InvertedLogTransform(base)[source]
 
Bases: matplotlib.transforms.Transform  Parameters 
 
shorthand_namestr


A string representing the "name" of the transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True.       has_inverse=True
 
True if this transform has a corresponding inverse transform. 
   input_dims=1
 
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. 
   inverted()[source]
 
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. 
   is_separable=True
 
True if this transform is separable in the x- and y- dimensions. 
   output_dims=1
 
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. 
   transform_non_affine(a)[source]
 
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.  Parameters 
 
valuesarray


The input values as NumPy array of length input_dims or shape (N x input_dims).    Returns 
 array

The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
transform_non_affine(a)[source]
 
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.  Parameters 
 
valuesarray


The input values as NumPy array of length input_dims or shape (N x input_dims).    Returns 
 array

The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
classmatplotlib.scale.InvertedSymmetricalLogTransform(base, linthresh, linscale)[source]
 
Bases: matplotlib.transforms.Transform  Parameters 
 
shorthand_namestr


A string representing the "name" of the transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True.       has_inverse=True
 
True if this transform has a corresponding inverse transform. 
   input_dims=1
 
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. 
   inverted()[source]
 
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. 
   is_separable=True
 
True if this transform is separable in the x- and y- dimensions. 
   output_dims=1
 
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. 
   transform_non_affine(a)[source]
 
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op.  Parameters 
 
valuesarray


The input values as NumPy array of length input_dims or shape (N x input_dims).    Returns 
 array

The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
classmatplotlib.scale.LinearScale(axis)[source]
 
Bases: matplotlib.scale.ScaleBase The default linear scale.   get_transform()[source]
 
Return the transform for linear scaling, which is just the IdentityTransform. 
   name='linear'

   set_default_locators_and_formatters(axis)[source]
 
Set the locators and formatters of axis to instances suitable for this scale.
get_transform()[source]
 
Return the transform for linear scaling, which is just the IdentityTransform.
classmatplotlib.scale.LogisticTransform(nonpositive='mask')[source]
 
Bases: matplotlib.transforms.Transform  Parameters 
 
shorthand_namestr


A string representing the "name" of the transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True.       has_inverse=True
 
True if this transform has a corresponding inverse transform. 
   input_dims=1
 
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. 
   inverted()[source]
 
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. 
   is_separable=True
 
True if this transform is separable in the x- and y- dimensions. 
   output_dims=1
 
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. 
   transform_non_affine(a)[source]
 
logistic transform (base 10)
transform_non_affine(a)[source]
 
logistic transform (base 10)