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Remove signatures that duplicate introspectable data. #13177

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Jan 19, 2019
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Remove signatures that duplicate introspectable data. #13177

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83 changes: 10 additions & 73 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6113,12 +6113,9 @@ def pcolorfast(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
"""
Create a pseudocolor plot with a non-regular rectangular grid.

Call signatures::
Call signature::

ax.pcolorfast(C, **kwargs)
ax.pcolorfast(xr, yr, C, **kwargs)
ax.pcolorfast(x, y, C, **kwargs)
ax.pcolorfast(X, Y, C, **kwargs)
ax.pcolorfast([X, Y], C, /, **kwargs)

This method is similar to ~.Axes.pcolor` and `~.Axes.pcolormesh`.
It's designed to provide the fastest pcolor-type plotting with the
Expand All @@ -6138,24 +6135,24 @@ def pcolorfast(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
Parameters
----------
C : array-like(M, N)
A scalar 2D array. The values will be color-mapped.
*C* may be a masked array.
A 2D array or masked array. The values will be color-mapped.
This argument can only be passed positionally.

x, y : tuple or array-like
X, Y : tuple or array-like, default: ``(0, N)``, ``(0, M)``
*X* and *Y* are used to specify the coordinates of the
quadilaterals. There are different ways to do this:

- Use tuples ``xr=(xmin, xmax)`` and ``yr=(ymin, ymax)`` to define
- Use tuples ``X=(xmin, xmax)`` and ``Y=(ymin, ymax)`` to define
a *uniform rectiangular grid*.

The tuples define the outer edges of the grid. All individual
quadrilaterals will be of the same size. This is the fastest
version.

- Use 1D arrays *x*, *y* to specify a *non-uniform rectangular
- Use 1D arrays *X*, *Y* to specify a *non-uniform rectangular
grid*.

In this case *x* and *y* have to be monotonic 1D arrays of length
In this case *X* and *Y* have to be monotonic 1D arrays of length
*N+1* and *M+1*, specifying the x and y boundaries of the cells.

The speed is intermediate. Note: The grid is checked, and if
Expand All @@ -6172,7 +6169,7 @@ def pcolorfast(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
produce faster and more compact output using ps, pdf, and
svg backends, however.

Leaving out *x* and *y* defaults to ``xr=(0, N)``, ``yr=(O, M)``.
These arguments can only be passed positionally.

cmap : str or `~matplotlib.colors.Colormap`, optional
A Colormap instance or registered colormap name. The colormap
Expand Down Expand Up @@ -6324,32 +6321,7 @@ def clabel(self, CS, *args, **kwargs):
return CS.clabel(*args, **kwargs)
clabel.__doc__ = mcontour.ContourSet.clabel.__doc__

@docstring.dedent_interpd
def table(self, **kwargs):
"""
Add a table to the current axes.

Call signature::

table(cellText=None, cellColours=None,
cellLoc='right', colWidths=None,
rowLabels=None, rowColours=None, rowLoc='left',
colLabels=None, colColours=None, colLoc='center',
loc='bottom', bbox=None)

Returns a :class:`matplotlib.table.Table` instance. Either `cellText`
or `cellColours` must be provided. For finer grained control over
tables, use the :class:`~matplotlib.table.Table` class and add it to
the axes with :meth:`~matplotlib.axes.Axes.add_table`.

Thanks to John Gill for providing the class and table.

kwargs control the :class:`~matplotlib.table.Table`
properties:

%(Table)s
"""
return mtable.table(self, **kwargs)
table = mtable.table

#### Data analysis

Expand Down Expand Up @@ -6986,12 +6958,6 @@ def psd(self, x, NFFT=None, Fs=None, Fc=None, detrend=None,
r"""
Plot the power spectral density.

Call signature::

psd(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, return_line=None, **kwargs)

The power spectral density :math:`P_{xx}` by Welch's average
periodogram method. The vector *x* is divided into *NFFT* length
segments. Each segment is detrended by function *detrend* and
Expand Down Expand Up @@ -7109,12 +7075,6 @@ def csd(self, x, y, NFFT=None, Fs=None, Fc=None, detrend=None,
"""
Plot the cross-spectral density.

Call signature::

csd(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, return_line=None, **kwargs)

The cross spectral density :math:`P_{xy}` by Welch's average
periodogram method. The vectors *x* and *y* are divided into
*NFFT* length segments. Each segment is detrended by function
Expand Down Expand Up @@ -7221,11 +7181,6 @@ def magnitude_spectrum(self, x, Fs=None, Fc=None, window=None,
"""
Plot the magnitude spectrum.

Call signature::

magnitude_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,
pad_to=None, sides='default', **kwargs)

Compute the magnitude spectrum of *x*. Data is padded to a
length of *pad_to* and the windowing function *window* is applied to
the signal.
Expand Down Expand Up @@ -7323,11 +7278,6 @@ def angle_spectrum(self, x, Fs=None, Fc=None, window=None,
"""
Plot the angle spectrum.

Call signature::

angle_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,
pad_to=None, sides='default', **kwargs)

Compute the angle spectrum (wrapped phase spectrum) of *x*.
Data is padded to a length of *pad_to* and the windowing function
*window* is applied to the signal.
Expand Down Expand Up @@ -7405,11 +7355,6 @@ def phase_spectrum(self, x, Fs=None, Fc=None, window=None,
"""
Plot the phase spectrum.

Call signature::

phase_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,
pad_to=None, sides='default', **kwargs)

Compute the phase spectrum (unwrapped angle spectrum) of *x*.
Data is padded to a length of *pad_to* and the windowing function
*window* is applied to the signal.
Expand Down Expand Up @@ -7554,14 +7499,6 @@ def specgram(self, x, NFFT=None, Fs=None, Fc=None, detrend=None,
"""
Plot a spectrogram.

Call signature::

specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=128,
cmap=None, xextent=None, pad_to=None, sides='default',
scale_by_freq=None, mode='default', scale='default',
**kwargs)

Compute and plot a spectrogram of data in *x*. Data are split into
*NFFT* length segments and the spectrum of each section is
computed. The windowing function *window* is applied to each
Expand Down
18 changes: 3 additions & 15 deletions lib/matplotlib/mlab.py
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Expand Up @@ -669,15 +669,15 @@ def _single_spectrum_helper(x, mode, Fs=None, window=None, pad_to=None,
This should *NOT* be used to get zero padding, or the scaling of the
result will be incorrect. Use *pad_to* for this instead.

detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable
detrend : {'none', 'mean', 'linear'} or callable, default 'none'
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see #13177 (comment).

The function applied to each segment before fft-ing, designed to
remove the mean or linear trend. Unlike in MATLAB, where the
*detrend* parameter is a vector, in Matplotlib is it a function.
The :mod:`~matplotlib.mlab` module defines `.detrend_none`,
`.detrend_mean`, and `.detrend_linear`, but you can use a custom
function as well. You can also use a string to choose one of the
functions. 'default', 'constant', and 'mean' call `.detrend_mean`.
'linear' calls `.detrend_linear`. 'none' calls `.detrend_none`.
functions: 'none' calls `.detrend_none`. 'mean' calls `.detrend_mean`.
'linear' calls `.detrend_linear`.

scale_by_freq : bool, optional
Specifies whether the resulting density values should be scaled
Expand All @@ -693,12 +693,6 @@ def psd(x, NFFT=None, Fs=None, detrend=None, window=None,
r"""
Compute the power spectral density.

Call signature::

psd(x, NFFT=256, Fs=2, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None)

The power spectral density :math:`P_{xx}` by Welch's average
periodogram method. The vector *x* is divided into *NFFT* length
segments. Each segment is detrended by function *detrend* and
Expand Down Expand Up @@ -756,12 +750,6 @@ def csd(x, y, NFFT=None, Fs=None, detrend=None, window=None,
"""
Compute the cross-spectral density.

Call signature::

csd(x, y, NFFT=256, Fs=2, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None)

The cross spectral density :math:`P_{xy}` by Welch's average
periodogram method. The vectors *x* and *y* are divided into
*NFFT* length segments. Each segment is detrended by function
Expand Down
20 changes: 14 additions & 6 deletions lib/matplotlib/pyplot.py
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Expand Up @@ -1445,8 +1445,7 @@ def xticks(ticks=None, labels=None, **kwargs):

Call signatures::

locs, labels = xticks() # Get locations and labels

locs, labels = xticks() # Get locations and labels
xticks(ticks, [labels], **kwargs) # Set locations and labels

Parameters
Expand Down Expand Up @@ -1522,8 +1521,7 @@ def yticks(ticks=None, labels=None, **kwargs):

Call signatures::

locs, labels = yticks() # Get locations and labels

locs, labels = yticks() # Get locations and labels
yticks(ticks, [labels], **kwargs) # Set locations and labels

Parameters
Expand Down Expand Up @@ -2923,8 +2921,18 @@ def streamplot(

# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
@docstring.copy_dedent(Axes.table)
def table(**kwargs):
return gca().table(**kwargs)
def table(
cellText=None, cellColours=None, cellLoc='right',
colWidths=None, rowLabels=None, rowColours=None,
rowLoc='left', colLabels=None, colColours=None,
colLoc='center', loc='bottom', bbox=None, edges='closed',
**kwargs):
return gca().table(
cellText=cellText, cellColours=cellColours, cellLoc=cellLoc,
colWidths=colWidths, rowLabels=rowLabels,
rowColours=rowColours, rowLoc=rowLoc, colLabels=colLabels,
colColours=colColours, colLoc=colLoc, loc=loc, bbox=bbox,
edges=edges, **kwargs)


# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
Expand Down
15 changes: 10 additions & 5 deletions lib/matplotlib/table.py
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Expand Up @@ -656,6 +656,10 @@ def get_celld(self):
return self._cells


docstring.interpd.update(Table=artist.kwdoc(Table))


@docstring.dedent_interpd
def table(ax,
cellText=None, cellColours=None,
cellLoc='right', colWidths=None,
Expand All @@ -675,6 +679,9 @@ def table(ax,
using *rowLabels*, *rowColours*, *rowLoc* and *colLabels*, *colColours*,
*colLoc* respectively.

For finer grained control over tables, use the `.Table` class and add it to
the axes with `.Axes.add_table`.

Parameters
----------
cellText : 2D list of str, optional
Expand Down Expand Up @@ -726,13 +733,14 @@ def table(ax,
Other Parameters
----------------
**kwargs
`.Artist` properties.
`.Table` properties.

%(Table)s

Returns
-------
table : `~matplotlib.table.Table`
The created table.

"""

if cellColours is None and cellText is None:
Expand Down Expand Up @@ -829,6 +837,3 @@ def table(ax,

ax.add_table(table)
return table


docstring.interpd.update(Table=artist.kwdoc(Table))
8 changes: 2 additions & 6 deletions lib/mpl_toolkits/mplot3d/axes3d.py
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Expand Up @@ -2734,19 +2734,15 @@ def calc_arrow(uvw, angle=15):

def voxels(self, *args, facecolors=None, edgecolors=None, **kwargs):
"""
ax.voxels([x, y, z,] /, filled, **kwargs)
ax.voxels([x, y, z,] /, filled, facecolors=None, edgecolors=None, \
**kwargs)

Plot a set of filled voxels

All voxels are plotted as 1x1x1 cubes on the axis, with filled[0,0,0]
placed with its lower corner at the origin. Occluded faces are not
plotted.

Call signatures::

voxels(filled, facecolors=fc, edgecolors=ec, **kwargs)
voxels(x, y, z, filled, facecolors=fc, edgecolors=ec, **kwargs)

.. versionadded:: 2.1

Parameters
Expand Down