Remove signatures that duplicate introspectable data.

anntzer · anntzer · commit fd7e88e524bd · 2019-01-14T08:26:06.000+01:00
(For the case of Axes.table, one can just directly use the
`matplotlib.table.table` function as the method.)
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -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
@@ -6141,21 +6138,21 @@ def pcolorfast(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
             A scalar 2D array. The values will be color-mapped.
             *C* may be a masked array.
 
-        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
@@ -6172,8 +6169,6 @@ 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)``.
-
         cmap : str or `~matplotlib.colors.Colormap`, optional
             A Colormap instance or registered colormap name. The colormap
             maps the *C* values to colors. Defaults to :rc:`image.cmap`.
@@ -6324,32 +6319,7 @@ 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
 
@@ -6986,12 +6956,6 @@ 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
@@ -7109,12 +7073,6 @@ 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
@@ -7221,11 +7179,6 @@ 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.
@@ -7323,11 +7276,6 @@ 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.
@@ -7405,11 +7353,6 @@ 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.
@@ -7554,14 +7497,6 @@ 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
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
@@ -727,12 +727,6 @@ 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
@@ -793,12 +787,6 @@ 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
diff --git a/lib/matplotlib/pyplot.py b/lib/matplotlib/pyplot.py
@@ -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
@@ -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
@@ -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.
diff --git a/lib/matplotlib/table.py b/lib/matplotlib/table.py
@@ -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,
@@ -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
@@ -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:
@@ -829,6 +837,3 @@ def table(ax,
 
     ax.add_table(table)
     return table
-
-
-docstring.interpd.update(Table=artist.kwdoc(Table))
diff --git a/lib/mpl_toolkits/mplot3d/axes3d.py b/lib/mpl_toolkits/mplot3d/axes3d.py
@@ -2735,19 +2735,14 @@ 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=fc, edgecolors=ec, **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
