Fix documentation warnings

doc/mpl_toolkits/mplot3d/tutorial.rst

@@ -68,7 +68,7 @@ Surface plots
 
 Tri-Surface plots
 =================
-.. automethod:: Axes3D.plot_trisurface
+.. automethod:: Axes3D.plot_trisurf
 
 .. plot:: mpl_examples/mplot3d/trisurf3d_demo.py
 

lib/matplotlib/animation.py

@@ -75,8 +75,6 @@ class MovieWriter(object):
     The use of the context manager ensures that setup and cleanup are
     performed as necessary.
 
-    Attributes
-    ----------
     frame_format: string
         The format used in writing frame data, defaults to 'rgba'
     '''
@@ -85,8 +83,6 @@ def __init__(self, fps=5, codec=None, bitrate=None, extra_args=None,
         '''
         Construct a new MovieWriter object.
 
-        Parameters
-        ----------
         fps: int
             Framerate for movie.
         codec: string or None, optional
@@ -103,7 +99,7 @@ def __init__(self, fps=5, codec=None, bitrate=None, extra_args=None,
             movie utiltiy. The default is None, which passes the additional
             argurments in the 'animation.extra_args' rcParam.
         metadata: dict of string:string or None
-            A dictionary of keys and values for metadata to include in the 
+            A dictionary of keys and values for metadata to include in the
             output file. Some keys that may be of use include:
             title, artist, genre, subject, copyright, srcform, comment.
         '''
@@ -140,9 +136,6 @@ def setup(self, fig, outfile, dpi, *args):
         '''
         Perform setup for writing the movie file.
 
-        Parameters
-        ----------
-
         fig: `matplotlib.Figure` instance
             The figure object that contains the information for frames
         outfile: string
@@ -164,7 +157,7 @@ def saving(self, *args):
         '''
         Context manager to facilitate writing the movie file.
 
-        *args are any parameters that should be passed to setup()
+        ``*args`` are any parameters that should be passed to `setup`.
         '''
         # This particular sequence is what contextlib.contextmanager wants
         self.setup(*args)
@@ -254,9 +247,6 @@ def setup(self, fig, outfile, dpi, frame_prefix='_tmp', clear_temp=True):
         '''
         Perform setup for writing the movie file.
 
-        Parameters
-        ----------
-
         fig: `matplotlib.Figure` instance
             The figure object that contains the information for frames
         outfile: string

lib/matplotlib/axes.py

@@ -4432,8 +4432,8 @@ def legend(self, *args, **kwargs):
             instance. If *prop* is a dictionary, a new instance will be
             created with *prop*. If *None*, use rc settings.
 
-          *fontsize*: [ size in points | 'xx-small' | 'x-small' | 'small' |
-                        'medium' | 'large' | 'x-large' | 'xx-large' ]
+          *fontsize*: [ size in points | 'xx-small' | 'x-small' |
+          'small' | 'medium' | 'large' | 'x-large' | 'xx-large' ]
             Set the font size.  May be either a size string, relative to
             the default font size, or an absolute font size in points. This
             argument is only used if prop is not specified.
@@ -5602,17 +5602,17 @@ def boxplot(self, x, notch=False, sym='b+', vert=True, whis=1.5,
 
         Returns a dictionary mapping each component of the boxplot
         to a list of the :class:`matplotlib.lines.Line2D`
-        instances created. That disctionary has the following keys
+        instances created. That dictionary has the following keys
         (assuming vertical boxplots):
-            boxes: the main body of the boxplot showing the quartiles
-                and the median's confidence intervals if enabled.
-            medians: horizonal lines at the median of each box.
-            whiskers: the vertical lines extending to the most extreme,
-                non-outlier data points.
-            caps: the horizontal lines at the ends of the whiskers.
-            fliers: points representing data that extend beyone the
-                whiskers (outliers).
 
+            - boxes: the main body of the boxplot showing the quartiles
+              and the median's confidence intervals if enabled.
+            - medians: horizonal lines at the median of each box.
+            - whiskers: the vertical lines extending to the most extreme,
+              n-outlier data points.
+            - caps: the horizontal lines at the ends of the whiskers.
+            - fliers: points representing data that extend beyone the
+              whiskers (outliers).
 
         **Example:**
 
@@ -7828,6 +7828,8 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
                 ax.hist(12+3*np.random.randn(1000), label='women', alpha=0.5)
                 ax.legend()
 
+            .
+
         kwargs are used to update the properties of the
         :class:`~matplotlib.patches.Patch` instances returned by *hist*:
 
@@ -8121,23 +8123,35 @@ def hist2d(self, x, y, bins = 10, range=None, normed=False, weights=None,
           hist2d(x, y, bins = None, range=None, weights=None, cmin=None, cmax=None **kwargs)
 
         Make a 2d histogram plot of *x* versus *y*, where *x*,
-        *y* are 1-D sequences of the same length
+        *y* are 1-D sequences of the same length.
 
-        The return value is (counts,xedges,yedges,Image)
+        The return value is ``(counts, xedges, yedges, Image)``.
 
         Optional keyword arguments:
         *bins*: [None | int | [int, int] | array_like | [array, array]]
+
             The bin specification:
-                If int, the number of bins for the two dimensions (nx=ny=bins).
-                If [int, int], the number of bins in each dimension (nx, ny = bins).
-                If array_like, the bin edges for the two dimensions (x_edges=y_edges=bins).
-                If [array, array], the bin edges in each dimension (x_edges, y_edges = bins).
+
+                - If int, the number of bins for the two dimensions
+                  (nx=ny=bins).
+
+                - If [int, int], the number of bins in each dimension
+                  (nx, ny = bins).
+
+                - If array_like, the bin edges for the two dimensions
+                  (x_edges=y_edges=bins).
+
+                - If [array, array], the bin edges in each dimension
+                  (x_edges, y_edges = bins).
+
             The default value is 10.
 
         *range*: [*None* | array_like shape(2,2)]
-             The leftmost and rightmost edges of the bins along each dimension (if not specified
-             explicitly in the bins parameters): [[xmin, xmax], [ymin, ymax]]. All values outside of
-             this range will be considered outliers and not tallied in the histogram.
+             The leftmost and rightmost edges of the bins along each
+             dimension (if not specified explicitly in the bins
+             parameters): [[xmin, xmax], [ymin, ymax]]. All values
+             outside of this range will be considered outliers and not
+             tallied in the histogram.
 
         *normed*:[True|False]
              Normalize histogram.
@@ -8147,12 +8161,15 @@ def hist2d(self, x, y, bins = 10, range=None, normed=False, weights=None,
             An array of values w_i weighing each sample (x_i, y_i).
 
         *cmin* : [None| scalar]
-             All bins that has count less than cmin will not be displayed
-             and these count values in the return value count histogram will also be set to nan upon return
+             All bins that has count less than cmin will not be
+             displayed and these count values in the return value
+             count histogram will also be set to nan upon return
 
         *cmax* : [None| scalar]
-             All bins that has count more than cmax will not be displayed (set to none before passing to imshow)
-             and these count values in the return value count histogram will also be set to nan upon return
+             All bins that has count more than cmax will not be
+             displayed (set to none before passing to imshow) and
+             these count values in the return value count histogram
+             will also be set to nan upon return
 
         Remaining keyword arguments are passed directly to :meth:pcolorfast
 

lib/matplotlib/projections/__init__.py

@@ -77,7 +77,7 @@ def projection_factory(projection, figure, rect, **kwargs):
     Any other kwargs are passed along to the specific projection
     constructor being used.
 
-    .. deprecated::
+    .. deprecated:: 1.3
 
         This routine is deprecated in favour of getting the projection
         class directly with :func:`get_projection_class` and initialising it

lib/matplotlib/stackplot.py

@@ -15,26 +15,25 @@
 def stackplot(axes, x, *args, **kwargs):
     """Draws a stacked area plot.
 
-    Parameters
-    ----------
     *x* : 1d array of dimension N
+
     *y* : 2d array of dimension MxN, OR any number 1d arrays each of dimension
           1xN. The data is assumed to be unstacked. Each of the following
-          calls is legal:
+          calls is legal::
 
-          stackplot(x, y)               # where y is MxN
-          staclplot(x, y1, y2, y3, y4)  # where y1, y2, y3, y4, are all 1xNm
+            stackplot(x, y)               # where y is MxN
+            stackplot(x, y1, y2, y3, y4)  # where y1, y2, y3, y4, are all 1xNm
 
     Keyword arguments:
+
     *colors* : A list or tuple of colors. These will be cycled through and
                used to colour the stacked areas.
                All other keyword arguments are passed to
                :func:`~matplotlib.Axes.fill_between`
 
-    Returns
-    -------
-    *r* : A list of :class:`~matplotlib.collections.PolyCollection`, one for
-          each element in the stacked area plot.
+    Returns *r* : A list of
+    :class:`~matplotlib.collections.PolyCollection`, one for each
+    element in the stacked area plot.
     """
 
     if len(args) == 1:

lib/matplotlib/streamplot.py

@@ -17,8 +17,6 @@ def streamplot(axes, x, y, u, v, density=1, linewidth=None, color=None,
                minlength=0.1):
     """Draws streamlines of a vector flow.
 
-    Parameters
-    ----------
     *x*, *y* : 1d arrays
         an *evenly spaced* grid.
     *u*, *v* : 2d arrays
@@ -48,9 +46,7 @@ def streamplot(axes, x, y, u, v, density=1, linewidth=None, color=None,
     *minlength* : float
         Minimum length of streamline in axes coordinates.
 
-    Returns
-    -------
-    *streamlines* : :class:`~matplotlib.collections.LineCollection`
+    Returns *streamlines* : :class:`~matplotlib.collections.LineCollection`
         Line collection with all streamlines as a series of line segments.
         Currently, there is no way to differentiate between line segments
         on different streamlines (other than manually checking that segments
@@ -552,4 +548,3 @@ def _gen_starting_points(shape):
             if y <= yfirst:
                 yfirst +=1
                 direction = 'right'
-

lib/matplotlib/tight_layout.py

@@ -212,20 +212,25 @@ def get_renderer(fig):
 def get_tight_layout_figure(fig, axes_list, renderer,
                             pad=1.08, h_pad=None, w_pad=None, rect=None):
     """
-    return subplot parameters for tigh-layouted- figure with
-    specified padding.
+    Return subplot parameters for tight-layouted-figure with specified
+    padding.
 
     Parameters:
 
       *fig* : figure instance
+
       *axes_list* : a list of axes
+
       *renderer* : renderer instance
+
       *pad* : float
         padding between the figure edge and the edges of subplots,
         as a fraction of the font-size.
+
       *h_pad*, *w_pad* : float
         padding (height/width) between edges of adjacent subplots.
         Defaults to `pad_inches`.
+
       *rect* : if rect is given, it is interpreted as a rectangle
         (left, bottom, right, top) in the normalized figure
         coordinate that the whole subplots area (including
@@ -323,4 +328,3 @@ def get_tight_layout_figure(fig, axes_list, renderer,
                                          rect=(left, bottom, right, top))
 
     return kwargs
-

lib/matplotlib/widgets.py

@@ -1518,8 +1518,8 @@ class Lasso(AxesWidget):
     """Selection curve of an arbitrary shape.
 
     The selected path can be used in conjunction with
-    :function:`~matplotlib.path.Path.contains_point` to select
-    data points from an image.
+    :func:`~matplotlib.path.Path.contains_point` to select data points
+    from an image.
 
     Unlike :class:`LassoSelector`, this must be initialized with a starting
     point `xy`, and the `Lasso` events are destroyed upon release.
@@ -1578,4 +1578,3 @@ def onmove(self, event):
             self.canvas.blit(self.ax.bbox)
         else:
             self.canvas.draw_idle()
-