matplotlib · jklymak · Jul 7, 2018 · Jul 6, 2018 · afvincent · Jul 7, 2018
diff --git a/examples/images_contours_and_fields/tricontour_smooth_delaunay.py b/examples/images_contours_and_fields/tricontour_smooth_delaunay.py
@@ -61,7 +61,7 @@ def experiment_res(x, y):
 
 min_circle_ratio = .01  # Minimum circle ratio - border triangles with circle
                         # ratio below this will be masked if they touch a
-                        # border. Suggested value 0.01 ; Use -1 to keep
+                        # border. Suggested value 0.01; use -1 to keep
                         # all triangles.
 
 # Random points

diff --git a/examples/misc/svg_filter_line.py b/examples/misc/svg_filter_line.py
@@ -61,7 +61,7 @@
 
 # filter definition for a gaussian blur
 filter_def = """
-  <defs  xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'>
+  <defs xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'>
     <filter id='dropshadow' height='1.2' width='1.2'>
       <feGaussianBlur result='blur' stdDeviation='3'/>
     </filter>

diff --git a/examples/misc/svg_filter_pie.py b/examples/misc/svg_filter_pie.py
@@ -58,7 +58,7 @@
 # that, Inkscape's exporting also may not support it.
 
 filter_def = """
-  <defs  xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'>
+  <defs xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'>
     <filter id='dropshadow' height='1.2' width='1.2'>
       <feGaussianBlur result='blur' stdDeviation='2'/>
     </filter>

diff --git a/examples/specialty_plots/leftventricle_bulleye.py b/examples/specialty_plots/leftventricle_bulleye.py
@@ -61,7 +61,7 @@ def bullseye_plot(ax, data, segBold=None, cmap=None, norm=None):
     for i in range(r.shape[0]):
         ax.plot(theta, np.repeat(r[i], theta.shape), '-k', lw=linewidth)
 
-    # Create the bounds for the segments  1-12
+    # Create the bounds for the segments 1-12
     for i in range(6):
         theta_i = np.deg2rad(i * 60)
         ax.plot([theta_i, theta_i], [r[1], 1], '-k', lw=linewidth)

diff --git a/examples/specialty_plots/radar_chart.py b/examples/specialty_plots/radar_chart.py
@@ -121,7 +121,7 @@ def unit_poly_verts(theta):
 
 def example_data():
     # The following data is from the Denver Aerosol Sources and Health study.
-    # See  doi:10.1016/j.atmosenv.2008.12.017
+    # See doi:10.1016/j.atmosenv.2008.12.017
     #
     # The data are pollution source profile estimates for five modeled
     # pollution sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical

diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -1136,7 +1136,7 @@ def plot(self, *args, scalex=True, scaley=True, **kwargs):
         >>> plot(y, 'r+')     # ditto, but with red plusses
 
         You can use `.Line2D` properties as keyword arguments for more
-        control on the  appearance. Line properties and *fmt* can be mixed.
+        control on the appearance. Line properties and *fmt* can be mixed.
         The following two calls yield identical results:
 
         >>> plot(x, y, 'go--', linewidth=2, markersize=12)
@@ -3155,16 +3155,15 @@ def boxplot(self, x, notch=None, sym=None, vert=None, whis=None,
 
         labels : sequence, optional
             Labels for each dataset. Length must be compatible with
-            dimensions  of ``x``.
+            dimensions of ``x``.
 
         manage_xticks : bool, optional (True)
             If the function should adjust the xlim and xtick locations.
 
         autorange : bool, optional (False)
-            When `True` and the data are distributed such that the  25th and
+            When `True` and the data are distributed such that the 25th and
             75th percentiles are equal, ``whis`` is set to ``'range'`` such
-            that the whisker ends are at the minimum and maximum of the
-            data.
+            that the whisker ends are at the minimum and maximum of the data.
 
         meanline : bool, optional (False)
             If `True` (and ``showmeans`` is `True`), will try to render
@@ -5728,7 +5727,7 @@ def pcolorfast(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
         It's designed to provide the fastest pcolor-type plotting with the
         Agg backend. To achieve this, it uses different algorithms internally
         depending on the complexity of the input grid (regular rectangular,
-        non-regular rectangular or arbitrary  quadrilateral).
+        non-regular rectangular or arbitrary quadrilateral).
 
         .. warning::
 

diff --git a/lib/matplotlib/backend_bases.py b/lib/matplotlib/backend_bases.py
@@ -2596,7 +2596,7 @@ class NavigationToolbar2(object):
     # )
     toolitems = (
         ('Home', 'Reset original view', 'home', 'home'),
-        ('Back', 'Back to  previous view', 'back', 'back'),
+        ('Back', 'Back to previous view', 'back', 'back'),
         ('Forward', 'Forward to next view', 'forward', 'forward'),
         (None, None, None, None),
         ('Pan', 'Pan axes with left mouse, zoom with right', 'move', 'pan'),

diff --git a/lib/matplotlib/backend_managers.py b/lib/matplotlib/backend_managers.py
@@ -22,7 +22,7 @@ def __init__(self, name, sender, tool, data=None):
 
 
 class ToolTriggerEvent(ToolEvent):
-    """Event to inform  that a tool has been triggered"""
+    """Event to inform that a tool has been triggered"""
     def __init__(self, name, sender, tool, canvasevent=None, data=None):
         ToolEvent.__init__(self, name, sender, tool, data)
         self.canvasevent = canvasevent

diff --git a/lib/matplotlib/backends/backend_ps.py b/lib/matplotlib/backends/backend_ps.py
@@ -1571,7 +1571,7 @@ def get_bbox_header(lbrt, rotated=False):
 
     l, b, r, t = lbrt
     if rotated:
-        rotate = "%.2f %.2f  translate\n90 rotate" % (l+r, 0)
+        rotate = "%.2f %.2f translate\n90 rotate" % (l+r, 0)
     else:
         rotate = ""
     bbox_info = '%%%%BoundingBox: %d %d %d %d' % (l, b, np.ceil(r), np.ceil(t))

diff --git a/lib/matplotlib/category.py b/lib/matplotlib/category.py
@@ -24,7 +24,7 @@ class StrCategoryConverter(units.ConversionInterface):
     @staticmethod
     def convert(value, unit, axis):
         """Converts strings in value to floats using
-        mapping information store in the  unit object
+        mapping information store in the unit object.
 
         Parameters
         ----------

diff --git a/lib/matplotlib/cbook/__init__.py b/lib/matplotlib/cbook/__init__.py
@@ -1177,10 +1177,9 @@ def boxplot_stats(X, whis=1.5, bootstrap=None, labels=None,
         dimensions of `X`.
 
     autorange : bool, optional (False)
-        When `True` and the data are distributed such that the  25th and
-        75th percentiles are equal, ``whis`` is set to ``'range'`` such
-        that the whisker ends are at the minimum and maximum of the
-        data.
+        When `True` and the data are distributed such that the 25th and 75th
+        percentiles are equal, ``whis`` is set to ``'range'`` such that the
+        whisker ends are at the minimum and maximum of the data.
 
     Returns
     -------

diff --git a/lib/matplotlib/cbook/deprecation.py b/lib/matplotlib/cbook/deprecation.py
@@ -55,7 +55,7 @@ def warn_deprecated(
         since, message='', name='', alternative='', pending=False,
         obj_type='attribute', addendum='', *, removal=''):
     """
-    Used to display deprecation  in a standard way.
+    Used to display deprecation in a standard way.
 
     Parameters
     ----------

diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
@@ -117,7 +117,7 @@
     Import record array from CSV file with type inspection
 
 :func:`rec_append_fields`
-    Adds  field(s)/array(s) to record array
+    Adds field(s)/array(s) to record array
 
 :func:`rec_drop_fields`
     Drop fields from record array

diff --git a/lib/matplotlib/offsetbox.py b/lib/matplotlib/offsetbox.py
@@ -842,7 +842,7 @@ def draw(self, renderer):
 
 class AuxTransformBox(OffsetBox):
     """
-    Offset Box with the aux_transform . Its children will be
+    Offset Box with the aux_transform. Its children will be
     transformed with the aux_transform first then will be
     offseted. The absolute coordinate of the aux_transform is meaning
     as it will be automatically adjust so that the left-lower corner

diff --git a/lib/matplotlib/patches.py b/lib/matplotlib/patches.py
@@ -2562,7 +2562,7 @@ def get_y(self):
         return self._y
 
     def get_width(self):
-        "Return the width of the  rectangle"
+        "Return the width of the rectangle"
         return self._width
 
     def get_height(self):

diff --git a/lib/matplotlib/tests/test_triangulation.py b/lib/matplotlib/tests/test_triangulation.py
@@ -562,14 +562,14 @@ def poisson_sparse_matrix(n, m):
 def test_triinterpcubic_geom_weights():
     # Tests to check computation of weights for _DOF_estimator_geom:
     # The weight sum per triangle can be 1. (in case all angles < 90 degrees)
-    # or (2*w_i) where w_i = 1-alpha_i/np.pi is the weight of apex i ; alpha_i
+    # or (2*w_i) where w_i = 1-alpha_i/np.pi is the weight of apex i; alpha_i
     # is the apex angle > 90 degrees.
     (ax, ay) = (0., 1.687)
     x = np.array([ax, 0.5*ax, 0., 1.])
     y = np.array([ay, -ay, 0., 0.])
     z = np.zeros(4, dtype=np.float64)
     triangles = [[0, 2, 3], [1, 3, 2]]
-    sum_w = np.zeros([4, 2])  # 4 possibilities ; 2 triangles
+    sum_w = np.zeros([4, 2])  # 4 possibilities; 2 triangles
     for theta in np.linspace(0., 2*np.pi, 14):  # rotating the figure...
         x_rot = np.cos(theta)*x + np.sin(theta)*y
         y_rot = -np.sin(theta)*x + np.cos(theta)*y
@@ -594,7 +594,7 @@ def test_triinterp_colinear():
     #
     # Note that the LinearTriInterpolator and the CubicTriInterpolator with
     # kind='min_E' or 'geom' still pass a linear patch test.
-    # We also test interpolation inside a  flat triangle, by forcing
+    # We also test interpolation inside a flat triangle, by forcing
     # *tri_index* in a call to :meth:`_interpolate_multikeys`.
 
     # If +ve, triangulation is OK, if -ve triangulation invalid,
@@ -604,7 +604,7 @@ def test_triinterp_colinear():
     x0 = np.array([1.5, 0,  1,  2, 3, 1.5,   1.5])
     y0 = np.array([-1,  0,  0,  0, 0, delta, 1])
 
-    # We test different affine transformations of the initial figure ; to
+    # We test different affine transformations of the initial figure; to
     # avoid issues related to round-off errors we only use integer
     # coefficients (otherwise the Triangulation might become invalid even with
     # delta == 0).

diff --git a/lib/matplotlib/tri/triinterpolate.py b/lib/matplotlib/tri/triinterpolate.py
@@ -76,9 +76,8 @@ def __init__(self, triangulation, z, trifinder=None):
         Returns
         -------
         z : np.ma.array
-            Masked array of the same shape as *x* and *y* ; values
-            corresponding to (*x*, *y*) points outside of the triangulation
-            are masked out.
+            Masked array of the same shape as *x* and *y*; values corresponding
+            to (*x*, *y*) points outside of the triangulation are masked out.
 
         """
 
@@ -95,7 +94,7 @@ def __init__(self, triangulation, z, trifinder=None):
         Returns
         -------
         dzdx, dzdy : np.ma.array
-            2  masked arrays of the same shape as *x* and *y* ; values
+            2 masked arrays of the same shape as *x* and *y*; values
             corresponding to (x,y) points outside of the triangulation
             are masked out.
             The first returned array contains the values of
@@ -466,7 +465,7 @@ def _compute_dof(self, kind, dz=None):
             Choice of the _DOF_estimator subclass to perform the gradient
             estimation.
         dz: tuple of array_likes (dzdx, dzdy), optional
-            Used only if *kind=user ; in this case passed to the
+            Used only if *kind*=user; in this case passed to the
             :class:`_DOF_estimator_user`.
 
         Returns
@@ -486,7 +485,7 @@ def _compute_dof(self, kind, dz=None):
         elif kind == 'min_E':
             TE = _DOF_estimator_min_E(self)
         else:
-            raise ValueError("CubicTriInterpolator *kind* proposed: {0} ; "
+            raise ValueError("CubicTriInterpolator *kind* proposed: {0}; "
                              "should be one of: "
                              "'user', 'geom', 'min_E'".format(kind))
         return TE.compute_dof_from_df()
@@ -919,7 +918,7 @@ def get_Hrot_from_J(self, J, return_area=False):
         to global coordinates.
         if *return_area* is True, returns also the triangle area (0.5*det(J))
         """
-        # Here we try to deal with the simplest colinear cases ; a null
+        # Here we try to deal with the simplest colinear cases; a null
         # energy and area is imposed.
         J_inv = _safe_inv22_vectorized(J)
         Ji00 = J_inv[:, 0, 0]
@@ -1138,7 +1137,7 @@ def compute_geom_weights(self):
             # In the below formula we could take modulo 2. but
             # modulo 1. is safer regarding round-off errors (flat triangles).
             angle = np.abs(np.mod((alpha2-alpha1) / np.pi, 1.))
-            # Weight proportional to angle up np.pi/2 ; null weight for
+            # Weight proportional to angle up np.pi/2; null weight for
             # degenerated cases 0. and np.pi (Note that `angle` is normalized
             # by np.pi)
             weights[:, ipt] = 0.5 - np.abs(angle-0.5)
@@ -1201,9 +1200,9 @@ def compute_dz(self):
             J, eccs, triangles, Uc)
 
         # Building sparse matrix and solving minimization problem
-        # We could use scipy.sparse direct solver ; however to avoid this
+        # We could use scipy.sparse direct solver; however to avoid this
         # external dependency an implementation of a simple PCG solver with
-        # a simplendiagonal Jocabi preconditioner is implemented.
+        # a simple diagonal Jacobi preconditioner is implemented.
         tol = 1.e-10
         n_dof = Ff.shape[0]
         Kff_coo = _Sparse_Matrix_coo(Kff_vals, Kff_rows, Kff_cols,
@@ -1435,7 +1434,7 @@ def _safe_inv22_vectorized(M):
     prod1 = M[:, 0, 0]*M[:, 1, 1]
     delta = prod1 - M[:, 0, 1]*M[:, 1, 0]
 
-    # We set delta_inv to 0. in case of a rank deficient matrix ; a
+    # We set delta_inv to 0. in case of a rank deficient matrix; a
     # rank-deficient input matrix *M* will lead to a null matrix in output
     rank2 = (np.abs(delta) > 1e-8*np.abs(prod1))
     if np.all(rank2):
@@ -1455,7 +1454,7 @@ def _safe_inv22_vectorized(M):
 
 def _pseudo_inv22sym_vectorized(M):
     """
-    Inversion of arrays of (2,2) SYMMETRIC matrices ; returns the
+    Inversion of arrays of (2,2) SYMMETRIC matrices; returns the
     (Moore-Penrose) pseudo-inverse for rank-deficient matrices.
 
     In case M is of rank 1, we have M = trace(M) x P where P is the orthogonal

diff --git a/lib/matplotlib/tri/trirefine.py b/lib/matplotlib/tri/trirefine.py
@@ -232,7 +232,7 @@ def _refine_triangulation_once(triangulation, ancestors=None):
         # Each edge belongs to 1 triangle (if border edge) or is shared by 2
         # masked_triangles (interior edge).
         # We first build 2 * ntri arrays of edge starting nodes (edge_elems,
-        # edge_apexes) ; we then extract only the masters to avoid overlaps.
+        # edge_apexes); we then extract only the masters to avoid overlaps.
         # The so-called 'master' is the triangle with biggest index
         # The 'slave' is the triangle with lower index
         # (can be -1 if border edge)
@@ -257,7 +257,7 @@ def _refine_triangulation_once(triangulation, ancestors=None):
         refi_x[npts:] = x_add
         refi_y[npts:] = y_add
 
-        # Building the new masked_triangles ; each old masked_triangles hosts
+        # Building the new masked_triangles; each old masked_triangles hosts
         # 4 new masked_triangles
         # there are 6 pts to identify per 'old' triangle, 3 new_pt_corner and
         # 3 new_pt_midside
@@ -267,7 +267,7 @@ def _refine_triangulation_once(triangulation, ancestors=None):
         #  of elem ielem ?
         # If ielem is the apex master: simple count, given the way refi_x was
         #  built.
-        # If ielem is the apex slave: yet we do not know ; but we will soon
+        # If ielem is the apex slave: yet we do not know; but we will soon
         # using the neighbors table.
         new_pt_midside = np.empty([ntri, 3], dtype=np.int32)
         cum_sum = npts

diff --git a/lib/matplotlib/tri/tritools.py b/lib/matplotlib/tri/tritools.py
@@ -127,14 +127,13 @@ def get_flat_tri_mask(self, min_circle_ratio=0.01, rescale=True):
         triangulation from its border-located flat triangles
         (according to their :meth:`circle_ratios`).
         This mask is meant to be subsequently applied to the triangulation
-        using :func:`matplotlib.tri.Triangulation.set_mask` .
+        using :func:`matplotlib.tri.Triangulation.set_mask`.
         *new_mask* is an extension of the initial triangulation mask
         in the sense that an initially masked triangle will remain masked.
 
-        The *new_mask* array is computed recursively ; at each step flat
-        triangles are removed only if they share a side with the current
-        mesh border. Thus no new holes in the triangulated domain will be
-        created.
+        The *new_mask* array is computed recursively; at each step flat
+        triangles are removed only if they share a side with the current mesh
+        border. Thus no new holes in the triangulated domain will be created.
 
         Parameters
         ----------

diff --git a/lib/mpl_toolkits/axes_grid1/axes_size.py b/lib/mpl_toolkits/axes_grid1/axes_size.py
@@ -53,7 +53,7 @@ def get_size(self, renderer):
 
 
 class Fixed(_Base):
-    "Simple fixed size  with absolute part = *fixed_size* and relative part = 0"
+    "Simple fixed size with absolute part = *fixed_size* and relative part = 0"
     def __init__(self, fixed_size):
         self.fixed_size = fixed_size
 

diff --git a/lib/mpl_toolkits/mplot3d/art3d.py b/lib/mpl_toolkits/mplot3d/art3d.py
@@ -486,7 +486,7 @@ def patch_collection_2d_to_3d(col, zs=0, zdir='z', depthshade=True):
     Parameters
     ----------
     za
-        The location or locations to place the patches in the  collection along
+        The location or locations to place the patches in the collection along
         the *zdir* axis. Default: 0.
     zdir
         The axis in which to place the patches. Default: "z".

diff --git a/lib/mpl_toolkits/mplot3d/axes3d.py b/lib/mpl_toolkits/mplot3d/axes3d.py
@@ -1364,7 +1364,7 @@ def locator_params(self, axis='both', tight=None, **kwargs):
         Convenience method for controlling tick locators.
 
         See :meth:`matplotlib.axes.Axes.locator_params` for full
-        documentation  Note that this is for Axes3D objects,
+        documentation.  Note that this is for Axes3D objects,
         therefore, setting *axis* to 'both' will result in the
         parameters being set for all three axes.  Also, *axis*
         can also take a value of 'z' to apply parameters to the

diff --git a/tutorials/introductory/pyplot.py b/tutorials/introductory/pyplot.py
@@ -12,7 +12,7 @@
 # ===============
 #
 # :mod:`matplotlib.pyplot` is a collection of command style functions
-# that make matplotlib  work like MATLAB.
+# that make matplotlib work like MATLAB.
 # Each ``pyplot`` function makes
 # some change to a figure: e.g., creates a figure, creates a plotting area
 # in a figure, plots some lines in a plotting area, decorates the plot

diff --git a/tutorials/introductory/sample_plots.py b/tutorials/introductory/sample_plots.py
@@ -11,7 +11,7 @@
 Line Plot
 =========
 
-Here's how to create a line plot with  text labels using
+Here's how to create a line plot with text labels using
 :func:`~matplotlib.pyplot.plot`.
 
 .. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_simple_plot_001.png
@@ -147,11 +147,10 @@
 Ellipses
 ========
 
-In support of the
-`Phoenix <http://www.jpl.nasa.gov/news/phoenix/main.php>`_ mission to
-Mars (which used Matplotlib to display ground tracking of spacecraft),
-Michael Droettboom built on work by Charlie Moad to provide an extremely
-accurate  8-spline approximation to elliptical arcs (see
+In support of the `Phoenix <http://www.jpl.nasa.gov/news/phoenix/main.php>`_
+mission to Mars (which used Matplotlib to display ground tracking of
+spacecraft), Michael Droettboom built on work by Charlie Moad to provide
+an extremely accurate 8-spline approximation to elliptical arcs (see
 :class:`~matplotlib.patches.Arc`), which are insensitive to zoom level.
 
 .. figure:: ../../gallery/shapes_and_collections/images/sphx_glr_ellipse_demo_001.png