Merge pull request matplotlib#18706 from timhoffm/2D

Consistently use 3D, 2D, 1D for dimensionality

Author: QuLogic

examples/images_contours_and_fields/image_transparency_blend.py

@@ -1,18 +1,18 @@
 """
-===========================================
-Blend transparency with color in 2-D images
-===========================================
+==========================================
+Blend transparency with color in 2D images
+==========================================
 
 Blend transparency with color to highlight parts of data with imshow.
 
-A common use for `matplotlib.pyplot.imshow` is to plot a 2-D statistical
-map. The function makes it easy to visualize a 2-D matrix as an image and add
+A common use for `matplotlib.pyplot.imshow` is to plot a 2D statistical
+map. The function makes it easy to visualize a 2D matrix as an image and add
 transparency to the output. For example, one can plot a statistic (such as a
 t-statistic) and color the transparency of each pixel according to its p-value.
 This example demonstrates how you can achieve this effect.
 
-First we will generate some data, in this case, we'll create two 2-D "blobs"
-in a 2-D grid. One blob will be positive, and the other negative.
+First we will generate some data, in this case, we'll create two 2D "blobs"
+in a 2D grid. One blob will be positive, and the other negative.
 """
 
 # sphinx_gallery_thumbnail_number = 3

examples/images_contours_and_fields/pcolor_demo.py

@@ -5,7 +5,7 @@
 
 Generating images with `~.axes.Axes.pcolor`.
 
-Pcolor allows you to generate 2-D image-style plots. Below we will show how
+Pcolor allows you to generate 2D image-style plots. Below we will show how
 to do so in Matplotlib.
 """
 import matplotlib.pyplot as plt

examples/images_contours_and_fields/pcolormesh_levels.py

@@ -3,7 +3,7 @@
 pcolormesh
 ==========
 
-`.axes.Axes.pcolormesh` allows you to generate 2-D image-style plots.  Note it
+`.axes.Axes.pcolormesh` allows you to generate 2D image-style plots.  Note it
 is faster than the similar `~.axes.Axes.pcolor`.
 
 """

examples/mplot3d/quiver3d.py

@@ -3,7 +3,7 @@
 3D quiver plot
 ==============
 
-Demonstrates plotting directional arrows at points on a 3d meshgrid.
+Demonstrates plotting directional arrows at points on a 3D meshgrid.
 """
 
 import matplotlib.pyplot as plt

examples/statistics/hexbin_demo.py

@@ -6,7 +6,7 @@
 Plotting hexbins with Matplotlib.
 
 Hexbin is an axes method or pyplot function that is essentially
-a pcolor of a 2-D histogram with hexagonal cells.  It can be
+a pcolor of a 2D histogram with hexagonal cells.  It can be
 much more informative than a scatter plot. In the first plot
 below, try substituting 'scatter' for 'hexbin'.
 """

lib/matplotlib/_cm.py

@@ -76,7 +76,7 @@ def cubehelix(gamma=1.0, s=0.5, r=-1.5, h=1.0):
     can be visualised as a squashed helix around the diagonal in the
     (r, g, b) color cube.
 
-    For a unit color cube (i.e. 3-D coordinates for (r, g, b) each in the
+    For a unit color cube (i.e. 3D coordinates for (r, g, b) each in the
     range 0 to 1) the color scheme starts at (r, g, b) = (0, 0, 0), i.e. black,
     and finishes at (r, g, b) = (1, 1, 1), i.e. white. For some fraction *x*,
     between 0 and 1, the color is the corresponding grey value at that

lib/matplotlib/axes/_axes.py

@@ -2028,11 +2028,11 @@ def step(self, x, y, *args, where='pre', data=None, **kwargs):
         Parameters
         ----------
         x : array-like
-            1-D sequence of x positions. It is assumed, but not checked, that
+            1D sequence of x positions. It is assumed, but not checked, that
             it is uniformly increasing.
 
         y : array-like
-            1-D sequence of y levels.
+            1D sequence of y levels.
 
         fmt : str, optional
             A format string, e.g. 'g' for a green line. See `.plot` for a more
@@ -4200,7 +4200,7 @@ def invalid_shape_exception(csize, xsize):
                             "RGBA sequence, which should be avoided as value-"
                             "mapping will have precedence in case its length "
                             "matches with *x* & *y*.  Please use the *color* "
-                            "keyword-argument or provide a 2-D array "
+                            "keyword-argument or provide a 2D array "
                             "with a single row if you intend to specify "
                             "the same RGB or RGBA value for all points.")
                     valid_shape = False
@@ -4253,14 +4253,14 @@ def scatter(self, x, y, s=None, c=None, marker=None, cmap=None, norm=None,
 
             - A scalar or sequence of n numbers to be mapped to colors using
               *cmap* and *norm*.
-            - A 2-D array in which the rows are RGB or RGBA.
+            - A 2D array in which the rows are RGB or RGBA.
             - A sequence of colors of length n.
             - A single color format string.
 
             Note that *c* should not be a single numeric RGB or RGBA sequence
             because that is indistinguishable from an array of values to be
             colormapped. If you want to specify the same RGB or RGBA value for
-            all points, use a 2-D array with a single row.  Otherwise, value-
+            all points, use a 2D array with a single row.  Otherwise, value-
             matching will have precedence in case of a size matching with *x*
             and *y*.
 
@@ -4341,7 +4341,7 @@ def scatter(self, x, y, s=None, c=None, marker=None, cmap=None, norm=None,
           case all masks will be combined and only unmasked points will be
           plotted.
 
-        * Fundamentally, scatter works with 1-D arrays; *x*, *y*, *s*, and *c*
+        * Fundamentally, scatter works with 1D arrays; *x*, *y*, *s*, and *c*
           may be input as N-D arrays, but within scatter they will be
           flattened. The exception is *c*, which will be flattened only if its
           size matches the size of *x* and *y*.
@@ -5597,7 +5597,7 @@ def pcolor(self, *args, shading=None, alpha=None, norm=None, cmap=None,
         Parameters
         ----------
         C : array-like
-            A scalar 2-D array. The values will be color-mapped.
+            A scalar 2D array. The values will be color-mapped.
 
         X, Y : array-like, optional
             The coordinates of the corners of quadrilaterals of a pcolormesh::
@@ -5623,7 +5623,7 @@ def pcolor(self, *args, shading=None, alpha=None, norm=None, cmap=None,
             color ``C[i, j]`` will be centered on ``(X[i, j], Y[i, j])``.
 
             If *X* and/or *Y* are 1-D arrays or column vectors they will be
-            expanded as needed into the appropriate 2-D arrays, making a
+            expanded as needed into the appropriate 2D arrays, making a
             rectangular grid.
 
         shading : {'flat', 'nearest', 'auto'}, optional
@@ -5841,7 +5841,7 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
         Parameters
         ----------
         C : array-like
-            A scalar 2-D array. The values will be color-mapped.
+            A scalar 2D array. The values will be color-mapped.
 
         X, Y : array-like, optional
             The coordinates of the corners of quadrilaterals of a pcolormesh::
@@ -5869,7 +5869,7 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
             interpolation is caried out between the quadrilateral corners.
 
             If *X* and/or *Y* are 1-D arrays or column vectors they will be
-            expanded as needed into the appropriate 2-D arrays, making a
+            expanded as needed into the appropriate 2D arrays, making a
             rectangular grid.
 
         cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`
@@ -5970,7 +5970,7 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
 
         **Differences between pcolor() and pcolormesh()**
 
-        Both methods are used to create a pseudocolor plot of a 2-D array
+        Both methods are used to create a pseudocolor plot of a 2D array
         using quadrilaterals.
 
         The main difference lies in the created object and internal data
@@ -6300,7 +6300,7 @@ def hist(self, x, bins=None, range=None, density=False, weights=None,
 
         Multiple data can be provided via *x* as a list of datasets
         of potentially different length ([*x0*, *x1*, ...]), or as
-        a 2-D ndarray in which each column is a dataset.  Note that
+        a 2D ndarray in which each column is a dataset.  Note that
         the ndarray form is transposed relative to the list form.
 
         Masked arrays are not supported.
@@ -7478,7 +7478,7 @@ def specgram(self, x, NFFT=None, Fs=None, Fc=None, detrend=None,
 
         Returns
         -------
-        spectrum : 2-D array
+        spectrum : 2D array
             Columns are the periodograms of successive segments.
 
         freqs : 1-D array

lib/matplotlib/axes/_base.py

@@ -430,7 +430,7 @@ def _plot_args(self, tup, kwargs, return_kwargs=False):
             raise ValueError(f"x and y must have same first dimension, but "
                              f"have shapes {x.shape} and {y.shape}")
         if x.ndim > 2 or y.ndim > 2:
-            raise ValueError(f"x and y can be no greater than 2-D, but have "
+            raise ValueError(f"x and y can be no greater than 2D, but have "
                              f"shapes {x.shape} and {y.shape}")
         if x.ndim == 1:
             x = x[:, np.newaxis]

lib/matplotlib/cbook/__init__.py

@@ -1300,7 +1300,7 @@ def _to_unmasked_float_array(x):
 
 
 def _check_1d(x):
-    """Convert scalars to 1d arrays; pass-through arrays as is."""
+    """Convert scalars to 1D arrays; pass-through arrays as is."""
     if not hasattr(x, 'shape') or len(x.shape) < 1:
         return np.atleast_1d(x)
     else:

lib/matplotlib/cm.py

@@ -295,7 +295,7 @@ def to_rgba(self, x, alpha=None, bytes=False, norm=True):
         """
         Return a normalized rgba array corresponding to *x*.
 
-        In the normal case, *x* is a 1-D or 2-D sequence of scalars, and
+        In the normal case, *x* is a 1D or 2D sequence of scalars, and
         the corresponding ndarray of rgba values will be returned,
         based on the norm and colormap set for this ScalarMappable.