matplotlib · NelleV · Jun 29, 2018 · Mar 27, 2018 · May 9, 2018 · May 10, 2018
diff --git a/examples/color/color_by_yvalue.py b/examples/color/color_by_yvalue.py
@@ -22,3 +22,17 @@
 fig, ax = plt.subplots()
 ax.plot(t, smiddle, t, slower, t, supper)
 plt.show()
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions, methods, classes and modules is shown
+# in this example:
+
+import matplotlib
+matplotlib.axes.Axes.plot
+matplotlib.pyplot.plot
diff --git a/examples/color/color_cycle_default.py b/examples/color/color_cycle_default.py
@@ -3,7 +3,8 @@
 Colors in the default property cycle
 ====================================
 
-Display the colors from the default prop_cycle.
+Display the colors from the default prop_cycle, which is obtained from the
+:ref:`rc parameters<sphx_glr_tutorials_introductory_customizing.py>`.
 """
 import numpy as np
 import matplotlib.pyplot as plt
@@ -38,3 +39,21 @@
 fig.suptitle('Colors in the default prop_cycle', fontsize='large')
 
 plt.show()
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions, methods, classes and modules is shown
+# in this example:
+
+import matplotlib
+matplotlib.axes.Axes.axhline
+matplotlib.axes.Axes.axvline
+matplotlib.pyplot.axhline
+matplotlib.pyplot.axvline
+matplotlib.axes.Axes.set_facecolor
+matplotlib.figure.Figure.suptitle
diff --git a/examples/color/color_cycler.py b/examples/color/color_cycler.py
@@ -8,8 +8,10 @@
 
 This example demonstrates two different APIs:
 
-    1. Setting the default rc parameter specifying the property cycle.
-       This affects all subsequent axes (but not axes already created).
+    1. Setting the default
+       :ref:`rc parameter<sphx_glr_tutorials_introductory_customizing.py>`
+       specifying the property cycle. This affects all subsequent axes
+       (but not axes already created).
     2. Setting the property cycle for a single pair of axes.
 """
 from cycler import cycler
@@ -39,3 +41,17 @@
 # Tweak spacing between subplots to prevent labels from overlapping
 fig.subplots_adjust(hspace=0.3)
 plt.show()
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions, methods, classes and modules is shown
+# in this example:
+
+import matplotlib
+matplotlib.axes.Axes.plot
+matplotlib.axes.Axes.set_prop_cycle
diff --git a/examples/color/color_demo.py b/examples/color/color_demo.py
@@ -3,32 +3,75 @@
 Color Demo
 ==========
 
-matplotlib gives you 5 ways to specify colors,
+Matplotlib gives you 8 ways to specify colors,
 
-    1) as a single letter string, ala MATLAB
+1) an RGB or RGBA tuple of float values in ``[0, 1]`` (e.g. ``(0.1, 0.2, 0.5)``
+   or  ``(0.1, 0.2, 0.5, 0.3)``).  RGBA is short for Red, Green, Blue, Alpha;
+2) a hex RGB or RGBA string (e.g., ``'#0F0F0F'`` or ``'#0F0F0F0F'``);
+3) a string representation of a float value in ``[0, 1]`` inclusive for gray
+   level (e.g., ``'0.5'``);
+4) a single letter string, i.e. one of
+   ``{'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'}``;
+5) a X11/CSS4 ("html") color name, e.g. ``"blue"``;
+6) a name from the `xkcd color survey <https://xkcd.com/color/rgb/>`__,
+   prefixed with ``'xkcd:'`` (e.g., ``'xkcd:sky blue'``);
+7) a "Cn" color spec, i.e. `'C'` followed by a single digit, which is an index
+   into the default property cycle
+   (``matplotlib.rcParams['axes.prop_cycle']``); the indexing occurs at artist
+   creation time and defaults to black if the cycle does not include color.
+8) one of ``{'tab:blue', 'tab:orange', 'tab:green',
+   'tab:red', 'tab:purple', 'tab:brown', 'tab:pink',
+   'tab:gray', 'tab:olive', 'tab:cyan'}`` which are the Tableau Colors from the
+   'tab10' categorical palette (which is the default color cycle);
 
-    2) as an html style hex string or html color name
+For more information on colors in matplotlib see
 
-    3) as an R,G,B tuple, where R,G,B, range from 0-1
-
-    4) as a string representing a floating point number
-       from 0 to 1, corresponding to shades of gray.
-
-    5) as a special color "Cn", where n is a number 0-9 specifying the
-       nth color in the currently active color cycle.
-
-See help(colors) for more info.
+* the :ref:`sphx_glr_tutorials_colors_colors.py` tutorial;
+* the `matplotlib.colors` API;
+* the :ref:`sphx_glr_gallery_color_named_colors.py` example.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 
-t = np.arange(0.0, 2.0, 0.01)
+t = np.linspace(0.0, 2.0, 201)
 s = np.sin(2 * np.pi * t)
 
-fig, ax = plt.subplots(facecolor='darkslategray')
-ax.plot(t, s, 'C1')
-ax.set_xlabel('time (s)', color='C1')
-ax.set_ylabel('voltage (mV)', color='0.5')  # grayscale color
-ax.set_title('About as silly as it gets, folks', color='#afeeee')
+# 1) RGB tuple:
+fig, ax = plt.subplots(facecolor=(.18, .31, .31))
+# 2) hex string:
+ax.set_facecolor('#eafff5')
+# 3) gray level string:
+ax.set_title('Voltage vs. time chart', color='0.7')
+# 4) single letter color string
+ax.set_xlabel('time (s)', color='c')
+# 5) a named color:
+ax.set_ylabel('voltage (mV)', color='peachpuff')
+# 6) a named xkcd color:
+ax.plot(t, s, 'xkcd:crimson')
+# 7) Cn notation:
+ax.plot(t, .7*s, color='C4', linestyle='--')
+# 8) tab notation:
+ax.tick_params(labelcolor='tab:orange')
+
 
 plt.show()
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions, methods, classes and modules is shown
+# in this example:
+
+import matplotlib
+matplotlib.colors
+matplotlib.axes.Axes.plot
+matplotlib.axes.Axes.set_facecolor
+matplotlib.axes.Axes.set_title
+matplotlib.axes.Axes.set_xlabel
+matplotlib.axes.Axes.set_ylabel
+matplotlib.axes.Axes.tick_params
diff --git a/examples/color/colorbar_basics.py b/examples/color/colorbar_basics.py
@@ -3,8 +3,9 @@
 Colorbar
 ========
 
-Use colorbar by specifying the mappable object (here
-the imshow returned object) and the axes to attach the colorbar to.
+Use `~.figure.Figure.colorbar` by specifying the mappable object (here
+the `~.matplotlib.image.AxesImage` returned by `~.axes.Axes.imshow`)
+and the axes to attach the colorbar to.
 """
 
 import numpy as np
@@ -35,3 +36,19 @@
 fig.colorbar(neg, ax=ax2)
 
 plt.show()
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions, methods, classes and modules is shown
+# in this example:
+
+import matplotlib
+matplotlib.axes.Axes.imshow
+matplotlib.pyplot.imshow
+matplotlib.figure.Figure.colorbar
+matplotlib.pyplot.colorbar
diff --git a/examples/color/colormap_reference.py b/examples/color/colormap_reference.py
@@ -65,3 +65,19 @@ def plot_color_gradients(cmap_category, cmap_list, nrows):
     plot_color_gradients(cmap_category, cmap_list, nrows)
 
 plt.show()
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions, methods, classes and modules is shown
+# in this example:
+
+import matplotlib
+matplotlib.colors
+matplotlib.axes.Axes.imshow
+matplotlib.figure.Figure.text
+matplotlib.axes.Axes.set_axis_off
diff --git a/examples/color/named_colors.py b/examples/color/named_colors.py
@@ -4,6 +4,12 @@
 ========================
 
 Simple plot example with the named colors and its visual representation.
+
+For more information on colors in matplotlib see
+
+* the :ref:`sphx_glr_tutorials_colors_colors.py` tutorial;
+* the `matplotlib.colors` API;
+* the :ref:`sphx_glr_gallery_color_color_demo.py`.
 """
 from __future__ import division
 
@@ -53,3 +59,22 @@
                     top=1, bottom=0,
                     hspace=0, wspace=0)
 plt.show()
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions, methods, classes and modules is shown
+# in this example:
+
+import matplotlib
+matplotlib.colors
+matplotlib.colors.rgb_to_hsv
+matplotlib.colors.to_rgba
+matplotlib.figure.Figure.get_size_inches
+matplotlib.figure.Figure.subplots_adjust
+matplotlib.axes.Axes.text
+matplotlib.axes.Axes.hlines
diff --git a/examples/images_contours_and_fields/affine_image.py b/examples/images_contours_and_fields/affine_image.py
@@ -3,6 +3,13 @@
 Affine transform of an image
 ============================
 
+
+Prepending an affine transformation (:class:`~.transforms.Affine2D`)
+to the :ref:`data transform <data-coords>`
+of an image allows to manipulate the image's shape and orientation.
+This is an example of the concept of
+:ref:`transform chaining <transformation-pipeline>`.
+
 For the backends that support draw_image with optional affine
 transform (e.g., agg, ps backend), the image of the output should
 have its boundary match the dashed yellow rectangle.
@@ -57,3 +64,19 @@ def do_plot(ax, Z, transform):
         rotate_deg(30).skew_deg(30, 15).scale(-1, .5).translate(.5, -1))
 
 plt.show()
+
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions, methods and classes is shown
+# in this example:
+
+import matplotlib
+matplotlib.axes.Axes.imshow
+matplotlib.pyplot.imshow
+matplotlib.transforms.Affine2D
diff --git a/examples/images_contours_and_fields/barb_demo.py b/examples/images_contours_and_fields/barb_demo.py
@@ -22,26 +22,23 @@
 data = np.array(data, dtype=[('x', np.float32), ('y', np.float32),
                              ('u', np.float32), ('v', np.float32)])
 
+fig1, axs1 = plt.subplots(nrows=2, ncols=2)
 # Default parameters, uniform grid
-ax = plt.subplot(2, 2, 1)
-ax.barbs(X, Y, U, V)
+axs1[0, 0].barbs(X, Y, U, V)
 
 # Arbitrary set of vectors, make them longer and change the pivot point
 # (point around which they're rotated) to be the middle
-ax = plt.subplot(2, 2, 2)
-ax.barbs(data['x'], data['y'], data['u'], data['v'], length=8, pivot='middle')
+axs1[0, 1].barbs(data['x'], data['y'], data['u'], data['v'], length=8, pivot='middle')
 
 # Showing colormapping with uniform grid.  Fill the circle for an empty barb,
 # don't round the values, and change some of the size parameters
-ax = plt.subplot(2, 2, 3)
-ax.barbs(X, Y, U, V, np.sqrt(U * U + V * V), fill_empty=True, rounding=False,
-         sizes=dict(emptybarb=0.25, spacing=0.2, height=0.3))
+axs1[1, 0].barbs(X, Y, U, V, np.sqrt(U * U + V * V), fill_empty=True, rounding=False,
+                 sizes=dict(emptybarb=0.25, spacing=0.2, height=0.3))
 
 # Change colors as well as the increments for parts of the barbs
-ax = plt.subplot(2, 2, 4)
-ax.barbs(data['x'], data['y'], data['u'], data['v'], flagcolor='r',
-         barbcolor=['b', 'g'],
-         barb_increments=dict(half=10, full=20, flag=100), flip_barb=True)
+axs1[1, 1].barbs(data['x'], data['y'], data['u'], data['v'], flagcolor='r',
+                 barbcolor=['b', 'g'], flip_barb=True,
+                 barb_increments=dict(half=10, full=20, flag=100))
 
 # Masked arrays are also supported
 masked_u = np.ma.masked_array(data['u'])
@@ -50,8 +47,21 @@
 
 # Identical plot to panel 2 in the first figure, but with the point at
 # (0.5, 0.25) missing (masked)
-fig2 = plt.figure()
-ax = fig2.add_subplot(1, 1, 1)
-ax.barbs(data['x'], data['y'], masked_u, data['v'], length=8, pivot='middle')
+fig2, ax2 = plt.subplots()
+ax2.barbs(data['x'], data['y'], masked_u, data['v'], length=8, pivot='middle')
 
 plt.show()
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions, methods and classes is shown
+# in this example:
+
+import matplotlib
+matplotlib.axes.Axes.barbs
+matplotlib.pyplot.barbs
diff --git a/examples/images_contours_and_fields/barcode_demo.py b/examples/images_contours_and_fields/barcode_demo.py
@@ -3,6 +3,7 @@
 Barcode Demo
 ============
 
+This demo shows how to produce a one-dimensional image, or "bar code".
 """
 import matplotlib.pyplot as plt
 import numpy as np
@@ -19,13 +20,27 @@
 
 fig = plt.figure()
 
-# a vertical barcode -- this is broken at present
-ax = fig.add_axes([0.1, 0.3, 0.1, 0.6], **axprops)
-ax.imshow(x.reshape((-1, 1)), **barprops)
+# a vertical barcode
+ax1 = fig.add_axes([0.1, 0.3, 0.1, 0.6], **axprops)
+ax1.imshow(x.reshape((-1, 1)), **barprops)
 
 # a horizontal barcode
-ax = fig.add_axes([0.3, 0.1, 0.6, 0.1], **axprops)
-ax.imshow(x.reshape((1, -1)), **barprops)
+ax2 = fig.add_axes([0.3, 0.1, 0.6, 0.1], **axprops)
+ax2.imshow(x.reshape((1, -1)), **barprops)
 
 
 plt.show()
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The use of the following functions, methods and classes is shown
+# in this example:
+
+import matplotlib
+matplotlib.axes.Axes.imshow
+matplotlib.pyplot.imshow