@@ -48,9 +48,9 @@
 # 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')
+ax.set_xlabel('Time [s]', color='c')
 # 5) a named color:
-ax.set_ylabel('voltage (mV)', color='peachpuff')
+ax.set_ylabel('Voltage [mV]', color='peachpuff')
 # 6) a named xkcd color:
 ax.plot(t, s, 'xkcd:crimson')
 # 7) Cn notation:
 
@@ -42,7 +42,7 @@
 
 If, as in this example, there are no discontinuities in the r, g, and b
 components, then it is quite simple: the second and third element of
-each tuple, above, is the same--call it "``y``".  The first element ("``x``")
+each tuple, above, is the same -- call it "``y``".  The first element ("``x``")
 defines interpolation intervals over the full range of 0 to 1, and it
 must span that whole range.  In other words, the values of ``x`` divide the
 0-to-1 range into a set of segments, and ``y`` gives the end-point color
 
@@ -16,19 +16,19 @@
 nse1 = np.random.randn(len(t))                 # white noise 1
 nse2 = np.random.randn(len(t))                 # white noise 2
 
-# Two signals with a coherent part at 10Hz and a random part
+# Two signals with a coherent part at 10 Hz and a random part
 s1 = np.sin(2 * np.pi * 10 * t) + nse1
 s2 = np.sin(2 * np.pi * 10 * t) + nse2
 
 fig, axs = plt.subplots(2, 1)
 axs[0].plot(t, s1, t, s2)
 axs[0].set_xlim(0, 2)
-axs[0].set_xlabel('time')
+axs[0].set_xlabel('Time')
 axs[0].set_ylabel('s1 and s2')
 axs[0].grid(True)
 
 cxy, f = axs[1].cohere(s1, s2, 256, 1. / dt)
-axs[1].set_ylabel('coherence')
+axs[1].set_ylabel('Coherence')
 
 fig.tight_layout()
 plt.show()
@@ -33,7 +33,7 @@
 
 ax1.plot(t, s1, t, s2)
 ax1.set_xlim(0, 5)
-ax1.set_xlabel('time')
+ax1.set_xlabel('Time')
 ax1.set_ylabel('s1 and s2')
 ax1.grid(True)
 
 
@@ -11,8 +11,8 @@
 fig = plt.figure()
 fig.subplots_adjust(top=0.8)
 ax1 = fig.add_subplot(211)
-ax1.set_ylabel('volts')
-ax1.set_title('a sine wave')
+ax1.set_ylabel('Voltage [V]')
+ax1.set_title('A sine wave')
 
 t = np.arange(0.0, 1.0, 0.01)
 s = np.sin(2 * np.pi * t)
@@ -23,7 +23,7 @@
 
 ax2 = fig.add_axes([0.15, 0.1, 0.7, 0.3])
 n, bins, patches = ax2.hist(np.random.randn(1000), 50)
-ax2.set_xlabel('time (s)')
+ax2.set_xlabel('Time [s]')
 
 plt.show()
 
 
@@ -16,8 +16,8 @@
 plt.text(1, -0.6, r'$\sum_{i=0}^\infty x_i$', fontsize=20)
 plt.text(0.6, 0.6, r'$\mathcal{A}\mathrm{sin}(2 \omega t)$',
          fontsize=20)
-plt.xlabel('time (s)')
-plt.ylabel('volts (mV)')
+plt.xlabel('Time [s]')
+plt.ylabel('Voltage [mV]')
 plt.show()
 
 #############################################################################
 
@@ -76,11 +76,11 @@
 # ==============
 #
 # Some of the path components require multiple vertices to specify them:
-# for example CURVE 3 is a `bézier
+# for example CURVE 3 is a `Bézier
 # <https://en.wikipedia.org/wiki/B%C3%A9zier_curve>`_ curve with one
 # control point and one end point, and CURVE4 has three vertices for the
 # two control points and the end point.  The example below shows a
-# CURVE4 Bézier spline -- the bézier curve will be contained in the
+# CURVE4 Bézier spline -- the Bézier curve will be contained in the
 # convex hull of the start point, the two control points, and the end
 # point
 
@@ -139,8 +139,8 @@
 # for each histogram bar: the rectangle width is the bin width and the
 # rectangle height is the number of datapoints in that bin.  First we'll
 # create some random normally distributed data and compute the
-# histogram.  Because numpy returns the bin edges and not centers, the
-# length of ``bins`` is 1 greater than the length of ``n`` in the
+# histogram.  Because NumPy returns the bin edges and not centers, the
+# length of ``bins`` is one greater than the length of ``n`` in the
 # example below::
 #
 #     # histogram our data with numpy
@@ -159,10 +159,10 @@
 #
 # Now we have to construct our compound path, which will consist of a
 # series of ``MOVETO``, ``LINETO`` and ``CLOSEPOLY`` for each rectangle.
-# For each rectangle, we need 5 vertices: 1 for the ``MOVETO``, 3 for
-# the ``LINETO``, and 1 for the ``CLOSEPOLY``.  As indicated in the
-# table above, the vertex for the closepoly is ignored but we still need
-# it to keep the codes aligned with the vertices::
+# For each rectangle, we need five vertices: one for the ``MOVETO``,
+# three for the ``LINETO``, and one for the ``CLOSEPOLY``.  As indicated
+# in the table above, the vertex for the closepoly is ignored but we still
+# need it to keep the codes aligned with the vertices::
 #
 #     nverts = nrects*(1+3+1)
 #     verts = np.zeros((nverts, 2))
 
@@ -123,8 +123,8 @@ class in the Matplotlib API, and the one you will be working with most
 fig = plt.figure()
 fig.subplots_adjust(top=0.8)
 ax1 = fig.add_subplot(211)
-ax1.set_ylabel('volts')
-ax1.set_title('a sine wave')
+ax1.set_ylabel('Voltage [V]')
+ax1.set_title('A sine wave')
 
 t = np.arange(0.0, 1.0, 0.01)
 s = np.sin(2*np.pi*t)
@@ -136,7 +136,7 @@ class in the Matplotlib API, and the one you will be working with most
 ax2 = fig.add_axes([0.15, 0.1, 0.7, 0.3])
 n, bins, patches = ax2.hist(np.random.randn(1000), 50,
                             facecolor='yellow', edgecolor='yellow')
-ax2.set_xlabel('time (s)')
+ax2.set_xlabel('Time [s]')
 
 plt.show()
 
 
@@ -263,7 +263,7 @@ def example_plot(ax, fontsize=12, hide_labels=False):
 
 ##########################################
 # If there are more than two columns, the *wspace* is shared between them,
-# so here the wspace is divided in 2, with a *wspace* of 0.1 between each
+# so here the wspace is divided in two, with a *wspace* of 0.1 between each
 # column:
 
 fig, axs = plt.subplots(2, 3, layout="constrained")
 
@@ -295,8 +295,10 @@ def f(t):
 #     plt.figure(2)                # a second figure
 #     plt.plot([4, 5, 6])          # creates a subplot() by default
 #
-#     plt.figure(1)                # figure 1 current; subplot(212) still current
-#     plt.subplot(211)             # make subplot(211) in figure1 current
+#     plt.figure(1)                # first figure current;
+#                                  # subplot(212) still current
+#     plt.subplot(211)             # make subplot(211) in the first figure
+#                                  # current
 #     plt.title('Easy as 1, 2, 3') # subplot 211 title
 #
 # You can clear the current figure with `~.pyplot.clf`
Original file line number	Diff line number	Diff line change
@@ -295,8 +295,10 @@ def f(t):
295	295	# plt.figure(2) # a second figure
296	296	# plt.plot([4, 5, 6]) # creates a subplot() by default
297	297	#
298		- # plt.figure(1) # figure 1 current; subplot(212) still current
299		- # plt.subplot(211) # make subplot(211) in figure1 current
	298	+ # plt.figure(1) # first figure current;
	299	+ # # subplot(212) still current
	300	+ # plt.subplot(211) # make subplot(211) in the first figure
	301	+ # # current
300	302	# plt.title('Easy as 1, 2, 3') # subplot 211 title
301	303	#
302	304	# You can clear the current figure with `~.pyplot.clf`