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Commit c73cbf7

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Merge pull request matplotlib#24007 from meeseeksmachine/auto-backport-of-pr-24004-on-v3.6.x
Backport PR matplotlib#24004 on branch v3.6.x (Increase consistency in tutorials and examples)
2 parents 8f39714 + f5e6927 commit c73cbf7

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11 files changed

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-32
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11 files changed

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‎examples/color/color_demo.py

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@@ -48,9 +48,9 @@
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# 3) gray level string:
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ax.set_title('Voltage vs. time chart', color='0.7')
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# 4) single letter color string
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ax.set_xlabel('time (s)', color='c')
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ax.set_xlabel('Time [s]', color='c')
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# 5) a named color:
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ax.set_ylabel('voltage (mV)', color='peachpuff')
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ax.set_ylabel('Voltage [mV]', color='peachpuff')
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# 6) a named xkcd color:
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ax.plot(t, s, 'xkcd:crimson')
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# 7) Cn notation:

‎examples/color/custom_cmap.py

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If, as in this example, there are no discontinuities in the r, g, and b
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components, then it is quite simple: the second and third element of
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each tuple, above, is the same--call it "``y``". The first element ("``x``")
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each tuple, above, is the same -- call it "``y``". The first element ("``x``")
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defines interpolation intervals over the full range of 0 to 1, and it
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must span that whole range. In other words, the values of ``x`` divide the
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0-to-1 range into a set of segments, and ``y`` gives the end-point color

‎examples/lines_bars_and_markers/cohere.py

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@@ -16,19 +16,19 @@
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nse1 = np.random.randn(len(t)) # white noise 1
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nse2 = np.random.randn(len(t)) # white noise 2
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# Two signals with a coherent part at 10Hz and a random part
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# Two signals with a coherent part at 10 Hz and a random part
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s1 = np.sin(2 * np.pi * 10 * t) + nse1
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s2 = np.sin(2 * np.pi * 10 * t) + nse2
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fig, axs = plt.subplots(2, 1)
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axs[0].plot(t, s1, t, s2)
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axs[0].set_xlim(0, 2)
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axs[0].set_xlabel('time')
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axs[0].set_xlabel('Time')
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axs[0].set_ylabel('s1 and s2')
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axs[0].grid(True)
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cxy, f = axs[1].cohere(s1, s2, 256, 1. / dt)
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axs[1].set_ylabel('coherence')
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axs[1].set_ylabel('Coherence')
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fig.tight_layout()
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plt.show()

‎examples/lines_bars_and_markers/csd_demo.py

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@@ -33,7 +33,7 @@
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ax1.plot(t, s1, t, s2)
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ax1.set_xlim(0, 5)
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ax1.set_xlabel('time')
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ax1.set_xlabel('Time')
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ax1.set_ylabel('s1 and s2')
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ax1.grid(True)
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‎examples/pyplots/fig_axes_labels_simple.py

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@@ -11,8 +11,8 @@
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fig = plt.figure()
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fig.subplots_adjust(top=0.8)
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ax1 = fig.add_subplot(211)
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ax1.set_ylabel('volts')
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ax1.set_title('a sine wave')
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ax1.set_ylabel('Voltage [V]')
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ax1.set_title('A sine wave')
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t = np.arange(0.0, 1.0, 0.01)
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s = np.sin(2 * np.pi * t)
@@ -23,7 +23,7 @@
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ax2 = fig.add_axes([0.15, 0.1, 0.7, 0.3])
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n, bins, patches = ax2.hist(np.random.randn(1000), 50)
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ax2.set_xlabel('time (s)')
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ax2.set_xlabel('Time [s]')
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plt.show()
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‎examples/pyplots/pyplot_mathtext.py

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@@ -16,8 +16,8 @@
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plt.text(1, -0.6, r'$\sum_{i=0}^\infty x_i$', fontsize=20)
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plt.text(0.6, 0.6, r'$\mathcal{A}\mathrm{sin}(2 \omega t)$',
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fontsize=20)
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plt.xlabel('time (s)')
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plt.ylabel('volts (mV)')
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plt.xlabel('Time [s]')
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plt.ylabel('Voltage [mV]')
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plt.show()
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#############################################################################

‎tutorials/advanced/path_tutorial.py

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# ==============
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#
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# Some of the path components require multiple vertices to specify them:
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# for example CURVE 3 is a `bézier
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# for example CURVE 3 is a `Bézier
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# <https://en.wikipedia.org/wiki/B%C3%A9zier_curve>`_ curve with one
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# control point and one end point, and CURVE4 has three vertices for the
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# two control points and the end point. The example below shows a
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# CURVE4 Bézier spline -- the bézier curve will be contained in the
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# CURVE4 Bézier spline -- the Bézier curve will be contained in the
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# convex hull of the start point, the two control points, and the end
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# point
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@@ -139,8 +139,8 @@
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# for each histogram bar: the rectangle width is the bin width and the
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# rectangle height is the number of datapoints in that bin. First we'll
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# create some random normally distributed data and compute the
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# histogram. Because numpy returns the bin edges and not centers, the
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# length of ``bins`` is 1 greater than the length of ``n`` in the
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# histogram. Because NumPy returns the bin edges and not centers, the
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# length of ``bins`` is one greater than the length of ``n`` in the
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# example below::
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#
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# # histogram our data with numpy
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#
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# Now we have to construct our compound path, which will consist of a
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# series of ``MOVETO``, ``LINETO`` and ``CLOSEPOLY`` for each rectangle.
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# For each rectangle, we need 5 vertices: 1 for the ``MOVETO``, 3 for
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# the ``LINETO``, and 1 for the ``CLOSEPOLY``. As indicated in the
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# table above, the vertex for the closepoly is ignored but we still need
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# it to keep the codes aligned with the vertices::
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# For each rectangle, we need five vertices: one for the ``MOVETO``,
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# three for the ``LINETO``, and one for the ``CLOSEPOLY``. As indicated
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# in the table above, the vertex for the closepoly is ignored but we still
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# need it to keep the codes aligned with the vertices::
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#
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# nverts = nrects*(1+3+1)
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# verts = np.zeros((nverts, 2))

‎tutorials/intermediate/artists.py

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@@ -123,8 +123,8 @@ class in the Matplotlib API, and the one you will be working with most
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fig = plt.figure()
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fig.subplots_adjust(top=0.8)
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ax1 = fig.add_subplot(211)
126-
ax1.set_ylabel('volts')
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ax1.set_title('a sine wave')
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ax1.set_ylabel('Voltage [V]')
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ax1.set_title('A sine wave')
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129129
t = np.arange(0.0, 1.0, 0.01)
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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
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ax2 = fig.add_axes([0.15, 0.1, 0.7, 0.3])
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n, bins, patches = ax2.hist(np.random.randn(1000), 50,
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facecolor='yellow', edgecolor='yellow')
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ax2.set_xlabel('time (s)')
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ax2.set_xlabel('Time [s]')
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plt.show()
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‎tutorials/intermediate/constrainedlayout_guide.py

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@@ -263,7 +263,7 @@ def example_plot(ax, fontsize=12, hide_labels=False):
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##########################################
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# If there are more than two columns, the *wspace* is shared between them,
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# so here the wspace is divided in 2, with a *wspace* of 0.1 between each
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# so here the wspace is divided in two, with a *wspace* of 0.1 between each
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# column:
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fig, axs = plt.subplots(2, 3, layout="constrained")

‎tutorials/introductory/pyplot.py

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@@ -295,8 +295,10 @@ def f(t):
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# plt.figure(2) # a second figure
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# plt.plot([4, 5, 6]) # creates a subplot() by default
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#
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# plt.figure(1) # figure 1 current; subplot(212) still current
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# plt.subplot(211) # make subplot(211) in figure1 current
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# plt.figure(1) # first figure current;
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# # subplot(212) still current
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# plt.subplot(211) # make subplot(211) in the first figure
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# # current
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# plt.title('Easy as 1, 2, 3') # subplot 211 title
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#
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# You can clear the current figure with `~.pyplot.clf`

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