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DOC: some minor fixes to the usage rewrite #21794

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Nov 29, 2021
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DOC: some minor fixes to the usage rewrite #21794

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fd83646
DOC: some minor fixes to the usage rewrite
jklymak Nov 29, 2021
94d2fd5
DOC: add show to each example
jklymak Nov 29, 2021
26fc6a1
DOC: suppress extra prints
jklymak Nov 29, 2021
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2 changes: 1 addition & 1 deletion .flake8
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Original file line number Diff line number Diff line change
Expand Up @@ -100,7 +100,7 @@ per-file-ignores =
tutorials/introductory/images.py: E402, E501
tutorials/introductory/pyplot.py: E402, E501
tutorials/introductory/sample_plots.py: E501
tutorials/introductory/usage.py: E501
tutorials/introductory/usage.py: E703
tutorials/text/annotations.py: E402, E501
tutorials/text/text_intro.py: E402
tutorials/text/text_props.py: E501
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79 changes: 39 additions & 40 deletions tutorials/introductory/usage.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@
# `.Axes.plot` to draw some data on the axes:

fig, ax = plt.subplots() # Create a figure containing a single axes.
ax.plot([1, 2, 3, 4], [1, 4, 2, 3]) # Plot some data on the axes.
ax.plot([1, 2, 3, 4], [1, 4, 2, 3]); # Plot some data on the axes.

###############################################################################
# .. _figure_parts:
Expand Down Expand Up @@ -123,14 +123,14 @@
fig, ax = plt.subplots(figsize=(5, 2.7), constrained_layout=True)
ax.scatter('a', 'b', c='c', s='d', data=data)
ax.set_xlabel('entry a')
ax.set_ylabel('entry b')
ax.set_ylabel('entry b');

##############################################################################
# .. _coding_styles:
#
# Coding styles
# =============

#
# The object-oriented and the pyplot interfaces
# ---------------------------------------------
#
Expand All @@ -153,7 +153,7 @@
ax.set_xlabel('x label') # Add an x-label to the axes.
ax.set_ylabel('y label') # Add a y-label to the axes.
ax.set_title("Simple Plot") # Add a title to the axes.
ax.legend() # Add a legend.
ax.legend(); # Add a legend.

###############################################################################
# or the pyplot-style:
Expand All @@ -167,19 +167,19 @@
plt.xlabel('x label')
plt.ylabel('y label')
plt.title("Simple Plot")
plt.legend()
plt.legend();

###############################################################################
# (In addition, there is a third approach, for the case when embedding
# Matplotlib in a GUI application, which completely drops pyplot, even for
# figure creation. See the corresponding section in the gallery for more info
# (:ref:`user_interfaces`).)
# figure creation. See the corresponding section in the gallery for more info:
# :ref:`user_interfaces`.)
#
# Matplotlib's documentation and examples use both the OO and the pyplot
# styles. In general, we suggest using the OO style, particularly for
# complicated plots, and functions and scripts that are intended to be reused
# as part of a larger project. However, the pyplot style can be very conveneient
# for quick interactive work.
# as part of a larger project. However, the pyplot style can be very
# conveneient for quick interactive work.
#
# .. note::
#
Expand All @@ -189,9 +189,9 @@
# Making a helper functions
# -------------------------
#
# If you need to make the same plots over and over again with different data sets,
# or want to easily wrap Matplotlib methods, use the recommended signature function
# below.
# If you need to make the same plots over and over again with different data
# sets, or want to easily wrap Matplotlib methods, use the recommended
# signature function below.


def my_plotter(ax, data1, data2, param_dict):
Expand All @@ -208,11 +208,9 @@ def my_plotter(ax, data1, data2, param_dict):
xdata = np.arange(len(data1)) # make an ordinal for this
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(5, 2.7))
my_plotter(ax1, data1, data2, {'marker': 'x'})
my_plotter(ax2, data3, data4, {'marker': 'o'})
my_plotter(ax2, data3, data4, {'marker': 'o'});

###############################################################################
# These examples provide convenience for more complex graphs.
#
# Note that if you want to install these as a python package, or any other
# customizations you could use use one of the many templates on the web;
# Matplotlib has one at `mpl-cookiecutter
Expand All @@ -232,7 +230,7 @@ def my_plotter(ax, data1, data2, param_dict):
x = np.arange(len(data1))
ax.plot(x, np.cumsum(data1), color='blue', linewidth=3, linestyle='--')
l, = ax.plot(x, np.cumsum(data2), color='orange', linewidth=2)
l.set_linestyle(':')
l.set_linestyle(':');

###############################################################################
# Colors
Expand All @@ -246,7 +244,7 @@ def my_plotter(ax, data1, data2, param_dict):

fig, ax = plt.subplots(figsize=(5, 2.7))
x = np.arange(len(data1))
ax.scatter(data1, data2, s=50, facecolor='C0', edgecolor='k')
ax.scatter(data1, data2, s=50, facecolor='C0', edgecolor='k');

###############################################################################
# Linewidths, linestyles, and markersizes
Expand All @@ -260,8 +258,8 @@ def my_plotter(ax, data1, data2, param_dict):
# Marker size depends on the method being used. `~.Axes.plot` specifies
# markersize in points, and is generally the "diameter" or width of the
# marker. `~.Axes.scatter` specifies markersize as approximately
# proportional to the visual area of the marker. There are also an array of
# markerstyles available as string codes (see :mod:`~.matplotlib.markers`) or
# proportional to the visual area of the marker. There is an array of
# markerstyles available as string codes (see :mod:`~.matplotlib.markers`), or
# users can define their own `~.MarkerStyle` (see
# :doc:`/gallery/lines_bars_and_markers/marker_reference`):

Expand All @@ -270,19 +268,19 @@ def my_plotter(ax, data1, data2, param_dict):
ax.plot(data2, 'd', label='data2')
ax.plot(data3, 'v', label='data3')
ax.plot(data4, 's', label='data4')
ax.legend()
ax.legend();

################################################################################
###############################################################################
#
# Labelling plots
# ===============
#
# Axes labels and text
# --------------------
#
# `~.Axes.set_xlabel`, `~.Axes.set_ylabel` and `~.Axes.set_title` are used to
# add text in the indicated locations (see :doc:`/tutorials/text/text_intro` for
# more discussion). Text can also be directly added to plots using
# `~.Axes.set_xlabel`, `~.Axes.set_ylabel`, and `~.Axes.set_title` are used to
# add text in the indicated locations (see :doc:`/tutorials/text/text_intro`
# for more discussion). Text can also be directly added to plots using
# `~.Axes.text`:

mu, sigma = 115, 15
Expand All @@ -296,8 +294,7 @@ def my_plotter(ax, data1, data2, param_dict):
ax.set_title('Aardvark lengths\n (not really)')
ax.text(75, .025, r'$\mu=115,\ \sigma=15$')
ax.axis([55, 175, 0, 0.03])
ax.grid(True)
plt.show()
ax.grid(True);

###############################################################################
# All of the `~.Axes.text` functions return a `matplotlib.text.Text`
Expand Down Expand Up @@ -341,7 +338,7 @@ def my_plotter(ax, data1, data2, param_dict):
ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05))

ax.set_ylim(-2, 2)
ax.set_ylim(-2, 2);

###############################################################################
# In this basic example, both *xy* and *xytext* are in data coordinates.
Expand All @@ -359,7 +356,7 @@ def my_plotter(ax, data1, data2, param_dict):
ax.plot(np.arange(len(data1)), data1, label='data1')
ax.plot(np.arange(len(data2)), data2, label='data2')
ax.plot(np.arange(len(data3)), data3, 'd', label='data3')
ax.legend()
ax.legend();

##############################################################################
# Legends in Matplotlib are quite flexible in layout, placement, and what
Expand All @@ -369,9 +366,9 @@ def my_plotter(ax, data1, data2, param_dict):
# Axis scales and ticks
# =====================
#
# Each Axes has two (or three) `~.axis.Axis` objects represnting the x- and y-axis.
# These control the *scale* of the axis, the tick *Locators* and the tick
# *Formatters*.
# Each Axes has two (or three) `~.axis.Axis` objects represnting the x- and
# y-axis. These control the *scale* of the axis, the tick *Locators* and the
# tick *Formatters*.
#
# Scales
# ------
Expand All @@ -388,7 +385,7 @@ def my_plotter(ax, data1, data2, param_dict):
axs[0].plot(xdata, data)

axs[1].set_yscale('log')
axs[1].plot(xdata, data)
axs[1].plot(xdata, data);

##############################################################################
# The scale sets the mapping from data values to spacing along the Axis. This
Expand All @@ -409,7 +406,7 @@ def my_plotter(ax, data1, data2, param_dict):
axs[1].plot(xdata, data1)
axs[1].set_xticks(np.arange(0, 100, 30), ['zero', '30', 'sixty', '90'])
axs[1].set_yticks([-1.5, 0, 1.5]) # note that we don't need to specify labels
axs[1].set_title('Manual ticks')
axs[1].set_title('Manual ticks');

##############################################################################
# Different scales can have different locators and formatters; for instance
Expand All @@ -421,15 +418,15 @@ def my_plotter(ax, data1, data2, param_dict):
# Plotting dates and strings
# --------------------------
#
# Matplotlib can handle plotting arrays of dates and arrays of strings as
# Matplotlib can handle plotting arrays of dates and arrays of strings, as
# well as floating point numbers. These get special locators and formatters
# as appropriate. For dates:

fig, ax = plt.subplots(figsize=(5, 3.7), constrained_layout=True)
dates = np.arange(np.datetime64('2021-11-15'), np.datetime64('2021-12-25'),
np.timedelta64(1, 'h'))
data = np.cumsum(np.random.randn(len(dates)))
ax.plot(dates, data)
ax.plot(dates, data);

##############################################################################
# For more information see the date examples
Expand All @@ -441,13 +438,13 @@ def my_plotter(ax, data1, data2, param_dict):
fig, ax = plt.subplots(figsize=(5, 2.7), constrained_layout=True)
categories = ['turnips', 'rutabega', 'cucumber', 'pumpkins']

ax.bar(categories, np.random.rand(len(categories)))
ax.bar(categories, np.random.rand(len(categories)));

##############################################################################
# One caveat about categorical plotting is that some methods of parsing
# text files return a list of strings, even if the strings all represent
# numbers or dates. If you pass 1000 strings Matplotlib will think you
# meant 1000 categories and will add 1000 ticks to your plot.
# numbers or dates. If you pass 1000 strings, Matplotlib will think you
# meant 1000 categories and will add 1000 ticks to your plot!
#
# Color mapped data
# =================
Expand All @@ -474,7 +471,7 @@ def my_plotter(ax, data1, data2, param_dict):

pc = axs[1, 1].scatter(data1, data2, c=data3, cmap='RdBu_r')
fig.colorbar(pc, ax=axs[1, 1], extend='both')
axs[1, 1].set_title('scatter()')
axs[1, 1].set_title('scatter()');

##############################################################################
# Colormaps
Expand Down Expand Up @@ -507,7 +504,9 @@ def my_plotter(ax, data1, data2, param_dict):
# :doc:`/gallery/subplots_axes_and_figures/colorbar_placement` for
# details. You can also change the appearance of colorbars with the
# *extend* keyword to add arrows to the ends, and *shrink* and *aspect* to
# control the size.
# control the size. Finally, the colorbar will have default Locators
# and Formatters appropriate to the Norm. These can be changed as for
# other axis objects.
#
#
# Working with multiple figures and axes
Expand Down