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Sep 18, 2020
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Prose copy edits #18474

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9b55f1f
Prose copy edits for Usage Guide
jeromefv Sep 14, 2020
e7ce534
DOCS: Copy edit for Usage Guide
jeromefv Sep 14, 2020
9e9562b
Removed usage.rst file
jeromefv Sep 14, 2020
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48 changes: 24 additions & 24 deletions tutorials/introductory/usage.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
# Matplotlib graphs your data on `~.figure.Figure`\s (i.e., windows, Jupyter
# widgets, etc.), each of which can contain one or more `~.axes.Axes` (i.e., an
# area where points can be specified in terms of x-y coordinates (or theta-r
# in a polar plot, or x-y-z in a 3D plot, etc.). The most simple way of
# in a polar plot, or x-y-z in a 3D plot, etc.). The simplest way of
# creating a figure with an axes is using `.pyplot.subplots`. We can then use
# `.Axes.plot` to draw some data on the axes:

Expand All @@ -39,7 +39,7 @@
# In fact, you can do the same in Matplotlib: for each `~.axes.Axes` graphing
# method, there is a corresponding function in the :mod:`matplotlib.pyplot`
# module that performs that plot on the "current" axes, creating that axes (and
# its parent figure) if they don't exist yet. So the previous example can be
# its parent figure) if they don't exist yet. So, the previous example can be
# written more shortly as

plt.plot([1, 2, 3, 4], [1, 4, 2, 3]) # Matplotlib plot.
Expand Down Expand Up @@ -108,7 +108,7 @@
# :class:`~matplotlib.artist.Artist`
# ----------------------------------
#
# Basically everything you can see on the figure is an artist (even the
# Basically, everything you can see on the figure is an artist (even the
# `.Figure`, `Axes <.axes.Axes>`, and `~.axis.Axis` objects). This includes
# `.Text` objects, `.Line2D` objects, :mod:`.collections` objects, `.Patch`
# objects ... (you get the idea). When the figure is rendered, all of the
Expand Down Expand Up @@ -175,7 +175,7 @@
plt.legend()

###############################################################################
# Actually there is a third approach, for the case where you are embedding
# 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. We won't discuss it here; see the corresponding section in
# the gallery for more info (:ref:`user_interfaces`).
Expand All @@ -200,7 +200,7 @@
# ...
#
# for an even more MATLAB-like style. This approach is strongly discouraged
# nowadays and deprecated; it is only mentioned here because you may still
# nowadays and deprecated. It is only mentioned here because you may still
# encounter it in the wild.
#
# Typically one finds oneself making the same plots over and over
Expand Down Expand Up @@ -266,17 +266,17 @@ def my_plotter(ax, data1, data2, param_dict):
#
# A lot of documentation on the website and in the mailing lists refers
# to the "backend" and many new users are confused by this term.
# matplotlib targets many different use cases and output formats. Some
# people use matplotlib interactively from the python shell and have
# Matplotlib targets many different use cases and output formats. Some
# people use Matplotlib interactively from the python shell and have
# plotting windows pop up when they type commands. Some people run
# `Jupyter <https://jupyter.org>`_ notebooks and draw inline plots for
# quick data analysis. Others embed matplotlib into graphical user
# quick data analysis. Others embed Matplotlib into graphical user
# interfaces like wxpython or pygtk to build rich applications. Some
# people use matplotlib in batch scripts to generate postscript images
# people use Matplotlib in batch scripts to generate postscript images
# from numerical simulations, and still others run web application
# servers to dynamically serve up graphs.
#
# To support all of these use cases, matplotlib can target different
# To support all of these use cases, Matplotlib can target different
# outputs, and each of these capabilities is called a backend; the
# "frontend" is the user facing code, i.e., the plotting code, whereas the
# "backend" does all the hard work behind-the-scenes to make the figure.
Expand Down Expand Up @@ -343,7 +343,7 @@ def my_plotter(ax, data1, data2, param_dict):
# import matplotlib
# matplotlib.use('qt5agg')
#
# This should be done before any figure is created; otherwise Matplotlib may
# This should be done before any figure is created, otherwise Matplotlib may
# fail to switch the backend and raise an ImportError.
#
# Using `~matplotlib.use` will require changes in your code if users want to
Expand All @@ -357,16 +357,16 @@ def my_plotter(ax, data1, data2, param_dict):
#
# By default, Matplotlib should automatically select a default backend which
# allows both interactive work and plotting from scripts, with output to the
# screen and/or to a file, so at least initially you will not need to worry
# screen and/or to a file, so at least initially, you will not need to worry
# about the backend. The most common exception is if your Python distribution
# comes without :mod:`tkinter` and you have no other GUI toolkit installed;
# this happens on certain Linux distributions, where you need to install a
# comes without :mod:`tkinter` and you have no other GUI toolkit installed.
# This happens on certain Linux distributions, where you need to install a
# Linux package named ``python-tk`` (or similar).
#
# If, however, you want to write graphical user interfaces, or a web
# application server (:ref:`howto-webapp`), or need a better
# understanding of what is going on, read on. To make things a little
# more customizable for graphical user interfaces, matplotlib separates
# more customizable for graphical user interfaces, Matplotlib separates
# the concept of the renderer (the thing that actually does the drawing)
# from the canvas (the place where the drawing goes). The canonical
# renderer for user interfaces is ``Agg`` which uses the `Anti-Grain
Expand All @@ -383,7 +383,7 @@ def my_plotter(ax, data1, data2, param_dict):
# generate a pixel representation of the line whose accuracy depends on a
# DPI setting.
#
# Here is a summary of the matplotlib renderers (there is an eponymous
# Here is a summary of the Matplotlib renderers (there is an eponymous
# backend for each; these are *non-interactive backends*, capable of
# writing to a file):
#
Expand Down Expand Up @@ -484,7 +484,7 @@ def my_plotter(ax, data1, data2, param_dict):
# ``pyside`` to use ``PyQt4`` or ``PySide``, respectively.
#
# Since the default value for the bindings to be used is ``PyQt4``, Matplotlib
# first tries to import it, if the import fails, it tries to import ``PySide``.
# first tries to import it. If the import fails, it tries to import ``PySide``.
#
# Using non-builtin backends
# --------------------------
Expand All @@ -505,20 +505,20 @@ def my_plotter(ax, data1, data2, param_dict):
# and whether a script or shell session continues after a plot
# is drawn on the screen, depends on the functions and methods
# that are called, and on a state variable that determines whether
# matplotlib is in "interactive mode". The default Boolean value is set
# Matplotlib is in "interactive mode". The default Boolean value is set
# by the :file:`matplotlibrc` file, and may be customized like any other
# configuration parameter (see :doc:`/tutorials/introductory/customizing`). It
# may also be set via :func:`matplotlib.interactive`, and its
# value may be queried via :func:`matplotlib.is_interactive`. Turning
# interactive mode on and off in the middle of a stream of plotting
# commands, whether in a script or in a shell, is rarely needed
# and potentially confusing, so in the following we will assume all
# and potentially confusing. In the following, we will assume all
# plotting is done with interactive mode either on or off.
#
# .. note::
# Major changes related to interactivity, and in particular the
# role and behavior of :func:`~matplotlib.pyplot.show`, were made in the
# transition to matplotlib version 1.0, and bugs were fixed in
# transition to Matplotlib version 1.0, and bugs were fixed in
# 1.0.1. Here we describe the version 1.0.1 behavior for the
# primary interactive backends, with the partial exception of
# *macosx*.
Expand Down Expand Up @@ -558,7 +558,7 @@ def my_plotter(ax, data1, data2, param_dict):
# ax.plot([3.1, 2.2])
#
# If you are using certain backends (like ``macosx``), or an older version
# of matplotlib, you may not see the new line added to the plot immediately.
# of Matplotlib, you may not see the new line added to the plot immediately.
# In this case, you need to explicitly call :func:`~matplotlib.pyplot.draw`
# in order to update the plot::
#
Expand Down Expand Up @@ -610,7 +610,7 @@ def my_plotter(ax, data1, data2, param_dict):
# plt.plot(np.random.rand(10))
# plt.show()
#
# which makes three plots, one at a time. I.e. the second plot will show up,
# This makes three plots, one at a time. I.e., the second plot will show up
# once the first plot is closed.
#
# Summary
Expand Down Expand Up @@ -714,7 +714,7 @@ def my_plotter(ax, data1, data2, param_dict):
#
# plt.plot(x, y, markevery=10)
#
# The markevery argument allows for naive subsampling, or an
# The ``markevery`` argument allows for naive subsampling, or an
# attempt at evenly spaced (along the *x* axis) sampling. See the
# :doc:`/gallery/lines_bars_and_markers/markevery_demo`
# for more information.
Expand Down Expand Up @@ -776,7 +776,7 @@ def my_plotter(ax, data1, data2, param_dict):
# import matplotlib.style as mplstyle
# mplstyle.use('fast')
#
# It is very light weight, so it plays nicely with other
# It is very lightweight, so it plays nicely with other
# styles, just make sure the fast style is applied last
# so that other styles do not overwrite the settings::
#
Expand Down