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"""
.. redirect-from:: /tutorials/introductory/usage
.. redirect-from:: /tutorials/introductory/quick_start
.. _quick_start:
*****************
Quick start guide
*****************
This tutorial covers some basic usage patterns and best practices to
help you get started with Matplotlib.
"""
import matplotlib.pyplot as plt
import numpy as np
# sphinx_gallery_thumbnail_number = 3
# %%
#
# A simple example
# ================
#
# Matplotlib graphs your data on `.Figure`\s (e.g., windows, Jupyter
# widgets, etc.), each of which can contain one or more `~.axes.Axes`, an
# area where points can be specified in terms of x-y coordinates (or theta-r
# in a polar plot, 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, and `~.pyplot.show` to display
# the figure:
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.
plt.show() # Show the figure.
# %%
#
# Depending on the environment you are working in, ``plt.show()`` can be left
# out. This is for example the case with Jupyter notebooks, which
# automatically show all figures created in a code cell.
#
# .. _figure_parts:
#
# Parts of a Figure
# =================
#
# Here are the components of a Matplotlib Figure.
#
# .. image:: ../../_static/anatomy.png
#
# :class:`~matplotlib.figure.Figure`
# ----------------------------------
#
# The **whole** figure. The Figure keeps
# track of all the child :class:`~matplotlib.axes.Axes`, a group of
# 'special' Artists (titles, figure legends, colorbars, etc.), and
# even nested subfigures.
#
# Typically, you'll create a new Figure through one of the following
# functions::
#
# fig = plt.figure() # an empty figure with no Axes
# fig, ax = plt.subplots() # a figure with a single Axes
# fig, axs = plt.subplots(2, 2) # a figure with a 2x2 grid of Axes
# # a figure with one Axes on the left, and two on the right:
# fig, axs = plt.subplot_mosaic([['left', 'right_top'],
# ['left', 'right_bottom']])
#
# `~.pyplot.subplots()` and `~.pyplot.subplot_mosaic` are convenience functions
# that additionally create Axes objects inside the Figure, but you can also
# manually add Axes later on.
#
# For more on Figures, including panning and zooming, see :ref:`figure-intro`.
#
# :class:`~matplotlib.axes.Axes`
# ------------------------------
#
# An Axes is an Artist attached to a Figure that contains a region for
# plotting data, and usually includes two (or three in the case of 3D)
# :class:`~matplotlib.axis.Axis` objects (be aware of the difference
# between **Axes** and **Axis**) that provide ticks and tick labels to
# provide scales for the data in the Axes. Each :class:`~.axes.Axes` also
# has a title
# (set via :meth:`~matplotlib.axes.Axes.set_title`), an x-label (set via
# :meth:`~matplotlib.axes.Axes.set_xlabel`), and a y-label set via
# :meth:`~matplotlib.axes.Axes.set_ylabel`).
#
# The `~.axes.Axes` methods are the primary interface for configuring
# most parts of your plot (adding data, controlling axis scales and
# limits, adding labels etc.).
#
# :class:`~matplotlib.axis.Axis`
# ------------------------------
#
# These objects set the scale and limits and generate ticks (the marks
# on the Axis) and ticklabels (strings labeling the ticks). The location
# of the ticks is determined by a `~matplotlib.ticker.Locator` object and the
# ticklabel strings are formatted by a `~matplotlib.ticker.Formatter`. The
# combination of the correct `.Locator` and `.Formatter` gives very fine
# control over the tick locations and labels.
#
# :class:`~matplotlib.artist.Artist`
# ----------------------------------
#
# Basically, everything visible on the Figure is an Artist (even
# `.Figure`, `Axes <.axes.Axes>`, and `~.axis.Axis` objects). This includes
# `.Text` objects, `.Line2D` objects, :mod:`.collections` objects, `.Patch`
# objects, etc. When the Figure is rendered, all of the
# Artists are drawn to the **canvas**. Most Artists are tied to an Axes; such
# an Artist cannot be shared by multiple Axes, or moved from one to another.
#
# .. _input_types:
#
# Types of inputs to plotting functions
# =====================================
#
# Plotting functions expect `numpy.array` or `numpy.ma.masked_array` as
# input, or objects that can be passed to `numpy.asarray`.
# Classes that are similar to arrays ('array-like') such as `pandas`
# data objects and `numpy.matrix` may not work as intended. Common convention
# is to convert these to `numpy.array` objects prior to plotting.
# For example, to convert a `numpy.matrix` ::
#
# b = np.matrix([[1, 2], [3, 4]])
# b_asarray = np.asarray(b)
#
# Most methods will also parse a string-indexable object like a *dict*, a
# `structured numpy array`_, or a `pandas.DataFrame`. Matplotlib allows you
# to provide the ``data`` keyword argument and generate plots passing the
# strings corresponding to the *x* and *y* variables.
#
# .. _structured numpy array: https://numpy.org/doc/stable/user/basics.rec.html#structured-arrays # noqa: E501
np.random.seed(19680801) # seed the random number generator.
data = {'a': np.arange(50),
'c': np.random.randint(0, 50, 50),
'd': np.random.randn(50)}
data['b'] = data['a'] + 10 * np.random.randn(50)
data['d'] = np.abs(data['d']) * 100
fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
ax.scatter('a', 'b', c='c', s='d', data=data)
ax.set_xlabel('entry a')
ax.set_ylabel('entry b')
# %%
# .. _coding_styles:
#
# Coding styles
# =============
#
# The explicit and the implicit interfaces
# ----------------------------------------
#
# As noted above, there are essentially two ways to use Matplotlib:
#
# - Explicitly create Figures and Axes, and call methods on them (the
# "object-oriented (OO) style").
# - Rely on pyplot to implicitly create and manage the Figures and Axes, and
# use pyplot functions for plotting.
#
# See :ref:`api_interfaces` for an explanation of the tradeoffs between the
# implicit and explicit interfaces.
#
# So one can use the OO-style
x = np.linspace(0, 2, 100) # Sample data.
# Note that even in the OO-style, we use `.pyplot.figure` to create the Figure.
fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
ax.plot(x, x, label='linear') # Plot some data on the Axes.
ax.plot(x, x**2, label='quadratic') # Plot more data on the Axes...
ax.plot(x, x**3, label='cubic') # ... and some more.
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.
# %%
# or the pyplot-style:
x = np.linspace(0, 2, 100) # Sample data.
plt.figure(figsize=(5, 2.7), layout='constrained')
plt.plot(x, x, label='linear') # Plot some data on the (implicit) Axes.
plt.plot(x, x**2, label='quadratic') # etc.
plt.plot(x, x**3, label='cubic')
plt.xlabel('x label')
plt.ylabel('y label')
plt.title("Simple Plot")
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`.)
#
# 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 convenient
# for quick interactive work.
#
# .. note::
#
# You may find older examples that use the ``pylab`` interface,
# via ``from pylab import *``. This approach is strongly deprecated.
#
# 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.
def my_plotter(ax, data1, data2, param_dict):
"""
A helper function to make a graph.
"""
out = ax.plot(data1, data2, **param_dict)
return out
# %%
# which you would then use twice to populate two subplots:
data1, data2, data3, data4 = np.random.randn(4, 100) # make 4 random data sets
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'})
# %%
# Note that if you want to install these as a python package, or any other
# customizations you could use one of the many templates on the web;
# Matplotlib has one at `mpl-cookiecutter
# <https://github.com/matplotlib/matplotlib-extension-cookiecutter>`_
#
#
# Styling Artists
# ===============
#
# Most plotting methods have styling options for the Artists, accessible either
# when a plotting method is called, or from a "setter" on the Artist. In the
# plot below we manually set the *color*, *linewidth*, and *linestyle* of the
# Artists created by `~.Axes.plot`, and we set the linestyle of the second line
# after the fact with `~.Line2D.set_linestyle`.
fig, ax = plt.subplots(figsize=(5, 2.7))
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(':')
# %%
# Colors
# ------
#
# Matplotlib has a very flexible array of colors that are accepted for most
# Artists; see :ref:`allowable color definitions <colors_def>` for a
# list of specifications. Some Artists will take multiple colors. i.e. for
# a `~.Axes.scatter` plot, the edge of the markers can be different colors
# from the interior:
fig, ax = plt.subplots(figsize=(5, 2.7))
ax.scatter(data1, data2, s=50, facecolor='C0', edgecolor='k')
# %%
# Linewidths, linestyles, and markersizes
# ---------------------------------------
#
# Line widths are typically in typographic points (1 pt = 1/72 inch) and
# available for Artists that have stroked lines. Similarly, stroked lines
# can have a linestyle. See the :doc:`linestyles example
# </gallery/lines_bars_and_markers/linestyles>`.
#
# 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 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`):
fig, ax = plt.subplots(figsize=(5, 2.7))
ax.plot(data1, 'o', label='data1')
ax.plot(data2, 'd', label='data2')
ax.plot(data3, 'v', label='data3')
ax.plot(data4, 's', label='data4')
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 :ref:`text_intro`
# for more discussion). Text can also be directly added to plots using
# `~.Axes.text`:
mu, sigma = 115, 15
x = mu + sigma * np.random.randn(10000)
fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
# the histogram of the data
n, bins, patches = ax.hist(x, 50, density=True, facecolor='C0', alpha=0.75)
ax.set_xlabel('Length [cm]')
ax.set_ylabel('Probability')
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)
# %%
# All of the `~.Axes.text` functions return a `matplotlib.text.Text`
# instance. Just as with lines above, you can customize the properties by
# passing keyword arguments into the text functions::
#
# t = ax.set_xlabel('my data', fontsize=14, color='red')
#
# These properties are covered in more detail in
# :ref:`text_props`.
#
# Using mathematical expressions in text
# --------------------------------------
#
# Matplotlib accepts TeX equation expressions in any text expression.
# For example to write the expression :math:`\sigma_i=15` in the title,
# you can write a TeX expression surrounded by dollar signs::
#
# ax.set_title(r'$\sigma_i=15$')
#
# where the ``r`` preceding the title string signifies that the string is a
# *raw* string and not to treat backslashes as python escapes.
# Matplotlib has a built-in TeX expression parser and
# layout engine, and ships its own math fonts – for details see
# :ref:`mathtext`. You can also use LaTeX directly to format
# your text and incorporate the output directly into your display figures or
# saved postscript – see :ref:`usetex`.
#
# Annotations
# -----------
#
# We can also annotate points on a plot, often by connecting an arrow pointing
# to *xy*, to a piece of text at *xytext*:
fig, ax = plt.subplots(figsize=(5, 2.7))
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2 * np.pi * t)
line, = ax.plot(t, s, lw=2)
ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05))
ax.set_ylim(-2, 2)
# %%
# In this basic example, both *xy* and *xytext* are in data coordinates.
# There are a variety of other coordinate systems one can choose -- see
# :ref:`annotations-tutorial` and :ref:`plotting-guide-annotation` for
# details. More examples also can be found in
# :doc:`/gallery/text_labels_and_annotations/annotation_demo`.
#
# Legends
# -------
#
# Often we want to identify lines or markers with a `.Axes.legend`:
fig, ax = plt.subplots(figsize=(5, 2.7))
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()
# %%
# Legends in Matplotlib are quite flexible in layout, placement, and what
# Artists they can represent. They are discussed in detail in
# :ref:`legend_guide`.
#
# Axis scales and ticks
# =====================
#
# Each Axes has two (or three) `~.axis.Axis` objects representing the x- and
# y-axis. These control the *scale* of the Axis, the tick *locators* and the
# tick *formatters*. Additional Axes can be attached to display further Axis
# objects.
#
# Scales
# ------
#
# In addition to the linear scale, Matplotlib supplies non-linear scales,
# such as a log-scale. Since log-scales are used so much there are also
# direct methods like `~.Axes.loglog`, `~.Axes.semilogx`, and
# `~.Axes.semilogy`. There are a number of scales (see
# :doc:`/gallery/scales/scales` for other examples). Here we set the scale
# manually:
fig, axs = plt.subplots(1, 2, figsize=(5, 2.7), layout='constrained')
xdata = np.arange(len(data1)) # make an ordinal for this
data = 10**data1
axs[0].plot(xdata, data)
axs[1].set_yscale('log')
axs[1].plot(xdata, data)
# %%
# The scale sets the mapping from data values to spacing along the Axis. This
# happens in both directions, and gets combined into a *transform*, which
# is the way that Matplotlib maps from data coordinates to Axes, Figure, or
# screen coordinates. See :ref:`transforms_tutorial`.
#
# Tick locators and formatters
# ----------------------------
#
# Each Axis has a tick *locator* and *formatter* that choose where along the
# Axis objects to put tick marks. A simple interface to this is
# `~.Axes.set_xticks`:
fig, axs = plt.subplots(2, 1, layout='constrained')
axs[0].plot(xdata, data1)
axs[0].set_title('Automatic ticks')
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')
# %%
# Different scales can have different locators and formatters; for instance
# the log-scale above uses `~.LogLocator` and `~.LogFormatter`. See
# :doc:`/gallery/ticks/tick-locators` and
# :doc:`/gallery/ticks/tick-formatters` for other formatters and
# locators and information for writing your own.
#
# Plotting dates and strings
# --------------------------
#
# 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:
from matplotlib.dates import ConciseDateFormatter
fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
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.xaxis.set_major_formatter(ConciseDateFormatter(ax.xaxis.get_major_locator()))
# %%
# For more information see the date examples
# (e.g. :doc:`/gallery/ticks/date`)
#
# For strings, we get categorical plotting (see:
# :doc:`/gallery/lines_bars_and_markers/categorical_variables`).
fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
categories = ['turnips', 'rutabaga', 'cucumber', 'pumpkins']
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!
#
#
# Additional Axis objects
# ------------------------
#
# Plotting data of different magnitude in one chart may require
# an additional y-axis. Such an Axis can be created by using
# `~.Axes.twinx` to add a new Axes with an invisible x-axis and a y-axis
# positioned at the right (analogously for `~.Axes.twiny`). See
# :doc:`/gallery/subplots_axes_and_figures/two_scales` for another example.
#
# Similarly, you can add a `~.Axes.secondary_xaxis` or
# `~.Axes.secondary_yaxis` having a different scale than the main Axis to
# represent the data in different scales or units. See
# :doc:`/gallery/subplots_axes_and_figures/secondary_axis` for further
# examples.
fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(7, 2.7), layout='constrained')
l1, = ax1.plot(t, s)
ax2 = ax1.twinx()
l2, = ax2.plot(t, range(len(t)), 'C1')
ax2.legend([l1, l2], ['Sine (left)', 'Straight (right)'])
ax3.plot(t, s)
ax3.set_xlabel('Angle [rad]')
ax4 = ax3.secondary_xaxis('top', (np.rad2deg, np.deg2rad))
ax4.set_xlabel('Angle [°]')
# %%
# Color mapped data
# =================
#
# Often we want to have a third dimension in a plot represented by colors in
# a colormap. Matplotlib has a number of plot types that do this:
from matplotlib.colors import LogNorm
X, Y = np.meshgrid(np.linspace(-3, 3, 128), np.linspace(-3, 3, 128))
Z = (1 - X/2 + X**5 + Y**3) * np.exp(-X**2 - Y**2)
fig, axs = plt.subplots(2, 2, layout='constrained')
pc = axs[0, 0].pcolormesh(X, Y, Z, vmin=-1, vmax=1, cmap='RdBu_r')
fig.colorbar(pc, ax=axs[0, 0])
axs[0, 0].set_title('pcolormesh()')
co = axs[0, 1].contourf(X, Y, Z, levels=np.linspace(-1.25, 1.25, 11))
fig.colorbar(co, ax=axs[0, 1])
axs[0, 1].set_title('contourf()')
pc = axs[1, 0].imshow(Z**2 * 100, cmap='plasma', norm=LogNorm(vmin=0.01, vmax=100))
fig.colorbar(pc, ax=axs[1, 0], extend='both')
axs[1, 0].set_title('imshow() with LogNorm()')
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()')
# %%
# Colormaps
# ---------
#
# These are all examples of Artists that derive from `~.ScalarMappable`
# objects. They all can set a linear mapping between *vmin* and *vmax* into
# the colormap specified by *cmap*. Matplotlib has many colormaps to choose
# from (:ref:`colormaps`) you can make your
# own (:ref:`colormap-manipulation`) or download as
# `third-party packages
# <https://matplotlib.org/mpl-third-party/#colormaps-and-styles>`_.
#
# Normalizations
# --------------
#
# Sometimes we want a non-linear mapping of the data to the colormap, as
# in the ``LogNorm`` example above. We do this by supplying the
# ScalarMappable with the *norm* argument instead of *vmin* and *vmax*.
# More normalizations are shown at :ref:`colormapnorms`.
#
# Colorbars
# ---------
#
# Adding a `~.Figure.colorbar` gives a key to relate the color back to the
# underlying data. Colorbars are figure-level Artists, and are attached to
# a ScalarMappable (where they get their information about the norm and
# colormap) and usually steal space from a parent Axes. Placement of
# colorbars can be complex: see
# :ref:`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. 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
# ======================================
#
# You can open multiple Figures with multiple calls to
# ``fig = plt.figure()`` or ``fig2, ax = plt.subplots()``. By keeping the
# object references you can add Artists to either Figure.
#
# Multiple Axes can be added a number of ways, but the most basic is
# ``plt.subplots()`` as used above. One can achieve more complex layouts,
# with Axes objects spanning columns or rows, using `~.pyplot.subplot_mosaic`.
fig, axd = plt.subplot_mosaic([['upleft', 'right'],
['lowleft', 'right']], layout='constrained')
axd['upleft'].set_title('upleft')
axd['lowleft'].set_title('lowleft')
axd['right'].set_title('right')
# %%
# Matplotlib has quite sophisticated tools for arranging Axes: See
# :ref:`arranging_axes` and :ref:`mosaic`.
#
#
# More reading
# ============
#
# For more plot types see :doc:`Plot types </plot_types/index>` and the
# :doc:`API reference </api/index>`, in particular the
# :doc:`Axes API </api/axes_api>`.
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