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"""

.. redirect-from:: /tutorials/introductory/pyplot

.. _pyplot_tutorial:

===============

Pyplot tutorial

===============

An introduction to the pyplot interface. Please also see

:ref:`quick_start` for an overview of how Matplotlib

works and :ref:`api_interfaces` for an explanation of the trade-offs between the

supported user APIs.

"""

# %%

# Introduction to pyplot

# ======================

# :mod:`matplotlib.pyplot` is a collection of functions that make matplotlib

# work like MATLAB. Each ``pyplot`` function makes some change to a figure:

# e.g., creates a figure, creates a plotting area in a figure, plots some lines

# in a plotting area, decorates the plot with labels, etc.

# In :mod:`matplotlib.pyplot` various states are preserved

# across function calls, so that it keeps track of things like

# the current figure and plotting area, and the plotting

# functions are directed to the current Axes (please note that we use uppercase

# Axes to refer to the `~.axes.Axes` concept, which is a central

# :ref:`part of a figure <figure_parts>`

# and not only the plural of *axis*).

# .. note::

# The implicit pyplot API is generally less verbose but also not as flexible as the

# explicit API. Most of the function calls you see here can also be called

# as methods from an ``Axes`` object. We recommend browsing the tutorials

# and examples to see how this works. See :ref:`api_interfaces` for an

# explanation of the trade-off of the supported user APIs.

# Generating visualizations with pyplot is very quick:

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4])

plt.ylabel('some numbers')

plt.show()

# %%

# You may be wondering why the x-axis ranges from 0-3 and the y-axis

# from 1-4. If you provide a single list or array to

# `~.pyplot.plot`, matplotlib assumes it is a

# sequence of y values, and automatically generates the x values for

# you. Since python ranges start with 0, the default x vector has the

# same length as y but starts with 0; therefore, the x data are

# ``[0, 1, 2, 3]``.

# `~.pyplot.plot` is a versatile function, and will take an arbitrary number of

# arguments. For example, to plot x versus y, you can write:

plt.plot([1, 2, 3, 4], [1, 4, 9, 16])

# %%

# Formatting the style of your plot

# ---------------------------------

# For every x, y pair of arguments, there is an optional third argument

# which is the format string that indicates the color and line type of

# the plot. The letters and symbols of the format string are from

# MATLAB, and you concatenate a color string with a line style string.

# The default format string is 'b-', which is a solid blue line. For

# example, to plot the above with red circles, you would issue

plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')

plt.axis((0, 6, 0, 20))

plt.show()

# %%

# See the `~.pyplot.plot` documentation for a complete

# list of line styles and format strings. The

# `~.pyplot.axis` function in the example above takes a

# list of ``[xmin, xmax, ymin, ymax]`` and specifies the viewport of the

# Axes.

# If matplotlib were limited to working with lists, it would be fairly

# useless for numeric processing. Generally, you will use `numpy

# <https://numpy.org/>`_ arrays. In fact, all sequences are

# converted to numpy arrays internally. The example below illustrates

# plotting several lines with different format styles in one function call

# using arrays.

import numpy as np

# evenly sampled time at 200ms intervals

t = np.arange(0., 5., 0.2)

# red dashes, blue squares and green triangles

plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')

plt.show()

# %%

# .. _plotting-with-keywords:

# Plotting with keyword strings

# =============================

# There are some instances where you have data in a format that lets you

# access particular variables with strings. For example, with `structured arrays`_

# or `pandas.DataFrame`.

# .. _structured arrays: https://numpy.org/doc/stable/user/basics.rec.html#structured-arrays

# Matplotlib allows you to provide such an object with

# the ``data`` keyword argument. If provided, then you may generate plots with

# the strings corresponding to these variables.

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

plt.scatter('a', 'b', c='c', s='d', data=data)

plt.xlabel('entry a')

plt.ylabel('entry b')

plt.show()

# %%

# .. _plotting-with-categorical-vars:

# Plotting with categorical variables

# ===================================

# It is also possible to create a plot using categorical variables.

# Matplotlib allows you to pass categorical variables directly to

# many plotting functions. For example:

names = ['group_a', 'group_b', 'group_c']

values = [1, 10, 100]

plt.figure(figsize=(9, 3))

plt.subplot(131)

plt.bar(names, values)

plt.subplot(132)

plt.scatter(names, values)

plt.subplot(133)

plt.plot(names, values)

plt.suptitle('Categorical Plotting')

plt.show()

# %%

# .. _controlling-line-properties:

# Controlling line properties

# ===========================

# Lines have many attributes that you can set: linewidth, dash style,

# antialiased, etc; see `matplotlib.lines.Line2D`. There are

# several ways to set line properties

# * Use keyword arguments::

# plt.plot(x, y, linewidth=2.0)

# * Use the setter methods of a ``Line2D`` instance. ``plot`` returns a list

# of ``Line2D`` objects; e.g., ``line1, line2 = plot(x1, y1, x2, y2)``. In the code

# below we will suppose that we have only

# one line so that the list returned is of length 1. We use tuple unpacking with

# ``line,`` to get the first element of that list::

# line, = plt.plot(x, y, '-')

# line.set_antialiased(False) # turn off antialiasing

# * Use `~.pyplot.setp`. The example below

# uses a MATLAB-style function to set multiple properties

# on a list of lines. ``setp`` works transparently with a list of objects

# or a single object. You can either use python keyword arguments or

# MATLAB-style string/value pairs::

# lines = plt.plot(x1, y1, x2, y2)

# # use keyword arguments

# plt.setp(lines, color='r', linewidth=2.0)

# # or MATLAB style string value pairs

# plt.setp(lines, 'color', 'r', 'linewidth', 2.0)

# Here are the available `~.lines.Line2D` properties.

# ====================== ==================================================

# Property Value Type

# ====================== ==================================================

# alpha float

# animated [True | False]

# antialiased or aa [True | False]

# clip_box a matplotlib.transform.Bbox instance

# clip_on [True | False]

# clip_path a Path instance and a Transform instance, a Patch

# color or c any matplotlib color

# contains the hit testing function

# dash_capstyle [``'butt'`` | ``'round'`` | ``'projecting'``]

# dash_joinstyle [``'miter'`` | ``'round'`` | ``'bevel'``]

# dashes sequence of on/off ink in points

# data (np.array xdata, np.array ydata)

# figure a matplotlib.figure.Figure instance

# label any string

# linestyle or ls [ ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'steps'`` | ...]

# linewidth or lw float value in points

# marker [ ``'+'`` | ``','`` | ``'.'`` | ``'1'`` | ``'2'`` | ``'3'`` | ``'4'`` ]

# markeredgecolor or mec any matplotlib color

# markeredgewidth or mew float value in points

# markerfacecolor or mfc any matplotlib color

# markersize or ms float

# markevery [ None | integer | (startind, stride) ]

# picker used in interactive line selection

# pickradius the line pick selection radius

# solid_capstyle [``'butt'`` | ``'round'`` | ``'projecting'``]

# solid_joinstyle [``'miter'`` | ``'round'`` | ``'bevel'``]

# transform a matplotlib.transforms.Transform instance

# visible [True | False]

# xdata np.array

# ydata np.array

# zorder any number

# ====================== ==================================================

# To get a list of settable line properties, call the

# `~.pyplot.setp` function with a line or lines as argument

# .. sourcecode:: ipython

# In [69]: lines = plt.plot([1, 2, 3])

# In [70]: plt.setp(lines)

# alpha: float

# animated: [True | False]

# antialiased or aa: [True | False]

# ...snip

# .. _multiple-figs-axes:

# Working with multiple figures and Axes

# ======================================

# MATLAB, and :mod:`.pyplot`, have the concept of the current figure

# and the current Axes. All plotting functions apply to the current

# Axes. The function `~.pyplot.gca` returns the current Axes (a

# `matplotlib.axes.Axes` instance), and `~.pyplot.gcf` returns the current

# figure (a `matplotlib.figure.Figure` instance). Normally, you don't have to

# worry about this, because it is all taken care of behind the scenes. Below

# is a script to create two subplots.

def f(t):

return np.exp(-t) * np.cos(2*np.pi*t)

t1 = np.arange(0.0, 5.0, 0.1)

t2 = np.arange(0.0, 5.0, 0.02)

plt.figure()

plt.subplot(211)

plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')

plt.subplot(212)

plt.plot(t2, np.cos(2*np.pi*t2), 'r--')

plt.show()

# %%

# The `~.pyplot.figure` call here is optional because a figure will be created

# if none exists, just as an Axes will be created (equivalent to an explicit

# ``subplot()`` call) if none exists.

# The `~.pyplot.subplot` call specifies ``numrows,

# numcols, plot_number`` where ``plot_number`` ranges from 1 to

# ``numrows*numcols``. The commas in the ``subplot`` call are

# optional if ``numrows*numcols<10``. So ``subplot(211)`` is identical

# to ``subplot(2, 1, 1)``.

# You can create an arbitrary number of subplots

# and Axes. If you want to place an Axes manually, i.e., not on a

# rectangular grid, use `~.pyplot.axes`,

# which allows you to specify the location as ``axes([left, bottom,

# width, height])`` where all values are in fractional (0 to 1)

# coordinates. See :doc:`/gallery/subplots_axes_and_figures/axes_demo` for an example of

# placing Axes manually and :doc:`/gallery/subplots_axes_and_figures/subplot` for an

# example with lots of subplots.

# You can create multiple figures by using multiple

# `~.pyplot.figure` calls with an increasing figure

# number. Of course, each figure can contain as many Axes and subplots

# as your heart desires::

# import matplotlib.pyplot as plt

# plt.figure(1) # the first figure

# plt.subplot(211) # the first subplot in the first figure

# plt.plot([1, 2, 3])

# plt.subplot(212) # the second subplot in the first figure

# plt.plot([4, 5, 6])

# plt.figure(2) # a second figure

# plt.plot([4, 5, 6]) # creates a subplot() by default

# 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`

# and the current Axes with `~.pyplot.cla`. If you find

# it annoying that states (specifically the current image, figure and Axes)

# are being maintained for you behind the scenes, don't despair: this is just a thin

# stateful wrapper around an object-oriented API, which you can use

# instead (see :ref:`artists_tutorial`)

# If you are making lots of figures, you need to be aware of one

# more thing: the memory required for a figure is not completely

# released until the figure is explicitly closed with

# `~.pyplot.close`. Deleting all references to the

# figure, and/or using the window manager to kill the window in which

# the figure appears on the screen, is not enough, because pyplot

# maintains internal references until `~.pyplot.close`

# is called.

# .. _working-with-text:

# Working with text

# =================

# `~.pyplot.text` can be used to add text in an arbitrary location, and

# `~.pyplot.xlabel`, `~.pyplot.ylabel` and `~.pyplot.title` are used to add

# text in the indicated locations (see :ref:`text_intro` for a

# more detailed example)

mu, sigma = 100, 15

x = mu + sigma * np.random.randn(10000)

# the histogram of the data

n, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)

plt.xlabel('Smarts')

plt.ylabel('Probability')

plt.title('Histogram of IQ')

plt.text(60, .025, r'$\mu=100,\ \sigma=15$')

plt.axis([40, 160, 0, 0.03])

plt.grid(True)

plt.show()

# %%

# All of the `~.pyplot.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 or using `~.pyplot.setp`::

# t = plt.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::

# plt.title(r'$\sigma_i=15$')

# The ``r`` preceding the title string is important -- it 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`. Thus, you can use mathematical text across

# platforms without requiring a TeX installation. For those who have LaTeX

# and dvipng installed, you can also use LaTeX to format your text and

# incorporate the output directly into your display figures or saved

# postscript -- see :ref:`usetex`.

# Annotating text

# ---------------

# The uses of the basic `~.pyplot.text` function above

# place text at an arbitrary position on the Axes. A common use for

# text is to annotate some feature of the plot, and the

# `~.pyplot.annotate` method provides helper

# functionality to make annotations easy. In an annotation, there are

# two points to consider: the location being annotated represented by

# the argument ``xy`` and the location of the text ``xytext``. Both of

# these arguments are ``(x, y)`` tuples.

ax = plt.subplot()

t = np.arange(0.0, 5.0, 0.01)

s = np.cos(2*np.pi*t)

line, = plt.plot(t, s, lw=2)

plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),

arrowprops=dict(facecolor='black', shrink=0.05),

)

plt.ylim(-2, 2)

plt.show()

# %%

# In this basic example, both the ``xy`` (arrow tip) and ``xytext``

# locations (text location) 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 can be found in

# :doc:`/gallery/text_labels_and_annotations/annotation_demo`.

# Logarithmic and other nonlinear axes

# ====================================

# :mod:`matplotlib.pyplot` supports not only linear axis scales, but also

# logarithmic and logit scales. This is commonly used if data spans many orders

# of magnitude. Changing the scale of an axis is easy::

# plt.xscale('log')

# An example of four plots with the same data and different scales for the y-axis

# is shown below.

# Fixing random state for reproducibility

np.random.seed(19680801)

# make up some data in the open interval (0, 1)

y = np.random.normal(loc=0.5, scale=0.4, size=1000)

y = y[(y > 0) & (y < 1)]

y.sort()

x = np.arange(len(y))

# plot with various axes scales

plt.figure()

# linear

plt.subplot(221)

plt.plot(x, y)

plt.yscale('linear')

plt.title('linear')

plt.grid(True)

# log

plt.subplot(222)

plt.plot(x, y)

plt.yscale('log')

plt.title('log')

plt.grid(True)

# symmetric log

plt.subplot(223)

plt.plot(x, y - y.mean())

plt.yscale('symlog', linthresh=0.01)

plt.title('symlog')

plt.grid(True)

# logit

plt.subplot(224)

plt.plot(x, y)

plt.yscale('logit')

plt.title('logit')

plt.grid(True)

# Adjust the subplot layout, because the logit one may take more space

# than usual, due to y-tick labels like "1 - 10^{-3}"

plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,

wspace=0.35)

plt.show()

# %%

# It is also possible to add your own scale, see `matplotlib.scale` for

# details.

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