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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

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<h3><a href="../contents.html">Table Of Contents</a></h3>

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<li><a class="reference internal" href="#">Pyplot tutorial</a><ul>

<li><a class="reference internal" href="#controlling-line-properties">Controlling line properties</a></li>

<li><a class="reference internal" href="#working-with-multiple-figures-and-axes">Working with multiple figures and axes</a></li>

<li><a class="reference internal" href="#working-with-text">Working with text</a><ul>

<li><a class="reference internal" href="#using-mathematical-expressions-in-text">Using mathematical expressions in text</a></li>

<li><a class="reference internal" href="#annotating-text">Annotating text</a></li>

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<h1>Pyplot tutorial<a class="headerlink" href="#pyplot-tutorial" title="Permalink to this headline">¶</a></h1>

<a class="reference internal" href="../api/pyplot_api.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><tt class="xref py py-mod docutils literal">matplotlib.pyplot</tt></a> is a collection of command style functions

that make matplotlib work like MATLAB.

Each <tt class="docutils literal">pyplot</tt> function makes

some change to a figure: eg, create a figure, create a plotting area

in a figure, plot some lines in a plotting area, decorate the plot

with labels, etc.... <a class="reference internal" href="../api/pyplot_api.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><tt class="xref py py-mod docutils literal">matplotlib.pyplot</tt></a> is stateful, in that it

keeps track of the current figure and plotting area, and the plotting

functions are directed to the current axes

<div class="highlight-python"><div class="highlight"><pre>import matplotlib.pyplot as plt

plt.ylabel('some numbers')

plt.show()

</pre></div>

</div>

(<a class="reference external" href="../pyplots/pyplot_simple.py">Source code</a>, <a class="reference external" href="../pyplots/pyplot_simple.png">png</a>, <a class="reference external" href="../pyplots/pyplot_simple.hires.png">hires.png</a>, <a class="reference external" href="../pyplots/pyplot_simple.pdf">pdf</a>)

</div>

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 the

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot"><tt class="xref py py-func docutils literal">plot()</tt></a> command, 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. Hence the x data are

<tt class="docutils literal">[0,1,2,3]</tt>.

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot"><tt class="xref py py-func docutils literal">plot()</tt></a> is a versatile command, and will take

an arbitrary number of arguments. For example, to plot x versus y,

you can issue the command:

<div class="highlight-python"><div class="highlight"><pre>plt.plot([1,2,3,4], [1,4,9,16])

</pre></div>

</div>

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

<div class="highlight-python"><div class="highlight"><pre>import matplotlib.pyplot as plt

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

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

plt.show()

</pre></div>

</div>

(<a class="reference external" href="../pyplots/pyplot_formatstr.py">Source code</a>, <a class="reference external" href="../pyplots/pyplot_formatstr.png">png</a>, <a class="reference external" href="../pyplots/pyplot_formatstr.hires.png">hires.png</a>, <a class="reference external" href="../pyplots/pyplot_formatstr.pdf">pdf</a>)

</div>

See the <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot"><tt class="xref py py-func docutils literal">plot()</tt></a> documentation for a complete

list of line styles and format strings. The

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.axis" title="matplotlib.pyplot.axis"><tt class="xref py py-func docutils literal">axis()</tt></a> command in the example above takes a

list of <tt class="docutils literal">[xmin, xmax, ymin, ymax]</tt> 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 <a class="reference external" href="http://numpy.scipy.org">numpy</a> arrays. In fact, all sequences are

converted to numpy arrays internally. The example below illustrates a

plotting several lines with different format styles in one command

using arrays.

<div class="highlight-python"><div class="highlight"><pre>import numpy as np

import matplotlib.pyplot as plt

# 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()

</pre></div>

</div>

(<a class="reference external" href="../pyplots/pyplot_three.py">Source code</a>, <a class="reference external" href="../pyplots/pyplot_three.png">png</a>, <a class="reference external" href="../pyplots/pyplot_three.hires.png">hires.png</a>, <a class="reference external" href="../pyplots/pyplot_three.pdf">pdf</a>)

</div>

<h2>Controlling line properties<a class="headerlink" href="#controlling-line-properties" title="Permalink to this headline">¶</a></h2>

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

antialiased, etc; see <a class="reference internal" href="../api/artist_api.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D"><tt class="xref py py-class docutils literal">matplotlib.lines.Line2D</tt></a>. There are

several ways to set line properties

<ul>

<li>Use keyword args:

<div class="highlight-python"><div class="highlight"><pre>plt.plot(x, y, linewidth=2.0)

</pre></div>

</div>

</li>

<li>Use the setter methods of the <tt class="docutils literal">Line2D</tt> instance. <tt class="docutils literal">plot</tt> returns a list

of lines; eg <tt class="docutils literal">line1, line2 = plot(x1,y1,x2,y2)</tt>. Below I have only

one line so it is a list of length 1. I use tuple unpacking in the

<tt class="docutils literal">line, = plot(x, y, 'o')</tt> to get the first element of the list:

<div class="highlight-python"><div class="highlight"><pre>line, = plt.plot(x, y, '-')

line.set_antialiased(False) # turn off antialising

</pre></div>

</div>

</li>

<li>Use the <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp"><tt class="xref py py-func docutils literal">setp()</tt></a> command. The example below

uses a MATLAB-style command to set multiple properties

on a list of lines. <tt class="docutils literal">setp</tt> works transparently with a list of objects

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

MATLAB-style string/value pairs:

<div class="highlight-python"><div class="highlight"><pre>lines = plt.plot(x1, y1, x2, y2)

# use keyword args

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

# or MATLAB style string value pairs

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

</pre></div>

</div>

</li>

</ul>

Here are the available <a class="reference internal" href="../api/artist_api.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D"><tt class="xref py py-class docutils literal">Line2D</tt></a> properties.

</colgroup>

<tr class="row-odd"><th class="head">Property</th>

<th class="head">Value Type</th>

</tr>

</thead>

<tr class="row-even"><td>alpha</td>

<td>float</td>

</tr>

<tr class="row-odd"><td>animated</td>

<td>[True | False]</td>

</tr>

<tr class="row-even"><td>antialiased or aa</td>

<td>[True | False]</td>

</tr>

<td>a matplotlib.transform.Bbox instance</td>

</tr>

<td>[True | False]</td>

</tr>

<td>a Path instance and a Transform instance, a Patch</td>

</tr>

<tr class="row-even"><td>color or c</td>

<td>any matplotlib color</td>

</tr>

<tr class="row-odd"><td>contains</td>

<td>the hit testing function</td>

</tr>

<tr class="row-even"><td>dash_capstyle</td>

<td>[‘butt’ | ‘round’ | ‘projecting’]</td>

</tr>

<tr class="row-odd"><td>dash_joinstyle</td>

<td>[‘miter’ | ‘round’ | ‘bevel’]</td>

</tr>

<tr class="row-even"><td>dashes</td>

<td>sequence of on/off ink in points</td>

</tr>

<td>(np.array xdata, np.array ydata)</td>

</tr>

<tr class="row-even"><td>figure</td>

<td>a matplotlib.figure.Figure instance</td>

</tr>

<tr class="row-odd"><td>label</td>

<td>any string</td>

</tr>

<tr class="row-even"><td>linestyle or ls</td>

<td>[ ‘-‘ | ‘–’ | ‘-.’ | ‘:’ | ‘steps’ | ...]</td>

</tr>

<tr class="row-odd"><td>linewidth or lw</td>

<td>float value in points</td>

</tr>

<td>[True | False]</td>

</tr>

<tr class="row-odd"><td>marker</td>

</tr>

<tr class="row-even"><td>markeredgecolor or mec</td>

<td>any matplotlib color</td>

</tr>

<tr class="row-odd"><td>markeredgewidth or mew</td>

<td>float value in points</td>

</tr>

<tr class="row-even"><td>markerfacecolor or mfc</td>

<td>any matplotlib color</td>

</tr>

<tr class="row-odd"><td>markersize or ms</td>

<td>float</td>

</tr>

<tr class="row-even"><td>markevery</td>

<td>None | integer | (startind, stride)</td>

</tr>

<tr class="row-odd"><td>picker</td>

<td>used in interactive line selection</td>

</tr>

<tr class="row-even"><td>pickradius</td>

<td>the line pick selection radius</td>

</tr>

<tr class="row-odd"><td>solid_capstyle</td>

<td>[‘butt’ | ‘round’ | ‘projecting’]</td>

</tr>

<tr class="row-even"><td>solid_joinstyle</td>

<td>[‘miter’ | ‘round’ | ‘bevel’]</td>

</tr>

<tr class="row-odd"><td>transform</td>

<td>a matplotlib.transforms.Transform instance</td>

</tr>

<tr class="row-even"><td>visible</td>

<td>[True | False]</td>

</tr>

<tr class="row-odd"><td>xdata</td>

<td>np.array</td>

</tr>

<tr class="row-even"><td>ydata</td>

<td>np.array</td>

</tr>

<tr class="row-odd"><td>zorder</td>

<td>any number</td>

</tr>

</tbody>

</table>

To get a list of settable line properties, call the

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp"><tt class="xref py py-func docutils literal">setp()</tt></a> function with a line or lines

as argument

<div class="highlight-ipython"><div class="highlight"><pre>In [69]: lines = plt.plot([1,2,3])

In [70]: plt.setp(lines)

alpha: float

animated: [True | False]

antialiased or aa: [True | False]

...snip

</pre></div>

</div>

<h2>Working with multiple figures and axes<a class="headerlink" href="#working-with-multiple-figures-and-axes" title="Permalink to this headline">¶</a></h2>

MATLAB, and <a class="reference internal" href="../api/pyplot_api.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><tt class="xref py py-mod docutils literal">pyplot</tt></a>, have the concept of the current

figure and the current axes. All plotting commands apply to the

current axes. The function <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.gca" title="matplotlib.pyplot.gca"><tt class="xref py py-func docutils literal">gca()</tt></a> returns the

current axes (a <a class="reference internal" href="../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><tt class="xref py py-class docutils literal">matplotlib.axes.Axes</tt></a> instance), and

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.gcf" title="matplotlib.pyplot.gcf"><tt class="xref py py-func docutils literal">gcf()</tt></a> returns the current figure

(<a class="reference internal" href="../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><tt class="xref py py-class docutils literal">matplotlib.figure.Figure</tt></a> 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.

<div class="highlight-python"><div class="highlight"><pre>import numpy as np

import matplotlib.pyplot as plt

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(1)

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()

</pre></div>

</div>

(<a class="reference external" href="../pyplots/pyplot_two_subplots.py">Source code</a>, <a class="reference external" href="../pyplots/pyplot_two_subplots.png">png</a>, <a class="reference external" href="../pyplots/pyplot_two_subplots.hires.png">hires.png</a>, <a class="reference external" href="../pyplots/pyplot_two_subplots.pdf">pdf</a>)

</div>

The <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure"><tt class="xref py py-func docutils literal">figure()</tt></a> command here is optional because

<tt class="docutils literal">figure(1)</tt> will be created by default, just as a <tt class="docutils literal">subplot(111)</tt>

will be created by default if you don’t manually specify an axes. The

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot"><tt class="xref py py-func docutils literal">subplot()</tt></a> command specifies <tt class="docutils literal">numrows,

numcols, fignum</tt> where <tt class="docutils literal">fignum</tt> ranges from 1 to

<tt class="docutils literal">numrows*numcols</tt>. The commas in the <tt class="docutils literal">subplot</tt> command are

optional if <tt class="docutils literal">numrows*numcols<10</tt>. So <tt class="docutils literal">subplot(211)</tt> is identical

to <tt class="docutils literal">subplot(2,1,1)</tt>. You can create an arbitrary number of subplots

and axes. If you want to place an axes manually, ie, not on a

rectangular grid, use the <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.axes" title="matplotlib.pyplot.axes"><tt class="xref py py-func docutils literal">axes()</tt></a> command,

which allows you to specify the location as <tt class="docutils literal">axes([left, bottom,

width, height])</tt> where all values are in fractional (0 to 1)

coordinates. See <a class="reference internal" href="../examples/pylab_examples/axes_demo.html#pylab-examples-axes-demo">pylab_examples example code: axes_demo.py</a> for an example of

placing axes manually and <a class="reference internal" href="../examples/pylab_examples/line_styles.html#pylab-examples-line-styles">pylab_examples example code: line_styles.py</a> for an

example with lots-o-subplots.

You can create multiple figures by using multiple

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure"><tt class="xref py py-func docutils literal">figure()</tt></a> calls with an increasing figure

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

as your heart desires:

<div class="highlight-python"><div class="highlight"><pre>import matplotlib.pyplot as plt

plt.figure(1) # the first figure

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

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

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

plt.figure(2) # a second figure

plt.figure(1) # figure 1 current; subplot(212) still current

plt.title('Easy as 1,2,3') # subplot 211 title

</pre></div>

</div>

You can clear the current figure with <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.clf" title="matplotlib.pyplot.clf"><tt class="xref py py-func docutils literal">clf()</tt></a>

and the current axes with <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.cla" title="matplotlib.pyplot.cla"><tt class="xref py py-func docutils literal">cla()</tt></a>. If you find

this statefulness, annoying, don’t despair, this is just a thin

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

instead (see <a class="reference internal" href="artists.html#artist-tutorial">Artist tutorial</a>)

If you are making a long sequence 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

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.close" title="matplotlib.pyplot.close"><tt class="xref py py-func docutils literal">close()</tt></a>. 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 <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.close" title="matplotlib.pyplot.close"><tt class="xref py py-func docutils literal">close()</tt></a>

is called.

</div>

<h2>Working with text<a class="headerlink" href="#working-with-text" title="Permalink to this headline">¶</a></h2>

The <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.text" title="matplotlib.pyplot.text"><tt class="xref py py-func docutils literal">text()</tt></a> command can be used to add text in

an arbitrary location, and the <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel"><tt class="xref py py-func docutils literal">xlabel()</tt></a>,

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.ylabel" title="matplotlib.pyplot.ylabel"><tt class="xref py py-func docutils literal">ylabel()</tt></a> and <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title"><tt class="xref py py-func docutils literal">title()</tt></a>

are used to add text in the indicated locations (see <a class="reference internal" href="text_intro.html#text-intro">Text introduction</a>

for a more detailed example)

<div class="highlight-python"><div class="highlight"><pre>import numpy as np

import matplotlib.pyplot as plt

mu, sigma = 100, 15

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

# the histogram of the data

n, bins, patches = plt.hist(x, 50, normed=1, 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()

</pre></div>

</div>

(<a class="reference external" href="../pyplots/pyplot_text.py">Source code</a>, <a class="reference external" href="../pyplots/pyplot_text.png">png</a>, <a class="reference external" href="../pyplots/pyplot_text.hires.png">hires.png</a>, <a class="reference external" href="../pyplots/pyplot_text.pdf">pdf</a>)

</div>

All of the <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.text" title="matplotlib.pyplot.text"><tt class="xref py py-func docutils literal">text()</tt></a> commands return an

<a class="reference internal" href="../api/artist_api.html#matplotlib.text.Text" title="matplotlib.text.Text"><tt class="xref py py-class docutils literal">matplotlib.text.Text</tt></a> instance. Just as with with lines

above, you can customize the properties by passing keyword arguments

into the text functions or using <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp"><tt class="xref py py-func docutils literal">setp()</tt></a>:

<div class="highlight-python"><div class="highlight"><pre>t = plt.xlabel('my data', fontsize=14, color='red')

</pre></div>

</div>

These properties are covered in more detail in <a class="reference internal" href="text_props.html#text-properties">Text properties and layout</a>.

<h3>Using mathematical expressions in text<a class="headerlink" href="#using-mathematical-expressions-in-text" title="Permalink to this headline">¶</a></h3>

matplotlib accepts TeX equation expressions in any text expression.

For example to write the expression <img src="../_images/mathmpl/math-4cd9a23707.png" style="position: relative; bottom: -7px"/> in the title,

you can write a TeX expression surrounded by dollar signs:

<div class="highlight-python"><div class="highlight"><pre>plt.title(r'$\sigma_i=15$')

</pre></div>

</div>

The <tt class="docutils literal">r</tt> preceding the title string is important – it signifies

that the string is a raw string and not to treat backslashes and

python escapes. matplotlib has a built-in TeX expression parser and

layout engine, and ships its own math fonts – for details see

<a class="reference internal" href="mathtext.html#mathtext-tutorial">Writing mathematical expressions</a>. 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 <a class="reference internal" href="usetex.html#usetex-tutorial">Text rendering With LaTeX</a>.

</div>

<h3>Annotating text<a class="headerlink" href="#annotating-text" title="Permalink to this headline">¶</a></h3>

The uses of the basic <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.text" title="matplotlib.pyplot.text"><tt class="xref py py-func docutils literal">text()</tt></a> command above

place text at an arbitrary position on the Axes. A common use case of

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

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.annotate" title="matplotlib.pyplot.annotate"><tt class="xref py py-func docutils literal">annotate()</tt></a> 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 <tt class="docutils literal">xy</tt> and the location of the text <tt class="docutils literal">xytext</tt>. Both of

these arguments are <tt class="docutils literal">(x,y)</tt> tuples.

<div class="highlight-python"><div class="highlight"><pre>import numpy as np

import matplotlib.pyplot as plt

ax = plt.subplot(111)

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()

</pre></div>

</div>

(<a class="reference external" href="../pyplots/pyplot_annotate.py">Source code</a>, <a class="reference external" href="../pyplots/pyplot_annotate.png">png</a>, <a class="reference external" href="../pyplots/pyplot_annotate.hires.png">hires.png</a>, <a class="reference external" href="../pyplots/pyplot_annotate.pdf">pdf</a>)

</div>

In this basic example, both the <tt class="docutils literal">xy</tt> (arrow tip) and <tt class="docutils literal">xytext</tt>

locations (text location) are in data coordinates. There are a

variety of other coordinate systems one can choose – see

<a class="reference internal" href="annotations_intro.html#annotations-tutorial">Annotating text</a> and <a class="reference internal" href="annotations_guide.html#plotting-guide-annotation">Annotating Axes</a> for

details. More examples can be found in

<a class="reference internal" href="../examples/pylab_examples/annotation_demo.html#pylab-examples-annotation-demo">pylab_examples example code: annotation_demo.py</a>.

</div>

</div>

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Last updated on Oct 10, 2013.

Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.2b2.

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