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‎doc/api/animation_api.rst

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:maxdepth: 1
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../examples/animation/basic_example
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../examples/animation/basic_example_writer
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../examples/animation/basic_example_writer_sgskip
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../examples/animation/dynamic_image2
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‎examples/README.txt

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Gallery
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=======
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Click on any image to see full size image and source code.
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This gallery contains examples of the many things you can do with
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Matplotlib. Click on any image to see the full image and source code.
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.. contents::

‎examples/animation/moviewriter_sgskip.py

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# -*- noplot -*-
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"""
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.. _animation-moviewriter:
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===========
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MovieWriter
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===========

‎examples/api/agg_oo_sgskip.py

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# -*- noplot -*-
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"""
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.. _api-agg_oo:
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=============================
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The object-oriented interface
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=============================
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A pure OO (look Ma, no pylab!) example using the agg backend
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"""
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from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
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from matplotlib.figure import Figure

‎examples/api/font_file_sgskip.py

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font_manager.FontProperties fname argument (for a more flexible
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solution, see the font_family_rc.py and fonts_demo.py examples).
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"""
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import sys
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import os

‎examples/axes_grid1/demo_axes_rgb.py

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"""
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=============
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Demo Axes Rgb
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Demo Axes RGB
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=============
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"""

‎examples/event_handling/pipong.py

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Pipong
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======
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A matplotlib based game of Pong illustrating one way to write interactive
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A Matplotlib based game of Pong illustrating one way to write interactive
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animation which are easily ported to multiple backends
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pipong.py was written by Paul Ivanov <http://pirsquared.org>
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"""

‎examples/event_handling/pong_gtk_sgskip.py

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"""
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========
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Pong Gtk
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Pong GTK
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========
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For detailed comments on animation and the techniques used here, see
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the wiki entry
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http://www.scipy.org/wikis/topical_software/MatplotlibAnimation
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A small game demo using GTK.
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"""
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from __future__ import print_function

‎examples/misc/README.txt

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Miscellaneous Examples
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======================

‎examples/misc/contour_manual.py

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"""
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==============
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Contour Manual
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Manual Contour
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==============
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Example of displaying your own contour lines and polygons using ContourSet.

‎examples/misc/image_thumbnail_sgskip.py

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# -*- noplot -*-
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"""
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.. _misc-image_thumbnail:
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===============
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Image Thumbnail
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===============

‎examples/misc/rc_traits_sgskip.py

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=========
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Here is some example code showing how to define some representative
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rc properties and construct a matplotlib artist using traits.
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matplotlib does not ship with enthought.traits, so you will need to
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rc properties and construct a Matplotlib artist using traits.
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Matplotlib does not ship with enthought.traits, so you will need to
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install it separately.
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"""

‎examples/misc/svg_filter_line.py

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"""
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===============
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Svg Filter Line
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SVG Filter Line
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===============
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Demonstrate SVG filtering effects which might be used with mpl.

‎examples/misc/svg_filter_pie.py

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"""
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==============
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Svg Filter Pie
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SVG Filter Pie
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==============
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Demonstrate SVG filtering effects which might be used with mpl.

‎examples/pylab_examples/annotation_demo.py

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Valid keys are
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width : the width of the arrow in points
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frac : the fraction of the arrow length occupied by the head
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headwidth : the width of the base of the arrow head in points
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shrink : move the tip and base some percent away from the
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annotated point and text
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any key for matplotlib.patches.polygon (e.g., facecolor)
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width : the width of the arrow in points
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frac : the fraction of the arrow length occupied by the head
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headwidth : the width of the base of the arrow head in points
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shrink : move the tip/base some % away from the annotated point and text
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any key for matplotlib.patches.polygon (e.g., facecolor)
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For physical coordinate systems (points or pixels) the origin is the
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(bottom, left) of the figure or axes.

‎examples/pylab_examples/anscombe.py

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"""
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========
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Anscombe
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========
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==================
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Anscombe's Quartet
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==================
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"""
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from __future__ import print_function

‎examples/pylab_examples/aspect_loglog.py

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"""
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=============
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Aspect Loglog
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Loglog Aspect
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=============
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"""

‎examples/pylab_examples/axis_equal_demo.py

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Axis Equal Demo
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===============
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This example is only interesting when ran in interactive mode
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This example is only interesting when run in interactive mode.
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"""
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‎examples/pylab_examples/bar_stacked.py

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Bar Stacked
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===========
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a stacked bar plot with errorbars
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A stacked bar plot with errorbars.
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"""
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import numpy as np
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import matplotlib.pyplot as plt

‎examples/pylab_examples/centered_ticklabels.py

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Centered Ticklabels
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===================
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sometimes it is nice to have ticklabels centered. mpl currently
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sometimes it is nice to have ticklabels centered. Matplotlib currently
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associates a label with a tick, and the label can be aligned
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'center', 'left', or 'right' using the horizontal alignment property:
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'center', 'left', or 'right' using the horizontal alignment property::
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for label in ax.xaxis.get_xticklabels():
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label.set_horizontalalignment('right')
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for label in ax.xaxis.get_xticklabels():
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label.set_horizontalalignment('right')
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but this doesn't help center the label between ticks. One solution
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is to "face it". Use the minor ticks to place a tick centered
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is to "fake it". Use the minor ticks to place a tick centered
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between the major ticks. Here is an example that labels the months,
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centered between the ticks
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"""

‎examples/pylab_examples/color_by_yvalue.py

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Color By Yvalue
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===============
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use masked arrays to plot a line with different colors by y-value
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Use masked arrays to plot a line with different colors by y-value.
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"""
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import numpy as np
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import matplotlib.pyplot as plt

‎examples/pylab_examples/colours_sgskip.py

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Some simple functions to generate colours.
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"""
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import numpy as np
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from matplotlib import colors as mcolors

‎examples/pylab_examples/contour_label_demo.py

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##################################################
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# Define our surface
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##################################################
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delta = 0.025
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x = np.arange(-3.0, 3.0, delta)
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y = np.arange(-2.0, 2.0, delta)
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##################################################
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# Make contour labels using creative float classes
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# Follows suggestion of Manuel Metz
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##################################################
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plt.figure()
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# Basic contour plot
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##################################################
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# Label contours with arbitrary strings using a
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# dictionary
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##################################################
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plt.figure()
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# Basic contour plot

‎examples/pylab_examples/coords_report.py

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Coords Report
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=============
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override the default reporting of coords
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Override the default reporting of coords.
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"""
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import matplotlib.pyplot as plt

‎examples/pylab_examples/cursor_demo_sgskip.py

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Cursor Demo
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===========
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"""
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"""
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This example shows how to use matplotlib to provide a data cursor. It
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uses matplotlib to draw the cursor and may be a slow since this
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requires redrawing the figure with every mouse move.
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The mpldatacursor and mplcursors third-party packages can be used to achieve a
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similar effect. See
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https://github.com/joferkington/mpldatacursor
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https://github.com/anntzer/mplcursors
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"""

‎examples/pylab_examples/custom_cmap.py

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"""
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===========
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Custom Cmap
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===========
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.colors import LinearSegmentedColormap
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"""
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=========================================
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Creating a colormap from a list of colors
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-----------------------------------------
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=========================================
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Creating a colormap from a list of colors can be done with the `from_list`
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method of `LinearSegmentedColormap`. You must pass a list of RGB tuples that
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define the mixture of colors from 0 to 1.
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Example: suppose you want red to increase from 0 to 1 over the bottom
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half, green to do the same over the middle half, and blue over the top
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half. Then you would use:
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half. Then you would use::
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cdict = {'red': ((0.0, 0.0, 0.0),
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(0.5, 1.0, 1.0),
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(1.0, 1.0, 1.0)),
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cdict = {'red': ((0.0, 0.0, 0.0),
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(0.5, 1.0, 1.0),
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(1.0, 1.0, 1.0)),
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'green': ((0.0, 0.0, 0.0),
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(0.25, 0.0, 0.0),
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(0.75, 1.0, 1.0),
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(1.0, 1.0, 1.0)),
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'green': ((0.0, 0.0, 0.0),
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(0.25, 0.0, 0.0),
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(0.75, 1.0, 1.0),
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(1.0, 1.0, 1.0)),
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'blue': ((0.0, 0.0, 0.0),
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(0.5, 0.0, 0.0),
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(1.0, 1.0, 1.0))}
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'blue': ((0.0, 0.0, 0.0),
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(0.5, 0.0, 0.0),
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(1.0, 1.0, 1.0))}
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If, as in this example, there are no discontinuities in the r, g, and b
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y1, it is saying that for x from 0 to 0.5, red increases from 0 to 1,
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0.7 to 1. Green ramps from 0 to 1 as x goes from 0 to 0.5, then jumps
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back to 0, and ramps back to 1 as x goes from 0.5 to 1.
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back to 0, and ramps back to 1 as x goes from 0.5 to 1.::
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row i: x y0 y1
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/
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/
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row i+1: x y0 y1
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row i: x y0 y1
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/
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/
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row i+1: x y0 y1
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Above is an attempt to show that for x in the range x[i] to x[i+1], the
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interpolation is between y1[i] and y0[i+1]. So, y0[0] and y1[-1] are
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never used.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.colors import LinearSegmentedColormap
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# Make some illustrative fake data:
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x = np.arange(0, np.pi, 0.1)

‎examples/pylab_examples/customize_rc.py

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If you like to work interactively, and need to create different sets
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of defaults for figures (e.g., one set of defaults for publication, one
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set for interactive exploration), you may want to define some
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functions in a custom module that set the defaults, e.g.,
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def set_pub():
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rc('font', weight='bold') # bold fonts are easier to see
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rc('tick', labelsize=15) # tick labels bigger
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rc('lines', lw=1, color='k') # thicker black lines (no budget for color!)
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rc('grid', c='0.5', ls='-', lw=0.5) # solid gray grid lines
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rc('savefig', dpi=300) # higher res outputs
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Then as you are working interactively, you just need to do
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>>> set_pub()
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>>> subplot(111)
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>>> plot([1,2,3])
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>>> savefig('myfig')
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>>> rcdefaults() # restore the defaults
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functions in a custom module that set the defaults, e.g.,::
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def set_pub():
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rc('font', weight='bold') # bold fonts are easier to see
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rc('tick', labelsize=15) # tick labels bigger
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rc('lines', lw=1, color='k') # thicker black lines (no budget for color!)
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rc('grid', c='0.5', ls='-', lw=0.5) # solid gray grid lines
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rc('savefig', dpi=300) # higher res outputs
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Then as you are working interactively, you just need to do::
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>>> set_pub()
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>>> subplot(111)
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>>> plot([1,2,3])
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>>> savefig('myfig')
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>>> rcdefaults() # restore the defaults
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"""
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import matplotlib.pyplot as plt

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