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Merge pull request matplotlib#1669 from toddrjen/eventplot
Add EventCollection and eventplot
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‎CHANGELOG

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2013-01-23 Add 'savefig.directory' to rcParams to remember and fill in the last
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directory saved to for figure save dialogs - Martin Spacek
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2013-01-13 Add eventplot method to axes and pyplot and EventCollection class
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to collections.
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2013-01-08 Added two extra titles to axes which are flush with the left and
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right edges of the plot respectively.
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Andrew Dawson

‎boilerplate.py

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@@ -108,6 +108,7 @@ def boilerplate_gen():
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'contourf',
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'csd',
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'errorbar',
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'eventplot',
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'fill',
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'fill_between',
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'fill_betweenx',

‎doc/users/whats_new.rst

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new in matplotlib-1.3
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=====================
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New eventplot plot type
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-------------------------------------
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Todd Jennings added a :func:`~matplotlib.pyplot.eventplot` function to
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create multiple rows or columns of identical line segments
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New EventCollection collections class
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-------------------------------------
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Todd Jennings created the new :class:`~matplotlib.collections.EventCollection`
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class that allows for plotting and manipulating rows or columns of identical
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line segments
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Baselines for stackplot
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-----------------------
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Till Stensitzki added non-zero baselines to :func:`~matplotlib.pyplot.stackplot`.

‎examples/pylab_examples/eventcollection_demo.py

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#!/usr/bin/env python
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# -*- Coding:utf-8 -*-
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'''Plot two curves, then use EventCollections to mark the locations of the x
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and y data points on the respective axes for each curve'''
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import matplotlib.pyplot as plt
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from matplotlib.collections import EventCollection
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import numpy as np
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# create random data
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np.random.seed(50)
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xdata = np.random.random([2, 10])
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# split the data into two parts
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xdata1 = xdata[0, :]
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xdata2 = xdata[1, :]
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# sort the data so it makes clean curves
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xdata1.sort()
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xdata2.sort()
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# create some y data points
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ydata1 = xdata1 ** 2
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ydata2 = 1 - xdata2 ** 3
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# plot the data
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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ax.plot(xdata1, ydata1, 'r', xdata2, ydata2, 'b')
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# create the events marking the x data points
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xevents1 = EventCollection(xdata1, color=[1, 0, 0], linelength=0.05)
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xevents2 = EventCollection(xdata2, color=[0, 0, 1], linelength=0.05)
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# create the events marking the y data points
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yevents1 = EventCollection(ydata1, color=[1, 0, 0], linelength=0.05,
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orientation='vertical')
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yevents2 = EventCollection(ydata2, color=[0, 0, 1], linelength=0.05,
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orientation='vertical')
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# add the events to the axis
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ax.add_collection(xevents1)
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ax.add_collection(xevents2)
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ax.add_collection(yevents1)
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ax.add_collection(yevents2)
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# set the limits
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ax.set_xlim([0, 1])
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ax.set_ylim([0, 1])
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ax.set_title('line plot with data points')
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# display the plot
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plt.show()

‎examples/pylab_examples/eventplot_demo.py

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#!/usr/bin/env python
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# -*- Coding:utf-8 -*-
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'''an eventplot showing sequences of events with various line properties
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the plot is shown in both horizontal and vertical orientations'''
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import matplotlib.pyplot as plt
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import numpy as np
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# set the random seed
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np.random.seed(0)
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# create random data
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data1 = np.random.random([6, 50])
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# set different colors for each set of positions
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colors1 = np.array([[1, 0, 0],
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[0, 1, 0],
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[0, 0, 1],
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[1, 1, 0],
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[1, 0, 1],
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[0, 1, 1]])
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# set different line properties for each set of positions
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# note that some overlap
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lineoffsets1 = np.array([-15, -3, 1, 1.5, 6, 10])
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linelengths1 = [5, 2, 1, 1, 3, 1.5]
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fig = plt.figure()
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# create a horizontal plot
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ax1 = fig.add_subplot(221)
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ax1.eventplot(data1, colors=colors1, lineoffsets=lineoffsets1,
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linelengths=linelengths1)
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ax1.set_title('horizontal eventplot 1')
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# create a vertical plot
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ax2 = fig.add_subplot(223)
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ax2.eventplot(data1, colors=colors1, lineoffsets=lineoffsets1,
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linelengths=linelengths1, orientation='vertical')
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ax2.set_title('vertical eventplot 1')
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# create another set of random data.
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# the gamma distribution is only used fo aesthetic purposes
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data2 = np.random.gamma(4, size=[60, 50])
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# use individual values for the parameters this time
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# these values will be used for all data sets (except lineoffsets2, which
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# sets the increment between each data set in this usage)
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colors2 = [[0, 0, 0]]
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lineoffsets2 = 1
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linelengths2 = 1
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# create a horizontal plot
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ax1 = fig.add_subplot(222)
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ax1.eventplot(data2, colors=colors2, lineoffsets=lineoffsets2,
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linelengths=linelengths2)
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ax1.set_title('horizontal eventplot 2')
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# create a vertical plot
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ax2 = fig.add_subplot(224)
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ax2.eventplot(data2, colors=colors2, lineoffsets=lineoffsets2,
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linelengths=linelengths2, orientation='vertical')
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ax2.set_title('vertical eventplot 2')
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plt.show()

‎lib/matplotlib/__init__.py

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'matplotlib.tests.test_basic',
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'matplotlib.tests.test_bbox_tight',
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'matplotlib.tests.test_cbook',
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'matplotlib.tests.test_collections',
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'matplotlib.tests.test_colorbar',
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'matplotlib.tests.test_colors',
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'matplotlib.tests.test_contour',

‎lib/matplotlib/axes.py

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return coll
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@docstring.dedent_interpd
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def eventplot(self, positions, orientation='horizontal', lineoffsets=1,
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linelengths=1, linewidths=None, colors=None,
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linestyles='solid', **kwargs):
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"""
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Plot identical parallel lines at specific positions.
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Call signature::
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eventplot(positions, orientation='horizontal', lineoffsets=0,
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linelengths=1, linewidths=None, color =None,
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linestyles='solid'
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Plot parallel lines at the given positions. positions should be a 1D
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or 2D array-like object, with each row corresponding to a row or column
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of lines.
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This type of plot is commonly used in neuroscience for representing
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neural events, where it is commonly called a spike raster, dot raster,
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or raster plot.
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However, it is useful in any situation where you wish to show the
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timing or position of multiple sets of discrete events, such as the
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arrival times of people to a business on each day of the month or the
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date of hurricanes each year of the last century.
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*orientation* : [ 'horizonal' | 'vertical' ]
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'horizonal' : the lines will be vertical and arranged in rows
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"vertical' : lines will be horizontal and arranged in columns
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*lineoffsets* :
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A float or array-like containing floats.
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*linelengths* :
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A float or array-like containing floats.
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*linewidths* :
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A float or array-like containing floats.
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*colors*
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must be a sequence of RGBA tuples (eg arbitrary color
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strings, etc, not allowed) or a list of such sequences
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*linestyles* :
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[ 'solid' | 'dashed' | 'dashdot' | 'dotted' ] or an array of these
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values
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For linelengths, linewidths, colors, and linestyles, if only a single
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value is given, that value is applied to all lines. If an array-like
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is given, it must have the same length as positions, and each value
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will be applied to the corresponding row or column in positions.
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Returns a list of :class:`matplotlib.collections.EventCollection`
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objects that were added.
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kwargs are :class:`~matplotlib.collections.LineCollection` properties:
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%(LineCollection)s
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**Example:**
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.. plot:: mpl_examples/pylab_examples/eventplot_demo.py
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"""
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self._process_unit_info(xdata=positions,
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ydata=[lineoffsets, linelengths],
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kwargs=kwargs)
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# We do the conversion first since not all unitized data is uniform
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positions = self.convert_xunits(positions)
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lineoffsets = self.convert_yunits(lineoffsets)
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linelengths = self.convert_yunits(linelengths)
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if not iterable(positions):
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positions = [positions]
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elif any(iterable(position) for position in positions):
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positions = [np.asanyarray(position) for position in positions]
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else:
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positions = [np.asanyarray(positions)]
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if len(positions) == 0:
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return []
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if not iterable(lineoffsets):
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lineoffsets = [lineoffsets]
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if not iterable(linelengths):
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linelengths = [linelengths]
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if not iterable(linewidths):
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linewidths = [linewidths]
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if not iterable(colors):
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colors = [colors]
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if hasattr(linestyles, 'lower') or not iterable(linestyles):
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linestyles = [linestyles]
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lineoffsets = np.asarray(lineoffsets)
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linelengths = np.asarray(linelengths)
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linewidths = np.asarray(linewidths)
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if len(lineoffsets) == 0:
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lineoffsets = [None]
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if len(linelengths) == 0:
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linelengths = [None]
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if len(linewidths) == 0:
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lineoffsets = [None]
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if len(linewidths) == 0:
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lineoffsets = [None]
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if len(colors) == 0:
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colors = [None]
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if len(lineoffsets) == 1 and len(positions) != 1:
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lineoffsets = np.tile(lineoffsets, len(positions))
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lineoffsets[0] = 0
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lineoffsets = np.cumsum(lineoffsets)
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if len(linelengths) == 1:
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linelengths = np.tile(linelengths, len(positions))
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if len(linewidths) == 1:
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linewidths = np.tile(linewidths, len(positions))
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if len(colors) == 1:
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colors = np.asanyarray(colors)
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colors = np.tile(colors, [len(positions), 1])
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if len(linestyles) == 1:
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linestyles = [linestyles] * len(positions)
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if len(lineoffsets) != len(positions):
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raise ValueError('lineoffsets and positions are unequal sized '
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'sequences')
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if len(linelengths) != len(positions):
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raise ValueError('linelengths and positions are unequal sized '
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'sequences')
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if len(linewidths) != len(positions):
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raise ValueError('linewidths and positions are unequal sized '
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'sequences')
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if len(colors) != len(positions):
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raise ValueError('colors and positions are unequal sized '
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'sequences')
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if len(linestyles) != len(positions):
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raise ValueError('linestyles and positions are unequal sized '
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'sequences')
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colls = []
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for position, lineoffset, linelength, linewidth, color, linestyle in \
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itertools.izip(positions, lineoffsets, linelengths, linewidths,
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colors, linestyles):
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coll = mcoll.EventCollection(position,
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orientation=orientation,
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lineoffset=lineoffset,
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linelength=linelength,
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linewidth=linewidth,
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color=color,
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linestyle=linestyle)
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self.add_collection(coll)
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coll.update(kwargs)
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colls.append(coll)
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if len(positions) > 0:
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minpos = min(position.min() for position in positions)
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maxpos = max(position.max() for position in positions)
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minline = (lineoffsets - linelengths).min()
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maxline = (lineoffsets + linelengths).max()
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if colls[0].is_horizontal():
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corners = (minpos, minline), (maxpos, maxline)
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else:
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corners = (minline, minpos), (maxline, maxpos)
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self.update_datalim(corners)
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self.autoscale_view()
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return colls
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#### Basic plotting
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@docstring.dedent_interpd
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def plot(self, *args, **kwargs):

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