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added two examples from Josh Hemann
svn path=/branches/v0_99_maint/; revision=7335
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‎examples/pylab_examples/barchart_demo2.py

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"""
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Thanks Josh Hemann for the example
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This examples comes from an application in which grade school gym
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teachers wanted to be able to show parents how their child did across
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a handful of fitness tests, and importantly, relative to how other
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children did. To extract the plotting code for demo purposes, we'll
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just make up some data for little Johnny Doe...
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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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import pylab
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from matplotlib.patches import Polygon
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from matplotlib.ticker import MaxNLocator
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student = 'Johnny Doe'
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grade = 2
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gender = 'boy'
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cohortSize = 62 #The number of other 2nd grade boys
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numTests = 5
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testNames = ['Pacer Test', 'Flexed Arm\n Hang', 'Mile Run', 'Agility',
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'Push Ups']
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testMeta = ['laps', 'sec', 'min:sec', 'sec', '']
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scores = ['7', '48', '12:52', '17', '14']
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rankings = np.round(np.random.uniform(0, 1, numTests)*100, 0)
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fig = plt.figure(figsize=(9,7))
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ax1 = fig.add_subplot(111)
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plt.subplots_adjust(left=0.115, right=0.88)
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fig.canvas.set_window_title('Eldorado K-8 Fitness Chart')
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pos = np.arange(numTests)+0.5 #Center bars on the Y-axis ticks
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rects = ax1.barh(pos, rankings, align='center', height=0.5, color='m')
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ax1.axis([0,100,0,5])
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pylab.yticks(pos, testNames)
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ax1.set_title('Johnny Doe')
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plt.text(50, -0.5, 'Cohort Size: ' + str(cohortSize),
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horizontalalignment='center', size='small')
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# Set the right-hand Y-axis ticks and labels and set X-axis tick marks at the
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# deciles
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ax2 = ax1.twinx()
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ax2.plot([100,100], [0, 5], 'white', alpha=0.1)
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ax2.xaxis.set_major_locator(MaxNLocator(11))
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xticks = pylab.setp(ax2, xticklabels=['0','10','20','30','40','50','60',
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'70',
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'80','90','100'])
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ax2.xaxis.grid(True, linestyle='--', which='major', color='grey',
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alpha=0.25)
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#Plot a solid vertical gridline to highlight the median position
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plt.plot([50,50], [0, 5], 'grey', alpha=0.25)
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# Build up the score labels for the right Y-axis by first appending a carriage
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# return to each string and then tacking on the appropriate meta information
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# (i.e., 'laps' vs 'seconds'). We want the labels centered on the ticks, so if
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# there is no meta info (like for pushups) then don't add the carriage return to
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# the string
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def withnew(i, scr):
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if testMeta[i] != '' : return '%s\n'%scr
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else: return scr
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scoreLabels = [withnew(i, scr) for i,scr in enumerate(scores)]
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scoreLabels = [i+j for i,j in zip(scoreLabels, testMeta)]
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pylab.yticks(pos, scoreLabels)
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ax2.set_ylabel('Test Scores')
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#Make list of numerical suffixes corresponding to position in a list
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# 0 1 2 3 4 5 6 7 8 9
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suffixes =['th', 'st', 'nd', 'rd', 'th', 'th', 'th', 'th', 'th', 'th']
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ax2.set_xlabel('Percentile Ranking Across ' + str(grade) + suffixes[grade] \
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+ ' Grade ' + gender.title() + 's')
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# Lastly, write in the ranking inside each bar to aid in interpretation
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for rect in rects:
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# Rectangle widths are already integer-valued but are floating
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# type, so it helps to remove the trailing decimal point and 0 by
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# converting width to int type
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width = int(rect.get_width())
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# Figure out what the last digit (width modulo 10) so we can add
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# the appropriate numerical suffix (e.g. 1st, 2nd, 3rd, etc)
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lastDigit = width % 10
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# Note that 11, 12, and 13 are special cases
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if (width == 11) or (width == 12) or (width == 13):
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suffix = 'th'
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else:
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suffix = suffixes[lastDigit]
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rankStr = str(width) + suffix
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if (width < 5): # The bars aren't wide enough to print the ranking inside
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xloc = width + 1 # Shift the text to the right side of the right edge
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clr = 'black' # Black against white background
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align = 'left'
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else:
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xloc = 0.98*width # Shift the text to the left side of the right edge
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clr = 'white' # White on magenta
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align = 'right'
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yloc = rect.get_y()+rect.get_height()/2.0 #Center the text vertically in the bar
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ax1.text(xloc, yloc, rankStr, horizontalalignment=align,
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verticalalignment='center', color=clr, weight='bold')
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plt.show()
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‎examples/pylab_examples/boxplot_demo2.py

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"""
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Thanks Josh Hemann for the example
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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.patches import Polygon
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# Generate some data from five different probability distributions,
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# each with different characteristics. We want to play with how an IID
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# bootstrap resample of the data preserves the distributional
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# properties of the original sample, and a boxplot is one visual tool
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# to make this assessment
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numDists = 5
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randomDists = ['Normal(1,1)',' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
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'Triangular(2,9,11)']
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N = 500
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norm = np.random.normal(1,1, N)
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logn = np.random.lognormal(1,1, N)
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expo = np.random.exponential(1, N)
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gumb = np.random.gumbel(6, 4, N)
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tria = np.random.triangular(2, 9, 11, N)
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# Generate some random indices that we'll use to resample the original data
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# arrays. For code brevity, just use the same random indices for each array
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bootstrapIndices = np.random.random_integers(0, N-1, N)
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normBoot = norm[bootstrapIndices]
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expoBoot = expo[bootstrapIndices]
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gumbBoot = gumb[bootstrapIndices]
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lognBoot = logn[bootstrapIndices]
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triaBoot = tria[bootstrapIndices]
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data = [norm, normBoot, logn, lognBoot, expo, expoBoot, gumb, gumbBoot,
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tria, triaBoot]
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fig = plt.figure(figsize=(10,6))
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fig.canvas.set_window_title('A Boxplot Example')
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ax1 = fig.add_subplot(111)
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plt.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)
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bp = plt.boxplot(data, notch=0, sym='+', vert=1, whis=1.5)
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plt.setp(bp['boxes'], color='black')
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plt.setp(bp['whiskers'], color='black')
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plt.setp(bp['fliers'], color='red', marker='+')
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# Add a horizontal grid to the plot, but make it very light in color
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# so we can use it for reading data values but not be distracting
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ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
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alpha=0.5)
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# Hide these grid behind plot objects
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ax1.set_axisbelow(True)
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ax1.set_title('Comparison of IID Bootstrap Resampling Across Five Distributions')
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ax1.set_xlabel('Distribution')
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ax1.set_ylabel('Value')
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# Now fill the boxes with desired colors
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boxColors = ['darkkhaki','royalblue']
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numBoxes = numDists*2
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medians = range(numBoxes)
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for i in range(numBoxes):
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box = bp['boxes'][i]
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boxX = []
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boxY = []
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for j in range(5):
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boxX.append(box.get_xdata()[j])
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boxY.append(box.get_ydata()[j])
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boxCoords = zip(boxX,boxY)
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# Alternate between Dark Khaki and Royal Blue
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k = i % 2
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boxPolygon = Polygon(boxCoords, facecolor=boxColors[k])
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ax1.add_patch(boxPolygon)
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# Now draw the median lines back over what we just filled in
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med = bp['medians'][i]
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medianX = []
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medianY = []
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for j in range(2):
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medianX.append(med.get_xdata()[j])
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medianY.append(med.get_ydata()[j])
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plt.plot(medianX, medianY, 'k')
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medians[i] = medianY[0]
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# Finally, overplot the sample averages, with horixzontal alignment
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# in the center of each box
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plt.plot([np.average(med.get_xdata())], [np.average(data[i])],
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color='w', marker='*', markeredgecolor='k')
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# Set the axes ranges and axes labels
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ax1.set_xlim(0.5, numBoxes+0.5)
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top = 40
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bottom = -5
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ax1.set_ylim(bottom, top)
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xtickNames = plt.setp(ax1, xticklabels=np.repeat(randomDists, 2))
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plt.setp(xtickNames, rotation=45, fontsize=8)
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# Due to the Y-axis scale being different across samples, it can be
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# hard to compare differences in medians across the samples. Add upper
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# X-axis tick labels with the sample medians to aid in comparison
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# (just use two decimal places of precision)
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pos = np.arange(numBoxes)+1
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upperLabels = [str(np.round(s, 2)) for s in medians]
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weights = ['bold', 'semibold']
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for tick,label in zip(range(numBoxes),ax1.get_xticklabels()):
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k = tick % 2
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ax1.text(pos[tick], top-(top*0.05), upperLabels[tick],
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horizontalalignment='center', size='x-small', weight=weights[k],
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color=boxColors[k])
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# Finally, add a basic legend
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plt.figtext(0.80, 0.08, str(N) + ' Random Numbers' ,
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backgroundcolor=boxColors[0], color='black', weight='roman',
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size='x-small')
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plt.figtext(0.80, 0.045, 'IID Bootstrap Resample',
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backgroundcolor=boxColors[1],
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color='white', weight='roman', size='x-small')
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plt.figtext(0.80, 0.015, '*', color='white', backgroundcolor='silver',
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weight='roman', size='medium')
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plt.figtext(0.815, 0.013, ' Average Value', color='black', weight='roman',
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size='x-small')
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plt.show()

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