I am submitting two possible gallery additions for bar charts and boxplots. The existing examples are good, but being relatively new to matplotlib I had to learn quite a bit to enhance these charts to fit my needs. Learning is good of course, but maybe these "more complete" examples will help others to get up to speed more quickly. Likewise, I am sure there are experts out there who can comment on better, more Pythonic ways of doing things, so please feel free to offer advice.
http://www.nabble.com/file/p24793965/barchartdemo.png http://www.nabble.com/file/p24793965/boxplotdemo.png ----------------------------- barchartdemo.py ----------------------------- import numpy as np import matplotlib.pyplot as plt import pylab from matplotlib.patches import Polygon from matplotlib.ticker import MaxNLocator #This examples comes from an application in which grade school gym teachers #wanted to be able to show parents how their child did across a handful of #fitness tests, and importantly, relative to how other children did. To extract #the plotting code for demo purposes, we'll just make up some data for little #Johnny Doe... student = 'Johnny Doe' grade = 2 gender = 'boy' cohortSize = 62 #The number of other 2nd grade boys numTests = 5 testNames = ['Pacer Test', 'Flexed Arm\n Hang', 'Mile Run', 'Agility', 'Push Ups'] testMeta = ['laps', 'sec', 'min:sec', 'sec', ''] scores = ['7', '48', '12:52', '17', '14'] rankings = np.round(np.random.uniform(0, 1, numTests)*100, 0) fig = plt.figure(figsize=(9,7)) ax1 = fig.add_subplot(111) plt.subplots_adjust(left=0.115, right=0.88) fig.canvas.set_window_title('Eldorado K-8 Fitness Chart') pos = np.arange(numTests)+0.5 #Center bars on the Y-axis ticks rects = ax1.barh(pos, rankings, align='center', height=0.5, color='m') ax1.axis([0,100,0,5]) pylab.yticks(pos, testNames) ax1.set_title('Johnny Doe') plt.text(50, -0.5, 'Cohort Size: ' + str(cohortSize), horizontalalignment='center', size='small') #Set the right-hand Y-axis ticks and labels and set X-axis tick marks at the #deciles ax2 = ax1.twinx() ax2.plot([100,100], [0, 5], 'white', alpha=0.1) ax2.xaxis.set_major_locator(MaxNLocator(11)) xticks = pylab.setp(ax2, xticklabels=['0','10','20','30','40','50','60', '70', '80','90','100']) ax2.xaxis.grid(True, linestyle='--', which='major', color='grey', alpha=0.25) #Plot a solid vertical gridline to highlight the median position plt.plot([50,50], [0, 5], 'grey', alpha=0.25) #Build up the score labels for the right Y-axis by first appending a carriage #return to each string and then tacking on the appropriate meta information #(i.e., 'laps' vs 'seconds'). We want the labels centered on the ticks, so if #there is no meta info (like for pushups) then don't add the carriage return to #the string scoreLabels = [(scr + '\n' if testMeta[i] != '' else scr) for i,scr in enumerate(scores)] scoreLabels = [i+j for i,j in zip(scoreLabels, testMeta)] pylab.yticks(pos, scoreLabels) ax2.set_ylabel('Test Scores') #Make list of numerical suffixes corresponding to position in a list # 0 1 2 3 4 5 6 7 8 9 suffixes =['th', 'st', 'nd', 'rd', 'th', 'th', 'th', 'th', 'th', 'th'] ax2.set_xlabel('Percentile Ranking Across ' + str(grade) + suffixes[grade] \ + ' Grade ' + gender.title() + 's') #Lastly, write in the ranking inside each bar to aid in interpretation for rect in rects: #Rectangle widths are already integer-valued but are floating type, so it #helps to remove the trailing decimal point and 0 by converting width to int #type width = int(rect.get_width()) #Figure out what the last digit (width modulo 10) so we can add the #appropriate numerical suffix (e.g. 1st, 2nd, 3rd, etc) lastDigit = width % 10 #Note that 11, 12, and 13 are special cases if (width == 11) or (width == 12) or (width == 13): suffix = 'th' else: suffix = suffixes[lastDigit] rankStr = str(width) + suffix if (width < 5): #The bars aren't wide enough to print the ranking inside xloc = width + 1 #Shift the text to the right side of the right edge clr = 'black' #Black against white background align = 'left' else: xloc = 0.98*width #Shift the text to the left side of the right edge clr = 'white' #White on magenta align = 'right' yloc = rect.get_y()+rect.get_height()/2.0 #Center the text vertically in the #bar ax1.text(xloc, yloc, rankStr, horizontalalignment=align, verticalalignment='center', color=clr, weight='bold') plt.show() ----------------------------- boxplotdemo.py ----------------------------- import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Polygon #Generate some data from five different probability distributions, each with #different characteristics. We want to play with how an IID bootstrap resample #of the data preserves the distributional properties of the original sample, and #a boxplot is one visual tool to make this assessment numDists = 5 randomDists = ['Normal(1,1)',' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)', 'Triangular(2,9,11)'] N = 500 norm = np.random.normal(1,1, N) logn = np.random.lognormal(1,1, N) expo = np.random.exponential(1, N) gumb = np.random.gumbel(6, 4, N) tria = np.random.triangular(2, 9, 11, N) #Generate some random indices that we'll use to resample the original data #arrays. For code brevity, just use the same random indices for each array bootstrapIndices = np.random.random_integers(0, N-1, N) normBoot = norm[bootstrapIndices] expoBoot = expo[bootstrapIndices] gumbBoot = gumb[bootstrapIndices] lognBoot = logn[bootstrapIndices] triaBoot = tria[bootstrapIndices] data = [norm, normBoot, logn, lognBoot, expo, expoBoot, gumb, gumbBoot, tria, triaBoot] fig = plt.figure(figsize=(10,6)) fig.canvas.set_window_title('A Boxplot Example') ax1 = fig.add_subplot(111) plt.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25) bp = plt.boxplot(data, notch=0, sym='+', vert=1, whis=1.5) plt.setp(bp['boxes'], color='black') plt.setp(bp['whiskers'], color='black') plt.setp(bp['fliers'], color='red', marker='+') #Add a horizontal grid to the plot, but make it very light in color so we can #use it for reading data values but not be distracting ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5) #Hide these grid behind plot objects ax1.set_axisbelow(True) ax1.set_title('Comparison of IID Bootstrap Resampling Across Five Distributions') ax1.set_xlabel('Distribution') ax1.set_ylabel('Value') #Now fill the boxes with desired colors boxColors = ['darkkhaki','royalblue'] numBoxes = numDists*2 medians = range(numBoxes) for i in range(numBoxes): box = bp['boxes'][i] boxX = [] boxY = [] for j in range(5): boxX.append(box.get_xdata()[j]) boxY.append(box.get_ydata()[j]) boxCoords = zip(boxX,boxY) #Alternate between Dark Khaki and Royal Blue k = i % 2 boxPolygon = Polygon(boxCoords, facecolor=boxColors[k]) ax1.add_patch(boxPolygon) #Now draw the median lines back over what we just filled in med = bp['medians'][i] medianX = [] medianY = [] for j in range(2): medianX.append(med.get_xdata()[j]) medianY.append(med.get_ydata()[j]) plt.plot(medianX, medianY, 'k') medians[i] = medianY[0] #Finally, overplot the sample averages, with horixzontal alignment in the #center of each box plt.plot([np.average(med.get_xdata().data)], [np.average(data[i])], color='w', marker='*', markeredgecolor='k') #Set the axes ranges and axes labels ax1.set_xlim(0.5, numBoxes+0.5) top = 40 bottom = -5 ax1.set_ylim(bottom, top) xtickNames = plt.setp(ax1, xticklabels=np.repeat(randomDists, 2)) plt.setp(xtickNames, rotation=45, fontsize=8) #Due to the Y-axis scale being different across samples, it can be hard to #compare differences in medians across the samples. Add upper X-axis tick labels #with the sample medians to aid in comparison (just use two decimal places of #precision) pos = np.arange(numBoxes)+1 upperLabels = [str(np.round(s, 2)) for s in medians] weights = ['bold', 'semibold'] for tick,label in zip(range(numBoxes),ax1.get_xticklabels()): k = tick % 2 ax1.text(pos[tick], top-(top*0.05), upperLabels[tick], horizontalalignment='center', size='x-small', weight=weights[k], color=boxColors[k]) #Finally, add a basic legend plt.figtext(0.80, 0.08, str(N) + ' Random Numbers' , backgroundcolor=boxColors[0], color='black', weight='roman', size='x-small') plt.figtext(0.80, 0.045, 'IID Bootstrap Resample', backgroundcolor=boxColors[1], color='white', weight='roman', size='x-small') plt.figtext(0.80, 0.015, '*', color='white', backgroundcolor='silver', weight='roman', size='medium') plt.figtext(0.815, 0.013, ' Average Value', color='black', weight='roman', size='x-small') plt.show() ----- Josh Hemann Statistical Advisor http://www.vni.com/ Visual Numerics jhem...@vni.com | P 720.407.4214 | F 720.407.4199 -- View this message in context: http://www.nabble.com/A-couple-of-new-gallery-examples--tp24793965p24793965.html Sent from the matplotlib - users mailing list archive at Nabble.com. ------------------------------------------------------------------------------ Let Crystal Reports handle the reporting - Free Crystal Reports 2008 30-Day trial. 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