python_matplotlib_example

1978 days ago by macieksk

# After # http://matplotlib.org/examples/index.html 
       
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] 
       
len(data) 
       
10
[len(d) for d in data] 
       
[500, 500, 500, 500, 500, 500, 500, 500, 500, 500]
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) plt.savefig('test.png') 
       
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') plt.savefig('test.png') 
       
# 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 horizontal alignment # in the center of each box plt.plot([np.average(med.get_xdata())], [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]) plt.savefig('test.png') 
       
# 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.savefig('test.png') 
       
###### 
       
import numpy as np import matplotlib.path as mpath import matplotlib.patches as mpatches import matplotlib.pyplot as plt Path = mpath.Path fig = plt.figure() ax = fig.add_subplot(111) pathdata = [ (Path.MOVETO, (1.58, -2.57)), (Path.CURVE4, (0.35, -1.1)), (Path.CURVE4, (-1.75, 2.0)), (Path.CURVE4, (0.375, 2.0)), (Path.LINETO, (0.85, 1.15)), (Path.CURVE4, (2.2, 3.2)), (Path.CURVE4, (3, 0.05)), (Path.CURVE4, (2.0, -0.5)), (Path.CLOSEPOLY, (1.58, -2.57)), ] codes, verts = zip(*pathdata) path = mpath.Path(verts, codes) patch = mpatches.PathPatch(path, facecolor='red', edgecolor='yellow', alpha=0.5) ax.add_patch(patch) x, y = zip(*path.vertices) line, = ax.plot(x, y, 'go-') ax.grid() ax.set_xlim(-3,4) ax.set_ylim(-3,4) ax.set_title('spline paths') #plt.show() plt.savefig('figure.png')