#!/usr/bin/env python ''' Use matplotlib to generate performance charts Copyright (C) 2018 The noVNC Authors Licensed under MPL-2.0 (see docs/LICENSE.MPL-2.0) ''' # a bar plot with errorbars import sys, json import numpy as np import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties def usage(): print "%s json_file level1 level2 level3 [legend_height]\n\n" % sys.argv[0] print "Description:\n" print "level1, level2, and level3 are one each of the following:\n"; print " select=ITEM - select only ITEM at this level"; print " bar - each item on this level becomes a graph bar"; print " group - items on this level become groups of bars"; print "\n"; print "json_file is a file containing json data in the following format:\n" print ' {'; print ' "conf": {'; print ' "order_l1": ['; print ' "level1_label1",'; print ' "level1_label2",'; print ' ...'; print ' ],'; print ' "order_l2": ['; print ' "level2_label1",'; print ' "level2_label2",'; print ' ...'; print ' ],'; print ' "order_l3": ['; print ' "level3_label1",'; print ' "level3_label2",'; print ' ...'; print ' ]'; print ' },'; print ' "stats": {'; print ' "level1_label1": {'; print ' "level2_label1": {'; print ' "level3_label1": [val1, val2, val3],'; print ' "level3_label2": [val1, val2, val3],'; print ' ...'; print ' },'; print ' "level2_label2": {'; print ' ...'; print ' },'; print ' },'; print ' "level1_label2": {'; print ' ...'; print ' },'; print ' ...'; print ' },'; print ' }'; sys.exit(2) def error(msg): print msg sys.exit(1) #colors = ['#ff0000', '#0863e9', '#00f200', '#ffa100', # '#800000', '#805100', '#013075', '#007900'] colors = ['#ff0000', '#00ff00', '#0000ff', '#dddd00', '#dd00dd', '#00dddd', '#dd6622', '#dd2266', '#66dd22', '#8844dd', '#44dd88', '#4488dd'] if len(sys.argv) < 5: usage() filename = sys.argv[1] L1 = sys.argv[2] L2 = sys.argv[3] L3 = sys.argv[4] if len(sys.argv) > 5: legendHeight = float(sys.argv[5]) else: legendHeight = 0.75 # Load the JSON data from the file data = json.loads(file(filename).read()) conf = data['conf'] stats = data['stats'] # Sanity check data hierarchy if len(conf['order_l1']) != len(stats.keys()): error("conf.order_l1 does not match stats level 1") for l1 in stats.keys(): if len(conf['order_l2']) != len(stats[l1].keys()): error("conf.order_l2 does not match stats level 2 for %s" % l1) if conf['order_l1'].count(l1) < 1: error("%s not found in conf.order_l1" % l1) for l2 in stats[l1].keys(): if len(conf['order_l3']) != len(stats[l1][l2].keys()): error("conf.order_l3 does not match stats level 3") if conf['order_l2'].count(l2) < 1: error("%s not found in conf.order_l2" % l2) for l3 in stats[l1][l2].keys(): if conf['order_l3'].count(l3) < 1: error("%s not found in conf.order_l3" % l3) # # Generate the data based on the level specifications # bar_labels = None group_labels = None bar_vals = [] bar_sdvs = [] if L3.startswith("select="): select_label = l3 = L3.split("=")[1] bar_labels = conf['order_l1'] group_labels = conf['order_l2'] bar_vals = [[0]*len(group_labels) for i in bar_labels] bar_sdvs = [[0]*len(group_labels) for i in bar_labels] for b in range(len(bar_labels)): l1 = bar_labels[b] for g in range(len(group_labels)): l2 = group_labels[g] bar_vals[b][g] = np.mean(stats[l1][l2][l3]) bar_sdvs[b][g] = np.std(stats[l1][l2][l3]) elif L2.startswith("select="): select_label = l2 = L2.split("=")[1] bar_labels = conf['order_l1'] group_labels = conf['order_l3'] bar_vals = [[0]*len(group_labels) for i in bar_labels] bar_sdvs = [[0]*len(group_labels) for i in bar_labels] for b in range(len(bar_labels)): l1 = bar_labels[b] for g in range(len(group_labels)): l3 = group_labels[g] bar_vals[b][g] = np.mean(stats[l1][l2][l3]) bar_sdvs[b][g] = np.std(stats[l1][l2][l3]) elif L1.startswith("select="): select_label = l1 = L1.split("=")[1] bar_labels = conf['order_l2'] group_labels = conf['order_l3'] bar_vals = [[0]*len(group_labels) for i in bar_labels] bar_sdvs = [[0]*len(group_labels) for i in bar_labels] for b in range(len(bar_labels)): l2 = bar_labels[b] for g in range(len(group_labels)): l3 = group_labels[g] bar_vals[b][g] = np.mean(stats[l1][l2][l3]) bar_sdvs[b][g] = np.std(stats[l1][l2][l3]) else: usage() # If group is before bar then flip (zip) the data if [L1, L2, L3].index("group") < [L1, L2, L3].index("bar"): bar_labels, group_labels = group_labels, bar_labels bar_vals = zip(*bar_vals) bar_sdvs = zip(*bar_sdvs) print "bar_vals:", bar_vals # # Now render the bar graph # ind = np.arange(len(group_labels)) # the x locations for the groups width = 0.8 * (1.0/len(bar_labels)) # the width of the bars fig = plt.figure(figsize=(10,6), dpi=80) plot = fig.add_subplot(1, 1, 1) rects = [] for i in range(len(bar_vals)): rects.append(plot.bar(ind+width*i, bar_vals[i], width, color=colors[i], yerr=bar_sdvs[i], align='center')) # add some plot.set_ylabel('Milliseconds (less is better)') plot.set_title("Javascript array test: %s" % select_label) plot.set_xticks(ind+width) plot.set_xticklabels( group_labels ) fontP = FontProperties() fontP.set_size('small') plot.legend( [r[0] for r in rects], bar_labels, prop=fontP, loc = 'center right', bbox_to_anchor = (1.0, legendHeight)) def autolabel(rects): # attach some text labels for rect in rects: height = rect.get_height() if np.isnan(height): height = 0.0 plot.text(rect.get_x()+rect.get_width()/2., height+20, '%d'%int(height), ha='center', va='bottom', size='7') for rect in rects: autolabel(rect) # Adjust axis sizes axis = list(plot.axis()) axis[0] = -width # Make sure left side has enough for bar #axis[1] = axis[1] * 1.20 # Add 20% to the right to make sure it fits axis[2] = 0 # Make y-axis start at 0 axis[3] = axis[3] * 1.10 # Add 10% to the top plot.axis(axis) plt.show()