def method_boxplot(data, xlabels, line_color=default_color, med_color=None, legend=None, x_offset=0.0): if not med_color: med_color = line_color ax.grid(axis='x', color='0.9', linestyle='-', linewidth=0.2) ax.set_axisbelow(True) ppl.boxplot(ax, data, positions=np.array(range(len(xlabels))) - x_offset, yticklabels=xlabels.tolist(), linewidth=0.3, widths=0.2, show_caps=False, sym='', vert=False, line_color=line_color, med_color=med_color) plt.xlabel('% cases used') ax.set_yticks(np.array(range(len(xlabels)))) ax.set_yticklabels(xlabels, fontsize=6) spines_to_remove = ['top', 'right', 'left'] for spine in spines_to_remove: ax.spines[spine].set_visible(False) if legend: rect = legend.get_frame() rect.set_facecolor(light_grey) rect.set_linewidth(0.0)
def boxplot(ax, data, xticklabels, ylabel): if isinstance(data, collections.Iterable): ppl.boxplot(ax, data, notch=True, xticklabels=xticklabels, sym="", vert=False) ax.set_ylabel(ylabel) else: sys.stderr.write("data has to be iterable\n") sys.exit(1) return ax
def test_boxplot(): # Set the random seed for consistency np.random.seed(10) data = np.random.randn(8, 4) labels = ['A', 'B', 'C', 'D'] fig, ax = plt.subplots() ppl.boxplot(ax, data, xticklabels=labels)
def boxplotEllipseVsExclReg(type_header, type_table, config, instanceType, figName = None): if config not in type_table: raise Exception("Config \""+config+"\" not found!") if instanceType not in type_table[config]: raise Exception("Instance type \""+instanceType+"\" not found!") fig, ax = plt.subplots(1) data = [] for size in sorted(type_table[config][instanceType].iterkeys()): byEllipse = np.array(map(float, type_table[config][instanceType][size]["excludedByEllipse"])) exclugReg = np.array(map(float, type_table[config][instanceType][size]["exclusionReg"])) blockedEdgesPropInc = ((byEllipse - exclugReg) / exclugReg) * 100.0 if size == 70: print "box:", byEllipse.mean(), exclugReg.mean() data.append(blockedEdgesPropInc) ret = ppl.boxplot(ax, data, xticklabels=map(str, sorted(type_table[config][instanceType].iterkeys())), widths=0.3, showmeans=True, meanprops=meanprops) ax.set_xlabel(u'# Pontos') ax.set_ylabel(u'Incremento % Arestas Bloqueadas') if figName != None: fig.savefig(figName, bbox_inches='tight')
def boxplotFixedPaths(type_header, type_table, config, instanceType, figName = None): if config not in type_table: raise Exception("Config \""+config+"\" not found!") if instanceType not in type_table[config]: raise Exception("Instance type \""+instanceType+"\" not found!") fig, ax = plt.subplots(1) data = [] for size in sorted(type_table[config][instanceType].iterkeys()): fixedpaths = np.array(map(int, type_table[config][instanceType][size]["preproc.fixedPaths"])) nPaths = (size*(size-1))/2.0 fixedPathsProp = fixedpaths/nPaths * 100.0 data.append(fixedPathsProp) ret = ppl.boxplot(ax, data, xticklabels=map(str, sorted(type_table[config][instanceType].iterkeys())), widths=0.3, showmeans=True, meanprops=meanprops) ax.set_xlabel(u'# Pontos') ax.set_ylabel(u'% Caminhos Fixados') if figName != None: fig.savefig(figName, bbox_inches='tight')
def boxplotFixedEdgesSols(type_header, type_table, config, instanceType, solsPath, solsExt, figName = None): if config not in type_table: raise Exception("Config \""+config+"\" not found!") if instanceType not in type_table[config]: raise Exception("Instance type \""+instanceType+"\" not found!") fig, ax = plt.subplots(1) data = [] for size in sorted(type_table[config][instanceType].iterkeys()): fixedEdges = np.array(map(int, type_table[config][instanceType][size]["preproc.fixedEdges"])) instanceNumbers = np.array(type_table[config][instanceType][size]["instanceNumber"]) fixedEdgesProp = [] for fEdges, instanceNumber in zip(fixedEdges, instanceNumbers): instanceName = "%s_%03d_%02d%s" % (instanceType, size, instanceNumber, solsExt) solEdges = getNEdges(os.path.join(solsPath, instanceName)) fEdgesProp = float(fEdges)/float(solEdges) * 100.0 fixedEdgesProp.append(fEdgesProp) data.append(fixedEdgesProp) ret = ppl.boxplot(ax, data, xticklabels=map(str, sorted(type_table[config][instanceType].iterkeys())), widths=0.3, showmeans=True, meanprops=meanprops) ax.set_xlabel(u'# Pontos') ax.set_ylabel(u'% Arestas Fixadas') if figName != None: fig.savefig(figName, bbox_inches='tight')
def method_boxplot(data,xlabels, line_color=default_color, med_color=None, legend=None, x_offset=0.0): if not med_color: med_color=line_color ax.grid(axis='x', color='0.9', linestyle='-', linewidth=0.2) ax.set_axisbelow(True) ppl.boxplot(ax,data, positions=np.array(range(len(xlabels)))-x_offset, yticklabels=xlabels.tolist(),linewidth=0.3, widths=0.2,show_caps=False,sym='',vert=False, line_color=line_color, med_color=med_color) plt.xlabel('% cases used') ax.set_yticks(np.array(range(len(xlabels)))) ax.set_yticklabels(xlabels,fontsize=6) spines_to_remove = ['top', 'right','left'] for spine in spines_to_remove: ax.spines[spine].set_visible(False) if legend: rect = legend.get_frame() rect.set_facecolor(light_grey) rect.set_linewidth(0.0)
def boxplotFixedEdgesSols(type_header, type_table, config, instanceType, solsPath, solsExt, figName=None): if config not in type_table: raise Exception("Config \"" + config + "\" not found!") if instanceType not in type_table[config]: raise Exception("Instance type \"" + instanceType + "\" not found!") fig, ax = plt.subplots(1) data = [] for size in sorted(type_table[config][instanceType].iterkeys()): fixedEdges = np.array( map(int, type_table[config][instanceType][size]["preproc.fixedEdges"])) instanceNumbers = np.array( type_table[config][instanceType][size]["instanceNumber"]) fixedEdgesProp = [] for fEdges, instanceNumber in zip(fixedEdges, instanceNumbers): instanceName = "%s_%03d_%02d%s" % (instanceType, size, instanceNumber, solsExt) solEdges = getNEdges(os.path.join(solsPath, instanceName)) fEdgesProp = float(fEdges) / float(solEdges) * 100.0 fixedEdgesProp.append(fEdgesProp) data.append(fixedEdgesProp) ret = ppl.boxplot(ax, data, xticklabels=map( str, sorted(type_table[config][instanceType].iterkeys())), widths=0.3, showmeans=True, meanprops=meanprops) ax.set_xlabel(u'# Pontos') ax.set_ylabel(u'% Arestas Fixadas') if figName != None: fig.savefig(figName, bbox_inches='tight')
def boxplotEllipseVsExclReg(type_header, type_table, config, instanceType, figName=None): if config not in type_table: raise Exception("Config \"" + config + "\" not found!") if instanceType not in type_table[config]: raise Exception("Instance type \"" + instanceType + "\" not found!") fig, ax = plt.subplots(1) data = [] for size in sorted(type_table[config][instanceType].iterkeys()): byEllipse = np.array( map(float, type_table[config][instanceType][size]["excludedByEllipse"])) exclugReg = np.array( map(float, type_table[config][instanceType][size]["exclusionReg"])) blockedEdgesPropInc = ((byEllipse - exclugReg) / exclugReg) * 100.0 if size == 70: print "box:", byEllipse.mean(), exclugReg.mean() data.append(blockedEdgesPropInc) ret = ppl.boxplot(ax, data, xticklabels=map( str, sorted(type_table[config][instanceType].iterkeys())), widths=0.3, showmeans=True, meanprops=meanprops) ax.set_xlabel(u'# Pontos') ax.set_ylabel(u'Incremento % Arestas Bloqueadas') if figName != None: fig.savefig(figName, bbox_inches='tight')
def boxplotFixedPaths(type_header, type_table, config, instanceType, figName=None): if config not in type_table: raise Exception("Config \"" + config + "\" not found!") if instanceType not in type_table[config]: raise Exception("Instance type \"" + instanceType + "\" not found!") fig, ax = plt.subplots(1) data = [] for size in sorted(type_table[config][instanceType].iterkeys()): fixedpaths = np.array( map(int, type_table[config][instanceType][size]["preproc.fixedPaths"])) nPaths = (size * (size - 1)) / 2.0 fixedPathsProp = fixedpaths / nPaths * 100.0 data.append(fixedPathsProp) ret = ppl.boxplot(ax, data, xticklabels=map( str, sorted(type_table[config][instanceType].iterkeys())), widths=0.3, showmeans=True, meanprops=meanprops) ax.set_xlabel(u'# Pontos') ax.set_ylabel(u'% Caminhos Fixados') if figName != None: fig.savefig(figName, bbox_inches='tight')
# Creates a Box plot of the Clean Height Data and the Raw Height Data plt.figure(4) labels = ['Height (Clean)', 'Height (Raw)'] dataHeight = pd.DataFrame({ 'Height (Clean)': cleanHeight['Height'], 'Height (Raw)': studentData['Height'] }) dataHeightArray = np.array(dataHeight) #print dataHeightArray ppl.boxplot(dataHeightArray, xticklabels=labels, fontsize=16) newAx = np.arange(0, 80, 5) plt.yticks(newAx) plt.ylabel('Height (inches)', fontsize=16) plt.title(s="Heights of Students", fontsize=20) plt.show() #Subset of Data that contains just the Gender Columns and Hair Dyed Columns studentHairDye = studentData.loc[:, ['Gender', 'Hair dyed?']] #Gets rid of the Missing values in the Hair Dyed Subset (the subset object called studentHairDye) )
for x in range(count): data[locat].append(value) print("data") for i in data: print(i[:10]) print("data") #np.random.seed(10) #data = np.random.randn(8, 4) u_labels = ['1e-7 mid','1e-7 end','1e-6 mid','1e-6 end','1e-5 mid','1e-5 end','1e-4 mid','1e-4 end','0.001 mid','0.001 end','0.005 mid','0.005 end','0.01 mid','0.01 end',\ '0.05 mid', '0.05 end','0.1 mid','0.1 end'] fig, ax = plt.subplots() ax.set_xticklabels(u_labels) ax.set_xticks(u_labels) ax.set_xlabel('Initial Mutation Rates', fontsize=9) ax.set_ylabel('End Proliferation Rates', fontsize=9) i = 0 print(ax.get_xticklabels()) ppl.boxplot(ax, data) #,xticklabels=u_labels) plt.title("Distribution of End Proliferation Rates by Initial Mutation Rate", fontsize=9) fig.savefig(opt.output + '_boxplot.png') print(opt.output + "DONE") #ppl.hist(ax,data) #fig.savefig('histogram_prettyplotlib_default.png')
weekday = calendar.weekday(date.year, date.month, date.day) if (weekday not in weekdaySteps.keys()): weekdaySteps[weekday] = [] weekdaySteps[weekday].append(entry[1]) if (weekday not in weekdayFloors.keys()): weekdayFloors[weekday] = [] weekdayFloors[weekday].append(entry[2]) reformattedData = np.asarray([[weekdaySteps[firstThing][secondThing] for firstThing in range(7)] for secondThing in range(51)]) xLabels = ["Mondays", "Tuesdays", "Wednesdays", "Thursdays", "Fridays", "Saturdays", "Sundays"] fig2 = plt.figure(figsize=(14,8)) ax2 = fig2.add_subplot(111) ppl.boxplot(ax2, reformattedData, xticklabels=xLabels) # ax2.xaxis.set_ticklabels(xLabels) saveAllTheFiles(fig2, "fitbit-per_weekday--boxplot") averages = [np.average(weekdaySteps[day]) for day in weekdaySteps] # np.std(weekdaySteps[0]) stdDeviations = [np.std(weekdaySteps[day]) for day in weekdaySteps] currentColor = next(color_cycle) currentColor = next(color_cycle) fig3 = plt.figure(figsize=(14,8)) ax3 = fig3.add_subplot(111) ax3.errorbar(range(1,8), averages, yerr=stdDeviations, color=currentColor, fmt='o', markeredgecolor=currentColor, elinewidth=1) ax3.xaxis.set_ticks(range(1,8)) ax3.xaxis.set_ticklabels(xLabels) ax3.set_xlim(left=0.5, right=7.5) ax3.set_ylim(bottom=0, top=35000) saveAllTheFiles(fig3, "fitbit-per_weekday--mean")
params = { 'axes.labelsize': 8, 'font.size': 8, 'legend.fontsize': 10, 'xtick.labelsize': 10, 'ytick.labelsize': 10, 'text.usetex': False, 'figure.figsize': [2.5, 4.5] } rcParams.update(params) def load(dir): f_list = glob.glob(dir + '/*/*/bestfit.dat') num_lines = sum(1 for line in open(f_list[0])) i = 0; data = np.zeros((len(f_list), num_lines)) for f in f_list: data[i, :] = np.loadtxt(f)[:,1] i += 1 return data data_low_mut = load('data/low_mut') data_high_mut = load('data/high_mut') low_mut_100 = data_low_mut[:, 100] high_mut_100 = data_high_mut[:, 100] fig, ax = plt.subplots() ppl.boxplot(ax, [low_mut_100, high_mut_100]) fig.savefig('boxplot_prettyplotlib_default.png')
import prettyplotlib as ppl import numpy as np import matplotlib.pyplot as plt np.random.seed(10) data = np.random.randn(8, 4) labels = ['A', 'B', 'C', 'D'] fig, ax = plt.subplots() ppl.boxplot(ax, data, xticklabels=labels) fig.savefig('boxplot_prettyplotlib_default.png')
####### End of code section that creates HUB food opinion graph based on CLEAN data ###### # Creates a Box plot of the Clean Height Data and the Raw Height Data plt.figure(4) labels = ['Height (Clean)','Height (Raw)'] dataHeight = pd.DataFrame({'Height (Clean)': cleanHeight['Height'],'Height (Raw)': studentData['Height']}) dataHeightArray = np.array(dataHeight) #print dataHeightArray ppl.boxplot(dataHeightArray, xticklabels= labels,fontsize= 16) newAx = np.arange(0,80,5) plt.yticks(newAx) plt.ylabel('Height (inches)',fontsize = 16) plt.title(s = "Heights of Students",fontsize = 20) plt.show() #Subset of Data that contains just the Gender Columns and Hair Dyed Columns