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
