def func(kei,y_l,y_up):
fns = os.listdir(PATH)
print fns
data = []
for fn in fns:
p = os.path.join(PATH,fn)
#print p
stat_d = load_obj(p)
target_l = stat_d[kei]
#target_l = [float(x) for x in target_l]
data.append(target_l)
data = [data[3],data[4],data[0],data[1],data[2]]
r = data[3]
r.sort()
print r
fig, ax1 = plt.subplots(figsize=(10,5))
fig.canvas.set_window_title(tag+' '+kei)
plt.subplots_adjust(left=0.15, right=1, top=0.8, bottom=0.2)
bp = plt.boxplot(data, notch=0, sym='o', vert=1, whis=1.5)
plt.setp(bp['boxes'], color='black')
plt.setp(bp['whiskers'], color='black')
plt.setp(bp['fliers'], color='lightgrey', marker='x')
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(tag+' '+kei)
ax1.set_xlabel('Time since the first post (min)',fontsize=LABEL_FONT)
ax1.set_ylabel(y_l,fontsize=LABEL_FONT)
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0),labelsize=LABEL_FONT)
plt.rc('font',size=18)
# Now fill the boxes with desired colors
boxColors = ['white','white']
numBoxes = len(fns)
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='black', marker='^', markeredgecolor='k')
ax1.set_xlim(0.5, numBoxes+0.5)
#top = np.average(data[i])*3
#top = 1.5
top = y_up
bottom = 0
ax1.set_ylim(bottom, top)
#xLabels = ['40','60','100','150','300'] #for time
xLabels = ['40','60','100','150','300'] #for size
xtickNames = plt.setp(ax1, xticklabels=np.repeat(xLabels, 1))
plt.setp(xtickNames, fontsize=TICK_FONT)
plt.yticks(fontsize = TICK_FONT)
pos = np.arange(numBoxes)+1
avgs = [ np.average(data[i]) for i in xrange(0,len(data))]
upperLabels = []
for i in xrange(0,len(avgs)):
upperLabels.append('Med' + str(round(medians[i],1))+'\n'+'Avg' + str(round(avgs[i],1)))
weights = ['normal', 'normal']
for tick,label in zip(range(numBoxes),ax1.get_xticklabels()):
k = tick % 2
ax1.text(pos[tick], top+(top*0.1), upperLabels[tick],
horizontalalignment='center', weight=weights[k],
color='black',fontsize = UPPERLABEL_FONT)
#path = os.path.join('C:\Users\rguo12\Documents\Asonam\\asonam\SNI_Paper\\',y_l.replace('#','no').replace('\n','').replace(' ','').replace('(','').replace(')','')+'.pdf')
path = 'C:\weibodataset\\for_box_plot_Mar20\\'+kei+'_'+tag+'.pdf'
#plt.savefig(path,format='pdf')
plt.show()
def func(kei,y_u,y_l,y_label ):
fns = os.listdir(PATH)
print fns
data = []
for fn in fns:
p = os.path.join(PATH,fn)
#print p
stat_d = load_obj(p)
target_l = stat_d[kei]
#target_l = [float(x) for x in target_l]
data.append(target_l)
data = [data[1],data[3],data[4],data[0],data[2]]
#data = [data[3],data[4],data[0],data[1],data[2]]
#for ele in data:
# print len(ele)
fig, ax1 = plt.subplots(figsize=(8.7,7))
fig.canvas.set_window_title(tag+' '+kei)
#plt.ticklabel_format(style='sci',axis='y',scilimits=(0,0))
plt.rc('font',size=18)
plt.subplots_adjust(left=0.15, right=0.98, top=0.8, bottom=0.15)
bp = plt.boxplot(data, notch=0, sym='o', vert=1, whis=1.5,positions=np.log10([10,25,50,100,200]))
if y_u > 1000:
formatter = FuncFormatter(thousands)
ax1.yaxis.set_major_formatter(formatter)
ax1.set_ylabel(y_label+r'($10^3$)',fontsize=LABEL_FONT,fontweight='bold')
elif y_u > 100:
formatter = FuncFormatter(hundreds)
ax1.yaxis.set_major_formatter(formatter)
ax1.set_ylabel(y_label+r'($10^2$)',fontsize=LABEL_FONT,fontweight='bold')
else:
ax1.set_ylabel(y_label,fontsize=LABEL_FONT,fontweight='bold')
plt.setp(bp['boxes'], color='black')
plt.setp(bp['whiskers'], color='black')
plt.setp(bp['fliers'], color='white', marker='x')
#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(title + '(' + tag + ')')
#ax1.set_xlabel('Time since the first post (min)')
ax1.set_xlabel('Number of Adopters',fontsize=LABEL_FONT,fontweight='bold')
#ax1.set_xlabel('Time Since the Original Post',fontsize=LABEL_FONT,fontweight='bold')
#ax1.set_yticks(fontsize=12)
# Now fill the boxes with desired colors
boxColors = ['white','white']
numBoxes = len(fns)
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, 'blue')
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='r', marker='^', markeredgecolor='k')
ax1.set_xlim(0.8, np.log10(200)+0.2)
#top = np.average(data[i]) * 2
#top = 1.5
bottom = 0
#ax1.set_ylim(bottom, top)
ax1.set_ylim(y_l,y_u)
#xLabels = ['40','60','100','150','300'] #for time
xLabels = ['10','30','50','100','200'] #for size
xtickNames = plt.setp(ax1, xticklabels=np.repeat(xLabels, 1))
plt.setp(xtickNames, rotation=0, fontsize=TICK_FONT)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(TICK_FONT)
#pos = np.arange(numBoxes)+1
pos = np.log10([10,25,50,100,200])
avgs = [ np.average(data[i]) for i in xrange(0,len(data))]
upperLabels = []
for i in xrange(0,len(avgs)):
if i==0:
upperLabels.append('M: '+str(smart_round(medians[i]))+'\n'+'A: '+str(smart_round(avgs[i])))
else:
upperLabels.append(str(smart_round(medians[i]))+'\n'+str(smart_round(avgs[i])))
#upperLabels.append(('M' + str(round(medians[i],1)),'A' + str(round(avgs[i],1))))
weights = ['bold', 'semibold']
for tick,label in zip(range(numBoxes),ax1.get_xticklabels()):
k = tick % 2
ax1.text(pos[tick],y_u+(y_u*0.1) , upperLabels[tick],fontsize=TICK_FONT-2,
horizontalalignment='center', weight=weights[k],
color='black')
#plt.set_size_inches(18.5,10.5)
#plt.figure(figsize=(8.0, 6.0))
if tag == 'viral':
vnv = 'v_'
else:
vnv = 'nv_'
plt.savefig('E:\\SNAM_2015\\for_box_plot_size_comparison_infomap\\svg\\'+vnv+kei+'.pdf',format='pdf')
def func(kei, y_u, y_l, y_label):
fns = os.listdir(PATH)
print fns
data = []
for fn in fns:
p = os.path.join(PATH, fn)
# print p
stat_d = load_obj(p)
target_l = stat_d[kei]
# target_l = [float(x) for x in target_l]
data.append(target_l)
# data = [data[1],data[3],data[4],data[0],data[2]]
data = [data[3], data[4], data[0], data[1], data[2]]
# for ele in data:
# print len(ele)
fig, ax1 = plt.subplots(figsize=(8.7, 7))
fig.canvas.set_window_title(tag + " " + kei)
# plt.ticklabel_format(style='sci',axis='y',scilimits=(0,0))
plt.rc("font", size=18)
plt.subplots_adjust(left=0.15, right=0.98, top=0.8, bottom=0.15)
bp = plt.boxplot(data, notch=0, sym="o", vert=1, whis=1.5, positions=np.log10([35, 60, 95, 150, 300]))
if y_u > 1000:
formatter = FuncFormatter(thousands)
ax1.yaxis.set_major_formatter(formatter)
ax1.set_ylabel(y_label + r"($10^3$)", fontsize=LABEL_FONT, fontweight="bold")
elif y_u > 100:
formatter = FuncFormatter(hundreds)
ax1.yaxis.set_major_formatter(formatter)
ax1.set_ylabel(y_label + r"($10^2$)", fontsize=LABEL_FONT, fontweight="bold")
else:
ax1.set_ylabel(y_label, fontsize=LABEL_FONT, fontweight="bold")
plt.setp(bp["boxes"], color="black")
plt.setp(bp["whiskers"], color="black")
plt.setp(bp["fliers"], color="white", marker="x")
# 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(title + '(' + tag + ')')
# ax1.set_xlabel('Time since the first post (min)')
# ax1.set_xlabel('Number of Adopters',fontsize=LABEL_FONT,fontweight='bold')
ax1.set_xlabel("Time Since the Original Post", fontsize=LABEL_FONT, fontweight="bold")
# ax1.set_yticks(fontsize=12)
# Now fill the boxes with desired colors
boxColors = ["white", "white"]
numBoxes = len(fns)
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="black", marker="^", markeredgecolor="k")
ax1.set_xlim(np.log10(40) - 0.2, np.log10(300) + 0.2)
# top = np.average(data[i]) * 2
# top = 1.5
bottom = 0
# ax1.set_ylim(bottom, top)
ax1.set_ylim(y_l, y_u)
# xLabels = ['40','60','100','150','300'] #for time
xLabels = ["40", "60", "100", "150", "300"] # for size
xtickNames = plt.setp(ax1, xticklabels=np.repeat(xLabels, 1))
plt.setp(xtickNames, rotation=0, fontsize=TICK_FONT)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(TICK_FONT)
# pos = np.arange(numBoxes)+1
pos = np.log10([35, 60, 95, 150, 300])
avgs = [np.average(data[i]) for i in xrange(0, len(data))]
upperLabels = []
for i in xrange(0, len(avgs)):
if i == 0:
upperLabels.append("M: " + str(smart_round(medians[i])) + "\n" + "A: " + str(smart_round(avgs[i])))
else:
upperLabels.append(str(smart_round(medians[i])) + "\n" + str(smart_round(avgs[i])))
# upperLabels.append(('M' + str(round(medians[i],1)),'A' + str(round(avgs[i],1))))
weights = ["bold", "semibold"]
for tick, label in zip(range(numBoxes), ax1.get_xticklabels()):
k = tick % 2
ax1.text(
pos[tick],
y_u + (y_u * 0.1),
upperLabels[tick],
fontsize=TICK_FONT - 2,
horizontalalignment="center",
weight=weights[k],
color="black",
)
# plt.set_size_inches(18.5,10.5)
# plt.figure(figsize=(8.0, 5.0))
if tag == "viral":
vnv = "v_"
else:
vnv = "nv_"
plt.savefig("C:\\Users\\rguo12\\Desktop\\SNAM_figures\\boxplt_time\\" + vnv + kei + ".pdf", format="pdf")
