def plotSubGroups(grouped, strColumnName, aggFunction, nXLim, nYLim):
#application mobility dynamics on user mobility
aggregatedSubgroup = grouped[strColumnName].agg(aggFunction)
fig = plt.figure()
fig_sub_group = fig.add_subplot(111)
fig_sub_group.set_color_cycle(['r','g','b','c','y','m','k'])
for i in [1,3,5,6,7,10,11,22,26]:
fig_sub_group.plot(aggregatedSubgroup.loc[i],'-')
fig_sub_group.set_xlim(0, nXLim)
fig_sub_group.set_ylim(0, nYLim)
fig.show()
def drawCDF(dfData, nXStart=0, nXStop=0, nXStep=1, strTitle="CDF"):
'''
Draw CDF for all the columns in the dfData
'''
fig = plt.figure()
spCDF = fig.add_subplot(111)
spCDF.set_color_cycle(['r','g','b','c','y','m','k'])
if (nXStop != 0):
spCDF.set_xticks(range(nXStart,nXStop, nXStep))
spCDF.set_xlim((nXStart, nXStop))
spCDF.set_ylim(0.0, 1.0)
spCDF.set_title(strTitle)
for col in dfData.columns:
cdf = dfData[col].order(ascending=False).cumsum()/dfData[col].sum()
spCDF.plot(cdf,'-',label=col)
spCDF.legend(loc='best')
fig.show()
def drawUserCDF(data):
#user cdf of user mobility
http_cumsum = data.groupby(['ServiceGroup', 'Mobility'])['UserNum'].sum()[1].cumsum()
im_cumsum = data.groupby(['ServiceGroup', 'Mobility'])['UserNum'].sum()[3].cumsum()
weibo_cumsum = data.groupby(['ServiceGroup', 'Mobility'])['UserNum'].sum()[5].cumsum()
video_cumsum = data.groupby(['ServiceGroup', 'Mobility'])['UserNum'].sum()[6].cumsum()
video_cumsum.add(data.groupby(['ServiceGroup', 'Mobility'])['UserNum'].sum()[7].cumsum(), fill_value=0)
game_cumsum = data.groupby(['ServiceGroup', 'Mobility'])['UserNum'].sum()[10].cumsum()
email_cumsum = data.groupby(['ServiceGroup', 'Mobility'])['UserNum'].sum()[11].cumsum()
shopping_cumsum = data.groupby(['ServiceGroup', 'Mobility'])['UserNum'].sum()[22].cumsum()
map_cumsum = data.groupby(['ServiceGroup', 'Mobility'])['UserNum'].sum()[26].cumsum()
http_cdf = http_cumsum/http_cumsum.iloc[-1]
im_cdf = im_cumsum/im_cumsum.iloc[-1]
weibo_cdf = weibo_cumsum/weibo_cumsum.iloc[-1]
video_cdf = video_cumsum/video_cumsum.iloc[-1]
game_cdf = game_cumsum/game_cumsum.iloc[-1]
email_cdf = email_cumsum/email_cumsum.iloc[-1]
shopping_cdf = shopping_cumsum/shopping_cumsum.iloc[-1]
map_cdf = map_cumsum/map_cumsum.iloc[-1]
fig = plt.figure()
fig_user_cdf = fig.add_subplot(111)
fig_user_cdf.plot(http_cdf, 'b-', label='http')
fig_user_cdf.plot(im_cdf, 'g-', label='im')
fig_user_cdf.plot(weibo_cdf, '-',color='0.71', label='weibo')
fig_user_cdf.plot(video_cdf, 'c-', label='video')
fig_user_cdf.plot(game_cdf, 'm-', label='game')
fig_user_cdf.plot(email_cdf, 'y-', label='email')
fig_user_cdf.plot(shopping_cdf, 'k-', label='shopping')
fig_user_cdf.plot(map_cdf, 'r-',label='map')
fig_user_cdf.set_xticks(range(10))
fig_user_cdf.set_xlim(0, 10)
fig_user_cdf.set_ylim(0.7, 1.0)
fig_user_cdf.set_xlabel('Moving speed level')
fig_user_cdf.set_title('Appilcation User Number CDF of User Moving Speed')
fig_user_cdf.legend(loc='best')
fig.show()
