def Output(self, dframe):
f = open('my_output.csv', 'w')
for rootNb, rootNm in enumerate(self.TruncNames):
print >> f, 'TruncDics: ', rootNb,' : ', rootNm
for attr in self.attrModif:
if self.OrderOrNot[attr] == True: # normal output
for i in range(1, self.CritDiff):
myPandaUtilities.myfilter(dframe,['RootNb',rootNb,'attrModif',attr,'branchWeight',i],['fileName','RootNb','attrModif','from','to']).to_csv('my_output.csv', mode='a', header=False)
else: # output range
print >> f ,'TruncDics: ', rootNb,' : ', rootNm, 'Range: ', self.OrderOrNot[attr][1][0], self.OrderOrNot[attr][1][1], self.OrderOrNot[attr][1][2]
for i in range(1, self.CritDiff):
myPandaUtilities.myfilter(dframe,['RootNb',rootNb,'attrModif',attr,'branchWeight',i],['fileName']).to_csv('my_output.csv', mode='a', header=False)
print >> f, 'FileNumber', 'FileName', 'RootNb', 'AttributeNb' ,'from', 'to'
def Study(self):
# Filter out data with a sufficiently big statistics
_count = []
_line = []
_trip = []
for line in [1, 2]: #self.LineIdList:
for trip in self.TripIdList:
df = myPandaUtilities.myfilter(
self.data, ['Line_id', line, 'Trip_id', trip])
tmpDF = df.describe()
if (tmpDF['Reven']['count'] > 0):
_count.append(tmpDF['Reven']['count'])
_line.append(line)
_trip.append(trip)
dframe = pd.DataFrame({
'count': _count,
'Line_id': _line,
'Trip_id': _trip
})
myPandaUtilities.myLazyDispl(dframe)
del _count, _line, _trip
dframe = dframe.sort('count', ascending=False)
dframe = dframe[dframe['count'] > self.CutOff]
return dframe[['Line_id', 'Trip_id']]
def plotAttrVSDays( self ):
tmp0 =[]
_line = 1
for tmp in self.AttrList1:
tmp0.append( str(tmp) +'_'+str(self._line) )
dff = []
for tmp1 in self.DaysList:
df = myPandaUtilities.myfilter(self.data,['Line_id',self._line,'Trip_id',tmp,'DayOfWeek',tmp1],['DayOfWeek','Ride_load'])
dff.append(df)
dff = pd.concat(dff)
dff['DayOfWeek'] = dff['DayOfWeek'].apply( fctChgDays )
dff=dff.sort('DayOfWeek')
if ( (dff.shape[0] != 0 ) & (len(dff['DayOfWeek'].unique()) > 2) ):
dfave = dff.groupby('DayOfWeek').mean() #
dfvar = dff.groupby('DayOfWeek').std() #
dfave.rename(columns={'Ride_load': 'mean'}, inplace=True) #
dfvar.rename(columns={'Ride_load': 'std'}, inplace=True) #
df = dfave.join(dfvar)
df = df.fillna(0.00000001)
print ' ---df--- ', df, dff['DayOfWeek'].unique()
print "tmp0=============", tmp0
plt.errorbar( dff['DayOfWeek'].unique(), df['mean'], df['std'], linestyle="dashed", marker="o",zorder=1)
plt.legend(tmp0, loc='upper left') #'lower left'
plt.draw()
def plotAttrVSDays( self ):
tmp0 =[]
_line = 1
for tmp in self.AttrList1:
tmp0.append( str(tmp) +'_'+str(self._line) )
dff = []
for tmp1 in self.DaysList:
df = myPandaUtilities.myfilter(self.data,['Line_id',self._line,'Trip_id',tmp,'DayOfWeek',tmp1],['DayOfWeek','Ride_load'])
dff.append(df)
dff = pd.concat(dff)
dff['DayOfWeek'] = dff['DayOfWeek'].apply( fctChgDays )
dff=dff.sort('DayOfWeek')
if ( (dff.shape[0] != 0 ) & (len(dff['DayOfWeek'].unique()) > 2) ):
dfave = dff.groupby('DayOfWeek').mean() #
dfvar = dff.groupby('DayOfWeek').std() #
dfave.rename(columns={'Ride_load': 'mean'}, inplace=True) #
dfvar.rename(columns={'Ride_load': 'std'}, inplace=True) #
df = dfave.join(dfvar)
df = df.fillna(0.00000001)
print ' ---df--- ', df, dff['DayOfWeek'].unique()
print "tmp0=============", tmp0
plt.errorbar( dff['DayOfWeek'].unique(), df['mean'], df['std'], linestyle="dashed", marker="o",zorder=1)
plt.legend(tmp0, loc='upper left') #'lower left'
plt.draw()
def plotHistoVSDays( self ):
legend = ''
dff = []
for day in self.DaysList:
df = myPandaUtilities.myfilter(self.data,['DayOfWeek',day],['DayOfWeek',str(self.testAttr)])
dff.append(df)
self.plotGeneration(dff, legend)
def plotHistoVSDays( self ):
legend = ''
dff = []
for day in self.DaysList:
df = myPandaUtilities.myfilter(self.data,['DayOfWeek',day],['DayOfWeek',str(self.testAttr)])
dff.append(df)
self.plotGeneration(dff, legend)
def plotAttrVSDays( self ):
legend =[]
for trip in self.AttrList1:
for line in self.AttrList2:
legend.append(str(trip)+'_'+str(line))
dff = []
for day in self.DaysList:
df = myPandaUtilities.myfilter(self.data,['Line_id',line,'Trip_id',trip,'DayOfWeek',day],['DayOfWeek',str(self.testAttr)]) #'Ride_load'])
dff.append(df)
self.plotGeneration(dff, legend)
def plotAttrVSDays( self ):
legend =[]
for trip in self.AttrList1:
for line in self.AttrList2:
legend.append(str(trip)+'_'+str(line))
dff = []
for day in self.DaysList:
df = myPandaUtilities.myfilter(self.data,['Line_id',line,'Trip_id',trip,'DayOfWeek',day],['DayOfWeek',str(self.testAttr)]) #'Ride_load'])
dff.append(df)
self.plotGeneration(dff, legend)
def plotLineVSDays( self ):
legend =[]
_line = 1
isplotted = False
for trip in self.AttrList1:
legend.append( str(trip) +'_'+str(self._line) )
dff = []
for day in self.DaysList:
df = myPandaUtilities.myfilter(self.data,['Line_id',self._line,'Trip_id',trip,'DayOfWeek',day],['DayOfWeek',str(self.testAttr)])
dff.append(df)
if (pd.concat(dff).shape[0] > 5):
self.plotGeneration(dff,legend)
isplotted = True
return isplotted
def plotLineVSDays( self ):
legend =[]
_line = 1
isplotted = False
for trip in self.AttrList1:
legend.append( str(trip) +'_'+str(self._line) )
dff = []
for day in self.DaysList:
df = myPandaUtilities.myfilter(self.data,['Line_id',self._line,'Trip_id',trip,'DayOfWeek',day],['DayOfWeek',str(self.testAttr)])
dff.append(df)
if (pd.concat(dff).shape[0] > 5):
self.plotGeneration(dff,legend)
isplotted = True
return isplotted
def Study(self):
# Filter out data with a sufficiently big statistics
_count = []
_line = []
_trip = []
for line in [1,2]: #self.LineIdList:
for trip in self.TripIdList:
df = myPandaUtilities.myfilter(self.data,['Line_id',line,'Trip_id',trip])
tmpDF = df.describe()
if ( tmpDF['Reven']['count'] > 0 ) :
_count.append( tmpDF['Reven']['count'] )
_line.append( line )
_trip.append( trip )
dframe = pd.DataFrame({ 'count' : _count , 'Line_id' : _line, 'Trip_id' : _trip })
myPandaUtilities.myLazyDispl(dframe)
del _count, _line, _trip
dframe=dframe.sort('count',ascending=False)
dframe = dframe[ dframe['count'] > self.CutOff ]
return dframe[ ['Line_id','Trip_id' ] ]
def plot2AttrVSDaysHisto( self ):
dff = []
for tmp1 in self.DaysList:
df = myPandaUtilities.myfilter(self.data,['DayOfWeek',tmp1],['DayOfWeek','Ride_load'])
dff.append(df)
dff = pd.concat(dff)
dff['DayOfWeek'] = dff['DayOfWeek'].apply( fctChgDays )
dff=dff.sort('DayOfWeek')
myPandaUtilities.myLazyDispl(dff)
dfave = dff.groupby('DayOfWeek').mean() #
dfvar = dff.groupby('DayOfWeek').std() #
dfave.rename(columns={'Ride_load': 'mean'}, inplace=True) #
dfvar.rename(columns={'Ride_load': 'std'}, inplace=True) #
df = dfave.join(dfvar)
df = df.fillna(0.00000001)
# Dframe
myPandaUtilities.myLazyDispl(df) #Dframe)
#print df.head(10)
plt.errorbar( dff['DayOfWeek'].unique(), df['mean'], df['std'], linestyle="dashed", marker="o",zorder=1)
plt.legend('histo', loc='upper left' ) #'lower left' title='Histooo',
plt.draw()
def plot2AttrVSDaysHisto( self ):
dff = []
for tmp1 in self.DaysList:
df = myPandaUtilities.myfilter(self.data,['DayOfWeek',tmp1],['DayOfWeek','Ride_load'])
dff.append(df)
dff = pd.concat(dff)
dff['DayOfWeek'] = dff['DayOfWeek'].apply( fctChgDays )
dff=dff.sort('DayOfWeek')
myPandaUtilities.myLazyDispl(dff)
dfave = dff.groupby('DayOfWeek').mean() #
dfvar = dff.groupby('DayOfWeek').std() #
dfave.rename(columns={'Ride_load': 'mean'}, inplace=True) #
dfvar.rename(columns={'Ride_load': 'std'}, inplace=True) #
df = dfave.join(dfvar)
df = df.fillna(0.00000001)
# Dframe
myPandaUtilities.myLazyDispl(df) #Dframe)
#print df.head(10)
plt.errorbar( dff['DayOfWeek'].unique(), df['mean'], df['std'], linestyle="dashed", marker="o",zorder=1)
plt.legend('histo', loc='upper left' ) #'lower left' title='Histooo',
plt.draw()
def AnalysisTree(self, dframe):
for n, trunc in enumerate([0]) : #enumerate(self.listTruncNbs):
#n = n + 1
print n, trunc
print self.truncCoords[n]
print 'DBG', n
df = dframe[dframe['RootNb']==n]
filNames1 = []
filCoords1 = []
filTos1 = []
filNames2 = []
filCoords2 = []
filTos2 = []
filNames3 = []
filCoords3 = []
filTos3 = []
for i in df['fileName'].unique():
coords = []
tos = []
#myfilter(df,arrFilter,arrFields = []):
tmp = myPandaUtilities.myfilter( df, ['fileName',i], ['fileNum','attrModif','toNb','branchWeight'] )
#myPandaUtilities.myLazyDispl(tmp)
if ( tmp.shape[0] == 1 ):
#print 'DBG0', tmp['fileNum']
filCoords1.append( [ tmp['attrModif'].iloc[0] ] )
filTos1.append( [ tmp['toNb'].iloc[0] ] ) # tmp.iloc[:,1] #
filNames1.append( i )
if ( tmp.shape[0] == 2 ):
filCoords2.append( [ tmp['attrModif'].iloc[0], tmp['attrModif'].iloc[1] ] )
filTos2.append( [ tmp['toNb'].iloc[0], tmp['toNb'].iloc[1]] )
filNames2.append( i )
if ( tmp.shape[0] == 3 ):
filCoords3.append( [ tmp['attrModif'].iloc[0], tmp['attrModif'].iloc[1], tmp['attrModif'].iloc[2] ] )
filTos3.append( [ tmp['toNb'].iloc[0], tmp['toNb'].iloc[1], tmp['toNb'].iloc[2] ] )
filNames3.append( i )
filNames = self.listTruncNbs[n]
filCoords = [self.attrModif[:]] #
filTos = self.truncCoords[n]
myTree = myPandaUtilities.DataTree( self.attrModif)
myTree.AnalysisDic(filNames, filCoords, filTos)
myTree.Analysis1D(filNames1, filCoords1, filTos1)
myTree.Analysis2D(filNames2, filCoords2, filTos2)
myTree.Analysis3D(filNames3, filCoords3, filTos3)
print '585555555555555555555555555555555555555555555555555555555555'
print myTree.points
print myTree.points[36]
print len(myTree.points)
print myTree.AlgorithmTree()
#[ 'fileName','fileNum','RootNb',\
# 'RootNm','attrModif','branchWeight','from','to' ]
################################################################################
