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preprocess.py
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"""
BigDataMining - Part 1
(A) - Data preprocessing
(B) - Data clean-up
(C) - Data visualization through gmplot
"""
from math import radians, sin, cos, asin, sqrt
import gmplot as gmplot
import config
import pandas as pd
import ast
import csv
########################################################################################################################
# Harvesine Distance Function #
########################################################################################################################
def haversine_dist(long1, lat1, long2, lat2):
long1, lat1, long2, lat2 = map(radians, [long1, lat1, long2, lat2])
difflong = long2 - long1
difflat = lat2 - lat1
a = sin(difflat/2)**2 + cos(lat1) * cos(lat2) * sin(difflong/2)**2
c = 2 * asin(sqrt(a))
r = 6371 # Earth's radius in km
return c * r
########################################################################################################################
# Preprocessing Function #
########################################################################################################################
def preprocessing():
df = pd.read_csv(config.trainsetPath)
df = df[pd.notnull(df['journeyPatternId'])] # Removes rows with null values
tripid=0
with open('trips.csv', 'wb') as csvfile:
fieldnames = ['tripId','journeyPatternId','trajectories']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
jlist=[]
for veh_id in df.vehicleID.unique():
flag=1
for i, row in df[df['vehicleID']==veh_id].iterrows():
if flag:
flag=0
jid=row['journeyPatternId']
curjid = row['journeyPatternId']
if jid!=curjid:
writer.writerow({'tripId': tripid , 'journeyPatternId': jid , 'trajectories': jlist})
jlist=[]
jlist.append([row['timestamp'], row['longitude'], row['latitude']])
tripid+=1
jid=curjid
else:
jlist.append([row['timestamp'], row['longitude'], row['latitude']])
writer.writerow({'tripId': tripid, 'journeyPatternId': jid, 'trajectories': jlist})
jlist=[]
tripid+=1
########################################################################################################################
# Cleaning Data Function #
########################################################################################################################
def cleandata():
maxfails = 0 # Fails due to max distance
totalfails = 0 # Fails due to total distance
df = pd.read_csv('trips.csv')
with open('tripsClean.csv', 'wb') as csvfile:
fieldnames = ['tripId','journeyPatternId','trajectories']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for i,row in df.iterrows():
maxdist = 0
totaldist = 0
trajectories = ast.literal_eval(row[2])
for j in range(1, len(trajectories)):
# Compute distance between the two points
harvdist = haversine_dist(float(trajectories[j-1][1]), float(trajectories[j-1][2]), float(trajectories[j][1]), float(trajectories[j][2]))
totaldist += harvdist
if harvdist > maxdist:
maxdist = harvdist
if maxdist > 2: maxfails+=1
if totaldist < 2: totalfails+=1
if( maxdist <=2 and totaldist >= 2):
writer.writerow({'tripId': row[0], 'journeyPatternId': row[1], 'trajectories': row[2]})
print ("Total Fails: %d (MaxDistance) | %d (TotalDistance)" % (maxfails,totalfails))
########################################################################################################################
# Plot Data Function #
########################################################################################################################
def plot_data():
df = pd.read_csv('tripsClean.csv')
plotcount = 0
for jid in df.journeyPatternId.unique()[::66]:
for i, row in df[df['journeyPatternId'] == jid].iterrows():
trajectory = ast.literal_eval(row[2])
longlist = []
latlist = []
for j in range(0, len(trajectory)):
longlist.append(float(trajectory[j][1]))
latlist.append(float(trajectory[j][2]))
gmap = gmplot.GoogleMapPlotter(latlist[0], longlist[0], 12, 'AIzaSyDf6Dk2_fg0p8XaEhQdFVCXg-AMlm54dAs')
gmap.plot(latlist, longlist, 'green', edge_width=5)
gmap.draw('Maps/gmplotMaps/map-tripID' + str(i) + '.html')
print jid
break
plotcount += 1
if plotcount == 5: break
########################################################################################################################
# preprocessing()
# cleandata()
# plot_data()