def df_stop_frequency(direction, for_lines=['4','5','6'], fname="default", write_df_root="stopFreq", dt=120):
D = load_df(fname)
get_stops = _get_stops(direction=direction, set_range="all")
D = filter_stops(D, get_stops, for_lines)
endpoint = "N"
origin = "S"
if lower(direction)[0] == "s":
endpoint = "S"
origin = "N"
elif lower(direction)[0] != "n":
print "Specified direction",direction,"not recognized; forcing NORTH"
#station_codes = get_stop_dict(direction)
times = D['timestamp'].unique()
t_max = times.max()
t_min = times.min()
print t_min,t_max
nbins = int((t_max - t_min)/dt) + 1
set_index = [int(t_min+n*dt) for n in range(nbins)]
stops = D['stop'].unique()
print "Filtered DF contains stops",stops
df_freq = pd.DataFrame(index=set_index, columns=stops)
df_freq = df_freq.fillna(0.)
for i in D.index:
t_bin = int((D.loc[i,'timestamp'] - t_min)/dt)*dt + int(t_min)
df_freq.loc[t_bin, D.loc[i,'stop']] = df_freq.loc[t_bin, D.loc[i,'stop']] + 1
df_freq.to_csv(write_df_root+".csv")
return df_freq
def df_trips_by_column(D, direction="N", for_lines=['4','5','6'], fname="default", write_df_root="tripData"):
D['tref'] = D['timestamp'].map(lambda t: get_TOD_reference(t))
# This is cumbersome and probably inefficient...
# But it's what I do to generate unique trip_ids to manipulate
# Probably want to end up doing this as a pre-processing step
D['long_id'] = D['id'] + "::" + D['tref'].astype('string')
stops = _get_stops(direction=direction, set_range="all")
D = filter_stops(D, stops, for_lines)
endpoint = "N"
origin = "S"
if lower(direction)[0] == "s":
endpoint = "S"
origin = "N"
elif lower(direction)[0] != "n":
print "Specified direction",direction,"not recognized; forcing NORTH"
station_codes = get_stop_dict(direction)
trips = D['long_id'].unique()
stops = D['stop'].unique()
tripCol = pd.DataFrame(index=['line','trip_time','tref']+list(stops), columns = trips)
tripTimes = pd.DataFrame(index = trips, columns = ['line','trip_time','time_of_day'])
stopCounts = pd.DataFrame(index=list(stops), columns = trips)
## Should profile these loops and speed it up
for trip in trips:
D_trip = D[D['long_id']==trip]
l = get_line(trip)
tripCol.loc['line',trip] = l
tripCol.loc['tref',trip] = D_trip['tref'].values[0]
tripTimes.loc[trip,'line'] = l
tripTimes.loc[trip,'time_of_day'] = l
for stop in D_trip['stop'].unique():
D_trip_for_stop = D_trip[D_trip['stop']==stop]
tripCol.loc[stop,trip] = D_trip_for_stop['timestamp'].max()
stopCounts.loc[stop,trip] = len(D_trip_for_stop)
trip_time = tripCol.loc[station_codes[stop_for(l,endpoint)],trip] - \
tripCol.loc[station_codes[stop_for(l,origin)],trip]
tripCol.loc['trip_time',trip] = trip_time
tripTimes.loc[trip,'trip_time'] = trip_time
tripTimes.loc[trip,'time_of_day'] = \
TOD_value(tripCol.loc[station_codes[stop_for(l,origin)],\
trip],tripCol.loc['tref',trip])/3600.
print "created DataFrames with data compiled by trip; writing to files with root", write_df_root
tripCol.to_csv(write_df_root + "_verbose.csv")
tripTimes.to_csv(write_df_root + "_trip_times.csv")
stopCounts.to_csv(write_df_root + "_stop_counts.csv")
return tripCol, tripTimes, stopCounts
