In [5]:

    

from __future__ import division
import datetime as dt
from collections import OrderedDict
import sys, os
import dateutil.relativedelta as rd
import json
from pathlib import Path

import utm
import pandas as pd
import numpy as np
import shapely.geometry as sg

DIR = Path('..')
sys.path.append(str(DIR))

import gtfstk as gt

%load_ext autoreload
%autoreload 2

DATA_DIR = DIR/'data'


    

    




The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

In [6]:

    

#path = DATA_DIR/'cairns_gtfs.zip'
path = "../../Desktop/IDFM_gtfs.zip"
feed = gt.read_gtfs(path, dist_units='km')


    

In [7]:

    

route_ids = feed.routes.loc[lambda x: x.agency_id == "372", "route_id"]
display(route_ids)
little_feed = feed.restrict_to_routes(route_ids=route_ids)


    

    






0     067167003:E
1     067067003:F
2     067167006:G
3     067167008:D
4     067167002:A
5     067167001:B
6     067067005:C
7     067167004:H
8     067067020:M
9     067167010:N
10    067167005:O
11    067067017:L
12    067167009:I
13    067067002:J
14    067067064:K
Name: route_id, dtype: object

In [8]:

    

vv = little_feed.validate()
vv


    

    
Out[8]:







  

    

      

      
type
      
message
      
table
      
rows
    
  
  

    

      
0
      
warning
      
Unrecognized column stop_time_desc
      
stop_times
      
[]
    
    

      
1
      
warning
      
Repeated pair (trip_id, departure_time)
      
stop_times
      
[17468, 17478, 17488, 17608, 17618, 17628, 176...
    
    

      
2
      
warning
      
Unrecognized column trip_desc
      
trips
      
[]
    
  

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freq = "8H"
f = gt.downsample(rts, freq=freq)
display(f)


    

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(
    rts
    .pipe(gt.unstack_time_series)
    .merge(feed.routes.filter(["route_id", "route_type"]), how="left")
    .groupby(["datetime", "indicator", "route_type"])
    .agg({"value": lambda x: x.sum(skipna=True, min_count=1)})  # Preserve NaNs
    .reset_index("datetime")
#     .fillna(-1)
#     .pivot_table(
#         index=["datetime"], columns=["indicator", "route_type"]
#     )
#     .value
#     .replace({-1: np.nan})
)


    

In [ ]:

    

# Slicing 
display(fts.loc[:, ("num_trips", slice(None))])
display(fts.xs("num_trips", axis="columns"))


    

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f = fts = feed.compute_feed_time_series(trip_stats, dates[:1], freq="12H", split_route_types=False)
display(f)
g = gt.unstack_time_series(f)

# columns = [c for c in g.columns if c not in ["datetime", "value"]]
# h = g.pivot_table(index="datetime", columns=columns).value.sort_index(
#     axis="columns"
# )

# hours = (h.index[1] - h.index[0]).components.hours
# if hours != 0:
#     freqy = f"{hours}H"
# else:
#     freqy = "D"

# print(freqy)

h = gt.restack_time_series(g)
h


    

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f.index


    

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delta.components.hours


    

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feed1 = feed.copy()
cal = feed1.calendar.copy()
cal["monday"] = 0
feed1.calendar = cal
feed1.compute_feed_time_series(trip_stats, dates, freq="12H")


    

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s1 = pd.Series({"hello": 12, "goodbye": 13})
s2 = s1.copy()
pd.DataFrame([s1, s2])


    

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feed.compute_stop_stats(['20140601', '20140603'])


    

In [ ]:

    

trip_stats = feed.compute_trip_stats()
feed = feed.append_dist_to_stop_times(trip_stats)

# Load screen line
with (DATA_DIR/'cairns_screen_line.geojson').open() as src:
    line = json.load(src)
    line = sg.shape(line['features'][0]['geometry'])


    

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f = feed.compute_screen_line_counts(line, dates[:7])
f[f['crossing_time'] < '06:00:00']


    

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feed.compute_feed_time_series(ts, dates[-2:], freq='12H')


    

In [ ]:

    

sd = False
#%time p1 = proto1(feed, ts, dates[:14], split_directions=sd)
%time p2 = proto2(feed, [dates[0], '20010101'], split_directions=sd, freq='12H')
p2


    

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feed.compute_feed_stats(ts, dates[0])


    

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feed.describe()


    

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feed.summarize()


    

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