def get_n_winning_targets_using_change_in_mean(self,
n,
measure,
distance=500,
threshold=10800,
losers=False,
include_list=None):
if losers:
order_by = "DESC"
else:
order_by = "ASC"
include_list = "(" + ",".join([str(x) for x in include_list]) + ")"
query = """SELECT t1.to_stop_I, t2.mean-t1.mean AS diff_mean FROM
(SELECT to_stop_I, avg(mean) AS mean FROM before.{measure}
WHERE mean <= {threshold} AND to_stop_I IN {include_list}
GROUP BY to_stop_I) t1,
(SELECT to_stop_I, avg(mean) AS mean FROM after.{measure}
WHERE mean <= {threshold} AND to_stop_I IN {include_list}
GROUP BY to_stop_I) t2
WHERE t1.to_stop_I=t2.to_stop_I
ORDER BY diff_mean {order_by}
""".format(measure=measure,
threshold=threshold,
order_by=order_by,
include_list=include_list)
df = pandas.read_sql_query(query, self.conn)
# exclude nearby stops
nearby_excluded_stops = []
stops_remaining = []
gtfs = GTFS(GTFS_PATH)
for value in df.itertuples():
if not value.to_stop_I in nearby_excluded_stops:
exclude_df = gtfs.get_stops_within_distance(
value.to_stop_I, distance)
nearby_excluded_stops += list(exclude_df["stop_I"])
stops_remaining.append(value.to_stop_I)
if len(stops_remaining) == n:
break
df = df.loc[df['to_stop_I'].isin(stops_remaining)]
return df
def analysis_zones(as_dict=False):
"""
returns data containers that pair zone type to a set of stops
:param as_dict:
:return:
"""
gtfs_old = GTFS(OLD_DICT["gtfs_dir"])
gtfs_lm = GTFS(LM_DICT["gtfs_dir"])
station_distance = 600
upstream_ratio = 0.5
df_old = gtfs_old.get_stops_for_route_type(1)
df_lm = gtfs_lm.get_stops_for_route_type(1)
new_metro = difference_of_pandas_dfs(df_old, df_lm, ["stop_I"])
old_metro = difference_of_pandas_dfs(new_metro, df_lm, ["stop_I"])
train = gtfs_lm.get_stops_for_route_type(2)
feeder_area = pd.DataFrame()
other_stops = gtfs_lm.stops()
jda = JourneyDataAnalyzer(LM_DICT["journey_dir"], LM_DICT["gtfs_dir"])
# jda = JourneyDataAnalyzer(OLD_DICT["journey_dir"], OLD_DICT["gtfs_dir"])
areas_to_remove = stops_to_exclude(return_sqlite_list=False)
df = jda.get_upstream_stops_ratio(
1040, [str(i.stop_I) for i in new_metro.itertuples()], upstream_ratio)
feeder_area = feeder_area.append(df)
# df = jda.get_upstream_stops_ratio(7193, 563, 0.7)
print("new metro")
for i in new_metro.itertuples():
df = gtfs_lm.get_stops_within_distance(i.stop_I, station_distance)
new_metro = new_metro.append(df)
print("old metro")
for i in old_metro.itertuples():
df = gtfs_lm.get_stops_within_distance(i.stop_I, station_distance)
old_metro = old_metro.append(df)
print("train")
for i in train.itertuples():
df = gtfs_lm.get_stops_within_distance(i.stop_I, station_distance)
train = train.append(df)
new_metro = new_metro.drop_duplicates().reset_index(drop=True)
old_metro = old_metro.drop_duplicates().reset_index(drop=True)
train = train.drop_duplicates().reset_index(drop=True)
feeder_area = feeder_area.drop_duplicates().reset_index(drop=True)
# cleaning up borders
new_metro = difference_of_pandas_dfs(old_metro, new_metro, ["stop_I"])
for zone in [new_metro, old_metro, areas_to_remove]:
train = difference_of_pandas_dfs(zone, train, ["stop_I"])
for zone in [new_metro, train, old_metro, areas_to_remove]:
feeder_area = difference_of_pandas_dfs(zone, feeder_area, ["stop_I"])
spec_areas = pd.concat(
[new_metro, old_metro, train, feeder_area, areas_to_remove])
other_stops = difference_of_pandas_dfs(spec_areas, other_stops, ["stop_I"])
old_metro = old_metro.assign(stop_cat=1)
new_metro = new_metro.assign(stop_cat=2)
train = train.assign(stop_cat=3)
feeder_area = feeder_area.assign(stop_cat=4)
other_stops = other_stops.assign(stop_cat=5)
all_stops = pd.concat(
[new_metro, old_metro, train, feeder_area,
other_stops]).reset_index(drop=True)
if as_dict:
all_dfs = {
"new_metro_stations": new_metro,
"feeder_bus_area": feeder_area,
"old_metro_stations": old_metro,
"commuter_train_stations": train,
"other_stops": other_stops
}
else:
all_dfs = [("new_metro_stations", new_metro),
("feeder_bus_area", feeder_area),
("old_metro_stations", old_metro),
("commuter_train_stations", train),
("other_stops", other_stops)]
return all_dfs, all_stops
