Apr open data

_shared_utils/shared_utils/gtfs_analytics_data.yml

@@ -64,7 +64,7 @@ digest_tables:
 
 stop_segments:
   dir: ${gcs_paths.SEGMENT_GCS}
-  stage1: ${speeds_tables.usable_vp_file}
+  stage1: ${speeds_tables.usable_vp}
   stage2: "nearest/nearest_vp_shape_segments"
   stage3: "stop_arrivals"
   stage4: "speeds_stop_segments"
@@ -77,7 +77,7 @@ stop_segments:
 
 rt_stop_times:
   dir: ${gcs_paths.SEGMENT_GCS}
-  stage1: ${speeds_tables.usable_vp_file}
+  stage1: ${speeds_tables.usable_vp}
   stage2: "nearest/nearest_vp_rt_stop_times"
   stage3: "rt_stop_times/stop_arrivals"
   stage4: "rt_stop_times/speeds"
@@ -92,7 +92,7 @@ rt_stop_times:
 
 road_segments:
   dir: ${gcs_paths.SEGMENT_GCS}
-  stage1: ${speeds_tables.usable_vp_file}
+  stage1: ${speeds_tables.usable_vp}
   stage2: "nearest/nearest_vp_roads"
   stage3: "road_segments/stop_arrivals"
   stage4: "speeds_road_segments"

_shared_utils/shared_utils/rt_dates.py

@@ -52,6 +52,7 @@
     "jan2024": "2024-01-17",
     "feb2024": "2024-02-14",
     "mar2024": "2024-03-13",
+    "apr2024": "2024-04-17",
 }
 
 y2023_dates = [

gtfs_funnel/Makefile

@@ -16,7 +16,7 @@ preprocess:
 	python cleanup.py
 	python vp_condenser.py
 	make route_typologies_data
-	python operator_scheduled_stats.py
+	python operator_scheduled_stats.py 
 
 
 route_typologies_data:
@@ -36,4 +36,4 @@ monthly_scheduled_data:
 
 
 funnel_gtfs_data: 
-	make download_gtfs_data && make preprocess && make timeseries_preprocessing
+	make download_gtfs_data && make preprocess && make timeseries_preprocessing

gtfs_funnel/cleanup.py

@@ -12,7 +12,7 @@
 
     for analysis_date in analysis_date_list:
 
-        INPUT_FILE = GTFS_DATA_DICT.speed_tables.usable_vp
+        INPUT_FILE = GTFS_DATA_DICT.speeds_tables.usable_vp
 
         helpers.if_exists_then_delete(
             f"{SEGMENT_GCS}{INPUT_FILE}_{analysis_date}_stage"

gtfs_funnel/logs/download_data.log

@@ -320,3 +320,19 @@
 2024-03-14 11:44:27.599 | INFO     | __main__:download_one_day:56 - execution time: 0:01:22.625555
 2024-03-19 16:50:51.742 | INFO     | __main__:download_one_year:35 - execution time: 0:00:43.062868
 2024-04-02 09:03:24.949 | INFO     | __main__:download_one_year:35 - execution time: 0:00:14.017626
+2024-04-18 10:27:32.761 | INFO     | __main__:download_one_day:45 - Analysis date: 2024-04-17
+2024-04-18 10:27:35.561 | INFO     | __main__:download_one_day:52 - # operators to run: 198
+2024-04-18 10:27:35.562 | INFO     | __main__:download_one_day:56 - *********** Download trips data ***********
+2024-04-18 10:28:15.325 | INFO     | __main__:download_one_day:86 - execution time: 0:00:42.561678
+2024-04-18 10:28:35.712 | INFO     | __main__:download_one_day:22 - Analysis date: 2024-04-17
+2024-04-18 10:28:38.053 | INFO     | __main__:download_one_day:29 - # operators to run: 198
+2024-04-18 10:28:38.054 | INFO     | __main__:download_one_day:33 - *********** Download stops data ***********
+2024-04-18 10:28:51.192 | INFO     | __main__:download_one_day:64 - execution time: 0:00:15.478316
+2024-04-18 10:29:14.197 | INFO     | __main__:download_one_day:22 - Analysis date: 2024-04-17
+2024-04-18 10:29:16.683 | INFO     | __main__:download_one_day:29 - # operators to run: 198
+2024-04-18 10:29:16.683 | INFO     | __main__:download_one_day:33 - *********** Download routelines data ***********
+2024-04-18 10:32:02.705 | INFO     | __main__:download_one_day:63 - execution time: 0:02:48.507170
+2024-04-18 10:32:26.078 | INFO     | __main__:download_one_day:21 - Analysis date: 2024-04-17
+2024-04-18 10:32:27.682 | INFO     | __main__:download_one_day:29 - # operators to run: 171
+2024-04-18 10:32:27.683 | INFO     | __main__:download_one_day:33 - *********** Download st data ***********
+2024-04-18 10:34:57.448 | INFO     | __main__:download_one_day:56 - execution time: 0:02:31.368995

gtfs_funnel/logs/download_vp_v2.log

@@ -207,3 +207,14 @@
 2024-03-14 11:58:41.151 | INFO     | __main__:<module>:110 - export concatenated vp: 0:03:05.913001
 2024-03-14 12:01:43.033 | INFO     | __main__:<module>:132 - remove batched parquets
 2024-03-14 12:01:43.035 | INFO     | __main__:<module>:135 - execution time: 0:06:14.791580
+2024-04-18 10:35:22.856 | INFO     | __main__:<module>:148 - Analysis date: 2024-04-17
+2024-04-18 10:38:25.816 | INFO     | __main__:loop_through_batches_and_download_vp:111 - exported batch 0 to GCS: 0:03:02.950348
+2024-04-18 10:39:56.253 | INFO     | __main__:loop_through_batches_and_download_vp:111 - exported batch 1 to GCS: 0:01:30.434267
+2024-04-18 10:45:13.007 | INFO     | __main__:loop_through_batches_and_download_vp:111 - exported batch 2 to GCS: 0:05:16.754172
+2024-04-18 10:47:08.452 | INFO     | __main__:loop_through_batches_and_download_vp:111 - exported batch 3 to GCS: 0:01:55.425286
+2024-04-18 10:47:08.453 | INFO     | __main__:<module>:155 - execution time: 0:11:45.586863
+2024-04-18 10:47:29.679 | INFO     | __main__:<module>:97 - Analysis date: 2024-04-17
+2024-04-18 10:47:37.286 | INFO     | __main__:<module>:105 - concat and filter batched data: 0:00:07.606835
+2024-04-18 10:51:44.225 | INFO     | __main__:<module>:112 - export concatenated vp: 0:04:06.939409
+2024-04-18 10:56:04.569 | INFO     | __main__:<module>:134 - remove batched parquets
+2024-04-18 10:56:04.570 | INFO     | __main__:<module>:137 - execution time: 0:08:34.890652

gtfs_funnel/logs/vp_preprocessing.log

@@ -20,3 +20,11 @@
 2024-03-14 12:16:08.742 | INFO     | __main__:<module>:202 - 2024-03-13: vp_direction script execution time: 0:07:35.556127
 2024-03-14 12:43:58.062 | INFO     | __main__:<module>:153 - 2024-03-13: condense vp for trip 0:04:45.267623
 2024-03-14 12:56:43.421 | INFO     | __main__:<module>:161 - 2024-03-13: prepare vp to use in nearest neighbor: 0:12:45.358549
+2024-04-18 11:04:43.747 | INFO     | __main__:<module>:169 - 2024-04-17: pare down vp: 0:02:22.947760
+2024-04-18 11:08:55.978 | INFO     | __main__:attach_prior_vp_add_direction:90 - persist vp gddf: 0:03:51.080485
+2024-04-18 11:13:14.285 | INFO     | __main__:attach_prior_vp_add_direction:122 - np vectorize arrays for direction: 0:04:18.306848
+2024-04-18 11:13:22.726 | INFO     | __main__:<module>:194 - 2024-04-17: export vp direction: 0:08:17.828237
+2024-04-18 11:14:49.190 | INFO     | __main__:<module>:200 - 2024-04-17: export usable vp with direction: 0:01:26.463779
+2024-04-18 11:14:49.191 | INFO     | __main__:<module>:203 - 2024-04-17: vp_direction script execution time: 0:09:44.292016
+2024-04-18 11:34:37.603 | INFO     | __main__:<module>:120 - 2024-04-17: condense vp for trip 0:06:16.809244
+2024-04-18 11:52:47.882 | INFO     | __main__:<module>:128 - 2024-04-17: prepare vp to use in nearest neighbor: 0:18:10.278588

gtfs_funnel/operator_scheduled_stats.py

@@ -121,26 +121,19 @@ def operator_typology_breakdown(df: pd.DataFrame) -> pd.DataFrame:
     Get a count of how many routes (not route-dir) 
     have a certain primary typology.
     """    
-    df2 = (df
-           .groupby(
-               ["schedule_gtfs_dataset_key", "primary_typology"])
-            .agg({"route_id": "nunique"})
-            .reset_index()
-    )
-
-    df_wide = df2.pivot(
-        index="schedule_gtfs_dataset_key", 
-        columns = "primary_typology",
-        values="route_id"
-    ).reset_index().fillna(0)
-
-    typology_values = ["downtown_local", "local",
-                       "rapid", "coverage"]
-
-    df_wide[typology_values] = df_wide[typology_values].astype(int)
+    typology_values = [
+        f"is_{i}" for i in 
+        ["downtown_local", "local", "rapid", "coverage"]
+    ]
 
-    rename_dict = {old_name: f"n_{old_name}_routes" 
+    df_wide = (df.groupby("schedule_gtfs_dataset_key")
+               .agg({**{c: "sum" for c in typology_values}})
+               .reset_index()
+              )
+
+    rename_dict = {old_name: f"n_{old_name.replace('is_', '')}_routes" 
                    for old_name in typology_values}
+
     df_wide = df_wide.rename(columns = rename_dict)
 
     return df_wide
@@ -152,7 +145,7 @@ def operator_typology_breakdown(df: pd.DataFrame) -> pd.DataFrame:
 
     ROUTE_TYPOLOGY = GTFS_DATA_DICT.schedule_tables.route_typologies
     OPERATOR_EXPORT = GTFS_DATA_DICT.schedule_tables.operator_scheduled_stats
-    OPERATOR_ROUTE_EXPORT = GTFS_DATA_DICT.schedule_tables.operator_route
+    OPERATOR_ROUTE_EXPORT = GTFS_DATA_DICT.schedule_tables.operator_routes
 
     for analysis_date in analysis_date_list:
         start = datetime.datetime.now()

gtfs_funnel/route_typologies.py

@@ -244,7 +244,7 @@ def primary_secondary_typology(
 
     start = datetime.datetime.now()
 
-    #roads = delayed(prep_roads)(GTFS_DATA_DICT)
+    roads = delayed(prep_roads)(GTFS_DATA_DICT)
     ROAD_BUFFER_METERS = 20
     TYPOLOGY_THRESHOLD = 0.10
 

gtfs_funnel/update_vars.py

@@ -4,7 +4,7 @@
              rt_dates.oct_week + rt_dates.apr_week)
 
 analysis_date_list = [
-    rt_dates.DATES["mar2024"]
+    rt_dates.DATES["apr2024"]
 ] 
 
 GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data")

high_quality_transit_areas/logs/hqta_processing.log

@@ -87,3 +87,10 @@
 2024-03-21 12:04:01.449 | INFO     | __main__:<module>:163 - C3_create_bus_hqta_types 2024-03-13 execution time: 0:00:19.553787
 2024-03-21 12:04:42.807 | INFO     | __main__:<module>:295 - D1_assemble_hqta_points 2024-03-13 execution time: 0:00:22.988739
 2024-03-21 12:05:20.102 | INFO     | __main__:<module>:167 - D2_assemble_hqta_polygons 2024-03-13 execution time: 0:00:19.166756
+2024-04-18 12:02:44.870 | INFO     | __main__:<module>:354 - A1_rail_ferry_brt_stops 2024-04-17 execution time: 0:00:59.115933
+2024-04-18 12:09:06.425 | INFO     | __main__:<module>:256 - B2_sjoin_stops_to_segments 2024-04-17 execution time: 0:00:50.678918
+2024-04-18 12:09:36.340 | INFO     | __main__:<module>:142 - C1_prep_pairwise_intersections 2024-04-17 execution time: 0:00:07.719892
+2024-04-18 12:10:31.226 | INFO     | __main__:<module>:125 - C2_find_intersections 2024-04-17 execution time: 0:00:33.802270
+2024-04-18 12:11:31.609 | INFO     | __main__:<module>:163 - C3_create_bus_hqta_types 2024-04-17 execution time: 0:00:37.330690
+2024-04-18 12:12:28.853 | INFO     | __main__:<module>:296 - D1_assemble_hqta_points 2024-04-17 execution time: 0:00:31.955298
+2024-04-18 12:13:36.294 | INFO     | __main__:<module>:167 - D2_assemble_hqta_polygons 2024-04-17 execution time: 0:00:40.596021

high_quality_transit_areas/update_vars.py

@@ -1,6 +1,6 @@
 from shared_utils import rt_dates
 
-analysis_date = rt_dates.DATES["mar2024"]
+analysis_date = rt_dates.DATES["apr2024"]
 
 GCS_FILE_PATH = ("gs://calitp-analytics-data/data-analyses/"
                  "high_quality_transit_areas/")

open_data/update_vars.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 from shared_utils import rt_dates
 
-analysis_date = rt_dates.DATES["mar2024"]
+analysis_date = rt_dates.DATES["apr2024"]
 
 GCS_FILE_PATH = "gs://calitp-analytics-data/data-analyses/"
 COMPILED_CACHED_VIEWS = f"{GCS_FILE_PATH}rt_delay/compiled_cached_views/"

rt_scheduled_v_ran/logs/rt_v_scheduled_route_metrics.log

@@ -49,3 +49,4 @@
 2024-03-29 12:11:15.513 | INFO     | __main__:route_metrics:47 - route aggregation 2023-04-14: 0:00:01.444968
 2024-03-29 12:11:16.851 | INFO     | __main__:route_metrics:47 - route aggregation 2023-04-15: 0:00:01.334524
 2024-03-29 12:11:18.456 | INFO     | __main__:route_metrics:47 - route aggregation 2023-04-16: 0:00:01.601076
+2024-04-18 13:34:04.718 | INFO     | __main__:route_metrics:74 - route aggregation 2024-04-17: 0:00:02.236708

rt_scheduled_v_ran/logs/rt_v_scheduled_trip_metrics.log

@@ -397,3 +397,7 @@
 2024-03-29 12:01:25.746 | INFO     | __main__:rt_schedule_trip_metrics:337 - Total run time for metrics on 2023-10-12: 0:01:03.980200
 2024-03-29 12:02:28.269 | INFO     | __main__:rt_schedule_trip_metrics:337 - Total run time for metrics on 2023-10-13: 0:01:02.513314
 2024-03-29 12:03:14.207 | INFO     | __main__:rt_schedule_trip_metrics:337 - Total run time for metrics on 2023-10-14: 0:00:45.927788
+2024-04-18 12:23:17.745 | INFO     | __main__:rt_schedule_trip_metrics:280 - tabular trip metrics 2024-04-17: 0:04:28.804318
+2024-04-18 13:02:41.637 | INFO     | __main__:rt_schedule_trip_metrics:280 - tabular trip metrics 2024-04-17: 0:03:45.566584
+2024-04-18 13:32:29.183 | INFO     | __main__:rt_schedule_trip_metrics:285 - spatial trip metrics 2024-04-17: 0:29:47.546487
+2024-04-18 13:33:34.374 | INFO     | __main__:rt_schedule_trip_metrics:333 - Total run time for metrics on 2024-04-17: 0:34:38.304007

rt_scheduled_v_ran/scripts/Makefile

@@ -4,5 +4,6 @@ rt_sched_pipeline:
 	python rt_v_scheduled_routes.py
 
 
+# this can be run after rt_segment_speeds make rt_stop_times_pipeline is run
 schedule_rt_stop_times_table:
 	python rt_stop_times.py

rt_scheduled_v_ran/scripts/rt_stop_times.py

@@ -6,7 +6,7 @@
 import pandas as pd
 
 from segment_speed_utils import helpers, segment_calcs
-from update_vars import SEGMENT_GCS, RT_SCHED_GCS
+from update_vars import GTFS_DATA_DICT, SEGMENT_GCS, RT_SCHED_GCS
 
 def prep_scheduled_stop_times(
     analysis_date: str

rt_scheduled_v_ran/scripts/rt_v_scheduled_trip.py

@@ -160,7 +160,7 @@ def buffer_shapes(
         crs=PROJECT_CRS,
         get_pandas=True,
         **kwargs
-    ).dropna(
+    ).pipe(helpers.remove_shapes_outside_ca).dropna(
         subset="geometry"
     ).query("shape_array_key not in @amtrak_outside_ca")
 

rt_scheduled_v_ran/scripts/update_vars.py

@@ -4,7 +4,9 @@
 oct_week = rt_dates.get_week("oct2023", exclude_wed=True)
 apr_week = rt_dates.get_week("apr2023", exclude_wed=True)
 
-analysis_date_list = rt_dates.y2024_dates + rt_dates.y2023_dates + oct_week + apr_week   
+analysis_date_list = [
+    rt_dates.DATES["apr2024"]
+]
 
 GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data")
 

rt_segment_speeds/logs/avg_speeds.log

@@ -132,3 +132,10 @@
 2024-03-28 18:45:04.105 | INFO     | __main__:<module>:304 - average rollups for ['2023-10-09', '2023-10-10', '2023-10-11', '2023-10-12', '2023-10-13', '2023-10-14', '2023-10-15']: 0:07:22.839496
 2024-03-28 18:52:09.308 | INFO     | __main__:multi_day_segment_averages:240 - multi day segment execution time: 0:07:05.201271
 2024-03-28 18:52:09.420 | INFO     | __main__:<module>:304 - average rollups for ['2023-04-10', '2023-04-11', '2023-04-12', '2023-04-13', '2023-04-14', '2023-04-15', '2023-04-16']: 0:07:05.312950
+2024-04-18 15:42:58.828 | INFO     | __main__:single_day_summary_averages:82 - trip avg 0:00:14.291404
+2024-04-18 15:43:09.833 | INFO     | __main__:single_day_summary_averages:122 - route dir avg: 0:00:11.005300
+2024-04-18 15:43:09.834 | INFO     | __main__:single_day_summary_averages:123 - single day summary speed execution time: 0:00:25.296704
+2024-04-18 15:43:09.912 | INFO     | __main__:<module>:246 - average rollups for 2024-04-17: 0:00:25.384157
+2024-04-18 15:49:30.073 | INFO     | __main__:single_day_segment_averages:167 - shape seg avg 0:05:04.862313
+2024-04-18 15:53:11.195 | INFO     | __main__:single_day_segment_averages:183 - route dir seg avg 0:03:41.121253
+2024-04-18 15:53:11.196 | INFO     | __main__:single_day_segment_averages:184 - single day segment execution time: 0:08:45.983566

rt_segment_speeds/logs/cut_stop_segments.log

@@ -1,2 +1,3 @@
 0:15:29.6603982024-02-15 13:36:56.379 | INFO     | __main__:<module>:156 - cut segments 2024-02-14: 0:13:55.835925
 2024-03-14 13:33:18.528 | INFO     | __main__:<module>:156 - cut segments 2024-03-13: 0:26:02.810762
+2024-04-18 12:44:11.808 | INFO     | __main__:<module>:155 - cut segments 2024-04-17: 0:33:24.198635

rt_segment_speeds/logs/interpolate_stop_arrival.log

@@ -1 +1,5 @@
 2024-02-15 14:18:47.082 | INFO     | __main__:interpolate_stop_arrivals:110 - get stop arrivals 2024-02-14: 0:09:37.655118
+2024-04-18 14:30:37.401 | INFO     | interpolate_stop_arrival:interpolate_stop_arrivals:203 - get stop arrivals 2024-04-17: 0:18:48.633416
+2024-04-18 14:36:29.213 | INFO     | interpolate_stop_arrival:interpolate_stop_arrivals:214 - interpolate arrivals for stop_segments 2024-04-17:  2024-04-17: 0:24:40.445216
+2024-04-18 15:40:32.431 | INFO     | interpolate_stop_arrival:interpolate_stop_arrivals:214 - interpolate arrivals for rt_stop_times 2024-04-17:  2024-04-17: 0:18:11.754862
+2024-04-18 15:42:16.358 | INFO     | 

rt_segment_speeds/logs/nearest_vp.log

@@ -1,2 +1,5 @@
 2024-02-15 13:48:26.270 | INFO     | __main__:nearest_neighbor_shape_segments:203 - shape segments 2024-02-14: 0:10:31.474349
 2024-02-15 14:08:52.783 | INFO     | __main__:nearest_neighbor_rt_stop_times:135 - RT stop times 2024-02-14: 0:11:07.883083
+2024-04-18 13:19:11.258 | INFO     | nearest_vp_to_stop:nearest_neighbor_for_stop:134 - nearest neighbor for stop_segments 2024-04-17: 0:27:28.612602
+2024-04-18 15:22:16.322 | INFO     | nearest_vp_to_stop:nearest_neighbor_for_stop:134 - nearest neighbor for rt_stop_times 2024-04-17: 0:17:44.714181
+

rt_segment_speeds/logs/speeds_by_segment_trip.log

@@ -1,3 +1,5 @@
 2024-02-15 14:19:36.965 | INFO     | __main__:calculate_speed_from_stop_arrivals:132 - speeds by segment: 2024-02-14: 0:00:31.989535
 2024-02-15 14:45:36.035 | INFO     | __main__:calculate_speed_from_stop_arrivals:132 - speeds by segment: 2024-01-17: 0:00:32.109610
 2024-03-28 13:11:29.272 | INFO     | __main__:calculate_speed_from_stop_arrivals:148 - speeds by segment for stop_segments 2024-03-13: 0:01:36.402022
+2024-04-18 15:04:52.606 | INFO     | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:148 - speeds by segment for stop_segments 2024-04-17: 0:01:48.092886
+2024-04-18 15:42:16.358 | INFO     | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:148 - speeds by segment for rt_stop_times 2024-04-17: 0:01:43.666310

rt_segment_speeds/scripts/average_segment_speeds.py

@@ -8,7 +8,6 @@
 
 from dask import delayed, compute
 from loguru import logger
-from pathlib import Path
 from typing import Literal
 
 from calitp_data_analysis.geography_utils import WGS84
@@ -240,33 +239,6 @@ def multi_day_segment_averages(analysis_date_list: list, dict_inputs: dict):
     logger.info(f"multi day segment execution time: {end - start}")
 
     return    
-
-def stage_open_data_exports(analysis_date: str, dict_inputs: dict):
-    """
-    For the datasets we publish to Geoportal, 
-    export them to a stable GCS URL so we can always 
-    read it in open_data/catalog.yml.
-    """
-    datasets = [
-        dict_inputs["route_dir_single_segment"],
-        dict_inputs["route_dir_single_summary"]
-    ]
-
-    for d in datasets:
-        gdf = gpd.read_parquet(
-            f"{SEGMENT_GCS}{d}_{analysis_date}.parquet"
-        )
-
-        utils.geoparquet_gcs_export(
-            gdf,
-            f"{SEGMENT_GCS}export/",
-            f"{Path(d).stem}"
-        )
-        del gdf
-
-    print(f"overwrite {datasets}")
-
-    return
 
 
 if __name__ == "__main__":
@@ -287,7 +259,6 @@ def stage_open_data_exports(analysis_date: str, dict_inputs: dict):
         start = datetime.datetime.now()
 
         single_day_segment_averages(analysis_date, STOP_SEG_DICT)
-        stage_open_data_exports(analysis_date, STOP_SEG_DICT)
 
         end = datetime.datetime.now()
 

rt_segment_speeds/scripts/cut_stop_segments.py

@@ -16,7 +16,7 @@
 
 from calitp_data_analysis import utils
 from calitp_data_analysis.geography_utils import WGS84
-from segment_speed_utils import gtfs_schedule_wrangling
+from segment_speed_utils import gtfs_schedule_wrangling, helpers
 from update_vars import GTFS_DATA_DICT, SEGMENT_GCS
 from segment_speed_utils.project_vars import PROJECT_CRS 
 

rt_segment_speeds/scripts/interpolate_stop_arrival.py

@@ -12,7 +12,7 @@
 from pathlib import Path
 from typing import Literal, Optional
 
-from segment_speed_utils import (array_utils, 
+from segment_speed_utils import (array_utils, helpers,
                                  segment_calcs, wrangle_shapes)
 from update_vars import SEGMENT_GCS, GTFS_DATA_DICT
 from segment_speed_utils.project_vars import PROJECT_CRS, SEGMENT_TYPES

rt_segment_speeds/scripts/pipe.py

@@ -4,6 +4,8 @@
 interpolate_stop_arrivals.py,
 and calculate_speed_from_stop_arrivals.py
 """
+import sys
+from loguru import logger
 from pathlib import Path
 from typing import Literal, Optional
 
@@ -24,6 +26,7 @@ def nearest_neigbor_to_speed(
     interpolation of stop arrival, deriving segment speeds 
     between stops.
     """
+
     LOG_FILE = "../logs/nearest_vp.log"
     logger.add(LOG_FILE, retention="3 months")
     logger.add(sys.stderr, 
@@ -47,7 +50,7 @@ def nearest_neigbor_to_speed(
         segment_type = segment_type, 
         config_path = config_path
     )
-
+    
     LOG_FILE = "../logs/speeds_by_segment_trip.log"
     logger.add(LOG_FILE, retention="3 months")
     logger.add(sys.stderr,