Merge pull request #1223 from cal-itp/private-datasets

Exclude private datasets from 6 geoportal datasets, GTFS digest

Author: tiffanychu90

_shared_utils/setup.py

@@ -4,7 +4,7 @@
 setup(
     name="shared_utils",
     packages=find_packages(),
-    version="2.5",
+    version="2.5.1",
     description="Shared utility functions for data analyses",
     author="Cal-ITP",
     license="Apache",

_shared_utils/shared_utils/gtfs_utils_v2.py

@@ -503,3 +503,23 @@ def get_stop_times(
         )
 
     return stop_times
+
+
+def filter_to_public_schedule_gtfs_dataset_keys(get_df: bool = False) -> list:
+    """
+    Return a list of schedule_gtfs_dataset_keys that have
+    private_dataset == None.
+    private_dataset holds values:True or None, no False.
+    """
+    dim_gtfs_datasets = schedule_rt_utils.filter_dim_gtfs_datasets(
+        keep_cols=["key", "name", "private_dataset"],
+        custom_filtering={
+            "type": ["schedule"],
+        },
+        get_df=True,
+    ) >> filter(_.private_dataset != True)
+
+    if get_df:
+        return dim_gtfs_datasets
+    else:
+        return dim_gtfs_datasets.gtfs_dataset_key.unique().tolist()

_shared_utils/shared_utils/publish_utils.py

@@ -3,6 +3,7 @@
 from typing import Union
 
 import gcsfs
+import pandas as pd
 
 fs = gcsfs.GCSFileSystem()
 PUBLIC_BUCKET = "gs://calitp-publish-data-analysis/"
@@ -47,3 +48,14 @@ def if_exists_then_delete(filepath: str):
             fs.rm(filepath)
 
     return
+
+
+def exclude_private_datasets(
+    df: pd.DataFrame,
+    col: str = "schedule_gtfs_dataset_key",
+    public_gtfs_dataset_keys: list = [],
+) -> pd.DataFrame:
+    """
+    Filter out private datasets.
+    """
+    return df[df[col].isin(public_gtfs_dataset_keys)].reset_index(drop=True)

gtfs_digest/merge_data.py

@@ -8,6 +8,7 @@
 
 from calitp_data_analysis import utils
 from segment_speed_utils import gtfs_schedule_wrangling, time_series_utils
+from shared_utils import gtfs_utils_v2, publish_utils
 from update_vars import GTFS_DATA_DICT, SEGMENT_GCS, RT_SCHED_GCS, SCHED_GCS
 
 route_time_cols = ["schedule_gtfs_dataset_key", 
@@ -220,16 +221,21 @@ def set_primary_typology(df: pd.DataFrame) -> pd.DataFrame:
 
     return df3
 
+
 if __name__ == "__main__":
 
     from shared_utils import rt_dates
 
-    analysis_date_list = (rt_dates.y2024_dates + rt_dates.y2023_dates
-            )
+    analysis_date_list = (
+        rt_dates.y2024_dates + rt_dates.y2023_dates
+    )
 
     DIGEST_RT_SCHED = GTFS_DATA_DICT.digest_tables.route_schedule_vp 
     DIGEST_SEGMENT_SPEEDS = GTFS_DATA_DICT.digest_tables.route_segment_speeds
 
+    # These are public schedule_gtfs_dataset_keys
+    public_feeds = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys()
+    
     # Get cardinal direction for each route
     df_sched = concatenate_schedule_by_route_direction(analysis_date_list)
 
@@ -274,11 +280,15 @@ def set_primary_typology(df: pd.DataFrame) -> pd.DataFrame:
         df_crosswalk,
         on = ["schedule_gtfs_dataset_key", "name", "service_date"],
         how = "left"
+    ).pipe(
+        # Find the most common cardinal direction
+        gtfs_schedule_wrangling.top_cardinal_direction
+    ).pipe(
+        # Drop any private datasets before exporting
+        publish_utils.exclude_private_datasets, 
+        public_gtfs_dataset_keys= public_feeds
     )
-    
-    # Find the most common cardinal direction
-    df = gtfs_schedule_wrangling.top_cardinal_direction(df)
-    
+     
     integrify = [
         "n_scheduled_trips", "n_vp_trips",
         "minutes_atleast1_vp", "minutes_atleast2_vp",
@@ -304,7 +314,9 @@ def set_primary_typology(df: pd.DataFrame) -> pd.DataFrame:
         primary_typology,
         on = route_time_cols,
         how = "left"
-    )
+    ).pipe(
+        publish_utils.exclude_private_datasets, 
+        public_gtfs_dataset_keys= public_feeds)
 
     utils.geoparquet_gcs_export(
         segment_speeds2,

gtfs_digest/merge_operator_data.py

@@ -8,6 +8,7 @@
 
 from calitp_data_analysis import utils
 from segment_speed_utils import time_series_utils
+from shared_utils import publish_utils
 from merge_data import merge_in_standardized_route_names
 from update_vars import GTFS_DATA_DICT, SCHED_GCS, RT_SCHED_GCS
 
@@ -100,6 +101,7 @@ def operator_category_counts_by_date() -> pd.DataFrame:
 
     return operator_category_counts
 
+
 if __name__ == "__main__":
 
     from shared_utils import rt_dates
@@ -111,6 +113,8 @@ def operator_category_counts_by_date() -> pd.DataFrame:
     SCHED_RT_CATEGORY = GTFS_DATA_DICT.digest_tables.operator_sched_rt
     CROSSWALK = GTFS_DATA_DICT.schedule_tables.gtfs_key_crosswalk
 
+    public_feeds = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys()
+    
     # Concat operator profiles
     df = concatenate_operator_stats(analysis_date_list)
 
@@ -141,14 +145,20 @@ def operator_category_counts_by_date() -> pd.DataFrame:
 
     # Merge
     merge_cols = ["schedule_gtfs_dataset_key", "service_date"]
-    op_profiles_df1 = pd.merge(df, 
-                               crosswalk_df, 
-                               on = merge_cols, 
-                               how = "left")
+    op_profiles_df1 = pd.merge(
+        df, 
+        crosswalk_df, 
+        on = merge_cols, 
+        how = "left"
+    )
 
     # Drop duplicates created after merging
     op_profiles_df2 = (op_profiles_df1
-                       .drop_duplicates(subset = list(op_profiles_df1.columns))
+                       .pipe(
+                           publish_utils.exclude_private_datasets, 
+                           col = "schedule_gtfs_dataset_key", 
+                           public_gtfs_dataset_keys = public_feeds
+                       ).drop_duplicates(subset = list(op_profiles_df1.columns))
                        .reset_index(drop = True))
 
     op_profiles_df2.to_parquet(
@@ -157,15 +167,26 @@ def operator_category_counts_by_date() -> pd.DataFrame:
 
     gdf = concatenate_operator_routes(
         analysis_date_list
-    ).pipe(merge_in_standardized_route_names)
+    ).pipe(
+        merge_in_standardized_route_names
+    ).pipe(
+        publish_utils.exclude_private_datasets, 
+        col = "schedule_gtfs_dataset_key", 
+        public_gtfs_dataset_keys = public_feeds
+    )
 
     utils.geoparquet_gcs_export(
         gdf,
         RT_SCHED_GCS,
         OPERATOR_ROUTE
     )
 
-    operator_category_counts = operator_category_counts_by_date()
+    operator_category_counts = operator_category_counts_by_date().pipe(
+        publish_utils.exclude_private_datasets, 
+        col = "schedule_gtfs_dataset_key", 
+        public_gtfs_dataset_keys = public_feeds    
+    )
+    
     operator_category_counts.to_parquet(
         f"{RT_SCHED_GCS}{SCHED_RT_CATEGORY}.parquet"
     )

gtfs_digest/merge_operator_service.py

@@ -1,19 +1,20 @@
-import pandas as pd
-import numpy as np
-from segment_speed_utils import helpers, time_series_utils, gtfs_schedule_wrangling
-from segment_speed_utils.project_vars import (COMPILED_CACHED_VIEWS, RT_SCHED_GCS, SCHED_GCS)
-
-from shared_utils import catalog_utils, rt_dates
-
-GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data")
-
 """
 Finding the total number of scheduled service hours for 
 an operator across its routes for a full week. The data is
 downloaded every 1/2 a year. 
 
 Grain is operator-service_date-route
 """
+import pandas as pd
+
+from segment_speed_utils import (gtfs_schedule_wrangling, helpers, 
+                                 time_series_utils) 
+from segment_speed_utils.project_vars import (
+    COMPILED_CACHED_VIEWS, weeks_available)
+from shared_utils import gtfs_utils_v2, publish_utils, rt_dates
+from update_vars import GTFS_DATA_DICT, RT_SCHED_GCS
+
+
 def concatenate_trips(
     date_list: list,
 ) -> pd.DataFrame:
@@ -44,29 +45,6 @@ def concatenate_trips(
 
     return df
 
-def get_day_type(date):
-    """
-    Function to return the day type (e.g., Monday, Tuesday, etc.) from a datetime object.
-    """
-    days_of_week = ["Monday", 
-                    "Tuesday", 
-                    "Wednesday", 
-                    "Thursday", 
-                    "Friday", 
-                    "Saturday", 
-                    "Sunday"]
-    return days_of_week[date.weekday()]
-
-def weekday_or_weekend(row):
-    """
-    Tag if a day is a weekday or Saturday/Sunday
-    """
-    if row.day_type == "Sunday":
-        return "Sunday"
-    if row.day_type == "Saturday":
-        return "Saturday"
-    else:
-        return "Weekday"
 
 def total_service_hours(date_list: list) -> pd.DataFrame:
     """
@@ -76,67 +54,81 @@ def total_service_hours(date_list: list) -> pd.DataFrame:
     # Combine all the days' data for a week.
     df = concatenate_trips(date_list)
 
-    # Find day type aka Monday, Tuesday, Wednesday based on service date.
-    df['day_type'] = df['service_date'].apply(get_day_type)
-    
-    # Tag if the day is a weekday, Saturday, or Sunday.
-    df["weekday_weekend"] = df.apply(weekday_or_weekend, axis=1)
+    WEEKDAY_DICT = {
+        **{k: "Weekday" for k in ["Monday", "Tuesday", "Wednesday",
+                             "Thursday", "Friday"]},
+        "Saturday": "Saturday",
+        "Sunday": "Sunday"
+    }
 
-    # df = gtfs_schedule_wrangling.add_weekday_weekend_column(df)
-    
-    # Find the minimum departure hour.
-    df["departure_hour"] = df.trip_first_departure_datetime_pacific.dt.hour
+    # Find day type (Monday, Tuesday, etc), departure hour, month_year, and weekday_weekend
+    df = df.assign(
+        day_type = df.service_date.dt.day_name(),
+        departure_hour = df.trip_first_departure_datetime_pacific.dt.hour.astype("Int64"),
+        # get month_year that's 2024-04 for Apr2024 format
+        month_year = (df.service_date.dt.year.astype(str) + 
+                      "-" +  df.service_date.dt.month.astype(str).str.zfill(2)),
+    ).pipe(
+        gtfs_schedule_wrangling.add_weekday_weekend_column, WEEKDAY_DICT
+    )
 
-    # Delete out the specific day, leave only month & year.
-    df["month"] = df.service_date.astype(str).str.slice(stop=7)
 
-    # Total up service hours by weekday, Sunday, and Saturday.
+    # Total up hourly service hours by weekday, Sunday, and Saturday.
     df2 = (
         df.groupby(["name", 
-                    "month", 
+                    "month_year", 
                     "weekday_weekend", 
                     "departure_hour"])
-        .agg(
-            {
-                "service_hours": "sum",
-            }
-        )
+        .agg({"service_hours": "sum"})
         .reset_index()
     )
 
-    # For weekday hours, divide by 5.
-    df2["weekday_service_hours"] = df2.service_hours/5
+    # weekday hours should be divided by 5, while keeping sat/sun intact
+    df2 = df2.assign(
+        daily_service_hours = df2.apply(
+            lambda x: round(x.service_hours / 5, 2) 
+            if x.weekday_weekend=="Weekday"
+            else round(x.service_hours, 2), axis=1
+        ),
+        service_hours = df2.service_hours.round(2),
+    )
 
-    # Rename projects.
-    df2 = df2.rename(columns = {'service_hours':'weekend_service_hours'})
     return df2
 
-def total_service_hours_all_months() -> pd.DataFrame:
+
+def total_service_hours_all_months(week_list: list[list]) -> pd.DataFrame:
     """
     Find service hours for a full week for one operator
     and for the months we have a full week's worth of data downloaded.
-    As of 5/2024, we have April 2023 and October 2023.
-    """
-    # Grab the dataframes with a full week's worth of data. 
-    apr_23week = rt_dates.get_week(month="apr2023", exclude_wed=False)
-    oct_23week = rt_dates.get_week(month="oct2023", exclude_wed=False)
-    apr_24week = rt_dates.get_week(month="apr2024", exclude_wed=False)
-    
-    # Sum up total service_hours
-    apr_23df = total_service_hours(apr_23week)
-    oct_23df = total_service_hours(oct_23week)
-    apr_24df = total_service_hours(apr_24week)
+    As of 5/2024, we have April 2023, October 2023, and April 2024.
+    """   
+    public_datasets = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys(
+        get_df=True
+    )
+    public_feeds = public_datasets.gtfs_dataset_name.unique().tolist()
 
     # Combine everything
-    all_df = pd.concat([apr_23df, oct_23df, apr_24df])
-   
+    all_df = pd.concat(
+        [total_service_hours(one_week) for one_week in week_list]
+    ).pipe(
+        publish_utils.exclude_private_datasets, 
+        col = "name", 
+        public_gtfs_dataset_keys = public_feeds
+    )
+
     return all_df
 
 
 if __name__ == "__main__":
 
-    # Save service hours.
-    SERVICE_EXPORT = f"{GTFS_DATA_DICT.digest_tables.dir}{GTFS_DATA_DICT.digest_tables.scheduled_service_hours}.parquet"
-    service_hours = total_service_hours_all_months()
-    service_hours.to_parquet(SERVICE_EXPORT) 
+    print(f"Aggregating for dates: {weeks_available}")
+    
+    # Save service hours
+    SERVICE_EXPORT = GTFS_DATA_DICT.digest_tables.scheduled_service_hours
+    
+    service_hours = total_service_hours_all_months(weeks_available)
+    
+    service_hours.to_parquet(
+        f"{RT_SCHED_GCS}{SERVICE_EXPORT}.parquet"
+    )