Segments compare

rt_segment_speeds/prep_comparison.py

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+"""
+Prep the data used to compare segment methodologies 
+between Eric and Tiffany.
+
+Get at why speeds are coming out differently.
+
+   - segments do not exactly match
+   - points over which speeds are calculated aren't exactly the same, 
+     since understanding of direction is not exactly the same
+   - peel all that back and start at the trip-level to see what's going into 
+     averages, start with simpler shapes (no loop, no inlining)
+"""
+import geopandas as gpd
+import pandas as pd
+
+from shared_utils import rt_dates, rt_utils
+from segment_speed_utils import helpers
+from segment_speed_utils.project_vars import SEGMENT_GCS, GCS_FILE_PATH
+from calitp_data_analysis import utils
+
+RT_DELAY_GCS = f"{GCS_FILE_PATH}rt_delay/v2_segment_speed_views/"
+analysis_date = rt_dates.DATES["sep2023"]
+
+def prep_eric_data(analysis_date: str) -> gpd.GeoDataFrame:
+    itp_ids = [
+        182,
+        300,
+    ]
+
+    # Don't narrow down time-of-day yet, we might select a trip from any 
+    # of these
+    time_of_day = [
+        "AM_Peak", "Midday", "PM_Peak"
+    ]
+
+    eric_dfs = [
+        gpd.read_parquet(
+            f"{RT_DELAY_GCS}{itp_id}_{analysis_date}_{time}.parquet")
+          for itp_id, time in zip(itp_ids, time_of_day)
+         ]
+
+    df_eric = pd.concat(eric_dfs, axis=0).reset_index(drop=True)
+
+    return df_eric
+
+
+def prep_tiff_data(
+    analysis_date: str, 
+    subset_df: gpd.GeoDataFrame
+) -> gpd.GeoDataFrame:
+
+    shape_trips = subset_df[["shape_id", "trip_id"]].drop_duplicates()
+
+    scheduled_trips = helpers.import_scheduled_trips(
+        analysis_date,
+        columns = [
+            "gtfs_dataset_key", "name", 
+            "trip_id", "trip_instance_key",
+            "shape_id", "shape_array_key",
+            "route_id", "direction_id"],
+        get_pandas = True
+    ).rename(columns = {"gtfs_dataset_key": "schedule_gtfs_dataset_key"})
+
+    # Grab the trip_instance_keys we need and use it
+    # to filter the speeds parquet down
+    subset_trips = scheduled_trips.merge(
+        shape_trips,
+        on = ["shape_id", "trip_id"],
+        how = "inner"
+    )
+
+    trip_instances = subset_trips.trip_instance_key.unique().tolist()
+    subset_shapes = subset_trips.shape_array_key.unique().tolist()
+
+    segments = gpd.read_parquet(
+        f"{SEGMENT_GCS}stop_segments_{analysis_date}.parquet",
+        filters = [[("shape_array_key", "in", subset_shapes)]]
+    ).drop(columns = ["geometry_arrowized", "district_name"])
+
+    filtered_trip_speeds = pd.read_parquet(
+        f"{SEGMENT_GCS}speeds_stop_segments_{analysis_date}.parquet",
+        filters = [[("trip_instance_key", "in", trip_instances)]]
+    ).merge(
+        subset_trips,
+        on = ["trip_instance_key", "shape_array_key"],
+        how = "inner"
+    )
+
+    df_tiff = pd.merge(
+        segments,
+        filtered_trip_speeds,
+        on = ["schedule_gtfs_dataset_key", "shape_array_key", "stop_sequence"],
+        how = "inner"
+    )
+
+    return df_tiff
+
+
+def map_one_trip(gdf: gpd.GeoDataFrame, one_trip: str):
+    gdf2 = gdf[gdf.trip_id==one_trip]
+
+    m1 = gdf2.explore(
+         "speed_mph", 
+        tiles = "CartoDB Positron",
+        cmap = rt_utils.ZERO_THIRTY_COLORSCALE
+    )
+
+    return m1
+
+if __name__ == "__main__":
+    df_eric = prep_eric_data(analysis_date)
+    df_tiff = prep_tiff_data(analysis_date, df_eric)
+
+    utils.geoparquet_gcs_export(
+        df_eric,
+        SEGMENT_GCS,
+        f"speeds_eric_{analysis_date}"
+    )
+
+    utils.geoparquet_gcs_export(
+        df_tiff,
+        SEGMENT_GCS,
+        f"speeds_tiff_{analysis_date}"
+    )
+
+    # stop_sequence doesn't exactly merge, but that's fine, 
+    # since Eric cuts shorter segments, so stop_sequence can have 
+    # values like 1.25, 1.50, etc.
+    # Leave it in the merge for now, and allow left_only merges
+    identifier_cols = [
+        "trip_id", "shape_id", "stop_id", "stop_sequence",
+        "route_id", "direction_id",
+    ]
+
+
+    speed_df = pd.merge(
+        df_eric[identifier_cols + ["speed_mph"]].rename(
+            columns = {"speed_mph": "eric_speed_mph"}),
+        df_tiff[identifier_cols + ["speed_mph"]].rename(
+            columns = {"speed_mph": "tiff_speed_mph"}),
+        on = identifier_cols,
+        how = "left",
+        indicator = True
+    )
+
+    speed_df.to_parquet(f"{SEGMENT_GCS}speeds_comparison_{analysis_date}.parquet")