create od truncation

scripts/od_truncation.py

@@ -0,0 +1,44 @@
+# %%
+from pathlib import Path
+import os
+import pandas as pd
+from tqdm import tqdm
+from nird.utils import load_config
+
+
+base_path = Path(load_config()["paths"]["base_path"])
+tqdm.pandas()
+
+od_df = pd.read_csv(
+    os.path.join(base_path, "census_datasets", "od_matrix", "od_gb_oa_2021_node.csv")
+)
+od_df = od_df[od_df["Car21"] > 0]
+
+od_df = od_df.sort_values(by=["Car21"], ascending=False)
+od_df["car_total"] = od_df.groupby(["origin_node"])["Car21"].transform("sum")
+od_df["car_cumsum"] = od_df.groupby(["origin_node"])["Car21"].transform("cumsum")
+od_df["fraction"] = od_df["car_cumsum"] / od_df["car_total"]
+od_df["count"] = od_df.groupby(["origin_node"])["origin_node"].transform("count")
+
+before = od_df["Car21"].sum()
+print("Before trunction...")
+print(f"The total number of trips: {before} ")
+print(f"The number of origins: {od_df.origin_node.unique().shape[0]}")
+print(f"The number of destinations: {od_df.shape[0]}")
+
+# od_df = od_df[(od_df["fraction"] < 0.7) | (od_df["count"] == 1)]
+
+od_df = od_df[
+    (od_df["Car21"] > 1)  # 32.6% (~1.79 millon)
+    | (od_df["count"] == 1)  # 32.7% (~1.81 million)
+    # | ((od_df["Car21"] == 1) & (od_df["fraction"] < 0.7))  # 69.2% (~7.74 million)
+]
+
+after = od_df["Car21"].sum()
+print("After trunction...")
+print(f"The total number of trips: {after} ")
+print(f"The number of origins: {od_df.origin_node.unique().shape[0]}")
+print(f"The number of destinations: {od_df.shape[0]}")
+
+print(f"The number of trip reduction: {before - after}")
+print(f"The percentage of trips retained: {1.0 * after / before}")