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BayAreaMetro · Jul 10, 2024 · d088122 · d088122
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Showing 3 changed files with 130 additions and 79 deletions.
diff --git a/scripts/compare_skims.py b/scripts/compare_skims.py
@@ -1,23 +1,35 @@
-#%%
+# %%
 import pandas as pd
 import openmatrix as omx
 from pathlib import Path
 
 import numpy as np
 
-network_fid_path = Path(r"Z:\MTC\US0024934.9168\Task_3_runtime_improvements\3.1_network_fidelity\run_result")
+network_fid_path = Path(
+    r"Z:\MTC\US0024934.9168\Task_3_runtime_improvements\3.1_network_fidelity\run_result"
+)
 # network_fid_path = Path(r"D:\TEMP\TM2.2.1.1-0.05")
 
-#%%
+# %%
+
 
 def read_matrix_as_long_df(path: Path, run_name):
     run = omx.open_file(path, "r")
     am_time = np.array(run["AM_da_time"])
     index_lables = list(range(am_time.shape[0]))
-    return pd.DataFrame(am_time, index=index_lables, columns=index_lables).stack().rename(run_name).to_frame()
+    return (
+        pd.DataFrame(am_time, index=index_lables, columns=index_lables)
+        .stack()
+        .rename(run_name)
+        .to_frame()
+    )
+
 
-a = read_matrix_as_long_df(r"D:\TEMP\TM2.2.1.1-New_network_rerun\TM2.2.1.1_new_taz\skim_matrices\highway\HWYSKMAM_taz.omx", "test")
-#%%
+a = read_matrix_as_long_df(
+    r"D:\TEMP\TM2.2.1.1-New_network_rerun\TM2.2.1.1_new_taz\skim_matrices\highway\HWYSKMAM_taz.omx",
+    "test",
+)
+# %%
 all_skims = []
 for skim_matrix_path in network_fid_path.rglob("*AM_taz.omx"):
     print(skim_matrix_path)
@@ -26,14 +38,17 @@ def read_matrix_as_long_df(path: Path, run_name):
 
 all_skims = pd.concat(all_skims, axis=1)
 # %%
-#%%%
-all_skims.to_csv(r"Z:\MTC\US0024934.9168\Task_3_runtime_improvements\3.1_network_fidelity\output_summaries\skim_data\skims.csv")
+# %%%
+all_skims.to_csv(
+    r"Z:\MTC\US0024934.9168\Task_3_runtime_improvements\3.1_network_fidelity\output_summaries\skim_data\skims.csv"
+)
 # %%
 # %%
 import geopandas as gpd
 from importlib import Path
-import pandas as pd 
-#%%
+import pandas as pd
+
+# %%
 output_paths_to_consolidate = Path(r"D:\TEMP\output_summaries")
 all_files = []
 for file in output_paths_to_consolidate.glob("*_roadway_network.geojson"):
@@ -42,15 +57,15 @@ def read_matrix_as_long_df(path: Path, run_name):
     specific_run = gpd.read_file(file)
     specific_run["run_number"] = run_name
     all_files.append(specific_run)
-#%%
+# %%
 all_files = pd.concat(all_files)
-#%%
+# %%
 all_files.to_file(output_paths_to_consolidate / "all_runs_concat.gdb")
- 
-#%%
- 
+
+# %%
+
 all_files.drop(columns="geometry").to_csv(output_paths_to_consolidate / "data.csv")
-#%%
+# %%
 to_be_shape = all_files[["geometry", "model_link_id"]].drop_duplicates()
 print("outputting")
-to_be_shape.to_file(output_paths_to_consolidate / "geom_package")
+to_be_shape.to_file(output_paths_to_consolidate / "geom_package")
diff --git a/scripts/compile_model_runs.py b/scripts/compile_model_runs.py
@@ -1,16 +1,17 @@
-#%%
+# %%
 import geopandas as gpd
 import pandas as pd
 import numpy as np
 from pathlib import Path
 from tqdm import tqdm
 from shapely.geometry import LineString
 
-input_dir = Path(r"Z:\MTC\US0024934.9168\Task_3_runtime_improvements\3.1_network_fidelity\run_result")
+input_dir = Path(
+    r"Z:\MTC\US0024934.9168\Task_3_runtime_improvements\3.1_network_fidelity\run_result"
+)
 output_dir = input_dir / "consolidated_3"
 
 
-
 # in_file = next(input_dir.rglob('emme_links.shp'))
 # print("reading", in_file)
 # input2 = gpd.read_file(in_file, engine="pyogrio", use_arrow=True)
@@ -20,10 +21,21 @@
 
 scenarios_to_consolidate = (11, 12, 13, 14, 15)
 runs_to_consolidate = (3, 4)
-#%%
+# %%
 
-def read_file_and_tag(path: Path, columns_to_filter = ("@ft", "VOLAU", "@capacity", "run_number", "scenario_number", "#link_id", "geometry")) -> pd.DataFrame:
-
+
+def read_file_and_tag(
+    path: Path,
+    columns_to_filter=(
+        "@ft",
+        "VOLAU",
+        "@capacity",
+        "run_number",
+        "scenario_number",
+        "#link_id",
+        "geometry",
+    ),
+) -> pd.DataFrame:
     scenario = file.parent.stem
     scenario_number = int(scenario.split("_")[-1])
     if scenario_number not in scenarios_to_consolidate:
@@ -40,19 +52,19 @@ def read_file_and_tag(path: Path, columns_to_filter = ("@ft", "VOLAU", "@capacit
     return_gdf["scenario_number"] = scenario_number
     return_gdf["run"] = run
     return_gdf["run_number"] = run_number
-    
+
     if "VOLAU" not in return_gdf.columns:
         print(return_gdf.columns)
         print("... No VOLAU, filling with zero")
-        return_gdf["VOLAU" ] = 0
-
+        return_gdf["VOLAU"] = 0
 
     return_gdf = return_gdf[list(columns_to_filter)]
 
     # assert return_gdf["#link_id"].is_unique
 
     return return_gdf
 
+
 def get_linestring_direction(linestring: LineString) -> str:
     if not isinstance(linestring, LineString) or len(linestring.coords) < 2:
         raise ValueError("Input must be a LineString with at least two coordinates")
@@ -73,31 +85,32 @@ def get_linestring_direction(linestring: LineString) -> str:
             return "North"
         else:
             return "South"
-#%%
+
+
+# %%
 
 print("Reading Links...", end="")
 all_links = []
-for file in tqdm(input_dir.rglob('run_*/Scenario_*/emme_links.shp')):
+for file in tqdm(input_dir.rglob("run_*/Scenario_*/emme_links.shp")):
     print(file)
     all_links.append(read_file_and_tag(file))
 links_table = pd.concat(all_links)
 
 print("done")
-#%%
-scen_map = {
-    11: "EA",
-    12: "AM",
-    13: "MD",
-    14: "PM",
-    15: "EV"
-}
+# %%
+scen_map = {11: "EA", 12: "AM", 13: "MD", 14: "PM", 15: "EV"}
+
 
 def get_return_first_gem(row):
     geom_columns = [col for col in row.index if "geometry" in col]
-    return [row[col] for col in geom_columns if (row[col] is not None) and (row[col] != np.NAN)][0]
+    return [
+        row[col]
+        for col in geom_columns
+        if (row[col] is not None) and (row[col] != np.NAN)
+    ][0]
+
 
 def combine_tables(dfs, columns_same):
-
     return_frame = dfs[0][columns_same]
 
     for df in dfs:
@@ -106,86 +119,108 @@ def combine_tables(dfs, columns_same):
         scen_number = df["scenario_number"].iloc[0]
         scen_number = scen_map[scen_number]
         df["saturation"] = df["VOLAU"] / df["@capacity"]
-
-        df = df[["#link_id", "@capacity", "VOLAU", "geometry", "@ft"]].rename(columns = {
-            "@capacity": f"capacity_run{run_number}_scen{scen_number}",
-            "VOLAU": f"@volau_run{run_number}_scen{scen_number}",
-            "saturation": f"@saturation_run{run_number}_scen{scen_number}",
-            "geometry": f"geometry_run{run_number}_scen{scen_number}",
-            "@ft": f"ft_run{run_number}_scen{scen_number}"
+
+        df = df[["#link_id", "@capacity", "VOLAU", "geometry", "@ft"]].rename(
+            columns={
+                "@capacity": f"capacity_run{run_number}_scen{scen_number}",
+                "VOLAU": f"@volau_run{run_number}_scen{scen_number}",
+                "saturation": f"@saturation_run{run_number}_scen{scen_number}",
+                "geometry": f"geometry_run{run_number}_scen{scen_number}",
+                "@ft": f"ft_run{run_number}_scen{scen_number}",
             }
         )
         # if there are link_ids that are not in the right frame
-        return_frame = return_frame.merge(df, how="outer", on="#link_id", validate="1:1")
+        return_frame = return_frame.merge(
+            df, how="outer", on="#link_id", validate="1:1"
+        )
         geometry = return_frame.apply(get_return_first_gem, axis=1)
         # remove geometries that are not main geometry
-        return_frame = return_frame.drop(columns=[col for col in return_frame.columns if "geometry_" in col])
+        return_frame = return_frame.drop(
+            columns=[col for col in return_frame.columns if "geometry_" in col]
+        )
         return_frame["geometry"] = geometry
 
     return return_frame
-all_links_no_none = [links for links in all_links if (links is not None) and (links["#link_id"].is_unique)]    
+
+
+all_links_no_none = [
+    links
+    for links in all_links
+    if (links is not None) and (links["#link_id"].is_unique)
+]
 links_wide_table = combine_tables(all_links_no_none, ["#link_id", "geometry"])
 
-links_wide_table["direction"] = links_wide_table["geometry"].apply(get_linestring_direction)
-#%%
+links_wide_table["direction"] = links_wide_table["geometry"].apply(
+    get_linestring_direction
+)
+# %%
 ft_cols = [col for col in links_wide_table.columns if "ft_" in col]
 
 links_wide_table["ft"] = links_wide_table[ft_cols].max(axis=1)
 links_wide_table = links_wide_table.drop(columns=ft_cols)
 
-#%%
+# %%
 links_wide_table.to_file(
-    Path(r"Z:\MTC\US0024934.9168\Task_3_runtime_improvements\3.1_network_fidelity\output_summaries\all_links_data")
-    / "all_data_wide.geojson")
-
-
-#%%
-num_iter = {
-    (3,11): 3,
-    (3,12): 10,
-    (3,13): 10,
-    (3,14): 19,
-    (3,15): 4,
-    (4,12): 20
-}
-#%%
-all_links_no_none = [links for links in all_links if (links is not None)] #and (links["#link_id"].is_unique)]    
+    Path(
+        r"Z:\MTC\US0024934.9168\Task_3_runtime_improvements\3.1_network_fidelity\output_summaries\all_links_data"
+    )
+    / "all_data_wide.geojson"
+)
+
+
+# %%
+num_iter = {(3, 11): 3, (3, 12): 10, (3, 13): 10, (3, 14): 19, (3, 15): 4, (4, 12): 20}
+# %%
+all_links_no_none = [
+    links for links in all_links if (links is not None)
+]  # and (links["#link_id"].is_unique)]
 for df in all_links_no_none:
     df["saturation"] = df["VOLAU"] / df["@capacity"]
-ft6_sat = [(link["run_number"].iloc[0], link["scenario_number"].iloc[0], (link.loc[link["@ft"] == 6, "saturation"] > 1).mean()) for link in all_links_no_none]
+ft6_sat = [
+    (
+        link["run_number"].iloc[0],
+        link["scenario_number"].iloc[0],
+        (link.loc[link["@ft"] == 6, "saturation"] > 1).mean(),
+    )
+    for link in all_links_no_none
+]
 
 y = [val for val in num_iter.values()]
 x = [x[-1] for x in ft6_sat]
 col = [val[0] for val in num_iter.keys()]
 
-#%%
+# %%
 import matplotlib.pyplot as plt
+
 plt.scatter(x, y, c=col)
 
 # Calculate the trendline
 z = np.polyfit(x, y, 1)
 p = np.poly1d(z)
 
 # Plot the trendline
-plt.plot(x, p(x), color='red')
+plt.plot(x, p(x), color="red")
 
-plt.xlabel('proportion of ft 6 with saturation > 1')
-plt.ylabel('number of iterations to solve')
-plt.title('Number of iterations to solve (relative gap = 0.05)')
+plt.xlabel("proportion of ft 6 with saturation > 1")
+plt.ylabel("number of iterations to solve")
+plt.title("Number of iterations to solve (relative gap = 0.05)")
 plt.show()
-#%%
+# %%
 import matplotlib.pyplot as plt
+
 data = [links_wide_table[col] for col in links_wide_table.iloc[:, 2:].columns]
 
 fig = plt.boxplot(data)
 fig.show()
 
 # --------------------------------------------------------------------------
-#%%
+# %%
 links_table["direction"] = links_table["geometry"].apply(get_linestring_direction)
 # %%
 links_table.to_file(output_dir / "all_data.geojson", index=False)
-#%%
+
+
+# %%
 def get_link_counts(df: pd.DataFrame):
     ret_val = df.value_counts("@ft").sort_index().to_frame().T
     total = ret_val.sum(axis=1)
@@ -197,6 +232,7 @@ def get_link_counts(df: pd.DataFrame):
     ret_val["scenario_number"] = df["scenario_number"].iloc[0]
     return ret_val
 
-pd.concat(
-    [get_link_counts(df) for df in all_links]
-).sort_values(by=["run_number", "scenario_number"])
+
+pd.concat([get_link_counts(df) for df in all_links]).sort_values(
+    by=["run_number", "scenario_number"]
+)
diff --git a/tm2py/components/network/create_tod_scenarios.py b/tm2py/components/network/create_tod_scenarios.py
@@ -628,7 +628,7 @@ def _set_capclass(network):
             area_type = link["@area_type"]
             if area_type < 0:
                 link["@capclass"] = -1
-            elif (link["@ft"] == 99):
+            elif link["@ft"] == 99:
                 link["@capclass"] = 10 * area_type + 7
             else:
                 link["@capclass"] = 10 * area_type + link["@ft"]