Custom Airport Data

Snakefile

@@ -1013,6 +1013,7 @@ rule prepare_ports:
 rule prepare_airports:
     params:
         airport_sizing_factor=config["sector"]["airport_sizing_factor"],
+        airport_custom_data=config["custom_data"]["airports"],
     output:
         ports="data/airports.csv",  # TODO move from data to resources
     script:

config.default.yaml

@@ -481,6 +481,7 @@ custom_data:
   custom_sectors: false
   gas_network: false # If "True" then a custom .csv file must be placed in "resources/custom_data/pipelines.csv" , If "False" the user can choose btw "greenfield" or Model built-in datasets. Please refer to ["sector"] below.
   export_ports: false # If "True" then a custom .csv file must be placed in "data/custom/export_ports.csv"
+  airports: false # If "True" then a custom .csv file must be placed in "data/custom/airports.csv". Data format for aiports must be in the format of the airports.csv file in the data folder.
 
 industry:
   reference_year: 2015

data/custom/airports.csv

@@ -0,0 +1,2 @@
+,ident,type,airport_size_nr,name,y,x,elevation_ft,continent,country,iso_region,municipality,scheduled_service,iata_code,length_ft,width_ft,surface,lighted,closed,Total_airport_size_nr,fraction
+0,5A8,medium_airport,1,Aleknagik / New Airport,59.2826004028,-158.617996216,66.0,,US,US-AK,Aleknagik,yes,WKK,2040.0,80.0,GRVL-DIRT-F,0.0,0.0,633,0.001579778830963665

scripts/prepare_airports.py

@@ -3,6 +3,9 @@
 #
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
+import shutil
+from pathlib import Path
+
 import numpy as np
 import pandas as pd
 
@@ -36,52 +39,10 @@ def download_airports():
     return (airports_csv, runways_csv)
 
 
-if __name__ == "__main__":
-    if "snakemake" not in globals():
-        from _helpers import mock_snakemake
-
-        snakemake = mock_snakemake("prepare_airports")
-    # configure_logging(snakemake)
-
-    # run = snakemake.config.get("run", {})
-    # RDIR = run["name"] + "/" if run.get("name") else ""
-    # store_path_data = Path.joinpath(Path().cwd(), "data")
-    # country_list = country_list_to_geofk(snakemake.config["countries"])'
-
-    # Prepare downloaded data
-    airports_csv = download_airports()[0].copy()
-    airports_csv = airports_csv[
-        [
-            "ident",
-            "type",
-            "name",
-            "latitude_deg",
-            "longitude_deg",
-            "elevation_ft",
-            "continent",
-            "iso_country",
-            "iso_region",
-            "municipality",
-            "scheduled_service",
-            "iata_code",
-        ]
-    ]
-    airports_csv.loc[airports_csv["iso_country"].isnull(), "iso_country"] = "NA"
-    airports_csv = airports_csv.rename(columns={"latitude_deg": "y"})
-    airports_csv = airports_csv.rename(columns={"longitude_deg": "x"})
-
-    runways_csv = download_airports()[1].copy()
-    runways_csv = runways_csv[
-        ["airport_ident", "length_ft", "width_ft", "surface", "lighted", "closed"]
-    ]
-    runways_csv = runways_csv.drop_duplicates(subset=["airport_ident"])
-
-    airports_original = pd.merge(
-        airports_csv, runways_csv, how="left", left_on="ident", right_on="airport_ident"
-    )
-    airports_original = airports_original.drop("airport_ident", axis=1)
-
-    df = airports_original.copy()
+def preprocess_airports(df):
+    """
+    Preprocess the airports data
+    """
 
     # Keep only airports that are of type medium and large
     df = df.loc[df["type"].isin(["large_airport", "medium_airport"])]
@@ -117,5 +78,67 @@ def download_airports():
     # Rename columns
     airports = airports.rename(columns={"iso_country": "country"})
 
-    # Save
-    airports.to_csv(snakemake.output[0], sep=",", encoding="utf-8", header="true")
+    return airports
+
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake("prepare_airports")
+    # configure_logging(snakemake)
+
+    # run = snakemake.config.get("run", {})
+    # RDIR = run["name"] + "/" if run.get("name") else ""
+    # store_path_data = Path.joinpath(Path().cwd(), "data")
+    # country_list = country_list_to_geofk(snakemake.config["countries"])'
+
+    if snakemake.params.airport_custom_data:
+        custom_airports = Path.joinpath("data", "custom", "airports.csv")
+        shutil.move(custom_airports, snakemake.output[0])
+    else:
+        # Prepare downloaded data
+        download_data = download_airports()
+
+        airports_csv = download_data[0].copy()
+        airports_csv = airports_csv[
+            [
+                "ident",
+                "type",
+                "name",
+                "latitude_deg",
+                "longitude_deg",
+                "elevation_ft",
+                "continent",
+                "iso_country",
+                "iso_region",
+                "municipality",
+                "scheduled_service",
+                "iata_code",
+            ]
+        ]
+        airports_csv.loc[airports_csv["iso_country"].isnull(), "iso_country"] = "NA"
+        airports_csv = airports_csv.rename(columns={"latitude_deg": "y"})
+        airports_csv = airports_csv.rename(columns={"longitude_deg": "x"})
+
+        runways_csv = download_data[1].copy()
+        runways_csv = runways_csv[
+            ["airport_ident", "length_ft", "width_ft", "surface", "lighted", "closed"]
+        ]
+        runways_csv = runways_csv.drop_duplicates(subset=["airport_ident"])
+
+        airports_original = pd.merge(
+            airports_csv,
+            runways_csv,
+            how="left",
+            left_on="ident",
+            right_on="airport_ident",
+        )
+        airports_original = airports_original.drop("airport_ident", axis=1)
+
+        df = airports_original.copy()
+
+        airports = preprocess_airports(df)
+
+        # Save
+        airports.to_csv(snakemake.output[0], sep=",", encoding="utf-8", header="true")

test/config.test_myopic.yaml

@@ -94,6 +94,8 @@ custom_data:
   custom_sectors: false
   gas_network: false # If "True" then a custom .csv file must be placed in "resources/custom_data/pipelines.csv" , If "False" the user can choose btw "greenfield" or Model built-in datasets. Please refer to ["sector"] below.
   export_ports: false # If "True" then a custom .csv file must be placed in "data/custom/export.csv"
+  airports: false # If "True" then a custom .csv file must be placed in "data/custom/airports.csv". Data format for aiports must be in the format of the airports.csv file in the data folder.
+
 
 
 costs: # Costs used in PyPSA-Earth-Sec. Year depends on the wildcard planning_horizon in the scenario section