add gsp to dno mapping

scripts/gsp_dno_mapping.py

@@ -0,0 +1,99 @@
+""" to make gsp to dno mappings """
+
+import json
+import os
+
+import geopandas as gpd
+import pandas as pd
+import plotly.graph_objects as go
+import requests
+
+# load gsp files
+url = (
+    "https://data.nationalgrideso.com/backend/dataset/2810092e-d4b2-472f-b955-d8bea01f9ec0/"
+    "resource/08534dae-5408-4e31-8639-b579c8f1c50b/download/gsp_regions_20220314.geojson"
+)
+
+response = requests.get(url)
+shape_gpd = gpd.read_file(response.text)
+
+# load gsp files from database
+url = os.getenv("DB_URL")
+gsp = pd.read_sql("select * from location order by gsp_id", url)
+
+shape_gpd["gsp_name"] = shape_gpd["GSPs"]
+shape_gpd = shape_gpd.merge(gsp, on="gsp_name", how="left")
+
+# GSPGROUP name, I think is DNO
+
+# load dno files just incase
+# url = "https://data.nationalgrideso.com/backend/dataset/0e377f16-95e9-4c15-a1fc-49e06a39cfa0/resource/e96db306-aaa8-45be-aecd-65b34d38923a/download/dno_license_areas_20200506.geojson"  # noqa
+# dno_shapes = gpd.read_file(url)
+
+# sort by gsp mapping
+shape_gpd.sort_values("GSPGroup", inplace=True)
+
+
+# group by GSPGroup
+def join_two_rows_together(x, y, gdf):
+    if x.GSPGroup == y.GSPGroup:
+        gdf = gdf.copy()
+        geom = x.geometry.union(y.geometry)
+        geom = gpd.GeoSeries(geom, index=[x.name])
+        gdf.loc[gdf.index == x.name, "geometry"] = geom.geometry
+        gdf.loc[gdf.index == x.name, "gsp_ids"] += "," + str(y.gsp_id)
+        gdf.loc[gdf.index == x.name, "installed_capacity_mw"] += y.installed_capacity_mw
+
+        # drop row
+        print(f"dropping {y.name}")
+        gdf = gdf.drop(y.name)
+
+        drop = True
+
+    else:
+        print(f"skipping {x.GSPGroup} and {y.GSPGroup} as they are different")
+        drop = False
+
+    return gdf.copy(), drop
+
+
+# shape_gpd_all = shape_gpd
+# shape_gpd = shape_gpd_all
+shape_gpd.reset_index(inplace=True)
+j = 0
+shape_gpd["gsp_ids"] = shape_gpd["gsp_id"].astype(str)
+for i in range(1, len(shape_gpd)):
+    print(j, i)
+
+    x = shape_gpd.iloc[j]
+    y = shape_gpd.iloc[j + 1]
+
+    shape_gpd, drop = join_two_rows_together(x, y, shape_gpd)
+    if not drop:
+        j = j + 1
+
+# save to csv
+gsp = shape_gpd[["GSPGroup", "gsp_ids", "installed_capacity_mw"]]
+gsp.to_csv("gsp_dno_mapping.csv", index=False)
+
+# lets plot it
+shape_gpd.reset_index(inplace=True, drop=True)
+shape_gpd = shape_gpd.to_crs(epsg=4326)
+shapes_dict = json.loads(shape_gpd.to_json())
+
+fig = go.Figure()
+fig.add_trace(
+    go.Choroplethmapbox(
+        geojson=shapes_dict,
+        locations=shape_gpd.index.values,
+        z=shape_gpd["installed_capacity_mw"].values,
+        colorscale="Viridis",
+    )
+)
+
+fig.update_layout(mapbox_style="carto-positron", mapbox_zoom=4, mapbox_center={"lat": 55, "lon": 0})
+fig.update_layout(margin={"r": 0, "t": 30, "l": 0, "b": 30})
+fig.show(renderer="browser")
+
+# save html
+fig.write_html("gsp_dno_mapping.html")