Merge pull request #81 from openclimatefix/create-site-fixes

add method to get gsp and dno automatically

requirements.txt

@@ -9,4 +9,5 @@ SQLAlchemy==1.4.46
 streamlit==1.17.0
 testcontainers==3.2.0
 uvicorn==0.17.6
+geopandas==0.11.1
 

scripts/get_gsp_and_dno.py

@@ -0,0 +1,5 @@
+from src.data.dno import download_dno
+from src.data.gsp import download_gsp
+
+download_dno()
+download_gsp()

src/data/dno.py

@@ -0,0 +1,76 @@
+""" This script adds the relevant GSP to the sites
+
+Might need to install nowcasting_dataset
+
+and we want to added the gsp as {gsp_id}|{gsp_nam} into the database
+
+1. Load in dno data from NG
+2. Load all sites
+3. For each site add dno
+
+"""
+import ssl
+import os
+
+import geopandas as gpd
+from shapely.geometry import Point
+
+from data.utils import lat_lon_to_osgb
+
+
+cwd = os.getcwd()
+if "src" not in cwd:
+    dno_local_file = "./src/data/dno"
+else:
+    dno_local_file = "./data/dno"
+
+
+def download_dno():
+
+    print("Getting dno file")
+    ssl._create_default_https_context = ssl._create_unverified_context
+    url = "https://data.nationalgrideso.com/backend/dataset/0e377f16-95e9-4c15-a1fc-49e06a39cfa0/resource/e96db306-aaa8-45be-aecd-65b34d38923a/download/dno_license_areas_20200506.geojson"
+    dno_shapes = gpd.read_file(url)
+
+    print("Saving dno file")
+    dno_shapes.to_file(dno_local_file)
+
+
+def get_dno(latitude, longitude) -> dict:
+    """
+    This function takes a latitude and longitude and returns the dno
+
+    :param latitude:
+    :param longitude:
+
+    :return: dno is this format {"dno_id": dno_id, "name": dno_name, "long_name": dno_long_name}=
+    """
+
+    # load file
+    dno = gpd.read_file(dno_local_file)
+
+    # change lat lon to osgb
+    x, y = lat_lon_to_osgb(lat=latitude, lon=longitude)
+    point = Point(x, y)
+
+    # select dno
+    mask = dno.contains(point)
+    dno = dno[mask]
+
+    # format dno
+    if len(dno) == 1:
+        dno = dno.iloc[0]
+
+        dno_id = dno["ID"]
+        name = dno["Name"]
+        long_name = dno["LongName"]
+
+        dno_dict = {"dno_id": str(dno_id), "name": name, "long_name": long_name}
+        print(dno_dict)
+    else:
+        dno_dict = {"dno_id": "999", "name": "unknown", "long_name": "unknown"}
+
+    return dno_dict
+
+
+#

src/data/dno/dno.cpg

@@ -0,0 +1 @@
+ISO-8859-1

src/data/dno/dno.prj

@@ -0,0 +1 @@
+PROJCS["British_National_Grid",GEOGCS["GCS_OSGB_1936",DATUM["D_OSGB_1936",SPHEROID["Airy_1830",6377563.396,299.3249646]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",400000.0],PARAMETER["False_Northing",-100000.0],PARAMETER["Central_Meridian",-2.0],PARAMETER["Scale_Factor",0.9996012717],PARAMETER["Latitude_Of_Origin",49.0],UNIT["Meter",1.0]]

src/data/gsp.py

@@ -0,0 +1,67 @@
+import os
+
+import geopandas as gpd
+import pandas as pd
+from shapely.geometry import Point
+
+from data.utils import lat_lon_to_osgb
+
+cwd = os.getcwd()
+if "src" not in cwd:
+    dir = "./src/data"
+else:
+    dir = "./data"
+
+gsp_names = pd.read_csv(f"{dir}/gsp_new_ids_and_names-edited.csv")
+gsp_local_file = f"{dir}/gsp"
+
+
+def download_gsp():
+
+    print("Getting gsp file")
+
+    url = (
+        "https://data.nationalgrideso.com/backend/dataset/2810092e-d4b2-472f-b955-d8bea01f9ec0/"
+        "resource/08534dae-5408-4e31-8639-b579c8f1c50b/download/gsp_regions_20220314.geojson"
+    )
+    gsp_shapes = gpd.read_file(url)
+
+    print("Saving gsp file")
+    gsp_shapes.to_file(gsp_local_file)
+
+
+def get_gsp(latitude, longitude) -> dict:
+    """
+    This function takes a latitude and longitude and returns the dno
+
+    :param latitude:
+    :param longitude:
+
+    :return: dno is this format {"dno_id": dno_id, "name": dno_name, "long_name": dno_long_name}=
+    """
+
+    # load file
+    gsp = gpd.read_file(gsp_local_file)
+
+    # change lat lon to osgb
+    x, y = lat_lon_to_osgb(lat=latitude, lon=longitude)
+    point = Point(x, y)
+
+    # select gsp
+    mask = gsp.contains(point)
+    gsp = gsp[mask]
+
+    # format gsp
+    if len(gsp) == 1:
+        gsp = gsp.iloc[0]
+        gsp_details = gsp_names[gsp_names["gsp_name"] == gsp.GSPs]
+        gsp_id = gsp_details.index[0]
+        gsp_details = gsp_details.iloc[0]
+        name = gsp_details["region_name"]
+
+        gsp_dict = {"gsp_id": str(gsp_id), "name": name}
+        print(gsp_dict)
+    else:
+        gsp_dict = {"gsp_id": "999", "name": "unknown"}
+
+    return gsp_dict

src/data/gsp/gsp.cpg

@@ -0,0 +1 @@
+ISO-8859-1

src/data/gsp/gsp.prj

@@ -0,0 +1 @@
+PROJCS["British_National_Grid",GEOGCS["GCS_OSGB_1936",DATUM["D_OSGB_1936",SPHEROID["Airy_1830",6377563.396,299.3249646]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",400000.0],PARAMETER["False_Northing",-100000.0],PARAMETER["Central_Meridian",-2.0],PARAMETER["Scale_Factor",0.9996012717],PARAMETER["Latitude_Of_Origin",49.0],UNIT["Meter",1.0]]