Change input file formats and input paths in chilled

message_ix_buildings/chilled/cities/example_coords.py

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+import numpy as np
+import pandas as pd
+import xarray as xr
+
+# Load example temperature data
+temp_file = "/Volumes/mengm.pdrv/watxene/ISIMIP/ISIMIP3b/InputData/climate_updated/bias-adjusted/ssp126/GFDL-ESM4/gfdl-esm4_r1i1p1f1_w5e5_ssp126_tas_global_daily_2015_2020.nc"
+
+# Read the temperature data
+temp = xr.open_dataarray(temp_file)
+
+# Example: List of city coordinates (lat, lon)
+city_coords = [
+    (51.5074, -0.1278),  # London
+    (40.7128, -74.0060),  # New York
+    (35.6895, 139.6917),  # Tokyo
+]
+
+# Convert to a pandas DataFrame for clarity
+city_df = pd.DataFrame(city_coords, columns=["lat", "lon"])
+
+# Step 1: Stack lat and lon into a single dimension 'locations'
+stacked_temp = temp.stack(locations=("lat", "lon"))
+
+# Step 2: Find the nearest indices manually by calculating the distances
+selected_points = []
+for _, row in city_df.iterrows():
+    lat, lon = row["lat"], row["lon"]
+    print("city: " + str(lat) + ", " + str(lon))
+
+    # Find the nearest index by calculating the distance between each point
+    # and the given coordinates
+    dist = np.sqrt((stacked_temp.lat - lat) ** 2 + (stacked_temp.lon - lon) ** 2)
+    nearest_idx = dist.argmin()  # index of the nearest point
+
+    # Select the nearest point based on the index
+    nearest_point = stacked_temp.isel(locations=nearest_idx)
+
+    # Add lat, lon as coordinates as city_lat, city_lon
+    nearest_point["city_location"] = "(" + str(lat) + ", " + str(lon) + ")"
+    nearest_point["city_lat"] = lat
+    nearest_point["city_lon"] = lon
+
+    # Append the selected point to the list
+    selected_points.append(nearest_point)
+
+# Step 3: Concatenate the selected points into a single 1D xarray
+selected_vector = xr.concat(selected_points, dim="locations")
+
+# Display results as a 1D array or convert to pandas DataFrame if preferred
+df = selected_vector.to_dataframe().reset_index()
+print(df)