Added hospital data conversion script

relecov_tools/assets/institutions_utils/README.md

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+# Spanish Hospitals JSON Converter
+
+This script converts a CSV file with hospital data in Spain into two structured JSON files.
+
+## Input
+
+- `Hospitales_de_Espaadb.csv`  
+  Must include columns like `NOMBRE`, `DIRECCION`, `EMAIL`, `FINALIDAD_ASISITENCIAL`, `COMUNIDADES`, `PROVINCIAS`, `MUNICIPIOS`, `pais`, `X`, and `Y`.
+
+## Output
+
+- `laboratory_address.json`: general info per hospital (address, region, purpose, etc.).
+- `geo_loc_centers.json`: geolocation info (latitude and longitude).
+
+## How it works
+
+- Reads the CSV with `pandas`.
+- Normalizes text to title case.
+- Fills missing values with defaults (`"Unknown"` / `"Spain"`).
+- Saves results as two JSON files (UTF-8, pretty-printed).
+
+## Run
+
+```bash
+python transform.py

relecov_tools/assets/institutions_utils/transform.py

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+import pandas as pd
+import json
+
+csv_file = "Hospitales_de_Espaadb.csv"
+df = pd.read_csv(csv_file)
+
+json_dict = {}
+
+
+def to_title_case(value, default="Desconocido"):
+    if pd.isna(value) or value == "":
+        return default
+    return str(value).strip().title()
+
+
+for index, row in df.iterrows():
+    hospital_name = to_title_case(row["NOMBRE"])
+    json_dict[hospital_name] = {
+        "collecting_institution_address": to_title_case(row["DIRECCION"]),
+        "collecting_institution_email": (
+            row["EMAIL"].strip() if not pd.isna(row["EMAIL"]) else "Desconocido"
+        ),
+        "collecting_institution_finalidad": to_title_case(
+            row["FINALIDAD_ASISITENCIAL"]
+        ),
+        "geo_loc_state": to_title_case(row["COMUNIDADES"]),
+        "geo_loc_region": to_title_case(row.get("PROVINCIAS", None)),
+        "geo_loc_city": to_title_case(row.get("MUNICIPIOS", None)),
+        "geo_loc_country": to_title_case(row.get("pais", None), default="Spain"),
+        "submitting_institution": "",
+        "submitting_institution_address": "",
+        "submitting_institution_email": "",
+    }
+
+output_file = "laboratory_address.json"
+with open(output_file, "w", encoding="utf-8") as json_file:
+    json.dump(json_dict, json_file, ensure_ascii=False, indent=4)
+
+print(f"Archivo JSON generado: {output_file}")
+
+json_dict_coords = {}
+
+
+for index, row in df.iterrows():
+    hospital_name = to_title_case(row["NOMBRE"])
+    json_dict_coords[hospital_name] = {
+        "geo_loc_latitude": f"{row['Y']:.4f}",
+        "geo_loc_longitude": f"{row['X']:.4f}",
+    }
+output_file_coords = "geo_loc_centers.json"
+with open(output_file_coords, "w", encoding="utf-8") as json_file:
+    json.dump(json_dict_coords, json_file, ensure_ascii=False, indent=4)
+
+print(f"Archivo JSON generado: {output_file_coords}")