Dast_project

data.py

@@ -0,0 +1,116 @@
+import rasterio
+import numpy as np
+import pandas as pd
+from datetime import datetime
+import os
+import glob
+
+
+def extract_dates_from_filename(filename):
+
+    try:
+        filename = os.path.basename(filename)
+
+        import re
+
+        dates = re.findall(r"20\d{6}", filename)
+
+        if len(dates) >= 2:
+            return dates[0], dates[1]
+
+        print(f"Date non trovate nel file {filename}")
+        return None, None
+    except Exception as e:
+        print(f"Errore nell'estrazione delle date dal file {filename}: {e}")
+        return None, None
+
+
+def read_tif_file(file_path):
+
+    try:
+        print(f"Elaborazione del file: {file_path}")
+
+        start_date, end_date = extract_dates_from_filename(os.path.basename(file_path))
+
+        with rasterio.open(file_path) as src:
+
+            raster_data = src.read(1)
+
+            transform = src.transform
+
+            height, width = raster_data.shape
+            rows, cols = np.mgrid[0:height, 0:width]
+
+            xs, ys = rasterio.transform.xy(transform, rows, cols)
+
+            lons = np.array(xs)
+            lats = np.array(ys)
+
+            df = pd.DataFrame(
+                {
+                    "longitude": lons.flatten(),
+                    "latitude": lats.flatten(),
+                    "precipitation": raster_data.flatten(),
+                    "start_date": start_date,
+                    "end_date": end_date,
+                    "source_file": os.path.basename(file_path),
+                }
+            )
+
+            df = df[df["precipitation"] != src.nodata]
+            df = df.dropna()
+
+            return df
+
+    except Exception as e:
+        print(f"Errore nella lettura del file {file_path}: {str(e)}")
+        return None
+
+
+def process_multiple_files(directory_path):
+
+    all_data = []
+
+    tif_files = glob.glob(os.path.join(directory_path, "GIOVANNI*precipitation*.tif"))
+
+    if not tif_files:
+        print(f"Nessun file TIF trovato in {directory_path}")
+        return None
+
+    print("File TIF trovati:")
+    for file in tif_files:
+        print(f"- {os.path.basename(file)}")
+
+    for file_path in tif_files:
+        df = read_tif_file(file_path)
+        if df is not None:
+            all_data.append(df)
+
+    if all_data:
+
+        combined_df = pd.concat(all_data, ignore_index=True)
+
+        combined_df.sort_values("start_date", inplace=True)
+
+        output_path = os.path.join(directory_path, "precipitation_timeseries.csv")
+        combined_df.to_csv(output_path, index=False)
+        print(f"\nDati salvati in: {output_path}")
+
+        print("\nStatistiche di base delle precipitazioni:")
+        print(combined_df.groupby("start_date")["precipitation"].describe())
+
+        return combined_df
+
+    return None
+
+
+if __name__ == "__main__":
+
+    directory_path = "."
+    print("Inizio elaborazione dei file TIF...")
+    data = process_multiple_files(directory_path)
+
+    if data is not None:
+        print("\nElaborazione completata con successo!")
+    else:
+        print("\nErrore nell'elaborazione dei file.")

plot/output_analysis/annual_statistics.csv

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+,precipitation,precipitation,precipitation,precipitation,precipitation
+,mean,std,min,max,count
+year,,,,,
+2018,2.822,0.38,1.983,3.96,1500
+2019,2.733,0.492,1.583,4.112,1500
+2020,1.766,0.42,0.958,2.973,1500
+2021,2.531,0.658,1.341,4.477,1500
+2022,2.018,0.442,0.97,3.128,1500
+2023,1.893,0.526,0.851,3.428,1500