fix: 🐛 Fixed time calculations for ETA & Completion

Also added a warning when the app finds existing CSV files in the combined folder

README.MD

@@ -15,11 +15,12 @@ The project consists of a main pipeline workflow that processes multiple modules
 
 ### main.py
 
-- Orchestrates the entire workflow pipeline
-- Uncompress the packed .gz.tar files to DAT files
-- Processes DAT files to ASC format
-- Generates timeseries data for specified locations 
-- Combines grouped CSV files into consolidated datasets formatted for Infoworks ICM
+- **Startup Safety Check**: Scans the `COMBINED_FOLDER` at startup and warns the user if existing files are found, offering a chance to abort to prevent accidental data mixing.
+- **Batch Processing**: Processes input tar files in configurable batches to manage resource usage.
+- **End-to-End Processing**: Extracts GZ files, processes DAT/ASC, and appends to CSV in a single thread per file.
+- **Concurrency**: Uses multi-threading to process individual GZ files within a batch concurrently.
+- **Cumulative Data**: Automatically appends new query results to the existing CSV files in `COMBINED_FOLDER` for each batch, ensuring no data is lost and columns are correctly aligned.
+- **Dynamic ETA**: Provides a real-time estimate of completion time.
 
 ### extract.py
 
@@ -73,6 +74,7 @@ The `config.py` file defines folder paths and file deletion options:
 - ASC_TOP_FOLDER = "./asc_files"
 - COMBINED_FOLDER = "./combined_files"
 - ZONE_FOLDER = "./zone_inputs"
+- BATCH_SIZE = 5 (Number of tar files to process per batch)
 
 Example of how the zone csv files should look:
 

config.py

@@ -11,3 +11,5 @@ class Config:
     delete_gz_after_processing = True
     delete_dat_after_processing = True
     delete_asc_after_processing = True
+
+    BATCH_SIZE = 5

main.py

@@ -4,6 +4,7 @@ import os
 import csv
 import concurrent.futures
 from pathlib import Path
+import shutil
 
 from config import Config
 from modules import BatchNimrod, GenerateTimeseries, Extract
@@ -13,14 +14,40 @@ logging.basicConfig(
 )
 
 
-def process_pipeline(dat_file):
-    # 1. Process DAT to ASC
-    asc_file = batch._process_single_file(dat_file)
-    if not asc_file:
+def process_pipeline(gz_file_path):
+    # 1. Extract GZ to DAT
+    gz_path = Path(gz_file_path)
+    # The dat file name is derived from the gz file name (removing .gz or .dat.gz)
+    # gz files are named like 'NAME.dat.gz' often.
+    dat_filename = gz_path.name.replace(".gz", "")
+    dat_path = Path(Config.DAT_TOP_FOLDER, dat_filename)
+
+    # Extract
+    try:
+        extraction.process_single_gz(gz_path, dat_path)
+    except Exception as e:
+        logging.error(f"Failed to extract {gz_path}: {e}")
         return None
 
-    # 2. Extract data from ASC
+    if not dat_path.exists():
+        logging.error(f"DAT file not found after extraction: {dat_path}")
+        return None
+
+    # 2. Process DAT to ASC
+    # BatchNimrod._process_single_file expects just the filename, not full path
+    asc_file = batch._process_single_file(dat_filename)
+    if not asc_file:
+        # Cleanup failed DAT file if needed (BatchNimrod might have done it or not)
+        if Config.delete_dat_after_processing and dat_path.exists():
+            try:
+                os.remove(dat_path)
+            except OSError:
+                pass
+        return None
+
+    # 3. Extract data from ASC
     file_results = timeseries.process_asc_file(asc_file, locations)
+
     return file_results
 
 
@@ -57,64 +84,140 @@ if __name__ == "__main__":
     logging.info(f"Count of 1km Grids: {len(locations)}")
     logging.info(f"Count of Zones: {len(zones)}")
 
+    # Check for existing combined files
+    existing_combined = os.listdir(Config.COMBINED_FOLDER)
+    if existing_combined:
+        logging.warning("!" * 80)
+        logging.warning(
+            f"Found {len(existing_combined)} files in {Config.COMBINED_FOLDER}"
+        )
+        logging.warning(
+            "You may want to remove these before continuing to avoid duplicates or messy data."
+        )
+        logging.warning("!" * 80)
+        response = input("Continue? (Y/N): ").strip().lower()
+        if response != "y":
+            logging.info("Aborting...")
+            exit(0)
+
     extraction = Extract(Config)
     batch = BatchNimrod(Config)
     timeseries = GenerateTimeseries(Config, locations)
 
     start = time.time()
     logging.info(
-        "Starting interleaved processing of DAT files and Timeseries generation"
+        "Starting interleaved processing of GZ files -> DAT -> ASC -> Timeseries"
     )
 
-    # Initialize results structure
-    results = {loc[0]: {"dates": [], "values": []} for loc in locations}
+    # Get list of all tar files
+    all_tar_files = [f for f in os.listdir(Config.TAR_TOP_FOLDER) if f.endswith(".tar")]
+    all_tar_files.sort()
+    total_tars = len(all_tar_files)
+    files_per_tar = 288
+    estimated_total_files = total_tars * files_per_tar
+    logging.info(f"Found {total_tars} tar files to process")
 
-    logging.info("Extracting tar and gz files")
-    extraction.run_extraction()
+    # Process in batches
+    for i in range(0, total_tars, Config.BATCH_SIZE):
+        batch_files = all_tar_files[i : i + Config.BATCH_SIZE]
+        logging.info(
+            f"Processing batch {i // Config.BATCH_SIZE + 1}: {len(batch_files)} tar files"
+        )
 
-    # Get list of DAT files
-    dat_files = [
-        f for f in os.listdir(Path(Config.DAT_TOP_FOLDER)) if not f.startswith(".")
-    ]
-    total_files = len(dat_files)
+        # Initialize results structure for this batch
+        results = {loc[0]: {"dates": [], "values": []} for loc in locations}
 
-    logging.info(f"Processing {total_files} files concurrently...")
+        # 1. Extract batch (TAR -> GZ)
+        logging.info("Extracting tar files for batch")
+        extraction.extract_tar_batch(batch_files)
+        # Note: We do NOT run extract_gz_batch anymore. We will find GZ files and process them.
 
-    with concurrent.futures.ThreadPoolExecutor() as executor:
-        future_to_file = {
-            executor.submit(process_pipeline, dat_file): dat_file
-            for dat_file in dat_files
-        }
+        # Get list of GZ files (recursively or flat?)
+        # extract_tar_batch puts them in GZ_TOP_FOLDER/tar_name_without_ext
+        # So we need to look there.
+        # Ideally we know where we put them.
 
-        completed_count = 0
-        try:
-            for future in concurrent.futures.as_completed(future_to_file):
-                file_results = future.result()
-                if file_results:
-                    for res in file_results:
-                        zone_id = res["zone_id"]
-                        results[zone_id]["dates"].append(res["date"])
-                        results[zone_id]["values"].append(res["value"])
+        gz_files_to_process = []
+        for tar_file in batch_files:
+            extract_folder = Path(Config.GZ_TOP_FOLDER, tar_file.replace(".tar", ""))
+            if extract_folder.exists():
+                for root, _, files in os.walk(extract_folder):
+                    for file in files:
+                        if file.endswith(".gz"):
+                            gz_files_to_process.append(Path(root, file))
 
-                completed_count += 1
-                if completed_count % 100 == 0:
-                    elapsed_time = time.time() - start
-                    files_per_minute = (completed_count / elapsed_time) * 60
-                    remaining_files = total_files - completed_count
-                    eta_minutes = remaining_files / (files_per_minute / 60) / 60
-                    logging.info(f"""Processed {completed_count} out of {total_files} files.
-    Speed: {files_per_minute:.2f} files/min. ETA: {eta_minutes:.2f} minutes""")
-        except KeyboardInterrupt:
-            logging.warning("KeyboardInterrupt received. Cancelling pending tasks...")
-            executor.shutdown(wait=False, cancel_futures=True)
-            raise
+        total_files = len(gz_files_to_process)
+        logging.info(f"Found {total_files} GZ files to process concurrently...")
 
-    elapsed_time = time.time() - start
-    logging.info(f"Interleaved processing completed in {elapsed_time:.2f} seconds")
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future_to_file = {
+                executor.submit(process_pipeline, gz_file): gz_file
+                for gz_file in gz_files_to_process
+            }
 
-    logging.info("Writing CSV files...")
-    timeseries.write_results_to_csv(results, locations)
+            completed_count = 0
+            try:
+                for future in concurrent.futures.as_completed(future_to_file):
+                    file_results = future.result()
+                    if file_results:
+                        for res in file_results:
+                            zone_id = res["zone_id"]
+                            results[zone_id]["dates"].append(res["date"])
+                            results[zone_id]["values"].append(res["value"])
+
+                    completed_count += 1
+                    if completed_count % 100 == 0:
+                        files_processed_previous = i * files_per_tar
+                        files_processed_so_far = (
+                            files_processed_previous + completed_count
+                        )
+                        
+                        elapsed_time = time.time() - start
+                        rate_per_second = files_processed_so_far / elapsed_time
+
+                        remaining_files = estimated_total_files - files_processed_so_far
+
+                        if rate_per_second > 0:
+                            eta_seconds = remaining_files / rate_per_second
+
+                            if eta_seconds < 60:
+                                eta_str = f"{int(eta_seconds)}s"
+                            elif eta_seconds < 3600:
+                                eta_str = f"{int(eta_seconds // 60)}m {int(eta_seconds % 60)}s"
+                            else:
+                                eta_str = f"{int(eta_seconds // 3600)}h {int((eta_seconds % 3600) // 60)}m"
+                        else:
+                            eta_str = "Unknown"
+
+                        logging.info(f"""Progress: {files_processed_so_far}/{estimated_total_files} files ({files_processed_so_far / estimated_total_files * 100:.1f}%)
+    Speed: {rate_per_second * 60:.2f} files/min. ETA: {eta_str}""")
+            except KeyboardInterrupt:
+                logging.warning(
+                    "KeyboardInterrupt received. Cancelling pending tasks..."
+                )
+                executor.shutdown(wait=False, cancel_futures=True)
+                raise
+
+        logging.info("Appending batch results to CSV files...")
+        timeseries.append_results_to_csv(results, locations)
+
+        # Cleanup GZ folders for this batch
+        # We loop through batch_files again to delete the folders we created
+        for tar_file in batch_files:
+            extract_folder = Path(Config.GZ_TOP_FOLDER, tar_file.replace(".tar", ""))
+            if extract_folder.exists():
+                try:
+                    shutil.rmtree(extract_folder)
+                except OSError as e:
+                    logging.warning(f"Failed to remove GZ folder {extract_folder}: {e}")
     end = time.time()
     elapsed_time = end - start
 
-    logging.info(f"All Complete total time {elapsed_time:.2f} seconds")
+    if elapsed_time < 60:
+        elapsed_time_str = f"{int(elapsed_time)}s"
+    elif elapsed_time < 3600:
+        elapsed_time_str = f"{int(elapsed_time // 60)}m {int(elapsed_time % 60)}s"
+    else:
+        elapsed_time_str = f"{int(elapsed_time // 3600)}h {int((elapsed_time % 3600) // 60)}m"
+
+    logging.info(f"All Complete total time {elapsed_time_str}")

modules/extract.py

@@ -3,6 +3,7 @@ import gzip
 import shutil
 import os
 from pathlib import Path
+import concurrent.futures
 
 
 class Extract:
@@ -10,12 +11,8 @@ class Extract:
     def __init__(self, Config):
         self.config = Config
 
-    def _extract_tar(self):
-        for tar_file in os.listdir(self.config.TAR_TOP_FOLDER):
-            # only handle .tar files
-            if not tar_file.endswith(".tar"):
-                pass
-
+    def extract_tar_batch(self, tar_files):
+        for tar_file in tar_files:
             tar_path = Path(self.config.TAR_TOP_FOLDER, tar_file)
 
             # Create a folder for extracted tar contents
@@ -31,32 +28,45 @@ class Extract:
             if self.config.delete_tar_after_processing:
                 os.remove(tar_path)
 
-    def _extract_gz(self):
+    def process_single_gz(self, gz_path, dat_path):
+        try:
+            with gzip.open(gz_path, "rb") as f_in:
+                with open(dat_path, "wb") as f_out:
+                    shutil.copyfileobj(f_in, f_out)
+
+            if self.config.delete_gz_after_processing:
+                os.remove(gz_path)
+        except Exception as e:
+            print(f"Error extracting {gz_path}: {e}")
+
+    def extract_gz_batch(self):
+        gz_tasks = []
         for root, _, files in os.walk(self.config.GZ_TOP_FOLDER):
             for file in files:
                 # only handle .gz files
                 if not file.endswith(".dat.gz"):
-                    pass  # adjust if extension differs
+                    continue
+
                 gz_path = Path(root, file)
                 dat_path = Path(self.config.DAT_TOP_FOLDER, file.replace(".gz", ""))
+                gz_tasks.append((gz_path, dat_path))
 
-                # Unzip .gz file
-                with gzip.open(gz_path, "rb") as f_in:
-                    with open(dat_path, "wb") as f_out:
-                        shutil.copyfileobj(f_in, f_out)
+        print(f"Extracting {len(gz_tasks)} gz files concurrently...")
 
-                if self.config.delete_gz_after_processing:
-                    os.remove(gz_path)
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            futures = [
+                executor.submit(self.process_single_gz, gz_path, dat_path)
+                for gz_path, dat_path in gz_tasks
+            ]
+            concurrent.futures.wait(futures)
 
         try:
             shutil.rmtree(self.config.GZ_TOP_FOLDER)
+            # Recreate the folder for the next batch
+            Path(self.config.GZ_TOP_FOLDER).mkdir(exist_ok=True)
             print("processing complete and GZ files deleted")
         except Exception as e:
             print(str(e))
             print(
                 f"processing complete but GZ folder delete failed. Please delete manually ({self.config.GZ_TOP_FOLDER})"
             )
-
-    def run_extraction(self):
-        self._extract_tar()
-        self._extract_gz()

modules/generate_timeseries.py

@@ -164,8 +164,8 @@ class GenerateTimeseries:
                 executor.shutdown(wait=False, cancel_futures=True)
                 raise
 
-    def write_results_to_csv(self, results, locations):
-        """Write extracted data to CSV files for each zone.
+    def append_results_to_csv(self, results, locations):
+        """Append extracted data to CSV files for each zone.
 
         Args:
             results (dict): Aggregated results {zone_id: {'dates': [], 'values': []}}
@@ -217,19 +217,41 @@ class GenerateTimeseries:
 
                 df_dict[grid_square] = aligned_values
 
-            df = pd.DataFrame(df_dict)
+            new_df = pd.DataFrame(df_dict)
 
-            # Sort by datetime (already sorted)
-            sorted_df = df.sort("datetime")
-
-            # Format datetime column
-            sorted_df = sorted_df.with_columns(
+            # Format datetime column in new_df
+            new_df = new_df.with_columns(
                 pd.col("datetime").dt.strftime("%Y-%m-%d %H:%M:%S")
             )
 
             output_path = (
                 Path(self.config.COMBINED_FOLDER) / f"{zone_name}_timeseries_data.csv"
             )
-            sorted_df.write_csv(output_path, float_precision=4)
 
-        logging.info("All CSV files written.")
+            if output_path.exists():
+                # Load existing CSV
+                existing_df = pd.read_csv(output_path)
+
+                # Reorder new_df to match existing_df
+                new_df = new_df.select(existing_df.columns)
+
+                # Concatenate
+                combined_df = pd.concat([existing_df, new_df])
+                # Sort by datetime
+                combined_df = combined_df.sort("datetime")
+                # Write back
+                combined_df.write_csv(output_path, float_precision=4)
+            else:
+                # Write new CSV
+                # Sort columns to ensure deterministic order (datetime first)
+                cols = new_df.columns
+                cols.remove("datetime")
+                cols.sort()
+                sorted_cols = ["datetime"] + cols
+                new_df = new_df.select(sorted_cols)
+
+                # Sort by datetime (already sorted but good practice)
+                sorted_df = new_df.sort("datetime")
+                sorted_df.write_csv(output_path, float_precision=4)
+
+        logging.info("All CSV files updated.")

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "met-office"
-version = "1.2.0"
+version = "1.3.1"
 description = "Convert .dat nimrod files to .asc files"
 readme = "README.md"
 requires-python = ">=3.14"

uv.lock

@@ -4,7 +4,7 @@ requires-python = ">=3.14"
 
 [[package]]
 name = "met-office"
-version = "1.2.0"
+version = "1.3.1"
 source = { virtual = "." }
 dependencies = [
     { name = "numpy" },