seperate dict into two lists

sed/loader/flash/loader.py

@@ -39,7 +39,7 @@ class FlashLoader(BaseLoader):
 
     __name__ = "flash"
 
-    supported_file_types = ["h5", "parquet"]
+    supported_file_types = ["h5"]
 
     def __init__(self, config: dict) -> None:
 
@@ -626,37 +626,35 @@ def buffer_file_handler(self, data_parquet_dir, detector):
             detector (str): Detector name.
 
         Returns:
-            Dict[Path, Path]: Dictionary mapping h5 file paths to corresponding parquet file paths.
+            Tuple[List[Path], List[Path]]: Two lists, one for h5 file paths and one for
+            corresponding parquet file paths.
 
         Raises:
             FileNotFoundError: If the conversion fails for any files or no data is available.
-
         """
 
         # Create the directory for buffer parquet files
         buffer_file_dir = data_parquet_dir.joinpath("buffer")
         buffer_file_dir.mkdir(parents=True, exist_ok=True)
 
-        # Create a dictionary to store the filenames of h5 and parquet files
-        filenames = {
-            Path(file): buffer_file_dir.joinpath(
-                Path(file).stem + detector,
-            )
-            for file in self.files
-        }
+        # Create two separate lists for h5 and parquet file paths
+        h5_filenames = [Path(file) for file in self.files]
+        parquet_filenames = [
+            buffer_file_dir.joinpath(Path(file).stem + detector) for file in self.files
+        ]
 
         # Raise a value error if no data is available after the conversion
-        if len(filenames) == 0:
-            raise ValueError(
-                "No data available. Probably failed reading all h5 files",
-            )
+        if len(h5_filenames) == 0:
+            raise ValueError("No data available. Probably failed reading all h5 files")
 
         # Choose files to read
-        files_to_read = {h_5: parquet for h_5, parquet in filenames.items() if not parquet.exists()}
+        files_to_read = [
+            (h5_path, parquet_path)
+            for h5_path, parquet_path in zip(h5_filenames, parquet_filenames)
+            if not parquet_path.exists()
+        ]
 
-        print(
-            f"Reading files: {len(files_to_read)} new files of {len(filenames)} total.",
-        )
+        print(f"Reading files: {len(files_to_read)} new files of {len(h5_filenames)} total.")
 
         # Initialize the indices for create_buffer_file conversion
         self.reset_multi_index()
@@ -665,19 +663,18 @@ def buffer_file_handler(self, data_parquet_dir, detector):
         if len(files_to_read) > 0:
             Parallel(n_jobs=len(files_to_read), verbose=10)(
                 delayed(self.create_buffer_file)(h5_path, parquet_path)
-                for h5_path, parquet_path in files_to_read.items()
+                for h5_path, parquet_path in files_to_read
             )
 
         # Raise an error if the conversion failed for any files
         if self.failed_files_error:
             raise FileNotFoundError(
-                "Conversion failed for the following files: \n"
-                + "\n".join(self.failed_files_error),
+                "Conversion failed for the following files:\n" + "\n".join(self.failed_files_error),
             )
 
         print("All files converted successfully!")
 
-        return filenames
+        return h5_filenames, parquet_filenames
 
     def fill_na(
         self,
@@ -782,14 +779,14 @@ def parquet_handler(self, data_parquet_dir, **kwds):
 
         else:
             # Obtain the filenames from the method which handles buffer file creation/reading
-            filenames = self.buffer_file_handler(
+            h5_filenames, parquet_filenames = self.buffer_file_handler(
                 data_parquet_dir,
                 detector,
             )
 
             # Read all parquet files using dask and concatenate into one dataframe after filling
             dataframe = self.fill_na(
-                [dd.read_parquet(file) for file in filenames.values()],
+                [dd.read_parquet(file) for file in parquet_filenames],
             )
 
             dataframe = dataframe.dropna(