Merge branch 'main' into 'feature/DB_for_FileReader_Repo'

# Conflicts:
#   instruments/filereader_registry.py
#   pipelines/data_integration.py
#   src/hdf5_writer.py

src/hdf5_writer.py

@@ -7,6 +7,7 @@ import pandas as pd
 import numpy as np
 import h5py
 import logging
+import json
 
 #try:
 #    from dima.utils import g5505_utils as utils
@@ -158,10 +159,21 @@ def create_hdf5_file_from_filesystem_path(path_to_input_directory: str,
                     stdout = inst
                     logging.error('Failed to create group %s into HDF5: %s', group_name, inst)
 
-                for filenumber, filename in enumerate(filtered_filenames_list):
+                if 'data_lineage_metadata.json' in filtered_filenames_list:
+                    idx = filtered_filenames_list.index('data_lineage_metadata.json') 
+                    data_lineage_file = filtered_filenames_list[idx]
+                    try:
+                        with open('/'.join([dirpath,data_lineage_file]),'r') as dlf:                        
+                            data_lineage_dict = json.load(dlf)
+                        filtered_filenames_list.pop(idx)
+                    except json.JSONDecodeError:
+                            data_lineage_dict = {}  # Start fresh if file is invalid
                         
-                    #file_ext = os.path.splitext(filename)[1]
-                    #try: 
+                else:
+                    data_lineage_dict = {}                
+                   
+
+                for filenumber, filename in enumerate(filtered_filenames_list):
                     
                     # hdf5 path to filename group 
                     dest_group_name = f'{group_name}/{filename}'

utils/g5505_utils.py

@@ -161,6 +161,8 @@ def convert_dataframe_to_np_structured_array(df: pd.DataFrame):
                 dtype.append((col, 'i4'))  # Assuming 32-bit integer
             elif pd.api.types.is_float_dtype(col_dtype):
                 dtype.append((col, 'f4'))  # Assuming 32-bit float
+            elif pd.api.types.is_bool_dtype(col_dtype):
+                dtype.append((col,bool))
             else:
                 # Handle unsupported data types
                 print(f"Unsupported dtype found in column '{col}': {col_data.dtype}")