import sys import os try: thisFilePath = os.path.abspath(__file__) except NameError: print("Error: __file__ is not available. Ensure the script is being run from a file.") print("[Notice] Path to DIMA package may not be resolved properly.") thisFilePath = os.getcwd() # Use current directory or specify a default dimaPath = os.path.normpath(os.path.join(thisFilePath, "..",'..','..')) # Move up to project root if dimaPath not in sys.path: # Avoid duplicate entries sys.path.insert(0,dimaPath) import pandas as pd import collections import yaml import h5py import argparse import logging import utils.g5505_utils as utils def read_acsm_files_as_dict(filename: str, instruments_dir: str = None, work_with_copy: bool = True): # If instruments_dir is not provided, use the default path relative to the module directory if not instruments_dir: # Assuming the instruments folder is one level up from the source module directory module_dir = os.path.dirname(__file__) instruments_dir = os.path.join(module_dir, '..') # Normalize the path (resolves any '..' in the path) instrument_configs_path = os.path.abspath(os.path.join(instruments_dir,'dictionaries','ACSM_TOFWARE.yaml')) with open(instrument_configs_path,'r') as stream: try: config_dict = yaml.load(stream, Loader=yaml.FullLoader) except yaml.YAMLError as exc: print(exc) # Verify if file can be read by available intrument configurations. #if not any(key in filename.replace(os.sep,'/') for key in config_dict.keys()): # return {} #TODO: this may be prone to error if assumed folder structure is non compliant description_dict = config_dict.get('table_header',{}) file_encoding = config_dict['config_text_reader'].get('file_encoding','utf-8') separator = config_dict['config_text_reader'].get('separator',None) table_header = config_dict['config_text_reader'].get('table_header',None) timestamp_variables = config_dict['config_text_reader'].get('timestamp',[]) datetime_format = config_dict['config_text_reader'].get('datetime_format',[]) # Read header as a dictionary and detect where data table starts header_dict = {} data_start = False # Work with copy of the file for safety if work_with_copy: tmp_filename = utils.make_file_copy(source_file_path=filename) else: tmp_filename = filename if not isinstance(table_header, list): table_header = [table_header] file_encoding = [file_encoding] separator = [separator] with open(tmp_filename,'rb') as f: table_preamble = [] for line_number, line in enumerate(f): for tb_idx, tb in enumerate(table_header): if tb in line.decode(file_encoding[tb_idx]): break if tb in line.decode(file_encoding[tb_idx]): list_of_substrings = line.decode(file_encoding[tb_idx]).split(separator[tb_idx].replace('\\t','\t')) # Count occurrences of each substring substring_counts = collections.Counter(list_of_substrings) data_start = True # Generate column names with appended index only for repeated substrings column_names = [f"{i}_{name.strip()}" if substring_counts[name] > 1 else name.strip() for i, name in enumerate(list_of_substrings)] #column_names = [str(i)+'_'+name.strip() for i, name in enumerate(list_of_substrings)] #column_names = [] #for i, name in enumerate(list_of_substrings): # column_names.append(str(i)+'_'+name) #print(line_number, len(column_names ),'\n') break # Subdivide line into words, and join them by single space. # I asumme this can produce a cleaner line that contains no weird separator characters \t \r or extra spaces and so on. list_of_substrings = line.decode(file_encoding[tb_idx]).split() # TODO: ideally we should use a multilinear string but the yalm parser is not recognizing \n as special character #line = ' '.join(list_of_substrings+['\n']) #line = ' '.join(list_of_substrings) table_preamble.append(' '.join([item for item in list_of_substrings]))# += new_line # TODO: it does not work with separator as none :(. fix for RGA try: df = pd.read_csv(tmp_filename, delimiter = separator[tb_idx].replace('\\t','\t'), header=line_number, #encoding='latin-1', encoding = file_encoding[tb_idx], names=column_names, skip_blank_lines=True) df_numerical_attrs = df.select_dtypes(include ='number') df_categorical_attrs = df.select_dtypes(exclude='number') numerical_variables = [item for item in df_numerical_attrs.columns] # Consolidate into single timestamp column the separate columns 'date' 'time' specified in text_data_source.yaml if timestamp_variables: #df_categorical_attrs['timestamps'] = [' '.join(df_categorical_attrs.loc[i,timestamp_variables].to_numpy()) for i in df.index] #df_categorical_attrs['timestamps'] = [ df_categorical_attrs.loc[i,'0_Date']+' '+df_categorical_attrs.loc[i,'1_Time'] for i in df.index] #df_categorical_attrs['timestamps'] = df_categorical_attrs[timestamp_variables].astype(str).agg(' '.join, axis=1) timestamps_name = ' '.join(timestamp_variables) df_categorical_attrs[ timestamps_name] = df_categorical_attrs[timestamp_variables].astype(str).agg(' '.join, axis=1) valid_indices = [] if datetime_format: df_categorical_attrs[ timestamps_name] = pd.to_datetime(df_categorical_attrs[ timestamps_name],format=datetime_format,errors='coerce') valid_indices = df_categorical_attrs.dropna(subset=[timestamps_name]).index df_categorical_attrs = df_categorical_attrs.loc[valid_indices,:] df_numerical_attrs = df_numerical_attrs.loc[valid_indices,:] df_categorical_attrs[timestamps_name] = df_categorical_attrs[timestamps_name].dt.strftime(config_dict['default']['desired_format']) startdate = df_categorical_attrs[timestamps_name].min() enddate = df_categorical_attrs[timestamps_name].max() df_categorical_attrs[timestamps_name] = df_categorical_attrs[timestamps_name].astype(str) #header_dict.update({'stastrrtdate':startdate,'enddate':enddate}) header_dict['startdate']= str(startdate) header_dict['enddate']=str(enddate) if len(timestamp_variables) > 1: df_categorical_attrs = df_categorical_attrs.drop(columns = timestamp_variables) #df_categorical_attrs.reindex(drop=True) #df_numerical_attrs.reindex(drop=True) categorical_variables = [item for item in df_categorical_attrs.columns] #### #elif 'RGA' in filename: # df_categorical_attrs = df_categorical_attrs.rename(columns={'0_Time(s)' : 'timestamps'}) ### file_dict = {} path_tail, path_head = os.path.split(tmp_filename) file_dict['name'] = path_head # TODO: review this header dictionary, it may not be the best way to represent header data file_dict['attributes_dict'] = header_dict file_dict['datasets'] = [] #### df = pd.concat((df_categorical_attrs,df_numerical_attrs),axis=1) #if numerical_variables: dataset = {} dataset['name'] = 'data_table'#_numerical_variables' dataset['data'] = utils.convert_dataframe_to_np_structured_array(df) #df_numerical_attrs.to_numpy() dataset['shape'] = dataset['data'].shape dataset['dtype'] = type(dataset['data']) #dataset['data_units'] = file_obj['wave']['data_units'] # # Create attribute descriptions based on description_dict dataset['attributes'] = {} # Annotate column headers if description_dict is non empty if description_dict: for column_name in df.columns: column_attr_dict = description_dict.get(column_name, {'note':'there was no description available. Review instrument files.'}) dataset['attributes'].update({column_name: utils.convert_attrdict_to_np_structured_array(column_attr_dict)}) #try: # dataset['attributes'] = description_dict['table_header'].copy() # for key in description_dict['table_header'].keys(): # if not key in numerical_variables: # dataset['attributes'].pop(key) # delete key # else: # dataset['attributes'][key] = utils.parse_attribute(dataset['attributes'][key]) # if timestamps_name in categorical_variables: # dataset['attributes'][timestamps_name] = utils.parse_attribute({'unit':'YYYY-MM-DD HH:MM:SS.ffffff'}) #except ValueError as err: # print(err) # Represent string values as fixed length strings in the HDF5 file, which need # to be decoded as string when we read them. It provides better control than variable strings, # at the expense of flexibility. # https://docs.h5py.org/en/stable/strings.html if table_preamble: #header_dict["table_preamble"] = utils.convert_string_to_bytes(table_preamble) tp_dataset = {} tp_dataset['name'] = "table_preamble" tp_dataset['data'] = utils.convert_string_to_bytes(table_preamble) tp_dataset['shape'] = tp_dataset['data'].shape tp_dataset['dtype'] = type(tp_dataset['data']) tp_dataset['attributes'] = {} file_dict['datasets'].append(tp_dataset) file_dict['datasets'].append(dataset) #if categorical_variables: # dataset = {} # dataset['name'] = 'table_categorical_variables' # dataset['data'] = dataframe_to_np_structured_array(df_categorical_attrs) #df_categorical_attrs.loc[:,categorical_variables].to_numpy() # dataset['shape'] = dataset['data'].shape # dataset['dtype'] = type(dataset['data']) # if timestamps_name in categorical_variables: # dataset['attributes'] = {timestamps_name: utils.parse_attribute({'unit':'YYYY-MM-DD HH:MM:SS.ffffff'})} # file_dict['datasets'].append(dataset) except: return {} return file_dict if __name__ == "__main__": from src.hdf5_ops import save_file_dict_to_hdf5 from utils.g5505_utils import created_at # Set up argument parsing parser = argparse.ArgumentParser(description="Data ingestion process to HDF5 files.") parser.add_argument('dst_file_path', type=str, help="Path to the target HDF5 file.") parser.add_argument('src_file_path', type=str, help="Relative path to source file to be saved to target HDF5 file.") parser.add_argument('dst_group_name', type=str, help="Group name '/instFolder/[category]/fileName' in the target HDF5 file.") args = parser.parse_args() hdf5_file_path = args.dst_file_path src_file_path = args.src_file_path dst_group_name = args.dst_group_name default_mode = 'r+' try: # Read source file and return an internal dictionary representation idr_dict = read_acsm_files_as_dict(src_file_path) if not os.path.exists(hdf5_file_path): default_mode = 'w' print(f'Opening HDF5 file: {hdf5_file_path} in mode {default_mode}') with h5py.File(hdf5_file_path, mode=default_mode, track_order=True) as hdf5_file_obj: try: # Create group if it does not exist if dst_group_name not in hdf5_file_obj: hdf5_file_obj.create_group(dst_group_name) hdf5_file_obj[dst_group_name].attrs['creation_date'] = created_at().encode('utf-8') print(f'Created new group: {dst_group_name}') else: print(f'Group {dst_group_name} already exists. Proceeding with data transfer...') except Exception as inst: logging.error('Failed to create group %s in HDF5: %s', dst_group_name, inst) # Save dictionary to HDF5 save_file_dict_to_hdf5(hdf5_file_obj, dst_group_name, idr_dict) print(f'Completed saving file dict with keys: {idr_dict.keys()}') except Exception as e: logging.error('File reader failed to process %s: %s', src_file_path, e) print(f'File reader failed to process {src_file_path}. See logs for details.')