mirror of
https://gitea.psi.ch/APOG/acsmnode.git
synced 2025-06-24 21:21:08 +02:00
Update to account for yaml file attribute renamings.
This commit is contained in:
@ -1,167 +1,169 @@
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import sys, os
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try:
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thisFilePath = os.path.abspath(__file__)
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print(thisFilePath)
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except NameError:
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print("[Notice] The __file__ attribute is unavailable in this environment (e.g., Jupyter or IDLE).")
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print("When using a terminal, make sure the working directory is set to the script's location to prevent path issues (for the DIMA submodule)")
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#print("Otherwise, path to submodule DIMA may not be resolved properly.")
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thisFilePath = os.getcwd() # Use current directory or specify a default
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import numpy as np
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import pandas as pd
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import argparse
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import yaml, json
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dimaPath = os.path.normpath(os.path.join(thisFilePath, "..", "..",'..')) # Move up to project root
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projectPath = os.path.normpath(os.path.join(dimaPath,'..'))
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print(dimaPath)
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sys.path.append(dimaPath)
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import dima.src.hdf5_ops as dataOps
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def create_flags_for_diagnostic_vars(data_table, variable_limits):
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"""
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Create indicator variables that check whether a particular diagnostic variable is within
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pre-specified/acceptable limits, which are defined by `variable_limits`.
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Parameters:
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data_table (pd.DataFrame): The input data table with variables to calibrate.
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variable_limits (dict): Dictionary mapping diagnostic-variables to their limits, e.g.,
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{
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'ABsamp': {
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'lower_lim': {'value': 20000, 'description': "not specified yet"},
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'upper_lim': {'value': 500000, 'description': "not specified yet"}
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}
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}
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Returns:
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pd.DataFrame: A new data table with calibrated variables, containing the original columns
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and additional indicator variables, representing flags.
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"""
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# Initialize a dictionary to store indicator variables
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indicator_variables = {}
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# Loop through the column names in the data table
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for diagnostic_variable in data_table.columns:
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# Skip if the diagnostic variable is not in variable_limits
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if diagnostic_variable not in variable_limits:
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print(f'Diagnostic variable {diagnostic_variable} has not defined limits in {variable_limits}.')
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continue
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# Get lower and upper limits for diagnostic_variable from variable limits dict
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lower_lim = variable_limits[diagnostic_variable]['lower_lim']['value']
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upper_lim = variable_limits[diagnostic_variable]['upper_lim']['value']
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# Create an indicator variable for the current diagnostic variable
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tmp = data_table[diagnostic_variable]
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indicator_variables['flag_'+diagnostic_variable] = ((tmp >= lower_lim) & (tmp <= upper_lim)).to_numpy()
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# Add indicator variables to the new data table
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new_data_table = pd.DataFrame(indicator_variables)
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return new_data_table
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# all_dat[VaporizerTemp_C >= heater_lower_lim & VaporizerTemp_C <= heater_upper_lim ,flag_heater_auto:="V"]
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# all_dat[ABsamp >= AB_lower_lim & ABsamp <= AB_upper_lim ,flag_AB_auto:="V"]
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# all_dat[FlowRate_ccs >= flow_lower_lim & FlowRate_ccs <= flow_upper_lim ,flag_flow_auto:="V"]
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# all_dat[FilamentEmission_mA >= filament_lower_lim & FilamentEmission_mA <= filament_upper_lim ,flag_filament_auto:="V"]
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if __name__ == '__main__':
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# Set up argument parsing
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parser = argparse.ArgumentParser(description="Calibrate species data using calibration factors.")
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parser.add_argument('data_file', type=str, help="Path to the input HDF5 file containing the data table.")
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parser.add_argument('dataset_name', type=str, help ='Relative path to data_table (i.e., dataset name) in HDF5 file')
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parser.add_argument('calibration_file', type=str, help="Path to the input YAML file containing calibration factors.")
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#parser.add_argument('output_file', type=str, help="Path to save the output calibrated data as a CSV file.")
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args = parser.parse_args()
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# Load input data and calibration factors
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try:
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#data_table = pd.read_json(args.data_file)
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print(args.data_file)
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dataManager = dataOps.HDF5DataOpsManager(args.data_file)
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dataManager.load_file_obj()
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dataset_name = '/'+args.dataset_name
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data_table = dataManager.extract_dataset_as_dataframe('/'+args.dataset_name)
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dataManager.extract_and_load_dataset_metadata()
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dataset_metadata_df = dataManager.dataset_metadata_df.copy()
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print(dataset_metadata_df.head())
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dataset_name_idx = dataset_metadata_df.index[(dataset_metadata_df['dataset_name']==args.dataset_name).to_numpy()]
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data_table_metadata = dataset_metadata_df.loc[dataset_name_idx,:]
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parent_instrument = data_table_metadata.loc[dataset_name_idx,'parent_instrument'].values[0]
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parent_file = data_table_metadata.loc[dataset_name_idx,'parent_file'].values[0]
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dataManager.unload_file_obj()
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print(args.calibration_file)
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with open(args.calibration_file, 'r') as stream:
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calibration_factors = yaml.load(stream, Loader=yaml.FullLoader)
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except Exception as e:
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print(f"Error loading input files: {e}")
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exit(1)
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path_to_output_dir, ext = os.path.splitext(args.data_file)
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print('Path to output directory :', path_to_output_dir)
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# Perform calibration
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try:
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processingScriptRelPath = os.path.relpath(thisFilePath,start=projectPath)
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print(processingScriptRelPath)
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metadata = {'actris_level' : 1, 'processing_script': processingScriptRelPath.replace(os.sep,'/')}
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path_to_output_file, ext = os.path.splitext('/'.join([path_to_output_dir,parent_instrument,parent_file]))
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path_to_calibrated_file = ''.join([path_to_output_file, '_flags.csv'])
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path_tail, path_head = os.path.split(path_to_calibrated_file)
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path_to_metadata_file = '/'.join([path_tail, 'data_lineage_metadata.json'])
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print('Path to output file :', path_to_calibrated_file)
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import dima.utils.g5505_utils as utils
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import json
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calibrated_table = create_flags_for_diagnostic_vars(data_table, calibration_factors)
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metadata['processing_date'] = utils.created_at()
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calibrated_table.to_csv(path_to_calibrated_file, index=False)
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# Ensure the file exists
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if not os.path.exists(path_to_metadata_file):
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with open(path_to_metadata_file, 'w') as f:
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json.dump({}, f) # Initialize empty JSON
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# Read the existing JSON
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with open(path_to_metadata_file, 'r') as metadata_file:
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try:
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json_dict = json.load(metadata_file)
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except json.JSONDecodeError:
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json_dict = {} # Start fresh if file is invalid
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# Update the JSON object
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outputfileRelPath = os.path.relpath(path_to_calibrated_file, start=projectPath).replace(os.sep, '/')
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json_dict[outputfileRelPath] = metadata
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# Write updated JSON back to the file
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with open(path_to_metadata_file, 'w') as metadata_file:
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json.dump(json_dict, metadata_file, indent=4)
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print(f"Calibrated data saved to {path_to_calibrated_file}")
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print(f"Metadata for calibrated data saved to {path_to_metadata_file}")
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except Exception as e:
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print(f"Error during calibration: {e}")
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import sys, os
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try:
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thisFilePath = os.path.abspath(__file__)
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print(thisFilePath)
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except NameError:
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print("[Notice] The __file__ attribute is unavailable in this environment (e.g., Jupyter or IDLE).")
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print("When using a terminal, make sure the working directory is set to the script's location to prevent path issues (for the DIMA submodule)")
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#print("Otherwise, path to submodule DIMA may not be resolved properly.")
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thisFilePath = os.getcwd() # Use current directory or specify a default
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import numpy as np
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import pandas as pd
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import argparse
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import yaml, json
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dimaPath = os.path.normpath(os.path.join(thisFilePath, "..", "..",'..')) # Move up to project root
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projectPath = os.path.normpath(os.path.join(dimaPath,'..'))
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print(dimaPath)
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sys.path.append(dimaPath)
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import dima.src.hdf5_ops as dataOps
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def create_flags_for_diagnostic_vars(data_table, validity_thresholds_dict):
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"""
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Create indicator variables that check whether a particular diagnostic variable is within
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pre-specified/acceptable limits, which are defined by `variable_limits`.
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Parameters:
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data_table (pd.DataFrame): The input data table with variables to calibrate.
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variable_limits (dict): Dictionary mapping diagnostic-variables to their limits, e.g.,
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{
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'ABsamp': {
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'lower_lim': {'value': 20000, 'description': "not specified yet"},
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'upper_lim': {'value': 500000, 'description': "not specified yet"}
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}
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}
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Returns:
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pd.DataFrame: A new data table with calibrated variables, containing the original columns
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and additional indicator variables, representing flags.
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"""
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# Initialize a dictionary to store indicator variables
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indicator_variables = {}
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# Loop through the column names in the data table
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for diagnostic_variable in data_table.columns:
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print(diagnostic_variable)
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# Skip if the diagnostic variable is not in variable_limits
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if diagnostic_variable not in validity_thresholds_dict['validity_thresholds']['variables']:
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print(f'Diagnostic variable {diagnostic_variable} has not defined limits in {validity_thresholds_dict}.')
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continue
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# Get lower and upper limits for diagnostic_variable from variable limits dict
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variable_ranges = validity_thresholds_dict['validity_thresholds']['variables'][diagnostic_variable]
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lower_lim = variable_ranges['lower_lim']
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upper_lim = variable_ranges['upper_lim']
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# Create an indicator variable for the current diagnostic variable
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tmp = data_table[diagnostic_variable]
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indicator_variables['flag_'+diagnostic_variable] = ((tmp >= lower_lim) & (tmp <= upper_lim)).to_numpy()
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# Add indicator variables to the new data table
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new_data_table = pd.DataFrame(indicator_variables)
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return new_data_table
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# all_dat[VaporizerTemp_C >= heater_lower_lim & VaporizerTemp_C <= heater_upper_lim ,flag_heater_auto:="V"]
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# all_dat[ABsamp >= AB_lower_lim & ABsamp <= AB_upper_lim ,flag_AB_auto:="V"]
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# all_dat[FlowRate_ccs >= flow_lower_lim & FlowRate_ccs <= flow_upper_lim ,flag_flow_auto:="V"]
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# all_dat[FilamentEmission_mA >= filament_lower_lim & FilamentEmission_mA <= filament_upper_lim ,flag_filament_auto:="V"]
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if __name__ == '__main__':
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# Set up argument parsing
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parser = argparse.ArgumentParser(description="Calibrate species data using calibration factors.")
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parser.add_argument('data_file', type=str, help="Path to the input HDF5 file containing the data table.")
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parser.add_argument('dataset_name', type=str, help ='Relative path to data_table (i.e., dataset name) in HDF5 file')
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parser.add_argument('calibration_file', type=str, help="Path to the input YAML file containing calibration factors.")
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#parser.add_argument('output_file', type=str, help="Path to save the output calibrated data as a CSV file.")
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args = parser.parse_args()
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# Load input data and calibration factors
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try:
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#data_table = pd.read_json(args.data_file)
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print(args.data_file)
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dataManager = dataOps.HDF5DataOpsManager(args.data_file)
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dataManager.load_file_obj()
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dataset_name = '/'+args.dataset_name
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data_table = dataManager.extract_dataset_as_dataframe('/'+args.dataset_name)
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dataManager.extract_and_load_dataset_metadata()
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dataset_metadata_df = dataManager.dataset_metadata_df.copy()
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print(dataset_metadata_df.head())
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dataset_name_idx = dataset_metadata_df.index[(dataset_metadata_df['dataset_name']==args.dataset_name).to_numpy()]
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data_table_metadata = dataset_metadata_df.loc[dataset_name_idx,:]
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parent_instrument = data_table_metadata.loc[dataset_name_idx,'parent_instrument'].values[0]
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parent_file = data_table_metadata.loc[dataset_name_idx,'parent_file'].values[0]
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dataManager.unload_file_obj()
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print(args.calibration_file)
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with open(args.calibration_file, 'r') as stream:
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calibration_factors = yaml.load(stream, Loader=yaml.FullLoader)
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except Exception as e:
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print(f"Error loading input files: {e}")
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exit(1)
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path_to_output_dir, ext = os.path.splitext(args.data_file)
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print('Path to output directory :', path_to_output_dir)
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# Perform calibration
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try:
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processingScriptRelPath = os.path.relpath(thisFilePath,start=projectPath)
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print(processingScriptRelPath)
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metadata = {'actris_level' : 1, 'processing_script': processingScriptRelPath.replace(os.sep,'/')}
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path_to_output_file, ext = os.path.splitext('/'.join([path_to_output_dir,parent_instrument,parent_file]))
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path_to_calibrated_file = ''.join([path_to_output_file, '_flags.csv'])
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path_tail, path_head = os.path.split(path_to_calibrated_file)
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path_to_metadata_file = '/'.join([path_tail, 'data_lineage_metadata.json'])
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print('Path to output file :', path_to_calibrated_file)
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import dima.utils.g5505_utils as utils
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import json
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print(calibration_factors.keys())
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calibrated_table = create_flags_for_diagnostic_vars(data_table, calibration_factors)
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metadata['processing_date'] = utils.created_at()
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calibrated_table.to_csv(path_to_calibrated_file, index=False)
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# Ensure the file exists
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if not os.path.exists(path_to_metadata_file):
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with open(path_to_metadata_file, 'w') as f:
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json.dump({}, f) # Initialize empty JSON
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# Read the existing JSON
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with open(path_to_metadata_file, 'r') as metadata_file:
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try:
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json_dict = json.load(metadata_file)
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except json.JSONDecodeError:
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json_dict = {} # Start fresh if file is invalid
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# Update the JSON object
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outputfileRelPath = os.path.relpath(path_to_calibrated_file, start=projectPath).replace(os.sep, '/')
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json_dict[outputfileRelPath] = metadata
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# Write updated JSON back to the file
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with open(path_to_metadata_file, 'w') as metadata_file:
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json.dump(json_dict, metadata_file, indent=4)
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print(f"Calibrated data saved to {path_to_calibrated_file}")
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print(f"Metadata for calibrated data saved to {path_to_metadata_file}")
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except Exception as e:
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print(f"Error during calibration: {e}")
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exit(1)
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