mirror of
https://gitea.psi.ch/APOG/acsm-fairifier.git
synced 2026-01-19 13:25:23 +01:00
426 lines
18 KiB
Python
426 lines
18 KiB
Python
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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projectPath = os.path.normpath(os.path.join(thisFilePath, "..", "..",'..')) # Move up to project root
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#print('Project path:', projectPath)
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dimaPath = os.path.normpath('/'.join([projectPath,'dima']))
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#print('DIMA path:', dimaPath)
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# Set up project root directory
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sys.path.insert(0,projectPath)
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sys.path.insert(0,dimaPath)
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import dima.src.hdf5_ops as dataOps
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import pipelines.steps.utils as stepUtils
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import dima.utils.g5505_utils as utils
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import json
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def compute_cpc_flags():
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# TODO: ask rob where to find this information.
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return 0
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def load_parameters(flag_type):
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# Implicit input
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if flag_type == 'diagnostics':
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flag_type_file = os.path.normpath(os.path.join(projectPath,'pipelines/params/validity_thresholds.yaml'))
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error_message = f"Error accessing validation thresholds at: {flag_type_file}"
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elif flag_type == 'species':
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flag_type_file = os.path.normpath(os.path.join(projectPath,'pipelines/params/calibration_params.yaml'))
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error_message = f"Error accessing calibration parameters at: {flag_type_file}"
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output_dict = {}
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try:
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with open(flag_type_file, 'r') as stream:
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output_dict = yaml.load(stream, Loader=yaml.FullLoader)
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except Exception as e:
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print(error_message)
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return {}
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# Get name of the specifies to flag based on diagnostics and manual flags
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#path_to_calib_params = os.path.normpath(os.path.join(projectPath,'pipelines/params/calibration_params.yaml'))
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#if not os.path.exists(path_to_calib_params):
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# raise FileNotFoundError(f'Calibration params file:{path_to_calib_params}')
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#with open(path_to_calib_params,'r') as stream:
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# calib_param_dict = yaml.safe_load(stream)
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return output_dict
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#def compute_diagnostic_variable_flags(data_table, validity_thresholds_dict):
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def generate_diagnostic_flags(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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# Define binary to ebas flag code map
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# Specify labeling function to create numbered EBAS flags. It maps a column indicator,
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# marking a time interval in which a particular flagging event occurred.
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binary_to_ebas_code = {False : 0, True : 456}
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# Initialize a dictionary to store indicator variables
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indicator_variables = {}
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indicator_variables['t_base'] = data_table['t_base']
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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] = np.logical_not(((tmp >= lower_lim) & (tmp <= upper_lim)).to_numpy())
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indicator_variables['numflag_'+diagnostic_variable] = np.array([binary_to_ebas_code[entry] for entry in indicator_variables['flag_'+diagnostic_variable]], dtype=np.int64)
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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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aggr_func = lambda x : max(x.values)
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new_data_table['numflag_any_diagnostic_flag'] = new_data_table.loc[:,['numflag_' in col for col in new_data_table.columns]].aggregate(aggr_func,axis='columns')
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aggr_func = lambda x : np.nan if x.isna().all() else any(x.dropna().values)
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new_data_table['flag_any_diagnostic_flag'] = new_data_table.loc[:,['flag_' in col for col in new_data_table.columns]].aggregate(aggr_func, axis='columns')
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#new_data_table['flag_any_diagnostic_flag'] = new_data_table.apply(lambda x : any(np.logical_not(x.values)), axis='columns')
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#new_data_table['flag_any_diagnostic'] = new_data_table.apply(
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# lambda x: np.nan if x.isna().all() else any(x.dropna().values), axis='columns'
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#)
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return new_data_table
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# TODO: abstract some of the code in the command line main
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def generate_species_flags(data_table : pd.DataFrame, calib_param_dict : dict):
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"""Generate flags for columns in data_table based on flags_table
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Returns
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-------
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_type_
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_description_
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"""
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predefined_species = calib_param_dict.get('variables',{}).get('species',[])
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if not predefined_species:
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raise RuntimeError("Undefined species. Input argument 'calib_param_dict' must contain a 'variables' : {'species' : ['example1',...,'examplen']} ")
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print('Predefined_species:', predefined_species)
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# Save output tables to csv file and save/or update data lineage record
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filename, ext = os.path.splitext(parent_file)
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path_to_flags_file = '/'.join([path_to_output_folder, f'{filename}_flags.csv'])
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variables_set = set(data_table.columns)
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print(variables_set)
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manual_json_flags = []
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csv_flags = []
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# Inspect flags folder
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for filename in os.listdir(path_to_output_folder):
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if all([filename.endswith('.json'), 'metadata' not in filename, 'flag' in filename]):
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manual_json_flags.append(filename)
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elif filename.endswith('.csv'):
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csv_flags.append(filename)
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if csv_flags:
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flags_table = pd.read_csv(os.path.join(path_to_output_folder, csv_flags[0]))
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if 'numflag_any_diagnostic_flag' in flags_table.columns:
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#renaming_map = {var: f'flag_{var}' for var in data_table.columns}
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#data_table[renaming_map.keys()] = flags_table['flag_any_diagnostic_flag'].values
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#data_table.rename(columns=renaming_map, inplace=True)
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renaming_map = {}
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for var in data_table.columns:
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#print(var)
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if (not datetime_var == var) and (var in predefined_species):
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renaming_map[var] = f'numflag_{var}'
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print(f'numflag_{var}')
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data_table[var] = pd.Series(flags_table['numflag_any_diagnostic_flag'].values,dtype=np.int64)
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print(renaming_map)
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data_table.rename(columns=renaming_map, inplace=True)
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else:
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raise FileNotFoundError("Automated diagnostic flag .csv not found. Hint: Run pipelines/step/generate_flags.py <campaignFile.h5> --flag-type diagnostics.")
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numflag_columns = [col for col in data_table.columns if 'numflag_' in col]
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print(numflag_columns)
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for flag_filename in manual_json_flags:
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#print(flag_filename)
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parts = os.path.splitext(flag_filename)[0].split('_')
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varname = '_'.join(parts[2:]) # Extract variable name from filename
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#if f'flag_{varname}' in data_table.columns:
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try:
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# Load manually generate flag
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with open(os.path.join(path_to_output_folder, flag_filename), 'r') as stream:
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flag_dict = json.load(stream)
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t1 = pd.to_datetime(flag_dict.get('startdate'))
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t2 = pd.to_datetime(flag_dict.get('enddate'))
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flag_code = flag_dict.get('flag_code', np.nan) # Default to NaN if missing
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if pd.isnull(t1) or pd.isnull(t2):
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continue # Skip if invalid timestamps
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if not data_table[datetime_var].is_monotonic_increasing:
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data_table.sort_values(by=datetime_var, inplace=True)
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data_table.reset_index(drop=True, inplace=True)
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t1_idx = int(abs(data_table[datetime_var] - t1).argmin())
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t2_idx = int(abs(data_table[datetime_var] - t2).argmin())
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#print(flag_code)
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#for col in data_table.columns:
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# if 'numflag_' in col:
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# print(col)
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data_table = reconcile_flags(data_table, flag_code, t1_idx, t2_idx, numflag_columns)
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# Apply the ranking logic efficiently
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#data_table.loc[t1_idx:t2_idx, numflag_columns] = data_table.loc[t1_idx:t2_idx, numflag_columns].map(
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# lambda x: reconcile_flags(x, flag_code)
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#)
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#if 456 <= flag_code <= 800:
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# data_table.loc[t1_idx:t2_idx, numflag_columns] = data_table.loc[t1_idx:t2_idx, numflag_columns].applymap(lambda x: max(x, flag_code))
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#else:
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# data_table.loc[t1_idx:t2_idx, numflag_columns] = flag_code
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except (KeyError, ValueError, FileNotFoundError) as e:
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print(f"Error processing {flag_filename}: {e}")
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continue
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return data_table.loc[:,[datetime_var] + numflag_columns]
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with open('app/flags/ebas_dict.yaml','r') as stream:
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ebas_dict = yaml.safe_load(stream)
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flag_ranking = ebas_dict['flag_ranking']
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# Vectorized function for getting the rank of a flag
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def get_rank(flag):
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return flag_ranking.get(flag, 0) # Default rank 0 for unknown flags
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# Vectorized function for reconciling flags
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def reconcile_flags(data_table, flag_code, t1_idx, t2_idx, numflag_columns):
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# Get the rank of the flag_code
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flag_code_rank = get_rank(flag_code)
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# Extract the relevant subtable for efficiency
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sub_table = data_table.loc[t1_idx:t2_idx, numflag_columns]
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# Get the ranks for the current values in the subtable, using np.vectorize to avoid applymap
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current_ranks = np.vectorize(get_rank)(sub_table.values) # Element-wise rank computation
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# Compare ranks: If the rank of the flag_code is higher, it will override the current value
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new_values = np.where(current_ranks < flag_code_rank, flag_code, sub_table.values)
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# Update the dataframe with the new values
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data_table.loc[t1_idx:t2_idx, numflag_columns] = new_values.astype(np.int64)
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return 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(
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"--flag-type",
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required=True,
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choices=["diagnostics", "species", "cpd"],
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help="Specify the flag type. Must be one of: diagnostics, species, cpd"
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)
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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('validity_thersholds_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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flag_type = args.flag_type
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data_file = args.data_file
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# Load input data and calibration factors
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try:
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dataManager = dataOps.HDF5DataOpsManager(args.data_file)
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dataManager.load_file_obj()
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base_name = '/ACSM_TOFWARE'
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if '/ACSM_TOFWARE' not in dataManager.file_obj:
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dataManager.unload_file_obj()
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print(f'Invalid data file: {data_file}. Missing instrument folder ACSM_TOFWARE.')
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raise ImportError(f'Instrument folder "/ACSM_TOFWARE" not found in data_file : {data_file}')
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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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# Find dataset associated with diagnostic channels
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if flag_type == 'diagnostics':
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keywords = ['ACSM_JFJ_','_meta.txt/data_table']
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find_keyword = [all(keyword in item for keyword in keywords) for item in dataset_metadata_df['dataset_name']]
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if flag_type == 'species':
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keywords = ['ACSM_JFJ_','_timeseries.txt/data_table']
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find_keyword = [all(keyword in item for keyword in keywords) for item in dataset_metadata_df['dataset_name']]
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#dataset_name = dataset_metadata_df['dataset_name'][find_keyword]
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#parent_file = dataset_metadata_df.loc[find_keyword,'parent_file']
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#parent_flag_file = '_'.join([os.path.splitext(parent_file),'flags.csv'])
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#parent_instrument = dataset_metadata_df.loc[find_keyword,'parent_instrument']
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dataset_name = dataset_metadata_df['dataset_name'][find_keyword]
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parent_file = dataset_metadata_df.loc[find_keyword,'parent_file']
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#parent_flag_file = '_'.join([os.path.splitext(parent_file)[0],'flags.csv']) # Expected name
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parent_instrument = dataset_metadata_df.loc[find_keyword,'parent_instrument']
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print(':)')
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if not (dataset_name.size == 1):
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raise ValueError(f'{flag_type} file is not uniquely identifiable: {parent_file}')
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else:
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dataset_name = dataset_name.values[0]
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parent_file = parent_file.values[0]
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parent_instrument = parent_instrument.values[0]
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data_table = dataManager.extract_dataset_as_dataframe(dataset_name)
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datetime_var, datetime_var_format = dataManager.infer_datetime_variable(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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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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flag_type = args.flag_type
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try:
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# Define output directory of apply_calibration_factors() step
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suffix = 'flags'
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if len(parent_instrument.split('/')) >= 2:
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instFolder = parent_instrument.split('/')[0]
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category = parent_instrument.split('/')[1]
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else:
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instFolder = parent_instrument.split('/')[0]
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category = ''
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path_to_output_folder, ext = os.path.splitext('/'.join([path_to_output_dir,f'{instFolder}_{suffix}',category]))
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processingScriptRelPath = os.path.relpath(thisFilePath,start=projectPath)
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if not os.path.exists(path_to_output_folder):
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os.makedirs(path_to_output_folder)
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print('Processing script:', processingScriptRelPath)
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print('Output directory:', path_to_output_folder)
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# Compute diagnostic flags based on validity thresholds defined in configuration_file_dict
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if flag_type == 'diagnostics':
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validity_thresholds_dict = load_parameters(flag_type)
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flags_table = generate_diagnostic_flags(data_table, validity_thresholds_dict)
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if flag_type == 'species':
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calib_param_dict = load_parameters(flag_type)
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flags_table = generate_species_flags(data_table,calib_param_dict)
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metadata = {'actris_level' : 1,
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'processing_script': processingScriptRelPath.replace(os.sep,'/'),
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'processing_date' : utils.created_at(),
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'flag_type' : flag_type,
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'datetime_var': datetime_var
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}
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# Save output tables to csv file and save/or update data lineage record
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filename, ext = os.path.splitext(parent_file)
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path_to_flags_file = '/'.join([path_to_output_folder, f'{filename}_flags.csv'])
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#path_to_calibration_factors_file = '/'.join([path_to_output_folder, f'{filename}_calibration_factors.csv'])
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flags_table.to_csv(path_to_flags_file, index=False)
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status = stepUtils.record_data_lineage(path_to_flags_file, projectPath, metadata)
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print(f"Flags saved to {path_to_flags_file}")
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print(f"Data lineage saved to {path_to_output_dir}")
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#flags_table.to_csv(path_to_flags_file, index=False)
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# Read json and assign numeric flag to column
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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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