axis=1) # export result output_result = os.path.join(os.path.dirname(args.output_file), '{}'.format(comparison)) logging.debug('Path to file: {}'.format(output_result)) h.export_result_to_csv(result_df, output_result) logger.info('Data for {} exported to csv'.format(comparison)) return if __name__ == "__main__": args = get_args() rule_params = h.load_json_parameter(args.file_id) filename = h.filename(args.input_file) # get logger logpath = os.path.join(paths.global_data_dir, args.file_id, 'log/divide.log') logger = h.get_logger(logpath) # get data data_df = pd.read_csv(args.input_file, header=0, index_col=None) # parameters values_cols_prefix = rule_params['all']['values_cols_prefix'] # get contrast matrix comparison_df = pd.read_csv(args.comparison_file, index_col=0, header=0)
data_col_str = rule_params['all']['value_col_prefix'] + '_' data_col = df.filter(regex=data_col_str) #data_col = data_col.apply(pd.to_numeric) # à déplacer res = pd.concat([df[metadata_col], data_col], axis=1) return res def suppress_panel_genes(df): res = df[df['Class Name'] == 'Endogenous'] return res if __name__ == "__main__": args = get_args() rule_params = h.load_json_parameter(args.project, args.version) filename = h.filename(args.input_file) logpath = os.path.join(rule_params['all']['logpath'], 'NanoString_data_format.log') logger = h.get_logger(logpath) input_df = pd.read_csv(args.input_file, header=0, index_col=None, sep='\t') print(input_df.head()) if args.mapping_file: # annotate columns based on mapping file information df_metadata, df_numeric_data = split_data(input_df) header = df_metadata.iloc[0] # discard rows with annotations data = pd.concat([header, df_numeric_data], axis=1)