diff --git a/openbis_lib.py b/openbis_lib.py deleted file mode 100644 index d9a34e9..0000000 --- a/openbis_lib.py +++ /dev/null @@ -1,270 +0,0 @@ -import pandas as pd -import logging -import os -import datetime -from pybis import Openbis -import hidden - -admissible_props_list = ['$name', 'filenumber', 'default_experiment.experimental_results', - 'dataquality', '$xmlcomments', '$annotations_state', - 'sample_name', 'position_x', 'position_y', 'position_z', 'temp', 'cell_pressure', 'gas_flow_setting', 'sample_notes', - 'beamline', 'photon_energy', 'slit_entrance_v', 'slit_exit_v', 'izero', - 'slit_exit_h', 'hos', 'cone', 'endstation', 'hof', - 'method_name', 'region', 'lens_mode', 'acq_mode', 'dwell_time', 'frames', 'passenergy', - 'iterations', 'sequenceiterations', 'ke_range_center', 'ke_step'] - - -def initialize_openbis_obj(): - - # TODO: implement a more secure authentication method. - openbis_obj = Openbis('https://openbis-psi.labnotebook.ch/openbis/webapp/eln-lims/?menuUniqueId=null&viewName=showBlancPage&viewData=null', verify_certificates=False) - openbis_obj.login(hidden.username,hidden.password) - - return openbis_obj - -def align_datetime_observation_windows(df_h5: pd.DataFrame, df_openbis: pd.DataFrame, h5_datetime_var: str = 'lastModifiedDatestr', ob_datetime_var: str = 'registrationDate') -> pd.DataFrame: - - """ returns filtered/reduced versions of 'df' and 'df_ref' with aligned datetime observation windows. - That is, the datetime variable range is the same for the returned dataframes.""" - #""returns a filtered or reduced version of 'df' by removing all rows that are outside the datetime variable overlapping region between 'df' and 'df_ref'. - #""" - - #df_h5['lastModifiedDatestr'] = df_h5['lastModifiedDatestr'].astype('datetime64[ns]') - #df_h5 = df_h5.sort_values(by='lastModifiedDatestr') - - if not (h5_datetime_var in df_h5.columns.to_list() and ob_datetime_var in df_openbis.columns.to_list()): - #TODO: Check if ValueError is the best type of error to raise here - raise ValueError("Dataframes 'df' and 'df_ref' must contain columns 'datetime_var' and 'datetime_var_ref', storing values in suitable datetime string format (e.g., yyyy-mm-dd hh:mm:ss).") - - df_h5[h5_datetime_var] = df_h5[h5_datetime_var].astype('datetime64[ns]') - df_openbis[ob_datetime_var] = df_openbis[ob_datetime_var].astype('datetime64[ns]') - - min_timestamp = max([df_openbis[ob_datetime_var].min(), df_h5[h5_datetime_var].min()]) - max_timestamp = min([df_openbis[ob_datetime_var].max(), df_h5[h5_datetime_var].max()]) - - # Determine overlap between df and df_ref, and filters out all rows from df with datetime variable outside the overlapping datetime region. - datetime_overlap_indicator = (df_h5[h5_datetime_var] >= min_timestamp) & (df_h5[h5_datetime_var] <= max_timestamp) - df_h5 = df_h5.loc[datetime_overlap_indicator,:] - - datetime_overlap_indicator = (df_openbis[ob_datetime_var] >= min_timestamp) & (df_openbis[ob_datetime_var] <= max_timestamp) - df_openbis = df_openbis.loc[datetime_overlap_indicator,:] - - df_h5 = df_h5.sort_values(by=h5_datetime_var) - df_openbis = df_openbis.sort_values(by=ob_datetime_var) - - return df_h5, df_openbis - -def reformat_openbis_dataframe_filenumber(df_openbis): - - if not 'FILENUMBER' in df_openbis.columns: - raise ValueError('df_openbis does not contain the column "FILENUMBER". Make sure you query (e.g., o.get_samples(props=["filenumbe"])) that before creating df_openbis.') - #if not 'name' in df.columns: - # raise ValueError("df does not contain the column 'name'. Ensure df complies with Throsten's Table's format.") - - # Augment df_openbis with 'name' column consitent with Thorsten's naming convention - name_list = ['0' + item.zfill(3) + item.zfill(3) for item in df_openbis['FILENUMBER']] - df_openbis['REFORMATED_FILENUMBER'] = pd.Series(name_list, index=df_openbis.index) - - return df_openbis - -def pair_openbis_and_h5_dataframes(df_openbis, df_h5, pairing_ob_var: str, pairing_h5_var: str): - - """ Pairs every row (or openbis sample) in 'df_openbis' with a set of rows (or measurements) in 'df_h5' by matching the i-th row in 'df_h5' - with the rows of 'df_h5' that satisfy the string df_openbis.loc[i,pairing_var_1] is contained in the string df_h5[i,pairing_var_2] - - Example: pairing_var_1, pairing_var_2 = reformated 'REFORMATED_FILENUMBER', 'name' - - """ - # Reformat openbis dataframe filenumber so that it can be used to find associated measurements in h5 dataframe - df_openbis = reformat_openbis_dataframe_filenumber(df_openbis) - - related_indices_list = [] - for sample_idx in df_openbis.index: - sample_value = df_openbis.loc[sample_idx,pairing_ob_var] - tmp_list = [sample_value in item[0:item.find('_')] for item in df_h5[pairing_h5_var]] - related_indices_list.append(df_h5.index[tmp_list]) - - print('Paring openbis sample: ' + df_openbis.loc[sample_idx,pairing_ob_var]) - print('with reformated FILENUMBER: ' + sample_value) - print('to following measurements in h5 dataframe:') - print(df_h5.loc[df_h5.index[tmp_list],'name']) - print('\n') - - df_openbis['related_h5_indices'] = pd.Series(related_indices_list, index=df_openbis.index) - - return df_openbis - - -def range_cols_2_string(df,lb_var,ub_var): - - if not sum(df.loc[:,ub_var]-df.loc[:,lb_var])==0: - #tmp_list = ['-'.join([str(round(df.loc[i,lb_var],2)),str(round(df.loc[i,ub_var],1))]) for i in df.index] - tmp_list = ['-'.join(["{:.1f}".format(df.loc[i,lb_var]),"{:.1f}".format(df.loc[i,ub_var])]) for i in df.index] - elif len(df.loc[:,lb_var].unique())>1: # check if values are different - #tmp_list = [str(round(df.loc[i,lb_var],2)) for i in df.index] - tmp_list = ["{:.1f}".format(df.loc[i,lb_var]) for i in df.index] - else: - #tmp_list = [str(round(df.loc[0,lb_var],2))] - tmp_list = ["{:.1f}".format(df.loc[0,lb_var])] - return '/'.join(tmp_list) - -def col_2_string(df,column_var): - - if not column_var in df.columns: - raise ValueError("'column var must belong in df.columns") - - #tmp_list = [str(round(item,1)) for item in df[column_var]] - tmp_list = ["{:.2f}".format(item) for item in df[column_var]] - if len(df[column_var].unique())==1: - tmp_list = [tmp_list[0]] - - return '/'.join(tmp_list) - - -def compute_openbis_sample_props_from_h5(df_openbis, df_h5, sample_idx): - - prop2attr = {'sample_name':'sample', # ask Throsten whether this assignment is correct or not - 'position_x':'smplX_mm', - 'position_y':'smplY_mm', - 'position_z':'smplZ_mm', - 'temp':'sampleTemp_dC', - 'cell_pressure':'cellPressure_mbar', - #'gas_flow_setting': '', - 'method_name':'regionName', # measurement type: XPS or NEXAFS - 'region':'regionName', # VB/N1s/C1s - 'passenergy':'regionName', # REAL - - 'photon_energy':'xRayEkinRange_eV', - 'dwell_time':'scientaDwellTime_ms', - 'acq_mode':'scientaAcquisitionMode', - 'ke_range_center':'scientaEkinRange_eV', - 'ke_step':'scientaEkinStep_eV', - 'lens_mode':'scientaLensMode' - } - - sample_identifier = df_openbis.loc[sample_idx,'identifier'] - props_dict = {'FILENUMBER' : df_openbis.loc[sample_idx,'FILENUMBER']} - - #props_dict = {} - - if not len(df_openbis.loc[sample_idx,'related_h5_indices']): - props_dict['identifier'] = sample_identifier - return props_dict - - reduced_df_h5 = df_h5.loc[df_openbis.loc[sample_idx,'related_h5_indices'],:] - reduced_df_h5 = reduced_df_h5.reset_index() - - # include related_samples key for validation purposes. Related samples are used to compute average and/or combined openbis properties. - related_sample_list = [reduced_df_h5['name'][index] for index in reduced_df_h5['name'].index] - related_samples = ' / '.join(related_sample_list) - props_dict['Subject_samples'] = related_samples - - props_dict['sample_name'] = reduced_df_h5['sample'].unique()[0] if len(reduced_df_h5['sample'].unique())==1 else '/'.join(reduced_df_h5['sample'].tolist()) - - if not 'NEXAFS' in reduced_df_h5['regionName'].iloc[0]: - props_dict['identifier'] = sample_identifier - props_dict['method_name'] = 'XPS' - for item_idx in reduced_df_h5.index: - item = reduced_df_h5.loc[item_idx,'regionName'] - if item_idx > 0: - props_dict['region'] = props_dict['region'] + '/' + item[0:item.find('_')] - #props_dict['dwell_time'] = props_dict['dwell_time'] + '/' + str(reduced_df_h5.loc[item_idx,'scientaDwellTime_ms']) - #props_dict['ke_range_center'] = props_dict['ke_range_center'] + '/' + str(round(reduced_df_h5.loc[item_idx,['scientaEkinRange_eV_1','scientaEkinRange_eV_2']].mean(),2)) - #props_dict['ke_step_center'] = props_dict['ke_step_center'] + '/' + str(reduced_df_h5.loc[item_idx,'scientaEkinStep_eV']) - #props_dict['passenergy'].append(float(item[item.find('_')+1:item.find('eV')])) - else: - props_dict['region'] = item[0:item.find('_')] - #props_dict['dwell_time'] = str(reduced_df_h5.loc[item_idx,'scientaDwellTime_ms']) - #props_dict['ke_range_center'] = str(round(reduced_df_h5.loc[item_idx,['scientaEkinRange_eV_1','scientaEkinRange_eV_2']].mean(),2)) - #props_dict['ke_step_center'] = str(reduced_df_h5.loc[item_idx,'scientaEkinStep_eV']) - - #props_dict['passenergy'] = reduced_df_h5.loc[:,'scientaPassEnergy_eV'].min() - - else: - props_dict = {'identifier':sample_identifier,'method_name':'NEXAFS'} - - - #props_dict['temp'] = round(reduced_df_h5['sampleTemp_dC'].mean(),2) - #props_dict['cell_pressure'] = round(reduced_df_h5['cellPressure_mbar'].mean(),2) - props_dict['temp'] = "{:.2f}".format(reduced_df_h5['sampleTemp_dC'].mean()) - props_dict['cell_pressure'] = "{:.2f}".format(reduced_df_h5['cellPressure_mbar'].mean()) - - reduced_df_h5['scientaDwellTime_ms'] = reduced_df_h5['scientaDwellTime_ms']*1e-3 # covert ms to seconds - props_dict['dwell_time'] = col_2_string(reduced_df_h5,'scientaDwellTime_ms') - props_dict['passenergy'] = col_2_string(reduced_df_h5,'scientaPassEnergy_eV') - props_dict['ke_step_center'] = col_2_string(reduced_df_h5,'scientaEkinStep_eV') - #props_dict['photon_energy'] =round(reduced_df_h5[['xRayEkinRange_eV_1','xRayEkinRange_eV_2']].mean(axis=1)[0],2) - props_dict['photon_energy'] = range_cols_2_string(reduced_df_h5,'xRayEkinRange_eV_1','xRayEkinRange_eV_2') - props_dict['ke_range_center'] = range_cols_2_string(reduced_df_h5,'scientaEkinRange_eV_1','scientaEkinRange_eV_2') - - props_dict['lens_mode'] = reduced_df_h5['scientaLensMode'][0] - props_dict['acq_mode'] = reduced_df_h5['scientaAcquisitionMode'][0] - - props_dict['position_x'] = "{:.2f}".format(reduced_df_h5.loc[:,'smplX_mm'].mean()) # round(reduced_df_h5.loc[:,'smplX_mm'].mean(),2) - props_dict['position_y'] = "{:.2f}".format(reduced_df_h5.loc[:,'smplY_mm'].mean()) - props_dict['position_z'] = "{:.2f}".format(reduced_df_h5.loc[:,'smplZ_mm'].mean()) - - - - return props_dict - - - -def single_sample_update(sample_props_dict,sample_collection,props_include_list): - - """ Updates sample in openbis database specified in sample_props_dict, which must belong in sample_collection (i.e., result of openbis_obj.get_samples(...)) """ - - try: - sample_path_identifier = sample_props_dict['identifier'] #path-like index - sample = sample_collection[sample_path_identifier] - for prop in sample_props_dict.keys(): - if (prop in admissible_props_list) and (prop in props_include_list): - sample.props[prop] = sample_props_dict[prop] - sample.save() - except Exception: - logging.error(Exception) - - return 0 - - -def sample_batch_update(openbis_obj,sample_collection,df_openbis,df_h5,props_include_list): - - """ See """ - - if not 'related_h5_indices' in df_openbis.columns: - raise ValueError("Input dataframe 'df_openbis' must contain a column named 'related_h5_indeces', resulting from suitable proprocessing steps.") - - # TODO: as a safeguard, create exclude list containing properties that must not be changed - exclude_list = ['filenumber','FILENUMBER','identifier'] - for item in props_include_list: - if item in exclude_list: - props_include_list.remove(item) - - trans = openbis_obj.new_transaction() - for sample_idx in len(range(df_openbis['identifier'])): - - props_dict = compute_openbis_sample_props_from_h5(df_openbis, df_h5, sample_idx) - sample_path_identifier = props_dict['identifier'] #path-like index - sample = sample_collection[sample_path_identifier] - - for prop in props_dict.keys(): - if prop in props_include_list: - sample.props[prop] = props_dict[prop] - - trans.add(sample) - - trans.commit() - - return 0 - -def conduct_dataframe_preprocessing_steps(df_h5, df_openbis): - - if not 'lastModifiedDatestr'in df_h5.columns: - raise ValueError('') - - df_h5, df_openbis = align_datetime_observation_windows(df_h5, df_openbis, 'lastModifiedDatestr' , 'registrationDate') - df_openbis = pair_openbis_and_h5_dataframes(df_openbis, df_h5, 'REFORMATED_FILENUMBER', 'name') - - return df_h5, df_openbis - -