nucml.exfor package

Submodules

nucml.exfor.data_utilities module

nucml.exfor.data_utilities.add_compound_nucleus_info(df, drop_q=False)[source]

Adds compound nucleus data to the original EXFOR DataFrame. This is performed by just appending the AME data by shifting the number of neutrons by 1.

Parameters

  • df (DataFrame) – Original EXFOR dataframe.

  • drop_q (bool, optional) – If True, it will drop all Q-value information. Defaults to False.

Returns

DataFrame

nucml.exfor.data_utilities.append_energy(e_array, df, Z, A, MT, nat_iso='I', one_hot=False, log=False, scale=False, scaler=None, to_scale=[], ignore_MT=False)[source]

The given energy array is appended to the passed DataFrame and feature values are coppied to these new rows.

Parameters

  • e_array (np.array) – Numpy array with the additional energy values to append.

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • MT (int) – Reaction channel (endf-coded).

  • nat_iso (str, optional) – “I” means isotopic while “N” means natural experimental campaigns. Defaults to “I”.

  • one_hot (bool, optional) – If True, the script assumes that the reaction channel is one-hot encoded. Defaults to False.

  • log (bool, optional) – If True, the log of both the Energy and Data features will be taken.

  • scale (bool, optional) – If True, the scaler object passed will be use to normalize the extracted information. Defaults to False.

  • scaler (object, optional) – Fitted scaler object. Defaults to None.

  • to_scale (list, optional) – List of feature names that are to be scaled. Defaults to [].

  • ignore_MT (bool, optional) – If True, the reaction channel is ignored and data for the total reaction is taken. Defaults to False.

Returns

DataFrame

nucml.exfor.data_utilities.expanding_dataset_energy(data, E_min, E_max, log, N, e_array=None)[source]

Expands a given DataFrames energy points by a given number of energy points between E_min and E_max.

Parameters

  • data (DataFrame) – DataFrame for which the Energy will be expanded.

  • E_min (int) – Starting point for energy expansion.

  • E_max (float) – Ending point for energy expansion.

  • log (bool) – If True, it assumes the Energy in the passed data is already in log form.

  • N (int) – Number of datapoints between E_min and E_max to create.

  • e_array (np.array, optional) – If e_array is provided, this gets appended overriding any other parameters. Defaults to None.

Returns

DataFrame

nucml.exfor.data_utilities.get_csv_for_ace(df, Z, A, model, scaler, to_scale, model_type=None, saving_dir=None, saving_filename=None, scale=True)[source]

Creates a CSV with the model predictions for a particular isotope in the appropiate format for the ACE utilities. The function returns a DataFrame which can then be saved as a CSV. The saving_dir argument provides a direct method by which to save the CSV file in the process.

Parameters

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • model (object) – Trained model object.

  • scaler (object) – Fitted scaler object.

  • to_scale (list) – List of feature names that are to be scaled.

  • model_type (str) – Type of model. Options include None meaning scikit-learn models, “tf”, for tensorflow, and “xgb” for gradient boosting machines.

  • saving_dir (str, optional) – Path-like string on where to save the CSV file. If given, the CSV file will be saved. Defaults to None.

  • saving_filename (str, optional) – Name for the CSV file to be saved. Defaults to None.

Returns

DataFrame

nucml.exfor.data_utilities.get_error_endf_exfor(endf, exfor_sample)[source]

Calculates error between an ENDF and EXFOR sample.

Parameters

  • endf (DataFrame) – ENDF DataFrame sample for the relevant isotope and reaction channel.

  • exfor_sample (DataFrame) – EXFOR DataFrame sample for the relevant isotope and reaction channel.

Returns

first dataframe contains original values while the second one

the calculated errors.

Return type

DataFrame, DataFrame

nucml.exfor.data_utilities.get_error_endf_new(endf, new_data)[source]

Calculates the error between a given dataframe of experimental datapoints to ENDF.

Parameters

  • endf (DataFrame) – DataFrame containing the ENDF datapoints.

  • new_data (DataFrame) – DataFrame containing the new experimental data points.

Returns

first dataframe contains original values while the second one

the calculated errors.

Return type

DataFrame, DataFrame

nucml.exfor.data_utilities.get_mt_error_exfor_endf(df, Z, A, scaler, to_scale)[source]

Calculates the error between EXFOR and ENDF for a given isotope.

Parameters

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • scaler (object) – Fitted scaler object.

  • to_scale (list) – List of feature names that are to be scaled.

Returns

DataFrame

nucml.exfor.data_utilities.get_mt_errors_exfor_ml(df, Z, A, scaler, to_scale, model)[source]

Calculates the error between EXFOR and ML predictions for a given isotope.

Parameters

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • scaler (object) – Fitted scaler object.

  • to_scale (list) – List of feature names that are to be scaled.

  • model (object) – Machine learning object.

Returns

DataFrame

nucml.exfor.data_utilities.load_element(df, Z, nat_iso='I', one_hot=False, scale=False, scaler=None, to_scale=[])[source]

Loads all datapoints avaliable for a particular element.

Parameters

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • nat_iso (str, optional) – “I” means isotopic while “N” means natural experimental campaigns. Defaults to “I”.

  • one_hot (bool, optional) – If True, the script assumes that the reaction channel is one-hot encoded. Defaults to False.

  • scale (bool, optional) – If True, the scaler object passed will be use to normalize the extracted information. Defaults to False.

  • scaler (object, optional) – Fitted scaler object. Defaults to None.

  • to_scale (list, optional) – List of feature names that are to be scaled. Defaults to [].

Returns

DataFrame

nucml.exfor.data_utilities.load_isotope(df, Z, A, nat_iso='I', one_hot=False, scale=False, scaler=None, to_scale=[])[source]

Loads all datapoints avaliable for a particular isotope.

Parameters

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • nat_iso (str, optional) – “I” means isotopic while “N” means natural experimental campaigns. Defaults to “I”.

  • one_hot (bool, optional) – If True, the script assumes that the reaction channel is one-hot encoded. Defaults to False.

  • scale (bool, optional) – If True, the scaler object passed will be use to normalize the extracted information. Defaults to False.

  • scaler (object, optional) – Fitted scaler object. Defaults to None.

  • to_scale (list, optional) – List of feature names that are to be scaled. Defaults to [].

Returns

DataFrame

nucml.exfor.data_utilities.load_newdata(datapath, df, Z, A, MT, nat_iso='I', one_hot=False, log=False, scale=False, scaler=None, to_scale=[])[source]

Loads new measurments and appends the appropiate EXFOR isotopic data. Assumes new data only have two columns: Energy and Data.

Parameters

  • datapath (str) – Path-like string of the new data file.

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • MT (int) – Reaction channel (endf-coded).

  • nat_iso (str, optional) – “I” means isotopic while “N” means natural experimental campaigns. Defaults to “I”.

  • one_hot (bool, optional) – If True, the script assumes that the reaction channel is one-hot encoded. Defaults to False.

  • log (bool, optional) – If True, the log of both the Energy and Data features will be taken.

  • scale (bool, optional) – If True, the scaler object passed will be use to normalize the extracted information. Defaults to False.

  • scaler (object, optional) – Fitted scaler object. Defaults to None.

  • to_scale (list, optional) – List of feature names that are to be scaled. Defaults to [].

Returns

DataFrame

nucml.exfor.data_utilities.load_samples(df, Z, A, MT, nat_iso='I', one_hot=False, scale=False, scaler=None, to_scale=[], mt_for='EXFOR')[source]

Extracts datapoints belonging to a particular isotope-reaction channel pair.

Parameters

  • df (DataFrame) – DataFrame containing all avaliable datapoints from where to extract the information.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • MT (int) – Reaction channel (ENDF-coded).

  • nat_iso (str, optional) – “I” means isotopic while “N” means natural experimental campaigns. Defaults to “I”.

  • one_hot (bool, optional) – If True, the script assumes that the reaction channel is one-hot encoded. Defaults to False.

  • scale (bool, optional) – If True, the scaler object passed will be use to normalize the extracted information. Defaults to False.

  • scaler (object, optional) – Fitted scaler object. Defaults to None.

  • to_scale (list, optional) – List of feature names that are to be scaled. Defaults to [].

Returns

DataFrame

nucml.exfor.data_utilities.make_predictions_from_df(df, Z, A, MT, model, model_type, scaler, to_scale, log=False, show=False)[source]

Returns predictions for all avaliable datapoints for a particular isotope-reaction channel pair.

Parameters

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • MT (int) – Reaction channel (endf-coded).

  • model (object) – Trained model object.

  • model_type (str) – Type of model. Options include None meaning scikit-learn models, “tf”, for tensorflow, and “xgb” for gradient boosting machines.

  • scaler (object) – Fitted scaler object.

  • to_scale (list) – List of feature names that are to be scaled.

  • log (bool, optional) – If True, the log of both the Energy and Data features will be taken.

  • show (bool, optional) – If True, a plot of the predictions will be rendered. Defaults to False.

Returns

np.array

nucml.exfor.data_utilities.make_predictions_w_energy(e_array, df, Z, A, MT, model, model_type, scaler, to_scale, one_hot=True, log=False, show=False, scale=True)[source]

Given an energy array and isotopic information, Predictions using a model are performed at the given energy points in the e_array for a given isotope.

Parameters

  • e_array (np.array) – Numpy array representing energy points at which inferences will be made.

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • MT (int) – Reaction channel (endf-coded).

  • model (object) – Trained model object.

  • model_type (str) – Type of model. Options include None meaning scikit-learn models, “tf”, for tensorflow, and “xgb” for gradient boosting machines.

  • scaler (object) – Fitted scaler object.

  • to_scale (list) – List of feature names that are to be scaled.

  • one_hot (bool, optional) – If True, the script assumes that the reaction channel is one-hot encoded. Defaults to False.

  • log (bool, optional) – If True, the log of both the Energy and Data features will be taken.

  • show (bool, optional) – If True, a plot of the predictions will be rendered. Defaults to False.

Returns

np.array

nucml.exfor.data_utilities.plot_exfor_w_references(df, Z, A, MT, nat_iso='I', new_data=Empty DataFrame Columns: [] Index: [], endf=Empty DataFrame Columns: [] Index: [], error=False, get_endf=True, reverse_log=False, legend_size=21, save=False, interpolate=False, legend=False, alpha=0.7, one_hot=False, log_plot=False, path='', ref=False, new_data_label='Additional Data', dpi=300, figure_size=(14, 10))[source]

Plots Cross Section for a particular Isotope with or without references.

Parameters

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • MT (int) – Reaction channel (endf-coded).

  • nat_iso (str, optional) – “I” means isotopic while “N” means natural experimental campaigns. Defaults to “I”.

  • new_data (DataFrame, optional) – New data for which to make predictions, get errors, and plot. Assumes it has all needed information. Defaults to empty_df.

  • endf (DataFrame, optional) – DataFrame containing the appropiate ENDF data to plot against. Defaults to empty_df.

  • error (bool, optional) – If True, error between the EXFOR and ENDF datapoints are calculated. Defaults to False.

  • get_endf (bool, optional) – If True, the endf file will be extracted to calculate errors and create plots. Defaults to False.

  • reverse_log (bool, optional) – If True, the log in Energy and Data is first removed from the passed dataframe. Defaults to False.

  • legend_size (int, optional) – Legend size in plots. Useful when there are many experimental campaigns. Defaults to 21.

  • save (bool, optional) – If True, the plot will be saved. Defaults to False.

  • interpolate (bool, optional) – If True, the EXFOR will be ploted as a line rather than scatter points. Defaults to False.

  • legend (bool, optional) – If True, a legend will appear in the image. Defaults to False.

  • alpha (float, optional) – Level of transparency of ENDF and EXFOR plots. Defaults to 0.7.

  • one_hot (bool, optional) – If True, the passed dataframe is assumed to be preprocessed. Defaults to False.

  • log_plot (bool, optional) – If True, log scales will be applied. Defaults to False.

  • path (str, optional) – Path-like string on which to save the rendered plots. Defaults to “”.

  • ref (bool, optional) – If True, EXFOR will be ploted per experimental campaign (one color for each). Defaults to False.

  • new_data_label (str, optional) – If new data is provided, this sets the label in the legend. Defaults to “Additional Data”.

Returns

All information requested including original data and errors are contained in a python dictionary.

Return type

dict

nucml.exfor.data_utilities.predicting_nuclear_xs_v2(df, Z, A, MT, model, to_scale, scaler, e_array='ace', log=False, model_type=None, new_data=Empty DataFrame Columns: [] Index: [], nat_iso='I', get_endf=False, inv_trans=False, show=False, plotter='plotly', save=False, path='', save_both=True, order_dict={})[source]

Predicts values for a given isotope-reaction channel pair. This all-in-one function allows to not only get predictions but also calculate the errors relative to the EXFOR and ENDF datapoints (if avaliable). In addition, the plotting capabilities allow the user to inspect the predictions in a typical cross section plot. In addition to predicting values at the original exfor datapoint energies, the .ACE energy grid is used for further comparison.

Parameters

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • MT (int) – Reaction channel (endf-coded).

  • model (object) – Trained model object.

  • to_scale (list) – List of feature names that are to be scaled.

  • scaler (object) – Fitted scaler object.

  • e_array (str, optional) – If “ace”, the energy grid from the appropiate ACE file is appended. An alternative is to provide a specific energy array. Defaults to “ace”.

  • log (bool, optional) – If True, it assumes the Energy is already in a log form. Defaults to False.

  • model_type (str) – Type of model. Options include None meaning scikit-learn models, “tf”, for tensorflow, and “xgb” for gradient boosting machines.

  • html (bool, optional) – If True, the plot will be rendered in an interactive browser tab. Defaults to False.

  • new_data (DataFrame, optional) – New data for which to make predictions, get errors, and plot. Assumes it has all needed information. Defaults to empty_df.

  • save (bool, optional) – If True, the plot will be saved. Defaults to False.

  • show (bool, optional) – If True, a plot of the predictions will be rendered. Defaults to False.

  • path (str, optional) – Path-like string on which to save the rendered plots. Defaults to “”.

  • nat_iso (str, optional) – “I” means isotopic while “N” means natural experimental campaigns. Defaults to “I”.

  • order_dict (dict, optional) – Order in which to plot the different lines. See plotly_ml_results() for more info. Defaults to {}.

  • get_endf (bool, optional) – If True, the endf file will be extracted to calculate errors and create plots. Defaults to False.

  • inv_trans (bool, optional) – If True, the returned data will be in its original form (not scaled). Defaults to False.

Returns

contains a variety of information including predictions, errors, and more.

Return type

dict

nucml.exfor.data_utilities.predicting_nuclear_xs_v2_no_norm(df, Z, A, MT, model, e_array='ace', log=False, model_type=None, new_data=Empty DataFrame Columns: [] Index: [], nat_iso='I', get_endf=False, show=False, plotter='plotly', save=False, path='', save_both=True, order_dict={})[source]

Predicts values for a given isotope-reaction channel pair. This all-in-one function allows to not only get predictions but also calculate the errors relative to the EXFOR and ENDF datapoints (if avaliable). In addition, the plotting capabilities allow the user to inspect the predictions in a typical cross section plot. In addition to predicting values at the original exfor datapoint energies, the .ACE energy grid is used for further comparison.

Parameters

  • df (DataFrame) – All avaliable experimental datapoints.

  • Z (int) – Number of protons.

  • A (int) – Atomic mass number.

  • MT (int) – Reaction channel (endf-coded).

  • model (object) – Trained model object.

  • to_scale (list) – List of feature names that are to be scaled.

  • scaler (object) – Fitted scaler object.

  • e_array (str, optional) – If “ace”, the energy grid from the appropiate ACE file is appended. An alternative is to provide a specific energy array. Defaults to “ace”.

  • log (bool, optional) – If True, it assumes the Energy is already in a log form. Defaults to False.

  • model_type (str) – Type of model. Options include None meaning scikit-learn models, “tf”, for tensorflow, and “xgb” for gradient boosting machines.

  • html (bool, optional) – If True, the plot will be rendered in an interactive browser tab. Defaults to False.

  • new_data (DataFrame, optional) – New data for which to make predictions, get errors, and plot. Assumes it has all needed information. Defaults to empty_df.

  • save (bool, optional) – If True, the plot will be saved. Defaults to False.

  • show (bool, optional) – If True, a plot of the predictions will be rendered. Defaults to False.

  • path (str, optional) – Path-like string on which to save the rendered plots. Defaults to “”.

  • nat_iso (str, optional) – “I” means isotopic while “N” means natural experimental campaigns. Defaults to “I”.

  • order_dict (dict, optional) – Order in which to plot the different lines. See plotly_ml_results() for more info. Defaults to {}.

  • get_endf (bool, optional) – If True, the endf file will be extracted to calculate errors and create plots. Defaults to False.

  • inv_trans (bool, optional) – If True, the returned data will be in its original form (not scaled). Defaults to False.

Returns

contains a variety of information including predictions, errors, and more.

Return type

dict

nucml.exfor.parsing module

nucml.exfor.parsing.csv_creator(heavy_path, tmp_path, mode, append_ame=True)[source]

Creates various CSV files from the information extracted using the get_all() function. This function is usually called after the get_all() function. The following CSV files will be created:

  • EXFOR_mode_ORIGINAL.csv: Contains the EXFOR database in it’s original state.

  • EXFOR_mode_ORIGINAL_w_AME.csv: Contains the same features as in the original CSV

    file plus AME data appended to each row. The appended AME is the original data therefore not containing natural element data.

Note: mode refers to the type of projectile selected in the ‘mode’ argument.

Parameters

  • heavy_path (str) – Path to directory where heavy files generated by the get_all() function are stored. This directory will also be used to store the resulting CSV files.

  • tmp_path (str) – Path to directory where temporary files are to be saved.

  • mode (str) – The reaction projectile of the originally provided C4 files.

  • append_ame (bool) – If True, the AME data will be appended to the final EXFOR CSV files. It is recommended this is always set to True. The features can be afterwards eliminated if wanted. Defaults to True.

Returns

None

nucml.exfor.parsing.get_all(c4_list, heavy_path, tmp_path, mode='neutrons')[source]

Retrieves all avaliable information from all .c4 files. This function combines the proccesses defined on:

  • get_c4_names()

  • get_raw_datapoints()

  • get_authors()

  • get_years()

  • get_institutes()

  • get_dates()

  • get_titles()

  • get_references()

  • get_reaction_notation()

  • get_datapoints_per_experiment()

It is optimized to run faster than running the individual functions.

Parameters

  • c4_list (list) – List containing paths to all .c4 files.

  • heavy_path (str) – Path to directory where heavy files are to be saved.

  • tmp_path (str) – Path to directory where temporary files are to be saved.

  • mode (str) – The reaction projectile of the provided C4 files.

Returns

None

Raises

FileNotFoundError – If no .c4 files are in the provided list, then an error is raised.

nucml.exfor.parsing.get_c4_names(c4_directory)[source]

Searches given directory for EXFOR-generated C4 files. It returns a list of relative paths for each found file.

Parameters

c4_directory (str) – Path to the directory containing all .c4 files.

Returns

Contains relative paths to each encountered .c4 file.

Return type

list

Raises

FileNotFoundError – If no C4 files are found an error will be raised.

nucml.exfor.parsing.impute_original_exfor(heavy_path, tmp_path, mode, append_ame=True, MF_number='3')[source]

Imputes missing values in the CSV files created using the csv_creator() function. It generates a new CSV files with filled missing values. The main features with considerable missing values are the Uncertainties in both Energy and Cross Section. It also limits the dataset to a particular type (MF ENDF code).

  • EXFOR_mode_MF3_AME_no_RawNaN: is a dataset created for personal use. It contains no

    missing values in both the EXFOR and the AME data entries. Furthermore, it is limited to reaction data rows (MF=3).

Parameters

  • heavy_path (str) – Path to directory where heavy files generated by the csv_creator() function are stored. This directory will also be used to store the resulting CSV files.

  • tmp_path (str) – Path to directory where temporary files are to be saved.

  • mode (str) – The reaction projectile of the originally provided C4 files.

  • append_ame (bool) – If True, the AME data will be appended to the final EXFOR CSV files. It is recommended this is always set to True. The features can be afterwards eliminated if wanted. Defaults to True.

  • MF_number (str, optional) – The MT ENDF code of data to retrieve and impute. Defaults to “3”.

Returns

None

nucml.exfor.plot module

nucml.exfor.plot.make_chlorine_paper_figure(df, dt_model, dt_scaler, knn_model, knn_scaler, to_scale, save=False, saving_dir='')[source]

Personal function used to create the Chlorine figure used in a conference summary.

Parameters

  • df (pd.DataFrame) – Dataframe containing all the relevant chlorine datapoints.

  • dt_model (object) – Trained scikit-learn Decision Tree regressor model.

  • dt_scaler (object) – Trained scikit-learn scaler object used to scale data for the dt_model.

  • knn_model (object) – Trained scikit-learn KNN regressor model.

  • knn_scaler (object) – Trained scikit-learn scaler object used to scale data fro the knn_model.

  • to_scale (list) – Features subject to transformation/normalization by the applicable scalers.

Returns

None

nucml.exfor.plot.ml_results(results_dict, order_dict={}, save_dir='', save=False, render_browser=False, show=False, paper=False, log=True, plot_type='plotly')[source]

Plots the machine learning predictions from the dictionary generated by the

Parameters

  • results_dict (dict) – Generated dictionary from the .

  • order_dict (dict, optional) – Order of plots. Defaults to {}.

  • save_dir (str, optional) – Path-like string where the figure will be saved. Defaults to ‘’.

  • save (bool, optional) – If True, the figure will be saved. Defaults to False.

  • render_browser (bool, optional) – If True, the plotly plot will be render in a new tab. Defaults to False.

  • show (bool, optional) – If True, the plot will be render. Defaults to False.

  • paper (bool, optional) – If True, the plot will be resized to 600 by 700 pixels. Defaults to False.

Returns

Plotly graph object.

Return type

object

nucml.exfor.plot.plot_limits(data, endf, new_data, y_hat, y_hat2, y_hat3)[source]

Sets new plot limits based on plotted data. It is a known issue that matplotlib “looses” the limits when thousand of scatter points are plotted. In these cases the limits need to be set manually. These occurs often for highly researched isotopes like U-235. ML-predictions can sometimes be outsied the default plot limits.

Note: Internal Function.

Parameters

  • data (DataFrame) – DataFrame containing the EXFOR or Predicted data.

  • endf (DataFrame) – DataFrame containing the ENDF data.

  • new_data (DataFrame) – DataFrame containing new external data if applicable.

  • y_hat (np.array) – Numpy array containing the ML-predictions for EXFOR energy points.

  • y_hat2 (np.array) – Numpy array containing the ML-predictions for ENDF energy points.

  • y_hat3 (np.array) – Numpy array containing the ML-predicitons for the new data points.

Returns

None

nucml.exfor.plot.plot_limits_ref(exfor, endf, new_data)[source]

Sets new plot limits based on plotted data. It is a known issue that matplotlib “looses” the limits when thousand of scatter points are plotted. In these cases the limits need to be set manually. These occurs often for highly researched isotopes like U-235.

Note: Internal Function.

Parameters

  • exfor_sample (pd.DataFrame) – Contains the EXFOR datapoints being plotted.

  • endf (pd.DataFrame) – Contains the ENDF datapoints being plotted.

  • new_data (pd.DataFrame) – Contains the New Datapoints being plotted if applicable.

Returns

None

Module contents