"""Translate ODEs in the YAML format into PEtab."""
import argparse
import os
import warnings

import libsbml as sbml
import pandas as pd
import petab
import yaml
from pathlib import Path


from .yaml2sbml import _parse_yaml_dict, _load_yaml_file
from .yaml_validation import _validate_yaml_from_dict


def yaml2petab(yaml_dir: str,
               output_dir: str,
               sbml_name: str,
               petab_yaml_name: str = None,
               measurement_table_name: str = None):
    """
    Translate a YAML model into a PEtab model.

    Takes a YAML file with the ODE specification, parses it, converts
    it into SBML format, and writes the SBML file. Further it translates the
    given information into PEtab tables.

    If a `petab_yaml_name` is given, a YAML file is created, that organizes
    the PEtab problem. If additionally a `measurement_table_file_name` is
    specified, this file name is written into the created YAML file.

    Arguments:
        yaml_dir : path to the YAML file with the ODEs specification
        output_dir: path the output file(s) are be written out
        sbml_name: name of SBML model
        petab_yaml_name: name of YAML organizing the PEtab problem.
        measurement_table_name: Name of measurement table
    """
    yaml_model_dict = _load_yaml_file(yaml_dir)
    _yaml2petab(yaml_model_dict,
                output_dir,
                sbml_name,
                petab_yaml_name,
                measurement_table_name)


def _yaml2petab(yaml_model_dict: dict,
                output_dir: str,
                model_name: str,
                petab_yaml_name: str = None,
                measurement_table_name: str = None):
    """
    Similar to 'yaml2petab', but takes a yaml_model_dict as input.

    If a petab_yaml_name is given, a .yaml file is created, that organizes
    the petab problem. If additionally a measurement_table_file_name is
    specified, this file name is written into the created .yaml file.

    Arguments:
        yaml_model_dict : dictionary, containing the yaml model
        output_dir: path the output file(s) are be written out
        model_name: name of SBML model
        petab_yaml_name: name of yaml organizing the PEtab problem.
        measurement_table_name: Name of measurement table
    """
    # validate yaml
    _validate_yaml_from_dict(yaml_model_dict)

    # output make directory, if it doesn't exist yet.
    if not os.path.exists(output_dir):
        os.mkdir(output_dir)

    if model_name.endswith('.xml') or model_name.endswith('.sbml'):
        sbml_dir = os.path.join(output_dir, model_name)
    else:
        sbml_dir = os.path.join(output_dir, model_name + '.xml')

    with warnings.catch_warnings():
        warnings.simplefilter("ignore", category=UserWarning)
        sbml_as_string = _parse_yaml_dict(yaml_model_dict,
                                          model_name)

    with open(sbml_dir, 'w') as f_out:
        f_out.write(sbml_as_string)

    # create petab tsv files:
    if model_name.endswith('.xml') or model_name.endswith('.sbml'):
        model_name = Path(model_name).stem

    _create_petab_tables_from_yaml(yaml_model_dict,
                                   output_dir,
                                   model_name)

    # create yaml file, that organizes the petab problem:
    if (petab_yaml_name is None) and (measurement_table_name is not None):

        warnings.warn('Since no petab_yaml_file_name is specified, the '
                      'specified measurement_table_name will have no effect.',
                      RuntimeWarning)

    elif petab_yaml_name is not None:
        _create_petab_problem_yaml(yaml_model_dict,
                                   output_dir,
                                   sbml_dir,
                                   petab_yaml_name,
                                   model_name,
                                   measurement_table_name)
    # validate PEtab tables:
    validate_petab_tables(sbml_dir, output_dir)


def _create_petab_tables_from_yaml(yaml_dict: dict,
                                   output_dir: str,
                                   model_name: str):
    """
    Parse the YAML dict to a PEtab observable/parameter table.

    The files will be named (if the corresponding information is in the YAML):
    `parameters_<model_name>.tsv`,
    `observables_<model_name>.tsv` and
    `experimental_conditions_<model_name>.tsv`.

    Arguments:
        yaml_dict: dict, that contains the yaml file.
        output_dir: directory, where the PEtab tables should be written.
    """
    parameter_table = _create_parameter_table(yaml_dict)
    parameter_table.to_csv(os.path.join(output_dir,
                                        f'parameters_{model_name}.tsv'),
                           sep='\t',
                           index=False)

    # create PEtab observable table, if observables occur in the yaml file.
    if 'observables' in yaml_dict.keys():
        observable_table = _create_observable_table(yaml_dict)
        observable_table.to_csv(os.path.join(output_dir,
                                             f'observables_{model_name}.tsv'),
                                sep='\t',
                                index=False)

    # create PEtab condition table, if conditions occur in the yaml file.
    if 'conditions' in yaml_dict.keys():
        condition_table = _create_condition_table(yaml_dict)
        condition_table.to_csv(
            os.path.join(output_dir,
                         f'experimental_conditions_{model_name}.tsv'),
            sep='\t',
            index=False)


def _create_petab_problem_yaml(yaml_dict: dict,
                               output_dir: str,
                               sbml_dir: str,
                               petab_yaml_name: str,
                               model_name: str,
                               measurement_table_name: str = None):
    """
    Create a YAML file, that can be used for defining a PEtab problem.

    Arguments:
        yaml_dict: dict, that contains the YAML file.
        output_dir: directory, where the PEtab tables should be written.
        sbml_dir: directory of the SBML model.
        petab_yaml_name: name of file, where PEtab YAML is written.
        model_name: name of the model, in order to name the PEtab tables.
        measurement_table_name: directory of the  measurement table.
    """
    petab_yaml_dict = {'format_version': 1,
                       'parameter_file': f'parameters_{model_name}.tsv',
                       'problems': [{'sbml_files':
                                         [os.path.basename(sbml_dir)]}]}

    # fill the corresponding entries, if they are contained in the yaml/input.

    if 'observables' in yaml_dict.keys():
        petab_yaml_dict['problems'][0]['observable_files'] = \
            [f'observables_{model_name}.tsv']

    if 'conditions' in yaml_dict.keys():
        petab_yaml_dict['problems'][0]['condition_files'] = \
            [f'experimental_conditions_{model_name}.tsv']

    if measurement_table_name is not None:
        petab_yaml_dict['problems'][0]['measurement_files'] = \
            [measurement_table_name]

    petab_yaml_dir = os.path.join(output_dir, petab_yaml_name)

    with open(petab_yaml_dir, 'w') as file:
        yaml.dump(petab_yaml_dict, file)


def _create_parameter_table(yaml_dict: dict):
    """
    Create a parameter table from the parameter block in `yaml_dict.

    Arguments:`
        yaml_dict

    Returns:
        parameter_table: pandas data frame containing the parameter table.
    """
    return _create_petab_table(yaml_dict['parameters'],
                               petab.PARAMETER_DF_REQUIRED_COLS,
                               petab.PARAMETER_DF_OPTIONAL_COLS)


def _create_observable_table(yaml_dict: dict):
    """
    Create an observable table from the observable block in `yaml_dict`.

    Arguments:
        yaml_dict

    Returns:
        observable_table: pandas data frame containing the observable table.
            (if observable block is not empty, else None)
    """
    return _create_petab_table(yaml_dict['observables'],
                               petab.OBSERVABLE_DF_REQUIRED_COLS,
                               petab.OBSERVABLE_DF_OPTIONAL_COLS)


def _create_condition_table(yaml_dict: dict):
    """
    Create a condition table from the condition block in `yaml_dict.

    Arguments:
        yaml_dict

    Returns:
        condition_table: pandas data frame containing the condition table.
            (if condition block is not empty, else None)
    """
    mandatory_id_list = [petab.CONDITION_ID]

    optional_id_list = [petab.CONDITION_NAME] + \
                       [p['parameterId'] for p in yaml_dict['parameters']] + \
                       [ode['stateId'] for ode in yaml_dict['odes']]

    return _create_petab_table(yaml_dict['conditions'],
                               mandatory_id_list,
                               optional_id_list)


def validate_petab_tables(sbml_dir: str,
                          output_dir: str):
    """
    Validate the PEtab tables via `petab.lint`.

    Throws an error if the PEtab tables do not follow the PEtab format
    standard.

    Arguments:
        sbml_dir: directory of the sbml
        output_dir: output directory for petab files

    Raises:
        Errors are raised by lint, if PEtab files are invalid...
    """
    model = sbml.readSBML(sbml_dir).getModel()
    model_name = model.getId()

    parameter_file_dir = \
        os.path.join(output_dir, f'parameters_{model_name}.tsv')
    observable_file_dir = \
        os.path.join(output_dir, f'observables_{model_name}.tsv')
    condition_table_dir = \
        os.path.join(output_dir, f'experimental_conditions_{model_name}.tsv')

    # check observable table, if the table exists
    if os.path.exists(observable_file_dir):
        observable_df = pd.read_csv(observable_file_dir,
                                    sep='\t',
                                    index_col='observableId')
        petab.lint.check_observable_df(observable_df)

    else:
        observable_df = None

    # check condition table, if the table exists
    if os.path.exists(condition_table_dir):
        condition_df = pd.read_csv(condition_table_dir,
                                   sep='\t',
                                   index_col='conditionId')
        petab.lint.check_condition_df(condition_df, model)

    # check parameter table
    parameter_df = pd.read_csv(parameter_file_dir,
                               sep='\t',
                               index_col='parameterId')

    petab.lint.check_parameter_df(parameter_df,
                                  sbml_model=model,
                                  observable_df=observable_df)


def _create_petab_table(block_list: list,
                        mandatory_id_list: list,
                        optional_id_list: id):
    """
    Create a PEtab table from the block_list in the yaml_dict.

    Arguments:
        block_list: entry from yaml_dict.
        mandatory_id_list: list of mandatory ids in the PEtab table
        optional_id_list: list of optional ids in the PEtab table

    Returns:
        petab_table: pandas data frame containing the petab table.
    """
    petab_table = pd.DataFrame({col_id: [] for col_id in mandatory_id_list})

    for row in block_list:
        _petab_table_add_row(petab_table, row)

    # check if every column is part of PEtab standard.
    for col_name in petab_table.head():
        if not (col_name in mandatory_id_list or col_name in optional_id_list):
            warnings.warn(f'PEtab warning: {col_name} is not part of the '
                          f'PEtab standard and hence might have noe effect.')
    return petab_table


def _petab_table_add_row(petab_table: pd.DataFrame, row_dict: dict):
    """
    Add a row defined by row_dict to the petab_table given by petab_table.

    After the function call the last row of petab_table has the entries

        petab_table[-1, key] = row_dict[key]

    for all keys of row_dict.

    Arguments:
        petab_table: pd.DataFrame containing the current petab table.
        row_dict: dict, that contains the values of the new row.
    """
    n_rows = petab_table.shape[0]

    for key in row_dict.keys():
        petab_table.loc[n_rows + 1, key] = row_dict[key]


def main():
    """Command-Line Interface."""
    parser = argparse.ArgumentParser(description='Takes in an ODE model in '
                                     '.yaml and converts it to a PEtab file.')

    parser.add_argument('yaml_file', type=str, help='Input yaml directory')
    parser.add_argument('output_dir', type=str, help='Output directory')
    parser.add_argument('model_name', type=str,
                        help='name of created SBML model.')
    parser.add_argument('-y', '--petab_yaml', type=str,
                        help='Optional argument, creates a petab .yml')
    parser.add_argument('-m', '--measurement_table', type=str,
                        help='Optional argument, path to measurement table')

    args = parser.parse_args()

    print(f'Path to yaml file: {args.yaml_file}')
    print(f'Output directory: {args.output_dir}')
    print(f'Path to sbml/petab files: {args.model_name}')

    print('Converting...')

    yaml2petab(args.yaml_file,
               args.output_dir,
               args.model_name,
               args.petab_yaml,
               args.measurement_table)


if __name__ == '__main__':
    main()

"""Translate ODEs in the YAML format into PEtab."""
import argparse
import os
import warnings

import libsbml as sbml
import pandas as pd
import petab
import yaml
from pathlib import Path


from .yaml2sbml import _parse_yaml_dict, _load_yaml_file
from .yaml_validation import _validate_yaml_from_dict


def yaml2petab(yaml_dir: str,
               output_dir: str,
               sbml_name: str,
               petab_yaml_name: str = None,
               measurement_table_name: str = None):
    """
    Translate a YAML model into a PEtab model.

    Takes a YAML file with the ODE specification, parses it, converts
    it into SBML format, and writes the SBML file. Further it translates the
    given information into PEtab tables.

    If a `petab_yaml_name` is given, a YAML file is created, that organizes
    the PEtab problem. If additionally a `measurement_table_file_name` is
    specified, this file name is written into the created YAML file.

    Arguments:
        yaml_dir : path to the YAML file with the ODEs specification
        output_dir: path the output file(s) are be written out
        sbml_name: name of SBML model
        petab_yaml_name: name of YAML organizing the PEtab problem.
        measurement_table_name: Name of measurement table
    """
    yaml_model_dict = _load_yaml_file(yaml_dir)
    _yaml2petab(yaml_model_dict,
                output_dir,
                sbml_name,
                petab_yaml_name,
                measurement_table_name)


def _yaml2petab(yaml_model_dict: dict,
                output_dir: str,
                model_name: str,
                petab_yaml_name: str = None,
                measurement_table_name: str = None):
    """
    Similar to 'yaml2petab', but takes a yaml_model_dict as input.

    If a petab_yaml_name is given, a .yaml file is created, that organizes
    the petab problem. If additionally a measurement_table_file_name is
    specified, this file name is written into the created .yaml file.

    Arguments:
        yaml_model_dict : dictionary, containing the yaml model
        output_dir: path the output file(s) are be written out
        model_name: name of SBML model
        petab_yaml_name: name of yaml organizing the PEtab problem.
        measurement_table_name: Name of measurement table
    """
    # validate yaml
    _validate_yaml_from_dict(yaml_model_dict)

    # output make directory, if it doesn't exist yet.
    if not os.path.exists(output_dir):
        os.mkdir(output_dir)

    if model_name.endswith('.xml') or model_name.endswith('.sbml'):
        sbml_dir = os.path.join(output_dir, model_name)
    else:
        sbml_dir = os.path.join(output_dir, model_name + '.xml')

    with warnings.catch_warnings():
        warnings.simplefilter("ignore", category=UserWarning)
        sbml_as_string = _parse_yaml_dict(yaml_model_dict,
                                          model_name)

    with open(sbml_dir, 'w') as f_out:
        f_out.write(sbml_as_string)

    # create petab tsv files:
    if model_name.endswith('.xml') or model_name.endswith('.sbml'):
        model_name = Path(model_name).stem

    _create_petab_tables_from_yaml(yaml_model_dict,
                                   output_dir,
                                   model_name)

    # create yaml file, that organizes the petab problem:
    if (petab_yaml_name is None) and (measurement_table_name is not None):

        warnings.warn('Since no petab_yaml_file_name is specified, the '
                      'specified measurement_table_name will have no effect.',
                      RuntimeWarning)

    elif petab_yaml_name is not None:
        _create_petab_problem_yaml(yaml_model_dict,
                                   output_dir,
                                   sbml_dir,
                                   petab_yaml_name,
                                   model_name,
                                   measurement_table_name)
    # validate PEtab tables:
    validate_petab_tables(sbml_dir, output_dir)


def _create_petab_tables_from_yaml(yaml_dict: dict,
                                   output_dir: str,
                                   model_name: str):
    """
    Parse the YAML dict to a PEtab observable/parameter table.

    The files will be named (if the corresponding information is in the YAML):
    `parameters_<model_name>.tsv`,
    `observables_<model_name>.tsv` and
    `experimental_conditions_<model_name>.tsv`.

    Arguments:
        yaml_dict: dict, that contains the yaml file.
        output_dir: directory, where the PEtab tables should be written.
    """
    parameter_table = _create_parameter_table(yaml_dict)
    parameter_table.to_csv(os.path.join(output_dir,
                                        f'parameters_{model_name}.tsv'),
                           sep='\t',
                           index=False)

    # create PEtab observable table, if observables occur in the yaml file.
    if 'observables' in yaml_dict.keys():
        observable_table = _create_observable_table(yaml_dict)
        observable_table.to_csv(os.path.join(output_dir,
                                             f'observables_{model_name}.tsv'),
                                sep='\t',
                                index=False)

    # create PEtab condition table, if conditions occur in the yaml file.
    if 'conditions' in yaml_dict.keys():
        condition_table = _create_condition_table(yaml_dict)
        condition_table.to_csv(
            os.path.join(output_dir,
                         f'experimental_conditions_{model_name}.tsv'),
            sep='\t',
            index=False)


def _create_petab_problem_yaml(yaml_dict: dict,
                               output_dir: str,
                               sbml_dir: str,
                               petab_yaml_name: str,
                               model_name: str,
                               measurement_table_name: str = None):
    """
    Create a YAML file, that can be used for defining a PEtab problem.

    Arguments:
        yaml_dict: dict, that contains the YAML file.
        output_dir: directory, where the PEtab tables should be written.
        sbml_dir: directory of the SBML model.
        petab_yaml_name: name of file, where PEtab YAML is written.
        model_name: name of the model, in order to name the PEtab tables.
        measurement_table_name: directory of the  measurement table.
    """
    petab_yaml_dict = {'format_version': 1,
                       'parameter_file': f'parameters_{model_name}.tsv',
                       'problems': [{'sbml_files':
                                         [os.path.basename(sbml_dir)]}]}

    # fill the corresponding entries, if they are contained in the yaml/input.

    if 'observables' in yaml_dict.keys():
        petab_yaml_dict['problems'][0]['observable_files'] = \
            [f'observables_{model_name}.tsv']

    if 'conditions' in yaml_dict.keys():
        petab_yaml_dict['problems'][0]['condition_files'] = \
            [f'experimental_conditions_{model_name}.tsv']

    if measurement_table_name is not None:
        petab_yaml_dict['problems'][0]['measurement_files'] = \
            [measurement_table_name]

    petab_yaml_dir = os.path.join(output_dir, petab_yaml_name)

    with open(petab_yaml_dir, 'w') as file:
        yaml.dump(petab_yaml_dict, file)


def _create_parameter_table(yaml_dict: dict):
    """
    Create a parameter table from the parameter block in `yaml_dict.

    Arguments:`
        yaml_dict

    Returns:
        parameter_table: pandas data frame containing the parameter table.
    """
    return _create_petab_table(yaml_dict['parameters'],
                               petab.PARAMETER_DF_REQUIRED_COLS,
                               petab.PARAMETER_DF_OPTIONAL_COLS)


def _create_observable_table(yaml_dict: dict):
    """
    Create an observable table from the observable block in `yaml_dict`.

    Arguments:
        yaml_dict

    Returns:
        observable_table: pandas data frame containing the observable table.
            (if observable block is not empty, else None)
    """
    return _create_petab_table(yaml_dict['observables'],
                               petab.OBSERVABLE_DF_REQUIRED_COLS,
                               petab.OBSERVABLE_DF_OPTIONAL_COLS)


def _create_condition_table(yaml_dict: dict):
    """
    Create a condition table from the condition block in `yaml_dict.

    Arguments:
        yaml_dict

    Returns:
        condition_table: pandas data frame containing the condition table.
            (if condition block is not empty, else None)
    """
    mandatory_id_list = [petab.CONDITION_ID]

    optional_id_list = [petab.CONDITION_NAME] + \
                       [p['parameterId'] for p in yaml_dict['parameters']] + \
                       [ode['stateId'] for ode in yaml_dict['odes']]

    return _create_petab_table(yaml_dict['conditions'],
                               mandatory_id_list,
                               optional_id_list)


def validate_petab_tables(sbml_dir: str,
                          output_dir: str):
    """
    Validate the PEtab tables via `petab.lint`.

    Throws an error if the PEtab tables do not follow the PEtab format
    standard.

    Arguments:
        sbml_dir: directory of the sbml
        output_dir: output directory for petab files

    Raises:
        Errors are raised by lint, if PEtab files are invalid...
    """
    model = sbml.readSBML(sbml_dir).getModel()
    model_name = model.getId()

    parameter_file_dir = \
        os.path.join(output_dir, f'parameters_{model_name}.tsv')
    observable_file_dir = \
        os.path.join(output_dir, f'observables_{model_name}.tsv')
    condition_table_dir = \
        os.path.join(output_dir, f'experimental_conditions_{model_name}.tsv')

    # check observable table, if the table exists
    if os.path.exists(observable_file_dir):
        observable_df = pd.read_csv(observable_file_dir,
                                    sep='\t',
                                    index_col='observableId')
        petab.lint.check_observable_df(observable_df)

    else:
        observable_df = None

    # check condition table, if the table exists
    if os.path.exists(condition_table_dir):
        condition_df = pd.read_csv(condition_table_dir,
                                   sep='\t',
                                   index_col='conditionId')
        petab.lint.check_condition_df(condition_df, model)

    # check parameter table
    parameter_df = pd.read_csv(parameter_file_dir,
                               sep='\t',
                               index_col='parameterId')

    petab.lint.check_parameter_df(parameter_df,
                                  sbml_model=model,
                                  observable_df=observable_df)


def _create_petab_table(block_list: list,
                        mandatory_id_list: list,
                        optional_id_list: id):
    """
    Create a PEtab table from the block_list in the yaml_dict.

    Arguments:
        block_list: entry from yaml_dict.
        mandatory_id_list: list of mandatory ids in the PEtab table
        optional_id_list: list of optional ids in the PEtab table

    Returns:
        petab_table: pandas data frame containing the petab table.
    """
    petab_table = pd.DataFrame({col_id: [] for col_id in mandatory_id_list})

    for row in block_list:
        _petab_table_add_row(petab_table, row)

    # check if every column is part of PEtab standard.
    for col_name in petab_table.head():
        if not (col_name in mandatory_id_list or col_name in optional_id_list):
            warnings.warn(f'PEtab warning: {col_name} is not part of the '
                          f'PEtab standard and hence might have noe effect.')
    return petab_table


def _petab_table_add_row(petab_table: pd.DataFrame, row_dict: dict):
    """
    Add a row defined by row_dict to the petab_table given by petab_table.

    After the function call the last row of petab_table has the entries

        petab_table[-1, key] = row_dict[key]

    for all keys of row_dict.

    Arguments:
        petab_table: pd.DataFrame containing the current petab table.
        row_dict: dict, that contains the values of the new row.
    """
    n_rows = petab_table.shape[0]

    for key in row_dict.keys():
        petab_table.loc[n_rows + 1, key] = row_dict[key]


def main():
    """Command-Line Interface."""
    parser = argparse.ArgumentParser(description='Takes in an ODE model in '
                                     '.yaml and converts it to a PEtab file.')

    parser.add_argument('yaml_file', type=str, help='Input yaml directory')
    parser.add_argument('output_dir', type=str, help='Output directory')
    parser.add_argument('model_name', type=str,
                        help='name of created SBML model.')
    parser.add_argument('-y', '--petab_yaml', type=str,
                        help='Optional argument, creates a petab .yml')
    parser.add_argument('-m', '--measurement_table', type=str,
                        help='Optional argument, path to measurement table')

    args = parser.parse_args()

    print(f'Path to yaml file: {args.yaml_file}')
    print(f'Output directory: {args.output_dir}')
    print(f'Path to sbml/petab files: {args.model_name}')

    print('Converting...')

    yaml2petab(args.yaml_file,
               args.output_dir,
               args.model_name,
               args.petab_yaml,
               args.measurement_table)


if __name__ == '__main__':
    main()

Translate ODEs in the YAML format into PEtab. Translate a YAML model into a PEtab model.      Takes a YAML file with the ODE specification, parses it, converts     it into SBML format, and writes the SBML file. Further it translates the     given information into PEtab tables.      If a `petab_yaml_name` is given, a YAML file is created, that organizes     the PEtab problem. If additionally a `measurement_table_file_name` is     specified, this file name is written into the created YAML file.      Arguments:         yaml_dir : path to the YAML file with the ODEs specification         output_dir: path the output file(s) are be written out         sbml_name: name of SBML model         petab_yaml_name: name of YAML organizing the PEtab problem.         measurement_table_name: Name of measurement table Similar to 'yaml2petab', but takes a yaml_model_dict as input.      If a petab_yaml_name is given, a .yaml file is created, that organizes     the petab problem. If additionally a measurement_table_file_name is     specified, this file name is written into the created .yaml file.      Arguments:         yaml_model_dict : dictionary, containing the yaml model         output_dir: path the output file(s) are be written out         model_name: name of SBML model         petab_yaml_name: name of yaml organizing the PEtab problem.         measurement_table_name: Name of measurement table # validate yaml # output make directory, if it doesn't exist yet. # create petab tsv files: # create yaml file, that organizes the petab problem: # validate PEtab tables: Parse the YAML dict to a PEtab observable/parameter table.      The files will be named (if the corresponding information is in the YAML):     `parameters_<model_name>.tsv`,     `observables_<model_name>.tsv` and     `experimental_conditions_<model_name>.tsv`.      Arguments:         yaml_dict: dict, that contains the yaml file.         output_dir: directory, where the PEtab tables should be written. # create PEtab observable table, if observables occur in the yaml file. # create PEtab condition table, if conditions occur in the yaml file. Create a YAML file, that can be used for defining a PEtab problem.      Arguments:         yaml_dict: dict, that contains the YAML file.         output_dir: directory, where the PEtab tables should be written.         sbml_dir: directory of the SBML model.         petab_yaml_name: name of file, where PEtab YAML is written.         model_name: name of the model, in order to name the PEtab tables.         measurement_table_name: directory of the  measurement table. # fill the corresponding entries, if they are contained in the yaml/input. Create a parameter table from the parameter block in `yaml_dict.      Arguments:`         yaml_dict      Returns:         parameter_table: pandas data frame containing the parameter table. Create an observable table from the observable block in `yaml_dict`.      Arguments:         yaml_dict      Returns:         observable_table: pandas data frame containing the observable table.             (if observable block is not empty, else None) Create a condition table from the condition block in `yaml_dict.      Arguments:         yaml_dict      Returns:         condition_table: pandas data frame containing the condition table.             (if condition block is not empty, else None) Validate the PEtab tables via `petab.lint`.      Throws an error if the PEtab tables do not follow the PEtab format     standard.      Arguments:         sbml_dir: directory of the sbml         output_dir: output directory for petab files      Raises:         Errors are raised by lint, if PEtab files are invalid... # check observable table, if the table exists # check condition table, if the table exists # check parameter table Create a PEtab table from the block_list in the yaml_dict.      Arguments:         block_list: entry from yaml_dict.         mandatory_id_list: list of mandatory ids in the PEtab table         optional_id_list: list of optional ids in the PEtab table      Returns:         petab_table: pandas data frame containing the petab table. # check if every column is part of PEtab standard. Add a row defined by row_dict to the petab_table given by petab_table.      After the function call the last row of petab_table has the entries          petab_table[-1, key] = row_dict[key]      for all keys of row_dict.      Arguments:         petab_table: pd.DataFrame containing the current petab table.         row_dict: dict, that contains the values of the new row. Command-Line Interface.