pycotools3.model.InsertParameters

class pycotools3.model.InsertParameters(model, parameter_dict=None, df=None, parameter_path=None, index=0, quantity_type='concentration', inplace=False)

Parse parameters into a copasi model

Insert parameters from a file, dictionary or a pandas dataframe into a copasi file.

__init__(model, parameter_dict=None, df=None, parameter_path=None, index=0, quantity_type='concentration', inplace=False)

Parameters:

• model (Model) – The model to parse parameters into
• parameter_dict (dict) – Default None. If not None, dict[parameter_name] = parameter_value
• df (pandas.DataFrame) – Default None. If not None, a dataframe containing parameters to insert
• parameter_path (str) – Default None. If not None a path to parameter estimation output file
• index (int) – Default 0 (best RSS). When multiple parameter sets available, rank of best fit you want to insert
• quantity_type (str) – concentration (default) or particle_numbers
• inplace (bool) – Whether to operate inplace or return a new model

Methods

__init__(model[, parameter_dict, df, …])	param model:The model to parse parameters into
insert()	User other methods defined in this class to insert parameters into the model :return:
insert_compartments()	insert new parameters into compartment :return:
insert_global_quantities()	insert new parameters into compartment :return:
insert_locals()	return:
insert_metabolites()	insert new parameters into compartment :return:
read_model(m)	param m:
to_dict()	Args:

Attributes

parameters

Get parameters depending on the type of input.

insert()

User other methods defined in this class to insert parameters into the model :return:

Args:

Returns:

insert_compartments()

insert new parameters into compartment :return:

Args:

Returns:

insert_global_quantities()

insert new parameters into compartment :return:

Args:

Returns:

insert_locals()

Returns:

insert_metabolites()

insert new parameters into compartment :return:

Args:

Returns:

parameters

Get parameters depending on the type of input. Converge on a pandas dataframe. Columns = parameters, rows = parameter sets

Use check parameter consistency to see whether headers have been pruned or not. If not try pruning them

Args:

Returns:

to_dict()

Args:

Returns:return: