Parameter Estimation (import / export)
This example describes how to export the parameter estimation setup to a YaML file (or string), so that it can be easily modified later, and then loaded back (or applied to a different model).
When saving the file, it will be exported as a sequence, of experiments of the following form:
name: Experiment filename: data.txt type: Time-Course separator: "\t" first_row: 1 last_row: 102 header_row: 1 weight_method: Mean Square normalize_per_experiment: true mapping: - column: '# Time' type: time - column: Values[F16BP_obs] type: dependent cn: CN=Root, ... object: '[Fru1,6-P2]'
the individual fields are:
name: the name of the experiment
Time-Coursefor time course data (requires a mapping of type
timeto be specified), or
Steady-Statefor steady state data.
separator: the separator being used
first_row: the beginning of the experiment in the file (1 based)
last_row: the last row of the experiment
header_row: (optional) row with header information that can be later used in the
columnfield of the mappings.
weight_method: one of:
normalize_per_experiment: boolean indicating whether experiments should be scaled individual (
True) or over all defined experimentes (
mapping: sequence of column mappings described as follows.
The mapping descriptions contain the fields:
column: either an integer index (zero based), describing which column the mapping applies to. If the experiment has header information, the column may be a string with the (case sensitive) header the mapping applies to.
type: the type of the mapping for this column. One of
object: display name of the element to map to
cn: (optional) the CN to the reference to map to.
For columns of type
independentat least on of
cnneeds to be defined. (The
cnvalue takes preference).
weight: may be used for columns of type
dependentto customize the scale to be applied to the column. If not specified it will be automatically calculated based on the selected
We start by importing basico as usual (should that fail for you just
!pip install copasi-basico:
from basico import *
here we load an existing parameter estimation example, included with the distribution as example:
dm = load_example('PK')
now we can directly export the setup of the experimental data files as yaml string (or if you supply a filename to the function, it will be saved as file):
yaml_str = save_experiments_to_yaml() print("\n".join(yaml_str.split('\n')[:24])) # just restricting the amount of yaml to be printed here to the first experiment
- name: Experiment filename: e:/development/basico/basico/data\data_2.txt type: Time-Course separator: "\t" first_row: 1 last_row: 102 weight_method: Mean Square normalize_per_experiment: true header_row: 1 mapping: - column: '# Time' type: time - column: Values[F16BP_obs] type: dependent cn: CN=Root,Model=Pritchard2002_glycolysis,Vector=Compartments[cytosol],Vector=Metabolites[Fru1\,6-P2],Reference=Concentration object: '[Fru1,6-P2]' - column: Values[Glu_obs] type: dependent cn: CN=Root,Model=Pritchard2002_glycolysis,Vector=Compartments[cytosol],Vector=Metabolites[Glc(int)],Reference=Concentration object: '[Glc(int)]' - column: Values[Pyr_obs] type: dependent cn: CN=Root,Model=Pritchard2002_glycolysis,Vector=Compartments[cytosol],Vector=Metabolites[pyruvate],Reference=Concentration object: '[pyruvate]'
at this point you’d make modifications to you’d want to it. Remember, that the
cn is optional. The key points to keep in mind:
ensure that the row number for the experiment are consistent with the changes you make
if it is a time course experiment, ensure you have a column of type
the column specifier is either the index of the column, or the name if the experiment has headers.
once the changes are made, you can load the setup back into the model, using the
load_experiments_from_yaml function. This will remove all existing experiments from the file first.