pyabc.copasi
Copasi
Simulations via COPASI (http://copasi.org).
- class pyabc.copasi.BasicoModel(sbml_file: str, changes: Dict[str, float] = None, change_unit: bool = True, method: str = 'stochastic', t0: float = None, duration: float = None, num_steps: int = None, automatic: bool = True, use_numbers: bool = False, output: List[str] = None, model_name: str = None)[source]
Bases:
Model
COPASI time series simulations via BasiCO.
This class is a
pyabc.Model
compliant wrapper around basico.run_time_course, allowing to update model parameters and use various simulation methods. BasiCO (https://basico.rtfd.io) is a simple Python interface to COPASI (http://copasi.org).The implementation is derived from an implementation by Frank Bergmann at https://github.com/fbergmann/pyabc-copasi.
- __call__(pars: Dict[str, float], return_raw: bool = False)[source]
Simulate data for given parameters.
Calls the time course and returns the selected result.
- __init__(sbml_file: str, changes: Dict[str, float] = None, change_unit: bool = True, method: str = 'stochastic', t0: float = None, duration: float = None, num_steps: int = None, automatic: bool = True, use_numbers: bool = False, output: List[str] = None, model_name: str = None)[source]
- Parameters:
sbml_file – SBML file containing the model definition.
changes – Parametric changes to apply to the SBML model.
change_unit – Whether to change units to 1, useful for discrete simulations (particle numbers).
method –
Simulation method, can be any method supported by basico.run_time_course, in particular:
deterministic, lsoda: the LSODA implementation
stochastic: the Gibson & Bruck Gillespie implementation
directMethod: Gillespie Direct Method
others: hybridode45, hybridlsoda, adaptivesa, tauleap, radau5, sde
t0 – Initial time point, duration, number of steps. Definition and combination as in basico.run_time_course.
duration – Initial time point, duration, number of steps. Definition and combination as in basico.run_time_course.
num_steps – Initial time point, duration, number of steps. Definition and combination as in basico.run_time_course.
automatic – Whether to use automatic steps, or the specified interval / number of steps.
use_numbers – Whether to return all elements collected.
output – Species to output. Defaults to all.
model_name – Model name, for identification e.g. in the database.
- apply_parameters(pars: Dict[str, float])[source]
Set the parameters of the model.
- Parameters:
pars – Parameters to apply, id-value dictionary. Local parameters are assumed to be named something like (reaction).local_parameter, where reaction is the name of the reaction, and local_parameter the local parameter. Specifically, local parameters are identified by the presence of brackets. Otherwise the parameter is expected to be a global one.