Convert notebooks

Author: flo-schu

docs/source/user_guide/framework_overview.ipynb

@@ -13,7 +13,7 @@
    "id": "eda0dec4",
    "metadata": {},
    "source": [
-    "Pymob is built around {class}`pymob.simulation.Simulation`, which is the object where all necessary information are pooled. For configuration, `pymob` relies on {class}`pymob.sim.config.Config`, which uses [`pydantic`](https://docs.pydantic.dev/latest/) to validate the configuration, before it is used to set up the simulation. "
+    "Pymob is built around {class}`pymob.simulation.SimulationBase`, which is the object where all necessary information are pooled. For configuration, `pymob` relies on {class}`pymob.sim.config.Config`, which uses [`pydantic`](https://docs.pydantic.dev/latest/) to validate the configuration, before it is used to set up the simulation. "
    ]
   },
   {

docs/source/user_guide/framework_overview.md

@@ -1,6 +1,6 @@
 # Framework overview
 
-Pymob is built around {class}`pymob.simulation.Simulation`, which is the object where all necessary information are pooled. For configuration, `pymob` relies on {class}`pymob.sim.config.Config`, which uses [`pydantic`](https://docs.pydantic.dev/latest/) to validate the configuration, before it is used to set up the simulation. 
+Pymob is built around {class}`pymob.simulation.SimulationBase`, which is the object where all necessary information are pooled. For configuration, `pymob` relies on {class}`pymob.sim.config.Config`, which uses [`pydantic`](https://docs.pydantic.dev/latest/) to validate the configuration, before it is used to set up the simulation. 
 
 
 ```python
@@ -16,7 +16,7 @@ sim.config
 
 
 
-    Config(case_study=Casestudy(init_root='/home/flo-schu/projects/pymob/docs/source/user_guide', root='.', name='quickstart', scenario='test', package='case_studies', modules=['sim', 'mod', 'prob', 'data', 'plot'], simulation='Simulation', output=None, data=None, observations='observations.nc', logging='DEBUG', logfile=None, output_path='case_studies/quickstart/results/test', data_path='case_studies/quickstart/data', default_settings_path='case_studies/quickstart/scenarios/test/settings.cfg'), simulation=Simulation(model=None, solver=None, y0=[], x_in=[], input_files=[], n_ode_states=1, batch_dimension='batch_id', x_dimension='time', modeltype='deterministic', solver_post_processing=None, seed=1), data_structure=Datastructure(y=DataVariable(dimensions=['x'], min=-5.690912333645177, max=5.891166954282328, observed=True, dimensions_evaluator=None)), solverbase=Solverbase(x_dim='time', exclude_kwargs_model=('t', 'time', 'x_in', 'y', 'x', 'Y', 'X'), exclude_kwargs_postprocessing=('t', 'time', 'interpolation', 'results')), jaxsolver=Jaxsolver(diffrax_solver='Dopri5', rtol=1e-06, atol=1e-07, pcoeff=0.0, icoeff=1.0, dcoeff=0.0, max_steps=100000, throw_exception=True), inference=Inference(eps=1e-08, objective_function='total_average', n_objectives=1, objective_names=[], backend=None, extra_vars=[], plot=None, n_predictions=100), model_parameters=Modelparameters(a=Param(name=None, value=0.0, dims=(), prior=None, min=None, max=None, step=None, hyper=False, free=False), b=Param(name=None, value=3.0, dims=(), prior=RandomVariable(distribution='lognorm', parameters={'scale': 1, 's': 1}, obs=None, obs_inv=None), min=None, max=None, step=None, hyper=False, free=True), sigma_y=Param(name=None, value=0.0, dims=(), prior=RandomVariable(distribution='lognorm', parameters={'scale': 1, 's': 1}, obs=None, obs_inv=None), min=None, max=None, step=None, hyper=False, free=True)), error_model=Errormodel(y=RandomVariable(distribution='normal', parameters={'loc': y, 'scale': sigma_y}, obs=None, obs_inv=None)), multiprocessing=Multiprocessing(cores=1), inference_pyabc=Pyabc(sampler='SingleCoreSampler', population_size=100, minimum_epsilon=0.0, min_eps_diff=0.0, max_nr_populations=1000, database_path='/tmp/pyabc.db'), inference_pyabc_redis=Redis(password='nopassword', port=1111, n_predictions=50, history_id=-1, model_id=0), inference_pymoo=Pymoo(algortihm='UNSGA3', population_size=100, max_nr_populations=1000, ftol=1e-05, xtol=1e-07, cvtol=1e-07, verbose=True), inference_numpyro=Numpyro(user_defined_probability_model=None, user_defined_error_model=None, user_defined_preprocessing=None, gaussian_base_distribution=False, kernel='nuts', init_strategy='init_to_uniform', chains=1, draws=2000, warmup=1000, thinning=1, nuts_draws=2000, nuts_step_size=0.8, nuts_max_tree_depth=10, nuts_target_accept_prob=0.8, nuts_dense_mass=True, sa_adapt_state_size=None, svi_iterations=10000, svi_learning_rate=0.0001))
+    Config(case_study=Casestudy(init_root='/home/flo-schu/projects/pymob/docs/source/user_guide', root='.', name='quickstart', version=None, pymob_version='0.5.3', scenario='test', package='case_studies', modules=['sim', 'mod', 'prob', 'data', 'plot'], simulation='Simulation', output=None, data=None, observations='observations.nc', logging='DEBUG', logfile=None, output_path='case_studies/quickstart/results/test', data_path='case_studies/quickstart/data', default_settings_path='case_studies/quickstart/scenarios/test/settings.cfg'), simulation=Simulation(model=None, solver=None, y0=[], x_in=[], input_files=[], n_ode_states=1, batch_dimension='batch_id', x_dimension='x', modeltype='deterministic', solver_post_processing=None, seed=1), data_structure=Datastructure(y=DataVariable(dimensions=['x'], min=-5.690912333645177, max=5.891166954282328, observed=True, dimensions_evaluator=None)), solverbase=Solverbase(x_dim='time', exclude_kwargs_model=('t', 'time', 'x_in', 'y', 'x', 'Y', 'X'), exclude_kwargs_postprocessing=('t', 'time', 'interpolation', 'results')), jaxsolver=Jaxsolver(diffrax_solver='Dopri5', rtol=1e-06, atol=1e-07, pcoeff=0.0, icoeff=1.0, dcoeff=0.0, max_steps=100000, throw_exception=True), inference=Inference(eps=1e-08, objective_function='total_average', n_objectives=1, objective_names=[], backend=None, extra_vars=[], plot=None, n_predictions=100), model_parameters=Modelparameters(a=Param(name=None, value=0.0, dims=(), prior=None, min=None, max=None, step=None, hyper=False, free=False), b=Param(name=None, value=3.0, dims=(), prior=RandomVariable(distribution='lognorm', parameters={'scale': 1, 's': 1}, obs=None, obs_inv=None), min=-5.0, max=5.0, step=None, hyper=False, free=True), sigma_y=Param(name=None, value=0.0, dims=(), prior=RandomVariable(distribution='lognorm', parameters={'scale': 1, 's': 1}, obs=None, obs_inv=None), min=0.0, max=1.0, step=None, hyper=False, free=True)), error_model=Errormodel(y=RandomVariable(distribution='normal', parameters={'loc': y, 'scale': sigma_y}, obs=None, obs_inv=None)), multiprocessing=Multiprocessing(cores=1), inference_pyabc=Pyabc(sampler='SingleCoreSampler', population_size=100, minimum_epsilon=0.0, min_eps_diff=0.0, max_nr_populations=1000, database_path='/tmp/pyabc.db'), inference_pyabc_redis=Redis(password='nopassword', port=1111, n_predictions=50, history_id=-1, model_id=0), inference_pymoo=Pymoo(algortihm='UNSGA3', population_size=100, max_nr_populations=1000, ftol=1e-05, xtol=1e-07, cvtol=1e-07, verbose=True), inference_numpyro=Numpyro(user_defined_probability_model=None, user_defined_error_model=None, user_defined_preprocessing=None, gaussian_base_distribution=False, kernel='nuts', init_strategy='init_to_uniform', chains=1, draws=2000, warmup=1000, thinning=1, nuts_draws=2000, nuts_step_size=0.8, nuts_max_tree_depth=10, nuts_target_accept_prob=0.8, nuts_dense_mass=True, nuts_adapt_step_size=True, nuts_adapt_mass_matrix=True, sa_adapt_state_size=None, svi_iterations=10000, svi_learning_rate=0.0001), report=Report(table_parameter_estimates=True, table_parameter_estimates_format='csv', table_parameter_estimates_error_metric='sd', table_parameter_estimates_parameters_as_rows=True, table_parameter_estimates_with_batch_dim_vars=False, table_parameter_estimates_override_names={}, plot_trace=True, plot_parameter_pairs=True))