def create_parameter_table(problem: petab.Problem, nominal_parameters):
"""Create PEtab parameter table"""
df = petab.create_parameter_df(problem.sbml_model,
problem.condition_df,
problem.observable_df,
problem.measurement_df,
include_optional=True,
lower_bound=1e-3,
upper_bound=1e5)
df['hierarchicalOptimization'] = 0
df.loc['scaling_x1_common', 'hierarchicalOptimization'] = 1
df.loc['offset_x2_batch_0', 'hierarchicalOptimization'] = 1
df.loc['offset_x2_batch_1', 'hierarchicalOptimization'] = 1
df.loc['x1withsigma_sigma', 'hierarchicalOptimization'] = 1
for pid, val in nominal_parameters.items():
if pid in df.index:
df.loc[pid, ptc.NOMINAL_VALUE] = val
df.loc[pid, ptc.PARAMETER_SCALE] = ptc.LOG10
df.loc[pid, ptc.ESTIMATE] = 1
elif pid.startswith('noiseParameter') \
or pid.startswith('observableParameter'):
continue
else:
print("extra parameter", pid, val)
# offsets can be negative: adapt scaling and bounds:
offsets = df.index.str.startswith('offset_')
df.loc[offsets, ptc.PARAMETER_SCALE] = ptc.LIN
problem.parameter_df = df
def create_parameter_table(problem: petab.Problem, parameter_file,
nominal_parameters):
"""Create PEtab parameter table"""
df = problem.create_parameter_df(lower_bound=-3, upper_bound=5)
# TODO: move to peTAB
df['hierarchicalOptimization'] = 0
df.loc['scaling_x1_common', 'hierarchicalOptimization'] = 1
df.loc['offset_x2_batch-0', 'hierarchicalOptimization'] = 1
df.loc['offset_x2_batch-1', 'hierarchicalOptimization'] = 1
df.loc['x1withsigma_sigma', 'hierarchicalOptimization'] = 1
#df.parameterScale = 'lin'
#df.estimate = 0
print(nominal_parameters)
for pid, val in nominal_parameters.items():
if pid in df.index:
df.loc[pid, 'nominalValue'] = val
df.loc[pid, 'parameterScale'] = 'log10'
df.loc[pid, 'estimate'] = 1
elif pid.startswith('noiseParameter') \
or pid.startswith('observableParameter'):
continue
else:
print("extra parameter", pid, val)
# offsets can be negative: adapt scaling and bounds:
offsets = df.index.str.startswith('offset_')
df.loc[offsets, 'parameterScale'] = 'lin'
df.loc[offsets, 'lowerBound'] = np.power(10.0, df.loc[offsets,
'lowerBound'])
df.loc[offsets, 'upperBound'] = np.power(10.0, df.loc[offsets,
'upperBound'])
problem.parameter_df = df
df.to_csv(parameter_file, sep="\t", index=True)
