def constructEdataFromDataFrame(df, model, condition):
""" Constructs an ExpData instance according to the provided Model and DataFrame
Arguments:
df: pd.DataFrame with Observable Names/Ids as columns
standard deviations may be specified by appending '_std' as suffix
model: Model instance
condition: pd.Series with FixedParameter Names/Ids as columns
preequilibration conditions may be specified by appending '_preeq' as suffix
presimulation conditions may be specified by appending '_presim' as suffix
Returns:
ExpData instance
Raises:
"""
edata = ExpData(model.get())
# timepoints
df = df.sort_values(by='time', ascending=True)
edata.setTimepoints(df['time'].values)
overwrite_preeq = {}
overwrite_presim = {}
for par in list(_get_names_or_ids(model, 'FixedParameter')):
if par + '_preeq' in condition.keys() \
and not math.isnan(condition[par + '_preeq']):
overwrite_preeq[par] = condition[par + '_preeq']
if par + '_presim' in condition.keys() \
and not math.isnan(condition[par + '_presim']):
overwrite_presim[par] = condition[par + '_presim']
# fixedParameters
edata.fixedParameters = \
condition[_get_names_or_ids(model, 'FixedParameter')].values
if any([overwrite_preeq[key] != condition[key] for key in
overwrite_preeq.keys()]):
edata.fixedParametersPreequilibration = \
_get_specialized_fixed_parameters(model, condition,overwrite_preeq)
elif len(overwrite_preeq.keys()):
edata.fixedParametersPreequilibration = copy.deepcopy(
edata.fixedParameters
)
if any([overwrite_presim[key] != condition[key] for key in
overwrite_presim.keys()]):
edata.fixedParametersPresimulation = _get_specialized_fixed_parameters(
model, condition,overwrite_presim
)
elif len(overwrite_presim.keys()):
edata.fixedParametersPresimulation = copy.deepcopy(
edata.fixedParameters
)
if 't_presim' in condition.keys():
edata.t_presim = condition['t_presim']
# data
for obs_index, obs in enumerate(_get_names_or_ids(model, 'Observable')):
if obs in df.keys():
edata.setObservedData(df[obs].values,
obs_index)
if obs + '_std' in df.keys():
edata.setObservedDataStdDev(
df[obs + '_std'].values,
obs_index
)
return edata
def constructEdataFromDataFrame(df, model, condition, by_id=False):
""" Constructs an ExpData instance according to the provided Model and DataFrame.
Arguments:
df: pd.DataFrame with Observable Names/Ids as columns.
Standard deviations may be specified by appending '_std' as suffix.
model: Model instance.
condition: pd.Series with FixedParameter Names/Ids as columns.
Preequilibration conditions may be specified by appending '_preeq' as suffix.
Presimulation conditions may be specified by appending '_presim' as suffix.
by_id: bool, optional (default = False)
Indicate whether in the arguments, column headers are based on ids or names.
This should correspond to the way `df` and `condition` was created in the
first place.
Returns:
ExpData instance.
Raises:
"""
# initialize edata
edata = ExpData(model.get())
# timepoints
df = df.sort_values(by='time', ascending=True)
edata.setTimepoints(df['time'].values)
# get fixed parameters from condition
overwrite_preeq = {}
overwrite_presim = {}
for par in list(_get_names_or_ids(model, 'FixedParameter', by_id=by_id)):
if par + '_preeq' in condition.keys() \
and not math.isnan(condition[par + '_preeq']):
overwrite_preeq[par] = condition[par + '_preeq']
if par + '_presim' in condition.keys() \
and not math.isnan(condition[par + '_presim']):
overwrite_presim[par] = condition[par + '_presim']
# fill in fixed parameters
edata.fixedParameters = \
condition[_get_names_or_ids(model, 'FixedParameter', by_id=by_id)].values
# fill in preequilibration parameters
if any([
overwrite_preeq[key] != condition[key]
for key in overwrite_preeq.keys()
]):
edata.fixedParametersPreequilibration = \
_get_specialized_fixed_parameters(
model, condition, overwrite_preeq, by_id=by_id)
elif len(overwrite_preeq.keys()):
edata.fixedParametersPreequilibration = copy.deepcopy(
edata.fixedParameters)
# fill in presimulation parameters
if any([
overwrite_presim[key] != condition[key]
for key in overwrite_presim.keys()
]):
edata.fixedParametersPresimulation = _get_specialized_fixed_parameters(
model, condition, overwrite_presim, by_id=by_id)
elif len(overwrite_presim.keys()):
edata.fixedParametersPresimulation = copy.deepcopy(
edata.fixedParameters)
# fill in presimulation time
if 't_presim' in condition.keys():
edata.t_presim = condition['t_presim']
# fill in data and stds
for obs_index, obs in enumerate(
_get_names_or_ids(model, 'Observable', by_id=by_id)):
if obs in df.keys():
edata.setObservedData(df[obs].values, obs_index)
if obs + '_std' in df.keys():
edata.setObservedDataStdDev(df[obs + '_std'].values, obs_index)
return edata