def transform(ind):
from neuronunit.optimization.data_transport_container import DataTC
dtc = DataTC()
print(dtc)
param_dict = {}
for i, j in enumerate(ind):
param_dict[td[i]] = str(j)
dtc.attrs = param_dict
dtc.evaluated = False
return dtc
def transform(ind):
import dask.bag as db
from neuronunit.optimization.data_transport_container import DataTC
dtc = DataTC()
import neuronunit
LEMS_MODEL_PATH = str(neuronunit.__path__[0])+str('/models/NeuroML2/LEMS_2007One.xml')
if backend is not None:
dtc.backend = backend
else:
dtc.backend = 'NEURON'
dtc.attrs = {}
for i,j in enumerate(ind):
dtc.attrs[str(td[i])] = j
dtc.evaluated = False
return dtc
def transform(xargs):
(ind, td, backend) = xargs
# The merits of defining a function in a function
# is that it yields a semi global scoped variables.
# conisider refactoring outer function as a decorator.
dtc = DataTC()
LEMS_MODEL_PATH = str(
neuronunit.__path__[0]) + str('/models/NeuroML2/LEMS_2007One.xml')
dtc.backend = 'RAW'
dtc.attrs = {}
for i, j in enumerate(ind):
dtc.attrs[str(td[i])] = j
dtc.evaluated = False
return dtc
def transform(ind):
# The merits of defining a function in a function
# is that it yields a semi global scoped variables.
#
dtc = DataTC()
LEMS_MODEL_PATH = str(neuronunit.__path__[0])+str('/models/NeuroML2/LEMS_2007One.xml')
if backend is not None:
dtc.backend = backend
else:
dtc.backend = 'NEURON'
dtc.attrs = {}
if isinstance(ind, Iterable):
for i,j in enumerate(ind):
dtc.attrs[str(td[i])] = j
else:
dtc.attrs[str(td[0])] = ind
dtc.evaluated = False
return dtc