def clone_and_change_morph_file(param_cond_file, model_path, model, neuron_type, non_conds, sample_name=''):
''' Inputs param_cond_file == string => Absolute path of cond_file.
model_path == Path objects => model Path objects.
model == string => model name.
neuron_type == string => Neuron model.
'''
from ajustador.basic_simulation import morph_morph_file
from ajustador.helpers.copy_param.process_common import get_file_abs_path
from ajustador.helpers.copy_param.process_common import get_file_name_with_version
from ajustador.helpers.copy_param.process_common import make_model_path_obj
from ajustador.helpers.copy_param.process_param_cond import extract_morph_file_from_cond
morph_features = ('RM', 'Eleak', 'RA', 'CM')
model_obj = make_model_path_obj(model_path, model)
logger.debug("\n{} \n{}".format(param_cond_file, neuron_type))
morph_file = extract_morph_file_from_cond(param_cond_file, neuron_type) #PROBLEM HERE. NOT RETURNING CORRECT FILE FOR D2?
logger.debug('\n{} \n{}'.format(model_path, morph_file))
src_morph_file_path = get_file_abs_path(model_path, morph_file)
if 'conductance_save' in src_morph_file_path:
new_morph_file_path = get_file_name_with_version(src_morph_file_path)
morph_morph_file(model_obj, neuron_type, src_morph_file_path, new_file = open(new_morph_file_path,'w'),
**{k:v for k,v in non_conds.items() if k in morph_features})
return new_morph_file_path
new_morph_file_path = str(model_path/'conductance_save'/morph_file)[:-2]+'_'+sample_name+'.p'
morph_morph_file(model_obj, neuron_type, src_morph_file_path, new_file = open(new_morph_file_path, 'w'),
**{k:v for k,v in non_conds.items() if k in morph_features})
return new_morph_file_path
def clone_param_cond_file(src_path, src_file, dest_file):
''' Inputs param_cond_file == string => Absolute path of cond_file.
model_path == Path objects => model Path objects.
model == string => model name.
neuron_type == string => Neuron model.
'''
from ajustador.helpers.copy_param.process_common import get_file_abs_path
from ajustador.helpers.copy_param.process_common import get_file_name_with_version
src_abs_path = get_file_abs_path(src_path, src_file)
if Path(dest_file).is_file(
): # Creates a version file of destination file.
dest_file = get_file_name_with_version(dest_file)
shutil.copy(src_abs_path, dest_file)
return dest_file
shutil.copy(src_abs_path, dest_file)
return dest_file
def create_npz_param(npz_file,
model,
neuron_type,
store_param_path=None,
fitnum=None,
cond_file=None,
chan_file=None):
"""Main function to be executed to generate parameter file from npz_file.
Inputs => npz_file -> *.npz file;
model -> 'gp', 'd1d2', 'ep' or 'ca1' soon;
neuron_type -> 'proto', 'D1' or 'D2' soon;
store_param_spath -> User intended path to store neuron parameter files;
fitnum -> user desired fitnumber to extract from npz file;
cond_file -> Pure file name no path prefixes,
Note** Program searches for cond_file in model folder and conductance_save in-order.
Note** If *.p file in cond_file should be present in the same directory for proper execution.
Note** Block comments in param_chan.py and param_cond.py must be of <'''>.
"""
model_path = Path(moose_nerp.__file__.rpartition('/')[0]) / model
logger.info("START STEP 1!!!\n loading npz file: {}.".format(npz_file))
data = np.load(npz_file)
logger.info("START STEP 2!!! Prepare param conductances.")
fit_number, param_data_list = get_least_fitness_params(data, fitnum)
header_line = "# Generated from npzfile: {} of fit number: {}\n".format(
npz_file.rpartition('/')[2], fit_number)
sample_label = npz_file.rpartition('/')[2].rstrip('.npz').split('_')[-1]
logger.debug("Param_data: {}".format(param_data_list))
conds = get_params(param_data_list, 'Cond_')
non_conds = get_params(param_data_list, 'Cond_', exclude_flag=True)
# Create new path to save param_cond.py and *.p
new_param_path = create_path(
store_param_path) if store_param_path else create_path(
model_path / 'conductance_save')
if cond_file is None:
cond_file = 'param_cond.py'
new_param_cond = make_new_file_name_from_npz(data, npz_file,
str(new_param_path),
neuron_type, cond_file)
new_cond_file_name = check_version_build_file_path(str(new_param_cond),
neuron_type, fit_number)
logger.info("START STEP 3!!! Copy \n source : {} \n dest: {}".format(
get_file_abs_path(model_path, cond_file), new_cond_file_name))
new_param_cond = clone_file(src_path=model_path,
src_file=cond_file,
dest_file=new_cond_file_name)
logger.info("START STEP 4!!! Extract and modify morph_file from {}".format(
new_param_cond))
morph_file = clone_and_change_morph_file(new_param_cond, model_path, model,
neuron_type, non_conds,
sample_label)
#NOTE: created param_cond.py file in conductance_save directory of moose_nerp squid model.
#NOTE: created and updated morph file.
logger.info("START STEP 5!!! Renaming morph file after checking version.")
new_morph_file_name = check_version_build_file_path(
morph_file, neuron_type, fit_number)
Path(str(new_param_path / morph_file)).rename(str(new_morph_file_name))
logger.info("START STEP 6!!! Renaming morph file after checking version.")
update_morph_file_name_in_cond(new_cond_file_name, neuron_type,
new_morph_file_name.rpartition('/')[2])
write_header(header_line, new_param_cond)
start_param_cond_block = get_namedict_block_start(new_param_cond,
neuron_type)
end_param_cond_block = get_block_end(new_param_cond,
start_param_cond_block, r"\)")
conds_dict = reshape_conds_to_dict(conds)
update_conductance_param(new_param_cond, conds_dict,
start_param_cond_block, end_param_cond_block)
logger.info("STEP 8!!! start channel processing.")
chans = get_params(param_data_list, 'Chan_')
logger.debug('{}'.format(chans))
if chan_file is None:
chan_file = 'param_chan.py'
new_param_chan = make_new_file_name_from_npz(data, npz_file,
str(new_param_path),
neuron_type, chan_file)
new_chan_file_name = check_version_build_file_path(str(new_param_chan),
neuron_type, fit_number)
logger.info("START STEP 9!!! Copy \n source : {} \n dest: {}".format(
get_file_abs_path(model_path, chan_file), new_chan_file_name))
new_param_chan = clone_file(src_path=model_path,
src_file=chan_file,
dest_file=new_chan_file_name)
logger.info("START STEP 10!!! Preparing channel and gateparams relations.")
start_param_chan_block = get_namedict_block_start(new_param_chan,
'Channels')
end_param_chan_block = get_block_end(new_param_chan,
start_param_chan_block, r"^(\s*\))")
chans_dict = reshape_chans_to_dict(chans)
logger.info(
"START STEP 11!!! import parameters from param_chan.py. and apply scale Tau and delay SS"
)
py_param_chan = import_param_chan(
model) # import param_chan.py file from model.
chanset = py_param_chan.Channels # Get Channels set from the imported param_chan.py.
for key, value in chans_dict.items():
chan_name, opt, gate = key
if opt == 'taumul':
scale_voltage_dependents_tau_muliplier(chanset, chan_name, gate,
np.float(value))
elif opt == 'vshift':
offset_voltage_dependents_vshift(chanset, chan_name, gate,
np.float(value))
chan_param_name_relation = create_chan_param_relation(
new_param_chan, start_param_chan_block, end_param_chan_block)
param_location = chan_param_locator(new_param_chan,
chan_param_name_relation)
update_chan_param(
new_param_chan, chan_param_name_relation, chanset,
param_location) #Update new param_chan files with new channel params.
write_header(header_line,
new_param_chan) # Write header to the new param_chan.py
logger.info(
"THE END!!! New files names \n morph: {1} \n param_cond file: {0} \n param_chan file: {2}"
.format(new_cond_file_name, new_morph_file_name, new_chan_file_name))