def main():
from nems.main import fit_single_model
import argparse
import pylab as pl
parser = argparse.ArgumentParser(description='Fit single model')
parser.add_argument('batch', type=int, help='Batch to use')
parser.add_argument('cell', type=str, help='Cell from batch to fit')
parser.add_argument('model', type=str, help='Model to fit')
args = parser.parse_args()
fit_single_model(args.cell, args.batch, args.model, autoplot=True)
pl.show()
@author: svd This is meant to illustrate the range of uses either in place or that would be nice to support """ import nems.main as nems import nems.utilities as nu # CASE 1 # vanilla NAT model on "nice" linear cell cellid = "chn029d-a1" batch = 271 #A1 modelname = "fb18ch100_wcg01_fir15_fit01" stack = nems.fit_single_model(cellid, batch, modelname, saveInDB=False) # CASE 2 # pupil-dependent gain - stretch out all trials (rather than averaging across # stimulus repetitions). that way you can adjust gain according to pupil at # each point in time cellid = "BOL006b-48-1" batch = 293 # tone pip (PPS) + pupil modelname = "parm50_wc01_fir10_pupgain_fit01_nested5" stack = nems.fit_single_model(cellid, batch, modelname, saveInDB=False) # CASE 3 # pupil-dependent AND task-dependent gain. as in CASE 3, stretched out in # time. gain on output of STRF is now a function of both pupil and task # condition typically would want to add "_nested10" or something on the end.
if queueid:
print("Starting QUEUEID={}".format(queueid))
nd.update_job_start(queueid)
if len(sys.argv) < 4:
print('syntax: nems_fit_single cellid batch modelname')
exit(-1)
cellid = sys.argv[1]
batch = sys.argv[2]
modelname = sys.argv[3]
print("Running fit_single_model({0},{1},{2})".format(
cellid, batch, modelname))
stack = nems.fit_single_model(cellid, batch, modelname, autoplot=False)
print("Done with fit.")
# Edit: added code to save preview image. -Jacob 7/6/2017
preview_file = stack.quick_plot_save(mode="png")
print("Preview saved to: {0}".format(preview_file))
if db_exists:
if queueid:
pass
else:
queueid = None
r_id = nd.save_results(stack, preview_file, queueid=queueid)
print("Fit results saved to NarfResults, id={0}".format(r_id))
def get_stacks(cell_ids='all',
method='load',
from_file='default',
modelnames=[
'env100e_fir20_fit01_ssa', "env100e_stp1pc_fir20_fit01_ssa"
]):
# defines path to load from or save to the stacks
if from_file == 'default':
filename = '/home/mateo/nems/SSA_batch_296/171109_refreshed_full_batch_stacks'
elif from_file != 'default' and isinstance(from_file, str):
filename = '/home/mateo/nems/SSA_batch_296/{}'.format(from_file)
else:
raise ValueError(
'invalid from_file value, chose either "default" or a path')
#defines models to be used, batch, default 296, and list of cell ids,
modelnames = modelnames
batch = 296
if cell_ids == 'all':
d = ndb.get_batch_cells(batch=batch)
input_cells = d['cellid'].tolist()
elif isinstance(cell_ids, list):
input_cells = cell_ids
else:
raise ValueError('cell_ids has to be "all" or ar list of cell ids')
all_stacks = dict()
problem_cells = dict()
if method == 'load':
for mn in modelnames:
w_stacks = list()
w_p_cells = list()
for cellid in input_cells:
try:
print(
'############\n reloading {} \n########## \n '.format(
cellid))
loaded_stack = nu.io.load_single_model(cellid, batch, mn)
del_idx = nu.utils.find_modules(
loaded_stack, mod_name='metrics.ssa_index')[0]
loaded_stack.remove(del_idx)
del loaded_stack.data[del_idx]
loaded_stack.insert(nmet.ssa_index,
idx=del_idx,
z_score='bootstrap',
significant_bins='window')
w_stacks.append(loaded_stack)
except:
try:
fitted_stack = nm.fit_single_model(cellid,
batch,
mn,
autoplot=False)
w_stacks.append(fitted_stack)
except:
print('reloading of {} failed, skipping to next cell'.
format(cellid))
w_p_cells.append(cellid)
all_stacks[mn] = w_stacks
problem_cells[mn] = w_p_cells
jl.dump(all_stacks, filename)
elif method == 'fit':
for mn in modelnames:
w_stacks = list()
w_p_cells = list()
for cellid in input_cells:
try:
print('############\n locally fitting {} \n########## \n '.
format(cellid))
fitted_stack = fit_single_cell(cellid, batch, mn)
w_stacks.append(fitted_stack)
except:
print('fitting of {} failed, skipping to next cell'.format(
cellid))
w_p_cells.append(cellid)
all_stacks[mn] = w_stacks
problem_cells[mn] = w_p_cells
jl.dump(all_stacks, filename)
elif method == 'joblib':
all_stacks = jl.load(filename)
else:
raise ValueError(
'method {} not suported, options are: "load", "joblib", "fit"'.
format(method))
return all_stacks