def load_model( filename=None, rgctype='off parasol' ):
# filename += rgctype
print 'Loading model', filename
indices = extract( linear_stats(rgctype,(5,0)), ['sparse_index', 'subunit_index'] )
indices['N_subunits'] = len(cones)
spikes = which_spikes( rgctype )
data_generator = retina.read_stimulus( spikes,
stimulus_pattern='cone_input_%d.mat' ,
skip_pattern=(-5,0) )
stimdata = data_generator.next()
print 'stimdata', stimdata.keys()
model = kb.load(filename)
for n,v in model.items():
if isinstance(v,type({})):
model.update(v)
return forward_LQLEP( stimdata['stimulus'], stimdata['spikes'], model, indices)
def optimize_LQLEP( rgc_type, filename=None, maxiter=maxiter, indices=None, description='',
unknowns=['sv1','V2','u','uc','c','ud','d'],
vardict = LQLEP_wBarrier( **LQLEP( **thetaM( **u2c_parameterization())))):
# unknowns = {'sv1':sv1, 'V2':init_V2 , 'u':old['u'], 'uc':old['u'], 'c':init_V2}
defaults = extract( { 'sv1':init_sv1( rgc_type ), 'V2':init_V2 , 'u':init_u,
'uc':numpy.zeros_like(init_u), 'c':init_V2,
'ud':0.001*numpy.ones_like(init_u), 'd':0.0001+init_V2},
list( set(unknowns).intersection( set(vardict.keys()) ) ) + ['sv1'])
if filename is not None:
print 'Re-optimizing',filename
unknowns = kb.load(filename)
for name in unknowns.keys():
if not defaults.has_key(name): del unknowns[name]
default(unknowns,defaults)
else:
unknowns = defaults
# if rgc_type[:3] == 'off':
# unknowns['u'] = -0.01*numpy.abs(unknowns['u'])
if vardict.has_key('barrier_positiveV1'):
unknowns['sv1'] = numpy.abs(unknowns['sv1'])
else:
unknowns['sv1'] = unknowns['sv1']*0.01
train_LQLEP = global_objective( unknowns, {}, vardict, run=linear_stats(rgc_type,( 5,0)), indices=indices)
test_LQLEP = global_objective( unknowns, {}, vardict, run=linear_stats(rgc_type,(-5,0)), indices=indices)
test_LQLEP.description = 'Test_LQLEP'
train_LQLEP.with_callback(partial(callback,other=
{'Test_LNP':test_LNP(rgc_type)['LL'], 'nonlinearity':test_LQLEP.nonlinearity},
objectives=[test_LQLEP]))
train_LQLEP.description = description+rgc_type
unknowns['V2'] = unknowns['V2']*0.001
trained = optimize_objective( train_LQLEP, unknowns, gtol=1e-10 , maxiter=maxiter)
print 'RGC type:', rgc_type
test_global_objective( train_LQLEP, trained )
train_LQLEP.callback( trained, force=True )
train_LQLEP.callback( train_LQLEP.unflat( trained ), force=True )
return trained
from QuadPoiss import LQLEP_wBarrier, LQLEP, thetaM, UV12_input, UVi, \
linear_reparameterization
from IPython.Debugger import Tracer; debug_here = Tracer()
# Memoizing results using joblib; makes life easier
from joblib import Memory
memory = Memory(cachedir='/Users/kolia/Documents/joblibcache', verbose=0)
data = retina.read_data()
cones = data['cone_locations']
possible_subunits = cones
from kolia_base import load
stats = load('Linear_localization')
def n_largest( values , keep=10 ):
sorted_values = numpy.abs(values)
sorted_values.sort()
cutoff = sorted_values[-keep]
return numpy.nonzero( numpy.abs(values) >= cutoff )[0]
@memory.cache
def fit_U_stats( rgc_type='off midget', keep=15, stats=stats ):
which_rgc = [i for i,ind in enumerate(data['rgc_ids'])
if ind in data['rgc_types'][rgc_type]]
spikes = data['spikes'][which_rgc,:]
sparse_index = [n_largest( sta, keep=keep ) for sta in stats['STA']]
stats['rgc_type'] = rgc_type
stats['rgc_index'] = which_rgc
