def __init__(self, parameter_storage, comm=None):

PObject.__init__(self, parameter_storage, comm)

def prepare_tuning_prop(self):

tuning_prop = utils.set_tuning_prop(self.params, mode='hexgrid', v_max=self.params['v_max']) # set the tuning properties of exc cells: space (x, y) and velocity (u, v)

if self.pc_id == 0:

print "Creating tuning properties", self.pc_id, self.params['tuning_prop_means_fn']

print "Saving tuning_prop to file:", self.pc_id, self.params['tuning_prop_means_fn']

np.savetxt(self.params['tuning_prop_means_fn'], tuning_prop)

# write inital bias values to file

np.savetxt(self.params['bias_values_fn_base']+'0.dat', np.zeros(self.params['n_exc']))

if self.comm != None:

print 'Pid %d at Barrier in Preparer.prepare_tuning_params' % self.pc_id

sys.stdout.flush()

self.comm.barrier()

return tuning_prop

def prepare_spiketrains(self, tuning_prop):

if (type(tuning_prop) == type('')):

# try:

tp = np.loadtxt(tuning_prop)

# except:

# print 'Pid %d fails to load the file ...' % self.pc_id

# tp = self.prepare_tuning_prop(self.params)

elif (type(tuning_prop) == type(np.array([]))):

tp = tuning_prop

else:

raise TypeError, 'Only filename or numpy array accepted for tuning_prop, given %s' % (str(type(tuning_prop)))

my_units = utils.distribute_n(self.params['n_exc'], self.n_proc, self.pc_id)

input_spike_trains = utils.create_spike_trains_for_motion(tp, self.params, contrast=.9, my_units=my_units) # write to paths defined in the params dictionary

if self.comm != None:

self.comm.barrier() #

def sort_gids(self):

self.tuning_prop = np.loadtxt(self.params['tuning_prop_means_fn'])

# sort the cells by their proximity to the stimulus into 'good_gids' and the 'rest'

# cell in 'good_gids' should have the highest response to the stimulus

all_gids, all_distances = utils.sort_gids_by_distance_to_stimulus(self.tuning_prop, self.params['motion_params'])

n_good = self.params['n_exc'] * 0.02

self.good_gids, self.good_distances = all_gids[0:n_good], all_distances[0:n_good]

self.rest_gids = range(self.params['n_exc'])

for gid in self.good_gids:

self.rest_gids.remove(gid)

print 'Saving gids to record to', self.params['gids_to_record_fn']

np.savetxt(self.params['gids_to_record_fn'], np.array(self.good_gids), fmt='%d')