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() #
print 'n_cells=%d\tn_exc=%d\tn_inh=%d' % (params['n_cells'], params['n_exc'], params['n_inh'])
print 'Blur', params['blur_X'], params['blur_V']
scale_input_frequency = False
if scale_input_frequency:
scaling_factor = utils.scale_input_frequency(params['blur_X'])
params['f_max_stim'] *= scaling_factor
try:
from mpi4py import MPI
USE_MPI = True
comm = MPI.COMM_WORLD
pc_id, n_proc = comm.rank, comm.size
print "USE_MPI:", USE_MPI, 'pc_id, n_proc:', pc_id, n_proc
except:
USE_MPI = False
pc_id, n_proc, comm = 0, 1, None
print "MPI not used"
try:
tuning_prop = np.loadtxt(params['tuning_prop_means_fn'])
except:
print 'File with tuning properties missing: %s\nPlease run: \nmpirun -np [N] python prepare_tuning_prop.py\nOR\npython prepare_tuning_prop.py' % params['tuning_prop_means_fn']
exit(1)
my_units = utils.distribute_n(params['n_exc'], n_proc, pc_id)
utils.create_spike_trains_for_motion(tuning_prop, params, contrast=.9, my_units=my_units, seed=seed) # write to paths defined in the params dictionary
if comm != None:
comm.barrier()
