def main():
parser.description = 'Sparsenoise stimulus generator.'
parser.add_argument(
'fraction_on',
type=float,
nargs='*',
help='fraction of pixels on per presentation frame (between 0 and 1)')
parser.add_argument('--frames_per_run',
type=int,
help='number of frames per run presentation',
default=60000)
args = parser.parse_args()
fracs = args.fraction_on
frames_total = args.nframes
frames_per_run = args.frames_per_run
n_runs = -(-frames_total // frames_per_run) # ceiling division.
assert n_runs > len(fracs)
if n_runs > (frames_total // frames_per_run):
print(
'WARNING: {} frames were requested, but {} frames will be presented due to rounding.'
.format(frames_total, n_runs * frames_per_run))
if n_runs < len(fracs) * 2:
print(
'WARNING: each sparsity fraction will be presented in only one contiguous run of {} frames'
.format(frames_per_run))
runs_per_frac = -(-n_runs // len(fracs)) # ceiling division
print('Running each sparsity fraction {} times...'.format(runs_per_frac))
frac_list_weighted = []
for frac in fracs: # check to make sure fractions are valid.
if frac > 1. or frac < 0.:
errst = 'Fraction argument must be between 0 and 1.'
raise ValueError(errst)
else:
frac_list_weighted.extend([frac] * runs_per_frac)
frac_list_permuted = np.random.permutation(frac_list_weighted)
savefile_base, _ = os.path.splitext(args.savefile)
presented = 0
for i in range(n_runs):
savefile_str = "{}_r{:03d}.h5".format(savefile_base, i)
print('Starting run number {} of {}.'.format(i + 1, n_runs))
frac = frac_list_permuted[i]
seq_gen = sparsenoise_function_generator(frac)
presented += frames_per_run
run_presentations(frames_per_run,
savefile_str,
seq_gen,
file_overwrite=args.overwrite,
seq_debug=False,
image_scale=args.scale,
picture_time=args.pic_time,
mask_filepath=args.maskfile)
def main():
parser.description = 'Whitenoise (50%) stimulus generator.'
parser.add_argument(
'--debug',
action='store_true',
help='display sequential numbers on DMD to debug sequence and saving')
args = parser.parse_args()
run_presentations(args.nframes,
args.savefile,
generate_whitenoise_sequence,
file_overwrite=args.overwrite,
seq_debug=args.debug,
picture_time=args.pic_time,
mask_filepath=args.maskfile,
image_scale=args.scale)
def main():
parser.description = 'Sparsenoise stimulus generator.'
parser.add_argument(
'fraction_on',
type=float,
help='fraction of pixels on per presentation frame (between 0 and 1)')
args = parser.parse_args()
frac = args.fraction_on
if frac > 1. or frac < 0.:
errst = 'Fraction argument must be between 0 and 1.'
raise ValueError(errst)
else:
generate_sparsenoise_sequences = sparsenoise_function_generator(frac)
run_presentations(args.nframes,
args.savefile,
generate_sparsenoise_sequences,
file_overwrite=args.overwrite,
seq_debug=False,
image_scale=args.scale,
picture_time=args.pic_time,
mask_filepath=args.maskfile)
seq_array[:] *= mask # mask is 1 in areas we want to stimulate and 0 otherwise. This is faster than alternatives.
def main():
parser.description = 'Whitenoise (50%) stimulus generator.'
parser.add_argument(
'--debug',
action='store_true',
help='display sequential numbers on DMD to debug sequence and saving')
args = parser.parse_args()
run_presentations(args.nframes,
args.savefile,
generate_whitenoise_sequence,
file_overwrite=args.overwrite,
seq_debug=args.debug,
picture_time=args.pic_time,
mask_filepath=args.maskfile,
image_scale=args.scale)
if __name__ == '__main__':
pth = r"D:\patters\test22.h5"
mskfile = r"D:\patters\mouse_11103\sess_001\mask.npy"
run_presentations(1 * 10**6,
pth,
generate_whitenoise_sequence,
file_overwrite=False,
picture_time=10 * 1000,
mask_filepath=mskfile,
image_scale=8)
else:
generate_sparsenoise_sequences = sparsenoise_function_generator(frac)
run_presentations(args.nframes,
args.savefile,
generate_sparsenoise_sequences,
file_overwrite=args.overwrite,
seq_debug=False,
image_scale=args.scale,
picture_time=args.pic_time,
mask_filepath=args.maskfile)
if __name__ == '__main__':
import os
d = r"D:"
pth = os.path.join(d, 'test.h5')
# mskfile = os.path.join(d, 'mask.npy')
mskfile = r"D:\patters\mouse_11113\sess_001\mask.npy"
if os.path.exists(d) and os.path.exists(
mskfile) and not os.path.exists(pth):
generate_sparsenoise_sequences = sparsenoise_function_generator(.005)
run_presentations(.7 * 10**6,
pth,
file_overwrite=False,
seq_debug=False,
picture_time=10 * 1000,
mask_filepath=mskfile)
elif not os.path.exists(mskfile):
raise FileNotFoundError('Mask file not found ({})'.format(mskfile))
elif os.path.exists(pth):
raise FileExistsError("Patterns file already exists {}.".format(pth))