def gen_input_norfscreen(config):
steps = config['General']['steps']
input_file = config['Photoreceptor']['input_file']
input_type = config['Photoreceptor']['single_intype']
input_obj = get_singleinput_cls(input_type)(config)
with Timer('getting photoreceptor inputs'):
# although not configurable one can use
# alternatives like .get_flat_image()
# if available by manually
# changing command below
photor_inputs = input_obj.get_input(steps)
config['General']['steps'] = photor_inputs[:, 0].size
sio.write_array(photor_inputs, filename=input_file)
def gen_input(config):
cuda.init()
ctx = cuda.Device(0).make_context()
atexit.register(ctx.pop)
suffix = config['General']['file_suffix']
eye_num = config['General']['eye_num']
eulerangles = config['Retina']['eulerangles']
radius = config['Retina']['radius']
rings = config['Retina']['rings']
steps = config['General']['steps']
screen_write_step = config['Retina']['screen_write_step']
config['Retina']['screen_write_step'] = 1
screen_type = config['Retina']['screentype']
screen_cls = cls_map.get_screen_cls(screen_type)
for i in range(eye_num):
screen = screen_cls(config)
screen_file = 'intensities_tmp{}.h5'.format(i)
screen.setup_file(screen_file)
retina_elev_file = 'retina_elev{}.h5'.format(i)
retina_azim_file = 'retina_azim{}.h5'.format(i)
screen_dima_file = 'grid_dima{}.h5'.format(i)
screen_dimb_file = 'grid_dimb{}.h5'.format(i)
retina_dima_file = 'retina_dima{}.h5'.format(i)
retina_dimb_file = 'retina_dimb{}.h5'.format(i)
input_file = 'retina_input{}.h5'.format(i)
transform = AlbersProjectionMap(radius,
eulerangles[3*i:3*(i+1)]).invmap
hexagon = hx.HexagonArray(num_rings=rings, radius=radius,
transform=transform)
retina = ret.RetinaArray(hexagon, config)
print('Acceptance angle: {}'.format(retina.acceptance_angle))
print('Neurons: {}'.format(retina.num_photoreceptors))
elev_v, azim_v = retina.get_ommatidia_pos()
rfs = _get_receptive_fields(retina, screen, screen_type)
steps_count = steps
write_mode = 'w'
while (steps_count > 0):
steps_batch = min(100, steps_count)
im = screen.get_screen_intensity_steps(steps_batch)
photor_inputs = rfs.filter(im)
sio.write_array(photor_inputs, filename=input_file, mode=write_mode)
steps_count -= steps_batch
write_mode = 'a'
tmp = sio.read_array(screen_file)
sio.write_array(tmp[::screen_write_step],
'intensities{}{}.h5'.format(suffix, i),
complevel = 9)
del tmp
os.remove(screen_file)
for data, filename in [(elev_v, retina_elev_file),
(azim_v, retina_azim_file),
(screen.grid[0], screen_dima_file),
(screen.grid[1], screen_dimb_file),
(rfs.refa, retina_dima_file),
(rfs.refb, retina_dimb_file)]:
sio.write_array(data, filename)
def gen_input(config):
cuda.init()
ctx = cuda.Device(0).make_context()
atexit.register(ctx.pop)
suffix = config['General']['file_suffix']
eye_num = config['General']['eye_num']
rings = config['Retina']['rings']
steps = config['General']['steps']
input_filename = config['Retina']['input_file']
screen_write_step = config['Retina']['screen_write_step']
config['Retina']['screen_write_step'] = 1
screen_type = config['Retina']['screentype']
screen_cls = cls_map.get_screen_cls(screen_type)
eulerangles = config['Retina']['eulerangles']
radius = config['Retina']['radius']
for i in range(eye_num):
screen = screen_cls(config)
screen_file = 'intensities_tmp{}.h5'.format(i)
screen.setup_file(screen_file)
retina_elev_file = 'retina_elev{}.h5'.format(i)
retina_azim_file = 'retina_azim{}.h5'.format(i)
screen_dima_file = 'grid_dima{}.h5'.format(i)
screen_dimb_file = 'grid_dimb{}.h5'.format(i)
retina_dima_file = 'retina_dima{}.h5'.format(i)
retina_dimb_file = 'retina_dimb{}.h5'.format(i)
input_file = '{}{}{}.h5'.format(input_filename, i, suffix)
transform = AlbersProjectionMap(radius,
eulerangles[3*i:3*(i+1)]).invmap
hexagon = hx.HexagonArray(num_rings=rings, radius=radius,
transform=transform)
retina = ret.RetinaArray(hexagon, config=config)
print('Acceptance angle: {}'.format(retina.acceptance_angle))
print('Neurons: {}'.format(retina.num_photoreceptors))
elev_v, azim_v = retina.get_ommatidia_pos()
rfs = _get_receptive_fields(retina, screen, screen_type)
steps_count = steps
write_mode = 'w'
while (steps_count > 0):
steps_batch = min(100, steps_count)
im = screen.get_screen_intensity_steps(steps_batch)
photor_inputs = rfs.filter(im)
sio.write_array(photor_inputs, filename=input_file, mode=write_mode)
steps_count -= steps_batch
write_mode = 'a'
tmp = sio.read_array(screen_file)
sio.write_array(tmp[::screen_write_step],
'intensities{}{}.h5'.format(suffix, i),
complevel = 9)
del tmp
os.remove(screen_file)
for data, filename in [(elev_v, retina_elev_file),
(azim_v, retina_azim_file),
(screen.grid[0], screen_dima_file),
(screen.grid[1], screen_dimb_file),
(rfs.refa, retina_dima_file),
(rfs.refb, retina_dimb_file)]:
sio.write_array(data, filename)