def run():
data_folder = r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project" \
r"\180404-M360495-2p\2p_movie\reorged"
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
mc.motion_correction(input_folder=data_folder,
input_path_identifier='.tif',
process_num=6,
output_folder=os.path.join(data_folder, 'corrected'),
anchor_frame_ind_chunk=10,
anchor_frame_ind_projection=0,
iteration_chunk=10,
iteration_projection=10,
max_offset_chunk=(40., 40.),
max_offset_projection=(40., 40.),
align_func=mc.phase_correlation,
preprocessing_type=0,
fill_value=0.)
offsets_path = os.path.join(data_folder, 'corrected', 'correction_offsets.hdf5')
fns = [f for f in os.listdir(data_folder) if f[-4:] == '.tif']
fns.sort()
f_paths = [os.path.join(data_folder, f) for f in fns]
print('\nfile paths:')
print('\n'.join(f_paths))
mc.apply_correction_offsets(offsets_path=offsets_path,
path_pairs=zip(f_paths, f_paths),
output_folder=os.path.join(data_folder, 'corrected'),
process_num=6,
fill_value=0.,
avi_downsample_rate=10,
is_equalizing_histogram=True)
def run():
data_folder = r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project" \
r"\180104-M361012-2p\FOV1_injection_site_00001"
ref_ch_n = 'red'
apply_ch_ns = ['green', 'red']
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
ref_data_folder = os.path.join(data_folder, ref_ch_n)
mc.motion_correction(input_folder=ref_data_folder,
input_path_identifier='.tif',
process_num=3,
output_folder=os.path.join(ref_data_folder,
'corrected'),
anchor_frame_ind_chunk=10,
anchor_frame_ind_projection=0,
iteration_chunk=10,
iteration_projection=10,
max_offset_chunk=(20., 20.),
max_offset_projection=(20., 20.),
align_func=mc.phase_correlation,
preprocessing_type=0,
fill_value=0.)
offsets_path = os.path.join(ref_data_folder, 'corrected',
'correction_offsets.hdf5')
ref_fns = [f for f in os.listdir(ref_data_folder) if f[-4:] == '.tif']
ref_fns.sort()
ref_paths = [os.path.join(ref_data_folder, f) for f in ref_fns]
print('\nreference paths:')
print('\n'.join(ref_paths))
for apply_ch_i, apply_ch_n in enumerate(apply_ch_ns):
apply_data_folder = os.path.join(data_folder, apply_ch_n)
apply_fns = [
f for f in os.listdir(apply_data_folder) if f[-4:] == '.tif'
]
apply_fns.sort()
apply_paths = [os.path.join(apply_data_folder, f) for f in apply_fns]
print('\napply paths:')
print('\n'.join(apply_paths))
mc.apply_correction_offsets(offsets_path=offsets_path,
path_pairs=zip(ref_paths, apply_paths),
output_folder=os.path.join(
apply_data_folder, 'corrected'),
process_num=3,
fill_value=0.,
avi_downsample_rate=20,
is_equalizing_histogram=False)
def correct(data_folder):
ref_ch_n = 'red'
apply_ch_ns = ['green', 'red']
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
ref_data_folder = os.path.join(data_folder, ref_ch_n)
mc.motion_correction(input_folder=ref_data_folder,
input_path_identifier='.tif',
process_num=6,
output_folder=os.path.join(ref_data_folder,
'corrected'),
anchor_frame_ind_chunk=10,
anchor_frame_ind_projection=0,
iteration_chunk=10,
iteration_projection=10,
max_offset_chunk=(100., 100.),
max_offset_projection=(100., 100.),
align_func=mc.phase_correlation,
preprocessing_type=6,
fill_value=0.)
offsets_path = os.path.join(ref_data_folder, 'corrected',
'correction_offsets.hdf5')
ref_fns = [f for f in os.listdir(ref_data_folder) if f[-4:] == '.tif']
ref_fns.sort()
ref_paths = [os.path.join(ref_data_folder, f) for f in ref_fns]
print('\nreference paths:')
print('\n'.join(ref_paths))
for apply_ch_i, apply_ch_n in enumerate(apply_ch_ns):
apply_data_folder = os.path.join(data_folder, apply_ch_n)
apply_fns = [
f for f in os.listdir(apply_data_folder) if f[-4:] == '.tif'
]
apply_fns.sort()
apply_paths = [os.path.join(apply_data_folder, f) for f in apply_fns]
print('\napply paths:')
print('\n'.join(apply_paths))
mc.apply_correction_offsets(offsets_path=offsets_path,
path_pairs=zip(ref_paths, apply_paths),
output_folder=os.path.join(
apply_data_folder, 'corrected'),
process_num=6,
fill_value=0.,
avi_downsample_rate=10,
is_equalizing_histogram=False)
def correct_single_step(param):
folder_ref, anchor_frame_ind_chunk, iteration_chunk, max_offset_chunk, preprocessing_type, fill_value,\
is_apply, avi_downsample_rate, is_equalizing_histogram= param
step_n = os.path.split(folder_ref)[1]
print('\nStart correcting step {} ...'.format(step_n))
mov_ref_n = [f for f in os.listdir(folder_ref) if f[-4:] == '.tif' and step_n in f]
if len(mov_ref_n) != 1:
warn('step {}: number of green movie does not equal 1.'.format(step_n))
return
mov_paths, _ = mc.motion_correction(input_folder=folder_ref,
input_path_identifier='.tif',
process_num=1,
output_folder=folder_ref,
anchor_frame_ind_chunk=anchor_frame_ind_chunk,
anchor_frame_ind_projection=0,
iteration_chunk=iteration_chunk,
iteration_projection=10,
max_offset_chunk=max_offset_chunk,
max_offset_projection=(30., 30.),
align_func=mc.phase_correlation,
preprocessing_type=preprocessing_type,
fill_value=fill_value)
if is_apply:
offsets_path = os.path.join(folder_ref, 'correction_offsets.hdf5')
offsets_f = h5py.File(offsets_path)
ref_path = offsets_f['file_0000'].attrs['path']
offsets_f.close()
movie_path = mov_paths[0]
mc.apply_correction_offsets(offsets_path=offsets_path,
path_pairs=[[ref_path, movie_path]],
output_folder=folder_ref,
process_num=1,
fill_value=fill_value,
avi_downsample_rate=avi_downsample_rate,
is_equalizing_histogram=is_equalizing_histogram)
def run():
data_folder = r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project" \
r"\180328-M360495-deepscope\04\04_"
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
plane_ns = [p for p in os.listdir(data_folder) if os.path.isdir(os.path.join(data_folder, p))]
plane_ns.sort()
print('planes:')
print('\n'.join(plane_ns))
for plane_n in plane_ns:
print('\nprocessing plane: {}'.format(plane_n))
plane_folder = os.path.join(data_folder, plane_n)
f_paths, _ = mc.motion_correction(input_folder=plane_folder,
input_path_identifier='.tif',
process_num=3,
output_folder=os.path.join(plane_folder, 'corrected'),
anchor_frame_ind_chunk=10,
anchor_frame_ind_projection=0,
iteration_chunk=10,
iteration_projection=10,
max_offset_chunk=(50., 50.),
max_offset_projection=(50., 50.),
align_func=mc.phase_correlation,
preprocessing_type=0,
fill_value=0.)
offsets_path = os.path.join(plane_folder, 'corrected', 'correction_offsets.hdf5')
mc.apply_correction_offsets(offsets_path=offsets_path,
path_pairs=zip(f_paths, f_paths),
output_folder=os.path.join(plane_folder, 'corrected'),
process_num=3,
fill_value=0.,
avi_downsample_rate=20,
is_equalizing_histogram=True)
avi_downsample_rate = 20
sub_folder_ns = [f for f in os.listdir(data_folder) if os.path.isdir(os.path.join(data_folder, f))]
sub_folder_ns.sort()
print('\n'.join(sub_folder_ns))
for sub_folder_n in sub_folder_ns:
sub_folder = os.path.join(data_folder, sub_folder_n)
f_paths, _ = mc.motion_correction(input_folder=sub_folder,
input_path_identifier=input_path_identifier,
process_num=process_num,
output_folder=sub_folder,
anchor_frame_ind_chunk=anchor_frame_ind_chunk,
anchor_frame_ind_projection=anchor_frame_ind_projection,
iteration_chunk=iteration_chunk,
iteration_projection=iteration_projection,
max_offset_chunk=max_offset_chunk,
max_offset_projection=max_offset_projection,
align_func=align_func,
fill_value=fill_value)
print('\n'.join(f_paths))
offsets_path = os.path.join(sub_folder, 'correction_offsets.hdf5')
path_pairs = zip(f_paths, f_paths)
mc.apply_correction_offsets(offsets_path=offsets_path,
path_pairs=path_pairs,
process_num=process_num,
fill_value=fill_value,
output_folder=sub_folder,