def test_ROI_binary_overlap(self):
roi1 = np.zeros((10, 10))
roi1[4:8, 3:7] = 1
roi1 = ia.ROI(roi1)
roi2 = np.zeros((10, 10))
roi2[5:9, 5:8] = 1
roi2 = ia.ROI(roi2)
assert(roi1.binary_overlap(roi2) == 6)
def test_ROI(self):
a = np.zeros((10, 10))
a[5:7, 3:6] = 1
a[8:9, 7:10] = np.nan
roi = ia.ROI(a)
# plt.imshow(roi.get_binary_mask(),interpolation='nearest')
assert (list(roi.get_center()) == [5.5, 4.])
def test_ROI_getBinaryTrace(self):
mov = np.random.rand(5, 4, 4)
mask = np.zeros((4, 4))
mask[2, 3] = 1
trace1 = mov[:, 2, 3]
roi = ia.ROI(mask)
trace2 = roi.get_binary_trace(mov)
assert (np.array_equal(trace1, trace2))
def test_plot_ROIs(self):
aa = np.zeros((50, 50));
aa[15:20, 30:35] = np.random.rand(5, 5)
roi1 = ia.ROI(aa)
_ = roi1.plot_binary_mask_border();
_ = roi1.plot_binary_mask()
roi2 = ia.WeightedROI(aa)
_ = roi2.plot_binary_mask_border();
_ = roi2.plot_binary_mask();
_ = roi2.plot_weighted_mask()
def get_traces(params):
t0 = time.time()
chunk_ind, chunk_start, chunk_end, nwb_path, data_path, curr_folder, center_array, surround_array = params
nwb_f = h5py.File(nwb_path, 'r')
print('\nstart analyzing chunk: {}'.format(chunk_ind))
curr_mov = nwb_f[data_path][chunk_start:chunk_end]
nwb_f.close()
# print 'extracting traces'
curr_traces_center = np.empty((center_array.shape[0], curr_mov.shape[0]),
dtype=np.float32)
curr_traces_surround = np.empty((center_array.shape[0], curr_mov.shape[0]),
dtype=np.float32)
for i in range(center_array.shape[0]):
curr_center = ia.WeightedROI(center_array[i])
curr_surround = ia.ROI(surround_array[i])
curr_traces_center[i, :] = curr_center.get_weighted_trace_pixelwise(
curr_mov)
# scale surround trace to be similar as center trace
mean_center_weight = curr_center.get_mean_weight()
curr_traces_surround[i, :] = curr_surround.get_binary_trace_pixelwise(
curr_mov) * mean_center_weight
# print 'saveing chunk {} ...'.format(chunk_ind)
chunk_folder = os.path.join(curr_folder, 'chunks')
if not os.path.isdir(chunk_folder):
os.mkdir(chunk_folder)
chunk_f = h5py.File(
os.path.join(chunk_folder,
'chunk_temp_' + ft.int2str(chunk_ind, 4) + '.hdf5'))
chunk_f['traces_center'] = curr_traces_center
chunk_f['traces_surround'] = curr_traces_surround
chunk_f.close()
print('\n\t{:06d} seconds: chunk: {}; demixing finished.'.format(
int(time.time() - t0), chunk_ind))
return None
def add_rois_and_traces(
data_folder,
nwb_f,
plane_n,
imaging_depth,
mov_path='/processing/motion_correction/MotionCorrection'):
mov_grp = nwb_f.file_pointer[mov_path + '/' + plane_n + '/corrected']
data_f = h5py.File(os.path.join(data_folder, 'rois_and_traces.hdf5'), 'r')
mask_arr_c = data_f['masks_center'].value
mask_arr_s = data_f['masks_surround'].value
traces_center_raw = data_f['traces_center_raw'].value
# traces_center_demixed = data_f['traces_center_demixed'].value
traces_center_subtracted = data_f['traces_center_subtracted'].value
# traces_center_dff = data_f['traces_center_dff'].value
traces_surround_raw = data_f['traces_surround_raw'].value
neuropil_r = data_f['neuropil_r'].value
neuropil_err = data_f['neuropil_err'].value
data_f.close()
if traces_center_raw.shape[1] != mov_grp['num_samples'].value:
raise ValueError(
'number of trace time points ({}) does not match frame number of '
'corresponding movie ({}).'.format(traces_center_raw.shape[1],
mov_grp['num_samples'].value))
# traces_center_raw = traces_center_raw[:, :mov_grp['num_samples'].value]
# traces_center_subtracted = traces_center_subtracted[:, :mov_grp['num_samples'].value]
# traces_surround_raw = traces_surround_raw[:, :mov_grp['num_samples'].value]
rf_img_max = tf.imread(
os.path.join(data_folder, 'corrected_max_projection.tif'))
rf_img_mean = tf.imread(
os.path.join(data_folder, 'corrected_mean_projection.tif'))
print 'adding segmentation results ...'
rt_mo = nwb_f.create_module('rois_and_traces_' + plane_n)
rt_mo.set_value('imaging_depth_micron', imaging_depth)
is_if = rt_mo.create_interface('ImageSegmentation')
is_if.create_imaging_plane('imaging_plane', description='')
is_if.add_reference_image('imaging_plane', 'max_projection', rf_img_max)
is_if.add_reference_image('imaging_plane', 'mean_projection', rf_img_mean)
for i in range(mask_arr_c.shape[0]):
curr_cen = mask_arr_c[i]
curr_cen_n = 'roi_' + ft.int2str(i, 4)
curr_cen_roi = ia.WeightedROI(curr_cen)
curr_cen_pixels_yx = curr_cen_roi.get_pixel_array()
curr_cen_pixels_xy = np.array(
[curr_cen_pixels_yx[:, 1], curr_cen_pixels_yx[:, 0]]).transpose()
is_if.add_roi_mask_pixels(image_plane='imaging_plane',
roi_name=curr_cen_n,
desc='',
pixel_list=curr_cen_pixels_xy,
weights=curr_cen_roi.weights,
width=512,
height=512)
curr_sur = mask_arr_s[i]
curr_sur_n = 'surround_' + ft.int2str(i, 4)
curr_sur_roi = ia.ROI(curr_sur)
curr_sur_pixels_yx = curr_sur_roi.get_pixel_array()
curr_sur_pixels_xy = np.array(
[curr_sur_pixels_yx[:, 1], curr_sur_pixels_yx[:, 0]]).transpose()
is_if.add_roi_mask_pixels(image_plane='imaging_plane',
roi_name=curr_sur_n,
desc='',
pixel_list=curr_sur_pixels_xy,
weights=None,
width=512,
height=512)
is_if.finalize()
trace_f_if = rt_mo.create_interface('Fluorescence')
seg_if_path = '/processing/rois_and_traces_' + plane_n + '/ImageSegmentation/imaging_plane'
# print seg_if_path
ts_path = mov_path + '/' + plane_n + '/corrected'
print 'adding center fluorescence raw'
trace_raw_ts = nwb_f.create_timeseries('RoiResponseSeries', 'f_center_raw')
trace_raw_ts.set_data(traces_center_raw,
unit='au',
conversion=np.nan,
resolution=np.nan)
trace_raw_ts.set_value('data_format', 'roi (row) x time (column)')
trace_raw_ts.set_value('data_range', '[-8192, 8191]')
trace_raw_ts.set_description(
'fluorescence traces extracted from the center region of each roi')
trace_raw_ts.set_time_as_link(ts_path)
trace_raw_ts.set_value_as_link('segmentation_interface', seg_if_path)
roi_names = [
'roi_' + ft.int2str(ind, 4)
for ind in range(traces_center_raw.shape[0])
]
trace_raw_ts.set_value('roi_names', roi_names)
trace_raw_ts.set_value('num_samples', traces_center_raw.shape[1])
trace_f_if.add_timeseries(trace_raw_ts)
trace_raw_ts.finalize()
print 'adding neuropil fluorescence raw'
trace_sur_ts = nwb_f.create_timeseries('RoiResponseSeries',
'f_surround_raw')
trace_sur_ts.set_data(traces_surround_raw,
unit='au',
conversion=np.nan,
resolution=np.nan)
trace_sur_ts.set_value('data_format', 'roi (row) x time (column)')
trace_sur_ts.set_value('data_range', '[-8192, 8191]')
trace_sur_ts.set_description(
'neuropil traces extracted from the surroud region of each roi')
trace_sur_ts.set_time_as_link(ts_path)
trace_sur_ts.set_value_as_link('segmentation_interface', seg_if_path)
sur_names = [
'surround_' + ft.int2str(ind, 4)
for ind in range(traces_center_raw.shape[0])
]
trace_sur_ts.set_value('roi_names', sur_names)
trace_sur_ts.set_value('num_samples', traces_surround_raw.shape[1])
trace_f_if.add_timeseries(trace_sur_ts)
trace_sur_ts.finalize()
roi_center_n_path = '/processing/rois_and_traces_' + plane_n + '/Fluorescence/f_center_raw/roi_names'
# print 'adding center fluorescence demixed'
# trace_demix_ts = nwb_f.create_timeseries('RoiResponseSeries', 'f_center_demixed')
# trace_demix_ts.set_data(traces_center_demixed, unit='au', conversion=np.nan, resolution=np.nan)
# trace_demix_ts.set_value('data_format', 'roi (row) x time (column)')
# trace_demix_ts.set_description('center traces after overlapping demixing for each roi')
# trace_demix_ts.set_time_as_link(mov_path + '/' + plane_n + '/corrected')
# trace_demix_ts.set_value_as_link('segmentation_interface', seg_if_path)
# trace_demix_ts.set_value('roi_names', roi_names)
# trace_demix_ts.set_value('num_samples', traces_center_demixed.shape[1])
# trace_f_if.add_timeseries(trace_demix_ts)
# trace_demix_ts.finalize()
print 'adding center fluorescence after neuropil subtraction'
trace_sub_ts = nwb_f.create_timeseries('RoiResponseSeries',
'f_center_subtracted')
trace_sub_ts.set_data(traces_center_subtracted,
unit='au',
conversion=np.nan,
resolution=np.nan)
trace_sub_ts.set_value('data_format', 'roi (row) x time (column)')
trace_sub_ts.set_description(
'center traces after overlap demixing and neuropil subtraction for each roi'
)
trace_sub_ts.set_time_as_link(mov_path + '/' + plane_n + '/corrected')
trace_sub_ts.set_value_as_link('segmentation_interface', seg_if_path)
trace_sub_ts.set_value_as_link('roi_names', roi_center_n_path)
trace_sub_ts.set_value('num_samples', traces_center_subtracted.shape[1])
trace_sub_ts.set_value('r', neuropil_r, dtype='float32')
trace_sub_ts.set_value('rmse', neuropil_err, dtype='float32')
trace_sub_ts.set_comments(
'value "r": neuropil contribution ratio for each roi. '
'value "rmse": RMS error of neuropil subtraction for each roi')
trace_f_if.add_timeseries(trace_sub_ts)
trace_sub_ts.finalize()
trace_f_if.finalize()
# print 'adding global dF/F traces for each roi'
# trace_dff_if = rt_mo.create_interface('DfOverF')
#
# trace_dff_ts = nwb_f.create_timeseries('RoiResponseSeries', 'dff_center')
# trace_dff_ts.set_data(traces_center_dff, unit='au', conversion=np.nan, resolution=np.nan)
# trace_dff_ts.set_value('data_format', 'roi (row) x time (column)')
# trace_dff_ts.set_description('global df/f traces for each roi center, input fluorescence is the trace after demixing'
# ' and neuropil subtraction. global df/f is calculated by '
# 'allensdk.brain_observatory.dff.compute_dff() function.')
# trace_dff_ts.set_time_as_link(ts_path)
# trace_dff_ts.set_value_as_link('segmentation_interface', seg_if_path)
# trace_dff_ts.set_value('roi_names', roi_names)
# trace_dff_ts.set_value('num_samples', traces_center_dff.shape[1])
# trace_dff_if.add_timeseries(trace_dff_ts)
# trace_dff_ts.finalize()
# trace_dff_if.finalize()
rt_mo.finalize()