import tifffile as tf
import matplotlib.pyplot as plt
import NeuroAnalysisTools.core.ImageAnalysis as ia
data_fn = 'zstack_2p_zoom2_red_aligned.tif'
save_fn = '2018-08-16-M376019-depth-profile-red.png'
start_depth = 50 # micron
step_depth = 2 # micron
pix_size = 0.7 # sutter scope, zoom2, 512 x 512
resolution = 512
curr_folder = os.path.dirname(os.path.abspath(__file__))
os.chdir(curr_folder)
data = tf.imread(data_fn)
dp = ia.array_nor(np.mean(data, axis=1))
depth_i = np.array(range(0, dp.shape[0], 50))
depth_l = depth_i * step_depth + start_depth
f = plt.figure(figsize=(8, 8))
ax = f.add_subplot(111)
ax.imshow(dp, vmin=0, vmax=1, cmap='magma', aspect=step_depth / pix_size)
ax.set_xticks([0, resolution-1])
ax.set_xticklabels(['0', '{:7.2f}'.format(resolution*pix_size)])
ax.set_yticks(depth_i)
ax.set_yticklabels(depth_l)
ax.set_xlabel('horizontal dis (um)')
ax.set_ylabel('depth (um)')
plt.show()
filter_sigma = 0. # parameters only used if filter the rois
# dilation_iterations = 1. # parameters only used if filter the rois
cut_thr = 2.5 # low for more rois, high for less rois
bg_fn = "corrected_mean_projections.tif"
save_folder = 'figures'
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
data_f = h5py.File('caiman_segmentation_results.hdf5')
masks = data_f['masks'].value
data_f.close()
bg = ia.array_nor(np.max(tf.imread(bg_fn), axis=0))
final_roi_dict = {}
for i, mask in enumerate(masks):
if is_filter:
mask_nor = (mask - np.mean(mask.flatten())) / np.abs(
np.std(mask.flatten()))
mask_nor_f = ni.filters.gaussian_filter(mask_nor, filter_sigma)
mask_bin = np.zeros(mask_nor_f.shape, dtype=np.uint8)
mask_bin[mask_nor_f > cut_thr] = 1
else:
mask_bin = np.zeros(mask.shape, dtype=np.uint8)
mask_bin[mask > 0] = 1
def run():
# pixels, masks with center location within this pixel region at the image border will be discarded
center_margin = [
10, 30, 35, 10
] # [top margin, bottom margin, left margin, right margin]
# area range, range of number of pixels of a valid roi
area_range = [5, 500] # [10, 100] for bouton, [150, 1000] for soma
# for the two masks that are overlapping, if the ratio between overlap and the area of the smaller mask is larger than
# this value, the smaller mask will be discarded.
overlap_thr = 0 # 0.2
save_folder = 'figures'
data_file_name = 'cells.hdf5'
save_file_name = 'cells_refined.hdf5'
background_file_name = "corrected_mean_projections.tif"
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
# read cells
dfile = h5py.File(data_file_name, 'r')
cells = {}
for cellname in dfile.keys():
cells.update({cellname: ia.WeightedROI.from_h5_group(dfile[cellname])})
print('total number of cells:', len(cells))
# get the names of cells which are on the edge
edge_cells = []
for cellname, cellmask in cells.items():
dimension = cellmask.dimension
center = cellmask.get_center()
if center[0] < center_margin[0] or \
center[0] > dimension[0] - center_margin[1] or \
center[1] < center_margin[2] or \
center[1] > dimension[1] - center_margin[3]:
# cellmask.plot_binary_mask_border(color='#ff0000', borderWidth=1)
# plt.title(cellname)
# plt.show()
edge_cells.append(cellname)
print('\ncells to be removed because they are on the edges:')
print('\n'.join(edge_cells))
# remove edge cells
for edge_cell in edge_cells:
_ = cells.pop(edge_cell)
# get dictionary of cell areas
cell_areas = {}
for cellname, cellmask in cells.items():
cell_areas.update({cellname: cellmask.get_binary_area()})
# remove cellnames that have area outside of the area_range
invalid_cell_ns = []
for cellname, cellarea in cell_areas.items():
if cellarea < area_range[0] or cellarea > area_range[1]:
invalid_cell_ns.append(cellname)
print("cells to be removed because they do not meet area criterion:")
print("\n".join(invalid_cell_ns))
for invalid_cell_n in invalid_cell_ns:
cell_areas.pop(invalid_cell_n)
# sort cells with their binary area
cell_areas_sorted = sorted(cell_areas.items(), key=operator.itemgetter(1))
cell_areas_sorted.reverse()
cell_names_sorted = [c[0] for c in cell_areas_sorted]
# print '\n'.join([str(c) for c in cell_areas_sorted])
# get the name of cells that needs to be removed because of overlapping
retain_cells = []
remove_cells = []
for cell1_name in cell_names_sorted:
cell1_mask = cells[cell1_name]
is_remove = 0
cell1_area = cell1_mask.get_binary_area()
for cell2_name in retain_cells:
cell2_mask = cells[cell2_name]
cell2_area = cell2_mask.get_binary_area()
curr_overlap = cell1_mask.binary_overlap(cell2_mask)
if float(curr_overlap) / cell1_area > overlap_thr:
remove_cells.append(cell1_name)
is_remove = 1
print(cell1_name, ':', cell1_mask.get_binary_area(),
': removed')
# f = plt.figure(figsize=(10,10))
# ax = f.add_subplot(111)
# cell1_mask.plot_binary_mask_border(plotAxis=ax, color='#ff0000', borderWidth=1)
# cell2_mask.plot_binary_mask_border(plotAxis=ax, color='#0000ff', borderWidth=1)
# ax.set_title('red:'+cell1_name+'; blue:'+cell2_name)
# plt.show()
break
if is_remove == 0:
retain_cells.append(cell1_name)
print(cell1_name, ':', cell1_mask.get_binary_area(), ': retained')
print('\ncells to be removed because of overlapping:')
print('\n'.join(remove_cells))
print('\ntotal number of reatined cells:', len(retain_cells))
# plotting
colors = pt.random_color(len(cells.keys()))
bgImg = ia.array_nor(np.max(tf.imread(background_file_name), axis=0))
f = plt.figure(figsize=(10, 10))
ax = f.add_subplot(111)
ax.imshow(ia.array_nor(bgImg),
cmap='gray',
vmin=0,
vmax=0.5,
interpolation='nearest')
f2 = plt.figure(figsize=(10, 10))
ax2 = f2.add_subplot(111)
ax2.imshow(np.zeros(bgImg.shape, dtype=np.uint8),
vmin=0,
vmax=1,
cmap='gray',
interpolation='nearest')
i = 0
for retain_cell in retain_cells:
cells[retain_cell].plot_binary_mask_border(plotAxis=ax,
color=colors[i],
borderWidth=1)
cells[retain_cell].plot_binary_mask_border(plotAxis=ax2,
color=colors[i],
borderWidth=1)
i += 1
# plt.show()
# save figures
pt.save_figure_without_borders(f,
os.path.join(
save_folder,
'2P_refined_ROIs_with_background.png'),
dpi=300)
pt.save_figure_without_borders(
f2,
os.path.join(save_folder, '2P_refined_ROIs_without_background.png'),
dpi=300)
# save h5 file
save_file = h5py.File(save_file_name, 'x')
i = 0
for retain_cell in retain_cells:
print(retain_cell, ':', cells[retain_cell].get_binary_area())
currGroup = save_file.create_group('cell' + ft.int2str(i, 4))
currGroup.attrs['name'] = retain_cell
roiGroup = currGroup.create_group('roi')
cells[retain_cell].to_h5_group(roiGroup)
i += 1
for attr, value in dfile.attrs.items():
save_file.attrs[attr] = value
save_file.close()
dfile.close()
# cell2_mask.plot_binary_mask_border(plotAxis=ax, color='#0000ff', borderWidth=1)
# ax.set_title('red:'+cell1_name+'; blue:'+cell2_name)
# plt.show()
break
if is_remove == 0:
retain_cells.append(cell1_name)
print(cell1_name, ':', cell1_mask.get_binary_area(), ': retained')
print('\ncells to be removed because of overlapping:')
print('\n'.join(remove_cells))
print('\ntotal number of reatined cells:', len(retain_cells))
# plotting
colors = pt.random_color(len(cells.keys()))
bgImg = ia.array_nor(np.max(tf.imread(background_file_name), axis=0))
f = plt.figure(figsize=(10, 10))
ax = f.add_subplot(111)
ax.imshow(ia.array_nor(bgImg),
cmap='gray',
vmin=0,
vmax=0.5,
interpolation='nearest')
f2 = plt.figure(figsize=(10, 10))
ax2 = f2.add_subplot(111)
ax2.imshow(np.zeros(bgImg.shape, dtype=np.uint8),
vmin=0,
vmax=1,
cmap='gray',
if 'path_list' in offsets_keys:
offsets_keys.remove('path_list')
offsets_keys.sort()
offsets = []
for offsets_key in offsets_keys:
offsets.append(offsets_f[offsets_key].value)
offsets = np.concatenate(offsets, axis=0)
offsets = np.array(zip(offsets[:, 1], offsets[:, 0]))
offsets_f.close()
mean_projection = tf.imread(
os.path.join(plane_n, 'corrected_mean_projection.tif'))
max_projection = tf.imread(
os.path.join(plane_n, 'corrected_max_projections.tif'))
max_projection = ia.array_nor(np.max(max_projection, axis=0))
input_dict = {
'field_name':
plane_n,
'original_timeseries_path':
'/acquisition/timeseries/2p_movie_plane' + str(i),
'corrected_file_path':
movie_2p_fn,
'corrected_dataset_path':
plane_n,
'xy_translation_offsets':
offsets,
'mean_projection':
mean_projection,
'max_projection':
def run():
isSave = True
filter_sigma = 2. # 2. for soma, 1. for bouton
thr_high = 0.0
thr_low = 0.1
bg_fn = "corrected_mean_projections.tif"
save_folder = 'figures'
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
data_f = h5py.File('caiman_segmentation_results.hdf5', 'r')
masks = data_f['masks'].value
data_f.close()
bg = ia.array_nor(np.max(tf.imread(bg_fn), axis=0))
final_roi_dict = {}
roi_ind = 0
for i, mask in enumerate(masks):
mask_dict = hl.threshold_mask_by_energy(mask,
sigma=filter_sigma,
thr_high=thr_high,
thr_low=thr_low)
for mask_roi in mask_dict.values():
final_roi_dict.update({'roi_{:04d}'.format(roi_ind): mask_roi})
roi_ind += 1
print('Total number of ROIs:', len(final_roi_dict))
f = plt.figure(figsize=(15, 8))
ax1 = f.add_subplot(121)
ax1.imshow(bg, vmin=0, vmax=0.5, cmap='gray', interpolation='nearest')
colors1 = pt.random_color(masks.shape[0])
for i, mask in enumerate(masks):
pt.plot_mask_borders(mask, plotAxis=ax1, color=colors1[i])
ax1.set_title('original ROIs')
ax1.set_axis_off()
ax2 = f.add_subplot(122)
ax2.imshow(ia.array_nor(bg),
vmin=0,
vmax=0.5,
cmap='gray',
interpolation='nearest')
colors2 = pt.random_color(len(final_roi_dict))
i = 0
for roi in final_roi_dict.values():
pt.plot_mask_borders(roi.get_binary_mask(),
plotAxis=ax2,
color=colors2[i])
i = i + 1
ax2.set_title('filtered ROIs')
ax2.set_axis_off()
# plt.show()
if isSave:
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
f.savefig(os.path.join(save_folder,
'caiman_segmentation_filtering.pdf'),
dpi=300)
cell_file = h5py.File('cells.hdf5', 'w')
i = 0
for key, value in sorted(final_roi_dict.items()):
curr_grp = cell_file.create_group('cell{:04d}'.format(i))
curr_grp.attrs['name'] = key
value.to_h5_group(curr_grp)
i += 1
cell_file.close()
'vasmap_wf_rotated': 'wide field surface vasculature map through cranial window rotated',
'vasmap_2p_green': '2p surface vasculature map through cranial window green original, zoom1',
'vasmap_2p_green_rotated': '2p surface vasculature map through cranial window green rotated, zoom1',
'vasmap_2p_red': '2p surface vasculature map through cranial window red original, zoom1',
'vasmap_2p_red_rotated': '2p surface vasculature map through cranial window red rotated, zoom1'
}
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
nwb_fn = [f for f in os.listdir(curr_folder) if f[-4:] == '.nwb'][0]
nwb_f = nt.RecordedFile(nwb_fn)
for mn, des in vasmap_dict.items():
try:
curr_m = ia.array_nor(tf.imread(mn + '.tif'))
if is_plot:
f = plt.figure(figsize=(10, 10))
ax = f.add_subplot(111)
ax.imshow(curr_m, vmin=0., vmax=1., cmap='gray', interpolation='nearest')
ax.set_axis_off()
ax.set_title(mn)
plt.show()
print('adding {} to nwb file.'.format(mn))
nwb_f.add_acquisition_image(mn, curr_m, description=des)
except Exception as e:
print(e)
def run():
data_file_name = 'cells_refined.hdf5'
background_file_name = "corrected_mean_projections.tif"
save_folder = 'figures'
overlap_threshold = 0.9
surround_limit = [1, 8]
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
print('reading cells file ...')
data_f = h5py.File(data_file_name, 'r')
cell_ns = data_f.keys()
cell_ns.sort()
binary_mask_array = []
weight_mask_array = []
for cell_n in cell_ns:
curr_roi = ia.ROI.from_h5_group(data_f[cell_n]['roi'])
binary_mask_array.append(curr_roi.get_binary_mask())
weight_mask_array.append(curr_roi.get_weighted_mask())
data_f.close()
binary_mask_array = np.array(binary_mask_array)
weight_mask_array = np.array(weight_mask_array)
print('starting mask_array shape:', weight_mask_array.shape)
print('getting total mask ...')
total_mask = np.zeros((binary_mask_array.shape[1], binary_mask_array.shape[2]), dtype=np.uint8)
for curr_mask in binary_mask_array:
total_mask = np.logical_or(total_mask, curr_mask)
total_mask = np.logical_not(total_mask)
plt.imshow(total_mask, interpolation='nearest')
plt.title('total_mask')
# plt.show()
print('getting and surround masks ...')
binary_surround_array = []
for binary_center in binary_mask_array:
curr_surround = np.logical_xor(ni.binary_dilation(binary_center, iterations=surround_limit[1]),
ni.binary_dilation(binary_center, iterations=surround_limit[0]))
curr_surround = np.logical_and(curr_surround, total_mask).astype(np.uint8)
binary_surround_array.append(curr_surround)
# plt.imshow(curr_surround)
# plt.show()
binary_surround_array = np.array(binary_surround_array)
print("saving rois ...")
center_areas = []
surround_areas = []
for mask_ind in range(binary_mask_array.shape[0]):
center_areas.append(np.sum(binary_mask_array[mask_ind].flat))
surround_areas.append(np.sum(binary_surround_array[mask_ind].flat))
roi_f = h5py.File('rois_and_traces.hdf5')
roi_f['masks_center'] = weight_mask_array
roi_f['masks_surround'] = binary_surround_array
roi_f.close()
print('minimum surround area:', min(surround_areas), 'pixels.')
f = plt.figure(figsize=(10, 10))
ax_center = f.add_subplot(211)
ax_center.hist(center_areas, bins=30)
ax_center.set_title('roi center area distribution')
ax_surround = f.add_subplot(212)
ax_surround.hist(surround_areas, bins=30)
ax_surround.set_title('roi surround area distribution')
# plt.show()
print('plotting ...')
colors = pt.random_color(weight_mask_array.shape[0])
bg = ia.array_nor(np.max(tf.imread(background_file_name), axis=0))
f_c_bg = plt.figure(figsize=(10, 10))
ax_c_bg = f_c_bg.add_subplot(111)
ax_c_bg.imshow(bg, cmap='gray', vmin=0, vmax=0.5, interpolation='nearest')
f_c_nbg = plt.figure(figsize=(10, 10))
ax_c_nbg = f_c_nbg.add_subplot(111)
ax_c_nbg.imshow(np.zeros(bg.shape,dtype=np.uint8),vmin=0,vmax=1,cmap='gray',interpolation='nearest')
f_s_nbg = plt.figure(figsize=(10, 10))
ax_s_nbg = f_s_nbg.add_subplot(111)
ax_s_nbg.imshow(np.zeros(bg.shape,dtype=np.uint8),vmin=0,vmax=1,cmap='gray',interpolation='nearest')
i = 0
for mask_ind in range(binary_mask_array.shape[0]):
pt.plot_mask_borders(binary_mask_array[mask_ind], plotAxis=ax_c_bg, color=colors[i], borderWidth=1)
pt.plot_mask_borders(binary_mask_array[mask_ind], plotAxis=ax_c_nbg, color=colors[i], borderWidth=1)
pt.plot_mask_borders(binary_surround_array[mask_ind], plotAxis=ax_s_nbg, color=colors[i], borderWidth=1)
i += 1
# plt.show()
print('saving figures ...')
pt.save_figure_without_borders(f_c_bg, os.path.join(save_folder, '2P_ROIs_with_background.png'), dpi=300)
pt.save_figure_without_borders(f_c_nbg, os.path.join(save_folder, '2P_ROIs_without_background.png'), dpi=300)
pt.save_figure_without_borders(f_s_nbg, os.path.join(save_folder, '2P_ROI_surrounds_background.png'), dpi=300)
f.savefig(os.path.join(save_folder, 'roi_area_distribution.pdf'), dpi=300)
import tifffile as tf
import matplotlib.pyplot as plt
import NeuroAnalysisTools.core.ImageAnalysis as ia
data_folder = r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data\190822-M471944-deepscope\movie"
identifier = '110_LSNDGCUC'
start_ind = 121228
frame_num = 3
fns = []
for ind in np.arange(frame_num, dtype=np.int) + start_ind:
if ind < 100000:
fns.append('{}_{:05d}_00001.tif'.format(identifier, ind))
elif ind < 1000000:
fns.append('{}_{:06d}_00001.tif'.format(identifier, ind))
elif ind < 10000000:
fns.append('{}_{:07d}_00001.tif'.format(identifier, ind))
f = plt.figure(figsize=(5, 12))
for frame_i in range(frame_num):
ax = f.add_subplot(frame_num, 1, frame_i+1)
ax.imshow(ia.array_nor(tf.imread(os.path.join(data_folder, fns[frame_i]))), cmap='gray',
vmin=0, vmax=0.5, interpolation='nearest')
ax.set_title(fns[frame_i])
ax.set_axis_off()
plt.tight_layout()
plt.show()