def on_draw(self):
""" Redraws the figure
"""
self.axes.clear()
if type(self.ReferenceVasMap) != type(None):
width = self.ReferenceVasMap.shape[1]
height = self.ReferenceVasMap.shape[0]
elif type(self.MatchingVasMapAfterChange) != type(None):
width = self.MatchingVasMapAfterChange.shape[1]
height = self.MatchingVasMapAfterChange.shape[0]
elif type(self.MatchingVasMap) != type(None):
width = self.MatchingVasMap.shape[1]
height = self.MatchingVasMap.shape[0]
else:
width = 1344
height = 1024
if (type(self.ReferenceVasMap) !=
type(None)) and (self.radiobutton_reference.isChecked()
or self.radiobutton_both.isChecked()):
greenChannel = ia.resize_image(self.ReferenceVasMap,
(height, width))
greenChannel = (
np.power(ia.array_nor(greenChannel), self.reference_contrast) *
255).astype(np.uint8)
else:
greenChannel = np.zeros((height, width)).astype(np.uint8)
if (self.radiobutton_matching.isChecked()
or self.radiobutton_both.isChecked()):
if type(self.MatchingVasMapAfterChange) != type(None):
redChannel = ia.resize_image(self.MatchingVasMapAfterChange,
(height, width))
redChannel = (np.power(ia.array_nor(redChannel),
self.matching_contrast) * 255).astype(
np.uint8)
elif type(self.MatchingVasMap) != type(None):
redChannel = ia.resize_image(self.MatchingVasMap,
(height, width))
redChannel = (np.power(ia.array_nor(redChannel),
self.matching_contrast) * 255).astype(
np.uint8)
else:
redChannel = np.zeros((height, width)).astype(np.uint8)
else:
redChannel = np.zeros((height, width)).astype(np.uint8)
blueChannel = np.zeros((height, width)).astype(np.uint8)
pltImg = cv2.merge((redChannel, greenChannel, blueChannel))
self.axes.imshow(pltImg)
self.axes.set_xlim([0, width])
self.axes.set_ylim([0, height])
self.axes.invert_yaxis()
self.canvas.draw()
data_folder = r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project\180404-M360495-2p\vasmap_2p"
zoom1_paths = [
os.path.join(data_folder, f) for f in os.listdir(data_folder)
if f[-12:] == '_rotated.tif' and '_zoom1_' in f
]
# zoom2_paths = [os.path.join(data_folder, f) for f in os.listdir(data_folder)
# if f[-12:] == '_rotated.tif' and '_zoom2_' in f]
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
vas_map_zoom1 = []
# vas_map_zoom2 = []
for zoom1_path in zoom1_paths:
curr_vasmap = np.mean(tf.imread(zoom1_path), axis=0)
vas_map_zoom1.append(curr_vasmap)
# for zoom2_path in zoom2_paths:
# curr_vasmap = np.mean(tf.imread(zoom2_path), axis=0)
# vas_map_zoom2.append(curr_vasmap)
vas_map_zoom1 = ia.array_nor(np.mean(vas_map_zoom1, axis=0))
# vas_map_zoom2 = ia.array_nor(np.mean(vas_map_zoom2, axis=0))
tf.imsave('vas_map_focused_2p_zoom1.tif', vas_map_zoom1.astype(np.float32))
# tf.imsave('vas_map_focused_2p_zoom2.tif', vas_map_zoom2.astype(np.float32))
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
import os
import corticalmapping.NwbTools as nt
import corticalmapping.core.ImageAnalysis as ia
import matplotlib.pyplot as plt
import tifffile as tf
vasmap_name_wf = 'vas_map_focused_wf.tif'
vasmap_name_2p_zoom1 = 'vas_map_focused_2p_zoom1.tif'
# vasmap_name_2p_zoom2 = 'vas_map_focused_2p_zoom2.tif'
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
vasmap_wf = tf.imread(vasmap_name_wf)
vasmap_2p_zoom1 = ia.array_nor(tf.imread(vasmap_name_2p_zoom1))
# vasmap_2p_zoom2 = ia.array_nor(tf.imread(vasmap_name_2p_zoom2))
f = plt.figure(figsize=(15, 7))
ax1 = f.add_subplot(121)
ax1.imshow(vasmap_wf, cmap='gray', interpolation='nearest')
ax1.set_title('wide field surface vasculature')
ax2 = f.add_subplot(122)
ax2.imshow(vasmap_2p_zoom1,
vmin=0,
vmax=0.1,
cmap='gray',
interpolation='nearest')
ax2.set_title('two photon surface vasculature')
plt.show()
nwb_fn = [f for f in os.listdir(curr_folder) if f[-4:] == '.nwb'][0]
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':
else:
mask_bin = np.zeros(mask.shape, dtype=np.uint8)
mask_bin[mask > 0] = 1
mask_labeled, mask_num = ni.label(mask_bin)
curr_mask_dict = ia.get_masks(labeled=mask_labeled,
keyPrefix='caiman_mask_{:03d}'.format(i),
labelLength=5)
for roi_key, roi_mask in curr_mask_dict.items():
final_roi_dict.update({roi_key: ia.WeightedROI(roi_mask * mask)})
print 'Total number of ROIs:', len(final_roi_dict)
f = plt.figure(figsize=(15, 8))
ax1 = f.add_subplot(121)
ax1.imshow(ia.array_nor(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')
vas_map_paths = [
r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project"
r"\180404-M360495-2p\vasmap_wf\180404JCamF100",
r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project"
r"\180404-M360495-2p\vasmap_wf\180404JCamF101",
r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project"
r"\180404-M360495-2p\vasmap_wf\180404JCamF102",
]
saveFolder = os.path.dirname(os.path.realpath(__file__))
os.chdir(saveFolder)
vas_maps = []
for vas_map_path in vas_map_paths:
vas_map_focused, _, _ = ft.importRawJCamF(vas_map_path,
column=1024,
row=1024,
headerLength=116,
tailerLength=452)
vas_map_focused = vas_map_focused[2:]
vas_map_focused = vas_map_focused[:, ::-1, :]
vas_map_focused[vas_map_focused > 50000] = 400
vas_map_focused = np.mean(vas_map_focused, axis=0)
vas_maps.append(ia.array_nor(vas_map_focused))
vas_map = ia.array_nor(np.mean(vas_maps, axis=0))
tf.imsave('vas_map_focused_wf.tif', vas_map.astype(np.float32))
if len(curr_vasmap.shape) == 2:
if len(channels) == 1:
vasmaps[channels[0]].append(np.array([curr_vasmap]))
else:
raise ValueError(
'recorded file is 2d, cannot be deinterleved into {} channels.'
.format(len(channels)))
else:
if len(curr_vasmap.shape) != 3:
raise ValueError(
'shape of recorded file: {}. should be either 2d or 3d.'.
format(curr_vasmap.shape))
for ch_i, ch_n in enumerate(channels):
curr_vasmap_ch = curr_vasmap[ch_i::len(channels)]
curr_vasmap_ch = ia.array_nor(np.mean(curr_vasmap_ch, axis=0))
if is_equalize:
curr_vasmap_ch = (curr_vasmap_ch * 255).astype(np.uint8)
curr_vasmap_ch = cv2.equalizeHist(curr_vasmap_ch).astype(
np.float32)
vasmaps[ch_n].append(curr_vasmap_ch)
for ch_n, ch_vasmap in vasmaps.items():
# save_vasmap = np.concatenate(ch_vasmap, axis=0)
# print(save_vasmap.shape)
# save_vasmap = ia.array_nor(np.mean(save_vasmap, axis=0))
# print(save_vasmap.shape)
save_vasmap = ia.array_nor(np.mean(ch_vasmap, axis=0))
if scope == 'scientifica':
]
save_name = 'vasmap_2p_zoom1'
channels = ['green', 'red']
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
vasmaps = {}
for chn in channels:
vasmaps.update({chn: []})
for file_n in file_ns:
print(file_n)
curr_vasmap = tf.imread(os.path.join(data_folder, file_n))
for ch_i, ch_n in enumerate(channels):
curr_vasmap_ch = curr_vasmap[ch_i::len(channels)]
vasmaps[ch_n].append(curr_vasmap_ch.transpose((0, 2, 1))[:, ::-1, :])
# print(curr_vasmap_ch.shape)
for ch_n, ch_vasmap in vasmaps.items():
save_vasmap = np.concatenate(ch_vasmap, axis=0)
# print(save_vasmap.shape)
save_vasmap = ia.array_nor(np.mean(save_vasmap, axis=0))
# print(save_vasmap.shape)
tf.imsave('{}_{}.tif'.format(save_name, ch_n),
save_vasmap.astype(np.float32))
import os import numpy as np import tifffile as tf import corticalmapping.core.ImageAnalysis as ia import matplotlib.pyplot as plt data_folder = r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project\180322-M360495-2p" file_name = "vasmap_zoom1_00001_00001.tif" save_name = 'vasmap_2p_zoom1.tif' curr_folder = os.path.dirname(os.path.realpath(__file__)) os.chdir(curr_folder) vasmap = tf.imread(os.path.join(data_folder, file_name)) vasmap = np.mean(vasmap.transpose((0, 2, 1))[:, ::-1, :], axis=0) vasmap = ia.array_nor(vasmap) tf.imsave(save_name, vasmap.astype(np.float32))
def run():
isSave = True
filter_sigma = 2. # parameters only used if filter the rois
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')
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.iteritems()):
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()
def run():
# pixels, masks with center location within this pixel region at the image border will be discarded
center_margin = [10, 20, 25, 10] # [top margin, bottom margin, left margin, right margin]
# area range, range of number of pixels of a valid roi
area_range = [150, 1000]
# 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 = 0.2
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)
cells = {}
for cellname in dfile.iterkeys():
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.iteritems():
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.iteritems():
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, 'w')
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.iteritems():
save_file.attrs[attr] = value
save_file.close()
dfile.close()
import matplotlib.pyplot as plt
import corticalmapping.core.ImageAnalysis as ia
vasmap_wf_folder = r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data\181102-M412052-deepscope\vasmap_wf"
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
map_fns = [f for f in os.listdir(vasmap_wf_folder) if f[-4:]=='.tif']
map_fns.sort()
print('\n'.join(map_fns))
map_wf = []
for map_fn in map_fns:
curr_map = tf.imread(os.path.join(vasmap_wf_folder, map_fn)).astype(np.float32)
map_wf.append(ia.array_nor(curr_map))
map_wf = ia.array_nor(np.mean(map_wf, axis=0))
map_wf_r = ia.array_nor(ia.rigid_transform_cv2(map_wf, rotation=140)[:, ::-1])
f = plt.figure(figsize=(12, 6))
ax_wf = f.add_subplot(121)
ax_wf.imshow(map_wf, vmin=0., vmax=1., cmap='gray', interpolation='nearest')
ax_wf.set_axis_off()
ax_wf.set_title('vasmap widefield')
ax_wf_r = f.add_subplot(122)
ax_wf_r.imshow(map_wf_r, vmin=0., vmax=1., cmap='gray', interpolation='nearest')
ax_wf_r.set_axis_off()
ax_wf_r.set_title('vasmap widefield rotated')
import tifffile as tf
import matplotlib.pyplot as plt
import corticalmapping.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()
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)
for i, mask in enumerate(masks):
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
mask_labeled, mask_num = ni.label(mask_bin)
curr_mask_dict = ia.get_masks(labeled=mask_labeled, keyPrefix='caiman_mask_{:03d}'.format(i), labelLength=5)
for roi_key, roi_mask in curr_mask_dict.items():
final_roi_dict.update({roi_key: ia.WeightedROI(roi_mask * mask)})
print 'Total number of ROIs:',len(final_roi_dict)
f = plt.figure(figsize=(15, 8))
ax1 = f.add_subplot(121)
ax1.imshow(ia.array_nor(bg), vmin=0, vmax=0.1, 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=0.1, 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()
# 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',
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()
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 = tf.imread(background_file_name)
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:
isRectify=False # should the fft method be applied to a rectify signal or not
#wrap experiment parameters
isAnesthetized=False
visualStimType='KSstim'
visualStimBackground='gray'
analysisParams ={}
if vasMapPaths:
vasMap = hl.getVasMap(vasMapPaths,dtype=vasMapDtype,headerLength=vasMapHeaderLength,tailerLength=vasMapTailerLength,
column=vasMapColumn,row=vasMapRow,frame=vasMapFrame,crop=vasMapCrop,mergeMethod=vasMapMergeMethod)
else:
print 'No vasculature map find. Taking first frame of movie as vasculature map.'
vasMap = BinarySlicer(movPath)[0,:,:]
vasMap = ia.array_nor(vasMap).astype(np.float32)
tf.imsave(os.path.join(saveFolder,dateRecorded+'_M'+mouseID+'_Trial'+trialNum+'_vasMap.tif'),vasMap)
_, jphys = ft.importRawNewJPhys(jphysPath,dtype=jphysDtype,headerLength=jphysHeaderLength,channels=jphysChannels,sf=jphysFs)
pd = jphys['photodiode']
displayOnsets = hl.segmentPhotodiodeSignal(pd, digitizeThr=pdDigitizeThr, filterSize=pdFilterSize, segmentThr=pdSegmentThr, Fs=jphysFs)
imgFrameTS = ta.get_onset_timeStamps(jphys['read'], Fs=jphysFs, threshold=readThreshold, onsetType=readOnsetType)
logPath = hl.findLogPath(date=dateRecorded,mouseID=mouseID,stimulus='KSstimAllDir',userID=userID,fileNumber=str(fileNum),displayFolder=dataFolder)
displayInfo = hl.analysisMappingDisplayLog(logPath)
node_grp0 = node_grp
if node1 in node_grp:
node_grp1 = node_grp
if node_grp0 != node_grp1:
node_grp0.update(node_grp1)
node_grps.remove(node_grp1)
return node_grps
save_plot_dir = os.path.join(curr_folder, 'figures', 'dff_extraction')
if not os.path.isdir(save_plot_dir):
os.makedirs(save_plot_dir)
bg = ia.array_nor(tf.imread('corrected_mean_projection.tif'))
data_f = h5py.File('rois_and_traces.hdf5')
traces_subtracted = data_f['traces_center_subtracted'].value
masks = data_f['masks_center'].value
f, axs = plt.subplots(1, 2, figsize=(16, 5))
cor_mat = np.corrcoef(traces_subtracted)
fig = axs[0].imshow(cor_mat,
vmin=-1,
vmax=1,
cmap='jet',
interpolation='nearest')
axs[0].set_title('coriance matrix')
f.colorbar(fig, ax=axs[0])
vasmap_wf_path = r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project" \
r"\180502-M376019-deepscope\Widefield.tif"
vasmap_2p_zoom1_path = r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project" \
r"\180502-M376019-deepscope\01\01_00001.tif"
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
vasmap_wf = io.imread(vasmap_wf_path, as_grey=True)
vasmap_wf = vasmap_wf.transpose()[::-1, ::-1]
vasmap_2p_zoom1 = tf.imread(vasmap_2p_zoom1_path).astype(np.float32)
vasmap_2p_zoom1 = np.mean(vasmap_2p_zoom1, axis=0)
vasmap_2p_zoom1 = vasmap_2p_zoom1.transpose()[::-1, ::-1]
f = plt.figure(figsize=(12, 5))
ax_wf = f.add_subplot(121)
ax_wf.imshow(ia.array_nor(vasmap_wf), vmin=0., vmax=1., cmap='gray', interpolation='nearest')
ax_wf.set_title('vasmap wide field')
ax_wf.set_axis_off()
ax_2p = f.add_subplot(122)
ax_2p.imshow(ia.array_nor(vasmap_2p_zoom1), vmin=0., vmax=0.15, cmap='gray', interpolation='nearest')
ax_2p.set_title('vasmap 2p zoom1')
ax_2p.set_axis_off()
plt.show()
tf.imsave('vasmap_wf.tif', vasmap_wf)
tf.imsave('vasmap_2p_zoom1.tif', vasmap_2p_zoom1)
vasmap_2p_zoom1_path = r"\\allen\programs\braintv\workgroups\nc-ophys\Jun\raw_data_rabies_project" \
r"\180328-M360495-deepscope\01\01_00001.tif"
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
vasmap_wf = io.imread(vasmap_wf_path, as_grey=True)
vasmap_wf = vasmap_wf.transpose()[::-1, ::-1]
vasmap_2p_zoom1 = tf.imread(vasmap_2p_zoom1_path).astype(np.float32)
vasmap_2p_zoom1 = np.mean(vasmap_2p_zoom1, axis=0)
vasmap_2p_zoom1 = vasmap_2p_zoom1.transpose()[::-1, ::-1]
f = plt.figure(figsize=(12, 5))
ax_wf = f.add_subplot(121)
ax_wf.imshow(ia.array_nor(vasmap_wf),
vmin=0.,
vmax=1.,
cmap='gray',
interpolation='nearest')
ax_wf.set_title('vasmap wide field')
ax_wf.set_axis_off()
ax_2p = f.add_subplot(122)
ax_2p.imshow(ia.array_nor(vasmap_2p_zoom1),
vmin=0.,
vmax=0.15,
cmap='gray',
interpolation='nearest')
ax_2p.set_title('vasmap 2p zoom1')
ax_2p.set_axis_off()