def run_gui(path=None, data=None):
"""
Loads PCF or CNMF data object from a provided path and loads the CaImAn GUI for component inspection.
This GUI was tweaked to not require any storage-intensive mmap files, but can therefore not show individual frames.
:param path: optional str, directory of the PCF or CNMF object from which component data should be loaded. If None
and data=None, a window prompt will open to select a directory where to look for a CNMF/PCF file.
:param data: optional, data in form of an already loaded cnm object can be provided directly
:return:
"""
try:
cv2.setNumThreads(1)
except:
print('Open CV is naturally single threaded')
try:
if __IPYTHON__:
# print(1)
# this is used for debugging purposes only. allows to reload classes
# when changed
get_ipython().magic('load_ext autoreload')
get_ipython().magic('autoreload 2')
except NameError:
print('Not launched under iPython')
def make_color_img(img, gain=255, min_max=None, out_type=np.uint8):
if min_max is None:
min_ = img.min()
max_ = img.max()
else:
min_, max_ = min_max
img = (img - min_) / (max_ - min_) * gain
img = img.astype(out_type)
img = np.dstack([img] * 3)
return img
### FIND DATA ###
if data is None: # different conditions on file loading (not necessary if data was already provided)
if path is None: # if no path has been given, open a window prompt to select a directory
F = FileDialog()
# load object saved by CNMF
path = F.getExistingDirectory(
caption='Select folder from which to load a PCF or CNMF file')
try: # first try to get CNMF data from a PCF object (should be most up-to-date)
cnm_obj = pipe.load_pcf(path).cnmf
except FileNotFoundError:
try:
cnm_obj = pipe.load_cnmf(path)
except FileNotFoundError:
raise FileNotFoundError(
f'Could not find data to load in {path}!')
else:
cnm_obj = data
# movie NOT NEEDED IN VERSION WITHOUT MMAP FILE
# if not os.path.exists(cnm_obj.mmap_file):
# M = FileDialog()
# cnm_obj.mmap_file = M.getOpenFileName(caption='Load memory mapped file', filter='*.mmap')[0]
#
# if fpath[-3:] == 'nwb':
# mov = cm.load(cnm_obj.mmap_file, var_name_hdf5='acquisition/TwoPhotonSeries')
# else:
# mov = cm.load(cnm_obj.mmap_file)
estimates = cnm_obj.estimates
params_obj = cnm_obj.params
# min_mov = np.min(mov)
# max_mov = np.max(mov)
if not hasattr(estimates, 'Cn'):
if not os.path.exists(cnm_obj.mmap_file):
M = FileDialog()
cnm_obj.mmap_file = M.getOpenFileName(
caption='Load memory mapped file', filter='*.mmap')[0]
mov = cm.load(cnm_obj.mmap_file)
estimates.Cn = cm.local_correlations(mov, swap_dim=False)
Cn = estimates.Cn
# min_mov_denoise = np.min(estimates.A)*estimates.C.min()
# max_mov_denoise = np.max(estimates.A)*estimates.C.max()
background_num = -1
neuron_selected = False
nr_index = 0
min_background = np.min(estimates.b, axis=0) * np.min(estimates.f, axis=1)
max_background = np.max(estimates.b, axis=0) * np.max(estimates.f, axis=1)
if not hasattr(estimates, 'accepted_list'):
# if estimates.discarded_components.A.shape[-1] > 0:
# estimates.restore_discarded_components()
estimates.accepted_list = np.array([], dtype=np.int)
estimates.rejected_list = np.array([], dtype=np.int)
estimates.img_components = estimates.A.toarray().reshape(
(estimates.dims[0], estimates.dims[1], -1),
order='F').transpose([2, 0, 1])
estimates.cms = np.array([
scipy.ndimage.measurements.center_of_mass(comp)
for comp in estimates.img_components
])
estimates.idx_components = np.arange(estimates.nr)
estimates.idx_components_bad = np.array([])
estimates.background_image = make_color_img(estimates.Cn)
# Generate image data
estimates.img_components /= estimates.img_components.max(
axis=(1, 2))[:, None, None]
estimates.img_components *= 255
estimates.img_components = estimates.img_components.astype(np.uint8)
def draw_contours_overall(md):
if md is "reset":
draw_contours()
elif md is "neurons":
if neuron_selected is True:
#if a specific neuron has been selected, only one contour should be changed while thrshcomp_line is changing
if nr_index is 0:
#if user does not start to move through the frames
draw_contours_update(estimates.background_image, img)
draw_contours_update(comp2_scaled, img2)
else:
# NEVER CALLED IN THIS VERSION WITHOUT MMAP SINCE NR_INDEX NEVER CHANGES (NO NR_VLINE)
draw_contours_update(raw_mov_scaled, img)
draw_contours_update(frame_denoise_scaled, img2)
else:
#if no specific neuron has been selected, all the contours are changing
draw_contours()
else:
#md is "background":
return
def draw_contours():
global thrshcomp_line, estimates, img
bkgr_contours = estimates.background_image.copy()
if len(estimates.idx_components) > 0:
contours = [
cv2.findContours(
cv2.threshold(img, np.int(thrshcomp_line.value()), 255,
0)[1], cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)[0]
for img in estimates.img_components[estimates.idx_components]
]
SNRs = np.array(estimates.r_values)
iidd = np.array(estimates.idx_components)
idx1 = np.where(SNRs[iidd] < 0.1)[0]
idx2 = np.where((SNRs[iidd] >= 0.1) & (SNRs[iidd] < 0.25))[0]
idx3 = np.where((SNRs[iidd] >= 0.25) & (SNRs[iidd] < 0.5))[0]
idx4 = np.where((SNRs[iidd] >= 0.5) & (SNRs[iidd] < 0.75))[0]
idx5 = np.where((SNRs[iidd] >= 0.75) & (SNRs[iidd] < 0.9))[0]
idx6 = np.where(SNRs[iidd] >= 0.9)[0]
cv2.drawContours(bkgr_contours,
sum([contours[jj] for jj in idx1], []), -1,
(255, 0, 0), 1)
cv2.drawContours(bkgr_contours,
sum([contours[jj] for jj in idx2], []), -1,
(0, 255, 0), 1)
cv2.drawContours(bkgr_contours,
sum([contours[jj] for jj in idx3], []), -1,
(0, 0, 255), 1)
cv2.drawContours(bkgr_contours,
sum([contours[jj] for jj in idx4], []), -1,
(255, 255, 0), 1)
cv2.drawContours(bkgr_contours,
sum([contours[jj] for jj in idx5], []), -1,
(255, 0, 255), 1)
cv2.drawContours(bkgr_contours,
sum([contours[jj] for jj in idx6], []), -1,
(0, 255, 255), 1)
img.setImage(bkgr_contours, autoLevels=False)
# pg.setConfigOptions(imageAxisOrder='row-major')
def draw_contours_update(cf, im):
global thrshcomp_line, estimates
curFrame = cf.copy()
if len(estimates.idx_components) > 0:
contours = [
cv2.findContours(
cv2.threshold(img, np.int(thrshcomp_line.value()), 255,
0)[1], cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)[0]
for img in estimates.img_components[estimates.idx_components]
]
SNRs = np.array(estimates.r_values)
iidd = np.array(estimates.idx_components)
idx1 = np.where(SNRs[iidd] < 0.1)[0]
idx2 = np.where((SNRs[iidd] >= 0.1) & (SNRs[iidd] < 0.25))[0]
idx3 = np.where((SNRs[iidd] >= 0.25) & (SNRs[iidd] < 0.5))[0]
idx4 = np.where((SNRs[iidd] >= 0.5) & (SNRs[iidd] < 0.75))[0]
idx5 = np.where((SNRs[iidd] >= 0.75) & (SNRs[iidd] < 0.9))[0]
idx6 = np.where(SNRs[iidd] >= 0.9)[0]
if min_dist_comp in idx1:
cv2.drawContours(curFrame, contours[min_dist_comp], -1,
(255, 0, 0), 1)
if min_dist_comp in idx2:
cv2.drawContours(curFrame, contours[min_dist_comp], -1,
(0, 255, 0), 1)
if min_dist_comp in idx3:
cv2.drawContours(curFrame, contours[min_dist_comp], -1,
(0, 0, 255), 1)
if min_dist_comp in idx4:
cv2.drawContours(curFrame, contours[min_dist_comp], -1,
(255, 255, 0), 1)
if min_dist_comp in idx5:
cv2.drawContours(curFrame, contours[min_dist_comp], -1,
(255, 0, 255), 1)
if min_dist_comp in idx6:
cv2.drawContours(curFrame, contours[min_dist_comp], -1,
(0, 255, 255), 1)
im.setImage(curFrame, autoLevels=False)
#%% START BUILDING THE APPLICATION WINDOW
# Always start by initializing Qt (only once per application)
app = QtGui.QApplication([])
# Define a top-level widget to hold everything
w = QtGui.QWidget()
# Create some widgets to be placed inside
btn = QtGui.QPushButton('press me')
text = QtGui.QLineEdit('enter text')
# Histogram controller (win)
win = pg.GraphicsLayoutWidget()
win.setMaximumWidth(300)
win.setMinimumWidth(200)
hist = pg.HistogramLUTItem() # Contrast/color control
win.addItem(hist)
# Plotting windows
p1 = pg.PlotWidget(
) # raw movie window (top-mid), all contours are drawn here
p2 = pg.PlotWidget(
) # trace window (bottom-mid), calcium trace of the selected component
p3 = pg.PlotWidget(
) # denoised movie window (top-right), only selected contour is drawn here
# parameter table for online evaluation and mode change
t = ParameterTree()
# parameter table for neuron selection/sorting
t_action = ParameterTree()
action_layout = QtGui.QGridLayout()
## Create a grid layout to manage the widgets size and position
layout = QtGui.QGridLayout()
w.setLayout(layout)
# A plot area (ViewBox + axes) for displaying the image
#p1 = win.addPlot(title="Image here")
# Item for displaying image data
img = pg.ImageItem()
p1.addItem(img)
img2 = pg.ImageItem()
p3.addItem(img2)
hist.setImageItem(img)
# Draggable line for setting isocurve level
thrshcomp_line = pg.InfiniteLine(angle=0, movable=True, pen='g')
hist.vb.addItem(thrshcomp_line)
hist.vb.setMouseEnabled(y=False) # makes user interaction a little easier
thrshcomp_line.setValue(100)
thrshcomp_line.setZValue(1000) # bring iso line above contrast controls
## Add widgets to the layout in their proper positions
layout.addWidget(win, 1, 0) # histogram
layout.addWidget(p3, 0, 2) # denoised movie
layout.addWidget(t, 0, 0) # upper-right table
layout.addWidget(t_action, 1, 2) # bottom-right table
layout.addWidget(p1, 0, 1) # raw movie
layout.addWidget(p2, 1, 1) # calcium trace window
#enable only horizontal zoom for the traces component
p2.setMouseEnabled(x=True, y=False)
## Display the widget as a new window
w.show()
## Start the Qt event loop
app.exec_()
draw_contours()
hist.setLevels(estimates.background_image.min(),
estimates.background_image.max())
# Another plot area for displaying ROI data
#win.nextRow()
#p2 = win.addPlot(colspan=2)
p2.setMaximumHeight(250)
#win.resize(800, 800)
#win.show()
# set position and scale of image
img.scale(1, 1)
# img.translate(-50, 0)
# zoom to fit image
p1.autoRange()
mode = "reset"
p2.setTitle("mode: %s" % (mode))
thrshcomp_line.sigDragged.connect(lambda: draw_contours_overall(mode))
def imageHoverEvent(event):
#Show the position, pixel, and value under the mouse cursor.
global x, y, i, j, val
pos = event.pos()
i, j = pos.y(), pos.x()
i = int(np.clip(i, 0, estimates.background_image.shape[0] - 1))
j = int(np.clip(j, 0, estimates.background_image.shape[1] - 1))
val = estimates.background_image[i, j, 0]
ppos = img.mapToParent(pos)
x, y = ppos.x(), ppos.y()
# Monkey-patch the image to use our custom hover function.
# This is generally discouraged (you should subclass ImageItem instead),
# but it works for a very simple use like this.
img.hoverEvent = imageHoverEvent
def mouseClickEvent(event):
global mode
global x, y, i, j, val
pos = img.mapFromScene(event.pos())
x = int(pos.x())
y = int(pos.y())
if x < 0 or x > mov.shape[1] or y < 0 or y > mov.shape[2]:
# if the user click outside of the movie, do nothing and jump out of the function
return
i, j = pos.y(), pos.x()
i = int(np.clip(i, 0, estimates.background_image.shape[0] - 1))
j = int(np.clip(j, 0, estimates.background_image.shape[1] - 1))
val = estimates.background_image[i, j, 0]
if mode is "neurons":
show_neurons_clicked()
p1.mousePressEvent = mouseClickEvent
#A general rule in Qt is that if you override one mouse event handler, you must override all of them.
def release(event):
pass
p1.mouseReleaseEvent = release
def move(event):
pass
p1.mouseMoveEvent = move
## PARAMS
params = [{
'name': 'min_cnn_thr',
'type': 'float',
'value': 0.99,
'limits': (0, 1),
'step': 0.01
}, {
'name': 'cnn_lowest',
'type': 'float',
'value': 0.1,
'limits': (0, 1),
'step': 0.01
}, {
'name': 'rval_thr',
'type': 'float',
'value': 0.85,
'limits': (-1, 1),
'step': 0.01
}, {
'name': 'rval_lowest',
'type': 'float',
'value': -1,
'limits': (-1, 1),
'step': 0.01
}, {
'name': 'min_SNR',
'type': 'float',
'value': 2,
'limits': (0, 20),
'step': 0.1
}, {
'name': 'SNR_lowest',
'type': 'float',
'value': 0,
'limits': (0, 20),
'step': 0.1
}, {
'name': 'RESET',
'type': 'action'
}, {
'name': 'SHOW BACKGROUND',
'type': 'action'
}, {
'name': 'SHOW NEURONS',
'type': 'action'
}]
## Create tree of Parameter objects
pars = Parameter.create(name='params', type='group', children=params)
params_action = [{
'name': 'Filter components',
'type': 'bool',
'value': True,
'tip': "Filter components"
}, {
'name': 'View components',
'type': 'list',
'values': ['All', 'Accepted', 'Rejected', 'Unassigned'],
'value': 'All'
}, {
'name': 'ADD GROUP',
'type': 'action'
}, {
'name': 'REMOVE GROUP',
'type': 'action'
}, {
'name': 'ADD SINGLE',
'type': 'action'
}, {
'name': 'REMOVE SINGLE',
'type': 'action'
}, {
'name': 'SAVE OBJECT',
'type': 'action'
}]
pars_action = Parameter.create(name='params_action',
type='group',
children=params_action)
t_action.setParameters(pars_action, showTop=False)
t_action.setWindowTitle('Parameter Action')
def reset_button():
global mode
mode = "reset"
p2.setTitle("mode: %s" % (mode))
#clear the upper right image
zeros = np.asarray([[0] * 80 for _ in range(60)])
img2.setImage(make_color_img(zeros), autoLevels=False)
draw_contours()
pars.param('RESET').sigActivated.connect(reset_button)
def show_background_button():
global bg_vline, min_background, max_background, background_num
global mode, background_first_frame_scaled
#clear thhe upper right image
zeros = np.asarray([[0] * 80 for _ in range(60)])
img2.setImage(make_color_img(zeros), autoLevels=False)
background_num = (background_num + 1) % estimates.f.shape[0]
mode = "background"
p2.setTitle("mode: %s %d" % (mode, background_num))
# display the first frame of the background
background_first_frame = estimates.b[:, background_num].reshape(
estimates.dims, order='F')
min_background_first_frame = np.min(background_first_frame)
max_background_first_frame = np.max(background_first_frame)
background_first_frame_scaled = make_color_img(
background_first_frame,
min_max=(min_background_first_frame, max_background_first_frame))
img.setImage(background_first_frame_scaled, autoLevels=False)
# draw the trace and the infinite line
trace_background = estimates.f[background_num]
p2.plot(trace_background, clear=True)
bg_vline = pg.InfiniteLine(angle=90, movable=True)
p2.addItem(bg_vline, ignoreBounds=True)
bg_vline.setValue(0)
bg_vline.sigPositionChanged.connect(show_background_update)
def show_background_update():
global bg_index, min_background, max_background, background_scaled
bg_index = int(bg_vline.value())
if bg_index > -1 and bg_index < estimates.f.shape[-1]:
# upper left component scrolls through the frames of the background
background = estimates.b[:, background_num].dot(
estimates.f[background_num, bg_index]).reshape(estimates.dims,
order='F')
background_scaled = make_color_img(
background,
min_max=(min_background[background_num],
max_background[background_num]))
img.setImage(background_scaled, autoLevels=False)
pars.param('SHOW BACKGROUND').sigActivated.connect(show_background_button)
def show_neurons_button():
global mode, neuron_selected
mode = "neurons"
neuron_selected = False
p2.setTitle("mode: %s" % (mode))
#clear the upper right image
zeros = np.asarray([[0] * 80 for _ in range(60)])
img2.setImage(make_color_img(zeros), autoLevels=False)
def show_neurons_clicked():
global nr_index
global x, y, i, j, val, min_dist_comp, contour_single, neuron_selected, comp2_scaled
neuron_selected = True
distances = np.sum(
((x, y) - estimates.cms[estimates.idx_components])**2, axis=1)**0.5
min_dist_comp = np.argmin(distances)
contour_all = [
cv2.threshold(img, np.int(thrshcomp_line.value()), 255, 0)[1]
for img in estimates.img_components[estimates.idx_components]
]
contour_single = contour_all[min_dist_comp]
# draw the traces (lower left component)
estimates.components_to_plot = estimates.idx_components[min_dist_comp]
p2.plot(estimates.C[estimates.components_to_plot] +
estimates.YrA[estimates.components_to_plot],
clear=True)
# plot img (upper left component)
img.setImage(estimates.background_image, autoLevels=False)
draw_contours_update(estimates.background_image, img)
# plot img2 (upper right component)
comp2 = np.multiply(estimates.Cn, contour_single > 0)
comp2_scaled = make_color_img(comp2,
min_max=(np.min(comp2), np.max(comp2)))
img2.setImage(comp2_scaled, autoLevels=False)
draw_contours_update(comp2_scaled, img2)
# set title for the upper two components
p3.setTitle("pos: (%0.1f, %0.1f) component: %d value: %g dist:%f" %
(x, y, estimates.components_to_plot, val,
distances[min_dist_comp]))
p1.setTitle("pos: (%0.1f, %0.1f) component: %d value: %g dist:%f" %
(x, y, estimates.components_to_plot, val,
distances[min_dist_comp]))
# draw the infinite line (INACTIVE IN THIS VERSION WITHOUT MMAP FILES)
# nr_vline = pg.InfiniteLine(angle=90, movable=True)
# p2.addItem(nr_vline, ignoreBounds=True)
# nr_vline.setValue(0)
# nr_vline.sigPositionChanged.connect(show_neurons_update)
nr_index = 0
def show_neurons_update(): # NOT CALLED IN THIS VERSION
global nr_index, frame_denoise_scaled, estimates, raw_mov_scaled
global min_mov, max_mov, min_mov_denoise, max_mov_denoise
if neuron_selected is False:
return
nr_index = int(nr_vline.value())
if nr_index > 0 and nr_index < mov[:, 0, 0].shape[0]:
# upper left component scrolls through the raw movie
raw_mov = mov[nr_index, :, :]
raw_mov_scaled = make_color_img(raw_mov,
min_max=(min_mov, max_mov))
img.setImage(raw_mov_scaled, autoLevels=False)
draw_contours_update(raw_mov_scaled, img)
# upper right component scrolls through the denoised movie
frame_denoise = estimates.A[:, estimates.idx_components].dot(
estimates.C[estimates.idx_components,
nr_index]).reshape(estimates.dims, order='F')
frame_denoise_scaled = make_color_img(frame_denoise,
min_max=(min_mov_denoise,
max_mov_denoise))
img2.setImage(frame_denoise_scaled, autoLevels=False)
draw_contours_update(frame_denoise_scaled, img2)
pars.param('SHOW NEURONS').sigActivated.connect(show_neurons_button)
def add_group():
estimates.accepted_list = np.union1d(estimates.accepted_list,
estimates.idx_components)
estimates.rejected_list = np.setdiff1d(estimates.rejected_list,
estimates.idx_components)
change(None, None)
pars_action.param('ADD GROUP').sigActivated.connect(add_group)
def remove_group():
estimates.rejected_list = np.union1d(estimates.rejected_list,
estimates.idx_components)
estimates.accepted_list = np.setdiff1d(estimates.accepted_list,
estimates.idx_components)
change(None, None)
pars_action.param('REMOVE GROUP').sigActivated.connect(remove_group)
def add_single():
estimates.accepted_list = np.union1d(estimates.accepted_list,
estimates.components_to_plot)
estimates.rejected_list = np.setdiff1d(estimates.rejected_list,
estimates.components_to_plot)
change(None, None)
pars_action.param('ADD SINGLE').sigActivated.connect(add_single)
def remove_single():
estimates.rejected_list = np.union1d(estimates.rejected_list,
estimates.components_to_plot)
estimates.accepted_list = np.setdiff1d(estimates.accepted_list,
estimates.components_to_plot)
change(None, None)
pars_action.param('REMOVE SINGLE').sigActivated.connect(remove_single)
def save_object():
print('Saving')
ffll = F.getSaveFileName(filter='*.hdf5')
print(ffll[0])
cnm_obj.estimates = estimates
cnm_obj.save(ffll[0])
pars_action.param('SAVE OBJECT').sigActivated.connect(save_object)
def action_pars_activated(param, changes):
change(None, None)
pars_action.sigTreeStateChanged.connect(action_pars_activated)
## If anything changes in the tree, print a message
def change(param, changes):
global estimates, pars, pars_action
set_par = pars.getValues()
if pars_action.param('Filter components').value():
for keyy in set_par.keys():
params_obj.quality.update({keyy: set_par[keyy][0]})
else:
params_obj.quality.update({
'cnn_lowest': .1,
'min_cnn_thr': 0.99,
'rval_thr': 0.85,
'rval_lowest': -1,
'min_SNR': 2,
'SNR_lowest': 0
})
estimates.filter_components(
mov,
params_obj,
dview=None,
select_mode=pars_action.param('View components').value())
if mode is "background":
return
else:
draw_contours()
pars.sigTreeStateChanged.connect(change)
change(None, None) # set params to default
t.setParameters(pars, showTop=False)
t.setWindowTitle('Parameter Quality')
## END PARAMS
## Display the widget as a new window
w.show()
## Start the Qt event loop
app.exit(app.exec_())
def main():
# Prompt user for directory containing files to be analyzed
F = FileDialog() # Calls Qt backend script to create a file dialog object
mcdir = F.getExistingDirectory(caption='Select Motion Corrected Video Directory')
fvids=[]
for file in os.listdir(mcdir):
if file.endswith("_mc.tif"):
fvids.append(os.path.join(mcdir, file))
# Set up a variable to determine which sections are lightsheet and which
# are epi. This is a horrible way to handle it - need to write new code to
# either automatically determine or prompt user for input.
# Use 1 for lightsheet and 0 for epi.
lsepi = [1, 0, 1, 0, 1, 0, 0, 1, 0]
# Grab the first two masks and assume that they are representative of every
# lightsheet and epi neuron. Also generate an overlap mask by logical ANDing
# the two masks together.
maskLSin = np.transpose(np.load(fvids[0]+'.npy'), axes=(2,1,0))
maskEpiin = np.transpose(np.load(fvids[1]+'.npy'), axes=(2,1,0))
maskLSt = []
maskEpit = []
for mask in maskLSin:
if (np.argwhere(mask).size > 0):
maskLSt.append(mask)
for mask in maskEpiin:
if (np.argwhere(mask).size > 0):
maskEpit.append(mask)
maskLS = np.asarray(maskLSt)
maskEpi = np.asarray(maskEpit)
# Take the first lightsheet vid, find the top N neurons, do the same for
# the first epi vid. Take the 2N masks corresponding to this, find the set
# of unique ones, and then use the remaining masks to go through all of the
# other vids.
N = 14
# Lightsheet:
vid = cm.load(fvids[0])
LSdff = calculate_dff_set(vid, maskLS)
# Epi:
vid = cm.load(fvids[1])
EPdff = calculate_dff_set(vid, maskEpi)
# Sort by top, get the overlap:
threshold = 10
topsLS = argsort_traces(LSdff)
topsEpi = argsort_traces(EPdff)
masks = mask_union(maskLS[topsLS[-N:]], maskEpi[topsEpi[-N:]], threshold)
masksTopLS = mask_disjoint(maskLS[topsLS[-N:]], masks, threshold)
masksTopEpi = mask_disjoint(maskEpi[topsEpi[-N:]], masks, threshold)
maskov = mask_joint(maskLS[topsLS[-N:]], maskEpi[topsEpi[-N:]], threshold)
print(masksTopLS.shape)
print(masksTopEpi.shape)
print(maskov.shape)
# The variable tops now contains the non-overlapping union of the top N
# neurons from epi and from light sheet. Now run through the rest of the
# analyses using only these masks.
# Can grab the top epi and top lightsheet values for each neuron. Can also
# grab on a neuron-by-neuron basis whether the peak dF/F was lightsheet or
# epi.
dff = np.empty((masks.shape[0], 0))
divs = np.zeros(len(fvids))
max_idx = np.zeros((masks.shape[0], 1))
max_val = np.zeros((masks.shape[0], 1))
maxepi_idx = np.zeros((masks.shape[0], 1))
maxepi_val = np.zeros((masks.shape[0], 1))
maxls_idx = np.zeros((masks.shape[0], 1))
maxls_val = np.zeros((masks.shape[0], 1))
flatmask = flatten_masks(masks)
lspeaks = [[] for k in range(masks.shape[0])]
lspeakvals = np.empty((0))
epipeaks = [[] for k in range(masks.shape[0])]
epipeakvals = np.empty((0))
rawtraces = np.empty((masks.shape[0], 0))
rawbckgnd = np.empty((masks.shape[0], 0))
for i, fvid in enumerate(fvids):
vid = cm.load(fvid)
traces = np.empty((masks.shape[0], vid.shape[0]))
bval = np.empty((masks.shape[0], vid.shape[0]))
dff_i = np.empty((masks.shape[0], vid.shape[0]))
for j, mask in enumerate(masks):
traces[j], bval[j] = extract_neuron_trace_uniform(vid, mask, flatmask, 2)
dff_i[j] = trace_df_f(traces[j], bval[j])
peaks, props = scipy.signal.find_peaks(dff_i[j], distance=10, prominence=(0.1, None))
if (peaks.size > 0):
if lsepi[i]:
lspeaks[j].append(peaks)
lspeakvals = np.append(lspeakvals, dff_i[j][peaks])
else:
epipeaks[j].append(peaks)
epipeakvals = np.append(epipeakvals, dff_i[j][peaks])
if (max(dff_i[j]) > max_val[j]):
max_idx[j] = i
max_val[j] = max(dff_i[j])
if lsepi[i]:
maxls_idx[j] = i
maxls_val[j] = max_val[j]
else:
maxepi_idx[j] = i
maxepi_val[j] = max_val[j]
dff = np.concatenate([dff, dff_i], axis = 1)
rawtraces = np.concatenate([rawtraces, traces], axis = 1)
rawbckgnd = np.concatenate([rawbckgnd, bval], axis = 1)
divs[i] = dff.shape[1]
# Save generated values for post-post-processing
masksout = np.transpose(masks, axes=(2,1,0))
np.save(os.path.join(mcdir, 'top_masks_out.npy'), masksout)
np.save(os.path.join(mcdir, 'top_epi_sections.npy'), maxepi_idx)
np.save(os.path.join(mcdir, 'top_epi_values.npy'), maxepi_val)
np.save(os.path.join(mcdir, 'top_ls_sections.npy'), maxls_idx)
np.save(os.path.join(mcdir, 'top_ls_values.npy'), maxls_val)
np.save(os.path.join(mcdir, 'ls_peak_vals.npy'), lspeakvals)
np.save(os.path.join(mcdir, 'epi_peak_vals.npy'), epipeakvals)
np.save(os.path.join(mcdir, 'dff_traces.npy'), dff)
np.save(os.path.join(mcdir, 'dff_div_points.npy'), divs)
# Plot out the dF/F traces, and put vertical markers at the dividers
# between video segments. User would have to manually label them as there's
# no real way to determine what segments are what.
nrow = 6
ncol = 1
dffig, ax = plt.subplots(nrow, ncol, sharex = True, sharey = True)
ll = dff[0].shape[0]
axrange = np.linspace(0, (ll-1)/10, num=ll)
for i, dfft in enumerate(dff):
if i == nrow:
ax[i-1].set_xlabel('Time (seconds)')
break
ax[i].plot(axrange, dfft)
ax[i].set_ylabel(str(i+1))
for div in divs:
ax[i].axvline(div/10, color='r', linestyle='--')
dffig.suptitle('Neuron dF/F Curves')
plt.show()
tfig, ax = plt.subplots(nrow, ncol, sharex = True, sharey = True)
ll = rawtraces[0].shape[0]
axrange = np.linspace(0, (ll-1)/10, num=ll)
for i, tr in enumerate(rawtraces):
if i >= nrow:
ax[i-1].set_xlabel('Time (seconds)')
break
ax[i].plot(axrange, np.add(tr, rawbckgnd[i]))
ax[i].plot(axrange, rawbckgnd[i])
ax[i].set_ylabel(str(i+1))
for div in divs:
ax[i].axvline(div/10, color='r', linestyle='--')
tfig.suptitle('Neuron Raw Traces + Background')
plt.show()
"""
# Next do line plot with averages + error bars.
# Set up lines for a given neuron, showing increase or decrease of max
# intensity on that neuron between lightsheet and epi.
#
intensity_lineplot = np.concatenate([maxepi_val, maxls_val], axis=1).T
avg_ls = np.mean(maxls_val)
std_ls = np.std(maxls_val)/math.sqrt(maxls_val.shape[0])
avg_epi = np.mean(maxepi_val)
std_epi = np.std(maxepi_val)/math.sqrt(maxepi_val.shape[0])
binplot, ax = plt.subplots()
plt.plot(intensity_lineplot)
ax.bar([0, 1], [avg_epi, avg_ls], yerr=[std_epi, std_ls], align='center', capsize=10, alpha=0.5)
ax.set_ylabel('Peak dF/F')
ax.set_xticks([0, 1])
ax.set_xticklabels(['Epi', 'Light-sheet'])
ax.set_title('Contrast change, Epi vs. LS')
plt.show()
"""
# Histogram of spike intensities.
histfig, ax = plt.subplots()
if not (lspeakvals.size>0):
lspeakvals = np.zeros(1)
if not (epipeakvals.size>0):
epipeakvals = np.zeros(1)
binrange = np.amax(np.concatenate([lspeakvals, epipeakvals]))
binrange = 1.5 if binrange >1.5 else math.ceil(binrange*10)/10
binset = np.linspace(0, binrange, num=int(binrange*10+1))
nls = lspeakvals.shape[0]
nepi = epipeakvals.shape[0]
epi_n, epi_bins, epi_patches = ax.hist(epipeakvals, bins=binset, alpha=0.5, label='Epi-illumination', histtype='barstacked', ec='black', lw=0, color='#7f86c1')
ls_n, ls_bins, ls_patches = ax.hist(lspeakvals, bins=binset, alpha=0.5, label='Light-sheet', histtype='barstacked', ec='black', lw=0, color='#f48466')
plt.legend(loc='upper right')
histfig.suptitle('Lightsheet vs. Epi-illumination dF/F')
plt.xlabel('dF/F')
plt.ylabel('Spike Count')
plt.show()
# Plot the image with the contours (outlines of neurons), labeled
Asparse = scipy.sparse.csc_matrix(masksout.reshape((masksout.shape[1]*masksout.shape[0], masksout.shape[2])))
lstop = np.transpose(masksTopLS, axes=(2,1,0))
epitop = np.transpose(masksTopEpi, axes=(2,1,0))
ovtop = np.transpose(maskov, axes=(2,1,0))
AsparseLS = scipy.sparse.csc_matrix(lstop.reshape((lstop.shape[1]*lstop.shape[0], lstop.shape[2])))
AsparseEpi = scipy.sparse.csc_matrix(epitop.reshape((epitop.shape[1]*epitop.shape[0], epitop.shape[2])))
AsparseOv = scipy.sparse.csc_matrix(ovtop.reshape((ovtop.shape[1]*ovtop.shape[0], ovtop.shape[2])))
vid = cm.load(fvids[0])
#Cn = cm.local_correlations(vid.transpose(1,2,0))
Cn = np.zeros((vid.shape[1], vid.shape[2]))
Cn[np.isnan(Cn)] = 0
out=plt.figure()
cm.utils.visualization.plot_contours(Asparse, Cn)
out=plt.figure()
cm.utils.visualization.plot_contours(AsparseLS, Cn)
out=plt.figure()
cm.utils.visualization.plot_contours(AsparseEpi, Cn)
out=plt.figure()
cm.utils.visualization.plot_contours(AsparseOv, Cn)
scipy.io.savemat(os.path.join(mcdir, 'epi_histogram.mat'), {'n':epi_n, 'bins':epi_bins, 'patches':epi_patches})
scipy.io.savemat(os.path.join(mcdir, 'ls_histogram.mat'), {'n':ls_n, 'bins':ls_bins, 'patches':ls_patches})
scipy.io.savemat(os.path.join(mcdir, 'epi_spike_values.mat'), {'epispikes':epipeakvals})
scipy.io.savemat(os.path.join(mcdir, 'ls_spike_values.mat'), {'lsspikes':lspeakvals})
scipy.io.savemat(os.path.join(mcdir, 'df_over_f.mat'), {'data':dff, 'indices_between_ls_or_epi':divs})
scipy.io.savemat(os.path.join(mcdir, 'rawtraces.mat'), {'data':rawtraces, 'indices_between_ls_or_epi':divs})
scipy.io.savemat(os.path.join(mcdir, 'rawbackground.mat'), {'data':rawbckgnd, 'indices_between_ls_or_epi':divs})
def test():
def make_color_img(img, gain=255, min_max=None,out_type=np.uint8):
if min_max is None:
min_ = img.min()
max_ = img.max()
else:
min_, max_ = min_max
img = (img-min_)/(max_-min_)*gain
img = img.astype(out_type)
img = np.dstack([img]*3)
return img
data=None
path=r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M18\20191121b\N4'
### FIND DATA ###
if data is None: # different conditions on file loading (not necessary if data was already provided)
if path is None: # if no path has been given, open a window prompt to select a directory
F = FileDialog()
# load object saved by CNMF
path = F.getExistingDirectory(caption='Select folder from which to load a PCF or CNMF file')
try: # first try to get CNMF data from a PCF object (should be most up-to-date)
cnm_obj = pipe.load_pcf(path).cnmf
except FileNotFoundError:
try:
cnm_obj = pipe.load_cnmf(path)
except FileNotFoundError:
raise FileNotFoundError(f'Could not find data to load in {path}!')
else:
cnm_obj = data
def draw_contours_overall(md):
if md is "reset":
draw_contours()
elif md is "neurons":
if neuron_selected is True:
#if a specific neuron has been selected, only one contour should be changed while thrshcomp_line is changing
if nr_index is 0:
#if user does not start to move through the frames
draw_contours_update(estimates.background_image, img)
draw_contours_update(comp2_scaled, img2)
else:
# NEVER CALLED IN THIS VERSION WITHOUT MMAP SINCE NR_INDEX NEVER CHANGES (NO NR_VLINE)
draw_contours_update(raw_mov_scaled, img)
draw_contours_update(frame_denoise_scaled, img2)
else:
#if no specific neuron has been selected, all the contours are changing
draw_contours()
else:
#md is "background":
return
def draw_contours():
global thrshcomp_line, estimates, img
bkgr_contours = estimates.background_image.copy()
if len(estimates.idx_components) > 0:
contours = [cv2.findContours(cv2.threshold(img, np.int(thrshcomp_line.value()), 255, 0)[1], cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)[0] for img in estimates.img_components[estimates.idx_components]]
SNRs = np.array(estimates.r_values)
iidd = np.array(estimates.idx_components)
idx1 = np.where(SNRs[iidd] < 0.1)[0]
idx2 = np.where((SNRs[iidd] >= 0.1) &
(SNRs[iidd] < 0.25))[0]
idx3 = np.where((SNRs[iidd] >= 0.25) &
(SNRs[iidd] < 0.5))[0]
idx4 = np.where((SNRs[iidd] >= 0.5) &
(SNRs[iidd] < 0.75))[0]
idx5 = np.where((SNRs[iidd] >= 0.75) &
(SNRs[iidd] < 0.9))[0]
idx6 = np.where(SNRs[iidd] >= 0.9)[0]
cv2.drawContours(bkgr_contours, sum([contours[jj] for jj in idx1], []), -1, (255, 0, 0), 1)
cv2.drawContours(bkgr_contours, sum([contours[jj] for jj in idx2], []), -1, (0, 255, 0), 1)
cv2.drawContours(bkgr_contours, sum([contours[jj] for jj in idx3], []), -1, (0, 0, 255), 1)
cv2.drawContours(bkgr_contours, sum([contours[jj] for jj in idx4], []), -1, (255, 255, 0), 1)
cv2.drawContours(bkgr_contours, sum([contours[jj] for jj in idx5], []), -1, (255, 0, 255), 1)
cv2.drawContours(bkgr_contours, sum([contours[jj] for jj in idx6], []), -1, (0, 255, 255), 1)
img.setImage(bkgr_contours, autoLevels=False)
# pg.setConfigOptions(imageAxisOrder='row-major')
def draw_contours_update(cf, im):
global thrshcomp_line, estimates
curFrame = cf.copy()
if len(estimates.idx_components) > 0:
contours = [cv2.findContours(cv2.threshold(img, np.int(thrshcomp_line.value()), 255, 0)[1], cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)[0] for img in estimates.img_components[estimates.idx_components]]
SNRs = np.array(estimates.r_values)
iidd = np.array(estimates.idx_components)
idx1 = np.where(SNRs[iidd] < 0.1)[0]
idx2 = np.where((SNRs[iidd] >= 0.1) &
(SNRs[iidd] < 0.25))[0]
idx3 = np.where((SNRs[iidd] >= 0.25) &
(SNRs[iidd] < 0.5))[0]
idx4 = np.where((SNRs[iidd] >= 0.5) &
(SNRs[iidd] < 0.75))[0]
idx5 = np.where((SNRs[iidd] >= 0.75) &
(SNRs[iidd] < 0.9))[0]
idx6 = np.where(SNRs[iidd] >= 0.9)[0]
if min_dist_comp in idx1:
cv2.drawContours(curFrame, contours[min_dist_comp], -1, (255, 0, 0), 1)
if min_dist_comp in idx2:
cv2.drawContours(curFrame, contours[min_dist_comp], -1, (0, 255, 0), 1)
if min_dist_comp in idx3:
cv2.drawContours(curFrame, contours[min_dist_comp], -1, (0, 0, 255), 1)
if min_dist_comp in idx4:
cv2.drawContours(curFrame, contours[min_dist_comp], -1, (255, 255, 0), 1)
if min_dist_comp in idx5:
cv2.drawContours(curFrame, contours[min_dist_comp], -1, (255, 0, 255), 1)
if min_dist_comp in idx6:
cv2.drawContours(curFrame, contours[min_dist_comp], -1, (0, 255, 255), 1)
im.setImage(curFrame, autoLevels=False)
# Always start by initializing Qt (only once per application)
app = QtGui.QApplication([])
try:
cv2.setNumThreads(1)
except:
print('Open CV is naturally single threaded')
## Define a top-level widget to hold everything
w = QtGui.QWidget()
## Create some widgets to be placed inside
btn = QtGui.QPushButton('press me')
text = QtGui.QLineEdit('enter text')
win = pg.GraphicsLayoutWidget()
win.setMaximumWidth(300)
win.setMinimumWidth(200)
hist = pg.HistogramLUTItem() # Contrast/color control
win.addItem(hist)
p1 = pg.PlotWidget()
p2 = pg.PlotWidget()
p3 = pg.PlotWidget()
t = ParameterTree()
t_action = ParameterTree()
action_layout = QtGui.QGridLayout()
## Create a grid layout to manage the widgets size and position
layout = QtGui.QGridLayout()
w.setLayout(layout)
# A plot area (ViewBox + axes) for displaying the image
#p1 = win.addPlot(title="Image here")
# Item for displaying image data
img = pg.ImageItem()
p1.addItem(img)
img2 = pg.ImageItem()
p3.addItem(img2)
hist.setImageItem(img)
# Draggable line for setting isocurve level (for setting contour threshold)
thrshcomp_line = pg.InfiniteLine(angle=0, movable=True, pen='g')
hist.vb.addItem(thrshcomp_line)
hist.vb.setMouseEnabled(y=False) # makes user interaction a little easier
thrshcomp_line.setValue(100)
thrshcomp_line.setZValue(1000) # bring iso line above contrast controls
## Add widgets to the layout in their proper positions
layout.addWidget(win, 1, 0) # histogram
layout.addWidget(p3, 0, 2) # denoised movie
layout.addWidget(t, 0, 0) # upper-right table
layout.addWidget(t_action, 1, 2) # bottom-right table
layout.addWidget(p1, 0, 1) # raw movie
layout.addWidget(p2, 1, 1) # calcium trace window
#enable only horizontal zoom for the traces component
p2.setMouseEnabled(x=True, y=False)
# draw something in the raw-movie field and set the histogram borders correspondingly
test_img_file = r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M18\20191121b\N4\local_correlation_image.png'
test_img = plt.imread(test_img_file)
img.setImage(np.rot90(test_img[:, :, 0], 3))
hist.setLevels(test_img[:, :, 0].min(), test_img[:, :, 0].max())
p2.setMouseEnabled(x=True, y=False)
# Another plot area for displaying ROI data
p2.setMaximumHeight(250)
# set position and scale of image
img.scale(1, 1)
# zoom to fit image
p1.autoRange()
mode = "reset"
p2.setTitle("mode: %s" % (mode))
## Display the widget as a new window
w.show()
## Start the Qt event loop
app.exec_()
def main():
# Prompt user for directory containing files to be analyzed
F = FileDialog() # Calls Qt backend script to create a file dialog object
mcdir = F.getExistingDirectory(
caption='Select Motion Corrected Video Directory')
fvids = []
for file in os.listdir(mcdir):
if file.endswith("_mc.tif"):
fvids.append(os.path.join(mcdir, file))
# Set up a variable to determine which sections are lightsheet and which
# are epi. This is a horrible way to handle it - need to write new code to
# either automatically determine or prompt user for input.
# Use 1 for lightsheet and 0 for epi.
lsepi = [1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0]
# Threshold for non-binary masks to convert to binary
th = 0.05
# Load masks, videos, and dF/F curves
lsmasks = None
epimasks = None
lsunion = None
epiunion = None
lsvidconcat = None
epividconcat = None
lsvidconcattimes = [0]
epividconcattimes = [0]
for i, file in enumerate(fvids):
vid = cm.load(file)
mask = load_masks(file)
if lsepi[i]:
if lsmasks is None:
lsmasks = np.empty((0, mask.shape[1], mask.shape[2]))
lsunion = mask > th
lsvidconcat = np.empty((0, vid.shape[1], vid.shape[2]))
lsvidconcat = cm.concatenate([lsvidconcat, vid], axis=0)
lsmasks = np.concatenate((lsmasks, mask))
lsunion = mask_union(lsunion, mask > th, 10)
lsvidconcattimes.append(lsvidconcat.shape[0])
else:
if epimasks is None:
epimasks = np.empty((0, mask.shape[1], mask.shape[2]))
epiunion = mask > th
epividconcat = np.empty((0, vid.shape[1], vid.shape[2]))
epividconcat = cm.concatenate([epividconcat, vid], axis=0)
epimasks = np.concatenate((epimasks, mask))
epiunion = mask_union(epiunion, mask > th, 10)
epividconcattimes.append(epividconcat.shape[0])
print(epividconcattimes)
print(epividconcat.shape)
# Plot out the flattened masks for light-sheet and epi, and count the
# number of unique detected neurons
flFig, flAx = plt.subplots(1, 2)
unFig, unAx = plt.subplots(1, 2)
flsunion = np.zeros((lsmasks.shape[1], lsmasks.shape[2]))
ff = np.zeros((lsmasks.shape[1], lsmasks.shape[2]))
for lsmask in lsmasks:
ff = np.add(ff, lsmask > th)
for unionmask in lsunion:
flsunion = np.add(flsunion, unionmask)
flAx[0].imshow(ff)
unAx[0].imshow(flsunion)
print('Number of ls neurons: ' + str(lsunion.shape[0]))
ff = np.zeros((lsmasks.shape[1], lsmasks.shape[2]))
fepiunion = np.zeros((lsmasks.shape[1], lsmasks.shape[2]))
for epimask in epimasks:
ff = np.add(ff, epimask > th)
for unionmask in epiunion:
fepiunion = np.add(fepiunion, unionmask)
flAx[1].imshow(ff)
unAx[1].imshow(fepiunion)
print('Number of epi neurons: ' + str(epiunion.shape[0]))
# Mask operations to create the various sets and then plot them all out
sharedneurons = mask_joint(lsunion, epiunion, 10)
lsunique = mask_disjoint(sharedneurons, lsunion, 10)
epunique = mask_disjoint(sharedneurons, epiunion, 10)
allFig, allAx = plt.subplots(1, 3)
allAx[0].imshow(np.sum(lsunique, axis=0))
allAx[1].imshow(np.sum(sharedneurons, axis=0))
allAx[2].imshow(np.sum(epunique, axis=0))
print('Number of unique-to-ls neurons: ' + str(lsunique.shape[0]))
print('Number of unique-to-epi neurons: ' + str(epunique.shape[0]))
print('Number of shared neurons: ' + str(sharedneurons.shape[0]))
lsallmasks = mask_union(sharedneurons, lsunique, 10)
epallmasks = mask_union(sharedneurons, epunique, 10)
# Plot out df/F traces, custom calculated, for 'zz' number of elements
#zz=-1
#lslsdff = calculate_dff_set(lsvidconcat, lsallmasks)
#lsepdff = calculate_dff_set(lsvidconcat, epunique[0:zz])
#epepdff = calculate_dff_set(epvidconcat, epallmasks)
#eplsdff = calculate_dff_set(epvidconcat, lsunique[0:zz])
lslsdff = np.empty((lsallmasks.shape[0], 0))
for i, el in enumerate(lsvidconcattimes):
if not i == 0:
start = lsvidconcattimes[i - 1] + 1
end = lsvidconcattimes[i]
lslsdff = np.concatenate(
(lslsdff, calculate_dff_set(lsvidconcat[start:end],
lsallmasks)),
axis=1)
print(lslsdff.shape)
lslsdff = np.clip(lslsdff, 0, None)
epepdff = np.empty((epallmasks.shape[0], 0))
for i, el in enumerate(epividconcattimes):
if not i == 0:
start = epividconcattimes[i - 1] + 1
end = epividconcattimes[i]
epepdff = np.concatenate(
(epepdff, calculate_dff_set(epividconcat[start:end],
epallmasks)),
axis=1)
print(epepdff.shape)
epepdff = np.clip(epepdff, 0, None)
lspeakmax = np.zeros(lslsdff.shape[0])
lspeakcount = np.zeros(lslsdff.shape[0])
for i, lsdff in enumerate(lslsdff):
peaks, props = scipy.signal.find_peaks(lsdff,
distance=10,
prominence=(0.05, None),
width=(3, None),
height=(0.1, None))
lspeakmax[i] = max(lsdff)
if peaks.size > 0:
lspeakcount[i] = peaks.size
eppeakmax = np.zeros(epepdff.shape[0])
eppeakcount = np.zeros(epepdff.shape[0])
for i, epdff in enumerate(epepdff):
peaks, props = scipy.signal.find_peaks(epdff,
distance=10,
prominence=(0.05, None),
width=(3, None),
height=(0.1, None))
eppeakmax[i] = max(epdff)
if peaks.size > 0:
eppeakcount[i] = peaks.size
toplscount_idxs = np.argsort(lspeakcount)
toplspeak_idxs = np.argsort(lspeakmax)
topls_idxs = np.concatenate((toplscount_idxs[-4:], toplspeak_idxs[-2:]))
topsixlscount = lslsdff[topls_idxs]
topepcount_idxs = np.argsort(eppeakcount)
topeppeak_idxs = np.argsort(eppeakmax)
topep_idxs = np.concatenate((topepcount_idxs[-4:], topeppeak_idxs[-2:]))
topsixepcount = epepdff[topep_idxs]
lsmaxheatmap = np.zeros(lsallmasks[0].shape)
lscountheatmap = np.zeros(lsallmasks[0].shape)
for i, mask in enumerate(lsallmasks):
lsmaxheatmap = np.add(lsmaxheatmap, mask * lspeakmax[i])
lscountheatmap = np.add(lscountheatmap, mask * lspeakcount[i])
epmaxheatmap = np.zeros(epallmasks[0].shape)
epcountheatmap = np.zeros(epallmasks[0].shape)
for i, mask in enumerate(epallmasks):
epmaxheatmap = np.add(epmaxheatmap, mask * eppeakmax[i])
epcountheatmap = np.add(epcountheatmap, mask * eppeakcount[i])
lsrankedheatmap = np.zeros(lsallmasks[0].shape)
for i, idx in enumerate(topls_idxs):
lsrankedheatmap = np.add(lsrankedheatmap, lsallmasks[idx] * 2)
eprankedheatmap = np.zeros(epallmasks[0].shape)
for i, idx in enumerate(topep_idxs):
eprankedheatmap = np.add(eprankedheatmap, epallmasks[idx] * 2)
imageio.imwrite('ls_topmasks.png', lsrankedheatmap)
imageio.imwrite('epi_topmasks.png', eprankedheatmap)
ff, ax = plt.subplots()
ax.imshow(lsrankedheatmap)
ff, ax = plt.subplots()
ax.imshow(eprankedheatmap)
# Setting up plot information
cmap = plt.get_cmap('jet')
# Light-sheet, maximum dF/F figure
ff, ax = plt.subplots()
ax.imshow(lsmaxheatmap, cmap='jet')
vmin = math.floor(
np.min(lsmaxheatmap[np.nonzero(lsmaxheatmap)]) * 100) / 100
vmax = math.ceil(
np.max(lsmaxheatmap[np.nonzero(lsmaxheatmap)]) * 100) / 100
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
colors = cmap(np.linspace(1. - (vmax - vmin) / float(vmax), 1, cmap.N))
color_map = matplotlib.colors.LinearSegmentedColormap.from_list(
'cut_jet', colors)
cax, _ = matplotlib.colorbar.make_axes(plt.gca())
cbar = matplotlib.colorbar.ColorbarBase(
cax,
cmap=color_map,
norm=norm,
)
cbar.set_ticks([vmin, (vmax + vmin) / 2, vmax])
cbar.set_ticklabels([vmin, (vmax + vmin) / 2, vmax])
#cax.setlabel('Max. dF/F')
ax.axis('off')
ff.suptitle('Heatmap of light-sheet neurons by maximum dF/F')
plt.show()
# Light-sheet, spike count figure
ff, ax = plt.subplots()
ax.imshow(lscountheatmap, cmap='jet')
vmin = np.min(lscountheatmap[np.nonzero(lscountheatmap)])
vmax = np.max(lscountheatmap[np.nonzero(lscountheatmap)])
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
colors = cmap(np.linspace(1. - (vmax - vmin) / float(vmax), 1, cmap.N))
color_map = matplotlib.colors.LinearSegmentedColormap.from_list(
'cut_jet', colors)
cax, _ = matplotlib.colorbar.make_axes(plt.gca())
cbar = matplotlib.colorbar.ColorbarBase(
cax,
cmap=color_map,
norm=norm,
)
cbar.set_ticks([vmin, math.floor((vmax + vmin) / 2), vmax])
cbar.set_ticklabels([vmin, math.floor((vmax + vmin) / 2), vmax])
#cax.setlabel('Event Count')
ax.axis('off')
ff.suptitle('Heatmap of light-sheet neurons by spike count')
ff.show()
# Epi-illumination, maximum dF/F figure
ff, ax = plt.subplots()
ax.imshow(epmaxheatmap, cmap='jet')
vmin = math.floor(
np.min(epmaxheatmap[np.nonzero(epmaxheatmap)]) * 100) / 100
vmax = math.ceil(
np.max(epmaxheatmap[np.nonzero(epmaxheatmap)]) * 100) / 100
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
colors = cmap(np.linspace(1. - (vmax - vmin) / float(vmax), 1, cmap.N))
color_map = matplotlib.colors.LinearSegmentedColormap.from_list(
'cut_jet', colors)
cax, _ = matplotlib.colorbar.make_axes(plt.gca())
cbar = matplotlib.colorbar.ColorbarBase(
cax,
cmap=color_map,
norm=norm,
)
cbar.set_ticks([vmin, (vmax + vmin) / 2, vmax])
cbar.set_ticklabels([vmin, (vmax + vmin) / 2, vmax])
#cbar.ax.setlabel('Max. dF/F')
ax.axis('off')
ff.suptitle('Heatmap of epi-illuminated neurons by maximum dF/F')
ff.show()
# Epi-illumination, spike count figure
ff, ax = plt.subplots()
ax.imshow(epcountheatmap, cmap='jet')
vmin = np.min(epcountheatmap[np.nonzero(epcountheatmap)])
vmax = np.max(epcountheatmap[np.nonzero(epcountheatmap)])
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
colors = cmap(np.linspace(1. - (vmax - vmin) / float(vmax), 1, cmap.N))
color_map = matplotlib.colors.LinearSegmentedColormap.from_list(
'cut_jet', colors)
cax, _ = matplotlib.colorbar.make_axes(plt.gca())
cbar = matplotlib.colorbar.ColorbarBase(
cax,
cmap=color_map,
norm=norm,
)
cbar.set_ticks([vmin, math.floor((vmax + vmin) / 2), vmax])
cbar.set_ticklabels([vmin, math.floor((vmax + vmin) / 2), vmax])
#cax.setlabel('Event Count')
ax.axis('off')
ff.suptitle('Heatmap of epi-illuminated neurons by spike count')
ff.show()
ffls, axls = plt.subplots(6, 1)
ffep, axep = plt.subplots(6, 1)
for i in range(6):
axls[i].plot(topsixlscount[i])
axep[i].plot(topsixepcount[i])
ffls.suptitle('Top 6 Lightsheet neurons')
ffep.suptitle('Top 6 Epi neurons')
# export data (df/f traces and concat times) to be plotted in matlab
scipy.io.savemat(os.path.join(mcdir, 'lightsheet_dff_data.mat'), {
'trace': lslsdff,
'timebreaks': lsvidconcattimes
})
scipy.io.savemat(os.path.join(mcdir, 'epi_dff_data.mat'), {
'trace': epepdff,
'timebreaks': epividconcattimes
})
scipy.io.savemat(os.path.join(mcdir, 'lightsheet_dff_datatops.mat'), {
'trace': topsixlscount,
'timebreaks': lsvidconcattimes,
'idxs': topls_idxs
})
scipy.io.savemat(
os.path.join(mcdir, 'epi_dff_datatops.mat'), {
'trace': topsixepcount,
'timebreaks': epividconcattimes,
'idxs': topep_idxs
})
scipy.io.savemat(os.path.join(mcdir, 'epi_masks.mat'),
{'mask': epallmasks})
scipy.io.savemat(os.path.join(mcdir, 'ls_masks.mat'), {'mask': lsallmasks})