def make_mask_figures(self):
# self.ops_plot[1] = 0
# self.ops_plot[2] = 0
self.ops_plot[3] = []
self.ops_plot[4] = []
self.ops_plot[5] = []
self.ops_plot[6] = []
ncells = len(self.stat)
for n in range(0, ncells):
ypix = self.stat[n]["ypix"].flatten()
xpix = self.stat[n]["xpix"].flatten()
iext = fig.boundary(ypix, xpix)
self.stat[n]["yext"] = ypix[iext]
self.stat[n]["xext"] = xpix[iext]
ycirc, xcirc = fig.circle(self.stat[n]["med"],
self.stat[n]["radius"])
goodi = ((ycirc >= 0)
& (xcirc >= 0)
& (ycirc < self.ops["Ly"])
& (xcirc < self.ops["Lx"]))
self.stat[n]["ycirc"] = ycirc[goodi]
self.stat[n]["xcirc"] = xcirc[goodi]
# # make color arrays for various views
fig.make_colors(self)
# # colorbar
self.colormat = fig.make_colorbar()
self.init_all_masks()
def make_masks_and_buttons(self):
self.loadBeh.setEnabled(True)
self.bloaded = False
self.ROI_remove()
self.isROI = False
self.ops_plot[1] = 0
self.ops_plot[2] = 0
self.ops_plot[3] = []
self.ops_plot[4] = []
self.setWindowTitle(self.fname)
# set bin size to be 0.5s by default
self.bin = int(self.ops['tau'] * self.ops['fs'] / 2)
self.binedit.setText(str(self.bin))
# add boundaries to stat for ROI overlays
ncells = len(self.stat)
for n in range(0, ncells):
ypix = self.stat[n]['ypix'].flatten()
xpix = self.stat[n]['xpix'].flatten()
iext = fig.boundary(ypix, xpix)
self.stat[n]['yext'] = ypix[iext]
self.stat[n]['xext'] = xpix[iext]
ycirc, xcirc = fig.circle(self.stat[n]['med'],
self.stat[n]['radius'])
goodi = (ycirc >= 0) & (xcirc >= 0) & (ycirc < self.ops['Ly']) & (
xcirc < self.ops['Lx'])
self.stat[n]['ycirc'] = ycirc[goodi]
self.stat[n]['xcirc'] = xcirc[goodi]
# enable buttons
self.enable_views_and_classifier()
# make color arrays for various views
fig.make_colors(self)
self.ichosen = int(0)
self.imerge = [int(0)]
self.iflip = int(0)
self.ichosen_stats()
self.comboBox.setCurrentIndex(2)
# colorbar
self.colormat = fig.make_colorbar()
fig.plot_colorbar(self, self.ops_plot[2])
tic = time.time()
fig.init_masks(self)
print(time.time() - tic)
M = fig.draw_masks(self)
fig.plot_masks(self, M)
self.lcell1.setText('%d' % (ncells - self.iscell.sum()))
self.lcell0.setText('%d' % (self.iscell.sum()))
fig.init_range(self)
fig.plot_trace(self)
if (type(self.ops['diameter'])
is not int) and (len(self.ops['diameter']) > 1):
self.xyrat = self.ops['diameter'][0] / self.ops['diameter'][1]
else:
self.xyrat = 1.0
self.p1.setAspectLocked(lock=True, ratio=self.xyrat)
self.p2.setAspectLocked(lock=True, ratio=self.xyrat)
self.loaded = True
self.mode_change(2)
self.show()
# no classifier loaded
classifier.activate(self, False)
def activity_stats(parent):
i0 = int(1 - parent.iscell[parent.ichosen])
ypix = np.array([])
xpix = np.array([])
lam = np.array([])
footprints = np.array([])
F = np.zeros((0, parent.Fcell.shape[1]), np.float32)
Fneu = np.zeros((0, parent.Fcell.shape[1]), np.float32)
for n in np.array(parent.imerge):
ypix = np.append(ypix, parent.stat[n]["ypix"])
xpix = np.append(xpix, parent.stat[n]["xpix"])
lam = np.append(lam, parent.stat[n]["lam"])
footprints = np.append(footprints, parent.stat[n]["footprint"])
F = np.append(F, parent.Fcell[n, :][np.newaxis, :], axis=0)
Fneu = np.append(Fneu, parent.Fneu[n, :][np.newaxis, :], axis=0)
# remove overlaps
ipix = np.concatenate((ypix[:, np.newaxis], xpix[:, np.newaxis]), axis=1)
_, goodi = np.unique(ipix, return_index=True, axis=0)
ypix = ypix[goodi]
xpix = xpix[goodi]
lam = lam[goodi]
stat0 = {}
stat0["ypix"] = ypix.astype(np.int32)
stat0["xpix"] = xpix.astype(np.int32)
stat0["lam"] = lam
stat0['med'] = [np.median(stat0["ypix"]), np.median(stat0["xpix"])]
stat0["npix"] = ypix.size
d0 = parent.ops["diameter"]
radius = utils.fitMVGaus(ypix / d0[0], xpix / d0[1], lam, 2)[2]
stat0["radius"] = radius[0] * d0.mean()
stat0["aspect_ratio"] = 2 * radius[0] / (.01 + radius[0] + radius[1])
npix = np.array([parent.stat[n]['npix']
for n in range(len(parent.stat))]).astype('float32')
stat0["npix_norm"] = stat0["npix"] / npix.mean()
# compactness
rs, dy, dx = sparsedetect.circleMask(d0)
rsort = np.sort(rs.flatten())
r2 = ((ypix - stat0["med"][0]) / d0[0])**2 + (
(xpix - stat0["med"][1]) / d0[1])**2
r2 = r2**.5
stat0["mrs"] = np.mean(r2)
stat0["mrs0"] = np.mean(rsort[:r2.size])
stat0["compact"] = stat0["mrs"] / (1e-10 + stat0["mrs0"])
# footprint
stat0["footprint"] = footprints.mean()
F = F.mean(axis=0)
Fneu = Fneu.mean(axis=0)
dF = F - parent.ops["neucoeff"] * Fneu
# activity stats
stat0["skew"] = stats.skew(dF)
stat0["std"] = dF.std()
# compute outline and circle around cell
iext = fig.boundary(ypix, xpix)
stat0["yext"] = ypix[iext]
stat0["xext"] = xpix[iext]
ycirc, xcirc = fig.circle(stat0["med"], stat0["radius"])
goodi = ((ycirc >= 0)
& (xcirc >= 0)
& (ycirc < parent.ops["Ly"])
& (xcirc < parent.ops["Lx"]))
stat0["ycirc"] = ycirc[goodi]
stat0["xcirc"] = xcirc[goodi]
# deconvolve activity
spks = dcnv.oasis(dF[np.newaxis, :], parent.ops)
# add cell to structs
parent.stat = np.concatenate((parent.stat, np.array([stat0])), axis=0)
print(parent.stat[-1]["ypix"].shape)
parent.Fcell = np.concatenate((parent.Fcell, F[np.newaxis, :]), axis=0)
parent.Fneu = np.concatenate((parent.Fneu, Fneu[np.newaxis, :]), axis=0)
parent.Spks = np.concatenate((parent.Spks, spks), axis=0)
iscell = np.array([parent.iscell[parent.ichosen]], dtype=bool)
parent.iscell = np.concatenate((parent.iscell, iscell), axis=0)
def merge_activity_masks(parent):
print('merging activity... this may take some time')
i0 = int(1-parent.iscell[parent.ichosen])
ypix = np.array([])
xpix = np.array([])
lam = np.array([])
footprints = np.array([])
F = np.zeros((0,parent.Fcell.shape[1]), np.float32)
Fneu = np.zeros((0,parent.Fcell.shape[1]), np.float32)
probcell = []
probredcell = []
merged_cells = []
remove_merged = []
for n in np.array(parent.imerge):
if len(parent.stat[n]['imerge']) > 0:
remove_merged.append(n)
for k in parent.stat[n]['imerge']:
merged_cells.append(k)
else:
merged_cells.append(n)
merged_cells = np.unique(np.array(merged_cells))
for n in merged_cells:
ypix = np.append(ypix, parent.stat[n]["ypix"])
xpix = np.append(xpix, parent.stat[n]["xpix"])
lam = np.append(lam, parent.stat[n]["lam"])
footprints = np.append(footprints, parent.stat[n]["footprint"])
F = np.append(F, parent.Fcell[n,:][np.newaxis,:], axis=0)
Fneu = np.append(Fneu, parent.Fneu[n,:][np.newaxis,:], axis=0)
probcell.append(parent.probcell[n])
probredcell.append(parent.probredcell[n])
probcell = np.array(probcell)
probredcell = np.array(probredcell)
pmean = probcell.mean()
prmean = probredcell.mean()
# remove overlaps
ipix = np.concatenate((ypix[:,np.newaxis], xpix[:,np.newaxis]), axis=1)
_, goodi = np.unique(ipix, return_index=True, axis=0)
ypix = ypix[goodi]
xpix = xpix[goodi]
lam = lam[goodi]
stat0 = {}
if 'aspect' in parent.ops:
d0 = np.array([int(parent.ops['aspect']*10), 10])
else:
d0 = parent.ops['diameter']
if isinstance(d0, int):
d0 = [d0,d0]
### compute statistics of merges
stat0["imerge"] = merged_cells
stat0["ypix"] = ypix.astype(np.int32)
stat0["xpix"] = xpix.astype(np.int32)
stat0["lam"] = lam / lam.sum() * merged_cells.size
stat0['med'] = [np.median(stat0["ypix"]), np.median(stat0["xpix"])]
stat0["npix"] = ypix.size
radius = utils.fitMVGaus(ypix/d0[0], xpix/d0[1], lam, 2)[2]
stat0["radius"] = radius[0] * d0.mean()
stat0["aspect_ratio"] = 2 * radius[0]/(.01 + radius[0] + radius[1])
npix = np.array([parent.stat[n]['npix'] for n in range(len(parent.stat))]).astype('float32')
stat0["npix_norm"] = stat0["npix"] / npix.mean()
# compactness
rs,dy,dx = sparsedetect.circleMask(d0)
rsort = np.sort(rs.flatten())
r2 = ((ypix - stat0["med"][0])/d0[0])**2 + ((xpix - stat0["med"][1])/d0[1])**2
r2 = r2**.5
stat0["mrs"] = np.mean(r2)
stat0["mrs0"] = np.mean(rsort[:r2.size])
stat0["compact"] = stat0["mrs"] / (1e-10 + stat0["mrs0"])
# footprint
stat0["footprint"] = footprints.mean()
# inmerge
stat0["inmerge"] = 0
### compute activity of merged cells
F = F.mean(axis=0)
Fneu = Fneu.mean(axis=0)
dF = F - parent.ops["neucoeff"]*Fneu
# activity stats
stat0["skew"] = stats.skew(dF)
stat0["std"] = dF.std()
### for GUI drawing
# compute outline and circle around cell
iext = fig.boundary(ypix, xpix)
stat0["yext"] = ypix[iext].astype(np.int32)
stat0["xext"] = xpix[iext].astype(np.int32)
ycirc, xcirc = fig.circle(stat0["med"], stat0["radius"])
goodi = (
(ycirc >= 0)
& (xcirc >= 0)
& (ycirc < parent.ops["Ly"])
& (xcirc < parent.ops["Lx"])
)
stat0["ycirc"] = ycirc[goodi]
stat0["xcirc"] = xcirc[goodi]
# deconvolve activity
spks = dcnv.oasis(dF[np.newaxis, :], parent.ops)
### remove previously merged cell (do not replace)
for k in remove_merged:
remove_mask(parent, k)
np.delete(parent.stat, k, 0)
np.delete(parent.Fcell, k, 0)
np.delete(parent.Fneu, k, 0)
np.delete(parent.Spks, k, 0)
np.delete(parent.iscell, k, 0)
np.delete(parent.probcell, k, 0)
np.delete(parent.probredcell, k, 0)
np.delete(parent.redcell, k, 0)
np.delete(parent.notmerged, k, 0)
# add cell to structs
parent.stat = np.concatenate((parent.stat, np.array([stat0])), axis=0)
parent.stat = sparsedetect.get_overlaps(parent.stat, parent.ops)
parent.stat = np.array(parent.stat)
parent.Fcell = np.concatenate((parent.Fcell, F[np.newaxis,:]), axis=0)
parent.Fneu = np.concatenate((parent.Fneu, Fneu[np.newaxis,:]), axis=0)
parent.Spks = np.concatenate((parent.Spks, spks), axis=0)
iscell = np.array([parent.iscell[parent.ichosen]], dtype=bool)
parent.iscell = np.concatenate((parent.iscell, iscell), axis=0)
parent.probcell = np.append(parent.probcell, pmean)
parent.probredcell = np.append(parent.probredcell, prmean)
parent.redcell = np.append(parent.redcell, prmean > parent.chan2prob)
parent.notmerged = np.append(parent.notmerged, False)
# recompute binned F
parent.mode_change(parent.activityMode)
for n in merged_cells:
parent.stat[n]['inmerge'] = parent.stat.size-1
add_mask(parent, parent.iscell.size-1)