def deconvolve(fluor, pos, prctile=10, A0=0.15, lamb0=0.15, do_plot=True):
nc, nt = fluor.shape
# euclidean distances
dist = all_distances(pos)
ij, distvec = submission.adjacency2vec(dist)
# Pearson correlation coefficients for small fluorescence values
corr = threshold_corr(fluor, prctile)
ij, corrvec = submission.adjacency2vec(corr)
# from Stetter et al 2012
# A = 0.15
# lamb = 0.15
A, lamb = fit_gauss_blur(distvec, corrvec, A0, lamb0)
# convolution matrix (nc x nc)
C = gauss((A / 2., lamb), dist) # why divide by 2?
# # we set the diagonal to zero, since we don't consider a cell's own
# # fluorescence
# C[np.diag_indices(nc)] = 0
# F + CF = F_sc
# (I + C)F = F_sc
deconv = np.linalg.solve((np.eye(nc) + C), fluor)
if do_plot:
corr2 = threshold_corr(deconv, prctile)
ij, corrvec2 = submission.adjacency2vec(corr2)
A2, lamb2 = fit_gauss_blur(distvec, corrvec2, A0, lamb0)
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, sharey=True,
figsize=(8, 8))
plot_hist_fit(distvec, corrvec, (A, lamb), ax=ax1)
plot_hist_fit(distvec, corrvec2, (A2, lamb2), ax=ax2)
ax1.set_title('Original', fontsize=18)
ax2.set_title( 'Deconvolved', fontsize=18)
ax2.set_xlabel('Distance (mm)', fontsize=14)
ax1.set_ylabel('Correlation coefficient', fontsize=14)
ax2.set_ylabel('Correlation coefficient', fontsize=14)
cax, kw = colorbar.make_axes((ax1, ax2))
ax2.images[0].set_clim(ax1.images[0].get_clim())
cb = plt.colorbar(ax1.images[0], cax=cax, **kw)
cb.set_label('Density')
plt.show()
return deconv
def run(f, name, network=None, network_pos=None, ncells=None, sim_hours=1,
overwrite=False):
try:
try:
print ">> Creating new group: '/%s'" % name
g = f.create_group(f.root, name)
except tables.NodeError as e:
if overwrite:
# recursively remove existing group
f.remove_node('/' + name, recursive=True)
g = f.create_group(f.root, name)
else:
raise e
if network is None:
print ">> Creating new connectivity matrix"
# create an adjacency matrix for ncells, with default params
adjacency = fake_network.create_network(ncells)
# reshape the adjacency matrix into a vector form that is
# compatible with submission.run_auc()
ij, connected = submission.adjacency2vec(adjacency)
network = np.vstack((ij, connected)).T
del ij, connected
f.create_carray(g, 'network', obj=network, filters=FILTERS)
f.flush()
else:
# convert existing network to an adjacency matrix
ij, connected = submission.real2dense(network, ncells)
adjacency = submission.vec2adjacency(ij, connected)
f.create_carray(g, 'network', obj=network[:], filters=FILTERS)
if network_pos is None:
print ">> Generating fake cell positions"
network_pos = fake_blur.fake_positions(ncells)
f.create_carray(g, 'network_pos', obj=network_pos, filters=FILTERS)
f.flush()
else:
f.create_carray(g, 'network_pos', obj=network_pos[:],
filters=FILTERS)
print ">> Running spiking network simulation"
# run a NEST simulation using this adjacency matrix. the synaptic
# scaling parameter will be adjusted automatically to achieve a target
# burst rate according to default params
simtime = sim_hours * 60 * 60 * 1000 # time in ms
(spike_times, cell_indices,
resampled_spikes) = fake_spikes.run_simulation(adjacency,
simtime=simtime,
verbose=True)
f.create_carray(g, 'spike_times', obj=spike_times, filters=FILTERS)
f.create_carray(g, 'spike_cell_indices', obj=cell_indices,
filters=FILTERS)
f.create_carray(g, 'resampled_spikes', obj=resampled_spikes,
filters=FILTERS)
f.flush()
del spike_times, cell_indices
print ">> Modelling calcium dynamics"
calcium = fake_ca.fast_ca_from_spikes(resampled_spikes)
f.create_carray(g, 'calcium', obj=calcium, filters=FILTERS)
f.flush()
del resampled_spikes
print ">> Modelling dye saturation"
no_noise_fluor = fake_ca.fluor_from_ca(calcium)
f.create_carray(g, 'no_noise_fluor', obj=no_noise_fluor,
filters=FILTERS)
f.flush()
del calcium
print ">> Modelling fluorescence noise"
noisy_fluor = fake_ca.noisy_from_no_noise(no_noise_fluor)
f.create_carray(g, 'noisy_fluor', obj=noisy_fluor, filters=FILTERS)
f.flush()
del no_noise_fluor
print ">> Modelling optical blurring"
fluor = fake_blur.apply_gaussian_blur(network_pos, noisy_fluor)
f.create_carray(g, 'fluorescence', obj=fluor, filters=FILTERS)
f.flush()
print ">> Done."
except:
import traceback
# print the traceback for the exception
traceback.print_exc()
# make sure the file is closed, otherwise we won't have access
# to the object and we won't be able to close it without
# restarting ipython. close() calls flush() first, so no need to
# call it explicitly.
print 'Closing file "%s"' % f.filename
f.close()
def run(f,
name,
network=None,
network_pos=None,
ncells=None,
sim_hours=1,
overwrite=False):
try:
try:
print ">> Creating new group: '/%s'" % name
g = f.create_group(f.root, name)
except tables.NodeError as e:
if overwrite:
# recursively remove existing group
f.remove_node('/' + name, recursive=True)
g = f.create_group(f.root, name)
else:
raise e
if network is None:
print ">> Creating new connectivity matrix"
# create an adjacency matrix for ncells, with default params
adjacency = fake_network.create_network(ncells)
# reshape the adjacency matrix into a vector form that is
# compatible with submission.run_auc()
ij, connected = submission.adjacency2vec(adjacency)
network = np.vstack((ij, connected)).T
del ij, connected
f.create_carray(g, 'network', obj=network, filters=FILTERS)
f.flush()
else:
# convert existing network to an adjacency matrix
ij, connected = submission.real2dense(network, ncells)
adjacency = submission.vec2adjacency(ij, connected)
f.create_carray(g, 'network', obj=network[:], filters=FILTERS)
if network_pos is None:
print ">> Generating fake cell positions"
network_pos = fake_blur.fake_positions(ncells)
f.create_carray(g, 'network_pos', obj=network_pos, filters=FILTERS)
f.flush()
else:
f.create_carray(g,
'network_pos',
obj=network_pos[:],
filters=FILTERS)
print ">> Running spiking network simulation"
# run a NEST simulation using this adjacency matrix. the synaptic
# scaling parameter will be adjusted automatically to achieve a target
# burst rate according to default params
simtime = sim_hours * 60 * 60 * 1000 # time in ms
(spike_times, cell_indices,
resampled_spikes) = fake_spikes.run_simulation(adjacency,
simtime=simtime,
verbose=True)
f.create_carray(g, 'spike_times', obj=spike_times, filters=FILTERS)
f.create_carray(g,
'spike_cell_indices',
obj=cell_indices,
filters=FILTERS)
f.create_carray(g,
'resampled_spikes',
obj=resampled_spikes,
filters=FILTERS)
f.flush()
del spike_times, cell_indices
print ">> Modelling calcium dynamics"
calcium = fake_ca.fast_ca_from_spikes(resampled_spikes)
f.create_carray(g, 'calcium', obj=calcium, filters=FILTERS)
f.flush()
del resampled_spikes
print ">> Modelling dye saturation"
no_noise_fluor = fake_ca.fluor_from_ca(calcium)
f.create_carray(g,
'no_noise_fluor',
obj=no_noise_fluor,
filters=FILTERS)
f.flush()
del calcium
print ">> Modelling fluorescence noise"
noisy_fluor = fake_ca.noisy_from_no_noise(no_noise_fluor)
f.create_carray(g, 'noisy_fluor', obj=noisy_fluor, filters=FILTERS)
f.flush()
del no_noise_fluor
print ">> Modelling optical blurring"
fluor = fake_blur.apply_gaussian_blur(network_pos, noisy_fluor)
f.create_carray(g, 'fluorescence', obj=fluor, filters=FILTERS)
f.flush()
print ">> Done."
except:
import traceback
# print the traceback for the exception
traceback.print_exc()
# make sure the file is closed, otherwise we won't have access
# to the object and we won't be able to close it without
# restarting ipython. close() calls flush() first, so no need to
# call it explicitly.
print 'Closing file "%s"' % f.filename
f.close()
