def test_estfr():
T = 100
dt = 1e-2
# test an empty array
bspk = np.zeros(T,)
time = np.arange(0, 1, dt)
fr = spk.estfr(bspk, time, sigma=0.01)
assert np.allclose(fr, bspk)
# test a single spike
bspk[T // 2] = 1.
fr = spk.estfr(bspk, time, sigma=0.01)
assert (fr.sum() * dt) == bspk.sum()
def create_default_fake_rates():
"""Return the default firing rates."""
spikes = np.arange(10)
time = np.linspace(0, 10, 100)
binned = spiketools.binspikes(spikes, time)
rate = spiketools.estfr(binned, time)
return rate
def create_default_fake_rates():
"""Return the default firing rates."""
spikes = np.arange(10)
time = np.linspace(0, 10, 100)
binned = spiketools.binspikes(spikes, time)
rate = spiketools.estfr(binned, time)
return rate
def test_play_rates():
"""Test playing firing rates for cells as a movie."""
nx, ny, nt = 10, 10, 50
sta = utils.create_spatiotemporal_filter(nx, ny, nt)[-1]
time = np.linspace(0, 10, 100)
spikes = np.arange(10)
binned_spikes = spiketools.binspikes(spikes, time)
rate = spiketools.estfr(binned_spikes, time)
# Plot cell
fig, axes = viz.ellipse(sta)
patch = plt.findobj(axes, Ellipse)[0]
anim = viz.play_rates(rate, patch)
filename = os.path.join(IMG_DIR, 'test-rates-movie.png')
frame = 10
anim._func(frame)
plt.savefig(filename)
assert not compare_images(
os.path.join(IMG_DIR, 'baseline-rates-movie-frame.png'), filename, 1)
os.remove(filename)
plt.close('all')
all_nrepeats = []
for test_stim in stim_types:
full_path = data_dir + expt + '/'
f = h5py.File(full_path + test_stim, 'r')
bspk = np.array(f['train/response/binned'])
tax = np.array(f['train/time'])
ncells = bspk.shape[0]
# estimate firing rate with 1 second Gaussian filter
frs = np.zeros_like(bspk)
# estimate the cross-correlogram between trials
nrepeats = np.array(f['test/repeats/cell01']).shape[0]
correlations = np.zeros((ncells, nrepeats, nrepeats))
for c in range(ncells):
# estimate firing rate with 2 second Gaussian
frs[c,:] = spktools.estfr(tax, bspk[c,:], 2.0)
cell_label = 'cell%02i' %(c+1)
repeats = np.array(f['test/repeats/' + cell_label])
for i in range(nrepeats):
for j in range(i+1): #(i, nrepeats):
correlations[c,i,j] = cc(repeats[i,:], repeats[j,:])
all_ccs.append(correlations)
all_frs.append(frs)
all_nrepeats.append(nrepeats)
# generate plots
for c in range(ncells):
cell_label = 'cell%02i' %(c+1)