def test_rfsize():
np.random.seed(0)
filter_length = 100
nx, ny = 10, 10
true_filter = utils.create_spatiotemporal_filter(nx, ny, filter_length)[1]
xsize, ysize = flt.rfsize(true_filter, 1., 1.)
assert np.allclose(xsize, 3., 0.1) # 1 SD is about 3 units
assert np.allclose(ysize, 3., 0.1)
def test_ellipse():
"""Test plotting an ellipse fitted to an RF."""
nx, ny, nt = 10, 10, 50
sta = utils.create_spatiotemporal_filter(nx, ny, nt)[-1]
filename = os.path.join(IMG_DIR, 'test-ellipse.png')
viz.ellipse(sta)
plt.savefig(filename)
assert not compare_images(os.path.join(IMG_DIR, 'baseline-ellipse.png'),
filename, 1)
os.remove(filename)
plt.close('all')
def test_spatial_filter():
"""Test plotting a spatial filter from a full 3D spatiotemporal STA."""
nx, ny, nt = 10, 10, 50
time = np.arange(nt)
sta = utils.create_spatiotemporal_filter(nx, ny, nt)[-1]
viz.spatial(sta)
filename = os.path.join(IMG_DIR, 'test-spatial-filter.png')
plt.savefig(filename)
assert not compare_images(
os.path.join(IMG_DIR, 'baseline-spatial-filter.png'), filename, 1)
os.remove(filename)
plt.close('all')
def test_anim_to_html():
"""Test converting an animation to HTML."""
try:
from IPython.display import HTML
except ImportError:
pytest.skip('Cannot convert movie to HTML without IPython.')
nx, ny, nt = 10, 10, 50
sta = utils.create_spatiotemporal_filter(nx, ny, nt)[-1]
anim = viz.play_sta(sta)
html = viz.anim_to_html(viz.play_sta(sta))
assert isinstance(html, HTML)
def test_normalize_spatial():
"""Test normalizing a noisy filter."""
np.random.seed(0)
filter_length = 100
nx, ny = 10, 10
true_filter = utils.create_spatiotemporal_filter(nx, ny, filter_length)[1]
noise_std = 0.01
noisy_filter = true_filter + 1.0 + np.random.randn(*true_filter.shape) * 0.01
normalized = flt.normalize_spatial(noisy_filter)
normalized /= np.linalg.norm(normalized)
atol = 0.1
assert np.allclose(normalized, true_filter, atol=atol)
def test_plot_cells():
"""Test plotting ellipses for multiple cells on the same axes."""
nx, ny, nt = 10, 10, 50
stas = []
ncells = 2
for cell in range(ncells):
stas.append(utils.create_spatiotemporal_filter(nx, ny, nt)[-1])
filename = os.path.join(IMG_DIR, 'test-plotcells.png')
np.random.seed(0) # plot_cells() uses random colors for each cell
viz.plot_cells(stas)
plt.savefig(filename)
assert not compare_images(os.path.join(IMG_DIR, 'baseline-plotcells.png'),
filename, 1)
os.remove(filename)
plt.close('all')
def test_decompose():
"""Tests computing a rank-1 approximation to a filter.
Note that this tests both filtertools.decompose() and filtertools.lowranksta().
"""
np.random.seed(0)
filter_length = 50
nx, ny = 10, 10
temporal, spatial, true_filter = utils.create_spatiotemporal_filter(nx, ny, filter_length)
noise_std = 0.01
true_filter += np.random.randn(*true_filter.shape) * noise_std
s, t = flt.decompose(true_filter)
tol = 0.1
assert np.allclose(temporal, t, atol=tol)
assert np.allclose(spatial, s, atol=tol)
def test_revcorr_nd():
np.random.seed(0)
"""Test computation of 3D linear filter by reverse correlation"""
# Create fake filter
filter_length = 100
nx, ny = 10, 10
true_filter = utils.create_spatiotemporal_filter(nx, ny, filter_length)[-1]
# Compute linear response
stim_length = 10000
stimulus = np.random.randn(stim_length, nx, ny)
response = flt.linear_prediction(true_filter, stimulus)
# Reverse correlation
filt = flt.revcorr(response, stimulus, filter_length)
filt /= np.linalg.norm(filt)
tol = 0.1
assert np.allclose(true_filter, filt, atol=tol)
def test_play_sta():
"""Test playing an STA as a movie.
Matplotlib doesn't yet have a way to compare movies, and the formats
and precise bytes written by different encoding libraries are too variable
to test reliably. Instead, we write a specific frame of the animation
to disk as an image, and compare it with a baseline.
"""
nx, ny, nt = 10, 10, 50
sta = utils.create_spatiotemporal_filter(nx, ny, nt)[-1]
anim = viz.play_sta(sta)
filename = os.path.join(IMG_DIR, 'test-sta-movie.png')
frame = 10
anim._func(frame)
plt.savefig(filename)
assert not compare_images(
os.path.join(IMG_DIR, 'baseline-sta-movie-frame.png'), filename, 1)
os.remove(filename)
plt.close('all')
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')
def test_revcorr_nd():
np.random.seed(0)
"""Test computation of 3D linear filter by reverse correlation"""
# Create fake filter
filter_length = 100
nx, ny = 10, 10
true_filter = utils.create_spatiotemporal_filter(nx, ny, filter_length)[-1]
# Compute linear response
stim_length = 10000
stimulus = np.random.randn(stim_length, nx, ny)
response = flt.linear_response(true_filter, stimulus)
# Reverse correlation, pad response with zeros to so that
# stim and response match. These will be ignored by
# filtertools.revcorr anyway.
padded_response = np.concatenate((np.zeros((filter_length - 1,)),
response), axis=0)
filt = flt.revcorr(stimulus, padded_response, filter_length)[0]
filt /= np.linalg.norm(filt)
tol = 0.1
assert np.allclose(true_filter, filt, atol=tol)
def test_spatiotemporal_filter():
"""Test plotting a full 3D spatiotemporal STA."""
nx, ny, nt = 10, 10, 50
time = np.arange(nt)
t, s, sta = utils.create_spatiotemporal_filter(nx, ny, nt)
# Test plotting temporal component
filename = os.path.join(IMG_DIR, 'test-temporal-filter.png')
viz.plot_sta(time, t)
plt.savefig(filename)
assert not compare_images(
os.path.join(IMG_DIR,
'baseline-temporal-from-spatiotemporal-filter.png'),
filename, 1)
os.remove(filename)
plt.close('all')
# Test plotting spatial component
filename = os.path.join(IMG_DIR, 'test-temporal-filter.png')
viz.plot_sta(time, s)
plt.savefig(filename)
assert not compare_images(
os.path.join(IMG_DIR,
'baseline-spatial-from-spatiotemporal-filter.png'),
filename, 1)
os.remove(filename)
plt.close('all')
# Test plotting both spatial/temporal components
filename = os.path.join(IMG_DIR, 'test-full-spatiotemporal-filter.png')
viz.plot_sta(time, sta)
plt.savefig(filename)
assert not compare_images(
os.path.join(IMG_DIR, 'baseline-full-spatiotemporal-filter.png'),
filename, 1)
os.remove(filename)
plt.close('all')
