def test_strf():
n_spatial_basis = 36
n_temporal_basis = 7
patch_size = 50
# Instantiate strf object
strf_ = STRF(patch_size=patch_size, sigma=5,
n_spatial_basis=n_spatial_basis,
n_temporal_basis=n_temporal_basis)
# Design a spatial basis
spatial_basis = strf_.make_cosine_basis()
assert_equal(len(spatial_basis), 2)
for basis in spatial_basis:
assert_equal(basis.shape[0], patch_size)
assert_equal(basis.shape[1], patch_size)
spatial_basis = strf_.make_gaussian_basis()
assert_equal(len(spatial_basis), n_spatial_basis)
# Visualize spatial basis
strf_.visualize_gaussian_basis(spatial_basis)
# Design temporal basis
time_points = np.linspace(-100., 100., 10.)
centers = [-75., -50., -25., 0, 25., 50., 75.]
width = 10.
temporal_basis = strf_.make_raised_cosine_temporal_basis(
time_points=time_points,
centers=centers,
widths=width * np.ones(7))
assert_equal(temporal_basis.shape[0], len(time_points))
assert_equal(temporal_basis.shape[1], n_temporal_basis)
# Project to spatial basis
I = np.zeros(shape=(patch_size, patch_size))
row = 5
col = 10
I[row, col] = 1
basis_projection = strf_.project_to_spatial_basis(I, spatial_basis)
assert_equal(len(basis_projection), n_spatial_basis)
# Recover image from basis projection
weights = np.random.normal(size=n_spatial_basis)
RF = strf_.make_image_from_spatial_basis(spatial_basis, weights)
assert_equal(RF.shape[0], patch_size)
assert_equal(RF.shape[1], patch_size)
# Convolve with temporal basis
n_samples = 100
n_features = n_spatial_basis
design_matrix = np.random.normal(size=(n_samples, n_features))
features = strf_.convolve_with_temporal_basis(
design_matrix, temporal_basis)
assert_equal(features.shape[0], n_samples)
assert_equal(features.shape[1], n_features * n_temporal_basis)
# Design prior covariance
PriorCov = strf_.design_prior_covariance(
sigma_temporal=3., sigma_spatial=5.)
assert_equal(PriorCov.shape[0], PriorCov.shape[1])
assert_equal(PriorCov.shape[0], n_spatial_basis * n_temporal_basis)
and (probe_col - fix_col) > -n_shape / 2
and (probe_col - fix_col) < n_shape / 2):
# Get probe timestamp relative to fixation
probe_time = probes_df.loc[probe_id]['t_probe']
probe_bin = np.where(
bin_template < (probe_time - fixation_time))[0][-1]
# Define an image based on the relative locations
img = np.zeros(shape=(n_shape, n_shape))
row = int(-np.round(probe_row - fix_row) + n_shape / 2 - 1)
col = int(np.round(probe_col - fix_col) + n_shape / 2 - 1)
img[row, col] = 1
# Compute projection
basis_projection = strf_model.project_to_spatial_basis(
img, spatial_basis)
this_fixation_spatial_features[probe_bin, :] = basis_projection
# Count spikes in window of interest relative to fixation
bins = fixation_time + bin_template
searchsorted_idx = np.searchsorted(
spiketimes, [fixation_time + window[0], fixation_time + window[1]])
this_fixation_spike_counts = np.histogram(
spiketimes[searchsorted_idx[0]:searchsorted_idx[1]], bins)[0]
# Accumulate
fixation_id = np.concatenate((fixation_id, this_fixation_id), axis=0)
spatial_features = np.concatenate(
(spatial_features, this_fixation_spatial_features), axis=0)
spike_counts = np.concatenate((spike_counts, this_fixation_spike_counts),
axis=0)
def test_strf():
n_spatial_basis = 36
n_temporal_basis = 7
patch_size = 50
# Instantiate strf object
strf_ = STRF(patch_size=patch_size,
sigma=5,
n_spatial_basis=n_spatial_basis,
n_temporal_basis=n_temporal_basis)
# Design a spatial basis
spatial_basis = strf_.make_cosine_basis()
assert_equal(len(spatial_basis), 2)
for basis in spatial_basis:
assert_equal(basis.shape[0], patch_size)
assert_equal(basis.shape[1], patch_size)
spatial_basis = strf_.make_gaussian_basis()
assert_equal(len(spatial_basis), n_spatial_basis)
# Visualize spatial basis
strf_.visualize_gaussian_basis(spatial_basis)
# Design temporal basis
time_points = np.linspace(-100., 100., 10.)
centers = [-75., -50., -25., 0, 25., 50., 75.]
width = 10.
temporal_basis = strf_.make_raised_cosine_temporal_basis(
time_points=time_points, centers=centers, widths=width * np.ones(7))
assert_equal(temporal_basis.shape[0], len(time_points))
assert_equal(temporal_basis.shape[1], n_temporal_basis)
# Project to spatial basis
I = np.zeros(shape=(patch_size, patch_size))
row = 5
col = 10
I[row, col] = 1
basis_projection = strf_.project_to_spatial_basis(I, spatial_basis)
assert_equal(len(basis_projection), n_spatial_basis)
# Recover image from basis projection
weights = np.random.normal(size=n_spatial_basis)
RF = strf_.make_image_from_spatial_basis(spatial_basis, weights)
assert_equal(RF.shape[0], patch_size)
assert_equal(RF.shape[1], patch_size)
# Convolve with temporal basis
n_samples = 100
n_features = n_spatial_basis
design_matrix = np.random.normal(size=(n_samples, n_features))
features = strf_.convolve_with_temporal_basis(design_matrix,
temporal_basis)
assert_equal(features.shape[0], n_samples)
assert_equal(features.shape[1], n_features * n_temporal_basis)
# Design prior covariance
PriorCov = strf_.design_prior_covariance(sigma_temporal=3.,
sigma_spatial=5.)
assert_equal(PriorCov.shape[0], PriorCov.shape[1])
assert_equal(PriorCov.shape[0], n_spatial_basis * n_temporal_basis)