class TestsEphysAlignment(unittest.TestCase):
def setUp(self):
self.ephysalign = EphysAlignment(xyz_picks)
self.feature = self.ephysalign.feature_init
self.track = self.ephysalign.track_init
def test_no_scaling(self):
xyz_channels = self.ephysalign.get_channel_locations(self.feature,
self.track,
depths=depths)
coords = np.r_[[xyz_picks[-1, :]], [xyz_channels[0, :]]]
dist_to_fist_electrode = np.around(_cumulative_distance(coords)[-1], 5)
assert np.isclose(dist_to_fist_electrode, (TIP_SIZE_UM + 20) / 1e6)
def test_offset(self):
feature_val = 500 / 1e6
track_val = 1000 / 1e6
tracks = np.sort(np.r_[self.track[[0, -1]], track_val])
track_new = self.ephysalign.feature2track(tracks, self.feature,
self.track)
feature_new = np.sort(np.r_[self.feature[[0, -1]], feature_val])
track_new = self.ephysalign.adjust_extremes_uniform(
feature_new, track_new)
xyz_channels = self.ephysalign.get_channel_locations(feature_new,
track_new,
depths=depths)
coords = np.r_[[xyz_picks[-1, :]], [xyz_channels[0, :]]]
dist_to_fist_electrode = np.around(_cumulative_distance(coords)[-1], 5)
assert np.isclose(dist_to_fist_electrode,
((TIP_SIZE_UM + 20) / 1e6 + feature_val))
track_val = self.ephysalign.track2feature(track_val, feature_new,
track_new)
self.assertTrue(np.all(np.isclose(track_val, feature_val)))
region_new, _ = self.ephysalign.scale_histology_regions(
feature_new, track_new)
_, scale_factor = self.ephysalign.get_scale_factor(region_new)
self.assertTrue(np.all(np.isclose(scale_factor, 1)))
def test_uniform_scaling(self):
feature_val = np.array([500, 700, 2000]) / 1e6
track_val = np.array([1000, 1300, 2700]) / 1e6
tracks = np.sort(np.r_[self.track[[0, -1]], track_val])
track_new = self.ephysalign.feature2track(tracks, self.feature,
self.track)
feature_new = np.sort(np.r_[self.feature[[0, -1]], feature_val])
track_new = self.ephysalign.adjust_extremes_uniform(
feature_new, track_new)
region_new, _ = self.ephysalign.scale_histology_regions(
feature_new, track_new)
_, scale_factor = self.ephysalign.get_scale_factor(region_new)
self.assertTrue(np.isclose(scale_factor[0], 1))
self.assertTrue(np.isclose(scale_factor[-1], 1))
def test_linear_scaling(self):
feature_val = np.array([500, 700, 2000]) / 1e6
track_val = np.array([1000, 1300, 2700]) / 1e6
tracks = np.sort(np.r_[self.track[[0, -1]], track_val])
track_new = self.ephysalign.feature2track(tracks, self.feature,
self.track)
feature_new = np.sort(np.r_[self.feature[[0, -1]], feature_val])
fit = np.polyfit(feature_new[1:-1], track_new[1:-1], 1)
linear_fit = np.around(1 / fit[0], 3)
feature_new, track_new = self.ephysalign.adjust_extremes_linear(
feature_new, track_new, extend_feature=1)
region_new, _ = self.ephysalign.scale_histology_regions(
feature_new, track_new)
_, scale_factor = self.ephysalign.get_scale_factor(region_new)
self.assertTrue(np.isclose(np.around(scale_factor[0], 3), linear_fit))
self.assertTrue(np.isclose(np.around(scale_factor[-1], 3), linear_fit))
# Find brain region that each channel is located in
brain_regions = ephysalign.get_brain_locations(xyz_channels)
# Add extra keys to store all useful information as one bunch object
brain_regions['xyz'] = xyz_channels
brain_regions['lateral'] = chn_coords[:, 0]
brain_regions['axial'] = chn_coords[:, 1]
# Store brain regions result in channels dict with same key as in alignment
channel_info = {key: brain_regions}
channels.update(channel_info)
# For plotting -> extract the boundaries of the brain regions, as well as CCF label and colour
region, region_label, region_colour, _ = ephysalign.get_histology_regions(
xyz_channels, depths)
channel_depths_track = (ephysalign.feature2track(depths, feature, track) -
ephysalign.track_extent[0])
# Make plot that shows the brain regions that channels pass through
ax_regions = fig.axes[iK * 2]
for reg, col in zip(region, region_colour):
height = np.abs(reg[1] - reg[0])
bottom = reg[0]
color = col / 255
ax_regions.bar(x=0.5,
height=height,
width=1,
color=color,
bottom=reg[0],
edgecolor='w')
ax_regions.set_yticks(region_label[:, 0].astype(int))