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))
for reg, col in zip(region_scaled, scale_factor):
height = np.abs(reg[1] - reg[0])
color = np.array(mapper.to_rgba(col, bytes=True)) / 255
ax.bar(x=1.1, height=height, width=0.2, color=color, bottom=reg[0], edgecolor='w')
sec_ax = ax.secondary_yaxis('right')
sec_ax.set_yticks(np.mean(region, axis=1))
sec_ax.set_yticklabels(np.around(scale, 2))
sec_ax.tick_params(axis="y", direction="in")
sec_ax.set_ylim([20, 3840])
fig, ax = plt.subplots(1, len(alignments) + 1, figsize=(15, 15))
ephysalign = EphysAlignment(xyz_picks, depths, brain_atlas=brain_atlas)
feature, track, _ = ephysalign.get_track_and_feature()
channels_orig = ephysalign.get_channel_locations(feature, track)
region, region_label = ephysalign.scale_histology_regions(feature, track)
region_scaled, scale_factor = ephysalign.get_scale_factor(region)
region_colour = ephysalign.region_colour
norm = matplotlib.colors.Normalize(vmin=0.5, vmax=1.5, clip=True)
mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=matplotlib.cm.seismic)
ax_i = fig.axes[0]
plot_regions(region, region_label, region_colour, ax_i)
plot_scaling(region_scaled, scale_factor, mapper, ax_i)
ax_i.set_title('Original')
for iK, key in enumerate(alignments):
# Location of reference lines used for alignmnet
feature = np.array(alignments[key][0])
track = np.array(alignments[key][1])
name=traj['probe_name'])
xyz_picks = np.array(insertion[0]['json']['xyz_picks']) / 1e6
session_info = traj['session']['start_time'][:10] + '_' + traj['probe_name']
for iK, key in enumerate(alignments):
# Location of reference lines used for alignmnet
feature = np.array(alignments[key][0])
track = np.array(alignments[key][1])
user = key[:19]
# Instantiate EphysAlignment object
ephysalign = EphysAlignment(xyz_picks,
depths,
track_prev=track,
feature_prev=feature)
region_scaled, _ = ephysalign.scale_histology_regions(feature, track)
_, scale_factor = ephysalign.get_scale_factor(region_scaled)
if np.all(np.round(np.diff(scale_factor), 3) == 0):
# Case where there is no scaling but just an offset
scale_factor = np.array([1])
avg_sf = 1
else:
if feature.size > 4:
# Case where 3 or more reference lines have been placed so take gradient of
# linear fit to represent average scaling factor
avg_sf = scale_factor[0]
else:
# Case where 2 reference lines have been used. Only have local scaling between
# two reference lines, everywhere else scaling is 1. Use the local scaling as the
# average scaling factor