Source code for ibllib.tests.test_histology

from pathlib import Path
import unittest

import numpy as np

from ibllib.pipes import histology
from ibllib.pipes.ephys_alignment import (EphysAlignment, TIP_SIZE_UM, _cumulative_distance)
import ibllib.atlas as atlas

# TODO Place this in setUpModule()
brain_atlas = atlas.AllenAtlas(res_um=25)


[docs]class TestHistology(unittest.TestCase):
[docs] def setUp(self) -> None: self.path_tracks = Path(__file__).parent.joinpath('fixtures', 'histology', 'tracks')
[docs] def test_histology_get_brain_regions(self): # first part of the test is to check on an actual track file for file_track in self.path_tracks.rglob("*_pts.csv"): xyz = histology.load_track_csv(file_track) channels, ins = histology.get_brain_regions(xyz=xyz, brain_atlas=brain_atlas) # also check that it works from an insertion channels, ins2 = histology.get_brain_regions(xyz=ins.xyz, brain_atlas=brain_atlas) self.assertTrue(channels.acronym[-1] == 'VISpm1') self.assertTrue(channels.acronym[0] == 'APN') a = np.array([ins.x, ins.y, ins.z, ins.phi, ins.theta, ins.depth]) b = np.array([ins2.x, ins2.y, ins2.z, ins2.phi, ins2.theta, ins2.depth]) self.assertTrue(np.all(np.isclose(a, b)))
[docs] def test_histology_insertion_from_track(self): for file_track in self.path_tracks.rglob("*_pts.csv"): xyz = histology.load_track_csv(file_track) insertion = atlas.Insertion.from_track(xyz, brain_atlas=brain_atlas) # checks that the tip coordinate is not the deepest point but its projection self.assertFalse(np.all(np.isclose(insertion.tip, xyz[-1])))
[docs] def test_get_brain_exit_entry(self): traj = atlas.Trajectory.fit(np.array([[0, 0, 0], [0, 0, 0.005]])) top = atlas.Insertion.get_brain_entry(traj, brain_atlas=brain_atlas) bottom = atlas.Insertion.get_brain_exit(traj, brain_atlas=brain_atlas) ix, iy = (brain_atlas.bc.x2i(0), brain_atlas.bc.y2i(0)) self.assertTrue(np.isclose(brain_atlas.top[iy, ix], top[2])) self.assertTrue(np.isclose(brain_atlas.bottom[iy, ix], bottom[2]))
[docs] def test_filename_parser(self): tdata = [ {'input': Path("/gna/electrode_tracks_SWC_014/2019-12-12_SWC_014_001_probe01_fit.csv"), 'output': {'date': '2019-12-12', 'experiment_number': 1, 'name': 'probe01', 'subject': 'SWC_014'}}, {'input': Path("/gna/datadisk/Data/Histology/" "tracks/ZM_2407/2019-11-06_ZM_2407_001_probe_00_pts.csv"), 'output': {'date': '2019-11-06', 'experiment_number': 1, 'name': 'probe_00', 'subject': 'ZM_2407'}}, {'input': Path("/gna/2019-12-06_KS023_001_probe01_pts.csv"), 'output': {'date': '2019-12-06', 'experiment_number': 1, 'name': 'probe01', 'subject': 'KS023'}}, ] for t in tdata: track_file = t['input'] assert t['output'] == histology._parse_filename(track_file)
# Load in data for ephys alignment testing data = np.load(Path(Path(__file__).parent.joinpath('fixtures', 'ephysalignment'), 'alignment_data.npz'), allow_pickle=True) xyz_picks = data['xyz_picks'] feature_prev = data['feature_prev'] track_prev = data['track_prev'] xyz_channels_ref = data['xyz_channels_ref'] brain_regions_ref = data['brain_regions_ref'] depths = np.arange(20, 3860, 20) / 1e6
[docs]class TestsEphysAlignment(unittest.TestCase):
[docs] def setUp(self): self.ephysalign = EphysAlignment(xyz_picks, brain_atlas=brain_atlas) self.feature = self.ephysalign.feature_init self.track = self.ephysalign.track_init
[docs] 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)
[docs] 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)))
[docs] 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))
[docs] 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))
[docs]class TestsEphysReconstruction(unittest.TestCase):
[docs] def setUp(self): self.ephysalign = EphysAlignment(xyz_picks, track_prev=track_prev, feature_prev=feature_prev, brain_atlas=brain_atlas) self.feature = self.ephysalign.feature_init self.track = self.ephysalign.track_init
[docs] def test_channel_locations(self): xyz_channels = self.ephysalign.get_channel_locations(self.feature, self.track, depths=depths) self.assertTrue(np.all(np.isclose(xyz_channels[0, :], xyz_channels_ref[0]))) self.assertTrue(np.all(np.isclose(xyz_channels[-1, :], xyz_channels_ref[-1]))) brain_regions = self.ephysalign.get_brain_locations(xyz_channels) self.assertTrue(np.all(np.equal(np.unique(brain_regions.acronym), brain_regions_ref)))
if __name__ == "__main__": unittest.main(exit=False, verbosity=2)