Coverage for ibllib/qc/alignment_qc.py: 67%
230 statements
« prev ^ index » next coverage.py v7.7.0, created at 2025-03-17 15:25 +0000
1import logging
3import numpy as np
4from pathlib import Path
5from one.alf.spec import QC
6from datetime import date
8from neuropixel import trace_header
9import spikeglx
11from iblatlas.atlas import AllenAtlas
12from ibllib.pipes import histology
13from ibllib.pipes.ephys_alignment import EphysAlignment
14from ibllib.qc import base
15from ibllib.oneibl.patcher import FTPPatcher
17_log = logging.getLogger(__name__)
18CRITERIA = {"PASS": 0.8}
21class AlignmentQC(base.QC):
22 """
23 Class that is used to update the extended_qc of the probe insertion fields with the results
24 from the ephys alignment procedure
25 """
26 def __init__(self, probe_id, one=None, brain_atlas=None, channels=True, collection=None):
27 super().__init__(probe_id, one=one, log=_log, endpoint='insertions') 1ab
29 # Data
30 self.alignments = None 1ab
31 self.xyz_picks = None 1ab
32 self.depths = None 1ab
33 self.cluster_chns = None 1ab
34 self.chn_coords = None 1ab
35 self.align_keys_sorted = None 1ab
37 # Metrics and passed trials
38 self.sim_matrix = None 1ab
39 self.criteria = CRITERIA 1ab
41 # Get the brain atlas
42 self.brain_atlas = brain_atlas or AllenAtlas(25) 1ab
43 # Flag for uploading channels to alyx. For testing purposes
44 self.channels_flag = channels 1ab
46 self.insertion = self.one.alyx.get(f'/insertions/{self.eid}', clobber=True) 1ab
47 self.resolved = (self.insertion.get('json', {'temp': 0}).get('extended_qc'). 1ab
48 get('alignment_resolved', False))
50 self.probe_collection = collection 1ab
52 def load_data(self, prev_alignments=None, xyz_picks=None, depths=None, cluster_chns=None,
53 chn_coords=None):
54 """"
55 Load data required to assess alignment qc and compute similarity matrix. If no arguments
56 are given load_data will fetch all the relevant data required
57 """
58 if not np.any(prev_alignments): 1ab
59 aligned_traj = self.one.alyx.get(f'/trajectories?&probe_insertion={self.eid}' 1a
60 '&provenance=Ephys aligned histology track',
61 clobber=True)
62 if len(aligned_traj) > 0: 1a
63 self.alignments = aligned_traj[0].get('json', {}) 1a
64 else:
65 self.alignments = {} 1a
66 return 1a
67 else:
68 self.alignments = prev_alignments 1ab
70 align_keys = [*self.alignments.keys()] 1ab
71 self.align_keys_sorted = sorted(align_keys, reverse=True) 1ab
73 if not np.any(xyz_picks): 1ab
74 self.xyz_picks = np.array(self.insertion['json']['xyz_picks']) / 1e6 1a
75 else:
76 self.xyz_picks = xyz_picks 1ab
78 if len(self.alignments) < 2: 1ab
79 return 1a
81 if not self.probe_collection: 1ab
82 all_collections = self.one.list_collections(self.insertion['session']) 1ab
83 if f'alf/{self.insertion["name"]}/pykilosort' in all_collections: 1ab
84 self.probe_collection = f'alf/{self.insertion["name"]}/pykilosort'
85 else:
86 self.probe_collection = f'alf/{self.insertion["name"]}' 1ab
88 if not np.any(chn_coords): 1ab
89 self.chn_coords = self.one.load_dataset(self.insertion['session'],
90 'channels.localCoordinates.npy',
91 collection=self.probe_collection)
92 else:
93 self.chn_coords = chn_coords 1ab
95 if not np.any(depths): 1ab
96 self.depths = self.chn_coords[:, 1]
97 else:
98 self.depths = depths 1ab
100 if not np.any(cluster_chns): 1ab
101 self.cluster_chns = self.one.load_dataset(self.insertion['session'],
102 'clusters.channels.npy',
103 collection=self.probe_collection)
104 else:
105 self.cluster_chns = cluster_chns 1ab
107 def compute(self):
108 """
109 Computes the similarity matrix if > 2 alignments. If no data loaded, wraps around load_data
110 to get all relevant data needed
111 """
113 if self.alignments is None: 1ab
114 self.load_data() 1a
116 if len(self.alignments) < 2: 1ab
117 self.log.info(f"Insertion {self.eid}: One or less alignment found...") 1a
118 self.sim_matrix = np.array([len(self.alignments)]) 1a
119 else:
120 self.log.info(f"Insertion {self.eid}: Running QC on alignment data...") 1ab
121 self.sim_matrix = self.compute_similarity_matrix() 1ab
123 return self.sim_matrix 1ab
125 def run(self, update=True, upload_alyx=True, upload_flatiron=True):
126 """
127 Compute alignment_qc for a specified probe insertion and updates extended qc field in alyx.
128 If alignment is resolved and upload flags set to True channels from resolved
129 alignment will be updated to alyx and datasets sent to ibl-ftp-patcher to be uploaded to
130 flatiron
131 """
132 if self.sim_matrix is None: 1ab
133 self.compute() 1ab
135 # Case where the alignment has already been resolved
136 if self.resolved: 1ab
137 self.log.info(f"Alignment for insertion {self.eid} already resolved, channels won't be"
138 f" updated. To force update of channels use "
139 f"resolve_manual method with force=True")
140 results = {'alignment_count': len(self.alignments)}
141 if update:
142 self.update_extended_qc(results)
143 results.update({'alignment_resolved': True})
145 # Case where no alignments have been made
146 elif np.all(self.sim_matrix == 0) and self.sim_matrix.shape[0] == 1: 1ab
147 # We don't update database
148 results = {'alignment_resolved': False} 1a
150 # Case where only one alignment
151 elif np.all(self.sim_matrix == 1) and self.sim_matrix.shape[0] == 1: 1ab
152 results = {'alignment_count': len(self.alignments), 1a
153 'alignment_stored': self.align_keys_sorted[0],
154 'alignment_resolved': False}
155 if update: 1a
156 self.update_extended_qc(results) 1a
158 # Case where 2 or more alignments and alignments haven't been resolved
159 else:
160 results = self.compute_alignment_status() 1ab
162 if update: 1ab
163 self.update_extended_qc(results) 1ab
165 if results['alignment_resolved'] and np.bitwise_or(upload_alyx, upload_flatiron): 1ab
166 self.upload_channels(results['alignment_stored'], upload_alyx, upload_flatiron) 1a
168 return results 1ab
170 def resolve_manual(self, align_key, update=True, upload_alyx=True, upload_flatiron=False,
171 force=False):
172 """
173 Method to manually resolve the alignment of a probe insertion with a given alignment
174 regardless of the number of alignments or the alignment qc value. Channels from specified
175 alignment will be uploaded to alyx and datasets sent to ibl-ftp-patcher to be uploaded to
176 flatiron. If alignment already resolved will only upload if force flag set to True
177 """
179 if self.sim_matrix is None: 1b
180 self.compute() 1b
181 assert align_key in self.align_keys_sorted, 'align key not recognised' 1b
183 if self.resolved == 1 and not force: 1b
184 self.log.info(f"Alignment for insertion {self.eid} already resolved, channels won't be"
185 f"updated. To overwrite stored channels with alignment {align_key} "
186 f"set 'force=True'")
187 file_paths = []
188 else:
189 if self.sim_matrix.shape[0] > 1: 1b
190 results = self.compute_alignment_status() 1b
191 else:
192 results = {'alignment_count': self.sim_matrix.shape[0]}
194 results['alignment_resolved'] = True 1b
195 results['alignment_stored'] = align_key 1b
196 results['alignment_resolved_by'] = 'experimenter' 1b
197 results['alignment_resolved_date'] = date.today().isoformat() 1b
199 if update: 1b
200 self.update_extended_qc(results) 1b
201 file_paths = [] 1b
203 if upload_alyx or upload_flatiron: 1b
204 file_paths = self.upload_channels(align_key, upload_alyx, upload_flatiron) 1b
206 return file_paths 1b
208 def compute_similarity_matrix(self):
209 """
210 Computes the similarity matrix between each alignment stored in the ephys aligned
211 trajectory. Similarity matrix based on number of clusters that share brain region and
212 parent brain region
213 """
215 r = self.brain_atlas.regions 1ab
217 clusters = dict() 1ab
218 for iK, key in enumerate(self.align_keys_sorted): 1ab
219 # Location of reference lines used for alignment
220 feature = np.array(self.alignments[key][0]) 1ab
221 track = np.array(self.alignments[key][1]) 1ab
223 # Instantiate EphysAlignment object
224 ephysalign = EphysAlignment(self.xyz_picks, self.depths, track_prev=track, 1ab
225 feature_prev=feature,
226 brain_atlas=self.brain_atlas)
228 # Find xyz location of all channels
229 xyz_channels = ephysalign.get_channel_locations(feature, track) 1ab
230 brain_regions = ephysalign.get_brain_locations(xyz_channels) 1ab
232 # Find the location of clusters along the alignment
233 cluster_info = dict() 1ab
234 cluster_info['brain_id'] = brain_regions['id'][self.cluster_chns] 1ab
235 cluster_info['parent_id'] = r.get(ids=cluster_info['brain_id']).parent.astype(int) 1ab
236 clusters.update({key: cluster_info}) 1ab
238 sim_matrix = np.zeros((len(self.align_keys_sorted), len(self.align_keys_sorted))) 1ab
240 for ik, key in enumerate(self.align_keys_sorted): 1ab
241 for ikk, key2 in enumerate(self.align_keys_sorted): 1ab
242 same_id = np.where(clusters[key]['brain_id'] == clusters[key2]['brain_id'])[0] 1ab
243 not_same_id = \ 1ab
244 np.where(clusters[key]['brain_id'] != clusters[key2]['brain_id'])[0]
245 same_parent = np.where(clusters[key]['parent_id'][not_same_id] == 1ab
246 clusters[key2]['parent_id'][not_same_id])[0]
247 sim_matrix[ik, ikk] = len(same_id) + (len(same_parent) * 0.5) 1ab
248 # Normalise
249 sim_matrix_norm = sim_matrix / np.max(sim_matrix) 1ab
251 return sim_matrix_norm 1ab
253 def compute_alignment_status(self):
254 """
255 Determine whether alignments agree based on value in similarity matrix. If any alignments
256 have similarity of 0.8 set the alignment to be resolved
257 """
258 # Set diagonals to zero so we don't use those to find max
259 np.fill_diagonal(self.sim_matrix, 0) 1ab
261 max_sim = np.max(self.sim_matrix) 1ab
263 results = {'alignment_qc': max_sim, 1ab
264 'alignment_count': self.sim_matrix.shape[0]}
266 if max_sim > CRITERIA['PASS']: 1ab
267 location = np.where(self.sim_matrix == max_sim) 1a
268 results.update({'alignment_stored': self.align_keys_sorted[np.min(location)]}) 1a
269 results.update({'alignment_resolved': True}) 1a
270 results.update({'alignment_resolved_by': 'qc'}) 1a
272 else:
273 results.update({'alignment_stored': self.align_keys_sorted[0]}) 1ab
274 results.update({'alignment_resolved': False}) 1ab
276 return results 1ab
278 def upload_channels(self, alignment_key, upload_alyx, upload_flatiron):
279 """
280 Upload channels to alyx and flatiron based on the alignment specified by the alignment key
281 """
283 feature = np.array(self.alignments[alignment_key][0]) 1ab
284 track = np.array(self.alignments[alignment_key][1]) 1ab
286 try: 1ab
287 meta_dset = self.one.list_datasets(self.insertion['session'], '*ap.meta', 1ab
288 collection=f'raw_ephys_data/{self.insertion["name"]}')
290 meta_file = self.one.load_dataset(self.insertion['session'], meta_dset[0].split('/')[-1], 1ab
291 collection=f'raw_ephys_data/{self.insertion["name"]}',
292 download_only=True)
293 geometry = spikeglx.read_geometry(meta_file)
294 chns = np.c_[geometry['x'], geometry['y']]
295 except Exception as err: 1ab
296 self.log.warning(f"Could not compute channel locations from meta file, errored with message: {err}. " 1ab
297 f"Will use default Neuropixel 1 channels")
298 geometry = trace_header(version=1) 1ab
299 chns = np.c_[geometry['x'], geometry['y']] 1ab
301 ephysalign = EphysAlignment(self.xyz_picks, chns[:, 1], 1ab
302 track_prev=track,
303 feature_prev=feature,
304 brain_atlas=self.brain_atlas)
305 channels_mlapdv = np.int32(ephysalign.get_channel_locations(feature, track) * 1e6) 1ab
306 channels_atlas_id = ephysalign.get_brain_locations(channels_mlapdv / 1e6)['id'] 1ab
308 # Need to change channels stored on alyx as well as the stored key is not the same as the latest key
309 if upload_alyx: 1ab
310 if alignment_key != self.align_keys_sorted[0]: 1ab
311 histology.register_aligned_track(self.eid, channels_mlapdv / 1e6, 1ab
312 chn_coords=chns, one=self.one,
313 overwrite=True, channels=self.channels_flag,
314 brain_atlas=self.brain_atlas)
316 ephys_traj = self.one.alyx.get(f'/trajectories?&probe_insertion={self.eid}' 1ab
317 '&provenance=Ephys aligned histology track',
318 clobber=True)
319 patch_dict = {'probe_insertion': self.eid, 'json': self.alignments} 1ab
320 self.one.alyx.rest('trajectories', 'partial_update', id=ephys_traj[0]['id'], 1ab
321 data=patch_dict)
323 files_to_register = [] 1ab
324 if upload_flatiron: 1ab
325 ftp_patcher = FTPPatcher(one=self.one)
327 alf_path = self.one.eid2path(self.insertion['session']).joinpath('alf', self.insertion["name"])
328 alf_path.mkdir(exist_ok=True, parents=True)
330 f_name = alf_path.joinpath('electrodeSites.mlapdv.npy')
331 np.save(f_name, channels_mlapdv)
332 files_to_register.append(f_name)
334 f_name = alf_path.joinpath('electrodeSites.brainLocationIds_ccf_2017.npy')
335 np.save(f_name, channels_atlas_id)
336 files_to_register.append(f_name)
338 f_name = alf_path.joinpath('electrodeSites.localCoordinates.npy')
339 np.save(f_name, chns)
340 files_to_register.append(f_name)
342 probe_collections = self.one.list_collections(self.insertion['session'], filename='channels*',
343 collection=f'alf/{self.insertion["name"]}*')
345 for collection in probe_collections:
346 chns = self.one.load_dataset(self.insertion['session'], 'channels.localCoordinates', collection=collection)
347 ephysalign = EphysAlignment(self.xyz_picks, chns[:, 1],
348 track_prev=track,
349 feature_prev=feature,
350 brain_atlas=self.brain_atlas)
351 channels_mlapdv = np.int32(ephysalign.get_channel_locations(feature, track) * 1e6)
352 channels_atlas_id = ephysalign.get_brain_locations(channels_mlapdv / 1e6)['id']
354 alf_path = self.one.eid2path(self.insertion['session']).joinpath(collection)
355 alf_path.mkdir(exist_ok=True, parents=True)
357 f_name = alf_path.joinpath('channels.mlapdv.npy')
358 np.save(f_name, channels_mlapdv)
359 files_to_register.append(f_name)
361 f_name = alf_path.joinpath('channels.brainLocationIds_ccf_2017.npy')
362 np.save(f_name, channels_atlas_id)
363 files_to_register.append(f_name)
365 self.log.info("Writing datasets to FlatIron")
366 ftp_patcher.create_dataset(path=files_to_register,
367 created_by=self.one.alyx.user)
369 return files_to_register 1ab
371 def update_experimenter_evaluation(self, prev_alignments=None, override=False):
373 if not np.any(prev_alignments) and not np.any(self.alignments):
374 aligned_traj = self.one.alyx.get(f'/trajectories?&probe_insertion={self.eid}'
375 '&provenance=Ephys aligned histology track',
376 clobber=True)
377 if len(aligned_traj) > 0:
378 self.alignments = aligned_traj[0].get('json', {})
379 else:
380 self.alignments = {}
381 return
382 else:
383 self.alignments = prev_alignments
385 outcomes = [align[2].split(':')[0] for key, align in self.alignments.items()
386 if len(align) == 3]
387 if len(outcomes) > 0:
388 vals = list(map(QC.validate, outcomes))
389 max_qc = np.argmax(vals)
390 outcome = outcomes[max_qc]
391 self.update(outcome, namespace='experimenter', override=override)
392 else:
393 self.log.warning(f'No experimenter qc found, qc field of probe insertion {self.eid} '
394 f'will not be updated')
397def get_aligned_channels(ins, chn_coords, one, ba=None, save_dir=None):
399 ba = ba or AllenAtlas(25)
400 depths = chn_coords[:, 1]
401 xyz = np.array(ins['json']['xyz_picks']) / 1e6
402 traj = one.alyx.rest('trajectories', 'list', probe_insertion=ins['id'],
403 provenance='Ephys aligned histology track')[0]
404 align_key = ins['json']['extended_qc']['alignment_stored']
405 feature = traj['json'][align_key][0]
406 track = traj['json'][align_key][1]
407 ephysalign = EphysAlignment(xyz, depths, track_prev=track,
408 feature_prev=feature,
409 brain_atlas=ba, speedy=True)
410 channels_mlapdv = np.int32(ephysalign.get_channel_locations(feature, track) * 1e6)
411 channels_atlas_ids = ephysalign.get_brain_locations(channels_mlapdv / 1e6)['id']
413 out_files = []
414 if save_dir is not None:
415 f_name = Path(save_dir).joinpath('channels.mlapdv.npy')
416 np.save(f_name, channels_mlapdv)
417 out_files.append(f_name)
419 f_name = Path(save_dir).joinpath('channels.brainLocationIds_ccf_2017.npy')
420 np.save(f_name, channels_atlas_ids)
421 out_files.append(f_name)
423 return out_files