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View Code? Open in Web Editor NEWKA-Search: Rapid and exhaustive sequence identity search of known antibodies
License: BSD 3-Clause "New" or "Revised" License
KA-Search: Rapid and exhaustive sequence identity search of known antibodies
License: BSD 3-Clause "New" or "Revised" License
The PrepareOASdb class used to prepare OAS data for KA-Search creates invalide URLs to the OAS data in the id_to_study.txt file. The 'unpaired' folder occurs twice in the path.
Dear Developer,
I downloaded oasdb_small to my computer and tried to run search locally, but noticed there is a heavy wifi traffic. Is it normal? the search is done in a remote server or in a local computer? Thanks!
Hi!
I've run your example notebooks both locally and on GoogleColab, and I always get a KeyError: -1 when doing the EasySearch on the new database.
I think the error may be due to how the database is generated because I'm able to run EasySearch when using oas-aligned-tiny.
Here is the traceback error that I get on GoogleColab:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-60-5ac3149581ac>](https://localhost:8080/#) in <cell line: 5>()
3 query = 'QVQLQQSGAELARPGASVKLSCKASGYTFTSYWMQWVKQRPGQGLEWIGAIYPGDGDTRYTQKFKGKATLTADKSSSTAYMQLSSLASEDSAVYYCARGGLRRGAWFAYWGQGTLVTVS'
4
----> 5 results = EasySearch(query,
6 keep_best_n=10,
7 database_path=path_to_save_new_db,
5 frames
[/usr/local/lib/python3.10/site-packages/kasearch/easy_search.py](https://localhost:8080/#) in EasySearch(query, keep_best_n, database_path, allowed_chain, allowed_species, regions, length_matched, include_ends, local_oas_path, n_jobs)
54 targetdb.search(querydb[:1], keep_best_n=keep_best_n)
55
---> 56 return targetdb.get_meta(n_query=0, n_region=0, n_sequences='all', n_jobs=n_jobs)
[/usr/local/lib/python3.10/site-packages/kasearch/kasearch.py](https://localhost:8080/#) in get_meta(self, n_query, n_region, n_sequences, n_jobs)
147 assert n_sequences > 0
148
--> 149 metadf = self._extract_meta(self.current_best_ids[n_query, :n_sequences, n_region], n_jobs=n_jobs)
150 metadf['Identity'] = self.current_best_identities[n_query, :n_sequences, n_region]
151 return metadf
[/usr/local/lib/python3.10/site-packages/kasearch/meta_extract.py](https://localhost:8080/#) in _extract_meta(self, idxs, n_jobs)
77 n_jobs = n_groups if n_groups < n_jobs else n_jobs
78 chunksize= n_groups // n_jobs
---> 79
80 fetched_metadata = pd.concat(Parallel(n_jobs=n_jobs)(delayed(self._get_single_study_meta)(group) for group in groups))
81
[/usr/local/lib/python3.10/site-packages/joblib/parallel.py](https://localhost:8080/#) in __call__(self, iterable)
1853 output = self._get_sequential_output(iterable)
1854 next(output)
-> 1855 return output if self.return_generator else list(output)
1856
1857 # Let's create an ID that uniquely identifies the current call. If the
[/usr/local/lib/python3.10/site-packages/joblib/parallel.py](https://localhost:8080/#) in _get_sequential_output(self, iterable)
1782 self.n_dispatched_batches += 1
1783 self.n_dispatched_tasks += 1
-> 1784 res = func(*args, **kwargs)
1785 self.n_completed_tasks += 1
1786 self.print_progress()
[/usr/local/lib/python3.10/site-packages/kasearch/meta_extract.py](https://localhost:8080/#) in _get_single_study_meta(self, idxs)
44 """
45 print('hi')
---> 46 study_id, line_ids = idxs[0,0], idxs[:,1]
47 study_file = self.id_to_study[study_id]
48
KeyError: -1
Thanks!
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