Removed some leftover comments

Author: jacobpennington

kilosort/gui/sorter.py

@@ -60,103 +60,4 @@ def run(self):
             # Hard-coded `False` is for "save_extra_vars", which isn't an option
             # in the GUI right now (and isn't likely to be added).
 
-            # try:
-            #     logger.info(f"Kilosort version {kilosort.__version__}")
-            #     logger.info(f"Sorting {self.data_path}")
-            #     clear_cache = settings['clear_cache']
-            #     if clear_cache:
-            #         logger.info('clear_cache=True')
-            #     logger.info('-'*40)
-
-            #     tic0 = time.time()
-
-            #     if probe['chanMap'].max() >= settings['n_chan_bin']:
-            #         raise ValueError(
-            #             f'Largest value of chanMap exceeds channel count of data, '
-            #             'make sure chanMap is 0-indexed.'
-            #         )
-
-            #     if settings['nt0min'] is None:
-            #         settings['nt0min'] = int(20 * settings['nt']/61)
-            #     data_dtype = settings['data_dtype']
-            #     device = self.device
-            #     save_preprocessed_copy = settings['save_preprocessed_copy']
-            #     do_CAR = settings['do_CAR']
-            #     invert_sign = settings['invert_sign']
-            #     if not do_CAR:
-            #         logger.info("Skipping common average reference.")
-
-            #     ops = initialize_ops(settings, probe, data_dtype, do_CAR,
-            #                          invert_sign, device, save_preprocessed_copy)
-
-            #     # Pretty-print ops and probe for log
-            #     logger.debug(f"Initial ops:\n\n{ops_as_string(ops)}\n")
-            #     logger.debug(f"Probe dictionary:\n\n{probe_as_string(ops['probe'])}\n")
-
-            #     # TODO: add support for file object through data conversion
-            #     # Set preprocessing and drift correction parameters
-            #     ops = compute_preprocessing(ops, self.device, tic0=tic0,
-            #                                 file_object=self.file_object)
-            #     np.random.seed(1)
-            #     torch.cuda.manual_seed_all(1)
-            #     torch.random.manual_seed(1)
-            #     ops, bfile, st0 = compute_drift_correction(
-            #         ops, self.device, tic0=tic0, progress_bar=self.progress_bar,
-            #         file_object=self.file_object, clear_cache=clear_cache
-            #         )
-
-            #     # Check scale of data for log file
-            #     b1 = bfile.padded_batch_to_torch(0).cpu().numpy()
-            #     logger.debug(f"First batch min, max: {b1.min(), b1.max()}")
-
-            #     if save_preprocessed_copy:
-            #         save_preprocessing(results_dir / 'temp_wh.dat', ops, bfile)
-
-            #     # Will be None if nblocks = 0 (no drift correction)
-            #     if st0 is not None:
-            #         self.dshift = ops['dshift']
-            #         self.st0 = st0
-            #         self.plotDataReady.emit('drift')
-
-            #     # Sort spikes and save results
-            #     st, tF, Wall0, clu0 = detect_spikes(
-            #         ops, self.device, bfile, tic0=tic0,
-            #         progress_bar=self.progress_bar, clear_cache=clear_cache,
-            #         verbose=verbose
-            #         )
-
-            #     self.Wall0 = Wall0
-            #     self.wPCA = torch.clone(ops['wPCA'].cpu()).numpy()
-            #     self.clu0 = clu0
-            #     self.plotDataReady.emit('diagnostics')
-
-            #     clu, Wall, _ = cluster_spikes(
-            #         st, tF, ops, self.device, bfile, tic0=tic0,
-            #         progress_bar=self.progress_bar, clear_cache=clear_cache,
-            #         verbose=verbose
-            #         )
-            #     ops, similar_templates, is_ref, est_contam_rate, kept_spikes = \
-            #         save_sorting(ops, results_dir, st, clu, tF, Wall, bfile.imin, tic0)
-
-            # except Exception as e:
-            #     if isinstance(e, torch.cuda.OutOfMemoryError):
-            #         logger.exception('Out of memory error, printing performance...')
-            #         log_performance(logger, level='info')
-            #         log_cuda_details(logger)
-            #     # This makes sure the full traceback is written to log file.
-            #     logger.exception('Encountered error in `run_kilosort`:')
-            #     # Annoyingly, this will print the error message twice for console
-            #     # but I haven't found a good way around that.
-            #     raise
-
-            # finally:
-            #     close_logger()
-
-            # self.ops = ops
-            # self.st = st[kept_spikes]
-            # self.clu = clu[kept_spikes]
-            # self.tF = tF[kept_spikes]
-            # self.is_refractory = is_ref
-            # self.plotDataReady.emit('probe')
-
             self.finishedSpikesort.emit(self.context)