BUG: ZeroDivisionError in KSv4.1.1 #1002
Description
Describe the issue:
Hi! I'm having an issue similar to #724 and #963, but with the latest update of KS. Have sucessfully sorted other probes/recordings, even those with terrible stepwise drift. Happy to roll back KS versions if needed, but couldn't find a discernable cause for this - the drift map for this particular probe/recording looks acceptable to me as well. Let me know if I can provide any more information/send data anywhere.
Reproduce the bug:
Error message:
2025-10-31 06:02:11,762 kilosort.run_kilosort INFO Kilosort version 4.1.1 2025-10-31 06:02:11,762 kilosort.run_kilosort INFO Python version 3.9.21 2025-10-31 06:02:11,762 kilosort.run_kilosort INFO ---------------------------------------- 2025-10-31 06:02:11,762 kilosort.run_kilosort INFO System information: 2025-10-31 06:02:11,780 kilosort.run_kilosort INFO Windows-10-10.0.26100-SP0 AMD64 2025-10-31 06:02:11,780 kilosort.run_kilosort INFO Intel64 Family 6 Model 183 Stepping 1, GenuineIntel 2025-10-31 06:02:11,846 kilosort.run_kilosort INFO Using GPU for PyTorch computations. Specify device to change this. 2025-10-31 06:02:11,850 kilosort.run_kilosort INFO Using CUDA device: NVIDIA GeForce RTX 3060 Ti 8.00GB 2025-10-31 06:02:11,850 kilosort.run_kilosort INFO ---------------------------------------- 2025-10-31 06:02:11,850 kilosort.run_kilosort INFO Sorting [WindowsPath('E:/LocalProcessed/ecephys/catgt_CLF29_103025_keicontrastlick_record3_g0/CLF29_103025_keicontrastlick_record3_g0_imec1/CLF29_103025_keicontrastlick_record3_g0_tcat.imec1.ap.bin')] 2025-10-31 06:02:11,851 kilosort.run_kilosort INFO Skipping common average reference. 2025-10-31 06:02:11,851 kilosort.run_kilosort INFO clear_cache=True 2025-10-31 06:02:11,851 kilosort.run_kilosort DEBUG Initial ops:
ops = { 'n_chan_bin': 385, 'fs': 30000.015062761508, 'batch_size': 60000, 'nblocks': 6, 'Th_universal': 8.0, 'Th_learned': 9.0, 'tmin': 0.0, 'tmax': inf, 'nt': 61, 'shift': None, 'scale': None, 'artifact_threshold': inf, 'nskip': 25, 'whitening_range': 43, 'highpass_cutoff': 300, 'binning_depth': 5, 'sig_interp': 20.0, 'drift_smoothing': [0.5, 0.5, 0.5], 'nt0min': 20, 'dmin': None, 'dminx': 32, 'min_template_size': 10.0, 'template_sizes': 5, 'nearest_chans': 10, 'nearest_templates': 100, 'max_channel_distance': 32, 'max_peels': 100, 'templates_from_data': True, 'n_templates': 6, 'n_pcs': 6, 'Th_single_ch': 8.0, 'acg_threshold': 0.2, 'ccg_threshold': 0.25, 'cluster_neighbors': 10, 'cluster_downsampling': 1, 'max_cluster_subset': 25000, 'x_centers': None, 'duplicate_spike_ms': 0.25, 'position_limit': 100, 'filename': [ WindowsPath('E:/LocalProcessed/ecephys/catgt_CLF29_103025_keicontrastlick_record3_g0/CLF29_103025_keicontrastlick_record3_g0_imec1/CLF29_103025_keicontrastlick_record3_g0_tcat.imec1.ap.bin')], 'data_dir': WindowsPath('E:/LocalProcessed/ecephys/catgt_CLF29_103025_keicontrastlick_record3_g0/CLF29_103025_keicontrastlick_record3_g0_imec1'), 'data_dtype': 'int16', 'do_CAR': False, 'invert_sign': False, 'NTbuff': 60122, 'Nchan': 384, 'duplicate_spike_bins': 7, 'torch_device': 'cuda', 'save_preprocessed_copy': False }
2025-10-31 06:02:11,856 kilosort.run_kilosort DEBUG Probe dictionary:
probe = { 'xc': np.array([ 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 27., 59., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 277., 309., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 527., 559., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809., 777., 809.], dtype=np.float32), 'yc': np.array([ 0., 0., 15., 15., 30., 30., 45., 45., 60., 60., 75., 75., 90., 90., 105., 105., 120., 120., 135., 135., 150., 150., 165., 165., 180., 180., 195., 195., 210., 210., 225., 225., 240., 240., 255., 255., 270., 270., 285., 285., 300., 300., 315., 315., 330., 330., 345., 345., 0., 0., 15., 15., 30., 30., 45., 45., 60., 60., 75., 75., 90., 90., 105., 105., 120., 120., 135., 135., 150., 150., 165., 165., 180., 180., 195., 195., 210., 210., 225., 225., 240., 240., 255., 255., 270., 270., 285., 285., 300., 300., 315., 315., 330., 330., 345., 345., 360., 360., 375., 375., 390., 390., 405., 405., 420., 420., 435., 435., 450., 450., 465., 465., 480., 480., 495., 495., 510., 510., 525., 525., 540., 540., 555., 555., 570., 570., 585., 585., 600., 600., 615., 615., 630., 630., 645., 645., 660., 660., 675., 675., 690., 690., 705., 705., 360., 360., 375., 375., 390., 390., 405., 405., 420., 420., 435., 435., 450., 450., 465., 465., 480., 480., 495., 495., 510., 510., 525., 525., 540., 540., 555., 555., 570., 570., 585., 585., 600., 600., 615., 615., 630., 630., 645., 645., 660., 660., 675., 675., 690., 690., 705., 705., 0., 0., 15., 15., 30., 30., 45., 45., 60., 60., 75., 75., 90., 90., 105., 105., 120., 120., 135., 135., 150., 150., 165., 165., 180., 180., 195., 195., 210., 210., 225., 225., 240., 240., 255., 255., 270., 270., 285., 285., 300., 300., 315., 315., 330., 330., 345., 345., 0., 0., 15., 15., 30., 30., 45., 45., 60., 60., 75., 75., 90., 90., 105., 105., 120., 120., 135., 135., 150., 150., 165., 165., 180., 180., 195., 195., 210., 210., 225., 225., 240., 240., 255., 255., 270., 270., 285., 285., 300., 300., 315., 315., 330., 330., 345., 345., 360., 360., 375., 375., 390., 390., 405., 405., 420., 420., 435., 435., 450., 450., 465., 465., 480., 480., 495., 495., 510., 510., 525., 525., 540., 540., 555., 555., 570., 570., 585., 585., 600., 600., 615., 615., 630., 630., 645., 645., 660., 660., 675., 675., 690., 690., 705., 705., 360., 360., 375., 375., 390., 390., 405., 405., 420., 420., 435., 435., 450., 450., 465., 465., 480., 480., 495., 495., 510., 510., 525., 525., 540., 540., 555., 555., 570., 570., 585., 585., 600., 600., 615., 615., 630., 630., 645., 645., 660., 660., 675., 675., 690., 690., 705., 705.], dtype=np.float32), 'kcoords': np.array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.], dtype=np.float32), 'chanMap': np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383]), 'n_chan': 384 }
2025-10-31 06:02:12,017 kilosort.run_kilosort INFO
2025-10-31 06:02:12,017 kilosort.run_kilosort INFO Resource usage before sorting 2025-10-31 06:02:12,017 kilosort.run_kilosort INFO ******************************************************** 2025-10-31 06:02:12,017 kilosort.run_kilosort INFO CPU usage: 8.50 % 2025-10-31 06:02:12,017 kilosort.run_kilosort INFO Mem used: 51.30 % | 16.26 GB 2025-10-31 06:02:12,018 kilosort.run_kilosort INFO Mem avail: 15.43 / 31.70 GB 2025-10-31 06:02:12,018 kilosort.run_kilosort INFO ------------------------------------------------------ 2025-10-31 06:02:12,018 kilosort.run_kilosort INFO GPU usage: conda install pynvml for GPU usage 2025-10-31 06:02:12,018 kilosort.run_kilosort INFO GPU memory: 12.98 % | 1.04 / 8.00 GB 2025-10-31 06:02:12,018 kilosort.run_kilosort INFO Allocated: 0.00 % | 0.00 / 8.00 GB 2025-10-31 06:02:12,018 kilosort.run_kilosort INFO Max alloc: 0.00 % | 0.00 / 8.00 GB 2025-10-31 06:02:12,018 kilosort.run_kilosort INFO ******************************************************** 2025-10-31 06:02:12,018 kilosort.run_kilosort
OMITTED THIS SECTION FOR LENGTH REASONS
2025-10-31 08:10:46,852 kilosort.run_kilosort INFO
2025-10-31 08:10:46,852 kilosort.run_kilosort INFO Resource usage after spike detect (learned) 2025-10-31 08:10:46,852 kilosort.run_kilosort INFO ******************************************************** 2025-10-31 08:10:46,852 kilosort.run_kilosort INFO CPU usage: 0.00 % 2025-10-31 08:10:46,852 kilosort.run_kilosort INFO Mem used: 60.00 % | 19.02 GB 2025-10-31 08:10:46,852 kilosort.run_kilosort INFO Mem avail: 12.67 / 31.70 GB 2025-10-31 08:10:46,852 kilosort.run_kilosort INFO ------------------------------------------------------ 2025-10-31 08:10:46,852 kilosort.run_kilosort INFO GPU usage: conda install pynvml for GPU usage 2025-10-31 08:10:46,852 kilosort.run_kilosort INFO GPU memory: 40.21 % | 3.22 / 8.00 GB 2025-10-31 08:10:46,853 kilosort.run_kilosort INFO Allocated: 0.21 % | 0.02 / 8.00 GB 2025-10-31 08:10:46,853 kilosort.run_kilosort INFO Max alloc: 20.43 % | 1.63 / 8.00 GB 2025-10-31 08:10:46,853 kilosort.run_kilosort INFO ******************************************************** 2025-10-31 08:10:46,896 kilosort.run_kilosort INFO Generating diagnostic plots ... 2025-10-31 08:10:53,078 kilosort.run_kilosort INFO
2025-10-31 08:10:53,079 kilosort.run_kilosort INFO Final clustering 2025-10-31 08:10:53,079 kilosort.run_kilosort INFO ---------------------------------------- 2025-10-31 08:10:53,124 kilosort.clustering_qr DEBUG Center 0 | Xd shape: torch.Size([18328, 60]) | ntemp: 2 2025-10-31 08:10:55,892 kilosort.clustering_qr DEBUG Center 1 | Xd shape: torch.Size([5782, 60]) | ntemp: 2 2025-10-31 08:10:56,324 kilosort.clustering_qr DEBUG Center 2 | Xd shape: torch.Size([10923, 60]) | ntemp: 2 2025-10-31 08:10:56,884 kilosort.clustering_qr DEBUG Center 3 | Xd shape: torch.Size([16407, 60]) | ntemp: 2 2025-10-31 08:10:57,598 kilosort.clustering_qr DEBUG Center 4 | Xd shape: torch.Size([23163, 60]) | ntemp: 2 2025-10-31 08:10:58,550 kilosort.clustering_qr DEBUG Center 5 | Xd shape: torch.Size([116791, 72]) | ntemp: 4 2025-10-31 08:11:01,635 kilosort.clustering_qr DEBUG Center 6 | Xd shape: torch.Size([34301, 60]) | ntemp: 3 2025-10-31 08:11:02,879 kilosort.clustering_qr DEBUG Center 7 | Xd shape: torch.Size([36862, 60]) | ntemp: 4 2025-10-31 08:11:04,165 kilosort.clustering_qr DEBUG Center 8 | Xd shape: torch.Size([13465, 72]) | ntemp: 3 2025-10-31 08:11:04,726 kilosort.clustering_qr DEBUG
2025-10-31 08:11:04,727 kilosort.clustering_qr DEBUG Cluster center: 9 (10/96) 2025-10-31 08:11:04,727 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:11:04,727 kilosort.clustering_qr DEBUG CPU usage: 13.30 % 2025-10-31 08:11:04,727 kilosort.clustering_qr DEBUG Mem used: 58.10 % | 18.43 GB 2025-10-31 08:11:04,727 kilosort.clustering_qr DEBUG Mem avail: 13.27 / 31.70 GB 2025-10-31 08:11:04,728 kilosort.clustering_qr DEBUG ------------------------------------------------------ 2025-10-31 08:11:04,728 kilosort.clustering_qr DEBUG GPU usage: conda install pynvml for GPU usage 2025-10-31 08:11:04,728 kilosort.clustering_qr DEBUG GPU memory: 14.59 % | 1.17 / 8.00 GB 2025-10-31 08:11:04,728 kilosort.clustering_qr DEBUG Allocated: 0.13 % | 0.01 / 8.00 GB 2025-10-31 08:11:04,728 kilosort.clustering_qr DEBUG Max alloc: 4.13 % | 0.33 / 8.00 GB 2025-10-31 08:11:04,728 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:11:04,748 kilosort.clustering_qr DEBUG Center 9 | Xd shape: torch.Size([66702, 72]) | ntemp: 6 2025-10-31 08:11:06,759 kilosort.clustering_qr DEBUG Center 10 | Xd shape: torch.Size([26608, 72]) | ntemp: 5 2025-10-31 08:11:07,865 kilosort.clustering_qr DEBUG Center 11 | Xd shape: torch.Size([61875, 60]) | ntemp: 4 2025-10-31 08:11:09,853 kilosort.clustering_qr DEBUG Center 12 | Xd shape: torch.Size([99694, 72]) | ntemp: 10 2025-10-31 08:11:13,065 kilosort.clustering_qr DEBUG Center 13 | Xd shape: torch.Size([145249, 72]) | ntemp: 26 2025-10-31 08:11:17,440 kilosort.clustering_qr DEBUG Center 14 | Xd shape: torch.Size([161386, 72]) | ntemp: 24 2025-10-31 08:11:22,128 kilosort.clustering_qr DEBUG Center 15 | Xd shape: torch.Size([231595, 72]) | ntemp: 26 2025-10-31 08:11:28,615 kilosort.clustering_qr DEBUG Center 16 | Xd shape: torch.Size([365228, 72]) | ntemp: 46 2025-10-31 08:11:37,784 kilosort.clustering_qr DEBUG Center 17 | Xd shape: torch.Size([275891, 72]) | ntemp: 31 2025-10-31 08:11:45,489 kilosort.clustering_qr DEBUG Center 18 | Xd shape: torch.Size([3937, 60]) | ntemp: 3 2025-10-31 08:11:45,870 kilosort.clustering_qr DEBUG
2025-10-31 08:11:45,870 kilosort.clustering_qr DEBUG Cluster center: 19 (20/96) 2025-10-31 08:11:45,871 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:11:45,871 kilosort.clustering_qr DEBUG CPU usage: 19.60 % 2025-10-31 08:11:45,872 kilosort.clustering_qr DEBUG Mem used: 58.70 % | 18.61 GB 2025-10-31 08:11:45,872 kilosort.clustering_qr DEBUG Mem avail: 13.09 / 31.70 GB 2025-10-31 08:11:45,872 kilosort.clustering_qr DEBUG ------------------------------------------------------ 2025-10-31 08:11:45,872 kilosort.clustering_qr DEBUG GPU usage: conda install pynvml for GPU usage 2025-10-31 08:11:45,873 kilosort.clustering_qr DEBUG GPU memory: 14.59 % | 1.17 / 8.00 GB 2025-10-31 08:11:45,873 kilosort.clustering_qr DEBUG Allocated: 0.13 % | 0.01 / 8.00 GB 2025-10-31 08:11:45,873 kilosort.clustering_qr DEBUG Max alloc: 12.51 % | 1.00 / 8.00 GB 2025-10-31 08:11:45,873 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:11:45,887 kilosort.clustering_qr DEBUG Center 19 | Xd shape: torch.Size([38939, 72]) | ntemp: 4 2025-10-31 08:11:47,274 kilosort.clustering_qr DEBUG Center 20 | Xd shape: torch.Size([47096, 72]) | ntemp: 5 2025-10-31 08:11:48,811 kilosort.clustering_qr DEBUG Center 21 | Xd shape: torch.Size([2712, 72]) | ntemp: 2 2025-10-31 08:11:49,113 kilosort.clustering_qr DEBUG Center 22 | Xd shape: torch.Size([11630, 72]) | ntemp: 4 2025-10-31 08:11:49,615 kilosort.clustering_qr DEBUG Center 23 | Xd shape: torch.Size([95316, 60]) | ntemp: 4 2025-10-31 08:11:52,125 kilosort.clustering_qr DEBUG Center 24 | Xd shape: torch.Size([21626, 60]) | ntemp: 3 2025-10-31 08:11:53,033 kilosort.clustering_qr DEBUG Center 25 | Xd shape: torch.Size([22838, 60]) | ntemp: 2 2025-10-31 08:11:53,939 kilosort.clustering_qr DEBUG Center 26 | Xd shape: torch.Size([39940, 60]) | ntemp: 2 2025-10-31 08:11:55,343 kilosort.clustering_qr DEBUG Center 27 | Xd shape: torch.Size([54462, 60]) | ntemp: 2 2025-10-31 08:11:56,940 kilosort.clustering_qr DEBUG Center 28 | Xd shape: torch.Size([39769, 72]) | ntemp: 3 2025-10-31 08:11:58,252 kilosort.clustering_qr DEBUG
2025-10-31 08:11:58,253 kilosort.clustering_qr DEBUG Cluster center: 29 (30/96) 2025-10-31 08:11:58,254 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:11:58,254 kilosort.clustering_qr DEBUG CPU usage: 15.40 % 2025-10-31 08:11:58,254 kilosort.clustering_qr DEBUG Mem used: 58.30 % | 18.47 GB 2025-10-31 08:11:58,254 kilosort.clustering_qr DEBUG Mem avail: 13.23 / 31.70 GB 2025-10-31 08:11:58,255 kilosort.clustering_qr DEBUG ------------------------------------------------------ 2025-10-31 08:11:58,255 kilosort.clustering_qr DEBUG GPU usage: conda install pynvml for GPU usage 2025-10-31 08:11:58,255 kilosort.clustering_qr DEBUG GPU memory: 14.59 % | 1.17 / 8.00 GB 2025-10-31 08:11:58,256 kilosort.clustering_qr DEBUG Allocated: 0.13 % | 0.01 / 8.00 GB 2025-10-31 08:11:58,256 kilosort.clustering_qr DEBUG Max alloc: 12.51 % | 1.00 / 8.00 GB 2025-10-31 08:11:58,256 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:11:58,268 kilosort.clustering_qr DEBUG Center 29 | Xd shape: torch.Size([32248, 60]) | ntemp: 3 2025-10-31 08:11:59,484 kilosort.clustering_qr DEBUG Center 30 | Xd shape: torch.Size([36624, 72]) | ntemp: 4 2025-10-31 08:12:00,750 kilosort.clustering_qr DEBUG Center 31 | Xd shape: torch.Size([33953, 60]) | ntemp: 2 2025-10-31 08:12:01,944 kilosort.clustering_qr DEBUG Center 32 | Xd shape: torch.Size([21854, 60]) | ntemp: 2 2025-10-31 08:12:02,813 kilosort.clustering_qr DEBUG Center 33 | Xd shape: torch.Size([42658, 60]) | ntemp: 5 2025-10-31 08:12:04,300 kilosort.clustering_qr DEBUG Center 34 | Xd shape: torch.Size([124648, 72]) | ntemp: 7 2025-10-31 08:12:07,812 kilosort.clustering_qr DEBUG Center 35 | Xd shape: torch.Size([113152, 60]) | ntemp: 8 2025-10-31 08:12:11,074 kilosort.clustering_qr DEBUG Center 36 | Xd shape: torch.Size([442335, 72]) | ntemp: 25 2025-10-31 08:12:22,111 kilosort.clustering_qr DEBUG Center 37 | Xd shape: torch.Size([142715, 72]) | ntemp: 17 2025-10-31 08:12:26,361 kilosort.clustering_qr DEBUG Center 38 | Xd shape: torch.Size([254921, 72]) | ntemp: 38 2025-10-31 08:12:33,108 kilosort.clustering_qr DEBUG
2025-10-31 08:12:33,108 kilosort.clustering_qr DEBUG Cluster center: 39 (40/96) 2025-10-31 08:12:33,108 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:12:33,108 kilosort.clustering_qr DEBUG CPU usage: 18.60 % 2025-10-31 08:12:33,109 kilosort.clustering_qr DEBUG Mem used: 58.50 % | 18.55 GB 2025-10-31 08:12:33,109 kilosort.clustering_qr DEBUG Mem avail: 13.15 / 31.70 GB 2025-10-31 08:12:33,109 kilosort.clustering_qr DEBUG ------------------------------------------------------ 2025-10-31 08:12:33,109 kilosort.clustering_qr DEBUG GPU usage: conda install pynvml for GPU usage 2025-10-31 08:12:33,109 kilosort.clustering_qr DEBUG GPU memory: 14.62 % | 1.17 / 8.00 GB 2025-10-31 08:12:33,109 kilosort.clustering_qr DEBUG Allocated: 0.14 % | 0.01 / 8.00 GB 2025-10-31 08:12:33,109 kilosort.clustering_qr DEBUG Max alloc: 15.13 % | 1.21 / 8.00 GB 2025-10-31 08:12:33,109 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:12:33,174 kilosort.clustering_qr DEBUG Center 39 | Xd shape: torch.Size([414505, 72]) | ntemp: 22 2025-10-31 08:12:43,274 kilosort.clustering_qr DEBUG Center 40 | Xd shape: torch.Size([118558, 72]) | ntemp: 5 2025-10-31 08:12:46,599 kilosort.clustering_qr DEBUG Center 41 | Xd shape: torch.Size([239636, 60]) | ntemp: 7 2025-10-31 08:12:52,403 kilosort.clustering_qr DEBUG Center 42 | Xd shape: torch.Size([30240, 72]) | ntemp: 16 2025-10-31 08:12:53,480 kilosort.clustering_qr DEBUG Center 43 | Xd shape: torch.Size([599, 60]) | ntemp: 2 2025-10-31 08:12:53,492 kilosort.clustering_qr DEBUG Center 44 | Xd shape: torch.Size([3388, 72]) | ntemp: 3 2025-10-31 08:12:53,786 kilosort.clustering_qr DEBUG Center 45 | Xd shape: torch.Size([42, 60]) | ntemp: 2 2025-10-31 08:12:53,796 kilosort.clustering_qr DEBUG Center 46 | Xd shape: torch.Size([755, 72]) | ntemp: 3 2025-10-31 08:12:53,806 kilosort.clustering_qr DEBUG Center 47 | Xd shape: torch.Size([1358, 60]) | ntemp: 2 2025-10-31 08:12:54,189 kilosort.clustering_qr DEBUG Center 48 | Xd shape: torch.Size([16671, 60]) | ntemp: 2 2025-10-31 08:12:54,871 kilosort.clustering_qr DEBUG
2025-10-31 08:12:54,872 kilosort.clustering_qr DEBUG Cluster center: 49 (50/96) 2025-10-31 08:12:54,872 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:12:54,872 kilosort.clustering_qr DEBUG CPU usage: 17.70 % 2025-10-31 08:12:54,872 kilosort.clustering_qr DEBUG Mem used: 58.20 % | 18.45 GB 2025-10-31 08:12:54,872 kilosort.clustering_qr DEBUG Mem avail: 13.25 / 31.70 GB 2025-10-31 08:12:54,872 kilosort.clustering_qr DEBUG ------------------------------------------------------ 2025-10-31 08:12:54,873 kilosort.clustering_qr DEBUG GPU usage: conda install pynvml for GPU usage 2025-10-31 08:12:54,873 kilosort.clustering_qr DEBUG GPU memory: 14.59 % | 1.17 / 8.00 GB 2025-10-31 08:12:54,873 kilosort.clustering_qr DEBUG Allocated: 0.13 % | 0.01 / 8.00 GB 2025-10-31 08:12:54,873 kilosort.clustering_qr DEBUG Max alloc: 15.13 % | 1.21 / 8.00 GB 2025-10-31 08:12:54,873 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:12:54,889 kilosort.clustering_qr DEBUG Center 49 | Xd shape: torch.Size([50600, 60]) | ntemp: 4 2025-10-31 08:12:56,529 kilosort.clustering_qr DEBUG Center 50 | Xd shape: torch.Size([25293, 72]) | ntemp: 4 2025-10-31 08:12:57,521 kilosort.clustering_qr DEBUG Center 51 | Xd shape: torch.Size([42455, 72]) | ntemp: 5 2025-10-31 08:12:59,031 kilosort.clustering_qr DEBUG Center 52 | Xd shape: torch.Size([54417, 60]) | ntemp: 2 2025-10-31 08:13:00,734 kilosort.clustering_qr DEBUG Center 53 | Xd shape: torch.Size([76332, 60]) | ntemp: 2 2025-10-31 08:13:02,891 kilosort.clustering_qr DEBUG Center 54 | Xd shape: torch.Size([50138, 60]) | ntemp: 2 2025-10-31 08:13:04,452 kilosort.clustering_qr DEBUG Center 55 | Xd shape: torch.Size([103204, 60]) | ntemp: 5 2025-10-31 08:13:07,247 kilosort.clustering_qr DEBUG Center 56 | Xd shape: torch.Size([232440, 72]) | ntemp: 11 2025-10-31 08:13:13,253 kilosort.clustering_qr DEBUG Center 57 | Xd shape: torch.Size([221073, 72]) | ntemp: 10 2025-10-31 08:13:19,097 kilosort.clustering_qr DEBUG Center 58 | Xd shape: torch.Size([174669, 72]) | ntemp: 14 2025-10-31 08:13:24,079 kilosort.clustering_qr DEBUG
2025-10-31 08:13:24,080 kilosort.clustering_qr DEBUG Cluster center: 59 (60/96) 2025-10-31 08:13:24,080 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:13:24,080 kilosort.clustering_qr DEBUG CPU usage: 17.80 % 2025-10-31 08:13:24,080 kilosort.clustering_qr DEBUG Mem used: 58.40 % | 18.53 GB 2025-10-31 08:13:24,080 kilosort.clustering_qr DEBUG Mem avail: 13.17 / 31.70 GB 2025-10-31 08:13:24,080 kilosort.clustering_qr DEBUG ------------------------------------------------------ 2025-10-31 08:13:24,080 kilosort.clustering_qr DEBUG GPU usage: conda install pynvml for GPU usage 2025-10-31 08:13:24,080 kilosort.clustering_qr DEBUG GPU memory: 14.62 % | 1.17 / 8.00 GB 2025-10-31 08:13:24,080 kilosort.clustering_qr DEBUG Allocated: 0.14 % | 0.01 / 8.00 GB 2025-10-31 08:13:24,081 kilosort.clustering_qr DEBUG Max alloc: 15.13 % | 1.21 / 8.00 GB 2025-10-31 08:13:24,081 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:13:24,115 kilosort.clustering_qr DEBUG Center 59 | Xd shape: torch.Size([194265, 72]) | ntemp: 17 2025-10-31 08:13:29,701 kilosort.clustering_qr DEBUG Center 60 | Xd shape: torch.Size([203171, 72]) | ntemp: 12 2025-10-31 08:13:35,102 kilosort.clustering_qr DEBUG Center 61 | Xd shape: torch.Size([36421, 72]) | ntemp: 4 2025-10-31 08:13:36,391 kilosort.clustering_qr DEBUG Center 62 | Xd shape: torch.Size([46315, 72]) | ntemp: 3 2025-10-31 08:13:37,948 kilosort.clustering_qr DEBUG Center 63 | Xd shape: torch.Size([16787, 72]) | ntemp: 5 2025-10-31 08:13:38,722 kilosort.clustering_qr DEBUG Center 64 | Xd shape: torch.Size([312217, 72]) | ntemp: 6 2025-10-31 08:13:45,882 kilosort.clustering_qr DEBUG Center 65 | Xd shape: torch.Size([11331, 72]) | ntemp: 2 2025-10-31 08:13:46,376 kilosort.clustering_qr DEBUG Center 66 | Xd shape: torch.Size([888, 60]) | ntemp: 3 2025-10-31 08:13:46,413 kilosort.clustering_qr DEBUG Center 67 | Xd shape: torch.Size([213, 72]) | ntemp: 3 2025-10-31 08:13:46,422 kilosort.clustering_qr DEBUG Center 68 | Xd shape: torch.Size([64, 60]) | ntemp: 2 2025-10-31 08:13:46,424 kilosort.clustering_qr DEBUG
2025-10-31 08:13:46,424 kilosort.clustering_qr DEBUG Cluster center: 69 (70/96) 2025-10-31 08:13:46,424 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:13:46,424 kilosort.clustering_qr DEBUG CPU usage: 17.70 % 2025-10-31 08:13:46,424 kilosort.clustering_qr DEBUG Mem used: 58.60 % | 18.58 GB 2025-10-31 08:13:46,424 kilosort.clustering_qr DEBUG Mem avail: 13.12 / 31.70 GB 2025-10-31 08:13:46,424 kilosort.clustering_qr DEBUG ------------------------------------------------------ 2025-10-31 08:13:46,424 kilosort.clustering_qr DEBUG GPU usage: conda install pynvml for GPU usage 2025-10-31 08:13:46,425 kilosort.clustering_qr DEBUG GPU memory: 14.59 % | 1.17 / 8.00 GB 2025-10-31 08:13:46,425 kilosort.clustering_qr DEBUG Allocated: 0.13 % | 0.01 / 8.00 GB 2025-10-31 08:13:46,425 kilosort.clustering_qr DEBUG Max alloc: 15.13 % | 1.21 / 8.00 GB 2025-10-31 08:13:46,425 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:13:46,434 kilosort.clustering_qr DEBUG Center 69 | Xd shape: torch.Size([87, 60]) | ntemp: 2 2025-10-31 08:13:46,445 kilosort.clustering_qr DEBUG Center 70 | Xd shape: torch.Size([34171, 60]) | ntemp: 2 2025-10-31 08:13:47,587 kilosort.clustering_qr DEBUG Center 71 | Xd shape: torch.Size([21909, 60]) | ntemp: 3 2025-10-31 08:13:48,427 kilosort.clustering_qr DEBUG Center 72 | Xd shape: torch.Size([168550, 60]) | ntemp: 3 2025-10-31 08:13:52,790 kilosort.clustering_qr DEBUG Center 73 | Xd shape: torch.Size([159264, 72]) | ntemp: 6 2025-10-31 08:13:57,171 kilosort.clustering_qr DEBUG Center 74 | Xd shape: torch.Size([251005, 72]) | ntemp: 13 2025-10-31 08:14:03,674 kilosort.clustering_qr DEBUG Center 75 | Xd shape: torch.Size([268498, 72]) | ntemp: 9 2025-10-31 08:14:10,745 kilosort.clustering_qr DEBUG Center 76 | Xd shape: torch.Size([109291, 72]) | ntemp: 6 2025-10-31 08:14:14,089 kilosort.clustering_qr DEBUG Center 77 | Xd shape: torch.Size([446250, 72]) | ntemp: 26 2025-10-31 08:14:24,878 kilosort.clustering_qr DEBUG Center 78 | Xd shape: torch.Size([616193, 72]) | ntemp: 28 2025-10-31 08:14:39,150 kilosort.clustering_qr DEBUG
2025-10-31 08:14:39,150 kilosort.clustering_qr DEBUG Cluster center: 79 (80/96) 2025-10-31 08:14:39,151 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:14:39,151 kilosort.clustering_qr DEBUG CPU usage: 19.90 % 2025-10-31 08:14:39,152 kilosort.clustering_qr DEBUG Mem used: 59.10 % | 18.73 GB 2025-10-31 08:14:39,152 kilosort.clustering_qr DEBUG Mem avail: 12.97 / 31.70 GB 2025-10-31 08:14:39,152 kilosort.clustering_qr DEBUG ------------------------------------------------------ 2025-10-31 08:14:39,152 kilosort.clustering_qr DEBUG GPU usage: conda install pynvml for GPU usage 2025-10-31 08:14:39,152 kilosort.clustering_qr DEBUG GPU memory: 14.59 % | 1.17 / 8.00 GB 2025-10-31 08:14:39,152 kilosort.clustering_qr DEBUG Allocated: 0.16 % | 0.01 / 8.00 GB 2025-10-31 08:14:39,152 kilosort.clustering_qr DEBUG Max alloc: 21.03 % | 1.68 / 8.00 GB 2025-10-31 08:14:39,152 kilosort.clustering_qr DEBUG ******************************************************** 2025-10-31 08:14:39,214 kilosort.clustering_qr DEBUG Center 79 | Xd shape: torch.Size([395169, 72]) | ntemp: 18 2025-10-31 08:14:48,963 kilosort.clustering_qr DEBUG Center 80 | Xd shape: torch.Size([363756, 60]) | ntemp: 5 2025-10-31 08:14:57,186 kilosort.clustering_qr DEBUG Center 81 | Xd shape: torch.Size([40142, 72]) | ntemp: 3 2025-10-31 08:14:58,486 kilosort.clustering_qr DEBUG Center 82 | Xd shape: torch.Size([22580, 72]) | ntemp: 9 2025-10-31 08:14:59,339 kilosort.clustering_qr DEBUG Center 83 | Xd shape: torch.Size([348459, 72]) | ntemp: 11 2025-10-31 08:15:07,298 kilosort.clustering_qr DEBUG Center 84 | Xd shape: torch.Size([4615, 72]) | ntemp: 4 2025-10-31 08:15:07,616 kilosort.clustering_qr DEBUG Center 85 | Xd shape: torch.Size([211, 60]) | ntemp: 2 2025-10-31 08:15:07,627 kilosort.clustering_qr DEBUG Center 86 | Xd shape: torch.Size([810, 72]) | ntemp: 3 2025-10-31 08:15:07,636 kilosort.clustering_qr DEBUG Center 87 | Xd shape: torch.Size([2314, 60]) | ntemp: 2 2025-10-31 08:15:08,168 kilosort.clustering_qr ERROR Error in clustering_qr.run on center 87 Traceback (most recent call last): File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\clustering_qr.py", line 509, in run xtree, tstat = swarmsplitter.split( File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\swarmsplitter.py", line 105, in split criterion = refractoriness(meta[ix1],meta[ix2]) File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\swarmsplitter.py", line 65, in refractoriness is_refractory = check_CCG(st1, st2)[1] File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\swarmsplitter.py", line 57, in check_CCG R12, Q12, Q00 = CCG_metrics(st1, st2, K, T, nbins = nbins, tbin = tbin) File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\CCG.py", line 44, in CCG_metrics R00 = K[irange1].sum() / (len(irange1) * tbin * len(st1) * len(st2) /T) ZeroDivisionError: float division by zero 2025-10-31 08:15:08,170 kilosort.clustering_qr DEBUG Xd shape: torch.Size([2314, 60]) 2025-10-31 08:15:08,170 kilosort.clustering_qr DEBUG Nfilt: 1 2025-10-31 08:15:08,170 kilosort.clustering_qr DEBUG num spikes: 9497554 2025-10-31 08:15:08,170 kilosort.clustering_qr DEBUG iclust shape: (2314,) 2025-10-31 08:15:08,171 kilosort.run_kilosort ERROR Encountered error in run_kilosort: Traceback (most recent call last): File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\run_kilosort.py", line 308, in _sort clu, Wall, st, tF = cluster_spikes( File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\run_kilosort.py", line 884, in cluster_spikes clu, Wall = clustering_qr.run( File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\clustering_qr.py", line 509, in run xtree, tstat = swarmsplitter.split( File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\swarmsplitter.py", line 105, in split criterion = refractoriness(meta[ix1],meta[ix2]) File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\swarmsplitter.py", line 65, in refractoriness is_refractory = check_CCG(st1, st2)[1] File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\swarmsplitter.py", line 57, in check_CCG R12, Q12, Q00 = CCG_metrics(st1, st2, K, T, nbins = nbins, tbin = tbin) File "C:\Users\Chelsea\miniconda3\envs\ks4_ece\lib\site-packages\kilosort\CCG.py", line 44, in CCG_metrics R00 = K[irange1].sum() / (len(irange1) * tbin * len(st1) * len(st2) /T) ZeroDivisionError: float division by zeroVersion information:
Kilosort version 4.1.1
Python version 3.9.21