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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 46 new columns ({'trace_category', 'arias_component_snr_window_seconds', 'arias_bandpass_high_hz', 'arias_energy_percent', 'net', 'source_distance_km', 'arias90_component_bandpass_0p5_20hz', 'raw_s_index', 'trace_name', 'arias90_cutoff_method_bandpass_0p5_20hz', 'shift_samples', 'arias90_manual_absdiff_seconds_bandpass_0p5_20hz', 's_decay_diff', 's_decay', 'arias90_component_snr_bandpass_0p5_20hz', 'station_key', 's_decay_original', 'station', 's_decay_measured_original', 'arias_envelope_window_seconds', 'coda_end_sample_stead', 'tend_index_arias90_bandpass_0p5_20hz', 'station_code', 'keep_original_positions', 'arias90_e_snr_bandpass_0p5_20hz', 'tend_index_original', 'arias90_manual_close_10s_bandpass_0p5_20hz', 's_decay_measured', 'dataset_name', 'is_noise', 'trace_start_time', 's_decay_stead', 'source_magnitude', 'tend_index', 'raw_p_index', 'arias90_manual_close_5s_bandpass_0p5_20hz', 's_decay_from_s_arias90_bandpass_0p5_20hz', 'chunk', 'arias90_cutoff_arias_bandpass_0p5_20hz', 's_decay_arias90_bandpass_0p5_20hz', 'arias90_manual_diff_seconds_bandpass_0p5_20hz', 'arias90_n_snr_bandpass_0p5_20hz', 'arias90_manual_close_15s_bandpass_0p5_20hz', 'raw_tend_index', 'tend_index_measured', 'arias_bandpass_low_hz'}) and 1 missing columns ({'target_split'}).

This happened while the csv dataset builder was generating data using

hf://datasets/DancingNow/swag-processed-data/stead/train/stead_100hz_60s_train_meta.csv (at revision 7af570f888cca9e5bc0fdec334a445289bbbd8b1), ['hf://datasets/DancingNow/swag-processed-data@7af570f888cca9e5bc0fdec334a445289bbbd8b1/iquique/train/stead_100hz_60s_train_meta.csv', 'hf://datasets/DancingNow/swag-processed-data@7af570f888cca9e5bc0fdec334a445289bbbd8b1/stead/train/stead_100hz_60s_train_meta.csv', 'hf://datasets/DancingNow/swag-processed-data@7af570f888cca9e5bc0fdec334a445289bbbd8b1/stead/train/stead_100hz_60s_train_meta_uniform10k.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              sample_id: string
              dataset_name: string
              chunk: string
              trace_name: string
              trace_category: string
              station_code: int64
              station_key: string
              net: string
              station: string
              is_noise: int64
              trace_start_time: string
              p_index: int64
              s_index: int64
              s_decay: int64
              s_decay_measured: int64
              tend_index: int64
              tend_index_measured: int64
              coda_end_sample_stead: double
              s_decay_stead: double
              s_decay_diff: double
              source_distance_km: double
              source_magnitude: double
              raw_p_index: int64
              raw_s_index: int64
              raw_tend_index: int64
              shift_samples: int64
              keep_original_positions: int64
              s_decay_original: int64
              s_decay_measured_original: int64
              tend_index_original: int64
              s_decay_arias90_bandpass_0p5_20hz: int64
              s_decay_from_s_arias90_bandpass_0p5_20hz: int64
              tend_index_arias90_bandpass_0p5_20hz: int64
              arias90_component_bandpass_0p5_20hz: string
              arias90_component_snr_bandpass_0p5_20hz: double
              arias90_e_snr_bandpass_0p5_20hz: double
              arias90_n_snr_bandpass_0p5_20hz: double
              arias90_cutoff_arias_bandpass_0p5_20hz: double
              arias90_cutoff_method_bandpass_0p5_20hz: string
              arias_energy_percent: double
              arias_envelope_window_seconds: double
              arias_component_snr_window_seconds: double
              arias_bandpass_low_hz: double
              arias_bandpass_high_hz: double
              arias90_manual_diff_seconds_bandpass_0p5_20hz: double
              arias90_manual_absdiff_seconds_bandpass_0p5_20hz: double
              arias90_manual_close_5s_bandpass_0p5_20hz: int64
              arias90_manual_close_10s_bandpass_0p5_20hz: int64
              arias90_manual_close_15s_bandpass_0p5_20hz: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 7233
              to
              {'sample_id': Value('string'), 'p_index': Value('int64'), 's_index': Value('int64'), 'target_split': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 46 new columns ({'trace_category', 'arias_component_snr_window_seconds', 'arias_bandpass_high_hz', 'arias_energy_percent', 'net', 'source_distance_km', 'arias90_component_bandpass_0p5_20hz', 'raw_s_index', 'trace_name', 'arias90_cutoff_method_bandpass_0p5_20hz', 'shift_samples', 'arias90_manual_absdiff_seconds_bandpass_0p5_20hz', 's_decay_diff', 's_decay', 'arias90_component_snr_bandpass_0p5_20hz', 'station_key', 's_decay_original', 'station', 's_decay_measured_original', 'arias_envelope_window_seconds', 'coda_end_sample_stead', 'tend_index_arias90_bandpass_0p5_20hz', 'station_code', 'keep_original_positions', 'arias90_e_snr_bandpass_0p5_20hz', 'tend_index_original', 'arias90_manual_close_10s_bandpass_0p5_20hz', 's_decay_measured', 'dataset_name', 'is_noise', 'trace_start_time', 's_decay_stead', 'source_magnitude', 'tend_index', 'raw_p_index', 'arias90_manual_close_5s_bandpass_0p5_20hz', 's_decay_from_s_arias90_bandpass_0p5_20hz', 'chunk', 'arias90_cutoff_arias_bandpass_0p5_20hz', 's_decay_arias90_bandpass_0p5_20hz', 'arias90_manual_diff_seconds_bandpass_0p5_20hz', 'arias90_n_snr_bandpass_0p5_20hz', 'arias90_manual_close_15s_bandpass_0p5_20hz', 'raw_tend_index', 'tend_index_measured', 'arias_bandpass_low_hz'}) and 1 missing columns ({'target_split'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/DancingNow/swag-processed-data/stead/train/stead_100hz_60s_train_meta.csv (at revision 7af570f888cca9e5bc0fdec334a445289bbbd8b1), ['hf://datasets/DancingNow/swag-processed-data@7af570f888cca9e5bc0fdec334a445289bbbd8b1/iquique/train/stead_100hz_60s_train_meta.csv', 'hf://datasets/DancingNow/swag-processed-data@7af570f888cca9e5bc0fdec334a445289bbbd8b1/stead/train/stead_100hz_60s_train_meta.csv', 'hf://datasets/DancingNow/swag-processed-data@7af570f888cca9e5bc0fdec334a445289bbbd8b1/stead/train/stead_100hz_60s_train_meta_uniform10k.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

sample_id
string
p_index
int64
s_index
int64
target_split
string
bucket6$654,:3,:14361
780
1,140
train
bucket5$90,:3,:14362
766
1,127
train
bucket10$680,:3,:14427
914
1,340
train
bucket5$422,:3,:14447
641
1,087
train
bucket1$27,:3,:14451
747
1,197
train
bucket1$372,:3,:14452
664
1,115
train
bucket8$455,:3,:14453
870
1,322
train
bucket6$725,:3,:14454
563
1,016
train
bucket5$765,:3,:14462
845
1,306
train
bucket3$950,:3,:14470
503
972
train
bucket5$120,:3,:14489
534
1,022
train
bucket10$161,:3,:14500
947
1,446
train
bucket8$427,:3,:14501
821
1,322
train
bucket6$467,:3,:14505
656
1,160
train
bucket9$948,:3,:14507
568
1,074
train
bucket4$895,:3,:14513
988
1,500
train
bucket10$967,:3,:14516
607
1,122
train
bucket5$624,:3,:14517
741
1,258
train
bucket8$789,:3,:14520
549
1,068
train
bucket6$628,:3,:14520
508
1,028
train
bucket12$473,:3,:14523
786
1,309
train
bucket10$268,:3,:14531
925
1,455
train
bucket7$352,:3,:14533
524
1,056
train
bucket4$515,:3,:14536
584
1,119
train
bucket5$61,:3,:14541
611
1,151
train
bucket9$454,:3,:14547
560
1,106
train
bucket9$178,:3,:14552
560
1,111
train
bucket12$762,:3,:14554
667
1,220
train
bucket7$188,:3,:14556
987
1,542
train
bucket1$637,:3,:14556
537
1,092
train
bucket5$568,:3,:14557
821
1,377
train
bucket8$357,:3,:14559
515
1,073
train
bucket10$290,:3,:14572
827
1,398
train
bucket9$253,:3,:14573
622
1,194
train
bucket6$399,:3,:14575
869
1,443
train
bucket0$114,:3,:14575
936
1,510
train
bucket5$763,:3,:14578
833
1,410
train
bucket8$317,:3,:14578
973
1,550
train
bucket7$824,:3,:14580
865
1,444
train
bucket7$22,:3,:14581
549
1,129
train
bucket9$965,:3,:14582
908
1,489
train
bucket3$28,:3,:14583
530
1,112
train
bucket9$104,:3,:14584
931
1,514
train
bucket8$665,:3,:14585
879
1,463
train
bucket4$783,:3,:14585
604
1,188
train
bucket6$684,:3,:14585
838
1,422
train
bucket3$525,:3,:14586
627
1,212
train
bucket7$535,:3,:14587
752
1,338
train
bucket9$451,:3,:14587
656
1,242
train
bucket9$155,:3,:14587
708
1,294
train
bucket2$750,:3,:14588
772
1,359
train
bucket8$677,:3,:14589
727
1,315
train
bucket7$310,:3,:14589
608
1,196
train
bucket3$569,:3,:14589
559
1,148
train
bucket7$101,:3,:14589
567
1,156
train
bucket2$248,:3,:14591
523
1,113
train
bucket7$276,:3,:14591
940
1,530
train
bucket6$314,:3,:14592
749
1,341
train
bucket7$763,:3,:14592
983
1,575
train
bucket3$646,:3,:14594
814
1,407
train
bucket1$640,:3,:14595
903
1,497
train
bucket10$159,:3,:14597
722
1,318
train
bucket8$123,:3,:14597
920
1,516
train
bucket1$417,:3,:14597
874
1,470
train
bucket2$730,:3,:14598
530
1,127
train
bucket5$178,:3,:14598
971
1,568
train
bucket6$492,:3,:14600
995
1,594
train
bucket6$490,:3,:14601
906
1,506
train
bucket7$977,:3,:14601
595
1,195
train
bucket5$612,:3,:14602
648
1,249
train
bucket7$503,:3,:14603
650
1,252
train
bucket10$45,:3,:14605
766
1,370
train
bucket1$218,:3,:14607
901
1,507
train
bucket7$43,:3,:14608
783
1,390
train
bucket3$864,:3,:14611
701
1,311
train
bucket4$868,:3,:14612
764
1,375
train
bucket8$125,:3,:14613
749
1,361
train
bucket12$823,:3,:14614
732
1,345
train
bucket12$490,:3,:14614
937
1,550
train
bucket8$643,:3,:14615
918
1,532
train
bucket10$965,:3,:14616
725
1,340
train
bucket7$889,:3,:14616
817
1,432
train
bucket9$733,:3,:14617
599
1,215
train
bucket2$567,:3,:14617
995
1,611
train
bucket9$684,:3,:14617
539
1,156
train
bucket7$712,:3,:14618
646
1,263
train
bucket10$677,:3,:14617
745
1,362
train
bucket5$394,:3,:14617
783
1,400
train
bucket12$837,:3,:14619
531
1,149
train
bucket1$885,:3,:14620
844
1,464
train
bucket4$893,:3,:14622
708
1,329
train
bucket2$353,:3,:14623
966
1,588
train
bucket9$224,:3,:14623
996
1,619
train
bucket7$407,:3,:14624
534
1,157
train
bucket9$866,:3,:14626
755
1,380
train
bucket3$481,:3,:14626
645
1,270
train
bucket7$932,:3,:14628
626
1,253
train
bucket2$547,:3,:14630
837
1,466
train
bucket6$91,:3,:14630
951
1,580
train
bucket7$286,:3,:14632
626
1,257
train
End of preview.

SWaG processed waveform data

This dataset contains processed waveform files used by the SWaG diffusion training workflow. It does not contain the original raw STEAD or Iquique archives.

File format

  • HDF5 data: float32, shape [N, 6000, 3], component order ENZ
  • HDF5 labels: float32, shape [N, 2], [P arrival sample, S arrival sample]
  • Sampling rate: 100 Hz, 60 seconds per window
  • Each waveform is standardized per window to zero mean and unit standard deviation
  • No bandpass filter is applied

The STEAD processed split contains valid local-earthquake records only; noise records are excluded. The Iquique split contains the P/S-labeled processed windows used for transfer-learning experiments. Metadata CSV files and dataset manifests are included beside the HDF5 files.

Please review the original STEAD and Iquique dataset licenses and citation requirements before making this repository public.

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