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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 3 new columns ({'decoded_frame', 'media_time_s', 'source_frame'}) and 1 missing columns ({'frame'}).
This happened while the csv dataset builder was generating data using
hf://datasets/winvswon78/G1_test/2026-10-27-19-19-03/raw/g1/raw/ego_view_video_index.csv (at revision fc703ec1b5caf82a69a64be4c966cb6d99ad29cb), ['hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/g1/raw/ego_view_frames.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/g1/raw/ego_view_video_index.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/g1/raw/events.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/optitrack/Take 2026-10-27 07.15.20 PM.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/motive/motive_frames.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/302899467_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/303008978_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/304203361_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/304866636_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/306087165_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/306714077_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/306854655_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/308314642_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/qr_reads.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/timeline.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_ego.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_g1.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_motive.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_optitrack_export.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_pose.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_zed.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_302899467.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_303008978.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_304203361.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_304866636.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_306087165.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_306714077.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_306854655.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_308314642.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 1856, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, 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 822, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
source_frame: int64
decoded_frame: int64
media_time_s: double
t_recv: double
t_mono: double
t_camera: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 993
to
{'frame': Value('int64'), 't_recv': Value('float64'), 't_mono': Value('float64'), 't_camera': Value('float64')}
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 1379, 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 956, 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 1702, 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 1858, 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 3 new columns ({'decoded_frame', 'media_time_s', 'source_frame'}) and 1 missing columns ({'frame'}).
This happened while the csv dataset builder was generating data using
hf://datasets/winvswon78/G1_test/2026-10-27-19-19-03/raw/g1/raw/ego_view_video_index.csv (at revision fc703ec1b5caf82a69a64be4c966cb6d99ad29cb), ['hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/g1/raw/ego_view_frames.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/g1/raw/ego_view_video_index.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/g1/raw/events.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/optitrack/Take 2026-10-27 07.15.20 PM.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/motive/motive_frames.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/302899467_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/303008978_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/304203361_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/304866636_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/306087165_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/306714077_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/306854655_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/raw/room_zed_cams/zed/308314642_imu.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/qr_reads.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/timeline.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_ego.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_g1.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_motive.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_optitrack_export.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_pose.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/sync/times_zed.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_302899467.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_303008978.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_304203361.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_304866636.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_306087165.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_306714077.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_306854655.csv', 'hf://datasets/winvswon78/G1_test@fc703ec1b5caf82a69a64be4c966cb6d99ad29cb/2026-10-27-19-19-03/videos/zed_308314642.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.
frame int64 | t_recv float64 | t_mono float64 | t_camera float64 |
|---|---|---|---|
0 | 1,793,099,944.119771 | 542,745.235184 | 1,793,099,943.929669 |
1 | 1,793,099,944.153017 | 542,745.268431 | 1,793,099,943.960213 |
2 | 1,793,099,944.186316 | 542,745.301729 | 1,793,099,943.996177 |
3 | 1,793,099,944.220103 | 542,745.335516 | 1,793,099,944.026876 |
4 | 1,793,099,944.253459 | 542,745.368872 | 1,793,099,944.06017 |
5 | 1,793,099,944.286751 | 542,745.402164 | 1,793,099,944.096747 |
6 | 1,793,099,944.319926 | 542,745.435338 | 1,793,099,944.129279 |
7 | 1,793,099,944.353348 | 542,745.468762 | 1,793,099,944.16015 |
8 | 1,793,099,944.386686 | 542,745.502099 | 1,793,099,944.196497 |
9 | 1,793,099,944.419945 | 542,745.535358 | 1,793,099,944.227095 |
10 | 1,793,099,944.453145 | 542,745.568558 | 1,793,099,944.260418 |
11 | 1,793,099,944.486156 | 542,745.601569 | 1,793,099,944.293566 |
12 | 1,793,099,944.519887 | 542,745.6353 | 1,793,099,944.326855 |
13 | 1,793,099,944.553233 | 542,745.668646 | 1,793,099,944.362179 |
14 | 1,793,099,944.586252 | 542,745.701666 | 1,793,099,944.396224 |
15 | 1,793,099,944.620095 | 542,745.735508 | 1,793,099,944.426853 |
16 | 1,793,099,944.65336 | 542,745.768773 | 1,793,099,944.460177 |
17 | 1,793,099,944.68667 | 542,745.802083 | 1,793,099,944.494757 |
18 | 1,793,099,944.719993 | 542,745.835406 | 1,793,099,944.529317 |
19 | 1,793,099,944.753608 | 542,745.869021 | 1,793,099,944.561315 |
20 | 1,793,099,944.786724 | 542,745.902137 | 1,793,099,944.596657 |
21 | 1,793,099,944.819986 | 542,745.935399 | 1,793,099,944.627153 |
22 | 1,793,099,944.853622 | 542,745.969035 | 1,793,099,944.661339 |
23 | 1,793,099,944.881726 | 542,745.997139 | 1,793,099,944.693839 |
24 | 1,793,099,944.917367 | 542,746.032781 | 1,793,099,944.726037 |
25 | 1,793,099,944.953071 | 542,746.068484 | 1,793,099,944.759442 |
26 | 1,793,099,944.98641 | 542,746.101824 | 1,793,099,944.796019 |
27 | 1,793,099,945.019577 | 542,746.13499 | 1,793,099,944.829164 |
28 | 1,793,099,945.052678 | 542,746.168092 | 1,793,099,944.860247 |
29 | 1,793,099,945.08597 | 542,746.201383 | 1,793,099,944.896042 |
30 | 1,793,099,945.119584 | 542,746.234997 | 1,793,099,944.926885 |
31 | 1,793,099,945.153049 | 542,746.268463 | 1,793,099,944.960684 |
32 | 1,793,099,945.186277 | 542,746.30169 | 1,793,099,944.993532 |
33 | 1,793,099,945.219602 | 542,746.335015 | 1,793,099,945.029394 |
34 | 1,793,099,945.253203 | 542,746.368616 | 1,793,099,945.062639 |
35 | 1,793,099,945.286436 | 542,746.401849 | 1,793,099,945.095895 |
36 | 1,793,099,945.319421 | 542,746.434834 | 1,793,099,945.126866 |
37 | 1,793,099,945.352901 | 542,746.468314 | 1,793,099,945.160987 |
38 | 1,793,099,945.386194 | 542,746.501607 | 1,793,099,945.194062 |
39 | 1,793,099,945.419428 | 542,746.534841 | 1,793,099,945.227368 |
40 | 1,793,099,945.452671 | 542,746.568085 | 1,793,099,945.261099 |
41 | 1,793,099,945.486187 | 542,746.6016 | 1,793,099,945.294401 |
42 | 1,793,099,945.519467 | 542,746.63488 | 1,793,099,945.32739 |
43 | 1,793,099,945.552811 | 542,746.668224 | 1,793,099,945.361059 |
44 | 1,793,099,945.586125 | 542,746.701538 | 1,793,099,945.393996 |
45 | 1,793,099,945.619689 | 542,746.735101 | 1,793,099,945.427498 |
46 | 1,793,099,945.653058 | 542,746.768471 | 1,793,099,945.462717 |
47 | 1,793,099,945.686243 | 542,746.801656 | 1,793,099,945.496024 |
48 | 1,793,099,945.71929 | 542,746.834703 | 1,793,099,945.526954 |
49 | 1,793,099,945.752631 | 542,746.868044 | 1,793,099,945.560669 |
50 | 1,793,099,945.780377 | 542,746.895791 | 1,793,099,945.595933 |
51 | 1,793,099,945.814102 | 542,746.929515 | 1,793,099,945.626188 |
52 | 1,793,099,945.847682 | 542,746.963096 | 1,793,099,945.659266 |
53 | 1,793,099,945.888043 | 542,747.003457 | 1,793,099,945.692843 |
54 | 1,793,099,945.920214 | 542,747.035627 | 1,793,099,945.726071 |
55 | 1,793,099,945.953598 | 542,747.069012 | 1,793,099,945.760758 |
56 | 1,793,099,945.986823 | 542,747.102236 | 1,793,099,945.79486 |
57 | 1,793,099,946.020005 | 542,747.135418 | 1,793,099,945.827187 |
58 | 1,793,099,946.053513 | 542,747.168926 | 1,793,099,945.861444 |
59 | 1,793,099,946.086685 | 542,747.202098 | 1,793,099,945.896594 |
60 | 1,793,099,946.120138 | 542,747.235551 | 1,793,099,945.929647 |
61 | 1,793,099,946.153593 | 542,747.269006 | 1,793,099,945.961365 |
62 | 1,793,099,946.186793 | 542,747.302206 | 1,793,099,945.994758 |
63 | 1,793,099,946.220088 | 542,747.335501 | 1,793,099,946.027154 |
64 | 1,793,099,946.253564 | 542,747.368978 | 1,793,099,946.060417 |
65 | 1,793,099,946.286755 | 542,747.402169 | 1,793,099,946.094787 |
66 | 1,793,099,946.320076 | 542,747.435489 | 1,793,099,946.12706 |
67 | 1,793,099,946.353531 | 542,747.468945 | 1,793,099,946.160206 |
68 | 1,793,099,946.386768 | 542,747.502181 | 1,793,099,946.194786 |
69 | 1,793,099,946.420032 | 542,747.535445 | 1,793,099,946.228149 |
70 | 1,793,099,946.453487 | 542,747.5689 | 1,793,099,946.260415 |
71 | 1,793,099,946.486717 | 542,747.60213 | 1,793,099,946.294775 |
72 | 1,793,099,946.520127 | 542,747.63554 | 1,793,099,946.328085 |
73 | 1,793,099,946.553537 | 542,747.668951 | 1,793,099,946.361315 |
74 | 1,793,099,946.586834 | 542,747.702247 | 1,793,099,946.393538 |
75 | 1,793,099,946.620213 | 542,747.735626 | 1,793,099,946.428054 |
76 | 1,793,099,946.65352 | 542,747.768934 | 1,793,099,946.461336 |
77 | 1,793,099,946.686787 | 542,747.8022 | 1,793,099,946.494764 |
78 | 1,793,099,946.720099 | 542,747.835512 | 1,793,099,946.52804 |
79 | 1,793,099,946.753545 | 542,747.868958 | 1,793,099,946.560432 |
80 | 1,793,099,946.786692 | 542,747.902105 | 1,793,099,946.596613 |
81 | 1,793,099,946.820075 | 542,747.935487 | 1,793,099,946.626837 |
82 | 1,793,099,946.853539 | 542,747.968953 | 1,793,099,946.660426 |
83 | 1,793,099,946.880878 | 542,747.996291 | 1,793,099,946.696748 |
84 | 1,793,099,946.91753 | 542,748.032943 | 1,793,099,946.726059 |
85 | 1,793,099,946.953074 | 542,748.068487 | 1,793,099,946.76006 |
86 | 1,793,099,946.986285 | 542,748.101698 | 1,793,099,946.793504 |
87 | 1,793,099,947.019439 | 542,748.134852 | 1,793,099,946.826856 |
88 | 1,793,099,947.053022 | 542,748.168436 | 1,793,099,946.860987 |
89 | 1,793,099,947.08629 | 542,748.201704 | 1,793,099,946.894106 |
90 | 1,793,099,947.119921 | 542,748.235334 | 1,793,099,946.927678 |
91 | 1,793,099,947.153103 | 542,748.268516 | 1,793,099,946.960986 |
92 | 1,793,099,947.186125 | 542,748.301538 | 1,793,099,946.994399 |
93 | 1,793,099,947.219386 | 542,748.334799 | 1,793,099,947.027736 |
94 | 1,793,099,947.252959 | 542,748.368373 | 1,793,099,947.060177 |
95 | 1,793,099,947.286195 | 542,748.401608 | 1,793,099,947.09597 |
96 | 1,793,099,947.319486 | 542,748.434899 | 1,793,099,947.129154 |
97 | 1,793,099,947.352713 | 542,748.468127 | 1,793,099,947.162496 |
98 | 1,793,099,947.386137 | 542,748.501551 | 1,793,099,947.195627 |
99 | 1,793,099,947.419316 | 542,748.534729 | 1,793,099,947.226853 |
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