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Datasets:
ydai17
/
TIDE-ou_relam90_256_fp64

Tags:
turbulence
computational-fluid-dynamics
direct-numerical-simulation
navier-stokes
scientific-machine-learning
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
mode: string
stats: struct<velocity: struct<absmax: double, mean: double, std: double, vmin: double, vmax: double, n: in (... 104 chars omitted)
  child 0, velocity: struct<absmax: double, mean: double, std: double, vmin: double, vmax: double, n: int64>
      child 0, absmax: double
      child 1, mean: double
      child 2, std: double
      child 3, vmin: double
      child 4, vmax: double
      child 5, n: int64
  child 1, pressure: struct<absmax: double, mean: double, std: double, vmin: double, vmax: double, n: int64>
      child 0, absmax: double
      child 1, mean: double
      child 2, std: double
      child 3, vmin: double
      child 4, vmax: double
      child 5, n: int64
n_frames: int64
note: string
per_seed_cross_range: list<item: double>
  child 0, item: double
seed_scan: list<item: struct<n_seed: int64, n_frames: int64, cross_pct: double, comp_pct: double, C_fit: double (... 2 chars omitted)
  child 0, item: struct<n_seed: int64, n_frames: int64, cross_pct: double, comp_pct: double, C_fit: double>
      child 0, n_seed: int64
      child 1, n_frames: int64
      child 2, cross_pct: double
      child 3, comp_pct: double
      child 4, C_fit: double
verdict: string
pooled_A10_cross_pct: double
n_seeds: int64
per_seed_cross_mean_pct: double
case: string
per_seed_cross_std_pct: double
n_seeds_individually_over_2pct: int64
pooled_A10_comp_pct: double
source: string
thresholds: struct<cross: double, comp: double>
  child 0, cross: double
  child 1, comp: double
to
{'case': Value('string'), 'source': Value('string'), 'n_seeds': Value('int64'), 'n_frames': Value('int64'), 'seed_scan': List({'n_seed': Value('int64'), 'n_frames': Value('int64'), 'cross_pct': Value('float64'), 'comp_pct': Value('float64'), 'C_fit': Value('float64')}), 'pooled_A10_cross_pct': Value('float64'), 'pooled_A10_comp_pct': Value('float64'), 'per_seed_cross_mean_pct': Value('float64'), 'per_seed_cross_std_pct': Value('float64'), 'per_seed_cross_range': List(Value('float64')), 'n_seeds_individually_over_2pct': Value('int64'), 'thresholds': {'cross': Value('float64'), 'comp': Value('float64')}, 'verdict': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              mode: string
              stats: struct<velocity: struct<absmax: double, mean: double, std: double, vmin: double, vmax: double, n: in (... 104 chars omitted)
                child 0, velocity: struct<absmax: double, mean: double, std: double, vmin: double, vmax: double, n: int64>
                    child 0, absmax: double
                    child 1, mean: double
                    child 2, std: double
                    child 3, vmin: double
                    child 4, vmax: double
                    child 5, n: int64
                child 1, pressure: struct<absmax: double, mean: double, std: double, vmin: double, vmax: double, n: int64>
                    child 0, absmax: double
                    child 1, mean: double
                    child 2, std: double
                    child 3, vmin: double
                    child 4, vmax: double
                    child 5, n: int64
              n_frames: int64
              note: string
              per_seed_cross_range: list<item: double>
                child 0, item: double
              seed_scan: list<item: struct<n_seed: int64, n_frames: int64, cross_pct: double, comp_pct: double, C_fit: double (... 2 chars omitted)
                child 0, item: struct<n_seed: int64, n_frames: int64, cross_pct: double, comp_pct: double, C_fit: double>
                    child 0, n_seed: int64
                    child 1, n_frames: int64
                    child 2, cross_pct: double
                    child 3, comp_pct: double
                    child 4, C_fit: double
              verdict: string
              pooled_A10_cross_pct: double
              n_seeds: int64
              per_seed_cross_mean_pct: double
              case: string
              per_seed_cross_std_pct: double
              n_seeds_individually_over_2pct: int64
              pooled_A10_comp_pct: double
              source: string
              thresholds: struct<cross: double, comp: double>
                child 0, cross: double
                child 1, comp: double
              to
              {'case': Value('string'), 'source': Value('string'), 'n_seeds': Value('int64'), 'n_frames': Value('int64'), 'seed_scan': List({'n_seed': Value('int64'), 'n_frames': Value('int64'), 'cross_pct': Value('float64'), 'comp_pct': Value('float64'), 'C_fit': Value('float64')}), 'pooled_A10_cross_pct': Value('float64'), 'pooled_A10_comp_pct': Value('float64'), 'per_seed_cross_mean_pct': Value('float64'), 'per_seed_cross_std_pct': Value('float64'), 'per_seed_cross_range': List(Value('float64')), 'n_seeds_individually_over_2pct': Value('int64'), 'thresholds': {'cross': Value('float64'), 'comp': Value('float64')}, 'verdict': Value('string')}
              because column names don't match

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TIDE — ou_relam90_256_fp64

One configuration of TIDE (Turbulent Incompressible DNS Ensembles): a 256^3 fp64 direct numerical simulation of the incompressible Navier-Stokes equations, shipped as independent realizations and released only after passing a fixed acceptance standard of statistical gates and equation-level residual checks.

huggingface-cli download ydai17/TIDE-ou_relam90_256_fp64 --repo-type dataset --local-dir ./tide-data/corpus

import zarr
z = zarr.open("./tide-data/corpus/ou_relam90_256_fp64.zarr", mode="r")
u = z["u"][0]   # (3, 256, 256, 256) velocity of the first frame

Each store holds velocity [N,3,256^3], pressure [N,256^3], the optional scalar/buoyancy channel, and per-frame t, k_max_eta, seed. Fields are computed in fp64 and stored in fp32; frames are exported every 0.05 T_L. Data under CC-BY-4.0.
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