Dataset Viewer
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
kind: string
code: string
species: list<item: string>
child 0, item: string
steps: int64
dt: double
ky: list<item: double>
child 0, item: double
gamma: list<item: double>
child 0, item: double
omega: list<item: double>
child 0, item: double
gamma_spread: list<item: double>
child 0, item: double
omega_spread: list<item: double>
child 0, item: double
t_converged: double
criterion: string
history: struct<time: list<item: double>, gamma: list<item: list<item: double>>, omega: list<item: list<item: (... 10 chars omitted)
child 0, time: list<item: double>
child 0, item: double
child 1, gamma: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 2, omega: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
mode_fluxes: struct<phi2_ky: list<item: double>, flux_ky: struct<ion: struct<particle: list<item: double>, heat: (... 169 chars omitted)
child 0, phi2_ky: list<item: double>
child 0, item: double
child 1, flux_ky: struct<ion: struct<particle: list<item: double>, heat: list<item: double>, momentum: list<item: doub (... 5 chars omitted)
child 0, ion: struct<particle: list<item: double>, heat: list<item: double>, momentum: list<item: double>>
child 0, particle: list<item: double>
child 0, item: double
child 1, heat: list<item: double>
child 0, item: double
child 2, momentum: list<i
...
child 0, item: double
child 2, drift: list<item: double>
child 0, item: double
child 3, frames: int64
heat_flux_ky: struct<ion: list<item: list<item: double>>, electron: list<item: list<item: double>>>
child 0, ion: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 1, electron: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
ky_spectrum: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
kx_spectrum: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
fluxes: struct<ion: struct<particle: list<item: double>, heat: list<item: double>, momentum: list<item: doub (... 109 chars omitted)
child 0, ion: struct<particle: list<item: double>, heat: list<item: double>, momentum: list<item: double>>
child 0, particle: list<item: double>
child 0, item: double
child 1, heat: list<item: double>
child 0, item: double
child 2, momentum: list<item: double>
child 0, item: double
child 1, electron: struct<particle: list<item: double>, heat: list<item: double>, momentum: list<item: double>>
child 0, particle: list<item: double>
child 0, item: double
child 1, heat: list<item: double>
child 0, item: double
child 2, momentum: list<item: double>
child 0, item: double
to
{'kind': Value('string'), 'code': Value('string'), 'species': List(Value('string')), 'steps': Value('int64'), 'dt_mean': Value('float64'), 'time': List(Value('float64')), 'fluxes': {'ion': {'particle': List(Value('float64')), 'heat': List(Value('float64')), 'momentum': List(Value('float64'))}, 'electron': {'particle': List(Value('float64')), 'heat': List(Value('float64')), 'momentum': List(Value('float64'))}}, 'ky': List(Value('float64')), 'kx': List(Value('float64')), 'ky_spectrum': List(List(Value('float64'))), 'kx_spectrum': List(List(Value('float64'))), 'growth': List(List(Value('float64'))), 'heat_flux_ky': {'ion': List(List(Value('float64'))), 'electron': List(List(Value('float64')))}, 'average': {'window': List(Value('float64')), 'fluxes': {'ion': {'particle': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}, 'heat': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}, 'momentum': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}}, 'electron': {'particle': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}, 'heat': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}, 'momentum': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}}}, 'ky_spectrum': {'mean': List(Value('float64')), 'err': List(Value('float64')), 'drift': List(Value('float64')), 'frames': Value('int64')}, 'kx_spectrum': {'mean': List(Value('float64')), 'err': List(Value('float64')), 'drift': List(Value('float64')), 'frames': Value('int64')}, 'heat_flux_ky': {'ion': {'mean': List(Value('float64')), 'err': List(Value('float64')), 'drift': List(Value('float64')), 'frames': Value('int64')}, 'electron': {'mean': List(Value('float64')), 'err': List(Value('float64')), 'drift': List(Value('float64')), 'frames': Value('int64')}}}}
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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 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
kind: string
code: string
species: list<item: string>
child 0, item: string
steps: int64
dt: double
ky: list<item: double>
child 0, item: double
gamma: list<item: double>
child 0, item: double
omega: list<item: double>
child 0, item: double
gamma_spread: list<item: double>
child 0, item: double
omega_spread: list<item: double>
child 0, item: double
t_converged: double
criterion: string
history: struct<time: list<item: double>, gamma: list<item: list<item: double>>, omega: list<item: list<item: (... 10 chars omitted)
child 0, time: list<item: double>
child 0, item: double
child 1, gamma: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 2, omega: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
mode_fluxes: struct<phi2_ky: list<item: double>, flux_ky: struct<ion: struct<particle: list<item: double>, heat: (... 169 chars omitted)
child 0, phi2_ky: list<item: double>
child 0, item: double
child 1, flux_ky: struct<ion: struct<particle: list<item: double>, heat: list<item: double>, momentum: list<item: doub (... 5 chars omitted)
child 0, ion: struct<particle: list<item: double>, heat: list<item: double>, momentum: list<item: double>>
child 0, particle: list<item: double>
child 0, item: double
child 1, heat: list<item: double>
child 0, item: double
child 2, momentum: list<i
...
child 0, item: double
child 2, drift: list<item: double>
child 0, item: double
child 3, frames: int64
heat_flux_ky: struct<ion: list<item: list<item: double>>, electron: list<item: list<item: double>>>
child 0, ion: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 1, electron: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
ky_spectrum: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
kx_spectrum: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
fluxes: struct<ion: struct<particle: list<item: double>, heat: list<item: double>, momentum: list<item: doub (... 109 chars omitted)
child 0, ion: struct<particle: list<item: double>, heat: list<item: double>, momentum: list<item: double>>
child 0, particle: list<item: double>
child 0, item: double
child 1, heat: list<item: double>
child 0, item: double
child 2, momentum: list<item: double>
child 0, item: double
child 1, electron: struct<particle: list<item: double>, heat: list<item: double>, momentum: list<item: double>>
child 0, particle: list<item: double>
child 0, item: double
child 1, heat: list<item: double>
child 0, item: double
child 2, momentum: list<item: double>
child 0, item: double
to
{'kind': Value('string'), 'code': Value('string'), 'species': List(Value('string')), 'steps': Value('int64'), 'dt_mean': Value('float64'), 'time': List(Value('float64')), 'fluxes': {'ion': {'particle': List(Value('float64')), 'heat': List(Value('float64')), 'momentum': List(Value('float64'))}, 'electron': {'particle': List(Value('float64')), 'heat': List(Value('float64')), 'momentum': List(Value('float64'))}}, 'ky': List(Value('float64')), 'kx': List(Value('float64')), 'ky_spectrum': List(List(Value('float64'))), 'kx_spectrum': List(List(Value('float64'))), 'growth': List(List(Value('float64'))), 'heat_flux_ky': {'ion': List(List(Value('float64'))), 'electron': List(List(Value('float64')))}, 'average': {'window': List(Value('float64')), 'fluxes': {'ion': {'particle': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}, 'heat': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}, 'momentum': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}}, 'electron': {'particle': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}, 'heat': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}, 'momentum': {'mean': Value('float64'), 'err': Value('float64'), 'drift': Value('float64'), 'frames': Value('int64')}}}, 'ky_spectrum': {'mean': List(Value('float64')), 'err': List(Value('float64')), 'drift': List(Value('float64')), 'frames': Value('int64')}, 'kx_spectrum': {'mean': List(Value('float64')), 'err': List(Value('float64')), 'drift': List(Value('float64')), 'frames': Value('int64')}, 'heat_flux_ky': {'ion': {'mean': List(Value('float64')), 'err': List(Value('float64')), 'drift': List(Value('float64')), 'frames': Value('int64')}, 'electron': {'mean': List(Value('float64')), 'err': List(Value('float64')), 'drift': List(Value('float64')), 'frames': Value('int64')}}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
gyaradax GKW test samples
Reference runs of the GKW flux-tube code (version 0.4-b1-1269), used by the test suite of gyaradax to check it against GKW. Every test case is a short excerpt of a longer GKW run: three pairs of consecutive distribution dumps, early, mid and late in the run, plus the files needed to rebuild the run.
All cases are electrostatic and collisionless, in circular geometry with $\varepsilon = 0.19$, on a $(n_{v_\parallel}, n_\mu, n_s, n_{k_x}, n_{k_y})$ = (32, 8, 16, 85, 32) grid. Times are in GKW units, $R/v_{th}$.
Layout
manifest.json cases, dump pairs, size and SHA-256 of every file
nonlinear/<case>/ nonlinear runs, adiabatic electrons
linear/<case>/ linear runs, adiabatic electrons
kinetic/<case>/ nonlinear runs, kinetic electrons (two species)
long_horizon/<case>/ long runs: configs and diagnostics as JSON, no dumps
Each case directory is a GKW run directory in GKW's own format:
input.dat: the GKW input, andconfig.yaml, the same run as agyaradaxconfig (gyaradax convert). Itsrun.data_diris., so run it from the case directory.- the distribution dumps (
K01,K02, ... or numbered133,134, ...), each with its.datheader holding the time and time step. Two consecutive dumps are 1.2 $R/v_{th}$ apart for adiabatic electrons and about 0.64 $R/v_{th}$ for kinetic ones. geom.datand the grid files (sgrid,krho,kxrh,intvp.dat,intmu.dat,vpgr.dat,vperp.dat,mode_label,kx_connect.dat).- the time traces of the whole run:
time.dat,fluxes.dat,kxspec,kyspec,growth.dat, and for linear runsgrowth_rates_all_modes. nonlinear.jsonorlinear.json: the diagnostics of the whole GKW run in the format oflong_horizon/(flux and spectrum traces with time averages, or the converged growth rates, frequencies and eigenmodes). Seelong_horizon/README.md.
Cases
Nonlinear, adiabatic electrons, fixed step 0.01. Pairs at $t \approx$ 1.2, 160 and 318.
| case | $q$ | $\hat s$ | $R/L_{T_i}$ | $R/L_n$ |
|---|---|---|---|---|
itg_steep_density |
7.73 | 2.14 | 11.89 | 6.16 |
itg_high_shear |
4.57 | 3.08 | 10.17 | 2.61 |
itg_flat_density |
8.65 | 1.61 | 9.14 | 0.10 |
itg_weak_drive |
8.99 | 1.67 | 7.37 | 1.59 |
itg_low_shear_strong_drive |
5.45 | 1.31 | 11.94 | 0.89 |
Linear, adiabatic electrons, fixed step 0.01. Pairs at $t \approx$ 1.2, 59 and 118. GKW normalises every connected $k_x$ chain to unit amplitude.
| case | $q$ | $\hat s$ | $R/L_{T_i}$ | $R/L_n$ |
|---|---|---|---|---|
itg_weak_high_shear |
6.30 | 1.95 | 6.36 | 0.62 |
itg_strong_low_shear |
8.06 | 0.57 | 10.91 | 0.94 |
itg_flat_density |
5.40 | 1.44 | 9.77 | 0.05 |
Nonlinear, kinetic electrons (mass ratio 2.72e-4), adaptive step from 0.004. Pairs at $t \approx$ 0.6, 85 and 170. The three cases differ only in the electron temperature gradient.
| case | $q$ | $\hat s$ | $R/L_{T_i}$ | $R/L_{T_e}$ | $R/L_n$ |
|---|---|---|---|---|---|
kinetic_low_te_gradient |
3.49 | 2.75 | 5.39 | 3.45 | 3.03 |
kinetic_mid_te_gradient |
3.49 | 2.75 | 5.39 | 6.90 | 3.03 |
kinetic_high_te_gradient |
3.49 | 2.75 | 5.39 | 13.80 | 3.03 |
Long-horizon references
long_horizon/ holds six parameter points, two adiabatic (GKW) and four kinetic (gyaradax), each with a long nonlinear run and a converged linear run. There are no distribution functions, only the configs and the diagnostics as JSON:
- flux and spectrum traces with their time averages and errors,
- the converged growth rates and frequencies, with the eigenmode along the field line.
A modified code should reproduce these statistics over long runs, not the trajectories. They are not part of the test suite. See long_horizon/README.md. 7 MB in total.
Use
From a gyaradax checkout:
pip install -e ".[test]"
python -m pytest tests/ --download-test-data # fetches the cases the tests need
hf download gerkone/gyaradax-tests --repo-type dataset --local-dir ~/.cache/gyaradax/test_data # everything
hf download gerkone/gyaradax-tests --repo-type dataset --local-dir ~/.cache/gyaradax/test_data --include "*.json" "*.yaml" "*input*.dat" # diagnostics and configs only
The files go to $GYARADAX_TEST_DATA, or ~/.cache/gyaradax/test_data when it is not set. One case is 1.1 GB (adiabatic) or 2.1 GB (kinetic), and the whole dataset 15 GB.
The dumps are read with gyaradax.io.gkw.load_gkw_k_dump(path, (nvpar, nmu, ns, nkx, nky), n_species=...).
License
MIT.
- Downloads last month
- 205