Datasets:

mjablonska
/

harps-interp-t02-grid

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
attributes: struct<title: string, generator: string, created_utc: timestamp[s], synthesis_timestamp: timestamp[s (... 860 chars omitted)
  child 0, title: string
  child 1, generator: string
  child 2, created_utc: timestamp[s]
  child 3, synthesis_timestamp: timestamp[s]
  child 4, flux_definition: string
  child 5, wavelength_unit: string
  child 6, flux_unit: string
  child 7, physics_hash: string
  child 8, git_commit: string
  child 9, git_sha: string
  child 10, contact: string
  child 11, schema_version: string
  child 12, n_models: int64
  child 13, n_lambda: int64
  child 14, n_params: int64
  child 15, continuum_present: bool
  child 16, runs_merged: int64
  child 17, source_runs: list<item: string>
      child 0, item: string
  child 18, source_physics_hashes: list<item: string>
      child 0, item: string
  child 19, source_config_hashes: list<item: string>
      child 0, item: string
  child 20, source_input_hashes: list<item: string>
      child 0, item: string
  child 21, run_rows: list<item: int64>
      child 0, item: int64
  child 22, run_rows_kept: list<item: int64>
      child 0, item: int64
  child 23, param_names: list<item: string>
      child 0, item: string
  child 24, strict_params: bool
  child 25, dedup_by_model_id: bool
  child 26, total_input_rows: int64
  child 27, output_mode_values: list<item: string>
      child 0, item: string
  child 28, snap_filter_applied: bool
  child 29, config_hash: string
  child 30, grid_definition_hash: string
  chi
...
: int64, checksum: bool>>
                  child 0, name: string
                  child 1, configuration: struct<level: int64, checksum: bool>
                      child 0, level: int64
                      child 1, checksum: bool
          child 6, attributes: struct<>
          child 7, zarr_format: int64
          child 8, node_type: string
          child 9, storage_transformers: list<item: null>
              child 0, item: null
node_type: string
storage_transformers: list<item: null>
  child 0, item: null
fill_value: string
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
  child 0, name: string
  child 1, configuration: struct<separator: string>
      child 0, separator: string
codecs: list<item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>> (... 1 chars omitted)
  child 0, item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>>
      child 0, name: string
      child 1, configuration: struct<endian: string, level: int64, checksum: bool>
          child 0, endian: string
          child 1, level: int64
          child 2, checksum: bool
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
  child 0, name: string
  child 1, configuration: struct<chunk_shape: list<item: int64>>
      child 0, chunk_shape: list<item: int64>
          child 0, item: int64
shape: list<item: int64>
  child 0, item: int64
data_type: string
to
{'shape': List(Value('int64')), 'data_type': Value('string'), 'chunk_grid': {'name': Value('string'), 'configuration': {'chunk_shape': List(Value('int64'))}}, 'chunk_key_encoding': {'name': Value('string'), 'configuration': {'separator': Value('string')}}, 'fill_value': Value('string'), 'codecs': List({'name': Value('string'), 'configuration': {'endian': Value('string'), 'level': Value('int64'), 'checksum': Value('bool')}}), 'attributes': {}, 'zarr_format': Value('int64'), 'node_type': Value('string'), 'storage_transformers': List(Value('null'))}
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
              attributes: struct<title: string, generator: string, created_utc: timestamp[s], synthesis_timestamp: timestamp[s (... 860 chars omitted)
                child 0, title: string
                child 1, generator: string
                child 2, created_utc: timestamp[s]
                child 3, synthesis_timestamp: timestamp[s]
                child 4, flux_definition: string
                child 5, wavelength_unit: string
                child 6, flux_unit: string
                child 7, physics_hash: string
                child 8, git_commit: string
                child 9, git_sha: string
                child 10, contact: string
                child 11, schema_version: string
                child 12, n_models: int64
                child 13, n_lambda: int64
                child 14, n_params: int64
                child 15, continuum_present: bool
                child 16, runs_merged: int64
                child 17, source_runs: list<item: string>
                    child 0, item: string
                child 18, source_physics_hashes: list<item: string>
                    child 0, item: string
                child 19, source_config_hashes: list<item: string>
                    child 0, item: string
                child 20, source_input_hashes: list<item: string>
                    child 0, item: string
                child 21, run_rows: list<item: int64>
                    child 0, item: int64
                child 22, run_rows_kept: list<item: int64>
                    child 0, item: int64
                child 23, param_names: list<item: string>
                    child 0, item: string
                child 24, strict_params: bool
                child 25, dedup_by_model_id: bool
                child 26, total_input_rows: int64
                child 27, output_mode_values: list<item: string>
                    child 0, item: string
                child 28, snap_filter_applied: bool
                child 29, config_hash: string
                child 30, grid_definition_hash: string
                chi
              ...
              : int64, checksum: bool>>
                                child 0, name: string
                                child 1, configuration: struct<level: int64, checksum: bool>
                                    child 0, level: int64
                                    child 1, checksum: bool
                        child 6, attributes: struct<>
                        child 7, zarr_format: int64
                        child 8, node_type: string
                        child 9, storage_transformers: list<item: null>
                            child 0, item: null
              node_type: string
              storage_transformers: list<item: null>
                child 0, item: null
              fill_value: string
              chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
                child 0, name: string
                child 1, configuration: struct<separator: string>
                    child 0, separator: string
              codecs: list<item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>> (... 1 chars omitted)
                child 0, item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>>
                    child 0, name: string
                    child 1, configuration: struct<endian: string, level: int64, checksum: bool>
                        child 0, endian: string
                        child 1, level: int64
                        child 2, checksum: bool
              chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
                child 0, name: string
                child 1, configuration: struct<chunk_shape: list<item: int64>>
                    child 0, chunk_shape: list<item: int64>
                        child 0, item: int64
              shape: list<item: int64>
                child 0, item: int64
              data_type: string
              to
              {'shape': List(Value('int64')), 'data_type': Value('string'), 'chunk_grid': {'name': Value('string'), 'configuration': {'chunk_shape': List(Value('int64'))}}, 'chunk_key_encoding': {'name': Value('string'), 'configuration': {'separator': Value('string')}}, 'fill_value': Value('string'), 'codecs': List({'name': Value('string'), 'configuration': {'endian': Value('string'), 'level': Value('int64'), 'checksum': Value('bool')}}), 'attributes': {}, 'zarr_format': Value('int64'), 'node_type': Value('string'), 'storage_transformers': List(Value('null'))}
              because column names don't match

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HARPS-range synthetic intensity grid on interpolated MARCS atmospheres (`harps-interp-t02`)

299,986 LTE Turbospectrum spectra of the emergent specific intensity I(lambda, mu), 3780.00-6910.00 A at 0.010 A sampling (313,001 pixels), not convolved with any instrumental or rotational profile. Training set for the SPICE spectral emulator (https://arxiv.org/abs/2511.10998).

How the spectra were made

Parameters: a Latin hypercube over Teff 3000-8000 K, log g -0.5 to 5.25, [Fe/H] -5 to +1, vmicro 1-5 km/s and six abundance offsets (table below), one random exact mu per spectrum; points without a complete MARCS bracket were removed.
Atmospheres: MARCS standard-composition models interpolated to each (Teff, log g, [Fe/H]) with Masseron's interpol_modeles (linear in Teff, log g, [Fe/H] between the 8 bracketing models) instead of the nearest node. Fixed microturbulence label t02 (2 km/s) for all atmospheres; spherical 1 Msun models below log g 3, plane-parallel at and above. Brackets with missing (unconverged) MARCS models are widened by up to 2 node steps; marcs_interp records it (counts: 1: 238495, 2: 38021, 3: 23470). 144,732 spectra have log g < 3 (spherical).
Synthesis: Turbospectrum (babsma + bsyn) in Intensity mode, LTE; solar abundances Magg et al. (2022). Line lists: Gaia-ESO atomic list (nlte_ges_linelist_jmg16jan2026_I_II, used in LTE), VALD 3700-4200 A, hydrogen lines (Hlinedata) and the molecular lists restricted to 3760-6930 A.
Continuum: continuum = flux / normalised flux as printed by Turbospectrum (normalised flux has 5 decimals, so below ~3150 K the continuum is coarse in saturated pixels).

One Zarr v3 store, harps_interp_t02.zarr:

array	shape
`flux`	(299986, 313001) float32	emergent intensity at `mu` (unnormalized_intensity, relative)
`continuum`	(299986, 313001) float32	continuum intensity
`wavelength`	(313001,) float32	A
`params` / `param_names`	(299986, 22) float32	per-spectrum parameters (below)
`model_id`, `run_index`, `source_physics_hash`	(299986,)	row identity and lineage
`provenance/`		configs, line-list / atmosphere / software manifests of the synthesis runs

Chunks hold 128 spectra x all wavelengths (Blosc zstd). Read with e.g. zarr.open_group("harps_interp_t02.zarr", mode="r")["flux"][i0:i1].

parameter	min	max	meaning
`teff`	3000	8000	effective temperature (K); also the atmosphere's (interpolated) Teff
`logg`	-0.5	5.25	surface gravity log g (cgs dex); also the atmosphere's
`feh`	-5	1	[Fe/H]: metallicity of the line formation and of the (interpolated) atmosphere
`vmicro`	1	5	microturbulence of the line formation (km/s); the atmosphere is always the xi = 2 km/s MARCS model
`turbvel`	1	5	(bookkeeping)
`t_value`	2	2	(bookkeeping)
`a`	-0.4	0.8	[alpha/Fe] (bsyn ALPHA/Fe keyword: O Ne Mg Si S Ar Ca Ti)
`c`	-1	1	[C/Fe]: A(C) = 8.56 + [Fe/H] + c
`n`	-0.5	1	[N/Fe]: A(N) = 7.98 + [Fe/H] + n
`o`	-0.4	0.8	oxygen relative to the alpha enhancement: A(O) = 8.77 + [Fe/H] + a + o (so [O/Fe] = a + o)
`r`	-0.4	1	[r/Fe] (bsyn R-PROCESS keyword)
`s`	-0.5	1	[s/Fe] (bsyn S-PROCESS keyword)
`mu`	0.01	1	cosine of the viewing angle of the emergent intensity (one exact mu per spectrum)
`marcs_fe_h`	-5	1	atmosphere [Fe/H] (= feh)
`marcs_a_fe`	0	0.4	[alpha/Fe] of the MARCS standard composition at this [Fe/H] (blended between nodes)
`marcs_c_fe`	0	0	(bookkeeping)
`marcs_n_fe`	0	0	(bookkeeping)
`marcs_o_fe`	0	0.4	(bookkeeping)
`marcs_teff`	3000	8000	atmosphere Teff (= teff: interpolated at the requested point)
`marcs_logg`	-0.5	5.25	atmosphere log g (= logg)
`marcs_turb`	2	2	atmosphere microturbulence label (always 2 km/s)
`marcs_interp`	1	3	atmosphere bracket: 0 = MARCS node used as is, 1 + k = interpolated with k widening steps

Known gaps

Rows whose Turbospectrum chemical equilibrium did not converge (cool stars near C/O = 1) are absent (a handful).
20 rows that failed in an early version of the atmosphere interpolation will be added in a later commit.

Provenance

Generated by the Turbospectrum_NLTE pipeline (git 96fe916441aa), schema 1.0.0; see provenance/ and the store attributes for the full configuration. Contact: Maja Jablonska.

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Total file size:

515 GB

Paper for mjablonska/harps-interp-t02-grid

SPICE -- modelling synthetic spectra of stars with non-homogeneous surfaces

Paper • 2511.10998 • Published Nov 14, 2025

HARPS-range synthetic intensity grid on interpolated MARCS atmospheres (harps-interp-t02)

How the spectra were made

Contents

Known gaps

Provenance

Paper for mjablonska/harps-interp-t02-grid

HARPS-range synthetic intensity grid on interpolated MARCS atmospheres (`harps-interp-t02`)