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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x89 in position 99: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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/csv/csv.py", line 196, in _generate_tables
                  csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/streaming.py", line 73, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1274, in xpandas_read_csv
                  return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
                         ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
                  return _read(filepath_or_buffer, kwds)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 620, in _read
                  parser = TextFileReader(filepath_or_buffer, **kwds)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
                  self._engine = self._make_engine(f, self.engine)
                                 ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1898, in _make_engine
                  return mapping[engine](f, **self.options)
                         ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in __init__
                  self._reader = parsers.TextReader(src, **kwds)
                                 ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "pandas/_libs/parsers.pyx", line 574, in pandas._libs.parsers.TextReader.__cinit__
                File "pandas/_libs/parsers.pyx", line 663, in pandas._libs.parsers.TextReader._get_header
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2053, in pandas._libs.parsers.raise_parser_error
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 99: invalid start byte

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SimPhy datasets for a 100k–1M scalability sweep

Simulated species trees and gene trees for two sweeps used to benchmark species-tree inference at scale (QRAFT, ASTRAL-X, STELAR-X):

  • taxa sweep: 100,000 to 1,000,000 taxa in steps of 100,000, each with 1,000 gene trees;
  • gene sweep: 100,000 to 1,000,000 gene trees in steps of 100,000, each with 1,000 taxa.

Every size has 5 replicates (R1–R5), each a species tree (s_tree.trees) and its gene trees (all_gt.tre, one Newick tree per line).

Model and command

The data were generated by the benchmark harness phylo-bench (commit 7aa3c87e49bfa6b6032ff81759b21740327385fb), unchanged, with the simphy-x engine in compat mode, which produces trees byte-identical to SimPhy 1.0.2. For every size T taxa × G genes:

./scripts/sim.sh -rs 5 --simphy-data-dir DATA --seed 42 --engine simphy-x-compat \
    --sim-threads all -t T -g G --sb 0.000001 --spmin 100000 --spmax 200000

which runs (see provenance/simphy-commands.txt for each data set's recorded command line):

simphy-x -XM compat -sb f:0.000001 -ld f:0 -lb f:0 -lt f:0 -rs 5 -rl f:G -rg 1 -o OUT \
    -sp u:100000,200000 -su ln:-17.27461,0.6931472 -sg f:1 -sl f:T -st ln:16.2,1 \
    -om 1 -v 2 -od 1 -op 1 -oc 1 -on 1 -cs 42

then concatenates each replicate's gene trees into all_gt.tre (dropping SimPhy's _0_0 label suffixes) with the harness's concat_gene_trees.py and reorganize_trees.py. Speciation rate 1e-6, no duplication, loss or transfer, effective population size uniform in [100,000, 200,000], species-tree height lognormal(16.2, 1), seed 42.

The 100k, 200k and 300k data sets of both sweeps are identical to those of the ASTRAL-X benchmark (imAniksahA/blab, ph/d/simulated/astralx-datasets/raw/): every tree file has the same CRC-32 and size. Only SimPhy's .command, .params and .db files differ, since they record the binary's and the output's paths.

Layout

raw/t_T_g_G_sb_0.000001_spmin_100000_spmax_200000.zip           (T × G ≤ 0.6e9)
raw/t_T_g_G_sb_0.000001_spmin_100000_spmax_200000.R1-R3.zip     (T × G ≥ 0.7e9: two parts,
raw/t_T_g_G_sb_0.000001_spmin_100000_spmax_200000.R4-R5.zip      under the 50 GB file limit)
provenance/

Each zip holds the data set's directory with SimPhy's .command, .params and .db files and its replicates, R<n>/all_gt.tre and R<n>/s_tree.trees. Unzip both parts of a split data set into the same directory to get R1–R5:

unzip t_1000000_g_1000_sb_0.000001_spmin_100000_spmax_200000.R1-R3.zip
unzip -o t_1000000_g_1000_sb_0.000001_spmin_100000_spmax_200000.R4-R5.zip

Check the trees against provenance/sha256-trees.txt (sha256sum -c after cd into the extraction directory).

Provenance

  • sim-commands.log: every command run (sim.sh and zip), verbatim, with start and end time, working directory and exit status (JSON lines).
  • simphy-commands.txt: the SimPhy command line each data set recorded.
  • sha256-trees.txt, sha256-zips.txt: sha256 of every tree file and every zip (for a zip packed more than once, the last line is the uploaded one).
  • environment-helper*.txt: the machines, the engine version and the sha256 of the harness scripts (identical on every machine).
  • NOTES.txt: what happened during the run, in order.
  • sim1m.py (driver), zip-parallel.py (multi-core zip -r), reorg_guard.py (memory guard), cleanup_restart.sh, pack_manual.py: the scripts that ran around the harness.
  • logs/helper<n>-*.tgz: each machine's harness simulation logs and driver logs.

The data sets were simulated on rented vast.ai machines, with the same harness scripts (same sha256) on each:

Machine Data sets
helper 1: Ryzen 9 9950X, Minnesota taxa 100k, 200k, 300k, 500k–900k; genes 100k, 800k
helper 2: Ryzen 9 9950X, Japan none (its disk stalled; abandoned)
helper 3: EPYC 7B13, Nebraska taxa 1M; genes 900k, 1M
helper 4: Ryzen 9 9950X, Utah taxa 400k; genes 200k–700k

Helper 1 rebooted twice, and helper 3's filesystem stalled while deleting SimPhy's per-locus files, after its three data sets were complete. Data sets in progress at a failure were simulated again from the start; completed ones were kept. NOTES.txt records every step. Zips were made with Info-ZIP 3.0 or, later, with zip-parallel.py: same layout and deflate format, slightly different bytes; the tree files are identical either way.

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