The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
# inputs of angio (contents are not mirrored): string
vs
algorithm: string
input_file: string
output_file: string
running_time_s: double
max_cpu_mb: double
max_gpu_mb: double
score: int64
rf_rate: double
exit_code: int64
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 575, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 5012, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
# inputs of angio (contents are not mirrored): string
vs
algorithm: string
input_file: string
output_file: string
running_time_s: double
max_cpu_mb: double
max_gpu_mb: double
score: int64
rf_rate: double
exit_code: int64Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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-corezip -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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