scigantic-headers

Read the metadata of a scientific file and return its fields (dimensions, data type, pixel size, columns, row count) as a dict, without reading the rest of the file. Decodes formats across cryo-EM, imaging, arrays, tabular data, flow cytometry, mass spec, sequencing, genome annotation, and structural biology (the full list is below). The decode functions have no dependencies; separate reader functions fetch the bytes from a file or a URL (the leading bytes for most formats, the trailing bytes for Parquet, whose schema is in a footer).

from scigantic_headers import decode_file
hdr = decode_file("session/frame.mrc")
hdr.summary            # "MRC stack 4096x4096x16, float32"
hdr.fields["dtype"]    # "float32"
hdr.fields["nz"]       # 16   (frames)

Zero runtime dependencies and header-only reads, so it runs air-gapped, on amd64 or arm64, and installs into a slim image with nothing else to pull.

Install

pip install scigantic-headers

Published to PyPI on a v* tag by .github/workflows/publish.yml. Pin the version in anything that consumes it (scigantic-headers==X.Y.Z). With no package index at build time, install from a wheel or a checkout instead:

pip install ./scigantic-headers

Why it exists

You often want to know what a file is, its shape, dtype, and key acquisition fields, without reading the whole thing. A scientific file's first ~1 KiB is a structured header that already says so. This reads that, and nothing more.

Decoders today:

• MRC / MRCS: cryo-EM micrographs, movie stacks, tilt series, EMDB maps.
• NPY: NumPy arrays. Cross-checked against numpy.
• NIfTI-1: neuroimaging volumes, .nii and .nii.gz. Cross-checked against nibabel. Big- and little-endian.
• CryoSPARC .cs: a structured-array dataset. Reports record count and the field schema.
• Parquet: reads the footer, not a leading header, and returns the column names, physical types, and row count. Cross-checked against pyarrow.
• FCS: flow cytometry (Flow Cytometry Standard). Returns parameter and event counts, data type, and per-channel names. Cross-checked against flowio.
• mzML: mass spectrometry. Returns the spectrum count, instrument model, software, source file, and run start time from the XML preamble. Cross-checked against a compliant XML parse.
• FASTQ: sequencing reads. Parses the Illumina read-name convention in the first record for instrument, run, flowcell, and lane, plus the read length.
• Illumina run: RunInfo.xml / RunParameters.xml. Instrument, flowcell, run id, date, and read structure. Dispatched by file name, not extension.
• VCF: variant calls. Version, sample names, reference, and meta-line counts from the header.
• SAM: alignment header. Version, sort order, reference sequences, read groups, and programs.
• PDB: structure header. PDB id, classification, deposition date, title, experiment method, and resolution.
• mmCIF / CIF: structures (the modern PDB format). Entry id, title, keywords, experiment method, deposition date, and resolution (including the cryo-EM resolution item).
• GenBank: annotated sequence records. LOCUS (name, length, molecule type, topology), definition, accession, version, and organism.
• GFF3 / GTF: genome annotation. Version and a preview of the sources and feature types.
• BED: genome intervals. BED flavor (column count) and track-line presence.
• DICOM: medical images. Modality, dimensions, bit depth, manufacturer, and study/series description. Reads technical fields only, never patient identifiers.

.gz is transparent. The reader decompresses just the leading block, so a gzipped .nii.gz or .mrc.gz decodes without inflating the whole file, and dispatch sees the inner format.

Adding a format is a pure bytes -> DecodedHeader | None function plus one register_decoder call. That call also declares how many bytes the decoder needs and from which end (read=Read(leading=...), or read=Read(footer=...) for a schema in the tail like Parquet), so the read strategy lives next to the decoder, not in a table elsewhere. Nothing about the dispatch, the readers, or the batch path is format-specific: the readers ask the registry how to read and never name a format. NPY and NIfTI landed with zero plumbing changes.

Pixel size for raw movies

A raw MRC movie header has no pixel size: CELLA is 0, so decode_* returns pixelSizeA as None. The value is in the RELION STAR or CryoSPARC .cs file the workflow writes in the session, and the library reads it. read_session_optics finds that file next to (or above) the data file and returns the optics:

from scigantic_headers import decode_file, read_session_optics

hdr = decode_file("Movies/frame.mrc")       # header fields; pixelSizeA is None
opt = read_session_optics("Movies/frame.mrc")
# {'pixelSizeA': 1.05, 'voltageKv': 300.0, 'source': 'relion-star:particles.star'}

read_star_optics(path) and read_cryosparc_optics(path) read a specific optics file when you already know which one. The RELION 3.1 data_optics loop, the older key-value form, optics columns in the main data loop, and the legacy detector-pixel-size / magnification pair are all handled.

Layout

src/scigantic_headers/
  decoders.py   pure core: registry + MRC / NPY / NIfTI / .cs decoders
  parquet.py    Parquet footer decode (small Thrift-compact reader)
  fcs.py        FCS (flow cytometry) header + TEXT-segment decode
  mzml.py       mzML (mass spec) XML-preamble metadata decode
  fastq.py      FASTQ first-record / read-name decode
  illumina.py   Illumina RunInfo.xml / RunParameters.xml decode
  vcf.py        VCF header decode
  sam.py        SAM alignment-header decode
  pdb.py        PDB structure-header decode
  mmcif.py      mmCIF / CIF structure-header decode
  genbank.py    GenBank flat-file header decode
  gff.py        GFF3 / GTF annotation-header decode
  bed.py        BED interval-file decode
  dicom.py      DICOM technical-metadata decode (no patient data)
  sources.py    read leading bytes from file or URL; bounded parallel batch
  star.py       RELION STAR optics reader
  cryosparc.py  CryoSPARC .cs optics reader
  optics.py     read_session_optics: find a data file's optics file
  cli.py        scigantic-headers <file|url|--dir>
  benchmark.py  scigantic-headers-bench, measures the speed levers
tests/          pytest (208 tests, incl. fuzz + golden fixtures)

Robustness

A decoder is handed the leading bytes of arbitrary files, so it must be total: for any input it returns None or a valid, JSON-safe DecodedHeader. It never raises and never emits NaN or inf. This is enforced, not assumed.

• Fuzz (test_fuzz.py): thousands of random and magic-seeded byte buffers through every decoder. Each result must serialize with allow_nan=False.
• Golden fixtures (fixtures/mrc-cases.json): hex input to exact decode. A change to a decoder that the fixture does not expect fails a test rather than passing silently.
• Non-finite floats (a garbage NIfTI pixdim, an inf MRC cell) sanitize to null.

Speed

The decode is microseconds: a 1 KiB read and a few struct unpacks. Optimizing that is a rounding error. All the speed is in I/O, and two things carry it. Both are measured, not asserted (run scigantic-headers-bench).

Read the header, not the file. The read is bounded (HEADER_BYTES, 1 KiB, for most formats; a few hundred KB for text formats with long headers, declared per decoder), so its cost is independent of file size and a multi-GB movie decodes as fast as a small one.

header read + decode (1 KiB):        15 us
full-file read + decode (200 MB):  22.7 ms      (about 1,500x cheaper)

Parallelize the I/O, bounded. Header reads across files are independent and I/O-bound, so a thread pool overlaps their latency (the read releases the GIL). Measured over 8 headers pulled by HTTP Range from a remote archive:

serial   (1 worker):   25.7 s
parallel (8 workers):   0.7 s        (about 35x)

That 35x is large because the server's per-request latency is about 3 s, so overlapping eight requests recovers a lot of dead wait. On low-latency local storage the speedup is smaller. The win scales with per-read latency, which is why the pool is bounded and tunable (default 8; past a point more connections hit server throttling or disk-queue thrash and get slower).

Two more, by construction:

• Filter before I/O. has_decoder_for is a cheap extension check. The batch and walk paths use it to skip a file before any syscall.
• Zero deps, fast import. The dtype table is a plain dict, not numpy. There is nothing to install and nothing to import, and it runs air-gapped.

The biggest system-level lever lives in the caller, not here: decode once when a file lands, cache the small result, and answer every later query from it without re-decoding.

Use

from scigantic_headers import decode_file, decode_paths, decode_urls, iter_decodable_files

decode_file("s/frame.mrc")                              # one local file
decode_paths(iter_decodable_files("/data/sessions"))    # a tree, parallel
decode_urls([...], workers=8)                           # remote, by Range

# CLI
scigantic-headers session/frame.mrc
scigantic-headers https://example.org/path/img.mrcs
scigantic-headers --dir /data/sessions --workers 8

pytest
scigantic-headers-bench            # reproduce the numbers above

Limitations

• Pointer-based formats are not supported. HDF5 and TIFF store their layout behind internal pointers, so reading the header or footer alone is not enough. Parquet (a footer) is supported; formats that need to chase offsets through the file are not. Plain gzip is handled (the reader inflates the leading block), but BAM's BGZF framing and per-record structure are not.
• Output is not a standard. The fields are an ad-hoc dict, not an interchange format such as Allotrope ASM. Map the dict to a standard if you need one.

License

MIT. See LICENSE.