Changelog¶

All notable changes to connecto.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning. Below 1.0 a minor bump can still change behaviour you were relying on; those changes are listed under Changed rather than left for you to find.

0.2.0 - 2026-08-21¶

Added¶

• connecto.precomputed: an in-house reader for neuroglancer precomputed and graphene volumes over plain HTTPS. Sharded containers (both hash functions, delta-encoded minishard indices, compressed morton codes, per-shard index caching), compressed_segmentation, multi-resolution draco meshes, and graphene - watershed reads, label decoding, and meshes assembled from static shards plus the fragments re-meshed since a proofreader last touched the neuron, with the seams welded. Read-only by design: no writing, no image codecs, no cloud SDK credentials.
• Per-synapse neurotransmitters for BANC, male-CNS and MANC through neuPrint. Their Synapse nodes carry the eight-class probability vector, so Cap.NT_PER_SYNAPSE is no longer denied backend-wide - synapses(transmitters=True) and transmitters() work on all three.
• BackendSpec.nt_columns and BackendSpec.nt_table declare where transmitters live for a given (dataset, backend). A dataset that claims NT_PER_SYNAPSE without either is now rejected at import rather than returning a frame with no nt column.
• Synapse frames carry a transmitter provenance stamp in df.attrs["connecto"]["transmitters"]. BANC's two doors serve different runs of the same model - they agree on the neuron-level call but on only ~57% of shared synapses - so a frame now says which run it holds.
• nt_source column on neuron-level annotations, naming the column each value came from. known_nt is somebody's immunostaining, top_nt is a CNN's argmax; coalescing them silently let "this neuron is GABAergic" mean either.
• AnnotationSource.fields: per-source column spellings, and a way for a source to declare that it has not got a field at all.
• neuPrint annotation sources for FlyWire and BANC. fetch_neuprint now borrows the spec's neuPrint door instead of assuming the query backend is neuPrint, so annotation source and backend are a genuine cross-product: FlyWire(backend="cave", annotations="neuprint") is a sentence, not a crash.
• DracoPy as a direct dependency (draco has no reasonable pure-Python fallback), and compressed-segmentation in the voxels extra - about 25x quicker per chunk than the numpy decoder in connecto.precomputed.codecs, which stays exact and is used otherwise.
• The capability tables in the README and on the docs landing page are now checked against the registry, per cell and per door, so a table that says it is generated has to prove it. NT is split into NT/syn (the capability) and NT/neuron (an annotation column, marked dense / sparse / none), and the vox column was added.

Changed¶

• FlyWire and BANC default to their neuPrint annotations. For BANC the mirror is the better table almost everywhere: 153,984 neurons with a type against codex's 118,446, 154,830 with a transmitter against 115,601, plus somas and status codex has not got. Both datasets' previous sources are one annotations= argument away.
• BANC's neuPrint source declares side absent - it covers 8,153 bodies against codex's 158,250. ids("PFNd", side="left") now refuses and names the source that can answer, instead of returning 0 where the truth is 18.
• Annotation field precedence is stated in one resolver, in one order: spec, then source, then the handle's fields=, then the call's. The handle's fields= is the escape hatch resolve_criteria names by name, so it now outranks the source.
• Annotations.fields reports the priorities in force for the source the handle will actually read, not spec.fields.
• male-CNS's neuron-level nt prefers consensusNt over predictedNt.
• neuPrint meshes are read from the published bucket directly rather than through navis, and mesh fetching is threaded instead of one neuron at a time.
• PrecomputedMeta.resolution no longer rounds to int. FANC's mip 0 is 17.2 x 17.2 x 45 nm, and rounding to 17 is a 1.2% error - a hundred voxels at the far edge of the volume, far enough to land a point lookup inside the neighbouring neuron.
• Detecting list-valued annotation columns only scans object columns and stops at the first hit: 0.9 s -> 0.3 s on a 175k x 50 frame, which two datasets now read by default.
• borrow(ds, kind) in connecto.sources replaces two hand-rolled copies of "reach the dataset's other backend". The CAVE side now gets the cached client, the real version rules and connecto's error translation rather than its own token lookup and a version guess.

Fixed¶

• neuPrint synapses() and synapse_counts() silently undercounted, by up to 5x: the unconstrained side was passed as None, which neuprint-python turns into :Neuron-only criteria, dropping every :Segment fragment partner (hemibrain 2,018 -> 4,903; male-CNS 1,186 -> 4,491; BANC 187 -> 994; MANC 3,190 -> 8,044). They now match edges() exactly.
• soma_x/y/z were all-null in neuPrint annotation frames on hemibrain, male-CNS, MANC and fish2. somaLocation comes back as a raw Neo4j point on the unfiltered query, not the list fetch_neurons hands back for a body-ID list.
• FlyWire's neuPrint door claimed NT_PER_SYNAPSE without having the columns behind it, so transmitters=True returned a frame with no nt column and no error. It now denies the capability on its own BackendSpec.
• A source's "declared empty" field rename runs after spec.derive, so a suppressed column is no longer recreated from a regex two lines later.
• The "another source has this column" suggestion carries the caller's backend= through; annotation source and backend are independent axes, and it was resetting one.
• get_dataset("banc", fields={"side": (...)}) was silently overruled by the annotation source that declared side absent.

Removed¶

• cloud-volume is no longer a runtime dependency. Reading is a much smaller job than reading and writing to half a dozen cloud backends, and paying for the latter cost 56 packages and ~78 MB of a default install (boto3, the Google Cloud SDK, gevent, protobuf and the rest). It stays a dev dependency: tests/test_precomputed.py reads the same cutouts and meshes through both readers and asserts they agree, so cloud-volume is the oracle the new reader is checked against.

0.1.1 - 2026-07-20¶

Added¶

• ds.voxels: sparse volumes - every voxel belonging to a neuron - as navis.VoxelNeurons, or as raw (N, 3) arrays or rle runs, in voxel or nm coordinates. ds.voxels.scales() lists the pyramid levels a dataset actually serves.
• Cap.VOXELS, kept separate from SEGMENTATION because "there is a segmentation volume" and "you can get one neuron's voxels out of it without reading the whole brain" are different claims. DVID keeps a live per-body index and answers in one request; a chunkedgraph keeps none, so the same question degrades to reading dense blocks and masking them - 100-1000x more voxels touched than kept.
• ds.voxels.estimate(), and a refusal rather than an apparent hang when a read would transfer billions of voxels. voxels.get() also picks a sensible default scale, since scale 0 for a hemibrain neuron is 1.17 billion voxels.
• SparseVolSource, for datasets served by an external per-body index - with the scales it really serves and a per-axis downsample factor, because fly pyramids routinely halve X and Y while leaving Z alone.
• DVID servers and nodes are discovered at runtime from neuPrint and clio metadata, so nothing is hard-coded and the node always matches the snapshot the rest of the query came from.
• NoSuchBodyError, which is also a KeyError so except KeyError around a per-neuron loop keeps working.
• The voxels extra, installing fastremap to speed up masking a dense cutout down to one root. connecto.voxels.pcg falls back to numpy without it.

0.1.0 - 2026-07-16¶

Initial release: one interface over CAVE and neuPrint.

Added¶

• Dataset handles for FlyWire, Hemibrain, BANC, FANC, MANC, male-CNS, MICrONS, Aedes and fish2, plus generic CAVE and NeuPrint constructors and register() for pointing connecto at any datastack or neuPrint server. Handles are immutable; ds.at(version) gives you another one.
• Namespaces with normalised output: annotations, connectivity, skeletons, meshes, rois, somas, viz, and - where the backend has them - segmentation, proofreading and l2. Edges are always pre, post, weight as int64, int64, int32, positions are always nanometres, side is always left/right/center, and skeletons and meshes are always navis neurons.
• The capability model: Cap, DatasetSpec/BackendSpec, ds.supports() and capability_matrix(). A capability belongs to a (dataset, backend) pair, so FlyWire(backend="neuprint") no longer claims a chunkedgraph it cannot reach. Nothing degrades silently - an explicit request a dataset cannot honour raises CapabilityError, and whole namespaces are absent rather than broken, so hasattr works as feature detection.
• NeuronCriteria and the mini-language it desugars from, so the same selection reads the same on every dataset.
• Version handling: materialization versions and neuPrint dataset tags behind one Version, ds.versions(), ds.find_version(), and an error rather than a guess when a request is ambiguous.
• Annotation sources as a separate axis from the backend - CAVE tables, neuPrint, GitHub TSVs, SeaTable/FlyTable and clio - with per-source freshness tokens.
• An on-disk cache under ~/.connecto/cache whose key includes the materialization, so it can never serve you the wrong version; live sources join a freshness token to the key and keep one entry rather than one per edit.
• Neuroglancer scene building via ds.viz.
• Token handling (set_token, get_token, auth_status) and server status helpers (server_status, wait_until_available), which wait only when you ask - connecto never silently retries a 503.
• ds.cite(), because connecto knows exactly which dataset produced the numbers in your figure.