The same query, the same frame¶

Connectomic datasets each speak their own dialect. CAVE has materialization versions, root IDs that change under you, and per-datastack table names. neuPrint has immutable body IDs, Cypher, and dataset strings like hemibrain:v1.2.1. And every dataset has its own idea of what "type" or "side" means.

connecto is one interface over all of it.

import connecto as cn

fw = cn.FlyWire()      # neuPrint (backend="cave" for the other door)
hb = cn.Hemibrain()    # neuPrint

fw.connectivity.edges("DA1_lPN")     # pre, post, weight
hb.connectivity.edges("DA1_lPN")     # the same frame - different backend, different species conventions

Both calls return pre, post, weight as int64, int64, int32. Positions are always nanometres. Side is always left / right / center. Skeletons and meshes are always navis neurons.

Two promises¶

cave = cn.BANC(backend="cave").connectivity.edges(ids)
np_  = cn.BANC(backend="neuprint").connectivity.edges(ids)

pd.testing.assert_frame_equal(cave, np_)   # passes

fw.connectivity.synapses(x, min_score=100)   # fine
mic.connectivity.synapses(x, min_score=100)  # CapabilityError

hasattr(fw, "segmentation")   # True
hasattr(hb, "segmentation")   # False

One mini-language for selecting neurons¶

ds.ids(720575940604407468)   # a root/body ID
ds.ids("DA1_lPN")            # a type
ds.ids("/^AOTU00.*")         # a regex
ds.ids("super_class:visual_projection")
ds.ids("DA1_lPN", side="left")

ds.connectivity.edges(ids, version="auto")

skels = ds.skeletons.get("DA1_lPN")   # navis.NeuronList
navis.plot3d(skels)

Datasets¶

The capability columns come from cn.capability_matrix(), and a · there means the call raises — not that it returns something subtly wrong.

NT/syn and NT/neuron are different claims, and the difference matters. NT/syn is a capability: a prediction per synapse, so synapses(x, transmitters=True) and connectivity.transmitters(x) work, and raise where they can't. NT/neuron is one call per neuron and is not a capability — it arrives as the nt column of annotations.get(), and it depends on the annotation source rather than the backend, so it has no cell in capability_matrix(). Ask ds.annotations.fields["nt"] for the columns behind it.

NT/neuron has a third mark, because "this dataset has transmitters" hides a difference you cannot afford to miss when you aggregate:

aedes is the ◐: 6,981 of its 17,397 neurons carry a transmitter, every one of them traceable to a paper, and no model has ever run on that volume. hemibrain is the ·. Treating those two the same — or treating aedes's 40% like FlyWire's 99% — is how a "GABAergic fraction" ends up being a statement about annotation effort. nt_source names the exact column behind every value, so the distinction survives into the frame.

aedes's CAVE datastack ships no annotation table — so its cell typing comes from FlyTable (the lab's SeaTable) instead, keyed by root ID. It is lab-internal, so it needs a SEATABLE_TOKEN: without one you get a clear missing-token error, not a silently empty frame. What aedes still has no · for is a per-synapse score — its synapse table records size, not a confidence — so min_score= raises.

Anything not listed works too, without writing a class:

ca3  = cn.CAVE("zheng_ca3", fields={"type": ("cell_type",)})
wasp = cn.NeuPrint("wasp3:v0.8", server="neuprint-pre.janelia.org")

Adding a dataset →

Get going¶

pip install connecto

What it doesn't do¶

Data fetching only. No clustering, no matching, no connectivity vectors, no curation policy — connecto will backfill a type from the columns your dataset declares, but it will never decide that some types are "bad" and null them out. That is analysis, and it belongs upstream (see cocoa).