fruitloops 0.1.9

Agent-friendly CLI for querying local connectome analysis tables.

fruitloops

Agent-friendly CLI for offline-first olfactory connectome queries across hemibrain and FlyWire.

The repository keeps generated CSV products in a predictable layout:

data/
  manifest.csv
  hemibrain/
  flywire/
  comparison/

Quick Use

Choose one install method.

Install with Homebrew:

brew tap gumadeiras/tap
brew install fruitloops

Install from PyPI:

python -m pip install fruitloops

Both installs include:

• the generated CSV snapshot for status, table, find, and partners
• Python dependencies for local CSV queries, bulk imports, live access, and plotting

Nothing else is required for local snapshot queries:

fruitloops status
fruitloops table --flywire --contains summary --csv

Run setup when you want the larger local offline stores. It downloads/imports the practical bulk connection tables, creates the DuckDB store, creates the live cache directory, and builds derived olfaction tables:

fruitloops setup

Setup prints numbered progress updates to stderr while keeping the final summary on stdout. The default output is a compact list; use --csv or --json when you need full file paths and DuckDB store locations. Use --no-progress for fully quiet pipeline runs. Re-running setup skips current downloads, imports, optimization, and derived olfaction tables when local source fingerprints still match the stored setup state.

Run directly from the repository:

python -m fruitloops status
python -m fruitloops olf glomerulus DM1 --flywire --csv
python -m fruitloops olf inputs --target-class PN --source-class ORN --glomerulus DM1 --by-side --csv
python -m fruitloops table --flywire --contains summary --csv
python -m fruitloops table flywire:analysis_outputs/full_summary --head
python -m fruitloops table comparison:matched_ln_class_similarity --contains LN_class=il3LN6 --json
python -m fruitloops find il3LN6 --flywire --json
python -m fruitloops partners il3LN6 --flywire --orn --csv
python -m fruitloops examples

For editable installation:

python -m pip install -e .
fruitloops status
fruitloops setup

Fruitloops does not depend on the current working directory. Inspect the active paths with:

fruitloops status

Path overrides:

• FRUITLOOPS_DATA_DIR: generated CSV snapshot directory with manifest.csv
• FRUITLOOPS_BULK_DIR: downloaded bulk files and extracted archives
• FRUITLOOPS_DUCKDB_PATH: imported DuckDB database path
• FRUITLOOPS_CACHE_DIR: live-query cache root

Table References

Tables can be referenced as:

• dataset:relative/path/without_csv
• dataset:collection/file_stem
• file_id from data/manifest.csv

Examples:

fruitloops table flywire:analysis_outputs/full_summary --schema --csv
fruitloops table hemibrain:analysis_outputs/full_summary --select bodyId,LN_type,input_preference
fruitloops table flywire:source_audit/ln_observations_by_hemisphere --where LN_type=il3LN6
fruitloops table flywire:source_audit/orn_partner_counts_by_hemisphere --where LN_type=il3LN6 --csv
fruitloops table comparison:matched_ln_class_similarity --path

Common Agent Queries

After fruitloops setup, use fruitloops olf for broad olfaction questions:

fruitloops olf classes --flywire --region AL --csv
fruitloops olf glomerulus DM1 --flywire --csv
fruitloops olf pns --glomerulus DM1 --hemibrain --csv
fruitloops olf inputs --target-class PN --source-class ORN --glomerulus DM1 --by-side --flywire --csv
fruitloops olf outputs --source-class PN --target-class KC --region MB --flywire --csv
fruitloops olf pathway ORN LN --region AL --flywire --csv
fruitloops olf pathway PN KC --region MB --flywire --csv

Question recipes:

# Which PNs exist for one glomerulus?
fruitloops olf pns --flywire --glomerulus DA2 --csv
fruitloops olf pns --hemibrain --glomerulus DA2 --csv

# Which lateral horn neurons receive direct DA2 PN input?
fruitloops olf outputs --flywire --source-class PN --target-class LHN --glomerulus DA2 --region LH --by-side --csv
fruitloops olf outputs --hemibrain --source-class PN --target-class LHN --glomerulus DA2 --region LH --by-side --csv

# Which mushroom body neurons receive direct DA2 PN input?
fruitloops olf outputs --flywire --source-class PN --target-class KC --glomerulus DA2 --region MB --by-side --csv
fruitloops olf outputs --hemibrain --source-class PN --target-class KC --glomerulus DA2 --region MB --by-side --csv

# Same DA2 PN target search, without requiring target class annotations.
fruitloops olf outputs --flywire --source-class PN --glomerulus DA2 --region LH --by-side --csv
fruitloops olf outputs --flywire --source-class PN --glomerulus DA2 --region MB --by-side --csv

Use table aggregation when you need legacy generated CSV products:

fruitloops table flywire:source_audit/orn_partner_counts_by_hemisphere \
  --where LN_type=il3LN6 \
  --by LN_type,analysis_hemisphere,input_relation \
  --sum n_synapses \
  --csv

Specialized LN partner summaries are still available:

fruitloops partners il3LN6 --flywire --orn --csv
fruitloops partners il3LN6 --flywire --pn --csv
fruitloops partners il3LN6 --hemibrain --orn --csv
fruitloops partners il3LN6 --hemibrain --pn --csv

Pull the reconciled hemibrain/FlyWire comparison:

fruitloops table comparison:matched_ln_class_similarity --contains LN_class=il3LN6 --json

Useful LN workflow:

fruitloops find il3LN6 --csv
fruitloops table flywire:source_audit/ln_observations_by_hemisphere --where LN_type=il3LN6 --csv
fruitloops table flywire:source_audit/orn_partner_counts_by_hemisphere \
  --where LN_type=il3LN6 \
  --by analysis_hemisphere,input_relation \
  --sum n_synapses \
  --csv
fruitloops table comparison:matched_ln_class_similarity --contains LN_class=il3LN6 --jsonl

Legacy commands such as datasets, files, schema, head, query, aggregate, and ln remain available for scripts. New interactive use should prefer status, setup, olf, table, find, partners, and examples.

Olfaction Offline Cache

Build derived AL/LH/MB tables after importing bulk connectivity:

fruitloops setup
fruitloops olf tables

For complete names/classes/glomeruli, cache annotations once from live APIs and rebuild:

fruitloops olf cache-annotations --dataset hemibrain
fruitloops olf cache-annotations --dataset flywire

The builder creates olf_edges_by_neuropil, olf_edges_total aggregated over AL/LH/MB, olf_neuropil_membership, olf_neurons, olf_annotations, olf_neuron_regions, olf_pathway_edges, olf_pathway_summary, olf_cell_type_summary, and olf_provenance in the DuckDB store. It uses imported annotation tables when available:

• hemibrain_olfaction_neuron_annotations or hemibrain_traced_neurons
• flywire_hierarchical_neuron_annotations
• flywire_neuron_information_v2

Example olfaction queries:

fruitloops olf neurons --dataset flywire --region AL --class ORN --format csv
fruitloops olf classes --dataset flywire --region AL --format csv
fruitloops olf glomerulus DM1 --dataset flywire --format csv
fruitloops olf inputs --dataset hemibrain --target-class PN --source-class ORN --glomerulus DM3 --by-side --format csv
fruitloops olf outputs --dataset flywire --source-class PN --target-class KC --region MB --format csv
fruitloops olf pathway PN KC --dataset flywire --region MB --format csv
fruitloops olf edges --dataset flywire --region LH --min-synapses 5 --format csv

There is not yet a separate docs site or command reference. The current documentation lives in this README, AGENTS.md, RELEASE.md, and CLI help from fruitloops --help / fruitloops olf --help.

Generic Plotting

Plotting is reusable and table-agnostic.

Render from any fruitloops table reference:

fruitloops admin plot \
  --table comparison:matched_ln_class_similarity \
  --kind scatter \
  --x hemibrain_mean_contra_preference \
  --y flywire_mean_contra_preference \
  --label LN_class \
  --top-labels 8 \
  --output outputs/contra_preference_scatter \
  --formats png,svg

Or render from any CSV path:

fruitloops admin plot \
  --csv path/to/table.csv \
  --kind scatter \
  --x x_column \
  --y y_column \
  --output outputs/my_scatter

Other generic plot kinds:

fruitloops admin plot --table comparison:matched_ln_class_similarity --kind bar --x LN_class --y orn_input_distribution_correlation --output outputs/orn_corr_bar
fruitloops admin plot --table flywire:source_audit/orn_partner_counts_by_hemisphere --kind violin --x input_relation --value n_synapses --where LN_type=il3LN6 --output outputs/il3ln6_orn_violin
fruitloops admin plot --table flywire:source_audit/orn_partner_counts_by_hemisphere --kind heatmap --x glomerulus --y input_relation --value n_synapses --where LN_type=il3LN6 --output outputs/il3ln6_orn_heatmap
fruitloops admin plot --table comparison:matched_ln_class_similarity --kind bubble --x orn_input_distribution_correlation --y pn_output_distribution_correlation --size flywire_orn_input_total --color flywire_contra_fraction --label LN_class --output outputs/similarity_bubble

The wrapper script is equivalent:

python scripts/plot_csv.py --csv path/to/table.csv --kind hist --value score --output outputs/score_hist

Live Connectome Access

Live database access is optional. Credentials come from environment variables or from a local .env file. .env is ignored by git; start from .env.example.

cp .env.example .env

Use a different env file with --env-file path/to/file.env.

Hemibrain uses neuprint-python:

export NEUPRINT_SERVER=neuprint.janelia.org
export NEUPRINT_DATASET=hemibrain:v1.2.1
export NEUPRINT_APPLICATION_CREDENTIALS=<neuprint-token>

fruitloops admin live hemibrain neurons --type-contains il3LN6 --limit 5 --format csv
fruitloops admin live hemibrain connections --upstream-body-id 5813018460 --limit 20 --format json
fruitloops admin live hemibrain cypher --query 'MATCH (n:Neuron) RETURN n.bodyId AS bodyId, n.type AS type LIMIT 5'

FlyWire uses caveclient:

export FLYWIRE_DATASTACK=flywire_fafb_public
export CAVE_AUTH_TOKEN=<cave-token>

fruitloops admin live flywire tables --format csv
fruitloops admin live flywire table --table synapses_nt_v1 --in pre_pt_root_id=720575940623636701 --limit 10 --format csv
fruitloops admin live flywire synapses --pre-root-id 720575940623636701 --limit 10 --format json

Script shortcuts are equivalent:

python scripts/live_hemibrain.py neurons --type-contains il3LN6 --limit 5
python scripts/live_flywire.py tables

Offline-First Live Cache

Use offline fetch when you want local data first and live APIs only on cache miss. Results are saved under cache/live/, which is ignored by git.

fruitloops admin offline fetch \
  --dataset flywire \
  --action synapses \
  --pre-root-id 720575940623636701 \
  --limit 10 \
  --format csv

Repeat the same command to read the cached CSV. Use --offline-only to fail instead of hitting the network, or --refresh to force a live re-fetch.

fruitloops admin offline list
fruitloops admin offline fetch --dataset flywire --action tables --offline-only
fruitloops admin offline fetch --dataset hemibrain --action neurons --type-contains il3LN6 --limit 5

Bulk Offline Releases

Bulk releases should be the primary offline source when you need broad connectivity, with live/cache queries only filling gaps.

List known public release files:

fruitloops admin bulk sources

Download the practical FlyWire connection table first:

fruitloops admin bulk download --dataset flywire --kind proofread-connections

Optional larger downloads:

fruitloops admin bulk download --dataset hemibrain --kind compact-adjacencies
fruitloops admin bulk download --dataset flywire --kind synapses
fruitloops admin bulk download --dataset hemibrain --kind neo4j-inputs

Import CSV/Parquet/Feather into local DuckDB:

flywire_path=$(fruitloops admin bulk download --dataset flywire --kind proofread-connections)
fruitloops admin bulk import --path "$flywire_path" --table flywire_proofread_connections --replace
fruitloops admin bulk tables
fruitloops admin bulk query --table flywire_proofread_connections --limit 10 --format csv

Optimize imported connection tables before repeated partner queries:

fruitloops admin bulk optimize --table flywire_proofread_connections --prefix flywire
fruitloops admin bulk optimize --table hemibrain_traced_roi_connections --prefix hemibrain

Agent-facing wrappers infer common pre/post/weight/ROI column names:

fruitloops admin bulk schema --table flywire_proofread_connections
fruitloops admin bulk connections --table flywire_proofread_connections --pre-id ROOT --limit 20 --format csv
fruitloops admin bulk inputs --table flywire_proofread_connections --body-id ROOT --format csv
fruitloops admin bulk outputs --table flywire_proofread_connections --body-id ROOT --format csv
fruitloops admin bulk partners --table flywire_proofread_connections --body-id ROOT --format json
fruitloops admin bulk views --table flywire_proofread_connections --prefix flywire
fruitloops admin bulk optimize --table flywire_proofread_connections --prefix flywire

Hemibrain's compact adjacency and Neo4j bundles are CSV archives; extract first, then import the CSVs you need:

hemibrain_path=$(fruitloops admin bulk download --dataset hemibrain --kind compact-adjacencies)
fruitloops admin bulk extract --path "$hemibrain_path"
fruitloops admin bulk import \
  --path "$(fruitloops status --csv | awk -F, '$2=="bulk_dir"{print $4}')/extracted/exported-traced-adjacencies-v1.2/traced-roi-connections.csv" \
  --table hemibrain_traced_roi_connections \
  --replace
fruitloops admin bulk import \
  --path "$(fruitloops status --csv | awk -F, '$2=="bulk_dir"{print $4}')/extracted/exported-traced-adjacencies-v1.2/traced-total-connections.csv" \
  --table hemibrain_traced_total_connections \
  --replace
fruitloops admin bulk import \
  --path "$(fruitloops status --csv | awk -F, '$2=="bulk_dir"{print $4}')/extracted/exported-traced-adjacencies-v1.2/traced-neurons.csv" \
  --table hemibrain_traced_neurons \
  --replace
neo4j_path=$(fruitloops admin bulk download --dataset hemibrain --kind neo4j-inputs)
fruitloops admin bulk extract --path "$neo4j_path"

End-to-end offline setup:

fruitloops setup
fruitloops admin bulk tables

flywire_synapses_783.feather is much larger than the proofread connection table. Fruitloops streams Feather imports through Arrow record batches, but the resulting DuckDB database still needs enough local disk for the imported table and indexes.

Output Formats

Most commands support --format table, --format csv, --format json, or --format jsonl. CSV and JSONL are intended for downstream agent pipelines.

Rebuilding the Data Snapshot

Fruitloops needs a generated CSV snapshot for status, table, find, partners, and comparison commands. Release builds ship that snapshot. For a source checkout or custom package without data/, point FRUITLOOPS_DATA_DIR at a snapshot or rebuild it from the paper repository.

From the fruitloops repository root:

python scripts/build_data_snapshot.py \
  --source "/path/to/widespread-direction-selectivity" \
  --dest "$(fruitloops status --csv | awk -F, '$2=="data_dir"{print $4}')"

The script copies generated CSVs and rewrites data/manifest.csv.

Test

python -m unittest discover -s tests