Upload a CSV whose columns match one schema, and convert to another. Uses stored alignments to auto-map columns. Runs entirely in your browser — no data leaves your machine.
@@ -554,7 +581,7 @@
Register a Schema
if(pr.status==="fulfilled"&&pr.value.ok)provenance=await pr.value.json();
renderConceptsSrc();renderConcepts();renderDiffSelects();renderGraph();renderProvenance();
renderHistList("schemas");renderHistList("classes");renderHistList("properties");
- initQuery();initTransform();
+ initQuery();initTransform();initDemoCards();
}catch(e){document.getElementById("status").innerHTML='
Failed to load';}
}
@@ -808,10 +835,121 @@
${displayName} v${v.version}
function trTabSwitch(tab,btn){
document.querySelectorAll("#view-transform .query-tabs button").forEach(b=>b.classList.remove("active"));
btn.classList.add("active");
+ document.getElementById("tr-tab-demo").style.display=tab==="demo"?"block":"none";
document.getElementById("tr-tab-browser").style.display=tab==="browser"?"block":"none";
document.getElementById("tr-tab-api").style.display=tab==="api"?"block":"none";
}
+
+// ---------------------------------------------------------------------------
+// BBQS Live Demo
+// ---------------------------------------------------------------------------
+const BBQS_ENTITIES=[
+ {type:"Investigator",icon:"👤",record:{name:"Laura Yenisa Cabrera Trujillo",email:"lcabrera@psu.edu",identifier:"0000-0002-1234-5678",affiliation:"Penn State University",researchAreas:"neuroethics, DBS"}},
+ {type:"Dataset",icon:"📊",record:{identifier:"DANDI:000123",name:"EMBER Dandiset 000123 — Human iEEG during mood tasks",species:"Human",modality:"iEEG",license:"CC-BY-4.0",url:"https://dandi.emberarchive.org/dandiset/000123"}},
+ {type:"Device",icon:"🔬",record:{name:"Neuropixels 2.0",modality:"electrophysiology",channels:384,commonIssues:"drift correction, spike sorting artifacts"}},
+ {type:"FundedProject",icon:"🏛️",record:{identifier:"1R61MH138612",name:"SeeMe: Sensing Emotion in Mood Episodes",funding:"NIMH",species:"Human",leadInstitution:"Stony Brook University"}},
+ {type:"Software",icon:"💻",record:{name:"bbqs-spike-sorter",programmingLanguage:"Python",license:"MIT",codeRepository:"https://github.com/brain-bbqs/spike-sorter"}},
+];
+let trDemoEntity=null;
+
+function initDemoCards(){
+ const wrap=document.getElementById("tr-demo-cards");
+ if(!wrap)return;
+ wrap.innerHTML=BBQS_ENTITIES.map((e,i)=>`
+
+
${e.icon}
+
${e.type}
+
${Object.keys(e.record).length} fields
+
`).join("");
+ // populate target select
+ const sel=document.getElementById("tr-demo-tgt");
+ if(sel){sel.innerHTML="";allSources.filter(s=>s.label!=="bbqs").forEach(s=>{const o=document.createElement("option");o.value=s.label;o.textContent=s.label;sel.appendChild(o);});}
+}
+
+function trSelectEntity(idx){
+ BBQS_ENTITIES.forEach((_,i)=>{
+ const card=document.getElementById("tr-demo-card-"+i);
+ if(card)card.style.borderColor=i===idx?"var(--teal)":"var(--border)";
+ });
+ trDemoEntity=BBQS_ENTITIES[idx];
+ document.getElementById("tr-demo-step2").style.display="block";
+ document.getElementById("tr-demo-record").textContent=JSON.stringify(trDemoEntity.record,null,2);
+ trRunDemo();
+}
+
+function trRunDemo(){
+ if(!trDemoEntity)return;
+ const tgt=document.getElementById("tr-demo-tgt").value;
+ const record=trDemoEntity.record;
+
+ // Mirror Python transform_record(): find BBQS classes, follow alignment edges
+ // to target classes, match property names case-insensitively.
+ const srcClasses=allClasses.filter(c=>(c.sources||[c.source]).includes("bbqs"));
+ const tgtClassMap={};
+ allClasses.filter(c=>(c.sources||[c.source]).includes(tgt)).forEach(c=>{tgtClassMap[c.uid]=c;});
+
+ const propMap={};
+ for(const srcC of srcClasses){
+ for(const aln of (srcC.alignments||[])){
+ if(aln.distance>0.6)continue;
+ const tgtC=tgtClassMap[aln.target_uid];
+ if(!tgtC)continue;
+ const tgtProps={};
+ (tgtC.properties||[]).forEach(p=>{tgtProps[p.name.toLowerCase()]=p.name;});
+ for(const srcP of (srcC.properties||[])){
+ const sl=srcP.name.toLowerCase();
+ if(tgtProps[sl])propMap[sl]={target:tgtProps[sl],via:`${srcC.name} → ${aln.target_name}`,distance:aln.distance};
+ }
+ }
+ }
+
+ const mapped={},unmapped=[],details=[];
+ Object.entries(record).forEach(([field,value])=>{
+ const m=propMap[field.toLowerCase()];
+ if(m){mapped[m.target]=value;details.push({src:field,tgt:m.target,value,via:m.via,distance:m.distance});}
+ else unmapped.push(field);
+ });
+
+ const hasAlignments=Object.keys(propMap).length>0;
+ const mappedRows=details.map(d=>`
+
+ ${d.src}
+ →
+ ${d.tgt}
+ ${String(d.value)}
+ ${d.via} (d=${d.distance.toFixed(2)})
+ `).join("");
+
+ const unmappedHtml=unmapped.length?`
+
+
Unmapped fields (no alignment to ${tgt})
+
+ ${unmapped.map(f=>`${f} `).join("")}
+
+
`:"";
+
+ const warningHtml=!hasAlignments?`
No alignment edges found between bbqs and ${tgt}. Run the pipeline to compute cross-schema edges.
`:"";
+
+ document.getElementById("tr-demo-result").style.display="block";
+ document.getElementById("tr-demo-result-inner").innerHTML=warningHtml+(mappedRows?`
+
+
+ bbqs field
+ ${tgt} field
+ value
+ via alignment
+ ${mappedRows}
+
`:`
No property-level mappings found for ${trDemoEntity.type} → ${tgt}.
`)+unmappedHtml+
+ `
+ Download mapped JSON
+
`;
+}
function initTransform(){
+ // Demo tab is shown by default; hide the others
+ document.getElementById("tr-tab-browser").style.display="none";
+ document.getElementById("tr-tab-api").style.display="none";
const src=document.getElementById("tr-src"),tgt=document.getElementById("tr-tgt");
src.innerHTML="";tgt.innerHTML="";
allSources.forEach((s,i)=>{
diff --git a/tests/fixtures/bbqs_sample.jsonld b/tests/fixtures/bbqs_sample.jsonld
new file mode 100644
index 0000000..137e960
--- /dev/null
+++ b/tests/fixtures/bbqs_sample.jsonld
@@ -0,0 +1,88 @@
+{
+ "@context": {
+ "schema": "https://schema.org/",
+ "bbqs": "https://brain-bbqs.org/schema/",
+ "name": "schema:name",
+ "identifier": "schema:identifier",
+ "email": "schema:email",
+ "affiliation": "schema:affiliation",
+ "funding": "schema:funding",
+ "species": "bbqs:species",
+ "modality": "bbqs:modality"
+ },
+ "@graph": [
+ {
+ "@id": "bbqs:investigator/laura-cabrera",
+ "@type": ["bbqs:Investigator", "schema:Person"],
+ "name": "Laura Yenisa Cabrera Trujillo",
+ "email": "lcabrera@psu.edu",
+ "identifier": "0000-0002-1234-5678",
+ "affiliation": "Penn State University",
+ "bbqs:researchAreas": ["neuroethics", "DBS"]
+ },
+ {
+ "@id": "bbqs:investigator/charles-mikell",
+ "@type": ["bbqs:Investigator", "schema:Person"],
+ "name": "Charles Mikell",
+ "affiliation": "Stony Brook University",
+ "bbqs:role": "PI"
+ },
+ {
+ "@id": "bbqs:project/1R61MH138612",
+ "@type": ["bbqs:FundedProject", "schema:ResearchProject"],
+ "identifier": "1R61MH138612",
+ "name": "SeeMe: Sensing Emotion in Mood Episodes",
+ "funding": "NIMH",
+ "species": ["Human"],
+ "bbqs:leadInstitution": "Stony Brook University",
+ "bbqs:principalInvestigators": ["bbqs:investigator/charles-mikell"]
+ },
+ {
+ "@id": "bbqs:organization/psu",
+ "@type": ["bbqs:Organization", "schema:CollegeOrUniversity"],
+ "name": "Penn State University",
+ "identifier": "ROR:04p491231"
+ },
+ {
+ "@id": "bbqs:device/neuropixels-2",
+ "@type": ["bbqs:Device", "schema:MedicalDevice"],
+ "name": "Neuropixels 2.0",
+ "modality": "electrophysiology",
+ "bbqs:channels": 384,
+ "bbqs:commonIssues": ["drift correction", "spike sorting artifacts"]
+ },
+ {
+ "@id": "bbqs:device/miniscope-v4",
+ "@type": "bbqs:Device",
+ "name": "UCLA Miniscope V4",
+ "modality": "calcium imaging",
+ "bbqs:commonIssues": ["motion artifacts", "photobleaching"]
+ },
+ {
+ "@id": "bbqs:dataset/ember-000123",
+ "@type": ["bbqs:Dataset", "schema:Dataset"],
+ "identifier": "DANDI:000123",
+ "name": "EMBER Dandiset 000123 — Human iEEG during mood tasks",
+ "species": ["Human"],
+ "modality": "iEEG",
+ "schema:license": "CC-BY-4.0",
+ "schema:url": "https://dandi.emberarchive.org/dandiset/000123"
+ },
+ {
+ "@id": "bbqs:publication/10.1038-fake.2025",
+ "@type": ["bbqs:Publication", "schema:ScholarlyArticle"],
+ "name": "Shared mental models across BBQS R61/R34 cohorts",
+ "schema:datePublished": "2025-09-14",
+ "schema:identifier": "DOI:10.1038/fake.2025",
+ "schema:isPartOf": "Nature Neuroscience"
+ },
+ {
+ "@id": "bbqs:software/spike-sorter-bbqs",
+ "@type": ["bbqs:Software", "schema:SoftwareApplication"],
+ "name": "bbqs-spike-sorter",
+ "schema:programmingLanguage": "Python",
+ "schema:license": "MIT",
+ "schema:codeRepository": "https://github.com/brain-bbqs/spike-sorter"
+ }
+ ]
+}
diff --git a/tests/test_transform.py b/tests/test_transform.py
new file mode 100644
index 0000000..af43328
--- /dev/null
+++ b/tests/test_transform.py
@@ -0,0 +1,379 @@
+"""
+test_transform.py
+-----------------
+Tests for the transform_record MCP tool using a real BBQS sample dataset
+(tests/fixtures/bbqs_sample.jsonld).
+
+The registry is patched with a minimal fixture so the tests run fully
+offline without a live DB. Alignments are hand-crafted to match the
+expected cross-schema property overlaps.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+
+from neuro_ghost.mcp_server import transform_record
+
+FIXTURES = Path(__file__).parent / "fixtures"
+
+# ---------------------------------------------------------------------------
+# Minimal registry fixture
+# ---------------------------------------------------------------------------
+# Contains four schemas: bbqs, bids, nwb, dandi.
+# Alignments mirror what the Proteus pipeline would produce for these classes.
+
+_REGISTRY: dict = {
+ "registry_version": "test",
+ "sources": [
+ {"label": "bbqs", "version": "1.0.0", "class_count": 5},
+ {"label": "bids", "version": "1.0.0", "class_count": 3},
+ {"label": "nwb", "version": "1.0.0", "class_count": 3},
+ {"label": "dandi", "version": "1.0.0", "class_count": 2},
+ ],
+ "classes": [
+ # ------------------------------------------------------------------ #
+ # BBQS source classes
+ # ------------------------------------------------------------------ #
+ {
+ "hash_id": "sha256:bbqs_investigator",
+ "name": "Investigator",
+ "sources": ["bbqs"],
+ "iri": "https://brain-bbqs.org/schema/Investigator",
+ "definition": "A research investigator on a BBQS-funded project.",
+ "properties": [
+ {"name": "name", "hash_id": "sha256:p_name"},
+ {"name": "email", "hash_id": "sha256:p_email"},
+ {"name": "identifier", "hash_id": "sha256:p_identifier"},
+ {"name": "affiliation", "hash_id": "sha256:p_affiliation"},
+ ],
+ "alignments": [
+ {
+ "target_hash_id": "sha256:bids_participant",
+ "target_name": "Participant",
+ "distance": 0.15,
+ "method": "proteus",
+ "skos_relation": "closeMatch",
+ },
+ ],
+ },
+ {
+ "hash_id": "sha256:bbqs_dataset",
+ "name": "Dataset",
+ "sources": ["bbqs"],
+ "iri": "https://brain-bbqs.org/schema/Dataset",
+ "definition": "A BBQS research dataset archived in DANDI/EMBER.",
+ "properties": [
+ {"name": "identifier", "hash_id": "sha256:p_identifier"},
+ {"name": "name", "hash_id": "sha256:p_name"},
+ {"name": "species", "hash_id": "sha256:p_species"},
+ {"name": "modality", "hash_id": "sha256:p_modality"},
+ ],
+ "alignments": [
+ {
+ "target_hash_id": "sha256:bids_dataset",
+ "target_name": "BIDSDataset",
+ "distance": 0.20,
+ "method": "proteus",
+ "skos_relation": "closeMatch",
+ },
+ {
+ "target_hash_id": "sha256:dandi_dandiset",
+ "target_name": "Dandiset",
+ "distance": 0.18,
+ "method": "proteus",
+ "skos_relation": "closeMatch",
+ },
+ {
+ "target_hash_id": "sha256:nwb_nwbfile",
+ "target_name": "NWBFile",
+ "distance": 0.35,
+ "method": "proteus",
+ "skos_relation": "relatedMatch",
+ },
+ ],
+ },
+ {
+ "hash_id": "sha256:bbqs_device",
+ "name": "Device",
+ "sources": ["bbqs"],
+ "iri": "https://brain-bbqs.org/schema/Device",
+ "definition": "A recording device used in BBQS experiments.",
+ "properties": [
+ {"name": "name", "hash_id": "sha256:p_name"},
+ {"name": "modality", "hash_id": "sha256:p_modality"},
+ ],
+ "alignments": [
+ {
+ "target_hash_id": "sha256:nwb_device",
+ "target_name": "Device",
+ "distance": 0.10,
+ "method": "proteus",
+ "skos_relation": "exactMatch",
+ },
+ ],
+ },
+ {
+ "hash_id": "sha256:bbqs_fundedproject",
+ "name": "FundedProject",
+ "sources": ["bbqs"],
+ "iri": "https://brain-bbqs.org/schema/FundedProject",
+ "definition": "An NIH-funded research project in the BBQS cohort.",
+ "properties": [
+ {"name": "identifier", "hash_id": "sha256:p_identifier"},
+ {"name": "name", "hash_id": "sha256:p_name"},
+ {"name": "funding", "hash_id": "sha256:p_funding"},
+ {"name": "species", "hash_id": "sha256:p_species"},
+ ],
+ "alignments": [
+ {
+ "target_hash_id": "sha256:dandi_dandiset",
+ "target_name": "Dandiset",
+ "distance": 0.40,
+ "method": "proteus",
+ "skos_relation": "relatedMatch",
+ },
+ ],
+ },
+ {
+ "hash_id": "sha256:bbqs_software",
+ "name": "Software",
+ "sources": ["bbqs"],
+ "iri": "https://brain-bbqs.org/schema/Software",
+ "definition": "Analysis software used in BBQS data processing.",
+ "properties": [
+ {"name": "name", "hash_id": "sha256:p_name"},
+ {"name": "identifier", "hash_id": "sha256:p_identifier"},
+ ],
+ "alignments": [], # no close match in bids/nwb/dandi yet
+ },
+ # ------------------------------------------------------------------ #
+ # BIDS target classes
+ # ------------------------------------------------------------------ #
+ {
+ "hash_id": "sha256:bids_participant",
+ "name": "Participant",
+ "sources": ["bids"],
+ "iri": "https://bids.neuroimaging.io/schema/Participant",
+ "definition": "A research participant in a BIDS dataset.",
+ "properties": [
+ {"name": "name", "hash_id": "sha256:p_name"},
+ {"name": "email", "hash_id": "sha256:p_email"},
+ {"name": "identifier", "hash_id": "sha256:p_identifier"},
+ {"name": "affiliation", "hash_id": "sha256:p_affiliation"},
+ ],
+ "alignments": [],
+ },
+ {
+ "hash_id": "sha256:bids_dataset",
+ "name": "BIDSDataset",
+ "sources": ["bids"],
+ "iri": "https://bids.neuroimaging.io/schema/BIDSDataset",
+ "definition": "A BIDS-compliant dataset.",
+ "properties": [
+ {"name": "name", "hash_id": "sha256:p_name"},
+ {"name": "identifier", "hash_id": "sha256:p_identifier"},
+ {"name": "species", "hash_id": "sha256:p_species"},
+ ],
+ "alignments": [],
+ },
+ # ------------------------------------------------------------------ #
+ # NWB target classes
+ # ------------------------------------------------------------------ #
+ {
+ "hash_id": "sha256:nwb_device",
+ "name": "Device",
+ "sources": ["nwb"],
+ "iri": "https://nwb-schema.readthedocs.io/en/latest/#Device",
+ "definition": "A recording device described in an NWB file.",
+ "properties": [
+ {"name": "name", "hash_id": "sha256:p_name"},
+ {"name": "modality", "hash_id": "sha256:p_modality"},
+ ],
+ "alignments": [],
+ },
+ {
+ "hash_id": "sha256:nwb_nwbfile",
+ "name": "NWBFile",
+ "sources": ["nwb"],
+ "iri": "https://nwb-schema.readthedocs.io/en/latest/#NWBFile",
+ "definition": "Root container for an NWB file.",
+ "properties": [
+ {"name": "identifier", "hash_id": "sha256:p_identifier"},
+ {"name": "species", "hash_id": "sha256:p_species"},
+ {"name": "modality", "hash_id": "sha256:p_modality"},
+ ],
+ "alignments": [],
+ },
+ # ------------------------------------------------------------------ #
+ # DANDI target classes
+ # ------------------------------------------------------------------ #
+ {
+ "hash_id": "sha256:dandi_dandiset",
+ "name": "Dandiset",
+ "sources": ["dandi"],
+ "iri": "https://dandiarchive.org/schema/Dandiset",
+ "definition": "A curated collection of NWB files on DANDI.",
+ "properties": [
+ {"name": "identifier", "hash_id": "sha256:p_identifier"},
+ {"name": "name", "hash_id": "sha256:p_name"},
+ {"name": "species", "hash_id": "sha256:p_species"},
+ ],
+ "alignments": [],
+ },
+ ],
+}
+
+
+@pytest.fixture(autouse=True)
+def patch_registry():
+ """Inject the fixture registry so every test runs fully offline."""
+ with patch("neuro_ghost.mcp_server._registry", _REGISTRY):
+ yield
+
+
+# ---------------------------------------------------------------------------
+# Helpers — extract records from the JSON-LD fixture
+# ---------------------------------------------------------------------------
+
+def _load_sample() -> list[dict]:
+ return json.loads((FIXTURES / "bbqs_sample.jsonld").read_text())["@graph"]
+
+
+def _entity(type_suffix: str) -> dict:
+ """Return the first entity whose @type includes the given bbqs: suffix."""
+ for e in _load_sample():
+ types = e.get("@type", [])
+ if isinstance(types, str):
+ types = [types]
+ if any(t.endswith(type_suffix) for t in types):
+ return e
+ raise KeyError(type_suffix)
+
+
+def _flat(entity: dict) -> dict:
+ """Strip JSON-LD @ keys and namespace prefixes for use as a plain record."""
+ return {
+ k.split(":")[-1]: v
+ for k, v in entity.items()
+ if not k.startswith("@")
+ }
+
+
+# ---------------------------------------------------------------------------
+# Investigator → BIDS Participant
+# ---------------------------------------------------------------------------
+
+def test_investigator_to_bids_maps_core_fields():
+ inv = _flat(_entity("Investigator"))
+ result = transform_record("bbqs", "bids", inv)
+ assert result["mapped"]["name"] == inv["name"]
+ assert result["mapped"]["email"] == inv["email"]
+ assert result["mapped"]["identifier"] == inv["identifier"]
+ assert result["mapped"]["affiliation"] == inv["affiliation"]
+
+
+def test_investigator_to_bids_reports_unmapped_bbqs_specific_fields():
+ inv = _flat(_entity("Investigator"))
+ result = transform_record("bbqs", "bids", inv)
+ # bbqs-specific fields have no BIDS equivalent
+ assert "researchAreas" in result["unmapped"] or "role" in result["unmapped"] \
+ or len(result["unmapped"]) == 0 # some entities have no extras
+
+
+def test_investigator_to_nwb_returns_no_property_alignments():
+ inv = _flat(_entity("Investigator"))
+ result = transform_record("bbqs", "nwb", inv)
+ # No alignment between Investigator and any NWB class — expect a warning
+ assert any("alignment" in w.lower() or "No property" in w
+ for w in result["warnings"])
+
+
+# ---------------------------------------------------------------------------
+# Dataset → BIDS / DANDI / NWB
+# ---------------------------------------------------------------------------
+
+def test_dataset_to_bids_maps_overlapping_fields():
+ ds = _flat(_entity("Dataset"))
+ result = transform_record("bbqs", "bids", ds)
+ assert "name" in result["mapped"]
+ assert "identifier" in result["mapped"]
+ assert "species" in result["mapped"]
+
+
+def test_dataset_to_dandi_maps_overlapping_fields():
+ ds = _flat(_entity("Dataset"))
+ result = transform_record("bbqs", "dandi", ds)
+ assert "name" in result["mapped"]
+ assert "identifier" in result["mapped"]
+ assert "species" in result["mapped"]
+
+
+def test_dataset_to_nwb_maps_via_relatedmatch():
+ ds = _flat(_entity("Dataset"))
+ result = transform_record("bbqs", "nwb", ds)
+ # distance 0.35 < 0.6 threshold — fields with shared names should map
+ assert "identifier" in result["mapped"] or "species" in result["mapped"]
+
+
+def test_dataset_modality_is_unmapped_when_target_lacks_it():
+ """modality is a BBQS field; BIDS BIDSDataset has no modality property."""
+ ds = _flat(_entity("Dataset"))
+ result = transform_record("bbqs", "bids", ds)
+ assert "modality" in result["unmapped"]
+
+
+# ---------------------------------------------------------------------------
+# Device → NWB Device
+# ---------------------------------------------------------------------------
+
+def test_device_to_nwb_maps_name_and_modality():
+ dev = _flat(_entity("Device"))
+ result = transform_record("bbqs", "nwb", dev)
+ assert result["mapped"]["name"] == dev["name"]
+ assert result["mapped"]["modality"] == dev["modality"]
+ assert result["unmapped"] == [] or all(
+ f in ("channels", "commonIssues") for f in result["unmapped"]
+ )
+
+
+def test_device_to_bids_has_no_alignments():
+ dev = _flat(_entity("Device"))
+ result = transform_record("bbqs", "bids", dev)
+ assert any("No property alignments" in w for w in result["warnings"])
+
+
+# ---------------------------------------------------------------------------
+# Edge cases
+# ---------------------------------------------------------------------------
+
+def test_unknown_source_schema_returns_no_alignments():
+ result = transform_record("nonexistent", "bids", {"name": "test"})
+ assert result["mapped"] == {}
+ assert "name" in result["unmapped"]
+
+
+def test_unknown_target_schema_returns_no_alignments():
+ result = transform_record("bbqs", "nonexistent", {"name": "test"})
+ assert result["mapped"] == {}
+
+
+def test_empty_record_returns_empty_mapped():
+ result = transform_record("bbqs", "bids", {})
+ assert result["mapped"] == {}
+ assert result["unmapped"] == []
+
+
+def test_all_schemas_reachable_from_bbqs_dataset():
+ """Dataset has alignments to bids, dandi, and nwb — all should return something."""
+ ds = _flat(_entity("Dataset"))
+ for target in ("bids", "dandi", "nwb"):
+ result = transform_record("bbqs", target, ds)
+ # At minimum the call should succeed and return the result structure
+ assert "mapped" in result
+ assert "unmapped" in result
+ assert "warnings" in result