Deploy Python probe API

.gitattributes

@@ -1,35 +1,6 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
 *.npy filter=lfs diff=lfs merge=lfs -text
 *.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.obj filter=lfs diff=lfs merge=lfs -text
+*.nii filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,4 @@
+.git
+__pycache__
+*.pyc
+.env

README.md

@@ -1,53 +1,10 @@
----
+---
 title: MindVisualizer API
 sdk: docker
 app_port: 7860
 pinned: false
 ---
 
-# MindVisualizer Python API
-
-Small FastAPI adapter for the hosted web page.
-
-The GitHub Pages frontend renders particles locally. This backend is only for algorithm-sensitive operations:
-
-- recomputing probe trajectories with the original Python `TriLinearSampler`
-- mapping a trajectory to ROI vectors with the original `ManifoldToROIKNN`
-- building the original `ROIFlowAnalyzer` context
-- optionally calling the original `ROIFlowLLM` if `OPENAI_API_KEY` is configured
-
-## Cheapest hosting shape
-
-Use a free Python container host and keep the service public:
-
-1. Bundle the minimal `mindVisualizer` source/data folder into the Space as `/app/mindVisualizer`.
-2. Deploy this directory as a Docker/FastAPI service.
-3. Set `MINDVIS_REPO=/app/mindVisualizer`.
-4. Set `CORS_ORIGINS=https://pixedar.github.io,http://localhost:3000`.
-5. In the GitHub Pages build, set `NEXT_PUBLIC_MINDVIS_API_URL=https://your-backend-url`.
-6. Optional: set `OPENAI_API_KEY` as a Space secret and `OPENAI_MODEL` as a Space variable.
-
-Current Space:
-
-- https://huggingface.co/spaces/Pixedar/mindvisualizer-api
-- https://pixedar-mindvisualizer-api.hf.space
-
-Do not install the full `mindVisualizer` `pyproject.toml` on a tiny free host; it pulls desktop/VTK dependencies. This adapter uses `PYTHONPATH` and installs only the packages needed for the API path.
-
-## Local run
-
-```bash
-cd backend/mindvis-api
-python -m venv .venv
-.venv\Scripts\activate
-pip install -r requirements.txt
-$env:MINDVIS_REPO="C:\Users\assas\PycharmProjects\mindVisualizer"
-uvicorn app:app --reload --port 7860
-```
-
-Then build/run the web page with:
+# MindVisualizer API
 
-```bash
-$env:NEXT_PUBLIC_MINDVIS_API_URL="http://localhost:7860"
-pnpm dev
-```
+Python-backed probe trajectory and explanation service.

app.py

@@ -1,4 +1,3 @@
-import json
 import os
 import sys
 from functools import lru_cache
@@ -11,20 +10,20 @@ from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel, Field
 
 
-FieldMode = Literal["mean", "gated", "mu1", "mu2"]
+FieldMode = Literal["mean", "gated", "mu1", "mu2", "mu3", "mu4", "mu5", "mu6"]
 
 
 class TrajectoryRequest(BaseModel):
     seed: list[float] = Field(default_factory=lambda: [0.5, 0.5, 0.5], min_length=3, max_length=3)
-    fieldMode: FieldMode = "gated"
-    steps: int = Field(default=220, ge=8, le=1200)
-    dt: float = Field(default=0.018, gt=0, le=0.1)
-    speedScale: float = Field(default=0.48, gt=0, le=4)
+    fieldMode: FieldMode = "mean"
+    steps: int = Field(default=700, ge=8, le=1600)
+    dt: float = Field(default=1.0, gt=0, le=8.0)
+    speedScale: float = Field(default=1.0, gt=0, le=8.0)
 
 
 class ExplainRequest(BaseModel):
     trajectory: list[list[float]] = Field(min_length=2)
-    fieldMode: FieldMode = "gated"
+    fieldMode: FieldMode = "mean"
     callLlm: bool = True
 
 
@@ -40,49 +39,80 @@ def mindvis_repo() -> Path:
 @lru_cache(maxsize=1)
 def services():
     repo = mindvis_repo()
+    data = repo / "data"
 
     from src.field_loader import TriLinearSampler, load_field
-    from src.roi_flow import ManifoldToROIKNN, ROIFlowAnalyzer
+    from src.mesh_overlay import FlowMeshOverlay
 
-    data = repo / "data"
-    flow = load_field(data / "roi_flow" / "mdn_universal_raw_grid64_meta.json")
+    flow = load_field(data / "mdn" / "mdn_particles_rdcim_teacher_edge_hq_grid125_meta.json")
     mean = flow["mean"]
-    mu1 = flow["mus"][0]
-    mu2 = flow["mus"][1]
+    mus = flow["mus"]
     pi = flow["pi"]
-    gated = np.where(np.argmax(pi, axis=-1)[..., None] == 0, mu1, mu2).astype(np.float32)
-
-    samplers = {
-        "mean": TriLinearSampler(mean, flow["amin"], flow["amax"]),
-        "gated": TriLinearSampler(gated, flow["amin"], flow["amax"]),
-        "mu1": TriLinearSampler(mu1, flow["amin"], flow["amax"]),
-        "mu2": TriLinearSampler(mu2, flow["amin"], flow["amax"]),
-    }
-
-    embed = np.load(data / "roi_flow" / "probe_embed.npy").astype(np.float32)
-    roi = np.load(data / "roi_flow" / "probe_roi.npy").astype(np.float32)
-    centers = np.load(data / "roi_flow" / "probe_roi_centers.npy").astype(np.float32)
-    structures = json.loads((data / "structures.json").read_text(encoding="utf-8"))
-
-    names = []
-    for idx in range(roi.shape[1]):
-        item = structures[idx] if idx < len(structures) else {}
-        names.append(item.get("name") or item.get("acronym") or f"ROI {idx + 1}")
+    ent = flow["ENT"]
+    amin = flow["amin"]
+    amax = flow["amax"]
+    span = amax - amin
+    diag = float(np.linalg.norm(span))
+    target_step = 0.01 * max(diag, 1e-6)
+
+    winner = np.argmax(pi, axis=-1)
+    gated = np.zeros_like(mean, dtype=np.float32)
+    for idx, mu in enumerate(mus):
+        gated += mu * (winner[..., None] == idx).astype(np.float32)
+
+    fields = {"mean": mean, "gated": gated}
+    fields.update({f"mu{idx + 1}": mu for idx, mu in enumerate(mus)})
+    samplers = {name: TriLinearSampler(field, amin, amax) for name, field in fields.items()}
+
+    def vmax(field):
+        mag = np.linalg.norm(field.reshape(-1, 3), axis=1)
+        nz = mag[mag > 0]
+        return float(np.percentile(nz, 100.0)) if nz.size else 1.0
+
+    vmax_by_name = {name: vmax(field) for name, field in fields.items()}
+
+    # The original Python app uses FlowMeshOverlay + its cached label grid for
+    # probe interpretation. This API intentionally reuses that path.
+    import vtk
+
+    ren = vtk.vtkRenderer()
+    win = vtk.vtkRenderWindow()
+    win.OffScreenRenderingOn()
+    win.AddRenderer(ren)
+    mesh = FlowMeshOverlay(ren=ren, win=win, mesh_dir=data / "meshes", alignment_file=data / "brain_alignment.json")
+    cache = data / "label_grid_cache.npz"
+    if not mesh.load_label_grid(cache):
+        raise RuntimeError("Python label_grid_cache is missing; run setup_brain_data.py in mindVisualizer first.")
+
+    # Optional finer parcellation, exactly as the desktop flow mode auto-detects.
+    try:
+        from src.extra_parcellation import ExtraParcellation
+
+        atlas = data / "extra_parcellation" / "combined_atlas.nii.gz"
+        labels = data / "extra_parcellation" / "combined_atlas_labels.json"
+        if atlas.exists():
+            extra = ExtraParcellation(atlas, labels if labels.exists() else None)
+            if extra.load():
+                mesh.set_extra_parcellation(extra)
+    except Exception as exc:
+        print(f"[extra-parcellation] disabled: {exc}")
 
-    mapper = ManifoldToROIKNN(embed, roi, k=256)
-    analyzer = ROIFlowAnalyzer(names, centers[: len(names)])
+    ent_sampler = TriLinearSampler(mean, amin, amax) if ent is not None else None
 
     return {
-        "amin": flow["amin"],
-        "amax": flow["amax"],
-        "span": flow["amax"] - flow["amin"],
+        "amin": amin,
+        "amax": amax,
+        "span": span,
         "samplers": samplers,
-        "mapper": mapper,
-        "analyzer": analyzer,
+        "vmax": vmax_by_name,
+        "target_step": target_step,
+        "mesh": mesh,
+        "entropy": ent,
+        "entropy_sampler": ent_sampler,
     }
 
 
-app = FastAPI(title="MindVisualizer API", version="0.1.0")
+app = FastAPI(title="MindVisualizer API", version="1.0.0")
 
 origins = [origin.strip() for origin in os.environ.get("CORS_ORIGINS", "*").split(",") if origin.strip()]
 app.add_middleware(
@@ -105,10 +135,16 @@ def health():
 @app.post("/api/mindvis/trajectory")
 def trajectory(req: TrajectoryRequest):
     svc = services()
-    path = integrate_path(req.seed, req.fieldMode, req.steps, req.dt, req.speedScale)
+    world, mags, stopped = integrate_probe(req, svc)
+    transitions = analyze_regions(world, mags, req.callLlm if hasattr(req, "callLlm") else False)
     return {
         "fieldMode": req.fieldMode,
-        "trajectory": normalized(path, svc["amin"], svc["span"]).tolist(),
+        "trajectory": normalized(world, svc["amin"], svc["span"]).tolist(),
+        "worldTrajectory": world.tolist(),
+        "fieldMagnitudes": mags.tolist(),
+        "stopped": stopped,
+        "transitions": transitions,
+        "regionText": format_transition_text(transitions),
     }
 
 
@@ -120,42 +156,82 @@ def explain(req: ExplainRequest):
         raise HTTPException(status_code=400, detail="trajectory must be an array of [x,y,z] points")
 
     world = denormalized(np.clip(traj, 0.0, 1.0), svc["amin"], svc["span"])
-    mapper = svc["mapper"]
-    analyzer = svc["analyzer"]
-    start_roi = mapper.query(world[0])
-    end_roi = mapper.query(world[-1])
-    delta = analyzer.compute_delta(start_roi, end_roi)
-    context = analyzer.build_llm_context(delta)
-
-    text = ""
-    if req.callLlm and os.environ.get("OPENAI_API_KEY"):
-        text = call_openai_interpreter(context)
-
-    if not text:
-        analysis = analyzer.analyze_flow_pattern(delta)
-        text = (
-            f"Pattern: {analysis['pattern_type']}. "
-            f"Bulk direction: {analysis['bulk_direction']}. "
-            f"Positive ROIs: {analysis['n_significant_positive']}; "
-            f"negative ROIs: {analysis['n_significant_negative']}."
+    sampler = svc["samplers"].get(req.fieldMode, svc["samplers"]["mean"])
+    mags = np.linalg.norm(sampler.sample_vec(world), axis=1).astype(np.float32)
+    transitions = analyze_regions(world, mags, req.callLlm)
+    text = format_transition_text(transitions)
+
+    if req.callLlm:
+        from src.region_analyzer import analyze_with_gpt
+
+        text = analyze_with_gpt(
+            transitions,
+            use_rag=os.environ.get("MINDVIS_USE_RAG", "0") == "1",
+            model=os.environ.get("OPENAI_MODEL", "gpt-5.4-mini"),
+            debug=os.environ.get("MINDVIS_DEBUG", "0") == "1",
         )
 
-    return {"text": text, "context": context}
+    return {"text": text, "transitions": transitions, "regionText": format_transition_text(transitions)}
+
+
+def integrate_probe(req: TrajectoryRequest, svc: dict) -> tuple[np.ndarray, np.ndarray, bool]:
+    p = denormalized(np.asarray(req.seed[:3], dtype=np.float32)[None, :], svc["amin"], svc["span"])
+    sampler = svc["samplers"].get(req.fieldMode, svc["samplers"]["mean"])
+    step = req.dt * req.speedScale * (svc["target_step"] / max(svc["vmax"].get(req.fieldMode, 1.0), 1e-9))
+    path = []
+    mags = []
+    stopped = False
+
+    for _ in range(req.steps):
+        path.append(p[0].copy())
+        velocity = sampler.sample_vec(p)[0]
+        mags.append(float(np.linalg.norm(velocity)))
+        new_pos = np.clip(p[0] + velocity * step, svc["amin"], svc["amax"])
+
+        # Original probe boundary behavior: if a step leaves labelled brain
+        # space, try half-step; otherwise stop instead of wrapping.
+        if not is_valid_probe_position(new_pos, svc["mesh"]):
+            half = np.clip(p[0] + velocity * step * 0.5, svc["amin"], svc["amax"])
+            if is_valid_probe_position(half, svc["mesh"]):
+                new_pos = half
+            else:
+                stopped = True
+                break
 
+        if np.linalg.norm(new_pos - p[0]) < 1e-5:
+            stopped = True
+            break
+        p[0] = new_pos.astype(np.float32)
 
-def integrate_path(seed: list[float], field_mode: FieldMode, steps: int, dt: float, speed_scale: float) -> np.ndarray:
+    return np.asarray(path, dtype=np.float32), np.asarray(mags, dtype=np.float32), stopped
+
+
+def is_valid_probe_position(point: np.ndarray, mesh) -> bool:
+    key = mesh.get_region_at_point(point)
+    if key is None:
+        key = mesh.find_nearest_region(point, search_radius=2, max_distance_mm=5.0)
+    return key is not None
+
+
+def analyze_regions(world: np.ndarray, mags: np.ndarray, call_llm: bool) -> list[dict]:
     svc = services()
-    p = denormalized(np.asarray(seed[:3], dtype=np.float32)[None, :], svc["amin"], svc["span"])
-    sampler = svc["samplers"][field_mode]
-    path = np.empty((steps, 3), dtype=np.float32)
+    from src.region_analyzer import analyze_probe_path
+
+    transitions = analyze_probe_path(
+        world,
+        svc["mesh"],
+        sample_every=5,
+        field_mags=mags,
+        entropy_sampler=svc["entropy_sampler"],
+        entropy_field=svc["entropy"],
+    )
+    return make_json_safe(transitions)
+
 
-    for idx in range(steps):
-        path[idx] = p[0]
-        v = sampler.sample_vec(p)
-        p = p + v * (dt * speed_scale)
-        p = ((p - svc["amin"]) % svc["span"]) + svc["amin"]
+def format_transition_text(transitions: list[dict]) -> str:
+    from src.region_analyzer import format_transitions_text
 
-    return path
+    return format_transitions_text(transitions)
 
 
 def denormalized(points: np.ndarray, amin: np.ndarray, span: np.ndarray) -> np.ndarray:
@@ -166,44 +242,15 @@ def normalized(points: np.ndarray, amin: np.ndarray, span: np.ndarray) -> np.nda
     return (points.astype(np.float32) - amin[None, :]) / span[None, :]
 
 
-def call_openai_interpreter(context: str) -> str:
-    from openai import OpenAI
-
-    model = os.environ.get("OPENAI_MODEL", "gpt-4o-mini")
-    instructions = (
-        "You are a neuroscientist interpreting brain state dynamics from a "
-        "manifold flow simulation. The user traced a path through a learned "
-        "neural manifold. You are given how each ROI's contribution changed "
-        "along this path.\n\n"
-        "The delta values do not mean simple activation. They measure how each "
-        "ROI's contribution to the overall brain state shifted. Positive means "
-        "the region became a stronger contributor; negative means it became a "
-        "weaker contributor.\n\n"
-        "Do not list every ROI. Synthesize the spatial pattern, directionality, "
-        "and network shift into one coherent interpretation. Keep the response "
-        "to 2 concise paragraphs."
-    )
-
-    try:
-        client = OpenAI(timeout=60.0)
-        if hasattr(client, "responses"):
-            response = client.responses.create(
-                model=model,
-                instructions=instructions,
-                input=context,
-                max_output_tokens=900,
-            )
-            return (response.output_text or "").strip()
-
-        response = client.chat.completions.create(
-            model=model,
-            messages=[
-                {"role": "system", "content": instructions},
-                {"role": "user", "content": context},
-            ],
-            max_tokens=900,
-            temperature=0.3,
-        )
-        return (response.choices[0].message.content or "").strip()
-    except Exception as exc:
-        return f"[GPT ERROR] {exc}"
+def make_json_safe(value):
+    if isinstance(value, np.ndarray):
+        return value.tolist()
+    if isinstance(value, np.generic):
+        return value.item()
+    if isinstance(value, dict):
+        return {str(k): make_json_safe(v) for k, v in value.items() if not str(k).startswith("_")}
+    if isinstance(value, list):
+        return [make_json_safe(v) for v in value]
+    if isinstance(value, tuple):
+        return [make_json_safe(v) for v in value]
+    return value

mindVisualizer/data/brain_alignment.json

@@ -0,0 +1,28 @@
+{
+  "matrix": [
+    [
+      -0.03483789662196803,
+      -0.1654855793300339,
+      0.9305766368497694,
+      -68.4377205759064
+    ],
+    [
+      -0.9422190580860553,
+      -0.06752555868994387,
+      -0.047281905216520856,
+      106.73199000287599
+    ],
+    [
+      0.0747101104018069,
+      -0.9287770749837014,
+      -0.16236864674663903,
+      115.80117022013123
+    ],
+    [
+      0.0,
+      0.0,
+      0.0,
+      1.0
+    ]
+  ]
+}

mindVisualizer/data/extra_parcellation/README.md

@@ -0,0 +1,53 @@
+# Extra Parcellation Data
+
+Provides fine-grained subregion labels alongside the primary Allen atlas meshes.
+
+## Setup (recommended)
+
+Run the setup script to build the combined atlas automatically:
+
+```bash
+python scripts/setup_extra_parcellation.py
+```
+
+This downloads and merges three complementary MNI-space atlases via nilearn:
+
+| Layer | Source | Regions | Role |
+|-------|--------|---------|------|
+| 1 | Harvard-Oxford Cortical | 48 | Broad cortical coverage |
+| 2 | Harvard-Oxford Subcortical | 17 | Thalamus, putamen, caudate, hippocampus, amygdala, etc. |
+| 3 | **Julich-Brain** (highest priority) | 62 | Cytoarchitectonic: motor BA4a/4p, somatosensory BA1-3, visual V1-V5, auditory, Broca's, hippocampal subfields |
+
+**Total: 127 labeled regions**, Julich-Brain labels override coarser Harvard-Oxford
+where available.
+
+Output files (auto-detected by flow mode):
+- `combined_atlas.nii.gz` — NIfTI volume (~700KB)
+- `combined_atlas_labels.json` — label ID to name map
+
+## How it works
+
+The combined atlas is in MNI152 1mm space. The Allen atlas scene space is
+approximately MNI-aligned, so probe coordinates can be used directly for lookups.
+When the exact voxel is unlabeled, a nearby-search fallback finds the closest
+labeled region within a few mm.
+
+When both the primary Allen mesh and the extra parcellation cover a probe position,
+both are highlighted: the Allen mesh in red, the extra parcellation subregion in
+orange. The subregion name is included in the LLM context as additional detail.
+
+## Custom atlas
+
+You can also provide any NIfTI atlas in MNI space:
+
+```bash
+python examples/flow_probe_example.py \
+    --extra-parcellation my_atlas.nii.gz \
+    --extra-parcellation-labels my_atlas_labels.json
+```
+
+## Requirements
+
+```bash
+pip install nilearn nibabel scikit-image
+```

mindVisualizer/data/{roi_flow/mdn_training_all_points.npy → extra_parcellation/combined_atlas.nii.gz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bf047435bda457ab7eb4d59940ae8463555bb92cffadaed9d13aad234123887f
-size 223208
+oid sha256:68b7dbb0b9949e0e3021d60db9d6f1499615560cba955e0a2f6ab2e41962d25a
+size 693799

mindVisualizer/data/extra_parcellation/combined_atlas_labels.json

@@ -0,0 +1,129 @@
+{
+  "1": "Frontal Pole",
+  "2": "Insular Cortex",
+  "3": "Superior Frontal Gyrus",
+  "4": "Middle Frontal Gyrus",
+  "5": "Inferior Frontal Gyrus, pars triangularis",
+  "6": "Inferior Frontal Gyrus, pars opercularis",
+  "7": "Precentral Gyrus",
+  "8": "Temporal Pole",
+  "9": "Superior Temporal Gyrus, anterior division",
+  "10": "Superior Temporal Gyrus, posterior division",
+  "11": "Middle Temporal Gyrus, anterior division",
+  "12": "Middle Temporal Gyrus, posterior division",
+  "13": "Middle Temporal Gyrus, temporooccipital part",
+  "14": "Inferior Temporal Gyrus, anterior division",
+  "15": "Inferior Temporal Gyrus, posterior division",
+  "16": "Inferior Temporal Gyrus, temporooccipital part",
+  "17": "Postcentral Gyrus",
+  "18": "Superior Parietal Lobule",
+  "19": "Supramarginal Gyrus, anterior division",
+  "20": "Supramarginal Gyrus, posterior division",
+  "21": "Angular Gyrus",
+  "22": "Lateral Occipital Cortex, superior division",
+  "23": "Lateral Occipital Cortex, inferior division",
+  "24": "Intracalcarine Cortex",
+  "25": "Frontal Medial Cortex",
+  "26": "Juxtapositional Lobule Cortex (formerly Supplementary Motor Cortex)",
+  "27": "Subcallosal Cortex",
+  "28": "Paracingulate Gyrus",
+  "29": "Cingulate Gyrus, anterior division",
+  "30": "Cingulate Gyrus, posterior division",
+  "31": "Precuneous Cortex",
+  "32": "Cuneal Cortex",
+  "33": "Frontal Orbital Cortex",
+  "34": "Parahippocampal Gyrus, anterior division",
+  "35": "Parahippocampal Gyrus, posterior division",
+  "36": "Lingual Gyrus",
+  "37": "Temporal Fusiform Cortex, anterior division",
+  "38": "Temporal Fusiform Cortex, posterior division",
+  "39": "Temporal Occipital Fusiform Cortex",
+  "40": "Occipital Fusiform Gyrus",
+  "41": "Frontal Opercular Cortex",
+  "42": "Central Opercular Cortex",
+  "43": "Parietal Opercular Cortex",
+  "44": "Planum Polare",
+  "45": "Heschl's Gyrus (includes H1 and H2)",
+  "46": "Planum Temporale",
+  "47": "Supracalcarine Cortex",
+  "48": "Occipital Pole",
+  "103": "Left Lateral Ventricle",
+  "104": "Left Thalamus",
+  "105": "Left Caudate",
+  "106": "Left Putamen",
+  "107": "Left Pallidum",
+  "108": "Brain-Stem",
+  "109": "Left Hippocampus",
+  "110": "Left Amygdala",
+  "111": "Left Accumbens",
+  "114": "Right Lateral Ventricle",
+  "115": "Right Thalamus",
+  "116": "Right Caudate",
+  "117": "Right Putamen",
+  "118": "Right Pallidum",
+  "119": "Right Hippocampus",
+  "120": "Right Amygdala",
+  "121": "Right Accumbens",
+  "201": "Amygdala_centromedial group",
+  "202": "Amygdala_laterobasal group",
+  "203": "Amygdala_superficial group",
+  "204": "Anterior intra-parietal sulcus hIP1",
+  "205": "Anterior intra-parietal sulcus hIP2",
+  "206": "Anterior intra-parietal sulcus hIP3",
+  "207": "Broca's area BA44",
+  "208": "Broca's area BA45",
+  "209": "Hippocampus cornu ammonis",
+  "210": "Hippocampus dentate gyrus",
+  "211": "Hippocampus entorhinal cortex",
+  "212": "Hippocampus hippocampal-amygdaloid transition area",
+  "213": "Hippocampus subiculum",
+  "214": "Inferior parietal lobule PF",
+  "215": "Inferior parietal lobule PFcm",
+  "216": "Inferior parietal lobule PFm",
+  "217": "Inferior parietal lobule PFop",
+  "218": "Inferior parietal lobule PFt",
+  "219": "Inferior parietal lobule PGp",
+  "220": "Inferior parietal lobule Pga",
+  "221": "Insula Id1",
+  "222": "Insula Ig1",
+  "223": "Insula Ig2",
+  "224": "Lateral geniculate body",
+  "225": "Mamillary body",
+  "226": "Medial geniculate body",
+  "227": "Premotor cortex BA6",
+  "228": "Primary auditory cortex TE1.0",
+  "229": "Primary auditory cortex TE1.1",
+  "230": "Primary auditory cortex TE1.2",
+  "231": "Primary motor cortex BA4a",
+  "232": "Primary motor cortex BA4p",
+  "233": "Primary somatosensory cortex BA1",
+  "234": "Primary somatosensory cortex BA2",
+  "235": "Primary somatosensory cortex BA3a",
+  "236": "Primary somatosensory cortex BA3b",
+  "237": "Secondary somatosensory cortex / Parietal operculum OP1",
+  "238": "Secondary somatosensory cortex / Parietal operculum OP2",
+  "239": "Secondary somatosensory cortex / Parietal operculum OP3",
+  "240": "Secondary somatosensory cortex / Parietal operculum OP4",
+  "241": "Superior parietal lobule 5Ci",
+  "242": "Superior parietal lobule 5L",
+  "243": "Superior parietal lobule 5M",
+  "244": "Superior parietal lobule 7A",
+  "245": "Superior parietal lobule 7M",
+  "246": "Superior parietal lobule 7P",
+  "247": "Superior parietal lobule 7PC",
+  "248": "Visual cortex V1 BA17",
+  "249": "Visual cortex V2 BA18",
+  "250": "Visual cortex V3V",
+  "251": "Visual cortex V4",
+  "252": "Visual cortex V5",
+  "253": "WM: Acoustic radiation",
+  "254": "WM: Callosal body",
+  "255": "WM: Cingulum",
+  "256": "WM: Corticospinal tract",
+  "257": "WM: Fornix",
+  "258": "WM: Inferior occipito-frontal fascicle",
+  "259": "WM: Optic radiation",
+  "260": "WM: Superior longitudinal fascicle",
+  "261": "WM: Superior occipito-frontal fascicle",
+  "262": "WM: Uncinate fascicle"
+}

mindVisualizer/data/label_grid_cache.keys.json

@@ -0,0 +1 @@
+["", "10159", "10292", "10306", "10307", "10308", "10311", "10313", "10327", "10329", "10330", "10331", "10332", "10333", "10334", "10335", "10336", "10337", "10338", "10339", "10342", "10343", "10344", "10346", "10349", "10361", "10383", "10384", "10389", "10390", "10465", "10466", "10467", "10595", "10596", "10597", "10598", "10599", "10600", "10602", "10648", "10649", "10650", "10651", "10653", "10654", "10655", "10656", "10657", "10658", "10659", "10660", "10661", "10662", "10669", "12114", "12115", "12116", "12117", "12118", "12119", "12121", "12122", "12123", "12124", "12125", "12126", "12127", "12132", "12133", "12136", "12137", "12138", "12140", "12142", "12143", "12144", "12145", "12146", "12147", "12148", "12149", "12150", "12151", "12152", "12153", "12154", "12155", "12156", "12157", "12158", "12159", "12160", "12162", "12163", "12164", "12165", "12166", "12170", "12171", "12173", "12174", "12176", "12177", "12178", "12179", "12181", "12195", "12291", "12292", "12305", "12330", "12354", "12369", "12384", "12390", "12405", "12416", "12535", "12538", "12805", "12826", "13229", "13230", "146034872", "146034876", "146034884", "146034888", "266441657"]

mindVisualizer/data/{roi_flow/probe_embed.npy → label_grid_cache.npz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:baecae7d26b1d76bace68d96f005b0dfc0e737dd83383345e819b669259b5af1
-size 113384
+oid sha256:ab71af20799b4cefb9986791337fcb609105b4653141cde9285d486cbfaa4791
+size 101223

mindVisualizer/data/{roi_flow/mdn_universal_all_grid64_entropy.bin → mdn/mdn_particles_rdcim_teacher_edge_hq_grid125_entropy.bin}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5ef8fb7dace16f77b06b262e09dbbbd0070c21d5ac71e49b280a4af5e34fee2e
-size 1048576
+oid sha256:2dc61d9b05f915dad3e8ade5e5d9e5f1df27983cbeaf34c0232010ab7e0008d7
+size 7812500

mindVisualizer/data/mdn/mdn_particles_rdcim_teacher_edge_hq_grid125_mean_xyz3.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34525f6bf4a68ac06f87801aa180b57cfea10c8c8559ea67d23d66ea54865109
+size 23437500

mindVisualizer/data/mdn/mdn_particles_rdcim_teacher_edge_hq_grid125_meta.json

@@ -0,0 +1 @@
+{"grid":125,"axis_min":[-69.5,-104.5625,-54.25],"axis_max":[70.0625,76.09375,81.8125],"files":{"mean_xyz3":"mdn_particles_rdcim_teacher_edge_hq_grid125_mean_xyz3.bin","pi":"mdn_particles_rdcim_teacher_edge_hq_grid125_pi_xyz6.bin","entropy":"mdn_particles_rdcim_teacher_edge_hq_grid125_entropy.bin","mu1_xyz3":"mdn_particles_rdcim_teacher_edge_hq_grid125_mu1_xyz3.bin","mu2_xyz3":"mdn_particles_rdcim_teacher_edge_hq_grid125_mu2_xyz3.bin","mu3_xyz3":"mdn_particles_rdcim_teacher_edge_hq_grid125_mu3_xyz3.bin","mu4_xyz3":"mdn_particles_rdcim_teacher_edge_hq_grid125_mu4_xyz3.bin","mu5_xyz3":"mdn_particles_rdcim_teacher_edge_hq_grid125_mu5_xyz3.bin","mu6_xyz3":"mdn_particles_rdcim_teacher_edge_hq_grid125_mu6_xyz3.bin"},"dtype":"float32","layout":"C-order (x,y,z,3) for *_xyz3; (x,y,z,K) for pi_xyzK; (x,y,z) for entropy","endianness":"little","mixture_mean_field":true,"model":{"quality":"hq","components":6},"mdn_source":"particles/rdcim_teacher_edge","bounds_mode":"full_brain","mode":"field","notes":"Trained on teacher segments (XYZ→ΔS) with optional rDCM conditioning and ROI embeddings","training_points_npy":"mdn_particles_rdcim_teacher_edge_hq_training_points.npy"}

mindVisualizer/data/mdn/mdn_particles_rdcim_teacher_edge_hq_grid125_mu1_xyz3.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44c45d1ccd4c2afb936d7a4622493b1d363df9664f6ce53cb26cd5bd892b79e8
+size 23437500

mindVisualizer/data/mdn/mdn_particles_rdcim_teacher_edge_hq_grid125_mu2_xyz3.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f1b9505a5ae611528425d57b09387e35dd921c20377a3b232a4267133b19baf
+size 23437500

mindVisualizer/data/mdn/mdn_particles_rdcim_teacher_edge_hq_grid125_mu3_xyz3.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53b0999649dfccf31f8726695c4d5a738009f408389a16954144be89d2a58c87
+size 23437500

mindVisualizer/data/mdn/mdn_particles_rdcim_teacher_edge_hq_grid125_mu4_xyz3.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:afd81e6a49823e2ddb42f8e4256f2bf58f268f5fbf83d95fbf0dc0f0eedef559
+size 23437500

mindVisualizer/data/mdn/mdn_particles_rdcim_teacher_edge_hq_grid125_mu5_xyz3.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68c1f5eceb934435f58ea77c3b623abb114256cfc23affaa2dba1a202139a4aa
+size 23437500

mindVisualizer/data/mdn/mdn_particles_rdcim_teacher_edge_hq_grid125_mu6_xyz3.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9827f9f7a923994c84e1a44773fc2fe88dd91fd7905e718083dc752d24a31f4d
+size 23437500

mindVisualizer/data/mdn/mdn_particles_rdcim_teacher_edge_hq_grid125_pi_xyz6.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ffb8a88b783e3a9aefeb8b7c14cd925769fcd3b4ef7d9ca029e4a8f71cd25265
+size 46875000

mindVisualizer/data/mdn/mdn_particles_rdcim_teacher_edge_hq_training_points.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0f4ac819327e772ab01ed85b81150d2a28111df1ba4ebd9b2b3af200a011849b
+size 14400128

mindVisualizer/data/{roi_flow/mdn_universal_all_grid64_mean_xyz3.bin → meshes_obj/10153.obj}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4157011b84e35e0a861730baa16de6a97b40ca42b5a0001356760d02a4cddaef
-size 3145728
+oid sha256:03245194c13e6081c41f81bfafb018f9aa819af57642f1729d6830db236912cb
+size 9316827

mindVisualizer/data/meshes_obj/10154.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0026eafd072ba11e02f3f4eab7f768ebcf89f32f5a0144fa38d8774576a1baff
+size 13523828

mindVisualizer/data/meshes_obj/10155.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89e07d4dc266bd9c69404da5b1ce8a1ea23dd2a41560e30f350b3c49a23e4c65
+size 13523828

mindVisualizer/data/meshes_obj/10156.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8dd907bcc3ed5f654395b9b2bec702f2f00355d6e7a9d208e18e01766f2782eb
+size 12090622

mindVisualizer/data/{roi_flow/mdn_universal_all_grid64_mu1_xyz3.bin → meshes_obj/10157.obj}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0e64c8f46eaa1c0d3fa269440687f9ff24b561567feaa7fdbb7f4d1aa259976f
-size 3145728
+oid sha256:c1432ccc092ed2e7b1495e506a11179420fcfb0919519824bb35329ee8171002
+size 3291242

mindVisualizer/data/meshes_obj/10158.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be09e3ec39b5cacb24af0e9f7d8e69086fd35853899daea7729bf7f048bb5c6d
+size 1992516

mindVisualizer/data/meshes_obj/10159.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa7c48344b0af6f41f069450f319e9c360ef8b45fe8f36dded24cb45e7ba0aef
+size 149484

mindVisualizer/data/meshes_obj/10292.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c4b4771c184c4512cfadef75b152e0bcbe9d816a62102d7b93349c967d0d955
+size 112083

mindVisualizer/data/meshes_obj/10306.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aef8de5471788784415bdd77b44e5c532cabc79f5fc3f3c4be37e9dd6c5222e8
+size 112083

mindVisualizer/data/{roi_flow/probe_roi_centers.npy → meshes_obj/10307.obj}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8a64ab89787f423cb3d4ac4e953a5fe056c4be9a7b3fe4738be529d14fac7b6b
-size 12128
+oid sha256:25a071fd3a955681fcd64f6d28fd917c921e9558b792f622649f93ebace11aa6
+size 19205

mindVisualizer/data/meshes_obj/10308.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:83c6c01f3d02a6980403f340692930dbe47339c03784b4c6a41d6cc8bcf45dd2
+size 28542

mindVisualizer/data/meshes_obj/10311.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e94088b6a00b31e0077ecb75f28d21621f8b8f4ae1a033b76756713b66737fe
+size 57711

mindVisualizer/data/meshes_obj/10313.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22a69c74d7c982a8372ac63c49df436bf8bc0fc1edb401710c4939c85fd9866f
+size 48263

mindVisualizer/data/meshes_obj/10327.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61602d7e56749544c6773467d7b8af22aa7788de7640de8c0b18e6fc9630239a
+size 48263

mindVisualizer/data/meshes_obj/10329.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f145efd0531e791a5f2059f89a4c649a83b12ffee4bd06648569cc36396bbd3c
+size 17345

mindVisualizer/data/meshes_obj/10330.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d96c21416298bac87134b1f41f762ac122854cd66a93f53bd97a8d833f7b99c
+size 36168

mindVisualizer/data/meshes_obj/10331.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e092fe926797259b4568d792ea0439daf36297ab5c9946a689892abe4c3b9aa
+size 1853173

mindVisualizer/data/meshes_obj/10332.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5030b54f80cad2316a5cc57db2649ac44e38d31d61677bda1710ebb29e1418f
+size 1027691

mindVisualizer/data/meshes_obj/10333.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df7e6a6ae97116b644f5090984b56ddcf5da574b5387c22bb8c554f9fff089ce
+size 949235

mindVisualizer/data/meshes_obj/10334.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a16d8d4d1fcb2cc0ac4be610e5f200c6efd910b3de982492432f563c39f6c2b9
+size 498360

mindVisualizer/data/meshes_obj/10335.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f4c64f991bb22248463cf2415ab33c6cf89235bb85fa26425672958c6362708
+size 225016

mindVisualizer/data/meshes_obj/10336.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88b46eb320fe5c91aa1da562277d436caaabace45b0547a1b0d71e13cfa86d81
+size 157144

mindVisualizer/data/meshes_obj/10337.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f60b0f68b013cfca7c50fc2afd63db26e64098d8e6476990d7cbc339613d68eb
+size 162134

mindVisualizer/data/meshes_obj/10338.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0a913a6bd57ddc20209c2a7d0c873705c234eb0f116ae0dfbb7fa88a7074d017
+size 374785

mindVisualizer/data/meshes_obj/10339.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0521f9f55fd1c10d2d9560fff80cb2385a6a9fab003bea1c524e4db1a154bcbb
+size 74905

mindVisualizer/data/meshes_obj/10342.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0864087632dbee936a66dd7d0ab48f208db7fc57118bdef6a1b37e0dbe38b162
+size 142593

mindVisualizer/data/meshes_obj/10343.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c02b9faf662b97b9275ec64a0da4818c28d5c60f0c7299b0f02212b1f0cf2ea
+size 121130

mindVisualizer/data/meshes_obj/10344.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf6753d2b9f64cd0f2d6aa8202d6bcdddea7faf10fc4daf6e7c30892c89a2c45
+size 53546

mindVisualizer/data/meshes_obj/10346.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2af0bc2a2c14637a896b13b5ccee911dd9f0d214353ed87aaebbe4b686cdc3d0
+size 396936