feat(oid): joint analysis — OID mode zone partition, action-OID, SR validation
P3.1: Zone energy partition from OID spatial modes P3.3: Three-layer overlap (action-OID, force-OID, signature-OID) - Key finding: action is near-orthogonal to both force and sig OID modes - Confirms OID finds observable-relevant, not action-relevant structures P3.5: SR formula → OID coordinate validation - Karman: corr(OID_z1, Cl_tot) = -0.82 (strong) Fix: Restore ROI masking in phase1_correction_pod.py (OOM prevention) Co-authored-by: Cursor <cursoragent@cursor.com>
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src/OID_analysis/analysis/phase3a_oid_mode_viz.py
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src/OID_analysis/analysis/phase3a_oid_mode_viz.py
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# OID_analysis/analysis/phase3a_oid_mode_viz.py
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"""
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Phase 3a: OID Force/Signature Mode Spatial Visualization & Zone Partition
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Loads OID spatial modes from derived/oid/, reshapes to velocity/vorticity fields,
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projects onto CCD's three-zone masks, and outputs:
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- Partition energy table JSON
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- Multi-panel figure: (force+sig) x 3 modes x (ux, uy, vorticity)
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Pure CPU. No GPU. Derived data only (small).
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Usage:
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PYTHONPATH="src:$PYTHONPATH" python3 src/OID_analysis/analysis/phase3a_oid_mode_viz.py
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"""
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import json, os, sys
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import numpy as np
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_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
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if _REPO not in sys.path:
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sys.path.insert(0, _REPO)
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from OID_analysis.configs import DATA_DIR, get_scene # noqa: E402
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# ROI coords used in Phase 1 POD (hardcoded consistently)
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ROI_X0, ROI_X1 = 400, 1000
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ROI_Y0, ROI_Y1 = 100, 400
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NY_ROI = ROI_Y1 - ROI_Y0 # 300
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NX_ROI = ROI_X1 - ROI_X0 # 600
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SCENES = ["steady_cloak", "karman_re100",
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"illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
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DERIVED = os.path.join(DATA_DIR, "derived")
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# ---------------------------------------------------------------------------
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# Zone mask definitions (in ROI pixel coordinates)
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# ---------------------------------------------------------------------------
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def build_zone_masks(scene_key: str):
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"""Build near_body, body_wake, sensor_zone bool masks in ROI frame."""
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cfg = get_scene(scene_key)
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L0 = 20.0
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front_x = cfg["pinball_front_x"] * L0 # lattice coords
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rear_x = cfg.get("pinball_rear_x", front_x + 1.3) * L0
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sensor_x = cfg["sensor_x"] * L0
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# Convert global -> ROI-local coords
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def gx(x): return x - ROI_X0
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def gy(y): return y - ROI_Y0
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ny, nx = NY_ROI, NX_ROI
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center_y = 255.5 # global CENTER_Y
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masks = {}
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# near_body: pinball ± 2D
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m = np.zeros((ny, nx), dtype=bool)
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x0 = max(0, int(gx(front_x - 2*L0)))
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x1 = min(nx, int(gx(rear_x + 2*L0)))
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y0 = max(0, int(gy(center_y - 2*L0)))
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y1 = min(ny, int(gy(center_y + 2*L0)))
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m[y0:y1, x0:x1] = True
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masks["near_body"] = m
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# body_wake: from right of near_body to sensors
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m = np.zeros((ny, nx), dtype=bool)
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x0 = int(gx(rear_x + 2*L0))
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x1 = min(nx, int(gx(sensor_x - L0)))
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y0 = max(0, int(gy(center_y - 3*L0)))
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y1 = min(ny, int(gy(center_y + 3*L0)))
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if x1 > x0:
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m[y0:y1, x0:x1] = True
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masks["body_wake"] = m
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# sensor_zone: ±2D around sensor x
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m = np.zeros((ny, nx), dtype=bool)
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x0 = max(0, int(gx(sensor_x - 2*L0)))
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x1 = min(nx, int(gx(sensor_x + 2*L0)))
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y0 = max(0, int(gy(center_y - 3*L0)))
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y1 = min(ny, int(gy(center_y + 3*L0)))
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m[y0:y1, x0:x1] = True
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masks["sensor_zone"] = m
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return masks
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# ---------------------------------------------------------------------------
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# OID mode loading & reshaping
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# ---------------------------------------------------------------------------
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def load_oid_mode_fields(oid_type: str, scene_key: str, k: int):
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"""Load OID mode k as (ux, uy, vorticity) arrays of shape (NY_ROI, NX_ROI)."""
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fpath = os.path.join(DERIVED, "oid", oid_type, scene_key,
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f"{oid_type}_oid_modes.npz")
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if not os.path.isfile(fpath):
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raise FileNotFoundError(f"Missing: {fpath}")
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data = np.load(fpath)
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# 'modes' key: shape (DOF_ROI, r)
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modes_all = data["modes"] # (DOF, r)
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dof_half = NY_ROI * NX_ROI
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ux = modes_all[:dof_half, k].reshape(NY_ROI, NX_ROI)
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uy = modes_all[dof_half:, k].reshape(NY_ROI, NX_ROI)
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# Vorticity via central difference
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vort = np.gradient(uy, axis=1) - np.gradient(ux, axis=0)
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return ux, uy, vort
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# ---------------------------------------------------------------------------
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# Zone energy partitioning
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# ---------------------------------------------------------------------------
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def zone_energy_fraction(mode_field: np.ndarray, masks: dict) -> dict:
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"""Fraction of L2 energy of mode_field in each zone."""
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total = np.sum(mode_field ** 2)
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if total < 1e-30:
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return {k: 0.0 for k in masks}
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return {k: float(np.sum(mode_field[masks[k]] ** 2) / total) for k in masks}
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def compute_all():
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results = {}
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for scene in SCENES:
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masks = build_zone_masks(scene)
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scene_result = {}
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for otype in ["force", "signature"]:
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fpath = os.path.join(DERIVED, "oid", otype, scene,
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f"{otype}_oid_modes.npz")
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if not os.path.isfile(fpath):
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print(f" SKIP {scene}/{otype}: no modes file")
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continue
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for km in range(3): # modes 0,1,2
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try:
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ux, uy, vort = load_oid_mode_fields(otype, scene, km)
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except Exception as e:
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print(f" SKIP {scene}/{otype}/mode{km}: {e}")
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continue
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key = f"{otype}_m{km}"
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scene_result[key] = {
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"ux_energy": zone_energy_fraction(ux, masks),
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"uy_energy": zone_energy_fraction(uy, masks),
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"vort_energy": zone_energy_fraction(vort, masks),
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}
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results[scene] = scene_result
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n = len(scene_result)
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print(f" {scene}: {n} mode-fields computed")
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# Save
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out_dir = os.path.join(DERIVED, "oid_viz")
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os.makedirs(out_dir, exist_ok=True)
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with open(os.path.join(out_dir, "zone_energy.json"), "w") as f:
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json.dump(results, f, indent=2)
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print(f"\nSaved to {out_dir}/zone_energy.json")
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# Print key table
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print("\nForce-OID mode 1 ux energy partition:")
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print(f"{'Scene':20s} {'near_body':>10s} {'body_wake':>10s} {'sensor_zone':>12s}")
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for scene in SCENES:
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if "force_m0" in results.get(scene, {}):
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r = results[scene]["force_m0"]["ux_energy"]
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print(f"{scene:20s} {r['near_body']:10.4f} {r['body_wake']:10.4f} "
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f"{r['sensor_zone']:12.4f}")
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print("\nSignature-OID mode 1 ux energy partition:")
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print(f"{'Scene':20s} {'near_body':>10s} {'body_wake':>10s} {'sensor_zone':>12s}")
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for scene in SCENES:
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if "signature_m0" in results.get(scene, {}):
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r = results[scene]["signature_m0"]["ux_energy"]
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print(f"{scene:20s} {r['near_body']:10.4f} {r['body_wake']:10.4f} "
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f"{r['sensor_zone']:12.4f}")
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return results
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if __name__ == "__main__":
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compute_all()
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