## Cleanup (v0.5.0) - Delete 102+ orphaned MCP session temp files - Remove build artifacts (htmlcov, dist, __pycache__) - Archive superseded plan docs (RALPH_LOOP V2/V3, CANVAS V3, etc.) - Move debug/analysis scripts from tests/ to tools/analysis/ - Archive redundant NX journals to archive/nx_journals/ - Archive monolithic PROTOCOL.md to docs/archive/ - Update .gitignore with missing patterns - Clean old study files (optimization_log_old.txt, run_optimization_old.py) ## Canvas UX (Phases 7-9) - Phase 7: Resizable panels with localStorage persistence - Left sidebar: 200-400px, Right panel: 280-600px - New useResizablePanel hook and ResizeHandle component - Phase 8: Enable all palette items - All 8 node types now draggable - Singleton logic for model/solver/algorithm/surrogate - Phase 9: Solver configuration - Add SolverEngine type (nxnastran, mscnastran, python, etc.) - Add NastranSolutionType (SOL101-SOL200) - Engine/solution dropdowns in config panel - Python script path support ## Documentation - Update CHANGELOG.md with recent versions - Update docs/00_INDEX.md - Create examples/README.md - Add docs/plans/CANVAS_UX_IMPROVEMENTS.md
84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
"""Debug script to compare figure.dat vs BDF node coordinates."""
|
|
import sys
|
|
from pathlib import Path
|
|
sys.path.insert(0, str(Path(__file__).parent.parent))
|
|
|
|
import numpy as np
|
|
import logging
|
|
logging.disable(logging.WARNING)
|
|
|
|
study_dir = Path(r"c:\Users\antoi\Atomizer\studies\M1_Mirror\m1_mirror_cost_reduction_V9")
|
|
|
|
# Load figure.dat
|
|
from optimization_engine.extractors.extract_zernike_figure import load_figure_geometry
|
|
fig_geo = load_figure_geometry(study_dir / "1_setup/model/figure.dat")
|
|
fig_nids = set(fig_geo.keys())
|
|
|
|
# Find OP2 and BDF
|
|
op2_file = list(study_dir.glob("3_results/best_design_archive/**/*.op2"))[0]
|
|
bdf_file = op2_file.with_suffix(".dat")
|
|
|
|
# Load BDF
|
|
from pyNastran.bdf.bdf import BDF
|
|
bdf = BDF(log=None, debug=False)
|
|
bdf.read_bdf(str(bdf_file))
|
|
bdf_nids = set(bdf.nodes.keys())
|
|
|
|
# Load OP2
|
|
from pyNastran.op2.op2 import OP2
|
|
op2 = OP2(log=None, debug=False)
|
|
op2.read_op2(str(op2_file))
|
|
disps = op2.displacements
|
|
first_key = list(disps.keys())[0]
|
|
op2_nids = set(int(n) for n in disps[first_key].node_gridtype[:,0])
|
|
|
|
print(f"Figure.dat nodes: {len(fig_nids)}")
|
|
print(f"BDF nodes: {len(bdf_nids)}")
|
|
print(f"OP2 nodes: {len(op2_nids)}")
|
|
|
|
print()
|
|
print(f"Figure ^ BDF: {len(fig_nids & bdf_nids)}")
|
|
print(f"Figure ^ OP2: {len(fig_nids & op2_nids)}")
|
|
print(f"BDF ^ OP2: {len(bdf_nids & op2_nids)}")
|
|
|
|
# Sample coords - use a node in all three
|
|
common_nids = list(fig_nids & bdf_nids & op2_nids)[:5]
|
|
print()
|
|
print("Sample common node coords comparison:")
|
|
z_diffs = []
|
|
for nid in common_nids:
|
|
fig_pos = fig_geo[nid]
|
|
bdf_pos = bdf.nodes[nid].get_position()
|
|
diff = np.array(fig_pos) - bdf_pos
|
|
z_diffs.append(diff[2])
|
|
print(f" Node {nid}:")
|
|
print(f" Figure: ({fig_pos[0]:.6f}, {fig_pos[1]:.6f}, {fig_pos[2]:.9f})")
|
|
print(f" BDF: ({bdf_pos[0]:.6f}, {bdf_pos[1]:.6f}, {bdf_pos[2]:.9f})")
|
|
print(f" Z diff: {diff[2]*1e6:.3f} nm")
|
|
|
|
# Statistics on all matching nodes
|
|
all_common = fig_nids & bdf_nids
|
|
all_z_diffs = []
|
|
all_xy_diffs = []
|
|
for nid in all_common:
|
|
fig_pos = np.array(fig_geo[nid])
|
|
bdf_pos = bdf.nodes[nid].get_position()
|
|
diff = fig_pos - bdf_pos
|
|
all_z_diffs.append(diff[2])
|
|
all_xy_diffs.append(np.sqrt(diff[0]**2 + diff[1]**2))
|
|
|
|
all_z_diffs = np.array(all_z_diffs)
|
|
all_xy_diffs = np.array(all_xy_diffs)
|
|
|
|
print()
|
|
print(f"=== ALL {len(all_common)} COMMON NODES ===")
|
|
print(f"Z difference (figure - BDF):")
|
|
print(f" Min: {all_z_diffs.min()*1e6:.3f} nm")
|
|
print(f" Max: {all_z_diffs.max()*1e6:.3f} nm")
|
|
print(f" Mean: {all_z_diffs.mean()*1e6:.3f} nm")
|
|
print(f" RMS: {np.sqrt(np.mean(all_z_diffs**2))*1e6:.3f} nm")
|
|
print()
|
|
print(f"XY difference (figure - BDF):")
|
|
print(f" Max: {all_xy_diffs.max()*1e3:.6f} um")
|
|
print(f" RMS: {np.sqrt(np.mean(all_xy_diffs**2))*1e3:.6f} um")
|