Collect paired moving-pinball Fig2 research material
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Frank14f
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# Fig. 2 material collection: modern moving plant
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Research-only child of `76873d911c4e2caba1bacc25aaa1495ea768453f`.
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No production/kernel/manuscript or DynamisLab edits. These are fresh common-release
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paired experiments, separate from the continuous OFF/ON/OFF demonstration videos.
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## Fixed plant and cases
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All cases use D40, Uref=.005, nu=.004, MRT/FP32/unshifted double buffering,
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the existing mean-parabolic/regularized inlet, NEQ outlet and bounce-back walls.
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One transverse assembly DOF: m*=4 based on all three disks, fnD/U=.2, zeta=0.
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No force filter or retuning. Existing +/-D, speed.025, nonfinite and no-donor stops.
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Structure RHS is previous native-step sum of pinball CME_y+Wen_y. Policy uses
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SI-mean packed CME and fixed lab probe signals; these are different force paths.
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SI=3200; EMA alpha=.9**.25 resets every SI as in the acquisition contract.
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Steady video used SI4000; its descriptive numbers are not the new collection.
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| case | prelude/release clock | controller | collection |
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|---|---|---|---|
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| steady | new static pinball, 2,048,000 steps | constant wall speeds (0,-5.1,5.1)U, no PPO | 400 SI,160 D/U |
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| Karman | verified saved static release at4,576,000 | historical `karman_re100` PPO; actual pinball Re_D50, scale8/bias(0,-4,4) | 400 SI,160 D/U |
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| Taylor y0 | clean512,000 then static pinball512,000; clock1,024,000 | `vortex_taylor` PPO, scale4/samebias | 150 SI,60 D/U |
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Zero/control share exact current/spare populations, flags, clock and zero initial
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structural state. Target is a separate no-pinball reference at the SAME absolute
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clocks, not a copy of a solid-containing release. Its different geometry is the
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definition of the reference, not identical microscopic initial conditions.
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Karman target must run to4,576,000; old target.npy is from earlier clocks and is
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not reused for same-clock field errors. Model/norm files are verified by SHA256
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in the driver and written with all source hashes into case.json.
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## Taylor injection
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Exactly one event at release in every role, inside the owning synchronized CUDA
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context: centre(600,510.5), R80, strength **.000075**. Reuse the existing initializer
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unchanged. It rewrites ALL interior cells to equilibrium and uploads BOTH buffers;
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this removes existing nonequilibrium globally. The impulse and first two SI of
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native force/displacement remain in the record. This is a declared limitation,
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not an independently qualified physical vortex generator.
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Strength has inverse-time units: u_theta=(s*pi/2)r exp(-r²/R²). D20 R40/U.01/s.0003
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maps to D40 R80/U.005/s.000075 under diffusive scaling. Using .03*U at D40 would
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double u/U and quadruple p/U²; actual-function CPU positive/negative controls lock it.
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Current Horsea `acquisition.py` SHA
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`ea674080501407d00ded6d23467423c516cd37e04d166d5adda16465100204d3`
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defines EIGHT offsets(-20,-15,-10,-5,0,5,10,15). `legacy_test/acquire.py` SHA
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`91cd29a368dfc53a0804ec37397d2e5f25f0f8721f01b560a0f4328b8fd1dd9c`
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selects the controlled centre-uy event: median first20, peak over38..87 inclusive.
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All roles use that common event index; own target/role peak delays are separately
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reported. The earlier five-offset remote summary is superseded by actual source.
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## Estimands and storage
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Primary: unaligned same-clock/event velocity-vector errors, Eq1
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`sqrt(mean_ROI((dux²+duy²)/Uref²))`. No target-energy denominator. Lab ROI is
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front-equilibrium-centre-relative x/D=[4,24], y/D=[-5,5], equivalent to sensor-plane
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x/D=[-6,14]; D40 inclusive columns1360..2160 and rows311..710. All ROI cells must
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be fluid and finite in every compared field; record support coverage.
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Motion uses native last80 D/U for steady/Karman, whole60 D/U for Taylor; mean,
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centered RMS and half-range remain distinct. Karman complete-cycle means and
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eight independently phased nearest snapshots reuse actual acquisition helpers,
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centre-uy column3 and minimum crossing gap. Missing crossings yield unavailable
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phase diagnostics, never fabricated phase slots. Steady retains its last snapshot
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and separately labeled fixed-window mean. Field collection for steady/Karman is
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the last160 SI boundaries (96.4..160 D/U); motion window remains80..160 D/U.
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Taylor retains its eight common event snapshots. Fixed-window mean error differs
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from mean instantaneous error; both are labeled. Native six-channel DTW is a
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supplementary training-space metric, never field recovery or physical delay;
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its actual legacy lag uses flat sensor column1, unlike the phase helper's column3.
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Use one FP32 full-grid RAM cache per role, plus compact ROI RAM histories for
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same-clock comparisons. Admit only with >=16GiB MemAvailable; no3.35GB disk field
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cache. Save only selected fields, means, explicit masks, scalars and source receipts.
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Nonfluid placeholders are zero-filled for helpers but always masked in plots and
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scores; nonfinite FLUID is a hard failure. Serialize compilation/GPU work. Verify
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compressed native histories before removing only redundant task scratch copies.
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Plot standards: vorticity curl(u)D/U, five-point fluid stencil, common RdBu_r21bins
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with fixed[-3,3] limits and nearest interpolation; lab-fixed coordinates and ROI.
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Mean plots exclude cells that were not fluid at every collected boundary. Selected
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snapshots use actual step geometry. Raw arrays and scalar errors are not clipped.
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## V5 boundary
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Standard V5 `kar_re100` seeds41..45 DO exist (actual pinball Re_D50), but use
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2000x600 uniform/free-slip and different absolute geometry/normalization/action
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contracts. These are not silently transferred onto the present Legacy-contract
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plant. No verified retained trained V5 steady/Taylor bundle was found; a vortex
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environment prototype is not a trained policy. Existing V5 within-case results
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may be indexed separately; cross-generation scores cannot imply an identical plant.
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## Execution
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First run focused CPU contracts, independent source review, then each scene with
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`--smoke` (three32-step SI per role; no physical scores). Only the reviewed exact
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SHA may run physical cases, one at a time, new outputs outside the checkout:
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```
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CUDA_VISIBLE_DEVICES=3 PYTHONPATH=src python research/fig2_collection/run.py --scene karman --out /tmp/celeris-results-fig2-collection/karman
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python research/fig2_collection/analyze.py /tmp/celeris-results-fig2-collection/karman
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python research/fig2_collection/plot.py /tmp/celeris-results-fig2-collection/karman
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```
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Use the same invocation for steady/taylor with new output directories. No automatic
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extension or parameter sweep. All results are developmental material, not asserted
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settled/physically qualified. Preserve partial failures and distinguish missing
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evidence from passing gates.
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"""Offline Fig2 estimands; preserve raw same-clock errors beside registration.
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No thresholds are fitted here. Probe DTW is the actual Legacy helper, not a
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field metric; independent wake phase is a diagnostic, not a time correction.
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"""
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import argparse
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import gc
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import importlib.util
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import json
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from pathlib import Path
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import sys
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import types
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import numpy as np
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ROOT=Path(__file__).resolve().parents[2]
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sys.path.insert(0,str(ROOT))
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from research.fig2_collection import run
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from research.fig2_materials.metrics import field_error, motion_summary
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def frequency(signal,dt):
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"""Largest non-DC rectangular-window FFT bin, descriptive resolution only."""
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a=np.asarray(signal,float);a=a-a.mean()
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if len(a)<3 or np.max(np.abs(a))<1e-12:return None
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power=np.abs(np.fft.rfft(a))**2
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i=1+int(np.argmax(power[1:]))
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return dict(f_D_U=float(np.fft.rfftfreq(len(a),dt)[i]),bin_width_D_U=1/(len(a)*dt),
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method='demeaned rectangular window; dominant non-DC bin; not a confidence interval')
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def native_summary(rows,scene):
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start=max(0,len(rows)-640000) if scene!='taylor' else 0
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a=rows[start:]
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y=a[:,1]/40.;cy=a[:,3:9].sum(axis=1)/(.5*.005**2*40)
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return dict(window_D_U=[start*.005/40,len(rows)*.005/40],
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motion=motion_summary(y),motion_frequency=frequency(y,.005/40),
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structural_RHS_Cy_RMS=float(np.sqrt(np.mean(cy**2))),
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structural_RHS_Cy_mean=float(cy.mean()),
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max_native_delta_Cy=float(np.abs(np.diff(cy)).max()) if len(cy)>1 else None,
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force_frequency=frequency(cy,.005/40),
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no_donor=float(rows[:,13].sum()),eq_only=float(rows[:,12].sum()),
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crossings=float(rows[:,14].sum()),
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force_convention='body force, sum pinball CME_y+Wen_y; native previous-step RHS, no filtering')
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def compare_selected(target,current,u0=.005):
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"""Fixed lab ROI; all actual compared roles must be fluid and finite here."""
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ys,xs=slice(311,711),slice(1360,2161)
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t,c=target['ux'][:,ys,xs],current['ux'][:,ys,xs]
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tv,cv=target['uy'][:,ys,xs],current['uy'][:,ys,xs]
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if t.shape!=c.shape or tv.shape!=cv.shape:raise ValueError('selected shape mismatch')
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for fields in (target,current):
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if not np.all((fields['flags'][:,ys,xs]&1)!=0):raise ValueError('nonfluid selected ROI')
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if not all(np.isfinite(a).all() for a in (t,c,tv,cv)):raise ValueError('nonfinite selected ROI')
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e2=((c.astype(float)-t)/u0)**2+((cv.astype(float)-tv)/u0)**2
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return dict(per_slot_E=np.sqrt(e2.mean(axis=(1,2))),aggregate_E=float(np.sqrt(e2.mean())))
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def main():
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ap=argparse.ArgumentParser(description=__doc__);ap.add_argument('directory',type=Path)
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args=ap.parse_args();p=args.directory
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case=json.loads((p/'case.json').read_text());term=json.loads((p/'terminal.json').read_text())
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if case['plan']['smoke'] or term['status']!='three_roles_completed_not_qualified':
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raise ValueError('physical analysis requires all three complete roles; smoke/partial is unscored')
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helpers=run.load_helpers()
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# Reuse the canonical pure module under the same isolated read-only package.
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native=run.load_file('_fig2_readonly_drl.legacy_test.metrics',
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run.ACQUISITION.parent/'legacy_test/metrics.py')
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reports={k:json.loads((p/k/'report.json').read_text()) for k in ('target','zero','controlled')}
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sig={}
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result=dict(scene=case['scene'],source=case['source'],Re_D=50.,roles={},
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primary='unaligned same-clock/event U-normalized vector errors; common lab ROI',
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periodic_phase='independently registered centre-uy phase diagnostic, not a shared-clock comparison',
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notes=['Modern moving-plant data; no Legacy numerical equivalence or physical qualification',
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'Motion, native structural force, policy SI means and probe DTW are different estimands'])
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for role in ('target','zero','controlled'):
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with np.load(p/role/'signals.npz') as z:sig[role]={k:z[k] for k in z.files}
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if role!='target':
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with np.load(p/role/'native.npz') as z:
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result['roles'][role]=native_summary(z['rows'],case['scene'])
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t=sig['target']['sensors']
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for role in ('zero','controlled'):
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s=sig[role]['sensors']
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if not np.array_equal(sig[role]['absolute_steps'],sig['target']['absolute_steps']):
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raise ValueError('probe absolute clocks mismatch')
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result['roles'][role]['unaligned_probe_area_velocity_RMS_over_U']=float(np.sqrt(np.mean(((s/156-t/156)/.005)**2)))
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result['roles'][role]['probe_channel_correlations']=native.channel_correlations(t,s)
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if case['scene']=='taylor':
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spec=types.SimpleNamespace(scene='vortex',conv_len=30)
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dtw=native.native_similarity(spec,t,s,len(s)-1)
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result['roles'][role]['native_probe_DTW']=dict(similarity=dtw,definition='source vortex training-window roll; supplementary, not event field recovery',conv_len=30)
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own=helpers.select_vortex_event(s,'vortex_taylor')
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target_event=helpers.select_vortex_event(t,'vortex_taylor')
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result['roles'][role]['own_event_peak']=int(own['peak_index'])
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result['roles'][role]['event_peak_delay_D_U']=(int(own['peak_index'])-int(target_event['peak_index']))*.4
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else:
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result['roles'][role]['native_probe_DTW']=native.cycle_similarity(t,s,30)
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result['roles'][role]['native_probe_DTW'].update(definition='canonical six-channel legacy cycle DTW, training D20 units',
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lag_column=1,target_template_indices=[30,60],state_window_indices=[len(s)-30,len(s)],
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registration_only=True,lag_identifiable=bool(case['scene']=='karman' and np.std(t[:,1])>1e-10))
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data={}
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for role in ('target','zero','controlled'):
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with np.load(p/role/'fields.npz') as z:data[role]={k:z[k] for k in z.files}
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for role in ('zero','controlled'):
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name={'karman':'independent_phase8_diagnostic','steady':'late_snapshot','taylor':'common_event8'}[case['scene']]
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if case['scene']!='karman' or all(len(data[k]['ux'])==8 for k in (role,'target')):
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result['roles'][role][name]=compare_selected(data['target'],data[role])
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else:
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result['roles'][role][name]=dict(status='unavailable_insufficient_periodic_crossings')
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result['roles'][role]['selection_metadata']=reports[role]['selection']
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result['roles'][role]['field_window_tU_D']=reports[role]['field_window_tU_D']
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if case['scene']=='karman' and all('cycle_mean_ux' in data[k] for k in (role,'target')):
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support=np.zeros(data[role]['cycle_mean_ux'].shape,bool);support[311:711,1360:2161]=True
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result['roles'][role]['E_complete_cycle_mean']=field_error(
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data[role]['cycle_mean_ux'],data[role]['cycle_mean_uy'],
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data['target']['cycle_mean_ux'],data['target']['cycle_mean_uy'],.005,support)
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if case['scene']=='taylor':
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clocks=[data[k]['absolute_steps'] for k in data]
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if not all(np.array_equal(clocks[0],v) for v in clocks[1:]):raise ValueError('event snapshot clocks differ')
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result['Taylor_injection']=dict(operator='all interior equilibrium rewrite; both buffers, identical event clock',
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strength=.000075,center=[600.,510.5],radius=80.,impulse_window='first two SI; retained native history',
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source_offsets=list(helpers.VORTEX_EVENT_OFFSETS))
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result['same_clock_field_errors']=json.loads((p/'same_clock_errors.json').read_text())
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run.write_json(p/'statistics.json',result)
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del data;gc.collect()
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print(json.dumps(dict(scene=case['scene'],statistics=str(p/'statistics.json'))))
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if __name__=='__main__':main()
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"""Shared scientific plots of retained fields; no solver or synthetic fields."""
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import argparse
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import json
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from pathlib import Path
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import sys
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import numpy as np
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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from matplotlib.colors import BoundaryNorm
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from matplotlib.patches import Circle,Rectangle
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ROOT=Path(__file__).resolve().parents[2]
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sys.path.insert(0,str(ROOT))
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from research.continuous_viv.render import vorticity
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def decorate(ax,scene,role,y,*,mean=False):
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if not mean:
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if scene=='karman':ax.add_patch(Circle((-20,0),1,facecolor='white',edgecolor='#253544',lw=.7))
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if role!='target':
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for x,dy in ((0,0),(1.3,.75),(1.3,-.75)):
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ax.add_patch(Circle((x,dy+y),.5,facecolor='white',edgecolor='#253544',lw=.7))
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ax.add_patch(Rectangle((4,-5),20,10,fill=False,ls='--',lw=.6,edgecolor='#555'))
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ax.set_xlim(-23.6,27.6);ax.set_ylim(-6.4,6.4)
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ax.set_xlabel('(x - pinball-front equilibrium centre) / D',fontsize=8)
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ax.set_ylabel('(y - channel centre) / D',fontsize=8)
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ax.tick_params(labelsize=8)
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def main():
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ap=argparse.ArgumentParser(description=__doc__);ap.add_argument('directory',type=Path)
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args=ap.parse_args();p=args.directory
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case=json.loads((p/'case.json').read_text());scene=case['scene']
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cmap=plt.get_cmap('RdBu_r',21).copy();cmap.set_bad('#DDE2E5')
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norm=BoundaryNorm(np.linspace(-3,3,22),21,clip=True)
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prefix='SMOKE — plumbing only | ' if case['plan']['smoke'] else ''
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roles=('target','zero','controlled')
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data={};motion={}
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for role in roles:
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with np.load(p/role/'fields.npz') as z:data[role]={k:z[k] for k in z.files}
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with np.load(p/role/'native.npz') as z:
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motion[role]=z['rows'][:,1].copy()/40.
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shape=data['target']['ux'].shape[-2:]
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extent=((-0.5-1200)/40,(shape[1]-.5-1200)/40,
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(-.5-510.5)/40,(shape[0]-.5-510.5)/40)
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fig,axs=plt.subplots(3,1,figsize=(13,10),constrained_layout=True)
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for ax,role in zip(axs,roles):
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d=data[role];flags=d['common_fluid_all_boundaries'].astype(np.uint16)
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w=vorticity(d['fixed_window_mean_ux'],d['fixed_window_mean_uy'],flags)
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image=ax.imshow(w,origin='lower',extent=extent,cmap=cmap,norm=norm,interpolation='nearest')
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decorate(ax,scene,role,0,mean=True)
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label='no pinball reference' if role=='target' else ('zero rotation' if role=='zero' else ('constant rotations' if scene=='steady' else 'frozen PPO'))
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ax.set_title(f'{role}: {label} | arithmetic fixed-window mean',fontsize=10)
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fig.suptitle(prefix+f'{scene}: modern Re_D=50, m*=4, zero structural damping\n'
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'Gray: cells outside the common fluid support; mean fields are not instantaneous geometry',fontsize=12)
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fig.colorbar(image,ax=list(axs),shrink=.65,label='vorticity D/U, fixed limits [-3,3]')
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fig.savefig(p/'mean-flow.png',dpi=160);plt.close(fig)
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slots=max(len(data[r]['ux']) for r in roles)
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fig,axs=plt.subplots(3,slots,figsize=(max(8,3*slots),7),squeeze=False,constrained_layout=True)
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for row,role in enumerate(roles):
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d=data[role]
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for col,ax in enumerate(axs[row]):
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if col>=len(d['ux']):ax.axis('off');continue
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w=vorticity(d['ux'][col],d['uy'][col],d['flags'][col])
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image=ax.imshow(w,origin='lower',extent=extent,cmap=cmap,norm=norm,interpolation='nearest')
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step=int(d['selected_response_steps'][col]);y=float(motion[role][step-1])
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decorate(ax,scene,role,y)
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ax.set_xlim(-3,14);ax.set_ylim(-3,3)
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ax.set_xlabel('x/D relative to equilibrium front',fontsize=7)
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ax.set_title(f'{role} | slot {col+1}\ntU/D={step*.005/40:.2f}',fontsize=8)
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family={'karman':'independently phased diagnostic snapshots (different real times)',
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'steady':'late snapshots at matching real times',
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'taylor':'common event-relative snapshots at matching real times'}[scene]
|
||||
fig.suptitle(prefix+f'{scene}: {family}\nFixed lab coordinates; same vorticity normalization/limits; source '+case['source'][:8],fontsize=12)
|
||||
fig.colorbar(image,ax=list(axs.ravel()),shrink=.7,label='vorticity D/U [-3,3]')
|
||||
fig.savefig(p/'selected-flow.png',dpi=160);plt.close(fig)
|
||||
fig,ax=plt.subplots(figsize=(11,3.5),constrained_layout=True)
|
||||
for role,color in (('zero','#5A6570'),('controlled','#147D73')):
|
||||
y=motion[role];stride=max(1,len(y)//12000)
|
||||
time=(np.arange(len(y))+1)*.005/40
|
||||
ax.plot(time[::stride],y[::stride],lw=.8,label=role,color=color)
|
||||
ax.set(xlabel='response time tU/D',ylabel='transverse y/D',ylim=(-1,1))
|
||||
ax.legend();ax.grid(alpha=.2)
|
||||
ax.set_title(prefix+scene+': independent paired releases; full retained duration')
|
||||
fig.savefig(p/'motion.png',dpi=160);plt.close(fig)
|
||||
print(json.dumps(dict(scene=scene,plots=['mean-flow.png','selected-flow.png','motion.png'],
|
||||
vorticity_limits=[-3,3],normalization='curl(u) D/U; five-node fluid stencil',
|
||||
mean_mask='intersection over collected boundary fields',display_interpolation='nearest')))
|
||||
|
||||
|
||||
if __name__=='__main__':main()
|
||||
@@ -0,0 +1,548 @@
|
||||
"""Research-only Fig. 2 acquisition on the modern moving plant.
|
||||
|
||||
These paired releases are new experiments, not Legacy numerical reproductions.
|
||||
Nothing here qualifies a steady state or automatically extends/retunes a run.
|
||||
GPU dependencies are lazy so the collection contracts can be checked on CPU.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import ast
|
||||
import gc
|
||||
import hashlib
|
||||
import importlib.util
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
import platform
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
import types
|
||||
|
||||
import numpy as np
|
||||
|
||||
sys.dont_write_bytecode = True
|
||||
ROOT = Path(__file__).resolve().parents[2]
|
||||
D, U, NU, SI = 40., .005, .004, 3200
|
||||
BASELINE = '76873d911c4e2caba1bacc25aaa1495ea768453f'
|
||||
CENTRES = ((1200., 510.5), (1252., 540.5), (1252., 480.5))
|
||||
PROBES = ((1600., 590.5), (1600., 510.5), (1600., 430.5))
|
||||
BIAS = np.array([0., -4., 4.])
|
||||
STEADY_SURFACE = np.array([0., -5.1, 5.1])*U
|
||||
MIN_AVAILABLE = 16*1024**3
|
||||
ACQUISITION = Path('/home/frank14f/DynamisLab/src/drl_pinball/acquisition.py')
|
||||
K_RELEASE = Path('/tmp/celeris-results-karman-viv-516975e/pair')
|
||||
ASSETS = {
|
||||
'karman': (Path('/tmp/celeris-karman-viv-assets/model.zip'),
|
||||
'148240c1dcb8b11d8e5a0a9e991f6874c4e799c9744707bf0fb32a8efeb0468d',
|
||||
Path('/tmp/celeris-karman-viv-assets/norm.json'),
|
||||
'534fda818c030a56960e95d0f3ac1e350d887d03db7f80f1d6453195a502b7f0'),
|
||||
'taylor': (Path('/home/frank14f/DynamisLab/models/old/vortex_taylor.zip'),
|
||||
'58882721900f1eb0779f64aea0a2e233d307a94b640b9aaf33efa036db3c79e3',
|
||||
Path('/home/frank14f/DynamisLab/src/SR_analysis/data/vortex/vortex_taylor/norm.json'),
|
||||
'bd38b4cf2b6da56b5cf378e646d673b97a606531c68163460313b330c9957b86'),
|
||||
}
|
||||
NATIVE_COLUMNS = ('response_step', 'y', 'v', 'cme_F', 'cme_U', 'cme_L',
|
||||
'wen_F', 'wen_U', 'wen_L', 'node_F', 'node_U', 'node_L',
|
||||
'eq_only', 'no_donor', 'crossings', 'omega_F', 'omega_U', 'omega_L')
|
||||
|
||||
|
||||
def sha(path):
|
||||
digest = hashlib.sha256()
|
||||
with Path(path).open('rb') as stream:
|
||||
for block in iter(lambda: stream.read(1024*1024), b''):
|
||||
digest.update(block)
|
||||
return digest.hexdigest()
|
||||
|
||||
|
||||
def json_value(value):
|
||||
if isinstance(value, dict):
|
||||
return {str(k): json_value(v) for k, v in value.items()}
|
||||
if isinstance(value, (tuple, list)):
|
||||
return [json_value(v) for v in value]
|
||||
if isinstance(value, np.ndarray):
|
||||
return json_value(value.tolist())
|
||||
if isinstance(value, np.generic):
|
||||
return value.item()
|
||||
if isinstance(value, Path):
|
||||
return str(value.resolve())
|
||||
return value
|
||||
|
||||
|
||||
def write_json(path, value):
|
||||
Path(path).write_text(json.dumps(json_value(value), indent=2, allow_nan=False)+'\n')
|
||||
|
||||
|
||||
def load_file(name, path):
|
||||
spec = importlib.util.spec_from_file_location(name, path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
sys.modules[name] = module
|
||||
spec.loader.exec_module(module)
|
||||
return module
|
||||
|
||||
|
||||
def load_helpers(path=ACQUISITION):
|
||||
"""Load read-only CPU helpers under an isolated package; no original imports.
|
||||
|
||||
Empty namespace packages avoid executing application __init__ modules. The
|
||||
acquisition module and its pure DTW dependency execute directly from source.
|
||||
Event functions are AST-selected verbatim to avoid acquire.py's heavy imports;
|
||||
offsets come from that acquisition module, never a copied/fabricated selector.
|
||||
"""
|
||||
path = Path(path)
|
||||
package = '_fig2_readonly_drl'
|
||||
for suffix, directory in (('', path.parent), ('.legacy_test', path.parent/'legacy_test'),
|
||||
('.legacy_test.core', path.parent/'legacy_test/core')):
|
||||
module = types.ModuleType(package+suffix)
|
||||
module.__path__ = [str(directory)]
|
||||
sys.modules[package+suffix] = module
|
||||
acquisition = load_file(package+'.acquisition', path)
|
||||
event_path = path.parent/'legacy_test/acquire.py'
|
||||
source = ast.parse(event_path.read_text(), filename=str(event_path))
|
||||
names = {'_vortex_event_channel', '_select_vortex_event'}
|
||||
nodes = [node for node in source.body if isinstance(node, ast.FunctionDef) and node.name in names]
|
||||
if {node.name for node in nodes} != names:
|
||||
raise ValueError('authoritative event selector functions missing')
|
||||
namespace = dict(np=np, VORTEX_EVENT_OFFSETS=acquisition.VORTEX_EVENT_OFFSETS)
|
||||
exec(compile(ast.Module(body=nodes, type_ignores=[]), str(event_path), 'exec'), namespace)
|
||||
acquisition.select_vortex_event = namespace['_select_vortex_event']
|
||||
return acquisition
|
||||
|
||||
|
||||
def schedule(scene, smoke=False):
|
||||
if scene not in ('karman', 'steady', 'taylor'):
|
||||
raise ValueError('unknown scene')
|
||||
intervals = 150 if scene == 'taylor' else 400
|
||||
release = {'karman': 4576000, 'steady': 2048000, 'taylor': 1024000}[scene]
|
||||
return dict(si=32 if smoke else SI, intervals=3 if smoke else intervals,
|
||||
field_start=0 if smoke or scene == 'taylor' else 240,
|
||||
release_clock=(64 if scene == 'taylor' else 32) if smoke else release,
|
||||
smoke=bool(smoke))
|
||||
|
||||
|
||||
def available_memory(path=Path('/proc/meminfo')):
|
||||
for line in Path(path).read_text().splitlines():
|
||||
if line.startswith('MemAvailable:'):
|
||||
return int(line.split()[1])*1024
|
||||
raise RuntimeError('MemAvailable unavailable; RAM admission cannot be verified')
|
||||
|
||||
|
||||
def admit_memory(smoke=False):
|
||||
if smoke:
|
||||
return None
|
||||
available = available_memory()
|
||||
if available < MIN_AVAILABLE:
|
||||
raise MemoryError(f'MemAvailable {available} < required {MIN_AVAILABLE}')
|
||||
return available
|
||||
|
||||
|
||||
def wall_surface(scene, role, action=None):
|
||||
if role == 'target' or role == 'zero':
|
||||
return np.zeros(3, np.float64) # exactly zero physical rotation
|
||||
if scene == 'steady':
|
||||
return STEADY_SURFACE.copy()
|
||||
action = np.asarray(action)
|
||||
if action.shape != (3,) or not np.isfinite(action).all() or (np.abs(action) > 1).any():
|
||||
raise ValueError('invalid deterministic policy action')
|
||||
return ((8. if scene == 'karman' else 4.)*action+BIAS)*U
|
||||
|
||||
|
||||
def check_structure(y, v, previous_y):
|
||||
if not np.isfinite([y, v]).all() or abs(y) > D or abs(v) > .025 or abs(y-previous_y) > .025:
|
||||
raise RuntimeError('nonfinite structure, travel > D or speed > .025; no retuning')
|
||||
|
||||
|
||||
def clean_field(fields, flags):
|
||||
"""Canonical modern bit-0 fluid mask; hard-fail before nonfluid zero fill."""
|
||||
ux, uy = np.asarray(fields['ux']), np.asarray(fields['uy'])
|
||||
rho = np.asarray(fields['rho'])
|
||||
if ux.ndim != 2 or uy.shape != ux.shape or rho.shape != ux.shape:
|
||||
raise ValueError('canonical shared (ny,nx) macroscopic arrays required')
|
||||
flags = np.asarray(flags)
|
||||
if flags.size != ux.size or flags.dtype != np.uint16:
|
||||
raise ValueError('modern uint16 flags matching field required')
|
||||
flags = flags.reshape(ux.shape)
|
||||
fluid = (flags & 1) != 0
|
||||
if any(not np.isfinite(value[fluid]).all() for value in (rho, ux, uy)):
|
||||
raise RuntimeError('nonfinite FLUID macroscopic field')
|
||||
result = np.zeros((2, *ux.shape), np.float32)
|
||||
result[0, fluid], result[1, fluid] = ux[fluid], uy[fluid]
|
||||
return result, flags.copy(), fluid
|
||||
|
||||
|
||||
def select_fields(scene, plan, helpers, times, sensors, cache, event=None):
|
||||
"""Fixed-window means remain separate from independently phased diagnostics."""
|
||||
mean = helpers.accumulate_mean_fields(cache[:, 0], cache[:, 1], np.arange(len(cache)))
|
||||
result = dict(fixed_window_mean_ux=mean['mean_ux'], fixed_window_mean_uy=mean['mean_uy'])
|
||||
metadata = dict(fixed_window_count=len(cache), periodic_phase_status='not_requested')
|
||||
indices = np.array([len(cache)-1], np.int64)
|
||||
if plan['smoke']:
|
||||
metadata['selection'] = 'smoke_last_snapshot_not_physical'
|
||||
elif scene == 'taylor':
|
||||
if event is None:
|
||||
event = helpers.select_vortex_event(sensors, 'vortex_taylor')
|
||||
indices = np.asarray(event['field_indices'], np.int64)
|
||||
if (indices < 0).any() or (indices >= len(cache)).any():
|
||||
raise ValueError('authoritative event offsets exceed collected fields')
|
||||
metadata.update(selection='source_vortex_event', event=event)
|
||||
elif scene == 'karman':
|
||||
try:
|
||||
phase = helpers.assign_periodic_phase(times, sensors, minimum_crossing_interval=.5*30*SI)
|
||||
complete = helpers.complete_cycle_field_indices(times, phase['crossing_times'])
|
||||
chosen = helpers.select_phase_fields(times, phase['crossing_times'])
|
||||
cycle_mean = helpers.accumulate_mean_fields(cache[:, 0], cache[:, 1], complete['field_indices'])
|
||||
result.update(cycle_mean_ux=cycle_mean['mean_ux'], cycle_mean_uy=cycle_mean['mean_uy'])
|
||||
indices = chosen['field_indices']
|
||||
metadata.update(periodic_phase_status='available_diagnostic_only', phase_selection=chosen,
|
||||
complete_cycles=complete, crossing_times=phase['crossing_times'],
|
||||
rejected_crossing_times=phase['rejected_crossing_times'])
|
||||
except ValueError as exc:
|
||||
metadata.update(periodic_phase_status='insufficient_crossings', phase_reason=str(exc))
|
||||
metadata['selection'] = 'independent_center_probe_uy_diagnostic'
|
||||
else:
|
||||
metadata.update(selection='last_snapshot', periodic_phase_status='not_assumed_steady')
|
||||
result.update(ux=cache[indices, 0], uy=cache[indices, 1], selected_index=indices,
|
||||
selected_response_steps=times[indices])
|
||||
return result, metadata, event
|
||||
|
||||
|
||||
def add_scene(sim, scene, *, moving=False, pinball=True):
|
||||
from CelerisLab.body.motion import Translation
|
||||
if scene == 'karman':
|
||||
sim.add_body('circle', (400., 510.5), 40.)
|
||||
probes = [sim.add_body('sensor', point, 10.) for point in PROBES]
|
||||
envelope = Translation((0., 0., -D, D), .025, shared=True) if moving else None
|
||||
bodies = [sim.add_body('circle', point, 20., translation=envelope) for point in CENTRES] if pinball else []
|
||||
return probes, bodies
|
||||
|
||||
|
||||
def inject_taylor(sim):
|
||||
from CelerisLab.lbm.initializers import add_vortex
|
||||
clock = int(sim.stepper.step_count)
|
||||
with sim.ctx.activate():
|
||||
sim.stream.synchronize()
|
||||
add_vortex(sim.field, (600., 510.5), 80., .000075, 'taylor')
|
||||
sim.stream.synchronize()
|
||||
if sim.stepper.step_count != clock:
|
||||
raise RuntimeError('vortex initializer advanced response clock')
|
||||
return dict(absolute_clock=clock, response_clock=0, center=[600., 510.5], radius=80.,
|
||||
strength=.000075, type='taylor', count=1,
|
||||
operator='unmodified add_vortex: all interior reset to equilibrium; both buffers uploaded',
|
||||
impulse='full equilibrium reset discards pre-existing non-equilibrium on all interior cells')
|
||||
|
||||
|
||||
def make_release(scene, plan, cfgpath, base, out, started, cap, release_dir):
|
||||
from CelerisLab import Simulation
|
||||
if scene == 'karman' and not plan['smoke']:
|
||||
seed, force = base.saved_release(release_dir)
|
||||
return seed, force, dict(kind='verified_saved_static_release', path=release_dir,
|
||||
release_npz_sha256=sha(release_dir/'release.npz'))
|
||||
with Simulation(str(cfgpath)) as sim:
|
||||
_, bodies = add_scene(sim, scene, pinball=scene != 'taylor')
|
||||
sim.initialize()
|
||||
warm = 32 if plan['smoke'] else (512000 if scene == 'taylor' else 2048000)
|
||||
if scene == 'taylor':
|
||||
base.warmup(sim, warm, started, cap)
|
||||
bodies = [sim.add_body('circle', point, 20.) for point in CENTRES]
|
||||
sim.sync_bodies()
|
||||
base.warmup(sim, warm-1, started, cap)
|
||||
sim.run(1, zero_obs=True, sync_obs=True)
|
||||
force = sum(float(sim.read_force(body)[1]) for body in bodies)
|
||||
seed = base.capture_seed(sim)
|
||||
if seed[3] != plan['release_clock'] or not np.isfinite(force):
|
||||
raise RuntimeError('unexpected static release clock/force')
|
||||
np.savez_compressed(out/'release.npz', current=seed[0], spare=seed[1], flags=seed[2],
|
||||
clock=seed[3], force=force)
|
||||
return seed, force, dict(kind='fresh_static_full_two_buffer_release',
|
||||
release_npz_sha256=sha(out/'release.npz'))
|
||||
|
||||
|
||||
def compress_native(path, rows, completed):
|
||||
"""Bounded disk memmap; preserve raw file until parent elects removal."""
|
||||
rows.flush()
|
||||
compressed = path.with_suffix('.npz')
|
||||
np.savez_compressed(compressed, rows=rows[:completed])
|
||||
with np.load(compressed) as data:
|
||||
retained = data['rows']
|
||||
if retained.shape != (completed, len(NATIVE_COLUMNS)):
|
||||
raise RuntimeError('compressed native shape mismatch')
|
||||
for start in range(0, completed, 10000):
|
||||
end = min(start+10000, completed)
|
||||
if not np.array_equal(retained[start:end], rows[start:end]):
|
||||
raise RuntimeError('compressed native verification failed')
|
||||
return dict(raw_path=path.name, compressed_path=compressed.name, verified=True,
|
||||
compressed_sha256=sha(compressed), raw_preserved_for_parent_cleanup=True)
|
||||
|
||||
|
||||
def run_role(scene, role, plan, cfgpath, base, helpers, metrics, seed, initial_force,
|
||||
model, norm, out, started, cap, event):
|
||||
from CelerisLab import Simulation
|
||||
from CelerisLab.body.motion import MovingTables
|
||||
directory = out/role
|
||||
directory.mkdir()
|
||||
steps = plan['si']*plan['intervals']
|
||||
rows = np.lib.format.open_memmap(directory/'native.npy', mode='w+', dtype=np.float64,
|
||||
shape=(steps, len(NATIVE_COLUMNS)))
|
||||
signals = {key: np.zeros((plan['intervals'], width), np.float32) for key, width in
|
||||
[('sensor_area_mean', 6), ('sensors', 6), ('forces', 6), ('actions', 3),
|
||||
('observations', 12), ('policy_input', 12), ('raw_observation', 12)]}
|
||||
structure = base.Structure(4., 2.5e-5, 0., D)
|
||||
columns = {name: i for i, name in enumerate(MovingTables.DIAG)}
|
||||
completed = completed_si = 0
|
||||
duration = steps*U/D
|
||||
report = dict(status='running', role=role, target_is_free_body=False if role == 'target' else None,
|
||||
columns=NATIVE_COLUMNS,
|
||||
motion_window=dict(start_tU_D=max(0., duration-80.), end_tU_D=duration,
|
||||
interpretation='smoke_short_not_physical' if plan['smoke'] else
|
||||
('entire_60DU_event_not_settled' if scene == 'taylor' else 'declared_last_80DU')),
|
||||
structure=None if role == 'target' else dict(mass=structure.mass, fn=structure.frequency,
|
||||
mass_ratio=4., zeta=0., y0=0., v0=0.))
|
||||
cache, common_fluid, selected_flags, roi_cache = None, None, {}, None
|
||||
obs, force = np.zeros(12, np.float32), initial_force
|
||||
try:
|
||||
report['MemAvailable_admission_bytes'] = admit_memory(plan['smoke'])
|
||||
with Simulation(str(cfgpath)) as sim:
|
||||
probes, bodies = add_scene(sim, scene, moving=role != 'target', pinball=role != 'target')
|
||||
sim.initialize()
|
||||
if role == 'target':
|
||||
base.warmup(sim, plan['release_clock'], started, cap)
|
||||
report['initialization'] = 'own no-pinball initialization at matching absolute clock'
|
||||
else:
|
||||
report['release_transfer'] = base.install_seed(sim, seed)
|
||||
if sim.stepper.step_count != plan['release_clock']:
|
||||
raise RuntimeError('role release clocks differ')
|
||||
if scene == 'taylor':
|
||||
report['injection'] = inject_taylor(sim)
|
||||
count = plan['intervals']-plan['field_start']
|
||||
shape = (sim.field.ny, sim.field.nx)
|
||||
cache = np.empty((count, 2, *shape), np.float32)
|
||||
common_fluid = np.ones(shape, bool)
|
||||
roi = metrics.roi_slices(shape, D=D, xs=1600., y0=510.5,
|
||||
downstream_solid_extent=1272.)
|
||||
# Only compact ROI histories survive a role; full-grid histories never reach disk.
|
||||
roi_shape = cache[(slice(None), slice(None), *roi)].shape
|
||||
roi_cache = np.empty(roi_shape, np.float32)
|
||||
retain = set(event['field_indices'].tolist()) if event is not None else set()
|
||||
if scene == 'taylor' and event is None and not plan['smoke']:
|
||||
# Authoritative selector searches 38..87; any imported offset can be selected.
|
||||
retain = {i+int(offset) for i in range(38, 88) for offset in helpers.VORTEX_EVENT_OFFSETS}
|
||||
# K phase unknown until the final signal: retain flags for every boundary in RAM.
|
||||
flag_cache = np.empty((count, *shape), np.uint16) if scene == 'karman' else None
|
||||
for interval in range(plan['intervals']):
|
||||
base.check_time(started, cap)
|
||||
action = np.zeros(3, np.float32)
|
||||
signals['policy_input'][interval] = obs
|
||||
if role == 'controlled' and scene != 'steady':
|
||||
action = np.asarray(model.predict(obs, deterministic=True)[0], np.float32)
|
||||
target, effective = wall_surface(scene, role, action), np.zeros(3)
|
||||
sensor_sum, force_sum = np.zeros(6), np.zeros(6)
|
||||
for native in range(plan['si']):
|
||||
if native % 1000 == 0:
|
||||
base.check_time(started, cap)
|
||||
y, v = (0., 0.) if role == 'target' else structure.candidate(force)
|
||||
check_structure(y, v, structure.y)
|
||||
effective = .9**.25*effective + (1.-.9**.25)*target
|
||||
omega = effective/20.
|
||||
for body, w in zip(bodies, omega):
|
||||
sim.set_body(body, omega=float(w), offset=(0., y), vy=y-structure.y)
|
||||
sim.run(1, zero_obs=True, sync_obs=True)
|
||||
if bodies:
|
||||
diag = sim.moving_diagnostics()
|
||||
cme, wen = diag[:, columns['cme_y']], diag[:, columns['wen_y']]
|
||||
node = diag[:, columns['cover_jy']]-diag[:, columns['uncover_jy']]
|
||||
counters = [diag[:, columns[key]].sum() for key in ('eq_only', 'no_donor', 'crossings')]
|
||||
packed = np.array([value for body in bodies for value in sim.read_force(body)[:2]])
|
||||
else:
|
||||
cme, wen, node, counters, packed = np.zeros(3), np.zeros(3), np.zeros(3), [0., 0., 0.], np.zeros(6)
|
||||
sensors = np.array([value for probe in probes for value in sim.read_sensor(probe)[:2]])
|
||||
row = np.array([completed+1, y, v, *cme, *wen, *node, *counters, *omega])
|
||||
if not np.isfinite(row).all() or counters[1] != 0 or not np.isfinite([packed, sensors]).all():
|
||||
raise RuntimeError('nonfinite native diagnostics/observations or no_donor')
|
||||
rows[completed] = row
|
||||
completed += 1
|
||||
structure.y, structure.v = y, v
|
||||
force = float(cme.sum(dtype=np.float64)+wen.sum(dtype=np.float64))
|
||||
sensor_sum += sensors
|
||||
force_sum += packed
|
||||
sensors, packed = sensor_sum/plan['si'], force_sum/plan['si']
|
||||
raw = np.r_[sensors*156., packed*2.].astype(np.float32)
|
||||
if norm is not None:
|
||||
raw, obs, clipped = base.observation(sensors, packed, 2., norm)
|
||||
report['clipped_channels'] = report.get('clipped_channels', 0)+clipped
|
||||
for key, value in [('sensor_area_mean', sensors), ('sensors', sensors*156.),
|
||||
('forces', packed*2.), ('actions', action), ('observations', obs),
|
||||
('raw_observation', raw)]:
|
||||
signals[key][interval] = value
|
||||
if interval >= plan['field_start']:
|
||||
index = interval-plan['field_start']
|
||||
clean, flags, fluid = clean_field(sim.get_macroscopic(), sim.get_flags())
|
||||
if not fluid[roi].all():
|
||||
raise RuntimeError('prescribed downstream ROI contains nonfluid cells')
|
||||
cache[index] = clean
|
||||
common_fluid &= fluid
|
||||
roi_cache[index] = clean[(slice(None), *roi)]
|
||||
if flag_cache is not None:
|
||||
flag_cache[index] = flags
|
||||
elif index in retain or index == count-1:
|
||||
selected_flags[index] = flags
|
||||
completed_si += 1
|
||||
if completed_si % 10 == 0 or completed_si == plan['intervals']:
|
||||
rows.flush()
|
||||
print(json.dumps(dict(event='progress', scene=scene, role=role, interval=completed_si,
|
||||
absolute_clock=sim.stepper.step_count, y_D=y/D)), flush=True)
|
||||
times = np.arange(plan['field_start']+1, plan['intervals']+1, dtype=np.int64)*plan['si']
|
||||
selected, selection, event = select_fields(scene, plan, helpers, times,
|
||||
signals['sensors'][plan['field_start']:], cache, event)
|
||||
indices = selected['selected_index']
|
||||
selected['flags'] = flag_cache[indices] if flag_cache is not None else np.stack([selected_flags[int(i)] for i in indices])
|
||||
selected['common_fluid_all_boundaries'] = common_fluid
|
||||
selected['absolute_steps'] = selected['selected_response_steps']+plan['release_clock']
|
||||
np.savez_compressed(directory/'fields.npz', **selected)
|
||||
report.update(selection=selection, field_response_steps=times,
|
||||
field_absolute_steps=times+plan['release_clock'],
|
||||
field_window_tU_D=[int(times[0])*U/D, int(times[-1])*U/D],
|
||||
status='smoke_completed_not_physical' if plan['smoke'] else 'duration_completed_not_qualified')
|
||||
roi_mask = common_fluid[roi].copy()
|
||||
report['motion_summary'] = metrics.motion_summary(rows[max(0, completed-640000):completed, 1]/D)
|
||||
return dict(roi=roi_cache, mask=roi_mask, times=times, event=event)
|
||||
except BaseException as exc:
|
||||
report['status'] = f'error: {type(exc).__name__}: {exc}'
|
||||
raise
|
||||
finally:
|
||||
report.update(completed_native_steps=completed, completed_intervals=completed_si)
|
||||
np.savez_compressed(directory/'signals.npz', response_steps=np.arange(1, completed_si+1)*plan['si'],
|
||||
absolute_steps=plan['release_clock']+np.arange(1, completed_si+1)*plan['si'],
|
||||
**{key: value[:completed_si] for key, value in signals.items()})
|
||||
if completed:
|
||||
report['native_archive'] = compress_native(directory/'native.npy', rows, completed)
|
||||
write_json(directory/'report.json', report)
|
||||
del cache, rows
|
||||
gc.collect()
|
||||
|
||||
|
||||
def score_roles(roles, metrics):
|
||||
target = roles['target']
|
||||
common = np.logical_and.reduce([role['mask'] for role in roles.values()])
|
||||
if not common.all():
|
||||
raise ValueError('prescribed downstream ROI must be entirely common fluid')
|
||||
result = dict(definition='sqrt(mean common ROI (dux^2+duy^2)/U^2)',
|
||||
alignment='same absolute native clock; no lag/phase alignment',
|
||||
roi_xy_inclusive=[1360, 2160, 311, 710], support_cells=int(common.sum()),
|
||||
support_coverage_fraction=1.)
|
||||
for name in ('zero', 'controlled'):
|
||||
role = roles[name]
|
||||
if not np.array_equal(role['times'], target['times']):
|
||||
raise ValueError('unmatched response clocks')
|
||||
errors = np.array([metrics.field_error(*a, *b, U, common) for a, b in zip(role['roi'], target['roi'])])
|
||||
# FP64 reduction without duplicating entire history.
|
||||
mean = [a.mean(axis=0, dtype=np.float64) for a in (role['roi'], target['roi'])]
|
||||
result[name] = dict(same_clock_instantaneous_E=errors,
|
||||
mean_instantaneous_E=float(errors.mean()),
|
||||
E_of_fixed_window_mean=metrics.field_error(*mean[0], *mean[1], U, common))
|
||||
return result, common
|
||||
|
||||
|
||||
def source_manifest(base, helpers):
|
||||
files = [Path(__file__), ROOT/'research/fig2_materials/metrics.py', Path(base.__file__),
|
||||
ROOT/'research/steady_pinball/run.py', ROOT/'research/steady_pinball/config.json',
|
||||
Path(helpers.__file__), Path(helpers.__file__).parent/'legacy_test/acquire.py',
|
||||
Path(helpers.__file__).parent/'legacy_test/core/dtw_metrics.py']
|
||||
files += sorted((ROOT/'src/CelerisLab').rglob('*.py'))
|
||||
for suffix in ('*.cu', '*.cuh', '*.h'):
|
||||
files += sorted((ROOT/'src/CelerisLab').rglob(suffix))
|
||||
return {str(path.resolve()): sha(path) for path in files}
|
||||
|
||||
|
||||
def main(argv=None):
|
||||
ap = argparse.ArgumentParser(description=__doc__)
|
||||
ap.add_argument('--scene', choices=('karman', 'steady', 'taylor'), required=True)
|
||||
ap.add_argument('--out', type=Path, required=True)
|
||||
ap.add_argument('--smoke', action='store_true', help='32-step warmups; 3 SI of 32; no physical score')
|
||||
ap.add_argument('--D', type=int, choices=(40,), default=40)
|
||||
ap.add_argument('--model', type=Path)
|
||||
ap.add_argument('--norm', type=Path)
|
||||
ap.add_argument('--release-dir', type=Path, default=K_RELEASE)
|
||||
ap.add_argument('--acquisition', type=Path, default=ACQUISITION)
|
||||
ap.add_argument('--wall-seconds', type=float, default=43200.)
|
||||
args = ap.parse_args(argv)
|
||||
if not np.isfinite(args.wall_seconds) or args.wall_seconds <= 0:
|
||||
ap.error('positive finite wall cap required')
|
||||
if args.scene == 'steady' and (args.model or args.norm):
|
||||
ap.error('steady constant control has no PPO or normalizer')
|
||||
out = args.out.resolve()
|
||||
if out == ROOT or ROOT in out.parents:
|
||||
ap.error('fresh output must be outside checkout')
|
||||
sys.path.insert(0, str(ROOT))
|
||||
from research.karman_viv import run as base
|
||||
metrics = load_file('_fig2_metrics', ROOT/'research/fig2_materials/metrics.py')
|
||||
helpers = load_helpers(args.acquisition)
|
||||
model = norm = None
|
||||
inputs = {}
|
||||
if args.scene != 'steady':
|
||||
model_path, model_sha, norm_path, norm_sha = ASSETS[args.scene]
|
||||
model_path, norm_path = args.model or model_path, args.norm or norm_path
|
||||
if sha(model_path) != model_sha or sha(norm_path) != norm_sha:
|
||||
ap.error('model/normalizer hashes differ from frozen inputs')
|
||||
norm = json.loads(norm_path.read_text())
|
||||
inputs = dict(model_path=model_path, model_sha256=model_sha,
|
||||
norm_path=norm_path, norm_sha256=norm_sha)
|
||||
model = base.load_policy(model_path)
|
||||
sources = source_manifest(base, helpers)
|
||||
source = subprocess.check_output(['git', '-C', str(ROOT), 'rev-parse', 'HEAD'], text=True).strip()
|
||||
dirty = subprocess.check_output(['git', '-C', str(ROOT), 'status', '--porcelain'], text=True)
|
||||
plan = schedule(args.scene, args.smoke)
|
||||
admit_memory(args.smoke)
|
||||
out.mkdir(parents=True, exist_ok=False)
|
||||
cfgpath = out/'config.json'
|
||||
write_json(cfgpath, base.config(40))
|
||||
receipt = dict(scene=args.scene, plan=plan, D=D, U=U, nu=NU, actual_Re_D=50.,
|
||||
historical_case='karman_re100' if args.scene == 'karman' else args.scene,
|
||||
expected_baseline=BASELINE, source=source, git_dirty=bool(dirty), git_status=dirty,
|
||||
source_sha256=sources, inputs=inputs, centres=CENTRES, probes=PROBES,
|
||||
environment=dict(python=sys.version, platform=platform.platform(), numpy=np.__version__,
|
||||
conda_env=os.environ.get('CONDA_DEFAULT_ENV'),
|
||||
cuda_visible_devices=os.environ.get('CUDA_VISIBLE_DEVICES')),
|
||||
acquisition_event_offsets=list(helpers.VORTEX_EVENT_OFFSETS),
|
||||
event_selector='source _select_vortex_event AST verbatim; actual acquisition offsets',
|
||||
structure=dict(mass_ratio=4., fn_native=2.5e-5, fn_D_U=.2, zeta=0., travel_D=1., speed=.025),
|
||||
rhs='previous native-step sum CME_y+Wen_y; initial force last static native step',
|
||||
policy='SI mean packed CME *2; area-mean sensors *156; frozen norm; first input zeros',
|
||||
control='steady constant (0,-5.1,5.1)*U; K scale8 or Taylor scale4, bias(0,-4,4)*U',
|
||||
ema=dict(alpha=.9**.25, reset='every SI'),
|
||||
storage='one full-grid float32 RAM cache at a time; compact ROI histories in RAM; no full history field disk',
|
||||
limitation='Modern moving-plant experiment; no Legacy numerical equivalence or steady/convergence claim')
|
||||
write_json(out/'case.json', receipt)
|
||||
started, status = time.monotonic(), 'running'
|
||||
try:
|
||||
seed, force, release = make_release(args.scene, plan, cfgpath, base, out, started,
|
||||
args.wall_seconds, args.release_dir)
|
||||
receipt['release'] = release
|
||||
receipt['release'].update(clock=int(seed[3]), initial_force=force,
|
||||
current_sha256=base.array_sha(seed[0]),
|
||||
spare_sha256=base.array_sha(seed[1]), flags_sha256=base.array_sha(seed[2]))
|
||||
write_json(out/'case.json', receipt)
|
||||
roles, event = {}, None
|
||||
order = ('controlled', 'zero', 'target') if args.scene == 'taylor' else ('target', 'zero', 'controlled')
|
||||
for role in order:
|
||||
roles[role] = run_role(args.scene, role, plan, cfgpath, base, helpers, metrics,
|
||||
seed, force, model, norm, out, started, args.wall_seconds, event)
|
||||
if args.scene == 'taylor':
|
||||
event = roles[role]['event']
|
||||
if not args.smoke:
|
||||
scores, support = score_roles(roles, metrics)
|
||||
write_json(out/'same_clock_errors.json', scores)
|
||||
np.savez_compressed(out/'common_roi_support.npz', support=support)
|
||||
if source_manifest(base, helpers) != sources:
|
||||
raise RuntimeError('source files changed during collection')
|
||||
status = 'smoke_completed_not_physical' if args.smoke else 'three_roles_completed_not_qualified'
|
||||
except BaseException as exc:
|
||||
status = f'error: {type(exc).__name__}: {exc}'
|
||||
raise
|
||||
finally:
|
||||
write_json(out/'terminal.json', dict(status=status, elapsed_s=time.monotonic()-started,
|
||||
physical_score_claim=False if args.smoke else None))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -0,0 +1,94 @@
|
||||
"""Pure NumPy Fig2 primitives; no solver, phase selection, or probe DTW.
|
||||
|
||||
Adapted formula and inclusive ROI bounds from DynamisLab
|
||||
src/drl_pinball/eval/wake_l2.py::mean_field_metric, roi_slices, inspected
|
||||
2026-11-02 at HEAD 609116ec26f19766f5e9ce2b0df427d4e24faae8
|
||||
(working-tree source, not asserted clean). The explicit common-fluid/finite
|
||||
mask and motion summaries are additions. legacy_test/metrics.py::cycle_similarity
|
||||
was inspected only: its six probe channels are not a field recovery metric.
|
||||
"""
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
|
||||
def roi_slices(shape, *, D, xs, y0=None, downstream_solid_extent):
|
||||
"""Lab-fixed ROI: sensor-relative x/D=-6..14, y/D=-5..5, inclusive.
|
||||
|
||||
Equivalent to pinball-front-relative x/D=4..24 when xs=front+10D.
|
||||
Supply actual geometry, e.g. D=40, xs=1600, y0=510.5.
|
||||
"""
|
||||
ny, nx = shape
|
||||
y0 = (ny - 1) / 2 if y0 is None else float(y0)
|
||||
if D <= 0 or not all(np.isfinite(v) for v in (D, xs, y0, downstream_solid_extent)):
|
||||
raise ValueError("invalid geometry")
|
||||
x0, x1 = math.ceil(xs - 6 * D), math.floor(xs + 14 * D)
|
||||
lo, hi = math.ceil(y0 - 5 * D), math.floor(y0 + 5 * D)
|
||||
if x0 < 0 or lo < 0 or x1 >= nx or hi >= ny:
|
||||
raise ValueError("ROI outside grid")
|
||||
if x0 <= downstream_solid_extent:
|
||||
raise ValueError("ROI not downstream of solid extent")
|
||||
return slice(lo, hi + 1), slice(x0, x1 + 1)
|
||||
|
||||
|
||||
def common_fluid_roi_mask(fluid_masks, fields, roi):
|
||||
"""Intersect explicit boolean fluid masks, ROI, and finite 2-D fields.
|
||||
|
||||
Pass every compared role/component/sample to freeze a common support over
|
||||
time. Zero velocity never implies solid. Masks must share the lab grid.
|
||||
"""
|
||||
fields = [np.asarray(a, dtype=float) for a in fields]
|
||||
masks = [np.asarray(a) for a in fluid_masks]
|
||||
if not fields or not masks:
|
||||
raise ValueError("fields and explicit fluid masks required")
|
||||
shape = fields[0].shape
|
||||
if len(shape) != 2 or any(a.shape != shape for a in fields + masks):
|
||||
raise ValueError("equal-shape 2-D arrays required")
|
||||
if any(a.dtype != np.dtype(bool) for a in masks):
|
||||
raise ValueError("fluid masks must be boolean")
|
||||
support = np.zeros(shape, dtype=bool)
|
||||
ys, xs = roi
|
||||
for s, n in zip((ys, xs), shape):
|
||||
if not isinstance(s, slice) or s.step not in (None, 1) or not 0 <= (s.start or 0) < (n if s.stop is None else s.stop) <= n:
|
||||
raise ValueError("invalid ROI slices")
|
||||
support[ys, xs] = True
|
||||
for a in masks:
|
||||
support &= a
|
||||
for a in fields:
|
||||
support &= np.isfinite(a)
|
||||
if not support.any():
|
||||
raise ValueError("empty common finite fluid ROI")
|
||||
return support
|
||||
|
||||
|
||||
def field_error(rux, ruy, tux, tuy, u0, support):
|
||||
"""Eq1: sqrt(mean_support((dux**2 + duy**2)/U0**2)).
|
||||
|
||||
For E(mean field), pass arithmetic mean fields from matched windows.
|
||||
Mean instantaneous E requires separate calls per snapshot and then a mean;
|
||||
these operations are not interchangeable. No target-energy denominator.
|
||||
"""
|
||||
a = [np.asarray(v, dtype=float) for v in (rux, ruy, tux, tuy)]
|
||||
support = np.asarray(support)
|
||||
if a[0].ndim != 2 or any(v.shape != a[0].shape for v in a) or support.shape != a[0].shape or support.dtype != np.dtype(bool):
|
||||
raise ValueError("equal-shape 2-D fields and boolean support required")
|
||||
if not np.isfinite(u0) or u0 <= 0 or not support.any():
|
||||
raise ValueError("positive finite U0 and nonempty support required")
|
||||
if any(not np.isfinite(v[support]).all() for v in a):
|
||||
raise ValueError("nonfinite field on fixed support")
|
||||
du = (a[0][support] - a[2][support]) / u0
|
||||
dv = (a[1][support] - a[3][support]) / u0
|
||||
return float(np.sqrt(np.mean(du * du + dv * dv)))
|
||||
|
||||
|
||||
def motion_summary(displacement):
|
||||
"""Arithmetic mean, centered RMS, half peak-to-peak along time axis 0.
|
||||
|
||||
Input is displacement in caller-declared units; no implicit D scaling.
|
||||
Use the same retained window as field evaluation. No sinusoid assumption.
|
||||
"""
|
||||
a = np.asarray(displacement, dtype=float)
|
||||
if a.ndim == 0 or a.shape[0] == 0 or not np.isfinite(a).all():
|
||||
raise ValueError("nonempty finite time samples required")
|
||||
mean = a.mean(axis=0)
|
||||
return {"mean": mean, "centered_rms": np.sqrt(np.mean((a - mean) ** 2, axis=0)),
|
||||
"half_range": (a.max(axis=0) - a.min(axis=0)) / 2}
|
||||
@@ -0,0 +1,36 @@
|
||||
"""Independent analytic data checks for offline estimands, no CFD context."""
|
||||
import unittest
|
||||
import numpy as np
|
||||
from research.fig2_collection.analyze import frequency, native_summary, compare_selected
|
||||
|
||||
|
||||
class AnalysisTests(unittest.TestCase):
|
||||
def test_frequency_units_and_no_dc(self):
|
||||
dt=.005/40;n=640000
|
||||
t=np.arange(n)*dt
|
||||
result=frequency(4+np.sin(2*np.pi*.2*t),dt)
|
||||
self.assertAlmostEqual(result['f_D_U'],.2)
|
||||
self.assertAlmostEqual(result['bin_width_D_U'],1/80)
|
||||
self.assertIsNone(frequency(np.ones(20),dt))
|
||||
|
||||
def test_motion_and_native_rhs_are_distinct_from_node_impulse(self):
|
||||
a=np.zeros((10,18));a[:,0]=np.arange(1,11);a[:,1]=40*np.tile([.5,-.5],5)
|
||||
a[:,3]=.001;a[:,6]=.002;a[:,9]=999
|
||||
result=native_summary(a,'taylor')
|
||||
self.assertEqual(result['motion']['centered_rms'],.5)
|
||||
self.assertEqual(result['motion']['half_range'],.5)
|
||||
self.assertAlmostEqual(result['structural_RHS_Cy_RMS'],6.)
|
||||
self.assertEqual(result['window_D_U'],[0.,10*.005/40])
|
||||
|
||||
def test_selected_vector_error_uses_common_roi_and_actual_flags(self):
|
||||
shape=(1,712,2162)
|
||||
target=dict(ux=np.zeros(shape,np.float32),uy=np.zeros(shape,np.float32),flags=np.ones(shape,np.uint16))
|
||||
current={k:v.copy() for k,v in target.items()}
|
||||
current['ux'][:,311:711,1360:2161]=.003
|
||||
current['uy'][:,311:711,1360:2161]=.004
|
||||
self.assertAlmostEqual(compare_selected(target,current)['aggregate_E'],1.,places=6)
|
||||
current['flags'][0,400,1500]=2
|
||||
with self.assertRaises(ValueError):compare_selected(target,current)
|
||||
|
||||
|
||||
if __name__=='__main__':unittest.main()
|
||||
@@ -0,0 +1,225 @@
|
||||
"""CPU contracts for Fig. 2 acquisition; no CUDA or simulation import required."""
|
||||
import importlib.util
|
||||
import json
|
||||
from pathlib import Path
|
||||
import sys
|
||||
import tempfile
|
||||
import types
|
||||
import unittest
|
||||
from unittest.mock import patch
|
||||
|
||||
import numpy as np
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[2]
|
||||
SPEC = importlib.util.spec_from_file_location('fig2_collection_tested', ROOT/'research/fig2_collection/run.py')
|
||||
driver = importlib.util.module_from_spec(SPEC)
|
||||
SPEC.loader.exec_module(driver)
|
||||
METRIC_SPEC = importlib.util.spec_from_file_location('fig2_collection_metrics', ROOT/'research/fig2_materials/metrics.py')
|
||||
metrics = importlib.util.module_from_spec(METRIC_SPEC)
|
||||
METRIC_SPEC.loader.exec_module(metrics)
|
||||
|
||||
|
||||
class CollectionContracts(unittest.TestCase):
|
||||
def test_frozen_clocks_and_windows(self):
|
||||
for scene, release, count, first in [('karman', 4576000, 400, 240),
|
||||
('steady', 2048000, 400, 240),
|
||||
('taylor', 1024000, 150, 0)]:
|
||||
plan = driver.schedule(scene)
|
||||
self.assertEqual((plan['release_clock'], plan['intervals'], plan['field_start']),
|
||||
(release, count, first))
|
||||
self.assertEqual(plan['si'], 3200)
|
||||
self.assertEqual((400-240)*3200*driver.U/driver.D, 64.)
|
||||
self.assertEqual(400*3200*driver.U/driver.D, 160.)
|
||||
self.assertEqual(150*3200*driver.U/driver.D, 60.)
|
||||
|
||||
def test_smoke_has_no_physical_timing(self):
|
||||
for scene in ('karman', 'steady', 'taylor'):
|
||||
plan = driver.schedule(scene, True)
|
||||
self.assertEqual((plan['si'], plan['intervals'], plan['field_start']), (32, 3, 0))
|
||||
self.assertEqual(plan['release_clock'], 64 if scene == 'taylor' else 32)
|
||||
|
||||
def test_zero_is_exact_physical_zero_despite_bias(self):
|
||||
for scene in ('karman', 'steady', 'taylor'):
|
||||
np.testing.assert_array_equal(driver.wall_surface(scene, 'zero'), np.zeros(3))
|
||||
np.testing.assert_array_equal(driver.wall_surface(scene, 'target'), np.zeros(3))
|
||||
np.testing.assert_allclose(driver.wall_surface('karman', 'controlled', [.5, -.5, 1]),
|
||||
[.02, -.04, .06])
|
||||
np.testing.assert_allclose(driver.wall_surface('taylor', 'controlled', [.5, -.5, 1]),
|
||||
[.01, -.03, .04])
|
||||
np.testing.assert_allclose(driver.wall_surface('steady', 'controlled'), [0, -.0255, .0255])
|
||||
with self.assertRaises(ValueError):
|
||||
driver.wall_surface('karman', 'controlled', [0, 0, np.nan])
|
||||
|
||||
def test_structure_hard_stops(self):
|
||||
driver.check_structure(40., 0., 40.)
|
||||
for y, v, old in [(40.01, 0, 40), (0, .026, 0), (.026, 0, 0), (np.nan, 0, 0)]:
|
||||
with self.subTest(y=y, v=v), self.assertRaises(RuntimeError):
|
||||
driver.check_structure(y, v, old)
|
||||
|
||||
def test_fluid_nonfinite_fails_nonfluid_only_zero_filled(self):
|
||||
flags = np.array([[1, 257, 2]], np.uint16)
|
||||
fields = dict(rho=np.array([[1., 1., np.nan]]), ux=np.array([[0., .1, np.nan]]),
|
||||
uy=np.array([[0., -.1, np.inf]]))
|
||||
clean, retained, fluid = driver.clean_field(fields, flags)
|
||||
np.testing.assert_array_equal(fluid, [[True, True, False]])
|
||||
np.testing.assert_array_equal(retained, flags)
|
||||
self.assertTrue(np.isfinite(clean).all())
|
||||
self.assertEqual(clean[0, 0, 0], 0.) # zero flow remains fluid
|
||||
fields['ux'][0, 1] = np.nan
|
||||
with self.assertRaisesRegex(RuntimeError, 'FLUID'):
|
||||
driver.clean_field(fields, flags)
|
||||
|
||||
def test_memory_admission_uses_available_not_free(self):
|
||||
with tempfile.TemporaryDirectory() as directory:
|
||||
path = Path(directory)/'meminfo'
|
||||
path.write_text('MemFree: 1 kB\nMemAvailable: 16777216 kB\n')
|
||||
self.assertEqual(driver.available_memory(path), driver.MIN_AVAILABLE)
|
||||
with patch.object(driver, 'available_memory', return_value=driver.MIN_AVAILABLE-1):
|
||||
with self.assertRaises(MemoryError):
|
||||
driver.admit_memory()
|
||||
self.assertIsNone(driver.admit_memory(True))
|
||||
|
||||
def test_roi_bounds_match_full_downstream_support(self):
|
||||
roi = metrics.roi_slices((1022, 2559), D=40, xs=1600, y0=510.5,
|
||||
downstream_solid_extent=1272.)
|
||||
self.assertEqual(roi, (slice(311, 711), slice(1360, 2161)))
|
||||
|
||||
def test_same_clock_error_and_mean_are_distinct(self):
|
||||
shape = (2, 2, 2, 3)
|
||||
target = np.zeros(shape, np.float32)
|
||||
changed = target.copy()
|
||||
changed[0, 0], changed[1, 0] = driver.U, -driver.U
|
||||
role = lambda fields: dict(roi=fields, mask=np.ones((2, 3), bool), times=np.array([3200, 6400]))
|
||||
roles = dict(target=role(target), zero=role(changed), controlled=role(target))
|
||||
result, mask = driver.score_roles(roles, metrics)
|
||||
self.assertAlmostEqual(result['zero']['mean_instantaneous_E'], 1., places=7)
|
||||
self.assertEqual(result['zero']['E_of_fixed_window_mean'], 0.)
|
||||
roles['zero']['times'] = np.array([6400, 9600])
|
||||
with self.assertRaisesRegex(ValueError, 'clocks'):
|
||||
driver.score_roles(roles, metrics)
|
||||
roles['zero']['times'] = roles['target']['times']
|
||||
roles['zero']['mask'][0, 0] = False
|
||||
with self.assertRaisesRegex(ValueError, 'entirely common fluid'):
|
||||
driver.score_roles(roles, metrics)
|
||||
|
||||
def test_native_archive_verified_raw_retained(self):
|
||||
with tempfile.TemporaryDirectory() as directory:
|
||||
path = Path(directory)/'native.npy'
|
||||
rows = np.lib.format.open_memmap(path, mode='w+', dtype=np.float64,
|
||||
shape=(9, len(driver.NATIVE_COLUMNS)))
|
||||
rows[:] = np.arange(rows.size).reshape(rows.shape)
|
||||
report = driver.compress_native(path, rows, 7)
|
||||
self.assertTrue(report['verified'])
|
||||
self.assertTrue(path.exists())
|
||||
with np.load(path.with_suffix('.npz')) as data:
|
||||
np.testing.assert_array_equal(data['rows'], rows[:7])
|
||||
|
||||
def test_readonly_import_reuses_actual_offsets_and_functions(self):
|
||||
with tempfile.TemporaryDirectory() as directory:
|
||||
root = Path(directory)
|
||||
(root/'legacy_test/core').mkdir(parents=True)
|
||||
# Package __init__ must never be executed by the isolated source loader.
|
||||
for folder in (root, root/'legacy_test', root/'legacy_test/core'):
|
||||
(folder/'__init__.py').write_text("raise RuntimeError('application import forbidden')\n")
|
||||
(root/'legacy_test/core/dtw_metrics.py').write_text('calc_dtw_sim = None\ncalc_lag = None\n')
|
||||
(root/'acquisition.py').write_text(
|
||||
'from .legacy_test.core.dtw_metrics import calc_dtw_sim, calc_lag\n'
|
||||
'VORTEX_EVENT_OFFSETS = (-20,-15,-10,-5,0,5,10,15)\n')
|
||||
(root/'legacy_test/acquire.py').write_text(
|
||||
"raise RuntimeError('heavy imports forbidden')\n"
|
||||
'def _vortex_event_channel(case_name):\n return 3, "center_sensor_uy"\n'
|
||||
'def _select_vortex_event(sensors, case_name):\n'
|
||||
' return dict(offsets=VORTEX_EVENT_OFFSETS, channel=_vortex_event_channel(case_name))\n')
|
||||
helper = driver.load_helpers(root/'acquisition.py')
|
||||
result = helper.select_vortex_event(None, 'vortex_taylor')
|
||||
self.assertEqual(result['offsets'], (-20,-15,-10,-5,0,5,10,15))
|
||||
self.assertEqual(result['channel'], (3, 'center_sensor_uy'))
|
||||
self.assertFalse(list(root.rglob('*.pyc')))
|
||||
|
||||
def test_geometry_and_shared_translation_envelope(self):
|
||||
calls, translations = [], []
|
||||
def translation(*args, **kwargs):
|
||||
item = types.SimpleNamespace(args=args, kwargs=kwargs)
|
||||
translations.append(item)
|
||||
return item
|
||||
def add_body(*args, **kwargs):
|
||||
calls.append((args, kwargs))
|
||||
return len(calls)-1
|
||||
motion = types.ModuleType('CelerisLab.body.motion')
|
||||
motion.Translation = translation
|
||||
with patch.dict(sys.modules, {'CelerisLab.body.motion': motion}):
|
||||
probes, bodies = driver.add_scene(types.SimpleNamespace(add_body=add_body), 'karman', moving=True)
|
||||
self.assertEqual(calls[0][0], ('circle', (400.,510.5),40.))
|
||||
self.assertEqual(probes, [1,2,3])
|
||||
self.assertEqual(bodies, [4,5,6])
|
||||
self.assertEqual(translations[0].args, ((0.,0.,-40.,40.), .025))
|
||||
self.assertTrue(translations[0].kwargs['shared'])
|
||||
self.assertTrue(all(calls[i][1]['translation'] is translations[0] for i in bodies))
|
||||
calls.clear()
|
||||
with patch.dict(sys.modules, {'CelerisLab.body.motion': motion}):
|
||||
driver.add_scene(types.SimpleNamespace(add_body=add_body), 'steady', pinball=False)
|
||||
self.assertEqual(len(calls), 3)
|
||||
self.assertTrue(all(call[0][0] == 'sensor' for call in calls))
|
||||
|
||||
def test_taylor_operator_called_once_inside_context_at_response_zero(self):
|
||||
events = []
|
||||
class Context:
|
||||
def __enter__(self):
|
||||
events.append('enter')
|
||||
def __exit__(self, *args):
|
||||
events.append('exit')
|
||||
initializer = types.ModuleType('CelerisLab.lbm.initializers')
|
||||
initializer.add_vortex = lambda *args: events.append(args)
|
||||
field = object()
|
||||
sim = types.SimpleNamespace(field=field, stepper=types.SimpleNamespace(step_count=1024000),
|
||||
ctx=types.SimpleNamespace(activate=Context),
|
||||
stream=types.SimpleNamespace(synchronize=lambda: events.append('sync')))
|
||||
with patch.dict(sys.modules, {'CelerisLab.lbm.initializers': initializer}):
|
||||
receipt = driver.inject_taylor(sim)
|
||||
self.assertEqual(events, ['enter', 'sync', (field, (600.,510.5),80.,.000075,'taylor'), 'sync', 'exit'])
|
||||
self.assertEqual((receipt['response_clock'], receipt['absolute_clock'], receipt['count']),
|
||||
(0,1024000,1))
|
||||
|
||||
|
||||
class SelectionContracts(unittest.TestCase):
|
||||
@staticmethod
|
||||
def mean(ux, uy, indices):
|
||||
return dict(mean_ux=ux[indices].mean(axis=0), mean_uy=uy[indices].mean(axis=0))
|
||||
|
||||
def test_karman_missing_cycles_preserves_fixed_mean_and_last(self):
|
||||
helper = types.SimpleNamespace(accumulate_mean_fields=self.mean)
|
||||
def no_crossing(*args, **kwargs):
|
||||
raise ValueError('at least two accepted rising crossings are required')
|
||||
helper.assign_periodic_phase = no_crossing
|
||||
cache = np.arange(24, dtype=np.float32).reshape(3, 2, 2, 2)
|
||||
arrays, metadata, event = driver.select_fields('karman', dict(smoke=False), helper,
|
||||
np.array([3200, 6400, 9600]), np.zeros((3, 6)), cache)
|
||||
self.assertEqual(metadata['periodic_phase_status'], 'insufficient_crossings')
|
||||
self.assertNotIn('cycle_mean_ux', arrays)
|
||||
np.testing.assert_array_equal(arrays['ux'][0], cache[-1, 0])
|
||||
np.testing.assert_allclose(arrays['fixed_window_mean_ux'], cache[:, 0].mean(axis=0))
|
||||
|
||||
def test_taylor_common_controlled_event_not_reselected(self):
|
||||
def forbidden(*args):
|
||||
raise AssertionError('other roles must not select their own event')
|
||||
helper = types.SimpleNamespace(accumulate_mean_fields=self.mean, select_vortex_event=forbidden)
|
||||
cache = np.zeros((150, 2, 2, 2), np.float32)
|
||||
event = dict(field_indices=np.array([20,25,30,35,40,45,50,55]))
|
||||
arrays, meta, returned = driver.select_fields('taylor', dict(smoke=False), helper,
|
||||
np.arange(1,151)*3200, np.zeros((150,6)), cache, event)
|
||||
self.assertIs(returned, event)
|
||||
np.testing.assert_array_equal(arrays['selected_index'], event['field_indices'])
|
||||
self.assertEqual(len(arrays['ux']), 8)
|
||||
|
||||
def test_smoke_selection_has_no_event_or_phase_claim(self):
|
||||
helper = types.SimpleNamespace(accumulate_mean_fields=self.mean)
|
||||
for scene in ('karman', 'steady', 'taylor'):
|
||||
arrays, metadata, event = driver.select_fields(scene, dict(smoke=True), helper,
|
||||
np.array([32,64,96]), np.zeros((3,6)),
|
||||
np.zeros((3,2,2,2), np.float32))
|
||||
self.assertEqual(metadata['selection'], 'smoke_last_snapshot_not_physical')
|
||||
self.assertIsNone(event)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@@ -0,0 +1,49 @@
|
||||
"""CPU-only analytic checks, runnable with stdlib unittest (or pytest)."""
|
||||
import unittest
|
||||
import numpy as np
|
||||
from research.fig2_materials.metrics import (
|
||||
common_fluid_roi_mask, field_error, motion_summary, roi_slices,
|
||||
)
|
||||
|
||||
|
||||
class MetricsTests(unittest.TestCase):
|
||||
def test_u0_normalization_not_target_energy(self):
|
||||
zero = np.zeros((2, 2)); support = np.ones((2, 2), bool)
|
||||
self.assertEqual(field_error(zero + 3, zero + 4, zero, zero, 2, support), 2.5)
|
||||
self.assertEqual(field_error(zero + 13, zero + 4, zero + 10, zero, 2, support), 2.5)
|
||||
for u0 in (0, -1, np.nan):
|
||||
with self.assertRaises(ValueError):
|
||||
field_error(zero, zero, zero, zero, u0, support)
|
||||
|
||||
def test_common_mask_roi_finite_and_fluid(self):
|
||||
a = np.zeros((3, 4)); b = a.copy(); b[1, 2] = np.nan
|
||||
m = np.ones(a.shape, bool); n = m.copy(); n[1, 1] = False
|
||||
support = common_fluid_roi_mask([m, n], [a, b], (slice(1, 3), slice(1, 4)))
|
||||
self.assertEqual(int(support.sum()), 4)
|
||||
self.assertFalse(support[1, 1]); self.assertFalse(support[1, 2])
|
||||
self.assertTrue(support[2, 1]) # zero velocity is fluid
|
||||
self.assertEqual(field_error(a, a, b, a, 1, support), 0)
|
||||
with self.assertRaises(ValueError):
|
||||
common_fluid_roi_mask([m & False], [a], (slice(None), slice(None)))
|
||||
with self.assertRaises(ValueError):
|
||||
field_error(a, a, b, a, 1, m) # fixed support must be finite
|
||||
|
||||
def test_mean_field_differs_from_mean_instantaneous_error(self):
|
||||
snapshots = np.array([[[2.]], [[-2.]]]); z = np.zeros((1, 1)); mask = np.ones((1, 1), bool)
|
||||
self.assertEqual(field_error(snapshots.mean(0), z, z, z, 1, mask), 0)
|
||||
self.assertEqual(np.mean([field_error(s, z, z, z, 1, mask) for s in snapshots]), 2)
|
||||
|
||||
def test_actual_geometry_and_motion_bias(self):
|
||||
roi = roi_slices((1022, 2400), D=40, xs=1600, y0=510.5, downstream_solid_extent=1350)
|
||||
self.assertEqual(roi, (slice(311, 711), slice(1360, 2161)))
|
||||
m = motion_summary([8, 10, 12])
|
||||
self.assertEqual(m['mean'], 10)
|
||||
self.assertAlmostEqual(m['centered_rms'], np.sqrt(8 / 3))
|
||||
self.assertEqual(m['half_range'], 2)
|
||||
for a in ([], [np.nan]):
|
||||
with self.assertRaises(ValueError):
|
||||
motion_summary(a)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user