surpyval 0.11 compatibility + conditional survival cs()#29
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non_repairable.py imported NonParametric, Parametric and ExactEventTime from submodule paths that were reorganised in surpyval 0.11. Import them from the top-level surpyval namespace instead, which works on both the old and new layouts, so RePyability can be used alongside the current surpyval that the Reliafy backend depends on. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013LT4iFs4HRQraP52HwVmgR
Bump the pinned dependencies to the latest surpyval (0.11.1), which requires numpy>=2.0,<3 and scipy>=1.13, and fix the incompatibilities that the newer surpyval/NumPy stack exposed: - NonRepairable._cost_rate: surpyval 0.11 returns 1-element arrays from sf/mean/qf, and scipy.optimize.minimize passes the time as a 1-element array. Coerce it to a float so the scipy.integrate.quad bound in avg_replacement_time() is a scalar (quad rejects array bounds). Fixes find_optimal_replacement() for parametric models. - NonRepairableRBD.random(): take the scalar from .random(1) before ordering the event queue and assigning into the output array (NumPy 2 rejects assigning a 1-element array to a scalar slot). Fixes the Monte-Carlo mean()/mean_time_to_failure(). - NonRepairableRBD.ff_by_node(): add the missing @check_x so it returns arrays like sf_by_node() rather than a scalar. Tests: relax the exact ff == 1 - sf assertion to approx (surpyval 0.11 computes ff(t) directly, so it can differ from 1 - sf(t) by a ULP), and add a system-level MTTF regression test. Full suite: 201 passed. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013LT4iFs4HRQraP52HwVmgR
Implement cs(x, X) = sf(X + x) / sf(X) — the probability of surviving a further x given survival to X — as a first-class method: - a shared conditional_survival(model, x, X) helper (utils.wrappers) that works for anything exposing sf, clipping to [0, 1] and returning 0 where the item has all but surely failed by X; - NonRepairableRBD.cs (passes through sf() args like working/broken nodes and method); - StandbyModel, RepeatedNode, RepeatedStandbyNode; - PerfectReliability (always 1) and PerfectUnreliability (always 0). This matches SurPyval's own model.cs definition, so conditional survival can be computed uniformly across systems and every node type. Full suite: 201 passing plus a new cs test. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013LT4iFs4HRQraP52HwVmgR
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Makes RePyability work with the latest SurPyval and adds first-class conditional survival.
Changes
NonParametric/Parametric/ExactEventTimeimports; coerce scalars inNonRepairable._cost_rateandNonRepairableRBD.random()(NumPy 2 no longer accepts 1-element arrays as scalars); add the missing@check_xonff_by_node. Pins bumped tosurpyval==0.11.1,numpy>=2.0,<3.cs(x, X): probability of surviving a furtherxgiven survival toX, i.e.sf(X + x) / sf(X). Implemented as a sharedconditional_survival()helper and exposed onNonRepairableRBD,StandbyModel,RepeatedNode,RepeatedStandbyNode, and thePerfectReliability/PerfectUnreliabilityhelpers — matching SurPyval's ownmodel.csdefinition.Tests
Full suite: 201 passing, plus a new conditional-survival test and an updated tolerance for the now more-precise
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