worstcase 0.2.1

Worst case analysis and sensitivity studies using Extreme Value and/or Monte Carlo methods.

worstcase

Overview

pip install worstcase

Worst case analysis and sensitivity studies using Extreme Value and/or Monte Carlo methods.

This package coexists alongside far more capable uncertainty analysis and error propagation packages such as uncertainties (first-order propagation), soerp (second-order propagation), and mcerp (Monte Carlo propagation).

This package is designed for engineering applications where worst case analysis computations are often done using the Extreme Value method over single-valued functions while falling back to the Monte Carlo method when the worst case is known to not exist at the extremes. The Extreme Value method is implemented as a brute-force search; the Monte Carlo method is implemented with Latin Hypercube Sampling over a uniform distribution.

This package does not transparently handle parameter covariance across functions. Instead, the graph of parameter dependence must be a tree (and therefore acyclic). Constructing a worst case analysis requires explicit definition of what the underlying parameter dependencies are.

Usage

import worstcase as wca
import numpy as np

wca.config.n = 2000  # Number of Monte Carlo runs. (default: 5000)
wca.config.sigfig = 4  # Number of significant firues to print. (default: 3)

The primitive varying parameter is a Param. Params are constructed either by tolerance (absolute or relative) or by range. Params may be given units from the default unit registry of Pint (default is unitless). A tag for printing is also optional (default is an empty string). A Param has the fields nom (nominal value), lb (lower bound), and ub (upper bound).

spd_initial = wca.param.bytol(nom=2, tol=0.1, rel=True, unit=wca.unit("m/s"), tag="v0")
accel = wca.param.byrange(nom=0.2, lb=0.1, ub=0.5, unit=wca.unit("m/s**2"), tag="a")
distance = wca.param.byrange(nom=1, lb=0.8, ub=1.1, unit=wca.unit.km, tag="x")

print([spd_initial, accel, distance])

[v0: 2 m/s (nom), 1.8 m/s (lb), 2.2 m/s (ub),
 a: 200 mm/s² (nom), 100 mm/s² (lb), 500 mm/s² (ub),
 x: 1 km (nom), 800 m (lb), 1.1 km (ub)]

A more complex parameter is built up as a ParamBuilder. ParamBuilders are constructed by decorating single-valued functions by ev (Extreme Value) or mc (Monte Carlo). The arguments passed to the ParamBuilder are partially bound to the underlying function. A parameter dependency tree can be drawn using the assigned tags; ParamBuilder will assume a tag corresponding to the function name as a default.

@wca.param.ev(spd_initial, accel, distance)
def spd_final(v, a, x):
    return np.sqrt(v ** 2 + 2 * a * x)


print(spd_final)

spd_final (ev)
├── a
├── v0
└── x

ParamBuilder is a callable and the returned value depends on the arguments supplied. If no arguments are supplied, the parameter is built and a Param is returned.

print(spd_final())

spd_final: 20.1 m/s (nom), 12.78 m/s (lb), 33.24 m/s (ub)

Alternatively, the ParamBuilder binding to the underlying function can be updated and a new ParamBuilder is returned.

spd_final_noaccel = spd_final(a=0 * wca.unit("m/s**2"), tag="spd_noaccel")
print(spd_final_noaccel)

spd_noaccel (ev)
├── v0
└── x

Finally, if the ParamBuilder binding is updated such that no arguments are varying parameters then the underlying function will be called to return a single value.

result = spd_final_noaccel(3 * wca.unit("m/s"), x=10 * wca.unit.m)
print(result)

3.0 meter / second

ParamBuilders can be used to construct other ParamBuilders.

spd_rel = wca.param.bytol(nom=20, tol=1, rel=False, unit=wca.unit("mi/hr"), tag="vrel")


@wca.param.mc(spd_final, spd_rel)
def spd_total(vf, vr):
    return vf + vr


print(spd_total)
print(spd_total())

spd_total (mc)
├── spd_final (ev)
│   ├── a
│   ├── v0
│   └── x
└── vrel

spd_total: 29.04 m/s (nom), 21.36 m/s (lb), 42.52 m/s (ub)

ParamBuilders can be modified with the ss method to perform a sensitivity study. By supplying a Param or ParamBuilder (or a list of them), a new ParamBuilder is returned where all other varying parameters are set to their nominal value. A few examples below.

accel_sens = spd_total.ss(accel, tag="accel-sens")
print(accel_sens)
print(accel_sens())

accel-sens (mc)
└── spd_final (ev)
    └── a

accel-sens: 29.04 m/s (nom), 23.23 m/s (lb), 40.62 m/s (ub)

accel_distance_sens = spd_total.ss([accel, distance], tag="accel/distance-sens")
print(accel_distance_sens)
print(accel_distance_sens())

accel/distance-sens (mc)
└── spd_final (ev)
    ├── a
    └── x

accel/distance-sens: 29.04 m/s (nom), 21.75 m/s (lb), 42.16 m/s (ub)

finalspd_sens = spd_total.ss(spd_final, tag="finalspd-sens")
print(finalspd_sens)
print(finalspd_sens())

finalspd-sens (mc)
└── spd_final (ev)
    ├── a
    ├── v0
    └── x

finalspd-sens: 29.04 m/s (nom), 21.73 m/s (lb), 42.18 m/s (ub)

relspd_sens = spd_total.ss(spd_rel, tag="relspd-sens")
print(relspd_sens)
print(relspd_sens())

relspd-sens (mc)
└── vrel

relspd-sens: 29.04 m/s (nom), 28.59 m/s (lb), 29.49 m/s (ub)