pyUngewiss

Python librarY for Uncertainty aNalysis in liGhtwEight desiGn with IntervalS and fuzzy numberS

Python-Bibliothek zur Unsicherheitsanalyse im Leichtbau mit Intervallen und unscharfen Zahlen

Libreria Python per l'analisi dell'incertezza nella costruzione leggera con intervalli e numeri sfocati

Installation

Prerequisites

Python 3 and you can install the necessary libraries via PIP:

pip install scipy
pip install numpy
pip install matplotlib
pip install pygmo
pip install cma

Further, for the use of gradient-based optimizers, you will need the package pyOpt.

svn checkout http://svn.pyopt.org/trunk pyopt
cd pyopt
python -m pip install -U .

For details see www.pyopt.org

Note to PyGMO: the PIP installation is currently not working. Therefore PaGMO and then PyGMO must be compiled to use the algorithms in that package.

Install

python -m pip install -U .

PIP

You can also install pyUngewiss via PIP

pip install pyUngewiss

Getting started

See iPython notebooks and Python files under examples.

Set up uncertain function with uncertain parameters and further parameters as input:

def Eigenfrequency1DoF(p, x):
    m = p[0]
    k = p[1]
    omega0 = np.sqrt(k/m)
    f0 = omega0/2/np.pi
    return(f0)

Then define the uncertain parameters -- here as intervals -- and combine in one list:

m = pu.UncertainNumber([2., 2.5])
k = pu.UncertainNumber([40000, 60000])
pUnc = [m, k]

Initialize the uncertain problem and set parameter options:

Prob = pu.UncertainAnalysis(Eigenfrequence1DoF, pUnc)
Prob.deltax = 1e-3
Prob.epsStop = 1e-3

Calculate:

Prob.calculate()

Print and plot results:

m.printValue()
k.printValue()
plt, _ = pu.plotIntervals([m.Value, k.Value],
                 labels=["mass $m$ [kg]", "stiffness $k$ [N/mm]"])
plt.show()

Author

E. J. Wehrle