pystra.analysis.AnalysisOptions#

class AnalysisOptions[source]#

Bases: object

Options

Options for the structural reliability analysis.

Methods

`getBlockSize`
`getDiffMode`
`getE1`
`getE2`
`getFlagSens`
`getImax`
`getMultiProc`
`getPrintOutput`
`getRandomGenerator`
`getSamples`	Return the number of samples used in MCS
`getSimulationCov`
`getSimulationPoint`
`getSimulationStdv`
`getStepSize`
`getTransform`
`getffdpara`
`setBins`
`setBlockSize`
`setDiffMode`
`setE1`
`setE2`
`setImax`
`setMultiProc`
`setPrintOutput`
`setSamples`	Set the number of samples used in MCS
`setStepSize`
`setTransform`
`setffdpara`

Attributes

`transf_type`	Type of joint distribution
`Ro_method`	Method for computation of the modified Nataf correlation matrix
`flag_sens`	Flag for computation of sensitivities
`print_output`	Print output to the console during calculation
`multi_proc`	Amount of g-calls
`block_size`	Block size
`i_max`	Maximum number of iterations allowed in the search algorithm
`e1`	Tolerance on how close design point is to limit-state surface
`e2`	Tolerance on how accurately the gradient points towards the origin
`step_size`	Step size
`diff_mode`	Kind of differentiation
`ffdpara`	Parameter for computation
`samples`	Number of samples (MC,IS)
`random_generator`	Kind of Random generator
`sim_point`	Start point for the simulation
`stdv_sim`	Standard deviation of sampling distribution in simulation analysis
`target_cov`	Target coefficient of variation for failure probability
`bins`	Amount on bins for the histogram

transf_type#

Type of joint distribution

Type:

1: jointly normal (no longer supported)
2: independent non-normal (no longer supported)
3: Nataf joint distribution (only available option)

Ro_method#

Method for computation of the modified Nataf correlation matrix

Methods:

0: use of approximations from ADK’s paper (no longer supported)
1: exact, solved numerically

flag_sens#

Flag for computation of sensitivities

w.r.t. means, standard deviations, parameters and correlation coefficients

Flag:

1: all sensitivities assessed,
0: no sensitivities assessment

print_output#

Print output to the console during calculation

Values:

True: prints output to the console (useful, e.g. spyder),
False: does not print out (e.g. jupyter notebook)

multi_proc#

Amount of g-calls

1: block_size g-calls sent simultaneously 0: g-calls sent sequentially

block_size#

Block size

Number of g-calls to be sent simultaneously

i_max#: Maximum number of iterations allowed in the search algorithm

e1#: Tolerance on how close design point is to limit-state surface

e2#: Tolerance on how accurately the gradient points towards the origin

step_size#

Step size

0: step size by Armijo rule, otherwise: given value is the step size

diff_mode#

Kind of differentiation

Type:

‘ddm’: direct differentiation,
‘ffd’: forward finite difference

ffdpara#

Parameter for computation

Parameter for computation of FFD estimates of gradients - Perturbation = stdv/analysisopt.ffdpara

Values:

1000 for basic limit-state functions,
50 for FE-based limit-state functions

samples#

Number of samples (MC,IS)

Number of samples per subset step (SS) or number of directions (DS)

random_generator#

Kind of Random generator

Type:

0: default rand matlab function,
1: Mersenne Twister (to be preferred)

sim_point#

Start point for the simulation

Start:

‘dspt’: design point,
‘origin’: origin in standard normal space (simulation analysis)

stdv_sim#: Standard deviation of sampling distribution in simulation analysis

target_cov#: Target coefficient of variation for failure probability

bins#: Amount on bins for the histogram

getSamples()[source]#: Return the number of samples used in MCS

setSamples(samples)[source]#: Set the number of samples used in MCS