Profile Configuration Reference
This page provides a complete reference for all options available in YAML profile files. Profiles control analysis parameters, plotting options, and reporting settings.
Profile Structure
A profile YAML file contains four main sections:
reporting_options:
# Report generation settings
rfad_plot_options:
# Reliability-based Failure Assessment Diagram settings
lsf_plot_options:
# Limit State Function plot settings
reliability_options:
# Reliability analysis, SORM, Monte Carlo, and optimization settings
Reporting Options
Options for saving analysis results and visualizations.
| Option | Type | Default | Description |
|---|---|---|---|
save_plots_to_pdf |
bool | true |
Whether to save all generated plots in PDF format |
save_plots_to_pickle |
bool | true |
Whether to save figure objects in pickle format for later retrieval and editing |
save_excel_summary |
bool | true |
Whether to save the numerical results summary to an Excel file |
Example:
reporting_options:
save_plots_to_pdf: true
save_plots_to_pickle: true
save_excel_summary: true
RFAD Plot Options
Options for Reliability-based Failure Assessment Diagram (RFAD) visualization.
| Option | Type | Default | Range/Values | Description |
|---|---|---|---|---|
n_x_points |
int | 30 |
[5, 60] | Number of grid points along the first variable axis |
n_y_points |
int | 29 |
[5, 60] | Number of grid points along the second variable axis |
plot_beta |
bool | true |
- | Whether to plot the safety index (reliability index beta) contours |
plot_alphas |
bool | true |
- | Whether to plot alpha values (normalized sensitivity factors) for each variable |
plot_base_point |
bool | true |
- | Whether to plot the base point (mean of input variables) on the diagram |
with_labels |
bool | true |
- | Whether to add text labels to the base point on the diagram |
ignore_axis_funcs |
bool | false |
- | Whether to ignore axis transformation functions and plot in original variable space |
view |
str or tuple | "auto" |
"auto", "default", "XY", "XZ", "YZ", "-XY", "-XZ", "-YZ" (case-insensitive) or tuple of 3 floats (elevation, azimuth, roll) in degrees |
View orientation for 3D perspective |
cmap |
str or list | "plasma" |
Valid matplotlib colormap name or list of shape (n, 4) with RGBA values in [0, 1] where 1 ≤ n ≤ 256 | Colormap specification |
type |
str | "surface" |
"contour", "surface" |
Plot visualization type |
Example:
rfad_plot_options:
n_x_points: 30
n_y_points: 29
plot_beta: true
plot_alphas: true
plot_base_point: true
with_labels: true
ignore_axis_funcs: false
view: "auto"
cmap: "plasma"
type: "surface"
LSF Plot Options
Options for Limit State Function (LSF) 3D visualization.
| Option | Type | Default | Range/Values | Description |
|---|---|---|---|---|
n_x_points |
int | 30 |
[5, 60] | Number of grid points along the first variable axis |
n_y_points |
int | 29 |
[5, 60] | Number of grid points along the second variable axis |
plot_base_point |
bool | true |
- | Whether to plot the base point (mean of input variables) on the LSF surface |
plot_failure_point |
bool | true |
- | Whether to plot the design point (Most Probable Point of Failure) on the LSF surface |
with_labels |
bool | true |
- | Whether to add text labels to the base and failure points |
ignore_axis_funcs |
bool | false |
- | Whether to ignore axis transformation functions and plot in original variable space |
view |
str or tuple | "auto" |
"auto", "default", "XY", "XZ", "YZ", "-XY", "-XZ", "-YZ" (case-insensitive) or tuple of 3 floats (elevation, azimuth, roll) in degrees |
View orientation for 3D perspective |
cmap |
str or list | "plasma" |
Valid matplotlib colormap name or list of shape (n, 4) with RGBA values in [0, 1] where 1 ≤ n ≤ 256 | Colormap specification |
type |
str | "surface" |
"contour", "surface" |
Plot visualization type |
Example:
lsf_plot_options:
n_x_points: 30
n_y_points: 29
plot_base_point: true
plot_failure_point: true
with_labels: true
ignore_axis_funcs: false
view: "auto"
cmap: "plasma"
type: "surface"
Reliability Options
Options for reliability calculations and related numerical settings. This section collects tolerances, algorithm choices, finite-difference settings, plotting flags, Monte Carlo controls and various checks used across FORM, SORM, inverse problems and design optimization routines.
FORM Settings
| Option | Type | Default | Range | Description |
|---|---|---|---|---|
form_xtol |
float | 0.0001 |
[1e-8, 1e-2] | Tolerance for FORM termination (variable changes) |
form_gtol |
float | 0.0001 |
[1e-8, 1e-2] | Tolerance for FORM termination (Lagrangian gradient norm changes) |
form_maxiter |
int | 1000 |
[1, ∞) | Maximum iterations for FORM |
form_random_start |
bool | false |
- | Use a random starting point for FORM/Inverse FORM |
form_seed |
int or null | null |
- | Random seed for FORM/Inverse FORM random starts |
Design Optimization Settings
| Option | Type | Default | Range | Description |
|---|---|---|---|---|
design_xtol |
float | 0.001 |
[1e-8, 1e-2] | Tolerance for design optimization termination (variable changes) |
design_gtol |
float | 0.001 |
[1e-8, 1e-2] | Tolerance for design optimization termination (Lagrangian gradient norm changes) |
design_maxiter |
int | 1000 |
[1, ∞) | Maximum iterations for design optimization |
design_random_start |
bool | false |
- | Use a random starting point for design optimization |
General Reliability Settings
| Option | Type | Default | Values | Description |
|---|---|---|---|---|
alpha_direction |
str | "outward" |
"outward", "inward" |
Direction convention for alpha (importance factors) |
use_nearest_correlation |
bool | false |
- | Use nearest PSD correlation representation for stochastic variables |
qn_epsilon |
float | 1e-8 |
[1e-8, 1e-2] | Tolerance used by quasi-Newton routines |
SORM Settings
| Option | Type | Default | Values | Description |
|---|---|---|---|---|
sor_method |
str | "SOSPA_H" |
"Breitung", "Tvedt3T", "TvedtSI", "TvedtDI", "Hohenbichler", "Koyluoglu", "ZhaoEmpirical", "SOSPA_L", "SOSPA_H", "IFFT" |
SORM method to use |
sor_approximation |
str | "Taylor2" |
"Paraboloid", "Taylor2" |
SORM approximation type |
sor_fit_method |
str or null | null |
"FiniteDiff", "ZhaoPF", "Kiureghian", "SR1", "AnalyticAll", null (auto) |
Method for SORM limit state function fitting |
sor_fit_delta_factor |
float | 1.0 |
[0.1, 10.0] | Factor to scale delta when numerically estimating SORM fit parameters for ZhaoPF and Kiureghian methods |
sor_fdm |
str | "forward" |
"central", "forward", "backward" |
Finite-difference method for gradient and Hessian |
sor_fdm_hess_form |
str | "full" |
"full", "diagonal" |
Hessian form for finite-difference approximation |
force_curvatures |
bool | false |
- | Force curvature computations even if not strictly required by selected SORM options |
SORM References
Use these references as starting points for method details and derivation nuances. Keep in mind that explanations on this page are intentionally brief and implementation-focused.
sor_method
- Breitung, K. (1984). "Asymptotic approximations for multinormal integrals." J. Eng. Mech., ASCE, 110(3), 357–366. →
- Der Kiureghian, A., Lin, H.-Z., and Hwang, S.-J. (1987). "Second-order reliability approximations." J. Eng. Mech., ASCE, 113(8), 1208–1225. →
- Hohenbichler, M., and Rackwitz, R. (1988). "Improvement of second-order reliability estimates by importance sampling." J. Eng. Mech., ASCE, 114(12), 2195–2199. →
- Tvedt, L. (1990). "Distribution of quadratic forms in normal space—application to structural reliability." J. Eng. Mech., ASCE, 116(6), 1183–1197. →
- Köylüoğlu, H. U., and Nielsen, S. R. K. (1994). "New approximations for SORM integrals." Structural Safety, 13(4), 235–246. →
- Zhao, Y.-G., and Ono, T. (1999a). "New approximations for SORM: Part 1." J. Eng. Mech., ASCE, 125(1), 79–85. →
- Zhao, Y.-G., and Ono, T. (1999b). "New approximations for SORM: Part 2." J. Eng. Mech., ASCE, 125(1), 86–93. →
- Du, X., and Sudjianto, A. (2004). "A saddlepoint approximation method for uncertainty analysis." Proc. ASME IDETC/CIE 2004, Paper DETC2004-57269, pp. 445–452. →
- Hu, Z., and Du, X. (2018). "Saddlepoint approximation reliability method for quadratic functions in normal variables." Structural Safety, 71, 24–32. →
- Additional SOSPA reference
sor_approximation
- Both
ParaboloidandTaylor2types are discussed in Der Kiureghian, Lin, and Hwang (1987) and Tvedt (1990) above.
sor_fit_method
FiniteDiff: numerical differentiation tooling — numdifftoolsSR1: quasi-Newton symmetric rank-one update — Wikipedia: Symmetric rank-oneKiureghian: point-fitting procedure — Der Kiureghian, Lin, and Hwang (1987) above.ZhaoPF: point-fitting procedure — Zhao and Ono (1999a) above.AnalyticAll: uses user-provided analytic gradient and Hessian from the problem definition.null(auto): Reliafy selects the most appropriate fit strategy based on available problem information.
sor_fit_delta_factor
- Applies to
ZhaoPFandKiureghianfit methods as a scaling factor for the point-fitting step size. Keep default unless Reliafy diagnostics suggest adjustment.
IFFT Settings (for IFFT-based SORM)
| Option | Type | Default | Range | Description |
|---|---|---|---|---|
ifft_std_domain |
float | 16.0 |
[2, 24] | Standard deviations covered when computing IFFT of characteristic functions |
ifft_n |
int | 4096 |
[2^8, 2^16], powers of 2 | Number of points used in IFFT |
plot_ifft_funcs |
bool | true |
- | Generate diagnostic plots for IFFT operations |
SOSPA Settings (for SOSPA-based SORM)
| Option | Type | Default | Description |
|---|---|---|---|
plot_sospa_funcs |
bool | true |
Generate diagnostic plots for SOSPA characteristic functions and derivatives |
Derivative Checking
These options validate user-provided derivatives against numerical finite differences.
| Option | Type | Default | Values | Description |
|---|---|---|---|---|
check_lsf_diffs_wrtx |
bool | false |
- | Validate user-provided LSF derivatives against numerical diffs w.r.t. real-space variables |
check_lsf_diffs_wrtu |
bool | true |
- | Validate user-provided LSF derivatives against numerical diffs w.r.t. standard-normal (U-space) variables |
lsf_diffs_check_method |
str | "forward" |
"central", "forward", "backward", "complex" |
Method for LSF derivative checks |
check_varscf_diffs_wrtx |
bool | false |
- | Validate user-provided stochastic variables constraints (real-space) |
check_varscf_diffs_wrtu |
bool | true |
- | Validate user-provided stochastic variables constraints (U-space) |
varscf_diffs_check_method |
str | "forward" |
"central", "forward", "backward", "complex" |
Method for stochastic variables constraint derivative checks |
check_dof_diffs |
bool | true |
- | Validate user-provided design objective function derivatives against numerical diffs |
dof_diffs_check_method |
str | "forward" |
"central", "forward", "backward", "complex" |
Method for design objective function derivative checks |
check_dcf_diffs |
bool | true |
- | Validate user-provided design constraint function derivatives against numerical diffs |
dcf_diffs_check_method |
str | "forward" |
"central", "forward", "backward", "complex" |
Method for design constraint function derivative checks |
Monte Carlo Settings
| Option | Type | Default | Range | Description |
|---|---|---|---|---|
mc_n |
float or null | null |
[1e3, 1e8] or null (auto) |
Monte Carlo sample size (null means automatic) |
mc_max_cv |
float | 0.05 |
[0.01, 1.0] | Maximum coefficient of variation per MC estimate allowed |
mc_seed |
int or null | null |
- | Random seed for Monte Carlo simulations |
mc_remove_oob |
bool | true |
- | Remove out-of-bounds samples during MC simulation (bounds or constraint violations) |
Importance Sampling Settings
| Option | Type | Default | Values/Range | Description |
|---|---|---|---|---|
is_method |
str | "kde" |
"kde", "mixture", "mcmc", "mpp_normal" |
Importance sampling proposal method |
is_kde_bandwidth |
str or float | "scott" |
"scott", "silverman" or positive float |
Bandwidth selection method for KDE or numeric value |
is_mixture_components |
int | 6 |
[1, 16] | Number of mixture components for mixture IS methods |
is_mixture_cov_type |
str | "full" |
"full", "tied", "diag", "spherical" |
Covariance type for mixture IS methods |
is_fit_samples |
int | 2000 |
[100, 100000] | Number of samples to collect for fitting the proposal distribution (for KDE and mixture IS) |
MCMC Importance Sampling Settings
| Option | Type | Default | Range | Description |
|---|---|---|---|---|
is_mcmc_chains |
int | 5 |
[1, 100] | Number of parallel Metropolis chains to run during MCMC proposal fitting |
is_mcmc_chain_length |
int | 500 |
[100, 10000] | Length of each MCMC chain during proposal fitting |
is_mcmc_thinning |
int | 3 |
[1, 100] | Thinning factor for MCMC chains during proposal fitting |
is_mcmc_burnin |
int | 200 |
[100, 1000] | Number of samples to discard as burn-in during MCMC proposal fitting |
is_mcmc_fit_method |
str | "kde" |
"kde", "mixture" |
Method used to fit the MCMC proposal distribution |
Example:
IS References
Use these references as starting points for method details and implementation background. Keep in mind that explanations on this page are intentionally brief and implementation-focused.
is_method: "kde"
KernelDensityfrom scikit-learn — sklearn.neighbors.KernelDensity. Bandwidth selection follows theis_kde_bandwidthoption ("scott"or"silverman"rules, or a numeric value).
is_method: "mixture"
BayesianGaussianMixturefrom scikit-learn — sklearn.mixture.BayesianGaussianMixture. Number of components and covariance type are controlled byis_mixture_componentsandis_mixture_cov_type.
is_method: "mcmc"
- Xiao, S., and Nowak, W. (2022). "Reliability sensitivity analysis based on a two-stage Markov chain Monte Carlo simulation." Aerospace Science and Technology, 130, 107938. →
- Cotter, S. L., Roberts, G. O., Stuart, A. M., and White, D. (2013). "MCMC methods for functions: modifying old algorithms to make them faster." Statistical Science, 28(3), 424–446. →
- Roberts, G. O., and Rosenthal, J. S. (2007). "Coupling and ergodicity of adaptive Markov chain Monte Carlo algorithms." Journal of Applied Probability, 44(2), 458–475. →
is_method: "mpp_normal"
- Centers the importance sampling proposal distribution on the Most Probable Point (MPP), also called the design point or failure point. The MPP is the point on the limit-state surface closest to the origin in standard normal space, found by FORM. A Gaussian proposal centered there concentrates samples near the dominant failure region. No external reference; see the FORM method and the textbooks listed on the home page.
reliability_options:
# FORM
form_xtol: 0.0001
form_gtol: 0.0001
form_maxiter: 1000
form_random_start: false
form_seed: null
# Design
design_xtol: 0.001
design_gtol: 0.001
design_maxiter: 1000
design_random_start: false
# General
alpha_direction: "outward"
use_nearest_correlation: false
qn_epsilon: 1e-8
# SORM
sor_method: "SOSPA_H"
sor_approximation: "Taylor2"
sor_fit_method: null
sor_fit_delta_factor: 1.0
sor_fdm: "forward"
sor_fdm_hess_form: "full"
force_curvatures: false
# IFFT
ifft_std_domain: 16.0
ifft_n: 4096
plot_ifft_funcs: true
# SOSPA
plot_sospa_funcs: true
# Derivative checking
check_lsf_diffs_wrtx: false
check_lsf_diffs_wrtu: true
lsf_diffs_check_method: "forward"
check_varscf_diffs_wrtx: false
check_varscf_diffs_wrtu: true
varscf_diffs_check_method: "forward"
check_dof_diffs: true
dof_diffs_check_method: "forward"
check_dcf_diffs: true
dcf_diffs_check_method: "forward"
# Monte Carlo
mc_n: null
mc_max_cv: 0.05
mc_seed: null
mc_remove_oob: true
# Importance Sampling
is_method: "kde"
is_kde_bandwidth: "scott"
is_mixture_components: 6
is_mixture_cov_type: "full"
is_fit_samples: 2000
is_mcmc_chains: 5
is_mcmc_chain_length: 500
is_mcmc_thinning: 3
is_mcmc_burnin: 200
is_mcmc_fit_method: "kde"
Notes
- All enum-style fields accept either the enum member name (case-insensitive) or the enum value
- Validators normalize to the enum name automatically
- Range constraints are enforced at validation time
- Fields marked with
exclude=Truein the data model are internal runtime controls and not part of the profile YAML