How rte-france/relife works
ReLife is a Python reliability and lifetime-data analysis library for infrastructure asset decisions. A principal workflow is to load lifetime observations, fit or select a lifetime model, evaluate replacement policies and their expected costs, then optionally sample the resulting stochastic processes. The package also supports nonparametric and semiparametric lifetime analysis, renewal and non-homogeneous Poisson processes, and sampling. The graph reflects README-level capabilities where source excerpts were unavailable; those subsystem links are therefore omitted when implementation wiring is uncertain.
Main entry points: _distributions.py Lifetime Distributions · _renewal_processes.py Renewal Processes · _iterators.py Sampling Iterators · _non_homogeneous_poisson_process.py Poisson Processes
Components 19
Lifetime Data
- Example Datasets
src/relife/datasets/load.py - Lifetime Data
src/relife/lifetime_models/_base.py
Lifetime Models
- Lifetime Distributions
src/relife/lifetime_models/_distributions.py - Parametric Regression
src/relife/lifetime_models/_parametric_regressions.py - Semiparametric Regression
src/relife/lifetime_models/_semi_parametric_regressions.py - Nonparametric Models
src/relife/lifetime_models/_non_parametric_models.py - Model Fitting
src/relife/lifetime_models/_base.py
Processes And Sampling
- Renewal Processes
src/relife/stochastic_processes/_renewal_processes.py - Poisson Processes
src/relife/stochastic_processes/_non_homogeneous_poisson_process.py - Virtual Age Processes
src/relife/stochastic_processes/_kijima_processes.py - Process Sampling
src/relife/sampling/_base.py - Sampling Iterators
src/relife/sampling/_iterators.py
Maintenance Decisions
- Replacement Policies
src/relife/policies/_preventive_age_replacement_policies.py - Run-To-Failure Policies
src/relife/policies/_run_to_failure_policies.py - Policy Cost Methods
src/relife/policies/_base.py
Numerical Core
- Parameter And Fit Core
src/relife/base.py - Numerical Quadrature
src/relife/quadratures.py
Other components
- Analyst
- Analysis And Cost Results
Connections 29
- Analyst → Example Datasets: loads examples
- Example Datasets → Lifetime Data: provides observations
- Analyst → Lifetime Distributions: selects model
- Lifetime Data → Model Fitting: fits from data
- Lifetime Distributions → Model Fitting: creates likelihood
- Model Fitting → Parameter And Fit Core: uses fit utilities
- Parametric Regression → Lifetime Distributions: uses baseline
- Semiparametric Regression → Lifetime Distributions: uses baseline
- Analyst → Nonparametric Models: analyzes lifetimes
- Analyst → Replacement Policies: evaluates policy
- Replacement Policies → Renewal Processes: computes rewards
- Policy Cost Methods → Analysis And Cost Results: returns cost measures
- Replacement Policies → Analysis And Cost Results: returns policy metrics
- Run-To-Failure Policies → Policy Cost Methods: uses cost methods
- Renewal Processes → Lifetime Distributions: evaluates lifetimes
- Poisson Processes → Lifetime Distributions: evaluates hazard
- Virtual Age Processes → Lifetime Distributions: uses lifetime model
- Analyst → Process Sampling: requests samples
- Process Sampling → Sampling Iterators: creates iterators
- Sampling Iterators → Renewal Processes: samples process
- Sampling Iterators → Poisson Processes: samples process
- Sampling Iterators → Virtual Age Processes: samples process
- Process Sampling → Analysis And Cost Results: yields sampled data
- Analyst → Parametric Regression: fits regression
- Analyst → Semiparametric Regression: fits regression
- Analyst → Renewal Processes: evaluates process
- Analyst → Poisson Processes: analyzes events
- Renewal Processes → Analysis And Cost Results: returns expectations
- Poisson Processes → Analysis And Cost Results: returns process measures