Welcome to CoFI!

Welcome to CoFI!#

Welcome to the CoFI Documentation! CoFI, the Common Framework for Inference, is an open-source tool that bridges the gap between domain expertise and inference, within and beyond the field of geoscience.

Whether you’re a student, an educator, or an industry professional, CoFI is your go-to tool. It simplifies the process of applying inference techniques, allowing for a wide range of applications from academic research and teaching to solving real-world geoscience problems in the industry. With its user-friendly, flexible and accessible interface, CoFI empowers you to focus on what truly matters - the science.

graph TD; cofi(CoFI - Common Framework for Inference):::cls_cofi; parameter_estimation(Parameter estimation):::cls_parameter_estimation; linear(Matrix based solvers):::cls_parameter_estimation; non_linear(Optimization):::cls_parameter_estimation; linear_system_solvers(Linear system solvers):::cls_parameter_estimation; linear_solverlist(scipy.linalg.lstsq ...):::cls_solvers; optimization(Non linear):::cls_parameter_estimation; optimization2(Linear):::cls_parameter_estimation; opt_solverlist(scipy.optimize.minimize torch.optim ROL ...):::cls_solvers; ensemble_methods(Ensemble methods):::cls_ensemble_methods; direct_search(Direct Search):::cls_ensemble_methods; amc(Monte Carlo):::cls_ensemble_methods; amc_solverlist(Neighbourhood Algorithm Bayesian Optimization Slime mold algorithm Border Collie Optimization ...):::cls_solvers; ng(Deterministic):::cls_ensemble_methods; ng_solverlist(Nested grids Hilbert Curves ...):::cls_solvers; bs(Bayesian Sampling):::cls_ensemble_methods; mcmc(McMC samplers):::cls_ensemble_methods; mcmc_solverlist(Basic metropolis Affine Invariance sampler emcee pyMC ...):::cls_solvers; rjmcmc(Trans-D McMC):::cls_ensemble_methods; rjmcmc_solverlist(Basic Trans-D BayesBay RJ-mcmc):::cls_solvers; cofi --> parameter_estimation; parameter_estimation --> linear; linear --> linear_system_solvers; linear_system_solvers -.- linear_solverlist; parameter_estimation --> non_linear; non_linear --> optimization; non_linear --> optimization2; optimization -.- opt_solverlist; optimization2 -.- opt_solverlist; cofi --> ensemble_methods; ensemble_methods --> direct_search; direct_search --> amc; amc -.- amc_solverlist; direct_search --> ng; ng -.- ng_solverlist; ensemble_methods --> bs; bs --> mcmc; mcmc -.- mcmc_solverlist; bs --> rjmcmc; rjmcmc -.- rjmcmc_solverlist; classDef cls_cofi fill: #d4a373, stroke-width:0; classDef cls_parameter_estimation fill: #ccd5ae, stroke-width:0; classDef cls_ensemble_methods fill: #e9edc9, stroke-width:0; classDef cls_solvers fill: #faedcd, stroke-width:0;

# CoFI API has flexible ways of defining an inversion problem. For instance:
import cofi

inv_problem = cofi.BaseProblem()
inv_problem.set_objective(my_objective_func)
inv_problem.set_initial_model(my_starting_point)

# Once a problem is defined, `cofi` can tell you what inference tools you can
# use based on what level of information you've provided:
inv_problem.suggest_tools()   # a tree will be printed

# Run an inversion with these lines:
inv_options = cofi.InversionOptions()
inv_options.set_tool("torch.optim")
inv_options.set_params(options={"num_iterations": 50, "algorithm": "Adam"})

inv = cofi.Inversion(inv_problem, inv_options)
result = inv.run()
print(result.success)
print(result.model)