diff --git a/CHANGELOG.md b/CHANGELOG.md index 40df2d85..62df06a1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,7 @@ PowerModels.jl Change Log ### Staged - Relax tests to allow `INFEASIBLE_POINT` (#976) +- Silence Memento logger during precompilation (#980) ### v0.21.4 - Fix InexactError in `compute_ac_pf` (#939) diff --git a/src/PowerModels.jl b/src/PowerModels.jl index 0790e8f1..4d72e104 100644 --- a/src/PowerModels.jl +++ b/src/PowerModels.jl @@ -21,12 +21,12 @@ __init__() = Memento.register(_LOGGER) function silence() Memento.info(_LOGGER, "Suppressing information and warning messages for the rest of this session. Use the Memento package for more fine-grained control of logging.") Memento.setlevel!(Memento.getlogger(_IM), "error") - Memento.setlevel!(Memento.getlogger(PowerModels), "error") + Memento.setlevel!(_LOGGER, "error") end "alows the user to set the logging level without the need to add Memento" function logger_config!(level) - Memento.config!(Memento.getlogger("PowerModels"), level) + Memento.setlevel!(_LOGGER, level) end const _pm_global_keys = Set(["time_series", "per_unit"]) @@ -86,6 +86,7 @@ include("util/flow_limit_cuts.jl") include("core/export.jl") PrecompileTools.@setup_workload begin + logger_config!("error") # Turn off logging for this precompile block case3 = joinpath(dirname(@__DIR__), "test/data/matpower/case3.m") case9 = joinpath(dirname(@__DIR__), "test/data/matpower/case9.m") PrecompileTools.@compile_workload begin @@ -99,6 +100,7 @@ PrecompileTools.@setup_workload begin _ = compute_ac_pf(case9) _ = compute_dc_pf(case9) end + logger_config!("info") # Re-enable default logging end # Deprecations to be removed in the next breaking release diff --git a/test/runtests.jl b/test/runtests.jl index 18e09f4a..a82a31e9 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -13,8 +13,7 @@ import SCS import SparseArrays # Suppress warnings during testing. -Memento.setlevel!(Memento.getlogger(InfrastructureModels), "error") -PowerModels.logger_config!("error") +PowerModels.silence() # default setup for solvers nlp_solver = JuMP.optimizer_with_attributes(