Merge pull request #19 from JuliaDiffEq/myb/info

Replace `@progress` with Base logging

Author: YingboMa

REQUIRE

@@ -6,4 +6,3 @@ DiffEqPDEBase 0.4.0
 NLsolve 0.14.1
 DiffEqMonteCarlo
 DiffEqNoiseProcess
-Juno

src/DiffEqDevTools.jl

@@ -3,7 +3,7 @@ __precompile__()
 module DiffEqDevTools
 
 using DiffEqBase, RecipesBase, RecursiveArrayTools, DiffEqPDEBase,
-      DiffEqMonteCarlo, DiffEqNoiseProcess, Juno
+      DiffEqMonteCarlo, DiffEqNoiseProcess
 
 using LinearAlgebra, Distributed
 

src/benchmark.jl

@@ -301,7 +301,8 @@ function WorkPrecisionSet(prob::AbstractRODEProblem,abstols,reltols,setups,test_
       @error_calculation
     end
   elseif parallel_type == :none
-    @progress for i in 1:numruns_error
+    for i in 1:numruns_error
+      @info "Error calculation: $i/$numruns_error"
       @error_calculation
     end
   end
@@ -388,7 +389,8 @@ function get_sample_errors(prob::AbstractRODEProblem,test_dt=nothing;
       @sample_errors
     end
   elseif parallel_type == :none
-    @progress for i in 1:_std_estimation_runs
+    for i in 1:_std_estimation_runs
+      @info "Standard deviation estimation: $i/$_std_estimation_runs"
       @sample_errors
     end
   end

src/convergence.jl

@@ -63,7 +63,8 @@ function analyticless_test_convergence(dts::AbstractArray,
                           weak_timeseries_errors=false,weak_dense_errors=false,kwargs...)
   _solutions = []
   tmp_solutions = Array{Any}(undef,numMonte,length(dts))
-  @progress for j in 1:numMonte
+  for j in 1:numMonte
+    @info "Monte Carlo iteration: $j/$numMonte"
     t = prob.tspan[1]:test_dt:prob.tspan[2]
     brownian_values = cumsum([[zeros(size(prob.u0))];[sqrt(test_dt)*randn(size(prob.u0)) for i in 1:length(t)-1]])
     brownian_values2 = cumsum([[zeros(size(prob.u0))];[sqrt(test_dt)*randn(size(prob.u0)) for i in 1:length(t)-1]])

test/REQUIRE

@@ -2,5 +2,4 @@ DiffEqProblemLibrary
 OrdinaryDiffEq 1.0.0
 DelayDiffEq
 StochasticDiffEq 0.0.5
-FiniteElementDiffEq 0.0.5
 ParameterizedFunctions

test/benchmark_tests.jl

@@ -48,6 +48,7 @@ set[1][:]
 
 ## WorkPrecision Tests
 println("WorkPrecision Tests")
+println("Test DP5")
 wp = WorkPrecision(prob,DP5(),abstols,reltols;name="Dormand-Prince 4/5")
 
 wp[1]
@@ -72,6 +73,7 @@ reltols = 1 ./10 .^(0:4)
 
 setups = [Dict(:alg=>DP5())
           Dict(:alg=>Tsit5())]
+println("Test DP5 and Tsit5")
 wp = WorkPrecisionSet(prob,abstols,reltols,setups;save_everystep=false)
 
 function lotka(du,u,p,t)
@@ -90,6 +92,7 @@ setups = [Dict(:alg=>DP5())
           Dict(:alg=>Tsit5())
           Dict(:alg=>Vern6())
           ]
+println("Test DP5, Tsit5, and Vern6")
 wp = WorkPrecisionSet(prob,abstols,reltols,setups;appxsol=test_sol,save_everystep=false,numruns=20,maxiters=10000)
 
 # DDE problem
@@ -102,4 +105,5 @@ test_sol = TestSolution(sol)
 
 setups = [Dict(:alg => MethodOfSteps(BS3()))
           Dict(:alg => MethodOfSteps(Tsit5()))]
+println("Test MethodOfSteps BS3 and Tsit5")
 wp = WorkPrecisionSet(prob, abstols, reltols, setups; appxsol = test_sol)