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  • ndcn0236/mribuilder.jl
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...@@ -171,7 +171,7 @@ function optimise_with_cost_func!(jump_model::Model, cost_func, n_attempts, prev ...@@ -171,7 +171,7 @@ function optimise_with_cost_func!(jump_model::Model, cost_func, n_attempts, prev
#println(solution_summary(jump_model)) #println(solution_summary(jump_model))
if termination_status(jump_model) in (LOCALLY_SOLVED, OPTIMAL) if termination_status(jump_model) in (LOCALLY_SOLVED, OPTIMAL)
nsuccess += 1 nsuccess += 1
if isapprox(objective_value(jump_model), min_objective, rtol=1e-3) if isapprox(objective_value(jump_model), min_objective, rtol=1e-3) || nsuccess > min(5, n_attempts)
consistent_minimum = true consistent_minimum = true
end end
if objective_value(jump_model) < min_objective if objective_value(jump_model) < min_objective
......
...@@ -102,13 +102,13 @@ ...@@ -102,13 +102,13 @@
@test variables.duration(seq[1]) > 1. @test variables.duration(seq[1]) > 1.
for index in 1:9 for index in 1:9
if index in [2, 6, 8] if index in [2, 6, 8]
@test abs(variables.duration(seq[index])) < 1e-6 @test abs(variables.duration(seq[index])) < 1e-4
else else
@test abs(variables.duration(seq[index])) > 1 @test abs(variables.duration(seq[index])) > 1
end end
end end
@test variables.duration(seq[:gradient]) variables.duration(seq[:gradient2]) @test variables.duration(seq[:gradient]) variables.duration(seq[:gradient2])
@test variables.bval(seq) 2. rtol=1e-6 @test variables.bval(seq) 2. rtol=1e-4
@test length(variables.readout_times(seq)) > 50 @test length(variables.readout_times(seq)) > 50
@test 67 < variables.TE(seq) < 68 @test 67 < variables.TE(seq) < 68
@test 72 < variables.duration(seq) < 73 @test 72 < variables.duration(seq) < 73
......