fix a few issues raised by dr. louboutin

slimgroup · Jan 6, 2023 · 6c693a2 · 6c693a2
1 parent 8594806
commit 6c693a2
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Showing 3 changed files with 4 additions and 5 deletions.
diff --git a/src/TimeModeling/LinearOperators/lazy.jl b/src/TimeModeling/LinearOperators/lazy.jl
@@ -130,7 +130,7 @@ end
 size(jA::jAdjoint) = (jA.op.n, jA.op.m)
 display(P::jAdjoint) = println("Adjoint($(P.op))")
 display(P::judiProjection{D}) where D = println("JUDI projection operator $(repr(P.n)) -> $(repr(P.m))")
-display(P::judiWavelet{T}) where T = println("JUDI wavelet injected at every grid point")
+display(P::judiWavelet{T}) where T = println("JUDI wavelet")
 
 ############################################################################################################################
 # Indexing

diff --git a/src/rrules.jl b/src/rrules.jl
@@ -30,8 +30,8 @@ function eval_prop(F::LazyPropagation)
     return F.post(F.val)
 end
 Base.collect(F::LazyPropagation) = eval_prop(F)
-LazyPropagation(post, F::judiPropagator, q) = LazyPropagation(post, F, q, nothing)
-LazyPropagation(F::judiPropagator, q) = LazyPropagation(identity, F, q)
+LazyPropagation(post::Function, F::judiPropagator, q) = LazyPropagation(post, F, q, nothing)
+LazyPropagation(F::judiPropagator, q) = LazyPropagation(identity, F, q, nothing)
 
 # Only a few arithmetic operation are supported
 
@@ -78,7 +78,6 @@ dot(x::AbstractArray, F::LazyPropagation) = dot(x, eval_prop(F))
 dot(F::LazyPropagation, x::AbstractArray) = dot(x, F)
 norm(F::LazyPropagation, p::Real=2) = norm(eval_prop(F), p)
 adjoint(F::JUDI.LazyPropagation) = F
-length(F::JUDI.LazyPropagation) = size(F.F, 1)
 
 ############################ Two params rules ############################################
 function rrule(F::judiPropagator{T, O}, m::AbstractArray{T}, q::AbstractArray{T}) where {T, O}

diff --git a/test/test_rrules.jl b/test/test_rrules.jl
@@ -32,7 +32,7 @@ perturb(x::judiVector) = judiVector(x.geometry, [randx(x.data[i]) for i=1:x.nsrc
 reverse(x::judiVector) = judiVector(x.geometry, [x.data[i][end:-1:1, :] for i=1:x.nsrc])
 
 misfit_objective_2p(d_obs, q0, m0, F) = .5f0*norm(F(m0, q0) - d_obs)^2
-misfit_objective_1p(d_obs, q0, m0, F) = .5f0*norm(F(1f0*m0)*q0 - d_obs)^2
+misfit_objective_1p(d_obs, q0, m0, F) = .5f0*norm(F(1f0.*m0)*q0 - d_obs)^2
 
 function loss(misfit, d_obs, q0, m0, F)
     local ϕ