Replace n_traj with ntraj for QuTiP compatibility

benchmarks/timeevolution.jl

@@ -47,7 +47,7 @@ function benchmark_timeevolution!(SUITE)
         $ψ0,
         $tlist,
         $c_ops,
-        n_traj = 100,
+        ntraj = 100,
         e_ops = $e_ops,
         progress_bar = Val(false),
         ensemble_method = EnsembleSerial(),
@@ -57,7 +57,7 @@ function benchmark_timeevolution!(SUITE)
         $ψ0,
         $tlist,
         $c_ops,
-        n_traj = 100,
+        ntraj = 100,
         e_ops = $e_ops,
         progress_bar = Val(false),
         ensemble_method = EnsembleThreads(),

docs/src/users_guide/steadystate.md

@@ -94,7 +94,7 @@ exp_ss = real(expect(e_ops[1], ρ_ss))
 tlist = LinRange(0, 50, 100)
 
 # monte-carlo
-sol_mc = mcsolve(H, ψ0, tlist, c_op_list, e_ops=e_ops, n_traj=100, progress_bar=false)
+sol_mc = mcsolve(H, ψ0, tlist, c_op_list, e_ops=e_ops, ntraj=100, progress_bar=false)
 exp_mc = real(sol_mc.expect[1, :])
 
 # master eq.

src/time_evolution/mcsolve.jl

@@ -402,7 +402,7 @@
         e_ops::Union{Nothing,AbstractVector}=nothing,
         H_t::Union{Nothing,Function,TimeDependentOperatorSum}=nothing,
         params::NamedTuple=NamedTuple(),
-        n_traj::Int=1,
+        ntraj::Int=1,
         ensemble_method=EnsembleThreads(),
         jump_callback::TJC=ContinuousLindbladJumpCallback(),
         kwargs...)
@@ -452,7 +452,7 @@
 - `H_t::Union{Nothing,Function,TimeDependentOperatorSum}`: Time-dependent part of the Hamiltonian.
 - `params::NamedTuple`: Dictionary of parameters to pass to the solver.
 - `seeds::Union{Nothing, Vector{Int}}`: List of seeds for the random number generator. Length must be equal to the number of trajectories provided.
-- `n_traj::Int`: Number of trajectories to use.
+- `ntraj::Int`: Number of trajectories to use.
 - `ensemble_method`: Ensemble method to use.
 - `jump_callback::LindbladJumpCallbackType`: The Jump Callback type: Discrete or Continuous.
 - `prob_func::Function`: Function to use for generating the ODEProblem.
@@ -482,15 +482,15 @@
     H_t::Union{Nothing,Function,TimeDependentOperatorSum} = nothing,
     params::NamedTuple = NamedTuple(),
     seeds::Union{Nothing,Vector{Int}} = nothing,
-    n_traj::Int = 1,
+    ntraj::Int = 1,
     ensemble_method = EnsembleThreads(),
     jump_callback::TJC = ContinuousLindbladJumpCallback(),
     prob_func::Function = _mcsolve_prob_func,
     output_func::Function = _mcsolve_output_func,
     kwargs...,
 ) where {MT1<:AbstractMatrix,T2,TJC<:LindbladJumpCallbackType}
-    if !isnothing(seeds) && length(seeds) != n_traj
-        throw(ArgumentError("Length of seeds must match n_traj ($n_traj), but got $(length(seeds))"))
+    if !isnothing(seeds) && length(seeds) != ntraj
+        throw(ArgumentError("Length of seeds must match ntraj ($ntraj), but got $(length(seeds))"))
     end
 
     ens_prob_mc = mcsolveEnsembleProblem(
@@ -509,16 +509,16 @@
         kwargs...,
     )
 
-    return mcsolve(ens_prob_mc; alg = alg, n_traj = n_traj, ensemble_method = ensemble_method)
+    return mcsolve(ens_prob_mc; alg = alg, ntraj = ntraj, ensemble_method = ensemble_method)
 end
 
 function mcsolve(
     ens_prob_mc::EnsembleProblem;
     alg::OrdinaryDiffEqAlgorithm = Tsit5(),
-    n_traj::Int = 1,
+    ntraj::Int = 1,
     ensemble_method = EnsembleThreads(),
 )
-    sol = solve(ens_prob_mc, alg, ensemble_method, trajectories = n_traj)
+    sol = solve(ens_prob_mc, alg, ensemble_method, trajectories = ntraj)
     _sol_1 = sol[:, 1]
 
     expvals_all = Array{ComplexF64}(undef, length(sol), size(_sol_1.prob.p.expvals)...)
@@ -536,7 +536,7 @@
     expvals = dropdims(sum(expvals_all, dims = 1), dims = 1) ./ length(sol)
 
     return TimeEvolutionMCSol(
-        n_traj,
+        ntraj,
         times,
         states,
         expvals,

src/time_evolution/ssesolve.jl

@@ -293,7 +293,7 @@ end
         e_ops::Union{Nothing,AbstractVector}=nothing,
         H_t::Union{Nothing,Function,TimeDependentOperatorSum}=nothing,
         params::NamedTuple=NamedTuple(),
-        n_traj::Int=1,
+        ntraj::Int=1,
         ensemble_method=EnsembleThreads(),
         prob_func::Function=_mcsolve_prob_func,
         output_func::Function=_mcsolve_output_func,
@@ -334,7 +334,7 @@ Above, `C_n` is the `n`-th collapse operator and  `dW_j(t)` is the real Wiener i
 - `H_t::Union{Nothing,Function,TimeDependentOperatorSum}`: Time-dependent part of the Hamiltonian.
 - `params::NamedTuple`: Dictionary of parameters to pass to the solver.
 - `seeds::Union{Nothing, Vector{Int}}`: List of seeds for the random number generator. Length must be equal to the number of trajectories provided.
-- `n_traj::Int`: Number of trajectories to use.
+- `ntraj::Int`: Number of trajectories to use.
 - `ensemble_method`: Ensemble method to use.
 - `prob_func::Function`: Function to use for generating the SDEProblem.
 - `output_func::Function`: Function to use for generating the output of a single trajectory.
@@ -362,7 +362,7 @@ function ssesolve(
     e_ops::Union{Nothing,AbstractVector} = nothing,
     H_t::Union{Nothing,Function,TimeDependentOperatorSum} = nothing,
     params::NamedTuple = NamedTuple(),
-    n_traj::Int = 1,
+    ntraj::Int = 1,
     ensemble_method = EnsembleThreads(),
     prob_func::Function = _ssesolve_prob_func,
     output_func::Function = _ssesolve_output_func,
@@ -382,16 +382,16 @@ function ssesolve(
         kwargs...,
     )
 
-    return ssesolve(ens_prob; alg = alg, n_traj = n_traj, ensemble_method = ensemble_method)
+    return ssesolve(ens_prob; alg = alg, ntraj = ntraj, ensemble_method = ensemble_method)
 end
 
 function ssesolve(
     ens_prob::EnsembleProblem;
     alg::StochasticDiffEqAlgorithm = SRA1(),
-    n_traj::Int = 1,
+    ntraj::Int = 1,
     ensemble_method = EnsembleThreads(),
 )
-    sol = solve(ens_prob, alg, ensemble_method, trajectories = n_traj)
+    sol = solve(ens_prob, alg, ensemble_method, trajectories = ntraj)
     _sol_1 = sol[:, 1]
 
     expvals_all = Array{ComplexF64}(undef, length(sol), size(_sol_1.prob.p.expvals)...)
@@ -403,7 +403,7 @@ function ssesolve(
     expvals = dropdims(sum(expvals_all, dims = 1), dims = 1) ./ length(sol)
 
     return TimeEvolutionSSESol(
-        n_traj,
+        ntraj,
         _sol_1.t,
         states,
         expvals,

src/time_evolution/time_evolution.jl

@@ -50,7 +50,7 @@ A structure storing the results and some information from solving quantum trajec
 
 # Fields (Attributes)
 
-- `n_traj::Int`: Number of trajectories
+- `ntraj::Int`: Number of trajectories
 - `times::AbstractVector`: The time list of the evolution in each trajectory.
 - `states::Vector{Vector{QuantumObject}}`: The list of result states in each trajectory.
 - `expect::Matrix`: The expectation values (averaging all trajectories) corresponding to each time point in `times`.
@@ -70,7 +70,7 @@ struct TimeEvolutionMCSol{
     TJT<:Vector{<:Vector{<:Real}},
     TJW<:Vector{<:Vector{<:Integer}},
 }
-    n_traj::Int
+    ntraj::Int
     times::TT
     states::TS
     expect::TE
@@ -87,7 +87,7 @@ function Base.show(io::IO, sol::TimeEvolutionMCSol)
     print(io, "Solution of quantum trajectories\n")
     print(io, "(converged: $(sol.converged))\n")
     print(io, "--------------------------------\n")
-    print(io, "num_trajectories = $(sol.n_traj)\n")
+    print(io, "num_trajectories = $(sol.ntraj)\n")
     print(io, "num_states = $(length(sol.states[1]))\n")
     print(io, "num_expect = $(size(sol.expect, 1))\n")
     print(io, "ODE alg.: $(sol.alg)\n")
@@ -100,7 +100,7 @@ end
      struct TimeEvolutionSSESol
  A structure storing the results and some information from solving trajectories of the Stochastic Shrodinger equation time evolution.
  # Fields (Attributes)
- - `n_traj::Int`: Number of trajectories
+ - `ntraj::Int`: Number of trajectories
  - `times::AbstractVector`: The time list of the evolution in each trajectory.
  - `states::Vector{Vector{QuantumObject}}`: The list of result states in each trajectory.
  - `expect::Matrix`: The expectation values (averaging all trajectories) corresponding to each time point in `times`.
@@ -118,7 +118,7 @@ struct TimeEvolutionSSESol{
     T1<:Real,
     T2<:Real,
 }
-    n_traj::Int
+    ntraj::Int
     times::TT
     states::TS
     expect::TE
@@ -133,7 +133,7 @@ function Base.show(io::IO, sol::TimeEvolutionSSESol)
     print(io, "Solution of quantum trajectories\n")
     print(io, "(converged: $(sol.converged))\n")
     print(io, "--------------------------------\n")
-    print(io, "num_trajectories = $(sol.n_traj)\n")
+    print(io, "num_trajectories = $(sol.ntraj)\n")
     print(io, "num_states = $(length(sol.states[1]))\n")
     print(io, "num_expect = $(size(sol.expect, 1))\n")
     print(io, "SDE alg.: $(sol.alg)\n")

src/time_evolution/time_evolution_dynamical.jl

@@ -687,7 +687,7 @@ end
         H_t::Union{Nothing,Function,TimeDependentOperatorSum}=nothing,
         params::NamedTuple=NamedTuple(),
         δα_list::Vector{<:Real}=fill(0.2, length(op_list)),
-        n_traj::Int=1,
+        ntraj::Int=1,
         ensemble_method=EnsembleThreads(),
         jump_callback::LindbladJumpCallbackType=ContinuousLindbladJumpCallback(),
         krylov_dim::Int=max(6, min(10, cld(length(ket2dm(ψ0).data), 4))),
@@ -712,7 +712,7 @@ function dsf_mcsolve(
     H_t::Union{Nothing,Function,TimeDependentOperatorSum} = nothing,
     params::NamedTuple = NamedTuple(),
     δα_list::Vector{<:Real} = fill(0.2, length(op_list)),
-    n_traj::Int = 1,
+    ntraj::Int = 1,
     ensemble_method = EnsembleThreads(),
     jump_callback::TJC = ContinuousLindbladJumpCallback(),
     krylov_dim::Int = min(5, cld(length(ψ0.data), 3)),
@@ -736,5 +736,5 @@ function dsf_mcsolve(
         kwargs...,
     )
 
-    return mcsolve(ens_prob_mc; alg = alg, n_traj = n_traj, ensemble_method = ensemble_method)
+    return mcsolve(ens_prob_mc; alg = alg, ntraj = ntraj, ensemble_method = ensemble_method)
 end

test/core-test/dynamical-shifted-fock.jl

@@ -60,7 +60,7 @@
         dsf_params,
         e_ops = e_ops_dsf,
         progress_bar = Val(false),
-        n_traj = 500,
+        ntraj = 500,
     )
     val_ss = abs2(sol0.expect[1, end])
     @test sum(abs2.(sol0.expect[1, :] .- sol_dsf_me.expect[1, :])) / (val_ss * length(tlist)) < 0.1
@@ -140,7 +140,7 @@
         dsf_params,
         e_ops = e_ops_dsf2,
         progress_bar = Val(false),
-        n_traj = 500,
+        ntraj = 500,
     )
 
     val_ss = abs2(sol0.expect[1, end])

test/core-test/time_evolution.jl

@@ -54,9 +54,9 @@
         sol_me = mesolve(H, psi0, t_l, c_ops, e_ops = e_ops, progress_bar = Val(false))
         sol_me2 = mesolve(H, psi0, t_l, c_ops, progress_bar = Val(false))
         sol_me3 = mesolve(H, psi0, t_l, c_ops, e_ops = e_ops, saveat = t_l, progress_bar = Val(false))
-        sol_mc = mcsolve(H, psi0, t_l, c_ops, n_traj = 500, e_ops = e_ops, progress_bar = Val(false))
-        sol_mc_states = mcsolve(H, psi0, t_l, c_ops, n_traj = 500, saveat = t_l, progress_bar = Val(false))
-        sol_sse = ssesolve(H, psi0, t_l, c_ops, n_traj = 500, e_ops = e_ops, progress_bar = Val(false))
+        sol_mc = mcsolve(H, psi0, t_l, c_ops, ntraj = 500, e_ops = e_ops, progress_bar = Val(false))
+        sol_mc_states = mcsolve(H, psi0, t_l, c_ops, ntraj = 500, saveat = t_l, progress_bar = Val(false))
+        sol_sse = ssesolve(H, psi0, t_l, c_ops, ntraj = 500, e_ops = e_ops, progress_bar = Val(false))
 
         ρt_mc = [ket2dm.(normalize.(states)) for states in sol_mc_states.states]
         expect_mc_states = mapreduce(states -> expect.(Ref(e_ops[1]), states), hcat, ρt_mc)
@@ -87,7 +87,7 @@
               "Solution of quantum trajectories\n" *
               "(converged: $(sol_mc.converged))\n" *
               "--------------------------------\n" *
-              "num_trajectories = $(sol_mc.n_traj)\n" *
+              "num_trajectories = $(sol_mc.ntraj)\n" *
               "num_states = $(length(sol_mc.states[1]))\n" *
               "num_expect = $(size(sol_mc.expect, 1))\n" *
               "ODE alg.: $(sol_mc.alg)\n" *
@@ -97,7 +97,7 @@
               "Solution of quantum trajectories\n" *
               "(converged: $(sol_sse.converged))\n" *
               "--------------------------------\n" *
-              "num_trajectories = $(sol_sse.n_traj)\n" *
+              "num_trajectories = $(sol_sse.ntraj)\n" *
               "num_states = $(length(sol_sse.states[1]))\n" *
               "num_expect = $(size(sol_sse.expect, 1))\n" *
               "SDE alg.: $(sol_sse.alg)\n" *
@@ -114,19 +114,11 @@
         end
 
         @testset "Type Inference mcsolve" begin
-            @inferred mcsolveEnsembleProblem(
-                H,
-                psi0,
-                t_l,
-                c_ops,
-                n_traj = 500,
-                e_ops = e_ops,
-                progress_bar = Val(false),
-            )
-            @inferred mcsolve(H, psi0, t_l, c_ops, n_traj = 500, e_ops = e_ops, progress_bar = Val(false))
-            @inferred mcsolve(H, psi0, t_l, c_ops, n_traj = 500, progress_bar = Val(true))
-            @inferred mcsolve(H, psi0, [0, 10], c_ops, n_traj = 500, progress_bar = Val(false))
-            @inferred mcsolve(H, Qobj(zeros(Int64, N)), t_l, c_ops, n_traj = 500, progress_bar = Val(false))
+            @inferred mcsolveEnsembleProblem(H, psi0, t_l, c_ops, ntraj = 500, e_ops = e_ops, progress_bar = Val(false))
+            @inferred mcsolve(H, psi0, t_l, c_ops, ntraj = 500, e_ops = e_ops, progress_bar = Val(false))
+            @inferred mcsolve(H, psi0, t_l, c_ops, ntraj = 500, progress_bar = Val(true))
+            @inferred mcsolve(H, psi0, [0, 10], c_ops, ntraj = 500, progress_bar = Val(false))
+            @inferred mcsolve(H, Qobj(zeros(Int64, N)), t_l, c_ops, ntraj = 500, progress_bar = Val(false))
         end
 
         @testset "Type Inference ssesolve" begin
@@ -135,12 +127,12 @@
                 psi0,
                 t_l,
                 c_ops,
-                n_traj = 500,
+                ntraj = 500,
                 e_ops = e_ops,
                 progress_bar = Val(false),
             )
-            @inferred ssesolve(H, psi0, t_l, c_ops, n_traj = 500, e_ops = e_ops, progress_bar = Val(false))
-            @inferred ssesolve(H, psi0, t_l, c_ops, n_traj = 500, progress_bar = Val(true))
+            @inferred ssesolve(H, psi0, t_l, c_ops, ntraj = 500, e_ops = e_ops, progress_bar = Val(false))
+            @inferred ssesolve(H, psi0, t_l, c_ops, ntraj = 500, progress_bar = Val(true))
         end
     end
 
@@ -179,7 +171,7 @@
         psi0 = kron(psi0_1, psi0_2)
         t_l = LinRange(0, 20 / γ1, 1000)
         sol_me = mesolve(H, psi0, t_l, c_ops, e_ops = [sp1 * sm1, sp2 * sm2], progress_bar = false) # Here we don't put Val(false) because we want to test the support for Bool type
-        sol_mc = mcsolve(H, psi0, t_l, c_ops, n_traj = 500, e_ops = [sp1 * sm1, sp2 * sm2], progress_bar = Val(false))
+        sol_mc = mcsolve(H, psi0, t_l, c_ops, ntraj = 500, e_ops = [sp1 * sm1, sp2 * sm2], progress_bar = Val(false))
         @test sum(abs.(sol_mc.expect[1:2, :] .- sol_me.expect[1:2, :])) / length(t_l) < 0.1
         @test expect(sp1 * sm1, sol_me.states[end]) ≈ expect(sigmap() * sigmam(), ptrace(sol_me.states[end], 1))
     end