Use ProgressMeter.jl rather than internal implementation

albertomercurio · albertomercurio · commit e720c9fbb90d · 2025-10-23T12:52:35.000+02:00
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "QuantumToolbox"
 uuid = "6c2fb7c5-b903-41d2-bc5e-5a7c320b9fab"
-authors = ["Alberto Mercurio", "Yi-Te Huang"]
 version = "0.37.0"
+authors = ["Alberto Mercurio", "Yi-Te Huang"]
 
 [deps]
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
@@ -18,6 +18,7 @@ LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 OrdinaryDiffEqCore = "bbf590c4-e513-4bbe-9b18-05decba2e5d8"
 OrdinaryDiffEqTsit5 = "b1df2697-797e-41e3-8120-5422d3b24e4a"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLOperators = "c0aeaf25-5076-4817-a8d5-81caf7dfa961"
@@ -60,6 +61,7 @@ Makie = "0.24"
 OrdinaryDiffEqCore = "1"
 OrdinaryDiffEqTsit5 = "1"
 Pkg = "1"
+ProgressMeter = "1.11.0"
 Random = "1"
 SciMLBase = "2.105"
 SciMLOperators = "1.4"
diff --git a/src/QuantumToolbox.jl b/src/QuantumToolbox.jl
@@ -62,6 +62,7 @@ import Graphs: connected_components, DiGraph
 import IncompleteLU: ilu
 import LaTeXStrings: @L_str
 import Pkg
+import ProgressMeter: Progress, next!
 import Random: AbstractRNG, default_rng, seed!
 import SpecialFunctions: loggamma
 import StaticArraysCore: SVector, MVector
@@ -81,7 +82,6 @@ export cache_operator, iscached, isconstant
 include("settings.jl")
 include("utilities.jl")
 include("versioninfo.jl")
-include("progress_bar.jl")
 include("linear_maps.jl")
 
 # Quantum Object
diff --git a/src/progress_bar.jl b/src/progress_bar.jl
diff --git a/src/time_evolution/callback_helpers/callback_helpers.jl b/src/time_evolution/callback_helpers/callback_helpers.jl
@@ -57,7 +57,7 @@ end
 function _generate_save_callback(e_ops, tlist, progress_bar, method)
     e_ops_data = e_ops isa Nothing ? nothing : _get_e_ops_data(e_ops, method)
 
-    progr = getVal(progress_bar) ? ProgressBar(length(tlist), enable = getVal(progress_bar)) : nothing
+    progr = getVal(progress_bar) ? Progress(length(tlist), showspeed=true, enabled = getVal(progress_bar)) : nothing
 
     expvals = e_ops isa Nothing ? nothing : Array{ComplexF64}(undef, length(e_ops), length(tlist))
 
@@ -69,7 +69,7 @@ function _generate_stochastic_save_callback(e_ops, sc_ops, tlist, store_measurem
     e_ops_data = e_ops isa Nothing ? nothing : _get_e_ops_data(e_ops, method)
     m_ops_data = _get_m_ops_data(sc_ops, method)
 
-    progr = getVal(progress_bar) ? ProgressBar(length(tlist), enable = getVal(progress_bar)) : nothing
+    progr = getVal(progress_bar) ? Progress(length(tlist), showspeed=true, enabled = getVal(progress_bar)) : nothing
 
     expvals = e_ops isa Nothing ? nothing : Array{ComplexF64}(undef, length(e_ops), length(tlist))
     m_expvals = getVal(store_measurement) ? Array{Float64}(undef, length(sc_ops), length(tlist) - 1) : nothing
diff --git a/src/time_evolution/callback_helpers/mcsolve_callback_helpers.jl b/src/time_evolution/callback_helpers/mcsolve_callback_helpers.jl
@@ -106,7 +106,7 @@ function _generate_mcsolve_kwargs(ψ0, T, e_ops, tlist, c_ops, jump_callback, rn
     end
 
     if e_ops isa Nothing
-        # We are implicitly saying that we don't have a `ProgressBar`
+        # We are implicitly saying that we don't have a `Progress`
         kwargs2 =
             haskey(kwargs, :callback) ? merge(kwargs, (callback = CallbackSet(cb1, kwargs.callback),)) :
             merge(kwargs, (callback = cb1,))
diff --git a/src/time_evolution/callback_helpers/mesolve_callback_helpers.jl b/src/time_evolution/callback_helpers/mesolve_callback_helpers.jl
@@ -2,7 +2,7 @@
 Helper functions for the mesolve callbacks.
 =#
 
-struct SaveFuncMESolve{TE,PT<:Union{Nothing,ProgressBar},IT,TEXPV<:Union{Nothing,AbstractMatrix}} <: AbstractSaveFunc
+struct SaveFuncMESolve{TE,PT<:Union{Nothing,Progress},IT,TEXPV<:Union{Nothing,AbstractMatrix}} <: AbstractSaveFunc
     e_ops::TE
     progr::PT
     iter::IT
diff --git a/src/time_evolution/callback_helpers/sesolve_callback_helpers.jl b/src/time_evolution/callback_helpers/sesolve_callback_helpers.jl
@@ -2,7 +2,7 @@
 Helper functions for the sesolve callbacks.
 =#
 
-struct SaveFuncSESolve{TE,PT<:Union{Nothing,ProgressBar},IT,TEXPV<:Union{Nothing,AbstractMatrix}} <: AbstractSaveFunc
+struct SaveFuncSESolve{TE,PT<:Union{Nothing,Progress},IT,TEXPV<:Union{Nothing,AbstractMatrix}} <: AbstractSaveFunc
     e_ops::TE
     progr::PT
     iter::IT
diff --git a/src/time_evolution/callback_helpers/smesolve_callback_helpers.jl b/src/time_evolution/callback_helpers/smesolve_callback_helpers.jl
@@ -6,7 +6,7 @@ struct SaveFuncSMESolve{
     SM,
     TE,
     TE2,
-    PT<:Union{Nothing,ProgressBar},
+    PT<:Union{Nothing,Progress},
     IT,
     TEXPV<:Union{Nothing,AbstractMatrix},
     TMEXPV<:Union{Nothing,AbstractMatrix},
diff --git a/src/time_evolution/callback_helpers/ssesolve_callback_helpers.jl b/src/time_evolution/callback_helpers/ssesolve_callback_helpers.jl
@@ -6,7 +6,7 @@ struct SaveFuncSSESolve{
     SM,
     TE,
     TE2,
-    PT<:Union{Nothing,ProgressBar},
+    PT<:Union{Nothing,Progress},
     IT,
     TEXPV<:Union{Nothing,AbstractMatrix},
     TMEXPV<:Union{Nothing,AbstractMatrix},
diff --git a/src/time_evolution/mcsolve.jl b/src/time_evolution/mcsolve.jl
@@ -205,7 +205,7 @@ If the environmental measurements register a quantum jump, the wave function und
 - `jump_callback`: The Jump Callback type: Discrete or Continuous. The default is `ContinuousLindbladJumpCallback()`, which is more precise.
 - `progress_bar`: Whether to show the progress bar. Using non-`Val` types might lead to type instabilities.
 - `prob_func`: Function to use for generating the ODEProblem.
-- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `ProgressBar` object, and the (optional) `RemoteChannel` object.
+- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `Progress` object, and the (optional) `RemoteChannel` object.
 - `kwargs`: The keyword arguments for the ODEProblem.
 
 # Notes
@@ -332,7 +332,7 @@ If the environmental measurements register a quantum jump, the wave function und
 - `jump_callback`: The Jump Callback type: Discrete or Continuous. The default is `ContinuousLindbladJumpCallback()`, which is more precise.
 - `progress_bar`: Whether to show the progress bar. Using non-`Val` types might lead to type instabilities.
 - `prob_func`: Function to use for generating the ODEProblem.
-- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `ProgressBar` object, and the (optional) `RemoteChannel` object.
+- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `Progress` object, and the (optional) `RemoteChannel` object.
 - `keep_runs_results`: Whether to save the results of each trajectory. Default to `Val(false)`.
 - `normalize_states`: Whether to normalize the states. Default to `Val(true)`.
 - `kwargs`: The keyword arguments for the ODEProblem.
diff --git a/src/time_evolution/mesolve.jl b/src/time_evolution/mesolve.jl
@@ -351,7 +351,7 @@ mesolve_map(
 # this method is for advanced usage
 # User can define their own iterator structure, prob_func and output_func
 #   - `prob_func`: Function to use for generating the ODEProblem.
-#   - `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `ProgressBar` object, and the (optional) `RemoteChannel` object.
+#   - `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `Progress` object, and the (optional) `RemoteChannel` object.
 #
 # Return: An array of TimeEvolutionSol objects with the size same as the given iter.
 function mesolve_map(
diff --git a/src/time_evolution/sesolve.jl b/src/time_evolution/sesolve.jl
@@ -260,7 +260,7 @@ sesolve_map(H::Union{AbstractQuantumObject{Operator},Tuple}, ψ0::QuantumObject{
 # this method is for advanced usage
 # User can define their own iterator structure, prob_func and output_func
 #   - `prob_func`: Function to use for generating the ODEProblem.
-#   - `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `ProgressBar` object, and the (optional) `RemoteChannel` object.
+#   - `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `Progress` object, and the (optional) `RemoteChannel` object.
 #
 # Return: An array of TimeEvolutionSol objects with the size same as the given iter.
 function sesolve_map(
diff --git a/src/time_evolution/smesolve.jl b/src/time_evolution/smesolve.jl
@@ -107,8 +107,6 @@ function smesolveProblem(
         to_dense(_complex_float_type(T), mat2vec(ket2dm(ψ0).data))
     end
 
-    progr = ProgressBar(length(tlist), enable = getVal(progress_bar))
-
     sc_ops_evo_data = Tuple(map(get_data ∘ QobjEvo, sc_ops_list))
 
     K = get_data(L_evo)
@@ -203,7 +201,7 @@ Above, ``\hat{C}_i`` represent the collapse operators related to pure dissipatio
 - `ntraj`: Number of trajectories to use. Default is `500`.
 - `ensemblealg`: Ensemble method to use. Default to `EnsembleThreads()`.
 - `prob_func`: Function to use for generating the SDEProblem.
-- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `ProgressBar` object, and the (optional) `RemoteChannel` object.
+- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `Progress` object, and the (optional) `RemoteChannel` object.
 - `progress_bar`: Whether to show the progress bar. Using non-`Val` types might lead to type instabilities.
 - `store_measurement`: Whether to store the measurement expectation values. Default is `Val(false)`.
 - `kwargs`: The keyword arguments for the ODEProblem.
@@ -332,7 +330,7 @@ Above, ``\hat{C}_i`` represent the collapse operators related to pure dissipatio
 - `ntraj`: Number of trajectories to use. Default is `500`.
 - `ensemblealg`: Ensemble method to use. Default to `EnsembleThreads()`.
 - `prob_func`: Function to use for generating the SDEProblem.
-- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `ProgressBar` object, and the (optional) `RemoteChannel` object.
+- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `Progress` object, and the (optional) `RemoteChannel` object.
 - `progress_bar`: Whether to show the progress bar. Using non-`Val` types might lead to type instabilities.
 - `keep_runs_results`: Whether to save the results of each trajectory. Default to `Val(false)`.
 - `store_measurement`: Whether to store the measurement expectation values. Default is `Val(false)`.
diff --git a/src/time_evolution/ssesolve.jl b/src/time_evolution/ssesolve.jl
@@ -103,7 +103,7 @@ function ssesolveProblem(
 
     ψ0 = to_dense(_complex_float_type(ψ0), get_data(ψ0))
 
-    progr = ProgressBar(length(tlist), enable = getVal(progress_bar))
+    progr = Progress(length(tlist), showspeed=true, enabled = getVal(progress_bar))
 
     sc_ops_evo_data = Tuple(map(get_data ∘ QobjEvo, sc_ops_list))
 
@@ -198,7 +198,7 @@ Above, ``\hat{S}_n`` are the stochastic collapse operators and  ``dW_n(t)`` is t
 - `ensemblealg`: Ensemble method to use. Default to `EnsembleThreads()`.
 - `jump_callback`: The Jump Callback type: Discrete or Continuous. The default is `ContinuousLindbladJumpCallback()`, which is more precise.
 - `prob_func`: Function to use for generating the SDEProblem.
-- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `ProgressBar` object, and the (optional) `RemoteChannel` object.
+- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `Progress` object, and the (optional) `RemoteChannel` object.
 - `progress_bar`: Whether to show the progress bar. Using non-`Val` types might lead to type instabilities.
 - `store_measurement`: Whether to store the measurement results. Default is `Val(false)`.
 - `kwargs`: The keyword arguments for the ODEProblem.
@@ -327,7 +327,7 @@ Above, ``\hat{S}_n`` are the stochastic collapse operators and ``dW_n(t)`` is th
 - `ntraj`: Number of trajectories to use. Default is `500`.
 - `ensemblealg`: Ensemble method to use. Default to `EnsembleThreads()`.
 - `prob_func`: Function to use for generating the SDEProblem.
-- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `ProgressBar` object, and the (optional) `RemoteChannel` object.
+- `output_func`: a `Tuple` containing the `Function` to use for generating the output of a single trajectory, the (optional) `Progress` object, and the (optional) `RemoteChannel` object.
 - `progress_bar`: Whether to show the progress bar. Using non-`Val` types might lead to type instabilities.
 - `keep_runs_results`: Whether to save the results of each trajectory. Default to `Val(false)`.
 - `store_measurement`: Whether to store the measurement results. Default is `Val(false)`.
diff --git a/src/time_evolution/time_evolution.jl b/src/time_evolution/time_evolution.jl
@@ -376,7 +376,7 @@ function _ensemble_dispatch_output_func(
     output_func,
 ) where {ET<:Union{EnsembleSerial,EnsembleThreads}}
     if getVal(progress_bar)
-        progr = ProgressBar(ntraj, enable = getVal(progress_bar))
+        progr = Progress(ntraj, showspeed=true, enabled = getVal(progress_bar))
         f = (sol, i) -> _ensemble_output_func_progress(sol, i, progr, output_func)
         return (f, progr, nothing)
     else
@@ -390,7 +390,7 @@ function _ensemble_dispatch_output_func(
     output_func,
 ) where {ET<:Union{EnsembleSplitThreads,EnsembleDistributed}}
     if getVal(progress_bar)
-        progr = ProgressBar(ntraj, enable = getVal(progress_bar))
+        progr = Progress(ntraj, showspeed=true, enabled = getVal(progress_bar))
         progr_channel::RemoteChannel{Channel{Bool}} = RemoteChannel(() -> Channel{Bool}(1))
 
         f = (sol, i) -> _ensemble_output_func_distributed(sol, i, progr_channel, output_func)
diff --git a/test/core-test/progress_bar.jl b/test/core-test/progress_bar.jl
