docs: fix examples

NQCD · Nov 3, 2021 · 764f71f · 764f71f
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diff --git a/docs/src/dynamicssimulations/dynamicsmethods/nrpmd.md b/docs/src/dynamicssimulations/dynamicsmethods/nrpmd.md
@@ -142,7 +142,7 @@ like to average the results so we use the `MeanReduction`.
 
 ```@example nrpmd
 output = Ensembles.OutputDiabaticPopulation(sim)
-reduction = Ensembles.MeanReduction()
+reduction = Ensembles.MeanReduction([zeros(2) for _ in 1:0.1:30])
 nothing # hide
 ```
 

diff --git a/docs/src/dynamicssimulations/dynamicsmethods/rpsh.md b/docs/src/dynamicssimulations/dynamicsmethods/rpsh.md
@@ -59,7 +59,8 @@ and it will be averaged over all trajectories by the `MeanReduction`.
 ```@example rpsh
 solution = Ensembles.run_ensemble(sim, (0.0, 3000.0), distribution;
     saveat=50, trajectories=5e2, dt=1,
-    output=Ensembles.OutputDiabaticPopulation(sim), reduction=Ensembles.MeanReduction())
+    output=Ensembles.OutputDiabaticPopulation(sim),
+    reduction=Ensembles.MeanReduction([zeros(2) for _=0:3000]))
 ```
 
 !!! note

diff --git a/docs/src/ensemble_simulations.md b/docs/src/ensemble_simulations.md
@@ -37,7 +37,7 @@ Here we choose to output the populations of each diabatic state and reduce by av
 the results over all trajectories.
 ```@example ensemble
 output = Ensembles.OutputDiabaticPopulation(sim)
-reduction = Ensembles.MeanReduction()
+reduction = Ensembles.MeanReduction([zeros(2) for _ in 0:10:3000])
 nothing # hide
 ```
 

diff --git a/docs/src/examples/spinboson.md b/docs/src/examples/spinboson.md
@@ -33,7 +33,7 @@ analytic form and we can use the distributions included in the package.
 ```@example spinboson
 position = PositionHarmonicWigner.(model.ωⱼ, β, 1)
 velocity = VelocityHarmonicWigner.(model.ωⱼ, β, 1)
-distribution = DynamicalDistribution(velocity, position, (1, 100))) * SingleState(1)
+distribution = DynamicalDistribution(velocity, position, (1, 100)) * SingleState(1)
 nothing # hide
 ```
 

diff --git a/docs/src/examples/threestatemorse.md b/docs/src/examples/threestatemorse.md
@@ -66,14 +66,15 @@ The [`OutputDiabaticPopulation`](@ref Ensembles.OutputDiabaticPopulation) will e
 the diabatic population estimator at each timestep.
 
 ```@example threestatemorse
+reduction = Ensembles.MeanReduction([zeros(3) for _=1:10:3000])
 sim = Simulation{FSSH}(atoms, model)
 fssh_result = Ensembles.run_ensemble(sim, (0.0, 3000.0), distribution;
     saveat=10, trajectories=1e3,
-    output=Ensembles.OutputDiabaticPopulation(sim), reduction=Ensembles.MeanReduction(), dt=1)
+    output=Ensembles.OutputDiabaticPopulation(sim), reduction=reduction, dt=1)
 sim = Simulation{Ehrenfest}(atoms, model)
 ehrenfest_result = Ensembles.run_ensemble(sim, (0.0, 3000.0), distribution;
     saveat=10, trajectories=1e3,
-    output=Ensembles.OutputDiabaticPopulation(sim), reduction=Ensembles.MeanReduction(), dt=1)
+    output=Ensembles.OutputDiabaticPopulation(sim), reduction=reduction, dt=1)
 
 fig = Figure()
 ax = Axis(fig[1,1], xlabel="Time /a.u.", ylabel="Population")

diff --git a/docs/src/examples/tully_scattering.md b/docs/src/examples/tully_scattering.md
@@ -16,8 +16,14 @@ We can use our [`Ensembles`](@ref) setup to run a set of trajectories for every
 momentum value.
 Firstly, we can prepare the parts that will be the same for every ensemble:
 ```@example tullymodeltwo
+using ComponentArrays: ComponentVector
+
 output = Ensembles.OutputStateResolvedScattering1D(sim, :adiabatic)
-reduction = Ensembles.MeanReduction()
+template = ComponentVector(
+    reflection=zeros(2),
+    transmission=zeros(2)
+)
+reduction = Ensembles.MeanReduction(templates)
 ```
 Here we are using the
 [`OutputStateResolvedScattering1D`](@ref Ensembles.OutputStateResolvedScattering1D)