export GramSchmidt

src/Rimu.jl

@@ -73,7 +73,7 @@ export TimeStepStrategy, ConstantTimeStep, OvershootControl
 export localpart, walkernumber
 export smart_logger, default_logger
 export ProjectorMonteCarloProblem, SimulationPlan, StateVectors
-export FCIQMC, num_replicas, num_spectral_states
+export FCIQMC, num_replicas, num_spectral_states, GramSchmidt
 
 function __init__()
     # Turn on smart logging once at runtime. Turn off with `default_logger()`.

src/lomc.jl

@@ -1,3 +1,5 @@
+# This file contains deprecated code. This code will be removed in a future release.
+
 """
     lomc!(ham::AbstractHamiltonian, [v]; kwargs...) -> df, state
     lomc!(state::ReplicaState, [df]; kwargs...) -> df, state

src/strategies_and_params/spectralstrategy.jl

@@ -21,4 +21,8 @@ Use the Gram-Schmidt procedure to orthogonalize the excited states. A total of `
 states are used in the simulation.
 """
 struct GramSchmidt{S} <: SpectralStrategy{S} end
-GramSchmidt(num_spectral_states = 1) = GramSchmidt{num_spectral_states}()
+
+function GramSchmidt(num_spectral_states = 1)
+    num_spectral_states > 1 && throw(ArgumentError("num_spectral_states > 1 is currently not supported."))
+    GramSchmidt{num_spectral_states}()
+end

test/Interfaces.jl

@@ -61,5 +61,5 @@ end
     @test eigs.values[1] ≈ last_shift rtol = 0.01
     @test df.shift[end] ≈ eigs.values[1] rtol=0.01
     @test df.hproj[end] / df.vproj[end] ≈ eigs.values[1] rtol=0.01
-    @test normalize(state_vectors(sm)[1]) ≈ DVec(pairs(eigs.vectors[:, 1])) rtol = 0.01
+    @test normalize(StateVectors(sm)[1]) ≈ DVec(pairs(eigs.vectors[:, 1])) rtol = 0.01
 end

test/lomc.jl

@@ -34,20 +34,17 @@ Random.seed!(1234)
         @test state.spectral_states[1].single_states[1].pv !== v
         @test state.spectral_states[1].single_states[1].pv !== wm
 
-        # @set state.simulation_plan.last_step = 10
         df = lomc!(state, df, laststep=10).df
         @test_broken state.spectral_states[1].single_states[1].v === wm
         @test state.spectral_states[1].single_states[1].pv === v
 
         @test size(df, 1) == 10
         @test state.step[] == 10
 
-        # @set state.simulation_plan.last_step = 100
         df, state = lomc!(state, df, laststep=100)
         @test size(df, 1) == 100
 
         state.step[] = 0
-        # state = @set state.simulation_plan.starting_step = 0
         df, state = lomc!(state, df)
         @test size(df, 1) == 200
         @test df.steps == [1:100; 1:100]