Fix doctests

Author: albertomercurio

docs/src/getting_started/type_stability.md

@@ -226,6 +226,8 @@ Which is an innocuous but huge difference in terms of performance. Hence, we hig
 In some functions of `QuantumToolbox.jl`, you may find the use of the [`Val`](https://docs.julialang.org/en/v1/base/base/#Base.Val) type in the arguments. This is a trick to pass a value at compile time, and it is very useful to avoid type instabilities. Let's make a very simple example, where we want to create a Fock state ``|j\rangle`` of a given dimension `N`, and we give the possibility to create it as a sparse or dense vector. At first, we can write the function without using `Val`:
 
 ```@example type-stability
+using SparseArrays
+
 function my_fock(N::Int, j::Int = 0; sparse::Bool = false)
     if sparse
         array = sparsevec([j + 1], [1.0 + 0im], N)

docs/src/resources/api.md

@@ -1,5 +1,11 @@
 ```@meta
 CurrentModule = QuantumToolbox
+
+DocTestSetup = quote
+    using LinearAlgebra
+    using SparseArrays
+    using QuantumToolbox
+end
 ```
 
 # [API](@id doc-API)

docs/src/users_guide/QuantumObject/QuantumObject.md

@@ -46,7 +46,7 @@ M = rand(ComplexF64, 4, 4)
 Qobj(M, dims = [2, 2])  # dims as Vector
 Qobj(M, dims = (2, 2))  # dims as Tuple (recommended)
 
-import QuantumObject: SVector # or using StaticArrays
+import QuantumToolbox: SVector # or using StaticArrays
 Qobj(M, dims = SVector(2, 2)) # dims as StaticArrays.SVector (recommended)
 ```
 
@@ -197,6 +197,8 @@ Vector{Int64}(v_d)
 ```
 
 ```@example Qobj
+using SparseArrays
+
 v_s = SparseVector(v_d)
 ```
 

docs/src/users_guide/QuantumObject/QuantumObject_functions.md

@@ -43,8 +43,6 @@ Here is a table that summarizes all the supported linear algebra functions and a
 
 - [`eigenenergies`](@ref): return eigenenergies (eigenvalues)
 - [`eigenstates`](@ref): return [`EigsolveResult`](@ref) (contains eigenvalues and eigenvectors)
-- [`eigvals`](@ref): return eigenvalues
-- [`eigen`](@ref): using dense eigen solver and return [`EigsolveResult`](@ref) (contains eigenvalues and eigenvectors)
 - [`eigsolve`](@ref): using sparse eigen solver and return [`EigsolveResult`](@ref) (contains eigenvalues and eigenvectors)
 - [`eigsolve_al`](@ref): using the Arnoldi-Lindblad eigen solver and return [`EigsolveResult`](@ref) (contains eigenvalues and eigenvectors)
 

src/qobj/eigsolve.jl

@@ -444,6 +444,8 @@ julia> a = destroy(5);
 
 julia> H = a + a';
 
+julia> using LinearAlgebra;
+
 julia> E, ψ, U = eigen(H)
 EigsolveResult:   type=Operator   dims=[5]
 values:

test/runtests.jl

@@ -12,16 +12,16 @@ const testdir = dirname(@__FILE__)
 
 # Put core tests in alphabetical order
 core_tests = [
-    # "block_diagonal_form.jl",
-    # "correlations_and_spectrum.jl",
-    # "dynamical_fock_dimension_mesolve.jl",
-    # "dynamical-shifted-fock.jl",
-    # "eigenvalues_and_operators.jl",
-    # "entropy_and_metric.jl",
-    # "generalized_master_equation.jl",
-    # "low_rank_dynamics.jl",
-    # "negativity_and_partial_transpose.jl",
-    # "progress_bar.jl",
+    "block_diagonal_form.jl",
+    "correlations_and_spectrum.jl",
+    "dynamical_fock_dimension_mesolve.jl",
+    "dynamical-shifted-fock.jl",
+    "eigenvalues_and_operators.jl",
+    "entropy_and_metric.jl",
+    "generalized_master_equation.jl",
+    "low_rank_dynamics.jl",
+    "negativity_and_partial_transpose.jl",
+    "progress_bar.jl",
     "quantum_objects.jl",
     "quantum_objects_evo.jl",
     "states_and_operators.jl",