diff --git a/Extras/ApproxFun Arbitrary Grid with AAA.ipynb b/Extras/ApproxFun_Arbitrary_Grid_with_AAA.ipynb
similarity index 100%
rename from Extras/ApproxFun Arbitrary Grid with AAA.ipynb
rename to Extras/ApproxFun_Arbitrary_Grid_with_AAA.ipynb
diff --git a/Extras/Delay Equation.jl b/Extras/Delay_Equation.jl
similarity index 100%
rename from Extras/Delay Equation.jl
rename to Extras/Delay_Equation.jl
diff --git a/Extras/Dual Numbers.jl b/Extras/Dual_Numbers.jl
similarity index 94%
rename from Extras/Dual Numbers.jl
rename to Extras/Dual_Numbers.jl
index c85ffa1..1c03112 100644
--- a/Extras/Dual Numbers.jl	
+++ b/Extras/Dual_Numbers.jl
@@ -3,8 +3,10 @@
 # changes in parameters
 ##
 
+using DualNumbers
+using ApproxFun
+using LinearAlgebra
 
-using DualNumbers, ApproxFun
 # What is the derivative of the function (without differentiating the Chebyshev expansion)?
 f=Fun(x->exp(dual(x,1)),-1..1)
 dualpart(f)
diff --git a/Extras/Fractional Integral Equations.ipynb b/Extras/Fractional_Integral_Equations.ipynb
similarity index 100%
rename from Extras/Fractional Integral Equations.ipynb
rename to Extras/Fractional_Integral_Equations.ipynb
diff --git a/Extras/Fractional derivatives.jl b/Extras/Fractional_derivatives.jl
similarity index 100%
rename from Extras/Fractional derivatives.jl
rename to Extras/Fractional_derivatives.jl
diff --git a/Extras/GalerkinOrthogonalPolynomials.jl b/Extras/GalerkinOrthogonalPolynomials.jl
index 2797d80..a36b542 100644
--- a/Extras/GalerkinOrthogonalPolynomials.jl
+++ b/Extras/GalerkinOrthogonalPolynomials.jl
@@ -1,4 +1,6 @@
-using ApproxFun, Plots
+using ApproxFun
+using Plots
+using LinearAlgebra
 
 # In this example, we numerically compute with Galerkin orthogonal polynomials.
 # If H is a separable Hilbert space and B contains ν linear functional constraints,
diff --git a/Extras/Inverse eigenvalue problem.jl b/Extras/Inverse eigenvalue problem.jl
deleted file mode 100644
index f5a548b..0000000
--- a/Extras/Inverse eigenvalue problem.jl	
+++ /dev/null
@@ -1,56 +0,0 @@
-using ApproxFun, LinearAlgebra, DualNumbers
-import ApproxFun: bandwidth
-
-#
-# Consider the following Sturm--Liouville problem with polynomial p, q, and w.
-#
-# [(-𝒟)(p𝒟) + q] u = λ w u,  (u±bu')(±1) = 0.
-#
-# For which value of b, the Neumann part of the Robin boundary conditions,
-# is λ₀ equal to 1?
-#
-# To answer this question, we use a dual number to observe the sensitivity in λ₀
-# with respect to perturbations in b. The `realpart` of the objective function
-#
-#    f(λ(b)) = λ(b) - 1,
-#
-# extracts the difference, whereas the `dualpart` evaluates to ∂f/∂b (λ(b)).
-#
-
-n = 100
-λ = dual(0.0, 0.0)
-b = dual(0.0, 1.0)
-v = zeros(Dual{Float64}, n)
-v[1] = 1
-v .+= 0.003.*randn(Float64, n)
-for _ in 1:8
-    d = Segment(-1..1)
-    S = Ultraspherical(0.5, d)
-    NS = NormalizedPolynomialSpace(S)
-    D = Derivative(S)
-    p = Fun(x->1+x^2, S)
-    q = Fun(x->x^3, S)
-    L = -D*(p*D)+q
-    w = Fun(x->1+x/4, S)
-    M = Multiplication(w, S)
-    C = Conversion(domainspace(L), rangespace(L))
-    B = [Evaluation(S, -1, 0) - b*Evaluation(S, -1, 1); Evaluation(S, 1, 0) + b*Evaluation(S, 1, 1)]
-    QS = QuotientSpace(B)
-    Q = Conversion(QS, S)
-    D1 = Conversion(S, NS)
-    D2 = Conversion(NS, S)
-    R = D1*Q
-    P = cache(PartialInverseOperator(C, (0, bandwidth(L, 1) + bandwidth(R, 1) + bandwidth(C, 2))))
-    A = R'D1*P*L*D2*R
-    B = R'D1*M*D2*R
-    SA = Symmetric(A[1:n,1:n], :L)
-    SB = Symmetric(B[1:n,1:n], :L)
-    for _ = 1:7
-        global λ = dot(v, SA*v)/dot(v, SB*v)
-        ldiv!(ldlt!(SA-λ*SB), v)
-        global v = v/norm(v)
-        println("\tThis is λ: ", λ)
-    end
-    global b -= (realpart(λ)-1)/dualpart(λ)
-    println("This is b: ", b)
-end
diff --git a/Extras/Inverse_eigenvalue_problem.jl b/Extras/Inverse_eigenvalue_problem.jl
new file mode 100644
index 0000000..8c10dfe
--- /dev/null
+++ b/Extras/Inverse_eigenvalue_problem.jl
@@ -0,0 +1,60 @@
+using ApproxFun, LinearAlgebra, DualNumbers
+import ApproxFun: bandwidth
+
+#
+# Consider the following Sturm--Liouville problem with polynomial p, q, and w.
+#
+# [(-𝒟)(p𝒟) + q] u = λ w u,  (u±bu')(±1) = 0.
+#
+# For which value of b, the Neumann part of the Robin boundary conditions,
+# is λ₀ equal to 1?
+#
+# To answer this question, we use a dual number to observe the sensitivity in λ₀
+# with respect to perturbations in b. The `realpart` of the objective function
+#
+#    f(λ(b)) = λ(b) - 1,
+#
+# extracts the difference, whereas the `dualpart` evaluates to ∂f/∂b (λ(b)).
+#
+
+function main()
+    n = 100
+    λ = dual(0.0, 0.0)
+    b = dual(0.0, 1.0)
+    v = zeros(Dual{Float64}, n)
+    v[1] = 1
+    v .+= 0.003.*randn(Float64, n)
+    for _ in 1:8
+        d = Segment(-1..1)
+        S = Ultraspherical(0.5, d)
+        NS = NormalizedPolynomialSpace(S)
+        D = Derivative(S)
+        p = Fun(x->1+x^2, S)
+        q = Fun(x->x^3, S)
+        L = -D*(p*D)+q
+        w = Fun(x->1+x/4, S)
+        M = Multiplication(w, S)
+        C = Conversion(domainspace(L), rangespace(L))
+        B = [Evaluation(S, -1, 0) - b*Evaluation(S, -1, 1); Evaluation(S, 1, 0) + b*Evaluation(S, 1, 1)]
+        QS = QuotientSpace(B)
+        Q = Conversion(QS, S)
+        D1 = Conversion(S, NS)
+        D2 = Conversion(NS, S)
+        R = D1*Q
+        P = cache(PartialInverseOperator(C, (0, bandwidth(L, 1) + bandwidth(R, 1) + bandwidth(C, 2))))
+        A = R'D1*P*L*D2*R
+        B = R'D1*M*D2*R
+        SA = Symmetric(A[1:n,1:n], :L)
+        SB = Symmetric(B[1:n,1:n], :L)
+        for _ = 1:7
+            global λ = dot(v, SA*v)/dot(v, SB*v)
+            ldiv!(ldlt!(SA-λ*SB), v)
+            global v = v/norm(v)
+            println("\tThis is λ: ", λ)
+        end
+        global b -= (realpart(λ)-1)/dualpart(λ)
+        println("This is b: ", b)
+    end
+end
+
+main()
diff --git a/Extras/Manual 3D interpolation.jl b/Extras/Manual_3D_interpolation.jl
similarity index 100%
rename from Extras/Manual 3D interpolation.jl
rename to Extras/Manual_3D_interpolation.jl
diff --git a/Manifest.toml b/Manifest.toml
index 40e5a4a..7eb8e2c 100644
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -1,495 +1,1357 @@
 # This file is machine-generated - editing it directly is not advised
 
-[[AbstractFFTs]]
-deps = ["LinearAlgebra"]
-git-tree-sha1 = "051c95d6836228d120f5f4b984dd5aba1624f716"
+julia_version = "1.7.3"
+manifest_format = "2.0"
+
+[[deps.AbstractFFTs]]
+deps = ["ChainRulesCore", "LinearAlgebra"]
+git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409"
 uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
-version = "0.5.0"
+version = "1.2.1"
+
+[[deps.Adapt]]
+deps = ["LinearAlgebra"]
+git-tree-sha1 = "195c5505521008abea5aee4f96930717958eac6f"
+uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+version = "3.4.0"
 
-[[ApproxFun]]
-deps = ["AbstractFFTs", "ApproxFunBase", "ApproxFunFourier", "ApproxFunOrthogonalPolynomials", "ApproxFunSingularities", "Calculus", "DomainSets", "DualNumbers", "FFTW", "FastTransforms", "LinearAlgebra", "RecipesBase", "Reexport"]
-git-tree-sha1 = "3bd77aef8177c523341466ddafa80a0ceddce67b"
+[[deps.ApproxFun]]
+deps = ["AbstractFFTs", "ApproxFunBase", "ApproxFunFourier", "ApproxFunOrthogonalPolynomials", "ApproxFunSingularities", "Calculus", "DomainSets", "DualNumbers", "FFTW", "FastTransforms", "LinearAlgebra", "RecipesBase", "Reexport", "SpecialFunctions"]
+git-tree-sha1 = "4e3f24696adc72fa6f71982f5482364a4dd97b45"
 uuid = "28f2ccd6-bb30-5033-b560-165f7b14dc2f"
-version = "0.11.9"
+version = "0.13.7"
 
-[[ApproxFunBase]]
-deps = ["AbstractFFTs", "BandedMatrices", "BlockArrays", "BlockBandedMatrices", "Calculus", "DSP", "DomainSets", "DualNumbers", "FFTW", "FastGaussQuadrature", "FillArrays", "InfiniteArrays", "IntervalSets", "LazyArrays", "LinearAlgebra", "LowRankApprox", "SparseArrays", "SpecialFunctions", "StaticArrays", "Statistics", "Test", "ToeplitzMatrices"]
-git-tree-sha1 = "1e16001890bcaa058fcdca357564e2ee91d77cea"
+[[deps.ApproxFunBase]]
+deps = ["AbstractFFTs", "BandedMatrices", "BlockArrays", "BlockBandedMatrices", "Calculus", "DSP", "DomainSets", "DualNumbers", "FFTW", "FastGaussQuadrature", "FillArrays", "InfiniteArrays", "InfiniteLinearAlgebra", "IntervalSets", "LazyArrays", "LinearAlgebra", "LowRankApprox", "SparseArrays", "SpecialFunctions", "StaticArrays", "Statistics", "Test", "ToeplitzMatrices"]
+git-tree-sha1 = "27605659500a28b7124b7c88a86b4c63a2984fe8"
 uuid = "fbd15aa5-315a-5a7d-a8a4-24992e37be05"
-version = "0.3.1"
+version = "0.6.5"
 
-[[ApproxFunFourier]]
+[[deps.ApproxFunFourier]]
 deps = ["AbstractFFTs", "ApproxFunBase", "DomainSets", "FFTW", "FastTransforms", "InfiniteArrays", "IntervalSets", "LinearAlgebra", "Reexport"]
-git-tree-sha1 = "2cfbb9bf45ba0ba2f0a29f9c206b27a4e7199f2e"
+git-tree-sha1 = "3209c243eff4ced9d4b422512d23d0a22b2b4cdd"
 uuid = "59844689-9c9d-51bf-9583-5b794ec66d30"
-version = "0.2.1"
+version = "0.2.15"
 
-[[ApproxFunOrthogonalPolynomials]]
+[[deps.ApproxFunOrthogonalPolynomials]]
 deps = ["AbstractFFTs", "ApproxFunBase", "BandedMatrices", "BlockArrays", "BlockBandedMatrices", "DomainSets", "FFTW", "FastGaussQuadrature", "FastTransforms", "FillArrays", "IntervalSets", "LinearAlgebra", "Reexport", "SpecialFunctions", "Statistics"]
-git-tree-sha1 = "bb7faf853a51cb2020aa3efb2feae8065df1386a"
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 uuid = "b70543e2-c0d9-56b8-a290-0d4d6d4de211"
-version = "0.3.1"
+version = "0.4.12"
 
-[[ApproxFunSingularities]]
+[[deps.ApproxFunSingularities]]
 deps = ["ApproxFunBase", "ApproxFunOrthogonalPolynomials", "DomainSets", "IntervalSets", "LinearAlgebra", "Reexport", "Statistics"]
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 uuid = "f8fcb915-6b99-5be2-b79a-d6dbef8e6e7e"
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+
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-[[ArrayLayouts]]
-deps = ["FillArrays", "LinearAlgebra"]
-git-tree-sha1 = "bc779df8d73be70e4e05a63727d3a4dfb4c52b1f"
+[[deps.ArrayInterface]]
+deps = ["ArrayInterfaceCore", "Compat", "IfElse", "LinearAlgebra", "Static"]
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+
+[[deps.ArrayInterfaceCore]]
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+
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 uuid = "4c555306-a7a7-4459-81d9-ec55ddd5c99a"
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 deps = ["ArrayLayouts", "FillArrays", "LinearAlgebra", "Random", "SparseArrays"]
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 uuid = "aae01518-5342-5314-be14-df237901396f"
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 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 
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-
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 uuid = "8e7c35d0-a365-5155-bbbb-fb81a777f24e"
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-git-tree-sha1 = "0c6a7e6e2dc861205f99d1312364568b07b8e792"
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-
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+
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+[[deps.Compat]]
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 uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
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+[[deps.CompilerSupportLibraries_jll]]
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 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
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+[[deps.CompositeTypes]]
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+
+[[deps.Conda]]
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 uuid = "8f4d0f93-b110-5947-807f-2305c1781a2d"
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-[[Contour]]
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+[[deps.Contour]]
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 uuid = "d38c429a-6771-53c6-b99e-75d170b6e991"
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+[[deps.DSP]]
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diff --git a/ODEs/Airy equation.jl b/ODEs/Airy_equation.jl
similarity index 95%
rename from ODEs/Airy equation.jl
rename to ODEs/Airy_equation.jl
index b1d1bbc..5b5faf3 100644
--- a/ODEs/Airy equation.jl	
+++ b/ODEs/Airy_equation.jl
@@ -1,4 +1,6 @@
 using ApproxFun
+using SpecialFunctions
+using LinearAlgebra
 
 #The following solves the Airy ODE with dirichlet boundary conditions
 
diff --git a/ODEs/Blasius and Falkner-Skan.jl b/ODEs/Blasius_and_Falkner-Skan.jl
similarity index 97%
rename from ODEs/Blasius and Falkner-Skan.jl
rename to ODEs/Blasius_and_Falkner-Skan.jl
index 343a42a..e7d85ea 100644
--- a/ODEs/Blasius and Falkner-Skan.jl	
+++ b/ODEs/Blasius_and_Falkner-Skan.jl
@@ -1,4 +1,6 @@
-using Plots, ApproxFun
+using Plots
+using ApproxFun
+using LinearAlgebra
 # The Blasius boundary layer problem (https://en.wikipedia.org/wiki/Blasius_boundary_layer)
 # describes the boundary layer on a semi-infinite plane with a head-on steady fluid flow.
 # When properly non-dimensionalized, it presents itself as a relatively simple nonlinear BVP.
diff --git a/ODEs/Lee & Greengard equation.jl b/ODEs/Lee_and_Greengard_equation.jl
similarity index 82%
rename from ODEs/Lee & Greengard equation.jl
rename to ODEs/Lee_and_Greengard_equation.jl
index 53ac531..0bbf577 100644
--- a/ODEs/Lee & Greengard equation.jl	
+++ b/ODEs/Lee_and_Greengard_equation.jl
@@ -1,4 +1,5 @@
 using ApproxFun
+using LinearAlgebra
 
 x=Fun(identity)
 D=Derivative()
diff --git a/ODEs/Multivariate newton iteration.jl b/ODEs/Multivariate newton iteration.jl
deleted file mode 100644
index b78520b..0000000
--- a/ODEs/Multivariate newton iteration.jl	
+++ /dev/null
@@ -1,23 +0,0 @@
-using ApproxFun, DualNumbers, LinearAlgebra, Plots
-    import ApproxFun: DualFun, jacobian
-
-x=Fun(-π..π)
-N = (T,u) -> [u(-π)-1;u(π)-1;u'(π)-1;u'' - u - sin(T*x/π)]
-
-T0=Tk=0.
-    u0=uk=one(x)
-
-    (T,u)=(T0,u0)
-    for k=1:4
-        global T,u
-        J1=jacobian.(N(DualFun(Fun(T)),u))
-        J2=jacobian.(N(T,DualFun(u)))
-        J=Operator[J1 J2]
-        Tk,uk=J\N(T,u)
-        @show Tk
-        T,u=T-Number(Tk),u-uk
-    end
-
-
-plot(u)
-norm(N(T,u)[end])  #9e-17
diff --git a/ODEs/Multivariate_newton_iteration.jl b/ODEs/Multivariate_newton_iteration.jl
new file mode 100644
index 0000000..87c453d
--- /dev/null
+++ b/ODEs/Multivariate_newton_iteration.jl
@@ -0,0 +1,23 @@
+using ApproxFun, DualNumbers, LinearAlgebra, Plots
+import ApproxFun: DualFun, jacobian
+
+x=Fun(-π..π)
+N = (T,u) -> [u(-π)-1;u(π)-1;u'(π)-1;u'' - u - sin(T*x/π)]
+
+T0=Tk=0.
+u0=uk=one(x)
+
+(T,u)=(T0,u0)
+for k=1:4
+    global T,u
+    J1=jacobian.(N(DualFun(Fun(T)),u))
+    J2=jacobian.(N(T,DualFun(u)))
+    J=Operator[J1 J2]
+    Tk,uk=J\N(T,u)
+    @show Tk
+    T,u=T-Number(Tk),u-uk
+end
+
+
+plot(u)
+norm(N(T,u)[end])  #9e-17
diff --git a/ODEs/Newton iteration for Nonlinear BVPs.jl b/ODEs/Newton_iteration_for_Nonlinear_BVPs.jl
similarity index 100%
rename from ODEs/Newton iteration for Nonlinear BVPs.jl
rename to ODEs/Newton_iteration_for_Nonlinear_BVPs.jl
diff --git a/ODEs/Picard Iteration.jl b/ODEs/Picard_Iteration.jl
similarity index 100%
rename from ODEs/Picard Iteration.jl
rename to ODEs/Picard_Iteration.jl
diff --git a/ODEs/Quantum states.jl b/ODEs/Quantum_states.jl
similarity index 76%
rename from ODEs/Quantum states.jl
rename to ODEs/Quantum_states.jl
index 579e938..968955f 100644
--- a/ODEs/Quantum states.jl	
+++ b/ODEs/Quantum_states.jl
@@ -8,7 +8,7 @@ B = [Dirichlet(S); continuity(S,0:1)]
 λ, v = ApproxFun.eigs(B, -D^2 + V, 500,tolerance=1E-10)
 
 p = plot(V; legend=false)
-    for k=1:20
-        plot!(real(v[k]/norm(v[k]) + λ[k]))
-    end
-    p
+for k=1:20
+    plot!(real(v[k]/norm(v[k]) + λ[k]))
+end
+p
diff --git a/ODEs/Tenth order ODE.jl b/ODEs/Tenth_order_ODE.jl
similarity index 100%
rename from ODEs/Tenth order ODE.jl
rename to ODEs/Tenth_order_ODE.jl
diff --git a/PDEs/Heat equation.jl b/PDEs/Heat_equation.jl
similarity index 91%
rename from PDEs/Heat equation.jl
rename to PDEs/Heat_equation.jl
index 694080c..c02270d 100644
--- a/PDEs/Heat equation.jl	
+++ b/PDEs/Heat_equation.jl
@@ -22,8 +22,8 @@ n = 10
 # sub block is identity
 #
 B = [ldirichlet(S); rneumann(S)]
-    B = B[1:2,1:n]
-    B = B[1:2,1:2]\B
+B = B[1:2,1:n]
+B = B[1:2,1:2]\B
 
 
 
@@ -53,8 +53,8 @@ L = D^2
 
 
 M = C[1:n-2,1:n]
-    M = M - M[:,1:2]*B # remove degrees of freedom
-    A = M[:,3:end]\L[1:n-2,3:n]
+M = M - M[:,1:2]*B # remove degrees of freedom
+A = M[:,3:end]\L[1:n-2,3:n]
 
 
 u₀ = Fun(exp, S)
diff --git a/PDEs/Heat equation image method.jl b/PDEs/Heat_equation_image_method.jl
similarity index 95%
rename from PDEs/Heat equation image method.jl
rename to PDEs/Heat_equation_image_method.jl
index f130f26..6cf99e9 100644
--- a/PDEs/Heat equation image method.jl	
+++ b/PDEs/Heat_equation_image_method.jl
@@ -1,4 +1,4 @@
-using ApproxFun, LinearAlgebra, Plots
+using ApproxFun, LinearAlgebra, Plots, Interact
 
 # In this example, we solve the heat equation with left Neumann and
 # right Dirichlet boundary conditions.
@@ -35,7 +35,7 @@ f = Fun(S, c₀) # Likely not a good approximation due to boundary discontinuiti
    scatter!(xarray, u₀, markersize=0.2)
 
 L = Derivative(S, 2)
-   L = Matrix(L[1:2m,1:2m])
+L = Matrix(L[1:2m,1:2m])
 
 @manipulate for T=[zeros(5); 0.0:0.01:1]
    u_t = Fun(S, exp(T*L)*c₀) + r # calculate solution and add back homogenisation term
diff --git a/PDEs/Linear KdV.jl b/PDEs/Linear_KdV.jl
similarity index 100%
rename from PDEs/Linear KdV.jl
rename to PDEs/Linear_KdV.jl
diff --git a/PDEs/Manipulate ODEs and PDEs.ipynb b/PDEs/Manipulate_ODEs_and_PDEs.ipynb
similarity index 100%
rename from PDEs/Manipulate ODEs and PDEs.ipynb
rename to PDEs/Manipulate_ODEs_and_PDEs.ipynb
diff --git a/PDEs/Periodic PDEs.ipynb b/PDEs/Periodic_PDEs.ipynb
similarity index 100%
rename from PDEs/Periodic PDEs.ipynb
rename to PDEs/Periodic_PDEs.ipynb
diff --git a/PDEs/Rectangle PDEs.ipynb b/PDEs/Rectangle_PDEs.ipynb
similarity index 100%
rename from PDEs/Rectangle PDEs.ipynb
rename to PDEs/Rectangle_PDEs.ipynb
diff --git a/Project.toml b/Project.toml
index 51b4db2..c2ecf89 100644
--- a/Project.toml
+++ b/Project.toml
@@ -7,7 +7,24 @@ version = "0.1.0"
 ApproxFun = "28f2ccd6-bb30-5033-b560-165f7b14dc2f"
 BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
 BlockBandedMatrices = "ffab5731-97b5-5995-9138-79e8c1846df0"
+DualNumbers = "fa6b7ba4-c1ee-5f82-b5fc-ecf0adba8f74"
+Interact = "c601a237-2ae4-5e1e-952c-7a85b0c7eef1"
 LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
+RandomMatrices = "2576dda1-a324-5b11-aa66-c48ed7e3c618"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
+
+[compat]
+ApproxFun = "0.13"
+BandedMatrices = "0.17"
+BlockBandedMatrices = "0.11"
+DualNumbers = "0.6"
+Interact = "0.10"
+LazyArrays = "0.22"
+Plots = "1"
+PyPlot = "2"
+RandomMatrices = "0.5"
+SpecialFunctions = "1, 2"
+julia = "1.7"