Update test_monic.jl

Author: dlfivefifty

test/test_monic.jl

@@ -2,57 +2,60 @@ using ClassicalOrthogonalPolynomials
 using Test
 using ClassicalOrthogonalPolynomials: Monic, _p0, orthogonalityweight, recurrencecoefficients
 
-@testset "Basic definition" begin
-    P1 = Legendre()
-    P2 = Normalized(P1)
-    P3 = Monic(P1)
-    @test P3.P == P2
-    @test Monic(P3) === P3
-    @test axes(P3) == axes(Legendre())
-    @test Normalized(P3) === P3.P
-    @test _p0(P3) == 1
-    @test orthogonalityweight(P3) == orthogonalityweight(P1)
-    @test sprint(show, MIME"text/plain"(), P3) == "Monic(Legendre())"
-end
+@testset "Monic" begin
+    @testset "Basic definition" begin
+        P1 = Legendre()
+        P2 = Normalized(P1)
+        P3 = Monic(P1)
+        @test P3.P == P2
+        @test Monic(P3) === P3
+        @test axes(P3) == axes(Legendre())
+        @test Normalized(P3) === P3.P
+        @test _p0(P3) == 1
+        @test orthogonalityweight(P3) == orthogonalityweight(P1)
+        @test sprint(show, MIME"text/plain"(), P3) == "Monic(Legendre())"
+    end
 
-@testset "evaluation" begin
-    function _pochhammer(x, n)
-        y = one(x)
-        for i in 0:(n-1)
-            y *= (x + i)
+    @testset "evaluation" begin
+        function _pochhammer(x, n)
+            y = one(x)
+            for i in 0:(n-1)
+                y *= (x + i)
+            end
+            return y
+        end
+        jacobi_kn = (α, β, n) -> _pochhammer(n + α + β + 1, n) / (2.0^n * factorial(n))
+        ultra_kn = (λ, n) -> 2^n * _pochhammer(λ, n) / factorial(n)
+        chebt_kn = n -> n == 0 ? 1.0 : 2.0 .^ (n - 1)
+        chebu_kn = n -> 2.0^n
+        leg_kn = n -> 2.0^n * _pochhammer(1 / 2, n) / factorial(n)
+        lag_kn = n -> (-1)^n / factorial(n)
+        herm_kn = n -> 2.0^n
+        _Jacobi(α, β, x, n) = Jacobi(α, β)[x, n+1] / jacobi_kn(α, β, n)
+        _Ultraspherical(λ, x, n) = Ultraspherical(λ)[x, n+1] / ultra_kn(λ, n)
+        _ChebyshevT(x, n) = ChebyshevT()[x, n+1] / chebt_kn(n)
+        _ChebyshevU(x, n) = ChebyshevU()[x, n+1] / chebu_kn(n)
+        _Legendre(x, n) = Legendre()[x, n+1] / leg_kn(n)
+        _Laguerre(α, x, n) = Laguerre(α)[x, n+1] / lag_kn(n)
+        _Hermite(x, n) = Hermite()[x, n+1] / herm_kn(n)
+        Ps = [
+            Jacobi(2.0, 5.0) (x, n)->_Jacobi(2.0, 5.0, x, n)
+            Ultraspherical(1.7) (x, n)->_Ultraspherical(1.7, x, n)
+            ChebyshevT() _ChebyshevT
+            ChebyshevU() _ChebyshevU
+            Legendre() _Legendre
+            Laguerre(1.5) (x, n)->_Laguerre(1.5, x, n)
+            Hermite() _Hermite
+        ]
+        for (P, _P) in eachrow(Ps)
+            @show P
+            Q = Monic(P)
+            @test Q[0.2, 1] == 1.0
+            @test Q[0.25, 2] ≈ _P(0.25, 1)
+            @test Q[0.17, 3] ≈ _P(0.17, 2)
+            @test Q[0.4, 17] ≈ _P(0.4, 16)
+            @test Q[0.9, 21] ≈ _P(0.9, 20)
+            # @inferred Q[0.2, 5] # no longer inferred
         end
-        return y
-    end
-    jacobi_kn = (α, β, n) -> _pochhammer(n + α + β + 1, n) / (2.0^n * factorial(n))
-    ultra_kn = (λ, n) -> 2^n * _pochhammer(λ, n) / factorial(n)
-    chebt_kn = n -> n == 0 ? 1.0 : 2.0 .^ (n - 1)
-    chebu_kn = n -> 2.0^n
-    leg_kn = n -> 2.0^n * _pochhammer(1 / 2, n) / factorial(n)
-    lag_kn = n -> (-1)^n / factorial(n)
-    herm_kn = n -> 2.0^n
-    _Jacobi(α, β, x, n) = Jacobi(α, β)[x, n+1] / jacobi_kn(α, β, n)
-    _Ultraspherical(λ, x, n) = Ultraspherical(λ)[x, n+1] / ultra_kn(λ, n)
-    _ChebyshevT(x, n) = ChebyshevT()[x, n+1] / chebt_kn(n)
-    _ChebyshevU(x, n) = ChebyshevU()[x, n+1] / chebu_kn(n)
-    _Legendre(x, n) = Legendre()[x, n+1] / leg_kn(n)
-    _Laguerre(α, x, n) = Laguerre(α)[x, n+1] / lag_kn(n)
-    _Hermite(x, n) = Hermite()[x, n+1] / herm_kn(n)
-    Ps = [
-        Jacobi(2.0, 5.0) (x, n)->_Jacobi(2.0, 5.0, x, n)
-        Ultraspherical(1.7) (x, n)->_Ultraspherical(1.7, x, n)
-        ChebyshevT() _ChebyshevT
-        ChebyshevU() _ChebyshevU
-        Legendre() _Legendre
-        Laguerre(1.5) (x, n)->_Laguerre(1.5, x, n)
-        Hermite() _Hermite
-    ]
-    for (P, _P) in eachrow(Ps)
-        Q = Monic(P)
-        @test Q[0.2, 1] == 1.0
-        @test Q[0.25, 2] ≈ _P(0.25, 1)
-        @test Q[0.17, 3] ≈ _P(0.17, 2)
-        @test Q[0.4, 17] ≈ _P(0.4, 16)
-        @test Q[0.9, 21] ≈ _P(0.9, 20)
-        # @inferred Q[0.2, 5] # no longer inferred
     end
 end