Implement ren sv and apply to massive functions

project/theory_cards/1_20mur.yaml

@@ -0,0 +1,6 @@
+order: 2
+fns: "our"
+grids: False
+mass: 4.92
+mur_ratio: 2.0
+muf_ratio: 1.0

src/dis_tp/MassiveCoeffFunc.py

@@ -5,11 +5,11 @@
 from eko.constants import TR
 from scipy.integrate import quad
 
-from . import Initialize
+from . import Initialize, scale_variations
 
 
 # F2
-def Cb_2_m_reg(z, Q, p, _nf):
+def Cb_2_m_reg(z, Q, p, _nf, mur_ratio=1.0):
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
@@ -21,20 +21,22 @@ def Cb_2_m_reg(z, Q, p, _nf):
     eta = xi / 4.0 * (1.0 / z - 1.0) - 1.0
     eta = min(eta, 1e5)
     FHprefactor = Q2 / (np.pi * m_b**2) * e_h**2
-    return FHprefactor / z * (4.0 * np.pi) ** 2 * LeProHQ.dq1("F2", "VV", xi, eta)
+    bare_res = FHprefactor / z * (4.0 * np.pi) ** 2 * LeProHQ.dq1("F2", "VV", xi, eta)
+    return scale_variations.apply_rensv_kernel(0, 2, [bare_res], mur_ratio, _nf)
 
 
-def Cb_2_m_loc(_z, Q, p, _nf):
+def Cb_2_m_loc(_z, Q, p, _nf, mur_ratio=1.0):
     l = quad(
         lambda x: Cb_2_m_reg(x, Q, p, _nf),
         0.0,
         1.0,
         points=(0.0, 1.0),
     )
-    return -l[0]
+    bare_res = -l[0]
+    return scale_variations.apply_rensv_kernel(0, 2, [bare_res], mur_ratio, _nf)
 
 
-def Cg_1_m_reg(z, Q, p, _nf):
+def Cg_1_m_reg(z, Q, p, _nf, mur_ratio=1.0):
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
@@ -43,7 +45,7 @@ def Cg_1_m_reg(z, Q, p, _nf):
     if thre > 1.0:
         return 0
     v = np.sqrt(1 - thre)
-    return (
+    bare_res = (
         4
         * TR
         * e_h
@@ -54,9 +56,10 @@ def Cg_1_m_reg(z, Q, p, _nf):
             * (z * z + (1 - z) ** 2 + 4 * z * eps * (1 - 3 * z) - 8 * z * z * eps * eps)
         )
     )
+    return scale_variations.apply_rensv_kernel(0, 1, [bare_res], mur_ratio, _nf)
 
 
-def Cg_2_m_reg(z, Q, p, _nf):
+def Cg_2_m_reg(z, Q, p, _nf, mur_ratio=1.0):
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b**2 / Q2
@@ -70,7 +73,7 @@ def Cg_2_m_reg(z, Q, p, _nf):
     if xi > 2499.9999999999995:
         # FH grids are not defined above this
         return 0.0
-    return (
+    bare_res = (
         FHprefactor
         / z
         * (4.0 * np.pi) ** 2
@@ -79,20 +82,33 @@ def Cg_2_m_reg(z, Q, p, _nf):
             + LeProHQ.cgBar1("F2", "VV", xi, eta) * np.log(xi)
         )
     )
+    return scale_variations.apply_rensv_kernel(
+        1, 1, [bare_res, Cg_1_m_reg(z, Q, p, _nf, mur_ratio=1.0)], mur_ratio, _nf
+    )
 
 
-def Cg_3_m_reg(z, Q, p, nf):
+def Cg_3_m_reg(z, Q, p, nf, mur_ratio=1.0):
     e_h = p[-1]
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
     thre = 4.0 * eps * z / (1 - z)
     if thre > 1.0:
         return 0.0
-    return e_h**2 * Initialize.Cg3m[nf - 4](z, Q)[0]
+    bare_res = e_h**2 * Initialize.Cg3m[nf - 4](z, Q)[0]
+    return scale_variations.apply_rensv_kernel(
+        2,
+        1,
+        [
+            bare_res,
+            Cg_2_m_reg(z, Q, p, nf, mur_ratio=1.0),
+            Cg_1_m_reg(z, Q, p, nf, mur_ratio=1.0),
+        ],
+        mur_ratio,
+    )
 
 
-def Cq_2_m_reg(z, Q, p, _nf):
+def Cq_2_m_reg(z, Q, p, _nf, mur_ratio=1.0):
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
@@ -103,7 +119,7 @@ def Cq_2_m_reg(z, Q, p, _nf):
     xi = 1 / eps
     eta = xi / 4.0 * (1.0 / z - 1.0) - 1.0
     FHprefactor = Q2 / (np.pi * m_b**2) * e_h**2
-    return (
+    bare_res = (
         FHprefactor
         / z
         * (4.0 * np.pi) ** 2
@@ -112,21 +128,25 @@ def Cq_2_m_reg(z, Q, p, _nf):
             + LeProHQ.cqBarF1("F2", "VV", xi, eta) * np.log(xi)
         )
     )
+    return scale_variations.apply_rensv_kernel(0, 2, [bare_res], mur_ratio, _nf)
 
 
-def Cq_3_m_reg(z, Q, p, nf):
+def Cq_3_m_reg(z, Q, p, nf, mur_ratio=1.0):
     e_h = p[-1]
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
     thre = 4.0 * eps * z / (1 - z)
     if thre > 1.0:
         return 0.0
-    return e_h**2 * Initialize.Cq3m[nf - 4](z, Q)[0]
+    bare_res = e_h**2 * Initialize.Cq3m[nf - 4](z, Q)[0]
+    return scale_variations.apply_rensv_kernel(
+        1, 2, [bare_res, Cq_2_m_reg(z, Q, p, nf, mur_ratio=1.0)], mur_ratio, nf
+    )
 
 
 # FL
-def CLb_2_m_reg(z, Q, p, _nf):
+def CLb_2_m_reg(z, Q, p, _nf, mur_ratio=1.0):
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
@@ -137,10 +157,11 @@ def CLb_2_m_reg(z, Q, p, _nf):
     xi = 1 / eps
     eta = xi / 4.0 * (1.0 / z - 1.0) - 1.0
     FHprefactor = Q2 / (np.pi * m_b**2) * e_h**2
-    return FHprefactor / z * (4.0 * np.pi) ** 2 * LeProHQ.dq1("FL", "VV", xi, eta)
+    bare_res = FHprefactor / z * (4.0 * np.pi) ** 2 * LeProHQ.dq1("FL", "VV", xi, eta)
+    return scale_variations.apply_rensv_kernel(0, 2, [bare_res], mur_ratio, _nf)
 
 
-def CLg_1_m_reg(z, Q, p, _nf):
+def CLg_1_m_reg(z, Q, p, _nf, mur_ratio=1.0):
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
@@ -150,16 +171,17 @@ def CLg_1_m_reg(z, Q, p, _nf):
     if thre > 1.0:
         return 0
     v = np.sqrt(1 - thre)
-    return (
+    bare_res = (
         4
         * TR
         * e_h
         * e_h
         * (-8 * eps * z2 * np.log((1 + v) / (1 - v)) + 4 * v * z * (1 - z))
     )
+    return scale_variations.apply_rensv_kernel(0, 1, [bare_res], mur_ratio, _nf)
 
 
-def CLg_2_m_reg(z, Q, p, _nf):
+def CLg_2_m_reg(z, Q, p, _nf, mur_ratio=1.0):
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
@@ -173,7 +195,7 @@ def CLg_2_m_reg(z, Q, p, _nf):
     if xi > 2499.9999999999995:
         # FH grids are not defined above this
         return 0.0
-    return (
+    bare_res = (
         FHprefactor
         / z
         * (4.0 * np.pi) ** 2
@@ -182,20 +204,34 @@ def CLg_2_m_reg(z, Q, p, _nf):
             + LeProHQ.cgBar1("FL", "VV", xi, eta) * np.log(xi)
         )
     )
+    return scale_variations.apply_rensv_kernel(
+        1, 1, [bare_res, CLg_1_m_reg(z, Q, p, _nf, mur_ratio=1.0)], mur_ratio, _nf
+    )
 
 
-def CLg_3_m_reg(z, Q, p, nf):
+def CLg_3_m_reg(z, Q, p, nf, mur_ratio=1.0):
     e_h = p[-1]
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
     thre = 4.0 * eps * z / (1 - z)
     if thre > 1.0:
         return 0
-    return e_h**2 * Initialize.CLg3m[nf - 4](z, Q)[0]
+    bare_res = e_h**2 * Initialize.CLg3m[nf - 4](z, Q)[0]
+    return scale_variations.apply_rensv_kernel(
+        2,
+        1,
+        [
+            bare_res,
+            CLg_2_m_reg(z, Q, p, nf, mur_ratio=1.0),
+            CLg_1_m_reg(z, Q, p, nf, mur_ratio=1.0),
+        ],
+        mur_ratio,
+        nf,
+    )
 
 
-def CLq_2_m_reg(z, Q, p, _nf):
+def CLq_2_m_reg(z, Q, p, _nf, mur_ratio=1.0):
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
@@ -206,7 +242,7 @@ def CLq_2_m_reg(z, Q, p, _nf):
     xi = 1 / eps
     eta = xi / 4.0 * (1.0 / z - 1.0) - 1.0
     FHprefactor = Q2 / (np.pi * m_b**2) * e_h**2
-    return (
+    bare_res = (
         FHprefactor
         / z
         * (4.0 * np.pi) ** 2
@@ -215,14 +251,18 @@ def CLq_2_m_reg(z, Q, p, _nf):
             + LeProHQ.cqBarF1("FL", "VV", xi, eta) * np.log(xi)
         )
     )
+    return scale_variations.apply_rensv_kernel(0, 2, [bare_res], mur_ratio, _nf)
 
 
-def CLq_3_m_reg(z, Q, p, nf):
+def CLq_3_m_reg(z, Q, p, nf, mur_ratio=1.0):
     e_h = p[-1]
     Q2 = Q * Q
     m_b = p[0]
     eps = m_b * m_b / Q2
     thre = 4.0 * eps * z / (1 - z)
     if thre > 1.0:
         return 0.0
-    return e_h**2 * Initialize.CLq3m[nf - 4](z, Q)[0]
+    bare_res = e_h**2 * Initialize.CLq3m[nf - 4](z, Q)[0]
+    return scale_variations.apply_rensv_kernel(
+        1, 2, [bare_res, CLq_2_m_reg(z, Q, p, nf, mur_ratio=1.0)], mur_ratio, nf
+    )