custom CG coef, faster

This commit is contained in:
Ryan Tang 2025-02-20 20:04:06 -05:00
parent 05204a6934
commit 5313494e9c
3 changed files with 119 additions and 24 deletions

View File

@ -5,22 +5,22 @@ from boundState import BoundState
from solveSE import WoodsSaxonPot, CoulombPotential, SpinOrbit_Pot, WS_SurfacePot
import matplotlib.pyplot as plt
boundState = BoundState(16, 8, 1, 0, 1, 0, 0.5, -3.273)
boundState.SetPotential(1.25, 0.65, -6, 1.10, 0.65, 1.30)
boundState.FindPotentialDepth(-75, -40, 0.1)
# boundState.PrintWF()
boundState.PlotBoundState()
# boundState = BoundState(16, 8, 1, 0, 1, 0, 0.5, -3.273)
# boundState.SetPotential(1.25, 0.65, -6, 1.10, 0.65, 1.30)
# boundState.FindPotentialDepth(-75, -40, 0.1)
# # boundState.PrintWF()
# boundState.PlotBoundState()
exit()
# exit()
from distortedWave import DistortedWave
# dw = DistortedWave("60Ni", "p", 30)
# dw.ClearPotential()
# dw.AddPotential(WoodsSaxonPot(-47.937-2.853j, 1.20, 0.669), False)
# dw.AddPotential(WS_SurfacePot(-6.878j, 1.28, 0.550), False)
# dw.AddPotential(SpinOrbit_Pot(-5.250 + 0.162j, 1.02, 0.590), False)
# dw.AddPotential(CoulombPotential(1.258), False)
dw = DistortedWave("60Ni", "p", 30)
dw.ClearPotential()
dw.AddPotential(WoodsSaxonPot(-47.937-2.853j, 1.20, 0.669), False)
dw.AddPotential(WS_SurfacePot(-6.878j, 1.28, 0.550), False)
dw.AddPotential(SpinOrbit_Pot(-5.250 + 0.162j, 1.02, 0.590), False)
dw.AddPotential(CoulombPotential(1.258), False)
# dw = DistortedWave("60Ni", "d", 60)
# dw.PrintInput()
@ -32,10 +32,12 @@ from distortedWave import DistortedWave
# dw.AddPotential(CoulombPotential(1.303), False)
# dw.CalScatteringMatrix()
dw.CalScatteringMatrix()
# dw.PrintScatteringMatrix()
# dw.PlotDCSUnpolarized(180, 1)
dw.PlotDCSUnpolarized(180, 1)
exit()
# for i in range(1, 19):
# theta = 10*i

89
Raphael/clebschGordan.py Executable file
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@ -0,0 +1,89 @@
#!/usr/bin/env python3
import numpy as np
from scipy.special import gamma
# from sympy.physics.quantum.cg import CG
# from sympy import S
# def clebsch_gordan(j1, m1, j2, m2, j, m):
# cg = CG(S(j1), S(m1), S(j2), S(m2), S(j), S(m))
# result = cg.doit()
# return np.complex128(result)
import numpy as np
from math import sqrt
def quantum_factorial(n):
"""
Calculate factorial for integer or half-integer numbers using gamma function.
For integer n: n! = n * (n-1) * ... * 1
For half-integer n: n! = Γ(n + 1)
"""
if n < 0:
return 0.0
return gamma(n + 1)
def clebsch_gordan(j1, m1,j2, m2, j, m):
"""
Calculate Clebsch-Gordan coefficient <j1 m1 j2 m2 | j m>
Parameters:
j1, j2: angular momentum quantum numbers
m1, m2: magnetic quantum numbers
j: total angular momentum quantum number
m: total magnetic quantum number
Returns:
float: Clebsch-Gordan coefficient value
"""
# Check validity of inputs using triangular inequalities and conservation
if not np.isclose(m, m1 + m2, atol=1e-10):
return 0.0
if abs(m1) > j1 or abs(m2) > j2 or abs(m) > j:
return 0.0
if not (abs(j1 - j2) <= j <= j1 + j2):
return 0.0
if j1 < 0 or j2 < 0 or j < 0:
return 0.0
# Ensure all quantum numbers are either integer or half-integer
if not (np.mod(2*j1, 1) < 1e-10 or np.isclose(np.mod(2*j1, 1), 1, atol=1e-10)):
return 0.0
if not (np.mod(2*j2, 1) < 1e-10 or np.isclose(np.mod(2*j2, 1), 1, atol=1e-10)):
return 0.0
if not (np.mod(2*j, 1) < 1e-10 or np.isclose(np.mod(2*j, 1), 1, atol=1e-10)):
return 0.0
# Calculate the coefficient
prefactor = sqrt((2*j + 1) * quantum_factorial(j1 + j2 - j) *
quantum_factorial(j1 - j2 + j) *
quantum_factorial(-j1 + j2 + j) /
quantum_factorial(j1 + j2 + j + 1))
prefactor *= sqrt(quantum_factorial(j + m) * quantum_factorial(j - m) *
quantum_factorial(j1 - m1) * quantum_factorial(j1 + m1) *
quantum_factorial(j2 - m2) * quantum_factorial(j2 + m2))
# Sum over k
sum_result = 0.0
k_min = max(0, max(j2 - j - m1, j1 + m2 - j))
k_max = min(j1 + j2 - j, min(j1 - m1, j2 + m2))
# Ensure k_min and k_max are integers for the range
k_min = int(np.ceil(k_min))
k_max = int(np.floor(k_max))
for k in range(k_min, k_max + 1):
denominator = (quantum_factorial(k) *
quantum_factorial(j1 + j2 - j - k) *
quantum_factorial(j1 - m1 - k) *
quantum_factorial(j2 + m2 - k) *
quantum_factorial(j - j2 + m1 + k) *
quantum_factorial(j - j1 - m2 + k))
if np.isclose(denominator, 0, atol=1e-10):
continue
term = (-1)**k / denominator
sum_result += term
return prefactor * sum_result

View File

@ -17,12 +17,12 @@ def SevenPointsSlope(data, n):
def FivePointsSlope(data, n):
return ( data[n + 2] - 8 * data[n + 1] + 8 * data[n - 1] - data[n - 2] ) / 12
from sympy.physics.quantum.cg import CG
from sympy import S
def clebsch_gordan(j1, m1, j2, m2, j, m):
cg = CG(S(j1), S(m1), S(j2), S(m2), S(j), S(m))
result = cg.doit()
return np.complex128(result)
# from sympy.physics.quantum.cg import CG
# from sympy import S
# def clebsch_gordan(j1, m1, j2, m2, j, m):
# cg = CG(S(j1), S(m1), S(j2), S(m2), S(j), S(m))
# result = cg.doit()
# return np.complex128(result)
def KroneckerDelta(i, j):
if i == j:
@ -30,6 +30,8 @@ def KroneckerDelta(i, j):
else:
return 0
from clebschGordan import clebsch_gordan
############################################################
class DistortedWave(SolvingSE):
def __init__(self, target, projectile, ELab):
@ -181,8 +183,9 @@ class DistortedWave(SolvingSE):
value = 0
for J in np.arange(Jmin, Jmax + 1, 1):
index = int(J - Jmin)
value += clebsch_gordan(l, 0, self.S, v0, J, v0) * clebsch_gordan(l, v0-v, self.S, v, J, v0) * self.ScatMatrix[l][index]
cg1 = clebsch_gordan(l, 0, self.S, v0, J, v0)
cg2 = clebsch_gordan(l, v0 - v, self.S, v, J, v0)
value += cg1 * cg2 * self.ScatMatrix[l][index]
return value - KroneckerDelta(v, v0)
def CalLegendre(self, theta_deg, maxL = None, maxM = None):
@ -224,7 +227,7 @@ class DistortedWave(SolvingSE):
value = value / (2 * self.S + 1)
return value
def PlotDCSUnpolarized(self, thetaRange = 180, thetaStepDeg = 0.2, maxL = None):
def PlotDCSUnpolarized(self, thetaRange = 180, thetaStepDeg = 0.2, maxL = None, verbose = False):
theta_values = np.linspace(0, thetaRange, int(thetaRange/thetaStepDeg)+1)
thetaTick = 30
@ -237,6 +240,7 @@ class DistortedWave(SolvingSE):
y_values.append(1)
else:
y_values.append(self.DCSUnpolarized(theta, maxL)/ self.RutherFord(theta))
if verbose :
print(f"{theta:6.2f}, {y_values[-1]:10.6f}")
plt.figure(figsize=(8, 6))