New submodule dirac

This commit is contained in:
Fabian Joswig 2021-10-21 09:07:04 +01:00
parent b5c9738d52
commit e7efa822b0
4 changed files with 65 additions and 61 deletions

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@ -1,5 +1,6 @@
from .pyerrors import *
from . import correlators
from . import dirac
from . import fits
from . import linalg
from . import misc

61
pyerrors/dirac.py Normal file
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@ -0,0 +1,61 @@
import numpy as np
gammaX = np.array(
[[0, 0, 0, 1j], [0, 0, 1j, 0], [0, -1j, 0, 0], [-1j, 0, 0, 0]],
dtype=complex)
gammaY = np.array(
[[0, 0, 0, -1], [0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0]],
dtype=complex)
gammaZ = np.array(
[[0, 0, 1j, 0], [0, 0, 0, -1j], [-1j, 0, 0, 0], [0, 1j, 0, 0]],
dtype=complex)
gammaT = np.array(
[[0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0]],
dtype=complex)
gamma = np.array([gammaX, gammaY, gammaZ, gammaT])
gamma5 = np.array(
[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, -1, 0], [0, 0, 0, -1]],
dtype=complex)
identity = np.array(
[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
dtype=complex)
def Grid_gamma(gamma_tag):
"""Returns gamma matrix in Grid labeling."""
if gamma_tag == 'Identity':
g = identity
elif gamma_tag == 'Gamma5':
g = gamma5
elif gamma_tag == 'GammaX':
g = gamma[0]
elif gamma_tag == 'GammaY':
g = gamma[1]
elif gamma_tag == 'GammaZ':
g = gamma[2]
elif gamma_tag == 'GammaT':
g = gamma[3]
elif gamma_tag == 'GammaXGamma5':
g = gamma[0] @ gamma5
elif gamma_tag == 'GammaYGamma5':
g = gamma[1] @ gamma5
elif gamma_tag == 'GammaZGamma5':
g = gamma[2] @ gamma5
elif gamma_tag == 'GammaTGamma5':
g = gamma[3] @ gamma5
elif gamma_tag == 'SigmaXT':
g = 0.5 * (gamma[0] @ gamma[3] - gamma[3] @ gamma[0])
elif gamma_tag == 'SigmaXY':
g = 0.5 * (gamma[0] @ gamma[1] - gamma[1] @ gamma[0])
elif gamma_tag == 'SigmaXZ':
g = 0.5 * (gamma[0] @ gamma[2] - gamma[2] @ gamma[0])
elif gamma_tag == 'SigmaYT':
g = 0.5 * (gamma[1] @ gamma[3] - gamma[3] @ gamma[1])
elif gamma_tag == 'SigmaYZ':
g = 0.5 * (gamma[1] @ gamma[2] - gamma[2] @ gamma[1])
elif gamma_tag == 'SigmaZT':
g = 0.5 * (gamma[2] @ gamma[3] - gamma[3] @ gamma[2])
else:
raise Exception('Unkown gamma structure', gamma_tag)
return g

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@ -2,70 +2,12 @@ import warnings
import numpy as np
import autograd.numpy as anp
from .linalg import mat_mat_op
from .dirac import gamma, gamma5
L = None
T = None
gammaX = np.array(
[[0, 0, 0, 1j], [0, 0, 1j, 0], [0, -1j, 0, 0], [-1j, 0, 0, 0]],
dtype=complex)
gammaY = np.array(
[[0, 0, 0, -1], [0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0]],
dtype=complex)
gammaZ = np.array(
[[0, 0, 1j, 0], [0, 0, 0, -1j], [-1j, 0, 0, 0], [0, 1j, 0, 0]],
dtype=complex)
gammaT = np.array(
[[0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0]],
dtype=complex)
gamma = np.array([gammaX, gammaY, gammaZ, gammaT])
gamma5 = np.array(
[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, -1, 0], [0, 0, 0, -1]],
dtype=complex)
identity = np.array(
[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
dtype=complex)
def Grid_gamma(gamma_tag):
"""Returns gamma matrix in Grid labeling."""
if gamma_tag == 'Identity':
g = identity
elif gamma_tag == 'Gamma5':
g = gamma5
elif gamma_tag == 'GammaX':
g = gamma[0]
elif gamma_tag == 'GammaY':
g = gamma[1]
elif gamma_tag == 'GammaZ':
g = gamma[2]
elif gamma_tag == 'GammaT':
g = gamma[3]
elif gamma_tag == 'GammaXGamma5':
g = gamma[0] @ gamma5
elif gamma_tag == 'GammaYGamma5':
g = gamma[1] @ gamma5
elif gamma_tag == 'GammaZGamma5':
g = gamma[2] @ gamma5
elif gamma_tag == 'GammaTGamma5':
g = gamma[3] @ gamma5
elif gamma_tag == 'SigmaXT':
g = 0.5 * (gamma[0] @ gamma[3] - gamma[3] @ gamma[0])
elif gamma_tag == 'SigmaXY':
g = 0.5 * (gamma[0] @ gamma[1] - gamma[1] @ gamma[0])
elif gamma_tag == 'SigmaXZ':
g = 0.5 * (gamma[0] @ gamma[2] - gamma[2] @ gamma[0])
elif gamma_tag == 'SigmaYT':
g = 0.5 * (gamma[1] @ gamma[3] - gamma[3] @ gamma[1])
elif gamma_tag == 'SigmaYZ':
g = 0.5 * (gamma[1] @ gamma[2] - gamma[2] @ gamma[1])
elif gamma_tag == 'SigmaZT':
g = 0.5 * (gamma[2] @ gamma[3] - gamma[3] @ gamma[2])
else:
raise Exception('Unkown gamma structure', gamma_tag)
return g
class Npr_matrix(np.ndarray):

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@ -6,7 +6,7 @@ np.random.seed(0)
def test_gamma_matrices():
for matrix in pe.npr.gamma:
for matrix in pe.dirac.gamma:
assert np.allclose(matrix @ matrix, np.identity(4))
assert np.allclose(matrix, matrix.T.conj())
assert np.allclose(pe.npr.gamma5, pe.npr.gamma[0] @ pe.npr.gamma[1] @ pe.npr.gamma[2] @ pe.npr.gamma[3])
assert np.allclose(pe.dirac.gamma5, pe.dirac.gamma[0] @ pe.dirac.gamma[1] @ pe.dirac.gamma[2] @ pe.dirac.gamma[3])