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added attribute sigma to zero within error, npr module improved

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This commit is contained in:
Fabian Joswig 2021-10-20 13:12:58 +01:00
parent f95123bd18
commit e45f330e9d
2 changed files with 14 additions and 10 deletions
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16
pyerrors/npr.py
View file

@ -1,23 +1,23 @@
import numpy as np
gamma = [0, 0, 0, 0]
gamma[0] = np.array(
gammaX = np.array(
[[0, 0, 0, 1j], [0, 0, 1j, 0], [0, -1j, 0, 0], [-1j, 0, 0, 0]],
dtype=complex)
gamma[1] = np.array(
gammaY = np.array(
[[0, 0, 0, -1], [0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0]],
dtype=complex)
gamma[2] = np.array(
gammaZ = np.array(
[[0, 0, 1j, 0], [0, 0, 0, -1j], [-1j, 0, 0, 0], [0, 1j, 0, 0]],
dtype=complex)
gamma[3] = np.array(
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)
imat = np.array(
identity = np.array(
[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
dtype=complex)
@ -25,7 +25,7 @@ imat = np.array(
def Grid_gamma(gamma_tag):
"""Returns gamma matrix in Grid labeling."""
if gamma_tag == 'Identity':
g = imat
g = identity
elif gamma_tag == 'Gamma5':
g = gamma5
elif gamma_tag == 'GammaX':
@ -87,7 +87,7 @@ class Npr_matrix(np.ndarray):
def _propagate_mom(self, other, name):
s_mom = getattr(self, name, None)
o_mom = getattr(other, name, None)
if s_mom and o_mom:
if s_mom is not None and o_mom is not None:
if not np.allclose(s_mom, o_mom):
raise Exception(name + ' does not match.')
return o_mom if o_mom else s_mom

8
pyerrors/pyerrors.py
View file

@ -337,8 +337,12 @@ class Obs:
for e_name in self.e_names:
print(e_name, ':', self.e_content[e_name])
def is_zero_within_error(self):
return np.abs(self.value) <= self.dvalue
def is_zero_within_error(self, sigma=1):
""" Checks whether the observable is zero within 'sigma' standard errors.
Works only properly when the gamma method was run.
"""
return np.abs(self.value) <= sigma * self.dvalue
def is_zero(self):
return np.isclose(0.0, self.value) and all(np.allclose(0.0, delta) for delta in self.deltas.values())