def setUp(self):
"""
Using (0.03, 0.03), (-1, -1) from 0.004 32c
"""
self.time_extent = 64
self.corr = [pp_flat(t, 1.0, 1.0, self.time_extent) for t in range(self.time_extent)]
self.corrs = [[pp_flat(t, 1.0, 1.0, self.time_extent) + random.normal(scale=1e-6)
for t in range(self.time_extent)] for _ in range(200)]
self.iwasaki_folder = os.path.join('testfiles', 'parse', 'f1')
self.fit_func = lambda t, m, c: pp_flat(t, m, c, self.time_extent)
def test_serialize_json(self):
data = [[pp_flat(t, 1.0, 1.0, self.time_extent) + random.normal(scale=1e-4)
for t in range(self.time_extent)] for _ in range(50)]
had = PseudoscalarMeson(data, masses=(0.01, 0.01))
j = had.json()
had2 = PseudoscalarMeson.from_json(j)
self.assertEqual(had2.masses, [0.01, 0.01])
def __init__(self, data=None, config_numbers=None, masses=None,
charges=None, **kwargs):
super(PseudoscalarChargedMeson, self).__init__(data, config_numbers,
masses, **kwargs)
self.charges = charges
self.fit_func = lambda t, c, m: pp_flat(t, c, m,
self.time_extent)
def test_fit_hadron(self):
data = [[pp_flat(t, 1.0, 1.0, self.time_extent) + random.normal(scale=1e-4)
for t in range(self.time_extent)] for _ in range(50)]
had = PseudoscalarMeson(data, masses=(0.01, 0.01))
fp = fit_hadron(had, fit_range=range(1, 32+1),
initial_value=[0.8, 1.5], covariant=False)
self.assertAlmostEqual(fp.average_params['m'], 1.0, 2)
def chi_sq(m, c):
ff = pp_flat(fit_range, m, c)
# pared_data = np.array([data[t] for t in fit_range])
v = np.array(ff - data)
m = np.array(inverse_covariance)
r = np.dot(m, v)
c2 = np.dot(v, r)
return c2 / len(fit_range)
def test_serialize_json(self):
data = [[
pp_flat(t, 1.0, 1.0, self.time_extent) + random.normal(scale=1e-4)
for t in range(self.time_extent)
] for _ in range(50)]
had = PseudoscalarMeson(data, masses=(0.01, 0.01))
j = had.json()
had2 = PseudoscalarMeson.from_json(j)
self.assertEqual(had2.masses, [0.01, 0.01])
def __init__(self,
data=None,
config_numbers=None,
masses=None,
charges=None,
**kwargs):
super(PseudoscalarChargedMeson, self).__init__(data, config_numbers,
masses, **kwargs)
self.charges = charges
self.fit_func = lambda t, c, m: pp_flat(t, c, m, self.time_extent)
def __init__(self, data=None, config_numbers=None, masses=None, **kwargs):
"""
Creates an uncharged pseudoscalar meson.
:param data: The measurements of the correlation functions.
:type data: list of floats
:param masses: The bare masses of the quarks of the meson.
:type masses: tuple of floats e.g. (0.01, 0.005)
"""
super(PseudoscalarMeson, self).__init__(data, config_numbers, masses,
**kwargs)
self.fit_func = lambda t, c, m: pp_flat(t, c, m, self.time_extent)
def __init__(self, data=None, config_numbers=None, masses=None, **kwargs):
"""
Creates an uncharged pseudoscalar meson.
:param data: The measurements of the correlation functions.
:type data: list of floats
:param masses: The bare masses of the quarks of the meson.
:type masses: tuple of floats e.g. (0.01, 0.005)
"""
super(PseudoscalarMeson, self).__init__(data, config_numbers, masses,
**kwargs)
self.fit_func = lambda t, c, m: pp_flat(t, c, m,
self.time_extent)
def setUp(self):
self.time_extent = 64
self.fit_func = lambda t, m, c: pp_flat(t, m, c, self.time_extent)
def setUp(self):
self.time_extent = 64
self.fit_func = lambda t, m, c: pp_flat(t, m, c, self.time_extent)
def chi_sq(m, c):
ff = pp_flat(fit_range, m, c)
return sum((data - ff)**2 / errors**2) / len(fit_range)
def test_make_correlator(self):
corr = [pp_flat(t, 1.0, 1.0, self.time_extent) for t in range(self.time_extent)]
self.failUnlessEqual(corr[0], 1.)
self.failIfEqual(corr[0], 2.)
def test_eff_mass_hadron(self):
data = [[pp_flat(t, 1.0, 1.0, self.time_extent) + random.normal(scale=1e-9)
for t in range(self.time_extent)] for _ in range(10)]
had = PseudoscalarMeson(data, masses=(0.01, 0.01))
plt = plot_hadron(had, effective_mass=True)
plt.show()
def test_make_hadron(self):
data = [[pp_flat(t, 1.0, 1.0, self.time_extent) + random.normal(scale=1e-4)
for t in range(self.time_extent)] for _ in range(10)]
had = PseudoscalarMeson(data, masses=(0.01, 0.01))
self.failUnlessEqual(had.data[0][0], data[0][0])
self.failUnlessEqual(had.masses, (0.01, 0.01))
def setUp(self):
self.time_extent = 64
self.fit_func = lambda t, m, c: pp_flat(t, m, c, self.time_extent)
self.iwasaki_folder = os.path.join('testfiles', 'parse', 'f1')