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Bugfix for covobs, fit tests for covobs added

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Simon Kuberski 2021-11-30 13:32:50 +01:00
parent 950fb17c84
commit 359c9c06da
3 changed files with 68 additions and 17 deletions
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2
pyerrors/covobs.py
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@ -41,7 +41,7 @@ class Covobs:
raise Exception('Have to specify position of cov-element belonging to mean!')
else:
if pos > self.N:
raise Exception('pos %d too large for covariance matrix with dimension %dx%d!' % (pos, self.N, self.N))
raise Exception('pos %d too large for covariance matrix with dimension %dx%d!' % (pos, self.N, self.N))
self.grad = np.zeros((self.N, 1))
self.grad[pos] = 1.
else:

32
pyerrors/obs.py
View file

@ -80,7 +80,7 @@ class Obs:
raise TypeError('All names have to be strings.')
if min(len(x) for x in samples) <= 4:
raise Exception('Samples have to have at least 5 entries.')
if names:
self.names = sorted(names)
else:
@ -324,19 +324,19 @@ class Obs:
self.e_windowsize[e_name] = n
break
self._dvalue += self.e_dvalue[e_name] ** 2
self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
self._dvalue += self.e_dvalue[e_name] ** 2
self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
for e_name in self.cov_names:
self.e_dvalue[e_name] = np.sqrt(self.covobs[e_name].errsq())
self.e_ddvalue[e_name] = 0
self._dvalue += self.e_dvalue[e_name]**2
self._dvalue = np.sqrt(self.dvalue)
self._dvalue = np.sqrt(self._dvalue)
if self._dvalue == 0.0:
self.ddvalue = 0.0
else:
self.ddvalue = np.sqrt(self.ddvalue) / self.dvalue
self.ddvalue = np.sqrt(self.ddvalue) / self._dvalue
return
def _calc_gamma(self, deltas, idx, shape, w_max, fft):
@ -388,8 +388,8 @@ class Obs:
if self.value == 0.0:
percentage = np.nan
else:
percentage = np.abs(self.dvalue / self.value) * 100
print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self.dvalue, self.ddvalue, percentage))
percentage = np.abs(self._dvalue / self.value) * 100
print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self._dvalue, self.ddvalue, percentage))
if len(self.e_names) > 1:
print(' Ensemble errors:')
for e_name in self.mc_names:
@ -447,7 +447,7 @@ class Obs:
Works only properly when the gamma method was run.
"""
return self.is_zero() or np.abs(self.value) <= sigma * self.dvalue
return self.is_zero() or np.abs(self.value) <= sigma * self._dvalue
def is_zero(self, rtol=1.e-5, atol=1.e-8):
"""Checks whether the observable is zero within a given tolerance.
@ -575,10 +575,10 @@ class Obs:
ensemble to the error and returns a dictionary containing the fractions."""
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
if self.dvalue == 0.0:
if self._dvalue == 0.0:
raise Exception('Error is 0.0')
labels = self.e_names
sizes = [i ** 2 for i in list(self.e_dvalue.values())] / self.dvalue ** 2
sizes = [i ** 2 for i in list(self.e_dvalue.values())] / self._dvalue ** 2
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, startangle=90, normalize=True)
ax1.axis('equal')
@ -636,15 +636,15 @@ class Obs:
return 'Obs[' + str(self) + ']'
def __str__(self):
if self.dvalue == 0.0:
if self._dvalue == 0.0:
return str(self.value)
fexp = np.floor(np.log10(self.dvalue))
fexp = np.floor(np.log10(self._dvalue))
if fexp < 0.0:
return '{:{form}}({:2.0f})'.format(self.value, self.dvalue * 10 ** (-fexp + 1), form='.' + str(-int(fexp) + 1) + 'f')
return '{:{form}}({:2.0f})'.format(self.value, self._dvalue * 10 ** (-fexp + 1), form='.' + str(-int(fexp) + 1) + 'f')
elif fexp == 0.0:
return '{:.1f}({:1.1f})'.format(self.value, self.dvalue)
return '{:.1f}({:1.1f})'.format(self.value, self._dvalue)
else:
return '{:.0f}({:2.0f})'.format(self.value, self.dvalue)
return '{:.0f}({:2.0f})'.format(self.value, self._dvalue)
# Overload comparisons
def __lt__(self, other):
@ -1151,7 +1151,7 @@ def derived_observable(func, data, **kwargs):
else:
new_deltas[name] = new_deltas.get(name, 0) + deriv[i_val + j_obs] * _expand_deltas_for_merge(obs.deltas[name], obs.idl[name], obs.shape[name], new_idl_d[name])
new_covobs = {name: Covobs(obs.covobs[name].value, obs.covobs[name].cov, obs.covobs[name].name, grad=new_grad[name]) for name in new_grad}
new_covobs = {name: Covobs(0, allcov[name], name, grad=new_grad[name]) for name in new_grad}
new_samples = []
new_means = []

51
tests/fits_test.py
View file

@ -46,6 +46,19 @@ def test_least_squares():
assert((out.fit_parameters[0] - beta[0]).is_zero())
assert((out.fit_parameters[1] - beta[1]).is_zero())
oyc = []
for i, item in enumerate(x):
oyc.append(pe.cov_Obs(y[i], yerr[i]**2, 'cov' + str(i)))
outc = pe.least_squares(x, oyc, func)
betac = outc.fit_parameters
for i in range(2):
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, popt[i], abs_tol=1e-5)
assert math.isclose(pcov[i, i], betac[i].dvalue ** 2, abs_tol=1e-3)
assert math.isclose(pe.covariance(betac[0], betac[1]), pcov[0, 1], abs_tol=1e-3)
num_samples = 400
N = 10
@ -135,6 +148,44 @@ def test_total_least_squares():
assert(diff / beta[0] < 1e-3 * beta[0].dvalue)
assert((out.fit_parameters[1] - beta[1]).is_zero())
oxc = []
for i, item in enumerate(x):
oxc.append(pe.cov_Obs(x[i], xerr[i]**2, 'covx' + str(i)))
oyc = []
for i, item in enumerate(x):
oyc.append(pe.cov_Obs(y[i], yerr[i]**2, 'covy' + str(i)))
outc = pe.total_least_squares(oxc, oyc, func)
betac = outc.fit_parameters
for i in range(2):
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
assert math.isclose(pe.covariance(betac[0], betac[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
outc = pe.total_least_squares(oxc, oyc, func, const_par=[betac[1]])
diffc = outc.fit_parameters[0] - betac[0]
assert(diffc / betac[0] < 1e-3 * betac[0].dvalue)
assert((outc.fit_parameters[1] - betac[1]).is_zero())
outc = pe.total_least_squares(oxc, oy, func)
betac = outc.fit_parameters
for i in range(2):
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
assert math.isclose(pe.covariance(betac[0], betac[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
outc = pe.total_least_squares(oxc, oy, func, const_par=[betac[1]])
diffc = outc.fit_parameters[0] - betac[0]
assert(diffc / betac[0] < 1e-3 * betac[0].dvalue)
assert((outc.fit_parameters[1] - betac[1]).is_zero())
def test_odr_derivatives():
x = []