def test2grid_minimize(self):
""" I sigproc.fit.Minimizer Jacobian grid_minimize """
result = fit.grid_minimize(self.x, self.y, self.model, points=100)
names = self.model.parameters.name
val = self.model.parameters.value
err = self.model.parameters.stderr
valr = [2.4272, 1.2912, 0.4928]
errr = [0.4345, 0.3796, 0.0921]
print(self.model.param2str(accuracy=4, output='result'))
msg = 'Resulting value for {:s} is not correct within {:.0f}\%'
pc = 0.05 # 5% difference is allowed
for i in range(3):
self.assertAlmostEqual(val[i],
valr[i],
delta=pc * valr[i],
msg=msg.format(names[i], pc * 100))
msg = 'Resulting std-error for {:s} is not correct within {:.0f}\%'
pc = 0.1 # 10% difference is allowed
for i in range(3):
self.assertAlmostEqual(err[i],
errr[i],
delta=pc * errr[i],
msg=msg.format(names[i], pc * 100))
def test2grid_minimize(self):
""" I sigproc.fit.Minimizer Function grid_minimize """
model = self.model
points = 100
fitters, newmodels, chisqrs = fit.grid_minimize(self.x, self.y, model,
parameters=self.pnames, points=points,
return_all=True, verbose=False)
values = newmodels[0].get_parameters()[0]
pars1 = [mod_.parameters['ampl'] for mod_ in newmodels]
msg = 'Not correct amount of fitting points'
self.assertEqual(len(fitters), points, msg=msg)
self.assertEqual(len(newmodels), points, msg=msg)
self.assertEqual(len(chisqrs), points, msg=msg)
msg = 'Parameters not well distributed'
for i, j in zip(np.random.randint(0,points, size=50), np.random.randint(0,points, size=50)):
if i != j:
self.assertNotEqual(pars1[i], pars1[j], msg=msg)
msg = 'Chi2 is not ordened correctly'
self.assertTrue(chisqrs[0] == np.min(chisqrs), msg=msg)
self.assertTrue(np.all(np.diff(chisqrs) >= 0), msg=msg)
msg = 'Fit did not converge to the correct values'
self.assertArrayAlmostEqual(values[0:2], self.value[0:2], places=2, msg=msg)
def test2grid_minimize(self):
""" I sigproc.fit.Minimizer Function grid_minimize """
model = self.model
points = 100
fitters, newmodels, chisqrs = fit.grid_minimize(self.x,
self.y,
model,
parameters=self.pnames,
points=points,
return_all=True,
verbose=False)
values = newmodels[0].get_parameters()[0]
pars1 = [mod_.parameters['ampl'] for mod_ in newmodels]
msg = 'Not correct amount of fitting points'
self.assertEqual(len(fitters), points, msg=msg)
self.assertEqual(len(newmodels), points, msg=msg)
self.assertEqual(len(chisqrs), points, msg=msg)
msg = 'Parameters not well distributed'
for i, j in zip(np.random.randint(0, points, size=50),
np.random.randint(0, points, size=50)):
if i != j:
self.assertNotEqual(pars1[i], pars1[j], msg=msg)
msg = 'Chi2 is not ordened correctly'
self.assertTrue(chisqrs[0] == np.min(chisqrs), msg=msg)
self.assertTrue(np.all(np.diff(chisqrs) >= 0), msg=msg)
msg = 'Fit did not converge to the correct values'
self.assertArrayAlmostEqual(values[0:2],
self.value[0:2],
places=2,
msg=msg)
def test2grid_minimize(self):
""" I sigproc.fit.Minimizer Jacobian grid_minimize """
result = fit.grid_minimize(self.x, self.y, self.model, points=100)
names = self.model.parameters.name
val = self.model.parameters.value
err = self.model.parameters.stderr
valr = [2.4272, 1.2912, 0.4928]
errr = [0.4345, 0.3796, 0.0921]
print self.model.param2str(accuracy=4, output='result')
msg = 'Resulting value for {:s} is not correct within {:.0f}\%'
pc = 0.05 # 5% difference is allowed
for i in range(3):
self.assertAlmostEqual(val[i], valr[i], delta = pc*valr[i],
msg=msg.format(names[i], pc*100))
msg = 'Resulting std-error for {:s} is not correct within {:.0f}\%'
pc = 0.1 # 10% difference is allowed
for i in range(3):
self.assertAlmostEqual(err[i], errr[i], delta = pc*errr[i],
msg=msg.format(names[i], pc*100))
