def test_noise_type_input(self):
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(period,
photon_count32,
noise_type='foo')
with self.assertRaises(TypeError):
flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32, noise_type={"foo": "bar"})
def test_unused_noise_types(self):
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32, noise_type='NOISE_GAUSSIAN_FIT')
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(period,
photon_count32,
noise_type='NOISE_MLE')
def test_photon_count(self):
flimlib.GCI_triple_integral_fitting_engine(
period, photon_count64) #passing float 64 should be fine
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(
period, photon_count2d) #passing 2d array will fail
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(period,
5.7) #must be 1d array
flimlib.GCI_triple_integral_fitting_engine(period, [1, 2])
def test_noise_const_and_given(self):
#the result should be the same if noise given is constant
result_const = flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32, noise_type='NOISE_CONST', sig=1.0)
result_noise_given = flimlib.GCI_triple_integral_fitting_engine(
period,
photon_count32,
noise_type='NOISE_GIVEN',
sig=np.ones(samples))
self.assertAlmostEqual(result_const.chisq, result_noise_given.chisq)
self.assertAlmostEqual(result_const.A, result_noise_given.A)
self.assertAlmostEqual(result_const.Z, result_noise_given.Z)
self.assertAlmostEqual(result_const.tau, result_noise_given.tau)
def test_noise_const(self):
flimlib.GCI_triple_integral_fitting_engine(period,
photon_count32,
noise_type='NOISE_CONST',
sig=2)
flimlib.GCI_triple_integral_fitting_engine(period,
photon_count32,
noise_type='NOISE_CONST',
sig=[5])
with self.assertRaises(TypeError):
flimlib.GCI_triple_integral_fitting_engine(
period,
photon_count32,
noise_type='NOISE_CONST',
sig=[1, 2, 3, 4, 5])
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32, noise_type='NOISE_CONST', sig='foo')
def test_output_margin(self):
result = flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32)
# test to an integer precision since the triple integral fit isn't as rigorous
self.assertAlmostEqual(result.A, a_in, 0)
self.assertAlmostEqual(result.tau, tau_in, 0)
self.assertAlmostEqual(result.Z, 0.0, 0)
self.assertTrue(result.tries > 0)
self.assertAlmostEqual(result.fitted[0], a_in, 0)
self.assertAlmostEqual(result.residuals[0], 0.0, 0)
self.assertAlmostEqual(result.residuals[-1], 0.0, 0)
def test_noise_given(self):
flimlib.GCI_triple_integral_fitting_engine(period,
photon_count32,
noise_type='NOISE_GIVEN',
sig=list(range(samples)))
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32, noise_type='NOISE_GIVEN', sig=2)
with self.assertRaises(TypeError):
flimlib.GCI_triple_integral_fitting_engine(
period,
photon_count32,
noise_type='NOISE_GIVEN',
sig={"foo": "bar"})
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32, noise_type='NOISE_GIVEN', sig='foo')
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(
period,
photon_count32,
noise_type='NOISE_GIVEN',
sig=[1, 2, 3, 4, 5])
def test_instr(self):
flimlib.GCI_triple_integral_fitting_engine(period,
photon_count32,
instr=[1, 2, 3, 4, 5])
flimlib.GCI_triple_integral_fitting_engine(period,
photon_count32,
instr=["42"])
flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32, instr=list(range(300))
) # no errors??? ask mark/jenu. ok we should actually just check that it's the right length
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(period,
photon_count32,
instr=5)
with self.assertRaises(ValueError):
flimlib.GCI_triple_integral_fitting_engine(period,
photon_count32,
instr="foo")
with self.assertRaises(TypeError):
flimlib.GCI_triple_integral_fitting_engine(period,
photon_count32,
instr={"foo": "bar"})
def test_chisq_target(self):
result = flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32, chisq_target=0.0
) #impossible target causes it to run more than once and give up
self.assertTrue(result.tries > 1)
def test_noise_poisson_fit(self):
flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32, noise_type='NOISE_POISSON_FIT')
# this should print a warning
flimlib.GCI_triple_integral_fitting_engine(
period, photon_count32, noise_type='NOISE_POISSON_FIT', sig=2)
