def test_mcmc():
x0 = generic_params
bounds = generic_bounds
f = np.linspace(0, 288, 100)
model = asy_peakbag.asymp_spec_model(f, 2)
snr = np.ones(len(f))
prior = asy_peakbag.Prior(generic_bounds, generic_gaussian)
mcmc = asy_peakbag.mcmc(f, snr, model, x0, prior)
def test_mcmc_call():
x0 = generic_params
bounds = generic_bounds
f = np.linspace(0, 288, 100)
model = asy_peakbag.asymp_spec_model(f, 2)
snr = model(x0)
prior = asy_peakbag.Prior(generic_bounds, generic_gaussian)
mcmc = asy_peakbag.mcmc(f, snr, model, x0, prior)
samples = mcmc(10, 20)
def test_model_call():
model = asy_peakbag.asymp_spec_model(np.linspace(1, 10, 10), 2)
asy = model(generic_params)
def test_model_asy():
"""Can I generate a complete model realization"""
model = asy_peakbag.asymp_spec_model(np.linspace(1, 10, 10), 2)
asy = model.model(*generic_params)
def test_model_pair():
"""Can I define a pair of modes in a model instance"""
model = asy_peakbag.asymp_spec_model(np.linspace(1, 10, 10), 2)
pair = model.pair(6, 10, 0.1, 2.0)
assert_almost_equal(pair[5], 10.0, 0.1)
def test_model_lorentzian():
''' Very simple lorentzian test '''
model = asy_peakbag.asymp_spec_model(np.linspace(1, 200, 3), 2)
lor = model.lor(1, 10.0, np.log10(1.0))
assert_almost_equal(lor[0], 10.0, 0.1)
assert_almost_equal(lor[1], 0.0, 0.1)
def test_model():
"""Can I initialize a model instance"""
model = asy_peakbag.asymp_spec_model(np.linspace(1, 2, 3), 2)