def test_clear_na_n(self):
obs = np.arange(9, dtype=float)
obs.shape = (3, 3)
obs[0, 2] = np.nan
res = Melding.clearNaN(obs)
print res, obs
assert_equal(res[0, 2], np.mean([obs[0, 0], obs[0, 1]]) )
def test_clear_na_n(self):
obs = np.arange(9, dtype=float)
obs.shape = (3, 3)
obs[0, 2] = np.nan
res = Melding.clearNaN(obs)
print(res, obs)
assert_equal(res[0, 2], np.mean([obs[0, 0], obs[0, 1]]))
def setUp(self):
self.meld = Melding.Meld(3000,
1000,
Melding.model,
2,
1,
alpha=0.5,
verbose=0,
viz=False)
self.meld.setTheta(['r', 'p0'], [st.uniform, st.uniform], [(2, 4),
(0, 5)],
[(0, 10), (0, 10)])
self.meld.setPhi(['p'], [st.uniform], [(6, 9)], [(0, 10)])
def test_basicfit(self):
assert_almost_equal(4.6666, Melding.basicfit(self.s1, self.s2), 1)
def test_basicfit(self):
assert_almost_equal(4.6666, Melding.basicfit(self.s1, self.s2), 1)
'''ODE model'''
S, I, R = y
return [
-beta * I * S, # dS/dt
beta * I * S - tau * I, # dI/dt
tau * I, # dR/dt
]
y = odeint(sir, inits, np.arange(0, tf, step))
return y
fit_model = Melding.FitModel(K,
model,
inits,
tf,
thetanames,
phinames,
verbose=verbose,
burnin=10)
class TestFitModel(unittest.TestCase):
def setUp(self):
nt = len(thetanames)
np = len(phinames)
fit_model.set_priors(tdists=nt * [st.uniform],
tpars=[(0, 4), (0, 2)],
tlims=[(0, 4), (0, 2)],
pdists=np * [st.uniform],
ppars=np * [(0, 1)],
plims=np * [(0, 1)])
