def test_time_arrays() -> None:
t = dca.get_time()
assert is_monotonic_increasing(t)
int_t = dca.get_time_monthly_vol()
thm = dca.THM(1000, 0.5, 2.0, 1.0, 30.0)
def check_transient_model_rate_cum(model: dca.THM) -> bool:
# these are computationally expensive, so check separately
t = dca.get_time()
with warnings.catch_warnings(record=True) as w:
t_N = model.transient_cum(t)
assert is_float_array_like(t_N, t)
# assert is_monotonic_nondecreasing(t_N)
assert np.all(np.isfinite(t_N))
t_q = model.transient_rate(t)
assert is_float_array_like(t_q, t)
# assert is_monotonic_nonincreasing(t_q)
assert np.all(np.isfinite(t_q))
return True
def test_nulls() -> None:
t = dca.get_time()
primary = dca.NullPrimaryPhase()
assert np.allclose(primary.rate(t), 0.0)
assert np.allclose(primary.cum(t), 0.0)
assert np.allclose(primary.D(t), 0.0)
assert np.allclose(primary.beta(t), 0.0)
assert np.allclose(primary.b(t), 0.0)
assert np.allclose(primary._Dfn2(t), 0.0)
secondary = dca.NullAssociatedPhase()
assert np.allclose(secondary.gor(t), 0.0)
assert np.allclose(secondary.cgr(t), 0.0)
assert np.allclose(secondary.wor(t), 0.0)
assert np.allclose(secondary.rate(t), 0.0)
assert np.allclose(secondary.cum(t), 0.0)
assert np.allclose(secondary.D(t), 0.0)
assert np.allclose(secondary.beta(t), 0.0)
assert np.allclose(secondary.b(t), 0.0)
assert np.allclose(secondary._Dfn2(t), 0.0)
def check_transient_model(model: dca.THM) -> bool:
t = dca.get_time()
with warnings.catch_warnings(record=True) as w:
t_D = model.transient_D(t)
assert is_float_array_like(t_D, t)
# assert is_monotonic_nonincreasing(t_D)
assert np.all(np.isfinite(t_D))
t_beta = model.transient_beta(t)
assert is_float_array_like(t_beta, t)
# assert is_monotonic_nonincreasing(t_beta)
assert np.all(np.isfinite(t_beta))
t_b = model.transient_b(t)
assert is_float_array_like(t_b, t)
# assert is_monotonic_nonincreasing(t_b)
assert np.all(np.isfinite(t_b))
return True
def check_model(model: dca.DeclineCurve, qi: float) -> bool:
t = dca.get_time()
with warnings.catch_warnings(record=True) as w:
if isinstance(model, dca.Duong):
t0 = 1e-3
assert np.isclose(model.rate(np.array(1.0)), qi, atol=1e-10)
assert np.isclose(model.cum(np.array(1.0)),
qi / model.a,
atol=1e-10)
else:
t0 = 0.0
assert np.isclose(model.rate(np.array(0.0)), qi, atol=1e-10)
assert np.isclose(model.cum(np.array(0.0)), 0.0, atol=1e-10)
rate = model.rate(t)
assert is_float_array_like(rate, t)
# assert is_monotonic_nonincreasing(rate)
assert np.all(np.isfinite(rate))
cum = model.cum(t)
assert is_float_array_like(cum, t)
# if not isinstance(model, dca.PLE):
# exclude PLE as it is numerically integrated
# assert is_monotonic_nondecreasing(cum)
assert np.all(np.isfinite(cum))
mvolume = model.monthly_vol(t)
mavg_rate = np.gradient(mvolume, t)
# assert is_float_array_like(mvolume, t)
# assert is_monotonic_nonincreasing(mavg_rate)
assert np.all(np.isfinite(mvolume))
assert np.all(np.isfinite(mavg_rate))
ivolume = model.interval_vol(t)
iavg_rate = np.gradient(ivolume, t)
# assert is_float_array_like(ivolume, t)
# assert is_monotonic_nonincreasing(iavg_rate)
assert np.all(np.isfinite(ivolume))
assert np.all(np.isfinite(iavg_rate))
evolume = model.monthly_vol_equiv(t)
mavg_rate = np.gradient(evolume, t)
# assert is_float_array_like(evolume, t)
# assert is_monotonic_nonincreasing(mavg_rate)
assert np.all(np.isfinite(evolume))
assert np.all(np.isfinite(mavg_rate))
D = model.D(t)
assert is_float_array_like(D, t)
# assert is_monotonic_nonincreasing(D)
assert np.all(np.isfinite(D))
D2 = model._Dfn2(t)
assert is_float_array_like(D2, t)
# assert is_monotonic_nonincreasing(D2)
assert np.all(np.isfinite(D2))
beta = model.beta(t)
assert is_float_array_like(beta, t)
# TODO: what are the invariants for beta?
D_inferred = beta / t
# assert is_monotonic_nonincreasing(D_inferred)
assert np.all(np.isfinite(beta))
b = model.b(t)
assert is_float_array_like(b, t)
assert np.all(np.isfinite(b))
return True
def check_yield_model(model: Union[dca.SecondaryPhase, dca.WaterPhase],
phase: str, qi: float) -> bool:
t = dca.get_time()
with warnings.catch_warnings(record=True) as w:
t0 = 0.0
assert np.isclose(model.cum(np.array(0.0)), 0.0, atol=1e-10)
if phase == 'secondary' and isinstance(model, dca.SecondaryPhase):
gor = model.gor(t)
assert is_float_array_like(gor, t)
assert np.all(np.isfinite(gor))
cgr = model.cgr(t)
assert is_float_array_like(cgr, t)
assert np.all(np.isfinite(cgr))
with pytest.raises(ValueError) as e:
wor = model.wor(t) # type: ignore
assert is_float_array_like(wor, t)
assert np.all(np.isfinite(wor))
elif phase == 'water' and isinstance(model, dca.WaterPhase):
with pytest.raises(ValueError) as e:
gor = model.gor(t) # type: ignore
assert is_float_array_like(gor, t)
assert np.all(np.isfinite(gor))
cgr = model.cgr(t) # type: ignore
assert is_float_array_like(cgr, t)
assert np.all(np.isfinite(cgr))
wor = model.wor(t)
assert is_float_array_like(wor, t)
assert np.all(np.isfinite(wor))
rate = model.rate(t)
assert is_float_array_like(rate, t)
# assert is_monotonic_nonincreasing(rate)
assert np.all(np.isfinite(rate))
cum = model.cum(t)
assert is_float_array_like(cum, t)
# if not isinstance(model, dca.PLE):
# exclude PLE as it is numerically integrated
# assert is_monotonic_nondecreasing(cum)
assert np.all(np.isfinite(cum))
mvolume = model.monthly_vol(t)
mavg_rate = np.gradient(mvolume, t)
# assert is_float_array_like(mvolume, t)
# assert is_monotonic_nonincreasing(mavg_rate)
assert np.all(np.isfinite(mvolume))
assert np.all(np.isfinite(mavg_rate))
ivolume = model.interval_vol(t, t0=t0)
iavg_rate = np.gradient(ivolume, t)
# assert is_float_array_like(ivolume, t)
# assert is_monotonic_nonincreasing(iavg_rate)
assert np.all(np.isfinite(ivolume))
assert np.all(np.isfinite(iavg_rate))
evolume = model.monthly_vol_equiv(t)
mavg_rate = np.gradient(evolume, t)
# assert is_float_array_like(evolume, t)
# assert is_monotonic_nonincreasing(mavg_rate)
assert np.all(np.isfinite(evolume))
assert np.all(np.isfinite(mavg_rate))
D = model.D(t)
assert is_float_array_like(D, t)
# assert is_monotonic_nonincreasing(D)
# assert np.all(np.isfinite(D))
D2 = model._Dfn2(t)
assert is_float_array_like(D2, t)
# assert is_monotonic_nonincreasing(D2)
# assert np.all(np.isfinite(D2))
beta = model.beta(t)
assert is_float_array_like(beta, t)
# TODO: what are the invariants for beta?
# D_inferred = beta / t
# assert is_monotonic_nonincreasing(D_inferred)
# assert np.all(np.isfinite(beta))
b = model.b(t)
assert is_float_array_like(b, t)
assert np.all(np.isfinite(b))
# der = model._derfn(np.array([0.0]))
# NN = model._NNfn(np.array([0.0]))
return True
from petbox import dca # ftime = dca.get_time() # thm = dca.THM(qi=750, Di=.8, bi=2, bf=.5, telf=28) q_trans = thm.transient_rate(ftime) N_trans = thm.transient_cum(ftime) D_trans = thm.transient_D(ftime) b_trans = thm.transient_b(ftime) beta_trans = thm.transient_beta(ftime) # q_thm = thm.rate(ftime) N_thm = thm.cum(ftime) D_thm = thm.D(ftime) b_thm = thm.b(ftime) beta_thm = thm.beta(ftime) # mh = dca.MH(qi=725, Di=0.85, bi=0.6, Dterm=0.2) q_mh = mh.rate(ftime) N_mh = mh.cum(ftime) N_mh = mh.monthly_vol(ftime) N_mh = mh.interval_vol(ftime) D_mh = mh.D(ftime) b_mh = mh.b(ftime) beta_mh = mh.beta(ftime) #
