def test_cal_method1(input2, input3, input4, input5, input6, expectation):
with expectation:
assert calibrator(emu=emu_test,
y=input2,
x=input3,
thetaprior=input4,
method=input5,
yvar=input6) is not None
def test_cal_MLcal(input1, input2, expectation):
with expectation:
assert calibrator(emu=input1,
y=y,
x=x_std,
thetaprior=prior_balldrop,
method='directbayes',
yvar=obsvar,
args=input2) is not None
def test_cal_emu(input1, input2, input3, input4, input5, expectation):
with expectation:
assert calibrator(emu=input1,
y=input2,
x=input3,
thetaprior=input4,
method='directbayes',
yvar=input5,
args=args1) is not None
def test_cal_predict(input1, expectation):
cal_test = calibrator(emu=emulator_f_1,
y=y,
x=x_std,
thetaprior=prior_balldrop,
method='directbayes',
yvar=obsvar,
args=args2)
with expectation:
assert cal_test.predict(x=input1) is not None
def test_call(expectation):
cal = calibrator(emu=emu_test,
y=y,
x=x,
thetaprior=priorphys_lin,
method='directbayes',
yvar=obsvar,
args=args1)
with expectation:
assert cal(x=x) is not None
def test_call(expectation):
cal = calibrator(emu=emulator_f_1,
y=y,
x=x_std,
thetaprior=prior_balldrop,
method='directbayes',
yvar=obsvar,
args=args2)
pred_test = cal.predict(x=x_std)
with expectation:
assert pred_test() is not None
def test_prediction_thetalpdf(cmdopt2):
emu = emulator(x=x, theta=theta, f=f, method='PCGPwM')
cal = calibrator(emu=emu,
y=y,
x=x,
thetaprior=prior_balldrop,
method=cmdopt2,
yvar=obsvar)
try:
cal.theta.lpdf()
except:
pytest.fail('theta.lpdf() functionality does not exist in the method')
def test_supplement_cal(expectation):
emu = emulator(x=x, theta=theta, f=f, method='PCGPwM')
args1 = {
'theta0': np.array([[0, 9]]),
'numsamp': 50,
'stepType': 'normal',
'stepParam': [0.1, 1]
}
cal = calibrator(emu=emu,
y=y,
x=x,
thetaprior=priorphys_lin,
method='directbayes',
yvar=obsvar,
args=args1)
with expectation:
assert emu.supplement(size=10, cal=cal) is not None
axs[3].plot(xrep[0:21].reshape(21), median, color='black')
axs[3].fill_between(xrep[0:21].reshape(21), lower, upper, color='grey')
axs[3].plot(xrep, y, 'ro', markersize=5, color='red')
plt.show()
# %%
obsvar = np.maximum(0.2 * y, 0.1)
# Fit a calibrator with emulator 1 via via method = 'directbayes' and 'sampler' = 'metropolis_hastings'
cal_1 = calibrator(emu=emulator_1,
y=y,
x=xrep_std,
thetaprior=prior_balldrop,
method='directbayes',
yvar=obsvar,
args={
'theta0': np.array([[0.4]]),
'numsamp': 1000,
'stepType': 'normal',
'stepParam': [0.3]
})
plot_pred(x_std, xrep, y, cal_1, theta_range)
# %%
# Fit a calibrator via method = 'directbayeswoodbury' and 'sampler' : 'LMC'
cal_2 = calibrator(emu=emulator_1,
y=y,
x=xrep_std,
thetaprior=prior_balldrop,
method='directbayeswoodbury',
# %%
obsvar = np.maximum(0.2 * real_data, 5)
# %%
# Calibrator 1
cal_1 = calibrator(
emu=emulator_1,
y=real_data,
x=x,
thetaprior=prior_covid,
method='directbayes',
yvar=obsvar,
args={
'theta0':
np.array([[2, 4, 4, 1.875, 14, 18, 20, 14, 13, 12]]),
'numsamp':
100,
'stepType':
'normal',
'stepParam':
np.array([0.01, 0.01, 0.01, 0.01, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03])
})
plot_pred_interval(cal_1)
# %%
# Calibrator 2
cal_2 = calibrator(emu=emulator_1,
y=real_data,
upper = np.percentile(rndm_m[:, inds], 97.5, axis=0)
lower = np.percentile(rndm_m[:, inds], 2.5, axis=0)
axs.fill_between(xv[inds, 0], lower, upper, color='grey', alpha=0.25)
axs.plot(xv[inds, 0], y[inds], 'ro', markersize=5, color='red')
return (axs)
# %% [markdown]
# ### Calibrators for Model 1
# %%
cal_grav_1 = calibrator(emu=emu_grav,
y=y,
x=x,
thetaprior=priorphys_grav,
method='directbayes',
yvar=obsvar,
args={
'stepType': 'normal',
'stepParam': np.array([1])
})
# %%
cal_grav_2 = calibrator(emu=emu_grav,
y=y,
x=x,
thetaprior=priorphys_grav,
method='directbayes',
yvar=obsvar,
args={'sampler': 'LMC'})
# %%
rndm_m = post.rnd(s=1000)
upper = np.percentile(rndm_m, 97.5, axis=0)
lower = np.percentile(rndm_m, 2.5, axis=0)
median = np.percentile(rndm_m, 50, axis=0)
axs[3].plot(xrep[0:21].reshape(21), median, color='black')
axs[3].fill_between(xrep[0:21].reshape(21), lower, upper, color='grey')
axs[3].plot(xrep, y, 'ro', markersize=5, color='red')
plt.show()
# %%
obsvar = np.maximum(0.2 * y, 0.1)
# Fit a calibrator with emulator 1 via via method = 'directbayes' and 'sampler' = 'metropolis_hastings'
cal_1 = calibrator(emu=emulator_1,
y=y,
x=x_std,
thetaprior=prior_balldrop,
method='directbayes',
yvar=obsvar,
args={
'theta0': np.array([[0.4]]),
'numsamp': 1000,
'stepType': 'normal',
'stepParam': [0.3]
})
plot_pred(x_std, x, y, cal_1, theta_range)