def test_absolute_and_relative_error(self):
v_right = Variable(data=np.arange(10).reshape(2, 5), unit="m")
v_wrong = Variable(data=np.arange(10).reshape(2, 5) + 1, unit="m")
abs_err = v_wrong.absolute_error(v_right)
self.assertTrue(np.all(abs_err.data == np.ones(10).reshape(2, 5)))
self.assertEqual(abs_err.unit, "m")
rel_err = v_wrong.relative_error(v_right)
res = 100 / np.ma.arange(10).reshape(2, 5)
self.assertTrue(np.all(rel_err.data == res.data))
self.assertEqual(rel_err.unit, "%")
## convertible units
v_right = Variable(data=np.arange(10).reshape(2, 5) * 1000, unit="m")
v_wrong = Variable(data=np.arange(10).reshape(2, 5) + 1, unit="km")
abs_err = v_wrong.absolute_error(v_right)
self.assertTrue(np.all(abs_err.data == np.ones(10).reshape(2, 5) * 1000))
self.assertEqual(abs_err.unit, "m")
rel_err = v_wrong.relative_error(v_right)
res = 100 / np.ma.arange(10).reshape(2, 5)
self.assertTrue(np.all(rel_err.data == res.data))
self.assertEqual(rel_err.unit, "%")
## unconvertible units
v_right = Variable(data=np.arange(10).reshape(2, 5) * 1000, unit="m")
v_wrong = Variable(data=np.arange(10).reshape(2, 5) + 1, unit="km/s")
with self.assertRaises(ValueError):
abs_err = v_wrong.absolute_error(v_right)
with self.assertRaises(ValueError):
rel_err = v_wrong.relative_error(v_right)
## incompatible shapes
v_right = Variable(data=np.arange(10) * 1000, unit="m")
v_wrong = Variable(data=np.arange(10).reshape(2, 5) + 1, unit="km/s")
with self.assertRaises(AssertionError):
abs_err = v_wrong.absolute_error(v_right)
with self.assertRaises(AssertionError):
rel_err = v_wrong.relative_error(v_right)
def create_discrete_model_run(self, errors=False):
out = self.load_xs_Us_Fs_Rs()
xs, Us, Fs, Rs = out
start_values = xs.data[0, :]
if (xs.data.mask.sum() + Fs.data.mask.sum() + Us.data.mask.sum() +
Rs.data.mask.sum() == 0):
# fixme hm 2020-04-21:
# which reconstruction version is the right choice?
# dmr = DMR.reconstruct_from_data(
# self.time_agg.data.filled(),
# start_values.filled(),
# xs.data.filled(),
# Fs.data.filled(),
# Rs.data.filled(),
# Us.data.filled()
# )
# dmr = DMR.reconstruct_from_fluxes_and_solution(
dmr = DMRWGF.reconstruct_from_fluxes_and_solution(
self.time_agg.data.filled(),
xs.data.filled(),
Fs.data.filled(),
Rs.data.filled(),
Us.data.filled(),
Fs.data.filled(),
Rs.data.filled(),
)
else:
dmr = None
if errors:
soln_dmr = Variable(data=dmr.solve(), unit=self.stock_unit)
abs_err = soln_dmr.absolute_error(xs)
rel_err = soln_dmr.relative_error(xs)
return dmr, abs_err, rel_err
else:
return dmr
def create_model_run(self, errors=False):
out = self.load_xs_Us_Fs_Rs()
xs, Us, Fs, Rs = out
# print(self.time_agg.data)
# print(xs.data)
# print(Fs.data)
# print(Rs.data)
# print(Us.data)
# input()
times = self.time_agg.data.filled()
# times = np.arange(len(self.time_agg.data))
if (xs.data.mask.sum() + Fs.data.mask.sum() + Us.data.mask.sum() +
Rs.data.mask.sum() == 0):
pwc_mr_fd = PWCMRFD.from_gross_fluxes(
symbols("t"),
times,
xs.data.filled()[0],
# xs.data.filled(),
Us.data.filled(),
Fs.data.filled(),
Rs.data.filled(),
# xs.data.filled()
)
else:
pwc_mr_fd = None
if errors:
err_dict = {}
soln = pwc_mr_fd.solve()
soln_pwc_mr_fd = Variable(data=soln, unit=self.stock_unit)
abs_err = soln_pwc_mr_fd.absolute_error(xs)
rel_err = soln_pwc_mr_fd.relative_error(xs)
err_dict["stocks"] = {"abs_err": abs_err, "rel_err": rel_err}
Us_pwc_mr_fd = Variable(
name="acc_gross_external_input_vector",
data=pwc_mr_fd.acc_gross_external_input_vector(),
unit=self.stock_unit,
)
abs_err = Us_pwc_mr_fd.absolute_error(Us)
rel_err = Us_pwc_mr_fd.relative_error(Us)
err_dict["acc_gross_external_inputs"] = {
"abs_err": abs_err,
"rel_err": rel_err,
}
Rs_pwc_mr_fd = Variable(
name="acc_gross_external_output_vector",
data=pwc_mr_fd.acc_gross_external_output_vector(),
unit=self.stock_unit,
)
abs_err = Rs_pwc_mr_fd.absolute_error(Rs)
rel_err = Rs_pwc_mr_fd.relative_error(Rs)
err_dict["acc_gross_external_outputs"] = {
"abs_err": abs_err,
"rel_err": rel_err,
}
Fs_pwc_mr_fd = Variable(
name="acc_gross_internal_flux_matrix",
data=pwc_mr_fd.acc_gross_internal_flux_matrix(),
unit=self.stock_unit,
)
abs_err = Fs_pwc_mr_fd.absolute_error(Fs)
rel_err = Fs_pwc_mr_fd.relative_error(Fs)
err_dict["acc_gross_internal_fluxes"] = {
"abs_err": abs_err,
"rel_err": rel_err,
}
return pwc_mr_fd, err_dict
else:
return pwc_mr_fd
def create_model_run(self,
integration_method='solve_ivp',
nr_nodes=None,
errors=False,
check_success=True):
out = self.load_xs_Us_Fs_Rs()
xs, Us, Fs, Rs = out
# print(self.time_agg.data)
# print(xs.data)
# print(Fs.data)
# print(Rs.data)
# print(Us.data)
# input()
times = self.time_agg.data.filled()
# times = np.arange(len(self.time_agg.data))
if (xs.data.mask.sum() + Fs.data.mask.sum() + Us.data.mask.sum() +
Rs.data.mask.sum() == 0):
pwc_mr_fd = PWCMRFD.from_gross_fluxes(symbols("t"), times,
xs.data.filled()[0],
Us.data.filled(),
Fs.data.filled(),
Rs.data.filled(),
xs.data.filled(),
integration_method, nr_nodes,
check_success)
else:
pwc_mr_fd = None
if errors:
# print('Computing reconstruction errors')
err_dict = {}
# print(' solution error')
soln = pwc_mr_fd.solve()
soln_pwc_mr_fd = Variable(name="stocks",
data=soln,
unit=self.stock_unit)
abs_err = soln_pwc_mr_fd.absolute_error(xs)
rel_err = soln_pwc_mr_fd.relative_error(xs)
err_dict["stocks"] = {
"abs_err": abs_err.max(),
"rel_err": rel_err.max()
}
# print(' input fluxes error')
Us_pwc_mr_fd = Variable(
name="acc_gross_external_input_vector",
data=pwc_mr_fd.acc_gross_external_input_vector(),
unit=self.stock_unit,
)
abs_err = Us_pwc_mr_fd.absolute_error(Us)
rel_err = Us_pwc_mr_fd.relative_error(Us)
err_dict["acc_gross_external_inputs"] = {
"abs_err": abs_err.max(),
"rel_err": rel_err.max(),
}
# print(' output fluxes error')
Rs_pwc_mr_fd = Variable(
name="acc_gross_external_output_vector",
data=pwc_mr_fd.acc_gross_external_output_vector(),
unit=self.stock_unit,
)
abs_err = Rs_pwc_mr_fd.absolute_error(Rs)
rel_err = Rs_pwc_mr_fd.relative_error(Rs)
err_dict["acc_gross_external_outputs"] = {
"abs_err": abs_err.max(),
"rel_err": rel_err.max(),
}
# print(' internal fluxes error')
Fs_pwc_mr_fd = Variable(
name="acc_gross_internal_flux_matrix",
data=pwc_mr_fd.acc_gross_internal_flux_matrix(),
unit=self.stock_unit,
)
abs_err = Fs_pwc_mr_fd.absolute_error(Fs)
rel_err = Fs_pwc_mr_fd.relative_error(Fs)
err_dict["acc_gross_internal_fluxes"] = {
"abs_err": abs_err.max(),
"rel_err": rel_err.max(),
}
abs_err.argmax()
rel_err.argmax()
# print('done')
return pwc_mr_fd, err_dict
else:
return pwc_mr_fd, dict()
