def test_pmodel_class_c3(request, values, soilmstress, ftemp_kphio, luevcmax_method, variables):
# Get the main vars
kwargs = {k: values[v] for k, v in variables.items()}
if soilmstress:
soilmstress = pmodel.calc_soilmstress(values['soilm_sc'], values['meanalpha_sc'])
else:
soilmstress = None
ret = pmodel.PModel(**kwargs,
kphio=0.05,
soilmstress=soilmstress,
do_ftemp_kphio=ftemp_kphio,
method_jmaxlim=luevcmax_method)
# Find the expected values, extracting the combination from the request
name = request.node.name
name = name[(name.find('[') + 1):-1]
expected = values['rpmodel-c3-' + name + '-unitiabs']
# Test values - two values calculated in main rpmodel function
# so can only test here - ci and iwue
assert np.allclose(ret.iwue, expected['iwue'])
assert np.allclose(ret.optchi.ci, expected['ci'])
# - and six values that are scaled by IABS - rpmodel enforces scaling
# where PModel can do it post hoc from unit_iabs values, so two
# rpmodel runs are used to test the unit values and scaled.
assert np.allclose(ret.unit_iabs.lue, expected['lue'])
assert np.allclose(ret.unit_iabs.vcmax, expected['vcmax'])
assert np.allclose(ret.unit_iabs.vcmax25, expected['vcmax25'])
assert np.allclose(ret.unit_iabs.rd, expected['rd'])
assert np.allclose(ret.unit_iabs.gs, expected['gs'])
# TODO - Numerical instability in the Jmax calculation - as denominator
# approaches 1, results --> infinity unpredictably with rounding
# so currently excluding Jmax in combinations where this occurs.
if 'none-fkphio-off-sm-off' not in name:
assert np.allclose(ret.unit_iabs.jmax, expected['jmax'])
else:
warnings.warn('Skipping Jmax test for cases with numerical instability')
# Check Iabs scaling
iabs = ret.unit_iabs.scale_iabs(values['fapar_sc'], values['ppfd_sc'])
# Find the expected values, extracting the combination from the request
expected = values['rpmodel-c3-' + name + '-iabs']
assert np.allclose(iabs.gpp, expected['gpp'])
assert np.allclose(iabs.vcmax, expected['vcmax'])
assert np.allclose(iabs.vcmax25, expected['vcmax25'])
assert np.allclose(iabs.rd, expected['rd'])
assert np.allclose(iabs.gs, expected['gs'])
if 'none-fkphio-off-sm-off' not in name:
assert np.allclose(iabs.jmax, expected['jmax'])
else:
warnings.warn('Skipping Jmax test for cases with numerical instability')
def test_pmodel_class_c4(request, values, soilmstress, ftemp_kphio, variables):
# Get the main vars
kwargs = {k: values[v] for k, v in variables.items()}
if soilmstress:
soilmstress = pmodel.calc_soilmstress(values['soilm_sc'], values['meanalpha_sc'])
else:
soilmstress = None
ret = pmodel.PModel(**kwargs,
kphio=0.05,
soilmstress=soilmstress,
do_ftemp_kphio=ftemp_kphio,
method_jmaxlim='none', # enforced in rpmodel.
c4=True)
# Find the expected values, extracting the combination from the request
name = request.node.name
name = name[(name.find('[') + 1):-1]
expected = values['rpmodel-c4-' + name + '-unitiabs']
# Test values - two values calculated in main rpmodel function
# so can only test here - ci and iwue
assert np.allclose(ret.iwue, expected['iwue'])
assert np.allclose(ret.optchi.ci, expected['ci'])
# - and six values that are scaled by IABS - rpmodel enforces scaling
# where PModel can do it post hoc from unit_iabs values, so two
# rpmodel runs are used to test the unit values and scaled.
unit_iabs = ret.unit_iabs.scale_iabs(1, 1)
assert np.allclose(unit_iabs.lue, expected['lue'])
assert np.allclose(unit_iabs.vcmax, expected['vcmax'])
assert np.allclose(unit_iabs.vcmax25, expected['vcmax25'])
assert np.allclose(unit_iabs.rd, expected['rd'])
assert np.allclose(unit_iabs.jmax, expected['jmax'])
assert np.allclose(unit_iabs.gs, expected['gs'])
# Check Iabs scaling
iabs = ret.unit_iabs.scale_iabs(values['fapar_sc'], values['ppfd_sc'])
# Find the expected values, extracting the combination from the request
expected = values['rpmodel-c4-' + name + '-iabs']
assert np.allclose(iabs.gpp, expected['gpp'])
assert np.allclose(iabs.vcmax, expected['vcmax'])
assert np.allclose(iabs.vcmax25, expected['vcmax25'])
assert np.allclose(iabs.rd, expected['rd'])
assert np.allclose(iabs.jmax, expected['jmax'])
assert np.allclose(iabs.gs, expected['gs'])
def test_pmodel_class_c3(request, values, pmodelenv, soilmstress, ftemp_kphio,
environ):
if soilmstress:
soilmstress = pmodel.calc_soilmstress(values['soilm_sc'],
values['meanalpha_sc'])
else:
soilmstress = None
ret = pmodel.PModel(
pmodelenv[environ],
kphio=0.05,
soilmstress=soilmstress,
do_ftemp_kphio=ftemp_kphio,
method_jmaxlim='none', # enforced in rpmodel.
c4=True)
# Find the expected values, extracting the combination from the request
name = request.node.name
name = name[(name.find('[') + 1):-1]
expected = values['rpmodel-c4-' + name + '-unitiabs']
# Test values - two values calculated in main rpmodel function
# so can only test here - ci and iwue
assert np.allclose(ret.iwue, expected['iwue'])
assert np.allclose(ret.optchi.ci, expected['ci'])
# - and six values that are scaled by IABS - rpmodel enforces scaling
# where PModel can do it post hoc from unit_iabs values, so two
# rpmodel runs are used to test the unit values and scaled.
ret.estimate_productivity() # defaults of fapar=1, ppfd=1
assert np.allclose(ret.lue, expected['lue'])
assert np.allclose(ret.vcmax, expected['vcmax'])
assert np.allclose(ret.vcmax25, expected['vcmax25'])
assert np.allclose(ret.rd, expected['rd'])
assert np.allclose(ret.jmax, expected['jmax'])
assert np.allclose(ret.gs, expected['gs'])
# Check Iabs scaling
ret.estimate_productivity(fapar=values['fapar_sc'], ppfd=values['ppfd_sc'])
# Find the expected values, extracting the combination from the request
expected = values['rpmodel-c4-' + name + '-iabs']
assert np.allclose(ret.gpp, expected['gpp'])
assert np.allclose(ret.vcmax, expected['vcmax'])
assert np.allclose(ret.vcmax25, expected['vcmax25'])
assert np.allclose(ret.rd, expected['rd'])
assert np.allclose(ret.jmax, expected['jmax'])
assert np.allclose(ret.gs, expected['gs'])
def test_calc_lue_vcmax(request, values, soilmstress, ftemp_kphio,
luevcmax_method, optchi, c4):
# ftemp_kphio needs to know the original tc inputs to optchi - these have
# all been synchronised so that anything with type 'mx' or 'ar' used the
# tc_ar input
if c4:
oc_method = 'c4'
pytest.skip(
'Not currently testing C4 outputs because of param in rpmodel')
else:
oc_method = 'prentice14'
if not ftemp_kphio:
ftemp_kphio = 1.0
elif optchi['kmm'] == 'kmm_sc':
ftemp_kphio = pmodel.calc_ftemp_kphio(tc=values['tc_sc'], c4=c4)
else:
ftemp_kphio = pmodel.calc_ftemp_kphio(tc=values['tc_ar'], c4=c4)
# Optimal Chi
kwargs = {k: values[v] for k, v in optchi.items()}
optchi = pmodel.CalcOptimalChi(**kwargs, method=oc_method)
# Soilmstress
if soilmstress['soilm'] is None:
soilmstress = 1.0
else:
soilmstress = pmodel.calc_soilmstress(
soilm=values[soilmstress['soilm']],
meanalpha=values[soilmstress['meanalpha']])
ret = pmodel.CalcLUEVcmax(optchi,
kphio=0.05,
ftemp_kphio=ftemp_kphio,
soilmstress=soilmstress,
method=luevcmax_method)
# Find the expected values, extracting the combination from the request
name = request.node.name
name = name[(name.find('[') + 1):-1]
expected = values['jmax-' + name]
assert np.allclose(ret.lue, expected['lue'])
assert np.allclose(ret.vcmax, expected['vcmax_unitiabs'])
if luevcmax_method == 'smith19':
assert np.allclose(ret.omega, expected['omega'])
assert np.allclose(ret.omega_star, expected['omega_star'])
def test_calc_soilmstress(values, ctrl):
with ctrl['cmng']:
kwargs = {k: values[v] for k, v in ctrl['args'].items()}
ret = pmodel.calc_soilmstress(**kwargs)
assert np.allclose(ret, values[ctrl['out']])