def test_get_dz(self):
mdo = self.mdo
ds = mdo.dataset
dz = mdo.get_dz("CWD")
self.assertEqual(dz.name, "dz")
self.assertEqual(dz.unit, "m")
res2 = ds["dz"]
self.assertTrue(np.all(dz.data == res2[:]))
## no layers, no grid
mdo = self.mdos_inc[0]
dz = mdo.get_dz("CWD")
self.assertTrue(np.all(dz.data == np.array([1])))
self.assertEqual(dz.unit, "1")
## test manually given dz variable
mdo = self.mdo
dz = Variable(name="dz", data=np.cumsum(np.arange(10)), unit="km")
time = Variable(data=np.arange(10), unit="d")
mdo_dz = ModelDataObject(
model_structure=mdo.model_structure,
dataset=mdo.dataset,
dz_var_names={"dz": dz},
nstep=mdo.nstep,
stock_unit=mdo.stock_unit,
time=time,
)
dz = mdo_dz.get_dz("CWD")
self.assertEqual(dz.name, "dz")
self.assertEqual(dz.unit, "km")
res2 = np.cumsum(np.arange(10))
self.assertTrue(np.all(dz.data == res2[:]))
def check_data_consistency(ds_single_site, time_step_in_days):
ms = load_model_structure_with_vegetation()
# data_test.py says tht for labile 31.0 is constantly right
time = Variable(name="time",
data=np.arange(len(ds_single_site.time)) *
time_step_in_days,
unit="d")
mdo = ModelDataObject(model_structure=ms,
dataset=ds_single_site,
stock_unit="gC/m2",
time=time)
abs_err, rel_err = mdo.check_data_consistency()
return abs_err, rel_err
def load_mdo_12C(parameter_set):
# dataset = parameter_set['dataset']
ds_filename = parameter_set["ds_filename"]
stock_unit = parameter_set["stock_unit"]
cftime_unit_src = parameter_set["cftime_unit_src"]
calendar_src = parameter_set["calendar_src"]
nr_layers = parameter_set["nr_layers"]
dz_var_name = parameter_set.get("dz_var_name", None)
dz_var_names = parameter_set.get("dz_var_names", dict())
min_time = parameter_set.get("min_time", 0)
max_time = parameter_set.get("max_time", None)
nstep = parameter_set.get("nstep", 1)
time_shift = parameter_set.get("time_shift", 0)
mdo_12C = ModelDataObject(
model_structure=model_structure,
# dataset = dataset,
ds_filename=ds_filename,
nstep=nstep,
stock_unit=stock_unit,
cftime_unit_src=cftime_unit_src,
calendar_src=calendar_src,
min_time=min_time,
max_time=max_time,
time_shift=time_shift,
dz_var_names=dz_var_names,
)
return mdo_12C
def _load_mdo(ds_dict, time_step_in_days, check_units=True): # time step in days
ms = load_model_structure()
# no unit support for dictionary version
time = Variable(
name="time",
data=np.arange(len(ds_dict['time'])) * time_step_in_days,
unit="d"
# unit="1"
)
mdo = ModelDataObject(
model_structure=ms,
dataset=ds_dict,
stock_unit="gC/m2",
# stock_unit="1",
time=time,
check_units=check_units
)
return mdo
def load_mdo(ds, parameter_set):
nstep = parameter_set.get("nstep", 1)
stock_unit = parameter_set["stock_unit"]
nr_layers = parameter_set["nr_layers"]
dz_var_name = parameter_set["dz_var_name"]
dz_var_names = parameter_set["dz_var_names"]
ms = load_model_structure(nr_layers, dz_var_name)
time = Variable(name="time", data=np.arange(len(ds.time)), unit="d")
mdo = ModelDataObject(
model_structure=ms,
dataset=ds,
nstep=nstep,
stock_unit=stock_unit,
time=time,
dz_var_names=dz_var_names,
)
return mdo
def load_mdo(ds):
# days_per_month = np.array(np.diff(ds.time), dtype='timedelta64[D]').astype(float)
ms = load_model_structure()
# bring fluxes from gC/m2/day to gC/m2/month
## all months are supposed to comprise 365.25/12 days
days_per_month = 365.25 / 12.0
time = Variable(
name="time",
data=np.arange(len(ds.time)) * days_per_month,
unit="d"
)
mdo = ModelDataObject(
model_structure=ms,
dataset=ds,
stock_unit="gC/m2",
time=time
)
return mdo
def load_mdo_14C(parameter_set):
# dataset = parameter_set['dataset']
ds_filename = parameter_set["ds_filename"]
stock_unit = parameter_set["stock_unit"]
cftime_unit_src = parameter_set["cftime_unit_src"]
calendar_src = parameter_set["calendar_src"]
nr_layers = parameter_set["nr_layers"]
dz_var_name = parameter_set.get("dz_var_name", None)
dz_var_names = parameter_set.get("dz_var_names", dict())
min_time = parameter_set.get("min_time", 0)
max_time = parameter_set.get("max_time", None)
nstep = parameter_set.get("nstep", 1)
time_shift = parameter_set.get("time_shift", 0)
pool_structure_14C = [
{
"pool_name": "CWD",
"stock_var": "C14_CWDC_vr",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Litter 1",
"stock_var": "C14_LITR1C_vr",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Litter 2",
"stock_var": "C14_LITR2C_vr",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Litter 3",
"stock_var": "C14_LITR3C_vr",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Soil 1",
"stock_var": "C14_SOIL1C_vr",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Soil 2",
"stock_var": "C14_SOIL2C_vr",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Soil 3",
"stock_var": "C14_SOIL3C_vr",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
]
external_input_structure_14C = {
"CWD": [
"C14_fire_mortality_c_to_cwdc",
"C14_gap_mortality_c_to_cwdc",
"C14_harvest_c_to_cwdc",
],
"Litter 1": [
"C14_gap_mortality_c_to_litr_met_c",
"C14_harvest_c_to_litr_met_c",
"C14_m_c_to_litr_met_fire",
"C14_phenology_c_to_litr_met_c",
],
"Litter 2": [
"C14_gap_mortality_c_to_litr_cel_c",
"C14_harvest_c_to_litr_cel_c",
"C14_m_c_to_litr_cel_fire",
"C14_phenology_c_to_litr_cel_c",
],
"Litter 3": [
"C14_gap_mortality_c_to_litr_lig_c",
"C14_harvest_c_to_litr_lig_c",
"C14_m_c_to_litr_lig_fire",
"C14_phenology_c_to_litr_lig_c",
],
}
external_output_structure_14C = {
"CWD":
["C14_CWD_HR_L2_vr", "C14_CWD_HR_L3_vr", "C14_M_CWDC_TO_FIRE_vr"],
"Litter 1": ["C14_LITR1_HR_vr", "C14_M_LITR1C_TO_FIRE_vr"],
"Litter 2": ["C14_LITR2_HR_vr", "C14_M_LITR2C_TO_FIRE_vr"],
"Litter 3": ["C14_LITR3_HR_vr", "C14_M_LITR2C_TO_FIRE_vr"],
"Soil 1": ["C14_SOIL1_HR_S2_vr", "C14_SOIL1_HR_S3_vr"],
"Soil 2": ["C14_SOIL2_HR_S1_vr", "C14_SOIL2_HR_S3_vr"],
"Soil 3": ["C14_SOIL3_HR_vr"],
}
model_structure_14C = ModelStructure(
pool_structure=pool_structure_14C,
external_input_structure=external_input_structure_14C,
horizontal_structure=[],
external_output_structure=external_output_structure_14C,
)
mdo_14C = ModelDataObject(
model_structure=model_structure_14C,
# dataset = dataset,
ds_filename=ds_filename,
nstep=nstep,
stock_unit=stock_unit,
cftime_unit_src=cftime_unit_src,
calendar_src=calendar_src,
min_time=min_time,
max_time=max_time,
time_shift=time_shift,
dz_var_names=dz_var_names,
)
return mdo_14C
def MDO_3pools_3layers():
## set up test model strcture
nr_layers = 3
dz_var_name = "dz"
pool_structure = [
{
"pool_name": "CWD",
"stock_var": "CWDC",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Litter",
"stock_var": "LITRC",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Soil",
"stock_var": "SOILC",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
]
external_input_structure = {
"CWD": ["fire_CWD", "gap_CWD", "harvest_CWD"],
"Litter": ["gap_LITR", "harvest_LITR", "m_c_LITR", "phenology_LITR"],
}
horizontal_structure = {
("CWD", "Litter"): ["CWD_TO_LITR"],
("Litter", "Soil"): ["LITR_TO_SOIL"],
}
vertical_structure = {
"Litter": {
"to_below": ["LITR_flux_down_tb"],
"from_below": ["LITR_flux_up_fb"],
"to_above": [],
"from_above": [],
},
"Soil": {
"to_below": [],
"from_below": [],
"to_above": ["SOIL_flux_up_ta"],
"from_above": ["SOIL_flux_down_fa"],
},
}
external_output_structure = {
"CWD": ["CWD_HR1", "CWD_HR2"],
"Litter": ["LITR_HR"],
"Soil": ["SOIL_HR1", "SOIL_HR2"],
}
model_structure = ModelStructure(
pool_structure=pool_structure,
external_input_structure=external_input_structure,
horizontal_structure=horizontal_structure,
vertical_structure=vertical_structure,
external_output_structure=external_output_structure,
)
## set up a test dataset
time = np.arange(10)
dz = np.arange(nr_layers) + 1
ds = Dataset("test_dataset.nc", "w", diskless=True, persist=False)
ds.createDimension("time", len(time))
ds.createDimension("level", nr_layers)
time_var = ds.createVariable("time", "f8", ("time", ))
time_var[:] = time
time_var.units = "d"
dz_var = ds.createVariable("dz", "f4", ("level"))
dz_var[:] = dz
dz_var.units = "m"
## introduce some test variables to test dataset
## stocks
var = ds.createVariable("CWDC", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers).reshape((len(time), nr_layers))
var[...] = data
var.units = "gC/m^3"
var.cell_methods = "time: instantaneous"
var = ds.createVariable("LITRC", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.1).reshape(
(len(time), nr_layers))
var[...] = data
var.units = "gC/m^3"
var.cell_methods = "time: instantaneous"
var = ds.createVariable("SOILC", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.2).reshape(
(len(time), nr_layers))
var[...] = data.copy()
var.units = "gC/m^3"
var.cell_methods = "time: instantaneous"
## external input fluxes
var = ds.createVariable("fire_CWD", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("gap_CWD", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.01).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("harvest_CWD", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.02).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("gap_LITR", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.10).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("harvest_LITR", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.11).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("m_c_LITR", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.12).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("phenology_LITR", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.13).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
## horizontal fluxes
var = ds.createVariable("CWD_TO_LITR", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("LITR_TO_SOIL", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.1).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
## vertical fluxes
var = ds.createVariable("LITR_flux_down_tb", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^2/s"
var.cell_methods = "time: mean"
var = ds.createVariable("LITR_flux_up_fb", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers).reshape(
(len(time), nr_layers)) * 1e-04
var[...] = data
var.units = "gC/m^2/s"
var.cell_methods = "time: mean"
var = ds.createVariable("SOIL_flux_down_fa", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^2/s"
var.cell_methods = "time: mean"
var = ds.createVariable("SOIL_flux_up_ta", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers).reshape(
(len(time), nr_layers)) * 1e-04
var[...] = data
var.units = "gC/m^2/s"
var.cell_methods = "time: mean"
## external_output_fluxes
var = ds.createVariable("CWD_HR1", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("CWD_HR2", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.01).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("LITR_HR", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.10).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("SOIL_HR1", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.20).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("SOIL_HR2", "f8", ("time", "level"))
data = (np.arange(len(time) * nr_layers) + 0.21).reshape(
(len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
## set up a ModelDataObject
nstep = 2
stock_unit = "g/m^2"
# cftime_unit_src = 'days since 1851-01-01 00:00:00'
# calendar_src = 'noleap'
# time_shift = 5
# max_time = 8
time = Variable(data=time, unit="d")
mdo = ModelDataObject(
model_structure=model_structure,
dataset=ds,
nstep=nstep,
stock_unit=stock_unit,
time=time
# cftime_unit_src = cftime_unit_src,
# calendar_src = calendar_src,
# time_shift = time_shift,
# max_time = max_time
)
return mdo
def MDO_3pools_3layers_discretizable():
## set up test model strcture
nr_layers = 3
dz_var_name = "dz"
pool_structure = [
{
"pool_name": "CWD",
"stock_var": "CWDC",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Litter",
"stock_var": "LITRC",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Soil",
"stock_var": "SOILC",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
]
external_input_structure = {"CWD": ["input_CWD"], "Litter": ["input_LITR"]}
horizontal_structure = {
("CWD", "Litter"): ["CWD_TO_LITR"],
("Litter", "Soil"): ["LITR_TO_SOIL"],
}
external_output_structure = {
"CWD": ["CWD_HR"],
"Litter": ["LITR_HR"],
"Soil": ["SOIL_HR"],
}
model_structure = ModelStructure(
pool_structure=pool_structure,
external_input_structure=external_input_structure,
horizontal_structure=horizontal_structure,
# vertical_structure = vertical_structure,
external_output_structure=external_output_structure,
)
## set up a test dataset
time = np.arange(10)
dz = np.arange(nr_layers) + 1
ds = Dataset("test_dataset_discretizable1.nc",
"w",
diskless=True,
persist=False)
ds.createDimension("time", len(time))
ds.createDimension("level", nr_layers)
time_var = ds.createVariable("time", "f8", ("time", ))
time_var[:] = time
time_var.units = "d"
dz_var = ds.createVariable("dz", "f4", ("level"))
dz_var[:] = dz
dz_var.units = "m"
## introduce some test variables to test dataset
## stocks
var = ds.createVariable("CWDC", "f8", ("time", "level"))
data = np.ones((len(time), nr_layers)) * 1e05
var[...] = data
var.units = "gC/m^3"
var.cell_methods = "time: instantaneous"
var = ds.createVariable("LITRC", "f8", ("time", "level"))
data = np.ones((len(time), nr_layers)) * 1e05
var[...] = data
var.units = "gC/m^3"
var.cell_methods = "time: instantaneous"
var = ds.createVariable("SOILC", "f8", ("time", "level"))
data = np.ones((
len(time),
nr_layers,
)) * 1e05
var[...] = data.copy()
var.units = "gC/m^3"
var.cell_methods = "time: instantaneous"
## external input fluxes
var = ds.createVariable("input_CWD", "f8", ("time", "level"))
data = np.ones((len(time), nr_layers)) * 1e-02
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("input_LITR", "f8", ("time", "level"))
data = np.ones((len(time), nr_layers)) * 1e-02
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
## horizontal fluxes
var = ds.createVariable("CWD_TO_LITR", "f8", ("time", "level"))
data = np.ones((len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("LITR_TO_SOIL", "f8", ("time", "level"))
data = np.ones((len(time), nr_layers)) * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
## external_output_fluxes
var = ds.createVariable("CWD_HR", "f8", ("time", "level"))
data = np.ones((len(time), nr_layers)) * 2 * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("LITR_HR", "f8", ("time", "level"))
data = np.ones((len(time), nr_layers)) * 2 * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
var = ds.createVariable("SOIL_HR", "f8", ("time", "level"))
data = np.ones((len(time), nr_layers)) * 2 * 1e-03
var[...] = data
var.units = "gC/m^3/s"
var.cell_methods = "time: mean"
## set up a ModelDataObject
nstep = 2
stock_unit = "g/m^2"
# cftime_unit_src = 'days since 1850-01-01 00:00:00'
# calendar_src = 'noleap'
# max_time = 8
time = Variable(data=time, unit="d")
mdo = ModelDataObject(
model_structure=model_structure,
dataset=ds,
nstep=nstep,
stock_unit=stock_unit,
time=time
# cftime_unit_src = cftime_unit_src,
# calendar_src = calendar_src,
# max_time = max_time
)
return mdo
def MDO_3pools_3layers_nogrid():
## set up test model strcture
nr_layers = 3
dz_var_name = "dz"
pool_structure = [
{
"pool_name": "CWD",
"stock_var": "CWDC",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Litter",
"stock_var": "LITRC",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
{
"pool_name": "Soil",
"stock_var": "SOILC",
"nr_layers": nr_layers,
"dz_var": dz_var_name,
},
]
external_input_structure = {
"CWD": ["fire_CWD"],
}
horizontal_structure = {("CWD", "Litter"): ["CWD_TO_LITR"]}
external_output_structure = {
"CWD": ["CWD_HR"],
}
model_structure = ModelStructure(
pool_structure=pool_structure,
external_input_structure=external_input_structure,
horizontal_structure=horizontal_structure,
external_output_structure=external_output_structure,
)
## set up a test dataset
time = np.arange(10)
dz = np.arange(nr_layers) + 1
ds = Dataset("test_dataset4.nc", "w", diskless=True, persist=False)
ds.createDimension("time", len(time))
ds.createDimension("level", nr_layers)
time_var = ds.createVariable("time", "f8", ("time", ))
time_var[:] = time
time_var.units = "d"
dz_var = ds.createVariable("dz", "f4", ("level"))
dz_var[:] = dz
dz_var.units = "m"
## introduce some test variables to test dataset
## stocks
var = ds.createVariable("CWDC", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers)
var[...] = data.reshape(len(time), nr_layers)
var.units = "gC14/m^3"
var.cell_methods = "time: instantaneous"
var = ds.createVariable("LITRC", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers) + 0.1
var[...] = data.reshape(len(time), nr_layers)
var.units = "gC14/m^3"
var.cell_methods = "time: instantaneous"
var = ds.createVariable("SOILC", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers) + 0.2
var[...] = data.reshape(len(time), nr_layers)
var.units = "gC14/m^3"
var.cell_methods = "time: instantaneous"
## external input fluxes
var = ds.createVariable("fire_CWD", "f8", ("time", "level"))
masked_data = np.ma.masked_array(
data=np.arange(len(time) * nr_layers) * 1e-03,
mask=np.zeros(len(time) * nr_layers),
)
masked_data.mask[21] = 1
var[...] = masked_data.reshape(len(time), nr_layers)
var.units = "gC14/m^3/s"
var.cell_methods = "time: mean"
## horizontal fluxes
var = ds.createVariable("CWD_TO_LITR", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers) * 1e-03
var[...] = data.reshape(len(time), nr_layers)
var.units = "gC14/m^3/s"
var.cell_methods = "time: mean"
## external_output_fluxes
var = ds.createVariable("CWD_HR", "f8", ("time", "level"))
data = np.arange(len(time) * nr_layers)
var[...] = data.reshape(len(time), nr_layers)
var.units = "gC14/m^3/s"
var.cell_methods = "time: mean"
## set up a ModelDataObject
nstep = 2
stock_unit = "g/m^2"
# cftime_unit_src = 'days since 1900-01-01 00:00:00'
# calendar_src = 'noleap'
# max_time = 8
time = Variable(data=time, unit="d")
mdo = ModelDataObject(
model_structure=model_structure,
dataset=ds,
nstep=nstep,
stock_unit=stock_unit,
time=time
# cftime_unit_src = cftime_unit_src,
# calendar_src = calendar_src,
# max_time = max_time
)
return mdo
def MDO_3pools_nolayers_nogrid():
## set up test model strcture
pool_structure = [
{
"pool_name": "CWD",
"stock_var": "CWDC",
},
{
"pool_name": "Litter",
"stock_var": "LITRC",
},
{
"pool_name": "Soil",
"stock_var": "SOILC",
},
]
external_input_structure = {
"CWD": ["fire_CWD"],
}
horizontal_structure = {
("CWD", "Litter"): ["CWD_TO_LITR"],
}
external_output_structure = {
"CWD": ["CWD_HR"],
}
model_structure = ModelStructure(
pool_structure=pool_structure,
external_input_structure=external_input_structure,
horizontal_structure=horizontal_structure,
external_output_structure=external_output_structure,
)
## set up a test dataset
time = np.arange(10)
ds = Dataset("test_dataset2.nc", "w", diskless=True, persist=False)
ds.createDimension("time", len(time))
time_var = ds.createVariable("time", "f8", ("time", ))
time_var[:] = time
time_var.units = "d"
## introduce some test variables to test dataset
## stocks
var = ds.createVariable("CWDC", "f8", ("time", ))
data = np.arange(len(time))
var[...] = data
var.units = "gC/m^2"
var.cell_methods = "time: instantaneous"
var = ds.createVariable("LITRC", "f8", ("time", ))
data = np.arange(len(time)) + 0.1
var[...] = data
var.units = "gC/m^2"
var.cell_methods = "time: instantaneous"
var = ds.createVariable("SOILC", "f8", ("time", ))
data = np.arange(len(time)) + 0.2
var[...] = data.copy()
var.units = "gC/m^2"
var.cell_methods = "time: instantaneous"
## external input fluxes
var = ds.createVariable("fire_CWD", "f8", ("time", ))
data = np.arange(len(time)) * 1e-03
var[...] = data
var.units = "gC/m^2/s"
var.cell_methods = "time: mean"
## horizontal fluxes
var = ds.createVariable("CWD_TO_LITR", "f8", ("time", ))
data = np.arange(len(time)) * 1e-03
var[...] = data
var.units = "gC/m^2/s"
var.cell_methods = "time: mean"
## external_output_fluxes
var = ds.createVariable("CWD_HR", "f8", ("time", ))
data = np.arange(len(time))
var[...] = data
var.units = "gC/m^2/s"
var.cell_methods = "time: mean"
## set up a ModelDataObject
nstep = 2
stock_unit = "g/m^2"
# cftime_unit_src = 'days since 1850-01-01 00:00:00'
# calendar_src = 'noleap'
# max_time = 8
time = Variable(data=time, unit="d")
mdo = ModelDataObject(
model_structure=model_structure,
dataset=ds,
nstep=nstep,
stock_unit=stock_unit,
time=time
# cftime_unit_src = cftime_unit_src,
# calendar_src = calendar_src,
# max_time = max_time
)
return mdo