def read_soil_parameters(self):
soil_parameters = [
'EvapZsurf', 'EvapZmin', 'EvapZmax', 'Kex', 'fevap', 'fWrelExp',
'fwcc', 'CN', 'zCN', 'zGerm', 'zRes', 'fshape_cr'
]
for param in soil_parameters:
try:
arr = open_hmdataarray(
self.model.config.SOIL_PARAMETERS['filename'],
param,
self.model.domain,
self.model.config.SOIL_PARAMETERS['is_1d'],
self.model.config.SOIL_PARAMETERS['xy_dimname'],
)
vars(self.model)[param] = np.require(
arr.values, requirements=['A', 'O', 'W', 'F'])
except:
try:
parameter_value = read_parameter_from_sqlite(
self.model.SoilParameterDatabase, param)
parameter_value = np.array(parameter_value[0],
dtype=np.float64)
vars(self.model)[param] = np.require(
np.full(self.model.domain.nxy, parameter_value),
requirements=['A', 'O', 'W', 'F'])
except:
pass
def test_open_hmdataarray():
# os.chdir('test/release')
modeltime = hm.set_modeltime(
datetime.datetime(2003,6,1),
datetime.datetime(2003,6,30),
datetime.timedelta(days=1)
)
domain = hm.set_domain(
'test_data/qrparm.soil_HWSD_cont_cosbyNew.nc',
modeltime,
'field342',
is_1d=True,
xy_dimname='land'
)
# domain.mask
# domain.starttime
# domain.endtime
# domain.n_timestep
# domain.dt
da = hm.open_hmdataarray(
'test_data/SWdown_WFDEI_land_200306.nc',
'SWdown',
domain,
True,
'land'
)
da = da.select(
time=slice(
datetime.datetime(2003,6,1),
datetime.datetime(2003,6,10)
)
)
def test_open_hmdataarray():
modeltime = hm.set_modeltime(datetime.datetime(2003, 6, 1, 0, 0),
datetime.datetime(2003, 6, 10, 0, 0),
datetime.timedelta(days=1))
domain = hm.set_domain('test_data/ghana_landmask_0pt25degree.tif',
modeltime)
da = hm.open_hmdataarray('test_data/AgMERRA_2003_tavg.nc4', 'tavg', domain)
da.select(time=pd.Timestamp(2003, 6, 1), method='nearest')
def set_initial_condition_from_file(self):
self.var.th_init = open_hmdataarray(
self.var.config.INITIAL_WATER_CONTENT['filename'],
'th',
self.var.domain,
self.var.config.INITIAL_WATER_CONTENT['is_1d'],
self.var.config.INITIAL_WATER_CONTENT['xy_dimname'],
skip=['depth'])
self.interpolate_initial_condition_to_compartments()
self.var.th = np.broadcast_to(
self.var.th_init.values[None, None, ...],
(self.var.nFarm, self.var.nCrop, self.var.nComp,
self.var.domain.nxy)).copy()
def read_soil_hydraulic_parameters(self):
# TODO: optionally read directly from config file
soil_hydraulic_parameters = {
'k_sat':
self.model.config.SOIL_HYDRAULIC_PARAMETERS['k_sat_varname'],
'th_sat':
self.model.config.SOIL_HYDRAULIC_PARAMETERS['th_sat_varname'],
'th_fc':
self.model.config.SOIL_HYDRAULIC_PARAMETERS['th_fc_varname'],
'th_wilt':
self.model.config.SOIL_HYDRAULIC_PARAMETERS['th_wilt_varname']
}
for param, varname in soil_hydraulic_parameters.items():
if param in self.model.config.SOIL_HYDRAULIC_PARAMETERS.keys():
# 1 - Try to read from config file
parameter_values = np.array(
self.model.config.SOIL_HYDRAULIC_PARAMETERS[param])
if (len(parameter_values) == self.model.nLayer):
vars(self.model)[param] = np.require(np.broadcast_to(
parameter_values,
(self.model.nLayer, self.model.domain.nxy)),
requirements=[
'A', 'O', 'W', 'F'
])
else:
raise ValueError("Error reading parameter " + param +
" from configuration file: length" +
" of parameter list must equal number" +
" of farms in simulation")
else:
# 2 - Try to read from netCDF file
try:
arr = open_hmdataarray(
self.model.config.
SOIL_HYDRAULIC_PARAMETERS['filename'],
varname,
self.model.domain,
self.model.config.SOIL_HYDRAULIC_PARAMETERS['is_1d'],
self.model.config.
SOIL_HYDRAULIC_PARAMETERS['xy_dimname'],
)
vars(self.model)[param] = np.require(
arr.values, requirements=['A', 'O', 'W', 'F'])
except:
raise ValueError() # TODO
def read(self):
# PlantPop currently real in Fortran
# ETadj should be an option provided in config
int_crop_params = [
'CropType', 'PlantingDate', 'HarvestDate', 'PolHeatStress',
'PolColdStress', 'BioTempStress', 'PlantPop', 'Determinant',
'ETadj', 'LagAer'
]
flt_crop_params = [
'Emergence', 'MaxRooting', 'Senescence', 'Maturity', 'HIstart',
'Flowering', 'YldForm', 'Tbase', 'Tupp', 'Tmax_up', 'Tmax_lo',
'Tmin_up', 'Tmin_lo', 'GDD_up', 'GDD_lo', 'fshape_b', 'PctZmin',
'Zmin', 'Zmax', 'fshape_r', 'fshape_ex', 'SxTopQ', 'SxBotQ',
'a_Tr', 'SeedSize', 'CCmin', 'CCx', 'CDC', 'CGC', 'Kcb', 'fage',
'WP', 'WPy', 'fsink', 'bsted', 'bface', 'HI0', 'HIini', 'dHI_pre',
'a_HI', 'b_HI', 'dHI0', 'exc', 'MaxFlowPct', 'p_up1', 'p_up2',
'p_up3', 'p_up4', 'p_lo1', 'p_lo2', 'p_lo3', 'p_lo4', 'fshape_w1',
'fshape_w2', 'fshape_w3', 'fshape_w4', 'Aer', 'beta', 'GermThr'
]
crop_params_to_read = int_crop_params + flt_crop_params
if len(crop_params_to_read) > 0:
for param in crop_params_to_read:
if param in int_crop_params:
datatype = np.int32
else:
datatype = np.float64
# 1 Try to read from configuration file:
if param in self.config.keys():
# TODO
parameter_values = np.array(self.config[param])
# ensure the parameter has a farm dimension
if len(parameter_values.shape) == 1:
if len(parameter_values) == self.model.nCrop:
parameter_values = np.broadcast_to(
parameter_values,
(self.model.nFarm, self.model.nCrop))
if (parameter_values.shape[0] == self.model.nFarm) & (
parameter_values.shape[1] == self.model.nCrop):
vars(self.model)[param] = np.require(
np.broadcast_to(
parameter_values[:, None],
(self.model.nFarm, self.model.nCrop,
self.model.domain.nxy)),
dtype=datatype,
requirements=['A', 'O', 'W', 'F'])
else:
raise ValueError(
"Error reading parameter " + param +
" from configuration file: length" +
" of parameter list must equal number" +
" of crops in simulation")
else:
# 2 Try to read from netCDF file
try:
arr = open_hmdataarray(
self.model.config.CROP_PARAMETERS['filename'],
param,
self.model.domain,
self.model.config.CROP_PARAMETERS['is_1d'],
self.model.config.CROP_PARAMETERS['xy_dimname'],
sample=self.model.currentSampleNumber)
vars(self.model)[param] = np.require(np.broadcast_to(
arr.values, (self.model.nFarm, self.model.nCrop,
self.model.domain.nxy)),
dtype=datatype,
requirements=[
'A', 'O', 'W',
'F'
])
# 3 - Read from default parameter database
except:
try:
parameter_values = np.zeros((self.model.nCrop))
for index, crop_id in enumerate(self.model.CropID):
parameter_values[
index] = read_crop_parameter_from_sqlite(
self.model.CropParameterDatabase,
crop_id, param)[0]
vars(self.model)[param] = np.require(
np.broadcast_to(
parameter_values[None, :, None],
(self.model.nFarm, self.model.nCrop,
self.model.domain.nxy)),
dtype=datatype,
requirements=['A', 'O', 'W', 'F'])
except:
raise KeyError("Error reading parameter " + param +
" from crop parameter database")