outvarname = options.outvarname
# varlist = ['hurs', 'psl', 'rls', 'rss', 'sfcWind', 'tasmax', 'tasmin', 'tas']
# pd = dict([[v,
# "/Users/nelereyniers/data/toydata/toydata_3x3/{}_rcp85_land-rcm_uk_12km_01_day_19801201-20801130.nc".format(v)]
# for v in varlist])
# outvarname = 'evpot-fao'
# outpath = "/Users/nelereyniers/data/toydata/toydata_3x3/{}_rcp85_land-rcm_uk_12km_01_day_19801201-20801130.nc".format(outvarname)
# ---------------------------------------------------------------------
pd = dict([[v, input_path_format.format(var=v)] for v in varlist])
outpath = output_path_format.format(outvarname)
ds_ukcp18 = prepare_input_from_ukcp18(pd, concat_dim=False)
kwargs_ukcp18 = get_ukcp18_kwargs()
et_ukcp18 = ETo_xr()
et_ukcp18.param_est_xr(ds_ukcp18, **kwargs_ukcp18)
et_ukcp18_fao = et_ukcp18.eto_fao_xr(remove_extreme_values=False, round_decimals=2)
attributes = {
'creation_script': sys.argv[0] + " (github user mullenkamp and N. Reyniers)",
'creation_time': datetime.now().strftime("%d-%m-%Y %H:%M"),
'standard_name': 'water_potential_evaporation_amount',
'long_name': 'FAO reference crop evaporation amount',
'units': 'kg m-2',
'label_units': 'kg m-2',
'plot_label': 'FAO56-Penman-Monteith ETo (kg m-2)',
'description': 'FAO56-Penman-Monteith reference crop potential evaporation (kg m-2)',
'units_equivalent': 'mm',
'PE_calculation_method': 'FAO56 Penman-Monteith (Allen et al., 1998)',
'references': 'Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines \
This computes FAO56 PM well-watered grass PE.
"""
from eto import ETo_xr, datasets
from eto.util_xr import get_ukcp18_kwargs, prepare_input_from_ukcp18
import pandas as pd
import numpy as np
# test on generic dataset.
# this dataset contains:
# R_sm
# T_max
# T_min
# e_a
# along with date.
et1 = ETo_xr()
ex1_path = datasets.get_path('example_daily')
dftemp = pd.read_csv(ex1_path,
parse_dates=True,
infer_datetime_format=True,
index_col='date')
ds = dftemp.to_xarray().assign_coords({
'projection_x_coordinate': 20000,
'projection_y_coordinate': 50000
}).expand_dims(['projection_x_coordinate', 'projection_y_coordinate'])
z_msl = 500 # known Elevation of the met station above mean sea level (m) (only needed if P is not in df).
lat = -43.6 # knwon The latitude of the met station (dec deg) (only needed if R_s or R_n are not in df).
lon = 172 # known The longitude of the met station (dec deg) (only needed if calculating ETo hourly)
TZ_lon = 173 # known The longitude of the center of the time zone (dec deg) (only needed if calculating ETo hourly).
# z_u = 2: The height of the wind speed measurement (m). Default is 2 m.
def test_eto_fao_daily():
et1 = ETo_xr(ds, 'D', z_msl, lat, lon, TZ_lon)
eto1 = et1.eto_fao_xr(remove_extreme_values=False, round_decimals=2).sum()
res1 = tsresults['ETo_FAO_mm'].sum()
assert eto1 == res1
### Tests
tsdata = pd.read_csv(example1,
parse_dates=True,
infer_datetime_format=True,
index_col='date')
tsresults = pd.read_csv(results1,
parse_dates=True,
infer_datetime_format=True,
index_col='date')
ds = tsdata.to_xarray().assign_coords({
'projection_x_coordinate': 20000,
'projection_y_coordinate': 50000
}).expand_dims(['projection_x_coordinate', 'projection_y_coordinate'])
self = ETo_xr(ds, 'D', z_msl, lat, lon, TZ_lon)
# eto1 = et1.eto_fao().sum()
# res1 = tsresults['ETo_FAO_mm'].sum()
print("self:\n", self)
remove_extreme_values = False
round_decimals = 2
max_ETo = 15
min_ETo = 0
interp = False
maxgap = 15
# def eto_fao(self, max_ETo=15, min_ETo=0, interp=False, maxgap=15, remove_extreme_values=True, round_decimals=2):
"""
Function to estimate reference ET (ETo) from the `FAO 56 paper <http://www.fao.org/docrep/X0490E/X0490E00.htm>`_ [1]_
using a minimum of T_min and T_max for daily estimates and T_mean and RH_mean for hourly, but optionally utilising
the maximum number of available met parameters.
The function prioritizes the estimation of specific parameters based on the available input data.
'projection_y_coordinate': 50000
}).expand_dims(['projection_x_coordinate', 'projection_y_coordinate'])
def test_eto_fao_daily():
et1 = ETo_xr(ds, 'D', z_msl, lat, lon, TZ_lon)
eto1 = et1.eto_fao_xr(remove_extreme_values=False, round_decimals=2).sum()
res1 = tsresults['ETo_FAO_mm'].sum()
assert eto1 == res1
et1 = ETo_xr(ds,
'D',
z_msl,
lat,
lon,
TZ_lon,
remove_extreme_values=False,
round_decimals=2)
# # TODO
# def test_eto_har_daily():
# pass
# eto2 = et1.eto_hargreaves_xr().sum()
# res1 = tsresults['ETo_Har_mm'].sum()
#
# assert eto2 == res1
# # TODO
# def test_eto_fao_hourly():
# pass