def harvest_and_save_nordnesfjelet(from_string, to_string):
stnr = 91500 # Nordnesfjellet (met)
from_date = dt.datetime.strptime(from_string, "%Y-%m-%d")
to_date = dt.datetime.strptime(to_string, "%Y-%m-%d")
#elems_blind = gws.getElementsFromTimeserieTypeStation(stnr, 2, output='csv')
qli = gws.getMetData(stnr, 'QLI', from_date, to_date, 2, output='raw list')
qli_test_message = we.test_for_missing_elements(qli, from_date, to_date, time_step=60*60)
ta = gws.getMetData(stnr, 'ta', from_date, to_date, 2, output='raw list')
ta_test_message = we.test_for_missing_elements(ta, from_date, to_date, time_step=3600)
rr_1 = gws.getMetData(stnr, 'rr_1', from_date, to_date, 2, output='raw list')
rr_1 = we.millimeter_from_meter(rr_1)
rr_test_message = we.test_for_missing_elements(rr_1, from_date, to_date, time_step=3600)
file_name = '{2}QLI TA RR Nordnesfjellet 91500 {0} to {1}.txt'.format(from_string, to_string, env.data_path)
WSYS = "github/Ice-modelling/rundataharvester.py"
OPER = "Ragnar Ekker"
DCHA = ['QLI [Wh/m2] avg pr hr on Nordnesfjellet 91500 from eklima.met.no',
'TA [C] avg pr hr on Nordnesfjellet 91500 from eklima.met.no',
'RR_1 [mm/hr] on Nordnesfjellet 91500 from eklima.met.no']
mfd.write_vardat2(file_name, [qli, ta, rr_1], WSYS, OPER, DCHA)
return
def harvest_and_save_blindern(from_string, to_string):
stnr_met_blind = 18700 # Blindern (met)
from_date = dt.datetime.strptime(from_string, "%Y-%m-%d")
to_date = dt.datetime.strptime(to_string, "%Y-%m-%d")
#elems_blind = gws.getElementsFromTimeserieTypeStation(stnr_met_blind, 2, output='csv')
rr = gws.getMetData(stnr_met_blind, 'RR', from_date, to_date, 0, output='raw list')
rr = we.millimeter_from_meter(rr)
rr_test_message = we.test_for_missing_elements(rr, from_date, to_date)
tam = gws.getMetData(stnr_met_blind, 'TAM', from_date, to_date, 0, output='raw list')
tam_test_message = we.test_for_missing_elements(tam, from_date, to_date)
nnm = gws.getMetData(stnr_met_blind, 'NNM', from_date, to_date, 0, output='raw list')
nnm_test_message = we.test_for_missing_elements(nnm, from_date, to_date)
file_name = '{2}RR TAM NNM Blindern 18700 {0} to {1}.txt'.format(from_string, to_string, env.data_path)
WSYS = "github/Ice-modelling/rundataharvester.py"
OPER = "Ragnar Ekker"
DCHA = ['RR [mm/day] on Blindern 18700 from eklima.met.no',
'TAM [C] daily avg on Blindern 18700 from eklima.met.no',
'NNM [%/100] daily avg on Blindern 18700 from eklima.met.no']
mfd.write_vardat2(file_name, [rr, tam, nnm], WSYS, OPER, DCHA)
return
def add_Global_rad(self, Global_rad_inn):
if Global_rad_inn is None:
self.Global_rad = None
else:
messages = we.test_for_missing_elements(Global_rad_inn, self.from_date, self.to_date)
self.metadata += messages
self.Global_rad = Global_rad_inn
def harvest_and_save_blindern(from_string, to_string):
stnr_met_blind = 18700 # Blindern (met)
from_date = dt.datetime.strptime(from_string, "%Y-%m-%d")
to_date = dt.datetime.strptime(to_string, "%Y-%m-%d")
#elems_blind = gws.getElementsFromTimeserieTypeStation(stnr_met_blind, 2, output='csv')
rr = gws.getMetData(stnr_met_blind,
'RR',
from_date,
to_date,
0,
output='raw list')
rr = we.millimeter_from_meter(rr)
rr_test_message = we.test_for_missing_elements(rr, from_date, to_date)
tam = gws.getMetData(stnr_met_blind,
'TAM',
from_date,
to_date,
0,
output='raw list')
tam_test_message = we.test_for_missing_elements(tam, from_date, to_date)
nnm = gws.getMetData(stnr_met_blind,
'NNM',
from_date,
to_date,
0,
output='raw list')
nnm_test_message = we.test_for_missing_elements(nnm, from_date, to_date)
file_name = '{2}RR TAM NNM Blindern 18700 {0} to {1}.txt'.format(
from_string, to_string, env.data_path)
WSYS = "github/Ice-modelling/rundataharvester.py"
OPER = "Ragnar Ekker"
DCHA = [
'RR [mm/day] on Blindern 18700 from eklima.met.no',
'TAM [C] daily avg on Blindern 18700 from eklima.met.no',
'NNM [%/100] daily avg on Blindern 18700 from eklima.met.no'
]
mfd.write_vardat2(file_name, [rr, tam, nnm], WSYS, OPER, DCHA)
return
def harvest_and_save_nordnesfjelet(from_string, to_string):
stnr = 91500 # Nordnesfjellet (met)
from_date = dt.datetime.strptime(from_string, "%Y-%m-%d")
to_date = dt.datetime.strptime(to_string, "%Y-%m-%d")
#elems_blind = gws.getElementsFromTimeserieTypeStation(stnr, 2, output='csv')
qli = gws.getMetData(stnr, 'QLI', from_date, to_date, 2, output='raw list')
qli_test_message = we.test_for_missing_elements(qli,
from_date,
to_date,
time_step=60 * 60)
ta = gws.getMetData(stnr, 'ta', from_date, to_date, 2, output='raw list')
ta_test_message = we.test_for_missing_elements(ta,
from_date,
to_date,
time_step=3600)
rr_1 = gws.getMetData(stnr,
'rr_1',
from_date,
to_date,
2,
output='raw list')
rr_1 = we.millimeter_from_meter(rr_1)
rr_test_message = we.test_for_missing_elements(rr_1,
from_date,
to_date,
time_step=3600)
file_name = '{2}QLI TA RR Nordnesfjellet 91500 {0} to {1}.txt'.format(
from_string, to_string, env.data_path)
WSYS = "github/Ice-modelling/rundataharvester.py"
OPER = "Ragnar Ekker"
DCHA = [
'QLI [Wh/m2] avg pr hr on Nordnesfjellet 91500 from eklima.met.no',
'TA [C] avg pr hr on Nordnesfjellet 91500 from eklima.met.no',
'RR_1 [mm/hr] on Nordnesfjellet 91500 from eklima.met.no'
]
mfd.write_vardat2(file_name, [qli, ta, rr_1], WSYS, OPER, DCHA)
return
def add_Global_rad(self, Global_rad_inn):
if Global_rad_inn is None:
self.Global_rad = None
else:
messages = we.test_for_missing_elements(Global_rad_inn,
self.from_date,
self.to_date)
self.metadata += messages
self.Global_rad = Global_rad_inn
def add_Inflow_DOC(self, Inflow_DOC_inn):
messages = we.test_for_missing_elements(Inflow_DOC_inn, self.from_date, self.to_date)
self.metadata += messages
self.Inflow_DOC = Inflow_DOC_inn
def add_Precipitation(self, Precipitation_inn):
messages = we.test_for_missing_elements(Precipitation_inn, self.from_date, self.to_date)
self.metadata += messages
self.Precipitation = Precipitation_inn
def add_Wind_speed(self, Wind_speed_inn):
messages = we.test_for_missing_elements(Wind_speed_inn, self.from_date, self.to_date)
self.metadata += messages
self.Wind_speed = Wind_speed_inn
def add_Air_press(self, Air_press_inn):
messages = we.test_for_missing_elements(Air_press_inn, self.from_date, self.to_date)
self.metadata += messages
self.Air_press = Air_press_inn
def add_Relat_hum(self, Relat_hum_inn):
messages = we.test_for_missing_elements(Relat_hum_inn, self.from_date, self.to_date)
self.metadata += messages
self.Relat_hum = Relat_hum_inn
def add_Cloud_cov(self, Cloud_cov_inn):
messages = we.test_for_missing_elements(Cloud_cov_inn, self.from_date, self.to_date)
self.metadata += messages
self.Cloud_cov = Cloud_cov_inn
def add_Inflow_DOC(self, Inflow_DOC_inn):
messages = we.test_for_missing_elements(Inflow_DOC_inn, self.from_date,
self.to_date)
self.metadata += messages
self.Inflow_DOC = Inflow_DOC_inn
def add_Precipitation(self, Precipitation_inn):
messages = we.test_for_missing_elements(Precipitation_inn,
self.from_date, self.to_date)
self.metadata += messages
self.Precipitation = Precipitation_inn
def add_Wind_speed(self, Wind_speed_inn):
messages = we.test_for_missing_elements(Wind_speed_inn, self.from_date,
self.to_date)
self.metadata += messages
self.Wind_speed = Wind_speed_inn
def add_Air_press(self, Air_press_inn):
messages = we.test_for_missing_elements(Air_press_inn, self.from_date,
self.to_date)
self.metadata += messages
self.Air_press = Air_press_inn
def add_Relat_hum(self, Relat_hum_inn):
messages = we.test_for_missing_elements(Relat_hum_inn, self.from_date,
self.to_date)
self.metadata += messages
self.Relat_hum = Relat_hum_inn
def add_Cloud_cov(self, Cloud_cov_inn):
messages = we.test_for_missing_elements(Cloud_cov_inn, self.from_date,
self.to_date)
self.metadata += messages
self.Cloud_cov = Cloud_cov_inn