def estimate_fao56_daily(self, day_of_year, temp_c, temp_c_min, temp_c_max,
tdew, elevation, latitude, rh, wind_m_s,
atmos_pres):
""" Estimate fao56 from weather """
sha = pyeto.sunset_hour_angle(pyeto.deg2rad(latitude),
pyeto.sol_dec(day_of_year))
daylight_hours = pyeto.daylight_hours(sha)
sunshine_hours = 0.8 * daylight_hours
ird = pyeto.inv_rel_dist_earth_sun(day_of_year)
et_rad = pyeto.et_rad(pyeto.deg2rad(latitude),
pyeto.sol_dec(day_of_year), sha, ird)
sol_rad = pyeto.sol_rad_from_sun_hours(daylight_hours, sunshine_hours,
et_rad)
net_in_sol_rad = pyeto.net_in_sol_rad(sol_rad=sol_rad, albedo=0.23)
cs_rad = pyeto.cs_rad(elevation, et_rad)
avp = pyeto.avp_from_tdew(tdew)
net_out_lw_rad = pyeto.net_out_lw_rad(
pyeto.convert.celsius2kelvin(temp_c_min),
pyeto.convert.celsius2kelvin(temp_c_max), sol_rad, cs_rad, avp)
net_rad = pyeto.net_rad(net_in_sol_rad, net_out_lw_rad)
eto = pyeto.fao56_penman_monteith(
net_rad=net_rad,
t=pyeto.convert.celsius2kelvin(temp_c),
ws=wind_m_s,
svp=pyeto.svp_from_t(temp_c),
avp=avp,
delta_svp=pyeto.delta_svp(temp_c),
psy=pyeto.psy_const(atmos_pres))
return eto
def calcPET(lat, time, tmin, tmax, tmean):
'''
Calculates Potential Evapotranspiration using
Hargreaves equation (Hargreaves and Samani, 1985)
'''
latrad = pt.deg2rad(lat) #Latitude to radians
dayofyear = pd.Series(time).dt.day.values
etrad = []
pet = []
# Calculate ET radiation
for x in np.nditer(dayofyear):
soldec = pt.sol_dec(x) #Solar declination
sha = pt.sunset_hour_angle(latrad, soldec) #Sunset hour aingle
ird = pt.inv_rel_dist_earth_sun(
x) #Inverse relative distance Earth-Sun
etrad.append(pt.et_rad(latrad, soldec, sha,
ird)) #Extraterrestrial radiation
# Calculate PET using hargreaves
for x in range(0, len(etrad)):
pet.append(pt.hargreaves(tmin[x], tmax[x], tmean[x], etrad[x]))
pet = np.array(pet)
return (pet)
def thorn(year, latitude, df, model):
data = df.loc[df['yil'] == year][model].values
# lat = 37
lat = deg2rad(latitude)
mmdlh = monthly_mean_daylight_hours(lat, year)
evapo = np.array(thornthwaite(data, mmdlh))
return evapo
def thorn_clima(year, latitude, df, model, senario):
data = df.loc[(df['Yıl'] == year) & (df['Model'] == model) &
(df['Senaryo'] == senario), 'Ortalama_Sıcaklık'].values
# lat = 37
if len(data) != 12:
data = data[0:12]
lat = deg2rad(latitude)
mmdlh = monthly_mean_daylight_hours(lat, year)
evapo = np.array(thornthwaite(data, mmdlh))
return data, evapo
def main(lat, temp_mean, precip_mean, max_tsmd):
'''get evapotranspiration''' # taken from Richards pyeto, see doc in same folder for more informatios
pyeto_lat = pyeto.deg2rad(lat) # converts the degree to radians
mean_month_list = [temp_mean[i] for i in temp_mean]
monthly_mean_daylight = pyeto.monthly_mean_daylight_hours(pyeto_lat)
eto = pyeto.thornthwaite(mean_month_list, monthly_mean_daylight)
'''make a "month_number" : "evapo" dic'''
eto_dict = {}
for n,i in enumerate(eto):
eto_dict[n+1] = eto[n]
print "\nWaterbalance with maximum deficientcy of", max_tsmd
acc_TSMD = {}
acc_factor = 0
budget = {}
'''The following part is still a bit hacky.
Two for loops that first calculates the the water buget for every mongth
and then starts a second loop to substract the value of the month before from the current month
'''
print "month \t rain \t eto \t\t TSMD"
for month, rain, etom in zip(range(1,13),precip_mean, eto_dict):
TSMD = precip_mean[rain] - ( eto_dict[etom] * 0.75 )
if TSMD <= max_tsmd:
TSMD = max_tsmd
print month,"\t", precip_mean[rain], "\t", eto_dict[etom],"\t", TSMD
budget[month] = TSMD if TSMD < 0 else 0
for month, rain, etom in zip(range(1,13),precip_mean, eto_dict):
TSMD = precip_mean[rain] - ( eto_dict[etom] * 0.75 )
if month == 1:
acc_TSMD[month] = TSMD + budget[12]
else:
acc_TSMD[month] = TSMD + budget[month -1]
if acc_TSMD[month] > 0 :
acc_TSMD[month] = 0
if acc_TSMD[month] < max_tsmd:
acc_TSMD[month] = max_tsmd
print "\nWater budget"
p(budget)
print ""
return acc_TSMD
def get_evap_i(lat, elev, wind, srad, tmin, tmax, tavg, month):
if month == 1:
J = 15
else:
J = 15 + (month - 1) * 30
latitude = pyeto.deg2rad(lat)
atmosphericVapourPressure = pyeto.avp_from_tmin(tmin)
saturationVapourPressure = pyeto.svp_from_t(tavg)
ird = pyeto.inv_rel_dist_earth_sun(J)
solarDeclination = pyeto.sol_dec(J)
sha = [pyeto.sunset_hour_angle(l, solarDeclination) for l in latitude]
extraterrestrialRad = [
pyeto.et_rad(x, solarDeclination, y, ird)
for x, y in zip(latitude, sha)
]
clearSkyRad = pyeto.cs_rad(elev, extraterrestrialRad)
netInSolRadnet = pyeto.net_in_sol_rad(srad * 0.001, albedo=0.23)
netOutSolRadnet = pyeto.net_out_lw_rad(tmin, tmax, srad * 0.001,
clearSkyRad,
atmosphericVapourPressure)
netRadiation = pyeto.net_rad(netInSolRadnet, netOutSolRadnet)
tempKelvin = pyeto.celsius2kelvin(tavg)
windSpeed2m = pyeto.wind_speed_2m(wind, 10)
slopeSvp = pyeto.delta_svp(tavg)
atmPressure = pyeto.atm_pressure(elev)
psyConstant = pyeto.psy_const(atmPressure)
return pyeto.fao56_penman_monteith(netRadiation,
tempKelvin,
windSpeed2m,
saturationVapourPressure,
atmosphericVapourPressure,
slopeSvp,
psyConstant,
shf=0.0)
import pandas as pd
import numpy as np
import os
from pyeto import thornthwaite, monthly_mean_daylight_hours, deg2rad
os.chdir(r"C:\Users\PC3\Desktop\Hydro")
df = pd.read_csv('data.csv')
df['Date'] = pd.to_datetime(df['Date'])
df['Year'] = df['Date'].dt.year
df = df.set_index('Date')
df_m = df.resample("M").mean()
data = []
lat = deg2rad(41.3)
for i in range(2015,2019):
mmdlh = monthly_mean_daylight_hours(lat, i)
monthly_t = df_m['Temp'][(df_m.Year == i)]
PET = thornthwaite(monthly_t.values.tolist(), mmdlh)
data.append(PET)
print("a")
def calculate_precipitation(self, d):
if "rain" in d:
self.rain_day = float(d["rain"])
if "snow" in d:
self.snow_day = float(d["snow"])
# def calculate_ev_fao56_factor(self, d):
dt = d['dt']
factor = 0.0
if dt > d['sunrise']:
if dt < d['sunset']:
factor = min(float(dt - d['sunrise'])/3600.0, 1.0)
else:
if dt > d['sunset']:
factor = (dt - d['sunrise'])/3600.0
if factor < 1.0:
factor = 1.0 - factor
return factor
#def estimate_fao56_hourly(self, day_of_year, temp_c, tdew, elevation, latitude, rh, wind_m_s, atmos_pres):
""" Estimate fao56 from weather """
sha = pyeto.sunset_hour_angle(pyeto.deg2rad(latitude),
pyeto.sol_dec(day_of_year))
daylight_hours = pyeto.daylight_hours(sha)
sunshine_hours = 0.8 * daylight_hours
ird = pyeto.inv_rel_dist_earth_sun(day_of_year)
et_rad = pyeto.et_rad(pyeto.deg2rad(latitude),
pyeto.sol_dec(day_of_year), sha, ird)
sol_rad = pyeto.sol_rad_from_sun_hours(daylight_hours, sunshine_hours,
et_rad)
net_in_sol_rad = pyeto.net_in_sol_rad(sol_rad=sol_rad, albedo=0.23)
cs_rad = pyeto.cs_rad(elevation, et_rad)
avp = pyeto.avp_from_tdew(tdew)
#not sure if I trust this net_out_lw_rad calculation here!
net_out_lw_rad = pyeto.net_out_lw_rad(temp_c-1, temp_c, sol_rad,
cs_rad, avp)
net_rad = pyeto.net_rad(net_in_sol_rad, net_out_lw_rad)
eto = pyeto.fao56_penman_monteith(
net_rad=net_rad,
t=pyeto.convert.celsius2kelvin(temp_c),
ws=wind_m_s,
svp=pyeto.svp_from_t(temp_c),
avp=avp,
delta_svp=pyeto.delta_svp(temp_c),
psy=pyeto.psy_const(atmos_pres))
return eto
#def calculate_fao56_hourly(self, d):
day_of_year = datetime.datetime.now().timetuple().tm_yday
T_hr = d['temp']
t_dew = float(d["dew_point"])
pressure = d['pressure']
RH_hr = d['humidity']
u_2 = d['wind_speed']
#print("CALCULATE_FAO56:")
#print("T_hr: {}".format(T_hr))
#print("t_dew: {}".format(t_dew))
#print("RH_hr: {}".format(RH_hr))
#print("u_2: {}".format(u_2))
#print("pressure: {}".format(pressure))
fao56 = self.estimate_fao56_hourly(day_of_year,
T_hr,
t_dew,
self.elevation,
LAT,
RH_hr,
u_2,
pressure)
return fao56
#watering system check for amount of rain.
Forever
check watering parameters
if <10cm then
no action
if >10cm then
initiate watering system
# this data should be from nws.
import pyeto
latitude_deg = 38.01
latitude = pyeto.deg2rad(latitude_deg)
day_of_year = 206
tmin = 37
tmax = 53
coastal = True
altitude = 147
rh_min = 13
rh_max = 88
ws = 1.3
tmean = pyeto.daily_mean_t(tmin, tmax)
atmos_pres = pyeto.atm_pressure(altitude)
psy = pyeto.psy_const(atmos_pres)
# Humidity
svp_tmin = pyeto.svp_from_t(tmin)
svp_tmax = pyeto.svp_from_t(tmax)
delta_svp = pyeto.delta_svp(tmean)
svp = pyeto.mean_svp(tmin, tmax)
avp = pyeto.avp_from_rhmin_rhmax(svp_tmin, svp_tmax, rh_min, rh_max)
def get_data_from_WU():
###array to store the reports
wu_weather_reports = []
## today and last 6 days definition
day1 = now - datetime.timedelta(days=6)
day2 = now - datetime.timedelta(days=5)
day3 = now - datetime.timedelta(days=4)
day4 = now - datetime.timedelta(days=3)
day5 = now - datetime.timedelta(days=2)
day6 = now - datetime.timedelta(days=1)
day7 = now
#### convert dates to WU required format
days = {
'day1': day1.strftime('%Y%m%d'),
'day2': day2.strftime('%Y%m%d'),
'day3': day3.strftime('%Y%m%d'),
'day4': day4.strftime('%Y%m%d'),
'day5': day5.strftime('%Y%m%d'),
'day6': day6.strftime('%Y%m%d'),
'day7': day7.strftime('%Y%m%d')
}
### make API wather hisotry call for each day
for day in days:
url = 'http://api.wunderground.com/api/7c2ab99a0ccee978/history_{0}/q/95316.json'.format(
days[day])
headers = {'content-type': 'application/JSON; charset=utf8'}
response = requests.get(url, headers=headers)
data = json.loads(response.text)
#ETo calculation for the day using FAO-56 Penman-Monteith method
lat = pyeto.deg2rad(37.585652)
altitude = 38
julian_day = datetime.datetime.strptime(days.get(day),
'%Y%m%d').timetuple().tm_yday
sol_dec = pyeto.sol_dec(julian_day)
sha = pyeto.sunset_hour_angle(lat, sol_dec)
ird = pyeto.inv_rel_dist_earth_sun(julian_day)
### net radiation calculator
net_rad = pyeto.et_rad(lat, sol_dec, sha, ird)
temp_c = float(data["history"]["observations"][1]["tempm"])
temp_k = float(data["history"]["observations"][1]["tempi"])
humidity = float(data["history"]["observations"][1]["hum"])
dew_point = float(data["history"]["observations"][1]["dewptm"])
ws = float(data["history"]["observations"][1]["wspdm"])
#actual and saturated vapour pressure in kPH
svp = pyeto.svp_from_t(temp_c)
avp = pyeto.avp_from_tdew(dew_point)
delta_svp = pyeto.delta_svp(temp_c)
atm_pressure = pyeto.atm_pressure(altitude)
psy = pyeto.psy_const(atm_pressure)
#### the ETo plugin retun results in mm, it was converted to inched
ETo_in_mm = pyeto.fao56_penman_monteith(net_rad,
temp_k,
ws,
svp,
avp,
delta_svp,
psy,
shf=0.0)
ETo = ETo_in_mm * 0.039370
## insert eto value to day weather report
data["history"]["observations"][1].update(
{"ETo": "{0:.2f}".format(ETo)})
###add report to report collector array
wu_weather_reports.append(data["history"]["observations"][1])
#return all reports
return wu_weather_reports
def etp_Thornthwaite(self, anosCalculoETP, inicioPlantioTuple):
latitudeRad = deg2rad(self.estacao.latitude)
temperaturaMensalAcc = np.array([]).reshape(0, 12)
temperaturaDecendioAcc = np.array([]).reshape(12, 3, 0)
ETPs = np.zeros([len(anosCalculoETP), 13, 4])
self.anosCalculo = anosCalculoETP
# O ETP é calculado anualmente e o resultado final é a média desses cálculos
# anosCalculoETP grava os anos selecionados pelo usuário para o cálculo do ETP
if anosCalculoETP:
for ano in anosCalculoETP:
#ano = min(anosCalculoETP)
### Calcula medias mensais e decendiais de temperatura para um ano ###
diaAtual = date(ano, 1, 1)
temperaturaAcum = 0
temperaturaAcumMes = 0
diasNoDecendio = 0
diasNoMes = 0
temperaturaMensal = []
temperaturaDecendio = np.zeros((12, 3))
#final = date(min(anosCalculoETP)+1, inicioPlantioTuple[0], inicioPlantioTuple[1])
#print(anosCalculoETP)
while (diaAtual.year == ano):
#while (diaAtual.year in anosCalculoETP) and ((diaAtual)!=final):
#print(diaAtual)
if not np.isnan(
self.dadosMeteorologicos['tmed'][diaAtual]):
#print(self.dadosMeteorologicos['tmed'][diaAtual])
temperaturaAcum += self.dadosMeteorologicos['tmed'][
diaAtual]
temperaturaAcumMes += self.dadosMeteorologicos['tmed'][
diaAtual]
diasNoDecendio += 1
diasNoMes += 1
amanha = diaAtual + timedelta(days=1)
#print(temperaturaAcum)
if amanha.month != diaAtual.month:
#print('trocou mes')
#print(amanha.month)
if diasNoMes > 0:
# Corrigir temperaturas negativas
temperaturaMensal.append(temperaturaAcumMes /
diasNoMes *
(temperaturaAcumMes >= 0))
else:
temperaturaMensal.append(25)
temperaturaAcumMes = 0
diasNoMes = 0
if amanha.day == 1 or amanha.day == 11 or amanha.day == 21:
decendioAtual = calculosDecendiais.converterToDataDecendio(
diaAtual)
if diasNoDecendio > 0:
temperaturaDecendio[
decendioAtual[0] - 1, decendioAtual[1] -
1] = (temperaturaAcum /
diasNoDecendio) * (temperaturaAcum >= 0)
else:
temperaturaDecendio[decendioAtual[0] - 1,
decendioAtual[1] - 1] = 25
temperaturaAcum = 0
diasNoDecendio = 0
#print(diaAtual)
diaAtual = amanha
###########
#print('temp Acumulada e temp Mensal')
#print(temperaturaMensalAcc)
#print(temperaturaDecendioAcc)
temperaturaMensalAcc = np.vstack(
(temperaturaMensalAcc, temperaturaMensal))
temperaturaDecendioAcc = np.dstack(
(temperaturaDecendioAcc, temperaturaDecendio))
temperaturaMensalMedia = temperaturaMensal
temperaturaDecendioMedia = temperaturaDecendio
#print(temperaturaMensalAcc)
#print(temperaturaDecendioAcc)
#print(temperaturaMensalAcc)
# Calcula o heat index a partir das temperaturas
I = 0.0
for Tai in temperaturaMensalMedia:
if Tai / 5.0 > 0.0:
I += (Tai / 5.0)**1.514
a = (6.75e-07 * I**3) - (7.71e-05 * I**2) + (1.792e-02 *
I) + 0.49239
#print(I)
#print(a)
horasDeSolAcum = 0
diasNoDecendio = 0
diaAtual = date(ano, 1, 1)
while (diaAtual.year == ano):
# Calcula o valor dos ETPs decendiais
#while diaAtual.year == 2000:
sd = sol_dec(int(diaAtual.strftime('%j')))
sha = sunset_hour_angle(latitudeRad, sd)
horasDeSolAcum += daylight_hours(sha)
idx = anosCalculoETP.index(ano)
diasNoDecendio += 1
amanha = diaAtual + timedelta(days=1)
if amanha.day == 1 or amanha.day == 11 or amanha.day == 21:
horasDeSolMedia = horasDeSolAcum / diasNoDecendio
decendioAtual = calculosDecendiais.converterToDataDecendio(
diaAtual)
#print(idx)
#print(decendioAtual)
#print(temperaturaDecendioMedia)
ETPs[idx][decendioAtual] = 16 * (
horasDeSolMedia /
12.0) * (diasNoDecendio / 30.0) * (
(10.0 *
temperaturaDecendioMedia[decendioAtual[0] - 1,
decendioAtual[1] - 1]
/ I)**a)
horasDeSolAcum = 0
diasNoDecendio = 0
diaAtual = amanha
self.temperaturaMensalMedia = temperaturaMensalAcc
return ETPs
def etp_Thornthwaite(self, anosCalculoETP):
latitudeRad = deg2rad(self.estacao.latitude)
temperaturaMensalAcc = np.array([]).reshape(0, 12)
temperaturaDecendioAcc = np.array([]).reshape(12, 3, 0)
ETPs = {}
# O ETP é calculado anualmente e o resultado final é a média desses cálculos
# anosCalculoETP grava os anos selecionados pelo usuário para o cálculo do ETP
if anosCalculoETP:
for ano in anosCalculoETP:
### Calcula medias mensais e decendiais de temperatura para um ano ###
diaAtual = date(ano, 1, 1)
temperaturaAcum = 0
temperaturaAcumMes = 0
diasNoDecendio = 0
diasNoMes = 0
temperaturaMensal = []
temperaturaDecendio = np.zeros((12, 3))
while diaAtual.year == ano:
if self.dadosMeteorologicos['tmed'][diaAtual] is not None:
temperaturaAcum += self.dadosMeteorologicos['tmed'][
diaAtual]
temperaturaAcumMes += self.dadosMeteorologicos['tmed'][
diaAtual]
diasNoDecendio += 1
diasNoMes += 1
amanha = diaAtual + timedelta(days=1)
if amanha.month != diaAtual.month:
if diasNoMes > 0:
# Corrigir temperaturas negativas
temperaturaMensal.append(temperaturaAcumMes /
diasNoMes *
(temperaturaAcumMes >= 0))
else:
temperaturaMensal.append(25)
temperaturaAcumMes = 0
diasNoMes = 0
if amanha.day == 1 or amanha.day == 11 or amanha.day == 21:
decendioAtual = calculosDecendiais.converterToDataDecendio(
diaAtual)
if diasNoDecendio > 0:
temperaturaDecendio[
decendioAtual[0] - 1, decendioAtual[1] -
1] = (temperaturaAcum /
diasNoDecendio) * (temperaturaAcum >= 0)
else:
temperaturaDecendio[decendioAtual[0] - 1,
decendioAtual[1] - 1] = 25
temperaturaAcum = 0
diasNoDecendio = 0
diaAtual = amanha
###########
temperaturaMensalAcc = np.vstack(
(temperaturaMensalAcc, temperaturaMensal))
temperaturaDecendioAcc = np.dstack(
(temperaturaDecendioAcc, temperaturaDecendio))
temperaturaMensalMedia = np.mean(temperaturaMensalAcc, axis=0)
temperaturaDecendioMedia = np.mean(temperaturaDecendioAcc, axis=2)
# Calcula o heat index a partir das temperaturas
self.temperaturaMensalMedia = temperaturaMensalMedia
I = 0.0
for Tai in temperaturaMensalMedia:
if Tai / 5.0 > 0.0:
I += (Tai / 5.0)**1.514
a = (6.75e-07 * I**3) - (7.71e-05 * I**2) + (1.792e-02 *
I) + 0.49239
diaAtual = date(2000, 1, 1)
horasDeSolAcum = 0
diasNoDecendio = 0
# Calcula o valor dos ETPs decendiais
while diaAtual.year == 2000:
sd = sol_dec(int(diaAtual.strftime('%j')))
sha = sunset_hour_angle(latitudeRad, sd)
horasDeSolAcum += daylight_hours(sha)
diasNoDecendio += 1
amanha = diaAtual + timedelta(days=1)
if amanha.day == 1 or amanha.day == 11 or amanha.day == 21:
horasDeSolMedia = horasDeSolAcum / diasNoDecendio
decendioAtual = calculosDecendiais.converterToDataDecendio(
diaAtual)
ETPs[decendioAtual] = 1.6 * (horasDeSolMedia / 12.0) * (
diasNoDecendio / 30.0) * (
(10.0 *
temperaturaDecendioMedia[decendioAtual[0] - 1,
decendioAtual[1] - 1] /
I)**a) * 10.0
horasDeSolAcum = 0
diasNoDecendio = 0
diaAtual = amanha
return ETPs
def exec(self):
log.info('[START] {}'.format("exec"))
try:
if (platform.system() == 'Windows'):
# 옵션 설정
sysOpt = {
# 시작/종료 시간
'srtDate': '2020-09-01',
'endDate': '2020-09-03'
# 경도 최소/최대/간격
,
'lonMin': 0,
'lonMax': 360,
'lonInv': 0.5
# 위도 최소/최대/간격
,
'latMin': -90,
'latMax': 90,
'latInv': 0.5
}
else:
# 옵션 설정
sysOpt = {
# 시작/종료 시간
# 'srtDate': globalVar['srtDate']
# , 'endDate': globalVar['endDate']
}
# globalVar['outPath'] = 'F:/Global Temp/aski'
modelList = ['MRI-ESM2-0']
for i, modelInfo in enumerate(modelList):
log.info("[CHECK] modelInfo : {}".format(modelInfo))
inpFile = '{}/{}/{} ssp585 2015-2100_*.nc'.format(
globalVar['inpPath'], serviceName, modelInfo)
fileList = sorted(glob.glob(inpFile))
dsData = xr.open_mfdataset(fileList)
# dsData = dsData.sel(lon=slice(120, 150), time=slice('2015-01', '2016-12'))
# dsData = dsData.sel(lat=slice(50, 50), lon=slice(120, 120), time=slice('2015-01', '2015-12'))
# 월별 시간 변환
dsData['time'] = pd.to_datetime(pd.to_datetime(
dsData['time'].values).strftime("%Y-%m"),
format='%Y-%m')
# 단위 설정
dsData['tasmin'].attrs['units'] = 'degC'
dsData['tasmax'].attrs['units'] = 'degC'
dsData['tas'].attrs['units'] = 'degC'
# 단위 환산을 위한 매월 마지막 날 계산
lon1D = dsData['lon'].values
lat1D = dsData['lat'].values
time1D = dsData['time'].values
timeEndMonth = []
timeYear = dsData['time.year'].values
timeMonth = dsData['time.month'].values
for i in range(0, len(timeYear)):
timeEndMonth.append(
calendar.monthrange(timeYear[i], timeMonth[i])[1])
latRad1D = pyeto.deg2rad(dsData['lat'])
# 2022.08.13 doy 순서 변경
# 시작 순서 1, 32, ...
dayOfYear1D = dsData['time.dayofyear']
latRad3D = np.tile(
np.transpose(np.tile(latRad1D, (len(lon1D), 1))),
(len(time1D), 1, 1))
dayOfYear3D = np.transpose(
np.tile(dayOfYear1D, (len(lon1D), len(lat1D), 1)))
timeEndMonth3D = np.transpose(
np.tile(timeEndMonth, (len(lon1D), len(lat1D), 1)))
tmpData = xr.Dataset(
{
'timeEndMonth':
(('time', 'lat', 'lon'), (timeEndMonth3D).reshape(
len(time1D), len(lat1D), len(lon1D))),
'latRad': (('time', 'lat', 'lon'), (latRad3D).reshape(
len(time1D), len(lat1D), len(lon1D)))
# , 'dayOfYear': (('time', 'lat', 'lon'), (dayOfYear3D).reshape(len(time1D), len(lat1D), len(lon1D)))
,
'doy': (('time', 'lat', 'lon'), (dayOfYear3D).reshape(
len(time1D), len(lat1D), len(lon1D)))
},
coords={
'lat': lat1D,
'lon': lon1D,
'time': time1D
})
# 마지막 순서 31, 59, ...
# tmpData['dayOfYear'] = tmpData['doy']
tmpData['dayOfYear'] = tmpData['doy'] + timeEndMonth3D
# ********************************************************************************************
# FAO-56 Penman-Monteith 방법
# ********************************************************************************************
# https://pyeto.readthedocs.io/en/latest/fao56_penman_monteith.html 매뉴얼 참조
# 1 W/m2 = 1 J/m2를 기준으로 MJ/day 변환
# dsData['rsdsMJ'] = dsData['rsds'] * 86400 / (10 ** 6)
dsData['rsdsMJ'] = dsData['rsds'] * 2592000 / (10**6)
# 섭씨 to 켈빈
dsData['tasKel'] = dsData['tas'] + 273.15
dsData['tasminKel'] = dsData['tasmin'] + 273.15
dsData['tasmaxKel'] = dsData['tasmax'] + 273.15
dsData['svp'] = svp_from_t(dsData['tas'])
dsData['svpMax'] = svp_from_t(dsData['tasmax'])
dsData['svpMin'] = svp_from_t(dsData['tasmin'])
tmpData['solDec'] = sol_dec(tmpData['dayOfYear'])
tmpData['sha'] = sunset_hour_angle(tmpData['latRad'],
tmpData['solDec'])
tmpData['dayLightHour'] = daylight_hours(tmpData['sha'])
tmpData['ird'] = inv_rel_dist_earth_sun(tmpData['dayOfYear'])
# tmpData['etRad'] = et_rad(tmpData['latRad'], tmpData['solDec'], tmpData['sha'], tmpData['ird'])
tmpData['etRad'] = dsData['rsdsMJ']
tmpData['csRad'] = fao.cs_rad(altitude=1.5,
et_rad=tmpData['etRad'])
dsData['deltaSvp'] = delta_svp(dsData['tas'])
# 대기 온도 1.5 m 가정
psy = fao.psy_const(atmos_pres=fao.atm_pressure(altitude=15))
dsData['avp'] = fao.avp_from_rhmin_rhmax(
dsData['svpMax'], dsData['svpMin'], dsData['hurs'].min(),
dsData['hurs'].max())
niSwRad = pyeto.net_in_sol_rad(dsData['rsdsMJ'], albedo=0.23)
niLwRad = net_out_lw_rad(dsData['tasminKel'],
dsData['tasmaxKel'], dsData['rsdsMJ'],
tmpData['csRad'], dsData['avp'])
dsData['net_rad'] = fao.net_rad(ni_sw_rad=niSwRad,
no_lw_rad=niLwRad)
# 2022.08.13
# 상수
# shfData = 0.336
# 이전, 현재 대기온도를 통해 계산
timeList = dsData['time'].values
shfDataL1 = xr.Dataset()
for i, timeInfo in enumerate(timeList):
prevYmd = (pd.to_datetime(timeInfo) +
pd.DateOffset(months=-1)).strftime('%Y-%m-%d')
nowYmd = pd.to_datetime(timeInfo).strftime('%Y-%m-%d')
prevData = dsData['tas'].interp(
time=prevYmd,
method='nearest',
kwargs={'fill_value': 'extrapolate'})
nowData = dsData['tas'].interp(
time=nowYmd,
method='nearest',
kwargs={'fill_value': 'extrapolate'})
shfData = pyeto.monthly_soil_heat_flux2(
prevData, nowData).rename('shf')
shfData['time'] = pd.to_datetime(timeInfo)
if (i == 0):
shfDataL1 = shfData
else:
shfDataL1 = xr.concat([shfDataL1, shfData], dim='time')
dsData = xr.merge([dsData, shfDataL1])
# Daily eto
# faoRes = fao.fao56_penman_monteith(dsData['net_rad'], dsData['tasKel'], dsData['sfcWind'], dsData['svp'], dsData['avp'], dsData['deltaSvp'], psy, shf = 0)
# Monthly eto
faoRes = fao.fao56_penman_monteith(dsData['net_rad'],
dsData['tasKel'],
dsData['sfcWind'],
dsData['svp'],
dsData['avp'],
dsData['deltaSvp'],
psy,
shf=dsData['shf'])
# ********************************************************************************************
# Hargreaves 방법
# ********************************************************************************************
# https://xclim.readthedocs.io/en/stable/indicators_api.html 매뉴얼 참조
# harRes = xclim.indices.potential_evapotranspiration( dsData['tasmin'], dsData['tasmax'], dsData['tas'], dsData['lat'], method='hargreaves85')
# 1 kg/m2/s = 86400 mm/day를 기준으로 mm/month 변환
# harResL1 = harRes * 86400.0 * tmpData['timeEndMonth']
# https://pyeto.readthedocs.io/en/latest/thornthwaite.html 매뉴얼 참조
harRes = pyeto.hargreaves(dsData['tasmin'], dsData['tasmax'],
dsData['tas'], tmpData['etRad'])
harResL1 = harRes
# ********************************************************************************************
# Thornthwaite 방법
# ********************************************************************************************
# https://xclim.readthedocs.io/en/stable/indicators_api.html 매뉴얼 참조
# thwRes = xclim.indices.potential_evapotranspiration(dsData['tasmin'], dsData['tasmax'], dsData['tas'], dsData['lat'], method ='thornthwaite48')
# 1 kg/m2/s = 86400 mm/day를 기준으로 mm/month 변환
# thwResL1 = thwRes * 86400.0 * tmpData['timeEndMonth']
dsData['tasAdj'] = xr.where((dsData['tas'] >= 0),
dsData['tas'], 0)
dsData['heatIdx'] = (dsData['tasAdj'] / 5.0)**1.514
sumHeatIdx = dsData['heatIdx'].groupby('time.year').sum(
skipna=True)
sumHeatIdxData = xr.Dataset()
timeList = dsData['time'].values
for i, timeInfo in enumerate(timeList):
iYear = int(pd.to_datetime(timeInfo).strftime('%Y'))
selHeatIdx = sumHeatIdx.sel(year=iYear).rename(
{'year': 'time'})
selHeatIdx['time'] = timeInfo
if (i == 0):
sumHeatIdxData = selHeatIdx
else:
sumHeatIdxData = xr.concat(
[sumHeatIdxData, selHeatIdx], dim='time')
# 2022.08.15
# dsData['thwConst'] = (6.75e-07 * dsData['heatIdx'] ** 3) - (7.71e-05 * dsData['heatIdx'] ** 2) + (1.792e-02 * dsData['heatIdx']) + 0.49239
# thwRes = 1.6 * (tmpData['dayLightHour'] / 12.0) * (tmpData['timeEndMonth'] / 30.0) * ((10.0 * dsData['tasAdj'] / dsData['heatIdx']) ** dsData['thwConst']) * 10.0
dsData['thwConst'] = (6.75e-07 * sumHeatIdxData**3) - (
7.71e-05 * sumHeatIdxData**2) + (1.792e-02 *
sumHeatIdxData) + 0.49239
thwRes = 1.6 * (tmpData['dayLightHour'] /
12.0) * (tmpData['timeEndMonth'] / 30.0) * (
(10.0 * dsData['tasAdj'] / sumHeatIdxData)
**dsData['thwConst']) * 10.0
# 0보다 작은 경우 0으로 대체
thwRes = xr.where((thwRes >= 0), thwRes, 0)
# ********************************************************************************************
# 데이터 병합
# ********************************************************************************************
etoData = xr.Dataset(
{
'hargreaves':
(('time', 'lat', 'lon'), (harResL1.values).reshape(
len(time1D), len(lat1D), len(lon1D))),
'thornthwaite':
(('time', 'lat', 'lon'), (thwRes.values).reshape(
len(time1D), len(lat1D), len(lon1D))),
'penman-monteith':
(('time', 'lat', 'lon'), (faoRes.values).reshape(
len(time1D), len(lat1D), len(lon1D)))
},
coords={
'lat': lat1D,
'lon': lon1D,
'time': time1D
})
# NetCDF 파일 저장
saveFile = '{}/{}/{}_eto.nc'.format(globalVar['outPath'],
serviceName, modelInfo)
os.makedirs(os.path.dirname(saveFile), exist_ok=True)
etoData.to_netcdf(saveFile)
log.info('[CHECK] saveFile : {}'.format(saveFile))
except Exception as e:
log.error("Exception : {}".format(e))
raise e
finally:
log.info('[END] {}'.format("exec"))
def test_deg2rad(self):
self.assertEqual(pyeto.deg2rad(0), 0.0)
# Test values obtained form online conversion calculator
self.assertAlmostEqual(pyeto.deg2rad(-90), -1.5707963268, 10)
self.assertAlmostEqual(pyeto.deg2rad(90), 1.5707963268, 10)
self.assertAlmostEqual(pyeto.deg2rad(360), 6.2831853072, 10)
def test_monthly_mean_daylight_hours(self):
# Test against values for latitude 20 deg N from Bautista et al (2009)
# Calibration of the equations of Hargreaves and Thornthwaite to
# estimate the potential evapotranspiration in semi-arid and subhumid
# tropical climates for regional applications. Atmosfera 22(4), 331-
# 348.
test_mmdlh = [
10.9, # Jan
11.3, # Feb
11.9, # Mar
12.5, # Apr
12.9, # May
13.2, # Jun
13.1, # Jul
12.7, # Aug
12.1, # Sep
11.5, # Oct
11.0, # Nov
10.8, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(20.0))
# Values were only quoted to 1 decimal place so check they are accurate
# to within 12 minutes (0.2 hours)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], test_mmdlh[m], delta=0.15)
# Test against values for latitude 46 deg N from Mimikou M. and
# Baltas E., Technical hydrology, Second edition, NTUA, 2002.
# cited in PAPADOPOULOU E., VARANOU E., BALTAS E., DASSAKLIS A., and
# MIMIKOU M. (2003) ESTIMATING POTENTIAL EVAPOTRANSPIRATION AND ITS
# SPATIAL DISTRIBUTION IN GREECE USING EMPIRICAL METHODS.
test_mmdlh = [
8.9, # Jan
10.1, # Feb
11.6, # Mar
13.3, # Apr
14.7, # May
15.5, # Jun
15.2, # Jul
13.9, # Aug
12.3, # Sep
10.7, # Oct
9.2, # Nov
8.5, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(46.0))
# Values were only quoted to 1 decimal place so check they are accurate
# to within 12 minutes (0.2 hours)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], test_mmdlh[m], delta=0.15)
# Test against values obtained for Los Angles, California,
# latitude 34 deg 05' N, from
# http://aa.usno.navy.mil/data/docs/Dur_OneYear.php
latitude = pyeto.deg2rad(34.0833333)
la_mmdlh = [
10.182, # Jan
10.973, # Feb
11.985, # Mar
13.046, # Apr
13.940, # May
14.388, # Jun
14.163, # Jul
13.404, # Aug
12.374, # Sep
11.320, # Oct
10.401, # Nov
9.928, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(latitude)
# Check that the 2 methods are almost the same (within 15 minutes)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], la_mmdlh[m], delta=0.25)
# Test with year set to a non-leap year
non_leap = pyeto.monthly_mean_daylight_hours(latitude, 2015)
for m in range(12):
self.assertEqual(mmdlh[m], non_leap[m])
# Test with year set to a leap year
leap = pyeto.monthly_mean_daylight_hours(latitude, 2016)
for m in range(12):
if m == 0:
self.assertEqual(leap[m], non_leap[m])
elif m == 1: # Feb
# Because Feb extends further into year in a leap year it
# should have a slightly longer mean day length in northern
# hemisphere
self.assertGreater(leap[m], non_leap[m])
else:
# All months after Feb in a lieap year will be composed of
# diffent Julian days (days of the year) compared to a
# non-leap year so will have different mean daylengths.
self.assertNotEqual(leap[m], non_leap[m])
# Test with bad latitude
with self.assertRaises(ValueError):
_ = pyeto.monthly_mean_daylight_hours(
pyeto.deg2rad(90.01))
with self.assertRaises(ValueError):
_ = pyeto.monthly_mean_daylight_hours(
pyeto.deg2rad(-90.01))
# Test limits of latitude
_ = pyeto.monthly_mean_daylight_hours(
pyeto.deg2rad(90.0))
_ = pyeto.monthly_mean_daylight_hours(
pyeto.deg2rad(-90.0))
def test_monthly_mean_daylight_hours(self):
# Test against values for latitude 20 deg N from Bautista et al (2009)
# Calibration of the equations of Hargreaves and Thornthwaite to
# estimate the potential evapotranspiration in semi-arid and subhumid
# tropical climates for regional applications. Atmosfera 22(4), 331-
# 348.
test_mmdlh = [
10.9, # Jan
11.3, # Feb
11.9, # Mar
12.5, # Apr
12.9, # May
13.2, # Jun
13.1, # Jul
12.7, # Aug
12.1, # Sep
11.5, # Oct
11.0, # Nov
10.8, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(20.0))
# Values were only quoted to 1 decimal place so check they are accurate
# to within 12 minutes (0.2 hours)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], test_mmdlh[m], delta=0.15)
# Test against values for latitude 46 deg N from Mimikou M. and
# Baltas E., Technical hydrology, Second edition, NTUA, 2002.
# cited in PAPADOPOULOU E., VARANOU E., BALTAS E., DASSAKLIS A., and
# MIMIKOU M. (2003) ESTIMATING POTENTIAL EVAPOTRANSPIRATION AND ITS
# SPATIAL DISTRIBUTION IN GREECE USING EMPIRICAL METHODS.
test_mmdlh = [
8.9, # Jan
10.1, # Feb
11.6, # Mar
13.3, # Apr
14.7, # May
15.5, # Jun
15.2, # Jul
13.9, # Aug
12.3, # Sep
10.7, # Oct
9.2, # Nov
8.5, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(46.0))
# Values were only quoted to 1 decimal place so check they are accurate
# to within 12 minutes (0.2 hours)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], test_mmdlh[m], delta=0.15)
# Test against values obtained for Los Angles, California,
# latitude 34 deg 05' N, from
# http://aa.usno.navy.mil/data/docs/Dur_OneYear.php
latitude = pyeto.deg2rad(34.0833333)
la_mmdlh = [
10.182, # Jan
10.973, # Feb
11.985, # Mar
13.046, # Apr
13.940, # May
14.388, # Jun
14.163, # Jul
13.404, # Aug
12.374, # Sep
11.320, # Oct
10.401, # Nov
9.928, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(latitude)
# Check that the 2 methods are almost the same (within 15 minutes)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], la_mmdlh[m], delta=0.25)
# Test with year set to a non-leap year
non_leap = pyeto.monthly_mean_daylight_hours(latitude, 2015)
for m in range(12):
self.assertEqual(mmdlh[m], non_leap[m])
# Test with year set to a leap year
leap = pyeto.monthly_mean_daylight_hours(latitude, 2016)
for m in range(12):
if m == 0:
self.assertEqual(leap[m], non_leap[m])
elif m == 1: # Feb
# Because Feb extends further into year in a leap year it
# should have a slightly longer mean day length in northern
# hemisphere
self.assertGreater(leap[m], non_leap[m])
else:
# All months after Feb in a lieap year will be composed of
# diffent Julian days (days of the year) compared to a
# non-leap year so will have different mean daylengths.
self.assertNotEqual(leap[m], non_leap[m])
# Test with bad latitude
with self.assertRaises(ValueError):
_ = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(90.01))
with self.assertRaises(ValueError):
_ = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(-90.01))
# Test limits of latitude
_ = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(90.0))
_ = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(-90.0))
def get_data_from_WU():
###array to store the reports
wu_weather_reports = []
## today and last 6 days definition
day1 = now - datetime.timedelta(days=6)
day2 = now - datetime.timedelta(days=5)
day3 = now - datetime.timedelta(days=4)
day4 = now - datetime.timedelta(days=3)
day5 = now - datetime.timedelta(days=2)
day6 = now - datetime.timedelta(days=1)
day7 = now
#### convert dates to WU required format
days = {
'day1': day1.strftime('%Y%m%d'),
'day2': day2.strftime('%Y%m%d'),
'day3': day3.strftime('%Y%m%d'),
'day4': day4.strftime('%Y%m%d'),
'day5': day5.strftime('%Y%m%d'),
'day6': day6.strftime('%Y%m%d'),
'day7': day7.strftime('%Y%m%d')
}
### make API wather hisotry call for each day
for day in days:
url = 'http://api.wunderground.com/api/7c2ab99a0ccee978/history_{0}/q/95316.json'.format(days[day])
headers = {'content-type': 'application/JSON; charset=utf8'}
response = requests.get(url, headers=headers)
data = json.loads(response.text)
#ETo calculation for the day using FAO-56 Penman-Monteith method
lat = pyeto.deg2rad(37.585652)
altitude = 38
julian_day = datetime.datetime.strptime(days.get(day), '%Y%m%d').timetuple().tm_yday
sol_dec = pyeto.sol_dec(julian_day)
sha = pyeto.sunset_hour_angle(lat, sol_dec)
ird = pyeto.inv_rel_dist_earth_sun(julian_day)
### net radiation calculator
net_rad = pyeto.et_rad(lat, sol_dec, sha, ird)
temp_c = float(data["history"]["observations"][1]["tempm"])
temp_k = float(data["history"]["observations"][1]["tempi"])
humidity = float(data["history"]["observations"][1]["hum"])
dew_point = float(data["history"]["observations"][1]["dewptm"])
ws = float(data["history"]["observations"][1]["wspdm"])
#actual and saturated vapour pressure in kPH
svp = pyeto.svp_from_t(temp_c)
avp = pyeto.avp_from_tdew(dew_point)
delta_svp = pyeto.delta_svp(temp_c)
atm_pressure = pyeto.atm_pressure(altitude)
psy = pyeto.psy_const(atm_pressure)
#### the ETo plugin retun results in mm, it was converted to inched
ETo_in_mm = pyeto.fao56_penman_monteith(net_rad, temp_k, ws, svp, avp, delta_svp, psy, shf=0.0)
ETo = ETo_in_mm * 0.039370
## insert eto value to day weather report
data["history"]["observations"][1].update({"ETo": "{0:.2f}".format(ETo)})
###add report to report collector array
wu_weather_reports.append(data["history"]["observations"][1])
#return all reports
return wu_weather_reports
def pet(event, context):
latitude = event['latitude']
print("Latitude: " + str(latitude))
longitude = event['longitude']
print("Longitude: " + str(longitude))
if latitude is None or longitude is None:
print("Unknown lat/lng")
return {}
if latitude == 0 and longitude == 0:
print("Zeroed lat/lng")
return {}
lat = pyeto.deg2rad(latitude)
# convert lat to radians
#pass google maps api info
mapKey = os.environ['GOOGLE_MAPS_API_KEY']
#pass lat/long and API key variables
GOOGLE_MAPS_API = 'https://maps.googleapis.com/maps/api/elevation/json?locations=' + str(
latitude) + ',' + str(longitude) + '&key=' + str(mapKey)
# Test: curl 'https://maps.googleapis.com/maps/api/elevation/json?locations=29.405708,-82.140176&key='
req = requests.get(GOOGLE_MAPS_API)
res = req.json()
if res['status'] == 'REQUEST_DENIED':
print(f"Error: Google Maps API request denied. {res['error_message']}")
return {}
results = res["results"]
if len(results) == 0:
print(
f"Error: Google Maps API request returned no results. Status: {res['status']}, Error: {res['error_message']}"
)
return {}
#print(results)
#ELEVATION / ALTITUDE DATA
alt = results[0]['elevation']
deviceName = event['deviceName']
print("deviceName: " + str(deviceName))
#set table parameters for sensor data
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table(os.environ['READINGS_TABLE_NAME'])
# time parameters
current_time = datetime.datetime.now(datetime.timezone.utc)
unix_timestamp = current_time.timestamp()
# remove decimal when passing in unix timestamp
currentTime = int(unix_timestamp)
#1440 minutes in a day * 60 seconds
#format timestamp to remove float...
dayPast = int(unix_timestamp - (1440 * 60))
# let's sample air temperature now
tempSample = table.query(
ReturnConsumedCapacity='TOTAL',
ProjectionExpression="weatherData.temperature",
KeyConditionExpression=Key('deviceName').eq(deviceName)
& Key('epoch').between(dayPast, currentTime))
tempList = [conversion.obj_to_json(i) for i in tempSample[u'Items']]
#print(tempList)
day1Temp = [ast.literal_eval(tempList[i]) for i in range(len(tempList))]
#print(day1Temp)
day1TempFormat = []
for dic in day1Temp:
for val in dic.values():
#print(val['temperature'])
day1TempFormat.append(val['temperature'])
print(day1TempFormat)
# calculate temperature avg, min, max for last 24 hrs
# convert to Celcius because default from DarkSky is F
tempListLength = len(day1TempFormat)
print("Temp List Length: " + str(tempListLength))
day1TempAvg = 0
day1TempMin = 0
day1TempMax = 0
if tempListLength != 0:
day1TempAvg = round(sum(day1TempFormat) / tempListLength, 2)
day1TempAvg = round((day1TempAvg - 32) * (5 / 9), 2)
day1TempMin = min(day1TempFormat)
day1TempMin = round((day1TempMin - 32) * (5 / 9), 2)
day1TempMax = max(day1TempFormat)
day1TempMax = round((day1TempMax - 32) * (5 / 9), 2)
print("Temp Avg (C): " + str(day1TempAvg))
print("Temp Min (C): " + str(day1TempMin))
print("Temp Max (C): " + str(day1TempMax))
# calculate dew point average
dewSample = table.query(
ReturnConsumedCapacity='TOTAL',
ProjectionExpression="weatherData.dewPoint",
KeyConditionExpression=Key('deviceName').eq(deviceName)
& Key('epoch').between(dayPast, currentTime))
dewList = [conversion.obj_to_json(i) for i in dewSample[u'Items']]
day1Dew = [ast.literal_eval(dewList[i]) for i in range(len(dewList))]
day1DewFormat = []
for dic in day1Dew:
for val in dic.values():
day1DewFormat.append(val['dewPoint'])
#print(day1DewFormat)
dewListLength = len(day1DewFormat)
print("Dew List Length: " + str(dewListLength))
day1DewAvg = 0
if dewListLength != 0:
day1DewAvg = round(sum(day1DewFormat) / dewListLength, 2)
# convert F to C (F-32) x 5/9
#AVERAGE ACTUAL DAILY VAPOUR PRESSURE
day1DewAvg = round((day1DewAvg - 32) * (5 / 9), 2)
print("Dew Point Avg (C): " + str(day1DewAvg))
# dew point is temp at which concentration of water vapour in air at concentration, convert to C down below because DarkSky is F
# now calculate wind speed
windSample = table.query(
ReturnConsumedCapacity='TOTAL',
ProjectionExpression="weatherData.windSpeed",
KeyConditionExpression=Key('deviceName').eq(deviceName)
& Key('epoch').between(dayPast, currentTime))
windList = [conversion.obj_to_json(i) for i in windSample[u'Items']]
day1Wind = [ast.literal_eval(windList[i]) for i in range(len(windList))]
day1WindFormat = []
for dic in day1Wind:
for val in dic.values():
day1WindFormat.append(val['windSpeed'])
windListLength = len(day1WindFormat)
#print(windListLength)
if windListLength <= 0:
windListLength = 1
day1windSpeed = round(sum(day1WindFormat) / windListLength, 2)
print("Wind Speed (knots): " + str(day1windSpeed))
#HUMIDITY
# pull in dewpoint temperature data from DarkSky
vaporPressure = fao.avp_from_tdew(day1DewAvg)
#SATURATION VAPOUR --> estimated from air temperature
satVapor = fao.svp_from_t(day1TempAvg)
#SLOPE OF SATURATION VAPOR CURVE
satVaporSlope = fao.delta_svp(day1TempAvg)
#ATMOSPHERIC PRESSURE
atmosphericPressure = fao.atm_pressure(alt)
#PSYCHOMETRIC CONSTANT - Eq 8 Allen, 1998
psychroConstant = fao.psy_const(atmosphericPressure)
#RADIATION
#day of year
day_of_year = time.localtime().tm_yday
#DAILY NET RADIATION
#SOLAR DECLINATION - FAO equation 24
sol_declination = fao.sol_dec(day_of_year)
# SUNSET HOUR ANGLE
sunsetHourAngle = fao.sunset_hour_angle(lat, sol_declination)
#calculate daylight hours
daylightHours = fao.daylight_hours(sunsetHourAngle)
#inverse rel distance - sun-earth
earthSunDist = fao.inv_rel_dist_earth_sun(day_of_year)
# ESTIMATED DAILY EXTRATERRESTRIAL RADIATION (top of atmosphere)
# Eq 21 (Allen et al 1998)
#def et_rad(latitude, sol_dec, sha, ird):
extraTerRad = fao.et_rad(lat, sol_declination, sunsetHourAngle,
earthSunDist)
#ESTIMATED CLEAR SKY RADIATION
# Eq 37 Allen et al 1998)
clearSkyRad = fao.cs_rad(alt, extraTerRad)
#daily min and max temperatures expressed as degrees C
grossRadiation = fao.sol_rad_from_t(extraTerRad,
clearSkyRad,
day1TempMin,
day1TempMax,
coastal=False)
#NET INCOMING SOLAR (shortwave rad) --> FAO equation 38
#albedo is of crop as proporiton of gross incoming solar radiation reflected by surgace --> 0.23 default set by FAO for grass crop
#incomingRadiation = net_in_sol_rad(sol_rad, albedo=0.23)
netIncomingRad = fao.net_in_sol_rad(grossRadiation, albedo=0.23)
#NET OUTGOING LONGWAVE ENERGY leaving earth's surface (Eq 39 in Allen et al 1998)
# def net_out_lw_rad(tmin, tmax, sol_rad, cs_rad, avp)
netOutgoingRad = fao.net_out_lw_rad(day1TempMin, day1TempMax,
grossRadiation, clearSkyRad,
vaporPressure)
#DAILY NET RADIATION AT CROP SURCACE (assuming grass ref crop)
#Eq 40 in Allen et al (1998)
dailyNetRadiation = fao.net_rad(netIncomingRad, netOutgoingRad)
#convert air temp from C to degrees Kelvin
day1TempAvg = day1TempAvg + 273.15
#FAO-56 ENMAN-MONTIETCH EQUATION to estimate reference evapotranspiration (ETo) from short grass reference surface
# Eq 6 in Allen et al 1998
#def fao56_penman_monteith(net_rad, t, ws, svp, avp, delta_svp, psy, shf=0.0):
penmanMontOutput = round(
fao.fao56_penman_monteith(dailyNetRadiation,
day1TempAvg,
day1windSpeed,
satVapor,
vaporPressure,
satVaporSlope,
psychroConstant,
shf=0.0), 2)
print("Reference ETo [mm/day]: " + str(penmanMontOutput))
# convert from mm/day to in/day
pET = round((penmanMontOutput * 0.0393701), 4)
if pET <= 0:
pET = 0
# now multiply by crop coefficients to find ETc (crop evapotranspiration)
ret = {"ETo": pET}
try:
ETc = None # Default value if missing or error
cc = cropET.calculateCropCoef(deviceName)
if cc is not None:
Kc = cc["KcMAIN"]
cropName = cc["cropName"]
if Kc is not None:
ETc = round((pET * Kc), 4)
ret.update({"ETc": ETc, "crop": cropName})
except:
traceback.print_exc()
return ret
# this data should be from nws. import pyeto latitude_deg = 38.01 latitude = pyeto.deg2rad(latitude_deg) day_of_year = 206 tmin = 37 tmax = 53 coastal = True altitude = 147 rh_min = 13 rh_max = 88 ws = 1.3 tmean = pyeto.daily_mean_t(tmin, tmax) atmos_pres = pyeto.atm_pressure(altitude) psy = pyeto.psy_const(atmos_pres) # Humidity svp_tmin = pyeto.svp_from_t(tmin) svp_tmax = pyeto.svp_from_t(tmax) delta_svp = pyeto.delta_svp(tmean) svp = pyeto.mean_svp(tmin, tmax) avp = pyeto.avp_from_rhmin_rhmax(svp_tmin, svp_tmax, rh_min, rh_max) # Radiation sol_dec = pyeto.sol_dec(day_of_year) sha = pyeto.sunset_hour_angle(latitude, sol_dec) ird = pyeto.inv_rel_dist_earth_sun(day_of_year) et_rad = pyeto.et_rad(latitude, sol_dec, sha, ird) cs_rad = pyeto.cs_rad(altitude, et_rad)
def exec(self):
log.info('[START] {}'.format("exec"))
try:
if (platform.system() == 'Windows'):
# 옵션 설정
sysOpt = {
# 시작/종료 시간
'srtDate': '2020-09-01',
'endDate': '2020-09-03'
# 경도 최소/최대/간격
,
'lonMin': 0,
'lonMax': 360,
'lonInv': 0.5
# 위도 최소/최대/간격
,
'latMin': -90,
'latMax': 90,
'latInv': 0.5
}
else:
# 옵션 설정
sysOpt = {
# 시작/종료 시간
# 'srtDate': globalVar['srtDate']
# , 'endDate': globalVar['endDate']
}
# globalVar['outPath'] = 'F:/Global Temp/aski'
modelList = ['MRI-ESM2-0']
for i, modelInfo in enumerate(modelList):
log.info("[CHECK] modelInfo : {}".format(modelInfo))
inpFile = '{}/{}/{} ssp585 2015-2100_*.nc'.format(
globalVar['inpPath'], serviceName, modelInfo)
fileList = sorted(glob.glob(inpFile))
dsData = xr.open_mfdataset(fileList)
# 월별 시간 변환
dsData['time'] = pd.to_datetime(pd.to_datetime(
dsData['time'].values).strftime("%Y-%m"),
format='%Y-%m')
# 단위 설정
dsData['tasmin'].attrs['units'] = 'degC'
dsData['tasmax'].attrs['units'] = 'degC'
dsData['tas'].attrs['units'] = 'degC'
# 단위 환산을 위한 매월 마지막 날 계산
lon1D = dsData['lon'].values
lat1D = dsData['lat'].values
time1D = dsData['time'].values
timeEndMonth = []
timeYear = dsData['time.year'].values
timeMonth = dsData['time.month'].values
for i in range(0, len(timeYear)):
timeEndMonth.append(
calendar.monthrange(timeYear[i], timeMonth[i])[1])
latRad1D = pyeto.deg2rad(dsData['lat'])
dayOfYear1D = dsData['time.dayofyear']
latRad3D = np.tile(
np.transpose(np.tile(latRad1D, (len(lon1D), 1))),
(len(time1D), 1, 1))
dayOfYear3D = np.transpose(
np.tile(dayOfYear1D, (len(lon1D), len(lat1D), 1)))
timeEndMonth3D = np.transpose(
np.tile(timeEndMonth, (len(lon1D), len(lat1D), 1)))
tmpData = xr.Dataset(
{
'timeEndMonth':
(('time', 'lat', 'lon'), (timeEndMonth3D).reshape(
len(time1D), len(lat1D), len(lon1D))),
'latRad': (('time', 'lat', 'lon'), (latRad3D).reshape(
len(time1D), len(lat1D), len(lon1D))),
'dayOfYear':
(('time', 'lat', 'lon'), (dayOfYear3D).reshape(
len(time1D), len(lat1D), len(lon1D)))
},
coords={
'lat': lat1D,
'lon': lon1D,
'time': time1D
})
# ********************************************************************************************
# Hargreaves 방법
# ********************************************************************************************
# https://xclim.readthedocs.io/en/stable/indicators_api.html 매뉴얼 참조
harRes = xclim.indices.potential_evapotranspiration(
dsData['tasmin'],
dsData['tasmax'],
dsData['tas'],
dsData['lat'],
method='hargreaves85')
# 1 kg/m2/s = 86400 mm/day를 기준으로 mm/month 변환
harResL1 = harRes * 86400.0 * tmpData['timeEndMonth']
# ********************************************************************************************
# Thornthwaite 방법
# ********************************************************************************************
# https://xclim.readthedocs.io/en/stable/indicators_api.html 매뉴얼 참조
thwRes = xclim.indices.potential_evapotranspiration(
dsData['tasmin'],
dsData['tasmax'],
dsData['tas'],
dsData['lat'],
method='thornthwaite48')
# 1 kg/m2/s = 86400 mm/day를 기준으로 mm/month 변환
thwResL1 = thwRes * 86400.0 * tmpData['timeEndMonth']
# ********************************************************************************************
# FAO-56 Penman-Monteith 방법
# ********************************************************************************************
# https://pyeto.readthedocs.io/en/latest/fao56_penman_monteith.html 매뉴얼 참조
# 1 W/m2 = 1 J/m2를 기준으로 MJ/day 변환
dsData['rsdsMJ'] = dsData['rsds'] * 86400 / (10**6)
# 섭씨 to 켈빈
dsData['tasKel'] = dsData['tas'] + 273.15
dsData['tasminKel'] = dsData['tasmin'] + 273.15
dsData['tasmaxKel'] = dsData['tasmax'] + 273.15
dsData['svp'] = svp_from_t(dsData['tas'])
dsData['svpMax'] = svp_from_t(dsData['tasmax'])
dsData['svpMin'] = svp_from_t(dsData['tasmin'])
tmpData['solDec'] = sol_dec(tmpData['dayOfYear'])
tmpData['sha'] = sunset_hour_angle(tmpData['latRad'],
tmpData['solDec'])
tmpData['ird'] = inv_rel_dist_earth_sun(tmpData['dayOfYear'])
tmpData['etRad'] = et_rad(tmpData['latRad'], tmpData['solDec'],
tmpData['sha'], tmpData['ird'])
tmpData['csRad'] = fao.cs_rad(altitude=1.5,
et_rad=tmpData['etRad'])
dsData['deltaSvp'] = delta_svp(dsData['tas'])
# 대기 온도 1.5 m 가정
psy = fao.psy_const(atmos_pres=fao.atm_pressure(altitude=15))
dsData['avp'] = fao.avp_from_rhmin_rhmax(
dsData['svpMax'], dsData['svpMin'], dsData['hurs'].min(),
dsData['hurs'].max())
niSwRad = pyeto.net_in_sol_rad(dsData['rsdsMJ'], albedo=0.23)
niLwRad = net_out_lw_rad(dsData['tasminKel'],
dsData['tasmaxKel'], dsData['rsdsMJ'],
tmpData['csRad'], dsData['avp'])
dsData['net_rad'] = fao.net_rad(ni_sw_rad=niSwRad,
no_lw_rad=niLwRad)
faoRes = fao.fao56_penman_monteith(dsData['net_rad'],
dsData['tasKel'],
dsData['sfcWind'],
dsData['svp'],
dsData['avp'],
dsData['deltaSvp'],
psy,
shf=0)
# ********************************************************************************************
# 데이터 병합
# ********************************************************************************************
data = xr.Dataset(
{
'hargreaves':
(('time', 'lat', 'lon'), (harResL1.values).reshape(
len(time1D), len(lat1D), len(lon1D))),
'thornthwaite':
(('time', 'lat', 'lon'), (thwResL1.values).reshape(
len(time1D), len(lat1D), len(lon1D))),
'penman-monteith':
(('time', 'lat', 'lon'), (faoRes.values).reshape(
len(time1D), len(lat1D), len(lon1D)))
},
coords={
'lat': lat1D,
'lon': lon1D,
'time': time1D
})
dataL1 = xr.merge([data, dsData])
# # NetCDF 파일 저장
saveFile = '{}/{}/{}_eto.nc'.format(globalVar['outPath'],
serviceName, modelInfo)
os.makedirs(os.path.dirname(saveFile), exist_ok=True)
dataL1.to_netcdf(saveFile)
log.info('[CHECK] saveFile : {}'.format(saveFile))
except Exception as e:
log.error("Exception : {}".format(e))
raise e
finally:
log.info('[END] {}'.format("exec"))
import netCDF4
sys.path.append('/home/pu17449/gitsrc/PyETo')
import pyeto
inpath = '/home/pu17449/data2/worldclim_precip/'
outfile = '/home/pu17449/data2/worldclim_precip/pet_yearmean_v3.nc'
#f_tiffout = '/home/pu17449/data2/worldclim_precip/pet_yearmean_v2.tif'
# Construct lat and lon arrays (centrepoints)
step = 0.25/30.
lons = np.arange(-180,180,step)+step/2.
lats = np.arange(90,-90,-1*step)-step/2.
nlats = len(lats)
nlons = len(lons)
lats_rad = pyeto.deg2rad(lats)
#lat2D = np.repeat(lats_rad[:,np.newaxis],len(lons),axis=1)
# Data array to store time/lon slices of PET
pet_timeslice = np.zeros([12,nlons],dtype=np.float32)
pet = np.zeros([nlons],dtype=np.float32) #yearly mean
###############################################################################
# Write output file for modsim:
# Follows format of variables needed in 'domain' file for metsim e.g. '/home/bridge/pu17449/src/MetSim/metsim/data/domain.nc'
with netCDF4.Dataset(outfile,'w') as f_out:
f_out.createDimension('lat',nlats)
f_out.createVariable('lat',np.float,('lat'))
f_out.variables['lat'].standard_name = "latitude"
VV=pd.read_csv(arch, names=["0"])
arch=esta[i]+"_HR.csv"
HR=pd.read_csv(arch, names=["0"])
#Inciacion de los valores de ETo
lat=LAT[i]
altitude=ALT[i]
tmin=[]
tmax=[]
t=[]
rh_mean=[]
ws=[]
lat=pyeto.deg2rad(lat)
day=1
sunshine_hours=[]
ETO=[]
#Completar los valores de ETo
for j in range(0,len(Tmean["0"])):
tmin=(Tmin["0"][j])
tmink=pyeto.celsius2kelvin(tmin)
tmax=(Tmax["0"][j])
tmaxk=pyeto.celsius2kelvin(tmax)
t=(Tmean["0"][j])
tk=pyeto.celsius2kelvin(t)
rh_mean=(HR["0"][j])
ws=(VV["0"][j])
def test_deg2rad(self):
self.assertEqual(pyeto.deg2rad(0), 0.0)
# Test values obtained form online conversion calculator
self.assertAlmostEqual(pyeto.deg2rad(-90), -1.5707963268, 10)
self.assertAlmostEqual(pyeto.deg2rad(90), 1.5707963268, 10)
self.assertAlmostEqual(pyeto.deg2rad(360), 6.2831853072, 10)
