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 test_thornthwaite(self):
# Test values obtained from a worked example in Hydrology: An
# Environmental Approach, pp 435-436 by Ian Watson.
test_monthly_t = [
2.1, 2.5, 4.8, 7.1, 8.3, 10.7, 13.4, 14.5, 11.1, 8.2, 5.4, 3.7
]
test_monthly_mean_dlh = [
9.4, 10.6, 11.9, 13.4, 14.6, 15.2, 14.9, 13.9, 12.6, 11.1, 9.8, 9.1
]
test_pet = [
10.67, 14.08, 28.49, 45.85, 57.47, 75.20, 89.91, 90.29, 64.26,
43.34, 26.24, 17.31
]
# NOTE: The test PET was calculated using rounded coefficients, rounded
# intermediate values and doesn't adjust for the number of days in
# the month. This results in a small difference in estimated monthly
# PET of up to +/- 4 mm.
pet = pyeto.thornthwaite(test_monthly_t, test_monthly_mean_dlh)
for m in range(12):
diff = abs(pet[m] - test_pet[m])
self.assertLess(diff, 4)
# Test with non-leap year
pet_non_leap = pyeto.thornthwaite(test_monthly_t,
test_monthly_mean_dlh,
year=2015)
# Test results are same as above when year argument is set
for m in range(12):
self.assertEqual(pet[m], pet_non_leap[m])
# Test with leap year
pet_leap = pyeto.thornthwaite(test_monthly_t,
test_monthly_mean_dlh,
year=2016)
for m in range(12):
# 29 days in Feb so PET should be higher than in non-leap year
# results
if m == 1: # Feb
self.assertGreater(pet_leap[m], pet_non_leap[m])
else:
self.assertEqual(pet_leap[m], pet_non_leap[m])
# Test with wrong length args
with self.assertRaises(ValueError):
_ = pyeto.thornthwaite(list(range(11)), test_monthly_mean_dlh)
with self.assertRaises(ValueError):
_ = pyeto.thornthwaite(list(range(13)), test_monthly_mean_dlh)
with self.assertRaises(ValueError):
_ = pyeto.thornthwaite(test_monthly_t, list(range(11)))
with self.assertRaises(ValueError):
_ = pyeto.thornthwaite(test_monthly_t, list(range(13)))
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 test_thornthwaite(self):
# Test values obtained from a worked example in Hydrology: An
# Environmental Approach, pp 435-436 by Ian Watson.
test_monthly_t = [
2.1, 2.5, 4.8, 7.1, 8.3, 10.7, 13.4, 14.5, 11.1, 8.2, 5.4, 3.7]
test_monthly_mean_dlh = [
9.4, 10.6, 11.9, 13.4, 14.6, 15.2, 14.9, 13.9, 12.6, 11.1, 9.8, 9.1]
test_pet = [
10.67, 14.08, 28.49, 45.85, 57.47, 75.20, 89.91, 90.29, 64.26,
43.34, 26.24, 17.31]
# NOTE: The test PET was calculated using rounded coefficients, rounded
# intermediate values and doesn't adjust for the number of days in
# the month. This results in a small difference in estimated monthly
# PET of up to +/- 4 mm.
pet = pyeto.thornthwaite(test_monthly_t, test_monthly_mean_dlh)
for m in range(12):
diff = abs(pet[m] - test_pet[m])
self.assertLess(diff, 4)
# Test with non-leap year
pet_non_leap = pyeto.thornthwaite(
test_monthly_t, test_monthly_mean_dlh, year=2015)
# Test results are same as above when year argument is set
for m in range(12):
self.assertEqual(pet[m], pet_non_leap[m])
# Test with leap year
pet_leap = pyeto.thornthwaite(
test_monthly_t, test_monthly_mean_dlh, year=2016)
for m in range(12):
# 29 days in Feb so PET should be higher than in non-leap year
# results
if m == 1: # Feb
self.assertGreater(pet_leap[m], pet_non_leap[m])
else:
self.assertEqual(pet_leap[m], pet_non_leap[m])
# Test with wrong length args
with self.assertRaises(ValueError):
_ = pyeto.thornthwaite(list(range(11)), test_monthly_mean_dlh)
with self.assertRaises(ValueError):
_ = pyeto.thornthwaite(list(range(13)), test_monthly_mean_dlh)
with self.assertRaises(ValueError):
_ = pyeto.thornthwaite(test_monthly_t, list(range(11)))
with self.assertRaises(ValueError):
_ = pyeto.thornthwaite(test_monthly_t, list(range(13)))
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")