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import pandas as pd
user_cols = ['Year', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec', 'Annual']
Table710 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/710/precipitation-tabular.txt',
skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),
names=user_cols)
Table709 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/709/precipitation-tabular.txt',
skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),
names=user_cols)
Table807 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/807/precipitation-tabular.txt',
skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),
names=user_cols)
Table808 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/808/precipitation-tabular.txt',
skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),
names=user_cols)
Table809 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/809/precipitation-tabular.txt',
skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),
names=user_cols)
Table810 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/810/precipitation-tabular.txt',
skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),
names=user_cols)
Table908 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/908/precipitation-tabular.txt',
skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),
names=user_cols)
Winter709 = []
Winter710 = []
Winter807 = []
Winter808 = []
Winter809 = []
Winter810 = []
Winter908 = []
Spring709 = []
Spring710 = []
Spring807 = []
Spring808 = []
Spring809 = []
Spring810 = []
Spring908 = []
Summer709 = []
Summer710 = []
Summer807 = []
Summer808 = []
Summer809 = []
Summer810 = []
Summer908 = []
Fall709 = []
Fall710 = []
Fall807 = []
Fall808 = []
Fall809 = []
Fall810 = []
Fall908 = []
WinterAvg = []
SpringAvg = []
SummerAvg = []
FallAvg = []
#Combines the months into seasons from the Tables
for i in range(1,37):
a = Table709.Mar[i] + Table709.Apr[i] + Table709.May[i]
b = Table710.Mar[i] + Table710.Apr[i] + Table710.May[i]
c = Table807.Mar[i] + Table807.Apr[i] + Table807.May[i]
d = Table808.Mar[i] + Table808.Apr[i] + Table808.May[i]
e = Table809.Mar[i] + Table809.Apr[i] + Table809.May[i]
f = Table810.Mar[i] + Table810.Apr[i] + Table810.May[i]
g = Table908.Mar[i] + Table908.Apr[i] + Table908.May[i]
Spring709.insert(i-1,a)
Spring710.insert(i-1,b)
Spring807.insert(i-1,c)
Spring808.insert(i-1,d)
Spring809.insert(i-1,e)
Spring810.insert(i-1,f)
Spring908.insert(i-1,g)
i = i + 1
for i in range(1,37):
h = Table709.Jun[i] + Table709.Jul[i] + Table709.Aug[i]
j = Table710.Jun[i] + Table710.Jul[i] + Table710.Aug[i]
k = Table807.Jun[i] + Table807.Jul[i] + Table807.Aug[i]
l = Table808.Jun[i] + Table808.Jul[i] + Table808.Aug[i]
m = Table809.Jun[i] + Table809.Jul[i] + Table809.Aug[i]
n = Table810.Jun[i] + Table810.Jul[i] + Table810.Aug[i]
cc = Table908.Jun[i] + Table908.Jul[i] + Table908.Aug[i]
Summer709.insert(i-1,h)
Summer710.insert(i-1,j)
Summer807.insert(i-1, k)
Summer808.insert(i-1, l)
Summer809.insert(i-1, m)
Summer810.insert(i-1, n)
Summer908.insert(i-1, cc)
i = i + 1
for i in range(1,37):
o = Table709.Sept[i] + Table709.Oct[i] + Table709.Nov[i]
p = Table710.Sept[i] + Table710.Oct[i] + Table710.Nov[i]
q = Table807.Sept[i] + Table807.Oct[i] + Table807.Nov[i]
r = Table808.Sept[i] + Table808.Oct[i] + Table808.Nov[i]
s = Table809.Sept[i] + Table809.Oct[i] + Table809.Nov[i]
t = Table810.Sept[i] + Table810.Oct[i] + Table810.Nov[i]
u = Table908.Sept[i] + Table908.Oct[i] + Table908.Nov[i]
Fall709.insert(i-1,o)
Fall710.insert(i-1,p)
Fall807.insert(i-1,q)
Fall808.insert(i-1,r)
Fall809.insert(i-1,s)
Fall810.insert(i-1,t)
Fall908.insert(i-1,u)
i = i + 1
#Uses i-1 since we want Dec. 1980 and 1980 is the first row
for i in range(1,37):
v = Table709.Dec[i-1] + Table709.Jan[i] + Table709.Feb[i]
w = Table710.Dec[i-1] + Table710.Jan[i] + Table710.Feb[i]
x = Table807.Dec[i-1] + Table807.Jan[i] + Table807.Feb[i]
y = Table808.Dec[i-1] + Table808.Jan[i] + Table808.Feb[i]
z = Table809.Dec[i-1] + Table809.Jan[i] + Table809.Feb[i]
aa = Table810.Dec[i-1] + Table810.Jan[i] + Table810.Feb[i]
bb = Table908.Dec[i-1] + Table908.Jan[i] + Table908.Feb[i]
Winter709.insert(i-1,v)
Winter710.insert(i-1,w)
Winter807.insert(i-1,x)
Winter808.insert(i-1,y)
Winter809.insert(i-1,z)
Winter810.insert(i-1, aa)
Winter908.insert(i-1, bb)
i = i + 1
#Linear addition of the selected polygons and an average is taken to get rainfall for our CWA
for i in range(0,36):
wtotal = ((Winter709[i] + Winter710[i] + Winter807[i] + Winter808[i] + Winter809[i] + Winter810[i] + Winter908[i])/7.0)
sptotal = ((Spring709[i] + Spring710[i] + Spring807[i] + Spring808[i] + Spring809[i] + Spring810[i] + Spring908[i])/7.0)
sutotal = ((Summer709[i] + Summer710[i] + Summer807[i] + Summer808[i] + Summer809[i] + Summer810[i] + Summer908[i])/7.0)
ftotal = ((Fall709[i] + Fall710[i] + Fall807[i] + Fall808[i] + Fall809[i] + Fall810[i] + Fall908[i])/7.0)
WinterAvg.insert(i, wtotal)
SpringAvg.insert(i, sptotal)
SummerAvg.insert(i, sutotal)
FallAvg.insert(i, ftotal)
i = i + 1
df1 = pd.DataFrame({'Winter': WinterAvg})
df2 = pd.DataFrame({'Spring': SpringAvg})
df3 = pd.DataFrame({'Summer': SummerAvg})
df4 = pd.DataFrame({'Fall': FallAvg})
dftot = pd.concat([df1, df2, df3, df4], axis=1)
#dftot.to_csv('LCRA Rainfall.csv')
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