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Edit and run



In [1]:

    
%cd /vagrant/source/charistools/test/test_files
%ls









    



/vagrant/source/charistools/test/test_files
Kta_hypso_100m.3col.csv
Kta_hypso_100m.csv
test.3x4.bin
test_basin.500.ALBEDO_MCD.0035.ablation.dat
test_basin.500.ALBEDO_MCD.0035.ablation.dat~
test_basin.500.ALBEDO_MCD.0035.snow_off_ice.dat
test_basin.500.ALBEDO_MCD.0035.snow_off_ice.dat~
test_basin.500.ALBEDO_MCD.0035.snow_on_ice.dat
test_basin.500.ALBEDO_MCD.0035.snow_on_ice.dat~
test_basin.500.temperature.dat
test_basin.500.temperature.dat~
test.sca_by_elev.txt



In [2]:

    
from charistools.hypsometry import Hypsometry
import numpy as np
import pandas as pd



In [3]:

    
sol_hyps = Hypsometry('test_basin.500.ALBEDO_MCD.0035.snow_off_ice.dat')
t_hyps = Hypsometry('test_basin.500.temperature.dat')



In [4]:

    
sol = sol_hyps.data.copy()
t = t_hyps.data.copy()



In [5]:

    
sol, t









    Out[5]:





(            3000.  3500.  4000.  4500.  5000.
 Date                                         
 2001-04-01   3605   9002  17390      0   1000
 2001-05-01   1130   5689  14800  23382   1000
 2001-06-01    100   1507   9089  18394  21466
 2001-07-01      9    255   2514  10954  14766
 2001-08-01     12     92    175   3324   9067
 2001-09-01     28    270   3275  12291  19570,
               3000.    3500.   4000.   4500.   5000.
 Date                                                
 2001-04-01   2.9000   1.6000  0.3000  0.0000  0.0000
 2001-05-01   6.7097   4.4839  1.9032  0.3548 -1.0000
 2001-06-01   9.8667   7.6000  4.5667  1.8667  0.4000
 2001-07-01  13.0000  10.5806  7.4839  4.3226  1.4516
 2001-08-01  13.3871  10.9355  7.7742  4.5484  1.3871
 2001-09-01   9.6000   7.2333  4.2000  1.6000  0.2333)



In [14]:

    
t[['4000.','5000.']]



In [18]:

    
#melt = sol.multiply(t[['4000.','5000.']],fill_value=0.)
melt = sol * t[['4000.','5000.']]
melt



In [6]:

    
sol.index









    Out[6]:





DatetimeIndex(['2001-04-01', '2001-05-01', '2001-06-01', '2001-07-01',
               '2001-08-01', '2001-09-01'],
              dtype='datetime64[ns]', name=u'Date', freq=None)



In [ ]:

    
ddfs = np.arange(len(sol.index))
ddf_series = pd.Series(data=ddfs, index=sol.index, name="Snow_DDF_mm_pday_pdegC")
ddf_series



In [ ]:

    
sol.multiply(ddf_series, axis=0)



In [21]:

    
sol



In [22]:

    
sol * 10



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doy = sol.index.dayofyear



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doy



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type(sol.index)



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tmp = sol.index
tmp



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test = tmp[0].replace(year=2000)
test
new = [test]
new



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import calendar



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leaptest = [calendar.isleap(year) for year in sol.index.year]
leaptest



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leaptest[0] = True
leaptest



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daysinyear = [366 if x else 365 for x in leaptest]



In [ ]:

    
print(isleap)
print(daysinyear)



In [ ]:

    
for x in isleap:
    print x



In [ ]:

    
[1 if isleap[x] else 365 for x in isleap]



In [33]:

    
pow(10.,-4)









    Out[33]:





0.0001



In [ ]:

	3000.	3500.	4000.	4500.	5000.
Date
2001-04-01	NaN	NaN	5217.0000	NaN	0.0000
2001-05-01	NaN	NaN	28167.3600	NaN	-1000.0000
2001-06-01	NaN	NaN	41506.7363	NaN	8586.4000
2001-07-01	NaN	NaN	18814.5246	NaN	21434.3256
2001-08-01	NaN	NaN	1360.4850	NaN	12576.8357
2001-09-01	NaN	NaN	13755.0000	NaN	4565.6810

	3000.	3500.	4000.	4500.	5000.
Date
2001-04-01	36050	90020	173900	0	10000
2001-05-01	11300	56890	148000	233820	10000
2001-06-01	1000	15070	90890	183940	214660
2001-07-01	90	2550	25140	109540	147660
2001-08-01	120	920	1750	33240	90670
2001-09-01	280	2700	32750	122910	195700

	4000.	5000.
Date
2001-04-01	0.3000	0.0000
2001-05-01	1.9032	-1.0000
2001-06-01	4.5667	0.4000
2001-07-01	7.4839	1.4516
2001-08-01	7.7742	1.3871
2001-09-01	4.2000	0.2333

	3000.	3500.	4000.	4500.	5000.
Date
2001-04-01	3605	9002	17390	0	1000
2001-05-01	1130	5689	14800	23382	1000
2001-06-01	100	1507	9089	18394	21466
2001-07-01	9	255	2514	10954	14766
2001-08-01	12	92	175	3324	9067
2001-09-01	28	270	3275	12291	19570