In [1]:

    

%pylab notebook


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

from charistools.charisTimeseries import CharisTimeseries
import glob
import matplotlib.pyplot as plt


    

In [10]:

    

%cd /projects/CHARIS/charistools_test_data/rainfall
list = sort(glob.glob("*csv"))
list


    

    




/projects/CHARIS/charistools_test_data/rainfall






    
Out[10]:





array(['AM_Vakhsh_at_Komsomolabad.APHRODITE_rainfall_km3.monthly.csv',
       'GA_Karnali_at_Benighat.APHRODITE_rainfall_km3.monthly.csv',
       'GA_Narayani_at_Devghat.APHRODITE_rainfall_km3.monthly.csv',
       'GA_SaptaKosi_at_Chatara.APHRODITE_rainfall_km3.monthly.csv',
       'IN_Hunza_at_Danyour.APHRODITE_rainfall_km3.monthly.csv'], 
      dtype='|S60')

In [11]:

    

fig, ax = plt.subplots(5, 1, sharex=True, sharey=True, figsize=(8,8))
#fig, ax = plt.subplots(5, 1, sharex=True, figsize=(8,8))
for filename_idx, filename in enumerate(list):
    data = CharisTimeseries(filename=filename)
    tdf = data.data[data.data > -9999.99]
    tdf['rainfall'].loc['2001-01-01':'2014-12-31'].plot(ax=ax[filename_idx], title=filename)
plt.tight_layout()


    

    















    












    




parse_date_format: ['%Y', '%m', '%d']
colnames:   ['year', 'month', 'day', 'doy', 'rainfall']
_datetime_cols:  ['year', 'month', 'day']
parse_date_format: ['%Y', '%m', '%d']
colnames:   ['year', 'month', 'day', 'doy', 'rainfall']
_datetime_cols:  ['year', 'month', 'day']
parse_date_format: ['%Y', '%m', '%d']
colnames:   ['year', 'month', 'day', 'doy', 'rainfall']
_datetime_cols:  ['year', 'month', 'day']
parse_date_format: ['%Y', '%m', '%d']
colnames:   ['year', 'month', 'day', 'doy', 'rainfall']
_datetime_cols:  ['year', 'month', 'day']
parse_date_format: ['%Y', '%m', '%d']
colnames:   ['year', 'month', 'day', 'doy', 'rainfall']
_datetime_cols:  ['year', 'month', 'day']

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