In [2]:

    

%pylab notebook


    

    




Populating the interactive namespace from numpy and matplotlib

In [3]:

    

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


    

In [4]:

    

%cd /projects/CHARIS/charistools_test_data/streamflow
list = sort(glob.glob("??_*dat"))
list


    

    




/projects/CHARIS/charistools_test_data/streamflow






    
Out[4]:





array(['AM_Vakhsh_at_Komsomolabad.month_runoff.dat',
       'GA_Karnali_at_Benighat.month_runoff.dat',
       'GA_Narayani_at_Devghat.month_runoff.dat',
       'GA_SaptaKosi_at_Chatara.month_runoff.dat',
       'IN_Hunza_at_DainyorBridge.month_runoff.dat'], 
      dtype='|S42')

In [5]:

    

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


    

    















    












    




parse_date_format: ['%Y', '%m']
colnames:   ['year', 'month', 'runoff']
_datetime_cols:  ['year', 'month']
parse_date_format: ['%Y', '%m']
colnames:   ['year', 'month', 'runoff']
_datetime_cols:  ['year', 'month']
parse_date_format: ['%Y', '%m']
colnames:   ['year', 'month', 'runoff']
_datetime_cols:  ['year', 'month']
parse_date_format: ['%Y', '%m']
colnames:   ['year', 'month', 'runoff']
_datetime_cols:  ['year', 'month']
parse_date_format: ['%Y', '%m']
colnames:   ['year', 'month', 'runoff']
_datetime_cols:  ['year', 'month']

In [5]:

    

for filename in list:
    data = CharisTimeseries(filename=filename)
    tdf = data.data[data.data > -9999.99]
    tdf.plot()
    tdf.loc['2001-01-01':'2010-12-31'].plot()


    

    




parse_date_format: ['%Y', '%m']
colnames:   ['year', 'month', 'runoff']
_datetime_cols:  ['year', 'month']

In [6]:

    

help(data)


    

    




Help on CharisTimeseries in module charistools.charisTimeseries object:

class CharisTimeseries(__builtin__.object)
 |  Class to represent timeseries data for CHARIS (discharge, runoff,
 |  precipitation, etc.).  Expected file format is ASCII text, with header
 |  lines beginning with '#', and data in two whitespace-separated columns:
 |  year and annual runoff (km^3) or mean annual discharge (m^3/s).
 |  
 |  Returns an object containing a pandas timeseries object in "data", and
 |  header comments in "comments" with the leading hash ('#') characters
 |  retained.
 |  
 |  Methods defined here:
 |  
 |  __init__(self, filename, default_year=2000)
 |  
 |  as_text(self)
 |  
 |  comments(self)
 |  
 |  comments_array(self)
 |  
 |  summary(self)
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)

In [7]:

    

data.comments()


    

    
Out[7]:





'# Station ID: 35749301\n# Basin: Hunza at Dainyor Bridge\n# Latitude: 35.92778\n# Longitude: 74.37639\n# Elevation: 1370 m\n# Area: 13157 km**2\n# Units: km**3\n# COLUMNS: year month runoff'

In [8]:

    

data.comments_array()


    

    
Out[8]:





['# Station ID: 35749301',
 '# Basin: Hunza at Dainyor Bridge',
 '# Latitude: 35.92778',
 '# Longitude: 74.37639',
 '# Elevation: 1370 m',
 '# Area: 13157 km**2',
 '# Units: km**3',
 '# COLUMNS: year month runoff']

In [9]:

    

data.data


    

    
Out[9]:






  

    

      

      
runoff
    
    

      
datetime
      

    
  
  

    

      
1966-01-01
      
0.15
    
    

      
1966-02-01
      
0.12
    
    

      
1966-03-01
      
0.10
    
    

      
1966-04-01
      
0.13
    
    

      
1966-05-01
      
0.29
    
    

      
1966-06-01
      
2.01
    
    

      
1966-07-01
      
3.16
    
    

      
1966-08-01
      
3.49
    
    

      
1966-09-01
      
1.61
    
    

      
1966-10-01
      
0.45
    
    

      
1966-11-01
      
0.20
    
    

      
1966-12-01
      
0.16
    
    

      
1967-01-01
      
0.15
    
    

      
1967-02-01
      
0.13
    
    

      
1967-03-01
      
0.15
    
    

      
1967-04-01
      
0.18
    
    

      
1967-05-01
      
0.29
    
    

      
1967-06-01
      
1.60
    
    

      
1967-07-01
      
3.93
    
    

      
1967-08-01
      
3.64
    
    

      
1967-09-01
      
1.62
    
    

      
1967-10-01
      
0.38
    
    

      
1967-11-01
      
0.19
    
    

      
1967-12-01
      
0.14
    
    

      
1968-01-01
      
0.11
    
    

      
1968-02-01
      
0.10
    
    

      
1968-03-01
      
0.10
    
    

      
1968-04-01
      
0.12
    
    

      
1968-05-01
      
0.33
    
    

      
1968-06-01
      
1.93
    
    

      
...
      
...
    
    

      
2011-07-01
      
-9999.99
    
    

      
2011-08-01
      
-9999.99
    
    

      
2011-09-01
      
-9999.99
    
    

      
2011-10-01
      
-9999.99
    
    

      
2011-11-01
      
-9999.99
    
    

      
2011-12-01
      
-9999.99
    
    

      
2012-01-01
      
-9999.99
    
    

      
2012-02-01
      
-9999.99
    
    

      
2012-03-01
      
-9999.99
    
    

      
2012-04-01
      
-9999.99
    
    

      
2012-05-01
      
-9999.99
    
    

      
2012-06-01
      
-9999.99
    
    

      
2012-07-01
      
-9999.99
    
    

      
2012-08-01
      
-9999.99
    
    

      
2012-09-01
      
-9999.99
    
    

      
2012-10-01
      
-9999.99
    
    

      
2012-11-01
      
-9999.99
    
    

      
2012-12-01
      
-9999.99
    
    

      
2013-01-01
      
-9999.99
    
    

      
2013-02-01
      
-9999.99
    
    

      
2013-03-01
      
-9999.99
    
    

      
2013-04-01
      
-9999.99
    
    

      
2013-05-01
      
-9999.99
    
    

      
2013-06-01
      
-9999.99
    
    

      
2013-07-01
      
-9999.99
    
    

      
2013-08-01
      
-9999.99
    
    

      
2013-09-01
      
-9999.99
    
    

      
2013-10-01
      
-9999.99
    
    

      
2013-11-01
      
-9999.99
    
    

      
2013-12-01
      
-9999.99
    
  

576 rows × 1 columns

In [19]:

    

tdf = data.data[data.data > -9999.99]
tdf.plot()


    

    















    












    
Out[19]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fec3e8ad890>

In [20]:

    

tdf.loc['2001-01-01':'2010-12-31'].plot()


    

    















    












    
Out[20]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fec3e3ad650>

In [18]:

    

#tdf.loc['2001-01-01':'2004-12-31'].plot()
tdf.plot()


    

    















    












    
Out[18]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fec3ec49490>

In [ ]: