In [1]:

    

%matplotlib inline
import numpy as np
import pandas as pd
import xray
from netCDF4 import num2date
import matplotlib.pyplot as plt


    

In [3]:

    

print("numpy version  : ", np.__version__)
print("pandas version : ", pd.__version__)
print("xray version   : ", xray.version.version)


    

    




numpy version  :  1.10.4
pandas version :  0.18.0
xray version   :  0.7.2

In [4]:

    

dpm = {'noleap': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
       '365_day': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
       'standard': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
       'gregorian': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
       'proleptic_gregorian': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
       'all_leap': [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
       '366_day': [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
       '360_day': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30]}


    

In [7]:

    

def leap_year(year, calendar='standard'):
    """Determine if year is a leap year"""
    leap = False
    if ((calendar in ['standard', 'gregorian',
        'proleptic_gregorian', 'julian']) and
        (year % 4 == 0)):
        leap = True
        if ((calendar == 'proleptic_gregorian') and
            (year % 100 == 0) and
            (year % 400 != 0)):
            leap = False
        elif ((calendar in ['standard', 'gregorian']) and
                 (year % 100 == 0) and (year % 400 != 0) and
                 (year < 1583)):
            leap = False
    return leap

def get_dpm(time, calendar='standard'):
    """
    return a array of days per month corresponding to the months provided in `months`
    """
    month_length = np.zeros(len(time), dtype=np.int)

    cal_days = dpm[calendar]

    for i, (month, year) in enumerate(zip(time.month, time.year)):
        month_length[i] = cal_days[month]
        if leap_year(year, calendar=calendar):
            month_length[i] += 1
    return month_length


    

In [5]:

    

monthly_mean_file = 'RASM_example_data.nc'
ds = xray.open_dataset(monthly_mean_file, decode_coords=False)
print(ds)


    

    




<xarray.Dataset>
Dimensions:  (time: 36, x: 275, y: 205)
Coordinates:
  * time     (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ...
  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...
  * y        (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...
Data variables:
    Tair     (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ...
Attributes:
    title: /workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc
    institution: U.W.
    source: RACM R1002RBRxaaa01a
    output_frequency: daily
    output_mode: averaged
    convention: CF-1.4
    references: Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.
    comment: Output from the Variable Infiltration Capacity (VIC) model.
    nco_openmp_thread_number: 1
    NCO: 4.3.7
    history: history deleted for brevity

In [8]:

    

month_length = xray.DataArray(get_dpm(ds.time.to_index(),
                                      calendar='noleap'),
                              coords=[ds.time], name='month_length')


    

In [9]:

    

weights = month_length.groupby('time.season') / month_length.astype(float).groupby('time.season').sum()


    

In [10]:

    

np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4))


    

In [11]:

    

ds_weighted = (ds * weights).groupby('time.season').sum(dim='time')


    

In [12]:

    

ds_weighted


    

    
Out[12]:





<xarray.Dataset>
Dimensions:  (season: 4, x: 275, y: 205)
Coordinates:
  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...
  * y        (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...
  * season   (season) object 'DJF' 'JJA' 'MAM' 'SON'
Data variables:
    Tair     (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...

In [24]:

    

ds.groupby('time.season').mean('time').plot()


    

    




---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-24-37f5e4019e08> in <module>()
----> 1 ds.groupby('time.season').mean('time').plot()

C:\Miniconda3\envs\conda-xarray\lib\site-packages\xarray\core\common.py in __getattr__(self, name)
    192                     return source[name]
    193         raise AttributeError("%r object has no attribute %r" %
--> 194                              (type(self).__name__, name))
    195 
    196     def __setattr__(self, name, value):

AttributeError: 'Dataset' object has no attribute 'plot'

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