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

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

"""
Explanation: Calculating Seasonal Averages from Timeseries of Monthly Means
Author: Joe Hamman
The data used for this example can be found in the xray-data repository.  You may need to change the path to RASM_example_data.nc below.
Suppose we have a netCDF or xray Dataset of monthly mean data and we want to calculate the seasonal average.  To do this properly, we need to calculate the weighted average considering that each month has a different number of days.
End of explanation
"""


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]} 

"""
Explanation: Some calendar information so we can support any netCDF calendar.
End of explanation
"""


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

"""
Explanation: A few calendar functions to determine the number of days in each month
If you were just using the standard calendar, it would be easy to use the calendar.month_range function.
End of explanation
"""


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

"""
Explanation: Open the Dataset
End of explanation
"""


# Make a DataArray with the number of days in each month, size = len(time)
month_length = xray.DataArray(get_dpm(ds.time.to_index(), calendar='noleap'),
                              coords=[ds.time], name='month_length')

# Calculate the weights by grouping by 'time.season'.
# Conversion to float type ('astype(float)') only necessary for Python 2.x
weights = month_length.groupby('time.season') / month_length.astype(float).groupby('time.season').sum()

# Test that the sum of the weights for each season is 1.0
np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4))

# Calculate the weighted average
ds_weighted = (ds * weights).groupby('time.season').sum(dim='time')

print(ds_weighted)

# only used for comparisons
ds_unweighted = ds.groupby('time.season').mean('time')
ds_diff = ds_weighted - ds_unweighted

# Quick plot to show the results
is_null = np.isnan(ds_unweighted['Tair'][0].values)

fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(14,12))
for i, season in enumerate(('DJF', 'MAM', 'JJA', 'SON')):
    plt.sca(axes[i, 0])
    plt.pcolormesh(np.ma.masked_where(is_null, ds_weighted['Tair'].sel(season=season).values),
                   vmin=-30, vmax=30, cmap='Spectral_r')
    plt.colorbar(extend='both')
    
    plt.sca(axes[i, 1])
    plt.pcolormesh(np.ma.masked_where(is_null, ds_unweighted['Tair'].sel(season=season).values),
                   vmin=-30, vmax=30, cmap='Spectral_r')
    plt.colorbar(extend='both')

    plt.sca(axes[i, 2])
    plt.pcolormesh(np.ma.masked_where(is_null, ds_diff['Tair'].sel(season=season).values),
                   vmin=-0.1, vmax=.1, cmap='RdBu_r')
    plt.colorbar(extend='both')
    for j in range(3):
        axes[i, j].axes.get_xaxis().set_ticklabels([])
        axes[i, j].axes.get_yaxis().set_ticklabels([])
        axes[i, j].axes.axis('tight')
    
    axes[i, 0].set_ylabel(season)
    
axes[0, 0].set_title('Weighted by DPM')
axes[0, 1].set_title('Equal Weighting')
axes[0, 2].set_title('Difference')
        
plt.tight_layout()

fig.suptitle('Seasonal Surface Air Temperature', fontsize=16, y=1.02)

# Wrap it into a simple function
def season_mean(ds, calendar='standard'):
    # Make a DataArray of season/year groups
    year_season = xray.DataArray(ds.time.to_index().to_period(freq='Q-NOV').to_timestamp(how='E'),
                                 coords=[ds.time], name='year_season')

    # Make a DataArray with the number of days in each month, size = len(time)
    month_length = xray.DataArray(get_dpm(ds.time.to_index(), calendar=calendar),
                                  coords=[ds.time], name='month_length')
    # Calculate the weights by grouping by 'time.season'
    weights = month_length.groupby('time.season') / month_length.groupby('time.season').sum()

    # Test that the sum of the weights for each season is 1.0
    np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4))

    # Calculate the weighted average
    return (ds * weights).groupby('time.season').sum(dim='time')

"""
Explanation: Now for the heavy lifting:
We first have to come up with the weights,
- calculate the month lengths for each monthly data record
- calculate weights using groupby('time.season')
Finally, we just need to multiply our weights by the Dataset and sum allong the time dimension.
End of explanation
"""


