%matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt 

"""
Explanation: Calculating Seasonal Averages from Timeseries of Monthly Means
Author: Joe Hamman
The data used for this example can be found in the xarray-data repository. You may need to change the path to rasm.nc below.
Suppose we have a netCDF or xarray.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
"""


ds = xr.tutorial.open_dataset('rasm').load()
ds

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


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

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
notnull = pd.notnull(ds_unweighted['Tair'][0])

fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(14,12))
for i, season in enumerate(('DJF', 'MAM', 'JJA', 'SON')):
    ds_weighted['Tair'].sel(season=season).where(notnull).plot.pcolormesh(
        ax=axes[i, 0], vmin=-30, vmax=30, cmap='Spectral_r', 
        add_colorbar=True, extend='both')
    
    ds_unweighted['Tair'].sel(season=season).where(notnull).plot.pcolormesh(
        ax=axes[i, 1], vmin=-30, vmax=30, cmap='Spectral_r', 
        add_colorbar=True, extend='both')

    ds_diff['Tair'].sel(season=season).where(notnull).plot.pcolormesh(
        ax=axes[i, 2], vmin=-0.1, vmax=.1, cmap='RdBu_r',
        add_colorbar=True, extend='both')

    axes[i, 0].set_ylabel(season)
    axes[i, 1].set_ylabel('')
    axes[i, 2].set_ylabel('')

for ax in axes.flat:
    ax.axes.get_xaxis().set_ticklabels([])
    ax.axes.get_yaxis().set_ticklabels([])
    ax.axes.axis('tight')
    ax.set_xlabel('')
    
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 with the number of days in each month, size = len(time)
    month_length = ds.time.dt.days_in_month

    # 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.  Creating a DataArray for the month length is as easy as using the days_in_month accessor on the time coordinate.  The calendar type, in this case 'noleap', is automatically considered in this operation.
End of explanation
"""


