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Mar 3, 2015
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updated monthly mean averaging example using new virtual variable syntax #357

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76 changes: 38 additions & 38 deletions doc/examples/monthly-means.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ different number of days.

numpy version : 1.9.1
pandas version : 0.15.2
xray version : 0.3.2
xray version : 0.4rc1-20-g52bbca3


Some calendar information so we can support any netCDF calendar.
Expand Down Expand Up @@ -99,7 +99,7 @@ Open the ``Dataset``
* depth (depth) int64 0 1 2
* x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ...
* y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ...
Variables:
Data variables:
Precipitation (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
Evap (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
Runoff (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan nan nan ...
Expand Down Expand Up @@ -165,35 +165,35 @@ allong the time dimension.
.. parsed-literal::

<xray.Dataset>
Dimensions: (depth: 3, time.season: 4, x: 275, y: 205)
Dimensions: (depth: 3, season: 4, x: 275, y: 205)
Coordinates:
* depth (depth) int64 0 1 2
* x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ...
* y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ...
* time.season (time.season) int32 1 2 3 4
Variables:
Lwnet (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Tair (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Surft (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Senht (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Tsoil (time.season, depth, y, x) float64 nan nan nan nan nan nan nan nan nan ...
Netrad (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Evap (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Latht (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Wind (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Precipitation (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Soilw (time.season, depth, y, x) float64 nan nan nan nan nan nan nan nan nan ...
Relhum (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Swd (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Swnet (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Swq (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Swin (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Albedo (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Lwin (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Radt (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Runoff (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Grdht (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
Baseflow (time.season, y, x) float64 nan nan nan nan nan nan nan nan nan nan nan ...
* x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ...
* depth (depth) int64 0 1 2
* season (season) object 'DJF' 'JJA' 'MAM' 'SON'
Data variables:
Baseflow (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Tsoil (season, depth, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Wind (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Swin (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Swq (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Netrad (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Albedo (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Evap (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Swd (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Radt (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Lwin (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Relhum (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Soilw (season, depth, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Lwnet (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Senht (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Surft (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Latht (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Runoff (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
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 0.0 0.0 ...
Grdht (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Swnet (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Precipitation (season, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...


.. code:: python
Expand All @@ -204,23 +204,23 @@ allong the time dimension.
.. code:: python

# Quick plot to show the results
is_null = np.isnan(ds_weighted['Tair'][0].values)
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'][i].values),
vmin=-30, vmax=30, cmap='Spectral_r')
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'][i].values),
vmin=-30, vmax=30, cmap='Spectral_r')
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'][i].values),
vmin=-0.1, vmax=.1, cmap='RdBu_r')
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([])
Expand All @@ -230,7 +230,7 @@ allong the time dimension.
axes[i, 0].set_ylabel(season)

axes[0, 0].set_title('Weighted by DPM')
axes[0, 1].set_title('No Weighting')
axes[0, 1].set_title('Equal Weighting')
axes[0, 2].set_title('Difference')

plt.tight_layout()
Expand Down Expand Up @@ -259,4 +259,4 @@ allong the time dimension.
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')
return (ds * weights).groupby('time.season').sum(dim='time')
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