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Sep 21, 2017
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DOC: Added examples to rolling.py #1574

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Added examples to doc
Sep 17, 2017
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Revert back to original rolling.py
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DOC: added an example in def rolling()
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Revert back to latest rolling.py
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DOC: rolling.py example
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Update common.py
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DOC: rolling.py example
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DOC: rolling.py example
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DOC: add rolling.py example
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74 changes: 74 additions & 0 deletions xarray/core/rolling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,80 @@ def __init__(self, obj, min_periods=None, center=False, **windows):
Returns
-------
rolling : type of input argument

Examples
-------
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This should have one more -.

Create rolling seasonal average of monthly data e.g. DJF, JFM, ..., SON

>>> lat = np.linspace(-90, 90, num=181)
>>> lon = np.linspace(0, 359, num=360)
>>> time = pd.date_range('15/12/1999',
periods=12, freq=pd.DateOffset(months=1))
>>> a = np.repeat(np.repeat(np.linspace(0,11,num=12)[:, np.newaxis],
len(lat), axis=1)[:, :, np.newaxis], len(lon), axis=2)
>>> da = xr.DataArray(a, coords=[time, lat, lon], dims=['time','lat','lon'])
>>> da
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This is a fine example, but it takes a lot of space to show it because of the unused lat/lon dimensions. Maybe it would be better to drop those dimensions and make the array only go along time?

<xarray.DataArray (time: 12, lat: 181, lon: 360)>
array([[[ 0., 0., ..., 0., 0.],
[ 0., 0., ..., 0., 0.],
...,
[ 0., 0., ..., 0., 0.],
[ 0., 0., ..., 0., 0.]],

[[ 1., 1., ..., 1., 1.],
[ 1., 1., ..., 1., 1.],
...,
[ 1., 1., ..., 1., 1.],
[ 1., 1., ..., 1., 1.]],

...,
[[ 10., 10., ..., 10., 10.],
[ 10., 10., ..., 10., 10.],
...,
[ 10., 10., ..., 10., 10.],
[ 10., 10., ..., 10., 10.]],

[[ 11., 11., ..., 11., 11.],
[ 11., 11., ..., 11., 11.],
...,
[ 11., 11., ..., 11., 11.],
[ 11., 11., ..., 11., 11.]]])
Coordinates:
* time (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...
* lat (lat) float64 -90.0 -89.0 -88.0 -87.0 -86.0 -85.0 -84.0 -83.0 ...
* lon (lon) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 ...
>>> da_seasonal_avg = da.rolling(time=3).mean().dropna('time')
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For examples in docstrings, we usually try to keep things as simple as possible. The dropna() and reassignment below are good tips for restructuring the result of rolling, but we should show the intermediate output first, e.g., by directly printing da.rolling(time=3).mean().

>>> da_seasonal_avg['time'] = time[1:len(time)-1]
>>> da_seasonal_avg
<xarray.DataArray (time: 10, lat: 181, lon: 360)>
array([[[ 1., 1., ..., 1., 1.],
[ 1., 1., ..., 1., 1.],
...,
[ 1., 1., ..., 1., 1.],
[ 1., 1., ..., 1., 1.]],

[[ 2., 2., ..., 2., 2.],
[ 2., 2., ..., 2., 2.],
...,
[ 2., 2., ..., 2., 2.],
[ 2., 2., ..., 2., 2.]],

...,
[[ 9., 9., ..., 9., 9.],
[ 9., 9., ..., 9., 9.],
...,
[ 9., 9., ..., 9., 9.],
[ 9., 9., ..., 9., 9.]],

[[ 10., 10., ..., 10., 10.],
[ 10., 10., ..., 10., 10.],
...,
[ 10., 10., ..., 10., 10.],
[ 10., 10., ..., 10., 10.]]])
Coordinates:
* time (time) datetime64[ns] 2000-01-15 2000-02-15 2000-03-15 ...
* lat (lat) float64 -90.0 -89.0 -88.0 -87.0 -86.0 -85.0 -84.0 -83.0 ...
* lon (lon) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 ...
"""

if (has_bottleneck and
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