html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/557#issuecomment-427291294,https://api.github.com/repos/pydata/xarray/issues/557,427291294,MDEyOklzc3VlQ29tbWVudDQyNzI5MTI5NA==,6883049,2018-10-05T08:46:12Z,2018-10-05T08:46:12Z,CONTRIBUTOR,"I though this issue was long forgotten ; ) The fact is that today I still call this seltime function very often in my code so I am glad to see it back, thank you.
I like the .isin(elnino_years) syntax, and I see that it is consistent with pandas. Something like dataset.time.year.isin(elnino_years) would be very nice too, right now is just a ""to_index()"" away, as this works: dataset.time.to_index().year.isin(elnino_years). However I am not sure about how this is related with multidimensional indexing, as time is only one dimension.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,104484316
https://github.com/pydata/xarray/issues/557#issuecomment-137478562,https://api.github.com/repos/pydata/xarray/issues/557,137478562,MDEyOklzc3VlQ29tbWVudDEzNzQ3ODU2Mg==,6883049,2015-09-03T15:05:35Z,2015-09-04T09:49:54Z,CONTRIBUTOR,"I agree with your arguments against creating too many datetime specific methods. However, I am not convinced about using .sel with 'time.year', it looks a bit hackish to me. Maybe all ""CDO methods"" can be integrated in one single ""seltimes"" method. Please look at the function below. The time_coord arguments lets you choose the time coordinate that you want to filter, and by using numpy.logical_and is it possible to do the whole operation in one single function call. It could be turned into a method. What do you think?
``` python
%matplotlib inline
import xray
import numpy as np
from matplotlib import pyplot as plt
ifile = ""HadCRUT.4.3.0.0.median.nc""
ixd = xray.open_dataset(ifile)
```
You can download the example file from here http://www.metoffice.gov.uk/hadobs/hadcrut4/data/4.3.0.0/gridded_fields/HadCRUT.4.3.0.0.median_netcdf.zip Now comes the function.
``` python
def seltime(ixd, time_coord, **kwargs):
""""""
Select time steps by groups of years, months, etc.
Parameters
----------
ixd : xray.Dataset or xray.DataArray, input dataset. Will be ""self"" if
the function is turned into a method.
time_coord: str, name of the time coordinate to use. Needed to avoid ambiguities.
**kwargs: String or interable: Currently years, months, days and hours are supported.
Returns
----------
xray.Dataset or xrat.DataArray filtered by the constraints defined
by the kwargs.
""""""
ntimes = len(ixd.coords[time_coord])
time_mask = np.repeat(True, ntimes)
for time_unit, time_values in kwargs.iteritems():
if time_unit not in (""year"", ""month"", ""day"", ""hour""):
raise KeyError(time_unit)
time_str = ""{}.{}"".format(time_coord, time_unit)
time_mask_temp = np.in1d(ixd[time_str], time_values)
time_mask = np.logical_and(time_mask, time_mask_temp)
oxd = ixd.sel(**{time_coord : time_mask})
return oxd
```
It works, and it looks fast enough.
``` python
elnino_years = (1958, 1973, 1983, 1987, 1998)
months = (1, 2, 3, 4)
%timeit elnino_xd = seltime(ixd, ""time"", year=elnino_years, month=months)
elnino_xd.time
```
```
1000 loops, best of 3: 1.05 ms per loop
array(['1958-01-16T12:00:00.000000000Z', '1958-02-15T00:00:00.000000000Z',
'1958-03-16T12:00:00.000000000Z', '1958-04-16T00:00:00.000000000Z',
'1973-01-16T13:00:00.000000000+0100',
'1973-02-15T01:00:00.000000000+0100',
'1973-03-16T13:00:00.000000000+0100',
'1973-04-16T01:00:00.000000000+0100',
'1983-01-16T13:00:00.000000000+0100',
'1983-02-15T01:00:00.000000000+0100',
'1983-03-16T13:00:00.000000000+0100',
'1983-04-16T02:00:00.000000000+0200',
'1987-01-16T13:00:00.000000000+0100',
'1987-02-15T01:00:00.000000000+0100',
'1987-03-16T13:00:00.000000000+0100',
'1987-04-16T02:00:00.000000000+0200',
'1998-01-16T13:00:00.000000000+0100',
'1998-02-15T01:00:00.000000000+0100',
'1998-03-16T13:00:00.000000000+0100',
'1998-04-16T02:00:00.000000000+0200'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1958-01-16T12:00:00 1958-02-15 ...
Attributes:
start_month: 1
start_year: 1850
end_month: 4
end_year: 2015
long_name: time
standard_name: time
axis: T
```
If we average in time we can see the warm tonge in the pacific.
``` python
plt.figure(figsize=(12, 5))
elnino_xd.temperature_anomaly.mean(""time"").plot()
plt.gca().invert_yaxis()
```
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