Reputation: 415
I have two xarray dataset that have matching and unmatching time series. I would like to drop time series from dataset 2 that doesn't match with time-series of dataset 1.
ds1
<xarray.Dataset>
Dimensions: (time: 149, x: 311, y: 266)
Coordinates:
* y (y) float64 -3.256e+06 -3.256e+06 ... -3.263e+06 -3.263e+06
spatial_ref int32 3577
* time (time) datetime64[ns] 2016-01-01T00:09:15.704000 ... 2020-12...
* x (x) float64 1.913e+06 1.913e+06 1.913e+06 ... 1.92e+06 1.92e+06
Data variables:
FMCOB (time, y, x) float64 78.63 48.68 85.0 ... 42.16 91.27 52.36
Forest (x, y) int64 0 0 0 3 3 3 3 3 0 0 0 0 ... 0 0 3 3 3 3 3 3 3 3 3
Grass (x, y) int64 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0
Shrub (x, y) int64 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0
Attributes:
crs: EPSG:3577
grid_mapping: spatial_ref
units: % dry matter
ds2
<xarray.Dataset>
Dimensions: (time: 155, x: 76, y: 47)
Coordinates:
* y (y) float64 -3.257e+06 -3.257e+06 ... -3.258e+06 -3.258e+06
spatial_ref int32 3577
* time (time) datetime64[ns] 2016-01-01T00:09:15.704000 ... 2020-12...
* x (x) float64 1.919e+06 1.919e+06 ... 1.921e+06 1.921e+06
Data variables:
FMCOB (time, y, x) float64 81.67 87.5 74.4 95.0 ... nan 58.39 85.96
Forest (x, y) int64 0 0 0 0 0 0 3 3 3 3 3 3 ... 0 0 0 0 0 0 0 0 0 0 0
Grass (x, y) int64 0 0 1 1 1 1 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0
Shrub (x, y) int64 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 2 0 0 0 0 2
Attributes:
crs: EPSG:3577
grid_mapping: spatial_ref
units: % dry matter
What I tried is following:
for i in ds1.time:
for k in ds2.time:
if k!=i:
ds2.drop_sel(time = np.datetime64(k))
But this throws following error:
ValueError Traceback (most recent call last)
<ipython-input-226-70fe5e9f97a4> in <module>
4 for k in ds2.time:
5 if k!=i:
----> 6 ds2.drop_sel(time = np.datetime64(k))
ValueError: Could not convert object to NumPy datetime
Upvotes: 2
Views: 552
Reputation: 7023
If you want to select all timeslices from ds2
which are also present in ds1
you can do
time_ix = np.isin(ds2.time, ds1.time)
ds2_sel = ds2.sel(time=time_ix)
where time_ix
is a simple boolean array with True
for each element in ds2.time
that also occurs in ds1.time
.
Upvotes: 4