Open dcherian opened 6 years ago
There is a related question on SO.
I think it is a good idea to add an example to our doc. Agreed for the question 1. I did not yet examine the question 2, but I think simple examples are generally nice.
Edit: Copied and pasted from a duplicate issue I opened. Closing that and moving convo here.
@jhamman's SO answer circa 2018 helped me this week https://stackoverflow.com/a/51714004/6046019
I wonder if it's worth (not sure where) providing an example of how to use preprocesses
with open_mfdataset
?
Add an Examples entry to the doc string? (http://xarray.pydata.org/en/latest/generated/xarray.open_mfdataset.html / https://github.com/pydata/xarray/blob/5296ed18272a856d478fbbb3d3253205508d1c2d/xarray/backends/api.py#L895)
While not a small example (as the remote files are large) this is how I used it:
import xarray as xr
import s3fs
def preprocess(ds):
return ds.expand_dims('time')
fs = s3fs.S3FileSystem(anon=True)
f1 = fs.open('s3://fmi-opendata-rcrhirlam-surface-grib/2021/02/03/00/numerical-hirlam74-forecast-MaximumWind-20210203T000000Z.grb2')
f2 = fs.open('s3://fmi-opendata-rcrhirlam-surface-grib/2021/02/03/06/numerical-hirlam74-forecast-MaximumWind-20210203T060000Z.grb2')
ds = xr.open_mfdataset([f1, f2], engine="cfgrib", preprocess=preprocess, parallel=True)
with one file looking like:
xr.open_dataset("LOCAL_numerical-hirlam74-forecast-MaximumWind-20210203T000000Z.grb2", engine="cfgrib")
<xarray.Dataset>
Dimensions: (latitude: 947, longitude: 5294, step: 55)
Coordinates:
time datetime64[ns] ...
* step (step) timedelta64[ns] 01:00:00 ... 2 days 07:00:00
heightAboveGround int64 ...
* latitude (latitude) float64 25.65 25.72 25.78 ... 89.86 89.93 90.0
* longitude (longitude) float64 -180.0 -179.9 -179.9 ... 179.9 180.0
valid_time (step) datetime64[ns] ...
Data variables:
fg10 (step, latitude, longitude) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: ecmf
GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
GRIB_subCentre: 0
Conventions: CF-1.7
institution: European Centre for Medium-Range Weather Forecasts
history: 2021-02-12T18:06:52 GRIB to CDM+CF via cfgrib-0....
A smaller example could be (WIP; note I was hoping ds would concat along t but it doesn't do what I expect)
import numpy as np
import xarray as xr
f1 = xr.DataArray(np.arange(2), coords=[np.arange(2)], dims=["a"], name="f1")
f1 = f1.assign_coords(t=0)
f1.to_dataset().to_zarr("f1.zarr") # What's the best way to store small files to open again with mf_dataset? csv via xarray objects? can you use open_mfdataset on pkl objects?
f2 = xr.DataArray(np.arange(2), coords=[np.arange(2)], dims=["a"], name="f2")
f2 = f2.assign_coords(t=1)
f2.to_dataset().to_zarr("f2.zarr")
# Concat along t
def preprocess(ds):
return ds.expand_dims('t')
ds = xr.open_mfdataset(["f1.zarr", "f2.zarr"], engine="zarr", concat_dim="t", preprocess=preprocess)
>>> ds
<xarray.Dataset>
Dimensions: (a: 2, t: 1)
Coordinates:
* t (t) int64 0
* a (a) int64 0 1
Data variables:
f1 (t, a) int64 dask.array<chunksize=(1, 2), meta=np.ndarray>
f2 (t, a) int64 dask.array<chunksize=(1, 2), meta=np.ndarray>
Seconding @dcherian's comment in #4901 on an example for .encoding['source']
. Working off @raybellwaves' example, something like this would have been useful to me:
>>> import xarray as xr
>>> import numpy as np
>>> model1 = xr.DataArray(np.arange(2), coords=[np.arange(2)], name="f")
>>> model1.to_dataset().to_netcdf("model1.nc")
>>> model2 = xr.DataArray(np.arange(2), coords=[np.arange(2)], name="f")
>>> model2.to_dataset().to_netcdf("model2.nc")
>>> ds = xr.open_mfdataset(
... ["model1.nc", "model2.nc"],
... preprocess=lambda ds: ds.expand_dims(
... {"model_name": [ds.encoding["source"].split("/")[-1].split(".")[0]]}
... ),
... )
>>> ds
<xarray.Dataset>
Dimensions: (dim_0: 2, model_name: 2)
Coordinates:
* dim_0 (dim_0) int64 0 1
* model_name (model_name) object 'model1' 'model2'
Data variables:
f (model_name, dim_0) int64 dask.array<chunksize=(1, 2), meta=np.ndarray>
On that note, the example above seems to work with some slight changes:
>>> import numpy as np
>>> import xarray as xr
>>>
>>> f1 = xr.DataArray(np.arange(2), coords=[np.arange(2)], dims=["a"], name="f1")
>>> f1 = f1.assign_coords(t='t0')
>>> f1.to_dataset().to_netcdf("f1.nc")
>>>
>>> f2 = xr.DataArray(np.arange(2), coords=[np.arange(2)], dims=["a"], name="f2")
>>> f2 = f2.assign_coords(t='t1')
>>> f2.to_dataset().to_netcdf("f2.nc")
>>>
>>> # Concat along t
>>> def preprocess(ds):
... return ds.expand_dims("t")
...
>>>
>>> ds = xr.open_mfdataset(["f1.nc", "f2.nc"], concat_dim="t", preprocess=preprocess)
>>> ds
<xarray.Dataset>
Dimensions: (a: 2, t: 2)
Coordinates:
* t (t) object 't0' 't1'
* a (a) int64 0 1
Data variables:
f1 (t, a) float64 dask.array<chunksize=(2, 2), meta=np.ndarray>
f2 (t, a) float64 dask.array<chunksize=(2, 2), meta=np.ndarray>
Hello:
I have to find maximum precipitation of each year (for example: 2007 and 2008, Dataset link are: 2007 and 2008). I have done this using resample method (i.e. .resample(time='Y').max()
) after concatenating it along time dimension.
Following along SO, I am wondering if I can use preprocess to find maximum (or minimum or average) for each file first and then concatenate it using time dimension. I tried the following code and was not successful. Can someone help me with this?
import numpy as np
import xarray as xr
from dask.distributed import Client
client = Client()
client
def preprocess_func(ds):
'''Get maximum (or minimum or average) from each file and concatenate along time'''
return ds.precip.max('time')
prec_ds=xr.open_mfdataset([prec_2007,prec_2008],
chunks={"lat": 25,"lon": 25,"time": -1,},
preprocess=preprocess_func,
concat_dim='time')```
I bet you need to ds.precip.max("time", keepdims=True)
so that the returned value has the time
dimension. Please ask usage questions at https://github.com/pydata/xarray/discussions next time
I'll like to work on this @TomNicholas, where do I start from?
I wrote this little notebook today while trying to get some satellite data in form that was nice to work with: https://gist.github.com/dcherian/66269bc2b36c2bc427897590d08472d7
I think it would make a useful example for the docs.
A few questions:
Is this the recommended way of adding meaningful co-ordinates, expanding dims etc.? The main bit is this function:
Also open to other feedback...