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Any Metview function that normally returns a vector will return a numPy array when called from Python. For example, the follownig fieldset functions return numPy arrays:
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a = mv.read('my_data.grib') # returns a Fieldset
lats = mv.latitudes(a) # returns a numPy array
lons = mv.longitudes(a) # returns a numPy array
vals = mv.values(a) # returns a numPy array |
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The Fieldset object has an additional method, to_dataset(), which produces an xarray Dataset object from the given fieldset. This is an N-dimensional data array based on the Common Data Model used in netCDF. For example:
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import metview as mv t2m_fc = mv.retrieve( type = 'fc', levtype = 'sfc', param = ['2t', '2d'], date = -5, step = list(range(0, 48+1, 6)), grid = [1,1] ) xa = t2m_fc.to_dataset() print(xa) |
will produce the following output:
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<xarray.Dataset>
Dimensions: (latitude: 181, longitude: 360, step: 9, time: 1)
Coordinates:
* time (time) datetime64[ns] 2018-05-10T12:00:00
* step (step) timedelta64[ns] 0 days 00:00:00 0 days 06:00:00 ...
* latitude (latitude) float64 90.0 89.0 88.0 87.0 86.0 85.0 84.0 83.0 ...
* longitude (longitude) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 ...
Data variables:
2t (time, step, latitude, longitude) float32 ...
2d (time, step, latitude, longitude) float32 ...
Attributes:
Conventions: CF-1.7
comment: GRIB to CF translation performed by xarray-grib |
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