CODEWITHSUNDEEP
pythonjupyter-notebookpython-xarray
hasna
hasna

Reputation: 11

I have a series of netcdf files of sst data. I want to compute sst gradient to locate the oceanic front

`sst_gradient = xr.Dataset({'sst_gradient':(['lat','lon','time'],sst_gradient)},/error in this line
                   coords={'lat':(selected_sst.lat.values),
                           'lon':(selected_sst.lon.values),
                           'time':(selected_sst.time.values)})
`

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) ~/anaconda3/envs/myenv/lib/python3.6/site-packages/xarray/core/variable.py in as_variable(obj, name) 106 try: --> 107 obj = Variable(*obj) 108 except (TypeError, ValueError) as error:

~/anaconda3/envs/myenv/lib/python3.6/site-packages/xarray/core/variable.py in init(self, dims, data, attrs, encoding, fastpath) 308 self._data = as_compatible_data(data, fastpath=fastpath) --> 309 self._dims = self._parse_dimensions(dims) 310 self._attrs = None

~/anaconda3/envs/myenv/lib/python3.6/site-packages/xarray/core/variable.py in _parse_dimensions(self, dims) 499 "dimensions %s must have the same length as the " --> 500 "number of data dimensions, ndim=%s" % (dims, self.ndim) 501 )

ValueError: dimensions ('lat', 'lon', 'time') must have the same length as the number of data dimensions, ndim=0

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last) in 3 coords={'lats':(selected_sst.lat.values), 4 'lons':(selected_sst.lon.values), ----> 5 'times':(selected_sst.time.values)}) 6 7

~/anaconda3/envs/myenv/lib/python3.6/site-packages/xarray/core/dataset.py in init(self, data_vars, coords, attrs, compat) 533 534 variables, coord_names, dims, indexes = merge_data_and_coords( --> 535 data_vars, coords, compat=compat 536 ) 537

~/anaconda3/envs/myenv/lib/python3.6/site-packages/xarray/core/merge.py in merge_data_and_coords(data, coords, compat, join) 465 indexes = dict(_extract_indexes_from_coords(coords)) 466 return merge_core( --> 467 objects, compat, join, explicit_coords=explicit_coords, indexes=indexes 468 ) 469

~/anaconda3/envs/myenv/lib/python3.6/site-packages/xarray/core/merge.py in merge_core(objects, compat, join, priority_arg, explicit_coords, indexes, fill_value) 550 coerced, join=join, copy=False, indexes=indexes, fill_value=fill_value 551 ) --> 552 collected = collect_variables_and_indexes(aligned) 553 554 prioritized = _get_priority_vars_and_indexes(aligned, priority_arg, compat=compat)

~/anaconda3/envs/myenv/lib/python3.6/site-packages/xarray/core/merge.py in collect_variables_and_indexes(list_of_mappings) 275 append_all(coords, indexes) 276 --> 277 variable = as_variable(variable, name=name) 278 if variable.dims == (name,): 279 variable = variable.to_index_variable()

~/anaconda3/envs/myenv/lib/python3.6/site-packages/xarray/core/variable.py in as_variable(obj, name) 111 "Could not convert tuple of form " 112 "(dims, data[, attrs, encoding]): " --> 113 "{} to Variable.".format(obj) 114 ) 115 elif utils.is_scalar(obj):

ValueError: Could not convert tuple of form (dims, data[, attrs, encoding]): (['lat', 'lon', 'time'], Dimensions:
(lat: 600, lon: 4320, sst.lat: 72, sst.lon: 600, sst.time: 4320, time: 72) Coordinates: * lat (lat) float32 -40.041668 -40.12501 ... -89.87501 -89.958336 * lon (lon) float32 -179.95833 -179.875 ... 179.87502 179.95836 * time (time) datetime64[ns] 2005-01-15 2005-02-15 ... 2010-12-15 Dimensions without coordinates: sst.lat, sst.lon, sst.time Data variables: sst_gradient (sst.lat, sst.lon, sst.time) float32 2.7785575e-08 ... nan) to Variable.

Upvotes: 0

Views: 1289

Answers (1)

dl.meteo
dl.meteo

Reputation: 1786

The equivalent function for numpy.gradient is xarray.DataSet.differentiate.

You can find details here: xarray differentiate

Upvotes: 0

Related Questions