Salem is a small library to do geoscientific data processing and plotting. It extendsxarrayto add geolocalised subsetting, masking, and plotting operations to xarray'sDataArrayandDataSetstructures. Documentation
You've created a new variable named combined with dimension name combined and assigned an index to combined. But there's no dimension named combined on the data array! From above: But it's not a dimension, the array lists <xarray.DataArray (time: 6)> Size: 48B as the dimensions. What...
Due to the fact that the NetCDF data model is much more free and extensible than the GRIB one, it is not possible to write a generic xarray.Dataset to a GRIB file. The aim for cfgrib is to implement write support for a subset of carefull...
127 + series files into a single dataset for conversion. 128 + - **Selective Variable Conversion**: Users can choose specific variables or 129 + groups of variables (e.g., ``allOnCells``) for conversion. 130 + 131 + Opening Files in ParaView 132 + === 133 + Once the conver...
The WRF projection is helpfully added in the wrf_projection variable but that can't be stored in a netCDF file, so you have to drop it before writing your output. ds = ( xr.open_dataset( "/path/to/wrfout" ) .xwrf.postprocess() .xwrf.destagger() ) # Do some more processing.....
N-D labeled arrays and datasets in Python. Contribute to pydata/xarray development by creating an account on GitHub.
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import xarray as xr logger = logging.getLogger(__name__) def extrapolate_wind_speed(ds, to_height, from_height=None): if TYPE_CHECKING: from typing import Literal def extrapolate_wind_speed( ds: xr.Dataset, to_height: int | float, from_height: int | None = None, method: Literal["lo...
dataset_to_icechunk( virtualizarr/writers/icechunk.py:112: in dataset_to_icechunkreturnwrite_variables_to_icechunk_group( virtualizarr/writers/icechunk.py:156: in write_variables_to_icechunk_group write_virtual_variable_to_icechunk( virtualizarr/writers/icechunk.py:246: in write_virtual_varia...
"Load the data into an `xarray.Dataset`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data1 = sims[0].load_data()\n", "data2 = sims[1].load_data()" ] }, { "cell_type": "code", "execution_count": null, ...