| /dports/devel/py-xarray/xarray-0.20.1/xarray/tests/ |
| H A D | test_backends.py | 296 return dataset.to_netcdf(
2427 fobj = data.to_netcdf()
2734 data.to_netcdf(
2755 data.to_netcdf(
2766 data.to_netcdf(
3170 ds.to_netcdf(f)
3199 ds.to_netcdf(f)
3214 ds.to_netcdf(f)
3478 ds1.to_netcdf(tmp1)
3741 ds.to_netcdf(tmp)
[all …]
|
| H A D | test_distributed.py | 93 original.to_netcdf(
98 original.to_netcdf(tmp_netcdf_filename, engine=engine, format=nc_format)
122 original.to_netcdf(tmp_netcdf_filename, engine=engine, format=nc_format)
|
| H A D | test_formatting.py | 489 arr.to_netcdf(tmp_path / "test.nc", engine="netcdf4")
|
| H A D | test_dask.py | 1542 map_ds.to_netcdf(tmp_file)
|
| /dports/math/py-arviz/arviz-0.11.4/arviz/data/ |
| H A D | io_netcdf.py | 28 def to_netcdf(data, filename, *, group="posterior", coords=None, dims=None): function
52 file_name = inference_data.to_netcdf(filename)
|
| H A D | __init__.py | 11 from .io_netcdf import from_netcdf, to_netcdf
|
| H A D | inference_data.py | 360 def to_netcdf( member in InferenceData
392 data.to_netcdf(filename, mode=mode, group=group, **kwargs)
|
| /dports/devel/py-xarray/xarray-0.20.1/asv_bench/benchmarks/ |
| H A D | dataset_io.py | 102 self.ds.to_netcdf("test_netcdf4_write.nc", engine="netcdf4", format=self.format)
105 self.ds.to_netcdf("test_scipy_write.nc", engine="scipy", format=self.format)
115 self.ds.to_netcdf(self.filepath, format=self.format)
136 self.ds.to_netcdf(self.filepath, format=self.format)
159 self.ds.to_netcdf(self.filepath, format=self.format)
199 self.ds.to_netcdf(self.filepath, format=self.format)
444 return ds.to_netcdf("file.nc", engine="netcdf4", compute=False)
|
| H A D | indexing.py | 141 ).to_netcdf(self.filepath, format="NETCDF4")
|
| /dports/devel/py-xarray/xarray-0.20.1/doc/ |
| H A D | howdoi.rst | 61 …- :py:func:`Dataset.to_netcdf`, :py:func:`DataArray.to_netcdf` specifying ``engine="h5netcdf", inv…
|
| H A D | api.rst | 698 Dataset.to_netcdf
727 DataArray.to_netcdf
|
| H A D | whats-new.rst | 2873 - Fixed a bug in :py:meth:`~Dataset.to_netcdf` which prevented writing
2970 ``dtype=str`` in ``encoding`` with ``to_netcdf()`` raised an error
3067 - New ``compute`` option in :py:meth:`~xarray.Dataset.to_netcdf`,
3338 :py:meth:`~Dataset.to_netcdf` (:issue:`1763`).
3564 ``xarray.to_netcdf``, and :py:func:`~xarray.save_mfdataset`
4827 - New ``encoding`` argument in ``xray.Dataset.to_netcdf`` for writing
5288 - ``xray.open_dataset`` and ``xray.Dataset.to_netcdf`` now
5428 bytestring) into ``xray.Dataset.to_netcdf`` (:issue:`333`).
5432 - ``xray.open_dataset`` and ``xray.Dataset.to_netcdf`` now
5475 ``xray.Dataset.to_netcdf``.
[all …]
|
| /dports/astro/py-metpy/MetPy-1.1.0/tutorials/ |
| H A D | xarray_tutorial.py | 319 subset.metpy.dequantify().drop_vars('metpy_crs').to_netcdf('500hPa_analysis.nc')
|
| /dports/math/py-arviz/arviz-0.11.4/arviz/tests/base_tests/ |
| H A D | test_data.py | 28 to_netcdf,
964 first.to_netcdf(filename)
977 first.to_netcdf(filename)
1208 to_netcdf(inference_data, filepath)
1243 inference_data.to_netcdf(
1269 inference_data.to_netcdf(filepath)
|
| /dports/devel/py-xarray/xarray-0.20.1/doc/user-guide/ |
| H A D | dask.rst | 76 ds.to_netcdf("example-data.nc")
113 fit into memory back to disk by using :py:meth:`~xarray.Dataset.to_netcdf` in the
118 ds.to_netcdf("manipulated-example-data.nc")
120 By setting the ``compute`` argument to ``False``, :py:meth:`~xarray.Dataset.to_netcdf`
128 delayed_obj = ds.to_netcdf("manipulated-example-data.nc", compute=False)
549 2. Save intermediate results to disk as a netCDF files (using ``to_netcdf()``) and then load them a…
|
| H A D | io.rst | 52 :py:meth:`Dataset.to_netcdf` method:
65 ds.to_netcdf("saved_on_disk.nc")
88 :py:meth:`DataArray.to_netcdf` method, and loaded
100 pass ``mode='a'`` to ``to_netcdf`` to ensure that each call does not delete the
157 :py:meth:`Dataset.to_netcdf` method to write to a group
160 :py:meth:`Dataset.to_netcdf` to ensure that each call does not delete the file.
475 This feature is available through :py:meth:`DataArray.to_netcdf` and
476 :py:meth:`Dataset.to_netcdf` when used with ``engine="h5netcdf"``
484 da.to_netcdf("complex.nc", engine="h5netcdf", invalid_netcdf=True)
|
| H A D | weather-climate.rst | 201 da.to_netcdf("example-no-leap.nc")
|
| /dports/devel/py-xarray/xarray-0.20.1/doc/getting-started-guide/ |
| H A D | quick-overview.rst | 212 You can directly read and write xarray objects to disk using :py:meth:`~xarray.Dataset.to_netcdf`, …
217 ds.to_netcdf("example.nc")
|
| /dports/devel/py-xarray/xarray-0.20.1/xarray/backends/ |
| H A D | api.py | 976 def to_netcdf( function
1222 to_netcdf(
|
| /dports/science/py-cdo/cdo-1.5.5/ |
| H A D | cdo.py | 376 infile.to_netcdf(tmpfile)
432 kwargs["input"].to_netcdf(tmpfile)
|
| /dports/devel/py-xarray/xarray-0.20.1/doc/examples/ |
| H A D | ROMS_ocean_model.ipynb | 58 " xi_rho=slice(300, None)).to_netcdf('ROMS_example.nc', mode='w')\n",
|
| /dports/graphics/py-pygeoapi/pygeoapi-0.11.0/pygeoapi/provider/ |
| H A D | xarray_.py | 298 fp.write(data.to_netcdf())
|
| /dports/devel/py-xarray/xarray-0.20.1/xarray/core/ |
| H A D | dataarray.py | 2808 def to_netcdf(self, *args, **kwargs) -> Union[bytes, "Delayed", None]: member in DataArray
2839 return dataset.to_netcdf(*args, **kwargs)
|
| H A D | dataset.py | 1819 def to_netcdf( member in Dataset
1900 from ..backends.api import to_netcdf
1902 return to_netcdf(
|
| /dports/science/py-cdo/cdo-1.5.5/test/ |
| H A D | test_cdo.py | 431 dataSet.to_netcdf(xarrayFile.name)
|