wradlib.io.xarray.OdimH5¶
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class
wradlib.io.xarray.OdimH5(filename=None, flavour=None, **kwargs)¶ Class for xarray based retrieval of ODIM_H5 data files
Initialize xarray structure from hdf5 data structure.
Parameters: Keyword Arguments: - decode_times (bool) – If True, decode cf times to np.datetime64. Defaults to True.
- decode_coords (bool) – If True, use the ‘coordinates’ attribute on variable to assign coordinates. Defaults to True.
- mask_and_scale (bool) – If True, lazily scale (using scale_factor and add_offset) and mask (using _FillValue). Defaults to True.
- chunks (int | dict, optional) – If chunks is provided, it used to load the new dataset into dask arrays. chunks={} loads the dataset with dask using a single chunk for all arrays.
- georef (bool) – If True, adds 2D AEQD x,y,z-coordinates, ground_range (gr) and 2D (rays,bins)-coordinates for easy georeferencing (eg. cartopy)
- standard (str) –
- none - data is read as verbatim as possible, no metadata
- odim - data is read, odim metadata added to datasets
- cf-mandatory - data is read according to cfradial2 standard importing mandatory metadata
- cf-full - data is read according to cfradial2 standard importing all available cfradial2 metadata (not fully implemented)
- dim0 (str) –
- name of the ray-dimension of DataArrays and Dataset:
- time - cfradial2 standard
- azimuth - better for working with xarray
assign_data(filename[, flavour]) |
Assign xarray dataset from hdf5 data structure. |
assign_root() |
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clear() |
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georeference([sweeps]) |
Georeference sweeps |
get(k[,d]) |
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items() |
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keys() |
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pop(k[,d]) |
If key is not found, d is returned if given, otherwise KeyError is raised. |
popitem() |
as a 2-tuple; but raise KeyError if D is empty. |
setdefault(k[,d]) |
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to_cfradial2(filename) |
Save volume to CfRadial2.0 compliant file. |
to_odim(filename) |
Save volume to ODIM_H5/V2_2 compliant file. |
update([E, ]**F) |
If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v |
values() |
Conventions |
Return CF/ODIM Conventions. |
location |
Return location of data source. |
root |
Return root dataset. |
sweep |
Return sweep dimension count. |
sweep_angles |
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sweep_names |
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sweeps |
Return zip sweep names, sweep_angles |
version |
Return CF/ODIM version |