sup3r.preprocessing.cachers.base.Cacher

class Cacher(data: Sup3rX | Sup3rDataset, cache_kwargs: Dict | None = None)

Bases: Container

Base cacher object. Simply writes given data to H5 or NETCDF files. By default every feature will be written to a separate file. To write multiple features to the same file call write_netcdf() or write_h5() directly

Parameters:

• data (Union[Sup3rX, Sup3rDataset]) – Data to write to file

• cache_kwargs (dict) – Dictionary with kwargs for caching wrangled data. This should at minimum include a ‘cache_pattern’ key, value. This pattern must have a {feature} format key and either a h5 or nc file extension, based on desired output type.

  Can also include a max_workers key and chunks key. max_workers is an inteeger specifying number of threads to use for writing chunks to output files and chunks is a dictionary of dictionaries for each feature (or a single dictionary to use for all features). e.g. .. code-block:: JSON

  {‘cache_pattern’: …,

  ‘chunks’: {

  ‘u_10m’: {

  ‘time’: 20, ‘south_north’: 100, ‘west_east’: 100

  }

  }

  }

Note

This is only for saving cached data. If you want to reload the cached files load them with a Loader object. DataHandler objects can cache and reload from cache automatically.

Methods

add_coord_meta(out_file, data)	Add flattened coordinate meta to out_file.
cache_data(cache_pattern[, chunks, ...])	Cache data to file with file type based on user provided cache_pattern.
get_chunk_slices(chunks, shape)	Get slices used to write xarray data to netcdf file in chunks.
get_chunksizes(dset, data, chunks)	Get chunksizes after rechunking (could be undetermined beforehand if chunks == 'auto') and return rechunked data.
parse_chunks(feature, chunks, dims)	Parse chunks input to Cacher.
post_init_log([args_dict])	Log additional arguments after initialization.
wrap(data)	Return a Sup3rDataset object or tuple of such.
write_chunk(out_file, dset, chunk_slice, ...)	Add chunk to netcdf file.
write_h5(out_file, data[, features, chunks, ...])	Cache data to h5 file using user provided chunks value.
write_netcdf(out_file, data[, features, ...])	Cache data to a netcdf file.
write_netcdf_chunks(out_file, feature, data)	Write netcdf chunks with delayed dask tasks.

Attributes

data	Return underlying data.
shape	Get shape of underlying data.

cache_data(cache_pattern, chunks=None, max_workers=None, mode='w', attrs=None, verbose=False)

Cache data to file with file type based on user provided cache_pattern.

Parameters:

• cache_pattern (str) – Cache file pattern. Must have a {feature} format key. The extension (.h5 or .nc) specifies which format to use for caching.

• chunks (dict | None) – Chunk sizes for coordinate dimensions. e.g. {'u_10m': {'time': 10, 'south_north': 100, 'west_east': 100}}

• max_workers (int | None) – Number of workers to use for parallel writing of chunks

• mode (str) – Write mode for out_file. Defaults to write.

• attrs (dict | None) – Optional attributes to write to file. Can specify dataset specific attributes by adding a dictionary with the dataset name as a key. e.g. {**global_attrs, dset: {…}}

• verbose (bool) – Whether to log progress for each chunk written to output files.

static parse_chunks(feature, chunks, dims)

Parse chunks input to Cacher. Needs to be a dictionary of dimensions and chunk values but parsed to a tuple for H5 caching.

classmethod get_chunksizes(dset, data, chunks)

Get chunksizes after rechunking (could be undetermined beforehand if chunks == 'auto') and return rechunked data.

classmethod add_coord_meta(out_file, data)

Add flattened coordinate meta to out_file. This is used for h5 caching.

classmethod write_h5(out_file, data, features='all', chunks=None, max_workers=None, mode='w', attrs=None, verbose=False)

Cache data to h5 file using user provided chunks value.

Parameters:

• out_file (str) – Name of file to write. Must have a .h5 extension.

• data (Sup3rDataset | Sup3rX | xr.Dataset) – Data to write to file. Comes from self.data, so an xr.Dataset like object with .dims and .coords

• features (str | list) – Name of feature(s) to write to file.

• chunks (dict | None) – Chunk sizes for coordinate dimensions. e.g. {'u_10m': {'time': 10, 'south_north': 100, 'west_east': 100}}

• max_workers (int | None) – Number of workers to use for parallel writing of chunks

• mode (str) – Write mode for out_file. Defaults to write.

• attrs (dict | None) – Optional attributes to write to file. Can specify dataset specific attributes by adding a dictionary with the dataset name as a key. e.g. {**global_attrs, dset: {…}}

• verbose (bool) – Dummy arg to match write_netcdf signature

static get_chunk_slices(chunks, shape)

Get slices used to write xarray data to netcdf file in chunks.

static write_chunk(out_file, dset, chunk_slice, chunk_data, msg=None)

Add chunk to netcdf file.

classmethod write_netcdf_chunks(out_file, feature, data, chunks=None, max_workers=None, verbose=False)

Write netcdf chunks with delayed dask tasks.

classmethod write_netcdf(out_file, data, features='all', chunks=None, max_workers=None, mode='w', attrs=None, verbose=False)

Cache data to a netcdf file.

Parameters:

• out_file (str) – Name of file to write. Must have a .nc extension.

• data (Sup3rDataset) – Data to write to file. Comes from self.data, so a Sup3rDataset with coords attributes

• features (str | list) – Names of feature(s) to write to file.

• chunks (dict | None) – Chunk sizes for coordinate dimensions. e.g. {'south_north': 100, 'west_east': 100, 'time': 10} Can also include dataset specific values. e.g. {'windspeed': {'south_north': 100, 'west_east': 100, 'time': 10}}

• max_workers (int | None) – Number of workers to use for parallel writing of chunks

• mode (str) – Write mode for out_file. Defaults to write.

• attrs (dict | None) – Optional attributes to write to file. Can specify dataset specific attributes by adding a dictionary with the dataset name as a key. e.g. {**global_attrs, dset: {…}}

• verbose (bool) – Whether to log output after each chunk is written.

property data

Return underlying data.

Returns:

Sup3rDataset

See also

wrap()

post_init_log(args_dict=None)

Log additional arguments after initialization.

property shape

Get shape of underlying data.

wrap(data)

Return a Sup3rDataset object or tuple of such. This is a tuple when the .data attribute belongs to a Collection object like BatchHandler. Otherwise this is Sup3rDataset object, which is either a wrapped 2-tuple or 1-tuple (e.g. len(data) == 2 or len(data) == 1). This is a 2-tuple when .data belongs to a dual container object like DualSampler and a 1-tuple otherwise.