nbodykit.source.catalog.species.MultipleSpeciesCatalog(names, *species, **kwargs)[source]¶Bases: nbodykit.base.catalog.CatalogSourceBase
A CatalogSource interface for handling multiples species of particles.
| Parameters: |
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Examples
>>> source1 = UniformCatalog(nbar=3e-5, BoxSize=512., seed=42)
>>> source2 = UniformCatalog(nbar=3e-5, BoxSize=512., seed=84)
>>> cat = MultipleSpeciesCatalog(['data', 'randoms'], source1, source2)
Attributes
attrs |
A dictionary storing relevant meta-data about the CatalogSource. |
columns |
All columns in the CatalogSource, including those hard-coded into the class’s defintion and override columns provided by the user. |
hardcolumns |
A list of the hard-coded columns in the CatalogSource. |
use_cache |
If set to True, use the built-in caching features of dask to cache data in memory. |
Methods
compute(*args, **kwargs) |
Our version of dask.compute() that computes multiple delayed dask collections at once. |
get_hardcolumn(col) |
Construct and return a hard-coded column. |
make_column(array) |
Utility function to convert a numpy array to a dask.array.Array. |
read(columns) |
Return the requested columns as dask arrays. |
save(output, columns[, datasets, header]) |
Save the CatalogSource to a bigfile.BigFile. |
to_mesh([Nmesh, BoxSize, dtype, interlaced, …]) |
Convert the catalog to a mesh, which knows how to “paint” the the combined density field, summed over all particle species. |
__delitem__(col)¶Delete a column; cannot delete a “hard-coded” column
__getitem__(key)[source]¶This modifies the behavior of CatalogSourceBase.__getitem__()
such that if key is a species name, a
CatalogCopy will be returned that
holds that data only for the species.
__setitem__(col, value)[source]¶Add columns to any of the species catalogs.
Note
New column names should be prefixed by ‘species/’ where
‘species’ is a name in the species attribute.
attrs¶A dictionary storing relevant meta-data about the CatalogSource.
columns¶All columns in the CatalogSource, including those hard-coded into the class’s defintion and override columns provided by the user.
compute(*args, **kwargs)¶Our version of dask.compute() that computes
multiple delayed dask collections at once.
This should be called on the return value of read()
to converts any dask arrays to numpy arrays.
If use_cache is True, this internally caches data, using
dask’s built-in cache features.
| Parameters: | args (object) – Any number of objects. If the object is a dask collection, it’s computed and the result is returned. Otherwise it’s passed through unchanged. |
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Notes
The dask default optimizer induces too many (unnecesarry) IO calls – we turn this off feature off by default. Eventually we want our own optimizer probably.
get_hardcolumn(col)¶Construct and return a hard-coded column.
These are usually produced by calling member functions marked by the @column decorator.
Subclasses may override this method and the hardcolumns attribute to bypass the decorator logic.
hardcolumns¶A list of the hard-coded columns in the CatalogSource.
These columns are usually member functions marked by @column decorator.
Subclasses may override this method and use get_hardcolumn() to
bypass the decorator logic.
logger = <logging.Logger object>¶make_column(array)¶Utility function to convert a numpy array to a dask.array.Array.
read(columns)¶Return the requested columns as dask arrays.
| Parameters: | columns (list of str) – the names of the requested columns |
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| Returns: | the list of column data, in the form of dask arrays |
| Return type: | list of dask.array.Array |
save(output, columns, datasets=None, header='Header')¶Save the CatalogSource to a bigfile.BigFile.
Only the selected columns are saved and attrs are saved in
header. The attrs of columns are stored in the datasets.
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to_mesh(Nmesh=None, BoxSize=None, dtype='f4', interlaced=False, compensated=False, window='cic', weight='Weight', selection='Selection', value='Value', position='Position')[source]¶Convert the catalog to a mesh, which knows how to “paint” the the combined density field, summed over all particle species.
| Parameters: |
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use_cache¶If set to True, use the built-in caching features of dask
to cache data in memory.