nbodykit.source.catalog.lognormal.LogNormalCatalog(Plin, nbar, BoxSize, Nmesh, bias=2.0, seed=None, cosmo=None, redshift=None, comm=None, use_cache=False)[source]¶Bases: nbodykit.base.catalog.CatalogSource
A CatalogSource containing biased particles that have been Poisson-sampled from a log-normal density field.
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References
Cole and Jones, 1991 Agrawal et al. 2017
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. |
csize |
The total, collective size of the CatalogSource, i.e., summed across all ranks. |
hardcolumns |
A list of the hard-coded columns in the CatalogSource. |
size |
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use_cache |
If set to True, use the built-in caching features of dask to cache data in memory. |
Methods
Position() |
Position assumed to be in Mpc/h |
Selection() |
A boolean column that selects a subset slice of the CatalogSource. |
Value() |
When interpolating a CatalogSource on to a mesh, the value of this array is used as the Value that each particle contributes to a given mesh cell. |
Velocity() |
Velocity in km/s |
VelocityOffset() |
The corresponding RSD offset, in Mpc/h |
Weight() |
The column giving the weight to use for each particle on the mesh. |
compute(*args, **kwargs) |
Our version of dask.compute() that computes multiple delayed dask collections at once. |
copy() |
Return a copy of the CatalogSource object |
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 CatalogSource to a MeshSource, using the specified parameters. |
update_csize() |
Set the collective size, csize. |
Selection()¶A boolean column that selects a subset slice of the CatalogSource.
By default, this column is set to True for all particles.
Value()¶When interpolating a CatalogSource on to a mesh, the value of this array is used as the Value that each particle contributes to a given mesh cell.
The mesh field is a weighted average of Value, with the weights
given by Weight.
By default, this array is set to unity for all particles.
Weight()¶The column giving the weight to use for each particle on the mesh.
The mesh field is a weighted average of Value, with the weights
given by Weight.
By default, this array is set to unity for all particles.
__delitem__(col)¶Delete a column; cannot delete a “hard-coded” column
__getitem__(sel)¶The following types of indexing are supported:
__len__()¶The local size of the CatalogSource on a given rank.
__setitem__(col, value)¶Add columns to the CatalogSource, overriding any existing columns
with the name col.
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.
copy()¶Return a copy of the CatalogSource object
| Returns: | the new CatalogSource object holding the copied data columns |
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| Return type: | CatalogCopy |
csize¶The total, collective size of the CatalogSource, i.e., summed across all ranks.
It is the sum of size across all available ranks.
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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size¶to_mesh(Nmesh=None, BoxSize=None, dtype='f4', interlaced=False, compensated=False, window='cic', weight='Weight', value='Value', selection='Selection', position='Position')¶Convert the CatalogSource to a MeshSource, using the specified parameters.
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| Returns: | mesh – a mesh object that provides an interface for gridding particle data onto a specified mesh |
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update_csize()¶Set the collective size, csize.
This function should be called in __init__() of a subclass,
after size has been set to a valid value (not NotImplemented)
use_cache¶If set to True, use the built-in caching features of dask
to cache data in memory.