def test_constarray(comm):
a = ConstantArray(1.0, 1, chunks=1000)
assert len(a) == 1
assert a.shape == (1,)
a = ConstantArray([1.0, 1.0], 1, chunks=1000)
assert a.shape == (1, 2)
a = ConstantArray([1.0, 1.0], 3, chunks=1000)
assert a.shape == (3, 2)
def run(self):
"""
Run the algorithm, which computes the histogram. This function
does not return anything, but adds the following attributes
to the class:
- :attr:`bin_edges`
- :attr:`bin_centers`
- :attr:`dV`
- :attr:`nbar`
.. note::
All ranks store the same result attributes.
Attributes
----------
bin_edges : array_like
the edges of the redshift bins
bin_centers : array_like
the center values of each redshift bin
dV : array_like
the volume of each redshift shell in units of :math:`(\mathrm{Mpc}/h)^3`
nbar : array_like
the values of the redshift histogram, normalized to
number density (in units of :math:`(\mathrm{Mpc}/h)^{-3}`)
"""
edges = self.attrs['edges']
# get the columns
redshift = self.source[self.attrs['redshift']]
if self.attrs['weight'] is not None:
weight = self.source[self.attrs['weight']]
if self.comm.rank == 0:
self.logger.info("computing histogram using weights from '%s' column" %self.attrs['weight'])
else:
weight = ConstantArray(1.0, self.source.size)
# compute the numpy arrays from dask
redshift, weight = self.source.compute(redshift, weight)
# do the bin count, using the specified weight values
dig = numpy.searchsorted(edges, redshift, "right")
N = numpy.bincount(dig, weights=weight, minlength=len(edges)+1)[1:-1]
# now sum across all ranks
N = self.comm.allreduce(N)
# compute the volume
if self.comm.rank == 0:
self.logger.info("using cosmology %s to compute volume in units of (Mpc/h)^3" %str(self.cosmo))
self.logger.info("sky fraction used in volume calculation: %.4f" %self.attrs['fsky'])
R_hi = self.cosmo.comoving_distance(edges[1:]) # in Mpc/h
R_lo = self.cosmo.comoving_distance(edges[:-1]) # in Mpc/h
dV = (4./3.)*numpy.pi*(R_hi**3 - R_lo**3) * self.attrs['fsky']
# store the results
self.bin_edges = edges
self.bin_centers = 0.5*(edges[:-1] + edges[1:])
self.dV = dV
self.nbar = 1.*N/dV
def Selection(self):
"""
A boolean column that selects a subset slice of the CatalogSource.
By default, this column is set to ``True`` for all particles, and
all CatalogSource objects will contain this column.
"""
return ConstantArray(True, self.size, chunks=_global_options['dask_chunk_size'])
def Weight(self):
"""
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, and
all CatalogSource objects will contain this column.
"""
return ConstantArray(1.0, self.size, chunks=_global_options['dask_chunk_size'])
def Value(self):
"""
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, and
all CatalogSource objects will contain this column.
"""
return ConstantArray(1.0, self.size, chunks=_global_options['dask_chunk_size'])
def to_catalog(self, **kwargs):
from nbodykit.lab import ArrayCatalog
from nbodykit.transform import ConstantArray
Omega = self.Omega(self.a['S'])
source = ArrayCatalog(
{
'Position': self.X,
'Velocity': self.V,
'Weight': ConstantArray(Omega, len(self.X))
},
BoxSize=self.BoxSize,
Omega=Omega,
Omega0=self.Omega(1.0),
Time=self.a['S'],
comm=self.comm,
**kwargs)
return source
def to_catalog(STATE, **kwargs):
from nbodykit.lab import ArrayCatalog
from nbodykit.transform import ConstantArray
Omega = 0.27 #Omega(self.a['S'])
source = ArrayCatalog(
{
'Position': STATE[0, 0],
'Velocity': STATE[1, 0],
'Weight': ConstantArray(Omega, len(STATE[0, 0]))
},
BoxSize=[64, 64, 64],
Nmesh=[64, 64, 64],
Omega=Omega,
Omega0=0.27,
Time=0.228,
**kwargs)
return source
def __setitem__(self, col, value):
"""
Add columns to the CatalogSource, overriding any existing columns
with the name ``col``.
"""
# handle scalar values
if numpy.isscalar(value):
assert self.size is not NotImplemented, "size is not implemented! cannot set scalar array"
value = ConstantArray(value, self.size, chunks=_global_options['dask_chunk_size'])
# check the correct size, if we know the size
if self.size is not NotImplemented:
args = (col, self.size, len(value))
msg = "error setting '%s' column, data must be array of size %d, not %d" % args
assert len(value) == self.size, msg
# call the base __setitem__
CatalogSourceBase.__setitem__(self, col, value)
