nbodykit.source.catalog.uniform¶

Classes

class nbodykit.source.catalog.uniform.MPIRandomState(comm, seed, N)[source]¶

A wrapper around numpy.random.RandomState that can return random numbers in parallel, independent of the number of ranks.

Parameters:	comm (MPI communicator) – the MPI communicator seed (int) – the global seed that seeds all other random seeds N (int) – the total size of the random numbers to generate; we return chunks of the total on each CPU, based on the CPU’s rank

Methods

beta(a, b[, size])	Draw samples from a Beta distribution.
binomial(n, p[, size])	Draw samples from a binomial distribution.
bytes(length)	Return random bytes.
chisquare(df[, size])	Draw samples from a chi-square distribution.
choice(a[, size, replace, p])	Generates a random sample from a given 1-D array
dirichlet(alpha[, size])	Draw samples from the Dirichlet distribution.
exponential([scale, size])	Draw samples from an exponential distribution.
f(dfnum, dfden[, size])	Draw samples from an F distribution.
gamma(shape[, scale, size])	Draw samples from a Gamma distribution.
geometric(p[, size])	Draw samples from the geometric distribution.
get_state()	Return a tuple representing the internal state of the generator.
gumbel([loc, scale, size])	Draw samples from a Gumbel distribution.
hypergeometric(ngood, nbad, nsample[, size])	Draw samples from a Hypergeometric distribution.
laplace([loc, scale, size])	Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
logistic([loc, scale, size])	Draw samples from a logistic distribution.
lognormal([mean, sigma, size])	Draw samples from a log-normal distribution.
logseries(p[, size])	Draw samples from a logarithmic series distribution.
multinomial(n, pvals[, size])	Draw samples from a multinomial distribution.
multivariate_normal(mean, cov[, size, …)	Draw random samples from a multivariate normal distribution.
negative_binomial(n, p[, size])	Draw samples from a negative binomial distribution.
noncentral_chisquare(df, nonc[, size])	Draw samples from a noncentral chi-square distribution.
noncentral_f(dfnum, dfden, nonc[, size])	Draw samples from the noncentral F distribution.
normal([loc, scale, size])	Draw random samples from a normal (Gaussian) distribution.
pareto(a[, size])	Draw samples from a Pareto II or Lomax distribution with specified shape.
permutation(x)	Randomly permute a sequence, or return a permuted range.
poisson([lam, size])	Draw samples from a Poisson distribution.
power(a[, size])	Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
rand(d0, d1, …, dn)	Random values in a given shape.
randint(low[, high, size, dtype])	Return random integers from low (inclusive) to high (exclusive).
randn(d0, d1, …, dn)	Return a sample (or samples) from the “standard normal” distribution.
random_integers(low[, high, size])	Random integers of type np.int between low and high, inclusive.
random_sample([size])	Return random floats in the half-open interval [0.0, 1.0).
rayleigh([scale, size])	Draw samples from a Rayleigh distribution.
seed([seed])	Seed the generator.
seeded_context(seed)	A context manager to set and then restore the random seed
set_state(state)	Set the internal state of the generator from a tuple.
shuffle(x)	Modify a sequence in-place by shuffling its contents.
standard_cauchy([size])	Draw samples from a standard Cauchy distribution with mode = 0.
standard_exponential([size])	Draw samples from the standard exponential distribution.
standard_gamma(shape[, size])	Draw samples from a standard Gamma distribution.
standard_normal([size])	Draw samples from a standard Normal distribution (mean=0, stdev=1).
standard_t(df[, size])	Draw samples from a standard Student’s t distribution with df degrees of freedom.
tomaxint([size])	Random integers between 0 and sys.maxint, inclusive.
triangular(left, mode, right[, size])	Draw samples from the triangular distribution over the interval [left, right].
uniform([low, high, size])	Draw samples from a uniform distribution.
vonmises(mu, kappa[, size])	Draw samples from a von Mises distribution.
wald(mean, scale[, size])	Draw samples from a Wald, or inverse Gaussian, distribution.
weibull(a[, size])	Draw samples from a Weibull distribution.
zipf(a[, size])	Draw samples from a Zipf distribution.

__dir__()[source]¶

Explicitly set the attributes as those of the RandomState class too

__getattribute__(name)[source]¶

Decorate callable functions of RandomState such that they return chunks of the total N random numbers generated

seeded_context(seed)[source]¶

A context manager to set and then restore the random seed

class nbodykit.source.catalog.uniform.RandomCatalog(csize, seed=None, comm=None, use_cache=False)[source]¶

A CatalogSource that can have columns added via a collective random number generator.

The random number generator stored as rng behaves as numpy.random.RandomState but generates random numbers only on the local rank in a manner independent of the number of ranks.

Parameters:	csize (int) – the desired collective size of the Source seed (int, optional) – the global seed for the random number generator comm (MPI communicator) – the MPI communicator; set automatically if None

Attributes

Index	The attribute giving the global index rank of each particle in the list.
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.
rng	A MPIRandomState that behaves as numpy.random.RandomState but generates random numbers in a manner independent of the number of ranks.
size	The number of objects in the CatalogSource on the local rank.
use_cache	If set to True, use the built-in caching features of dask to cache data in memory.

Methods

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.
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 shallow copy of the object, where each column is a reference of the corresponding column in self.
get_hardcolumn(col)	Construct and return a hard-coded column.
gslice(start, stop[, end, redistribute])	Execute a global slice of a CatalogSource.
make_column(array)	Utility function to convert an array-like object 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.
sort(keys[, reverse, usecols])	Return a CatalogSource, sorted globally across all MPI ranks in ascending order by the input keys.
to_mesh([Nmesh, BoxSize, dtype, interlaced, …])	Convert the CatalogSource to a MeshSource, using the specified parameters.
view([type])	Return a “view” of the CatalogSource object, with the returned type set by type.

rng¶

A MPIRandomState that behaves as numpy.random.RandomState but generates random numbers in a manner independent of the number of ranks.

class nbodykit.source.catalog.uniform.UniformCatalog(nbar, BoxSize, seed=None, comm=None, use_cache=False)[source]¶

A CatalogSource that has uniformly-distributed Position and Velocity columns.

The random numbers generated do not depend on the number of available ranks.

Parameters:	nbar (float) – the desired number density of particles in the box BoxSize (float, 3-vector) – the size of the box seed (int, optional) – the random seed comm – the MPI communicator use_cache (bool, optional) – whether to cache data on disk

Attributes

Index	The attribute giving the global index rank of each particle in the list.
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.
rng	A MPIRandomState that behaves as numpy.random.RandomState but generates random numbers in a manner independent of the number of ranks.
size	The number of objects in the CatalogSource on the local rank.
use_cache	If set to True, use the built-in caching features of dask to cache data in memory.

Methods

Position()	The position of particles, uniformly distributed in BoxSize
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()	The velocity of particles, uniformly distributed in 0.01 x BoxSize
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 shallow copy of the object, where each column is a reference of the corresponding column in self.
get_hardcolumn(col)	Construct and return a hard-coded column.
gslice(start, stop[, end, redistribute])	Execute a global slice of a CatalogSource.
make_column(array)	Utility function to convert an array-like object 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.
sort(keys[, reverse, usecols])	Return a CatalogSource, sorted globally across all MPI ranks in ascending order by the input keys.
to_mesh([Nmesh, BoxSize, dtype, interlaced, …])	Convert the CatalogSource to a MeshSource, using the specified parameters.
view([type])	Return a “view” of the CatalogSource object, with the returned type set by type.

Position()[source]¶

The position of particles, uniformly distributed in BoxSize

Velocity()[source]¶

The velocity of particles, uniformly distributed in 0.01 x BoxSize