phileas.iteration.leaf

This module defines abstract and concrete iteration leaves, which are the actual data sources of an iteration tree, alongside their iterators.

Classes

GeneratorWrapper(generator_function, ...)	Wrapper around a generator function, which can be used in order not to have to implement a new iteration leave, and its iterator.
GeneratorWrapperIterator(tree)
GeometricRange(start, end, default_value, steps)	Generate steps values geometrically spaced between start and end, both included.
IntegerRange(start, end, default_value, step)	Generate integer values step spaced, between start and end, both included.
IntegerRangeIterator(tree)
IterationLiteral(value)	Wrapper around a data tree.
LinearRange(start, end, default_value, steps)	Generate steps values linearly spaced between start and end, both included.
LiteralIterator(tree)
NumericRange(start, end, default_value)	Represents a range of numeric values.
NumpyRNG(seed, size, default_value, ...)	Random iteration leaf based on the RNG of numpy.
NumpyRNGIterator(tree)	Iterator that generates random numbers by reseeding a numpy bit generator, and getting its first returned values.
PrimeRng(seed, size, default_value, ...)	Random iteration leaf generating prime numbers.
PrimeRngIterator(tree)	Iterator that generates random prime numbers by uniformly generating an number with the big integer RNG, before finding a neighboring prime with sympy prevprime() and nextprime().
RandomIterationLeaf(seed, size, ...)	Deterministic pseudo-random elements generator.
Sequence(elements, ...)	Non-empty sequence of data trees.
SequenceIterator(tree)
UniformBigIntegerRng(seed, size, ...)	Random iteration leaf generating arbitrarily big integers.
UniformBigIntegerRngIterator(tree)	Iterator that generates random numbers by reseeding a numpy byte generator.