Streams

This module provides lazy implementations of basic operators on streams. The classes implemented in this module can be used to build up more complex streams for different kinds of series (Laurent, Dirichlet, etc).

EXAMPLES:

Streams can be used as data structure for lazy Laurent series:

sage: L.<z> = LazyLaurentSeriesRing(ZZ)
sage: f = L(lambda n: n, valuation=0)
sage: f
z + 2*z^2 + 3*z^3 + 4*z^4 + 5*z^5 + 6*z^6 + O(z^7)
sage: type(f._coeff_stream)
<class 'sage.data_structures.stream.Stream_function'>

There are basic unary and binary operators available for streams. For example, we can add two streams:

sage: from sage.data_structures.stream import *
sage: f = Stream_function(lambda n: n, QQ, True, 0)
sage: [f[i] for i in range(10)]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
sage: g = Stream_function(lambda n: 1, QQ, True, 0)
sage: [g[i] for i in range(10)]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
sage: h = Stream_add(f, g)
sage: [h[i] for i in range(10)]
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

We can subtract one stream from another:

sage: h = Stream_sub(f, g)
sage: [h[i] for i in range(10)]
[-1, 0, 1, 2, 3, 4, 5, 6, 7, 8]

There is a Cauchy product on streams:

sage: h = Stream_cauchy_mul(f, g)
sage: [h[i] for i in range(10)]
[0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

We can compute the inverse corresponding to the Cauchy product:

sage: ginv = Stream_cauchy_invert(g)
sage: h = Stream_cauchy_mul(f, ginv)
sage: [h[i] for i in range(10)]
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1]

Two streams can be composed:

sage: g = Stream_function(lambda n: n, QQ, True, 1)
sage: h = Stream_cauchy_compose(f, g)
sage: [h[i] for i in range(10)]
[0, 1, 4, 14, 46, 145, 444, 1331, 3926, 11434]

There is a unary negation operator:

sage: h = Stream_neg(f)
sage: [h[i] for i in range(10)]
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]

More generally, we can multiply by a scalar:

sage: h = Stream_lmul(f, 2)
sage: [h[i] for i in range(10)]
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

Finally, we can apply an arbitrary functions to the elements of a stream:

sage: h = Stream_map_coefficients(f, lambda n: n^2, QQ)
sage: [h[i] for i in range(10)]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

AUTHORS:

  • Kwankyu Lee (2019-02-24): initial version

  • Tejasvi Chebrolu, Martin Rubey, Travis Scrimshaw (2021-08): refactored and expanded functionality

class sage.data_structures.stream.Stream(sparse, approximate_order)

Bases: object

Abstract base class for all streams.

INPUT:

  • sparse – boolean; whether the implementation of the stream is sparse

  • approximate_order – integer; a lower bound for the order of the stream

is_nonzero()

Return True if and only if this stream is known to be nonzero.

The default implementation is False.

EXAMPLES:

sage: from sage.data_structures.stream import Stream
sage: CS = Stream(True, 1)
sage: CS.is_nonzero()
False
class sage.data_structures.stream.Stream_add(left, right)

Bases: sage.data_structures.stream.Stream_binaryCommutative

Operator for addition of two coefficient streams.

INPUT:

  • left – stream of coefficients on the left side of the operator

  • right – stream of coefficients on the right side of the operator

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_add, Stream_function)
sage: f = Stream_function(lambda n: n, ZZ, True, 0)
sage: g = Stream_function(lambda n: 1, ZZ, True, 0)
sage: h = Stream_add(f, g)
sage: [h[i] for i in range(10)]
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
sage: u = Stream_add(g, f)
sage: [u[i] for i in range(10)]
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_function, Stream_add)
sage: f = Stream_function(lambda n: n, ZZ, True, 0)
sage: g = Stream_function(lambda n: n^2, ZZ, True, 0)
sage: h = Stream_add(f, g)
sage: h.get_coefficient(5)
30
sage: [h.get_coefficient(i) for i in range(10)]
[0, 2, 6, 12, 20, 30, 42, 56, 72, 90]
class sage.data_structures.stream.Stream_binary(left, right, *args, **kwargs)

Bases: sage.data_structures.stream.Stream_inexact

Base class for binary operators on coefficient streams.

INPUT:

  • leftStream for the left side of the operator

  • rightStream for the right side of the operator

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_function, Stream_add, Stream_sub)
sage: f = Stream_function(lambda n: 2*n, ZZ, True, 0)
sage: g = Stream_function(lambda n: n, ZZ, True, 1)
sage: h = Stream_add(f, g)
sage: [h[i] for i in range(10)]
[0, 3, 6, 9, 12, 15, 18, 21, 24, 27]
sage: h = Stream_sub(f, g)
sage: [h[i] for i in range(10)]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
class sage.data_structures.stream.Stream_binaryCommutative(left, right, *args, **kwargs)

Bases: sage.data_structures.stream.Stream_binary

Base class for commutative binary operators on coefficient streams.

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_function, Stream_add)
sage: f = Stream_function(lambda n: 2*n, ZZ, True, 0)
sage: g = Stream_function(lambda n: n, ZZ, True, 1)
sage: h = Stream_add(f, g)
sage: [h[i] for i in range(10)]
[0, 3, 6, 9, 12, 15, 18, 21, 24, 27]
sage: u = Stream_add(g, f)
sage: [u[i] for i in range(10)]
[0, 3, 6, 9, 12, 15, 18, 21, 24, 27]
sage: h == u
True
class sage.data_structures.stream.Stream_cauchy_compose(f, g)

Bases: sage.data_structures.stream.Stream_binary

Return f composed by g.

This is the composition \((f \circ g)(z) = f(g(z))\).

INPUT:

EXAMPLES:

sage: from sage.data_structures.stream import Stream_cauchy_compose, Stream_function
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_function(lambda n: 1, ZZ, True, 1)
sage: h = Stream_cauchy_compose(f, g)
sage: [h[i] for i in range(10)]
[0, 1, 3, 8, 20, 48, 112, 256, 576, 1280]
sage: u = Stream_cauchy_compose(g, f)
sage: [u[i] for i in range(10)]
[0, 1, 3, 8, 21, 55, 144, 377, 987, 2584]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import Stream_function, Stream_cauchy_compose
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_function(lambda n: n^2, ZZ, True, 1)
sage: h = Stream_cauchy_compose(f, g)
sage: h.get_coefficient(5)
527
sage: [h.get_coefficient(i) for i in range(10)]
[0, 1, 6, 28, 124, 527, 2172, 8755, 34704, 135772]
class sage.data_structures.stream.Stream_cauchy_invert(series)

Bases: sage.data_structures.stream.Stream_unary

Operator for multiplicative inverse of the stream.

INPUT:

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_cauchy_invert, Stream_function)
sage: f = Stream_function(lambda n: 1, ZZ, True, 1)
sage: g = Stream_cauchy_invert(f)
sage: [g[i] for i in range(10)]
[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_cauchy_invert, Stream_function)
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_cauchy_invert(f)
sage: g.get_coefficient(5)
0
sage: [g.get_coefficient(i) for i in range(10)]
[-2, 1, 0, 0, 0, 0, 0, 0, 0, 0]
is_nonzero()

Return True if and only if this stream is known to be nonzero.

An assumption of this class is that it is nonzero.

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_cauchy_invert, Stream_function)
sage: f = Stream_function(lambda n: n^2, ZZ, False, 1)
sage: g = Stream_cauchy_invert(f)
sage: g.is_nonzero()
True
iterate_coefficients()

A generator for the coefficients of self.

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_cauchy_invert, Stream_function)
sage: f = Stream_function(lambda n: n^2, ZZ, False, 1)
sage: g = Stream_cauchy_invert(f)
sage: n = g.iterate_coefficients()
sage: [next(n) for i in range(10)]
[1, -4, 7, -8, 8, -8, 8, -8, 8, -8]
class sage.data_structures.stream.Stream_cauchy_mul(left, right)

Bases: sage.data_structures.stream.Stream_binary

Operator for multiplication of two coefficient streams using the Cauchy product.

We are not assuming commutativity of the coefficient ring here, only that the coefficient ring commutes with the (implicit) variable.

INPUT:

  • left – stream of coefficients on the left side of the operator

  • right – stream of coefficients on the right side of the operator

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_cauchy_mul, Stream_function)
sage: f = Stream_function(lambda n: n, ZZ, True, 0)
sage: g = Stream_function(lambda n: 1, ZZ, True, 0)
sage: h = Stream_cauchy_mul(f, g)
sage: [h[i] for i in range(10)]
[0, 1, 3, 6, 10, 15, 21, 28, 36, 45]
sage: u = Stream_cauchy_mul(g, f)
sage: [u[i] for i in range(10)]
[0, 1, 3, 6, 10, 15, 21, 28, 36, 45]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_function, Stream_cauchy_mul)
sage: f = Stream_function(lambda n: n, ZZ, True, 0)
sage: g = Stream_function(lambda n: n^2, ZZ, True, 0)
sage: h = Stream_cauchy_mul(f, g)
sage: h.get_coefficient(5)
50
sage: [h.get_coefficient(i) for i in range(10)]
[0, 0, 1, 6, 20, 50, 105, 196, 336, 540]
is_nonzero()

Return True if and only if this stream is known to be nonzero.

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_function,
....:     Stream_cauchy_mul, Stream_cauchy_invert)
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_cauchy_mul(f, f)
sage: g.is_nonzero()
False
sage: fi = Stream_cauchy_invert(f)
sage: h = Stream_cauchy_mul(fi, fi)
sage: h.is_nonzero()
True
class sage.data_structures.stream.Stream_dirichlet_convolve(left, right)

Bases: sage.data_structures.stream.Stream_binary

Operator for the Dirichlet convolution of two streams.

INPUT:

  • left – stream of coefficients on the left side of the operator

  • right – stream of coefficients on the right side of the operator

The coefficient of \(n^{-s}\) in the convolution of \(l\) and \(r\) equals \(\sum_{k | n} l_k r_{n/k}\).

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_dirichlet_convolve, Stream_function, Stream_exact)
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_exact([0], True, constant=1)
sage: h = Stream_dirichlet_convolve(f, g)
sage: [h[i] for i in range(1, 10)]
[1, 3, 4, 7, 6, 12, 8, 15, 13]
sage: [sigma(n) for n in range(1, 10)]
[1, 3, 4, 7, 6, 12, 8, 15, 13]

sage: u = Stream_dirichlet_convolve(g, f)
sage: [u[i] for i in range(1, 10)]
[1, 3, 4, 7, 6, 12, 8, 15, 13]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_dirichlet_convolve, Stream_function, Stream_exact)
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_exact([0], True, constant=1)
sage: h = Stream_dirichlet_convolve(f, g)
sage: h.get_coefficient(7)
8
sage: [h[i] for i in range(1, 10)]
[1, 3, 4, 7, 6, 12, 8, 15, 13]
class sage.data_structures.stream.Stream_dirichlet_invert(series)

Bases: sage.data_structures.stream.Stream_unary

Operator for inverse with respect to Dirichlet convolution of the stream.

INPUT:

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_dirichlet_invert, Stream_function)
sage: f = Stream_function(lambda n: 1, ZZ, True, 1)
sage: g = Stream_dirichlet_invert(f)
sage: [g[i] for i in range(10)]
[0, 1, -1, -1, 0, -1, 1, -1, 0, 0]
sage: [moebius(i) for i in range(10)]
[0, 1, -1, -1, 0, -1, 1, -1, 0, 0]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_exact, Stream_dirichlet_invert)
sage: f = Stream_exact([0, 3], True, constant=2)
sage: g = Stream_dirichlet_invert(f)
sage: g.get_coefficient(6)
2/27
sage: [g[i] for i in range(8)]
[0, 1/3, -2/9, -2/9, -2/27, -2/9, 2/27, -2/9]
class sage.data_structures.stream.Stream_exact(initial_coefficients, is_sparse, constant=None, degree=None, order=None)

Bases: sage.data_structures.stream.Stream

A stream of eventually constant coefficients.

INPUT:

  • initial_values – a list of initial values

  • is_sparse – boolean; specifies whether the stream is sparse

  • order – integer (default: 0); determining the degree of the first element of initial_values

  • degree – integer (optional); determining the degree of the first element which is known to be equal to constant

  • constant – integer (default: 0); the coefficient of every index larger than or equal to degree

is_nonzero()

Return True if and only if this stream is known to be nonzero.

An assumption of this class is that it is nonzero.

EXAMPLES:

sage: from sage.data_structures.stream import Stream_exact
sage: s = Stream_exact([2], False, order=-1, degree=2, constant=1)
sage: s.is_nonzero()
True
order()

Return the order of self, which is the minimum index n such that self[n] is nonzero.

EXAMPLES:

sage: from sage.data_structures.stream import Stream_exact
sage: s = Stream_exact([1], False)
sage: s.order()
0
class sage.data_structures.stream.Stream_function(function, ring, is_sparse, approximate_order)

Bases: sage.data_structures.stream.Stream_inexact

Class that creates a stream from a function on the integers.

INPUT:

  • function – a function that generates the coefficients of the stream

  • ring – the base ring

  • is_sparse – boolean; specifies whether the stream is sparse

  • approximate_order – integer; a lower bound for the order of the stream

EXAMPLES:

sage: from sage.data_structures.stream import Stream_function
sage: f = Stream_function(lambda n: n^2, ZZ, False, 1)
sage: f[3]
9
sage: [f[i] for i in range(10)]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import Stream_function
sage: f = Stream_function(lambda n: n, QQ, True, 0)
sage: f.get_coefficient(4)
4
iterate_coefficients()

A generator for the coefficients of self.

EXAMPLES:

sage: from sage.data_structures.stream import Stream_function
sage: f = Stream_function(lambda n: 1, QQ, False, 0)
sage: n = f.iterate_coefficients()
sage: [next(n) for _ in range(10)]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
class sage.data_structures.stream.Stream_inexact(is_sparse, approximate_order)

Bases: sage.data_structures.stream.Stream

An abstract base class for the stream when we do not know it is eventually constant.

INPUT:

  • sparse – boolean; whether the implementation of the stream is sparse

  • approximate_order – integer; a lower bound for the order of the stream

is_nonzero()

Return True if and only if the cache contains a nonzero element.

EXAMPLES:

sage: from sage.data_structures.stream import Stream_function
sage: CS = Stream_function(lambda n: 1/n, ZZ, False, 1)
sage: CS.is_nonzero()
False
sage: CS[1]
1
sage: CS.is_nonzero()
True
iterate_coefficients()

A generator for the coefficients of self.

EXAMPLES:

sage: from sage.data_structures.stream import Stream_function, Stream_cauchy_compose
sage: f = Stream_function(lambda n: 1, ZZ, False, 1)
sage: g = Stream_function(lambda n: n^3, ZZ, False, 1)
sage: h = Stream_cauchy_compose(f, g)
sage: n = h.iterate_coefficients()
sage: [next(n) for i in range(10)]
[1, 9, 44, 207, 991, 4752, 22769, 109089, 522676, 2504295]
order()

Return the order of self, which is the minimum index n such that self[n] is nonzero.

EXAMPLES:

sage: from sage.data_structures.stream import Stream_function
sage: f = Stream_function(lambda n: n, QQ, True, 0)
sage: f.order()
1
class sage.data_structures.stream.Stream_lmul(series, scalar)

Bases: sage.data_structures.stream.Stream_scalar

Operator for multiplying a coefficient stream with a scalar as self * scalar.

INPUT:

  • series – a Stream

  • scalar – a scalar

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_lmul, Stream_function)
sage: W = algebras.DifferentialWeyl(QQ, names=('x',))
sage: x, dx = W.gens()
sage: f = Stream_function(lambda n: x^n, W, True, 1)
sage: g = Stream_lmul(f, dx)
sage: [g[i] for i in range(5)]
[0, x*dx, x^2*dx, x^3*dx, x^4*dx]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_lmul, Stream_function)
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_lmul(f, 3)
sage: g.get_coefficient(5)
15
sage: [g.get_coefficient(i) for i in range(10)]
[0, 3, 6, 9, 12, 15, 18, 21, 24, 27]
class sage.data_structures.stream.Stream_map_coefficients(series, function, ring)

Bases: sage.data_structures.stream.Stream_inexact

The stream with function applied to each nonzero coefficient of series.

INPUT:

  • series – a Stream

  • function – a function that modifies the elements of the stream

  • ring – the base ring of the stream

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_map_coefficients, Stream_function)
sage: f = Stream_function(lambda n: 1, ZZ, True, 1)
sage: g = Stream_map_coefficients(f, lambda n: -n, ZZ)
sage: [g[i] for i in range(10)]
[0, -1, -1, -1, -1, -1, -1, -1, -1, -1]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_map_coefficients, Stream_function)
sage: f = Stream_function(lambda n: n, ZZ, True, -1)
sage: g = Stream_map_coefficients(f, lambda n: n^2 + 1, ZZ)
sage: g.get_coefficient(5)
26
sage: [g.get_coefficient(i) for i in range(-1, 10)]
[2, 0, 2, 5, 10, 17, 26, 37, 50, 65, 82]

sage: R.<x,y> = ZZ[]
sage: f = Stream_function(lambda n: n, ZZ, True, -1)
sage: g = Stream_map_coefficients(f, lambda n: n.degree() + 1, R)
sage: [g.get_coefficient(i) for i in range(-1, 3)]
[1, 0, 1, 1]

sage: f = Stream_function(lambda n: n, ZZ, True, 0)
sage: g = Stream_map_coefficients(f, lambda n: 5, GF(3))
sage: [g.get_coefficient(i) for i in range(10)]
[0, 5, 5, 0, 5, 5, 0, 5, 5, 0]
class sage.data_structures.stream.Stream_neg(series)

Bases: sage.data_structures.stream.Stream_unary

Operator for negative of the stream.

INPUT:

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_neg, Stream_function)
sage: f = Stream_function(lambda n: 1, ZZ, True, 1)
sage: g = Stream_neg(f)
sage: [g[i] for i in range(10)]
[0, -1, -1, -1, -1, -1, -1, -1, -1, -1]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_neg, Stream_function)
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_neg(f)
sage: g.get_coefficient(5)
-5
sage: [g.get_coefficient(i) for i in range(10)]
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
is_nonzero()

Return True if and only if this stream is known to be nonzero.

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_neg, Stream_function)
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_neg(f)
sage: g.is_nonzero()
False

sage: from sage.data_structures.stream import Stream_cauchy_invert
sage: fi = Stream_cauchy_invert(f)
sage: g = Stream_neg(fi)
sage: g.is_nonzero()
True
class sage.data_structures.stream.Stream_rmul(series, scalar)

Bases: sage.data_structures.stream.Stream_scalar

Operator for multiplying a coefficient stream with a scalar as scalar * self.

INPUT:

  • series – a Stream

  • scalar – a scalar

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_rmul, Stream_function)
sage: W = algebras.DifferentialWeyl(QQ, names=('x',))
sage: x, dx = W.gens()
sage: f = Stream_function(lambda n: x^n, W, True, 1)
sage: g = Stream_rmul(f, dx)
sage: [g[i] for i in range(5)]
[0, x*dx + 1, x^2*dx + 2*x, x^3*dx + 3*x^2, x^4*dx + 4*x^3]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_rmul, Stream_function)
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_rmul(f, 3)
sage: g.get_coefficient(5)
15
sage: [g.get_coefficient(i) for i in range(10)]
[0, 3, 6, 9, 12, 15, 18, 21, 24, 27]
class sage.data_structures.stream.Stream_scalar(series, scalar)

Bases: sage.data_structures.stream.Stream_inexact

Base class for operators multiplying a coeffeicient stream by a scalar.

is_nonzero()

Return True if and only if this stream is known to be nonzero.

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_rmul, Stream_function)
sage: f = Stream_function(lambda n: n, ZZ, True, 1)
sage: g = Stream_rmul(f, 2)
sage: g.is_nonzero()
False

sage: from sage.data_structures.stream import Stream_cauchy_invert
sage: fi = Stream_cauchy_invert(f)
sage: g = Stream_rmul(fi, 2)
sage: g.is_nonzero()
True
class sage.data_structures.stream.Stream_shift(series, shift)

Bases: sage.data_structures.stream.Stream_inexact

Operator for shifting the stream.

INPUT:

  • series – a Stream

  • shift – an integer

is_nonzero()

Return True if and only if this stream is known to be nonzero.

An assumption of this class is that it is nonzero.

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_cauchy_invert, Stream_function)
sage: f = Stream_function(lambda n: n^2, ZZ, False, 1)
sage: g = Stream_cauchy_invert(f)
sage: g.is_nonzero()
True
class sage.data_structures.stream.Stream_sub(left, right)

Bases: sage.data_structures.stream.Stream_binary

Operator for subtraction of two coefficient streams.

INPUT:

  • left – stream of coefficients on the left side of the operator

  • right – stream of coefficients on the right side of the operator

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_sub, Stream_function)
sage: f = Stream_function(lambda n: n, ZZ, True, 0)
sage: g = Stream_function(lambda n: 1, ZZ, True, 0)
sage: h = Stream_sub(f, g)
sage: [h[i] for i in range(10)]
[-1, 0, 1, 2, 3, 4, 5, 6, 7, 8]
sage: u = Stream_sub(g, f)
sage: [u[i] for i in range(10)]
[1, 0, -1, -2, -3, -4, -5, -6, -7, -8]
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_function, Stream_sub)
sage: f = Stream_function(lambda n: n, ZZ, True, 0)
sage: g = Stream_function(lambda n: n^2, ZZ, True, 0)
sage: h = Stream_sub(f, g)
sage: h.get_coefficient(5)
-20
sage: [h.get_coefficient(i) for i in range(10)]
[0, 0, -2, -6, -12, -20, -30, -42, -56, -72]
class sage.data_structures.stream.Stream_unary(series, *args, **kwargs)

Bases: sage.data_structures.stream.Stream_inexact

Base class for unary operators on coefficient streams.

INPUT:

  • seriesStream the operator acts on

EXAMPLES:

sage: from sage.data_structures.stream import (Stream_function, Stream_cauchy_invert, Stream_lmul)
sage: f = Stream_function(lambda n: 2*n, ZZ, False, 1)
sage: g = Stream_cauchy_invert(f)
sage: [g[i] for i in range(10)]
[-1, 1/2, 0, 0, 0, 0, 0, 0, 0, 0]
sage: g = Stream_lmul(f, 2)
sage: [g[i] for i in range(10)]
[0, 4, 8, 12, 16, 20, 24, 28, 32, 36]
class sage.data_structures.stream.Stream_uninitialized(is_sparse, approximate_order)

Bases: sage.data_structures.stream.Stream_inexact

Coefficient stream for an uninitialized series.

INPUT:

  • is_sparse – boolean; which specifies whether the stream is sparse

  • approximate_order – integer; a lower bound for the order of the stream

EXAMPLES:

sage: from sage.data_structures.stream import Stream_uninitialized
sage: from sage.data_structures.stream import Stream_exact
sage: one = Stream_exact([1], True)
sage: C = Stream_uninitialized(True, 0)
sage: C._target
sage: C._target = one
sage: C.get_coefficient(4)
0
get_coefficient(n)

Return the n-th coefficient of self.

INPUT:

  • n – integer; the degree for the coefficient

EXAMPLES:

sage: from sage.data_structures.stream import Stream_uninitialized
sage: from sage.data_structures.stream import Stream_exact
sage: one = Stream_exact([1], True)
sage: C = Stream_uninitialized(True, 0)
sage: C._target
sage: C._target = one
sage: C.get_coefficient(0)
1
iterate_coefficients()

A generator for the coefficients of self.

EXAMPLES:

sage: from sage.data_structures.stream import Stream_uninitialized
sage: from sage.data_structures.stream import Stream_exact
sage: z = Stream_exact([1], True, order=1)
sage: C = Stream_uninitialized(True, 0)
sage: C._target
sage: C._target = z
sage: n = C.iterate_coefficients()
sage: [next(n) for _ in range(10)]
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
class sage.data_structures.stream.Stream_zero(sparse)

Bases: sage.data_structures.stream.Stream

A coefficient stream that is exactly equal to zero.

INPUT:

  • sparse – boolean; whether the coefficient stream is sparse or not

EXAMPLES:

sage: from sage.data_structures.stream import Stream_zero
sage: s = Stream_zero(True)
sage: s[5]
0
order()

Return the order of self, which is infinity.

EXAMPLES:

sage: from sage.data_structures.stream import Stream_zero
sage: s = Stream_zero(True)
sage: s.order()
+Infinity