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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/expressions/tests/test_trace.py
|
from sympy.core import Lambda, S, symbols
from sympy.concrete import Sum
from sympy.functions import adjoint, conjugate, transpose
from sympy.matrices import eye, Matrix, ShapeError, ImmutableMatrix
from sympy.matrices.expressions import (
Adjoint, Identity, FunctionMatrix, MatrixExpr, MatrixSymbol, Trace,
ZeroMatrix, trace, MatPow, MatAdd, MatMul
)
from sympy.utilities.pytest import raises, XFAIL
n = symbols('n', integer=True)
A = MatrixSymbol('A', n, n)
B = MatrixSymbol('B', n, n)
C = MatrixSymbol('C', 3, 4)
def test_Trace():
assert isinstance(Trace(A), Trace)
assert not isinstance(Trace(A), MatrixExpr)
raises(ShapeError, lambda: Trace(C))
assert trace(eye(3)) == 3
assert trace(Matrix(3, 3, [1, 2, 3, 4, 5, 6, 7, 8, 9])) == 15
assert adjoint(Trace(A)) == trace(Adjoint(A))
assert conjugate(Trace(A)) == trace(Adjoint(A))
assert transpose(Trace(A)) == Trace(A)
A / Trace(A) # Make sure this is possible
# Some easy simplifications
assert trace(Identity(5)) == 5
assert trace(ZeroMatrix(5, 5)) == 0
assert trace(2*A*B) == 2*Trace(A*B)
assert trace(A.T) == trace(A)
i, j = symbols('i j')
F = FunctionMatrix(3, 3, Lambda((i, j), i + j))
assert trace(F) == (0 + 0) + (1 + 1) + (2 + 2)
raises(TypeError, lambda: Trace(S.One))
assert Trace(A).arg is A
assert str(trace(A)) == str(Trace(A).doit())
def test_Trace_A_plus_B():
assert trace(A + B) == Trace(A) + Trace(B)
assert Trace(A + B).arg == MatAdd(A, B)
assert Trace(A + B).doit() == Trace(A) + Trace(B)
def test_Trace_MatAdd_doit():
# See issue #9028
X = ImmutableMatrix([[1, 2, 3]]*3)
Y = MatrixSymbol('Y', 3, 3)
q = MatAdd(X, 2*X, Y, -3*Y)
assert Trace(q).arg == q
assert Trace(q).doit() == 18 - 2*Trace(Y)
def test_Trace_MatPow_doit():
X = Matrix([[1, 2], [3, 4]])
assert Trace(X).doit() == 5
q = MatPow(X, 2)
assert Trace(q).arg == q
assert Trace(q).doit() == 29
def test_Trace_MutableMatrix_plus():
# See issue #9043
X = Matrix([[1, 2], [3, 4]])
assert Trace(X) + Trace(X) == 2*Trace(X)
def test_Trace_doit_deep_False():
X = Matrix([[1, 2], [3, 4]])
q = MatPow(X, 2)
assert Trace(q).doit(deep=False).arg == q
q = MatAdd(X, 2*X)
assert Trace(q).doit(deep=False).arg == q
q = MatMul(X, 2*X)
assert Trace(q).doit(deep=False).arg == q
def test_trace_constant_factor():
# Issue 9052: gave 2*Trace(MatMul(A)) instead of 2*Trace(A)
assert trace(2*A) == 2*Trace(A)
X = ImmutableMatrix([[1, 2], [3, 4]])
assert trace(MatMul(2, X)) == 10
@XFAIL
def test_rewrite():
assert isinstance(trace(A).rewrite(Sum), Sum)
| 2,693 | 27.0625 | 71 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/expressions/tests/test_diagonal.py
|
from sympy.matrices.expressions import MatrixSymbol
from sympy.matrices.expressions.diagonal import DiagonalMatrix, DiagonalOf
from sympy import Symbol, ask, Q, KroneckerDelta
from sympy.utilities.pytest import raises
n = Symbol('n')
m = Symbol('m')
def test_DiagonalMatrix():
x = MatrixSymbol('x', n, m)
D = DiagonalMatrix(x)
assert D.diagonal_length is None
assert D.shape == (n, m)
x = MatrixSymbol('x', n, n)
D = DiagonalMatrix(x)
assert D.diagonal_length == n
assert D.shape == (n, n)
assert D[1, 2] == 0
assert D[1, 1] == x[1, 1]
i = Symbol('i')
j = Symbol('j')
x = MatrixSymbol('x', 3, 3)
ij = DiagonalMatrix(x)[i, j]
assert ij != 0
assert ij.subs({i:0, j:0}) == x[0, 0]
assert ij.subs({i:0, j:1}) == 0
assert ij.subs({i:1, j:1}) == x[1, 1]
assert ask(Q.diagonal(D)) # affirm that D is diagonal
x = MatrixSymbol('x', n, 3)
D = DiagonalMatrix(x)
assert D.diagonal_length == 3
assert D.shape == (n, 3)
assert D[2, m] == KroneckerDelta(2, m)*x[2, m]
assert D[3, m] == 0
raises(IndexError, lambda: D[m, 3])
x = MatrixSymbol('x', 3, n)
D = DiagonalMatrix(x)
assert D.diagonal_length == 3
assert D.shape == (3, n)
assert D[m, 2] == KroneckerDelta(m, 2)*x[m, 2]
assert D[m, 3] == 0
raises(IndexError, lambda: D[3, m])
x = MatrixSymbol('x', n, m)
D = DiagonalMatrix(x)
assert D.diagonal_length is None
assert D.shape == (n, m)
assert D[m, 4] != 0
x = MatrixSymbol('x', 3, 4)
assert [DiagonalMatrix(x)[i] for i in range(12)] == [
x[0, 0], 0, 0, 0, 0, x[1, 1], 0, 0, 0, 0, x[2, 2], 0]
# shape is retained, issue 12427
assert (
DiagonalMatrix(MatrixSymbol('x', 3, 4))*
DiagonalMatrix(MatrixSymbol('x', 4, 2))).shape == (3, 2)
def test_DiagonalOf():
x = MatrixSymbol('x', n, n)
d = DiagonalOf(x)
assert d.shape == (n, 1)
assert d.diagonal_length == n
assert d[2, 0] == d[2] == x[2, 2]
x = MatrixSymbol('x', n, m)
d = DiagonalOf(x)
assert d.shape == (None, 1)
assert d.diagonal_length is None
assert d[2, 0] == d[2] == x[2, 2]
d = DiagonalOf(MatrixSymbol('x', 4, 3))
assert d.shape == (3, 1)
d = DiagonalOf(MatrixSymbol('x', n, 3))
assert d.shape == (3, 1)
d = DiagonalOf(MatrixSymbol('x', 3, n))
assert d.shape == (3, 1)
x = MatrixSymbol('x', n, m)
assert [DiagonalOf(x)[i] for i in range(4)] ==[
x[0, 0], x[1, 1], x[2, 2], x[3, 3]]
| 2,515 | 27.91954 | 74 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/test_normalforms.py
|
from __future__ import print_function, division
from sympy import Symbol, Poly
from sympy.polys.solvers import RawMatrix as Matrix
from sympy.matrices.normalforms import invariant_factors, smith_normal_form
from sympy.polys.domains import ZZ, QQ
def test_smith_normal():
m = Matrix([[12, 6, 4,8],[3,9,6,12],[2,16,14,28],[20,10,10,20]])
setattr(m, 'ring', ZZ)
smf = Matrix([[1, 0, 0, 0], [0, 10, 0, 0], [0, 0, -30, 0], [0, 0, 0, 0]])
assert smith_normal_form(m) == smf
x = Symbol('x')
m = Matrix([[Poly(x-1), Poly(1, x),Poly(-1,x)],
[0, Poly(x), Poly(-1,x)],
[Poly(0,x),Poly(-1,x),Poly(x)]])
setattr(m, 'ring', QQ[x])
invs = (Poly(1, x), Poly(x - 1), Poly(x**2 - 1))
assert invariant_factors(m) == invs
| 773 | 35.857143 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/test_densearith.py
|
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
from sympy.matrices.densetools import eye
from sympy.matrices.densearith import add, sub, mulmatmat, mulmatscaler
from sympy import ZZ
def test_add():
a = [[ZZ(3), ZZ(7), ZZ(4)], [ZZ(2), ZZ(4), ZZ(5)], [ZZ(6), ZZ(2), ZZ(3)]]
b = [[ZZ(5), ZZ(4), ZZ(9)], [ZZ(3), ZZ(7), ZZ(1)], [ZZ(12), ZZ(13), ZZ(14)]]
c = [[ZZ(12)], [ZZ(17)], [ZZ(21)]]
d = [[ZZ(3)], [ZZ(4)], [ZZ(5)]]
e = [[ZZ(12), ZZ(78)], [ZZ(56), ZZ(79)]]
f = [[ZZ.zero, ZZ.zero], [ZZ.zero, ZZ.zero]]
assert add(a, b, ZZ) == [[ZZ(8), ZZ(11), ZZ(13)], [ZZ(5), ZZ(11), ZZ(6)], [ZZ(18), ZZ(15), ZZ(17)]]
assert add(c, d, ZZ) == [[ZZ(15)], [ZZ(21)], [ZZ(26)]]
assert add(e, f, ZZ) == e
def test_sub():
a = [[ZZ(3), ZZ(7), ZZ(4)], [ZZ(2), ZZ(4), ZZ(5)], [ZZ(6), ZZ(2), ZZ(3)]]
b = [[ZZ(5), ZZ(4), ZZ(9)], [ZZ(3), ZZ(7), ZZ(1)], [ZZ(12), ZZ(13), ZZ(14)]]
c = [[ZZ(12)], [ZZ(17)], [ZZ(21)]]
d = [[ZZ(3)], [ZZ(4)], [ZZ(5)]]
e = [[ZZ(12), ZZ(78)], [ZZ(56), ZZ(79)]]
f = [[ZZ.zero, ZZ.zero], [ZZ.zero, ZZ.zero]]
assert sub(a, b, ZZ) == [[ZZ(-2), ZZ(3), ZZ(-5)], [ZZ(-1), ZZ(-3), ZZ(4)], [ZZ(-6), ZZ(-11), ZZ(-11)]]
assert sub(c, d, ZZ) == [[ZZ(9)], [ZZ(13)], [ZZ(16)]]
assert sub(e, f, ZZ) == e
def test_mulmatmat():
a = [[ZZ(3), ZZ(4)], [ZZ(5), ZZ(6)]]
b = [[ZZ(1), ZZ(2)], [ZZ(7), ZZ(8)]]
c = eye(2, ZZ)
d = [[ZZ(6)], [ZZ(7)]]
assert mulmatmat(a, b, ZZ) == [[ZZ(31), ZZ(38)], [ZZ(47), ZZ(58)]]
assert mulmatmat(b, d, ZZ) == [[ZZ(20)], [ZZ(98)]]
def test_mulmatscaler():
a = eye(3, ZZ)
b = [[ZZ(3), ZZ(7), ZZ(4)], [ZZ(2), ZZ(4), ZZ(5)], [ZZ(6), ZZ(2), ZZ(3)]]
assert mulmatscaler(a, ZZ(4), ZZ) == [[ZZ(4), ZZ(0), ZZ(0)], [ZZ(0), ZZ(4), ZZ(0)], [ZZ(0), ZZ(0), ZZ(4)]]
assert mulmatscaler(b, ZZ(1), ZZ) == [[ZZ(3), ZZ(7), ZZ(4)], [ZZ(2), ZZ(4), ZZ(5)], [ZZ(6), ZZ(2), ZZ(3)]]
| 1,936 | 36.25 | 110 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/test_matrices.py
|
import collections
import random
import warnings
from sympy import (
Abs, Add, E, Float, I, Integer, Max, Min, N, Poly, Pow, PurePoly, Rational,
S, Symbol, cos, exp, oo, pi, signsimp, simplify, sin, sqrt, symbols,
sympify, trigsimp, tan, sstr, diff)
from sympy.matrices.matrices import (ShapeError, MatrixError,
NonSquareMatrixError, DeferredVector, _find_reasonable_pivot_naive,
_simplify)
from sympy.matrices import (
GramSchmidt, ImmutableMatrix, ImmutableSparseMatrix, Matrix,
SparseMatrix, casoratian, diag, eye, hessian,
matrix_multiply_elementwise, ones, randMatrix, rot_axis1, rot_axis2,
rot_axis3, wronskian, zeros, MutableDenseMatrix, ImmutableDenseMatrix)
from sympy.core.compatibility import long, iterable, range
from sympy.core import Tuple
from sympy.utilities.iterables import flatten, capture
from sympy.utilities.pytest import raises, XFAIL, slow, skip
from sympy.utilities.exceptions import SymPyDeprecationWarning
from sympy.solvers import solve
from sympy.assumptions import Q
from sympy.abc import a, b, c, d, x, y, z
# don't re-order this list
classes = (Matrix, SparseMatrix, ImmutableMatrix, ImmutableSparseMatrix)
def test_args():
for c, cls in enumerate(classes):
m = cls.zeros(3, 2)
# all should give back the same type of arguments, e.g. ints for shape
assert m.shape == (3, 2) and all(type(i) is int for i in m.shape)
assert m.rows == 3 and type(m.rows) is int
assert m.cols == 2 and type(m.cols) is int
if not c % 2:
assert type(m._mat) in (list, tuple, Tuple)
else:
assert type(m._smat) is dict
def test_division():
v = Matrix(1, 2, [x, y])
assert v.__div__(z) == Matrix(1, 2, [x/z, y/z])
assert v.__truediv__(z) == Matrix(1, 2, [x/z, y/z])
assert v/z == Matrix(1, 2, [x/z, y/z])
def test_sum():
m = Matrix([[1, 2, 3], [x, y, x], [2*y, -50, z*x]])
assert m + m == Matrix([[2, 4, 6], [2*x, 2*y, 2*x], [4*y, -100, 2*z*x]])
n = Matrix(1, 2, [1, 2])
raises(ShapeError, lambda: m + n)
def test_abs():
m = Matrix(1, 2, [-3, x])
n = Matrix(1, 2, [3, Abs(x)])
assert abs(m) == n
def test_addition():
a = Matrix((
(1, 2),
(3, 1),
))
b = Matrix((
(1, 2),
(3, 0),
))
assert a + b == a.add(b) == Matrix([[2, 4], [6, 1]])
def test_fancy_index_matrix():
for M in (Matrix, SparseMatrix):
a = M(3, 3, range(9))
assert a == a[:, :]
assert a[1, :] == Matrix(1, 3, [3, 4, 5])
assert a[:, 1] == Matrix([1, 4, 7])
assert a[[0, 1], :] == Matrix([[0, 1, 2], [3, 4, 5]])
assert a[[0, 1], 2] == a[[0, 1], [2]]
assert a[2, [0, 1]] == a[[2], [0, 1]]
assert a[:, [0, 1]] == Matrix([[0, 1], [3, 4], [6, 7]])
assert a[0, 0] == 0
assert a[0:2, :] == Matrix([[0, 1, 2], [3, 4, 5]])
assert a[:, 0:2] == Matrix([[0, 1], [3, 4], [6, 7]])
assert a[::2, 1] == a[[0, 2], 1]
assert a[1, ::2] == a[1, [0, 2]]
a = M(3, 3, range(9))
assert a[[0, 2, 1, 2, 1], :] == Matrix([
[0, 1, 2],
[6, 7, 8],
[3, 4, 5],
[6, 7, 8],
[3, 4, 5]])
assert a[:, [0,2,1,2,1]] == Matrix([
[0, 2, 1, 2, 1],
[3, 5, 4, 5, 4],
[6, 8, 7, 8, 7]])
a = SparseMatrix.zeros(3)
a[1, 2] = 2
a[0, 1] = 3
a[2, 0] = 4
assert a.extract([1, 1], [2]) == Matrix([
[2],
[2]])
assert a.extract([1, 0], [2, 2, 2]) == Matrix([
[2, 2, 2],
[0, 0, 0]])
assert a.extract([1, 0, 1, 2], [2, 0, 1, 0]) == Matrix([
[2, 0, 0, 0],
[0, 0, 3, 0],
[2, 0, 0, 0],
[0, 4, 0, 4]])
def test_multiplication():
a = Matrix((
(1, 2),
(3, 1),
(0, 6),
))
b = Matrix((
(1, 2),
(3, 0),
))
c = a*b
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
try:
eval('c = a @ b')
except SyntaxError:
pass
else:
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
h = matrix_multiply_elementwise(a, c)
assert h == a.multiply_elementwise(c)
assert h[0, 0] == 7
assert h[0, 1] == 4
assert h[1, 0] == 18
assert h[1, 1] == 6
assert h[2, 0] == 0
assert h[2, 1] == 0
raises(ShapeError, lambda: matrix_multiply_elementwise(a, b))
c = b * Symbol("x")
assert isinstance(c, Matrix)
assert c[0, 0] == x
assert c[0, 1] == 2*x
assert c[1, 0] == 3*x
assert c[1, 1] == 0
c2 = x * b
assert c == c2
c = 5 * b
assert isinstance(c, Matrix)
assert c[0, 0] == 5
assert c[0, 1] == 2*5
assert c[1, 0] == 3*5
assert c[1, 1] == 0
try:
eval('c = 5 @ b')
except SyntaxError:
pass
else:
assert isinstance(c, Matrix)
assert c[0, 0] == 5
assert c[0, 1] == 2*5
assert c[1, 0] == 3*5
assert c[1, 1] == 0
def test_power():
raises(NonSquareMatrixError, lambda: Matrix((1, 2))**2)
R = Rational
A = Matrix([[2, 3], [4, 5]])
assert (A**-3)[:] == [R(-269)/8, R(153)/8, R(51)/2, R(-29)/2]
assert (A**5)[:] == [6140, 8097, 10796, 14237]
A = Matrix([[2, 1, 3], [4, 2, 4], [6, 12, 1]])
assert (A**3)[:] == [290, 262, 251, 448, 440, 368, 702, 954, 433]
assert A**0 == eye(3)
assert A**1 == A
assert (Matrix([[2]]) ** 100)[0, 0] == 2**100
assert eye(2)**10000000 == eye(2)
assert Matrix([[1, 2], [3, 4]])**Integer(2) == Matrix([[7, 10], [15, 22]])
A = Matrix([[33, 24], [48, 57]])
assert (A**(S(1)/2))[:] == [5, 2, 4, 7]
A = Matrix([[0, 4], [-1, 5]])
assert (A**(S(1)/2))**2 == A
assert Matrix([[1, 0], [1, 1]])**(S(1)/2) == Matrix([[1, 0], [S.Half, 1]])
assert Matrix([[1, 0], [1, 1]])**0.5 == Matrix([[1.0, 0], [0.5, 1.0]])
from sympy.abc import a, b, n
assert Matrix([[1, a], [0, 1]])**n == Matrix([[1, a*n], [0, 1]])
assert Matrix([[b, a], [0, b]])**n == Matrix([[b**n, a*b**(n-1)*n], [0, b**n]])
assert Matrix([[a, 1, 0], [0, a, 1], [0, 0, a]])**n == Matrix([
[a**n, a**(n-1)*n, a**(n-2)*(n-1)*n/2],
[0, a**n, a**(n-1)*n],
[0, 0, a**n]])
assert Matrix([[a, 1, 0], [0, a, 0], [0, 0, b]])**n == Matrix([
[a**n, a**(n-1)*n, 0],
[0, a**n, 0],
[0, 0, b**n]])
A = Matrix([[1, 0], [1, 7]])
assert A._matrix_pow_by_jordan_blocks(3) == A._eval_pow_by_recursion(3)
A = Matrix([[2]])
assert A**10 == Matrix([[2**10]]) == A._matrix_pow_by_jordan_blocks(10) == \
A._eval_pow_by_recursion(10)
# testing a matrix that cannot be jordan blocked issue 11766
m = Matrix([[3, 0, 0, 0, -3], [0, -3, -3, 0, 3], [0, 3, 0, 3, 0], [0, 0, 3, 0, 3], [3, 0, 0, 3, 0]])
raises(MatrixError, lambda: m._matrix_pow_by_jordan_blocks(10))
# test issue 11964
raises(ValueError, lambda: Matrix([[1, 1], [3, 3]])._matrix_pow_by_jordan_blocks(-10))
A = Matrix([[0, 1, 0], [0, 0, 1], [0, 0, 0]]) # Nilpotent jordan block size 3
assert A**10.0 == Matrix([[0, 0, 0], [0, 0, 0], [0, 0, 0]])
raises(ValueError, lambda: A**2.1)
raises(ValueError, lambda: A**(S(3)/2))
A = Matrix([[8, 1], [3, 2]])
assert A**10.0 == Matrix([[1760744107, 272388050], [817164150, 126415807]])
A = Matrix([[0, 0, 1], [0, 0, 1], [0, 0, 1]]) # Nilpotent jordan block size 1
assert A**10.2 == Matrix([[0, 0, 1], [0, 0, 1], [0, 0, 1]])
A = Matrix([[0, 1, 0], [0, 0, 1], [0, 0, 1]]) # Nilpotent jordan block size 2
assert A**10.0 == Matrix([[0, 0, 1], [0, 0, 1], [0, 0, 1]])
n = Symbol('n', integer=True)
raises(ValueError, lambda: A**n)
n = Symbol('n', integer=True, nonnegative=True)
raises(ValueError, lambda: A**n)
assert A**(n + 2) == Matrix([[0, 0, 1], [0, 0, 1], [0, 0, 1]])
raises(ValueError, lambda: A**(S(3)/2))
A = Matrix([[0, 0, 1], [3, 0, 1], [4, 3, 1]])
assert A**5.0 == Matrix([[168, 72, 89], [291, 144, 161], [572, 267, 329]])
assert A**5.0 == A**5
def test_creation():
raises(ValueError, lambda: Matrix(5, 5, range(20)))
raises(ValueError, lambda: Matrix(5, -1, []))
raises(IndexError, lambda: Matrix((1, 2))[2])
with raises(IndexError):
Matrix((1, 2))[1:2] = 5
with raises(IndexError):
Matrix((1, 2))[3] = 5
assert Matrix() == Matrix([]) == Matrix([[]]) == Matrix(0, 0, [])
a = Matrix([[x, 0], [0, 0]])
m = a
assert m.cols == m.rows
assert m.cols == 2
assert m[:] == [x, 0, 0, 0]
b = Matrix(2, 2, [x, 0, 0, 0])
m = b
assert m.cols == m.rows
assert m.cols == 2
assert m[:] == [x, 0, 0, 0]
assert a == b
assert Matrix(b) == b
c = Matrix((
Matrix((
(1, 2, 3),
(4, 5, 6)
)),
(7, 8, 9)
))
assert c.cols == 3
assert c.rows == 3
assert c[:] == [1, 2, 3, 4, 5, 6, 7, 8, 9]
assert Matrix(eye(2)) == eye(2)
assert ImmutableMatrix(ImmutableMatrix(eye(2))) == ImmutableMatrix(eye(2))
assert ImmutableMatrix(c) == c.as_immutable()
assert Matrix(ImmutableMatrix(c)) == ImmutableMatrix(c).as_mutable()
assert c is not Matrix(c)
def test_tolist():
lst = [[S.One, S.Half, x*y, S.Zero], [x, y, z, x**2], [y, -S.One, z*x, 3]]
m = Matrix(lst)
assert m.tolist() == lst
def test_as_mutable():
assert zeros(0, 3).as_mutable() == zeros(0, 3)
assert zeros(0, 3).as_immutable() == ImmutableMatrix(zeros(0, 3))
assert zeros(3, 0).as_immutable() == ImmutableMatrix(zeros(3, 0))
def test_determinant():
for M in [Matrix(), Matrix([[1]])]:
assert (
M.det() ==
M._eval_det_bareiss() ==
M._eval_det_berkowitz() ==
M._eval_det_lu() ==
1)
M = Matrix(( (-3, 2),
( 8, -5) ))
assert M.det(method="bareiss") == -1
assert M.det(method="berkowitz") == -1
M = Matrix(( (x, 1),
(y, 2*y) ))
assert M.det(method="bareiss") == 2*x*y - y
assert M.det(method="berkowitz") == 2*x*y - y
M = Matrix(( (1, 1, 1),
(1, 2, 3),
(1, 3, 6) ))
assert M.det(method="bareiss") == 1
assert M.det(method="berkowitz") == 1
M = Matrix(( ( 3, -2, 0, 5),
(-2, 1, -2, 2),
( 0, -2, 5, 0),
( 5, 0, 3, 4) ))
assert M.det(method="bareiss") == -289
assert M.det(method="berkowitz") == -289
M = Matrix(( ( 1, 2, 3, 4),
( 5, 6, 7, 8),
( 9, 10, 11, 12),
(13, 14, 15, 16) ))
assert M.det(method="bareiss") == 0
assert M.det(method="berkowitz") == 0
M = Matrix(( (3, 2, 0, 0, 0),
(0, 3, 2, 0, 0),
(0, 0, 3, 2, 0),
(0, 0, 0, 3, 2),
(2, 0, 0, 0, 3) ))
assert M.det(method="bareiss") == 275
assert M.det(method="berkowitz") == 275
M = Matrix(( (1, 0, 1, 2, 12),
(2, 0, 1, 1, 4),
(2, 1, 1, -1, 3),
(3, 2, -1, 1, 8),
(1, 1, 1, 0, 6) ))
assert M.det(method="bareiss") == -55
assert M.det(method="berkowitz") == -55
M = Matrix(( (-5, 2, 3, 4, 5),
( 1, -4, 3, 4, 5),
( 1, 2, -3, 4, 5),
( 1, 2, 3, -2, 5),
( 1, 2, 3, 4, -1) ))
assert M.det(method="bareiss") == 11664
assert M.det(method="berkowitz") == 11664
M = Matrix(( ( 2, 7, -1, 3, 2),
( 0, 0, 1, 0, 1),
(-2, 0, 7, 0, 2),
(-3, -2, 4, 5, 3),
( 1, 0, 0, 0, 1) ))
assert M.det(method="bareiss") == 123
assert M.det(method="berkowitz") == 123
M = Matrix(( (x, y, z),
(1, 0, 0),
(y, z, x) ))
assert M.det(method="bareiss") == z**2 - x*y
assert M.det(method="berkowitz") == z**2 - x*y
def test_det_LU_decomposition():
for M in [Matrix(), Matrix([[1]])]:
assert M.det(method="lu") == 1
M = Matrix(( (-3, 2),
( 8, -5) ))
assert M.det(method="lu") == -1
M = Matrix(( (x, 1),
(y, 2*y) ))
assert M.det(method="lu") == 2*x*y - y
M = Matrix(( (1, 1, 1),
(1, 2, 3),
(1, 3, 6) ))
assert M.det(method="lu") == 1
M = Matrix(( ( 3, -2, 0, 5),
(-2, 1, -2, 2),
( 0, -2, 5, 0),
( 5, 0, 3, 4) ))
assert M.det(method="lu") == -289
M = Matrix(( (3, 2, 0, 0, 0),
(0, 3, 2, 0, 0),
(0, 0, 3, 2, 0),
(0, 0, 0, 3, 2),
(2, 0, 0, 0, 3) ))
assert M.det(method="lu") == 275
M = Matrix(( (1, 0, 1, 2, 12),
(2, 0, 1, 1, 4),
(2, 1, 1, -1, 3),
(3, 2, -1, 1, 8),
(1, 1, 1, 0, 6) ))
assert M.det(method="lu") == -55
M = Matrix(( (-5, 2, 3, 4, 5),
( 1, -4, 3, 4, 5),
( 1, 2, -3, 4, 5),
( 1, 2, 3, -2, 5),
( 1, 2, 3, 4, -1) ))
assert M.det(method="lu") == 11664
M = Matrix(( ( 2, 7, -1, 3, 2),
( 0, 0, 1, 0, 1),
(-2, 0, 7, 0, 2),
(-3, -2, 4, 5, 3),
( 1, 0, 0, 0, 1) ))
assert M.det(method="lu") == 123
M = Matrix(( (x, y, z),
(1, 0, 0),
(y, z, x) ))
assert M.det(method="lu") == z**2 - x*y
def test_slicing():
m0 = eye(4)
assert m0[:3, :3] == eye(3)
assert m0[2:4, 0:2] == zeros(2)
m1 = Matrix(3, 3, lambda i, j: i + j)
assert m1[0, :] == Matrix(1, 3, (0, 1, 2))
assert m1[1:3, 1] == Matrix(2, 1, (2, 3))
m2 = Matrix([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]])
assert m2[:, -1] == Matrix(4, 1, [3, 7, 11, 15])
assert m2[-2:, :] == Matrix([[8, 9, 10, 11], [12, 13, 14, 15]])
def test_submatrix_assignment():
m = zeros(4)
m[2:4, 2:4] = eye(2)
assert m == Matrix(((0, 0, 0, 0),
(0, 0, 0, 0),
(0, 0, 1, 0),
(0, 0, 0, 1)))
m[:2, :2] = eye(2)
assert m == eye(4)
m[:, 0] = Matrix(4, 1, (1, 2, 3, 4))
assert m == Matrix(((1, 0, 0, 0),
(2, 1, 0, 0),
(3, 0, 1, 0),
(4, 0, 0, 1)))
m[:, :] = zeros(4)
assert m == zeros(4)
m[:, :] = [(1, 2, 3, 4), (5, 6, 7, 8), (9, 10, 11, 12), (13, 14, 15, 16)]
assert m == Matrix(((1, 2, 3, 4),
(5, 6, 7, 8),
(9, 10, 11, 12),
(13, 14, 15, 16)))
m[:2, 0] = [0, 0]
assert m == Matrix(((0, 2, 3, 4),
(0, 6, 7, 8),
(9, 10, 11, 12),
(13, 14, 15, 16)))
def test_extract():
m = Matrix(4, 3, lambda i, j: i*3 + j)
assert m.extract([0, 1, 3], [0, 1]) == Matrix(3, 2, [0, 1, 3, 4, 9, 10])
assert m.extract([0, 3], [0, 0, 2]) == Matrix(2, 3, [0, 0, 2, 9, 9, 11])
assert m.extract(range(4), range(3)) == m
raises(IndexError, lambda: m.extract([4], [0]))
raises(IndexError, lambda: m.extract([0], [3]))
def test_reshape():
m0 = eye(3)
assert m0.reshape(1, 9) == Matrix(1, 9, (1, 0, 0, 0, 1, 0, 0, 0, 1))
m1 = Matrix(3, 4, lambda i, j: i + j)
assert m1.reshape(
4, 3) == Matrix(((0, 1, 2), (3, 1, 2), (3, 4, 2), (3, 4, 5)))
assert m1.reshape(2, 6) == Matrix(((0, 1, 2, 3, 1, 2), (3, 4, 2, 3, 4, 5)))
def test_applyfunc():
m0 = eye(3)
assert m0.applyfunc(lambda x: 2*x) == eye(3)*2
assert m0.applyfunc(lambda x: 0) == zeros(3)
def test_expand():
m0 = Matrix([[x*(x + y), 2], [((x + y)*y)*x, x*(y + x*(x + y))]])
# Test if expand() returns a matrix
m1 = m0.expand()
assert m1 == Matrix(
[[x*y + x**2, 2], [x*y**2 + y*x**2, x*y + y*x**2 + x**3]])
a = Symbol('a', real=True)
assert Matrix([exp(I*a)]).expand(complex=True) == \
Matrix([cos(a) + I*sin(a)])
assert Matrix([[0, 1, 2], [0, 0, -1], [0, 0, 0]]).exp() == Matrix([
[1, 1, Rational(3, 2)],
[0, 1, -1],
[0, 0, 1]]
)
def test_refine():
m0 = Matrix([[Abs(x)**2, sqrt(x**2)],
[sqrt(x**2)*Abs(y)**2, sqrt(y**2)*Abs(x)**2]])
m1 = m0.refine(Q.real(x) & Q.real(y))
assert m1 == Matrix([[x**2, Abs(x)], [y**2*Abs(x), x**2*Abs(y)]])
m1 = m0.refine(Q.positive(x) & Q.positive(y))
assert m1 == Matrix([[x**2, x], [x*y**2, x**2*y]])
m1 = m0.refine(Q.negative(x) & Q.negative(y))
assert m1 == Matrix([[x**2, -x], [-x*y**2, -x**2*y]])
def test_random():
M = randMatrix(3, 3)
M = randMatrix(3, 3, seed=3)
assert M == randMatrix(3, 3, seed=3)
M = randMatrix(3, 4, 0, 150)
M = randMatrix(3, seed=4, symmetric=True)
assert M == randMatrix(3, seed=4, symmetric=True)
S = M.copy()
S.simplify()
assert S == M # doesn't fail when elements are Numbers, not int
rng = random.Random(4)
assert M == randMatrix(3, symmetric=True, prng=rng)
def test_LUdecomp():
testmat = Matrix([[0, 2, 5, 3],
[3, 3, 7, 4],
[8, 4, 0, 2],
[-2, 6, 3, 4]])
L, U, p = testmat.LUdecomposition()
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - testmat == zeros(4)
testmat = Matrix([[6, -2, 7, 4],
[0, 3, 6, 7],
[1, -2, 7, 4],
[-9, 2, 6, 3]])
L, U, p = testmat.LUdecomposition()
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - testmat == zeros(4)
# non-square
testmat = Matrix([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12]])
L, U, p = testmat.LUdecomposition(rankcheck=False)
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - testmat == zeros(4, 3)
# square and singular
testmat = Matrix([[1, 2, 3],
[2, 4, 6],
[4, 5, 6]])
L, U, p = testmat.LUdecomposition(rankcheck=False)
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - testmat == zeros(3)
M = Matrix(((1, x, 1), (2, y, 0), (y, 0, z)))
L, U, p = M.LUdecomposition()
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - M == zeros(3)
mL = Matrix((
(1, 0, 0),
(2, 3, 0),
))
assert mL.is_lower is True
assert mL.is_upper is False
mU = Matrix((
(1, 2, 3),
(0, 4, 5),
))
assert mU.is_lower is False
assert mU.is_upper is True
# test FF LUdecomp
M = Matrix([[1, 3, 3],
[3, 2, 6],
[3, 2, 2]])
P, L, Dee, U = M.LUdecompositionFF()
assert P*M == L*Dee.inv()*U
M = Matrix([[1, 2, 3, 4],
[3, -1, 2, 3],
[3, 1, 3, -2],
[6, -1, 0, 2]])
P, L, Dee, U = M.LUdecompositionFF()
assert P*M == L*Dee.inv()*U
M = Matrix([[0, 0, 1],
[2, 3, 0],
[3, 1, 4]])
P, L, Dee, U = M.LUdecompositionFF()
assert P*M == L*Dee.inv()*U
def test_LUsolve():
A = Matrix([[2, 3, 5],
[3, 6, 2],
[8, 3, 6]])
x = Matrix(3, 1, [3, 7, 5])
b = A*x
soln = A.LUsolve(b)
assert soln == x
A = Matrix([[0, -1, 2],
[5, 10, 7],
[8, 3, 4]])
x = Matrix(3, 1, [-1, 2, 5])
b = A*x
soln = A.LUsolve(b)
assert soln == x
def test_QRsolve():
A = Matrix([[2, 3, 5],
[3, 6, 2],
[8, 3, 6]])
x = Matrix(3, 1, [3, 7, 5])
b = A*x
soln = A.QRsolve(b)
assert soln == x
x = Matrix([[1, 2], [3, 4], [5, 6]])
b = A*x
soln = A.QRsolve(b)
assert soln == x
A = Matrix([[0, -1, 2],
[5, 10, 7],
[8, 3, 4]])
x = Matrix(3, 1, [-1, 2, 5])
b = A*x
soln = A.QRsolve(b)
assert soln == x
x = Matrix([[7, 8], [9, 10], [11, 12]])
b = A*x
soln = A.QRsolve(b)
assert soln == x
def test_inverse():
A = eye(4)
assert A.inv() == eye(4)
assert A.inv(method="LU") == eye(4)
assert A.inv(method="ADJ") == eye(4)
A = Matrix([[2, 3, 5],
[3, 6, 2],
[8, 3, 6]])
Ainv = A.inv()
assert A*Ainv == eye(3)
assert A.inv(method="LU") == Ainv
assert A.inv(method="ADJ") == Ainv
# test that immutability is not a problem
cls = ImmutableMatrix
m = cls([[48, 49, 31],
[ 9, 71, 94],
[59, 28, 65]])
assert all(type(m.inv(s)) is cls for s in 'GE ADJ LU'.split())
cls = ImmutableSparseMatrix
m = cls([[48, 49, 31],
[ 9, 71, 94],
[59, 28, 65]])
assert all(type(m.inv(s)) is cls for s in 'CH LDL'.split())
def test_matrix_inverse_mod():
A = Matrix(2, 1, [1, 0])
raises(NonSquareMatrixError, lambda: A.inv_mod(2))
A = Matrix(2, 2, [1, 0, 0, 0])
raises(ValueError, lambda: A.inv_mod(2))
A = Matrix(2, 2, [1, 2, 3, 4])
Ai = Matrix(2, 2, [1, 1, 0, 1])
assert A.inv_mod(3) == Ai
A = Matrix(2, 2, [1, 0, 0, 1])
assert A.inv_mod(2) == A
def test_util():
R = Rational
v1 = Matrix(1, 3, [1, 2, 3])
v2 = Matrix(1, 3, [3, 4, 5])
assert v1.norm() == sqrt(14)
assert v1.project(v2) == Matrix(1, 3, [R(39)/25, R(52)/25, R(13)/5])
assert Matrix.zeros(1, 2) == Matrix(1, 2, [0, 0])
assert ones(1, 2) == Matrix(1, 2, [1, 1])
assert v1.copy() == v1
# cofactor
assert eye(3) == eye(3).cofactor_matrix()
test = Matrix([[1, 3, 2], [2, 6, 3], [2, 3, 6]])
assert test.cofactor_matrix() == \
Matrix([[27, -6, -6], [-12, 2, 3], [-3, 1, 0]])
test = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
assert test.cofactor_matrix() == \
Matrix([[-3, 6, -3], [6, -12, 6], [-3, 6, -3]])
def test_jacobian_hessian():
L = Matrix(1, 2, [x**2*y, 2*y**2 + x*y])
syms = [x, y]
assert L.jacobian(syms) == Matrix([[2*x*y, x**2], [y, 4*y + x]])
L = Matrix(1, 2, [x, x**2*y**3])
assert L.jacobian(syms) == Matrix([[1, 0], [2*x*y**3, x**2*3*y**2]])
f = x**2*y
syms = [x, y]
assert hessian(f, syms) == Matrix([[2*y, 2*x], [2*x, 0]])
f = x**2*y**3
assert hessian(f, syms) == \
Matrix([[2*y**3, 6*x*y**2], [6*x*y**2, 6*x**2*y]])
f = z + x*y**2
g = x**2 + 2*y**3
ans = Matrix([[0, 2*y],
[2*y, 2*x]])
assert ans == hessian(f, Matrix([x, y]))
assert ans == hessian(f, Matrix([x, y]).T)
assert hessian(f, (y, x), [g]) == Matrix([
[ 0, 6*y**2, 2*x],
[6*y**2, 2*x, 2*y],
[ 2*x, 2*y, 0]])
def test_QR():
A = Matrix([[1, 2], [2, 3]])
Q, S = A.QRdecomposition()
R = Rational
assert Q == Matrix([
[ 5**R(-1, 2), (R(2)/5)*(R(1)/5)**R(-1, 2)],
[2*5**R(-1, 2), (-R(1)/5)*(R(1)/5)**R(-1, 2)]])
assert S == Matrix([[5**R(1, 2), 8*5**R(-1, 2)], [0, (R(1)/5)**R(1, 2)]])
assert Q*S == A
assert Q.T * Q == eye(2)
A = Matrix([[1, 1, 1], [1, 1, 3], [2, 3, 4]])
Q, R = A.QRdecomposition()
assert Q.T * Q == eye(Q.cols)
assert R.is_upper
assert A == Q*R
def test_QR_non_square():
A = Matrix([[9, 0, 26], [12, 0, -7], [0, 4, 4], [0, -3, -3]])
Q, R = A.QRdecomposition()
assert Q.T * Q == eye(Q.cols)
assert R.is_upper
assert A == Q*R
A = Matrix([[1, -1, 4], [1, 4, -2], [1, 4, 2], [1, -1, 0]])
Q, R = A.QRdecomposition()
assert Q.T * Q == eye(Q.cols)
assert R.is_upper
assert A == Q*R
def test_nullspace():
# first test reduced row-ech form
R = Rational
M = Matrix([[5, 7, 2, 1],
[1, 6, 2, -1]])
out, tmp = M.rref()
assert out == Matrix([[1, 0, -R(2)/23, R(13)/23],
[0, 1, R(8)/23, R(-6)/23]])
M = Matrix([[-5, -1, 4, -3, -1],
[ 1, -1, -1, 1, 0],
[-1, 0, 0, 0, 0],
[ 4, 1, -4, 3, 1],
[-2, 0, 2, -2, -1]])
assert M*M.nullspace()[0] == Matrix(5, 1, [0]*5)
M = Matrix([[ 1, 3, 0, 2, 6, 3, 1],
[-2, -6, 0, -2, -8, 3, 1],
[ 3, 9, 0, 0, 6, 6, 2],
[-1, -3, 0, 1, 0, 9, 3]])
out, tmp = M.rref()
assert out == Matrix([[1, 3, 0, 0, 2, 0, 0],
[0, 0, 0, 1, 2, 0, 0],
[0, 0, 0, 0, 0, 1, R(1)/3],
[0, 0, 0, 0, 0, 0, 0]])
# now check the vectors
basis = M.nullspace()
assert basis[0] == Matrix([-3, 1, 0, 0, 0, 0, 0])
assert basis[1] == Matrix([0, 0, 1, 0, 0, 0, 0])
assert basis[2] == Matrix([-2, 0, 0, -2, 1, 0, 0])
assert basis[3] == Matrix([0, 0, 0, 0, 0, R(-1)/3, 1])
# issue 4797; just see that we can do it when rows > cols
M = Matrix([[1, 2], [2, 4], [3, 6]])
assert M.nullspace()
def test_columnspace():
M = Matrix([[ 1, 2, 0, 2, 5],
[-2, -5, 1, -1, -8],
[ 0, -3, 3, 4, 1],
[ 3, 6, 0, -7, 2]])
# now check the vectors
basis = M.columnspace()
assert basis[0] == Matrix([1, -2, 0, 3])
assert basis[1] == Matrix([2, -5, -3, 6])
assert basis[2] == Matrix([2, -1, 4, -7])
#check by columnspace definition
a, b, c, d, e = symbols('a b c d e')
X = Matrix([a, b, c, d, e])
for i in range(len(basis)):
eq=M*X-basis[i]
assert len(solve(eq, X)) != 0
#check if rank-nullity theorem holds
assert M.rank() == len(basis)
assert len(M.nullspace()) + len(M.columnspace()) == M.cols
def test_wronskian():
assert wronskian([cos(x), sin(x)], x) == cos(x)**2 + sin(x)**2
assert wronskian([exp(x), exp(2*x)], x) == exp(3*x)
assert wronskian([exp(x), x], x) == exp(x) - x*exp(x)
assert wronskian([1, x, x**2], x) == 2
w1 = -6*exp(x)*sin(x)*x + 6*cos(x)*exp(x)*x**2 - 6*exp(x)*cos(x)*x - \
exp(x)*cos(x)*x**3 + exp(x)*sin(x)*x**3
assert wronskian([exp(x), cos(x), x**3], x).expand() == w1
assert wronskian([exp(x), cos(x), x**3], x, method='berkowitz').expand() \
== w1
w2 = -x**3*cos(x)**2 - x**3*sin(x)**2 - 6*x*cos(x)**2 - 6*x*sin(x)**2
assert wronskian([sin(x), cos(x), x**3], x).expand() == w2
assert wronskian([sin(x), cos(x), x**3], x, method='berkowitz').expand() \
== w2
assert wronskian([], x) == 1
def test_eigen():
R = Rational
assert eye(3).charpoly(x) == Poly((x - 1)**3, x)
assert eye(3).charpoly(y) == Poly((y - 1)**3, y)
M = Matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
assert M.eigenvals(multiple=False) == {S.One: 3}
assert M.eigenvects() == (
[(1, 3, [Matrix([1, 0, 0]),
Matrix([0, 1, 0]),
Matrix([0, 0, 1])])])
assert M.left_eigenvects() == (
[(1, 3, [Matrix([[1, 0, 0]]),
Matrix([[0, 1, 0]]),
Matrix([[0, 0, 1]])])])
M = Matrix([[0, 1, 1],
[1, 0, 0],
[1, 1, 1]])
assert M.eigenvals() == {2*S.One: 1, -S.One: 1, S.Zero: 1}
assert M.eigenvects() == (
[
(-1, 1, [Matrix([-1, 1, 0])]),
( 0, 1, [Matrix([0, -1, 1])]),
( 2, 1, [Matrix([R(2, 3), R(1, 3), 1])])
])
assert M.left_eigenvects() == (
[
(-1, 1, [Matrix([[-2, 1, 1]])]),
(0, 1, [Matrix([[-1, -1, 1]])]),
(2, 1, [Matrix([[1, 1, 1]])])
])
a = Symbol('a')
M = Matrix([[a, 0],
[0, 1]])
assert M.eigenvals() == {a: 1, S.One: 1}
M = Matrix([[1, -1],
[1, 3]])
assert M.eigenvects() == ([(2, 2, [Matrix(2, 1, [-1, 1])])])
assert M.left_eigenvects() == ([(2, 2, [Matrix([[1, 1]])])])
M = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a = R(15, 2)
b = 3*33**R(1, 2)
c = R(13, 2)
d = (R(33, 8) + 3*b/8)
e = (R(33, 8) - 3*b/8)
def NS(e, n):
return str(N(e, n))
r = [
(a - b/2, 1, [Matrix([(12 + 24/(c - b/2))/((c - b/2)*e) + 3/(c - b/2),
(6 + 12/(c - b/2))/e, 1])]),
( 0, 1, [Matrix([1, -2, 1])]),
(a + b/2, 1, [Matrix([(12 + 24/(c + b/2))/((c + b/2)*d) + 3/(c + b/2),
(6 + 12/(c + b/2))/d, 1])]),
]
r1 = [(NS(r[i][0], 2), NS(r[i][1], 2),
[NS(j, 2) for j in r[i][2][0]]) for i in range(len(r))]
r = M.eigenvects()
r2 = [(NS(r[i][0], 2), NS(r[i][1], 2),
[NS(j, 2) for j in r[i][2][0]]) for i in range(len(r))]
assert sorted(r1) == sorted(r2)
eps = Symbol('eps', real=True)
M = Matrix([[abs(eps), I*eps ],
[-I*eps, abs(eps) ]])
assert M.eigenvects() == (
[
( 0, 1, [Matrix([[-I*eps/abs(eps)], [1]])]),
( 2*abs(eps), 1, [ Matrix([[I*eps/abs(eps)], [1]]) ] ),
])
assert M.left_eigenvects() == (
[
(0, 1, [Matrix([[I*eps/Abs(eps), 1]])]),
(2*Abs(eps), 1, [Matrix([[-I*eps/Abs(eps), 1]])])
])
M = Matrix(3, 3, [1, 2, 0, 0, 3, 0, 2, -4, 2])
M._eigenvects = M.eigenvects(simplify=False)
assert max(i.q for i in M._eigenvects[0][2][0]) > 1
M._eigenvects = M.eigenvects(simplify=True)
assert max(i.q for i in M._eigenvects[0][2][0]) == 1
M = Matrix([[S(1)/4, 1], [1, 1]])
assert M.eigenvects(simplify=True) == [
(S(5)/8 + sqrt(73)/8, 1, [Matrix([[-S(3)/8 + sqrt(73)/8], [1]])]),
(-sqrt(73)/8 + S(5)/8, 1, [Matrix([[-sqrt(73)/8 - S(3)/8], [1]])])]
assert M.eigenvects(simplify=False) ==[(S(5)/8 + sqrt(73)/8, 1, [Matrix([
[-1/(-sqrt(73)/8 - S(3)/8)],
[ 1]])]), (-sqrt(73)/8 + S(5)/8, 1, [Matrix([
[-1/(-S(3)/8 + sqrt(73)/8)],
[ 1]])])]
m = Matrix([[1, .6, .6], [.6, .9, .9], [.9, .6, .6]])
evals = {-sqrt(385)/20 + S(5)/4: 1, sqrt(385)/20 + S(5)/4: 1, S.Zero: 1}
assert m.eigenvals() == evals
nevals = list(sorted(m.eigenvals(rational=False).keys()))
sevals = list(sorted(evals.keys()))
assert all(abs(nevals[i] - sevals[i]) < 1e-9 for i in range(len(nevals)))
# issue 10719
assert Matrix([]).eigenvals() == {}
assert Matrix([]).eigenvects() == []
def test_subs():
assert Matrix([[1, x], [x, 4]]).subs(x, 5) == Matrix([[1, 5], [5, 4]])
assert Matrix([[x, 2], [x + y, 4]]).subs([[x, -1], [y, -2]]) == \
Matrix([[-1, 2], [-3, 4]])
assert Matrix([[x, 2], [x + y, 4]]).subs([(x, -1), (y, -2)]) == \
Matrix([[-1, 2], [-3, 4]])
assert Matrix([[x, 2], [x + y, 4]]).subs({x: -1, y: -2}) == \
Matrix([[-1, 2], [-3, 4]])
assert Matrix([x*y]).subs({x: y - 1, y: x - 1}, simultaneous=True) == \
Matrix([(x - 1)*(y - 1)])
for cls in classes:
assert Matrix([[2, 0], [0, 2]]) == cls.eye(2).subs(1, 2)
def test_xreplace():
assert Matrix([[1, x], [x, 4]]).xreplace({x: 5}) == \
Matrix([[1, 5], [5, 4]])
assert Matrix([[x, 2], [x + y, 4]]).xreplace({x: -1, y: -2}) == \
Matrix([[-1, 2], [-3, 4]])
for cls in classes:
assert Matrix([[2, 0], [0, 2]]) == cls.eye(2).xreplace({1: 2})
def test_simplify():
f, n = symbols('f, n')
M = Matrix([[ 1/x + 1/y, (x + x*y) / x ],
[ (f(x) + y*f(x))/f(x), 2 * (1/n - cos(n * pi)/n) / pi ]])
M.simplify()
assert M == Matrix([[ (x + y)/(x * y), 1 + y ],
[ 1 + y, 2*((1 - 1*cos(pi*n))/(pi*n)) ]])
eq = (1 + x)**2
M = Matrix([[eq]])
M.simplify()
assert M == Matrix([[eq]])
M.simplify(ratio=oo) == M
assert M == Matrix([[eq.simplify(ratio=oo)]])
def test_transpose():
M = Matrix([[1, 2, 3, 4, 5, 6, 7, 8, 9, 0],
[1, 2, 3, 4, 5, 6, 7, 8, 9, 0]])
assert M.T == Matrix( [ [1, 1],
[2, 2],
[3, 3],
[4, 4],
[5, 5],
[6, 6],
[7, 7],
[8, 8],
[9, 9],
[0, 0] ])
assert M.T.T == M
assert M.T == M.transpose()
def test_conjugate():
M = Matrix([[0, I, 5],
[1, 2, 0]])
assert M.T == Matrix([[0, 1],
[I, 2],
[5, 0]])
assert M.C == Matrix([[0, -I, 5],
[1, 2, 0]])
assert M.C == M.conjugate()
assert M.H == M.T.C
assert M.H == Matrix([[ 0, 1],
[-I, 2],
[ 5, 0]])
def test_conj_dirac():
raises(AttributeError, lambda: eye(3).D)
M = Matrix([[1, I, I, I],
[0, 1, I, I],
[0, 0, 1, I],
[0, 0, 0, 1]])
assert M.D == Matrix([[ 1, 0, 0, 0],
[-I, 1, 0, 0],
[-I, -I, -1, 0],
[-I, -I, I, -1]])
def test_trace():
M = Matrix([[1, 0, 0],
[0, 5, 0],
[0, 0, 8]])
assert M.trace() == 14
def test_shape():
M = Matrix([[x, 0, 0],
[0, y, 0]])
assert M.shape == (2, 3)
def test_col_row_op():
M = Matrix([[x, 0, 0],
[0, y, 0]])
M.row_op(1, lambda r, j: r + j + 1)
assert M == Matrix([[x, 0, 0],
[1, y + 2, 3]])
M.col_op(0, lambda c, j: c + y**j)
assert M == Matrix([[x + 1, 0, 0],
[1 + y, y + 2, 3]])
# neither row nor slice give copies that allow the original matrix to
# be changed
assert M.row(0) == Matrix([[x + 1, 0, 0]])
r1 = M.row(0)
r1[0] = 42
assert M[0, 0] == x + 1
r1 = M[0, :-1] # also testing negative slice
r1[0] = 42
assert M[0, 0] == x + 1
c1 = M.col(0)
assert c1 == Matrix([x + 1, 1 + y])
c1[0] = 0
assert M[0, 0] == x + 1
c1 = M[:, 0]
c1[0] = 42
assert M[0, 0] == x + 1
def test_zip_row_op():
for cls in classes[:2]: # XXX: immutable matrices don't support row ops
M = cls.eye(3)
M.zip_row_op(1, 0, lambda v, u: v + 2*u)
assert M == cls([[1, 0, 0],
[2, 1, 0],
[0, 0, 1]])
M = cls.eye(3)*2
M[0, 1] = -1
M.zip_row_op(1, 0, lambda v, u: v + 2*u); M
assert M == cls([[2, -1, 0],
[4, 0, 0],
[0, 0, 2]])
def test_issue_3950():
m = Matrix([1, 2, 3])
a = Matrix([1, 2, 3])
b = Matrix([2, 2, 3])
assert not (m in [])
assert not (m in [1])
assert m != 1
assert m == a
assert m != b
def test_issue_3981():
class Index1(object):
def __index__(self):
return 1
class Index2(object):
def __index__(self):
return 2
index1 = Index1()
index2 = Index2()
m = Matrix([1, 2, 3])
assert m[index2] == 3
m[index2] = 5
assert m[2] == 5
m = Matrix([[1, 2, 3], [4, 5, 6]])
assert m[index1, index2] == 6
assert m[1, index2] == 6
assert m[index1, 2] == 6
m[index1, index2] = 4
assert m[1, 2] == 4
m[1, index2] = 6
assert m[1, 2] == 6
m[index1, 2] = 8
assert m[1, 2] == 8
def test_evalf():
a = Matrix([sqrt(5), 6])
assert all(a.evalf()[i] == a[i].evalf() for i in range(2))
assert all(a.evalf(2)[i] == a[i].evalf(2) for i in range(2))
assert all(a.n(2)[i] == a[i].n(2) for i in range(2))
def test_is_symbolic():
a = Matrix([[x, x], [x, x]])
assert a.is_symbolic() is True
a = Matrix([[1, 2, 3, 4], [5, 6, 7, 8]])
assert a.is_symbolic() is False
a = Matrix([[1, 2, 3, 4], [5, 6, x, 8]])
assert a.is_symbolic() is True
a = Matrix([[1, x, 3]])
assert a.is_symbolic() is True
a = Matrix([[1, 2, 3]])
assert a.is_symbolic() is False
a = Matrix([[1], [x], [3]])
assert a.is_symbolic() is True
a = Matrix([[1], [2], [3]])
assert a.is_symbolic() is False
def test_is_upper():
a = Matrix([[1, 2, 3]])
assert a.is_upper is True
a = Matrix([[1], [2], [3]])
assert a.is_upper is False
a = zeros(4, 2)
assert a.is_upper is True
def test_is_lower():
a = Matrix([[1, 2, 3]])
assert a.is_lower is False
a = Matrix([[1], [2], [3]])
assert a.is_lower is True
def test_is_nilpotent():
a = Matrix(4, 4, [0, 2, 1, 6, 0, 0, 1, 2, 0, 0, 0, 3, 0, 0, 0, 0])
assert a.is_nilpotent()
a = Matrix([[1, 0], [0, 1]])
assert not a.is_nilpotent()
a = Matrix([])
assert a.is_nilpotent()
def test_zeros_ones_fill():
n, m = 3, 5
a = zeros(n, m)
a.fill( 5 )
b = 5 * ones(n, m)
assert a == b
assert a.rows == b.rows == 3
assert a.cols == b.cols == 5
assert a.shape == b.shape == (3, 5)
assert zeros(2) == zeros(2, 2)
assert ones(2) == ones(2, 2)
assert zeros(2, 3) == Matrix(2, 3, [0]*6)
assert ones(2, 3) == Matrix(2, 3, [1]*6)
def test_empty_zeros():
a = zeros(0)
assert a == Matrix()
a = zeros(0, 2)
assert a.rows == 0
assert a.cols == 2
a = zeros(2, 0)
assert a.rows == 2
assert a.cols == 0
def test_issue_3749():
a = Matrix([[x**2, x*y], [x*sin(y), x*cos(y)]])
assert a.diff(x) == Matrix([[2*x, y], [sin(y), cos(y)]])
assert Matrix([
[x, -x, x**2],
[exp(x), 1/x - exp(-x), x + 1/x]]).limit(x, oo) == \
Matrix([[oo, -oo, oo], [oo, 0, oo]])
assert Matrix([
[(exp(x) - 1)/x, 2*x + y*x, x**x ],
[1/x, abs(x), abs(sin(x + 1))]]).limit(x, 0) == \
Matrix([[1, 0, 1], [oo, 0, sin(1)]])
assert a.integrate(x) == Matrix([
[Rational(1, 3)*x**3, y*x**2/2],
[x**2*sin(y)/2, x**2*cos(y)/2]])
def test_inv_iszerofunc():
A = eye(4)
A.col_swap(0, 1)
for method in "GE", "LU":
assert A.inv(method=method, iszerofunc=lambda x: x == 0) == \
A.inv(method="ADJ")
def test_jacobian_metrics():
rho, phi = symbols("rho,phi")
X = Matrix([rho*cos(phi), rho*sin(phi)])
Y = Matrix([rho, phi])
J = X.jacobian(Y)
assert J == X.jacobian(Y.T)
assert J == (X.T).jacobian(Y)
assert J == (X.T).jacobian(Y.T)
g = J.T*eye(J.shape[0])*J
g = g.applyfunc(trigsimp)
assert g == Matrix([[1, 0], [0, rho**2]])
def test_jacobian2():
rho, phi = symbols("rho,phi")
X = Matrix([rho*cos(phi), rho*sin(phi), rho**2])
Y = Matrix([rho, phi])
J = Matrix([
[cos(phi), -rho*sin(phi)],
[sin(phi), rho*cos(phi)],
[ 2*rho, 0],
])
assert X.jacobian(Y) == J
def test_issue_4564():
X = Matrix([exp(x + y + z), exp(x + y + z), exp(x + y + z)])
Y = Matrix([x, y, z])
for i in range(1, 3):
for j in range(1, 3):
X_slice = X[:i, :]
Y_slice = Y[:j, :]
J = X_slice.jacobian(Y_slice)
assert J.rows == i
assert J.cols == j
for k in range(j):
assert J[:, k] == X_slice
def test_nonvectorJacobian():
X = Matrix([[exp(x + y + z), exp(x + y + z)],
[exp(x + y + z), exp(x + y + z)]])
raises(TypeError, lambda: X.jacobian(Matrix([x, y, z])))
X = X[0, :]
Y = Matrix([[x, y], [x, z]])
raises(TypeError, lambda: X.jacobian(Y))
raises(TypeError, lambda: X.jacobian(Matrix([ [x, y], [x, z] ])))
def test_vec():
m = Matrix([[1, 3], [2, 4]])
m_vec = m.vec()
assert m_vec.cols == 1
for i in range(4):
assert m_vec[i] == i + 1
def test_vech():
m = Matrix([[1, 2], [2, 3]])
m_vech = m.vech()
assert m_vech.cols == 1
for i in range(3):
assert m_vech[i] == i + 1
m_vech = m.vech(diagonal=False)
assert m_vech[0] == 2
m = Matrix([[1, x*(x + y)], [y*x + x**2, 1]])
m_vech = m.vech(diagonal=False)
assert m_vech[0] == x*(x + y)
m = Matrix([[1, x*(x + y)], [y*x, 1]])
m_vech = m.vech(diagonal=False, check_symmetry=False)
assert m_vech[0] == y*x
def test_vech_errors():
m = Matrix([[1, 3]])
raises(ShapeError, lambda: m.vech())
m = Matrix([[1, 3], [2, 4]])
raises(ValueError, lambda: m.vech())
raises(ShapeError, lambda: Matrix([ [1, 3] ]).vech())
raises(ValueError, lambda: Matrix([ [1, 3], [2, 4] ]).vech())
def test_diag():
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
assert diag(a, b, b) == Matrix([
[1, 2, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0],
[0, 0, 3, x, 0, 0],
[0, 0, y, 3, 0, 0],
[0, 0, 0, 0, 3, x],
[0, 0, 0, 0, y, 3],
])
assert diag(a, b, c) == Matrix([
[1, 2, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[0, 0, 3, x, 0, 0, 0],
[0, 0, y, 3, 0, 0, 0],
[0, 0, 0, 0, 3, x, 3],
[0, 0, 0, 0, y, 3, z],
[0, 0, 0, 0, x, y, z],
])
assert diag(a, c, b) == Matrix([
[1, 2, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[0, 0, 3, x, 3, 0, 0],
[0, 0, y, 3, z, 0, 0],
[0, 0, x, y, z, 0, 0],
[0, 0, 0, 0, 0, 3, x],
[0, 0, 0, 0, 0, y, 3],
])
a = Matrix([x, y, z])
b = Matrix([[1, 2], [3, 4]])
c = Matrix([[5, 6]])
assert diag(a, 7, b, c) == Matrix([
[x, 0, 0, 0, 0, 0],
[y, 0, 0, 0, 0, 0],
[z, 0, 0, 0, 0, 0],
[0, 7, 0, 0, 0, 0],
[0, 0, 1, 2, 0, 0],
[0, 0, 3, 4, 0, 0],
[0, 0, 0, 0, 5, 6],
])
assert diag(1, [2, 3], [[4, 5]]) == Matrix([
[1, 0, 0, 0],
[0, 2, 0, 0],
[0, 3, 0, 0],
[0, 0, 4, 5]])
def test_get_diag_blocks1():
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
assert a.get_diag_blocks() == [a]
assert b.get_diag_blocks() == [b]
assert c.get_diag_blocks() == [c]
def test_get_diag_blocks2():
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
assert diag(a, b, b).get_diag_blocks() == [a, b, b]
assert diag(a, b, c).get_diag_blocks() == [a, b, c]
assert diag(a, c, b).get_diag_blocks() == [a, c, b]
assert diag(c, c, b).get_diag_blocks() == [c, c, b]
def test_inv_block():
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
A = diag(a, b, b)
assert A.inv(try_block_diag=True) == diag(a.inv(), b.inv(), b.inv())
A = diag(a, b, c)
assert A.inv(try_block_diag=True) == diag(a.inv(), b.inv(), c.inv())
A = diag(a, c, b)
assert A.inv(try_block_diag=True) == diag(a.inv(), c.inv(), b.inv())
A = diag(a, a, b, a, c, a)
assert A.inv(try_block_diag=True) == diag(
a.inv(), a.inv(), b.inv(), a.inv(), c.inv(), a.inv())
assert A.inv(try_block_diag=True, method="ADJ") == diag(
a.inv(method="ADJ"), a.inv(method="ADJ"), b.inv(method="ADJ"),
a.inv(method="ADJ"), c.inv(method="ADJ"), a.inv(method="ADJ"))
def test_creation_args():
"""
Check that matrix dimensions can be specified using any reasonable type
(see issue 4614).
"""
raises(ValueError, lambda: zeros(3, -1))
raises(TypeError, lambda: zeros(1, 2, 3, 4))
assert zeros(long(3)) == zeros(3)
assert zeros(Integer(3)) == zeros(3)
assert zeros(3.) == zeros(3)
assert eye(long(3)) == eye(3)
assert eye(Integer(3)) == eye(3)
assert eye(3.) == eye(3)
assert ones(long(3), Integer(4)) == ones(3, 4)
raises(TypeError, lambda: Matrix(5))
raises(TypeError, lambda: Matrix(1, 2))
def test_diagonal_symmetrical():
m = Matrix(2, 2, [0, 1, 1, 0])
assert not m.is_diagonal()
assert m.is_symmetric()
assert m.is_symmetric(simplify=False)
m = Matrix(2, 2, [1, 0, 0, 1])
assert m.is_diagonal()
m = diag(1, 2, 3)
assert m.is_diagonal()
assert m.is_symmetric()
m = Matrix(3, 3, [1, 0, 0, 0, 2, 0, 0, 0, 3])
assert m == diag(1, 2, 3)
m = Matrix(2, 3, zeros(2, 3))
assert not m.is_symmetric()
assert m.is_diagonal()
m = Matrix(((5, 0), (0, 6), (0, 0)))
assert m.is_diagonal()
m = Matrix(((5, 0, 0), (0, 6, 0)))
assert m.is_diagonal()
m = Matrix(3, 3, [1, x**2 + 2*x + 1, y, (x + 1)**2, 2, 0, y, 0, 3])
assert m.is_symmetric()
assert not m.is_symmetric(simplify=False)
assert m.expand().is_symmetric(simplify=False)
def test_diagonalization():
m = Matrix(3, 2, [-3, 1, -3, 20, 3, 10])
assert not m.is_diagonalizable()
assert not m.is_symmetric()
raises(NonSquareMatrixError, lambda: m.diagonalize())
# diagonalizable
m = diag(1, 2, 3)
(P, D) = m.diagonalize()
assert P == eye(3)
assert D == m
m = Matrix(2, 2, [0, 1, 1, 0])
assert m.is_symmetric()
assert m.is_diagonalizable()
(P, D) = m.diagonalize()
assert P.inv() * m * P == D
m = Matrix(2, 2, [1, 0, 0, 3])
assert m.is_symmetric()
assert m.is_diagonalizable()
(P, D) = m.diagonalize()
assert P.inv() * m * P == D
assert P == eye(2)
assert D == m
m = Matrix(2, 2, [1, 1, 0, 0])
assert m.is_diagonalizable()
(P, D) = m.diagonalize()
assert P.inv() * m * P == D
m = Matrix(3, 3, [1, 2, 0, 0, 3, 0, 2, -4, 2])
assert m.is_diagonalizable()
(P, D) = m.diagonalize()
assert P.inv() * m * P == D
for i in P:
assert i.as_numer_denom()[1] == 1
m = Matrix(2, 2, [1, 0, 0, 0])
assert m.is_diagonal()
assert m.is_diagonalizable()
(P, D) = m.diagonalize()
assert P.inv() * m * P == D
assert P == Matrix([[0, 1], [1, 0]])
# diagonalizable, complex only
m = Matrix(2, 2, [0, 1, -1, 0])
assert not m.is_diagonalizable(True)
raises(MatrixError, lambda: m.diagonalize(True))
assert m.is_diagonalizable()
(P, D) = m.diagonalize()
assert P.inv() * m * P == D
# not diagonalizable
m = Matrix(2, 2, [0, 1, 0, 0])
assert not m.is_diagonalizable()
raises(MatrixError, lambda: m.diagonalize())
m = Matrix(3, 3, [-3, 1, -3, 20, 3, 10, 2, -2, 4])
assert not m.is_diagonalizable()
raises(MatrixError, lambda: m.diagonalize())
# symbolic
a, b, c, d = symbols('a b c d')
m = Matrix(2, 2, [a, c, c, b])
assert m.is_symmetric()
assert m.is_diagonalizable()
@XFAIL
def test_eigen_vects():
m = Matrix(2, 2, [1, 0, 0, I])
raises(NotImplementedError, lambda: m.is_diagonalizable(True))
# !!! bug because of eigenvects() or roots(x**2 + (-1 - I)*x + I, x)
# see issue 5292
assert not m.is_diagonalizable(True)
raises(MatrixError, lambda: m.diagonalize(True))
(P, D) = m.diagonalize(True)
def test_jordan_form():
m = Matrix(3, 2, [-3, 1, -3, 20, 3, 10])
raises(NonSquareMatrixError, lambda: m.jordan_form())
# diagonalizable
m = Matrix(3, 3, [7, -12, 6, 10, -19, 10, 12, -24, 13])
Jmust = Matrix(3, 3, [-1, 0, 0, 0, 1, 0, 0, 0, 1])
P, J = m.jordan_form()
assert Jmust == J
assert Jmust == m.diagonalize()[1]
# m = Matrix(3, 3, [0, 6, 3, 1, 3, 1, -2, 2, 1])
# m.jordan_form() # very long
# m.jordan_form() #
# diagonalizable, complex only
# Jordan cells
# complexity: one of eigenvalues is zero
m = Matrix(3, 3, [0, 1, 0, -4, 4, 0, -2, 1, 2])
# The blocks are ordered according to the value of their eigenvalues,
# in order to make the matrix compatible with .diagonalize()
Jmust = Matrix(3, 3, [2, 1, 0, 0, 2, 0, 0, 0, 2])
P, J = m.jordan_form()
assert Jmust == J
# complexity: all of eigenvalues are equal
m = Matrix(3, 3, [2, 6, -15, 1, 1, -5, 1, 2, -6])
# Jmust = Matrix(3, 3, [-1, 0, 0, 0, -1, 1, 0, 0, -1])
# same here see 1456ff
Jmust = Matrix(3, 3, [-1, 1, 0, 0, -1, 0, 0, 0, -1])
P, J = m.jordan_form()
assert Jmust == J
# complexity: two of eigenvalues are zero
m = Matrix(3, 3, [4, -5, 2, 5, -7, 3, 6, -9, 4])
Jmust = Matrix(3, 3, [0, 1, 0, 0, 0, 0, 0, 0, 1])
P, J = m.jordan_form()
assert Jmust == J
m = Matrix(4, 4, [6, 5, -2, -3, -3, -1, 3, 3, 2, 1, -2, -3, -1, 1, 5, 5])
Jmust = Matrix(4, 4, [2, 1, 0, 0,
0, 2, 0, 0,
0, 0, 2, 1,
0, 0, 0, 2]
)
P, J = m.jordan_form()
assert Jmust == J
m = Matrix(4, 4, [6, 2, -8, -6, -3, 2, 9, 6, 2, -2, -8, -6, -1, 0, 3, 4])
# Jmust = Matrix(4, 4, [2, 0, 0, 0, 0, 2, 1, 0, 0, 0, 2, 0, 0, 0, 0, -2])
# same here see 1456ff
Jmust = Matrix(4, 4, [-2, 0, 0, 0,
0, 2, 1, 0,
0, 0, 2, 0,
0, 0, 0, 2])
P, J = m.jordan_form()
assert Jmust == J
m = Matrix(4, 4, [5, 4, 2, 1, 0, 1, -1, -1, -1, -1, 3, 0, 1, 1, -1, 2])
assert not m.is_diagonalizable()
Jmust = Matrix(4, 4, [1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 4, 1, 0, 0, 0, 4])
P, J = m.jordan_form()
assert Jmust == J
def test_jordan_form_complex_issue_9274():
A = Matrix([[ 2, 4, 1, 0],
[-4, 2, 0, 1],
[ 0, 0, 2, 4],
[ 0, 0, -4, 2]])
p = 2 - 4*I;
q = 2 + 4*I;
Jmust1 = Matrix([[p, 1, 0, 0],
[0, p, 0, 0],
[0, 0, q, 1],
[0, 0, 0, q]])
Jmust2 = Matrix([[q, 1, 0, 0],
[0, q, 0, 0],
[0, 0, p, 1],
[0, 0, 0, p]])
P, J = A.jordan_form()
assert J == Jmust1 or J == Jmust2
assert simplify(P*J*P.inv()) == A
def test_issue_10220():
# two non-orthogonal Jordan blocks with eigenvalue 1
M = Matrix([[1, 0, 0, 1],
[0, 1, 1, 0],
[0, 0, 1, 1],
[0, 0, 0, 1]])
P, J = M.jordan_form()
assert P == Matrix([[0, 1, 0, 1],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]])
assert J == Matrix([
[1, 1, 0, 0],
[0, 1, 1, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]])
def test_Matrix_berkowitz_charpoly():
UA, K_i, K_w = symbols('UA K_i K_w')
A = Matrix([[-K_i - UA + K_i**2/(K_i + K_w), K_i*K_w/(K_i + K_w)],
[ K_i*K_w/(K_i + K_w), -K_w + K_w**2/(K_i + K_w)]])
charpoly = A.charpoly(x)
assert charpoly == \
Poly(x**2 + (K_i*UA + K_w*UA + 2*K_i*K_w)/(K_i + K_w)*x +
K_i*K_w*UA/(K_i + K_w), x, domain='ZZ(K_i,K_w,UA)')
assert type(charpoly) is PurePoly
A = Matrix([[1, 3], [2, 0]])
assert A.charpoly() == A.charpoly(x) == PurePoly(x**2 - x - 6)
def test_exp():
m = Matrix([[3, 4], [0, -2]])
m_exp = Matrix([[exp(3), -4*exp(-2)/5 + 4*exp(3)/5], [0, exp(-2)]])
assert m.exp() == m_exp
assert exp(m) == m_exp
m = Matrix([[1, 0], [0, 1]])
assert m.exp() == Matrix([[E, 0], [0, E]])
assert exp(m) == Matrix([[E, 0], [0, E]])
def test_has():
A = Matrix(((x, y), (2, 3)))
assert A.has(x)
assert not A.has(z)
assert A.has(Symbol)
A = A.subs(x, 2)
assert not A.has(x)
def test_LUdecomposition_Simple_iszerofunc():
# Test if callable passed to matrices.LUdecomposition_Simple() as iszerofunc keyword argument is used inside
# matrices.LUdecomposition_Simple()
magic_string = "I got passed in!"
def goofyiszero(value):
raise ValueError(magic_string)
try:
lu, p = Matrix([[1, 0], [0, 1]]).LUdecomposition_Simple(iszerofunc=goofyiszero)
except ValueError as err:
assert magic_string == err.args[0]
return
assert False
def test_LUdecomposition_iszerofunc():
# Test if callable passed to matrices.LUdecomposition() as iszerofunc keyword argument is used inside
# matrices.LUdecomposition_Simple()
magic_string = "I got passed in!"
def goofyiszero(value):
raise ValueError(magic_string)
try:
l, u, p = Matrix([[1, 0], [0, 1]]).LUdecomposition(iszerofunc=goofyiszero)
except ValueError as err:
assert magic_string == err.args[0]
return
assert False
def test_find_reasonable_pivot_naive_finds_guaranteed_nonzero1():
# Test if matrices._find_reasonable_pivot_naive()
# finds a guaranteed non-zero pivot when the
# some of the candidate pivots are symbolic expressions.
# Keyword argument: simpfunc=None indicates that no simplifications
# should be performed during the search.
x = Symbol('x')
column = Matrix(3, 1, [x, cos(x)**2 + sin(x)**2, Rational(1, 2)])
pivot_offset, pivot_val, pivot_assumed_nonzero, simplified =\
_find_reasonable_pivot_naive(column)
assert pivot_val == Rational(1, 2)
def test_find_reasonable_pivot_naive_finds_guaranteed_nonzero2():
# Test if matrices._find_reasonable_pivot_naive()
# finds a guaranteed non-zero pivot when the
# some of the candidate pivots are symbolic expressions.
# Keyword argument: simpfunc=_simplify indicates that the search
# should attempt to simplify candidate pivots.
x = Symbol('x')
column = Matrix(3, 1,
[x,
cos(x)**2+sin(x)**2+x**2,
cos(x)**2+sin(x)**2])
pivot_offset, pivot_val, pivot_assumed_nonzero, simplified =\
_find_reasonable_pivot_naive(column, simpfunc=_simplify)
assert pivot_val == 1
def test_find_reasonable_pivot_naive_simplifies():
# Test if matrices._find_reasonable_pivot_naive()
# simplifies candidate pivots, and reports
# their offsets correctly.
x = Symbol('x')
column = Matrix(3, 1,
[x,
cos(x)**2+sin(x)**2+x,
cos(x)**2+sin(x)**2])
pivot_offset, pivot_val, pivot_assumed_nonzero, simplified =\
_find_reasonable_pivot_naive(column, simpfunc=_simplify)
assert len(simplified) == 2
assert simplified[0][0] == 1
assert simplified[0][1] == 1+x
assert simplified[1][0] == 2
assert simplified[1][1] == 1
def test_errors():
raises(ValueError, lambda: Matrix([[1, 2], [1]]))
raises(IndexError, lambda: Matrix([[1, 2]])[1.2, 5])
raises(IndexError, lambda: Matrix([[1, 2]])[1, 5.2])
raises(ValueError, lambda: randMatrix(3, c=4, symmetric=True))
raises(ValueError, lambda: Matrix([1, 2]).reshape(4, 6))
raises(ShapeError,
lambda: Matrix([[1, 2], [3, 4]]).copyin_matrix([1, 0], Matrix([1, 2])))
raises(TypeError, lambda: Matrix([[1, 2], [3, 4]]).copyin_list([0,
1], set([])))
raises(NonSquareMatrixError, lambda: Matrix([[1, 2, 3], [2, 3, 0]]).inv())
raises(ShapeError,
lambda: Matrix(1, 2, [1, 2]).row_join(Matrix([[1, 2], [3, 4]])))
raises(
ShapeError, lambda: Matrix([1, 2]).col_join(Matrix([[1, 2], [3, 4]])))
raises(ShapeError, lambda: Matrix([1]).row_insert(1, Matrix([[1,
2], [3, 4]])))
raises(ShapeError, lambda: Matrix([1]).col_insert(1, Matrix([[1,
2], [3, 4]])))
raises(NonSquareMatrixError, lambda: Matrix([1, 2]).trace())
raises(TypeError, lambda: Matrix([1]).applyfunc(1))
raises(ShapeError, lambda: Matrix([1]).LUsolve(Matrix([[1, 2], [3, 4]])))
raises(MatrixError, lambda: Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]
]).QRdecomposition())
raises(MatrixError, lambda: Matrix(1, 2, [1, 2]).QRdecomposition())
raises(ValueError, lambda: Matrix([[1, 2], [3, 4]]).minor(4, 5))
raises(ValueError, lambda: Matrix([[1, 2], [3, 4]]).minor_submatrix(4, 5))
raises(TypeError, lambda: Matrix([1, 2, 3]).cross(1))
raises(TypeError, lambda: Matrix([1, 2, 3]).dot(1))
raises(ShapeError, lambda: Matrix([1, 2, 3]).dot(Matrix([1, 2])))
raises(ShapeError, lambda: Matrix([1, 2]).dot([]))
raises(TypeError, lambda: Matrix([1, 2]).dot('a'))
raises(NonSquareMatrixError, lambda: Matrix([1, 2, 3]).exp())
raises(ShapeError, lambda: Matrix([[1, 2], [3, 4]]).normalized())
raises(ValueError, lambda: Matrix([1, 2]).inv(method='not a method'))
raises(NonSquareMatrixError, lambda: Matrix([1, 2]).inverse_GE())
raises(ValueError, lambda: Matrix([[1, 2], [1, 2]]).inverse_GE())
raises(NonSquareMatrixError, lambda: Matrix([1, 2]).inverse_ADJ())
raises(ValueError, lambda: Matrix([[1, 2], [1, 2]]).inverse_ADJ())
raises(NonSquareMatrixError, lambda: Matrix([1, 2]).inverse_LU())
raises(NonSquareMatrixError, lambda: Matrix([1, 2]).is_nilpotent())
raises(NonSquareMatrixError, lambda: Matrix([1, 2]).det())
raises(ValueError,
lambda: Matrix([[1, 2], [3, 4]]).det(method='Not a real method'))
raises(ValueError,
lambda: hessian(Matrix([[1, 2], [3, 4]]), Matrix([[1, 2], [2, 1]])))
raises(ValueError, lambda: hessian(Matrix([[1, 2], [3, 4]]), []))
raises(ValueError, lambda: hessian(Symbol('x')**2, 'a'))
raises(IndexError, lambda: eye(3)[5, 2])
raises(IndexError, lambda: eye(3)[2, 5])
M = Matrix(((1, 2, 3, 4), (5, 6, 7, 8), (9, 10, 11, 12), (13, 14, 15, 16)))
raises(ValueError, lambda: M.det('method=LU_decomposition()'))
def test_len():
assert len(Matrix()) == 0
assert len(Matrix([[1, 2]])) == len(Matrix([[1], [2]])) == 2
assert len(Matrix(0, 2, lambda i, j: 0)) == \
len(Matrix(2, 0, lambda i, j: 0)) == 0
assert len(Matrix([[0, 1, 2], [3, 4, 5]])) == 6
assert Matrix([1]) == Matrix([[1]])
assert not Matrix()
assert Matrix() == Matrix([])
def test_integrate():
A = Matrix(((1, 4, x), (y, 2, 4), (10, 5, x**2)))
assert A.integrate(x) == \
Matrix(((x, 4*x, x**2/2), (x*y, 2*x, 4*x), (10*x, 5*x, x**3/3)))
assert A.integrate(y) == \
Matrix(((y, 4*y, x*y), (y**2/2, 2*y, 4*y), (10*y, 5*y, y*x**2)))
def test_limit():
A = Matrix(((1, 4, sin(x)/x), (y, 2, 4), (10, 5, x**2 + 1)))
assert A.limit(x, 0) == Matrix(((1, 4, 1), (y, 2, 4), (10, 5, 1)))
def test_diff():
A = MutableDenseMatrix(((1, 4, x), (y, 2, 4), (10, 5, x**2 + 1)))
assert A.diff(x) == MutableDenseMatrix(((0, 0, 1), (0, 0, 0), (0, 0, 2*x)))
assert A.diff(y) == MutableDenseMatrix(((0, 0, 0), (1, 0, 0), (0, 0, 0)))
assert diff(A, x) == MutableDenseMatrix(((0, 0, 1), (0, 0, 0), (0, 0, 2*x)))
assert diff(A, y) == MutableDenseMatrix(((0, 0, 0), (1, 0, 0), (0, 0, 0)))
A_imm = A.as_immutable()
assert A_imm.diff(x) == ImmutableDenseMatrix(((0, 0, 1), (0, 0, 0), (0, 0, 2*x)))
assert A_imm.diff(y) == ImmutableDenseMatrix(((0, 0, 0), (1, 0, 0), (0, 0, 0)))
assert diff(A_imm, x) == ImmutableDenseMatrix(((0, 0, 1), (0, 0, 0), (0, 0, 2*x)))
assert diff(A_imm, y) == ImmutableDenseMatrix(((0, 0, 0), (1, 0, 0), (0, 0, 0)))
def test_getattr():
A = Matrix(((1, 4, x), (y, 2, 4), (10, 5, x**2 + 1)))
raises(AttributeError, lambda: A.nonexistantattribute)
assert getattr(A, 'diff')(x) == Matrix(((0, 0, 1), (0, 0, 0), (0, 0, 2*x)))
def test_hessenberg():
A = Matrix([[3, 4, 1], [2, 4, 5], [0, 1, 2]])
assert A.is_upper_hessenberg
A = A.T
assert A.is_lower_hessenberg
A[0, -1] = 1
assert A.is_lower_hessenberg is False
A = Matrix([[3, 4, 1], [2, 4, 5], [3, 1, 2]])
assert not A.is_upper_hessenberg
A = zeros(5, 2)
assert A.is_upper_hessenberg
def test_cholesky():
raises(NonSquareMatrixError, lambda: Matrix((1, 2)).cholesky())
raises(ValueError, lambda: Matrix(((1, 2), (3, 4))).cholesky())
A = Matrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11)))
assert A.cholesky() * A.cholesky().T == A
assert A.cholesky().is_lower
assert A.cholesky() == Matrix([[5, 0, 0], [3, 3, 0], [-1, 1, 3]])
def test_LDLdecomposition():
raises(NonSquareMatrixError, lambda: Matrix((1, 2)).LDLdecomposition())
raises(ValueError, lambda: Matrix(((1, 2), (3, 4))).LDLdecomposition())
A = Matrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11)))
L, D = A.LDLdecomposition()
assert L * D * L.T == A
assert L.is_lower
assert L == Matrix([[1, 0, 0], [ S(3)/5, 1, 0], [S(-1)/5, S(1)/3, 1]])
assert D.is_diagonal()
assert D == Matrix([[25, 0, 0], [0, 9, 0], [0, 0, 9]])
def test_cholesky_solve():
A = Matrix([[2, 3, 5],
[3, 6, 2],
[8, 3, 6]])
x = Matrix(3, 1, [3, 7, 5])
b = A*x
soln = A.cholesky_solve(b)
assert soln == x
A = Matrix([[0, -1, 2],
[5, 10, 7],
[8, 3, 4]])
x = Matrix(3, 1, [-1, 2, 5])
b = A*x
soln = A.cholesky_solve(b)
assert soln == x
def test_LDLsolve():
A = Matrix([[2, 3, 5],
[3, 6, 2],
[8, 3, 6]])
x = Matrix(3, 1, [3, 7, 5])
b = A*x
soln = A.LDLsolve(b)
assert soln == x
A = Matrix([[0, -1, 2],
[5, 10, 7],
[8, 3, 4]])
x = Matrix(3, 1, [-1, 2, 5])
b = A*x
soln = A.LDLsolve(b)
assert soln == x
def test_lower_triangular_solve():
raises(NonSquareMatrixError,
lambda: Matrix([1, 0]).lower_triangular_solve(Matrix([0, 1])))
raises(ShapeError,
lambda: Matrix([[1, 0], [0, 1]]).lower_triangular_solve(Matrix([1])))
raises(ValueError,
lambda: Matrix([[2, 1], [1, 2]]).lower_triangular_solve(
Matrix([[1, 0], [0, 1]])))
A = Matrix([[1, 0], [0, 1]])
B = Matrix([[x, y], [y, x]])
C = Matrix([[4, 8], [2, 9]])
assert A.lower_triangular_solve(B) == B
assert A.lower_triangular_solve(C) == C
def test_upper_triangular_solve():
raises(NonSquareMatrixError,
lambda: Matrix([1, 0]).upper_triangular_solve(Matrix([0, 1])))
raises(TypeError,
lambda: Matrix([[1, 0], [0, 1]]).upper_triangular_solve(Matrix([1])))
raises(TypeError,
lambda: Matrix([[2, 1], [1, 2]]).upper_triangular_solve(
Matrix([[1, 0], [0, 1]])))
A = Matrix([[1, 0], [0, 1]])
B = Matrix([[x, y], [y, x]])
C = Matrix([[2, 4], [3, 8]])
assert A.upper_triangular_solve(B) == B
assert A.upper_triangular_solve(C) == C
def test_diagonal_solve():
raises(TypeError, lambda: Matrix([1, 1]).diagonal_solve(Matrix([1])))
A = Matrix([[1, 0], [0, 1]])*2
B = Matrix([[x, y], [y, x]])
assert A.diagonal_solve(B) == B/2
def test_matrix_norm():
# Vector Tests
# Test columns and symbols
x = Symbol('x', real=True)
v = Matrix([cos(x), sin(x)])
assert trigsimp(v.norm(2)) == 1
assert v.norm(10) == Pow(cos(x)**10 + sin(x)**10, S(1)/10)
# Test Rows
A = Matrix([[5, Rational(3, 2)]])
assert A.norm() == Pow(25 + Rational(9, 4), S(1)/2)
assert A.norm(oo) == max(A._mat)
assert A.norm(-oo) == min(A._mat)
# Matrix Tests
# Intuitive test
A = Matrix([[1, 1], [1, 1]])
assert A.norm(2) == 2
assert A.norm(-2) == 0
assert A.norm('frobenius') == 2
assert eye(10).norm(2) == eye(10).norm(-2) == 1
# Test with Symbols and more complex entries
A = Matrix([[3, y, y], [x, S(1)/2, -pi]])
assert (A.norm('fro')
== sqrt(S(37)/4 + 2*abs(y)**2 + pi**2 + x**2))
# Check non-square
A = Matrix([[1, 2, -3], [4, 5, Rational(13, 2)]])
assert A.norm(2) == sqrt(S(389)/8 + sqrt(78665)/8)
assert A.norm(-2) == S(0)
assert A.norm('frobenius') == sqrt(389)/2
# Test properties of matrix norms
# http://en.wikipedia.org/wiki/Matrix_norm#Definition
# Two matrices
A = Matrix([[1, 2], [3, 4]])
B = Matrix([[5, 5], [-2, 2]])
C = Matrix([[0, -I], [I, 0]])
D = Matrix([[1, 0], [0, -1]])
L = [A, B, C, D]
alpha = Symbol('alpha', real=True)
for order in ['fro', 2, -2]:
# Zero Check
assert zeros(3).norm(order) == S(0)
# Check Triangle Inequality for all Pairs of Matrices
for X in L:
for Y in L:
dif = (X.norm(order) + Y.norm(order) -
(X + Y).norm(order))
assert (dif >= 0)
# Scalar multiplication linearity
for M in [A, B, C, D]:
dif = simplify((alpha*M).norm(order) -
abs(alpha) * M.norm(order))
assert dif == 0
# Test Properties of Vector Norms
# http://en.wikipedia.org/wiki/Vector_norm
# Two column vectors
a = Matrix([1, 1 - 1*I, -3])
b = Matrix([S(1)/2, 1*I, 1])
c = Matrix([-1, -1, -1])
d = Matrix([3, 2, I])
e = Matrix([Integer(1e2), Rational(1, 1e2), 1])
L = [a, b, c, d, e]
alpha = Symbol('alpha', real=True)
for order in [1, 2, -1, -2, S.Infinity, S.NegativeInfinity, pi]:
# Zero Check
if order > 0:
assert Matrix([0, 0, 0]).norm(order) == S(0)
# Triangle inequality on all pairs
if order >= 1: # Triangle InEq holds only for these norms
for X in L:
for Y in L:
dif = (X.norm(order) + Y.norm(order) -
(X + Y).norm(order))
assert simplify(dif >= 0) is S.true
# Linear to scalar multiplication
if order in [1, 2, -1, -2, S.Infinity, S.NegativeInfinity]:
for X in L:
dif = simplify((alpha*X).norm(order) -
(abs(alpha) * X.norm(order)))
assert dif == 0
def test_condition_number():
x = Symbol('x', real=True)
A = eye(3)
A[0, 0] = 10
A[2, 2] = S(1)/10
assert A.condition_number() == 100
A[1, 1] = x
assert A.condition_number() == Max(10, Abs(x)) / Min(S(1)/10, Abs(x))
M = Matrix([[cos(x), sin(x)], [-sin(x), cos(x)]])
Mc = M.condition_number()
assert all(Float(1.).epsilon_eq(Mc.subs(x, val).evalf()) for val in
[Rational(1, 5), Rational(1, 2), Rational(1, 10), pi/2, pi, 7*pi/4 ])
#issue 10782
assert Matrix([]).condition_number() == 0
def test_equality():
A = Matrix(((1, 2, 3), (4, 5, 6), (7, 8, 9)))
B = Matrix(((9, 8, 7), (6, 5, 4), (3, 2, 1)))
assert A == A[:, :]
assert not A != A[:, :]
assert not A == B
assert A != B
assert A != 10
assert not A == 10
# A SparseMatrix can be equal to a Matrix
C = SparseMatrix(((1, 0, 0), (0, 1, 0), (0, 0, 1)))
D = Matrix(((1, 0, 0), (0, 1, 0), (0, 0, 1)))
assert C == D
assert not C != D
def test_col_join():
assert eye(3).col_join(Matrix([[7, 7, 7]])) == \
Matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[7, 7, 7]])
def test_row_insert():
r4 = Matrix([[4, 4, 4]])
for i in range(-4, 5):
l = [1, 0, 0]
l.insert(i, 4)
assert flatten(eye(3).row_insert(i, r4).col(0).tolist()) == l
def test_col_insert():
c4 = Matrix([4, 4, 4])
for i in range(-4, 5):
l = [0, 0, 0]
l.insert(i, 4)
assert flatten(zeros(3).col_insert(i, c4).row(0).tolist()) == l
def test_normalized():
assert Matrix([3, 4]).normalized() == \
Matrix([Rational(3, 5), Rational(4, 5)])
def test_print_nonzero():
assert capture(lambda: eye(3).print_nonzero()) == \
'[X ]\n[ X ]\n[ X]\n'
assert capture(lambda: eye(3).print_nonzero('.')) == \
'[. ]\n[ . ]\n[ .]\n'
def test_zeros_eye():
assert Matrix.eye(3) == eye(3)
assert Matrix.zeros(3) == zeros(3)
assert ones(3, 4) == Matrix(3, 4, [1]*12)
i = Matrix([[1, 0], [0, 1]])
z = Matrix([[0, 0], [0, 0]])
for cls in classes:
m = cls.eye(2)
assert i == m # but m == i will fail if m is immutable
assert i == eye(2, cls=cls)
assert type(m) == cls
m = cls.zeros(2)
assert z == m
assert z == zeros(2, cls=cls)
assert type(m) == cls
def test_is_zero():
assert Matrix().is_zero
assert Matrix([[0, 0], [0, 0]]).is_zero
assert zeros(3, 4).is_zero
assert not eye(3).is_zero
assert Matrix([[x, 0], [0, 0]]).is_zero == None
assert SparseMatrix([[x, 0], [0, 0]]).is_zero == None
assert ImmutableMatrix([[x, 0], [0, 0]]).is_zero == None
assert ImmutableSparseMatrix([[x, 0], [0, 0]]).is_zero == None
assert Matrix([[x, 1], [0, 0]]).is_zero == False
a = Symbol('a', nonzero=True)
assert Matrix([[a, 0], [0, 0]]).is_zero == False
def test_rotation_matrices():
# This tests the rotation matrices by rotating about an axis and back.
theta = pi/3
r3_plus = rot_axis3(theta)
r3_minus = rot_axis3(-theta)
r2_plus = rot_axis2(theta)
r2_minus = rot_axis2(-theta)
r1_plus = rot_axis1(theta)
r1_minus = rot_axis1(-theta)
assert r3_minus*r3_plus*eye(3) == eye(3)
assert r2_minus*r2_plus*eye(3) == eye(3)
assert r1_minus*r1_plus*eye(3) == eye(3)
# Check the correctness of the trace of the rotation matrix
assert r1_plus.trace() == 1 + 2*cos(theta)
assert r2_plus.trace() == 1 + 2*cos(theta)
assert r3_plus.trace() == 1 + 2*cos(theta)
# Check that a rotation with zero angle doesn't change anything.
assert rot_axis1(0) == eye(3)
assert rot_axis2(0) == eye(3)
assert rot_axis3(0) == eye(3)
def test_DeferredVector():
assert str(DeferredVector("vector")[4]) == "vector[4]"
assert sympify(DeferredVector("d")) == DeferredVector("d")
def test_DeferredVector_not_iterable():
assert not iterable(DeferredVector('X'))
def test_DeferredVector_Matrix():
raises(TypeError, lambda: Matrix(DeferredVector("V")))
def test_GramSchmidt():
R = Rational
m1 = Matrix(1, 2, [1, 2])
m2 = Matrix(1, 2, [2, 3])
assert GramSchmidt([m1, m2]) == \
[Matrix(1, 2, [1, 2]), Matrix(1, 2, [R(2)/5, R(-1)/5])]
assert GramSchmidt([m1.T, m2.T]) == \
[Matrix(2, 1, [1, 2]), Matrix(2, 1, [R(2)/5, R(-1)/5])]
# from wikipedia
assert GramSchmidt([Matrix([3, 1]), Matrix([2, 2])], True) == [
Matrix([3*sqrt(10)/10, sqrt(10)/10]),
Matrix([-sqrt(10)/10, 3*sqrt(10)/10])]
def test_casoratian():
assert casoratian([1, 2, 3, 4], 1) == 0
assert casoratian([1, 2, 3, 4], 1, zero=False) == 0
def test_zero_dimension_multiply():
assert (Matrix()*zeros(0, 3)).shape == (0, 3)
assert zeros(3, 0)*zeros(0, 3) == zeros(3, 3)
assert zeros(0, 3)*zeros(3, 0) == Matrix()
def test_slice_issue_2884():
m = Matrix(2, 2, range(4))
assert m[1, :] == Matrix([[2, 3]])
assert m[-1, :] == Matrix([[2, 3]])
assert m[:, 1] == Matrix([[1, 3]]).T
assert m[:, -1] == Matrix([[1, 3]]).T
raises(IndexError, lambda: m[2, :])
raises(IndexError, lambda: m[2, 2])
def test_slice_issue_3401():
assert zeros(0, 3)[:, -1].shape == (0, 1)
assert zeros(3, 0)[0, :] == Matrix(1, 0, [])
def test_copyin():
s = zeros(3, 3)
s[3] = 1
assert s[:, 0] == Matrix([0, 1, 0])
assert s[3] == 1
assert s[3: 4] == [1]
s[1, 1] = 42
assert s[1, 1] == 42
assert s[1, 1:] == Matrix([[42, 0]])
s[1, 1:] = Matrix([[5, 6]])
assert s[1, :] == Matrix([[1, 5, 6]])
s[1, 1:] = [[42, 43]]
assert s[1, :] == Matrix([[1, 42, 43]])
s[0, 0] = 17
assert s[:, :1] == Matrix([17, 1, 0])
s[0, 0] = [1, 1, 1]
assert s[:, 0] == Matrix([1, 1, 1])
s[0, 0] = Matrix([1, 1, 1])
assert s[:, 0] == Matrix([1, 1, 1])
s[0, 0] = SparseMatrix([1, 1, 1])
assert s[:, 0] == Matrix([1, 1, 1])
def test_invertible_check():
# sometimes a singular matrix will have a pivot vector shorter than
# the number of rows in a matrix...
assert Matrix([[1, 2], [1, 2]]).rref() == (Matrix([[1, 2], [0, 0]]), (0,))
raises(ValueError, lambda: Matrix([[1, 2], [1, 2]]).inv())
m = Matrix([
[-1, -1, 0],
[ x, 1, 1],
[ 1, x, -1],
])
assert len(m.rref()[1]) != m.rows
# in addition, unless simplify=True in the call to rref, the identity
# matrix will be returned even though m is not invertible
assert m.rref()[0] != eye(3)
assert m.rref(simplify=signsimp)[0] != eye(3)
raises(ValueError, lambda: m.inv(method="ADJ"))
raises(ValueError, lambda: m.inv(method="GE"))
raises(ValueError, lambda: m.inv(method="LU"))
@XFAIL
def test_issue_3959():
x, y = symbols('x, y')
e = x*y
assert e.subs(x, Matrix([3, 5, 3])) == Matrix([3, 5, 3])*y
def test_issue_5964():
assert str(Matrix([[1, 2], [3, 4]])) == 'Matrix([[1, 2], [3, 4]])'
def test_issue_7604():
x, y = symbols(u"x y")
assert sstr(Matrix([[x, 2*y], [y**2, x + 3]])) == \
'Matrix([\n[ x, 2*y],\n[y**2, x + 3]])'
def test_is_Identity():
assert eye(3).is_Identity
assert eye(3).as_immutable().is_Identity
assert not zeros(3).is_Identity
assert not ones(3).is_Identity
# issue 6242
assert not Matrix([[1, 0, 0]]).is_Identity
# issue 8854
assert SparseMatrix(3,3, {(0,0):1, (1,1):1, (2,2):1}).is_Identity
assert not SparseMatrix(2,3, range(6)).is_Identity
assert not SparseMatrix(3,3, {(0,0):1, (1,1):1}).is_Identity
assert not SparseMatrix(3,3, {(0,0):1, (1,1):1, (2,2):1, (0,1):2, (0,2):3}).is_Identity
def test_dot():
assert ones(1, 3).dot(ones(3, 1)) == 3
assert ones(1, 3).dot([1, 1, 1]) == 3
def test_dual():
B_x, B_y, B_z, E_x, E_y, E_z = symbols(
'B_x B_y B_z E_x E_y E_z', real=True)
F = Matrix((
( 0, E_x, E_y, E_z),
(-E_x, 0, B_z, -B_y),
(-E_y, -B_z, 0, B_x),
(-E_z, B_y, -B_x, 0)
))
Fd = Matrix((
( 0, -B_x, -B_y, -B_z),
(B_x, 0, E_z, -E_y),
(B_y, -E_z, 0, E_x),
(B_z, E_y, -E_x, 0)
))
assert F.dual().equals(Fd)
assert eye(3).dual().equals(zeros(3))
assert F.dual().dual().equals(-F)
def test_anti_symmetric():
assert Matrix([1, 2]).is_anti_symmetric() is False
m = Matrix(3, 3, [0, x**2 + 2*x + 1, y, -(x + 1)**2, 0, x*y, -y, -x*y, 0])
assert m.is_anti_symmetric() is True
assert m.is_anti_symmetric(simplify=False) is False
assert m.is_anti_symmetric(simplify=lambda x: x) is False
# tweak to fail
m[2, 1] = -m[2, 1]
assert m.is_anti_symmetric() is False
# untweak
m[2, 1] = -m[2, 1]
m = m.expand()
assert m.is_anti_symmetric(simplify=False) is True
m[0, 0] = 1
assert m.is_anti_symmetric() is False
def test_normalize_sort_diogonalization():
A = Matrix(((1, 2), (2, 1)))
P, Q = A.diagonalize(normalize=True)
assert P*P.T == P.T*P == eye(P.cols)
P, Q = A.diagonalize(normalize=True, sort=True)
assert P*P.T == P.T*P == eye(P.cols)
assert P*Q*P.inv() == A
def test_issue_5321():
raises(ValueError, lambda: Matrix([[1, 2, 3], Matrix(0, 1, [])]))
def test_issue_5320():
assert Matrix.hstack(eye(2), 2*eye(2)) == Matrix([
[1, 0, 2, 0],
[0, 1, 0, 2]
])
assert Matrix.vstack(eye(2), 2*eye(2)) == Matrix([
[1, 0],
[0, 1],
[2, 0],
[0, 2]
])
cls = SparseMatrix
assert cls.hstack(cls(eye(2)), cls(2*eye(2))) == Matrix([
[1, 0, 2, 0],
[0, 1, 0, 2]
])
def test_issue_11944():
A = Matrix([[1]])
AIm = sympify(A)
assert Matrix.hstack(AIm, A) == Matrix([[1, 1]])
assert Matrix.vstack(AIm, A) == Matrix([[1], [1]])
def test_cross():
a = [1, 2, 3]
b = [3, 4, 5]
col = Matrix([-2, 4, -2])
row = col.T
def test(M, ans):
assert ans == M
assert type(M) == cls
for cls in classes:
A = cls(a)
B = cls(b)
test(A.cross(B), col)
test(A.cross(B.T), col)
test(A.T.cross(B.T), row)
test(A.T.cross(B), row)
raises(ShapeError, lambda:
Matrix(1, 2, [1, 1]).cross(Matrix(1, 2, [1, 1])))
def test_hash():
for cls in classes[-2:]:
s = {cls.eye(1), cls.eye(1)}
assert len(s) == 1 and s.pop() == cls.eye(1)
# issue 3979
for cls in classes[:2]:
assert not isinstance(cls.eye(1), collections.Hashable)
@XFAIL
def test_issue_3979():
# when this passes, delete this and change the [1:2]
# to [:2] in the test_hash above for issue 3979
cls = classes[0]
raises(AttributeError, lambda: hash(cls.eye(1)))
def test_adjoint():
dat = [[0, I], [1, 0]]
ans = Matrix([[0, 1], [-I, 0]])
for cls in classes:
assert ans == cls(dat).adjoint()
def test_simplify_immutable():
from sympy import simplify, sin, cos
assert simplify(ImmutableMatrix([[sin(x)**2 + cos(x)**2]])) == \
ImmutableMatrix([[1]])
def test_rank():
from sympy.abc import x
m = Matrix([[1, 2], [x, 1 - 1/x]])
assert m.rank() == 2
n = Matrix(3, 3, range(1, 10))
assert n.rank() == 2
p = zeros(3)
assert p.rank() == 0
def test_issue_11434():
ax, ay, bx, by, cx, cy, dx, dy, ex, ey, t0, t1 = \
symbols('a_x a_y b_x b_y c_x c_y d_x d_y e_x e_y t_0 t_1')
M = Matrix([[ax, ay, ax*t0, ay*t0, 0],
[bx, by, bx*t0, by*t0, 0],
[cx, cy, cx*t0, cy*t0, 1],
[dx, dy, dx*t0, dy*t0, 1],
[ex, ey, 2*ex*t1 - ex*t0, 2*ey*t1 - ey*t0, 0]])
assert M.rank() == 4
def test_rank_regression_from_so():
# see:
# http://stackoverflow.com/questions/19072700/why-does-sympy-give-me-the-wrong-answer-when-i-row-reduce-a-symbolic-matrix
nu, lamb = symbols('nu, lambda')
A = Matrix([[-3*nu, 1, 0, 0],
[ 3*nu, -2*nu - 1, 2, 0],
[ 0, 2*nu, (-1*nu) - lamb - 2, 3],
[ 0, 0, nu + lamb, -3]])
expected_reduced = Matrix([[1, 0, 0, 1/(nu**2*(-lamb - nu))],
[0, 1, 0, 3/(nu*(-lamb - nu))],
[0, 0, 1, 3/(-lamb - nu)],
[0, 0, 0, 0]])
expected_pivots = (0, 1, 2)
reduced, pivots = A.rref()
assert simplify(expected_reduced - reduced) == zeros(*A.shape)
assert pivots == expected_pivots
def test_replace():
from sympy import symbols, Function, Matrix
F, G = symbols('F, G', cls=Function)
K = Matrix(2, 2, lambda i, j: G(i+j))
M = Matrix(2, 2, lambda i, j: F(i+j))
N = M.replace(F, G)
assert N == K
def test_replace_map():
from sympy import symbols, Function, Matrix
F, G = symbols('F, G', cls=Function)
K = Matrix(2, 2, [(G(0), {F(0): G(0)}), (G(1), {F(1): G(1)}), (G(1), {F(1)\
: G(1)}), (G(2), {F(2): G(2)})])
M = Matrix(2, 2, lambda i, j: F(i+j))
N = M.replace(F, G, True)
assert N == K
def test_atoms():
m = Matrix([[1, 2], [x, 1 - 1/x]])
assert m.atoms() == {S(1),S(2),S(-1), x}
assert m.atoms(Symbol) == {x}
@slow
def test_pinv():
# Pseudoinverse of an invertible matrix is the inverse.
A1 = Matrix([[a, b], [c, d]])
assert simplify(A1.pinv()) == simplify(A1.inv())
# Test the four properties of the pseudoinverse for various matrices.
As = [Matrix([[13, 104], [2212, 3], [-3, 5]]),
Matrix([[1, 7, 9], [11, 17, 19]]),
Matrix([a, b])]
for A in As:
A_pinv = A.pinv()
AAp = A * A_pinv
ApA = A_pinv * A
assert simplify(AAp * A) == A
assert simplify(ApA * A_pinv) == A_pinv
assert AAp.H == AAp
assert ApA.H == ApA
def test_pinv_solve():
# Fully determined system (unique result, identical to other solvers).
A = Matrix([[1, 5], [7, 9]])
B = Matrix([12, 13])
assert A.pinv_solve(B) == A.cholesky_solve(B)
assert A.pinv_solve(B) == A.LDLsolve(B)
assert A.pinv_solve(B) == Matrix([sympify('-43/26'), sympify('71/26')])
assert A * A.pinv() * B == B
# Fully determined, with two-dimensional B matrix.
B = Matrix([[12, 13, 14], [15, 16, 17]])
assert A.pinv_solve(B) == A.cholesky_solve(B)
assert A.pinv_solve(B) == A.LDLsolve(B)
assert A.pinv_solve(B) == Matrix([[-33, -37, -41], [69, 75, 81]]) / 26
assert A * A.pinv() * B == B
# Underdetermined system (infinite results).
A = Matrix([[1, 0, 1], [0, 1, 1]])
B = Matrix([5, 7])
solution = A.pinv_solve(B)
w = {}
for s in solution.atoms(Symbol):
# Extract dummy symbols used in the solution.
w[s.name] = s
assert solution == Matrix([[w['w0_0']/3 + w['w1_0']/3 - w['w2_0']/3 + 1],
[w['w0_0']/3 + w['w1_0']/3 - w['w2_0']/3 + 3],
[-w['w0_0']/3 - w['w1_0']/3 + w['w2_0']/3 + 4]])
assert A * A.pinv() * B == B
# Overdetermined system (least squares results).
A = Matrix([[1, 0], [0, 0], [0, 1]])
B = Matrix([3, 2, 1])
assert A.pinv_solve(B) == Matrix([3, 1])
# Proof the solution is not exact.
assert A * A.pinv() * B != B
@XFAIL
def test_pinv_rank_deficient():
# Test the four properties of the pseudoinverse for various matrices.
As = [Matrix([[1, 1, 1], [2, 2, 2]]),
Matrix([[1, 0], [0, 0]])]
for A in As:
A_pinv = A.pinv()
AAp = A * A_pinv
ApA = A_pinv * A
assert simplify(AAp * A) == A
assert simplify(ApA * A_pinv) == A_pinv
assert AAp.H == AAp
assert ApA.H == ApA
# Test solving with rank-deficient matrices.
A = Matrix([[1, 0], [0, 0]])
# Exact, non-unique solution.
B = Matrix([3, 0])
solution = A.pinv_solve(B)
w1 = solution.atoms(Symbol).pop()
assert w1.name == 'w1_0'
assert solution == Matrix([3, w1])
assert A * A.pinv() * B == B
# Least squares, non-unique solution.
B = Matrix([3, 1])
solution = A.pinv_solve(B)
w1 = solution.atoms(Symbol).pop()
assert w1.name == 'w1_0'
assert solution == Matrix([3, w1])
assert A * A.pinv() * B != B
def test_gauss_jordan_solve():
# Square, full rank, unique solution
A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 10]])
b = Matrix([3, 6, 9])
sol, params = A.gauss_jordan_solve(b)
assert sol == Matrix([[-1], [2], [0]])
assert params == Matrix(0, 1, [])
# Square, reduced rank, parametrized solution
A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = Matrix([3, 6, 9])
sol, params, freevar = A.gauss_jordan_solve(b, freevar=True)
w = {}
for s in sol.atoms(Symbol):
# Extract dummy symbols used in the solution.
w[s.name] = s
assert sol == Matrix([[w['tau0'] - 1], [-2*w['tau0'] + 2], [w['tau0']]])
assert params == Matrix([[w['tau0']]])
assert freevar == [2]
# Square, reduced rank, parametrized solution
A = Matrix([[1, 2, 3], [2, 4, 6], [3, 6, 9]])
b = Matrix([0, 0, 0])
sol, params = A.gauss_jordan_solve(b)
w = {}
for s in sol.atoms(Symbol):
w[s.name] = s
assert sol == Matrix([[-2*w['tau0'] - 3*w['tau1']],
[w['tau0']], [w['tau1']]])
assert params == Matrix([[w['tau0']], [w['tau1']]])
# Square, reduced rank, parametrized solution
A = Matrix([[0, 0, 0], [0, 0, 0], [0, 0, 0]])
b = Matrix([0, 0, 0])
sol, params = A.gauss_jordan_solve(b)
w = {}
for s in sol.atoms(Symbol):
w[s.name] = s
assert sol == Matrix([[w['tau0']], [w['tau1']], [w['tau2']]])
assert params == Matrix([[w['tau0']], [w['tau1']], [w['tau2']]])
# Square, reduced rank, no solution
A = Matrix([[1, 2, 3], [2, 4, 6], [3, 6, 9]])
b = Matrix([0, 0, 1])
raises(ValueError, lambda: A.gauss_jordan_solve(b))
# Rectangular, tall, full rank, unique solution
A = Matrix([[1, 5, 3], [2, 1, 6], [1, 7, 9], [1, 4, 3]])
b = Matrix([0, 0, 1, 0])
sol, params = A.gauss_jordan_solve(b)
assert sol == Matrix([[-S(1)/2], [0], [S(1)/6]])
assert params == Matrix(0, 1, [])
# Rectangular, tall, full rank, no solution
A = Matrix([[1, 5, 3], [2, 1, 6], [1, 7, 9], [1, 4, 3]])
b = Matrix([0, 0, 0, 1])
raises(ValueError, lambda: A.gauss_jordan_solve(b))
# Rectangular, tall, reduced rank, parametrized solution
A = Matrix([[1, 5, 3], [2, 10, 6], [3, 15, 9], [1, 4, 3]])
b = Matrix([0, 0, 0, 1])
sol, params = A.gauss_jordan_solve(b)
w = {}
for s in sol.atoms(Symbol):
w[s.name] = s
assert sol == Matrix([[-3*w['tau0'] + 5], [-1], [w['tau0']]])
assert params == Matrix([[w['tau0']]])
# Rectangular, tall, reduced rank, no solution
A = Matrix([[1, 5, 3], [2, 10, 6], [3, 15, 9], [1, 4, 3]])
b = Matrix([0, 0, 1, 1])
raises(ValueError, lambda: A.gauss_jordan_solve(b))
# Rectangular, wide, full rank, parametrized solution
A = Matrix([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 1, 12]])
b = Matrix([1, 1, 1])
sol, params = A.gauss_jordan_solve(b)
w = {}
for s in sol.atoms(Symbol):
w[s.name] = s
assert sol == Matrix([[2*w['tau0'] - 1], [-3*w['tau0'] + 1], [0],
[w['tau0']]])
assert params == Matrix([[w['tau0']]])
# Rectangular, wide, reduced rank, parametrized solution
A = Matrix([[1, 2, 3, 4], [5, 6, 7, 8], [2, 4, 6, 8]])
b = Matrix([0, 1, 0])
sol, params = A.gauss_jordan_solve(b)
w = {}
for s in sol.atoms(Symbol):
w[s.name] = s
assert sol == Matrix([[w['tau0'] + 2*w['tau1'] + 1/S(2)],
[-2*w['tau0'] - 3*w['tau1'] - 1/S(4)],
[w['tau0']], [w['tau1']]])
assert params == Matrix([[w['tau0']], [w['tau1']]])
# Rectangular, wide, reduced rank, no solution
A = Matrix([[1, 2, 3, 4], [5, 6, 7, 8], [2, 4, 6, 8]])
b = Matrix([1, 1, 1])
raises(ValueError, lambda: A.gauss_jordan_solve(b))
def test_issue_7201():
assert ones(0, 1) + ones(0, 1) == Matrix(0, 1, [])
assert ones(1, 0) + ones(1, 0) == Matrix(1, 0, [])
def test_free_symbols():
for M in ImmutableMatrix, ImmutableSparseMatrix, Matrix, SparseMatrix:
assert M([[x], [0]]).free_symbols == {x}
def test_from_ndarray():
"""See issue 7465."""
try:
from numpy import array
except ImportError:
skip('NumPy must be available to test creating matrices from ndarrays')
assert Matrix(array([1, 2, 3])) == Matrix([1, 2, 3])
assert Matrix(array([[1, 2, 3]])) == Matrix([[1, 2, 3]])
assert Matrix(array([[1, 2, 3], [4, 5, 6]])) == \
Matrix([[1, 2, 3], [4, 5, 6]])
assert Matrix(array([x, y, z])) == Matrix([x, y, z])
raises(NotImplementedError, lambda: Matrix(array([[
[1, 2], [3, 4]], [[5, 6], [7, 8]]])))
def test_hermitian():
a = Matrix([[1, I], [-I, 1]])
assert a.is_hermitian
a[0, 0] = 2*I
assert a.is_hermitian is False
a[0, 0] = x
assert a.is_hermitian is None
a[0, 1] = a[1, 0]*I
assert a.is_hermitian is False
def test_doit():
a = Matrix([[Add(x,x, evaluate=False)]])
assert a[0] != 2*x
assert a.doit() == Matrix([[2*x]])
def test_issue_9457_9467_9876():
# for row_del(index)
M = Matrix([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
M.row_del(1)
assert M == Matrix([[1, 2, 3], [3, 4, 5]])
N = Matrix([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
N.row_del(-2)
assert N == Matrix([[1, 2, 3], [3, 4, 5]])
O = Matrix([[1, 2, 3], [5, 6, 7], [9, 10, 11]])
O.row_del(-1)
assert O == Matrix([[1, 2, 3], [5, 6, 7]])
P = Matrix([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
raises(IndexError, lambda: P.row_del(10))
Q = Matrix([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
raises(IndexError, lambda: Q.row_del(-10))
# for col_del(index)
M = Matrix([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
M.col_del(1)
assert M == Matrix([[1, 3], [2, 4], [3, 5]])
N = Matrix([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
N.col_del(-2)
assert N == Matrix([[1, 3], [2, 4], [3, 5]])
P = Matrix([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
raises(IndexError, lambda: P.col_del(10))
Q = Matrix([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
raises(IndexError, lambda: Q.col_del(-10))
def test_issue_9422():
x, y = symbols('x y', commutative=False)
a, b = symbols('a b')
M = eye(2)
M1 = Matrix(2, 2, [x, y, y, z])
assert y*x*M != x*y*M
assert b*a*M == a*b*M
assert x*M1 != M1*x
assert a*M1 == M1*a
assert y*x*M == Matrix([[y*x, 0], [0, y*x]])
def test_issue_10770():
M = Matrix([])
a = ['col_insert', 'row_join'], Matrix([9, 6, 3])
b = ['row_insert', 'col_join'], a[1].T
c = ['row_insert', 'col_insert'], Matrix([[1, 2], [3, 4]])
for ops, m in (a, b, c):
for op in ops:
f = getattr(M, op)
new = f(m) if 'join' in op else f(42, m)
assert new == m and id(new) != id(m)
def test_issue_10658():
A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
assert A.extract([0, 1, 2], [True, True, False]) == \
Matrix([[1, 2], [4, 5], [7, 8]])
assert A.extract([0, 1, 2], [True, False, False]) == Matrix([[1], [4], [7]])
assert A.extract([True, False, False], [0, 1, 2]) == Matrix([[1, 2, 3]])
assert A.extract([True, False, True], [0, 1, 2]) == \
Matrix([[1, 2, 3], [7, 8, 9]])
assert A.extract([0, 1, 2], [False, False, False]) == Matrix(3, 0, [])
assert A.extract([False, False, False], [0, 1, 2]) == Matrix(0, 3, [])
assert A.extract([True, False, True], [False, True, False]) == \
Matrix([[2], [8]])
def test_opportunistic_simplification():
# this test relates to issue #10718, #9480, #11434
# issue #9480
m = Matrix([[-5 + 5*sqrt(2), -5], [-5*sqrt(2)/2 + 5, -5*sqrt(2)/2]])
assert m.rank() == 1
# issue #10781
m = Matrix([[3+3*sqrt(3)*I, -9],[4,-3+3*sqrt(3)*I]])
assert simplify(m.rref()[0] - Matrix([[1, -9/(3 + 3*sqrt(3)*I)], [0, 0]])) == zeros(2, 2)
# issue #11434
ax,ay,bx,by,cx,cy,dx,dy,ex,ey,t0,t1 = symbols('a_x a_y b_x b_y c_x c_y d_x d_y e_x e_y t_0 t_1')
m = Matrix([[ax,ay,ax*t0,ay*t0,0],[bx,by,bx*t0,by*t0,0],[cx,cy,cx*t0,cy*t0,1],[dx,dy,dx*t0,dy*t0,1],[ex,ey,2*ex*t1-ex*t0,2*ey*t1-ey*t0,0]])
assert m.rank() == 4
def test_partial_pivoting():
# example from https://en.wikipedia.org/wiki/Pivot_element
# partial pivoting with back subsitution gives a perfect result
# naive pivoting give an error ~1e-13, so anything better than
# 1e-15 is good
mm=Matrix([[0.003 ,59.14, 59.17],[ 5.291, -6.13,46.78]])
assert (mm.rref()[0] - Matrix([[1.0, 0, 10.0], [ 0, 1.0, 1.0]])).norm() < 1e-15
# issue #11549
m_mixed = Matrix([[6e-17, 1.0, 4],[ -1.0, 0, 8],[ 0, 0, 1]])
m_float = Matrix([[6e-17, 1.0, 4.],[ -1.0, 0., 8.],[ 0., 0., 1.]])
m_inv = Matrix([[ 0, -1.0, 8.0],[1.0, 6.0e-17, -4.0],[ 0, 0, 1]])
# this example is numerically unstable and involves a matrix with a norm >= 8,
# this comparing the difference of the results with 1e-15 is numerically sound.
assert (m_mixed.inv() - m_inv).norm() < 1e-15
assert (m_float.inv() - m_inv).norm() < 1e-15
def test_iszero_substitution():
""" When doing numerical computations, all elements that pass
the iszerofunc test should be set to numerically zero if they
aren't already. """
# Matrix from issue #9060
m = Matrix([[0.9, -0.1, -0.2, 0],[-0.8, 0.9, -0.4, 0],[-0.1, -0.8, 0.6, 0]])
m_rref = m.rref(iszerofunc=lambda x: abs(x)<6e-15)[0]
m_correct = Matrix([[1.0, 0, -0.301369863013699, 0],[ 0, 1.0, -0.712328767123288, 0],[ 0, 0, 0, 0]])
m_diff = m_rref - m_correct
assert m_diff.norm() < 1e-15
# if a zero-substitution wasn't made, this entry will be -1.11022302462516e-16
assert m_rref[2,2] == 0
@slow
def test_issue_11238():
from sympy import Point
xx = 8*tan(13*pi/45)/(tan(13*pi/45) + sqrt(3))
yy = (-8*sqrt(3)*tan(13*pi/45)**2 + 24*tan(13*pi/45))/(-3 + tan(13*pi/45)**2)
p1 = Point(0, 0)
p2 = Point(1, -sqrt(3))
p0 = Point(xx,yy)
m1 = Matrix([p1 - simplify(p0), p2 - simplify(p0)])
m2 = Matrix([p1 - p0, p2 - p0])
m3 = Matrix([simplify(p1 - p0), simplify(p2 - p0)])
assert m1.rank(simplify=True) == 1
assert m2.rank(simplify=True) == 1
assert m3.rank(simplify=True) == 1
def test_as_real_imag():
m1 = Matrix(2,2,[1,2,3,4])
m2 = m1*S.ImaginaryUnit
m3 = m1 + m2
for kls in classes:
a,b = kls(m3).as_real_imag()
assert list(a) == list(m1)
assert list(b) == list(m1)
def test_deprecated():
# Maintain tests for deprecated functions. We must capture
# the deprecation warnings. When the deprecated functionality is
# removed, the corresponding tests should be removed.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
m = Matrix(3, 3, [0, 1, 0, -4, 4, 0, -2, 1, 2])
P, Jcells = m.jordan_cells()
assert Jcells[1] == Matrix(1, 1, [2])
assert Jcells[0] == Matrix(2, 2, [2, 1, 0, 2])
| 93,139 | 30.637228 | 143 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/test_sparse.py
|
from sympy import Abs, S, Symbol, I, Rational, PurePoly
from sympy.matrices import Matrix, SparseMatrix, eye, zeros, ShapeError
from sympy.utilities.pytest import raises
def test_sparse_matrix():
def sparse_eye(n):
return SparseMatrix.eye(n)
def sparse_zeros(n):
return SparseMatrix.zeros(n)
# creation args
raises(TypeError, lambda: SparseMatrix(1, 2))
a = SparseMatrix((
(1, 0),
(0, 1)
))
assert SparseMatrix(a) == a
from sympy.matrices import MutableSparseMatrix, MutableDenseMatrix
a = MutableSparseMatrix([])
b = MutableDenseMatrix([1, 2])
assert a.row_join(b) == b
assert a.col_join(b) == b
assert type(a.row_join(b)) == type(a)
assert type(a.col_join(b)) == type(a)
# make sure 0 x n matrices get stacked correctly
sparse_matrices = [SparseMatrix.zeros(0, n) for n in range(4)]
assert SparseMatrix.hstack(*sparse_matrices) == Matrix(0, 6, [])
sparse_matrices = [SparseMatrix.zeros(n, 0) for n in range(4)]
assert SparseMatrix.vstack(*sparse_matrices) == Matrix(6, 0, [])
# test element assignment
a = SparseMatrix((
(1, 0),
(0, 1)
))
a[3] = 4
assert a[1, 1] == 4
a[3] = 1
a[0, 0] = 2
assert a == SparseMatrix((
(2, 0),
(0, 1)
))
a[1, 0] = 5
assert a == SparseMatrix((
(2, 0),
(5, 1)
))
a[1, 1] = 0
assert a == SparseMatrix((
(2, 0),
(5, 0)
))
assert a._smat == {(0, 0): 2, (1, 0): 5}
# test_multiplication
a = SparseMatrix((
(1, 2),
(3, 1),
(0, 6),
))
b = SparseMatrix((
(1, 2),
(3, 0),
))
c = a*b
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
try:
eval('c = a @ b')
except SyntaxError:
pass
else:
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
x = Symbol("x")
c = b * Symbol("x")
assert isinstance(c, SparseMatrix)
assert c[0, 0] == x
assert c[0, 1] == 2*x
assert c[1, 0] == 3*x
assert c[1, 1] == 0
c = 5 * b
assert isinstance(c, SparseMatrix)
assert c[0, 0] == 5
assert c[0, 1] == 2*5
assert c[1, 0] == 3*5
assert c[1, 1] == 0
#test_power
A = SparseMatrix([[2, 3], [4, 5]])
assert (A**5)[:] == [6140, 8097, 10796, 14237]
A = SparseMatrix([[2, 1, 3], [4, 2, 4], [6, 12, 1]])
assert (A**3)[:] == [290, 262, 251, 448, 440, 368, 702, 954, 433]
# test_creation
x = Symbol("x")
a = SparseMatrix([[x, 0], [0, 0]])
m = a
assert m.cols == m.rows
assert m.cols == 2
assert m[:] == [x, 0, 0, 0]
b = SparseMatrix(2, 2, [x, 0, 0, 0])
m = b
assert m.cols == m.rows
assert m.cols == 2
assert m[:] == [x, 0, 0, 0]
assert a == b
S = sparse_eye(3)
S.row_del(1)
assert S == SparseMatrix([
[1, 0, 0],
[0, 0, 1]])
S = sparse_eye(3)
S.col_del(1)
assert S == SparseMatrix([
[1, 0],
[0, 0],
[0, 1]])
S = SparseMatrix.eye(3)
S[2, 1] = 2
S.col_swap(1, 0)
assert S == SparseMatrix([
[0, 1, 0],
[1, 0, 0],
[2, 0, 1]])
a = SparseMatrix(1, 2, [1, 2])
b = a.copy()
c = a.copy()
assert a[0] == 1
a.row_del(0)
assert a == SparseMatrix(0, 2, [])
b.col_del(1)
assert b == SparseMatrix(1, 1, [1])
# test_determinant
x, y = Symbol('x'), Symbol('y')
assert SparseMatrix(1, 1, [0]).det() == 0
assert SparseMatrix([[1]]).det() == 1
assert SparseMatrix(((-3, 2), (8, -5))).det() == -1
assert SparseMatrix(((x, 1), (y, 2*y))).det() == 2*x*y - y
assert SparseMatrix(( (1, 1, 1),
(1, 2, 3),
(1, 3, 6) )).det() == 1
assert SparseMatrix(( ( 3, -2, 0, 5),
(-2, 1, -2, 2),
( 0, -2, 5, 0),
( 5, 0, 3, 4) )).det() == -289
assert SparseMatrix(( ( 1, 2, 3, 4),
( 5, 6, 7, 8),
( 9, 10, 11, 12),
(13, 14, 15, 16) )).det() == 0
assert SparseMatrix(( (3, 2, 0, 0, 0),
(0, 3, 2, 0, 0),
(0, 0, 3, 2, 0),
(0, 0, 0, 3, 2),
(2, 0, 0, 0, 3) )).det() == 275
assert SparseMatrix(( (1, 0, 1, 2, 12),
(2, 0, 1, 1, 4),
(2, 1, 1, -1, 3),
(3, 2, -1, 1, 8),
(1, 1, 1, 0, 6) )).det() == -55
assert SparseMatrix(( (-5, 2, 3, 4, 5),
( 1, -4, 3, 4, 5),
( 1, 2, -3, 4, 5),
( 1, 2, 3, -2, 5),
( 1, 2, 3, 4, -1) )).det() == 11664
assert SparseMatrix(( ( 2, 7, -1, 3, 2),
( 0, 0, 1, 0, 1),
(-2, 0, 7, 0, 2),
(-3, -2, 4, 5, 3),
( 1, 0, 0, 0, 1) )).det() == 123
# test_slicing
m0 = sparse_eye(4)
assert m0[:3, :3] == sparse_eye(3)
assert m0[2:4, 0:2] == sparse_zeros(2)
m1 = SparseMatrix(3, 3, lambda i, j: i + j)
assert m1[0, :] == SparseMatrix(1, 3, (0, 1, 2))
assert m1[1:3, 1] == SparseMatrix(2, 1, (2, 3))
m2 = SparseMatrix(
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]])
assert m2[:, -1] == SparseMatrix(4, 1, [3, 7, 11, 15])
assert m2[-2:, :] == SparseMatrix([[8, 9, 10, 11], [12, 13, 14, 15]])
assert SparseMatrix([[1, 2], [3, 4]])[[1], [1]] == Matrix([[4]])
# test_submatrix_assignment
m = sparse_zeros(4)
m[2:4, 2:4] = sparse_eye(2)
assert m == SparseMatrix([(0, 0, 0, 0),
(0, 0, 0, 0),
(0, 0, 1, 0),
(0, 0, 0, 1)])
assert len(m._smat) == 2
m[:2, :2] = sparse_eye(2)
assert m == sparse_eye(4)
m[:, 0] = SparseMatrix(4, 1, (1, 2, 3, 4))
assert m == SparseMatrix([(1, 0, 0, 0),
(2, 1, 0, 0),
(3, 0, 1, 0),
(4, 0, 0, 1)])
m[:, :] = sparse_zeros(4)
assert m == sparse_zeros(4)
m[:, :] = ((1, 2, 3, 4), (5, 6, 7, 8), (9, 10, 11, 12), (13, 14, 15, 16))
assert m == SparseMatrix((( 1, 2, 3, 4),
( 5, 6, 7, 8),
( 9, 10, 11, 12),
(13, 14, 15, 16)))
m[:2, 0] = [0, 0]
assert m == SparseMatrix((( 0, 2, 3, 4),
( 0, 6, 7, 8),
( 9, 10, 11, 12),
(13, 14, 15, 16)))
# test_reshape
m0 = sparse_eye(3)
assert m0.reshape(1, 9) == SparseMatrix(1, 9, (1, 0, 0, 0, 1, 0, 0, 0, 1))
m1 = SparseMatrix(3, 4, lambda i, j: i + j)
assert m1.reshape(4, 3) == \
SparseMatrix([(0, 1, 2), (3, 1, 2), (3, 4, 2), (3, 4, 5)])
assert m1.reshape(2, 6) == \
SparseMatrix([(0, 1, 2, 3, 1, 2), (3, 4, 2, 3, 4, 5)])
# test_applyfunc
m0 = sparse_eye(3)
assert m0.applyfunc(lambda x: 2*x) == sparse_eye(3)*2
assert m0.applyfunc(lambda x: 0 ) == sparse_zeros(3)
# test__eval_Abs
assert abs(SparseMatrix(((x, 1), (y, 2*y)))) == SparseMatrix(((Abs(x), 1), (Abs(y), 2*Abs(y))))
# test_LUdecomp
testmat = SparseMatrix([[ 0, 2, 5, 3],
[ 3, 3, 7, 4],
[ 8, 4, 0, 2],
[-2, 6, 3, 4]])
L, U, p = testmat.LUdecomposition()
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - testmat == sparse_zeros(4)
testmat = SparseMatrix([[ 6, -2, 7, 4],
[ 0, 3, 6, 7],
[ 1, -2, 7, 4],
[-9, 2, 6, 3]])
L, U, p = testmat.LUdecomposition()
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - testmat == sparse_zeros(4)
x, y, z = Symbol('x'), Symbol('y'), Symbol('z')
M = Matrix(((1, x, 1), (2, y, 0), (y, 0, z)))
L, U, p = M.LUdecomposition()
assert L.is_lower
assert U.is_upper
assert (L*U).permute_rows(p, 'backward') - M == sparse_zeros(3)
# test_LUsolve
A = SparseMatrix([[2, 3, 5],
[3, 6, 2],
[8, 3, 6]])
x = SparseMatrix(3, 1, [3, 7, 5])
b = A*x
soln = A.LUsolve(b)
assert soln == x
A = SparseMatrix([[0, -1, 2],
[5, 10, 7],
[8, 3, 4]])
x = SparseMatrix(3, 1, [-1, 2, 5])
b = A*x
soln = A.LUsolve(b)
assert soln == x
# test_inverse
A = sparse_eye(4)
assert A.inv() == sparse_eye(4)
assert A.inv(method="CH") == sparse_eye(4)
assert A.inv(method="LDL") == sparse_eye(4)
A = SparseMatrix([[2, 3, 5],
[3, 6, 2],
[7, 2, 6]])
Ainv = SparseMatrix(Matrix(A).inv())
assert A*Ainv == sparse_eye(3)
assert A.inv(method="CH") == Ainv
assert A.inv(method="LDL") == Ainv
A = SparseMatrix([[2, 3, 5],
[3, 6, 2],
[5, 2, 6]])
Ainv = SparseMatrix(Matrix(A).inv())
assert A*Ainv == sparse_eye(3)
assert A.inv(method="CH") == Ainv
assert A.inv(method="LDL") == Ainv
# test_cross
v1 = Matrix(1, 3, [1, 2, 3])
v2 = Matrix(1, 3, [3, 4, 5])
assert v1.cross(v2) == Matrix(1, 3, [-2, 4, -2])
assert v1.norm(2)**2 == 14
# conjugate
a = SparseMatrix(((1, 2 + I), (3, 4)))
assert a.C == SparseMatrix([
[1, 2 - I],
[3, 4]
])
# mul
assert a*Matrix(2, 2, [1, 0, 0, 1]) == a
assert a + Matrix(2, 2, [1, 1, 1, 1]) == SparseMatrix([
[2, 3 + I],
[4, 5]
])
# col join
assert a.col_join(sparse_eye(2)) == SparseMatrix([
[1, 2 + I],
[3, 4],
[1, 0],
[0, 1]
])
# symmetric
assert not a.is_symmetric(simplify=False)
# test_cofactor
assert sparse_eye(3) == sparse_eye(3).cofactor_matrix()
test = SparseMatrix([[1, 3, 2], [2, 6, 3], [2, 3, 6]])
assert test.cofactor_matrix() == \
SparseMatrix([[27, -6, -6], [-12, 2, 3], [-3, 1, 0]])
test = SparseMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
assert test.cofactor_matrix() == \
SparseMatrix([[-3, 6, -3], [6, -12, 6], [-3, 6, -3]])
# test_jacobian
x = Symbol('x')
y = Symbol('y')
L = SparseMatrix(1, 2, [x**2*y, 2*y**2 + x*y])
syms = [x, y]
assert L.jacobian(syms) == Matrix([[2*x*y, x**2], [y, 4*y + x]])
L = SparseMatrix(1, 2, [x, x**2*y**3])
assert L.jacobian(syms) == SparseMatrix([[1, 0], [2*x*y**3, x**2*3*y**2]])
# test_QR
A = Matrix([[1, 2], [2, 3]])
Q, S = A.QRdecomposition()
R = Rational
assert Q == Matrix([
[ 5**R(-1, 2), (R(2)/5)*(R(1)/5)**R(-1, 2)],
[2*5**R(-1, 2), (-R(1)/5)*(R(1)/5)**R(-1, 2)]])
assert S == Matrix([
[5**R(1, 2), 8*5**R(-1, 2)],
[ 0, (R(1)/5)**R(1, 2)]])
assert Q*S == A
assert Q.T * Q == sparse_eye(2)
R = Rational
# test nullspace
# first test reduced row-ech form
M = SparseMatrix([[5, 7, 2, 1],
[1, 6, 2, -1]])
out, tmp = M.rref()
assert out == Matrix([[1, 0, -R(2)/23, R(13)/23],
[0, 1, R(8)/23, R(-6)/23]])
M = SparseMatrix([[ 1, 3, 0, 2, 6, 3, 1],
[-2, -6, 0, -2, -8, 3, 1],
[ 3, 9, 0, 0, 6, 6, 2],
[-1, -3, 0, 1, 0, 9, 3]])
out, tmp = M.rref()
assert out == Matrix([[1, 3, 0, 0, 2, 0, 0],
[0, 0, 0, 1, 2, 0, 0],
[0, 0, 0, 0, 0, 1, R(1)/3],
[0, 0, 0, 0, 0, 0, 0]])
# now check the vectors
basis = M.nullspace()
assert basis[0] == Matrix([-3, 1, 0, 0, 0, 0, 0])
assert basis[1] == Matrix([0, 0, 1, 0, 0, 0, 0])
assert basis[2] == Matrix([-2, 0, 0, -2, 1, 0, 0])
assert basis[3] == Matrix([0, 0, 0, 0, 0, R(-1)/3, 1])
# test eigen
x = Symbol('x')
y = Symbol('y')
sparse_eye3 = sparse_eye(3)
assert sparse_eye3.charpoly(x) == PurePoly(((x - 1)**3))
assert sparse_eye3.charpoly(y) == PurePoly(((y - 1)**3))
# test values
M = Matrix([( 0, 1, -1),
( 1, 1, 0),
(-1, 0, 1)])
vals = M.eigenvals()
assert sorted(vals.keys()) == [-1, 1, 2]
R = Rational
M = Matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
assert M.eigenvects() == [(1, 3, [
Matrix([1, 0, 0]),
Matrix([0, 1, 0]),
Matrix([0, 0, 1])])]
M = Matrix([[5, 0, 2],
[3, 2, 0],
[0, 0, 1]])
assert M.eigenvects() == [(1, 1, [Matrix([R(-1)/2, R(3)/2, 1])]),
(2, 1, [Matrix([0, 1, 0])]),
(5, 1, [Matrix([1, 1, 0])])]
assert M.zeros(3, 5) == SparseMatrix(3, 5, {})
A = SparseMatrix(10, 10, {(0, 0): 18, (0, 9): 12, (1, 4): 18, (2, 7): 16, (3, 9): 12, (4, 2): 19, (5, 7): 16, (6, 2): 12, (9, 7): 18})
assert A.row_list() == [(0, 0, 18), (0, 9, 12), (1, 4, 18), (2, 7, 16), (3, 9, 12), (4, 2, 19), (5, 7, 16), (6, 2, 12), (9, 7, 18)]
assert A.col_list() == [(0, 0, 18), (4, 2, 19), (6, 2, 12), (1, 4, 18), (2, 7, 16), (5, 7, 16), (9, 7, 18), (0, 9, 12), (3, 9, 12)]
assert SparseMatrix.eye(2).nnz() == 2
def test_transpose():
assert SparseMatrix(((1, 2), (3, 4))).transpose() == \
SparseMatrix(((1, 3), (2, 4)))
def test_trace():
assert SparseMatrix(((1, 2), (3, 4))).trace() == 5
assert SparseMatrix(((0, 0), (0, 4))).trace() == 4
def test_CL_RL():
assert SparseMatrix(((1, 2), (3, 4))).row_list() == \
[(0, 0, 1), (0, 1, 2), (1, 0, 3), (1, 1, 4)]
assert SparseMatrix(((1, 2), (3, 4))).col_list() == \
[(0, 0, 1), (1, 0, 3), (0, 1, 2), (1, 1, 4)]
def test_add():
assert SparseMatrix(((1, 0), (0, 1))) + SparseMatrix(((0, 1), (1, 0))) == \
SparseMatrix(((1, 1), (1, 1)))
a = SparseMatrix(100, 100, lambda i, j: int(j != 0 and i % j == 0))
b = SparseMatrix(100, 100, lambda i, j: int(i != 0 and j % i == 0))
assert (len(a._smat) + len(b._smat) - len((a + b)._smat) > 0)
def test_errors():
raises(ValueError, lambda: SparseMatrix(1.4, 2, lambda i, j: 0))
raises(TypeError, lambda: SparseMatrix([1, 2, 3], [1, 2]))
raises(ValueError, lambda: SparseMatrix([[1, 2], [3, 4]])[(1, 2, 3)])
raises(IndexError, lambda: SparseMatrix([[1, 2], [3, 4]])[5])
raises(ValueError, lambda: SparseMatrix([[1, 2], [3, 4]])[1, 2, 3])
raises(TypeError,
lambda: SparseMatrix([[1, 2], [3, 4]]).copyin_list([0, 1], set([])))
raises(
IndexError, lambda: SparseMatrix([[1, 2], [3, 4]])[1, 2])
raises(TypeError, lambda: SparseMatrix([1, 2, 3]).cross(1))
raises(IndexError, lambda: SparseMatrix(1, 2, [1, 2])[3])
raises(ShapeError,
lambda: SparseMatrix(1, 2, [1, 2]) + SparseMatrix(2, 1, [2, 1]))
def test_len():
assert not SparseMatrix()
assert SparseMatrix() == SparseMatrix([])
assert SparseMatrix() == SparseMatrix([[]])
def test_sparse_zeros_sparse_eye():
assert SparseMatrix.eye(3) == eye(3, cls=SparseMatrix)
assert len(SparseMatrix.eye(3)._smat) == 3
assert SparseMatrix.zeros(3) == zeros(3, cls=SparseMatrix)
assert len(SparseMatrix.zeros(3)._smat) == 0
def test_copyin():
s = SparseMatrix(3, 3, {})
s[1, 0] = 1
assert s[:, 0] == SparseMatrix(Matrix([0, 1, 0]))
assert s[3] == 1
assert s[3: 4] == [1]
s[1, 1] = 42
assert s[1, 1] == 42
assert s[1, 1:] == SparseMatrix([[42, 0]])
s[1, 1:] = Matrix([[5, 6]])
assert s[1, :] == SparseMatrix([[1, 5, 6]])
s[1, 1:] = [[42, 43]]
assert s[1, :] == SparseMatrix([[1, 42, 43]])
s[0, 0] = 17
assert s[:, :1] == SparseMatrix([17, 1, 0])
s[0, 0] = [1, 1, 1]
assert s[:, 0] == SparseMatrix([1, 1, 1])
s[0, 0] = Matrix([1, 1, 1])
assert s[:, 0] == SparseMatrix([1, 1, 1])
s[0, 0] = SparseMatrix([1, 1, 1])
assert s[:, 0] == SparseMatrix([1, 1, 1])
def test_sparse_solve():
from sympy.matrices import SparseMatrix
A = SparseMatrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11)))
assert A.cholesky() == Matrix([
[ 5, 0, 0],
[ 3, 3, 0],
[-1, 1, 3]])
assert A.cholesky() * A.cholesky().T == Matrix([
[25, 15, -5],
[15, 18, 0],
[-5, 0, 11]])
A = SparseMatrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11)))
L, D = A.LDLdecomposition()
assert 15*L == Matrix([
[15, 0, 0],
[ 9, 15, 0],
[-3, 5, 15]])
assert D == Matrix([
[25, 0, 0],
[ 0, 9, 0],
[ 0, 0, 9]])
assert L * D * L.T == A
A = SparseMatrix(((3, 0, 2), (0, 0, 1), (1, 2, 0)))
assert A.inv() * A == SparseMatrix(eye(3))
A = SparseMatrix([
[ 2, -1, 0],
[-1, 2, -1],
[ 0, 0, 2]])
ans = SparseMatrix([
[S(2)/3, S(1)/3, S(1)/6],
[S(1)/3, S(2)/3, S(1)/3],
[ 0, 0, S(1)/2]])
assert A.inv(method='CH') == ans
assert A.inv(method='LDL') == ans
assert A * ans == SparseMatrix(eye(3))
s = A.solve(A[:, 0], 'LDL')
assert A*s == A[:, 0]
s = A.solve(A[:, 0], 'CH')
assert A*s == A[:, 0]
A = A.col_join(A)
s = A.solve_least_squares(A[:, 0], 'CH')
assert A*s == A[:, 0]
s = A.solve_least_squares(A[:, 0], 'LDL')
assert A*s == A[:, 0]
def test_hermitian():
x = Symbol('x')
a = SparseMatrix([[0, I], [-I, 0]])
assert a.is_hermitian
a = SparseMatrix([[1, I], [-I, 1]])
assert a.is_hermitian
a[0, 0] = 2*I
assert a.is_hermitian is False
a[0, 0] = x
assert a.is_hermitian is None
a[0, 1] = a[1, 0]*I
assert a.is_hermitian is False
| 18,362 | 29.810403 | 139 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/test_commonmatrix.py
|
import collections
import random
from sympy import (
Abs, Add, E, Float, I, Integer, Max, Min, N, Poly, Pow, PurePoly, Rational,
S, Symbol, cos, exp, oo, pi, signsimp, simplify, sin, sqrt, symbols,
sympify, trigsimp, tan, sstr, diff)
from sympy.matrices.common import (ShapeError, MatrixError, NonSquareMatrixError,
_MinimalMatrix, MatrixShaping, MatrixProperties, MatrixOperations, MatrixArithmetic,
MatrixSpecial)
from sympy.matrices.matrices import (DeferredVector, MatrixDeterminant,
MatrixReductions, MatrixSubspaces, MatrixEigen, MatrixCalculus)
from sympy.matrices import (
GramSchmidt, ImmutableMatrix, ImmutableSparseMatrix, Matrix,
SparseMatrix, casoratian, diag, eye, hessian,
matrix_multiply_elementwise, ones, randMatrix, rot_axis1, rot_axis2,
rot_axis3, wronskian, zeros, MutableDenseMatrix, ImmutableDenseMatrix)
from sympy.core.compatibility import long, iterable, range
from sympy.utilities.iterables import flatten, capture
from sympy.utilities.pytest import raises, XFAIL, slow, skip
from sympy.solvers import solve
from sympy.assumptions import Q
from sympy.abc import a, b, c, d, x, y, z
# classes to test the basic matrix classes
class ShapingOnlyMatrix(_MinimalMatrix, MatrixShaping):
pass
def eye_Shaping(n):
return ShapingOnlyMatrix(n, n, lambda i, j: int(i == j))
def zeros_Shaping(n):
return ShapingOnlyMatrix(n, n, lambda i, j: 0)
class PropertiesOnlyMatrix(_MinimalMatrix, MatrixProperties):
pass
def eye_Properties(n):
return PropertiesOnlyMatrix(n, n, lambda i, j: int(i == j))
def zeros_Properties(n):
return PropertiesOnlyMatrix(n, n, lambda i, j: 0)
class OperationsOnlyMatrix(_MinimalMatrix, MatrixOperations):
pass
def eye_Operations(n):
return OperationsOnlyMatrix(n, n, lambda i, j: int(i == j))
def zeros_Operations(n):
return OperationsOnlyMatrix(n, n, lambda i, j: 0)
class ArithmeticOnlyMatrix(_MinimalMatrix, MatrixArithmetic):
pass
def eye_Arithmetic(n):
return ArithmeticOnlyMatrix(n, n, lambda i, j: int(i == j))
def zeros_Arithmetic(n):
return ArithmeticOnlyMatrix(n, n, lambda i, j: 0)
class DeterminantOnlyMatrix(_MinimalMatrix, MatrixDeterminant):
pass
def eye_Determinant(n):
return DeterminantOnlyMatrix(n, n, lambda i, j: int(i == j))
def zeros_Determinant(n):
return DeterminantOnlyMatrix(n, n, lambda i, j: 0)
class ReductionsOnlyMatrix(_MinimalMatrix, MatrixReductions):
pass
def eye_Reductions(n):
return ReductionsOnlyMatrix(n, n, lambda i, j: int(i == j))
def zeros_Reductions(n):
return ReductionsOnlyMatrix(n, n, lambda i, j: 0)
class SpecialOnlyMatrix(_MinimalMatrix, MatrixSpecial):
pass
class SubspaceOnlyMatrix(_MinimalMatrix, MatrixSubspaces):
pass
class EigenOnlyMatrix(_MinimalMatrix, MatrixEigen):
pass
class CalculusOnlyMatrix(_MinimalMatrix, MatrixCalculus):
pass
def test__MinimalMatrix():
x = _MinimalMatrix(2,3,[1,2,3,4,5,6])
assert x.rows == 2
assert x.cols == 3
assert x[2] == 3
assert x[1,1] == 5
assert list(x) == [1,2,3,4,5,6]
assert list(x[1,:]) == [4,5,6]
assert list(x[:,1]) == [2,5]
assert list(x[:,:]) == list(x)
assert x[:,:] == x
assert _MinimalMatrix(x) == x
assert _MinimalMatrix([[1, 2, 3], [4, 5, 6]]) == x
assert not (_MinimalMatrix([[1, 2], [3, 4], [5, 6]]) == x)
# ShapingOnlyMatrix tests
def test_vec():
m = ShapingOnlyMatrix(2, 2, [1, 3, 2, 4])
m_vec = m.vec()
assert m_vec.cols == 1
for i in range(4):
assert m_vec[i] == i + 1
def test_tolist():
lst = [[S.One, S.Half, x*y, S.Zero], [x, y, z, x**2], [y, -S.One, z*x, 3]]
flat_lst = [S.One, S.Half, x*y, S.Zero, x, y, z, x**2, y, -S.One, z*x, 3]
m = ShapingOnlyMatrix(3, 4, flat_lst)
assert m.tolist() == lst
def test_row_col_del():
e = ShapingOnlyMatrix(3, 3, [1, 2, 3, 4, 5, 6, 7, 8, 9])
raises(ValueError, lambda: e.row_del(5))
raises(ValueError, lambda: e.row_del(-5))
raises(ValueError, lambda: e.col_del(5))
raises(ValueError, lambda: e.col_del(-5))
assert e.row_del(2) == e.row_del(-1) == Matrix([[1, 2, 3], [4, 5, 6]])
assert e.col_del(2) == e.col_del(-1) == Matrix([[1, 2], [4, 5], [7, 8]])
assert e.row_del(1) == e.row_del(-2) == Matrix([[1, 2, 3], [7, 8, 9]])
assert e.col_del(1) == e.col_del(-2) == Matrix([[1, 3], [4, 6], [7, 9]])
def test_get_diag_blocks1():
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
assert a.get_diag_blocks() == [a]
assert b.get_diag_blocks() == [b]
assert c.get_diag_blocks() == [c]
def test_get_diag_blocks2():
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
A, B, C, D = diag(a, b, b), diag(a, b, c), diag(a, c, b), diag(c, c, b)
A = ShapingOnlyMatrix(A.rows, A.cols, A)
B = ShapingOnlyMatrix(B.rows, B.cols, B)
C = ShapingOnlyMatrix(C.rows, C.cols, C)
D = ShapingOnlyMatrix(D.rows, D.cols, D)
assert A.get_diag_blocks() == [a, b, b]
assert B.get_diag_blocks() == [a, b, c]
assert C.get_diag_blocks() == [a, c, b]
assert D.get_diag_blocks() == [c, c, b]
def test_shape():
m = ShapingOnlyMatrix(1, 2, [0, 0])
m.shape == (1, 2)
def test_reshape():
m0 = eye_Shaping(3)
assert m0.reshape(1, 9) == Matrix(1, 9, (1, 0, 0, 0, 1, 0, 0, 0, 1))
m1 = ShapingOnlyMatrix(3, 4, lambda i, j: i + j)
assert m1.reshape(
4, 3) == Matrix(((0, 1, 2), (3, 1, 2), (3, 4, 2), (3, 4, 5)))
assert m1.reshape(2, 6) == Matrix(((0, 1, 2, 3, 1, 2), (3, 4, 2, 3, 4, 5)))
def test_row_col():
m = ShapingOnlyMatrix(3, 3, [1, 2, 3, 4, 5, 6, 7, 8, 9])
assert m.row(0) == Matrix(1, 3, [1, 2, 3])
assert m.col(0) == Matrix(3, 1, [1, 4, 7])
def test_row_join():
assert eye_Shaping(3).row_join(Matrix([7, 7, 7])) == \
Matrix([[1, 0, 0, 7],
[0, 1, 0, 7],
[0, 0, 1, 7]])
def test_col_join():
assert eye_Shaping(3).col_join(Matrix([[7, 7, 7]])) == \
Matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[7, 7, 7]])
def test_row_insert():
r4 = Matrix([[4, 4, 4]])
for i in range(-4, 5):
l = [1, 0, 0]
l.insert(i, 4)
assert flatten(eye_Shaping(3).row_insert(i, r4).col(0).tolist()) == l
def test_col_insert():
c4 = Matrix([4, 4, 4])
for i in range(-4, 5):
l = [0, 0, 0]
l.insert(i, 4)
assert flatten(zeros_Shaping(3).col_insert(i, c4).row(0).tolist()) == l
def test_extract():
m = ShapingOnlyMatrix(4, 3, lambda i, j: i*3 + j)
assert m.extract([0, 1, 3], [0, 1]) == Matrix(3, 2, [0, 1, 3, 4, 9, 10])
assert m.extract([0, 3], [0, 0, 2]) == Matrix(2, 3, [0, 0, 2, 9, 9, 11])
assert m.extract(range(4), range(3)) == m
raises(IndexError, lambda: m.extract([4], [0]))
raises(IndexError, lambda: m.extract([0], [3]))
def test_hstack():
m = ShapingOnlyMatrix(4, 3, lambda i, j: i*3 + j)
m2 = ShapingOnlyMatrix(3, 4, lambda i, j: i*3 + j)
assert m == m.hstack(m)
assert m.hstack(m, m, m) == ShapingOnlyMatrix.hstack(m, m, m) == Matrix([
[0, 1, 2, 0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5, 3, 4, 5],
[6, 7, 8, 6, 7, 8, 6, 7, 8],
[9, 10, 11, 9, 10, 11, 9, 10, 11]])
raises(ShapeError, lambda: m.hstack(m, m2))
assert Matrix.hstack() == Matrix()
# test regression #12938
M1 = Matrix.zeros(0, 0)
M2 = Matrix.zeros(0, 1)
M3 = Matrix.zeros(0, 2)
M4 = Matrix.zeros(0, 3)
m = ShapingOnlyMatrix.hstack(M1, M2, M3, M4)
assert m.rows == 0 and m.cols == 6
def test_vstack():
m = ShapingOnlyMatrix(4, 3, lambda i, j: i*3 + j)
m2 = ShapingOnlyMatrix(3, 4, lambda i, j: i*3 + j)
assert m == m.vstack(m)
assert m.vstack(m, m, m) == ShapingOnlyMatrix.vstack(m, m, m) == Matrix([
[0, 1, 2],
[3, 4, 5],
[6, 7, 8],
[9, 10, 11],
[0, 1, 2],
[3, 4, 5],
[6, 7, 8],
[9, 10, 11],
[0, 1, 2],
[3, 4, 5],
[6, 7, 8],
[9, 10, 11]])
raises(ShapeError, lambda: m.vstack(m, m2))
assert Matrix.vstack() == Matrix()
# PropertiesOnlyMatrix tests
def test_atoms():
m = PropertiesOnlyMatrix(2, 2, [1, 2, x, 1 - 1/x])
assert m.atoms() == {S(1),S(2),S(-1), x}
assert m.atoms(Symbol) == {x}
def test_free_symbols():
assert PropertiesOnlyMatrix([[x], [0]]).free_symbols == {x}
def test_has():
A = PropertiesOnlyMatrix(((x, y), (2, 3)))
assert A.has(x)
assert not A.has(z)
assert A.has(Symbol)
A = PropertiesOnlyMatrix(((2, y), (2, 3)))
assert not A.has(x)
def test_is_anti_symmetric():
x = symbols('x')
assert PropertiesOnlyMatrix(2, 1, [1, 2]).is_anti_symmetric() is False
m = PropertiesOnlyMatrix(3, 3, [0, x**2 + 2*x + 1, y, -(x + 1)**2, 0, x*y, -y, -x*y, 0])
assert m.is_anti_symmetric() is True
assert m.is_anti_symmetric(simplify=False) is False
assert m.is_anti_symmetric(simplify=lambda x: x) is False
m = PropertiesOnlyMatrix(3, 3, [x.expand() for x in m])
assert m.is_anti_symmetric(simplify=False) is True
m = PropertiesOnlyMatrix(3, 3, [x.expand() for x in [S.One] + list(m)[1:]])
assert m.is_anti_symmetric() is False
def test_diagonal_symmetrical():
m = PropertiesOnlyMatrix(2, 2, [0, 1, 1, 0])
assert not m.is_diagonal()
assert m.is_symmetric()
assert m.is_symmetric(simplify=False)
m = PropertiesOnlyMatrix(2, 2, [1, 0, 0, 1])
assert m.is_diagonal()
m = PropertiesOnlyMatrix(3, 3, diag(1, 2, 3))
assert m.is_diagonal()
assert m.is_symmetric()
m = PropertiesOnlyMatrix(3, 3, [1, 0, 0, 0, 2, 0, 0, 0, 3])
assert m == diag(1, 2, 3)
m = PropertiesOnlyMatrix(2, 3, zeros(2, 3))
assert not m.is_symmetric()
assert m.is_diagonal()
m = PropertiesOnlyMatrix(((5, 0), (0, 6), (0, 0)))
assert m.is_diagonal()
m = PropertiesOnlyMatrix(((5, 0, 0), (0, 6, 0)))
assert m.is_diagonal()
m = Matrix(3, 3, [1, x**2 + 2*x + 1, y, (x + 1)**2, 2, 0, y, 0, 3])
assert m.is_symmetric()
assert not m.is_symmetric(simplify=False)
assert m.expand().is_symmetric(simplify=False)
def test_is_hermitian():
a = PropertiesOnlyMatrix([[1, I], [-I, 1]])
assert a.is_hermitian
a = PropertiesOnlyMatrix([[2*I, I], [-I, 1]])
assert a.is_hermitian is False
a = PropertiesOnlyMatrix([[x, I], [-I, 1]])
assert a.is_hermitian is None
a = PropertiesOnlyMatrix([[x, 1], [-I, 1]])
assert a.is_hermitian is False
def test_is_Identity():
assert eye_Properties(3).is_Identity
assert not PropertiesOnlyMatrix(zeros(3)).is_Identity
assert not PropertiesOnlyMatrix(ones(3)).is_Identity
# issue 6242
assert not PropertiesOnlyMatrix([[1, 0, 0]]).is_Identity
def test_is_symbolic():
a = PropertiesOnlyMatrix([[x, x], [x, x]])
assert a.is_symbolic() is True
a = PropertiesOnlyMatrix([[1, 2, 3, 4], [5, 6, 7, 8]])
assert a.is_symbolic() is False
a = PropertiesOnlyMatrix([[1, 2, 3, 4], [5, 6, x, 8]])
assert a.is_symbolic() is True
a = PropertiesOnlyMatrix([[1, x, 3]])
assert a.is_symbolic() is True
a = PropertiesOnlyMatrix([[1, 2, 3]])
assert a.is_symbolic() is False
a = PropertiesOnlyMatrix([[1], [x], [3]])
assert a.is_symbolic() is True
a = PropertiesOnlyMatrix([[1], [2], [3]])
assert a.is_symbolic() is False
def test_is_upper():
a = PropertiesOnlyMatrix([[1, 2, 3]])
assert a.is_upper is True
a = PropertiesOnlyMatrix([[1], [2], [3]])
assert a.is_upper is False
def test_is_lower():
a = PropertiesOnlyMatrix([[1, 2, 3]])
assert a.is_lower is False
a = PropertiesOnlyMatrix([[1], [2], [3]])
assert a.is_lower is True
def test_is_square():
m = PropertiesOnlyMatrix([[1],[1]])
m2 = PropertiesOnlyMatrix([[2,2],[2,2]])
assert not m.is_square
assert m2.is_square
def test_is_symmetric():
m = PropertiesOnlyMatrix(2, 2, [0, 1, 1, 0])
assert m.is_symmetric()
m = PropertiesOnlyMatrix(2, 2, [0, 1, 0, 1])
assert not m.is_symmetric()
def test_is_hessenberg():
A = PropertiesOnlyMatrix([[3, 4, 1], [2, 4, 5], [0, 1, 2]])
assert A.is_upper_hessenberg
A = PropertiesOnlyMatrix(3, 3, [3, 2, 0, 4, 4, 1, 1, 5, 2])
assert A.is_lower_hessenberg
A = PropertiesOnlyMatrix(3, 3, [3, 2, -1, 4, 4, 1, 1, 5, 2])
assert A.is_lower_hessenberg is False
assert A.is_upper_hessenberg is False
A = PropertiesOnlyMatrix([[3, 4, 1], [2, 4, 5], [3, 1, 2]])
assert not A.is_upper_hessenberg
def test_is_zero():
assert PropertiesOnlyMatrix(0, 0, []).is_zero
assert PropertiesOnlyMatrix([[0, 0], [0, 0]]).is_zero
assert PropertiesOnlyMatrix(zeros(3, 4)).is_zero
assert not PropertiesOnlyMatrix(eye(3)).is_zero
assert PropertiesOnlyMatrix([[x, 0], [0, 0]]).is_zero == None
assert PropertiesOnlyMatrix([[x, 1], [0, 0]]).is_zero == False
a = Symbol('a', nonzero=True)
assert PropertiesOnlyMatrix([[a, 0], [0, 0]]).is_zero == False
def test_values():
assert set(PropertiesOnlyMatrix(2,2,[0,1,2,3]).values()) == set([1,2,3])
x = Symbol('x', real=True)
assert set(PropertiesOnlyMatrix(2,2,[x,0,0,1]).values()) == set([x,1])
# OperationsOnlyMatrix tests
def test_applyfunc():
m0 = OperationsOnlyMatrix(eye(3))
assert m0.applyfunc(lambda x: 2*x) == eye(3)*2
assert m0.applyfunc(lambda x: 0) == zeros(3)
assert m0.applyfunc(lambda x: 1) == ones(3)
def test_adjoint():
dat = [[0, I], [1, 0]]
ans = OperationsOnlyMatrix([[0, 1], [-I, 0]])
assert ans.adjoint() == Matrix(dat)
def test_as_real_imag():
m1 = OperationsOnlyMatrix(2,2,[1,2,3,4])
m3 = OperationsOnlyMatrix(2,2,[1+S.ImaginaryUnit,2+2*S.ImaginaryUnit,3+3*S.ImaginaryUnit,4+4*S.ImaginaryUnit])
a,b = m3.as_real_imag()
assert a == m1
assert b == m1
def test_conjugate():
M = OperationsOnlyMatrix([[0, I, 5],
[1, 2, 0]])
assert M.T == Matrix([[0, 1],
[I, 2],
[5, 0]])
assert M.C == Matrix([[0, -I, 5],
[1, 2, 0]])
assert M.C == M.conjugate()
assert M.H == M.T.C
assert M.H == Matrix([[ 0, 1],
[-I, 2],
[ 5, 0]])
def test_doit():
a = OperationsOnlyMatrix([[Add(x,x, evaluate=False)]])
assert a[0] != 2*x
assert a.doit() == Matrix([[2*x]])
def test_evalf():
a = OperationsOnlyMatrix(2, 1, [sqrt(5), 6])
assert all(a.evalf()[i] == a[i].evalf() for i in range(2))
assert all(a.evalf(2)[i] == a[i].evalf(2) for i in range(2))
assert all(a.n(2)[i] == a[i].n(2) for i in range(2))
def test_expand():
m0 = OperationsOnlyMatrix([[x*(x + y), 2], [((x + y)*y)*x, x*(y + x*(x + y))]])
# Test if expand() returns a matrix
m1 = m0.expand()
assert m1 == Matrix(
[[x*y + x**2, 2], [x*y**2 + y*x**2, x*y + y*x**2 + x**3]])
a = Symbol('a', real=True)
assert OperationsOnlyMatrix(1, 1, [exp(I*a)]).expand(complex=True) == \
Matrix([cos(a) + I*sin(a)])
def test_refine():
m0 = OperationsOnlyMatrix([[Abs(x)**2, sqrt(x**2)],
[sqrt(x**2)*Abs(y)**2, sqrt(y**2)*Abs(x)**2]])
m1 = m0.refine(Q.real(x) & Q.real(y))
assert m1 == Matrix([[x**2, Abs(x)], [y**2*Abs(x), x**2*Abs(y)]])
m1 = m0.refine(Q.positive(x) & Q.positive(y))
assert m1 == Matrix([[x**2, x], [x*y**2, x**2*y]])
m1 = m0.refine(Q.negative(x) & Q.negative(y))
assert m1 == Matrix([[x**2, -x], [-x*y**2, -x**2*y]])
def test_replace():
from sympy import symbols, Function, Matrix
F, G = symbols('F, G', cls=Function)
K = OperationsOnlyMatrix(2, 2, lambda i, j: G(i+j))
M = OperationsOnlyMatrix(2, 2, lambda i, j: F(i+j))
N = M.replace(F, G)
assert N == K
def test_replace_map():
from sympy import symbols, Function, Matrix
F, G = symbols('F, G', cls=Function)
K = OperationsOnlyMatrix(2, 2, [(G(0), {F(0): G(0)}), (G(1), {F(1): G(1)}), (G(1), {F(1) \
: G(1)}), (G(2), {F(2): G(2)})])
M = OperationsOnlyMatrix(2, 2, lambda i, j: F(i+j))
N = M.replace(F, G, True)
assert N == K
def test_simplify():
f, n = symbols('f, n')
M = OperationsOnlyMatrix([[ 1/x + 1/y, (x + x*y) / x ],
[ (f(x) + y*f(x))/f(x), 2 * (1/n - cos(n * pi)/n) / pi ]])
assert M.simplify() == Matrix([[ (x + y)/(x * y), 1 + y ],
[ 1 + y, 2*((1 - 1*cos(pi*n))/(pi*n)) ]])
eq = (1 + x)**2
M = OperationsOnlyMatrix([[eq]])
assert M.simplify() == Matrix([[eq]])
assert M.simplify(ratio=oo) == Matrix([[eq.simplify(ratio=oo)]])
def test_subs():
assert OperationsOnlyMatrix([[1, x], [x, 4]]).subs(x, 5) == Matrix([[1, 5], [5, 4]])
assert OperationsOnlyMatrix([[x, 2], [x + y, 4]]).subs([[x, -1], [y, -2]]) == \
Matrix([[-1, 2], [-3, 4]])
assert OperationsOnlyMatrix([[x, 2], [x + y, 4]]).subs([(x, -1), (y, -2)]) == \
Matrix([[-1, 2], [-3, 4]])
assert OperationsOnlyMatrix([[x, 2], [x + y, 4]]).subs({x: -1, y: -2}) == \
Matrix([[-1, 2], [-3, 4]])
assert OperationsOnlyMatrix([[x*y]]).subs({x: y - 1, y: x - 1}, simultaneous=True) == \
Matrix([[(x - 1)*(y - 1)]])
def test_trace():
M = OperationsOnlyMatrix([[1, 0, 0],
[0, 5, 0],
[0, 0, 8]])
assert M.trace() == 14
def test_xreplace():
assert OperationsOnlyMatrix([[1, x], [x, 4]]).xreplace({x: 5}) == \
Matrix([[1, 5], [5, 4]])
assert OperationsOnlyMatrix([[x, 2], [x + y, 4]]).xreplace({x: -1, y: -2}) == \
Matrix([[-1, 2], [-3, 4]])
def test_permute():
a = OperationsOnlyMatrix(3, 4, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
raises(IndexError, lambda: a.permute([[0,5]]))
b = a.permute_rows([[0, 2], [0, 1]])
assert a.permute([[0, 2], [0, 1]]) == b == Matrix([
[5, 6, 7, 8],
[9, 10, 11, 12],
[1, 2, 3, 4]])
b = a.permute_cols([[0, 2], [0, 1]])
assert a.permute([[0, 2], [0, 1]], orientation='cols') == b ==\
Matrix([
[ 2, 3, 1, 4],
[ 6, 7, 5, 8],
[10, 11, 9, 12]])
b = a.permute_cols([[0, 2], [0, 1]], direction='backward')
assert a.permute([[0, 2], [0, 1]], orientation='cols', direction='backward') == b ==\
Matrix([
[ 3, 1, 2, 4],
[ 7, 5, 6, 8],
[11, 9, 10, 12]])
assert a.permute([1, 2, 0, 3]) == Matrix([
[5, 6, 7, 8],
[9, 10, 11, 12],
[1, 2, 3, 4]])
from sympy.combinatorics import Permutation
assert a.permute(Permutation([1, 2, 0, 3])) == Matrix([
[5, 6, 7, 8],
[9, 10, 11, 12],
[1, 2, 3, 4]])
# ArithmeticOnlyMatrix tests
def test_abs():
m = ArithmeticOnlyMatrix([[1, -2], [x, y]])
assert abs(m) == ArithmeticOnlyMatrix([[1, 2], [Abs(x), Abs(y)]])
def test_add():
m = ArithmeticOnlyMatrix([[1, 2, 3], [x, y, x], [2*y, -50, z*x]])
assert m + m == ArithmeticOnlyMatrix([[2, 4, 6], [2*x, 2*y, 2*x], [4*y, -100, 2*z*x]])
n = ArithmeticOnlyMatrix(1, 2, [1, 2])
raises(ShapeError, lambda: m + n)
def test_multiplication():
a = ArithmeticOnlyMatrix((
(1, 2),
(3, 1),
(0, 6),
))
b = ArithmeticOnlyMatrix((
(1, 2),
(3, 0),
))
raises(ShapeError, lambda: b*a)
raises(TypeError, lambda: a*{})
c = a*b
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
try:
eval('c = a @ b')
except SyntaxError:
pass
else:
assert c[0, 0] == 7
assert c[0, 1] == 2
assert c[1, 0] == 6
assert c[1, 1] == 6
assert c[2, 0] == 18
assert c[2, 1] == 0
h = a.multiply_elementwise(c)
assert h == matrix_multiply_elementwise(a, c)
assert h[0, 0] == 7
assert h[0, 1] == 4
assert h[1, 0] == 18
assert h[1, 1] == 6
assert h[2, 0] == 0
assert h[2, 1] == 0
raises(ShapeError, lambda: a.multiply_elementwise(b))
c = b * Symbol("x")
assert isinstance(c, ArithmeticOnlyMatrix)
assert c[0, 0] == x
assert c[0, 1] == 2*x
assert c[1, 0] == 3*x
assert c[1, 1] == 0
c2 = x * b
assert c == c2
c = 5 * b
assert isinstance(c, ArithmeticOnlyMatrix)
assert c[0, 0] == 5
assert c[0, 1] == 2*5
assert c[1, 0] == 3*5
assert c[1, 1] == 0
try:
eval('c = 5 @ b')
except SyntaxError:
pass
else:
assert isinstance(c, ArithmeticOnlyMatrix)
assert c[0, 0] == 5
assert c[0, 1] == 2*5
assert c[1, 0] == 3*5
assert c[1, 1] == 0
def test_power():
raises(NonSquareMatrixError, lambda: Matrix((1, 2))**2)
A = ArithmeticOnlyMatrix([[2, 3], [4, 5]])
assert (A**5)[:] == (6140, 8097, 10796, 14237)
A = ArithmeticOnlyMatrix([[2, 1, 3], [4, 2, 4], [6, 12, 1]])
assert (A**3)[:] == (290, 262, 251, 448, 440, 368, 702, 954, 433)
assert A**0 == eye(3)
assert A**1 == A
assert (ArithmeticOnlyMatrix([[2]]) ** 100)[0, 0] == 2**100
assert ArithmeticOnlyMatrix([[1, 2], [3, 4]])**Integer(2) == ArithmeticOnlyMatrix([[7, 10], [15, 22]])
def test_neg():
n = ArithmeticOnlyMatrix(1, 2, [1, 2])
assert -n == ArithmeticOnlyMatrix(1, 2, [-1, -2])
def test_sub():
n = ArithmeticOnlyMatrix(1, 2, [1, 2])
assert n - n == ArithmeticOnlyMatrix(1, 2, [0, 0])
def test_div():
n = ArithmeticOnlyMatrix(1, 2, [1, 2])
assert n/2 == ArithmeticOnlyMatrix(1, 2, [1/2, 2/2])
# DeterminantOnlyMatrix tests
def test_det():
a = DeterminantOnlyMatrix(2,3,[1,2,3,4,5,6])
raises(NonSquareMatrixError, lambda: a.det())
z = zeros_Determinant(2)
ey = eye_Determinant(2)
assert z.det() == 0
assert ey.det() == 1
x = Symbol('x')
a = DeterminantOnlyMatrix(0,0,[])
b = DeterminantOnlyMatrix(1,1,[5])
c = DeterminantOnlyMatrix(2,2,[1,2,3,4])
d = DeterminantOnlyMatrix(3,3,[1,2,3,4,5,6,7,8,8])
e = DeterminantOnlyMatrix(4,4,[x,1,2,3,4,5,6,7,2,9,10,11,12,13,14,14])
# the method keyword for `det` doesn't kick in until 4x4 matrices,
# so there is no need to test all methods on smaller ones
assert a.det() == 1
assert b.det() == 5
assert c.det() == -2
assert d.det() == 3
assert e.det() == 4*x - 24
assert e.det(method='bareiss') == 4*x - 24
assert e.det(method='berkowitz') == 4*x - 24
def test_adjugate():
x = Symbol('x')
e = DeterminantOnlyMatrix(4,4,[x,1,2,3,4,5,6,7,2,9,10,11,12,13,14,14])
adj = Matrix([
[ 4, -8, 4, 0],
[ 76, -14*x - 68, 14*x - 8, -4*x + 24],
[-122, 17*x + 142, -21*x + 4, 8*x - 48],
[ 48, -4*x - 72, 8*x, -4*x + 24]])
assert e.adjugate() == adj
assert e.adjugate(method='bareiss') == adj
assert e.adjugate(method='berkowitz') == adj
a = DeterminantOnlyMatrix(2,3,[1,2,3,4,5,6])
raises(NonSquareMatrixError, lambda: a.adjugate())
def test_cofactor_and_minors():
x = Symbol('x')
e = DeterminantOnlyMatrix(4,4,[x,1,2,3,4,5,6,7,2,9,10,11,12,13,14,14])
m = Matrix([
[ x, 1, 3],
[ 2, 9, 11],
[12, 13, 14]])
cm = Matrix([
[ 4, 76, -122, 48],
[-8, -14*x - 68, 17*x + 142, -4*x - 72],
[ 4, 14*x - 8, -21*x + 4, 8*x],
[ 0, -4*x + 24, 8*x - 48, -4*x + 24]])
sub = Matrix([
[x, 1, 2],
[4, 5, 6],
[2, 9, 10]])
assert e.minor_submatrix(1,2) == m
assert e.minor_submatrix(-1,-1) == sub
assert e.minor(1,2) == -17*x - 142
assert e.cofactor(1,2) == 17*x + 142
assert e.cofactor_matrix() == cm
assert e.cofactor_matrix(method="bareiss") == cm
assert e.cofactor_matrix(method="berkowitz") == cm
raises(ValueError, lambda: e.cofactor(4,5))
raises(ValueError, lambda: e.minor(4,5))
raises(ValueError, lambda: e.minor_submatrix(4,5))
a = DeterminantOnlyMatrix(2,3,[1,2,3,4,5,6])
assert a.minor_submatrix(0,0) == Matrix([[5, 6]])
raises(ValueError, lambda: DeterminantOnlyMatrix(0,0,[]).minor_submatrix(0,0))
raises(NonSquareMatrixError, lambda: a.cofactor(0,0))
raises(NonSquareMatrixError, lambda: a.minor(0,0))
raises(NonSquareMatrixError, lambda: a.cofactor_matrix())
def test_charpoly():
x, y = Symbol('x'), Symbol('y')
m = DeterminantOnlyMatrix(3,3,[1,2,3,4,5,6,7,8,9])
assert eye_Determinant(3).charpoly(x) == Poly((x - 1)**3, x)
assert eye_Determinant(3).charpoly(y) == Poly((y - 1)**3, y)
assert m.charpoly() == Poly(x**3 - 15*x**2 - 18*x, x)
# ReductionsOnlyMatrix tests
def test_row_op():
e = eye_Reductions(3)
raises(ValueError, lambda: e.elementary_row_op("abc"))
raises(ValueError, lambda: e.elementary_row_op())
raises(ValueError, lambda: e.elementary_row_op('n->kn', row=5, k=5))
raises(ValueError, lambda: e.elementary_row_op('n->kn', row=-5, k=5))
raises(ValueError, lambda: e.elementary_row_op('n<->m', row1=1, row2=5))
raises(ValueError, lambda: e.elementary_row_op('n<->m', row1=5, row2=1))
raises(ValueError, lambda: e.elementary_row_op('n<->m', row1=-5, row2=1))
raises(ValueError, lambda: e.elementary_row_op('n<->m', row1=1, row2=-5))
raises(ValueError, lambda: e.elementary_row_op('n->n+km', row1=1, row2=5, k=5))
raises(ValueError, lambda: e.elementary_row_op('n->n+km', row1=5, row2=1, k=5))
raises(ValueError, lambda: e.elementary_row_op('n->n+km', row1=-5, row2=1, k=5))
raises(ValueError, lambda: e.elementary_row_op('n->n+km', row1=1, row2=-5, k=5))
raises(ValueError, lambda: e.elementary_row_op('n->n+km', row1=1, row2=1, k=5))
# test various ways to set arguments
assert e.elementary_row_op("n->kn", 0, 5) == Matrix([[5, 0, 0], [0, 1, 0], [0, 0, 1]])
assert e.elementary_row_op("n->kn", 1, 5) == Matrix([[1, 0, 0], [0, 5, 0], [0, 0, 1]])
assert e.elementary_row_op("n->kn", row=1, k=5) == Matrix([[1, 0, 0], [0, 5, 0], [0, 0, 1]])
assert e.elementary_row_op("n->kn", row1=1, k=5) == Matrix([[1, 0, 0], [0, 5, 0], [0, 0, 1]])
assert e.elementary_row_op("n<->m", 0, 1) == Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
assert e.elementary_row_op("n<->m", row1=0, row2=1) == Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
assert e.elementary_row_op("n<->m", row=0, row2=1) == Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
assert e.elementary_row_op("n->n+km", 0, 5, 1) == Matrix([[1, 5, 0], [0, 1, 0], [0, 0, 1]])
assert e.elementary_row_op("n->n+km", row=0, k=5, row2=1) == Matrix([[1, 5, 0], [0, 1, 0], [0, 0, 1]])
assert e.elementary_row_op("n->n+km", row1=0, k=5, row2=1) == Matrix([[1, 5, 0], [0, 1, 0], [0, 0, 1]])
# make sure the matrix doesn't change size
a = ReductionsOnlyMatrix(2, 3, [0]*6)
assert a.elementary_row_op("n->kn", 1, 5) == Matrix(2, 3, [0]*6)
assert a.elementary_row_op("n<->m", 0, 1) == Matrix(2, 3, [0]*6)
assert a.elementary_row_op("n->n+km", 0, 5, 1) == Matrix(2, 3, [0]*6)
def test_col_op():
e = eye_Reductions(3)
raises(ValueError, lambda: e.elementary_col_op("abc"))
raises(ValueError, lambda: e.elementary_col_op())
raises(ValueError, lambda: e.elementary_col_op('n->kn', col=5, k=5))
raises(ValueError, lambda: e.elementary_col_op('n->kn', col=-5, k=5))
raises(ValueError, lambda: e.elementary_col_op('n<->m', col1=1, col2=5))
raises(ValueError, lambda: e.elementary_col_op('n<->m', col1=5, col2=1))
raises(ValueError, lambda: e.elementary_col_op('n<->m', col1=-5, col2=1))
raises(ValueError, lambda: e.elementary_col_op('n<->m', col1=1, col2=-5))
raises(ValueError, lambda: e.elementary_col_op('n->n+km', col1=1, col2=5, k=5))
raises(ValueError, lambda: e.elementary_col_op('n->n+km', col1=5, col2=1, k=5))
raises(ValueError, lambda: e.elementary_col_op('n->n+km', col1=-5, col2=1, k=5))
raises(ValueError, lambda: e.elementary_col_op('n->n+km', col1=1, col2=-5, k=5))
raises(ValueError, lambda: e.elementary_col_op('n->n+km', col1=1, col2=1, k=5))
# test various ways to set arguments
assert e.elementary_col_op("n->kn", 0, 5) == Matrix([[5, 0, 0], [0, 1, 0], [0, 0, 1]])
assert e.elementary_col_op("n->kn", 1, 5) == Matrix([[1, 0, 0], [0, 5, 0], [0, 0, 1]])
assert e.elementary_col_op("n->kn", col=1, k=5) == Matrix([[1, 0, 0], [0, 5, 0], [0, 0, 1]])
assert e.elementary_col_op("n->kn", col1=1, k=5) == Matrix([[1, 0, 0], [0, 5, 0], [0, 0, 1]])
assert e.elementary_col_op("n<->m", 0, 1) == Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
assert e.elementary_col_op("n<->m", col1=0, col2=1) == Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
assert e.elementary_col_op("n<->m", col=0, col2=1) == Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
assert e.elementary_col_op("n->n+km", 0, 5, 1) == Matrix([[1, 0, 0], [5, 1, 0], [0, 0, 1]])
assert e.elementary_col_op("n->n+km", col=0, k=5, col2=1) == Matrix([[1, 0, 0], [5, 1, 0], [0, 0, 1]])
assert e.elementary_col_op("n->n+km", col1=0, k=5, col2=1) == Matrix([[1, 0, 0], [5, 1, 0], [0, 0, 1]])
# make sure the matrix doesn't change size
a = ReductionsOnlyMatrix(2, 3, [0]*6)
assert a.elementary_col_op("n->kn", 1, 5) == Matrix(2, 3, [0]*6)
assert a.elementary_col_op("n<->m", 0, 1) == Matrix(2, 3, [0]*6)
assert a.elementary_col_op("n->n+km", 0, 5, 1) == Matrix(2, 3, [0]*6)
def test_is_echelon():
zro = zeros_Reductions(3)
ident = eye_Reductions(3)
assert zro.is_echelon
assert ident.is_echelon
a = ReductionsOnlyMatrix(0, 0, [])
assert a.is_echelon
a = ReductionsOnlyMatrix(2, 3, [3, 2, 1, 0, 0, 6])
assert a.is_echelon
a = ReductionsOnlyMatrix(2, 3, [0, 0, 6, 3, 2, 1])
assert not a.is_echelon
x = Symbol('x')
a = ReductionsOnlyMatrix(3, 1, [x, 0, 0])
assert a.is_echelon
a = ReductionsOnlyMatrix(3, 1, [x, x, 0])
assert not a.is_echelon
a = ReductionsOnlyMatrix(3, 3, [0, 0, 0, 1, 2, 3, 0, 0, 0])
assert not a.is_echelon
def test_echelon_form():
# echelon form is not unique, but the result
# must be row-equivalent to the original matrix
# and it must be in echelon form.
a = zeros_Reductions(3)
e = eye_Reductions(3)
# we can assume the zero matrix and the identity matrix shouldn't change
assert a.echelon_form() == a
assert e.echelon_form() == e
a = ReductionsOnlyMatrix(0, 0, [])
assert a.echelon_form() == a
a = ReductionsOnlyMatrix(1, 1, [5])
assert a.echelon_form() == a
# now we get to the real tests
def verify_row_null_space(mat, rows, nulls):
for v in nulls:
assert all(t.is_zero for t in a_echelon*v)
for v in rows:
if not all(t.is_zero for t in v):
assert not all(t.is_zero for t in a_echelon*v.transpose())
a = ReductionsOnlyMatrix(3, 3, [1, 2, 3, 4, 5, 6, 7, 8, 9])
nulls = [Matrix([
[ 1],
[-2],
[ 1]])]
rows = [a[i,:] for i in range(a.rows)]
a_echelon = a.echelon_form()
assert a_echelon.is_echelon
verify_row_null_space(a, rows, nulls)
a = ReductionsOnlyMatrix(3, 3, [1, 2, 3, 4, 5, 6, 7, 8, 8])
nulls = []
rows = [a[i,:] for i in range(a.rows)]
a_echelon = a.echelon_form()
assert a_echelon.is_echelon
verify_row_null_space(a, rows, nulls)
a = ReductionsOnlyMatrix(3, 3, [2, 1, 3, 0, 0, 0, 2, 1, 3])
nulls = [Matrix([
[-1/2],
[ 1],
[ 0]]),
Matrix([
[-3/2],
[ 0],
[ 1]])]
rows = [a[i,:] for i in range(a.rows)]
a_echelon = a.echelon_form()
assert a_echelon.is_echelon
verify_row_null_space(a, rows, nulls)
# this one requires a row swap
a = ReductionsOnlyMatrix(3, 3, [2, 1, 3, 0, 0, 0, 1, 1, 3])
nulls = [Matrix([
[ 0],
[ -3],
[ 1]])]
rows = [a[i,:] for i in range(a.rows)]
a_echelon = a.echelon_form()
assert a_echelon.is_echelon
verify_row_null_space(a, rows, nulls)
a = ReductionsOnlyMatrix(3, 3, [0, 3, 3, 0, 2, 2, 0, 1, 1])
nulls = [Matrix([
[1],
[0],
[0]]),
Matrix([
[ 0],
[-1],
[ 1]])]
rows = [a[i,:] for i in range(a.rows)]
a_echelon = a.echelon_form()
assert a_echelon.is_echelon
verify_row_null_space(a, rows, nulls)
a = ReductionsOnlyMatrix(2, 3, [2, 2, 3, 3, 3, 0])
nulls = [Matrix([
[-1],
[1],
[0]])]
rows = [a[i,:] for i in range(a.rows)]
a_echelon = a.echelon_form()
assert a_echelon.is_echelon
verify_row_null_space(a, rows, nulls)
def test_rref():
e = ReductionsOnlyMatrix(0, 0, [])
assert e.rref(pivots=False) == e
e = ReductionsOnlyMatrix(1, 1, [1])
a = ReductionsOnlyMatrix(1, 1, [5])
assert e.rref(pivots=False) == a.rref(pivots=False) == e
a = ReductionsOnlyMatrix(3, 1, [1, 2, 3])
assert a.rref(pivots=False) == Matrix([[1], [0], [0]])
a = ReductionsOnlyMatrix(1, 3, [1, 2, 3])
assert a.rref(pivots=False) == Matrix([[1, 2, 3]])
a = ReductionsOnlyMatrix(3, 3, [1, 2, 3, 4, 5, 6, 7, 8, 9])
assert a.rref(pivots=False) == Matrix([
[1, 0, -1],
[0, 1, 2],
[0, 0, 0]])
a = ReductionsOnlyMatrix(3, 3, [1, 2, 3, 1, 2, 3, 1, 2, 3])
b = ReductionsOnlyMatrix(3, 3, [1, 2, 3, 0, 0, 0, 0, 0, 0])
c = ReductionsOnlyMatrix(3, 3, [0, 0, 0, 1, 2, 3, 0, 0, 0])
d = ReductionsOnlyMatrix(3, 3, [0, 0, 0, 0, 0, 0, 1, 2, 3])
assert a.rref(pivots=False) == \
b.rref(pivots=False) == \
c.rref(pivots=False) == \
d.rref(pivots=False) == b
e = eye_Reductions(3)
z = zeros_Reductions(3)
assert e.rref(pivots=False) == e
assert z.rref(pivots=False) == z
a = ReductionsOnlyMatrix([
[ 0, 0, 1, 2, 2, -5, 3],
[-1, 5, 2, 2, 1, -7, 5],
[ 0, 0, -2, -3, -3, 8, -5],
[-1, 5, 0, -1, -2, 1, 0]])
mat, pivot_offsets = a.rref()
assert mat == Matrix([
[1, -5, 0, 0, 1, 1, -1],
[0, 0, 1, 0, 0, -1, 1],
[0, 0, 0, 1, 1, -2, 1],
[0, 0, 0, 0, 0, 0, 0]])
assert pivot_offsets == (0, 2, 3)
a = ReductionsOnlyMatrix([[S(1)/19, S(1)/5, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[ 12, 13, 14, 15]])
assert a.rref(pivots=False) == Matrix([
[1, 0, 0, -S(76)/157],
[0, 1, 0, -S(5)/157],
[0, 0, 1, S(238)/157],
[0, 0, 0, 0]])
x = Symbol('x')
a = ReductionsOnlyMatrix(2, 3, [x, 1, 1, sqrt(x), x, 1])
for i, j in zip(a.rref(pivots=False),
[1, 0, sqrt(x)*(-x + 1)/(-x**(S(5)/2) + x),
0, 1, 1/(sqrt(x) + x + 1)]):
assert simplify(i - j).is_zero
# SpecialOnlyMatrix tests
def test_eye():
assert list(SpecialOnlyMatrix.eye(2,2)) == [1, 0, 0, 1]
assert list(SpecialOnlyMatrix.eye(2)) == [1, 0, 0, 1]
assert type(SpecialOnlyMatrix.eye(2)) == SpecialOnlyMatrix
assert type(SpecialOnlyMatrix.eye(2, cls=Matrix)) == Matrix
def test_ones():
assert list(SpecialOnlyMatrix.ones(2,2)) == [1, 1, 1, 1]
assert list(SpecialOnlyMatrix.ones(2)) == [1, 1, 1, 1]
assert SpecialOnlyMatrix.ones(2,3) == Matrix([[1, 1, 1], [1, 1, 1]])
assert type(SpecialOnlyMatrix.ones(2)) == SpecialOnlyMatrix
assert type(SpecialOnlyMatrix.ones(2, cls=Matrix)) == Matrix
def test_zeros():
assert list(SpecialOnlyMatrix.zeros(2,2)) == [0, 0, 0, 0]
assert list(SpecialOnlyMatrix.zeros(2)) == [0, 0, 0, 0]
assert SpecialOnlyMatrix.zeros(2,3) == Matrix([[0, 0, 0], [0, 0, 0]])
assert type(SpecialOnlyMatrix.zeros(2)) == SpecialOnlyMatrix
assert type(SpecialOnlyMatrix.zeros(2, cls=Matrix)) == Matrix
def test_diag():
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
assert SpecialOnlyMatrix.diag(a, b, b) == Matrix([
[1, 2, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0],
[0, 0, 3, x, 0, 0],
[0, 0, y, 3, 0, 0],
[0, 0, 0, 0, 3, x],
[0, 0, 0, 0, y, 3],
])
assert SpecialOnlyMatrix.diag(a, b, c) == Matrix([
[1, 2, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[0, 0, 3, x, 0, 0, 0],
[0, 0, y, 3, 0, 0, 0],
[0, 0, 0, 0, 3, x, 3],
[0, 0, 0, 0, y, 3, z],
[0, 0, 0, 0, x, y, z],
])
assert SpecialOnlyMatrix.diag(a, c, b) == Matrix([
[1, 2, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[0, 0, 3, x, 3, 0, 0],
[0, 0, y, 3, z, 0, 0],
[0, 0, x, y, z, 0, 0],
[0, 0, 0, 0, 0, 3, x],
[0, 0, 0, 0, 0, y, 3],
])
a = Matrix([x, y, z])
b = Matrix([[1, 2], [3, 4]])
c = Matrix([[5, 6]])
assert SpecialOnlyMatrix.diag(a, 7, b, c) == Matrix([
[x, 0, 0, 0, 0, 0],
[y, 0, 0, 0, 0, 0],
[z, 0, 0, 0, 0, 0],
[0, 7, 0, 0, 0, 0],
[0, 0, 1, 2, 0, 0],
[0, 0, 3, 4, 0, 0],
[0, 0, 0, 0, 5, 6],
])
assert SpecialOnlyMatrix.diag([2, 3]) == Matrix([
[2, 0],
[0, 3]])
assert SpecialOnlyMatrix.diag(Matrix([2, 3])) == Matrix([
[2],
[3]])
assert SpecialOnlyMatrix.diag(1, rows=3, cols=2) == Matrix([
[1, 0],
[0, 0],
[0, 0]])
assert type(SpecialOnlyMatrix.diag(1)) == SpecialOnlyMatrix
assert type(SpecialOnlyMatrix.diag(1, cls=Matrix)) == Matrix
def test_jordan_block():
assert SpecialOnlyMatrix.jordan_block(3, 2) == SpecialOnlyMatrix.jordan_block(3, eigenvalue=2) \
== SpecialOnlyMatrix.jordan_block(size=3, eigenvalue=2) \
== SpecialOnlyMatrix.jordan_block(rows=3, eigenvalue=2) \
== SpecialOnlyMatrix.jordan_block(cols=3, eigenvalue=2) \
== SpecialOnlyMatrix.jordan_block(3, 2, band='upper') == Matrix([
[2, 1, 0],
[0, 2, 1],
[0, 0, 2]])
assert SpecialOnlyMatrix.jordan_block(3, 2, band='lower') == Matrix([
[2, 0, 0],
[1, 2, 0],
[0, 1, 2]])
# missing eigenvalue
raises(ValueError, lambda: SpecialOnlyMatrix.jordan_block(2))
# non-integral size
raises(ValueError, lambda: SpecialOnlyMatrix.jordan_block(3.5, 2))
# SubspaceOnlyMatrix tests
def test_columnspace():
m = SubspaceOnlyMatrix([[ 1, 2, 0, 2, 5],
[-2, -5, 1, -1, -8],
[ 0, -3, 3, 4, 1],
[ 3, 6, 0, -7, 2]])
basis = m.columnspace()
assert basis[0] == Matrix([1, -2, 0, 3])
assert basis[1] == Matrix([2, -5, -3, 6])
assert basis[2] == Matrix([2, -1, 4, -7])
assert len(basis) == 3
assert Matrix.hstack(m, *basis).columnspace() == basis
def test_rowspace():
m = SubspaceOnlyMatrix([[ 1, 2, 0, 2, 5],
[-2, -5, 1, -1, -8],
[ 0, -3, 3, 4, 1],
[ 3, 6, 0, -7, 2]])
basis = m.rowspace()
assert basis[0] == Matrix([[1, 2, 0, 2, 5]])
assert basis[1] == Matrix([[0, -1, 1, 3, 2]])
assert basis[2] == Matrix([[0, 0, 0, 5, 5]])
assert len(basis) == 3
def test_nullspace():
m = SubspaceOnlyMatrix([[ 1, 2, 0, 2, 5],
[-2, -5, 1, -1, -8],
[ 0, -3, 3, 4, 1],
[ 3, 6, 0, -7, 2]])
basis = m.nullspace()
assert basis[0] == Matrix([-2, 1, 1, 0, 0])
assert basis[1] == Matrix([-1, -1, 0, -1, 1])
# make sure the null space is really gets zeroed
assert all(e.is_zero for e in m*basis[0])
assert all(e.is_zero for e in m*basis[1])
# EigenOnlyMatrix tests
def test_eigenvals():
M = EigenOnlyMatrix([[0, 1, 1],
[1, 0, 0],
[1, 1, 1]])
assert M.eigenvals() == {2*S.One: 1, -S.One: 1, S.Zero: 1}
# if we cannot factor the char poly, we raise an error
m = Matrix([[3, 0, 0, 0, -3], [0, -3, -3, 0, 3], [0, 3, 0, 3, 0], [0, 0, 3, 0, 3], [3, 0, 0, 3, 0]])
raises(MatrixError, lambda: m.eigenvals())
def test_eigenvects():
M = EigenOnlyMatrix([[0, 1, 1],
[1, 0, 0],
[1, 1, 1]])
vecs = M.eigenvects()
for val, mult, vec_list in vecs:
assert len(vec_list) == 1
assert M*vec_list[0] == val*vec_list[0]
def test_left_eigenvects():
M = EigenOnlyMatrix([[0, 1, 1],
[1, 0, 0],
[1, 1, 1]])
vecs = M.left_eigenvects()
for val, mult, vec_list in vecs:
assert len(vec_list) == 1
assert vec_list[0]*M == val*vec_list[0]
def test_diagonalize():
m = EigenOnlyMatrix(2, 2, [0, -1, 1, 0])
raises(MatrixError, lambda: m.diagonalize(reals_only=True))
P, D = m.diagonalize()
assert D.is_diagonal()
assert D == Matrix([
[-I, 0],
[ 0, I]])
# make sure we use floats out if floats are passed in
m = EigenOnlyMatrix(2, 2, [0, .5, .5, 0])
P, D = m.diagonalize()
assert all(isinstance(e, Float) for e in D.values())
assert all(isinstance(e, Float) for e in P.values())
_, D2 = m.diagonalize(reals_only=True)
assert D == D2
def test_is_diagonalizable():
a, b, c = symbols('a b c')
m = EigenOnlyMatrix(2, 2, [a, c, c, b])
assert m.is_symmetric()
assert m.is_diagonalizable()
assert not EigenOnlyMatrix(2, 2, [1, 1, 0, 1]).is_diagonalizable()
m = EigenOnlyMatrix(2, 2, [0, -1, 1, 0])
assert m.is_diagonalizable()
assert not m.is_diagonalizable(reals_only=True)
def test_jordan_form():
m = Matrix(3, 2, [-3, 1, -3, 20, 3, 10])
raises(NonSquareMatrixError, lambda: m.jordan_form())
# the next two tests test the cases where the old
# algorithm failed due to the fact that the block structure can
# *NOT* be determined from algebraic and geometric multiplicity alone
# This can be seen most easily when one lets compute the J.c.f. of a matrix that
# is in J.c.f already.
m = EigenOnlyMatrix(4, 4, [2, 1, 0, 0,
0, 2, 1, 0,
0, 0, 2, 0,
0, 0, 0, 2
])
P, J = m.jordan_form()
assert m == J
m = EigenOnlyMatrix(4, 4, [2, 1, 0, 0,
0, 2, 0, 0,
0, 0, 2, 1,
0, 0, 0, 2
])
P, J = m.jordan_form()
assert m == J
A = Matrix([[ 2, 4, 1, 0],
[-4, 2, 0, 1],
[ 0, 0, 2, 4],
[ 0, 0, -4, 2]])
P, J = A.jordan_form()
assert simplify(P*J*P.inv()) == A
assert EigenOnlyMatrix(1,1,[1]).jordan_form() == (Matrix([1]), Matrix([1]))
assert EigenOnlyMatrix(1,1,[1]).jordan_form(calc_transform=False) == Matrix([1])
# make sure if we cannot factor the characteristic polynomial, we raise an error
m = Matrix([[3, 0, 0, 0, -3], [0, -3, -3, 0, 3], [0, 3, 0, 3, 0], [0, 0, 3, 0, 3], [3, 0, 0, 3, 0]])
raises(MatrixError, lambda: m.jordan_form())
# make sure that if the input has floats, the output does too
m = Matrix([
[ 0.6875, 0.125 + 0.1875*sqrt(3)],
[0.125 + 0.1875*sqrt(3), 0.3125]])
P, J = m.jordan_form()
assert all(isinstance(x, Float) or x == 0 for x in P)
assert all(isinstance(x, Float) or x == 0 for x in J)
def test_singular_values():
x = Symbol('x', real=True)
A = EigenOnlyMatrix([[0, 1*I], [2, 0]])
# if singular values can be sorted, they should be in decreasing order
assert A.singular_values() == [2, 1]
A = eye(3)
A[1, 1] = x
A[2, 2] = 5
vals = A.singular_values()
# since Abs(x) cannot be sorted, test set equality
assert set(vals) == set([5, 1, Abs(x)])
A = EigenOnlyMatrix([[sin(x), cos(x)], [-cos(x), sin(x)]])
vals = [sv.trigsimp() for sv in A.singular_values()]
assert vals == [S(1), S(1)]
# CalculusOnlyMatrix tests
def test_diff():
x, y = symbols('x y')
m = CalculusOnlyMatrix(2, 1, [x, y])
assert m.diff(x) == Matrix(2, 1, [1, 0])
def test_integrate():
x, y = symbols('x y')
m = CalculusOnlyMatrix(2, 1, [x, y])
assert m.integrate(x) == Matrix(2, 1, [x**2/2, y*x])
def test_jacobian2():
rho, phi = symbols("rho,phi")
X = CalculusOnlyMatrix(3, 1, [rho*cos(phi), rho*sin(phi), rho**2])
Y = CalculusOnlyMatrix(2, 1, [rho, phi])
J = Matrix([
[cos(phi), -rho*sin(phi)],
[sin(phi), rho*cos(phi)],
[ 2*rho, 0],
])
assert X.jacobian(Y) == J
m = CalculusOnlyMatrix(2, 2, [1, 2, 3, 4])
m2 = CalculusOnlyMatrix(4, 1, [1, 2, 3, 4])
raises(TypeError, lambda: m.jacobian(Matrix([1,2])))
raises(TypeError, lambda: m2.jacobian(m))
def test_limit():
x, y = symbols('x y')
m = CalculusOnlyMatrix(2, 1, [1/x, y])
assert m.limit(x, 5) == Matrix(2, 1, [S(1)/5, y])
| 46,582 | 33.945986 | 114 |
py
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/test_immutable.py
|
from itertools import product
from sympy import (ImmutableMatrix, Matrix, eye, zeros, S, Equality,
Unequality, ImmutableSparseMatrix, SparseMatrix, sympify,
integrate)
from sympy.abc import x, y
from sympy.utilities.pytest import raises
IM = ImmutableMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ieye = ImmutableMatrix(eye(3))
def test_immutable_creation():
assert IM.shape == (3, 3)
assert IM[1, 2] == 6
assert IM[2, 2] == 9
def test_immutability():
with raises(TypeError):
IM[2, 2] = 5
def test_slicing():
assert IM[1, :] == ImmutableMatrix([[4, 5, 6]])
assert IM[:2, :2] == ImmutableMatrix([[1, 2], [4, 5]])
def test_subs():
A = ImmutableMatrix([[1, 2], [3, 4]])
B = ImmutableMatrix([[1, 2], [x, 4]])
C = ImmutableMatrix([[-x, x*y], [-(x + y), y**2]])
assert B.subs(x, 3) == A
assert (x*B).subs(x, 3) == 3*A
assert (x*eye(2) + B).subs(x, 3) == 3*eye(2) + A
assert C.subs([[x, -1], [y, -2]]) == A
assert C.subs([(x, -1), (y, -2)]) == A
assert C.subs({x: -1, y: -2}) == A
assert C.subs({x: y - 1, y: x - 1}, simultaneous=True) == \
ImmutableMatrix([[1 - y, (x - 1)*(y - 1)], [2 - x - y, (x - 1)**2]])
def test_as_immutable():
X = Matrix([[1, 2], [3, 4]])
assert sympify(X) == X.as_immutable() == ImmutableMatrix([[1, 2], [3, 4]])
X = SparseMatrix(5, 5, {})
assert sympify(X) == X.as_immutable() == ImmutableSparseMatrix(
[[0 for i in range(5)] for i in range(5)])
def test_function_return_types():
# Lets ensure that decompositions of immutable matrices remain immutable
# I.e. do MatrixBase methods return the correct class?
X = ImmutableMatrix([[1, 2], [3, 4]])
Y = ImmutableMatrix([[1], [0]])
q, r = X.QRdecomposition()
assert (type(q), type(r)) == (ImmutableMatrix, ImmutableMatrix)
assert type(X.LUsolve(Y)) == ImmutableMatrix
assert type(X.QRsolve(Y)) == ImmutableMatrix
X = ImmutableMatrix([[1, 2], [2, 1]])
assert X.T == X
assert X.is_symmetric
assert type(X.cholesky()) == ImmutableMatrix
L, D = X.LDLdecomposition()
assert (type(L), type(D)) == (ImmutableMatrix, ImmutableMatrix)
assert X.is_diagonalizable()
assert X.det() == -3
assert X.norm(2) == 3
assert type(X.eigenvects()[0][2][0]) == ImmutableMatrix
assert type(zeros(3, 3).as_immutable().nullspace()[0]) == ImmutableMatrix
X = ImmutableMatrix([[1, 0], [2, 1]])
assert type(X.lower_triangular_solve(Y)) == ImmutableMatrix
assert type(X.T.upper_triangular_solve(Y)) == ImmutableMatrix
assert type(X.minor_submatrix(0, 0)) == ImmutableMatrix
# issue 6279
# https://github.com/sympy/sympy/issues/6279
# Test that Immutable _op_ Immutable => Immutable and not MatExpr
def test_immutable_evaluation():
X = ImmutableMatrix(eye(3))
A = ImmutableMatrix(3, 3, range(9))
assert isinstance(X + A, ImmutableMatrix)
assert isinstance(X * A, ImmutableMatrix)
assert isinstance(X * 2, ImmutableMatrix)
assert isinstance(2 * X, ImmutableMatrix)
assert isinstance(A**2, ImmutableMatrix)
def test_deterimant():
assert ImmutableMatrix(4, 4, lambda i, j: i + j).det() == 0
def test_Equality():
assert Equality(IM, IM) is S.true
assert Unequality(IM, IM) is S.false
assert Equality(IM, IM.subs(1, 2)) is S.false
assert Unequality(IM, IM.subs(1, 2)) is S.true
assert Equality(IM, 2) is S.false
assert Unequality(IM, 2) is S.true
M = ImmutableMatrix([x, y])
assert Equality(M, IM) is S.false
assert Unequality(M, IM) is S.true
assert Equality(M, M.subs(x, 2)).subs(x, 2) is S.true
assert Unequality(M, M.subs(x, 2)).subs(x, 2) is S.false
assert Equality(M, M.subs(x, 2)).subs(x, 3) is S.false
assert Unequality(M, M.subs(x, 2)).subs(x, 3) is S.true
def test_integrate():
intIM = integrate(IM, x)
assert intIM.shape == IM.shape
assert all([intIM[i, j] == (1 + j + 3*i)*x for i, j in
product(range(3), range(3))])
| 4,013 | 31.634146 | 78 |
py
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/test_interactions.py
|
"""
We have a few different kind of Matrices
Matrix, ImmutableMatrix, MatrixExpr
Here we test the extent to which they cooperate
"""
from sympy import symbols
from sympy.matrices import (Matrix, MatrixSymbol, eye, Identity,
ImmutableMatrix)
from sympy.core.compatibility import range
from sympy.matrices.expressions import MatrixExpr, MatAdd
from sympy.matrices.matrices import classof
from sympy.utilities.pytest import raises
SM = MatrixSymbol('X', 3, 3)
SV = MatrixSymbol('v', 3, 1)
MM = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
IM = ImmutableMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
meye = eye(3)
imeye = ImmutableMatrix(eye(3))
ideye = Identity(3)
a, b, c = symbols('a,b,c')
def test_IM_MM():
assert isinstance(MM + IM, ImmutableMatrix)
assert isinstance(IM + MM, ImmutableMatrix)
assert isinstance(2*IM + MM, ImmutableMatrix)
assert MM.equals(IM)
def test_ME_MM():
assert isinstance(Identity(3) + MM, MatrixExpr)
assert isinstance(SM + MM, MatAdd)
assert isinstance(MM + SM, MatAdd)
assert (Identity(3) + MM)[1, 1] == 6
def test_equality():
a, b, c = Identity(3), eye(3), ImmutableMatrix(eye(3))
for x in [a, b, c]:
for y in [a, b, c]:
assert x.equals(y)
def test_matrix_symbol_MM():
X = MatrixSymbol('X', 3, 3)
Y = eye(3) + X
assert Y[1, 1] == 1 + X[1, 1]
def test_matrix_symbol_vector_matrix_multiplication():
A = MM * SV
B = IM * SV
assert A == B
C = (SV.T * MM.T).T
assert B == C
D = (SV.T * IM.T).T
assert C == D
def test_indexing_interactions():
assert (a * IM)[1, 1] == 5*a
assert (SM + IM)[1, 1] == SM[1, 1] + IM[1, 1]
assert (SM * IM)[1, 1] == SM[1, 0]*IM[0, 1] + SM[1, 1]*IM[1, 1] + \
SM[1, 2]*IM[2, 1]
def test_classof():
A = Matrix(3, 3, range(9))
B = ImmutableMatrix(3, 3, range(9))
C = MatrixSymbol('C', 3, 3)
assert classof(A, A) == Matrix
assert classof(B, B) == ImmutableMatrix
assert classof(A, B) == ImmutableMatrix
assert classof(B, A) == ImmutableMatrix
raises(TypeError, lambda: classof(A, C))
| 2,101 | 25.607595 | 71 |
py
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/test_densetools.py
|
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
from sympy.matrices.densetools import trace, transpose
from sympy.matrices.densetools import eye
from sympy import ZZ
def test_trace():
a = [[ZZ(3), ZZ(7), ZZ(4)], [ZZ(2), ZZ(4), ZZ(5)], [ZZ(6), ZZ(2), ZZ(3)]]
b = eye(2, ZZ)
assert trace(a, ZZ) == ZZ(10)
assert trace(b, ZZ) == ZZ(2)
def test_transpose():
a = [[ZZ(3), ZZ(7), ZZ(4)], [ZZ(2), ZZ(4), ZZ(5)], [ZZ(6), ZZ(2), ZZ(3)]]
b = eye(4, ZZ)
assert transpose(a, ZZ) == ([[ZZ(3), ZZ(2), ZZ(6)], [ZZ(7), ZZ(4), ZZ(2)], [ZZ(4), ZZ(5), ZZ(3)]])
assert transpose(b, ZZ) == b
| 652 | 28.681818 | 102 |
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/test_densesolve.py
|
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
from sympy.matrices.densesolve import LU_solve, rref_solve, cholesky_solve
from sympy import Dummy
from sympy import QQ
def test_LU_solve():
x, y, z = Dummy('x'), Dummy('y'), Dummy('z')
assert LU_solve([[QQ(2), QQ(-1), QQ(-2)], [QQ(-4), QQ(6), QQ(3)], [QQ(-4), QQ(-2), QQ(8)]], [[x], [y], [z]], [[QQ(-1)], [QQ(13)], [QQ(-6)]], QQ) == [[QQ(2,1)], [QQ(3, 1)], [QQ(1, 1)]]
def test_cholesky_solve():
x, y, z = Dummy('x'), Dummy('y'), Dummy('z')
assert cholesky_solve([[QQ(25), QQ(15), QQ(-5)], [QQ(15), QQ(18), QQ(0)], [QQ(-5), QQ(0), QQ(11)]], [[x], [y], [z]], [[QQ(2)], [QQ(3)], [QQ(1)]], QQ) == [[QQ(-1, 225)], [QQ(23, 135)], [QQ(4, 45)]]
def test_rref_solve():
x, y, z = Dummy('x'), Dummy('y'), Dummy('z')
assert rref_solve([[QQ(25), QQ(15), QQ(-5)], [QQ(15), QQ(18), QQ(0)], [QQ(-5), QQ(0), QQ(11)]], [[x], [y], [z]], [[QQ(2)], [QQ(3)], [QQ(1)]], QQ) == [[QQ(-1, 225)], [QQ(23, 135)], [QQ(4, 45)]]
| 1,019 | 43.347826 | 200 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/matrices/tests/test_sparsetools.py
|
from sympy.matrices.sparsetools import _doktocsr, _csrtodok
from sympy import SparseMatrix
def test_doktocsr():
a = SparseMatrix([[1, 2, 0, 0], [0, 3, 9, 0], [0, 1, 4, 0]])
b = SparseMatrix(4, 6, [10, 20, 0, 0, 0, 0, 0, 30, 0, 40, 0, 0, 0, 0, 50,
60, 70, 0, 0, 0, 0, 0, 0, 80])
c = SparseMatrix(4, 4, [0, 0, 0, 0, 0, 12, 0, 2, 15, 0, 12, 0, 0, 0, 0, 4])
d = SparseMatrix(10, 10, {(1, 1): 12, (3, 5): 7, (7, 8): 12})
e = SparseMatrix([[0, 0, 0], [1, 0, 2], [3, 0, 0]])
f = SparseMatrix(7, 8, {(2, 3): 5, (4, 5):12})
assert _doktocsr(a) == [[1, 2, 3, 9, 1, 4], [0, 1, 1, 2, 1, 2],
[0, 2, 4, 6], [3, 4]]
assert _doktocsr(b) == [[10, 20, 30, 40, 50, 60, 70, 80],
[0, 1, 1, 3, 2, 3, 4, 5], [0, 2, 4, 7, 8], [4, 6]]
assert _doktocsr(c) == [[12, 2, 15, 12, 4], [1, 3, 0, 2, 3],
[0, 0, 2, 4, 5], [4, 4]]
assert _doktocsr(d) == [[12, 7, 12], [1, 5, 8],
[0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 3], [10, 10]]
assert _doktocsr(e) == [[1, 2, 3], [0, 2, 0], [0, 0, 2, 3], [3, 3]]
assert _doktocsr(f) == [[5, 12], [3, 5], [0, 0, 0, 1, 1, 2, 2, 2], [7, 8]]
def test_csrtodok():
h = [[5, 7, 5], [2, 1, 3], [0, 1, 1, 3], [3, 4]]
g = [[12, 5, 4], [2, 4, 2], [0, 1, 2, 3], [3, 7]]
i = [[1, 3, 12], [0, 2, 4], [0, 2, 3], [2, 5]]
j = [[11, 15, 12, 15], [2, 4, 1, 2], [0, 1, 1, 2, 3, 4], [5, 8]]
k = [[1, 3], [2, 1], [0, 1, 1, 2], [3, 3]]
assert _csrtodok(h) == SparseMatrix(3, 4,
{(0, 2): 5, (2, 1): 7, (2, 3): 5})
assert _csrtodok(g) == SparseMatrix(3, 7,
{(0, 2): 12, (1, 4): 5, (2, 2): 4})
assert _csrtodok(i) == SparseMatrix([[1, 0, 3, 0, 0], [0, 0, 0, 0, 12]])
assert _csrtodok(j) == SparseMatrix(5, 8,
{(0, 2): 11, (2, 4): 15, (3, 1): 12, (4, 2): 15})
assert _csrtodok(k) == SparseMatrix(3, 3, {(0, 2): 1, (2, 1): 3})
| 1,849 | 46.435897 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/sandbox/indexed_integrals.py
|
from sympy.tensor import Indexed
from sympy import Integral, Dummy, sympify, Tuple
class IndexedIntegral(Integral):
"""
Experimental class to test integration by indexed variables.
Usage is analogue to ``Integral``, it simply adds awareness of
integration over indices.
Contraction of non-identical index symbols referring to the same
``IndexedBase`` is not yet supported.
Examples
========
>>> from sympy.sandbox.indexed_integrals import IndexedIntegral
>>> from sympy import IndexedBase, symbols
>>> A = IndexedBase('A')
>>> i, j = symbols('i j', integer=True)
>>> ii = IndexedIntegral(A[i], A[i])
>>> ii
Integral(_A[i], _A[i])
>>> ii.doit()
A[i]**2/2
If the indices are different, indexed objects are considered to be
different variables:
>>> i2 = IndexedIntegral(A[j], A[i])
>>> i2
Integral(A[j], _A[i])
>>> i2.doit()
A[i]*A[j]
"""
def __new__(cls, function, *limits, **assumptions):
repl, limits = IndexedIntegral._indexed_process_limits(limits)
function = sympify(function)
function = function.xreplace(repl)
obj = Integral.__new__(cls, function, *limits, **assumptions)
obj._indexed_repl = repl
obj._indexed_reverse_repl = dict((val, key) for key, val in repl.items())
return obj
def doit(self):
res = super(IndexedIntegral, self).doit()
return res.xreplace(self._indexed_reverse_repl)
@staticmethod
def _indexed_process_limits(limits):
repl = {}
newlimits = []
for i in limits:
if isinstance(i, (tuple, list, Tuple)):
v = i[0]
vrest = i[1:]
else:
v = i
vrest = ()
if isinstance(v, Indexed):
if v not in repl:
r = Dummy(str(v))
repl[v] = r
newlimits.append((r,)+vrest)
else:
newlimits.append(i)
return repl, newlimits
| 2,056 | 28.385714 | 81 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/sandbox/__init__.py
|
"""
Sandbox module of SymPy.
This module contains experimental code, use at your own risk!
There is no warranty that this code will still be located here in future
versions of SymPy.
"""
| 189 | 20.111111 | 72 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/sandbox/tests/test_indexed_integrals.py
|
from sympy.sandbox.indexed_integrals import IndexedIntegral
from sympy import IndexedBase, Idx, symbols, sin, cos
def test_indexed_integrals():
A = IndexedBase('A')
i, j = symbols('i j', integer=True)
a1, a2 = symbols('a1:3', cls=Idx)
assert isinstance(a1, Idx)
assert IndexedIntegral(1, A[i]).doit() == A[i]
assert IndexedIntegral(A[i], A[i]).doit() == A[i] ** 2 / 2
assert IndexedIntegral(A[j], A[i]).doit() == A[i] * A[j]
assert IndexedIntegral(A[i] * A[j], A[i]).doit() == A[i] ** 2 * A[j] / 2
assert IndexedIntegral(sin(A[i]), A[i]).doit() == -cos(A[i])
assert IndexedIntegral(sin(A[j]), A[i]).doit() == sin(A[j]) * A[i]
assert IndexedIntegral(1, A[a1]).doit() == A[a1]
assert IndexedIntegral(A[a1], A[a1]).doit() == A[a1] ** 2 / 2
assert IndexedIntegral(A[a2], A[a1]).doit() == A[a1] * A[a2]
assert IndexedIntegral(A[a1] * A[a2], A[a1]).doit() == A[a1] ** 2 * A[a2] / 2
assert IndexedIntegral(sin(A[a1]), A[a1]).doit() == -cos(A[a1])
assert IndexedIntegral(sin(A[a2]), A[a1]).doit() == sin(A[a2]) * A[a1]
| 1,079 | 44 | 81 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/sandbox/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/generators.py
|
from __future__ import print_function, division
from sympy.combinatorics.permutations import Permutation
from sympy.utilities.iterables import variations, rotate_left
from sympy.core.symbol import symbols
from sympy.matrices import Matrix
from sympy.core.compatibility import range
def symmetric(n):
"""
Generates the symmetric group of order n, Sn.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.generators import symmetric
>>> list(symmetric(3))
[(2), (1 2), (2)(0 1), (0 1 2), (0 2 1), (0 2)]
"""
for perm in variations(list(range(n)), n):
yield Permutation(perm)
def cyclic(n):
"""
Generates the cyclic group of order n, Cn.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.generators import cyclic
>>> list(cyclic(5))
[(4), (0 1 2 3 4), (0 2 4 1 3),
(0 3 1 4 2), (0 4 3 2 1)]
See Also
========
dihedral
"""
gen = list(range(n))
for i in range(n):
yield Permutation(gen)
gen = rotate_left(gen, 1)
def alternating(n):
"""
Generates the alternating group of order n, An.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.generators import alternating
>>> list(alternating(3))
[(2), (0 1 2), (0 2 1)]
"""
for perm in variations(list(range(n)), n):
p = Permutation(perm)
if p.is_even:
yield p
def dihedral(n):
"""
Generates the dihedral group of order 2n, Dn.
The result is given as a subgroup of Sn, except for the special cases n=1
(the group S2) and n=2 (the Klein 4-group) where that's not possible
and embeddings in S2 and S4 respectively are given.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.generators import dihedral
>>> list(dihedral(3))
[(2), (0 2), (0 1 2), (1 2), (0 2 1), (2)(0 1)]
See Also
========
cyclic
"""
if n == 1:
yield Permutation([0, 1])
yield Permutation([1, 0])
elif n == 2:
yield Permutation([0, 1, 2, 3])
yield Permutation([1, 0, 3, 2])
yield Permutation([2, 3, 0, 1])
yield Permutation([3, 2, 1, 0])
else:
gen = list(range(n))
for i in range(n):
yield Permutation(gen)
yield Permutation(gen[::-1])
gen = rotate_left(gen, 1)
def rubik_cube_generators():
"""Return the permutations of the 3x3 Rubik's cube, see
http://www.gap-system.org/Doc/Examples/rubik.html
"""
a = [
[(1, 3, 8, 6), (2, 5, 7, 4), (9, 33, 25, 17), (10, 34, 26, 18),
(11, 35, 27, 19)],
[(9, 11, 16, 14), (10, 13, 15, 12), (1, 17, 41, 40), (4, 20, 44, 37),
(6, 22, 46, 35)],
[(17, 19, 24, 22), (18, 21, 23, 20), (6, 25, 43, 16), (7, 28, 42, 13),
(8, 30, 41, 11)],
[(25, 27, 32, 30), (26, 29, 31, 28), (3, 38, 43, 19), (5, 36, 45, 21),
(8, 33, 48, 24)],
[(33, 35, 40, 38), (34, 37, 39, 36), (3, 9, 46, 32), (2, 12, 47, 29),
(1, 14, 48, 27)],
[(41, 43, 48, 46), (42, 45, 47, 44), (14, 22, 30, 38),
(15, 23, 31, 39), (16, 24, 32, 40)]
]
return [Permutation([[i - 1 for i in xi] for xi in x], size=48) for x in a]
def rubik(n):
"""Return permutations for an nxn Rubik's cube.
Permutations returned are for rotation of each of the slice
from the face up to the last face for each of the 3 sides (in this order):
front, right and bottom. Hence, the first n - 1 permutations are for the
slices from the front.
"""
if n < 2:
raise ValueError('dimension of cube must be > 1')
# 1-based reference to rows and columns in Matrix
def getr(f, i):
return faces[f].col(n - i)
def getl(f, i):
return faces[f].col(i - 1)
def getu(f, i):
return faces[f].row(i - 1)
def getd(f, i):
return faces[f].row(n - i)
def setr(f, i, s):
faces[f][:, n - i] = Matrix(n, 1, s)
def setl(f, i, s):
faces[f][:, i - 1] = Matrix(n, 1, s)
def setu(f, i, s):
faces[f][i - 1, :] = Matrix(1, n, s)
def setd(f, i, s):
faces[f][n - i, :] = Matrix(1, n, s)
# motion of a single face
def cw(F, r=1):
for _ in range(r):
face = faces[F]
rv = []
for c in range(n):
for r in range(n - 1, -1, -1):
rv.append(face[r, c])
faces[F] = Matrix(n, n, rv)
def ccw(F):
cw(F, 3)
# motion of plane i from the F side;
# fcw(0) moves the F face, fcw(1) moves the plane
# just behind the front face, etc...
def fcw(i, r=1):
for _ in range(r):
if i == 0:
cw(F)
i += 1
temp = getr(L, i)
setr(L, i, list((getu(D, i))))
setu(D, i, list(reversed(getl(R, i))))
setl(R, i, list((getd(U, i))))
setd(U, i, list(reversed(temp)))
i -= 1
def fccw(i):
fcw(i, 3)
# motion of the entire cube from the F side
def FCW(r=1):
for _ in range(r):
cw(F)
ccw(B)
cw(U)
t = faces[U]
cw(L)
faces[U] = faces[L]
cw(D)
faces[L] = faces[D]
cw(R)
faces[D] = faces[R]
faces[R] = t
def FCCW():
FCW(3)
# motion of the entire cube from the U side
def UCW(r=1):
for _ in range(r):
cw(U)
ccw(D)
t = faces[F]
faces[F] = faces[R]
faces[R] = faces[B]
faces[B] = faces[L]
faces[L] = t
def UCCW():
UCW(3)
# defining the permutations for the cube
U, F, R, B, L, D = names = symbols('U, F, R, B, L, D')
# the faces are represented by nxn matrices
faces = {}
count = 0
for fi in range(6):
f = []
for a in range(n**2):
f.append(count)
count += 1
faces[names[fi]] = Matrix(n, n, f)
# this will either return the value of the current permutation
# (show != 1) or else append the permutation to the group, g
def perm(show=0):
# add perm to the list of perms
p = []
for f in names:
p.extend(faces[f])
if show:
return p
g.append(Permutation(p))
g = [] # container for the group's permutations
I = list(range(6*n**2)) # the identity permutation used for checking
# define permutations corresponding to cw rotations of the planes
# up TO the last plane from that direction; by not including the
# last plane, the orientation of the cube is maintained.
# F slices
for i in range(n - 1):
fcw(i)
perm()
fccw(i) # restore
assert perm(1) == I
# R slices
# bring R to front
UCW()
for i in range(n - 1):
fcw(i)
# put it back in place
UCCW()
# record
perm()
# restore
# bring face to front
UCW()
fccw(i)
# restore
UCCW()
assert perm(1) == I
# D slices
# bring up bottom
FCW()
UCCW()
FCCW()
for i in range(n - 1):
# turn strip
fcw(i)
# put bottom back on the bottom
FCW()
UCW()
FCCW()
# record
perm()
# restore
# bring up bottom
FCW()
UCCW()
FCCW()
# turn strip
fccw(i)
# put bottom back on the bottom
FCW()
UCW()
FCCW()
assert perm(1) == I
return g
| 8,006 | 24.663462 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/permutations.py
|
from __future__ import print_function, division
import random
from collections import defaultdict
from sympy.core import Basic
from sympy.core.compatibility import is_sequence, reduce, range, as_int
from sympy.utilities.iterables import (flatten, has_variety, minlex,
has_dups, runs)
from sympy.polys.polytools import lcm
from sympy.matrices import zeros
from mpmath.libmp.libintmath import ifac
def _af_rmul(a, b):
"""
Return the product b*a; input and output are array forms. The ith value
is a[b[i]].
Examples
========
>>> from sympy.combinatorics.permutations import _af_rmul, Permutation
>>> Permutation.print_cyclic = False
>>> a, b = [1, 0, 2], [0, 2, 1]
>>> _af_rmul(a, b)
[1, 2, 0]
>>> [a[b[i]] for i in range(3)]
[1, 2, 0]
This handles the operands in reverse order compared to the ``*`` operator:
>>> a = Permutation(a)
>>> b = Permutation(b)
>>> list(a*b)
[2, 0, 1]
>>> [b(a(i)) for i in range(3)]
[2, 0, 1]
See Also
========
rmul, _af_rmuln
"""
return [a[i] for i in b]
def _af_rmuln(*abc):
"""
Given [a, b, c, ...] return the product of ...*c*b*a using array forms.
The ith value is a[b[c[i]]].
Examples
========
>>> from sympy.combinatorics.permutations import _af_rmul, Permutation
>>> Permutation.print_cyclic = False
>>> a, b = [1, 0, 2], [0, 2, 1]
>>> _af_rmul(a, b)
[1, 2, 0]
>>> [a[b[i]] for i in range(3)]
[1, 2, 0]
This handles the operands in reverse order compared to the ``*`` operator:
>>> a = Permutation(a); b = Permutation(b)
>>> list(a*b)
[2, 0, 1]
>>> [b(a(i)) for i in range(3)]
[2, 0, 1]
See Also
========
rmul, _af_rmul
"""
a = abc
m = len(a)
if m == 3:
p0, p1, p2 = a
return [p0[p1[i]] for i in p2]
if m == 4:
p0, p1, p2, p3 = a
return [p0[p1[p2[i]]] for i in p3]
if m == 5:
p0, p1, p2, p3, p4 = a
return [p0[p1[p2[p3[i]]]] for i in p4]
if m == 6:
p0, p1, p2, p3, p4, p5 = a
return [p0[p1[p2[p3[p4[i]]]]] for i in p5]
if m == 7:
p0, p1, p2, p3, p4, p5, p6 = a
return [p0[p1[p2[p3[p4[p5[i]]]]]] for i in p6]
if m == 8:
p0, p1, p2, p3, p4, p5, p6, p7 = a
return [p0[p1[p2[p3[p4[p5[p6[i]]]]]]] for i in p7]
if m == 1:
return a[0][:]
if m == 2:
a, b = a
return [a[i] for i in b]
if m == 0:
raise ValueError("String must not be empty")
p0 = _af_rmuln(*a[:m//2])
p1 = _af_rmuln(*a[m//2:])
return [p0[i] for i in p1]
def _af_parity(pi):
"""
Computes the parity of a permutation in array form.
The parity of a permutation reflects the parity of the
number of inversions in the permutation, i.e., the
number of pairs of x and y such that x > y but p[x] < p[y].
Examples
========
>>> from sympy.combinatorics.permutations import _af_parity
>>> _af_parity([0, 1, 2, 3])
0
>>> _af_parity([3, 2, 0, 1])
1
See Also
========
Permutation
"""
n = len(pi)
a = [0] * n
c = 0
for j in range(n):
if a[j] == 0:
c += 1
a[j] = 1
i = j
while pi[i] != j:
i = pi[i]
a[i] = 1
return (n - c) % 2
def _af_invert(a):
"""
Finds the inverse, ~A, of a permutation, A, given in array form.
Examples
========
>>> from sympy.combinatorics.permutations import _af_invert, _af_rmul
>>> A = [1, 2, 0, 3]
>>> _af_invert(A)
[2, 0, 1, 3]
>>> _af_rmul(_, A)
[0, 1, 2, 3]
See Also
========
Permutation, __invert__
"""
inv_form = [0] * len(a)
for i, ai in enumerate(a):
inv_form[ai] = i
return inv_form
def _af_pow(a, n):
"""
Routine for finding powers of a permutation.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation, _af_pow
>>> Permutation.print_cyclic = False
>>> p = Permutation([2, 0, 3, 1])
>>> p.order()
4
>>> _af_pow(p._array_form, 4)
[0, 1, 2, 3]
"""
if n == 0:
return list(range(len(a)))
if n < 0:
return _af_pow(_af_invert(a), -n)
if n == 1:
return a[:]
elif n == 2:
b = [a[i] for i in a]
elif n == 3:
b = [a[a[i]] for i in a]
elif n == 4:
b = [a[a[a[i]]] for i in a]
else:
# use binary multiplication
b = list(range(len(a)))
while 1:
if n & 1:
b = [b[i] for i in a]
n -= 1
if not n:
break
if n % 4 == 0:
a = [a[a[a[i]]] for i in a]
n = n // 4
elif n % 2 == 0:
a = [a[i] for i in a]
n = n // 2
return b
def _af_commutes_with(a, b):
"""
Checks if the two permutations with array forms
given by ``a`` and ``b`` commute.
Examples
========
>>> from sympy.combinatorics.permutations import _af_commutes_with
>>> _af_commutes_with([1, 2, 0], [0, 2, 1])
False
See Also
========
Permutation, commutes_with
"""
return not any(a[b[i]] != b[a[i]] for i in range(len(a) - 1))
class Cycle(dict):
"""
Wrapper around dict which provides the functionality of a disjoint cycle.
A cycle shows the rule to use to move subsets of elements to obtain
a permutation. The Cycle class is more flexible than Permutation in
that 1) all elements need not be present in order to investigate how
multiple cycles act in sequence and 2) it can contain singletons:
>>> from sympy.combinatorics.permutations import Perm, Cycle
A Cycle will automatically parse a cycle given as a tuple on the rhs:
>>> Cycle(1, 2)(2, 3)
(1 3 2)
The identity cycle, Cycle(), can be used to start a product:
>>> Cycle()(1, 2)(2, 3)
(1 3 2)
The array form of a Cycle can be obtained by calling the list
method (or passing it to the list function) and all elements from
0 will be shown:
>>> a = Cycle(1, 2)
>>> a.list()
[0, 2, 1]
>>> list(a)
[0, 2, 1]
If a larger (or smaller) range is desired use the list method and
provide the desired size -- but the Cycle cannot be truncated to
a size smaller than the largest element that is out of place:
>>> b = Cycle(2, 4)(1, 2)(3, 1, 4)(1, 3)
>>> b.list()
[0, 2, 1, 3, 4]
>>> b.list(b.size + 1)
[0, 2, 1, 3, 4, 5]
>>> b.list(-1)
[0, 2, 1]
Singletons are not shown when printing with one exception: the largest
element is always shown -- as a singleton if necessary:
>>> Cycle(1, 4, 10)(4, 5)
(1 5 4 10)
>>> Cycle(1, 2)(4)(5)(10)
(1 2)(10)
The array form can be used to instantiate a Permutation so other
properties of the permutation can be investigated:
>>> Perm(Cycle(1, 2)(3, 4).list()).transpositions()
[(1, 2), (3, 4)]
Notes
=====
The underlying structure of the Cycle is a dictionary and although
the __iter__ method has been redefined to give the array form of the
cycle, the underlying dictionary items are still available with the
such methods as items():
>>> list(Cycle(1, 2).items())
[(1, 2), (2, 1)]
See Also
========
Permutation
"""
def __missing__(self, arg):
"""Enter arg into dictionary and return arg."""
arg = as_int(arg)
self[arg] = arg
return arg
def __iter__(self):
for i in self.list():
yield i
def __call__(self, *other):
"""Return product of cycles processed from R to L.
Examples
========
>>> from sympy.combinatorics.permutations import Cycle as C
>>> from sympy.combinatorics.permutations import Permutation as Perm
>>> C(1, 2)(2, 3)
(1 3 2)
An instance of a Cycle will automatically parse list-like
objects and Permutations that are on the right. It is more
flexible than the Permutation in that all elements need not
be present:
>>> a = C(1, 2)
>>> a(2, 3)
(1 3 2)
>>> a(2, 3)(4, 5)
(1 3 2)(4 5)
"""
rv = Cycle(*other)
for k, v in zip(list(self.keys()), [rv[self[k]] for k in self.keys()]):
rv[k] = v
return rv
def list(self, size=None):
"""Return the cycles as an explicit list starting from 0 up
to the greater of the largest value in the cycles and size.
Truncation of trailing unmoved items will occur when size
is less than the maximum element in the cycle; if this is
desired, setting ``size=-1`` will guarantee such trimming.
Examples
========
>>> from sympy.combinatorics.permutations import Cycle
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> p = Cycle(2, 3)(4, 5)
>>> p.list()
[0, 1, 3, 2, 5, 4]
>>> p.list(10)
[0, 1, 3, 2, 5, 4, 6, 7, 8, 9]
Passing a length too small will trim trailing, unchanged elements
in the permutation:
>>> Cycle(2, 4)(1, 2, 4).list(-1)
[0, 2, 1]
"""
if not self and size is None:
raise ValueError('must give size for empty Cycle')
if size is not None:
big = max([i for i in self.keys() if self[i] != i] + [0])
size = max(size, big + 1)
else:
size = self.size
return [self[i] for i in range(size)]
def __repr__(self):
"""We want it to print as a Cycle, not as a dict.
Examples
========
>>> from sympy.combinatorics import Cycle
>>> Cycle(1, 2)
(1 2)
>>> print(_)
(1 2)
>>> list(Cycle(1, 2).items())
[(1, 2), (2, 1)]
"""
if not self:
return 'Cycle()'
cycles = Permutation(self).cyclic_form
s = ''.join(str(tuple(c)) for c in cycles)
big = self.size - 1
if not any(i == big for c in cycles for i in c):
s += '(%s)' % big
return 'Cycle%s' % s
def __str__(self):
"""We want it to be printed in a Cycle notation with no
comma in-between.
Examples
========
>>> from sympy.combinatorics import Cycle
>>> Cycle(1, 2)
(1 2)
>>> Cycle(1, 2, 4)(5, 6)
(1 2 4)(5 6)
"""
if not self:
return '()'
cycles = Permutation(self).cyclic_form
s = ''.join(str(tuple(c)) for c in cycles)
big = self.size - 1
if not any(i == big for c in cycles for i in c):
s += '(%s)' % big
s = s.replace(',', '')
return s
def __init__(self, *args):
"""Load up a Cycle instance with the values for the cycle.
Examples
========
>>> from sympy.combinatorics.permutations import Cycle
>>> Cycle(1, 2, 6)
(1 2 6)
"""
if not args:
return
if len(args) == 1:
if isinstance(args[0], Permutation):
for c in args[0].cyclic_form:
self.update(self(*c))
return
elif isinstance(args[0], Cycle):
for k, v in args[0].items():
self[k] = v
return
args = [as_int(a) for a in args]
if any(i < 0 for i in args):
raise ValueError('negative integers are not allowed in a cycle.')
if has_dups(args):
raise ValueError('All elements must be unique in a cycle.')
for i in range(-len(args), 0):
self[args[i]] = args[i + 1]
@property
def size(self):
if not self:
return 0
return max(self.keys()) + 1
def copy(self):
return Cycle(self)
class Permutation(Basic):
"""
A permutation, alternatively known as an 'arrangement number' or 'ordering'
is an arrangement of the elements of an ordered list into a one-to-one
mapping with itself. The permutation of a given arrangement is given by
indicating the positions of the elements after re-arrangement [2]_. For
example, if one started with elements [x, y, a, b] (in that order) and
they were reordered as [x, y, b, a] then the permutation would be
[0, 1, 3, 2]. Notice that (in SymPy) the first element is always referred
to as 0 and the permutation uses the indices of the elements in the
original ordering, not the elements (a, b, etc...) themselves.
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = False
Permutations Notation
=====================
Permutations are commonly represented in disjoint cycle or array forms.
Array Notation and 2-line Form
------------------------------------
In the 2-line form, the elements and their final positions are shown
as a matrix with 2 rows:
[0 1 2 ... n-1]
[p(0) p(1) p(2) ... p(n-1)]
Since the first line is always range(n), where n is the size of p,
it is sufficient to represent the permutation by the second line,
referred to as the "array form" of the permutation. This is entered
in brackets as the argument to the Permutation class:
>>> p = Permutation([0, 2, 1]); p
Permutation([0, 2, 1])
Given i in range(p.size), the permutation maps i to i^p
>>> [i^p for i in range(p.size)]
[0, 2, 1]
The composite of two permutations p*q means first apply p, then q, so
i^(p*q) = (i^p)^q which is i^p^q according to Python precedence rules:
>>> q = Permutation([2, 1, 0])
>>> [i^p^q for i in range(3)]
[2, 0, 1]
>>> [i^(p*q) for i in range(3)]
[2, 0, 1]
One can use also the notation p(i) = i^p, but then the composition
rule is (p*q)(i) = q(p(i)), not p(q(i)):
>>> [(p*q)(i) for i in range(p.size)]
[2, 0, 1]
>>> [q(p(i)) for i in range(p.size)]
[2, 0, 1]
>>> [p(q(i)) for i in range(p.size)]
[1, 2, 0]
Disjoint Cycle Notation
-----------------------
In disjoint cycle notation, only the elements that have shifted are
indicated. In the above case, the 2 and 1 switched places. This can
be entered in two ways:
>>> Permutation(1, 2) == Permutation([[1, 2]]) == p
True
Only the relative ordering of elements in a cycle matter:
>>> Permutation(1,2,3) == Permutation(2,3,1) == Permutation(3,1,2)
True
The disjoint cycle notation is convenient when representing
permutations that have several cycles in them:
>>> Permutation(1, 2)(3, 5) == Permutation([[1, 2], [3, 5]])
True
It also provides some economy in entry when computing products of
permutations that are written in disjoint cycle notation:
>>> Permutation(1, 2)(1, 3)(2, 3)
Permutation([0, 3, 2, 1])
>>> _ == Permutation([[1, 2]])*Permutation([[1, 3]])*Permutation([[2, 3]])
True
Caution: when the cycles have common elements
between them then the order in which the
permutations are applied matters. The
convention is that the permutations are
applied from *right to left*. In the following, the
transposition of elements 2 and 3 is followed
by the transposition of elements 1 and 2:
>>> Permutation(1, 2)(2, 3) == Permutation([(1, 2), (2, 3)])
True
>>> Permutation(1, 2)(2, 3).list()
[0, 3, 1, 2]
If the first and second elements had been
swapped first, followed by the swapping of the second
and third, the result would have been [0, 2, 3, 1].
If, for some reason, you want to apply the cycles
in the order they are entered, you can simply reverse
the order of cycles:
>>> Permutation([(1, 2), (2, 3)][::-1]).list()
[0, 2, 3, 1]
Entering a singleton in a permutation is a way to indicate the size of the
permutation. The ``size`` keyword can also be used.
Array-form entry:
>>> Permutation([[1, 2], [9]])
Permutation([0, 2, 1], size=10)
>>> Permutation([[1, 2]], size=10)
Permutation([0, 2, 1], size=10)
Cyclic-form entry:
>>> Permutation(1, 2, size=10)
Permutation([0, 2, 1], size=10)
>>> Permutation(9)(1, 2)
Permutation([0, 2, 1], size=10)
Caution: no singleton containing an element larger than the largest
in any previous cycle can be entered. This is an important difference
in how Permutation and Cycle handle the __call__ syntax. A singleton
argument at the start of a Permutation performs instantiation of the
Permutation and is permitted:
>>> Permutation(5)
Permutation([], size=6)
A singleton entered after instantiation is a call to the permutation
-- a function call -- and if the argument is out of range it will
trigger an error. For this reason, it is better to start the cycle
with the singleton:
The following fails because there is is no element 3:
>>> Permutation(1, 2)(3)
Traceback (most recent call last):
...
IndexError: list index out of range
This is ok: only the call to an out of range singleton is prohibited;
otherwise the permutation autosizes:
>>> Permutation(3)(1, 2)
Permutation([0, 2, 1, 3])
>>> Permutation(1, 2)(3, 4) == Permutation(3, 4)(1, 2)
True
Equality testing
----------------
The array forms must be the same in order for permutations to be equal:
>>> Permutation([1, 0, 2, 3]) == Permutation([1, 0])
False
Identity Permutation
--------------------
The identity permutation is a permutation in which no element is out of
place. It can be entered in a variety of ways. All the following create
an identity permutation of size 4:
>>> I = Permutation([0, 1, 2, 3])
>>> all(p == I for p in [
... Permutation(3),
... Permutation(range(4)),
... Permutation([], size=4),
... Permutation(size=4)])
True
Watch out for entering the range *inside* a set of brackets (which is
cycle notation):
>>> I == Permutation([range(4)])
False
Permutation Printing
====================
There are a few things to note about how Permutations are printed.
1) If you prefer one form (array or cycle) over another, you can set that
with the print_cyclic flag.
>>> Permutation(1, 2)(4, 5)(3, 4)
Permutation([0, 2, 1, 4, 5, 3])
>>> p = _
>>> Permutation.print_cyclic = True
>>> p
(1 2)(3 4 5)
>>> Permutation.print_cyclic = False
2) Regardless of the setting, a list of elements in the array for cyclic
form can be obtained and either of those can be copied and supplied as
the argument to Permutation:
>>> p.array_form
[0, 2, 1, 4, 5, 3]
>>> p.cyclic_form
[[1, 2], [3, 4, 5]]
>>> Permutation(_) == p
True
3) Printing is economical in that as little as possible is printed while
retaining all information about the size of the permutation:
>>> Permutation([1, 0, 2, 3])
Permutation([1, 0, 2, 3])
>>> Permutation([1, 0, 2, 3], size=20)
Permutation([1, 0], size=20)
>>> Permutation([1, 0, 2, 4, 3, 5, 6], size=20)
Permutation([1, 0, 2, 4, 3], size=20)
>>> p = Permutation([1, 0, 2, 3])
>>> Permutation.print_cyclic = True
>>> p
(3)(0 1)
>>> Permutation.print_cyclic = False
The 2 was not printed but it is still there as can be seen with the
array_form and size methods:
>>> p.array_form
[1, 0, 2, 3]
>>> p.size
4
Short introduction to other methods
===================================
The permutation can act as a bijective function, telling what element is
located at a given position
>>> q = Permutation([5, 2, 3, 4, 1, 0])
>>> q.array_form[1] # the hard way
2
>>> q(1) # the easy way
2
>>> {i: q(i) for i in range(q.size)} # showing the bijection
{0: 5, 1: 2, 2: 3, 3: 4, 4: 1, 5: 0}
The full cyclic form (including singletons) can be obtained:
>>> p.full_cyclic_form
[[0, 1], [2], [3]]
Any permutation can be factored into transpositions of pairs of elements:
>>> Permutation([[1, 2], [3, 4, 5]]).transpositions()
[(1, 2), (3, 5), (3, 4)]
>>> Permutation.rmul(*[Permutation([ti], size=6) for ti in _]).cyclic_form
[[1, 2], [3, 4, 5]]
The number of permutations on a set of n elements is given by n! and is
called the cardinality.
>>> p.size
4
>>> p.cardinality
24
A given permutation has a rank among all the possible permutations of the
same elements, but what that rank is depends on how the permutations are
enumerated. (There are a number of different methods of doing so.) The
lexicographic rank is given by the rank method and this rank is used to
increment a permutation with addition/subtraction:
>>> p.rank()
6
>>> p + 1
Permutation([1, 0, 3, 2])
>>> p.next_lex()
Permutation([1, 0, 3, 2])
>>> _.rank()
7
>>> p.unrank_lex(p.size, rank=7)
Permutation([1, 0, 3, 2])
The product of two permutations p and q is defined as their composition as
functions, (p*q)(i) = q(p(i)) [6]_.
>>> p = Permutation([1, 0, 2, 3])
>>> q = Permutation([2, 3, 1, 0])
>>> list(q*p)
[2, 3, 0, 1]
>>> list(p*q)
[3, 2, 1, 0]
>>> [q(p(i)) for i in range(p.size)]
[3, 2, 1, 0]
The permutation can be 'applied' to any list-like object, not only
Permutations:
>>> p(['zero', 'one', 'four', 'two'])
['one', 'zero', 'four', 'two']
>>> p('zo42')
['o', 'z', '4', '2']
If you have a list of arbitrary elements, the corresponding permutation
can be found with the from_sequence method:
>>> Permutation.from_sequence('SymPy')
Permutation([1, 3, 2, 0, 4])
See Also
========
Cycle
References
==========
.. [1] Skiena, S. 'Permutations.' 1.1 in Implementing Discrete Mathematics
Combinatorics and Graph Theory with Mathematica. Reading, MA:
Addison-Wesley, pp. 3-16, 1990.
.. [2] Knuth, D. E. The Art of Computer Programming, Vol. 4: Combinatorial
Algorithms, 1st ed. Reading, MA: Addison-Wesley, 2011.
.. [3] Wendy Myrvold and Frank Ruskey. 2001. Ranking and unranking
permutations in linear time. Inf. Process. Lett. 79, 6 (September 2001),
281-284. DOI=10.1016/S0020-0190(01)00141-7
.. [4] D. L. Kreher, D. R. Stinson 'Combinatorial Algorithms'
CRC Press, 1999
.. [5] Graham, R. L.; Knuth, D. E.; and Patashnik, O.
Concrete Mathematics: A Foundation for Computer Science, 2nd ed.
Reading, MA: Addison-Wesley, 1994.
.. [6] http://en.wikipedia.org/wiki/Permutation#Product_and_inverse
.. [7] http://en.wikipedia.org/wiki/Lehmer_code
"""
is_Permutation = True
_array_form = None
_cyclic_form = None
_cycle_structure = None
_size = None
_rank = None
def __new__(cls, *args, **kwargs):
"""
Constructor for the Permutation object from a list or a
list of lists in which all elements of the permutation may
appear only once.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
Permutations entered in array-form are left unaltered:
>>> Permutation([0, 2, 1])
Permutation([0, 2, 1])
Permutations entered in cyclic form are converted to array form;
singletons need not be entered, but can be entered to indicate the
largest element:
>>> Permutation([[4, 5, 6], [0, 1]])
Permutation([1, 0, 2, 3, 5, 6, 4])
>>> Permutation([[4, 5, 6], [0, 1], [19]])
Permutation([1, 0, 2, 3, 5, 6, 4], size=20)
All manipulation of permutations assumes that the smallest element
is 0 (in keeping with 0-based indexing in Python) so if the 0 is
missing when entering a permutation in array form, an error will be
raised:
>>> Permutation([2, 1])
Traceback (most recent call last):
...
ValueError: Integers 0 through 2 must be present.
If a permutation is entered in cyclic form, it can be entered without
singletons and the ``size`` specified so those values can be filled
in, otherwise the array form will only extend to the maximum value
in the cycles:
>>> Permutation([[1, 4], [3, 5, 2]], size=10)
Permutation([0, 4, 3, 5, 1, 2], size=10)
>>> _.array_form
[0, 4, 3, 5, 1, 2, 6, 7, 8, 9]
"""
size = kwargs.pop('size', None)
if size is not None:
size = int(size)
#a) ()
#b) (1) = identity
#c) (1, 2) = cycle
#d) ([1, 2, 3]) = array form
#e) ([[1, 2]]) = cyclic form
#f) (Cycle) = conversion to permutation
#g) (Permutation) = adjust size or return copy
ok = True
if not args: # a
return cls._af_new(list(range(size or 0)))
elif len(args) > 1: # c
return cls._af_new(Cycle(*args).list(size))
if len(args) == 1:
a = args[0]
if isinstance(a, cls): # g
if size is None or size == a.size:
return a
return cls(a.array_form, size=size)
if isinstance(a, Cycle): # f
return cls._af_new(a.list(size))
if not is_sequence(a): # b
return cls._af_new(list(range(a + 1)))
if has_variety(is_sequence(ai) for ai in a):
ok = False
else:
ok = False
if not ok:
raise ValueError("Permutation argument must be a list of ints, "
"a list of lists, Permutation or Cycle.")
# safe to assume args are valid; this also makes a copy
# of the args
args = list(args[0])
is_cycle = args and is_sequence(args[0])
if is_cycle: # e
args = [[int(i) for i in c] for c in args]
else: # d
args = [int(i) for i in args]
# if there are n elements present, 0, 1, ..., n-1 should be present
# unless a cycle notation has been provided. A 0 will be added
# for convenience in case one wants to enter permutations where
# counting starts from 1.
temp = flatten(args)
if has_dups(temp) and not is_cycle:
raise ValueError('there were repeated elements.')
temp = set(temp)
if not is_cycle and \
any(i not in temp for i in range(len(temp))):
raise ValueError("Integers 0 through %s must be present." %
max(temp))
if is_cycle:
# it's not necessarily canonical so we won't store
# it -- use the array form instead
c = Cycle()
for ci in args:
c = c(*ci)
aform = c.list()
else:
aform = list(args)
if size and size > len(aform):
# don't allow for truncation of permutation which
# might split a cycle and lead to an invalid aform
# but do allow the permutation size to be increased
aform.extend(list(range(len(aform), size)))
return cls._af_new(aform)
@classmethod
def _af_new(cls, perm):
"""A method to produce a Permutation object from a list;
the list is bound to the _array_form attribute, so it must
not be modified; this method is meant for internal use only;
the list ``a`` is supposed to be generated as a temporary value
in a method, so p = Perm._af_new(a) is the only object
to hold a reference to ``a``::
Examples
========
>>> from sympy.combinatorics.permutations import Perm
>>> Perm.print_cyclic = False
>>> a = [2,1,3,0]
>>> p = Perm._af_new(a)
>>> p
Permutation([2, 1, 3, 0])
"""
p = Basic.__new__(cls, perm)
p._array_form = perm
p._size = len(perm)
return p
def _hashable_content(self):
# the array_form (a list) is the Permutation arg, so we need to
# return a tuple, instead
return tuple(self.array_form)
@property
def array_form(self):
"""
Return a copy of the attribute _array_form
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> p = Permutation([[2, 0], [3, 1]])
>>> p.array_form
[2, 3, 0, 1]
>>> Permutation([[2, 0, 3, 1]]).array_form
[3, 2, 0, 1]
>>> Permutation([2, 0, 3, 1]).array_form
[2, 0, 3, 1]
>>> Permutation([[1, 2], [4, 5]]).array_form
[0, 2, 1, 3, 5, 4]
"""
return self._array_form[:]
def __repr__(self):
from sympy.combinatorics.permutations import Permutation, Cycle
if Permutation.print_cyclic:
if not self.size:
return 'Permutation()'
# before taking Cycle notation, see if the last element is
# a singleton and move it to the head of the string
s = Cycle(self)(self.size - 1).__repr__()[len('Cycle'):]
last = s.rfind('(')
if not last == 0 and ',' not in s[last:]:
s = s[last:] + s[:last]
return 'Permutation%s' %s
else:
s = self.support()
if not s:
if self.size < 5:
return 'Permutation(%s)' % str(self.array_form)
return 'Permutation([], size=%s)' % self.size
trim = str(self.array_form[:s[-1] + 1]) + ', size=%s' % self.size
use = full = str(self.array_form)
if len(trim) < len(full):
use = trim
return 'Permutation(%s)' % use
def list(self, size=None):
"""Return the permutation as an explicit list, possibly
trimming unmoved elements if size is less than the maximum
element in the permutation; if this is desired, setting
``size=-1`` will guarantee such trimming.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> p = Permutation(2, 3)(4, 5)
>>> p.list()
[0, 1, 3, 2, 5, 4]
>>> p.list(10)
[0, 1, 3, 2, 5, 4, 6, 7, 8, 9]
Passing a length too small will trim trailing, unchanged elements
in the permutation:
>>> Permutation(2, 4)(1, 2, 4).list(-1)
[0, 2, 1]
>>> Permutation(3).list(-1)
[]
"""
if not self and size is None:
raise ValueError('must give size for empty Cycle')
rv = self.array_form
if size is not None:
if size > self.size:
rv.extend(list(range(self.size, size)))
else:
# find first value from rhs where rv[i] != i
i = self.size - 1
while rv:
if rv[-1] != i:
break
rv.pop()
i -= 1
return rv
@property
def cyclic_form(self):
"""
This is used to convert to the cyclic notation
from the canonical notation. Singletons are omitted.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> p = Permutation([0, 3, 1, 2])
>>> p.cyclic_form
[[1, 3, 2]]
>>> Permutation([1, 0, 2, 4, 3, 5]).cyclic_form
[[0, 1], [3, 4]]
See Also
========
array_form, full_cyclic_form
"""
if self._cyclic_form is not None:
return list(self._cyclic_form)
array_form = self.array_form
unchecked = [True] * len(array_form)
cyclic_form = []
for i in range(len(array_form)):
if unchecked[i]:
cycle = []
cycle.append(i)
unchecked[i] = False
j = i
while unchecked[array_form[j]]:
j = array_form[j]
cycle.append(j)
unchecked[j] = False
if len(cycle) > 1:
cyclic_form.append(cycle)
assert cycle == list(minlex(cycle, is_set=True))
cyclic_form.sort()
self._cyclic_form = cyclic_form[:]
return cyclic_form
@property
def full_cyclic_form(self):
"""Return permutation in cyclic form including singletons.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation([0, 2, 1]).full_cyclic_form
[[0], [1, 2]]
"""
need = set(range(self.size)) - set(flatten(self.cyclic_form))
rv = self.cyclic_form
rv.extend([[i] for i in need])
rv.sort()
return rv
@property
def size(self):
"""
Returns the number of elements in the permutation.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation([[3, 2], [0, 1]]).size
4
See Also
========
cardinality, length, order, rank
"""
return self._size
def support(self):
"""Return the elements in permutation, P, for which P[i] != i.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> p = Permutation([[3, 2], [0, 1], [4]])
>>> p.array_form
[1, 0, 3, 2, 4]
>>> p.support()
[0, 1, 2, 3]
"""
a = self.array_form
return [i for i, e in enumerate(a) if a[i] != i]
def __add__(self, other):
"""Return permutation that is other higher in rank than self.
The rank is the lexicographical rank, with the identity permutation
having rank of 0.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> I = Permutation([0, 1, 2, 3])
>>> a = Permutation([2, 1, 3, 0])
>>> I + a.rank() == a
True
See Also
========
__sub__, inversion_vector
"""
rank = (self.rank() + other) % self.cardinality
rv = self.unrank_lex(self.size, rank)
rv._rank = rank
return rv
def __sub__(self, other):
"""Return the permutation that is other lower in rank than self.
See Also
========
__add__
"""
return self.__add__(-other)
@staticmethod
def rmul(*args):
"""
Return product of Permutations [a, b, c, ...] as the Permutation whose
ith value is a(b(c(i))).
a, b, c, ... can be Permutation objects or tuples.
Examples
========
>>> from sympy.combinatorics.permutations import _af_rmul, Permutation
>>> Permutation.print_cyclic = False
>>> a, b = [1, 0, 2], [0, 2, 1]
>>> a = Permutation(a); b = Permutation(b)
>>> list(Permutation.rmul(a, b))
[1, 2, 0]
>>> [a(b(i)) for i in range(3)]
[1, 2, 0]
This handles the operands in reverse order compared to the ``*`` operator:
>>> a = Permutation(a); b = Permutation(b)
>>> list(a*b)
[2, 0, 1]
>>> [b(a(i)) for i in range(3)]
[2, 0, 1]
Notes
=====
All items in the sequence will be parsed by Permutation as
necessary as long as the first item is a Permutation:
>>> Permutation.rmul(a, [0, 2, 1]) == Permutation.rmul(a, b)
True
The reverse order of arguments will raise a TypeError.
"""
rv = args[0]
for i in range(1, len(args)):
rv = args[i]*rv
return rv
@classmethod
def rmul_with_af(cls, *args):
"""
same as rmul, but the elements of args are Permutation objects
which have _array_form
"""
a = [x._array_form for x in args]
rv = cls._af_new(_af_rmuln(*a))
return rv
def mul_inv(self, other):
"""
other*~self, self and other have _array_form
"""
a = _af_invert(self._array_form)
b = other._array_form
return self._af_new(_af_rmul(a, b))
def __rmul__(self, other):
"""This is needed to coerce other to Permutation in rmul."""
cls = type(self)
return cls(other)*self
def __mul__(self, other):
"""
Return the product a*b as a Permutation; the ith value is b(a(i)).
Examples
========
>>> from sympy.combinatorics.permutations import _af_rmul, Permutation
>>> Permutation.print_cyclic = False
>>> a, b = [1, 0, 2], [0, 2, 1]
>>> a = Permutation(a); b = Permutation(b)
>>> list(a*b)
[2, 0, 1]
>>> [b(a(i)) for i in range(3)]
[2, 0, 1]
This handles operands in reverse order compared to _af_rmul and rmul:
>>> al = list(a); bl = list(b)
>>> _af_rmul(al, bl)
[1, 2, 0]
>>> [al[bl[i]] for i in range(3)]
[1, 2, 0]
It is acceptable for the arrays to have different lengths; the shorter
one will be padded to match the longer one:
>>> b*Permutation([1, 0])
Permutation([1, 2, 0])
>>> Permutation([1, 0])*b
Permutation([2, 0, 1])
It is also acceptable to allow coercion to handle conversion of a
single list to the left of a Permutation:
>>> [0, 1]*a # no change: 2-element identity
Permutation([1, 0, 2])
>>> [[0, 1]]*a # exchange first two elements
Permutation([0, 1, 2])
You cannot use more than 1 cycle notation in a product of cycles
since coercion can only handle one argument to the left. To handle
multiple cycles it is convenient to use Cycle instead of Permutation:
>>> [[1, 2]]*[[2, 3]]*Permutation([]) # doctest: +SKIP
>>> from sympy.combinatorics.permutations import Cycle
>>> Cycle(1, 2)(2, 3)
(1 3 2)
"""
a = self.array_form
# __rmul__ makes sure the other is a Permutation
b = other.array_form
if not b:
perm = a
else:
b.extend(list(range(len(b), len(a))))
perm = [b[i] for i in a] + b[len(a):]
return self._af_new(perm)
def commutes_with(self, other):
"""
Checks if the elements are commuting.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> a = Permutation([1, 4, 3, 0, 2, 5])
>>> b = Permutation([0, 1, 2, 3, 4, 5])
>>> a.commutes_with(b)
True
>>> b = Permutation([2, 3, 5, 4, 1, 0])
>>> a.commutes_with(b)
False
"""
a = self.array_form
b = other.array_form
return _af_commutes_with(a, b)
def __pow__(self, n):
"""
Routine for finding powers of a permutation.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> p = Permutation([2,0,3,1])
>>> p.order()
4
>>> p**4
Permutation([0, 1, 2, 3])
"""
if isinstance(n, Permutation):
raise NotImplementedError(
'p**p is not defined; do you mean p^p (conjugate)?')
n = int(n)
return self._af_new(_af_pow(self.array_form, n))
def __rxor__(self, i):
"""Return self(i) when ``i`` is an int.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> p = Permutation(1, 2, 9)
>>> 2^p == p(2) == 9
True
"""
if int(i) == i:
return self(i)
else:
raise NotImplementedError(
"i^p = p(i) when i is an integer, not %s." % i)
def __xor__(self, h):
"""Return the conjugate permutation ``~h*self*h` `.
If ``a`` and ``b`` are conjugates, ``a = h*b*~h`` and
``b = ~h*a*h`` and both have the same cycle structure.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = True
>>> p = Permutation(1, 2, 9)
>>> q = Permutation(6, 9, 8)
>>> p*q != q*p
True
Calculate and check properties of the conjugate:
>>> c = p^q
>>> c == ~q*p*q and p == q*c*~q
True
The expression q^p^r is equivalent to q^(p*r):
>>> r = Permutation(9)(4, 6, 8)
>>> q^p^r == q^(p*r)
True
If the term to the left of the conjugate operator, i, is an integer
then this is interpreted as selecting the ith element from the
permutation to the right:
>>> all(i^p == p(i) for i in range(p.size))
True
Note that the * operator as higher precedence than the ^ operator:
>>> q^r*p^r == q^(r*p)^r == Permutation(9)(1, 6, 4)
True
Notes
=====
In Python the precedence rule is p^q^r = (p^q)^r which differs
in general from p^(q^r)
>>> q^p^r
(9)(1 4 8)
>>> q^(p^r)
(9)(1 8 6)
For a given r and p, both of the following are conjugates of p:
~r*p*r and r*p*~r. But these are not necessarily the same:
>>> ~r*p*r == r*p*~r
True
>>> p = Permutation(1, 2, 9)(5, 6)
>>> ~r*p*r == r*p*~r
False
The conjugate ~r*p*r was chosen so that ``p^q^r`` would be equivalent
to ``p^(q*r)`` rather than ``p^(r*q)``. To obtain r*p*~r, pass ~r to
this method:
>>> p^~r == r*p*~r
True
"""
if self.size != h.size:
raise ValueError("The permutations must be of equal size.")
a = [None]*self.size
h = h._array_form
p = self._array_form
for i in range(self.size):
a[h[i]] = h[p[i]]
return self._af_new(a)
def transpositions(self):
"""
Return the permutation decomposed into a list of transpositions.
It is always possible to express a permutation as the product of
transpositions, see [1]
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([[1, 2, 3], [0, 4, 5, 6, 7]])
>>> t = p.transpositions()
>>> t
[(0, 7), (0, 6), (0, 5), (0, 4), (1, 3), (1, 2)]
>>> print(''.join(str(c) for c in t))
(0, 7)(0, 6)(0, 5)(0, 4)(1, 3)(1, 2)
>>> Permutation.rmul(*[Permutation([ti], size=p.size) for ti in t]) == p
True
References
==========
1. http://en.wikipedia.org/wiki/Transposition_%28mathematics%29#Properties
"""
a = self.cyclic_form
res = []
for x in a:
nx = len(x)
if nx == 2:
res.append(tuple(x))
elif nx > 2:
first = x[0]
for y in x[nx - 1:0:-1]:
res.append((first, y))
return res
@classmethod
def from_sequence(self, i, key=None):
"""Return the permutation needed to obtain ``i`` from the sorted
elements of ``i``. If custom sorting is desired, a key can be given.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> Permutation.from_sequence('SymPy')
(4)(0 1 3)
>>> _(sorted("SymPy"))
['S', 'y', 'm', 'P', 'y']
>>> Permutation.from_sequence('SymPy', key=lambda x: x.lower())
(4)(0 2)(1 3)
"""
ic = list(zip(i, list(range(len(i)))))
if key:
ic.sort(key=lambda x: key(x[0]))
else:
ic.sort()
return ~Permutation([i[1] for i in ic])
def __invert__(self):
"""
Return the inverse of the permutation.
A permutation multiplied by its inverse is the identity permutation.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([[2,0], [3,1]])
>>> ~p
Permutation([2, 3, 0, 1])
>>> _ == p**-1
True
>>> p*~p == ~p*p == Permutation([0, 1, 2, 3])
True
"""
return self._af_new(_af_invert(self._array_form))
def __iter__(self):
"""Yield elements from array form.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> list(Permutation(range(3)))
[0, 1, 2]
"""
for i in self.array_form:
yield i
def __call__(self, *i):
"""
Allows applying a permutation instance as a bijective function.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([[2, 0], [3, 1]])
>>> p.array_form
[2, 3, 0, 1]
>>> [p(i) for i in range(4)]
[2, 3, 0, 1]
If an array is given then the permutation selects the items
from the array (i.e. the permutation is applied to the array):
>>> from sympy.abc import x
>>> p([x, 1, 0, x**2])
[0, x**2, x, 1]
"""
# list indices can be Integer or int; leave this
# as it is (don't test or convert it) because this
# gets called a lot and should be fast
if len(i) == 1:
i = i[0]
try:
# P(1)
return self._array_form[i]
except TypeError:
try:
# P([a, b, c])
return [i[j] for j in self._array_form]
except Exception:
raise TypeError('unrecognized argument')
else:
# P(1, 2, 3)
return self*Permutation(Cycle(*i), size=self.size)
def atoms(self):
"""
Returns all the elements of a permutation
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation([0, 1, 2, 3, 4, 5]).atoms()
{0, 1, 2, 3, 4, 5}
>>> Permutation([[0, 1], [2, 3], [4, 5]]).atoms()
{0, 1, 2, 3, 4, 5}
"""
return set(self.array_form)
def next_lex(self):
"""
Returns the next permutation in lexicographical order.
If self is the last permutation in lexicographical order
it returns None.
See [4] section 2.4.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([2, 3, 1, 0])
>>> p = Permutation([2, 3, 1, 0]); p.rank()
17
>>> p = p.next_lex(); p.rank()
18
See Also
========
rank, unrank_lex
"""
perm = self.array_form[:]
n = len(perm)
i = n - 2
while perm[i + 1] < perm[i]:
i -= 1
if i == -1:
return None
else:
j = n - 1
while perm[j] < perm[i]:
j -= 1
perm[j], perm[i] = perm[i], perm[j]
i += 1
j = n - 1
while i < j:
perm[j], perm[i] = perm[i], perm[j]
i += 1
j -= 1
return self._af_new(perm)
@classmethod
def unrank_nonlex(self, n, r):
"""
This is a linear time unranking algorithm that does not
respect lexicographic order [3].
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> Permutation.unrank_nonlex(4, 5)
Permutation([2, 0, 3, 1])
>>> Permutation.unrank_nonlex(4, -1)
Permutation([0, 1, 2, 3])
See Also
========
next_nonlex, rank_nonlex
"""
def _unrank1(n, r, a):
if n > 0:
a[n - 1], a[r % n] = a[r % n], a[n - 1]
_unrank1(n - 1, r//n, a)
id_perm = list(range(n))
n = int(n)
r = r % ifac(n)
_unrank1(n, r, id_perm)
return self._af_new(id_perm)
def rank_nonlex(self, inv_perm=None):
"""
This is a linear time ranking algorithm that does not
enforce lexicographic order [3].
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 1, 2, 3])
>>> p.rank_nonlex()
23
See Also
========
next_nonlex, unrank_nonlex
"""
def _rank1(n, perm, inv_perm):
if n == 1:
return 0
s = perm[n - 1]
t = inv_perm[n - 1]
perm[n - 1], perm[t] = perm[t], s
inv_perm[n - 1], inv_perm[s] = inv_perm[s], t
return s + n*_rank1(n - 1, perm, inv_perm)
if inv_perm is None:
inv_perm = (~self).array_form
if not inv_perm:
return 0
perm = self.array_form[:]
r = _rank1(len(perm), perm, inv_perm)
return r
def next_nonlex(self):
"""
Returns the next permutation in nonlex order [3].
If self is the last permutation in this order it returns None.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> p = Permutation([2, 0, 3, 1]); p.rank_nonlex()
5
>>> p = p.next_nonlex(); p
Permutation([3, 0, 1, 2])
>>> p.rank_nonlex()
6
See Also
========
rank_nonlex, unrank_nonlex
"""
r = self.rank_nonlex()
if r == ifac(self.size) - 1:
return None
return self.unrank_nonlex(self.size, r + 1)
def rank(self):
"""
Returns the lexicographic rank of the permutation.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 1, 2, 3])
>>> p.rank()
0
>>> p = Permutation([3, 2, 1, 0])
>>> p.rank()
23
See Also
========
next_lex, unrank_lex, cardinality, length, order, size
"""
if not self._rank is None:
return self._rank
rank = 0
rho = self.array_form[:]
n = self.size - 1
size = n + 1
psize = int(ifac(n))
for j in range(size - 1):
rank += rho[j]*psize
for i in range(j + 1, size):
if rho[i] > rho[j]:
rho[i] -= 1
psize //= n
n -= 1
self._rank = rank
return rank
@property
def cardinality(self):
"""
Returns the number of all possible permutations.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 1, 2, 3])
>>> p.cardinality
24
See Also
========
length, order, rank, size
"""
return int(ifac(self.size))
def parity(self):
"""
Computes the parity of a permutation.
The parity of a permutation reflects the parity of the
number of inversions in the permutation, i.e., the
number of pairs of x and y such that ``x > y`` but ``p[x] < p[y]``.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 1, 2, 3])
>>> p.parity()
0
>>> p = Permutation([3, 2, 0, 1])
>>> p.parity()
1
See Also
========
_af_parity
"""
if self._cyclic_form is not None:
return (self.size - self.cycles) % 2
return _af_parity(self.array_form)
@property
def is_even(self):
"""
Checks if a permutation is even.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 1, 2, 3])
>>> p.is_even
True
>>> p = Permutation([3, 2, 1, 0])
>>> p.is_even
True
See Also
========
is_odd
"""
return not self.is_odd
@property
def is_odd(self):
"""
Checks if a permutation is odd.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 1, 2, 3])
>>> p.is_odd
False
>>> p = Permutation([3, 2, 0, 1])
>>> p.is_odd
True
See Also
========
is_even
"""
return bool(self.parity() % 2)
@property
def is_Singleton(self):
"""
Checks to see if the permutation contains only one number and is
thus the only possible permutation of this set of numbers
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation([0]).is_Singleton
True
>>> Permutation([0, 1]).is_Singleton
False
See Also
========
is_Empty
"""
return self.size == 1
@property
def is_Empty(self):
"""
Checks to see if the permutation is a set with zero elements
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation([]).is_Empty
True
>>> Permutation([0]).is_Empty
False
See Also
========
is_Singleton
"""
return self.size == 0
@property
def is_Identity(self):
"""
Returns True if the Permutation is an identity permutation.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([])
>>> p.is_Identity
True
>>> p = Permutation([[0], [1], [2]])
>>> p.is_Identity
True
>>> p = Permutation([0, 1, 2])
>>> p.is_Identity
True
>>> p = Permutation([0, 2, 1])
>>> p.is_Identity
False
See Also
========
order
"""
af = self.array_form
return not af or all(i == af[i] for i in range(self.size))
def ascents(self):
"""
Returns the positions of ascents in a permutation, ie, the location
where p[i] < p[i+1]
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([4, 0, 1, 3, 2])
>>> p.ascents()
[1, 2]
See Also
========
descents, inversions, min, max
"""
a = self.array_form
pos = [i for i in range(len(a) - 1) if a[i] < a[i + 1]]
return pos
def descents(self):
"""
Returns the positions of descents in a permutation, ie, the location
where p[i] > p[i+1]
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([4, 0, 1, 3, 2])
>>> p.descents()
[0, 3]
See Also
========
ascents, inversions, min, max
"""
a = self.array_form
pos = [i for i in range(len(a) - 1) if a[i] > a[i + 1]]
return pos
def max(self):
"""
The maximum element moved by the permutation.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([1, 0, 2, 3, 4])
>>> p.max()
1
See Also
========
min, descents, ascents, inversions
"""
max = 0
a = self.array_form
for i in range(len(a)):
if a[i] != i and a[i] > max:
max = a[i]
return max
def min(self):
"""
The minimum element moved by the permutation.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 1, 4, 3, 2])
>>> p.min()
2
See Also
========
max, descents, ascents, inversions
"""
a = self.array_form
min = len(a)
for i in range(len(a)):
if a[i] != i and a[i] < min:
min = a[i]
return min
def inversions(self):
"""
Computes the number of inversions of a permutation.
An inversion is where i > j but p[i] < p[j].
For small length of p, it iterates over all i and j
values and calculates the number of inversions.
For large length of p, it uses a variation of merge
sort to calculate the number of inversions.
References
==========
[1] http://www.cp.eng.chula.ac.th/~piak/teaching/algo/algo2008/count-inv.htm
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 1, 2, 3, 4, 5])
>>> p.inversions()
0
>>> Permutation([3, 2, 1, 0]).inversions()
6
See Also
========
descents, ascents, min, max
"""
inversions = 0
a = self.array_form
n = len(a)
if n < 130:
for i in range(n - 1):
b = a[i]
for c in a[i + 1:]:
if b > c:
inversions += 1
else:
k = 1
right = 0
arr = a[:]
temp = a[:]
while k < n:
i = 0
while i + k < n:
right = i + k * 2 - 1
if right >= n:
right = n - 1
inversions += _merge(arr, temp, i, i + k, right)
i = i + k * 2
k = k * 2
return inversions
def commutator(self, x):
"""Return the commutator of self and x: ``~x*~self*x*self``
If f and g are part of a group, G, then the commutator of f and g
is the group identity iff f and g commute, i.e. fg == gf.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> p = Permutation([0, 2, 3, 1])
>>> x = Permutation([2, 0, 3, 1])
>>> c = p.commutator(x); c
Permutation([2, 1, 3, 0])
>>> c == ~x*~p*x*p
True
>>> I = Permutation(3)
>>> p = [I + i for i in range(6)]
>>> for i in range(len(p)):
... for j in range(len(p)):
... c = p[i].commutator(p[j])
... if p[i]*p[j] == p[j]*p[i]:
... assert c == I
... else:
... assert c != I
...
References
==========
http://en.wikipedia.org/wiki/Commutator
"""
a = self.array_form
b = x.array_form
n = len(a)
if len(b) != n:
raise ValueError("The permutations must be of equal size.")
inva = [None]*n
for i in range(n):
inva[a[i]] = i
invb = [None]*n
for i in range(n):
invb[b[i]] = i
return self._af_new([a[b[inva[i]]] for i in invb])
def signature(self):
"""
Gives the signature of the permutation needed to place the
elements of the permutation in canonical order.
The signature is calculated as (-1)^<number of inversions>
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 1, 2])
>>> p.inversions()
0
>>> p.signature()
1
>>> q = Permutation([0,2,1])
>>> q.inversions()
1
>>> q.signature()
-1
See Also
========
inversions
"""
if self.is_even:
return 1
return -1
def order(self):
"""
Computes the order of a permutation.
When the permutation is raised to the power of its
order it equals the identity permutation.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> p = Permutation([3, 1, 5, 2, 4, 0])
>>> p.order()
4
>>> (p**(p.order()))
Permutation([], size=6)
See Also
========
identity, cardinality, length, rank, size
"""
return reduce(lcm, [len(cycle) for cycle in self.cyclic_form], 1)
def length(self):
"""
Returns the number of integers moved by a permutation.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation([0, 3, 2, 1]).length()
2
>>> Permutation([[0, 1], [2, 3]]).length()
4
See Also
========
min, max, support, cardinality, order, rank, size
"""
return len(self.support())
@property
def cycle_structure(self):
"""Return the cycle structure of the permutation as a dictionary
indicating the multiplicity of each cycle length.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> Permutation(3).cycle_structure
{1: 4}
>>> Permutation(0, 4, 3)(1, 2)(5, 6).cycle_structure
{2: 2, 3: 1}
"""
if self._cycle_structure:
rv = self._cycle_structure
else:
rv = defaultdict(int)
singletons = self.size
for c in self.cyclic_form:
rv[len(c)] += 1
singletons -= len(c)
if singletons:
rv[1] = singletons
self._cycle_structure = rv
return dict(rv) # make a copy
@property
def cycles(self):
"""
Returns the number of cycles contained in the permutation
(including singletons).
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation([0, 1, 2]).cycles
3
>>> Permutation([0, 1, 2]).full_cyclic_form
[[0], [1], [2]]
>>> Permutation(0, 1)(2, 3).cycles
2
See Also
========
sympy.functions.combinatorial.numbers.stirling
"""
return len(self.full_cyclic_form)
def index(self):
"""
Returns the index of a permutation.
The index of a permutation is the sum of all subscripts j such
that p[j] is greater than p[j+1].
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([3, 0, 2, 1, 4])
>>> p.index()
2
"""
a = self.array_form
return sum([j for j in range(len(a) - 1) if a[j] > a[j + 1]])
def runs(self):
"""
Returns the runs of a permutation.
An ascending sequence in a permutation is called a run [5].
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([2, 5, 7, 3, 6, 0, 1, 4, 8])
>>> p.runs()
[[2, 5, 7], [3, 6], [0, 1, 4, 8]]
>>> q = Permutation([1,3,2,0])
>>> q.runs()
[[1, 3], [2], [0]]
"""
return runs(self.array_form)
def inversion_vector(self):
"""Return the inversion vector of the permutation.
The inversion vector consists of elements whose value
indicates the number of elements in the permutation
that are lesser than it and lie on its right hand side.
The inversion vector is the same as the Lehmer encoding of a
permutation.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([4, 8, 0, 7, 1, 5, 3, 6, 2])
>>> p.inversion_vector()
[4, 7, 0, 5, 0, 2, 1, 1]
>>> p = Permutation([3, 2, 1, 0])
>>> p.inversion_vector()
[3, 2, 1]
The inversion vector increases lexicographically with the rank
of the permutation, the -ith element cycling through 0..i.
>>> p = Permutation(2)
>>> while p:
... print('%s %s %s' % (p, p.inversion_vector(), p.rank()))
... p = p.next_lex()
...
Permutation([0, 1, 2]) [0, 0] 0
Permutation([0, 2, 1]) [0, 1] 1
Permutation([1, 0, 2]) [1, 0] 2
Permutation([1, 2, 0]) [1, 1] 3
Permutation([2, 0, 1]) [2, 0] 4
Permutation([2, 1, 0]) [2, 1] 5
See Also
========
from_inversion_vector
"""
self_array_form = self.array_form
n = len(self_array_form)
inversion_vector = [0] * (n - 1)
for i in range(n - 1):
val = 0
for j in range(i + 1, n):
if self_array_form[j] < self_array_form[i]:
val += 1
inversion_vector[i] = val
return inversion_vector
def rank_trotterjohnson(self):
"""
Returns the Trotter Johnson rank, which we get from the minimal
change algorithm. See [4] section 2.4.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 1, 2, 3])
>>> p.rank_trotterjohnson()
0
>>> p = Permutation([0, 2, 1, 3])
>>> p.rank_trotterjohnson()
7
See Also
========
unrank_trotterjohnson, next_trotterjohnson
"""
if self.array_form == [] or self.is_Identity:
return 0
if self.array_form == [1, 0]:
return 1
perm = self.array_form
n = self.size
rank = 0
for j in range(1, n):
k = 1
i = 0
while perm[i] != j:
if perm[i] < j:
k += 1
i += 1
j1 = j + 1
if rank % 2 == 0:
rank = j1*rank + j1 - k
else:
rank = j1*rank + k - 1
return rank
@classmethod
def unrank_trotterjohnson(cls, size, rank):
"""
Trotter Johnson permutation unranking. See [4] section 2.4.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.unrank_trotterjohnson(5, 10)
Permutation([0, 3, 1, 2, 4])
See Also
========
rank_trotterjohnson, next_trotterjohnson
"""
perm = [0]*size
r2 = 0
n = ifac(size)
pj = 1
for j in range(2, size + 1):
pj *= j
r1 = (rank * pj) // n
k = r1 - j*r2
if r2 % 2 == 0:
for i in range(j - 1, j - k - 1, -1):
perm[i] = perm[i - 1]
perm[j - k - 1] = j - 1
else:
for i in range(j - 1, k, -1):
perm[i] = perm[i - 1]
perm[k] = j - 1
r2 = r1
return cls._af_new(perm)
def next_trotterjohnson(self):
"""
Returns the next permutation in Trotter-Johnson order.
If self is the last permutation it returns None.
See [4] section 2.4. If it is desired to generate all such
permutations, they can be generated in order more quickly
with the ``generate_bell`` function.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> p = Permutation([3, 0, 2, 1])
>>> p.rank_trotterjohnson()
4
>>> p = p.next_trotterjohnson(); p
Permutation([0, 3, 2, 1])
>>> p.rank_trotterjohnson()
5
See Also
========
rank_trotterjohnson, unrank_trotterjohnson, sympy.utilities.iterables.generate_bell
"""
pi = self.array_form[:]
n = len(pi)
st = 0
rho = pi[:]
done = False
m = n-1
while m > 0 and not done:
d = rho.index(m)
for i in range(d, m):
rho[i] = rho[i + 1]
par = _af_parity(rho[:m])
if par == 1:
if d == m:
m -= 1
else:
pi[st + d], pi[st + d + 1] = pi[st + d + 1], pi[st + d]
done = True
else:
if d == 0:
m -= 1
st += 1
else:
pi[st + d], pi[st + d - 1] = pi[st + d - 1], pi[st + d]
done = True
if m == 0:
return None
return self._af_new(pi)
def get_precedence_matrix(self):
"""
Gets the precedence matrix. This is used for computing the
distance between two permutations.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation.josephus(3, 6, 1)
>>> p
Permutation([2, 5, 3, 1, 4, 0])
>>> p.get_precedence_matrix()
Matrix([
[0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 0],
[1, 1, 0, 1, 1, 1],
[1, 1, 0, 0, 1, 0],
[1, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 1, 0]])
See Also
========
get_precedence_distance, get_adjacency_matrix, get_adjacency_distance
"""
m = zeros(self.size)
perm = self.array_form
for i in range(m.rows):
for j in range(i + 1, m.cols):
m[perm[i], perm[j]] = 1
return m
def get_precedence_distance(self, other):
"""
Computes the precedence distance between two permutations.
Suppose p and p' represent n jobs. The precedence metric
counts the number of times a job j is preceded by job i
in both p and p'. This metric is commutative.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([2, 0, 4, 3, 1])
>>> q = Permutation([3, 1, 2, 4, 0])
>>> p.get_precedence_distance(q)
7
>>> q.get_precedence_distance(p)
7
See Also
========
get_precedence_matrix, get_adjacency_matrix, get_adjacency_distance
"""
if self.size != other.size:
raise ValueError("The permutations must be of equal size.")
self_prec_mat = self.get_precedence_matrix()
other_prec_mat = other.get_precedence_matrix()
n_prec = 0
for i in range(self.size):
for j in range(self.size):
if i == j:
continue
if self_prec_mat[i, j] * other_prec_mat[i, j] == 1:
n_prec += 1
d = self.size * (self.size - 1)//2 - n_prec
return d
def get_adjacency_matrix(self):
"""
Computes the adjacency matrix of a permutation.
If job i is adjacent to job j in a permutation p
then we set m[i, j] = 1 where m is the adjacency
matrix of p.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation.josephus(3, 6, 1)
>>> p.get_adjacency_matrix()
Matrix([
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0]])
>>> q = Permutation([0, 1, 2, 3])
>>> q.get_adjacency_matrix()
Matrix([
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 0, 0, 0]])
See Also
========
get_precedence_matrix, get_precedence_distance, get_adjacency_distance
"""
m = zeros(self.size)
perm = self.array_form
for i in range(self.size - 1):
m[perm[i], perm[i + 1]] = 1
return m
def get_adjacency_distance(self, other):
"""
Computes the adjacency distance between two permutations.
This metric counts the number of times a pair i,j of jobs is
adjacent in both p and p'. If n_adj is this quantity then
the adjacency distance is n - n_adj - 1 [1]
[1] Reeves, Colin R. Landscapes, Operators and Heuristic search, Annals
of Operational Research, 86, pp 473-490. (1999)
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 3, 1, 2, 4])
>>> q = Permutation.josephus(4, 5, 2)
>>> p.get_adjacency_distance(q)
3
>>> r = Permutation([0, 2, 1, 4, 3])
>>> p.get_adjacency_distance(r)
4
See Also
========
get_precedence_matrix, get_precedence_distance, get_adjacency_matrix
"""
if self.size != other.size:
raise ValueError("The permutations must be of the same size.")
self_adj_mat = self.get_adjacency_matrix()
other_adj_mat = other.get_adjacency_matrix()
n_adj = 0
for i in range(self.size):
for j in range(self.size):
if i == j:
continue
if self_adj_mat[i, j] * other_adj_mat[i, j] == 1:
n_adj += 1
d = self.size - n_adj - 1
return d
def get_positional_distance(self, other):
"""
Computes the positional distance between two permutations.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> p = Permutation([0, 3, 1, 2, 4])
>>> q = Permutation.josephus(4, 5, 2)
>>> r = Permutation([3, 1, 4, 0, 2])
>>> p.get_positional_distance(q)
12
>>> p.get_positional_distance(r)
12
See Also
========
get_precedence_distance, get_adjacency_distance
"""
a = self.array_form
b = other.array_form
if len(a) != len(b):
raise ValueError("The permutations must be of the same size.")
return sum([abs(a[i] - b[i]) for i in range(len(a))])
@classmethod
def josephus(cls, m, n, s=1):
"""Return as a permutation the shuffling of range(n) using the Josephus
scheme in which every m-th item is selected until all have been chosen.
The returned permutation has elements listed by the order in which they
were selected.
The parameter ``s`` stops the selection process when there are ``s``
items remaining and these are selected by continuing the selection,
counting by 1 rather than by ``m``.
Consider selecting every 3rd item from 6 until only 2 remain::
choices chosen
======== ======
012345
01 345 2
01 34 25
01 4 253
0 4 2531
0 25314
253140
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.josephus(3, 6, 2).array_form
[2, 5, 3, 1, 4, 0]
References
==========
1. http://en.wikipedia.org/wiki/Flavius_Josephus
2. http://en.wikipedia.org/wiki/Josephus_problem
3. http://www.wou.edu/~burtonl/josephus.html
"""
from collections import deque
m -= 1
Q = deque(list(range(n)))
perm = []
while len(Q) > max(s, 1):
for dp in range(m):
Q.append(Q.popleft())
perm.append(Q.popleft())
perm.extend(list(Q))
return cls(perm)
@classmethod
def from_inversion_vector(cls, inversion):
"""
Calculates the permutation from the inversion vector.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> Permutation.from_inversion_vector([3, 2, 1, 0, 0])
Permutation([3, 2, 1, 0, 4, 5])
"""
size = len(inversion)
N = list(range(size + 1))
perm = []
try:
for k in range(size):
val = N[inversion[k]]
perm.append(val)
N.remove(val)
except IndexError:
raise ValueError("The inversion vector is not valid.")
perm.extend(N)
return cls._af_new(perm)
@classmethod
def random(cls, n):
"""
Generates a random permutation of length ``n``.
Uses the underlying Python pseudo-random number generator.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.random(2) in (Permutation([1, 0]), Permutation([0, 1]))
True
"""
perm_array = list(range(n))
random.shuffle(perm_array)
return cls._af_new(perm_array)
@classmethod
def unrank_lex(cls, size, rank):
"""
Lexicographic permutation unranking.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> a = Permutation.unrank_lex(5, 10)
>>> a.rank()
10
>>> a
Permutation([0, 2, 4, 1, 3])
See Also
========
rank, next_lex
"""
perm_array = [0] * size
psize = 1
for i in range(size):
new_psize = psize*(i + 1)
d = (rank % new_psize) // psize
rank -= d*psize
perm_array[size - i - 1] = d
for j in range(size - i, size):
if perm_array[j] > d - 1:
perm_array[j] += 1
psize = new_psize
return cls._af_new(perm_array)
# global flag to control how permutations are printed
# when True, Permutation([0, 2, 1, 3]) -> Cycle(1, 2)
# when False, Permutation([0, 2, 1, 3]) -> Permutation([0, 2, 1])
print_cyclic = True
def _merge(arr, temp, left, mid, right):
"""
Merges two sorted arrays and calculates the inversion count.
Helper function for calculating inversions. This method is
for internal use only.
"""
i = k = left
j = mid
inv_count = 0
while i < mid and j <= right:
if arr[i] < arr[j]:
temp[k] = arr[i]
k += 1
i += 1
else:
temp[k] = arr[j]
k += 1
j += 1
inv_count += (mid -i)
while i < mid:
temp[k] = arr[i]
k += 1
i += 1
if j <= right:
k += right - j + 1
j += right - j + 1
arr[left:k + 1] = temp[left:k + 1]
else:
arr[left:right + 1] = temp[left:right + 1]
return inv_count
Perm = Permutation
_af_new = Perm._af_new
| 80,155 | 27.075657 | 91 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/free_groups.py
|
# -*- coding: utf-8 -*-
from __future__ import print_function, division
from sympy.core.basic import Basic
from sympy.core.compatibility import is_sequence, as_int, string_types
from sympy.core.expr import Expr
from sympy.core.symbol import Symbol, symbols as _symbols
from sympy.core.sympify import CantSympify
from mpmath import isint
from sympy.core import S
from sympy.printing.defaults import DefaultPrinting
from sympy.utilities import public
from sympy.utilities.iterables import flatten
from sympy.utilities.magic import pollute
from sympy import sign
@public
def free_group(symbols):
"""Construct a free group returning ``(FreeGroup, (f_0, f_1, ..., f_(n-1))``.
Parameters
----------
symbols : str, Symbol/Expr or sequence of str, Symbol/Expr (may be empty)
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y, z = free_group("x, y, z")
>>> F
<free group on the generators (x, y, z)>
>>> x**2*y**-1
x**2*y**-1
>>> type(_)
<class 'sympy.combinatorics.free_groups.FreeGroupElement'>
"""
_free_group = FreeGroup(symbols)
return (_free_group,) + tuple(_free_group.generators)
@public
def xfree_group(symbols):
"""Construct a free group returning ``(FreeGroup, (f_0, f_1, ..., f_(n-1)))``.
Parameters
----------
symbols : str, Symbol/Expr or sequence of str, Symbol/Expr (may be empty)
Examples
========
>>> from sympy.combinatorics.free_groups import xfree_group
>>> F, (x, y, z) = xfree_group("x, y, z")
>>> F
<free group on the generators (x, y, z)>
>>> y**2*x**-2*z**-1
y**2*x**-2*z**-1
>>> type(_)
<class 'sympy.combinatorics.free_groups.FreeGroupElement'>
"""
_free_group = FreeGroup(symbols)
return (_free_group, _free_group.generators)
@public
def vfree_group(symbols):
"""Construct a free group and inject ``f_0, f_1, ..., f_(n-1)`` as symbols
into the global namespace.
Parameters
----------
symbols : str, Symbol/Expr or sequence of str, Symbol/Expr (may be empty)
Examples
========
>>> from sympy.combinatorics.free_groups import vfree_group
>>> vfree_group("x, y, z")
<free group on the generators (x, y, z)>
>>> x**2*y**-2*z
x**2*y**-2*z
>>> type(_)
<class 'sympy.combinatorics.free_groups.FreeGroupElement'>
"""
_free_group = FreeGroup(symbols)
pollute([sym.name for sym in _free_group.symbols], _free_group.generators)
return _free_group
def _parse_symbols(symbols):
if not symbols:
return tuple()
if isinstance(symbols, string_types):
return _symbols(symbols, seq=True)
elif isinstance(symbols, Expr or FreeGroupElement):
return (symbols,)
elif is_sequence(symbols):
if all(isinstance(s, string_types) for s in symbols):
return _symbols(symbols)
elif all(isinstance(s, Expr) for s in symbols):
return symbols
raise ValueError("The type of `symbols` must be one of the following: "
"a str, Symbol/Expr or a sequence of "
"one of these types")
##############################################################################
# FREE GROUP #
##############################################################################
_free_group_cache = {}
class FreeGroup(DefaultPrinting):
"""
Free group with finite or infinite number of generators. Its input API
is that of a str, Symbol/Expr or a sequence of one of
these types (which may be empty)
References
==========
[1] http://www.gap-system.org/Manuals/doc/ref/chap37.html
[2] https://en.wikipedia.org/wiki/Free_group
See Also
========
sympy.polys.rings.PolyRing
"""
is_associative = True
is_group = True
is_FreeGroup = True
is_PermutationGroup = False
def __new__(cls, symbols):
symbols = tuple(_parse_symbols(symbols))
rank = len(symbols)
_hash = hash((cls.__name__, symbols, rank))
obj = _free_group_cache.get(_hash)
if obj is None:
obj = object.__new__(cls)
obj._hash = _hash
obj._rank = rank
# dtype method is used to create new instances of FreeGroupElement
obj.dtype = type("FreeGroupElement", (FreeGroupElement,), {"group": obj})
obj.symbols = symbols
obj.generators = obj._generators()
obj._gens_set = set(obj.generators)
for symbol, generator in zip(obj.symbols, obj.generators):
if isinstance(symbol, Symbol):
name = symbol.name
if hasattr(obj, name):
setattr(obj, name, generator)
_free_group_cache[_hash] = obj
return obj
def _generators(group):
"""Returns the generators of the FreeGroup.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y, z = free_group("x, y, z")
>>> F.generators
(x, y, z)
"""
gens = []
for sym in group.symbols:
elm = ((sym, 1),)
gens.append(group.dtype(elm))
return tuple(gens)
def clone(self, symbols=None):
return self.__class__(symbols or self.symbols)
def __contains__(self, i):
"""Return True if ``i`` is contained in FreeGroup."""
if not isinstance(i, FreeGroupElement):
raise TypeError("FreeGroup contains only FreeGroupElement as elements "
", not elements of type %s" % type(i))
group = i.group
return self == group
def __hash__(self):
return self._hash
def __len__(self):
return self.rank
def __str__(self):
if self.rank > 30:
str_form = "<free group with %s generators>" % self.rank
else:
str_form = "<free group on the generators "
gens = self.generators
str_form += str(gens) + ">"
return str_form
__repr__ = __str__
def __getitem__(self, index):
symbols = self.symbols[index]
return self.clone(symbols=symbols)
def __eq__(self, other):
"""No ``FreeGroup`` is equal to any "other" ``FreeGroup``.
"""
return self is other
def index(self, gen):
"""Returns the index of the generator `gen` from ``(f_0, ..., f_(n-1))``.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y = free_group("x, y")
>>> F.index(y)
1
"""
if isinstance(gen, self.dtype):
return self.generators.index(gen)
else:
raise ValueError("expected a generator of Free Group %s, got %s" % (self, gen))
def order(self):
"""Returns the order of the free group."""
if self.rank == 0:
return 1
else:
return S.Infinity
@property
def elements(self):
if self.rank == 0:
# A set containing Identity element of `FreeGroup` self is returned
return {self.identity}
else:
raise ValueError("Group contains infinitely many elements"
", hence can't be represented")
@property
def rank(self):
r"""
In group theory, the `rank` of a group `G`, denoted `G.rank`,
can refer to the smallest cardinality of a generating set
for G, that is
\operatorname{rank}(G)=\min\{ |X|: X\subseteq G, \langle X\rangle =G\}.
"""
return self._rank
def _symbol_index(self, symbol):
"""Returns the index of a generator for free group `self`, while
returns the -ve index of the inverse generator.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy import Symbol
>>> F, x, y = free_group("x, y")
>>> F._symbol_index(-Symbol('x'))
0
"""
try:
return self.symbols.index(symbol)
except ValueError:
return -self.symbols.index(-symbol)
@property
def is_abelian(self):
"""Returns if the group is Abelian.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, x, y, z = free_group("x y z")
>>> f.is_abelian
False
"""
if self.rank == 0 or self.rank == 1:
return True
else:
return False
@property
def identity(self):
"""Returns the identity element of free group."""
return self.dtype()
def contains(self, g):
"""Tests if Free Group element ``g`` belong to self, ``G``.
In mathematical terms any linear combination of generators
of a Free Group is contained in it.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, x, y, z = free_group("x y z")
>>> f.contains(x**3*y**2)
True
"""
if not isinstance(g, FreeGroupElement):
return False
elif self != g.group:
return False
else:
return True
def is_subgroup(self, F):
"""Return True if all elements of `self` belong to `F`."""
return F.is_group and all([self.contains(gen) for gen in F.generators])
def center(self):
"""Returns the center of the free group `self`."""
return {self.identity}
############################################################################
# FreeGroupElement #
############################################################################
class FreeGroupElement(CantSympify, DefaultPrinting, tuple):
"""Used to create elements of FreeGroup. It can not be used directly to
create a free group element. It is called by the `dtype` method of the
`FreeGroup` class.
"""
is_assoc_word = True
def new(self, init):
return self.__class__(init)
_hash = None
def __hash__(self):
_hash = self._hash
if _hash is None:
self._hash = _hash = hash((self.group, frozenset(tuple(self))))
return _hash
def copy(self):
return self.new(self)
@property
def is_identity(self):
if self.array_form == tuple():
return True
else:
return False
@property
def array_form(self):
"""
SymPy provides two different internal kinds of representation
of associative words. The first one is called the `array_form`
which is a tuple containing `tuples` as its elements, where the
size of each tuple is two. At the first position the tuple
contains the `symbol-generator`, while at the second position
of tuple contains the exponent of that generator at the position.
Since elements (i.e. words) don't commute, the indexing of tuple
makes that property to stay.
The structure in ``array_form`` of ``FreeGroupElement`` is of form:
``( ( symbol_of_gen , exponent ), ( , ), ... ( , ) )``
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, x, y, z = free_group("x y z")
>>> (x*z).array_form
((x, 1), (z, 1))
>>> (x**2*z*y*x**2).array_form
((x, 2), (z, 1), (y, 1), (x, 2))
See Also
========
letter_repr
"""
return tuple(self)
@property
def letter_form(self):
"""
The letter representation of a ``FreeGroupElement`` is a tuple
of generator symbols, with each entry corresponding to a group
generator. Inverses of the generators are represented by
negative generator symbols.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, a, b, c, d = free_group("a b c d")
>>> (a**3).letter_form
(a, a, a)
>>> (a**2*d**-2*a*b**-4).letter_form
(a, a, -d, -d, a, -b, -b, -b, -b)
>>> (a**-2*b**3*d).letter_form
(-a, -a, b, b, b, d)
See Also
========
array_form
"""
return tuple(flatten([(i,)*j if j > 0 else (-i,)*(-j)
for i, j in self.array_form]))
def __getitem__(self, i):
group = self.group
r = self.letter_form[i]
if r.is_Symbol:
return group.dtype(((r, 1),))
else:
return group.dtype(((-r, -1),))
def index(self, gen):
if len(gen) != 1:
raise ValueError()
return (self.letter_form).index(gen.letter_form[0])
@property
def letter_form_elm(self):
"""
"""
group = self.group
r = self.letter_form
return [group.dtype(((elm,1),)) if elm.is_Symbol \
else group.dtype(((-elm,-1),)) for elm in r]
@property
def ext_rep(self):
"""This is called the External Representation of ``FreeGroupElement``
"""
return tuple(flatten(self.array_form))
def __contains__(self, gen):
return gen.array_form[0][0] in tuple([r[0] for r in self.array_form])
def __str__(self):
if self.is_identity:
return "<identity>"
symbols = self.group.symbols
str_form = ""
array_form = self.array_form
for i in range(len(array_form)):
if i == len(array_form) - 1:
if array_form[i][1] == 1:
str_form += str(array_form[i][0])
else:
str_form += str(array_form[i][0]) + \
"**" + str(array_form[i][1])
else:
if array_form[i][1] == 1:
str_form += str(array_form[i][0]) + "*"
else:
str_form += str(array_form[i][0]) + \
"**" + str(array_form[i][1]) + "*"
return str_form
__repr__ = __str__
def __pow__(self, n):
n = as_int(n)
group = self.group
if n == 0:
return group.identity
if n < 0:
n = -n
return (self.inverse())**n
result = self
for i in range(n - 1):
result = result*self
# this method can be improved instead of just returning the
# multiplication of elements
return result
def __mul__(self, other):
"""Returns the product of elements belonging to the same ``FreeGroup``.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, x, y, z = free_group("x y z")
>>> x*y**2*y**-4
x*y**-2
>>> z*y**-2
z*y**-2
>>> x**2*y*y**-1*x**-2
<identity>
"""
group = self.group
if not isinstance(other, group.dtype):
raise TypeError("only FreeGroup elements of same FreeGroup can "
"be multiplied")
if self.is_identity:
return other
if other.is_identity:
return self
r = list(self.array_form + other.array_form)
zero_mul_simp(r, len(self.array_form) - 1)
return group.dtype(tuple(r))
def __div__(self, other):
group = self.group
if not isinstance(other, group.dtype):
raise TypeError("only FreeGroup elements of same FreeGroup can "
"be multiplied")
return self*(other.inverse())
def __rdiv__(self, other):
group = self.group
if not isinstance(other, group.dtype):
raise TypeError("only FreeGroup elements of same FreeGroup can "
"be multiplied")
return other*(self.inverse())
__truediv__ = __div__
__rtruediv__ = __rdiv__
def __add__(self, other):
return NotImplemented
def inverse(self):
"""
Returns the inverse of a ``FreeGroupElement`` element
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, x, y, z = free_group("x y z")
>>> x.inverse()
x**-1
>>> (x*y).inverse()
y**-1*x**-1
"""
group = self.group
r = tuple([(i, -j) for i, j in self.array_form[::-1]])
return group.dtype(r)
def order(self):
"""Find the order of a ``FreeGroupElement``.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, x, y = free_group("x y")
>>> (x**2*y*y**-1*x**-2).order()
1
"""
if self.is_identity:
return 1
else:
return S.Infinity
def commutator(self, other):
"""
Return the commutator of `self` and `x`: ``~x*~self*x*self``
"""
group = self.group
if not isinstance(other, group.dtype):
raise ValueError("commutator of only FreeGroupElement of the same "
"FreeGroup exists")
else:
return self.inverse()*other.inverse()*self*other
def eliminate_words(self, words, _all=False):
'''
Replace each subword from the dictionary `words` by words[subword].
If words is a list, replace the words by the identity.
'''
new = self
if isinstance(words, dict):
for sub in words:
new = new.eliminate_word(sub, words[sub], _all=_all)
else:
for sub in words:
new = new.eliminate_word(sub, _all=_all)
return new
def eliminate_word(self, gen, by=None, _all=False):
"""
For an associative word `self`, a subword `gen`, and an associative
word `by` (identity by default), return the associative word obtained by
replacing each occurrence of `gen` in `self` by `by`. If `_all = True`,
the occurrences of `gen` that may appear after the first substitution will
also be replaced and so on until no occurrences are found. This might not
always terminate (e.g. `(x).eliminate_word(x, x**2, _all=True)`).
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, x, y = free_group("x y")
>>> w = x**5*y*x**2*y**-4*x
>>> w.eliminate_word( x, x**2 )
x**10*y*x**4*y**-4*x**2
>>> w.eliminate_word( x, y**-1 )
y**-11
>>> w.eliminate_word(x**5)
y*x**2*y**-4*x
>>> w.eliminate_word(x*y, y)
x**4*y*x**2*y**-4*x
See Also
========
substituted_word
"""
if by == None:
by = self.group.identity
if self.is_independent(gen) or gen == by:
return self
if gen == self:
return by
if gen**-1 == by:
_all = False
word = self
l = len(gen)
try:
i = word.subword_index(gen)
k = 1
except ValueError:
try:
i = word.subword_index(gen**-1)
k = -1
except ValueError:
return word
word = word.subword(0, i)*by**k*word.subword(i+l, len(word)).eliminate_word(gen, by)
if _all:
return word.eliminate_word(gen, by, _all=True)
else:
return word
def __len__(self):
"""
For an associative word `self`, returns the number of letters in it.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, a, b = free_group("a b")
>>> w = a**5*b*a**2*b**-4*a
>>> len(w)
13
>>> len(a**17)
17
>>> len(w**0)
0
"""
return sum([abs(j) for (i, j) in self])
def __eq__(self, other):
"""
Two associative words are equal if they are words over the
same alphabet and if they are sequences of the same letters.
This is equivalent to saying that the external representations
of the words are equal.
There is no "universal" empty word, every alphabet has its own
empty word.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, swapnil0, swapnil1 = free_group("swapnil0 swapnil1")
>>> f
<free group on the generators (swapnil0, swapnil1)>
>>> g, swap0, swap1 = free_group("swap0 swap1")
>>> g
<free group on the generators (swap0, swap1)>
>>> swapnil0 == swapnil1
False
>>> swapnil0*swapnil1 == swapnil1/swapnil1*swapnil0*swapnil1
True
>>> swapnil0*swapnil1 == swapnil1*swapnil0
False
>>> swapnil1**0 == swap0**0
False
"""
group = self.group
if not isinstance(other, group.dtype):
return False
return tuple.__eq__(self, other)
def __lt__(self, other):
"""
The ordering of associative words is defined by length and
lexicography (this ordering is called short-lex ordering), that
is, shorter words are smaller than longer words, and words of the
same length are compared w.r.t. the lexicographical ordering induced
by the ordering of generators. Generators are sorted according
to the order in which they were created. If the generators are
invertible then each generator `g` is larger than its inverse `g^{-1}`,
and `g^{-1}` is larger than every generator that is smaller than `g`.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, a, b = free_group("a b")
>>> b < a
False
>>> a < a.inverse()
False
"""
group = self.group
if not isinstance(other, group.dtype):
raise TypeError("only FreeGroup elements of same FreeGroup can "
"be compared")
l = len(self)
m = len(other)
# implement lenlex order
if l < m:
return True
elif l > m:
return False
a = self.letter_form
b = other.letter_form
for i in range(l):
p = group._symbol_index(a[i])
q = group._symbol_index(b[i])
if abs(p) < abs(q):
return True
elif abs(p) > abs(q):
return False
elif p < q:
return True
elif p > q:
return False
return False
def __le__(self, other):
return (self == other or self < other)
def __gt__(self, other):
"""
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, x, y, z = free_group("x y z")
>>> y**2 > x**2
True
>>> y*z > z*y
False
>>> x > x.inverse()
True
"""
group = self.group
if not isinstance(other, group.dtype):
raise TypeError("only FreeGroup elements of same FreeGroup can "
"be compared")
return not self <= other
def __ge__(self, other):
return not self < other
def exponent_sum(self, gen):
"""
For an associative word `self` and a generator or inverse of generator
`gen`, ``exponent_sum`` returns the number of times `gen` appears in
`self` minus the number of times its inverse appears in `self`. If
neither `gen` nor its inverse occur in `self` then 0 is returned.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y = free_group("x, y")
>>> w = x**2*y**3
>>> w.exponent_sum(x)
2
>>> w.exponent_sum(x**-1)
-2
>>> w = x**2*y**4*x**-3
>>> w.exponent_sum(x)
-1
See Also
========
generator_count
"""
if len(gen) != 1:
raise ValueError("gen must be a generator or inverse of a generator")
s = gen.array_form[0]
return s[1]*sum([i[1] for i in self.array_form if i[0] == s[0]])
def generator_count(self, gen):
"""
For an associative word `self` and a generator `gen`,
``generator_count`` returns the multiplicity of generator
`gen` in `self`.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y = free_group("x, y")
>>> w = x**2*y**3
>>> w.generator_count(x)
2
>>> w = x**2*y**4*x**-3
>>> w.generator_count(x)
5
See Also
========
exponent_sum
"""
if len(gen) != 1 or gen.array_form[0][1] < 0:
raise ValueError("gen must be a generator")
s = gen.array_form[0]
return s[1]*sum([abs(i[1]) for i in self.array_form if i[0] == s[0]])
def subword(self, from_i, to_j):
"""
For an associative word `self` and two positive integers `from_i` and
`to_j`, `subword` returns the subword of `self` that begins at position
`from_i` and ends at `to_j - 1`, indexing is done with origin 0.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, a, b = free_group("a b")
>>> w = a**5*b*a**2*b**-4*a
>>> w.subword(2, 6)
a**3*b
"""
group = self.group
if from_i < 0 or to_j > len(self):
raise ValueError("`from_i`, `to_j` must be positive and no greater than "
"the length of associative word")
if to_j <= from_i:
return group.identity
else:
letter_form = self.letter_form[from_i: to_j]
array_form = letter_form_to_array_form(letter_form, group)
return group.dtype(array_form)
def subword_index(self, word, start = 0):
'''
Find the index of `word` in `self`.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, a, b = free_group("a b")
>>> w = a**2*b*a*b**3
>>> w.subword_index(a*b*a*b)
1
'''
l = len(word)
self_lf = self.letter_form
word_lf = word.letter_form
index = None
for i in range(start,len(self_lf)-l+1):
if self_lf[i:i+l] == word_lf:
index = i
break
if index is not None:
return index
else:
raise ValueError("The given word is not a subword of self")
def is_dependent(self, word):
"""
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y = free_group("x, y")
>>> (x**4*y**-3).is_dependent(x**4*y**-2)
True
>>> (x**2*y**-1).is_dependent(x*y)
False
>>> (x*y**2*x*y**2).is_dependent(x*y**2)
True
>>> (x**12).is_dependent(x**-4)
True
See Also
========
is_independent
"""
try:
return self.subword_index(word) != None
except ValueError:
pass
try:
return self.subword_index(word**-1) != None
except ValueError:
return False
def is_independent(self, word):
"""
See Also
========
is_dependent
"""
return not self.is_dependent(word)
def contains_generators(self):
"""
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y, z = free_group("x, y, z")
>>> (x**2*y**-1).contains_generators()
{x, y}
>>> (x**3*z).contains_generators()
{x, z}
"""
group = self.group
gens = set()
for syllable in self.array_form:
gens.add(group.dtype(((syllable[0], 1),)))
return set(gens)
def cyclic_subword(self, from_i, to_j):
group = self.group
l = len(self)
letter_form = self.letter_form
period1 = int(from_i/l)
if from_i >= l:
from_i -= l*period1
to_j -= l*period1
diff = to_j - from_i
word = letter_form[from_i: to_j]
period2 = int(to_j/l) - 1
word += letter_form*period2 + letter_form[:diff-l+from_i-l*period2]
word = letter_form_to_array_form(word, group)
return group.dtype(word)
def cyclic_conjugates(self):
"""Returns a words which are cyclic to the word `self`.
References
==========
http://planetmath.org/cyclicpermutation
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y = free_group("x, y")
>>> w = x*y*x*y*x
>>> w.cyclic_conjugates()
{x*y*x**2*y, x**2*y*x*y, y*x*y*x**2, y*x**2*y*x, x*y*x*y*x}
>>> s = x*y*x**2*y*x
>>> s.cyclic_conjugates()
{x**2*y*x**2*y, y*x**2*y*x**2, x*y*x**2*y*x}
"""
return {self.cyclic_subword(i, i+len(self)) for i in range(len(self))}
def is_cyclic_conjugate(self, w):
"""
Checks whether words ``self``, ``w`` are cyclic conjugates.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y = free_group("x, y")
>>> w1 = x**2*y**5
>>> w2 = x*y**5*x
>>> w1.is_cyclic_conjugate(w2)
True
>>> w3 = x**-1*y**5*x**-1
>>> w3.is_cyclic_conjugate(w2)
False
"""
l1 = len(self)
l2 = len(w)
if l1 != l2:
return False
w1 = self.identity_cyclic_reduction()
w2 = w.identity_cyclic_reduction()
letter1 = w1.letter_form
letter2 = w2.letter_form
str1 = ' '.join(map(str, letter1))
str2 = ' '.join(map(str, letter2))
if len(str1) != len(str2):
return False
return str1 in str2 + ' ' + str2
def number_syllables(self):
"""Returns the number of syllables of the associative word `self`.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, swapnil0, swapnil1 = free_group("swapnil0 swapnil1")
>>> (swapnil1**3*swapnil0*swapnil1**-1).number_syllables()
3
"""
return len(self.array_form)
def exponent_syllable(self, i):
"""
Returns the exponent of the `i`-th syllable of the associative word
`self`.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, a, b = free_group("a b")
>>> w = a**5*b*a**2*b**-4*a
>>> w.exponent_syllable( 2 )
2
"""
return self.array_form[i][1]
def generator_syllable(self, i):
"""
Returns the symbol of the generator that is involved in the
i-th syllable of the associative word `self`.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, a, b = free_group("a b")
>>> w = a**5*b*a**2*b**-4*a
>>> w.generator_syllable( 3 )
b
"""
return self.array_form[i][0]
def sub_syllables(self, from_i, to_j):
"""
`sub_syllables` returns the subword of the associative word `self` that
consists of syllables from positions `from_to` to `to_j`, where
`from_to` and `to_j` must be positive integers and indexing is done
with origin 0.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> f, a, b = free_group("a, b")
>>> w = a**5*b*a**2*b**-4*a
>>> w.sub_syllables(1, 2)
b
>>> w.sub_syllables(3, 3)
<identity>
"""
if not isinstance(from_i, int) or not isinstance(to_j, int):
raise ValueError("both arguments should be integers")
group = self.group
if to_j <= from_i:
return group.identity
else:
r = tuple(self.array_form[from_i: to_j])
return group.dtype(r)
def substituted_word(self, from_i, to_j, by):
"""
Returns the associative word obtained by replacing the subword of
`self` that begins at position `from_i` and ends at position `to_j - 1`
by the associative word `by`. `from_i` and `to_j` must be positive
integers, indexing is done with origin 0. In other words,
`w.substituted_word(w, from_i, to_j, by)` is the product of the three
words: `w.subword(0, from_i)`, `by`, and
`w.subword(to_j len(w))`.
See Also
========
eliminate_word
"""
lw = len(self)
if from_i >= to_j or from_i > lw or to_j > lw:
raise ValueError("values should be within bounds")
# otherwise there are four possibilities
# first if from=1 and to=lw then
if from_i == 0 and to_j == lw:
return by
elif from_i == 0: # second if from_i=1 (and to_j < lw) then
return by*self.subword(to_j, lw)
elif to_j == lw: # third if to_j=1 (and from_i > 1) then
return self.subword(0, from_i)*by
else: # finally
return self.subword(0, from_i)*by*self.subword(to_j, lw)
def is_cyclically_reduced(self):
r"""Returns whether the word is cyclically reduced or not.
A word is cyclically reduced if by forming the cycle of the
word, the word is not reduced, i.e a word w = `a_1 ... a_n`
is called cyclically reduced if `a_1 \ne a_n^{−1}`.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y = free_group("x, y")
>>> (x**2*y**-1*x**-1).is_cyclically_reduced()
False
>>> (y*x**2*y**2).is_cyclically_reduced()
True
"""
if not self:
return True
return self[0] != self[-1]**-1
def identity_cyclic_reduction(self):
"""Return a unique cyclically reduced version of the word.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y = free_group("x, y")
>>> (x**2*y**2*x**-1).identity_cyclic_reduction()
x*y**2
>>> (x**-3*y**-1*x**5).identity_cyclic_reduction()
x**2*y**-1
References
==========
http://planetmath.org/cyclicallyreduced
"""
word = self.copy()
group = self.group
while not word.is_cyclically_reduced():
exp1 = word.exponent_syllable(0)
exp2 = word.exponent_syllable(-1)
r = exp1 + exp2
if r == 0:
rep = word.array_form[1: word.number_syllables() - 1]
else:
rep = ((word.generator_syllable(0), exp1 + exp2),) + \
word.array_form[1: word.number_syllables() - 1]
word = group.dtype(rep)
return word
def letter_form_to_array_form(array_form, group):
"""
This method converts a list given with possible repetitions of elements in
it. It returns a new list such that repetitions of consecutive elements is
removed and replace with a tuple element of size two such that the first
index contains `value` and the second index contains the number of
consecutive repetitions of `value`.
"""
a = list(array_form[:])
new_array = []
n = 1
symbols = group.symbols
for i in range(len(a)):
if i == len(a) - 1:
if a[i] == a[i - 1]:
if (-a[i]) in symbols:
new_array.append((-a[i], -n))
else:
new_array.append((a[i], n))
else:
if (-a[i]) in symbols:
new_array.append((-a[i], -1))
else:
new_array.append((a[i], 1))
return new_array
elif a[i] == a[i + 1]:
n += 1
else:
if (-a[i]) in symbols:
new_array.append((-a[i], -n))
else:
new_array.append((a[i], n))
n = 1
def zero_mul_simp(l, index):
"""Used to combine two reduced words."""
while index >=0 and index < len(l) - 1 and l[index][0] is l[index + 1][0]:
exp = l[index][1] + l[index + 1][1]
base = l[index][0]
l[index] = (base, exp)
del l[index + 1]
if l[index][1] == 0:
del l[index]
index -= 1
| 37,152 | 28.580414 | 92 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/named_groups.py
|
from __future__ import print_function, division
from sympy.core.compatibility import range
from sympy.combinatorics.perm_groups import PermutationGroup
from sympy.combinatorics.group_constructs import DirectProduct
from sympy.combinatorics.permutations import Permutation
_af_new = Permutation._af_new
def AbelianGroup(*cyclic_orders):
"""
Returns the direct product of cyclic groups with the given orders.
According to the structure theorem for finite abelian groups ([1]),
every finite abelian group can be written as the direct product of
finitely many cyclic groups.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.named_groups import AbelianGroup
>>> AbelianGroup(3, 4)
PermutationGroup([
(6)(0 1 2),
(3 4 5 6)])
>>> _.is_group
True
See Also
========
DirectProduct
References
==========
[1] http://groupprops.subwiki.org/wiki/Structure_theorem_for_finitely_generated_abelian_groups
"""
groups = []
degree = 0
order = 1
for size in cyclic_orders:
degree += size
order *= size
groups.append(CyclicGroup(size))
G = DirectProduct(*groups)
G._is_abelian = True
G._degree = degree
G._order = order
return G
def AlternatingGroup(n):
"""
Generates the alternating group on ``n`` elements as a permutation group.
For ``n > 2``, the generators taken are ``(0 1 2), (0 1 2 ... n-1)`` for
``n`` odd
and ``(0 1 2), (1 2 ... n-1)`` for ``n`` even (See [1], p.31, ex.6.9.).
After the group is generated, some of its basic properties are set.
The cases ``n = 1, 2`` are handled separately.
Examples
========
>>> from sympy.combinatorics.named_groups import AlternatingGroup
>>> G = AlternatingGroup(4)
>>> G.is_group
True
>>> a = list(G.generate_dimino())
>>> len(a)
12
>>> all(perm.is_even for perm in a)
True
See Also
========
SymmetricGroup, CyclicGroup, DihedralGroup
References
==========
[1] Armstrong, M. "Groups and Symmetry"
"""
# small cases are special
if n in (1, 2):
return PermutationGroup([Permutation([0])])
a = list(range(n))
a[0], a[1], a[2] = a[1], a[2], a[0]
gen1 = a
if n % 2:
a = list(range(1, n))
a.append(0)
gen2 = a
else:
a = list(range(2, n))
a.append(1)
a.insert(0, 0)
gen2 = a
gens = [gen1, gen2]
if gen1 == gen2:
gens = gens[:1]
G = PermutationGroup([_af_new(a) for a in gens], dups=False)
if n < 4:
G._is_abelian = True
G._is_nilpotent = True
else:
G._is_abelian = False
G._is_nilpotent = False
if n < 5:
G._is_solvable = True
else:
G._is_solvable = False
G._degree = n
G._is_transitive = True
G._is_alt = True
return G
def CyclicGroup(n):
"""
Generates the cyclic group of order ``n`` as a permutation group.
The generator taken is the ``n``-cycle ``(0 1 2 ... n-1)``
(in cycle notation). After the group is generated, some of its basic
properties are set.
Examples
========
>>> from sympy.combinatorics.named_groups import CyclicGroup
>>> G = CyclicGroup(6)
>>> G.is_group
True
>>> G.order()
6
>>> list(G.generate_schreier_sims(af=True))
[[0, 1, 2, 3, 4, 5], [1, 2, 3, 4, 5, 0], [2, 3, 4, 5, 0, 1],
[3, 4, 5, 0, 1, 2], [4, 5, 0, 1, 2, 3], [5, 0, 1, 2, 3, 4]]
See Also
========
SymmetricGroup, DihedralGroup, AlternatingGroup
"""
a = list(range(1, n))
a.append(0)
gen = _af_new(a)
G = PermutationGroup([gen])
G._is_abelian = True
G._is_nilpotent = True
G._is_solvable = True
G._degree = n
G._is_transitive = True
G._order = n
return G
def DihedralGroup(n):
r"""
Generates the dihedral group `D_n` as a permutation group.
The dihedral group `D_n` is the group of symmetries of the regular
``n``-gon. The generators taken are the ``n``-cycle ``a = (0 1 2 ... n-1)``
(a rotation of the ``n``-gon) and ``b = (0 n-1)(1 n-2)...``
(a reflection of the ``n``-gon) in cycle rotation. It is easy to see that
these satisfy ``a**n = b**2 = 1`` and ``bab = ~a`` so they indeed generate
`D_n` (See [1]). After the group is generated, some of its basic properties
are set.
Examples
========
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> G = DihedralGroup(5)
>>> G.is_group
True
>>> a = list(G.generate_dimino())
>>> [perm.cyclic_form for perm in a]
[[], [[0, 1, 2, 3, 4]], [[0, 2, 4, 1, 3]],
[[0, 3, 1, 4, 2]], [[0, 4, 3, 2, 1]], [[0, 4], [1, 3]],
[[1, 4], [2, 3]], [[0, 1], [2, 4]], [[0, 2], [3, 4]],
[[0, 3], [1, 2]]]
See Also
========
SymmetricGroup, CyclicGroup, AlternatingGroup
References
==========
[1] http://en.wikipedia.org/wiki/Dihedral_group
"""
# small cases are special
if n == 1:
return PermutationGroup([Permutation([1, 0])])
if n == 2:
return PermutationGroup([Permutation([1, 0, 3, 2]),
Permutation([2, 3, 0, 1]), Permutation([3, 2, 1, 0])])
a = list(range(1, n))
a.append(0)
gen1 = _af_new(a)
a = list(range(n))
a.reverse()
gen2 = _af_new(a)
G = PermutationGroup([gen1, gen2])
# if n is a power of 2, group is nilpotent
if n & (n-1) == 0:
G._is_nilpotent = True
else:
G._is_nilpotent = False
G._is_abelian = False
G._is_solvable = True
G._degree = n
G._is_transitive = True
G._order = 2*n
return G
def SymmetricGroup(n):
"""
Generates the symmetric group on ``n`` elements as a permutation group.
The generators taken are the ``n``-cycle
``(0 1 2 ... n-1)`` and the transposition ``(0 1)`` (in cycle notation).
(See [1]). After the group is generated, some of its basic properties
are set.
Examples
========
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> G = SymmetricGroup(4)
>>> G.is_group
True
>>> G.order()
24
>>> list(G.generate_schreier_sims(af=True))
[[0, 1, 2, 3], [1, 2, 3, 0], [2, 3, 0, 1], [3, 1, 2, 0], [0, 2, 3, 1],
[1, 3, 0, 2], [2, 0, 1, 3], [3, 2, 0, 1], [0, 3, 1, 2], [1, 0, 2, 3],
[2, 1, 3, 0], [3, 0, 1, 2], [0, 1, 3, 2], [1, 2, 0, 3], [2, 3, 1, 0],
[3, 1, 0, 2], [0, 2, 1, 3], [1, 3, 2, 0], [2, 0, 3, 1], [3, 2, 1, 0],
[0, 3, 2, 1], [1, 0, 3, 2], [2, 1, 0, 3], [3, 0, 2, 1]]
See Also
========
CyclicGroup, DihedralGroup, AlternatingGroup
References
==========
[1] http://en.wikipedia.org/wiki/Symmetric_group#Generators_and_relations
"""
if n == 1:
G = PermutationGroup([Permutation([0])])
elif n == 2:
G = PermutationGroup([Permutation([1, 0])])
else:
a = list(range(1, n))
a.append(0)
gen1 = _af_new(a)
a = list(range(n))
a[0], a[1] = a[1], a[0]
gen2 = _af_new(a)
G = PermutationGroup([gen1, gen2])
if n < 3:
G._is_abelian = True
G._is_nilpotent = True
else:
G._is_abelian = False
G._is_nilpotent = False
if n < 5:
G._is_solvable = True
else:
G._is_solvable = False
G._degree = n
G._is_transitive = True
G._is_sym = True
return G
def RubikGroup(n):
"""Return a group of Rubik's cube generators
>>> from sympy.combinatorics.named_groups import RubikGroup
>>> RubikGroup(2).is_group
True
"""
from sympy.combinatorics.generators import rubik
if n <= 1:
raise ValueError("Invalid cube . n has to be greater than 1")
return PermutationGroup(rubik(n))
| 7,906 | 24.588997 | 98 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/graycode.py
|
from __future__ import print_function, division
from sympy.core import Basic
from sympy.core.compatibility import range
import random
class GrayCode(Basic):
"""
A Gray code is essentially a Hamiltonian walk on
a n-dimensional cube with edge length of one.
The vertices of the cube are represented by vectors
whose values are binary. The Hamilton walk visits
each vertex exactly once. The Gray code for a 3d
cube is ['000','100','110','010','011','111','101',
'001'].
A Gray code solves the problem of sequentially
generating all possible subsets of n objects in such
a way that each subset is obtained from the previous
one by either deleting or adding a single object.
In the above example, 1 indicates that the object is
present, and 0 indicates that its absent.
Gray codes have applications in statistics as well when
we want to compute various statistics related to subsets
in an efficient manner.
References:
[1] Nijenhuis,A. and Wilf,H.S.(1978).
Combinatorial Algorithms. Academic Press.
[2] Knuth, D. (2011). The Art of Computer Programming, Vol 4
Addison Wesley
Examples
========
>>> from sympy.combinatorics.graycode import GrayCode
>>> a = GrayCode(3)
>>> list(a.generate_gray())
['000', '001', '011', '010', '110', '111', '101', '100']
>>> a = GrayCode(4)
>>> list(a.generate_gray())
['0000', '0001', '0011', '0010', '0110', '0111', '0101', '0100', \
'1100', '1101', '1111', '1110', '1010', '1011', '1001', '1000']
"""
_skip = False
_current = 0
_rank = None
def __new__(cls, n, *args, **kw_args):
"""
Default constructor.
It takes a single argument ``n`` which gives the dimension of the Gray
code. The starting Gray code string (``start``) or the starting ``rank``
may also be given; the default is to start at rank = 0 ('0...0').
Examples
========
>>> from sympy.combinatorics.graycode import GrayCode
>>> a = GrayCode(3)
>>> a
GrayCode(3)
>>> a.n
3
>>> a = GrayCode(3, start='100')
>>> a.current
'100'
>>> a = GrayCode(4, rank=4)
>>> a.current
'0110'
>>> a.rank
4
"""
if n < 1 or int(n) != n:
raise ValueError(
'Gray code dimension must be a positive integer, not %i' % n)
n = int(n)
args = (n,) + args
obj = Basic.__new__(cls, *args)
if 'start' in kw_args:
obj._current = kw_args["start"]
if len(obj._current) > n:
raise ValueError('Gray code start has length %i but '
'should not be greater than %i' % (len(obj._current), n))
elif 'rank' in kw_args:
if int(kw_args["rank"]) != kw_args["rank"]:
raise ValueError('Gray code rank must be a positive integer, '
'not %i' % kw_args["rank"])
obj._rank = int(kw_args["rank"]) % obj.selections
obj._current = obj.unrank(n, obj._rank)
return obj
def next(self, delta=1):
"""
Returns the Gray code a distance ``delta`` (default = 1) from the
current value in canonical order.
Examples
========
>>> from sympy.combinatorics.graycode import GrayCode
>>> a = GrayCode(3, start='110')
>>> a.next().current
'111'
>>> a.next(-1).current
'010'
"""
return GrayCode(self.n, rank=(self.rank + delta) % self.selections)
@property
def selections(self):
"""
Returns the number of bit vectors in the Gray code.
Examples
========
>>> from sympy.combinatorics.graycode import GrayCode
>>> a = GrayCode(3)
>>> a.selections
8
"""
return 2**self.n
@property
def n(self):
"""
Returns the dimension of the Gray code.
Examples
========
>>> from sympy.combinatorics.graycode import GrayCode
>>> a = GrayCode(5)
>>> a.n
5
"""
return self.args[0]
def generate_gray(self, **hints):
"""
Generates the sequence of bit vectors of a Gray Code.
[1] Knuth, D. (2011). The Art of Computer Programming,
Vol 4, Addison Wesley
Examples
========
>>> from sympy.combinatorics.graycode import GrayCode
>>> a = GrayCode(3)
>>> list(a.generate_gray())
['000', '001', '011', '010', '110', '111', '101', '100']
>>> list(a.generate_gray(start='011'))
['011', '010', '110', '111', '101', '100']
>>> list(a.generate_gray(rank=4))
['110', '111', '101', '100']
See Also
========
skip
"""
bits = self.n
start = None
if "start" in hints:
start = hints["start"]
elif "rank" in hints:
start = GrayCode.unrank(self.n, hints["rank"])
if start is not None:
self._current = start
current = self.current
graycode_bin = gray_to_bin(current)
if len(graycode_bin) > self.n:
raise ValueError('Gray code start has length %i but should '
'not be greater than %i' % (len(graycode_bin), bits))
self._current = int(current, 2)
graycode_int = int(''.join(graycode_bin), 2)
for i in range(graycode_int, 1 << bits):
if self._skip:
self._skip = False
else:
yield self.current
bbtc = (i ^ (i + 1))
gbtc = (bbtc ^ (bbtc >> 1))
self._current = (self._current ^ gbtc)
self._current = 0
def skip(self):
"""
Skips the bit generation.
Examples
========
>>> from sympy.combinatorics.graycode import GrayCode
>>> a = GrayCode(3)
>>> for i in a.generate_gray():
... if i == '010':
... a.skip()
... print(i)
...
000
001
011
010
111
101
100
See Also
========
generate_gray
"""
self._skip = True
@property
def rank(self):
"""
Ranks the Gray code.
A ranking algorithm determines the position (or rank)
of a combinatorial object among all the objects w.r.t.
a given order. For example, the 4 bit binary reflected
Gray code (BRGC) '0101' has a rank of 6 as it appears in
the 6th position in the canonical ordering of the family
of 4 bit Gray codes.
References:
[1] http://statweb.stanford.edu/~susan/courses/s208/node12.html
Examples
========
>>> from sympy.combinatorics.graycode import GrayCode
>>> a = GrayCode(3)
>>> list(a.generate_gray())
['000', '001', '011', '010', '110', '111', '101', '100']
>>> GrayCode(3, start='100').rank
7
>>> GrayCode(3, rank=7).current
'100'
See Also
========
unrank
"""
if self._rank is None:
self._rank = int(gray_to_bin(self.current), 2)
return self._rank
@property
def current(self):
"""
Returns the currently referenced Gray code as a bit string.
Examples
========
>>> from sympy.combinatorics.graycode import GrayCode
>>> GrayCode(3, start='100').current
'100'
"""
rv = self._current or '0'
if type(rv) is not str:
rv = bin(rv)[2:]
return rv.rjust(self.n, '0')
@classmethod
def unrank(self, n, rank):
"""
Unranks an n-bit sized Gray code of rank k. This method exists
so that a derivative GrayCode class can define its own code of
a given rank.
The string here is generated in reverse order to allow for tail-call
optimization.
Examples
========
>>> from sympy.combinatorics.graycode import GrayCode
>>> GrayCode(5, rank=3).current
'00010'
>>> GrayCode.unrank(5, 3)
'00010'
See Also
========
rank
"""
def _unrank(k, n):
if n == 1:
return str(k % 2)
m = 2**(n - 1)
if k < m:
return '0' + _unrank(k, n - 1)
return '1' + _unrank(m - (k % m) - 1, n - 1)
return _unrank(rank, n)
def random_bitstring(n):
"""
Generates a random bitlist of length n.
Examples
========
>>> from sympy.combinatorics.graycode import random_bitstring
>>> random_bitstring(3) # doctest: +SKIP
100
"""
return ''.join([random.choice('01') for i in range(n)])
def gray_to_bin(bin_list):
"""
Convert from Gray coding to binary coding.
We assume big endian encoding.
Examples
========
>>> from sympy.combinatorics.graycode import gray_to_bin
>>> gray_to_bin('100')
'111'
See Also
========
bin_to_gray
"""
b = [bin_list[0]]
for i in range(1, len(bin_list)):
b += str(int(b[i - 1] != bin_list[i]))
return ''.join(b)
def bin_to_gray(bin_list):
"""
Convert from binary coding to gray coding.
We assume big endian encoding.
Examples
========
>>> from sympy.combinatorics.graycode import bin_to_gray
>>> bin_to_gray('111')
'100'
See Also
========
gray_to_bin
"""
b = [bin_list[0]]
for i in range(0, len(bin_list) - 1):
b += str(int(bin_list[i]) ^ int(b[i - 1]))
return ''.join(b)
def get_subset_from_bitstring(super_set, bitstring):
"""
Gets the subset defined by the bitstring.
Examples
========
>>> from sympy.combinatorics.graycode import get_subset_from_bitstring
>>> get_subset_from_bitstring(['a', 'b', 'c', 'd'], '0011')
['c', 'd']
>>> get_subset_from_bitstring(['c', 'a', 'c', 'c'], '1100')
['c', 'a']
See Also
========
graycode_subsets
"""
if len(super_set) != len(bitstring):
raise ValueError("The sizes of the lists are not equal")
return [super_set[i] for i, j in enumerate(bitstring)
if bitstring[i] == '1']
def graycode_subsets(gray_code_set):
"""
Generates the subsets as enumerated by a Gray code.
Examples
========
>>> from sympy.combinatorics.graycode import graycode_subsets
>>> list(graycode_subsets(['a', 'b', 'c']))
[[], ['c'], ['b', 'c'], ['b'], ['a', 'b'], ['a', 'b', 'c'], \
['a', 'c'], ['a']]
>>> list(graycode_subsets(['a', 'b', 'c', 'c']))
[[], ['c'], ['c', 'c'], ['c'], ['b', 'c'], ['b', 'c', 'c'], \
['b', 'c'], ['b'], ['a', 'b'], ['a', 'b', 'c'], ['a', 'b', 'c', 'c'], \
['a', 'b', 'c'], ['a', 'c'], ['a', 'c', 'c'], ['a', 'c'], ['a']]
See Also
========
get_subset_from_bitstring
"""
for bitstring in list(GrayCode(len(gray_code_set)).generate_gray()):
yield get_subset_from_bitstring(gray_code_set, bitstring)
| 11,208 | 26.009639 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/partitions.py
|
from __future__ import print_function, division
from sympy.core import Basic, Dict, sympify
from sympy.core.compatibility import as_int, default_sort_key, range
from sympy.functions.combinatorial.numbers import bell
from sympy.matrices import zeros
from sympy.sets.sets import FiniteSet
from sympy.utilities.iterables import has_dups, flatten, group
from collections import defaultdict
class Partition(FiniteSet):
"""
This class represents an abstract partition.
A partition is a set of disjoint sets whose union equals a given set.
See Also
========
sympy.utilities.iterables.partitions,
sympy.utilities.iterables.multiset_partitions
"""
_rank = None
_partition = None
def __new__(cls, *partition):
"""
Generates a new partition object.
This method also verifies if the arguments passed are
valid and raises a ValueError if they are not.
Examples
========
>>> from sympy.combinatorics.partitions import Partition
>>> a = Partition([1, 2], [3])
>>> a
{{3}, {1, 2}}
>>> a.partition
[[1, 2], [3]]
>>> len(a)
2
>>> a.members
(1, 2, 3)
"""
args = partition
if not all(isinstance(part, (list, FiniteSet)) for part in args):
raise ValueError(
"Each argument to Partition should be a list or a FiniteSet")
# sort so we have a canonical reference for RGS
partition = sorted(sum(partition, []), key=default_sort_key)
if has_dups(partition):
raise ValueError("Partition contained duplicated elements.")
obj = FiniteSet.__new__(cls, *[FiniteSet(*x) for x in args])
obj.members = tuple(partition)
obj.size = len(partition)
return obj
def sort_key(self, order=None):
"""Return a canonical key that can be used for sorting.
Ordering is based on the size and sorted elements of the partition
and ties are broken with the rank.
Examples
========
>>> from sympy.utilities.iterables import default_sort_key
>>> from sympy.combinatorics.partitions import Partition
>>> from sympy.abc import x
>>> a = Partition([1, 2])
>>> b = Partition([3, 4])
>>> c = Partition([1, x])
>>> d = Partition(list(range(4)))
>>> l = [d, b, a + 1, a, c]
>>> l.sort(key=default_sort_key); l
[{{1, 2}}, {{1}, {2}}, {{1, x}}, {{3, 4}}, {{0, 1, 2, 3}}]
"""
if order is None:
members = self.members
else:
members = tuple(sorted(self.members,
key=lambda w: default_sort_key(w, order)))
return list(map(default_sort_key, (self.size, members, self.rank)))
@property
def partition(self):
"""Return partition as a sorted list of lists.
Examples
========
>>> from sympy.combinatorics.partitions import Partition
>>> Partition([1], [2, 3]).partition
[[1], [2, 3]]
"""
if self._partition is None:
self._partition = sorted([sorted(p, key=default_sort_key)
for p in self.args])
return self._partition
def __add__(self, other):
"""
Return permutation whose rank is ``other`` greater than current rank,
(mod the maximum rank for the set).
Examples
========
>>> from sympy.combinatorics.partitions import Partition
>>> a = Partition([1, 2], [3])
>>> a.rank
1
>>> (a + 1).rank
2
>>> (a + 100).rank
1
"""
other = as_int(other)
offset = self.rank + other
result = RGS_unrank((offset) %
RGS_enum(self.size),
self.size)
return Partition.from_rgs(result, self.members)
def __sub__(self, other):
"""
Return permutation whose rank is ``other`` less than current rank,
(mod the maximum rank for the set).
Examples
========
>>> from sympy.combinatorics.partitions import Partition
>>> a = Partition([1, 2], [3])
>>> a.rank
1
>>> (a - 1).rank
0
>>> (a - 100).rank
1
"""
return self.__add__(-other)
def __le__(self, other):
"""
Checks if a partition is less than or equal to
the other based on rank.
Examples
========
>>> from sympy.combinatorics.partitions import Partition
>>> a = Partition([1, 2], [3, 4, 5])
>>> b = Partition([1], [2, 3], [4], [5])
>>> a.rank, b.rank
(9, 34)
>>> a <= a
True
>>> a <= b
True
"""
return self.sort_key() <= sympify(other).sort_key()
def __lt__(self, other):
"""
Checks if a partition is less than the other.
Examples
========
>>> from sympy.combinatorics.partitions import Partition
>>> a = Partition([1, 2], [3, 4, 5])
>>> b = Partition([1], [2, 3], [4], [5])
>>> a.rank, b.rank
(9, 34)
>>> a < b
True
"""
return self.sort_key() < sympify(other).sort_key()
@property
def rank(self):
"""
Gets the rank of a partition.
Examples
========
>>> from sympy.combinatorics.partitions import Partition
>>> a = Partition([1, 2], [3], [4, 5])
>>> a.rank
13
"""
if self._rank is not None:
return self._rank
self._rank = RGS_rank(self.RGS)
return self._rank
@property
def RGS(self):
"""
Returns the "restricted growth string" of the partition.
The RGS is returned as a list of indices, L, where L[i] indicates
the block in which element i appears. For example, in a partition
of 3 elements (a, b, c) into 2 blocks ([c], [a, b]) the RGS is
[1, 1, 0]: "a" is in block 1, "b" is in block 1 and "c" is in block 0.
Examples
========
>>> from sympy.combinatorics.partitions import Partition
>>> a = Partition([1, 2], [3], [4, 5])
>>> a.members
(1, 2, 3, 4, 5)
>>> a.RGS
(0, 0, 1, 2, 2)
>>> a + 1
{{3}, {4}, {5}, {1, 2}}
>>> _.RGS
(0, 0, 1, 2, 3)
"""
rgs = {}
partition = self.partition
for i, part in enumerate(partition):
for j in part:
rgs[j] = i
return tuple([rgs[i] for i in sorted(
[i for p in partition for i in p], key=default_sort_key)])
@classmethod
def from_rgs(self, rgs, elements):
"""
Creates a set partition from a restricted growth string.
The indices given in rgs are assumed to be the index
of the element as given in elements *as provided* (the
elements are not sorted by this routine). Block numbering
starts from 0. If any block was not referenced in ``rgs``
an error will be raised.
Examples
========
>>> from sympy.combinatorics.partitions import Partition
>>> Partition.from_rgs([0, 1, 2, 0, 1], list('abcde'))
{{c}, {a, d}, {b, e}}
>>> Partition.from_rgs([0, 1, 2, 0, 1], list('cbead'))
{{e}, {a, c}, {b, d}}
>>> a = Partition([1, 4], [2], [3, 5])
>>> Partition.from_rgs(a.RGS, a.members)
{{2}, {1, 4}, {3, 5}}
"""
if len(rgs) != len(elements):
raise ValueError('mismatch in rgs and element lengths')
max_elem = max(rgs) + 1
partition = [[] for i in range(max_elem)]
j = 0
for i in rgs:
partition[i].append(elements[j])
j += 1
if not all(p for p in partition):
raise ValueError('some blocks of the partition were empty.')
return Partition(*partition)
class IntegerPartition(Basic):
"""
This class represents an integer partition.
In number theory and combinatorics, a partition of a positive integer,
``n``, also called an integer partition, is a way of writing ``n`` as a
list of positive integers that sum to n. Two partitions that differ only
in the order of summands are considered to be the same partition; if order
matters then the partitions are referred to as compositions. For example,
4 has five partitions: [4], [3, 1], [2, 2], [2, 1, 1], and [1, 1, 1, 1];
the compositions [1, 2, 1] and [1, 1, 2] are the same as partition
[2, 1, 1].
See Also
========
sympy.utilities.iterables.partitions,
sympy.utilities.iterables.multiset_partitions
Reference: http://en.wikipedia.org/wiki/Partition_%28number_theory%29
"""
_dict = None
_keys = None
def __new__(cls, partition, integer=None):
"""
Generates a new IntegerPartition object from a list or dictionary.
The partition can be given as a list of positive integers or a
dictionary of (integer, multiplicity) items. If the partition is
preceeded by an integer an error will be raised if the partition
does not sum to that given integer.
Examples
========
>>> from sympy.combinatorics.partitions import IntegerPartition
>>> a = IntegerPartition([5, 4, 3, 1, 1])
>>> a
IntegerPartition(14, (5, 4, 3, 1, 1))
>>> print(a)
[5, 4, 3, 1, 1]
>>> IntegerPartition({1:3, 2:1})
IntegerPartition(5, (2, 1, 1, 1))
If the value that the partion should sum to is given first, a check
will be made to see n error will be raised if there is a discrepancy:
>>> IntegerPartition(10, [5, 4, 3, 1])
Traceback (most recent call last):
...
ValueError: The partition is not valid
"""
if integer is not None:
integer, partition = partition, integer
if isinstance(partition, (dict, Dict)):
_ = []
for k, v in sorted(list(partition.items()), reverse=True):
if not v:
continue
k, v = as_int(k), as_int(v)
_.extend([k]*v)
partition = tuple(_)
else:
partition = tuple(sorted(map(as_int, partition), reverse=True))
sum_ok = False
if integer is None:
integer = sum(partition)
sum_ok = True
else:
integer = as_int(integer)
if not sum_ok and sum(partition) != integer:
raise ValueError("Partition did not add to %s" % integer)
if any(i < 1 for i in partition):
raise ValueError("The summands must all be positive.")
obj = Basic.__new__(cls, integer, partition)
obj.partition = list(partition)
obj.integer = integer
return obj
def prev_lex(self):
"""Return the previous partition of the integer, n, in lexical order,
wrapping around to [1, ..., 1] if the partition is [n].
Examples
========
>>> from sympy.combinatorics.partitions import IntegerPartition
>>> p = IntegerPartition([4])
>>> print(p.prev_lex())
[3, 1]
>>> p.partition > p.prev_lex().partition
True
"""
d = defaultdict(int)
d.update(self.as_dict())
keys = self._keys
if keys == [1]:
return IntegerPartition({self.integer: 1})
if keys[-1] != 1:
d[keys[-1]] -= 1
if keys[-1] == 2:
d[1] = 2
else:
d[keys[-1] - 1] = d[1] = 1
else:
d[keys[-2]] -= 1
left = d[1] + keys[-2]
new = keys[-2]
d[1] = 0
while left:
new -= 1
if left - new >= 0:
d[new] += left//new
left -= d[new]*new
return IntegerPartition(self.integer, d)
def next_lex(self):
"""Return the next partition of the integer, n, in lexical order,
wrapping around to [n] if the partition is [1, ..., 1].
Examples
========
>>> from sympy.combinatorics.partitions import IntegerPartition
>>> p = IntegerPartition([3, 1])
>>> print(p.next_lex())
[4]
>>> p.partition < p.next_lex().partition
True
"""
d = defaultdict(int)
d.update(self.as_dict())
key = self._keys
a = key[-1]
if a == self.integer:
d.clear()
d[1] = self.integer
elif a == 1:
if d[a] > 1:
d[a + 1] += 1
d[a] -= 2
else:
b = key[-2]
d[b + 1] += 1
d[1] = (d[b] - 1)*b
d[b] = 0
else:
if d[a] > 1:
if len(key) == 1:
d.clear()
d[a + 1] = 1
d[1] = self.integer - a - 1
else:
a1 = a + 1
d[a1] += 1
d[1] = d[a]*a - a1
d[a] = 0
else:
b = key[-2]
b1 = b + 1
d[b1] += 1
need = d[b]*b + d[a]*a - b1
d[a] = d[b] = 0
d[1] = need
return IntegerPartition(self.integer, d)
def as_dict(self):
"""Return the partition as a dictionary whose keys are the
partition integers and the values are the multiplicity of that
integer.
Examples
========
>>> from sympy.combinatorics.partitions import IntegerPartition
>>> IntegerPartition([1]*3 + [2] + [3]*4).as_dict()
{1: 3, 2: 1, 3: 4}
"""
if self._dict is None:
groups = group(self.partition, multiple=False)
self._keys = [g[0] for g in groups]
self._dict = dict(groups)
return self._dict
@property
def conjugate(self):
"""
Computes the conjugate partition of itself.
Examples
========
>>> from sympy.combinatorics.partitions import IntegerPartition
>>> a = IntegerPartition([6, 3, 3, 2, 1])
>>> a.conjugate
[5, 4, 3, 1, 1, 1]
"""
j = 1
temp_arr = list(self.partition) + [0]
k = temp_arr[0]
b = [0]*k
while k > 0:
while k > temp_arr[j]:
b[k - 1] = j
k -= 1
j += 1
return b
def __lt__(self, other):
"""Return True if self is less than other when the partition
is listed from smallest to biggest.
Examples
========
>>> from sympy.combinatorics.partitions import IntegerPartition
>>> a = IntegerPartition([3, 1])
>>> a < a
False
>>> b = a.next_lex()
>>> a < b
True
>>> a == b
False
"""
return list(reversed(self.partition)) < list(reversed(other.partition))
def __le__(self, other):
"""Return True if self is less than other when the partition
is listed from smallest to biggest.
Examples
========
>>> from sympy.combinatorics.partitions import IntegerPartition
>>> a = IntegerPartition([4])
>>> a <= a
True
"""
return list(reversed(self.partition)) <= list(reversed(other.partition))
def as_ferrers(self, char='#'):
"""
Prints the ferrer diagram of a partition.
Examples
========
>>> from sympy.combinatorics.partitions import IntegerPartition
>>> print(IntegerPartition([1, 1, 5]).as_ferrers())
#####
#
#
"""
return "\n".join([char*i for i in self.partition])
def __str__(self):
return str(list(self.partition))
def random_integer_partition(n, seed=None):
"""
Generates a random integer partition summing to ``n`` as a list
of reverse-sorted integers.
Examples
========
>>> from sympy.combinatorics.partitions import random_integer_partition
For the following, a seed is given so a known value can be shown; in
practice, the seed would not be given.
>>> random_integer_partition(100, seed=[1, 1, 12, 1, 2, 1, 85, 1])
[85, 12, 2, 1]
>>> random_integer_partition(10, seed=[1, 2, 3, 1, 5, 1])
[5, 3, 1, 1]
>>> random_integer_partition(1)
[1]
"""
from sympy.utilities.randtest import _randint
n = as_int(n)
if n < 1:
raise ValueError('n must be a positive integer')
randint = _randint(seed)
partition = []
while (n > 0):
k = randint(1, n)
mult = randint(1, n//k)
partition.append((k, mult))
n -= k*mult
partition.sort(reverse=True)
partition = flatten([[k]*m for k, m in partition])
return partition
def RGS_generalized(m):
"""
Computes the m + 1 generalized unrestricted growth strings
and returns them as rows in matrix.
Examples
========
>>> from sympy.combinatorics.partitions import RGS_generalized
>>> RGS_generalized(6)
Matrix([
[ 1, 1, 1, 1, 1, 1, 1],
[ 1, 2, 3, 4, 5, 6, 0],
[ 2, 5, 10, 17, 26, 0, 0],
[ 5, 15, 37, 77, 0, 0, 0],
[ 15, 52, 151, 0, 0, 0, 0],
[ 52, 203, 0, 0, 0, 0, 0],
[203, 0, 0, 0, 0, 0, 0]])
"""
d = zeros(m + 1)
for i in range(0, m + 1):
d[0, i] = 1
for i in range(1, m + 1):
for j in range(m):
if j <= m - i:
d[i, j] = j * d[i - 1, j] + d[i - 1, j + 1]
else:
d[i, j] = 0
return d
def RGS_enum(m):
"""
RGS_enum computes the total number of restricted growth strings
possible for a superset of size m.
Examples
========
>>> from sympy.combinatorics.partitions import RGS_enum
>>> from sympy.combinatorics.partitions import Partition
>>> RGS_enum(4)
15
>>> RGS_enum(5)
52
>>> RGS_enum(6)
203
We can check that the enumeration is correct by actually generating
the partitions. Here, the 15 partitions of 4 items are generated:
>>> a = Partition(list(range(4)))
>>> s = set()
>>> for i in range(20):
... s.add(a)
... a += 1
...
>>> assert len(s) == 15
"""
if (m < 1):
return 0
elif (m == 1):
return 1
else:
return bell(m)
def RGS_unrank(rank, m):
"""
Gives the unranked restricted growth string for a given
superset size.
Examples
========
>>> from sympy.combinatorics.partitions import RGS_unrank
>>> RGS_unrank(14, 4)
[0, 1, 2, 3]
>>> RGS_unrank(0, 4)
[0, 0, 0, 0]
"""
if m < 1:
raise ValueError("The superset size must be >= 1")
if rank < 0 or RGS_enum(m) <= rank:
raise ValueError("Invalid arguments")
L = [1] * (m + 1)
j = 1
D = RGS_generalized(m)
for i in range(2, m + 1):
v = D[m - i, j]
cr = j*v
if cr <= rank:
L[i] = j + 1
rank -= cr
j += 1
else:
L[i] = int(rank / v + 1)
rank %= v
return [x - 1 for x in L[1:]]
def RGS_rank(rgs):
"""
Computes the rank of a restricted growth string.
Examples
========
>>> from sympy.combinatorics.partitions import RGS_rank, RGS_unrank
>>> RGS_rank([0, 1, 2, 1, 3])
42
>>> RGS_rank(RGS_unrank(4, 7))
4
"""
rgs_size = len(rgs)
rank = 0
D = RGS_generalized(rgs_size)
for i in range(1, rgs_size):
n = len(rgs[(i + 1):])
m = max(rgs[0:i])
rank += D[n, m + 1] * rgs[i]
return rank
| 19,983 | 27.386364 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/testutil.py
|
from __future__ import print_function, division
from sympy.core.compatibility import range
from sympy.combinatorics.util import _distribute_gens_by_base
from sympy.combinatorics import Permutation
rmul = Permutation.rmul
def _cmp_perm_lists(first, second):
"""
Compare two lists of permutations as sets.
This is used for testing purposes. Since the array form of a
permutation is currently a list, Permutation is not hashable
and cannot be put into a set.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.testutil import _cmp_perm_lists
>>> a = Permutation([0, 2, 3, 4, 1])
>>> b = Permutation([1, 2, 0, 4, 3])
>>> c = Permutation([3, 4, 0, 1, 2])
>>> ls1 = [a, b, c]
>>> ls2 = [b, c, a]
>>> _cmp_perm_lists(ls1, ls2)
True
"""
return {tuple(a) for a in first} == \
{tuple(a) for a in second}
def _naive_list_centralizer(self, other, af=False):
from sympy.combinatorics.perm_groups import PermutationGroup
"""
Return a list of elements for the centralizer of a subgroup/set/element.
This is a brute force implementation that goes over all elements of the
group and checks for membership in the centralizer. It is used to
test ``.centralizer()`` from ``sympy.combinatorics.perm_groups``.
Examples
========
>>> from sympy.combinatorics.testutil import _naive_list_centralizer
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> D = DihedralGroup(4)
>>> _naive_list_centralizer(D, D)
[Permutation([0, 1, 2, 3]), Permutation([2, 3, 0, 1])]
See Also
========
sympy.combinatorics.perm_groups.centralizer
"""
from sympy.combinatorics.permutations import _af_commutes_with
if hasattr(other, 'generators'):
elements = list(self.generate_dimino(af=True))
gens = [x._array_form for x in other.generators]
commutes_with_gens = lambda x: all(_af_commutes_with(x, gen) for gen in gens)
centralizer_list = []
if not af:
for element in elements:
if commutes_with_gens(element):
centralizer_list.append(Permutation._af_new(element))
else:
for element in elements:
if commutes_with_gens(element):
centralizer_list.append(element)
return centralizer_list
elif hasattr(other, 'getitem'):
return _naive_list_centralizer(self, PermutationGroup(other), af)
elif hasattr(other, 'array_form'):
return _naive_list_centralizer(self, PermutationGroup([other]), af)
def _verify_bsgs(group, base, gens):
"""
Verify the correctness of a base and strong generating set.
This is a naive implementation using the definition of a base and a strong
generating set relative to it. There are other procedures for
verifying a base and strong generating set, but this one will
serve for more robust testing.
Examples
========
>>> from sympy.combinatorics.named_groups import AlternatingGroup
>>> from sympy.combinatorics.testutil import _verify_bsgs
>>> A = AlternatingGroup(4)
>>> A.schreier_sims()
>>> _verify_bsgs(A, A.base, A.strong_gens)
True
See Also
========
sympy.combinatorics.perm_groups.PermutationGroup.schreier_sims
"""
from sympy.combinatorics.perm_groups import PermutationGroup
strong_gens_distr = _distribute_gens_by_base(base, gens)
current_stabilizer = group
for i in range(len(base)):
candidate = PermutationGroup(strong_gens_distr[i])
if current_stabilizer.order() != candidate.order():
return False
current_stabilizer = current_stabilizer.stabilizer(base[i])
if current_stabilizer.order() != 1:
return False
return True
def _verify_centralizer(group, arg, centr=None):
"""
Verify the centralizer of a group/set/element inside another group.
This is used for testing ``.centralizer()`` from
``sympy.combinatorics.perm_groups``
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... AlternatingGroup)
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.testutil import _verify_centralizer
>>> S = SymmetricGroup(5)
>>> A = AlternatingGroup(5)
>>> centr = PermutationGroup([Permutation([0, 1, 2, 3, 4])])
>>> _verify_centralizer(S, A, centr)
True
See Also
========
_naive_list_centralizer,
sympy.combinatorics.perm_groups.PermutationGroup.centralizer,
_cmp_perm_lists
"""
if centr is None:
centr = group.centralizer(arg)
centr_list = list(centr.generate_dimino(af=True))
centr_list_naive = _naive_list_centralizer(group, arg, af=True)
return _cmp_perm_lists(centr_list, centr_list_naive)
def _verify_normal_closure(group, arg, closure=None):
from sympy.combinatorics.perm_groups import PermutationGroup
"""
Verify the normal closure of a subgroup/subset/element in a group.
This is used to test
sympy.combinatorics.perm_groups.PermutationGroup.normal_closure
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... AlternatingGroup)
>>> from sympy.combinatorics.testutil import _verify_normal_closure
>>> S = SymmetricGroup(3)
>>> A = AlternatingGroup(3)
>>> _verify_normal_closure(S, A, closure=A)
True
See Also
========
sympy.combinatorics.perm_groups.PermutationGroup.normal_closure
"""
if closure is None:
closure = group.normal_closure(arg)
conjugates = set()
if hasattr(arg, 'generators'):
subgr_gens = arg.generators
elif hasattr(arg, '__getitem__'):
subgr_gens = arg
elif hasattr(arg, 'array_form'):
subgr_gens = [arg]
for el in group.generate_dimino():
for gen in subgr_gens:
conjugates.add(gen ^ el)
naive_closure = PermutationGroup(list(conjugates))
return closure.is_subgroup(naive_closure)
def canonicalize_naive(g, dummies, sym, *v):
"""
Canonicalize tensor formed by tensors of the different types
g permutation representing the tensor
dummies list of dummy indices
msym symmetry of the metric
v is a list of (base_i, gens_i, n_i, sym_i) for tensors of type `i`
base_i, gens_i BSGS for tensors of this type
n_i number ot tensors of type `i`
sym_i symmetry under exchange of two component tensors of type `i`
None no symmetry
0 commuting
1 anticommuting
Return 0 if the tensor is zero, else return the array form of
the permutation representing the canonical form of the tensor.
Examples
========
>>> from sympy.combinatorics.testutil import canonicalize_naive
>>> from sympy.combinatorics.tensor_can import get_symmetric_group_sgs
>>> from sympy.combinatorics import Permutation, PermutationGroup
>>> g = Permutation([1, 3, 2, 0, 4, 5])
>>> base2, gens2 = get_symmetric_group_sgs(2)
>>> canonicalize_naive(g, [2, 3], 0, (base2, gens2, 2, 0))
[0, 2, 1, 3, 4, 5]
"""
from sympy.combinatorics.perm_groups import PermutationGroup
from sympy.combinatorics.tensor_can import gens_products, dummy_sgs
from sympy.combinatorics.permutations import Permutation, _af_rmul
v1 = []
for i in range(len(v)):
base_i, gens_i, n_i, sym_i = v[i]
v1.append((base_i, gens_i, [[]]*n_i, sym_i))
size, sbase, sgens = gens_products(*v1)
dgens = dummy_sgs(dummies, sym, size-2)
if isinstance(sym, int):
num_types = 1
dummies = [dummies]
sym = [sym]
else:
num_types = len(sym)
dgens = []
for i in range(num_types):
dgens.extend(dummy_sgs(dummies[i], sym[i], size - 2))
S = PermutationGroup(sgens)
D = PermutationGroup([Permutation(x) for x in dgens])
dlist = list(D.generate(af=True))
g = g.array_form
st = set()
for s in S.generate(af=True):
h = _af_rmul(g, s)
for d in dlist:
q = tuple(_af_rmul(d, h))
st.add(q)
a = list(st)
a.sort()
prev = (0,)*size
for h in a:
if h[:-2] == prev[:-2]:
if h[-1] != prev[-1]:
return 0
prev = h
return list(a[0])
def graph_certificate(gr):
"""
Return a certificate for the graph
gr adjacency list
The graph is assumed to be unoriented and without
external lines.
Associate to each vertex of the graph a symmetric tensor with
number of indices equal to the degree of the vertex; indices
are contracted when they correspond to the same line of the graph.
The canonical form of the tensor gives a certificate for the graph.
This is not an efficient algorithm to get the certificate of a graph.
Examples
========
>>> from sympy.combinatorics.testutil import graph_certificate
>>> gr1 = {0:[1, 2, 3, 5], 1:[0, 2, 4], 2:[0, 1, 3, 4], 3:[0, 2, 4], 4:[1, 2, 3, 5], 5:[0, 4]}
>>> gr2 = {0:[1, 5], 1:[0, 2, 3, 4], 2:[1, 3, 5], 3:[1, 2, 4, 5], 4:[1, 3, 5], 5:[0, 2, 3, 4]}
>>> c1 = graph_certificate(gr1)
>>> c2 = graph_certificate(gr2)
>>> c1
[0, 2, 4, 6, 1, 8, 10, 12, 3, 14, 16, 18, 5, 9, 15, 7, 11, 17, 13, 19, 20, 21]
>>> c1 == c2
True
"""
from sympy.combinatorics.permutations import _af_invert
from sympy.combinatorics.tensor_can import get_symmetric_group_sgs, canonicalize
items = list(gr.items())
items.sort(key=lambda x: len(x[1]), reverse=True)
pvert = [x[0] for x in items]
pvert = _af_invert(pvert)
# the indices of the tensor are twice the number of lines of the graph
num_indices = 0
for v, neigh in items:
num_indices += len(neigh)
# associate to each vertex its indices; for each line
# between two vertices assign the
# even index to the vertex which comes first in items,
# the odd index to the other vertex
vertices = [[] for i in items]
i = 0
for v, neigh in items:
for v2 in neigh:
if pvert[v] < pvert[v2]:
vertices[pvert[v]].append(i)
vertices[pvert[v2]].append(i+1)
i += 2
g = []
for v in vertices:
g.extend(v)
assert len(g) == num_indices
g += [num_indices, num_indices + 1]
size = num_indices + 2
assert sorted(g) == list(range(size))
g = Permutation(g)
vlen = [0]*(len(vertices[0])+1)
for neigh in vertices:
vlen[len(neigh)] += 1
v = []
for i in range(len(vlen)):
n = vlen[i]
if n:
base, gens = get_symmetric_group_sgs(i)
v.append((base, gens, n, 0))
v.reverse()
dummies = list(range(num_indices))
can = canonicalize(g, dummies, 0, *v)
return can
| 11,004 | 31.463127 | 98 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/util.py
|
from __future__ import print_function, division
from sympy.ntheory import isprime
from sympy.combinatorics.permutations import Permutation, _af_invert, _af_rmul
from sympy.core.compatibility import range
rmul = Permutation.rmul
_af_new = Permutation._af_new
############################################
#
# Utilities for computational group theory
#
############################################
def _base_ordering(base, degree):
r"""
Order `\{0, 1, ..., n-1\}` so that base points come first and in order.
Parameters
==========
``base`` - the base
``degree`` - the degree of the associated permutation group
Returns
=======
A list ``base_ordering`` such that ``base_ordering[point]`` is the
number of ``point`` in the ordering.
Examples
========
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> from sympy.combinatorics.util import _base_ordering
>>> S = SymmetricGroup(4)
>>> S.schreier_sims()
>>> _base_ordering(S.base, S.degree)
[0, 1, 2, 3]
Notes
=====
This is used in backtrack searches, when we define a relation `<<` on
the underlying set for a permutation group of degree `n`,
`\{0, 1, ..., n-1\}`, so that if `(b_1, b_2, ..., b_k)` is a base we
have `b_i << b_j` whenever `i<j` and `b_i << a` for all
`i\in\{1,2, ..., k\}` and `a` is not in the base. The idea is developed
and applied to backtracking algorithms in [1], pp.108-132. The points
that are not in the base are taken in increasing order.
References
==========
[1] Holt, D., Eick, B., O'Brien, E.
"Handbook of computational group theory"
"""
base_len = len(base)
ordering = [0]*degree
for i in range(base_len):
ordering[base[i]] = i
current = base_len
for i in range(degree):
if i not in base:
ordering[i] = current
current += 1
return ordering
def _check_cycles_alt_sym(perm):
"""
Checks for cycles of prime length p with n/2 < p < n-2.
Here `n` is the degree of the permutation. This is a helper function for
the function is_alt_sym from sympy.combinatorics.perm_groups.
Examples
========
>>> from sympy.combinatorics.util import _check_cycles_alt_sym
>>> from sympy.combinatorics.permutations import Permutation
>>> a = Permutation([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [11, 12]])
>>> _check_cycles_alt_sym(a)
False
>>> b = Permutation([[0, 1, 2, 3, 4, 5, 6], [7, 8, 9, 10]])
>>> _check_cycles_alt_sym(b)
True
See Also
========
sympy.combinatorics.perm_groups.PermutationGroup.is_alt_sym
"""
n = perm.size
af = perm.array_form
current_len = 0
total_len = 0
used = set()
for i in range(n//2):
if not i in used and i < n//2 - total_len:
current_len = 1
used.add(i)
j = i
while(af[j] != i):
current_len += 1
j = af[j]
used.add(j)
total_len += current_len
if current_len > n//2 and current_len < n - 2 and isprime(current_len):
return True
return False
def _distribute_gens_by_base(base, gens):
"""
Distribute the group elements ``gens`` by membership in basic stabilizers.
Notice that for a base `(b_1, b_2, ..., b_k)`, the basic stabilizers
are defined as `G^{(i)} = G_{b_1, ..., b_{i-1}}` for
`i \in\{1, 2, ..., k\}`.
Parameters
==========
``base`` - a sequence of points in `\{0, 1, ..., n-1\}`
``gens`` - a list of elements of a permutation group of degree `n`.
Returns
=======
List of length `k`, where `k` is
the length of ``base``. The `i`-th entry contains those elements in
``gens`` which fix the first `i` elements of ``base`` (so that the
`0`-th entry is equal to ``gens`` itself). If no element fixes the first
`i` elements of ``base``, the `i`-th element is set to a list containing
the identity element.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> from sympy.combinatorics.util import _distribute_gens_by_base
>>> D = DihedralGroup(3)
>>> D.schreier_sims()
>>> D.strong_gens
[(0 1 2), (0 2), (1 2)]
>>> D.base
[0, 1]
>>> _distribute_gens_by_base(D.base, D.strong_gens)
[[(0 1 2), (0 2), (1 2)],
[(1 2)]]
See Also
========
_strong_gens_from_distr, _orbits_transversals_from_bsgs,
_handle_precomputed_bsgs
"""
base_len = len(base)
degree = gens[0].size
stabs = [[] for _ in range(base_len)]
max_stab_index = 0
for gen in gens:
j = 0
while j < base_len - 1 and gen._array_form[base[j]] == base[j]:
j += 1
if j > max_stab_index:
max_stab_index = j
for k in range(j + 1):
stabs[k].append(gen)
for i in range(max_stab_index + 1, base_len):
stabs[i].append(_af_new(list(range(degree))))
return stabs
def _handle_precomputed_bsgs(base, strong_gens, transversals=None,
basic_orbits=None, strong_gens_distr=None):
"""
Calculate BSGS-related structures from those present.
The base and strong generating set must be provided; if any of the
transversals, basic orbits or distributed strong generators are not
provided, they will be calculated from the base and strong generating set.
Parameters
==========
``base`` - the base
``strong_gens`` - the strong generators
``transversals`` - basic transversals
``basic_orbits`` - basic orbits
``strong_gens_distr`` - strong generators distributed by membership in basic
stabilizers
Returns
=======
``(transversals, basic_orbits, strong_gens_distr)`` where ``transversals``
are the basic transversals, ``basic_orbits`` are the basic orbits, and
``strong_gens_distr`` are the strong generators distributed by membership
in basic stabilizers.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> from sympy.combinatorics.util import _handle_precomputed_bsgs
>>> D = DihedralGroup(3)
>>> D.schreier_sims()
>>> _handle_precomputed_bsgs(D.base, D.strong_gens,
... basic_orbits=D.basic_orbits)
([{0: (2), 1: (0 1 2), 2: (0 2)}, {1: (2), 2: (1 2)}], [[0, 1, 2], [1, 2]], [[(0 1 2), (0 2), (1 2)], [(1 2)]])
See Also
========
_orbits_transversals_from_bsgs, distribute_gens_by_base
"""
if strong_gens_distr is None:
strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
if transversals is None:
if basic_orbits is None:
basic_orbits, transversals = \
_orbits_transversals_from_bsgs(base, strong_gens_distr)
else:
transversals = \
_orbits_transversals_from_bsgs(base, strong_gens_distr,
transversals_only=True)
else:
if basic_orbits is None:
base_len = len(base)
basic_orbits = [None]*base_len
for i in range(base_len):
basic_orbits[i] = list(transversals[i].keys())
return transversals, basic_orbits, strong_gens_distr
def _orbits_transversals_from_bsgs(base, strong_gens_distr,
transversals_only=False):
"""
Compute basic orbits and transversals from a base and strong generating set.
The generators are provided as distributed across the basic stabilizers.
If the optional argument ``transversals_only`` is set to True, only the
transversals are returned.
Parameters
==========
``base`` - the base
``strong_gens_distr`` - strong generators distributed by membership in basic
stabilizers
``transversals_only`` - a flag switching between returning only the
transversals/ both orbits and transversals
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> from sympy.combinatorics.util import _orbits_transversals_from_bsgs
>>> from sympy.combinatorics.util import (_orbits_transversals_from_bsgs,
... _distribute_gens_by_base)
>>> S = SymmetricGroup(3)
>>> S.schreier_sims()
>>> strong_gens_distr = _distribute_gens_by_base(S.base, S.strong_gens)
>>> _orbits_transversals_from_bsgs(S.base, strong_gens_distr)
([[0, 1, 2], [1, 2]], [{0: (2), 1: (0 1 2), 2: (0 2 1)}, {1: (2), 2: (1 2)}])
See Also
========
_distribute_gens_by_base, _handle_precomputed_bsgs
"""
from sympy.combinatorics.perm_groups import _orbit_transversal
base_len = len(base)
degree = strong_gens_distr[0][0].size
transversals = [None]*base_len
if transversals_only is False:
basic_orbits = [None]*base_len
for i in range(base_len):
transversals[i] = dict(_orbit_transversal(degree, strong_gens_distr[i],
base[i], pairs=True))
if transversals_only is False:
basic_orbits[i] = list(transversals[i].keys())
if transversals_only:
return transversals
else:
return basic_orbits, transversals
def _remove_gens(base, strong_gens, basic_orbits=None, strong_gens_distr=None):
"""
Remove redundant generators from a strong generating set.
Parameters
==========
``base`` - a base
``strong_gens`` - a strong generating set relative to ``base``
``basic_orbits`` - basic orbits
``strong_gens_distr`` - strong generators distributed by membership in basic
stabilizers
Returns
=======
A strong generating set with respect to ``base`` which is a subset of
``strong_gens``.
Examples
========
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.util import _remove_gens
>>> from sympy.combinatorics.testutil import _verify_bsgs
>>> S = SymmetricGroup(15)
>>> base, strong_gens = S.schreier_sims_incremental()
>>> new_gens = _remove_gens(base, strong_gens)
>>> len(new_gens)
14
>>> _verify_bsgs(S, base, new_gens)
True
Notes
=====
This procedure is outlined in [1],p.95.
References
==========
[1] Holt, D., Eick, B., O'Brien, E.
"Handbook of computational group theory"
"""
from sympy.combinatorics.perm_groups import _orbit
base_len = len(base)
degree = strong_gens[0].size
if strong_gens_distr is None:
strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
temp = strong_gens_distr[:]
if basic_orbits is None:
basic_orbits = []
for i in range(base_len):
basic_orbit = _orbit(degree, strong_gens_distr[i], base[i])
basic_orbits.append(basic_orbit)
strong_gens_distr.append([])
res = strong_gens[:]
for i in range(base_len - 1, -1, -1):
gens_copy = strong_gens_distr[i][:]
for gen in strong_gens_distr[i]:
if gen not in strong_gens_distr[i + 1]:
temp_gens = gens_copy[:]
temp_gens.remove(gen)
if temp_gens == []:
continue
temp_orbit = _orbit(degree, temp_gens, base[i])
if temp_orbit == basic_orbits[i]:
gens_copy.remove(gen)
res.remove(gen)
return res
def _strip(g, base, orbits, transversals):
"""
Attempt to decompose a permutation using a (possibly partial) BSGS
structure.
This is done by treating the sequence ``base`` as an actual base, and
the orbits ``orbits`` and transversals ``transversals`` as basic orbits and
transversals relative to it.
This process is called "sifting". A sift is unsuccessful when a certain
orbit element is not found or when after the sift the decomposition
doesn't end with the identity element.
The argument ``transversals`` is a list of dictionaries that provides
transversal elements for the orbits ``orbits``.
Parameters
==========
``g`` - permutation to be decomposed
``base`` - sequence of points
``orbits`` - a list in which the ``i``-th entry is an orbit of ``base[i]``
under some subgroup of the pointwise stabilizer of `
`base[0], base[1], ..., base[i - 1]``. The groups themselves are implicit
in this function since the only information we need is encoded in the orbits
and transversals
``transversals`` - a list of orbit transversals associated with the orbits
``orbits``.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.util import _strip
>>> S = SymmetricGroup(5)
>>> S.schreier_sims()
>>> g = Permutation([0, 2, 3, 1, 4])
>>> _strip(g, S.base, S.basic_orbits, S.basic_transversals)
((4), 5)
Notes
=====
The algorithm is described in [1],pp.89-90. The reason for returning
both the current state of the element being decomposed and the level
at which the sifting ends is that they provide important information for
the randomized version of the Schreier-Sims algorithm.
References
==========
[1] Holt, D., Eick, B., O'Brien, E.
"Handbook of computational group theory"
See Also
========
sympy.combinatorics.perm_groups.PermutationGroup.schreier_sims
sympy.combinatorics.perm_groups.PermutationGroup.schreier_sims_random
"""
h = g._array_form
base_len = len(base)
for i in range(base_len):
beta = h[base[i]]
if beta == base[i]:
continue
if beta not in orbits[i]:
return _af_new(h), i + 1
u = transversals[i][beta]._array_form
h = _af_rmul(_af_invert(u), h)
return _af_new(h), base_len + 1
def _strip_af(h, base, orbits, transversals, j):
"""
optimized _strip, with h, transversals and result in array form
if the stripped elements is the identity, it returns False, base_len + 1
j h[base[i]] == base[i] for i <= j
"""
base_len = len(base)
for i in range(j+1, base_len):
beta = h[base[i]]
if beta == base[i]:
continue
if beta not in orbits[i]:
return h, i + 1
u = transversals[i][beta]
if h == u:
return False, base_len + 1
h = _af_rmul(_af_invert(u), h)
return h, base_len + 1
def _strong_gens_from_distr(strong_gens_distr):
"""
Retrieve strong generating set from generators of basic stabilizers.
This is just the union of the generators of the first and second basic
stabilizers.
Parameters
==========
``strong_gens_distr`` - strong generators distributed by membership in basic
stabilizers
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> from sympy.combinatorics.util import (_strong_gens_from_distr,
... _distribute_gens_by_base)
>>> S = SymmetricGroup(3)
>>> S.schreier_sims()
>>> S.strong_gens
[(0 1 2), (2)(0 1), (1 2)]
>>> strong_gens_distr = _distribute_gens_by_base(S.base, S.strong_gens)
>>> _strong_gens_from_distr(strong_gens_distr)
[(0 1 2), (2)(0 1), (1 2)]
See Also
========
_distribute_gens_by_base
"""
if len(strong_gens_distr) == 1:
return strong_gens_distr[0][:]
else:
result = strong_gens_distr[0]
for gen in strong_gens_distr[1]:
if gen not in result:
result.append(gen)
return result
| 16,221 | 30.196154 | 115 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/polyhedron.py
|
from __future__ import print_function, division
from sympy.core import Basic, Tuple
from sympy.sets import FiniteSet
from sympy.core.compatibility import as_int, range
from sympy.combinatorics import Permutation as Perm
from sympy.combinatorics.perm_groups import PermutationGroup
from sympy.utilities.iterables import (minlex, unflatten, flatten)
rmul = Perm.rmul
class Polyhedron(Basic):
"""
Represents the polyhedral symmetry group (PSG).
The PSG is one of the symmetry groups of the Platonic solids.
There are three polyhedral groups: the tetrahedral group
of order 12, the octahedral group of order 24, and the
icosahedral group of order 60.
All doctests have been given in the docstring of the
constructor of the object.
References
==========
http://mathworld.wolfram.com/PolyhedralGroup.html
"""
_edges = None
def __new__(cls, corners, faces=[], pgroup=[]):
"""
The constructor of the Polyhedron group object.
It takes up to three parameters: the corners, faces, and
allowed transformations.
The corners/vertices are entered as a list of arbitrary
expressions that are used to identify each vertex.
The faces are entered as a list of tuples of indices; a tuple
of indices identifies the vertices which define the face. They
should be entered in a cw or ccw order; they will be standardized
by reversal and rotation to be give the lowest lexical ordering.
If no faces are given then no edges will be computed.
>>> from sympy.combinatorics.polyhedron import Polyhedron
>>> Polyhedron(list('abc'), [(1, 2, 0)]).faces
{(0, 1, 2)}
>>> Polyhedron(list('abc'), [(1, 0, 2)]).faces
{(0, 1, 2)}
The allowed transformations are entered as allowable permutations
of the vertices for the polyhedron. Instance of Permutations
(as with faces) should refer to the supplied vertices by index.
These permutation are stored as a PermutationGroup.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> Permutation.print_cyclic = False
>>> from sympy.abc import w, x, y, z
Here we construct the Polyhedron object for a tetrahedron.
>>> corners = [w, x, y, z]
>>> faces = [(0, 1, 2), (0, 2, 3), (0, 3, 1), (1, 2, 3)]
Next, allowed transformations of the polyhedron must be given. This
is given as permutations of vertices.
Although the vertices of a tetrahedron can be numbered in 24 (4!)
different ways, there are only 12 different orientations for a
physical tetrahedron. The following permutations, applied once or
twice, will generate all 12 of the orientations. (The identity
permutation, Permutation(range(4)), is not included since it does
not change the orientation of the vertices.)
>>> pgroup = [Permutation([[0, 1, 2], [3]]), \
Permutation([[0, 1, 3], [2]]), \
Permutation([[0, 2, 3], [1]]), \
Permutation([[1, 2, 3], [0]]), \
Permutation([[0, 1], [2, 3]]), \
Permutation([[0, 2], [1, 3]]), \
Permutation([[0, 3], [1, 2]])]
The Polyhedron is now constructed and demonstrated:
>>> tetra = Polyhedron(corners, faces, pgroup)
>>> tetra.size
4
>>> tetra.edges
{(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)}
>>> tetra.corners
(w, x, y, z)
It can be rotated with an arbitrary permutation of vertices, e.g.
the following permutation is not in the pgroup:
>>> tetra.rotate(Permutation([0, 1, 3, 2]))
>>> tetra.corners
(w, x, z, y)
An allowed permutation of the vertices can be constructed by
repeatedly applying permutations from the pgroup to the vertices.
Here is a demonstration that applying p and p**2 for every p in
pgroup generates all the orientations of a tetrahedron and no others:
>>> all = ( (w, x, y, z), \
(x, y, w, z), \
(y, w, x, z), \
(w, z, x, y), \
(z, w, y, x), \
(w, y, z, x), \
(y, z, w, x), \
(x, z, y, w), \
(z, y, x, w), \
(y, x, z, w), \
(x, w, z, y), \
(z, x, w, y) )
>>> got = []
>>> for p in (pgroup + [p**2 for p in pgroup]):
... h = Polyhedron(corners)
... h.rotate(p)
... got.append(h.corners)
...
>>> set(got) == set(all)
True
The make_perm method of a PermutationGroup will randomly pick
permutations, multiply them together, and return the permutation that
can be applied to the polyhedron to give the orientation produced
by those individual permutations.
Here, 3 permutations are used:
>>> tetra.pgroup.make_perm(3) # doctest: +SKIP
Permutation([0, 3, 1, 2])
To select the permutations that should be used, supply a list
of indices to the permutations in pgroup in the order they should
be applied:
>>> use = [0, 0, 2]
>>> p002 = tetra.pgroup.make_perm(3, use)
>>> p002
Permutation([1, 0, 3, 2])
Apply them one at a time:
>>> tetra.reset()
>>> for i in use:
... tetra.rotate(pgroup[i])
...
>>> tetra.vertices
(x, w, z, y)
>>> sequentially = tetra.vertices
Apply the composite permutation:
>>> tetra.reset()
>>> tetra.rotate(p002)
>>> tetra.corners
(x, w, z, y)
>>> tetra.corners in all and tetra.corners == sequentially
True
Notes
=====
Defining permutation groups
---------------------------
It is not necessary to enter any permutations, nor is necessary to
enter a complete set of transformations. In fact, for a polyhedron,
all configurations can be constructed from just two permutations.
For example, the orientations of a tetrahedron can be generated from
an axis passing through a vertex and face and another axis passing
through a different vertex or from an axis passing through the
midpoints of two edges opposite of each other.
For simplicity of presentation, consider a square --
not a cube -- with vertices 1, 2, 3, and 4:
1-----2 We could think of axes of rotation being:
| | 1) through the face
| | 2) from midpoint 1-2 to 3-4 or 1-3 to 2-4
3-----4 3) lines 1-4 or 2-3
To determine how to write the permutations, imagine 4 cameras,
one at each corner, labeled A-D:
A B A B
1-----2 1-----3 vertex index:
| | | | 1 0
| | | | 2 1
3-----4 2-----4 3 2
C D C D 4 3
original after rotation
along 1-4
A diagonal and a face axis will be chosen for the "permutation group"
from which any orientation can be constructed.
>>> pgroup = []
Imagine a clockwise rotation when viewing 1-4 from camera A. The new
orientation is (in camera-order): 1, 3, 2, 4 so the permutation is
given using the *indices* of the vertices as:
>>> pgroup.append(Permutation((0, 2, 1, 3)))
Now imagine rotating clockwise when looking down an axis entering the
center of the square as viewed. The new camera-order would be
3, 1, 4, 2 so the permutation is (using indices):
>>> pgroup.append(Permutation((2, 0, 3, 1)))
The square can now be constructed:
** use real-world labels for the vertices, entering them in
camera order
** for the faces we use zero-based indices of the vertices
in *edge-order* as the face is traversed; neither the
direction nor the starting point matter -- the faces are
only used to define edges (if so desired).
>>> square = Polyhedron((1, 2, 3, 4), [(0, 1, 3, 2)], pgroup)
To rotate the square with a single permutation we can do:
>>> square.rotate(square.pgroup[0])
>>> square.corners
(1, 3, 2, 4)
To use more than one permutation (or to use one permutation more
than once) it is more convenient to use the make_perm method:
>>> p011 = square.pgroup.make_perm([0, 1, 1]) # diag flip + 2 rotations
>>> square.reset() # return to initial orientation
>>> square.rotate(p011)
>>> square.corners
(4, 2, 3, 1)
Thinking outside the box
------------------------
Although the Polyhedron object has a direct physical meaning, it
actually has broader application. In the most general sense it is
just a decorated PermutationGroup, allowing one to connect the
permutations to something physical. For example, a Rubik's cube is
not a proper polyhedron, but the Polyhedron class can be used to
represent it in a way that helps to visualize the Rubik's cube.
>>> from sympy.utilities.iterables import flatten, unflatten
>>> from sympy import symbols
>>> from sympy.combinatorics import RubikGroup
>>> facelets = flatten([symbols(s+'1:5') for s in 'UFRBLD'])
>>> def show():
... pairs = unflatten(r2.corners, 2)
... print(pairs[::2])
... print(pairs[1::2])
...
>>> r2 = Polyhedron(facelets, pgroup=RubikGroup(2))
>>> show()
[(U1, U2), (F1, F2), (R1, R2), (B1, B2), (L1, L2), (D1, D2)]
[(U3, U4), (F3, F4), (R3, R4), (B3, B4), (L3, L4), (D3, D4)]
>>> r2.rotate(0) # cw rotation of F
>>> show()
[(U1, U2), (F3, F1), (U3, R2), (B1, B2), (L1, D1), (R3, R1)]
[(L4, L2), (F4, F2), (U4, R4), (B3, B4), (L3, D2), (D3, D4)]
Predefined Polyhedra
====================
For convenience, the vertices and faces are defined for the following
standard solids along with a permutation group for transformations.
When the polyhedron is oriented as indicated below, the vertices in
a given horizontal plane are numbered in ccw direction, starting from
the vertex that will give the lowest indices in a given face. (In the
net of the vertices, indices preceded by "-" indicate replication of
the lhs index in the net.)
tetrahedron, tetrahedron_faces
------------------------------
4 vertices (vertex up) net:
0 0-0
1 2 3-1
4 faces:
(0, 1, 2) (0, 2, 3) (0, 3, 1) (1, 2, 3)
cube, cube_faces
----------------
8 vertices (face up) net:
0 1 2 3-0
4 5 6 7-4
6 faces:
(0, 1, 2, 3)
(0, 1, 5, 4) (1, 2, 6, 5) (2, 3, 7, 6) (0, 3, 7, 4)
(4, 5, 6, 7)
octahedron, octahedron_faces
----------------------------
6 vertices (vertex up) net:
0 0 0-0
1 2 3 4-1
5 5 5-5
8 faces:
(0, 1, 2) (0, 2, 3) (0, 3, 4) (0, 1, 4)
(1, 2, 5) (2, 3, 5) (3, 4, 5) (1, 4, 5)
dodecahedron, dodecahedron_faces
--------------------------------
20 vertices (vertex up) net:
0 1 2 3 4 -0
5 6 7 8 9 -5
14 10 11 12 13-14
15 16 17 18 19-15
12 faces:
(0, 1, 2, 3, 4) (0, 1, 6, 10, 5) (1, 2, 7, 11, 6)
(2, 3, 8, 12, 7) (3, 4, 9, 13, 8) (0, 4, 9, 14, 5)
(5, 10, 16, 15, 14) (6, 10, 16, 17, 11) (7, 11, 17, 18, 12)
(8, 12, 18, 19, 13) (9, 13, 19, 15, 14)(15, 16, 17, 18, 19)
icosahedron, icosahedron_faces
------------------------------
12 vertices (face up) net:
0 0 0 0 -0
1 2 3 4 5 -1
6 7 8 9 10 -6
11 11 11 11 -11
20 faces:
(0, 1, 2) (0, 2, 3) (0, 3, 4)
(0, 4, 5) (0, 1, 5) (1, 2, 6)
(2, 3, 7) (3, 4, 8) (4, 5, 9)
(1, 5, 10) (2, 6, 7) (3, 7, 8)
(4, 8, 9) (5, 9, 10) (1, 6, 10)
(6, 7, 11) (7, 8, 11) (8, 9, 11)
(9, 10, 11) (6, 10, 11)
>>> from sympy.combinatorics.polyhedron import cube
>>> cube.edges
{(0, 1), (0, 3), (0, 4), '...', (4, 7), (5, 6), (6, 7)}
If you want to use letters or other names for the corners you
can still use the pre-calculated faces:
>>> corners = list('abcdefgh')
>>> Polyhedron(corners, cube.faces).corners
(a, b, c, d, e, f, g, h)
References
==========
[1] www.ocf.berkeley.edu/~wwu/articles/platonicsolids.pdf
"""
faces = [minlex(f, directed=False, is_set=True) for f in faces]
corners, faces, pgroup = args = \
[Tuple(*a) for a in (corners, faces, pgroup)]
obj = Basic.__new__(cls, *args)
obj._corners = tuple(corners) # in order given
obj._faces = FiniteSet(*faces)
if pgroup and pgroup[0].size != len(corners):
raise ValueError("Permutation size unequal to number of corners.")
# use the identity permutation if none are given
obj._pgroup = PermutationGroup((
pgroup or [Perm(range(len(corners)))] ))
return obj
@property
def corners(self):
"""
Get the corners of the Polyhedron.
The method ``vertices`` is an alias for ``corners``.
Examples
========
>>> from sympy.combinatorics import Polyhedron
>>> from sympy.abc import a, b, c, d
>>> p = Polyhedron(list('abcd'))
>>> p.corners == p.vertices == (a, b, c, d)
True
See Also
========
array_form, cyclic_form
"""
return self._corners
vertices = corners
@property
def array_form(self):
"""Return the indices of the corners.
The indices are given relative to the original position of corners.
Examples
========
>>> from sympy.combinatorics import Permutation, Cycle
>>> from sympy.combinatorics.polyhedron import tetrahedron
>>> tetrahedron.array_form
[0, 1, 2, 3]
>>> tetrahedron.rotate(0)
>>> tetrahedron.array_form
[0, 2, 3, 1]
>>> tetrahedron.pgroup[0].array_form
[0, 2, 3, 1]
See Also
========
corners, cyclic_form
"""
corners = list(self.args[0])
return [corners.index(c) for c in self.corners]
@property
def cyclic_form(self):
"""Return the indices of the corners in cyclic notation.
The indices are given relative to the original position of corners.
See Also
========
corners, array_form
"""
return Perm._af_new(self.array_form).cyclic_form
@property
def size(self):
"""
Get the number of corners of the Polyhedron.
"""
return len(self._corners)
@property
def faces(self):
"""
Get the faces of the Polyhedron.
"""
return self._faces
@property
def pgroup(self):
"""
Get the permutations of the Polyhedron.
"""
return self._pgroup
@property
def edges(self):
"""
Given the faces of the polyhedra we can get the edges.
Examples
========
>>> from sympy.combinatorics import Polyhedron
>>> from sympy.abc import a, b, c
>>> corners = (a, b, c)
>>> faces = [(0, 1, 2)]
>>> Polyhedron(corners, faces).edges
{(0, 1), (0, 2), (1, 2)}
"""
if self._edges is None:
output = set()
for face in self.faces:
for i in range(len(face)):
edge = tuple(sorted([face[i], face[i - 1]]))
output.add(edge)
self._edges = FiniteSet(*output)
return self._edges
def rotate(self, perm):
"""
Apply a permutation to the polyhedron *in place*. The permutation
may be given as a Permutation instance or an integer indicating
which permutation from pgroup of the Polyhedron should be
applied.
This is an operation that is analogous to rotation about
an axis by a fixed increment.
Notes
=====
When a Permutation is applied, no check is done to see if that
is a valid permutation for the Polyhedron. For example, a cube
could be given a permutation which effectively swaps only 2
vertices. A valid permutation (that rotates the object in a
physical way) will be obtained if one only uses
permutations from the ``pgroup`` of the Polyhedron. On the other
hand, allowing arbitrary rotations (applications of permutations)
gives a way to follow named elements rather than indices since
Polyhedron allows vertices to be named while Permutation works
only with indices.
Examples
========
>>> from sympy.combinatorics import Polyhedron, Permutation
>>> from sympy.combinatorics.polyhedron import cube
>>> cube.corners
(0, 1, 2, 3, 4, 5, 6, 7)
>>> cube.rotate(0)
>>> cube.corners
(1, 2, 3, 0, 5, 6, 7, 4)
A non-physical "rotation" that is not prohibited by this method:
>>> cube.reset()
>>> cube.rotate(Permutation([[1, 2]], size=8))
>>> cube.corners
(0, 2, 1, 3, 4, 5, 6, 7)
Polyhedron can be used to follow elements of set that are
identified by letters instead of integers:
>>> shadow = h5 = Polyhedron(list('abcde'))
>>> p = Permutation([3, 0, 1, 2, 4])
>>> h5.rotate(p)
>>> h5.corners
(d, a, b, c, e)
>>> _ == shadow.corners
True
>>> copy = h5.copy()
>>> h5.rotate(p)
>>> h5.corners == copy.corners
False
"""
if not isinstance(perm, Perm):
perm = self.pgroup[perm]
# and we know it's valid
else:
if perm.size != self.size:
raise ValueError('Polyhedron and Permutation sizes differ.')
a = perm.array_form
corners = [self.corners[a[i]] for i in range(len(self.corners))]
self._corners = tuple(corners)
def reset(self):
"""Return corners to their original positions.
Examples
========
>>> from sympy.combinatorics.polyhedron import tetrahedron as T
>>> T.corners
(0, 1, 2, 3)
>>> T.rotate(0)
>>> T.corners
(0, 2, 3, 1)
>>> T.reset()
>>> T.corners
(0, 1, 2, 3)
"""
self._corners = self.args[0]
def _pgroup_calcs():
"""Return the permutation groups for each of the polyhedra and the face
definitions: tetrahedron, cube, octahedron, dodecahedron, icosahedron,
tetrahedron_faces, cube_faces, octahedron_faces, dodecahedron_faces,
icosahedron_faces
(This author didn't find and didn't know of a better way to do it though
there likely is such a way.)
Although only 2 permutations are needed for a polyhedron in order to
generate all the possible orientations, a group of permutations is
provided instead. A set of permutations is called a "group" if::
a*b = c (for any pair of permutations in the group, a and b, their
product, c, is in the group)
a*(b*c) = (a*b)*c (for any 3 permutations in the group associativity holds)
there is an identity permutation, I, such that I*a = a*I for all elements
in the group
a*b = I (the inverse of each permutation is also in the group)
None of the polyhedron groups defined follow these definitions of a group.
Instead, they are selected to contain those permutations whose powers
alone will construct all orientations of the polyhedron, i.e. for
permutations ``a``, ``b``, etc... in the group, ``a, a**2, ..., a**o_a``,
``b, b**2, ..., b**o_b``, etc... (where ``o_i`` is the order of
permutation ``i``) generate all permutations of the polyhedron instead of
mixed products like ``a*b``, ``a*b**2``, etc....
Note that for a polyhedron with n vertices, the valid permutations of the
vertices exclude those that do not maintain its faces. e.g. the
permutation BCDE of a square's four corners, ABCD, is a valid
permutation while CBDE is not (because this would twist the square).
Examples
========
The is_group checks for: closure, the presence of the Identity permutation,
and the presence of the inverse for each of the elements in the group. This
confirms that none of the polyhedra are true groups:
>>> from sympy.combinatorics.polyhedron import (
... tetrahedron, cube, octahedron, dodecahedron, icosahedron)
...
>>> polyhedra = (tetrahedron, cube, octahedron, dodecahedron, icosahedron)
>>> [h.pgroup.is_group for h in polyhedra]
...
[True, True, True, True, True]
Although tests in polyhedron's test suite check that powers of the
permutations in the groups generate all permutations of the vertices
of the polyhedron, here we also demonstrate the powers of the given
permutations create a complete group for the tetrahedron:
>>> from sympy.combinatorics import Permutation, PermutationGroup
>>> for h in polyhedra[:1]:
... G = h.pgroup
... perms = set()
... for g in G:
... for e in range(g.order()):
... p = tuple((g**e).array_form)
... perms.add(p)
...
... perms = [Permutation(p) for p in perms]
... assert PermutationGroup(perms).is_group
In addition to doing the above, the tests in the suite confirm that the
faces are all present after the application of each permutation.
References
==========
http://dogschool.tripod.com/trianglegroup.html
"""
def _pgroup_of_double(polyh, ordered_faces, pgroup):
n = len(ordered_faces[0])
# the vertices of the double which sits inside a give polyhedron
# can be found by tracking the faces of the outer polyhedron.
# A map between face and the vertex of the double is made so that
# after rotation the position of the vertices can be located
fmap = dict(zip(ordered_faces,
range(len(ordered_faces))))
flat_faces = flatten(ordered_faces)
new_pgroup = []
for i, p in enumerate(pgroup):
h = polyh.copy()
h.rotate(p)
c = h.corners
# reorder corners in the order they should appear when
# enumerating the faces
reorder = unflatten([c[j] for j in flat_faces], n)
# make them canonical
reorder = [tuple(map(as_int,
minlex(f, directed=False, is_set=True)))
for f in reorder]
# map face to vertex: the resulting list of vertices are the
# permutation that we seek for the double
new_pgroup.append(Perm([fmap[f] for f in reorder]))
return new_pgroup
tetrahedron_faces = [
(0, 1, 2), (0, 2, 3), (0, 3, 1), # upper 3
(1, 2, 3), # bottom
]
# cw from top
#
_t_pgroup = [
Perm([[1, 2, 3], [0]]), # cw from top
Perm([[0, 1, 2], [3]]), # cw from front face
Perm([[0, 3, 2], [1]]), # cw from back right face
Perm([[0, 3, 1], [2]]), # cw from back left face
Perm([[0, 1], [2, 3]]), # through front left edge
Perm([[0, 2], [1, 3]]), # through front right edge
Perm([[0, 3], [1, 2]]), # through back edge
]
tetrahedron = Polyhedron(
range(4),
tetrahedron_faces,
_t_pgroup)
cube_faces = [
(0, 1, 2, 3), # upper
(0, 1, 5, 4), (1, 2, 6, 5), (2, 3, 7, 6), (0, 3, 7, 4), # middle 4
(4, 5, 6, 7), # lower
]
# U, D, F, B, L, R = up, down, front, back, left, right
_c_pgroup = [Perm(p) for p in
[
[1, 2, 3, 0, 5, 6, 7, 4], # cw from top, U
[4, 0, 3, 7, 5, 1, 2, 6], # cw from F face
[4, 5, 1, 0, 7, 6, 2, 3], # cw from R face
[1, 0, 4, 5, 2, 3, 7, 6], # cw through UF edge
[6, 2, 1, 5, 7, 3, 0, 4], # cw through UR edge
[6, 7, 3, 2, 5, 4, 0, 1], # cw through UB edge
[3, 7, 4, 0, 2, 6, 5, 1], # cw through UL edge
[4, 7, 6, 5, 0, 3, 2, 1], # cw through FL edge
[6, 5, 4, 7, 2, 1, 0, 3], # cw through FR edge
[0, 3, 7, 4, 1, 2, 6, 5], # cw through UFL vertex
[5, 1, 0, 4, 6, 2, 3, 7], # cw through UFR vertex
[5, 6, 2, 1, 4, 7, 3, 0], # cw through UBR vertex
[7, 4, 0, 3, 6, 5, 1, 2], # cw through UBL
]]
cube = Polyhedron(
range(8),
cube_faces,
_c_pgroup)
octahedron_faces = [
(0, 1, 2), (0, 2, 3), (0, 3, 4), (0, 1, 4), # top 4
(1, 2, 5), (2, 3, 5), (3, 4, 5), (1, 4, 5), # bottom 4
]
octahedron = Polyhedron(
range(6),
octahedron_faces,
_pgroup_of_double(cube, cube_faces, _c_pgroup))
dodecahedron_faces = [
(0, 1, 2, 3, 4), # top
(0, 1, 6, 10, 5), (1, 2, 7, 11, 6), (2, 3, 8, 12, 7), # upper 5
(3, 4, 9, 13, 8), (0, 4, 9, 14, 5),
(5, 10, 16, 15, 14), (6, 10, 16, 17, 11), (7, 11, 17, 18,
12), # lower 5
(8, 12, 18, 19, 13), (9, 13, 19, 15, 14),
(15, 16, 17, 18, 19) # bottom
]
def _string_to_perm(s):
rv = [Perm(range(20))]
p = None
for si in s:
if si not in '01':
count = int(si) - 1
else:
count = 1
if si == '0':
p = _f0
elif si == '1':
p = _f1
rv.extend([p]*count)
return Perm.rmul(*rv)
# top face cw
_f0 = Perm([
1, 2, 3, 4, 0, 6, 7, 8, 9, 5, 11,
12, 13, 14, 10, 16, 17, 18, 19, 15])
# front face cw
_f1 = Perm([
5, 0, 4, 9, 14, 10, 1, 3, 13, 15,
6, 2, 8, 19, 16, 17, 11, 7, 12, 18])
# the strings below, like 0104 are shorthand for F0*F1*F0**4 and are
# the remaining 4 face rotations, 15 edge permutations, and the
# 10 vertex rotations.
_dodeca_pgroup = [_f0, _f1] + [_string_to_perm(s) for s in '''
0104 140 014 0410
010 1403 03104 04103 102
120 1304 01303 021302 03130
0412041 041204103 04120410 041204104 041204102
10 01 1402 0140 04102 0412 1204 1302 0130 03120'''.strip().split()]
dodecahedron = Polyhedron(
range(20),
dodecahedron_faces,
_dodeca_pgroup)
icosahedron_faces = [
[0, 1, 2], [0, 2, 3], [0, 3, 4], [0, 4, 5], [0, 1, 5],
[1, 6, 7], [1, 2, 7], [2, 7, 8], [2, 3, 8], [3, 8, 9],
[3, 4, 9], [4, 9, 10 ], [4, 5, 10], [5, 6, 10], [1, 5, 6],
[6, 7, 11], [7, 8, 11], [8, 9, 11], [9, 10, 11], [6, 10, 11]]
icosahedron = Polyhedron(
range(12),
icosahedron_faces,
_pgroup_of_double(
dodecahedron, dodecahedron_faces, _dodeca_pgroup))
return (tetrahedron, cube, octahedron, dodecahedron, icosahedron,
tetrahedron_faces, cube_faces, octahedron_faces,
dodecahedron_faces, icosahedron_faces)
(tetrahedron, cube, octahedron, dodecahedron, icosahedron,
tetrahedron_faces, cube_faces, octahedron_faces,
dodecahedron_faces, icosahedron_faces) = _pgroup_calcs()
| 28,370 | 33.389091 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/perm_groups.py
|
from __future__ import print_function, division
from random import randrange, choice
from math import log
from sympy.core import Basic
from sympy.core.compatibility import range
from sympy.combinatorics import Permutation
from sympy.combinatorics.permutations import (_af_commutes_with, _af_invert,
_af_rmul, _af_rmuln, _af_pow, Cycle)
from sympy.combinatorics.util import (_check_cycles_alt_sym,
_distribute_gens_by_base, _orbits_transversals_from_bsgs,
_handle_precomputed_bsgs, _base_ordering, _strong_gens_from_distr,
_strip, _strip_af)
from sympy.functions.combinatorial.factorials import factorial
from sympy.ntheory import sieve
from sympy.utilities.iterables import has_variety, is_sequence, uniq
from sympy.utilities.randtest import _randrange
from itertools import islice
rmul = Permutation.rmul_with_af
_af_new = Permutation._af_new
class PermutationGroup(Basic):
"""The class defining a Permutation group.
PermutationGroup([p1, p2, ..., pn]) returns the permutation group
generated by the list of permutations. This group can be supplied
to Polyhedron if one desires to decorate the elements to which the
indices of the permutation refer.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.permutations import Cycle
>>> from sympy.combinatorics.polyhedron import Polyhedron
>>> from sympy.combinatorics.perm_groups import PermutationGroup
The permutations corresponding to motion of the front, right and
bottom face of a 2x2 Rubik's cube are defined:
>>> F = Permutation(2, 19, 21, 8)(3, 17, 20, 10)(4, 6, 7, 5)
>>> R = Permutation(1, 5, 21, 14)(3, 7, 23, 12)(8, 10, 11, 9)
>>> D = Permutation(6, 18, 14, 10)(7, 19, 15, 11)(20, 22, 23, 21)
These are passed as permutations to PermutationGroup:
>>> G = PermutationGroup(F, R, D)
>>> G.order()
3674160
The group can be supplied to a Polyhedron in order to track the
objects being moved. An example involving the 2x2 Rubik's cube is
given there, but here is a simple demonstration:
>>> a = Permutation(2, 1)
>>> b = Permutation(1, 0)
>>> G = PermutationGroup(a, b)
>>> P = Polyhedron(list('ABC'), pgroup=G)
>>> P.corners
(A, B, C)
>>> P.rotate(0) # apply permutation 0
>>> P.corners
(A, C, B)
>>> P.reset()
>>> P.corners
(A, B, C)
Or one can make a permutation as a product of selected permutations
and apply them to an iterable directly:
>>> P10 = G.make_perm([0, 1])
>>> P10('ABC')
['C', 'A', 'B']
See Also
========
sympy.combinatorics.polyhedron.Polyhedron,
sympy.combinatorics.permutations.Permutation
References
==========
[1] Holt, D., Eick, B., O'Brien, E.
"Handbook of Computational Group Theory"
[2] Seress, A.
"Permutation Group Algorithms"
[3] http://en.wikipedia.org/wiki/Schreier_vector
[4] http://en.wikipedia.org/wiki/Nielsen_transformation
#Product_replacement_algorithm
[5] Frank Celler, Charles R.Leedham-Green, Scott H.Murray,
Alice C.Niemeyer, and E.A.O'Brien. "Generating Random
Elements of a Finite Group"
[6] http://en.wikipedia.org/wiki/Block_%28permutation_group_theory%29
[7] http://www.algorithmist.com/index.php/Union_Find
[8] http://en.wikipedia.org/wiki/Multiply_transitive_group#Multiply_transitive_groups
[9] http://en.wikipedia.org/wiki/Center_%28group_theory%29
[10] http://en.wikipedia.org/wiki/Centralizer_and_normalizer
[11] http://groupprops.subwiki.org/wiki/Derived_subgroup
[12] http://en.wikipedia.org/wiki/Nilpotent_group
[13] http://www.math.colostate.edu/~hulpke/CGT/cgtnotes.pdf
"""
is_group = True
def __new__(cls, *args, **kwargs):
"""The default constructor. Accepts Cycle and Permutation forms.
Removes duplicates unless ``dups`` keyword is ``False``.
"""
if not args:
args = [Permutation()]
else:
args = list(args[0] if is_sequence(args[0]) else args)
if any(isinstance(a, Cycle) for a in args):
args = [Permutation(a) for a in args]
if has_variety(a.size for a in args):
degree = kwargs.pop('degree', None)
if degree is None:
degree = max(a.size for a in args)
for i in range(len(args)):
if args[i].size != degree:
args[i] = Permutation(args[i], size=degree)
if kwargs.pop('dups', True):
args = list(uniq([_af_new(list(a)) for a in args]))
obj = Basic.__new__(cls, *args, **kwargs)
obj._generators = args
obj._order = None
obj._center = []
obj._is_abelian = None
obj._is_transitive = None
obj._is_sym = None
obj._is_alt = None
obj._is_primitive = None
obj._is_nilpotent = None
obj._is_solvable = None
obj._is_trivial = None
obj._transitivity_degree = None
obj._max_div = None
obj._r = len(obj._generators)
obj._degree = obj._generators[0].size
# these attributes are assigned after running schreier_sims
obj._base = []
obj._strong_gens = []
obj._basic_orbits = []
obj._transversals = []
# these attributes are assigned after running _random_pr_init
obj._random_gens = []
return obj
def __getitem__(self, i):
return self._generators[i]
def __contains__(self, i):
"""Return ``True`` if `i` is contained in PermutationGroup.
Examples
========
>>> from sympy.combinatorics import Permutation, PermutationGroup
>>> p = Permutation(1, 2, 3)
>>> Permutation(3) in PermutationGroup(p)
True
"""
if not isinstance(i, Permutation):
raise TypeError("A PermutationGroup contains only Permutations as "
"elements, not elements of type %s" % type(i))
return self.contains(i)
def __len__(self):
return len(self._generators)
def __eq__(self, other):
"""Return ``True`` if PermutationGroup generated by elements in the
group are same i.e they represent the same PermutationGroup.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> p = Permutation(0, 1, 2, 3, 4, 5)
>>> G = PermutationGroup([p, p**2])
>>> H = PermutationGroup([p**2, p])
>>> G.generators == H.generators
False
>>> G == H
True
"""
if not isinstance(other, PermutationGroup):
return False
set_self_gens = set(self.generators)
set_other_gens = set(other.generators)
# before reaching the general case there are also certain
# optimisation and obvious cases requiring less or no actual
# computation.
if set_self_gens == set_other_gens:
return True
# in the most general case it will check that each generator of
# one group belongs to the other PermutationGroup and vice-versa
for gen1 in set_self_gens:
if not other.contains(gen1):
return False
for gen2 in set_other_gens:
if not self.contains(gen2):
return False
return True
def __hash__(self):
return super(PermutationGroup, self).__hash__()
def __mul__(self, other):
"""Return the direct product of two permutation groups as a permutation
group.
This implementation realizes the direct product by shifting the index
set for the generators of the second group: so if we have `G` acting
on `n1` points and `H` acting on `n2` points, `G*H` acts on `n1 + n2`
points.
Examples
========
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.named_groups import CyclicGroup
>>> G = CyclicGroup(5)
>>> H = G*G
>>> H
PermutationGroup([
(9)(0 1 2 3 4),
(5 6 7 8 9)])
>>> H.order()
25
"""
gens1 = [perm._array_form for perm in self.generators]
gens2 = [perm._array_form for perm in other.generators]
n1 = self._degree
n2 = other._degree
start = list(range(n1))
end = list(range(n1, n1 + n2))
for i in range(len(gens2)):
gens2[i] = [x + n1 for x in gens2[i]]
gens2 = [start + gen for gen in gens2]
gens1 = [gen + end for gen in gens1]
together = gens1 + gens2
gens = [_af_new(x) for x in together]
return PermutationGroup(gens)
def _random_pr_init(self, r, n, _random_prec_n=None):
r"""Initialize random generators for the product replacement algorithm.
The implementation uses a modification of the original product
replacement algorithm due to Leedham-Green, as described in [1],
pp. 69-71; also, see [2], pp. 27-29 for a detailed theoretical
analysis of the original product replacement algorithm, and [4].
The product replacement algorithm is used for producing random,
uniformly distributed elements of a group `G` with a set of generators
`S`. For the initialization ``_random_pr_init``, a list ``R`` of
`\max\{r, |S|\}` group generators is created as the attribute
``G._random_gens``, repeating elements of `S` if necessary, and the
identity element of `G` is appended to ``R`` - we shall refer to this
last element as the accumulator. Then the function ``random_pr()``
is called ``n`` times, randomizing the list ``R`` while preserving
the generation of `G` by ``R``. The function ``random_pr()`` itself
takes two random elements ``g, h`` among all elements of ``R`` but
the accumulator and replaces ``g`` with a randomly chosen element
from `\{gh, g(~h), hg, (~h)g\}`. Then the accumulator is multiplied
by whatever ``g`` was replaced by. The new value of the accumulator is
then returned by ``random_pr()``.
The elements returned will eventually (for ``n`` large enough) become
uniformly distributed across `G` ([5]). For practical purposes however,
the values ``n = 50, r = 11`` are suggested in [1].
Notes
=====
THIS FUNCTION HAS SIDE EFFECTS: it changes the attribute
self._random_gens
See Also
========
random_pr
"""
deg = self.degree
random_gens = [x._array_form for x in self.generators]
k = len(random_gens)
if k < r:
for i in range(k, r):
random_gens.append(random_gens[i - k])
acc = list(range(deg))
random_gens.append(acc)
self._random_gens = random_gens
# handle randomized input for testing purposes
if _random_prec_n is None:
for i in range(n):
self.random_pr()
else:
for i in range(n):
self.random_pr(_random_prec=_random_prec_n[i])
def _union_find_merge(self, first, second, ranks, parents, not_rep):
"""Merges two classes in a union-find data structure.
Used in the implementation of Atkinson's algorithm as suggested in [1],
pp. 83-87. The class merging process uses union by rank as an
optimization. ([7])
Notes
=====
THIS FUNCTION HAS SIDE EFFECTS: the list of class representatives,
``parents``, the list of class sizes, ``ranks``, and the list of
elements that are not representatives, ``not_rep``, are changed due to
class merging.
See Also
========
minimal_block, _union_find_rep
References
==========
[1] Holt, D., Eick, B., O'Brien, E.
"Handbook of computational group theory"
[7] http://www.algorithmist.com/index.php/Union_Find
"""
rep_first = self._union_find_rep(first, parents)
rep_second = self._union_find_rep(second, parents)
if rep_first != rep_second:
# union by rank
if ranks[rep_first] >= ranks[rep_second]:
new_1, new_2 = rep_first, rep_second
else:
new_1, new_2 = rep_second, rep_first
total_rank = ranks[new_1] + ranks[new_2]
if total_rank > self.max_div:
return -1
parents[new_2] = new_1
ranks[new_1] = total_rank
not_rep.append(new_2)
return 1
return 0
def _union_find_rep(self, num, parents):
"""Find representative of a class in a union-find data structure.
Used in the implementation of Atkinson's algorithm as suggested in [1],
pp. 83-87. After the representative of the class to which ``num``
belongs is found, path compression is performed as an optimization
([7]).
Notes
=====
THIS FUNCTION HAS SIDE EFFECTS: the list of class representatives,
``parents``, is altered due to path compression.
See Also
========
minimal_block, _union_find_merge
References
==========
[1] Holt, D., Eick, B., O'Brien, E.
"Handbook of computational group theory"
[7] http://www.algorithmist.com/index.php/Union_Find
"""
rep, parent = num, parents[num]
while parent != rep:
rep = parent
parent = parents[rep]
# path compression
temp, parent = num, parents[num]
while parent != rep:
parents[temp] = rep
temp = parent
parent = parents[temp]
return rep
@property
def base(self):
"""Return a base from the Schreier-Sims algorithm.
For a permutation group `G`, a base is a sequence of points
`B = (b_1, b_2, ..., b_k)` such that no element of `G` apart
from the identity fixes all the points in `B`. The concepts of
a base and strong generating set and their applications are
discussed in depth in [1], pp. 87-89 and [2], pp. 55-57.
An alternative way to think of `B` is that it gives the
indices of the stabilizer cosets that contain more than the
identity permutation.
Examples
========
>>> from sympy.combinatorics import Permutation, PermutationGroup
>>> G = PermutationGroup([Permutation(0, 1, 3)(2, 4)])
>>> G.base
[0, 2]
See Also
========
strong_gens, basic_transversals, basic_orbits, basic_stabilizers
"""
if self._base == []:
self.schreier_sims()
return self._base
def baseswap(self, base, strong_gens, pos, randomized=False,
transversals=None, basic_orbits=None, strong_gens_distr=None):
r"""Swap two consecutive base points in base and strong generating set.
If a base for a group `G` is given by `(b_1, b_2, ..., b_k)`, this
function returns a base `(b_1, b_2, ..., b_{i+1}, b_i, ..., b_k)`,
where `i` is given by ``pos``, and a strong generating set relative
to that base. The original base and strong generating set are not
modified.
The randomized version (default) is of Las Vegas type.
Parameters
==========
base, strong_gens
The base and strong generating set.
pos
The position at which swapping is performed.
randomized
A switch between randomized and deterministic version.
transversals
The transversals for the basic orbits, if known.
basic_orbits
The basic orbits, if known.
strong_gens_distr
The strong generators distributed by basic stabilizers, if known.
Returns
=======
(base, strong_gens)
``base`` is the new base, and ``strong_gens`` is a generating set
relative to it.
Examples
========
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> from sympy.combinatorics.testutil import _verify_bsgs
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> S = SymmetricGroup(4)
>>> S.schreier_sims()
>>> S.base
[0, 1, 2]
>>> base, gens = S.baseswap(S.base, S.strong_gens, 1, randomized=False)
>>> base, gens
([0, 2, 1],
[(0 1 2 3), (3)(0 1), (1 3 2),
(2 3), (1 3)])
check that base, gens is a BSGS
>>> S1 = PermutationGroup(gens)
>>> _verify_bsgs(S1, base, gens)
True
See Also
========
schreier_sims
Notes
=====
The deterministic version of the algorithm is discussed in
[1], pp. 102-103; the randomized version is discussed in [1], p.103, and
[2], p.98. It is of Las Vegas type.
Notice that [1] contains a mistake in the pseudocode and
discussion of BASESWAP: on line 3 of the pseudocode,
`|\beta_{i+1}^{\left\langle T\right\rangle}|` should be replaced by
`|\beta_{i}^{\left\langle T\right\rangle}|`, and the same for the
discussion of the algorithm.
"""
# construct the basic orbits, generators for the stabilizer chain
# and transversal elements from whatever was provided
transversals, basic_orbits, strong_gens_distr = \
_handle_precomputed_bsgs(base, strong_gens, transversals,
basic_orbits, strong_gens_distr)
base_len = len(base)
degree = self.degree
# size of orbit of base[pos] under the stabilizer we seek to insert
# in the stabilizer chain at position pos + 1
size = len(basic_orbits[pos])*len(basic_orbits[pos + 1]) \
//len(_orbit(degree, strong_gens_distr[pos], base[pos + 1]))
# initialize the wanted stabilizer by a subgroup
if pos + 2 > base_len - 1:
T = []
else:
T = strong_gens_distr[pos + 2][:]
# randomized version
if randomized is True:
stab_pos = PermutationGroup(strong_gens_distr[pos])
schreier_vector = stab_pos.schreier_vector(base[pos + 1])
# add random elements of the stabilizer until they generate it
while len(_orbit(degree, T, base[pos])) != size:
new = stab_pos.random_stab(base[pos + 1],
schreier_vector=schreier_vector)
T.append(new)
# deterministic version
else:
Gamma = set(basic_orbits[pos])
Gamma.remove(base[pos])
if base[pos + 1] in Gamma:
Gamma.remove(base[pos + 1])
# add elements of the stabilizer until they generate it by
# ruling out member of the basic orbit of base[pos] along the way
while len(_orbit(degree, T, base[pos])) != size:
gamma = next(iter(Gamma))
x = transversals[pos][gamma]
temp = x._array_form.index(base[pos + 1]) # (~x)(base[pos + 1])
if temp not in basic_orbits[pos + 1]:
Gamma = Gamma - _orbit(degree, T, gamma)
else:
y = transversals[pos + 1][temp]
el = rmul(x, y)
if el(base[pos]) not in _orbit(degree, T, base[pos]):
T.append(el)
Gamma = Gamma - _orbit(degree, T, base[pos])
# build the new base and strong generating set
strong_gens_new_distr = strong_gens_distr[:]
strong_gens_new_distr[pos + 1] = T
base_new = base[:]
base_new[pos], base_new[pos + 1] = base_new[pos + 1], base_new[pos]
strong_gens_new = _strong_gens_from_distr(strong_gens_new_distr)
for gen in T:
if gen not in strong_gens_new:
strong_gens_new.append(gen)
return base_new, strong_gens_new
@property
def basic_orbits(self):
"""
Return the basic orbits relative to a base and strong generating set.
If `(b_1, b_2, ..., b_k)` is a base for a group `G`, and
`G^{(i)} = G_{b_1, b_2, ..., b_{i-1}}` is the ``i``-th basic stabilizer
(so that `G^{(1)} = G`), the ``i``-th basic orbit relative to this base
is the orbit of `b_i` under `G^{(i)}`. See [1], pp. 87-89 for more
information.
Examples
========
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> S = SymmetricGroup(4)
>>> S.basic_orbits
[[0, 1, 2, 3], [1, 2, 3], [2, 3]]
See Also
========
base, strong_gens, basic_transversals, basic_stabilizers
"""
if self._basic_orbits == []:
self.schreier_sims()
return self._basic_orbits
@property
def basic_stabilizers(self):
"""
Return a chain of stabilizers relative to a base and strong generating
set.
The ``i``-th basic stabilizer `G^{(i)}` relative to a base
`(b_1, b_2, ..., b_k)` is `G_{b_1, b_2, ..., b_{i-1}}`. For more
information, see [1], pp. 87-89.
Examples
========
>>> from sympy.combinatorics.named_groups import AlternatingGroup
>>> A = AlternatingGroup(4)
>>> A.schreier_sims()
>>> A.base
[0, 1]
>>> for g in A.basic_stabilizers:
... print(g)
...
PermutationGroup([
(3)(0 1 2),
(1 2 3)])
PermutationGroup([
(1 2 3)])
See Also
========
base, strong_gens, basic_orbits, basic_transversals
"""
if self._transversals == []:
self.schreier_sims()
strong_gens = self._strong_gens
base = self._base
if not base: # e.g. if self is trivial
return []
strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
basic_stabilizers = []
for gens in strong_gens_distr:
basic_stabilizers.append(PermutationGroup(gens))
return basic_stabilizers
@property
def basic_transversals(self):
"""
Return basic transversals relative to a base and strong generating set.
The basic transversals are transversals of the basic orbits. They
are provided as a list of dictionaries, each dictionary having
keys - the elements of one of the basic orbits, and values - the
corresponding transversal elements. See [1], pp. 87-89 for more
information.
Examples
========
>>> from sympy.combinatorics.named_groups import AlternatingGroup
>>> A = AlternatingGroup(4)
>>> A.basic_transversals
[{0: (3), 1: (3)(0 1 2), 2: (3)(0 2 1), 3: (0 3 1)}, {1: (3), 2: (1 2 3), 3: (1 3 2)}]
See Also
========
strong_gens, base, basic_orbits, basic_stabilizers
"""
if self._transversals == []:
self.schreier_sims()
return self._transversals
def coset_transversal(self, H):
"""Return a transversal of the right cosets of self by its subgroup H
using the second method described in [1], Subsection 4.6.7
"""
if not H.is_subgroup(self):
raise ValueError("The argument must be a subgroup")
if H.order() == 1:
return self._elements
self._schreier_sims(base=H.base) # make G.base an extension of H.base
base = self.base
base_ordering = _base_ordering(base, self.degree)
identity = Permutation(self.degree - 1)
transversals = self.basic_transversals[:]
# transversals is a list of dictionaries. Get rid of the keys
# so that it is a list of lists and sort each list in
# the increasing order of base[l]^x
for l, t in enumerate(transversals):
transversals[l] = sorted(t.values(),
key = lambda x: base_ordering[base[l]^x])
orbits = H.basic_orbits
h_stabs = H.basic_stabilizers
g_stabs = self.basic_stabilizers
indices = [x.order()//y.order() for x, y in zip(g_stabs, h_stabs)]
# T^(l) should be a right transversal of H^(l) in G^(l) for
# 1<=l<=len(base). While H^(l) is the trivial group, T^(l)
# contains all the elements of G^(l) so we might just as well
# start with l = len(h_stabs)-1
T = g_stabs[len(h_stabs)]._elements
t_len = len(T)
l = len(h_stabs)-1
while l > -1:
T_next = []
for u in transversals[l]:
if u == identity:
continue
b = base_ordering[base[l]^u]
for t in T:
p = t*u
if all([base_ordering[h^p] >= b for h in orbits[l]]):
T_next.append(p)
if t_len + len(T_next) == indices[l]:
break
if t_len + len(T_next) == indices[l]:
break
T += T_next
t_len += len(T_next)
l -= 1
return T
def _coset_representative(self, g, H):
"""Return the representative of Hg from the transversal that
would be computed by `self.coset_transversal(H)`.
"""
if H.order() == 1:
return g
# The base of self must be an extension of H.base.
if not(self.base[:len(H.base)] == H.base):
self._schreier_sims(base=H.base)
orbits = H.basic_orbits[:]
h_transversals = [list(_.values()) for _ in H.basic_transversals]
transversals = [list(_.values()) for _ in self.basic_transversals]
base = self.base
base_ordering = _base_ordering(base, self.degree)
def step(l, x):
gamma = sorted(orbits[l], key = lambda y: base_ordering[y^x])[0]
i = [base[l]^h for h in h_transversals[l]].index(gamma)
x = h_transversals[l][i]*x
if l < len(orbits)-1:
for u in transversals[l]:
if base[l]^u == base[l]^x:
break
x = step(l+1, x*u**-1)*u
return x
return step(0, g)
def coset_table(self, H):
"""Return the standardised (right) coset table of self in H as
a list of lists.
"""
# Maybe this should be made to return an instance of CosetTable
# from fp_groups.py but the class would need to be changed first
# to be compatible with PermutationGroups
from itertools import chain, product
if not H.is_subgroup(self):
raise ValueError("The argument must be a subgroup")
T = self.coset_transversal(H)
n = len(T)
A = list(chain.from_iterable((gen, gen**-1)
for gen in self.generators))
table = []
for i in range(n):
row = [self._coset_representative(T[i]*x, H) for x in A]
row = [T.index(r) for r in row]
table.append(row)
# standardize (this is the same as the algorithm used in fp_groups)
# If CosetTable is made compatible with PermutationGroups, this
# should be replaced by table.standardize()
A = range(len(A))
gamma = 1
for alpha, a in product(range(n), A):
beta = table[alpha][a]
if beta >= gamma:
if beta > gamma:
for x in A:
z = table[gamma][x]
table[gamma][x] = table[beta][x]
table[beta][x] = z
for i in range(n):
if table[i][x] == beta:
table[i][x] = gamma
elif table[i][x] == gamma:
table[i][x] = beta
gamma += 1
if gamma == n-1:
return table
def center(self):
r"""
Return the center of a permutation group.
The center for a group `G` is defined as
`Z(G) = \{z\in G | \forall g\in G, zg = gz \}`,
the set of elements of `G` that commute with all elements of `G`.
It is equal to the centralizer of `G` inside `G`, and is naturally a
subgroup of `G` ([9]).
Examples
========
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> D = DihedralGroup(4)
>>> G = D.center()
>>> G.order()
2
See Also
========
centralizer
Notes
=====
This is a naive implementation that is a straightforward application
of ``.centralizer()``
"""
return self.centralizer(self)
def centralizer(self, other):
r"""
Return the centralizer of a group/set/element.
The centralizer of a set of permutations ``S`` inside
a group ``G`` is the set of elements of ``G`` that commute with all
elements of ``S``::
`C_G(S) = \{ g \in G | gs = sg \forall s \in S\}` ([10])
Usually, ``S`` is a subset of ``G``, but if ``G`` is a proper subgroup of
the full symmetric group, we allow for ``S`` to have elements outside
``G``.
It is naturally a subgroup of ``G``; the centralizer of a permutation
group is equal to the centralizer of any set of generators for that
group, since any element commuting with the generators commutes with
any product of the generators.
Parameters
==========
other
a permutation group/list of permutations/single permutation
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... CyclicGroup)
>>> S = SymmetricGroup(6)
>>> C = CyclicGroup(6)
>>> H = S.centralizer(C)
>>> H.is_subgroup(C)
True
See Also
========
subgroup_search
Notes
=====
The implementation is an application of ``.subgroup_search()`` with
tests using a specific base for the group ``G``.
"""
if hasattr(other, 'generators'):
if other.is_trivial or self.is_trivial:
return self
degree = self.degree
identity = _af_new(list(range(degree)))
orbits = other.orbits()
num_orbits = len(orbits)
orbits.sort(key=lambda x: -len(x))
long_base = []
orbit_reps = [None]*num_orbits
orbit_reps_indices = [None]*num_orbits
orbit_descr = [None]*degree
for i in range(num_orbits):
orbit = list(orbits[i])
orbit_reps[i] = orbit[0]
orbit_reps_indices[i] = len(long_base)
for point in orbit:
orbit_descr[point] = i
long_base = long_base + orbit
base, strong_gens = self.schreier_sims_incremental(base=long_base)
strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
i = 0
for i in range(len(base)):
if strong_gens_distr[i] == [identity]:
break
base = base[:i]
base_len = i
for j in range(num_orbits):
if base[base_len - 1] in orbits[j]:
break
rel_orbits = orbits[: j + 1]
num_rel_orbits = len(rel_orbits)
transversals = [None]*num_rel_orbits
for j in range(num_rel_orbits):
rep = orbit_reps[j]
transversals[j] = dict(
other.orbit_transversal(rep, pairs=True))
trivial_test = lambda x: True
tests = [None]*base_len
for l in range(base_len):
if base[l] in orbit_reps:
tests[l] = trivial_test
else:
def test(computed_words, l=l):
g = computed_words[l]
rep_orb_index = orbit_descr[base[l]]
rep = orbit_reps[rep_orb_index]
im = g._array_form[base[l]]
im_rep = g._array_form[rep]
tr_el = transversals[rep_orb_index][base[l]]
# using the definition of transversal,
# base[l]^g = rep^(tr_el*g);
# if g belongs to the centralizer, then
# base[l]^g = (rep^g)^tr_el
return im == tr_el._array_form[im_rep]
tests[l] = test
def prop(g):
return [rmul(g, gen) for gen in other.generators] == \
[rmul(gen, g) for gen in other.generators]
return self.subgroup_search(prop, base=base,
strong_gens=strong_gens, tests=tests)
elif hasattr(other, '__getitem__'):
gens = list(other)
return self.centralizer(PermutationGroup(gens))
elif hasattr(other, 'array_form'):
return self.centralizer(PermutationGroup([other]))
def commutator(self, G, H):
"""
Return the commutator of two subgroups.
For a permutation group ``K`` and subgroups ``G``, ``H``, the
commutator of ``G`` and ``H`` is defined as the group generated
by all the commutators `[g, h] = hgh^{-1}g^{-1}` for ``g`` in ``G`` and
``h`` in ``H``. It is naturally a subgroup of ``K`` ([1], p.27).
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... AlternatingGroup)
>>> S = SymmetricGroup(5)
>>> A = AlternatingGroup(5)
>>> G = S.commutator(S, A)
>>> G.is_subgroup(A)
True
See Also
========
derived_subgroup
Notes
=====
The commutator of two subgroups `H, G` is equal to the normal closure
of the commutators of all the generators, i.e. `hgh^{-1}g^{-1}` for `h`
a generator of `H` and `g` a generator of `G` ([1], p.28)
"""
ggens = G.generators
hgens = H.generators
commutators = []
for ggen in ggens:
for hgen in hgens:
commutator = rmul(hgen, ggen, ~hgen, ~ggen)
if commutator not in commutators:
commutators.append(commutator)
res = self.normal_closure(commutators)
return res
def coset_factor(self, g, factor_index=False):
"""Return ``G``'s (self's) coset factorization of ``g``
If ``g`` is an element of ``G`` then it can be written as the product
of permutations drawn from the Schreier-Sims coset decomposition,
The permutations returned in ``f`` are those for which
the product gives ``g``: ``g = f[n]*...f[1]*f[0]`` where ``n = len(B)``
and ``B = G.base``. f[i] is one of the permutations in
``self._basic_orbits[i]``.
If factor_index==True,
returns a tuple ``[b[0],..,b[n]]``, where ``b[i]``
belongs to ``self._basic_orbits[i]``
Examples
========
>>> from sympy.combinatorics import Permutation, PermutationGroup
>>> Permutation.print_cyclic = True
>>> a = Permutation(0, 1, 3, 7, 6, 4)(2, 5)
>>> b = Permutation(0, 1, 3, 2)(4, 5, 7, 6)
>>> G = PermutationGroup([a, b])
Define g:
>>> g = Permutation(7)(1, 2, 4)(3, 6, 5)
Confirm that it is an element of G:
>>> G.contains(g)
True
Thus, it can be written as a product of factors (up to
3) drawn from u. See below that a factor from u1 and u2
and the Identity permutation have been used:
>>> f = G.coset_factor(g)
>>> f[2]*f[1]*f[0] == g
True
>>> f1 = G.coset_factor(g, True); f1
[0, 4, 4]
>>> tr = G.basic_transversals
>>> f[0] == tr[0][f1[0]]
True
If g is not an element of G then [] is returned:
>>> c = Permutation(5, 6, 7)
>>> G.coset_factor(c)
[]
see util._strip
"""
if isinstance(g, (Cycle, Permutation)):
g = g.list()
if len(g) != self._degree:
# this could either adjust the size or return [] immediately
# but we don't choose between the two and just signal a possible
# error
raise ValueError('g should be the same size as permutations of G')
I = list(range(self._degree))
basic_orbits = self.basic_orbits
transversals = self._transversals
factors = []
base = self.base
h = g
for i in range(len(base)):
beta = h[base[i]]
if beta == base[i]:
factors.append(beta)
continue
if beta not in basic_orbits[i]:
return []
u = transversals[i][beta]._array_form
h = _af_rmul(_af_invert(u), h)
factors.append(beta)
if h != I:
return []
if factor_index:
return factors
tr = self.basic_transversals
factors = [tr[i][factors[i]] for i in range(len(base))]
return factors
def coset_rank(self, g):
"""rank using Schreier-Sims representation
The coset rank of ``g`` is the ordering number in which
it appears in the lexicographic listing according to the
coset decomposition
The ordering is the same as in G.generate(method='coset').
If ``g`` does not belong to the group it returns None.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation(0, 1, 3, 7, 6, 4)(2, 5)
>>> b = Permutation(0, 1, 3, 2)(4, 5, 7, 6)
>>> G = PermutationGroup([a, b])
>>> c = Permutation(7)(2, 4)(3, 5)
>>> G.coset_rank(c)
16
>>> G.coset_unrank(16)
(7)(2 4)(3 5)
See Also
========
coset_factor
"""
factors = self.coset_factor(g, True)
if not factors:
return None
rank = 0
b = 1
transversals = self._transversals
base = self._base
basic_orbits = self._basic_orbits
for i in range(len(base)):
k = factors[i]
j = basic_orbits[i].index(k)
rank += b*j
b = b*len(transversals[i])
return rank
def coset_unrank(self, rank, af=False):
"""unrank using Schreier-Sims representation
coset_unrank is the inverse operation of coset_rank
if 0 <= rank < order; otherwise it returns None.
"""
if rank < 0 or rank >= self.order():
return None
base = self._base
transversals = self._transversals
basic_orbits = self._basic_orbits
m = len(base)
v = [0]*m
for i in range(m):
rank, c = divmod(rank, len(transversals[i]))
v[i] = basic_orbits[i][c]
a = [transversals[i][v[i]]._array_form for i in range(m)]
h = _af_rmuln(*a)
if af:
return h
else:
return _af_new(h)
@property
def degree(self):
"""Returns the size of the permutations in the group.
The number of permutations comprising the group is given by
``len(group)``; the number of permutations that can be generated
by the group is given by ``group.order()``.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([1, 0, 2])
>>> G = PermutationGroup([a])
>>> G.degree
3
>>> len(G)
1
>>> G.order()
2
>>> list(G.generate())
[(2), (2)(0 1)]
See Also
========
order
"""
return self._degree
@property
def elements(self):
"""Returns all the elements of the permutation group as a set
Examples
========
>>> from sympy.combinatorics import Permutation, PermutationGroup
>>> p = PermutationGroup(Permutation(1, 3), Permutation(1, 2))
>>> p.elements
{(3), (2 3), (3)(1 2), (1 2 3), (1 3 2), (1 3)}
"""
return set(self._elements)
@property
def _elements(self):
"""Returns all the elements of the permutation group as a list
Examples
========
>>> from sympy.combinatorics import Permutation, PermutationGroup
>>> p = PermutationGroup(Permutation(1, 3), Permutation(1, 2))
>>> p._elements
[(3), (3)(1 2), (1 3), (2 3), (1 2 3), (1 3 2)]
"""
return list(islice(self.generate(), None))
def derived_series(self):
r"""Return the derived series for the group.
The derived series for a group `G` is defined as
`G = G_0 > G_1 > G_2 > \ldots` where `G_i = [G_{i-1}, G_{i-1}]`,
i.e. `G_i` is the derived subgroup of `G_{i-1}`, for
`i\in\mathbb{N}`. When we have `G_k = G_{k-1}` for some
`k\in\mathbb{N}`, the series terminates.
Returns
=======
A list of permutation groups containing the members of the derived
series in the order `G = G_0, G_1, G_2, \ldots`.
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... AlternatingGroup, DihedralGroup)
>>> A = AlternatingGroup(5)
>>> len(A.derived_series())
1
>>> S = SymmetricGroup(4)
>>> len(S.derived_series())
4
>>> S.derived_series()[1].is_subgroup(AlternatingGroup(4))
True
>>> S.derived_series()[2].is_subgroup(DihedralGroup(2))
True
See Also
========
derived_subgroup
"""
res = [self]
current = self
next = self.derived_subgroup()
while not current.is_subgroup(next):
res.append(next)
current = next
next = next.derived_subgroup()
return res
def derived_subgroup(self):
"""Compute the derived subgroup.
The derived subgroup, or commutator subgroup is the subgroup generated
by all commutators `[g, h] = hgh^{-1}g^{-1}` for `g, h\in G` ; it is
equal to the normal closure of the set of commutators of the generators
([1], p.28, [11]).
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([1, 0, 2, 4, 3])
>>> b = Permutation([0, 1, 3, 2, 4])
>>> G = PermutationGroup([a, b])
>>> C = G.derived_subgroup()
>>> list(C.generate(af=True))
[[0, 1, 2, 3, 4], [0, 1, 3, 4, 2], [0, 1, 4, 2, 3]]
See Also
========
derived_series
"""
r = self._r
gens = [p._array_form for p in self.generators]
gens_inv = [_af_invert(p) for p in gens]
set_commutators = set()
degree = self._degree
rng = list(range(degree))
for i in range(r):
for j in range(r):
p1 = gens[i]
p2 = gens[j]
c = list(range(degree))
for k in rng:
c[p2[p1[k]]] = p1[p2[k]]
ct = tuple(c)
if not ct in set_commutators:
set_commutators.add(ct)
cms = [_af_new(p) for p in set_commutators]
G2 = self.normal_closure(cms)
return G2
def generate(self, method="coset", af=False):
"""Return iterator to generate the elements of the group
Iteration is done with one of these methods::
method='coset' using the Schreier-Sims coset representation
method='dimino' using the Dimino method
If af = True it yields the array form of the permutations
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics import PermutationGroup
>>> from sympy.combinatorics.polyhedron import tetrahedron
The permutation group given in the tetrahedron object is also
true groups:
>>> G = tetrahedron.pgroup
>>> G.is_group
True
Also the group generated by the permutations in the tetrahedron
pgroup -- even the first two -- is a proper group:
>>> H = PermutationGroup(G[0], G[1])
>>> J = PermutationGroup(list(H.generate())); J
PermutationGroup([
(0 1)(2 3),
(3),
(1 2 3),
(1 3 2),
(0 3 1),
(0 2 3),
(0 3)(1 2),
(0 1 3),
(3)(0 2 1),
(0 3 2),
(3)(0 1 2),
(0 2)(1 3)])
>>> _.is_group
True
"""
if method == "coset":
return self.generate_schreier_sims(af)
elif method == "dimino":
return self.generate_dimino(af)
else:
raise NotImplementedError('No generation defined for %s' % method)
def generate_dimino(self, af=False):
"""Yield group elements using Dimino's algorithm
If af == True it yields the array form of the permutations
References
==========
[1] The Implementation of Various Algorithms for Permutation Groups in
the Computer Algebra System: AXIOM, N.J. Doye, M.Sc. Thesis
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([0, 2, 1, 3])
>>> b = Permutation([0, 2, 3, 1])
>>> g = PermutationGroup([a, b])
>>> list(g.generate_dimino(af=True))
[[0, 1, 2, 3], [0, 2, 1, 3], [0, 2, 3, 1],
[0, 1, 3, 2], [0, 3, 2, 1], [0, 3, 1, 2]]
"""
idn = list(range(self.degree))
order = 0
element_list = [idn]
set_element_list = {tuple(idn)}
if af:
yield idn
else:
yield _af_new(idn)
gens = [p._array_form for p in self.generators]
for i in range(len(gens)):
# D elements of the subgroup G_i generated by gens[:i]
D = element_list[:]
N = [idn]
while N:
A = N
N = []
for a in A:
for g in gens[:i + 1]:
ag = _af_rmul(a, g)
if tuple(ag) not in set_element_list:
# produce G_i*g
for d in D:
order += 1
ap = _af_rmul(d, ag)
if af:
yield ap
else:
p = _af_new(ap)
yield p
element_list.append(ap)
set_element_list.add(tuple(ap))
N.append(ap)
self._order = len(element_list)
def generate_schreier_sims(self, af=False):
"""Yield group elements using the Schreier-Sims representation
in coset_rank order
If ``af = True`` it yields the array form of the permutations
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([0, 2, 1, 3])
>>> b = Permutation([0, 2, 3, 1])
>>> g = PermutationGroup([a, b])
>>> list(g.generate_schreier_sims(af=True))
[[0, 1, 2, 3], [0, 2, 1, 3], [0, 3, 2, 1],
[0, 1, 3, 2], [0, 2, 3, 1], [0, 3, 1, 2]]
"""
n = self._degree
u = self.basic_transversals
basic_orbits = self._basic_orbits
if len(u) == 0:
for x in self.generators:
if af:
yield x._array_form
else:
yield x
return
if len(u) == 1:
for i in basic_orbits[0]:
if af:
yield u[0][i]._array_form
else:
yield u[0][i]
return
u = list(reversed(u))
basic_orbits = basic_orbits[::-1]
# stg stack of group elements
stg = [list(range(n))]
posmax = [len(x) for x in u]
n1 = len(posmax) - 1
pos = [0]*n1
h = 0
while 1:
# backtrack when finished iterating over coset
if pos[h] >= posmax[h]:
if h == 0:
return
pos[h] = 0
h -= 1
stg.pop()
continue
p = _af_rmul(u[h][basic_orbits[h][pos[h]]]._array_form, stg[-1])
pos[h] += 1
stg.append(p)
h += 1
if h == n1:
if af:
for i in basic_orbits[-1]:
p = _af_rmul(u[-1][i]._array_form, stg[-1])
yield p
else:
for i in basic_orbits[-1]:
p = _af_rmul(u[-1][i]._array_form, stg[-1])
p1 = _af_new(p)
yield p1
stg.pop()
h -= 1
@property
def generators(self):
"""Returns the generators of the group.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([0, 2, 1])
>>> b = Permutation([1, 0, 2])
>>> G = PermutationGroup([a, b])
>>> G.generators
[(1 2), (2)(0 1)]
"""
return self._generators
def contains(self, g, strict=True):
"""Test if permutation ``g`` belong to self, ``G``.
If ``g`` is an element of ``G`` it can be written as a product
of factors drawn from the cosets of ``G``'s stabilizers. To see
if ``g`` is one of the actual generators defining the group use
``G.has(g)``.
If ``strict`` is not ``True``, ``g`` will be resized, if necessary,
to match the size of permutations in ``self``.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation(1, 2)
>>> b = Permutation(2, 3, 1)
>>> G = PermutationGroup(a, b, degree=5)
>>> G.contains(G[0]) # trivial check
True
>>> elem = Permutation([[2, 3]], size=5)
>>> G.contains(elem)
True
>>> G.contains(Permutation(4)(0, 1, 2, 3))
False
If strict is False, a permutation will be resized, if
necessary:
>>> H = PermutationGroup(Permutation(5))
>>> H.contains(Permutation(3))
False
>>> H.contains(Permutation(3), strict=False)
True
To test if a given permutation is present in the group:
>>> elem in G.generators
False
>>> G.has(elem)
False
See Also
========
coset_factor, has, in
"""
if not isinstance(g, Permutation):
return False
if g.size != self.degree:
if strict:
return False
g = Permutation(g, size=self.degree)
if g in self.generators:
return True
return bool(self.coset_factor(g.array_form, True))
@property
def is_abelian(self):
"""Test if the group is Abelian.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([0, 2, 1])
>>> b = Permutation([1, 0, 2])
>>> G = PermutationGroup([a, b])
>>> G.is_abelian
False
>>> a = Permutation([0, 2, 1])
>>> G = PermutationGroup([a])
>>> G.is_abelian
True
"""
if self._is_abelian is not None:
return self._is_abelian
self._is_abelian = True
gens = [p._array_form for p in self.generators]
for x in gens:
for y in gens:
if y <= x:
continue
if not _af_commutes_with(x, y):
self._is_abelian = False
return False
return True
def is_alt_sym(self, eps=0.05, _random_prec=None):
r"""Monte Carlo test for the symmetric/alternating group for degrees
>= 8.
More specifically, it is one-sided Monte Carlo with the
answer True (i.e., G is symmetric/alternating) guaranteed to be
correct, and the answer False being incorrect with probability eps.
Notes
=====
The algorithm itself uses some nontrivial results from group theory and
number theory:
1) If a transitive group ``G`` of degree ``n`` contains an element
with a cycle of length ``n/2 < p < n-2`` for ``p`` a prime, ``G`` is the
symmetric or alternating group ([1], pp. 81-82)
2) The proportion of elements in the symmetric/alternating group having
the property described in 1) is approximately `\log(2)/\log(n)`
([1], p.82; [2], pp. 226-227).
The helper function ``_check_cycles_alt_sym`` is used to
go over the cycles in a permutation and look for ones satisfying 1).
Examples
========
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> D = DihedralGroup(10)
>>> D.is_alt_sym()
False
See Also
========
_check_cycles_alt_sym
"""
if _random_prec is None:
n = self.degree
if n < 8:
return False
if not self.is_transitive():
return False
if n < 17:
c_n = 0.34
else:
c_n = 0.57
d_n = (c_n*log(2))/log(n)
N_eps = int(-log(eps)/d_n)
for i in range(N_eps):
perm = self.random_pr()
if _check_cycles_alt_sym(perm):
return True
return False
else:
for i in range(_random_prec['N_eps']):
perm = _random_prec[i]
if _check_cycles_alt_sym(perm):
return True
return False
@property
def is_nilpotent(self):
"""Test if the group is nilpotent.
A group `G` is nilpotent if it has a central series of finite length.
Alternatively, `G` is nilpotent if its lower central series terminates
with the trivial group. Every nilpotent group is also solvable
([1], p.29, [12]).
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... CyclicGroup)
>>> C = CyclicGroup(6)
>>> C.is_nilpotent
True
>>> S = SymmetricGroup(5)
>>> S.is_nilpotent
False
See Also
========
lower_central_series, is_solvable
"""
if self._is_nilpotent is None:
lcs = self.lower_central_series()
terminator = lcs[len(lcs) - 1]
gens = terminator.generators
degree = self.degree
identity = _af_new(list(range(degree)))
if all(g == identity for g in gens):
self._is_solvable = True
self._is_nilpotent = True
return True
else:
self._is_nilpotent = False
return False
else:
return self._is_nilpotent
def is_normal(self, gr, strict=True):
"""Test if ``G=self`` is a normal subgroup of ``gr``.
G is normal in gr if
for each g2 in G, g1 in gr, ``g = g1*g2*g1**-1`` belongs to G
It is sufficient to check this for each g1 in gr.generators and
g2 in G.generators.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([1, 2, 0])
>>> b = Permutation([1, 0, 2])
>>> G = PermutationGroup([a, b])
>>> G1 = PermutationGroup([a, Permutation([2, 0, 1])])
>>> G1.is_normal(G)
True
"""
d_self = self.degree
d_gr = gr.degree
new_self = self.copy()
if not strict and d_self != d_gr:
if d_self < d_gr:
new_self = PermGroup(new_self.generators + [Permutation(d_gr - 1)])
else:
gr = PermGroup(gr.generators + [Permutation(d_self - 1)])
gens2 = [p._array_form for p in new_self.generators]
gens1 = [p._array_form for p in gr.generators]
for g1 in gens1:
for g2 in gens2:
p = _af_rmuln(g1, g2, _af_invert(g1))
if not new_self.coset_factor(p, True):
return False
return True
def is_primitive(self, randomized=True):
"""Test if a group is primitive.
A permutation group ``G`` acting on a set ``S`` is called primitive if
``S`` contains no nontrivial block under the action of ``G``
(a block is nontrivial if its cardinality is more than ``1``).
Notes
=====
The algorithm is described in [1], p.83, and uses the function
minimal_block to search for blocks of the form `\{0, k\}` for ``k``
ranging over representatives for the orbits of `G_0`, the stabilizer of
``0``. This algorithm has complexity `O(n^2)` where ``n`` is the degree
of the group, and will perform badly if `G_0` is small.
There are two implementations offered: one finds `G_0`
deterministically using the function ``stabilizer``, and the other
(default) produces random elements of `G_0` using ``random_stab``,
hoping that they generate a subgroup of `G_0` with not too many more
orbits than `G_0` (this is suggested in [1], p.83). Behavior is changed
by the ``randomized`` flag.
Examples
========
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> D = DihedralGroup(10)
>>> D.is_primitive()
False
See Also
========
minimal_block, random_stab
"""
if self._is_primitive is not None:
return self._is_primitive
n = self.degree
if randomized:
random_stab_gens = []
v = self.schreier_vector(0)
for i in range(len(self)):
random_stab_gens.append(self.random_stab(0, v))
stab = PermutationGroup(random_stab_gens)
else:
stab = self.stabilizer(0)
orbits = stab.orbits()
for orb in orbits:
x = orb.pop()
if x != 0 and self.minimal_block([0, x]) != [0]*n:
self._is_primitive = False
return False
self._is_primitive = True
return True
@property
def is_solvable(self):
"""Test if the group is solvable.
``G`` is solvable if its derived series terminates with the trivial
group ([1], p.29).
Examples
========
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> S = SymmetricGroup(3)
>>> S.is_solvable
True
See Also
========
is_nilpotent, derived_series
"""
if self._is_solvable is None:
ds = self.derived_series()
terminator = ds[len(ds) - 1]
gens = terminator.generators
degree = self.degree
identity = _af_new(list(range(degree)))
if all(g == identity for g in gens):
self._is_solvable = True
return True
else:
self._is_solvable = False
return False
else:
return self._is_solvable
def is_subgroup(self, G, strict=True):
"""Return ``True`` if all elements of ``self`` belong to ``G``.
If ``strict`` is ``False`` then if ``self``'s degree is smaller
than ``G``'s, the elements will be resized to have the same degree.
Examples
========
>>> from sympy.combinatorics import Permutation, PermutationGroup
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... CyclicGroup)
Testing is strict by default: the degree of each group must be the
same:
>>> p = Permutation(0, 1, 2, 3, 4, 5)
>>> G1 = PermutationGroup([Permutation(0, 1, 2), Permutation(0, 1)])
>>> G2 = PermutationGroup([Permutation(0, 2), Permutation(0, 1, 2)])
>>> G3 = PermutationGroup([p, p**2])
>>> assert G1.order() == G2.order() == G3.order() == 6
>>> G1.is_subgroup(G2)
True
>>> G1.is_subgroup(G3)
False
>>> G3.is_subgroup(PermutationGroup(G3[1]))
False
>>> G3.is_subgroup(PermutationGroup(G3[0]))
True
To ignore the size, set ``strict`` to ``False``:
>>> S3 = SymmetricGroup(3)
>>> S5 = SymmetricGroup(5)
>>> S3.is_subgroup(S5, strict=False)
True
>>> C7 = CyclicGroup(7)
>>> G = S5*C7
>>> S5.is_subgroup(G, False)
True
>>> C7.is_subgroup(G, 0)
False
"""
if not isinstance(G, PermutationGroup):
return False
if self == G:
return True
if G.order() % self.order() != 0:
return False
if self.degree == G.degree or \
(self.degree < G.degree and not strict):
gens = self.generators
else:
return False
return all(G.contains(g, strict=strict) for g in gens)
def is_transitive(self, strict=True):
"""Test if the group is transitive.
A group is transitive if it has a single orbit.
If ``strict`` is ``False`` the group is transitive if it has
a single orbit of length different from 1.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([0, 2, 1, 3])
>>> b = Permutation([2, 0, 1, 3])
>>> G1 = PermutationGroup([a, b])
>>> G1.is_transitive()
False
>>> G1.is_transitive(strict=False)
True
>>> c = Permutation([2, 3, 0, 1])
>>> G2 = PermutationGroup([a, c])
>>> G2.is_transitive()
True
>>> d = Permutation([1, 0, 2, 3])
>>> e = Permutation([0, 1, 3, 2])
>>> G3 = PermutationGroup([d, e])
>>> G3.is_transitive() or G3.is_transitive(strict=False)
False
"""
if self._is_transitive: # strict or not, if True then True
return self._is_transitive
if strict:
if self._is_transitive is not None: # we only store strict=True
return self._is_transitive
ans = len(self.orbit(0)) == self.degree
self._is_transitive = ans
return ans
got_orb = False
for x in self.orbits():
if len(x) > 1:
if got_orb:
return False
got_orb = True
return got_orb
@property
def is_trivial(self):
"""Test if the group is the trivial group.
This is true if the group contains only the identity permutation.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> G = PermutationGroup([Permutation([0, 1, 2])])
>>> G.is_trivial
True
"""
if self._is_trivial is None:
self._is_trivial = len(self) == 1 and self[0].is_Identity
return self._is_trivial
def lower_central_series(self):
r"""Return the lower central series for the group.
The lower central series for a group `G` is the series
`G = G_0 > G_1 > G_2 > \ldots` where
`G_k = [G, G_{k-1}]`, i.e. every term after the first is equal to the
commutator of `G` and the previous term in `G1` ([1], p.29).
Returns
=======
A list of permutation groups in the order `G = G_0, G_1, G_2, \ldots`
Examples
========
>>> from sympy.combinatorics.named_groups import (AlternatingGroup,
... DihedralGroup)
>>> A = AlternatingGroup(4)
>>> len(A.lower_central_series())
2
>>> A.lower_central_series()[1].is_subgroup(DihedralGroup(2))
True
See Also
========
commutator, derived_series
"""
res = [self]
current = self
next = self.commutator(self, current)
while not current.is_subgroup(next):
res.append(next)
current = next
next = self.commutator(self, current)
return res
@property
def max_div(self):
"""Maximum proper divisor of the degree of a permutation group.
Notes
=====
Obviously, this is the degree divided by its minimal proper divisor
(larger than ``1``, if one exists). As it is guaranteed to be prime,
the ``sieve`` from ``sympy.ntheory`` is used.
This function is also used as an optimization tool for the functions
``minimal_block`` and ``_union_find_merge``.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> G = PermutationGroup([Permutation([0, 2, 1, 3])])
>>> G.max_div
2
See Also
========
minimal_block, _union_find_merge
"""
if self._max_div is not None:
return self._max_div
n = self.degree
if n == 1:
return 1
for x in sieve:
if n % x == 0:
d = n//x
self._max_div = d
return d
def minimal_block(self, points):
r"""For a transitive group, finds the block system generated by
``points``.
If a group ``G`` acts on a set ``S``, a nonempty subset ``B`` of ``S``
is called a block under the action of ``G`` if for all ``g`` in ``G``
we have ``gB = B`` (``g`` fixes ``B``) or ``gB`` and ``B`` have no
common points (``g`` moves ``B`` entirely). ([1], p.23; [6]).
The distinct translates ``gB`` of a block ``B`` for ``g`` in ``G``
partition the set ``S`` and this set of translates is known as a block
system. Moreover, we obviously have that all blocks in the partition
have the same size, hence the block size divides ``|S|`` ([1], p.23).
A ``G``-congruence is an equivalence relation ``~`` on the set ``S``
such that ``a ~ b`` implies ``g(a) ~ g(b)`` for all ``g`` in ``G``.
For a transitive group, the equivalence classes of a ``G``-congruence
and the blocks of a block system are the same thing ([1], p.23).
The algorithm below checks the group for transitivity, and then finds
the ``G``-congruence generated by the pairs ``(p_0, p_1), (p_0, p_2),
..., (p_0,p_{k-1})`` which is the same as finding the maximal block
system (i.e., the one with minimum block size) such that
``p_0, ..., p_{k-1}`` are in the same block ([1], p.83).
It is an implementation of Atkinson's algorithm, as suggested in [1],
and manipulates an equivalence relation on the set ``S`` using a
union-find data structure. The running time is just above
`O(|points||S|)`. ([1], pp. 83-87; [7]).
Examples
========
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> D = DihedralGroup(10)
>>> D.minimal_block([0, 5])
[0, 6, 2, 8, 4, 0, 6, 2, 8, 4]
>>> D.minimal_block([0, 1])
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
See Also
========
_union_find_rep, _union_find_merge, is_transitive, is_primitive
"""
if not self.is_transitive():
return False
n = self.degree
gens = self.generators
# initialize the list of equivalence class representatives
parents = list(range(n))
ranks = [1]*n
not_rep = []
k = len(points)
# the block size must divide the degree of the group
if k > self.max_div:
return [0]*n
for i in range(k - 1):
parents[points[i + 1]] = points[0]
not_rep.append(points[i + 1])
ranks[points[0]] = k
i = 0
len_not_rep = k - 1
while i < len_not_rep:
temp = not_rep[i]
i += 1
for gen in gens:
# find has side effects: performs path compression on the list
# of representatives
delta = self._union_find_rep(temp, parents)
# union has side effects: performs union by rank on the list
# of representatives
temp = self._union_find_merge(gen(temp), gen(delta), ranks,
parents, not_rep)
if temp == -1:
return [0]*n
len_not_rep += temp
for i in range(n):
# force path compression to get the final state of the equivalence
# relation
self._union_find_rep(i, parents)
return parents
def normal_closure(self, other, k=10):
r"""Return the normal closure of a subgroup/set of permutations.
If ``S`` is a subset of a group ``G``, the normal closure of ``A`` in ``G``
is defined as the intersection of all normal subgroups of ``G`` that
contain ``A`` ([1], p.14). Alternatively, it is the group generated by
the conjugates ``x^{-1}yx`` for ``x`` a generator of ``G`` and ``y`` a
generator of the subgroup ``\left\langle S\right\rangle`` generated by
``S`` (for some chosen generating set for ``\left\langle S\right\rangle``)
([1], p.73).
Parameters
==========
other
a subgroup/list of permutations/single permutation
k
an implementation-specific parameter that determines the number
of conjugates that are adjoined to ``other`` at once
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... CyclicGroup, AlternatingGroup)
>>> S = SymmetricGroup(5)
>>> C = CyclicGroup(5)
>>> G = S.normal_closure(C)
>>> G.order()
60
>>> G.is_subgroup(AlternatingGroup(5))
True
See Also
========
commutator, derived_subgroup, random_pr
Notes
=====
The algorithm is described in [1], pp. 73-74; it makes use of the
generation of random elements for permutation groups by the product
replacement algorithm.
"""
if hasattr(other, 'generators'):
degree = self.degree
identity = _af_new(list(range(degree)))
if all(g == identity for g in other.generators):
return other
Z = PermutationGroup(other.generators[:])
base, strong_gens = Z.schreier_sims_incremental()
strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
basic_orbits, basic_transversals = \
_orbits_transversals_from_bsgs(base, strong_gens_distr)
self._random_pr_init(r=10, n=20)
_loop = True
while _loop:
Z._random_pr_init(r=10, n=10)
for i in range(k):
g = self.random_pr()
h = Z.random_pr()
conj = h^g
res = _strip(conj, base, basic_orbits, basic_transversals)
if res[0] != identity or res[1] != len(base) + 1:
gens = Z.generators
gens.append(conj)
Z = PermutationGroup(gens)
strong_gens.append(conj)
temp_base, temp_strong_gens = \
Z.schreier_sims_incremental(base, strong_gens)
base, strong_gens = temp_base, temp_strong_gens
strong_gens_distr = \
_distribute_gens_by_base(base, strong_gens)
basic_orbits, basic_transversals = \
_orbits_transversals_from_bsgs(base,
strong_gens_distr)
_loop = False
for g in self.generators:
for h in Z.generators:
conj = h^g
res = _strip(conj, base, basic_orbits,
basic_transversals)
if res[0] != identity or res[1] != len(base) + 1:
_loop = True
break
if _loop:
break
return Z
elif hasattr(other, '__getitem__'):
return self.normal_closure(PermutationGroup(other))
elif hasattr(other, 'array_form'):
return self.normal_closure(PermutationGroup([other]))
def orbit(self, alpha, action='tuples'):
r"""Compute the orbit of alpha `\{g(\alpha) | g \in G\}` as a set.
The time complexity of the algorithm used here is `O(|Orb|*r)` where
`|Orb|` is the size of the orbit and ``r`` is the number of generators of
the group. For a more detailed analysis, see [1], p.78, [2], pp. 19-21.
Here alpha can be a single point, or a list of points.
If alpha is a single point, the ordinary orbit is computed.
if alpha is a list of points, there are three available options:
'union' - computes the union of the orbits of the points in the list
'tuples' - computes the orbit of the list interpreted as an ordered
tuple under the group action ( i.e., g((1,2,3)) = (g(1), g(2), g(3)) )
'sets' - computes the orbit of the list interpreted as a sets
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([1, 2, 0, 4, 5, 6, 3])
>>> G = PermutationGroup([a])
>>> G.orbit(0)
{0, 1, 2}
>>> G.orbit([0, 4], 'union')
{0, 1, 2, 3, 4, 5, 6}
See Also
========
orbit_transversal
"""
return _orbit(self.degree, self.generators, alpha, action)
def orbit_rep(self, alpha, beta, schreier_vector=None):
"""Return a group element which sends ``alpha`` to ``beta``.
If ``beta`` is not in the orbit of ``alpha``, the function returns
``False``. This implementation makes use of the schreier vector.
For a proof of correctness, see [1], p.80
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.named_groups import AlternatingGroup
>>> G = AlternatingGroup(5)
>>> G.orbit_rep(0, 4)
(0 4 1 2 3)
See Also
========
schreier_vector
"""
if schreier_vector is None:
schreier_vector = self.schreier_vector(alpha)
if schreier_vector[beta] is None:
return False
k = schreier_vector[beta]
gens = [x._array_form for x in self.generators]
a = []
while k != -1:
a.append(gens[k])
beta = gens[k].index(beta) # beta = (~gens[k])(beta)
k = schreier_vector[beta]
if a:
return _af_new(_af_rmuln(*a))
else:
return _af_new(list(range(self._degree)))
def orbit_transversal(self, alpha, pairs=False):
r"""Computes a transversal for the orbit of ``alpha`` as a set.
For a permutation group `G`, a transversal for the orbit
`Orb = \{g(\alpha) | g \in G\}` is a set
`\{g_\beta | g_\beta(\alpha) = \beta\}` for `\beta \in Orb`.
Note that there may be more than one possible transversal.
If ``pairs`` is set to ``True``, it returns the list of pairs
`(\beta, g_\beta)`. For a proof of correctness, see [1], p.79
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> G = DihedralGroup(6)
>>> G.orbit_transversal(0)
[(5), (0 1 2 3 4 5), (0 5)(1 4)(2 3), (0 2 4)(1 3 5), (5)(0 4)(1 3), (0 3)(1 4)(2 5)]
See Also
========
orbit
"""
return _orbit_transversal(self._degree, self.generators, alpha, pairs)
def orbits(self, rep=False):
"""Return the orbits of ``self``, ordered according to lowest element
in each orbit.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation(1, 5)(2, 3)(4, 0, 6)
>>> b = Permutation(1, 5)(3, 4)(2, 6, 0)
>>> G = PermutationGroup([a, b])
>>> G.orbits()
[{0, 2, 3, 4, 6}, {1, 5}]
"""
return _orbits(self._degree, self._generators)
def order(self):
"""Return the order of the group: the number of permutations that
can be generated from elements of the group.
The number of permutations comprising the group is given by
``len(group)``; the length of each permutation in the group is
given by ``group.size``.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([1, 0, 2])
>>> G = PermutationGroup([a])
>>> G.degree
3
>>> len(G)
1
>>> G.order()
2
>>> list(G.generate())
[(2), (2)(0 1)]
>>> a = Permutation([0, 2, 1])
>>> b = Permutation([1, 0, 2])
>>> G = PermutationGroup([a, b])
>>> G.order()
6
See Also
========
degree
"""
if self._order != None:
return self._order
if self._is_sym:
n = self._degree
self._order = factorial(n)
return self._order
if self._is_alt:
n = self._degree
self._order = factorial(n)/2
return self._order
basic_transversals = self.basic_transversals
m = 1
for x in basic_transversals:
m *= len(x)
self._order = m
return m
def pointwise_stabilizer(self, points, incremental=True):
r"""Return the pointwise stabilizer for a set of points.
For a permutation group `G` and a set of points
`\{p_1, p_2,\ldots, p_k\}`, the pointwise stabilizer of
`p_1, p_2, \ldots, p_k` is defined as
`G_{p_1,\ldots, p_k} =
\{g\in G | g(p_i) = p_i \forall i\in\{1, 2,\ldots,k\}\}` ([1],p20).
It is a subgroup of `G`.
Examples
========
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> S = SymmetricGroup(7)
>>> Stab = S.pointwise_stabilizer([2, 3, 5])
>>> Stab.is_subgroup(S.stabilizer(2).stabilizer(3).stabilizer(5))
True
See Also
========
stabilizer, schreier_sims_incremental
Notes
=====
When incremental == True,
rather than the obvious implementation using successive calls to
``.stabilizer()``, this uses the incremental Schreier-Sims algorithm
to obtain a base with starting segment - the given points.
"""
if incremental:
base, strong_gens = self.schreier_sims_incremental(base=points)
stab_gens = []
degree = self.degree
for gen in strong_gens:
if [gen(point) for point in points] == points:
stab_gens.append(gen)
if not stab_gens:
stab_gens = _af_new(list(range(degree)))
return PermutationGroup(stab_gens)
else:
gens = self._generators
degree = self.degree
for x in points:
gens = _stabilizer(degree, gens, x)
return PermutationGroup(gens)
def make_perm(self, n, seed=None):
"""
Multiply ``n`` randomly selected permutations from
pgroup together, starting with the identity
permutation. If ``n`` is a list of integers, those
integers will be used to select the permutations and they
will be applied in L to R order: make_perm((A, B, C)) will
give CBA(I) where I is the identity permutation.
``seed`` is used to set the seed for the random selection
of permutations from pgroup. If this is a list of integers,
the corresponding permutations from pgroup will be selected
in the order give. This is mainly used for testing purposes.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a, b = [Permutation([1, 0, 3, 2]), Permutation([1, 3, 0, 2])]
>>> G = PermutationGroup([a, b])
>>> G.make_perm(1, [0])
(0 1)(2 3)
>>> G.make_perm(3, [0, 1, 0])
(0 2 3 1)
>>> G.make_perm([0, 1, 0])
(0 2 3 1)
See Also
========
random
"""
if is_sequence(n):
if seed is not None:
raise ValueError('If n is a sequence, seed should be None')
n, seed = len(n), n
else:
try:
n = int(n)
except TypeError:
raise ValueError('n must be an integer or a sequence.')
randrange = _randrange(seed)
# start with the identity permutation
result = Permutation(list(range(self.degree)))
m = len(self)
for i in range(n):
p = self[randrange(m)]
result = rmul(result, p)
return result
def random(self, af=False):
"""Return a random group element
"""
rank = randrange(self.order())
return self.coset_unrank(rank, af)
def random_pr(self, gen_count=11, iterations=50, _random_prec=None):
"""Return a random group element using product replacement.
For the details of the product replacement algorithm, see
``_random_pr_init`` In ``random_pr`` the actual 'product replacement'
is performed. Notice that if the attribute ``_random_gens``
is empty, it needs to be initialized by ``_random_pr_init``.
See Also
========
_random_pr_init
"""
if self._random_gens == []:
self._random_pr_init(gen_count, iterations)
random_gens = self._random_gens
r = len(random_gens) - 1
# handle randomized input for testing purposes
if _random_prec is None:
s = randrange(r)
t = randrange(r - 1)
if t == s:
t = r - 1
x = choice([1, 2])
e = choice([-1, 1])
else:
s = _random_prec['s']
t = _random_prec['t']
if t == s:
t = r - 1
x = _random_prec['x']
e = _random_prec['e']
if x == 1:
random_gens[s] = _af_rmul(random_gens[s], _af_pow(random_gens[t], e))
random_gens[r] = _af_rmul(random_gens[r], random_gens[s])
else:
random_gens[s] = _af_rmul(_af_pow(random_gens[t], e), random_gens[s])
random_gens[r] = _af_rmul(random_gens[s], random_gens[r])
return _af_new(random_gens[r])
def random_stab(self, alpha, schreier_vector=None, _random_prec=None):
"""Random element from the stabilizer of ``alpha``.
The schreier vector for ``alpha`` is an optional argument used
for speeding up repeated calls. The algorithm is described in [1], p.81
See Also
========
random_pr, orbit_rep
"""
if schreier_vector is None:
schreier_vector = self.schreier_vector(alpha)
if _random_prec is None:
rand = self.random_pr()
else:
rand = _random_prec['rand']
beta = rand(alpha)
h = self.orbit_rep(alpha, beta, schreier_vector)
return rmul(~h, rand)
def schreier_sims(self):
"""Schreier-Sims algorithm.
It computes the generators of the chain of stabilizers
`G > G_{b_1} > .. > G_{b1,..,b_r} > 1`
in which `G_{b_1,..,b_i}` stabilizes `b_1,..,b_i`,
and the corresponding ``s`` cosets.
An element of the group can be written as the product
`h_1*..*h_s`.
We use the incremental Schreier-Sims algorithm.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> a = Permutation([0, 2, 1])
>>> b = Permutation([1, 0, 2])
>>> G = PermutationGroup([a, b])
>>> G.schreier_sims()
>>> G.basic_transversals
[{0: (2)(0 1), 1: (2), 2: (1 2)},
{0: (2), 2: (0 2)}]
"""
if self._transversals:
return
self._schreier_sims()
return
def _schreier_sims(self, base=None):
base, strong_gens = self.schreier_sims_incremental(base=base)
self._base = base
self._strong_gens = strong_gens
if not base:
self._transversals = []
self._basic_orbits = []
return
strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
basic_orbits, transversals = _orbits_transversals_from_bsgs(base,\
strong_gens_distr)
self._transversals = transversals
self._basic_orbits = [sorted(x) for x in basic_orbits]
def schreier_sims_incremental(self, base=None, gens=None):
"""Extend a sequence of points and generating set to a base and strong
generating set.
Parameters
==========
base
The sequence of points to be extended to a base. Optional
parameter with default value ``[]``.
gens
The generating set to be extended to a strong generating set
relative to the base obtained. Optional parameter with default
value ``self.generators``.
Returns
=======
(base, strong_gens)
``base`` is the base obtained, and ``strong_gens`` is the strong
generating set relative to it. The original parameters ``base``,
``gens`` remain unchanged.
Examples
========
>>> from sympy.combinatorics.named_groups import AlternatingGroup
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.testutil import _verify_bsgs
>>> A = AlternatingGroup(7)
>>> base = [2, 3]
>>> seq = [2, 3]
>>> base, strong_gens = A.schreier_sims_incremental(base=seq)
>>> _verify_bsgs(A, base, strong_gens)
True
>>> base[:2]
[2, 3]
Notes
=====
This version of the Schreier-Sims algorithm runs in polynomial time.
There are certain assumptions in the implementation - if the trivial
group is provided, ``base`` and ``gens`` are returned immediately,
as any sequence of points is a base for the trivial group. If the
identity is present in the generators ``gens``, it is removed as
it is a redundant generator.
The implementation is described in [1], pp. 90-93.
See Also
========
schreier_sims, schreier_sims_random
"""
if base is None:
base = []
if gens is None:
gens = self.generators[:]
degree = self.degree
id_af = list(range(degree))
# handle the trivial group
if len(gens) == 1 and gens[0].is_Identity:
return base, gens
# prevent side effects
_base, _gens = base[:], gens[:]
# remove the identity as a generator
_gens = [x for x in _gens if not x.is_Identity]
# make sure no generator fixes all base points
for gen in _gens:
if all(x == gen._array_form[x] for x in _base):
for new in id_af:
if gen._array_form[new] != new:
break
else:
assert None # can this ever happen?
_base.append(new)
# distribute generators according to basic stabilizers
strong_gens_distr = _distribute_gens_by_base(_base, _gens)
# initialize the basic stabilizers, basic orbits and basic transversals
orbs = {}
transversals = {}
base_len = len(_base)
for i in range(base_len):
transversals[i] = dict(_orbit_transversal(degree, strong_gens_distr[i],
_base[i], pairs=True, af=True))
orbs[i] = list(transversals[i].keys())
# main loop: amend the stabilizer chain until we have generators
# for all stabilizers
i = base_len - 1
while i >= 0:
# this flag is used to continue with the main loop from inside
# a nested loop
continue_i = False
# test the generators for being a strong generating set
db = {}
for beta, u_beta in list(transversals[i].items()):
for gen in strong_gens_distr[i]:
gb = gen._array_form[beta]
u1 = transversals[i][gb]
g1 = _af_rmul(gen._array_form, u_beta)
if g1 != u1:
# test if the schreier generator is in the i+1-th
# would-be basic stabilizer
y = True
try:
u1_inv = db[gb]
except KeyError:
u1_inv = db[gb] = _af_invert(u1)
schreier_gen = _af_rmul(u1_inv, g1)
h, j = _strip_af(schreier_gen, _base, orbs, transversals, i)
if j <= base_len:
# new strong generator h at level j
y = False
elif h:
# h fixes all base points
y = False
moved = 0
while h[moved] == moved:
moved += 1
_base.append(moved)
base_len += 1
strong_gens_distr.append([])
if y is False:
# if a new strong generator is found, update the
# data structures and start over
h = _af_new(h)
for l in range(i + 1, j):
strong_gens_distr[l].append(h)
transversals[l] =\
dict(_orbit_transversal(degree, strong_gens_distr[l],
_base[l], pairs=True, af=True))
orbs[l] = list(transversals[l].keys())
i = j - 1
# continue main loop using the flag
continue_i = True
if continue_i is True:
break
if continue_i is True:
break
if continue_i is True:
continue
i -= 1
# build the strong generating set
strong_gens = list(uniq(i for gens in strong_gens_distr for i in gens))
return _base, strong_gens
def schreier_sims_random(self, base=None, gens=None, consec_succ=10,
_random_prec=None):
r"""Randomized Schreier-Sims algorithm.
The randomized Schreier-Sims algorithm takes the sequence ``base``
and the generating set ``gens``, and extends ``base`` to a base, and
``gens`` to a strong generating set relative to that base with
probability of a wrong answer at most `2^{-consec\_succ}`,
provided the random generators are sufficiently random.
Parameters
==========
base
The sequence to be extended to a base.
gens
The generating set to be extended to a strong generating set.
consec_succ
The parameter defining the probability of a wrong answer.
_random_prec
An internal parameter used for testing purposes.
Returns
=======
(base, strong_gens)
``base`` is the base and ``strong_gens`` is the strong generating
set relative to it.
Examples
========
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.testutil import _verify_bsgs
>>> from sympy.combinatorics.named_groups import SymmetricGroup
>>> S = SymmetricGroup(5)
>>> base, strong_gens = S.schreier_sims_random(consec_succ=5)
>>> _verify_bsgs(S, base, strong_gens) #doctest: +SKIP
True
Notes
=====
The algorithm is described in detail in [1], pp. 97-98. It extends
the orbits ``orbs`` and the permutation groups ``stabs`` to
basic orbits and basic stabilizers for the base and strong generating
set produced in the end.
The idea of the extension process
is to "sift" random group elements through the stabilizer chain
and amend the stabilizers/orbits along the way when a sift
is not successful.
The helper function ``_strip`` is used to attempt
to decompose a random group element according to the current
state of the stabilizer chain and report whether the element was
fully decomposed (successful sift) or not (unsuccessful sift). In
the latter case, the level at which the sift failed is reported and
used to amend ``stabs``, ``base``, ``gens`` and ``orbs`` accordingly.
The halting condition is for ``consec_succ`` consecutive successful
sifts to pass. This makes sure that the current ``base`` and ``gens``
form a BSGS with probability at least `1 - 1/\text{consec\_succ}`.
See Also
========
schreier_sims
"""
if base is None:
base = []
if gens is None:
gens = self.generators
base_len = len(base)
n = self.degree
# make sure no generator fixes all base points
for gen in gens:
if all(gen(x) == x for x in base):
new = 0
while gen._array_form[new] == new:
new += 1
base.append(new)
base_len += 1
# distribute generators according to basic stabilizers
strong_gens_distr = _distribute_gens_by_base(base, gens)
# initialize the basic stabilizers, basic transversals and basic orbits
transversals = {}
orbs = {}
for i in range(base_len):
transversals[i] = dict(_orbit_transversal(n, strong_gens_distr[i],
base[i], pairs=True))
orbs[i] = list(transversals[i].keys())
# initialize the number of consecutive elements sifted
c = 0
# start sifting random elements while the number of consecutive sifts
# is less than consec_succ
while c < consec_succ:
if _random_prec is None:
g = self.random_pr()
else:
g = _random_prec['g'].pop()
h, j = _strip(g, base, orbs, transversals)
y = True
# determine whether a new base point is needed
if j <= base_len:
y = False
elif not h.is_Identity:
y = False
moved = 0
while h(moved) == moved:
moved += 1
base.append(moved)
base_len += 1
strong_gens_distr.append([])
# if the element doesn't sift, amend the strong generators and
# associated stabilizers and orbits
if y is False:
for l in range(1, j):
strong_gens_distr[l].append(h)
transversals[l] = dict(_orbit_transversal(n,
strong_gens_distr[l], base[l], pairs=True))
orbs[l] = list(transversals[l].keys())
c = 0
else:
c += 1
# build the strong generating set
strong_gens = strong_gens_distr[0][:]
for gen in strong_gens_distr[1]:
if gen not in strong_gens:
strong_gens.append(gen)
return base, strong_gens
def schreier_vector(self, alpha):
"""Computes the schreier vector for ``alpha``.
The Schreier vector efficiently stores information
about the orbit of ``alpha``. It can later be used to quickly obtain
elements of the group that send ``alpha`` to a particular element
in the orbit. Notice that the Schreier vector depends on the order
in which the group generators are listed. For a definition, see [3].
Since list indices start from zero, we adopt the convention to use
"None" instead of 0 to signify that an element doesn't belong
to the orbit.
For the algorithm and its correctness, see [2], pp.78-80.
Examples
========
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.permutations import Permutation
>>> a = Permutation([2, 4, 6, 3, 1, 5, 0])
>>> b = Permutation([0, 1, 3, 5, 4, 6, 2])
>>> G = PermutationGroup([a, b])
>>> G.schreier_vector(0)
[-1, None, 0, 1, None, 1, 0]
See Also
========
orbit
"""
n = self.degree
v = [None]*n
v[alpha] = -1
orb = [alpha]
used = [False]*n
used[alpha] = True
gens = self.generators
r = len(gens)
for b in orb:
for i in range(r):
temp = gens[i]._array_form[b]
if used[temp] is False:
orb.append(temp)
used[temp] = True
v[temp] = i
return v
def stabilizer(self, alpha):
r"""Return the stabilizer subgroup of ``alpha``.
The stabilizer of `\alpha` is the group `G_\alpha =
\{g \in G | g(\alpha) = \alpha\}`.
For a proof of correctness, see [1], p.79.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> G = DihedralGroup(6)
>>> G.stabilizer(5)
PermutationGroup([
(5)(0 4)(1 3),
(5)])
See Also
========
orbit
"""
return PermGroup(_stabilizer(self._degree, self._generators, alpha))
@property
def strong_gens(self):
"""Return a strong generating set from the Schreier-Sims algorithm.
A generating set `S = \{g_1, g_2, ..., g_t\}` for a permutation group
`G` is a strong generating set relative to the sequence of points
(referred to as a "base") `(b_1, b_2, ..., b_k)` if, for
`1 \leq i \leq k` we have that the intersection of the pointwise
stabilizer `G^{(i+1)} := G_{b_1, b_2, ..., b_i}` with `S` generates
the pointwise stabilizer `G^{(i+1)}`. The concepts of a base and
strong generating set and their applications are discussed in depth
in [1], pp. 87-89 and [2], pp. 55-57.
Examples
========
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> D = DihedralGroup(4)
>>> D.strong_gens
[(0 1 2 3), (0 3)(1 2), (1 3)]
>>> D.base
[0, 1]
See Also
========
base, basic_transversals, basic_orbits, basic_stabilizers
"""
if self._strong_gens == []:
self.schreier_sims()
return self._strong_gens
def subgroup(self, gens):
"""
Return the subgroup generated by `gens` which is a list of
elements of the group
"""
if not all([g in self for g in gens]):
raise ValueError("The group doesn't contain the supplied generators")
G = PermutationGroup(gens)
return G
def subgroup_search(self, prop, base=None, strong_gens=None, tests=None,
init_subgroup=None):
"""Find the subgroup of all elements satisfying the property ``prop``.
This is done by a depth-first search with respect to base images that
uses several tests to prune the search tree.
Parameters
==========
prop
The property to be used. Has to be callable on group elements
and always return ``True`` or ``False``. It is assumed that
all group elements satisfying ``prop`` indeed form a subgroup.
base
A base for the supergroup.
strong_gens
A strong generating set for the supergroup.
tests
A list of callables of length equal to the length of ``base``.
These are used to rule out group elements by partial base images,
so that ``tests[l](g)`` returns False if the element ``g`` is known
not to satisfy prop base on where g sends the first ``l + 1`` base
points.
init_subgroup
if a subgroup of the sought group is
known in advance, it can be passed to the function as this
parameter.
Returns
=======
res
The subgroup of all elements satisfying ``prop``. The generating
set for this group is guaranteed to be a strong generating set
relative to the base ``base``.
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... AlternatingGroup)
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.testutil import _verify_bsgs
>>> S = SymmetricGroup(7)
>>> prop_even = lambda x: x.is_even
>>> base, strong_gens = S.schreier_sims_incremental()
>>> G = S.subgroup_search(prop_even, base=base, strong_gens=strong_gens)
>>> G.is_subgroup(AlternatingGroup(7))
True
>>> _verify_bsgs(G, base, G.generators)
True
Notes
=====
This function is extremely lenghty and complicated and will require
some careful attention. The implementation is described in
[1], pp. 114-117, and the comments for the code here follow the lines
of the pseudocode in the book for clarity.
The complexity is exponential in general, since the search process by
itself visits all members of the supergroup. However, there are a lot
of tests which are used to prune the search tree, and users can define
their own tests via the ``tests`` parameter, so in practice, and for
some computations, it's not terrible.
A crucial part in the procedure is the frequent base change performed
(this is line 11 in the pseudocode) in order to obtain a new basic
stabilizer. The book mentiones that this can be done by using
``.baseswap(...)``, however the current imlementation uses a more
straightforward way to find the next basic stabilizer - calling the
function ``.stabilizer(...)`` on the previous basic stabilizer.
"""
# initialize BSGS and basic group properties
def get_reps(orbits):
# get the minimal element in the base ordering
return [min(orbit, key = lambda x: base_ordering[x]) \
for orbit in orbits]
def update_nu(l):
temp_index = len(basic_orbits[l]) + 1 -\
len(res_basic_orbits_init_base[l])
# this corresponds to the element larger than all points
if temp_index >= len(sorted_orbits[l]):
nu[l] = base_ordering[degree]
else:
nu[l] = sorted_orbits[l][temp_index]
if base is None:
base, strong_gens = self.schreier_sims_incremental()
base_len = len(base)
degree = self.degree
identity = _af_new(list(range(degree)))
base_ordering = _base_ordering(base, degree)
# add an element larger than all points
base_ordering.append(degree)
# add an element smaller than all points
base_ordering.append(-1)
# compute BSGS-related structures
strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
basic_orbits, transversals = _orbits_transversals_from_bsgs(base,
strong_gens_distr)
# handle subgroup initialization and tests
if init_subgroup is None:
init_subgroup = PermutationGroup([identity])
if tests is None:
trivial_test = lambda x: True
tests = []
for i in range(base_len):
tests.append(trivial_test)
# line 1: more initializations.
res = init_subgroup
f = base_len - 1
l = base_len - 1
# line 2: set the base for K to the base for G
res_base = base[:]
# line 3: compute BSGS and related structures for K
res_base, res_strong_gens = res.schreier_sims_incremental(
base=res_base)
res_strong_gens_distr = _distribute_gens_by_base(res_base,
res_strong_gens)
res_generators = res.generators
res_basic_orbits_init_base = \
[_orbit(degree, res_strong_gens_distr[i], res_base[i])\
for i in range(base_len)]
# initialize orbit representatives
orbit_reps = [None]*base_len
# line 4: orbit representatives for f-th basic stabilizer of K
orbits = _orbits(degree, res_strong_gens_distr[f])
orbit_reps[f] = get_reps(orbits)
# line 5: remove the base point from the representatives to avoid
# getting the identity element as a generator for K
orbit_reps[f].remove(base[f])
# line 6: more initializations
c = [0]*base_len
u = [identity]*base_len
sorted_orbits = [None]*base_len
for i in range(base_len):
sorted_orbits[i] = basic_orbits[i][:]
sorted_orbits[i].sort(key=lambda point: base_ordering[point])
# line 7: initializations
mu = [None]*base_len
nu = [None]*base_len
# this corresponds to the element smaller than all points
mu[l] = degree + 1
update_nu(l)
# initialize computed words
computed_words = [identity]*base_len
# line 8: main loop
while True:
# apply all the tests
while l < base_len - 1 and \
computed_words[l](base[l]) in orbit_reps[l] and \
base_ordering[mu[l]] < \
base_ordering[computed_words[l](base[l])] < \
base_ordering[nu[l]] and \
tests[l](computed_words):
# line 11: change the (partial) base of K
new_point = computed_words[l](base[l])
res_base[l] = new_point
new_stab_gens = _stabilizer(degree, res_strong_gens_distr[l],
new_point)
res_strong_gens_distr[l + 1] = new_stab_gens
# line 12: calculate minimal orbit representatives for the
# l+1-th basic stabilizer
orbits = _orbits(degree, new_stab_gens)
orbit_reps[l + 1] = get_reps(orbits)
# line 13: amend sorted orbits
l += 1
temp_orbit = [computed_words[l - 1](point) for point
in basic_orbits[l]]
temp_orbit.sort(key=lambda point: base_ordering[point])
sorted_orbits[l] = temp_orbit
# lines 14 and 15: update variables used minimality tests
new_mu = degree + 1
for i in range(l):
if base[l] in res_basic_orbits_init_base[i]:
candidate = computed_words[i](base[i])
if base_ordering[candidate] > base_ordering[new_mu]:
new_mu = candidate
mu[l] = new_mu
update_nu(l)
# line 16: determine the new transversal element
c[l] = 0
temp_point = sorted_orbits[l][c[l]]
gamma = computed_words[l - 1]._array_form.index(temp_point)
u[l] = transversals[l][gamma]
# update computed words
computed_words[l] = rmul(computed_words[l - 1], u[l])
# lines 17 & 18: apply the tests to the group element found
g = computed_words[l]
temp_point = g(base[l])
if l == base_len - 1 and \
base_ordering[mu[l]] < \
base_ordering[temp_point] < base_ordering[nu[l]] and \
temp_point in orbit_reps[l] and \
tests[l](computed_words) and \
prop(g):
# line 19: reset the base of K
res_generators.append(g)
res_base = base[:]
# line 20: recalculate basic orbits (and transversals)
res_strong_gens.append(g)
res_strong_gens_distr = _distribute_gens_by_base(res_base,
res_strong_gens)
res_basic_orbits_init_base = \
[_orbit(degree, res_strong_gens_distr[i], res_base[i]) \
for i in range(base_len)]
# line 21: recalculate orbit representatives
# line 22: reset the search depth
orbit_reps[f] = get_reps(orbits)
l = f
# line 23: go up the tree until in the first branch not fully
# searched
while l >= 0 and c[l] == len(basic_orbits[l]) - 1:
l = l - 1
# line 24: if the entire tree is traversed, return K
if l == -1:
return PermutationGroup(res_generators)
# lines 25-27: update orbit representatives
if l < f:
# line 26
f = l
c[l] = 0
# line 27
temp_orbits = _orbits(degree, res_strong_gens_distr[f])
orbit_reps[f] = get_reps(temp_orbits)
# line 28: update variables used for minimality testing
mu[l] = degree + 1
temp_index = len(basic_orbits[l]) + 1 - \
len(res_basic_orbits_init_base[l])
if temp_index >= len(sorted_orbits[l]):
nu[l] = base_ordering[degree]
else:
nu[l] = sorted_orbits[l][temp_index]
# line 29: set the next element from the current branch and update
# accorndingly
c[l] += 1
if l == 0:
gamma = sorted_orbits[l][c[l]]
else:
gamma = computed_words[l - 1]._array_form.index(sorted_orbits[l][c[l]])
u[l] = transversals[l][gamma]
if l == 0:
computed_words[l] = u[l]
else:
computed_words[l] = rmul(computed_words[l - 1], u[l])
@property
def transitivity_degree(self):
"""Compute the degree of transitivity of the group.
A permutation group `G` acting on `\Omega = \{0, 1, ..., n-1\}` is
``k``-fold transitive, if, for any k points
`(a_1, a_2, ..., a_k)\in\Omega` and any k points
`(b_1, b_2, ..., b_k)\in\Omega` there exists `g\in G` such that
`g(a_1)=b_1, g(a_2)=b_2, ..., g(a_k)=b_k`
The degree of transitivity of `G` is the maximum ``k`` such that
`G` is ``k``-fold transitive. ([8])
Examples
========
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.permutations import Permutation
>>> a = Permutation([1, 2, 0])
>>> b = Permutation([1, 0, 2])
>>> G = PermutationGroup([a, b])
>>> G.transitivity_degree
3
See Also
========
is_transitive, orbit
"""
if self._transitivity_degree is None:
n = self.degree
G = self
# if G is k-transitive, a tuple (a_0,..,a_k)
# can be brought to (b_0,...,b_(k-1), b_k)
# where b_0,...,b_(k-1) are fixed points;
# consider the group G_k which stabilizes b_0,...,b_(k-1)
# if G_k is transitive on the subset excluding b_0,...,b_(k-1)
# then G is (k+1)-transitive
for i in range(n):
orb = G.orbit((i))
if len(orb) != n - i:
self._transitivity_degree = i
return i
G = G.stabilizer(i)
self._transitivity_degree = n
return n
else:
return self._transitivity_degree
def _orbit(degree, generators, alpha, action='tuples'):
r"""Compute the orbit of alpha `\{g(\alpha) | g \in G\}` as a set.
The time complexity of the algorithm used here is `O(|Orb|*r)` where
`|Orb|` is the size of the orbit and ``r`` is the number of generators of
the group. For a more detailed analysis, see [1], p.78, [2], pp. 19-21.
Here alpha can be a single point, or a list of points.
If alpha is a single point, the ordinary orbit is computed.
if alpha is a list of points, there are three available options:
'union' - computes the union of the orbits of the points in the list
'tuples' - computes the orbit of the list interpreted as an ordered
tuple under the group action ( i.e., g((1, 2, 3)) = (g(1), g(2), g(3)) )
'sets' - computes the orbit of the list interpreted as a sets
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup, _orbit
>>> a = Permutation([1, 2, 0, 4, 5, 6, 3])
>>> G = PermutationGroup([a])
>>> _orbit(G.degree, G.generators, 0)
{0, 1, 2}
>>> _orbit(G.degree, G.generators, [0, 4], 'union')
{0, 1, 2, 3, 4, 5, 6}
See Also
========
orbit, orbit_transversal
"""
if not hasattr(alpha, '__getitem__'):
alpha = [alpha]
gens = [x._array_form for x in generators]
if len(alpha) == 1 or action == 'union':
orb = alpha
used = [False]*degree
for el in alpha:
used[el] = True
for b in orb:
for gen in gens:
temp = gen[b]
if used[temp] == False:
orb.append(temp)
used[temp] = True
return set(orb)
elif action == 'tuples':
alpha = tuple(alpha)
orb = [alpha]
used = {alpha}
for b in orb:
for gen in gens:
temp = tuple([gen[x] for x in b])
if temp not in used:
orb.append(temp)
used.add(temp)
return set(orb)
elif action == 'sets':
alpha = frozenset(alpha)
orb = [alpha]
used = {alpha}
for b in orb:
for gen in gens:
temp = frozenset([gen[x] for x in b])
if temp not in used:
orb.append(temp)
used.add(temp)
return {tuple(x) for x in orb}
def _orbits(degree, generators):
"""Compute the orbits of G.
If ``rep=False`` it returns a list of sets else it returns a list of
representatives of the orbits
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup, _orbits
>>> a = Permutation([0, 2, 1])
>>> b = Permutation([1, 0, 2])
>>> _orbits(a.size, [a, b])
[{0, 1, 2}]
"""
seen = set() # elements that have already appeared in orbits
orbs = []
sorted_I = list(range(degree))
I = set(sorted_I)
while I:
i = sorted_I[0]
orb = _orbit(degree, generators, i)
orbs.append(orb)
# remove all indices that are in this orbit
I -= orb
sorted_I = [i for i in sorted_I if i not in orb]
return orbs
def _orbit_transversal(degree, generators, alpha, pairs, af=False):
r"""Computes a transversal for the orbit of ``alpha`` as a set.
generators generators of the group ``G``
For a permutation group ``G``, a transversal for the orbit
`Orb = \{g(\alpha) | g \in G\}` is a set
`\{g_\beta | g_\beta(\alpha) = \beta\}` for `\beta \in Orb`.
Note that there may be more than one possible transversal.
If ``pairs`` is set to ``True``, it returns the list of pairs
`(\beta, g_\beta)`. For a proof of correctness, see [1], p.79
if ``af`` is ``True``, the transversal elements are given in
array form.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> from sympy.combinatorics.perm_groups import _orbit_transversal
>>> G = DihedralGroup(6)
>>> _orbit_transversal(G.degree, G.generators, 0, False)
[(5), (0 1 2 3 4 5), (0 5)(1 4)(2 3), (0 2 4)(1 3 5), (5)(0 4)(1 3), (0 3)(1 4)(2 5)]
"""
tr = [(alpha, list(range(degree)))]
used = [False]*degree
used[alpha] = True
gens = [x._array_form for x in generators]
for x, px in tr:
for gen in gens:
temp = gen[x]
if used[temp] == False:
tr.append((temp, _af_rmul(gen, px)))
used[temp] = True
if pairs:
if not af:
tr = [(x, _af_new(y)) for x, y in tr]
return tr
if af:
return [y for _, y in tr]
return [_af_new(y) for _, y in tr]
def _stabilizer(degree, generators, alpha):
r"""Return the stabilizer subgroup of ``alpha``.
The stabilizer of `\alpha` is the group `G_\alpha =
\{g \in G | g(\alpha) = \alpha\}`.
For a proof of correctness, see [1], p.79.
degree : degree of G
generators : generators of G
Examples
========
>>> from sympy.combinatorics import Permutation
>>> Permutation.print_cyclic = True
>>> from sympy.combinatorics.perm_groups import _stabilizer
>>> from sympy.combinatorics.named_groups import DihedralGroup
>>> G = DihedralGroup(6)
>>> _stabilizer(G.degree, G.generators, 5)
[(5)(0 4)(1 3), (5)]
See Also
========
orbit
"""
orb = [alpha]
table = {alpha: list(range(degree))}
table_inv = {alpha: list(range(degree))}
used = [False]*degree
used[alpha] = True
gens = [x._array_form for x in generators]
stab_gens = []
for b in orb:
for gen in gens:
temp = gen[b]
if used[temp] is False:
gen_temp = _af_rmul(gen, table[b])
orb.append(temp)
table[temp] = gen_temp
table_inv[temp] = _af_invert(gen_temp)
used[temp] = True
else:
schreier_gen = _af_rmuln(table_inv[temp], gen, table[b])
if schreier_gen not in stab_gens:
stab_gens.append(schreier_gen)
return [_af_new(x) for x in stab_gens]
PermGroup = PermutationGroup
| 123,426 | 33.935466 | 94 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/__init__.py
|
from sympy.combinatorics.permutations import Permutation, Cycle
from sympy.combinatorics.prufer import Prufer
from sympy.combinatorics.generators import cyclic, alternating, symmetric, dihedral
from sympy.combinatorics.subsets import Subset
from sympy.combinatorics.partitions import (Partition, IntegerPartition,
RGS_rank, RGS_unrank, RGS_enum)
from sympy.combinatorics.polyhedron import (Polyhedron, tetrahedron, cube,
octahedron, dodecahedron, icosahedron)
from sympy.combinatorics.perm_groups import PermutationGroup
from sympy.combinatorics.group_constructs import DirectProduct
from sympy.combinatorics.graycode import GrayCode
from sympy.combinatorics.named_groups import (SymmetricGroup, DihedralGroup,
CyclicGroup, AlternatingGroup, AbelianGroup, RubikGroup)
| 780 | 54.785714 | 83 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tensor_can.py
|
from __future__ import print_function, division
from sympy.core.compatibility import range
from sympy.combinatorics.permutations import Permutation, _af_rmul, \
_af_invert, _af_new
from sympy.combinatorics.perm_groups import PermutationGroup, _orbit, \
_orbit_transversal
from sympy.combinatorics.util import _distribute_gens_by_base, \
_orbits_transversals_from_bsgs
"""
References for tensor canonicalization:
[1] R. Portugal "Algorithmic simplification of tensor expressions",
J. Phys. A 32 (1999) 7779-7789
[2] R. Portugal, B.F. Svaiter "Group-theoretic Approach for Symbolic
Tensor Manipulation: I. Free Indices"
arXiv:math-ph/0107031v1
[3] L.R.U. Manssur, R. Portugal "Group-theoretic Approach for Symbolic
Tensor Manipulation: II. Dummy Indices"
arXiv:math-ph/0107032v1
[4] xperm.c part of XPerm written by J. M. Martin-Garcia
http://www.xact.es/index.html
"""
def dummy_sgs(dummies, sym, n):
"""
Return the strong generators for dummy indices
Parameters
==========
dummies : list of dummy indices
`dummies[2k], dummies[2k+1]` are paired indices
sym : symmetry under interchange of contracted dummies::
* None no symmetry
* 0 commuting
* 1 anticommuting
n : number of indices
in base form the dummy indices are always in consecutive positions
Examples
========
>>> from sympy.combinatorics.tensor_can import dummy_sgs
>>> dummy_sgs(range(2, 8), 0, 8)
[[0, 1, 3, 2, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 5, 4, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 7, 6, 8, 9], [0, 1, 4, 5, 2, 3, 6, 7, 8, 9],
[0, 1, 2, 3, 6, 7, 4, 5, 8, 9]]
"""
if len(dummies) > n:
raise ValueError("List too large")
res = []
# exchange of contravariant and covariant indices
if sym is not None:
for j in dummies[::2]:
a = list(range(n + 2))
if sym == 1:
a[n] = n + 1
a[n + 1] = n
a[j], a[j + 1] = a[j + 1], a[j]
res.append(a)
# rename dummy indices
for j in dummies[:-3:2]:
a = list(range(n + 2))
a[j:j + 4] = a[j + 2], a[j + 3], a[j], a[j + 1]
res.append(a)
return res
def _min_dummies(dummies, sym, indices):
"""
Return list of minima of the orbits of indices in group of dummies
see `double_coset_can_rep` for the description of `dummies` and `sym`
indices is the initial list of dummy indices
Examples
========
>>> from sympy.combinatorics.tensor_can import _min_dummies
>>> _min_dummies([list(range(2, 8))], [0], list(range(10)))
[0, 1, 2, 2, 2, 2, 2, 2, 8, 9]
"""
num_types = len(sym)
m = []
for dx in dummies:
if dx:
m.append(min(dx))
else:
m.append(None)
res = indices[:]
for i in range(num_types):
for c, i in enumerate(indices):
for j in range(num_types):
if i in dummies[j]:
res[c] = m[j]
break
return res
def _trace_S(s, j, b, S_cosets):
"""
Return the representative h satisfying s[h[b]] == j
If there is not such a representative return None
"""
for h in S_cosets[b]:
if s[h[b]] == j:
return h
return None
def _trace_D(gj, p_i, Dxtrav):
"""
Return the representative h satisfying h[gj] == p_i
If there is not such a representative return None
"""
for h in Dxtrav:
if h[gj] == p_i:
return h
return None
def _dumx_remove(dumx, dumx_flat, p0):
"""
remove p0 from dumx
"""
res = []
for dx in dumx:
if p0 not in dx:
res.append(dx)
continue
k = dx.index(p0)
if k % 2 == 0:
p0_paired = dx[k + 1]
else:
p0_paired = dx[k - 1]
dx.remove(p0)
dx.remove(p0_paired)
dumx_flat.remove(p0)
dumx_flat.remove(p0_paired)
res.append(dx)
def transversal2coset(size, base, transversal):
a = []
j = 0
for i in range(size):
if i in base:
a.append(sorted(transversal[j].values()))
j += 1
else:
a.append([list(range(size))])
j = len(a) - 1
while a[j] == [list(range(size))]:
j -= 1
return a[:j + 1]
def double_coset_can_rep(dummies, sym, b_S, sgens, S_transversals, g):
"""
Butler-Portugal algorithm for tensor canonicalization with dummy indices
dummies
list of lists of dummy indices,
one list for each type of index;
the dummy indices are put in order contravariant, covariant
[d0, -d0, d1, -d1, ...].
sym
list of the symmetries of the index metric for each type.
possible symmetries of the metrics
* 0 symmetric
* 1 antisymmetric
* None no symmetry
b_S
base of a minimal slot symmetry BSGS.
sgens
generators of the slot symmetry BSGS.
S_transversals
transversals for the slot BSGS.
g
permutation representing the tensor.
Return 0 if the tensor is zero, else return the array form of
the permutation representing the canonical form of the tensor.
A tensor with dummy indices can be represented in a number
of equivalent ways which typically grows exponentially with
the number of indices. To be able to establish if two tensors
with many indices are equal becomes computationally very slow
in absence of an efficient algorithm.
The Butler-Portugal algorithm [3] is an efficient algorithm to
put tensors in canonical form, solving the above problem.
Portugal observed that a tensor can be represented by a permutation,
and that the class of tensors equivalent to it under slot and dummy
symmetries is equivalent to the double coset `D*g*S`
(Note: in this documentation we use the conventions for multiplication
of permutations p, q with (p*q)(i) = p[q[i]] which is opposite
to the one used in the Permutation class)
Using the algorithm by Butler to find a representative of the
double coset one can find a canonical form for the tensor.
To see this correspondence,
let `g` be a permutation in array form; a tensor with indices `ind`
(the indices including both the contravariant and the covariant ones)
can be written as
`t = T(ind[g[0],..., ind[g[n-1]])`,
where `n= len(ind)`;
`g` has size `n + 2`, the last two indices for the sign of the tensor
(trick introduced in [4]).
A slot symmetry transformation `s` is a permutation acting on the slots
`t -> T(ind[(g*s)[0]],..., ind[(g*s)[n-1]])`
A dummy symmetry transformation acts on `ind`
`t -> T(ind[(d*g)[0]],..., ind[(d*g)[n-1]])`
Being interested only in the transformations of the tensor under
these symmetries, one can represent the tensor by `g`, which transforms
as
`g -> d*g*s`, so it belongs to the coset `D*g*S`.
Let us explain the conventions by an example.
Given a tensor `T^{d3 d2 d1}{}_{d1 d2 d3}` with the slot symmetries
`T^{a0 a1 a2 a3 a4 a5} = -T^{a2 a1 a0 a3 a4 a5}`
`T^{a0 a1 a2 a3 a4 a5} = -T^{a4 a1 a2 a3 a0 a5}`
and symmetric metric, find the tensor equivalent to it which
is the lowest under the ordering of indices:
lexicographic ordering `d1, d2, d3` then and contravariant index
before covariant index; that is the canonical form of the tensor.
The canonical form is `-T^{d1 d2 d3}{}_{d1 d2 d3}`
obtained using `T^{a0 a1 a2 a3 a4 a5} = -T^{a2 a1 a0 a3 a4 a5}`.
To convert this problem in the input for this function,
use the following labelling of the index names
(- for covariant for short) `d1, -d1, d2, -d2, d3, -d3`
`T^{d3 d2 d1}{}_{d1 d2 d3}` corresponds to `g = [4, 2, 0, 1, 3, 5, 6, 7]`
where the last two indices are for the sign
`sgens = [Permutation(0, 2)(6, 7), Permutation(0, 4)(6, 7)]`
sgens[0] is the slot symmetry `-(0, 2)`
`T^{a0 a1 a2 a3 a4 a5} = -T^{a2 a1 a0 a3 a4 a5}`
sgens[1] is the slot symmetry `-(0, 4)`
`T^{a0 a1 a2 a3 a4 a5} = -T^{a4 a1 a2 a3 a0 a5}`
The dummy symmetry group D is generated by the strong base generators
`[(0, 1), (2, 3), (4, 5), (0, 1)(2, 3),(2, 3)(4, 5)]`
The dummy symmetry acts from the left
`d = [1, 0, 2, 3, 4, 5, 6, 7]` exchange `d1 -> -d1`
`T^{d3 d2 d1}{}_{d1 d2 d3} == T^{d3 d2}{}_{d1}{}^{d1}{}_{d2 d3}`
`g=[4, 2, 0, 1, 3, 5, 6, 7] -> [4, 2, 1, 0, 3, 5, 6, 7] = _af_rmul(d, g)`
which differs from `_af_rmul(g, d)`.
The slot symmetry acts from the right
`s = [2, 1, 0, 3, 4, 5, 7, 6]` exchanges slots 0 and 2 and changes sign
`T^{d3 d2 d1}{}_{d1 d2 d3} == -T^{d1 d2 d3}{}_{d1 d2 d3}`
`g=[4,2,0,1,3,5,6,7] -> [0, 2, 4, 1, 3, 5, 7, 6] = _af_rmul(g, s)`
Example in which the tensor is zero, same slot symmetries as above:
`T^{d3}{}_{d1,d2}{}^{d1}{}_{d3}{}^{d2}`
`= -T^{d3}{}_{d1,d3}{}^{d1}{}_{d2}{}^{d2}` under slot symmetry `-(2,4)`;
`= T_{d3 d1}{}^{d3}{}^{d1}{}_{d2}{}^{d2}` under slot symmetry `-(0,2)`;
`= T^{d3}{}_{d1 d3}{}^{d1}{}_{d2}{}^{d2}` symmetric metric;
`= 0` since two of these lines have tensors differ only for the sign.
The double coset D*g*S consists of permutations `h = d*g*s` corresponding
to equivalent tensors; if there are two `h` which are the same apart
from the sign, return zero; otherwise
choose as representative the tensor with indices
ordered lexicographically according to `[d1, -d1, d2, -d2, d3, -d3]`
that is `rep = min(D*g*S) = min([d*g*s for d in D for s in S])`
The indices are fixed one by one; first choose the lowest index
for slot 0, then the lowest remaining index for slot 1, etc.
Doing this one obtains a chain of stabilizers
`S -> S_{b0} -> S_{b0,b1} -> ...` and
`D -> D_{p0} -> D_{p0,p1} -> ...`
where `[b0, b1, ...] = range(b)` is a base of the symmetric group;
the strong base `b_S` of S is an ordered sublist of it;
therefore it is sufficient to compute once the
strong base generators of S using the Schreier-Sims algorithm;
the stabilizers of the strong base generators are the
strong base generators of the stabilizer subgroup.
`dbase = [p0, p1, ...]` is not in general in lexicographic order,
so that one must recompute the strong base generators each time;
however this is trivial, there is no need to use the Schreier-Sims
algorithm for D.
The algorithm keeps a TAB of elements `(s_i, d_i, h_i)`
where `h_i = d_i*g*s_i` satisfying `h_i[j] = p_j` for `0 <= j < i`
starting from `s_0 = id, d_0 = id, h_0 = g`.
The equations `h_0[0] = p_0, h_1[1] = p_1,...` are solved in this order,
choosing each time the lowest possible value of p_i
For `j < i`
`d_i*g*s_i*S_{b_0,...,b_{i-1}}*b_j = D_{p_0,...,p_{i-1}}*p_j`
so that for dx in `D_{p_0,...,p_{i-1}}` and sx in
`S_{base[0],...,base[i-1]}` one has `dx*d_i*g*s_i*sx*b_j = p_j`
Search for dx, sx such that this equation holds for `j = i`;
it can be written as `s_i*sx*b_j = J, dx*d_i*g*J = p_j`
`sx*b_j = s_i**-1*J; sx = trace(s_i**-1, S_{b_0,...,b_{i-1}})`
`dx**-1*p_j = d_i*g*J; dx = trace(d_i*g*J, D_{p_0,...,p_{i-1}})`
`s_{i+1} = s_i*trace(s_i**-1*J, S_{b_0,...,b_{i-1}})`
`d_{i+1} = trace(d_i*g*J, D_{p_0,...,p_{i-1}})**-1*d_i`
`h_{i+1}*b_i = d_{i+1}*g*s_{i+1}*b_i = p_i`
`h_n*b_j = p_j` for all j, so that `h_n` is the solution.
Add the found `(s, d, h)` to TAB1.
At the end of the iteration sort TAB1 with respect to the `h`;
if there are two consecutive `h` in TAB1 which differ only for the
sign, the tensor is zero, so return 0;
if there are two consecutive `h` which are equal, keep only one.
Then stabilize the slot generators under `i` and the dummy generators
under `p_i`.
Assign `TAB = TAB1` at the end of the iteration step.
At the end `TAB` contains a unique `(s, d, h)`, since all the slots
of the tensor `h` have been fixed to have the minimum value according
to the symmetries. The algorithm returns `h`.
It is important that the slot BSGS has lexicographic minimal base,
otherwise there is an `i` which does not belong to the slot base
for which `p_i` is fixed by the dummy symmetry only, while `i`
is not invariant from the slot stabilizer, so `p_i` is not in
general the minimal value.
This algorithm differs slightly from the original algorithm [3]:
the canonical form is minimal lexicographically, and
the BSGS has minimal base under lexicographic order.
Equal tensors `h` are eliminated from TAB.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.tensor_can import double_coset_can_rep, get_transversals
>>> gens = [Permutation(x) for x in [[2, 1, 0, 3, 4, 5, 7, 6], [4, 1, 2, 3, 0, 5, 7, 6]]]
>>> base = [0, 2]
>>> g = Permutation([4, 2, 0, 1, 3, 5, 6, 7])
>>> transversals = get_transversals(base, gens)
>>> double_coset_can_rep([list(range(6))], [0], base, gens, transversals, g)
[0, 1, 2, 3, 4, 5, 7, 6]
>>> g = Permutation([4, 1, 3, 0, 5, 2, 6, 7])
>>> double_coset_can_rep([list(range(6))], [0], base, gens, transversals, g)
0
"""
size = g.size
g = g.array_form
num_dummies = size - 2
indices = list(range(num_dummies))
all_metrics_with_sym = all([_ is not None for _ in sym])
num_types = len(sym)
dumx = dummies[:]
dumx_flat = []
for dx in dumx:
dumx_flat.extend(dx)
b_S = b_S[:]
sgensx = [h._array_form for h in sgens]
if b_S:
S_transversals = transversal2coset(size, b_S, S_transversals)
# strong generating set for D
dsgsx = []
for i in range(num_types):
dsgsx.extend(dummy_sgs(dumx[i], sym[i], num_dummies))
ginv = _af_invert(g)
idn = list(range(size))
# TAB = list of entries (s, d, h) where h = _af_rmuln(d,g,s)
# for short, in the following d*g*s means _af_rmuln(d,g,s)
TAB = [(idn, idn, g)]
for i in range(size - 2):
b = i
testb = b in b_S and sgensx
if testb:
sgensx1 = [_af_new(_) for _ in sgensx]
deltab = _orbit(size, sgensx1, b)
else:
deltab = {b}
# p1 = min(IMAGES) = min(Union D_p*h*deltab for h in TAB)
if all_metrics_with_sym:
md = _min_dummies(dumx, sym, indices)
else:
md = [min(_orbit(size, [_af_new(
ddx) for ddx in dsgsx], ii)) for ii in range(size - 2)]
p_i = min([min([md[h[x]] for x in deltab]) for s, d, h in TAB])
dsgsx1 = [_af_new(_) for _ in dsgsx]
Dxtrav = _orbit_transversal(size, dsgsx1, p_i, False, af=True) \
if dsgsx else None
if Dxtrav:
Dxtrav = [_af_invert(x) for x in Dxtrav]
# compute the orbit of p_i
for ii in range(num_types):
if p_i in dumx[ii]:
# the orbit is made by all the indices in dum[ii]
if sym[ii] is not None:
deltap = dumx[ii]
else:
# the orbit is made by all the even indices if p_i
# is even, by all the odd indices if p_i is odd
p_i_index = dumx[ii].index(p_i) % 2
deltap = dumx[ii][p_i_index::2]
break
else:
deltap = [p_i]
TAB1 = []
nTAB = len(TAB)
while TAB:
s, d, h = TAB.pop()
if min([md[h[x]] for x in deltab]) != p_i:
continue
deltab1 = [x for x in deltab if md[h[x]] == p_i]
# NEXT = s*deltab1 intersection (d*g)**-1*deltap
dg = _af_rmul(d, g)
dginv = _af_invert(dg)
sdeltab = [s[x] for x in deltab1]
gdeltap = [dginv[x] for x in deltap]
NEXT = [x for x in sdeltab if x in gdeltap]
# d, s satisfy
# d*g*s*base[i-1] = p_{i-1}; using the stabilizers
# d*g*s*S_{base[0],...,base[i-1]}*base[i-1] =
# D_{p_0,...,p_{i-1}}*p_{i-1}
# so that to find d1, s1 satisfying d1*g*s1*b = p_i
# one can look for dx in D_{p_0,...,p_{i-1}} and
# sx in S_{base[0],...,base[i-1]}
# d1 = dx*d; s1 = s*sx
# d1*g*s1*b = dx*d*g*s*sx*b = p_i
for j in NEXT:
if testb:
# solve s1*b = j with s1 = s*sx for some element sx
# of the stabilizer of ..., base[i-1]
# sx*b = s**-1*j; sx = _trace_S(s, j,...)
# s1 = s*trace_S(s**-1*j,...)
s1 = _trace_S(s, j, b, S_transversals)
if not s1:
continue
else:
s1 = [s[ix] for ix in s1]
else:
s1 = s
# assert s1[b] == j # invariant
# solve d1*g*j = p_i with d1 = dx*d for some element dg
# of the stabilizer of ..., p_{i-1}
# dx**-1*p_i = d*g*j; dx**-1 = trace_D(d*g*j,...)
# d1 = trace_D(d*g*j,...)**-1*d
# to save an inversion in the inner loop; notice we did
# Dxtrav = [perm_af_invert(x) for x in Dxtrav] out of the loop
if Dxtrav:
d1 = _trace_D(dg[j], p_i, Dxtrav)
if not d1:
continue
else:
if p_i != dg[j]:
continue
d1 = idn
assert d1[dg[j]] == p_i # invariant
d1 = [d1[ix] for ix in d]
h1 = [d1[g[ix]] for ix in s1]
# assert h1[b] == p_i # invariant
TAB1.append((s1, d1, h1))
# if TAB contains equal permutations, keep only one of them;
# if TAB contains equal permutations up to the sign, return 0
TAB1.sort(key=lambda x: x[-1])
nTAB1 = len(TAB1)
prev = [0] * size
while TAB1:
s, d, h = TAB1.pop()
if h[:-2] == prev[:-2]:
if h[-1] != prev[-1]:
return 0
else:
TAB.append((s, d, h))
prev = h
# stabilize the SGS
sgensx = [h for h in sgensx if h[b] == b]
if b in b_S:
b_S.remove(b)
_dumx_remove(dumx, dumx_flat, p_i)
dsgsx = []
for i in range(num_types):
dsgsx.extend(dummy_sgs(dumx[i], sym[i], num_dummies))
return TAB[0][-1]
def canonical_free(base, gens, g, num_free):
"""
canonicalization of a tensor with respect to free indices
choosing the minimum with respect to lexicographical ordering
in the free indices
``base``, ``gens`` BSGS for slot permutation group
``g`` permutation representing the tensor
``num_free`` number of free indices
The indices must be ordered with first the free indices
see explanation in double_coset_can_rep
The algorithm is a variation of the one given in [2].
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.tensor_can import canonical_free
>>> gens = [[1, 0, 2, 3, 5, 4], [2, 3, 0, 1, 4, 5],[0, 1, 3, 2, 5, 4]]
>>> gens = [Permutation(h) for h in gens]
>>> base = [0, 2]
>>> g = Permutation([2, 1, 0, 3, 4, 5])
>>> canonical_free(base, gens, g, 4)
[0, 3, 1, 2, 5, 4]
Consider the product of Riemann tensors
``T = R^{a}_{d0}^{d1,d2}*R_{d2,d1}^{d0,b}``
The order of the indices is ``[a, b, d0, -d0, d1, -d1, d2, -d2]``
The permutation corresponding to the tensor is
``g = [0, 3, 4, 6, 7, 5, 2, 1, 8, 9]``
In particular ``a`` is position ``0``, ``b`` is in position ``9``.
Use the slot symmetries to get `T` is a form which is the minimal
in lexicographic order in the free indices ``a`` and ``b``, e.g.
``-R^{a}_{d0}^{d1,d2}*R^{b,d0}_{d2,d1}`` corresponding to
``[0, 3, 4, 6, 1, 2, 7, 5, 9, 8]``
>>> from sympy.combinatorics.tensor_can import riemann_bsgs, tensor_gens
>>> base, gens = riemann_bsgs
>>> size, sbase, sgens = tensor_gens(base, gens, [[], []], 0)
>>> g = Permutation([0, 3, 4, 6, 7, 5, 2, 1, 8, 9])
>>> canonical_free(sbase, [Permutation(h) for h in sgens], g, 2)
[0, 3, 4, 6, 1, 2, 7, 5, 9, 8]
"""
g = g.array_form
size = len(g)
if not base:
return g[:]
transversals = get_transversals(base, gens)
m = len(base)
for x in sorted(g[:-2]):
if x not in base:
base.append(x)
h = g
for i, transv in enumerate(transversals):
b = base[i]
h_i = [size]*num_free
# find the element s in transversals[i] such that
# _af_rmul(h, s) has its free elements with the lowest position in h
s = None
for sk in transv.values():
h1 = _af_rmul(h, sk)
hi = [h1.index(ix) for ix in range(num_free)]
if hi < h_i:
h_i = hi
s = sk
if s:
h = _af_rmul(h, s)
return h
def _get_map_slots(size, fixed_slots):
res = list(range(size))
pos = 0
for i in range(size):
if i in fixed_slots:
continue
res[i] = pos
pos += 1
return res
def _lift_sgens(size, fixed_slots, free, s):
a = []
j = k = 0
fd = list(zip(fixed_slots, free))
fd = [y for x, y in sorted(fd)]
num_free = len(free)
for i in range(size):
if i in fixed_slots:
a.append(fd[k])
k += 1
else:
a.append(s[j] + num_free)
j += 1
return a
def canonicalize(g, dummies, msym, *v):
"""
canonicalize tensor formed by tensors
Parameters
==========
g : permutation representing the tensor
dummies : list representing the dummy indices
it can be a list of dummy indices of the same type
or a list of lists of dummy indices, one list for each
type of index;
the dummy indices must come after the free indices,
and put in order contravariant, covariant
[d0, -d0, d1,-d1,...]
msym : symmetry of the metric(s)
it can be an integer or a list;
in the first case it is the symmetry of the dummy index metric;
in the second case it is the list of the symmetries of the
index metric for each type
v : list, (base_i, gens_i, n_i, sym_i) for tensors of type `i`
base_i, gens_i : BSGS for tensors of this type.
The BSGS should have minimal base under lexicographic ordering;
if not, an attempt is made do get the minimal BSGS;
in case of failure,
canonicalize_naive is used, which is much slower.
n_i : number of tensors of type `i`.
sym_i : symmetry under exchange of component tensors of type `i`.
Both for msym and sym_i the cases are
* None no symmetry
* 0 commuting
* 1 anticommuting
Returns
=======
0 if the tensor is zero, else return the array form of
the permutation representing the canonical form of the tensor.
Algorithm
=========
First one uses canonical_free to get the minimum tensor under
lexicographic order, using only the slot symmetries.
If the component tensors have not minimal BSGS, it is attempted
to find it; if the attempt fails canonicalize_naive
is used instead.
Compute the residual slot symmetry keeping fixed the free indices
using tensor_gens(base, gens, list_free_indices, sym).
Reduce the problem eliminating the free indices.
Then use double_coset_can_rep and lift back the result reintroducing
the free indices.
Examples
========
one type of index with commuting metric;
`A_{a b}` and `B_{a b}` antisymmetric and commuting
`T = A_{d0 d1} * B^{d0}{}_{d2} * B^{d2 d1}`
`ord = [d0,-d0,d1,-d1,d2,-d2]` order of the indices
g = [1, 3, 0, 5, 4, 2, 6, 7]
`T_c = 0`
>>> from sympy.combinatorics.tensor_can import get_symmetric_group_sgs, canonicalize, bsgs_direct_product
>>> from sympy.combinatorics import Permutation
>>> base2a, gens2a = get_symmetric_group_sgs(2, 1)
>>> t0 = (base2a, gens2a, 1, 0)
>>> t1 = (base2a, gens2a, 2, 0)
>>> g = Permutation([1, 3, 0, 5, 4, 2, 6, 7])
>>> canonicalize(g, range(6), 0, t0, t1)
0
same as above, but with `B_{a b}` anticommuting
`T_c = -A^{d0 d1} * B_{d0}{}^{d2} * B_{d1 d2}`
can = [0,2,1,4,3,5,7,6]
>>> t1 = (base2a, gens2a, 2, 1)
>>> canonicalize(g, range(6), 0, t0, t1)
[0, 2, 1, 4, 3, 5, 7, 6]
two types of indices `[a,b,c,d,e,f]` and `[m,n]`, in this order,
both with commuting metric
`f^{a b c}` antisymmetric, commuting
`A_{m a}` no symmetry, commuting
`T = f^c{}_{d a} * f^f{}_{e b} * A_m{}^d * A^{m b} * A_n{}^a * A^{n e}`
ord = [c,f,a,-a,b,-b,d,-d,e,-e,m,-m,n,-n]
g = [0,7,3, 1,9,5, 11,6, 10,4, 13,2, 12,8, 14,15]
The canonical tensor is
`T_c = -f^{c a b} * f^{f d e} * A^m{}_a * A_{m d} * A^n{}_b * A_{n e}`
can = [0,2,4, 1,6,8, 10,3, 11,7, 12,5, 13,9, 15,14]
>>> base_f, gens_f = get_symmetric_group_sgs(3, 1)
>>> base1, gens1 = get_symmetric_group_sgs(1)
>>> base_A, gens_A = bsgs_direct_product(base1, gens1, base1, gens1)
>>> t0 = (base_f, gens_f, 2, 0)
>>> t1 = (base_A, gens_A, 4, 0)
>>> dummies = [range(2, 10), range(10, 14)]
>>> g = Permutation([0, 7, 3, 1, 9, 5, 11, 6, 10, 4, 13, 2, 12, 8, 14, 15])
>>> canonicalize(g, dummies, [0, 0], t0, t1)
[0, 2, 4, 1, 6, 8, 10, 3, 11, 7, 12, 5, 13, 9, 15, 14]
"""
from sympy.combinatorics.testutil import canonicalize_naive
if not isinstance(msym, list):
if not msym in [0, 1, None]:
raise ValueError('msym must be 0, 1 or None')
num_types = 1
else:
num_types = len(msym)
if not all(msymx in [0, 1, None] for msymx in msym):
raise ValueError('msym entries must be 0, 1 or None')
if len(dummies) != num_types:
raise ValueError(
'dummies and msym must have the same number of elements')
size = g.size
num_tensors = 0
v1 = []
for i in range(len(v)):
base_i, gens_i, n_i, sym_i = v[i]
# check that the BSGS is minimal;
# this property is used in double_coset_can_rep;
# if it is not minimal use canonicalize_naive
if not _is_minimal_bsgs(base_i, gens_i):
mbsgs = get_minimal_bsgs(base_i, gens_i)
if not mbsgs:
can = canonicalize_naive(g, dummies, msym, *v)
return can
base_i, gens_i = mbsgs
v1.append((base_i, gens_i, [[]] * n_i, sym_i))
num_tensors += n_i
if num_types == 1 and not isinstance(msym, list):
dummies = [dummies]
msym = [msym]
flat_dummies = []
for dumx in dummies:
flat_dummies.extend(dumx)
if flat_dummies and flat_dummies != list(range(flat_dummies[0], flat_dummies[-1] + 1)):
raise ValueError('dummies is not valid')
# slot symmetry of the tensor
size1, sbase, sgens = gens_products(*v1)
if size != size1:
raise ValueError(
'g has size %d, generators have size %d' % (size, size1))
free = [i for i in range(size - 2) if i not in flat_dummies]
num_free = len(free)
# g1 minimal tensor under slot symmetry
g1 = canonical_free(sbase, sgens, g, num_free)
if not flat_dummies:
return g1
# save the sign of g1
sign = 0 if g1[-1] == size - 1 else 1
# the free indices are kept fixed.
# Determine free_i, the list of slots of tensors which are fixed
# since they are occupied by free indices, which are fixed.
start = 0
for i in range(len(v)):
free_i = []
base_i, gens_i, n_i, sym_i = v[i]
len_tens = gens_i[0].size - 2
# for each component tensor get a list od fixed islots
for j in range(n_i):
# get the elements corresponding to the component tensor
h = g1[start:(start + len_tens)]
fr = []
# get the positions of the fixed elements in h
for k in free:
if k in h:
fr.append(h.index(k))
free_i.append(fr)
start += len_tens
v1[i] = (base_i, gens_i, free_i, sym_i)
# BSGS of the tensor with fixed free indices
# if tensor_gens fails in gens_product, use canonicalize_naive
size, sbase, sgens = gens_products(*v1)
# reduce the permutations getting rid of the free indices
pos_dummies = [g1.index(x) for x in flat_dummies]
pos_free = [g1.index(x) for x in range(num_free)]
size_red = size - num_free
g1_red = [x - num_free for x in g1 if x in flat_dummies]
if sign:
g1_red.extend([size_red - 1, size_red - 2])
else:
g1_red.extend([size_red - 2, size_red - 1])
map_slots = _get_map_slots(size, pos_free)
sbase_red = [map_slots[i] for i in sbase if i not in pos_free]
sgens_red = [_af_new([map_slots[i] for i in y._array_form if i not in pos_free]) for y in sgens]
dummies_red = [[x - num_free for x in y] for y in dummies]
transv_red = get_transversals(sbase_red, sgens_red)
g1_red = _af_new(g1_red)
g2 = double_coset_can_rep(
dummies_red, msym, sbase_red, sgens_red, transv_red, g1_red)
if g2 == 0:
return 0
# lift to the case with the free indices
g3 = _lift_sgens(size, pos_free, free, g2)
return g3
def perm_af_direct_product(gens1, gens2, signed=True):
"""
direct products of the generators gens1 and gens2
Examples
========
>>> from sympy.combinatorics.tensor_can import perm_af_direct_product
>>> gens1 = [[1, 0, 2, 3], [0, 1, 3, 2]]
>>> gens2 = [[1, 0]]
>>> perm_af_direct_product(gens1, gens2, False)
[[1, 0, 2, 3, 4, 5], [0, 1, 3, 2, 4, 5], [0, 1, 2, 3, 5, 4]]
>>> gens1 = [[1, 0, 2, 3, 5, 4], [0, 1, 3, 2, 4, 5]]
>>> gens2 = [[1, 0, 2, 3]]
>>> perm_af_direct_product(gens1, gens2, True)
[[1, 0, 2, 3, 4, 5, 7, 6], [0, 1, 3, 2, 4, 5, 6, 7], [0, 1, 2, 3, 5, 4, 6, 7]]
"""
gens1 = [list(x) for x in gens1]
gens2 = [list(x) for x in gens2]
s = 2 if signed else 0
n1 = len(gens1[0]) - s
n2 = len(gens2[0]) - s
start = list(range(n1))
end = list(range(n1, n1 + n2))
if signed:
gens1 = [gen[:-2] + end + [gen[-2] + n2, gen[-1] + n2]
for gen in gens1]
gens2 = [start + [x + n1 for x in gen] for gen in gens2]
else:
gens1 = [gen + end for gen in gens1]
gens2 = [start + [x + n1 for x in gen] for gen in gens2]
res = gens1 + gens2
return res
def bsgs_direct_product(base1, gens1, base2, gens2, signed=True):
"""
direct product of two BSGS
base1 base of the first BSGS.
gens1 strong generating sequence of the first BSGS.
base2, gens2 similarly for the second BSGS.
signed flag for signed permutations.
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.tensor_can import (get_symmetric_group_sgs, bsgs_direct_product)
>>> Permutation.print_cyclic = True
>>> base1, gens1 = get_symmetric_group_sgs(1)
>>> base2, gens2 = get_symmetric_group_sgs(2)
>>> bsgs_direct_product(base1, gens1, base2, gens2)
([1], [(4)(1 2)])
"""
s = 2 if signed else 0
n1 = gens1[0].size - s
base = list(base1)
base += [x + n1 for x in base2]
gens1 = [h._array_form for h in gens1]
gens2 = [h._array_form for h in gens2]
gens = perm_af_direct_product(gens1, gens2, signed)
size = len(gens[0])
id_af = list(range(size))
gens = [h for h in gens if h != id_af]
if not gens:
gens = [id_af]
return base, [_af_new(h) for h in gens]
def get_symmetric_group_sgs(n, antisym=False):
"""
Return base, gens of the minimal BSGS for (anti)symmetric tensor
``n`` rank of the tensor
``antisym = False`` symmetric tensor
``antisym = True`` antisymmetric tensor
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.tensor_can import get_symmetric_group_sgs
>>> Permutation.print_cyclic = True
>>> get_symmetric_group_sgs(3)
([0, 1], [(4)(0 1), (4)(1 2)])
"""
if n == 1:
return [], [_af_new(list(range(3)))]
gens = [Permutation(n - 1)(i, i + 1)._array_form for i in range(n - 1)]
if antisym == 0:
gens = [x + [n, n + 1] for x in gens]
else:
gens = [x + [n + 1, n] for x in gens]
base = list(range(n - 1))
return base, [_af_new(h) for h in gens]
riemann_bsgs = [0, 2], [Permutation(0, 1)(4, 5), Permutation(2, 3)(4, 5),
Permutation(5)(0, 2)(1, 3)]
def get_transversals(base, gens):
"""
Return transversals for the group with BSGS base, gens
"""
if not base:
return []
stabs = _distribute_gens_by_base(base, gens)
orbits, transversals = _orbits_transversals_from_bsgs(base, stabs)
transversals = [{x: h._array_form for x, h in y.items()} for y in
transversals]
return transversals
def _is_minimal_bsgs(base, gens):
"""
Check if the BSGS has minimal base under lexigographic order.
base, gens BSGS
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.tensor_can import riemann_bsgs, _is_minimal_bsgs
>>> _is_minimal_bsgs(*riemann_bsgs)
True
>>> riemann_bsgs1 = ([2, 0], ([Permutation(5)(0, 1)(4, 5), Permutation(5)(0, 2)(1, 3)]))
>>> _is_minimal_bsgs(*riemann_bsgs1)
False
"""
base1 = []
sgs1 = gens[:]
size = gens[0].size
for i in range(size):
if not all(h._array_form[i] == i for h in sgs1):
base1.append(i)
sgs1 = [h for h in sgs1 if h._array_form[i] == i]
return base1 == base
def get_minimal_bsgs(base, gens):
"""
Compute a minimal GSGS
base, gens BSGS
If base, gens is a minimal BSGS return it; else return a minimal BSGS
if it fails in finding one, it returns None
TODO: use baseswap in the case in which if it fails in finding a
minimal BSGS
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.tensor_can import get_minimal_bsgs
>>> Permutation.print_cyclic = True
>>> riemann_bsgs1 = ([2, 0], ([Permutation(5)(0, 1)(4, 5), Permutation(5)(0, 2)(1, 3)]))
>>> get_minimal_bsgs(*riemann_bsgs1)
([0, 2], [(0 1)(4 5), (5)(0 2)(1 3), (2 3)(4 5)])
"""
G = PermutationGroup(gens)
base, gens = G.schreier_sims_incremental()
if not _is_minimal_bsgs(base, gens):
return None
return base, gens
def tensor_gens(base, gens, list_free_indices, sym=0):
"""
Returns size, res_base, res_gens BSGS for n tensors of the
same type
base, gens BSGS for tensors of this type
list_free_indices list of the slots occupied by fixed indices
for each of the tensors
sym symmetry under commutation of two tensors
sym None no symmetry
sym 0 commuting
sym 1 anticommuting
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.tensor_can import tensor_gens, get_symmetric_group_sgs
>>> Permutation.print_cyclic = True
two symmetric tensors with 3 indices without free indices
>>> base, gens = get_symmetric_group_sgs(3)
>>> tensor_gens(base, gens, [[], []])
(8, [0, 1, 3, 4], [(7)(0 1), (7)(1 2), (7)(3 4), (7)(4 5), (7)(0 3)(1 4)(2 5)])
two symmetric tensors with 3 indices with free indices in slot 1 and 0
>>> tensor_gens(base, gens, [[1], [0]])
(8, [0, 4], [(7)(0 2), (7)(4 5)])
four symmetric tensors with 3 indices, two of which with free indices
"""
def _get_bsgs(G, base, gens, free_indices):
"""
return the BSGS for G.pointwise_stabilizer(free_indices)
"""
if not free_indices:
return base[:], gens[:]
else:
H = G.pointwise_stabilizer(free_indices)
base, sgs = H.schreier_sims_incremental()
return base, sgs
# if not base there is no slot symmetry for the component tensors
# if list_free_indices.count([]) < 2 there is no commutation symmetry
# so there is no resulting slot symmetry
if not base and list_free_indices.count([]) < 2:
n = len(list_free_indices)
size = gens[0].size
size = n * (gens[0].size - 2) + 2
return size, [], [_af_new(list(range(size)))]
# if any(list_free_indices) one needs to compute the pointwise
# stabilizer, so G is needed
if any(list_free_indices):
G = PermutationGroup(gens)
else:
G = None
# no_free list of lists of indices for component tensors without fixed
# indices
no_free = []
size = gens[0].size
id_af = list(range(size))
num_indices = size - 2
if not list_free_indices[0]:
no_free.append(list(range(num_indices)))
res_base, res_gens = _get_bsgs(G, base, gens, list_free_indices[0])
for i in range(1, len(list_free_indices)):
base1, gens1 = _get_bsgs(G, base, gens, list_free_indices[i])
res_base, res_gens = bsgs_direct_product(res_base, res_gens,
base1, gens1, 1)
if not list_free_indices[i]:
no_free.append(list(range(size - 2, size - 2 + num_indices)))
size += num_indices
nr = size - 2
res_gens = [h for h in res_gens if h._array_form != id_af]
# if sym there are no commuting tensors stop here
if sym is None or not no_free:
if not res_gens:
res_gens = [_af_new(id_af)]
return size, res_base, res_gens
# if the component tensors have moinimal BSGS, so is their direct
# product P; the slot symmetry group is S = P*C, where C is the group
# to (anti)commute the component tensors with no free indices
# a stabilizer has the property S_i = P_i*C_i;
# the BSGS of P*C has SGS_P + SGS_C and the base is
# the ordered union of the bases of P and C.
# If P has minimal BSGS, so has S with this base.
base_comm = []
for i in range(len(no_free) - 1):
ind1 = no_free[i]
ind2 = no_free[i + 1]
a = list(range(ind1[0]))
a.extend(ind2)
a.extend(ind1)
base_comm.append(ind1[0])
a.extend(list(range(ind2[-1] + 1, nr)))
if sym == 0:
a.extend([nr, nr + 1])
else:
a.extend([nr + 1, nr])
res_gens.append(_af_new(a))
res_base = list(res_base)
# each base is ordered; order the union of the two bases
for i in base_comm:
if i not in res_base:
res_base.append(i)
res_base.sort()
if not res_gens:
res_gens = [_af_new(id_af)]
return size, res_base, res_gens
def gens_products(*v):
"""
Returns size, res_base, res_gens BSGS for n tensors of different types
v is a sequence of (base_i, gens_i, free_i, sym_i)
where
base_i, gens_i BSGS of tensor of type `i`
free_i list of the fixed slots for each of the tensors
of type `i`; if there are `n_i` tensors of type `i`
and none of them have fixed slots, `free = [[]]*n_i`
sym 0 (1) if the tensors of type `i` (anti)commute among themselves
Examples
========
>>> from sympy.combinatorics import Permutation
>>> from sympy.combinatorics.tensor_can import get_symmetric_group_sgs, gens_products
>>> Permutation.print_cyclic = True
>>> base, gens = get_symmetric_group_sgs(2)
>>> gens_products((base, gens, [[], []], 0))
(6, [0, 2], [(5)(0 1), (5)(2 3), (5)(0 2)(1 3)])
>>> gens_products((base, gens, [[1], []], 0))
(6, [2], [(5)(2 3)])
"""
res_size, res_base, res_gens = tensor_gens(*v[0])
for i in range(1, len(v)):
size, base, gens = tensor_gens(*v[i])
res_base, res_gens = bsgs_direct_product(res_base, res_gens, base,
gens, 1)
res_size = res_gens[0].size
id_af = list(range(res_size))
res_gens = [h for h in res_gens if h != id_af]
if not res_gens:
res_gens = [id_af]
return res_size, res_base, res_gens
| 40,933 | 33.456229 | 109 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/subsets.py
|
from __future__ import print_function, division
from itertools import combinations
from sympy.core import Basic
from sympy.combinatorics.graycode import GrayCode
from sympy.core.compatibility import range
class Subset(Basic):
"""
Represents a basic subset object.
We generate subsets using essentially two techniques,
binary enumeration and lexicographic enumeration.
The Subset class takes two arguments, the first one
describes the initial subset to consider and the second
describes the superset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.next_binary().subset
['b']
>>> a.prev_binary().subset
['c']
"""
_rank_binary = None
_rank_lex = None
_rank_graycode = None
_subset = None
_superset = None
def __new__(cls, subset, superset):
"""
Default constructor.
It takes the subset and its superset as its parameters.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.subset
['c', 'd']
>>> a.superset
['a', 'b', 'c', 'd']
>>> a.size
2
"""
if len(subset) > len(superset):
raise ValueError('Invalid arguments have been provided. The superset must be larger than the subset.')
for elem in subset:
if elem not in superset:
raise ValueError('The superset provided is invalid as it does not contain the element %i' % elem)
obj = Basic.__new__(cls)
obj._subset = subset
obj._superset = superset
return obj
def iterate_binary(self, k):
"""
This is a helper function. It iterates over the
binary subsets by k steps. This variable can be
both positive or negative.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.iterate_binary(-2).subset
['d']
>>> a = Subset(['a', 'b', 'c'], ['a', 'b', 'c', 'd'])
>>> a.iterate_binary(2).subset
[]
See Also
========
next_binary, prev_binary
"""
bin_list = Subset.bitlist_from_subset(self.subset, self.superset)
n = (int(''.join(bin_list), 2) + k) % 2**self.superset_size
bits = bin(n)[2:].rjust(self.superset_size, '0')
return Subset.subset_from_bitlist(self.superset, bits)
def next_binary(self):
"""
Generates the next binary ordered subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.next_binary().subset
['b']
>>> a = Subset(['a', 'b', 'c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.next_binary().subset
[]
See Also
========
prev_binary, iterate_binary
"""
return self.iterate_binary(1)
def prev_binary(self):
"""
Generates the previous binary ordered subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset([], ['a', 'b', 'c', 'd'])
>>> a.prev_binary().subset
['a', 'b', 'c', 'd']
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.prev_binary().subset
['c']
See Also
========
next_binary, iterate_binary
"""
return self.iterate_binary(-1)
def next_lexicographic(self):
"""
Generates the next lexicographically ordered subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.next_lexicographic().subset
['d']
>>> a = Subset(['d'], ['a', 'b', 'c', 'd'])
>>> a.next_lexicographic().subset
[]
See Also
========
prev_lexicographic
"""
i = self.superset_size - 1
indices = Subset.subset_indices(self.subset, self.superset)
if i in indices:
if i - 1 in indices:
indices.remove(i - 1)
else:
indices.remove(i)
i = i - 1
while not i in indices and i >= 0:
i = i - 1
if i >= 0:
indices.remove(i)
indices.append(i+1)
else:
while i not in indices and i >= 0:
i = i - 1
indices.append(i + 1)
ret_set = []
super_set = self.superset
for i in indices:
ret_set.append(super_set[i])
return Subset(ret_set, super_set)
def prev_lexicographic(self):
"""
Generates the previous lexicographically ordered subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset([], ['a', 'b', 'c', 'd'])
>>> a.prev_lexicographic().subset
['d']
>>> a = Subset(['c','d'], ['a', 'b', 'c', 'd'])
>>> a.prev_lexicographic().subset
['c']
See Also
========
next_lexicographic
"""
i = self.superset_size - 1
indices = Subset.subset_indices(self.subset, self.superset)
while i not in indices and i >= 0:
i = i - 1
if i - 1 in indices or i == 0:
indices.remove(i)
else:
if i >= 0:
indices.remove(i)
indices.append(i - 1)
indices.append(self.superset_size - 1)
ret_set = []
super_set = self.superset
for i in indices:
ret_set.append(super_set[i])
return Subset(ret_set, super_set)
def iterate_graycode(self, k):
"""
Helper function used for prev_gray and next_gray.
It performs k step overs to get the respective Gray codes.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset([1, 2, 3], [1, 2, 3, 4])
>>> a.iterate_graycode(3).subset
[1, 4]
>>> a.iterate_graycode(-2).subset
[1, 2, 4]
See Also
========
next_gray, prev_gray
"""
unranked_code = GrayCode.unrank(self.superset_size,
(self.rank_gray + k) % self.cardinality)
return Subset.subset_from_bitlist(self.superset,
unranked_code)
def next_gray(self):
"""
Generates the next Gray code ordered subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset([1, 2, 3], [1, 2, 3, 4])
>>> a.next_gray().subset
[1, 3]
See Also
========
iterate_graycode, prev_gray
"""
return self.iterate_graycode(1)
def prev_gray(self):
"""
Generates the previous Gray code ordered subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset([2, 3, 4], [1, 2, 3, 4, 5])
>>> a.prev_gray().subset
[2, 3, 4, 5]
See Also
========
iterate_graycode, next_gray
"""
return self.iterate_graycode(-1)
@property
def rank_binary(self):
"""
Computes the binary ordered rank.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset([], ['a','b','c','d'])
>>> a.rank_binary
0
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.rank_binary
3
See Also
========
iterate_binary, unrank_binary
"""
if self._rank_binary is None:
self._rank_binary = int("".join(
Subset.bitlist_from_subset(self.subset,
self.superset)), 2)
return self._rank_binary
@property
def rank_lexicographic(self):
"""
Computes the lexicographic ranking of the subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.rank_lexicographic
14
>>> a = Subset([2, 4, 5], [1, 2, 3, 4, 5, 6])
>>> a.rank_lexicographic
43
"""
if self._rank_lex is None:
def _ranklex(self, subset_index, i, n):
if subset_index == [] or i > n:
return 0
if i in subset_index:
subset_index.remove(i)
return 1 + _ranklex(self, subset_index, i + 1, n)
return 2**(n - i - 1) + _ranklex(self, subset_index, i + 1, n)
indices = Subset.subset_indices(self.subset, self.superset)
self._rank_lex = _ranklex(self, indices, 0, self.superset_size)
return self._rank_lex
@property
def rank_gray(self):
"""
Computes the Gray code ranking of the subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c','d'], ['a','b','c','d'])
>>> a.rank_gray
2
>>> a = Subset([2, 4, 5], [1, 2, 3, 4, 5, 6])
>>> a.rank_gray
27
See Also
========
iterate_graycode, unrank_gray
"""
if self._rank_graycode is None:
bits = Subset.bitlist_from_subset(self.subset, self.superset)
self._rank_graycode = GrayCode(len(bits), start=bits).rank
return self._rank_graycode
@property
def subset(self):
"""
Gets the subset represented by the current instance.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.subset
['c', 'd']
See Also
========
superset, size, superset_size, cardinality
"""
return self._subset
@property
def size(self):
"""
Gets the size of the subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.size
2
See Also
========
subset, superset, superset_size, cardinality
"""
return len(self.subset)
@property
def superset(self):
"""
Gets the superset of the subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.superset
['a', 'b', 'c', 'd']
See Also
========
subset, size, superset_size, cardinality
"""
return self._superset
@property
def superset_size(self):
"""
Returns the size of the superset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.superset_size
4
See Also
========
subset, superset, size, cardinality
"""
return len(self.superset)
@property
def cardinality(self):
"""
Returns the number of all possible subsets.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
>>> a.cardinality
16
See Also
========
subset, superset, size, superset_size
"""
return 2**(self.superset_size)
@classmethod
def subset_from_bitlist(self, super_set, bitlist):
"""
Gets the subset defined by the bitlist.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> Subset.subset_from_bitlist(['a', 'b', 'c', 'd'], '0011').subset
['c', 'd']
See Also
========
bitlist_from_subset
"""
if len(super_set) != len(bitlist):
raise ValueError("The sizes of the lists are not equal")
ret_set = []
for i in range(len(bitlist)):
if bitlist[i] == '1':
ret_set.append(super_set[i])
return Subset(ret_set, super_set)
@classmethod
def bitlist_from_subset(self, subset, superset):
"""
Gets the bitlist corresponding to a subset.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> Subset.bitlist_from_subset(['c', 'd'], ['a', 'b', 'c', 'd'])
'0011'
See Also
========
subset_from_bitlist
"""
bitlist = ['0'] * len(superset)
if type(subset) is Subset:
subset = subset.args[0]
for i in Subset.subset_indices(subset, superset):
bitlist[i] = '1'
return ''.join(bitlist)
@classmethod
def unrank_binary(self, rank, superset):
"""
Gets the binary ordered subset of the specified rank.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> Subset.unrank_binary(4, ['a', 'b', 'c', 'd']).subset
['b']
See Also
========
iterate_binary, rank_binary
"""
bits = bin(rank)[2:].rjust(len(superset), '0')
return Subset.subset_from_bitlist(superset, bits)
@classmethod
def unrank_gray(self, rank, superset):
"""
Gets the Gray code ordered subset of the specified rank.
Examples
========
>>> from sympy.combinatorics.subsets import Subset
>>> Subset.unrank_gray(4, ['a', 'b', 'c']).subset
['a', 'b']
>>> Subset.unrank_gray(0, ['a', 'b', 'c']).subset
[]
See Also
========
iterate_graycode, rank_gray
"""
graycode_bitlist = GrayCode.unrank(len(superset), rank)
return Subset.subset_from_bitlist(superset, graycode_bitlist)
@classmethod
def subset_indices(self, subset, superset):
"""Return indices of subset in superset in a list; the list is empty
if all elements of subset are not in superset.
Examples
========
>>> from sympy.combinatorics import Subset
>>> superset = [1, 3, 2, 5, 4]
>>> Subset.subset_indices([3, 2, 1], superset)
[1, 2, 0]
>>> Subset.subset_indices([1, 6], superset)
[]
>>> Subset.subset_indices([], superset)
[]
"""
a, b = superset, subset
sb = set(b)
d = {}
for i, ai in enumerate(a):
if ai in sb:
d[ai] = i
sb.remove(ai)
if not sb:
break
else:
return list()
return [d[bi] for bi in b]
def ksubsets(superset, k):
"""
Finds the subsets of size k in lexicographic order.
This uses the itertools generator.
Examples
========
>>> from sympy.combinatorics.subsets import ksubsets
>>> list(ksubsets([1, 2, 3], 2))
[(1, 2), (1, 3), (2, 3)]
>>> list(ksubsets([1, 2, 3, 4, 5], 2))
[(1, 2), (1, 3), (1, 4), (1, 5), (2, 3), (2, 4), \
(2, 5), (3, 4), (3, 5), (4, 5)]
See Also
========
class:Subset
"""
return combinations(superset, k)
| 15,802 | 25.784746 | 114 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/fp_groups.py
|
# -*- coding: utf-8 -*-
"""Finitely Presented Groups and its algorithms. """
from __future__ import print_function, division
from sympy.core.basic import Basic
from sympy.core import Symbol, Mod
from sympy.printing.defaults import DefaultPrinting
from sympy.utilities import public
from sympy.utilities.iterables import flatten
from sympy.combinatorics.free_groups import FreeGroupElement, free_group, zero_mul_simp
from itertools import chain, product
from bisect import bisect_left
@public
def fp_group(fr_grp, relators=[]):
_fp_group = FpGroup(fr_grp, relators)
return (_fp_group,) + tuple(_fp_group._generators)
@public
def xfp_group(fr_grp, relators=[]):
_fp_group = FpGroup(fr_grp, relators)
return (_fp_group, _fp_group._generators)
@public
def vfp_group(fr_grpm, relators):
_fp_group = FpGroup(symbols, relators)
pollute([sym.name for sym in _fp_group.symbols], _fp_group.generators)
return _fp_group
def _parse_relators(rels):
"""Parse the passed relators."""
return rels
###############################################################################
# FINITELY PRESENTED GROUPS #
###############################################################################
class FpGroup(DefaultPrinting):
"""
The FpGroup would take a FreeGroup and a list/tuple of relators, the
relators would be specified in such a way that each of them be equal to the
identity of the provided free group.
"""
is_group = True
is_FpGroup = True
is_PermutationGroup = False
def __new__(cls, fr_grp, relators):
relators = _parse_relators(relators)
# return the corresponding FreeGroup if no relators are specified
if not relators:
return fr_grp
obj = object.__new__(cls)
obj.free_group = fr_grp
obj.relators = relators
obj.generators = obj._generators()
obj.dtype = type("FpGroupElement", (FpGroupElement,), {"group": obj})
# CosetTable instance on identity subgroup
obj._coset_table = None
# returns whether coset table on identity subgroup
# has been standardized
obj._is_standardized = False
obj._order = None
obj._center = None
return obj
def _generators(self):
return self.free_group.generators
def subgroup(self, gens, C=None):
'''
Return the subgroup generated by `gens` using the
Reidemeister-Schreier algorithm
'''
if not all([isinstance(g, FreeGroupElement) for g in gens]):
raise ValueError("Generators must be `FreeGroupElement`s")
if not all([g.group == self.free_group for g in gens]):
raise ValueError("Given generators are not members of the group")
g, rels = reidemeister_presentation(self, gens, C=C)
g = FpGroup(g[0].group, rels)
return g
def coset_enumeration(self, H, strategy="relator_based", max_cosets=None):
"""
Return an instance of ``coset table``, when Todd-Coxeter algorithm is
run over the ``self`` with ``H`` as subgroup, using ``strategy``
argument as strategy. The returned coset table is compressed but not
standardized.
"""
if not max_cosets:
max_cosets = CosetTable.coset_table_max_limit
if strategy == 'relator_based':
C = coset_enumeration_r(self, H, max_cosets=max_cosets)
else:
C = coset_enumeration_c(self, H, max_cosets=max_cosets)
C.compress()
return C
def standardize_coset_table(self):
"""
Standardized the coset table ``self`` and makes the internal variable
``_is_standardized`` equal to ``True``.
"""
self._coset_table.standardize()
self._is_standardized = True
def coset_table(self, H, strategy="relator_based"):
"""
Return the mathematical coset table of ``self`` in ``H``.
"""
if not H:
if self._coset_table != None:
if not self._is_standardized:
self.standardize_coset_table()
else:
C = self.coset_enumeration([], strategy)
self._coset_table = C
self.standardize_coset_table()
return self._coset_table.table
else:
C = self.coset_enumeration(H, strategy)
C.standardize()
return C.table
def order(self, strategy="relator_based"):
"""
Returns the order of the finitely presented group ``self``. It uses
the coset enumeration with identity group as subgroup, i.e ``H=[]``.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy.combinatorics.fp_groups import FpGroup
>>> F, x, y = free_group("x, y")
>>> f = FpGroup(F, [x, y**2])
>>> f.order(strategy="coset_table_based")
2
"""
from sympy import S, gcd
if self._order != None:
return self._order
if self._coset_table != None:
self._order = len(self._coset_table.table)
elif len(self.generators) == 1:
self._order = gcd([r.array_form[0][1] for r in self.relators])
elif self._is_infinite():
self._order = S.Infinity
else:
gens, C = self._finite_index_subgroup()
if C:
ind = len(C.table)
self._order = ind*self.subgroup(gens, C=C).order()
else:
self._order = self.index([])
return self._order
def _is_infinite(self):
'''
Test if the group is infinite. Return `True` if the test succeeds
and `None` otherwise
'''
# Abelianisation test: check is the abelianisation is infinite
abelian_rels = []
from sympy.polys.solvers import RawMatrix as Matrix
from sympy.polys.domains import ZZ
from sympy.matrices.normalforms import invariant_factors
for rel in self.relators:
abelian_rels.append([rel.exponent_sum(g) for g in self.generators])
m = Matrix(abelian_rels)
setattr(m, "ring", ZZ)
if 0 in invariant_factors(m):
return True
else:
return None
def _finite_index_subgroup(self, s=[]):
'''
Find the elements of `self` that generate a finite index subgroup
and, if found, return the list of elements and the coset table of `self` by
the subgroup, otherwise return `(None, None)`
'''
gen = self.most_frequent_generator()
rels = list(self.generators)
rels.extend(self.relators)
if not s:
rand = self.free_group.identity
i = 0
while (rand in rels or rand**-1 in rels or rand.is_identity
or rand in rels) and i<10:
rand = self.random_element()
i += 1
s = [gen, rand] + [g for g in self.generators if g != gen]
mid = (len(s)+1)//2
half1 = s[:mid]
half2 = s[mid:]
m = 200
C = None
while not C and (m/2 < CosetTable.coset_table_max_limit):
m = min(m, CosetTable.coset_table_max_limit)
try:
C = self.coset_enumeration(half1, max_cosets=m)
half = half1
except ValueError:
pass
if not C:
try:
C = self.coset_enumeration(half2, max_cosets=m)
half = half2
except ValueError:
m *= 2
continue
if not C:
return None, None
return half, C
def most_frequent_generator(self):
gens = self.generators
rels = self.relators
freqs = [sum([r.generator_count(g) for r in rels]) for g in gens]
return gens[freqs.index(max(freqs))]
def random_element(self):
import random
r = self.free_group.identity
for i in range(random.randint(2,3)):
r = r*random.choice(self.generators)**random.choice([1,-1])
return r
def index(self, H, strategy="relator_based"):
"""
Return the index of subgroup ``H`` in group ``self``.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy.combinatorics.fp_groups import FpGroup
>>> F, x, y = free_group("x, y")
>>> f = FpGroup(F, [x**5, y**4, y*x*y**3*x**3])
>>> f.index([x])
4
"""
# TODO: use |G:H| = |G|/|H| (currently H can't be made into a group)
# when we know |G| and |H|
if H == []:
return self.order()
else:
C = self.coset_enumeration(H, strategy)
return len(C.table)
def __str__(self):
if self.free_group.rank > 30:
str_form = "<fp group with %s generators>" % self.free_group.rank
else:
str_form = "<fp group on the generators %s>" % str(self.generators)
return str_form
__repr__ = __str__
###############################################################################
# COSET TABLE #
###############################################################################
class CosetTable(DefaultPrinting):
# coset_table: Mathematically a coset table
# represented using a list of lists
# alpha: Mathematically a coset (precisely, a live coset)
# represented by an integer between i with 1 <= i <= n
# α ∈ c
# x: Mathematically an element of "A" (set of generators and
# their inverses), represented using "FpGroupElement"
# fp_grp: Finitely Presented Group with < X|R > as presentation.
# H: subgroup of fp_grp.
# NOTE: We start with H as being only a list of words in generators
# of "fp_grp". Since `.subgroup` method has not been implemented.
r"""
Properties
==========
[1] `0 \in \Omega` and `\tau(1) = \epsilon`
[2] `\alpha^x = \beta \Leftrightarrow \beta^{x^{-1}} = \alpha`
[3] If `\alpha^x = \beta`, then `H \tau(\alpha)x = H \tau(\beta)`
[4] `\forall \alpha \in \Omega, 1^{\tau(\alpha)} = \alpha`
References
==========
[1] Holt, D., Eick, B., O'Brien, E.
"Handbook of Computational Group Theory"
[2] John J. Cannon; Lucien A. Dimino; George Havas; Jane M. Watson
Mathematics of Computation, Vol. 27, No. 123. (Jul., 1973), pp. 463-490.
"Implementation and Analysis of the Todd-Coxeter Algorithm"
"""
# default limit for the number of cosets allowed in a
# coset enumeration.
coset_table_max_limit = 4096000
# maximum size of deduction stack above or equal to
# which it is emptied
max_stack_size = 500
def __init__(self, fp_grp, subgroup, max_cosets=None):
if not max_cosets:
max_cosets = CosetTable.coset_table_max_limit
self.fp_group = fp_grp
self.subgroup = subgroup
self.coset_table_max_limit = max_cosets
# "p" is setup independent of Ω and n
self.p = [0]
# a list of the form `[gen_1, gen_1^{-1}, ... , gen_k, gen_k^{-1}]`
self.A = list(chain.from_iterable((gen, gen**-1) \
for gen in self.fp_group.generators))
# the mathematical coset table which is a list of lists
self.table = [[None]*len(self.A)]
self.A_dict = {x: self.A.index(x) for x in self.A}
self.A_dict_inv = {}
for x, index in self.A_dict.items():
if index % 2 == 0:
self.A_dict_inv[x] = self.A_dict[x] + 1
else:
self.A_dict_inv[x] = self.A_dict[x] - 1
# used in the coset-table based method of coset enumeration. Each of
# the element is called a "deduction" which is the form (α, x) whenever
# a value is assigned to α^x during a definition or "deduction process"
self.deduction_stack = []
@property
def omega(self):
"""Set of live cosets. """
return [coset for coset in range(len(self.p)) if self.p[coset] == coset]
def copy(self):
"""
Return a shallow copy of Coset Table instance ``self``.
"""
self_copy = self.__class__(self.fp_group, self.subgroup)
self_copy.table = [list(perm_rep) for perm_rep in self.table]
self_copy.p = list(self.p)
self_copy.deduction_stack = list(self.deduction_stack)
return self_copy
def __str__(self):
return "Coset Table on %s with %s as subgroup generators" \
% (self.fp_group, self.subgroup)
__repr__ = __str__
@property
def n(self):
"""The number `n` represents the length of the sublist containing the
live cosets.
"""
if not self.table:
return 0
return max(self.omega) + 1
# Pg. 152 [1]
def is_complete(self):
r"""
The coset table is called complete if it has no undefined entries
on the live cosets; that is, `\alpha^x` is defined for all
`\alpha \in \Omega` and `x \in A`.
"""
return not any([None in self.table[coset] for coset in self.omega])
# Pg. 153 [1]
def define(self, alpha, x, max_cosets=coset_table_max_limit):
r"""
This routine is used in the relator-based strategy of Todd-Coxeter
algorithm if some `\alpha^x` is undefined. We check whether there is
space available for defining a new coset. If there is enough space
then we remedy this by adjoining a new coset `\beta` to `\Omega`
(i.e to set of live cosets) and put that equal to `\alpha^x`, then
make an assignment satisfying Property[1]. If there is not enough space
then we halt the Coset Table creation. The maximum amount of space that
can be used by Coset Table can be manipulated using the class variable
``CosetTable.coset_table_max_limit``.
See Also
========
define_c
"""
A = self.A
table = self.table
len_table = len(table)
if len_table == self.coset_table_max_limit:
# abort the further generation of cosets
raise ValueError("the coset enumeration has defined more than "
"%s cosets. Try with a greater value max number of cosets "
% self.coset_table_max_limit)
table.append([None]*len(A))
# beta is the new coset generated
beta = len_table
self.p.append(beta)
table[alpha][self.A_dict[x]] = beta
table[beta][self.A_dict_inv[x]] = alpha
def define_c(self, alpha, x, max_cosets=coset_table_max_limit):
r"""
A variation of ``define`` routine, described on Pg. 165 [1], used in
the coset table-based strategy of Todd-Coxeter algorithm. It differs
from ``define`` routine in that for each definition it also adds the
tuple `(\alpha, x)` to the deduction stack.
See Also
========
define
"""
A = self.A
table = self.table
len_table = len(table)
if len_table == self.coset_table_max_limit:
# abort the further generation of cosets
raise ValueError("the coset enumeration has defined more than "
"%s cosets. Try with a greater value max number of cosets "
% max_cosets)
table.append([None]*len(A))
# beta is the new coset generated
beta = len_table
self.p.append(beta)
table[alpha][self.A_dict[x]] = beta
table[beta][self.A_dict_inv[x]] = alpha
# append to deduction stack
self.deduction_stack.append((alpha, x))
def scan_c(self, alpha, word):
"""
A variation of ``scan`` routine, described on pg. 165 of [1], which
puts at tuple, whenever a deduction occurs, to deduction stack.
See Also
========
scan, scan_check, scan_and_fill, scan_and_fill_c
"""
# α is an integer representing a "coset"
# since scanning can be in two cases
# 1. for α=0 and w in Y (i.e generating set of H)
# 2. α in Ω (set of live cosets), w in R (relators)
A_dict = self.A_dict
A_dict_inv = self.A_dict_inv
table = self.table
f = alpha
i = 0
r = len(word)
b = alpha
j = r - 1
# list of union of generators and their inverses
while i <= j and table[f][A_dict[word[i]]] is not None:
f = table[f][A_dict[word[i]]]
i += 1
if i > j:
if f != b:
self.coincidence_c(f, b)
return
while j >= i and table[b][A_dict_inv[word[j]]] is not None:
b = table[b][A_dict_inv[word[j]]]
j -= 1
if j < i:
# we have an incorrect completed scan with coincidence f ~ b
# run the "coincidence" routine
self.coincidence_c(f, b)
elif j == i:
# deduction process
table[f][A_dict[word[i]]] = b
table[b][A_dict_inv[word[i]]] = f
self.deduction_stack.append((f, word[i]))
# otherwise scan is incomplete and yields no information
# α, β coincide, i.e. α, β represent the pair of cosets where
# coincidence occurs
def coincidence_c(self, alpha, beta):
"""
A variation of ``coincidence`` routine used in the coset-table based
method of coset enumeration. The only difference being on addition of
a new coset in coset table(i.e new coset introduction), then it is
appended to ``deduction_stack``.
See Also
========
coincidence
"""
A_dict = self.A_dict
A_dict_inv = self.A_dict_inv
table = self.table
p = self.p
l = 0
# behaves as a queue
q = []
self.merge(alpha, beta, q)
while len(q) > 0:
gamma = q.pop(0)
for x in A_dict:
delta = table[gamma][A_dict[x]]
if delta is not None:
table[delta][A_dict_inv[x]] = None
# only line of difference from ``coincidence`` routine
self.deduction_stack.append((delta, x**-1))
mu = self.rep(gamma)
nu = self.rep(delta)
if table[mu][A_dict[x]] is not None:
self.merge(nu, table[mu][A_dict[x]], q)
elif table[nu][A_dict_inv[x]] is not None:
self.merge(mu, table[nu][A_dict_inv[x]], q)
else:
table[mu][A_dict[x]] = nu
table[nu][A_dict_inv[x]] = mu
def scan(self, alpha, word):
r"""
``scan`` performs a scanning process on the input ``word``.
It first locates the largest prefix ``s`` of ``word`` for which
`\alpha^s` is defined (i.e is not ``None``), ``s`` may be empty. Let
``word=sv``, let ``t`` be the longest suffix of ``v`` for which
`\alpha^{t^{-1}}` is defined, and let ``v=ut``. Then three
possibilities are there:
1. If ``t=v``, then we say that the scan completes, and if, in addition
`\alpha^s = \alpha^{t^{-1}}`, then we say that the scan completes
correctly.
2. It can also happen that scan does not complete, but `|u|=1`; that
is, the word ``u`` consists of a single generator `x \in A`. In that
case, if `\alpha^s = \beta` and `\alpha^{t^{-1}} = \gamma`, then we can
set `\beta^x = \gamma` and `\gamma^{x^{-1}} = \beta`. These assignments
are known as deductions and enable the scan to complete correctly.
3. See ``coicidence`` routine for explanation of third condition.
Notes
=====
The code for the procedure of scanning `\alpha \in \Omega`
under `w \in A*` is defined on pg. 155 [1]
See Also
========
scan_c, scan_check, scan_and_fill, scan_and_fill_c
"""
# α is an integer representing a "coset"
# since scanning can be in two cases
# 1. for α=0 and w in Y (i.e generating set of H)
# 2. α in Ω (set of live cosets), w in R (relators)
A_dict = self.A_dict
A_dict_inv = self.A_dict_inv
table = self.table
f = alpha
i = 0
r = len(word)
b = alpha
j = r - 1
while i <= j and table[f][A_dict[word[i]]] is not None:
f = table[f][A_dict[word[i]]]
i += 1
if i > j:
if f != b:
self.coincidence(f, b)
return
while j >= i and table[b][A_dict_inv[word[j]]] is not None:
b = table[b][A_dict_inv[word[j]]]
j -= 1
if j < i:
# we have an incorrect completed scan with coincidence f ~ b
# run the "coincidence" routine
self.coincidence(f, b)
elif j == i:
# deduction process
table[f][A_dict[word[i]]] = b
table[b][A_dict_inv[word[i]]] = f
# otherwise scan is incomplete and yields no information
# used in the low-index subgroups algorithm
def scan_check(self, alpha, word):
r"""
Another version of ``scan`` routine, described on, it checks whether
`\alpha` scans correctly under `word`, it is a straightforward
modification of ``scan``. ``scan_check`` returns ``False`` (rather than
calling ``coincidence``) if the scan completes incorrectly; otherwise
it returns ``True``.
See Also
========
scan, scan_c, scan_and_fill, scan_and_fill_c
"""
# α is an integer representing a "coset"
# since scanning can be in two cases
# 1. for α=0 and w in Y (i.e generating set of H)
# 2. α in Ω (set of live cosets), w in R (relators)
A_dict = self.A_dict
A_dict_inv = self.A_dict_inv
table = self.table
f = alpha
i = 0
r = len(word)
b = alpha
j = r - 1
while i <= j and table[f][A_dict[word[i]]] is not None:
f = table[f][A_dict[word[i]]]
i += 1
if i > j:
return f == b
while j >= i and table[b][A_dict_inv[word[j]]] is not None:
b = table[b][A_dict_inv[word[j]]]
j -= 1
if j < i:
# we have an incorrect completed scan with coincidence f ~ b
# return False, instead of calling coincidence routine
return False
elif j == i:
# deduction process
table[f][A_dict[word[i]]] = b
table[b][A_dict_inv[word[i]]] = f
return True
def merge(self, k, lamda, q):
"""
Input: 'k', 'lamda' being the two class representatives to be merged.
=====
Merge two classes with representatives ``k`` and ``lamda``, described
on Pg. 157 [1] (for pseudocode), start by putting ``p[k] = lamda``.
It is more efficient to choose the new representative from the larger
of the two classes being merged, i.e larger among ``k`` and ``lamda``.
procedure ``merge`` performs the merging operation, adds the deleted
class representative to the queue ``q``.
Notes
=====
Pg. 86-87 [1] contains a description of this method.
See Also
========
coincidence, rep
"""
p = self.p
phi = self.rep(k)
psi = self.rep(lamda)
if phi != psi:
mu = min(phi, psi)
v = max(phi, psi)
p[v] = mu
q.append(v)
def rep(self, k):
r"""
Input: `k \in [0 \ldots n-1]`, as for ``self`` only array ``p`` is used
=====
Output: Representative of the class containing ``k``.
======
Returns the representative of `\sim` class containing ``k``, it also
makes some modification to array ``p`` of ``self`` to ease further
computations, described on Pg. 157 [1].
The information on classes under `\sim` is stored in array `p` of
``self`` argument, which will always satisfy the property:
`p[\alpha] \sim \alpha` and `p[\alpha]=\alpha \iff \alpha=rep(\alpha)`
`\forall \in [0 \ldots n-1]`.
So, for `\alpha \in [0 \ldots n-1]`, we find `rep(self, \alpha)` by
continually replacing `\alpha` by `p[\alpha]` until it becomes
constant (i.e satisfies `p[\alpha] = \alpha`):w
To increase the efficiency of later ``rep`` calculations, whenever we
find `rep(self, \alpha)=\beta`, we set
`p[\gamma] = \beta \forall \gamma \in p-chain` from `\alpha` to `\beta`
Notes
=====
``rep`` routine is also described on Pg. 85-87 [1] in Atkinson's
algorithm, this results from the fact that ``coincidence`` routine
introduces functionality similar to that introduced by the
``minimal_block`` routine on Pg. 85-87 [1].
See also
========
coincidence, merge
"""
p = self.p
lamda = k
rho = p[lamda]
while rho != lamda:
lamda = rho
rho = p[lamda]
mu = k
rho = p[mu]
while rho != lamda:
p[mu] = lamda
mu = rho
rho = p[mu]
return lamda
# α, β coincide, i.e. α, β represent the pair of cosets
# where coincidence occurs
def coincidence(self, alpha, beta):
r"""
The third situation described in ``scan`` routine is handled by this
routine, described on Pg. 156-161 [1].
The unfortunate situation when the scan completes but not correctly,
then ``coincidence`` routine is run. i.e when for some `i` with
`1 \le i \le r+1`, we have `w=st` with `s=x_1*x_2 ... x_{i-1}`,
`t=x_i*x_{i+1} ... x_r`, and `\beta = \alpha^s` and
`\gamma = \alph^{t-1}` are defined but unequal. This means that
`\beta` and `\gamma` represent the same coset of `H` in `G`. Described
on Pg. 156 [1]. ``rep``
See Also
========
scan
"""
A_dict = self.A_dict
A_dict_inv = self.A_dict_inv
table = self.table
p = self.p
l = 0
# behaves as a queue
q = []
self.merge(alpha, beta, q)
while len(q) > 0:
gamma = q.pop(0)
for x in A_dict:
delta = table[gamma][A_dict[x]]
if delta is not None:
table[delta][A_dict_inv[x]] = None
mu = self.rep(gamma)
nu = self.rep(delta)
if table[mu][A_dict[x]] is not None:
self.merge(nu, table[mu][A_dict[x]], q)
elif table[nu][A_dict_inv[x]] is not None:
self.merge(mu, table[nu][A_dict_inv[x]], q)
else:
table[mu][A_dict[x]] = nu
table[nu][A_dict_inv[x]] = mu
# method used in the HLT strategy
def scan_and_fill(self, alpha, word, max_cosets=coset_table_max_limit):
"""
A modified version of ``scan`` routine used in the relator-based
method of coset enumeration, described on pg. 162-163 [1], which
follows the idea that whenever the procedure is called and the scan
is incomplete then it makes new definitions to enable the scan to
complete; i.e it fills in the gaps in the scan of the relator or
subgroup generator.
"""
A_dict = self.A_dict
A_dict_inv = self.A_dict_inv
table = self.table
r = len(word)
f = alpha
i = 0
b = alpha
j = r - 1
# loop until it has filled the α row in the table.
while True:
# do the forward scanning
while i <= j and table[f][A_dict[word[i]]] is not None:
f = table[f][A_dict[word[i]]]
i += 1
if i > j:
if f != b:
self.coincidence(f, b)
return
# forward scan was incomplete, scan backwards
while j >= i and table[b][A_dict_inv[word[j]]] is not None:
b = table[b][A_dict_inv[word[j]]]
j -= 1
if j < i:
self.coincidence(f, b)
elif j == i:
table[f][A_dict[word[i]]] = b
table[b][A_dict_inv[word[i]]] = f
else:
self.define(f, word[i], max_cosets=max_cosets)
def scan_and_fill_c(self, alpha, word, max_cosets=coset_table_max_limit):
"""
A modified version of ``scan`` routine, described on Pg. 165 second
para. [1], with modification similar to that of ``scan_anf_fill`` the
only difference being it calls the coincidence procedure used in the
coset-table based method i.e. the routine ``coincidence_c`` is used.
Also See
========
scan, scan_and_fill
"""
A_dict = self.A_dict
A_dict_inv = self.A_dict_inv
table = self.table
r = len(word)
f = alpha
i = 0
b = alpha
j = r - 1
# loop until it has filled the α row in the table.
while True:
# do the forward scanning
while i <= j and table[f][A_dict[word[i]]] is not None:
f = table[f][A_dict[word[i]]]
i += 1
if i > j:
if f != b:
self.coincidence_c(f, b)
return
# forward scan was incomplete, scan backwards
while j >= i and table[b][A_dict_inv[word[j]]] is not None:
b = table[b][A_dict_inv[word[j]]]
j -= 1
if j < i:
self.coincidence_c(f, b)
elif j == i:
table[f][A_dict[word[i]]] = b
table[b][A_dict_inv[word[i]]] = f
self.deduction_stack.append((f, word[i]))
else:
self.define_c(f, word[i], max_cosets=max_cosets)
# method used in the HLT strategy
def look_ahead(self):
"""
When combined with the HLT method this is known as HLT+Lookahead
method of coset enumeration, described on pg. 164 [1]. Whenever
``define`` aborts due to lack of space available this procedure is
executed. This routine helps in recovering space resulting from
"coincidence" of cosets.
"""
R = self.fp_group.relators
p = self.p
# complete scan all relators under all cosets(obviously live)
# without making new definitions
for beta in self.omega:
for w in R:
self.scan(beta, w)
if p[beta] < beta:
break
# Pg. 166
def process_deductions(self, R_c_x, R_c_x_inv):
"""
Processes the deductions that have been pushed onto ``deduction_stack``,
described on Pg. 166 [1] and is used in coset-table based enumeration.
See Also
========
deduction_stack
"""
p = self.p
table = self.table
while len(self.deduction_stack) > 0:
if len(self.deduction_stack) >= CosetTable.max_stack_size:
self.look_ahead()
del self.deduction_stack[:]
else:
alpha, x = self.deduction_stack.pop()
if p[alpha] == alpha:
for w in R_c_x:
self.scan_c(alpha, w)
if p[alpha] < alpha:
break
beta = table[alpha][self.A_dict[x]]
if beta is not None and p[beta] == beta:
for w in R_c_x_inv:
self.scan_c(beta, w)
if p[beta] < beta:
break
def process_deductions_check(self, R_c_x, R_c_x_inv):
"""
A variation of ``process_deductions``, this calls ``scan_check``
wherever ``process_deductions`` calls ``scan``, described on Pg. [1].
See Also
========
process_deductions
"""
p = self.p
table = self.table
while len(self.deduction_stack) > 0:
alpha, x = self.deduction_stack.pop()
for w in R_c_x:
if not self.scan_check(alpha, w):
return False
beta = table[alpha][self.A_dict[x]]
if beta is not None:
for w in R_c_x_inv:
if not self.scan_check(beta, w):
return False
return True
def switch(self, beta, gamma):
r"""Switch the elements `\beta, \gamma \in \Omega` of ``self``, used
by the ``standardize`` procedure, described on Pg. 167 [1].
See Also
========
standardize
"""
A = self.A
A_dict = self.A_dict
table = self.table
for x in A:
z = table[gamma][A_dict[x]]
table[gamma][A_dict[x]] = table[beta][A_dict[x]]
table[beta][A_dict[x]] = z
for alpha in range(len(self.p)):
if self.p[alpha] == alpha:
if table[alpha][A_dict[x]] == beta:
table[alpha][A_dict[x]] = gamma
elif table[alpha][A_dict[x]] == gamma:
table[alpha][A_dict[x]] = beta
def standardize(self):
"""
A coset table is standardized if when running through the cosets and
within each coset through the generator images (ignoring generator
inverses), the cosets appear in order of the integers
`0, 1, , \ldots, n`. "Standardize" reorders the elements of `\Omega`
such that, if we scan the coset table first by elements of `\Omega`
and then by elements of A, then the cosets occur in ascending order.
``standardize()`` is used at the end of an enumeration to permute the
cosets so that they occur in some sort of standard order.
Notes
=====
procedure is described on pg. 167-168 [1], it also makes use of the
``switch`` routine to replace by smaller integer value.
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy.combinatorics.fp_groups import FpGroup, coset_enumeration_r
>>> F, x, y = free_group("x, y")
# Example 5.3 from [1]
>>> f = FpGroup(F, [x**2*y**2, x**3*y**5])
>>> C = coset_enumeration_r(f, [])
>>> C.compress()
>>> C.table
[[1, 3, 1, 3], [2, 0, 2, 0], [3, 1, 3, 1], [0, 2, 0, 2]]
>>> C.standardize()
>>> C.table
[[1, 2, 1, 2], [3, 0, 3, 0], [0, 3, 0, 3], [2, 1, 2, 1]]
"""
A = self.A
A_dict = self.A_dict
A_dict_inv = self.A_dict_inv
gamma = 1
for alpha, x in product(range(self.n), A):
beta = self.table[alpha][A_dict[x]]
if beta >= gamma:
if beta > gamma:
self.switch(gamma, beta)
gamma += 1
if gamma == self.n:
return
# Compression of a Coset Table
def compress(self):
"""Removes the non-live cosets from the coset table, described on
pg. 167 [1].
"""
gamma = -1
A = self.A
A_dict = self.A_dict
A_dict_inv = self.A_dict_inv
table = self.table
chi = tuple([i for i in range(len(self.p)) if self.p[i] != i])
for alpha in self.omega:
gamma += 1
if gamma != alpha:
# replace α by γ in coset table
for x in A:
beta = table[alpha][A_dict[x]]
table[gamma][A_dict[x]] = beta
table[beta][A_dict_inv[x]] == gamma
# all the cosets in the table are live cosets
self.p = list(range(gamma + 1))
# delete the useless coloumns
del table[len(self.p):]
# re-define values
for row in table:
for j in range(len(self.A)):
row[j] -= bisect_left(chi, row[j])
def conjugates(self, R):
R_c = list(chain.from_iterable((rel.cyclic_conjugates(), \
(rel**-1).cyclic_conjugates()) for rel in R))
R_set = set()
for conjugate in R_c:
R_set = R_set.union(conjugate)
R_c_list = []
for x in self.A:
r = set([word for word in R_set if word[0] == x])
R_c_list.append(r)
R_set.difference_update(r)
return R_c_list
###############################################################################
# COSET ENUMERATION #
###############################################################################
# relator-based method
def coset_enumeration_r(fp_grp, Y, max_cosets=None):
"""
This is easier of the two implemented methods of coset enumeration.
and is often called the HLT method, after Hazelgrove, Leech, Trotter
The idea is that we make use of ``scan_and_fill`` makes new definitions
whenever the scan is incomplete to enable the scan to complete; this way
we fill in the gaps in the scan of the relator or subgroup generator,
that's why the name relator-based method.
# TODO: complete the docstring
See Also
========
scan_and_fill,
References
==========
[1] Holt, D., Eick, B., O'Brien, E.
"Handbook of computational group theory"
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy.combinatorics.fp_groups import FpGroup, coset_enumeration_r
>>> F, x, y = free_group("x, y")
# Example 5.1 from [1]
>>> f = FpGroup(F, [x**3, y**3, x**-1*y**-1*x*y])
>>> C = coset_enumeration_r(f, [x])
>>> for i in range(len(C.p)):
... if C.p[i] == i:
... print(C.table[i])
[0, 0, 1, 2]
[1, 1, 2, 0]
[2, 2, 0, 1]
>>> C.p
[0, 1, 2, 1, 1]
# Example from exercises Q2 [1]
>>> f = FpGroup(F, [x**2*y**2, y**-1*x*y*x**-3])
>>> C = coset_enumeration_r(f, [])
>>> C.compress(); C.standardize()
>>> C.table
[[1, 2, 3, 4],
[5, 0, 6, 7],
[0, 5, 7, 6],
[7, 6, 5, 0],
[6, 7, 0, 5],
[2, 1, 4, 3],
[3, 4, 2, 1],
[4, 3, 1, 2]]
# Example 5.2
>>> f = FpGroup(F, [x**2, y**3, (x*y)**3])
>>> Y = [x*y]
>>> C = coset_enumeration_r(f, Y)
>>> for i in range(len(C.p)):
... if C.p[i] == i:
... print(C.table[i])
[1, 1, 2, 1]
[0, 0, 0, 2]
[3, 3, 1, 0]
[2, 2, 3, 3]
# Example 5.3
>>> f = FpGroup(F, [x**2*y**2, x**3*y**5])
>>> Y = []
>>> C = coset_enumeration_r(f, Y)
>>> for i in range(len(C.p)):
... if C.p[i] == i:
... print(C.table[i])
[1, 3, 1, 3]
[2, 0, 2, 0]
[3, 1, 3, 1]
[0, 2, 0, 2]
# Example 5.4
>>> F, a, b, c, d, e = free_group("a, b, c, d, e")
>>> f = FpGroup(F, [a*b*c**-1, b*c*d**-1, c*d*e**-1, d*e*a**-1, e*a*b**-1])
>>> Y = [a]
>>> C = coset_enumeration_r(f, Y)
>>> for i in range(len(C.p)):
... if C.p[i] == i:
... print(C.table[i])
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
# example of "compress" method
>>> C.compress()
>>> C.table
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
# Exercises Pg. 161, Q2.
>>> F, x, y = free_group("x, y")
>>> f = FpGroup(F, [x**2*y**2, y**-1*x*y*x**-3])
>>> Y = []
>>> C = coset_enumeration_r(f, Y)
>>> C.compress()
>>> C.standardize()
>>> C.table
[[1, 2, 3, 4],
[5, 0, 6, 7],
[0, 5, 7, 6],
[7, 6, 5, 0],
[6, 7, 0, 5],
[2, 1, 4, 3],
[3, 4, 2, 1],
[4, 3, 1, 2]]
# John J. Cannon; Lucien A. Dimino; George Havas; Jane M. Watson
# Mathematics of Computation, Vol. 27, No. 123. (Jul., 1973), pp. 463-490
# from 1973chwd.pdf
# Table 1. Ex. 1
>>> F, r, s, t = free_group("r, s, t")
>>> E1 = FpGroup(F, [t**-1*r*t*r**-2, r**-1*s*r*s**-2, s**-1*t*s*t**-2])
>>> C = coset_enumeration_r(E1, [r])
>>> for i in range(len(C.p)):
... if C.p[i] == i:
... print(C.table[i])
[0, 0, 0, 0, 0, 0]
Ex. 2
>>> F, a, b = free_group("a, b")
>>> Cox = FpGroup(F, [a**6, b**6, (a*b)**2, (a**2*b**2)**2, (a**3*b**3)**5])
>>> C = coset_enumeration_r(Cox, [a])
>>> index = 0
>>> for i in range(len(C.p)):
... if C.p[i] == i:
... index += 1
>>> index
500
# Ex. 3
>>> F, a, b = free_group("a, b")
>>> B_2_4 = FpGroup(F, [a**4, b**4, (a*b)**4, (a**-1*b)**4, (a**2*b)**4, \
(a*b**2)**4, (a**2*b**2)**4, (a**-1*b*a*b)**4, (a*b**-1*a*b)**4])
>>> C = coset_enumeration_r(B_2_4, [a])
>>> index = 0
>>> for i in range(len(C.p)):
... if C.p[i] == i:
... index += 1
>>> index
1024
"""
# 1. Initialize a coset table C for < X|R >
C = CosetTable(fp_grp, Y, max_cosets=max_cosets)
R = fp_grp.relators
A_dict = C.A_dict
A_dict_inv = C.A_dict_inv
p = C.p
for w in Y:
C.scan_and_fill(0, w)
alpha = 0
while alpha < C.n:
if p[alpha] == alpha:
for w in R:
C.scan_and_fill(alpha, w)
# if α was eliminated during the scan then break
if p[alpha] < alpha:
break
if p[alpha] == alpha:
for x in A_dict:
if C.table[alpha][A_dict[x]] is None:
C.define(alpha, x)
alpha += 1
return C
# Pg. 166
# coset-table based method
def coset_enumeration_c(fp_grp, Y, max_cosets=None):
"""
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy.combinatorics.fp_groups import FpGroup, coset_enumeration_c
>>> F, x, y = free_group("x, y")
>>> f = FpGroup(F, [x**3, y**3, x**-1*y**-1*x*y])
>>> C = coset_enumeration_c(f, [x])
>>> C.table
[[0, 0, 1, 2], [1, 1, 2, 0], [2, 2, 0, 1]]
"""
# Initialize a coset table C for < X|R >
C = CosetTable(fp_grp, Y)
X = fp_grp.generators
R = fp_grp.relators
A = C.A
# replace all the elements by cyclic reductions
R_cyc_red = [rel.identity_cyclic_reduction() for rel in R]
R_c = list(chain.from_iterable((rel.cyclic_conjugates(), (rel**-1).cyclic_conjugates()) \
for rel in R_cyc_red))
R_set = set()
for conjugate in R_c:
R_set = R_set.union(conjugate)
# a list of subsets of R_c whose words start with "x".
R_c_list = []
for x in C.A:
r = set([word for word in R_set if word[0] == x])
R_c_list.append(r)
R_set.difference_update(r)
for w in Y:
C.scan_and_fill_c(0, w)
for x in A:
C.process_deductions(R_c_list[C.A_dict[x]], R_c_list[C.A_dict_inv[x]])
alpha = 0
while alpha < len(C.table):
if C.p[alpha] == alpha:
for x in C.A:
if C.p[alpha] != alpha:
break
if C.table[alpha][C.A_dict[x]] is None:
C.define_c(alpha, x)
C.process_deductions(R_c_list[C.A_dict[x]], R_c_list[C.A_dict_inv[x]])
alpha += 1
return C
###############################################################################
# LOW INDEX SUBGROUPS #
###############################################################################
def low_index_subgroups(G, N, Y=[]):
"""
Implements the Low Index Subgroups algorithm, i.e find all subgroups of
``G`` upto a given index ``N``. This implements the method described in
[Sim94]. This procedure involves a backtrack search over incomplete Coset
Tables, rather than over forced coincidences.
G: An FpGroup < X|R >
N: positive integer, representing the maximun index value for subgroups
Y: (an optional argument) specifying a list of subgroup generators, such
that each of the resulting subgroup contains the subgroup generated by Y.
References
==========
[1] Holt, D., Eick, B., O'Brien, E.
"Handbook of Computational Group Theory"
Section 5.4
[2] Marston Conder and Peter Dobcsanyi
"Applications and Adaptions of the Low Index Subgroups Procedure"
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy.combinatorics.fp_groups import FpGroup, low_index_subgroups
>>> F, x, y = free_group("x, y")
>>> f = FpGroup(F, [x**2, y**3, (x*y)**4])
>>> L = low_index_subgroups(f, 4)
>>> for coset_table in L:
... print(coset_table.table)
[[0, 0, 0, 0]]
[[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 3, 3]]
[[0, 0, 1, 2], [2, 2, 2, 0], [1, 1, 0, 1]]
[[1, 1, 0, 0], [0, 0, 1, 1]]
"""
C = CosetTable(G, [])
R = G.relators
# length chosen for the length of the short relators
len_short_rel = 5
# elements of R2 only checked at the last step for complete
# coset tables
R2 = set([rel for rel in R if len(rel) > len_short_rel])
# elements of R1 are used in inner parts of the process to prune
# branches of the search tree,
R1 = set([rel.identity_cyclic_reduction() for rel in set(R) - R2])
R1_c_list = C.conjugates(R1)
S = []
descendant_subgroups(S, C, R1_c_list, C.A[0], R2, N, Y)
return S
def descendant_subgroups(S, C, R1_c_list, x, R2, N, Y):
A_dict = C.A_dict
A_dict_inv = C.A_dict_inv
if C.is_complete():
# if C is complete then it only needs to test
# whether the relators in R2 are satisfied
for w, alpha in product(R2, C.omega):
if not C.scan_check(alpha, w):
return
# relators in R2 are satisfied, append the table to list
S.append(C)
else:
# find the first undefined entry in Coset Table
for alpha, x in product(range(len(C.table)), C.A):
if C.table[alpha][A_dict[x]] is None:
# this is "x" in pseudo-code (using "y" makes it clear)
undefined_coset, undefined_gen = alpha, x
break
# for filling up the undefine entry we try all possible values
# of β ∈ Ω or β = n where β^(undefined_gen^-1) is undefined
reach = C.omega + [C.n]
for beta in reach:
if beta < N:
if beta == C.n or C.table[beta][A_dict_inv[undefined_gen]] is None:
try_descendant(S, C, R1_c_list, R2, N, undefined_coset, \
undefined_gen, beta, Y)
def try_descendant(S, C, R1_c_list, R2, N, alpha, x, beta, Y):
r"""
Solves the problem of trying out each individual possibility
for `\alpha^x.
"""
D = C.copy()
A_dict = D.A_dict
if beta == D.n and beta < N:
D.table.append([None]*len(D.A))
D.p.append(beta)
D.table[alpha][D.A_dict[x]] = beta
D.table[beta][D.A_dict_inv[x]] = alpha
D.deduction_stack.append((alpha, x))
if not D.process_deductions_check(R1_c_list[D.A_dict[x]], \
R1_c_list[D.A_dict_inv[x]]):
return
for w in Y:
if not D.scan_check(0, w):
return
if first_in_class(D, Y):
descendant_subgroups(S, D, R1_c_list, x, R2, N, Y)
def first_in_class(C, Y=[]):
"""
Checks whether the subgroup ``H=G1`` corresponding to the Coset Table
could possibly be the canonical representative of its conjugacy class.
Parameters
==========
C: CosetTable
Returns
=======
bool: True/False
If this returns False, then no descendant of C can have that property, and
so we can abandon C. If it returns True, then we need to process further
the node of the search tree corresponding to C, and so we call
``descendant_subgroups`` recursively on C.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy.combinatorics.fp_groups import FpGroup, CosetTable, first_in_class
>>> F, x, y = free_group("x, y")
>>> f = FpGroup(F, [x**2, y**3, (x*y)**4])
>>> C = CosetTable(f, [])
>>> C.table = [[0, 0, None, None]]
>>> first_in_class(C)
True
>>> C.table = [[1, 1, 1, None], [0, 0, None, 1]]; C.p = [0, 1]
>>> first_in_class(C)
True
>>> C.table = [[1, 1, 2, 1], [0, 0, 0, None], [None, None, None, 0]]
>>> C.p = [0, 1, 2]
>>> first_in_class(C)
False
>>> C.table = [[1, 1, 1, 2], [0, 0, 2, 0], [2, None, 0, 1]]
>>> first_in_class(C)
False
# TODO:: Sims points out in [Sim94] that performance can be improved by
# remembering some of the information computed by ``first_in_class``. If
# the ``continue α`` statement is executed at line 14, then the same thing
# will happen for that value of α in any descendant of the table C, and so
# the values the values of α for which this occurs could profitably be
# stored and passed through to the descendants of C. Of course this would
# make the code more complicated.
# The code below is taken directly from the function on page 208 of [Sim94]
# ν[α]
"""
n = C.n
# lamda is the largest numbered point in Ω_c_α which is currently defined
lamda = -1
# for α ∈ Ω_c, ν[α] is the point in Ω_c_α corresponding to α
nu = [None]*n
# for α ∈ Ω_c_α, μ[α] is the point in Ω_c corresponding to α
mu = [None]*n
# mutually ν and μ are the mutually-inverse equivalence maps between
# Ω_c_α and Ω_c
next_alpha = False
# For each 0≠α ∈ [0 .. nc-1], we start by constructing the equivalent
# standardized coset table C_α corresponding to H_α
for alpha in range(1, n):
# reset ν to "None" after previous value of α
for beta in range(lamda+1):
nu[mu[beta]] = None
# we only want to reject our current table in favour of a preceding
# table in the ordering in which 1 is replaced by α, if the subgroup
# G_α corresponding to this preceding table definitely contains the
# given subgroup
for w in Y:
# TODO: this should support input of a list of general words
# not just the words which are in "A" (i.e gen and gen^-1)
if C.table[alpha][C.A_dict[w]] != alpha:
# continue with α
next_alpha = True
break
if next_alpha:
next_alpha = False
continue
# try α as the new point 0 in Ω_C_α
mu[0] = alpha
nu[alpha] = 0
# compare corresponding entries in C and C_α
lamda = 0
for beta in range(n):
for x in C.A:
gamma = C.table[beta][C.A_dict[x]]
delta = C.table[mu[beta]][C.A_dict[x]]
# if either of the entries is undefined,
# we move with next α
if gamma is None or delta is None:
# continue with α
next_alpha = True
break
if nu[delta] is None:
# delta becomes the next point in Ω_C_α
lamda += 1
nu[delta] = lamda
mu[lamda] = delta
if nu[delta] < gamma:
return False
if nu[delta] > gamma:
# continue with α
next_alpha = True
break
if next_alpha:
next_alpha = False
break
return True
###############################################################################
# SUBGROUP PRESENTATIONS #
###############################################################################
# Pg 175 [1]
def define_schreier_generators(C):
y = []
gamma = 1
f = C.fp_group
X = f.generators
C.P = [[None]*len(C.A) for i in range(C.n)]
for alpha, x in product(C.omega, C.A):
beta = C.table[alpha][C.A_dict[x]]
if beta == gamma:
C.P[alpha][C.A_dict[x]] = "<identity>"
C.P[beta][C.A_dict_inv[x]] = "<identity>"
gamma += 1
elif x in X and C.P[alpha][C.A_dict[x]] is None:
y_alpha_x = '%s_%s' % (x, alpha)
y.append(y_alpha_x)
C.P[alpha][C.A_dict[x]] = y_alpha_x
grp_gens = list(free_group(', '.join(y)))
C._schreier_free_group = grp_gens.pop(0)
C._schreier_generators = grp_gens
# replace all elements of P by, free group elements
for i, j in product(range(len(C.P)), range(len(C.A))):
# if equals "<identity>", replace by identity element
if C.P[i][j] == "<identity>":
C.P[i][j] = C._schreier_free_group.identity
elif isinstance(C.P[i][j], str):
r = C._schreier_generators[y.index(C.P[i][j])]
C.P[i][j] = r
beta = C.table[i][j]
C.P[beta][j + 1] = r**-1
def reidemeister_relators(C):
R = C.fp_group.relators
rels = [rewrite(C, coset, word) for word in R for coset in range(C.n)]
identity = C._schreier_free_group.identity
order_1_gens = set([i for i in rels if len(i) == 1])
# remove all the order 1 generators from relators
rels = list(filter(lambda rel: rel not in order_1_gens, rels))
# replace order 1 generators by identity element in reidemeister relators
for i in range(len(rels)):
w = rels[i]
for gen in order_1_gens:
w = w.eliminate_word(gen, identity)
rels[i] = w
C._schreier_generators = [i for i in C._schreier_generators if i not in order_1_gens]
# Tietze transformation 1 i.e TT_1
# remove cyclic conjugate elements from relators
i = 0
while i < len(rels):
w = rels[i]
j = i + 1
while j < len(rels):
if w.is_cyclic_conjugate(rels[j]):
del rels[j]
else:
j += 1
i += 1
C._reidemeister_relators = rels
def rewrite(C, alpha, w):
"""
Parameters
----------
C: CosetTable
α: A live coset
w: A word in `A*`
Returns
-------
ρ(τ(α), w)
Examples
========
>>> from sympy.combinatorics.fp_groups import FpGroup, CosetTable, define_schreier_generators, rewrite
>>> from sympy.combinatorics.free_groups import free_group
>>> F, x, y = free_group("x ,y")
>>> f = FpGroup(F, [x**2, y**3, (x*y)**6])
>>> C = CosetTable(f, [])
>>> C.table = [[1, 1, 2, 3], [0, 0, 4, 5], [4, 4, 3, 0], [5, 5, 0, 2], [2, 2, 5, 1], [3, 3, 1, 4]]
>>> C.p = [0, 1, 2, 3, 4, 5]
>>> define_schreier_generators(C)
>>> rewrite(C, 0, (x*y)**6)
x_4*y_2*x_3*x_1*x_2*y_4*x_5
"""
v = C._schreier_free_group.identity
for i in range(len(w)):
x_i = w[i]
v = v*C.P[alpha][C.A_dict[x_i]]
alpha = C.table[alpha][C.A_dict[x_i]]
return v
# Pg 350, section 2.5.1 from [2]
def elimination_technique_1(C):
rels = C._reidemeister_relators
# the shorter relators are examined first so that generators selected for
# elimination will have shorter strings as equivalent
rels.sort()
gens = C._schreier_generators
redundant_gens = {}
redundant_rels = []
used_gens = set()
# examine each relator in relator list for any generator occuring exactly
# once
for rel in rels:
# don't look for a redundant generator in a relator which
# depends on previously found ones
contained_gens = rel.contains_generators()
if any([g in contained_gens for g in redundant_gens]):
continue
contained_gens = list(contained_gens)
contained_gens.sort(reverse = True)
for gen in contained_gens:
if rel.generator_count(gen) == 1 and gen not in used_gens:
k = rel.exponent_sum(gen)
gen_index = rel.index(gen**k)
bk = rel.subword(gen_index + 1, len(rel))
fw = rel.subword(0, gen_index)
chi = (bk*fw).identity_cyclic_reduction()
redundant_gens[gen] = chi**(-1*k)
used_gens.update(chi.contains_generators())
redundant_rels.append(rel)
break
rels = [r for r in rels if r not in redundant_rels]
# eliminate the redundant generators from remaining relators
rels = [r.eliminate_words(redundant_gens, _all = True).identity_cyclic_reduction() for r in rels]
rels = list(set(rels))
try:
rels.remove(C._schreier_free_group.identity)
except ValueError:
pass
gens = [g for g in gens if g not in redundant_gens]
C._reidemeister_relators = rels
C._schreier_generators = gens
# Pg 350, section 2.5.2 from [2]
def elimination_technique_2(C):
"""
This technique eliminates one generator at a time. Heuristically this
seems superior in that we may select for elimination the generator with
shortest equivalent string at each stage.
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy.combinatorics.fp_groups import FpGroup, coset_enumeration_r, \
reidemeister_relators, define_schreier_generators, elimination_technique_2
>>> F, x, y = free_group("x, y")
>>> f = FpGroup(F, [x**3, y**5, (x*y)**2]); H = [x*y, x**-1*y**-1*x*y*x]
>>> C = coset_enumeration_r(f, H)
>>> C.compress(); C.standardize()
>>> define_schreier_generators(C)
>>> reidemeister_relators(C)
>>> elimination_technique_2(C)
([y_1, y_2], [y_2**-3, y_2*y_1*y_2*y_1*y_2*y_1, y_1**2])
"""
rels = C._reidemeister_relators
rels.sort(reverse=True)
gens = C._schreier_generators
for i in range(len(gens) - 1, -1, -1):
rel = rels[i]
for j in range(len(gens) - 1, -1, -1):
gen = gens[j]
if rel.generator_count(gen) == 1:
k = rel.exponent_sum(gen)
gen_index = rel.index(gen**k)
bk = rel.subword(gen_index + 1, len(rel))
fw = rel.subword(0, gen_index)
rep_by = (bk*fw)**(-1*k)
del rels[i]; del gens[j]
for l in range(len(rels)):
rels[l] = rels[l].eliminate_word(gen, rep_by)
break
C._reidemeister_relators = rels
C._schreier_generators = gens
return C._schreier_generators, C._reidemeister_relators
def simplify_presentation(C):
"""Relies upon ``_simplification_technique_1`` for its functioning. """
rels = C._reidemeister_relators
group = C._schreier_free_group
rels = list(set(_simplification_technique_1(rels)))
rels.sort()
rels = [r.identity_cyclic_reduction() for r in rels]
try:
rels.remove(C._schreier_free_group.identity)
except ValueError:
pass
C._reidemeister_relators = rels
def _simplification_technique_1(rels):
"""
All relators are checked to see if they are of the form `gen^n`. If any
such relators are found then all other relators are processed for strings
in the `gen` known order.
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy.combinatorics.fp_groups import _simplification_technique_1
>>> F, x, y = free_group("x, y")
>>> w1 = [x**2*y**4, x**3]
>>> _simplification_technique_1(w1)
[x**-1*y**4, x**3]
>>> w2 = [x**2*y**-4*x**5, x**3, x**2*y**8, y**5]
>>> _simplification_technique_1(w2)
[x**-1*y*x**-1, x**3, x**-1*y**-2, y**5]
>>> w3 = [x**6*y**4, x**4]
>>> _simplification_technique_1(w3)
[x**2*y**4, x**4]
"""
from sympy import gcd
rels = rels[:]
# dictionary with "gen: n" where gen^n is one of the relators
exps = {}
for i in range(len(rels)):
rel = rels[i]
if rel.number_syllables() == 1:
g = rel[0]
exp = abs(rel.array_form[0][1])
if rel.array_form[0][1] < 0:
rels[i] = rels[i]**-1
g = g**-1
if g in exps:
exp = gcd(exp, exps[g].array_form[0][1])
exps[g] = g**exp
one_syllables_words = exps.values()
# decrease some of the exponents in relators, making use of the single
# syllable relators
for i in range(len(rels)):
rel = rels[i]
if rel in one_syllables_words:
continue
rel = rel.eliminate_words(one_syllables_words, _all = True)
# if rels[i] contains g**n where abs(n) is greater than half of the power p
# of g in exps, g**n can be replaced by g**(n-p) (or g**(p-n) if n<0)
for g in rel.contains_generators():
if g in exps:
exp = exps[g].array_form[0][1]
max_exp = (exp + 1)//2
rel = rel.eliminate_word(g**(max_exp), g**(max_exp-exp), _all = True)
rel = rel.eliminate_word(g**(-max_exp), g**(-(max_exp-exp)), _all = True)
rels[i] = rel
rels = [r.identity_cyclic_reduction() for r in rels]
return rels
def reidemeister_presentation(fp_grp, H, C=None):
"""
fp_group: A finitely presented group, an instance of FpGroup
H: A subgroup whose presentation is to be found, given as a list
of words in generators of `fp_grp`
Examples
========
>>> from sympy.combinatorics.free_groups import free_group
>>> from sympy.combinatorics.fp_groups import FpGroup, reidemeister_presentation
>>> F, x, y = free_group("x, y")
Example 5.6 Pg. 177 from [1]
>>> f = FpGroup(F, [x**3, y**5, (x*y)**2])
>>> H = [x*y, x**-1*y**-1*x*y*x]
>>> reidemeister_presentation(f, H)
((y_1, y_2), (y_1**2, y_2**3, y_2*y_1*y_2*y_1*y_2*y_1))
Example 5.8 Pg. 183 from [1]
>>> f = FpGroup(F, [x**3, y**3, (x*y)**3])
>>> H = [x*y, x*y**-1]
>>> reidemeister_presentation(f, H)
((x_0, y_0), (x_0**3, y_0**3, x_0*y_0*x_0*y_0*x_0*y_0))
Exercises Q2. Pg 187 from [1]
>>> f = FpGroup(F, [x**2*y**2, y**-1*x*y*x**-3])
>>> H = [x]
>>> reidemeister_presentation(f, H)
((x_0,), (x_0**4,))
Example 5.9 Pg. 183 from [1]
>>> f = FpGroup(F, [x**3*y**-3, (x*y)**3, (x*y**-1)**2])
>>> H = [x]
>>> reidemeister_presentation(f, H)
((x_0,), (x_0**6,))
"""
if not C:
C = coset_enumeration_r(fp_grp, H)
C.compress(); C.standardize()
define_schreier_generators(C)
reidemeister_relators(C)
prev_gens = []
prev_rels = []
while not set(prev_rels) == set(C._reidemeister_relators):
prev_rels = C._reidemeister_relators
while not set(prev_gens) == set(C._schreier_generators):
prev_gens = C._schreier_generators
elimination_technique_1(C)
simplify_presentation(C)
syms = [g.array_form[0][0] for g in C._schreier_generators]
g = free_group(syms)[0]
subs = dict(zip(syms,g.generators))
C._schreier_generators = g.generators
for j in range(len(C._reidemeister_relators)):
r = C._reidemeister_relators[j]
a = r.array_form
rel = g.identity
for i in range(len(a)):
rel = rel*subs[a[i][0]]**a[i][1]
C._reidemeister_relators[j] = rel
C.schreier_generators = tuple(C._schreier_generators)
C.reidemeister_relators = tuple(C._reidemeister_relators)
return C.schreier_generators, C.reidemeister_relators
FpGroupElement = FreeGroupElement
| 63,977 | 34.094899 | 106 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/group_constructs.py
|
from __future__ import print_function, division
from sympy.combinatorics.perm_groups import PermutationGroup
from sympy.combinatorics.permutations import Permutation
from sympy.utilities.iterables import uniq
from sympy.core.compatibility import range
_af_new = Permutation._af_new
def DirectProduct(*groups):
"""
Returns the direct product of several groups as a permutation group.
This is implemented much like the __mul__ procedure for taking the direct
product of two permutation groups, but the idea of shifting the
generators is realized in the case of an arbitrary number of groups.
A call to DirectProduct(G1, G2, ..., Gn) is generally expected to be faster
than a call to G1*G2*...*Gn (and thus the need for this algorithm).
Examples
========
>>> from sympy.combinatorics.group_constructs import DirectProduct
>>> from sympy.combinatorics.named_groups import CyclicGroup
>>> C = CyclicGroup(4)
>>> G = DirectProduct(C, C, C)
>>> G.order()
64
See Also
========
__mul__
"""
degrees = []
gens_count = []
total_degree = 0
total_gens = 0
for group in groups:
current_deg = group.degree
current_num_gens = len(group.generators)
degrees.append(current_deg)
total_degree += current_deg
gens_count.append(current_num_gens)
total_gens += current_num_gens
array_gens = []
for i in range(total_gens):
array_gens.append(list(range(total_degree)))
current_gen = 0
current_deg = 0
for i in range(len(gens_count)):
for j in range(current_gen, current_gen + gens_count[i]):
gen = ((groups[i].generators)[j - current_gen]).array_form
array_gens[j][current_deg:current_deg + degrees[i]] = \
[x + current_deg for x in gen]
current_gen += gens_count[i]
current_deg += degrees[i]
perm_gens = list(uniq([_af_new(list(a)) for a in array_gens]))
return PermutationGroup(perm_gens, dups=False)
| 2,030 | 32.295082 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/prufer.py
|
from __future__ import print_function, division
from sympy.core import Basic
from sympy.core.compatibility import iterable, as_int, range
from sympy.utilities.iterables import flatten
from collections import defaultdict
class Prufer(Basic):
"""
The Prufer correspondence is an algorithm that describes the
bijection between labeled trees and the Prufer code. A Prufer
code of a labeled tree is unique up to isomorphism and has
a length of n - 2.
Prufer sequences were first used by Heinz Prufer to give a
proof of Cayley's formula.
References
==========
.. [1] http://mathworld.wolfram.com/LabeledTree.html
"""
_prufer_repr = None
_tree_repr = None
_nodes = None
_rank = None
@property
def prufer_repr(self):
"""Returns Prufer sequence for the Prufer object.
This sequence is found by removing the highest numbered vertex,
recording the node it was attached to, and continuing until only
two vertices remain. The Prufer sequence is the list of recorded nodes.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> Prufer([[0, 3], [1, 3], [2, 3], [3, 4], [4, 5]]).prufer_repr
[3, 3, 3, 4]
>>> Prufer([1, 0, 0]).prufer_repr
[1, 0, 0]
See Also
========
to_prufer
"""
if self._prufer_repr is None:
self._prufer_repr = self.to_prufer(self._tree_repr[:], self.nodes)
return self._prufer_repr
@property
def tree_repr(self):
"""Returns the tree representation of the Prufer object.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> Prufer([[0, 3], [1, 3], [2, 3], [3, 4], [4, 5]]).tree_repr
[[0, 3], [1, 3], [2, 3], [3, 4], [4, 5]]
>>> Prufer([1, 0, 0]).tree_repr
[[1, 2], [0, 1], [0, 3], [0, 4]]
See Also
========
to_tree
"""
if self._tree_repr is None:
self._tree_repr = self.to_tree(self._prufer_repr[:])
return self._tree_repr
@property
def nodes(self):
"""Returns the number of nodes in the tree.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> Prufer([[0, 3], [1, 3], [2, 3], [3, 4], [4, 5]]).nodes
6
>>> Prufer([1, 0, 0]).nodes
5
"""
return self._nodes
@property
def rank(self):
"""Returns the rank of the Prufer sequence.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> p = Prufer([[0, 3], [1, 3], [2, 3], [3, 4], [4, 5]])
>>> p.rank
778
>>> p.next(1).rank
779
>>> p.prev().rank
777
See Also
========
prufer_rank, next, prev, size
"""
if self._rank is None:
self._rank = self.prufer_rank()
return self._rank
@property
def size(self):
"""Return the number of possible trees of this Prufer object.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> Prufer([0]*4).size == Prufer([6]*4).size == 1296
True
See Also
========
prufer_rank, rank, next, prev
"""
return self.prev(self.rank).prev().rank + 1
@staticmethod
def to_prufer(tree, n):
"""Return the Prufer sequence for a tree given as a list of edges where
``n`` is the number of nodes in the tree.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> a = Prufer([[0, 1], [0, 2], [0, 3]])
>>> a.prufer_repr
[0, 0]
>>> Prufer.to_prufer([[0, 1], [0, 2], [0, 3]], 4)
[0, 0]
See Also
========
prufer_repr: returns Prufer sequence of a Prufer object.
"""
d = defaultdict(int)
L = []
for edge in tree:
# Increment the value of the corresponding
# node in the degree list as we encounter an
# edge involving it.
d[edge[0]] += 1
d[edge[1]] += 1
for i in range(n - 2):
# find the smallest leaf
for x in range(n):
if d[x] == 1:
break
# find the node it was connected to
y = None
for edge in tree:
if x == edge[0]:
y = edge[1]
elif x == edge[1]:
y = edge[0]
if y is not None:
break
# record and update
L.append(y)
for j in (x, y):
d[j] -= 1
if not d[j]:
d.pop(j)
tree.remove(edge)
return L
@staticmethod
def to_tree(prufer):
"""Return the tree (as a list of edges) of the given Prufer sequence.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> a = Prufer([0, 2], 4)
>>> a.tree_repr
[[0, 1], [0, 2], [2, 3]]
>>> Prufer.to_tree([0, 2])
[[0, 1], [0, 2], [2, 3]]
References
==========
- https://hamberg.no/erlend/posts/2010-11-06-prufer-sequence-compact-tree-representation.html
See Also
========
tree_repr: returns tree representation of a Prufer object.
"""
tree = []
last = []
n = len(prufer) + 2
d = defaultdict(lambda: 1)
for p in prufer:
d[p] += 1
for i in prufer:
for j in range(n):
# find the smallest leaf (degree = 1)
if d[j] == 1:
break
# (i, j) is the new edge that we append to the tree
# and remove from the degree dictionary
d[i] -= 1
d[j] -= 1
tree.append(sorted([i, j]))
last = [i for i in range(n) if d[i] == 1] or [0, 1]
tree.append(last)
return tree
@staticmethod
def edges(*runs):
"""Return a list of edges and the number of nodes from the given runs
that connect nodes in an integer-labelled tree.
All node numbers will be shifted so that the minimum node is 0. It is
not a problem if edges are repeated in the runs; only unique edges are
returned. There is no assumption made about what the range of the node
labels should be, but all nodes from the smallest through the largest
must be present.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> Prufer.edges([1, 2, 3], [2, 4, 5]) # a T
([[0, 1], [1, 2], [1, 3], [3, 4]], 5)
Duplicate edges are removed:
>>> Prufer.edges([0, 1, 2, 3], [1, 4, 5], [1, 4, 6]) # a K
([[0, 1], [1, 2], [1, 4], [2, 3], [4, 5], [4, 6]], 7)
"""
e = set()
nmin = runs[0][0]
for r in runs:
for i in range(len(r) - 1):
a, b = r[i: i + 2]
if b < a:
a, b = b, a
e.add((a, b))
rv = []
got = set()
nmin = nmax = None
for ei in e:
for i in ei:
got.add(i)
nmin = min(ei[0], nmin) if nmin is not None else ei[0]
nmax = max(ei[1], nmax) if nmax is not None else ei[1]
rv.append(list(ei))
missing = set(range(nmin, nmax + 1)) - got
if missing:
missing = [i + nmin for i in missing]
if len(missing) == 1:
msg = 'Node %s is missing.' % missing.pop()
else:
msg = 'Nodes %s are missing.' % list(sorted(missing))
raise ValueError(msg)
if nmin != 0:
for i, ei in enumerate(rv):
rv[i] = [n - nmin for n in ei]
nmax -= nmin
return sorted(rv), nmax + 1
def prufer_rank(self):
"""Computes the rank of a Prufer sequence.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> a = Prufer([[0, 1], [0, 2], [0, 3]])
>>> a.prufer_rank()
0
See Also
========
rank, next, prev, size
"""
r = 0
p = 1
for i in range(self.nodes - 3, -1, -1):
r += p*self.prufer_repr[i]
p *= self.nodes
return r
@classmethod
def unrank(self, rank, n):
"""Finds the unranked Prufer sequence.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> Prufer.unrank(0, 4)
Prufer([0, 0])
"""
n, rank = as_int(n), as_int(rank)
L = defaultdict(int)
for i in range(n - 3, -1, -1):
L[i] = rank % n
rank = (rank - L[i])//n
return Prufer([L[i] for i in range(len(L))])
def __new__(cls, *args, **kw_args):
"""The constructor for the Prufer object.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
A Prufer object can be constructed from a list of edges:
>>> a = Prufer([[0, 1], [0, 2], [0, 3]])
>>> a.prufer_repr
[0, 0]
If the number of nodes is given, no checking of the nodes will
be performed; it will be assumed that nodes 0 through n - 1 are
present:
>>> Prufer([[0, 1], [0, 2], [0, 3]], 4)
Prufer([[0, 1], [0, 2], [0, 3]], 4)
A Prufer object can be constructed from a Prufer sequence:
>>> b = Prufer([1, 3])
>>> b.tree_repr
[[0, 1], [1, 3], [2, 3]]
"""
ret_obj = Basic.__new__(cls, *args, **kw_args)
args = [list(args[0])]
if args[0] and iterable(args[0][0]):
if not args[0][0]:
raise ValueError(
'Prufer expects at least one edge in the tree.')
if len(args) > 1:
nnodes = args[1]
else:
nodes = set(flatten(args[0]))
nnodes = max(nodes) + 1
if nnodes != len(nodes):
missing = set(range(nnodes)) - nodes
if len(missing) == 1:
msg = 'Node %s is missing.' % missing.pop()
else:
msg = 'Nodes %s are missing.' % list(sorted(missing))
raise ValueError(msg)
ret_obj._tree_repr = [list(i) for i in args[0]]
ret_obj._nodes = nnodes
else:
ret_obj._prufer_repr = args[0]
ret_obj._nodes = len(ret_obj._prufer_repr) + 2
return ret_obj
def next(self, delta=1):
"""Generates the Prufer sequence that is delta beyond the current one.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> a = Prufer([[0, 1], [0, 2], [0, 3]])
>>> b = a.next(1) # == a.next()
>>> b.tree_repr
[[0, 2], [0, 1], [1, 3]]
>>> b.rank
1
See Also
========
prufer_rank, rank, prev, size
"""
return Prufer.unrank(self.rank + delta, self.nodes)
def prev(self, delta=1):
"""Generates the Prufer sequence that is -delta before the current one.
Examples
========
>>> from sympy.combinatorics.prufer import Prufer
>>> a = Prufer([[0, 1], [1, 2], [2, 3], [1, 4]])
>>> a.rank
36
>>> b = a.prev()
>>> b
Prufer([1, 2, 0])
>>> b.rank
35
See Also
========
prufer_rank, rank, next, size
"""
return Prufer.unrank(self.rank -delta, self.nodes)
| 11,915 | 26.456221 | 101 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_partitions.py
|
from sympy.core.compatibility import range
from sympy.combinatorics.partitions import (Partition, IntegerPartition,
RGS_enum, RGS_unrank, RGS_rank,
random_integer_partition)
from sympy.utilities.pytest import raises
from sympy.utilities.iterables import default_sort_key, partitions
def test_partition():
from sympy.abc import x
raises(ValueError, lambda: Partition(*list(range(3))))
raises(ValueError, lambda: Partition([1, 1, 2]))
a = Partition([1, 2, 3], [4])
b = Partition([1, 2], [3, 4])
c = Partition([x])
l = [a, b, c]
l.sort(key=default_sort_key)
assert l == [c, a, b]
l.sort(key=lambda w: default_sort_key(w, order='rev-lex'))
assert l == [c, a, b]
assert (a == b) is False
assert a <= b
assert (a > b) is False
assert a != b
assert (a + 2).partition == [[1, 2], [3, 4]]
assert (b - 1).partition == [[1, 2, 4], [3]]
assert (a - 1).partition == [[1, 2, 3, 4]]
assert (a + 1).partition == [[1, 2, 4], [3]]
assert (b + 1).partition == [[1, 2], [3], [4]]
assert a.rank == 1
assert b.rank == 3
assert a.RGS == (0, 0, 0, 1)
assert b.RGS == (0, 0, 1, 1)
def test_integer_partition():
# no zeros in partition
raises(ValueError, lambda: IntegerPartition(list(range(3))))
# check fails since 1 + 2 != 100
raises(ValueError, lambda: IntegerPartition(100, list(range(1, 3))))
a = IntegerPartition(8, [1, 3, 4])
b = a.next_lex()
c = IntegerPartition([1, 3, 4])
d = IntegerPartition(8, {1: 3, 3: 1, 2: 1})
assert a == c
assert a.integer == d.integer
assert a.conjugate == [3, 2, 2, 1]
assert (a == b) is False
assert a <= b
assert (a > b) is False
assert a != b
for i in range(1, 11):
next = set()
prev = set()
a = IntegerPartition([i])
ans = {IntegerPartition(p) for p in partitions(i)}
n = len(ans)
for j in range(n):
next.add(a)
a = a.next_lex()
IntegerPartition(i, a.partition) # check it by giving i
for j in range(n):
prev.add(a)
a = a.prev_lex()
IntegerPartition(i, a.partition) # check it by giving i
assert next == ans
assert prev == ans
assert IntegerPartition([1, 2, 3]).as_ferrers() == '###\n##\n#'
assert IntegerPartition([1, 1, 3]).as_ferrers('o') == 'ooo\no\no'
assert str(IntegerPartition([1, 1, 3])) == '[3, 1, 1]'
assert IntegerPartition([1, 1, 3]).partition == [3, 1, 1]
raises(ValueError, lambda: random_integer_partition(-1))
assert random_integer_partition(1) == [1]
assert random_integer_partition(10, seed=[1, 3, 2, 1, 5, 1]
) == [5, 2, 1, 1, 1]
def test_rgs():
raises(ValueError, lambda: RGS_unrank(-1, 3))
raises(ValueError, lambda: RGS_unrank(3, 0))
raises(ValueError, lambda: RGS_unrank(10, 1))
raises(ValueError, lambda: Partition.from_rgs(list(range(3)), list(range(2))))
raises(ValueError, lambda: Partition.from_rgs(list(range(1, 3)), list(range(2))))
assert RGS_enum(-1) == 0
assert RGS_enum(1) == 1
assert RGS_unrank(7, 5) == [0, 0, 1, 0, 2]
assert RGS_unrank(23, 14) == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 2]
assert RGS_rank(RGS_unrank(40, 100)) == 40
| 3,377 | 32.78 | 85 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_tensor_can.py
|
from sympy.core.compatibility import range
from sympy.combinatorics.permutations import Permutation, Perm
from sympy.combinatorics.tensor_can import (perm_af_direct_product, dummy_sgs,
riemann_bsgs, get_symmetric_group_sgs, canonicalize, bsgs_direct_product)
from sympy.combinatorics.testutil import canonicalize_naive, graph_certificate
from sympy.utilities.pytest import skip, XFAIL
def test_perm_af_direct_product():
gens1 = [[1,0,2,3], [0,1,3,2]]
gens2 = [[1,0]]
assert perm_af_direct_product(gens1, gens2, 0) == [[1, 0, 2, 3, 4, 5], [0, 1, 3, 2, 4, 5], [0, 1, 2, 3, 5, 4]]
gens1 = [[1,0,2,3,5,4], [0,1,3,2,4,5]]
gens2 = [[1,0,2,3]]
assert [[1, 0, 2, 3, 4, 5, 7, 6], [0, 1, 3, 2, 4, 5, 6, 7], [0, 1, 2, 3, 5, 4, 6, 7]]
def test_dummy_sgs():
a = dummy_sgs([1,2], 0, 4)
assert a == [[0,2,1,3,4,5]]
a = dummy_sgs([2,3,4,5], 0, 8)
assert a == [x._array_form for x in [Perm(9)(2,3), Perm(9)(4,5),
Perm(9)(2,4)(3,5)]]
a = dummy_sgs([2,3,4,5], 1, 8)
assert a == [x._array_form for x in [Perm(2,3)(8,9), Perm(4,5)(8,9),
Perm(9)(2,4)(3,5)]]
def test_get_symmetric_group_sgs():
assert get_symmetric_group_sgs(2) == ([0], [Permutation(3)(0,1)])
assert get_symmetric_group_sgs(2, 1) == ([0], [Permutation(0,1)(2,3)])
assert get_symmetric_group_sgs(3) == ([0,1], [Permutation(4)(0,1), Permutation(4)(1,2)])
assert get_symmetric_group_sgs(3, 1) == ([0,1], [Permutation(0,1)(3,4), Permutation(1,2)(3,4)])
assert get_symmetric_group_sgs(4) == ([0,1,2], [Permutation(5)(0,1), Permutation(5)(1,2), Permutation(5)(2,3)])
assert get_symmetric_group_sgs(4, 1) == ([0,1,2], [Permutation(0,1)(4,5), Permutation(1,2)(4,5), Permutation(2,3)(4,5)])
def test_canonicalize_no_slot_sym():
# cases in which there is no slot symmetry after fixing the
# free indices; here and in the following if the symmetry of the
# metric is not specified, it is assumed to be symmetric.
# If it is not specified, tensors are commuting.
# A_d0 * B^d0; g = [1,0, 2,3]; T_c = A^d0*B_d0; can = [0,1,2,3]
base1, gens1 = get_symmetric_group_sgs(1)
dummies = [0, 1]
g = Permutation([1,0,2,3])
can = canonicalize(g, dummies, 0, (base1,gens1,1,0), (base1,gens1,1,0))
assert can == [0,1,2,3]
# equivalently
can = canonicalize(g, dummies, 0, (base1, gens1, 2, None))
assert can == [0,1,2,3]
# with antisymmetric metric; T_c = -A^d0*B_d0; can = [0,1,3,2]
can = canonicalize(g, dummies, 1, (base1,gens1,1,0), (base1,gens1,1,0))
assert can == [0,1,3,2]
# A^a * B^b; ord = [a,b]; g = [0,1,2,3]; can = g
g = Permutation([0,1,2,3])
dummies = []
t0 = t1 = (base1, gens1, 1, 0)
can = canonicalize(g, dummies, 0, t0, t1)
assert can == [0,1,2,3]
# B^b * A^a
g = Permutation([1,0,2,3])
can = canonicalize(g, dummies, 0, t0, t1)
assert can == [1,0,2,3]
# A symmetric
# A^{b}_{d0}*A^{d0, a} order a,b,d0,-d0; T_c = A^{a d0}*A{b}_{d0}
# g = [1,3,2,0,4,5]; can = [0,2,1,3,4,5]
base2, gens2 = get_symmetric_group_sgs(2)
dummies = [2,3]
g = Permutation([1,3,2,0,4,5])
can = canonicalize(g, dummies, 0, (base2, gens2, 2, 0))
assert can == [0, 2, 1, 3, 4, 5]
# with antisymmetric metric
can = canonicalize(g, dummies, 1, (base2, gens2, 2, 0))
assert can == [0, 2, 1, 3, 4, 5]
# A^{a}_{d0}*A^{d0, b}
g = Permutation([0,3,2,1,4,5])
can = canonicalize(g, dummies, 1, (base2, gens2, 2, 0))
assert can == [0, 2, 1, 3, 5, 4]
# A, B symmetric
# A^b_d0*B^{d0,a}; g=[1,3,2,0,4,5]
# T_c = A^{b,d0}*B_{a,d0}; can = [1,2,0,3,4,5]
dummies = [2,3]
g = Permutation([1,3,2,0,4,5])
can = canonicalize(g, dummies, 0, (base2,gens2,1,0), (base2,gens2,1,0))
assert can == [1,2,0,3,4,5]
# same with antisymmetric metric
can = canonicalize(g, dummies, 1, (base2,gens2,1,0), (base2,gens2,1,0))
assert can == [1,2,0,3,5,4]
# A^{d1}_{d0}*B^d0*C_d1 ord=[d0,-d0,d1,-d1]; g = [2,1,0,3,4,5]
# T_c = A^{d0 d1}*B_d0*C_d1; can = [0,2,1,3,4,5]
base1, gens1 = get_symmetric_group_sgs(1)
base2, gens2 = get_symmetric_group_sgs(2)
g = Permutation([2,1,0,3,4,5])
dummies = [0,1,2,3]
t0 = (base2, gens2, 1, 0)
t1 = t2 = (base1, gens1, 1, 0)
can = canonicalize(g, dummies, 0, t0, t1, t2)
assert can == [0, 2, 1, 3, 4, 5]
# A without symmetry
# A^{d1}_{d0}*B^d0*C_d1 ord=[d0,-d0,d1,-d1]; g = [2,1,0,3,4,5]
# T_c = A^{d0 d1}*B_d1*C_d0; can = [0,2,3,1,4,5]
g = Permutation([2,1,0,3,4,5])
dummies = [0,1,2,3]
t0 = ([], [Permutation(list(range(4)))], 1, 0)
can = canonicalize(g, dummies, 0, t0, t1, t2)
assert can == [0,2,3,1,4,5]
# A, B without symmetry
# A^{d1}_{d0}*B_{d1}^{d0}; g = [2,1,3,0,4,5]
# T_c = A^{d0 d1}*B_{d0 d1}; can = [0,2,1,3,4,5]
t0 = t1 = ([], [Permutation(list(range(4)))], 1, 0)
dummies = [0,1,2,3]
g = Permutation([2,1,3,0,4,5])
can = canonicalize(g, dummies, 0, t0, t1)
assert can == [0, 2, 1, 3, 4, 5]
# A_{d0}^{d1}*B_{d1}^{d0}; g = [1,2,3,0,4,5]
# T_c = A^{d0 d1}*B_{d1 d0}; can = [0,2,3,1,4,5]
g = Permutation([1,2,3,0,4,5])
can = canonicalize(g, dummies, 0, t0, t1)
assert can == [0,2,3,1,4,5]
# A, B, C without symmetry
# A^{d1 d0}*B_{a d0}*C_{d1 b} ord=[a,b,d0,-d0,d1,-d1]
# g=[4,2,0,3,5,1,6,7]
# T_c=A^{d0 d1}*B_{a d1}*C_{d0 b}; can = [2,4,0,5,3,1,6,7]
t0 = t1 = t2 = ([], [Permutation(list(range(4)))], 1, 0)
dummies = [2,3,4,5]
g = Permutation([4,2,0,3,5,1,6,7])
can = canonicalize(g, dummies, 0, t0, t1, t2)
assert can == [2,4,0,5,3,1,6,7]
# A symmetric, B and C without symmetry
# A^{d1 d0}*B_{a d0}*C_{d1 b} ord=[a,b,d0,-d0,d1,-d1]
# g=[4,2,0,3,5,1,6,7]
# T_c = A^{d0 d1}*B_{a d0}*C_{d1 b}; can = [2,4,0,3,5,1,6,7]
t0 = (base2,gens2,1,0)
t1 = t2 = ([], [Permutation(list(range(4)))], 1, 0)
dummies = [2,3,4,5]
g = Permutation([4,2,0,3,5,1,6,7])
can = canonicalize(g, dummies, 0, t0, t1, t2)
assert can == [2,4,0,3,5,1,6,7]
# A and C symmetric, B without symmetry
# A^{d1 d0}*B_{a d0}*C_{d1 b} ord=[a,b,d0,-d0,d1,-d1]
# g=[4,2,0,3,5,1,6,7]
# T_c = A^{d0 d1}*B_{a d0}*C_{b d1}; can = [2,4,0,3,1,5,6,7]
t0 = t2 = (base2,gens2,1,0)
t1 = ([], [Permutation(list(range(4)))], 1, 0)
dummies = [2,3,4,5]
g = Permutation([4,2,0,3,5,1,6,7])
can = canonicalize(g, dummies, 0, t0, t1, t2)
assert can == [2,4,0,3,1,5,6,7]
# A symmetric, B without symmetry, C antisymmetric
# A^{d1 d0}*B_{a d0}*C_{d1 b} ord=[a,b,d0,-d0,d1,-d1]
# g=[4,2,0,3,5,1,6,7]
# T_c = -A^{d0 d1}*B_{a d0}*C_{b d1}; can = [2,4,0,3,1,5,7,6]
t0 = (base2,gens2, 1, 0)
t1 = ([], [Permutation(list(range(4)))], 1, 0)
base2a, gens2a = get_symmetric_group_sgs(2, 1)
t2 = (base2a, gens2a, 1, 0)
dummies = [2,3,4,5]
g = Permutation([4,2,0,3,5,1,6,7])
can = canonicalize(g, dummies, 0, t0, t1, t2)
assert can == [2,4,0,3,1,5,7,6]
def test_canonicalize_no_dummies():
base1, gens1 = get_symmetric_group_sgs(1)
base2, gens2 = get_symmetric_group_sgs(2)
base2a, gens2a = get_symmetric_group_sgs(2, 1)
# A commuting
# A^c A^b A^a; ord = [a,b,c]; g = [2,1,0,3,4]
# T_c = A^a A^b A^c; can = list(range(5))
g = Permutation([2,1,0,3,4])
can = canonicalize(g, [], 0, (base1, gens1, 3, 0))
assert can == list(range(5))
# A anticommuting
# A^c A^b A^a; ord = [a,b,c]; g = [2,1,0,3,4]
# T_c = -A^a A^b A^c; can = [0,1,2,4,3]
g = Permutation([2,1,0,3,4])
can = canonicalize(g, [], 0, (base1, gens1, 3, 1))
assert can == [0,1,2,4,3]
# A commuting and symmetric
# A^{b,d}*A^{c,a}; ord = [a,b,c,d]; g = [1,3,2,0,4,5]
# T_c = A^{a c}*A^{b d}; can = [0,2,1,3,4,5]
g = Permutation([1,3,2,0,4,5])
can = canonicalize(g, [], 0, (base2, gens2, 2, 0))
assert can == [0,2,1,3,4,5]
# A anticommuting and symmetric
# A^{b,d}*A^{c,a}; ord = [a,b,c,d]; g = [1,3,2,0,4,5]
# T_c = -A^{a c}*A^{b d}; can = [0,2,1,3,5,4]
g = Permutation([1,3,2,0,4,5])
can = canonicalize(g, [], 0, (base2, gens2, 2, 1))
assert can == [0,2,1,3,5,4]
# A^{c,a}*A^{b,d} ; g = [2,0,1,3,4,5]
# T_c = A^{a c}*A^{b d}; can = [0,2,1,3,4,5]
g = Permutation([2,0,1,3,4,5])
can = canonicalize(g, [], 0, (base2, gens2, 2, 1))
assert can == [0,2,1,3,4,5]
def test_no_metric_symmetry():
# no metric symmetry
# A^d1_d0 * A^d0_d1; ord = [d0,-d0,d1,-d1]; g= [2,1,0,3,4,5]
# T_c = A^d0_d1 * A^d1_d0; can = [0,3,2,1,4,5]
g = Permutation([2,1,0,3,4,5])
can = canonicalize(g, list(range(4)), None, [[], [Permutation(list(range(4)))], 2, 0])
assert can == [0,3,2,1,4,5]
# A^d1_d2 * A^d0_d3 * A^d2_d1 * A^d3_d0
# ord = [d0,-d0,d1,-d1,d2,-d2,d3,-d3]
# 0 1 2 3 4 5 6 7
# g = [2,5,0,7,4,3,6,1,8,9]
# T_c = A^d0_d1 * A^d1_d0 * A^d2_d3 * A^d3_d2
# can = [0,3,2,1,4,7,6,5,8,9]
g = Permutation([2,5,0,7,4,3,6,1,8,9])
#can = canonicalize(g, list(range(8)), 0, [[], [list(range(4))], 4, 0])
#assert can == [0, 2, 3, 1, 4, 6, 7, 5, 8, 9]
can = canonicalize(g, list(range(8)), None, [[], [Permutation(list(range(4)))], 4, 0])
assert can == [0, 3, 2, 1, 4, 7, 6, 5, 8, 9]
# A^d0_d2 * A^d1_d3 * A^d3_d0 * A^d2_d1
# g = [0,5,2,7,6,1,4,3,8,9]
# T_c = A^d0_d1 * A^d1_d2 * A^d2_d3 * A^d3_d0
# can = [0,3,2,5,4,7,6,1,8,9]
g = Permutation([0,5,2,7,6,1,4,3,8,9])
can = canonicalize(g, list(range(8)), None, [[], [Permutation(list(range(4)))], 4, 0])
assert can == [0,3,2,5,4,7,6,1,8,9]
g = Permutation([12,7,10,3,14,13,4,11,6,1,2,9,0,15,8,5,16,17])
can = canonicalize(g, list(range(16)), None, [[], [Permutation(list(range(4)))], 8, 0])
assert can == [0,3,2,5,4,7,6,1,8,11,10,13,12,15,14,9,16,17]
def test_canonical_free():
# t = A^{d0 a1}*A_d0^a0
# ord = [a0,a1,d0,-d0]; g = [2,1,3,0,4,5]; dummies = [[2,3]]
# t_c = A_d0^a0*A^{d0 a1}
# can = [3,0, 2,1, 4,5]
base = [0]
gens = [Permutation(5)(0,2)(1,3)]
g = Permutation([2,1,3,0,4,5])
num_free = 2
dummies = [[2,3]]
can = canonicalize(g, dummies, [None], ([], [Permutation(3)], 2, 0))
assert can == [3,0, 2,1, 4,5]
def test_canonicalize1():
base1, gens1 = get_symmetric_group_sgs(1)
base1a, gens1a = get_symmetric_group_sgs(1, 1)
base2, gens2 = get_symmetric_group_sgs(2)
base3, gens3 = get_symmetric_group_sgs(3)
base2a, gens2a = get_symmetric_group_sgs(2, 1)
base3a, gens3a = get_symmetric_group_sgs(3, 1)
# A_d0*A^d0; ord = [d0,-d0]; g = [1,0,2,3]
# T_c = A^d0*A_d0; can = [0,1,2,3]
g = Permutation([1,0,2,3])
can = canonicalize(g, [0, 1], 0, (base1, gens1, 2, 0))
assert can == list(range(4))
# A commuting
# A_d0*A_d1*A_d2*A^d2*A^d1*A^d0; ord=[d0,-d0,d1,-d1,d2,-d2]
# g = [1,3,5,4,2,0,6,7]
# T_c = A^d0*A_d0*A^d1*A_d1*A^d2*A_d2; can = list(range(8))
g = Permutation([1,3,5,4,2,0,6,7])
can = canonicalize(g, list(range(6)), 0, (base1, gens1, 6, 0))
assert can == list(range(8))
# A anticommuting
# A_d0*A_d1*A_d2*A^d2*A^d1*A^d0; ord=[d0,-d0,d1,-d1,d2,-d2]
# g = [1,3,5,4,2,0,6,7]
# T_c 0; can = 0
g = Permutation([1,3,5,4,2,0,6,7])
can = canonicalize(g, list(range(6)), 0, (base1, gens1, 6, 1))
assert can == 0
can1 = canonicalize_naive(g, list(range(6)), 0, (base1, gens1, 6, 1))
assert can1 == 0
# A commuting symmetric
# A^{d0 b}*A^a_d1*A^d1_d0; ord=[a,b,d0,-d0,d1,-d1]
# g = [2,1,0,5,4,3,6,7]
# T_c = A^{a d0}*A^{b d1}*A_{d0 d1}; can = [0,2,1,4,3,5,6,7]
g = Permutation([2,1,0,5,4,3,6,7])
can = canonicalize(g, list(range(2,6)), 0, (base2, gens2, 3, 0))
assert can == [0,2,1,4,3,5,6,7]
# A, B commuting symmetric
# A^{d0 b}*A^d1_d0*B^a_d1; ord=[a,b,d0,-d0,d1,-d1]
# g = [2,1,4,3,0,5,6,7]
# T_c = A^{b d0}*A_d0^d1*B^a_d1; can = [1,2,3,4,0,5,6,7]
g = Permutation([2,1,4,3,0,5,6,7])
can = canonicalize(g, list(range(2,6)), 0, (base2,gens2,2,0), (base2,gens2,1,0))
assert can == [1,2,3,4,0,5,6,7]
# A commuting symmetric
# A^{d1 d0 b}*A^{a}_{d1 d0}; ord=[a,b, d0,-d0,d1,-d1]
# g = [4,2,1,0,5,3,6,7]
# T_c = A^{a d0 d1}*A^{b}_{d0 d1}; can = [0,2,4,1,3,5,6,7]
g = Permutation([4,2,1,0,5,3,6,7])
can = canonicalize(g, list(range(2,6)), 0, (base3, gens3, 2, 0))
assert can == [0,2,4,1,3,5,6,7]
# A^{d3 d0 d2}*A^a0_{d1 d2}*A^d1_d3^a1*A^{a2 a3}_d0
# ord = [a0,a1,a2,a3,d0,-d0,d1,-d1,d2,-d2,d3,-d3]
# 0 1 2 3 4 5 6 7 8 9 10 11
# g = [10,4,8, 0,7,9, 6,11,1, 2,3,5, 12,13]
# T_c = A^{a0 d0 d1}*A^a1_d0^d2*A^{a2 a3 d3}*A_{d1 d2 d3}
# can = [0,4,6, 1,5,8, 2,3,10, 7,9,11, 12,13]
g = Permutation([10,4,8, 0,7,9, 6,11,1, 2,3,5, 12,13])
can = canonicalize(g, list(range(4,12)), 0, (base3, gens3, 4, 0))
assert can == [0,4,6, 1,5,8, 2,3,10, 7,9,11, 12,13]
# A commuting symmetric, B antisymmetric
# A^{d0 d1 d2} * A_{d2 d3 d1} * B_d0^d3
# ord = [d0,-d0,d1,-d1,d2,-d2,d3,-d3]
# g = [0,2,4,5,7,3,1,6,8,9]
# in this esxample and in the next three,
# renaming dummy indices and using symmetry of A,
# T = A^{d0 d1 d2} * A_{d0 d1 d3} * B_d2^d3
# can = 0
g = Permutation([0,2,4,5,7,3,1,6,8,9])
can = canonicalize(g, list(range(8)), 0, (base3, gens3,2,0), (base2a,gens2a,1,0))
assert can == 0
# A anticommuting symmetric, B anticommuting
# A^{d0 d1 d2} * A_{d2 d3 d1} * B_d0^d3
# T_c = A^{d0 d1 d2} * A_{d0 d1}^d3 * B_{d2 d3}
# can = [0,2,4, 1,3,6, 5,7, 8,9]
can = canonicalize(g, list(range(8)), 0, (base3, gens3,2,1), (base2a,gens2a,1,0))
assert can == [0,2,4, 1,3,6, 5,7, 8,9]
# A anticommuting symmetric, B antisymmetric commuting, antisymmetric metric
# A^{d0 d1 d2} * A_{d2 d3 d1} * B_d0^d3
# T_c = -A^{d0 d1 d2} * A_{d0 d1}^d3 * B_{d2 d3}
# can = [0,2,4, 1,3,6, 5,7, 9,8]
can = canonicalize(g, list(range(8)), 1, (base3, gens3,2,1), (base2a,gens2a,1,0))
assert can == [0,2,4, 1,3,6, 5,7, 9,8]
# A anticommuting symmetric, B anticommuting anticommuting,
# no metric symmetry
# A^{d0 d1 d2} * A_{d2 d3 d1} * B_d0^d3
# T_c = A^{d0 d1 d2} * A_{d0 d1 d3} * B_d2^d3
# can = [0,2,4, 1,3,7, 5,6, 8,9]
can = canonicalize(g, list(range(8)), None, (base3, gens3,2,1), (base2a,gens2a,1,0))
assert can == [0,2,4,1,3,7,5,6,8,9]
# Gamma anticommuting
# Gamma_{mu nu} * gamma^rho * Gamma^{nu mu alpha}
# ord = [alpha, rho, mu,-mu,nu,-nu]
# g = [3,5,1,4,2,0,6,7]
# T_c = -Gamma^{mu nu} * gamma^rho * Gamma_{alpha mu nu}
# can = [2,4,1,0,3,5,7,6]]
g = Permutation([3,5,1,4,2,0,6,7])
t0 = (base2a, gens2a, 1, None)
t1 = (base1, gens1, 1, None)
t2 = (base3a, gens3a, 1, None)
can = canonicalize(g, list(range(2, 6)), 0, t0, t1, t2)
assert can == [2,4,1,0,3,5,7,6]
# Gamma_{mu nu} * Gamma^{gamma beta} * gamma_rho * Gamma^{nu mu alpha}
# ord = [alpha, beta, gamma, -rho, mu,-mu,nu,-nu]
# 0 1 2 3 4 5 6 7
# g = [5,7,2,1,3,6,4,0,8,9]
# T_c = Gamma^{mu nu} * Gamma^{beta gamma} * gamma_rho * Gamma^alpha_{mu nu} # can = [4,6,1,2,3,0,5,7,8,9]
t0 = (base2a, gens2a, 2, None)
g = Permutation([5,7,2,1,3,6,4,0,8,9])
can = canonicalize(g, list(range(4, 8)), 0, t0, t1, t2)
assert can == [4,6,1,2,3,0,5,7,8,9]
# f^a_{b,c} antisymmetric in b,c; A_mu^a no symmetry
# f^c_{d a} * f_{c e b} * A_mu^d * A_nu^a * A^{nu e} * A^{mu b}
# ord = [mu,-mu,nu,-nu,a,-a,b,-b,c,-c,d,-d, e, -e]
# 0 1 2 3 4 5 6 7 8 9 10 11 12 13
# g = [8,11,5, 9,13,7, 1,10, 3,4, 2,12, 0,6, 14,15]
# T_c = -f^{a b c} * f_a^{d e} * A^mu_b * A_{mu d} * A^nu_c * A_{nu e}
# can = [4,6,8, 5,10,12, 0,7, 1,11, 2,9, 3,13, 15,14]
g = Permutation([8,11,5, 9,13,7, 1,10, 3,4, 2,12, 0,6, 14,15])
base_f, gens_f = bsgs_direct_product(base1, gens1, base2a, gens2a)
base_A, gens_A = bsgs_direct_product(base1, gens1, base1, gens1)
t0 = (base_f, gens_f, 2, 0)
t1 = (base_A, gens_A, 4, 0)
can = canonicalize(g, [list(range(4)), list(range(4, 14))], [0, 0], t0, t1)
assert can == [4,6,8, 5,10,12, 0,7, 1,11, 2,9, 3,13, 15,14]
def test_riemann_invariants():
baser, gensr = riemann_bsgs
# R^{d0 d1}_{d1 d0}; ord = [d0,-d0,d1,-d1]; g = [0,2,3,1,4,5]
# T_c = -R^{d0 d1}_{d0 d1}; can = [0,2,1,3,5,4]
g = Permutation([0,2,3,1,4,5])
can = canonicalize(g, list(range(2, 4)), 0, (baser, gensr, 1, 0))
assert can == [0,2,1,3,5,4]
# use a non minimal BSGS
can = canonicalize(g, list(range(2, 4)), 0, ([2, 0], [Permutation([1,0,2,3,5,4]), Permutation([2,3,0,1,4,5])], 1, 0))
assert can == [0,2,1,3,5,4]
"""
The following tests in test_riemann_invariants and in
test_riemann_invariants1 have been checked using xperm.c from XPerm in
in [1] and with an older version contained in [2]
[1] xperm.c part of xPerm written by J. M. Martin-Garcia
http://www.xact.es/index.html
[2] test_xperm.cc in cadabra by Kasper Peeters, http://cadabra.phi-sci.com/
"""
# R_d11^d1_d0^d5 * R^{d6 d4 d0}_d5 * R_{d7 d2 d8 d9} *
# R_{d10 d3 d6 d4} * R^{d2 d7 d11}_d1 * R^{d8 d9 d3 d10}
# ord: contravariant d_k ->2*k, covariant d_k -> 2*k+1
# T_c = R^{d0 d1 d2 d3} * R_{d0 d1}^{d4 d5} * R_{d2 d3}^{d6 d7} *
# R_{d4 d5}^{d8 d9} * R_{d6 d7}^{d10 d11} * R_{d8 d9 d10 d11}
g = Permutation([23,2,1,10,12,8,0,11,15,5,17,19,21,7,13,9,4,14,22,3,16,18,6,20,24,25])
can = canonicalize(g, list(range(24)), 0, (baser, gensr, 6, 0))
assert can == [0,2,4,6,1,3,8,10,5,7,12,14,9,11,16,18,13,15,20,22,17,19,21,23,24,25]
# use a non minimal BSGS
can = canonicalize(g, list(range(24)), 0, ([2, 0], [Permutation([1,0,2,3,5,4]), Permutation([2,3,0,1,4,5])], 6, 0))
assert can == [0,2,4,6,1,3,8,10,5,7,12,14,9,11,16,18,13,15,20,22,17,19,21,23,24,25]
g = Permutation([0,2,5,7,4,6,9,11,8,10,13,15,12,14,17,19,16,18,21,23,20,22,25,27,24,26,29,31,28,30,33,35,32,34,37,39,36,38,1,3,40,41])
can = canonicalize(g, list(range(40)), 0, (baser, gensr, 10, 0))
assert can == [0,2,4,6,1,3,8,10,5,7,12,14,9,11,16,18,13,15,20,22,17,19,24,26,21,23,28,30,25,27,32,34,29,31,36,38,33,35,37,39,40,41]
@XFAIL
def test_riemann_invariants1():
skip('takes too much time')
baser, gensr = riemann_bsgs
g = Permutation([17, 44, 11, 3, 0, 19, 23, 15, 38, 4, 25, 27, 43, 36, 22, 14, 8, 30, 41, 20, 2, 10, 12, 28, 18, 1, 29, 13, 37, 42, 33, 7, 9, 31, 24, 26, 39, 5, 34, 47, 32, 6, 21, 40, 35, 46, 45, 16, 48, 49])
can = canonicalize(g, list(range(48)), 0, (baser, gensr, 12, 0))
assert can == [0, 2, 4, 6, 1, 3, 8, 10, 5, 7, 12, 14, 9, 11, 16, 18, 13, 15, 20, 22, 17, 19, 24, 26, 21, 23, 28, 30, 25, 27, 32, 34, 29, 31, 36, 38, 33, 35, 40, 42, 37, 39, 44, 46, 41, 43, 45, 47, 48, 49]
g = Permutation([0,2,4,6, 7,8,10,12, 14,16,18,20, 19,22,24,26, 5,21,28,30, 32,34,36,38, 40,42,44,46, 13,48,50,52, 15,49,54,56, 17,33,41,58, 9,23,60,62, 29,35,63,64, 3,45,66,68, 25,37,47,57, 11,31,69,70, 27,39,53,72, 1,59,73,74, 55,61,67,76, 43,65,75,78, 51,71,77,79, 80,81])
can = canonicalize(g, list(range(80)), 0, (baser, gensr, 20, 0))
assert can == [0,2,4,6, 1,8,10,12, 3,14,16,18, 5,20,22,24, 7,26,28,30, 9,15,32,34, 11,36,23,38, 13,40,42,44, 17,39,29,46, 19,48,43,50, 21,45,52,54, 25,56,33,58, 27,60,53,62, 31,51,64,66, 35,65,47,68, 37,70,49,72, 41,74,57,76, 55,67,59,78, 61,69,71,75, 63,79,73,77, 80,81]
def test_riemann_products():
baser, gensr = riemann_bsgs
base1, gens1 = get_symmetric_group_sgs(1)
base2, gens2 = get_symmetric_group_sgs(2)
base2a, gens2a = get_symmetric_group_sgs(2, 1)
# R^{a b d0}_d0 = 0
g = Permutation([0,1,2,3,4,5])
can = canonicalize(g, list(range(2,4)), 0, (baser, gensr, 1, 0))
assert can == 0
# R^{d0 b a}_d0 ; ord = [a,b,d0,-d0}; g = [2,1,0,3,4,5]
# T_c = -R^{a d0 b}_d0; can = [0,2,1,3,5,4]
g = Permutation([2,1,0,3,4,5])
can = canonicalize(g, list(range(2, 4)), 0, (baser, gensr, 1, 0))
assert can == [0,2,1,3,5,4]
# R^d1_d2^b_d0 * R^{d0 a}_d1^d2; ord=[a,b,d0,-d0,d1,-d1,d2,-d2]
# g = [4,7,1,3,2,0,5,6,8,9]
# T_c = -R^{a d0 d1 d2}* R^b_{d0 d1 d2}
# can = [0,2,4,6,1,3,5,7,9,8]
g = Permutation([4,7,1,3,2,0,5,6,8,9])
can = canonicalize(g, list(range(2,8)), 0, (baser, gensr, 2, 0))
assert can == [0,2,4,6,1,3,5,7,9,8]
can1 = canonicalize_naive(g, list(range(2,8)), 0, (baser, gensr, 2, 0))
assert can == can1
# A symmetric commuting
# R^{d6 d5}_d2^d1 * R^{d4 d0 d2 d3} * A_{d6 d0} A_{d3 d1} * A_{d4 d5}
# g = [12,10,5,2, 8,0,4,6, 13,1, 7,3, 9,11,14,15]
# T_c = -R^{d0 d1 d2 d3} * R_d0^{d4 d5 d6} * A_{d1 d4}*A_{d2 d5}*A_{d3 d6}
g = Permutation([12,10,5,2,8,0,4,6,13,1,7,3,9,11,14,15])
can = canonicalize(g, list(range(14)), 0, ((baser,gensr,2,0)), (base2,gens2,3,0))
assert can == [0, 2, 4, 6, 1, 8, 10, 12, 3, 9, 5, 11, 7, 13, 15, 14]
# R^{d2 a0 a2 d0} * R^d1_d2^{a1 a3} * R^{a4 a5}_{d0 d1}
# ord = [a0,a1,a2,a3,a4,a5,d0,-d0,d1,-d1,d2,-d2]
# 0 1 2 3 4 5 6 7 8 9 10 11
# can = [0, 6, 2, 8, 1, 3, 7, 10, 4, 5, 9, 11, 12, 13]
# T_c = R^{a0 d0 a2 d1}*R^{a1 a3}_d0^d2*R^{a4 a5}_{d1 d2}
g = Permutation([10,0,2,6,8,11,1,3,4,5,7,9,12,13])
can = canonicalize(g, list(range(6,12)), 0, (baser, gensr, 3, 0))
assert can == [0, 6, 2, 8, 1, 3, 7, 10, 4, 5, 9, 11, 12, 13]
#can1 = canonicalize_naive(g, list(range(6,12)), 0, (baser, gensr, 3, 0))
#assert can == can1
# A^n_{i, j} antisymmetric in i,j
# A_m0^d0_a1 * A_m1^a0_d0; ord = [m0,m1,a0,a1,d0,-d0]
# g = [0,4,3,1,2,5,6,7]
# T_c = -A_{m a1}^d0 * A_m1^a0_d0
# can = [0,3,4,1,2,5,7,6]
base, gens = bsgs_direct_product(base1, gens1, base2a, gens2a)
dummies = list(range(4, 6))
g = Permutation([0,4,3,1,2,5,6,7])
can = canonicalize(g, dummies, 0, (base, gens, 2, 0))
assert can == [0, 3, 4, 1, 2, 5, 7, 6]
# A^n_{i, j} symmetric in i,j
# A^m0_a0^d2 * A^n0_d2^d1 * A^n1_d1^d0 * A_{m0 d0}^a1
# ordering: first the free indices; then first n, then d
# ord=[n0,n1,a0,a1, m0,-m0,d0,-d0,d1,-d1,d2,-d2]
# 0 1 2 3 4 5 6 7 8 9 10 11]
# g = [4,2,10, 0,11,8, 1,9,6, 5,7,3, 12,13]
# if the dummy indices m_i and d_i were separated,
# one gets
# T_c = A^{n0 d0 d1} * A^n1_d0^d2 * A^m0^a0_d1 * A_m0^a1_d2
# can = [0, 6, 8, 1, 7, 10, 4, 2, 9, 5, 3, 11, 12, 13]
# If they are not, so can is
# T_c = A^{n0 m0 d0} A^n1_m0^d1 A^{d2 a0}_d0 A_d2^a1_d1
# can = [0, 4, 6, 1, 5, 8, 10, 2, 7, 11, 3, 9, 12, 13]
# case with single type of indices
base, gens = bsgs_direct_product(base1, gens1, base2, gens2)
dummies = list(range(4, 12))
g = Permutation([4,2,10, 0,11,8, 1,9,6, 5,7,3, 12,13])
can = canonicalize(g, dummies, 0, (base, gens, 4, 0))
assert can == [0, 4, 6, 1, 5, 8, 10, 2, 7, 11, 3, 9, 12, 13]
# case with separated indices
dummies = [list(range(4, 6)), list(range(6,12))]
sym = [0, 0]
can = canonicalize(g, dummies, sym, (base, gens, 4, 0))
assert can == [0, 6, 8, 1, 7, 10, 4, 2, 9, 5, 3, 11, 12, 13]
# case with separated indices with the second type of index
# with antisymmetric metric: there is a sign change
sym = [0, 1]
can = canonicalize(g, dummies, sym, (base, gens, 4, 0))
assert can == [0, 6, 8, 1, 7, 10, 4, 2, 9, 5, 3, 11, 13, 12]
def test_graph_certificate():
# test tensor invariants constructed from random regular graphs;
# checked graph isomorphism with networkx
import random
def randomize_graph(size, g):
p = list(range(size))
random.shuffle(p)
g1a = {}
for k, v in g1.items():
g1a[p[k]] = [p[i] for i in v]
return g1a
g1 = {0: [2, 3, 7], 1: [4, 5, 7], 2: [0, 4, 6], 3: [0, 6, 7], 4: [1, 2, 5], 5: [1, 4, 6], 6: [2, 3, 5], 7: [0, 1, 3]}
g2 = {0: [2, 3, 7], 1: [2, 4, 5], 2: [0, 1, 5], 3: [0, 6, 7], 4: [1, 5, 6], 5: [1, 2, 4], 6: [3, 4, 7], 7: [0, 3, 6]}
c1 = graph_certificate(g1)
c2 = graph_certificate(g2)
assert c1 != c2
g1a = randomize_graph(8, g1)
c1a = graph_certificate(g1a)
assert c1 == c1a
g1 = {0: [8, 1, 9, 7], 1: [0, 9, 3, 4], 2: [3, 4, 6, 7], 3: [1, 2, 5, 6], 4: [8, 1, 2, 5], 5: [9, 3, 4, 7], 6: [8, 2, 3, 7], 7: [0, 2, 5, 6], 8: [0, 9, 4, 6], 9: [8, 0, 5, 1]}
g2 = {0: [1, 2, 5, 6], 1: [0, 9, 5, 7], 2: [0, 4, 6, 7], 3: [8, 9, 6, 7], 4: [8, 2, 6, 7], 5: [0, 9, 8, 1], 6: [0, 2, 3, 4], 7: [1, 2, 3, 4], 8: [9, 3, 4, 5], 9: [8, 1, 3, 5]}
c1 = graph_certificate(g1)
c2 = graph_certificate(g2)
assert c1 != c2
g1a = randomize_graph(10, g1)
c1a = graph_certificate(g1a)
assert c1 == c1a
| 24,791 | 42.879646 | 278 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_fp_groups.py
|
# -*- coding: utf-8 -*-
from sympy import S
from sympy.combinatorics.fp_groups import (FpGroup, CosetTable, low_index_subgroups,
coset_enumeration_r, coset_enumeration_c, reidemeister_presentation)
from sympy.combinatorics.free_groups import free_group
"""
References
==========
[1] Holt, D., Eick, B., O'Brien, E.
"Handbook of Computational Group Theory"
[2] John J. Cannon; Lucien A. Dimino; George Havas; Jane M. Watson
Mathematics of Computation, Vol. 27, No. 123. (Jul., 1973), pp. 463-490.
"Implementation and Analysis of the Todd-Coxeter Algorithm"
[3] PROC. SECOND INTERNAT. CONF. THEORY OF GROUPS, CANBERRA 1973,
pp. 347-356. "A Reidemeister-Schreier program" by George Havas.
http://staff.itee.uq.edu.au/havas/1973cdhw.pdf
"""
def test_scan_1():
# Example 5.1 from [1]
F, x, y = free_group("x, y")
f = FpGroup(F, [x**3, y**3, x**-1*y**-1*x*y])
c = CosetTable(f, [x])
c.scan_and_fill(0, x)
assert c.table == [[0, 0, None, None]]
assert c.p == [0]
assert c.n == 1
assert c.omega == [0]
c.scan_and_fill(0, x**3)
assert c.table == [[0, 0, None, None]]
assert c.p == [0]
assert c.n == 1
assert c.omega == [0]
c.scan_and_fill(0, y**3)
assert c.table == [[0, 0, 1, 2], [None, None, 2, 0], [None, None, 0, 1]]
assert c.p == [0, 1, 2]
assert c.n == 3
assert c.omega == [0, 1, 2]
c.scan_and_fill(0, x**-1*y**-1*x*y)
assert c.table == [[0, 0, 1, 2], [None, None, 2, 0], [2, 2, 0, 1]]
assert c.p == [0, 1, 2]
assert c.n == 3
assert c.omega == [0, 1, 2]
c.scan_and_fill(1, x**3)
assert c.table == [[0, 0, 1, 2], [3, 4, 2, 0], [2, 2, 0, 1], \
[4, 1, None, None], [1, 3, None, None]]
assert c.p == [0, 1, 2, 3, 4]
assert c.n == 5
assert c.omega == [0, 1, 2, 3, 4]
c.scan_and_fill(1, y**3)
assert c.table == [[0, 0, 1, 2], [3, 4, 2, 0], [2, 2, 0, 1], \
[4, 1, None, None], [1, 3, None, None]]
assert c.p == [0, 1, 2, 3, 4]
assert c.n == 5
assert c.omega == [0, 1, 2, 3, 4]
c.scan_and_fill(1, x**-1*y**-1*x*y)
assert c.table == [[0, 0, 1, 2], [1, 1, 2, 0], [2, 2, 0, 1], \
[None, 1, None, None], [1, 3, None, None]]
assert c.p == [0, 1, 2, 1, 1]
assert c.n == 3
assert c.omega == [0, 1, 2]
# Example 5.2 from [1]
f = FpGroup(F, [x**2, y**3, (x*y)**3])
c = CosetTable(f, [x*y])
c.scan_and_fill(0, x*y)
assert c.table == [[1, None, None, 1], [None, 0, 0, None]]
assert c.p == [0, 1]
assert c.n == 2
assert c.omega == [0, 1]
c.scan_and_fill(0, x**2)
assert c.table == [[1, 1, None, 1], [0, 0, 0, None]]
assert c.p == [0, 1]
assert c.n == 2
assert c.omega == [0, 1]
c.scan_and_fill(0, y**3)
assert c.table == [[1, 1, 2, 1], [0, 0, 0, 2], [None, None, 1, 0]]
assert c.p == [0, 1, 2]
assert c.n == 3
assert c.omega == [0, 1, 2]
c.scan_and_fill(0, (x*y)**3)
assert c.table == [[1, 1, 2, 1], [0, 0, 0, 2], [None, None, 1, 0]]
assert c.p == [0, 1, 2]
assert c.n == 3
assert c.omega == [0, 1, 2]
c.scan_and_fill(1, x**2)
assert c.table == [[1, 1, 2, 1], [0, 0, 0, 2], [None, None, 1, 0]]
assert c.p == [0, 1, 2]
assert c.n == 3
assert c.omega == [0, 1, 2]
c.scan_and_fill(1, y**3)
assert c.table == [[1, 1, 2, 1], [0, 0, 0, 2], [None, None, 1, 0]]
assert c.p == [0, 1, 2]
assert c.n == 3
assert c.omega == [0, 1, 2]
c.scan_and_fill(1, (x*y)**3)
assert c.table == [[1, 1, 2, 1], [0, 0, 0, 2], [3, 4, 1, 0], [None, 2, 4, None], [2, None, None, 3]]
assert c.p == [0, 1, 2, 3, 4]
assert c.n == 5
assert c.omega == [0, 1, 2, 3, 4]
c.scan_and_fill(2, x**2)
assert c.table == [[1, 1, 2, 1], [0, 0, 0, 2], [3, 3, 1, 0], [2, 2, 3, 3], [2, None, None, 3]]
assert c.p == [0, 1, 2, 3, 3]
assert c.n == 4
assert c.omega == [0, 1, 2, 3]
def test_coset_enumeration():
# this test function contains the combined tests for the two strategies
# i.e. HLT and Felsch strategies.
# Example 5.1 from [1]
F, x, y = free_group("x, y")
f = FpGroup(F, [x**3, y**3, x**-1*y**-1*x*y])
C_r = coset_enumeration_r(f, [x])
C_r.compress(); C_r.standardize()
C_c = coset_enumeration_c(f, [x])
C_c.compress(); C_c.standardize()
table1 = [[0, 0, 1, 2], [1, 1, 2, 0], [2, 2, 0, 1]]
assert C_r.table == table1
assert C_c.table == table1
# E₁ from [2] Pg. 474
F, r, s, t = free_group("r, s, t")
E1 = FpGroup(F, [t**-1*r*t*r**-2, r**-1*s*r*s**-2, s**-1*t*s*t**-2])
C_r = coset_enumeration_r(E1, [])
C_r.compress()
C_c = coset_enumeration_c(E1, [])
C_c.compress()
table2 = [[0, 0, 0, 0, 0, 0]]
assert C_r.table == table2
# test for issue #11449
assert C_c.table == table2
# Cox group from [2] Pg. 474
F, a, b = free_group("a, b")
Cox = FpGroup(F, [a**6, b**6, (a*b)**2, (a**2*b**2)**2, (a**3*b**3)**5])
C_r = coset_enumeration_r(Cox, [a])
C_r.compress(); C_r.standardize()
C_c = coset_enumeration_c(Cox, [a])
C_c.compress(); C_c.standardize()
table3 = [[0, 0, 1, 2],
[2, 3, 4, 0],
[5, 1, 0, 6],
[1, 7, 8, 9],
[9, 10, 11, 1],
[12, 2, 9, 13],
[14, 9, 2, 11],
[3, 12, 15, 16],
[16, 17, 18, 3],
[6, 4, 3, 5],
[4, 19, 20, 21],
[21, 22, 6, 4],
[7, 5, 23, 24],
[25, 23, 5, 18],
[19, 6, 22, 26],
[24, 27, 28, 7],
[29, 8, 7, 30],
[8, 31, 32, 33],
[33, 34, 13, 8],
[10, 14, 35, 35],
[35, 36, 37, 10],
[30, 11, 10, 29],
[11, 38, 39, 14],
[13, 39, 38, 12],
[40, 15, 12, 41],
[42, 13, 34, 43],
[44, 35, 14, 45],
[15, 46, 47, 34],
[34, 48, 49, 15],
[50, 16, 21, 51],
[52, 21, 16, 49],
[17, 50, 53, 54],
[54, 55, 56, 17],
[41, 18, 17, 40],
[18, 28, 27, 25],
[26, 20, 19, 19],
[20, 57, 58, 59],
[59, 60, 51, 20],
[22, 52, 61, 23],
[23, 62, 63, 22],
[64, 24, 33, 65],
[48, 33, 24, 61],
[62, 25, 54, 66],
[67, 54, 25, 68],
[57, 26, 59, 69],
[70, 59, 26, 63],
[27, 64, 71, 72],
[72, 73, 68, 27],
[28, 41, 74, 75],
[75, 76, 30, 28],
[31, 29, 77, 78],
[79, 77, 29, 37],
[38, 30, 76, 80],
[78, 81, 82, 31],
[43, 32, 31, 42],
[32, 83, 84, 85],
[85, 86, 65, 32],
[36, 44, 87, 88],
[88, 89, 90, 36],
[45, 37, 36, 44],
[37, 82, 81, 79],
[80, 74, 41, 38],
[39, 42, 91, 92],
[92, 93, 45, 39],
[46, 40, 94, 95],
[96, 94, 40, 56],
[97, 91, 42, 82],
[83, 43, 98, 99],
[100, 98, 43, 47],
[101, 87, 44, 90],
[82, 45, 93, 97],
[95, 102, 103, 46],
[104, 47, 46, 105],
[47, 106, 107, 100],
[61, 108, 109, 48],
[105, 49, 48, 104],
[49, 110, 111, 52],
[51, 111, 110, 50],
[112, 53, 50, 113],
[114, 51, 60, 115],
[116, 61, 52, 117],
[53, 118, 119, 60],
[60, 70, 66, 53],
[55, 67, 120, 121],
[121, 122, 123, 55],
[113, 56, 55, 112],
[56, 103, 102, 96],
[69, 124, 125, 57],
[115, 58, 57, 114],
[58, 126, 127, 128],
[128, 128, 69, 58],
[66, 129, 130, 62],
[117, 63, 62, 116],
[63, 125, 124, 70],
[65, 109, 108, 64],
[131, 71, 64, 132],
[133, 65, 86, 134],
[135, 66, 70, 136],
[68, 130, 129, 67],
[137, 120, 67, 138],
[132, 68, 73, 131],
[139, 69, 128, 140],
[71, 141, 142, 86],
[86, 143, 144, 71],
[145, 72, 75, 146],
[147, 75, 72, 144],
[73, 145, 148, 120],
[120, 149, 150, 73],
[74, 151, 152, 94],
[94, 153, 146, 74],
[76, 147, 154, 77],
[77, 155, 156, 76],
[157, 78, 85, 158],
[143, 85, 78, 154],
[155, 79, 88, 159],
[160, 88, 79, 161],
[151, 80, 92, 162],
[163, 92, 80, 156],
[81, 157, 164, 165],
[165, 166, 161, 81],
[99, 107, 106, 83],
[134, 84, 83, 133],
[84, 167, 168, 169],
[169, 170, 158, 84],
[87, 171, 172, 93],
[93, 163, 159, 87],
[89, 160, 173, 174],
[174, 175, 176, 89],
[90, 90, 89, 101],
[91, 177, 178, 98],
[98, 179, 162, 91],
[180, 95, 100, 181],
[179, 100, 95, 152],
[153, 96, 121, 148],
[182, 121, 96, 183],
[177, 97, 165, 184],
[185, 165, 97, 172],
[186, 99, 169, 187],
[188, 169, 99, 178],
[171, 101, 174, 189],
[190, 174, 101, 176],
[102, 180, 191, 192],
[192, 193, 183, 102],
[103, 113, 194, 195],
[195, 196, 105, 103],
[106, 104, 197, 198],
[199, 197, 104, 109],
[110, 105, 196, 200],
[198, 201, 133, 106],
[107, 186, 202, 203],
[203, 204, 181, 107],
[108, 116, 205, 206],
[206, 207, 132, 108],
[109, 133, 201, 199],
[200, 194, 113, 110],
[111, 114, 208, 209],
[209, 210, 117, 111],
[118, 112, 211, 212],
[213, 211, 112, 123],
[214, 208, 114, 125],
[126, 115, 215, 216],
[217, 215, 115, 119],
[218, 205, 116, 130],
[125, 117, 210, 214],
[212, 219, 220, 118],
[136, 119, 118, 135],
[119, 221, 222, 217],
[122, 182, 223, 224],
[224, 225, 226, 122],
[138, 123, 122, 137],
[123, 220, 219, 213],
[124, 139, 227, 228],
[228, 229, 136, 124],
[216, 222, 221, 126],
[140, 127, 126, 139],
[127, 230, 231, 232],
[232, 233, 140, 127],
[129, 135, 234, 235],
[235, 236, 138, 129],
[130, 132, 207, 218],
[141, 131, 237, 238],
[239, 237, 131, 150],
[167, 134, 240, 241],
[242, 240, 134, 142],
[243, 234, 135, 220],
[221, 136, 229, 244],
[149, 137, 245, 246],
[247, 245, 137, 226],
[220, 138, 236, 243],
[244, 227, 139, 221],
[230, 140, 233, 248],
[238, 249, 250, 141],
[251, 142, 141, 252],
[142, 253, 254, 242],
[154, 255, 256, 143],
[252, 144, 143, 251],
[144, 257, 258, 147],
[146, 258, 257, 145],
[259, 148, 145, 260],
[261, 146, 153, 262],
[263, 154, 147, 264],
[148, 265, 266, 153],
[246, 267, 268, 149],
[260, 150, 149, 259],
[150, 250, 249, 239],
[162, 269, 270, 151],
[262, 152, 151, 261],
[152, 271, 272, 179],
[159, 273, 274, 155],
[264, 156, 155, 263],
[156, 270, 269, 163],
[158, 256, 255, 157],
[275, 164, 157, 276],
[277, 158, 170, 278],
[279, 159, 163, 280],
[161, 274, 273, 160],
[281, 173, 160, 282],
[276, 161, 166, 275],
[283, 162, 179, 284],
[164, 285, 286, 170],
[170, 188, 184, 164],
[166, 185, 189, 173],
[173, 287, 288, 166],
[241, 254, 253, 167],
[278, 168, 167, 277],
[168, 289, 290, 291],
[291, 292, 187, 168],
[189, 293, 294, 171],
[280, 172, 171, 279],
[172, 295, 296, 185],
[175, 190, 297, 297],
[297, 298, 299, 175],
[282, 176, 175, 281],
[176, 294, 293, 190],
[184, 296, 295, 177],
[284, 178, 177, 283],
[178, 300, 301, 188],
[181, 272, 271, 180],
[302, 191, 180, 303],
[304, 181, 204, 305],
[183, 266, 265, 182],
[306, 223, 182, 307],
[303, 183, 193, 302],
[308, 184, 188, 309],
[310, 189, 185, 311],
[187, 301, 300, 186],
[305, 202, 186, 304],
[312, 187, 292, 313],
[314, 297, 190, 315],
[191, 316, 317, 204],
[204, 318, 319, 191],
[320, 192, 195, 321],
[322, 195, 192, 319],
[193, 320, 323, 223],
[223, 324, 325, 193],
[194, 326, 327, 211],
[211, 328, 321, 194],
[196, 322, 329, 197],
[197, 330, 331, 196],
[332, 198, 203, 333],
[318, 203, 198, 329],
[330, 199, 206, 334],
[335, 206, 199, 336],
[326, 200, 209, 337],
[338, 209, 200, 331],
[201, 332, 339, 240],
[240, 340, 336, 201],
[202, 341, 342, 292],
[292, 343, 333, 202],
[205, 344, 345, 210],
[210, 338, 334, 205],
[207, 335, 346, 237],
[237, 347, 348, 207],
[208, 349, 350, 215],
[215, 351, 337, 208],
[352, 212, 217, 353],
[351, 217, 212, 327],
[328, 213, 224, 323],
[354, 224, 213, 355],
[349, 214, 228, 356],
[357, 228, 214, 345],
[358, 216, 232, 359],
[360, 232, 216, 350],
[344, 218, 235, 361],
[362, 235, 218, 348],
[219, 352, 363, 364],
[364, 365, 355, 219],
[222, 358, 366, 367],
[367, 368, 353, 222],
[225, 354, 369, 370],
[370, 371, 372, 225],
[307, 226, 225, 306],
[226, 268, 267, 247],
[227, 373, 374, 233],
[233, 360, 356, 227],
[229, 357, 361, 234],
[234, 375, 376, 229],
[248, 231, 230, 230],
[231, 377, 378, 379],
[379, 380, 359, 231],
[236, 362, 381, 245],
[245, 382, 383, 236],
[384, 238, 242, 385],
[340, 242, 238, 346],
[347, 239, 246, 381],
[386, 246, 239, 387],
[388, 241, 291, 389],
[343, 291, 241, 339],
[375, 243, 364, 390],
[391, 364, 243, 383],
[373, 244, 367, 392],
[393, 367, 244, 376],
[382, 247, 370, 394],
[395, 370, 247, 396],
[377, 248, 379, 397],
[398, 379, 248, 374],
[249, 384, 399, 400],
[400, 401, 387, 249],
[250, 260, 402, 403],
[403, 404, 252, 250],
[253, 251, 405, 406],
[407, 405, 251, 256],
[257, 252, 404, 408],
[406, 409, 277, 253],
[254, 388, 410, 411],
[411, 412, 385, 254],
[255, 263, 413, 414],
[414, 415, 276, 255],
[256, 277, 409, 407],
[408, 402, 260, 257],
[258, 261, 416, 417],
[417, 418, 264, 258],
[265, 259, 419, 420],
[421, 419, 259, 268],
[422, 416, 261, 270],
[271, 262, 423, 424],
[425, 423, 262, 266],
[426, 413, 263, 274],
[270, 264, 418, 422],
[420, 427, 307, 265],
[266, 303, 428, 425],
[267, 386, 429, 430],
[430, 431, 396, 267],
[268, 307, 427, 421],
[269, 283, 432, 433],
[433, 434, 280, 269],
[424, 428, 303, 271],
[272, 304, 435, 436],
[436, 437, 284, 272],
[273, 279, 438, 439],
[439, 440, 282, 273],
[274, 276, 415, 426],
[285, 275, 441, 442],
[443, 441, 275, 288],
[289, 278, 444, 445],
[446, 444, 278, 286],
[447, 438, 279, 294],
[295, 280, 434, 448],
[287, 281, 449, 450],
[451, 449, 281, 299],
[294, 282, 440, 447],
[448, 432, 283, 295],
[300, 284, 437, 452],
[442, 453, 454, 285],
[309, 286, 285, 308],
[286, 455, 456, 446],
[450, 457, 458, 287],
[311, 288, 287, 310],
[288, 454, 453, 443],
[445, 456, 455, 289],
[313, 290, 289, 312],
[290, 459, 460, 461],
[461, 462, 389, 290],
[293, 310, 463, 464],
[464, 465, 315, 293],
[296, 308, 466, 467],
[467, 468, 311, 296],
[298, 314, 469, 470],
[470, 471, 472, 298],
[315, 299, 298, 314],
[299, 458, 457, 451],
[452, 435, 304, 300],
[301, 312, 473, 474],
[474, 475, 309, 301],
[316, 302, 476, 477],
[478, 476, 302, 325],
[341, 305, 479, 480],
[481, 479, 305, 317],
[324, 306, 482, 483],
[484, 482, 306, 372],
[485, 466, 308, 454],
[455, 309, 475, 486],
[487, 463, 310, 458],
[454, 311, 468, 485],
[486, 473, 312, 455],
[459, 313, 488, 489],
[490, 488, 313, 342],
[491, 469, 314, 472],
[458, 315, 465, 487],
[477, 492, 485, 316],
[463, 317, 316, 468],
[317, 487, 493, 481],
[329, 447, 464, 318],
[468, 319, 318, 463],
[319, 467, 448, 322],
[321, 448, 467, 320],
[475, 323, 320, 466],
[432, 321, 328, 437],
[438, 329, 322, 434],
[323, 474, 452, 328],
[483, 494, 486, 324],
[466, 325, 324, 475],
[325, 485, 492, 478],
[337, 422, 433, 326],
[437, 327, 326, 432],
[327, 436, 424, 351],
[334, 426, 439, 330],
[434, 331, 330, 438],
[331, 433, 422, 338],
[333, 464, 447, 332],
[449, 339, 332, 440],
[465, 333, 343, 469],
[413, 334, 338, 418],
[336, 439, 426, 335],
[441, 346, 335, 415],
[440, 336, 340, 449],
[416, 337, 351, 423],
[339, 451, 470, 343],
[346, 443, 450, 340],
[480, 493, 487, 341],
[469, 342, 341, 465],
[342, 491, 495, 490],
[361, 407, 414, 344],
[418, 345, 344, 413],
[345, 417, 408, 357],
[381, 446, 442, 347],
[415, 348, 347, 441],
[348, 414, 407, 362],
[356, 408, 417, 349],
[423, 350, 349, 416],
[350, 425, 420, 360],
[353, 424, 436, 352],
[479, 363, 352, 435],
[428, 353, 368, 476],
[355, 452, 474, 354],
[488, 369, 354, 473],
[435, 355, 365, 479],
[402, 356, 360, 419],
[405, 361, 357, 404],
[359, 420, 425, 358],
[476, 366, 358, 428],
[427, 359, 380, 482],
[444, 381, 362, 409],
[363, 481, 477, 368],
[368, 393, 390, 363],
[365, 391, 394, 369],
[369, 490, 480, 365],
[366, 478, 483, 380],
[380, 398, 392, 366],
[371, 395, 496, 497],
[497, 498, 489, 371],
[473, 372, 371, 488],
[372, 486, 494, 484],
[392, 400, 403, 373],
[419, 374, 373, 402],
[374, 421, 430, 398],
[390, 411, 406, 375],
[404, 376, 375, 405],
[376, 403, 400, 393],
[397, 430, 421, 377],
[482, 378, 377, 427],
[378, 484, 497, 499],
[499, 499, 397, 378],
[394, 461, 445, 382],
[409, 383, 382, 444],
[383, 406, 411, 391],
[385, 450, 443, 384],
[492, 399, 384, 453],
[457, 385, 412, 493],
[387, 442, 446, 386],
[494, 429, 386, 456],
[453, 387, 401, 492],
[389, 470, 451, 388],
[493, 410, 388, 457],
[471, 389, 462, 495],
[412, 390, 393, 399],
[462, 394, 391, 410],
[401, 392, 398, 429],
[396, 445, 461, 395],
[498, 496, 395, 460],
[456, 396, 431, 494],
[431, 397, 499, 496],
[399, 477, 481, 412],
[429, 483, 478, 401],
[410, 480, 490, 462],
[496, 497, 484, 431],
[489, 495, 491, 459],
[495, 460, 459, 471],
[460, 489, 498, 498],
[472, 472, 471, 491]]
C_r.table == table3
C_c.table == table3
# Group denoted by B₂,₄ from [2] Pg. 474
F, a, b = free_group("a, b")
B_2_4 = FpGroup(F, [a**4, b**4, (a*b)**4, (a**-1*b)**4, (a**2*b)**4, \
(a*b**2)**4, (a**2*b**2)**4, (a**-1*b*a*b)**4, (a*b**-1*a*b)**4])
C_r = coset_enumeration_r(B_2_4, [a])
C_c = coset_enumeration_c(B_2_4, [a])
index_r = 0
for i in range(len(C_r.p)):
if C_r.p[i] == i:
index_r += 1
assert index_r == 1024
index_c = 0
for i in range(len(C_c.p)):
if C_c.p[i] == i:
index_c += 1
assert index_c == 1024
# trivial Macdonald group G(2,2) from [2] Pg. 480
M = FpGroup(F, [b**-1*a**-1*b*a*b**-1*a*b*a**-2, a**-1*b**-1*a*b*a**-1*b*a*b**-2])
C_r = coset_enumeration_r(M, [a])
C_r.compress(); C_r.standardize()
C_c = coset_enumeration_c(M, [a])
C_c.compress(); C_c.standardize()
table4 = [[0, 0, 0, 0]]
assert C_r.table == table4
assert C_c.table == table4
def test_look_ahead():
# Section 3.2 [Test Example] Example (d) from [2]
F, a, b, c = free_group("a, b, c")
f = FpGroup(F, [a**11, b**5, c**4, (a*c)**3, b**2*c**-1*b**-1*c, a**4*b**-1*a**-1*b])
H = [c, b, c**2]
table0 = [[1, 2, 0, 0, 0, 0],
[3, 0, 4, 5, 6, 7],
[0, 8, 9, 10, 11, 12],
[5, 1, 10, 13, 14, 15],
[16, 5, 16, 1, 17, 18],
[4, 3, 1, 8, 19, 20],
[12, 21, 22, 23, 24, 1],
[25, 26, 27, 28, 1, 24],
[2, 10, 5, 16, 22, 28],
[10, 13, 13, 2, 29, 30]]
CosetTable.max_stack_size = 10
C_c = coset_enumeration_c(f, H)
C_c.compress(); C_c.standardize()
assert C_c.table[: 10] == table0
def test_low_index_subgroups():
F, x, y = free_group("x, y")
# Example 5.10 from [1] Pg. 194
f = FpGroup(F, [x**2, y**3, (x*y)**4])
L = low_index_subgroups(f, 4)
t1 = [[[0, 0, 0, 0]],
[[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 3, 3]],
[[0, 0, 1, 2], [2, 2, 2, 0], [1, 1, 0, 1]],
[[1, 1, 0, 0], [0, 0, 1, 1]]]
for i in range(len(t1)):
assert L[i].table == t1[i]
f = FpGroup(F, [x**2, y**3, (x*y)**7])
L = low_index_subgroups(f, 15)
t2 = [[[0, 0, 0, 0]],
[[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5],
[4, 4, 5, 3], [6, 6, 3, 4], [5, 5, 6, 6]],
[[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5],
[6, 6, 5, 3], [5, 5, 3, 4], [4, 4, 6, 6]],
[[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5],
[6, 6, 5, 3], [7, 7, 3, 4], [4, 4, 8, 9], [5, 5, 10, 11],
[11, 11, 9, 6], [9, 9, 6, 8], [12, 12, 11, 7], [8, 8, 7, 10],
[10, 10, 13, 14], [14, 14, 14, 12], [13, 13, 12, 13]],
[[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5],
[6, 6, 5, 3], [7, 7, 3, 4], [4, 4, 8, 9], [5, 5, 10, 11],
[11, 11, 9, 6], [12, 12, 6, 8], [10, 10, 11, 7], [8, 8, 7, 10],
[9, 9, 13, 14], [14, 14, 14, 12], [13, 13, 12, 13]],
[[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5],
[6, 6, 5, 3], [7, 7, 3, 4], [4, 4, 8, 9], [5, 5, 10, 11],
[11, 11, 9, 6], [12, 12, 6, 8], [13, 13, 11, 7], [8, 8, 7, 10],
[9, 9, 12, 12], [10, 10, 13, 13]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 3, 3], [2, 2, 5, 6]
, [7, 7, 6, 4], [8, 8, 4, 5], [5, 5, 8, 9], [6, 6, 9, 7],
[10, 10, 7, 8], [9, 9, 11, 12], [11, 11, 12, 10], [13, 13, 10, 11],
[12, 12, 13, 13]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 3, 3], [2, 2, 5, 6]
, [7, 7, 6, 4], [8, 8, 4, 5], [5, 5, 8, 9], [6, 6, 9, 7],
[10, 10, 7, 8], [9, 9, 11, 12], [13, 13, 12, 10], [12, 12, 10, 11],
[11, 11, 13, 13]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 4, 4]
, [7, 7, 6, 3], [8, 8, 3, 5], [5, 5, 8, 9], [6, 6, 9, 7],
[10, 10, 7, 8], [9, 9, 11, 12], [13, 13, 12, 10], [12, 12, 10, 11],
[11, 11, 13, 13]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
, [5, 5, 6, 3], [9, 9, 3, 5], [10, 10, 8, 4], [8, 8, 4, 7],
[6, 6, 10, 11], [7, 7, 11, 9], [12, 12, 9, 10], [11, 11, 13, 14],
[14, 14, 14, 12], [13, 13, 12, 13]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
, [6, 6, 6, 3], [5, 5, 3, 5], [8, 8, 8, 4], [7, 7, 4, 7]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
, [9, 9, 6, 3], [6, 6, 3, 5], [10, 10, 8, 4], [11, 11, 4, 7],
[5, 5, 10, 12], [7, 7, 12, 9], [8, 8, 11, 11], [13, 13, 9, 10],
[12, 12, 13, 13]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
, [9, 9, 6, 3], [6, 6, 3, 5], [10, 10, 8, 4], [11, 11, 4, 7],
[5, 5, 12, 11], [7, 7, 10, 10], [8, 8, 9, 12], [13, 13, 11, 9],
[12, 12, 13, 13]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
, [9, 9, 6, 3], [10, 10, 3, 5], [7, 7, 8, 4], [11, 11, 4, 7],
[5, 5, 9, 9], [6, 6, 11, 12], [8, 8, 12, 10], [13, 13, 10, 11],
[12, 12, 13, 13]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
, [9, 9, 6, 3], [10, 10, 3, 5], [7, 7, 8, 4], [11, 11, 4, 7],
[5, 5, 12, 11], [6, 6, 10, 10], [8, 8, 9, 12], [13, 13, 11, 9],
[12, 12, 13, 13]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
, [9, 9, 6, 3], [10, 10, 3, 5], [11, 11, 8, 4], [12, 12, 4, 7],
[5, 5, 9, 9], [6, 6, 12, 13], [7, 7, 11, 11], [8, 8, 13, 10],
[13, 13, 10, 12]],
[[1, 1, 0, 0], [0, 0, 2, 3], [4, 4, 3, 1], [5, 5, 1, 2], [2, 2, 4, 4]
, [3, 3, 6, 7], [7, 7, 7, 5], [6, 6, 5, 6]]]
for i in range(len(t2)):
assert L[i].table == t2[i]
f = FpGroup(F, [x**2, y**3, (x*y)**7])
L = low_index_subgroups(f, 10, [x])
t3 = [[[0, 0, 0, 0]],
[[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5], [4, 4, 5, 3],
[6, 6, 3, 4], [5, 5, 6, 6]],
[[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5], [6, 6, 5, 3],
[5, 5, 3, 4], [4, 4, 6, 6]],
[[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8],
[6, 6, 6, 3], [5, 5, 3, 5], [8, 8, 8, 4], [7, 7, 4, 7]]]
for i in range(len(t3)):
assert L[i].table == t3[i]
def test_subgroup_presentations():
F, x, y = free_group("x, y")
f = FpGroup(F, [x**3, y**5, (x*y)**2])
H = [x*y, x**-1*y**-1*x*y*x]
p1 = reidemeister_presentation(f, H)
assert str(p1) == "((y_1, y_2), (y_1**2, y_2**3, y_2*y_1*y_2*y_1*y_2*y_1))"
H = f.subgroup(H)
assert (H.generators, H.relators) == p1
f = FpGroup(F, [x**3, y**3, (x*y)**3])
H = [x*y, x*y**-1]
p2 = reidemeister_presentation(f, H)
assert str(p2) == "((x_0, y_0), (x_0**3, y_0**3, x_0*y_0*x_0*y_0*x_0*y_0))"
f = FpGroup(F, [x**2*y**2, y**-1*x*y*x**-3])
H = [x]
p3 = reidemeister_presentation(f, H)
assert str(p3) == "((x_0,), (x_0**4,))"
f = FpGroup(F, [x**3*y**-3, (x*y)**3, (x*y**-1)**2])
H = [x]
p4 = reidemeister_presentation(f, H)
assert str(p4) == "((x_0,), (x_0**6,))"
# this presentation can be improved, the most simplified form
# of presentation is <a, b | a^11, b^2, (a*b)^3, (a^4*b*a^-5*b)^2>
# See [2] Pg 474 group PSL_2(11)
# This is the group PSL_2(11)
F, a, b, c = free_group("a, b, c")
f = FpGroup(F, [a**11, b**5, c**4, (b*c**2)**2, (a*b*c)**3, (a**4*c**2)**3, b**2*c**-1*b**-1*c, a**4*b**-1*a**-1*b])
H = [a, b, c**2]
gens, rels = reidemeister_presentation(f, H)
assert str(gens) == "(b_1, c_3)"
assert len(rels) == 18
def test_order():
from sympy import S
F, x, y = free_group("x, y")
f = FpGroup(F, [x**4, y**2, x*y*x**-1*y])
assert f.order() == 8
f = FpGroup(F, [x*y*x**-1*y**-1, y**2])
assert f.order() == S.Infinity
F, a, b, c = free_group("a, b, c")
f = FpGroup(F, [a**250, b**2, c*b*c**-1*b, c**4, c**-1*a**-1*c*a, a**-1*b**-1*a*b])
assert f.order() == 2000
| 30,528 | 34.748244 | 120 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_util.py
|
from sympy.core.compatibility import range
from sympy.combinatorics.named_groups import SymmetricGroup, DihedralGroup,\
AlternatingGroup
from sympy.combinatorics.permutations import Permutation
from sympy.combinatorics.util import _check_cycles_alt_sym, _strip,\
_distribute_gens_by_base, _strong_gens_from_distr,\
_orbits_transversals_from_bsgs, _handle_precomputed_bsgs, _base_ordering,\
_remove_gens
from sympy.combinatorics.testutil import _verify_bsgs
def test_check_cycles_alt_sym():
perm1 = Permutation([[0, 1, 2, 3, 4, 5, 6], [7], [8], [9]])
perm2 = Permutation([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9]])
perm3 = Permutation([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
assert _check_cycles_alt_sym(perm1) is True
assert _check_cycles_alt_sym(perm2) is False
assert _check_cycles_alt_sym(perm3) is False
def test_strip():
D = DihedralGroup(5)
D.schreier_sims()
member = Permutation([4, 0, 1, 2, 3])
not_member1 = Permutation([0, 1, 4, 3, 2])
not_member2 = Permutation([3, 1, 4, 2, 0])
identity = Permutation([0, 1, 2, 3, 4])
res1 = _strip(member, D.base, D.basic_orbits, D.basic_transversals)
res2 = _strip(not_member1, D.base, D.basic_orbits, D.basic_transversals)
res3 = _strip(not_member2, D.base, D.basic_orbits, D.basic_transversals)
assert res1[0] == identity
assert res1[1] == len(D.base) + 1
assert res2[0] == not_member1
assert res2[1] == len(D.base) + 1
assert res3[0] != identity
assert res3[1] == 2
def test_distribute_gens_by_base():
base = [0, 1, 2]
gens = [Permutation([0, 1, 2, 3]), Permutation([0, 1, 3, 2]),
Permutation([0, 2, 3, 1]), Permutation([3, 2, 1, 0])]
assert _distribute_gens_by_base(base, gens) == [gens,
[Permutation([0, 1, 2, 3]),
Permutation([0, 1, 3, 2]),
Permutation([0, 2, 3, 1])],
[Permutation([0, 1, 2, 3]),
Permutation([0, 1, 3, 2])]]
def test_strong_gens_from_distr():
strong_gens_distr = [[Permutation([0, 2, 1]), Permutation([1, 2, 0]),
Permutation([1, 0, 2])], [Permutation([0, 2, 1])]]
assert _strong_gens_from_distr(strong_gens_distr) == \
[Permutation([0, 2, 1]),
Permutation([1, 2, 0]),
Permutation([1, 0, 2])]
def test_orbits_transversals_from_bsgs():
S = SymmetricGroup(4)
S.schreier_sims()
base = S.base
strong_gens = S.strong_gens
strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
result = _orbits_transversals_from_bsgs(base, strong_gens_distr)
orbits = result[0]
transversals = result[1]
base_len = len(base)
for i in range(base_len):
for el in orbits[i]:
assert transversals[i][el](base[i]) == el
for j in range(i):
assert transversals[i][el](base[j]) == base[j]
order = 1
for i in range(base_len):
order *= len(orbits[i])
assert S.order() == order
def test_handle_precomputed_bsgs():
A = AlternatingGroup(5)
A.schreier_sims()
base = A.base
strong_gens = A.strong_gens
result = _handle_precomputed_bsgs(base, strong_gens)
strong_gens_distr = _distribute_gens_by_base(base, strong_gens)
assert strong_gens_distr == result[2]
transversals = result[0]
orbits = result[1]
base_len = len(base)
for i in range(base_len):
for el in orbits[i]:
assert transversals[i][el](base[i]) == el
for j in range(i):
assert transversals[i][el](base[j]) == base[j]
order = 1
for i in range(base_len):
order *= len(orbits[i])
assert A.order() == order
def test_base_ordering():
base = [2, 4, 5]
degree = 7
assert _base_ordering(base, degree) == [3, 4, 0, 5, 1, 2, 6]
def test_remove_gens():
S = SymmetricGroup(10)
base, strong_gens = S.schreier_sims_incremental()
new_gens = _remove_gens(base, strong_gens)
assert _verify_bsgs(S, base, new_gens) is True
A = AlternatingGroup(7)
base, strong_gens = A.schreier_sims_incremental()
new_gens = _remove_gens(base, strong_gens)
assert _verify_bsgs(A, base, new_gens) is True
D = DihedralGroup(2)
base, strong_gens = D.schreier_sims_incremental()
new_gens = _remove_gens(base, strong_gens)
assert _verify_bsgs(D, base, new_gens) is True
| 4,542 | 36.237705 | 78 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_prufer.py
|
from sympy.combinatorics.prufer import Prufer
from sympy.utilities.pytest import raises
def test_prufer():
# number of nodes is optional
assert Prufer([[0, 1], [0, 2], [0, 3], [0, 4]], 5).nodes == 5
assert Prufer([[0, 1], [0, 2], [0, 3], [0, 4]]).nodes == 5
a = Prufer([[0, 1], [0, 2], [0, 3], [0, 4]])
assert a.rank == 0
assert a.nodes == 5
assert a.prufer_repr == [0, 0, 0]
a = Prufer([[2, 4], [1, 4], [1, 3], [0, 5], [0, 4]])
assert a.rank == 924
assert a.nodes == 6
assert a.tree_repr == [[2, 4], [1, 4], [1, 3], [0, 5], [0, 4]]
assert a.prufer_repr == [4, 1, 4, 0]
assert Prufer.edges([0, 1, 2, 3], [1, 4, 5], [1, 4, 6]) == \
([[0, 1], [1, 2], [1, 4], [2, 3], [4, 5], [4, 6]], 7)
assert Prufer([0]*4).size == Prufer([6]*4).size == 1296
# accept iterables but convert to list of lists
tree = [(0, 1), (1, 5), (0, 3), (0, 2), (2, 6), (4, 7), (2, 4)]
tree_lists = [list(t) for t in tree]
assert Prufer(tree).tree_repr == tree_lists
assert sorted(Prufer(set(tree)).tree_repr) == sorted(tree_lists)
raises(ValueError, lambda: Prufer([[1, 2], [3, 4]])) # 0 is missing
assert Prufer(*Prufer.edges([1, 2], [3, 4])).prufer_repr == [1, 3]
raises(ValueError, lambda: Prufer.edges(
[1, 3], [3, 4])) # a broken tree but edges doesn't care
raises(ValueError, lambda: Prufer.edges([1, 2], [5, 6]))
def test_round_trip():
def doit(t, b):
e, n = Prufer.edges(*t)
t = Prufer(e, n)
a = sorted(t.tree_repr)
b = [i - 1 for i in b]
assert t.prufer_repr == b
assert sorted(Prufer(b).tree_repr) == a
assert Prufer.unrank(t.rank, n).prufer_repr == b
doit([[1, 2]], [])
doit([[2, 1, 3]], [1])
doit([[1, 3, 2]], [3])
doit([[1, 2, 3]], [2])
doit([[2, 1, 4], [1, 3]], [1, 1])
doit([[3, 2, 1, 4]], [2, 1])
doit([[3, 2, 1], [2, 4]], [2, 2])
doit([[1, 3, 2, 4]], [3, 2])
doit([[1, 4, 2, 3]], [4, 2])
doit([[3, 1, 4, 2]], [4, 1])
doit([[4, 2, 1, 3]], [1, 2])
doit([[1, 2, 4, 3]], [2, 4])
doit([[1, 3, 4, 2]], [3, 4])
doit([[2, 4, 1], [4, 3]], [4, 4])
doit([[1, 2, 3, 4]], [2, 3])
doit([[2, 3, 1], [3, 4]], [3, 3])
doit([[1, 4, 3, 2]], [4, 3])
doit([[2, 1, 4, 3]], [1, 4])
doit([[2, 1, 3, 4]], [1, 3])
doit([[6, 2, 1, 4], [1, 3, 5, 8], [3, 7]], [1, 2, 1, 3, 3, 5])
| 2,396 | 34.25 | 72 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_graycode.py
|
from sympy.combinatorics.graycode import (GrayCode, bin_to_gray,
random_bitstring, get_subset_from_bitstring, graycode_subsets)
def test_graycode():
g = GrayCode(2)
got = []
for i in g.generate_gray():
if i.startswith('0'):
g.skip()
got.append(i)
assert got == '00 11 10'.split()
a = GrayCode(6)
assert a.current == '0'*6
assert a.rank == 0
assert len(list(a.generate_gray())) == 64
codes = ['011001', '011011', '011010',
'011110', '011111', '011101', '011100', '010100', '010101', '010111',
'010110', '010010', '010011', '010001', '010000', '110000', '110001',
'110011', '110010', '110110', '110111', '110101', '110100', '111100',
'111101', '111111', '111110', '111010', '111011', '111001', '111000',
'101000', '101001', '101011', '101010', '101110', '101111', '101101',
'101100', '100100', '100101', '100111', '100110', '100010', '100011',
'100001', '100000']
assert list(a.generate_gray(start='011001')) == codes
assert list(
a.generate_gray(rank=GrayCode(6, start='011001').rank)) == codes
assert a.next().current == '000001'
assert a.next(2).current == '000011'
assert a.next(-1).current == '100000'
a = GrayCode(5, start='10010')
assert a.rank == 28
a = GrayCode(6, start='101000')
assert a.rank == 48
assert GrayCode(6, rank=4).current == '000110'
assert GrayCode(6, rank=4).rank == 4
assert [GrayCode(4, start=s).rank for s in
GrayCode(4).generate_gray()] == [0, 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15]
a = GrayCode(15, rank=15)
assert a.current == '000000000001000'
assert bin_to_gray('111') == '100'
a = random_bitstring(5)
assert type(a) is str
assert len(a) == 5
assert all(i in ['0', '1'] for i in a)
assert get_subset_from_bitstring(
['a', 'b', 'c', 'd'], '0011') == ['c', 'd']
assert get_subset_from_bitstring('abcd', '1001') == ['a', 'd']
assert list(graycode_subsets(['a', 'b', 'c'])) == \
[[], ['c'], ['b', 'c'], ['b'], ['a', 'b'], ['a', 'b', 'c'],
['a', 'c'], ['a']]
| 2,173 | 36.482759 | 73 |
py
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_perm_groups.py
|
from sympy.core.compatibility import range
from sympy.combinatorics.perm_groups import PermutationGroup
from sympy.combinatorics.named_groups import SymmetricGroup, CyclicGroup,\
DihedralGroup, AlternatingGroup, AbelianGroup, RubikGroup
from sympy.combinatorics.permutations import Permutation
from sympy.utilities.pytest import skip, XFAIL
from sympy.combinatorics.generators import rubik_cube_generators
from sympy.combinatorics.polyhedron import tetrahedron as Tetra, cube
from sympy.combinatorics.testutil import _verify_bsgs, _verify_centralizer,\
_verify_normal_closure
from sympy.utilities.pytest import raises
rmul = Permutation.rmul
def test_has():
a = Permutation([1, 0])
G = PermutationGroup([a])
assert G.is_abelian
a = Permutation([2, 0, 1])
b = Permutation([2, 1, 0])
G = PermutationGroup([a, b])
assert not G.is_abelian
G = PermutationGroup([a])
assert G.has(a)
assert not G.has(b)
a = Permutation([2, 0, 1, 3, 4, 5])
b = Permutation([0, 2, 1, 3, 4])
assert PermutationGroup(a, b).degree == \
PermutationGroup(a, b).degree == 6
def test_generate():
a = Permutation([1, 0])
g = list(PermutationGroup([a]).generate())
assert g == [Permutation([0, 1]), Permutation([1, 0])]
assert len(list(PermutationGroup(Permutation((0, 1))).generate())) == 1
g = PermutationGroup([a]).generate(method='dimino')
assert list(g) == [Permutation([0, 1]), Permutation([1, 0])]
a = Permutation([2, 0, 1])
b = Permutation([2, 1, 0])
G = PermutationGroup([a, b])
g = G.generate()
v1 = [p.array_form for p in list(g)]
v1.sort()
assert v1 == [[0, 1, 2], [0, 2, 1], [1, 0, 2], [1, 2, 0], [2, 0,
1], [2, 1, 0]]
v2 = list(G.generate(method='dimino', af=True))
assert v1 == sorted(v2)
a = Permutation([2, 0, 1, 3, 4, 5])
b = Permutation([2, 1, 3, 4, 5, 0])
g = PermutationGroup([a, b]).generate(af=True)
assert len(list(g)) == 360
def test_order():
a = Permutation([2, 0, 1, 3, 4, 5, 6, 7, 8, 9])
b = Permutation([2, 1, 3, 4, 5, 6, 7, 8, 9, 0])
g = PermutationGroup([a, b])
assert g.order() == 1814400
assert PermutationGroup().order() == 1
def test_equality():
p_1 = Permutation(0, 1, 3)
p_2 = Permutation(0, 2, 3)
p_3 = Permutation(0, 1, 2)
p_4 = Permutation(0, 1, 3)
g_1 = PermutationGroup(p_1, p_2)
g_2 = PermutationGroup(p_3, p_4)
g_3 = PermutationGroup(p_2, p_1)
assert g_1 == g_2
assert g_1.generators != g_2.generators
assert g_1 == g_3
def test_stabilizer():
S = SymmetricGroup(2)
H = S.stabilizer(0)
assert H.generators == [Permutation(1)]
a = Permutation([2, 0, 1, 3, 4, 5])
b = Permutation([2, 1, 3, 4, 5, 0])
G = PermutationGroup([a, b])
G0 = G.stabilizer(0)
assert G0.order() == 60
gens_cube = [[1, 3, 5, 7, 0, 2, 4, 6], [1, 3, 0, 2, 5, 7, 4, 6]]
gens = [Permutation(p) for p in gens_cube]
G = PermutationGroup(gens)
G2 = G.stabilizer(2)
assert G2.order() == 6
G2_1 = G2.stabilizer(1)
v = list(G2_1.generate(af=True))
assert v == [[0, 1, 2, 3, 4, 5, 6, 7], [3, 1, 2, 0, 7, 5, 6, 4]]
gens = (
(1, 2, 0, 4, 5, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19),
(0, 1, 2, 3, 4, 5, 19, 6, 8, 9, 10, 11, 12, 13, 14,
15, 16, 7, 17, 18),
(0, 1, 2, 3, 4, 5, 6, 7, 9, 18, 16, 11, 12, 13, 14, 15, 8, 17, 10, 19))
gens = [Permutation(p) for p in gens]
G = PermutationGroup(gens)
G2 = G.stabilizer(2)
assert G2.order() == 181440
S = SymmetricGroup(3)
assert [G.order() for G in S.basic_stabilizers] == [6, 2]
def test_center():
# the center of the dihedral group D_n is of order 2 for even n
for i in (4, 6, 10):
D = DihedralGroup(i)
assert (D.center()).order() == 2
# the center of the dihedral group D_n is of order 1 for odd n>2
for i in (3, 5, 7):
D = DihedralGroup(i)
assert (D.center()).order() == 1
# the center of an abelian group is the group itself
for i in (2, 3, 5):
for j in (1, 5, 7):
for k in (1, 1, 11):
G = AbelianGroup(i, j, k)
assert G.center().is_subgroup(G)
# the center of a nonabelian simple group is trivial
for i in(1, 5, 9):
A = AlternatingGroup(i)
assert (A.center()).order() == 1
# brute-force verifications
D = DihedralGroup(5)
A = AlternatingGroup(3)
C = CyclicGroup(4)
G.is_subgroup(D*A*C)
assert _verify_centralizer(G, G)
def test_centralizer():
# the centralizer of the trivial group is the entire group
S = SymmetricGroup(2)
assert S.centralizer(Permutation(list(range(2)))).is_subgroup(S)
A = AlternatingGroup(5)
assert A.centralizer(Permutation(list(range(5)))).is_subgroup(A)
# a centralizer in the trivial group is the trivial group itself
triv = PermutationGroup([Permutation([0, 1, 2, 3])])
D = DihedralGroup(4)
assert triv.centralizer(D).is_subgroup(triv)
# brute-force verifications for centralizers of groups
for i in (4, 5, 6):
S = SymmetricGroup(i)
A = AlternatingGroup(i)
C = CyclicGroup(i)
D = DihedralGroup(i)
for gp in (S, A, C, D):
for gp2 in (S, A, C, D):
if not gp2.is_subgroup(gp):
assert _verify_centralizer(gp, gp2)
# verify the centralizer for all elements of several groups
S = SymmetricGroup(5)
elements = list(S.generate_dimino())
for element in elements:
assert _verify_centralizer(S, element)
A = AlternatingGroup(5)
elements = list(A.generate_dimino())
for element in elements:
assert _verify_centralizer(A, element)
D = DihedralGroup(7)
elements = list(D.generate_dimino())
for element in elements:
assert _verify_centralizer(D, element)
# verify centralizers of small groups within small groups
small = []
for i in (1, 2, 3):
small.append(SymmetricGroup(i))
small.append(AlternatingGroup(i))
small.append(DihedralGroup(i))
small.append(CyclicGroup(i))
for gp in small:
for gp2 in small:
if gp.degree == gp2.degree:
assert _verify_centralizer(gp, gp2)
def test_coset_rank():
gens_cube = [[1, 3, 5, 7, 0, 2, 4, 6], [1, 3, 0, 2, 5, 7, 4, 6]]
gens = [Permutation(p) for p in gens_cube]
G = PermutationGroup(gens)
i = 0
for h in G.generate(af=True):
rk = G.coset_rank(h)
assert rk == i
h1 = G.coset_unrank(rk, af=True)
assert h == h1
i += 1
assert G.coset_unrank(48) == None
assert G.coset_unrank(G.coset_rank(gens[0])) == gens[0]
def test_coset_factor():
a = Permutation([0, 2, 1])
G = PermutationGroup([a])
c = Permutation([2, 1, 0])
assert not G.coset_factor(c)
assert G.coset_rank(c) is None
a = Permutation([2, 0, 1, 3, 4, 5])
b = Permutation([2, 1, 3, 4, 5, 0])
g = PermutationGroup([a, b])
assert g.order() == 360
d = Permutation([1, 0, 2, 3, 4, 5])
assert not g.coset_factor(d.array_form)
assert not g.contains(d)
assert Permutation(2) in G
c = Permutation([1, 0, 2, 3, 5, 4])
v = g.coset_factor(c, True)
tr = g.basic_transversals
p = Permutation.rmul(*[tr[i][v[i]] for i in range(len(g.base))])
assert p == c
v = g.coset_factor(c)
p = Permutation.rmul(*v)
assert p == c
assert g.contains(c)
G = PermutationGroup([Permutation([2, 1, 0])])
p = Permutation([1, 0, 2])
assert G.coset_factor(p) == []
def test_orbits():
a = Permutation([2, 0, 1])
b = Permutation([2, 1, 0])
g = PermutationGroup([a, b])
assert g.orbit(0) == {0, 1, 2}
assert g.orbits() == [{0, 1, 2}]
assert g.is_transitive() and g.is_transitive(strict=False)
assert g.orbit_transversal(0) == \
[Permutation(
[0, 1, 2]), Permutation([2, 0, 1]), Permutation([1, 2, 0])]
assert g.orbit_transversal(0, True) == \
[(0, Permutation([0, 1, 2])), (2, Permutation([2, 0, 1])),
(1, Permutation([1, 2, 0]))]
a = Permutation(list(range(1, 100)) + [0])
G = PermutationGroup([a])
assert [min(o) for o in G.orbits()] == [0]
G = PermutationGroup(rubik_cube_generators())
assert [min(o) for o in G.orbits()] == [0, 1]
assert not G.is_transitive() and not G.is_transitive(strict=False)
G = PermutationGroup([Permutation(0, 1, 3), Permutation(3)(0, 1)])
assert not G.is_transitive() and G.is_transitive(strict=False)
assert PermutationGroup(
Permutation(3)).is_transitive(strict=False) is False
def test_is_normal():
gens_s5 = [Permutation(p) for p in [[1, 2, 3, 4, 0], [2, 1, 4, 0, 3]]]
G1 = PermutationGroup(gens_s5)
assert G1.order() == 120
gens_a5 = [Permutation(p) for p in [[1, 0, 3, 2, 4], [2, 1, 4, 3, 0]]]
G2 = PermutationGroup(gens_a5)
assert G2.order() == 60
assert G2.is_normal(G1)
gens3 = [Permutation(p) for p in [[2, 1, 3, 0, 4], [1, 2, 0, 3, 4]]]
G3 = PermutationGroup(gens3)
assert not G3.is_normal(G1)
assert G3.order() == 12
G4 = G1.normal_closure(G3.generators)
assert G4.order() == 60
gens5 = [Permutation(p) for p in [[1, 2, 3, 0, 4], [1, 2, 0, 3, 4]]]
G5 = PermutationGroup(gens5)
assert G5.order() == 24
G6 = G1.normal_closure(G5.generators)
assert G6.order() == 120
assert G1.is_subgroup(G6)
assert not G1.is_subgroup(G4)
assert G2.is_subgroup(G4)
s4 = PermutationGroup(Permutation(0, 1, 2, 3), Permutation(3)(0, 1))
s6 = PermutationGroup(Permutation(0, 1, 2, 3, 5), Permutation(5)(0, 1))
assert s6.is_normal(s4, strict=False)
assert not s4.is_normal(s6, strict=False)
def test_eq():
a = [[1, 2, 0, 3, 4, 5], [1, 0, 2, 3, 4, 5], [2, 1, 0, 3, 4, 5], [
1, 2, 0, 3, 4, 5]]
a = [Permutation(p) for p in a + [[1, 2, 3, 4, 5, 0]]]
g = Permutation([1, 2, 3, 4, 5, 0])
G1, G2, G3 = [PermutationGroup(x) for x in [a[:2], a[2:4], [g, g**2]]]
assert G1.order() == G2.order() == G3.order() == 6
assert G1.is_subgroup(G2)
assert not G1.is_subgroup(G3)
G4 = PermutationGroup([Permutation([0, 1])])
assert not G1.is_subgroup(G4)
assert G4.is_subgroup(G1, 0)
assert PermutationGroup(g, g).is_subgroup(PermutationGroup(g))
assert SymmetricGroup(3).is_subgroup(SymmetricGroup(4), 0)
assert SymmetricGroup(3).is_subgroup(SymmetricGroup(3)*CyclicGroup(5), 0)
assert not CyclicGroup(5).is_subgroup(SymmetricGroup(3)*CyclicGroup(5), 0)
assert CyclicGroup(3).is_subgroup(SymmetricGroup(3)*CyclicGroup(5), 0)
def test_derived_subgroup():
a = Permutation([1, 0, 2, 4, 3])
b = Permutation([0, 1, 3, 2, 4])
G = PermutationGroup([a, b])
C = G.derived_subgroup()
assert C.order() == 3
assert C.is_normal(G)
assert C.is_subgroup(G, 0)
assert not G.is_subgroup(C, 0)
gens_cube = [[1, 3, 5, 7, 0, 2, 4, 6], [1, 3, 0, 2, 5, 7, 4, 6]]
gens = [Permutation(p) for p in gens_cube]
G = PermutationGroup(gens)
C = G.derived_subgroup()
assert C.order() == 12
def test_is_solvable():
a = Permutation([1, 2, 0])
b = Permutation([1, 0, 2])
G = PermutationGroup([a, b])
assert G.is_solvable
a = Permutation([1, 2, 3, 4, 0])
b = Permutation([1, 0, 2, 3, 4])
G = PermutationGroup([a, b])
assert not G.is_solvable
def test_rubik1():
gens = rubik_cube_generators()
gens1 = [gens[-1]] + [p**2 for p in gens[1:]]
G1 = PermutationGroup(gens1)
assert G1.order() == 19508428800
gens2 = [p**2 for p in gens]
G2 = PermutationGroup(gens2)
assert G2.order() == 663552
assert G2.is_subgroup(G1, 0)
C1 = G1.derived_subgroup()
assert C1.order() == 4877107200
assert C1.is_subgroup(G1, 0)
assert not G2.is_subgroup(C1, 0)
G = RubikGroup(2)
assert G.order() == 3674160
@XFAIL
def test_rubik():
skip('takes too much time')
G = PermutationGroup(rubik_cube_generators())
assert G.order() == 43252003274489856000
G1 = PermutationGroup(G[:3])
assert G1.order() == 170659735142400
assert not G1.is_normal(G)
G2 = G.normal_closure(G1.generators)
assert G2.is_subgroup(G)
def test_direct_product():
C = CyclicGroup(4)
D = DihedralGroup(4)
G = C*C*C
assert G.order() == 64
assert G.degree == 12
assert len(G.orbits()) == 3
assert G.is_abelian is True
H = D*C
assert H.order() == 32
assert H.is_abelian is False
def test_orbit_rep():
G = DihedralGroup(6)
assert G.orbit_rep(1, 3) in [Permutation([2, 3, 4, 5, 0, 1]),
Permutation([4, 3, 2, 1, 0, 5])]
H = CyclicGroup(4)*G
assert H.orbit_rep(1, 5) is False
def test_schreier_vector():
G = CyclicGroup(50)
v = [0]*50
v[23] = -1
assert G.schreier_vector(23) == v
H = DihedralGroup(8)
assert H.schreier_vector(2) == [0, 1, -1, 0, 0, 1, 0, 0]
L = SymmetricGroup(4)
assert L.schreier_vector(1) == [1, -1, 0, 0]
def test_random_pr():
D = DihedralGroup(6)
r = 11
n = 3
_random_prec_n = {}
_random_prec_n[0] = {'s': 7, 't': 3, 'x': 2, 'e': -1}
_random_prec_n[1] = {'s': 5, 't': 5, 'x': 1, 'e': -1}
_random_prec_n[2] = {'s': 3, 't': 4, 'x': 2, 'e': 1}
D._random_pr_init(r, n, _random_prec_n=_random_prec_n)
assert D._random_gens[11] == [0, 1, 2, 3, 4, 5]
_random_prec = {'s': 2, 't': 9, 'x': 1, 'e': -1}
assert D.random_pr(_random_prec=_random_prec) == \
Permutation([0, 5, 4, 3, 2, 1])
def test_is_alt_sym():
G = DihedralGroup(10)
assert G.is_alt_sym() is False
S = SymmetricGroup(10)
N_eps = 10
_random_prec = {'N_eps': N_eps,
0: Permutation([[2], [1, 4], [0, 6, 7, 8, 9, 3, 5]]),
1: Permutation([[1, 8, 7, 6, 3, 5, 2, 9], [0, 4]]),
2: Permutation([[5, 8], [4, 7], [0, 1, 2, 3, 6, 9]]),
3: Permutation([[3], [0, 8, 2, 7, 4, 1, 6, 9, 5]]),
4: Permutation([[8], [4, 7, 9], [3, 6], [0, 5, 1, 2]]),
5: Permutation([[6], [0, 2, 4, 5, 1, 8, 3, 9, 7]]),
6: Permutation([[6, 9, 8], [4, 5], [1, 3, 7], [0, 2]]),
7: Permutation([[4], [0, 2, 9, 1, 3, 8, 6, 5, 7]]),
8: Permutation([[1, 5, 6, 3], [0, 2, 7, 8, 4, 9]]),
9: Permutation([[8], [6, 7], [2, 3, 4, 5], [0, 1, 9]])}
assert S.is_alt_sym(_random_prec=_random_prec) is True
A = AlternatingGroup(10)
_random_prec = {'N_eps': N_eps,
0: Permutation([[1, 6, 4, 2, 7, 8, 5, 9, 3], [0]]),
1: Permutation([[1], [0, 5, 8, 4, 9, 2, 3, 6, 7]]),
2: Permutation([[1, 9, 8, 3, 2, 5], [0, 6, 7, 4]]),
3: Permutation([[6, 8, 9], [4, 5], [1, 3, 7, 2], [0]]),
4: Permutation([[8], [5], [4], [2, 6, 9, 3], [1], [0, 7]]),
5: Permutation([[3, 6], [0, 8, 1, 7, 5, 9, 4, 2]]),
6: Permutation([[5], [2, 9], [1, 8, 3], [0, 4, 7, 6]]),
7: Permutation([[1, 8, 4, 7, 2, 3], [0, 6, 9, 5]]),
8: Permutation([[5, 8, 7], [3], [1, 4, 2, 6], [0, 9]]),
9: Permutation([[4, 9, 6], [3, 8], [1, 2], [0, 5, 7]])}
assert A.is_alt_sym(_random_prec=_random_prec) is False
def test_minimal_block():
D = DihedralGroup(6)
block_system = D.minimal_block([0, 3])
for i in range(3):
assert block_system[i] == block_system[i + 3]
S = SymmetricGroup(6)
assert S.minimal_block([0, 1]) == [0, 0, 0, 0, 0, 0]
assert Tetra.pgroup.minimal_block([0, 1]) == [0, 0, 0, 0]
def test_max_div():
S = SymmetricGroup(10)
assert S.max_div == 5
def test_is_primitive():
S = SymmetricGroup(5)
assert S.is_primitive() is True
C = CyclicGroup(7)
assert C.is_primitive() is True
def test_random_stab():
S = SymmetricGroup(5)
_random_el = Permutation([1, 3, 2, 0, 4])
_random_prec = {'rand': _random_el}
g = S.random_stab(2, _random_prec=_random_prec)
assert g == Permutation([1, 3, 2, 0, 4])
h = S.random_stab(1)
assert h(1) == 1
def test_transitivity_degree():
perm = Permutation([1, 2, 0])
C = PermutationGroup([perm])
assert C.transitivity_degree == 1
gen1 = Permutation([1, 2, 0, 3, 4])
gen2 = Permutation([1, 2, 3, 4, 0])
# alternating group of degree 5
Alt = PermutationGroup([gen1, gen2])
assert Alt.transitivity_degree == 3
def test_schreier_sims_random():
assert sorted(Tetra.pgroup.base) == [0, 1]
S = SymmetricGroup(3)
base = [0, 1]
strong_gens = [Permutation([1, 2, 0]), Permutation([1, 0, 2]),
Permutation([0, 2, 1])]
assert S.schreier_sims_random(base, strong_gens, 5) == (base, strong_gens)
D = DihedralGroup(3)
_random_prec = {'g': [Permutation([2, 0, 1]), Permutation([1, 2, 0]),
Permutation([1, 0, 2])]}
base = [0, 1]
strong_gens = [Permutation([1, 2, 0]), Permutation([2, 1, 0]),
Permutation([0, 2, 1])]
assert D.schreier_sims_random([], D.generators, 2,
_random_prec=_random_prec) == (base, strong_gens)
def test_baseswap():
S = SymmetricGroup(4)
S.schreier_sims()
base = S.base
strong_gens = S.strong_gens
assert base == [0, 1, 2]
deterministic = S.baseswap(base, strong_gens, 1, randomized=False)
randomized = S.baseswap(base, strong_gens, 1)
assert deterministic[0] == [0, 2, 1]
assert _verify_bsgs(S, deterministic[0], deterministic[1]) is True
assert randomized[0] == [0, 2, 1]
assert _verify_bsgs(S, randomized[0], randomized[1]) is True
def test_schreier_sims_incremental():
identity = Permutation([0, 1, 2, 3, 4])
TrivialGroup = PermutationGroup([identity])
base, strong_gens = TrivialGroup.schreier_sims_incremental(base=[0, 1, 2])
assert _verify_bsgs(TrivialGroup, base, strong_gens) is True
S = SymmetricGroup(5)
base, strong_gens = S.schreier_sims_incremental(base=[0, 1, 2])
assert _verify_bsgs(S, base, strong_gens) is True
D = DihedralGroup(2)
base, strong_gens = D.schreier_sims_incremental(base=[1])
assert _verify_bsgs(D, base, strong_gens) is True
A = AlternatingGroup(7)
gens = A.generators[:]
gen0 = gens[0]
gen1 = gens[1]
gen1 = rmul(gen1, ~gen0)
gen0 = rmul(gen0, gen1)
gen1 = rmul(gen0, gen1)
base, strong_gens = A.schreier_sims_incremental(base=[0, 1], gens=gens)
assert _verify_bsgs(A, base, strong_gens) is True
C = CyclicGroup(11)
gen = C.generators[0]
base, strong_gens = C.schreier_sims_incremental(gens=[gen**3])
assert _verify_bsgs(C, base, strong_gens) is True
def _subgroup_search(i, j, k):
prop_true = lambda x: True
prop_fix_points = lambda x: [x(point) for point in points] == points
prop_comm_g = lambda x: rmul(x, g) == rmul(g, x)
prop_even = lambda x: x.is_even
for i in range(i, j, k):
S = SymmetricGroup(i)
A = AlternatingGroup(i)
C = CyclicGroup(i)
Sym = S.subgroup_search(prop_true)
assert Sym.is_subgroup(S)
Alt = S.subgroup_search(prop_even)
assert Alt.is_subgroup(A)
Sym = S.subgroup_search(prop_true, init_subgroup=C)
assert Sym.is_subgroup(S)
points = [7]
assert S.stabilizer(7).is_subgroup(S.subgroup_search(prop_fix_points))
points = [3, 4]
assert S.stabilizer(3).stabilizer(4).is_subgroup(
S.subgroup_search(prop_fix_points))
points = [3, 5]
fix35 = A.subgroup_search(prop_fix_points)
points = [5]
fix5 = A.subgroup_search(prop_fix_points)
assert A.subgroup_search(prop_fix_points, init_subgroup=fix35
).is_subgroup(fix5)
base, strong_gens = A.schreier_sims_incremental()
g = A.generators[0]
comm_g = \
A.subgroup_search(prop_comm_g, base=base, strong_gens=strong_gens)
assert _verify_bsgs(comm_g, base, comm_g.generators) is True
assert [prop_comm_g(gen) is True for gen in comm_g.generators]
def test_subgroup_search():
_subgroup_search(10, 15, 2)
@XFAIL
def test_subgroup_search2():
skip('takes too much time')
_subgroup_search(16, 17, 1)
def test_normal_closure():
# the normal closure of the trivial group is trivial
S = SymmetricGroup(3)
identity = Permutation([0, 1, 2])
closure = S.normal_closure(identity)
assert closure.is_trivial
# the normal closure of the entire group is the entire group
A = AlternatingGroup(4)
assert A.normal_closure(A).is_subgroup(A)
# brute-force verifications for subgroups
for i in (3, 4, 5):
S = SymmetricGroup(i)
A = AlternatingGroup(i)
D = DihedralGroup(i)
C = CyclicGroup(i)
for gp in (A, D, C):
assert _verify_normal_closure(S, gp)
# brute-force verifications for all elements of a group
S = SymmetricGroup(5)
elements = list(S.generate_dimino())
for element in elements:
assert _verify_normal_closure(S, element)
# small groups
small = []
for i in (1, 2, 3):
small.append(SymmetricGroup(i))
small.append(AlternatingGroup(i))
small.append(DihedralGroup(i))
small.append(CyclicGroup(i))
for gp in small:
for gp2 in small:
if gp2.is_subgroup(gp, 0) and gp2.degree == gp.degree:
assert _verify_normal_closure(gp, gp2)
def test_derived_series():
# the derived series of the trivial group consists only of the trivial group
triv = PermutationGroup([Permutation([0, 1, 2])])
assert triv.derived_series()[0].is_subgroup(triv)
# the derived series for a simple group consists only of the group itself
for i in (5, 6, 7):
A = AlternatingGroup(i)
assert A.derived_series()[0].is_subgroup(A)
# the derived series for S_4 is S_4 > A_4 > K_4 > triv
S = SymmetricGroup(4)
series = S.derived_series()
assert series[1].is_subgroup(AlternatingGroup(4))
assert series[2].is_subgroup(DihedralGroup(2))
assert series[3].is_trivial
def test_lower_central_series():
# the lower central series of the trivial group consists of the trivial
# group
triv = PermutationGroup([Permutation([0, 1, 2])])
assert triv.lower_central_series()[0].is_subgroup(triv)
# the lower central series of a simple group consists of the group itself
for i in (5, 6, 7):
A = AlternatingGroup(i)
assert A.lower_central_series()[0].is_subgroup(A)
# GAP-verified example
S = SymmetricGroup(6)
series = S.lower_central_series()
assert len(series) == 2
assert series[1].is_subgroup(AlternatingGroup(6))
def test_commutator():
# the commutator of the trivial group and the trivial group is trivial
S = SymmetricGroup(3)
triv = PermutationGroup([Permutation([0, 1, 2])])
assert S.commutator(triv, triv).is_subgroup(triv)
# the commutator of the trivial group and any other group is again trivial
A = AlternatingGroup(3)
assert S.commutator(triv, A).is_subgroup(triv)
# the commutator is commutative
for i in (3, 4, 5):
S = SymmetricGroup(i)
A = AlternatingGroup(i)
D = DihedralGroup(i)
assert S.commutator(A, D).is_subgroup(S.commutator(D, A))
# the commutator of an abelian group is trivial
S = SymmetricGroup(7)
A1 = AbelianGroup(2, 5)
A2 = AbelianGroup(3, 4)
triv = PermutationGroup([Permutation([0, 1, 2, 3, 4, 5, 6])])
assert S.commutator(A1, A1).is_subgroup(triv)
assert S.commutator(A2, A2).is_subgroup(triv)
# examples calculated by hand
S = SymmetricGroup(3)
A = AlternatingGroup(3)
assert S.commutator(A, S).is_subgroup(A)
def test_is_nilpotent():
# every abelian group is nilpotent
for i in (1, 2, 3):
C = CyclicGroup(i)
Ab = AbelianGroup(i, i + 2)
assert C.is_nilpotent
assert Ab.is_nilpotent
Ab = AbelianGroup(5, 7, 10)
assert Ab.is_nilpotent
# A_5 is not solvable and thus not nilpotent
assert AlternatingGroup(5).is_nilpotent is False
def test_is_trivial():
for i in range(5):
triv = PermutationGroup([Permutation(list(range(i)))])
assert triv.is_trivial
def test_pointwise_stabilizer():
S = SymmetricGroup(2)
stab = S.pointwise_stabilizer([0])
assert stab.generators == [Permutation(1)]
S = SymmetricGroup(5)
points = []
stab = S
for point in (2, 0, 3, 4, 1):
stab = stab.stabilizer(point)
points.append(point)
assert S.pointwise_stabilizer(points).is_subgroup(stab)
def test_make_perm():
assert cube.pgroup.make_perm(5, seed=list(range(5))) == \
Permutation([4, 7, 6, 5, 0, 3, 2, 1])
assert cube.pgroup.make_perm(7, seed=list(range(7))) == \
Permutation([6, 7, 3, 2, 5, 4, 0, 1])
def test_elements():
p = Permutation(2, 3)
assert PermutationGroup(p).elements == {Permutation(3), Permutation(2, 3)}
def test_is_group():
assert PermutationGroup(Permutation(1,2), Permutation(2,4)).is_group == True
assert SymmetricGroup(4).is_group == True
def test_PermutationGroup():
assert PermutationGroup() == PermutationGroup(Permutation())
def test_coset_transvesal():
G = AlternatingGroup(5)
H = PermutationGroup(Permutation(0,1,2),Permutation(1,2)(3,4))
assert G.coset_transversal(H) == \
[Permutation(4), Permutation(2, 3, 4), Permutation(2, 4, 3),
Permutation(1, 2, 4), Permutation(4)(1, 2, 3), Permutation(1, 3)(2, 4),
Permutation(0, 1, 2, 3, 4), Permutation(0, 1, 2, 4, 3),
Permutation(0, 1, 3, 2, 4), Permutation(0, 2, 4, 1, 3)]
def test_coset_table():
G = PermutationGroup(Permutation(0,1,2,3), Permutation(0,1,2),
Permutation(0,4,2,7), Permutation(5,6), Permutation(0,7));
H = PermutationGroup(Permutation(0,1,2,3), Permutation(0,7))
assert G.coset_table(H) == \
[[0, 0, 0, 0, 1, 2, 3, 3, 0, 0], [4, 5, 2, 5, 6, 0, 7, 7, 1, 1],
[5, 4, 5, 1, 0, 6, 8, 8, 6, 6], [3, 3, 3, 3, 7, 8, 0, 0, 3, 3],
[2, 1, 4, 4, 4, 4, 9, 9, 4, 4], [1, 2, 1, 2, 5, 5, 10, 10, 5, 5],
[6, 6, 6, 6, 2, 1, 11, 11, 2, 2], [9, 10, 8, 10, 11, 3, 1, 1, 7, 7],
[10, 9, 10, 7, 3, 11, 2, 2, 11, 11], [8, 7, 9, 9, 9, 9, 4, 4, 9, 9],
[7, 8, 7, 8, 10, 10, 5, 5, 10, 10], [11, 11, 11, 11, 8, 7, 6, 6, 8, 8]]
def test_subgroup():
G = PermutationGroup(Permutation(0,1,2), Permutation(0,2,3))
H = G.subgroup([Permutation(0,1,3)])
assert H.is_subgroup(G)
| 26,443 | 34.025166 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_generators.py
|
from sympy.combinatorics.generators import symmetric, cyclic, alternating, dihedral
from sympy.combinatorics.permutations import Permutation
def test_generators():
assert list(cyclic(6)) == [
Permutation([0, 1, 2, 3, 4, 5]),
Permutation([1, 2, 3, 4, 5, 0]),
Permutation([2, 3, 4, 5, 0, 1]),
Permutation([3, 4, 5, 0, 1, 2]),
Permutation([4, 5, 0, 1, 2, 3]),
Permutation([5, 0, 1, 2, 3, 4])]
assert list(cyclic(10)) == [
Permutation([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
Permutation([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]),
Permutation([2, 3, 4, 5, 6, 7, 8, 9, 0, 1]),
Permutation([3, 4, 5, 6, 7, 8, 9, 0, 1, 2]),
Permutation([4, 5, 6, 7, 8, 9, 0, 1, 2, 3]),
Permutation([5, 6, 7, 8, 9, 0, 1, 2, 3, 4]),
Permutation([6, 7, 8, 9, 0, 1, 2, 3, 4, 5]),
Permutation([7, 8, 9, 0, 1, 2, 3, 4, 5, 6]),
Permutation([8, 9, 0, 1, 2, 3, 4, 5, 6, 7]),
Permutation([9, 0, 1, 2, 3, 4, 5, 6, 7, 8])]
assert list(alternating(4)) == [
Permutation([0, 1, 2, 3]),
Permutation([0, 2, 3, 1]),
Permutation([0, 3, 1, 2]),
Permutation([1, 0, 3, 2]),
Permutation([1, 2, 0, 3]),
Permutation([1, 3, 2, 0]),
Permutation([2, 0, 1, 3]),
Permutation([2, 1, 3, 0]),
Permutation([2, 3, 0, 1]),
Permutation([3, 0, 2, 1]),
Permutation([3, 1, 0, 2]),
Permutation([3, 2, 1, 0])]
assert list(symmetric(3)) == [
Permutation([0, 1, 2]),
Permutation([0, 2, 1]),
Permutation([1, 0, 2]),
Permutation([1, 2, 0]),
Permutation([2, 0, 1]),
Permutation([2, 1, 0])]
assert list(symmetric(4)) == [
Permutation([0, 1, 2, 3]),
Permutation([0, 1, 3, 2]),
Permutation([0, 2, 1, 3]),
Permutation([0, 2, 3, 1]),
Permutation([0, 3, 1, 2]),
Permutation([0, 3, 2, 1]),
Permutation([1, 0, 2, 3]),
Permutation([1, 0, 3, 2]),
Permutation([1, 2, 0, 3]),
Permutation([1, 2, 3, 0]),
Permutation([1, 3, 0, 2]),
Permutation([1, 3, 2, 0]),
Permutation([2, 0, 1, 3]),
Permutation([2, 0, 3, 1]),
Permutation([2, 1, 0, 3]),
Permutation([2, 1, 3, 0]),
Permutation([2, 3, 0, 1]),
Permutation([2, 3, 1, 0]),
Permutation([3, 0, 1, 2]),
Permutation([3, 0, 2, 1]),
Permutation([3, 1, 0, 2]),
Permutation([3, 1, 2, 0]),
Permutation([3, 2, 0, 1]),
Permutation([3, 2, 1, 0])]
assert list(dihedral(1)) == [
Permutation([0, 1]), Permutation([1, 0])]
assert list(dihedral(2)) == [
Permutation([0, 1, 2, 3]),
Permutation([1, 0, 3, 2]),
Permutation([2, 3, 0, 1]),
Permutation([3, 2, 1, 0])]
assert list(dihedral(3)) == [
Permutation([0, 1, 2]),
Permutation([2, 1, 0]),
Permutation([1, 2, 0]),
Permutation([0, 2, 1]),
Permutation([2, 0, 1]),
Permutation([1, 0, 2])]
assert list(dihedral(5)) == [
Permutation([0, 1, 2, 3, 4]),
Permutation([4, 3, 2, 1, 0]),
Permutation([1, 2, 3, 4, 0]),
Permutation([0, 4, 3, 2, 1]),
Permutation([2, 3, 4, 0, 1]),
Permutation([1, 0, 4, 3, 2]),
Permutation([3, 4, 0, 1, 2]),
Permutation([2, 1, 0, 4, 3]),
Permutation([4, 0, 1, 2, 3]),
Permutation([3, 2, 1, 0, 4])]
| 3,473 | 32.728155 | 83 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_free_groups.py
|
from sympy.combinatorics.free_groups import free_group, FreeGroup
from sympy.core import Symbol
from sympy.utilities.pytest import raises
from sympy import oo
F, x, y, z = free_group("x, y, z")
def test_FreeGroup__init__():
x, y, z = map(Symbol, "xyz")
assert len(FreeGroup("x, y, z").generators) == 3
assert len(FreeGroup(x).generators) == 1
assert len(FreeGroup(("x", "y", "z"))) == 3
assert len(FreeGroup((x, y, z)).generators) == 3
def test_free_group():
G, a, b, c = free_group("a, b, c")
assert F.generators == (x, y, z)
assert x*z**2 in F
assert x in F
assert y*z**-1 in F
assert (y*z)**0 in F
assert a not in F
assert a**0 not in F
assert len(F) == 3
assert str(F) == '<free group on the generators (x, y, z)>'
assert not F == G
assert F.order() == oo
assert F.is_abelian == False
assert F.center() == set([F.identity])
(e,) = free_group("")
assert e.order() == 1
assert e.generators == ()
assert e.elements == set([e.identity])
assert e.is_abelian == True
def test_FreeGroup__hash__():
assert hash(F)
def test_FreeGroup__eq__():
assert free_group("x, y, z")[0] == free_group("x, y, z")[0]
assert free_group("x, y, z")[0] is free_group("x, y, z")[0]
assert free_group("x, y, z")[0] != free_group("a, x, y")[0]
assert free_group("x, y, z")[0] is not free_group("a, x, y")[0]
assert free_group("x, y")[0] != free_group("x, y, z")[0]
assert free_group("x, y")[0] is not free_group("x, y, z")[0]
assert free_group("x, y, z")[0] != free_group("x, y")[0]
assert free_group("x, y, z")[0] is not free_group("x, y")[0]
def test_FreeGroup__getitem__():
assert F[0:] == FreeGroup("x, y, z")
assert F[1:] == FreeGroup("y, z")
assert F[2:] == FreeGroup("z")
def test_FreeGroupElm__hash__():
assert hash(x*y*z)
def test_FreeGroupElm_copy():
f = x*y*z**3
g = f.copy()
h = x*y*z**7
assert f == g
assert f != h
def test_FreeGroupElm_inverse():
assert x.inverse() == x**-1
assert (x*y).inverse() == y**-1*x**-1
assert (y*x*y**-1).inverse() == y*x**-1*y**-1
assert (y**2*x**-1).inverse() == x*y**-2
def test_FreeGroupElm_type_error():
raises(TypeError, lambda: 2/x)
raises(TypeError, lambda: x**2 + y**2)
raises(TypeError, lambda: x/2)
def test_FreeGroupElm_methods():
assert (x**0).order() == 1
assert (y**2).order() == oo
assert (x**-1*y).commutator(x) == y**-1*x**-1*y*x
assert len(x**2*y**-1) == 3
assert len(x**-1*y**3*z) == 5
def test_FreeGroupElm_eliminate_word():
w = x**5*y*x**2*y**-4*x
assert w.eliminate_word( x, x**2 ) == x**10*y*x**4*y**-4*x**2
w3 = x**2*y**3*x**-1*y
assert w3.eliminate_word(x, x**2) == x**4*y**3*x**-2*y
assert w3.eliminate_word(x, y) == y**5
assert w3.eliminate_word(x, y**4) == y**8
assert w3.eliminate_word(y, x**-1) == x**-3
assert w3.eliminate_word(x, y*z) == y*z*y*z*y**3*z**-1
assert (y**-3).eliminate_word(y, x**-1*z**-1) == z*x*z*x*z*x
#assert w3.eliminate_word(x, y*x) == y*x*y*x**2*y*x*y*x*y*x*z**3
#assert w3.eliminate_word(x, x*y) == x*y*x**2*y*x*y*x*y*x*y*z**3
def test_FreeGroupElm_array_form():
assert (x*z).array_form == ((Symbol('x'), 1), (Symbol('z'), 1))
assert (x**2*z*y*x**-2).array_form == \
((Symbol('x'), 2), (Symbol('z'), 1), (Symbol('y'), 1), (Symbol('x'), -2))
assert (x**-2*y**-1).array_form == ((Symbol('x'), -2), (Symbol('y'), -1))
def test_FreeGroupElm_letter_form():
assert (x**3).letter_form == (Symbol('x'), Symbol('x'), Symbol('x'))
assert (x**2*z**-2*x).letter_form == \
(Symbol('x'), Symbol('x'), -Symbol('z'), -Symbol('z'), Symbol('x'))
def test_FreeGroupElm_ext_rep():
assert (x**2*z**-2*x).ext_rep == \
(Symbol('x'), 2, Symbol('z'), -2, Symbol('x'), 1)
assert (x**-2*y**-1).ext_rep == (Symbol('x'), -2, Symbol('y'), -1)
assert (x*z).ext_rep == (Symbol('x'), 1, Symbol('z'), 1)
def test_FreeGroupElm__mul__pow__():
assert (x**2*y*x**-2)**4 == x**2*y**4*x**-2
assert (x**2)**2 == x**4
assert (x**-1)**-1 == x
assert (x**-1)**0 == F.identity
assert (y**2)**-2 == y**-4
assert x**2*x**-1 == x
assert x**2*y**2*y**-1 == x**2*y
assert x*x**-1 == F.identity
assert x/x == F.identity
assert x/x**2 == x**-1
assert (x**2*y)/(x**2*y**-1) == x**2*y**2*x**-2
assert (x**2*y)/(y**-1*x**2) == x**2*y*x**-2*y
assert x*(x**-1*y*z*y**-1) == y*z*y**-1
assert x**2*(x**-2*y**-1*z**2*y) == y**-1*z**2*y
def test_FreeGroupElm__len__():
assert len(x**5*y*x**2*y**-4*x) == 13
assert len(x**17) == 17
assert len(y**0) == 0
def test_FreeGroupElm_comparison():
assert not (x*y == y*x)
assert x**0 == y**0
assert x**2 < y**3
assert not x**3 < y**2
assert x*y < x**2*y
assert x**2*y**2 < y**4
assert not y**4 < y**-4
assert not y**4 < x**-4
assert y**-2 < y**2
assert x**2 <= y**2
assert x**2 <= x**2
assert not y*z > z*y
assert x > x**-1
assert not x**2 >= y**2
def test_FreeGroupElm_syllables():
w = x**5*y*x**2*y**-4*x
assert w.number_syllables() == 5
assert w.exponent_syllable(2) == 2
assert w.generator_syllable(3) == Symbol('y')
assert w.sub_syllables(1, 2) == y
assert w.sub_syllables(3, 3) == F.identity
def test_FreeGroup_exponents():
w1 = x**2*y**3
assert w1.exponent_sum(x) == 2
assert w1.exponent_sum(x**-1) == -2
assert w1.generator_count(x) == 2
w2 = x**2*y**4*x**-3
assert w2.exponent_sum(x) == -1
assert w2.generator_count(x) == 5
def test_FreeGroup_generators():
assert (x**2*y**4*z**-1).contains_generators() == {x, y, z}
assert (x**-1*y**3).contains_generators() == {x, y}
def test_FreeGroupElm_words():
w = x**5*y*x**2*y**-4*x
assert w.subword(2, 6) == x**3*y
assert w.subword(3, 2) == F.identity
assert w.subword(6, 10) == x**2*y**-2
assert w.substituted_word(0, 7, y**-1) == y**-1*x*y**-4*x
assert w.substituted_word(0, 7, y**2*x) == y**2*x**2*y**-4*x
| 6,091 | 27.600939 | 81 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_permutations.py
|
from itertools import permutations
from sympy.core.compatibility import range
from sympy.core.symbol import Symbol
from sympy.combinatorics.permutations import (Permutation, _af_parity,
_af_rmul, _af_rmuln, Cycle)
from sympy.utilities.pytest import raises
rmul = Permutation.rmul
a = Symbol('a', integer=True)
def test_Permutation():
# don't auto fill 0
raises(ValueError, lambda: Permutation([1]))
p = Permutation([0, 1, 2, 3])
# call as bijective
assert [p(i) for i in range(p.size)] == list(p)
# call as operator
assert p(list(range(p.size))) == list(p)
# call as function
assert list(p(1, 2)) == [0, 2, 1, 3]
# conversion to list
assert list(p) == list(range(4))
assert Permutation(size=4) == Permutation(3)
assert Permutation(Permutation(3), size=5) == Permutation(4)
# cycle form with size
assert Permutation([[1, 2]], size=4) == Permutation([[1, 2], [0], [3]])
# random generation
assert Permutation.random(2) in (Permutation([1, 0]), Permutation([0, 1]))
p = Permutation([2, 5, 1, 6, 3, 0, 4])
q = Permutation([[1], [0, 3, 5, 6, 2, 4]])
assert len({p, p}) == 1
r = Permutation([1, 3, 2, 0, 4, 6, 5])
ans = Permutation(_af_rmuln(*[w.array_form for w in (p, q, r)])).array_form
assert rmul(p, q, r).array_form == ans
# make sure no other permutation of p, q, r could have given
# that answer
for a, b, c in permutations((p, q, r)):
if (a, b, c) == (p, q, r):
continue
assert rmul(a, b, c).array_form != ans
assert p.support() == list(range(7))
assert q.support() == [0, 2, 3, 4, 5, 6]
assert Permutation(p.cyclic_form).array_form == p.array_form
assert p.cardinality == 5040
assert q.cardinality == 5040
assert q.cycles == 2
assert rmul(q, p) == Permutation([4, 6, 1, 2, 5, 3, 0])
assert rmul(p, q) == Permutation([6, 5, 3, 0, 2, 4, 1])
assert _af_rmul(p.array_form, q.array_form) == \
[6, 5, 3, 0, 2, 4, 1]
assert rmul(Permutation([[1, 2, 3], [0, 4]]),
Permutation([[1, 2, 4], [0], [3]])).cyclic_form == \
[[0, 4, 2], [1, 3]]
assert q.array_form == [3, 1, 4, 5, 0, 6, 2]
assert q.cyclic_form == [[0, 3, 5, 6, 2, 4]]
assert q.full_cyclic_form == [[0, 3, 5, 6, 2, 4], [1]]
assert p.cyclic_form == [[0, 2, 1, 5], [3, 6, 4]]
t = p.transpositions()
assert t == [(0, 5), (0, 1), (0, 2), (3, 4), (3, 6)]
assert Permutation.rmul(*[Permutation(Cycle(*ti)) for ti in (t)])
assert Permutation([1, 0]).transpositions() == [(0, 1)]
assert p**13 == p
assert q**0 == Permutation(list(range(q.size)))
assert q**-2 == ~q**2
assert q**2 == Permutation([5, 1, 0, 6, 3, 2, 4])
assert q**3 == q**2*q
assert q**4 == q**2*q**2
a = Permutation(1, 3)
b = Permutation(2, 0, 3)
I = Permutation(3)
assert ~a == a**-1
assert a*~a == I
assert a*b**-1 == a*~b
ans = Permutation(0, 5, 3, 1, 6)(2, 4)
assert (p + q.rank()).rank() == ans.rank()
assert (p + q.rank())._rank == ans.rank()
assert (q + p.rank()).rank() == ans.rank()
raises(TypeError, lambda: p + Permutation(list(range(10))))
assert (p - q.rank()).rank() == Permutation(0, 6, 3, 1, 2, 5, 4).rank()
assert p.rank() - q.rank() < 0 # for coverage: make sure mod is used
assert (q - p.rank()).rank() == Permutation(1, 4, 6, 2)(3, 5).rank()
assert p*q == Permutation(_af_rmuln(*[list(w) for w in (q, p)]))
assert p*Permutation([]) == p
assert Permutation([])*p == p
assert p*Permutation([[0, 1]]) == Permutation([2, 5, 0, 6, 3, 1, 4])
assert Permutation([[0, 1]])*p == Permutation([5, 2, 1, 6, 3, 0, 4])
pq = p ^ q
assert pq == Permutation([5, 6, 0, 4, 1, 2, 3])
assert pq == rmul(q, p, ~q)
qp = q ^ p
assert qp == Permutation([4, 3, 6, 2, 1, 5, 0])
assert qp == rmul(p, q, ~p)
raises(ValueError, lambda: p ^ Permutation([]))
assert p.commutator(q) == Permutation(0, 1, 3, 4, 6, 5, 2)
assert q.commutator(p) == Permutation(0, 2, 5, 6, 4, 3, 1)
assert p.commutator(q) == ~q.commutator(p)
raises(ValueError, lambda: p.commutator(Permutation([])))
assert len(p.atoms()) == 7
assert q.atoms() == {0, 1, 2, 3, 4, 5, 6}
assert p.inversion_vector() == [2, 4, 1, 3, 1, 0]
assert q.inversion_vector() == [3, 1, 2, 2, 0, 1]
assert Permutation.from_inversion_vector(p.inversion_vector()) == p
assert Permutation.from_inversion_vector(q.inversion_vector()).array_form\
== q.array_form
raises(ValueError, lambda: Permutation.from_inversion_vector([0, 2]))
assert Permutation([i for i in range(500, -1, -1)]).inversions() == 125250
s = Permutation([0, 4, 1, 3, 2])
assert s.parity() == 0
_ = s.cyclic_form # needed to create a value for _cyclic_form
assert len(s._cyclic_form) != s.size and s.parity() == 0
assert not s.is_odd
assert s.is_even
assert Permutation([0, 1, 4, 3, 2]).parity() == 1
assert _af_parity([0, 4, 1, 3, 2]) == 0
assert _af_parity([0, 1, 4, 3, 2]) == 1
s = Permutation([0])
assert s.is_Singleton
assert Permutation([]).is_Empty
r = Permutation([3, 2, 1, 0])
assert (r**2).is_Identity
assert rmul(~p, p).is_Identity
assert (~p)**13 == Permutation([5, 2, 0, 4, 6, 1, 3])
assert ~(r**2).is_Identity
assert p.max() == 6
assert p.min() == 0
q = Permutation([[6], [5], [0, 1, 2, 3, 4]])
assert q.max() == 4
assert q.min() == 0
p = Permutation([1, 5, 2, 0, 3, 6, 4])
q = Permutation([[1, 2, 3, 5, 6], [0, 4]])
assert p.ascents() == [0, 3, 4]
assert q.ascents() == [1, 2, 4]
assert r.ascents() == []
assert p.descents() == [1, 2, 5]
assert q.descents() == [0, 3, 5]
assert Permutation(r.descents()).is_Identity
assert p.inversions() == 7
# test the merge-sort with a longer permutation
big = list(p) + list(range(p.max() + 1, p.max() + 130))
assert Permutation(big).inversions() == 7
assert p.signature() == -1
assert q.inversions() == 11
assert q.signature() == -1
assert rmul(p, ~p).inversions() == 0
assert rmul(p, ~p).signature() == 1
assert p.order() == 6
assert q.order() == 10
assert (p**(p.order())).is_Identity
assert p.length() == 6
assert q.length() == 7
assert r.length() == 4
assert p.runs() == [[1, 5], [2], [0, 3, 6], [4]]
assert q.runs() == [[4], [2, 3, 5], [0, 6], [1]]
assert r.runs() == [[3], [2], [1], [0]]
assert p.index() == 8
assert q.index() == 8
assert r.index() == 3
assert p.get_precedence_distance(q) == q.get_precedence_distance(p)
assert p.get_adjacency_distance(q) == p.get_adjacency_distance(q)
assert p.get_positional_distance(q) == p.get_positional_distance(q)
p = Permutation([0, 1, 2, 3])
q = Permutation([3, 2, 1, 0])
assert p.get_precedence_distance(q) == 6
assert p.get_adjacency_distance(q) == 3
assert p.get_positional_distance(q) == 8
p = Permutation([0, 3, 1, 2, 4])
q = Permutation.josephus(4, 5, 2)
assert p.get_adjacency_distance(q) == 3
raises(ValueError, lambda: p.get_adjacency_distance(Permutation([])))
raises(ValueError, lambda: p.get_positional_distance(Permutation([])))
raises(ValueError, lambda: p.get_precedence_distance(Permutation([])))
a = [Permutation.unrank_nonlex(4, i) for i in range(5)]
iden = Permutation([0, 1, 2, 3])
for i in range(5):
for j in range(i + 1, 5):
assert a[i].commutes_with(a[j]) == \
(rmul(a[i], a[j]) == rmul(a[j], a[i]))
if a[i].commutes_with(a[j]):
assert a[i].commutator(a[j]) == iden
assert a[j].commutator(a[i]) == iden
a = Permutation(3)
b = Permutation(0, 6, 3)(1, 2)
assert a.cycle_structure == {1: 4}
assert b.cycle_structure == {2: 1, 3: 1, 1: 2}
def test_Permutation_subclassing():
# Subclass that adds permutation application on iterables
class CustomPermutation(Permutation):
def __call__(self, *i):
try:
return super(CustomPermutation, self).__call__(*i)
except TypeError:
pass
try:
perm_obj = i[0]
return [self._array_form[j] for j in perm_obj]
except Exception:
raise TypeError('unrecognized argument')
def __eq__(self, other):
if isinstance(other, Permutation):
return self._hashable_content() == other._hashable_content()
else:
return super(CustomPermutation, self).__eq__(other)
def __hash__(self):
return super(CustomPermutation, self).__hash__()
p = CustomPermutation([1, 2, 3, 0])
q = Permutation([1, 2, 3, 0])
assert p == q
raises(TypeError, lambda: q([1, 2]))
assert [2, 3] == p([1, 2])
assert type(p * q) == CustomPermutation
assert type(q * p) == Permutation # True because q.__mul__(p) is called!
# Run all tests for the Permutation class also on the subclass
def wrapped_test_Permutation():
# Monkeypatch the class definition in the globals
globals()['__Perm'] = globals()['Permutation']
globals()['Permutation'] = CustomPermutation
test_Permutation()
globals()['Permutation'] = globals()['__Perm'] # Restore
del globals()['__Perm']
wrapped_test_Permutation()
def test_josephus():
assert Permutation.josephus(4, 6, 1) == Permutation([3, 1, 0, 2, 5, 4])
assert Permutation.josephus(1, 5, 1).is_Identity
def test_ranking():
assert Permutation.unrank_lex(5, 10).rank() == 10
p = Permutation.unrank_lex(15, 225)
assert p.rank() == 225
p1 = p.next_lex()
assert p1.rank() == 226
assert Permutation.unrank_lex(15, 225).rank() == 225
assert Permutation.unrank_lex(10, 0).is_Identity
p = Permutation.unrank_lex(4, 23)
assert p.rank() == 23
assert p.array_form == [3, 2, 1, 0]
assert p.next_lex() is None
p = Permutation([1, 5, 2, 0, 3, 6, 4])
q = Permutation([[1, 2, 3, 5, 6], [0, 4]])
a = [Permutation.unrank_trotterjohnson(4, i).array_form for i in range(5)]
assert a == [[0, 1, 2, 3], [0, 1, 3, 2], [0, 3, 1, 2], [3, 0, 1,
2], [3, 0, 2, 1] ]
assert [Permutation(pa).rank_trotterjohnson() for pa in a] == list(range(5))
assert Permutation([0, 1, 2, 3]).next_trotterjohnson() == \
Permutation([0, 1, 3, 2])
assert q.rank_trotterjohnson() == 2283
assert p.rank_trotterjohnson() == 3389
assert Permutation([1, 0]).rank_trotterjohnson() == 1
a = Permutation(list(range(3)))
b = a
l = []
tj = []
for i in range(6):
l.append(a)
tj.append(b)
a = a.next_lex()
b = b.next_trotterjohnson()
assert a == b is None
assert {tuple(a) for a in l} == {tuple(a) for a in tj}
p = Permutation([2, 5, 1, 6, 3, 0, 4])
q = Permutation([[6], [5], [0, 1, 2, 3, 4]])
assert p.rank() == 1964
assert q.rank() == 870
assert Permutation([]).rank_nonlex() == 0
prank = p.rank_nonlex()
assert prank == 1600
assert Permutation.unrank_nonlex(7, 1600) == p
qrank = q.rank_nonlex()
assert qrank == 41
assert Permutation.unrank_nonlex(7, 41) == Permutation(q.array_form)
a = [Permutation.unrank_nonlex(4, i).array_form for i in range(24)]
assert a == [
[1, 2, 3, 0], [3, 2, 0, 1], [1, 3, 0, 2], [1, 2, 0, 3], [2, 3, 1, 0],
[2, 0, 3, 1], [3, 0, 1, 2], [2, 0, 1, 3], [1, 3, 2, 0], [3, 0, 2, 1],
[1, 0, 3, 2], [1, 0, 2, 3], [2, 1, 3, 0], [2, 3, 0, 1], [3, 1, 0, 2],
[2, 1, 0, 3], [3, 2, 1, 0], [0, 2, 3, 1], [0, 3, 1, 2], [0, 2, 1, 3],
[3, 1, 2, 0], [0, 3, 2, 1], [0, 1, 3, 2], [0, 1, 2, 3]]
N = 10
p1 = Permutation(a[0])
for i in range(1, N+1):
p1 = p1*Permutation(a[i])
p2 = Permutation.rmul_with_af(*[Permutation(h) for h in a[N::-1]])
assert p1 == p2
ok = []
p = Permutation([1, 0])
for i in range(3):
ok.append(p.array_form)
p = p.next_nonlex()
if p is None:
ok.append(None)
break
assert ok == [[1, 0], [0, 1], None]
assert Permutation([3, 2, 0, 1]).next_nonlex() == Permutation([1, 3, 0, 2])
assert [Permutation(pa).rank_nonlex() for pa in a] == list(range(24))
def test_mul():
a, b = [0, 2, 1, 3], [0, 1, 3, 2]
assert _af_rmul(a, b) == [0, 2, 3, 1]
assert _af_rmuln(a, b, list(range(4))) == [0, 2, 3, 1]
assert rmul(Permutation(a), Permutation(b)).array_form == [0, 2, 3, 1]
a = Permutation([0, 2, 1, 3])
b = (0, 1, 3, 2)
c = (3, 1, 2, 0)
assert Permutation.rmul(a, b, c) == Permutation([1, 2, 3, 0])
assert Permutation.rmul(a, c) == Permutation([3, 2, 1, 0])
raises(TypeError, lambda: Permutation.rmul(b, c))
n = 6
m = 8
a = [Permutation.unrank_nonlex(n, i).array_form for i in range(m)]
h = list(range(n))
for i in range(m):
h = _af_rmul(h, a[i])
h2 = _af_rmuln(*a[:i + 1])
assert h == h2
def test_args():
p = Permutation([(0, 3, 1, 2), (4, 5)])
assert p._cyclic_form is None
assert Permutation(p) == p
assert p.cyclic_form == [[0, 3, 1, 2], [4, 5]]
assert p._array_form == [3, 2, 0, 1, 5, 4]
p = Permutation((0, 3, 1, 2))
assert p._cyclic_form is None
assert p._array_form == [0, 3, 1, 2]
assert Permutation([0]) == Permutation((0, ))
assert Permutation([[0], [1]]) == Permutation(((0, ), (1, ))) == \
Permutation(((0, ), [1]))
assert Permutation([[1, 2]]) == Permutation([0, 2, 1])
assert Permutation([[1], [4, 2]]) == Permutation([0, 1, 4, 3, 2])
assert Permutation([[1], [4, 2]], size=1) == Permutation([0, 1, 4, 3, 2])
assert Permutation(
[[1], [4, 2]], size=6) == Permutation([0, 1, 4, 3, 2, 5])
assert Permutation([[0, 1], [0, 2]]) == Permutation(0, 1, 2)
assert Permutation([], size=3) == Permutation([0, 1, 2])
assert Permutation(3).list(5) == [0, 1, 2, 3, 4]
assert Permutation(3).list(-1) == []
assert Permutation(5)(1, 2).list(-1) == [0, 2, 1]
assert Permutation(5)(1, 2).list() == [0, 2, 1, 3, 4, 5]
raises(ValueError, lambda: Permutation([1, 2], [0]))
# enclosing brackets needed
raises(ValueError, lambda: Permutation([[1, 2], 0]))
# enclosing brackets needed on 0
raises(ValueError, lambda: Permutation([1, 1, 0]))
raises(ValueError, lambda: Permutation([4, 5], size=10)) # where are 0-3?
# but this is ok because cycles imply that only those listed moved
assert Permutation(4, 5) == Permutation([0, 1, 2, 3, 5, 4])
def test_Cycle():
assert str(Cycle()) == '()'
assert Cycle(Cycle(1,2)) == Cycle(1, 2)
assert Cycle(1,2).copy() == Cycle(1,2)
assert list(Cycle(1, 3, 2)) == [0, 3, 1, 2]
assert Cycle(1, 2)(2, 3) == Cycle(1, 3, 2)
assert Cycle(1, 2)(2, 3)(4, 5) == Cycle(1, 3, 2)(4, 5)
assert Permutation(Cycle(1, 2)(2, 1, 0, 3)).cyclic_form, Cycle(0, 2, 1)
raises(ValueError, lambda: Cycle().list())
assert Cycle(1, 2).list() == [0, 2, 1]
assert Cycle(1, 2).list(4) == [0, 2, 1, 3]
assert Cycle(3).list(2) == [0, 1]
assert Cycle(3).list(6) == [0, 1, 2, 3, 4, 5]
assert Permutation(Cycle(1, 2), size=4) == \
Permutation([0, 2, 1, 3])
assert str(Cycle(1, 2)(4, 5)) == '(1 2)(4 5)'
assert str(Cycle(1, 2)) == '(1 2)'
assert Cycle(Permutation(list(range(3)))) == Cycle()
assert Cycle(1, 2).list() == [0, 2, 1]
assert Cycle(1, 2).list(4) == [0, 2, 1, 3]
assert Cycle().size == 0
raises(ValueError, lambda: Cycle((1, 2)))
raises(ValueError, lambda: Cycle(1, 2, 1))
raises(TypeError, lambda: Cycle(1, 2)*{})
raises(ValueError, lambda: Cycle(4)[a])
raises(ValueError, lambda: Cycle(2, -4, 3))
# check round-trip
p = Permutation([[1, 2], [4, 3]], size=5)
assert Permutation(Cycle(p)) == p
def test_from_sequence():
assert Permutation.from_sequence('SymPy') == Permutation(4)(0, 1, 3)
assert Permutation.from_sequence('SymPy', key=lambda x: x.lower()) == \
Permutation(4)(0, 2)(1, 3)
def test_printing_cyclic():
Permutation.print_cyclic = True
p1 = Permutation([0, 2, 1])
assert repr(p1) == 'Permutation(1, 2)'
assert str(p1) == '(1 2)'
p2 = Permutation()
assert repr(p2) == 'Permutation()'
assert str(p2) == '()'
p3 = Permutation([1, 2, 0, 3])
assert repr(p3) == 'Permutation(3)(0, 1, 2)'
def test_printing_non_cyclic():
Permutation.print_cyclic = False
p1 = Permutation([0, 1, 2, 3, 4, 5])
assert repr(p1) == 'Permutation([], size=6)'
assert str(p1) == 'Permutation([], size=6)'
p2 = Permutation([0, 1, 2])
assert repr(p2) == 'Permutation([0, 1, 2])'
assert str(p2) == 'Permutation([0, 1, 2])'
p3 = Permutation([0, 2, 1])
assert repr(p3) == 'Permutation([0, 2, 1])'
assert str(p3) == 'Permutation([0, 2, 1])'
p4 = Permutation([0, 1, 3, 2, 4, 5, 6, 7])
assert repr(p4) == 'Permutation([0, 1, 3, 2], size=8)'
| 17,100 | 35.540598 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_testutil.py
|
from sympy.combinatorics.named_groups import SymmetricGroup, AlternatingGroup,\
CyclicGroup
from sympy.combinatorics.testutil import _verify_bsgs, _cmp_perm_lists,\
_naive_list_centralizer, _verify_centralizer,\
_verify_normal_closure
from sympy.combinatorics.permutations import Permutation
from sympy.combinatorics.perm_groups import PermutationGroup
from random import shuffle
def test_cmp_perm_lists():
S = SymmetricGroup(4)
els = list(S.generate_dimino())
other = els[:]
shuffle(other)
assert _cmp_perm_lists(els, other) is True
def test_naive_list_centralizer():
# verified by GAP
S = SymmetricGroup(3)
A = AlternatingGroup(3)
assert _naive_list_centralizer(S, S) == [Permutation([0, 1, 2])]
assert PermutationGroup(_naive_list_centralizer(S, A)).is_subgroup(A)
def test_verify_bsgs():
S = SymmetricGroup(5)
S.schreier_sims()
base = S.base
strong_gens = S.strong_gens
assert _verify_bsgs(S, base, strong_gens) is True
assert _verify_bsgs(S, base[:-1], strong_gens) is False
assert _verify_bsgs(S, base, S.generators) is False
def test_verify_centralizer():
# verified by GAP
S = SymmetricGroup(3)
A = AlternatingGroup(3)
triv = PermutationGroup([Permutation([0, 1, 2])])
assert _verify_centralizer(S, S, centr=triv)
assert _verify_centralizer(S, A, centr=A)
def test_verify_normal_closure():
# verified by GAP
S = SymmetricGroup(3)
A = AlternatingGroup(3)
assert _verify_normal_closure(S, A, closure=A)
S = SymmetricGroup(5)
A = AlternatingGroup(5)
C = CyclicGroup(5)
assert _verify_normal_closure(S, A, closure=A)
assert _verify_normal_closure(S, C, closure=A)
| 1,718 | 29.696429 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_polyhedron.py
|
from sympy.core.compatibility import range
from sympy import symbols, FiniteSet
from sympy.combinatorics.polyhedron import (Polyhedron,
tetrahedron, cube as square, octahedron, dodecahedron, icosahedron,
cube_faces)
from sympy.combinatorics.permutations import Permutation
from sympy.combinatorics.perm_groups import PermutationGroup
from sympy.utilities.pytest import raises
rmul = Permutation.rmul
def test_polyhedron():
raises(ValueError, lambda: Polyhedron(list('ab'),
pgroup=[Permutation([0])]))
pgroup = [Permutation([[0, 7, 2, 5], [6, 1, 4, 3]]),
Permutation([[0, 7, 1, 6], [5, 2, 4, 3]]),
Permutation([[3, 6, 0, 5], [4, 1, 7, 2]]),
Permutation([[7, 4, 5], [1, 3, 0], [2], [6]]),
Permutation([[1, 3, 2], [7, 6, 5], [4], [0]]),
Permutation([[4, 7, 6], [2, 0, 3], [1], [5]]),
Permutation([[1, 2, 0], [4, 5, 6], [3], [7]]),
Permutation([[4, 2], [0, 6], [3, 7], [1, 5]]),
Permutation([[3, 5], [7, 1], [2, 6], [0, 4]]),
Permutation([[2, 5], [1, 6], [0, 4], [3, 7]]),
Permutation([[4, 3], [7, 0], [5, 1], [6, 2]]),
Permutation([[4, 1], [0, 5], [6, 2], [7, 3]]),
Permutation([[7, 2], [3, 6], [0, 4], [1, 5]]),
Permutation([0, 1, 2, 3, 4, 5, 6, 7])]
corners = tuple(symbols('A:H'))
faces = cube_faces
cube = Polyhedron(corners, faces, pgroup)
assert cube.edges == FiniteSet(*(
(0, 1), (6, 7), (1, 2), (5, 6), (0, 3), (2, 3),
(4, 7), (4, 5), (3, 7), (1, 5), (0, 4), (2, 6)))
for i in range(3): # add 180 degree face rotations
cube.rotate(cube.pgroup[i]**2)
assert cube.corners == corners
for i in range(3, 7): # add 240 degree axial corner rotations
cube.rotate(cube.pgroup[i]**2)
assert cube.corners == corners
cube.rotate(1)
raises(ValueError, lambda: cube.rotate(Permutation([0, 1])))
assert cube.corners != corners
assert cube.array_form == [7, 6, 4, 5, 3, 2, 0, 1]
assert cube.cyclic_form == [[0, 7, 1, 6], [2, 4, 3, 5]]
cube.reset()
assert cube.corners == corners
def check(h, size, rpt, target):
assert len(h.faces) + len(h.vertices) - len(h.edges) == 2
assert h.size == size
got = set()
for p in h.pgroup:
# make sure it restores original
P = h.copy()
hit = P.corners
for i in range(rpt):
P.rotate(p)
if P.corners == hit:
break
else:
print('error in permutation', p.array_form)
for i in range(rpt):
P.rotate(p)
got.add(tuple(P.corners))
c = P.corners
f = [[c[i] for i in f] for f in P.faces]
assert h.faces == Polyhedron(c, f).faces
assert len(got) == target
assert PermutationGroup([Permutation(g) for g in got]).is_group
for h, size, rpt, target in zip(
(tetrahedron, square, octahedron, dodecahedron, icosahedron),
(4, 8, 6, 20, 12),
(3, 4, 4, 5, 5),
(12, 24, 24, 60, 60)):
check(h, size, rpt, target)
| 3,243 | 36.72093 | 71 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_named_groups.py
|
from sympy.combinatorics.named_groups import (SymmetricGroup, CyclicGroup,
DihedralGroup, AlternatingGroup, AbelianGroup)
def test_SymmetricGroup():
G = SymmetricGroup(5)
elements = list(G.generate())
assert (G.generators[0]).size == 5
assert len(elements) == 120
assert G.is_solvable is False
assert G.is_abelian is False
assert G.is_nilpotent is False
assert G.is_transitive() is True
H = SymmetricGroup(1)
assert H.order() == 1
L = SymmetricGroup(2)
assert L.order() == 2
def test_CyclicGroup():
G = CyclicGroup(10)
elements = list(G.generate())
assert len(elements) == 10
assert (G.derived_subgroup()).order() == 1
assert G.is_abelian is True
assert G.is_solvable is True
assert G.is_nilpotent is True
H = CyclicGroup(1)
assert H.order() == 1
L = CyclicGroup(2)
assert L.order() == 2
def test_DihedralGroup():
G = DihedralGroup(6)
elements = list(G.generate())
assert len(elements) == 12
assert G.is_transitive() is True
assert G.is_abelian is False
assert G.is_solvable is True
assert G.is_nilpotent is False
H = DihedralGroup(1)
assert H.order() == 2
L = DihedralGroup(2)
assert L.order() == 4
assert L.is_abelian is True
assert L.is_nilpotent is True
def test_AlternatingGroup():
G = AlternatingGroup(5)
elements = list(G.generate())
assert len(elements) == 60
assert [perm.is_even for perm in elements] == [True]*60
H = AlternatingGroup(1)
assert H.order() == 1
L = AlternatingGroup(2)
assert L.order() == 1
def test_AbelianGroup():
A = AbelianGroup(3, 3, 3)
assert A.order() == 27
assert A.is_abelian is True
| 1,716 | 25.415385 | 74 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_subsets.py
|
from sympy.combinatorics import Subset
def test_subset():
a = Subset(['c', 'd'], ['a', 'b', 'c', 'd'])
assert a.next_binary() == Subset(['b'], ['a', 'b', 'c', 'd'])
assert a.prev_binary() == Subset(['c'], ['a', 'b', 'c', 'd'])
assert a.next_lexicographic() == Subset(['d'], ['a', 'b', 'c', 'd'])
assert a.prev_lexicographic() == Subset(['c'], ['a', 'b', 'c', 'd'])
assert a.next_gray() == Subset(['c'], ['a', 'b', 'c', 'd'])
assert a.prev_gray() == Subset(['d'], ['a', 'b', 'c', 'd'])
assert a.rank_binary == 3
assert a.rank_lexicographic == 14
assert a.rank_gray == 2
assert a.cardinality == 16
a = Subset([2, 5, 7], [1, 2, 3, 4, 5, 6, 7])
assert a.next_binary() == Subset([2, 5, 6], [1, 2, 3, 4, 5, 6, 7])
assert a.prev_binary() == Subset([2, 5], [1, 2, 3, 4, 5, 6, 7])
assert a.next_lexicographic() == Subset([2, 6], [1, 2, 3, 4, 5, 6, 7])
assert a.prev_lexicographic() == Subset([2, 5, 6, 7], [1, 2, 3, 4, 5, 6, 7])
assert a.next_gray() == Subset([2, 5, 6, 7], [1, 2, 3, 4, 5, 6, 7])
assert a.prev_gray() == Subset([2, 5], [1, 2, 3, 4, 5, 6, 7])
assert a.rank_binary == 37
assert a.rank_lexicographic == 93
assert a.rank_gray == 57
assert a.cardinality == 128
superset = ['a', 'b', 'c', 'd']
assert Subset.unrank_binary(4, superset).rank_binary == 4
assert Subset.unrank_gray(10, superset).rank_gray == 10
superset = [1, 2, 3, 4, 5, 6, 7, 8, 9]
assert Subset.unrank_binary(33, superset).rank_binary == 33
assert Subset.unrank_gray(25, superset).rank_gray == 25
a = Subset([], ['a', 'b', 'c', 'd'])
i = 1
while a.subset != Subset(['d'], ['a', 'b', 'c', 'd']).subset:
a = a.next_lexicographic()
i = i + 1
assert i == 16
i = 1
while a.subset != Subset([], ['a', 'b', 'c', 'd']).subset:
a = a.prev_lexicographic()
i = i + 1
assert i == 16
| 1,918 | 38.163265 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/combinatorics/tests/test_group_constructs.py
|
from sympy.combinatorics.group_constructs import DirectProduct
from sympy.combinatorics.named_groups import CyclicGroup, DihedralGroup
def test_direct_product_n():
C = CyclicGroup(4)
D = DihedralGroup(4)
G = DirectProduct(C, C, C)
assert G.order() == 64
assert G.degree == 12
assert len(G.orbits()) == 3
assert G.is_abelian is True
H = DirectProduct(D, C)
assert H.order() == 32
assert H.is_abelian is False
| 450 | 27.1875 | 71 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/integrals.py
|
from __future__ import print_function, division
from sympy.concrete.expr_with_limits import AddWithLimits
from sympy.core.add import Add
from sympy.core.basic import Basic
from sympy.core.compatibility import is_sequence, range
from sympy.core.containers import Tuple
from sympy.core.expr import Expr
from sympy.core.function import diff
from sympy.core.mul import Mul
from sympy.core.numbers import oo
from sympy.core.relational import Eq
from sympy.core.singleton import S
from sympy.core.symbol import (Dummy, Symbol, Wild)
from sympy.core.sympify import sympify
from sympy.integrals.manualintegrate import manualintegrate
from sympy.integrals.trigonometry import trigintegrate
from sympy.integrals.meijerint import meijerint_definite, meijerint_indefinite
from sympy.matrices import MatrixBase
from sympy.utilities.misc import filldedent
from sympy.polys import Poly, PolynomialError
from sympy.functions import Piecewise, sqrt, sign
from sympy.functions.elementary.exponential import log
from sympy.series import limit
from sympy.series.order import Order
class Integral(AddWithLimits):
"""Represents unevaluated integral."""
__slots__ = ['is_commutative']
def __new__(cls, function, *symbols, **assumptions):
"""Create an unevaluated integral.
Arguments are an integrand followed by one or more limits.
If no limits are given and there is only one free symbol in the
expression, that symbol will be used, otherwise an error will be
raised.
>>> from sympy import Integral
>>> from sympy.abc import x, y
>>> Integral(x)
Integral(x, x)
>>> Integral(y)
Integral(y, y)
When limits are provided, they are interpreted as follows (using
``x`` as though it were the variable of integration):
(x,) or x - indefinite integral
(x, a) - "evaluate at" integral is an abstract antiderivative
(x, a, b) - definite integral
The ``as_dummy`` method can be used to see which symbols cannot be
targeted by subs: those with a preppended underscore cannot be
changed with ``subs``. (Also, the integration variables themselves --
the first element of a limit -- can never be changed by subs.)
>>> i = Integral(x, x)
>>> at = Integral(x, (x, x))
>>> i.as_dummy()
Integral(x, x)
>>> at.as_dummy()
Integral(_x, (_x, x))
"""
#This will help other classes define their own definitions
#of behaviour with Integral.
if hasattr(function, '_eval_Integral'):
return function._eval_Integral(*symbols, **assumptions)
obj = AddWithLimits.__new__(cls, function, *symbols, **assumptions)
return obj
def __getnewargs__(self):
return (self.function,) + tuple([tuple(xab) for xab in self.limits])
@property
def free_symbols(self):
"""
This method returns the symbols that will exist when the
integral is evaluated. This is useful if one is trying to
determine whether an integral depends on a certain
symbol or not.
Examples
========
>>> from sympy import Integral
>>> from sympy.abc import x, y
>>> Integral(x, (x, y, 1)).free_symbols
{y}
See Also
========
function, limits, variables
"""
return AddWithLimits.free_symbols.fget(self)
def _eval_is_zero(self):
# This is a very naive and quick test, not intended to do the integral to
# answer whether it is zero or not, e.g. Integral(sin(x), (x, 0, 2*pi))
# is zero but this routine should return None for that case. But, like
# Mul, there are trivial situations for which the integral will be
# zero so we check for those.
if self.function.is_zero:
return True
got_none = False
for l in self.limits:
if len(l) == 3:
z = (l[1] == l[2]) or (l[1] - l[2]).is_zero
if z:
return True
elif z is None:
got_none = True
free = self.function.free_symbols
for xab in self.limits:
if len(xab) == 1:
free.add(xab[0])
continue
if len(xab) == 2 and xab[0] not in free:
if xab[1].is_zero:
return True
elif xab[1].is_zero is None:
got_none = True
# take integration symbol out of free since it will be replaced
# with the free symbols in the limits
free.discard(xab[0])
# add in the new symbols
for i in xab[1:]:
free.update(i.free_symbols)
if self.function.is_zero is False and got_none is False:
return False
def transform(self, x, u):
r"""
Performs a change of variables from `x` to `u` using the relationship
given by `x` and `u` which will define the transformations `f` and `F`
(which are inverses of each other) as follows:
1) If `x` is a Symbol (which is a variable of integration) then `u`
will be interpreted as some function, f(u), with inverse F(u).
This, in effect, just makes the substitution of x with f(x).
2) If `u` is a Symbol then `x` will be interpreted as some function,
F(x), with inverse f(u). This is commonly referred to as
u-substitution.
Once f and F have been identified, the transformation is made as
follows:
.. math:: \int_a^b x \mathrm{d}x \rightarrow \int_{F(a)}^{F(b)} f(x)
\frac{\mathrm{d}}{\mathrm{d}x}
where `F(x)` is the inverse of `f(x)` and the limits and integrand have
been corrected so as to retain the same value after integration.
Notes
=====
The mappings, F(x) or f(u), must lead to a unique integral. Linear
or rational linear expression, `2*x`, `1/x` and `sqrt(x)`, will
always work; quadratic expressions like `x**2 - 1` are acceptable
as long as the resulting integrand does not depend on the sign of
the solutions (see examples).
The integral will be returned unchanged if `x` is not a variable of
integration.
`x` must be (or contain) only one of of the integration variables. If
`u` has more than one free symbol then it should be sent as a tuple
(`u`, `uvar`) where `uvar` identifies which variable is replacing
the integration variable.
XXX can it contain another integration variable?
Examples
========
>>> from sympy.abc import a, b, c, d, x, u, y
>>> from sympy import Integral, S, cos, sqrt
>>> i = Integral(x*cos(x**2 - 1), (x, 0, 1))
transform can change the variable of integration
>>> i.transform(x, u)
Integral(u*cos(u**2 - 1), (u, 0, 1))
transform can perform u-substitution as long as a unique
integrand is obtained:
>>> i.transform(x**2 - 1, u)
Integral(cos(u)/2, (u, -1, 0))
This attempt fails because x = +/-sqrt(u + 1) and the
sign does not cancel out of the integrand:
>>> Integral(cos(x**2 - 1), (x, 0, 1)).transform(x**2 - 1, u)
Traceback (most recent call last):
...
ValueError:
The mapping between F(x) and f(u) did not give a unique integrand.
transform can do a substitution. Here, the previous
result is transformed back into the original expression
using "u-substitution":
>>> ui = _
>>> _.transform(sqrt(u + 1), x) == i
True
We can accomplish the same with a regular substitution:
>>> ui.transform(u, x**2 - 1) == i
True
If the `x` does not contain a symbol of integration then
the integral will be returned unchanged. Integral `i` does
not have an integration variable `a` so no change is made:
>>> i.transform(a, x) == i
True
When `u` has more than one free symbol the symbol that is
replacing `x` must be identified by passing `u` as a tuple:
>>> Integral(x, (x, 0, 1)).transform(x, (u + a, u))
Integral(a + u, (u, -a, -a + 1))
>>> Integral(x, (x, 0, 1)).transform(x, (u + a, a))
Integral(a + u, (a, -u, -u + 1))
See Also
========
variables : Lists the integration variables
as_dummy : Replace integration variables with dummy ones
"""
from sympy.solvers.solvers import solve, posify
d = Dummy('d')
xfree = x.free_symbols.intersection(self.variables)
if len(xfree) > 1:
raise ValueError(
'F(x) can only contain one of: %s' % self.variables)
xvar = xfree.pop() if xfree else d
if xvar not in self.variables:
return self
u = sympify(u)
if isinstance(u, Expr):
ufree = u.free_symbols
if len(ufree) != 1:
raise ValueError(filldedent('''
When f(u) has more than one free symbol, the one replacing x
must be identified: pass f(u) as (f(u), u)'''))
uvar = ufree.pop()
else:
u, uvar = u
if uvar not in u.free_symbols:
raise ValueError(filldedent('''
Expecting a tuple (expr, symbol) where symbol identified
a free symbol in expr, but symbol is not in expr's free
symbols.'''))
if not isinstance(uvar, Symbol):
raise ValueError(filldedent('''
Expecting a tuple (expr, symbol) but didn't get
a symbol; got %s''' % uvar))
if x.is_Symbol and u.is_Symbol:
return self.xreplace({x: u})
if not x.is_Symbol and not u.is_Symbol:
raise ValueError('either x or u must be a symbol')
if uvar == xvar:
return self.transform(x, (u.subs(uvar, d), d)).xreplace({d: uvar})
if uvar in self.limits:
raise ValueError(filldedent('''
u must contain the same variable as in x
or a variable that is not already an integration variable'''))
if not x.is_Symbol:
F = [x.subs(xvar, d)]
soln = solve(u - x, xvar, check=False)
if not soln:
raise ValueError('no solution for solve(F(x) - f(u), x)')
f = [fi.subs(uvar, d) for fi in soln]
else:
f = [u.subs(uvar, d)]
pdiff, reps = posify(u - x)
puvar = uvar.subs([(v, k) for k, v in reps.items()])
soln = [s.subs(reps) for s in solve(pdiff, puvar)]
if not soln:
raise ValueError('no solution for solve(F(x) - f(u), u)')
F = [fi.subs(xvar, d) for fi in soln]
newfuncs = set([(self.function.subs(xvar, fi)*fi.diff(d)
).subs(d, uvar) for fi in f])
if len(newfuncs) > 1:
raise ValueError(filldedent('''
The mapping between F(x) and f(u) did not give
a unique integrand.'''))
newfunc = newfuncs.pop()
def _calc_limit_1(F, a, b):
"""
replace d with a, using subs if possible, otherwise limit
where sign of b is considered
"""
wok = F.subs(d, a)
if wok is S.NaN or wok.is_finite is False and a.is_finite:
return limit(sign(b)*F, d, a)
return wok
def _calc_limit(a, b):
"""
replace d with a, using subs if possible, otherwise limit
where sign of b is considered
"""
avals = list({_calc_limit_1(Fi, a, b) for Fi in F})
if len(avals) > 1:
raise ValueError(filldedent('''
The mapping between F(x) and f(u) did not
give a unique limit.'''))
return avals[0]
newlimits = []
for xab in self.limits:
sym = xab[0]
if sym == xvar:
if len(xab) == 3:
a, b = xab[1:]
a, b = _calc_limit(a, b), _calc_limit(b, a)
if a - b > 0:
a, b = b, a
newfunc = -newfunc
newlimits.append((uvar, a, b))
elif len(xab) == 2:
a = _calc_limit(xab[1], 1)
newlimits.append((uvar, a))
else:
newlimits.append(uvar)
else:
newlimits.append(xab)
return self.func(newfunc, *newlimits)
def doit(self, **hints):
"""
Perform the integration using any hints given.
Examples
========
>>> from sympy import Integral
>>> from sympy.abc import x, i
>>> Integral(x**i, (i, 1, 3)).doit()
Piecewise((2, Eq(log(x), 0)), (x**3/log(x) - x/log(x), True))
See Also
========
sympy.integrals.trigonometry.trigintegrate
sympy.integrals.risch.heurisch
sympy.integrals.rationaltools.ratint
as_sum : Approximate the integral using a sum
"""
if not hints.get('integrals', True):
return self
deep = hints.get('deep', True)
meijerg = hints.get('meijerg', None)
conds = hints.get('conds', 'piecewise')
risch = hints.get('risch', None)
manual = hints.get('manual', None)
if conds not in ['separate', 'piecewise', 'none']:
raise ValueError('conds must be one of "separate", "piecewise", '
'"none", got: %s' % conds)
if risch and any(len(xab) > 1 for xab in self.limits):
raise ValueError('risch=True is only allowed for indefinite integrals.')
# check for the trivial zero
if self.is_zero:
return S.Zero
# now compute and check the function
function = self.function
if deep:
function = function.doit(**hints)
if function.is_zero:
return S.Zero
if isinstance(function, MatrixBase):
return function.applyfunc(lambda f: self.func(f, self.limits).doit(**hints))
# There is no trivial answer, so continue
undone_limits = []
# ulj = free symbols of any undone limits' upper and lower limits
ulj = set()
for xab in self.limits:
# compute uli, the free symbols in the
# Upper and Lower limits of limit I
if len(xab) == 1:
uli = set(xab[:1])
elif len(xab) == 2:
uli = xab[1].free_symbols
elif len(xab) == 3:
uli = xab[1].free_symbols.union(xab[2].free_symbols)
# this integral can be done as long as there is no blocking
# limit that has been undone. An undone limit is blocking if
# it contains an integration variable that is in this limit's
# upper or lower free symbols or vice versa
if xab[0] in ulj or any(v[0] in uli for v in undone_limits):
undone_limits.append(xab)
ulj.update(uli)
function = self.func(*([function] + [xab]))
factored_function = function.factor()
if not isinstance(factored_function, Integral):
function = factored_function
continue
# There are a number of tradeoffs in using the Meijer G method.
# It can sometimes be a lot faster than other methods, and
# sometimes slower. And there are certain types of integrals for
# which it is more likely to work than others.
# These heuristics are incorporated in deciding what integration
# methods to try, in what order.
# See the integrate() docstring for details.
def try_meijerg(function, xab):
ret = None
if len(xab) == 3 and meijerg is not False:
x, a, b = xab
try:
res = meijerint_definite(function, x, a, b)
except NotImplementedError:
from sympy.integrals.meijerint import _debug
_debug('NotImplementedError from meijerint_definite')
res = None
if res is not None:
f, cond = res
if conds == 'piecewise':
ret = Piecewise((f, cond),
(self.func(function, (x, a, b)), True))
elif conds == 'separate':
if len(self.limits) != 1:
raise ValueError('conds=separate not supported in '
'multiple integrals')
ret = f, cond
else:
ret = f
return ret
meijerg1 = meijerg
if len(xab) == 3 and xab[1].is_real and xab[2].is_real \
and not function.is_Poly and \
(xab[1].has(oo, -oo) or xab[2].has(oo, -oo)):
ret = try_meijerg(function, xab)
if ret is not None:
function = ret
continue
else:
meijerg1 = False
# If the special meijerg code did not succeed in finding a definite
# integral, then the code using meijerint_indefinite will not either
# (it might find an antiderivative, but the answer is likely to be
# nonsensical).
# Thus if we are requested to only use Meijer G-function methods,
# we give up at this stage. Otherwise we just disable G-function
# methods.
if meijerg1 is False and meijerg is True:
antideriv = None
else:
antideriv = self._eval_integral(
function, xab[0],
meijerg=meijerg1, risch=risch, manual=manual,
conds=conds)
if antideriv is None and meijerg1 is True:
ret = try_meijerg(function, xab)
if ret is not None:
function = ret
continue
if antideriv is None:
undone_limits.append(xab)
function = self.func(*([function] + [xab])).factor()
factored_function = function.factor()
if not isinstance(factored_function, Integral):
function = factored_function
continue
else:
if len(xab) == 1:
function = antideriv
else:
if len(xab) == 3:
x, a, b = xab
elif len(xab) == 2:
x, b = xab
a = None
else:
raise NotImplementedError
if deep:
if isinstance(a, Basic):
a = a.doit(**hints)
if isinstance(b, Basic):
b = b.doit(**hints)
if antideriv.is_Poly:
gens = list(antideriv.gens)
gens.remove(x)
antideriv = antideriv.as_expr()
function = antideriv._eval_interval(x, a, b)
function = Poly(function, *gens)
else:
def is_indef_int(g, x):
return (isinstance(g, Integral) and
any(i == (x,) for i in g.limits))
def eval_factored(f, x, a, b):
# _eval_interval for integrals with
# (constant) factors
# a single indefinite integral is assumed
args = []
for g in Mul.make_args(f):
if is_indef_int(g, x):
args.append(g._eval_interval(x, a, b))
else:
args.append(g)
return Mul(*args)
integrals, others = [], []
for f in Add.make_args(antideriv):
if any(is_indef_int(g, x)
for g in Mul.make_args(f)):
integrals.append(f)
else:
others.append(f)
uneval = Add(*[eval_factored(f, x, a, b)
for f in integrals])
try:
evalued = Add(*others)._eval_interval(x, a, b)
function = uneval + evalued
except NotImplementedError:
# This can happen if _eval_interval depends in a
# complicated way on limits that cannot be computed
undone_limits.append(xab)
function = self.func(*([function] + [xab]))
factored_function = function.factor()
if not isinstance(factored_function, Integral):
function = factored_function
return function
def _eval_derivative(self, sym):
"""Evaluate the derivative of the current Integral object by
differentiating under the integral sign [1], using the Fundamental
Theorem of Calculus [2] when possible.
Whenever an Integral is encountered that is equivalent to zero or
has an integrand that is independent of the variable of integration
those integrals are performed. All others are returned as Integral
instances which can be resolved with doit() (provided they are integrable).
References:
[1] http://en.wikipedia.org/wiki/Differentiation_under_the_integral_sign
[2] http://en.wikipedia.org/wiki/Fundamental_theorem_of_calculus
Examples
========
>>> from sympy import Integral
>>> from sympy.abc import x, y
>>> i = Integral(x + y, y, (y, 1, x))
>>> i.diff(x)
Integral(x + y, (y, x)) + Integral(1, y, (y, 1, x))
>>> i.doit().diff(x) == i.diff(x).doit()
True
>>> i.diff(y)
0
The previous must be true since there is no y in the evaluated integral:
>>> i.free_symbols
{x}
>>> i.doit()
2*x**3/3 - x/2 - 1/6
"""
# differentiate under the integral sign; we do not
# check for regularity conditions (TODO), see issue 4215
# get limits and the function
f, limits = self.function, list(self.limits)
# the order matters if variables of integration appear in the limits
# so work our way in from the outside to the inside.
limit = limits.pop(-1)
if len(limit) == 3:
x, a, b = limit
elif len(limit) == 2:
x, b = limit
a = None
else:
a = b = None
x = limit[0]
if limits: # f is the argument to an integral
f = self.func(f, *tuple(limits))
# assemble the pieces
def _do(f, ab):
dab_dsym = diff(ab, sym)
if not dab_dsym:
return S.Zero
if isinstance(f, Integral):
limits = [(x, x) if (len(l) == 1 and l[0] == x) else l
for l in f.limits]
f = self.func(f.function, *limits)
return f.subs(x, ab)*dab_dsym
rv = 0
if b is not None:
rv += _do(f, b)
if a is not None:
rv -= _do(f, a)
if len(limit) == 1 and sym == x:
# the dummy variable *is* also the real-world variable
arg = f
rv += arg
else:
# the dummy variable might match sym but it's
# only a dummy and the actual variable is determined
# by the limits, so mask off the variable of integration
# while differentiating
u = Dummy('u')
arg = f.subs(x, u).diff(sym).subs(u, x)
rv += self.func(arg, Tuple(x, a, b))
return rv
def _eval_integral(self, f, x, meijerg=None, risch=None, manual=None,
conds='piecewise'):
"""
Calculate the anti-derivative to the function f(x).
The following algorithms are applied (roughly in this order):
1. Simple heuristics (based on pattern matching and integral table):
- most frequently used functions (e.g. polynomials, products of trig functions)
2. Integration of rational functions:
- A complete algorithm for integrating rational functions is
implemented (the Lazard-Rioboo-Trager algorithm). The algorithm
also uses the partial fraction decomposition algorithm
implemented in apart() as a preprocessor to make this process
faster. Note that the integral of a rational function is always
elementary, but in general, it may include a RootSum.
3. Full Risch algorithm:
- The Risch algorithm is a complete decision
procedure for integrating elementary functions, which means that
given any elementary function, it will either compute an
elementary antiderivative, or else prove that none exists.
Currently, part of transcendental case is implemented, meaning
elementary integrals containing exponentials, logarithms, and
(soon!) trigonometric functions can be computed. The algebraic
case, e.g., functions containing roots, is much more difficult
and is not implemented yet.
- If the routine fails (because the integrand is not elementary, or
because a case is not implemented yet), it continues on to the
next algorithms below. If the routine proves that the integrals
is nonelementary, it still moves on to the algorithms below,
because we might be able to find a closed-form solution in terms
of special functions. If risch=True, however, it will stop here.
4. The Meijer G-Function algorithm:
- This algorithm works by first rewriting the integrand in terms of
very general Meijer G-Function (meijerg in SymPy), integrating
it, and then rewriting the result back, if possible. This
algorithm is particularly powerful for definite integrals (which
is actually part of a different method of Integral), since it can
compute closed-form solutions of definite integrals even when no
closed-form indefinite integral exists. But it also is capable
of computing many indefinite integrals as well.
- Another advantage of this method is that it can use some results
about the Meijer G-Function to give a result in terms of a
Piecewise expression, which allows to express conditionally
convergent integrals.
- Setting meijerg=True will cause integrate() to use only this
method.
5. The "manual integration" algorithm:
- This algorithm tries to mimic how a person would find an
antiderivative by hand, for example by looking for a
substitution or applying integration by parts. This algorithm
does not handle as many integrands but can return results in a
more familiar form.
- Sometimes this algorithm can evaluate parts of an integral; in
this case integrate() will try to evaluate the rest of the
integrand using the other methods here.
- Setting manual=True will cause integrate() to use only this
method.
6. The Heuristic Risch algorithm:
- This is a heuristic version of the Risch algorithm, meaning that
it is not deterministic. This is tried as a last resort because
it can be very slow. It is still used because not enough of the
full Risch algorithm is implemented, so that there are still some
integrals that can only be computed using this method. The goal
is to implement enough of the Risch and Meijer G-function methods
so that this can be deleted.
"""
from sympy.integrals.deltafunctions import deltaintegrate
from sympy.integrals.singularityfunctions import singularityintegrate
from sympy.integrals.heurisch import heurisch, heurisch_wrapper
from sympy.integrals.rationaltools import ratint
from sympy.integrals.risch import risch_integrate
if risch:
try:
return risch_integrate(f, x, conds=conds)
except NotImplementedError:
return None
if manual:
try:
result = manualintegrate(f, x)
if result is not None and result.func != Integral:
return result
except (ValueError, PolynomialError):
pass
# if it is a poly(x) then let the polynomial integrate itself (fast)
#
# It is important to make this check first, otherwise the other code
# will return a sympy expression instead of a Polynomial.
#
# see Polynomial for details.
if isinstance(f, Poly) and not meijerg:
return f.integrate(x)
# Piecewise antiderivatives need to call special integrate.
if f.func is Piecewise:
return f._eval_integral(x)
# let's cut it short if `f` does not depend on `x`
if not f.has(x):
return f*x
# try to convert to poly(x) and then integrate if successful (fast)
poly = f.as_poly(x)
if poly is not None and not meijerg:
return poly.integrate().as_expr()
if risch is not False:
try:
result, i = risch_integrate(f, x, separate_integral=True, conds=conds)
except NotImplementedError:
pass
else:
if i:
# There was a nonelementary integral. Try integrating it.
return result + i.doit(risch=False)
else:
return result
# since Integral(f=g1+g2+...) == Integral(g1) + Integral(g2) + ...
# we are going to handle Add terms separately,
# if `f` is not Add -- we only have one term
# Note that in general, this is a bad idea, because Integral(g1) +
# Integral(g2) might not be computable, even if Integral(g1 + g2) is.
# For example, Integral(x**x + x**x*log(x)). But many heuristics only
# work term-wise. So we compute this step last, after trying
# risch_integrate. We also try risch_integrate again in this loop,
# because maybe the integral is a sum of an elementary part and a
# nonelementary part (like erf(x) + exp(x)). risch_integrate() is
# quite fast, so this is acceptable.
parts = []
args = Add.make_args(f)
for g in args:
coeff, g = g.as_independent(x)
# g(x) = const
if g is S.One and not meijerg:
parts.append(coeff*x)
continue
# g(x) = expr + O(x**n)
order_term = g.getO()
if order_term is not None:
h = self._eval_integral(g.removeO(), x)
if h is not None:
h_order_expr = self._eval_integral(order_term.expr, x)
if h_order_expr is not None:
h_order_term = order_term.func(
h_order_expr, *order_term.variables)
parts.append(coeff*(h + h_order_term))
continue
# NOTE: if there is O(x**n) and we fail to integrate then there is
# no point in trying other methods because they will fail anyway.
return None
# c
# g(x) = (a*x+b)
if g.is_Pow and not g.exp.has(x) and not meijerg:
a = Wild('a', exclude=[x])
b = Wild('b', exclude=[x])
M = g.base.match(a*x + b)
if M is not None:
if g.exp == -1:
h = log(g.base)
elif conds != 'piecewise':
h = g.base**(g.exp + 1) / (g.exp + 1)
else:
h1 = log(g.base)
h2 = g.base**(g.exp + 1) / (g.exp + 1)
h = Piecewise((h1, Eq(g.exp, -1)), (h2, True))
parts.append(coeff * h / M[a])
continue
# poly(x)
# g(x) = -------
# poly(x)
if g.is_rational_function(x) and not meijerg:
parts.append(coeff * ratint(g, x))
continue
if not meijerg:
# g(x) = Mul(trig)
h = trigintegrate(g, x, conds=conds)
if h is not None:
parts.append(coeff * h)
continue
# g(x) has at least a DiracDelta term
h = deltaintegrate(g, x)
if h is not None:
parts.append(coeff * h)
continue
# g(x) has at least a Singularity Function term
h = singularityintegrate(g, x)
if h is not None:
parts.append(coeff * h)
continue
# Try risch again.
if risch is not False:
try:
h, i = risch_integrate(g, x, separate_integral=True, conds=conds)
except NotImplementedError:
h = None
else:
if i:
h = h + i.doit(risch=False)
parts.append(coeff*h)
continue
# fall back to heurisch
try:
if conds == 'piecewise':
h = heurisch_wrapper(g, x, hints=[])
else:
h = heurisch(g, x, hints=[])
except PolynomialError:
# XXX: this exception means there is a bug in the
# implementation of heuristic Risch integration
# algorithm.
h = None
else:
h = None
if meijerg is not False and h is None:
# rewrite using G functions
try:
h = meijerint_indefinite(g, x)
except NotImplementedError:
from sympy.integrals.meijerint import _debug
_debug('NotImplementedError from meijerint_definite')
res = None
if h is not None:
parts.append(coeff * h)
continue
if h is None and manual is not False:
try:
result = manualintegrate(g, x)
if result is not None and not isinstance(result, Integral):
if result.has(Integral):
# try to have other algorithms do the integrals
# manualintegrate can't handle
result = result.func(*[
arg.doit(manual=False) if arg.has(Integral) else arg
for arg in result.args
]).expand(multinomial=False,
log=False,
power_exp=False,
power_base=False)
if not result.has(Integral):
parts.append(coeff * result)
continue
except (ValueError, PolynomialError):
# can't handle some SymPy expressions
pass
# if we failed maybe it was because we had
# a product that could have been expanded,
# so let's try an expansion of the whole
# thing before giving up; we don't try this
# at the outset because there are things
# that cannot be solved unless they are
# NOT expanded e.g., x**x*(1+log(x)). There
# should probably be a checker somewhere in this
# routine to look for such cases and try to do
# collection on the expressions if they are already
# in an expanded form
if not h and len(args) == 1:
f = f.expand(mul=True, deep=False)
if f.is_Add:
# Note: risch will be identical on the expanded
# expression, but maybe it will be able to pick out parts,
# like x*(exp(x) + erf(x)).
return self._eval_integral(f, x, meijerg=meijerg, risch=risch, conds=conds)
if h is not None:
parts.append(coeff * h)
else:
return None
return Add(*parts)
def _eval_lseries(self, x, logx):
expr = self.as_dummy()
symb = x
for l in expr.limits:
if x in l[1:]:
symb = l[0]
break
for term in expr.function.lseries(symb, logx):
yield integrate(term, *expr.limits)
def _eval_nseries(self, x, n, logx):
expr = self.as_dummy()
symb = x
for l in expr.limits:
if x in l[1:]:
symb = l[0]
break
terms, order = expr.function.nseries(
x=symb, n=n, logx=logx).as_coeff_add(Order)
order = [o.subs(symb, x) for o in order]
return integrate(terms, *expr.limits) + Add(*order)*x
def _eval_as_leading_term(self, x):
series_gen = self.args[0].lseries(x)
for leading_term in series_gen:
if leading_term != 0:
break
return integrate(leading_term, *self.args[1:])
def as_sum(self, n, method="midpoint"):
"""
Approximates the definite integral by a sum.
method ... one of: left, right, midpoint, trapezoid
These are all basically the rectangle method [1], the only difference
is where the function value is taken in each interval to define the
rectangle.
[1] http://en.wikipedia.org/wiki/Rectangle_method
Examples
========
>>> from sympy import sin, sqrt
>>> from sympy.abc import x
>>> from sympy.integrals import Integral
>>> e = Integral(sin(x), (x, 3, 7))
>>> e
Integral(sin(x), (x, 3, 7))
For demonstration purposes, this interval will only be split into 2
regions, bounded by [3, 5] and [5, 7].
The left-hand rule uses function evaluations at the left of each
interval:
>>> e.as_sum(2, 'left')
2*sin(5) + 2*sin(3)
The midpoint rule uses evaluations at the center of each interval:
>>> e.as_sum(2, 'midpoint')
2*sin(4) + 2*sin(6)
The right-hand rule uses function evaluations at the right of each
interval:
>>> e.as_sum(2, 'right')
2*sin(5) + 2*sin(7)
The trapezoid rule uses function evaluations on both sides of the
intervals. This is equivalent to taking the average of the left and
right hand rule results:
>>> e.as_sum(2, 'trapezoid')
2*sin(5) + sin(3) + sin(7)
>>> (e.as_sum(2, 'left') + e.as_sum(2, 'right'))/2 == _
True
All but the trapexoid method may be used when dealing with a function
with a discontinuity. Here, the discontinuity at x = 0 can be avoided
by using the midpoint or right-hand method:
>>> e = Integral(1/sqrt(x), (x, 0, 1))
>>> e.as_sum(5).n(4)
1.730
>>> e.as_sum(10).n(4)
1.809
>>> e.doit().n(4) # the actual value is 2
2.000
The left- or trapezoid method will encounter the discontinuity and
return oo:
>>> e.as_sum(5, 'left')
oo
>>> e.as_sum(5, 'trapezoid')
oo
See Also
========
Integral.doit : Perform the integration using any hints
"""
limits = self.limits
if len(limits) > 1:
raise NotImplementedError(
"Multidimensional midpoint rule not implemented yet")
else:
limit = limits[0]
if len(limit) != 3:
raise ValueError("Expecting a definite integral.")
if n <= 0:
raise ValueError("n must be > 0")
if n == oo:
raise NotImplementedError("Infinite summation not yet implemented")
sym, lower_limit, upper_limit = limit
dx = (upper_limit - lower_limit)/n
if method == 'trapezoid':
l = self.function.limit(sym, lower_limit)
r = self.function.limit(sym, upper_limit, "-")
result = (l + r)/2
for i in range(1, n):
x = lower_limit + i*dx
result += self.function.subs(sym, x)
return result*dx
elif method not in ('left', 'right', 'midpoint'):
raise NotImplementedError("Unknown method %s" % method)
result = 0
for i in range(n):
if method == "midpoint":
xi = lower_limit + i*dx + dx/2
elif method == "left":
xi = lower_limit + i*dx
if i == 0:
result = self.function.limit(sym, lower_limit)
continue
elif method == "right":
xi = lower_limit + i*dx + dx
if i == n:
result += self.function.limit(sym, upper_limit, "-")
continue
result += self.function.subs(sym, xi)
return result*dx
def _sage_(self):
import sage.all as sage
f, limits = self.function._sage_(), list(self.limits)
for limit in limits:
if len(limit) == 1:
x = limit[0]
f = sage.integral(f,
x._sage_(),
hold=True)
elif len(limit) == 2:
x, b = limit
f = sage.integral(f,
x._sage_(),
b._sage_(),
hold=True)
else:
x, a, b = limit
f = sage.integral(f,
(x._sage_(),
a._sage_(),
b._sage_()),
hold=True)
return f
def integrate(*args, **kwargs):
"""integrate(f, var, ...)
Compute definite or indefinite integral of one or more variables
using Risch-Norman algorithm and table lookup. This procedure is
able to handle elementary algebraic and transcendental functions
and also a huge class of special functions, including Airy,
Bessel, Whittaker and Lambert.
var can be:
- a symbol -- indefinite integration
- a tuple (symbol, a) -- indefinite integration with result
given with `a` replacing `symbol`
- a tuple (symbol, a, b) -- definite integration
Several variables can be specified, in which case the result is
multiple integration. (If var is omitted and the integrand is
univariate, the indefinite integral in that variable will be performed.)
Indefinite integrals are returned without terms that are independent
of the integration variables. (see examples)
Definite improper integrals often entail delicate convergence
conditions. Pass conds='piecewise', 'separate' or 'none' to have
these returned, respectively, as a Piecewise function, as a separate
result (i.e. result will be a tuple), or not at all (default is
'piecewise').
**Strategy**
SymPy uses various approaches to definite integration. One method is to
find an antiderivative for the integrand, and then use the fundamental
theorem of calculus. Various functions are implemented to integrate
polynomial, rational and trigonometric functions, and integrands
containing DiracDelta terms.
SymPy also implements the part of the Risch algorithm, which is a decision
procedure for integrating elementary functions, i.e., the algorithm can
either find an elementary antiderivative, or prove that one does not
exist. There is also a (very successful, albeit somewhat slow) general
implementation of the heuristic Risch algorithm. This algorithm will
eventually be phased out as more of the full Risch algorithm is
implemented. See the docstring of Integral._eval_integral() for more
details on computing the antiderivative using algebraic methods.
The option risch=True can be used to use only the (full) Risch algorithm.
This is useful if you want to know if an elementary function has an
elementary antiderivative. If the indefinite Integral returned by this
function is an instance of NonElementaryIntegral, that means that the
Risch algorithm has proven that integral to be non-elementary. Note that
by default, additional methods (such as the Meijer G method outlined
below) are tried on these integrals, as they may be expressible in terms
of special functions, so if you only care about elementary answers, use
risch=True. Also note that an unevaluated Integral returned by this
function is not necessarily a NonElementaryIntegral, even with risch=True,
as it may just be an indication that the particular part of the Risch
algorithm needed to integrate that function is not yet implemented.
Another family of strategies comes from re-writing the integrand in
terms of so-called Meijer G-functions. Indefinite integrals of a
single G-function can always be computed, and the definite integral
of a product of two G-functions can be computed from zero to
infinity. Various strategies are implemented to rewrite integrands
as G-functions, and use this information to compute integrals (see
the ``meijerint`` module).
The option manual=True can be used to use only an algorithm that tries
to mimic integration by hand. This algorithm does not handle as many
integrands as the other algorithms implemented but may return results in
a more familiar form. The ``manualintegrate`` module has functions that
return the steps used (see the module docstring for more information).
In general, the algebraic methods work best for computing
antiderivatives of (possibly complicated) combinations of elementary
functions. The G-function methods work best for computing definite
integrals from zero to infinity of moderately complicated
combinations of special functions, or indefinite integrals of very
simple combinations of special functions.
The strategy employed by the integration code is as follows:
- If computing a definite integral, and both limits are real,
and at least one limit is +- oo, try the G-function method of
definite integration first.
- Try to find an antiderivative, using all available methods, ordered
by performance (that is try fastest method first, slowest last; in
particular polynomial integration is tried first, Meijer
G-functions second to last, and heuristic Risch last).
- If still not successful, try G-functions irrespective of the
limits.
The option meijerg=True, False, None can be used to, respectively:
always use G-function methods and no others, never use G-function
methods, or use all available methods (in order as described above).
It defaults to None.
Examples
========
>>> from sympy import integrate, log, exp, oo
>>> from sympy.abc import a, x, y
>>> integrate(x*y, x)
x**2*y/2
>>> integrate(log(x), x)
x*log(x) - x
>>> integrate(log(x), (x, 1, a))
a*log(a) - a + 1
>>> integrate(x)
x**2/2
Terms that are independent of x are dropped by indefinite integration:
>>> from sympy import sqrt
>>> integrate(sqrt(1 + x), (x, 0, x))
2*(x + 1)**(3/2)/3 - 2/3
>>> integrate(sqrt(1 + x), x)
2*(x + 1)**(3/2)/3
>>> integrate(x*y)
Traceback (most recent call last):
...
ValueError: specify integration variables to integrate x*y
Note that ``integrate(x)`` syntax is meant only for convenience
in interactive sessions and should be avoided in library code.
>>> integrate(x**a*exp(-x), (x, 0, oo)) # same as conds='piecewise'
Piecewise((gamma(a + 1), -re(a) < 1),
(Integral(x**a*exp(-x), (x, 0, oo)), True))
>>> integrate(x**a*exp(-x), (x, 0, oo), conds='none')
gamma(a + 1)
>>> integrate(x**a*exp(-x), (x, 0, oo), conds='separate')
(gamma(a + 1), -re(a) < 1)
See Also
========
Integral, Integral.doit
"""
meijerg = kwargs.pop('meijerg', None)
conds = kwargs.pop('conds', 'piecewise')
risch = kwargs.pop('risch', None)
manual = kwargs.pop('manual', None)
integral = Integral(*args, **kwargs)
if isinstance(integral, Integral):
return integral.doit(deep=False, meijerg=meijerg, conds=conds,
risch=risch, manual=manual)
else:
return integral
def line_integrate(field, curve, vars):
"""line_integrate(field, Curve, variables)
Compute the line integral.
Examples
========
>>> from sympy import Curve, line_integrate, E, ln
>>> from sympy.abc import x, y, t
>>> C = Curve([E**t + 1, E**t - 1], (t, 0, ln(2)))
>>> line_integrate(x + y, C, [x, y])
3*sqrt(2)
See Also
========
integrate, Integral
"""
from sympy.geometry import Curve
F = sympify(field)
if not F:
raise ValueError(
"Expecting function specifying field as first argument.")
if not isinstance(curve, Curve):
raise ValueError("Expecting Curve entity as second argument.")
if not is_sequence(vars):
raise ValueError("Expecting ordered iterable for variables.")
if len(curve.functions) != len(vars):
raise ValueError("Field variable size does not match curve dimension.")
if curve.parameter in vars:
raise ValueError("Curve parameter clashes with field parameters.")
# Calculate derivatives for line parameter functions
# F(r) -> F(r(t)) and finally F(r(t)*r'(t))
Ft = F
dldt = 0
for i, var in enumerate(vars):
_f = curve.functions[i]
_dn = diff(_f, curve.parameter)
# ...arc length
dldt = dldt + (_dn * _dn)
Ft = Ft.subs(var, _f)
Ft = Ft * sqrt(dldt)
integral = Integral(Ft, curve.limits).doit(deep=False)
return integral
| 52,199 | 37.724036 | 95 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/manualintegrate.py
|
"""Integration method that emulates by-hand techniques.
This module also provides functionality to get the steps used to evaluate a
particular integral, in the ``integral_steps`` function. This will return
nested namedtuples representing the integration rules used. The
``manualintegrate`` function computes the integral using those steps given
an integrand; given the steps, ``_manualintegrate`` will evaluate them.
The integrator can be extended with new heuristics and evaluation
techniques. To do so, write a function that accepts an ``IntegralInfo``
object and returns either a namedtuple representing a rule or
``None``. Then, write another function that accepts the namedtuple's fields
and returns the antiderivative, and decorate it with
``@evaluates(namedtuple_type)``. If the new technique requires a new
match, add the key and call to the antiderivative function to integral_steps.
To enable simple substitutions, add the match to find_substitutions.
"""
from __future__ import print_function, division
from collections import namedtuple
import sympy
from sympy.core.compatibility import reduce
from sympy.core.logic import fuzzy_not
from sympy.functions.elementary.trigonometric import TrigonometricFunction
from sympy.strategies.core import switch, do_one, null_safe, condition
def Rule(name, props=""):
# GOTCHA: namedtuple class name not considered!
def __eq__(self, other):
return self.__class__ == other.__class__ and tuple.__eq__(self, other)
__neq__ = lambda self, other: not __eq__(self, other)
cls = namedtuple(name, props + " context symbol")
cls.__eq__ = __eq__
cls.__ne__ = __neq__
return cls
ConstantRule = Rule("ConstantRule", "constant")
ConstantTimesRule = Rule("ConstantTimesRule", "constant other substep")
PowerRule = Rule("PowerRule", "base exp")
AddRule = Rule("AddRule", "substeps")
URule = Rule("URule", "u_var u_func constant substep")
PartsRule = Rule("PartsRule", "u dv v_step second_step")
CyclicPartsRule = Rule("CyclicPartsRule", "parts_rules coefficient")
TrigRule = Rule("TrigRule", "func arg")
ExpRule = Rule("ExpRule", "base exp")
ReciprocalRule = Rule("ReciprocalRule", "func")
ArcsinRule = Rule("ArcsinRule")
InverseHyperbolicRule = Rule("InverseHyperbolicRule", "func")
AlternativeRule = Rule("AlternativeRule", "alternatives")
DontKnowRule = Rule("DontKnowRule")
DerivativeRule = Rule("DerivativeRule")
RewriteRule = Rule("RewriteRule", "rewritten substep")
PiecewiseRule = Rule("PiecewiseRule", "subfunctions")
HeavisideRule = Rule("HeavisideRule", "harg ibnd substep")
TrigSubstitutionRule = Rule("TrigSubstitutionRule",
"theta func rewritten substep restriction")
ArctanRule = Rule("ArctanRule", "a b c")
ArccothRule = Rule("ArccothRule", "a b c")
ArctanhRule = Rule("ArctanhRule", "a b c")
IntegralInfo = namedtuple('IntegralInfo', 'integrand symbol')
evaluators = {}
def evaluates(rule):
def _evaluates(func):
func.rule = rule
evaluators[rule] = func
return func
return _evaluates
def contains_dont_know(rule):
if isinstance(rule, DontKnowRule):
return True
else:
for val in rule:
if isinstance(val, tuple):
if contains_dont_know(val):
return True
elif isinstance(val, list):
if any(contains_dont_know(i) for i in val):
return True
return False
def manual_diff(f, symbol):
"""Derivative of f in form expected by find_substitutions
SymPy's derivatives for some trig functions (like cot) aren't in a form
that works well with finding substitutions; this replaces the
derivatives for those particular forms with something that works better.
"""
if f.args:
arg = f.args[0]
if isinstance(f, sympy.tan):
return arg.diff(symbol) * sympy.sec(arg)**2
elif isinstance(f, sympy.cot):
return -arg.diff(symbol) * sympy.csc(arg)**2
elif isinstance(f, sympy.sec):
return arg.diff(symbol) * sympy.sec(arg) * sympy.tan(arg)
elif isinstance(f, sympy.csc):
return -arg.diff(symbol) * sympy.csc(arg) * sympy.cot(arg)
elif isinstance(f, sympy.Add):
return sum([manual_diff(arg, symbol) for arg in f.args])
elif isinstance(f, sympy.Mul):
if len(f.args) == 2 and isinstance(f.args[0], sympy.Number):
return f.args[0] * manual_diff(f.args[1], symbol)
return f.diff(symbol)
# Method based on that on SIN, described in "Symbolic Integration: The
# Stormy Decade"
def find_substitutions(integrand, symbol, u_var):
results = []
def test_subterm(u, u_diff):
substituted = integrand / u_diff
if symbol not in substituted.free_symbols:
# replaced everything already
return False
substituted = substituted.subs(u, u_var).cancel()
if symbol not in substituted.free_symbols:
return substituted.as_independent(u_var, as_Add=False)
return False
def possible_subterms(term):
if isinstance(term, (TrigonometricFunction,
sympy.asin, sympy.acos, sympy.atan,
sympy.exp, sympy.log, sympy.Heaviside)):
return [term.args[0]]
elif isinstance(term, sympy.Mul):
r = []
for u in term.args:
r.append(u)
r.extend(possible_subterms(u))
return r
elif isinstance(term, sympy.Pow):
if term.args[1].is_constant(symbol):
return [term.args[0]]
elif term.args[0].is_constant(symbol):
return [term.args[1]]
elif isinstance(term, sympy.Add):
r = []
for arg in term.args:
r.append(arg)
r.extend(possible_subterms(arg))
return r
return []
for u in possible_subterms(integrand):
if u == symbol:
continue
u_diff = manual_diff(u, symbol)
new_integrand = test_subterm(u, u_diff)
if new_integrand is not False:
constant, new_integrand = new_integrand
if new_integrand == integrand.subs(symbol, u_var):
continue
substitution = (u, constant, new_integrand)
if substitution not in results:
results.append(substitution)
return results
def rewriter(condition, rewrite):
"""Strategy that rewrites an integrand."""
def _rewriter(integral):
integrand, symbol = integral
if condition(*integral):
rewritten = rewrite(*integral)
if rewritten != integrand:
substep = integral_steps(rewritten, symbol)
if not isinstance(substep, DontKnowRule) and substep:
return RewriteRule(
rewritten,
substep,
integrand, symbol)
return _rewriter
def proxy_rewriter(condition, rewrite):
"""Strategy that rewrites an integrand based on some other criteria."""
def _proxy_rewriter(criteria):
criteria, integral = criteria
integrand, symbol = integral
args = criteria + list(integral)
if condition(*args):
rewritten = rewrite(*args)
if rewritten != integrand:
return RewriteRule(
rewritten,
integral_steps(rewritten, symbol),
integrand, symbol)
return _proxy_rewriter
def multiplexer(conditions):
"""Apply the rule that matches the condition, else None"""
def multiplexer_rl(expr):
for key, rule in conditions.items():
if key(expr):
return rule(expr)
return multiplexer_rl
def alternatives(*rules):
"""Strategy that makes an AlternativeRule out of multiple possible results."""
def _alternatives(integral):
alts = []
for rule in rules:
result = rule(integral)
if (result and not isinstance(result, DontKnowRule) and
result != integral and result not in alts):
alts.append(result)
if len(alts) == 1:
return alts[0]
elif alts:
doable = [rule for rule in alts if not contains_dont_know(rule)]
if doable:
return AlternativeRule(doable, *integral)
else:
return AlternativeRule(alts, *integral)
return _alternatives
def constant_rule(integral):
integrand, symbol = integral
return ConstantRule(integral.integrand, *integral)
def power_rule(integral):
integrand, symbol = integral
base, exp = integrand.as_base_exp()
if symbol not in exp.free_symbols and isinstance(base, sympy.Symbol):
if sympy.simplify(exp + 1) == 0:
return ReciprocalRule(base, integrand, symbol)
return PowerRule(base, exp, integrand, symbol)
elif symbol not in base.free_symbols and isinstance(exp, sympy.Symbol):
rule = ExpRule(base, exp, integrand, symbol)
if fuzzy_not(sympy.log(base).is_zero):
return rule
elif sympy.log(base).is_zero:
return ConstantRule(1, 1, symbol)
return PiecewiseRule([
(ConstantRule(1, 1, symbol), sympy.Eq(sympy.log(base), 0)),
(rule, True)
], integrand, symbol)
def exp_rule(integral):
integrand, symbol = integral
if isinstance(integrand.args[0], sympy.Symbol):
return ExpRule(sympy.E, integrand.args[0], integrand, symbol)
def inverse_trig_rule(integral):
integrand, symbol = integral
base, exp = integrand.as_base_exp()
a = sympy.Wild('a', exclude=[symbol])
b = sympy.Wild('b', exclude=[symbol])
match = base.match(a + b*symbol**2)
if not match:
return
def negative(x):
return x.is_negative or x.could_extract_minus_sign()
def ArcsinhRule(integrand, symbol):
return InverseHyperbolicRule(sympy.asinh, integrand, symbol)
def ArccoshRule(integrand, symbol):
return InverseHyperbolicRule(sympy.acosh, integrand, symbol)
def make_inverse_trig(RuleClass, base_exp, a, sign_a, b, sign_b):
u_var = sympy.Dummy("u")
current_base = base
current_symbol = symbol
constant = u_func = u_constant = substep = None
factored = integrand
if a != 1:
constant = a**base_exp
current_base = sign_a + sign_b * (b/a) * current_symbol**2
factored = current_base ** base_exp
if (b/a) != 1:
u_func = sympy.sqrt(b/a) * symbol
u_constant = sympy.sqrt(a/b)
current_symbol = u_var
current_base = sign_a + sign_b * current_symbol**2
substep = RuleClass(current_base ** base_exp, current_symbol)
if u_func is not None:
if u_constant != 1 and substep is not None:
substep = ConstantTimesRule(
u_constant, current_base ** base_exp, substep,
u_constant * current_base ** base_exp, symbol)
substep = URule(u_var, u_func, u_constant, substep, factored, symbol)
if constant is not None and substep is not None:
substep = ConstantTimesRule(constant, factored, substep, integrand, symbol)
return substep
a, b = match[a], match[b]
# list of (rule, base_exp, a, sign_a, b, sign_b, condition)
possibilities = []
if sympy.simplify(2*exp + 1) == 0:
possibilities.append((ArcsinRule, exp, a, 1, -b, -1, sympy.And(a > 0, b < 0)))
possibilities.append((ArcsinhRule, exp, a, 1, b, 1, sympy.And(a > 0, b > 0)))
possibilities.append((ArccoshRule, exp, -a, -1, b, 1, sympy.And(a < 0, b > 0)))
possibilities = [p for p in possibilities if p[-1] is not sympy.false]
if a.is_number and b.is_number:
possibility = [p for p in possibilities if p[-1] is sympy.true]
if len(possibility) == 1:
return make_inverse_trig(*possibility[0][:-1])
elif possibilities:
return PiecewiseRule(
[(make_inverse_trig(*p[:-1]), p[-1]) for p in possibilities],
integrand, symbol)
def add_rule(integral):
integrand, symbol = integral
return AddRule(
[integral_steps(g, symbol)
for g in integrand.as_ordered_terms()],
integrand, symbol)
def mul_rule(integral):
integrand, symbol = integral
args = integrand.args
# Constant times function case
coeff, f = integrand.as_independent(symbol)
next_step = integral_steps(f, symbol)
if coeff != 1 and next_step is not None:
return ConstantTimesRule(
coeff, f,
next_step,
integrand, symbol)
def _parts_rule(integrand, symbol):
# LIATE rule:
# log, inverse trig, algebraic, trigonometric, exponential
def pull_out_algebraic(integrand):
integrand = integrand.cancel().together()
algebraic = [arg for arg in integrand.args if arg.is_algebraic_expr(symbol)]
if algebraic:
u = sympy.Mul(*algebraic)
dv = (integrand / u).cancel()
if not u.is_polynomial() and isinstance(dv, sympy.exp):
return
return u, dv
def pull_out_u(*functions):
def pull_out_u_rl(integrand):
if any([integrand.has(f) for f in functions]):
args = [arg for arg in integrand.args
if any(isinstance(arg, cls) for cls in functions)]
if args:
u = reduce(lambda a,b: a*b, args)
dv = integrand / u
return u, dv
return pull_out_u_rl
liate_rules = [pull_out_u(sympy.log), pull_out_u(sympy.atan, sympy.asin, sympy.acos),
pull_out_algebraic, pull_out_u(sympy.sin, sympy.cos),
pull_out_u(sympy.exp)]
dummy = sympy.Dummy("temporary")
# we can integrate log(x) and atan(x) by setting dv = 1
if isinstance(integrand, (sympy.log, sympy.atan, sympy.asin, sympy.acos)):
integrand = dummy * integrand
for index, rule in enumerate(liate_rules):
result = rule(integrand)
if result:
u, dv = result
# Don't pick u to be a constant if possible
if symbol not in u.free_symbols and not u.has(dummy):
return
u = u.subs(dummy, 1)
dv = dv.subs(dummy, 1)
for rule in liate_rules[index + 1:]:
r = rule(integrand)
# make sure dv is amenable to integration
if r and sympy.simplify(r[0].subs(dummy, 1)) == sympy.simplify(dv):
du = u.diff(symbol)
v_step = integral_steps(sympy.simplify(dv), symbol)
v = _manualintegrate(v_step)
return u, dv, v, du, v_step
def parts_rule(integral):
integrand, symbol = integral
constant, integrand = integrand.as_coeff_Mul()
result = _parts_rule(integrand, symbol)
steps = []
if result:
u, dv, v, du, v_step = result
steps.append(result)
if isinstance(v, sympy.Integral):
return
while True:
if (integrand / (v * du)).cancel() == 1:
break
if symbol not in (integrand / (v * du)).cancel().free_symbols:
coefficient = ((v * du) / integrand).cancel()
rule = CyclicPartsRule(
[PartsRule(u, dv, v_step, None, None, None)
for (u, dv, v, du, v_step) in steps],
(-1) ** len(steps) * coefficient,
integrand, symbol
)
if (constant != 1) and rule:
rule = ConstantTimesRule(constant, integrand, rule,
constant * integrand, symbol)
return rule
result = _parts_rule(v * du, symbol)
if result:
u, dv, v, du, v_step = result
steps.append(result)
else:
break
def make_second_step(steps, integrand):
if steps:
u, dv, v, du, v_step = steps[0]
return PartsRule(u, dv, v_step,
make_second_step(steps[1:], v * du),
integrand, symbol)
else:
steps = integral_steps(integrand, symbol)
if steps:
return steps
else:
return DontKnowRule(integrand, symbol)
if steps:
u, dv, v, du, v_step = steps[0]
rule = PartsRule(u, dv, v_step,
make_second_step(steps[1:], v * du),
integrand, symbol)
if (constant != 1) and rule:
rule = ConstantTimesRule(constant, integrand, rule,
constant * integrand, symbol)
return rule
def trig_rule(integral):
integrand, symbol = integral
if isinstance(integrand, sympy.sin) or isinstance(integrand, sympy.cos):
arg = integrand.args[0]
if not isinstance(arg, sympy.Symbol):
return # perhaps a substitution can deal with it
if isinstance(integrand, sympy.sin):
func = 'sin'
else:
func = 'cos'
return TrigRule(func, arg, integrand, symbol)
if integrand == sympy.sec(symbol)**2:
return TrigRule('sec**2', symbol, integrand, symbol)
elif integrand == sympy.csc(symbol)**2:
return TrigRule('csc**2', symbol, integrand, symbol)
if isinstance(integrand, sympy.tan):
rewritten = sympy.sin(*integrand.args) / sympy.cos(*integrand.args)
elif isinstance(integrand, sympy.cot):
rewritten = sympy.cos(*integrand.args) / sympy.sin(*integrand.args)
elif isinstance(integrand, sympy.sec):
arg = integrand.args[0]
rewritten = ((sympy.sec(arg)**2 + sympy.tan(arg) * sympy.sec(arg)) /
(sympy.sec(arg) + sympy.tan(arg)))
elif isinstance(integrand, sympy.csc):
arg = integrand.args[0]
rewritten = ((sympy.csc(arg)**2 + sympy.cot(arg) * sympy.csc(arg)) /
(sympy.csc(arg) + sympy.cot(arg)))
else:
return
return RewriteRule(
rewritten,
integral_steps(rewritten, symbol),
integrand, symbol
)
def trig_product_rule(integral):
integrand, symbol = integral
sectan = sympy.sec(symbol) * sympy.tan(symbol)
q = integrand / sectan
if symbol not in q.free_symbols:
rule = TrigRule('sec*tan', symbol, sectan, symbol)
if q != 1 and rule:
rule = ConstantTimesRule(q, sectan, rule, integrand, symbol)
return rule
csccot = -sympy.csc(symbol) * sympy.cot(symbol)
q = integrand / csccot
if symbol not in q.free_symbols:
rule = TrigRule('csc*cot', symbol, csccot, symbol)
if q != 1 and rule:
rule = ConstantTimesRule(q, csccot, rule, integrand, symbol)
return rule
def quadratic_denom_rule(integral):
integrand, symbol = integral
a = sympy.Wild('a', exclude=[symbol])
b = sympy.Wild('b', exclude=[symbol])
c = sympy.Wild('c', exclude=[symbol])
match = integrand.match(a / (b * symbol ** 2 + c))
if not match:
return
a, b, c = match[a], match[b], match[c]
return PiecewiseRule([(ArctanRule(a, b, c, integrand, symbol), sympy.Gt(c / b, 0)),
(ArccothRule(a, b, c, integrand, symbol), sympy.And(sympy.Gt(symbol ** 2, -c / b), sympy.Lt(c / b, 0))),
(ArctanhRule(a, b, c, integrand, symbol), sympy.And(sympy.Lt(symbol ** 2, -c / b), sympy.Lt(c / b, 0))),
], integrand, symbol)
def root_mul_rule(integral):
integrand, symbol = integral
a = sympy.Wild('a', exclude=[symbol])
b = sympy.Wild('b', exclude=[symbol])
c = sympy.Wild('c')
match = integrand.match(sympy.sqrt(a * symbol + b) * c)
if not match:
return
a, b, c = match[a], match[b], match[c]
d = sympy.Wild('d', exclude=[symbol])
e = sympy.Wild('e', exclude=[symbol])
f = sympy.Wild('f')
recursion_test = c.match(sympy.sqrt(d * symbol + e) * f)
if recursion_test:
return
u = sympy.Dummy('u')
u_func = sympy.sqrt(a * symbol + b)
integrand = integrand.subs(u_func, u)
integrand = integrand.subs(symbol, (u**2 - b) / a)
integrand = integrand * 2 * u / a
next_step = integral_steps(integrand, u)
if next_step:
return URule(u, u_func, None, next_step, integrand, symbol)
@sympy.cacheit
def make_wilds(symbol):
a = sympy.Wild('a', exclude=[symbol])
b = sympy.Wild('b', exclude=[symbol])
m = sympy.Wild('m', exclude=[symbol], properties=[lambda n: isinstance(n, sympy.Integer)])
n = sympy.Wild('n', exclude=[symbol], properties=[lambda n: isinstance(n, sympy.Integer)])
return a, b, m, n
@sympy.cacheit
def sincos_pattern(symbol):
a, b, m, n = make_wilds(symbol)
pattern = sympy.sin(a*symbol)**m * sympy.cos(b*symbol)**n
return pattern, a, b, m, n
@sympy.cacheit
def tansec_pattern(symbol):
a, b, m, n = make_wilds(symbol)
pattern = sympy.tan(a*symbol)**m * sympy.sec(b*symbol)**n
return pattern, a, b, m, n
@sympy.cacheit
def cotcsc_pattern(symbol):
a, b, m, n = make_wilds(symbol)
pattern = sympy.cot(a*symbol)**m * sympy.csc(b*symbol)**n
return pattern, a, b, m, n
@sympy.cacheit
def heaviside_pattern(symbol):
m = sympy.Wild('m', exclude=[symbol])
b = sympy.Wild('b', exclude=[symbol])
g = sympy.Wild('g')
pattern = sympy.Heaviside(m*symbol + b) * g
return pattern, m, b, g
def uncurry(func):
def uncurry_rl(args):
return func(*args)
return uncurry_rl
def trig_rewriter(rewrite):
def trig_rewriter_rl(args):
a, b, m, n, integrand, symbol = args
rewritten = rewrite(a, b, m, n, integrand, symbol)
if rewritten != integrand:
return RewriteRule(
rewritten,
integral_steps(rewritten, symbol),
integrand, symbol)
return trig_rewriter_rl
sincos_botheven_condition = uncurry(
lambda a, b, m, n, i, s: m.is_even and n.is_even and
m.is_nonnegative and n.is_nonnegative)
sincos_botheven = trig_rewriter(
lambda a, b, m, n, i, symbol: ( (((1 - sympy.cos(2*a*symbol)) / 2) ** (m / 2)) *
(((1 + sympy.cos(2*b*symbol)) / 2) ** (n / 2)) ))
sincos_sinodd_condition = uncurry(lambda a, b, m, n, i, s: m.is_odd and m >= 3)
sincos_sinodd = trig_rewriter(
lambda a, b, m, n, i, symbol: ( (1 - sympy.cos(a*symbol)**2)**((m - 1) / 2) *
sympy.sin(a*symbol) *
sympy.cos(b*symbol) ** n))
sincos_cosodd_condition = uncurry(lambda a, b, m, n, i, s: n.is_odd and n >= 3)
sincos_cosodd = trig_rewriter(
lambda a, b, m, n, i, symbol: ( (1 - sympy.sin(b*symbol)**2)**((n - 1) / 2) *
sympy.cos(b*symbol) *
sympy.sin(a*symbol) ** m))
tansec_seceven_condition = uncurry(lambda a, b, m, n, i, s: n.is_even and n >= 4)
tansec_seceven = trig_rewriter(
lambda a, b, m, n, i, symbol: ( (1 + sympy.tan(b*symbol)**2) ** (n/2 - 1) *
sympy.sec(b*symbol)**2 *
sympy.tan(a*symbol) ** m ))
tansec_tanodd_condition = uncurry(lambda a, b, m, n, i, s: m.is_odd)
tansec_tanodd = trig_rewriter(
lambda a, b, m, n, i, symbol: ( (sympy.sec(a*symbol)**2 - 1) ** ((m - 1) / 2) *
sympy.tan(a*symbol) *
sympy.sec(b*symbol) ** n ))
tan_tansquared_condition = uncurry(lambda a, b, m, n, i, s: m == 2 and n == 0)
tan_tansquared = trig_rewriter(
lambda a, b, m, n, i, symbol: ( sympy.sec(a*symbol)**2 - 1))
cotcsc_csceven_condition = uncurry(lambda a, b, m, n, i, s: n.is_even and n >= 4)
cotcsc_csceven = trig_rewriter(
lambda a, b, m, n, i, symbol: ( (1 + sympy.cot(b*symbol)**2) ** (n/2 - 1) *
sympy.csc(b*symbol)**2 *
sympy.cot(a*symbol) ** m ))
cotcsc_cotodd_condition = uncurry(lambda a, b, m, n, i, s: m.is_odd)
cotcsc_cotodd = trig_rewriter(
lambda a, b, m, n, i, symbol: ( (sympy.csc(a*symbol)**2 - 1) ** ((m - 1) / 2) *
sympy.cot(a*symbol) *
sympy.csc(b*symbol) ** n ))
def trig_sincos_rule(integral):
integrand, symbol = integral
if any(integrand.has(f) for f in (sympy.sin, sympy.cos)):
pattern, a, b, m, n = sincos_pattern(symbol)
match = integrand.match(pattern)
if match:
a, b, m, n = match.get(a, 0), match.get(b, 0), match.get(m, 0), match.get(n, 0)
return multiplexer({
sincos_botheven_condition: sincos_botheven,
sincos_sinodd_condition: sincos_sinodd,
sincos_cosodd_condition: sincos_cosodd
})((a, b, m, n, integrand, symbol))
def trig_tansec_rule(integral):
integrand, symbol = integral
integrand = integrand.subs({
1 / sympy.cos(symbol): sympy.sec(symbol)
})
if any(integrand.has(f) for f in (sympy.tan, sympy.sec)):
pattern, a, b, m, n = tansec_pattern(symbol)
match = integrand.match(pattern)
if match:
a, b, m, n = match.get(a, 0),match.get(b, 0), match.get(m, 0), match.get(n, 0)
return multiplexer({
tansec_tanodd_condition: tansec_tanodd,
tansec_seceven_condition: tansec_seceven,
tan_tansquared_condition: tan_tansquared
})((a, b, m, n, integrand, symbol))
def trig_cotcsc_rule(integral):
integrand, symbol = integral
integrand = integrand.subs({
1 / sympy.sin(symbol): sympy.csc(symbol),
1 / sympy.tan(symbol): sympy.cot(symbol),
sympy.cos(symbol) / sympy.tan(symbol): sympy.cot(symbol)
})
if any(integrand.has(f) for f in (sympy.cot, sympy.csc)):
pattern, a, b, m, n = cotcsc_pattern(symbol)
match = integrand.match(pattern)
if match:
a, b, m, n = match.get(a, 0),match.get(b, 0), match.get(m, 0), match.get(n, 0)
return multiplexer({
cotcsc_cotodd_condition: cotcsc_cotodd,
cotcsc_csceven_condition: cotcsc_csceven
})((a, b, m, n, integrand, symbol))
def trig_powers_products_rule(integral):
return do_one(null_safe(trig_sincos_rule),
null_safe(trig_tansec_rule),
null_safe(trig_cotcsc_rule))(integral)
def trig_substitution_rule(integral):
integrand, symbol = integral
a = sympy.Wild('a', exclude=[0, symbol])
b = sympy.Wild('b', exclude=[0, symbol])
theta = sympy.Dummy("theta")
matches = integrand.find(a + b*symbol**2)
if matches:
for expr in matches:
match = expr.match(a + b*symbol**2)
a = match[a]
b = match[b]
a_positive = ((a.is_number and a > 0) or a.is_positive)
b_positive = ((b.is_number and b > 0) or b.is_positive)
x_func = None
if a_positive and b_positive:
# a**2 + b*x**2. Assume sec(theta) > 0, -pi/2 < theta < pi/2
x_func = (sympy.sqrt(a)/sympy.sqrt(b)) * sympy.tan(theta)
# Do not restrict the domain: tan(theta) takes on any real
# value on the interval -pi/2 < theta < pi/2 so x takes on
# any value
restriction = True
elif a_positive and not b_positive:
# a**2 - b*x**2. Assume cos(theta) > 0, -pi/2 < theta < pi/2
constant = sympy.sqrt(a)/sympy.sqrt(-b)
x_func = constant * sympy.sin(theta)
restriction = sympy.And(symbol > -constant, symbol < constant)
elif not a_positive and b_positive:
# b*x**2 - a**2. Assume sin(theta) > 0, 0 < theta < pi
constant = sympy.sqrt(-a)/sympy.sqrt(b)
x_func = constant * sympy.sec(theta)
restriction = sympy.And(symbol > -constant, symbol < constant)
if x_func:
# Manually simplify sqrt(trig(theta)**2) to trig(theta)
# Valid due to assumed domain restriction
substitutions = {}
for f in [sympy.sin, sympy.cos, sympy.tan,
sympy.sec, sympy.csc, sympy.cot]:
substitutions[sympy.sqrt(f(theta)**2)] = f(theta)
substitutions[sympy.sqrt(f(theta)**(-2))] = 1/f(theta)
replaced = integrand.subs(symbol, x_func).trigsimp()
replaced = replaced.subs(substitutions)
if not replaced.has(symbol):
replaced *= manual_diff(x_func, theta)
replaced = replaced.trigsimp()
secants = replaced.find(1/sympy.cos(theta))
if secants:
replaced = replaced.xreplace({
1/sympy.cos(theta): sympy.sec(theta)
})
substep = integral_steps(replaced, theta)
if not contains_dont_know(substep):
return TrigSubstitutionRule(
theta, x_func, replaced, substep, restriction,
integrand, symbol)
def heaviside_rule(integral):
integrand, symbol = integral
pattern, m, b, g = heaviside_pattern(symbol)
match = integrand.match(pattern)
if match and 0 != match[g]:
# f = Heaviside(m*x + b)*g
v_step = integral_steps(match[g], symbol)
result = _manualintegrate(v_step)
m, b = match[m], match[b]
return HeavisideRule(m*symbol + b, -b/m, result, integrand, symbol)
def substitution_rule(integral):
integrand, symbol = integral
u_var = sympy.Dummy("u")
substitutions = find_substitutions(integrand, symbol, u_var)
if substitutions:
ways = []
for u_func, c, substituted in substitutions:
subrule = integral_steps(substituted, u_var)
if contains_dont_know(subrule):
continue
if sympy.simplify(c - 1) != 0:
_, denom = c.as_numer_denom()
if subrule:
subrule = ConstantTimesRule(c, substituted, subrule, substituted, u_var)
if denom.free_symbols:
piecewise = []
could_be_zero = []
if isinstance(denom, sympy.Mul):
could_be_zero = denom.args
else:
could_be_zero.append(denom)
for expr in could_be_zero:
if not fuzzy_not(expr.is_zero):
substep = integral_steps(integrand.subs(expr, 0), symbol)
if substep:
piecewise.append((
substep,
sympy.Eq(expr, 0)
))
piecewise.append((subrule, True))
subrule = PiecewiseRule(piecewise, substituted, symbol)
ways.append(URule(u_var, u_func, c,
subrule,
integrand, symbol))
if len(ways) > 1:
return AlternativeRule(ways, integrand, symbol)
elif ways:
return ways[0]
elif integrand.has(sympy.exp):
u_func = sympy.exp(symbol)
c = 1
substituted = integrand / u_func.diff(symbol)
substituted = substituted.subs(u_func, u_var)
if symbol not in substituted.free_symbols:
return URule(u_var, u_func, c,
integral_steps(substituted, u_var),
integrand, symbol)
partial_fractions_rule = rewriter(
lambda integrand, symbol: integrand.is_rational_function(),
lambda integrand, symbol: integrand.apart(symbol))
cancel_rule = rewriter(
# lambda integrand, symbol: integrand.is_algebraic_expr(),
# lambda integrand, symbol: isinstance(integrand, sympy.Mul),
lambda integrand, symbol: True,
lambda integrand, symbol: integrand.cancel())
distribute_expand_rule = rewriter(
lambda integrand, symbol: (
all(arg.is_Pow or arg.is_polynomial(symbol) for arg in integrand.args)
or isinstance(integrand, sympy.Pow)
or isinstance(integrand, sympy.Mul)),
lambda integrand, symbol: integrand.expand())
def derivative_rule(integral):
variables = integral[0].args[1:]
if variables[-1] == integral.symbol:
return DerivativeRule(*integral)
else:
return ConstantRule(integral.integrand, *integral)
def rewrites_rule(integral):
integrand, symbol = integral
if integrand.match(1/sympy.cos(symbol)):
rewritten = integrand.subs(1/sympy.cos(symbol), sympy.sec(symbol))
return RewriteRule(rewritten, integral_steps(rewritten, symbol), integrand, symbol)
def fallback_rule(integral):
return DontKnowRule(*integral)
# Cache is used to break cyclic integrals
_integral_cache = {}
def integral_steps(integrand, symbol, **options):
"""Returns the steps needed to compute an integral.
This function attempts to mirror what a student would do by hand as
closely as possible.
SymPy Gamma uses this to provide a step-by-step explanation of an
integral. The code it uses to format the results of this function can be
found at
https://github.com/sympy/sympy_gamma/blob/master/app/logic/intsteps.py.
Examples
========
>>> from sympy import exp, sin, cos
>>> from sympy.integrals.manualintegrate import integral_steps
>>> from sympy.abc import x
>>> print(repr(integral_steps(exp(x) / (1 + exp(2 * x)), x))) \
# doctest: +NORMALIZE_WHITESPACE
URule(u_var=_u, u_func=exp(x), constant=1,
substep=PiecewiseRule(subfunctions=[(ArctanRule(a=1, b=1, c=1, context=1/(_u**2 + 1), symbol=_u), True),
(ArccothRule(a=1, b=1, c=1, context=1/(_u**2 + 1), symbol=_u), False),
(ArctanhRule(a=1, b=1, c=1, context=1/(_u**2 + 1), symbol=_u), False)],
context=1/(_u**2 + 1), symbol=_u), context=exp(x)/(exp(2*x) + 1), symbol=x)
>>> print(repr(integral_steps(sin(x), x))) \
# doctest: +NORMALIZE_WHITESPACE
TrigRule(func='sin', arg=x, context=sin(x), symbol=x)
>>> print(repr(integral_steps((x**2 + 3)**2 , x))) \
# doctest: +NORMALIZE_WHITESPACE
RewriteRule(rewritten=x**4 + 6*x**2 + 9,
substep=AddRule(substeps=[PowerRule(base=x, exp=4, context=x**4, symbol=x),
ConstantTimesRule(constant=6, other=x**2,
substep=PowerRule(base=x, exp=2, context=x**2, symbol=x),
context=6*x**2, symbol=x),
ConstantRule(constant=9, context=9, symbol=x)],
context=x**4 + 6*x**2 + 9, symbol=x), context=(x**2 + 3)**2, symbol=x)
Returns
=======
rule : namedtuple
The first step; most rules have substeps that must also be
considered. These substeps can be evaluated using ``manualintegrate``
to obtain a result.
"""
cachekey = (integrand, symbol)
if cachekey in _integral_cache:
if _integral_cache[cachekey] is None:
# cyclic integral! null_safe will eliminate that path
return None
else:
return _integral_cache[cachekey]
else:
_integral_cache[cachekey] = None
integral = IntegralInfo(integrand, symbol)
def key(integral):
integrand = integral.integrand
if isinstance(integrand, TrigonometricFunction):
return TrigonometricFunction
elif isinstance(integrand, sympy.Derivative):
return sympy.Derivative
elif symbol not in integrand.free_symbols:
return sympy.Number
else:
for cls in (sympy.Pow, sympy.Symbol, sympy.exp, sympy.log,
sympy.Add, sympy.Mul, sympy.atan, sympy.asin, sympy.acos, sympy.Heaviside):
if isinstance(integrand, cls):
return cls
def integral_is_subclass(*klasses):
def _integral_is_subclass(integral):
k = key(integral)
return k and issubclass(k, klasses)
return _integral_is_subclass
result = do_one(
null_safe(switch(key, {
sympy.Pow: do_one(null_safe(power_rule), null_safe(inverse_trig_rule), \
null_safe(quadratic_denom_rule)),
sympy.Symbol: power_rule,
sympy.exp: exp_rule,
sympy.Add: add_rule,
sympy.Mul: do_one(null_safe(mul_rule), null_safe(trig_product_rule), \
null_safe(heaviside_rule), null_safe(quadratic_denom_rule), \
null_safe(root_mul_rule)),
sympy.Derivative: derivative_rule,
TrigonometricFunction: trig_rule,
sympy.Heaviside: heaviside_rule,
sympy.Number: constant_rule
})),
do_one(
null_safe(trig_rule),
null_safe(alternatives(
rewrites_rule,
substitution_rule,
condition(
integral_is_subclass(sympy.Mul, sympy.Pow),
partial_fractions_rule),
condition(
integral_is_subclass(sympy.Mul, sympy.Pow),
cancel_rule),
condition(
integral_is_subclass(sympy.Mul, sympy.log, sympy.atan, sympy.asin, sympy.acos),
parts_rule),
condition(
integral_is_subclass(sympy.Mul, sympy.Pow),
distribute_expand_rule),
trig_powers_products_rule
)),
null_safe(trig_substitution_rule)
),
fallback_rule)(integral)
del _integral_cache[cachekey]
return result
@evaluates(ConstantRule)
def eval_constant(constant, integrand, symbol):
return constant * symbol
@evaluates(ConstantTimesRule)
def eval_constanttimes(constant, other, substep, integrand, symbol):
return constant * _manualintegrate(substep)
@evaluates(PowerRule)
def eval_power(base, exp, integrand, symbol):
return sympy.Piecewise((sympy.log(base), sympy.Eq(exp, -1)),
((base**(exp + 1))/(exp + 1), True))
@evaluates(ExpRule)
def eval_exp(base, exp, integrand, symbol):
return integrand / sympy.ln(base)
@evaluates(AddRule)
def eval_add(substeps, integrand, symbol):
return sum(map(_manualintegrate, substeps))
@evaluates(URule)
def eval_u(u_var, u_func, constant, substep, integrand, symbol):
result = _manualintegrate(substep)
return result.subs(u_var, u_func)
@evaluates(PartsRule)
def eval_parts(u, dv, v_step, second_step, integrand, symbol):
v = _manualintegrate(v_step)
return u * v - _manualintegrate(second_step)
@evaluates(CyclicPartsRule)
def eval_cyclicparts(parts_rules, coefficient, integrand, symbol):
coefficient = 1 - coefficient
result = []
sign = 1
for rule in parts_rules:
result.append(sign * rule.u * _manualintegrate(rule.v_step))
sign *= -1
return sympy.Add(*result) / coefficient
@evaluates(TrigRule)
def eval_trig(func, arg, integrand, symbol):
if func == 'sin':
return -sympy.cos(arg)
elif func == 'cos':
return sympy.sin(arg)
elif func == 'sec*tan':
return sympy.sec(arg)
elif func == 'csc*cot':
return sympy.csc(arg)
elif func == 'sec**2':
return sympy.tan(arg)
elif func == 'csc**2':
return -sympy.cot(arg)
@evaluates(ArctanRule)
def eval_arctan(a, b, c, integrand, symbol):
return a / b * 1 / sympy.sqrt(c / b) * sympy.atan(symbol / sympy.sqrt(c / b))
@evaluates(ArccothRule)
def eval_arccoth(a, b, c, integrand, symbol):
return - a / b * 1 / sympy.sqrt(-c / b) * sympy.acoth(symbol / sympy.sqrt(-c / b))
@evaluates(ArctanhRule)
def eval_arctanh(a, b, c, integrand, symbol):
return - a / b * 1 / sympy.sqrt(-c / b) * sympy.atanh(symbol / sympy.sqrt(-c / b))
@evaluates(ReciprocalRule)
def eval_reciprocal(func, integrand, symbol):
return sympy.ln(func)
@evaluates(ArcsinRule)
def eval_arcsin(integrand, symbol):
return sympy.asin(symbol)
@evaluates(InverseHyperbolicRule)
def eval_inversehyperbolic(func, integrand, symbol):
return func(symbol)
@evaluates(AlternativeRule)
def eval_alternative(alternatives, integrand, symbol):
return _manualintegrate(alternatives[0])
@evaluates(RewriteRule)
def eval_rewrite(rewritten, substep, integrand, symbol):
return _manualintegrate(substep)
@evaluates(PiecewiseRule)
def eval_piecewise(substeps, integrand, symbol):
return sympy.Piecewise(*[(_manualintegrate(substep), cond)
for substep, cond in substeps])
@evaluates(TrigSubstitutionRule)
def eval_trigsubstitution(theta, func, rewritten, substep, restriction, integrand, symbol):
func = func.subs(sympy.sec(theta), 1/sympy.cos(theta))
trig_function = list(func.find(TrigonometricFunction))
assert len(trig_function) == 1
trig_function = trig_function[0]
relation = sympy.solve(symbol - func, trig_function)
assert len(relation) == 1
numer, denom = sympy.fraction(relation[0])
if isinstance(trig_function, sympy.sin):
opposite = numer
hypotenuse = denom
adjacent = sympy.sqrt(denom**2 - numer**2)
inverse = sympy.asin(relation[0])
elif isinstance(trig_function, sympy.cos):
adjacent = numer
hypotenuse = denom
opposite = sympy.sqrt(denom**2 - numer**2)
inverse = sympy.acos(relation[0])
elif isinstance(trig_function, sympy.tan):
opposite = numer
adjacent = denom
hypotenuse = sympy.sqrt(denom**2 + numer**2)
inverse = sympy.atan(relation[0])
substitution = [
(sympy.sin(theta), opposite/hypotenuse),
(sympy.cos(theta), adjacent/hypotenuse),
(sympy.tan(theta), opposite/adjacent),
(theta, inverse)
]
return sympy.Piecewise(
(_manualintegrate(substep).subs(substitution).trigsimp(), restriction)
)
@evaluates(DerivativeRule)
def eval_derivativerule(integrand, symbol):
# isinstance(integrand, Derivative) should be True
if len(integrand.args) == 2:
return integrand.args[0]
else:
return sympy.Derivative(integrand.args[0], *integrand.args[1:-1])
@evaluates(HeavisideRule)
def eval_heaviside(harg, ibnd, substep, integrand, symbol):
# If we are integrating over x and the integrand has the form
# Heaviside(m*x+b)*g(x) == Heaviside(harg)*g(symbol)
# then there needs to be continuity at -b/m == ibnd,
# so we subtract the appropriate term.
return sympy.Heaviside(harg)*(substep - substep.subs(symbol, ibnd))
@evaluates(DontKnowRule)
def eval_dontknowrule(integrand, symbol):
return sympy.Integral(integrand, symbol)
def _manualintegrate(rule):
evaluator = evaluators.get(rule.__class__)
if not evaluator:
raise ValueError("Cannot evaluate rule %s" % repr(rule))
return evaluator(*rule)
def manualintegrate(f, var):
"""manualintegrate(f, var)
Compute indefinite integral of a single variable using an algorithm that
resembles what a student would do by hand.
Unlike ``integrate``, var can only be a single symbol.
Examples
========
>>> from sympy import sin, cos, tan, exp, log, integrate
>>> from sympy.integrals.manualintegrate import manualintegrate
>>> from sympy.abc import x
>>> manualintegrate(1 / x, x)
log(x)
>>> integrate(1/x)
log(x)
>>> manualintegrate(log(x), x)
x*log(x) - x
>>> integrate(log(x))
x*log(x) - x
>>> manualintegrate(exp(x) / (1 + exp(2 * x)), x)
atan(exp(x))
>>> integrate(exp(x) / (1 + exp(2 * x)))
RootSum(4*_z**2 + 1, Lambda(_i, _i*log(2*_i + exp(x))))
>>> manualintegrate(cos(x)**4 * sin(x), x)
-cos(x)**5/5
>>> integrate(cos(x)**4 * sin(x), x)
-cos(x)**5/5
>>> manualintegrate(cos(x)**4 * sin(x)**3, x)
cos(x)**7/7 - cos(x)**5/5
>>> integrate(cos(x)**4 * sin(x)**3, x)
cos(x)**7/7 - cos(x)**5/5
>>> manualintegrate(tan(x), x)
-log(cos(x))
>>> integrate(tan(x), x)
-log(sin(x)**2 - 1)/2
See Also
========
sympy.integrals.integrals.integrate
sympy.integrals.integrals.Integral.doit
sympy.integrals.integrals.Integral
"""
return _manualintegrate(integral_steps(f, var))
| 44,953 | 35.727124 | 130 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/heurisch.py
|
from __future__ import print_function, division
from itertools import permutations
from sympy.core.add import Add
from sympy.core.basic import Basic
from sympy.core.mul import Mul
from sympy.core.symbol import Wild, Dummy
from sympy.core.basic import sympify
from sympy.core.numbers import Rational, pi
from sympy.core.relational import Eq
from sympy.core.singleton import S
from sympy.functions import exp, sin, cos, tan, cot, asin, atan
from sympy.functions import log, sinh, cosh, tanh, coth, asinh, acosh
from sympy.functions import sqrt, erf, erfi, li, Ei
from sympy.functions import besselj, bessely, besseli, besselk
from sympy.functions import hankel1, hankel2, jn, yn
from sympy.functions.elementary.exponential import LambertW
from sympy.functions.elementary.piecewise import Piecewise
from sympy.logic.boolalg import And
from sympy.utilities.iterables import uniq
from sympy.polys import quo, gcd, lcm, factor, cancel, PolynomialError
from sympy.polys.monomials import itermonomials
from sympy.polys.polyroots import root_factors
from sympy.polys.rings import PolyRing
from sympy.polys.solvers import solve_lin_sys
from sympy.polys.constructor import construct_domain
from sympy.core.compatibility import reduce, ordered
def components(f, x):
"""
Returns a set of all functional components of the given expression
which includes symbols, function applications and compositions and
non-integer powers. Fractional powers are collected with
minimal, positive exponents.
>>> from sympy import cos, sin
>>> from sympy.abc import x, y
>>> from sympy.integrals.heurisch import components
>>> components(sin(x)*cos(x)**2, x)
{x, sin(x), cos(x)}
See Also
========
heurisch
"""
result = set()
if x in f.free_symbols:
if f.is_Symbol:
result.add(f)
elif f.is_Function or f.is_Derivative:
for g in f.args:
result |= components(g, x)
result.add(f)
elif f.is_Pow:
result |= components(f.base, x)
if not f.exp.is_Integer:
if f.exp.is_Rational:
result.add(f.base**Rational(1, f.exp.q))
else:
result |= components(f.exp, x) | {f}
else:
for g in f.args:
result |= components(g, x)
return result
# name -> [] of symbols
_symbols_cache = {}
# NB @cacheit is not convenient here
def _symbols(name, n):
"""get vector of symbols local to this module"""
try:
lsyms = _symbols_cache[name]
except KeyError:
lsyms = []
_symbols_cache[name] = lsyms
while len(lsyms) < n:
lsyms.append( Dummy('%s%i' % (name, len(lsyms))) )
return lsyms[:n]
def heurisch_wrapper(f, x, rewrite=False, hints=None, mappings=None, retries=3,
degree_offset=0, unnecessary_permutations=None):
"""
A wrapper around the heurisch integration algorithm.
This method takes the result from heurisch and checks for poles in the
denominator. For each of these poles, the integral is reevaluated, and
the final integration result is given in terms of a Piecewise.
Examples
========
>>> from sympy.core import symbols
>>> from sympy.functions import cos
>>> from sympy.integrals.heurisch import heurisch, heurisch_wrapper
>>> n, x = symbols('n x')
>>> heurisch(cos(n*x), x)
sin(n*x)/n
>>> heurisch_wrapper(cos(n*x), x)
Piecewise((x, Eq(n, 0)), (sin(n*x)/n, True))
See Also
========
heurisch
"""
from sympy.solvers.solvers import solve, denoms
f = sympify(f)
if x not in f.free_symbols:
return f*x
res = heurisch(f, x, rewrite, hints, mappings, retries, degree_offset,
unnecessary_permutations)
if not isinstance(res, Basic):
return res
# We consider each denominator in the expression, and try to find
# cases where one or more symbolic denominator might be zero. The
# conditions for these cases are stored in the list slns.
slns = []
for d in denoms(res):
try:
slns += solve(d, dict=True, exclude=(x,))
except NotImplementedError:
pass
if not slns:
return res
slns = list(uniq(slns))
# Remove the solutions corresponding to poles in the original expression.
slns0 = []
for d in denoms(f):
try:
slns0 += solve(d, dict=True, exclude=(x,))
except NotImplementedError:
pass
slns = [s for s in slns if s not in slns0]
if not slns:
return res
if len(slns) > 1:
eqs = []
for sub_dict in slns:
eqs.extend([Eq(key, value) for key, value in sub_dict.items()])
slns = solve(eqs, dict=True, exclude=(x,)) + slns
# For each case listed in the list slns, we reevaluate the integral.
pairs = []
for sub_dict in slns:
expr = heurisch(f.subs(sub_dict), x, rewrite, hints, mappings, retries,
degree_offset, unnecessary_permutations)
cond = And(*[Eq(key, value) for key, value in sub_dict.items()])
pairs.append((expr, cond))
pairs.append((heurisch(f, x, rewrite, hints, mappings, retries,
degree_offset, unnecessary_permutations), True))
return Piecewise(*pairs)
class BesselTable(object):
"""
Derivatives of Bessel functions of orders n and n-1
in terms of each other.
See the docstring of DiffCache.
"""
def __init__(self):
self.table = {}
self.n = Dummy('n')
self.z = Dummy('z')
self._create_table()
def _create_table(t):
table, n, z = t.table, t.n, t.z
for f in (besselj, bessely, hankel1, hankel2):
table[f] = (f(n-1, z) - n*f(n, z)/z,
(n-1)*f(n-1, z)/z - f(n, z))
f = besseli
table[f] = (f(n-1, z) - n*f(n, z)/z,
(n-1)*f(n-1, z)/z + f(n, z))
f = besselk
table[f] = (-f(n-1, z) - n*f(n, z)/z,
(n-1)*f(n-1, z)/z - f(n, z))
for f in (jn, yn):
table[f] = (f(n-1, z) - (n+1)*f(n, z)/z,
(n-1)*f(n-1, z)/z - f(n, z))
def diffs(t, f, n, z):
if f in t.table:
diff0, diff1 = t.table[f]
repl = [(t.n, n), (t.z, z)]
return (diff0.subs(repl), diff1.subs(repl))
def has(t, f):
return f in t.table
_bessel_table = None
class DiffCache(object):
"""
Store for derivatives of expressions.
The standard form of the derivative of a Bessel function of order n
contains two Bessel functions of orders n-1 and n+1, respectively.
Such forms cannot be used in parallel Risch algorithm, because
there is a linear recurrence relation between the three functions
while the algorithm expects that functions and derivatives are
represented in terms of algebraically independent transcendentals.
The solution is to take two of the functions, e.g., those of orders
n and n-1, and to express the derivatives in terms of the pair.
To guarantee that the proper form is used the two derivatives are
cached as soon as one is encountered.
Derivatives of other functions are also cached at no extra cost.
All derivatives are with respect to the same variable `x`.
"""
def __init__(self, x):
self.cache = {}
self.x = x
global _bessel_table
if not _bessel_table:
_bessel_table = BesselTable()
def get_diff(self, f):
cache = self.cache
if f in cache:
pass
elif (not hasattr(f, 'func') or
not _bessel_table.has(f.func)):
cache[f] = cancel(f.diff(self.x))
else:
n, z = f.args
d0, d1 = _bessel_table.diffs(f.func, n, z)
dz = self.get_diff(z)
cache[f] = d0*dz
cache[f.func(n-1, z)] = d1*dz
return cache[f]
def heurisch(f, x, rewrite=False, hints=None, mappings=None, retries=3,
degree_offset=0, unnecessary_permutations=None):
"""
Compute indefinite integral using heuristic Risch algorithm.
This is a heuristic approach to indefinite integration in finite
terms using the extended heuristic (parallel) Risch algorithm, based
on Manuel Bronstein's "Poor Man's Integrator".
The algorithm supports various classes of functions including
transcendental elementary or special functions like Airy,
Bessel, Whittaker and Lambert.
Note that this algorithm is not a decision procedure. If it isn't
able to compute the antiderivative for a given function, then this is
not a proof that such a functions does not exist. One should use
recursive Risch algorithm in such case. It's an open question if
this algorithm can be made a full decision procedure.
This is an internal integrator procedure. You should use toplevel
'integrate' function in most cases, as this procedure needs some
preprocessing steps and otherwise may fail.
Specification
=============
heurisch(f, x, rewrite=False, hints=None)
where
f : expression
x : symbol
rewrite -> force rewrite 'f' in terms of 'tan' and 'tanh'
hints -> a list of functions that may appear in anti-derivate
- hints = None --> no suggestions at all
- hints = [ ] --> try to figure out
- hints = [f1, ..., fn] --> we know better
Examples
========
>>> from sympy import tan
>>> from sympy.integrals.heurisch import heurisch
>>> from sympy.abc import x, y
>>> heurisch(y*tan(x), x)
y*log(tan(x)**2 + 1)/2
See Manuel Bronstein's "Poor Man's Integrator":
[1] http://www-sop.inria.fr/cafe/Manuel.Bronstein/pmint/index.html
For more information on the implemented algorithm refer to:
[2] K. Geddes, L. Stefanus, On the Risch-Norman Integration
Method and its Implementation in Maple, Proceedings of
ISSAC'89, ACM Press, 212-217.
[3] J. H. Davenport, On the Parallel Risch Algorithm (I),
Proceedings of EUROCAM'82, LNCS 144, Springer, 144-157.
[4] J. H. Davenport, On the Parallel Risch Algorithm (III):
Use of Tangents, SIGSAM Bulletin 16 (1982), 3-6.
[5] J. H. Davenport, B. M. Trager, On the Parallel Risch
Algorithm (II), ACM Transactions on Mathematical
Software 11 (1985), 356-362.
See Also
========
sympy.integrals.integrals.Integral.doit
sympy.integrals.integrals.Integral
components
"""
f = sympify(f)
if x not in f.free_symbols:
return f*x
if not f.is_Add:
indep, f = f.as_independent(x)
else:
indep = S.One
rewritables = {
(sin, cos, cot): tan,
(sinh, cosh, coth): tanh,
}
if rewrite:
for candidates, rule in rewritables.items():
f = f.rewrite(candidates, rule)
else:
for candidates in rewritables.keys():
if f.has(*candidates):
break
else:
rewrite = True
terms = components(f, x)
if hints is not None:
if not hints:
a = Wild('a', exclude=[x])
b = Wild('b', exclude=[x])
c = Wild('c', exclude=[x])
for g in set(terms): # using copy of terms
if g.is_Function:
if g.func is li:
M = g.args[0].match(a*x**b)
if M is not None:
terms.add( x*(li(M[a]*x**M[b]) - (M[a]*x**M[b])**(-1/M[b])*Ei((M[b]+1)*log(M[a]*x**M[b])/M[b])) )
#terms.add( x*(li(M[a]*x**M[b]) - (x**M[b])**(-1/M[b])*Ei((M[b]+1)*log(M[a]*x**M[b])/M[b])) )
#terms.add( x*(li(M[a]*x**M[b]) - x*Ei((M[b]+1)*log(M[a]*x**M[b])/M[b])) )
#terms.add( li(M[a]*x**M[b]) - Ei((M[b]+1)*log(M[a]*x**M[b])/M[b]) )
elif g.func is exp:
M = g.args[0].match(a*x**2)
if M is not None:
if M[a].is_positive:
terms.add(erfi(sqrt(M[a])*x))
else: # M[a].is_negative or unknown
terms.add(erf(sqrt(-M[a])*x))
M = g.args[0].match(a*x**2 + b*x + c)
if M is not None:
if M[a].is_positive:
terms.add(sqrt(pi/4*(-M[a]))*exp(M[c] - M[b]**2/(4*M[a]))*
erfi(sqrt(M[a])*x + M[b]/(2*sqrt(M[a]))))
elif M[a].is_negative:
terms.add(sqrt(pi/4*(-M[a]))*exp(M[c] - M[b]**2/(4*M[a]))*
erf(sqrt(-M[a])*x - M[b]/(2*sqrt(-M[a]))))
M = g.args[0].match(a*log(x)**2)
if M is not None:
if M[a].is_positive:
terms.add(erfi(sqrt(M[a])*log(x) + 1/(2*sqrt(M[a]))))
if M[a].is_negative:
terms.add(erf(sqrt(-M[a])*log(x) - 1/(2*sqrt(-M[a]))))
elif g.is_Pow:
if g.exp.is_Rational and g.exp.q == 2:
M = g.base.match(a*x**2 + b)
if M is not None and M[b].is_positive:
if M[a].is_positive:
terms.add(asinh(sqrt(M[a]/M[b])*x))
elif M[a].is_negative:
terms.add(asin(sqrt(-M[a]/M[b])*x))
M = g.base.match(a*x**2 - b)
if M is not None and M[b].is_positive:
if M[a].is_positive:
terms.add(acosh(sqrt(M[a]/M[b])*x))
elif M[a].is_negative:
terms.add((-M[b]/2*sqrt(-M[a])*
atan(sqrt(-M[a])*x/sqrt(M[a]*x**2 - M[b]))))
else:
terms |= set(hints)
dcache = DiffCache(x)
for g in set(terms): # using copy of terms
terms |= components(dcache.get_diff(g), x)
# TODO: caching is significant factor for why permutations work at all. Change this.
V = _symbols('x', len(terms))
# sort mapping expressions from largest to smallest (last is always x).
mapping = list(reversed(list(zip(*ordered( #
[(a[0].as_independent(x)[1], a) for a in zip(terms, V)])))[1])) #
rev_mapping = {v: k for k, v in mapping} #
if mappings is None: #
# optimizing the number of permutations of mapping #
assert mapping[-1][0] == x # if not, find it and correct this comment
unnecessary_permutations = [mapping.pop(-1)]
mappings = permutations(mapping)
else:
unnecessary_permutations = unnecessary_permutations or []
def _substitute(expr):
return expr.subs(mapping)
for mapping in mappings:
mapping = list(mapping)
mapping = mapping + unnecessary_permutations
diffs = [ _substitute(dcache.get_diff(g)) for g in terms ]
denoms = [ g.as_numer_denom()[1] for g in diffs ]
if all(h.is_polynomial(*V) for h in denoms) and _substitute(f).is_rational_function(*V):
denom = reduce(lambda p, q: lcm(p, q, *V), denoms)
break
else:
if not rewrite:
result = heurisch(f, x, rewrite=True, hints=hints,
unnecessary_permutations=unnecessary_permutations)
if result is not None:
return indep*result
return None
numers = [ cancel(denom*g) for g in diffs ]
def _derivation(h):
return Add(*[ d * h.diff(v) for d, v in zip(numers, V) ])
def _deflation(p):
for y in V:
if not p.has(y):
continue
if _derivation(p) is not S.Zero:
c, q = p.as_poly(y).primitive()
return _deflation(c)*gcd(q, q.diff(y)).as_expr()
else:
return p
def _splitter(p):
for y in V:
if not p.has(y):
continue
if _derivation(y) is not S.Zero:
c, q = p.as_poly(y).primitive()
q = q.as_expr()
h = gcd(q, _derivation(q), y)
s = quo(h, gcd(q, q.diff(y), y), y)
c_split = _splitter(c)
if s.as_poly(y).degree() == 0:
return (c_split[0], q * c_split[1])
q_split = _splitter(cancel(q / s))
return (c_split[0]*q_split[0]*s, c_split[1]*q_split[1])
else:
return (S.One, p)
special = {}
for term in terms:
if term.is_Function:
if term.func is tan:
special[1 + _substitute(term)**2] = False
elif term.func is tanh:
special[1 + _substitute(term)] = False
special[1 - _substitute(term)] = False
elif term.func is LambertW:
special[_substitute(term)] = True
F = _substitute(f)
P, Q = F.as_numer_denom()
u_split = _splitter(denom)
v_split = _splitter(Q)
polys = set(list(v_split) + [ u_split[0] ] + list(special.keys()))
s = u_split[0] * Mul(*[ k for k, v in special.items() if v ])
polified = [ p.as_poly(*V) for p in [s, P, Q] ]
if None in polified:
return None
#--- definitions for _integrate
a, b, c = [ p.total_degree() for p in polified ]
poly_denom = (s * v_split[0] * _deflation(v_split[1])).as_expr()
def _exponent(g):
if g.is_Pow:
if g.exp.is_Rational and g.exp.q != 1:
if g.exp.p > 0:
return g.exp.p + g.exp.q - 1
else:
return abs(g.exp.p + g.exp.q)
else:
return 1
elif not g.is_Atom and g.args:
return max([ _exponent(h) for h in g.args ])
else:
return 1
A, B = _exponent(f), a + max(b, c)
if A > 1 and B > 1:
monoms = itermonomials(V, A + B - 1 + degree_offset)
else:
monoms = itermonomials(V, A + B + degree_offset)
poly_coeffs = _symbols('A', len(monoms))
poly_part = Add(*[ poly_coeffs[i]*monomial
for i, monomial in enumerate(monoms) ])
reducibles = set()
for poly in polys:
if poly.has(*V):
try:
factorization = factor(poly, greedy=True)
except PolynomialError:
factorization = poly
if factorization.is_Mul:
factors = factorization.args
else:
factors = (factorization, )
for fact in factors:
if fact.is_Pow:
reducibles.add(fact.base)
else:
reducibles.add(fact)
def _integrate(field=None):
irreducibles = set()
for poly in reducibles:
for z in poly.free_symbols:
if z in V:
break # should this be: `irreducibles |= \
else: # set(root_factors(poly, z, filter=field))`
continue # and the line below deleted?
# |
# V
irreducibles |= set(root_factors(poly, z, filter=field))
log_coeffs, log_part = [], []
B = _symbols('B', len(irreducibles))
# Note: the ordering matters here
for poly, b in reversed(list(ordered(zip(irreducibles, B)))):
if poly.has(*V):
poly_coeffs.append(b)
log_part.append(b * log(poly))
# TODO: Currently it's better to use symbolic expressions here instead
# of rational functions, because it's simpler and FracElement doesn't
# give big speed improvement yet. This is because cancelation is slow
# due to slow polynomial GCD algorithms. If this gets improved then
# revise this code.
candidate = poly_part/poly_denom + Add(*log_part)
h = F - _derivation(candidate) / denom
raw_numer = h.as_numer_denom()[0]
# Rewrite raw_numer as a polynomial in K[coeffs][V] where K is a field
# that we have to determine. We can't use simply atoms() because log(3),
# sqrt(y) and similar expressions can appear, leading to non-trivial
# domains.
syms = set(poly_coeffs) | set(V)
non_syms = set([])
def find_non_syms(expr):
if expr.is_Integer or expr.is_Rational:
pass # ignore trivial numbers
elif expr in syms:
pass # ignore variables
elif not expr.has(*syms):
non_syms.add(expr)
elif expr.is_Add or expr.is_Mul or expr.is_Pow:
list(map(find_non_syms, expr.args))
else:
# TODO: Non-polynomial expression. This should have been
# filtered out at an earlier stage.
raise PolynomialError
try:
find_non_syms(raw_numer)
except PolynomialError:
return None
else:
ground, _ = construct_domain(non_syms, field=True)
coeff_ring = PolyRing(poly_coeffs, ground)
ring = PolyRing(V, coeff_ring)
try:
numer = ring.from_expr(raw_numer)
except ValueError:
raise PolynomialError
solution = solve_lin_sys(numer.coeffs(), coeff_ring, _raw=False)
if solution is None:
return None
else:
return candidate.subs(solution).subs(
list(zip(poly_coeffs, [S.Zero]*len(poly_coeffs))))
if not (F.free_symbols - set(V)):
solution = _integrate('Q')
if solution is None:
solution = _integrate()
else:
solution = _integrate()
if solution is not None:
antideriv = solution.subs(rev_mapping)
antideriv = cancel(antideriv).expand(force=True)
if antideriv.is_Add:
antideriv = antideriv.as_independent(x)[1]
return indep*antideriv
else:
if retries >= 0:
result = heurisch(f, x, mappings=mappings, rewrite=rewrite, hints=hints, retries=retries - 1, unnecessary_permutations=unnecessary_permutations)
if result is not None:
return indep*result
return None
| 22,840 | 32.540382 | 156 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/meijerint.py
|
"""
Integrate functions by rewriting them as Meijer G-functions.
There are three user-visible functions that can be used by other parts of the
sympy library to solve various integration problems:
- meijerint_indefinite
- meijerint_definite
- meijerint_inversion
They can be used to compute, respectively, indefinite integrals, definite
integrals over intervals of the real line, and inverse laplace-type integrals
(from c-I*oo to c+I*oo). See the respective docstrings for details.
The main references for this are:
[L] Luke, Y. L. (1969), The Special Functions and Their Approximations,
Volume 1
[R] Kelly B. Roach. Meijer G Function Representations.
In: Proceedings of the 1997 International Symposium on Symbolic and
Algebraic Computation, pages 205-211, New York, 1997. ACM.
[P] A. P. Prudnikov, Yu. A. Brychkov and O. I. Marichev (1990).
Integrals and Series: More Special Functions, Vol. 3,.
Gordon and Breach Science Publisher
"""
from __future__ import print_function, division
from sympy.core import oo, S, pi, Expr
from sympy.core.exprtools import factor_terms
from sympy.core.function import expand, expand_mul, expand_power_base
from sympy.core.add import Add
from sympy.core.mul import Mul
from sympy.core.compatibility import range
from sympy.core.cache import cacheit
from sympy.core.symbol import Dummy, Wild
from sympy.simplify import hyperexpand, powdenest, collect
from sympy.logic.boolalg import And, Or, BooleanAtom
from sympy.functions.special.delta_functions import Heaviside
from sympy.functions.elementary.exponential import exp
from sympy.functions.elementary.piecewise import Piecewise, piecewise_fold
from sympy.functions.elementary.hyperbolic import \
_rewrite_hyperbolics_as_exp, HyperbolicFunction
from sympy.functions.special.hyper import meijerg
from sympy.utilities.iterables import multiset_partitions, ordered
from sympy.utilities.misc import debug as _debug
from sympy.utilities import default_sort_key
# keep this at top for easy reference
z = Dummy('z')
def _has(res, *f):
# return True if res has f; in the case of Piecewise
# only return True if *all* pieces have f
res = piecewise_fold(res)
if getattr(res, 'is_Piecewise', False):
return all(_has(i, *f) for i in res.args)
return res.has(*f)
def _create_lookup_table(table):
""" Add formulae for the function -> meijerg lookup table. """
def wild(n):
return Wild(n, exclude=[z])
p, q, a, b, c = list(map(wild, 'pqabc'))
n = Wild('n', properties=[lambda x: x.is_Integer and x > 0])
t = p*z**q
def add(formula, an, ap, bm, bq, arg=t, fac=S(1), cond=True, hint=True):
table.setdefault(_mytype(formula, z), []).append((formula,
[(fac, meijerg(an, ap, bm, bq, arg))], cond, hint))
def addi(formula, inst, cond, hint=True):
table.setdefault(
_mytype(formula, z), []).append((formula, inst, cond, hint))
def constant(a):
return [(a, meijerg([1], [], [], [0], z)),
(a, meijerg([], [1], [0], [], z))]
table[()] = [(a, constant(a), True, True)]
# [P], Section 8.
from sympy import unpolarify, Function, Not
class IsNonPositiveInteger(Function):
@classmethod
def eval(cls, arg):
arg = unpolarify(arg)
if arg.is_Integer is True:
return arg <= 0
# Section 8.4.2
from sympy import (gamma, pi, cos, exp, re, sin, sinc, sqrt, sinh, cosh,
factorial, log, erf, erfc, erfi, polar_lift)
# TODO this needs more polar_lift (c/f entry for exp)
add(Heaviside(t - b)*(t - b)**(a - 1), [a], [], [], [0], t/b,
gamma(a)*b**(a - 1), And(b > 0))
add(Heaviside(b - t)*(b - t)**(a - 1), [], [a], [0], [], t/b,
gamma(a)*b**(a - 1), And(b > 0))
add(Heaviside(z - (b/p)**(1/q))*(t - b)**(a - 1), [a], [], [], [0], t/b,
gamma(a)*b**(a - 1), And(b > 0))
add(Heaviside((b/p)**(1/q) - z)*(b - t)**(a - 1), [], [a], [0], [], t/b,
gamma(a)*b**(a - 1), And(b > 0))
add((b + t)**(-a), [1 - a], [], [0], [], t/b, b**(-a)/gamma(a),
hint=Not(IsNonPositiveInteger(a)))
add(abs(b - t)**(-a), [1 - a], [(1 - a)/2], [0], [(1 - a)/2], t/b,
2*sin(pi*a/2)*gamma(1 - a)*abs(b)**(-a), re(a) < 1)
add((t**a - b**a)/(t - b), [0, a], [], [0, a], [], t/b,
b**(a - 1)*sin(a*pi)/pi)
# 12
def A1(r, sign, nu):
return pi**(-S(1)/2)*(-sign*nu/2)**(1 - 2*r)
def tmpadd(r, sgn):
# XXX the a**2 is bad for matching
add((sqrt(a**2 + t) + sgn*a)**b/(a**2 + t)**r,
[(1 + b)/2, 1 - 2*r + b/2], [],
[(b - sgn*b)/2], [(b + sgn*b)/2], t/a**2,
a**(b - 2*r)*A1(r, sgn, b))
tmpadd(0, 1)
tmpadd(0, -1)
tmpadd(S(1)/2, 1)
tmpadd(S(1)/2, -1)
# 13
def tmpadd(r, sgn):
add((sqrt(a + p*z**q) + sgn*sqrt(p)*z**(q/2))**b/(a + p*z**q)**r,
[1 - r + sgn*b/2], [1 - r - sgn*b/2], [0, S(1)/2], [],
p*z**q/a, a**(b/2 - r)*A1(r, sgn, b))
tmpadd(0, 1)
tmpadd(0, -1)
tmpadd(S(1)/2, 1)
tmpadd(S(1)/2, -1)
# (those after look obscure)
# Section 8.4.3
add(exp(polar_lift(-1)*t), [], [], [0], [])
# TODO can do sin^n, sinh^n by expansion ... where?
# 8.4.4 (hyperbolic functions)
add(sinh(t), [], [1], [S(1)/2], [1, 0], t**2/4, pi**(S(3)/2))
add(cosh(t), [], [S(1)/2], [0], [S(1)/2, S(1)/2], t**2/4, pi**(S(3)/2))
# Section 8.4.5
# TODO can do t + a. but can also do by expansion... (XXX not really)
add(sin(t), [], [], [S(1)/2], [0], t**2/4, sqrt(pi))
add(cos(t), [], [], [0], [S(1)/2], t**2/4, sqrt(pi))
# Section 8.4.6 (sinc function)
add(sinc(t), [], [], [0], [S(-1)/2], t**2/4, sqrt(pi)/2)
# Section 8.5.5
def make_log1(subs):
N = subs[n]
return [((-1)**N*factorial(N),
meijerg([], [1]*(N + 1), [0]*(N + 1), [], t))]
def make_log2(subs):
N = subs[n]
return [(factorial(N),
meijerg([1]*(N + 1), [], [], [0]*(N + 1), t))]
# TODO these only hold for positive p, and can be made more general
# but who uses log(x)*Heaviside(a-x) anyway ...
# TODO also it would be nice to derive them recursively ...
addi(log(t)**n*Heaviside(1 - t), make_log1, True)
addi(log(t)**n*Heaviside(t - 1), make_log2, True)
def make_log3(subs):
return make_log1(subs) + make_log2(subs)
addi(log(t)**n, make_log3, True)
addi(log(t + a),
constant(log(a)) + [(S(1), meijerg([1, 1], [], [1], [0], t/a))],
True)
addi(log(abs(t - a)), constant(log(abs(a))) +
[(pi, meijerg([1, 1], [S(1)/2], [1], [0, S(1)/2], t/a))],
True)
# TODO log(x)/(x+a) and log(x)/(x-1) can also be done. should they
# be derivable?
# TODO further formulae in this section seem obscure
# Sections 8.4.9-10
# TODO
# Section 8.4.11
from sympy import Ei, I, expint, Si, Ci, Shi, Chi, fresnels, fresnelc
addi(Ei(t),
constant(-I*pi) + [(S(-1), meijerg([], [1], [0, 0], [],
t*polar_lift(-1)))],
True)
# Section 8.4.12
add(Si(t), [1], [], [S(1)/2], [0, 0], t**2/4, sqrt(pi)/2)
add(Ci(t), [], [1], [0, 0], [S(1)/2], t**2/4, -sqrt(pi)/2)
# Section 8.4.13
add(Shi(t), [S(1)/2], [], [0], [S(-1)/2, S(-1)/2], polar_lift(-1)*t**2/4,
t*sqrt(pi)/4)
add(Chi(t), [], [S(1)/2, 1], [0, 0], [S(1)/2, S(1)/2], t**2/4, -
pi**S('3/2')/2)
# generalized exponential integral
add(expint(a, t), [], [a], [a - 1, 0], [], t)
# Section 8.4.14
add(erf(t), [1], [], [S(1)/2], [0], t**2, 1/sqrt(pi))
# TODO exp(-x)*erf(I*x) does not work
add(erfc(t), [], [1], [0, S(1)/2], [], t**2, 1/sqrt(pi))
# This formula for erfi(z) yields a wrong(?) minus sign
#add(erfi(t), [1], [], [S(1)/2], [0], -t**2, I/sqrt(pi))
add(erfi(t), [S(1)/2], [], [0], [-S(1)/2], -t**2, t/sqrt(pi))
# Fresnel Integrals
add(fresnels(t), [1], [], [S(3)/4], [0, S(1)/4], pi**2*t**4/16, S(1)/2)
add(fresnelc(t), [1], [], [S(1)/4], [0, S(3)/4], pi**2*t**4/16, S(1)/2)
##### bessel-type functions #####
from sympy import besselj, bessely, besseli, besselk
# Section 8.4.19
add(besselj(a, t), [], [], [a/2], [-a/2], t**2/4)
# all of the following are derivable
#add(sin(t)*besselj(a, t), [S(1)/4, S(3)/4], [], [(1+a)/2],
# [-a/2, a/2, (1-a)/2], t**2, 1/sqrt(2))
#add(cos(t)*besselj(a, t), [S(1)/4, S(3)/4], [], [a/2],
# [-a/2, (1+a)/2, (1-a)/2], t**2, 1/sqrt(2))
#add(besselj(a, t)**2, [S(1)/2], [], [a], [-a, 0], t**2, 1/sqrt(pi))
#add(besselj(a, t)*besselj(b, t), [0, S(1)/2], [], [(a + b)/2],
# [-(a+b)/2, (a - b)/2, (b - a)/2], t**2, 1/sqrt(pi))
# Section 8.4.20
add(bessely(a, t), [], [-(a + 1)/2], [a/2, -a/2], [-(a + 1)/2], t**2/4)
# TODO all of the following should be derivable
#add(sin(t)*bessely(a, t), [S(1)/4, S(3)/4], [(1 - a - 1)/2],
# [(1 + a)/2, (1 - a)/2], [(1 - a - 1)/2, (1 - 1 - a)/2, (1 - 1 + a)/2],
# t**2, 1/sqrt(2))
#add(cos(t)*bessely(a, t), [S(1)/4, S(3)/4], [(0 - a - 1)/2],
# [(0 + a)/2, (0 - a)/2], [(0 - a - 1)/2, (1 - 0 - a)/2, (1 - 0 + a)/2],
# t**2, 1/sqrt(2))
#add(besselj(a, t)*bessely(b, t), [0, S(1)/2], [(a - b - 1)/2],
# [(a + b)/2, (a - b)/2], [(a - b - 1)/2, -(a + b)/2, (b - a)/2],
# t**2, 1/sqrt(pi))
#addi(bessely(a, t)**2,
# [(2/sqrt(pi), meijerg([], [S(1)/2, S(1)/2 - a], [0, a, -a],
# [S(1)/2 - a], t**2)),
# (1/sqrt(pi), meijerg([S(1)/2], [], [a], [-a, 0], t**2))],
# True)
#addi(bessely(a, t)*bessely(b, t),
# [(2/sqrt(pi), meijerg([], [0, S(1)/2, (1 - a - b)/2],
# [(a + b)/2, (a - b)/2, (b - a)/2, -(a + b)/2],
# [(1 - a - b)/2], t**2)),
# (1/sqrt(pi), meijerg([0, S(1)/2], [], [(a + b)/2],
# [-(a + b)/2, (a - b)/2, (b - a)/2], t**2))],
# True)
# Section 8.4.21 ?
# Section 8.4.22
add(besseli(a, t), [], [(1 + a)/2], [a/2], [-a/2, (1 + a)/2], t**2/4, pi)
# TODO many more formulas. should all be derivable
# Section 8.4.23
add(besselk(a, t), [], [], [a/2, -a/2], [], t**2/4, S(1)/2)
# TODO many more formulas. should all be derivable
# Complete elliptic integrals K(z) and E(z)
from sympy import elliptic_k, elliptic_e
add(elliptic_k(t), [S.Half, S.Half], [], [0], [0], -t, S.Half)
add(elliptic_e(t), [S.Half, 3*S.Half], [], [0], [0], -t, -S.Half/2)
####################################################################
# First some helper functions.
####################################################################
from sympy.utilities.timeutils import timethis
timeit = timethis('meijerg')
def _mytype(f, x):
""" Create a hashable entity describing the type of f. """
if x not in f.free_symbols:
return ()
elif f.is_Function:
return (type(f),)
else:
types = [_mytype(a, x) for a in f.args]
res = []
for t in types:
res += list(t)
res.sort()
return tuple(res)
class _CoeffExpValueError(ValueError):
"""
Exception raised by _get_coeff_exp, for internal use only.
"""
pass
def _get_coeff_exp(expr, x):
"""
When expr is known to be of the form c*x**b, with c and/or b possibly 1,
return c, b.
>>> from sympy.abc import x, a, b
>>> from sympy.integrals.meijerint import _get_coeff_exp
>>> _get_coeff_exp(a*x**b, x)
(a, b)
>>> _get_coeff_exp(x, x)
(1, 1)
>>> _get_coeff_exp(2*x, x)
(2, 1)
>>> _get_coeff_exp(x**3, x)
(1, 3)
"""
from sympy import powsimp
(c, m) = expand_power_base(powsimp(expr)).as_coeff_mul(x)
if not m:
return c, S(0)
[m] = m
if m.is_Pow:
if m.base != x:
raise _CoeffExpValueError('expr not of form a*x**b')
return c, m.exp
elif m == x:
return c, S(1)
else:
raise _CoeffExpValueError('expr not of form a*x**b: %s' % expr)
def _exponents(expr, x):
"""
Find the exponents of ``x`` (not including zero) in ``expr``.
>>> from sympy.integrals.meijerint import _exponents
>>> from sympy.abc import x, y
>>> from sympy import sin
>>> _exponents(x, x)
{1}
>>> _exponents(x**2, x)
{2}
>>> _exponents(x**2 + x, x)
{1, 2}
>>> _exponents(x**3*sin(x + x**y) + 1/x, x)
{-1, 1, 3, y}
"""
def _exponents_(expr, x, res):
if expr == x:
res.update([1])
return
if expr.is_Pow and expr.base == x:
res.update([expr.exp])
return
for arg in expr.args:
_exponents_(arg, x, res)
res = set()
_exponents_(expr, x, res)
return res
def _functions(expr, x):
""" Find the types of functions in expr, to estimate the complexity. """
from sympy import Function
return set(e.func for e in expr.atoms(Function) if x in e.free_symbols)
def _find_splitting_points(expr, x):
"""
Find numbers a such that a linear substitution x -> x + a would
(hopefully) simplify expr.
>>> from sympy.integrals.meijerint import _find_splitting_points as fsp
>>> from sympy import sin
>>> from sympy.abc import a, x
>>> fsp(x, x)
{0}
>>> fsp((x-1)**3, x)
{1}
>>> fsp(sin(x+3)*x, x)
{-3, 0}
"""
p, q = [Wild(n, exclude=[x]) for n in 'pq']
def compute_innermost(expr, res):
if not isinstance(expr, Expr):
return
m = expr.match(p*x + q)
if m and m[p] != 0:
res.add(-m[q]/m[p])
return
if expr.is_Atom:
return
for arg in expr.args:
compute_innermost(arg, res)
innermost = set()
compute_innermost(expr, innermost)
return innermost
def _split_mul(f, x):
"""
Split expression ``f`` into fac, po, g, where fac is a constant factor,
po = x**s for some s independent of s, and g is "the rest".
>>> from sympy.integrals.meijerint import _split_mul
>>> from sympy import sin
>>> from sympy.abc import s, x
>>> _split_mul((3*x)**s*sin(x**2)*x, x)
(3**s, x*x**s, sin(x**2))
"""
from sympy import polarify, unpolarify
fac = S(1)
po = S(1)
g = S(1)
f = expand_power_base(f)
args = Mul.make_args(f)
for a in args:
if a == x:
po *= x
elif x not in a.free_symbols:
fac *= a
else:
if a.is_Pow and x not in a.exp.free_symbols:
c, t = a.base.as_coeff_mul(x)
if t != (x,):
c, t = expand_mul(a.base).as_coeff_mul(x)
if t == (x,):
po *= x**a.exp
fac *= unpolarify(polarify(c**a.exp, subs=False))
continue
g *= a
return fac, po, g
def _mul_args(f):
"""
Return a list ``L`` such that Mul(*L) == f.
If f is not a Mul or Pow, L=[f].
If f=g**n for an integer n, L=[g]*n.
If f is a Mul, L comes from applying _mul_args to all factors of f.
"""
args = Mul.make_args(f)
gs = []
for g in args:
if g.is_Pow and g.exp.is_Integer:
n = g.exp
base = g.base
if n < 0:
n = -n
base = 1/base
gs += [base]*n
else:
gs.append(g)
return gs
def _mul_as_two_parts(f):
"""
Find all the ways to split f into a product of two terms.
Return None on failure.
Although the order is canonical from multiset_partitions, this is
not necessarily the best order to process the terms. For example,
if the case of len(gs) == 2 is removed and multiset is allowed to
sort the terms, some tests fail.
>>> from sympy.integrals.meijerint import _mul_as_two_parts
>>> from sympy import sin, exp, ordered
>>> from sympy.abc import x
>>> list(ordered(_mul_as_two_parts(x*sin(x)*exp(x))))
[(x, exp(x)*sin(x)), (x*exp(x), sin(x)), (x*sin(x), exp(x))]
"""
gs = _mul_args(f)
if len(gs) < 2:
return None
if len(gs) == 2:
return [tuple(gs)]
return [(Mul(*x), Mul(*y)) for (x, y) in multiset_partitions(gs, 2)]
def _inflate_g(g, n):
""" Return C, h such that h is a G function of argument z**n and
g = C*h. """
# TODO should this be a method of meijerg?
# See: [L, page 150, equation (5)]
def inflate(params, n):
""" (a1, .., ak) -> (a1/n, (a1+1)/n, ..., (ak + n-1)/n) """
res = []
for a in params:
for i in range(n):
res.append((a + i)/n)
return res
v = S(len(g.ap) - len(g.bq))
C = n**(1 + g.nu + v/2)
C /= (2*pi)**((n - 1)*g.delta)
return C, meijerg(inflate(g.an, n), inflate(g.aother, n),
inflate(g.bm, n), inflate(g.bother, n),
g.argument**n * n**(n*v))
def _flip_g(g):
""" Turn the G function into one of inverse argument
(i.e. G(1/x) -> G'(x)) """
# See [L], section 5.2
def tr(l):
return [1 - a for a in l]
return meijerg(tr(g.bm), tr(g.bother), tr(g.an), tr(g.aother), 1/g.argument)
def _inflate_fox_h(g, a):
r"""
Let d denote the integrand in the definition of the G function ``g``.
Consider the function H which is defined in the same way, but with
integrand d/Gamma(a*s) (contour conventions as usual).
If a is rational, the function H can be written as C*G, for a constant C
and a G-function G.
This function returns C, G.
"""
if a < 0:
return _inflate_fox_h(_flip_g(g), -a)
p = S(a.p)
q = S(a.q)
# We use the substitution s->qs, i.e. inflate g by q. We are left with an
# extra factor of Gamma(p*s), for which we use Gauss' multiplication
# theorem.
D, g = _inflate_g(g, q)
z = g.argument
D /= (2*pi)**((1 - p)/2)*p**(-S(1)/2)
z /= p**p
bs = [(n + 1)/p for n in range(p)]
return D, meijerg(g.an, g.aother, g.bm, list(g.bother) + bs, z)
_dummies = {}
def _dummy(name, token, expr, **kwargs):
"""
Return a dummy. This will return the same dummy if the same token+name is
requested more than once, and it is not already in expr.
This is for being cache-friendly.
"""
d = _dummy_(name, token, **kwargs)
if d in expr.free_symbols:
return Dummy(name, **kwargs)
return d
def _dummy_(name, token, **kwargs):
"""
Return a dummy associated to name and token. Same effect as declaring
it globally.
"""
global _dummies
if not (name, token) in _dummies:
_dummies[(name, token)] = Dummy(name, **kwargs)
return _dummies[(name, token)]
def _is_analytic(f, x):
""" Check if f(x), when expressed using G functions on the positive reals,
will in fact agree with the G functions almost everywhere """
from sympy import Heaviside, Abs
return not any(x in expr.free_symbols for expr in f.atoms(Heaviside, Abs))
def _condsimp(cond):
"""
Do naive simplifications on ``cond``.
Note that this routine is completely ad-hoc, simplification rules being
added as need arises rather than following any logical pattern.
>>> from sympy.integrals.meijerint import _condsimp as simp
>>> from sympy import Or, Eq, unbranched_argument as arg, And
>>> from sympy.abc import x, y, z
>>> simp(Or(x < y, z, Eq(x, y)))
z | (x <= y)
>>> simp(Or(x <= y, And(x < y, z)))
x <= y
"""
from sympy import (
symbols, Wild, Eq, unbranched_argument, exp_polar, pi, I,
periodic_argument, oo, polar_lift)
from sympy.logic.boolalg import BooleanFunction
if not isinstance(cond, BooleanFunction):
return cond
cond = cond.func(*list(map(_condsimp, cond.args)))
change = True
p, q, r = symbols('p q r', cls=Wild)
rules = [
(Or(p < q, Eq(p, q)), p <= q),
# The next two obviously are instances of a general pattern, but it is
# easier to spell out the few cases we care about.
(And(abs(unbranched_argument(p)) <= pi,
abs(unbranched_argument(exp_polar(-2*pi*I)*p)) <= pi),
Eq(unbranched_argument(exp_polar(-I*pi)*p), 0)),
(And(abs(unbranched_argument(p)) <= pi/2,
abs(unbranched_argument(exp_polar(-pi*I)*p)) <= pi/2),
Eq(unbranched_argument(exp_polar(-I*pi/2)*p), 0)),
(Or(p <= q, And(p < q, r)), p <= q)
]
while change:
change = False
for fro, to in rules:
if fro.func != cond.func:
continue
for n, arg in enumerate(cond.args):
if r in fro.args[0].free_symbols:
m = arg.match(fro.args[1])
num = 1
else:
num = 0
m = arg.match(fro.args[0])
if not m:
continue
otherargs = [x.subs(m) for x in fro.args[:num] + fro.args[num + 1:]]
otherlist = [n]
for arg2 in otherargs:
for k, arg3 in enumerate(cond.args):
if k in otherlist:
continue
if arg2 == arg3:
otherlist += [k]
break
if arg3.func is And and arg2.args[1] == r and \
arg2.func is And and arg2.args[0] in arg3.args:
otherlist += [k]
break
if arg3.func is And and arg2.args[0] == r and \
arg2.func is And and arg2.args[1] in arg3.args:
otherlist += [k]
break
if len(otherlist) != len(otherargs) + 1:
continue
newargs = [arg for (k, arg) in enumerate(cond.args)
if k not in otherlist] + [to.subs(m)]
cond = cond.func(*newargs)
change = True
break
# final tweak
def repl_eq(orig):
if orig.lhs == 0:
expr = orig.rhs
elif orig.rhs == 0:
expr = orig.lhs
else:
return orig
m = expr.match(unbranched_argument(polar_lift(p)**q))
if not m:
if expr.func is periodic_argument and not expr.args[0].is_polar \
and expr.args[1] == oo:
return (expr.args[0] > 0)
return orig
return (m[p] > 0)
return cond.replace(
lambda expr: expr.is_Relational and expr.rel_op == '==',
repl_eq)
def _eval_cond(cond):
""" Re-evaluate the conditions. """
if isinstance(cond, bool):
return cond
return _condsimp(cond.doit())
####################################################################
# Now the "backbone" functions to do actual integration.
####################################################################
def _my_principal_branch(expr, period, full_pb=False):
""" Bring expr nearer to its principal branch by removing superfluous
factors.
This function does *not* guarantee to yield the principal branch,
to avoid introducing opaque principal_branch() objects,
unless full_pb=True. """
from sympy import principal_branch
res = principal_branch(expr, period)
if not full_pb:
res = res.replace(principal_branch, lambda x, y: x)
return res
def _rewrite_saxena_1(fac, po, g, x):
"""
Rewrite the integral fac*po*g dx, from zero to infinity, as
integral fac*G, where G has argument a*x. Note po=x**s.
Return fac, G.
"""
_, s = _get_coeff_exp(po, x)
a, b = _get_coeff_exp(g.argument, x)
period = g.get_period()
a = _my_principal_branch(a, period)
# We substitute t = x**b.
C = fac/(abs(b)*a**((s + 1)/b - 1))
# Absorb a factor of (at)**((1 + s)/b - 1).
def tr(l):
return [a + (1 + s)/b - 1 for a in l]
return C, meijerg(tr(g.an), tr(g.aother), tr(g.bm), tr(g.bother),
a*x)
def _check_antecedents_1(g, x, helper=False):
r"""
Return a condition under which the mellin transform of g exists.
Any power of x has already been absorbed into the G function,
so this is just int_0^\infty g dx.
See [L, section 5.6.1]. (Note that s=1.)
If ``helper`` is True, only check if the MT exists at infinity, i.e. if
int_1^\infty g dx exists.
"""
# NOTE if you update these conditions, please update the documentation as well
from sympy import Eq, Not, ceiling, Ne, re, unbranched_argument as arg
delta = g.delta
eta, _ = _get_coeff_exp(g.argument, x)
m, n, p, q = S([len(g.bm), len(g.an), len(g.ap), len(g.bq)])
xi = m + n - p
if p > q:
def tr(l):
return [1 - x for x in l]
return _check_antecedents_1(meijerg(tr(g.bm), tr(g.bother),
tr(g.an), tr(g.aother), x/eta),
x)
tmp = []
for b in g.bm:
tmp += [-re(b) < 1]
for a in g.an:
tmp += [1 < 1 - re(a)]
cond_3 = And(*tmp)
for b in g.bother:
tmp += [-re(b) < 1]
for a in g.aother:
tmp += [1 < 1 - re(a)]
cond_3_star = And(*tmp)
cond_4 = (-re(g.nu) + (q + 1 - p)/2 > q - p)
def debug(*msg):
_debug(*msg)
debug('Checking antecedents for 1 function:')
debug(' delta=%s, eta=%s, m=%s, n=%s, p=%s, q=%s'
% (delta, eta, m, n, p, q))
debug(' ap = %s, %s' % (list(g.an), list(g.aother)))
debug(' bq = %s, %s' % (list(g.bm), list(g.bother)))
debug(' cond_3=%s, cond_3*=%s, cond_4=%s' % (cond_3, cond_3_star, cond_4))
conds = []
# case 1
case1 = []
tmp1 = [1 <= n, p < q, 1 <= m]
tmp2 = [1 <= p, 1 <= m, Eq(q, p + 1), Not(And(Eq(n, 0), Eq(m, p + 1)))]
tmp3 = [1 <= p, Eq(q, p)]
for k in range(ceiling(delta/2) + 1):
tmp3 += [Ne(abs(arg(eta)), (delta - 2*k)*pi)]
tmp = [delta > 0, abs(arg(eta)) < delta*pi]
extra = [Ne(eta, 0), cond_3]
if helper:
extra = []
for t in [tmp1, tmp2, tmp3]:
case1 += [And(*(t + tmp + extra))]
conds += case1
debug(' case 1:', case1)
# case 2
extra = [cond_3]
if helper:
extra = []
case2 = [And(Eq(n, 0), p + 1 <= m, m <= q,
abs(arg(eta)) < delta*pi, *extra)]
conds += case2
debug(' case 2:', case2)
# case 3
extra = [cond_3, cond_4]
if helper:
extra = []
case3 = [And(p < q, 1 <= m, delta > 0, Eq(abs(arg(eta)), delta*pi),
*extra)]
case3 += [And(p <= q - 2, Eq(delta, 0), Eq(abs(arg(eta)), 0), *extra)]
conds += case3
debug(' case 3:', case3)
# TODO altered cases 4-7
# extra case from wofram functions site:
# (reproduced verbatim from Prudnikov, section 2.24.2)
# http://functions.wolfram.com/HypergeometricFunctions/MeijerG/21/02/01/
case_extra = []
case_extra += [Eq(p, q), Eq(delta, 0), Eq(arg(eta), 0), Ne(eta, 0)]
if not helper:
case_extra += [cond_3]
s = []
for a, b in zip(g.ap, g.bq):
s += [b - a]
case_extra += [re(Add(*s)) < 0]
case_extra = And(*case_extra)
conds += [case_extra]
debug(' extra case:', [case_extra])
case_extra_2 = [And(delta > 0, abs(arg(eta)) < delta*pi)]
if not helper:
case_extra_2 += [cond_3]
case_extra_2 = And(*case_extra_2)
conds += [case_extra_2]
debug(' second extra case:', [case_extra_2])
# TODO This leaves only one case from the three listed by Prudnikov.
# Investigate if these indeed cover everything; if so, remove the rest.
return Or(*conds)
def _int0oo_1(g, x):
r"""
Evaluate int_0^\infty g dx using G functions,
assuming the necessary conditions are fulfilled.
>>> from sympy.abc import a, b, c, d, x, y
>>> from sympy import meijerg
>>> from sympy.integrals.meijerint import _int0oo_1
>>> _int0oo_1(meijerg([a], [b], [c], [d], x*y), x)
gamma(-a)*gamma(c + 1)/(y*gamma(-d)*gamma(b + 1))
"""
# See [L, section 5.6.1]. Note that s=1.
from sympy import gamma, combsimp, unpolarify
eta, _ = _get_coeff_exp(g.argument, x)
res = 1/eta
# XXX TODO we should reduce order first
for b in g.bm:
res *= gamma(b + 1)
for a in g.an:
res *= gamma(1 - a - 1)
for b in g.bother:
res /= gamma(1 - b - 1)
for a in g.aother:
res /= gamma(a + 1)
return combsimp(unpolarify(res))
def _rewrite_saxena(fac, po, g1, g2, x, full_pb=False):
"""
Rewrite the integral fac*po*g1*g2 from 0 to oo in terms of G functions
with argument c*x.
Return C, f1, f2 such that integral C f1 f2 from 0 to infinity equals
integral fac po g1 g2 from 0 to infinity.
>>> from sympy.integrals.meijerint import _rewrite_saxena
>>> from sympy.abc import s, t, m
>>> from sympy import meijerg
>>> g1 = meijerg([], [], [0], [], s*t)
>>> g2 = meijerg([], [], [m/2], [-m/2], t**2/4)
>>> r = _rewrite_saxena(1, t**0, g1, g2, t)
>>> r[0]
s/(4*sqrt(pi))
>>> r[1]
meijerg(((), ()), ((-1/2, 0), ()), s**2*t/4)
>>> r[2]
meijerg(((), ()), ((m/2,), (-m/2,)), t/4)
"""
from sympy.core.numbers import ilcm
def pb(g):
a, b = _get_coeff_exp(g.argument, x)
per = g.get_period()
return meijerg(g.an, g.aother, g.bm, g.bother,
_my_principal_branch(a, per, full_pb)*x**b)
_, s = _get_coeff_exp(po, x)
_, b1 = _get_coeff_exp(g1.argument, x)
_, b2 = _get_coeff_exp(g2.argument, x)
if (b1 < 0) == True:
b1 = -b1
g1 = _flip_g(g1)
if (b2 < 0) == True:
b2 = -b2
g2 = _flip_g(g2)
if not b1.is_Rational or not b2.is_Rational:
return
m1, n1 = b1.p, b1.q
m2, n2 = b2.p, b2.q
tau = ilcm(m1*n2, m2*n1)
r1 = tau//(m1*n2)
r2 = tau//(m2*n1)
C1, g1 = _inflate_g(g1, r1)
C2, g2 = _inflate_g(g2, r2)
g1 = pb(g1)
g2 = pb(g2)
fac *= C1*C2
a1, b = _get_coeff_exp(g1.argument, x)
a2, _ = _get_coeff_exp(g2.argument, x)
# arbitrarily tack on the x**s part to g1
# TODO should we try both?
exp = (s + 1)/b - 1
fac = fac/(abs(b) * a1**exp)
def tr(l):
return [a + exp for a in l]
g1 = meijerg(tr(g1.an), tr(g1.aother), tr(g1.bm), tr(g1.bother), a1*x)
g2 = meijerg(g2.an, g2.aother, g2.bm, g2.bother, a2*x)
return powdenest(fac, polar=True), g1, g2
def _check_antecedents(g1, g2, x):
""" Return a condition under which the integral theorem applies. """
from sympy import re, Eq, Ne, cos, I, exp, sin, sign, unpolarify
from sympy import arg as arg_, unbranched_argument as arg
# Yes, this is madness.
# XXX TODO this is a testing *nightmare*
# NOTE if you update these conditions, please update the documentation as well
# The following conditions are found in
# [P], Section 2.24.1
#
# They are also reproduced (verbatim!) at
# http://functions.wolfram.com/HypergeometricFunctions/MeijerG/21/02/03/
#
# Note: k=l=r=alpha=1
sigma, _ = _get_coeff_exp(g1.argument, x)
omega, _ = _get_coeff_exp(g2.argument, x)
s, t, u, v = S([len(g1.bm), len(g1.an), len(g1.ap), len(g1.bq)])
m, n, p, q = S([len(g2.bm), len(g2.an), len(g2.ap), len(g2.bq)])
bstar = s + t - (u + v)/2
cstar = m + n - (p + q)/2
rho = g1.nu + (u - v)/2 + 1
mu = g2.nu + (p - q)/2 + 1
phi = q - p - (v - u)
eta = 1 - (v - u) - mu - rho
psi = (pi*(q - m - n) + abs(arg(omega)))/(q - p)
theta = (pi*(v - s - t) + abs(arg(sigma)))/(v - u)
_debug('Checking antecedents:')
_debug(' sigma=%s, s=%s, t=%s, u=%s, v=%s, b*=%s, rho=%s'
% (sigma, s, t, u, v, bstar, rho))
_debug(' omega=%s, m=%s, n=%s, p=%s, q=%s, c*=%s, mu=%s,'
% (omega, m, n, p, q, cstar, mu))
_debug(' phi=%s, eta=%s, psi=%s, theta=%s' % (phi, eta, psi, theta))
def _c1():
for g in [g1, g2]:
for i in g.an:
for j in g.bm:
diff = i - j
if diff.is_integer and diff.is_positive:
return False
return True
c1 = _c1()
c2 = And(*[re(1 + i + j) > 0 for i in g1.bm for j in g2.bm])
c3 = And(*[re(1 + i + j) < 1 + 1 for i in g1.an for j in g2.an])
c4 = And(*[(p - q)*re(1 + i - 1) - re(mu) > -S(3)/2 for i in g1.an])
c5 = And(*[(p - q)*re(1 + i) - re(mu) > -S(3)/2 for i in g1.bm])
c6 = And(*[(u - v)*re(1 + i - 1) - re(rho) > -S(3)/2 for i in g2.an])
c7 = And(*[(u - v)*re(1 + i) - re(rho) > -S(3)/2 for i in g2.bm])
c8 = (abs(phi) + 2*re((rho - 1)*(q - p) + (v - u)*(q - p) + (mu -
1)*(v - u)) > 0)
c9 = (abs(phi) - 2*re((rho - 1)*(q - p) + (v - u)*(q - p) + (mu -
1)*(v - u)) > 0)
c10 = (abs(arg(sigma)) < bstar*pi)
c11 = Eq(abs(arg(sigma)), bstar*pi)
c12 = (abs(arg(omega)) < cstar*pi)
c13 = Eq(abs(arg(omega)), cstar*pi)
# The following condition is *not* implemented as stated on the wolfram
# function site. In the book of Prudnikov there is an additional part
# (the And involving re()). However, I only have this book in russian, and
# I don't read any russian. The following condition is what other people
# have told me it means.
# Worryingly, it is different from the condition implemented in REDUCE.
# The REDUCE implementation:
# https://reduce-algebra.svn.sourceforge.net/svnroot/reduce-algebra/trunk/packages/defint/definta.red
# (search for tst14)
# The Wolfram alpha version:
# http://functions.wolfram.com/HypergeometricFunctions/MeijerG/21/02/03/03/0014/
z0 = exp(-(bstar + cstar)*pi*I)
zos = unpolarify(z0*omega/sigma)
zso = unpolarify(z0*sigma/omega)
if zos == 1/zso:
c14 = And(Eq(phi, 0), bstar + cstar <= 1,
Or(Ne(zos, 1), re(mu + rho + v - u) < 1,
re(mu + rho + q - p) < 1))
else:
c14 = And(Eq(phi, 0), bstar - 1 + cstar <= 0,
Or(And(Ne(zos, 1), abs(arg_(1 - zos)) < pi),
And(re(mu + rho + v - u) < 1, Eq(zos, 1))))
def _cond():
'''
Note: if `zso` is 1 then tmp will be NaN. This raises a
TypeError on `NaN < pi`. Previously this gave `False` so
this behavior has been hardcoded here but someone should
check if this NaN is more serious! This NaN is triggered by
test_meijerint() in test_meijerint.py:
`meijerint_definite(exp(x), x, 0, I)`
'''
tmp = abs(arg_(1 - zso))
return False if tmp is S.NaN else tmp < pi
c14_alt = And(Eq(phi, 0), cstar - 1 + bstar <= 0,
Or(And(Ne(zso, 1), _cond()),
And(re(mu + rho + q - p) < 1, Eq(zso, 1))))
# Since r=k=l=1, in our case there is c14_alt which is the same as calling
# us with (g1, g2) = (g2, g1). The conditions below enumerate all cases
# (i.e. we don't have to try arguments reversed by hand), and indeed try
# all symmetric cases. (i.e. whenever there is a condition involving c14,
# there is also a dual condition which is exactly what we would get when g1,
# g2 were interchanged, *but c14 was unaltered*).
# Hence the following seems correct:
c14 = Or(c14, c14_alt)
'''
When `c15` is NaN (e.g. from `psi` being NaN as happens during
'test_issue_4992' and/or `theta` is NaN as in 'test_issue_6253',
both in `test_integrals.py`) the comparison to 0 formerly gave False
whereas now an error is raised. To keep the old behavior, the value
of NaN is replaced with False but perhaps a closer look at this condition
should be made: XXX how should conditions leading to c15=NaN be handled?
'''
try:
lambda_c = (q - p)*abs(omega)**(1/(q - p))*cos(psi) \
+ (v - u)*abs(sigma)**(1/(v - u))*cos(theta)
# the TypeError might be raised here, e.g. if lambda_c is NaN
if _eval_cond(lambda_c > 0) != False:
c15 = (lambda_c > 0)
else:
def lambda_s0(c1, c2):
return c1*(q - p)*abs(omega)**(1/(q - p))*sin(psi) \
+ c2*(v - u)*abs(sigma)**(1/(v - u))*sin(theta)
lambda_s = Piecewise(
((lambda_s0(+1, +1)*lambda_s0(-1, -1)),
And(Eq(arg(sigma), 0), Eq(arg(omega), 0))),
(lambda_s0(sign(arg(omega)), +1)*lambda_s0(sign(arg(omega)), -1),
And(Eq(arg(sigma), 0), Ne(arg(omega), 0))),
(lambda_s0(+1, sign(arg(sigma)))*lambda_s0(-1, sign(arg(sigma))),
And(Ne(arg(sigma), 0), Eq(arg(omega), 0))),
(lambda_s0(sign(arg(omega)), sign(arg(sigma))), True))
tmp = [lambda_c > 0,
And(Eq(lambda_c, 0), Ne(lambda_s, 0), re(eta) > -1),
And(Eq(lambda_c, 0), Eq(lambda_s, 0), re(eta) > 0)]
c15 = Or(*tmp)
except TypeError:
c15 = False
for cond, i in [(c1, 1), (c2, 2), (c3, 3), (c4, 4), (c5, 5), (c6, 6),
(c7, 7), (c8, 8), (c9, 9), (c10, 10), (c11, 11),
(c12, 12), (c13, 13), (c14, 14), (c15, 15)]:
_debug(' c%s:' % i, cond)
# We will return Or(*conds)
conds = []
def pr(count):
_debug(' case %s:' % count, conds[-1])
conds += [And(m*n*s*t != 0, bstar.is_positive is True, cstar.is_positive is True, c1, c2, c3, c10,
c12)] # 1
pr(1)
conds += [And(Eq(u, v), Eq(bstar, 0), cstar.is_positive is True, sigma.is_positive is True, re(rho) < 1,
c1, c2, c3, c12)] # 2
pr(2)
conds += [And(Eq(p, q), Eq(cstar, 0), bstar.is_positive is True, omega.is_positive is True, re(mu) < 1,
c1, c2, c3, c10)] # 3
pr(3)
conds += [And(Eq(p, q), Eq(u, v), Eq(bstar, 0), Eq(cstar, 0),
sigma.is_positive is True, omega.is_positive is True, re(mu) < 1, re(rho) < 1,
Ne(sigma, omega), c1, c2, c3)] # 4
pr(4)
conds += [And(Eq(p, q), Eq(u, v), Eq(bstar, 0), Eq(cstar, 0),
sigma.is_positive is True, omega.is_positive is True, re(mu + rho) < 1,
Ne(omega, sigma), c1, c2, c3)] # 5
pr(5)
conds += [And(p > q, s.is_positive is True, bstar.is_positive is True, cstar >= 0,
c1, c2, c3, c5, c10, c13)] # 6
pr(6)
conds += [And(p < q, t.is_positive is True, bstar.is_positive is True, cstar >= 0,
c1, c2, c3, c4, c10, c13)] # 7
pr(7)
conds += [And(u > v, m.is_positive is True, cstar.is_positive is True, bstar >= 0,
c1, c2, c3, c7, c11, c12)] # 8
pr(8)
conds += [And(u < v, n.is_positive is True, cstar.is_positive is True, bstar >= 0,
c1, c2, c3, c6, c11, c12)] # 9
pr(9)
conds += [And(p > q, Eq(u, v), Eq(bstar, 0), cstar >= 0, sigma.is_positive is True,
re(rho) < 1, c1, c2, c3, c5, c13)] # 10
pr(10)
conds += [And(p < q, Eq(u, v), Eq(bstar, 0), cstar >= 0, sigma.is_positive is True,
re(rho) < 1, c1, c2, c3, c4, c13)] # 11
pr(11)
conds += [And(Eq(p, q), u > v, bstar >= 0, Eq(cstar, 0), omega.is_positive is True,
re(mu) < 1, c1, c2, c3, c7, c11)] # 12
pr(12)
conds += [And(Eq(p, q), u < v, bstar >= 0, Eq(cstar, 0), omega.is_positive is True,
re(mu) < 1, c1, c2, c3, c6, c11)] # 13
pr(13)
conds += [And(p < q, u > v, bstar >= 0, cstar >= 0,
c1, c2, c3, c4, c7, c11, c13)] # 14
pr(14)
conds += [And(p > q, u < v, bstar >= 0, cstar >= 0,
c1, c2, c3, c5, c6, c11, c13)] # 15
pr(15)
conds += [And(p > q, u > v, bstar >= 0, cstar >= 0,
c1, c2, c3, c5, c7, c8, c11, c13, c14)] # 16
pr(16)
conds += [And(p < q, u < v, bstar >= 0, cstar >= 0,
c1, c2, c3, c4, c6, c9, c11, c13, c14)] # 17
pr(17)
conds += [And(Eq(t, 0), s.is_positive is True, bstar.is_positive is True, phi.is_positive is True, c1, c2, c10)] # 18
pr(18)
conds += [And(Eq(s, 0), t.is_positive is True, bstar.is_positive is True, phi.is_negative is True, c1, c3, c10)] # 19
pr(19)
conds += [And(Eq(n, 0), m.is_positive is True, cstar.is_positive is True, phi.is_negative is True, c1, c2, c12)] # 20
pr(20)
conds += [And(Eq(m, 0), n.is_positive is True, cstar.is_positive is True, phi.is_positive is True, c1, c3, c12)] # 21
pr(21)
conds += [And(Eq(s*t, 0), bstar.is_positive is True, cstar.is_positive is True,
c1, c2, c3, c10, c12)] # 22
pr(22)
conds += [And(Eq(m*n, 0), bstar.is_positive is True, cstar.is_positive is True,
c1, c2, c3, c10, c12)] # 23
pr(23)
# The following case is from [Luke1969]. As far as I can tell, it is *not*
# covered by Prudnikov's.
# Let G1 and G2 be the two G-functions. Suppose the integral exists from
# 0 to a > 0 (this is easy the easy part), that G1 is exponential decay at
# infinity, and that the mellin transform of G2 exists.
# Then the integral exists.
mt1_exists = _check_antecedents_1(g1, x, helper=True)
mt2_exists = _check_antecedents_1(g2, x, helper=True)
conds += [And(mt2_exists, Eq(t, 0), u < s, bstar.is_positive is True, c10, c1, c2, c3)]
pr('E1')
conds += [And(mt2_exists, Eq(s, 0), v < t, bstar.is_positive is True, c10, c1, c2, c3)]
pr('E2')
conds += [And(mt1_exists, Eq(n, 0), p < m, cstar.is_positive is True, c12, c1, c2, c3)]
pr('E3')
conds += [And(mt1_exists, Eq(m, 0), q < n, cstar.is_positive is True, c12, c1, c2, c3)]
pr('E4')
# Let's short-circuit if this worked ...
# the rest is corner-cases and terrible to read.
r = Or(*conds)
if _eval_cond(r) != False:
return r
conds += [And(m + n > p, Eq(t, 0), Eq(phi, 0), s.is_positive is True, bstar.is_positive is True, cstar.is_negative is True,
abs(arg(omega)) < (m + n - p + 1)*pi,
c1, c2, c10, c14, c15)] # 24
pr(24)
conds += [And(m + n > q, Eq(s, 0), Eq(phi, 0), t.is_positive is True, bstar.is_positive is True, cstar.is_negative is True,
abs(arg(omega)) < (m + n - q + 1)*pi,
c1, c3, c10, c14, c15)] # 25
pr(25)
conds += [And(Eq(p, q - 1), Eq(t, 0), Eq(phi, 0), s.is_positive is True, bstar.is_positive is True,
cstar >= 0, cstar*pi < abs(arg(omega)),
c1, c2, c10, c14, c15)] # 26
pr(26)
conds += [And(Eq(p, q + 1), Eq(s, 0), Eq(phi, 0), t.is_positive is True, bstar.is_positive is True,
cstar >= 0, cstar*pi < abs(arg(omega)),
c1, c3, c10, c14, c15)] # 27
pr(27)
conds += [And(p < q - 1, Eq(t, 0), Eq(phi, 0), s.is_positive is True, bstar.is_positive is True,
cstar >= 0, cstar*pi < abs(arg(omega)),
abs(arg(omega)) < (m + n - p + 1)*pi,
c1, c2, c10, c14, c15)] # 28
pr(28)
conds += [And(
p > q + 1, Eq(s, 0), Eq(phi, 0), t.is_positive is True, bstar.is_positive is True, cstar >= 0,
cstar*pi < abs(arg(omega)),
abs(arg(omega)) < (m + n - q + 1)*pi,
c1, c3, c10, c14, c15)] # 29
pr(29)
conds += [And(Eq(n, 0), Eq(phi, 0), s + t > 0, m.is_positive is True, cstar.is_positive is True, bstar.is_negative is True,
abs(arg(sigma)) < (s + t - u + 1)*pi,
c1, c2, c12, c14, c15)] # 30
pr(30)
conds += [And(Eq(m, 0), Eq(phi, 0), s + t > v, n.is_positive is True, cstar.is_positive is True, bstar.is_negative is True,
abs(arg(sigma)) < (s + t - v + 1)*pi,
c1, c3, c12, c14, c15)] # 31
pr(31)
conds += [And(Eq(n, 0), Eq(phi, 0), Eq(u, v - 1), m.is_positive is True, cstar.is_positive is True,
bstar >= 0, bstar*pi < abs(arg(sigma)),
abs(arg(sigma)) < (bstar + 1)*pi,
c1, c2, c12, c14, c15)] # 32
pr(32)
conds += [And(Eq(m, 0), Eq(phi, 0), Eq(u, v + 1), n.is_positive is True, cstar.is_positive is True,
bstar >= 0, bstar*pi < abs(arg(sigma)),
abs(arg(sigma)) < (bstar + 1)*pi,
c1, c3, c12, c14, c15)] # 33
pr(33)
conds += [And(
Eq(n, 0), Eq(phi, 0), u < v - 1, m.is_positive is True, cstar.is_positive is True, bstar >= 0,
bstar*pi < abs(arg(sigma)),
abs(arg(sigma)) < (s + t - u + 1)*pi,
c1, c2, c12, c14, c15)] # 34
pr(34)
conds += [And(
Eq(m, 0), Eq(phi, 0), u > v + 1, n.is_positive is True, cstar.is_positive is True, bstar >= 0,
bstar*pi < abs(arg(sigma)),
abs(arg(sigma)) < (s + t - v + 1)*pi,
c1, c3, c12, c14, c15)] # 35
pr(35)
return Or(*conds)
# NOTE An alternative, but as far as I can tell weaker, set of conditions
# can be found in [L, section 5.6.2].
def _int0oo(g1, g2, x):
"""
Express integral from zero to infinity g1*g2 using a G function,
assuming the necessary conditions are fulfilled.
>>> from sympy.integrals.meijerint import _int0oo
>>> from sympy.abc import s, t, m
>>> from sympy import meijerg, S
>>> g1 = meijerg([], [], [-S(1)/2, 0], [], s**2*t/4)
>>> g2 = meijerg([], [], [m/2], [-m/2], t/4)
>>> _int0oo(g1, g2, t)
4*meijerg(((1/2, 0), ()), ((m/2,), (-m/2,)), s**(-2))/s**2
"""
# See: [L, section 5.6.2, equation (1)]
eta, _ = _get_coeff_exp(g1.argument, x)
omega, _ = _get_coeff_exp(g2.argument, x)
def neg(l):
return [-x for x in l]
a1 = neg(g1.bm) + list(g2.an)
a2 = list(g2.aother) + neg(g1.bother)
b1 = neg(g1.an) + list(g2.bm)
b2 = list(g2.bother) + neg(g1.aother)
return meijerg(a1, a2, b1, b2, omega/eta)/eta
def _rewrite_inversion(fac, po, g, x):
""" Absorb ``po`` == x**s into g. """
_, s = _get_coeff_exp(po, x)
a, b = _get_coeff_exp(g.argument, x)
def tr(l):
return [t + s/b for t in l]
return (powdenest(fac/a**(s/b), polar=True),
meijerg(tr(g.an), tr(g.aother), tr(g.bm), tr(g.bother), g.argument))
def _check_antecedents_inversion(g, x):
""" Check antecedents for the laplace inversion integral. """
from sympy import re, im, Or, And, Eq, exp, I, Add, nan, Ne
_debug('Checking antecedents for inversion:')
z = g.argument
_, e = _get_coeff_exp(z, x)
if e < 0:
_debug(' Flipping G.')
# We want to assume that argument gets large as |x| -> oo
return _check_antecedents_inversion(_flip_g(g), x)
def statement_half(a, b, c, z, plus):
coeff, exponent = _get_coeff_exp(z, x)
a *= exponent
b *= coeff**c
c *= exponent
conds = []
wp = b*exp(I*re(c)*pi/2)
wm = b*exp(-I*re(c)*pi/2)
if plus:
w = wp
else:
w = wm
conds += [And(Or(Eq(b, 0), re(c) <= 0), re(a) <= -1)]
conds += [And(Ne(b, 0), Eq(im(c), 0), re(c) > 0, re(w) < 0)]
conds += [And(Ne(b, 0), Eq(im(c), 0), re(c) > 0, re(w) <= 0,
re(a) <= -1)]
return Or(*conds)
def statement(a, b, c, z):
""" Provide a convergence statement for z**a * exp(b*z**c),
c/f sphinx docs. """
return And(statement_half(a, b, c, z, True),
statement_half(a, b, c, z, False))
# Notations from [L], section 5.7-10
m, n, p, q = S([len(g.bm), len(g.an), len(g.ap), len(g.bq)])
tau = m + n - p
nu = q - m - n
rho = (tau - nu)/2
sigma = q - p
if sigma == 1:
epsilon = S(1)/2
elif sigma > 1:
epsilon = 1
else:
epsilon = nan
theta = ((1 - sigma)/2 + Add(*g.bq) - Add(*g.ap))/sigma
delta = g.delta
_debug(' m=%s, n=%s, p=%s, q=%s, tau=%s, nu=%s, rho=%s, sigma=%s' % (
m, n, p, q, tau, nu, rho, sigma))
_debug(' epsilon=%s, theta=%s, delta=%s' % (epsilon, theta, delta))
# First check if the computation is valid.
if not (g.delta >= e/2 or (p >= 1 and p >= q)):
_debug(' Computation not valid for these parameters.')
return False
# Now check if the inversion integral exists.
# Test "condition A"
for a in g.an:
for b in g.bm:
if (a - b).is_integer and a > b:
_debug(' Not a valid G function.')
return False
# There are two cases. If p >= q, we can directly use a slater expansion
# like [L], 5.2 (11). Note in particular that the asymptotics of such an
# expansion even hold when some of the parameters differ by integers, i.e.
# the formula itself would not be valid! (b/c G functions are cts. in their
# parameters)
# When p < q, we need to use the theorems of [L], 5.10.
if p >= q:
_debug(' Using asymptotic Slater expansion.')
return And(*[statement(a - 1, 0, 0, z) for a in g.an])
def E(z):
return And(*[statement(a - 1, 0, z) for a in g.an])
def H(z):
return statement(theta, -sigma, 1/sigma, z)
def Hp(z):
return statement_half(theta, -sigma, 1/sigma, z, True)
def Hm(z):
return statement_half(theta, -sigma, 1/sigma, z, False)
# [L], section 5.10
conds = []
# Theorem 1
conds += [And(1 <= n, p < q, 1 <= m, rho*pi - delta >= pi/2, delta > 0,
E(z*exp(I*pi*(nu + 1))))]
# Theorem 2, statements (2) and (3)
conds += [And(p + 1 <= m, m + 1 <= q, delta > 0, delta < pi/2, n == 0,
(m - p + 1)*pi - delta >= pi/2,
Hp(z*exp(I*pi*(q - m))), Hm(z*exp(-I*pi*(q - m))))]
# Theorem 2, statement (5)
conds += [And(p < q, m == q, n == 0, delta > 0,
(sigma + epsilon)*pi - delta >= pi/2, H(z))]
# Theorem 3, statements (6) and (7)
conds += [And(Or(And(p <= q - 2, 1 <= tau, tau <= sigma/2),
And(p + 1 <= m + n, m + n <= (p + q)/2)),
delta > 0, delta < pi/2, (tau + 1)*pi - delta >= pi/2,
Hp(z*exp(I*pi*nu)), Hm(z*exp(-I*pi*nu)))]
# Theorem 4, statements (10) and (11)
conds += [And(p < q, 1 <= m, rho > 0, delta > 0, delta + rho*pi < pi/2,
(tau + epsilon)*pi - delta >= pi/2,
Hp(z*exp(I*pi*nu)), Hm(z*exp(-I*pi*nu)))]
# Trivial case
conds += [m == 0]
# TODO
# Theorem 5 is quite general
# Theorem 6 contains special cases for q=p+1
return Or(*conds)
def _int_inversion(g, x, t):
"""
Compute the laplace inversion integral, assuming the formula applies.
"""
b, a = _get_coeff_exp(g.argument, x)
C, g = _inflate_fox_h(meijerg(g.an, g.aother, g.bm, g.bother, b/t**a), -a)
return C/t*g
####################################################################
# Finally, the real meat.
####################################################################
_lookup_table = None
@cacheit
@timeit
def _rewrite_single(f, x, recursive=True):
"""
Try to rewrite f as a sum of single G functions of the form
C*x**s*G(a*x**b), where b is a rational number and C is independent of x.
We guarantee that result.argument.as_coeff_mul(x) returns (a, (x**b,))
or (a, ()).
Returns a list of tuples (C, s, G) and a condition cond.
Returns None on failure.
"""
from sympy import polarify, unpolarify, oo, zoo, Tuple
global _lookup_table
if not _lookup_table:
_lookup_table = {}
_create_lookup_table(_lookup_table)
if isinstance(f, meijerg):
from sympy import factor
coeff, m = factor(f.argument, x).as_coeff_mul(x)
if len(m) > 1:
return None
m = m[0]
if m.is_Pow:
if m.base != x or not m.exp.is_Rational:
return None
elif m != x:
return None
return [(1, 0, meijerg(f.an, f.aother, f.bm, f.bother, coeff*m))], True
f_ = f
f = f.subs(x, z)
t = _mytype(f, z)
if t in _lookup_table:
l = _lookup_table[t]
for formula, terms, cond, hint in l:
subs = f.match(formula, old=True)
if subs:
subs_ = {}
for fro, to in subs.items():
subs_[fro] = unpolarify(polarify(to, lift=True),
exponents_only=True)
subs = subs_
if not isinstance(hint, bool):
hint = hint.subs(subs)
if hint == False:
continue
if not isinstance(cond, (bool, BooleanAtom)):
cond = unpolarify(cond.subs(subs))
if _eval_cond(cond) == False:
continue
if not isinstance(terms, list):
terms = terms(subs)
res = []
for fac, g in terms:
r1 = _get_coeff_exp(unpolarify(fac.subs(subs).subs(z, x),
exponents_only=True), x)
try:
g = g.subs(subs).subs(z, x)
except ValueError:
continue
# NOTE these substitutions can in principle introduce oo,
# zoo and other absurdities. It shouldn't matter,
# but better be safe.
if Tuple(*(r1 + (g,))).has(oo, zoo, -oo):
continue
g = meijerg(g.an, g.aother, g.bm, g.bother,
unpolarify(g.argument, exponents_only=True))
res.append(r1 + (g,))
if res:
return res, cond
# try recursive mellin transform
if not recursive:
return None
_debug('Trying recursive Mellin transform method.')
from sympy.integrals.transforms import (mellin_transform,
inverse_mellin_transform, IntegralTransformError,
MellinTransformStripError)
from sympy import oo, nan, zoo, simplify, cancel
def my_imt(F, s, x, strip):
""" Calling simplify() all the time is slow and not helpful, since
most of the time it only factors things in a way that has to be
un-done anyway. But sometimes it can remove apparent poles. """
# XXX should this be in inverse_mellin_transform?
try:
return inverse_mellin_transform(F, s, x, strip,
as_meijerg=True, needeval=True)
except MellinTransformStripError:
return inverse_mellin_transform(
simplify(cancel(expand(F))), s, x, strip,
as_meijerg=True, needeval=True)
f = f_
s = _dummy('s', 'rewrite-single', f)
# to avoid infinite recursion, we have to force the two g functions case
def my_integrator(f, x):
from sympy import Integral, hyperexpand
r = _meijerint_definite_4(f, x, only_double=True)
if r is not None:
res, cond = r
res = _my_unpolarify(hyperexpand(res, rewrite='nonrepsmall'))
return Piecewise((res, cond),
(Integral(f, (x, 0, oo)), True))
return Integral(f, (x, 0, oo))
try:
F, strip, _ = mellin_transform(f, x, s, integrator=my_integrator,
simplify=False, needeval=True)
g = my_imt(F, s, x, strip)
except IntegralTransformError:
g = None
if g is None:
# We try to find an expression by analytic continuation.
# (also if the dummy is already in the expression, there is no point in
# putting in another one)
a = _dummy_('a', 'rewrite-single')
if a not in f.free_symbols and _is_analytic(f, x):
try:
F, strip, _ = mellin_transform(f.subs(x, a*x), x, s,
integrator=my_integrator,
needeval=True, simplify=False)
g = my_imt(F, s, x, strip).subs(a, 1)
except IntegralTransformError:
g = None
if g is None or g.has(oo, nan, zoo):
_debug('Recursive Mellin transform failed.')
return None
args = Add.make_args(g)
res = []
for f in args:
c, m = f.as_coeff_mul(x)
if len(m) > 1:
raise NotImplementedError('Unexpected form...')
g = m[0]
a, b = _get_coeff_exp(g.argument, x)
res += [(c, 0, meijerg(g.an, g.aother, g.bm, g.bother,
unpolarify(polarify(
a, lift=True), exponents_only=True)
*x**b))]
_debug('Recursive Mellin transform worked:', g)
return res, True
def _rewrite1(f, x, recursive=True):
"""
Try to rewrite f using a (sum of) single G functions with argument a*x**b.
Return fac, po, g such that f = fac*po*g, fac is independent of x
and po = x**s.
Here g is a result from _rewrite_single.
Return None on failure.
"""
fac, po, g = _split_mul(f, x)
g = _rewrite_single(g, x, recursive)
if g:
return fac, po, g[0], g[1]
def _rewrite2(f, x):
"""
Try to rewrite f as a product of two G functions of arguments a*x**b.
Return fac, po, g1, g2 such that f = fac*po*g1*g2, where fac is
independent of x and po is x**s.
Here g1 and g2 are results of _rewrite_single.
Returns None on failure.
"""
fac, po, g = _split_mul(f, x)
if any(_rewrite_single(expr, x, False) is None for expr in _mul_args(g)):
return None
l = _mul_as_two_parts(g)
if not l:
return None
l = list(ordered(l, [
lambda p: max(len(_exponents(p[0], x)), len(_exponents(p[1], x))),
lambda p: max(len(_functions(p[0], x)), len(_functions(p[1], x))),
lambda p: max(len(_find_splitting_points(p[0], x)),
len(_find_splitting_points(p[1], x)))]))
for recursive in [False, True]:
for fac1, fac2 in l:
g1 = _rewrite_single(fac1, x, recursive)
g2 = _rewrite_single(fac2, x, recursive)
if g1 and g2:
cond = And(g1[1], g2[1])
if cond != False:
return fac, po, g1[0], g2[0], cond
def meijerint_indefinite(f, x):
"""
Compute an indefinite integral of ``f`` by rewriting it as a G function.
Examples
========
>>> from sympy.integrals.meijerint import meijerint_indefinite
>>> from sympy import sin
>>> from sympy.abc import x
>>> meijerint_indefinite(sin(x), x)
-cos(x)
"""
from sympy import hyper, meijerg
results = []
for a in sorted(_find_splitting_points(f, x) | {S(0)}, key=default_sort_key):
res = _meijerint_indefinite_1(f.subs(x, x + a), x)
if not res:
continue
res = res.subs(x, x - a)
if _has(res, hyper, meijerg):
results.append(res)
else:
return res
if f.has(HyperbolicFunction):
_debug('Try rewriting hyperbolics in terms of exp.')
rv = meijerint_indefinite(
_rewrite_hyperbolics_as_exp(f), x)
if rv:
if not type(rv) is list:
return collect(factor_terms(rv), rv.atoms(exp))
results.extend(rv)
if results:
return next(ordered(results))
def _meijerint_indefinite_1(f, x):
""" Helper that does not attempt any substitution. """
from sympy import Integral, piecewise_fold, nan, zoo
_debug('Trying to compute the indefinite integral of', f, 'wrt', x)
gs = _rewrite1(f, x)
if gs is None:
# Note: the code that calls us will do expand() and try again
return None
fac, po, gl, cond = gs
_debug(' could rewrite:', gs)
res = S(0)
for C, s, g in gl:
a, b = _get_coeff_exp(g.argument, x)
_, c = _get_coeff_exp(po, x)
c += s
# we do a substitution t=a*x**b, get integrand fac*t**rho*g
fac_ = fac * C / (b*a**((1 + c)/b))
rho = (c + 1)/b - 1
# we now use t**rho*G(params, t) = G(params + rho, t)
# [L, page 150, equation (4)]
# and integral G(params, t) dt = G(1, params+1, 0, t)
# (or a similar expression with 1 and 0 exchanged ... pick the one
# which yields a well-defined function)
# [R, section 5]
# (Note that this dummy will immediately go away again, so we
# can safely pass S(1) for ``expr``.)
t = _dummy('t', 'meijerint-indefinite', S(1))
def tr(p):
return [a + rho + 1 for a in p]
if any(b.is_integer and (b <= 0) == True for b in tr(g.bm)):
r = -meijerg(
tr(g.an), tr(g.aother) + [1], tr(g.bm) + [0], tr(g.bother), t)
else:
r = meijerg(
tr(g.an) + [1], tr(g.aother), tr(g.bm), tr(g.bother) + [0], t)
# The antiderivative is most often expected to be defined
# in the neighborhood of x = 0.
place = 0
if b < 0 or f.subs(x, 0).has(nan, zoo):
place = None
r = hyperexpand(r.subs(t, a*x**b), place=place)
# now substitute back
# Note: we really do want the powers of x to combine.
res += powdenest(fac_*r, polar=True)
def _clean(res):
"""This multiplies out superfluous powers of x we created, and chops off
constants:
>> _clean(x*(exp(x)/x - 1/x) + 3)
exp(x)
cancel is used before mul_expand since it is possible for an
expression to have an additive constant that doesn't become isolated
with simple expansion. Such a situation was identified in issue 6369:
>>> from sympy import sqrt, cancel
>>> from sympy.abc import x
>>> a = sqrt(2*x + 1)
>>> bad = (3*x*a**5 + 2*x - a**5 + 1)/a**2
>>> bad.expand().as_independent(x)[0]
0
>>> cancel(bad).expand().as_independent(x)[0]
1
"""
from sympy import cancel
res = expand_mul(cancel(res), deep=False)
return Add._from_args(res.as_coeff_add(x)[1])
res = piecewise_fold(res)
if res.is_Piecewise:
newargs = []
for expr, cond in res.args:
expr = _my_unpolarify(_clean(expr))
newargs += [(expr, cond)]
res = Piecewise(*newargs)
else:
res = _my_unpolarify(_clean(res))
return Piecewise((res, _my_unpolarify(cond)), (Integral(f, x), True))
@timeit
def meijerint_definite(f, x, a, b):
"""
Integrate ``f`` over the interval [``a``, ``b``], by rewriting it as a product
of two G functions, or as a single G function.
Return res, cond, where cond are convergence conditions.
Examples
========
>>> from sympy.integrals.meijerint import meijerint_definite
>>> from sympy import exp, oo
>>> from sympy.abc import x
>>> meijerint_definite(exp(-x**2), x, -oo, oo)
(sqrt(pi), True)
This function is implemented as a succession of functions
meijerint_definite, _meijerint_definite_2, _meijerint_definite_3,
_meijerint_definite_4. Each function in the list calls the next one
(presumably) several times. This means that calling meijerint_definite
can be very costly.
"""
# This consists of three steps:
# 1) Change the integration limits to 0, oo
# 2) Rewrite in terms of G functions
# 3) Evaluate the integral
#
# There are usually several ways of doing this, and we want to try all.
# This function does (1), calls _meijerint_definite_2 for step (2).
from sympy import arg, exp, I, And, DiracDelta, SingularityFunction
_debug('Integrating', f, 'wrt %s from %s to %s.' % (x, a, b))
if f.has(DiracDelta):
_debug('Integrand has DiracDelta terms - giving up.')
return None
if f.has(SingularityFunction):
_debug('Integrand has Singularity Function terms - giving up.')
return None
f_, x_, a_, b_ = f, x, a, b
# Let's use a dummy in case any of the boundaries has x.
d = Dummy('x')
f = f.subs(x, d)
x = d
if a == b:
return (S.Zero, True)
results = []
if a == -oo and b != oo:
return meijerint_definite(f.subs(x, -x), x, -b, -a)
elif a == -oo:
# Integrating -oo to oo. We need to find a place to split the integral.
_debug(' Integrating -oo to +oo.')
innermost = _find_splitting_points(f, x)
_debug(' Sensible splitting points:', innermost)
for c in sorted(innermost, key=default_sort_key, reverse=True) + [S(0)]:
_debug(' Trying to split at', c)
if not c.is_real:
_debug(' Non-real splitting point.')
continue
res1 = _meijerint_definite_2(f.subs(x, x + c), x)
if res1 is None:
_debug(' But could not compute first integral.')
continue
res2 = _meijerint_definite_2(f.subs(x, c - x), x)
if res2 is None:
_debug(' But could not compute second integral.')
continue
res1, cond1 = res1
res2, cond2 = res2
cond = _condsimp(And(cond1, cond2))
if cond == False:
_debug(' But combined condition is always false.')
continue
res = res1 + res2
return res, cond
elif a == oo:
return -meijerint_definite(f, x, b, oo)
elif (a, b) == (0, oo):
# This is a common case - try it directly first.
res = _meijerint_definite_2(f, x)
if res:
if _has(res[0], meijerg):
results.append(res)
else:
return res
else:
if b == oo:
for split in _find_splitting_points(f, x):
if (a - split >= 0) == True:
_debug('Trying x -> x + %s' % split)
res = _meijerint_definite_2(f.subs(x, x + split)
*Heaviside(x + split - a), x)
if res:
if _has(res[0], meijerg):
results.append(res)
else:
return res
f = f.subs(x, x + a)
b = b - a
a = 0
if b != oo:
phi = exp(I*arg(b))
b = abs(b)
f = f.subs(x, phi*x)
f *= Heaviside(b - x)*phi
b = oo
_debug('Changed limits to', a, b)
_debug('Changed function to', f)
res = _meijerint_definite_2(f, x)
if res:
if _has(res[0], meijerg):
results.append(res)
else:
return res
if f_.has(HyperbolicFunction):
_debug('Try rewriting hyperbolics in terms of exp.')
rv = meijerint_definite(
_rewrite_hyperbolics_as_exp(f_), x_, a_, b_)
if rv:
if not type(rv) is list:
rv = (collect(factor_terms(rv[0]), rv[0].atoms(exp)),) + rv[1:]
return rv
results.extend(rv)
if results:
return next(ordered(results))
def _guess_expansion(f, x):
""" Try to guess sensible rewritings for integrand f(x). """
from sympy import expand_trig
from sympy.functions.elementary.trigonometric import TrigonometricFunction
res = [(f, 'original integrand')]
orig = res[-1][0]
saw = {orig}
expanded = expand_mul(orig)
if expanded not in saw:
res += [(expanded, 'expand_mul')]
saw.add(expanded)
expanded = expand(orig)
if expanded not in saw:
res += [(expanded, 'expand')]
saw.add(expanded)
if orig.has(TrigonometricFunction, HyperbolicFunction):
expanded = expand_mul(expand_trig(orig))
if expanded not in saw:
res += [(expanded, 'expand_trig, expand_mul')]
saw.add(expanded)
return res
def _meijerint_definite_2(f, x):
"""
Try to integrate f dx from zero to infinty.
The body of this function computes various 'simplifications'
f1, f2, ... of f (e.g. by calling expand_mul(), trigexpand()
- see _guess_expansion) and calls _meijerint_definite_3 with each of
these in succession.
If _meijerint_definite_3 succeedes with any of the simplified functions,
returns this result.
"""
# This function does preparation for (2), calls
# _meijerint_definite_3 for (2) and (3) combined.
# use a positive dummy - we integrate from 0 to oo
# XXX if a nonnegative symbol is used there will be test failures
dummy = _dummy('x', 'meijerint-definite2', f, positive=True)
f = f.subs(x, dummy)
x = dummy
if f == 0:
return S(0), True
for g, explanation in _guess_expansion(f, x):
_debug('Trying', explanation)
res = _meijerint_definite_3(g, x)
if res:
return res
def _meijerint_definite_3(f, x):
"""
Try to integrate f dx from zero to infinity.
This function calls _meijerint_definite_4 to try to compute the
integral. If this fails, it tries using linearity.
"""
res = _meijerint_definite_4(f, x)
if res and res[1] != False:
return res
if f.is_Add:
_debug('Expanding and evaluating all terms.')
ress = [_meijerint_definite_4(g, x) for g in f.args]
if all(r is not None for r in ress):
conds = []
res = S(0)
for r, c in ress:
res += r
conds += [c]
c = And(*conds)
if c != False:
return res, c
def _my_unpolarify(f):
from sympy import unpolarify
return _eval_cond(unpolarify(f))
@timeit
def _meijerint_definite_4(f, x, only_double=False):
"""
Try to integrate f dx from zero to infinity.
This function tries to apply the integration theorems found in literature,
i.e. it tries to rewrite f as either one or a product of two G-functions.
The parameter ``only_double`` is used internally in the recursive algorithm
to disable trying to rewrite f as a single G-function.
"""
# This function does (2) and (3)
_debug('Integrating', f)
# Try single G function.
if not only_double:
gs = _rewrite1(f, x, recursive=False)
if gs is not None:
fac, po, g, cond = gs
_debug('Could rewrite as single G function:', fac, po, g)
res = S(0)
for C, s, f in g:
if C == 0:
continue
C, f = _rewrite_saxena_1(fac*C, po*x**s, f, x)
res += C*_int0oo_1(f, x)
cond = And(cond, _check_antecedents_1(f, x))
if cond == False:
break
cond = _my_unpolarify(cond)
if cond == False:
_debug('But cond is always False.')
else:
_debug('Result before branch substitutions is:', res)
return _my_unpolarify(hyperexpand(res)), cond
# Try two G functions.
gs = _rewrite2(f, x)
if gs is not None:
for full_pb in [False, True]:
fac, po, g1, g2, cond = gs
_debug('Could rewrite as two G functions:', fac, po, g1, g2)
res = S(0)
for C1, s1, f1 in g1:
for C2, s2, f2 in g2:
r = _rewrite_saxena(fac*C1*C2, po*x**(s1 + s2),
f1, f2, x, full_pb)
if r is None:
_debug('Non-rational exponents.')
return
C, f1_, f2_ = r
_debug('Saxena subst for yielded:', C, f1_, f2_)
cond = And(cond, _check_antecedents(f1_, f2_, x))
if cond == False:
break
res += C*_int0oo(f1_, f2_, x)
else:
continue
break
cond = _my_unpolarify(cond)
if cond == False:
_debug('But cond is always False (full_pb=%s).' % full_pb)
else:
_debug('Result before branch substitutions is:', res)
if only_double:
return res, cond
return _my_unpolarify(hyperexpand(res)), cond
def meijerint_inversion(f, x, t):
r"""
Compute the inverse laplace transform
:math:\int_{c+i\infty}^{c-i\infty} f(x) e^{tx) dx,
for real c larger than the real part of all singularities of f.
Note that ``t`` is always assumed real and positive.
Return None if the integral does not exist or could not be evaluated.
Examples
========
>>> from sympy.abc import x, t
>>> from sympy.integrals.meijerint import meijerint_inversion
>>> meijerint_inversion(1/x, x, t)
Heaviside(t)
"""
from sympy import I, Integral, exp, expand, log, Add, Mul, Heaviside
f_ = f
t_ = t
t = Dummy('t', polar=True) # We don't want sqrt(t**2) = abs(t) etc
f = f.subs(t_, t)
c = Dummy('c')
_debug('Laplace-inverting', f)
if not _is_analytic(f, x):
_debug('But expression is not analytic.')
return None
# We filter out exponentials here. If we are given an Add this will not
# work, but the calling code will take care of that.
shift = 0
if f.is_Mul:
args = list(f.args)
newargs = []
exponentials = []
while args:
arg = args.pop()
if isinstance(arg, exp):
arg2 = expand(arg)
if arg2.is_Mul:
args += arg2.args
continue
try:
a, b = _get_coeff_exp(arg.args[0], x)
except _CoeffExpValueError:
b = 0
if b == 1:
exponentials.append(a)
else:
newargs.append(arg)
elif arg.is_Pow:
arg2 = expand(arg)
if arg2.is_Mul:
args += arg2.args
continue
if x not in arg.base.free_symbols:
try:
a, b = _get_coeff_exp(arg.exp, x)
except _CoeffExpValueError:
b = 0
if b == 1:
exponentials.append(a*log(arg.base))
newargs.append(arg)
else:
newargs.append(arg)
shift = Add(*exponentials)
f = Mul(*newargs)
gs = _rewrite1(f, x)
if gs is not None:
fac, po, g, cond = gs
_debug('Could rewrite as single G function:', fac, po, g)
res = S(0)
for C, s, f in g:
C, f = _rewrite_inversion(fac*C, po*x**s, f, x)
res += C*_int_inversion(f, x, t)
cond = And(cond, _check_antecedents_inversion(f, x))
if cond == False:
break
cond = _my_unpolarify(cond)
if cond == False:
_debug('But cond is always False.')
else:
_debug('Result before branch substitution:', res)
res = _my_unpolarify(hyperexpand(res))
if not res.has(Heaviside):
res *= Heaviside(t)
res = res.subs(t, t + shift)
if not isinstance(cond, bool):
cond = cond.subs(t, t + shift)
return Piecewise((res.subs(t, t_), cond),
(Integral(f_*exp(x*t), (x, c - oo*I, c + oo*I)).subs(t, t_), True))
| 76,263 | 35.264384 | 127 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/quadrature.py
|
from __future__ import print_function, division
from sympy.core import S, Dummy, pi
from sympy.functions.combinatorial.factorials import factorial
from sympy.functions.elementary.trigonometric import sin, cos
from sympy.functions.elementary.miscellaneous import sqrt
from sympy.functions.special.gamma_functions import gamma
from sympy.polys.orthopolys import (legendre_poly, laguerre_poly,
hermite_poly, jacobi_poly)
from sympy.polys.rootoftools import RootOf
from sympy.core.compatibility import range
def gauss_legendre(n, n_digits):
r"""
Computes the Gauss-Legendre quadrature [1]_ points and weights.
The Gauss-Legendre quadrature approximates the integral:
.. math::
\int_{-1}^1 f(x)\,dx \approx \sum_{i=1}^n w_i f(x_i)
The nodes `x_i` of an order `n` quadrature rule are the roots of `P_n`
and the weights `w_i` are given by:
.. math::
w_i = \frac{2}{\left(1-x_i^2\right) \left(P'_n(x_i)\right)^2}
Parameters
==========
n : the order of quadrature
n_digits : number of significant digits of the points and weights to return
Returns
=======
(x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
The points `x_i` and weights `w_i` are returned as ``(x, w)``
tuple of lists.
Examples
========
>>> from sympy.integrals.quadrature import gauss_legendre
>>> x, w = gauss_legendre(3, 5)
>>> x
[-0.7746, 0, 0.7746]
>>> w
[0.55556, 0.88889, 0.55556]
>>> x, w = gauss_legendre(4, 5)
>>> x
[-0.86114, -0.33998, 0.33998, 0.86114]
>>> w
[0.34786, 0.65215, 0.65215, 0.34786]
See Also
========
gauss_laguerre, gauss_gen_laguerre, gauss_hermite, gauss_chebyshev_t, gauss_chebyshev_u, gauss_jacobi, gauss_lobatto
References
==========
.. [1] http://en.wikipedia.org/wiki/Gaussian_quadrature
.. [2] http://people.sc.fsu.edu/~jburkardt/cpp_src/legendre_rule/legendre_rule.html
"""
x = Dummy("x")
p = legendre_poly(n, x, polys=True)
pd = p.diff(x)
xi = []
w = []
for r in p.real_roots():
if isinstance(r, RootOf):
r = r.eval_rational(S(1)/10**(n_digits+2))
xi.append(r.n(n_digits))
w.append((2/((1-r**2) * pd.subs(x, r)**2)).n(n_digits))
return xi, w
def gauss_laguerre(n, n_digits):
r"""
Computes the Gauss-Laguerre quadrature [1]_ points and weights.
The Gauss-Laguerre quadrature approximates the integral:
.. math::
\int_0^{\infty} e^{-x} f(x)\,dx \approx \sum_{i=1}^n w_i f(x_i)
The nodes `x_i` of an order `n` quadrature rule are the roots of `L_n`
and the weights `w_i` are given by:
.. math::
w_i = \frac{x_i}{(n+1)^2 \left(L_{n+1}(x_i)\right)^2}
Parameters
==========
n : the order of quadrature
n_digits : number of significant digits of the points and weights to return
Returns
=======
(x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
The points `x_i` and weights `w_i` are returned as ``(x, w)``
tuple of lists.
Examples
========
>>> from sympy.integrals.quadrature import gauss_laguerre
>>> x, w = gauss_laguerre(3, 5)
>>> x
[0.41577, 2.2943, 6.2899]
>>> w
[0.71109, 0.27852, 0.010389]
>>> x, w = gauss_laguerre(6, 5)
>>> x
[0.22285, 1.1889, 2.9927, 5.7751, 9.8375, 15.983]
>>> w
[0.45896, 0.417, 0.11337, 0.010399, 0.00026102, 8.9855e-7]
See Also
========
gauss_legendre, gauss_gen_laguerre, gauss_hermite, gauss_chebyshev_t, gauss_chebyshev_u, gauss_jacobi, gauss_lobatto
References
==========
.. [1] http://en.wikipedia.org/wiki/Gauss%E2%80%93Laguerre_quadrature
.. [2] http://people.sc.fsu.edu/~jburkardt/cpp_src/laguerre_rule/laguerre_rule.html
"""
x = Dummy("x")
p = laguerre_poly(n, x, polys=True)
p1 = laguerre_poly(n+1, x, polys=True)
xi = []
w = []
for r in p.real_roots():
if isinstance(r, RootOf):
r = r.eval_rational(S(1)/10**(n_digits+2))
xi.append(r.n(n_digits))
w.append((r/((n+1)**2 * p1.subs(x, r)**2)).n(n_digits))
return xi, w
def gauss_hermite(n, n_digits):
r"""
Computes the Gauss-Hermite quadrature [1]_ points and weights.
The Gauss-Hermite quadrature approximates the integral:
.. math::
\int_{-\infty}^{\infty} e^{-x^2} f(x)\,dx \approx
\sum_{i=1}^n w_i f(x_i)
The nodes `x_i` of an order `n` quadrature rule are the roots of `H_n`
and the weights `w_i` are given by:
.. math::
w_i = \frac{2^{n-1} n! \sqrt{\pi}}{n^2 \left(H_{n-1}(x_i)\right)^2}
Parameters
==========
n : the order of quadrature
n_digits : number of significant digits of the points and weights to return
Returns
=======
(x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
The points `x_i` and weights `w_i` are returned as ``(x, w)``
tuple of lists.
Examples
========
>>> from sympy.integrals.quadrature import gauss_hermite
>>> x, w = gauss_hermite(3, 5)
>>> x
[-1.2247, 0, 1.2247]
>>> w
[0.29541, 1.1816, 0.29541]
>>> x, w = gauss_hermite(6, 5)
>>> x
[-2.3506, -1.3358, -0.43608, 0.43608, 1.3358, 2.3506]
>>> w
[0.00453, 0.15707, 0.72463, 0.72463, 0.15707, 0.00453]
See Also
========
gauss_legendre, gauss_laguerre, gauss_gen_laguerre, gauss_chebyshev_t, gauss_chebyshev_u, gauss_jacobi, gauss_lobatto
References
==========
.. [1] http://en.wikipedia.org/wiki/Gauss-Hermite_Quadrature
.. [2] http://people.sc.fsu.edu/~jburkardt/cpp_src/hermite_rule/hermite_rule.html
.. [3] http://people.sc.fsu.edu/~jburkardt/cpp_src/gen_hermite_rule/gen_hermite_rule.html
"""
x = Dummy("x")
p = hermite_poly(n, x, polys=True)
p1 = hermite_poly(n-1, x, polys=True)
xi = []
w = []
for r in p.real_roots():
if isinstance(r, RootOf):
r = r.eval_rational(S(1)/10**(n_digits+2))
xi.append(r.n(n_digits))
w.append(((2**(n-1) * factorial(n) * sqrt(pi)) /
(n**2 * p1.subs(x, r)**2)).n(n_digits))
return xi, w
def gauss_gen_laguerre(n, alpha, n_digits):
r"""
Computes the generalized Gauss-Laguerre quadrature [1]_ points and weights.
The generalized Gauss-Laguerre quadrature approximates the integral:
.. math::
\int_{0}^\infty x^{\alpha} e^{-x} f(x)\,dx \approx
\sum_{i=1}^n w_i f(x_i)
The nodes `x_i` of an order `n` quadrature rule are the roots of
`L^{\alpha}_n` and the weights `w_i` are given by:
.. math::
w_i = \frac{\Gamma(\alpha+n)}
{n \Gamma(n) L^{\alpha}_{n-1}(x_i) L^{\alpha+1}_{n-1}(x_i)}
Parameters
==========
n : the order of quadrature
alpha : the exponent of the singularity, `\alpha > -1`
n_digits : number of significant digits of the points and weights to return
Returns
=======
(x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
The points `x_i` and weights `w_i` are returned as ``(x, w)``
tuple of lists.
Examples
========
>>> from sympy import S
>>> from sympy.integrals.quadrature import gauss_gen_laguerre
>>> x, w = gauss_gen_laguerre(3, -S.Half, 5)
>>> x
[0.19016, 1.7845, 5.5253]
>>> w
[1.4493, 0.31413, 0.00906]
>>> x, w = gauss_gen_laguerre(4, 3*S.Half, 5)
>>> x
[0.97851, 2.9904, 6.3193, 11.712]
>>> w
[0.53087, 0.67721, 0.11895, 0.0023152]
See Also
========
gauss_legendre, gauss_laguerre, gauss_hermite, gauss_chebyshev_t, gauss_chebyshev_u, gauss_jacobi, gauss_lobatto
References
==========
.. [1] http://en.wikipedia.org/wiki/Gauss%E2%80%93Laguerre_quadrature
.. [2] http://people.sc.fsu.edu/~jburkardt/cpp_src/gen_laguerre_rule/gen_laguerre_rule.html
"""
x = Dummy("x")
p = laguerre_poly(n, x, alpha=alpha, polys=True)
p1 = laguerre_poly(n-1, x, alpha=alpha, polys=True)
p2 = laguerre_poly(n-1, x, alpha=alpha+1, polys=True)
xi = []
w = []
for r in p.real_roots():
if isinstance(r, RootOf):
r = r.eval_rational(S(1)/10**(n_digits+2))
xi.append(r.n(n_digits))
w.append((gamma(alpha+n) /
(n*gamma(n)*p1.subs(x, r)*p2.subs(x, r))).n(n_digits))
return xi, w
def gauss_chebyshev_t(n, n_digits):
r"""
Computes the Gauss-Chebyshev quadrature [1]_ points and weights of
the first kind.
The Gauss-Chebyshev quadrature of the first kind approximates the integral:
.. math::
\int_{-1}^{1} \frac{1}{\sqrt{1-x^2}} f(x)\,dx \approx
\sum_{i=1}^n w_i f(x_i)
The nodes `x_i` of an order `n` quadrature rule are the roots of `T_n`
and the weights `w_i` are given by:
.. math::
w_i = \frac{\pi}{n}
Parameters
==========
n : the order of quadrature
n_digits : number of significant digits of the points and weights to return
Returns
=======
(x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
The points `x_i` and weights `w_i` are returned as ``(x, w)``
tuple of lists.
Examples
========
>>> from sympy import S
>>> from sympy.integrals.quadrature import gauss_chebyshev_t
>>> x, w = gauss_chebyshev_t(3, 5)
>>> x
[0.86602, 0, -0.86602]
>>> w
[1.0472, 1.0472, 1.0472]
>>> x, w = gauss_chebyshev_t(6, 5)
>>> x
[0.96593, 0.70711, 0.25882, -0.25882, -0.70711, -0.96593]
>>> w
[0.5236, 0.5236, 0.5236, 0.5236, 0.5236, 0.5236]
See Also
========
gauss_legendre, gauss_laguerre, gauss_hermite, gauss_gen_laguerre, gauss_chebyshev_u, gauss_jacobi, gauss_lobatto
References
==========
.. [1] http://en.wikipedia.org/wiki/Chebyshev%E2%80%93Gauss_quadrature
.. [2] http://people.sc.fsu.edu/~jburkardt/cpp_src/chebyshev1_rule/chebyshev1_rule.html
"""
xi = []
w = []
for i in range(1, n+1):
xi.append((cos((2*i-S.One)/(2*n)*S.Pi)).n(n_digits))
w.append((S.Pi/n).n(n_digits))
return xi, w
def gauss_chebyshev_u(n, n_digits):
r"""
Computes the Gauss-Chebyshev quadrature [1]_ points and weights of
the second kind.
The Gauss-Chebyshev quadrature of the second kind approximates the
integral:
.. math::
\int_{-1}^{1} \sqrt{1-x^2} f(x)\,dx \approx \sum_{i=1}^n w_i f(x_i)
The nodes `x_i` of an order `n` quadrature rule are the roots of `U_n`
and the weights `w_i` are given by:
.. math::
w_i = \frac{\pi}{n+1} \sin^2 \left(\frac{i}{n+1}\pi\right)
Parameters
==========
n : the order of quadrature
n_digits : number of significant digits of the points and weights to return
Returns
=======
(x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
The points `x_i` and weights `w_i` are returned as ``(x, w)``
tuple of lists.
Examples
========
>>> from sympy import S
>>> from sympy.integrals.quadrature import gauss_chebyshev_u
>>> x, w = gauss_chebyshev_u(3, 5)
>>> x
[0.70711, 0, -0.70711]
>>> w
[0.3927, 0.7854, 0.3927]
>>> x, w = gauss_chebyshev_u(6, 5)
>>> x
[0.90097, 0.62349, 0.22252, -0.22252, -0.62349, -0.90097]
>>> w
[0.084489, 0.27433, 0.42658, 0.42658, 0.27433, 0.084489]
See Also
========
gauss_legendre, gauss_laguerre, gauss_hermite, gauss_gen_laguerre, gauss_chebyshev_t, gauss_jacobi, gauss_lobatto
References
==========
.. [1] http://en.wikipedia.org/wiki/Chebyshev%E2%80%93Gauss_quadrature
.. [2] http://people.sc.fsu.edu/~jburkardt/cpp_src/chebyshev2_rule/chebyshev2_rule.html
"""
xi = []
w = []
for i in range(1, n+1):
xi.append((cos(i/(n+S.One)*S.Pi)).n(n_digits))
w.append((S.Pi/(n+S.One)*sin(i*S.Pi/(n+S.One))**2).n(n_digits))
return xi, w
def gauss_jacobi(n, alpha, beta, n_digits):
r"""
Computes the Gauss-Jacobi quadrature [1]_ points and weights.
The Gauss-Jacobi quadrature of the first kind approximates the integral:
.. math::
\int_{-1}^1 (1-x)^\alpha (1+x)^\beta f(x)\,dx \approx
\sum_{i=1}^n w_i f(x_i)
The nodes `x_i` of an order `n` quadrature rule are the roots of
`P^{(\alpha,\beta)}_n` and the weights `w_i` are given by:
.. math::
w_i = -\frac{2n+\alpha+\beta+2}{n+\alpha+\beta+1}
\frac{\Gamma(n+\alpha+1)\Gamma(n+\beta+1)}
{\Gamma(n+\alpha+\beta+1)(n+1)!}
\frac{2^{\alpha+\beta}}{P'_n(x_i)
P^{(\alpha,\beta)}_{n+1}(x_i)}
Parameters
==========
n : the order of quadrature
alpha : the first parameter of the Jacobi Polynomial, `\alpha > -1`
beta : the second parameter of the Jacobi Polynomial, `\beta > -1`
n_digits : number of significant digits of the points and weights to return
Returns
=======
(x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
The points `x_i` and weights `w_i` are returned as ``(x, w)``
tuple of lists.
Examples
========
>>> from sympy import S
>>> from sympy.integrals.quadrature import gauss_jacobi
>>> x, w = gauss_jacobi(3, S.Half, -S.Half, 5)
>>> x
[-0.90097, -0.22252, 0.62349]
>>> w
[1.7063, 1.0973, 0.33795]
>>> x, w = gauss_jacobi(6, 1, 1, 5)
>>> x
[-0.87174, -0.5917, -0.2093, 0.2093, 0.5917, 0.87174]
>>> w
[0.050584, 0.22169, 0.39439, 0.39439, 0.22169, 0.050584]
See Also
========
gauss_legendre, gauss_laguerre, gauss_hermite, gauss_gen_laguerre, gauss_chebyshev_t, gauss_chebyshev_u, gauss_lobatto
References
==========
.. [1] http://en.wikipedia.org/wiki/Gauss%E2%80%93Jacobi_quadrature
.. [2] http://people.sc.fsu.edu/~jburkardt/cpp_src/jacobi_rule/jacobi_rule.html
.. [3] http://people.sc.fsu.edu/~jburkardt/cpp_src/gegenbauer_rule/gegenbauer_rule.html
"""
x = Dummy("x")
p = jacobi_poly(n, alpha, beta, x, polys=True)
pd = p.diff(x)
pn = jacobi_poly(n+1, alpha, beta, x, polys=True)
xi = []
w = []
for r in p.real_roots():
if isinstance(r, RootOf):
r = r.eval_rational(S(1)/10**(n_digits+2))
xi.append(r.n(n_digits))
w.append((
- (2*n+alpha+beta+2) / (n+alpha+beta+S.One) *
(gamma(n+alpha+1)*gamma(n+beta+1)) /
(gamma(n+alpha+beta+S.One)*gamma(n+2)) *
2**(alpha+beta) / (pd.subs(x, r) * pn.subs(x, r))).n(n_digits))
return xi, w
def gauss_lobatto(n, n_digits):
r"""
Computes the Gauss-Lobatto quadrature [1]_ points and weights.
The Gauss-Lobatto quadrature approximates the integral:
.. math::
\int_{-1}^1 f(x)\,dx \approx \sum_{i=1}^n w_i f(x_i)
The nodes `x_i` of an order `n` quadrature rule are the roots of `P'_(n-1)`
and the weights `w_i` are given by:
.. math::
&w_i = \frac{2}{n(n-1) \left[P_{n-1}(x_i)\right]^2},\quad x\neq\pm 1\\
&w_i = \frac{2}{n(n-1)},\quad x=\pm 1
Parameters
==========
n : the order of quadrature
n_digits : number of significant digits of the points and weights to return
Returns
=======
(x, w) : the ``x`` and ``w`` are lists of points and weights as Floats.
The points `x_i` and weights `w_i` are returned as ``(x, w)``
tuple of lists.
Examples
========
>>> from sympy.integrals.quadrature import gauss_lobatto
>>> x, w = gauss_lobatto(3, 5)
>>> x
[-1, 0, 1]
>>> w
[0.33333, 1.3333, 0.33333]
>>> x, w = gauss_lobatto(4, 5)
>>> x
[-1, -0.44721, 0.44721, 1]
>>> w
[0.16667, 0.83333, 0.83333, 0.16667]
See Also
========
gauss_legendre,gauss_laguerre, gauss_gen_laguerre, gauss_hermite, gauss_chebyshev_t, gauss_chebyshev_u, gauss_jacobi
References
==========
.. [1] http://en.wikipedia.org/wiki/Gaussian_quadrature#Gauss.E2.80.93Lobatto_rules
.. [2] http://people.math.sfu.ca/~cbm/aands/page_888.htm
"""
x = Dummy("x")
p = legendre_poly(n-1, x, polys=True)
pd = p.diff(x)
xi = []
w = []
for r in pd.real_roots():
if isinstance(r, RootOf):
r = r.eval_rational(S(1)/10**(n_digits+2))
xi.append(r.n(n_digits))
w.append((2/(n*(n-1) * p.subs(x, r)**2)).n(n_digits))
xi.insert(0, -1)
xi.append(1)
w.insert(0, (S(2)/(n*(n-1))).n(n_digits))
w.append((S(2)/(n*(n-1))).n(n_digits))
return xi, w
| 16,782 | 27.445763 | 122 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/deltafunctions.py
|
from __future__ import print_function, division
from sympy.core import Mul
from sympy.functions import DiracDelta, Heaviside
from sympy.core.compatibility import default_sort_key
from sympy.core.singleton import S
def change_mul(node, x):
"""change_mul(node, x)
Rearranges the operands of a product, bringing to front any simple
DiracDelta expression.
If no simple DiracDelta expression was found, then all the DiracDelta
expressions are simplified (using DiracDelta.expand(diracdelta=True, wrt=x)).
Return: (dirac, new node)
Where:
o dirac is either a simple DiracDelta expression or None (if no simple
expression was found);
o new node is either a simplified DiracDelta expressions or None (if it
could not be simplified).
Examples
========
>>> from sympy import DiracDelta, cos
>>> from sympy.integrals.deltafunctions import change_mul
>>> from sympy.abc import x, y
>>> change_mul(x*y*DiracDelta(x)*cos(x), x)
(DiracDelta(x), x*y*cos(x))
>>> change_mul(x*y*DiracDelta(x**2 - 1)*cos(x), x)
(None, x*y*cos(x)*DiracDelta(x - 1)/2 + x*y*cos(x)*DiracDelta(x + 1)/2)
>>> change_mul(x*y*DiracDelta(cos(x))*cos(x), x)
(None, None)
See Also
========
sympy.functions.special.delta_functions.DiracDelta
deltaintegrate
"""
new_args = []
dirac = None
#Sorting is needed so that we consistently collapse the same delta;
#However, we must preserve the ordering of non-commutative terms
c, nc = node.args_cnc()
sorted_args = sorted(c, key=default_sort_key)
sorted_args.extend(nc)
for arg in sorted_args:
if arg.is_Pow and arg.base.func is DiracDelta:
new_args.append(arg.func(arg.base, arg.exp - 1))
arg = arg.base
if dirac is None and (arg.func is DiracDelta and arg.is_simple(x)):
dirac = arg
else:
new_args.append(arg)
if not dirac: # there was no simple dirac
new_args = []
for arg in sorted_args:
if arg.func is DiracDelta:
new_args.append(arg.expand(diracdelta=True, wrt=x))
elif arg.is_Pow and arg.base.func is DiracDelta:
new_args.append(arg.func(arg.base.expand(diracdelta=True, wrt=x), arg.exp))
else:
new_args.append(arg)
if new_args != sorted_args:
nnode = Mul(*new_args).expand()
else: # if the node didn't change there is nothing to do
nnode = None
return (None, nnode)
return (dirac, Mul(*new_args))
def deltaintegrate(f, x):
"""
deltaintegrate(f, x)
The idea for integration is the following:
- If we are dealing with a DiracDelta expression, i.e. DiracDelta(g(x)),
we try to simplify it.
If we could simplify it, then we integrate the resulting expression.
We already know we can integrate a simplified expression, because only
simple DiracDelta expressions are involved.
If we couldn't simplify it, there are two cases:
1) The expression is a simple expression: we return the integral,
taking care if we are dealing with a Derivative or with a proper
DiracDelta.
2) The expression is not simple (i.e. DiracDelta(cos(x))): we can do
nothing at all.
- If the node is a multiplication node having a DiracDelta term:
First we expand it.
If the expansion did work, then we try to integrate the expansion.
If not, we try to extract a simple DiracDelta term, then we have two
cases:
1) We have a simple DiracDelta term, so we return the integral.
2) We didn't have a simple term, but we do have an expression with
simplified DiracDelta terms, so we integrate this expression.
Examples
========
>>> from sympy.abc import x, y, z
>>> from sympy.integrals.deltafunctions import deltaintegrate
>>> from sympy import sin, cos, DiracDelta, Heaviside
>>> deltaintegrate(x*sin(x)*cos(x)*DiracDelta(x - 1), x)
sin(1)*cos(1)*Heaviside(x - 1)
>>> deltaintegrate(y**2*DiracDelta(x - z)*DiracDelta(y - z), y)
z**2*DiracDelta(x - z)*Heaviside(y - z)
See Also
========
sympy.functions.special.delta_functions.DiracDelta
sympy.integrals.integrals.Integral
"""
if not f.has(DiracDelta):
return None
from sympy.integrals import Integral, integrate
from sympy.solvers import solve
# g(x) = DiracDelta(h(x))
if f.func == DiracDelta:
h = f.expand(diracdelta=True, wrt=x)
if h == f: # can't simplify the expression
#FIXME: the second term tells whether is DeltaDirac or Derivative
#For integrating derivatives of DiracDelta we need the chain rule
if f.is_simple(x):
if (len(f.args) <= 1 or f.args[1] == 0):
return Heaviside(f.args[0])
else:
return (DiracDelta(f.args[0], f.args[1] - 1) /
f.args[0].as_poly().LC())
else: # let's try to integrate the simplified expression
fh = integrate(h, x)
return fh
elif f.is_Mul or f.is_Pow: # g(x) = a*b*c*f(DiracDelta(h(x)))*d*e
g = f.expand()
if f != g: # the expansion worked
fh = integrate(g, x)
if fh is not None and not isinstance(fh, Integral):
return fh
else:
# no expansion performed, try to extract a simple DiracDelta term
deltaterm, rest_mult = change_mul(f, x)
if not deltaterm:
if rest_mult:
fh = integrate(rest_mult, x)
return fh
else:
deltaterm = deltaterm.expand(diracdelta=True, wrt=x)
if deltaterm.is_Mul: # Take out any extracted factors
deltaterm, rest_mult_2 = change_mul(deltaterm, x)
rest_mult = rest_mult*rest_mult_2
point = solve(deltaterm.args[0], x)[0]
# Return the largest hyperreal term left after
# repeated integration by parts. For example,
#
# integrate(y*DiracDelta(x, 1),x) == y*DiracDelta(x,0), not 0
#
# This is so Integral(y*DiracDelta(x).diff(x),x).doit()
# will return y*DiracDelta(x) instead of 0 or DiracDelta(x),
# both of which are correct everywhere the value is defined
# but give wrong answers for nested integration.
n = (0 if len(deltaterm.args)==1 else deltaterm.args[1])
m = 0
while n >= 0:
r = (-1)**n*rest_mult.diff(x, n).subs(x, point)
if r is S.Zero:
n -= 1
m += 1
else:
if m == 0:
return r*Heaviside(x - point)
else:
return r*DiracDelta(x,m-1)
# In some very weak sense, x=0 is still a singularity,
# but we hope will not be of any practial consequence.
return S.Zero
return None
| 7,395 | 36.165829 | 91 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/rationaltools.py
|
"""This module implements tools for integrating rational functions. """
from __future__ import print_function, division
from sympy import S, Symbol, symbols, I, log, atan, \
roots, RootSum, Lambda, cancel, Dummy
from sympy.polys import Poly, resultant, ZZ
from sympy.core.compatibility import range
def ratint(f, x, **flags):
"""Performs indefinite integration of rational functions.
Given a field :math:`K` and a rational function :math:`f = p/q`,
where :math:`p` and :math:`q` are polynomials in :math:`K[x]`,
returns a function :math:`g` such that :math:`f = g'`.
>>> from sympy.integrals.rationaltools import ratint
>>> from sympy.abc import x
>>> ratint(36/(x**5 - 2*x**4 - 2*x**3 + 4*x**2 + x - 2), x)
(12*x + 6)/(x**2 - 1) + 4*log(x - 2) - 4*log(x + 1)
References
==========
.. [Bro05] M. Bronstein, Symbolic Integration I: Transcendental
Functions, Second Edition, Springer-Verlag, 2005, pp. 35-70
See Also
========
sympy.integrals.integrals.Integral.doit
ratint_logpart, ratint_ratpart
"""
if type(f) is not tuple:
p, q = f.as_numer_denom()
else:
p, q = f
p, q = Poly(p, x, composite=False, field=True), Poly(q, x, composite=False, field=True)
coeff, p, q = p.cancel(q)
poly, p = p.div(q)
result = poly.integrate(x).as_expr()
if p.is_zero:
return coeff*result
g, h = ratint_ratpart(p, q, x)
P, Q = h.as_numer_denom()
P = Poly(P, x)
Q = Poly(Q, x)
q, r = P.div(Q)
result += g + q.integrate(x).as_expr()
if not r.is_zero:
symbol = flags.get('symbol', 't')
if not isinstance(symbol, Symbol):
t = Dummy(symbol)
else:
t = symbol.as_dummy()
L = ratint_logpart(r, Q, x, t)
real = flags.get('real')
if real is None:
if type(f) is not tuple:
atoms = f.atoms()
else:
p, q = f
atoms = p.atoms() | q.atoms()
for elt in atoms - {x}:
if not elt.is_real:
real = False
break
else:
real = True
eps = S(0)
if not real:
for h, q in L:
_, h = h.primitive()
eps += RootSum(
q, Lambda(t, t*log(h.as_expr())), quadratic=True)
else:
for h, q in L:
_, h = h.primitive()
R = log_to_real(h, q, x, t)
if R is not None:
eps += R
else:
eps += RootSum(
q, Lambda(t, t*log(h.as_expr())), quadratic=True)
result += eps
return coeff*result
def ratint_ratpart(f, g, x):
"""
Horowitz-Ostrogradsky algorithm.
Given a field K and polynomials f and g in K[x], such that f and g
are coprime and deg(f) < deg(g), returns fractions A and B in K(x),
such that f/g = A' + B and B has square-free denominator.
Examples
========
>>> from sympy.integrals.rationaltools import ratint_ratpart
>>> from sympy.abc import x, y
>>> from sympy import Poly
>>> ratint_ratpart(Poly(1, x, domain='ZZ'),
... Poly(x + 1, x, domain='ZZ'), x)
(0, 1/(x + 1))
>>> ratint_ratpart(Poly(1, x, domain='EX'),
... Poly(x**2 + y**2, x, domain='EX'), x)
(0, 1/(x**2 + y**2))
>>> ratint_ratpart(Poly(36, x, domain='ZZ'),
... Poly(x**5 - 2*x**4 - 2*x**3 + 4*x**2 + x - 2, x, domain='ZZ'), x)
((12*x + 6)/(x**2 - 1), 12/(x**2 - x - 2))
See Also
========
ratint, ratint_logpart
"""
from sympy import solve
f = Poly(f, x)
g = Poly(g, x)
u, v, _ = g.cofactors(g.diff())
n = u.degree()
m = v.degree()
A_coeffs = [ Dummy('a' + str(n - i)) for i in range(0, n) ]
B_coeffs = [ Dummy('b' + str(m - i)) for i in range(0, m) ]
C_coeffs = A_coeffs + B_coeffs
A = Poly(A_coeffs, x, domain=ZZ[C_coeffs])
B = Poly(B_coeffs, x, domain=ZZ[C_coeffs])
H = f - A.diff()*v + A*(u.diff()*v).quo(u) - B*u
result = solve(H.coeffs(), C_coeffs)
A = A.as_expr().subs(result)
B = B.as_expr().subs(result)
rat_part = cancel(A/u.as_expr(), x)
log_part = cancel(B/v.as_expr(), x)
return rat_part, log_part
def ratint_logpart(f, g, x, t=None):
"""
Lazard-Rioboo-Trager algorithm.
Given a field K and polynomials f and g in K[x], such that f and g
are coprime, deg(f) < deg(g) and g is square-free, returns a list
of tuples (s_i, q_i) of polynomials, for i = 1..n, such that s_i
in K[t, x] and q_i in K[t], and:
___ ___
d f d \ ` \ `
-- - = -- ) ) a log(s_i(a, x))
dx g dx /__, /__,
i=1..n a | q_i(a) = 0
Examples
========
>>> from sympy.integrals.rationaltools import ratint_logpart
>>> from sympy.abc import x
>>> from sympy import Poly
>>> ratint_logpart(Poly(1, x, domain='ZZ'),
... Poly(x**2 + x + 1, x, domain='ZZ'), x)
[(Poly(x + 3*_t/2 + 1/2, x, domain='QQ[_t]'),
...Poly(3*_t**2 + 1, _t, domain='ZZ'))]
>>> ratint_logpart(Poly(12, x, domain='ZZ'),
... Poly(x**2 - x - 2, x, domain='ZZ'), x)
[(Poly(x - 3*_t/8 - 1/2, x, domain='QQ[_t]'),
...Poly(-_t**2 + 16, _t, domain='ZZ'))]
See Also
========
ratint, ratint_ratpart
"""
f, g = Poly(f, x), Poly(g, x)
t = t or Dummy('t')
a, b = g, f - g.diff()*Poly(t, x)
res, R = resultant(a, b, includePRS=True)
res = Poly(res, t, composite=False)
assert res, "BUG: resultant(%s, %s) can't be zero" % (a, b)
R_map, H = {}, []
for r in R:
R_map[r.degree()] = r
def _include_sign(c, sqf):
if (c < 0) == True:
h, k = sqf[0]
sqf[0] = h*c, k
C, res_sqf = res.sqf_list()
_include_sign(C, res_sqf)
for q, i in res_sqf:
_, q = q.primitive()
if g.degree() == i:
H.append((g, q))
else:
h = R_map[i]
h_lc = Poly(h.LC(), t, field=True)
c, h_lc_sqf = h_lc.sqf_list(all=True)
_include_sign(c, h_lc_sqf)
for a, j in h_lc_sqf:
h = h.quo(Poly(a.gcd(q)**j, x))
inv, coeffs = h_lc.invert(q), [S(1)]
for coeff in h.coeffs()[1:]:
T = (inv*coeff).rem(q)
coeffs.append(T.as_expr())
h = Poly(dict(list(zip(h.monoms(), coeffs))), x)
H.append((h, q))
return H
def log_to_atan(f, g):
"""
Convert complex logarithms to real arctangents.
Given a real field K and polynomials f and g in K[x], with g != 0,
returns a sum h of arctangents of polynomials in K[x], such that:
dh d f + I g
-- = -- I log( ------- )
dx dx f - I g
Examples
========
>>> from sympy.integrals.rationaltools import log_to_atan
>>> from sympy.abc import x
>>> from sympy import Poly, sqrt, S
>>> log_to_atan(Poly(x, x, domain='ZZ'), Poly(1, x, domain='ZZ'))
2*atan(x)
>>> log_to_atan(Poly(x + S(1)/2, x, domain='QQ'),
... Poly(sqrt(3)/2, x, domain='EX'))
2*atan(2*sqrt(3)*x/3 + sqrt(3)/3)
See Also
========
log_to_real
"""
if f.degree() < g.degree():
f, g = -g, f
f = f.to_field()
g = g.to_field()
p, q = f.div(g)
if q.is_zero:
return 2*atan(p.as_expr())
else:
s, t, h = g.gcdex(-f)
u = (f*s + g*t).quo(h)
A = 2*atan(u.as_expr())
return A + log_to_atan(s, t)
def log_to_real(h, q, x, t):
"""
Convert complex logarithms to real functions.
Given real field K and polynomials h in K[t,x] and q in K[t],
returns real function f such that:
___
df d \ `
-- = -- ) a log(h(a, x))
dx dx /__,
a | q(a) = 0
Examples
========
>>> from sympy.integrals.rationaltools import log_to_real
>>> from sympy.abc import x, y
>>> from sympy import Poly, sqrt, S
>>> log_to_real(Poly(x + 3*y/2 + S(1)/2, x, domain='QQ[y]'),
... Poly(3*y**2 + 1, y, domain='ZZ'), x, y)
2*sqrt(3)*atan(2*sqrt(3)*x/3 + sqrt(3)/3)/3
>>> log_to_real(Poly(x**2 - 1, x, domain='ZZ'),
... Poly(-2*y + 1, y, domain='ZZ'), x, y)
log(x**2 - 1)/2
See Also
========
log_to_atan
"""
from sympy import collect
u, v = symbols('u,v', cls=Dummy)
H = h.as_expr().subs({t: u + I*v}).expand()
Q = q.as_expr().subs({t: u + I*v}).expand()
H_map = collect(H, I, evaluate=False)
Q_map = collect(Q, I, evaluate=False)
a, b = H_map.get(S(1), S(0)), H_map.get(I, S(0))
c, d = Q_map.get(S(1), S(0)), Q_map.get(I, S(0))
R = Poly(resultant(c, d, v), u)
R_u = roots(R, filter='R')
if len(R_u) != R.count_roots():
return None
result = S(0)
for r_u in R_u.keys():
C = Poly(c.subs({u: r_u}), v)
R_v = roots(C, filter='R')
if len(R_v) != C.count_roots():
return None
for r_v in R_v:
if not r_v.is_positive:
continue
D = d.subs({u: r_u, v: r_v})
if D.evalf(chop=True) != 0:
continue
A = Poly(a.subs({u: r_u, v: r_v}), x)
B = Poly(b.subs({u: r_u, v: r_v}), x)
AB = (A**2 + B**2).as_expr()
result += r_u*log(AB) + r_v*log_to_atan(A, B)
R_q = roots(q, filter='R')
if len(R_q) != q.count_roots():
return None
for r in R_q.keys():
result += r*log(h.as_expr().subs(t, r))
return result
| 10,072 | 25.095855 | 91 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/meijerint_doc.py
|
""" This module cooks up a docstring when imported. Its only purpose is to
be displayed in the sphinx documentation. """
from __future__ import print_function, division
from sympy.integrals.meijerint import _create_lookup_table
from sympy import latex, Eq, Add, Symbol
t = {}
_create_lookup_table(t)
doc = ""
for about, category in sorted(t.items()):
if about == ():
doc += 'Elementary functions:\n\n'
else:
doc += 'Functions involving ' + ', '.join('`%s`' % latex(
list(category[0][0].atoms(func))[0]) for func in about) + ':\n\n'
for formula, gs, cond, hint in category:
if not isinstance(gs, list):
g = Symbol('\\text{generated}')
else:
g = Add(*[fac*f for (fac, f) in gs])
obj = Eq(formula, g)
if cond is True:
cond = ""
else:
cond = ',\\text{ if } %s' % latex(cond)
doc += ".. math::\n %s%s\n\n" % (latex(obj), cond)
__doc__ = doc
| 983 | 28.818182 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/trigonometry.py
|
# -*- coding: utf-8 -*-
from __future__ import print_function, division
from sympy.core.compatibility import range
from sympy.core import cacheit, Dummy, Eq, Integer, Rational, S, Wild
from sympy.functions import binomial, sin, cos, Piecewise
# TODO sin(a*x)*cos(b*x) -> sin((a+b)x) + sin((a-b)x) ?
# creating, each time, Wild's and sin/cos/Mul is expensive. Also, our match &
# subs are very slow when not cached, and if we create Wild each time, we
# effectively block caching.
#
# so we cache the pattern
# need to use a function instead of lamda since hash of lambda changes on
# each call to _pat_sincos
def _integer_instance(n):
return isinstance(n , Integer)
@cacheit
def _pat_sincos(x):
a = Wild('a', exclude=[x])
n, m = [Wild(s, exclude=[x], properties=[_integer_instance])
for s in 'nm']
pat = sin(a*x)**n * cos(a*x)**m
return pat, a, n, m
_u = Dummy('u')
def trigintegrate(f, x, conds='piecewise'):
"""Integrate f = Mul(trig) over x
>>> from sympy import Symbol, sin, cos, tan, sec, csc, cot
>>> from sympy.integrals.trigonometry import trigintegrate
>>> from sympy.abc import x
>>> trigintegrate(sin(x)*cos(x), x)
sin(x)**2/2
>>> trigintegrate(sin(x)**2, x)
x/2 - sin(x)*cos(x)/2
>>> trigintegrate(tan(x)*sec(x), x)
1/cos(x)
>>> trigintegrate(sin(x)*tan(x), x)
-log(sin(x) - 1)/2 + log(sin(x) + 1)/2 - sin(x)
http://en.wikibooks.org/wiki/Calculus/Integration_techniques
See Also
========
sympy.integrals.integrals.Integral.doit
sympy.integrals.integrals.Integral
"""
from sympy.integrals.integrals import integrate
pat, a, n, m = _pat_sincos(x)
f = f.rewrite('sincos')
M = f.match(pat)
if M is None:
return
n, m = M[n], M[m]
if n is S.Zero and m is S.Zero:
return x
zz = x if n is S.Zero else S.Zero
a = M[a]
if n.is_odd or m.is_odd:
u = _u
n_, m_ = n.is_odd, m.is_odd
# take smallest n or m -- to choose simplest substitution
if n_ and m_:
n_ = n_ and (n < m) # NB: careful here, one of the
m_ = m_ and not (n < m) # conditions *must* be true
# n m u=C (n-1)/2 m
# S(x) * C(x) dx --> -(1-u^2) * u du
if n_:
ff = -(1 - u**2)**((n - 1)/2) * u**m
uu = cos(a*x)
# n m u=S n (m-1)/2
# S(x) * C(x) dx --> u * (1-u^2) du
elif m_:
ff = u**n * (1 - u**2)**((m - 1)/2)
uu = sin(a*x)
fi = integrate(ff, u) # XXX cyclic deps
fx = fi.subs(u, uu)
if conds == 'piecewise':
return Piecewise((zz, Eq(a, 0)), (fx / a, True))
return fx / a
# n & m are both even
#
# 2k 2m 2l 2l
# we transform S (x) * C (x) into terms with only S (x) or C (x)
#
# example:
# 100 4 100 2 2 100 4 2
# S (x) * C (x) = S (x) * (1-S (x)) = S (x) * (1 + S (x) - 2*S (x))
#
# 104 102 100
# = S (x) - 2*S (x) + S (x)
# 2k
# then S is integrated with recursive formula
# take largest n or m -- to choose simplest substitution
n_ = (abs(n) > abs(m))
m_ = (abs(m) > abs(n))
res = S.Zero
if n_:
# 2k 2 k i 2i
# C = (1 - S ) = sum(i, (-) * B(k, i) * S )
if m > 0:
for i in range(0, m//2 + 1):
res += ((-1)**i * binomial(m//2, i) *
_sin_pow_integrate(n + 2*i, x))
elif m == 0:
res = _sin_pow_integrate(n, x)
else:
# m < 0 , |n| > |m|
# /
# |
# | m n
# | cos (x) sin (x) dx =
# |
# |
#/
# /
# |
# -1 m+1 n-1 n - 1 | m+2 n-2
# ________ cos (x) sin (x) + _______ | cos (x) sin (x) dx
# |
# m + 1 m + 1 |
# /
res = (Rational(-1, m + 1) * cos(x)**(m + 1) * sin(x)**(n - 1) +
Rational(n - 1, m + 1) *
trigintegrate(cos(x)**(m + 2)*sin(x)**(n - 2), x))
elif m_:
# 2k 2 k i 2i
# S = (1 - C ) = sum(i, (-) * B(k, i) * C )
if n > 0:
# / /
# | |
# | m n | -m n
# | cos (x)*sin (x) dx or | cos (x) * sin (x) dx
# | |
# / /
#
# |m| > |n| ; m, n >0 ; m, n belong to Z - {0}
# n 2
# sin (x) term is expanded here in terms of cos (x),
# and then integrated.
#
for i in range(0, n//2 + 1):
res += ((-1)**i * binomial(n//2, i) *
_cos_pow_integrate(m + 2*i, x))
elif n == 0:
# /
# |
# | 1
# | _ _ _
# | m
# | cos (x)
# /
#
res = _cos_pow_integrate(m, x)
else:
# n < 0 , |m| > |n|
# /
# |
# | m n
# | cos (x) sin (x) dx =
# |
# |
#/
# /
# |
# 1 m-1 n+1 m - 1 | m-2 n+2
# _______ cos (x) sin (x) + _______ | cos (x) sin (x) dx
# |
# n + 1 n + 1 |
# /
res = (Rational(1, n + 1) * cos(x)**(m - 1)*sin(x)**(n + 1) +
Rational(m - 1, n + 1) *
trigintegrate(cos(x)**(m - 2)*sin(x)**(n + 2), x))
else:
if m == n:
##Substitute sin(2x)/2 for sin(x)cos(x) and then Integrate.
res = integrate((Rational(1, 2)*sin(2*x))**m, x)
elif (m == -n):
if n < 0:
# Same as the scheme described above.
# the function argument to integrate in the end will
# be 1 , this cannot be integrated by trigintegrate.
# Hence use sympy.integrals.integrate.
res = (Rational(1, n + 1) * cos(x)**(m - 1) * sin(x)**(n + 1) +
Rational(m - 1, n + 1) *
integrate(cos(x)**(m - 2) * sin(x)**(n + 2), x))
else:
res = (Rational(-1, m + 1) * cos(x)**(m + 1) * sin(x)**(n - 1) +
Rational(n - 1, m + 1) *
integrate(cos(x)**(m + 2)*sin(x)**(n - 2), x))
if conds == 'piecewise':
return Piecewise((zz, Eq(a, 0)), (res.subs(x, a*x) / a, True))
return res.subs(x, a*x) / a
def _sin_pow_integrate(n, x):
if n > 0:
if n == 1:
#Recursion break
return -cos(x)
# n > 0
# / /
# | |
# | n -1 n-1 n - 1 | n-2
# | sin (x) dx = ______ cos (x) sin (x) + _______ | sin (x) dx
# | |
# | n n |
#/ /
#
#
return (Rational(-1, n) * cos(x) * sin(x)**(n - 1) +
Rational(n - 1, n) * _sin_pow_integrate(n - 2, x))
if n < 0:
if n == -1:
##Make sure this does not come back here again.
##Recursion breaks here or at n==0.
return trigintegrate(1/sin(x), x)
# n < 0
# / /
# | |
# | n 1 n+1 n + 2 | n+2
# | sin (x) dx = _______ cos (x) sin (x) + _______ | sin (x) dx
# | |
# | n + 1 n + 1 |
#/ /
#
return (Rational(1, n + 1) * cos(x) * sin(x)**(n + 1) +
Rational(n + 2, n + 1) * _sin_pow_integrate(n + 2, x))
else:
#n == 0
#Recursion break.
return x
def _cos_pow_integrate(n, x):
if n > 0:
if n == 1:
#Recursion break.
return sin(x)
# n > 0
# / /
# | |
# | n 1 n-1 n - 1 | n-2
# | sin (x) dx = ______ sin (x) cos (x) + _______ | cos (x) dx
# | |
# | n n |
#/ /
#
return (Rational(1, n) * sin(x) * cos(x)**(n - 1) +
Rational(n - 1, n) * _cos_pow_integrate(n - 2, x))
if n < 0:
if n == -1:
##Recursion break
return trigintegrate(1/cos(x), x)
# n < 0
# / /
# | |
# | n -1 n+1 n + 2 | n+2
# | cos (x) dx = _______ sin (x) cos (x) + _______ | cos (x) dx
# | |
# | n + 1 n + 1 |
#/ /
#
return (Rational(-1, n + 1) * sin(x) * cos(x)**(n + 1) +
Rational(n + 2, n + 1) * _cos_pow_integrate(n + 2, x))
else:
# n == 0
#Recursion Break.
return x
| 10,631 | 32.539432 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/prde.py
|
"""
Algorithms for solving Parametric Risch Differential Equations.
The methods used for solving Parametric Risch Differential Equations parallel
those for solving Risch Differential Equations. See the outline in the
docstring of rde.py for more information.
The Parametric Risch Differential Equation problem is, given f, g1, ..., gm in
K(t), to determine if there exist y in K(t) and c1, ..., cm in Const(K) such
that Dy + f*y == Sum(ci*gi, (i, 1, m)), and to find such y and ci if they exist.
For the algorithms here G is a list of tuples of factions of the terms on the
right hand side of the equation (i.e., gi in k(t)), and Q is a list of terms on
the right hand side of the equation (i.e., qi in k[t]). See the docstring of
each function for more information.
"""
from __future__ import print_function, division
from sympy.core import Dummy, ilcm, Add, Mul, Pow, S, oo
from sympy.matrices import zeros, eye
from sympy.polys.polymatrix import PolyMatrix as Matrix
from sympy.solvers import solve
from sympy.polys import Poly, lcm, cancel, sqf_list
from sympy.integrals.risch import (gcdex_diophantine, frac_in, derivation,
NonElementaryIntegralException, residue_reduce, splitfactor,
residue_reduce_derivation, DecrementLevel, recognize_log_derivative)
from sympy.integrals.rde import (order_at, order_at_oo, weak_normalizer,
bound_degree, spde, solve_poly_rde)
from sympy.core.compatibility import reduce, range
from sympy.utilities.misc import debug
def prde_normal_denom(fa, fd, G, DE):
"""
Parametric Risch Differential Equation - Normal part of the denominator.
Given a derivation D on k[t] and f, g1, ..., gm in k(t) with f weakly
normalized with respect to t, return the tuple (a, b, G, h) such that
a, h in k[t], b in k<t>, G = [g1, ..., gm] in k(t)^m, and for any solution
c1, ..., cm in Const(k) and y in k(t) of Dy + f*y == Sum(ci*gi, (i, 1, m)),
q == y*h in k<t> satisfies a*Dq + b*q == Sum(ci*Gi, (i, 1, m)).
"""
dn, ds = splitfactor(fd, DE)
Gas, Gds = list(zip(*G))
gd = reduce(lambda i, j: i.lcm(j), Gds, Poly(1, DE.t))
en, es = splitfactor(gd, DE)
p = dn.gcd(en)
h = en.gcd(en.diff(DE.t)).quo(p.gcd(p.diff(DE.t)))
a = dn*h
c = a*h
ba = a*fa - dn*derivation(h, DE)*fd
ba, bd = ba.cancel(fd, include=True)
G = [(c*A).cancel(D, include=True) for A, D in G]
return (a, (ba, bd), G, h)
def real_imag(ba, bd, gen):
"""
Helper function, to get the real and imaginary part of a rational function
evaluated at sqrt(-1) without actually evaluating it at sqrt(-1)
Separates the even and odd power terms by checking the degree of terms wrt
mod 4. Returns a tuple (ba[0], ba[1], bd) where ba[0] is real part
of the numerator ba[1] is the imaginary part and bd is the denominator
of the rational function.
"""
bd = bd.as_poly(gen).as_dict()
ba = ba.as_poly(gen).as_dict()
denom_real = [value if key[0] % 4 == 0 else -value if key[0] % 4 == 2 else 0 for key, value in bd.items()]
denom_imag = [value if key[0] % 4 == 1 else -value if key[0] % 4 == 3 else 0 for key, value in bd.items()]
bd_real = sum(r for r in denom_real)
bd_imag = sum(r for r in denom_imag)
num_real = [value if key[0] % 4 == 0 else -value if key[0] % 4 == 2 else 0 for key, value in ba.items()]
num_imag = [value if key[0] % 4 == 1 else -value if key[0] % 4 == 3 else 0 for key, value in ba.items()]
ba_real = sum(r for r in num_real)
ba_imag = sum(r for r in num_imag)
ba = ((ba_real*bd_real + ba_imag*bd_imag).as_poly(gen), (ba_imag*bd_real - ba_real*bd_imag).as_poly(gen))
bd = (bd_real*bd_real + bd_imag*bd_imag).as_poly(gen)
return (ba[0], ba[1], bd)
def prde_special_denom(a, ba, bd, G, DE, case='auto'):
"""
Parametric Risch Differential Equation - Special part of the denominator.
case is one of {'exp', 'tan', 'primitive'} for the hyperexponential,
hypertangent, and primitive cases, respectively. For the hyperexponential
(resp. hypertangent) case, given a derivation D on k[t] and a in k[t],
b in k<t>, and g1, ..., gm in k(t) with Dt/t in k (resp. Dt/(t**2 + 1) in
k, sqrt(-1) not in k), a != 0, and gcd(a, t) == 1 (resp.
gcd(a, t**2 + 1) == 1), return the tuple (A, B, GG, h) such that A, B, h in
k[t], GG = [gg1, ..., ggm] in k(t)^m, and for any solution c1, ..., cm in
Const(k) and q in k<t> of a*Dq + b*q == Sum(ci*gi, (i, 1, m)), r == q*h in
k[t] satisfies A*Dr + B*r == Sum(ci*ggi, (i, 1, m)).
For case == 'primitive', k<t> == k[t], so it returns (a, b, G, 1) in this
case.
"""
# TODO: Merge this with the very similar special_denom() in rde.py
if case == 'auto':
case = DE.case
if case == 'exp':
p = Poly(DE.t, DE.t)
elif case == 'tan':
p = Poly(DE.t**2 + 1, DE.t)
elif case in ['primitive', 'base']:
B = ba.quo(bd)
return (a, B, G, Poly(1, DE.t))
else:
raise ValueError("case must be one of {'exp', 'tan', 'primitive', "
"'base'}, not %s." % case)
nb = order_at(ba, p, DE.t) - order_at(bd, p, DE.t)
nc = min([order_at(Ga, p, DE.t) - order_at(Gd, p, DE.t) for Ga, Gd in G])
n = min(0, nc - min(0, nb))
if not nb:
# Possible cancellation.
if case == 'exp':
dcoeff = DE.d.quo(Poly(DE.t, DE.t))
with DecrementLevel(DE): # We are guaranteed to not have problems,
# because case != 'base'.
alphaa, alphad = frac_in(-ba.eval(0)/bd.eval(0)/a.eval(0), DE.t)
etaa, etad = frac_in(dcoeff, DE.t)
A = parametric_log_deriv(alphaa, alphad, etaa, etad, DE)
if A is not None:
a, m, z = A
if a == 1:
n = min(n, m)
elif case == 'tan':
dcoeff = DE.d.quo(Poly(DE.t**2 + 1, DE.t))
with DecrementLevel(DE): # We are guaranteed to not have problems,
# because case != 'base'.
betaa, alphaa, alphad = real_imag(ba, bd*a, DE.t)
betad = alphad
etaa, etad = frac_in(dcoeff, DE.t)
if recognize_log_derivative(2*betaa, betad, DE):
A = parametric_log_deriv(alphaa, alphad, etaa, etad, DE)
B = parametric_log_deriv(betaa, betad, etaa, etad, DE)
if A is not None and B is not None:
a, s, z = A
# TODO: Add test
if a == 1:
n = min(n, s/2)
N = max(0, -nb)
pN = p**N
pn = p**-n # This is 1/h
A = a*pN
B = ba*pN.quo(bd) + Poly(n, DE.t)*a*derivation(p, DE).quo(p)*pN
G = [(Ga*pN*pn).cancel(Gd, include=True) for Ga, Gd in G]
h = pn
# (a*p**N, (b + n*a*Dp/p)*p**N, g1*p**(N - n), ..., gm*p**(N - n), p**-n)
return (A, B, G, h)
def prde_linear_constraints(a, b, G, DE):
"""
Parametric Risch Differential Equation - Generate linear constraints on the constants.
Given a derivation D on k[t], a, b, in k[t] with gcd(a, b) == 1, and
G = [g1, ..., gm] in k(t)^m, return Q = [q1, ..., qm] in k[t]^m and a
matrix M with entries in k(t) such that for any solution c1, ..., cm in
Const(k) and p in k[t] of a*Dp + b*p == Sum(ci*gi, (i, 1, m)),
(c1, ..., cm) is a solution of Mx == 0, and p and the ci satisfy
a*Dp + b*p == Sum(ci*qi, (i, 1, m)).
Because M has entries in k(t), and because Matrix doesn't play well with
Poly, M will be a Matrix of Basic expressions.
"""
m = len(G)
Gns, Gds = list(zip(*G))
d = reduce(lambda i, j: i.lcm(j), Gds)
d = Poly(d, field=True)
Q = [(ga*(d).quo(gd)).div(d) for ga, gd in G]
if not all([ri.is_zero for _, ri in Q]):
N = max([ri.degree(DE.t) for _, ri in Q])
M = Matrix(N + 1, m, lambda i, j: Q[j][1].nth(i))
else:
M = Matrix(0, m, []) # No constraints, return the empty matrix.
qs, _ = list(zip(*Q))
return (qs, M)
def poly_linear_constraints(p, d):
"""
Given p = [p1, ..., pm] in k[t]^m and d in k[t], return
q = [q1, ..., qm] in k[t]^m and a matrix M with entries in k such
that Sum(ci*pi, (i, 1, m)), for c1, ..., cm in k, is divisible
by d if and only if (c1, ..., cm) is a solution of Mx = 0, in
which case the quotient is Sum(ci*qi, (i, 1, m)).
"""
m = len(p)
q, r = zip(*[pi.div(d) for pi in p])
if not all([ri.is_zero for ri in r]):
n = max([ri.degree() for ri in r])
M = Matrix(n + 1, m, lambda i, j: r[j].nth(i))
else:
M = Matrix(0, m, []) # No constraints.
return q, M
def constant_system(A, u, DE):
"""
Generate a system for the constant solutions.
Given a differential field (K, D) with constant field C = Const(K), a Matrix
A, and a vector (Matrix) u with coefficients in K, returns the tuple
(B, v, s), where B is a Matrix with coefficients in C and v is a vector
(Matrix) such that either v has coefficients in C, in which case s is True
and the solutions in C of Ax == u are exactly all the solutions of Bx == v,
or v has a non-constant coefficient, in which case s is False Ax == u has no
constant solution.
This algorithm is used both in solving parametric problems and in
determining if an element a of K is a derivative of an element of K or the
logarithmic derivative of a K-radical using the structure theorem approach.
Because Poly does not play well with Matrix yet, this algorithm assumes that
all matrix entries are Basic expressions.
"""
if not A:
return A, u
Au = A.row_join(u)
Au = Au.rref(simplify=cancel, normalize_last=False)[0]
# Warning: This will NOT return correct results if cancel() cannot reduce
# an identically zero expression to 0. The danger is that we might
# incorrectly prove that an integral is nonelementary (such as
# risch_integrate(exp((sin(x)**2 + cos(x)**2 - 1)*x**2), x).
# But this is a limitation in computer algebra in general, and implicit
# in the correctness of the Risch Algorithm is the computability of the
# constant field (actually, this same correctness problem exists in any
# algorithm that uses rref()).
#
# We therefore limit ourselves to constant fields that are computable
# via the cancel() function, in order to prevent a speed bottleneck from
# calling some more complex simplification function (rational function
# coefficients will fall into this class). Furthermore, (I believe) this
# problem will only crop up if the integral explicitly contains an
# expression in the constant field that is identically zero, but cannot
# be reduced to such by cancel(). Therefore, a careful user can avoid this
# problem entirely by being careful with the sorts of expressions that
# appear in his integrand in the variables other than the integration
# variable (the structure theorems should be able to completely decide these
# problems in the integration variable).
Au = Au.applyfunc(cancel)
A, u = Au[:, :-1], Au[:, -1]
for j in range(A.cols):
for i in range(A.rows):
if A[i, j].has(*DE.T):
# This assumes that const(F(t0, ..., tn) == const(K) == F
Ri = A[i, :]
# Rm+1; m = A.rows
Rm1 = Ri.applyfunc(lambda x: derivation(x, DE, basic=True)/
derivation(A[i, j], DE, basic=True))
Rm1 = Rm1.applyfunc(cancel)
um1 = cancel(derivation(u[i], DE, basic=True)/
derivation(A[i, j], DE, basic=True))
for s in range(A.rows):
# A[s, :] = A[s, :] - A[s, i]*A[:, m+1]
Asj = A[s, j]
A.row_op(s, lambda r, jj: cancel(r - Asj*Rm1[jj]))
# u[s] = u[s] - A[s, j]*u[m+1
u.row_op(s, lambda r, jj: cancel(r - Asj*um1))
A = A.col_join(Rm1)
u = u.col_join(Matrix([um1]))
return (A, u)
def prde_spde(a, b, Q, n, DE):
"""
Special Polynomial Differential Equation algorithm: Parametric Version.
Given a derivation D on k[t], an integer n, and a, b, q1, ..., qm in k[t]
with deg(a) > 0 and gcd(a, b) == 1, return (A, B, Q, R, n1), with
Qq = [q1, ..., qm] and R = [r1, ..., rm], such that for any solution
c1, ..., cm in Const(k) and q in k[t] of degree at most n of
a*Dq + b*q == Sum(ci*gi, (i, 1, m)), p = (q - Sum(ci*ri, (i, 1, m)))/a has
degree at most n1 and satisfies A*Dp + B*p == Sum(ci*qi, (i, 1, m))
"""
R, Z = list(zip(*[gcdex_diophantine(b, a, qi) for qi in Q]))
A = a
B = b + derivation(a, DE)
Qq = [zi - derivation(ri, DE) for ri, zi in zip(R, Z)]
R = list(R)
n1 = n - a.degree(DE.t)
return (A, B, Qq, R, n1)
def prde_no_cancel_b_large(b, Q, n, DE):
"""
Parametric Poly Risch Differential Equation - No cancellation: deg(b) large enough.
Given a derivation D on k[t], n in ZZ, and b, q1, ..., qm in k[t] with
b != 0 and either D == d/dt or deg(b) > max(0, deg(D) - 1), returns
h1, ..., hr in k[t] and a matrix A with coefficients in Const(k) such that
if c1, ..., cm in Const(k) and q in k[t] satisfy deg(q) <= n and
Dq + b*q == Sum(ci*qi, (i, 1, m)), then q = Sum(dj*hj, (j, 1, r)), where
d1, ..., dr in Const(k) and A*Matrix([[c1, ..., cm, d1, ..., dr]]).T == 0.
"""
db = b.degree(DE.t)
m = len(Q)
H = [Poly(0, DE.t)]*m
for N in range(n, -1, -1): # [n, ..., 0]
for i in range(m):
si = Q[i].nth(N + db)/b.LC()
sitn = Poly(si*DE.t**N, DE.t)
H[i] = H[i] + sitn
Q[i] = Q[i] - derivation(sitn, DE) - b*sitn
if all(qi.is_zero for qi in Q):
dc = -1
M = zeros(0, 2)
else:
dc = max([qi.degree(DE.t) for qi in Q])
M = Matrix(dc + 1, m, lambda i, j: Q[j].nth(i))
A, u = constant_system(M, zeros(dc + 1, 1), DE)
c = eye(m)
A = A.row_join(zeros(A.rows, m)).col_join(c.row_join(-c))
return (H, A)
def prde_no_cancel_b_small(b, Q, n, DE):
"""
Parametric Poly Risch Differential Equation - No cancellation: deg(b) small enough.
Given a derivation D on k[t], n in ZZ, and b, q1, ..., qm in k[t] with
deg(b) < deg(D) - 1 and either D == d/dt or deg(D) >= 2, returns
h1, ..., hr in k[t] and a matrix A with coefficients in Const(k) such that
if c1, ..., cm in Const(k) and q in k[t] satisfy deg(q) <= n and
Dq + b*q == Sum(ci*qi, (i, 1, m)) then q = Sum(dj*hj, (j, 1, r)) where
d1, ..., dr in Const(k) and A*Matrix([[c1, ..., cm, d1, ..., dr]]).T == 0.
"""
m = len(Q)
H = [Poly(0, DE.t)]*m
for N in range(n, 0, -1): # [n, ..., 1]
for i in range(m):
si = Q[i].nth(N + DE.d.degree(DE.t) - 1)/(N*DE.d.LC())
sitn = Poly(si*DE.t**N, DE.t)
H[i] = H[i] + sitn
Q[i] = Q[i] - derivation(sitn, DE) - b*sitn
if b.degree(DE.t) > 0:
for i in range(m):
si = Poly(Q[i].nth(b.degree(DE.t))/b.LC(), DE.t)
H[i] = H[i] + si
Q[i] = Q[i] - derivation(si, DE) - b*si
if all(qi.is_zero for qi in Q):
dc = -1
M = Matrix()
else:
dc = max([qi.degree(DE.t) for qi in Q])
M = Matrix(dc + 1, m, lambda i, j: Q[j].nth(i))
A, u = constant_system(M, zeros(dc + 1, 1), DE)
c = eye(m)
A = A.row_join(zeros(A.rows, m)).col_join(c.row_join(-c))
return (H, A)
# else: b is in k, deg(qi) < deg(Dt)
t = DE.t
if DE.case != 'base':
with DecrementLevel(DE):
t0 = DE.t # k = k0(t0)
ba, bd = frac_in(b, t0, field=True)
Q0 = [frac_in(qi.TC(), t0, field=True) for qi in Q]
f, B = param_rischDE(ba, bd, Q0, DE)
# f = [f1, ..., fr] in k^r and B is a matrix with
# m + r columns and entries in Const(k) = Const(k0)
# such that Dy0 + b*y0 = Sum(ci*qi, (i, 1, m)) has
# a solution y0 in k with c1, ..., cm in Const(k)
# if and only y0 = Sum(dj*fj, (j, 1, r)) where
# d1, ..., dr ar in Const(k) and
# B*Matrix([c1, ..., cm, d1, ..., dr]) == 0.
# Transform fractions (fa, fd) in f into constant
# polynomials fa/fd in k[t].
# (Is there a better way?)
f = [Poly(fa.as_expr()/fd.as_expr(), t, field=True)
for fa, fd in f]
else:
# Base case. Dy == 0 for all y in k and b == 0.
# Dy + b*y = Sum(ci*qi) is solvable if and only if
# Sum(ci*qi) == 0 in which case the solutions are
# y = d1*f1 for f1 = 1 and any d1 in Const(k) = k.
f = [Poly(1, t, field=True)] # r = 1
B = Matrix([[qi.TC() for qi in Q] + [S(0)]])
# The condition for solvability is
# B*Matrix([c1, ..., cm, d1]) == 0
# There are no constraints on d1.
# Coefficients of t^j (j > 0) in Sum(ci*qi) must be zero.
d = max([qi.degree(DE.t) for qi in Q])
if d > 0:
M = Matrix(d, m, lambda i, j: Q[j].nth(i + 1))
A, _ = constant_system(M, zeros(d, 1), DE)
else:
# No constraints on the hj.
A = Matrix(0, m, [])
# Solutions of the original equation are
# y = Sum(dj*fj, (j, 1, r) + Sum(ei*hi, (i, 1, m)),
# where ei == ci (i = 1, ..., m), when
# A*Matrix([c1, ..., cm]) == 0 and
# B*Matrix([c1, ..., cm, d1, ..., dr]) == 0
# Build combined constraint matrix with m + r + m columns.
r = len(f)
I = eye(m)
A = A.row_join(zeros(A.rows, r + m))
B = B.row_join(zeros(B.rows, m))
C = I.row_join(zeros(m, r)).row_join(-I)
return f + H, A.col_join(B).col_join(C)
def prde_cancel_liouvillian(b, Q, n, DE):
"""
Pg, 237.
"""
H = []
# Why use DecrementLevel? Below line answers that:
# Assuming that we can solve such problems over 'k' (not k[t])
if DE.case == 'primitive':
with DecrementLevel(DE):
ba, bd = frac_in(b, DE.t, field=True)
for i in range(n, -1, -1):
if DE.case == 'exp': # this re-checking can be avoided
with DecrementLevel(DE):
ba, bd = frac_in(b + i*derivation(DE.t, DE)/DE.t,
DE.t, field=True)
with DecrementLevel(DE):
Qy = [frac_in(q.nth(i), DE.t, field=True) for q in Q]
fi, Ai = param_rischDE(ba, bd, Qy, DE)
fi = [Poly(fa.as_expr()/fd.as_expr(), DE.t, field=True)
for fa, fd in fi]
ri = len(fi)
if i == n:
M = Ai
else:
M = Ai.col_join(M.row_join(zeros(M.rows, ri)))
Fi, hi = [None]*ri, [None]*ri
# from eq. on top of p.238 (unnumbered)
for j in range(ri):
hji = fi[j]*DE.t**i
hi[j] = hji
# building up Sum(djn*(D(fjn*t^n) - b*fjnt^n))
Fi[j] = -(derivation(hji, DE) - b*hji)
H += hi
# in the next loop instead of Q it has
# to be Q + Fi taking its place
Q = Q + Fi
return (H, M)
def param_poly_rischDE(a, b, q, n, DE):
"""Polynomial solutions of a parametric Risch differential equation.
Given a derivation D in k[t], a, b in k[t] relatively prime, and q
= [q1, ..., qm] in k[t]^m, return h = [h1, ..., hr] in k[t]^r and
a matrix A with m + r columns and entries in Const(k) such that
a*Dp + b*p = Sum(ci*qi, (i, 1, m)) has a solution p of degree <= n
in k[t] with c1, ..., cm in Const(k) if and only if p = Sum(dj*hj,
(j, 1, r)) where d1, ..., dr are in Const(k) and (c1, ..., cm,
d1, ..., dr) is a solution of Ax == 0.
"""
m = len(q)
if n < 0:
# Only the trivial zero solution is possible.
# Find relations between the qi.
if all([qi.is_zero for qi in q]):
return [], zeros(1, m) # No constraints.
N = max([qi.degree(DE.t) for qi in q])
M = Matrix(N + 1, m, lambda i, j: q[j].nth(i))
A, _ = constant_system(M, zeros(M.rows, 1), DE)
return [], A
if a.is_ground:
# Normalization: a = 1.
a = a.LC()
b, q = b.quo_ground(a), [qi.quo_ground(a) for qi in q]
if not b.is_zero and (DE.case == 'base' or
b.degree() > max(0, DE.d.degree() - 1)):
return prde_no_cancel_b_large(b, q, n, DE)
elif ((b.is_zero or b.degree() < DE.d.degree() - 1)
and (DE.case == 'base' or DE.d.degree() >= 2)):
return prde_no_cancel_b_small(b, q, n, DE)
elif (DE.d.degree() >= 2 and
b.degree() == DE.d.degree() - 1 and
n > -b.as_poly().LC()/DE.d.as_poly().LC()):
raise NotImplementedError("prde_no_cancel_b_equal() is "
"not yet implemented.")
else:
# Liouvillian cases
if DE.case == 'primitive' or DE.case == 'exp':
return prde_cancel_liouvillian(b, q, n, DE)
else:
raise NotImplementedError("non-linear and hypertangent "
"cases have not yet been implemented")
# else: deg(a) > 0
# Iterate SPDE as long as possible cumulating coefficient
# and terms for the recovery of original solutions.
alpha, beta = 1, [0]*m
while n >= 0: # and a, b relatively prime
a, b, q, r, n = prde_spde(a, b, q, n, DE)
beta = [betai + alpha*ri for betai, ri in zip(beta, r)]
alpha *= a
# Solutions p of a*Dp + b*p = Sum(ci*qi) correspond to
# solutions alpha*p + Sum(ci*betai) of the initial equation.
d = a.gcd(b)
if not d.is_ground:
break
# a*Dp + b*p = Sum(ci*qi) may have a polynomial solution
# only if the sum is divisible by d.
qq, M = poly_linear_constraints(q, d)
# qq = [qq1, ..., qqm] where qqi = qi.quo(d).
# M is a matrix with m columns an entries in k.
# Sum(fi*qi, (i, 1, m)), where f1, ..., fm are elements of k, is
# divisible by d if and only if M*Matrix([f1, ..., fm]) == 0,
# in which case the quotient is Sum(fi*qqi).
A, _ = constant_system(M, zeros(M.rows, 1), DE)
# A is a matrix with m columns and entries in Const(k).
# Sum(ci*qqi) is Sum(ci*qi).quo(d), and the remainder is zero
# for c1, ..., cm in Const(k) if and only if
# A*Matrix([c1, ...,cm]) == 0.
V = A.nullspace()
# V = [v1, ..., vu] where each vj is a column matrix with
# entries aj1, ..., ajm in Const(k).
# Sum(aji*qi) is divisible by d with exact quotient Sum(aji*qqi).
# Sum(ci*qi) is divisible by d if and only if ci = Sum(dj*aji)
# (i = 1, ..., m) for some d1, ..., du in Const(k).
# In that case, solutions of
# a*Dp + b*p = Sum(ci*qi) = Sum(dj*Sum(aji*qi))
# are the same as those of
# (a/d)*Dp + (b/d)*p = Sum(dj*rj)
# where rj = Sum(aji*qqi).
if not V: # No non-trivial solution.
return [], eye(m) # Could return A, but this has
# the minimum number of rows.
Mqq = Matrix([qq]) # A single row.
r = [(Mqq*vj)[0] for vj in V] # [r1, ..., ru]
# Solutions of (a/d)*Dp + (b/d)*p = Sum(dj*rj) correspond to
# solutions alpha*p + Sum(Sum(dj*aji)*betai) of the initial
# equation. These are equal to alpha*p + Sum(dj*fj) where
# fj = Sum(aji*betai).
Mbeta = Matrix([beta])
f = [(Mbeta*vj)[0] for vj in V] # [f1, ..., fu]
#
# Solve the reduced equation recursively.
#
g, B = param_poly_rischDE(a.quo(d), b.quo(d), r, n, DE)
# g = [g1, ..., gv] in k[t]^v and and B is a matrix with u + v
# columns and entries in Const(k) such that
# (a/d)*Dp + (b/d)*p = Sum(dj*rj) has a solution p of degree <= n
# in k[t] if and only if p = Sum(ek*gk) where e1, ..., ev are in
# Const(k) and B*Matrix([d1, ..., du, e1, ..., ev]) == 0.
# The solutions of the original equation are then
# Sum(dj*fj, (j, 1, u)) + alpha*Sum(ek*gk, (k, 1, v)).
# Collect solution components.
h = f + [alpha*gk for gk in g]
# Build combined relation matrix.
A = -eye(m)
for vj in V:
A = A.row_join(vj)
A = A.row_join(zeros(m, len(g)))
A = A.col_join(zeros(B.rows, m).row_join(B))
return h, A
def param_rischDE(fa, fd, G, DE):
"""
Solve a Parametric Risch Differential Equation: Dy + f*y == Sum(ci*Gi, (i, 1, m)).
Given a derivation D in k(t), f in k(t), and G
= [G1, ..., Gm] in k(t)^m, return h = [h1, ..., hr] in k(t)^r and
a matrix A with m + r columns and entries in Const(k) such that
Dy + f*y = Sum(ci*Gi, (i, 1, m)) has a solution y
in k(t) with c1, ..., cm in Const(k) if and only if y = Sum(dj*hj,
(j, 1, r)) where d1, ..., dr are in Const(k) and (c1, ..., cm,
d1, ..., dr) is a solution of Ax == 0.
Elements of k(t) are tuples (a, d) with a and d in k[t].
"""
m = len(G)
q, (fa, fd) = weak_normalizer(fa, fd, DE)
# Solutions of the weakly normalized equation Dz + f*z = q*Sum(ci*Gi)
# correspond to solutions y = z/q of the original equation.
gamma = q
G = [(q*ga).cancel(gd, include=True) for ga, gd in G]
a, (ba, bd), G, hn = prde_normal_denom(fa, fd, G, DE)
# Solutions q in k<t> of a*Dq + b*q = Sum(ci*Gi) correspond
# to solutions z = q/hn of the weakly normalized equation.
gamma *= hn
A, B, G, hs = prde_special_denom(a, ba, bd, G, DE)
# Solutions p in k[t] of A*Dp + B*p = Sum(ci*Gi) correspond
# to solutions q = p/hs of the previous equation.
gamma *= hs
g = A.gcd(B)
a, b, g = A.quo(g), B.quo(g), [gia.cancel(gid*g, include=True) for
gia, gid in G]
# a*Dp + b*p = Sum(ci*gi) may have a polynomial solution
# only if the sum is in k[t].
q, M = prde_linear_constraints(a, b, g, DE)
# q = [q1, ..., qm] where qi in k[t] is the polynomial component
# of the partial fraction expansion of gi.
# M is a matrix with m columns and entries in k.
# Sum(fi*gi, (i, 1, m)), where f1, ..., fm are elements of k,
# is a polynomial if and only if M*Matrix([f1, ..., fm]) == 0,
# in which case the sum is equal to Sum(fi*qi).
M, _ = constant_system(M, zeros(M.rows, 1), DE)
# M is a matrix with m columns and entries in Const(k).
# Sum(ci*gi) is in k[t] for c1, ..., cm in Const(k)
# if and only if M*Matrix([c1, ..., cm]) == 0,
# in which case the sum is Sum(ci*qi).
## Reduce number of constants at this point
V = M.nullspace()
# V = [v1, ..., vu] where each vj is a column matrix with
# entries aj1, ..., ajm in Const(k).
# Sum(aji*gi) is in k[t] and equal to Sum(aji*qi) (j = 1, ..., u).
# Sum(ci*gi) is in k[t] if and only is ci = Sum(dj*aji)
# (i = 1, ..., m) for some d1, ..., du in Const(k).
# In that case,
# Sum(ci*gi) = Sum(ci*qi) = Sum(dj*Sum(aji*qi)) = Sum(dj*rj)
# where rj = Sum(aji*qi) (j = 1, ..., u) in k[t].
if not V: # No non-trivial solution
return [], eye(m)
Mq = Matrix([q]) # A single row.
r = [(Mq*vj)[0] for vj in V] # [r1, ..., ru]
# Solutions of a*Dp + b*p = Sum(dj*rj) correspond to solutions
# y = p/gamma of the initial equation with ci = Sum(dj*aji).
try:
# We try n=5. At least for prde_spde, it will always
# terminate no matter what n is.
n = bound_degree(a, b, r, DE, parametric=True)
except NotImplementedError:
# A temporary bound is set. Eventually, it will be removed.
# the currently added test case takes large time
# even with n=5, and much longer with large n's.
n = 5
h, B = param_poly_rischDE(a, b, r, n, DE)
# h = [h1, ..., hv] in k[t]^v and and B is a matrix with u + v
# columns and entries in Const(k) such that
# a*Dp + b*p = Sum(dj*rj) has a solution p of degree <= n
# in k[t] if and only if p = Sum(ek*hk) where e1, ..., ev are in
# Const(k) and B*Matrix([d1, ..., du, e1, ..., ev]) == 0.
# The solutions of the original equation for ci = Sum(dj*aji)
# (i = 1, ..., m) are then y = Sum(ek*hk, (k, 1, v))/gamma.
## Build combined relation matrix with m + u + v columns.
A = -eye(m)
for vj in V:
A = A.row_join(vj)
A = A.row_join(zeros(m, len(h)))
A = A.col_join(zeros(B.rows, m).row_join(B))
## Eliminate d1, ..., du.
W = A.nullspace()
# W = [w1, ..., wt] where each wl is a column matrix with
# entries blk (k = 1, ..., m + u + v) in Const(k).
# The vectors (bl1, ..., blm) generate the space of those
# constant families (c1, ..., cm) for which a solution of
# the equation Dy + f*y == Sum(ci*Gi) exists. They generate
# the space and form a basis except possibly when Dy + f*y == 0
# is solvable in k(t}. The corresponding solutions are
# y = Sum(blk'*hk, (k, 1, v))/gamma, where k' = k + m + u.
v = len(h)
M = Matrix([wl[:m] + wl[-v:] for wl in W]) # excise dj's.
N = M.nullspace()
# N = [n1, ..., ns] where the ni in Const(k)^(m + v) are column
# vectors generating the space of linear relations between
# c1, ..., cm, e1, ..., ev.
C = Matrix([ni[:] for ni in N]) # rows n1, ..., ns.
return [hk.cancel(gamma, include=True) for hk in h], C
def limited_integrate_reduce(fa, fd, G, DE):
"""
Simpler version of step 1 & 2 for the limited integration problem.
Given a derivation D on k(t) and f, g1, ..., gn in k(t), return
(a, b, h, N, g, V) such that a, b, h in k[t], N is a non-negative integer,
g in k(t), V == [v1, ..., vm] in k(t)^m, and for any solution v in k(t),
c1, ..., cm in C of f == Dv + Sum(ci*wi, (i, 1, m)), p = v*h is in k<t>, and
p and the ci satisfy a*Dp + b*p == g + Sum(ci*vi, (i, 1, m)). Furthermore,
if S1irr == Sirr, then p is in k[t], and if t is nonlinear or Liouvillian
over k, then deg(p) <= N.
So that the special part is always computed, this function calls the more
general prde_special_denom() automatically if it cannot determine that
S1irr == Sirr. Furthermore, it will automatically call bound_degree() when
t is linear and non-Liouvillian, which for the transcendental case, implies
that Dt == a*t + b with for some a, b in k*.
"""
dn, ds = splitfactor(fd, DE)
E = [splitfactor(gd, DE) for _, gd in G]
En, Es = list(zip(*E))
c = reduce(lambda i, j: i.lcm(j), (dn,) + En) # lcm(dn, en1, ..., enm)
hn = c.gcd(c.diff(DE.t))
a = hn
b = -derivation(hn, DE)
N = 0
# These are the cases where we know that S1irr = Sirr, but there could be
# others, and this algorithm will need to be extended to handle them.
if DE.case in ['base', 'primitive', 'exp', 'tan']:
hs = reduce(lambda i, j: i.lcm(j), (ds,) + Es) # lcm(ds, es1, ..., esm)
a = hn*hs
b = -derivation(hn, DE) - (hn*derivation(hs, DE)).quo(hs)
mu = min(order_at_oo(fa, fd, DE.t), min([order_at_oo(ga, gd, DE.t) for
ga, gd in G]))
# So far, all the above are also nonlinear or Liouvillian, but if this
# changes, then this will need to be updated to call bound_degree()
# as per the docstring of this function (DE.case == 'other_linear').
N = hn.degree(DE.t) + hs.degree(DE.t) + max(0, 1 - DE.d.degree(DE.t) - mu)
else:
# TODO: implement this
raise NotImplementedError
V = [(-a*hn*ga).cancel(gd, include=True) for ga, gd in G]
return (a, b, a, N, (a*hn*fa).cancel(fd, include=True), V)
def limited_integrate(fa, fd, G, DE):
"""
Solves the limited integration problem: f = Dv + Sum(ci*wi, (i, 1, n))
"""
fa, fd = fa*Poly(1/fd.LC(), DE.t), fd.monic()
# interpretting limited integration problem as a
# parametric Risch DE problem
Fa = Poly(0, DE.t)
Fd = Poly(1, DE.t)
G = [(fa, fd)] + G
h, A = param_rischDE(Fa, Fd, G, DE)
V = A.nullspace()
V = [v for v in V if v[0] != 0]
if not V:
return None
else:
# we can take any vector from V, we take V[0]
c0 = V[0][0]
# v = [-1, c1, ..., cm, d1, ..., dr]
v = V[0]/(-c0)
r = len(h)
m = len(v) - r - 1
C = list(v[1: m + 1])
y = -sum([v[m + 1 + i]*h[i][0].as_expr()/h[i][1].as_expr() \
for i in range(r)])
y_num, y_den = y.as_numer_denom()
Ya, Yd = Poly(y_num, DE.t), Poly(y_den, DE.t)
Y = Ya*Poly(1/Yd.LC(), DE.t), Yd.monic()
return Y, C
def parametric_log_deriv_heu(fa, fd, wa, wd, DE, c1=None):
"""
Parametric logarithmic derivative heuristic.
Given a derivation D on k[t], f in k(t), and a hyperexponential monomial
theta over k(t), raises either NotImplementedError, in which case the
heuristic failed, or returns None, in which case it has proven that no
solution exists, or returns a solution (n, m, v) of the equation
n*f == Dv/v + m*Dtheta/theta, with v in k(t)* and n, m in ZZ with n != 0.
If this heuristic fails, the structure theorem approach will need to be
used.
The argument w == Dtheta/theta
"""
# TODO: finish writing this and write tests
c1 = c1 or Dummy('c1')
p, a = fa.div(fd)
q, b = wa.div(wd)
B = max(0, derivation(DE.t, DE).degree(DE.t) - 1)
C = max(p.degree(DE.t), q.degree(DE.t))
if q.degree(DE.t) > B:
eqs = [p.nth(i) - c1*q.nth(i) for i in range(B + 1, C + 1)]
s = solve(eqs, c1)
if not s or not s[c1].is_Rational:
# deg(q) > B, no solution for c.
return None
N, M = s[c1].as_numer_denom() # N and M are integers
N, M = Poly(N, DE.t), Poly(M, DE.t)
nfmwa = N*fa*wd - M*wa*fd
nfmwd = fd*wd
Qv = is_log_deriv_k_t_radical_in_field(N*fa*wd - M*wa*fd, fd*wd, DE,
'auto')
if Qv is None:
# (N*f - M*w) is not the logarithmic derivative of a k(t)-radical.
return None
Q, e, v = Qv
if e != 1:
return None
if Q.is_zero or v.is_zero:
return None
return (Q*N, Q*M, v)
if p.degree(DE.t) > B:
return None
c = lcm(fd.as_poly(DE.t).LC(), wd.as_poly(DE.t).LC())
l = fd.monic().lcm(wd.monic())*Poly(c, DE.t)
ln, ls = splitfactor(l, DE)
z = ls*ln.gcd(ln.diff(DE.t))
if not z.has(DE.t):
# TODO: We treat this as 'no solution', until the structure
# theorem version of parametric_log_deriv is implemented.
return None
u1, r1 = (fa*l.quo(fd)).div(z) # (l*f).div(z)
u2, r2 = (wa*l.quo(wd)).div(z) # (l*w).div(z)
eqs = [r1.nth(i) - c1*r2.nth(i) for i in range(z.degree(DE.t))]
s = solve(eqs, c1)
if not s or not s[c1].is_Rational:
# deg(q) <= B, no solution for c.
return None
M, N = s[c1].as_numer_denom()
nfmwa = N.as_poly(DE.t)*fa*wd - M.as_poly(DE.t)*wa*fd
nfmwd = fd*wd
Qv = is_log_deriv_k_t_radical_in_field(nfmwa, nfmwd, DE)
if Qv is None:
# (N*f - M*w) is not the logarithmic derivative of a k(t)-radical.
return None
Q, v = Qv
if Q.is_zero or v.is_zero:
return None
return (Q*N, Q*M, v)
def parametric_log_deriv(fa, fd, wa, wd, DE):
# TODO: Write the full algorithm using the structure theorems.
# try:
A = parametric_log_deriv_heu(fa, fd, wa, wd, DE)
# except NotImplementedError:
# Heuristic failed, we have to use the full method.
# TODO: This could be implemented more efficiently.
# It isn't too worrisome, because the heuristic handles most difficult
# cases.
return A
def is_deriv_k(fa, fd, DE):
"""
Checks if Df/f is the derivative of an element of k(t).
a in k(t) is the derivative of an element of k(t) if there exists b in k(t)
such that a = Db. Either returns (ans, u), such that Df/f == Du, or None,
which means that Df/f is not the derivative of an element of k(t). ans is
a list of tuples such that Add(*[i*j for i, j in ans]) == u. This is useful
for seeing exactly which elements of k(t) produce u.
This function uses the structure theorem approach, which says that for any
f in K, Df/f is the derivative of a element of K if and only if there are ri
in QQ such that::
--- --- Dt
\ r * Dt + \ r * i Df
/ i i / i --- = --.
--- --- t f
i in L i in E i
K/C(x) K/C(x)
Where C = Const(K), L_K/C(x) = { i in {1, ..., n} such that t_i is
transcendental over C(x)(t_1, ..., t_i-1) and Dt_i = Da_i/a_i, for some a_i
in C(x)(t_1, ..., t_i-1)* } (i.e., the set of all indices of logarithmic
monomials of K over C(x)), and E_K/C(x) = { i in {1, ..., n} such that t_i
is transcendental over C(x)(t_1, ..., t_i-1) and Dt_i/t_i = Da_i, for some
a_i in C(x)(t_1, ..., t_i-1) } (i.e., the set of all indices of
hyperexponential monomials of K over C(x)). If K is an elementary extension
over C(x), then the cardinality of L_K/C(x) U E_K/C(x) is exactly the
transcendence degree of K over C(x). Furthermore, because Const_D(K) ==
Const_D(C(x)) == C, deg(Dt_i) == 1 when t_i is in E_K/C(x) and
deg(Dt_i) == 0 when t_i is in L_K/C(x), implying in particular that E_K/C(x)
and L_K/C(x) are disjoint.
The sets L_K/C(x) and E_K/C(x) must, by their nature, be computed
recursively using this same function. Therefore, it is required to pass
them as indices to D (or T). E_args are the arguments of the
hyperexponentials indexed by E_K (i.e., if i is in E_K, then T[i] ==
exp(E_args[i])). This is needed to compute the final answer u such that
Df/f == Du.
log(f) will be the same as u up to a additive constant. This is because
they will both behave the same as monomials. For example, both log(x) and
log(2*x) == log(x) + log(2) satisfy Dt == 1/x, because log(2) is constant.
Therefore, the term const is returned. const is such that
log(const) + f == u. This is calculated by dividing the arguments of one
logarithm from the other. Therefore, it is necessary to pass the arguments
of the logarithmic terms in L_args.
To handle the case where we are given Df/f, not f, use is_deriv_k_in_field().
"""
# Compute Df/f
dfa, dfd = fd*(fd*derivation(fa, DE) - fa*derivation(fd, DE)), fd**2*fa
dfa, dfd = dfa.cancel(dfd, include=True)
# Our assumption here is that each monomial is recursively transcendental
if len(DE.exts) != len(DE.D):
if [i for i in DE.cases if i == 'tan'] or \
(set([i for i in DE.cases if i == 'primitive']) -
set(DE.indices('log'))):
raise NotImplementedError("Real version of the structure "
"theorems with hypertangent support is not yet implemented.")
# TODO: What should really be done in this case?
raise NotImplementedError("Nonelementary extensions not supported "
"in the structure theorems.")
E_part = [DE.D[i].quo(Poly(DE.T[i], DE.T[i])).as_expr() for i in DE.indices('exp')]
L_part = [DE.D[i].as_expr() for i in DE.indices('log')]
lhs = Matrix([E_part + L_part])
rhs = Matrix([dfa.as_expr()/dfd.as_expr()])
A, u = constant_system(lhs, rhs, DE)
if not all(derivation(i, DE, basic=True).is_zero for i in u) or not A:
# If the elements of u are not all constant
# Note: See comment in constant_system
# Also note: derivation(basic=True) calls cancel()
return None
else:
if not all(i.is_Rational for i in u):
raise NotImplementedError("Cannot work with non-rational "
"coefficients in this case.")
else:
terms = ([DE.extargs[i] for i in DE.indices('exp')] +
[DE.T[i] for i in DE.indices('log')])
ans = list(zip(terms, u))
result = Add(*[Mul(i, j) for i, j in ans])
argterms = ([DE.T[i] for i in DE.indices('exp')] +
[DE.extargs[i] for i in DE.indices('log')])
l = []
ld = []
for i, j in zip(argterms, u):
# We need to get around things like sqrt(x**2) != x
# and also sqrt(x**2 + 2*x + 1) != x + 1
# Issue 10798: i need not be a polynomial
i, d = i.as_numer_denom()
icoeff, iterms = sqf_list(i)
l.append(Mul(*([Pow(icoeff, j)] + [Pow(b, e*j) for b, e in iterms])))
dcoeff, dterms = sqf_list(d)
ld.append(Mul(*([Pow(dcoeff, j)] + [Pow(b, e*j) for b, e in dterms])))
const = cancel(fa.as_expr()/fd.as_expr()/Mul(*l)*Mul(*ld))
return (ans, result, const)
def is_log_deriv_k_t_radical(fa, fd, DE, Df=True):
"""
Checks if Df is the logarithmic derivative of a k(t)-radical.
b in k(t) can be written as the logarithmic derivative of a k(t) radical if
there exist n in ZZ and u in k(t) with n, u != 0 such that n*b == Du/u.
Either returns (ans, u, n, const) or None, which means that Df cannot be
written as the logarithmic derivative of a k(t)-radical. ans is a list of
tuples such that Mul(*[i**j for i, j in ans]) == u. This is useful for
seeing exactly what elements of k(t) produce u.
This function uses the structure theorem approach, which says that for any
f in K, Df is the logarithmic derivative of a K-radical if and only if there
are ri in QQ such that::
--- --- Dt
\ r * Dt + \ r * i
/ i i / i --- = Df.
--- --- t
i in L i in E i
K/C(x) K/C(x)
Where C = Const(K), L_K/C(x) = { i in {1, ..., n} such that t_i is
transcendental over C(x)(t_1, ..., t_i-1) and Dt_i = Da_i/a_i, for some a_i
in C(x)(t_1, ..., t_i-1)* } (i.e., the set of all indices of logarithmic
monomials of K over C(x)), and E_K/C(x) = { i in {1, ..., n} such that t_i
is transcendental over C(x)(t_1, ..., t_i-1) and Dt_i/t_i = Da_i, for some
a_i in C(x)(t_1, ..., t_i-1) } (i.e., the set of all indices of
hyperexponential monomials of K over C(x)). If K is an elementary extension
over C(x), then the cardinality of L_K/C(x) U E_K/C(x) is exactly the
transcendence degree of K over C(x). Furthermore, because Const_D(K) ==
Const_D(C(x)) == C, deg(Dt_i) == 1 when t_i is in E_K/C(x) and
deg(Dt_i) == 0 when t_i is in L_K/C(x), implying in particular that E_K/C(x)
and L_K/C(x) are disjoint.
The sets L_K/C(x) and E_K/C(x) must, by their nature, be computed
recursively using this same function. Therefore, it is required to pass
them as indices to D (or T). L_args are the arguments of the logarithms
indexed by L_K (i.e., if i is in L_K, then T[i] == log(L_args[i])). This is
needed to compute the final answer u such that n*f == Du/u.
exp(f) will be the same as u up to a multiplicative constant. This is
because they will both behave the same as monomials. For example, both
exp(x) and exp(x + 1) == E*exp(x) satisfy Dt == t. Therefore, the term const
is returned. const is such that exp(const)*f == u. This is calculated by
subtracting the arguments of one exponential from the other. Therefore, it
is necessary to pass the arguments of the exponential terms in E_args.
To handle the case where we are given Df, not f, use
is_log_deriv_k_t_radical_in_field().
"""
H = []
if Df:
dfa, dfd = (fd*derivation(fa, DE) - fa*derivation(fd, DE)).cancel(fd**2,
include=True)
else:
dfa, dfd = fa, fd
# Our assumption here is that each monomial is recursively transcendental
if len(DE.exts) != len(DE.D):
if [i for i in DE.cases if i == 'tan'] or \
(set([i for i in DE.cases if i == 'primitive']) -
set(DE.indices('log'))):
raise NotImplementedError("Real version of the structure "
"theorems with hypertangent support is not yet implemented.")
# TODO: What should really be done in this case?
raise NotImplementedError("Nonelementary extensions not supported "
"in the structure theorems.")
E_part = [DE.D[i].quo(Poly(DE.T[i], DE.T[i])).as_expr() for i in DE.indices('exp')]
L_part = [DE.D[i].as_expr() for i in DE.indices('log')]
lhs = Matrix([E_part + L_part])
rhs = Matrix([dfa.as_expr()/dfd.as_expr()])
A, u = constant_system(lhs, rhs, DE)
if not all(derivation(i, DE, basic=True).is_zero for i in u) or not A:
# If the elements of u are not all constant
# Note: See comment in constant_system
# Also note: derivation(basic=True) calls cancel()
return None
else:
if not all(i.is_Rational for i in u):
# TODO: But maybe we can tell if they're not rational, like
# log(2)/log(3). Also, there should be an option to continue
# anyway, even if the result might potentially be wrong.
raise NotImplementedError("Cannot work with non-rational "
"coefficients in this case.")
else:
n = reduce(ilcm, [i.as_numer_denom()[1] for i in u])
u *= n
terms = ([DE.T[i] for i in DE.indices('exp')] +
[DE.extargs[i] for i in DE.indices('log')])
ans = list(zip(terms, u))
result = Mul(*[Pow(i, j) for i, j in ans])
# exp(f) will be the same as result up to a multiplicative
# constant. We now find the log of that constant.
argterms = ([DE.extargs[i] for i in DE.indices('exp')] +
[DE.T[i] for i in DE.indices('log')])
const = cancel(fa.as_expr()/fd.as_expr() -
Add(*[Mul(i, j/n) for i, j in zip(argterms, u)]))
return (ans, result, n, const)
def is_log_deriv_k_t_radical_in_field(fa, fd, DE, case='auto', z=None):
"""
Checks if f can be written as the logarithmic derivative of a k(t)-radical.
f in k(t) can be written as the logarithmic derivative of a k(t) radical if
there exist n in ZZ and u in k(t) with n, u != 0 such that n*f == Du/u.
Either returns (n, u) or None, which means that f cannot be written as the
logarithmic derivative of a k(t)-radical.
case is one of {'primitive', 'exp', 'tan', 'auto'} for the primitive,
hyperexponential, and hypertangent cases, respectively. If case is 'auto',
it will attempt to determine the type of the derivation automatically.
"""
fa, fd = fa.cancel(fd, include=True)
# f must be simple
n, s = splitfactor(fd, DE)
if not s.is_one:
pass
z = z or Dummy('z')
H, b = residue_reduce(fa, fd, DE, z=z)
if not b:
# I will have to verify, but I believe that the answer should be
# None in this case. This should never happen for the
# functions given when solving the parametric logarithmic
# derivative problem when integration elementary functions (see
# Bronstein's book, page 255), so most likely this indicates a bug.
return None
roots = [(i, i.real_roots()) for i, _ in H]
if not all(len(j) == i.degree() and all(k.is_Rational for k in j) for
i, j in roots):
# If f is the logarithmic derivative of a k(t)-radical, then all the
# roots of the resultant must be rational numbers.
return None
# [(a, i), ...], where i*log(a) is a term in the log-part of the integral
# of f
respolys, residues = list(zip(*roots)) or [[], []]
# Note: this might be empty, but everything below should work find in that
# case (it should be the same as if it were [[1, 1]])
residueterms = [(H[j][1].subs(z, i), i) for j in range(len(H)) for
i in residues[j]]
# TODO: finish writing this and write tests
p = cancel(fa.as_expr()/fd.as_expr() - residue_reduce_derivation(H, DE, z))
p = p.as_poly(DE.t)
if p is None:
# f - Dg will be in k[t] if f is the logarithmic derivative of a k(t)-radical
return None
if p.degree(DE.t) >= max(1, DE.d.degree(DE.t)):
return None
if case == 'auto':
case = DE.case
if case == 'exp':
wa, wd = derivation(DE.t, DE).cancel(Poly(DE.t, DE.t), include=True)
with DecrementLevel(DE):
pa, pd = frac_in(p, DE.t, cancel=True)
wa, wd = frac_in((wa, wd), DE.t)
A = parametric_log_deriv(pa, pd, wa, wd, DE)
if A is None:
return None
n, e, u = A
u *= DE.t**e
elif case == 'primitive':
with DecrementLevel(DE):
pa, pd = frac_in(p, DE.t)
A = is_log_deriv_k_t_radical_in_field(pa, pd, DE, case='auto')
if A is None:
return None
n, u = A
elif case == 'base':
# TODO: we can use more efficient residue reduction from ratint()
if not fd.is_sqf or fa.degree() >= fd.degree():
# f is the logarithmic derivative in the base case if and only if
# f = fa/fd, fd is square-free, deg(fa) < deg(fd), and
# gcd(fa, fd) == 1. The last condition is handled by cancel() above.
return None
# Note: if residueterms = [], returns (1, 1)
# f had better be 0 in that case.
n = reduce(ilcm, [i.as_numer_denom()[1] for _, i in residueterms], S(1))
u = Mul(*[Pow(i, j*n) for i, j in residueterms])
return (n, u)
elif case == 'tan':
raise NotImplementedError("The hypertangent case is "
"not yet implemented for is_log_deriv_k_t_radical_in_field()")
elif case in ['other_linear', 'other_nonlinear']:
# XXX: If these are supported by the structure theorems, change to NotImplementedError.
raise ValueError("The %s case is not supported in this function." % case)
else:
raise ValueError("case must be one of {'primitive', 'exp', 'tan', "
"'base', 'auto'}, not %s" % case)
common_denom = reduce(ilcm, [i.as_numer_denom()[1] for i in [j for _, j in
residueterms]] + [n], S(1))
residueterms = [(i, j*common_denom) for i, j in residueterms]
m = common_denom//n
if common_denom != n*m: # Verify exact division
raise ValueError("Inexact division")
u = cancel(u**m*Mul(*[Pow(i, j) for i, j in residueterms]))
return (common_denom, u)
| 50,896 | 39.298496 | 110 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/risch.py
|
"""
The Risch Algorithm for transcendental function integration.
The core algorithms for the Risch algorithm are here. The subproblem
algorithms are in the rde.py and prde.py files for the Risch
Differential Equation solver and the parametric problems solvers,
respectively. All important information concerning the differential extension
for an integrand is stored in a DifferentialExtension object, which in the code
is usually called DE. Throughout the code and Inside the DifferentialExtension
object, the conventions/attribute names are that the base domain is QQ and each
differential extension is x, t0, t1, ..., tn-1 = DE.t. DE.x is the variable of
integration (Dx == 1), DE.D is a list of the derivatives of
x, t1, t2, ..., tn-1 = t, DE.T is the list [x, t1, t2, ..., tn-1], DE.t is the
outer-most variable of the differential extension at the given level (the level
can be adjusted using DE.increment_level() and DE.decrement_level()),
k is the field C(x, t0, ..., tn-2), where C is the constant field. The
numerator of a fraction is denoted by a and the denominator by
d. If the fraction is named f, fa == numer(f) and fd == denom(f).
Fractions are returned as tuples (fa, fd). DE.d and DE.t are used to
represent the topmost derivation and extension variable, respectively.
The docstring of a function signifies whether an argument is in k[t], in
which case it will just return a Poly in t, or in k(t), in which case it
will return the fraction (fa, fd). Other variable names probably come
from the names used in Bronstein's book.
"""
from __future__ import print_function, division
from sympy import real_roots, default_sort_key
from sympy.abc import z
from sympy.core.function import Lambda
from sympy.core.numbers import ilcm, oo
from sympy.core.mul import Mul
from sympy.core.power import Pow
from sympy.core.relational import Eq
from sympy.core.singleton import S
from sympy.core.symbol import Symbol, Dummy
from sympy.core.compatibility import reduce, ordered, range
from sympy.integrals.heurisch import _symbols
from sympy.functions import (acos, acot, asin, atan, cos, cot, exp, log,
Piecewise, sin, tan)
from sympy.functions import sinh, cosh, tanh, coth
from sympy.integrals import Integral, integrate
from sympy.polys import gcd, cancel, PolynomialError, Poly, reduced, RootSum, DomainError
from sympy.utilities.iterables import numbered_symbols
from types import GeneratorType
def integer_powers(exprs):
"""
Rewrites a list of expressions as integer multiples of each other.
For example, if you have [x, x/2, x**2 + 1, 2*x/3], then you can rewrite
this as [(x/6) * 6, (x/6) * 3, (x**2 + 1) * 1, (x/6) * 4]. This is useful
in the Risch integration algorithm, where we must write exp(x) + exp(x/2)
as (exp(x/2))**2 + exp(x/2), but not as exp(x) + sqrt(exp(x)) (this is
because only the transcendental case is implemented and we therefore cannot
integrate algebraic extensions). The integer multiples returned by this
function for each term are the smallest possible (their content equals 1).
Returns a list of tuples where the first element is the base term and the
second element is a list of `(item, factor)` terms, where `factor` is the
integer multiplicative factor that must multiply the base term to obtain
the original item.
The easiest way to understand this is to look at an example:
>>> from sympy.abc import x
>>> from sympy.integrals.risch import integer_powers
>>> integer_powers([x, x/2, x**2 + 1, 2*x/3])
[(x/6, [(x, 6), (x/2, 3), (2*x/3, 4)]), (x**2 + 1, [(x**2 + 1, 1)])]
We can see how this relates to the example at the beginning of the
docstring. It chose x/6 as the first base term. Then, x can be written as
(x/2) * 2, so we get (0, 2), and so on. Now only element (x**2 + 1)
remains, and there are no other terms that can be written as a rational
multiple of that, so we get that it can be written as (x**2 + 1) * 1.
"""
# Here is the strategy:
# First, go through each term and determine if it can be rewritten as a
# rational multiple of any of the terms gathered so far.
# cancel(a/b).is_Rational is sufficient for this. If it is a multiple, we
# add its multiple to the dictionary.
terms = {}
for term in exprs:
for j in terms:
a = cancel(term/j)
if a.is_Rational:
terms[j].append((term, a))
break
else:
terms[term] = [(term, S(1))]
# After we have done this, we have all the like terms together, so we just
# need to find a common denominator so that we can get the base term and
# integer multiples such that each term can be written as an integer
# multiple of the base term, and the content of the integers is 1.
newterms = {}
for term in terms:
common_denom = reduce(ilcm, [i.as_numer_denom()[1] for _, i in
terms[term]])
newterm = term/common_denom
newmults = [(i, j*common_denom) for i, j in terms[term]]
newterms[newterm] = newmults
return sorted(iter(newterms.items()), key=lambda item: item[0].sort_key())
class DifferentialExtension(object):
"""
A container for all the information relating to a differential extension.
The attributes of this object are (see also the docstring of __init__):
- f: The original (Expr) integrand.
- x: The variable of integration.
- T: List of variables in the extension.
- D: List of derivations in the extension; corresponds to the elements of T.
- fa: Poly of the numerator of the integrand.
- fd: Poly of the denominator of the integrand.
- Tfuncs: Lambda() representations of each element of T (except for x).
For back-substitution after integration.
- backsubs: A (possibly empty) list of further substitutions to be made on
the final integral to make it look more like the integrand.
- exts:
- extargs:
- cases: List of string representations of the cases of T.
- t: The top level extension variable, as defined by the current level
(see level below).
- d: The top level extension derivation, as defined by the current
derivation (see level below).
- case: The string representation of the case of self.d.
(Note that self.T and self.D will always contain the complete extension,
regardless of the level. Therefore, you should ALWAYS use DE.t and DE.d
instead of DE.T[-1] and DE.D[-1]. If you want to have a list of the
derivations or variables only up to the current level, use
DE.D[:len(DE.D) + DE.level + 1] and DE.T[:len(DE.T) + DE.level + 1]. Note
that, in particular, the derivation() function does this.)
The following are also attributes, but will probably not be useful other
than in internal use:
- newf: Expr form of fa/fd.
- level: The number (between -1 and -len(self.T)) such that
self.T[self.level] == self.t and self.D[self.level] == self.d.
Use the methods self.increment_level() and self.decrement_level() to change
the current level.
"""
# __slots__ is defined mainly so we can iterate over all the attributes
# of the class easily (the memory use doesn't matter too much, since we
# only create one DifferentialExtension per integration). Also, it's nice
# to have a safeguard when debugging.
__slots__ = ('f', 'x', 'T', 'D', 'fa', 'fd', 'Tfuncs', 'backsubs',
'exts', 'extargs', 'cases', 'case', 't', 'd', 'newf', 'level',
'ts', 'dummy')
def __init__(self, f=None, x=None, handle_first='log', dummy=False, extension=None, rewrite_complex=False):
"""
Tries to build a transcendental extension tower from f with respect to x.
If it is successful, creates a DifferentialExtension object with, among
others, the attributes fa, fd, D, T, Tfuncs, and backsubs such that
fa and fd are Polys in T[-1] with rational coefficients in T[:-1],
fa/fd == f, and D[i] is a Poly in T[i] with rational coefficients in
T[:i] representing the derivative of T[i] for each i from 1 to len(T).
Tfuncs is a list of Lambda objects for back replacing the functions
after integrating. Lambda() is only used (instead of lambda) to make
them easier to test and debug. Note that Tfuncs corresponds to the
elements of T, except for T[0] == x, but they should be back-substituted
in reverse order. backsubs is a (possibly empty) back-substitution list
that should be applied on the completed integral to make it look more
like the original integrand.
If it is unsuccessful, it raises NotImplementedError.
You can also create an object by manually setting the attributes as a
dictionary to the extension keyword argument. You must include at least
D. Warning, any attribute that is not given will be set to None. The
attributes T, t, d, cases, case, x, and level are set automatically and
do not need to be given. The functions in the Risch Algorithm will NOT
check to see if an attribute is None before using it. This also does not
check to see if the extension is valid (non-algebraic) or even if it is
self-consistent. Therefore, this should only be used for
testing/debugging purposes.
"""
# XXX: If you need to debug this function, set the break point here
if extension:
if 'D' not in extension:
raise ValueError("At least the key D must be included with "
"the extension flag to DifferentialExtension.")
for attr in extension:
setattr(self, attr, extension[attr])
self._auto_attrs()
return
elif f is None or x is None:
raise ValueError("Either both f and x or a manual extension must "
"be given.")
from sympy.integrals.prde import is_deriv_k
if handle_first not in ['log', 'exp']:
raise ValueError("handle_first must be 'log' or 'exp', not %s." %
str(handle_first))
# f will be the original function, self.f might change if we reset
# (e.g., we pull out a constant from an exponential)
self.f = f
self.x = x
# setting the default value 'dummy'
self.dummy = dummy
self.reset()
exp_new_extension, log_new_extension = True, True
if rewrite_complex:
rewritables = {
(sin, cos, cot, tan, sinh, cosh, coth, tanh): exp,
(asin, acos, acot, atan): log,
}
# rewrite the trigonometric components
for candidates, rule in rewritables.items():
self.newf = self.newf.rewrite(candidates, rule)
else:
if any(i.has(x) for i in self.f.atoms(sin, cos, tan, atan, asin, acos)):
raise NotImplementedError("Trigonometric extensions are not "
"supported (yet!)")
def update(seq, atoms, func):
s = set(seq)
new = atoms - s
s = atoms.intersection(s)
s.update(list(filter(func, new)))
return list(s)
exps = set()
pows = set()
numpows = set()
sympows = set()
logs = set()
symlogs = set()
while True:
if self.newf.is_rational_function(*self.T):
break
if not exp_new_extension and not log_new_extension:
# We couldn't find a new extension on the last pass, so I guess
# we can't do it.
raise NotImplementedError("Couldn't find an elementary "
"transcendental extension for %s. Try using a " % str(f) +
"manual extension with the extension flag.")
# Pre-preparsing.
#################
# Get all exp arguments, so we can avoid ahead of time doing
# something like t1 = exp(x), t2 = exp(x/2) == sqrt(t1).
# Things like sqrt(exp(x)) do not automatically simplify to
# exp(x/2), so they will be viewed as algebraic. The easiest way
# to handle this is to convert all instances of (a**b)**Rational
# to a**(Rational*b) before doing anything else. Note that the
# _exp_part code can generate terms of this form, so we do need to
# do this at each pass (or else modify it to not do that).
ratpows = [i for i in self.newf.atoms(Pow).union(self.newf.atoms(exp))
if (i.base.is_Pow or i.base.func is exp and i.exp.is_Rational)]
ratpows_repl = [
(i, i.base.base**(i.exp*i.base.exp)) for i in ratpows]
self.backsubs += [(j, i) for i, j in ratpows_repl]
self.newf = self.newf.xreplace(dict(ratpows_repl))
# To make the process deterministic, the args are sorted
# so that functions with smaller op-counts are processed first.
# Ties are broken with the default_sort_key.
# XXX Although the method is deterministic no additional work
# has been done to guarantee that the simplest solution is
# returned and that it would be affected be using different
# variables. Though it is possible that this is the case
# one should know that it has not been done intentionally, so
# further improvements may be possible.
# TODO: This probably doesn't need to be completely recomputed at
# each pass.
exps = update(exps, self.newf.atoms(exp),
lambda i: i.exp.is_rational_function(*self.T) and
i.exp.has(*self.T))
pows = update(pows, self.newf.atoms(Pow),
lambda i: i.exp.is_rational_function(*self.T) and
i.exp.has(*self.T))
numpows = update(numpows, set(pows),
lambda i: not i.base.has(*self.T))
sympows = update(sympows, set(pows) - set(numpows),
lambda i: i.base.is_rational_function(*self.T) and
not i.exp.is_Integer)
# The easiest way to deal with non-base E powers is to convert them
# into base E, integrate, and then convert back.
for i in ordered(pows):
old = i
new = exp(i.exp*log(i.base))
# If exp is ever changed to automatically reduce exp(x*log(2))
# to 2**x, then this will break. The solution is to not change
# exp to do that :)
if i in sympows:
if i.exp.is_Rational:
raise NotImplementedError("Algebraic extensions are "
"not supported (%s)." % str(i))
# We can add a**b only if log(a) in the extension, because
# a**b == exp(b*log(a)).
basea, based = frac_in(i.base, self.t)
A = is_deriv_k(basea, based, self)
if A is None:
# Nonelementary monomial (so far)
# TODO: Would there ever be any benefit from just
# adding log(base) as a new monomial?
# ANSWER: Yes, otherwise we can't integrate x**x (or
# rather prove that it has no elementary integral)
# without first manually rewriting it as exp(x*log(x))
self.newf = self.newf.xreplace({old: new})
self.backsubs += [(new, old)]
log_new_extension = self._log_part([log(i.base)])
exps = update(exps, self.newf.atoms(exp), lambda i:
i.exp.is_rational_function(*self.T) and i.exp.has(*self.T))
continue
ans, u, const = A
newterm = exp(i.exp*(log(const) + u))
# Under the current implementation, exp kills terms
# only if they are of the form a*log(x), where a is a
# Number. This case should have already been killed by the
# above tests. Again, if this changes to kill more than
# that, this will break, which maybe is a sign that you
# shouldn't be changing that. Actually, if anything, this
# auto-simplification should be removed. See
# http://groups.google.com/group/sympy/browse_thread/thread/a61d48235f16867f
self.newf = self.newf.xreplace({i: newterm})
elif i not in numpows:
continue
else:
# i in numpows
newterm = new
# TODO: Just put it in self.Tfuncs
self.backsubs.append((new, old))
self.newf = self.newf.xreplace({old: newterm})
exps.append(newterm)
atoms = self.newf.atoms(log)
logs = update(logs, atoms,
lambda i: i.args[0].is_rational_function(*self.T) and
i.args[0].has(*self.T))
symlogs = update(symlogs, atoms,
lambda i: i.has(*self.T) and i.args[0].is_Pow and
i.args[0].base.is_rational_function(*self.T) and
not i.args[0].exp.is_Integer)
# We can handle things like log(x**y) by converting it to y*log(x)
# This will fix not only symbolic exponents of the argument, but any
# non-Integer exponent, like log(sqrt(x)). The exponent can also
# depend on x, like log(x**x).
for i in ordered(symlogs):
# Unlike in the exponential case above, we do not ever
# potentially add new monomials (above we had to add log(a)).
# Therefore, there is no need to run any is_deriv functions
# here. Just convert log(a**b) to b*log(a) and let
# log_new_extension() handle it from there.
lbase = log(i.args[0].base)
logs.append(lbase)
new = i.args[0].exp*lbase
self.newf = self.newf.xreplace({i: new})
self.backsubs.append((new, i))
# remove any duplicates
logs = sorted(set(logs), key=default_sort_key)
if handle_first == 'exp' or not log_new_extension:
exp_new_extension = self._exp_part(exps)
if exp_new_extension is None:
# reset and restart
self.f = self.newf
self.reset()
exp_new_extension = True
continue
if handle_first == 'log' or not exp_new_extension:
log_new_extension = self._log_part(logs)
self.fa, self.fd = frac_in(self.newf, self.t)
self._auto_attrs()
return
def __getattr__(self, attr):
# Avoid AttributeErrors when debugging
if attr not in self.__slots__:
raise AttributeError("%s has no attribute %s" % (repr(self), repr(attr)))
return None
def _auto_attrs(self):
"""
Set attributes that are generated automatically.
"""
if not self.T:
# i.e., when using the extension flag and T isn't given
self.T = [i.gen for i in self.D]
if not self.x:
self.x = self.T[0]
self.cases = [get_case(d, t) for d, t in zip(self.D, self.T)]
self.level = -1
self.t = self.T[self.level]
self.d = self.D[self.level]
self.case = self.cases[self.level]
def _exp_part(self, exps):
"""
Try to build an exponential extension.
Returns True if there was a new extension, False if there was no new
extension but it was able to rewrite the given exponentials in terms
of the existing extension, and None if the entire extension building
process should be restarted. If the process fails because there is no
way around an algebraic extension (e.g., exp(log(x)/2)), it will raise
NotImplementedError.
"""
from sympy.integrals.prde import is_log_deriv_k_t_radical
new_extension = False
restart = False
expargs = [i.exp for i in exps]
ip = integer_powers(expargs)
for arg, others in ip:
# Minimize potential problems with algebraic substitution
others.sort(key=lambda i: i[1])
arga, argd = frac_in(arg, self.t)
A = is_log_deriv_k_t_radical(arga, argd, self)
if A is not None:
ans, u, n, const = A
# if n is 1 or -1, it's algebraic, but we can handle it
if n == -1:
# This probably will never happen, because
# Rational.as_numer_denom() returns the negative term in
# the numerator. But in case that changes, reduce it to
# n == 1.
n = 1
u **= -1
const *= -1
ans = [(i, -j) for i, j in ans]
if n == 1:
# Example: exp(x + x**2) over QQ(x, exp(x), exp(x**2))
self.newf = self.newf.xreplace({exp(arg): exp(const)*Mul(*[
u**power for u, power in ans])})
self.newf = self.newf.xreplace(dict([(exp(p*exparg),
exp(const*p) * Mul(*[u**power for u, power in ans]))
for exparg, p in others]))
# TODO: Add something to backsubs to put exp(const*p)
# back together.
continue
else:
# Bad news: we have an algebraic radical. But maybe we
# could still avoid it by choosing a different extension.
# For example, integer_powers() won't handle exp(x/2 + 1)
# over QQ(x, exp(x)), but if we pull out the exp(1), it
# will. Or maybe we have exp(x + x**2/2), over
# QQ(x, exp(x), exp(x**2)), which is exp(x)*sqrt(exp(x**2)),
# but if we use QQ(x, exp(x), exp(x**2/2)), then they will
# all work.
#
# So here is what we do: If there is a non-zero const, pull
# it out and retry. Also, if len(ans) > 1, then rewrite
# exp(arg) as the product of exponentials from ans, and
# retry that. If const == 0 and len(ans) == 1, then we
# assume that it would have been handled by either
# integer_powers() or n == 1 above if it could be handled,
# so we give up at that point. For example, you can never
# handle exp(log(x)/2) because it equals sqrt(x).
if const or len(ans) > 1:
rad = Mul(*[term**(power/n) for term, power in ans])
self.newf = self.newf.xreplace(dict((exp(p*exparg),
exp(const*p)*rad) for exparg, p in others))
self.newf = self.newf.xreplace(dict(list(zip(reversed(self.T),
reversed([f(self.x) for f in self.Tfuncs])))))
restart = True
break
else:
# TODO: give algebraic dependence in error string
raise NotImplementedError("Cannot integrate over "
"algebraic extensions.")
else:
arga, argd = frac_in(arg, self.t)
darga = (argd*derivation(Poly(arga, self.t), self) -
arga*derivation(Poly(argd, self.t), self))
dargd = argd**2
darga, dargd = darga.cancel(dargd, include=True)
darg = darga.as_expr()/dargd.as_expr()
self.t = next(self.ts)
self.T.append(self.t)
self.extargs.append(arg)
self.exts.append('exp')
self.D.append(darg.as_poly(self.t, expand=False)*Poly(self.t,
self.t, expand=False))
if self.dummy:
i = Dummy("i")
else:
i = Symbol('i')
self.Tfuncs = self.Tfuncs + [Lambda(i, exp(arg.subs(self.x, i)))]
self.newf = self.newf.xreplace(
dict((exp(exparg), self.t**p) for exparg, p in others))
new_extension = True
if restart:
return None
return new_extension
def _log_part(self, logs):
"""
Try to build a logarithmic extension.
Returns True if there was a new extension and False if there was no new
extension but it was able to rewrite the given logarithms in terms
of the existing extension. Unlike with exponential extensions, there
is no way that a logarithm is not transcendental over and cannot be
rewritten in terms of an already existing extension in a non-algebraic
way, so this function does not ever return None or raise
NotImplementedError.
"""
from sympy.integrals.prde import is_deriv_k
new_extension = False
logargs = [i.args[0] for i in logs]
for arg in ordered(logargs):
# The log case is easier, because whenever a logarithm is algebraic
# over the base field, it is of the form a1*t1 + ... an*tn + c,
# which is a polynomial, so we can just replace it with that.
# In other words, we don't have to worry about radicals.
arga, argd = frac_in(arg, self.t)
A = is_deriv_k(arga, argd, self)
if A is not None:
ans, u, const = A
newterm = log(const) + u
self.newf = self.newf.xreplace({log(arg): newterm})
continue
else:
arga, argd = frac_in(arg, self.t)
darga = (argd*derivation(Poly(arga, self.t), self) -
arga*derivation(Poly(argd, self.t), self))
dargd = argd**2
darg = darga.as_expr()/dargd.as_expr()
self.t = next(self.ts)
self.T.append(self.t)
self.extargs.append(arg)
self.exts.append('log')
self.D.append(cancel(darg.as_expr()/arg).as_poly(self.t,
expand=False))
if self.dummy:
i = Dummy("i")
else:
i = Symbol('i')
self.Tfuncs = self.Tfuncs + [Lambda(i, log(arg.subs(self.x, i)))]
self.newf = self.newf.xreplace({log(arg): self.t})
new_extension = True
return new_extension
@property
def _important_attrs(self):
"""
Returns some of the more important attributes of self.
Used for testing and debugging purposes.
The attributes are (fa, fd, D, T, Tfuncs, backsubs,
exts, extargs).
"""
return (self.fa, self.fd, self.D, self.T, self.Tfuncs,
self.backsubs, self.exts, self.extargs)
# NOTE: this printing doesn't follow the Python's standard
# eval(repr(DE)) == DE, where DE is the DifferentialExtension object
# , also this printing is supposed to contain all the important
# attributes of a DifferentialExtension object
def __repr__(self):
# no need to have GeneratorType object printed in it
r = [(attr, getattr(self, attr)) for attr in self.__slots__
if not isinstance(getattr(self, attr), GeneratorType)]
return self.__class__.__name__ + '(dict(%r))' % (r)
# fancy printing of DifferentialExtension object
def __str__(self):
return (self.__class__.__name__ + '({fa=%s, fd=%s, D=%s})' %
(self.fa, self.fd, self.D))
# should only be used for debugging purposes, internally
# f1 = f2 = log(x) at different places in code execution
# may return D1 != D2 as True, since 'level' or other attribute
# may differ
def __eq__(self, other):
for attr in self.__class__.__slots__:
d1, d2 = getattr(self, attr), getattr(other, attr)
if not (isinstance(d1, GeneratorType) or d1 == d2):
return False
return True
def reset(self):
"""
Reset self to an initial state. Used by __init__.
"""
self.t = self.x
self.T = [self.x]
self.D = [Poly(1, self.x)]
self.level = -1
self.exts = [None]
self.extargs = [None]
if self.dummy:
self.ts = numbered_symbols('t', cls=Dummy)
else:
# For testing
self.ts = numbered_symbols('t')
# For various things that we change to make things work that we need to
# change back when we are done.
self.backsubs = []
self.Tfuncs = []
self.newf = self.f
def indices(self, extension):
"""
Args:
extension (str): represents a valid extension type.
Returns:
list: A list of indices of 'exts' where extension of
type 'extension' is present.
Examples
========
>>> from sympy.integrals.risch import DifferentialExtension
>>> from sympy import log, exp
>>> from sympy.abc import x
>>> DE = DifferentialExtension(log(x) + exp(x), x, handle_first='exp')
>>> DE.indices('log')
[2]
>>> DE.indices('exp')
[1]
"""
return [i for i, ext in enumerate(self.exts) if ext == extension]
def increment_level(self):
"""
Increment the level of self.
This makes the working differential extension larger. self.level is
given relative to the end of the list (-1, -2, etc.), so we don't need
do worry about it when building the extension.
"""
if self.level >= -1:
raise ValueError("The level of the differential extension cannot "
"be incremented any further.")
self.level += 1
self.t = self.T[self.level]
self.d = self.D[self.level]
self.case = self.cases[self.level]
return None
def decrement_level(self):
"""
Decrease the level of self.
This makes the working differential extension smaller. self.level is
given relative to the end of the list (-1, -2, etc.), so we don't need
do worry about it when building the extension.
"""
if self.level <= -len(self.T):
raise ValueError("The level of the differential extension cannot "
"be decremented any further.")
self.level -= 1
self.t = self.T[self.level]
self.d = self.D[self.level]
self.case = self.cases[self.level]
return None
class DecrementLevel(object):
"""
A context manager for decrementing the level of a DifferentialExtension.
"""
__slots__ = ('DE',)
def __init__(self, DE):
self.DE = DE
return
def __enter__(self):
self.DE.decrement_level()
def __exit__(self, exc_type, exc_value, traceback):
self.DE.increment_level()
class NonElementaryIntegralException(Exception):
"""
Exception used by subroutines within the Risch algorithm to indicate to one
another that the function being integrated does not have an elementary
integral in the given differential field.
"""
# TODO: Rewrite algorithms below to use this (?)
# TODO: Pass through information about why the integral was nonelementary,
# and store that in the resulting NonElementaryIntegral somehow.
pass
def gcdex_diophantine(a, b, c):
"""
Extended Euclidean Algorithm, Diophantine version.
Given a, b in K[x] and c in (a, b), the ideal generated by a and b,
return (s, t) such that s*a + t*b == c and either s == 0 or s.degree()
< b.degree().
"""
# Extended Euclidean Algorithm (Diophantine Version) pg. 13
# TODO: This should go in densetools.py.
# XXX: Bettter name?
s, g = a.half_gcdex(b)
q = c.exquo(g) # Inexact division means c is not in (a, b)
s = q*s
if not s.is_zero and b.degree() >= b.degree():
q, s = s.div(b)
t = (c - s*a).exquo(b)
return (s, t)
def frac_in(f, t, **kwargs):
"""
Returns the tuple (fa, fd), where fa and fd are Polys in t.
This is a common idiom in the Risch Algorithm functions, so we abstract
it out here. f should be a basic expresion, a Poly, or a tuple (fa, fd),
where fa and fd are either basic expressions or Polys, and f == fa/fd.
**kwargs are applied to Poly.
"""
cancel = kwargs.pop('cancel', False)
if type(f) is tuple:
fa, fd = f
f = fa.as_expr()/fd.as_expr()
fa, fd = f.as_expr().as_numer_denom()
fa, fd = fa.as_poly(t, **kwargs), fd.as_poly(t, **kwargs)
if cancel:
fa, fd = fa.cancel(fd, include=True)
if fa is None or fd is None:
raise ValueError("Could not turn %s into a fraction in %s." % (f, t))
return (fa, fd)
def as_poly_1t(p, t, z):
"""
(Hackish) way to convert an element p of K[t, 1/t] to K[t, z].
In other words, z == 1/t will be a dummy variable that Poly can handle
better.
See issue 5131.
Examples
========
>>> from sympy import random_poly
>>> from sympy.integrals.risch import as_poly_1t
>>> from sympy.abc import x, z
>>> p1 = random_poly(x, 10, -10, 10)
>>> p2 = random_poly(x, 10, -10, 10)
>>> p = p1 + p2.subs(x, 1/x)
>>> as_poly_1t(p, x, z).as_expr().subs(z, 1/x) == p
True
"""
# TODO: Use this on the final result. That way, we can avoid answers like
# (...)*exp(-x).
pa, pd = frac_in(p, t, cancel=True)
if not pd.is_monomial:
# XXX: Is there a better Poly exception that we could raise here?
# Either way, if you see this (from the Risch Algorithm) it indicates
# a bug.
raise PolynomialError("%s is not an element of K[%s, 1/%s]." % (p, t, t))
d = pd.degree(t)
one_t_part = pa.slice(0, d + 1)
r = pd.degree() - pa.degree()
t_part = pa - one_t_part
try:
t_part = t_part.to_field().exquo(pd)
except DomainError as e:
# issue 4950
raise NotImplementedError(e)
# Compute the negative degree parts.
one_t_part = Poly.from_list(reversed(one_t_part.rep.rep), *one_t_part.gens,
domain=one_t_part.domain)
if 0 < r < oo:
one_t_part *= Poly(t**r, t)
one_t_part = one_t_part.replace(t, z) # z will be 1/t
if pd.nth(d):
one_t_part *= Poly(1/pd.nth(d), z, expand=False)
ans = t_part.as_poly(t, z, expand=False) + one_t_part.as_poly(t, z,
expand=False)
return ans
def derivation(p, DE, coefficientD=False, basic=False):
"""
Computes Dp.
Given the derivation D with D = d/dx and p is a polynomial in t over
K(x), return Dp.
If coefficientD is True, it computes the derivation kD
(kappaD), which is defined as kD(sum(ai*Xi**i, (i, 0, n))) ==
sum(Dai*Xi**i, (i, 1, n)) (Definition 3.2.2, page 80). X in this case is
T[-1], so coefficientD computes the derivative just with respect to T[:-1],
with T[-1] treated as a constant.
If basic=True, the returns a Basic expression. Elements of D can still be
instances of Poly.
"""
if basic:
r = 0
else:
r = Poly(0, DE.t)
t = DE.t
if coefficientD:
if DE.level <= -len(DE.T):
# 'base' case, the answer is 0.
return r
DE.decrement_level()
D = DE.D[:len(DE.D) + DE.level + 1]
T = DE.T[:len(DE.T) + DE.level + 1]
for d, v in zip(D, T):
pv = p.as_poly(v)
if pv is None or basic:
pv = p.as_expr()
if basic:
r += d.as_expr()*pv.diff(v)
else:
r += (d*pv.diff(v)).as_poly(t)
if basic:
r = cancel(r)
if coefficientD:
DE.increment_level()
return r
def get_case(d, t):
"""
Returns the type of the derivation d.
Returns one of {'exp', 'tan', 'base', 'primitive', 'other_linear',
'other_nonlinear'}.
"""
if not d.has(t):
if d.is_one:
return 'base'
return 'primitive'
if d.rem(Poly(t, t)).is_zero:
return 'exp'
if d.rem(Poly(1 + t**2, t)).is_zero:
return 'tan'
if d.degree(t) > 1:
return 'other_nonlinear'
return 'other_linear'
def splitfactor(p, DE, coefficientD=False, z=None):
"""
Splitting factorization.
Given a derivation D on k[t] and p in k[t], return (p_n, p_s) in
k[t] x k[t] such that p = p_n*p_s, p_s is special, and each square
factor of p_n is normal.
Page. 100
"""
kinv = [1/x for x in DE.T[:DE.level]]
if z:
kinv.append(z)
One = Poly(1, DE.t, domain=p.get_domain())
Dp = derivation(p, DE, coefficientD=coefficientD)
# XXX: Is this right?
if p.is_zero:
return (p, One)
if not p.has(DE.t):
s = p.as_poly(*kinv).gcd(Dp.as_poly(*kinv)).as_poly(DE.t)
n = p.exquo(s)
return (n, s)
if not Dp.is_zero:
h = p.gcd(Dp).to_field()
g = p.gcd(p.diff(DE.t)).to_field()
s = h.exquo(g)
if s.degree(DE.t) == 0:
return (p, One)
q_split = splitfactor(p.exquo(s), DE, coefficientD=coefficientD)
return (q_split[0], q_split[1]*s)
else:
return (p, One)
def splitfactor_sqf(p, DE, coefficientD=False, z=None, basic=False):
"""
Splitting Square-free Factorization
Given a derivation D on k[t] and p in k[t], returns (N1, ..., Nm)
and (S1, ..., Sm) in k[t]^m such that p =
(N1*N2**2*...*Nm**m)*(S1*S2**2*...*Sm**m) is a splitting
factorization of p and the Ni and Si are square-free and coprime.
"""
# TODO: This algorithm appears to be faster in every case
# TODO: Verify this and splitfactor() for multiple extensions
kkinv = [1/x for x in DE.T[:DE.level]] + DE.T[:DE.level]
if z:
kkinv = [z]
S = []
N = []
p_sqf = p.sqf_list_include()
if p.is_zero:
return (((p, 1),), ())
for pi, i in p_sqf:
Si = pi.as_poly(*kkinv).gcd(derivation(pi, DE,
coefficientD=coefficientD,basic=basic).as_poly(*kkinv)).as_poly(DE.t)
pi = Poly(pi, DE.t)
Si = Poly(Si, DE.t)
Ni = pi.exquo(Si)
if not Si.is_one:
S.append((Si, i))
if not Ni.is_one:
N.append((Ni, i))
return (tuple(N), tuple(S))
def canonical_representation(a, d, DE):
"""
Canonical Representation.
Given a derivation D on k[t] and f = a/d in k(t), return (f_p, f_s,
f_n) in k[t] x k(t) x k(t) such that f = f_p + f_s + f_n is the
canonical representation of f (f_p is a polynomial, f_s is reduced
(has a special denominator), and f_n is simple (has a normal
denominator).
"""
# Make d monic
l = Poly(1/d.LC(), DE.t)
a, d = a.mul(l), d.mul(l)
q, r = a.div(d)
dn, ds = splitfactor(d, DE)
b, c = gcdex_diophantine(dn.as_poly(DE.t), ds.as_poly(DE.t), r.as_poly(DE.t))
b, c = b.as_poly(DE.t), c.as_poly(DE.t)
return (q, (b, ds), (c, dn))
def hermite_reduce(a, d, DE):
"""
Hermite Reduction - Mack's Linear Version.
Given a derivation D on k(t) and f = a/d in k(t), returns g, h, r in
k(t) such that f = Dg + h + r, h is simple, and r is reduced.
"""
# Make d monic
l = Poly(1/d.LC(), DE.t)
a, d = a.mul(l), d.mul(l)
fp, fs, fn = canonical_representation(a, d, DE)
a, d = fn
l = Poly(1/d.LC(), DE.t)
a, d = a.mul(l), d.mul(l)
ga = Poly(0, DE.t)
gd = Poly(1, DE.t)
dd = derivation(d, DE)
dm = gcd(d, dd).as_poly(DE.t)
ds, r = d.div(dm)
while dm.degree(DE.t)>0:
ddm = derivation(dm, DE)
dm2 = gcd(dm, ddm)
dms, r = dm.div(dm2)
ds_ddm = ds.mul(ddm)
ds_ddm_dm, r = ds_ddm.div(dm)
b, c = gcdex_diophantine(-ds_ddm_dm.as_poly(DE.t), dms.as_poly(DE.t), a.as_poly(DE.t))
b, c = b.as_poly(DE.t), c.as_poly(DE.t)
db = derivation(b, DE).as_poly(DE.t)
ds_dms, r = ds.div(dms)
a = c.as_poly(DE.t) - db.mul(ds_dms).as_poly(DE.t)
ga = ga*dm + b*gd
gd = gd*dm
ga, gd = ga.cancel(gd, include=True)
dm = dm2
d = ds
q, r = a.div(d)
ga, gd = ga.cancel(gd, include=True)
r, d = r.cancel(d, include=True)
rra = q*fs[1] + fp*fs[1] + fs[0]
rrd = fs[1]
rra, rrd = rra.cancel(rrd, include=True)
return ((ga, gd), (r, d), (rra, rrd))
def polynomial_reduce(p, DE):
"""
Polynomial Reduction.
Given a derivation D on k(t) and p in k[t] where t is a nonlinear
monomial over k, return q, r in k[t] such that p = Dq + r, and
deg(r) < deg_t(Dt).
"""
q = Poly(0, DE.t)
while p.degree(DE.t) >= DE.d.degree(DE.t):
m = p.degree(DE.t) - DE.d.degree(DE.t) + 1
q0 = Poly(DE.t**m, DE.t).mul(Poly(p.as_poly(DE.t).LC()/
(m*DE.d.LC()), DE.t))
q += q0
p = p - derivation(q0, DE)
return (q, p)
def laurent_series(a, d, F, n, DE):
"""
Contribution of F to the full partial fraction decomposition of A/D
Given a field K of characteristic 0 and A,D,F in K[x] with D monic,
nonzero, coprime with A, and F the factor of multiplicity n in the square-
free factorization of D, return the principal parts of the Laurent series of
A/D at all the zeros of F.
"""
if F.degree()==0:
return 0
Z = _symbols('z', n)
Z.insert(0, z)
delta_a = Poly(0, DE.t)
delta_d = Poly(1, DE.t)
E = d.quo(F**n)
ha, hd = (a, E*Poly(z**n, DE.t))
dF = derivation(F,DE)
B, G = gcdex_diophantine(E, F, Poly(1,DE.t))
C, G = gcdex_diophantine(dF, F, Poly(1,DE.t))
# initialization
F_store = F
V, DE_D_list, H_list= [], [], []
for j in range(0, n):
# jth derivative of z would be substituted with dfnth/(j+1) where dfnth =(d^n)f/(dx)^n
F_store = derivation(F_store, DE)
v = (F_store.as_expr())/(j + 1)
V.append(v)
DE_D_list.append(Poly(Z[j + 1],Z[j]))
DE_new = DifferentialExtension(extension = {'D': DE_D_list}) #a differential indeterminate
for j in range(0, n):
zEha = Poly(z**(n + j), DE.t)*E**(j + 1)*ha
zEhd = hd
Pa, Pd = cancel((zEha, zEhd))[1], cancel((zEha, zEhd))[2]
Q = Pa.quo(Pd)
for i in range(0, j + 1):
Q = Q.subs(Z[i], V[i])
Dha = hd*derivation(ha, DE, basic=True) + ha*derivation(hd, DE, basic=True)
Dha += hd*derivation(ha, DE_new, basic=True) + ha*derivation(hd, DE_new, basic=True)
Dhd = Poly(j + 1, DE.t)*hd**2
ha, hd = Dha, Dhd
Ff, Fr = F.div(gcd(F, Q))
F_stara, F_stard = frac_in(Ff, DE.t)
if F_stara.degree(DE.t) - F_stard.degree(DE.t) > 0:
QBC = Poly(Q, DE.t)*B**(1 + j)*C**(n + j)
H = QBC
H_list.append(H)
H = (QBC*F_stard).rem(F_stara)
alphas = real_roots(F_stara)
for alpha in list(alphas):
delta_a = delta_a*Poly((DE.t - alpha)**(n - j), DE.t) + Poly(H.eval(alpha), DE.t)
delta_d = delta_d*Poly((DE.t - alpha)**(n - j), DE.t)
return (delta_a, delta_d, H_list)
def recognize_derivative(a, d, DE, z=None):
"""
Compute the squarefree factorization of the denominator of f
and for each Di the polynomial H in K[x] (see Theorem 2.7.1), using the
LaurentSeries algorithm. Write Di = GiEi where Gj = gcd(Hn, Di) and
gcd(Ei,Hn) = 1. Since the residues of f at the roots of Gj are all 0, and
the residue of f at a root alpha of Ei is Hi(a) != 0, f is the derivative of a
rational function if and only if Ei = 1 for each i, which is equivalent to
Di | H[-1] for each i.
"""
flag =True
a, d = a.cancel(d, include=True)
q, r = a.div(d)
Np, Sp = splitfactor_sqf(d, DE, coefficientD=True, z=z)
j = 1
for (s, i) in Sp:
delta_a, delta_d, H = laurent_series(r, d, s, j, DE)
g = gcd(d, H[-1]).as_poly()
if g is not d:
flag = False
break
j = j + 1
return flag
def recognize_log_derivative(a, d, DE, z=None):
"""
There exists a v in K(x)* such that f = dv/v
where f a rational function if and only if f can be written as f = A/D
where D is squarefree,deg(A) < deg(D), gcd(A, D) = 1,
and all the roots of the Rothstein-Trager resultant are integers. In that case,
any of the Rothstein-Trager, Lazard-Rioboo-Trager or Czichowski algorithm
produces u in K(x) such that du/dx = uf.
"""
z = z or Dummy('z')
a, d = a.cancel(d, include=True)
p, a = a.div(d)
pz = Poly(z, DE.t)
Dd = derivation(d, DE)
q = a - pz*Dd
r, R = d.resultant(q, includePRS=True)
r = Poly(r, z)
Np, Sp = splitfactor_sqf(r, DE, coefficientD=True, z=z)
for s, i in Sp:
# TODO also consider the complex roots
# incase we have complex roots it should turn the flag false
a = real_roots(s.as_poly(z))
if any(not j.is_Integer for j in a):
return False
return True
def residue_reduce(a, d, DE, z=None, invert=True):
"""
Lazard-Rioboo-Rothstein-Trager resultant reduction.
Given a derivation D on k(t) and f in k(t) simple, return g
elementary over k(t) and a Boolean b in {True, False} such that f -
Dg in k[t] if b == True or f + h and f + h - Dg do not have an
elementary integral over k(t) for any h in k<t> (reduced) if b ==
False.
Returns (G, b), where G is a tuple of tuples of the form (s_i, S_i),
such that g = Add(*[RootSum(s_i, lambda z: z*log(S_i(z, t))) for
S_i, s_i in G]). f - Dg is the remaining integral, which is elementary
only if b == True, and hence the integral of f is elementary only if
b == True.
f - Dg is not calculated in this function because that would require
explicitly calculating the RootSum. Use residue_reduce_derivation().
"""
# TODO: Use log_to_atan() from rationaltools.py
# If r = residue_reduce(...), then the logarithmic part is given by:
# sum([RootSum(a[0].as_poly(z), lambda i: i*log(a[1].as_expr()).subs(z,
# i)).subs(t, log(x)) for a in r[0]])
z = z or Dummy('z')
a, d = a.cancel(d, include=True)
a, d = a.to_field().mul_ground(1/d.LC()), d.to_field().mul_ground(1/d.LC())
kkinv = [1/x for x in DE.T[:DE.level]] + DE.T[:DE.level]
if a.is_zero:
return ([], True)
p, a = a.div(d)
pz = Poly(z, DE.t)
Dd = derivation(d, DE)
q = a - pz*Dd
if Dd.degree(DE.t) <= d.degree(DE.t):
r, R = d.resultant(q, includePRS=True)
else:
r, R = q.resultant(d, includePRS=True)
R_map, H = {}, []
for i in R:
R_map[i.degree()] = i
r = Poly(r, z)
Np, Sp = splitfactor_sqf(r, DE, coefficientD=True, z=z)
for s, i in Sp:
if i == d.degree(DE.t):
s = Poly(s, z).monic()
H.append((s, d))
else:
h = R_map.get(i)
if h is None:
continue
h_lc = Poly(h.as_poly(DE.t).LC(), DE.t, field=True)
h_lc_sqf = h_lc.sqf_list_include(all=True)
for a, j in h_lc_sqf:
h = Poly(h, DE.t, field=True).exquo(Poly(gcd(a, s**j, *kkinv),
DE.t))
s = Poly(s, z).monic()
if invert:
h_lc = Poly(h.as_poly(DE.t).LC(), DE.t, field=True, expand=False)
inv, coeffs = h_lc.as_poly(z, field=True).invert(s), [S(1)]
for coeff in h.coeffs()[1:]:
L = reduced(inv*coeff, [s])[1]
coeffs.append(L.as_expr())
h = Poly(dict(list(zip(h.monoms(), coeffs))), DE.t)
H.append((s, h))
b = all([not cancel(i.as_expr()).has(DE.t, z) for i, _ in Np])
return (H, b)
def residue_reduce_to_basic(H, DE, z):
"""
Converts the tuple returned by residue_reduce() into a Basic expression.
"""
# TODO: check what Lambda does with RootOf
i = Dummy('i')
s = list(zip(reversed(DE.T), reversed([f(DE.x) for f in DE.Tfuncs])))
return sum((RootSum(a[0].as_poly(z), Lambda(i, i*log(a[1].as_expr()).subs(
{z: i}).subs(s))) for a in H))
def residue_reduce_derivation(H, DE, z):
"""
Computes the derivation of an expression returned by residue_reduce().
In general, this is a rational function in t, so this returns an
as_expr() result.
"""
# TODO: verify that this is correct for multiple extensions
i = Dummy('i')
return S(sum((RootSum(a[0].as_poly(z), Lambda(i, i*derivation(a[1],
DE).as_expr().subs(z, i)/a[1].as_expr().subs(z, i))) for a in H)))
def integrate_primitive_polynomial(p, DE):
"""
Integration of primitive polynomials.
Given a primitive monomial t over k, and p in k[t], return q in k[t],
r in k, and a bool b in {True, False} such that r = p - Dq is in k if b is
True, or r = p - Dq does not have an elementary integral over k(t) if b is
False.
"""
from sympy.integrals.prde import limited_integrate
Zero = Poly(0, DE.t)
q = Poly(0, DE.t)
if not p.has(DE.t):
return (Zero, p, True)
while True:
if not p.has(DE.t):
return (q, p, True)
Dta, Dtb = frac_in(DE.d, DE.T[DE.level - 1])
with DecrementLevel(DE): # We had better be integrating the lowest extension (x)
# with ratint().
a = p.LC()
aa, ad = frac_in(a, DE.t)
try:
rv = limited_integrate(aa, ad, [(Dta, Dtb)], DE)
if rv is None:
raise NonElementaryIntegralException
(ba, bd), c = rv
except NonElementaryIntegralException:
return (q, p, False)
m = p.degree(DE.t)
q0 = c[0].as_poly(DE.t)*Poly(DE.t**(m + 1)/(m + 1), DE.t) + \
(ba.as_expr()/bd.as_expr()).as_poly(DE.t)*Poly(DE.t**m, DE.t)
p = p - derivation(q0, DE)
q = q + q0
def integrate_primitive(a, d, DE, z=None):
"""
Integration of primitive functions.
Given a primitive monomial t over k and f in k(t), return g elementary over
k(t), i in k(t), and b in {True, False} such that i = f - Dg is in k if b
is True or i = f - Dg does not have an elementary integral over k(t) if b
is False.
This function returns a Basic expression for the first argument. If b is
True, the second argument is Basic expression in k to recursively integrate.
If b is False, the second argument is an unevaluated Integral, which has
been proven to be nonelementary.
"""
# XXX: a and d must be canceled, or this might return incorrect results
z = z or Dummy("z")
s = list(zip(reversed(DE.T), reversed([f(DE.x) for f in DE.Tfuncs])))
g1, h, r = hermite_reduce(a, d, DE)
g2, b = residue_reduce(h[0], h[1], DE, z=z)
if not b:
i = cancel(a.as_expr()/d.as_expr() - (g1[1]*derivation(g1[0], DE) -
g1[0]*derivation(g1[1], DE)).as_expr()/(g1[1]**2).as_expr() -
residue_reduce_derivation(g2, DE, z))
i = NonElementaryIntegral(cancel(i).subs(s), DE.x)
return ((g1[0].as_expr()/g1[1].as_expr()).subs(s) +
residue_reduce_to_basic(g2, DE, z), i, b)
# h - Dg2 + r
p = cancel(h[0].as_expr()/h[1].as_expr() - residue_reduce_derivation(g2,
DE, z) + r[0].as_expr()/r[1].as_expr())
p = p.as_poly(DE.t)
q, i, b = integrate_primitive_polynomial(p, DE)
ret = ((g1[0].as_expr()/g1[1].as_expr() + q.as_expr()).subs(s) +
residue_reduce_to_basic(g2, DE, z))
if not b:
# TODO: This does not do the right thing when b is False
i = NonElementaryIntegral(cancel(i.as_expr()).subs(s), DE.x)
else:
i = cancel(i.as_expr())
return (ret, i, b)
def integrate_hyperexponential_polynomial(p, DE, z):
"""
Integration of hyperexponential polynomials.
Given a hyperexponential monomial t over k and p in k[t, 1/t], return q in
k[t, 1/t] and a bool b in {True, False} such that p - Dq in k if b is True,
or p - Dq does not have an elementary integral over k(t) if b is False.
"""
from sympy.integrals.rde import rischDE
t1 = DE.t
dtt = DE.d.exquo(Poly(DE.t, DE.t))
qa = Poly(0, DE.t)
qd = Poly(1, DE.t)
b = True
if p.is_zero:
return(qa, qd, b)
with DecrementLevel(DE):
for i in range(-p.degree(z), p.degree(t1) + 1):
if not i:
continue
elif i < 0:
# If you get AttributeError: 'NoneType' object has no attribute 'nth'
# then this should really not have expand=False
# But it shouldn't happen because p is already a Poly in t and z
a = p.as_poly(z, expand=False).nth(-i)
else:
# If you get AttributeError: 'NoneType' object has no attribute 'nth'
# then this should really not have expand=False
a = p.as_poly(t1, expand=False).nth(i)
aa, ad = frac_in(a, DE.t, field=True)
aa, ad = aa.cancel(ad, include=True)
iDt = Poly(i, t1)*dtt
iDta, iDtd = frac_in(iDt, DE.t, field=True)
try:
va, vd = rischDE(iDta, iDtd, Poly(aa, DE.t), Poly(ad, DE.t), DE)
va, vd = frac_in((va, vd), t1)
except NonElementaryIntegralException:
b = False
else:
qa = qa*vd + va*Poly(t1**i)*qd
qd *= vd
return (qa, qd, b)
def integrate_hyperexponential(a, d, DE, z=None, conds='piecewise'):
"""
Integration of hyperexponential functions.
Given a hyperexponential monomial t over k and f in k(t), return g
elementary over k(t), i in k(t), and a bool b in {True, False} such that
i = f - Dg is in k if b is True or i = f - Dg does not have an elementary
integral over k(t) if b is False.
This function returns a Basic expression for the first argument. If b is
True, the second argument is Basic expression in k to recursively integrate.
If b is False, the second argument is an unevaluated Integral, which has
been proven to be nonelementary.
"""
# XXX: a and d must be canceled, or this might return incorrect results
z = z or Dummy("z")
s = list(zip(reversed(DE.T), reversed([f(DE.x) for f in DE.Tfuncs])))
g1, h, r = hermite_reduce(a, d, DE)
g2, b = residue_reduce(h[0], h[1], DE, z=z)
if not b:
i = cancel(a.as_expr()/d.as_expr() - (g1[1]*derivation(g1[0], DE) -
g1[0]*derivation(g1[1], DE)).as_expr()/(g1[1]**2).as_expr() -
residue_reduce_derivation(g2, DE, z))
i = NonElementaryIntegral(cancel(i.subs(s)), DE.x)
return ((g1[0].as_expr()/g1[1].as_expr()).subs(s) +
residue_reduce_to_basic(g2, DE, z), i, b)
# p should be a polynomial in t and 1/t, because Sirr == k[t, 1/t]
# h - Dg2 + r
p = cancel(h[0].as_expr()/h[1].as_expr() - residue_reduce_derivation(g2,
DE, z) + r[0].as_expr()/r[1].as_expr())
pp = as_poly_1t(p, DE.t, z)
qa, qd, b = integrate_hyperexponential_polynomial(pp, DE, z)
i = pp.nth(0, 0)
ret = ((g1[0].as_expr()/g1[1].as_expr()).subs(s) \
+ residue_reduce_to_basic(g2, DE, z))
qas = qa.as_expr().subs(s)
qds = qd.as_expr().subs(s)
if conds == 'piecewise' and DE.x not in qds.free_symbols:
# We have to be careful if the exponent is S.Zero!
# XXX: Does qd = 0 always necessarily correspond to the exponential
# equaling 1?
ret += Piecewise(
(integrate((p - i).subs(DE.t, 1).subs(s), DE.x), Eq(qds, 0)),
(qas/qds, True)
)
else:
ret += qas/qds
if not b:
i = p - (qd*derivation(qa, DE) - qa*derivation(qd, DE)).as_expr()/\
(qd**2).as_expr()
i = NonElementaryIntegral(cancel(i).subs(s), DE.x)
return (ret, i, b)
def integrate_hypertangent_polynomial(p, DE):
"""
Integration of hypertangent polynomials.
Given a differential field k such that sqrt(-1) is not in k, a
hypertangent monomial t over k, and p in k[t], return q in k[t] and
c in k such that p - Dq - c*D(t**2 + 1)/(t**1 + 1) is in k and p -
Dq does not have an elementary integral over k(t) if Dc != 0.
"""
# XXX: Make sure that sqrt(-1) is not in k.
q, r = polynomial_reduce(p, DE)
a = DE.d.exquo(Poly(DE.t**2 + 1, DE.t))
c = Poly(r.nth(1)/(2*a.as_expr()), DE.t)
return (q, c)
def integrate_nonlinear_no_specials(a, d, DE, z=None):
"""
Integration of nonlinear monomials with no specials.
Given a nonlinear monomial t over k such that Sirr ({p in k[t] | p is
special, monic, and irreducible}) is empty, and f in k(t), returns g
elementary over k(t) and a Boolean b in {True, False} such that f - Dg is
in k if b == True, or f - Dg does not have an elementary integral over k(t)
if b == False.
This function is applicable to all nonlinear extensions, but in the case
where it returns b == False, it will only have proven that the integral of
f - Dg is nonelementary if Sirr is empty.
This function returns a Basic expression.
"""
# TODO: Integral from k?
# TODO: split out nonelementary integral
# XXX: a and d must be canceled, or this might not return correct results
z = z or Dummy("z")
s = list(zip(reversed(DE.T), reversed([f(DE.x) for f in DE.Tfuncs])))
g1, h, r = hermite_reduce(a, d, DE)
g2, b = residue_reduce(h[0], h[1], DE, z=z)
if not b:
return ((g1[0].as_expr()/g1[1].as_expr()).subs(s) +
residue_reduce_to_basic(g2, DE, z), b)
# Because f has no specials, this should be a polynomial in t, or else
# there is a bug.
p = cancel(h[0].as_expr()/h[1].as_expr() - residue_reduce_derivation(g2,
DE, z).as_expr() + r[0].as_expr()/r[1].as_expr()).as_poly(DE.t)
q1, q2 = polynomial_reduce(p, DE)
if q2.has(DE.t):
b = False
else:
b = True
ret = (cancel(g1[0].as_expr()/g1[1].as_expr() + q1.as_expr()).subs(s) +
residue_reduce_to_basic(g2, DE, z))
return (ret, b)
class NonElementaryIntegral(Integral):
"""
Represents a nonelementary Integral.
If the result of integrate() is an instance of this class, it is
guaranteed to be nonelementary. Note that integrate() by default will try
to find any closed-form solution, even in terms of special functions which
may themselves not be elementary. To make integrate() only give
elementary solutions, or, in the cases where it can prove the integral to
be nonelementary, instances of this class, use integrate(risch=True).
In this case, integrate() may raise NotImplementedError if it cannot make
such a determination.
integrate() uses the deterministic Risch algorithm to integrate elementary
functions or prove that they have no elementary integral. In some cases,
this algorithm can split an integral into an elementary and nonelementary
part, so that the result of integrate will be the sum of an elementary
expression and a NonElementaryIntegral.
Examples
========
>>> from sympy import integrate, exp, log, Integral
>>> from sympy.abc import x
>>> a = integrate(exp(-x**2), x, risch=True)
>>> print(a)
Integral(exp(-x**2), x)
>>> type(a)
<class 'sympy.integrals.risch.NonElementaryIntegral'>
>>> expr = (2*log(x)**2 - log(x) - x**2)/(log(x)**3 - x**2*log(x))
>>> b = integrate(expr, x, risch=True)
>>> print(b)
-log(-x + log(x))/2 + log(x + log(x))/2 + Integral(1/log(x), x)
>>> type(b.atoms(Integral).pop())
<class 'sympy.integrals.risch.NonElementaryIntegral'>
"""
# TODO: This is useful in and of itself, because isinstance(result,
# NonElementaryIntegral) will tell if the integral has been proven to be
# elementary. But should we do more? Perhaps a no-op .doit() if
# elementary=True? Or maybe some information on why the integral is
# nonelementary.
pass
def risch_integrate(f, x, extension=None, handle_first='log',
separate_integral=False, rewrite_complex=False,
conds='piecewise'):
r"""
The Risch Integration Algorithm.
Only transcendental functions are supported. Currently, only exponentials
and logarithms are supported, but support for trigonometric functions is
forthcoming.
If this function returns an unevaluated Integral in the result, it means
that it has proven that integral to be nonelementary. Any errors will
result in raising NotImplementedError. The unevaluated Integral will be
an instance of NonElementaryIntegral, a subclass of Integral.
handle_first may be either 'exp' or 'log'. This changes the order in
which the extension is built, and may result in a different (but
equivalent) solution (for an example of this, see issue 5109). It is also
possible that the integral may be computed with one but not the other,
because not all cases have been implemented yet. It defaults to 'log' so
that the outer extension is exponential when possible, because more of the
exponential case has been implemented.
If separate_integral is True, the result is returned as a tuple (ans, i),
where the integral is ans + i, ans is elementary, and i is either a
NonElementaryIntegral or 0. This useful if you want to try further
integrating the NonElementaryIntegral part using other algorithms to
possibly get a solution in terms of special functions. It is False by
default.
Examples
========
>>> from sympy.integrals.risch import risch_integrate
>>> from sympy import exp, log, pprint
>>> from sympy.abc import x
First, we try integrating exp(-x**2). Except for a constant factor of
2/sqrt(pi), this is the famous error function.
>>> pprint(risch_integrate(exp(-x**2), x))
/
|
| 2
| -x
| e dx
|
/
The unevaluated Integral in the result means that risch_integrate() has
proven that exp(-x**2) does not have an elementary anti-derivative.
In many cases, risch_integrate() can split out the elementary
anti-derivative part from the nonelementary anti-derivative part.
For example,
>>> pprint(risch_integrate((2*log(x)**2 - log(x) - x**2)/(log(x)**3 -
... x**2*log(x)), x))
/
|
log(-x + log(x)) log(x + log(x)) | 1
- ---------------- + --------------- + | ------ dx
2 2 | log(x)
|
/
This means that it has proven that the integral of 1/log(x) is
nonelementary. This function is also known as the logarithmic integral,
and is often denoted as Li(x).
risch_integrate() currently only accepts purely transcendental functions
with exponentials and logarithms, though note that this can include
nested exponentials and logarithms, as well as exponentials with bases
other than E.
>>> pprint(risch_integrate(exp(x)*exp(exp(x)), x))
/ x\
\e /
e
>>> pprint(risch_integrate(exp(exp(x)), x))
/
|
| / x\
| \e /
| e dx
|
/
>>> pprint(risch_integrate(x*x**x*log(x) + x**x + x*x**x, x))
x
x*x
>>> pprint(risch_integrate(x**x, x))
/
|
| x
| x dx
|
/
>>> pprint(risch_integrate(-1/(x*log(x)*log(log(x))**2), x))
1
-----------
log(log(x))
"""
f = S(f)
DE = extension or DifferentialExtension(f, x, handle_first=handle_first,
dummy=True, rewrite_complex=rewrite_complex)
fa, fd = DE.fa, DE.fd
result = S(0)
for case in reversed(DE.cases):
if not fa.has(DE.t) and not fd.has(DE.t) and not case == 'base':
DE.decrement_level()
fa, fd = frac_in((fa, fd), DE.t)
continue
fa, fd = fa.cancel(fd, include=True)
if case == 'exp':
ans, i, b = integrate_hyperexponential(fa, fd, DE, conds=conds)
elif case == 'primitive':
ans, i, b = integrate_primitive(fa, fd, DE)
elif case == 'base':
# XXX: We can't call ratint() directly here because it doesn't
# handle polynomials correctly.
ans = integrate(fa.as_expr()/fd.as_expr(), DE.x, risch=False)
b = False
i = S(0)
else:
raise NotImplementedError("Only exponential and logarithmic "
"extensions are currently supported.")
result += ans
if b:
DE.decrement_level()
fa, fd = frac_in(i, DE.t)
else:
result = result.subs(DE.backsubs)
if not i.is_zero:
i = NonElementaryIntegral(i.function.subs(DE.backsubs),i.limits)
if not separate_integral:
result += i
return result
else:
if isinstance(i, NonElementaryIntegral):
return (result, i)
else:
return (result, 0)
| 66,185 | 36.627061 | 111 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/rde.py
|
"""
Algorithms for solving the Risch differential equation.
Given a differential field K of characteristic 0 that is a simple
monomial extension of a base field k and f, g in K, the Risch
Differential Equation problem is to decide if there exist y in K such
that Dy + f*y == g and to find one if there are some. If t is a
monomial over k and the coefficients of f and g are in k(t), then y is
in k(t), and the outline of the algorithm here is given as:
1. Compute the normal part n of the denominator of y. The problem is
then reduced to finding y' in k<t>, where y == y'/n.
2. Compute the special part s of the denominator of y. The problem is
then reduced to finding y'' in k[t], where y == y''/(n*s)
3. Bound the degree of y''.
4. Reduce the equation Dy + f*y == g to a similar equation with f, g in
k[t].
5. Find the solutions in k[t] of bounded degree of the reduced equation.
See Chapter 6 of "Symbolic Integration I: Transcendental Functions" by
Manuel Bronstein. See also the docstring of risch.py.
"""
from __future__ import print_function, division
from operator import mul
from sympy.core import oo
from sympy.core.compatibility import reduce
from sympy.core.symbol import Dummy
from sympy.polys import Poly, gcd, ZZ, cancel
from sympy.integrals.risch import (gcdex_diophantine, frac_in, derivation,
splitfactor, NonElementaryIntegralException, DecrementLevel)
# TODO: Add messages to NonElementaryIntegralException errors
def order_at(a, p, t):
"""
Computes the order of a at p, with respect to t.
For a, p in k[t], the order of a at p is defined as nu_p(a) = max({n
in Z+ such that p**n|a}), where a != 0. If a == 0, nu_p(a) = +oo.
To compute the order at a rational function, a/b, use the fact that
nu_p(a/b) == nu_p(a) - nu_p(b).
"""
if a.is_zero:
return oo
if p == Poly(t, t):
return a.as_poly(t).ET()[0][0]
# Uses binary search for calculating the power. power_list collects the tuples
# (p^k,k) where each k is some power of 2. After deciding the largest k
# such that k is power of 2 and p^k|a the loop iteratively calculates
# the actual power.
power_list = []
p1 = p
r = a.rem(p1)
tracks_power = 1
while r.is_zero:
power_list.append((p1,tracks_power))
p1 = p1*p1
tracks_power *= 2
r = a.rem(p1)
n = 0
product = Poly(1, t)
while len(power_list) != 0:
final = power_list.pop()
productf = product*final[0]
r = a.rem(productf)
if r.is_zero:
n += final[1]
product = productf
return n
def order_at_oo(a, d, t):
"""
Computes the order of a/d at oo (infinity), with respect to t.
For f in k(t), the order or f at oo is defined as deg(d) - deg(a), where
f == a/d.
"""
if a.is_zero:
return oo
return d.degree(t) - a.degree(t)
def weak_normalizer(a, d, DE, z=None):
"""
Weak normalization.
Given a derivation D on k[t] and f == a/d in k(t), return q in k[t]
such that f - Dq/q is weakly normalized with respect to t.
f in k(t) is said to be "weakly normalized" with respect to t if
residue_p(f) is not a positive integer for any normal irreducible p
in k[t] such that f is in R_p (Definition 6.1.1). If f has an
elementary integral, this is equivalent to no logarithm of
integral(f) whose argument depends on t has a positive integer
coefficient, where the arguments of the logarithms not in k(t) are
in k[t].
Returns (q, f - Dq/q)
"""
z = z or Dummy('z')
dn, ds = splitfactor(d, DE)
# Compute d1, where dn == d1*d2**2*...*dn**n is a square-free
# factorization of d.
g = gcd(dn, dn.diff(DE.t))
d_sqf_part = dn.quo(g)
d1 = d_sqf_part.quo(gcd(d_sqf_part, g))
a1, b = gcdex_diophantine(d.quo(d1).as_poly(DE.t), d1.as_poly(DE.t),
a.as_poly(DE.t))
r = (a - Poly(z, DE.t)*derivation(d1, DE)).as_poly(DE.t).resultant(
d1.as_poly(DE.t))
r = Poly(r, z)
if not r.has(z):
return (Poly(1, DE.t), (a, d))
N = [i for i in r.real_roots() if i in ZZ and i > 0]
q = reduce(mul, [gcd(a - Poly(n, DE.t)*derivation(d1, DE), d1) for n in N],
Poly(1, DE.t))
dq = derivation(q, DE)
sn = q*a - d*dq
sd = q*d
sn, sd = sn.cancel(sd, include=True)
return (q, (sn, sd))
def normal_denom(fa, fd, ga, gd, DE):
"""
Normal part of the denominator.
Given a derivation D on k[t] and f, g in k(t) with f weakly
normalized with respect to t, either raise NonElementaryIntegralException,
in which case the equation Dy + f*y == g has no solution in k(t), or the
quadruplet (a, b, c, h) such that a, h in k[t], b, c in k<t>, and for any
solution y in k(t) of Dy + f*y == g, q = y*h in k<t> satisfies
a*Dq + b*q == c.
This constitutes step 1 in the outline given in the rde.py docstring.
"""
dn, ds = splitfactor(fd, DE)
en, es = splitfactor(gd, DE)
p = dn.gcd(en)
h = en.gcd(en.diff(DE.t)).quo(p.gcd(p.diff(DE.t)))
a = dn*h
c = a*h
if c.div(en)[1]:
# en does not divide dn*h**2
raise NonElementaryIntegralException
ca = c*ga
ca, cd = ca.cancel(gd, include=True)
ba = a*fa - dn*derivation(h, DE)*fd
ba, bd = ba.cancel(fd, include=True)
# (dn*h, dn*h*f - dn*Dh, dn*h**2*g, h)
return (a, (ba, bd), (ca, cd), h)
def special_denom(a, ba, bd, ca, cd, DE, case='auto'):
"""
Special part of the denominator.
case is one of {'exp', 'tan', 'primitive'} for the hyperexponential,
hypertangent, and primitive cases, respectively. For the
hyperexponential (resp. hypertangent) case, given a derivation D on
k[t] and a in k[t], b, c, in k<t> with Dt/t in k (resp. Dt/(t**2 + 1) in
k, sqrt(-1) not in k), a != 0, and gcd(a, t) == 1 (resp.
gcd(a, t**2 + 1) == 1), return the quadruplet (A, B, C, 1/h) such that
A, B, C, h in k[t] and for any solution q in k<t> of a*Dq + b*q == c,
r = qh in k[t] satisfies A*Dr + B*r == C.
For case == 'primitive', k<t> == k[t], so it returns (a, b, c, 1) in
this case.
This constitutes step 2 of the outline given in the rde.py docstring.
"""
from sympy.integrals.prde import parametric_log_deriv
# TODO: finish writing this and write tests
if case == 'auto':
case = DE.case
if case == 'exp':
p = Poly(DE.t, DE.t)
elif case == 'tan':
p = Poly(DE.t**2 + 1, DE.t)
elif case in ['primitive', 'base']:
B = ba.to_field().quo(bd)
C = ca.to_field().quo(cd)
return (a, B, C, Poly(1, DE.t))
else:
raise ValueError("case must be one of {'exp', 'tan', 'primitive', "
"'base'}, not %s." % case)
nb = order_at(ba, p, DE.t) - order_at(bd, p, DE.t)
nc = order_at(ca, p, DE.t) - order_at(cd, p, DE.t)
n = min(0, nc - min(0, nb))
if not nb:
# Possible cancellation.
if case == 'exp':
dcoeff = DE.d.quo(Poly(DE.t, DE.t))
with DecrementLevel(DE): # We are guaranteed to not have problems,
# because case != 'base'.
alphaa, alphad = frac_in(-ba.eval(0)/bd.eval(0)/a.eval(0), DE.t)
etaa, etad = frac_in(dcoeff, DE.t)
A = parametric_log_deriv(alphaa, alphad, etaa, etad, DE)
if A is not None:
a, m, z = A
if a == 1:
n = min(n, m)
elif case == 'tan':
dcoeff = DE.d.quo(Poly(DE.t**2+1, DE.t))
with DecrementLevel(DE): # We are guaranteed to not have problems,
# because case != 'base'.
alphaa, alphad = frac_in(im(-ba.eval(sqrt(-1))/bd.eval(sqrt(-1))/a.eval(sqrt(-1))), DE.t)
betaa, betad = frac_in(re(-ba.eval(sqrt(-1))/bd.eval(sqrt(-1))/a.eval(sqrt(-1))), DE.t)
etaa, etad = frac_in(dcoeff, DE.t)
if recognize_log_derivative(2*betaa, betad, DE):
A = parametric_log_deriv(alphaa*sqrt(-1)*betad+alphad*betaa, alphad*betad, etaa, etad, DE)
if A is not None:
a, m, z = A
if a == 1:
n = min(n, m)
N = max(0, -nb, n - nc)
pN = p**N
pn = p**-n
A = a*pN
B = ba*pN.quo(bd) + Poly(n, DE.t)*a*derivation(p, DE).quo(p)*pN
C = (ca*pN*pn).quo(cd)
h = pn
# (a*p**N, (b + n*a*Dp/p)*p**N, c*p**(N - n), p**-n)
return (A, B, C, h)
def bound_degree(a, b, cQ, DE, case='auto', parametric=False):
"""
Bound on polynomial solutions.
Given a derivation D on k[t] and a, b, c in k[t] with a != 0, return
n in ZZ such that deg(q) <= n for any solution q in k[t] of
a*Dq + b*q == c, when parametric=False, or deg(q) <= n for any solution
c1, ..., cm in Const(k) and q in k[t] of a*Dq + b*q == Sum(ci*gi, (i, 1, m))
when parametric=True.
For parametric=False, cQ is c, a Poly; for parametric=True, cQ is Q ==
[q1, ..., qm], a list of Polys.
This constitutes step 3 of the outline given in the rde.py docstring.
"""
from sympy.integrals.prde import (parametric_log_deriv, limited_integrate,
is_log_deriv_k_t_radical_in_field)
# TODO: finish writing this and write tests
if case == 'auto':
case = DE.case
da = a.degree(DE.t)
db = b.degree(DE.t)
# The parametric and regular cases are identical, except for this part
if parametric:
dc = max([i.degree(DE.t) for i in cQ])
else:
dc = cQ.degree(DE.t)
alpha = cancel(-b.as_poly(DE.t).LC().as_expr()/
a.as_poly(DE.t).LC().as_expr())
if case == 'base':
n = max(0, dc - max(db, da - 1))
if db == da - 1 and alpha.is_Integer:
n = max(0, alpha, dc - db)
elif case == 'primitive':
if db > da:
n = max(0, dc - db)
else:
n = max(0, dc - da + 1)
etaa, etad = frac_in(DE.d, DE.T[DE.level - 1])
t1 = DE.t
with DecrementLevel(DE):
alphaa, alphad = frac_in(alpha, DE.t)
if db == da - 1:
# if alpha == m*Dt + Dz for z in k and m in ZZ:
try:
(za, zd), m = limited_integrate(alphaa, alphad, [(etaa, etad)],
DE)
except NonElementaryIntegralException:
pass
else:
if len(m) != 1:
raise ValueError("Length of m should be 1")
n = max(n, m[0])
elif db == da:
# if alpha == Dz/z for z in k*:
# beta = -lc(a*Dz + b*z)/(z*lc(a))
# if beta == m*Dt + Dw for w in k and m in ZZ:
# n = max(n, m)
A = is_log_deriv_k_t_radical_in_field(alphaa, alphad, DE)
if A is not None:
aa, z = A
if aa == 1:
beta = -(a*derivation(z, DE).as_poly(t1) +
b*z.as_poly(t1)).LC()/(z.as_expr()*a.LC())
betaa, betad = frac_in(beta, DE.t)
try:
(za, zd), m = limited_integrate(betaa, betad,
[(etaa, etad)], DE)
except NonElementaryIntegralException:
pass
else:
if len(m) != 1:
raise ValueError("Length of m should be 1")
n = max(n, m[0])
elif case == 'exp':
n = max(0, dc - max(db, da))
if da == db:
etaa, etad = frac_in(DE.d.quo(Poly(DE.t, DE.t)), DE.T[DE.level - 1])
with DecrementLevel(DE):
alphaa, alphad = frac_in(alpha, DE.t)
A = parametric_log_deriv(alphaa, alphad, etaa, etad, DE)
if A is not None:
# if alpha == m*Dt/t + Dz/z for z in k* and m in ZZ:
# n = max(n, m)
a, m, z = A
if a == 1:
n = max(n, m)
elif case in ['tan', 'other_nonlinear']:
delta = DE.d.degree(DE.t)
lam = DE.d.LC()
alpha = cancel(alpha/lam)
n = max(0, dc - max(da + delta - 1, db))
if db == da + delta - 1 and alpha.is_Integer:
n = max(0, alpha, dc - db)
else:
raise ValueError("case must be one of {'exp', 'tan', 'primitive', "
"'other_nonlinear', 'base'}, not %s." % case)
return n
def spde(a, b, c, n, DE):
"""
Rothstein's Special Polynomial Differential Equation algorithm.
Given a derivation D on k[t], an integer n and a, b, c in k[t] with
a != 0, either raise NonElementaryIntegralException, in which case the
equation a*Dq + b*q == c has no solution of degree at most n in
k[t], or return the tuple (B, C, m, alpha, beta) such that B, C,
alpha, beta in k[t], m in ZZ, and any solution q in k[t] of degree
at most n of a*Dq + b*q == c must be of the form
q == alpha*h + beta, where h in k[t], deg(h) <= m, and Dh + B*h == C.
This constitutes step 4 of the outline given in the rde.py docstring.
"""
zero = Poly(0, DE.t)
alpha = Poly(1, DE.t)
beta = Poly(0, DE.t)
while True:
if c.is_zero:
return (zero, zero, 0, zero, beta) # -1 is more to the point
if (n < 0) is True:
raise NonElementaryIntegralException
g = a.gcd(b)
if not c.rem(g).is_zero: # g does not divide c
raise NonElementaryIntegralException
a, b, c = a.quo(g), b.quo(g), c.quo(g)
if a.degree(DE.t) == 0:
b = b.to_field().quo(a)
c = c.to_field().quo(a)
return (b, c, n, alpha, beta)
r, z = gcdex_diophantine(b, a, c)
b += derivation(a, DE)
c = z - derivation(r, DE)
n -= a.degree(DE.t)
beta += alpha * r
alpha *= a
def no_cancel_b_large(b, c, n, DE):
"""
Poly Risch Differential Equation - No cancellation: deg(b) large enough.
Given a derivation D on k[t], n either an integer or +oo, and b, c
in k[t] with b != 0 and either D == d/dt or
deg(b) > max(0, deg(D) - 1), either raise NonElementaryIntegralException, in
which case the equation Dq + b*q == c has no solution of degree at
most n in k[t], or a solution q in k[t] of this equation with
deg(q) < n.
"""
q = Poly(0, DE.t)
while not c.is_zero:
m = c.degree(DE.t) - b.degree(DE.t)
if not 0 <= m <= n: # n < 0 or m < 0 or m > n
raise NonElementaryIntegralException
p = Poly(c.as_poly(DE.t).LC()/b.as_poly(DE.t).LC()*DE.t**m, DE.t,
expand=False)
q = q + p
n = m - 1
c = c - derivation(p, DE) - b*p
return q
def no_cancel_b_small(b, c, n, DE):
"""
Poly Risch Differential Equation - No cancellation: deg(b) small enough.
Given a derivation D on k[t], n either an integer or +oo, and b, c
in k[t] with deg(b) < deg(D) - 1 and either D == d/dt or
deg(D) >= 2, either raise NonElementaryIntegralException, in which case the
equation Dq + b*q == c has no solution of degree at most n in k[t],
or a solution q in k[t] of this equation with deg(q) <= n, or the
tuple (h, b0, c0) such that h in k[t], b0, c0, in k, and for any
solution q in k[t] of degree at most n of Dq + bq == c, y == q - h
is a solution in k of Dy + b0*y == c0.
"""
q = Poly(0, DE.t)
while not c.is_zero:
if n == 0:
m = 0
else:
m = c.degree(DE.t) - DE.d.degree(DE.t) + 1
if not 0 <= m <= n: # n < 0 or m < 0 or m > n
raise NonElementaryIntegralException
if m > 0:
p = Poly(c.as_poly(DE.t).LC()/(m*DE.d.as_poly(DE.t).LC())*DE.t**m,
DE.t, expand=False)
else:
if b.degree(DE.t) != c.degree(DE.t):
raise NonElementaryIntegralException
if b.degree(DE.t) == 0:
return (q, b.as_poly(DE.T[DE.level - 1]),
c.as_poly(DE.T[DE.level - 1]))
p = Poly(c.as_poly(DE.t).LC()/b.as_poly(DE.t).LC(), DE.t,
expand=False)
q = q + p
n = m - 1
c = c - derivation(p, DE) - b*p
return q
# TODO: better name for this function
def no_cancel_equal(b, c, n, DE):
"""
Poly Risch Differential Equation - No cancellation: deg(b) == deg(D) - 1
Given a derivation D on k[t] with deg(D) >= 2, n either an integer
or +oo, and b, c in k[t] with deg(b) == deg(D) - 1, either raise
NonElementaryIntegralException, in which case the equation Dq + b*q == c has
no solution of degree at most n in k[t], or a solution q in k[t] of
this equation with deg(q) <= n, or the tuple (h, m, C) such that h
in k[t], m in ZZ, and C in k[t], and for any solution q in k[t] of
degree at most n of Dq + b*q == c, y == q - h is a solution in k[t]
of degree at most m of Dy + b*y == C.
"""
q = Poly(0, DE.t)
lc = cancel(-b.as_poly(DE.t).LC()/DE.d.as_poly(DE.t).LC())
if lc.is_Integer and lc.is_positive:
M = lc
else:
M = -1
while not c.is_zero:
m = max(M, c.degree(DE.t) - DE.d.degree(DE.t) + 1)
if not 0 <= m <= n: # n < 0 or m < 0 or m > n
raise NonElementaryIntegralException
u = cancel(m*DE.d.as_poly(DE.t).LC() + b.as_poly(DE.t).LC())
if u.is_zero:
return (q, m, c)
if m > 0:
p = Poly(c.as_poly(DE.t).LC()/u*DE.t**m, DE.t, expand=False)
else:
if c.degree(DE.t) != DE.d.degree(DE.t) - 1:
raise NonElementaryIntegralException
else:
p = c.as_poly(DE.t).LC()/b.as_poly(DE.t).LC()
q = q + p
n = m - 1
c = c - derivation(p, DE) - b*p
return q
def cancel_primitive(b, c, n, DE):
"""
Poly Risch Differential Equation - Cancellation: Primitive case.
Given a derivation D on k[t], n either an integer or +oo, b in k, and
c in k[t] with Dt in k and b != 0, either raise
NonElementaryIntegralException, in which case the equation Dq + b*q == c
has no solution of degree at most n in k[t], or a solution q in k[t] of
this equation with deg(q) <= n.
"""
from sympy.integrals.prde import is_log_deriv_k_t_radical_in_field
with DecrementLevel(DE):
ba, bd = frac_in(b, DE.t)
A = is_log_deriv_k_t_radical_in_field(ba, bd, DE)
if A is not None:
n, z = A
if n == 1: # b == Dz/z
raise NotImplementedError("is_deriv_in_field() is required to "
" solve this problem.")
# if z*c == Dp for p in k[t] and deg(p) <= n:
# return p/z
# else:
# raise NonElementaryIntegralException
if c.is_zero:
return c # return 0
if n < c.degree(DE.t):
raise NonElementaryIntegralException
q = Poly(0, DE.t)
while not c.is_zero:
m = c.degree(DE.t)
if n < m:
raise NonElementaryIntegralException
with DecrementLevel(DE):
a2a, a2d = frac_in(c.LC(), DE.t)
sa, sd = rischDE(ba, bd, a2a, a2d, DE)
stm = Poly(sa.as_expr()/sd.as_expr()*DE.t**m, DE.t, expand=False)
q += stm
n = m - 1
c -= b*stm + derivation(stm, DE)
return q
def cancel_exp(b, c, n, DE):
"""
Poly Risch Differential Equation - Cancellation: Hyperexponential case.
Given a derivation D on k[t], n either an integer or +oo, b in k, and
c in k[t] with Dt/t in k and b != 0, either raise
NonElementaryIntegralException, in which case the equation Dq + b*q == c
has no solution of degree at most n in k[t], or a solution q in k[t] of
this equation with deg(q) <= n.
"""
from sympy.integrals.prde import parametric_log_deriv
eta = DE.d.quo(Poly(DE.t, DE.t)).as_expr()
with DecrementLevel(DE):
etaa, etad = frac_in(eta, DE.t)
ba, bd = frac_in(b, DE.t)
A = parametric_log_deriv(ba, bd, etaa, etad, DE)
if A is not None:
a, m, z = A
if a == 1:
raise NotImplementedError("is_deriv_in_field() is required to "
"solve this problem.")
# if c*z*t**m == Dp for p in k<t> and q = p/(z*t**m) in k[t] and
# deg(q) <= n:
# return q
# else:
# raise NonElementaryIntegralException
if c.is_zero:
return c # return 0
if n < c.degree(DE.t):
raise NonElementaryIntegralException
q = Poly(0, DE.t)
while not c.is_zero:
m = c.degree(DE.t)
if n < m:
raise NonElementaryIntegralException
# a1 = b + m*Dt/t
a1 = b.as_expr()
with DecrementLevel(DE):
# TODO: Write a dummy function that does this idiom
a1a, a1d = frac_in(a1, DE.t)
a1a = a1a*etad + etaa*a1d*Poly(m, DE.t)
a1d = a1d*etad
a2a, a2d = frac_in(c.LC(), DE.t)
sa, sd = rischDE(a1a, a1d, a2a, a2d, DE)
stm = Poly(sa.as_expr()/sd.as_expr()*DE.t**m, DE.t, expand=False)
q += stm
n = m - 1
c -= b*stm + derivation(stm, DE) # deg(c) becomes smaller
return q
def solve_poly_rde(b, cQ, n, DE, parametric=False):
"""
Solve a Polynomial Risch Differential Equation with degree bound n.
This constitutes step 4 of the outline given in the rde.py docstring.
For parametric=False, cQ is c, a Poly; for parametric=True, cQ is Q ==
[q1, ..., qm], a list of Polys.
"""
from sympy.integrals.prde import (prde_no_cancel_b_large,
prde_no_cancel_b_small)
# No cancellation
if not b.is_zero and (DE.case == 'base' or
b.degree(DE.t) > max(0, DE.d.degree(DE.t) - 1)):
if parametric:
return prde_no_cancel_b_large(b, cQ, n, DE)
return no_cancel_b_large(b, cQ, n, DE)
elif (b.is_zero or b.degree(DE.t) < DE.d.degree(DE.t) - 1) and \
(DE.case == 'base' or DE.d.degree(DE.t) >= 2):
if parametric:
return prde_no_cancel_b_small(b, cQ, n, DE)
R = no_cancel_b_small(b, cQ, n, DE)
if isinstance(R, Poly):
return R
else:
# XXX: Might k be a field? (pg. 209)
h, b0, c0 = R
with DecrementLevel(DE):
b0, c0 = b0.as_poly(DE.t), c0.as_poly(DE.t)
if b0 is None: # See above comment
raise ValueError("b0 should be a non-Null value")
if c0 is None:
raise ValueError("c0 should be a non-Null value")
y = solve_poly_rde(b0, c0, n, DE).as_poly(DE.t)
return h + y
elif DE.d.degree(DE.t) >= 2 and b.degree(DE.t) == DE.d.degree(DE.t) - 1 and \
n > -b.as_poly(DE.t).LC()/DE.d.as_poly(DE.t).LC():
# TODO: Is this check necessary, and if so, what should it do if it fails?
# b comes from the first element returned from spde()
if not b.as_poly(DE.t).LC().is_number:
raise TypeError("Result should be a number")
if parametric:
raise NotImplementedError("prde_no_cancel_b_equal() is not yet "
"implemented.")
R = no_cancel_equal(b, cQ, n, DE)
if isinstance(R, Poly):
return R
else:
h, m, C = R
# XXX: Or should it be rischDE()?
y = solve_poly_rde(b, C, m, DE)
return h + y
else:
# Cancellation
if b.is_zero:
raise NotImplementedError("Remaining cases for Poly (P)RDE are "
"not yet implemented (is_deriv_in_field() required).")
else:
if DE.case == 'exp':
if parametric:
raise NotImplementedError("Parametric RDE cancellation "
"hyperexponential case is not yet implemented.")
return cancel_exp(b, cQ, n, DE)
elif DE.case == 'primitive':
if parametric:
raise NotImplementedError("Parametric RDE cancellation "
"primitive case is not yet implemented.")
return cancel_primitive(b, cQ, n, DE)
else:
raise NotImplementedError("Other Poly (P)RDE cancellation "
"cases are not yet implemented (%s)." % case)
if parametric:
raise NotImplementedError("Remaining cases for Poly PRDE not yet "
"implemented.")
raise NotImplementedError("Remaining cases for Poly RDE not yet "
"implemented.")
def rischDE(fa, fd, ga, gd, DE):
"""
Solve a Risch Differential Equation: Dy + f*y == g.
See the outline in the docstring of rde.py for more information
about the procedure used. Either raise NonElementaryIntegralException, in
which case there is no solution y in the given differential field,
or return y in k(t) satisfying Dy + f*y == g, or raise
NotImplementedError, in which case, the algorithms necessary to
solve the given Risch Differential Equation have not yet been
implemented.
"""
_, (fa, fd) = weak_normalizer(fa, fd, DE)
a, (ba, bd), (ca, cd), hn = normal_denom(fa, fd, ga, gd, DE)
A, B, C, hs = special_denom(a, ba, bd, ca, cd, DE)
try:
# Until this is fully implemented, use oo. Note that this will almost
# certainly cause non-termination in spde() (unless A == 1), and
# *might* lead to non-termination in the next step for a nonelementary
# integral (I don't know for certain yet). Fortunately, spde() is
# currently written recursively, so this will just give
# RuntimeError: maximum recursion depth exceeded.
n = bound_degree(A, B, C, DE)
except NotImplementedError:
# Useful for debugging:
# import warnings
# warnings.warn("rischDE: Proceeding with n = oo; may cause "
# "non-termination.")
n = oo
B, C, m, alpha, beta = spde(A, B, C, n, DE)
if C.is_zero:
y = C
else:
y = solve_poly_rde(B, C, m, DE)
return (alpha*y + beta, hn*hs)
| 26,625 | 33.942257 | 110 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/__init__.py
|
"""Integration functions that integrates a sympy expression.
Examples
========
>>> from sympy import integrate, sin
>>> from sympy.abc import x
>>> integrate(1/x,x)
log(x)
>>> integrate(sin(x),x)
-cos(x)
"""
from .integrals import integrate, Integral, line_integrate
from .transforms import (mellin_transform, inverse_mellin_transform,
MellinTransform, InverseMellinTransform,
laplace_transform, inverse_laplace_transform,
LaplaceTransform, InverseLaplaceTransform,
fourier_transform, inverse_fourier_transform,
FourierTransform, InverseFourierTransform,
sine_transform, inverse_sine_transform,
SineTransform, InverseSineTransform,
cosine_transform, inverse_cosine_transform,
CosineTransform, InverseCosineTransform,
hankel_transform, inverse_hankel_transform,
HankelTransform, InverseHankelTransform)
from .singularityfunctions import singularityintegrate
| 1,155 | 41.814815 | 69 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/singularityfunctions.py
|
from __future__ import print_function, division
from sympy.functions import SingularityFunction, DiracDelta, Heaviside
from sympy.core import sympify
from sympy.integrals import integrate
def singularityintegrate(f, x):
"""
This function handles the indefinite integrations of Singularity functions.
The ``integrate`` function calls this function internally whenever an
instance of SingularityFunction is passed as argument.
The idea for integration is the following:
- If we are dealing with a SingularityFunction expression,
i.e. ``SingularityFunction(x, a, n)``, we just return
``SingularityFunction(x, a, n + 1)/(n + 1)`` if ``n >= 0`` and
``SingularityFunction(x, a, n + 1)`` if ``n < 0``.
- If the node is a multiplication or power node having a
SingularityFunction term we rewrite the whole expression in terms of
Heaviside and DiracDelta and then integrate the output. Lastly, we
rewrite the output of integration back in terms of SingularityFunction.
- If none of the above case arises, we return None.
Examples
========
>>> from sympy.integrals.singularityfunctions import singularityintegrate
>>> from sympy import SingularityFunction, symbols, Function
>>> x, a, n, y = symbols('x a n y')
>>> f = Function('f')
>>> singularityintegrate(SingularityFunction(x, a, 3), x)
SingularityFunction(x, a, 4)/4
>>> singularityintegrate(5*SingularityFunction(x, 5, -2), x)
5*SingularityFunction(x, 5, -1)
>>> singularityintegrate(6*SingularityFunction(x, 5, -1), x)
6*SingularityFunction(x, 5, 0)
>>> singularityintegrate(x*SingularityFunction(x, 0, -1), x)
0
>>> singularityintegrate(SingularityFunction(x, 1, -1) * f(x), x)
f(1)*SingularityFunction(x, 1, 0)
"""
if not f.has(SingularityFunction):
return None
if f.func == SingularityFunction:
x = sympify(f.args[0])
a = sympify(f.args[1])
n = sympify(f.args[2])
if n.is_positive or n.is_zero:
return SingularityFunction(x, a, n + 1)/(n + 1)
elif n == -1 or n == -2:
return SingularityFunction(x, a, n + 1)
if f.is_Mul or f.is_Pow:
expr = f.rewrite(DiracDelta)
expr = integrate(expr, x)
return expr.rewrite(SingularityFunction)
return None
| 2,352 | 35.2 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/transforms.py
|
""" Integral Transforms """
from __future__ import print_function, division
from sympy.core import S
from sympy.core.compatibility import reduce, range
from sympy.core.function import Function
from sympy.core.numbers import oo
from sympy.core.symbol import Dummy
from sympy.integrals import integrate, Integral
from sympy.integrals.meijerint import _dummy
from sympy.logic.boolalg import to_cnf, conjuncts, disjuncts, Or, And
from sympy.simplify import simplify
from sympy.utilities import default_sort_key
from sympy.matrices.matrices import MatrixBase
##########################################################################
# Helpers / Utilities
##########################################################################
class IntegralTransformError(NotImplementedError):
"""
Exception raised in relation to problems computing transforms.
This class is mostly used internally; if integrals cannot be computed
objects representing unevaluated transforms are usually returned.
The hint ``needeval=True`` can be used to disable returning transform
objects, and instead raise this exception if an integral cannot be
computed.
"""
def __init__(self, transform, function, msg):
super(IntegralTransformError, self).__init__(
"%s Transform could not be computed: %s." % (transform, msg))
self.function = function
class IntegralTransform(Function):
"""
Base class for integral transforms.
This class represents unevaluated transforms.
To implement a concrete transform, derive from this class and implement
the _compute_transform(f, x, s, **hints) and _as_integral(f, x, s)
functions. If the transform cannot be computed, raise IntegralTransformError.
Also set cls._name.
Implement self._collapse_extra if your function returns more than just a
number and possibly a convergence condition.
"""
@property
def function(self):
""" The function to be transformed. """
return self.args[0]
@property
def function_variable(self):
""" The dependent variable of the function to be transformed. """
return self.args[1]
@property
def transform_variable(self):
""" The independent transform variable. """
return self.args[2]
@property
def free_symbols(self):
"""
This method returns the symbols that will exist when the transform
is evaluated.
"""
return self.function.free_symbols.union({self.transform_variable}) \
- {self.function_variable}
def _compute_transform(self, f, x, s, **hints):
raise NotImplementedError
def _as_integral(self, f, x, s):
raise NotImplementedError
def _collapse_extra(self, extra):
cond = And(*extra)
if cond == False:
raise IntegralTransformError(self.__class__.name, None, '')
def doit(self, **hints):
"""
Try to evaluate the transform in closed form.
This general function handles linearity, but apart from that leaves
pretty much everything to _compute_transform.
Standard hints are the following:
- ``simplify``: whether or not to simplify the result
- ``noconds``: if True, don't return convergence conditions
- ``needeval``: if True, raise IntegralTransformError instead of
returning IntegralTransform objects
The default values of these hints depend on the concrete transform,
usually the default is
``(simplify, noconds, needeval) = (True, False, False)``.
"""
from sympy import Add, expand_mul, Mul
from sympy.core.function import AppliedUndef
needeval = hints.pop('needeval', False)
try_directly = not any(func.has(self.function_variable)
for func in self.function.atoms(AppliedUndef))
if try_directly:
try:
return self._compute_transform(self.function,
self.function_variable, self.transform_variable, **hints)
except IntegralTransformError:
pass
fn = self.function
if not fn.is_Add:
fn = expand_mul(fn)
if fn.is_Add:
hints['needeval'] = needeval
res = [self.__class__(*([x] + list(self.args[1:]))).doit(**hints)
for x in fn.args]
extra = []
ress = []
for x in res:
if not isinstance(x, tuple):
x = [x]
ress.append(x[0])
if len(x) > 1:
extra += [x[1:]]
res = Add(*ress)
if not extra:
return res
try:
extra = self._collapse_extra(extra)
return tuple([res]) + tuple(extra)
except IntegralTransformError:
pass
if needeval:
raise IntegralTransformError(
self.__class__._name, self.function, 'needeval')
# TODO handle derivatives etc
# pull out constant coefficients
coeff, rest = fn.as_coeff_mul(self.function_variable)
return coeff*self.__class__(*([Mul(*rest)] + list(self.args[1:])))
@property
def as_integral(self):
return self._as_integral(self.function, self.function_variable,
self.transform_variable)
def _eval_rewrite_as_Integral(self, *args):
return self.as_integral
from sympy.solvers.inequalities import _solve_inequality
def _simplify(expr, doit):
from sympy import powdenest, piecewise_fold
if doit:
return simplify(powdenest(piecewise_fold(expr), polar=True))
return expr
def _noconds_(default):
"""
This is a decorator generator for dropping convergence conditions.
Suppose you define a function ``transform(*args)`` which returns a tuple of
the form ``(result, cond1, cond2, ...)``.
Decorating it ``@_noconds_(default)`` will add a new keyword argument
``noconds`` to it. If ``noconds=True``, the return value will be altered to
be only ``result``, whereas if ``noconds=False`` the return value will not
be altered.
The default value of the ``noconds`` keyword will be ``default`` (i.e. the
argument of this function).
"""
def make_wrapper(func):
from sympy.core.decorators import wraps
@wraps(func)
def wrapper(*args, **kwargs):
noconds = kwargs.pop('noconds', default)
res = func(*args, **kwargs)
if noconds:
return res[0]
return res
return wrapper
return make_wrapper
_noconds = _noconds_(False)
##########################################################################
# Mellin Transform
##########################################################################
def _default_integrator(f, x):
return integrate(f, (x, 0, oo))
@_noconds
def _mellin_transform(f, x, s_, integrator=_default_integrator, simplify=True):
""" Backend function to compute Mellin transforms. """
from sympy import re, Max, Min, count_ops
# We use a fresh dummy, because assumptions on s might drop conditions on
# convergence of the integral.
s = _dummy('s', 'mellin-transform', f)
F = integrator(x**(s - 1) * f, x)
if not F.has(Integral):
return _simplify(F.subs(s, s_), simplify), (-oo, oo), True
if not F.is_Piecewise:
raise IntegralTransformError('Mellin', f, 'could not compute integral')
F, cond = F.args[0]
if F.has(Integral):
raise IntegralTransformError(
'Mellin', f, 'integral in unexpected form')
def process_conds(cond):
"""
Turn ``cond`` into a strip (a, b), and auxiliary conditions.
"""
a = -oo
b = oo
aux = True
conds = conjuncts(to_cnf(cond))
t = Dummy('t', real=True)
for c in conds:
a_ = oo
b_ = -oo
aux_ = []
for d in disjuncts(c):
d_ = d.replace(
re, lambda x: x.as_real_imag()[0]).subs(re(s), t)
if not d.is_Relational or \
d.rel_op in ('==', '!=') \
or d_.has(s) or not d_.has(t):
aux_ += [d]
continue
soln = _solve_inequality(d_, t)
if not soln.is_Relational or \
soln.rel_op in ('==', '!='):
aux_ += [d]
continue
if soln.lts == t:
b_ = Max(soln.gts, b_)
else:
a_ = Min(soln.lts, a_)
if a_ != oo and a_ != b:
a = Max(a_, a)
elif b_ != -oo and b_ != a:
b = Min(b_, b)
else:
aux = And(aux, Or(*aux_))
return a, b, aux
conds = [process_conds(c) for c in disjuncts(cond)]
conds = [x for x in conds if x[2] != False]
conds.sort(key=lambda x: (x[0] - x[1], count_ops(x[2])))
if not conds:
raise IntegralTransformError('Mellin', f, 'no convergence found')
a, b, aux = conds[0]
return _simplify(F.subs(s, s_), simplify), (a, b), aux
class MellinTransform(IntegralTransform):
"""
Class representing unevaluated Mellin transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute Mellin transforms, see the :func:`mellin_transform`
docstring.
"""
_name = 'Mellin'
def _compute_transform(self, f, x, s, **hints):
return _mellin_transform(f, x, s, **hints)
def _as_integral(self, f, x, s):
return Integral(f*x**(s - 1), (x, 0, oo))
def _collapse_extra(self, extra):
from sympy import Max, Min
a = []
b = []
cond = []
for (sa, sb), c in extra:
a += [sa]
b += [sb]
cond += [c]
res = (Max(*a), Min(*b)), And(*cond)
if (res[0][0] >= res[0][1]) == True or res[1] == False:
raise IntegralTransformError(
'Mellin', None, 'no combined convergence.')
return res
def mellin_transform(f, x, s, **hints):
r"""
Compute the Mellin transform `F(s)` of `f(x)`,
.. math :: F(s) = \int_0^\infty x^{s-1} f(x) \mathrm{d}x.
For all "sensible" functions, this converges absolutely in a strip
`a < \operatorname{Re}(s) < b`.
The Mellin transform is related via change of variables to the Fourier
transform, and also to the (bilateral) Laplace transform.
This function returns ``(F, (a, b), cond)``
where ``F`` is the Mellin transform of ``f``, ``(a, b)`` is the fundamental strip
(as above), and ``cond`` are auxiliary convergence conditions.
If the integral cannot be computed in closed form, this function returns
an unevaluated :class:`MellinTransform` object.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`. If ``noconds=False``,
then only `F` will be returned (i.e. not ``cond``, and also not the strip
``(a, b)``).
>>> from sympy.integrals.transforms import mellin_transform
>>> from sympy import exp
>>> from sympy.abc import x, s
>>> mellin_transform(exp(-x), x, s)
(gamma(s), (0, oo), True)
See Also
========
inverse_mellin_transform, laplace_transform, fourier_transform
hankel_transform, inverse_hankel_transform
"""
return MellinTransform(f, x, s).doit(**hints)
def _rewrite_sin(m_n, s, a, b):
"""
Re-write the sine function ``sin(m*s + n)`` as gamma functions, compatible
with the strip (a, b).
Return ``(gamma1, gamma2, fac)`` so that ``f == fac/(gamma1 * gamma2)``.
>>> from sympy.integrals.transforms import _rewrite_sin
>>> from sympy import pi, S
>>> from sympy.abc import s
>>> _rewrite_sin((pi, 0), s, 0, 1)
(gamma(s), gamma(-s + 1), pi)
>>> _rewrite_sin((pi, 0), s, 1, 0)
(gamma(s - 1), gamma(-s + 2), -pi)
>>> _rewrite_sin((pi, 0), s, -1, 0)
(gamma(s + 1), gamma(-s), -pi)
>>> _rewrite_sin((pi, pi/2), s, S(1)/2, S(3)/2)
(gamma(s - 1/2), gamma(-s + 3/2), -pi)
>>> _rewrite_sin((pi, pi), s, 0, 1)
(gamma(s), gamma(-s + 1), -pi)
>>> _rewrite_sin((2*pi, 0), s, 0, S(1)/2)
(gamma(2*s), gamma(-2*s + 1), pi)
>>> _rewrite_sin((2*pi, 0), s, S(1)/2, 1)
(gamma(2*s - 1), gamma(-2*s + 2), -pi)
"""
# (This is a separate function because it is moderately complicated,
# and I want to doctest it.)
# We want to use pi/sin(pi*x) = gamma(x)*gamma(1-x).
# But there is one comlication: the gamma functions determine the
# inegration contour in the definition of the G-function. Usually
# it would not matter if this is slightly shifted, unless this way
# we create an undefined function!
# So we try to write this in such a way that the gammas are
# eminently on the right side of the strip.
from sympy import expand_mul, pi, ceiling, gamma
m, n = m_n
m = expand_mul(m/pi)
n = expand_mul(n/pi)
r = ceiling(-m*a - n.as_real_imag()[0]) # Don't use re(n), does not expand
return gamma(m*s + n + r), gamma(1 - n - r - m*s), (-1)**r*pi
class MellinTransformStripError(ValueError):
"""
Exception raised by _rewrite_gamma. Mainly for internal use.
"""
pass
def _rewrite_gamma(f, s, a, b):
"""
Try to rewrite the product f(s) as a product of gamma functions,
so that the inverse Mellin transform of f can be expressed as a meijer
G function.
Return (an, ap), (bm, bq), arg, exp, fac such that
G((an, ap), (bm, bq), arg/z**exp)*fac is the inverse Mellin transform of f(s).
Raises IntegralTransformError or MellinTransformStripError on failure.
It is asserted that f has no poles in the fundamental strip designated by
(a, b). One of a and b is allowed to be None. The fundamental strip is
important, because it determines the inversion contour.
This function can handle exponentials, linear factors, trigonometric
functions.
This is a helper function for inverse_mellin_transform that will not
attempt any transformations on f.
>>> from sympy.integrals.transforms import _rewrite_gamma
>>> from sympy.abc import s
>>> from sympy import oo
>>> _rewrite_gamma(s*(s+3)*(s-1), s, -oo, oo)
(([], [-3, 0, 1]), ([-2, 1, 2], []), 1, 1, -1)
>>> _rewrite_gamma((s-1)**2, s, -oo, oo)
(([], [1, 1]), ([2, 2], []), 1, 1, 1)
Importance of the fundamental strip:
>>> _rewrite_gamma(1/s, s, 0, oo)
(([1], []), ([], [0]), 1, 1, 1)
>>> _rewrite_gamma(1/s, s, None, oo)
(([1], []), ([], [0]), 1, 1, 1)
>>> _rewrite_gamma(1/s, s, 0, None)
(([1], []), ([], [0]), 1, 1, 1)
>>> _rewrite_gamma(1/s, s, -oo, 0)
(([], [1]), ([0], []), 1, 1, -1)
>>> _rewrite_gamma(1/s, s, None, 0)
(([], [1]), ([0], []), 1, 1, -1)
>>> _rewrite_gamma(1/s, s, -oo, None)
(([], [1]), ([0], []), 1, 1, -1)
>>> _rewrite_gamma(2**(-s+3), s, -oo, oo)
(([], []), ([], []), 1/2, 1, 8)
"""
from itertools import repeat
from sympy import (Poly, gamma, Mul, re, CRootOf, exp as exp_, expand,
roots, ilcm, pi, sin, cos, tan, cot, igcd, exp_polar)
# Our strategy will be as follows:
# 1) Guess a constant c such that the inversion integral should be
# performed wrt s'=c*s (instead of plain s). Write s for s'.
# 2) Process all factors, rewrite them independently as gamma functions in
# argument s, or exponentials of s.
# 3) Try to transform all gamma functions s.t. they have argument
# a+s or a-s.
# 4) Check that the resulting G function parameters are valid.
# 5) Combine all the exponentials.
a_, b_ = S([a, b])
def left(c, is_numer):
"""
Decide whether pole at c lies to the left of the fundamental strip.
"""
# heuristically, this is the best chance for us to solve the inequalities
c = expand(re(c))
if a_ is None and b_ is oo:
return True
if a_ is None:
return c < b_
if b_ is None:
return c <= a_
if (c >= b_) == True:
return False
if (c <= a_) == True:
return True
if is_numer:
return None
if a_.free_symbols or b_.free_symbols or c.free_symbols:
return None # XXX
#raise IntegralTransformError('Inverse Mellin', f,
# 'Could not determine position of singularity %s'
# ' relative to fundamental strip' % c)
raise MellinTransformStripError('Pole inside critical strip?')
# 1)
s_multipliers = []
for g in f.atoms(gamma):
if not g.has(s):
continue
arg = g.args[0]
if arg.is_Add:
arg = arg.as_independent(s)[1]
coeff, _ = arg.as_coeff_mul(s)
s_multipliers += [coeff]
for g in f.atoms(sin, cos, tan, cot):
if not g.has(s):
continue
arg = g.args[0]
if arg.is_Add:
arg = arg.as_independent(s)[1]
coeff, _ = arg.as_coeff_mul(s)
s_multipliers += [coeff/pi]
s_multipliers = [abs(x) if x.is_real else x for x in s_multipliers]
common_coefficient = S(1)
for x in s_multipliers:
if not x.is_Rational:
common_coefficient = x
break
s_multipliers = [x/common_coefficient for x in s_multipliers]
if (any(not x.is_Rational for x in s_multipliers) or
not common_coefficient.is_real):
raise IntegralTransformError("Gamma", None, "Nonrational multiplier")
s_multiplier = common_coefficient/reduce(ilcm, [S(x.q)
for x in s_multipliers], S(1))
if s_multiplier == common_coefficient:
if len(s_multipliers) == 0:
s_multiplier = common_coefficient
else:
s_multiplier = common_coefficient \
*reduce(igcd, [S(x.p) for x in s_multipliers])
exponent = S(1)
fac = S(1)
f = f.subs(s, s/s_multiplier)
fac /= s_multiplier
exponent = 1/s_multiplier
if a_ is not None:
a_ *= s_multiplier
if b_ is not None:
b_ *= s_multiplier
# 2)
numer, denom = f.as_numer_denom()
numer = Mul.make_args(numer)
denom = Mul.make_args(denom)
args = list(zip(numer, repeat(True))) + list(zip(denom, repeat(False)))
facs = []
dfacs = []
# *_gammas will contain pairs (a, c) representing Gamma(a*s + c)
numer_gammas = []
denom_gammas = []
# exponentials will contain bases for exponentials of s
exponentials = []
def exception(fact):
return IntegralTransformError("Inverse Mellin", f, "Unrecognised form '%s'." % fact)
while args:
fact, is_numer = args.pop()
if is_numer:
ugammas, lgammas = numer_gammas, denom_gammas
ufacs, lfacs = facs, dfacs
else:
ugammas, lgammas = denom_gammas, numer_gammas
ufacs, lfacs = dfacs, facs
def linear_arg(arg):
""" Test if arg is of form a*s+b, raise exception if not. """
if not arg.is_polynomial(s):
raise exception(fact)
p = Poly(arg, s)
if p.degree() != 1:
raise exception(fact)
return p.all_coeffs()
# constants
if not fact.has(s):
ufacs += [fact]
# exponentials
elif fact.is_Pow or isinstance(fact, exp_):
if fact.is_Pow:
base = fact.base
exp = fact.exp
else:
base = exp_polar(1)
exp = fact.args[0]
if exp.is_Integer:
cond = is_numer
if exp < 0:
cond = not cond
args += [(base, cond)]*abs(exp)
continue
elif not base.has(s):
a, b = linear_arg(exp)
if not is_numer:
base = 1/base
exponentials += [base**a]
facs += [base**b]
else:
raise exception(fact)
# linear factors
elif fact.is_polynomial(s):
p = Poly(fact, s)
if p.degree() != 1:
# We completely factor the poly. For this we need the roots.
# Now roots() only works in some cases (low degree), and CRootOf
# only works without parameters. So try both...
coeff = p.LT()[1]
rs = roots(p, s)
if len(rs) != p.degree():
rs = CRootOf.all_roots(p)
ufacs += [coeff]
args += [(s - c, is_numer) for c in rs]
continue
a, c = p.all_coeffs()
ufacs += [a]
c /= -a
# Now need to convert s - c
if left(c, is_numer):
ugammas += [(S(1), -c + 1)]
lgammas += [(S(1), -c)]
else:
ufacs += [-1]
ugammas += [(S(-1), c + 1)]
lgammas += [(S(-1), c)]
elif isinstance(fact, gamma):
a, b = linear_arg(fact.args[0])
if is_numer:
if (a > 0 and (left(-b/a, is_numer) == False)) or \
(a < 0 and (left(-b/a, is_numer) == True)):
raise NotImplementedError(
'Gammas partially over the strip.')
ugammas += [(a, b)]
elif isinstance(fact, sin):
# We try to re-write all trigs as gammas. This is not in
# general the best strategy, since sometimes this is impossible,
# but rewriting as exponentials would work. However trig functions
# in inverse mellin transforms usually all come from simplifying
# gamma terms, so this should work.
a = fact.args[0]
if is_numer:
# No problem with the poles.
gamma1, gamma2, fac_ = gamma(a/pi), gamma(1 - a/pi), pi
else:
gamma1, gamma2, fac_ = _rewrite_sin(linear_arg(a), s, a_, b_)
args += [(gamma1, not is_numer), (gamma2, not is_numer)]
ufacs += [fac_]
elif isinstance(fact, tan):
a = fact.args[0]
args += [(sin(a, evaluate=False), is_numer),
(sin(pi/2 - a, evaluate=False), not is_numer)]
elif isinstance(fact, cos):
a = fact.args[0]
args += [(sin(pi/2 - a, evaluate=False), is_numer)]
elif isinstance(fact, cot):
a = fact.args[0]
args += [(sin(pi/2 - a, evaluate=False), is_numer),
(sin(a, evaluate=False), not is_numer)]
else:
raise exception(fact)
fac *= Mul(*facs)/Mul(*dfacs)
# 3)
an, ap, bm, bq = [], [], [], []
for gammas, plus, minus, is_numer in [(numer_gammas, an, bm, True),
(denom_gammas, bq, ap, False)]:
while gammas:
a, c = gammas.pop()
if a != -1 and a != +1:
# We use the gamma function multiplication theorem.
p = abs(S(a))
newa = a/p
newc = c/p
if not a.is_Integer:
raise TypeError("a is not an integer")
for k in range(p):
gammas += [(newa, newc + k/p)]
if is_numer:
fac *= (2*pi)**((1 - p)/2) * p**(c - S(1)/2)
exponentials += [p**a]
else:
fac /= (2*pi)**((1 - p)/2) * p**(c - S(1)/2)
exponentials += [p**(-a)]
continue
if a == +1:
plus.append(1 - c)
else:
minus.append(c)
# 4)
# TODO
# 5)
arg = Mul(*exponentials)
# for testability, sort the arguments
an.sort(key=default_sort_key)
ap.sort(key=default_sort_key)
bm.sort(key=default_sort_key)
bq.sort(key=default_sort_key)
return (an, ap), (bm, bq), arg, exponent, fac
@_noconds_(True)
def _inverse_mellin_transform(F, s, x_, strip, as_meijerg=False):
""" A helper for the real inverse_mellin_transform function, this one here
assumes x to be real and positive. """
from sympy import (expand, expand_mul, hyperexpand, meijerg,
arg, pi, re, factor, Heaviside, gamma, Add)
x = _dummy('t', 'inverse-mellin-transform', F, positive=True)
# Actually, we won't try integration at all. Instead we use the definition
# of the Meijer G function as a fairly general inverse mellin transform.
F = F.rewrite(gamma)
for g in [factor(F), expand_mul(F), expand(F)]:
if g.is_Add:
# do all terms separately
ress = [_inverse_mellin_transform(G, s, x, strip, as_meijerg,
noconds=False)
for G in g.args]
conds = [p[1] for p in ress]
ress = [p[0] for p in ress]
res = Add(*ress)
if not as_meijerg:
res = factor(res, gens=res.atoms(Heaviside))
return res.subs(x, x_), And(*conds)
try:
a, b, C, e, fac = _rewrite_gamma(g, s, strip[0], strip[1])
except IntegralTransformError:
continue
G = meijerg(a, b, C/x**e)
if as_meijerg:
h = G
else:
try:
h = hyperexpand(G)
except NotImplementedError as detail:
raise IntegralTransformError(
'Inverse Mellin', F, 'Could not calculate integral')
if h.is_Piecewise and len(h.args) == 3:
# XXX we break modularity here!
h = Heaviside(x - abs(C))*h.args[0].args[0] \
+ Heaviside(abs(C) - x)*h.args[1].args[0]
# We must ensure that the intgral along the line we want converges,
# and return that value.
# See [L], 5.2
cond = [abs(arg(G.argument)) < G.delta*pi]
# Note: we allow ">=" here, this corresponds to convergence if we let
# limits go to oo symetrically. ">" corresponds to absolute convergence.
cond += [And(Or(len(G.ap) != len(G.bq), 0 >= re(G.nu) + 1),
abs(arg(G.argument)) == G.delta*pi)]
cond = Or(*cond)
if cond == False:
raise IntegralTransformError(
'Inverse Mellin', F, 'does not converge')
return (h*fac).subs(x, x_), cond
raise IntegralTransformError('Inverse Mellin', F, '')
_allowed = None
class InverseMellinTransform(IntegralTransform):
"""
Class representing unevaluated inverse Mellin transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute inverse Mellin transforms, see the
:func:`inverse_mellin_transform` docstring.
"""
_name = 'Inverse Mellin'
_none_sentinel = Dummy('None')
_c = Dummy('c')
def __new__(cls, F, s, x, a, b, **opts):
if a is None:
a = InverseMellinTransform._none_sentinel
if b is None:
b = InverseMellinTransform._none_sentinel
return IntegralTransform.__new__(cls, F, s, x, a, b, **opts)
@property
def fundamental_strip(self):
a, b = self.args[3], self.args[4]
if a is InverseMellinTransform._none_sentinel:
a = None
if b is InverseMellinTransform._none_sentinel:
b = None
return a, b
def _compute_transform(self, F, s, x, **hints):
from sympy import postorder_traversal
global _allowed
if _allowed is None:
from sympy import (
exp, gamma, sin, cos, tan, cot, cosh, sinh, tanh,
coth, factorial, rf)
_allowed = set(
[exp, gamma, sin, cos, tan, cot, cosh, sinh, tanh, coth,
factorial, rf])
for f in postorder_traversal(F):
if f.is_Function and f.has(s) and f.func not in _allowed:
raise IntegralTransformError('Inverse Mellin', F,
'Component %s not recognised.' % f)
strip = self.fundamental_strip
return _inverse_mellin_transform(F, s, x, strip, **hints)
def _as_integral(self, F, s, x):
from sympy import I
c = self.__class__._c
return Integral(F*x**(-s), (s, c - I*oo, c + I*oo))
def inverse_mellin_transform(F, s, x, strip, **hints):
r"""
Compute the inverse Mellin transform of `F(s)` over the fundamental
strip given by ``strip=(a, b)``.
This can be defined as
.. math:: f(x) = \int_{c - i\infty}^{c + i\infty} x^{-s} F(s) \mathrm{d}s,
for any `c` in the fundamental strip. Under certain regularity
conditions on `F` and/or `f`,
this recovers `f` from its Mellin transform `F`
(and vice versa), for positive real `x`.
One of `a` or `b` may be passed as ``None``; a suitable `c` will be
inferred.
If the integral cannot be computed in closed form, this function returns
an unevaluated :class:`InverseMellinTransform` object.
Note that this function will assume x to be positive and real, regardless
of the sympy assumptions!
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`.
>>> from sympy.integrals.transforms import inverse_mellin_transform
>>> from sympy import oo, gamma
>>> from sympy.abc import x, s
>>> inverse_mellin_transform(gamma(s), s, x, (0, oo))
exp(-x)
The fundamental strip matters:
>>> f = 1/(s**2 - 1)
>>> inverse_mellin_transform(f, s, x, (-oo, -1))
(x/2 - 1/(2*x))*Heaviside(x - 1)
>>> inverse_mellin_transform(f, s, x, (-1, 1))
-x*Heaviside(-x + 1)/2 - Heaviside(x - 1)/(2*x)
>>> inverse_mellin_transform(f, s, x, (1, oo))
(-x/2 + 1/(2*x))*Heaviside(-x + 1)
See Also
========
mellin_transform
hankel_transform, inverse_hankel_transform
"""
return InverseMellinTransform(F, s, x, strip[0], strip[1]).doit(**hints)
##########################################################################
# Laplace Transform
##########################################################################
def _simplifyconds(expr, s, a):
r"""
Naively simplify some conditions occuring in ``expr``, given that `\operatorname{Re}(s) > a`.
>>> from sympy.integrals.transforms import _simplifyconds as simp
>>> from sympy.abc import x
>>> from sympy import sympify as S
>>> simp(abs(x**2) < 1, x, 1)
False
>>> simp(abs(x**2) < 1, x, 2)
False
>>> simp(abs(x**2) < 1, x, 0)
Abs(x**2) < 1
>>> simp(abs(1/x**2) < 1, x, 1)
True
>>> simp(S(1) < abs(x), x, 1)
True
>>> simp(S(1) < abs(1/x), x, 1)
False
>>> from sympy import Ne
>>> simp(Ne(1, x**3), x, 1)
True
>>> simp(Ne(1, x**3), x, 2)
True
>>> simp(Ne(1, x**3), x, 0)
Ne(1, x**3)
"""
from sympy.core.relational import ( StrictGreaterThan, StrictLessThan,
Unequality )
from sympy import Abs
def power(ex):
if ex == s:
return 1
if ex.is_Pow and ex.base == s:
return ex.exp
return None
def bigger(ex1, ex2):
""" Return True only if |ex1| > |ex2|, False only if |ex1| < |ex2|.
Else return None. """
if ex1.has(s) and ex2.has(s):
return None
if ex1.func is Abs:
ex1 = ex1.args[0]
if ex2.func is Abs:
ex2 = ex2.args[0]
if ex1.has(s):
try:
return bigger(1/ex2, 1/ex1)
except TypeError:
return None
n = power(ex2)
if n is None:
return None
if n > 0 and (abs(ex1) <= abs(a)**n) == True:
return False
if n < 0 and (abs(ex1) >= abs(a)**n) == True:
return True
def replie(x, y):
""" simplify x < y """
if not (x.is_positive or x.func is Abs) \
or not (y.is_positive or y.func is Abs):
return (x < y)
r = bigger(x, y)
if r is not None:
return not r
return (x < y)
def replue(x, y):
b = bigger(x, y)
if b == True or b == False:
return True
return Unequality(x, y)
def repl(ex, *args):
if ex == True or ex == False:
return bool(ex)
return ex.replace(*args)
expr = repl(expr, StrictLessThan, replie)
expr = repl(expr, StrictGreaterThan, lambda x, y: replie(y, x))
expr = repl(expr, Unequality, replue)
return expr
@_noconds
def _laplace_transform(f, t, s_, simplify=True):
""" The backend function for Laplace transforms. """
from sympy import (re, Max, exp, pi, Min, periodic_argument as arg,
cos, Wild, symbols, polar_lift)
s = Dummy('s')
F = integrate(exp(-s*t) * f, (t, 0, oo))
if not F.has(Integral):
return _simplify(F.subs(s, s_), simplify), -oo, True
if not F.is_Piecewise:
raise IntegralTransformError(
'Laplace', f, 'could not compute integral')
F, cond = F.args[0]
if F.has(Integral):
raise IntegralTransformError(
'Laplace', f, 'integral in unexpected form')
def process_conds(conds):
""" Turn ``conds`` into a strip and auxiliary conditions. """
a = -oo
aux = True
conds = conjuncts(to_cnf(conds))
u = Dummy('u', real=True)
p, q, w1, w2, w3, w4, w5 = symbols(
'p q w1 w2 w3 w4 w5', cls=Wild, exclude=[s])
for c in conds:
a_ = oo
aux_ = []
for d in disjuncts(c):
m = d.match(abs(arg((s + w3)**p*q, w1)) < w2)
if not m:
m = d.match(abs(arg((s + w3)**p*q, w1)) <= w2)
if not m:
m = d.match(abs(arg((polar_lift(s + w3))**p*q, w1)) < w2)
if not m:
m = d.match(abs(arg((polar_lift(s + w3))**p*q, w1)) <= w2)
if m:
if m[q].is_positive and m[w2]/m[p] == pi/2:
d = re(s + m[w3]) > 0
m = d.match(
0 < cos(abs(arg(s**w1*w5, q))*w2)*abs(s**w3)**w4 - p)
if not m:
m = d.match(0 < cos(abs(
arg(polar_lift(s)**w1*w5, q))*w2)*abs(s**w3)**w4 - p)
if m and all(m[wild].is_positive for wild in [w1, w2, w3, w4, w5]):
d = re(s) > m[p]
d_ = d.replace(
re, lambda x: x.expand().as_real_imag()[0]).subs(re(s), t)
if not d.is_Relational or \
d.rel_op in ('==', '!=') \
or d_.has(s) or not d_.has(t):
aux_ += [d]
continue
soln = _solve_inequality(d_, t)
if not soln.is_Relational or \
soln.rel_op in ('==', '!='):
aux_ += [d]
continue
if soln.lts == t:
raise IntegralTransformError('Laplace', f,
'convergence not in half-plane?')
else:
a_ = Min(soln.lts, a_)
if a_ != oo:
a = Max(a_, a)
else:
aux = And(aux, Or(*aux_))
return a, aux
conds = [process_conds(c) for c in disjuncts(cond)]
conds2 = [x for x in conds if x[1] != False and x[0] != -oo]
if not conds2:
conds2 = [x for x in conds if x[1] != False]
conds = conds2
def cnt(expr):
if expr == True or expr == False:
return 0
return expr.count_ops()
conds.sort(key=lambda x: (-x[0], cnt(x[1])))
if not conds:
raise IntegralTransformError('Laplace', f, 'no convergence found')
a, aux = conds[0]
def sbs(expr):
if expr == S.true or expr == S.false:
return bool(expr)
return expr.subs(s, s_)
if simplify:
F = _simplifyconds(F, s, a)
aux = _simplifyconds(aux, s, a)
return _simplify(F.subs(s, s_), simplify), sbs(a), sbs(aux)
class LaplaceTransform(IntegralTransform):
"""
Class representing unevaluated Laplace transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute Laplace transforms, see the :func:`laplace_transform`
docstring.
"""
_name = 'Laplace'
def _compute_transform(self, f, t, s, **hints):
return _laplace_transform(f, t, s, **hints)
def _as_integral(self, f, t, s):
from sympy import exp
return Integral(f*exp(-s*t), (t, 0, oo))
def _collapse_extra(self, extra):
from sympy import Max
conds = []
planes = []
for plane, cond in extra:
conds.append(cond)
planes.append(plane)
cond = And(*conds)
plane = Max(*planes)
if cond == False:
raise IntegralTransformError(
'Laplace', None, 'No combined convergence.')
return plane, cond
def laplace_transform(f, t, s, **hints):
r"""
Compute the Laplace Transform `F(s)` of `f(t)`,
.. math :: F(s) = \int_0^\infty e^{-st} f(t) \mathrm{d}t.
For all "sensible" functions, this converges absolutely in a
half plane `a < \operatorname{Re}(s)`.
This function returns ``(F, a, cond)``
where ``F`` is the Laplace transform of ``f``, `\operatorname{Re}(s) > a` is the half-plane
of convergence, and ``cond`` are auxiliary convergence conditions.
If the integral cannot be computed in closed form, this function returns
an unevaluated :class:`LaplaceTransform` object.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`. If ``noconds=True``,
only `F` will be returned (i.e. not ``cond``, and also not the plane ``a``).
>>> from sympy.integrals import laplace_transform
>>> from sympy.abc import t, s, a
>>> laplace_transform(t**a, t, s)
(s**(-a)*gamma(a + 1)/s, 0, -re(a) < 1)
See Also
========
inverse_laplace_transform, mellin_transform, fourier_transform
hankel_transform, inverse_hankel_transform
"""
if isinstance(f, MatrixBase) and hasattr(f, 'applyfunc'):
return f.applyfunc(lambda fij: laplace_transform(fij, t, s, **hints))
return LaplaceTransform(f, t, s).doit(**hints)
@_noconds_(True)
def _inverse_laplace_transform(F, s, t_, plane, simplify=True):
""" The backend function for inverse Laplace transforms. """
from sympy import exp, Heaviside, log, expand_complex, Integral, Piecewise
from sympy.integrals.meijerint import meijerint_inversion, _get_coeff_exp
# There are two strategies we can try:
# 1) Use inverse mellin transforms - related by a simple change of variables.
# 2) Use the inversion integral.
t = Dummy('t', real=True)
def pw_simp(*args):
""" Simplify a piecewise expression from hyperexpand. """
# XXX we break modularity here!
if len(args) != 3:
return Piecewise(*args)
arg = args[2].args[0].argument
coeff, exponent = _get_coeff_exp(arg, t)
e1 = args[0].args[0]
e2 = args[1].args[0]
return Heaviside(1/abs(coeff) - t**exponent)*e1 \
+ Heaviside(t**exponent - 1/abs(coeff))*e2
try:
f, cond = inverse_mellin_transform(F, s, exp(-t), (None, oo),
needeval=True, noconds=False)
except IntegralTransformError:
f = None
if f is None:
f = meijerint_inversion(F, s, t)
if f is None:
raise IntegralTransformError('Inverse Laplace', f, '')
if f.is_Piecewise:
f, cond = f.args[0]
if f.has(Integral):
raise IntegralTransformError('Inverse Laplace', f,
'inversion integral of unrecognised form.')
else:
cond = True
f = f.replace(Piecewise, pw_simp)
if f.is_Piecewise:
# many of the functions called below can't work with piecewise
# (b/c it has a bool in args)
return f.subs(t, t_), cond
u = Dummy('u')
def simp_heaviside(arg):
a = arg.subs(exp(-t), u)
if a.has(t):
return Heaviside(arg)
rel = _solve_inequality(a > 0, u)
if rel.lts == u:
k = log(rel.gts)
return Heaviside(t + k)
else:
k = log(rel.lts)
return Heaviside(-(t + k))
f = f.replace(Heaviside, simp_heaviside)
def simp_exp(arg):
return expand_complex(exp(arg))
f = f.replace(exp, simp_exp)
# TODO it would be nice to fix cosh and sinh ... simplify messes these
# exponentials up
return _simplify(f.subs(t, t_), simplify), cond
class InverseLaplaceTransform(IntegralTransform):
"""
Class representing unevaluated inverse Laplace transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute inverse Laplace transforms, see the
:func:`inverse_laplace_transform` docstring.
"""
_name = 'Inverse Laplace'
_none_sentinel = Dummy('None')
_c = Dummy('c')
def __new__(cls, F, s, x, plane, **opts):
if plane is None:
plane = InverseLaplaceTransform._none_sentinel
return IntegralTransform.__new__(cls, F, s, x, plane, **opts)
@property
def fundamental_plane(self):
plane = self.args[3]
if plane is InverseLaplaceTransform._none_sentinel:
plane = None
return plane
def _compute_transform(self, F, s, t, **hints):
return _inverse_laplace_transform(F, s, t, self.fundamental_plane, **hints)
def _as_integral(self, F, s, t):
from sympy import I, exp
c = self.__class__._c
return Integral(exp(s*t)*F, (s, c - I*oo, c + I*oo))
def inverse_laplace_transform(F, s, t, plane=None, **hints):
r"""
Compute the inverse Laplace transform of `F(s)`, defined as
.. math :: f(t) = \int_{c-i\infty}^{c+i\infty} e^{st} F(s) \mathrm{d}s,
for `c` so large that `F(s)` has no singularites in the
half-plane `\operatorname{Re}(s) > c-\epsilon`.
The plane can be specified by
argument ``plane``, but will be inferred if passed as None.
Under certain regularity conditions, this recovers `f(t)` from its
Laplace Transform `F(s)`, for non-negative `t`, and vice
versa.
If the integral cannot be computed in closed form, this function returns
an unevaluated :class:`InverseLaplaceTransform` object.
Note that this function will always assume `t` to be real,
regardless of the sympy assumption on `t`.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`.
>>> from sympy.integrals.transforms import inverse_laplace_transform
>>> from sympy import exp, Symbol
>>> from sympy.abc import s, t
>>> a = Symbol('a', positive=True)
>>> inverse_laplace_transform(exp(-a*s)/s, s, t)
Heaviside(-a + t)
See Also
========
laplace_transform
hankel_transform, inverse_hankel_transform
"""
if isinstance(F, MatrixBase) and hasattr(F, 'applyfunc'):
return F.applyfunc(lambda Fij: inverse_laplace_transform(Fij, s, t, plane, **hints))
return InverseLaplaceTransform(F, s, t, plane).doit(**hints)
##########################################################################
# Fourier Transform
##########################################################################
@_noconds_(True)
def _fourier_transform(f, x, k, a, b, name, simplify=True):
"""
Compute a general Fourier-type transform
F(k) = a int_-oo^oo exp(b*I*x*k) f(x) dx.
For suitable choice of a and b, this reduces to the standard Fourier
and inverse Fourier transforms.
"""
from sympy import exp, I
F = integrate(a*f*exp(b*I*x*k), (x, -oo, oo))
if not F.has(Integral):
return _simplify(F, simplify), True
if not F.is_Piecewise:
raise IntegralTransformError(name, f, 'could not compute integral')
F, cond = F.args[0]
if F.has(Integral):
raise IntegralTransformError(name, f, 'integral in unexpected form')
return _simplify(F, simplify), cond
class FourierTypeTransform(IntegralTransform):
""" Base class for Fourier transforms."""
def a(self):
raise NotImplementedError(
"Class %s must implement a(self) but does not" % self.__class__)
def b(self):
raise NotImplementedError(
"Class %s must implement b(self) but does not" % self.__class__)
def _compute_transform(self, f, x, k, **hints):
return _fourier_transform(f, x, k,
self.a(), self.b(),
self.__class__._name, **hints)
def _as_integral(self, f, x, k):
from sympy import exp, I
a = self.a()
b = self.b()
return Integral(a*f*exp(b*I*x*k), (x, -oo, oo))
class FourierTransform(FourierTypeTransform):
"""
Class representing unevaluated Fourier transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute Fourier transforms, see the :func:`fourier_transform`
docstring.
"""
_name = 'Fourier'
def a(self):
return 1
def b(self):
return -2*S.Pi
def fourier_transform(f, x, k, **hints):
r"""
Compute the unitary, ordinary-frequency Fourier transform of `f`, defined
as
.. math:: F(k) = \int_{-\infty}^\infty f(x) e^{-2\pi i x k} \mathrm{d} x.
If the transform cannot be computed in closed form, this
function returns an unevaluated :class:`FourierTransform` object.
For other Fourier transform conventions, see the function
:func:`sympy.integrals.transforms._fourier_transform`.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`.
Note that for this transform, by default ``noconds=True``.
>>> from sympy import fourier_transform, exp
>>> from sympy.abc import x, k
>>> fourier_transform(exp(-x**2), x, k)
sqrt(pi)*exp(-pi**2*k**2)
>>> fourier_transform(exp(-x**2), x, k, noconds=False)
(sqrt(pi)*exp(-pi**2*k**2), True)
See Also
========
inverse_fourier_transform
sine_transform, inverse_sine_transform
cosine_transform, inverse_cosine_transform
hankel_transform, inverse_hankel_transform
mellin_transform, laplace_transform
"""
return FourierTransform(f, x, k).doit(**hints)
class InverseFourierTransform(FourierTypeTransform):
"""
Class representing unevaluated inverse Fourier transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute inverse Fourier transforms, see the
:func:`inverse_fourier_transform` docstring.
"""
_name = 'Inverse Fourier'
def a(self):
return 1
def b(self):
return 2*S.Pi
def inverse_fourier_transform(F, k, x, **hints):
r"""
Compute the unitary, ordinary-frequency inverse Fourier transform of `F`,
defined as
.. math:: f(x) = \int_{-\infty}^\infty F(k) e^{2\pi i x k} \mathrm{d} k.
If the transform cannot be computed in closed form, this
function returns an unevaluated :class:`InverseFourierTransform` object.
For other Fourier transform conventions, see the function
:func:`sympy.integrals.transforms._fourier_transform`.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`.
Note that for this transform, by default ``noconds=True``.
>>> from sympy import inverse_fourier_transform, exp, sqrt, pi
>>> from sympy.abc import x, k
>>> inverse_fourier_transform(sqrt(pi)*exp(-(pi*k)**2), k, x)
exp(-x**2)
>>> inverse_fourier_transform(sqrt(pi)*exp(-(pi*k)**2), k, x, noconds=False)
(exp(-x**2), True)
See Also
========
fourier_transform
sine_transform, inverse_sine_transform
cosine_transform, inverse_cosine_transform
hankel_transform, inverse_hankel_transform
mellin_transform, laplace_transform
"""
return InverseFourierTransform(F, k, x).doit(**hints)
##########################################################################
# Fourier Sine and Cosine Transform
##########################################################################
from sympy import sin, cos, sqrt, pi
@_noconds_(True)
def _sine_cosine_transform(f, x, k, a, b, K, name, simplify=True):
"""
Compute a general sine or cosine-type transform
F(k) = a int_0^oo b*sin(x*k) f(x) dx.
F(k) = a int_0^oo b*cos(x*k) f(x) dx.
For suitable choice of a and b, this reduces to the standard sine/cosine
and inverse sine/cosine transforms.
"""
F = integrate(a*f*K(b*x*k), (x, 0, oo))
if not F.has(Integral):
return _simplify(F, simplify), True
if not F.is_Piecewise:
raise IntegralTransformError(name, f, 'could not compute integral')
F, cond = F.args[0]
if F.has(Integral):
raise IntegralTransformError(name, f, 'integral in unexpected form')
return _simplify(F, simplify), cond
class SineCosineTypeTransform(IntegralTransform):
"""
Base class for sine and cosine transforms.
Specify cls._kern.
"""
def a(self):
raise NotImplementedError(
"Class %s must implement a(self) but does not" % self.__class__)
def b(self):
raise NotImplementedError(
"Class %s must implement b(self) but does not" % self.__class__)
def _compute_transform(self, f, x, k, **hints):
return _sine_cosine_transform(f, x, k,
self.a(), self.b(),
self.__class__._kern,
self.__class__._name, **hints)
def _as_integral(self, f, x, k):
a = self.a()
b = self.b()
K = self.__class__._kern
return Integral(a*f*K(b*x*k), (x, 0, oo))
class SineTransform(SineCosineTypeTransform):
"""
Class representing unevaluated sine transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute sine transforms, see the :func:`sine_transform`
docstring.
"""
_name = 'Sine'
_kern = sin
def a(self):
return sqrt(2)/sqrt(pi)
def b(self):
return 1
def sine_transform(f, x, k, **hints):
r"""
Compute the unitary, ordinary-frequency sine transform of `f`, defined
as
.. math:: F(k) = \sqrt{\frac{2}{\pi}} \int_{0}^\infty f(x) \sin(2\pi x k) \mathrm{d} x.
If the transform cannot be computed in closed form, this
function returns an unevaluated :class:`SineTransform` object.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`.
Note that for this transform, by default ``noconds=True``.
>>> from sympy import sine_transform, exp
>>> from sympy.abc import x, k, a
>>> sine_transform(x*exp(-a*x**2), x, k)
sqrt(2)*k*exp(-k**2/(4*a))/(4*a**(3/2))
>>> sine_transform(x**(-a), x, k)
2**(-a + 1/2)*k**(a - 1)*gamma(-a/2 + 1)/gamma(a/2 + 1/2)
See Also
========
fourier_transform, inverse_fourier_transform
inverse_sine_transform
cosine_transform, inverse_cosine_transform
hankel_transform, inverse_hankel_transform
mellin_transform, laplace_transform
"""
return SineTransform(f, x, k).doit(**hints)
class InverseSineTransform(SineCosineTypeTransform):
"""
Class representing unevaluated inverse sine transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute inverse sine transforms, see the
:func:`inverse_sine_transform` docstring.
"""
_name = 'Inverse Sine'
_kern = sin
def a(self):
return sqrt(2)/sqrt(pi)
def b(self):
return 1
def inverse_sine_transform(F, k, x, **hints):
r"""
Compute the unitary, ordinary-frequency inverse sine transform of `F`,
defined as
.. math:: f(x) = \sqrt{\frac{2}{\pi}} \int_{0}^\infty F(k) \sin(2\pi x k) \mathrm{d} k.
If the transform cannot be computed in closed form, this
function returns an unevaluated :class:`InverseSineTransform` object.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`.
Note that for this transform, by default ``noconds=True``.
>>> from sympy import inverse_sine_transform, exp, sqrt, gamma, pi
>>> from sympy.abc import x, k, a
>>> inverse_sine_transform(2**((1-2*a)/2)*k**(a - 1)*
... gamma(-a/2 + 1)/gamma((a+1)/2), k, x)
x**(-a)
>>> inverse_sine_transform(sqrt(2)*k*exp(-k**2/(4*a))/(4*sqrt(a)**3), k, x)
x*exp(-a*x**2)
See Also
========
fourier_transform, inverse_fourier_transform
sine_transform
cosine_transform, inverse_cosine_transform
hankel_transform, inverse_hankel_transform
mellin_transform, laplace_transform
"""
return InverseSineTransform(F, k, x).doit(**hints)
class CosineTransform(SineCosineTypeTransform):
"""
Class representing unevaluated cosine transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute cosine transforms, see the :func:`cosine_transform`
docstring.
"""
_name = 'Cosine'
_kern = cos
def a(self):
return sqrt(2)/sqrt(pi)
def b(self):
return 1
def cosine_transform(f, x, k, **hints):
r"""
Compute the unitary, ordinary-frequency cosine transform of `f`, defined
as
.. math:: F(k) = \sqrt{\frac{2}{\pi}} \int_{0}^\infty f(x) \cos(2\pi x k) \mathrm{d} x.
If the transform cannot be computed in closed form, this
function returns an unevaluated :class:`CosineTransform` object.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`.
Note that for this transform, by default ``noconds=True``.
>>> from sympy import cosine_transform, exp, sqrt, cos
>>> from sympy.abc import x, k, a
>>> cosine_transform(exp(-a*x), x, k)
sqrt(2)*a/(sqrt(pi)*(a**2 + k**2))
>>> cosine_transform(exp(-a*sqrt(x))*cos(a*sqrt(x)), x, k)
a*exp(-a**2/(2*k))/(2*k**(3/2))
See Also
========
fourier_transform, inverse_fourier_transform,
sine_transform, inverse_sine_transform
inverse_cosine_transform
hankel_transform, inverse_hankel_transform
mellin_transform, laplace_transform
"""
return CosineTransform(f, x, k).doit(**hints)
class InverseCosineTransform(SineCosineTypeTransform):
"""
Class representing unevaluated inverse cosine transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute inverse cosine transforms, see the
:func:`inverse_cosine_transform` docstring.
"""
_name = 'Inverse Cosine'
_kern = cos
def a(self):
return sqrt(2)/sqrt(pi)
def b(self):
return 1
def inverse_cosine_transform(F, k, x, **hints):
r"""
Compute the unitary, ordinary-frequency inverse cosine transform of `F`,
defined as
.. math:: f(x) = \sqrt{\frac{2}{\pi}} \int_{0}^\infty F(k) \cos(2\pi x k) \mathrm{d} k.
If the transform cannot be computed in closed form, this
function returns an unevaluated :class:`InverseCosineTransform` object.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`.
Note that for this transform, by default ``noconds=True``.
>>> from sympy import inverse_cosine_transform, exp, sqrt, pi
>>> from sympy.abc import x, k, a
>>> inverse_cosine_transform(sqrt(2)*a/(sqrt(pi)*(a**2 + k**2)), k, x)
exp(-a*x)
>>> inverse_cosine_transform(1/sqrt(k), k, x)
1/sqrt(x)
See Also
========
fourier_transform, inverse_fourier_transform,
sine_transform, inverse_sine_transform
cosine_transform
hankel_transform, inverse_hankel_transform
mellin_transform, laplace_transform
"""
return InverseCosineTransform(F, k, x).doit(**hints)
##########################################################################
# Hankel Transform
##########################################################################
@_noconds_(True)
def _hankel_transform(f, r, k, nu, name, simplify=True):
r"""
Compute a general Hankel transform
.. math:: F_\nu(k) = \int_{0}^\infty f(r) J_\nu(k r) r \mathrm{d} r.
"""
from sympy import besselj
F = integrate(f*besselj(nu, k*r)*r, (r, 0, oo))
if not F.has(Integral):
return _simplify(F, simplify), True
if not F.is_Piecewise:
raise IntegralTransformError(name, f, 'could not compute integral')
F, cond = F.args[0]
if F.has(Integral):
raise IntegralTransformError(name, f, 'integral in unexpected form')
return _simplify(F, simplify), cond
class HankelTypeTransform(IntegralTransform):
"""
Base class for Hankel transforms.
"""
def doit(self, **hints):
return self._compute_transform(self.function,
self.function_variable,
self.transform_variable,
self.args[3],
**hints)
def _compute_transform(self, f, r, k, nu, **hints):
return _hankel_transform(f, r, k, nu, self._name, **hints)
def _as_integral(self, f, r, k, nu):
from sympy import besselj
return Integral(f*besselj(nu, k*r)*r, (r, 0, oo))
@property
def as_integral(self):
return self._as_integral(self.function,
self.function_variable,
self.transform_variable,
self.args[3])
class HankelTransform(HankelTypeTransform):
"""
Class representing unevaluated Hankel transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute Hankel transforms, see the :func:`hankel_transform`
docstring.
"""
_name = 'Hankel'
def hankel_transform(f, r, k, nu, **hints):
r"""
Compute the Hankel transform of `f`, defined as
.. math:: F_\nu(k) = \int_{0}^\infty f(r) J_\nu(k r) r \mathrm{d} r.
If the transform cannot be computed in closed form, this
function returns an unevaluated :class:`HankelTransform` object.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`.
Note that for this transform, by default ``noconds=True``.
>>> from sympy import hankel_transform, inverse_hankel_transform
>>> from sympy import gamma, exp, sinh, cosh
>>> from sympy.abc import r, k, m, nu, a
>>> ht = hankel_transform(1/r**m, r, k, nu)
>>> ht
2*2**(-m)*k**(m - 2)*gamma(-m/2 + nu/2 + 1)/gamma(m/2 + nu/2)
>>> inverse_hankel_transform(ht, k, r, nu)
r**(-m)
>>> ht = hankel_transform(exp(-a*r), r, k, 0)
>>> ht
a/(k**3*(a**2/k**2 + 1)**(3/2))
>>> inverse_hankel_transform(ht, k, r, 0)
exp(-a*r)
See Also
========
fourier_transform, inverse_fourier_transform
sine_transform, inverse_sine_transform
cosine_transform, inverse_cosine_transform
inverse_hankel_transform
mellin_transform, laplace_transform
"""
return HankelTransform(f, r, k, nu).doit(**hints)
class InverseHankelTransform(HankelTypeTransform):
"""
Class representing unevaluated inverse Hankel transforms.
For usage of this class, see the :class:`IntegralTransform` docstring.
For how to compute inverse Hankel transforms, see the
:func:`inverse_hankel_transform` docstring.
"""
_name = 'Inverse Hankel'
def inverse_hankel_transform(F, k, r, nu, **hints):
r"""
Compute the inverse Hankel transform of `F` defined as
.. math:: f(r) = \int_{0}^\infty F_\nu(k) J_\nu(k r) k \mathrm{d} k.
If the transform cannot be computed in closed form, this
function returns an unevaluated :class:`InverseHankelTransform` object.
For a description of possible hints, refer to the docstring of
:func:`sympy.integrals.transforms.IntegralTransform.doit`.
Note that for this transform, by default ``noconds=True``.
>>> from sympy import hankel_transform, inverse_hankel_transform, gamma
>>> from sympy import gamma, exp, sinh, cosh
>>> from sympy.abc import r, k, m, nu, a
>>> ht = hankel_transform(1/r**m, r, k, nu)
>>> ht
2*2**(-m)*k**(m - 2)*gamma(-m/2 + nu/2 + 1)/gamma(m/2 + nu/2)
>>> inverse_hankel_transform(ht, k, r, nu)
r**(-m)
>>> ht = hankel_transform(exp(-a*r), r, k, 0)
>>> ht
a/(k**3*(a**2/k**2 + 1)**(3/2))
>>> inverse_hankel_transform(ht, k, r, 0)
exp(-a*r)
See Also
========
fourier_transform, inverse_fourier_transform
sine_transform, inverse_sine_transform
cosine_transform, inverse_cosine_transform
hankel_transform
mellin_transform, laplace_transform
"""
return InverseHankelTransform(F, k, r, nu).doit(**hints)
| 62,138 | 32.176188 | 97 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/benchmarks/bench_integrate.py
|
from __future__ import print_function, division
from sympy import integrate, Symbol, sin
x = Symbol('x')
def bench_integrate_sin():
integrate(sin(x), x)
def bench_integrate_x1sin():
integrate(x**1*sin(x), x)
def bench_integrate_x2sin():
integrate(x**2*sin(x), x)
def bench_integrate_x3sin():
integrate(x**3*sin(x), x)
| 344 | 14.681818 | 47 |
py
|
cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/benchmarks/bench_trigintegrate.py
|
from __future__ import print_function, division
from sympy import Symbol, sin
from sympy.integrals.trigonometry import trigintegrate
x = Symbol('x')
def timeit_trigintegrate_sin3x():
trigintegrate(sin(x)**3, x)
def timeit_trigintegrate_x2():
trigintegrate(x**2, x) # -> None
| 290 | 18.4 | 54 |
py
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/benchmarks/__init__.py
| 0 | 0 | 0 |
py
|
|
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|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_integrals.py
|
from sympy import (
Abs, acos, acosh, Add, asin, asinh, atan, Ci, cos, sinh,
cosh, tanh, Derivative, diff, DiracDelta, E, exp, erf, erfi, EulerGamma,
Expr, factor, Function, I, Integral, integrate, Interval, Lambda,
LambertW, log, Matrix, O, oo, pi, Piecewise, Poly, Rational, S, simplify,
sin, tan, sqrt, sstr, Sum, Symbol, symbols, sympify, trigsimp, Tuple, nan,
And, Eq, Ne, re, im, polar_lift, meijerg, SingularityFunction
)
from sympy.functions.elementary.complexes import periodic_argument
from sympy.integrals.risch import NonElementaryIntegral
from sympy.physics import units
from sympy.core.compatibility import range
from sympy.utilities.pytest import XFAIL, raises, slow
from sympy.utilities.randtest import verify_numerically
x, y, a, t, x_1, x_2, z, s = symbols('x y a t x_1 x_2 z s')
n = Symbol('n', integer=True)
f = Function('f')
def diff_test(i):
"""Return the set of symbols, s, which were used in testing that
i.diff(s) agrees with i.doit().diff(s). If there is an error then
the assertion will fail, causing the test to fail."""
syms = i.free_symbols
for s in syms:
assert (i.diff(s).doit() - i.doit().diff(s)).expand() == 0
return syms
def test_improper_integral():
assert integrate(log(x), (x, 0, 1)) == -1
assert integrate(x**(-2), (x, 1, oo)) == 1
assert integrate(1/(1 + exp(x)), (x, 0, oo)) == log(2)
def test_constructor():
# this is shared by Sum, so testing Integral's constructor
# is equivalent to testing Sum's
s1 = Integral(n, n)
assert s1.limits == (Tuple(n),)
s2 = Integral(n, (n,))
assert s2.limits == (Tuple(n),)
s3 = Integral(Sum(x, (x, 1, y)))
assert s3.limits == (Tuple(y),)
s4 = Integral(n, Tuple(n,))
assert s4.limits == (Tuple(n),)
s5 = Integral(n, (n, Interval(1, 2)))
assert s5.limits == (Tuple(n, 1, 2),)
def test_basics():
assert Integral(0, x) != 0
assert Integral(x, (x, 1, 1)) != 0
assert Integral(oo, x) != oo
assert Integral(S.NaN, x) == S.NaN
assert diff(Integral(y, y), x) == 0
assert diff(Integral(x, (x, 0, 1)), x) == 0
assert diff(Integral(x, x), x) == x
assert diff(Integral(t, (t, 0, x)), x) == x + Integral(0, (t, 0, x))
e = (t + 1)**2
assert diff(integrate(e, (t, 0, x)), x) == \
diff(Integral(e, (t, 0, x)), x).doit().expand() == \
((1 + x)**2).expand()
assert diff(integrate(e, (t, 0, x)), t) == \
diff(Integral(e, (t, 0, x)), t) == 0
assert diff(integrate(e, (t, 0, x)), a) == \
diff(Integral(e, (t, 0, x)), a) == 0
assert diff(integrate(e, t), a) == diff(Integral(e, t), a) == 0
assert integrate(e, (t, a, x)).diff(x) == \
Integral(e, (t, a, x)).diff(x).doit().expand()
assert Integral(e, (t, a, x)).diff(x).doit() == ((1 + x)**2)
assert integrate(e, (t, x, a)).diff(x).doit() == (-(1 + x)**2).expand()
assert integrate(t**2, (t, x, 2*x)).diff(x) == 7*x**2
assert Integral(x, x).atoms() == {x}
assert Integral(f(x), (x, 0, 1)).atoms() == {S(0), S(1), x}
assert diff_test(Integral(x, (x, 3*y))) == {y}
assert diff_test(Integral(x, (a, 3*y))) == {x, y}
assert integrate(x, (x, oo, oo)) == 0 #issue 8171
assert integrate(x, (x, -oo, -oo)) == 0
# sum integral of terms
assert integrate(y + x + exp(x), x) == x*y + x**2/2 + exp(x)
assert Integral(x).is_commutative
n = Symbol('n', commutative=False)
assert Integral(n + x, x).is_commutative is False
def test_diff_wrt():
class Test(Expr):
_diff_wrt = True
is_commutative = True
t = Test()
assert integrate(t + 1, t) == t**2/2 + t
assert integrate(t + 1, (t, 0, 1)) == S(3)/2
raises(ValueError, lambda: integrate(x + 1, x + 1))
raises(ValueError, lambda: integrate(x + 1, (x + 1, 0, 1)))
def test_basics_multiple():
assert diff_test(Integral(x, (x, 3*x, 5*y), (y, x, 2*x))) == {x}
assert diff_test(Integral(x, (x, 5*y), (y, x, 2*x))) == {x}
assert diff_test(Integral(x, (x, 5*y), (y, y, 2*x))) == {x, y}
assert diff_test(Integral(y, y, x)) == {x, y}
assert diff_test(Integral(y*x, x, y)) == {x, y}
assert diff_test(Integral(x + y, y, (y, 1, x))) == {x}
assert diff_test(Integral(x + y, (x, x, y), (y, y, x))) == {x, y}
def test_conjugate_transpose():
A, B = symbols("A B", commutative=False)
x = Symbol("x", complex=True)
p = Integral(A*B, (x,))
assert p.adjoint().doit() == p.doit().adjoint()
assert p.conjugate().doit() == p.doit().conjugate()
assert p.transpose().doit() == p.doit().transpose()
x = Symbol("x", real=True)
p = Integral(A*B, (x,))
assert p.adjoint().doit() == p.doit().adjoint()
assert p.conjugate().doit() == p.doit().conjugate()
assert p.transpose().doit() == p.doit().transpose()
def test_integration():
assert integrate(0, (t, 0, x)) == 0
assert integrate(3, (t, 0, x)) == 3*x
assert integrate(t, (t, 0, x)) == x**2/2
assert integrate(3*t, (t, 0, x)) == 3*x**2/2
assert integrate(3*t**2, (t, 0, x)) == x**3
assert integrate(1/t, (t, 1, x)) == log(x)
assert integrate(-1/t**2, (t, 1, x)) == 1/x - 1
assert integrate(t**2 + 5*t - 8, (t, 0, x)) == x**3/3 + 5*x**2/2 - 8*x
assert integrate(x**2, x) == x**3/3
assert integrate((3*t*x)**5, x) == (3*t)**5 * x**6 / 6
b = Symbol("b")
c = Symbol("c")
assert integrate(a*t, (t, 0, x)) == a*x**2/2
assert integrate(a*t**4, (t, 0, x)) == a*x**5/5
assert integrate(a*t**2 + b*t + c, (t, 0, x)) == a*x**3/3 + b*x**2/2 + c*x
def test_multiple_integration():
assert integrate((x**2)*(y**2), (x, 0, 1), (y, -1, 2)) == Rational(1)
assert integrate((y**2)*(x**2), x, y) == Rational(1, 9)*(x**3)*(y**3)
assert integrate(1/(x + 3)/(1 + x)**3, x) == \
-S(1)/8*log(3 + x) + S(1)/8*log(1 + x) + x/(4 + 8*x + 4*x**2)
def test_issue_3532():
assert integrate(exp(-x), (x, 0, oo)) == 1
def test_issue_3560():
assert integrate(sqrt(x)**3, x) == 2*sqrt(x)**5/5
assert integrate(sqrt(x), x) == 2*sqrt(x)**3/3
assert integrate(1/sqrt(x)**3, x) == -2/sqrt(x)
def test_integrate_poly():
p = Poly(x + x**2*y + y**3, x, y)
qx = integrate(p, x)
qy = integrate(p, y)
assert isinstance(qx, Poly) is True
assert isinstance(qy, Poly) is True
assert qx.gens == (x, y)
assert qy.gens == (x, y)
assert qx.as_expr() == x**2/2 + x**3*y/3 + x*y**3
assert qy.as_expr() == x*y + x**2*y**2/2 + y**4/4
def test_integrate_poly_defined():
p = Poly(x + x**2*y + y**3, x, y)
Qx = integrate(p, (x, 0, 1))
Qy = integrate(p, (y, 0, pi))
assert isinstance(Qx, Poly) is True
assert isinstance(Qy, Poly) is True
assert Qx.gens == (y,)
assert Qy.gens == (x,)
assert Qx.as_expr() == Rational(1, 2) + y/3 + y**3
assert Qy.as_expr() == pi**4/4 + pi*x + pi**2*x**2/2
def test_integrate_omit_var():
y = Symbol('y')
assert integrate(x) == x**2/2
raises(ValueError, lambda: integrate(2))
raises(ValueError, lambda: integrate(x*y))
def test_integrate_poly_accurately():
y = Symbol('y')
assert integrate(x*sin(y), x) == x**2*sin(y)/2
# when passed to risch_norman, this will be a CPU hog, so this really
# checks, that integrated function is recognized as polynomial
assert integrate(x**1000*sin(y), x) == x**1001*sin(y)/1001
def test_issue_3635():
y = Symbol('y')
assert integrate(x**2, y) == x**2*y
assert integrate(x**2, (y, -1, 1)) == 2*x**2
# works in sympy and py.test but hangs in `setup.py test`
def test_integrate_linearterm_pow():
# check integrate((a*x+b)^c, x) -- issue 3499
y = Symbol('y', positive=True)
# TODO: Remove conds='none' below, let the assumption take care of it.
assert integrate(x**y, x, conds='none') == x**(y + 1)/(y + 1)
assert integrate((exp(y)*x + 1/y)**(1 + sin(y)), x, conds='none') == \
exp(-y)*(exp(y)*x + 1/y)**(2 + sin(y)) / (2 + sin(y))
def test_issue_3618():
assert integrate(pi*sqrt(x), x) == 2*pi*sqrt(x)**3/3
assert integrate(pi*sqrt(x) + E*sqrt(x)**3, x) == \
2*pi*sqrt(x)**3/3 + 2*E *sqrt(x)**5/5
def test_issue_3623():
assert integrate(cos((n + 1)*x), x) == Piecewise(
(x, Eq(n + 1, 0)), (sin((n + 1)*x)/(n + 1), True))
assert integrate(cos((n - 1)*x), x) == Piecewise(
(x, Eq(n - 1, 0)), (sin((n - 1)*x)/(n - 1), True))
assert integrate(cos((n + 1)*x) + cos((n - 1)*x), x) == \
Piecewise((x, Eq(n + 1, 0)), (sin((n + 1)*x)/(n + 1), True)) + \
Piecewise((x, Eq(n - 1, 0)), (sin((n - 1)*x)/(n - 1), True))
def test_issue_3664():
n = Symbol('n', integer=True, nonzero=True)
assert integrate(-1./2 * x * sin(n * pi * x/2), [x, -2, 0]) == \
2*cos(pi*n)/(pi*n)
assert integrate(-Rational(1)/2 * x * sin(n * pi * x/2), [x, -2, 0]) == \
2*cos(pi*n)/(pi*n)
def test_issue_3679():
# definite integration of rational functions gives wrong answers
assert NS(Integral(1/(x**2 - 8*x + 17), (x, 2, 4))) == '1.10714871779409'
def test_issue_3686(): # remove this when fresnel itegrals are implemented
from sympy import expand_func, fresnels
assert expand_func(integrate(sin(x**2), x)) == \
sqrt(2)*sqrt(pi)*fresnels(sqrt(2)*x/sqrt(pi))/2
def test_integrate_units():
m = units.m
s = units.s
assert integrate(x * m/s, (x, 1*s, 5*s)) == 12*m*s
def test_transcendental_functions():
assert integrate(LambertW(2*x), x) == \
-x + x*LambertW(2*x) + x/LambertW(2*x)
def test_issue_3740():
f = 4*log(x) - 2*log(x)**2
fid = diff(integrate(f, x), x)
assert abs(f.subs(x, 42).evalf() - fid.subs(x, 42).evalf()) < 1e-10
def test_issue_3788():
assert integrate(1/(1 + x**2), x) == atan(x)
def test_issue_3952():
f = sin(x)
assert integrate(f, x) == -cos(x)
raises(ValueError, lambda: integrate(f, 2*x))
def test_issue_4516():
assert integrate(2**x - 2*x, x) == 2**x/log(2) - x**2
def test_issue_7450():
ans = integrate(exp(-(1 + I)*x), (x, 0, oo))
assert re(ans) == S.Half and im(ans) == -S.Half
def test_matrices():
M = Matrix(2, 2, lambda i, j: (i + j + 1)*sin((i + j + 1)*x))
assert integrate(M, x) == Matrix([
[-cos(x), -cos(2*x)],
[-cos(2*x), -cos(3*x)],
])
def test_integrate_functions():
# issue 4111
assert integrate(f(x), x) == Integral(f(x), x)
assert integrate(f(x), (x, 0, 1)) == Integral(f(x), (x, 0, 1))
assert integrate(f(x)*diff(f(x), x), x) == f(x)**2/2
assert integrate(diff(f(x), x) / f(x), x) == log(f(x))
def test_integrate_derivatives():
assert integrate(Derivative(f(x), x), x) == f(x)
assert integrate(Derivative(f(y), y), x) == x*Derivative(f(y), y)
def test_transform():
a = Integral(x**2 + 1, (x, -1, 2))
fx = x
fy = 3*y + 1
assert a.doit() == a.transform(fx, fy).doit()
assert a.transform(fx, fy).transform(fy, fx) == a
fx = 3*x + 1
fy = y
assert a.transform(fx, fy).transform(fy, fx) == a
a = Integral(sin(1/x), (x, 0, 1))
assert a.transform(x, 1/y) == Integral(sin(y)/y**2, (y, 1, oo))
assert a.transform(x, 1/y).transform(y, 1/x) == a
a = Integral(exp(-x**2), (x, -oo, oo))
assert a.transform(x, 2*y) == Integral(2*exp(-4*y**2), (y, -oo, oo))
# < 3 arg limit handled properly
assert Integral(x, x).transform(x, a*y).doit() == \
Integral(y*a**2, y).doit()
_3 = S(3)
assert Integral(x, (x, 0, -_3)).transform(x, 1/y).doit() == \
Integral(-1/x**3, (x, -oo, -1/_3)).doit()
assert Integral(x, (x, 0, _3)).transform(x, 1/y) == \
Integral(y**(-3), (y, 1/_3, oo))
# issue 8400
i = Integral(x + y, (x, 1, 2), (y, 1, 2))
assert i.transform(x, (x + 2*y, x)).doit() == \
i.transform(x, (x + 2*z, x)).doit() == 3
def test_issue_4052():
f = S(1)/2*asin(x) + x*sqrt(1 - x**2)/2
assert integrate(cos(asin(x)), x) == f
assert integrate(sin(acos(x)), x) == f
def NS(e, n=15, **options):
return sstr(sympify(e).evalf(n, **options), full_prec=True)
@slow
def test_evalf_integrals():
assert NS(Integral(x, (x, 2, 5)), 15) == '10.5000000000000'
gauss = Integral(exp(-x**2), (x, -oo, oo))
assert NS(gauss, 15) == '1.77245385090552'
assert NS(gauss**2 - pi + E*Rational(
1, 10**20), 15) in ('2.71828182845904e-20', '2.71828182845905e-20')
# A monster of an integral from http://mathworld.wolfram.com/DefiniteIntegral.html
t = Symbol('t')
a = 8*sqrt(3)/(1 + 3*t**2)
b = 16*sqrt(2)*(3*t + 1)*sqrt(4*t**2 + t + 1)**3
c = (3*t**2 + 1)*(11*t**2 + 2*t + 3)**2
d = sqrt(2)*(249*t**2 + 54*t + 65)/(11*t**2 + 2*t + 3)**2
f = a - b/c - d
assert NS(Integral(f, (t, 0, 1)), 50) == \
NS((3*sqrt(2) - 49*pi + 162*atan(sqrt(2)))/12, 50)
# http://mathworld.wolfram.com/VardisIntegral.html
assert NS(Integral(log(log(1/x))/(1 + x + x**2), (x, 0, 1)), 15) == \
NS('pi/sqrt(3) * log(2*pi**(5/6) / gamma(1/6))', 15)
# http://mathworld.wolfram.com/AhmedsIntegral.html
assert NS(Integral(atan(sqrt(x**2 + 2))/(sqrt(x**2 + 2)*(x**2 + 1)), (x,
0, 1)), 15) == NS(5*pi**2/96, 15)
# http://mathworld.wolfram.com/AbelsIntegral.html
assert NS(Integral(x/((exp(pi*x) - exp(
-pi*x))*(x**2 + 1)), (x, 0, oo)), 15) == NS('log(2)/2-1/4', 15)
# Complex part trimming
# http://mathworld.wolfram.com/VardisIntegral.html
assert NS(Integral(log(log(sin(x)/cos(x))), (x, pi/4, pi/2)), 15, chop=True) == \
NS('pi/4*log(4*pi**3/gamma(1/4)**4)', 15)
#
# Endpoints causing trouble (rounding error in integration points -> complex log)
assert NS(
2 + Integral(log(2*cos(x/2)), (x, -pi, pi)), 17, chop=True) == NS(2, 17)
assert NS(
2 + Integral(log(2*cos(x/2)), (x, -pi, pi)), 20, chop=True) == NS(2, 20)
assert NS(
2 + Integral(log(2*cos(x/2)), (x, -pi, pi)), 22, chop=True) == NS(2, 22)
# Needs zero handling
assert NS(pi - 4*Integral(
'sqrt(1-x**2)', (x, 0, 1)), 15, maxn=30, chop=True) in ('0.0', '0')
# Oscillatory quadrature
a = Integral(sin(x)/x**2, (x, 1, oo)).evalf(maxn=15)
assert 0.49 < a < 0.51
assert NS(
Integral(sin(x)/x**2, (x, 1, oo)), quad='osc') == '0.504067061906928'
assert NS(Integral(
cos(pi*x + 1)/x, (x, -oo, -1)), quad='osc') == '0.276374705640365'
# indefinite integrals aren't evaluated
assert NS(Integral(x, x)) == 'Integral(x, x)'
assert NS(Integral(x, (x, y))) == 'Integral(x, (x, y))'
def test_evalf_issue_939():
# https://github.com/sympy/sympy/issues/4038
# The output form of an integral may differ by a step function between
# revisions, making this test a bit useless. This can't be said about
# other two tests. For now, all values of this evaluation are used here,
# but in future this should be reconsidered.
assert NS(integrate(1/(x**5 + 1), x).subs(x, 4), chop=True) in \
['-0.000976138910649103', '0.965906660135753', '1.93278945918216']
assert NS(Integral(1/(x**5 + 1), (x, 2, 4))) == '0.0144361088886740'
assert NS(
integrate(1/(x**5 + 1), (x, 2, 4)), chop=True) == '0.0144361088886740'
@XFAIL
def test_failing_integrals():
#---
# Double integrals not implemented
assert NS(Integral(
sqrt(x) + x*y, (x, 1, 2), (y, -1, 1)), 15) == '2.43790283299492'
# double integral + zero detection
assert NS(Integral(sin(x + x*y), (x, -1, 1), (y, -1, 1)), 15) == '0.0'
def test_integrate_SingularityFunction():
in_1 = SingularityFunction(x, a, 3) + SingularityFunction(x, 5, -1)
out_1 = SingularityFunction(x, a, 4)/4 + SingularityFunction(x, 5, 0)
assert integrate(in_1, x) == out_1
in_2 = 10*SingularityFunction(x, 4, 0) - 5*SingularityFunction(x, -6, -2)
out_2 = 10*SingularityFunction(x, 4, 1) - 5*SingularityFunction(x, -6, -1)
assert integrate(in_2, x) == out_2
in_3 = 2*x**2*y -10*SingularityFunction(x, -4, 7) - 2*SingularityFunction(y, 10, -2)
out_3_1 = 2*x**3*y/3 - 2*x*SingularityFunction(y, 10, -2) - 5*SingularityFunction(x, -4, 8)/4
out_3_2 = x**2*y**2 - 10*y*SingularityFunction(x, -4, 7) - 2*SingularityFunction(y, 10, -1)
assert integrate(in_3, x) == out_3_1
assert integrate(in_3, y) == out_3_2
assert Integral(in_3, x) == Integral(in_3, x)
assert Integral(in_3, x).doit() == out_3_1
in_4 = 10*SingularityFunction(x, -4, 7) - 2*SingularityFunction(x, 10, -2)
out_4 = 5*SingularityFunction(x, -4, 8)/4 - 2*SingularityFunction(x, 10, -1)
assert integrate(in_4, (x, -oo, x)) == out_4
assert integrate(SingularityFunction(x, 5, -1), x) == SingularityFunction(x, 5, 0)
assert integrate(SingularityFunction(x, 0, -1), (x, -oo, oo)) == 1
assert integrate(5*SingularityFunction(x, 5, -1), (x, -oo, oo)) == 5
assert integrate(SingularityFunction(x, 5, -1) * f(x), (x, -oo, oo)) == f(5)
def test_integrate_DiracDelta():
# This is here to check that deltaintegrate is being called, but also
# to test definite integrals. More tests are in test_deltafunctions.py
assert integrate(DiracDelta(x) * f(x), (x, -oo, oo)) == f(0)
assert integrate(DiracDelta(x)**2, (x, -oo, oo)) == DiracDelta(0)
# issue 4522
assert integrate(integrate((4 - 4*x + x*y - 4*y) * \
DiracDelta(x)*DiracDelta(y - 1), (x, 0, 1)), (y, 0, 1)) == 0
# issue 5729
p = exp(-(x**2 + y**2))/pi
assert integrate(p*DiracDelta(x - 10*y), (x, -oo, oo), (y, -oo, oo)) == \
integrate(p*DiracDelta(x - 10*y), (y, -oo, oo), (x, -oo, oo)) == \
integrate(p*DiracDelta(10*x - y), (x, -oo, oo), (y, -oo, oo)) == \
integrate(p*DiracDelta(10*x - y), (y, -oo, oo), (x, -oo, oo)) == \
1/sqrt(101*pi)
@XFAIL
def test_integrate_DiracDelta_fails():
# issue 6427
assert integrate(integrate(integrate(
DiracDelta(x - y - z), (z, 0, oo)), (y, 0, 1)), (x, 0, 1)) == S(1)/2
def test_integrate_returns_piecewise():
assert integrate(x**y, x) == Piecewise(
(log(x), Eq(y, -1)), (x**(y + 1)/(y + 1), True))
assert integrate(x**y, y) == Piecewise(
(y, Eq(log(x), 0)), (x**y/log(x), True))
assert integrate(exp(n*x), x) == Piecewise(
(x, Eq(n, 0)), (exp(n*x)/n, True))
assert integrate(x*exp(n*x), x) == Piecewise(
(x**2/2, Eq(n**3, 0)), ((x*n**2 - n)*exp(n*x)/n**3, True))
assert integrate(x**(n*y), x) == Piecewise(
(log(x), Eq(n*y, -1)), (x**(n*y + 1)/(n*y + 1), True))
assert integrate(x**(n*y), y) == Piecewise(
(y, Eq(n*log(x), 0)), (x**(n*y)/(n*log(x)), True))
assert integrate(cos(n*x), x) == Piecewise(
(x, Eq(n, 0)), (sin(n*x)/n, True))
assert integrate(cos(n*x)**2, x) == Piecewise(
(x, Eq(n, 0)), ((n*x/2 + sin(n*x)*cos(n*x)/2)/n, True))
assert integrate(x*cos(n*x), x) == Piecewise(
(x**2/2, Eq(n, 0)), (x*sin(n*x)/n + cos(n*x)/n**2, True))
assert integrate(sin(n*x), x) == Piecewise(
(0, Eq(n, 0)), (-cos(n*x)/n, True))
assert integrate(sin(n*x)**2, x) == Piecewise(
(0, Eq(n, 0)), ((n*x/2 - sin(n*x)*cos(n*x)/2)/n, True))
assert integrate(x*sin(n*x), x) == Piecewise(
(0, Eq(n, 0)), (-x*cos(n*x)/n + sin(n*x)/n**2, True))
assert integrate(exp(x*y),(x,0,z)) == Piecewise( \
(z, Eq(y,0)), (exp(y*z)/y - 1/y, True))
def test_subs1():
e = Integral(exp(x - y), x)
assert e.subs(y, 3) == Integral(exp(x - 3), x)
e = Integral(exp(x - y), (x, 0, 1))
assert e.subs(y, 3) == Integral(exp(x - 3), (x, 0, 1))
f = Lambda(x, exp(-x**2))
conv = Integral(f(x - y)*f(y), (y, -oo, oo))
assert conv.subs({x: 0}) == Integral(exp(-2*y**2), (y, -oo, oo))
def test_subs2():
e = Integral(exp(x - y), x, t)
assert e.subs(y, 3) == Integral(exp(x - 3), x, t)
e = Integral(exp(x - y), (x, 0, 1), (t, 0, 1))
assert e.subs(y, 3) == Integral(exp(x - 3), (x, 0, 1), (t, 0, 1))
f = Lambda(x, exp(-x**2))
conv = Integral(f(x - y)*f(y), (y, -oo, oo), (t, 0, 1))
assert conv.subs({x: 0}) == Integral(exp(-2*y**2), (y, -oo, oo), (t, 0, 1))
def test_subs3():
e = Integral(exp(x - y), (x, 0, y), (t, y, 1))
assert e.subs(y, 3) == Integral(exp(x - 3), (x, 0, 3), (t, 3, 1))
f = Lambda(x, exp(-x**2))
conv = Integral(f(x - y)*f(y), (y, -oo, oo), (t, x, 1))
assert conv.subs({x: 0}) == Integral(exp(-2*y**2), (y, -oo, oo), (t, 0, 1))
def test_subs4():
e = Integral(exp(x), (x, 0, y), (t, y, 1))
assert e.subs(y, 3) == Integral(exp(x), (x, 0, 3), (t, 3, 1))
f = Lambda(x, exp(-x**2))
conv = Integral(f(y)*f(y), (y, -oo, oo), (t, x, 1))
assert conv.subs({x: 0}) == Integral(exp(-2*y**2), (y, -oo, oo), (t, 0, 1))
def test_subs5():
e = Integral(exp(-x**2), (x, -oo, oo))
assert e.subs(x, 5) == e
e = Integral(exp(-x**2 + y), x)
assert e.subs(y, 5) == Integral(exp(-x**2 + 5), x)
e = Integral(exp(-x**2 + y), (x, x))
assert e.subs(x, 5) == Integral(exp(y - x**2), (x, 5))
assert e.subs(y, 5) == Integral(exp(-x**2 + 5), x)
e = Integral(exp(-x**2 + y), (y, -oo, oo), (x, -oo, oo))
assert e.subs(x, 5) == e
assert e.subs(y, 5) == e
# Test evaluation of antiderivatives
e = Integral(exp(-x**2), (x, x))
assert e.subs(x, 5) == Integral(exp(-x**2), (x, 5))
e = Integral(exp(x), x)
assert (e.subs(x,1) - e.subs(x,0) - Integral(exp(x), (x, 0, 1))
).doit().is_zero
def test_subs6():
a, b = symbols('a b')
e = Integral(x*y, (x, f(x), f(y)))
assert e.subs(x, 1) == Integral(x*y, (x, f(1), f(y)))
assert e.subs(y, 1) == Integral(x, (x, f(x), f(1)))
e = Integral(x*y, (x, f(x), f(y)), (y, f(x), f(y)))
assert e.subs(x, 1) == Integral(x*y, (x, f(1), f(y)), (y, f(1), f(y)))
assert e.subs(y, 1) == Integral(x*y, (x, f(x), f(y)), (y, f(x), f(1)))
e = Integral(x*y, (x, f(x), f(a)), (y, f(x), f(a)))
assert e.subs(a, 1) == Integral(x*y, (x, f(x), f(1)), (y, f(x), f(1)))
def test_subs7():
e = Integral(x, (x, 1, y), (y, 1, 2))
assert e.subs({x: 1, y: 2}) == e
e = Integral(sin(x) + sin(y), (x, sin(x), sin(y)),
(y, 1, 2))
assert e.subs(sin(y), 1) == e
assert e.subs(sin(x), 1) == Integral(sin(x) + sin(y), (x, 1, sin(y)),
(y, 1, 2))
def test_expand():
e = Integral(f(x)+f(x**2), (x, 1, y))
assert e.expand() == Integral(f(x), (x, 1, y)) + Integral(f(x**2), (x, 1, y))
def test_integration_variable():
raises(ValueError, lambda: Integral(exp(-x**2), 3))
raises(ValueError, lambda: Integral(exp(-x**2), (3, -oo, oo)))
def test_expand_integral():
assert Integral(cos(x**2)*(sin(x**2) + 1), (x, 0, 1)).expand() == \
Integral(cos(x**2)*sin(x**2), (x, 0, 1)) + \
Integral(cos(x**2), (x, 0, 1))
assert Integral(cos(x**2)*(sin(x**2) + 1), x).expand() == \
Integral(cos(x**2)*sin(x**2), x) + \
Integral(cos(x**2), x)
def test_as_sum_midpoint1():
e = Integral(sqrt(x**3 + 1), (x, 2, 10))
assert e.as_sum(1, method="midpoint") == 8*sqrt(217)
assert e.as_sum(2, method="midpoint") == 4*sqrt(65) + 12*sqrt(57)
assert e.as_sum(3, method="midpoint") == 8*sqrt(217)/3 + \
8*sqrt(3081)/27 + 8*sqrt(52809)/27
assert e.as_sum(4, method="midpoint") == 2*sqrt(730) + \
4*sqrt(7) + 4*sqrt(86) + 6*sqrt(14)
assert abs(e.as_sum(4, method="midpoint").n() - e.n()) < 0.5
e = Integral(sqrt(x**3 + y**3), (x, 2, 10), (y, 0, 10))
raises(NotImplementedError, lambda: e.as_sum(4))
def test_as_sum_midpoint2():
e = Integral((x + y)**2, (x, 0, 1))
assert e.as_sum(1, method="midpoint").expand() == S(1)/4 + y + y**2
assert e.as_sum(2, method="midpoint").expand() == S(5)/16 + y + y**2
assert e.as_sum(3, method="midpoint").expand() == S(35)/108 + y + y**2
assert e.as_sum(4, method="midpoint").expand() == S(21)/64 + y + y**2
def test_as_sum_left():
e = Integral((x + y)**2, (x, 0, 1))
assert e.as_sum(1, method="left").expand() == y**2
assert e.as_sum(2, method="left").expand() == S(1)/8 + y/2 + y**2
assert e.as_sum(3, method="left").expand() == S(5)/27 + 2*y/3 + y**2
assert e.as_sum(4, method="left").expand() == S(7)/32 + 3*y/4 + y**2
def test_as_sum_right():
e = Integral((x + y)**2, (x, 0, 1))
assert e.as_sum(1, method="right").expand() == 1 + 2*y + y**2
assert e.as_sum(2, method="right").expand() == S(5)/8 + 3*y/2 + y**2
assert e.as_sum(3, method="right").expand() == S(14)/27 + 4*y/3 + y**2
assert e.as_sum(4, method="right").expand() == S(15)/32 + 5*y/4 + y**2
def test_as_sum_raises():
e = Integral((x + y)**2, (x, 0, 1))
raises(ValueError, lambda: e.as_sum(-1))
raises(ValueError, lambda: e.as_sum(0))
raises(ValueError, lambda: Integral(x).as_sum(3))
raises(NotImplementedError, lambda: e.as_sum(oo))
raises(NotImplementedError, lambda: e.as_sum(3, method='xxxx2'))
def test_nested_doit():
e = Integral(Integral(x, x), x)
f = Integral(x, x, x)
assert e.doit() == f.doit()
def test_issue_4665():
# Allow only upper or lower limit evaluation
e = Integral(x**2, (x, None, 1))
f = Integral(x**2, (x, 1, None))
assert e.doit() == Rational(1, 3)
assert f.doit() == Rational(-1, 3)
assert Integral(x*y, (x, None, y)).subs(y, t) == Integral(x*t, (x, None, t))
assert Integral(x*y, (x, y, None)).subs(y, t) == Integral(x*t, (x, t, None))
assert integrate(x**2, (x, None, 1)) == Rational(1, 3)
assert integrate(x**2, (x, 1, None)) == Rational(-1, 3)
assert integrate("x**2", ("x", "1", None)) == Rational(-1, 3)
def test_integral_reconstruct():
e = Integral(x**2, (x, -1, 1))
assert e == Integral(*e.args)
def test_doit_integrals():
e = Integral(Integral(2*x), (x, 0, 1))
assert e.doit() == Rational(1, 3)
assert e.doit(deep=False) == Rational(1, 3)
f = Function('f')
# doesn't matter if the integral can't be performed
assert Integral(f(x), (x, 1, 1)).doit() == 0
# doesn't matter if the limits can't be evaluated
assert Integral(0, (x, 1, Integral(f(x), x))).doit() == 0
assert Integral(x, (a, 0)).doit() == 0
limits = ((a, 1, exp(x)), (x, 0))
assert Integral(a, *limits).doit() == S(1)/4
assert Integral(a, *list(reversed(limits))).doit() == 0
def test_issue_4884():
assert integrate(sqrt(x)*(1 + x)) == \
Piecewise(
(2*sqrt(x)*(x + 1)**2/5 - 2*sqrt(x)*(x + 1)/15 - 4*sqrt(x)/15,
Abs(x + 1) > 1),
(2*I*sqrt(-x)*(x + 1)**2/5 - 2*I*sqrt(-x)*(x + 1)/15 -
4*I*sqrt(-x)/15, True))
assert integrate(x**x*(1 + log(x))) == x**x
def test_is_number():
from sympy.abc import x, y, z
from sympy import cos, sin
assert Integral(x).is_number is False
assert Integral(1, x).is_number is False
assert Integral(1, (x, 1)).is_number is True
assert Integral(1, (x, 1, 2)).is_number is True
assert Integral(1, (x, 1, y)).is_number is False
assert Integral(1, (x, y)).is_number is False
assert Integral(x, y).is_number is False
assert Integral(x, (y, 1, x)).is_number is False
assert Integral(x, (y, 1, 2)).is_number is False
assert Integral(x, (x, 1, 2)).is_number is True
# `foo.is_number` should always be eqivalent to `not foo.free_symbols`
# in each of these cases, there are pseudo-free symbols
i = Integral(x, (y, 1, 1))
assert i.is_number is False and i.n() == 0
i = Integral(x, (y, z, z))
assert i.is_number is False and i.n() == 0
i = Integral(1, (y, z, z + 2))
assert i.is_number is False and i.n() == 2
assert Integral(x*y, (x, 1, 2), (y, 1, 3)).is_number is True
assert Integral(x*y, (x, 1, 2), (y, 1, z)).is_number is False
assert Integral(x, (x, 1)).is_number is True
assert Integral(x, (x, 1, Integral(y, (y, 1, 2)))).is_number is True
assert Integral(Sum(z, (z, 1, 2)), (x, 1, 2)).is_number is True
# it is possible to get a false negative if the integrand is
# actually an unsimplified zero, but this is true of is_number in general.
assert Integral(sin(x)**2 + cos(x)**2 - 1, x).is_number is False
assert Integral(f(x), (x, 0, 1)).is_number is True
def test_symbols():
from sympy.abc import x, y, z
assert Integral(0, x).free_symbols == {x}
assert Integral(x).free_symbols == {x}
assert Integral(x, (x, None, y)).free_symbols == {y}
assert Integral(x, (x, y, None)).free_symbols == {y}
assert Integral(x, (x, 1, y)).free_symbols == {y}
assert Integral(x, (x, y, 1)).free_symbols == {y}
assert Integral(x, (x, x, y)).free_symbols == {x, y}
assert Integral(x, x, y).free_symbols == {x, y}
assert Integral(x, (x, 1, 2)).free_symbols == set()
assert Integral(x, (y, 1, 2)).free_symbols == {x}
# pseudo-free in this case
assert Integral(x, (y, z, z)).free_symbols == {x, z}
assert Integral(x, (y, 1, 2), (y, None, None)).free_symbols == {x, y}
assert Integral(x, (y, 1, 2), (x, 1, y)).free_symbols == {y}
assert Integral(2, (y, 1, 2), (y, 1, x), (x, 1, 2)).free_symbols == set()
assert Integral(2, (y, x, 2), (y, 1, x), (x, 1, 2)).free_symbols == set()
assert Integral(2, (x, 1, 2), (y, x, 2), (y, 1, 2)).free_symbols == \
{x}
def test_is_zero():
from sympy.abc import x, m
assert Integral(0, (x, 1, x)).is_zero
assert Integral(1, (x, 1, 1)).is_zero
assert Integral(1, (x, 1, 2), (y, 2)).is_zero is False
assert Integral(x, (m, 0)).is_zero
assert Integral(x + m, (m, 0)).is_zero is None
i = Integral(m, (m, 1, exp(x)), (x, 0))
assert i.is_zero is None
assert Integral(m, (x, 0), (m, 1, exp(x))).is_zero is True
assert Integral(x, (x, oo, oo)).is_zero # issue 8171
assert Integral(x, (x, -oo, -oo)).is_zero
# this is zero but is beyond the scope of what is_zero
# should be doing
assert Integral(sin(x), (x, 0, 2*pi)).is_zero is None
def test_series():
from sympy.abc import x
i = Integral(cos(x), (x, x))
e = i.lseries(x)
assert i.nseries(x, n=8).removeO() == Add(*[next(e) for j in range(4)])
def test_issue_4403():
x = Symbol('x')
y = Symbol('y')
z = Symbol('z', positive=True)
assert integrate(sqrt(x**2 + z**2), x) == \
z**2*asinh(x/z)/2 + x*sqrt(x**2 + z**2)/2
assert integrate(sqrt(x**2 - z**2), x) == \
-z**2*acosh(x/z)/2 + x*sqrt(x**2 - z**2)/2
x = Symbol('x', real=True)
y = Symbol('y', positive=True)
assert integrate(1/(x**2 + y**2)**S('3/2'), x) == \
x/(y**2*sqrt(x**2 + y**2))
# If y is real and nonzero, we get x*Abs(y)/(y**3*sqrt(x**2 + y**2)),
# which results from sqrt(1 + x**2/y**2) = sqrt(x**2 + y**2)/|y|.
def test_issue_4403_2():
assert integrate(sqrt(-x**2 - 4), x) == \
-2*atan(x/sqrt(-4 - x**2)) + x*sqrt(-4 - x**2)/2
def test_issue_4100():
R = Symbol('R', positive=True)
assert integrate(sqrt(R**2 - x**2), (x, 0, R)) == pi*R**2/4
def test_issue_5167():
from sympy.abc import w, x, y, z
f = Function('f')
assert Integral(Integral(f(x), x), x) == Integral(f(x), x, x)
assert Integral(f(x)).args == (f(x), Tuple(x))
assert Integral(Integral(f(x))).args == (f(x), Tuple(x), Tuple(x))
assert Integral(Integral(f(x)), y).args == (f(x), Tuple(x), Tuple(y))
assert Integral(Integral(f(x), z), y).args == (f(x), Tuple(z), Tuple(y))
assert Integral(Integral(Integral(f(x), x), y), z).args == \
(f(x), Tuple(x), Tuple(y), Tuple(z))
assert integrate(Integral(f(x), x), x) == Integral(f(x), x, x)
assert integrate(Integral(f(x), y), x) == y*Integral(f(x), x)
assert integrate(Integral(f(x), x), y) in [Integral(y*f(x), x), y*Integral(f(x), x)]
assert integrate(Integral(2, x), x) == x**2
assert integrate(Integral(2, x), y) == 2*x*y
# don't re-order given limits
assert Integral(1, x, y).args != Integral(1, y, x).args
# do as many as possibble
assert Integral(f(x), y, x, y, x).doit() == y**2*Integral(f(x), x, x)/2
assert Integral(f(x), (x, 1, 2), (w, 1, x), (z, 1, y)).doit() == \
y*(x - 1)*Integral(f(x), (x, 1, 2)) - (x - 1)*Integral(f(x), (x, 1, 2))
def test_issue_4890():
z = Symbol('z', positive=True)
assert integrate(exp(-log(x)**2), x) == \
sqrt(pi)*exp(S(1)/4)*erf(log(x)-S(1)/2)/2
assert integrate(exp(log(x)**2), x) == \
sqrt(pi)*exp(-S(1)/4)*erfi(log(x)+S(1)/2)/2
assert integrate(exp(-z*log(x)**2), x) == \
sqrt(pi)*exp(1/(4*z))*erf(sqrt(z)*log(x) - 1/(2*sqrt(z)))/(2*sqrt(z))
def test_issue_4376():
n = Symbol('n', integer=True, positive=True)
assert simplify(integrate(n*(x**(1/n) - 1), (x, 0, S.Half)) -
(n**2 - 2**(1/n)*n**2 - n*2**(1/n))/(2**(1 + 1/n) + n*2**(1 + 1/n))) == 0
def test_issue_4517():
assert integrate((sqrt(x) - x**3)/x**Rational(1, 3), x) == \
6*x**Rational(7, 6)/7 - 3*x**Rational(11, 3)/11
def test_issue_4527():
k, m = symbols('k m', integer=True)
assert integrate(sin(k*x)*sin(m*x), (x, 0, pi)) == Piecewise(
(0, And(Eq(k, 0), Eq(m, 0))),
(-pi/2, Eq(k, -m)),
(pi/2, Eq(k, m)),
(0, True))
assert integrate(sin(k*x)*sin(m*x), (x,)) == Piecewise(
(0, And(Eq(k, 0), Eq(m, 0))),
(-x*sin(m*x)**2/2 - x*cos(m*x)**2/2 + sin(m*x)*cos(m*x)/(2*m), Eq(k, -m)),
(x*sin(m*x)**2/2 + x*cos(m*x)**2/2 - sin(m*x)*cos(m*x)/(2*m), Eq(k, m)),
(m*sin(k*x)*cos(m*x)/(k**2 - m**2) -
k*sin(m*x)*cos(k*x)/(k**2 - m**2), True))
def test_issue_4199():
ypos = Symbol('y', positive=True)
# TODO: Remove conds='none' below, let the assumption take care of it.
assert integrate(exp(-I*2*pi*ypos*x)*x, (x, -oo, oo), conds='none') == \
Integral(exp(-I*2*pi*ypos*x)*x, (x, -oo, oo))
@slow
def test_issue_3940():
a, b, c, d = symbols('a:d', positive=True, finite=True)
assert integrate(exp(-x**2 + I*c*x), x) == \
-sqrt(pi)*exp(-c**2/4)*erf(I*c/2 - x)/2
assert integrate(exp(a*x**2 + b*x + c), x) == \
sqrt(pi)*exp(c)*exp(-b**2/(4*a))*erfi(sqrt(a)*x + b/(2*sqrt(a)))/(2*sqrt(a))
from sympy import expand_mul
from sympy.abc import k
assert expand_mul(integrate(exp(-x**2)*exp(I*k*x), (x, -oo, oo))) == \
sqrt(pi)*exp(-k**2/4)
a, d = symbols('a d', positive=True)
assert expand_mul(integrate(exp(-a*x**2 + 2*d*x), (x, -oo, oo))) == \
sqrt(pi)*exp(d**2/a)/sqrt(a)
def test_issue_5413():
# Note that this is not the same as testing ratint() becuase integrate()
# pulls out the coefficient.
assert integrate(-a/(a**2 + x**2), x) == I*log(-I*a + x)/2 - I*log(I*a + x)/2
def test_issue_4892a():
A, z = symbols('A z')
c = Symbol('c', nonzero=True)
P1 = -A*exp(-z)
P2 = -A/(c*t)*(sin(x)**2 + cos(y)**2)
h1 = -sin(x)**2 - cos(y)**2
h2 = -sin(x)**2 + sin(y)**2 - 1
# there is still some non-deterministic behavior in integrate
# or trigsimp which permits one of the following
assert integrate(c*(P2 - P1), t) in [
c*(-A*(-h1)*log(c*t)/c + A*t*exp(-z)),
c*(-A*(-h2)*log(c*t)/c + A*t*exp(-z)),
c*( A* h1 *log(c*t)/c + A*t*exp(-z)),
c*( A* h2 *log(c*t)/c + A*t*exp(-z)),
(A*c*t - A*(-h1)*log(t)*exp(z))*exp(-z),
(A*c*t - A*(-h2)*log(t)*exp(z))*exp(-z),
]
def test_issue_4892b():
# Issues relating to issue 4596 are making the actual result of this hard
# to test. The answer should be something like
#
# (-sin(y) + sqrt(-72 + 48*cos(y) - 8*cos(y)**2)/2)*log(x + sqrt(-72 +
# 48*cos(y) - 8*cos(y)**2)/(2*(3 - cos(y)))) + (-sin(y) - sqrt(-72 +
# 48*cos(y) - 8*cos(y)**2)/2)*log(x - sqrt(-72 + 48*cos(y) -
# 8*cos(y)**2)/(2*(3 - cos(y)))) + x**2*sin(y)/2 + 2*x*cos(y)
expr = (sin(y)*x**3 + 2*cos(y)*x**2 + 12)/(x**2 + 2)
assert trigsimp(factor(integrate(expr, x).diff(x) - expr)) == 0
def test_issue_5178():
assert integrate(sin(x)*f(y, z), (x, 0, pi), (y, 0, pi), (z, 0, pi)) == \
2*Integral(f(y, z), (y, 0, pi), (z, 0, pi))
def test_integrate_series():
f = sin(x).series(x, 0, 10)
g = x**2/2 - x**4/24 + x**6/720 - x**8/40320 + x**10/3628800 + O(x**11)
assert integrate(f, x) == g
assert diff(integrate(f, x), x) == f
assert integrate(O(x**5), x) == O(x**6)
def test_atom_bug():
from sympy import meijerg
from sympy.integrals.heurisch import heurisch
assert heurisch(meijerg([], [], [1], [], x), x) is None
def test_limit_bug():
z = Symbol('z', zero=False)
assert integrate(sin(x*y*z), (x, 0, pi), (y, 0, pi)) == \
(log(z**2) + 2*EulerGamma + 2*log(pi))/(2*z) - \
(-log(pi*z) + log(pi**2*z**2)/2 + Ci(pi**2*z))/z + log(pi)/z
def test_issue_4703():
g = Function('g')
assert integrate(exp(x)*g(x), x).has(Integral)
def test_issue_1888():
f = Function('f')
assert integrate(f(x).diff(x)**2, x).has(Integral)
# The following tests work using meijerint.
def test_issue_3558():
from sympy import Si
assert integrate(cos(x*y), (x, -pi/2, pi/2), (y, 0, pi)) == 2*Si(pi**2/2)
def test_issue_4422():
assert integrate(1/sqrt(16 + 4*x**2), x) == asinh(x/2) / 2
def test_issue_4493():
from sympy import simplify
assert simplify(integrate(x*sqrt(1 + 2*x), x)) == \
sqrt(2*x + 1)*(6*x**2 + x - 1)/15
def test_issue_4737():
assert integrate(sin(x)/x, (x, -oo, oo)) == pi
assert integrate(sin(x)/x, (x, 0, oo)) == pi/2
def test_issue_4992():
# Note: psi in _check_antecedents becomes NaN.
from sympy import simplify, expand_func, polygamma, gamma
a = Symbol('a', positive=True)
assert simplify(expand_func(integrate(exp(-x)*log(x)*x**a, (x, 0, oo)))) == \
(a*polygamma(0, a) + 1)*gamma(a)
def test_issue_4487():
from sympy import lowergamma, simplify
assert simplify(integrate(exp(-x)*x**y, x)) == lowergamma(y + 1, x)
def test_issue_4215():
x = Symbol("x")
assert integrate(1/(x**2), (x, -1, 1)) == oo
def test_issue_4400():
n = Symbol('n', integer=True, positive=True)
assert integrate((x**n)*log(x), x) == \
n*x*x**n*log(x)/(n**2 + 2*n + 1) + x*x**n*log(x)/(n**2 + 2*n + 1) - \
x*x**n/(n**2 + 2*n + 1)
def test_issue_6253():
# Note: this used to raise NotImplementedError
# Note: psi in _check_antecedents becomes NaN.
assert integrate((sqrt(1 - x) + sqrt(1 + x))**2/x, x, meijerg=True) == \
Integral((sqrt(-x + 1) + sqrt(x + 1))**2/x, x)
def test_issue_4153():
assert integrate(1/(1 + x + y + z), (x, 0, 1), (y, 0, 1), (z, 0, 1)) in [
-12*log(3) - 3*log(6)/2 + 3*log(8)/2 + 5*log(2) + 7*log(4),
6*log(2) + 8*log(4) - 27*log(3)/2, 22*log(2) - 27*log(3)/2,
-12*log(3) - 3*log(6)/2 + 47*log(2)/2]
def test_issue_4326():
R, b, h = symbols('R b h')
# It doesn't matter if we can do the integral. Just make sure the result
# doesn't contain nan. This is really a test against _eval_interval.
assert not integrate(((h*(x - R + b))/b)*sqrt(R**2 - x**2), (x, R - b, R)).has(nan)
def test_powers():
assert integrate(2**x + 3**x, x) == 2**x/log(2) + 3**x/log(3)
def test_risch_option():
# risch=True only allowed on indefinite integrals
raises(ValueError, lambda: integrate(1/log(x), (x, 0, oo), risch=True))
assert integrate(exp(-x**2), x, risch=True) == NonElementaryIntegral(exp(-x**2), x)
assert integrate(log(1/x)*y, x, y, risch=True) == y**2*(x*log(1/x)/2 + x/2)
assert integrate(erf(x), x, risch=True) == Integral(erf(x), x)
# TODO: How to test risch=False?
def test_issue_6828():
f = 1/(1.08*x**2 - 4.3)
g = integrate(f, x).diff(x)
assert verify_numerically(f, g, tol=1e-12)
@XFAIL
def test_integrate_Piecewise_rational_over_reals():
f = Piecewise(
(0, t - 478.515625*pi < 0),
(13.2075145209219*pi/(0.000871222*t + 0.995)**2, t - 478.515625*pi >= 0))
assert integrate(f, (t, 0, oo)) == 15235.9375*pi
def test_issue_4803():
x_max = Symbol("x_max")
assert integrate(y/pi*exp(-(x_max - x)/cos(a)), x) == \
y*exp((x - x_max)/cos(a))*cos(a)/pi
def test_issue_4234():
assert integrate(1/sqrt(1 + tan(x)**2)) == tan(x) / sqrt(1 + tan(x)**2)
def test_issue_4492():
assert simplify(integrate(x**2 * sqrt(5 - x**2), x)) == Piecewise(
(I*(2*x**5 - 15*x**3 + 25*x - 25*sqrt(x**2 - 5)*acosh(sqrt(5)*x/5)) /
(8*sqrt(x**2 - 5)), 1 < Abs(x**2)/5),
((-2*x**5 + 15*x**3 - 25*x + 25*sqrt(-x**2 + 5)*asin(sqrt(5)*x/5)) /
(8*sqrt(-x**2 + 5)), True))
def test_issue_2708():
# This test needs to use an integration function that can
# not be evaluated in closed form. Update as needed.
f = 1/(a + z + log(z))
integral_f = NonElementaryIntegral(f, (z, 2, 3))
assert Integral(f, (z, 2, 3)).doit() == integral_f
assert integrate(f + exp(z), (z, 2, 3)) == integral_f - exp(2) + exp(3)
assert integrate(2*f + exp(z), (z, 2, 3)) == \
2*integral_f - exp(2) + exp(3)
assert integrate(exp(1.2*n*s*z*(-t + z)/t), (z, 0, x)) == \
1.0*NonElementaryIntegral(exp(-1.2*n*s*z)*exp(1.2*n*s*z**2/t),
(z, 0, x))
def test_issue_8368():
assert integrate(exp(-s*x)*cosh(x), (x, 0, oo)) == \
Piecewise(
( pi*Piecewise(
( -s/(pi*(-s**2 + 1)),
Abs(s**2) < 1),
( 1/(pi*s*(1 - 1/s**2)),
Abs(s**(-2)) < 1),
( meijerg(
((S(1)/2,), (0, 0)),
((0, S(1)/2), (0,)),
polar_lift(s)**2),
True)
),
And(
Abs(periodic_argument(polar_lift(s)**2, oo)) < pi,
cos(Abs(periodic_argument(polar_lift(s)**2, oo))/2)*sqrt(Abs(s**2)) - 1 > 0,
Ne(s**2, 1))
),
(
Integral(exp(-s*x)*cosh(x), (x, 0, oo)),
True))
assert integrate(exp(-s*x)*sinh(x), (x, 0, oo)) == \
Piecewise(
( -1/(s + 1)/2 - 1/(-s + 1)/2,
And(
Ne(1/s, 1),
Abs(periodic_argument(s, oo)) < pi/2,
Abs(periodic_argument(s, oo)) <= pi/2,
cos(Abs(periodic_argument(s, oo)))*Abs(s) - 1 > 0)),
( Integral(exp(-s*x)*sinh(x), (x, 0, oo)),
True))
def test_issue_8901():
assert integrate(sinh(1.0*x)) == 1.0*cosh(1.0*x)
assert integrate(tanh(1.0*x)) == 1.0*x - 1.0*log(tanh(1.0*x) + 1)
assert integrate(tanh(x)) == x - log(tanh(x) + 1)
@slow
def test_issue_7130():
i, L, a, b = symbols('i L a b')
integrand = (cos(pi*i*x/L)**2 / (a + b*x)).rewrite(exp)
assert x not in integrate(integrand, (x, 0, L)).free_symbols
def test_issue_10567():
a, b, c, t = symbols('a b c t')
vt = Matrix([a*t, b, c])
assert integrate(vt, t) == Integral(vt, t).doit()
assert integrate(vt, t) == Matrix([[a*t**2/2], [b*t], [c*t]])
def test_issue_4950():
assert integrate((-60*exp(x) - 19.2*exp(4*x))*exp(4*x), x) ==\
-2.4*exp(8*x) - 12.0*exp(5*x)
def test_issue_4968():
assert integrate(sin(log(x**2))) == x*sin(2*log(x))/5 - 2*x*cos(2*log(x))/5
def test_singularities():
assert integrate(1/x**2, (x, -oo, oo)) == oo
assert integrate(1/x**2, (x, -1, 1)) == oo
assert integrate(1/(x - 1)**2, (x, -2, 2)) == oo
assert integrate(1/x**2, (x, 1, -1)) == -oo
assert integrate(1/(x - 1)**2, (x, 2, -2)) == -oo
def test_issue_12677():
assert integrate(sin(x) / (cos(x)**3) , (x, 0, pi/6)) == Rational(1,6)
| 43,739 | 35.973795 | 97 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_risch.py
|
"""Most of these tests come from the examples in Bronstein's book."""
from sympy import (Poly, I, S, Function, log, symbols, exp, tan, sqrt,
Symbol, Lambda, sin, Eq, Piecewise, factor, expand_log, cancel,
expand, diff, pi)
from sympy.integrals.risch import (gcdex_diophantine, frac_in, as_poly_1t,
derivation, splitfactor, splitfactor_sqf, canonical_representation,
hermite_reduce, polynomial_reduce, residue_reduce, residue_reduce_to_basic,
integrate_primitive, integrate_hyperexponential_polynomial,
integrate_hyperexponential, integrate_hypertangent_polynomial,
integrate_nonlinear_no_specials, integer_powers, DifferentialExtension,
risch_integrate, DecrementLevel, NonElementaryIntegral, recognize_log_derivative,
recognize_derivative, laurent_series)
from sympy.utilities.pytest import raises
from sympy.abc import x, t, nu, z, a, y
t0, t1, t2 = symbols('t:3')
i = Symbol('i')
def test_gcdex_diophantine():
assert gcdex_diophantine(Poly(x**4 - 2*x**3 - 6*x**2 + 12*x + 15),
Poly(x**3 + x**2 - 4*x - 4), Poly(x**2 - 1)) == \
(Poly((-x**2 + 4*x - 3)/5), Poly((x**3 - 7*x**2 + 16*x - 10)/5))
def test_frac_in():
assert frac_in(Poly((x + 1)/x*t, t), x) == \
(Poly(t*x + t, x), Poly(x, x))
assert frac_in((x + 1)/x*t, x) == \
(Poly(t*x + t, x), Poly(x, x))
assert frac_in((Poly((x + 1)/x*t, t), Poly(t + 1, t)), x) == \
(Poly(t*x + t, x), Poly((1 + t)*x, x))
raises(ValueError, lambda: frac_in((x + 1)/log(x)*t, x))
assert frac_in(Poly((2 + 2*x + x*(1 + x))/(1 + x)**2, t), x, cancel=True) == \
(Poly(x + 2, x), Poly(x + 1, x))
def test_as_poly_1t():
assert as_poly_1t(2/t + t, t, z) in [
Poly(t + 2*z, t, z), Poly(t + 2*z, z, t)]
assert as_poly_1t(2/t + 3/t**2, t, z) in [
Poly(2*z + 3*z**2, t, z), Poly(2*z + 3*z**2, z, t)]
assert as_poly_1t(2/((exp(2) + 1)*t), t, z) in [
Poly(2/(exp(2) + 1)*z, t, z), Poly(2/(exp(2) + 1)*z, z, t)]
assert as_poly_1t(2/((exp(2) + 1)*t) + t, t, z) in [
Poly(t + 2/(exp(2) + 1)*z, t, z), Poly(t + 2/(exp(2) + 1)*z, z, t)]
assert as_poly_1t(S(0), t, z) == Poly(0, t, z)
def test_derivation():
p = Poly(4*x**4*t**5 + (-4*x**3 - 4*x**4)*t**4 + (-3*x**2 + 2*x**3)*t**3 +
(2*x + 7*x**2 + 2*x**3)*t**2 + (1 - 4*x - 4*x**2)*t - 1 + 2*x, t)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(-t**2 - 3/(2*x)*t + 1/(2*x), t)]})
assert derivation(p, DE) == Poly(-20*x**4*t**6 + (2*x**3 + 16*x**4)*t**5 +
(21*x**2 + 12*x**3)*t**4 + (7*x/2 - 25*x**2 - 12*x**3)*t**3 +
(-5 - 15*x/2 + 7*x**2)*t**2 - (3 - 8*x - 10*x**2 - 4*x**3)/(2*x)*t +
(1 - 4*x**2)/(2*x), t)
assert derivation(Poly(1, t), DE) == Poly(0, t)
assert derivation(Poly(t, t), DE) == DE.d
assert derivation(Poly(t**2 + 1/x*t + (1 - 2*x)/(4*x**2), t), DE) == \
Poly(-2*t**3 - 4/x*t**2 - (5 - 2*x)/(2*x**2)*t - (1 - 2*x)/(2*x**3), t, domain='ZZ(x)')
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t1), Poly(t, t)]})
assert derivation(Poly(x*t*t1, t), DE) == Poly(t*t1 + x*t*t1 + t, t)
assert derivation(Poly(x*t*t1, t), DE, coefficientD=True) == \
Poly((1 + t1)*t, t)
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
assert derivation(Poly(x, x), DE) == Poly(1, x)
# Test basic option
assert derivation((x + 1)/(x - 1), DE, basic=True) == -2/(1 - 2*x + x**2)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
assert derivation((t + 1)/(t - 1), DE, basic=True) == -2*t/(1 - 2*t + t**2)
assert derivation(t + 1, DE, basic=True) == t
def test_splitfactor():
p = Poly(4*x**4*t**5 + (-4*x**3 - 4*x**4)*t**4 + (-3*x**2 + 2*x**3)*t**3 +
(2*x + 7*x**2 + 2*x**3)*t**2 + (1 - 4*x - 4*x**2)*t - 1 + 2*x, t, field=True)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(-t**2 - 3/(2*x)*t + 1/(2*x), t)]})
assert splitfactor(p, DE) == (Poly(4*x**4*t**3 + (-8*x**3 - 4*x**4)*t**2 +
(4*x**2 + 8*x**3)*t - 4*x**2, t), Poly(t**2 + 1/x*t + (1 - 2*x)/(4*x**2), t, domain='ZZ(x)'))
assert splitfactor(Poly(x, t), DE) == (Poly(x, t), Poly(1, t))
r = Poly(-4*x**4*z**2 + 4*x**6*z**2 - z*x**3 - 4*x**5*z**3 + 4*x**3*z**3 + x**4 + z*x**5 - x**6, t)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
assert splitfactor(r, DE, coefficientD=True) == \
(Poly(x*z - x**2 - z*x**3 + x**4, t), Poly(-x**2 + 4*x**2*z**2, t))
assert splitfactor_sqf(r, DE, coefficientD=True) == \
(((Poly(x*z - x**2 - z*x**3 + x**4, t), 1),), ((Poly(-x**2 + 4*x**2*z**2, t), 1),))
assert splitfactor(Poly(0, t), DE) == (Poly(0, t), Poly(1, t))
assert splitfactor_sqf(Poly(0, t), DE) == (((Poly(0, t), 1),), ())
def test_canonical_representation():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1 + t**2, t)]})
assert canonical_representation(Poly(x - t, t), Poly(t**2, t), DE) == \
(Poly(0, t), (Poly(0, t),
Poly(1, t)), (Poly(-t + x, t),
Poly(t**2, t)))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**2 + 1, t)]})
assert canonical_representation(Poly(t**5 + t**3 + x**2*t + 1, t),
Poly((t**2 + 1)**3, t), DE) == \
(Poly(0, t), (Poly(t**5 + t**3 + x**2*t + 1, t),
Poly(t**6 + 3*t**4 + 3*t**2 + 1, t)), (Poly(0, t), Poly(1, t)))
def test_hermite_reduce():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**2 + 1, t)]})
assert hermite_reduce(Poly(x - t, t), Poly(t**2, t), DE) == \
((Poly(-x, t), Poly(t, t)), (Poly(0, t), Poly(1, t)), (Poly(-x, t), Poly(1, t)))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(-t**2 - t/x - (1 - nu**2/x**2), t)]})
assert hermite_reduce(
Poly(x**2*t**5 + x*t**4 - nu**2*t**3 - x*(x**2 + 1)*t**2 - (x**2 - nu**2)*t - x**5/4, t),
Poly(x**2*t**4 + x**2*(x**2 + 2)*t**2 + x**2 + x**4 + x**6/4, t), DE) == \
((Poly(-x**2 - 4, t), Poly(4*t**2 + 2*x**2 + 4, t)),
(Poly((-2*nu**2 - x**4)*t - (2*x**3 + 2*x), t), Poly(2*x**2*t**2 + x**4 + 2*x**2, t)),
(Poly(x*t + 1, t), Poly(x, t)))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
a = Poly((-2 + 3*x)*t**3 + (-1 + x)*t**2 + (-4*x + 2*x**2)*t + x**2, t)
d = Poly(x*t**6 - 4*x**2*t**5 + 6*x**3*t**4 - 4*x**4*t**3 + x**5*t**2, t)
assert hermite_reduce(a, d, DE) == \
((Poly(3*t**2 + t + 3*x, t), Poly(3*t**4 - 9*x*t**3 + 9*x**2*t**2 - 3*x**3*t, t)),
(Poly(0, t), Poly(1, t)),
(Poly(0, t), Poly(1, t)))
assert hermite_reduce(
Poly(-t**2 + 2*t + 2, t),
Poly(-x*t**2 + 2*x*t - x, t), DE) == \
((Poly(3, t), Poly(t - 1, t)),
(Poly(0, t), Poly(1, t)),
(Poly(1, t), Poly(x, t)))
assert hermite_reduce(
Poly(-x**2*t**6 + (-1 - 2*x**3 + x**4)*t**3 + (-3 - 3*x**4)*t**2 - 2*x*t - x - 3*x**2, t),
Poly(x**4*t**6 - 2*x**2*t**3 + 1, t), DE) == \
((Poly(x**3*t + x**4 + 1, t), Poly(x**3*t**3 - x, t)),
(Poly(0, t), Poly(1, t)),
(Poly(-1, t), Poly(x**2, t)))
assert hermite_reduce(
Poly((-2 + 3*x)*t**3 + (-1 + x)*t**2 + (-4*x + 2*x**2)*t + x**2, t),
Poly(x*t**6 - 4*x**2*t**5 + 6*x**3*t**4 - 4*x**4*t**3 + x**5*t**2, t), DE) == \
((Poly(3*t**2 + t + 3*x, t), Poly(3*t**4 - 9*x*t**3 + 9*x**2*t**2 - 3*x**3*t, t)),
(Poly(0, t), Poly(1, t)),
(Poly(0, t), Poly(1, t)))
def test_polynomial_reduce():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1 + t**2, t)]})
assert polynomial_reduce(Poly(1 + x*t + t**2, t), DE) == \
(Poly(t, t), Poly(x*t, t))
assert polynomial_reduce(Poly(0, t), DE) == \
(Poly(0, t), Poly(0, t))
def test_laurent_series():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1, t)]})
a = Poly(36, t)
d = Poly((t - 2)*(t**2 - 1)**2, t)
F = Poly(t**2 - 1, t)
n = 2
assert laurent_series(a, d, F, n, DE) == \
(Poly(-3*t**3 + 3*t**2 - 6*t - 8, t), Poly(t**5 + t**4 - 2*t**3 - 2*t**2 + t + 1, t),
[Poly(-3*t**3 - 6*t**2, t), Poly(2*t**6 + 6*t**5 - 8*t**3, t)])
def test_recognize_derivative():
DE = DifferentialExtension(extension={'D': [Poly(1, t)]})
a = Poly(36, t)
d = Poly((t - 2)*(t**2 - 1)**2, t)
assert recognize_derivative(a, d, DE) == False
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
a = Poly(2, t)
d = Poly(t**2 - 1, t)
assert recognize_derivative(a, d, DE) == False
assert recognize_derivative(Poly(x*t, t), Poly(1, t), DE) == True
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**2 + 1, t)]})
assert recognize_derivative(Poly(t, t), Poly(1, t), DE) == True
def test_recognize_log_derivative():
a = Poly(2*x**2 + 4*x*t - 2*t - x**2*t, t)
d = Poly((2*x + t)*(t + x**2), t)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
assert recognize_log_derivative(a, d, DE, z) == True
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
assert recognize_log_derivative(Poly(t + 1, t), Poly(t + x, t), DE) == True
assert recognize_log_derivative(Poly(2, t), Poly(t**2 - 1, t), DE) == True
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
assert recognize_log_derivative(Poly(1, x), Poly(x**2 - 2, x), DE) == False
assert recognize_log_derivative(Poly(1, x), Poly(x**2 + x, x), DE) == True
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**2 + 1, t)]})
assert recognize_log_derivative(Poly(1, t), Poly(t**2 - 2, t), DE) == False
assert recognize_log_derivative(Poly(1, t), Poly(t**2 + t, t), DE) == False
def test_residue_reduce():
a = Poly(2*t**2 - t - x**2, t)
d = Poly(t**3 - x**2*t, t)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)], 'Tfuncs': [log]})
assert residue_reduce(a, d, DE, z, invert=False) == \
([(Poly(z**2 - S(1)/4, z), Poly((1 + 3*x*z - 6*z**2 -
2*x**2 + 4*x**2*z**2)*t - x*z + x**2 + 2*x**2*z**2 - 2*z*x**3, t))], False)
assert residue_reduce(a, d, DE, z, invert=True) == \
([(Poly(z**2 - S(1)/4, z), Poly(t + 2*x*z, t))], False)
assert residue_reduce(Poly(-2/x, t), Poly(t**2 - 1, t,), DE, z, invert=False) == \
([(Poly(z**2 - 1, z), Poly(-2*z*t/x - 2/x, t))], True)
ans = residue_reduce(Poly(-2/x, t), Poly(t**2 - 1, t), DE, z, invert=True)
assert ans == ([(Poly(z**2 - 1, z), Poly(t + z, t))], True)
assert residue_reduce_to_basic(ans[0], DE, z) == -log(-1 + log(x)) + log(1 + log(x))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(-t**2 - t/x - (1 - nu**2/x**2), t)]})
# TODO: Skip or make faster
assert residue_reduce(Poly((-2*nu**2 - x**4)/(2*x**2)*t - (1 + x**2)/x, t),
Poly(t**2 + 1 + x**2/2, t), DE, z) == \
([(Poly(z + S(1)/2, z, domain='QQ'), Poly(t**2 + 1 + x**2/2, t, domain='EX'))], True)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1 + t**2, t)]})
assert residue_reduce(Poly(-2*x*t + 1 - x**2, t),
Poly(t**2 + 2*x*t + 1 + x**2, t), DE, z) == \
([(Poly(z**2 + S(1)/4, z), Poly(t + x + 2*z, t))], True)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
assert residue_reduce(Poly(t, t), Poly(t + sqrt(2), t), DE, z) == \
([(Poly(z - 1, z), Poly(t + sqrt(2), t))], True)
def test_integrate_hyperexponential():
# TODO: Add tests for integrate_hyperexponential() from the book
a = Poly((1 + 2*t1 + t1**2 + 2*t1**3)*t**2 + (1 + t1**2)*t + 1 + t1**2, t)
d = Poly(1, t)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1 + t1**2, t1),
Poly(t*(1 + t1**2), t)], 'Tfuncs': [tan, Lambda(i, exp(tan(i)))]})
assert integrate_hyperexponential(a, d, DE) == \
(exp(2*tan(x))*tan(x) + exp(tan(x)), 1 + t1**2, True)
a = Poly((t1**3 + (x + 1)*t1**2 + t1 + x + 2)*t, t)
assert integrate_hyperexponential(a, d, DE) == \
((x + tan(x))*exp(tan(x)), 0, True)
a = Poly(t, t)
d = Poly(1, t)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(2*x*t, t)],
'Tfuncs': [Lambda(i, exp(x**2))]})
assert integrate_hyperexponential(a, d, DE) == \
(0, NonElementaryIntegral(exp(x**2), x), False)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)], 'Tfuncs': [exp]})
assert integrate_hyperexponential(a, d, DE) == (exp(x), 0, True)
a = Poly(25*t**6 - 10*t**5 + 7*t**4 - 8*t**3 + 13*t**2 + 2*t - 1, t)
d = Poly(25*t**6 + 35*t**4 + 11*t**2 + 1, t)
assert integrate_hyperexponential(a, d, DE) == \
(-(11 - 10*exp(x))/(5 + 25*exp(2*x)) + log(1 + exp(2*x)), -1, True)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t0, t0), Poly(t0*t, t)],
'Tfuncs': [exp, Lambda(i, exp(exp(i)))]})
assert integrate_hyperexponential(Poly(2*t0*t**2, t), Poly(1, t), DE) == (exp(2*exp(x)), 0, True)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t0, t0), Poly(-t0*t, t)],
'Tfuncs': [exp, Lambda(i, exp(-exp(i)))]})
assert integrate_hyperexponential(Poly(-27*exp(9) - 162*t0*exp(9) +
27*x*t0*exp(9), t), Poly((36*exp(18) + x**2*exp(18) - 12*x*exp(18))*t, t), DE) == \
(27*exp(exp(x))/(-6*exp(9) + x*exp(9)), 0, True)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)], 'Tfuncs': [exp]})
assert integrate_hyperexponential(Poly(x**2/2*t, t), Poly(1, t), DE) == \
((2 - 2*x + x**2)*exp(x)/2, 0, True)
assert integrate_hyperexponential(Poly(1 + t, t), Poly(t, t), DE) == \
(-exp(-x), 1, True) # x - exp(-x)
assert integrate_hyperexponential(Poly(x, t), Poly(t + 1, t), DE) == \
(0, NonElementaryIntegral(x/(1 + exp(x)), x), False)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t0), Poly(2*x*t1, t1)],
'Tfuncs': [log, Lambda(i, exp(i**2))]})
elem, nonelem, b = integrate_hyperexponential(Poly((8*x**7 - 12*x**5 + 6*x**3 - x)*t1**4 +
(8*t0*x**7 - 8*t0*x**6 - 4*t0*x**5 + 2*t0*x**3 + 2*t0*x**2 - t0*x +
24*x**8 - 36*x**6 - 4*x**5 + 22*x**4 + 4*x**3 - 7*x**2 - x + 1)*t1**3
+ (8*t0*x**8 - 4*t0*x**6 - 16*t0*x**5 - 2*t0*x**4 + 12*t0*x**3 +
t0*x**2 - 2*t0*x + 24*x**9 - 36*x**7 - 8*x**6 + 22*x**5 + 12*x**4 -
7*x**3 - 6*x**2 + x + 1)*t1**2 + (8*t0*x**8 - 8*t0*x**6 - 16*t0*x**5 +
6*t0*x**4 + 10*t0*x**3 - 2*t0*x**2 - t0*x + 8*x**10 - 12*x**8 - 4*x**7
+ 2*x**6 + 12*x**5 + 3*x**4 - 9*x**3 - x**2 + 2*x)*t1 + 8*t0*x**7 -
12*t0*x**6 - 4*t0*x**5 + 8*t0*x**4 - t0*x**2 - 4*x**7 + 4*x**6 +
4*x**5 - 4*x**4 - x**3 + x**2, t1), Poly((8*x**7 - 12*x**5 + 6*x**3 -
x)*t1**4 + (24*x**8 + 8*x**7 - 36*x**6 - 12*x**5 + 18*x**4 + 6*x**3 -
3*x**2 - x)*t1**3 + (24*x**9 + 24*x**8 - 36*x**7 - 36*x**6 + 18*x**5 +
18*x**4 - 3*x**3 - 3*x**2)*t1**2 + (8*x**10 + 24*x**9 - 12*x**8 -
36*x**7 + 6*x**6 + 18*x**5 - x**4 - 3*x**3)*t1 + 8*x**10 - 12*x**8 +
6*x**6 - x**4, t1), DE)
assert factor(elem) == -((x - 1)*log(x)/((x + exp(x**2))*(2*x**2 - 1)))
assert (nonelem, b) == (NonElementaryIntegral(exp(x**2)/(exp(x**2) + 1), x), False)
def test_integrate_hyperexponential_polynomial():
# Without proper cancellation within integrate_hyperexponential_polynomial(),
# this will take a long time to complete, and will return a complicated
# expression
p = Poly((-28*x**11*t0 - 6*x**8*t0 + 6*x**9*t0 - 15*x**8*t0**2 +
15*x**7*t0**2 + 84*x**10*t0**2 - 140*x**9*t0**3 - 20*x**6*t0**3 +
20*x**7*t0**3 - 15*x**6*t0**4 + 15*x**5*t0**4 + 140*x**8*t0**4 -
84*x**7*t0**5 - 6*x**4*t0**5 + 6*x**5*t0**5 + x**3*t0**6 - x**4*t0**6 +
28*x**6*t0**6 - 4*x**5*t0**7 + x**9 - x**10 + 4*x**12)/(-8*x**11*t0 +
28*x**10*t0**2 - 56*x**9*t0**3 + 70*x**8*t0**4 - 56*x**7*t0**5 +
28*x**6*t0**6 - 8*x**5*t0**7 + x**4*t0**8 + x**12)*t1**2 +
(-28*x**11*t0 - 12*x**8*t0 + 12*x**9*t0 - 30*x**8*t0**2 +
30*x**7*t0**2 + 84*x**10*t0**2 - 140*x**9*t0**3 - 40*x**6*t0**3 +
40*x**7*t0**3 - 30*x**6*t0**4 + 30*x**5*t0**4 + 140*x**8*t0**4 -
84*x**7*t0**5 - 12*x**4*t0**5 + 12*x**5*t0**5 - 2*x**4*t0**6 +
2*x**3*t0**6 + 28*x**6*t0**6 - 4*x**5*t0**7 + 2*x**9 - 2*x**10 +
4*x**12)/(-8*x**11*t0 + 28*x**10*t0**2 - 56*x**9*t0**3 +
70*x**8*t0**4 - 56*x**7*t0**5 + 28*x**6*t0**6 - 8*x**5*t0**7 +
x**4*t0**8 + x**12)*t1 + (-2*x**2*t0 + 2*x**3*t0 + x*t0**2 -
x**2*t0**2 + x**3 - x**4)/(-4*x**5*t0 + 6*x**4*t0**2 - 4*x**3*t0**3 +
x**2*t0**4 + x**6), t1, z, expand=False)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t0), Poly(2*x*t1, t1)]})
assert integrate_hyperexponential_polynomial(p, DE, z) == (
Poly((x - t0)*t1**2 + (-2*t0 + 2*x)*t1, t1), Poly(-2*x*t0 + x**2 +
t0**2, t1), True)
DE = DifferentialExtension(extension={'D':[Poly(1, x), Poly(t0, t0)]})
assert integrate_hyperexponential_polynomial(Poly(0, t0), DE, z) == (
Poly(0, t0), Poly(1, t0), True)
def test_integrate_hyperexponential_returns_piecewise():
a, b = symbols('a b')
DE = DifferentialExtension(a**x, x)
assert integrate_hyperexponential(DE.fa, DE.fd, DE) == (Piecewise(
(x, Eq(log(a), 0)), (exp(x*log(a))/log(a), True)), 0, True)
DE = DifferentialExtension(a**(b*x), x)
assert integrate_hyperexponential(DE.fa, DE.fd, DE) == (Piecewise(
(x, Eq(b*log(a), 0)), (exp(b*x*log(a))/(b*log(a)), True)), 0, True)
DE = DifferentialExtension(exp(a*x), x)
assert integrate_hyperexponential(DE.fa, DE.fd, DE) == (Piecewise(
(x, Eq(a, 0)), (exp(a*x)/a, True)), 0, True)
DE = DifferentialExtension(x*exp(a*x), x)
assert integrate_hyperexponential(DE.fa, DE.fd, DE) == (Piecewise(
(x**2/2, Eq(a**3, 0)), ((x*a**2 - a)*exp(a*x)/a**3, True)), 0, True)
DE = DifferentialExtension(x**2*exp(a*x), x)
assert integrate_hyperexponential(DE.fa, DE.fd, DE) == (Piecewise(
(x**3/3, Eq(a**6, 0)),
((x**2*a**5 - 2*x*a**4 + 2*a**3)*exp(a*x)/a**6, True)), 0, True)
DE = DifferentialExtension(x**y + z, y)
assert integrate_hyperexponential(DE.fa, DE.fd, DE) == (Piecewise((y,
Eq(log(x), 0)), (exp(log(x)*y)/log(x), True)), z, True)
DE = DifferentialExtension(x**y + z + x**(2*y), y)
assert integrate_hyperexponential(DE.fa, DE.fd, DE) == (Piecewise((2*y,
Eq(2*log(x)**2, 0)), ((exp(2*log(x)*y)*log(x) +
2*exp(log(x)*y)*log(x))/(2*log(x)**2), True)), z, True)
# TODO: Add a test where two different parts of the extension use a
# Piecewise, like y**x + z**x.
def test_integrate_primitive():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)],
'Tfuncs': [log]})
assert integrate_primitive(Poly(t, t), Poly(1, t), DE) == (x*log(x), -1, True)
assert integrate_primitive(Poly(x, t), Poly(t, t), DE) == (0, NonElementaryIntegral(x/log(x), x), False)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t1), Poly(1/(x + 1), t2)],
'Tfuncs': [log, Lambda(i, log(i + 1))]})
assert integrate_primitive(Poly(t1, t2), Poly(t2, t2), DE) == \
(0, NonElementaryIntegral(log(x)/log(1 + x), x), False)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t1), Poly(1/(x*t1), t2)],
'Tfuncs': [log, Lambda(i, log(log(i)))]})
assert integrate_primitive(Poly(t2, t2), Poly(t1, t2), DE) == \
(0, NonElementaryIntegral(log(log(x))/log(x), x), False)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t0)],
'Tfuncs': [log]})
assert integrate_primitive(Poly(x**2*t0**3 + (3*x**2 + x)*t0**2 + (3*x**2
+ 2*x)*t0 + x**2 + x, t0), Poly(x**2*t0**4 + 4*x**2*t0**3 + 6*x**2*t0**2 +
4*x**2*t0 + x**2, t0), DE) == \
(-1/(log(x) + 1), NonElementaryIntegral(1/(log(x) + 1), x), False)
def test_integrate_hypertangent_polynomial():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**2 + 1, t)]})
assert integrate_hypertangent_polynomial(Poly(t**2 + x*t + 1, t), DE) == \
(Poly(t, t), Poly(x/2, t))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(a*(t**2 + 1), t)]})
assert integrate_hypertangent_polynomial(Poly(t**5, t), DE) == \
(Poly(1/(4*a)*t**4 - 1/(2*a)*t**2, t), Poly(1/(2*a), t))
def test_integrate_nonlinear_no_specials():
a, d, = Poly(x**2*t**5 + x*t**4 - nu**2*t**3 - x*(x**2 + 1)*t**2 - (x**2 -
nu**2)*t - x**5/4, t), Poly(x**2*t**4 + x**2*(x**2 + 2)*t**2 + x**2 + x**4 + x**6/4, t)
# f(x) == phi_nu(x), the logarithmic derivative of J_v, the Bessel function,
# which has no specials (see Chapter 5, note 4 of Bronstein's book).
f = Function('phi_nu')
DE = DifferentialExtension(extension={'D': [Poly(1, x),
Poly(-t**2 - t/x - (1 - nu**2/x**2), t)], 'Tfuncs': [f]})
assert integrate_nonlinear_no_specials(a, d, DE) == \
(-log(1 + f(x)**2 + x**2/2)/2 - (4 + x**2)/(4 + 2*x**2 + 4*f(x)**2), True)
assert integrate_nonlinear_no_specials(Poly(t, t), Poly(1, t), DE) == \
(0, False)
def test_integer_powers():
assert integer_powers([x, x/2, x**2 + 1, 2*x/3]) == [
(x/6, [(x, 6), (x/2, 3), (2*x/3, 4)]),
(1 + x**2, [(1 + x**2, 1)])]
def test_DifferentialExtension_exp():
assert DifferentialExtension(exp(x) + exp(x**2), x)._important_attrs == \
(Poly(t1 + t0, t1), Poly(1, t1), [Poly(1, x,), Poly(t0, t0),
Poly(2*x*t1, t1)], [x, t0, t1], [Lambda(i, exp(i)),
Lambda(i, exp(i**2))], [], [None, 'exp', 'exp'], [None, x, x**2])
assert DifferentialExtension(exp(x) + exp(2*x), x)._important_attrs == \
(Poly(t0**2 + t0, t0), Poly(1, t0), [Poly(1, x), Poly(t0, t0)], [x, t0],
[Lambda(i, exp(i))], [], [None, 'exp'], [None, x])
assert DifferentialExtension(exp(x) + exp(x/2), x)._important_attrs == \
(Poly(t0**2 + t0, t0), Poly(1, t0), [Poly(1, x), Poly(t0/2, t0)],
[x, t0], [Lambda(i, exp(i/2))], [], [None, 'exp'], [None, x/2])
assert DifferentialExtension(exp(x) + exp(x**2) + exp(x + x**2), x)._important_attrs == \
(Poly((1 + t0)*t1 + t0, t1), Poly(1, t1), [Poly(1, x), Poly(t0, t0),
Poly(2*x*t1, t1)], [x, t0, t1], [Lambda(i, exp(i)),
Lambda(i, exp(i**2))], [], [None, 'exp', 'exp'], [None, x, x**2])
assert DifferentialExtension(exp(x) + exp(x**2) + exp(x + x**2 + 1), x)._important_attrs == \
(Poly((1 + S.Exp1*t0)*t1 + t0, t1), Poly(1, t1), [Poly(1, x),
Poly(t0, t0), Poly(2*x*t1, t1)], [x, t0, t1], [Lambda(i, exp(i)),
Lambda(i, exp(i**2))], [], [None, 'exp', 'exp'], [None, x, x**2])
assert DifferentialExtension(exp(x) + exp(x**2) + exp(x/2 + x**2), x)._important_attrs == \
(Poly((t0 + 1)*t1 + t0**2, t1), Poly(1, t1), [Poly(1, x),
Poly(t0/2, t0), Poly(2*x*t1, t1)], [x, t0, t1],
[Lambda(i, exp(i/2)), Lambda(i, exp(i**2))],
[(exp(x/2), sqrt(exp(x)))], [None, 'exp', 'exp'], [None, x/2, x**2])
assert DifferentialExtension(exp(x) + exp(x**2) + exp(x/2 + x**2 + 3), x)._important_attrs == \
(Poly((t0*exp(3) + 1)*t1 + t0**2, t1), Poly(1, t1), [Poly(1, x),
Poly(t0/2, t0), Poly(2*x*t1, t1)], [x, t0, t1], [Lambda(i, exp(i/2)),
Lambda(i, exp(i**2))], [(exp(x/2), sqrt(exp(x)))], [None, 'exp', 'exp'],
[None, x/2, x**2])
assert DifferentialExtension(sqrt(exp(x)), x)._important_attrs == \
(Poly(t0, t0), Poly(1, t0), [Poly(1, x), Poly(t0/2, t0)], [x, t0],
[Lambda(i, exp(i/2))], [(exp(x/2), sqrt(exp(x)))], [None, 'exp'], [None, x/2])
assert DifferentialExtension(exp(x/2), x)._important_attrs == \
(Poly(t0, t0), Poly(1, t0), [Poly(1, x), Poly(t0/2, t0)], [x, t0],
[Lambda(i, exp(i/2))], [], [None, 'exp'], [None, x/2])
def test_DifferentialExtension_log():
assert DifferentialExtension(log(x)*log(x + 1)*log(2*x**2 + 2*x), x)._important_attrs == \
(Poly(t0*t1**2 + (t0*log(2) + t0**2)*t1, t1), Poly(1, t1),
[Poly(1, x), Poly(1/x, t0),
Poly(1/(x + 1), t1, expand=False)], [x, t0, t1],
[Lambda(i, log(i)), Lambda(i, log(i + 1))], [], [None, 'log', 'log'],
[None, x, x + 1])
assert DifferentialExtension(x**x*log(x), x)._important_attrs == \
(Poly(t0*t1, t1), Poly(1, t1), [Poly(1, x), Poly(1/x, t0),
Poly((1 + t0)*t1, t1)], [x, t0, t1], [Lambda(i, log(i)),
Lambda(i, exp(t0*i))], [(exp(x*log(x)), x**x)], [None, 'log', 'exp'],
[None, x, t0*x])
def test_DifferentialExtension_symlog():
# See comment on test_risch_integrate below
assert DifferentialExtension(log(x**x), x)._important_attrs == \
(Poly(t0*x, t1), Poly(1, t1), [Poly(1, x), Poly(1/x, t0), Poly((t0 +
1)*t1, t1)], [x, t0, t1], [Lambda(i, log(i)), Lambda(i, exp(i*t0))],
[(exp(x*log(x)), x**x)], [None, 'log', 'exp'], [None, x, t0*x])
assert DifferentialExtension(log(x**y), x)._important_attrs == \
(Poly(y*t0, t0), Poly(1, t0), [Poly(1, x), Poly(1/x, t0)], [x, t0],
[Lambda(i, log(i))], [(y*log(x), log(x**y))], [None, 'log'],
[None, x])
assert DifferentialExtension(log(sqrt(x)), x)._important_attrs == \
(Poly(t0, t0), Poly(2, t0), [Poly(1, x), Poly(1/x, t0)], [x, t0],
[Lambda(i, log(i))], [(log(x)/2, log(sqrt(x)))], [None, 'log'],
[None, x])
def test_DifferentialExtension_handle_first():
assert DifferentialExtension(exp(x)*log(x), x, handle_first='log')._important_attrs == \
(Poly(t0*t1, t1), Poly(1, t1), [Poly(1, x), Poly(1/x, t0),
Poly(t1, t1)], [x, t0, t1], [Lambda(i, log(i)), Lambda(i, exp(i))],
[], [None, 'log', 'exp'], [None, x, x])
assert DifferentialExtension(exp(x)*log(x), x, handle_first='exp')._important_attrs == \
(Poly(t0*t1, t1), Poly(1, t1), [Poly(1, x), Poly(t0, t0),
Poly(1/x, t1)], [x, t0, t1], [Lambda(i, exp(i)), Lambda(i, log(i))],
[], [None, 'exp', 'log'], [None, x, x])
# This one must have the log first, regardless of what we set it to
# (because the log is inside of the exponential: x**x == exp(x*log(x)))
assert DifferentialExtension(-x**x*log(x)**2 + x**x - x**x/x, x,
handle_first='exp')._important_attrs == \
DifferentialExtension(-x**x*log(x)**2 + x**x - x**x/x, x,
handle_first='log')._important_attrs == \
(Poly((-1 + x - x*t0**2)*t1, t1), Poly(x, t1),
[Poly(1, x), Poly(1/x, t0), Poly((1 + t0)*t1, t1)], [x, t0, t1],
[Lambda(i, log(i)), Lambda(i, exp(t0*i))], [(exp(x*log(x)), x**x)],
[None, 'log', 'exp'], [None, x, t0*x])
def test_DifferentialExtension_all_attrs():
# Test 'unimportant' attributes
DE = DifferentialExtension(exp(x)*log(x), x, handle_first='exp')
assert DE.f == exp(x)*log(x)
assert DE.newf == t0*t1
assert DE.x == x
assert DE.cases == ['base', 'exp', 'primitive']
assert DE.case == 'primitive'
assert DE.level == -1
assert DE.t == t1 == DE.T[DE.level]
assert DE.d == Poly(1/x, t1) == DE.D[DE.level]
raises(ValueError, lambda: DE.increment_level())
DE.decrement_level()
assert DE.level == -2
assert DE.t == t0 == DE.T[DE.level]
assert DE.d == Poly(t0, t0) == DE.D[DE.level]
assert DE.case == 'exp'
DE.decrement_level()
assert DE.level == -3
assert DE.t == x == DE.T[DE.level] == DE.x
assert DE.d == Poly(1, x) == DE.D[DE.level]
assert DE.case == 'base'
raises(ValueError, lambda: DE.decrement_level())
DE.increment_level()
DE.increment_level()
assert DE.level == -1
assert DE.t == t1 == DE.T[DE.level]
assert DE.d == Poly(1/x, t1) == DE.D[DE.level]
assert DE.case == 'primitive'
# Test methods
assert DE.indices('log') == [2]
assert DE.indices('exp') == [1]
def test_DifferentialExtension_extension_flag():
raises(ValueError, lambda: DifferentialExtension(extension={'T': [x, t]}))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
assert DE._important_attrs == (None, None, [Poly(1, x), Poly(t, t)], [x, t],
None, None, None, None)
assert DE.d == Poly(t, t)
assert DE.t == t
assert DE.level == -1
assert DE.cases == ['base', 'exp']
assert DE.x == x
assert DE.case == 'exp'
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)],
'exts': [None, 'exp'], 'extargs': [None, x]})
assert DE._important_attrs == (None, None, [Poly(1, x), Poly(t, t)], [x, t],
None, None, [None, 'exp'], [None, x])
raises(ValueError, lambda: DifferentialExtension())
def test_DifferentialExtension_misc():
# Odd ends
assert DifferentialExtension(sin(y)*exp(x), x)._important_attrs == \
(Poly(sin(y)*t0, t0, domain='ZZ[sin(y)]'), Poly(1, t0, domain='ZZ'),
[Poly(1, x, domain='ZZ'), Poly(t0, t0, domain='ZZ')], [x, t0],
[Lambda(i, exp(i))], [], [None, 'exp'], [None, x])
raises(NotImplementedError, lambda: DifferentialExtension(sin(x), x))
assert DifferentialExtension(10**x, x)._important_attrs == \
(Poly(t0, t0), Poly(1, t0), [Poly(1, x), Poly(log(10)*t0, t0)], [x, t0],
[Lambda(i, exp(i*log(10)))], [(exp(x*log(10)), 10**x)], [None, 'exp'],
[None, x*log(10)])
assert DifferentialExtension(log(x) + log(x**2), x)._important_attrs in [
(Poly(3*t0, t0), Poly(2, t0), [Poly(1, x), Poly(2/x, t0)], [x, t0],
[Lambda(i, log(i**2))], [], [None, ], [], [1], [x**2]),
(Poly(3*t0, t0), Poly(1, t0), [Poly(1, x), Poly(1/x, t0)], [x, t0],
[Lambda(i, log(i))], [], [None, 'log'], [None, x])]
assert DifferentialExtension(S.Zero, x)._important_attrs == \
(Poly(0, x), Poly(1, x), [Poly(1, x)], [x], [], [], [None], [None])
def test_DifferentialExtension_Rothstein():
# Rothstein's integral
f = (2581284541*exp(x) + 1757211400)/(39916800*exp(3*x) +
119750400*exp(x)**2 + 119750400*exp(x) + 39916800)*exp(1/(exp(x) + 1) - 10*x)
assert DifferentialExtension(f, x)._important_attrs == \
(Poly((1757211400 + 2581284541*t0)*t1, t1), Poly(39916800 +
119750400*t0 + 119750400*t0**2 + 39916800*t0**3, t1),
[Poly(1, x), Poly(t0, t0), Poly(-(10 + 21*t0 + 10*t0**2)/(1 + 2*t0 +
t0**2)*t1, t1, domain='ZZ(t0)')], [x, t0, t1],
[Lambda(i, exp(i)), Lambda(i, exp(1/(t0 + 1) - 10*i))], [],
[None, 'exp', 'exp'], [None, x, 1/(t0 + 1) - 10*x])
class TestingException(Exception):
"""Dummy Exception class for testing."""
pass
def test_DecrementLevel():
DE = DifferentialExtension(x*log(exp(x) + 1), x)
assert DE.level == -1
assert DE.t == t1
assert DE.d == Poly(t0/(t0 + 1), t1)
assert DE.case == 'primitive'
with DecrementLevel(DE):
assert DE.level == -2
assert DE.t == t0
assert DE.d == Poly(t0, t0)
assert DE.case == 'exp'
with DecrementLevel(DE):
assert DE.level == -3
assert DE.t == x
assert DE.d == Poly(1, x)
assert DE.case == 'base'
assert DE.level == -2
assert DE.t == t0
assert DE.d == Poly(t0, t0)
assert DE.case == 'exp'
assert DE.level == -1
assert DE.t == t1
assert DE.d == Poly(t0/(t0 + 1), t1)
assert DE.case == 'primitive'
# Test that __exit__ is called after an exception correctly
try:
with DecrementLevel(DE):
raise TestingException
except TestingException:
pass
else:
raise AssertionError("Did not raise.")
assert DE.level == -1
assert DE.t == t1
assert DE.d == Poly(t0/(t0 + 1), t1)
assert DE.case == 'primitive'
def test_risch_integrate():
assert risch_integrate(t0*exp(x), x) == t0*exp(x)
assert risch_integrate(sin(x), x, rewrite_complex=True) == -exp(I*x)/2 - exp(-I*x)/2
# From my GSoC writeup
assert risch_integrate((1 + 2*x**2 + x**4 + 2*x**3*exp(2*x**2))/
(x**4*exp(x**2) + 2*x**2*exp(x**2) + exp(x**2)), x) == \
NonElementaryIntegral(exp(-x**2), x) + exp(x**2)/(1 + x**2)
assert risch_integrate(0, x) == 0
# also tests prde_cancel()
e1 = log(x/exp(x) + 1)
ans1 = risch_integrate(e1, x)
assert ans1 == (x*log(x*exp(-x) + 1) + NonElementaryIntegral((x**2 - x)/(x + exp(x)), x))
assert cancel(diff(ans1, x) - e1) == 0
# also tests issue #10798
e2 = (log(-1/y)/2 - log(1/y)/2)/y - (log(1 - 1/y)/2 - log(1 + 1/y)/2)/y
ans2 = risch_integrate(e2, y)
assert ans2 == log(1/y)*log(1 - 1/y)/2 - log(1/y)*log(1 + 1/y)/2 + \
NonElementaryIntegral((I*pi*y**2 - 2*y*log(1/y) - I*pi)/(2*y**3 - 2*y), y)
assert expand_log(cancel(diff(ans2, y) - e2), force=True) == 0
# These are tested here in addition to in test_DifferentialExtension above
# (symlogs) to test that backsubs works correctly. The integrals should be
# written in terms of the original logarithms in the integrands.
# XXX: Unfortunately, making backsubs work on this one is a little
# trickier, because x**x is converted to exp(x*log(x)), and so log(x**x)
# is converted to x*log(x). (x**2*log(x)).subs(x*log(x), log(x**x)) is
# smart enough, the issue is that these splits happen at different places
# in the algorithm. Maybe a heuristic is in order
assert risch_integrate(log(x**x), x) == x**2*log(x)/2 - x**2/4
assert risch_integrate(log(x**y), x) == x*log(x**y) - x*y
assert risch_integrate(log(sqrt(x)), x) == x*log(sqrt(x)) - x/2
def test_risch_integrate_float():
assert risch_integrate((-60*exp(x) - 19.2*exp(4*x))*exp(4*x), x) == -2.4*exp(8*x) - 12.0*exp(5*x)
def test_NonElementaryIntegral():
assert isinstance(risch_integrate(exp(x**2), x), NonElementaryIntegral)
assert isinstance(risch_integrate(x**x*log(x), x), NonElementaryIntegral)
# Make sure methods of Integral still give back a NonElementaryIntegral
assert isinstance(NonElementaryIntegral(x**x*t0, x).subs(t0, log(x)), NonElementaryIntegral)
def test_xtothex():
a = risch_integrate(x**x, x)
assert a == NonElementaryIntegral(x**x, x)
assert isinstance(a, NonElementaryIntegral)
def test_DifferentialExtension_equality():
DE1 = DE2 = DifferentialExtension(log(x), x)
assert DE1 == DE2
def test_DifferentialExtension_printing():
DE = DifferentialExtension(exp(2*x**2) + log(exp(x**2) + 1), x)
assert repr(DE) == ("DifferentialExtension(dict([('f', exp(2*x**2) + log(exp(x**2) + 1)), "
"('x', x), ('T', [x, t0, t1]), ('D', [Poly(1, x, domain='ZZ'), Poly(2*x*t0, t0, domain='ZZ[x]'), "
"Poly(2*t0*x/(t0 + 1), t1, domain='ZZ(x,t0)')]), ('fa', Poly(t1 + t0**2, t1, domain='ZZ[t0]')), "
"('fd', Poly(1, t1, domain='ZZ')), ('Tfuncs', [Lambda(i, exp(i**2)), Lambda(i, log(t0 + 1))]), "
"('backsubs', []), ('exts', [None, 'exp', 'log']), ('extargs', [None, x**2, t0 + 1]), "
"('cases', ['base', 'exp', 'primitive']), ('case', 'primitive'), ('t', t1), "
"('d', Poly(2*t0*x/(t0 + 1), t1, domain='ZZ(x,t0)')), ('newf', t0**2 + t1), ('level', -1), "
"('dummy', False)]))")
assert str(DE) == ("DifferentialExtension({fa=Poly(t1 + t0**2, t1, domain='ZZ[t0]'), "
"fd=Poly(1, t1, domain='ZZ'), D=[Poly(1, x, domain='ZZ'), Poly(2*x*t0, t0, domain='ZZ[x]'), "
"Poly(2*t0*x/(t0 + 1), t1, domain='ZZ(x,t0)')]})")
| 35,501 | 48.932489 | 108 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_singularityfunctions.py
|
from sympy.integrals.singularityfunctions import singularityintegrate
from sympy import SingularityFunction, symbols, Function
x, a, n, y = symbols('x a n y')
f = Function('f')
def test_singularityintegrate():
assert singularityintegrate(x, x) is None
assert singularityintegrate(x + SingularityFunction(x, 9, 1), x) is None
assert 4*singularityintegrate(SingularityFunction(x, a, 3), x) == 4*SingularityFunction(x, a, 4)/4
assert singularityintegrate(5*SingularityFunction(x, 5, -2), x) == 5*SingularityFunction(x, 5, -1)
assert singularityintegrate(6*SingularityFunction(x, 5, -1), x) == 6*SingularityFunction(x, 5, 0)
assert singularityintegrate(x*SingularityFunction(x, 0, -1), x) == 0
assert singularityintegrate((x - 5)*SingularityFunction(x, 5, -1), x) == 0
assert singularityintegrate(SingularityFunction(x, 0, -1) * f(x), x) == f(0) * SingularityFunction(x, 0, 0)
assert singularityintegrate(SingularityFunction(x, 1, -1) * f(x), x) == f(1) * SingularityFunction(x, 1, 0)
assert singularityintegrate(y*SingularityFunction(x, 0, -1)**2, x) == \
y*SingularityFunction(0, 0, -1)*SingularityFunction(x, 0, 0)
| 1,166 | 54.571429 | 111 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_prde.py
|
"""Most of these tests come from the examples in Bronstein's book."""
from sympy.integrals.risch import (DifferentialExtension, NonElementaryIntegral,
derivation, risch_integrate)
from sympy.integrals.prde import (prde_normal_denom, prde_special_denom,
prde_linear_constraints, constant_system, prde_spde, prde_no_cancel_b_large,
prde_no_cancel_b_small, limited_integrate_reduce, limited_integrate,
is_deriv_k, is_log_deriv_k_t_radical, parametric_log_deriv_heu,
is_log_deriv_k_t_radical_in_field, param_poly_rischDE, param_rischDE,
prde_cancel_liouvillian)
from sympy.polys.polymatrix import PolyMatrix as Matrix
from sympy import Poly, S, symbols, integrate, log, I, pi, exp
from sympy.abc import x, t, n, y
t0, t1, t2, t3, k = symbols('t:4 k')
def test_prde_normal_denom():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1 + t**2, t)]})
fa = Poly(1, t)
fd = Poly(x, t)
G = [(Poly(t, t), Poly(1 + t**2, t)), (Poly(1, t), Poly(x + x*t**2, t))]
assert prde_normal_denom(fa, fd, G, DE) == \
(Poly(x, t), (Poly(1, t), Poly(1, t)), [(Poly(x*t, t),
Poly(t**2 + 1, t)), (Poly(1, t), Poly(t**2 + 1, t))], Poly(1, t))
G = [(Poly(t, t), Poly(t**2 + 2*t + 1, t)), (Poly(x*t, t),
Poly(t**2 + 2*t + 1, t)), (Poly(x*t**2, t), Poly(t**2 + 2*t + 1, t))]
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
assert prde_normal_denom(Poly(x, t), Poly(1, t), G, DE) == \
(Poly(t + 1, t), (Poly((-1 + x)*t + x, t), Poly(1, t)), [(Poly(t, t),
Poly(1, t)), (Poly(x*t, t), Poly(1, t)), (Poly(x*t**2, t),
Poly(1, t))], Poly(t + 1, t))
def test_prde_special_denom():
a = Poly(t + 1, t)
ba = Poly(t**2, t)
bd = Poly(1, t)
G = [(Poly(t, t), Poly(1, t)), (Poly(t**2, t), Poly(1, t)), (Poly(t**3, t), Poly(1, t))]
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
assert prde_special_denom(a, ba, bd, G, DE) == \
(Poly(t + 1, t), Poly(t**2, t), [(Poly(t, t), Poly(1, t)),
(Poly(t**2, t), Poly(1, t)), (Poly(t**3, t), Poly(1, t))], Poly(1, t))
G = [(Poly(t, t), Poly(1, t)), (Poly(1, t), Poly(t, t))]
assert prde_special_denom(Poly(1, t), Poly(t**2, t), Poly(1, t), G, DE) == \
(Poly(1, t), Poly(t**2 - 1, t), [(Poly(t**2, t), Poly(1, t)),
(Poly(1, t), Poly(1, t))], Poly(t, t))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(-2*x*t0, t0)]})
DE.decrement_level()
G = [(Poly(t, t), Poly(t**2, t)), (Poly(2*t, t), Poly(t, t))]
assert prde_special_denom(Poly(5*x*t + 1, t), Poly(t**2 + 2*x**3*t, t), Poly(t**3 + 2, t), G, DE) == \
(Poly(5*x*t + 1, t), Poly(0, t), [(Poly(t, t), Poly(t**2, t)),
(Poly(2*t, t), Poly(t, t))], Poly(1, x))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly((t**2 + 1)*2*x, t)]})
G = [(Poly(t + x, t), Poly(t*x, t)), (Poly(2*t, t), Poly(x**2, x))]
assert prde_special_denom(Poly(5*x*t + 1, t), Poly(t**2 + 2*x**3*t, t), Poly(t**3, t), G, DE) == \
(Poly(5*x*t + 1, t), Poly(0, t), [(Poly(t + x, t), Poly(x*t, t)),
(Poly(2*t, t, x), Poly(x**2, t, x))], Poly(1, t))
assert prde_special_denom(Poly(t + 1, t), Poly(t**2, t), Poly(t**3, t), G, DE) == \
(Poly(t + 1, t), Poly(0, t), [(Poly(t + x, t), Poly(x*t, t)), (Poly(2*t, t, x),
Poly(x**2, t, x))], Poly(1, t))
def test_prde_linear_constraints():
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
G = [(Poly(2*x**3 + 3*x + 1, x), Poly(x**2 - 1, x)), (Poly(1, x), Poly(x - 1, x)),
(Poly(1, x), Poly(x + 1, x))]
assert prde_linear_constraints(Poly(1, x), Poly(0, x), G, DE) == \
((Poly(2*x, x), Poly(0, x), Poly(0, x)), Matrix([[1, 1, -1], [5, 1, 1]]))
G = [(Poly(t, t), Poly(1, t)), (Poly(t**2, t), Poly(1, t)), (Poly(t**3, t), Poly(1, t))]
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
assert prde_linear_constraints(Poly(t + 1, t), Poly(t**2, t), G, DE) == \
((Poly(t, t), Poly(t**2, t), Poly(t**3, t)), Matrix(0, 3, []))
G = [(Poly(2*x, t), Poly(t, t)), (Poly(-x, t), Poly(t, t))]
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
prde_linear_constraints(Poly(1, t), Poly(0, t), G, DE) == \
((Poly(0, t), Poly(0, t)), Matrix([[2*x, -x]]))
def test_constant_system():
A = Matrix([[-(x + 3)/(x - 1), (x + 1)/(x - 1), 1],
[-x - 3, x + 1, x - 1],
[2*(x + 3)/(x - 1), 0, 0]])
u = Matrix([(x + 1)/(x - 1), x + 1, 0])
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
assert constant_system(A, u, DE) == \
(Matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 0],
[0, 0, 1]]), Matrix([0, 1, 0, 0]))
def test_prde_spde():
D = [Poly(x, t), Poly(-x*t, t)]
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
# TODO: when bound_degree() can handle this, test degree bound from that too
assert prde_spde(Poly(t, t), Poly(-1/x, t), D, n, DE) == \
(Poly(t, t), Poly(0, t), [Poly(2*x, t), Poly(-x, t)],
[Poly(-x**2, t), Poly(0, t)], n - 1)
def test_prde_no_cancel():
# b large
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
assert prde_no_cancel_b_large(Poly(1, x), [Poly(x**2, x), Poly(1, x)], 2, DE) == \
([Poly(x**2 - 2*x + 2, x), Poly(1, x)], Matrix([[1, 0, -1, 0],
[0, 1, 0, -1]]))
assert prde_no_cancel_b_large(Poly(1, x), [Poly(x**3, x), Poly(1, x)], 3, DE) == \
([Poly(x**3 - 3*x**2 + 6*x - 6, x), Poly(1, x)], Matrix([[1, 0, -1, 0],
[0, 1, 0, -1]]))
assert prde_no_cancel_b_large(Poly(x, x), [Poly(x**2, x), Poly(1, x)], 1, DE) == \
([Poly(x, x, domain='ZZ'), Poly(0, x, domain='ZZ')], Matrix([[1, -1, 0, 0],
[1, 0, -1, 0],
[0, 1, 0, -1]]))
# b small
# XXX: Is there a better example of a monomial with D.degree() > 2?
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**3 + 1, t)]})
# My original q was t**4 + t + 1, but this solution implies q == t**4
# (c1 = 4), with some of the ci for the original q equal to 0.
G = [Poly(t**6, t), Poly(x*t**5, t), Poly(t**3, t), Poly(x*t**2, t), Poly(1 + x, t)]
assert prde_no_cancel_b_small(Poly(x*t, t), G, 4, DE) == \
([Poly(t**4/4 - x/12*t**3 + x**2/24*t**2 + (-S(11)/12 - x**3/24)*t + x/24, t),
Poly(x/3*t**3 - x**2/6*t**2 + (-S(1)/3 + x**3/6)*t - x/6, t), Poly(t, t),
Poly(0, t), Poly(0, t)], Matrix([[1, 0, -1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, -S(1)/4, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, -1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, -1, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, -1, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, -1, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, -1]]))
# TODO: Add test for deg(b) <= 0 with b small
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1 + t**2, t)]})
b = Poly(-1/x**2, t, field=True) # deg(b) == 0
q = [Poly(x**i*t**j, t, field=True) for i in range(2) for j in range(3)]
h, A = prde_no_cancel_b_small(b, q, 3, DE)
V = A.nullspace()
assert len(V) == 1
assert V[0] == Matrix([-1/2, 0, 0, 1, 0, 0]*3)
assert (Matrix([h])*V[0][6:, :])[0] == Poly(x**2/2, t, domain='ZZ(x)')
assert (Matrix([q])*V[0][:6, :])[0] == Poly(x - 1/2, t, domain='QQ(x)')
def test_prde_cancel_liouvillian():
### 1. case == 'primitive'
# used when integrating f = log(x) - log(x - 1)
# Not taken from 'the' book
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
p0 = Poly(0, t, field=True)
h, A = prde_cancel_liouvillian(Poly(-1/(x - 1), t), [Poly(-x + 1, t), Poly(1, t)], 1, DE)
V = A.nullspace()
h == [p0, p0, Poly((x - 1)*t, t), p0, p0, p0, p0, p0, p0, p0, Poly(x - 1, t), Poly(-x**2 + x, t), p0, p0, p0, p0]
assert A.rank() == 16
assert (Matrix([h])*V[0][:16, :]) == Matrix([[Poly(0, t, domain='QQ(x)')]])
### 2. case == 'exp'
# used when integrating log(x/exp(x) + 1)
# Not taken from book
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(-t, t)]})
assert prde_cancel_liouvillian(Poly(0, t, domain='QQ[x]'), [Poly(1, t, domain='QQ(x)')], 0, DE) == \
([Poly(1, t, domain='QQ'), Poly(x, t)], Matrix([[-1, 0, 1]]))
def test_param_poly_rischDE():
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
a = Poly(x**2 - x, x, field=True)
b = Poly(1, x, field=True)
q = [Poly(x, x, field=True), Poly(x**2, x, field=True)]
h, A = param_poly_rischDE(a, b, q, 3, DE)
assert A.nullspace() == [Matrix([0, 1, 1, 1])] # c1, c2, d1, d2
# Solution of a*Dp + b*p = c1*q1 + c2*q2 = q2 = x**2
# is d1*h1 + d2*h2 = h1 + h2 = x.
assert h[0] + h[1] == Poly(x, x)
# a*Dp + b*p = q1 = x has no solution.
a = Poly(x**2 - x, x, field=True)
b = Poly(x**2 - 5*x + 3, x, field=True)
q = [Poly(1, x, field=True), Poly(x, x, field=True),
Poly(x**2, x, field=True)]
h, A = param_poly_rischDE(a, b, q, 3, DE)
assert A.nullspace() == [Matrix([3, -5, 1, -5, 1, 1])]
p = -5*h[0] + h[1] + h[2] # Poly(1, x)
assert a*derivation(p, DE) + b*p == Poly(x**2 - 5*x + 3, x)
def test_param_rischDE():
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
p1, px = Poly(1, x, field=True), Poly(x, x, field=True)
G = [(p1, px), (p1, p1), (px, p1)] # [1/x, 1, x]
h, A = param_rischDE(-p1, Poly(x**2, x, field=True), G, DE)
assert len(h) == 3
p = [hi[0].as_expr()/hi[1].as_expr() for hi in h]
V = A.nullspace()
assert len(V) == 2
assert V[0] == Matrix([-1, 1, 0, -1, 1, 0])
y = -p[0] + p[1] + 0*p[2] # x
assert y.diff(x) - y/x**2 == 1 - 1/x # Dy + f*y == -G0 + G1 + 0*G2
# the below test computation takes place while computing the integral
# of 'f = log(log(x + exp(x)))'
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
G = [(Poly(t + x, t, domain='ZZ(x)'), Poly(1, t, domain='QQ')), (Poly(0, t, domain='QQ'), Poly(1, t, domain='QQ'))]
h, A = param_rischDE(Poly(-t - 1, t, field=True), Poly(t + x, t, field=True), G, DE)
assert len(h) == 5
p = [hi[0].as_expr()/hi[1].as_expr() for hi in h]
V = A.nullspace()
assert len(V) == 3
assert V[0] == Matrix([0, 0, 0, 0, 1, 0, 0])
y = 0*p[0] + 0*p[1] + 1*p[2] + 0*p[3] + 0*p[4]
assert y.diff(t) - y/(t + x) == 0 # Dy + f*y = 0*G0 + 0*G1
def test_limited_integrate_reduce():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
assert limited_integrate_reduce(Poly(x, t), Poly(t**2, t), [(Poly(x, t),
Poly(t, t))], DE) == \
(Poly(t, t), Poly(-1/x, t), Poly(t, t), 1, (Poly(x, t), Poly(1, t)),
[(Poly(-x*t, t), Poly(1, t))])
def test_limited_integrate():
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
G = [(Poly(x, x), Poly(x + 1, x))]
assert limited_integrate(Poly(-(1 + x + 5*x**2 - 3*x**3), x),
Poly(1 - x - x**2 + x**3, x), G, DE) == \
((Poly(x**2 - x + 2, x), Poly(x - 1, x)), [2])
G = [(Poly(1, x), Poly(x, x))]
assert limited_integrate(Poly(5*x**2, x), Poly(3, x), G, DE) == \
((Poly(5*x**3/9, x), Poly(1, x)), [0])
def test_is_log_deriv_k_t_radical():
DE = DifferentialExtension(extension={'D': [Poly(1, x)], 'exts': [None],
'extargs': [None]})
assert is_log_deriv_k_t_radical(Poly(2*x, x), Poly(1, x), DE) is None
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(2*t1, t1), Poly(1/x, t2)],
'exts': [None, 'exp', 'log'], 'extargs': [None, 2*x, x]})
assert is_log_deriv_k_t_radical(Poly(x + t2/2, t2), Poly(1, t2), DE) == \
([(t1, 1), (x, 1)], t1*x, 2, 0)
# TODO: Add more tests
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t0, t0), Poly(1/x, t)],
'exts': [None, 'exp', 'log'], 'extargs': [None, x, x]})
assert is_log_deriv_k_t_radical(Poly(x + t/2 + 3, t), Poly(1, t), DE) == \
([(t0, 2), (x, 1)], x*t0**2, 2, 3)
def test_is_deriv_k():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t1), Poly(1/(x + 1), t2)],
'exts': [None, 'log', 'log'], 'extargs': [None, x, x + 1]})
assert is_deriv_k(Poly(2*x**2 + 2*x, t2), Poly(1, t2), DE) == \
([(t1, 1), (t2, 1)], t1 + t2, 2)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t1), Poly(t2, t2)],
'exts': [None, 'log', 'exp'], 'extargs': [None, x, x]})
assert is_deriv_k(Poly(x**2*t2**3, t2), Poly(1, t2), DE) == \
([(x, 3), (t1, 2)], 2*t1 + 3*x, 1)
# TODO: Add more tests, including ones with exponentials
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(2/x, t1)],
'exts': [None, 'log'], 'extargs': [None, x**2]})
assert is_deriv_k(Poly(x, t1), Poly(1, t1), DE) == \
([(t1, S(1)/2)], t1/2, 1)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(2/(1 + x), t0)],
'exts': [None, 'log'], 'extargs': [None, x**2 + 2*x + 1]})
assert is_deriv_k(Poly(1 + x, t0), Poly(1, t0), DE) == \
([(t0, S(1)/2)], t0/2, 1)
# Issue 10798
# DE = DifferentialExtension(log(1/x), x)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(-1/x, t)],
'exts': [None, 'log'], 'extargs': [None, 1/x]})
assert is_deriv_k(Poly(1, t), Poly(x, t), DE) == ([(t, 1)], t, 1)
def test_is_log_deriv_k_t_radical_in_field():
# NOTE: any potential constant factor in the second element of the result
# doesn't matter, because it cancels in Da/a.
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
assert is_log_deriv_k_t_radical_in_field(Poly(5*t + 1, t), Poly(2*t*x, t), DE) == \
(2, t*x**5)
assert is_log_deriv_k_t_radical_in_field(Poly(2 + 3*t, t), Poly(5*x*t, t), DE) == \
(5, x**3*t**2)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(-t/x**2, t)]})
assert is_log_deriv_k_t_radical_in_field(Poly(-(1 + 2*t), t),
Poly(2*x**2 + 2*x**2*t, t), DE) == \
(2, t + t**2)
assert is_log_deriv_k_t_radical_in_field(Poly(-1, t), Poly(x**2, t), DE) == \
(1, t)
assert is_log_deriv_k_t_radical_in_field(Poly(1, t), Poly(2*x**2, t), DE) == \
(2, 1/t)
def test_parametric_log_deriv():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
assert parametric_log_deriv_heu(Poly(5*t**2 + t - 6, t), Poly(2*x*t**2, t),
Poly(-1, t), Poly(x*t**2, t), DE) == \
(2, 6, t*x**5)
| 15,308 | 48.543689 | 119 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_rde.py
|
"""Most of these tests come from the examples in Bronstein's book."""
from sympy import Poly, S, symbols, oo, I
from sympy.integrals.risch import (DifferentialExtension,
NonElementaryIntegralException)
from sympy.integrals.rde import (order_at, order_at_oo, weak_normalizer,
normal_denom, special_denom, bound_degree, spde, solve_poly_rde,
no_cancel_equal, cancel_primitive, cancel_exp, rischDE)
from sympy.utilities.pytest import raises, XFAIL
from sympy.abc import x, t, z, n
t0, t1, t2, k = symbols('t:3 k')
def test_order_at():
a = Poly(t**4, t)
b = Poly((t**2 + 1)**3*t, t)
c = Poly((t**2 + 1)**6*t, t)
d = Poly((t**2 + 1)**10*t**10, t)
e = Poly((t**2 + 1)**100*t**37, t)
p1 = Poly(t, t)
p2 = Poly(1 + t**2, t)
assert order_at(a, p1, t) == 4
assert order_at(b, p1, t) == 1
assert order_at(c, p1, t) == 1
assert order_at(d, p1, t) == 10
assert order_at(e, p1, t) == 37
assert order_at(a, p2, t) == 0
assert order_at(b, p2, t) == 3
assert order_at(c, p2, t) == 6
assert order_at(d, p1, t) == 10
assert order_at(e, p2, t) == 100
assert order_at(Poly(0, t), Poly(t, t), t) == oo
assert order_at_oo(Poly(t**2 - 1, t), Poly(t + 1), t) == \
order_at_oo(Poly(t - 1, t), Poly(1, t), t) == -1
assert order_at_oo(Poly(0, t), Poly(1, t), t) == oo
def test_weak_normalizer():
a = Poly((1 + x)*t**5 + 4*t**4 + (-1 - 3*x)*t**3 - 4*t**2 + (-2 + 2*x)*t, t)
d = Poly(t**4 - 3*t**2 + 2, t)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
r = weak_normalizer(a, d, DE, z)
assert r == (Poly(t**5 - t**4 - 4*t**3 + 4*t**2 + 4*t - 4, t),
(Poly((1 + x)*t**2 + x*t, t), Poly(t + 1, t)))
assert weak_normalizer(r[1][0], r[1][1], DE) == (Poly(1, t), r[1])
r = weak_normalizer(Poly(1 + t**2), Poly(t**2 - 1, t), DE, z)
assert r == (Poly(t**4 - 2*t**2 + 1, t), (Poly(-3*t**2 + 1, t), Poly(t**2 - 1, t)))
assert weak_normalizer(r[1][0], r[1][1], DE, z) == (Poly(1, t), r[1])
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1 + t**2)]})
r = weak_normalizer(Poly(1 + t**2), Poly(t, t), DE, z)
assert r == (Poly(t, t), (Poly(0, t), Poly(1, t)))
assert weak_normalizer(r[1][0], r[1][1], DE, z) == (Poly(1, t), r[1])
def test_normal_denom():
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
raises(NonElementaryIntegralException, lambda: normal_denom(Poly(1, x), Poly(1, x),
Poly(1, x), Poly(x, x), DE))
fa, fd = Poly(t**2 + 1, t), Poly(1, t)
ga, gd = Poly(1, t), Poly(t**2, t)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**2 + 1, t)]})
assert normal_denom(fa, fd, ga, gd, DE) == \
(Poly(t, t), (Poly(t**3 - t**2 + t - 1, t), Poly(1, t)), (Poly(1, t),
Poly(1, t)), Poly(t, t))
def test_special_denom():
# TODO: add more tests here
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
assert special_denom(Poly(1, t), Poly(t**2, t), Poly(1, t), Poly(t**2 - 1, t),
Poly(t, t), DE) == \
(Poly(1, t), Poly(t**2 - 1, t), Poly(t**2 - 1, t), Poly(t, t))
# assert special_denom(Poly(1, t), Poly(2*x, t), Poly((1 + 2*x)*t, t), DE) == 1
# issue 3940
# Note, this isn't a very good test, because the denominator is just 1,
# but at least it tests the exp cancellation case
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(-2*x*t0, t0),
Poly(I*k*t1, t1)]})
DE.decrement_level()
assert special_denom(Poly(1, t0), Poly(I*k, t0), Poly(1, t0), Poly(t0, t0),
Poly(1, t0), DE) == \
(Poly(1, t0), Poly(I*k, t0), Poly(t0, t0), Poly(1, t0))
@XFAIL
def test_bound_degree_fail():
# Primitive
DE = DifferentialExtension(extension={'D': [Poly(1, x),
Poly(t0/x**2, t0), Poly(1/x, t)]})
assert bound_degree(Poly(t**2, t), Poly(-(1/x**2*t**2 + 1/x), t),
Poly((2*x - 1)*t**4 + (t0 + x)/x*t**3 - (t0 + 4*x**2)/2*x*t**2 + x*t,
t), DE) == 3
def test_bound_degree():
# Base
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
assert bound_degree(Poly(1, x), Poly(-2*x, x), Poly(1, x), DE) == 0
# Primitive (see above test_bound_degree_fail)
# TODO: Add test for when the degree bound becomes larger after limited_integrate
# TODO: Add test for db == da - 1 case
# Exp
# TODO: Add tests
# TODO: Add test for when the degree becomes larger after parametric_log_deriv()
# Nonlinear
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**2 + 1, t)]})
assert bound_degree(Poly(t, t), Poly((t - 1)*(t**2 + 1), t), Poly(1, t), DE) == 0
def test_spde():
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**2 + 1, t)]})
raises(NonElementaryIntegralException, lambda: spde(Poly(t, t), Poly((t - 1)*(t**2 + 1), t), Poly(1, t), 0, DE))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
assert spde(Poly(t**2 + x*t*2 + x**2, t), Poly(t**2/x**2 + (2/x - 1)*t, t),
Poly(t**2/x**2 + (2/x - 1)*t, t), 0, DE) == \
(Poly(0, t), Poly(0, t), 0, Poly(0, t), Poly(1, t))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t0/x**2, t0), Poly(1/x, t)]})
assert spde(Poly(t**2, t), Poly(-t**2/x**2 - 1/x, t),
Poly((2*x - 1)*t**4 + (t0 + x)/x*t**3 - (t0 + 4*x**2)/(2*x)*t**2 + x*t, t), 3, DE) == \
(Poly(0, t), Poly(0, t), 0, Poly(0, t),
Poly(t0*t**2/2 + x**2*t**2 - x**2*t, t))
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
assert spde(Poly(x**2 + x + 1, x), Poly(-2*x - 1, x), Poly(x**5/2 +
3*x**4/4 + x**3 - x**2 + 1, x), 4, DE) == \
(Poly(0, x), Poly(x/2 - S(1)/4, x), 2, Poly(x**2 + x + 1, x), Poly(5*x/4, x))
assert spde(Poly(x**2 + x + 1, x), Poly(-2*x - 1, x), Poly(x**5/2 +
3*x**4/4 + x**3 - x**2 + 1, x), n, DE) == \
(Poly(0, x), Poly(x/2 - S(1)/4, x), -2 + n, Poly(x**2 + x + 1, x), Poly(5*x/4, x))
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1, t)]})
raises(NonElementaryIntegralException, lambda: spde(Poly((t - 1)*(t**2 + 1)**2, t), Poly((t - 1)*(t**2 + 1), t), Poly(1, t), 0, DE))
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
assert spde(Poly(x**2 - x, x), Poly(1, x), Poly(9*x**4 - 10*x**3 + 2*x**2, x), 4, DE) == (Poly(0, x), Poly(0, x), 0, Poly(0, x), Poly(3*x**3 - 2*x**2, x))
assert spde(Poly(x**2 - x, x), Poly(x**2 - 5*x + 3, x), Poly(x**7 - x**6 - 2*x**4 + 3*x**3 - x**2, x), 5, DE) == \
(Poly(1, x), Poly(x + 1, x), 1, Poly(x**4 - x**3, x), Poly(x**3 - x**2, x))
def test_solve_poly_rde_no_cancel():
# deg(b) large
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1 + t**2, t)]})
assert solve_poly_rde(Poly(t**2 + 1, t), Poly(t**3 + (x + 1)*t**2 + t + x + 2, t),
oo, DE) == Poly(t + x, t)
# deg(b) small
DE = DifferentialExtension(extension={'D': [Poly(1, x)]})
assert solve_poly_rde(Poly(0, x), Poly(x/2 - S(1)/4, x), oo, DE) == \
Poly(x**2/4 - x/4, x)
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**2 + 1, t)]})
assert solve_poly_rde(Poly(2, t), Poly(t**2 + 2*t + 3, t), 1, DE) == \
Poly(t + 1, t, x)
# deg(b) == deg(D) - 1
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t**2 + 1, t)]})
assert no_cancel_equal(Poly(1 - t, t),
Poly(t**3 + t**2 - 2*x*t - 2*x, t), oo, DE) == \
(Poly(t**2, t), 1, Poly((-2 - 2*x)*t - 2*x, t))
def test_solve_poly_rde_cancel():
# exp
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
assert cancel_exp(Poly(2*x, t), Poly(2*x, t), 0, DE) == \
Poly(1, t)
assert cancel_exp(Poly(2*x, t), Poly((1 + 2*x)*t, t), 1, DE) == \
Poly(t, t)
# TODO: Add more exp tests, including tests that require is_deriv_in_field()
# primitive
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(1/x, t)]})
# If the DecrementLevel context manager is working correctly, this shouldn't
# cause any problems with the further tests.
raises(NonElementaryIntegralException, lambda: cancel_primitive(Poly(1, t), Poly(t, t), oo, DE))
assert cancel_primitive(Poly(1, t), Poly(t + 1/x, t), 2, DE) == \
Poly(t, t)
assert cancel_primitive(Poly(4*x, t), Poly(4*x*t**2 + 2*t/x, t), 3, DE) == \
Poly(t**2, t)
# TODO: Add more primitive tests, including tests that require is_deriv_in_field()
def test_rischDE():
# TODO: Add more tests for rischDE, including ones from the text
DE = DifferentialExtension(extension={'D': [Poly(1, x), Poly(t, t)]})
DE.decrement_level()
assert rischDE(Poly(-2*x, x), Poly(1, x), Poly(1 - 2*x - 2*x**2, x),
Poly(1, x), DE) == \
(Poly(x + 1, x), Poly(1, x))
| 8,769 | 45.157895 | 158 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_failing_integrals.py
|
# A collection of failing integrals from the issues.
from __future__ import division
from sympy import (
integrate, Integral, exp, oo, pi, sign, sqrt, sin, cos,
tan, S, log, gamma, sinh,
)
from sympy.utilities.pytest import XFAIL, SKIP, slow, skip, ON_TRAVIS
from sympy.abc import x, k, c, y, R, b, h, a, m
import signal
class TimeOutError(Exception):
pass
def timeout(signum, frame, time):
raise TimeOutError("Timed out after %d seconds" % time)
def run_with_timeout(test, time):
# Set the signal handler and a 5-second alarm
signal.signal(signal.SIGALRM, lambda s, f: timeout(s, f, time))
signal.alarm(time)
r = eval(test)
signal.alarm(0) # Disable the alarm
return r
@SKIP("Too slow for @slow")
@XFAIL
def test_issue_3880():
# integrate_hyperexponential(Poly(t*2*(1 - t0**2)*t0*(x**3 + x**2), t), Poly((1 + t0**2)**2*2*(x**2 + x + 1), t), [Poly(1, x), Poly(1 + t0**2, t0), Poly(t, t)], [x, t0, t], [exp, tan])
assert not integrate(exp(x)*cos(2*x)*sin(2*x) * (x**3 + x**2)/(2*(x**2 + x + 1)), x).has(Integral)
@XFAIL
def test_issue_4212():
assert not integrate(sign(x), x).has(Integral)
@XFAIL
def test_issue_4326():
assert integrate(((h*(x - R + b))/b)*sqrt(R**2 - x**2), (x, R - b, R)).has(Integral)
@XFAIL
def test_issue_4491():
assert not integrate(x*sqrt(x**2 + 2*x + 4), x).has(Integral)
@XFAIL
@slow
def test_issue_4511():
# This works, but gives a complicated answer. The correct answer is x - cos(x).
# The last one is what Maple gives. It is also quite slow.
assert integrate(cos(x)**2 / (1 - sin(x))) in [x - cos(x), 1 - cos(x) + x,
-2/(tan((S(1)/2)*x)**2 + 1) + x]
@XFAIL
def test_issue_4514():
# The correct answer is 2*sin(x)
assert not integrate(sin(2*x)/ sin(x)).has(Integral)
@XFAIL
def test_issue_4525():
# Warning: takes a long time
assert not integrate((x**m * (1 - x)**n * (a + b*x + c*x**2))/(1 + x**2), (x, 0, 1)).has(Integral)
@XFAIL
@slow
def test_issue_4540():
if ON_TRAVIS:
skip("Too slow for travis.")
# Note, this integral is probably nonelementary
assert not integrate(
(sin(1/x) - x*exp(x)) /
((-sin(1/x) + x*exp(x))*x + x*sin(1/x)), x).has(Integral)
@XFAIL
def test_issue_4551():
assert integrate(1/(x*sqrt(1 - x**2)), x).has(Integral)
@XFAIL
def test_issue_4737a():
# Implementation of Si()
assert integrate(sin(x)/x, x).has(Integral)
@XFAIL
def test_issue_1638b():
assert integrate(sin(x)/x, (x, -oo, oo)) == pi/2
@XFAIL
@slow
def test_issue_4891():
# Requires the hypergeometric function.
assert not integrate(cos(x)**y, x).has(Integral)
@XFAIL
@slow
def test_issue_1796a():
assert not integrate(exp(2*b*x)*exp(-a*x**2), x).has(Integral)
@XFAIL
def test_issue_4895b():
assert not integrate(exp(2*b*x)*exp(-a*x**2), (x, -oo, 0)).has(Integral)
@XFAIL
def test_issue_4895c():
assert not integrate(exp(2*b*x)*exp(-a*x**2), (x, -oo, oo)).has(Integral)
@XFAIL
def test_issue_4895d():
assert not integrate(exp(2*b*x)*exp(-a*x**2), (x, 0, oo)).has(Integral)
@XFAIL
@slow
def test_issue_4941():
if ON_TRAVIS:
skip("Too slow for travis.")
assert not integrate(sqrt(1 + sinh(x/20)**2), (x, -25, 25)).has(Integral)
@XFAIL
def test_issue_4992():
# Nonelementary integral. Requires hypergeometric/Meijer-G handling.
assert not integrate(log(x) * x**(k - 1) * exp(-x) / gamma(k), (x, 0, oo)).has(Integral)
| 3,488 | 23.062069 | 188 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_meijerint.py
|
from sympy import (meijerg, I, S, integrate, Integral, oo, gamma, cosh, sinc,
hyperexpand, exp, simplify, sqrt, pi, erf, erfc, sin, cos,
exp_polar, polygamma, hyper, log, expand_func)
from sympy.integrals.meijerint import (_rewrite_single, _rewrite1,
meijerint_indefinite, _inflate_g, _create_lookup_table,
meijerint_definite, meijerint_inversion)
from sympy.utilities import default_sort_key
from sympy.utilities.pytest import slow
from sympy.utilities.randtest import (verify_numerically,
random_complex_number as randcplx)
from sympy.core.compatibility import range
from sympy.abc import x, y, a, b, c, d, s, t, z
def test_rewrite_single():
def t(expr, c, m):
e = _rewrite_single(meijerg([a], [b], [c], [d], expr), x)
assert e is not None
assert isinstance(e[0][0][2], meijerg)
assert e[0][0][2].argument.as_coeff_mul(x) == (c, (m,))
def tn(expr):
assert _rewrite_single(meijerg([a], [b], [c], [d], expr), x) is None
t(x, 1, x)
t(x**2, 1, x**2)
t(x**2 + y*x**2, y + 1, x**2)
tn(x**2 + x)
tn(x**y)
def u(expr, x):
from sympy import Add, exp, exp_polar
r = _rewrite_single(expr, x)
e = Add(*[res[0]*res[2] for res in r[0]]).replace(
exp_polar, exp) # XXX Hack?
assert verify_numerically(e, expr, x)
u(exp(-x)*sin(x), x)
# The following has stopped working because hyperexpand changed slightly.
# It is probably not worth fixing
#u(exp(-x)*sin(x)*cos(x), x)
# This one cannot be done numerically, since it comes out as a g-function
# of argument 4*pi
# NOTE This also tests a bug in inverse mellin transform (which used to
# turn exp(4*pi*I*t) into a factor of exp(4*pi*I)**t instead of
# exp_polar).
#u(exp(x)*sin(x), x)
assert _rewrite_single(exp(x)*sin(x), x) == \
([(-sqrt(2)/(2*sqrt(pi)), 0,
meijerg(((-S(1)/2, 0, S(1)/4, S(1)/2, S(3)/4), (1,)),
((), (-S(1)/2, 0)), 64*exp_polar(-4*I*pi)/x**4))], True)
def test_rewrite1():
assert _rewrite1(x**3*meijerg([a], [b], [c], [d], x**2 + y*x**2)*5, x) == \
(5, x**3, [(1, 0, meijerg([a], [b], [c], [d], x**2*(y + 1)))], True)
def test_meijerint_indefinite_numerically():
def t(fac, arg):
g = meijerg([a], [b], [c], [d], arg)*fac
subs = {a: randcplx()/10, b: randcplx()/10 + I,
c: randcplx(), d: randcplx()}
integral = meijerint_indefinite(g, x)
assert integral is not None
assert verify_numerically(g.subs(subs), integral.diff(x).subs(subs), x)
t(1, x)
t(2, x)
t(1, 2*x)
t(1, x**2)
t(5, x**S('3/2'))
t(x**3, x)
t(3*x**S('3/2'), 4*x**S('7/3'))
def test_meijerint_definite():
v, b = meijerint_definite(x, x, 0, 0)
assert v.is_zero and b is True
v, b = meijerint_definite(x, x, oo, oo)
assert v.is_zero and b is True
def test_inflate():
subs = {a: randcplx()/10, b: randcplx()/10 + I, c: randcplx(),
d: randcplx(), y: randcplx()/10}
def t(a, b, arg, n):
from sympy import Mul
m1 = meijerg(a, b, arg)
m2 = Mul(*_inflate_g(m1, n))
# NOTE: (the random number)**9 must still be on the principal sheet.
# Thus make b&d small to create random numbers of small imaginary part.
return verify_numerically(m1.subs(subs), m2.subs(subs), x, b=0.1, d=-0.1)
assert t([[a], [b]], [[c], [d]], x, 3)
assert t([[a, y], [b]], [[c], [d]], x, 3)
assert t([[a], [b]], [[c, y], [d]], 2*x**3, 3)
def test_recursive():
from sympy import symbols
a, b, c = symbols('a b c', positive=True)
r = exp(-(x - a)**2)*exp(-(x - b)**2)
e = integrate(r, (x, 0, oo), meijerg=True)
assert simplify(e.expand()) == (
sqrt(2)*sqrt(pi)*(
(erf(sqrt(2)*(a + b)/2) + 1)*exp(-a**2/2 + a*b - b**2/2))/4)
e = integrate(exp(-(x - a)**2)*exp(-(x - b)**2)*exp(c*x), (x, 0, oo), meijerg=True)
assert simplify(e) == (
sqrt(2)*sqrt(pi)*(erf(sqrt(2)*(2*a + 2*b + c)/4) + 1)*exp(-a**2 - b**2
+ (2*a + 2*b + c)**2/8)/4)
assert simplify(integrate(exp(-(x - a - b - c)**2), (x, 0, oo), meijerg=True)) == \
sqrt(pi)/2*(1 + erf(a + b + c))
assert simplify(integrate(exp(-(x + a + b + c)**2), (x, 0, oo), meijerg=True)) == \
sqrt(pi)/2*(1 - erf(a + b + c))
@slow
def test_meijerint():
from sympy import symbols, expand, arg
s, t, mu = symbols('s t mu', real=True)
assert integrate(meijerg([], [], [0], [], s*t)
*meijerg([], [], [mu/2], [-mu/2], t**2/4),
(t, 0, oo)).is_Piecewise
s = symbols('s', positive=True)
assert integrate(x**s*meijerg([[], []], [[0], []], x), (x, 0, oo)) == \
gamma(s + 1)
assert integrate(x**s*meijerg([[], []], [[0], []], x), (x, 0, oo),
meijerg=True) == gamma(s + 1)
assert isinstance(integrate(x**s*meijerg([[], []], [[0], []], x),
(x, 0, oo), meijerg=False),
Integral)
assert meijerint_indefinite(exp(x), x) == exp(x)
# TODO what simplifications should be done automatically?
# This tests "extra case" for antecedents_1.
a, b = symbols('a b', positive=True)
assert simplify(meijerint_definite(x**a, x, 0, b)[0]) == \
b**(a + 1)/(a + 1)
# This tests various conditions and expansions:
meijerint_definite((x + 1)**3*exp(-x), x, 0, oo) == (16, True)
# Again, how about simplifications?
sigma, mu = symbols('sigma mu', positive=True)
i, c = meijerint_definite(exp(-((x - mu)/(2*sigma))**2), x, 0, oo)
assert simplify(i) == sqrt(pi)*sigma*(2 - erfc(mu/(2*sigma)))
assert c == True
i, _ = meijerint_definite(exp(-mu*x)*exp(sigma*x), x, 0, oo)
# TODO it would be nice to test the condition
assert simplify(i) == 1/(mu - sigma)
# Test substitutions to change limits
assert meijerint_definite(exp(x), x, -oo, 2) == (exp(2), True)
# Note: causes a NaN in _check_antecedents
assert expand(meijerint_definite(exp(x), x, 0, I)[0]) == exp(I) - 1
assert expand(meijerint_definite(exp(-x), x, 0, x)[0]) == \
1 - exp(-exp(I*arg(x))*abs(x))
# Test -oo to oo
assert meijerint_definite(exp(-x**2), x, -oo, oo) == (sqrt(pi), True)
assert meijerint_definite(exp(-abs(x)), x, -oo, oo) == (2, True)
assert meijerint_definite(exp(-(2*x - 3)**2), x, -oo, oo) == \
(sqrt(pi)/2, True)
assert meijerint_definite(exp(-abs(2*x - 3)), x, -oo, oo) == (1, True)
assert meijerint_definite(exp(-((x - mu)/sigma)**2/2)/sqrt(2*pi*sigma**2),
x, -oo, oo) == (1, True)
assert meijerint_definite(sinc(x)**2, x, -oo, oo) == (pi, True)
# Test one of the extra conditions for 2 g-functinos
assert meijerint_definite(exp(-x)*sin(x), x, 0, oo) == (S(1)/2, True)
# Test a bug
def res(n):
return (1/(1 + x**2)).diff(x, n).subs(x, 1)*(-1)**n
for n in range(6):
assert integrate(exp(-x)*sin(x)*x**n, (x, 0, oo), meijerg=True) == \
res(n)
# This used to test trigexpand... now it is done by linear substitution
assert simplify(integrate(exp(-x)*sin(x + a), (x, 0, oo), meijerg=True)
) == sqrt(2)*sin(a + pi/4)/2
# Test the condition 14 from prudnikov.
# (This is besselj*besselj in disguise, to stop the product from being
# recognised in the tables.)
a, b, s = symbols('a b s')
from sympy import And, re
assert meijerint_definite(meijerg([], [], [a/2], [-a/2], x/4)
*meijerg([], [], [b/2], [-b/2], x/4)*x**(s - 1), x, 0, oo) == \
(4*2**(2*s - 2)*gamma(-2*s + 1)*gamma(a/2 + b/2 + s)
/(gamma(-a/2 + b/2 - s + 1)*gamma(a/2 - b/2 - s + 1)
*gamma(a/2 + b/2 - s + 1)),
And(0 < -2*re(4*s) + 8, 0 < re(a/2 + b/2 + s), re(2*s) < 1))
# test a bug
assert integrate(sin(x**a)*sin(x**b), (x, 0, oo), meijerg=True) == \
Integral(sin(x**a)*sin(x**b), (x, 0, oo))
# test better hyperexpand
assert integrate(exp(-x**2)*log(x), (x, 0, oo), meijerg=True) == \
(sqrt(pi)*polygamma(0, S(1)/2)/4).expand()
# Test hyperexpand bug.
from sympy import lowergamma
n = symbols('n', integer=True)
assert simplify(integrate(exp(-x)*x**n, x, meijerg=True)) == \
lowergamma(n + 1, x)
# Test a bug with argument 1/x
alpha = symbols('alpha', positive=True)
assert meijerint_definite((2 - x)**alpha*sin(alpha/x), x, 0, 2) == \
(sqrt(pi)*alpha*gamma(alpha + 1)*meijerg(((), (alpha/2 + S(1)/2,
alpha/2 + 1)), ((0, 0, S(1)/2), (-S(1)/2,)), alpha**S(2)/16)/4, True)
# test a bug related to 3016
a, s = symbols('a s', positive=True)
assert simplify(integrate(x**s*exp(-a*x**2), (x, -oo, oo))) == \
a**(-s/2 - S(1)/2)*((-1)**s + 1)*gamma(s/2 + S(1)/2)/2
def test_bessel():
from sympy import besselj, besseli
assert simplify(integrate(besselj(a, z)*besselj(b, z)/z, (z, 0, oo),
meijerg=True, conds='none')) == \
2*sin(pi*(a/2 - b/2))/(pi*(a - b)*(a + b))
assert simplify(integrate(besselj(a, z)*besselj(a, z)/z, (z, 0, oo),
meijerg=True, conds='none')) == 1/(2*a)
# TODO more orthogonality integrals
assert simplify(integrate(sin(z*x)*(x**2 - 1)**(-(y + S(1)/2)),
(x, 1, oo), meijerg=True, conds='none')
*2/((z/2)**y*sqrt(pi)*gamma(S(1)/2 - y))) == \
besselj(y, z)
# Werner Rosenheinrich
# SOME INDEFINITE INTEGRALS OF BESSEL FUNCTIONS
assert integrate(x*besselj(0, x), x, meijerg=True) == x*besselj(1, x)
assert integrate(x*besseli(0, x), x, meijerg=True) == x*besseli(1, x)
# TODO can do higher powers, but come out as high order ... should they be
# reduced to order 0, 1?
assert integrate(besselj(1, x), x, meijerg=True) == -besselj(0, x)
assert integrate(besselj(1, x)**2/x, x, meijerg=True) == \
-(besselj(0, x)**2 + besselj(1, x)**2)/2
# TODO more besseli when tables are extended or recursive mellin works
assert integrate(besselj(0, x)**2/x**2, x, meijerg=True) == \
-2*x*besselj(0, x)**2 - 2*x*besselj(1, x)**2 \
+ 2*besselj(0, x)*besselj(1, x) - besselj(0, x)**2/x
assert integrate(besselj(0, x)*besselj(1, x), x, meijerg=True) == \
-besselj(0, x)**2/2
assert integrate(x**2*besselj(0, x)*besselj(1, x), x, meijerg=True) == \
x**2*besselj(1, x)**2/2
assert integrate(besselj(0, x)*besselj(1, x)/x, x, meijerg=True) == \
(x*besselj(0, x)**2 + x*besselj(1, x)**2 -
besselj(0, x)*besselj(1, x))
# TODO how does besselj(0, a*x)*besselj(0, b*x) work?
# TODO how does besselj(0, x)**2*besselj(1, x)**2 work?
# TODO sin(x)*besselj(0, x) etc come out a mess
# TODO can x*log(x)*besselj(0, x) be done?
# TODO how does besselj(1, x)*besselj(0, x+a) work?
# TODO more indefinite integrals when struve functions etc are implemented
# test a substitution
assert integrate(besselj(1, x**2)*x, x, meijerg=True) == \
-besselj(0, x**2)/2
def test_inversion():
from sympy import piecewise_fold, besselj, sqrt, sin, cos, Heaviside
def inv(f):
return piecewise_fold(meijerint_inversion(f, s, t))
assert inv(1/(s**2 + 1)) == sin(t)*Heaviside(t)
assert inv(s/(s**2 + 1)) == cos(t)*Heaviside(t)
assert inv(exp(-s)/s) == Heaviside(t - 1)
assert inv(1/sqrt(1 + s**2)) == besselj(0, t)*Heaviside(t)
# Test some antcedents checking.
assert meijerint_inversion(sqrt(s)/sqrt(1 + s**2), s, t) is None
assert inv(exp(s**2)) is None
assert meijerint_inversion(exp(-s**2), s, t) is None
@slow
def test_lookup_table():
from random import uniform, randrange
from sympy import Add
from sympy.integrals.meijerint import z as z_dummy
table = {}
_create_lookup_table(table)
for _, l in sorted(table.items()):
for formula, terms, cond, hint in sorted(l, key=default_sort_key):
subs = {}
for a in list(formula.free_symbols) + [z_dummy]:
if hasattr(a, 'properties') and a.properties:
# these Wilds match positive integers
subs[a] = randrange(1, 10)
else:
subs[a] = uniform(1.5, 2.0)
if not isinstance(terms, list):
terms = terms(subs)
# First test that hyperexpand can do this.
expanded = [hyperexpand(g) for (_, g) in terms]
assert all(x.is_Piecewise or not x.has(meijerg) for x in expanded)
# Now test that the meijer g-function is indeed as advertised.
expanded = Add(*[f*x for (f, x) in terms])
a, b = formula.n(subs=subs), expanded.n(subs=subs)
r = min(abs(a), abs(b))
if r < 1:
assert abs(a - b).n() <= 1e-10
else:
assert (abs(a - b)/r).n() <= 1e-10
def test_branch_bug():
from sympy import powdenest, lowergamma
# TODO combsimp cannot prove that the factor is unity
assert powdenest(integrate(erf(x**3), x, meijerg=True).diff(x),
polar=True) == 2*erf(x**3)*gamma(S(2)/3)/3/gamma(S(5)/3)
assert integrate(erf(x**3), x, meijerg=True) == \
2*x*erf(x**3)*gamma(S(2)/3)/(3*gamma(S(5)/3)) \
- 2*gamma(S(2)/3)*lowergamma(S(2)/3, x**6)/(3*sqrt(pi)*gamma(S(5)/3))
def test_linear_subs():
from sympy import besselj
assert integrate(sin(x - 1), x, meijerg=True) == -cos(1 - x)
assert integrate(besselj(1, x - 1), x, meijerg=True) == -besselj(0, 1 - x)
@slow
def test_probability():
# various integrals from probability theory
from sympy.abc import x, y
from sympy import symbols, Symbol, Abs, expand_mul, combsimp, powsimp, sin
mu1, mu2 = symbols('mu1 mu2', real=True, nonzero=True, finite=True)
sigma1, sigma2 = symbols('sigma1 sigma2', real=True, nonzero=True,
finite=True, positive=True)
rate = Symbol('lambda', real=True, positive=True, finite=True)
def normal(x, mu, sigma):
return 1/sqrt(2*pi*sigma**2)*exp(-(x - mu)**2/2/sigma**2)
def exponential(x, rate):
return rate*exp(-rate*x)
assert integrate(normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) == 1
assert integrate(x*normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) == \
mu1
assert integrate(x**2*normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) \
== mu1**2 + sigma1**2
assert integrate(x**3*normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) \
== mu1**3 + 3*mu1*sigma1**2
assert integrate(normal(x, mu1, sigma1)*normal(y, mu2, sigma2),
(x, -oo, oo), (y, -oo, oo), meijerg=True) == 1
assert integrate(x*normal(x, mu1, sigma1)*normal(y, mu2, sigma2),
(x, -oo, oo), (y, -oo, oo), meijerg=True) == mu1
assert integrate(y*normal(x, mu1, sigma1)*normal(y, mu2, sigma2),
(x, -oo, oo), (y, -oo, oo), meijerg=True) == mu2
assert integrate(x*y*normal(x, mu1, sigma1)*normal(y, mu2, sigma2),
(x, -oo, oo), (y, -oo, oo), meijerg=True) == mu1*mu2
assert integrate((x + y + 1)*normal(x, mu1, sigma1)*normal(y, mu2, sigma2),
(x, -oo, oo), (y, -oo, oo), meijerg=True) == 1 + mu1 + mu2
assert integrate((x + y - 1)*normal(x, mu1, sigma1)*normal(y, mu2, sigma2),
(x, -oo, oo), (y, -oo, oo), meijerg=True) == \
-1 + mu1 + mu2
i = integrate(x**2*normal(x, mu1, sigma1)*normal(y, mu2, sigma2),
(x, -oo, oo), (y, -oo, oo), meijerg=True)
assert not i.has(Abs)
assert simplify(i) == mu1**2 + sigma1**2
assert integrate(y**2*normal(x, mu1, sigma1)*normal(y, mu2, sigma2),
(x, -oo, oo), (y, -oo, oo), meijerg=True) == \
sigma2**2 + mu2**2
assert integrate(exponential(x, rate), (x, 0, oo), meijerg=True) == 1
assert integrate(x*exponential(x, rate), (x, 0, oo), meijerg=True) == \
1/rate
assert integrate(x**2*exponential(x, rate), (x, 0, oo), meijerg=True) == \
2/rate**2
def E(expr):
res1 = integrate(expr*exponential(x, rate)*normal(y, mu1, sigma1),
(x, 0, oo), (y, -oo, oo), meijerg=True)
res2 = integrate(expr*exponential(x, rate)*normal(y, mu1, sigma1),
(y, -oo, oo), (x, 0, oo), meijerg=True)
assert expand_mul(res1) == expand_mul(res2)
return res1
assert E(1) == 1
assert E(x*y) == mu1/rate
assert E(x*y**2) == mu1**2/rate + sigma1**2/rate
ans = sigma1**2 + 1/rate**2
assert simplify(E((x + y + 1)**2) - E(x + y + 1)**2) == ans
assert simplify(E((x + y - 1)**2) - E(x + y - 1)**2) == ans
assert simplify(E((x + y)**2) - E(x + y)**2) == ans
# Beta' distribution
alpha, beta = symbols('alpha beta', positive=True)
betadist = x**(alpha - 1)*(1 + x)**(-alpha - beta)*gamma(alpha + beta) \
/gamma(alpha)/gamma(beta)
assert integrate(betadist, (x, 0, oo), meijerg=True) == 1
i = integrate(x*betadist, (x, 0, oo), meijerg=True, conds='separate')
assert (combsimp(i[0]), i[1]) == (alpha/(beta - 1), 1 < beta)
j = integrate(x**2*betadist, (x, 0, oo), meijerg=True, conds='separate')
assert j[1] == (1 < beta - 1)
assert combsimp(j[0] - i[0]**2) == (alpha + beta - 1)*alpha \
/(beta - 2)/(beta - 1)**2
# Beta distribution
# NOTE: this is evaluated using antiderivatives. It also tests that
# meijerint_indefinite returns the simplest possible answer.
a, b = symbols('a b', positive=True)
betadist = x**(a - 1)*(-x + 1)**(b - 1)*gamma(a + b)/(gamma(a)*gamma(b))
assert simplify(integrate(betadist, (x, 0, 1), meijerg=True)) == 1
assert simplify(integrate(x*betadist, (x, 0, 1), meijerg=True)) == \
a/(a + b)
assert simplify(integrate(x**2*betadist, (x, 0, 1), meijerg=True)) == \
a*(a + 1)/(a + b)/(a + b + 1)
assert simplify(integrate(x**y*betadist, (x, 0, 1), meijerg=True)) == \
gamma(a + b)*gamma(a + y)/gamma(a)/gamma(a + b + y)
# Chi distribution
k = Symbol('k', integer=True, positive=True)
chi = 2**(1 - k/2)*x**(k - 1)*exp(-x**2/2)/gamma(k/2)
assert powsimp(integrate(chi, (x, 0, oo), meijerg=True)) == 1
assert simplify(integrate(x*chi, (x, 0, oo), meijerg=True)) == \
sqrt(2)*gamma((k + 1)/2)/gamma(k/2)
assert simplify(integrate(x**2*chi, (x, 0, oo), meijerg=True)) == k
# Chi^2 distribution
chisquared = 2**(-k/2)/gamma(k/2)*x**(k/2 - 1)*exp(-x/2)
assert powsimp(integrate(chisquared, (x, 0, oo), meijerg=True)) == 1
assert simplify(integrate(x*chisquared, (x, 0, oo), meijerg=True)) == k
assert simplify(integrate(x**2*chisquared, (x, 0, oo), meijerg=True)) == \
k*(k + 2)
assert combsimp(integrate(((x - k)/sqrt(2*k))**3*chisquared, (x, 0, oo),
meijerg=True)) == 2*sqrt(2)/sqrt(k)
# Dagum distribution
a, b, p = symbols('a b p', positive=True)
# XXX (x/b)**a does not work
dagum = a*p/x*(x/b)**(a*p)/(1 + x**a/b**a)**(p + 1)
assert simplify(integrate(dagum, (x, 0, oo), meijerg=True)) == 1
# XXX conditions are a mess
arg = x*dagum
assert simplify(integrate(arg, (x, 0, oo), meijerg=True, conds='none')
) == a*b*gamma(1 - 1/a)*gamma(p + 1 + 1/a)/(
(a*p + 1)*gamma(p))
assert simplify(integrate(x*arg, (x, 0, oo), meijerg=True, conds='none')
) == a*b**2*gamma(1 - 2/a)*gamma(p + 1 + 2/a)/(
(a*p + 2)*gamma(p))
# F-distribution
d1, d2 = symbols('d1 d2', positive=True)
f = sqrt(((d1*x)**d1 * d2**d2)/(d1*x + d2)**(d1 + d2))/x \
/gamma(d1/2)/gamma(d2/2)*gamma((d1 + d2)/2)
assert simplify(integrate(f, (x, 0, oo), meijerg=True)) == 1
# TODO conditions are a mess
assert simplify(integrate(x*f, (x, 0, oo), meijerg=True, conds='none')
) == d2/(d2 - 2)
assert simplify(integrate(x**2*f, (x, 0, oo), meijerg=True, conds='none')
) == d2**2*(d1 + 2)/d1/(d2 - 4)/(d2 - 2)
# TODO gamma, rayleigh
# inverse gaussian
lamda, mu = symbols('lamda mu', positive=True)
dist = sqrt(lamda/2/pi)*x**(-S(3)/2)*exp(-lamda*(x - mu)**2/x/2/mu**2)
mysimp = lambda expr: simplify(expr.rewrite(exp))
assert mysimp(integrate(dist, (x, 0, oo))) == 1
assert mysimp(integrate(x*dist, (x, 0, oo))) == mu
assert mysimp(integrate((x - mu)**2*dist, (x, 0, oo))) == mu**3/lamda
assert mysimp(integrate((x - mu)**3*dist, (x, 0, oo))) == 3*mu**5/lamda**2
# Levi
c = Symbol('c', positive=True)
assert integrate(sqrt(c/2/pi)*exp(-c/2/(x - mu))/(x - mu)**S('3/2'),
(x, mu, oo)) == 1
# higher moments oo
# log-logistic
distn = (beta/alpha)*x**(beta - 1)/alpha**(beta - 1)/ \
(1 + x**beta/alpha**beta)**2
assert simplify(integrate(distn, (x, 0, oo))) == 1
# NOTE the conditions are a mess, but correctly state beta > 1
assert simplify(integrate(x*distn, (x, 0, oo), conds='none')) == \
pi*alpha/beta/sin(pi/beta)
# (similar comment for conditions applies)
assert simplify(integrate(x**y*distn, (x, 0, oo), conds='none')) == \
pi*alpha**y*y/beta/sin(pi*y/beta)
# weibull
k = Symbol('k', positive=True)
n = Symbol('n', positive=True)
distn = k/lamda*(x/lamda)**(k - 1)*exp(-(x/lamda)**k)
assert simplify(integrate(distn, (x, 0, oo))) == 1
assert simplify(integrate(x**n*distn, (x, 0, oo))) == \
lamda**n*gamma(1 + n/k)
# rice distribution
from sympy import besseli
nu, sigma = symbols('nu sigma', positive=True)
rice = x/sigma**2*exp(-(x**2 + nu**2)/2/sigma**2)*besseli(0, x*nu/sigma**2)
assert integrate(rice, (x, 0, oo), meijerg=True) == 1
# can someone verify higher moments?
# Laplace distribution
mu = Symbol('mu', real=True)
b = Symbol('b', positive=True)
laplace = exp(-abs(x - mu)/b)/2/b
assert integrate(laplace, (x, -oo, oo), meijerg=True) == 1
assert integrate(x*laplace, (x, -oo, oo), meijerg=True) == mu
assert integrate(x**2*laplace, (x, -oo, oo), meijerg=True) == \
2*b**2 + mu**2
# TODO are there other distributions supported on (-oo, oo) that we can do?
# misc tests
k = Symbol('k', positive=True)
assert combsimp(expand_mul(integrate(log(x)*x**(k - 1)*exp(-x)/gamma(k),
(x, 0, oo)))) == polygamma(0, k)
def test_expint():
""" Test various exponential integrals. """
from sympy import (expint, unpolarify, Symbol, Ci, Si, Shi, Chi,
sin, cos, sinh, cosh, Ei)
assert simplify(unpolarify(integrate(exp(-z*x)/x**y, (x, 1, oo),
meijerg=True, conds='none'
).rewrite(expint).expand(func=True))) == expint(y, z)
assert integrate(exp(-z*x)/x, (x, 1, oo), meijerg=True,
conds='none').rewrite(expint).expand() == \
expint(1, z)
assert integrate(exp(-z*x)/x**2, (x, 1, oo), meijerg=True,
conds='none').rewrite(expint).expand() == \
expint(2, z).rewrite(Ei).rewrite(expint)
assert integrate(exp(-z*x)/x**3, (x, 1, oo), meijerg=True,
conds='none').rewrite(expint).expand() == \
expint(3, z).rewrite(Ei).rewrite(expint).expand()
t = Symbol('t', positive=True)
assert integrate(-cos(x)/x, (x, t, oo), meijerg=True).expand() == Ci(t)
assert integrate(-sin(x)/x, (x, t, oo), meijerg=True).expand() == \
Si(t) - pi/2
assert integrate(sin(x)/x, (x, 0, z), meijerg=True) == Si(z)
assert integrate(sinh(x)/x, (x, 0, z), meijerg=True) == Shi(z)
assert integrate(exp(-x)/x, x, meijerg=True).expand().rewrite(expint) == \
I*pi - expint(1, x)
assert integrate(exp(-x)/x**2, x, meijerg=True).rewrite(expint).expand() \
== expint(1, x) - exp(-x)/x - I*pi
u = Symbol('u', polar=True)
assert integrate(cos(u)/u, u, meijerg=True).expand().as_independent(u)[1] \
== Ci(u)
assert integrate(cosh(u)/u, u, meijerg=True).expand().as_independent(u)[1] \
== Chi(u)
assert integrate(expint(1, x), x, meijerg=True
).rewrite(expint).expand() == x*expint(1, x) - exp(-x)
assert integrate(expint(2, x), x, meijerg=True
).rewrite(expint).expand() == \
-x**2*expint(1, x)/2 + x*exp(-x)/2 - exp(-x)/2
assert simplify(unpolarify(integrate(expint(y, x), x,
meijerg=True).rewrite(expint).expand(func=True))) == \
-expint(y + 1, x)
assert integrate(Si(x), x, meijerg=True) == x*Si(x) + cos(x)
assert integrate(Ci(u), u, meijerg=True).expand() == u*Ci(u) - sin(u)
assert integrate(Shi(x), x, meijerg=True) == x*Shi(x) - cosh(x)
assert integrate(Chi(u), u, meijerg=True).expand() == u*Chi(u) - sinh(u)
assert integrate(Si(x)*exp(-x), (x, 0, oo), meijerg=True) == pi/4
assert integrate(expint(1, x)*sin(x), (x, 0, oo), meijerg=True) == log(2)/2
def test_messy():
from sympy import (laplace_transform, Si, Shi, Chi, atan, Piecewise,
acoth, E1, besselj, acosh, asin, And, re,
fourier_transform, sqrt)
assert laplace_transform(Si(x), x, s) == ((-atan(s) + pi/2)/s, 0, True)
assert laplace_transform(Shi(x), x, s) == (acoth(s)/s, 1, True)
# where should the logs be simplified?
assert laplace_transform(Chi(x), x, s) == \
((log(s**(-2)) - log((s**2 - 1)/s**2))/(2*s), 1, True)
# TODO maybe simplify the inequalities?
assert laplace_transform(besselj(a, x), x, s)[1:] == \
(0, And(S(0) < re(a/2) + S(1)/2, S(0) < re(a/2) + 1))
# NOTE s < 0 can be done, but argument reduction is not good enough yet
assert fourier_transform(besselj(1, x)/x, x, s, noconds=False) == \
(Piecewise((0, 4*abs(pi**2*s**2) > 1),
(2*sqrt(-4*pi**2*s**2 + 1), True)), s > 0)
# TODO FT(besselj(0,x)) - conditions are messy (but for acceptable reasons)
# - folding could be better
assert integrate(E1(x)*besselj(0, x), (x, 0, oo), meijerg=True) == \
log(1 + sqrt(2))
assert integrate(E1(x)*besselj(1, x), (x, 0, oo), meijerg=True) == \
log(S(1)/2 + sqrt(2)/2)
assert integrate(1/x/sqrt(1 - x**2), x, meijerg=True) == \
Piecewise((-acosh(1/x), 1 < abs(x**(-2))), (I*asin(1/x), True))
def test_issue_6122():
assert integrate(exp(-I*x**2), (x, -oo, oo), meijerg=True) == \
-I*sqrt(pi)*exp(I*pi/4)
def test_issue_6252():
expr = 1/x/(a + b*x)**(S(1)/3)
anti = integrate(expr, x, meijerg=True)
assert not expr.has(hyper)
# XXX the expression is a mess, but actually upon differentiation and
# putting in numerical values seems to work...
def test_issue_6348():
assert integrate(exp(I*x)/(1 + x**2), (x, -oo, oo)).simplify().rewrite(exp) \
== pi*exp(-1)
def test_fresnel():
from sympy import fresnels, fresnelc
assert expand_func(integrate(sin(pi*x**2/2), x)) == fresnels(x)
assert expand_func(integrate(cos(pi*x**2/2), x)) == fresnelc(x)
def test_issue_6860():
assert meijerint_indefinite(x**x**x, x) is None
def test_issue_7337():
f = meijerint_indefinite(x*sqrt(2*x + 3), x).together()
assert f == sqrt(2*x + 3)*(2*x**2 + x - 3)/5
assert f._eval_interval(x, S(-1), S(1)) == S(2)/5
def test_issue_8368():
assert meijerint_indefinite(cosh(x)*exp(-x*t), x) == (
(-t - 1)*exp(x) + (-t + 1)*exp(-x))*exp(-t*x)/2/(t**2 - 1)
def test_issue_10211():
from sympy.abc import h, w
assert integrate((1/sqrt(((y-x)**2 + h**2))**3), (x,0,w), (y,0,w)) == \
2*sqrt(1 + w**2/h**2)/h - 2/h
def test_issue_11806():
from sympy import symbols
y, L = symbols('y L', positive=True)
assert integrate(1/sqrt(x**2 + y**2)**3, (x, -L, L)) == \
2*L/(y**2*sqrt(L**2 + y**2))
def test_issue_10681():
from sympy import RR
from sympy.abc import R, r
f = integrate(r**2*(R**2-r**2)**0.5, r, meijerg=True)
g = (1.0/3)*R**1.0*r**3*hyper((-0.5, S(3)/2), (S(5)/2,),
r**2*exp_polar(2*I*pi)/R**2)
assert RR.almosteq((f/g).n(), 1.0, 1e-12)
| 28,412 | 41.031065 | 87 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_quadrature.py
|
from sympy.core import S
from sympy.integrals.quadrature import (gauss_legendre, gauss_laguerre,
gauss_hermite, gauss_gen_laguerre,
gauss_chebyshev_t, gauss_chebyshev_u,
gauss_jacobi, gauss_lobatto)
def test_legendre():
x, w = gauss_legendre(1, 17)
assert [str(r) for r in x] == ['0']
assert [str(r) for r in w] == ['2.0000000000000000']
x, w = gauss_legendre(2, 17)
assert [str(r) for r in x] == [
'-0.57735026918962576',
'0.57735026918962576']
assert [str(r) for r in w] == [
'1.0000000000000000',
'1.0000000000000000']
x, w = gauss_legendre(3, 17)
assert [str(r) for r in x] == [
'-0.77459666924148338',
'0',
'0.77459666924148338']
assert [str(r) for r in w] == [
'0.55555555555555556',
'0.88888888888888889',
'0.55555555555555556']
x, w = gauss_legendre(4, 17)
assert [str(r) for r in x] == [
'-0.86113631159405258',
'-0.33998104358485626',
'0.33998104358485626',
'0.86113631159405258']
assert [str(r) for r in w] == [
'0.34785484513745386',
'0.65214515486254614',
'0.65214515486254614',
'0.34785484513745386']
def test_legendre_precise():
x, w = gauss_legendre(3, 40)
assert [str(r) for r in x] == [
'-0.7745966692414833770358530799564799221666',
'0',
'0.7745966692414833770358530799564799221666']
assert [str(r) for r in w] == [
'0.5555555555555555555555555555555555555556',
'0.8888888888888888888888888888888888888889',
'0.5555555555555555555555555555555555555556']
def test_laguerre():
x, w = gauss_laguerre(1, 17)
assert [str(r) for r in x] == ['1.0000000000000000']
assert [str(r) for r in w] == ['1.0000000000000000']
x, w = gauss_laguerre(2, 17)
assert [str(r) for r in x] == [
'0.58578643762690495',
'3.4142135623730950']
assert [str(r) for r in w] == [
'0.85355339059327376',
'0.14644660940672624']
x, w = gauss_laguerre(3, 17)
assert [str(r) for r in x] == [
'0.41577455678347908',
'2.2942803602790417',
'6.2899450829374792',
]
assert [str(r) for r in w] == [
'0.71109300992917302',
'0.27851773356924085',
'0.010389256501586136',
]
x, w = gauss_laguerre(4, 17)
assert [str(r) for r in x] == [
'0.32254768961939231',
'1.7457611011583466',
'4.5366202969211280',
'9.3950709123011331']
assert [str(r) for r in w] == [
'0.60315410434163360',
'0.35741869243779969',
'0.038887908515005384',
'0.00053929470556132745']
x, w = gauss_laguerre(5, 17)
assert [str(r) for r in x] == [
'0.26356031971814091',
'1.4134030591065168',
'3.5964257710407221',
'7.0858100058588376',
'12.640800844275783']
assert [str(r) for r in w] == [
'0.52175561058280865',
'0.39866681108317593',
'0.075942449681707595',
'0.0036117586799220485',
'2.3369972385776228e-5']
def test_laguerre_precise():
x, w = gauss_laguerre(3, 40)
assert [str(r) for r in x] == [
'0.4157745567834790833115338731282744735466',
'2.294280360279041719822050361359593868960',
'6.289945082937479196866415765512131657493']
assert [str(r) for r in w] == [
'0.7110930099291730154495901911425944313094',
'0.2785177335692408488014448884567264810349',
'0.01038925650158613574896492040067908765572']
def test_hermite():
x, w = gauss_hermite(1, 17)
assert [str(r) for r in x] == ['0']
assert [str(r) for r in w] == ['1.7724538509055160']
x, w = gauss_hermite(2, 17)
assert [str(r) for r in x] == [
'-0.70710678118654752',
'0.70710678118654752']
assert [str(r) for r in w] == [
'0.88622692545275801',
'0.88622692545275801']
x, w = gauss_hermite(3, 17)
assert [str(r) for r in x] == [
'-1.2247448713915890',
'0',
'1.2247448713915890']
assert [str(r) for r in w] == [
'0.29540897515091934',
'1.1816359006036774',
'0.29540897515091934']
x, w = gauss_hermite(4, 17)
assert [str(r) for r in x] == [
'-1.6506801238857846',
'-0.52464762327529032',
'0.52464762327529032',
'1.6506801238857846']
assert [str(r) for r in w] == [
'0.081312835447245177',
'0.80491409000551284',
'0.80491409000551284',
'0.081312835447245177']
x, w = gauss_hermite(5, 17)
assert [str(r) for r in x] == [
'-2.0201828704560856',
'-0.95857246461381851',
'0',
'0.95857246461381851',
'2.0201828704560856']
assert [str(r) for r in w] == [
'0.019953242059045913',
'0.39361932315224116',
'0.94530872048294188',
'0.39361932315224116',
'0.019953242059045913']
def test_hermite_precise():
x, w = gauss_hermite(3, 40)
assert [str(r) for r in x] == [
'-1.224744871391589049098642037352945695983',
'0',
'1.224744871391589049098642037352945695983']
assert [str(r) for r in w] == [
'0.2954089751509193378830279138901908637996',
'1.181635900603677351532111655560763455198',
'0.2954089751509193378830279138901908637996']
def test_gen_laguerre():
x, w = gauss_gen_laguerre(1, -S.Half, 17)
assert [str(r) for r in x] == ['0.50000000000000000']
assert [str(r) for r in w] == ['1.7724538509055160']
x, w = gauss_gen_laguerre(2, -S.Half, 17)
assert [str(r) for r in x] == [
'0.27525512860841095',
'2.7247448713915890']
assert [str(r) for r in w] == [
'1.6098281800110257',
'0.16262567089449035']
x, w = gauss_gen_laguerre(3, -S.Half, 17)
assert [str(r) for r in x] == [
'0.19016350919348813',
'1.7844927485432516',
'5.5253437422632603']
assert [str(r) for r in w] == [
'1.4492591904487850',
'0.31413464064571329',
'0.0090600198110176913']
x, w = gauss_gen_laguerre(4, -S.Half, 17)
assert [str(r) for r in x] == [
'0.14530352150331709',
'1.3390972881263614',
'3.9269635013582872',
'8.5886356890120343']
assert [str(r) for r in w] == [
'1.3222940251164826',
'0.41560465162978376',
'0.034155966014826951',
'0.00039920814442273524']
x, w = gauss_gen_laguerre(5, -S.Half, 17)
assert [str(r) for r in x] == [
'0.11758132021177814',
'1.0745620124369040',
'3.0859374437175500',
'6.4147297336620305',
'11.807189489971737']
assert [str(r) for r in w] == [
'1.2217252674706516',
'0.48027722216462937',
'0.067748788910962126',
'0.0026872914935624654',
'1.5280865710465241e-5']
x, w = gauss_gen_laguerre(1, 2, 17)
assert [str(r) for r in x] == ['3.0000000000000000']
assert [str(r) for r in w] == ['2.0000000000000000']
x, w = gauss_gen_laguerre(2, 2, 17)
assert [str(r) for r in x] == [
'2.0000000000000000',
'6.0000000000000000']
assert [str(r) for r in w] == [
'1.5000000000000000',
'0.50000000000000000']
x, w = gauss_gen_laguerre(3, 2, 17)
assert [str(r) for r in x] == [
'1.5173870806774125',
'4.3115831337195203',
'9.1710297856030672']
assert [str(r) for r in w] == [
'1.0374949614904253',
'0.90575000470306537',
'0.056755033806509347']
x, w = gauss_gen_laguerre(4, 2, 17)
assert [str(r) for r in x] == [
'1.2267632635003021',
'3.4125073586969460',
'6.9026926058516134',
'12.458036771951139']
assert [str(r) for r in w] == [
'0.72552499769865438',
'1.0634242919791946',
'0.20669613102835355',
'0.0043545792937974889']
x, w = gauss_gen_laguerre(5, 2, 17)
assert [str(r) for r in x] == [
'1.0311091440933816',
'2.8372128239538217',
'5.6202942725987079',
'9.6829098376640271',
'15.828473921690062']
assert [str(r) for r in w] == [
'0.52091739683509184',
'1.0667059331592211',
'0.38354972366693113',
'0.028564233532974658',
'0.00026271280578124935']
def test_gen_laguerre_precise():
x, w = gauss_gen_laguerre(3, -S.Half, 40)
assert [str(r) for r in x] == [
'0.1901635091934881328718554276203028970878',
'1.784492748543251591186722461957367638500',
'5.525343742263260275941422110422329464413']
assert [str(r) for r in w] == [
'1.449259190448785048183829411195134343108',
'0.3141346406457132878326231270167565378246',
'0.009060019811017691281714945129254301865020']
x, w = gauss_gen_laguerre(3, 2, 40)
assert [str(r) for r in x] == [
'1.517387080677412495020323111016672547482',
'4.311583133719520302881184669723530562299',
'9.171029785603067202098492219259796890218']
assert [str(r) for r in w] == [
'1.037494961490425285817554606541269153041',
'0.9057500047030653669269785048806009945254',
'0.05675503380650934725546688857812985243312']
def test_chebyshev_t():
x, w = gauss_chebyshev_t(1, 17)
assert [str(r) for r in x] == ['0']
assert [str(r) for r in w] == ['3.1415926535897932']
x, w = gauss_chebyshev_t(2, 17)
assert [str(r) for r in x] == [
'0.70710678118654752',
'-0.70710678118654752']
assert [str(r) for r in w] == [
'1.5707963267948966',
'1.5707963267948966']
x, w = gauss_chebyshev_t(3, 17)
assert [str(r) for r in x] == [
'0.86602540378443865',
'0',
'-0.86602540378443865']
assert [str(r) for r in w] == [
'1.0471975511965977',
'1.0471975511965977',
'1.0471975511965977']
x, w = gauss_chebyshev_t(4, 17)
assert [str(r) for r in x] == [
'0.92387953251128676',
'0.38268343236508977',
'-0.38268343236508977',
'-0.92387953251128676']
assert [str(r) for r in w] == [
'0.78539816339744831',
'0.78539816339744831',
'0.78539816339744831',
'0.78539816339744831']
x, w = gauss_chebyshev_t(5, 17)
assert [str(r) for r in x] == [
'0.95105651629515357',
'0.58778525229247313',
'0',
'-0.58778525229247313',
'-0.95105651629515357']
assert [str(r) for r in w] == [
'0.62831853071795865',
'0.62831853071795865',
'0.62831853071795865',
'0.62831853071795865',
'0.62831853071795865']
def test_chebyshev_t_precise():
x, w = gauss_chebyshev_t(3, 40)
assert [str(r) for r in x] == [
'0.8660254037844386467637231707529361834714',
'0',
'-0.8660254037844386467637231707529361834714']
assert [str(r) for r in w] == [
'1.047197551196597746154214461093167628066',
'1.047197551196597746154214461093167628066',
'1.047197551196597746154214461093167628066']
def test_chebyshev_u():
x, w = gauss_chebyshev_u(1, 17)
assert [str(r) for r in x] == ['0']
assert [str(r) for r in w] == ['1.5707963267948966']
x, w = gauss_chebyshev_u(2, 17)
assert [str(r) for r in x] == [
'0.50000000000000000',
'-0.50000000000000000']
assert [str(r) for r in w] == [
'0.78539816339744831',
'0.78539816339744831']
x, w = gauss_chebyshev_u(3, 17)
assert [str(r) for r in x] == [
'0.70710678118654752',
'0',
'-0.70710678118654752']
assert [str(r) for r in w] == [
'0.39269908169872415',
'0.78539816339744831',
'0.39269908169872415']
x, w = gauss_chebyshev_u(4, 17)
assert [str(r) for r in x] == [
'0.80901699437494742',
'0.30901699437494742',
'-0.30901699437494742',
'-0.80901699437494742']
assert [str(r) for r in w] == [
'0.21707871342270599',
'0.56831944997474231',
'0.56831944997474231',
'0.21707871342270599']
x, w = gauss_chebyshev_u(5, 17)
assert [str(r) for r in x] == [
'0.86602540378443865',
'0.50000000000000000',
'0',
'-0.50000000000000000',
'-0.86602540378443865']
assert [str(r) for r in w] == [
'0.13089969389957472',
'0.39269908169872415',
'0.52359877559829887',
'0.39269908169872415',
'0.13089969389957472']
def test_chebyshev_u_precise():
x, w = gauss_chebyshev_u(3, 40)
assert [str(r) for r in x] == [
'0.7071067811865475244008443621048490392848',
'0',
'-0.7071067811865475244008443621048490392848']
assert [str(r) for r in w] == [
'0.3926990816987241548078304229099378605246',
'0.7853981633974483096156608458198757210493',
'0.3926990816987241548078304229099378605246']
def test_jacobi():
x, w = gauss_jacobi(1, -S.Half, S.Half, 17)
assert [str(r) for r in x] == ['0.50000000000000000']
assert [str(r) for r in w] == ['3.1415926535897932']
x, w = gauss_jacobi(2, -S.Half, S.Half, 17)
assert [str(r) for r in x] == [
'-0.30901699437494742',
'0.80901699437494742']
assert [str(r) for r in w] == [
'0.86831485369082398',
'2.2732777998989693']
x, w = gauss_jacobi(3, -S.Half, S.Half, 17)
assert [str(r) for r in x] == [
'-0.62348980185873353',
'0.22252093395631440',
'0.90096886790241913']
assert [str(r) for r in w] == [
'0.33795476356635433',
'1.0973322242791115',
'1.7063056657443274']
x, w = gauss_jacobi(4, -S.Half, S.Half, 17)
assert [str(r) for r in x] == [
'-0.76604444311897804',
'-0.17364817766693035',
'0.50000000000000000',
'0.93969262078590838']
assert [str(r) for r in w] == [
'0.16333179083642836',
'0.57690240318269103',
'1.0471975511965977',
'1.3541609083740761']
x, w = gauss_jacobi(5, -S.Half, S.Half, 17)
assert [str(r) for r in x] == [
'-0.84125353283118117',
'-0.41541501300188643',
'0.14231483827328514',
'0.65486073394528506',
'0.95949297361449739']
assert [str(r) for r in w] == [
'0.090675770007435371',
'0.33391416373675607',
'0.65248870981926643',
'0.94525424081394926',
'1.1192597692123861']
x, w = gauss_jacobi(1, 2, 3, 17)
assert [str(r) for r in x] == ['0.14285714285714286']
assert [str(r) for r in w] == ['1.0666666666666667']
x, w = gauss_jacobi(2, 2, 3, 17)
assert [str(r) for r in x] == [
'-0.24025307335204215',
'0.46247529557426437']
assert [str(r) for r in w] == [
'0.48514624517838660',
'0.58152042148828007']
x, w = gauss_jacobi(3, 2, 3, 17)
assert [str(r) for r in x] == [
'-0.46115870378089762',
'0.10438533038323902',
'0.62950064612493132']
assert [str(r) for r in w] == [
'0.17937613502213266',
'0.61595640991147154',
'0.27133412173306246']
x, w = gauss_jacobi(4, 2, 3, 17)
assert [str(r) for r in x] == [
'-0.59903470850824782',
'-0.14761105199952565',
'0.32554377081188859',
'0.72879429738819258']
assert [str(r) for r in w] == [
'0.067809641836772187',
'0.38956404952032481',
'0.47995970868024150',
'0.12933326662932816']
x, w = gauss_jacobi(5, 2, 3, 17)
assert [str(r) for r in x] == [
'-0.69045775012676106',
'-0.32651993134900065',
'0.082337849552034905',
'0.47517887061283164',
'0.79279429464422850']
assert [str(r) for r in w] == [
'0.027410178066337099',
'0.21291786060364828',
'0.43908437944395081',
'0.32220656547221822',
'0.065047683080512268']
def test_jacobi_precise():
x, w = gauss_jacobi(3, -S.Half, S.Half, 40)
assert [str(r) for r in x] == [
'-0.6234898018587335305250048840042398106323',
'0.2225209339563144042889025644967947594664',
'0.9009688679024191262361023195074450511659']
assert [str(r) for r in w] == [
'0.3379547635663543330553835737094171534907',
'1.097332224279111467485302294320899710461',
'1.706305665744327437921957515249186020246']
x, w = gauss_jacobi(3, 2, 3, 40)
assert [str(r) for r in x] == [
'-0.4611587037808976179121958105554375981274',
'0.1043853303832390210914918407615869143233',
'0.6295006461249313240934312425211234110769']
assert [str(r) for r in w] == [
'0.1793761350221326596137764371503859752628',
'0.6159564099114715430909548532229749439714',
'0.2713341217330624639619353762933057474325']
def test_lobatto():
x, w = gauss_lobatto(2, 17)
assert [str(r) for r in x] == [
'-1',
'1']
assert [str(r) for r in w] == [
'1.0000000000000000',
'1.0000000000000000']
x, w = gauss_lobatto(3, 17)
assert [str(r) for r in x] == [
'-1',
'0',
'1']
assert [str(r) for r in w] == [
'0.33333333333333333',
'1.3333333333333333',
'0.33333333333333333']
x, w = gauss_lobatto(4, 17)
assert [str(r) for r in x] == [
'-1',
'-0.44721359549995794',
'0.44721359549995794',
'1']
assert [str(r) for r in w] == [
'0.16666666666666667',
'0.83333333333333333',
'0.83333333333333333',
'0.16666666666666667']
x, w = gauss_lobatto(5, 17)
assert [str(r) for r in x] == [
'-1',
'-0.65465367070797714',
'0',
'0.65465367070797714',
'1']
assert [str(r) for r in w] == [
'0.10000000000000000',
'0.54444444444444444',
'0.71111111111111111',
'0.54444444444444444',
'0.10000000000000000']
def test_lobatto_precise():
x, w = gauss_lobatto(3, 40)
assert [str(r) for r in x] == [
'-1',
'0',
'1']
assert [str(r) for r in w] == [
'0.3333333333333333333333333333333333333333',
'1.333333333333333333333333333333333333333',
'0.3333333333333333333333333333333333333333']
| 19,813 | 31.913621 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_lineintegrals.py
|
from sympy import symbols, E, ln, Curve, line_integrate, sqrt
s, t, x, y, z = symbols('s,t,x,y,z')
def test_lineintegral():
c = Curve([E**t + 1, E**t - 1], (t, 0, ln(2)))
assert line_integrate(x + y, c, [x, y]) == 3*sqrt(2)
| 235 | 25.222222 | 61 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_trigonometry.py
|
from sympy.core import Eq, Rational, Symbol
from sympy.functions import sin, cos, tan, csc, sec, cot, log, Piecewise
from sympy.integrals.trigonometry import trigintegrate
x = Symbol('x')
def test_trigintegrate_odd():
assert trigintegrate(Rational(1), x) == x
assert trigintegrate(x, x) is None
assert trigintegrate(x**2, x) is None
assert trigintegrate(sin(x), x) == -cos(x)
assert trigintegrate(cos(x), x) == sin(x)
assert trigintegrate(sin(3*x), x) == -cos(3*x)/3
assert trigintegrate(cos(3*x), x) == sin(3*x)/3
y = Symbol('y')
assert trigintegrate(sin(y*x), x) == \
Piecewise((0, Eq(y, 0)), (-cos(y*x)/y, True))
assert trigintegrate(cos(y*x), x) == \
Piecewise((x, Eq(y, 0)), (sin(y*x)/y, True))
assert trigintegrate(sin(y*x)**2, x) == \
Piecewise((0, Eq(y, 0)), ((x*y/2 - sin(x*y)*cos(x*y)/2)/y, True))
assert trigintegrate(sin(y*x)*cos(y*x), x) == \
Piecewise((0, Eq(y, 0)), (sin(x*y)**2/(2*y), True))
assert trigintegrate(cos(y*x)**2, x) == \
Piecewise((x, Eq(y, 0)), ((x*y/2 + sin(x*y)*cos(x*y)/2)/y, True))
y = Symbol('y', positive=True)
# TODO: remove conds='none' below. For this to work we would have to rule
# out (e.g. by trying solve) the condition y = 0, incompatible with
# y.is_positive being True.
assert trigintegrate(sin(y*x), x, conds='none') == -cos(y*x)/y
assert trigintegrate(cos(y*x), x, conds='none') == sin(y*x)/y
assert trigintegrate(sin(x)*cos(x), x) == sin(x)**2/2
assert trigintegrate(sin(x)*cos(x)**2, x) == -cos(x)**3/3
assert trigintegrate(sin(x)**2*cos(x), x) == sin(x)**3/3
# check if it selects right function to substitute,
# so the result is kept simple
assert trigintegrate(sin(x)**7 * cos(x), x) == sin(x)**8/8
assert trigintegrate(sin(x) * cos(x)**7, x) == -cos(x)**8/8
assert trigintegrate(sin(x)**7 * cos(x)**3, x) == \
-sin(x)**10/10 + sin(x)**8/8
assert trigintegrate(sin(x)**3 * cos(x)**7, x) == \
cos(x)**10/10 - cos(x)**8/8
def test_trigintegrate_even():
assert trigintegrate(sin(x)**2, x) == x/2 - cos(x)*sin(x)/2
assert trigintegrate(cos(x)**2, x) == x/2 + cos(x)*sin(x)/2
assert trigintegrate(sin(3*x)**2, x) == x/2 - cos(3*x)*sin(3*x)/6
assert trigintegrate(cos(3*x)**2, x) == x/2 + cos(3*x)*sin(3*x)/6
assert trigintegrate(sin(x)**2 * cos(x)**2, x) == \
x/8 - sin(2*x)*cos(2*x)/16
assert trigintegrate(sin(x)**4 * cos(x)**2, x) == \
x/16 - sin(x) *cos(x)/16 - sin(x)**3*cos(x)/24 + \
sin(x)**5*cos(x)/6
assert trigintegrate(sin(x)**2 * cos(x)**4, x) == \
x/16 + cos(x) *sin(x)/16 + cos(x)**3*sin(x)/24 - \
cos(x)**5*sin(x)/6
assert trigintegrate(sin(x)**(-4), x) == -2*cos(x)/(3*sin(x)) \
- cos(x)/(3*sin(x)**3)
assert trigintegrate(cos(x)**(-6), x) == sin(x)/(5*cos(x)**5) \
+ 4*sin(x)/(15*cos(x)**3) + 8*sin(x)/(15*cos(x))
def test_trigintegrate_mixed():
assert trigintegrate(sin(x)*sec(x), x) == -log(sin(x)**2 - 1)/2
assert trigintegrate(sin(x)*csc(x), x) == x
assert trigintegrate(sin(x)*cot(x), x) == sin(x)
assert trigintegrate(cos(x)*sec(x), x) == x
assert trigintegrate(cos(x)*csc(x), x) == log(cos(x)**2 - 1)/2
assert trigintegrate(cos(x)*tan(x), x) == -cos(x)
assert trigintegrate(cos(x)*cot(x), x) == log(cos(x) - 1)/2 \
- log(cos(x) + 1)/2 + cos(x)
def test_trigintegrate_symbolic():
n = Symbol('n', integer=True)
assert trigintegrate(cos(x)**n, x) is None
assert trigintegrate(sin(x)**n, x) is None
assert trigintegrate(cot(x)**n, x) is None
| 3,657 | 37.914894 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_rationaltools.py
|
from sympy import S, symbols, I, atan, log, Poly, sqrt, simplify, integrate
from sympy.integrals.rationaltools import ratint, ratint_logpart, log_to_atan
from sympy.abc import a, b, x, t
half = S(1)/2
def test_ratint():
assert ratint(S(0), x) == 0
assert ratint(S(7), x) == 7*x
assert ratint(x, x) == x**2/2
assert ratint(2*x, x) == x**2
assert ratint(-2*x, x) == -x**2
assert ratint(8*x**7 + 2*x + 1, x) == x**8 + x**2 + x
f = S(1)
g = x + 1
assert ratint(f / g, x) == log(x + 1)
assert ratint((f, g), x) == log(x + 1)
f = x**3 - x
g = x - 1
assert ratint(f/g, x) == x**3/3 + x**2/2
f = x
g = (x - a)*(x + a)
assert ratint(f/g, x) == log(x**2 - a**2)/2
f = S(1)
g = x**2 + 1
assert ratint(f/g, x, real=None) == atan(x)
assert ratint(f/g, x, real=True) == atan(x)
assert ratint(f/g, x, real=False) == I*log(x + I)/2 - I*log(x - I)/2
f = S(36)
g = x**5 - 2*x**4 - 2*x**3 + 4*x**2 + x - 2
assert ratint(f/g, x) == \
-4*log(x + 1) + 4*log(x - 2) + (12*x + 6)/(x**2 - 1)
f = x**4 - 3*x**2 + 6
g = x**6 - 5*x**4 + 5*x**2 + 4
assert ratint(f/g, x) == \
atan(x) + atan(x**3) + atan(x/2 - 3*x**S(3)/2 + S(1)/2*x**5)
f = x**7 - 24*x**4 - 4*x**2 + 8*x - 8
g = x**8 + 6*x**6 + 12*x**4 + 8*x**2
assert ratint(f/g, x) == \
(4 + 6*x + 8*x**2 + 3*x**3)/(4*x + 4*x**3 + x**5) + log(x)
assert ratint((x**3*f)/(x*g), x) == \
-(12 - 16*x + 6*x**2 - 14*x**3)/(4 + 4*x**2 + x**4) - \
5*sqrt(2)*atan(x*sqrt(2)/2) + S(1)/2*x**2 - 3*log(2 + x**2)
f = x**5 - x**4 + 4*x**3 + x**2 - x + 5
g = x**4 - 2*x**3 + 5*x**2 - 4*x + 4
assert ratint(f/g, x) == \
x + S(1)/2*x**2 + S(1)/2*log(2 - x + x**2) - (4*x - 9)/(14 - 7*x + 7*x**2) + \
13*sqrt(7)*atan(-S(1)/7*sqrt(7) + 2*x*sqrt(7)/7)/49
assert ratint(1/(x**2 + x + 1), x) == \
2*sqrt(3)*atan(sqrt(3)/3 + 2*x*sqrt(3)/3)/3
assert ratint(1/(x**3 + 1), x) == \
-log(1 - x + x**2)/6 + log(1 + x)/3 + sqrt(3)*atan(-sqrt(3)
/3 + 2*x*sqrt(3)/3)/3
assert ratint(1/(x**2 + x + 1), x, real=False) == \
-I*3**half*log(half + x - half*I*3**half)/3 + \
I*3**half*log(half + x + half*I*3**half)/3
assert ratint(1/(x**3 + 1), x, real=False) == log(1 + x)/3 + \
(-S(1)/6 + I*3**half/6)*log(-half + x + I*3**half/2) + \
(-S(1)/6 - I*3**half/6)*log(-half + x - I*3**half/2)
# issue 4991
assert ratint(1/(x*(a + b*x)**3), x) == \
(3*a + 2*b*x)/(2*a**4 + 4*a**3*b*x + 2*a**2*b**2*x**2) + (
log(x) - log(a/b + x))/a**3
assert ratint(x/(1 - x**2), x) == -log(x**2 - 1)/2
assert ratint(-x/(1 - x**2), x) == log(x**2 - 1)/2
assert ratint((x/4 - 4/(1 - x)).diff(x), x) == x/4 + 4/(x - 1)
ans = atan(x)
assert ratint(1/(x**2 + 1), x, symbol=x) == ans
assert ratint(1/(x**2 + 1), x, symbol='x') == ans
assert ratint(1/(x**2 + 1), x, symbol=a) == ans
def test_ratint_logpart():
assert ratint_logpart(x, x**2 - 9, x, t) == \
[(Poly(x**2 - 9, x), Poly(-2*t + 1, t))]
assert ratint_logpart(x**2, x**3 - 5, x, t) == \
[(Poly(x**3 - 5, x), Poly(-3*t + 1, t))]
def test_issue_5414():
assert ratint(1/(x**2 + 16), x) == atan(x/4)/4
def test_issue_5249():
assert ratint(
1/(x**2 + a**2), x) == (-I*log(-I*a + x)/2 + I*log(I*a + x)/2)/a
def test_issue_5817():
a, b, c = symbols('a,b,c', positive=True)
assert simplify(ratint(a/(b*c*x**2 + a**2 + b*a), x)) == \
sqrt(a)*atan(sqrt(
b)*sqrt(c)*x/(sqrt(a)*sqrt(a + b)))/(sqrt(b)*sqrt(c)*sqrt(a + b))
def test_issue_5981():
u = symbols('u')
assert integrate(1/(u**2 + 1)) == atan(u)
def test_issue_10488():
a,b,c,x = symbols('a b c x', real=True, positive=True)
assert integrate(x/(a*x+b),x) == x/a - b*log(a*x + b)/a**2
def test_log_to_atan():
f, g = (Poly(x + S(1)/2, x, domain='QQ'), Poly(sqrt(3)/2, x, domain='EX'))
fg_ans = 2*atan(2*sqrt(3)*x/3 + sqrt(3)/3)
assert log_to_atan(f, g) == fg_ans
assert log_to_atan(g, f) == -fg_ans
| 4,133 | 28.319149 | 86 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_manual.py
|
from sympy import (sin, cos, tan, sec, csc, cot, log, exp, atan, asin, acos,
Symbol, Integral, integrate, pi, Dummy, Derivative,
diff, I, sqrt, erf, Piecewise, Eq, symbols, Rational,
And, Heaviside, S, asinh, acosh, atanh, acoth, expand)
from sympy.integrals.manualintegrate import manualintegrate, find_substitutions, \
_parts_rule
x, y, z, u, n, a, b, c = symbols('x y z u n a b c')
def test_find_substitutions():
assert find_substitutions((cot(x)**2 + 1)**2*csc(x)**2*cot(x)**2, x, u) == \
[(cot(x), 1, -u**6 - 2*u**4 - u**2)]
assert find_substitutions((sec(x)**2 + tan(x) * sec(x)) / (sec(x) + tan(x)),
x, u) == [(sec(x) + tan(x), 1, 1/u)]
assert find_substitutions(x * exp(-x**2), x, u) == [(-x**2, -S.Half, exp(u))]
def test_manualintegrate_polynomials():
assert manualintegrate(y, x) == x*y
assert manualintegrate(exp(2), x) == x * exp(2)
assert manualintegrate(x**2, x) == x**3 / 3
assert manualintegrate(3 * x**2 + 4 * x**3, x) == x**3 + x**4
assert manualintegrate((x + 2)**3, x) == (x + 2)**4 / 4
assert manualintegrate((3*x + 4)**2, x) == (3*x + 4)**3 / 9
assert manualintegrate((u + 2)**3, u) == (u + 2)**4 / 4
assert manualintegrate((3*u + 4)**2, u) == (3*u + 4)**3 / 9
def test_manualintegrate_exponentials():
assert manualintegrate(exp(2*x), x) == exp(2*x) / 2
assert manualintegrate(2**x, x) == (2 ** x) / log(2)
assert manualintegrate(1 / x, x) == log(x)
assert manualintegrate(1 / (2*x + 3), x) == log(2*x + 3) / 2
assert manualintegrate(log(x)**2 / x, x) == log(x)**3 / 3
def test_manualintegrate_parts():
assert manualintegrate(exp(x) * sin(x), x) == \
(exp(x) * sin(x)) / 2 - (exp(x) * cos(x)) / 2
assert manualintegrate(2*x*cos(x), x) == 2*x*sin(x) + 2*cos(x)
assert manualintegrate(x * log(x), x) == x**2*log(x)/2 - x**2/4
assert manualintegrate(log(x), x) == x * log(x) - x
assert manualintegrate((3*x**2 + 5) * exp(x), x) == \
3*x**2*exp(x) - 6*x*exp(x) + 11*exp(x)
assert manualintegrate(atan(x), x) == x*atan(x) - log(x**2 + 1)/2
# Make sure _parts_rule doesn't pick u = constant but can pick dv =
# constant if necessary, e.g. for integrate(atan(x))
assert _parts_rule(cos(x), x) == None
assert _parts_rule(exp(x), x) == None
assert _parts_rule(x**2, x) == None
result = _parts_rule(atan(x), x)
assert result[0] == atan(x) and result[1] == 1
def test_manualintegrate_trigonometry():
assert manualintegrate(sin(x), x) == -cos(x)
assert manualintegrate(tan(x), x) == -log(cos(x))
assert manualintegrate(sec(x), x) == log(sec(x) + tan(x))
assert manualintegrate(csc(x), x) == -log(csc(x) + cot(x))
assert manualintegrate(sin(x) * cos(x), x) in [sin(x) ** 2 / 2, -cos(x)**2 / 2]
assert manualintegrate(-sec(x) * tan(x), x) == -sec(x)
assert manualintegrate(csc(x) * cot(x), x) == -csc(x)
assert manualintegrate(sec(x)**2, x) == tan(x)
assert manualintegrate(csc(x)**2, x) == -cot(x)
assert manualintegrate(x * sec(x**2), x) == log(tan(x**2) + sec(x**2))/2
assert manualintegrate(cos(x)*csc(sin(x)), x) == -log(cot(sin(x)) + csc(sin(x)))
def test_manualintegrate_trigpowers():
assert manualintegrate(sin(x)**2 * cos(x), x) == sin(x)**3 / 3
assert manualintegrate(sin(x)**2 * cos(x) **2, x) == \
x / 8 - sin(4*x) / 32
assert manualintegrate(sin(x) * cos(x)**3, x) == -cos(x)**4 / 4
assert manualintegrate(sin(x)**3 * cos(x)**2, x) == \
cos(x)**5 / 5 - cos(x)**3 / 3
assert manualintegrate(tan(x)**3 * sec(x), x) == sec(x)**3/3 - sec(x)
assert manualintegrate(tan(x) * sec(x) **2, x) == sec(x)**2/2
assert manualintegrate(cot(x)**5 * csc(x), x) == \
-csc(x)**5/5 + 2*csc(x)**3/3 - csc(x)
assert manualintegrate(cot(x)**2 * csc(x)**6, x) == \
-cot(x)**7/7 - 2*cot(x)**5/5 - cot(x)**3/3
def test_manualintegrate_inversetrig():
# atan
assert manualintegrate(exp(x) / (1 + exp(2*x)), x) == atan(exp(x))
assert manualintegrate(1 / (4 + 9 * x**2), x) == atan(3 * x/2) / 6
assert manualintegrate(1 / (16 + 16 * x**2), x) == atan(x) / 16
assert manualintegrate(1 / (4 + x**2), x) == atan(x / 2) / 2
assert manualintegrate(1 / (1 + 4 * x**2), x) == atan(2*x) / 2
assert manualintegrate(1/(a + b*x**2), x) == \
Piecewise((atan(x/sqrt(a/b))/(b*sqrt(a/b)), a/b > 0), \
(-acoth(x/sqrt(-a/b))/(b*sqrt(-a/b)), And(a/b < 0, x**2 > -a/b)), \
(-atanh(x/sqrt(-a/b))/(b*sqrt(-a/b)), And(a/b < 0, x**2 < -a/b)))
assert manualintegrate(1/(4 + b*x**2), x) == \
Piecewise((atan(x/(2*sqrt(1/b)))/(2*b*sqrt(1/b)), 4/b > 0), \
(-acoth(x/(2*sqrt(-1/b)))/(2*b*sqrt(-1/b)), And(4/b < 0, x**2 > -4/b)), \
(-atanh(x/(2*sqrt(-1/b)))/(2*b*sqrt(-1/b)), And(4/b < 0, x**2 < -4/b)))
assert manualintegrate(1/(a + 4*x**2), x) == \
Piecewise((atan(2*x/sqrt(a))/(2*sqrt(a)), a/4 > 0), \
(-acoth(2*x/sqrt(-a))/(2*sqrt(-a)), And(a/4 < 0, x**2 > -a/4)), \
(-atanh(2*x/sqrt(-a))/(2*sqrt(-a)), And(a/4 < 0, x**2 < -a/4)))
assert manualintegrate(1/(4 + 4*x**2), x) == atan(x) / 4
# asin
assert manualintegrate(1/sqrt(1-x**2), x) == asin(x)
assert manualintegrate(1/sqrt(4-4*x**2), x) == asin(x)/2
assert manualintegrate(3/sqrt(1-9*x**2), x) == asin(3*x)
assert manualintegrate(1/sqrt(4-9*x**2), x) == asin(3*x/2)/3
# asinh
assert manualintegrate(1/sqrt(x**2 + 1), x) == \
asinh(x)
assert manualintegrate(1/sqrt(x**2 + 4), x) == \
asinh(x/2)
assert manualintegrate(1/sqrt(4*x**2 + 4), x) == \
asinh(x)/2
assert manualintegrate(1/sqrt(4*x**2 + 1), x) == \
asinh(2*x)/2
assert manualintegrate(1/sqrt(a*x**2 + 1), x) == \
Piecewise((sqrt(-1/a)*asin(x*sqrt(-a)), a < 0), (sqrt(1/a)*asinh(sqrt(a)*x), a > 0))
assert manualintegrate(1/sqrt(a + x**2), x) == \
Piecewise((asinh(x*sqrt(1/a)), a > 0), (acosh(x*sqrt(-1/a)), a < 0))
# acosh
assert manualintegrate(1/sqrt(x**2 - 1), x) == \
acosh(x)
assert manualintegrate(1/sqrt(x**2 - 4), x) == \
acosh(x/2)
assert manualintegrate(1/sqrt(4*x**2 - 4), x) == \
acosh(x)/2
assert manualintegrate(1/sqrt(9*x**2 - 1), x) == \
acosh(3*x)/3
assert manualintegrate(1/sqrt(a*x**2 - 4), x) == \
Piecewise((sqrt(1/a)*acosh(sqrt(a)*x/2), a > 0))
assert manualintegrate(1/sqrt(-a + 4*x**2), x) == \
Piecewise((asinh(2*x*sqrt(-1/a))/2, -a > 0), (acosh(2*x*sqrt(1/a))/2, -a < 0))
# piecewise
assert manualintegrate(1/sqrt(a-b*x**2), x) == \
Piecewise((sqrt(a/b)*asin(x*sqrt(b/a))/sqrt(a), And(-b < 0, a > 0)),
(sqrt(-a/b)*asinh(x*sqrt(-b/a))/sqrt(a), And(-b > 0, a > 0)),
(sqrt(a/b)*acosh(x*sqrt(b/a))/sqrt(-a), And(-b > 0, a < 0)))
assert manualintegrate(1/sqrt(a + b*x**2), x) == \
Piecewise((sqrt(-a/b)*asin(x*sqrt(-b/a))/sqrt(a), And(a > 0, b < 0)),
(sqrt(a/b)*asinh(x*sqrt(b/a))/sqrt(a), And(a > 0, b > 0)),
(sqrt(-a/b)*acosh(x*sqrt(-b/a))/sqrt(-a), And(a < 0, b > 0)))
def test_manualintegrate_trig_substitution():
assert manualintegrate(sqrt(16*x**2 - 9)/x, x) == \
Piecewise((sqrt(16*x**2 - 9) - 3*acos(3/(4*x)),
And(x < 3*S.One/4, x > -3*S.One/4)))
assert manualintegrate(1/(x**4 * sqrt(25-x**2)), x) == \
Piecewise((-sqrt(-x**2/25 + 1)/(125*x) -
(-x**2/25 + 1)**(3*S.Half)/(15*x**3), And(x < 5, x > -5)))
assert manualintegrate(x**7/(49*x**2 + 1)**(3 * S.Half), x) == \
((49*x**2 + 1)**(5*S.Half)/28824005 -
(49*x**2 + 1)**(3*S.Half)/5764801 +
3*sqrt(49*x**2 + 1)/5764801 + 1/(5764801*sqrt(49*x**2 + 1)))
def test_manualintegrate_trivial_substitution():
assert manualintegrate((exp(x) - exp(-x))/x, x) == \
-Integral(exp(-x)/x, x) + Integral(exp(x)/x, x)
def test_manualintegrate_rational():
assert manualintegrate(1/(4 - x**2), x) == Piecewise((acoth(x/2)/2, x**2 > 4), (atanh(x/2)/2, x**2 < 4))
assert manualintegrate(1/(-1 + x**2), x) == Piecewise((-acoth(x), x**2 > 1), (-atanh(x), x**2 < 1))
def test_manualintegrate_derivative():
assert manualintegrate(pi * Derivative(x**2 + 2*x + 3), x) == \
pi * ((x**2 + 2*x + 3))
assert manualintegrate(Derivative(x**2 + 2*x + 3, y), x) == \
x * Derivative(x**2 + 2*x + 3, y)
assert manualintegrate(Derivative(sin(x), x, x, y, x), x) == \
Derivative(sin(x), x, x, y)
def test_manualintegrate_Heaviside():
assert manualintegrate(Heaviside(x), x) == x*Heaviside(x)
assert manualintegrate(x*Heaviside(2), x) == x**2/2
assert manualintegrate(x*Heaviside(-2), x) == 0
assert manualintegrate(x*Heaviside( x), x) == x**2*Heaviside( x)/2
assert manualintegrate(x*Heaviside(-x), x) == x**2*Heaviside(-x)/2
assert manualintegrate(Heaviside(2*x + 4), x) == (x+2)*Heaviside(2*x + 4)
assert manualintegrate(x*Heaviside(x), x) == x**2*Heaviside(x)/2
assert manualintegrate(Heaviside(x + 1)*Heaviside(1 - x)*x**2, x) == \
((x**3/3 + S(1)/3)*Heaviside(x + 1) - S(2)/3)*Heaviside(-x + 1)
y = Symbol('y')
assert manualintegrate(sin(7 + x)*Heaviside(3*x - 7), x) == \
(- cos(x + 7) + cos(S(28)/3))*Heaviside(3*x - S(7))
assert manualintegrate(sin(y + x)*Heaviside(3*x - y), x) == \
(cos(4*y/3) - cos(x + y))*Heaviside(3*x - y)
def test_issue_6799():
r, x, phi = map(Symbol, 'r x phi'.split())
n = Symbol('n', integer=True, positive=True)
integrand = (cos(n*(x-phi))*cos(n*x))
limits = (x, -pi, pi)
assert manualintegrate(integrand, x).has(Integral)
assert r * integrate(integrand.expand(trig=True), limits) / pi == r * cos(n * phi)
assert not integrate(integrand, limits).has(Dummy)
def test_issue_12251():
assert manualintegrate(x**y, x) == Piecewise(
(log(x), Eq(y, -1)), (x**(y + 1)/(y + 1), True))
def test_issue_3796():
assert manualintegrate(diff(exp(x + x**2)), x) == exp(x + x**2)
assert integrate(x * exp(x**4), x, risch=False) == -I*sqrt(pi)*erf(I*x**2)/4
def test_manual_true():
assert integrate(exp(x) * sin(x), x, manual=True) == \
(exp(x) * sin(x)) / 2 - (exp(x) * cos(x)) / 2
assert integrate(sin(x) * cos(x), x, manual=True) in \
[sin(x) ** 2 / 2, -cos(x)**2 / 2]
def test_issue_6746():
y = Symbol('y')
n = Symbol('n')
assert manualintegrate(y**x, x) == \
Piecewise((x, Eq(log(y), 0)), (y**x/log(y), True))
assert manualintegrate(y**(n*x), x) == \
Piecewise(
(x, Eq(n, 0)),
(Piecewise(
(n*x, Eq(log(y), 0)),
(y**(n*x)/log(y), True))/n, True))
assert manualintegrate(exp(n*x), x) == \
Piecewise((x, Eq(n, 0)), (exp(n*x)/n, True))
y = Symbol('y', positive=True)
assert manualintegrate((y + 1)**x, x) == (y + 1)**x/log(y + 1)
y = Symbol('y', zero=True)
assert manualintegrate((y + 1)**x, x) == x
y = Symbol('y')
n = Symbol('n', nonzero=True)
assert manualintegrate(y**(n*x), x) == \
Piecewise((n*x, Eq(log(y), 0)), (y**(n*x)/log(y), True))/n
y = Symbol('y', positive=True)
assert manualintegrate((y + 1)**(n*x), x) == \
(y + 1)**(n*x)/(n*log(y + 1))
a = Symbol('a', negative=True)
assert manualintegrate(1 / (a + b*x**2), x) == \
Piecewise((atan(x/sqrt(a/b))/(b*sqrt(a/b)), a/b > 0), \
(-acoth(x/sqrt(-a/b))/(b*sqrt(-a/b)), And(a/b < 0, x**2 > -a/b)), \
(-atanh(x/sqrt(-a/b))/(b*sqrt(-a/b)), And(a/b < 0, x**2 < -a/b)))
def test_issue_2850():
assert manualintegrate(asin(x)*log(x), x) == -x*asin(x) - sqrt(-x**2 + 1) \
+ (x*asin(x) + sqrt(-x**2 + 1))*log(x) - Integral(sqrt(-x**2 + 1)/x, x)
assert manualintegrate(acos(x)*log(x), x) == -x*acos(x) + sqrt(-x**2 + 1) + \
(x*acos(x) - sqrt(-x**2 + 1))*log(x) + Integral(sqrt(-x**2 + 1)/x, x)
assert manualintegrate(atan(x)*log(x), x) == -x*atan(x) + (x*atan(x) - \
log(x**2 + 1)/2)*log(x) + log(x**2 + 1)/2 + Integral(log(x**2 + 1)/x, x)/2
def test_issue_9462():
assert manualintegrate(sin(2*x)*exp(x), x) == -3*exp(x)*sin(2*x) \
- 2*exp(x)*cos(2*x) + 4*Integral(2*exp(x)*cos(2*x), x)
assert manualintegrate((x - 3) / (x**2 - 2*x + 2)**2, x) == \
Integral(x/(x**4 - 4*x**3 + 8*x**2 - 8*x + 4), x) \
- 3*Integral(1/(x**4 - 4*x**3 + 8*x**2 - 8*x + 4), x)
def test_issue_10847():
assert manualintegrate(x**2 / (x**2 - c), x) == c*Piecewise((atan(x/sqrt(-c))/sqrt(-c), -c > 0), \
(-acoth(x/sqrt(c))/sqrt(c), And(-c < 0, x**2 > c)), \
(-atanh(x/sqrt(c))/sqrt(c), And(-c < 0, x**2 < c))) + x
assert manualintegrate(sqrt(x - y) * log(z / x), x) == 4*y**2*Piecewise((atan(sqrt(x - y)/sqrt(y))/sqrt(y), y > 0), \
(-acoth(sqrt(x - y)/sqrt(-y))/sqrt(-y), \
And(x - y > -y, y < 0)), \
(-atanh(sqrt(x - y)/sqrt(-y))/sqrt(-y), \
And(x - y < -y, y < 0)))/3 \
- 4*y*sqrt(x - y)/3 + 2*(x - y)**(3/2)*log(z/x)/3 \
+ 4*(x - y)**(3/2)/9
assert manualintegrate(sqrt(x) * log(x), x) == 2*x**(3/2)*log(x)/3 - 4*x**(3/2)/9
assert manualintegrate(sqrt(a*x + b) / x, x) == -2*b*Piecewise((-atan(sqrt(a*x + b)/sqrt(-b))/sqrt(-b), -b > 0), \
(acoth(sqrt(a*x + b)/sqrt(b))/sqrt(b), And(-b < 0, a*x + b > b)), \
(atanh(sqrt(a*x + b)/sqrt(b))/sqrt(b), And(-b < 0, a*x + b < b))) \
+ 2*sqrt(a*x + b)
assert expand(manualintegrate(sqrt(a*x + b) / (x + c), x)) == -2*a*c*Piecewise((atan(sqrt(a*x + b)/sqrt(a*c - b))/sqrt(a*c - b), \
a*c - b > 0), (-acoth(sqrt(a*x + b)/sqrt(-a*c + b))/sqrt(-a*c + b), And(a*c - b < 0, a*x + b > -a*c + b)), \
(-atanh(sqrt(a*x + b)/sqrt(-a*c + b))/sqrt(-a*c + b), And(a*c - b < 0, a*x + b < -a*c + b))) \
+ 2*b*Piecewise((atan(sqrt(a*x + b)/sqrt(a*c - b))/sqrt(a*c - b), a*c - b > 0), \
(-acoth(sqrt(a*x + b)/sqrt(-a*c + b))/sqrt(-a*c + b), And(a*c - b < 0, a*x + b > -a*c + b)), \
(-atanh(sqrt(a*x + b)/sqrt(-a*c + b))/sqrt(-a*c + b), And(a*c - b < 0, a*x + b < -a*c + b))) + 2*sqrt(a*x + b)
assert manualintegrate((4*x**4 + 4*x**3 + 16*x**2 + 12*x + 8) \
/ (x**6 + 2*x**5 + 3*x**4 + 4*x**3 + 3*x**2 + 2*x + 1), x) == \
2*x/(x**2 + 1) + 3*atan(x) - 1/(x**2 + 1) - 3/(x + 1)
assert manualintegrate(sqrt(2*x + 3) / (x + 1), x) == 2*sqrt(2*x + 3) - log(sqrt(2*x + 3) + 1) + log(sqrt(2*x + 3) - 1)
assert manualintegrate(sqrt(2*x + 3) / 2 * x, x) == (2*x + 3)**(5/2)/20 - (2*x + 3)**(3/2)/4
assert manualintegrate(x**Rational(3,2) * log(x), x) == 2*x**Rational(5,2)*log(x)/5 - 4*x**Rational(5/2)/25
assert manualintegrate(x**(-3) * log(x), x) == -log(x)/(2*x**2) - 1/(4*x**2)
assert manualintegrate(log(y)/(y**2*(1 - 1/y)), y) == (-log(y) + log(y - 1))*log(y) + log(y)**2/2 - Integral(log(y - 1)/y, y)
def test_constant_independent_of_symbol():
assert manualintegrate(Integral(y, (x, 1, 2)), x) == x*Integral(y, (x, 1, 2))
| 15,987 | 48.806854 | 134 |
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_deltafunctions.py
|
from sympy.core.compatibility import range
from sympy import cos, DiracDelta, Heaviside, Function, pi, S, sin, symbols
from sympy.integrals.deltafunctions import change_mul, deltaintegrate
f = Function("f")
x_1, x_2, x, y, z = symbols("x_1 x_2 x y z")
def test_change_mul():
assert change_mul(x, x) == (None, None)
assert change_mul(x*y, x) == (None, None)
assert change_mul(x*y*DiracDelta(x), x) == (DiracDelta(x), x*y)
assert change_mul(x*y*DiracDelta(x)*DiracDelta(y), x) == \
(DiracDelta(x), x*y*DiracDelta(y))
assert change_mul(DiracDelta(x)**2, x) == \
(DiracDelta(x), DiracDelta(x))
assert change_mul(y*DiracDelta(x)**2, x) == \
(DiracDelta(x), y*DiracDelta(x))
def test_deltaintegrate():
assert deltaintegrate(x, x) is None
assert deltaintegrate(x + DiracDelta(x), x) is None
assert deltaintegrate(DiracDelta(x, 0), x) == Heaviside(x)
for n in range(10):
assert deltaintegrate(DiracDelta(x, n + 1), x) == DiracDelta(x, n)
assert deltaintegrate(DiracDelta(x), x) == Heaviside(x)
assert deltaintegrate(DiracDelta(-x), x) == Heaviside(x)
assert deltaintegrate(DiracDelta(x - y), x) == Heaviside(x - y)
assert deltaintegrate(DiracDelta(y - x), x) == Heaviside(x - y)
assert deltaintegrate(x*DiracDelta(x), x) == 0
assert deltaintegrate((x - y)*DiracDelta(x - y), x) == 0
assert deltaintegrate(DiracDelta(x)**2, x) == DiracDelta(0)*Heaviside(x)
assert deltaintegrate(y*DiracDelta(x)**2, x) == \
y*DiracDelta(0)*Heaviside(x)
assert deltaintegrate(DiracDelta(x, 1), x) == DiracDelta(x, 0)
assert deltaintegrate(y*DiracDelta(x, 1), x) == y*DiracDelta(x, 0)
assert deltaintegrate(DiracDelta(x, 1)**2, x) == -DiracDelta(0, 2)*Heaviside(x)
assert deltaintegrate(y*DiracDelta(x, 1)**2, x) == -y*DiracDelta(0, 2)*Heaviside(x)
assert deltaintegrate(DiracDelta(x) * f(x), x) == f(0) * Heaviside(x)
assert deltaintegrate(DiracDelta(-x) * f(x), x) == f(0) * Heaviside(x)
assert deltaintegrate(DiracDelta(x - 1) * f(x), x) == f(1) * Heaviside(x - 1)
assert deltaintegrate(DiracDelta(1 - x) * f(x), x) == f(1) * Heaviside(x - 1)
assert deltaintegrate(DiracDelta(x**2 + x - 2), x) == \
Heaviside(x - 1)/3 + Heaviside(x + 2)/3
p = cos(x)*(DiracDelta(x) + DiracDelta(x**2 - 1))*sin(x)*(x - pi)
assert deltaintegrate(p, x) - (-pi*(cos(1)*Heaviside(-1 + x)*sin(1)/2 - \
cos(1)*Heaviside(1 + x)*sin(1)/2) + \
cos(1)*Heaviside(1 + x)*sin(1)/2 + \
cos(1)*Heaviside(-1 + x)*sin(1)/2) == 0
p = x_2*DiracDelta(x - x_2)*DiracDelta(x_2 - x_1)
assert deltaintegrate(p, x_2) == x*DiracDelta(x - x_1)*Heaviside(x_2 - x)
p = x*y**2*z*DiracDelta(y - x)*DiracDelta(y - z)*DiracDelta(x - z)
assert deltaintegrate(p, y) == x**3*z*DiracDelta(x - z)**2*Heaviside(y - x)
assert deltaintegrate((x + 1)*DiracDelta(2*x), x) == S(1)/2 * Heaviside(x)
assert deltaintegrate((x + 1)*DiracDelta(2*x/3 + 4/S(9)), x) == \
S(1)/2 * Heaviside(x + S(2)/3)
a, b, c = symbols('a b c', commutative=False)
assert deltaintegrate(DiracDelta(x - y)*f(x - b)*f(x - a), x) == \
f(y - b)*f(y - a)*Heaviside(x - y)
p = f(x - a)*DiracDelta(x - y)*f(x - c)*f(x - b)
assert deltaintegrate(p, x) == f(y - a)*f(y - c)*f(y - b)*Heaviside(x - y)
p = DiracDelta(x - z)*f(x - b)*f(x - a)*DiracDelta(x - y)
assert deltaintegrate(p, x) == DiracDelta(y - z)*f(y - b)*f(y - a) * \
Heaviside(x - y)
| 3,501 | 45.078947 | 87 |
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/__init__.py
| 0 | 0 | 0 |
py
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_transforms.py
|
from sympy.integrals.transforms import (mellin_transform,
inverse_mellin_transform, laplace_transform, inverse_laplace_transform,
fourier_transform, inverse_fourier_transform,
sine_transform, inverse_sine_transform,
cosine_transform, inverse_cosine_transform,
hankel_transform, inverse_hankel_transform,
LaplaceTransform, FourierTransform, SineTransform, CosineTransform,
InverseLaplaceTransform, InverseFourierTransform,
InverseSineTransform, InverseCosineTransform, IntegralTransformError)
from sympy import (
gamma, exp, oo, Heaviside, symbols, Symbol, re, factorial, pi,
cos, S, Abs, And, Or, sin, sqrt, I, log, tan, hyperexpand, meijerg,
EulerGamma, erf, erfc, besselj, bessely, besseli, besselk,
exp_polar, polar_lift, unpolarify, Function, expint, expand_mul,
combsimp, trigsimp, atan, sinh, cosh, Ne, periodic_argument, atan2, Abs)
from sympy.utilities.pytest import XFAIL, slow, skip, raises
from sympy.matrices import Matrix, eye
from sympy.abc import x, s, a, b, c, d
nu, beta, rho = symbols('nu beta rho')
def test_undefined_function():
from sympy import Function, MellinTransform
f = Function('f')
assert mellin_transform(f(x), x, s) == MellinTransform(f(x), x, s)
assert mellin_transform(f(x) + exp(-x), x, s) == \
(MellinTransform(f(x), x, s) + gamma(s), (0, oo), True)
assert laplace_transform(2*f(x), x, s) == 2*LaplaceTransform(f(x), x, s)
# TODO test derivative and other rules when implemented
def test_free_symbols():
from sympy import Function
f = Function('f')
assert mellin_transform(f(x), x, s).free_symbols == {s}
assert mellin_transform(f(x)*a, x, s).free_symbols == {s, a}
def test_as_integral():
from sympy import Function, Integral
f = Function('f')
assert mellin_transform(f(x), x, s).rewrite('Integral') == \
Integral(x**(s - 1)*f(x), (x, 0, oo))
assert fourier_transform(f(x), x, s).rewrite('Integral') == \
Integral(f(x)*exp(-2*I*pi*s*x), (x, -oo, oo))
assert laplace_transform(f(x), x, s).rewrite('Integral') == \
Integral(f(x)*exp(-s*x), (x, 0, oo))
assert str(inverse_mellin_transform(f(s), s, x, (a, b)).rewrite('Integral')) \
== "Integral(x**(-s)*f(s), (s, _c - oo*I, _c + oo*I))"
assert str(inverse_laplace_transform(f(s), s, x).rewrite('Integral')) == \
"Integral(f(s)*exp(s*x), (s, _c - oo*I, _c + oo*I))"
assert inverse_fourier_transform(f(s), s, x).rewrite('Integral') == \
Integral(f(s)*exp(2*I*pi*s*x), (s, -oo, oo))
# NOTE this is stuck in risch because meijerint cannot handle it
@slow
@XFAIL
def test_mellin_transform_fail():
skip("Risch takes forever.")
MT = mellin_transform
bpos = symbols('b', positive=True)
bneg = symbols('b', negative=True)
expr = (sqrt(x + b**2) + b)**a/sqrt(x + b**2)
# TODO does not work with bneg, argument wrong. Needs changes to matching.
assert MT(expr.subs(b, -bpos), x, s) == \
((-1)**(a + 1)*2**(a + 2*s)*bpos**(a + 2*s - 1)*gamma(a + s)
*gamma(1 - a - 2*s)/gamma(1 - s),
(-re(a), -re(a)/2 + S(1)/2), True)
expr = (sqrt(x + b**2) + b)**a
assert MT(expr.subs(b, -bpos), x, s) == \
(
2**(a + 2*s)*a*bpos**(a + 2*s)*gamma(-a - 2*
s)*gamma(a + s)/gamma(-s + 1),
(-re(a), -re(a)/2), True)
# Test exponent 1:
assert MT(expr.subs({b: -bpos, a: 1}), x, s) == \
(-bpos**(2*s + 1)*gamma(s)*gamma(-s - S(1)/2)/(2*sqrt(pi)),
(-1, -S(1)/2), True)
@slow
def test_mellin_transform():
from sympy import Max, Min
MT = mellin_transform
bpos = symbols('b', positive=True)
# 8.4.2
assert MT(x**nu*Heaviside(x - 1), x, s) == \
(-1/(nu + s), (-oo, -re(nu)), True)
assert MT(x**nu*Heaviside(1 - x), x, s) == \
(1/(nu + s), (-re(nu), oo), True)
assert MT((1 - x)**(beta - 1)*Heaviside(1 - x), x, s) == \
(gamma(beta)*gamma(s)/gamma(beta + s), (0, oo), re(-beta) < 0)
assert MT((x - 1)**(beta - 1)*Heaviside(x - 1), x, s) == \
(gamma(beta)*gamma(1 - beta - s)/gamma(1 - s),
(-oo, -re(beta) + 1), re(-beta) < 0)
assert MT((1 + x)**(-rho), x, s) == \
(gamma(s)*gamma(rho - s)/gamma(rho), (0, re(rho)), True)
# TODO also the conditions should be simplified
assert MT(abs(1 - x)**(-rho), x, s) == (
2*sin(pi*rho/2)*gamma(1 - rho)*
cos(pi*(rho/2 - s))*gamma(s)*gamma(rho-s)/pi,
(0, re(rho)), And(re(rho) - 1 < 0, re(rho) < 1))
mt = MT((1 - x)**(beta - 1)*Heaviside(1 - x)
+ a*(x - 1)**(beta - 1)*Heaviside(x - 1), x, s)
assert mt[1], mt[2] == ((0, -re(beta) + 1), True)
assert MT((x**a - b**a)/(x - b), x, s)[0] == \
pi*b**(a + s - 1)*sin(pi*a)/(sin(pi*s)*sin(pi*(a + s)))
assert MT((x**a - bpos**a)/(x - bpos), x, s) == \
(pi*bpos**(a + s - 1)*sin(pi*a)/(sin(pi*s)*sin(pi*(a + s))),
(Max(-re(a), 0), Min(1 - re(a), 1)), True)
expr = (sqrt(x + b**2) + b)**a
assert MT(expr.subs(b, bpos), x, s) == \
(-a*(2*bpos)**(a + 2*s)*gamma(s)*gamma(-a - 2*s)/gamma(-a - s + 1),
(0, -re(a)/2), True)
expr = (sqrt(x + b**2) + b)**a/sqrt(x + b**2)
assert MT(expr.subs(b, bpos), x, s) == \
(2**(a + 2*s)*bpos**(a + 2*s - 1)*gamma(s)
*gamma(1 - a - 2*s)/gamma(1 - a - s),
(0, -re(a)/2 + S(1)/2), True)
# 8.4.2
assert MT(exp(-x), x, s) == (gamma(s), (0, oo), True)
assert MT(exp(-1/x), x, s) == (gamma(-s), (-oo, 0), True)
# 8.4.5
assert MT(log(x)**4*Heaviside(1 - x), x, s) == (24/s**5, (0, oo), True)
assert MT(log(x)**3*Heaviside(x - 1), x, s) == (6/s**4, (-oo, 0), True)
assert MT(log(x + 1), x, s) == (pi/(s*sin(pi*s)), (-1, 0), True)
assert MT(log(1/x + 1), x, s) == (pi/(s*sin(pi*s)), (0, 1), True)
assert MT(log(abs(1 - x)), x, s) == (pi/(s*tan(pi*s)), (-1, 0), True)
assert MT(log(abs(1 - 1/x)), x, s) == (pi/(s*tan(pi*s)), (0, 1), True)
# TODO we cannot currently do these (needs summation of 3F2(-1))
# this also implies that they cannot be written as a single g-function
# (although this is possible)
mt = MT(log(x)/(x + 1), x, s)
assert mt[1:] == ((0, 1), True)
assert not hyperexpand(mt[0], allow_hyper=True).has(meijerg)
mt = MT(log(x)**2/(x + 1), x, s)
assert mt[1:] == ((0, 1), True)
assert not hyperexpand(mt[0], allow_hyper=True).has(meijerg)
mt = MT(log(x)/(x + 1)**2, x, s)
assert mt[1:] == ((0, 2), True)
assert not hyperexpand(mt[0], allow_hyper=True).has(meijerg)
# 8.4.14
assert MT(erf(sqrt(x)), x, s) == \
(-gamma(s + S(1)/2)/(sqrt(pi)*s), (-S(1)/2, 0), True)
@slow
def test_mellin_transform_bessel():
from sympy import Max
MT = mellin_transform
# 8.4.19
assert MT(besselj(a, 2*sqrt(x)), x, s) == \
(gamma(a/2 + s)/gamma(a/2 - s + 1), (-re(a)/2, S(3)/4), True)
assert MT(sin(sqrt(x))*besselj(a, sqrt(x)), x, s) == \
(2**a*gamma(-2*s + S(1)/2)*gamma(a/2 + s + S(1)/2)/(
gamma(-a/2 - s + 1)*gamma(a - 2*s + 1)), (
-re(a)/2 - S(1)/2, S(1)/4), True)
assert MT(cos(sqrt(x))*besselj(a, sqrt(x)), x, s) == \
(2**a*gamma(a/2 + s)*gamma(-2*s + S(1)/2)/(
gamma(-a/2 - s + S(1)/2)*gamma(a - 2*s + 1)), (
-re(a)/2, S(1)/4), True)
assert MT(besselj(a, sqrt(x))**2, x, s) == \
(gamma(a + s)*gamma(S(1)/2 - s)
/ (sqrt(pi)*gamma(1 - s)*gamma(1 + a - s)),
(-re(a), S(1)/2), True)
assert MT(besselj(a, sqrt(x))*besselj(-a, sqrt(x)), x, s) == \
(gamma(s)*gamma(S(1)/2 - s)
/ (sqrt(pi)*gamma(1 - a - s)*gamma(1 + a - s)),
(0, S(1)/2), True)
# NOTE: prudnikov gives the strip below as (1/2 - re(a), 1). As far as
# I can see this is wrong (since besselj(z) ~ 1/sqrt(z) for z large)
assert MT(besselj(a - 1, sqrt(x))*besselj(a, sqrt(x)), x, s) == \
(gamma(1 - s)*gamma(a + s - S(1)/2)
/ (sqrt(pi)*gamma(S(3)/2 - s)*gamma(a - s + S(1)/2)),
(S(1)/2 - re(a), S(1)/2), True)
assert MT(besselj(a, sqrt(x))*besselj(b, sqrt(x)), x, s) == \
(4**s*gamma(1 - 2*s)*gamma((a + b)/2 + s)
/ (gamma(1 - s + (b - a)/2)*gamma(1 - s + (a - b)/2)
*gamma( 1 - s + (a + b)/2)),
(-(re(a) + re(b))/2, S(1)/2), True)
assert MT(besselj(a, sqrt(x))**2 + besselj(-a, sqrt(x))**2, x, s)[1:] == \
((Max(re(a), -re(a)), S(1)/2), True)
# Section 8.4.20
assert MT(bessely(a, 2*sqrt(x)), x, s) == \
(-cos(pi*(a/2 - s))*gamma(s - a/2)*gamma(s + a/2)/pi,
(Max(-re(a)/2, re(a)/2), S(3)/4), True)
assert MT(sin(sqrt(x))*bessely(a, sqrt(x)), x, s) == \
(-4**s*sin(pi*(a/2 - s))*gamma(S(1)/2 - 2*s)
* gamma((1 - a)/2 + s)*gamma((1 + a)/2 + s)
/ (sqrt(pi)*gamma(1 - s - a/2)*gamma(1 - s + a/2)),
(Max(-(re(a) + 1)/2, (re(a) - 1)/2), S(1)/4), True)
assert MT(cos(sqrt(x))*bessely(a, sqrt(x)), x, s) == \
(-4**s*cos(pi*(a/2 - s))*gamma(s - a/2)*gamma(s + a/2)*gamma(S(1)/2 - 2*s)
/ (sqrt(pi)*gamma(S(1)/2 - s - a/2)*gamma(S(1)/2 - s + a/2)),
(Max(-re(a)/2, re(a)/2), S(1)/4), True)
assert MT(besselj(a, sqrt(x))*bessely(a, sqrt(x)), x, s) == \
(-cos(pi*s)*gamma(s)*gamma(a + s)*gamma(S(1)/2 - s)
/ (pi**S('3/2')*gamma(1 + a - s)),
(Max(-re(a), 0), S(1)/2), True)
assert MT(besselj(a, sqrt(x))*bessely(b, sqrt(x)), x, s) == \
(-4**s*cos(pi*(a/2 - b/2 + s))*gamma(1 - 2*s)
* gamma(a/2 - b/2 + s)*gamma(a/2 + b/2 + s)
/ (pi*gamma(a/2 - b/2 - s + 1)*gamma(a/2 + b/2 - s + 1)),
(Max((-re(a) + re(b))/2, (-re(a) - re(b))/2), S(1)/2), True)
# NOTE bessely(a, sqrt(x))**2 and bessely(a, sqrt(x))*bessely(b, sqrt(x))
# are a mess (no matter what way you look at it ...)
assert MT(bessely(a, sqrt(x))**2, x, s)[1:] == \
((Max(-re(a), 0, re(a)), S(1)/2), True)
# Section 8.4.22
# TODO we can't do any of these (delicate cancellation)
# Section 8.4.23
assert MT(besselk(a, 2*sqrt(x)), x, s) == \
(gamma(
s - a/2)*gamma(s + a/2)/2, (Max(-re(a)/2, re(a)/2), oo), True)
assert MT(besselj(a, 2*sqrt(2*sqrt(x)))*besselk(
a, 2*sqrt(2*sqrt(x))), x, s) == (4**(-s)*gamma(2*s)*
gamma(a/2 + s)/(2*gamma(a/2 - s + 1)), (Max(0, -re(a)/2), oo), True)
# TODO bessely(a, x)*besselk(a, x) is a mess
assert MT(besseli(a, sqrt(x))*besselk(a, sqrt(x)), x, s) == \
(gamma(s)*gamma(
a + s)*gamma(-s + S(1)/2)/(2*sqrt(pi)*gamma(a - s + 1)),
(Max(-re(a), 0), S(1)/2), True)
assert MT(besseli(b, sqrt(x))*besselk(a, sqrt(x)), x, s) == \
(2**(2*s - 1)*gamma(-2*s + 1)*gamma(-a/2 + b/2 + s)* \
gamma(a/2 + b/2 + s)/(gamma(-a/2 + b/2 - s + 1)* \
gamma(a/2 + b/2 - s + 1)), (Max(-re(a)/2 - re(b)/2, \
re(a)/2 - re(b)/2), S(1)/2), True)
# TODO products of besselk are a mess
mt = MT(exp(-x/2)*besselk(a, x/2), x, s)
mt0 = combsimp((trigsimp(combsimp(mt[0].expand(func=True)))))
assert mt0 == 2*pi**(S(3)/2)*cos(pi*s)*gamma(-s + S(1)/2)/(
(cos(2*pi*a) - cos(2*pi*s))*gamma(-a - s + 1)*gamma(a - s + 1))
assert mt[1:] == ((Max(-re(a), re(a)), oo), True)
# TODO exp(x/2)*besselk(a, x/2) [etc] cannot currently be done
# TODO various strange products of special orders
@slow
def test_expint():
from sympy import E1, expint, Max, re, lerchphi, Symbol, simplify, Si, Ci, Ei
aneg = Symbol('a', negative=True)
u = Symbol('u', polar=True)
assert mellin_transform(E1(x), x, s) == (gamma(s)/s, (0, oo), True)
assert inverse_mellin_transform(gamma(s)/s, s, x,
(0, oo)).rewrite(expint).expand() == E1(x)
assert mellin_transform(expint(a, x), x, s) == \
(gamma(s)/(a + s - 1), (Max(1 - re(a), 0), oo), True)
# XXX IMT has hickups with complicated strips ...
assert simplify(unpolarify(
inverse_mellin_transform(gamma(s)/(aneg + s - 1), s, x,
(1 - aneg, oo)).rewrite(expint).expand(func=True))) == \
expint(aneg, x)
assert mellin_transform(Si(x), x, s) == \
(-2**s*sqrt(pi)*gamma(s/2 + S(1)/2)/(
2*s*gamma(-s/2 + 1)), (-1, 0), True)
assert inverse_mellin_transform(-2**s*sqrt(pi)*gamma((s + 1)/2)
/(2*s*gamma(-s/2 + 1)), s, x, (-1, 0)) \
== Si(x)
assert mellin_transform(Ci(sqrt(x)), x, s) == \
(-2**(2*s - 1)*sqrt(pi)*gamma(s)/(s*gamma(-s + S(1)/2)), (0, 1), True)
assert inverse_mellin_transform(
-4**s*sqrt(pi)*gamma(s)/(2*s*gamma(-s + S(1)/2)),
s, u, (0, 1)).expand() == Ci(sqrt(u))
# TODO LT of Si, Shi, Chi is a mess ...
assert laplace_transform(Ci(x), x, s) == (-log(1 + s**2)/2/s, 0, True)
assert laplace_transform(expint(a, x), x, s) == \
(lerchphi(s*polar_lift(-1), 1, a), 0, S(0) < re(a))
assert laplace_transform(expint(1, x), x, s) == (log(s + 1)/s, 0, True)
assert laplace_transform(expint(2, x), x, s) == \
((s - log(s + 1))/s**2, 0, True)
assert inverse_laplace_transform(-log(1 + s**2)/2/s, s, u).expand() == \
Heaviside(u)*Ci(u)
assert inverse_laplace_transform(log(s + 1)/s, s, x).rewrite(expint) == \
Heaviside(x)*E1(x)
assert inverse_laplace_transform((s - log(s + 1))/s**2, s,
x).rewrite(expint).expand() == \
(expint(2, x)*Heaviside(x)).rewrite(Ei).rewrite(expint).expand()
@slow
def test_inverse_mellin_transform():
from sympy import (sin, simplify, Max, Min, expand,
powsimp, exp_polar, cos, cot)
IMT = inverse_mellin_transform
assert IMT(gamma(s), s, x, (0, oo)) == exp(-x)
assert IMT(gamma(-s), s, x, (-oo, 0)) == exp(-1/x)
assert simplify(IMT(s/(2*s**2 - 2), s, x, (2, oo))) == \
(x**2 + 1)*Heaviside(1 - x)/(4*x)
# test passing "None"
assert IMT(1/(s**2 - 1), s, x, (-1, None)) == \
-x*Heaviside(-x + 1)/2 - Heaviside(x - 1)/(2*x)
assert IMT(1/(s**2 - 1), s, x, (None, 1)) == \
-x*Heaviside(-x + 1)/2 - Heaviside(x - 1)/(2*x)
# test expansion of sums
assert IMT(gamma(s) + gamma(s - 1), s, x, (1, oo)) == (x + 1)*exp(-x)/x
# test factorisation of polys
r = symbols('r', real=True)
assert IMT(1/(s**2 + 1), s, exp(-x), (None, oo)
).subs(x, r).rewrite(sin).simplify() \
== sin(r)*Heaviside(1 - exp(-r))
# test multiplicative substitution
_a, _b = symbols('a b', positive=True)
assert IMT(_b**(-s/_a)*factorial(s/_a)/s, s, x, (0, oo)) == exp(-_b*x**_a)
assert IMT(factorial(_a/_b + s/_b)/(_a + s), s, x, (-_a, oo)) == x**_a*exp(-x**_b)
def simp_pows(expr):
return simplify(powsimp(expand_mul(expr, deep=False), force=True)).replace(exp_polar, exp)
# Now test the inverses of all direct transforms tested above
# Section 8.4.2
nu = symbols('nu', real=True, finite=True)
assert IMT(-1/(nu + s), s, x, (-oo, None)) == x**nu*Heaviside(x - 1)
assert IMT(1/(nu + s), s, x, (None, oo)) == x**nu*Heaviside(1 - x)
assert simp_pows(IMT(gamma(beta)*gamma(s)/gamma(s + beta), s, x, (0, oo))) \
== (1 - x)**(beta - 1)*Heaviside(1 - x)
assert simp_pows(IMT(gamma(beta)*gamma(1 - beta - s)/gamma(1 - s),
s, x, (-oo, None))) \
== (x - 1)**(beta - 1)*Heaviside(x - 1)
assert simp_pows(IMT(gamma(s)*gamma(rho - s)/gamma(rho), s, x, (0, None))) \
== (1/(x + 1))**rho
assert simp_pows(IMT(d**c*d**(s - 1)*sin(pi*c)
*gamma(s)*gamma(s + c)*gamma(1 - s)*gamma(1 - s - c)/pi,
s, x, (Max(-re(c), 0), Min(1 - re(c), 1)))) \
== (x**c - d**c)/(x - d)
assert simplify(IMT(1/sqrt(pi)*(-c/2)*gamma(s)*gamma((1 - c)/2 - s)
*gamma(-c/2 - s)/gamma(1 - c - s),
s, x, (0, -re(c)/2))) == \
(1 + sqrt(x + 1))**c
assert simplify(IMT(2**(a + 2*s)*b**(a + 2*s - 1)*gamma(s)*gamma(1 - a - 2*s)
/gamma(1 - a - s), s, x, (0, (-re(a) + 1)/2))) == \
b**(a - 1)*(sqrt(1 + x/b**2) + 1)**(a - 1)*(b**2*sqrt(1 + x/b**2) +
b**2 + x)/(b**2 + x)
assert simplify(IMT(-2**(c + 2*s)*c*b**(c + 2*s)*gamma(s)*gamma(-c - 2*s)
/ gamma(-c - s + 1), s, x, (0, -re(c)/2))) == \
b**c*(sqrt(1 + x/b**2) + 1)**c
# Section 8.4.5
assert IMT(24/s**5, s, x, (0, oo)) == log(x)**4*Heaviside(1 - x)
assert expand(IMT(6/s**4, s, x, (-oo, 0)), force=True) == \
log(x)**3*Heaviside(x - 1)
assert IMT(pi/(s*sin(pi*s)), s, x, (-1, 0)) == log(x + 1)
assert IMT(pi/(s*sin(pi*s/2)), s, x, (-2, 0)) == log(x**2 + 1)
assert IMT(pi/(s*sin(2*pi*s)), s, x, (-S(1)/2, 0)) == log(sqrt(x) + 1)
assert IMT(pi/(s*sin(pi*s)), s, x, (0, 1)) == log(1 + 1/x)
# TODO
def mysimp(expr):
from sympy import expand, logcombine, powsimp
return expand(
powsimp(logcombine(expr, force=True), force=True, deep=True),
force=True).replace(exp_polar, exp)
assert mysimp(mysimp(IMT(pi/(s*tan(pi*s)), s, x, (-1, 0)))) in [
log(1 - x)*Heaviside(1 - x) + log(x - 1)*Heaviside(x - 1),
log(x)*Heaviside(x - 1) + log(1 - 1/x)*Heaviside(x - 1) + log(-x +
1)*Heaviside(-x + 1)]
# test passing cot
assert mysimp(IMT(pi*cot(pi*s)/s, s, x, (0, 1))) in [
log(1/x - 1)*Heaviside(1 - x) + log(1 - 1/x)*Heaviside(x - 1),
-log(x)*Heaviside(-x + 1) + log(1 - 1/x)*Heaviside(x - 1) + log(-x +
1)*Heaviside(-x + 1), ]
# 8.4.14
assert IMT(-gamma(s + S(1)/2)/(sqrt(pi)*s), s, x, (-S(1)/2, 0)) == \
erf(sqrt(x))
# 8.4.19
assert simplify(IMT(gamma(a/2 + s)/gamma(a/2 - s + 1), s, x, (-re(a)/2, S(3)/4))) \
== besselj(a, 2*sqrt(x))
assert simplify(IMT(2**a*gamma(S(1)/2 - 2*s)*gamma(s + (a + 1)/2)
/ (gamma(1 - s - a/2)*gamma(1 - 2*s + a)),
s, x, (-(re(a) + 1)/2, S(1)/4))) == \
sin(sqrt(x))*besselj(a, sqrt(x))
assert simplify(IMT(2**a*gamma(a/2 + s)*gamma(S(1)/2 - 2*s)
/ (gamma(S(1)/2 - s - a/2)*gamma(1 - 2*s + a)),
s, x, (-re(a)/2, S(1)/4))) == \
cos(sqrt(x))*besselj(a, sqrt(x))
# TODO this comes out as an amazing mess, but simplifies nicely
assert simplify(IMT(gamma(a + s)*gamma(S(1)/2 - s)
/ (sqrt(pi)*gamma(1 - s)*gamma(1 + a - s)),
s, x, (-re(a), S(1)/2))) == \
besselj(a, sqrt(x))**2
assert simplify(IMT(gamma(s)*gamma(S(1)/2 - s)
/ (sqrt(pi)*gamma(1 - s - a)*gamma(1 + a - s)),
s, x, (0, S(1)/2))) == \
besselj(-a, sqrt(x))*besselj(a, sqrt(x))
assert simplify(IMT(4**s*gamma(-2*s + 1)*gamma(a/2 + b/2 + s)
/ (gamma(-a/2 + b/2 - s + 1)*gamma(a/2 - b/2 - s + 1)
*gamma(a/2 + b/2 - s + 1)),
s, x, (-(re(a) + re(b))/2, S(1)/2))) == \
besselj(a, sqrt(x))*besselj(b, sqrt(x))
# Section 8.4.20
# TODO this can be further simplified!
assert simplify(IMT(-2**(2*s)*cos(pi*a/2 - pi*b/2 + pi*s)*gamma(-2*s + 1) *
gamma(a/2 - b/2 + s)*gamma(a/2 + b/2 + s) /
(pi*gamma(a/2 - b/2 - s + 1)*gamma(a/2 + b/2 - s + 1)),
s, x,
(Max(-re(a)/2 - re(b)/2, -re(a)/2 + re(b)/2), S(1)/2))) == \
besselj(a, sqrt(x))*-(besselj(-b, sqrt(x)) -
besselj(b, sqrt(x))*cos(pi*b))/sin(pi*b)
# TODO more
# for coverage
assert IMT(pi/cos(pi*s), s, x, (0, S(1)/2)) == sqrt(x)/(x + 1)
@slow
def test_laplace_transform():
from sympy import fresnels, fresnelc
LT = laplace_transform
a, b, c, = symbols('a b c', positive=True)
t = symbols('t')
w = Symbol("w")
f = Function("f")
# Test unevaluated form
assert laplace_transform(f(t), t, w) == LaplaceTransform(f(t), t, w)
assert inverse_laplace_transform(
f(w), w, t, plane=0) == InverseLaplaceTransform(f(w), w, t, 0)
# test a bug
spos = symbols('s', positive=True)
assert LT(exp(t), t, spos)[:2] == (1/(spos - 1), True)
# basic tests from wikipedia
assert LT((t - a)**b*exp(-c*(t - a))*Heaviside(t - a), t, s) == \
((s + c)**(-b - 1)*exp(-a*s)*gamma(b + 1), -c, True)
assert LT(t**a, t, s) == (s**(-a - 1)*gamma(a + 1), 0, True)
assert LT(Heaviside(t), t, s) == (1/s, 0, True)
assert LT(Heaviside(t - a), t, s) == (exp(-a*s)/s, 0, True)
assert LT(1 - exp(-a*t), t, s) == (a/(s*(a + s)), 0, True)
assert LT((exp(2*t) - 1)*exp(-b - t)*Heaviside(t)/2, t, s, noconds=True) \
== exp(-b)/(s**2 - 1)
assert LT(exp(t), t, s)[:2] == (1/(s - 1), 1)
assert LT(exp(2*t), t, s)[:2] == (1/(s - 2), 2)
assert LT(exp(a*t), t, s)[:2] == (1/(s - a), a)
assert LT(log(t/a), t, s) == ((log(a*s) + EulerGamma)/s/-1, 0, True)
assert LT(erf(t), t, s) == ((erfc(s/2))*exp(s**2/4)/s, 0, True)
assert LT(sin(a*t), t, s) == (a/(a**2 + s**2), 0, True)
assert LT(cos(a*t), t, s) == (s/(a**2 + s**2), 0, True)
# TODO would be nice to have these come out better
assert LT(
exp(-a*t)*sin(b*t), t, s) == (b/(b**2 + (a + s)**2), -a, True)
assert LT(exp(-a*t)*cos(b*t), t, s) == \
((a + s)/(b**2 + (a + s)**2), -a, True)
assert LT(besselj(0, t), t, s) == (1/sqrt(1 + s**2), 0, True)
assert LT(besselj(1, t), t, s) == (1 - 1/sqrt(1 + 1/s**2), 0, True)
# TODO general order works, but is a *mess*
# TODO besseli also works, but is an even greater mess
# test a bug in conditions processing
# TODO the auxiliary condition should be recognised/simplified
assert LT(exp(t)*cos(t), t, s)[:-1] in [
((s - 1)/(s**2 - 2*s + 2), -oo),
((s - 1)/((s - 1)**2 + 1), -oo),
]
# Fresnel functions
assert laplace_transform(fresnels(t), t, s) == \
((-sin(s**2/(2*pi))*fresnels(s/pi) + sin(s**2/(2*pi))/2 -
cos(s**2/(2*pi))*fresnelc(s/pi) + cos(s**2/(2*pi))/2)/s, 0, True)
assert laplace_transform(fresnelc(t), t, s) == (
(sin(s**2/(2*pi))*fresnelc(s/pi)/s - cos(s**2/(2*pi))*fresnels(s/pi)/s
+ sqrt(2)*cos(s**2/(2*pi) + pi/4)/(2*s), 0, True))
assert LT(Matrix([[exp(t), t*exp(-t)], [t*exp(-t), exp(t)]]), t, s) ==\
Matrix([
[(1/(s - 1), 1, True), ((s + 1)**(-2), 0, True)],
[((s + 1)**(-2), 0, True), (1/(s - 1), 1, True)]
])
def test_issue_8368_7173():
LT = laplace_transform
# hyperbolic
assert LT(sinh(x), x, s) == (1/(s**2 - 1), 1, True)
assert LT(cosh(x), x, s) == (s/(s**2 - 1), 1, True)
assert LT(sinh(x + 3), x, s) == (
(-s + (s + 1)*exp(6) + 1)*exp(-3)/(s - 1)/(s + 1)/2, 1, True)
assert LT(sinh(x)*cosh(x), x, s) == (1/(s**2 - 4), 2, Ne(s/2, 1))
# trig (make sure they are not being rewritten in terms of exp)
assert LT(cos(x + 3), x, s) == ((s*cos(3) - sin(3))/(s**2 + 1), 0, True)
def test_inverse_laplace_transform():
from sympy import sinh, cosh, besselj, besseli, simplify, factor_terms
ILT = inverse_laplace_transform
a, b, c, = symbols('a b c', positive=True, finite=True)
t = symbols('t')
def simp_hyp(expr):
return factor_terms(expand_mul(expr)).rewrite(sin)
# just test inverses of all of the above
assert ILT(1/s, s, t) == Heaviside(t)
assert ILT(1/s**2, s, t) == t*Heaviside(t)
assert ILT(1/s**5, s, t) == t**4*Heaviside(t)/24
assert ILT(exp(-a*s)/s, s, t) == Heaviside(t - a)
assert ILT(exp(-a*s)/(s + b), s, t) == exp(b*(a - t))*Heaviside(-a + t)
assert ILT(a/(s**2 + a**2), s, t) == sin(a*t)*Heaviside(t)
assert ILT(s/(s**2 + a**2), s, t) == cos(a*t)*Heaviside(t)
# TODO is there a way around simp_hyp?
assert simp_hyp(ILT(a/(s**2 - a**2), s, t)) == sinh(a*t)*Heaviside(t)
assert simp_hyp(ILT(s/(s**2 - a**2), s, t)) == cosh(a*t)*Heaviside(t)
assert ILT(a/((s + b)**2 + a**2), s, t) == exp(-b*t)*sin(a*t)*Heaviside(t)
assert ILT(
(s + b)/((s + b)**2 + a**2), s, t) == exp(-b*t)*cos(a*t)*Heaviside(t)
# TODO sinh/cosh shifted come out a mess. also delayed trig is a mess
# TODO should this simplify further?
assert ILT(exp(-a*s)/s**b, s, t) == \
(t - a)**(b - 1)*Heaviside(t - a)/gamma(b)
assert ILT(exp(-a*s)/sqrt(1 + s**2), s, t) == \
Heaviside(t - a)*besselj(0, a - t) # note: besselj(0, x) is even
# XXX ILT turns these branch factor into trig functions ...
assert simplify(ILT(a**b*(s + sqrt(s**2 - a**2))**(-b)/sqrt(s**2 - a**2),
s, t).rewrite(exp)) == \
Heaviside(t)*besseli(b, a*t)
assert ILT(a**b*(s + sqrt(s**2 + a**2))**(-b)/sqrt(s**2 + a**2),
s, t).rewrite(exp) == \
Heaviside(t)*besselj(b, a*t)
assert ILT(1/(s*sqrt(s + 1)), s, t) == Heaviside(t)*erf(sqrt(t))
# TODO can we make erf(t) work?
assert ILT(1/(s**2*(s**2 + 1)),s,t) == (t - sin(t))*Heaviside(t)
assert ILT( (s * eye(2) - Matrix([[1, 0], [0, 2]])).inv(), s, t) ==\
Matrix([[exp(t)*Heaviside(t), 0], [0, exp(2*t)*Heaviside(t)]])
def test_fourier_transform():
from sympy import simplify, expand, expand_complex, factor, expand_trig
FT = fourier_transform
IFT = inverse_fourier_transform
def simp(x):
return simplify(expand_trig(expand_complex(expand(x))))
def sinc(x):
return sin(pi*x)/(pi*x)
k = symbols('k', real=True)
f = Function("f")
# TODO for this to work with real a, need to expand abs(a*x) to abs(a)*abs(x)
a = symbols('a', positive=True)
b = symbols('b', positive=True)
posk = symbols('posk', positive=True)
# Test unevaluated form
assert fourier_transform(f(x), x, k) == FourierTransform(f(x), x, k)
assert inverse_fourier_transform(
f(k), k, x) == InverseFourierTransform(f(k), k, x)
# basic examples from wikipedia
assert simp(FT(Heaviside(1 - abs(2*a*x)), x, k)) == sinc(k/a)/a
# TODO IFT is a *mess*
assert simp(FT(Heaviside(1 - abs(a*x))*(1 - abs(a*x)), x, k)) == sinc(k/a)**2/a
# TODO IFT
assert factor(FT(exp(-a*x)*Heaviside(x), x, k), extension=I) == \
1/(a + 2*pi*I*k)
# NOTE: the ift comes out in pieces
assert IFT(1/(a + 2*pi*I*x), x, posk,
noconds=False) == (exp(-a*posk), True)
assert IFT(1/(a + 2*pi*I*x), x, -posk,
noconds=False) == (0, True)
assert IFT(1/(a + 2*pi*I*x), x, symbols('k', negative=True),
noconds=False) == (0, True)
# TODO IFT without factoring comes out as meijer g
assert factor(FT(x*exp(-a*x)*Heaviside(x), x, k), extension=I) == \
1/(a + 2*pi*I*k)**2
assert FT(exp(-a*x)*sin(b*x)*Heaviside(x), x, k) == \
b/(b**2 + (a + 2*I*pi*k)**2)
assert FT(exp(-a*x**2), x, k) == sqrt(pi)*exp(-pi**2*k**2/a)/sqrt(a)
assert IFT(sqrt(pi/a)*exp(-(pi*k)**2/a), k, x) == exp(-a*x**2)
assert FT(exp(-a*abs(x)), x, k) == 2*a/(a**2 + 4*pi**2*k**2)
# TODO IFT (comes out as meijer G)
# TODO besselj(n, x), n an integer > 0 actually can be done...
# TODO are there other common transforms (no distributions!)?
def test_sine_transform():
from sympy import EulerGamma
t = symbols("t")
w = symbols("w")
a = symbols("a")
f = Function("f")
# Test unevaluated form
assert sine_transform(f(t), t, w) == SineTransform(f(t), t, w)
assert inverse_sine_transform(
f(w), w, t) == InverseSineTransform(f(w), w, t)
assert sine_transform(1/sqrt(t), t, w) == 1/sqrt(w)
assert inverse_sine_transform(1/sqrt(w), w, t) == 1/sqrt(t)
assert sine_transform(
(1/sqrt(t))**3, t, w) == sqrt(w)*gamma(S(1)/4)/(2*gamma(S(5)/4))
assert sine_transform(t**(-a), t, w) == 2**(
-a + S(1)/2)*w**(a - 1)*gamma(-a/2 + 1)/gamma((a + 1)/2)
assert inverse_sine_transform(2**(-a + S(
1)/2)*w**(a - 1)*gamma(-a/2 + 1)/gamma(a/2 + S(1)/2), w, t) == t**(-a)
assert sine_transform(
exp(-a*t), t, w) == sqrt(2)*w/(sqrt(pi)*(a**2 + w**2))
assert inverse_sine_transform(
sqrt(2)*w/(sqrt(pi)*(a**2 + w**2)), w, t) == exp(-a*t)
assert sine_transform(
log(t)/t, t, w) == -sqrt(2)*sqrt(pi)*(log(w**2) + 2*EulerGamma)/4
assert sine_transform(
t*exp(-a*t**2), t, w) == sqrt(2)*w*exp(-w**2/(4*a))/(4*a**(S(3)/2))
assert inverse_sine_transform(
sqrt(2)*w*exp(-w**2/(4*a))/(4*a**(S(3)/2)), w, t) == t*exp(-a*t**2)
def test_cosine_transform():
from sympy import Si, Ci
t = symbols("t")
w = symbols("w")
a = symbols("a")
f = Function("f")
# Test unevaluated form
assert cosine_transform(f(t), t, w) == CosineTransform(f(t), t, w)
assert inverse_cosine_transform(
f(w), w, t) == InverseCosineTransform(f(w), w, t)
assert cosine_transform(1/sqrt(t), t, w) == 1/sqrt(w)
assert inverse_cosine_transform(1/sqrt(w), w, t) == 1/sqrt(t)
assert cosine_transform(1/(
a**2 + t**2), t, w) == sqrt(2)*sqrt(pi)*exp(-a*w)/(2*a)
assert cosine_transform(t**(
-a), t, w) == 2**(-a + S(1)/2)*w**(a - 1)*gamma((-a + 1)/2)/gamma(a/2)
assert inverse_cosine_transform(2**(-a + S(
1)/2)*w**(a - 1)*gamma(-a/2 + S(1)/2)/gamma(a/2), w, t) == t**(-a)
assert cosine_transform(
exp(-a*t), t, w) == sqrt(2)*a/(sqrt(pi)*(a**2 + w**2))
assert inverse_cosine_transform(
sqrt(2)*a/(sqrt(pi)*(a**2 + w**2)), w, t) == exp(-a*t)
assert cosine_transform(exp(-a*sqrt(t))*cos(a*sqrt(
t)), t, w) == a*exp(-a**2/(2*w))/(2*w**(S(3)/2))
assert cosine_transform(1/(a + t), t, w) == sqrt(2)*(
(-2*Si(a*w) + pi)*sin(a*w)/2 - cos(a*w)*Ci(a*w))/sqrt(pi)
assert inverse_cosine_transform(sqrt(2)*meijerg(((S(1)/2, 0), ()), (
(S(1)/2, 0, 0), (S(1)/2,)), a**2*w**2/4)/(2*pi), w, t) == 1/(a + t)
assert cosine_transform(1/sqrt(a**2 + t**2), t, w) == sqrt(2)*meijerg(
((S(1)/2,), ()), ((0, 0), (S(1)/2,)), a**2*w**2/4)/(2*sqrt(pi))
assert inverse_cosine_transform(sqrt(2)*meijerg(((S(1)/2,), ()), ((0, 0), (S(1)/2,)), a**2*w**2/4)/(2*sqrt(pi)), w, t) == 1/(t*sqrt(a**2/t**2 + 1))
def test_hankel_transform():
from sympy import gamma, sqrt, exp
r = Symbol("r")
k = Symbol("k")
nu = Symbol("nu")
m = Symbol("m")
a = symbols("a")
assert hankel_transform(1/r, r, k, 0) == 1/k
assert inverse_hankel_transform(1/k, k, r, 0) == 1/r
assert hankel_transform(
1/r**m, r, k, 0) == 2**(-m + 1)*k**(m - 2)*gamma(-m/2 + 1)/gamma(m/2)
assert inverse_hankel_transform(
2**(-m + 1)*k**(m - 2)*gamma(-m/2 + 1)/gamma(m/2), k, r, 0) == r**(-m)
assert hankel_transform(1/r**m, r, k, nu) == (
2*2**(-m)*k**(m - 2)*gamma(-m/2 + nu/2 + 1)/gamma(m/2 + nu/2))
assert inverse_hankel_transform(2**(-m + 1)*k**(
m - 2)*gamma(-m/2 + nu/2 + 1)/gamma(m/2 + nu/2), k, r, nu) == r**(-m)
assert hankel_transform(r**nu*exp(-a*r), r, k, nu) == \
2**(nu + 1)*a*k**(-nu - 3)*(a**2/k**2 + 1)**(-nu - S(
3)/2)*gamma(nu + S(3)/2)/sqrt(pi)
assert inverse_hankel_transform(
2**(nu + 1)*a*k**(-nu - 3)*(a**2/k**2 + 1)**(-nu - S(3)/2)*gamma(
nu + S(3)/2)/sqrt(pi), k, r, nu) == r**nu*exp(-a*r)
def test_issue_7181():
assert mellin_transform(1/(1 - x), x, s) != None
def test_issue_8882():
# This is the original test.
# from sympy import diff, Integral, integrate
# r = Symbol('r')
# psi = 1/r*sin(r)*exp(-(a0*r))
# h = -1/2*diff(psi, r, r) - 1/r*psi
# f = 4*pi*psi*h*r**2
# assert integrate(f, (r, -oo, 3), meijerg=True).has(Integral) == True
# To save time, only the critical part is included.
F = -a**(-s + 1)*(4 + 1/a**2)**(-s/2)*sqrt(1/a**2)*exp(-s*I*pi)* \
sin(s*atan(sqrt(1/a**2)/2))*gamma(s)
raises(IntegralTransformError, lambda:
inverse_mellin_transform(F, s, x, (-1, oo),
**{'as_meijerg': True, 'needeval': True}))
def test_issue_7173():
assert laplace_transform(sinh(a*x)*cosh(a*x), x, s) == \
(a/(s**2 - 4*a**2), 0,
And(Or(Abs(periodic_argument(exp_polar(I*pi)*polar_lift(a), oo)) <
pi/2, Abs(periodic_argument(exp_polar(I*pi)*polar_lift(a), oo)) <=
pi/2), Or(Abs(periodic_argument(a, oo)) < pi/2,
Abs(periodic_argument(a, oo)) <= pi/2)))
def test_issue_8514():
from sympy import simplify
a, b, c, = symbols('a b c', positive=True)
t = symbols('t', positive=True)
ft = simplify(inverse_laplace_transform(1/(a*s**2+b*s+c),s, t))
assert ft == ((exp(t*(exp(I*atan2(0, -4*a*c + b**2)/2) -
exp(-I*atan2(0, -4*a*c + b**2)/2))*
sqrt(Abs(4*a*c - b**2))/(4*a))*exp(t*cos(atan2(0, -4*a*c + b**2)/2)
*sqrt(Abs(4*a*c - b**2))/a) + I*sin(t*sin(atan2(0, -4*a*c + b**2)/2)
*sqrt(Abs(4*a*c - b**2))/(2*a)) - cos(t*sin(atan2(0, -4*a*c + b**2)/2)
*sqrt(Abs(4*a*c - b**2))/(2*a)))*exp(-t*(b + cos(atan2(0, -4*a*c + b**2)/2)
*sqrt(Abs(4*a*c - b**2)))/(2*a))/sqrt(-4*a*c + b**2))
| 33,313 | 41.223067 | 151 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/integrals/tests/test_heurisch.py
|
from sympy import Rational, sqrt, symbols, sin, exp, log, sinh, cosh, cos, pi, \
I, erf, tan, asin, asinh, acos, Function, Derivative, diff, simplify, \
LambertW, Eq, Piecewise, Symbol, Add, ratsimp, Integral, Sum, \
besselj, besselk, bessely, jn
from sympy.integrals.heurisch import components, heurisch, heurisch_wrapper
from sympy.utilities.pytest import XFAIL, skip, slow, ON_TRAVIS
from sympy.integrals.integrals import integrate
x, y, z, nu = symbols('x,y,z,nu')
f = Function('f')
def test_components():
assert components(x*y, x) == {x}
assert components(1/(x + y), x) == {x}
assert components(sin(x), x) == {sin(x), x}
assert components(sin(x)*sqrt(log(x)), x) == \
{log(x), sin(x), sqrt(log(x)), x}
assert components(x*sin(exp(x)*y), x) == \
{sin(y*exp(x)), x, exp(x)}
assert components(x**Rational(17, 54)/sqrt(sin(x)), x) == \
{sin(x), x**Rational(1, 54), sqrt(sin(x)), x}
assert components(f(x), x) == \
{x, f(x)}
assert components(Derivative(f(x), x), x) == \
{x, f(x), Derivative(f(x), x)}
assert components(f(x)*diff(f(x), x), x) == \
{x, f(x), Derivative(f(x), x), Derivative(f(x), x)}
def test_issue_10680():
assert isinstance(integrate(x**log(x**log(x**log(x))),x), Integral)
def test_heurisch_polynomials():
assert heurisch(1, x) == x
assert heurisch(x, x) == x**2/2
assert heurisch(x**17, x) == x**18/18
def test_heurisch_fractions():
assert heurisch(1/x, x) == log(x)
assert heurisch(1/(2 + x), x) == log(x + 2)
assert heurisch(1/(x + sin(y)), x) == log(x + sin(y))
# Up to a constant, where C = 5*pi*I/12, Mathematica gives identical
# result in the first case. The difference is because sympy changes
# signs of expressions without any care.
# XXX ^ ^ ^ is this still correct?
assert heurisch(5*x**5/(
2*x**6 - 5), x) in [5*log(2*x**6 - 5) / 12, 5*log(-2*x**6 + 5) / 12]
assert heurisch(5*x**5/(2*x**6 + 5), x) == 5*log(2*x**6 + 5) / 12
assert heurisch(1/x**2, x) == -1/x
assert heurisch(-1/x**5, x) == 1/(4*x**4)
def test_heurisch_log():
assert heurisch(log(x), x) == x*log(x) - x
assert heurisch(log(3*x), x) == -x + x*log(3) + x*log(x)
assert heurisch(log(x**2), x) in [x*log(x**2) - 2*x, 2*x*log(x) - 2*x]
def test_heurisch_exp():
assert heurisch(exp(x), x) == exp(x)
assert heurisch(exp(-x), x) == -exp(-x)
assert heurisch(exp(17*x), x) == exp(17*x) / 17
assert heurisch(x*exp(x), x) == x*exp(x) - exp(x)
assert heurisch(x*exp(x**2), x) == exp(x**2) / 2
assert heurisch(exp(-x**2), x) is None
assert heurisch(2**x, x) == 2**x/log(2)
assert heurisch(x*2**x, x) == x*2**x/log(2) - 2**x*log(2)**(-2)
assert heurisch(Integral(x**z*y, (y, 1, 2), (z, 2, 3)).function, x) == (x*x**z*y)/(z+1)
assert heurisch(Sum(x**z, (z, 1, 2)).function, z) == x**z/log(x)
def test_heurisch_trigonometric():
assert heurisch(sin(x), x) == -cos(x)
assert heurisch(pi*sin(x) + 1, x) == x - pi*cos(x)
assert heurisch(cos(x), x) == sin(x)
assert heurisch(tan(x), x) in [
log(1 + tan(x)**2)/2,
log(tan(x) + I) + I*x,
log(tan(x) - I) - I*x,
]
assert heurisch(sin(x)*sin(y), x) == -cos(x)*sin(y)
assert heurisch(sin(x)*sin(y), y) == -cos(y)*sin(x)
# gives sin(x) in answer when run via setup.py and cos(x) when run via py.test
assert heurisch(sin(x)*cos(x), x) in [sin(x)**2 / 2, -cos(x)**2 / 2]
assert heurisch(cos(x)/sin(x), x) == log(sin(x))
assert heurisch(x*sin(7*x), x) == sin(7*x) / 49 - x*cos(7*x) / 7
assert heurisch(1/pi/4 * x**2*cos(x), x) == 1/pi/4*(x**2*sin(x) -
2*sin(x) + 2*x*cos(x))
assert heurisch(acos(x/4) * asin(x/4), x) == 2*x - (sqrt(16 - x**2))*asin(x/4) \
+ (sqrt(16 - x**2))*acos(x/4) + x*asin(x/4)*acos(x/4)
def test_heurisch_hyperbolic():
assert heurisch(sinh(x), x) == cosh(x)
assert heurisch(cosh(x), x) == sinh(x)
assert heurisch(x*sinh(x), x) == x*cosh(x) - sinh(x)
assert heurisch(x*cosh(x), x) == x*sinh(x) - cosh(x)
assert heurisch(
x*asinh(x/2), x) == x**2*asinh(x/2)/2 + asinh(x/2) - x*sqrt(4 + x**2)/4
def test_heurisch_mixed():
assert heurisch(sin(x)*exp(x), x) == exp(x)*sin(x)/2 - exp(x)*cos(x)/2
def test_heurisch_radicals():
assert heurisch(1/sqrt(x), x) == 2*sqrt(x)
assert heurisch(1/sqrt(x)**3, x) == -2/sqrt(x)
assert heurisch(sqrt(x)**3, x) == 2*sqrt(x)**5/5
assert heurisch(sin(x)*sqrt(cos(x)), x) == -2*sqrt(cos(x))**3/3
y = Symbol('y')
assert heurisch(sin(y*sqrt(x)), x) == 2/y**2*sin(y*sqrt(x)) - \
2*sqrt(x)*cos(y*sqrt(x))/y
assert heurisch_wrapper(sin(y*sqrt(x)), x) == Piecewise(
(0, Eq(y, 0)),
(-2*sqrt(x)*cos(sqrt(x)*y)/y + 2*sin(sqrt(x)*y)/y**2, True))
y = Symbol('y', positive=True)
assert heurisch_wrapper(sin(y*sqrt(x)), x) == 2/y**2*sin(y*sqrt(x)) - \
2*sqrt(x)*cos(y*sqrt(x))/y
def test_heurisch_special():
assert heurisch(erf(x), x) == x*erf(x) + exp(-x**2)/sqrt(pi)
assert heurisch(exp(-x**2)*erf(x), x) == sqrt(pi)*erf(x)**2 / 4
def test_heurisch_symbolic_coeffs():
assert heurisch(1/(x + y), x) == log(x + y)
assert heurisch(1/(x + sqrt(2)), x) == log(x + sqrt(2))
assert simplify(diff(heurisch(log(x + y + z), y), y)) == log(x + y + z)
def test_heurisch_symbolic_coeffs_1130():
y = Symbol('y')
assert heurisch_wrapper(1/(x**2 + y), x) == Piecewise(
(-1/x, Eq(y, 0)),
(-I*log(x - I*sqrt(y))/(2*sqrt(y)) + I*log(x + I*sqrt(y))/(2*sqrt(y)), True))
y = Symbol('y', positive=True)
assert heurisch_wrapper(1/(x**2 + y), x) in [I/sqrt(y)*log(x + sqrt(-y))/2 -
I/sqrt(y)*log(x - sqrt(-y))/2, I*log(x + I*sqrt(y)) /
(2*sqrt(y)) - I*log(x - I*sqrt(y))/(2*sqrt(y))]
def test_heurisch_hacking():
assert heurisch(sqrt(1 + 7*x**2), x, hints=[]) == \
x*sqrt(1 + 7*x**2)/2 + sqrt(7)*asinh(sqrt(7)*x)/14
assert heurisch(sqrt(1 - 7*x**2), x, hints=[]) == \
x*sqrt(1 - 7*x**2)/2 + sqrt(7)*asin(sqrt(7)*x)/14
assert heurisch(1/sqrt(1 + 7*x**2), x, hints=[]) == \
sqrt(7)*asinh(sqrt(7)*x)/7
assert heurisch(1/sqrt(1 - 7*x**2), x, hints=[]) == \
sqrt(7)*asin(sqrt(7)*x)/7
assert heurisch(exp(-7*x**2), x, hints=[]) == \
sqrt(7*pi)*erf(sqrt(7)*x)/14
assert heurisch(1/sqrt(9 - 4*x**2), x, hints=[]) == \
asin(2*x/3)/2
assert heurisch(1/sqrt(9 + 4*x**2), x, hints=[]) == \
asinh(2*x/3)/2
def test_heurisch_function():
assert heurisch(f(x), x) is None
@XFAIL
def test_heurisch_function_derivative():
# TODO: it looks like this used to work just by coincindence and
# thanks to sloppy implementation. Investigate why this used to
# work at all and if support for this can be restored.
df = diff(f(x), x)
assert heurisch(f(x)*df, x) == f(x)**2/2
assert heurisch(f(x)**2*df, x) == f(x)**3/3
assert heurisch(df/f(x), x) == log(f(x))
def test_heurisch_wrapper():
f = 1/(y + x)
assert heurisch_wrapper(f, x) == log(x + y)
f = 1/(y - x)
assert heurisch_wrapper(f, x) == -log(x - y)
f = 1/((y - x)*(y + x))
assert heurisch_wrapper(f, x) == \
Piecewise((1/x, Eq(y, 0)), (log(x + y)/2/y - log(x - y)/2/y, True))
# issue 6926
f = sqrt(x**2/((y - x)*(y + x)))
assert heurisch_wrapper(f, x) == x*sqrt(x**2)*sqrt(1/(-x**2 + y**2)) \
- y**2*sqrt(x**2)*sqrt(1/(-x**2 + y**2))/x
def test_issue_3609():
assert heurisch(1/(x * (1 + log(x)**2)), x) == I*log(log(x) + I)/2 - \
I*log(log(x) - I)/2
### These are examples from the Poor Man's Integrator
### http://www-sop.inria.fr/cafe/Manuel.Bronstein/pmint/examples/
def test_pmint_rat():
# TODO: heurisch() is off by a constant: -3/4. Possibly different permutation
# would give the optimal result?
def drop_const(expr, x):
if expr.is_Add:
return Add(*[ arg for arg in expr.args if arg.has(x) ])
else:
return expr
f = (x**7 - 24*x**4 - 4*x**2 + 8*x - 8)/(x**8 + 6*x**6 + 12*x**4 + 8*x**2)
g = (4 + 8*x**2 + 6*x + 3*x**3)/(x**5 + 4*x**3 + 4*x) + log(x)
assert drop_const(ratsimp(heurisch(f, x)), x) == g
def test_pmint_trig():
f = (x - tan(x)) / tan(x)**2 + tan(x)
g = -x**2/2 - x/tan(x) + log(tan(x)**2 + 1)/2
assert heurisch(f, x) == g
@slow # 8 seconds on 3.4 GHz
def test_pmint_logexp():
if ON_TRAVIS:
# See https://github.com/sympy/sympy/pull/12795
skip("Too slow for travis.")
f = (1 + x + x*exp(x))*(x + log(x) + exp(x) - 1)/(x + log(x) + exp(x))**2/x
g = log(x + exp(x) + log(x)) + 1/(x + exp(x) + log(x))
assert ratsimp(heurisch(f, x)) == g
@slow # 8 seconds on 3.4 GHz
@XFAIL # there's a hash dependent failure lurking here
def test_pmint_erf():
f = exp(-x**2)*erf(x)/(erf(x)**3 - erf(x)**2 - erf(x) + 1)
g = sqrt(pi)*log(erf(x) - 1)/8 - sqrt(pi)*log(erf(x) + 1)/8 - sqrt(pi)/(4*erf(x) - 4)
assert ratsimp(heurisch(f, x)) == g
def test_pmint_LambertW():
f = LambertW(x)
g = x*LambertW(x) - x + x/LambertW(x)
assert heurisch(f, x) == g
def test_pmint_besselj():
f = besselj(nu + 1, x)/besselj(nu, x)
g = nu*log(x) - log(besselj(nu, x))
assert heurisch(f, x) == g
f = (nu*besselj(nu, x) - x*besselj(nu + 1, x))/x
g = besselj(nu, x)
assert heurisch(f, x) == g
f = jn(nu + 1, x)/jn(nu, x)
g = nu*log(x) - log(jn(nu, x))
assert heurisch(f, x) == g
@slow
def test_pmint_bessel_products():
# Note: Derivatives of Bessel functions have many forms.
# Recurrence relations are needed for comparisons.
if ON_TRAVIS:
skip("Too slow for travis.")
f = x*besselj(nu, x)*bessely(nu, 2*x)
g = -2*x*besselj(nu, x)*bessely(nu - 1, 2*x)/3 + x*besselj(nu - 1, x)*bessely(nu, 2*x)/3
assert heurisch(f, x) == g
f = x*besselj(nu, x)*besselk(nu, 2*x)
g = -2*x*besselj(nu, x)*besselk(nu - 1, 2*x)/5 - x*besselj(nu - 1, x)*besselk(nu, 2*x)/5
assert heurisch(f, x) == g
@slow # 110 seconds on 3.4 GHz
def test_pmint_WrightOmega():
if ON_TRAVIS:
skip("Too slow for travis.")
def omega(x):
return LambertW(exp(x))
f = (1 + omega(x) * (2 + cos(omega(x)) * (x + omega(x))))/(1 + omega(x))/(x + omega(x))
g = log(x + LambertW(exp(x))) + sin(LambertW(exp(x)))
assert heurisch(f, x) == g
def test_RR():
# Make sure the algorithm does the right thing if the ring is RR. See
# issue 8685.
assert heurisch(sqrt(1 + 0.25*x**2), x, hints=[]) == \
0.5*x*sqrt(0.25*x**2 + 1) + 1.0*asinh(0.5*x)
# TODO: convert the rest of PMINT tests:
# Airy functions
# f = (x - AiryAi(x)*AiryAi(1, x)) / (x**2 - AiryAi(x)**2)
# g = Rational(1,2)*ln(x + AiryAi(x)) + Rational(1,2)*ln(x - AiryAi(x))
# f = x**2 * AiryAi(x)
# g = -AiryAi(x) + AiryAi(1, x)*x
# Whittaker functions
# f = WhittakerW(mu + 1, nu, x) / (WhittakerW(mu, nu, x) * x)
# g = x/2 - mu*ln(x) - ln(WhittakerW(mu, nu, x))
| 11,019 | 33.763407 | 92 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/series_class.py
|
"""
Contains the base class for series
Made using sequences in mind
"""
from __future__ import print_function, division
from sympy.core.expr import Expr
from sympy.core.singleton import S
from sympy.core.cache import cacheit
from sympy.core.compatibility import integer_types
class SeriesBase(Expr):
"""Base Class for series"""
@property
def interval(self):
"""The interval on which the series is defined"""
raise NotImplementedError("(%s).interval" % self)
@property
def start(self):
"""The starting point of the series. This point is included"""
raise NotImplementedError("(%s).start" % self)
@property
def stop(self):
"""The ending point of the series. This point is included"""
raise NotImplementedError("(%s).stop" % self)
@property
def length(self):
"""Length of the series expansion"""
raise NotImplementedError("(%s).length" % self)
@property
def variables(self):
"""Returns a tuple of variables that are bounded"""
return ()
@property
def free_symbols(self):
"""
This method returns the symbols in the object, excluding those
that take on a specific value (i.e. the dummy symbols).
"""
return (set(j for i in self.args for j in i.free_symbols)
.difference(self.variables))
@cacheit
def term(self, pt):
"""Term at point pt of a series"""
if pt < self.start or pt > self.stop:
raise IndexError("Index %s out of bounds %s" % (pt, self.interval))
return self._eval_term(pt)
def _eval_term(self, pt):
raise NotImplementedError("The _eval_term method should be added to"
"%s to return series term so it is available"
"when 'term' calls it."
% self.func)
def _ith_point(self, i):
"""
Returns the i'th point of a series
If start point is negative infinity, point is returned from the end.
Assumes the first point to be indexed zero.
Examples
========
TODO
"""
if self.start is S.NegativeInfinity:
initial = self.stop
step = -1
else:
initial = self.start
step = 1
return initial + i*step
def __iter__(self):
i = 0
while i < self.length:
pt = self._ith_point(i)
yield self.term(pt)
i += 1
def __getitem__(self, index):
if isinstance(index, integer_types):
index = self._ith_point(index)
return self.term(index)
elif isinstance(index, slice):
start, stop = index.start, index.stop
if start is None:
start = 0
if stop is None:
stop = self.length
return [self.term(self._ith_point(i)) for i in
range(start, stop, index.step or 1)]
| 3,031 | 28.436893 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/limits.py
|
from __future__ import print_function, division
from sympy.core import S, Symbol, Add, sympify, Expr, PoleError, Mul
from sympy.core.compatibility import string_types
from sympy.core.symbol import Dummy
from sympy.functions.combinatorial.factorials import factorial
from sympy.core.numbers import GoldenRatio
from sympy.functions.combinatorial.numbers import fibonacci
from sympy.functions.special.gamma_functions import gamma
from sympy.series.order import Order
from .gruntz import gruntz
from sympy.core.exprtools import factor_terms
from sympy.simplify.ratsimp import ratsimp
from sympy.polys import PolynomialError
def limit(e, z, z0, dir="+"):
"""
Compute the limit of e(z) at the point z0.
z0 can be any expression, including oo and -oo.
For dir="+" (default) it calculates the limit from the right
(z->z0+) and for dir="-" the limit from the left (z->z0-). For infinite
z0 (oo or -oo), the dir argument is determined from the direction
of the infinity (i.e., dir="-" for oo).
Examples
========
>>> from sympy import limit, sin, Symbol, oo
>>> from sympy.abc import x
>>> limit(sin(x)/x, x, 0)
1
>>> limit(1/x, x, 0, dir="+")
oo
>>> limit(1/x, x, 0, dir="-")
-oo
>>> limit(1/x, x, oo)
0
Notes
=====
First we try some heuristics for easy and frequent cases like "x", "1/x",
"x**2" and similar, so that it's fast. For all other cases, we use the
Gruntz algorithm (see the gruntz() function).
"""
return Limit(e, z, z0, dir).doit(deep=False)
def heuristics(e, z, z0, dir):
rv = None
if abs(z0) is S.Infinity:
rv = limit(e.subs(z, 1/z), z, S.Zero, "+" if z0 is S.Infinity else "-")
if isinstance(rv, Limit):
return
elif e.is_Mul or e.is_Add or e.is_Pow or e.is_Function:
r = []
for a in e.args:
l = limit(a, z, z0, dir)
if l.has(S.Infinity) and l.is_finite is None:
return
elif isinstance(l, Limit):
return
elif l is S.NaN:
return
else:
r.append(l)
if r:
rv = e.func(*r)
if rv is S.NaN:
try:
rat_e = ratsimp(e)
except PolynomialError:
return
if rat_e is S.NaN or rat_e == e:
return
return limit(rat_e, z, z0, dir)
return rv
class Limit(Expr):
"""Represents an unevaluated limit.
Examples
========
>>> from sympy import Limit, sin, Symbol
>>> from sympy.abc import x
>>> Limit(sin(x)/x, x, 0)
Limit(sin(x)/x, x, 0)
>>> Limit(1/x, x, 0, dir="-")
Limit(1/x, x, 0, dir='-')
"""
def __new__(cls, e, z, z0, dir="+"):
e = sympify(e)
z = sympify(z)
z0 = sympify(z0)
if z0 is S.Infinity:
dir = "-"
elif z0 is S.NegativeInfinity:
dir = "+"
if isinstance(dir, string_types):
dir = Symbol(dir)
elif not isinstance(dir, Symbol):
raise TypeError("direction must be of type basestring or Symbol, not %s" % type(dir))
if str(dir) not in ('+', '-'):
raise ValueError(
"direction must be either '+' or '-', not %s" % dir)
obj = Expr.__new__(cls)
obj._args = (e, z, z0, dir)
return obj
@property
def free_symbols(self):
e = self.args[0]
isyms = e.free_symbols
isyms.difference_update(self.args[1].free_symbols)
isyms.update(self.args[2].free_symbols)
return isyms
def doit(self, **hints):
"""Evaluates limit"""
from sympy.series.limitseq import limit_seq
from sympy.functions import RisingFactorial
e, z, z0, dir = self.args
if hints.get('deep', True):
e = e.doit(**hints)
z = z.doit(**hints)
z0 = z0.doit(**hints)
if e == z:
return z0
if not e.has(z):
return e
# gruntz fails on factorials but works with the gamma function
# If no factorial term is present, e should remain unchanged.
# factorial is defined to be zero for negative inputs (which
# differs from gamma) so only rewrite for positive z0.
if z0.is_positive:
e = e.rewrite([factorial, RisingFactorial], gamma)
if e.is_Mul:
if abs(z0) is S.Infinity:
e = factor_terms(e)
e = e.rewrite(fibonacci, GoldenRatio)
ok = lambda w: (z in w.free_symbols and
any(a.is_polynomial(z) or
any(z in m.free_symbols and m.is_polynomial(z)
for m in Mul.make_args(a))
for a in Add.make_args(w)))
if all(ok(w) for w in e.as_numer_denom()):
u = Dummy(positive=True)
if z0 is S.NegativeInfinity:
inve = e.subs(z, -1/u)
else:
inve = e.subs(z, 1/u)
r = limit(inve.as_leading_term(u), u, S.Zero, "+")
if isinstance(r, Limit):
return self
else:
return r
if e.is_Order:
return Order(limit(e.expr, z, z0), *e.args[1:])
try:
r = gruntz(e, z, z0, dir)
if r is S.NaN:
raise PoleError()
except (PoleError, ValueError):
r = heuristics(e, z, z0, dir)
if r is None:
return self
except NotImplementedError:
# Trying finding limits of sequences
if hints.get('sequence', True) and z0 is S.Infinity:
trials = hints.get('trials', 5)
r = limit_seq(e, z, trials)
if r is None:
raise NotImplementedError()
else:
raise NotImplementedError()
return r
| 6,153 | 30.080808 | 97 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/gruntz.py
|
"""
Limits
======
Implemented according to the PhD thesis
http://www.cybertester.com/data/gruntz.pdf, which contains very thorough
descriptions of the algorithm including many examples. We summarize here
the gist of it.
All functions are sorted according to how rapidly varying they are at
infinity using the following rules. Any two functions f and g can be
compared using the properties of L:
L=lim log|f(x)| / log|g(x)| (for x -> oo)
We define >, < ~ according to::
1. f > g .... L=+-oo
we say that:
- f is greater than any power of g
- f is more rapidly varying than g
- f goes to infinity/zero faster than g
2. f < g .... L=0
we say that:
- f is lower than any power of g
3. f ~ g .... L!=0, +-oo
we say that:
- both f and g are bounded from above and below by suitable integral
powers of the other
Examples
========
::
2 < x < exp(x) < exp(x**2) < exp(exp(x))
2 ~ 3 ~ -5
x ~ x**2 ~ x**3 ~ 1/x ~ x**m ~ -x
exp(x) ~ exp(-x) ~ exp(2x) ~ exp(x)**2 ~ exp(x+exp(-x))
f ~ 1/f
So we can divide all the functions into comparability classes (x and x^2
belong to one class, exp(x) and exp(-x) belong to some other class). In
principle, we could compare any two functions, but in our algorithm, we
don't compare anything below the class 2~3~-5 (for example log(x) is
below this), so we set 2~3~-5 as the lowest comparability class.
Given the function f, we find the list of most rapidly varying (mrv set)
subexpressions of it. This list belongs to the same comparability class.
Let's say it is {exp(x), exp(2x)}. Using the rule f ~ 1/f we find an
element "w" (either from the list or a new one) from the same
comparability class which goes to zero at infinity. In our example we
set w=exp(-x) (but we could also set w=exp(-2x) or w=exp(-3x) ...). We
rewrite the mrv set using w, in our case {1/w, 1/w^2}, and substitute it
into f. Then we expand f into a series in w::
f = c0*w^e0 + c1*w^e1 + ... + O(w^en), where e0<e1<...<en, c0!=0
but for x->oo, lim f = lim c0*w^e0, because all the other terms go to zero,
because w goes to zero faster than the ci and ei. So::
for e0>0, lim f = 0
for e0<0, lim f = +-oo (the sign depends on the sign of c0)
for e0=0, lim f = lim c0
We need to recursively compute limits at several places of the algorithm, but
as is shown in the PhD thesis, it always finishes.
Important functions from the implementation:
compare(a, b, x) compares "a" and "b" by computing the limit L.
mrv(e, x) returns list of most rapidly varying (mrv) subexpressions of "e"
rewrite(e, Omega, x, wsym) rewrites "e" in terms of w
leadterm(f, x) returns the lowest power term in the series of f
mrv_leadterm(e, x) returns the lead term (c0, e0) for e
limitinf(e, x) computes lim e (for x->oo)
limit(e, z, z0) computes any limit by converting it to the case x->oo
All the functions are really simple and straightforward except
rewrite(), which is the most difficult/complex part of the algorithm.
When the algorithm fails, the bugs are usually in the series expansion
(i.e. in SymPy) or in rewrite.
This code is almost exact rewrite of the Maple code inside the Gruntz
thesis.
Debugging
---------
Because the gruntz algorithm is highly recursive, it's difficult to
figure out what went wrong inside a debugger. Instead, turn on nice
debug prints by defining the environment variable SYMPY_DEBUG. For
example:
[user@localhost]: SYMPY_DEBUG=True ./bin/isympy
In [1]: limit(sin(x)/x, x, 0)
limitinf(_x*sin(1/_x), _x) = 1
+-mrv_leadterm(_x*sin(1/_x), _x) = (1, 0)
| +-mrv(_x*sin(1/_x), _x) = set([_x])
| | +-mrv(_x, _x) = set([_x])
| | +-mrv(sin(1/_x), _x) = set([_x])
| | +-mrv(1/_x, _x) = set([_x])
| | +-mrv(_x, _x) = set([_x])
| +-mrv_leadterm(exp(_x)*sin(exp(-_x)), _x, set([exp(_x)])) = (1, 0)
| +-rewrite(exp(_x)*sin(exp(-_x)), set([exp(_x)]), _x, _w) = (1/_w*sin(_w), -_x)
| +-sign(_x, _x) = 1
| +-mrv_leadterm(1, _x) = (1, 0)
+-sign(0, _x) = 0
+-limitinf(1, _x) = 1
And check manually which line is wrong. Then go to the source code and
debug this function to figure out the exact problem.
"""
from __future__ import print_function, division
from sympy.core import Basic, S, oo, Symbol, I, Dummy, Wild, Mul
from sympy.functions import log, exp
from sympy.series.order import Order
from sympy.simplify.powsimp import powsimp, powdenest
from sympy import cacheit
from sympy.core.compatibility import reduce
from sympy.utilities.timeutils import timethis
timeit = timethis('gruntz')
from sympy.utilities.misc import debug_decorator as debug
def compare(a, b, x):
"""Returns "<" if a<b, "=" for a == b, ">" for a>b"""
# log(exp(...)) must always be simplified here for termination
la, lb = log(a), log(b)
if isinstance(a, Basic) and a.func is exp:
la = a.args[0]
if isinstance(b, Basic) and b.func is exp:
lb = b.args[0]
c = limitinf(la/lb, x)
if c == 0:
return "<"
elif c.is_infinite:
return ">"
else:
return "="
class SubsSet(dict):
"""
Stores (expr, dummy) pairs, and how to rewrite expr-s.
The gruntz algorithm needs to rewrite certain expressions in term of a new
variable w. We cannot use subs, because it is just too smart for us. For
example::
> Omega=[exp(exp(_p - exp(-_p))/(1 - 1/_p)), exp(exp(_p))]
> O2=[exp(-exp(_p) + exp(-exp(-_p))*exp(_p)/(1 - 1/_p))/_w, 1/_w]
> e = exp(exp(_p - exp(-_p))/(1 - 1/_p)) - exp(exp(_p))
> e.subs(Omega[0],O2[0]).subs(Omega[1],O2[1])
-1/w + exp(exp(p)*exp(-exp(-p))/(1 - 1/p))
is really not what we want!
So we do it the hard way and keep track of all the things we potentially
want to substitute by dummy variables. Consider the expression::
exp(x - exp(-x)) + exp(x) + x.
The mrv set is {exp(x), exp(-x), exp(x - exp(-x))}.
We introduce corresponding dummy variables d1, d2, d3 and rewrite::
d3 + d1 + x.
This class first of all keeps track of the mapping expr->variable, i.e.
will at this stage be a dictionary::
{exp(x): d1, exp(-x): d2, exp(x - exp(-x)): d3}.
[It turns out to be more convenient this way round.]
But sometimes expressions in the mrv set have other expressions from the
mrv set as subexpressions, and we need to keep track of that as well. In
this case, d3 is really exp(x - d2), so rewrites at this stage is::
{d3: exp(x-d2)}.
The function rewrite uses all this information to correctly rewrite our
expression in terms of w. In this case w can be choosen to be exp(-x),
i.e. d2. The correct rewriting then is::
exp(-w)/w + 1/w + x.
"""
def __init__(self):
self.rewrites = {}
def __repr__(self):
return super(SubsSet, self).__repr__() + ', ' + self.rewrites.__repr__()
def __getitem__(self, key):
if not key in self:
self[key] = Dummy()
return dict.__getitem__(self, key)
def do_subs(self, e):
"""Substitute the variables with expressions"""
for expr, var in self.items():
e = e.subs(var, expr)
return e
def meets(self, s2):
"""Tell whether or not self and s2 have non-empty intersection"""
return set(self.keys()).intersection(list(s2.keys())) != set()
def union(self, s2, exps=None):
"""Compute the union of self and s2, adjusting exps"""
res = self.copy()
tr = {}
for expr, var in s2.items():
if expr in self:
if exps:
exps = exps.subs(var, res[expr])
tr[var] = res[expr]
else:
res[expr] = var
for var, rewr in s2.rewrites.items():
res.rewrites[var] = rewr.subs(tr)
return res, exps
def copy(self):
"""Create a shallow copy of SubsSet"""
r = SubsSet()
r.rewrites = self.rewrites.copy()
for expr, var in self.items():
r[expr] = var
return r
@debug
def mrv(e, x):
"""Returns a SubsSet of most rapidly varying (mrv) subexpressions of 'e',
and e rewritten in terms of these"""
e = powsimp(e, deep=True, combine='exp')
if not isinstance(e, Basic):
raise TypeError("e should be an instance of Basic")
if not e.has(x):
return SubsSet(), e
elif e == x:
s = SubsSet()
return s, s[x]
elif e.is_Mul or e.is_Add:
i, d = e.as_independent(x) # throw away x-independent terms
if d.func != e.func:
s, expr = mrv(d, x)
return s, e.func(i, expr)
a, b = d.as_two_terms()
s1, e1 = mrv(a, x)
s2, e2 = mrv(b, x)
return mrv_max1(s1, s2, e.func(i, e1, e2), x)
elif e.is_Pow:
b, e = e.as_base_exp()
if b == 1:
return SubsSet(), b
if e.has(x):
return mrv(exp(e * log(b)), x)
else:
s, expr = mrv(b, x)
return s, expr**e
elif e.func is log:
s, expr = mrv(e.args[0], x)
return s, log(expr)
elif e.func is exp:
# We know from the theory of this algorithm that exp(log(...)) may always
# be simplified here, and doing so is vital for termination.
if e.args[0].func is log:
return mrv(e.args[0].args[0], x)
# if a product has an infinite factor the result will be
# infinite if there is no zero, otherwise NaN; here, we
# consider the result infinite if any factor is infinite
li = limitinf(e.args[0], x)
if any(_.is_infinite for _ in Mul.make_args(li)):
s1 = SubsSet()
e1 = s1[e]
s2, e2 = mrv(e.args[0], x)
su = s1.union(s2)[0]
su.rewrites[e1] = exp(e2)
return mrv_max3(s1, e1, s2, exp(e2), su, e1, x)
else:
s, expr = mrv(e.args[0], x)
return s, exp(expr)
elif e.is_Function:
l = [mrv(a, x) for a in e.args]
l2 = [s for (s, _) in l if s != SubsSet()]
if len(l2) != 1:
# e.g. something like BesselJ(x, x)
raise NotImplementedError("MRV set computation for functions in"
" several variables not implemented.")
s, ss = l2[0], SubsSet()
args = [ss.do_subs(x[1]) for x in l]
return s, e.func(*args)
elif e.is_Derivative:
raise NotImplementedError("MRV set computation for derviatives"
" not implemented yet.")
return mrv(e.args[0], x)
raise NotImplementedError(
"Don't know how to calculate the mrv of '%s'" % e)
def mrv_max3(f, expsf, g, expsg, union, expsboth, x):
"""Computes the maximum of two sets of expressions f and g, which
are in the same comparability class, i.e. max() compares (two elements of)
f and g and returns either (f, expsf) [if f is larger], (g, expsg)
[if g is larger] or (union, expsboth) [if f, g are of the same class].
"""
if not isinstance(f, SubsSet):
raise TypeError("f should be an instance of SubsSet")
if not isinstance(g, SubsSet):
raise TypeError("g should be an instance of SubsSet")
if f == SubsSet():
return g, expsg
elif g == SubsSet():
return f, expsf
elif f.meets(g):
return union, expsboth
c = compare(list(f.keys())[0], list(g.keys())[0], x)
if c == ">":
return f, expsf
elif c == "<":
return g, expsg
else:
if c != "=":
raise ValueError("c should be =")
return union, expsboth
def mrv_max1(f, g, exps, x):
"""Computes the maximum of two sets of expressions f and g, which
are in the same comparability class, i.e. mrv_max1() compares (two elements of)
f and g and returns the set, which is in the higher comparability class
of the union of both, if they have the same order of variation.
Also returns exps, with the appropriate substitutions made.
"""
u, b = f.union(g, exps)
return mrv_max3(f, g.do_subs(exps), g, f.do_subs(exps),
u, b, x)
@debug
@cacheit
@timeit
def sign(e, x):
"""
Returns a sign of an expression e(x) for x->oo.
::
e > 0 for x sufficiently large ... 1
e == 0 for x sufficiently large ... 0
e < 0 for x sufficiently large ... -1
The result of this function is currently undefined if e changes sign
arbitarily often for arbitrarily large x (e.g. sin(x)).
Note that this returns zero only if e is *constantly* zero
for x sufficiently large. [If e is constant, of course, this is just
the same thing as the sign of e.]
"""
from sympy import sign as _sign
if not isinstance(e, Basic):
raise TypeError("e should be an instance of Basic")
if e.is_positive:
return 1
elif e.is_negative:
return -1
elif e.is_zero:
return 0
elif not e.has(x):
return _sign(e)
elif e == x:
return 1
elif e.is_Mul:
a, b = e.as_two_terms()
sa = sign(a, x)
if not sa:
return 0
return sa * sign(b, x)
elif e.func is exp:
return 1
elif e.is_Pow:
s = sign(e.base, x)
if s == 1:
return 1
if e.exp.is_Integer:
return s**e.exp
elif e.func is log:
return sign(e.args[0] - 1, x)
# if all else fails, do it the hard way
c0, e0 = mrv_leadterm(e, x)
return sign(c0, x)
@debug
@timeit
@cacheit
def limitinf(e, x):
"""Limit e(x) for x-> oo"""
# rewrite e in terms of tractable functions only
e = e.rewrite('tractable', deep=True)
if not e.has(x):
return e # e is a constant
if e.has(Order):
e = e.expand().removeO()
if not x.is_positive:
# We make sure that x.is_positive is True so we
# get all the correct mathematical behavior from the expression.
# We need a fresh variable.
p = Dummy('p', positive=True, finite=True)
e = e.subs(x, p)
x = p
c0, e0 = mrv_leadterm(e, x)
sig = sign(e0, x)
if sig == 1:
return S.Zero # e0>0: lim f = 0
elif sig == -1: # e0<0: lim f = +-oo (the sign depends on the sign of c0)
if c0.match(I*Wild("a", exclude=[I])):
return c0*oo
s = sign(c0, x)
# the leading term shouldn't be 0:
if s == 0:
raise ValueError("Leading term should not be 0")
return s*oo
elif sig == 0:
return limitinf(c0, x) # e0=0: lim f = lim c0
def moveup2(s, x):
r = SubsSet()
for expr, var in s.items():
r[expr.subs(x, exp(x))] = var
for var, expr in s.rewrites.items():
r.rewrites[var] = s.rewrites[var].subs(x, exp(x))
return r
def moveup(l, x):
return [e.subs(x, exp(x)) for e in l]
@debug
@timeit
def calculate_series(e, x, logx=None):
""" Calculates at least one term of the series of "e" in "x".
This is a place that fails most often, so it is in its own function.
"""
from sympy.polys import cancel
for t in e.lseries(x, logx=logx):
t = cancel(t)
if t.has(exp) and t.has(log):
t = powdenest(t)
if t.simplify():
break
return t
@debug
@timeit
@cacheit
def mrv_leadterm(e, x):
"""Returns (c0, e0) for e."""
Omega = SubsSet()
if not e.has(x):
return (e, S.Zero)
if Omega == SubsSet():
Omega, exps = mrv(e, x)
if not Omega:
# e really does not depend on x after simplification
series = calculate_series(e, x)
c0, e0 = series.leadterm(x)
if e0 != 0:
raise ValueError("e0 should be 0")
return c0, e0
if x in Omega:
# move the whole omega up (exponentiate each term):
Omega_up = moveup2(Omega, x)
e_up = moveup([e], x)[0]
exps_up = moveup([exps], x)[0]
# NOTE: there is no need to move this down!
e = e_up
Omega = Omega_up
exps = exps_up
#
# The positive dummy, w, is used here so log(w*2) etc. will expand;
# a unique dummy is needed in this algorithm
#
# For limits of complex functions, the algorithm would have to be
# improved, or just find limits of Re and Im components separately.
#
w = Dummy("w", real=True, positive=True, finite=True)
f, logw = rewrite(exps, Omega, x, w)
series = calculate_series(f, w, logx=logw)
return series.leadterm(w)
def build_expression_tree(Omega, rewrites):
r""" Helper function for rewrite.
We need to sort Omega (mrv set) so that we replace an expression before
we replace any expression in terms of which it has to be rewritten::
e1 ---> e2 ---> e3
\
-> e4
Here we can do e1, e2, e3, e4 or e1, e2, e4, e3.
To do this we assemble the nodes into a tree, and sort them by height.
This function builds the tree, rewrites then sorts the nodes.
"""
class Node:
def ht(self):
return reduce(lambda x, y: x + y,
[x.ht() for x in self.before], 1)
nodes = {}
for expr, v in Omega:
n = Node()
n.before = []
n.var = v
n.expr = expr
nodes[v] = n
for _, v in Omega:
if v in rewrites:
n = nodes[v]
r = rewrites[v]
for _, v2 in Omega:
if r.has(v2):
n.before.append(nodes[v2])
return nodes
@debug
@timeit
def rewrite(e, Omega, x, wsym):
"""e(x) ... the function
Omega ... the mrv set
wsym ... the symbol which is going to be used for w
Returns the rewritten e in terms of w and log(w). See test_rewrite1()
for examples and correct results.
"""
from sympy import ilcm
if not isinstance(Omega, SubsSet):
raise TypeError("Omega should be an instance of SubsSet")
if len(Omega) == 0:
raise ValueError("Length can not be 0")
# all items in Omega must be exponentials
for t in Omega.keys():
if not t.func is exp:
raise ValueError("Value should be exp")
rewrites = Omega.rewrites
Omega = list(Omega.items())
nodes = build_expression_tree(Omega, rewrites)
Omega.sort(key=lambda x: nodes[x[1]].ht(), reverse=True)
# make sure we know the sign of each exp() term; after the loop,
# g is going to be the "w" - the simplest one in the mrv set
for g, _ in Omega:
sig = sign(g.args[0], x)
if sig != 1 and sig != -1:
raise NotImplementedError('Result depends on the sign of %s' % sig)
if sig == 1:
wsym = 1/wsym # if g goes to oo, substitute 1/w
# O2 is a list, which results by rewriting each item in Omega using "w"
O2 = []
denominators = []
for f, var in Omega:
c = limitinf(f.args[0]/g.args[0], x)
if c.is_Rational:
denominators.append(c.q)
arg = f.args[0]
if var in rewrites:
if not rewrites[var].func is exp:
raise ValueError("Value should be exp")
arg = rewrites[var].args[0]
O2.append((var, exp((arg - c*g.args[0]).expand())*wsym**c))
# Remember that Omega contains subexpressions of "e". So now we find
# them in "e" and substitute them for our rewriting, stored in O2
# the following powsimp is necessary to automatically combine exponentials,
# so that the .subs() below succeeds:
# TODO this should not be necessary
f = powsimp(e, deep=True, combine='exp')
for a, b in O2:
f = f.subs(a, b)
for _, var in Omega:
assert not f.has(var)
# finally compute the logarithm of w (logw).
logw = g.args[0]
if sig == 1:
logw = -logw # log(w)->log(1/w)=-log(w)
# Some parts of sympy have difficulty computing series expansions with
# non-integral exponents. The following heuristic improves the situation:
exponent = reduce(ilcm, denominators, 1)
f = f.subs(wsym, wsym**exponent)
logw /= exponent
return f, logw
def gruntz(e, z, z0, dir="+"):
"""
Compute the limit of e(z) at the point z0 using the Gruntz algorithm.
z0 can be any expression, including oo and -oo.
For dir="+" (default) it calculates the limit from the right
(z->z0+) and for dir="-" the limit from the left (z->z0-). For infinite z0
(oo or -oo), the dir argument doesn't matter.
This algorithm is fully described in the module docstring in the gruntz.py
file. It relies heavily on the series expansion. Most frequently, gruntz()
is only used if the faster limit() function (which uses heuristics) fails.
"""
if not z.is_Symbol:
raise NotImplementedError("Second argument must be a Symbol")
# convert all limits to the limit z->oo; sign of z is handled in limitinf
r = None
if z0 == oo:
r = limitinf(e, z)
elif z0 == -oo:
r = limitinf(e.subs(z, -z), z)
else:
if str(dir) == "-":
e0 = e.subs(z, z0 - 1/z)
elif str(dir) == "+":
e0 = e.subs(z, z0 + 1/z)
else:
raise NotImplementedError("dir must be '+' or '-'")
r = limitinf(e0, z)
# This is a bit of a heuristic for nice results... we always rewrite
# tractable functions in terms of familiar intractable ones.
# It might be nicer to rewrite the exactly to what they were initially,
# but that would take some work to implement.
return r.rewrite('intractable', deep=True)
| 21,639 | 31.541353 | 83 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/approximants.py
|
from __future__ import print_function, division
from sympy.utilities import public
from sympy.core.compatibility import range
from sympy import Integer
from sympy.core import Symbol
@public
def approximants(l, X=Symbol('x'), simplify=False):
"""
Return a generator for consecutive Pade approximants for a series.
It can also be used for computing the rational generating function of a
series when possible, since the last approximant returned by the generator
will be the generating function (if any).
The input list can contain more complex expressions than integer or rational
numbers; symbols may also be involved in the computation. An example below
show how to compute the generating function of the whole Pascal triangle.
The generator can be asked to apply the sympy.simplify function on each
generated term, which will make the computation slower; however it may be
useful when symbols are involved in the expressions.
Examples
========
>>> from sympy.series import approximants
>>> from sympy import lucas, fibonacci, symbols, binomial
>>> g = [lucas(k) for k in range(16)]
>>> [e for e in approximants(g)]
[2, -4/(x - 2), (5*x - 2)/(3*x - 1), (x - 2)/(x**2 + x - 1)]
>>> h = [fibonacci(k) for k in range(16)]
>>> [e for e in approximants(h)]
[x, -x/(x - 1), (x**2 - x)/(2*x - 1), -x/(x**2 + x - 1)]
>>> x, t = symbols("x,t")
>>> p=[sum(binomial(k,i)*x**i for i in range(k+1)) for k in range(16)]
>>> y = approximants(p, t)
>>> for k in range(3): print(next(y))
1
(x + 1)/((-x - 1)*(t*(x + 1) + (x + 1)/(-x - 1)))
nan
>>> y = approximants(p, t, simplify=True)
>>> for k in range(3): print(next(y))
1
-1/(t*(x + 1) - 1)
nan
See also
========
See function sympy.concrete.guess.guess_generating_function_rational and
function mpmath.pade
"""
p1, q1 = [Integer(1)], [Integer(0)]
p2, q2 = [Integer(0)], [Integer(1)]
while len(l):
b = 0
while l[b]==0:
b += 1
if b == len(l):
return
m = [Integer(1)/l[b]]
for k in range(b+1, len(l)):
s = 0
for j in range(b, k):
s -= l[j+1] * m[b-j-1]
m.append(s/l[b])
l = m
a, l[0] = l[0], 0
p = [0] * max(len(p2), b+len(p1))
q = [0] * max(len(q2), b+len(q1))
for k in range(len(p2)):
p[k] = a*p2[k]
for k in range(b, b+len(p1)):
p[k] += p1[k-b]
for k in range(len(q2)):
q[k] = a*q2[k]
for k in range(b, b+len(q1)):
q[k] += q1[k-b]
while p[-1]==0: p.pop()
while q[-1]==0: q.pop()
p1, p2 = p2, p
q1, q2 = q2, q
# yield result
from sympy import denom, lcm, simplify as simp
c = 1
for x in p:
c = lcm(c, denom(x))
for x in q:
c = lcm(c, denom(x))
out = ( sum(c*e*X**k for k, e in enumerate(p))
/ sum(c*e*X**k for k, e in enumerate(q)) )
if simplify: yield(simp(out))
else: yield out
return
| 3,174 | 30.435644 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/fourier.py
|
"""Fourier Series"""
from __future__ import print_function, division
from sympy import pi, oo
from sympy.core.expr import Expr
from sympy.core.add import Add
from sympy.core.sympify import sympify
from sympy.core.singleton import S
from sympy.core.symbol import Dummy, Symbol
from sympy.core.compatibility import is_sequence
from sympy.core.containers import Tuple
from sympy.functions.elementary.trigonometric import sin, cos, sinc
from sympy.sets.sets import Interval
from sympy.series.series_class import SeriesBase
from sympy.series.sequences import SeqFormula
def fourier_cos_seq(func, limits, n):
"""Returns the cos sequence in a Fourier series"""
from sympy.integrals import integrate
x, L = limits[0], limits[2] - limits[1]
cos_term = cos(2*n*pi*x / L)
formula = 2 * cos_term * integrate(func * cos_term, limits) / L
a0 = formula.subs(n, S.Zero) / 2
return a0, SeqFormula(2 * cos_term * integrate(func * cos_term, limits)
/ L, (n, 1, oo))
def fourier_sin_seq(func, limits, n):
"""Returns the sin sequence in a Fourier series"""
from sympy.integrals import integrate
x, L = limits[0], limits[2] - limits[1]
sin_term = sin(2*n*pi*x / L)
return SeqFormula(2 * sin_term * integrate(func * sin_term, limits)
/ L, (n, 1, oo))
def _process_limits(func, limits):
"""
Limits should be of the form (x, start, stop).
x should be a symbol. Both start and stop should be bounded.
* If x is not given, x is determined from func.
* If limits is None. Limit of the form (x, -pi, pi) is returned.
Examples
========
>>> from sympy import pi
>>> from sympy.series.fourier import _process_limits as pari
>>> from sympy.abc import x
>>> pari(x**2, (x, -2, 2))
(x, -2, 2)
>>> pari(x**2, (-2, 2))
(x, -2, 2)
>>> pari(x**2, None)
(x, -pi, pi)
"""
def _find_x(func):
free = func.free_symbols
if len(func.free_symbols) == 1:
return free.pop()
elif len(func.free_symbols) == 0:
return Dummy('k')
else:
raise ValueError(
" specify dummy variables for %s. If the function contains"
" more than one free symbol, a dummy variable should be"
" supplied explicitly e.g. FourierSeries(m*n**2, (n, -pi, pi))"
% func)
x, start, stop = None, None, None
if limits is None:
x, start, stop = _find_x(func), -pi, pi
if is_sequence(limits, Tuple):
if len(limits) == 3:
x, start, stop = limits
elif len(limits) == 2:
x = _find_x(func)
start, stop = limits
if not isinstance(x, Symbol) or start is None or stop is None:
raise ValueError('Invalid limits given: %s' % str(limits))
unbounded = [S.NegativeInfinity, S.Infinity]
if start in unbounded or stop in unbounded:
raise ValueError("Both the start and end value should be bounded")
return sympify((x, start, stop))
class FourierSeries(SeriesBase):
r"""Represents Fourier sine/cosine series.
This class only represents a fourier series.
No computation is performed.
For how to compute Fourier series, see the :func:`fourier_series`
docstring.
See Also
========
sympy.series.fourier.fourier_series
"""
def __new__(cls, *args):
args = map(sympify, args)
return Expr.__new__(cls, *args)
@property
def function(self):
return self.args[0]
@property
def x(self):
return self.args[1][0]
@property
def period(self):
return (self.args[1][1], self.args[1][2])
@property
def a0(self):
return self.args[2][0]
@property
def an(self):
return self.args[2][1]
@property
def bn(self):
return self.args[2][2]
@property
def interval(self):
return Interval(0, oo)
@property
def start(self):
return self.interval.inf
@property
def stop(self):
return self.interval.sup
@property
def length(self):
return oo
def _eval_subs(self, old, new):
x = self.x
if old.has(x):
return self
def truncate(self, n=3):
"""
Return the first n nonzero terms of the series.
If n is None return an iterator.
Parameters
==========
n : int or None
Amount of non-zero terms in approximation or None.
Returns
=======
Expr or iterator
Approximation of function expanded into Fourier series.
Examples
========
>>> from sympy import fourier_series, pi
>>> from sympy.abc import x
>>> s = fourier_series(x, (x, -pi, pi))
>>> s.truncate(4)
2*sin(x) - sin(2*x) + 2*sin(3*x)/3 - sin(4*x)/2
See Also
========
sympy.series.fourier.FourierSeries.sigma_approximation
"""
if n is None:
return iter(self)
terms = []
for t in self:
if len(terms) == n:
break
if t is not S.Zero:
terms.append(t)
return Add(*terms)
def sigma_approximation(self, n=3):
r"""
Return :math:`\sigma`-approximation of Fourier series with respect
to order n.
Sigma approximation adjusts a Fourier summation to eliminate the Gibbs
phenomenon which would otherwise occur at discontinuities.
A sigma-approximated summation for a Fourier series of a T-periodical
function can be written as
.. math::
s(\theta) = \frac{1}{2} a_0 + \sum _{k=1}^{m-1}
\operatorname{sinc} \Bigl( \frac{k}{m} \Bigr) \cdot
\left[ a_k \cos \Bigl( \frac{2\pi k}{T} \theta \Bigr)
+ b_k \sin \Bigl( \frac{2\pi k}{T} \theta \Bigr) \right],
where :math:`a_0, a_k, b_k, k=1,\ldots,{m-1}` are standard Fourier
series coefficients and
:math:`\operatorname{sinc} \Bigl( \frac{k}{m} \Bigr)` is a Lanczos
:math:`\sigma` factor (expressed in terms of normalized
:math:`\operatorname{sinc}` function).
Parameters
==========
n : int
Highest order of the terms taken into account in approximation.
Returns
=======
Expr
Sigma approximation of function expanded into Fourier series.
Examples
========
>>> from sympy import fourier_series, pi
>>> from sympy.abc import x
>>> s = fourier_series(x, (x, -pi, pi))
>>> s.sigma_approximation(4)
2*sin(x)*sinc(pi/4) - 2*sin(2*x)/pi + 2*sin(3*x)*sinc(3*pi/4)/3
See Also
========
sympy.series.fourier.FourierSeries.truncate
Notes
=====
The behaviour of
:meth:`~sympy.series.fourier.FourierSeries.sigma_approximation`
is different from :meth:`~sympy.series.fourier.FourierSeries.truncate`
- it takes all nonzero terms of degree smaller than n, rather than
first n nonzero ones.
References
==========
.. [1] https://en.wikipedia.org/wiki/Gibbs_phenomenon
.. [2] https://en.wikipedia.org/wiki/Sigma_approximation
"""
terms = [sinc(pi * i / n) * t for i, t in enumerate(self[:n])
if t is not S.Zero]
return Add(*terms)
def shift(self, s):
"""Shift the function by a term independent of x.
f(x) -> f(x) + s
This is fast, if Fourier series of f(x) is already
computed.
Examples
========
>>> from sympy import fourier_series, pi
>>> from sympy.abc import x
>>> s = fourier_series(x**2, (x, -pi, pi))
>>> s.shift(1).truncate()
-4*cos(x) + cos(2*x) + 1 + pi**2/3
"""
s, x = sympify(s), self.x
if x in s.free_symbols:
raise ValueError("'%s' should be independent of %s" % (s, x))
a0 = self.a0 + s
sfunc = self.function + s
return self.func(sfunc, self.args[1], (a0, self.an, self.bn))
def shiftx(self, s):
"""Shift x by a term independent of x.
f(x) -> f(x + s)
This is fast, if Fourier series of f(x) is already
computed.
Examples
========
>>> from sympy import fourier_series, pi
>>> from sympy.abc import x
>>> s = fourier_series(x**2, (x, -pi, pi))
>>> s.shiftx(1).truncate()
-4*cos(x + 1) + cos(2*x + 2) + pi**2/3
"""
s, x = sympify(s), self.x
if x in s.free_symbols:
raise ValueError("'%s' should be independent of %s" % (s, x))
an = self.an.subs(x, x + s)
bn = self.bn.subs(x, x + s)
sfunc = self.function.subs(x, x + s)
return self.func(sfunc, self.args[1], (self.a0, an, bn))
def scale(self, s):
"""Scale the function by a term independent of x.
f(x) -> s * f(x)
This is fast, if Fourier series of f(x) is already
computed.
Examples
========
>>> from sympy import fourier_series, pi
>>> from sympy.abc import x
>>> s = fourier_series(x**2, (x, -pi, pi))
>>> s.scale(2).truncate()
-8*cos(x) + 2*cos(2*x) + 2*pi**2/3
"""
s, x = sympify(s), self.x
if x in s.free_symbols:
raise ValueError("'%s' should be independent of %s" % (s, x))
an = self.an.coeff_mul(s)
bn = self.bn.coeff_mul(s)
a0 = self.a0 * s
sfunc = self.args[0] * s
return self.func(sfunc, self.args[1], (a0, an, bn))
def scalex(self, s):
"""Scale x by a term independent of x.
f(x) -> f(s*x)
This is fast, if Fourier series of f(x) is already
computed.
Examples
========
>>> from sympy import fourier_series, pi
>>> from sympy.abc import x
>>> s = fourier_series(x**2, (x, -pi, pi))
>>> s.scalex(2).truncate()
-4*cos(2*x) + cos(4*x) + pi**2/3
"""
s, x = sympify(s), self.x
if x in s.free_symbols:
raise ValueError("'%s' should be independent of %s" % (s, x))
an = self.an.subs(x, x * s)
bn = self.bn.subs(x, x * s)
sfunc = self.function.subs(x, x * s)
return self.func(sfunc, self.args[1], (self.a0, an, bn))
def _eval_as_leading_term(self, x):
for t in self:
if t is not S.Zero:
return t
def _eval_term(self, pt):
if pt == 0:
return self.a0
return self.an.coeff(pt) + self.bn.coeff(pt)
def __neg__(self):
return self.scale(-1)
def __add__(self, other):
if isinstance(other, FourierSeries):
if self.period != other.period:
raise ValueError("Both the series should have same periods")
x, y = self.x, other.x
function = self.function + other.function.subs(y, x)
if self.x not in function.free_symbols:
return function
an = self.an + other.an
bn = self.bn + other.bn
a0 = self.a0 + other.a0
return self.func(function, self.args[1], (a0, an, bn))
return Add(self, other)
def __sub__(self, other):
return self.__add__(-other)
def fourier_series(f, limits=None):
"""Computes Fourier sine/cosine series expansion.
Returns a :class:`FourierSeries` object.
Examples
========
>>> from sympy import fourier_series, pi, cos
>>> from sympy.abc import x
>>> s = fourier_series(x**2, (x, -pi, pi))
>>> s.truncate(n=3)
-4*cos(x) + cos(2*x) + pi**2/3
Shifting
>>> s.shift(1).truncate()
-4*cos(x) + cos(2*x) + 1 + pi**2/3
>>> s.shiftx(1).truncate()
-4*cos(x + 1) + cos(2*x + 2) + pi**2/3
Scaling
>>> s.scale(2).truncate()
-8*cos(x) + 2*cos(2*x) + 2*pi**2/3
>>> s.scalex(2).truncate()
-4*cos(2*x) + cos(4*x) + pi**2/3
Notes
=====
Computing Fourier series can be slow
due to the integration required in computing
an, bn.
It is faster to compute Fourier series of a function
by using shifting and scaling on an already
computed Fourier series rather than computing
again.
e.g. If the Fourier series of ``x**2`` is known
the Fourier series of ``x**2 - 1`` can be found by shifting by ``-1``.
See Also
========
sympy.series.fourier.FourierSeries
References
==========
.. [1] mathworld.wolfram.com/FourierSeries.html
"""
f = sympify(f)
limits = _process_limits(f, limits)
x = limits[0]
if x not in f.free_symbols:
return f
n = Dummy('n')
neg_f = f.subs(x, -x)
if f == neg_f:
a0, an = fourier_cos_seq(f, limits, n)
bn = SeqFormula(0, (1, oo))
elif f == -neg_f:
a0 = S.Zero
an = SeqFormula(0, (1, oo))
bn = fourier_sin_seq(f, limits, n)
else:
a0, an = fourier_cos_seq(f, limits, n)
bn = fourier_sin_seq(f, limits, n)
return FourierSeries(f, limits, (a0, an, bn))
| 13,254 | 26.217659 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/kauers.py
|
from __future__ import print_function, division
def finite_diff(expression, variable, increment=1):
"""
Takes as input a polynomial expression and the variable used to construct
it and returns the difference between function's value when the input is
incremented to 1 and the original function value. If you want an increment
other than one supply it as a third argument.
Examples
========
>>> from sympy.abc import x, y, z, k, n
>>> from sympy.series.kauers import finite_diff
>>> from sympy import Sum
>>> finite_diff(x**2, x)
2*x + 1
>>> finite_diff(y**3 + 2*y**2 + 3*y + 4, y)
3*y**2 + 7*y + 6
>>> finite_diff(x**2 + 3*x + 8, x, 2)
4*x + 10
>>> finite_diff(z**3 + 8*z, z, 3)
9*z**2 + 27*z + 51
"""
expression = expression.expand()
expression2 = expression.subs(variable, variable + increment)
expression2 = expression2.expand()
return expression2 - expression
def finite_diff_kauers(sum):
"""
Takes as input a Sum instance and returns the difference between the sum
with the upper index incremented by 1 and the original sum. For example,
if S(n) is a sum, then finite_diff_kauers will return S(n + 1) - S(n).
Examples
========
>>> from sympy.series.kauers import finite_diff_kauers
>>> from sympy import Sum
>>> from sympy.abc import x, y, m, n, k
>>> finite_diff_kauers(Sum(k, (k, 1, n)))
n + 1
>>> finite_diff_kauers(Sum(1/k, (k, 1, n)))
1/(n + 1)
>>> finite_diff_kauers(Sum((x*y**2), (x, 1, n), (y, 1, m)))
(m + 1)**2*(n + 1)
>>> finite_diff_kauers(Sum((x*y), (x, 1, m), (y, 1, n)))
(m + 1)*(n + 1)
"""
function = sum.function
for l in sum.limits:
function = function.subs(l[0], l[- 1] + 1)
return function
| 1,807 | 31.285714 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/acceleration.py
|
"""
Convergence acceleration / extrapolation methods for series and
sequences.
References:
Carl M. Bender & Steven A. Orszag, "Advanced Mathematical Methods for
Scientists and Engineers: Asymptotic Methods and Perturbation Theory",
Springer 1999. (Shanks transformation: pp. 368-375, Richardson
extrapolation: pp. 375-377.)
"""
from __future__ import print_function, division
from sympy import factorial, Integer, S
from sympy.core.compatibility import range
def richardson(A, k, n, N):
"""
Calculate an approximation for lim k->oo A(k) using Richardson
extrapolation with the terms A(n), A(n+1), ..., A(n+N+1).
Choosing N ~= 2*n often gives good results.
A simple example is to calculate exp(1) using the limit definition.
This limit converges slowly; n = 100 only produces two accurate
digits:
>>> from sympy.abc import n
>>> e = (1 + 1/n)**n
>>> print(round(e.subs(n, 100).evalf(), 10))
2.7048138294
Richardson extrapolation with 11 appropriately chosen terms gives
a value that is accurate to the indicated precision:
>>> from sympy import E
>>> from sympy.series.acceleration import richardson
>>> print(round(richardson(e, n, 10, 20).evalf(), 10))
2.7182818285
>>> print(round(E.evalf(), 10))
2.7182818285
Another useful application is to speed up convergence of series.
Computing 100 terms of the zeta(2) series 1/k**2 yields only
two accurate digits:
>>> from sympy.abc import k, n
>>> from sympy import Sum
>>> A = Sum(k**-2, (k, 1, n))
>>> print(round(A.subs(n, 100).evalf(), 10))
1.6349839002
Richardson extrapolation performs much better:
>>> from sympy import pi
>>> print(round(richardson(A, n, 10, 20).evalf(), 10))
1.6449340668
>>> print(round(((pi**2)/6).evalf(), 10)) # Exact value
1.6449340668
"""
s = S.Zero
for j in range(0, N + 1):
s += A.subs(k, Integer(n + j)).doit() * (n + j)**N * (-1)**(j + N) / \
(factorial(j) * factorial(N - j))
return s
def shanks(A, k, n, m=1):
"""
Calculate an approximation for lim k->oo A(k) using the n-term Shanks
transformation S(A)(n). With m > 1, calculate the m-fold recursive
Shanks transformation S(S(...S(A)...))(n).
The Shanks transformation is useful for summing Taylor series that
converge slowly near a pole or singularity, e.g. for log(2):
>>> from sympy.abc import k, n
>>> from sympy import Sum, Integer
>>> from sympy.series.acceleration import shanks
>>> A = Sum(Integer(-1)**(k+1) / k, (k, 1, n))
>>> print(round(A.subs(n, 100).doit().evalf(), 10))
0.6881721793
>>> print(round(shanks(A, n, 25).evalf(), 10))
0.6931396564
>>> print(round(shanks(A, n, 25, 5).evalf(), 10))
0.6931471806
The correct value is 0.6931471805599453094172321215.
"""
table = [A.subs(k, Integer(j)).doit() for j in range(n + m + 2)]
table2 = table[:]
for i in range(1, m + 1):
for j in range(i, n + m + 1):
x, y, z = table[j - 1], table[j], table[j + 1]
table2[j] = (z*x - y**2) / (z + x - 2*y)
table = table2[:]
return table[n]
| 3,314 | 32.15 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/formal.py
|
"""Formal Power Series"""
from __future__ import print_function, division
from collections import defaultdict
from sympy import oo, zoo, nan
from sympy.core.expr import Expr
from sympy.core.add import Add
from sympy.core.mul import Mul
from sympy.core.function import Derivative, Function
from sympy.core.singleton import S
from sympy.core.sympify import sympify
from sympy.core.symbol import Wild, Dummy, symbols, Symbol
from sympy.core.relational import Eq
from sympy.core.numbers import Rational
from sympy.core.compatibility import iterable
from sympy.sets.sets import Interval
from sympy.functions.combinatorial.factorials import binomial, factorial, rf
from sympy.functions.elementary.piecewise import Piecewise
from sympy.functions.elementary.integers import floor, frac, ceiling
from sympy.functions.elementary.miscellaneous import Min, Max
from sympy.series.sequences import sequence
from sympy.series.series_class import SeriesBase
from sympy.series.order import Order
from sympy.series.limits import Limit
def rational_algorithm(f, x, k, order=4, full=False):
"""Rational algorithm for computing
formula of coefficients of Formal Power Series
of a function.
Applicable when f(x) or some derivative of f(x)
is a rational function in x.
:func:`rational_algorithm` uses :func:`apart` function for partial fraction
decomposition. :func:`apart` by default uses 'undetermined coefficients
method'. By setting ``full=True``, 'Bronstein's algorithm' can be used
instead.
Looks for derivative of a function up to 4'th order (by default).
This can be overriden using order option.
Returns
=======
formula : Expr
ind : Expr
Independent terms.
order : int
Examples
========
>>> from sympy import log, atan, I
>>> from sympy.series.formal import rational_algorithm as ra
>>> from sympy.abc import x, k
>>> ra(1 / (1 - x), x, k)
(1, 0, 0)
>>> ra(log(1 + x), x, k)
(-(-1)**(-k)/k, 0, 1)
>>> ra(atan(x), x, k, full=True)
((-I*(-I)**(-k)/2 + I*I**(-k)/2)/k, 0, 1)
Notes
=====
By setting ``full=True``, range of admissible functions to be solved using
``rational_algorithm`` can be increased. This option should be used
carefully as it can signifcantly slow down the computation as ``doit`` is
performed on the :class:`RootSum` object returned by the ``apart`` function.
Use ``full=False`` whenever possible.
See Also
========
sympy.polys.partfrac.apart
References
==========
.. [1] Formal Power Series - Dominik Gruntz, Wolfram Koepf
.. [2] Power Series in Computer Algebra - Wolfram Koepf
"""
from sympy.polys import RootSum, apart
from sympy.integrals import integrate
diff = f
ds = [] # list of diff
for i in range(order + 1):
if i:
diff = diff.diff(x)
if diff.is_rational_function(x):
coeff, sep = S.Zero, S.Zero
terms = apart(diff, x, full=full)
if terms.has(RootSum):
terms = terms.doit()
for t in Add.make_args(terms):
num, den = t.as_numer_denom()
if not den.has(x):
sep += t
else:
if isinstance(den, Mul):
# m*(n*x - a)**j -> (n*x - a)**j
ind = den.as_independent(x)
den = ind[1]
num /= ind[0]
# (n*x - a)**j -> (x - b)
den, j = den.as_base_exp()
a, xterm = den.as_coeff_add(x)
# term -> m/x**n
if not a:
sep += t
continue
xc = xterm[0].coeff(x)
a /= -xc
num /= xc**j
ak = ((-1)**j * num *
binomial(j + k - 1, k).rewrite(factorial) /
a**(j + k))
coeff += ak
# Hacky, better way?
if coeff is S.Zero:
return None
if (coeff.has(x) or coeff.has(zoo) or coeff.has(oo) or
coeff.has(nan)):
return None
for j in range(i):
coeff = (coeff / (k + j + 1))
sep = integrate(sep, x)
sep += (ds.pop() - sep).limit(x, 0) # constant of integration
return (coeff.subs(k, k - i), sep, i)
else:
ds.append(diff)
return None
def rational_independent(terms, x):
"""Returns a list of all the rationally independent terms.
Examples
========
>>> from sympy import sin, cos
>>> from sympy.series.formal import rational_independent
>>> from sympy.abc import x
>>> rational_independent([cos(x), sin(x)], x)
[cos(x), sin(x)]
>>> rational_independent([x**2, sin(x), x*sin(x), x**3], x)
[x**3 + x**2, x*sin(x) + sin(x)]
"""
if not terms:
return []
ind = terms[0:1]
for t in terms[1:]:
n = t.as_independent(x)[1]
for i, term in enumerate(ind):
d = term.as_independent(x)[1]
q = (n / d).cancel()
if q.is_rational_function(x):
ind[i] += t
break
else:
ind.append(t)
return ind
def simpleDE(f, x, g, order=4):
r"""Generates simple DE.
DE is of the form
.. math::
f^k(x) + \sum\limits_{j=0}^{k-1} A_j f^j(x) = 0
where :math:`A_j` should be rational function in x.
Generates DE's upto order 4 (default). DE's can also have free parameters.
By increasing order, higher order DE's can be found.
Yields a tuple of (DE, order).
"""
from sympy.solvers.solveset import linsolve
a = symbols('a:%d' % (order))
def _makeDE(k):
eq = f.diff(x, k) + Add(*[a[i]*f.diff(x, i) for i in range(0, k)])
DE = g(x).diff(x, k) + Add(*[a[i]*g(x).diff(x, i) for i in range(0, k)])
return eq, DE
eq, DE = _makeDE(order)
found = False
for k in range(1, order + 1):
eq, DE = _makeDE(k)
eq = eq.expand()
terms = eq.as_ordered_terms()
ind = rational_independent(terms, x)
if found or len(ind) == k:
sol = dict(zip(a, (i for s in linsolve(ind, a[:k]) for i in s)))
if sol:
found = True
DE = DE.subs(sol)
DE = DE.as_numer_denom()[0]
DE = DE.factor().as_coeff_mul(Derivative)[1][0]
yield DE.collect(Derivative(g(x))), k
def exp_re(DE, r, k):
"""Converts a DE with constant coefficients (explike) into a RE.
Performs the substitution:
.. math::
f^j(x) \\to r(k + j)
Normalises the terms so that lowest order of a term is always r(k).
Examples
========
>>> from sympy import Function, Derivative
>>> from sympy.series.formal import exp_re
>>> from sympy.abc import x, k
>>> f, r = Function('f'), Function('r')
>>> exp_re(-f(x) + Derivative(f(x)), r, k)
-r(k) + r(k + 1)
>>> exp_re(Derivative(f(x), x) + Derivative(f(x), x, x), r, k)
r(k) + r(k + 1)
See Also
========
sympy.series.formal.hyper_re
"""
RE = S.Zero
g = DE.atoms(Function).pop()
mini = None
for t in Add.make_args(DE):
coeff, d = t.as_independent(g)
if isinstance(d, Derivative):
j = len(d.args) - 1
else:
j = 0
if mini is None or j < mini:
mini = j
RE += coeff * r(k + j)
if mini:
RE = RE.subs(k, k - mini)
return RE
def hyper_re(DE, r, k):
"""Converts a DE into a RE.
Performs the substitution:
.. math::
x^l f^j(x) \\to (k + 1 - l)_j . a_{k + j - l}
Normalises the terms so that lowest order of a term is always r(k).
Examples
========
>>> from sympy import Function, Derivative
>>> from sympy.series.formal import hyper_re
>>> from sympy.abc import x, k
>>> f, r = Function('f'), Function('r')
>>> hyper_re(-f(x) + Derivative(f(x)), r, k)
(k + 1)*r(k + 1) - r(k)
>>> hyper_re(-x*f(x) + Derivative(f(x), x, x), r, k)
(k + 2)*(k + 3)*r(k + 3) - r(k)
See Also
========
sympy.series.formal.exp_re
"""
RE = S.Zero
g = DE.atoms(Function).pop()
x = g.atoms(Symbol).pop()
mini = None
for t in Add.make_args(DE.expand()):
coeff, d = t.as_independent(g)
c, v = coeff.as_independent(x)
l = v.as_coeff_exponent(x)[1]
if isinstance(d, Derivative):
j = len(d.args[1:])
else:
j = 0
RE += c * rf(k + 1 - l, j) * r(k + j - l)
if mini is None or j - l < mini:
mini = j - l
RE = RE.subs(k, k - mini)
m = Wild('m')
return RE.collect(r(k + m))
def _transformation_a(f, x, P, Q, k, m, shift):
f *= x**(-shift)
P = P.subs(k, k + shift)
Q = Q.subs(k, k + shift)
return f, P, Q, m
def _transformation_c(f, x, P, Q, k, m, scale):
f = f.subs(x, x**scale)
P = P.subs(k, k / scale)
Q = Q.subs(k, k / scale)
m *= scale
return f, P, Q, m
def _transformation_e(f, x, P, Q, k, m):
f = f.diff(x)
P = P.subs(k, k + 1) * (k + m + 1)
Q = Q.subs(k, k + 1) * (k + 1)
return f, P, Q, m
def _apply_shift(sol, shift):
return [(res, cond + shift) for res, cond in sol]
def _apply_scale(sol, scale):
return [(res, cond / scale) for res, cond in sol]
def _apply_integrate(sol, x, k):
return [(res / ((cond + 1)*(cond.as_coeff_Add()[1].coeff(k))), cond + 1)
for res, cond in sol]
def _compute_formula(f, x, P, Q, k, m, k_max):
"""Computes the formula for f."""
from sympy.polys import roots
sol = []
for i in range(k_max + 1, k_max + m + 1):
r = f.diff(x, i).limit(x, 0) / factorial(i)
if r is S.Zero:
continue
kterm = m*k + i
res = r
p = P.subs(k, kterm)
q = Q.subs(k, kterm)
c1 = p.subs(k, 1/k).leadterm(k)[0]
c2 = q.subs(k, 1/k).leadterm(k)[0]
res *= (-c1 / c2)**k
for r, mul in roots(p, k).items():
res *= rf(-r, k)**mul
for r, mul in roots(q, k).items():
res /= rf(-r, k)**mul
sol.append((res, kterm))
return sol
def _rsolve_hypergeometric(f, x, P, Q, k, m):
"""Recursive wrapper to rsolve_hypergeometric.
Returns a Tuple of (formula, series independent terms,
maximum power of x in independent terms) if successful
otherwise ``None``.
See :func:`rsolve_hypergeometric` for details.
"""
from sympy.polys import lcm, roots
from sympy.integrals import integrate
# tranformation - c
proots, qroots = roots(P, k), roots(Q, k)
all_roots = dict(proots)
all_roots.update(qroots)
scale = lcm([r.as_numer_denom()[1] for r, t in all_roots.items()
if r.is_rational])
f, P, Q, m = _transformation_c(f, x, P, Q, k, m, scale)
# transformation - a
qroots = roots(Q, k)
if qroots:
k_min = Min(*qroots.keys())
else:
k_min = S.Zero
shift = k_min + m
f, P, Q, m = _transformation_a(f, x, P, Q, k, m, shift)
l = (x*f).limit(x, 0)
if not isinstance(l, Limit) and l != 0: # Ideally should only be l != 0
return None
qroots = roots(Q, k)
if qroots:
k_max = Max(*qroots.keys())
else:
k_max = S.Zero
ind, mp = S.Zero, -oo
for i in range(k_max + m + 1):
r = f.diff(x, i).limit(x, 0) / factorial(i)
if r.is_finite is False:
old_f = f
f, P, Q, m = _transformation_a(f, x, P, Q, k, m, i)
f, P, Q, m = _transformation_e(f, x, P, Q, k, m)
sol, ind, mp = _rsolve_hypergeometric(f, x, P, Q, k, m)
sol = _apply_integrate(sol, x, k)
sol = _apply_shift(sol, i)
ind = integrate(ind, x)
ind += (old_f - ind).limit(x, 0) # constant of integration
mp += 1
return sol, ind, mp
elif r:
ind += r*x**(i + shift)
pow_x = Rational((i + shift), scale)
if pow_x > mp:
mp = pow_x # maximum power of x
ind = ind.subs(x, x**(1/scale))
sol = _compute_formula(f, x, P, Q, k, m, k_max)
sol = _apply_shift(sol, shift)
sol = _apply_scale(sol, scale)
return sol, ind, mp
def rsolve_hypergeometric(f, x, P, Q, k, m):
"""Solves RE of hypergeometric type.
Attempts to solve RE of the form
Q(k)*a(k + m) - P(k)*a(k)
Transformations that preserve Hypergeometric type:
a. x**n*f(x): b(k + m) = R(k - n)*b(k)
b. f(A*x): b(k + m) = A**m*R(k)*b(k)
c. f(x**n): b(k + n*m) = R(k/n)*b(k)
d. f(x**(1/m)): b(k + 1) = R(k*m)*b(k)
e. f'(x): b(k + m) = ((k + m + 1)/(k + 1))*R(k + 1)*b(k)
Some of these transformations have been used to solve the RE.
Returns
=======
formula : Expr
ind : Expr
Independent terms.
order : int
Examples
========
>>> from sympy import exp, ln, S
>>> from sympy.series.formal import rsolve_hypergeometric as rh
>>> from sympy.abc import x, k
>>> rh(exp(x), x, -S.One, (k + 1), k, 1)
(Piecewise((1/factorial(k), Eq(Mod(k, 1), 0)), (0, True)), 1, 1)
>>> rh(ln(1 + x), x, k**2, k*(k + 1), k, 1)
(Piecewise(((-1)**(k - 1)*factorial(k - 1)/RisingFactorial(2, k - 1),
Eq(Mod(k, 1), 0)), (0, True)), x, 2)
References
==========
.. [1] Formal Power Series - Dominik Gruntz, Wolfram Koepf
.. [2] Power Series in Computer Algebra - Wolfram Koepf
"""
result = _rsolve_hypergeometric(f, x, P, Q, k, m)
if result is None:
return None
sol_list, ind, mp = result
sol_dict = defaultdict(lambda: S.Zero)
for res, cond in sol_list:
j, mk = cond.as_coeff_Add()
c = mk.coeff(k)
if j.is_integer is False:
res *= x**frac(j)
j = floor(j)
res = res.subs(k, (k - j) / c)
cond = Eq(k % c, j % c)
sol_dict[cond] += res # Group together formula for same conditions
sol = []
for cond, res in sol_dict.items():
sol.append((res, cond))
sol.append((S.Zero, True))
sol = Piecewise(*sol)
if mp is -oo:
s = S.Zero
elif mp.is_integer is False:
s = ceiling(mp)
else:
s = mp + 1
# save all the terms of
# form 1/x**k in ind
if s < 0:
ind += sum(sequence(sol * x**k, (k, s, -1)))
s = S.Zero
return (sol, ind, s)
def _solve_hyper_RE(f, x, RE, g, k):
"""See docstring of :func:`rsolve_hypergeometric` for details."""
terms = Add.make_args(RE)
if len(terms) == 2:
gs = list(RE.atoms(Function))
P, Q = map(RE.coeff, gs)
m = gs[1].args[0] - gs[0].args[0]
if m < 0:
P, Q = Q, P
m = abs(m)
return rsolve_hypergeometric(f, x, P, Q, k, m)
def _solve_explike_DE(f, x, DE, g, k):
"""Solves DE with constant coefficients."""
from sympy.solvers import rsolve
for t in Add.make_args(DE):
coeff, d = t.as_independent(g)
if coeff.free_symbols:
return
RE = exp_re(DE, g, k)
init = {}
for i in range(len(Add.make_args(RE))):
if i:
f = f.diff(x)
init[g(k).subs(k, i)] = f.limit(x, 0)
sol = rsolve(RE, g(k), init)
if sol:
return (sol / factorial(k), S.Zero, S.Zero)
def _solve_simple(f, x, DE, g, k):
"""Converts DE into RE and solves using :func:`rsolve`."""
from sympy.solvers import rsolve
RE = hyper_re(DE, g, k)
init = {}
for i in range(len(Add.make_args(RE))):
if i:
f = f.diff(x)
init[g(k).subs(k, i)] = f.limit(x, 0) / factorial(i)
sol = rsolve(RE, g(k), init)
if sol:
return (sol, S.Zero, S.Zero)
def _transform_explike_DE(DE, g, x, order, syms):
"""Converts DE with free parameters into DE with constant coefficients."""
from sympy.solvers.solveset import linsolve
eq = []
highest_coeff = DE.coeff(Derivative(g(x), x, order))
for i in range(order):
coeff = DE.coeff(Derivative(g(x), x, i))
coeff = (coeff / highest_coeff).expand().collect(x)
for t in Add.make_args(coeff):
eq.append(t)
temp = []
for e in eq:
if e.has(x):
break
elif e.has(Symbol):
temp.append(e)
else:
eq = temp
if eq:
sol = dict(zip(syms, (i for s in linsolve(eq, list(syms)) for i in s)))
if sol:
DE = DE.subs(sol)
DE = DE.factor().as_coeff_mul(Derivative)[1][0]
DE = DE.collect(Derivative(g(x)))
return DE
def _transform_DE_RE(DE, g, k, order, syms):
"""Converts DE with free parameters into RE of hypergeometric type."""
from sympy.solvers.solveset import linsolve
RE = hyper_re(DE, g, k)
eq = []
for i in range(1, order):
coeff = RE.coeff(g(k + i))
eq.append(coeff)
sol = dict(zip(syms, (i for s in linsolve(eq, list(syms)) for i in s)))
if sol:
m = Wild('m')
RE = RE.subs(sol)
RE = RE.factor().as_numer_denom()[0].collect(g(k + m))
RE = RE.as_coeff_mul(g)[1][0]
for i in range(order): # smallest order should be g(k)
if RE.coeff(g(k + i)) and i:
RE = RE.subs(k, k - i)
break
return RE
def solve_de(f, x, DE, order, g, k):
"""Solves the DE.
Tries to solve DE by either converting into a RE containing two terms or
converting into a DE having constant coefficients.
Returns
=======
formula : Expr
ind : Expr
Independent terms.
order : int
Examples
========
>>> from sympy import Derivative as D
>>> from sympy import exp, ln
>>> from sympy.series.formal import solve_de
>>> from sympy.abc import x, k, f
>>> solve_de(exp(x), x, D(f(x), x) - f(x), 1, f, k)
(Piecewise((1/factorial(k), Eq(Mod(k, 1), 0)), (0, True)), 1, 1)
>>> solve_de(ln(1 + x), x, (x + 1)*D(f(x), x, 2) + D(f(x)), 2, f, k)
(Piecewise(((-1)**(k - 1)*factorial(k - 1)/RisingFactorial(2, k - 1),
Eq(Mod(k, 1), 0)), (0, True)), x, 2)
"""
sol = None
syms = DE.free_symbols.difference({g, x})
if syms:
RE = _transform_DE_RE(DE, g, k, order, syms)
else:
RE = hyper_re(DE, g, k)
if not RE.free_symbols.difference({k}):
sol = _solve_hyper_RE(f, x, RE, g, k)
if sol:
return sol
if syms:
DE = _transform_explike_DE(DE, g, x, order, syms)
if not DE.free_symbols.difference({x}):
sol = _solve_explike_DE(f, x, DE, g, k)
if sol:
return sol
def hyper_algorithm(f, x, k, order=4):
"""Hypergeometric algorithm for computing Formal Power Series.
Steps:
* Generates DE
* Convert the DE into RE
* Solves the RE
Examples
========
>>> from sympy import exp, ln
>>> from sympy.series.formal import hyper_algorithm
>>> from sympy.abc import x, k
>>> hyper_algorithm(exp(x), x, k)
(Piecewise((1/factorial(k), Eq(Mod(k, 1), 0)), (0, True)), 1, 1)
>>> hyper_algorithm(ln(1 + x), x, k)
(Piecewise(((-1)**(k - 1)*factorial(k - 1)/RisingFactorial(2, k - 1),
Eq(Mod(k, 1), 0)), (0, True)), x, 2)
See Also
========
sympy.series.formal.simpleDE
sympy.series.formal.solve_de
"""
g = Function('g')
des = [] # list of DE's
sol = None
for DE, i in simpleDE(f, x, g, order):
if DE is not None:
sol = solve_de(f, x, DE, i, g, k)
if sol:
return sol
if not DE.free_symbols.difference({x}):
des.append(DE)
# If nothing works
# Try plain rsolve
for DE in des:
sol = _solve_simple(f, x, DE, g, k)
if sol:
return sol
def _compute_fps(f, x, x0, dir, hyper, order, rational, full):
"""Recursive wrapper to compute fps.
See :func:`compute_fps` for details.
"""
if x0 in [S.Infinity, -S.Infinity]:
dir = S.One if x0 is S.Infinity else -S.One
temp = f.subs(x, 1/x)
result = _compute_fps(temp, x, 0, dir, hyper, order, rational, full)
if result is None:
return None
return (result[0], result[1].subs(x, 1/x), result[2].subs(x, 1/x))
elif x0 or dir == -S.One:
if dir == -S.One:
rep = -x + x0
rep2 = -x
rep2b = x0
else:
rep = x + x0
rep2 = x
rep2b = -x0
temp = f.subs(x, rep)
result = _compute_fps(temp, x, 0, S.One, hyper, order, rational, full)
if result is None:
return None
return (result[0], result[1].subs(x, rep2 + rep2b),
result[2].subs(x, rep2 + rep2b))
if f.is_polynomial(x):
return None
# Break instances of Add
# this allows application of different
# algorithms on different terms increasing the
# range of admissible functions.
if isinstance(f, Add):
result = False
ak = sequence(S.Zero, (0, oo))
ind, xk = S.Zero, None
for t in Add.make_args(f):
res = _compute_fps(t, x, 0, S.One, hyper, order, rational, full)
if res:
if not result:
result = True
xk = res[1]
if res[0].start > ak.start:
seq = ak
s, f = ak.start, res[0].start
else:
seq = res[0]
s, f = res[0].start, ak.start
save = Add(*[z[0]*z[1] for z in zip(seq[0:(f - s)], xk[s:f])])
ak += res[0]
ind += res[2] + save
else:
ind += t
if result:
return ak, xk, ind
return None
result = None
# from here on it's x0=0 and dir=1 handling
k = Dummy('k')
if rational:
result = rational_algorithm(f, x, k, order, full)
if result is None and hyper:
result = hyper_algorithm(f, x, k, order)
if result is None:
return None
ak = sequence(result[0], (k, result[2], oo))
xk = sequence(x**k, (k, 0, oo))
ind = result[1]
return ak, xk, ind
def compute_fps(f, x, x0=0, dir=1, hyper=True, order=4, rational=True,
full=False):
"""Computes the formula for Formal Power Series of a function.
Tries to compute the formula by applying the following techniques
(in order):
* rational_algorithm
* Hypergeomitric algorithm
Parameters
==========
x : Symbol
x0 : number, optional
Point to perform series expansion about. Default is 0.
dir : {1, -1, '+', '-'}, optional
If dir is 1 or '+' the series is calculated from the right and
for -1 or '-' the series is calculated from the left. For smooth
functions this flag will not alter the results. Default is 1.
hyper : {True, False}, optional
Set hyper to False to skip the hypergeometric algorithm.
By default it is set to False.
order : int, optional
Order of the derivative of ``f``, Default is 4.
rational : {True, False}, optional
Set rational to False to skip rational algorithm. By default it is set
to True.
full : {True, False}, optional
Set full to True to increase the range of rational algorithm.
See :func:`rational_algorithm` for details. By default it is set to
False.
Returns
=======
ak : sequence
Sequence of coefficients.
xk : sequence
Sequence of powers of x.
ind : Expr
Independent terms.
mul : Pow
Common terms.
See Also
========
sympy.series.formal.rational_algorithm
sympy.series.formal.hyper_algorithm
"""
f = sympify(f)
x = sympify(x)
if not f.has(x):
return None
x0 = sympify(x0)
if dir == '+':
dir = S.One
elif dir == '-':
dir = -S.One
elif dir not in [S.One, -S.One]:
raise ValueError("Dir must be '+' or '-'")
else:
dir = sympify(dir)
return _compute_fps(f, x, x0, dir, hyper, order, rational, full)
class FormalPowerSeries(SeriesBase):
"""Represents Formal Power Series of a function.
No computation is performed. This class should only to be used to represent
a series. No checks are performed.
For computing a series use :func:`fps`.
See Also
========
sympy.series.formal.fps
"""
def __new__(cls, *args):
args = map(sympify, args)
return Expr.__new__(cls, *args)
@property
def function(self):
return self.args[0]
@property
def x(self):
return self.args[1]
@property
def x0(self):
return self.args[2]
@property
def dir(self):
return self.args[3]
@property
def ak(self):
return self.args[4][0]
@property
def xk(self):
return self.args[4][1]
@property
def ind(self):
return self.args[4][2]
@property
def interval(self):
return Interval(0, oo)
@property
def start(self):
return self.interval.inf
@property
def stop(self):
return self.interval.sup
@property
def length(self):
return oo
@property
def infinite(self):
"""Returns an infinite representation of the series"""
from sympy.concrete import Sum
ak, xk = self.ak, self.xk
k = ak.variables[0]
inf_sum = Sum(ak.formula * xk.formula, (k, ak.start, ak.stop))
return self.ind + inf_sum
def _get_pow_x(self, term):
"""Returns the power of x in a term."""
xterm, pow_x = term.as_independent(self.x)[1].as_base_exp()
if not xterm.has(self.x):
return S.Zero
return pow_x
def polynomial(self, n=6):
"""Truncated series as polynomial.
Returns series sexpansion of ``f`` upto order ``O(x**n)``
as a polynomial(without ``O`` term).
"""
terms = []
for i, t in enumerate(self):
xp = self._get_pow_x(t)
if xp >= n:
break
elif xp.is_integer is True and i == n + 1:
break
elif t is not S.Zero:
terms.append(t)
return Add(*terms)
def truncate(self, n=6):
"""Truncated series.
Returns truncated series expansion of f upto
order ``O(x**n)``.
If n is ``None``, returns an infinite iterator.
"""
if n is None:
return iter(self)
x, x0 = self.x, self.x0
pt_xk = self.xk.coeff(n)
if x0 is S.NegativeInfinity:
x0 = S.Infinity
return self.polynomial(n) + Order(pt_xk, (x, x0))
def _eval_term(self, pt):
try:
pt_xk = self.xk.coeff(pt)
pt_ak = self.ak.coeff(pt).simplify() # Simplify the coefficients
except IndexError:
term = S.Zero
else:
term = (pt_ak * pt_xk)
if self.ind:
ind = S.Zero
for t in Add.make_args(self.ind):
pow_x = self._get_pow_x(t)
if pt == 0 and pow_x < 1:
ind += t
elif pow_x >= pt and pow_x < pt + 1:
ind += t
term += ind
return term.collect(self.x)
def _eval_subs(self, old, new):
x = self.x
if old.has(x):
return self
def _eval_as_leading_term(self, x):
for t in self:
if t is not S.Zero:
return t
def _eval_derivative(self, x):
f = self.function.diff(x)
ind = self.ind.diff(x)
pow_xk = self._get_pow_x(self.xk.formula)
ak = self.ak
k = ak.variables[0]
if ak.formula.has(x):
form = []
for e, c in ak.formula.args:
temp = S.Zero
for t in Add.make_args(e):
pow_x = self._get_pow_x(t)
temp += t * (pow_xk + pow_x)
form.append((temp, c))
form = Piecewise(*form)
ak = sequence(form.subs(k, k + 1), (k, ak.start - 1, ak.stop))
else:
ak = sequence((ak.formula * pow_xk).subs(k, k + 1),
(k, ak.start - 1, ak.stop))
return self.func(f, self.x, self.x0, self.dir, (ak, self.xk, ind))
def integrate(self, x=None):
"""Integrate Formal Power Series.
Examples
========
>>> from sympy import fps, sin
>>> from sympy.abc import x
>>> f = fps(sin(x))
>>> f.integrate(x).truncate()
-1 + x**2/2 - x**4/24 + O(x**6)
>>> f.integrate((x, 0, 1))
-cos(1) + 1
"""
from sympy.integrals import integrate
if x is None:
x = self.x
elif iterable(x):
return integrate(self.function, x)
f = integrate(self.function, x)
ind = integrate(self.ind, x)
ind += (f - ind).limit(x, 0) # constant of integration
pow_xk = self._get_pow_x(self.xk.formula)
ak = self.ak
k = ak.variables[0]
if ak.formula.has(x):
form = []
for e, c in ak.formula.args:
temp = S.Zero
for t in Add.make_args(e):
pow_x = self._get_pow_x(t)
temp += t / (pow_xk + pow_x + 1)
form.append((temp, c))
form = Piecewise(*form)
ak = sequence(form.subs(k, k - 1), (k, ak.start + 1, ak.stop))
else:
ak = sequence((ak.formula / (pow_xk + 1)).subs(k, k - 1),
(k, ak.start + 1, ak.stop))
return self.func(f, self.x, self.x0, self.dir, (ak, self.xk, ind))
def __add__(self, other):
other = sympify(other)
if isinstance(other, FormalPowerSeries):
if self.dir != other.dir:
raise ValueError("Both series should be calculated from the"
" same direction.")
elif self.x0 != other.x0:
raise ValueError("Both series should be calculated about the"
" same point.")
x, y = self.x, other.x
f = self.function + other.function.subs(y, x)
if self.x not in f.free_symbols:
return f
ak = self.ak + other.ak
if self.ak.start > other.ak.start:
seq = other.ak
s, e = other.ak.start, self.ak.start
else:
seq = self.ak
s, e = self.ak.start, other.ak.start
save = Add(*[z[0]*z[1] for z in zip(seq[0:(e - s)], self.xk[s:e])])
ind = self.ind + other.ind + save
return self.func(f, x, self.x0, self.dir, (ak, self.xk, ind))
elif not other.has(self.x):
f = self.function + other
ind = self.ind + other
return self.func(f, self.x, self.x0, self.dir,
(self.ak, self.xk, ind))
return Add(self, other)
def __radd__(self, other):
return self.__add__(other)
def __neg__(self):
return self.func(-self.function, self.x, self.x0, self.dir,
(-self.ak, self.xk, -self.ind))
def __sub__(self, other):
return self.__add__(-other)
def __rsub__(self, other):
return (-self).__add__(other)
def __mul__(self, other):
other = sympify(other)
if other.has(self.x):
return Mul(self, other)
f = self.function * other
ak = self.ak.coeff_mul(other)
ind = self.ind * other
return self.func(f, self.x, self.x0, self.dir, (ak, self.xk, ind))
def __rmul__(self, other):
return self.__mul__(other)
def fps(f, x=None, x0=0, dir=1, hyper=True, order=4, rational=True, full=False):
"""Generates Formal Power Series of f.
Returns the formal series expansion of ``f`` around ``x = x0``
with respect to ``x`` in the form of a ``FormalPowerSeries`` object.
Formal Power Series is represented using an explicit formula
computed using different algorithms.
See :func:`compute_fps` for the more details regarding the computation
of formula.
Parameters
==========
x : Symbol, optional
If x is None and ``f`` is univariate, the univariate symbols will be
supplied, otherwise an error will be raised.
x0 : number, optional
Point to perform series expansion about. Default is 0.
dir : {1, -1, '+', '-'}, optional
If dir is 1 or '+' the series is calculated from the right and
for -1 or '-' the series is calculated from the left. For smooth
functions this flag will not alter the results. Default is 1.
hyper : {True, False}, optional
Set hyper to False to skip the hypergeometric algorithm.
By default it is set to False.
order : int, optional
Order of the derivative of ``f``, Default is 4.
rational : {True, False}, optional
Set rational to False to skip rational algorithm. By default it is set
to True.
full : {True, False}, optional
Set full to True to increase the range of rational algorithm.
See :func:`rational_algorithm` for details. By default it is set to
False.
Examples
========
>>> from sympy import fps, O, ln, atan
>>> from sympy.abc import x
Rational Functions
>>> fps(ln(1 + x)).truncate()
x - x**2/2 + x**3/3 - x**4/4 + x**5/5 + O(x**6)
>>> fps(atan(x), full=True).truncate()
x - x**3/3 + x**5/5 + O(x**6)
See Also
========
sympy.series.formal.FormalPowerSeries
sympy.series.formal.compute_fps
"""
f = sympify(f)
if x is None:
free = f.free_symbols
if len(free) == 1:
x = free.pop()
elif not free:
return f
else:
raise NotImplementedError("multivariate formal power series")
result = compute_fps(f, x, x0, dir, hyper, order, rational, full)
if result is None:
return f
return FormalPowerSeries(f, x, x0, dir, result)
| 34,591 | 26.541401 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/residues.py
|
"""
This module implements the Residue function and related tools for working
with residues.
"""
from __future__ import print_function, division
from sympy import sympify
from sympy.utilities.timeutils import timethis
@timethis('residue')
def residue(expr, x, x0):
"""
Finds the residue of ``expr`` at the point x=x0.
The residue is defined as the coefficient of 1/(x-x0) in the power series
expansion about x=x0.
Examples
========
>>> from sympy import Symbol, residue, sin
>>> x = Symbol("x")
>>> residue(1/x, x, 0)
1
>>> residue(1/x**2, x, 0)
0
>>> residue(2/sin(x), x, 0)
2
This function is essential for the Residue Theorem [1].
References
==========
1. http://en.wikipedia.org/wiki/Residue_theorem
"""
# The current implementation uses series expansion to
# calculate it. A more general implementation is explained in
# the section 5.6 of the Bronstein's book {M. Bronstein:
# Symbolic Integration I, Springer Verlag (2005)}. For purely
# rational functions, the algorithm is much easier. See
# sections 2.4, 2.5, and 2.7 (this section actually gives an
# algorithm for computing any Laurent series coefficient for
# a rational function). The theory in section 2.4 will help to
# understand why the resultant works in the general algorithm.
# For the definition of a resultant, see section 1.4 (and any
# previous sections for more review).
from sympy import collect, Mul, Order, S
expr = sympify(expr)
if x0 != 0:
expr = expr.subs(x, x + x0)
for n in [0, 1, 2, 4, 8, 16, 32]:
if n == 0:
s = expr.series(x, n=0)
else:
s = expr.nseries(x, n=n)
if s.has(Order) and s.removeO() == 0:
# bug in nseries
continue
if not s.has(Order) or s.getn() >= 0:
break
if s.has(Order) and s.getn() < 0:
raise NotImplementedError('Bug in nseries?')
s = collect(s.removeO(), x)
if s.is_Add:
args = s.args
else:
args = [s]
res = S(0)
for arg in args:
c, m = arg.as_coeff_mul(x)
m = Mul(*m)
if not (m == 1 or m == x or (m.is_Pow and m.exp.is_Integer)):
raise NotImplementedError('term of unexpected form: %s' % m)
if m == 1/x:
res += c
return res
| 2,386 | 28.469136 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/limitseq.py
|
"""Limits of sequences"""
from __future__ import print_function, division
from sympy.core.sympify import sympify
from sympy.core.singleton import S
from sympy.core.add import Add
from sympy.core.function import PoleError
from sympy.series.limits import Limit
def difference_delta(expr, n=None, step=1):
"""Difference Operator.
Discrete analogous to differential operator.
Examples
========
>>> from sympy import difference_delta as dd
>>> from sympy.abc import n
>>> dd(n*(n + 1), n)
2*n + 2
>>> dd(n*(n + 1), n, 2)
4*n + 6
References
==========
.. [1] https://reference.wolfram.com/language/ref/DifferenceDelta.html
"""
expr = sympify(expr)
if n is None:
f = expr.free_symbols
if len(f) == 1:
n = f.pop()
elif len(f) == 0:
return S.Zero
else:
raise ValueError("Since there is more than one variable in the"
" expression, a variable must be supplied to"
" take the difference of %s" % expr)
step = sympify(step)
if step.is_number is False:
raise ValueError("Step should be a number.")
elif step in [S.Infinity, -S.Infinity]:
raise ValueError("Step should be bounded.")
if hasattr(expr, '_eval_difference_delta'):
result = expr._eval_difference_delta(n, step)
if result:
return result
return expr.subs(n, n + step) - expr
def dominant(expr, n):
"""Finds the most dominating term in an expression.
if limit(a/b, n, oo) is oo then a dominates b.
if limit(a/b, n, oo) is 0 then b dominates a.
else a and b are comparable.
returns the most dominant term.
If no unique domiant term, then returns ``None``.
Examples
========
>>> from sympy import Sum
>>> from sympy.series.limitseq import dominant
>>> from sympy.abc import n, k
>>> dominant(5*n**3 + 4*n**2 + n + 1, n)
5*n**3
>>> dominant(2**n + Sum(k, (k, 0, n)), n)
2**n
See Also
========
sympy.series.limitseq.dominant
"""
terms = Add.make_args(expr.expand(func=True))
term0 = terms[-1]
comp = [term0] # comparable terms
for t in terms[:-1]:
e = (term0 / t).combsimp()
l = limit_seq(e, n)
if l is S.Zero:
term0 = t
comp = [term0]
elif l is None:
return None
elif l not in [S.Infinity, -S.Infinity]:
comp.append(t)
if len(comp) > 1:
return None
return term0
def _limit_inf(expr, n):
try:
return Limit(expr, n, S.Infinity).doit(deep=False, sequence=False)
except (NotImplementedError, PoleError):
return None
def limit_seq(expr, n=None, trials=5):
"""Finds limits of terms having sequences at infinity.
Parameters
==========
expr : Expr
SymPy expression that is admissible (see section below).
n : Symbol
Find the limit wrt to n at infinity.
trials: int, optional
The algorithm is highly recursive. ``trials`` is a safeguard from
infinite recursion incase limit is not easily computed by the
algorithm. Try increasing ``trials`` if the algorithm returns ``None``.
Admissible Terms
================
The terms should be built from rational functions, indefinite sums,
and indefinite products over an indeterminate n. A term is admissible
if the scope of all product quantifiers are asymptotically positive.
Every admissible term is asymptoticically monotonous.
Examples
========
>>> from sympy import limit_seq, Sum, binomial
>>> from sympy.abc import n, k, m
>>> limit_seq((5*n**3 + 3*n**2 + 4) / (3*n**3 + 4*n - 5), n)
5/3
>>> limit_seq(binomial(2*n, n) / Sum(binomial(2*k, k), (k, 1, n)), n)
3/4
>>> limit_seq(Sum(k**2 * Sum(2**m/m, (m, 1, k)), (k, 1, n)) / (2**n*n), n)
4
See Also
========
sympy.series.limitseq.dominant
References
==========
.. [1] Computing Limits of Sequences - Manuel Kauers
"""
from sympy.concrete.summations import Sum
if n is None:
free = expr.free_symbols
if len(free) == 1:
n = free.pop()
elif not free:
return expr
else:
raise ValueError("expr %s has more than one variables. Please"
"specify a variable." % (expr))
elif n not in expr.free_symbols:
return expr
for i in range(trials):
if not expr.has(Sum):
result = _limit_inf(expr, n)
if result is not None:
return result
num, den = expr.as_numer_denom()
if not den.has(n) or not num.has(n):
result = _limit_inf(expr.doit(), n)
if result is not None:
return result
return None
num, den = (difference_delta(t.expand(), n) for t in [num, den])
expr = (num / den).combsimp()
if not expr.has(Sum):
result = _limit_inf(expr, n)
if result is not None:
return result
num, den = expr.as_numer_denom()
num = dominant(num, n)
if num is None:
return None
den = dominant(den, n)
if den is None:
return None
expr = (num / den).combsimp()
| 5,397 | 25.99 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/__init__.py
|
"""A module that handles series: find a limit, order the series etc.
"""
from .order import Order
from .limits import limit, Limit
from .gruntz import gruntz
from .series import series
from .approximants import approximants
from .residues import residue
from .sequences import (EmptySequence, SeqPer, SeqFormula, sequence, SeqAdd,
SeqMul)
from .fourier import fourier_series
from .formal import fps
from .limitseq import difference_delta, limit_seq
O = Order
__all__ = ['Order', 'O', 'limit', 'Limit', 'gruntz', 'series', 'residue',
'EmptySequence', 'SeqPer', 'SeqFormula', 'sequence',
'SeqAdd', 'SeqMul', 'fourier_series', 'fps', 'difference_delta',
'limit_seq']
| 723 | 33.47619 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/series.py
|
from __future__ import print_function, division
from sympy.core.sympify import sympify
def series(expr, x=None, x0=0, n=6, dir="+"):
"""Series expansion of expr around point `x = x0`.
See the doctring of Expr.series() for complete details of this wrapper.
"""
expr = sympify(expr)
return expr.series(x, x0, n, dir)
| 339 | 25.153846 | 75 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/sequences.py
|
from __future__ import print_function, division
from sympy.core.basic import Basic
from sympy.core.mul import Mul
from sympy.core.singleton import S, Singleton
from sympy.core.symbol import Dummy, Symbol
from sympy.core.compatibility import (range, integer_types, with_metaclass,
is_sequence, iterable, ordered)
from sympy.core.decorators import call_highest_priority
from sympy.core.cache import cacheit
from sympy.core.sympify import sympify
from sympy.core.containers import Tuple
from sympy.core.evaluate import global_evaluate
from sympy.polys import lcm, factor
from sympy.sets.sets import Interval, Intersection
from sympy.utilities.iterables import flatten
from sympy.tensor.indexed import Idx
from sympy.simplify import simplify
from sympy import expand
###############################################################################
# SEQUENCES #
###############################################################################
class SeqBase(Basic):
"""Base class for sequences"""
is_commutative = True
_op_priority = 15
@staticmethod
def _start_key(expr):
"""Return start (if possible) else S.Infinity.
adapted from Set._infimum_key
"""
try:
start = expr.start
except (NotImplementedError,
AttributeError, ValueError):
start = S.Infinity
return start
def _intersect_interval(self, other):
"""Returns start and stop.
Takes intersection over the two intervals.
"""
interval = Intersection(self.interval, other.interval)
return interval.inf, interval.sup
@property
def gen(self):
"""Returns the generator for the sequence"""
raise NotImplementedError("(%s).gen" % self)
@property
def interval(self):
"""The interval on which the sequence is defined"""
raise NotImplementedError("(%s).interval" % self)
@property
def start(self):
"""The starting point of the sequence. This point is included"""
raise NotImplementedError("(%s).start" % self)
@property
def stop(self):
"""The ending point of the sequence. This point is included"""
raise NotImplementedError("(%s).stop" % self)
@property
def length(self):
"""Length of the sequence"""
raise NotImplementedError("(%s).length" % self)
@property
def variables(self):
"""Returns a tuple of variables that are bounded"""
return ()
@property
def free_symbols(self):
"""
This method returns the symbols in the object, excluding those
that take on a specific value (i.e. the dummy symbols).
Examples
========
>>> from sympy import SeqFormula
>>> from sympy.abc import n, m
>>> SeqFormula(m*n**2, (n, 0, 5)).free_symbols
{m}
"""
return (set(j for i in self.args for j in i.free_symbols
.difference(self.variables)))
@cacheit
def coeff(self, pt):
"""Returns the coefficient at point pt"""
if pt < self.start or pt > self.stop:
raise IndexError("Index %s out of bounds %s" % (pt, self.interval))
return self._eval_coeff(pt)
def _eval_coeff(self, pt):
raise NotImplementedError("The _eval_coeff method should be added to"
"%s to return coefficient so it is available"
"when coeff calls it."
% self.func)
def _ith_point(self, i):
"""Returns the i'th point of a sequence.
If start point is negative infinity, point is returned from the end.
Assumes the first point to be indexed zero.
Examples
=========
>>> from sympy import oo
>>> from sympy.series.sequences import SeqPer
bounded
>>> SeqPer((1, 2, 3), (-10, 10))._ith_point(0)
-10
>>> SeqPer((1, 2, 3), (-10, 10))._ith_point(5)
-5
End is at infinity
>>> SeqPer((1, 2, 3), (0, oo))._ith_point(5)
5
Starts at negative infinity
>>> SeqPer((1, 2, 3), (-oo, 0))._ith_point(5)
-5
"""
if self.start is S.NegativeInfinity:
initial = self.stop
else:
initial = self.start
if self.start is S.NegativeInfinity:
step = -1
else:
step = 1
return initial + i*step
def _add(self, other):
"""
Should only be used internally.
self._add(other) returns a new, term-wise added sequence if self
knows how to add with other, otherwise it returns ``None``.
``other`` should only be a sequence object.
Used within :class:`SeqAdd` class.
"""
return None
def _mul(self, other):
"""
Should only be used internally.
self._mul(other) returns a new, term-wise multiplied sequence if self
knows how to multiply with other, otherwise it returns ``None``.
``other`` should only be a sequence object.
Used within :class:`SeqMul` class.
"""
return None
def coeff_mul(self, other):
"""
Should be used when ``other`` is not a sequence. Should be
defined to define custom behaviour.
Examples
========
>>> from sympy import S, oo, SeqFormula
>>> from sympy.abc import n
>>> SeqFormula(n**2).coeff_mul(2)
SeqFormula(2*n**2, (n, 0, oo))
Notes
=====
'*' defines multiplication of sequences with sequences only.
"""
return Mul(self, other)
def __add__(self, other):
"""Returns the term-wise addition of 'self' and 'other'.
``other`` should be a sequence.
Examples
========
>>> from sympy import S, oo, SeqFormula
>>> from sympy.abc import n
>>> SeqFormula(n**2) + SeqFormula(n**3)
SeqFormula(n**3 + n**2, (n, 0, oo))
"""
if not isinstance(other, SeqBase):
raise TypeError('cannot add sequence and %s' % type(other))
return SeqAdd(self, other)
@call_highest_priority('__add__')
def __radd__(self, other):
return self + other
def __sub__(self, other):
"""Returns the term-wise subtraction of 'self' and 'other'.
``other`` should be a sequence.
Examples
========
>>> from sympy import S, oo, SeqFormula
>>> from sympy.abc import n
>>> SeqFormula(n**2) - (SeqFormula(n))
SeqFormula(n**2 - n, (n, 0, oo))
"""
if not isinstance(other, SeqBase):
raise TypeError('cannot subtract sequence and %s' % type(other))
return SeqAdd(self, -other)
@call_highest_priority('__sub__')
def __rsub__(self, other):
return (-self) + other
def __neg__(self):
"""Negates the sequence.
Examples
========
>>> from sympy import S, oo, SeqFormula
>>> from sympy.abc import n
>>> -SeqFormula(n**2)
SeqFormula(-n**2, (n, 0, oo))
"""
return self.coeff_mul(-1)
def __mul__(self, other):
"""Returns the term-wise multiplication of 'self' and 'other'.
``other`` should be a sequence. For ``other`` not being a
sequence see :func:`coeff_mul` method.
Examples
========
>>> from sympy import S, oo, SeqFormula
>>> from sympy.abc import n
>>> SeqFormula(n**2) * (SeqFormula(n))
SeqFormula(n**3, (n, 0, oo))
"""
if not isinstance(other, SeqBase):
raise TypeError('cannot multiply sequence and %s' % type(other))
return SeqMul(self, other)
@call_highest_priority('__mul__')
def __rmul__(self, other):
return self * other
def __iter__(self):
for i in range(self.length):
pt = self._ith_point(i)
yield self.coeff(pt)
def __getitem__(self, index):
if isinstance(index, integer_types):
index = self._ith_point(index)
return self.coeff(index)
elif isinstance(index, slice):
start, stop = index.start, index.stop
if start is None:
start = 0
if stop is None:
stop = self.length
return [self.coeff(self._ith_point(i)) for i in
range(start, stop, index.step or 1)]
def find_linear_recurrence(self,n,d=None,gfvar=None):
r"""
Finds the shortest linear recurrence that satisfies the first n
terms of sequence of order `\leq` n/2 if possible.
If d is specified, find shortest linear recurrence of order
`\leq` min(d, n/2) if possible.
Returns list of coefficients ``[b(1), b(2), ...]`` corresponding to the
recurrence relation ``x(n) = b(1)*x(n-1) + b(2)*x(n-2) + ...``
Returns ``[]`` if no recurrence is found.
If gfvar is specified, also returns ordinary generating function as a
function of gfvar.
Examples
========
>>> from sympy import sequence, sqrt, oo, lucas
>>> from sympy.abc import n, x, y
>>> sequence(n**2).find_linear_recurrence(10, 2)
[]
>>> sequence(n**2).find_linear_recurrence(10)
[3, -3, 1]
>>> sequence(2**n).find_linear_recurrence(10)
[2]
>>> sequence(23*n**4+91*n**2).find_linear_recurrence(10)
[5, -10, 10, -5, 1]
>>> sequence(sqrt(5)*(((1 + sqrt(5))/2)**n - (-(1 + sqrt(5))/2)**(-n))/5).find_linear_recurrence(10)
[1, 1]
>>> sequence(x+y*(-2)**(-n), (n, 0, oo)).find_linear_recurrence(30)
[1/2, 1/2]
>>> sequence(3*5**n + 12).find_linear_recurrence(20,gfvar=x)
([6, -5], 3*(-21*x + 5)/((x - 1)*(5*x - 1)))
>>> sequence(lucas(n)).find_linear_recurrence(15,gfvar=x)
([1, 1], (x - 2)/(x**2 + x - 1))
"""
from sympy.matrices import Matrix
x = [simplify(expand(t)) for t in self[:n]]
lx = len(x)
if d == None:
r = lx//2
else:
r = min(d,lx//2)
coeffs = []
for l in range(1, r+1):
l2 = 2*l
mlist = []
for k in range(l):
mlist.append(x[k:k+l])
m = Matrix(mlist)
if m.det() != 0:
y = simplify(m.LUsolve(Matrix(x[l:l2])))
if lx == l2:
coeffs = flatten(y[::-1])
break
mlist = []
for k in range(l,lx-l):
mlist.append(x[k:k+l])
m = Matrix(mlist)
if m*y == Matrix(x[l2:]):
coeffs = flatten(y[::-1])
break
if gfvar == None:
return coeffs
else:
l = len(coeffs)
if l == 0:
return [], None
else:
n, d = x[l-1]*gfvar**(l-1), 1 - coeffs[l-1]*gfvar**l
for i in range(l-1):
n += x[i]*gfvar**i
for j in range(l-i-1):
n -= coeffs[i]*x[j]*gfvar**(i+j+1)
d -= coeffs[i]*gfvar**(i+1)
return coeffs, simplify(factor(n)/factor(d))
class EmptySequence(with_metaclass(Singleton, SeqBase)):
"""Represents an empty sequence.
The empty sequence is available as a
singleton as ``S.EmptySequence``.
Examples
========
>>> from sympy import S, SeqPer, oo
>>> from sympy.abc import x
>>> S.EmptySequence
EmptySequence()
>>> SeqPer((1, 2), (x, 0, 10)) + S.EmptySequence
SeqPer((1, 2), (x, 0, 10))
>>> SeqPer((1, 2)) * S.EmptySequence
EmptySequence()
>>> S.EmptySequence.coeff_mul(-1)
EmptySequence()
"""
@property
def interval(self):
return S.EmptySet
@property
def length(self):
return S.Zero
def coeff_mul(self, coeff):
"""See docstring of SeqBase.coeff_mul"""
return self
def __iter__(self):
return iter([])
class SeqExpr(SeqBase):
"""Sequence expression class.
Various sequences should inherit from this class.
Examples
========
>>> from sympy.series.sequences import SeqExpr
>>> from sympy.abc import x
>>> s = SeqExpr((1, 2, 3), (x, 0, 10))
>>> s.gen
(1, 2, 3)
>>> s.interval
Interval(0, 10)
>>> s.length
11
See Also
========
sympy.series.sequences.SeqPer
sympy.series.sequences.SeqFormula
"""
@property
def gen(self):
return self.args[0]
@property
def interval(self):
return Interval(self.args[1][1], self.args[1][2])
@property
def start(self):
return self.interval.inf
@property
def stop(self):
return self.interval.sup
@property
def length(self):
return self.stop - self.start + 1
@property
def variables(self):
return (self.args[1][0],)
class SeqPer(SeqExpr):
"""Represents a periodic sequence.
The elements are repeated after a given period.
Examples
========
>>> from sympy import SeqPer, oo
>>> from sympy.abc import k
>>> s = SeqPer((1, 2, 3), (0, 5))
>>> s.periodical
(1, 2, 3)
>>> s.period
3
For value at a particular point
>>> s.coeff(3)
1
supports slicing
>>> s[:]
[1, 2, 3, 1, 2, 3]
iterable
>>> list(s)
[1, 2, 3, 1, 2, 3]
sequence starts from negative infinity
>>> SeqPer((1, 2, 3), (-oo, 0))[0:6]
[1, 2, 3, 1, 2, 3]
Periodic formulas
>>> SeqPer((k, k**2, k**3), (k, 0, oo))[0:6]
[0, 1, 8, 3, 16, 125]
See Also
========
sympy.series.sequences.SeqFormula
"""
def __new__(cls, periodical, limits=None):
periodical = sympify(periodical)
def _find_x(periodical):
free = periodical.free_symbols
if len(periodical.free_symbols) == 1:
return free.pop()
else:
return Dummy('k')
x, start, stop = None, None, None
if limits is None:
x, start, stop = _find_x(periodical), 0, S.Infinity
if is_sequence(limits, Tuple):
if len(limits) == 3:
x, start, stop = limits
elif len(limits) == 2:
x = _find_x(periodical)
start, stop = limits
if not isinstance(x, (Symbol, Idx)) or start is None or stop is None:
raise ValueError('Invalid limits given: %s' % str(limits))
if start is S.NegativeInfinity and stop is S.Infinity:
raise ValueError("Both the start and end value"
"cannot be unbounded")
limits = sympify((x, start, stop))
if is_sequence(periodical, Tuple):
periodical = sympify(tuple(flatten(periodical)))
else:
raise ValueError("invalid period %s should be something "
"like e.g (1, 2) " % periodical)
if Interval(limits[1], limits[2]) is S.EmptySet:
return S.EmptySequence
return Basic.__new__(cls, periodical, limits)
@property
def period(self):
return len(self.gen)
@property
def periodical(self):
return self.gen
def _eval_coeff(self, pt):
if self.start is S.NegativeInfinity:
idx = (self.stop - pt) % self.period
else:
idx = (pt - self.start) % self.period
return self.periodical[idx].subs(self.variables[0], pt)
def _add(self, other):
"""See docstring of SeqBase._add"""
if isinstance(other, SeqPer):
per1, lper1 = self.periodical, self.period
per2, lper2 = other.periodical, other.period
per_length = lcm(lper1, lper2)
new_per = []
for x in range(per_length):
ele1 = per1[x % lper1]
ele2 = per2[x % lper2]
new_per.append(ele1 + ele2)
start, stop = self._intersect_interval(other)
return SeqPer(new_per, (self.variables[0], start, stop))
def _mul(self, other):
"""See docstring of SeqBase._mul"""
if isinstance(other, SeqPer):
per1, lper1 = self.periodical, self.period
per2, lper2 = other.periodical, other.period
per_length = lcm(lper1, lper2)
new_per = []
for x in range(per_length):
ele1 = per1[x % lper1]
ele2 = per2[x % lper2]
new_per.append(ele1 * ele2)
start, stop = self._intersect_interval(other)
return SeqPer(new_per, (self.variables[0], start, stop))
def coeff_mul(self, coeff):
"""See docstring of SeqBase.coeff_mul"""
coeff = sympify(coeff)
per = [x * coeff for x in self.periodical]
return SeqPer(per, self.args[1])
class SeqFormula(SeqExpr):
"""Represents sequence based on a formula.
Elements are generated using a formula.
Examples
========
>>> from sympy import SeqFormula, oo, Symbol
>>> n = Symbol('n')
>>> s = SeqFormula(n**2, (n, 0, 5))
>>> s.formula
n**2
For value at a particular point
>>> s.coeff(3)
9
supports slicing
>>> s[:]
[0, 1, 4, 9, 16, 25]
iterable
>>> list(s)
[0, 1, 4, 9, 16, 25]
sequence starts from negative infinity
>>> SeqFormula(n**2, (-oo, 0))[0:6]
[0, 1, 4, 9, 16, 25]
See Also
========
sympy.series.sequences.SeqPer
"""
def __new__(cls, formula, limits=None):
formula = sympify(formula)
def _find_x(formula):
free = formula.free_symbols
if len(formula.free_symbols) == 1:
return free.pop()
elif len(formula.free_symbols) == 0:
return Dummy('k')
else:
raise ValueError(
" specify dummy variables for %s. If the formula contains"
" more than one free symbol, a dummy variable should be"
" supplied explicitly e.g., SeqFormula(m*n**2, (n, 0, 5))"
% formula)
x, start, stop = None, None, None
if limits is None:
x, start, stop = _find_x(formula), 0, S.Infinity
if is_sequence(limits, Tuple):
if len(limits) == 3:
x, start, stop = limits
elif len(limits) == 2:
x = _find_x(formula)
start, stop = limits
if not isinstance(x, (Symbol, Idx)) or start is None or stop is None:
raise ValueError('Invalid limits given: %s' % str(limits))
if start is S.NegativeInfinity and stop is S.Infinity:
raise ValueError("Both the start and end value"
"cannot be unbounded")
limits = sympify((x, start, stop))
if Interval(limits[1], limits[2]) is S.EmptySet:
return S.EmptySequence
return Basic.__new__(cls, formula, limits)
@property
def formula(self):
return self.gen
def _eval_coeff(self, pt):
d = self.variables[0]
return self.formula.subs(d, pt)
def _add(self, other):
"""See docstring of SeqBase._add"""
if isinstance(other, SeqFormula):
form1, v1 = self.formula, self.variables[0]
form2, v2 = other.formula, other.variables[0]
formula = form1 + form2.subs(v2, v1)
start, stop = self._intersect_interval(other)
return SeqFormula(formula, (v1, start, stop))
def _mul(self, other):
"""See docstring of SeqBase._mul"""
if isinstance(other, SeqFormula):
form1, v1 = self.formula, self.variables[0]
form2, v2 = other.formula, other.variables[0]
formula = form1 * form2.subs(v2, v1)
start, stop = self._intersect_interval(other)
return SeqFormula(formula, (v1, start, stop))
def coeff_mul(self, coeff):
"""See docstring of SeqBase.coeff_mul"""
coeff = sympify(coeff)
formula = self.formula * coeff
return SeqFormula(formula, self.args[1])
def sequence(seq, limits=None):
"""Returns appropriate sequence object.
If ``seq`` is a sympy sequence, returns :class:`SeqPer` object
otherwise returns :class:`SeqFormula` object.
Examples
========
>>> from sympy import sequence, SeqPer, SeqFormula
>>> from sympy.abc import n
>>> sequence(n**2, (n, 0, 5))
SeqFormula(n**2, (n, 0, 5))
>>> sequence((1, 2, 3), (n, 0, 5))
SeqPer((1, 2, 3), (n, 0, 5))
See Also
========
sympy.series.sequences.SeqPer
sympy.series.sequences.SeqFormula
"""
seq = sympify(seq)
if is_sequence(seq, Tuple):
return SeqPer(seq, limits)
else:
return SeqFormula(seq, limits)
###############################################################################
# OPERATIONS #
###############################################################################
class SeqExprOp(SeqBase):
"""Base class for operations on sequences.
Examples
========
>>> from sympy.series.sequences import SeqExprOp, sequence
>>> from sympy.abc import n
>>> s1 = sequence(n**2, (n, 0, 10))
>>> s2 = sequence((1, 2, 3), (n, 5, 10))
>>> s = SeqExprOp(s1, s2)
>>> s.gen
(n**2, (1, 2, 3))
>>> s.interval
Interval(5, 10)
>>> s.length
6
See Also
========
sympy.series.sequences.SeqAdd
sympy.series.sequences.SeqMul
"""
@property
def gen(self):
"""Generator for the sequence.
returns a tuple of generators of all the argument sequences.
"""
return tuple(a.gen for a in self.args)
@property
def interval(self):
"""Sequence is defined on the intersection
of all the intervals of respective sequences
"""
return Intersection(a.interval for a in self.args)
@property
def start(self):
return self.interval.inf
@property
def stop(self):
return self.interval.sup
@property
def variables(self):
"""Cumulative of all the bound variables"""
return tuple(flatten([a.variables for a in self.args]))
@property
def length(self):
return self.stop - self.start + 1
class SeqAdd(SeqExprOp):
"""Represents term-wise addition of sequences.
Rules:
* The interval on which sequence is defined is the intersection
of respective intervals of sequences.
* Anything + :class:`EmptySequence` remains unchanged.
* Other rules are defined in ``_add`` methods of sequence classes.
Examples
========
>>> from sympy import S, oo, SeqAdd, SeqPer, SeqFormula
>>> from sympy.abc import n
>>> SeqAdd(SeqPer((1, 2), (n, 0, oo)), S.EmptySequence)
SeqPer((1, 2), (n, 0, oo))
>>> SeqAdd(SeqPer((1, 2), (n, 0, 5)), SeqPer((1, 2), (n, 6, 10)))
EmptySequence()
>>> SeqAdd(SeqPer((1, 2), (n, 0, oo)), SeqFormula(n**2, (n, 0, oo)))
SeqAdd(SeqFormula(n**2, (n, 0, oo)), SeqPer((1, 2), (n, 0, oo)))
>>> SeqAdd(SeqFormula(n**3), SeqFormula(n**2))
SeqFormula(n**3 + n**2, (n, 0, oo))
See Also
========
sympy.series.sequences.SeqMul
"""
def __new__(cls, *args, **kwargs):
evaluate = kwargs.get('evaluate', global_evaluate[0])
# flatten inputs
args = list(args)
# adapted from sympy.sets.sets.Union
def _flatten(arg):
if isinstance(arg, SeqBase):
if isinstance(arg, SeqAdd):
return sum(map(_flatten, arg.args), [])
else:
return [arg]
if iterable(arg):
return sum(map(_flatten, arg), [])
raise TypeError("Input must be Sequences or "
" iterables of Sequences")
args = _flatten(args)
args = [a for a in args if a is not S.EmptySequence]
# Addition of no sequences is EmptySequence
if not args:
return S.EmptySequence
if Intersection(a.interval for a in args) is S.EmptySet:
return S.EmptySequence
# reduce using known rules
if evaluate:
return SeqAdd.reduce(args)
args = list(ordered(args, SeqBase._start_key))
return Basic.__new__(cls, *args)
@staticmethod
def reduce(args):
"""Simplify :class:`SeqAdd` using known rules.
Iterates through all pairs and ask the constituent
sequences if they can simplify themselves with any other constituent.
Notes
=====
adapted from ``Union.reduce``
"""
new_args = True
while(new_args):
for id1, s in enumerate(args):
new_args = False
for id2, t in enumerate(args):
if id1 == id2:
continue
new_seq = s._add(t)
# This returns None if s does not know how to add
# with t. Returns the newly added sequence otherwise
if new_seq is not None:
new_args = [a for a in args if a not in (s, t)]
new_args.append(new_seq)
break
if new_args:
args = new_args
break
if len(args) == 1:
return args.pop()
else:
return SeqAdd(args, evaluate=False)
def _eval_coeff(self, pt):
"""adds up the coefficients of all the sequences at point pt"""
return sum(a.coeff(pt) for a in self.args)
class SeqMul(SeqExprOp):
r"""Represents term-wise multiplication of sequences.
Handles multiplication of sequences only. For multiplication
with other objects see :func:`SeqBase.coeff_mul`.
Rules:
* The interval on which sequence is defined is the intersection
of respective intervals of sequences.
* Anything \* :class:`EmptySequence` returns :class:`EmptySequence`.
* Other rules are defined in ``_mul`` methods of sequence classes.
Examples
========
>>> from sympy import S, oo, SeqMul, SeqPer, SeqFormula
>>> from sympy.abc import n
>>> SeqMul(SeqPer((1, 2), (n, 0, oo)), S.EmptySequence)
EmptySequence()
>>> SeqMul(SeqPer((1, 2), (n, 0, 5)), SeqPer((1, 2), (n, 6, 10)))
EmptySequence()
>>> SeqMul(SeqPer((1, 2), (n, 0, oo)), SeqFormula(n**2))
SeqMul(SeqFormula(n**2, (n, 0, oo)), SeqPer((1, 2), (n, 0, oo)))
>>> SeqMul(SeqFormula(n**3), SeqFormula(n**2))
SeqFormula(n**5, (n, 0, oo))
See Also
========
sympy.series.sequences.SeqAdd
"""
def __new__(cls, *args, **kwargs):
evaluate = kwargs.get('evaluate', global_evaluate[0])
# flatten inputs
args = list(args)
# adapted from sympy.sets.sets.Union
def _flatten(arg):
if isinstance(arg, SeqBase):
if isinstance(arg, SeqMul):
return sum(map(_flatten, arg.args), [])
else:
return [arg]
elif iterable(arg):
return sum(map(_flatten, arg), [])
raise TypeError("Input must be Sequences or "
" iterables of Sequences")
args = _flatten(args)
# Multiplication of no sequences is EmptySequence
if not args:
return S.EmptySequence
if Intersection(a.interval for a in args) is S.EmptySet:
return S.EmptySequence
# reduce using known rules
if evaluate:
return SeqMul.reduce(args)
args = list(ordered(args, SeqBase._start_key))
return Basic.__new__(cls, *args)
@staticmethod
def reduce(args):
"""Simplify a :class:`SeqMul` using known rules.
Iterates through all pairs and ask the constituent
sequences if they can simplify themselves with any other constituent.
Notes
=====
adapted from ``Union.reduce``
"""
new_args = True
while(new_args):
for id1, s in enumerate(args):
new_args = False
for id2, t in enumerate(args):
if id1 == id2:
continue
new_seq = s._mul(t)
# This returns None if s does not know how to multiply
# with t. Returns the newly multiplied sequence otherwise
if new_seq is not None:
new_args = [a for a in args if a not in (s, t)]
new_args.append(new_seq)
break
if new_args:
args = new_args
break
if len(args) == 1:
return args.pop()
else:
return SeqMul(args, evaluate=False)
def _eval_coeff(self, pt):
"""multiplies the coefficients of all the sequences at point pt"""
val = 1
for a in self.args:
val *= a.coeff(pt)
return val
| 29,580 | 28.00098 | 108 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/order.py
|
from __future__ import print_function, division
from sympy.core import S, sympify, Expr, Rational, Symbol, Dummy
from sympy.core import Add, Mul, expand_power_base, expand_log
from sympy.core.cache import cacheit
from sympy.core.compatibility import default_sort_key, is_sequence
from sympy.core.containers import Tuple
from sympy.utilities.iterables import uniq
from sympy.sets.sets import Complement
class Order(Expr):
r""" Represents the limiting behavior of some function
The order of a function characterizes the function based on the limiting
behavior of the function as it goes to some limit. Only taking the limit
point to be a number is currently supported. This is expressed in
big O notation [1]_.
The formal definition for the order of a function `g(x)` about a point `a`
is such that `g(x) = O(f(x))` as `x \rightarrow a` if and only if for any
`\delta > 0` there exists a `M > 0` such that `|g(x)| \leq M|f(x)|` for
`|x-a| < \delta`. This is equivalent to `\lim_{x \rightarrow a}
\sup |g(x)/f(x)| < \infty`.
Let's illustrate it on the following example by taking the expansion of
`\sin(x)` about 0:
.. math ::
\sin(x) = x - x^3/3! + O(x^5)
where in this case `O(x^5) = x^5/5! - x^7/7! + \cdots`. By the definition
of `O`, for any `\delta > 0` there is an `M` such that:
.. math ::
|x^5/5! - x^7/7! + ....| <= M|x^5| \text{ for } |x| < \delta
or by the alternate definition:
.. math ::
\lim_{x \rightarrow 0} | (x^5/5! - x^7/7! + ....) / x^5| < \infty
which surely is true, because
.. math ::
\lim_{x \rightarrow 0} | (x^5/5! - x^7/7! + ....) / x^5| = 1/5!
As it is usually used, the order of a function can be intuitively thought
of representing all terms of powers greater than the one specified. For
example, `O(x^3)` corresponds to any terms proportional to `x^3,
x^4,\ldots` and any higher power. For a polynomial, this leaves terms
proportional to `x^2`, `x` and constants.
Examples
========
>>> from sympy import O, oo, cos, pi
>>> from sympy.abc import x, y
>>> O(x + x**2)
O(x)
>>> O(x + x**2, (x, 0))
O(x)
>>> O(x + x**2, (x, oo))
O(x**2, (x, oo))
>>> O(1 + x*y)
O(1, x, y)
>>> O(1 + x*y, (x, 0), (y, 0))
O(1, x, y)
>>> O(1 + x*y, (x, oo), (y, oo))
O(x*y, (x, oo), (y, oo))
>>> O(1) in O(1, x)
True
>>> O(1, x) in O(1)
False
>>> O(x) in O(1, x)
True
>>> O(x**2) in O(x)
True
>>> O(x)*x
O(x**2)
>>> O(x) - O(x)
O(x)
>>> O(cos(x))
O(1)
>>> O(cos(x), (x, pi/2))
O(x - pi/2, (x, pi/2))
References
==========
.. [1] `Big O notation <http://en.wikipedia.org/wiki/Big_O_notation>`_
Notes
=====
In ``O(f(x), x)`` the expression ``f(x)`` is assumed to have a leading
term. ``O(f(x), x)`` is automatically transformed to
``O(f(x).as_leading_term(x),x)``.
``O(expr*f(x), x)`` is ``O(f(x), x)``
``O(expr, x)`` is ``O(1)``
``O(0, x)`` is 0.
Multivariate O is also supported:
``O(f(x, y), x, y)`` is transformed to
``O(f(x, y).as_leading_term(x,y).as_leading_term(y), x, y)``
In the multivariate case, it is assumed the limits w.r.t. the various
symbols commute.
If no symbols are passed then all symbols in the expression are used
and the limit point is assumed to be zero.
"""
is_Order = True
__slots__ = []
@cacheit
def __new__(cls, expr, *args, **kwargs):
expr = sympify(expr)
if not args:
if expr.is_Order:
variables = expr.variables
point = expr.point
else:
variables = list(expr.free_symbols)
point = [S.Zero]*len(variables)
else:
args = list(args if is_sequence(args) else [args])
variables, point = [], []
if is_sequence(args[0]):
for a in args:
v, p = list(map(sympify, a))
variables.append(v)
point.append(p)
else:
variables = list(map(sympify, args))
point = [S.Zero]*len(variables)
if not all(v.is_Symbol for v in variables):
raise TypeError('Variables are not symbols, got %s' % variables)
if len(list(uniq(variables))) != len(variables):
raise ValueError('Variables are supposed to be unique symbols, got %s' % variables)
if expr.is_Order:
expr_vp = dict(expr.args[1:])
new_vp = dict(expr_vp)
vp = dict(zip(variables, point))
for v, p in vp.items():
if v in new_vp.keys():
if p != new_vp[v]:
raise NotImplementedError(
"Mixing Order at different points is not supported.")
else:
new_vp[v] = p
if set(expr_vp.keys()) == set(new_vp.keys()):
return expr
else:
variables = list(new_vp.keys())
point = [new_vp[v] for v in variables]
if expr is S.NaN:
return S.NaN
if any(x in p.free_symbols for x in variables for p in point):
raise ValueError('Got %s as a point.' % point)
if variables:
if any(p != point[0] for p in point):
raise NotImplementedError
if point[0] is S.Infinity:
s = {k: 1/Dummy() for k in variables}
rs = {1/v: 1/k for k, v in s.items()}
elif point[0] is not S.Zero:
s = dict((k, Dummy() + point[0]) for k in variables)
rs = dict((v - point[0], k - point[0]) for k, v in s.items())
else:
s = ()
rs = ()
expr = expr.subs(s)
if expr.is_Add:
from sympy import expand_multinomial
expr = expand_multinomial(expr)
if s:
args = tuple([r[0] for r in rs.items()])
else:
args = tuple(variables)
if len(variables) > 1:
# XXX: better way? We need this expand() to
# workaround e.g: expr = x*(x + y).
# (x*(x + y)).as_leading_term(x, y) currently returns
# x*y (wrong order term!). That's why we want to deal with
# expand()'ed expr (handled in "if expr.is_Add" branch below).
expr = expr.expand()
if expr.is_Add:
lst = expr.extract_leading_order(args)
expr = Add(*[f.expr for (e, f) in lst])
elif expr:
expr = expr.as_leading_term(*args)
expr = expr.as_independent(*args, as_Add=False)[1]
expr = expand_power_base(expr)
expr = expand_log(expr)
if len(args) == 1:
# The definition of O(f(x)) symbol explicitly stated that
# the argument of f(x) is irrelevant. That's why we can
# combine some power exponents (only "on top" of the
# expression tree for f(x)), e.g.:
# x**p * (-x)**q -> x**(p+q) for real p, q.
x = args[0]
margs = list(Mul.make_args(
expr.as_independent(x, as_Add=False)[1]))
for i, t in enumerate(margs):
if t.is_Pow:
b, q = t.args
if b in (x, -x) and q.is_real and not q.has(x):
margs[i] = x**q
elif b.is_Pow and not b.exp.has(x):
b, r = b.args
if b in (x, -x) and r.is_real:
margs[i] = x**(r*q)
elif b.is_Mul and b.args[0] is S.NegativeOne:
b = -b
if b.is_Pow and not b.exp.has(x):
b, r = b.args
if b in (x, -x) and r.is_real:
margs[i] = x**(r*q)
expr = Mul(*margs)
expr = expr.subs(rs)
if expr is S.Zero:
return expr
if expr.is_Order:
expr = expr.expr
if not expr.has(*variables):
expr = S.One
# create Order instance:
vp = dict(zip(variables, point))
variables.sort(key=default_sort_key)
point = [vp[v] for v in variables]
args = (expr,) + Tuple(*zip(variables, point))
obj = Expr.__new__(cls, *args)
return obj
def _eval_nseries(self, x, n, logx):
return self
@property
def expr(self):
return self.args[0]
@property
def variables(self):
if self.args[1:]:
return tuple(x[0] for x in self.args[1:])
else:
return ()
@property
def point(self):
if self.args[1:]:
return tuple(x[1] for x in self.args[1:])
else:
return ()
@property
def free_symbols(self):
return self.expr.free_symbols | set(self.variables)
def _eval_power(b, e):
if e.is_Number and e.is_nonnegative:
return b.func(b.expr ** e, *b.args[1:])
if e == O(1):
return b
return
def as_expr_variables(self, order_symbols):
if order_symbols is None:
order_symbols = self.args[1:]
else:
if not all(o[1] == order_symbols[0][1] for o in order_symbols) and \
not all(p == self.point[0] for p in self.point):
raise NotImplementedError('Order at points other than 0 '
'or oo not supported, got %s as a point.' % point)
if order_symbols and order_symbols[0][1] != self.point[0]:
raise NotImplementedError(
"Multiplying Order at different points is not supported.")
order_symbols = dict(order_symbols)
for s, p in dict(self.args[1:]).items():
if s not in order_symbols.keys():
order_symbols[s] = p
order_symbols = sorted(order_symbols.items(), key=lambda x: default_sort_key(x[0]))
return self.expr, tuple(order_symbols)
def removeO(self):
return S.Zero
def getO(self):
return self
@cacheit
def contains(self, expr):
r"""
Return True if expr belongs to Order(self.expr, \*self.variables).
Return False if self belongs to expr.
Return None if the inclusion relation cannot be determined
(e.g. when self and expr have different symbols).
"""
from sympy import powsimp
if expr is S.Zero:
return True
if expr is S.NaN:
return False
if expr.is_Order:
if not all(p == expr.point[0] for p in expr.point) and \
not all(p == self.point[0] for p in self.point):
raise NotImplementedError('Order at points other than 0 '
'or oo not supported, got %s as a point.' % point)
else:
# self and/or expr is O(1):
if any(not p for p in [expr.point, self.point]):
point = self.point + expr.point
if point:
point = point[0]
else:
point = S.Zero
else:
point = self.point[0]
if expr.expr == self.expr:
# O(1) + O(1), O(1) + O(1, x), etc.
return all([x in self.args[1:] for x in expr.args[1:]])
if expr.expr.is_Add:
return all([self.contains(x) for x in expr.expr.args])
if self.expr.is_Add and point == S.Zero:
return any([self.func(x, *self.args[1:]).contains(expr)
for x in self.expr.args])
if self.variables and expr.variables:
common_symbols = tuple(
[s for s in self.variables if s in expr.variables])
elif self.variables:
common_symbols = self.variables
else:
common_symbols = expr.variables
if not common_symbols:
return None
if (self.expr.is_Pow and self.expr.base.is_Symbol
and self.expr.exp.is_positive):
if expr.expr.is_Pow and self.expr.base == expr.expr.base:
return not (self.expr.exp-expr.expr.exp).is_positive
if expr.expr.is_Mul:
for arg in expr.expr.args:
if (arg.is_Pow and self.expr.base == arg.base
and (expr.expr/arg).is_number):
r = (self.expr.exp-arg.exp).is_positive
if not (r is None):
return not r
r = None
ratio = self.expr/expr.expr
ratio = powsimp(ratio, deep=True, combine='exp')
for s in common_symbols:
l = ratio.limit(s, point)
from sympy.series.limits import Limit
if not isinstance(l, Limit):
l = l != 0
else:
l = None
if r is None:
r = l
else:
if r != l:
return
return r
if (self.expr.is_Pow and self.expr.base.is_Symbol
and self.expr.exp.is_positive):
if expr.is_Pow and self.expr.base == expr.base:
return not (self.expr.exp-expr.exp).is_positive
if expr.is_Mul:
for arg in expr.args:
if (arg.is_Pow and self.expr.base == arg.base
and (expr/arg).is_number):
r = (self.expr.exp-arg.exp).is_positive
if not (r is None):
return not r
obj = self.func(expr, *self.args[1:])
return self.contains(obj)
def __contains__(self, other):
result = self.contains(other)
if result is None:
raise TypeError('contains did not evaluate to a bool')
return result
def _eval_subs(self, old, new):
if old in self.variables:
newexpr = self.expr.subs(old, new)
i = self.variables.index(old)
newvars = list(self.variables)
newpt = list(self.point)
if new.is_Symbol:
newvars[i] = new
else:
syms = new.free_symbols
if len(syms) == 1 or old in syms:
if old in syms:
var = self.variables[i]
else:
var = syms.pop()
# First, try to substitute self.point in the "new"
# expr to see if this is a fixed point.
# E.g. O(y).subs(y, sin(x))
point = new.subs(var, self.point[i])
if point != self.point[i]:
from sympy.solvers.solveset import solveset
d = Dummy()
sol = solveset(old - new.subs(var, d), d)
if isinstance(sol, Complement):
e1 = sol.args[0]
e2 = sol.args[1]
sol = set(e1) - set(e2)
res = [dict(zip((d, ), sol))]
point = d.subs(res[0]).limit(old, self.point[i])
newvars[i] = var
newpt[i] = point
elif old not in syms:
del newvars[i], newpt[i]
if not syms and new == self.point[i]:
newvars.extend(syms)
newpt.extend([S.Zero]*len(syms))
else:
return
return Order(newexpr, *zip(newvars, newpt))
def _eval_conjugate(self):
expr = self.expr._eval_conjugate()
if expr is not None:
return self.func(expr, *self.args[1:])
def _eval_derivative(self, x):
return self.func(self.expr.diff(x), *self.args[1:]) or self
def _eval_transpose(self):
expr = self.expr._eval_transpose()
if expr is not None:
return self.func(expr, *self.args[1:])
def _sage_(self):
#XXX: SAGE doesn't have Order yet. Let's return 0 instead.
return Rational(0)._sage_()
O = Order
| 16,927 | 34.563025 | 95 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/benchmarks/bench_order.py
|
from __future__ import print_function, division
from sympy import Symbol, O, Add
x = Symbol('x')
l = list(x**i for i in range(1000))
l.append(O(x**1001))
def timeit_order_1x():
_ = Add(*l)
| 196 | 16.909091 | 47 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/benchmarks/bench_limit.py
|
from __future__ import print_function, division
from sympy import Symbol, limit, oo
x = Symbol('x')
def timeit_limit_1x():
limit(1/x, x, oo)
| 149 | 14 | 47 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/sympy/series/benchmarks/__init__.py
| 0 | 0 | 0 |
py
|
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