def test_ccode_sign():
expr1, ref1 = sign(x) * y, 'y*(((x) > 0) - ((x) < 0))'
expr2, ref2 = sign(cos(x)), '(((cos(x)) > 0) - ((cos(x)) < 0))'
expr3, ref3 = sign(2 * x + x**2) * x + x**2, 'pow(x, 2) + x*(((pow(x, 2) + 2*x) > 0) - ((pow(x, 2) + 2*x) < 0))'
assert ccode(expr1) == ref1
assert ccode(expr1, 'z') == 'z = %s;' % ref1
assert ccode(expr2) == ref2
assert ccode(expr3) == ref3
def test_ccode_sign():
expr1, ref1 = sign(x) * y, 'y*(((x) > 0) - ((x) < 0))'
expr2, ref2 = sign(cos(x)), '(((cos(x)) > 0) - ((cos(x)) < 0))'
expr3, ref3 = sign(2 * x + x**2) * x + x**2, 'pow(x, 2) + x*(((pow(x, 2) + 2*x) > 0) - ((pow(x, 2) + 2*x) < 0))'
assert ccode(expr1) == ref1
assert ccode(expr1, 'z') == 'z = %s;' % ref1
assert ccode(expr2) == ref2
assert ccode(expr3) == ref3
def test_sign():
expr = sign(x) * y
assert rust_code(expr) == "y*x.signum()"
assert rust_code(expr, assign_to='r') == "r = y*x.signum();"
expr = sign(x + y) + 42
assert rust_code(expr) == "(x + y).signum() + 42"
assert rust_code(expr, assign_to='r') == "r = (x + y).signum() + 42;"
expr = sign(cos(x))
assert rust_code(expr) == "x.cos().signum()"
def test_ccode_sign():
expr = sign(x) * y
assert ccode(expr) == 'y*(((x) > 0) - ((x) < 0))'
assert ccode(expr, 'z') == 'z = y*(((x) > 0) - ((x) < 0));'
assert ccode(sign(2 * x + x**2) * x + x**2) == \
'pow(x, 2) + x*(((pow(x, 2) + 2*x) > 0) - ((pow(x, 2) + 2*x) < 0))'
expr = sign(cos(x))
assert ccode(expr) == '(((cos(x)) > 0) - ((cos(x)) < 0))'
def test_ccode_sign():
expr = sign(x) * y
assert ccode(expr) == 'y*(((x) > 0) - ((x) < 0))'
assert ccode(expr, 'z') == 'z = y*(((x) > 0) - ((x) < 0));'
assert ccode(sign(2 * x + x**2) * x + x**2) == \
'pow(x, 2) + x*(((pow(x, 2) + 2*x) > 0) - ((pow(x, 2) + 2*x) < 0))'
expr = sign(cos(x))
assert ccode(expr) == '(((cos(x)) > 0) - ((cos(x)) < 0))'
def test_sign():
expr = sign(x) * y
assert rust_code(expr) == "y*x.signum()"
assert rust_code(expr, assign_to='r') == "r = y*x.signum();"
expr = sign(x + y) + 42
assert rust_code(expr) == "(x + y).signum() + 42"
assert rust_code(expr, assign_to='r') == "r = (x + y).signum() + 42;"
expr = sign(cos(x))
assert rust_code(expr) == "x.cos().signum()"
def test_ccode_sign():
expr1, ref1 = sign(x) * y, "y*(((x) > 0) - ((x) < 0))"
expr2, ref2 = sign(cos(x)), "(((cos(x)) > 0) - ((cos(x)) < 0))"
expr3, ref3 = (
sign(2 * x + x ** 2) * x + x ** 2,
"pow(x, 2) + x*(((pow(x, 2) + 2*x) > 0) - ((pow(x, 2) + 2*x) < 0))",
)
assert ccode(expr1) == ref1
assert ccode(expr1, "z") == "z = %s;" % ref1
assert ccode(expr2) == ref2
assert ccode(expr3) == ref3
def test_rcode_sgn():
expr = sign(x) * y
assert rcode(expr) == "y*sign(x)"
p = rcode(expr, "z")
assert p == "z = y*sign(x);"
p = rcode(sign(2 * x + x**2) * x + x**2)
assert p == "x^2 + x*sign(x^2 + 2*x)"
expr = sign(cos(x))
p = rcode(expr)
assert p == "sign(cos(x))"
def test_rcode_sgn():
expr = sign(x) * y
assert rcode(expr) == 'y*sign(x)'
p = rcode(expr, 'z')
assert p == 'z = y*sign(x);'
p = rcode(sign(2 * x + x**2) * x + x**2)
assert p == "x^2 + x*sign(x^2 + 2*x)"
expr = sign(cos(x))
p = rcode(expr)
assert p == 'sign(cos(x))'
def test_bounded():
x, y = symbols('xy')
assert ask(x, Q.bounded) == False
assert ask(x, Q.bounded, Assume(x, Q.bounded)) == True
assert ask(x, Q.bounded, Assume(y, Q.bounded)) == False
assert ask(x, Q.bounded, Assume(x, Q.complex)) == False
assert ask(x+1, Q.bounded) == False
assert ask(x+1, Q.bounded, Assume(x, Q.bounded)) == True
assert ask(x+y, Q.bounded) == None
assert ask(x+y, Q.bounded, Assume(x, Q.bounded)) == False
assert ask(x+1, Q.bounded, Assume(x, Q.bounded) & \
Assume(y, Q.bounded)) == True
assert ask(2*x, Q.bounded) == False
assert ask(2*x, Q.bounded, Assume(x, Q.bounded)) == True
assert ask(x*y, Q.bounded) == None
assert ask(x*y, Q.bounded, Assume(x, Q.bounded)) == False
assert ask(x*y, Q.bounded, Assume(x, Q.bounded) & \
Assume(y, Q.bounded)) == True
assert ask(x**2, Q.bounded) == False
assert ask(2**x, Q.bounded) == False
assert ask(2**x, Q.bounded, Assume(x, Q.bounded)) == True
assert ask(x**x, Q.bounded) == False
assert ask(Rational(1,2) ** x, Q.bounded) == True
assert ask(x ** Rational(1,2), Q.bounded) == False
# sign function
assert ask(sign(x), Q.bounded) == True
assert ask(sign(x), Q.bounded, Assume(x, Q.bounded, False)) == True
# exponential functions
assert ask(log(x), Q.bounded) == False
assert ask(log(x), Q.bounded, Assume(x, Q.bounded)) == True
assert ask(exp(x), Q.bounded) == False
assert ask(exp(x), Q.bounded, Assume(x, Q.bounded)) == True
assert ask(exp(2), Q.bounded) == True
# trigonometric functions
assert ask(sin(x), Q.bounded) == True
assert ask(sin(x), Q.bounded, Assume(x, Q.bounded, False)) == True
assert ask(cos(x), Q.bounded) == True
assert ask(cos(x), Q.bounded, Assume(x, Q.bounded, False)) == True
assert ask(2*sin(x), Q.bounded) == True
assert ask(sin(x)**2, Q.bounded) == True
assert ask(cos(x)**2, Q.bounded) == True
assert ask(cos(x) + sin(x), Q.bounded) == True
def test_bounded():
x, y = symbols('x,y')
assert ask(Q.bounded(x)) == False
assert ask(Q.bounded(x), Q.bounded(x)) == True
assert ask(Q.bounded(x), Q.bounded(y)) == False
assert ask(Q.bounded(x), Q.complex(x)) == False
assert ask(Q.bounded(x + 1)) == False
assert ask(Q.bounded(x + 1), Q.bounded(x)) == True
assert ask(Q.bounded(x + y)) == None
assert ask(Q.bounded(x + y), Q.bounded(x)) == False
assert ask(Q.bounded(x + 1), Q.bounded(x) & Q.bounded(y)) == True
assert ask(Q.bounded(2 * x)) == False
assert ask(Q.bounded(2 * x), Q.bounded(x)) == True
assert ask(Q.bounded(x * y)) == None
assert ask(Q.bounded(x * y), Q.bounded(x)) == False
assert ask(Q.bounded(x * y), Q.bounded(x) & Q.bounded(y)) == True
assert ask(Q.bounded(x**2)) == False
assert ask(Q.bounded(2**x)) == False
assert ask(Q.bounded(2**x), Q.bounded(x)) == True
assert ask(Q.bounded(x**x)) == False
assert ask(Q.bounded(Rational(1, 2)**x)) == None
assert ask(Q.bounded(Rational(1, 2)**x), Q.positive(x)) == True
assert ask(Q.bounded(Rational(1, 2)**x), Q.negative(x)) == False
assert ask(Q.bounded(sqrt(x))) == False
# sign function
assert ask(Q.bounded(sign(x))) == True
assert ask(Q.bounded(sign(x)), ~Q.bounded(x)) == True
# exponential functions
assert ask(Q.bounded(log(x))) == False
assert ask(Q.bounded(log(x)), Q.bounded(x)) == True
assert ask(Q.bounded(exp(x))) == False
assert ask(Q.bounded(exp(x)), Q.bounded(x)) == True
assert ask(Q.bounded(exp(2))) == True
# trigonometric functions
assert ask(Q.bounded(sin(x))) == True
assert ask(Q.bounded(sin(x)), ~Q.bounded(x)) == True
assert ask(Q.bounded(cos(x))) == True
assert ask(Q.bounded(cos(x)), ~Q.bounded(x)) == True
assert ask(Q.bounded(2 * sin(x))) == True
assert ask(Q.bounded(sin(x)**2)) == True
assert ask(Q.bounded(cos(x)**2)) == True
assert ask(Q.bounded(cos(x) + sin(x))) == True
def test_bounded():
x, y = symbols('x,y')
assert ask(Q.bounded(x)) == False
assert ask(Q.bounded(x), Q.bounded(x)) == True
assert ask(Q.bounded(x), Q.bounded(y)) == False
assert ask(Q.bounded(x), Q.complex(x)) == False
assert ask(Q.bounded(x+1)) == False
assert ask(Q.bounded(x+1), Q.bounded(x)) == True
assert ask(Q.bounded(x+y)) == None
assert ask(Q.bounded(x+y), Q.bounded(x)) == False
assert ask(Q.bounded(x+1), Q.bounded(x) & Q.bounded(y)) == True
assert ask(Q.bounded(2*x)) == False
assert ask(Q.bounded(2*x), Q.bounded(x)) == True
assert ask(Q.bounded(x*y)) == None
assert ask(Q.bounded(x*y), Q.bounded(x)) == False
assert ask(Q.bounded(x*y), Q.bounded(x) & Q.bounded(y)) == True
assert ask(Q.bounded(x**2)) == False
assert ask(Q.bounded(2**x)) == False
assert ask(Q.bounded(2**x), Q.bounded(x)) == True
assert ask(Q.bounded(x**x)) == False
assert ask(Q.bounded(Rational(1,2) ** x)) == None
assert ask(Q.bounded(Rational(1,2) ** x), Q.positive(x)) == True
assert ask(Q.bounded(Rational(1,2) ** x), Q.negative(x)) == False
assert ask(Q.bounded(sqrt(x))) == False
# sign function
assert ask(Q.bounded(sign(x))) == True
assert ask(Q.bounded(sign(x)), ~Q.bounded(x)) == True
# exponential functions
assert ask(Q.bounded(log(x))) == False
assert ask(Q.bounded(log(x)), Q.bounded(x)) == True
assert ask(Q.bounded(exp(x))) == False
assert ask(Q.bounded(exp(x)), Q.bounded(x)) == True
assert ask(Q.bounded(exp(2))) == True
# trigonometric functions
assert ask(Q.bounded(sin(x))) == True
assert ask(Q.bounded(sin(x)), ~Q.bounded(x)) == True
assert ask(Q.bounded(cos(x))) == True
assert ask(Q.bounded(cos(x)), ~Q.bounded(x)) == True
assert ask(Q.bounded(2*sin(x))) == True
assert ask(Q.bounded(sin(x)**2)) == True
assert ask(Q.bounded(cos(x)**2)) == True
assert ask(Q.bounded(cos(x) + sin(x))) == True
def calc_limit(a, b):
"""replace x with a, using subs if possible, otherwise limit
where sign of b is considered"""
wok = inverse_mapping.subs(x, a)
if wok is S.NaN or wok.is_bounded is False and a.is_bounded:
return limit(sign(b)*inverse_mapping, x, a)
return wok
def refine_Pow(expr, assumptions):
"""
Handler for instances of Pow.
>>> from sympy import Symbol, Assume, Q
>>> from sympy.assumptions.refine import refine_Pow
>>> from sympy.abc import x
>>> refine_Pow((-1)**x, Assume(x, Q.real))
>>> refine_Pow((-1)**x, Assume(x, Q.even))
1
>>> refine_Pow((-1)**x, Assume(x, Q.odd))
-1
"""
from sympy.core import Pow, Rational
from sympy.functions import sign
if ask(expr.base, Q.real, assumptions):
if expr.base.is_number:
if ask(expr.exp, Q.even, assumptions):
return abs(expr.base) ** expr.exp
if ask(expr.exp, Q.odd, assumptions):
return sign(expr.base) * abs(expr.base) ** expr.exp
if isinstance(expr.exp, Rational):
if type(expr.base) is Pow:
return abs(expr.base.base) ** (expr.base.exp * expr.exp)
def test_MpmathPrinter():
p = MpmathPrinter()
assert p.doprint(sign(x)) == 'mpmath.sign(x)'
assert p.doprint(Rational(1, 2)) == 'mpmath.mpf(1)/mpmath.mpf(2)'
assert p.doprint(loggamma(x)) == 'mpmath.loggamma(x)'
assert p.doprint(fresnels(x)) == 'mpmath.fresnels(x)'
assert p.doprint(fresnelc(x)) == 'mpmath.fresnelc(x)'
def test_PythonCodePrinter():
prntr = PythonCodePrinter()
assert not prntr.module_imports
assert prntr.doprint(x**y) == 'x**y'
assert prntr.doprint(Mod(x, 2)) == 'x % 2'
assert prntr.doprint(And(x, y)) == 'x and y'
assert prntr.doprint(Or(x, y)) == 'x or y'
assert not prntr.module_imports
assert prntr.doprint(pi) == 'math.pi'
assert prntr.module_imports == {'math': {'pi'}}
assert prntr.doprint(x**Rational(1, 2)) == 'math.sqrt(x)'
assert prntr.doprint(sqrt(x)) == 'math.sqrt(x)'
assert prntr.module_imports == {'math': {'pi', 'sqrt'}}
assert prntr.doprint(acos(x)) == 'math.acos(x)'
assert prntr.doprint(Assignment(x, 2)) == 'x = 2'
assert prntr.doprint(Piecewise(
(1, Eq(x, 0)),
(2, x > 6))) == '((1) if (x == 0) else (2) if (x > 6) else None)'
assert prntr.doprint(Piecewise((2, Le(x, 0)),
(3, Gt(x, 0)), evaluate=False)) == '((2) if (x <= 0) else'\
' (3) if (x > 0) else None)'
assert prntr.doprint(sign(x)) == '(0.0 if x == 0 else math.copysign(1, x))'
assert prntr.doprint(p[0, 1]) == 'p[0, 1]'
def calc_limit(a, b):
"""replace x with a, using subs if possible, otherwise limit
where sign of b is considered"""
wok = inverse_mapping.subs(x, a)
if wok is S.NaN or wok.is_bounded is False and a.is_bounded:
return limit(sign(b)*inverse_mapping, x, a)
return wok
def test_PythonCodePrinter():
prntr = PythonCodePrinter()
assert not prntr.module_imports
assert prntr.doprint(x**y) == "x**y"
assert prntr.doprint(Mod(x, 2)) == "x % 2"
assert prntr.doprint(And(x, y)) == "x and y"
assert prntr.doprint(Or(x, y)) == "x or y"
assert not prntr.module_imports
assert prntr.doprint(pi) == "math.pi"
assert prntr.module_imports == {"math": {"pi"}}
assert prntr.doprint(x**Rational(1, 2)) == "math.sqrt(x)"
assert prntr.doprint(sqrt(x)) == "math.sqrt(x)"
assert prntr.module_imports == {"math": {"pi", "sqrt"}}
assert prntr.doprint(acos(x)) == "math.acos(x)"
assert prntr.doprint(Assignment(x, 2)) == "x = 2"
assert (prntr.doprint(Piecewise(
(1, Eq(x, 0)),
(2, x > 6))) == "((1) if (x == 0) else (2) if (x > 6) else None)")
assert (prntr.doprint(
Piecewise((2, Le(x, 0)), (3, Gt(x, 0)),
evaluate=False)) == "((2) if (x <= 0) else"
" (3) if (x > 0) else None)")
assert prntr.doprint(sign(x)) == "(0.0 if x == 0 else math.copysign(1, x))"
assert prntr.doprint(p[0, 1]) == "p[0, 1]"
def calc_limit(a, b):
"""replace x with a, using subs if possible, otherwise limit
where sign of b is considered"""
wok = inverse_mapping.subs(x, a)
if not wok is S.NaN:
return wok
return limit(sign(b)*inverse_mapping, x, a)
def denester(nested):
"""
Denests a list of expressions that contain nested square roots.
This method should not be called directly - use 'sqrtdenest' instead.
This algorithm is based on <http://www.almaden.ibm.com/cs/people/fagin/symb85.pdf>.
It is assumed that all of the elements of 'nested' share the same
bottom-level radicand. (This is stated in the paper, on page 177, in
the paragraph immediately preceding the algorithm.)
When evaluating all of the arguments in parallel, the bottom-level
radicand only needs to be denested once. This means that calling
denester with x arguments results in a recursive invocation with x+1
arguments; hence denester has polynomial complexity.
However, if the arguments were evaluated separately, each call would
result in two recursive invocations, and the algorithm would have
exponential complexity.
This is discussed in the paper in the middle paragraph of page 179.
"""
if all((n**2).is_Number for n in nested): #If none of the arguments are nested
for f in subsets(len(nested)): #Test subset 'f' of nested
p = prod(nested[i]**2 for i in range(len(f)) if f[i]).expand()
if 1 in f and f.count(1) > 1 and f[-1]:
p = -p
if sqrt(p).is_Number:
return sqrt(p), f #If we got a perfect square, return its square root.
return nested[-1], [0]*len(nested) #Otherwise, return the radicand from the previous invocation.
else:
a, b, r, R = Wild('a'), Wild('b'), Wild('r'), None
values = [expr.match(sqrt(a + b * sqrt(r))) for expr in nested]
if any(v is None for v in values): # this pattern is not recognized
return nested[-1], [0]*len(nested) #Otherwise, return the radicand from the previous invocation.
for v in values:
if r in v: #Since if b=0, r is not defined
if R is not None:
assert R == v[r] #All the 'r's should be the same.
else:
R = v[r]
if R is None:
return nested[-1], [0]*len(nested) #return the radicand from the previous invocation.
d, f = denester([sqrt((v[a]**2).expand()-(R*v[b]**2).expand()) for v in values] + [sqrt(R)])
if all(fi == 0 for fi in f):
v = values[-1]
return sqrt(v[a] + v[b]*d), f
else:
v = prod(nested[i]**2 for i in range(len(nested)) if f[i]).expand().match(a+b*sqrt(r))
if 1 in f and f.index(1) < len(nested) - 1 and f[len(nested)-1]:
v[a] = -1 * v[a]
v[b] = -1 * v[b]
if not f[len(nested)]: #Solution denests with square roots
vad = (v[a] + d).expand()
if not vad:
return nested[-1], [0]*len(nested) #Otherwise, return the radicand from the previous invocation.
return (sqrt(vad/2) + sign(v[b])*sqrt((v[b]**2*R/(2*vad)).expand())).expand(), f
else: #Solution requires a fourth root
FR, s = (R.expand()**Rational(1,4)), sqrt((v[b]*R).expand()+d)
return (s/(sqrt(2)*FR) + v[a]*FR/(sqrt(2)*s)).expand(), f
def _sqrt_numeric_denest(a, b, r, d2):
r"""Helper that denest
$\sqrt{a + b \sqrt{r}}, d^2 = a^2 - b^2 r > 0$
If it cannot be denested, it returns ``None``.
"""
d = sqrt(d2)
s = a + d
# sqrt_depth(res) <= sqrt_depth(s) + 1
# sqrt_depth(expr) = sqrt_depth(r) + 2
# there is denesting if sqrt_depth(s) + 1 < sqrt_depth(r) + 2
# if s**2 is Number there is a fourth root
if sqrt_depth(s) < sqrt_depth(r) + 1 or (s**2).is_Rational:
s1, s2 = sign(s), sign(b)
if s1 == s2 == -1:
s1 = s2 = 1
res = (s1 * sqrt(a + d) + s2 * sqrt(a - d)) * sqrt(2) / 2
return res.expand()
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_bounded is False and a.is_bounded:
return limit(sign(b) * F, d, a)
return wok
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_bounded is False and a.is_bounded:
return limit(sign(b)*F, d, a)
return wok
def p_parameter(self):
"""P is a parameter of parabola.
Returns
=======
p : number or symbolic expression
Notes
=====
The absolute value of p is the focal length. The sign on p tells
which way the parabola faces. Vertical parabolas that open up
and horizontal that open right, give a positive value for p.
Vertical parabolas that open down and horizontal that open left,
give a negative value for p.
See Also
========
http://www.sparknotes.com/math/precalc/conicsections/section2.rhtml
Examples
========
>>> from sympy import Parabola, Point, Line
>>> p1 = Parabola(Point(0, 0), Line(Point(5, 8), Point(7, 8)))
>>> p1.p_parameter
-4
"""
m = self.directrix.slope
if m is S.Infinity:
x = self.directrix.coefficients[2]
p = sign(self.focus.args[0] + x)
elif m == 0:
y = self.directrix.coefficients[2]
p = sign(self.focus.args[1] + y)
else:
d = self.directrix.projection(self.focus)
p = sign(self.focus.x - d.x)
return p * self.focal_length
def test_MpmathPrinter():
p = MpmathPrinter()
assert p.doprint(sign(x)) == 'mpmath.sign(x)'
assert p.doprint(Rational(1, 2)) == 'mpmath.mpf(1)/mpmath.mpf(2)'
assert p.doprint(S.Exp1) == 'mpmath.e'
assert p.doprint(S.Pi) == 'mpmath.pi'
assert p.doprint(S.GoldenRatio) == 'mpmath.phi'
assert p.doprint(S.EulerGamma) == 'mpmath.euler'
assert p.doprint(S.NaN) == 'mpmath.nan'
assert p.doprint(S.Infinity) == 'mpmath.inf'
assert p.doprint(S.NegativeInfinity) == 'mpmath.ninf'
def p_parameter(self):
"""P is a parameter of parabola.
Returns
=======
p : number or symbolic expression
Notes
=====
The absolute value of p is the focal length. The sign on p tells
which way the parabola faces. Vertical parabolas that open up
and horizontal that open right, give a positive value for p.
Vertical parabolas that open down and horizontal that open left,
give a negative value for p.
See Also
========
http://www.sparknotes.com/math/precalc/conicsections/section2.rhtml
Examples
========
>>> from sympy import Parabola, Point, Line
>>> p1 = Parabola(Point(0, 0), Line(Point(5, 8), Point(7, 8)))
>>> p1.p_parameter
-4
"""
if self.axis_of_symmetry.slope == 0:
x = self.directrix.coefficients[2]
p = sign(self.focus.args[0] + x)
else:
y = self.directrix.coefficients[2]
p = sign(self.focus.args[1] + y)
return p * self.focal_length
def test_Function():
assert mcode(sin(x)**cos(x)) == "sin(x).^cos(x)"
assert mcode(sign(x)) == "sign(x)"
assert mcode(exp(x)) == "exp(x)"
assert mcode(log(x)) == "log(x)"
assert mcode(factorial(x)) == "factorial(x)"
assert mcode(floor(x)) == "floor(x)"
assert mcode(atan2(y, x)) == "atan2(y, x)"
assert mcode(beta(x, y)) == 'beta(x, y)'
assert mcode(polylog(x, y)) == 'polylog(x, y)'
assert mcode(harmonic(x)) == 'harmonic(x)'
assert mcode(bernoulli(x)) == "bernoulli(x)"
assert mcode(bernoulli(x, y)) == "bernoulli(x, y)"
def test_Function():
assert mcode(sin(x) ** cos(x)) == "sin(x).^cos(x)"
assert mcode(sign(x)) == "sign(x)"
assert mcode(exp(x)) == "exp(x)"
assert mcode(log(x)) == "log(x)"
assert mcode(factorial(x)) == "factorial(x)"
assert mcode(floor(x)) == "floor(x)"
assert mcode(atan2(y, x)) == "atan2(y, x)"
assert mcode(beta(x, y)) == 'beta(x, y)'
assert mcode(polylog(x, y)) == 'polylog(x, y)'
assert mcode(harmonic(x)) == 'harmonic(x)'
assert mcode(bernoulli(x)) == "bernoulli(x)"
assert mcode(bernoulli(x, y)) == "bernoulli(x, y)"
def test_NumPyPrinter():
from sympy import (
Lambda,
ZeroMatrix,
OneMatrix,
FunctionMatrix,
HadamardProduct,
KroneckerProduct,
Adjoint,
DiagonalOf,
DiagMatrix,
DiagonalMatrix,
)
from sympy.abc import a, b
p = NumPyPrinter()
assert p.doprint(sign(x)) == "numpy.sign(x)"
A = MatrixSymbol("A", 2, 2)
B = MatrixSymbol("B", 2, 2)
C = MatrixSymbol("C", 1, 5)
D = MatrixSymbol("D", 3, 4)
assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
assert p.doprint(Identity(3)) == "numpy.eye(3)"
u = MatrixSymbol("x", 2, 1)
v = MatrixSymbol("y", 2, 1)
assert p.doprint(MatrixSolve(A, u)) == "numpy.linalg.solve(A, x)"
assert p.doprint(MatrixSolve(A, u) + v) == "numpy.linalg.solve(A, x) + y"
assert p.doprint(ZeroMatrix(2, 3)) == "numpy.zeros((2, 3))"
assert p.doprint(OneMatrix(2, 3)) == "numpy.ones((2, 3))"
assert (p.doprint(FunctionMatrix(4, 5, Lambda(
(a, b), a + b))) == "numpy.fromfunction(lambda a, b: a + b, (4, 5))")
assert p.doprint(HadamardProduct(A, B)) == "numpy.multiply(A, B)"
assert p.doprint(KroneckerProduct(A, B)) == "numpy.kron(A, B)"
assert p.doprint(Adjoint(A)) == "numpy.conjugate(numpy.transpose(A))"
assert p.doprint(DiagonalOf(A)) == "numpy.reshape(numpy.diag(A), (-1, 1))"
assert p.doprint(DiagMatrix(C)) == "numpy.diagflat(C)"
assert p.doprint(DiagonalMatrix(D)) == "numpy.multiply(D, numpy.eye(3, 4))"
# Workaround for numpy negative integer power errors
assert p.doprint(x**-1) == "x**(-1.0)"
assert p.doprint(x**-2) == "x**(-2.0)"
assert p.doprint(S.Exp1) == "numpy.e"
assert p.doprint(S.Pi) == "numpy.pi"
assert p.doprint(S.EulerGamma) == "numpy.euler_gamma"
assert p.doprint(S.NaN) == "numpy.nan"
assert p.doprint(S.Infinity) == "numpy.PINF"
assert p.doprint(S.NegativeInfinity) == "numpy.NINF"
def test_NumPyPrinter():
p = NumPyPrinter()
assert p.doprint(sign(x)) == 'numpy.sign(x)'
A = MatrixSymbol("A", 2, 2)
assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
u = MatrixSymbol('x', 2, 1)
v = MatrixSymbol('y', 2, 1)
assert p.doprint(MatrixSolve(A, u)) == 'numpy.linalg.solve(A, x)'
assert p.doprint(MatrixSolve(A, u) + v) == 'numpy.linalg.solve(A, x) + y'
# Workaround for numpy negative integer power errors
assert p.doprint(x**-1) == 'x**(-1.0)'
assert p.doprint(x**-2) == 'x**(-2.0)'
def test_NumPyPrinter():
from sympy.core.function import Lambda
from sympy.matrices.expressions.adjoint import Adjoint
from sympy.matrices.expressions.diagonal import (DiagMatrix,
DiagonalMatrix,
DiagonalOf)
from sympy.matrices.expressions.funcmatrix import FunctionMatrix
from sympy.matrices.expressions.hadamard import HadamardProduct
from sympy.matrices.expressions.kronecker import KroneckerProduct
from sympy.matrices.expressions.special import (OneMatrix, ZeroMatrix)
from sympy.abc import a, b
p = NumPyPrinter()
assert p.doprint(sign(x)) == 'numpy.sign(x)'
A = MatrixSymbol("A", 2, 2)
B = MatrixSymbol("B", 2, 2)
C = MatrixSymbol("C", 1, 5)
D = MatrixSymbol("D", 3, 4)
assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
assert p.doprint(Identity(3)) == "numpy.eye(3)"
u = MatrixSymbol('x', 2, 1)
v = MatrixSymbol('y', 2, 1)
assert p.doprint(MatrixSolve(A, u)) == 'numpy.linalg.solve(A, x)'
assert p.doprint(MatrixSolve(A, u) + v) == 'numpy.linalg.solve(A, x) + y'
assert p.doprint(ZeroMatrix(2, 3)) == "numpy.zeros((2, 3))"
assert p.doprint(OneMatrix(2, 3)) == "numpy.ones((2, 3))"
assert p.doprint(FunctionMatrix(4, 5, Lambda((a, b), a + b))) == \
"numpy.fromfunction(lambda a, b: a + b, (4, 5))"
assert p.doprint(HadamardProduct(A, B)) == "numpy.multiply(A, B)"
assert p.doprint(KroneckerProduct(A, B)) == "numpy.kron(A, B)"
assert p.doprint(Adjoint(A)) == "numpy.conjugate(numpy.transpose(A))"
assert p.doprint(DiagonalOf(A)) == "numpy.reshape(numpy.diag(A), (-1, 1))"
assert p.doprint(DiagMatrix(C)) == "numpy.diagflat(C)"
assert p.doprint(DiagonalMatrix(D)) == "numpy.multiply(D, numpy.eye(3, 4))"
# Workaround for numpy negative integer power errors
assert p.doprint(x**-1) == 'x**(-1.0)'
assert p.doprint(x**-2) == 'x**(-2.0)'
expr = Pow(2, -1, evaluate=False)
assert p.doprint(expr) == "2**(-1.0)"
assert p.doprint(S.Exp1) == 'numpy.e'
assert p.doprint(S.Pi) == 'numpy.pi'
assert p.doprint(S.EulerGamma) == 'numpy.euler_gamma'
assert p.doprint(S.NaN) == 'numpy.nan'
assert p.doprint(S.Infinity) == 'numpy.PINF'
assert p.doprint(S.NegativeInfinity) == 'numpy.NINF'
def fits_in(unit, dimension, reciprocal):
"""
Checks if unit fits in given dimension
(just for use inside this module)
Args:
- unit
- dimension
- reciprocal: 1 or -1 to show if unit is supposed to be fitted in as reciprocal or not
"""
assert isinstance(unit, Unit)
assert isinstance(dimension, Dimension)
assert reciprocal is S.One or reciprocal is S.NegativeOne
for lookAtThis, unitExp in unit.dim.items():
dimExp = dimension.get(lookAtThis, 0)
if not unitExp == 0:
if not sign(unitExp) == sign(dimExp * reciprocal):
return False
if not abs(unitExp) <= abs(dimExp):
return False
return True
def _sqrt_numeric_denest(a, b, r, d2):
"""Helper that denest expr = a + b*sqrt(r), with d2 = a**2 - b**2*r > 0
or returns None if not denested.
"""
from sympy.simplify.simplify import radsimp
depthr = sqrt_depth(r)
d = sqrt(d2)
vad = a + d
# sqrt_depth(res) <= sqrt_depth(vad) + 1
# sqrt_depth(expr) = depthr + 2
# there is denesting if sqrt_depth(vad)+1 < depthr + 2
# if vad**2 is Number there is a fourth root
if sqrt_depth(vad) < depthr + 1 or (vad**2).is_Rational:
vad1 = radsimp(1/vad)
return (sqrt(vad/2) + sign(b)*sqrt((b**2*r*vad1/2).expand())).expand()
def _sqrt_numeric_denest(a, b, r, d2):
"""Helper that denest expr = a + b*sqrt(r), with d2 = a**2 - b**2*r > 0
or returns None if not denested.
"""
from sympy.simplify.simplify import radsimp
depthr = sqrt_depth(r)
d = sqrt(d2)
vad = a + d
# sqrt_depth(res) <= sqrt_depth(vad) + 1
# sqrt_depth(expr) = depthr + 2
# there is denesting if sqrt_depth(vad)+1 < depthr + 2
# if vad**2 is Number there is a fourth root
if sqrt_depth(vad) < depthr + 1 or (vad**2).is_Rational:
vad1 = radsimp(1/vad)
return (sqrt(vad/2) + sign(b)*sqrt((b**2*r*vad1/2).expand())).expand()
def test_PythonCodePrinter():
prntr = PythonCodePrinter()
assert not prntr.module_imports
assert prntr.doprint(x**y) == 'x**y'
assert prntr.doprint(Mod(x, 2)) == 'x % 2'
assert prntr.doprint(And(x, y)) == 'x and y'
assert prntr.doprint(Or(x, y)) == 'x or y'
assert not prntr.module_imports
assert prntr.doprint(pi) == 'math.pi'
assert prntr.module_imports == {'math': {'pi'}}
assert prntr.doprint(acos(x)) == 'math.acos(x)'
assert prntr.doprint(Assignment(x, 2)) == 'x = 2'
assert prntr.doprint(Piecewise((1, Eq(x, 0)),
(2, x>6))) == '((1) if (x == 0) else (2) if (x > 6) else None)'
assert prntr.doprint(Piecewise((2, Le(x, 0)),
(3, Gt(x, 0)), evaluate=False)) == '((2) if (x <= 0) else'\
' (3) if (x > 0) else None)'
assert prntr.doprint(sign(x)) == '(0.0 if x == 0 else math.copysign(1, x))'
def test_NumPyPrinter():
p = NumPyPrinter()
assert p.doprint(sign(x)) == 'numpy.sign(x)'
A = MatrixSymbol("A", 2, 2)
assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
assert p.doprint(Identity(3)) == "numpy.eye(3)"
u = MatrixSymbol('x', 2, 1)
v = MatrixSymbol('y', 2, 1)
assert p.doprint(MatrixSolve(A, u)) == 'numpy.linalg.solve(A, x)'
assert p.doprint(MatrixSolve(A, u) + v) == 'numpy.linalg.solve(A, x) + y'
# Workaround for numpy negative integer power errors
assert p.doprint(x**-1) == 'x**(-1.0)'
assert p.doprint(x**-2) == 'x**(-2.0)'
assert p.doprint(S.Exp1) == 'numpy.e'
assert p.doprint(S.Pi) == 'numpy.pi'
assert p.doprint(S.EulerGamma) == 'numpy.euler_gamma'
assert p.doprint(S.NaN) == 'numpy.nan'
assert p.doprint(S.Infinity) == 'numpy.PINF'
assert p.doprint(S.NegativeInfinity) == 'numpy.NINF'
def refine_Pow(expr, assumptions):
"""
Handler for instances of Pow.
>>> from sympy import Symbol, Q
>>> from sympy.assumptions.refine import refine_Pow
>>> from sympy.abc import x,y,z
>>> refine_Pow((-1)**x, Q.real(x))
>>> refine_Pow((-1)**x, Q.even(x))
1
>>> refine_Pow((-1)**x, Q.odd(x))
-1
For powers of -1, even parts of the exponent can be simplified:
>>> refine_Pow((-1)**(x+y), Q.even(x))
(-1)**y
>>> refine_Pow((-1)**(x+y+z), Q.odd(x) & Q.odd(z))
(-1)**y
>>> refine_Pow((-1)**(x+y+2), Q.odd(x))
(-1)**(y + 1)
>>> refine_Pow((-1)**(x+3), True)
(-1)**(x + 1)
"""
from sympy.core import Pow, Rational
from sympy.functions.elementary.complexes import Abs
from sympy.functions import sign
if isinstance(expr.base, Abs):
if ask(Q.real(expr.base.args[0]), assumptions) and \
ask(Q.even(expr.exp), assumptions):
return expr.base.args[0]**expr.exp
if ask(Q.real(expr.base), assumptions):
if expr.base.is_number:
if ask(Q.even(expr.exp), assumptions):
return abs(expr.base)**expr.exp
if ask(Q.odd(expr.exp), assumptions):
return sign(expr.base) * abs(expr.base)**expr.exp
if isinstance(expr.exp, Rational):
if type(expr.base) is Pow:
return abs(expr.base.base)**(expr.base.exp * expr.exp)
if expr.base is S.NegativeOne:
if expr.exp.is_Add:
old = expr
# For powers of (-1) we can remove
# - even terms
# - pairs of odd terms
# - a single odd term + 1
# - A numerical constant N can be replaced with mod(N,2)
coeff, terms = expr.exp.as_coeff_add()
terms = set(terms)
even_terms = set([])
odd_terms = set([])
initial_number_of_terms = len(terms)
for t in terms:
if ask(Q.even(t), assumptions):
even_terms.add(t)
elif ask(Q.odd(t), assumptions):
odd_terms.add(t)
terms -= even_terms
if len(odd_terms) % 2:
terms -= odd_terms
new_coeff = (coeff + S.One) % 2
else:
terms -= odd_terms
new_coeff = coeff % 2
if new_coeff != coeff or len(terms) < initial_number_of_terms:
terms.add(new_coeff)
expr = expr.base**(Add(*terms))
# Handle (-1)**((-1)**n/2 + m/2)
e2 = 2 * expr.exp
if ask(Q.even(e2), assumptions):
if e2.could_extract_minus_sign():
e2 *= expr.base
if e2.is_Add:
i, p = e2.as_two_terms()
if p.is_Pow and p.base is S.NegativeOne:
if ask(Q.integer(p.exp), assumptions):
i = (i + 1) / 2
if ask(Q.even(i), assumptions):
return expr.base**p.exp
elif ask(Q.odd(i), assumptions):
return expr.base**(p.exp + 1)
else:
return expr.base**(p.exp + i)
if old != expr:
return expr
def test_NumPyPrinter():
p = NumPyPrinter()
assert p.doprint(sign(x)) == 'numpy.sign(x)'
A = MatrixSymbol("A", 2, 2)
assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)"
assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"
def sqrtofsqrtsimp_(a=0, b=0, c=0): # 1/sqrt(a + b*sqrt(c))
q = sqrt(a**2 - b**2*c)
if not q.is_Rational:
return None
return 1/(sqrt((a+q)/2) + sign(b)*sqrt((a-q)/2))
def refine_Pow(expr, assumptions):
"""
Handler for instances of Pow.
>>> from sympy import Symbol, Q
>>> from sympy.assumptions.refine import refine_Pow
>>> from sympy.abc import x,y,z
>>> refine_Pow((-1)**x, Q.real(x))
>>> refine_Pow((-1)**x, Q.even(x))
1
>>> refine_Pow((-1)**x, Q.odd(x))
-1
For powers of -1, even parts of the exponent can be simplified:
>>> refine_Pow((-1)**(x+y), Q.even(x))
(-1)**y
>>> refine_Pow((-1)**(x+y+z), Q.odd(x) & Q.odd(z))
(-1)**y
>>> refine_Pow((-1)**(x+y+2), Q.odd(x))
(-1)**(y + 1)
>>> refine_Pow((-1)**(x+3), True)
(-1)**(x + 1)
"""
from sympy.core import Pow, Rational
from sympy.functions import sign
if ask(Q.real(expr.base), assumptions):
if expr.base.is_number:
if ask(Q.even(expr.exp), assumptions):
return abs(expr.base) ** expr.exp
if ask(Q.odd(expr.exp), assumptions):
return sign(expr.base) * abs(expr.base) ** expr.exp
if isinstance(expr.exp, Rational):
if type(expr.base) is Pow:
return abs(expr.base.base) ** (expr.base.exp * expr.exp)
if expr.base is S.NegativeOne:
if expr.exp.is_Add:
# For powers of (-1) we can remove
# - even terms
# - pairs of odd terms
# - a single odd term + 1
# - A numerical constant N can be replaced with mod(N,2)
coeff, terms = expr.exp.as_coeff_add()
terms = set(terms)
even_terms = set([])
odd_terms = set([])
initial_number_of_terms = len(terms)
for t in terms:
if ask(Q.even(t), assumptions):
even_terms.add(t)
elif ask(Q.odd(t), assumptions):
odd_terms.add(t)
terms -= even_terms
if len(odd_terms)%2:
terms -= odd_terms
new_coeff = (coeff + S.One) % 2
else:
terms -= odd_terms
new_coeff = coeff % 2
if new_coeff != coeff or len(terms) < initial_number_of_terms:
terms.add(new_coeff)
return expr.base**(Add(*terms))
def _denester(nested, av0, h, max_depth_level):
"""Denests a list of expressions that contain nested square roots.
Algorithm based on <http://www.almaden.ibm.com/cs/people/fagin/symb85.pdf>.
It is assumed that all of the elements of 'nested' share the same
bottom-level radicand. (This is stated in the paper, on page 177, in
the paragraph immediately preceding the algorithm.)
When evaluating all of the arguments in parallel, the bottom-level
radicand only needs to be denested once. This means that calling
_denester with x arguments results in a recursive invocation with x+1
arguments; hence _denester has polynomial complexity.
However, if the arguments were evaluated separately, each call would
result in two recursive invocations, and the algorithm would have
exponential complexity.
This is discussed in the paper in the middle paragraph of page 179.
"""
from sympy.simplify.simplify import radsimp
if h > max_depth_level:
return None, None
if av0[1] is None:
return None, None
if (av0[0] is None and
all(n.is_Number for n in nested)): # no arguments are nested
for f in subsets(len(nested)): # test subset 'f' of nested
p = _mexpand(Mul(*[nested[i] for i in range(len(f)) if f[i]]))
if f.count(1) > 1 and f[-1]:
p = -p
sqp = sqrt(p)
if sqp.is_Rational:
return sqp, f # got a perfect square so return its square root.
# Otherwise, return the radicand from the previous invocation.
return sqrt(nested[-1]), [0]*len(nested)
else:
R = None
if av0[0] is not None:
values = [av0[:2]]
R = av0[2]
nested2 = [av0[3], R]
av0[0] = None
else:
values = filter(None, [_sqrt_match(expr) for expr in nested])
for v in values:
if v[2]: #Since if b=0, r is not defined
if R is not None:
if R != v[2]:
av0[1] = None
return None, None
else:
R = v[2]
if R is None:
# return the radicand from the previous invocation
return sqrt(nested[-1]), [0]*len(nested)
nested2 = [_mexpand(v[0]**2) -
_mexpand(R*v[1]**2) for v in values] + [R]
d, f = _denester(nested2, av0, h + 1, max_depth_level)
if not f:
return None, None
if not any(f[i] for i in range(len(nested))):
v = values[-1]
return sqrt(v[0] + v[1]*d), f
else:
p = Mul(*[nested[i] for i in range(len(nested)) if f[i]])
v = _sqrt_match(p)
if 1 in f and f.index(1) < len(nested) - 1 and f[len(nested) - 1]:
v[0] = -v[0]
v[1] = -v[1]
if not f[len(nested)]: #Solution denests with square roots
vad = _mexpand(v[0] + d)
if vad <= 0:
# return the radicand from the previous invocation.
return sqrt(nested[-1]), [0]*len(nested)
if not(sqrt_depth(vad) < sqrt_depth(R) + 1 or
(vad**2).is_Number):
av0[1] = None
return None, None
vad1 = radsimp(1/vad)
return _mexpand(sqrt(vad/2) +
sign(v[1])*sqrt(_mexpand(v[1]**2*R*vad1/2))), f
else: #Solution requires a fourth root
s2 = _mexpand(v[1]*R) + d
if s2 <= 0:
return sqrt(nested[-1]), [0]*len(nested)
FR, s = root(_mexpand(R), 4), sqrt(s2)
return _mexpand(s/(sqrt(2)*FR) + v[0]*FR/(sqrt(2)*s)), f
def trpzs2aff(tvec=zeros3, rquat=Mat([1,0,0,0]), parity=1, zfac=ones3, stri=zeros3):
mat = rquat2rmat(rquat) * sign(parity) * zfac2zmat(zfac) * stri2smat(stri)
return augment(mat, tvec)
def __new__(cls, tvec=zeros3, rquat=Mat([1,0,0,0]), parity=1):
tvec = Mat(tvec)
rquat = Mat(rquat)
parity = sign(parity)
return Functor.__new__(cls, tvec, rquat, parity)
def refine_Pow(expr, assumptions):
"""
Handler for instances of Pow.
>>> from sympy import Symbol, Q
>>> from sympy.assumptions.refine import refine_Pow
>>> from sympy.abc import x,y,z
>>> refine_Pow((-1)**x, Q.real(x))
>>> refine_Pow((-1)**x, Q.even(x))
1
>>> refine_Pow((-1)**x, Q.odd(x))
-1
For powers of -1, even parts of the exponent can be simplified:
>>> refine_Pow((-1)**(x+y), Q.even(x))
(-1)**y
>>> refine_Pow((-1)**(x+y+z), Q.odd(x) & Q.odd(z))
(-1)**y
>>> refine_Pow((-1)**(x+y+2), Q.odd(x))
(-1)**(y + 1)
>>> refine_Pow((-1)**(x+3), True)
(-1)**(x + 1)
"""
from sympy.core import Pow, Rational
from sympy.functions.elementary.complexes import Abs
from sympy.functions import sign
if isinstance(expr.base, Abs):
if ask(Q.real(expr.base.args[0]), assumptions) and \
ask(Q.even(expr.exp), assumptions):
return expr.base.args[0] ** expr.exp
if ask(Q.real(expr.base), assumptions):
if expr.base.is_number:
if ask(Q.even(expr.exp), assumptions):
return abs(expr.base) ** expr.exp
if ask(Q.odd(expr.exp), assumptions):
return sign(expr.base) * abs(expr.base) ** expr.exp
if isinstance(expr.exp, Rational):
if type(expr.base) is Pow:
return abs(expr.base.base) ** (expr.base.exp * expr.exp)
if expr.base is S.NegativeOne:
if expr.exp.is_Add:
old = expr
# For powers of (-1) we can remove
# - even terms
# - pairs of odd terms
# - a single odd term + 1
# - A numerical constant N can be replaced with mod(N,2)
coeff, terms = expr.exp.as_coeff_add()
terms = set(terms)
even_terms = set([])
odd_terms = set([])
initial_number_of_terms = len(terms)
for t in terms:
if ask(Q.even(t), assumptions):
even_terms.add(t)
elif ask(Q.odd(t), assumptions):
odd_terms.add(t)
terms -= even_terms
if len(odd_terms) % 2:
terms -= odd_terms
new_coeff = (coeff + S.One) % 2
else:
terms -= odd_terms
new_coeff = coeff % 2
if new_coeff != coeff or len(terms) < initial_number_of_terms:
terms.add(new_coeff)
expr = expr.base**(Add(*terms))
# Handle (-1)**((-1)**n/2 + m/2)
e2 = 2*expr.exp
if ask(Q.even(e2), assumptions):
if e2.could_extract_minus_sign():
e2 *= expr.base
if e2.is_Add:
i, p = e2.as_two_terms()
if p.is_Pow and p.base is S.NegativeOne:
if ask(Q.integer(p.exp), assumptions):
i = (i + 1)/2
if ask(Q.even(i), assumptions):
return expr.base**p.exp
elif ask(Q.odd(i), assumptions):
return expr.base**(p.exp + 1)
else:
return expr.base**(p.exp + i)
if old != expr:
return expr
def test_MpmathPrinter():
p = MpmathPrinter()
assert p.doprint(sign(x)) == 'mpmath.sign(x)'
def test_MpmathPrinter():
p = MpmathPrinter()
assert p.doprint(sign(x)) == 'mpmath.sign(x)'
assert p.doprint(Rational(1, 2)) == 'mpmath.mpf(1)/mpmath.mpf(2)'
def SuperellipseYZ(n, a, b):
x = (Abs(cos(v)) ** (2/n)) * a * sign(cos(v))
y = (Abs(sin(v)) ** (2/n)) * b * sign(sin(v))
return VVF(0, -x, y)
def test_MpmathPrinter():
p = MpmathPrinter()
assert p.doprint(sign(x)) == 'mpmath.sign(x)'
assert p.doprint(Rational(1, 2)) == 'mpmath.mpf(1)/mpmath.mpf(2)'
def test_NumPyPrinter():
p = NumPyPrinter()
assert p.doprint(sign(x)) == 'numpy.sign(x)'
N = 15 # número de términos en la suma
#step_approx=lambdify(x,Sum(A(m).doit()*sp.legendre(2*m+1,x),(m,0,N)).doit(),"numpy")
step_approx = lambdify(
x,
Sum(A(m).evalf() * sp.legendre(2 * m + 1, x), (m, 0, N)).doit(), "numpy")
#Sum es una función de sympy. Recordar que m es un símbolo que también importamos desde simpy.
#Un ejemplo para entender la expresión de arriba es
from sympy.abc import p #notar que ya importamos m antes
Sum(p, (p, 0, 3))
print(Sum(p, (p, 0, 3)))
## Graficamos la aproximación y comparamos con la solución exacta. Note que si ponemos muchos términos la solución no converge.
fig = mpl.figure(figsize=(10, 10))
step = lambdify(x, sp.sign(x), "numpy")
f_vals = step(x_vals)
mpl.plot(x_vals, f_vals, color="k", label="Exact")
for N in [5, 15, 25]:
x_vals = np.linspace(-1, 1, 200)
step_approx = lambdify(
x,
Sum(A(m).evalf() * sp.legendre(2 * m + 1, x), (m, 0, N)).doit(),
"numpy")
z_vals = step_approx(x_vals)
mpl.plot(x_vals, z_vals, label=f"N = {N:d}")
mpl.ylabel("step")
mpl.ylim(-2, 2) #seteamos los límites del ploteo
mpl.legend() #para ver los labels
# mpl.show()
def Jump(x,x_i,Xi):
return 1./2.*(sign(x-Xi)-sign(x_i-Xi))
def test_tensorflow_math():
if not tf:
skip("TensorFlow not installed")
expr = Abs(x)
assert tensorflow_code(expr) == "tensorflow.math.abs(x)"
_compare_tensorflow_scalar((x, ), expr)
expr = sign(x)
assert tensorflow_code(expr) == "tensorflow.math.sign(x)"
_compare_tensorflow_scalar((x, ), expr)
expr = ceiling(x)
assert tensorflow_code(expr) == "tensorflow.math.ceil(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = floor(x)
assert tensorflow_code(expr) == "tensorflow.math.floor(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = exp(x)
assert tensorflow_code(expr) == "tensorflow.math.exp(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = sqrt(x)
assert tensorflow_code(expr) == "tensorflow.math.sqrt(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = x**4
assert tensorflow_code(expr) == "tensorflow.math.pow(x, 4)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = cos(x)
assert tensorflow_code(expr) == "tensorflow.math.cos(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = acos(x)
assert tensorflow_code(expr) == "tensorflow.math.acos(x)"
_compare_tensorflow_scalar((x, ),
expr,
rng=lambda: random.uniform(0, 0.95))
expr = sin(x)
assert tensorflow_code(expr) == "tensorflow.math.sin(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = asin(x)
assert tensorflow_code(expr) == "tensorflow.math.asin(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = tan(x)
assert tensorflow_code(expr) == "tensorflow.math.tan(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = atan(x)
assert tensorflow_code(expr) == "tensorflow.math.atan(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = atan2(y, x)
assert tensorflow_code(expr) == "tensorflow.math.atan2(y, x)"
_compare_tensorflow_scalar((y, x), expr, rng=lambda: random.random())
expr = cosh(x)
assert tensorflow_code(expr) == "tensorflow.math.cosh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = acosh(x)
assert tensorflow_code(expr) == "tensorflow.math.acosh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = sinh(x)
assert tensorflow_code(expr) == "tensorflow.math.sinh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = asinh(x)
assert tensorflow_code(expr) == "tensorflow.math.asinh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = tanh(x)
assert tensorflow_code(expr) == "tensorflow.math.tanh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = atanh(x)
assert tensorflow_code(expr) == "tensorflow.math.atanh(x)"
_compare_tensorflow_scalar((x, ),
expr,
rng=lambda: random.uniform(-.5, .5))
expr = erf(x)
assert tensorflow_code(expr) == "tensorflow.math.erf(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = loggamma(x)
assert tensorflow_code(expr) == "tensorflow.math.lgamma(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
def _denester(nested, av0, h, max_depth_level):
"""Denests a list of expressions that contain nested square roots.
Algorithm based on <http://www.almaden.ibm.com/cs/people/fagin/symb85.pdf>.
It is assumed that all of the elements of 'nested' share the same
bottom-level radicand. (This is stated in the paper, on page 177, in
the paragraph immediately preceding the algorithm.)
When evaluating all of the arguments in parallel, the bottom-level
radicand only needs to be denested once. This means that calling
_denester with x arguments results in a recursive invocation with x+1
arguments; hence _denester has polynomial complexity.
However, if the arguments were evaluated separately, each call would
result in two recursive invocations, and the algorithm would have
exponential complexity.
This is discussed in the paper in the middle paragraph of page 179.
"""
from sympy.simplify.simplify import radsimp
if h > max_depth_level:
return None, None
if av0[1] is None:
return None, None
if (av0[0] is None
and all(n.is_Number for n in nested)): # no arguments are nested
for f in subsets(len(nested)): # test subset 'f' of nested
p = _mexpand(Mul(*[nested[i] for i in range(len(f)) if f[i]]))
if f.count(1) > 1 and f[-1]:
p = -p
sqp = sqrt(p)
if sqp.is_Rational:
return sqp, f # got a perfect square so return its square root.
# Otherwise, return the radicand from the previous invocation.
return sqrt(nested[-1]), [0] * len(nested)
else:
R = None
if av0[0] is not None:
values = [av0[:2]]
R = av0[2]
nested2 = [av0[3], R]
av0[0] = None
else:
values = filter(None, [_sqrt_match(expr) for expr in nested])
for v in values:
if v[2]: #Since if b=0, r is not defined
if R is not None:
if R != v[2]:
av0[1] = None
return None, None
else:
R = v[2]
if R is None:
# return the radicand from the previous invocation
return sqrt(nested[-1]), [0] * len(nested)
nested2 = [
_mexpand(v[0]**2) - _mexpand(R * v[1]**2) for v in values
] + [R]
d, f = _denester(nested2, av0, h + 1, max_depth_level)
if not f:
return None, None
if not any(f[i] for i in range(len(nested))):
v = values[-1]
return sqrt(v[0] + v[1] * d), f
else:
p = Mul(*[nested[i] for i in range(len(nested)) if f[i]])
v = _sqrt_match(p)
if 1 in f and f.index(1) < len(nested) - 1 and f[len(nested) - 1]:
v[0] = -v[0]
v[1] = -v[1]
if not f[len(nested)]: #Solution denests with square roots
vad = _mexpand(v[0] + d)
if vad <= 0:
# return the radicand from the previous invocation.
return sqrt(nested[-1]), [0] * len(nested)
if not (sqrt_depth(vad) < sqrt_depth(R) + 1 or
(vad**2).is_Number):
av0[1] = None
return None, None
vad1 = radsimp(1 / vad)
return _mexpand(
sqrt(vad / 2) +
sign(v[1]) * sqrt(_mexpand(v[1]**2 * R * vad1 / 2))), f
else: #Solution requires a fourth root
s2 = _mexpand(v[1] * R) + d
if s2 <= 0:
return sqrt(nested[-1]), [0] * len(nested)
FR, s = root(_mexpand(R), 4), sqrt(s2)
return _mexpand(s / (sqrt(2) * FR) + v[0] * FR /
(sqrt(2) * s)), f
def test_NumPyPrinter():
p = NumPyPrinter()
assert p.doprint(sign(x)) == 'numpy.sign(x)'
