def inicializar(argumentos):
if len(argumentos) < 6:
print("Es necesario introducir 6 argumentos")
print("Uso: python SuperficieRevolucion.py <f(t)> <g(t)> <número de puntos> <inicio> <fin>")
sys.exit(-1)
global vertices, x_t, y_t, z_t
t = symbols('t')
x_t = sympify(argumentos[1])
y_t = sympify(argumentos[2])
z_t = sympify('0')
num_puntos = int(argumentos[3])
inicio = float(argumentos[4])
final = float(argumentos[5])
vueltas = 32
longitud = final - inicio
incremento = longitud / (num_puntos - 1)
curva = Matrix([x_t,y_t,z_t])
for indice_punto in range(num_puntos):
t_var = inicio + indice_punto*incremento
vertices.append([curva[0].subs(t,t_var),curva[1].subs(t,t_var),curva[2].subs(t,t_var)])
revoluciona(vueltas)
glEnable(GL_NORMALIZE)
glEnable(GL_MULTISAMPLE_ARB);
glClearColor( 1.0, 1.0, 1.0, 1.0 ) ;
glColor3f(0.0,0.0,0.0)
def test_sympyify_iterables():
ans = [Rational(3, 10), Rational(1, 5)]
assert sympify([".3", ".2"], rational=True) == ans
assert sympify(set([".3", ".2"]), rational=True) == set(ans)
assert sympify(tuple([".3", ".2"]), rational=True) == Tuple(*ans)
assert sympify(dict(x=0, y=1)) == {x: 0, y: 1}
assert sympify(["1", "2", ["3", "4"]]) == [S(1), S(2), [S(3), S(4)]]
def test_issue_10295():
if not numpy:
skip("numpy not installed.")
A = numpy.array([[1, 3, -1],
[0, 1, 7]])
sA = S(A)
assert sA.shape == (2, 3)
for (ri, ci), val in numpy.ndenumerate(A):
assert sA[ri, ci] == val
B = numpy.array([-7, x, 3*y**2])
sB = S(B)
assert B[0] == -7
assert B[1] == x
assert B[2] == 3*y**2
C = numpy.arange(0, 24)
C.resize(2,3,4)
sC = S(C)
assert sC[0, 0, 0].is_integer
assert sC[0, 0, 0] == 0
a1 = numpy.array([1, 2, 3])
a2 = numpy.array([i for i in range(24)])
a2.resize(2, 4, 3)
assert sympify(a1) == ImmutableDenseNDimArray([1, 2, 3])
assert sympify(a2) == ImmutableDenseNDimArray([i for i in range(24)], (2, 4, 3))
def __eval_cond(cls, cond):
"""Returns S.One if True, S.Zero if False, or None if undecidable."""
if type(cond) == bool or cond.is_Number:
return sympify(bool(cond))
if cond.args[0].is_Number and cond.args[1].is_Number:
return sympify(bool(cond))
return None
def gcd(self, other): # Factors
"""Return Factors of ``gcd(self, other)``. The keys are
the intersection of factors with the minimum exponent for
each factor.
Examples
========
>>> from sympy.core.exprtools import Factors
>>> from sympy.abc import x, y, z
>>> a = Factors((x*y**2).as_powers_dict())
>>> b = Factors((x*y/z).as_powers_dict())
>>> a.gcd(b)
Factors({x: 1, y: 1})
"""
if not isinstance(other, Factors):
other = Factors(other)
if other.is_zero:
return Factors(self.factors)
factors = {}
for factor, exp in self.factors.items():
factor, exp = sympify(factor), sympify(exp)
if factor in other.factors:
lt = (exp - other.factors[factor]).is_negative
if lt == True:
factors[factor] = exp
elif lt == False:
factors[factor] = other.factors[factor]
return Factors(factors)
def _evalf(func, points, derivatives=False, method="RK4"):
"""
Numerical methods for numerical integration along a given set of
points in the complex plane.
"""
ann = func.annihilator
a = ann.order
R = ann.parent.base
K = R.get_field()
if method == "Euler":
meth = _euler
else:
meth = _rk4
dmf = []
for j in ann.listofpoly:
dmf.append(K.new(j.rep))
red = [-dmf[i] / dmf[a] for i in range(a)]
y0 = func.y0
if len(y0) < a:
raise TypeError("Not Enough Initial Conditions")
x0 = func.x0
sol = [meth(red, x0, points[0], y0, a)]
for i, j in enumerate(points[1:]):
sol.append(meth(red, points[i], j, sol[-1], a))
if not derivatives:
return [sympify(i[0]) for i in sol]
else:
return sympify(sol)
def __getitem__(self, key):
if not isinstance(key, tuple) and isinstance(key, slice):
from sympy.matrices.expressions.slice import MatrixSlice
return MatrixSlice(self, key, (0, None, 1))
if isinstance(key, tuple) and len(key) == 2:
i, j = key
if isinstance(i, slice) or isinstance(j, slice):
from sympy.matrices.expressions.slice import MatrixSlice
return MatrixSlice(self, i, j)
i, j = sympify(i), sympify(j)
if self.valid_index(i, j) != False:
return self._entry(i, j)
else:
raise IndexError("Invalid indices (%s, %s)" % (i, j))
elif isinstance(key, (int, Integer)):
# row-wise decomposition of matrix
rows, cols = self.shape
if not (isinstance(rows, Integer) and isinstance(cols, Integer)):
raise IndexError("Single index only supported for "
"non-symbolic matrix shapes.")
key = sympify(key)
i = key // cols
j = key % cols
if self.valid_index(i, j) != False:
return self._entry(i, j)
else:
raise IndexError("Invalid index %s" % key)
elif isinstance(key, (Symbol, Expr)):
raise IndexError("Single index only supported for "
"non-symbolic indices.")
raise IndexError("Invalid index, wanted %s[i,j]" % self)
def __new__(cls, periodical, limits=None):
x, start, stop = None, None, None
if limits is None:
x, start, stop = Dummy("k"), 0, S.Infinity
if is_sequence(limits, Tuple):
if len(limits) == 3:
x, start, stop = limits
elif len(limits) == 2:
x = Dummy("k")
start, stop = limits
if not isinstance(x, Symbol) 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)
def test_sympyify_iterables():
ans = [Rational(3, 10), Rational(1, 5)]
assert sympify(['.3', '.2'], rational=True) == ans
assert sympify(set(['.3', '.2']), rational=True) == set(ans)
assert sympify(tuple(['.3', '.2']), rational=True) == Tuple(*ans)
assert sympify(dict(x=0, y=1)) == {x: 0, y: 1}
assert sympify(['1', '2', ['3', '4']]) == [S(1), S(2), [S(3), S(4)]]
def domain_check(f, symbol, p):
"""Returns False if point p is infinite or any subexpression of f
is infinite or becomes so after replacing symbol with p. If none of
these conditions is met then True will be returned.
Examples
========
>>> from sympy import Mul, oo
>>> from sympy.abc import x
>>> from sympy.solvers.solveset import domain_check
>>> g = 1/(1 + (1/(x + 1))**2)
>>> domain_check(g, x, -1)
False
>>> domain_check(x**2, x, 0)
True
>>> domain_check(1/x, x, oo)
False
* The function relies on the assumption that the original form
of the equation has not been changed by automatic simplification.
>>> domain_check(x/x, x, 0) # x/x is automatically simplified to 1
True
* To deal with automatic evaluations use evaluate=False:
>>> domain_check(Mul(x, 1/x, evaluate=False), x, 0)
False
"""
f, p = sympify(f), sympify(p)
if p.is_infinite:
return False
return _domain_check(f, symbol, p)
def __getitem__(self, key):
if not isinstance(key, tuple) and isinstance(key, slice):
from sympy.matrices.expressions.slice import MatrixSlice
return MatrixSlice(self, key, (0, None, 1))
if isinstance(key, tuple) and len(key) == 2:
i, j = key
if isinstance(i, slice) or isinstance(j, slice):
from sympy.matrices.expressions.slice import MatrixSlice
return MatrixSlice(self, i, j)
i, j = sympify(i), sympify(j)
if self.valid_index(i, j) != False:
return self._entry(i, j)
else:
raise IndexError("Invalid indices (%s, %s)" % (i, j))
elif isinstance(key, (SYMPY_INTS, Integer)):
# row-wise decomposition of matrix
rows, cols = self.shape
# allow single indexing if number of columns is known
if not isinstance(cols, Integer):
raise IndexError(filldedent('''
Single indexing is only supported when the number
of columns is known.'''))
key = sympify(key)
i = key // cols
j = key % cols
if self.valid_index(i, j) != False:
return self._entry(i, j)
else:
raise IndexError("Invalid index %s" % key)
elif isinstance(key, (Symbol, Expr)):
raise IndexError(filldedent('''
Only integers may be used when addressing the matrix
with a single index.'''))
raise IndexError("Invalid index, wanted %s[i,j]" % self)
def test_sympify3():
assert sympify("x**3") == x**3
assert sympify("x^3") == x**3
assert sympify("1/2") == Integer(1)/2
raises(SympifyError, "_sympify('x**3')")
raises(SympifyError, "_sympify('1/2')")
def test_lambda():
x = Symbol('x')
assert sympify('lambda : 1') == Lambda(x, 1)
assert sympify('lambda x: 2*x') == Lambda(x, 2*x)
assert sympify('lambda x, y: 2*x+y') == Lambda([x, y], 2*x+y)
raises(SympifyError, "_sympify('lambda : 1')")
def test_sympify3():
assert sympify("x**3") == x ** 3
assert sympify("x^3") == x ** 3
assert sympify("1/2") == Integer(1) / 2
raises(SympifyError, lambda: _sympify("x**3"))
raises(SympifyError, lambda: _sympify("1/2"))
def test_issue_15265():
from sympy.core.sympify import sympify
from sympy.core.singleton import S
matplotlib = import_module('matplotlib', min_module_version='1.1.0', catch=(RuntimeError,))
if not matplotlib:
skip("Matplotlib not the default backend")
x = Symbol('x')
eqn = sin(x)
p = plot(eqn, xlim=(-S.Pi, S.Pi), ylim=(-1, 1))
p._backend.close()
p = plot(eqn, xlim=(-1, 1), ylim=(-S.Pi, S.Pi))
p._backend.close()
p = plot(eqn, xlim=(-1, 1), ylim=(sympify('-3.14'), sympify('3.14')))
p._backend.close()
p = plot(eqn, xlim=(sympify('-3.14'), sympify('3.14')), ylim=(-1, 1))
p._backend.close()
raises(ValueError,
lambda: plot(eqn, xlim=(-S.ImaginaryUnit, 1), ylim=(-1, 1)))
raises(ValueError,
lambda: plot(eqn, xlim=(-1, 1), ylim=(-1, S.ImaginaryUnit)))
raises(ValueError,
lambda: plot(eqn, xlim=(-S.Infinity, 1), ylim=(-1, 1)))
raises(ValueError,
lambda: plot(eqn, xlim=(-1, 1), ylim=(-1, S.Infinity)))
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.
Returns (sequence of coefficients, sequence of x, independent terms,
common terms).
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.
See Also
========
sympy.series.rational_algorithm
sympy.series.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)
def __new__(cls, *args):
if len(args) == 2:
low, high = args
return Basic.__new__(cls, sympify(low), sympify(high))
elif len(args) == 0 or (len(args) == 1 and args[0] in (':', None)):
return Basic.__new__(cls) # assumed shape
else:
raise ValueError("Expected 0 or 2 args (or one argument == None or ':')")
def test_issue_4988_builtins():
C = Symbol('C')
vars = {'C': C}
exp1 = sympify('C')
assert exp1 == C # Make sure it did not get mixed up with sympy.C
exp2 = sympify('C', vars)
assert exp2 == C # Make sure it did not get mixed up with sympy.C
def test_issue1889_builtins():
C = Symbol('C')
vars = {}
vars['C'] = C
exp1 = sympify('C')
assert exp1 == C # Make sure it did not get mixed up with sympy.C
exp2 = sympify('C', vars)
assert exp2 == C # Make sure it did not get mixed up with sympy.C
def __new__(cls, *args, **assumptions):
if isinstance(args[0], cls):
expr = args[0].expr
cond = args[0].cond
elif len(args) == 2:
expr = sympify(args[0])
cond = sympify(args[1])
else:
raise TypeError("args must be a (expr, cond) pair")
return Basic.__new__(cls, expr, cond, **assumptions)
def test_sympify_gmpy():
try:
import gmpy
except ImportError:
pass
else:
value = sympify(gmpy.mpz(1000001))
assert value == Integer(1000001) and type(value) is Integer
value = sympify(gmpy.mpq(101, 127))
assert value == Rational(101, 127) and type(value) is Rational
def test_sympify_gmpy():
if HAS_GMPY:
if HAS_GMPY == 2:
import gmpy2 as gmpy
elif HAS_GMPY == 1:
import gmpy
value = sympify(gmpy.mpz(1000001))
assert value == Integer(1000001) and type(value) is Integer
value = sympify(gmpy.mpq(101, 127))
assert value == Rational(101, 127) and type(value) is Rational
def __new__(cls, recurrence, y, n, initial=None, start=0):
if not isinstance(y, UndefinedFunction):
raise TypeError("recurrence sequence must be an undefined function"
", found `{}`".format(y))
if not isinstance(n, Basic) or not n.is_symbol:
raise TypeError("recurrence variable must be a symbol"
", found `{}`".format(n))
k = Wild("k", exclude=(n,))
degree = 0
# Find all applications of y in the recurrence and check that:
# 1. The function y is only being used with a single argument; and
# 2. All arguments are n + k for constant negative integers k.
prev_ys = recurrence.find(y)
for prev_y in prev_ys:
if len(prev_y.args) != 1:
raise TypeError("Recurrence should be in a single variable")
shift = prev_y.args[0].match(n + k)[k]
if not (shift.is_constant() and shift.is_integer and shift < 0):
raise TypeError("Recurrence should have constant,"
" negative, integer shifts"
" (found {})".format(prev_y))
if -shift > degree:
degree = -shift
if not initial:
initial = [Dummy("c_{}".format(k)) for k in range(degree)]
if len(initial) != degree:
raise ValueError("Number of initial terms must equal degree")
degree = Integer(degree)
start = sympify(start)
initial = Tuple(*(sympify(x) for x in initial))
seq = Basic.__new__(cls, recurrence, y(n), initial, start)
seq.cache = {y(start + k): init for k, init in enumerate(initial)}
seq._start = start
seq.degree = degree
seq.y = y
seq.n = n
seq._recurrence = recurrence
return seq
def __init__(self, *args):
if len(args) == 1 and isinstance(args[0], SparseMatrix):
self.rows = args[0].rows
self.cols = args[0].cols
self._smat = dict(args[0]._smat)
return
self._smat = {}
if len(args) == 3:
self.rows = as_int(args[0])
self.cols = as_int(args[1])
if callable(args[2]):
op = args[2]
for i in range(self.rows):
for j in range(self.cols):
value = sympify(op(i, j))
if value:
self._smat[(i, j)] = value
elif isinstance(args[2], (dict, Dict)):
# manual copy, copy.deepcopy() doesn't work
for key in args[2].keys():
v = args[2][key]
if v:
self._smat[key] = v
elif is_sequence(args[2]):
if len(args[2]) != self.rows*self.cols:
raise ValueError(
'List length (%s) != rows*columns (%s)' %
(len(args[2]), self.rows*self.cols))
flat_list = args[2]
for i in range(self.rows):
for j in range(self.cols):
value = sympify(flat_list[i*self.cols + j])
if value:
self._smat[(i, j)] = value
else:
# handle full matrix forms with _handle_creation_inputs
r, c, _list = Matrix._handle_creation_inputs(*args)
self.rows = r
self.cols = c
for i in range(self.rows):
for j in range(self.cols):
value = _list[self.cols*i + j]
if value:
self._smat[(i, j)] = value
def __new__(cls, *args, **options):
args = (_sympify(arg) for arg in args)
try:
_args = frozenset(cls._new_args_filter(args))
except ShortCircuit:
return sympify(cls.zero)
if not _args:
return sympify(cls.identity)
elif len(_args) == 1:
return set(_args).pop()
else:
# XXX in almost every other case for __new__, *_args is
# passed along, but the expectation here is for _args
obj = super(AssocOp, cls).__new__(cls, _args)
obj._argset = _args
return obj
def _normalize(list_of, parent, negative=True):
"""
Normalize a given annihilator
"""
num = []
denom = []
base = parent.base
K = base.get_field()
R = ZZ.old_poly_ring(base.gens[0])
lcm_denom = R.from_sympy(S(1))
list_of_coeff = []
# convert polynomials to the elements of associated
# fraction field
for i, j in enumerate(list_of):
if isinstance(j, base.dtype):
list_of_coeff.append(K.new(j.rep))
elif not isinstance(j, K.dtype):
list_of_coeff.append(K.from_sympy(sympify(j)))
else:
list_of_coeff.append(j)
# corresponding numerators of the sequence of polynomials
num.append(base(list_of_coeff[i].num))
# corresponding denominators
den = list_of_coeff[i].den
if isinstance(den[0], PythonRational):
for i, j in enumerate(den):
den[i] = j.p
denom.append(R(den))
# lcm of denominators in the coefficients
for i in denom:
lcm_denom = i.lcm(lcm_denom)
if negative is True:
lcm_denom = -lcm_denom
lcm_denom = K.new(lcm_denom.rep)
# multiply the coefficients with lcm
for i, j in enumerate(list_of_coeff):
list_of_coeff[i] = j * lcm_denom
gcd_numer = base.from_FractionField(list_of_coeff[-1], K)
# gcd of numerators in the coefficients
for i in num:
gcd_numer = i.gcd(gcd_numer)
gcd_numer = K.new(gcd_numer.rep)
# divide all the coefficients by the gcd
for i, j in enumerate(list_of_coeff):
list_of_coeff[i] = base.from_FractionField(j / gcd_numer, K)
return DifferentialOperator(list_of_coeff, parent)
def to_dnf(expr, simplify=False):
"""
Convert a propositional logical sentence s to disjunctive normal form.
That is, of the form ((A & ~B & ...) | (B & C & ...) | ...)
If simplify is True, the expr is evaluated to its simplest DNF form.
Examples
========
>>> from sympy.logic.boolalg import to_dnf
>>> from sympy.abc import A, B, C
>>> to_dnf(B & (A | C))
Or(And(A, B), And(B, C))
>>> to_dnf((A & B) | (A & ~B) | (B & C) | (~B & C), True)
Or(A, C)
"""
expr = sympify(expr)
if not isinstance(expr, BooleanFunction):
return expr
if simplify:
return simplify_logic(expr, 'dnf', True)
# Don't convert unless we have to
if is_dnf(expr):
return expr
expr = eliminate_implications(expr)
return distribute_or_over_and(expr)
def wronskian(functions, var, method='bareis'):
"""
Compute Wronskian for [] of functions
::
| f1 f2 ... fn |
| f1' f2' ... fn' |
| . . . . |
W(f1, ..., fn) = | . . . . |
| . . . . |
| (n) (n) (n) |
| D (f1) D (f2) ... D (fn) |
see: http://en.wikipedia.org/wiki/Wronskian
See Also
========
sympy.matrices.mutable.Matrix.jacobian
hessian
"""
from .dense import Matrix
for index in range(0, len(functions)):
functions[index] = sympify(functions[index])
n = len(functions)
if n == 0:
return 1
W = Matrix(n, n, lambda i, j: functions[i].diff(var, j))
return W.det(method)
def to_cnf(expr, simplify=False):
"""
Convert a propositional logical sentence s to conjunctive normal form.
That is, of the form ((A | ~B | ...) & (B | C | ...) & ...)
If simplify is True, the expr is evaluated to its simplest CNF form.
Examples
========
>>> from sympy.logic.boolalg import to_cnf
>>> from sympy.abc import A, B, D
>>> to_cnf(~(A | B) | D)
And(Or(D, Not(A)), Or(D, Not(B)))
>>> to_cnf((A | B) & (A | ~A), True)
Or(A, B)
"""
expr = sympify(expr)
if not isinstance(expr, BooleanFunction):
return expr
if simplify:
return simplify_logic(expr, 'cnf', True)
# Don't convert unless we have to
if is_cnf(expr):
return expr
expr = eliminate_implications(expr)
return distribute_and_over_or(expr)
def eliminate_implications(expr):
"""
Change >>, <<, and Equivalent into &, |, and ~. That is, return an
expression that is equivalent to s, but has only &, |, and ~ as logical
operators.
Examples
========
>>> from sympy.logic.boolalg import Implies, Equivalent, \
eliminate_implications
>>> from sympy.abc import A, B, C
>>> eliminate_implications(Implies(A, B))
Or(B, Not(A))
>>> eliminate_implications(Equivalent(A, B))
And(Or(A, Not(B)), Or(B, Not(A)))
"""
expr = sympify(expr)
if expr.is_Atom:
return expr # (Atoms are unchanged.)
args = list(map(eliminate_implications, expr.args))
if expr.func is Implies:
a, b = args[0], args[-1]
return (~a) | b
elif expr.func is Equivalent:
a, b = args[0], args[-1]
return (a | Not(b)) & (b | Not(a))
else:
return expr.func(*args)
def __new__(cls, n):
return super(Identity, cls).__new__(cls, sympify(n))
def test_sympify1():
assert sympify("x") == Symbol("x")
assert sympify(" x") == Symbol("x")
assert sympify(" x ") == Symbol("x")
# issue 4877
n1 = Rational(1, 2)
assert sympify('--.5') == n1
assert sympify('-1/2') == -n1
assert sympify('-+--.5') == -n1
assert sympify('-.[3]') == Rational(-1, 3)
assert sympify('.[3]') == Rational(1, 3)
assert sympify('+.[3]') == Rational(1, 3)
assert sympify('+0.[3]*10**-2') == Rational(1, 300)
assert sympify('.[052631578947368421]') == Rational(1, 19)
assert sympify('.0[526315789473684210]') == Rational(1, 19)
assert sympify('.034[56]') == Rational(1711, 49500)
# options to make reals into rationals
assert sympify('1.22[345]', rational=True) == \
1 + Rational(22, 100) + Rational(345, 99900)
assert sympify('2/2.6', rational=True) == Rational(10, 13)
assert sympify('2.6/2', rational=True) == Rational(13, 10)
assert sympify('2.6e2/17', rational=True) == Rational(260, 17)
assert sympify('2.6e+2/17', rational=True) == Rational(260, 17)
assert sympify('2.6e-2/17', rational=True) == Rational(26, 17000)
assert sympify('2.1+3/4', rational=True) == \
Rational(21, 10) + Rational(3, 4)
assert sympify('2.234456', rational=True) == Rational(279307, 125000)
assert sympify('2.234456e23', rational=True) == 223445600000000000000000
assert sympify('2.234456e-23', rational=True) == \
Rational(279307, 12500000000000000000000000000)
assert sympify('-2.234456e-23', rational=True) == \
Rational(-279307, 12500000000000000000000000000)
assert sympify('12345678901/17', rational=True) == \
Rational(12345678901, 17)
assert sympify('1/.3 + x', rational=True) == Rational(10, 3) + x
# make sure longs in fractions work
assert sympify('222222222222/11111111111') == \
Rational(222222222222, 11111111111)
# ... even if they come from repetend notation
assert sympify('1/.2[123456789012]') == Rational(333333333333, 70781892967)
# ... or from high precision reals
assert sympify('.1234567890123456', rational=True) == \
Rational(19290123283179, 156250000000000)
def test_lambda():
x = Symbol('x')
assert sympify('lambda: 1') == Lambda((), 1)
assert sympify('lambda x: x') == Lambda(x, x)
assert sympify('lambda x: 2*x') == Lambda(x, 2 * x)
assert sympify('lambda x, y: 2*x+y') == Lambda([x, y], 2 * x + y)
def __new__(cls, *args):
return Basic.__new__(cls, *[sympify(arg) for arg in args])
def solveset_complex(f, symbol):
""" Solve a complex valued equation.
Parameters
==========
f : Expr
The target equation
symbol : Symbol
The variable for which the equation is solved
Returns
=======
Set
A set of values for `symbol` for which `f` equal to
zero. An `EmptySet` is returned if no solution is found.
`solveset_complex` claims to be complete in the solution set that
it returns.
Raises
======
NotImplementedError
The algorithms for to find the solution of the given equation are
not yet implemented.
ValueError
The input is not valid.
RuntimeError
It is a bug, please report to the github issue tracker.
See Also
========
solveset_real: solver for real domain
Examples
========
>>> from sympy import Symbol, exp
>>> from sympy.solvers.solveset import solveset_complex
>>> from sympy.abc import x, a, b, c
>>> solveset_complex(a*x**2 + b*x +c, x)
{-b/(2*a) - sqrt(-4*a*c + b**2)/(2*a), -b/(2*a) + sqrt(-4*a*c + b**2)/(2*a)}
* Due to the fact that complex extension of my real valued functions are
multivariate even some simple equations can have infinitely many
solution.
>>> solveset_complex(exp(x) - 1, x)
ImageSet(Lambda(_n, 2*_n*I*pi), Integers())
"""
if not symbol.is_Symbol:
raise ValueError(" %s is not a symbol" % (symbol))
f = sympify(f)
original_eq = f
if not isinstance(f, (Expr, Number)):
raise ValueError(" %s is not a valid sympy expression" % (f))
f = together(f)
# Without this equations like a + 4*x**2 - E keep oscillating
# into form a/4 + x**2 - E/4 and (a + 4*x**2 - E)/4
if not fraction(f)[1].has(symbol):
f = expand(f)
if f.is_zero:
raise NotImplementedError("S.Complex set is not yet implemented")
elif not f.has(symbol):
result = EmptySet()
elif f.is_Mul and all([_is_finite_with_finite_vars(m) for m in f.args]):
result = Union(*[solveset_complex(m, symbol) for m in f.args])
else:
lhs, rhs_s = invert_complex(f, 0, symbol)
if lhs == symbol:
result = rhs_s
elif isinstance(rhs_s, FiniteSet):
equations = [lhs - rhs for rhs in rhs_s]
result = EmptySet()
for equation in equations:
if equation == f:
if any(
_has_rational_power(g, symbol)[0]
for g in equation.args):
result += _solve_radical(equation, symbol,
solveset_complex)
else:
result += _solve_as_rational(
equation,
symbol,
solveset_solver=solveset_complex,
as_poly_solver=_solve_as_poly_complex)
else:
result += solveset_complex(equation, symbol)
else:
raise NotImplementedError
if isinstance(result, FiniteSet):
result = [
s for s in result
if isinstance(s, RootOf) or domain_check(original_eq, symbol, s)
]
return FiniteSet(*result)
else:
return result
def _printable(arg):
return String(arg) if isinstance(arg, string_types) else sympify(arg)
def test_issue_3538():
v = sympify("exp(x)")
assert v == exp(x)
assert type(v) == type(exp(x))
assert str(type(v)) == str(type(exp(x)))
def test_issue_4133():
a = sympify('Integer(4)')
assert a == Integer(4)
assert a.is_Integer
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
"""
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(set([_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 test_issue_3982():
a = [3, 2.0]
assert sympify(a) == [Integer(3), Float(2.0)]
assert sympify(tuple(a)) == Tuple(Integer(3), Float(2.0))
assert sympify(set(a)) == set([Integer(3), Float(2.0)])
def test_S_sympify():
assert S(1) / 2 == sympify(1) / 2
assert (-2)**(S(1) / 2) == sqrt(2) * I
def solveset(f, symbol=None, domain=S.Complexes):
"""Solves a given inequality or equation with set as output
Parameters
==========
f : Expr or a relational.
The target equation or inequality
symbol : Symbol
The variable for which the equation is solved
domain : Set
The domain over which the equation is solved
Returns
=======
Set
A set of values for `symbol` for which `f` is True or is equal to
zero. An `EmptySet` is returned if no solution is found.
A `ConditionSet` is returned as unsolved object if algorithms
to evaluatee complete solution are not yet implemented.
`solveset` claims to be complete in the solution set that it returns.
Raises
======
NotImplementedError
The algorithms to solve inequalities in complex domain are
not yet implemented.
ValueError
The input is not valid.
RuntimeError
It is a bug, please report to the github issue tracker.
`solveset` uses two underlying functions `solveset_real` and
`solveset_complex` to solve equations. They are the solvers for real and
complex domain respectively. `solveset` ignores the assumptions on the
variable being solved for and instead, uses the `domain` parameter to
decide which solver to use.
See Also
========
solveset_real: solver for real domain
solveset_complex: solver for complex domain
Examples
========
>>> from sympy import exp, Symbol, Eq, pprint, S, solveset
>>> from sympy.abc import x
* The default domain is complex. Not specifying a domain will lead to the
solving of the equation in the complex domain.
>>> pprint(solveset(exp(x) - 1, x), use_unicode=False)
{2*n*I*pi | n in Integers()}
* If you want to solve equation in real domain by the `solveset`
interface, then specify that the domain is real. Alternatively use
`solveset\_real`.
>>> x = Symbol('x')
>>> solveset(exp(x) - 1, x, S.Reals)
{0}
>>> solveset(Eq(exp(x), 1), x, S.Reals)
{0}
* Inequalities can be solved over the real domain only. Use of a complex
domain leads to a NotImplementedError.
>>> solveset(exp(x) > 1, x, S.Reals)
(0, oo)
"""
from sympy.solvers.inequalities import solve_univariate_inequality
if symbol is None:
free_symbols = f.free_symbols
if len(free_symbols) == 1:
symbol = free_symbols.pop()
else:
raise ValueError(
filldedent('''
The independent variable must be specified for a
multivariate equation.'''))
elif not symbol.is_Symbol:
raise ValueError('A Symbol must be given, not type %s: %s' %
(type(symbol), symbol))
f = sympify(f)
if f is S.false:
return EmptySet()
if f is S.true:
return domain
if isinstance(f, Eq):
from sympy.core import Add
f = Add(f.lhs, -f.rhs, evaluate=False)
if f.is_Relational:
if not domain.is_subset(S.Reals):
raise NotImplementedError("Inequalities in the complex domain are "
"not supported. Try the real domain by"
"setting domain=S.Reals")
try:
result = solve_univariate_inequality(
f, symbol, relational=False).intersection(domain)
except NotImplementedError:
result = ConditionSet(symbol, f, domain)
return result
if isinstance(f, (Expr, Number)):
if domain is S.Reals:
return solveset_real(f, symbol)
elif domain is S.Complexes:
return solveset_complex(f, symbol)
elif domain.is_subset(S.Reals):
return Intersection(solveset_real(f, symbol), domain)
else:
return Intersection(solveset_complex(f, symbol), domain)
def test_sympify_keywords():
raises(SympifyError, lambda: sympify('if'))
raises(SympifyError, lambda: sympify('for'))
raises(SympifyError, lambda: sympify('while'))
raises(SympifyError, lambda: sympify('lambda'))
def test_bug496():
assert sympify("a_") == Symbol("a_")
assert sympify("_a") == Symbol("_a")
def test_sympify_factorial():
assert sympify('x!') == factorial(x)
assert sympify('(x+1)!') == factorial(x + 1)
assert sympify('(1 + y*(x + 1))!') == factorial(1 + y * (x + 1))
assert sympify('(1 + y*(x + 1)!)^2') == (1 + y * factorial(x + 1))**2
assert sympify('y*x!') == y * factorial(x)
assert sympify('x!!') == factorial2(x)
assert sympify('(x+1)!!') == factorial2(x + 1)
assert sympify('(1 + y*(x + 1))!!') == factorial2(1 + y * (x + 1))
assert sympify('(1 + y*(x + 1)!!)^2') == (1 + y * factorial2(x + 1))**2
assert sympify('y*x!!') == y * factorial2(x)
assert sympify('factorial2(x)!') == factorial(factorial2(x))
raises(SympifyError, lambda: sympify("+!!"))
raises(SympifyError, lambda: sympify(")!!"))
raises(SympifyError, lambda: sympify("!"))
raises(SympifyError, lambda: sympify("(!)"))
raises(SympifyError, lambda: sympify("x!!!"))
def __new__(cls, name, n, m):
n, m = sympify(n), sympify(m)
obj = Basic.__new__(cls, name, n, m)
return obj
def test_sympify_float():
assert sympify("1e-64") != 0
assert sympify("1e-20000") != 0
def solveset(f, symbol=None):
"""Solves a given inequality or equation with set as output
Parameters
==========
f : Expr or a relational.
The target equation or inequality
symbol : Symbol
The variable for which the equation is solved
Returns
=======
Set
A set of values for `symbol` for which `f` is True or is equal to
zero. An `EmptySet` is returned if no solution is found.
`solveset` claims to be complete in the solution set that it returns.
Raises
======
NotImplementedError
The algorithms for to find the solution of the given equation are
not yet implemented.
ValueError
The input is not valid.
RuntimeError
It is a bug, please report to the github issue tracker.
`solveset` uses two underlying functions `solveset_real` and
`solveset_complex` to solve equations. They are
the solvers for real and complex domain respectively. The domain of
the solver is decided by the assumption on the variable for which the
equation is being solved.
See Also
========
solveset_real: solver for real domain
solveset_complex: solver for complex domain
Examples
========
>>> from sympy import exp, Symbol, Eq, pprint
>>> from sympy.solvers.solveset import solveset
>>> from sympy.abc import x
* Symbols in Sympy are complex by default. A complex variable
will lead to the solving of the equation in complex domain.
>>> pprint(solveset(exp(x) - 1, x), use_unicode=False)
{2*n*I*pi | n in Integers()}
* If you want to solve equation in real domain by the `solveset`
interface, then specify the variable to real. Alternatively use
`solveset\_real`.
>>> x = Symbol('x', real=True)
>>> solveset(exp(x) - 1, x)
{0}
>>> solveset(Eq(exp(x), 1), x)
{0}
* Inequalities are always solved in the real domain irrespective of
the assumption on the variable for which the inequality is solved.
>>> solveset(exp(x) > 1, x)
(0, oo)
"""
from sympy.solvers.inequalities import solve_univariate_inequality
if symbol is None:
free_symbols = f.free_symbols
if len(free_symbols) == 1:
symbol = free_symbols.pop()
else:
raise ValueError(
filldedent('''
The independent variable must be specified for a
multivariate equation.'''))
elif not symbol.is_Symbol:
raise ValueError('A Symbol must be given, not type %s: %s' %
(type(symbol), symbol))
real = (symbol.is_real is True)
f = sympify(f)
if isinstance(f, Eq):
f = f.lhs - f.rhs
if f.is_Relational:
if real is False:
warnings.warn(
filldedent('''
The variable you are solving for is complex
but will assumed to be real since solving complex
inequalities is not supported.
'''))
return solve_univariate_inequality(f, symbol, relational=False)
if isinstance(f, (Expr, Number)):
if real is True:
return solveset_real(f, symbol)
else:
return solveset_complex(f, symbol)
def test_sympify_poly():
p = Poly(x**2 + x + 1, x)
assert _sympify(p) is p
assert sympify(p) is p
def test_sympify_function():
assert sympify('factor(x**2-1, x)') == -(1 - x) * (x + 1)
assert sympify('sin(pi/2)*cos(pi)') == -Integer(1)
def test_geometry():
p = sympify(Point(0, 1))
assert p == Point(0, 1) and type(p) == Point
L = sympify(Line(p, (1, 0)))
assert L == Line((0, 1), (1, 0)) and type(L) == Line
def solveset_real(f, symbol):
""" Solves a real valued equation.
Parameters
==========
f : Expr
The target equation
symbol : Symbol
The variable for which the equation is solved
Returns
=======
Set
A set of values for `symbol` for which `f` is equal to
zero. An `EmptySet` is returned if no solution is found.
`solveset_real` claims to be complete in the set of the solution it
returns.
Raises
======
NotImplementedError
The algorithms for to find the solution of the given equation are
not yet implemented.
ValueError
The input is not valid.
RuntimeError
It is a bug, please report to the github issue tracker.
See Also
=======
solveset_complex : solver for complex domain
Examples
========
>>> from sympy import Symbol, exp, sin, sqrt, I
>>> from sympy.solvers.solveset import solveset_real
>>> x = Symbol('x', real=True)
>>> a = Symbol('a', real=True, finite=True, positive=True)
>>> solveset_real(x**2 - 1, x)
{-1, 1}
>>> solveset_real(sqrt(5*x + 6) - 2 - x, x)
{-1, 2}
>>> solveset_real(x - I, x)
EmptySet()
>>> solveset_real(x - a, x)
{a}
>>> solveset_real(exp(x) - a, x)
{log(a)}
* In case the equation has infinitely many solutions an infinitely indexed
`ImageSet` is returned.
>>> solveset_real(sin(x) - 1, x)
ImageSet(Lambda(_n, 2*_n*pi + pi/2), Integers())
* If the equation is true for any arbitrary value of the symbol a `S.Reals`
set is returned.
>>> solveset_real(x - x, x)
(-oo, oo)
"""
if not symbol.is_Symbol:
raise ValueError(" %s is not a symbol" % (symbol))
f = sympify(f)
if not isinstance(f, (Expr, Number)):
raise ValueError(" %s is not a valid sympy expression" % (f))
original_eq = f
f = together(f)
if f.has(Piecewise):
f = piecewise_fold(f)
result = EmptySet()
if f.expand().is_zero:
return S.Reals
elif not f.has(symbol):
return EmptySet()
elif f.is_Mul and all([_is_finite_with_finite_vars(m) for m in f.args]):
# if f(x) and g(x) are both finite we can say that the solution of
# f(x)*g(x) == 0 is same as Union(f(x) == 0, g(x) == 0) is not true in
# general. g(x) can grow to infinitely large for the values where
# f(x) == 0. To be sure that we not are silently allowing any
# wrong solutions we are using this technique only if both f and g and
# finite for a finite input.
result = Union(*[solveset_real(m, symbol) for m in f.args])
elif _is_function_class_equation(C.TrigonometricFunction, f, symbol) or \
_is_function_class_equation(C.HyperbolicFunction, f, symbol):
result = _solve_real_trig(f, symbol)
elif f.is_Piecewise:
result = EmptySet()
expr_set_pairs = f.as_expr_set_pairs()
for (expr, in_set) in expr_set_pairs:
solns = solveset_real(expr, symbol).intersect(in_set)
result = result + solns
else:
lhs, rhs_s = invert_real(f, 0, symbol)
if lhs == symbol:
result = rhs_s
elif isinstance(rhs_s, FiniteSet):
equations = [lhs - rhs for rhs in rhs_s]
for equation in equations:
if equation == f:
if any(
_has_rational_power(g, symbol)[0]
for g in equation.args):
result += _solve_radical(equation, symbol,
solveset_real)
elif equation.has(Abs):
result += _solve_abs(f, symbol)
else:
result += _solve_as_rational(
equation,
symbol,
solveset_solver=solveset_real,
as_poly_solver=_solve_as_poly_real)
else:
result += solveset_real(equation, symbol)
else:
raise NotImplementedError
if isinstance(result, FiniteSet):
result = [
s for s in result
if isinstance(s, RootOf) or domain_check(original_eq, symbol, s)
]
return FiniteSet(*result).intersect(S.Reals)
else:
return result.intersect(S.Reals)
def __sympifyit_wrapper(a, b):
try:
b = sympify(b, strict=True)
return func(a, b)
except SympifyError:
return retval
def roots(f, *gens, **flags):
"""
Computes symbolic roots of a univariate polynomial.
Given a univariate polynomial f with symbolic coefficients (or
a list of the polynomial's coefficients), returns a dictionary
with its roots and their multiplicities.
Only roots expressible via radicals will be returned. To get
a complete set of roots use RootOf class or numerical methods
instead. By default cubic and quartic formulas are used in
the algorithm. To disable them because of unreadable output
set ``cubics=False`` or ``quartics=False`` respectively. If cubic
roots are real but are expressed in terms of complex numbers
(casus irreducibilis [1]) the ``trig`` flag can be set to True to
have the solutions returned in terms of cosine and inverse cosine
functions.
To get roots from a specific domain set the ``filter`` flag with
one of the following specifiers: Z, Q, R, I, C. By default all
roots are returned (this is equivalent to setting ``filter='C'``).
By default a dictionary is returned giving a compact result in
case of multiple roots. However to get a list containing all
those roots set the ``multiple`` flag to True; the list will
have identical roots appearing next to each other in the result.
(For a given Poly, the all_roots method will give the roots in
sorted numerical order.)
Examples
========
>>> from sympy import Poly, roots
>>> from sympy.abc import x, y
>>> roots(x**2 - 1, x)
{-1: 1, 1: 1}
>>> p = Poly(x**2-1, x)
>>> roots(p)
{-1: 1, 1: 1}
>>> p = Poly(x**2-y, x, y)
>>> roots(Poly(p, x))
{-sqrt(y): 1, sqrt(y): 1}
>>> roots(x**2 - y, x)
{-sqrt(y): 1, sqrt(y): 1}
>>> roots([1, 0, -1])
{-1: 1, 1: 1}
References
==========
1. http://en.wikipedia.org/wiki/Cubic_function#Trigonometric_.28and_hyperbolic.29_method
"""
from sympy.polys.polytools import to_rational_coeffs
flags = dict(flags)
auto = flags.pop('auto', True)
cubics = flags.pop('cubics', True)
trig = flags.pop('trig', False)
quartics = flags.pop('quartics', True)
quintics = flags.pop('quintics', False)
multiple = flags.pop('multiple', False)
filter = flags.pop('filter', None)
predicate = flags.pop('predicate', None)
if isinstance(f, list):
if gens:
raise ValueError('redundant generators given')
x = Dummy('x')
poly, i = {}, len(f) - 1
for coeff in f:
poly[i], i = sympify(coeff), i - 1
f = Poly(poly, x, field=True)
else:
try:
f = Poly(f, *gens, **flags)
if f.length == 2 and f.degree() != 1:
# check for foo**n factors in the constant
n = f.degree()
npow_bases = []
expr = f.as_expr()
con = expr.as_independent(*gens)[0]
for p in Mul.make_args(con):
if p.is_Pow and not p.exp % n:
npow_bases.append(p.base**(p.exp / n))
else:
other.append(p)
if npow_bases:
b = Mul(*npow_bases)
B = Dummy()
d = roots(
Poly(expr - con + B**n * Mul(*others), *gens,
**flags), *gens, **flags)
rv = {}
for k, v in d.items():
rv[k.subs(B, b)] = v
return rv
except GeneratorsNeeded:
if multiple:
return []
else:
return {}
if f.is_multivariate:
raise PolynomialError('multivariate polynomials are not supported')
def _update_dict(result, root, k):
if root in result:
result[root] += k
else:
result[root] = k
def _try_decompose(f):
"""Find roots using functional decomposition. """
factors, roots = f.decompose(), []
for root in _try_heuristics(factors[0]):
roots.append(root)
for factor in factors[1:]:
previous, roots = list(roots), []
for root in previous:
g = factor - Poly(root, f.gen)
for root in _try_heuristics(g):
roots.append(root)
return roots
def _try_heuristics(f):
"""Find roots using formulas and some tricks. """
if f.is_ground:
return []
if f.is_monomial:
return [S(0)] * f.degree()
if f.length() == 2:
if f.degree() == 1:
return list(map(cancel, roots_linear(f)))
else:
return roots_binomial(f)
result = []
for i in [-1, 1]:
if not f.eval(i):
f = f.quo(Poly(f.gen - i, f.gen))
result.append(i)
break
n = f.degree()
if n == 1:
result += list(map(cancel, roots_linear(f)))
elif n == 2:
result += list(map(cancel, roots_quadratic(f)))
elif f.is_cyclotomic:
result += roots_cyclotomic(f)
elif n == 3 and cubics:
result += roots_cubic(f, trig=trig)
elif n == 4 and quartics:
result += roots_quartic(f)
elif n == 5 and quintics:
result += roots_quintic(f)
return result
(k, ), f = f.terms_gcd()
if not k:
zeros = {}
else:
zeros = {S(0): k}
coeff, f = preprocess_roots(f)
if auto and f.get_domain().is_Ring:
f = f.to_field()
rescale_x = None
translate_x = None
result = {}
if not f.is_ground:
if not f.get_domain().is_Exact:
for r in f.nroots():
_update_dict(result, r, 1)
elif f.degree() == 1:
result[roots_linear(f)[0]] = 1
elif f.length() == 2:
roots_fun = roots_quadratic if f.degree() == 2 else roots_binomial
for r in roots_fun(f):
_update_dict(result, r, 1)
else:
_, factors = Poly(f.as_expr()).factor_list()
if len(factors) == 1 and f.degree() == 2:
for r in roots_quadratic(f):
_update_dict(result, r, 1)
else:
if len(factors) == 1 and factors[0][1] == 1:
if f.get_domain().is_EX:
res = to_rational_coeffs(f)
if res:
if res[0] is None:
translate_x, f = res[2:]
else:
rescale_x, f = res[1], res[-1]
result = roots(f)
if not result:
for root in _try_decompose(f):
_update_dict(result, root, 1)
else:
for r in _try_heuristics(f):
_update_dict(result, r, 1)
else:
for root in _try_decompose(f):
_update_dict(result, root, 1)
else:
for factor, k in factors:
for r in _try_heuristics(
Poly(factor, f.gen, field=True)):
_update_dict(result, r, k)
if coeff is not S.One:
_result, result, = result, {}
for root, k in _result.items():
result[coeff * root] = k
result.update(zeros)
if filter not in [None, 'C']:
handlers = {
'Z': lambda r: r.is_Integer,
'Q': lambda r: r.is_Rational,
'R': lambda r: r.is_real,
'I': lambda r: r.is_imaginary,
}
try:
query = handlers[filter]
except KeyError:
raise ValueError("Invalid filter: %s" % filter)
for zero in dict(result).keys():
if not query(zero):
del result[zero]
if predicate is not None:
for zero in dict(result).keys():
if not predicate(zero):
del result[zero]
if rescale_x:
result1 = {}
for k, v in result.items():
result1[k * rescale_x] = v
result = result1
if translate_x:
result1 = {}
for k, v in result.items():
result1[k + translate_x] = v
result = result1
if not multiple:
return result
else:
zeros = []
for zero in ordered(result):
zeros.extend([zero] * result[zero])
return zeros
def test_sympify_raises():
raises(SympifyError, lambda: sympify("fx)"))
def linear_eq_to_matrix(equations, *symbols):
r"""
Converts a given System of Equations into Matrix form.
Here `equations` must be a linear system of equations in
`symbols`. The order of symbols in input `symbols` will
determine the order of coefficients in the returned
Matrix.
The Matrix form corresponds to the augmented matrix form.
For example:
.. math:: 4x + 2y + 3z = 1
.. math:: 3x + y + z = -6
.. math:: 2x + 4y + 9z = 2
This system would return `A` & `b` as given below:
::
[ 4 2 3 ] [ 1 ]
A = [ 3 1 1 ] b = [-6 ]
[ 2 4 9 ] [ 2 ]
Examples
========
>>> from sympy import linear_eq_to_matrix, symbols
>>> x, y, z = symbols('x, y, z')
>>> eqns = [x + 2*y + 3*z - 1, 3*x + y + z + 6, 2*x + 4*y + 9*z - 2]
>>> A, b = linear_eq_to_matrix(eqns, [x, y, z])
>>> A
Matrix([
[1, 2, 3],
[3, 1, 1],
[2, 4, 9]])
>>> b
Matrix([
[ 1],
[-6],
[ 2]])
>>> eqns = [x + z - 1, y + z, x - y]
>>> A, b = linear_eq_to_matrix(eqns, [x, y, z])
>>> A
Matrix([
[1, 0, 1],
[0, 1, 1],
[1, -1, 0]])
>>> b
Matrix([
[1],
[0],
[0]])
* Symbolic coefficients are also supported
>>> a, b, c, d, e, f = symbols('a, b, c, d, e, f')
>>> eqns = [a*x + b*y - c, d*x + e*y - f]
>>> A, B = linear_eq_to_matrix(eqns, x, y)
>>> A
Matrix([
[a, b],
[d, e]])
>>> B
Matrix([
[c],
[f]])
"""
if not symbols:
raise ValueError('Symbols must be given, for which coefficients \
are to be found.')
if hasattr(symbols[0], '__iter__'):
symbols = symbols[0]
M = Matrix([symbols])
# initialise Matrix with symbols + 1 columns
M = M.col_insert(len(symbols), Matrix([1]))
row_no = 1
for equation in equations:
f = sympify(equation)
if isinstance(f, Equality):
f = f.lhs - f.rhs
# Extract coeff of symbols
coeff_list = []
for symbol in symbols:
coeff_list.append(f.coeff(symbol))
# append constant term (term free from symbols)
coeff_list.append(-f.as_coeff_add(*symbols)[0])
# insert equations coeff's into rows
M = M.row_insert(row_no, Matrix([coeff_list]))
row_no += 1
# delete the initialised (Ist) trivial row
M.row_del(0)
A, b = M[:, :-1], M[:, -1:]
return A, b
def test_sympify_bool():
assert sympify(True) is true
assert sympify(False) is false
def test_int_float():
class F1_1(object):
def __float__(self):
return 1.1
class F1_1b(object):
"""
This class is still a float, even though it also implements __int__().
"""
def __float__(self):
return 1.1
def __int__(self):
return 1
class F1_1c(object):
"""
This class is still a float, because it implements _sympy_()
"""
def __float__(self):
return 1.1
def __int__(self):
return 1
def _sympy_(self):
return Float(1.1)
class I5(object):
def __int__(self):
return 5
class I5b(object):
"""
This class implements both __int__() and __float__(), so it will be
treated as Float in SymPy. One could change this behavior, by using
float(a) == int(a), but deciding that integer-valued floats represent
exact numbers is arbitrary and often not correct, so we do not do it.
If, in the future, we decide to do it anyway, the tests for I5b need to
be changed.
"""
def __float__(self):
return 5.0
def __int__(self):
return 5
class I5c(object):
"""
This class implements both __int__() and __float__(), but also
a _sympy_() method, so it will be Integer.
"""
def __float__(self):
return 5.0
def __int__(self):
return 5
def _sympy_(self):
return Integer(5)
i5 = I5()
i5b = I5b()
i5c = I5c()
f1_1 = F1_1()
f1_1b = F1_1b()
f1_1c = F1_1c()
assert sympify(i5) == 5
assert isinstance(sympify(i5), Integer)
assert sympify(i5b) == 5
assert isinstance(sympify(i5b), Float)
assert sympify(i5c) == 5
assert isinstance(sympify(i5c), Integer)
assert abs(sympify(f1_1) - 1.1) < 1e-5
assert abs(sympify(f1_1b) - 1.1) < 1e-5
assert abs(sympify(f1_1c) - 1.1) < 1e-5
assert _sympify(i5) == 5
assert isinstance(_sympify(i5), Integer)
assert _sympify(i5b) == 5
assert isinstance(_sympify(i5b), Float)
assert _sympify(i5c) == 5
assert isinstance(_sympify(i5c), Integer)
assert abs(_sympify(f1_1) - 1.1) < 1e-5
assert abs(_sympify(f1_1b) - 1.1) < 1e-5
assert abs(_sympify(f1_1c) - 1.1) < 1e-5
def checksol(f, symbol, sol=None, **flags):
"""Checks whether sol is a solution of equation f == 0.
Input can be either a single symbol and corresponding value
or a dictionary of symbols and values.
Examples:
---------
>>> from sympy import symbols
>>> from sympy.solvers import checksol
>>> x, y = symbols('x,y')
>>> checksol(x**4-1, x, 1)
True
>>> checksol(x**4-1, x, 0)
False
>>> checksol(x**2 + y**2 - 5**2, {x:3, y: 4})
True
None is returned if checksol() could not conclude.
flags:
'numerical=True (default)'
do a fast numerical check if f has only one symbol.
'minimal=True (default is False)'
a very fast, minimal testing.
'warning=True (default is False)'
print a warning if checksol() could not conclude.
'simplified=True (default)'
solution should be simplified before substituting into function
and function should be simplified after making substitution.
'force=True (default is False)'
make positive all symbols without assumptions regarding sign.
"""
if sol is not None:
sol = {symbol: sol}
elif isinstance(symbol, dict):
sol = symbol
else:
msg = 'Expecting sym, val or {sym: val}, None but got %s, %s'
raise ValueError(msg % (symbol, sol))
if hasattr(f, '__iter__') and hasattr(f, '__len__'):
if not f:
raise ValueError('no functions to check')
rv = set()
for fi in f:
check = checksol(fi, sol, **flags)
if check is False:
return False
rv.add(check)
if None in rv: # rv might contain True and/or None
return None
assert len(rv) == 1 # True
return True
if isinstance(f, Poly):
f = f.as_expr()
elif isinstance(f, Equality):
f = f.lhs - f.rhs
if not f:
return True
if not f.has(*sol.keys()):
return False
attempt = -1
numerical = flags.get('numerical', True)
while 1:
attempt += 1
if attempt == 0:
val = f.subs(sol)
elif attempt == 1:
if not val.atoms(Symbol) and numerical:
# val is a constant, so a fast numerical test may suffice
if val not in [S.Infinity, S.NegativeInfinity]:
# issue 2088 shows that +/-oo chops to 0
val = val.evalf(36).n(30, chop=True)
elif attempt == 2:
if flags.get('minimal', False):
return
# the flag 'simplified=False' is used in solve to avoid
# simplifying the solution. So if it is set to False there
# the simplification will not be attempted here, either. But
# if the simplification is done here then the flag should be
# set to False so it isn't done again there.
if flags.get('simplified', True):
for k in sol:
sol[k] = simplify(sympify(sol[k]))
flags['simplified'] = False
val = simplify(f.subs(sol))
if flags.get('force', False):
val = posify(val)[0]
elif attempt == 3:
val = powsimp(val)
elif attempt == 4:
val = cancel(val)
elif attempt == 5:
val = val.expand()
elif attempt == 6:
val = together(val)
elif attempt == 7:
val = powsimp(val)
else:
break
if val.is_zero:
return True
elif attempt > 0 and numerical and val.is_nonzero:
return False
if flags.get('warning', False):
print("Warning: could not verify solution %s." % sol)
def test_issue_3218():
assert sympify("x+\ny") == x + y
