def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = piecewise_fold(sympify(function))
if function is S.NaN:
return S.NaN
if symbols:
limits, sign = _process_limits(*symbols)
else:
# no symbols provided -- let's compute full anti-derivative
limits, sign = [Tuple(s) for s in function.free_symbols], 1
if len(limits) != 1:
raise ValueError(
"specify integration variables to integrate %s" % function)
while isinstance(function, Integral):
# denest the integrand
limits = list(function.limits) + limits
function = function.function
obj = Expr.__new__(cls, **assumptions)
arglist = [sign * function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = all(s.is_commutative for s in obj.free_symbols)
return obj
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
#
# This constructor only differs from ExprWithLimits
# in the application of the orientation variable. Perhaps merge?
function = piecewise_fold(sympify(function))
if function is S.NaN:
return S.NaN
if symbols:
limits, orientation = _process_limits(*symbols)
else:
# symbol not provided -- we can still try to compute a general form
free = function.free_symbols
if len(free) != 1:
raise ValueError("specify dummy variables for %s" % function)
limits, orientation = [Tuple(s) for s in free], 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
obj = Expr.__new__(cls, **assumptions)
arglist = [orientation * function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = function.is_commutative # limits already checked
return obj
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = piecewise_fold(sympify(function))
if function.is_Number:
if function is S.NaN:
return S.NaN
elif function is S.Infinity:
return S.Infinity
elif function is S.NegativeInfinity:
return S.NegativeInfinity
if symbols:
limits = []
for V in symbols:
if isinstance(V, Symbol):
limits.append(Tuple(V))
continue
elif isinstance(V, (tuple, list, Tuple)):
V = flatten(V)
newsymbol = sympify(V[0])
if len(V) == 3:
if isinstance(newsymbol, Symbol):
nlim = map(sympify, V[1:])
if V[1] is None and V[2] is not None:
nlim = [V[2]]
if V[2] is None and V[1] is not None:
function = -function
nlim = [V[1]]
if V[1] is None and V[2] is None:
nlim = []
limits.append( Tuple(newsymbol, *nlim ))
continue
elif len(V) == 1 or (len(V) == 2 and V[1] is None):
if isinstance(newsymbol, Symbol):
limits.append(Tuple(newsymbol))
continue
elif len(V) == 2:
if isinstance(newsymbol, Symbol):
limits.append(Tuple(newsymbol,V[1]))
continue
raise ValueError("Invalid integration variable or limits: %s" % str(symbols))
else:
# no symbols provided -- let's compute full anti-derivative
limits = [Tuple(symb) for symb in function.atoms(Symbol)]
if not limits:
return function
obj = Expr.__new__(cls, **assumptions)
arglist = [function]
arglist.extend(limits)
obj._args = tuple(arglist)
return obj
def __new__(cls, function, *symbols, **assumptions):
from sympy.integrals.integrals import _process_limits
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = piecewise_fold(sympify(function))
if function is S.NaN:
return S.NaN
if not symbols:
raise ValueError("Summation variables must be given")
limits, sign = _process_limits(*symbols)
# Only limits with lower and upper bounds are supported; the indefinite Sum
# is not supported
if any(len(l) != 3 or None in l for l in limits):
raise ValueError('Sum requires values for lower and upper bounds.')
obj = Expr.__new__(cls, **assumptions)
arglist = [sign*function]
arglist.extend(limits)
obj._args = tuple(arglist)
return obj
def _common_new(cls, function, *symbols, **assumptions):
"""Return either a special return value or the tuple,
(function, limits, orientation). This code is common to
both ExprWithLimits and AddWithLimits."""
function = sympify(function)
if isinstance(function, Equality):
# This transforms e.g. Integral(Eq(x, y)) to Eq(Integral(x), Integral(y))
# but that is only valid for definite integrals.
limits, orientation = _process_limits(*symbols)
if not (limits and all(len(limit) == 3 for limit in limits)):
SymPyDeprecationWarning(
feature='Integral(Eq(x, y))',
useinstead='Eq(Integral(x, z), Integral(y, z))',
issue=18053,
deprecated_since_version=1.6,
).warn()
lhs = function.lhs
rhs = function.rhs
return Equality(cls(lhs, *symbols, **assumptions), \
cls(rhs, *symbols, **assumptions))
if function is S.NaN:
return S.NaN
if symbols:
limits, orientation = _process_limits(*symbols)
for i, li in enumerate(limits):
if len(li) == 4:
function = function.subs(li[0], li[-1])
limits[i] = Tuple(*li[:-1])
else:
# symbol not provided -- we can still try to compute a general form
free = function.free_symbols
if len(free) != 1:
raise ValueError("specify dummy variables for %s" % function)
limits, orientation = [Tuple(s) for s in free], 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
# Any embedded piecewise functions need to be brought out to the
# top level. We only fold Piecewise that contain the integration
# variable.
reps = {}
symbols_of_integration = {i[0] for i in limits}
for p in function.atoms(Piecewise):
if not p.has(*symbols_of_integration):
reps[p] = Dummy()
# mask off those that don't
function = function.xreplace(reps)
# do the fold
function = piecewise_fold(function)
# remove the masking
function = function.xreplace({v: k for k, v in reps.items()})
return function, limits, orientation
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = piecewise_fold(sympify(function))
if function is S.NaN:
return S.NaN
if symbols:
limits, sign = _process_limits(*symbols)
else:
# no symbols provided -- let's compute full anti-derivative
limits, sign = [Tuple(s) for s in function.free_symbols], 1
if len(limits) != 1:
raise ValueError("specify integration variables to integrate %s" % function)
while isinstance(function, Integral):
# denest the integrand
limits = list(function.limits) + limits
function = function.function
obj = Expr.__new__(cls, **assumptions)
arglist = [sign*function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = all(s.is_commutative for s in obj.free_symbols)
return obj
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
#
# This constructor only differs from ExprWithLimits
# in the application of the orientation variable. Perhaps merge?
function = piecewise_fold(sympify(function))
if function is S.NaN:
return S.NaN
if symbols:
limits, orientation = _process_limits(*symbols)
else:
# symbol not provided -- we can still try to compute a general form
free = function.free_symbols
if len(free) != 1:
raise ValueError(
"specify dummy variables for %s" % function)
limits, orientation = [Tuple(s) for s in free], 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
obj = Expr.__new__(cls, **assumptions)
arglist = [orientation*function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = function.is_commutative # limits already checked
return obj
def __new__(cls, function, *symbols, **assumptions):
from sympy.integrals.integrals import _process_limits
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = piecewise_fold(sympify(function))
if function is S.NaN:
return S.NaN
if not symbols:
raise ValueError("Summation variables must be given")
limits, sign = _process_limits(*symbols)
# Only limits with lower and upper bounds are supported; the indefinite Sum
# is not supported
if any(len(l) != 3 or None in l for l in limits):
raise ValueError('Sum requires values for lower and upper bounds.')
obj = Expr.__new__(cls, **assumptions)
arglist = [sign * function]
arglist.extend(limits)
obj._args = tuple(arglist)
return obj
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = piecewise_fold(sympify(function))
if function is S.NaN:
return S.NaN
symbols = list(symbols)
if not symbols:
# no symbols provided -- let's compute full anti-derivative
symbols = sorted(function.free_symbols, Basic.compare)
if not symbols:
raise ValueError('An integration variable is required.')
while isinstance(function, Integral):
# denest the integrand
symbols = list(function.limits) + symbols
function = function.function
limits = []
for V in symbols:
if isinstance(V, Symbol):
limits.append(Tuple(V))
continue
elif isinstance(V, (tuple, list, Tuple)):
V = sympify(flatten(V))
if V[0].is_Symbol:
newsymbol = V[0]
if len(V) == 3:
if V[1] is None and V[2] is not None:
nlim = [V[2]]
elif V[1] is not None and V[2] is None:
function = -function
nlim = [V[1]]
elif V[1] is None and V[2] is None:
nlim = []
else:
nlim = V[1:]
limits.append(Tuple(newsymbol, *nlim ))
continue
elif len(V) == 1 or (len(V) == 2 and V[1] is None):
limits.append(Tuple(newsymbol))
continue
elif len(V) == 2:
limits.append(Tuple(newsymbol, V[1]))
continue
raise ValueError("Invalid integration variable or limits: %s" % str(symbols))
obj = Expr.__new__(cls, **assumptions)
obj._args = tuple([function] + limits)
obj.is_commutative = all(s.is_commutative for s in obj.free_symbols)
return obj
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = piecewise_fold(sympify(function))
if function.is_Number:
if function is S.NaN:
return S.NaN
elif function is S.Infinity:
return S.Infinity
elif function is S.NegativeInfinity:
return S.NegativeInfinity
if symbols:
limits = []
for V in symbols:
if isinstance(V, Symbol):
limits.append((V, None))
continue
elif isinstance(V, (tuple, list)):
V = flatten(V)
newsymbol = sympify(V[0])
if len(V) == 3:
if isinstance(newsymbol, Symbol):
nlim = map(sympify, V[1:])
if V[1] is None:
nlim[0] = None
if V[2] is None:
nlim[1] = None
limits.append((newsymbol, tuple(nlim)))
continue
elif len(V) == 1 or (len(V) == 2 and V[1] is None):
if isinstance(newsymbol, Symbol):
limits.append((newsymbol, None))
continue
raise ValueError("Invalid integration variable or limits: %s" %
str(symbols))
else:
# no symbols provided -- let's compute full anti-derivative
limits = [(symb, None) for symb in function.atoms(Symbol)]
if not limits:
return function
obj = Expr.__new__(cls, **assumptions)
obj._args = (function, tuple(limits))
return obj
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
#
# This constructor only differs from ExprWithLimits
# in the application of the orientation variable. Perhaps merge?
function = piecewise_fold(sympify(function))
if function is S.NaN:
return S.NaN
# delete dx, dy, dx, etc.
free = function.free_symbols
for f in free:
if len(f.name) > 1 and f.name[0] == "d":
function = function.subs(f, 1)
if symbols:
limits, orientation = _process_limits(*symbols)
else:
# symbol not provided -- we can still try to compute a general form
new_free = set()
limits = []
# if f is dx, then the variable is x
for f in free:
if len(f.name) > 1 and f.name[0] == "d":
limits.append((Symbol(f.name[1:]),))
else:
new_free.add(f)
free = new_free
del new_free
if len(limits) == 0:
if len(free) != 1:
raise ValueError(
"specify dummy variables for %s" % function)
limits = [Tuple(s) for s in free]
orientation = 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
obj = Expr.__new__(cls, **assumptions)
arglist = [orientation*function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = function.is_commutative # limits already checked
return obj
def _common_new(cls, function, *symbols, **assumptions):
"""Return either a special return value or the tuple,
(function, limits, orientation). This code is common to
both ExprWithLimits and AddWithLimits."""
function = sympify(function)
if hasattr(function, 'func') and isinstance(function, Equality):
lhs = function.lhs
rhs = function.rhs
return Equality(cls(lhs, *symbols, **assumptions), \
cls(rhs, *symbols, **assumptions))
if function is S.NaN:
return S.NaN
if symbols:
limits, orientation = _process_limits(*symbols)
for i, li in enumerate(limits):
if len(li) == 4:
function = function.subs(li[0], li[-1])
limits[i] = tuple(li[:-1])
else:
# symbol not provided -- we can still try to compute a general form
free = function.free_symbols
if len(free) != 1:
raise ValueError(
"specify dummy variables for %s" % function)
limits, orientation = [Tuple(s) for s in free], 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
# Any embedded piecewise functions need to be brought out to the
# top level. We only fold Piecewise that contain the integration
# variable.
reps = {}
symbols_of_integration = set([i[0] for i in limits])
for p in function.atoms(Piecewise):
if not p.has(*symbols_of_integration):
reps[p] = Dummy()
# mask off those that don't
function = function.xreplace(reps)
# do the fold
function = piecewise_fold(function)
# remove the masking
function = function.xreplace({v: k for k, v in reps.items()})
return function, limits, orientation
def _common_new(cls, function, *symbols, **assumptions):
"""Return either a special return value or the tuple,
(function, limits, orientation). This code is common to
both ExprWithLimits and AddWithLimits."""
function = sympify(function)
if hasattr(function, 'func') and isinstance(function, Equality):
lhs = function.lhs
rhs = function.rhs
return Equality(cls(lhs, *symbols, **assumptions), \
cls(rhs, *symbols, **assumptions))
if function is S.NaN:
return S.NaN
if symbols:
limits, orientation = _process_limits(*symbols)
for i, li in enumerate(limits):
if len(li) == 4:
function = function.subs(li[0], li[-1])
limits[i] = Tuple(*li[:-1])
else:
# symbol not provided -- we can still try to compute a general form
free = function.free_symbols
if len(free) != 1:
raise ValueError("specify dummy variables for %s" % function)
limits, orientation = [Tuple(s) for s in free], 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
# Any embedded piecewise functions need to be brought out to the
# top level. We only fold Piecewise that contain the integration
# variable.
reps = {}
symbols_of_integration = set([i[0] for i in limits])
for p in function.atoms(Piecewise):
if not p.has(*symbols_of_integration):
reps[p] = Dummy()
# mask off those that don't
function = function.xreplace(reps)
# do the fold
function = piecewise_fold(function)
# remove the masking
function = function.xreplace({v: k for k, v in reps.items()})
return function, limits, orientation
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
#
# This constructor only differs from ExprWithLimits
# in the application of the orientation variable. Perhaps merge?
function = sympify(function)
if hasattr(function, 'func') and function.func is Equality:
lhs = function.lhs
rhs = function.rhs
return Equality(cls(lhs, *symbols, **assumptions), \
cls(rhs, *symbols, **assumptions))
function = piecewise_fold(function)
if function is S.NaN:
return S.NaN
if symbols:
limits, orientation = _process_limits(*symbols)
else:
# symbol not provided -- we can still try to compute a general form
free = function.free_symbols
if len(free) != 1:
raise ValueError(
" specify dummy variables for %s. If the integrand contains"
" more than one free symbol, an integration variable should"
" be supplied explicitly e.g., integrate(f(x, y), x)" %
function)
limits, orientation = [Tuple(s) for s in free], 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
obj = Expr.__new__(cls, **assumptions)
arglist = [orientation * function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = function.is_commutative # limits already checked
return obj
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
#
# This constructor only differs from ExprWithLimits
# in the application of the orientation variable. Perhaps merge?
function = sympify(function)
if hasattr(function, 'func') and function.func is Equality:
lhs = function.lhs
rhs = function.rhs
return Equality(cls(lhs, *symbols, **assumptions), \
cls(rhs, *symbols, **assumptions))
function = piecewise_fold(function)
if function is S.NaN:
return S.NaN
if symbols:
limits, orientation = _process_limits(*symbols)
else:
# symbol not provided -- we can still try to compute a general form
free = function.free_symbols
if len(free) != 1:
raise ValueError(
" specify dummy variables for %s. If the integrand contains"
" more than one free symbol, an integration variable should"
" be supplied explicitly e.g., integrate(f(x, y), x)"
% function)
limits, orientation = [Tuple(s) for s in free], 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
obj = Expr.__new__(cls, **assumptions)
arglist = [orientation*function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = function.is_commutative # limits already checked
return obj
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = sympify(function)
if hasattr(function, 'func') and function.func is Equality:
lhs = function.lhs
rhs = function.rhs
return Equality(cls(lhs, *symbols, **assumptions), \
cls(rhs, *symbols, **assumptions))
function = piecewise_fold(function)
if function is S.NaN:
return S.NaN
if symbols:
limits, orientation = _process_limits(*symbols)
else:
# symbol not provided -- we can still try to compute a general form
free = function.free_symbols
if len(free) != 1:
raise ValueError("specify dummy variables for %s" % function)
limits, orientation = [Tuple(s) for s in free], 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
# Only limits with lower and upper bounds are supported; the indefinite form
# is not supported
if any(len(l) != 3 or None in l for l in limits):
raise ValueError(
'ExprWithLimits requires values for lower and upper bounds.')
obj = Expr.__new__(cls, **assumptions)
arglist = [function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = function.is_commutative # limits already checked
return obj
def __new__(cls, function, *symbols, **assumptions):
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = sympify(function)
if hasattr(function, 'func') and function.func is C.Equality:
lhs = function.lhs
rhs = function.rhs
return C.Equality(cls(lhs, *symbols, **assumptions), \
cls(rhs, *symbols, **assumptions))
function = piecewise_fold(function)
if function is S.NaN:
return S.NaN
if symbols:
limits, orientation = _process_limits(*symbols)
else:
# symbol not provided -- we can still try to compute a general form
free = function.free_symbols
if len(free) != 1:
raise ValueError(
"specify dummy variables for %s" % function)
limits, orientation = [Tuple(s) for s in free], 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
# Only limits with lower and upper bounds are supported; the indefinite form
# is not supported
if any(len(l) != 3 or None in l for l in limits):
raise ValueError('ExprWithLimits requires values for lower and upper bounds.')
obj = Expr.__new__(cls, **assumptions)
arglist = [function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = function.is_commutative # limits already checked
return obj
def solve(f, *symbols, **flags):
"""
Algebraically solves equations and systems of equations.
Currently supported are:
- univariate polynomial,
- transcendental
- piecewise combinations of the above
- systems of linear and polynomial equations
- sytems containing relational expressions.
Input is formed as:
f
- a single Expr or Poly that must be zero,
- an Equality
- a Relational expression or boolean
- iterable of one or more of the above
symbols (Symbol, Function or Derivative) specified as
- none given (all free symbols will be used)
- single symbol
- denested list of symbols
e.g. solve(f, x, y)
- ordered iterable of symbols
e.g. solve(f, [x, y])
flags
- ``simplified``, when False, will not simplify solutions
(default=True except for polynomials of
order 3 or greater)
- ``warning``, when True, will warn every time a solution can
not be checked, or assumptions about a variable
can't be verified for a solution.
The output varies according to the input and can be seen by example:
>>> from sympy import solve, Poly, Eq, Function, exp
>>> from sympy.abc import x, y, z, a, b
o boolean or univariate Relational
>>> solve(x < 3)
And(im(x) == 0, re(x) < 3)
o single expression and single symbol that is in the expression
>>> solve(x - y, x)
[y]
>>> solve(x - 3, x)
[3]
>>> solve(Eq(x, 3), x)
[3]
>>> solve(Poly(x - 3), x)
[3]
>>> solve(x**2 - y**2, x)
[y, -y]
>>> solve(x**4 - 1, x)
[1, -1, -I, I]
o single expression with no symbol that is in the expression
>>> solve(3, x)
[]
>>> solve(x - 3, y)
[]
o when no symbol is given then all free symbols will be used
and sorted with default_sort_key and the result will be the
same as above as if those symbols had been supplied
>>> solve(x - 3)
[3]
>>> solve(x**2 - y**2)
[y, -y]
o when a Function or Derivative is given as a symbol, it is isolated
algebraically and an implicit solution may be obtained
>>> f = Function('f')
>>> solve(f(x) - x, f(x))
[x]
>>> solve(f(x).diff(x) - f(x) - x, f(x).diff(x))
[x + f(x)]
o single expression and more than 1 symbol
when there is a linear solution
>>> solve(x - y**2, x, y)
{x: y**2}
>>> solve(x**2 - y, x, y)
{y: x**2}
when undetermined coefficients are identified
that are linear
>>> solve((a + b)*x - b + 2, a, b)
{a: -2, b: 2}
that are nonlinear
>>> solve((a + b)*x - b**2 + 2, a, b)
[(-2**(1/2), 2**(1/2)), (2**(1/2), -2**(1/2))]
if there is no linear solution then the first successful
attempt for a nonlinear solution will be returned
>>> solve(x**2 - y**2, x, y)
[y, -y]
>>> solve(x**2 - y**2/exp(x), x, y)
[x*exp(x/2), -x*exp(x/2)]
o iterable of one or more of the above
involving relationals or bools
>>> solve([x < 3, x - 2])
And(im(x) == 0, re(x) == 2)
>>> solve([x > 3, x - 2])
False
when the system is linear
with a solution
>>> solve([x - 3], x)
{x: 3}
>>> solve((x + 5*y - 2, -3*x + 6*y - 15), x, y)
{x: -3, y: 1}
>>> solve((x + 5*y - 2, -3*x + 6*y - 15), x, y, z)
{x: -3, y: 1}
>>> solve((x + 5*y - 2, -3*x + 6*y - z), z, x, y)
{x: -5*y + 2, z: 21*y - 6}
without a solution
>>> solve([x + 3, x - 3])
when the system is not linear
>>> solve([x**2 + y -2, y**2 - 4], x, y)
[(-2, -2), (0, 2), (0, 2), (2, -2)]
Warning: there is a possibility of obtaining ambiguous results
if no symbols are given for a nonlinear system of equations or
are given as a set since the symbols are not presently reported
with the solution. A warning will be issued in this situation.
>>> solve([x - 2, x**2 + y])
<BLANKLINE>
For nonlinear systems of equations, symbols should be
given as a list so as to avoid ambiguity in the results.
solve sorted the symbols as [x, y]
[(2, -4)]
>>> solve([x - 2, x**2 + f(x)], set([f(x), x]))
<BLANKLINE>
For nonlinear systems of equations, symbols should be
given as a list so as to avoid ambiguity in the results.
solve sorted the symbols as [x, f(x)]
[(2, -4)]
If two variables (or more) don't appear in the result, the assumptions
can't be checked.
>>> solve(z**2*x**2 - z**2*y**2/exp(x), x, y, z, warning=True)
<BLANKLINE>
Warning: assumptions can't be checked
(can't find for which variable equation was solved).
[x*exp(x/2), -x*exp(x/2)]
Presently, assumptions aren't checked either when `solve()` input
involves relationals or bools.
See also:
rsolve() for solving recurrence relationships
dsolve() for solving differential equations
"""
# make f and symbols into lists of sympified quantities
# keeping track of how f was passed since if it is a list
# a dictionary of results will be returned.
###########################################################################
def sympified_list(w):
return map(sympify, w if iterable(w) else [w])
bare_f = not iterable(f)
ordered_symbols = (symbols and
symbols[0] and
(isinstance(symbols[0], Symbol) or
is_sequence(symbols[0], include=GeneratorType)
)
)
f, symbols = (sympified_list(w) for w in [f, symbols])
# preprocess equation(s)
###########################################################################
for i, fi in enumerate(f):
if isinstance(fi, Equality):
f[i] = fi.lhs - fi.rhs
elif isinstance(fi, Poly):
f[i] = fi.as_expr()
elif isinstance(fi, bool) or fi.is_Relational:
return reduce_inequalities(f, assume=flags.get('assume'))
# Any embedded piecewise functions need to be brought out to the
# top level so that the appropriate strategy gets selected.
f[i] = piecewise_fold(f[i])
# preprocess symbol(s)
###########################################################################
if not symbols:
# get symbols from equations or supply dummy symbols so solve(3) behaves
# like solve(3, x).
symbols = set([])
for fi in f:
symbols |= fi.free_symbols or set([Dummy()])
ordered_symbols = False
elif len(symbols) == 1 and iterable(symbols[0]):
symbols = symbols[0]
if not ordered_symbols:
# we do this to make the results returned canonical in case f
# contains a system of nonlinear equations; all other cases should
# be unambiguous
symbols = sorted(symbols, key=lambda i: i.sort_key())
# we can solve for Function and Derivative instances by replacing them
# with Dummy symbols
symbols_new = []
symbol_swapped = False
symbols_passed = list(symbols)
for i, s in enumerate(symbols):
if s.is_Symbol:
s_new = s
elif s.is_Function:
symbol_swapped = True
s_new = Dummy('F%d' % i)
elif s.is_Derivative:
symbol_swapped = True
s_new = Dummy('D%d' % i)
else:
msg = 'expected Symbol, Function or Derivative but got %s'
raise TypeError(msg % type(s))
symbols_new.append(s_new)
if symbol_swapped:
swap_back_dict = dict(zip(symbols_new, symbols))
swap_dict = zip(symbols, symbols_new)
f = [fi.subs(swap_dict) for fi in f]
symbols = symbols_new
#
# try to get a solution
###########################################################################
if bare_f:
# pass f the way it was passed to solve; if it wasn't a list then
# a list of solutions will be returned, otherwise a dictionary is
# going to be returned
f = f[0]
solution = _solve(f, *symbols, **flags)
#
# postprocessing
###########################################################################
# Restore original Functions and Derivatives if a dictionary is returned.
# This is not necessary for
# - the single equation, single unknown case
# since the symbol will have been removed from the solution;
# - the nonlinear poly_system since that only support zero-dimensional
# systems and those results come back as a list
if symbol_swapped and type(solution) is dict:
solution = dict([(swap_back_dict[k], v.subs(swap_back_dict))
for k, v in solution.iteritems()])
# warn if ambiguous results are being obtained
# XXX agree on how to make this unambiguous
# see issue 2405 for logic in how Polys chooses ordering and
# for discussion of what to return see http://groups.google.com/group/sympy
# Apr 18, 2011 posting 'using results from solve'
elif (not ordered_symbols and
len(symbols) > 1 and
solution and
is_sequence(solution) and
is_sequence(solution[0]) and
any(len(set(s)) > 1 for s in solution)
):
msg = ('\n\tFor nonlinear systems of equations, symbols should be' +
'\n\tgiven as a list so as to avoid ambiguity in the results.' +
'\n\tsolve sorted the symbols as %s')
if symbol_swapped:
from itertools import izip
tmp = izip(*swap_dict) # separate for the benefit of 2to3
print msg % list(tmp.next())
else:
print msg % symbols
# Get assumptions about symbols, to filter solutions.
# Note that if assumptions about a solution can't be verified, it is still returned.
# XXX: Currently, there are some cases which are not handled,
# see issue 2098 comment 13: http://code.google.com/p/sympy/issues/detail?id=2098#c13.
warn = flags.get('warning', False)
if type(solution) is list:
if solution:
unchecked = []
filtered = []
if type(solution[0]) is tuple:
for sol in solution:
full_check = True
for symb, val in zip(symbols, sol):
test = check_assumptions(val, **symb.assumptions0)
if test is None:
full_check = False
if test is False: # not None nor True
break
if test is not False:
filtered.append(sol)
if not full_check:
unchecked.append(sol)
solution = filtered
else:
if len(symbols) != 1: # find which one was solved for
symbols = list(f.free_symbols - set.union(*(s.free_symbols for s in solution)))
if len(symbols) == 1:
for sol in solution:
test = check_assumptions(sol, **symbols[0].assumptions0)
if test is None:
unchecked.append(sol)
if test is not False: # None or True
filtered.append(sol)
solution = filtered
else:
if warn:
print("\n\tWarning: assumptions can't be checked"
"\n\t(can't find for which variable equation was solved).")
if warn and unchecked:
print("\n\tWarning: assumptions concerning following solution(s) can't be checked:"
+ '\n\t' + ', '.join(str(s) for s in unchecked))
elif type(solution) is dict:
full_check = True
for symb, val in solution.iteritems():
test = check_assumptions(val, **symb.assumptions0)
if test is None:
full_check = False
if test is False: # not None nor True
solution = None
break
if warn and not full_check:
print("\n\tWarning: assumptions concerning solution can't be checked.")
elif isinstance(solution, (Relational, And, Or)):
assert len(symbols) == 1
if warn and symbols[0].assumptions0:
print("\n\tWarning: assumptions about variable '%s' are not handled currently." %symbols[0])
# TODO: check also variable assumptions for inequalities
elif solution is not None:
raise TypeError('Unrecognized solution') # improve the checker to handle this
#
# done
###########################################################################
return solution
def _solve(f, *symbols, **flags):
"""Solves equations and systems of equations.
Currently supported are univariate polynomial, transcendental
equations, piecewise combinations thereof and systems of linear
and polynomial equations. Input is formed as a single expression
or an equation, or an iterable container in case of an equation
system. The type of output may vary and depends heavily on the
input. For more details refer to more problem specific functions.
By default all solutions are simplified to make the output more
readable. If this is not the expected behavior (e.g., because of
speed issues) set simplified=False in function arguments.
To solve equations and systems of equations like recurrence relations
or differential equations, use rsolve() or dsolve(), respectively.
>>> from sympy import I, solve
>>> from sympy.abc import x, y
Solve a polynomial equation:
>>> solve(x**4-1, x)
[1, -1, -I, I]
Solve a linear system:
>>> solve((x+5*y-2, -3*x+6*y-15), x, y)
{x: -3, y: 1}
"""
def sympified_list(w):
return map(sympify, iff(isinstance(w,(list, tuple, set)), w, [w]))
# make f and symbols into lists of sympified quantities
# keeping track of how f was passed since if it is a list
# a dictionary of results will be returned.
bare_f = not iterable(f)
f, symbols = (sympified_list(w) for w in [f, symbols])
for i, fi in enumerate(f):
if isinstance(fi, Equality):
f[i] = fi.lhs - fi.rhs
elif isinstance(fi, Poly):
f[i] = fi.as_expr()
elif isinstance(fi, bool) or fi.is_Relational:
return reduce_inequalities(f, assume=flags.get('assume'))
if not symbols:
#get symbols from equations or supply dummy symbols since
#solve(3,x) returns []...though it seems that it should raise some sort of error TODO
symbols = set([])
for fi in f:
symbols |= fi.free_symbols or set([Dummy('x')])
symbols = list(symbols)
symbols.sort(key=Basic.sort_key)
if len(symbols) == 1:
if isinstance(symbols[0], (list, tuple, set)):
symbols = symbols[0]
result = list()
# Begin code handling for Function and Derivative instances
# Basic idea: store all the passed symbols in symbols_passed, check to see
# if any of them are Function or Derivative types, if so, use a dummy
# symbol in their place, and set symbol_swapped = True so that other parts
# of the code can be aware of the swap. Once all swapping is done, the
# continue on with regular solving as usual, and swap back at the end of
# the routine, so that whatever was passed in symbols is what is returned.
symbols_new = []
symbol_swapped = False
symbols_passed = list(symbols)
for i, s in enumerate(symbols):
if s.is_Symbol:
s_new = s
elif s.is_Function:
symbol_swapped = True
s_new = Dummy('F%d' % i)
elif s.is_Derivative:
symbol_swapped = True
s_new = Dummy('D%d' % i)
else:
raise TypeError('not a Symbol or a Function')
symbols_new.append(s_new)
if symbol_swapped:
swap_back_dict = dict(zip(symbols_new, symbols))
# End code for handling of Function and Derivative instances
if bare_f:
f = f[0]
# Create a swap dictionary for storing the passed symbols to be solved
# for, so that they may be swapped back.
if symbol_swapped:
swap_dict = zip(symbols, symbols_new)
f = f.subs(swap_dict)
symbols = symbols_new
# Any embedded piecewise functions need to be brought out to the
# top level so that the appropriate strategy gets selected.
f = piecewise_fold(f)
if len(symbols) != 1:
soln = None
free = f.free_symbols
ex = free - set(symbols)
if len(ex) == 1:
ex = ex.pop()
try:
# may come back as dict or list (if non-linear)
soln = solve_undetermined_coeffs(f, symbols, ex)
except NotImplementedError:
pass
if soln is None:
n, d = solve_linear(f, x=symbols)
if n.is_Symbol:
soln = {n: cancel(d)}
if soln:
if symbol_swapped and isinstance(soln, dict):
return dict([(swap_back_dict[k],
v.subs(swap_back_dict))
for k, v in soln.iteritems()])
return soln
symbol = symbols[0]
# first see if it really depends on symbol and whether there
# is a linear solution
f_num, sol = solve_linear(f, x=symbols)
if not symbol in f_num.free_symbols:
return []
elif f_num.is_Symbol:
return [cancel(sol)]
strategy = guess_solve_strategy(f, symbol)
result = False # no solution was obtained
if strategy == GS_POLY:
poly = f.as_poly(symbol)
if poly is None:
msg = "Cannot solve equation %s for %s" % (f, symbol)
else:
# for cubics and quartics, if the flag wasn't set, DON'T do it
# by default since the results are quite long. Perhaps one could
# base this decision on a certain crtical length of the roots.
if poly.degree() > 2:
flags['simplified'] = flags.get('simplified', False)
result = roots(poly, cubics=True, quartics=True).keys()
elif strategy == GS_RATIONAL:
P, _ = f.as_numer_denom()
dens = denoms(f, x=symbols)
# reject any result that makes Q affirmatively 0;
# if in doubt, keep it
try:
soln = _solve(P, symbol, **flags)
except NotImplementedError:
msg = "Cannot solve equation %s for %s" % (P, symbol)
result = []
else:
if dens:
result = [s for s in soln if all(not checksol(den, {symbol: s}) for den in dens)]
else:
result = soln
elif strategy == GS_POLY_CV_1:
args = list(f.args)
if isinstance(f, Pow):
result = _solve(args[0], symbol, **flags)
elif isinstance(f, Add):
# we must search for a suitable change of variables
# collect exponents
exponents_denom = list()
for arg in args:
if isinstance(arg, Pow):
exponents_denom.append(arg.exp.q)
elif isinstance(arg, Mul):
for mul_arg in arg.args:
if isinstance(mul_arg, Pow):
exponents_denom.append(mul_arg.exp.q)
assert len(exponents_denom) > 0
if len(exponents_denom) == 1:
m = exponents_denom[0]
else:
# get the LCM of the denominators
m = reduce(ilcm, exponents_denom)
# x -> y**m.
# we assume positive for simplification purposes
t = Dummy('t', positive=True)
f_ = f.subs(symbol, t**m)
if guess_solve_strategy(f_, t) != GS_POLY:
msg = "Could not convert to a polynomial equation: %s" % f_
result = []
else:
soln = [s**m for s in _solve(f_, t)]
# we might have introduced solutions from another branch
# when changing variables; check and keep solutions
# unless they definitely aren't a solution
result = [s for s in soln if checksol(f, {symbol: s}) is not False]
elif isinstance(f, Mul):
result = []
for m in f.args:
result.extend(_solve(m, symbol, **flags) or [])
elif strategy == GS_POLY_CV_2:
m = 0
args = list(f.args)
if isinstance(f, Add):
for arg in args:
if isinstance(arg, Pow):
m = min(m, arg.exp)
elif isinstance(arg, Mul):
for mul_arg in arg.args:
if isinstance(mul_arg, Pow):
m = min(m, mul_arg.exp)
elif isinstance(f, Mul):
for mul_arg in args:
if isinstance(mul_arg, Pow):
m = min(m, mul_arg.exp)
if m and m != 1:
f_ = simplify(f*symbol**(-m))
try:
sols = _solve(f_, symbol)
except NotImplementedError:
msg = 'Could not solve %s for %s' % (f_, symbol)
else:
# we might have introduced unwanted solutions
# when multiplying by x**-m; check and keep solutions
# unless they definitely aren't a solution
if sols:
result = [s for s in sols if checksol(f, {symbol: s}) is not False]
else:
msg = 'CV_2 calculated %d but it should have been other than 0 or 1' % m
elif strategy == GS_PIECEWISE:
result = set()
for expr, cond in f.args:
candidates = _solve(expr, *symbols)
if isinstance(cond, bool) or cond.is_Number:
if not cond:
continue
# Only include solutions that do not match the condition
# of any of the other pieces.
for candidate in candidates:
matches_other_piece = False
for other_expr, other_cond in f.args:
if isinstance(other_cond, bool) \
or other_cond.is_Number:
continue
if bool(other_cond.subs(symbol, candidate)):
matches_other_piece = True
break
if not matches_other_piece:
result.add(candidate)
else:
for candidate in candidates:
if bool(cond.subs(symbol, candidate)):
result.add(candidate)
result = list(result)
elif strategy == -1:
raise ValueError('Could not parse expression %s' % f)
# this is the fallback for not getting any other solution
if result is False or strategy == GS_TRANSCENDENTAL:
# reject any result that makes any dens affirmatively 0,
# if in doubt, keep it
soln = tsolve(f_num, symbol)
dens = denoms(f, x=symbols)
if not dens:
result = soln
else:
result = [s for s in soln if all(not checksol(den, {symbol: s}) for den in dens)]
if result is False:
raise NotImplementedError(msg + "\nNo algorithms are implemented to solve equation %s" % f)
if flags.get('simplified', True) and strategy != GS_RATIONAL:
result = map(simplify, result)
return result
else:
if not f:
return []
else:
# Create a swap dictionary for storing the passed symbols to be
# solved for, so that they may be swapped back.
if symbol_swapped:
swap_dict = zip(symbols, symbols_new)
f = [fi.subs(swap_dict) for fi in f]
symbols = symbols_new
polys = []
for g in f:
poly = g.as_poly(*symbols, extension=True)
if poly is not None:
polys.append(poly)
else:
raise NotImplementedError()
if all(p.is_linear for p in polys):
n, m = len(f), len(symbols)
matrix = zeros((n, m + 1))
for i, poly in enumerate(polys):
for monom, coeff in poly.terms():
try:
j = list(monom).index(1)
matrix[i, j] = coeff
except ValueError:
matrix[i, m] = -coeff
# a dictionary of symbols: values or None
soln = solve_linear_system(matrix, *symbols, **flags)
# Use swap_dict to ensure we return the same type as what was
# passed; this is not necessary in the poly-system case which
# only supports zero-dimensional systems
if symbol_swapped and soln:
soln = dict([(swap_back_dict[k],
v.subs(swap_back_dict))
for k, v in soln.iteritems()])
return soln
else:
# a list of tuples, T, where T[i] [j] corresponds to the ith solution for symbols[j]
return solve_poly_system(polys)
def inv(f):
return piecewise_fold(meijerint_inversion(f, s, t))
def solve(f, *symbols, **flags):
"""Solves equations and systems of equations.
Currently supported are univariate polynomial, transcendental
equations, piecewise combinations thereof and systems of linear
and polynomial equations. Input is formed as a single expression
or an equation, or an iterable container in case of an equation
system. The type of output may vary and depends heavily on the
input. For more details refer to more problem specific functions.
By default all solutions are simplified to make the output more
readable. If this is not the expected behavior (e.g., because of
speed issues) set simplified=False in function arguments.
To solve equations and systems of equations like recurrence relations
or differential equations, use rsolve() or dsolve(), respectively.
>>> from sympy import I, solve
>>> from sympy.abc import x, y
Solve a polynomial equation:
>>> solve(x**4-1, x)
[1, -1, -I, I]
Solve a linear system:
>>> solve((x+5*y-2, -3*x+6*y-15), x, y)
{x: -3, y: 1}
"""
def sympit(w):
return map(sympify, iff(isinstance(w, (list, tuple, set)), w, [w]))
# make f and symbols into lists of sympified quantities
# keeping track of how f was passed since if it is a list
# a dictionary of results will be returned.
bare_f = not isinstance(f, (list, tuple, set))
f, symbols = (sympit(w) for w in [f, symbols])
for i, fi in enumerate(f):
if isinstance(fi, Equality):
f[i] = fi.lhs - fi.rhs
if not symbols:
#get symbols from equations or supply dummy symbols since
#solve(3,x) returns []...though it seems that it should raise some sort of error TODO
symbols = set([])
for fi in f:
symbols |= fi.atoms(Symbol) or set([Dummy('x')])
symbols = list(symbols)
if bare_f:
f = f[0]
if len(symbols) == 1:
if isinstance(symbols[0], (list, tuple, set)):
symbols = symbols[0]
result = list()
# Begin code handling for Function and Derivative instances
# Basic idea: store all the passed symbols in symbols_passed, check to see
# if any of them are Function or Derivative types, if so, use a dummy
# symbol in their place, and set symbol_swapped = True so that other parts
# of the code can be aware of the swap. Once all swapping is done, the
# continue on with regular solving as usual, and swap back at the end of
# the routine, so that whatever was passed in symbols is what is returned.
symbols_new = []
symbol_swapped = False
symbols_passed = list(symbols)
for i, s in enumerate(symbols):
if s.is_Symbol:
s_new = s
elif s.is_Function:
symbol_swapped = True
s_new = Dummy('F%d' % i)
elif s.is_Derivative:
symbol_swapped = True
s_new = Dummy('D%d' % i)
else:
raise TypeError('not a Symbol or a Function')
symbols_new.append(s_new)
if symbol_swapped:
swap_back_dict = dict(zip(symbols_new, symbols))
# End code for handling of Function and Derivative instances
if not isinstance(f, (tuple, list, set)):
# Create a swap dictionary for storing the passed symbols to be solved
# for, so that they may be swapped back.
if symbol_swapped:
swap_dict = zip(symbols, symbols_new)
f = f.subs(swap_dict)
symbols = symbols_new
# Any embedded piecewise functions need to be brought out to the
# top level so that the appropriate strategy gets selected.
f = piecewise_fold(f)
if len(symbols) != 1:
result = {}
for s in symbols:
result[s] = solve(f, s, **flags)
if flags.get('simplified', True):
for s, r in result.items():
result[s] = map(simplify, r)
return result
symbol = symbols[0]
strategy = guess_solve_strategy(f, symbol)
if strategy == GS_POLY:
poly = f.as_poly(symbol)
if poly is None:
raise NotImplementedError("Cannot solve equation " + str(f) +
" for " + str(symbol))
# for cubics and quartics, if the flag wasn't set, DON'T do it
# by default since the results are quite long. Perhaps one could
# base this decision on a certain crtical length of the roots.
if poly.degree > 2:
flags['simplified'] = flags.get('simplified', False)
result = roots(poly, cubics=True, quartics=True).keys()
elif strategy == GS_RATIONAL:
P, Q = f.as_numer_denom()
#TODO: check for Q != 0
result = solve(P, symbol, **flags)
elif strategy == GS_POLY_CV_1:
args = list(f.args)
if isinstance(f, Add):
# we must search for a suitable change of variable
# collect exponents
exponents_denom = list()
for arg in args:
if isinstance(arg, Pow):
exponents_denom.append(arg.exp.q)
elif isinstance(arg, Mul):
for mul_arg in arg.args:
if isinstance(mul_arg, Pow):
exponents_denom.append(mul_arg.exp.q)
assert len(exponents_denom) > 0
if len(exponents_denom) == 1:
m = exponents_denom[0]
else:
# get the LCM of the denominators
m = reduce(ilcm, exponents_denom)
# x -> y**m.
# we assume positive for simplification purposes
t = Dummy('t', positive=True)
f_ = f.subs(symbol, t**m)
if guess_solve_strategy(f_, t) != GS_POLY:
raise NotImplementedError(
"Could not convert to a polynomial equation: %s" % f_)
cv_sols = solve(f_, t)
for sol in cv_sols:
result.append(sol**m)
elif isinstance(f, Mul):
for mul_arg in args:
result.extend(solve(mul_arg, symbol))
elif strategy == GS_POLY_CV_2:
m = 0
args = list(f.args)
if isinstance(f, Add):
for arg in args:
if isinstance(arg, Pow):
m = min(m, arg.exp)
elif isinstance(arg, Mul):
for mul_arg in arg.args:
if isinstance(mul_arg, Pow):
m = min(m, mul_arg.exp)
elif isinstance(f, Mul):
for mul_arg in args:
if isinstance(mul_arg, Pow):
m = min(m, mul_arg.exp)
f1 = simplify(f * symbol**(-m))
result = solve(f1, symbol)
# TODO: we might have introduced unwanted solutions
# when multiplied by x**-m
elif strategy == GS_PIECEWISE:
result = set()
for expr, cond in f.args:
candidates = solve(expr, *symbols)
if isinstance(cond, bool) or cond.is_Number:
if not cond:
continue
# Only include solutions that do not match the condition
# of any of the other pieces.
for candidate in candidates:
matches_other_piece = False
for other_expr, other_cond in f.args:
if isinstance(other_cond, bool) \
or other_cond.is_Number:
continue
if bool(other_cond.subs(symbol, candidate)):
matches_other_piece = True
break
if not matches_other_piece:
result.add(candidate)
else:
for candidate in candidates:
if bool(cond.subs(symbol, candidate)):
result.add(candidate)
result = list(result)
elif strategy == GS_TRANSCENDENTAL:
#a, b = f.as_numer_denom()
# Let's throw away the denominator for now. When we have robust
# assumptions, it should be checked, that for the solution,
# b!=0.
result = tsolve(f, *symbols)
elif strategy == -1:
raise ValueError('Could not parse expression %s' % f)
else:
raise NotImplementedError(
"No algorithms are implemented to solve equation %s" % f)
# This symbol swap should not be necessary for the single symbol case: if you've
# solved for the symbol the it will not appear in the solution. Right now, however
# ode's are getting solutions for solve (even though they shouldn't be -- see the
# swap_back test in test_solvers).
if symbol_swapped:
result = [ri.subs(swap_back_dict) for ri in result]
if flags.get('simplified', True) and strategy != GS_RATIONAL:
return map(simplify, result)
else:
return result
else:
if not f:
return {}
else:
# Create a swap dictionary for storing the passed symbols to be
# solved for, so that they may be swapped back.
if symbol_swapped:
swap_dict = zip(symbols, symbols_new)
f = [fi.subs(swap_dict) for fi in f]
symbols = symbols_new
polys = []
for g in f:
poly = g.as_poly(*symbols)
if poly is not None:
polys.append(poly)
else:
raise NotImplementedError()
if all(p.is_linear for p in polys):
n, m = len(f), len(symbols)
matrix = zeros((n, m + 1))
for i, poly in enumerate(polys):
for monom, coeff in poly.terms():
try:
j = list(monom).index(1)
matrix[i, j] = coeff
except ValueError:
matrix[i, m] = -coeff
soln = solve_linear_system(matrix, *symbols, **flags)
else:
soln = solve_poly_system(polys)
# Use swap_dict to ensure we return the same type as what was
# passed
if symbol_swapped:
if isinstance(soln, dict):
res = {}
for k in soln.keys():
res.update({swap_back_dict[k]: soln[k]})
return res
else:
return soln
else:
return soln
def solve(f, *symbols, **flags):
"""
Algebraically solves equations and systems of equations.
Currently supported are:
- univariate polynomial,
- transcendental
- piecewise combinations of the above
- systems of linear and polynomial equations
- sytems containing relational expressions.
Input is formed as:
f
- a single Expr or Poly that must be zero,
- an Equality
- a Relational expression or boolean
- iterable of one or more of the above
symbols (Symbol, Function or Derivative) specified as
- none given (all free symbols will be used)
- single symbol
- denested list of symbols
e.g. solve(f, x, y)
- ordered iterable of symbols
e.g. solve(f, [x, y])
flags
- ``simplified``, when False, will not simplify solutions
(default=True except for polynomials of
order 3 or greater)
The output varies according to the input and can be seen by example:
>>> from sympy import solve, Poly, Eq, Function, exp
>>> from sympy.abc import x, y, z, a, b
o boolean or univariate Relational
>>> solve(x < 3)
And(im(x) == 0, re(x) < 3)
o single expression and single symbol that is in the expression
>>> solve(x - y, x)
[y]
>>> solve(x - 3, x)
[3]
>>> solve(Eq(x, 3), x)
[3]
>>> solve(Poly(x - 3), x)
[3]
>>> solve(x**2 - y**2, x)
[y, -y]
>>> solve(x**4 - 1, x)
[1, -1, -I, I]
o single expression with no symbol that is in the expression
>>> solve(3, x)
[]
>>> solve(x - 3, y)
[]
o when no symbol is given then all free symbols will be used
and sorted with default_sort_key and the result will be the
same as above as if those symbols had been supplied
>>> solve(x - 3)
[3]
>>> solve(x**2 - y**2)
[y, -y]
o when a Function or Derivative is given as a symbol, it is isolated
algebraically and an implicit solution may be obtained
>>> f = Function('f')
>>> solve(f(x) - x, f(x))
[x]
>>> solve(f(x).diff(x) - f(x) - x, f(x).diff(x))
[x + f(x)]
o single expression and more than 1 symbol
when there is a linear solution
>>> solve(x - y**2, x, y)
{x: y**2}
>>> solve(x**2 - y, x, y)
{y: x**2}
when undetermined coefficients are identified
that are linear
>>> solve((a + b)*x - b + 2, a, b)
{a: -2, b: 2}
that are nonlinear
>>> solve((a + b)*x - b**2 + 2, a, b)
[(-2**(1/2), 2**(1/2)), (2**(1/2), -2**(1/2))]
if there is no linear solution then the first successful
attempt for a nonlinear solution will be returned
>>> solve(x**2 - y**2, x, y)
[y, -y]
>>> solve(x**2 - y**2/exp(x), x, y)
[x*exp(x/2), -x*exp(x/2)]
o iterable of one or more of the above
involving relationals or bools
>>> solve([x < 3, x - 2])
And(im(x) == 0, re(x) == 2)
>>> solve([x > 3, x - 2])
False
when the system is linear
with a solution
>>> solve([x - 3], x)
{x: 3}
>>> solve((x + 5*y - 2, -3*x + 6*y - 15), x, y)
{x: -3, y: 1}
>>> solve((x + 5*y - 2, -3*x + 6*y - 15), x, y, z)
{x: -3, y: 1}
>>> solve((x + 5*y - 2, -3*x + 6*y - z), z, x, y)
{x: -5*y + 2, z: 21*y - 6}
without a solution
>>> solve([x + 3, x - 3])
when the system is not linear
>>> solve([x**2 + y -2, y**2 - 4], x, y)
[(-2, -2), (0, 2), (0, 2), (2, -2)]
Warning: there is a possibility of obtaining ambiguous results
if no symbols are given for a nonlinear system of equations or
are given as a set since the symbols are not presently reported
with the solution. A warning will be issued in this situation.
>>> solve([x - 2, x**2 + y])
<BLANKLINE>
For nonlinear systems of equations, symbols should be
given as a list so as to avoid ambiguity in the results.
solve sorted the symbols as [x, y]
[(2, -4)]
>>> solve([x - 2, x**2 + f(x)], set([f(x), x]))
<BLANKLINE>
For nonlinear systems of equations, symbols should be
given as a list so as to avoid ambiguity in the results.
solve sorted the symbols as [x, f(x)]
[(2, -4)]
See also:
rsolve() for solving recurrence relationships
dsolve() for solving differential equations
"""
# make f and symbols into lists of sympified quantities
# keeping track of how f was passed since if it is a list
# a dictionary of results will be returned.
###########################################################################
def sympified_list(w):
return map(sympify, iff(iterable(w), w, [w]))
bare_f = not iterable(f)
ordered_symbols = (symbols and symbols[0] and
(isinstance(symbols[0], Symbol)
or ordered_iter(symbols[0], include=GeneratorType)))
f, symbols = (sympified_list(w) for w in [f, symbols])
# preprocess equation(s)
###########################################################################
for i, fi in enumerate(f):
if isinstance(fi, Equality):
f[i] = fi.lhs - fi.rhs
elif isinstance(fi, Poly):
f[i] = fi.as_expr()
elif isinstance(fi, bool) or fi.is_Relational:
return reduce_inequalities(f, assume=flags.get('assume'))
# Any embedded piecewise functions need to be brought out to the
# top level so that the appropriate strategy gets selected.
f[i] = piecewise_fold(f[i])
# preprocess symbol(s)
###########################################################################
if not symbols:
# get symbols from equations or supply dummy symbols so solve(3) behaves
# like solve(3, x).
symbols = set([])
for fi in f:
symbols |= fi.free_symbols or set([Dummy()])
ordered_symbols = False
elif len(symbols) == 1 and iterable(symbols[0]):
symbols = symbols[0]
if not ordered_symbols:
# we do this to make the results returned canonical in case f
# contains a system of nonlinear equations; all other cases should
# be unambiguous
symbols = sorted(symbols, key=lambda i: i.sort_key())
# we can solve for Function and Derivative instances by replacing them
# with Dummy symbols
symbols_new = []
symbol_swapped = False
symbols_passed = list(symbols)
for i, s in enumerate(symbols):
if s.is_Symbol:
s_new = s
elif s.is_Function:
symbol_swapped = True
s_new = Dummy('F%d' % i)
elif s.is_Derivative:
symbol_swapped = True
s_new = Dummy('D%d' % i)
else:
msg = 'expected Symbol, Function or Derivative but got %s'
raise TypeError(msg % type(s))
symbols_new.append(s_new)
if symbol_swapped:
swap_back_dict = dict(zip(symbols_new, symbols))
swap_dict = zip(symbols, symbols_new)
f = [fi.subs(swap_dict) for fi in f]
symbols = symbols_new
#
# try to get a solution
###########################################################################
if bare_f:
# pass f the way it was passed to solve; if it wasn't a list then
# a list of solutions will be returned, otherwise a dictionary is
# going to be returned
f = f[0]
solution = _solve(f, *symbols, **flags)
#
# postprocessing
###########################################################################
# Restore original Functions and Derivatives if a dictionary is returned.
# This is not necessary for
# - the single equation, single unknown case
# since the symbol will have been removed from the solution;
# - the nonlinear poly_system since that only support zero-dimensional
# systems and those results come back as a list
if symbol_swapped and type(solution) is dict:
solution = dict([(swap_back_dict[k], v.subs(swap_back_dict))
for k, v in solution.iteritems()])
# warn if ambiguous results are being obtained
# XXX agree on how to make this unambiguous
# see issue 2405 for logic in how Polys chooses ordering and
# for discussion of what to return see http://groups.google.com/group/sympy
# Apr 18, 2011 posting 'using results from solve'
elif (not ordered_symbols and len(symbols) > 1 and solution
and ordered_iter(solution) and ordered_iter(solution[0])
and any(len(set(s)) > 1 for s in solution)):
msg = ('\n\tFor nonlinear systems of equations, symbols should be' +
'\n\tgiven as a list so as to avoid ambiguity in the results.' +
'\n\tsolve sorted the symbols as %s')
print msg % str(
bool(symbol_swapped) and list(zip(*swap_dict)[0]) or symbols)
#
# done
###########################################################################
return solution
def _common_new(cls, function, *symbols, discrete, **assumptions):
"""Return either a special return value or the tuple,
(function, limits, orientation). This code is common to
both ExprWithLimits and AddWithLimits."""
function = sympify(function)
if isinstance(function, Equality):
# This transforms e.g. Integral(Eq(x, y)) to Eq(Integral(x), Integral(y))
# but that is only valid for definite integrals.
limits, orientation = _process_limits(*symbols, discrete=discrete)
if not (limits and all(len(limit) == 3 for limit in limits)):
sympy_deprecation_warning(
"""
Creating a indefinite integral with an Eq() argument is
deprecated.
This is because indefinite integrals do not preserve equality
due to the arbitrary constants. If you want an equality of
indefinite integrals, use Eq(Integral(a, x), Integral(b, x))
explicitly.
""",
deprecated_since_version="1.6",
active_deprecations_target="deprecated-indefinite-integral-eq",
stacklevel=5,
)
lhs = function.lhs
rhs = function.rhs
return Equality(cls(lhs, *symbols, **assumptions), \
cls(rhs, *symbols, **assumptions))
if function is S.NaN:
return S.NaN
if symbols:
limits, orientation = _process_limits(*symbols, discrete=discrete)
for i, li in enumerate(limits):
if len(li) == 4:
function = function.subs(li[0], li[-1])
limits[i] = Tuple(*li[:-1])
else:
# symbol not provided -- we can still try to compute a general form
free = function.free_symbols
if len(free) != 1:
raise ValueError("specify dummy variables for %s" % function)
limits, orientation = [Tuple(s) for s in free], 1
# denest any nested calls
while cls == type(function):
limits = list(function.limits) + limits
function = function.function
# Any embedded piecewise functions need to be brought out to the
# top level. We only fold Piecewise that contain the integration
# variable.
reps = {}
symbols_of_integration = {i[0] for i in limits}
for p in function.atoms(Piecewise):
if not p.has(*symbols_of_integration):
reps[p] = Dummy()
# mask off those that don't
function = function.xreplace(reps)
# do the fold
function = piecewise_fold(function)
# remove the masking
function = function.xreplace({v: k for k, v in reps.items()})
return function, limits, orientation
def solve(f, *symbols, **flags):
"""Solves equations and systems of equations.
Currently supported are univariate polynomial, transcendental
equations, piecewise combinations thereof and systems of linear
and polynomial equations. Input is formed as a single expression
or an equation, or an iterable container in case of an equation
system. The type of output may vary and depends heavily on the
input. For more details refer to more problem specific functions.
By default all solutions are simplified to make the output more
readable. If this is not the expected behavior (e.g., because of
speed issues) set simplified=False in function arguments.
To solve equations and systems of equations like recurrence relations
or differential equations, use rsolve() or dsolve(), respectively.
>>> from sympy import I, solve
>>> from sympy.abc import x, y
Solve a polynomial equation:
>>> solve(x**4-1, x)
[1, -1, -I, I]
Solve a linear system:
>>> solve((x+5*y-2, -3*x+6*y-15), x, y)
{x: -3, y: 1}
"""
def sympit(w):
return map(sympify, iff(isinstance(w, (list, tuple, set)), w, [w]))
# make f and symbols into lists of sympified quantities
# keeping track of how f was passed since if it is a list
# a dictionary of results will be returned.
bare_f = not isinstance(f, (list, tuple, set))
f, symbols = (sympit(w) for w in [f, symbols])
if any(isinstance(fi, bool) or (fi.is_Relational and not fi.is_Equality) for fi in f):
return reduce_inequalities(f, assume=flags.get("assume"))
for i, fi in enumerate(f):
if fi.is_Equality:
f[i] = fi.lhs - fi.rhs
if not symbols:
# get symbols from equations or supply dummy symbols since
# solve(3,x) returns []...though it seems that it should raise some sort of error TODO
symbols = set([])
for fi in f:
symbols |= fi.atoms(Symbol) or set([Dummy("x")])
symbols = list(symbols)
if bare_f:
f = f[0]
if len(symbols) == 1:
if isinstance(symbols[0], (list, tuple, set)):
symbols = symbols[0]
result = list()
# Begin code handling for Function and Derivative instances
# Basic idea: store all the passed symbols in symbols_passed, check to see
# if any of them are Function or Derivative types, if so, use a dummy
# symbol in their place, and set symbol_swapped = True so that other parts
# of the code can be aware of the swap. Once all swapping is done, the
# continue on with regular solving as usual, and swap back at the end of
# the routine, so that whatever was passed in symbols is what is returned.
symbols_new = []
symbol_swapped = False
symbols_passed = list(symbols)
for i, s in enumerate(symbols):
if s.is_Symbol:
s_new = s
elif s.is_Function:
symbol_swapped = True
s_new = Dummy("F%d" % i)
elif s.is_Derivative:
symbol_swapped = True
s_new = Dummy("D%d" % i)
else:
raise TypeError("not a Symbol or a Function")
symbols_new.append(s_new)
if symbol_swapped:
swap_back_dict = dict(zip(symbols_new, symbols))
# End code for handling of Function and Derivative instances
if not isinstance(f, (tuple, list, set)):
# Create a swap dictionary for storing the passed symbols to be solved
# for, so that they may be swapped back.
if symbol_swapped:
swap_dict = zip(symbols, symbols_new)
f = f.subs(swap_dict)
symbols = symbols_new
# Any embedded piecewise functions need to be brought out to the
# top level so that the appropriate strategy gets selected.
f = piecewise_fold(f)
if len(symbols) != 1:
result = {}
for s in symbols:
result[s] = solve(f, s, **flags)
if flags.get("simplified", True):
for s, r in result.items():
result[s] = map(simplify, r)
return result
symbol = symbols[0]
strategy = guess_solve_strategy(f, symbol)
if strategy == GS_POLY:
poly = f.as_poly(symbol)
if poly is None:
raise NotImplementedError("Cannot solve equation " + str(f) + " for " + str(symbol))
# for cubics and quartics, if the flag wasn't set, DON'T do it
# by default since the results are quite long. Perhaps one could
# base this decision on a certain crtical length of the roots.
if poly.degree > 2:
flags["simplified"] = flags.get("simplified", False)
result = roots(poly, cubics=True, quartics=True).keys()
elif strategy == GS_RATIONAL:
P, Q = f.as_numer_denom()
# TODO: check for Q != 0
result = solve(P, symbol, **flags)
elif strategy == GS_POLY_CV_1:
args = list(f.args)
if isinstance(f, Add):
# we must search for a suitable change of variable
# collect exponents
exponents_denom = list()
for arg in args:
if isinstance(arg, Pow):
exponents_denom.append(arg.exp.q)
elif isinstance(arg, Mul):
for mul_arg in arg.args:
if isinstance(mul_arg, Pow):
exponents_denom.append(mul_arg.exp.q)
assert len(exponents_denom) > 0
if len(exponents_denom) == 1:
m = exponents_denom[0]
else:
# get the LCM of the denominators
m = reduce(ilcm, exponents_denom)
# x -> y**m.
# we assume positive for simplification purposes
t = Dummy("t", positive=True)
f_ = f.subs(symbol, t ** m)
if guess_solve_strategy(f_, t) != GS_POLY:
raise NotImplementedError("Could not convert to a polynomial equation: %s" % f_)
cv_sols = solve(f_, t)
for sol in cv_sols:
result.append(sol ** m)
elif isinstance(f, Mul):
for mul_arg in args:
result.extend(solve(mul_arg, symbol))
elif strategy == GS_POLY_CV_2:
m = 0
args = list(f.args)
if isinstance(f, Add):
for arg in args:
if isinstance(arg, Pow):
m = min(m, arg.exp)
elif isinstance(arg, Mul):
for mul_arg in arg.args:
if isinstance(mul_arg, Pow):
m = min(m, mul_arg.exp)
elif isinstance(f, Mul):
for mul_arg in args:
if isinstance(mul_arg, Pow):
m = min(m, mul_arg.exp)
f1 = simplify(f * symbol ** (-m))
result = solve(f1, symbol)
# TODO: we might have introduced unwanted solutions
# when multiplied by x**-m
elif strategy == GS_PIECEWISE:
result = set()
for expr, cond in f.args:
candidates = solve(expr, *symbols)
if isinstance(cond, bool) or cond.is_Number:
if not cond:
continue
# Only include solutions that do not match the condition
# of any of the other pieces.
for candidate in candidates:
matches_other_piece = False
for other_expr, other_cond in f.args:
if isinstance(other_cond, bool) or other_cond.is_Number:
continue
if bool(other_cond.subs(symbol, candidate)):
matches_other_piece = True
break
if not matches_other_piece:
result.add(candidate)
else:
for candidate in candidates:
if bool(cond.subs(symbol, candidate)):
result.add(candidate)
result = list(result)
elif strategy == GS_TRANSCENDENTAL:
# a, b = f.as_numer_denom()
# Let's throw away the denominator for now. When we have robust
# assumptions, it should be checked, that for the solution,
# b!=0.
result = tsolve(f, *symbols)
elif strategy == -1:
raise ValueError("Could not parse expression %s" % f)
else:
raise NotImplementedError("No algorithms are implemented to solve equation %s" % f)
# This symbol swap should not be necessary for the single symbol case: if you've
# solved for the symbol the it will not appear in the solution. Right now, however
# ode's are getting solutions for solve (even though they shouldn't be -- see the
# swap_back test in test_solvers).
if symbol_swapped:
result = [ri.subs(swap_back_dict) for ri in result]
if flags.get("simplified", True) and strategy != GS_RATIONAL:
return map(simplify, result)
else:
return result
else:
if not f:
return {}
else:
# Create a swap dictionary for storing the passed symbols to be
# solved for, so that they may be swapped back.
if symbol_swapped:
swap_dict = zip(symbols, symbols_new)
f = [fi.subs(swap_dict) for fi in f]
symbols = symbols_new
polys = []
for g in f:
poly = g.as_poly(*symbols)
if poly is not None:
polys.append(poly)
else:
raise NotImplementedError()
if all(p.is_linear for p in polys):
n, m = len(f), len(symbols)
matrix = zeros((n, m + 1))
for i, poly in enumerate(polys):
for monom, coeff in poly.terms():
try:
j = list(monom).index(1)
matrix[i, j] = coeff
except ValueError:
matrix[i, m] = -coeff
soln = solve_linear_system(matrix, *symbols, **flags)
else:
soln = solve_poly_system(polys)
# Use swap_dict to ensure we return the same type as what was
# passed
if symbol_swapped:
if isinstance(soln, dict):
res = {}
for k in soln.keys():
res.update({swap_back_dict[k]: soln[k]})
return res
else:
return soln
else:
return soln
def median(X, evaluate=True, **kwargs):
r"""
Calculuates the median of the probability distribution.
Explanation
===========
Mathematically, median of Probability distribution is defined as all those
values of `m` for which the following condition is satisfied
.. math::
P(X\leq m) \geq \frac{1}{2} \text{ and} \text{ } P(X\geq m)\geq \frac{1}{2}
Parameters
==========
X: The random expression whose median is to be calculated.
Returns
=======
The FiniteSet or an Interval which contains the median of the
random expression.
Examples
========
>>> from sympy.stats import Normal, Die, median
>>> N = Normal('N', 3, 1)
>>> median(N)
{3}
>>> D = Die('D')
>>> median(D)
{3, 4}
References
==========
.. [1] https://en.wikipedia.org/wiki/Median#Probability_distributions
"""
if not is_random(X):
return X
from sympy.stats.crv import ContinuousPSpace
from sympy.stats.drv import DiscretePSpace
from sympy.stats.frv import FinitePSpace
if isinstance(pspace(X), FinitePSpace):
cdf = pspace(X).compute_cdf(X)
result = []
for key, value in cdf.items():
if value>= Rational(1, 2) and (1 - value) + \
pspace(X).probability(Eq(X, key)) >= Rational(1, 2):
result.append(key)
return FiniteSet(*result)
if isinstance(pspace(X), (ContinuousPSpace, DiscretePSpace)):
cdf = pspace(X).compute_cdf(X)
x = Dummy('x')
result = solveset(piecewise_fold(cdf(x) - Rational(1, 2)), x,
pspace(X).set)
return result
raise NotImplementedError("The median of %s is not implemeted." %
str(pspace(X)))
def _eval_as_leading_term(self, x, logx=None, cdir=0):
from sympy.series.order import Order
from sympy.functions.elementary.exponential import log
from sympy.functions.elementary.piecewise import Piecewise, piecewise_fold
from .function import expand_mul
old = self
if old.has(Piecewise):
old = piecewise_fold(old)
# This expansion is the last part of expand_log. expand_log also calls
# expand_mul with factor=True, which would be more expensive
if any(isinstance(a, log) for a in self.args):
logflags = dict(deep=True, log=True, mul=False, power_exp=False,
power_base=False, multinomial=False, basic=False, force=False,
factor=False)
old = old.expand(**logflags)
expr = expand_mul(old)
if not expr.is_Add:
return expr.as_leading_term(x, logx=logx, cdir=cdir)
infinite = [t for t in expr.args if t.is_infinite]
leading_terms = [t.as_leading_term(x, logx=logx, cdir=cdir) for t in expr.args]
min, new_expr = Order(0), 0
try:
for term in leading_terms:
order = Order(term, x)
if not min or order not in min:
min = order
new_expr = term
elif min in order:
new_expr += term
except TypeError:
return expr
is_zero = new_expr.is_zero
if is_zero is None:
new_expr = new_expr.trigsimp().cancel()
is_zero = new_expr.is_zero
if is_zero is True:
# simple leading term analysis gave us cancelled terms but we have to send
# back a term, so compute the leading term (via series)
n0 = min.getn()
res = Order(1)
incr = S.One
while res.is_Order:
res = old._eval_nseries(x, n=n0+incr, logx=None, cdir=cdir).cancel().powsimp().trigsimp()
incr *= 2
return res.as_leading_term(x, logx=logx, cdir=cdir)
elif new_expr is S.NaN:
return old.func._from_args(infinite)
else:
return new_expr
def _solve(f, *symbols, **flags):
"""Solves equations and systems of equations.
Currently supported are univariate polynomial, transcendental
equations, piecewise combinations thereof and systems of linear
and polynomial equations. Input is formed as a single expression
or an equation, or an iterable container in case of an equation
system. The type of output may vary and depends heavily on the
input. For more details refer to more problem specific functions.
By default all solutions are simplified to make the output more
readable. If this is not the expected behavior (e.g., because of
speed issues) set simplified=False in function arguments.
To solve equations and systems of equations like recurrence relations
or differential equations, use rsolve() or dsolve(), respectively.
>>> from sympy import I, solve
>>> from sympy.abc import x, y
Solve a polynomial equation:
>>> solve(x**4-1, x)
[1, -1, -I, I]
Solve a linear system:
>>> solve((x+5*y-2, -3*x+6*y-15), x, y)
{x: -3, y: 1}
"""
def sympified_list(w):
return map(sympify, iff(isinstance(w, (list, tuple, set)), w, [w]))
# make f and symbols into lists of sympified quantities
# keeping track of how f was passed since if it is a list
# a dictionary of results will be returned.
bare_f = not iterable(f)
f, symbols = (sympified_list(w) for w in [f, symbols])
for i, fi in enumerate(f):
if isinstance(fi, Equality):
f[i] = fi.lhs - fi.rhs
elif isinstance(fi, Poly):
f[i] = fi.as_expr()
elif isinstance(fi, bool) or fi.is_Relational:
return reduce_inequalities(f, assume=flags.get('assume'))
if not symbols:
#get symbols from equations or supply dummy symbols since
#solve(3,x) returns []...though it seems that it should raise some sort of error TODO
symbols = set([])
for fi in f:
symbols |= fi.free_symbols or set([Dummy('x')])
symbols = list(symbols)
symbols.sort(key=Basic.sort_key)
if len(symbols) == 1:
if isinstance(symbols[0], (list, tuple, set)):
symbols = symbols[0]
result = list()
# Begin code handling for Function and Derivative instances
# Basic idea: store all the passed symbols in symbols_passed, check to see
# if any of them are Function or Derivative types, if so, use a dummy
# symbol in their place, and set symbol_swapped = True so that other parts
# of the code can be aware of the swap. Once all swapping is done, the
# continue on with regular solving as usual, and swap back at the end of
# the routine, so that whatever was passed in symbols is what is returned.
symbols_new = []
symbol_swapped = False
symbols_passed = list(symbols)
for i, s in enumerate(symbols):
if s.is_Symbol:
s_new = s
elif s.is_Function:
symbol_swapped = True
s_new = Dummy('F%d' % i)
elif s.is_Derivative:
symbol_swapped = True
s_new = Dummy('D%d' % i)
else:
raise TypeError('not a Symbol or a Function')
symbols_new.append(s_new)
if symbol_swapped:
swap_back_dict = dict(zip(symbols_new, symbols))
# End code for handling of Function and Derivative instances
if bare_f:
f = f[0]
# Create a swap dictionary for storing the passed symbols to be solved
# for, so that they may be swapped back.
if symbol_swapped:
swap_dict = zip(symbols, symbols_new)
f = f.subs(swap_dict)
symbols = symbols_new
# Any embedded piecewise functions need to be brought out to the
# top level so that the appropriate strategy gets selected.
f = piecewise_fold(f)
if len(symbols) != 1:
soln = None
free = f.free_symbols
ex = free - set(symbols)
if len(ex) == 1:
ex = ex.pop()
try:
# may come back as dict or list (if non-linear)
soln = solve_undetermined_coeffs(f, symbols, ex)
except NotImplementedError:
pass
if soln is None:
n, d = solve_linear(f, x=symbols)
if n.is_Symbol:
soln = {n: cancel(d)}
if soln:
if symbol_swapped and isinstance(soln, dict):
return dict([(swap_back_dict[k], v.subs(swap_back_dict))
for k, v in soln.iteritems()])
return soln
symbol = symbols[0]
# first see if it really depends on symbol and whether there
# is a linear solution
f_num, sol = solve_linear(f, x=symbols)
if not symbol in f_num.free_symbols:
return []
elif f_num.is_Symbol:
return [cancel(sol)]
strategy = guess_solve_strategy(f, symbol)
result = False # no solution was obtained
if strategy == GS_POLY:
poly = f.as_poly(symbol)
if poly is None:
msg = "Cannot solve equation %s for %s" % (f, symbol)
else:
# for cubics and quartics, if the flag wasn't set, DON'T do it
# by default since the results are quite long. Perhaps one could
# base this decision on a certain crtical length of the roots.
if poly.degree() > 2:
flags['simplified'] = flags.get('simplified', False)
result = roots(poly, cubics=True, quartics=True).keys()
elif strategy == GS_RATIONAL:
P, _ = f.as_numer_denom()
dens = denoms(f, x=symbols)
# reject any result that makes Q affirmatively 0;
# if in doubt, keep it
try:
soln = _solve(P, symbol, **flags)
except NotImplementedError:
msg = "Cannot solve equation %s for %s" % (P, symbol)
result = []
else:
if dens:
result = [
s for s in soln
if all(not checksol(den, {symbol: s}) for den in dens)
]
else:
result = soln
elif strategy == GS_POLY_CV_1:
args = list(f.args)
if isinstance(f, Pow):
result = _solve(args[0], symbol, **flags)
elif isinstance(f, Add):
# we must search for a suitable change of variables
# collect exponents
exponents_denom = list()
for arg in args:
if isinstance(arg, Pow):
exponents_denom.append(arg.exp.q)
elif isinstance(arg, Mul):
for mul_arg in arg.args:
if isinstance(mul_arg, Pow):
exponents_denom.append(mul_arg.exp.q)
assert len(exponents_denom) > 0
if len(exponents_denom) == 1:
m = exponents_denom[0]
else:
# get the LCM of the denominators
m = reduce(ilcm, exponents_denom)
# x -> y**m.
# we assume positive for simplification purposes
t = Dummy('t', positive=True)
f_ = f.subs(symbol, t**m)
if guess_solve_strategy(f_, t) != GS_POLY:
msg = "Could not convert to a polynomial equation: %s" % f_
result = []
else:
soln = [s**m for s in _solve(f_, t)]
# we might have introduced solutions from another branch
# when changing variables; check and keep solutions
# unless they definitely aren't a solution
result = [
s for s in soln
if checksol(f, {symbol: s}) is not False
]
elif isinstance(f, Mul):
result = []
for m in f.args:
result.extend(_solve(m, symbol, **flags) or [])
elif strategy == GS_POLY_CV_2:
m = 0
args = list(f.args)
if isinstance(f, Add):
for arg in args:
if isinstance(arg, Pow):
m = min(m, arg.exp)
elif isinstance(arg, Mul):
for mul_arg in arg.args:
if isinstance(mul_arg, Pow):
m = min(m, mul_arg.exp)
elif isinstance(f, Mul):
for mul_arg in args:
if isinstance(mul_arg, Pow):
m = min(m, mul_arg.exp)
if m and m != 1:
f_ = simplify(f * symbol**(-m))
try:
sols = _solve(f_, symbol)
except NotImplementedError:
msg = 'Could not solve %s for %s' % (f_, symbol)
else:
# we might have introduced unwanted solutions
# when multiplying by x**-m; check and keep solutions
# unless they definitely aren't a solution
if sols:
result = [
s for s in sols
if checksol(f, {symbol: s}) is not False
]
else:
msg = 'CV_2 calculated %d but it should have been other than 0 or 1' % m
elif strategy == GS_PIECEWISE:
result = set()
for expr, cond in f.args:
candidates = _solve(expr, *symbols)
if isinstance(cond, bool) or cond.is_Number:
if not cond:
continue
# Only include solutions that do not match the condition
# of any of the other pieces.
for candidate in candidates:
matches_other_piece = False
for other_expr, other_cond in f.args:
if isinstance(other_cond, bool) \
or other_cond.is_Number:
continue
if bool(other_cond.subs(symbol, candidate)):
matches_other_piece = True
break
if not matches_other_piece:
result.add(candidate)
else:
for candidate in candidates:
if bool(cond.subs(symbol, candidate)):
result.add(candidate)
result = list(result)
elif strategy == -1:
raise ValueError('Could not parse expression %s' % f)
# this is the fallback for not getting any other solution
if result is False or strategy == GS_TRANSCENDENTAL:
# reject any result that makes any dens affirmatively 0,
# if in doubt, keep it
soln = tsolve(f_num, symbol)
dens = denoms(f, x=symbols)
if not dens:
result = soln
else:
result = [
s for s in soln
if all(not checksol(den, {symbol: s}) for den in dens)
]
if result is False:
raise NotImplementedError(
msg +
"\nNo algorithms are implemented to solve equation %s" % f)
if flags.get('simplified', True) and strategy != GS_RATIONAL:
result = map(simplify, result)
return result
else:
if not f:
return []
else:
# Create a swap dictionary for storing the passed symbols to be
# solved for, so that they may be swapped back.
if symbol_swapped:
swap_dict = zip(symbols, symbols_new)
f = [fi.subs(swap_dict) for fi in f]
symbols = symbols_new
polys = []
for g in f:
poly = g.as_poly(*symbols, extension=True)
if poly is not None:
polys.append(poly)
else:
raise NotImplementedError()
if all(p.is_linear for p in polys):
n, m = len(f), len(symbols)
matrix = zeros((n, m + 1))
for i, poly in enumerate(polys):
for monom, coeff in poly.terms():
try:
j = list(monom).index(1)
matrix[i, j] = coeff
except ValueError:
matrix[i, m] = -coeff
# a dictionary of symbols: values or None
soln = solve_linear_system(matrix, *symbols, **flags)
# Use swap_dict to ensure we return the same type as what was
# passed; this is not necessary in the poly-system case which
# only supports zero-dimensional systems
if symbol_swapped and soln:
soln = dict([(swap_back_dict[k], v.subs(swap_back_dict))
for k, v in soln.iteritems()])
return soln
else:
# a list of tuples, T, where T[i] [j] corresponds to the ith solution for symbols[j]
return solve_poly_system(polys)
def __new__(cls, function, *symbols, **assumptions):
"""Create an unevaluated integral.
Arguments are an integrand followed by one or more limits.
If no limits are given and there is only one free symbol in the
expression, that symbol will be used, otherwise an error will be
raised.
>>> from sympy import Integral
>>> from sympy.abc import x, y
>>> Integral(x)
Integral(x, x)
>>> Integral(y)
Integral(y, y)
When limits are provided, they are interpreted as follows (using
``x`` as though it were the variable of integration):
(x,) or x - indefinite integral
(x, a) - "evaluate at" integral
(x, a, b) - definite integral
Although the same integral will be obtained from an indefinite
integral and an "evaluate at" integral when ``a == x``, they
respond differently to substitution:
>>> i = Integral(x, x)
>>> at = Integral(x, (x, x))
>>> i.doit() == at.doit()
True
>>> i.subs(x, 1)
Integral(1, x)
>>> at.subs(x, 1)
Integral(x, (x, 1))
The ``as_dummy`` method can be used to see which symbols cannot be
targeted by subs: those with a preppended underscore cannot be
changed with ``subs``. (Also, the integration variables themselves --
the first element of a limit -- can never be changed by subs.)
>>> i.as_dummy()
Integral(x, x)
>>> at.as_dummy()
Integral(_x, (_x, x))
"""
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = piecewise_fold(sympify(function))
if function is S.NaN:
return S.NaN
if symbols:
limits, sign = _process_limits(*symbols)
else:
# no symbols provided -- let's compute full anti-derivative
free = function.free_symbols
if len(free) != 1:
raise ValueError("specify variables of integration for %s" %
function)
limits, sign = [Tuple(s) for s in free], 1
while isinstance(function, Integral):
# denest the integrand
limits = list(function.limits) + limits
function = function.function
obj = Expr.__new__(cls, **assumptions)
arglist = [sign * function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = all(s.is_commutative for s in obj.free_symbols)
return obj
def __new__(cls, function, *symbols, **assumptions):
"""Create an unevaluated integral.
Arguments are an integrand followed by one or more limits.
If no limits are given and there is only one free symbol in the
expression, that symbol will be used, otherwise an error will be
raised.
>>> from sympy import Integral
>>> from sympy.abc import x, y
>>> Integral(x)
Integral(x, x)
>>> Integral(y)
Integral(y, y)
When limits are provided, they are interpreted as follows (using
``x`` as though it were the variable of integration):
(x,) or x - indefinite integral
(x, a) - "evaluate at" integral
(x, a, b) - definite integral
Although the same integral will be obtained from an indefinite
integral and an "evaluate at" integral when ``a == x``, they
respond differently to substitution:
>>> i = Integral(x, x)
>>> at = Integral(x, (x, x))
>>> i.doit() == at.doit()
True
>>> i.subs(x, 1)
Integral(1, x)
>>> at.subs(x, 1)
Integral(x, (x, 1))
The ``as_dummy`` method can be used to see which symbols cannot be
targeted by subs: those with a preppended underscore cannot be
changed with ``subs``. (Also, the integration variables themselves --
the first element of a limit -- can never be changed by subs.)
>>> i.as_dummy()
Integral(x, x)
>>> at.as_dummy()
Integral(_x, (_x, x))
"""
# Any embedded piecewise functions need to be brought out to the
# top level so that integration can go into piecewise mode at the
# earliest possible moment.
function = piecewise_fold(sympify(function))
if function is S.NaN:
return S.NaN
if symbols:
limits, sign = _process_limits(*symbols)
else:
# no symbols provided -- let's compute full anti-derivative
free = function.free_symbols
if len(free) != 1:
raise ValueError("specify variables of integration for %s" % function)
limits, sign = [Tuple(s) for s in free], 1
while isinstance(function, Integral):
# denest the integrand
limits = list(function.limits) + limits
function = function.function
obj = Expr.__new__(cls, **assumptions)
arglist = [sign*function]
arglist.extend(limits)
obj._args = tuple(arglist)
obj.is_commutative = function.is_commutative # limits already checked
return obj