Source code for diofant.core.expr

from collections import defaultdict
from functools import reduce

from mpmath.libmp import mpf_log, prec_to_dps

from .assumptions import ManagedProperties
from .basic import Atom, Basic
from .cache import cacheit
from .compatibility import as_int, default_sort_key
from .decorators import _sympifyit, call_highest_priority
from .evalf import EvalfMixin, PrecisionExhausted, pure_complex
from .singleton import S
from .sympify import sympify


[docs]class Expr(Basic, EvalfMixin, metaclass=ManagedProperties): """ Base class for algebraic expressions. Everything that requires arithmetic operations to be defined should subclass this class, instead of Basic (which should be used only for argument storage and expression manipulation, i.e. pattern matching, substitutions, etc). See Also ======== diofant.core.basic.Basic """ def __new__(cls, *args): obj = Basic.__new__(cls, *args) obj._assumptions = cls.default_assumptions return obj @property def _diff_wrt(self): """Is it allowed to take derivative wrt to this instance. This determines if it is allowed to take derivatives wrt this object. Subclasses such as Symbol, Function and Derivative should return True to enable derivatives wrt them. The implementation in Derivative separates the Symbol and non-Symbol _diff_wrt=True variables and temporarily converts the non-Symbol vars in Symbols when performing the differentiation. Notes ===== The expr.subs({yourclass: Symbol}) should be well-defined on a structural level, or this will lead to inconsistent results. Examples ======== >>> e = Expr() >>> e._diff_wrt False >>> class MyClass(Expr): ... _diff_wrt = True ... >>> (2*MyClass()).diff(MyClass()) 2 See Also ======== diofant.core.function.Derivative """ return False
[docs] @cacheit def sort_key(self, order=None): """Return a sort key.""" coeff, expr = self.as_coeff_Mul() if expr.is_Pow: expr, exp = expr.args else: expr, exp = expr, S.One if expr.is_Dummy: args = expr.sort_key(), elif expr.is_Atom: args = str(expr), else: if expr.is_Add: args = expr.as_ordered_terms(order=order) elif expr.is_Mul: args = expr.as_ordered_factors(order=order) else: args = expr.args args = tuple(default_sort_key(arg, order=order) for arg in args) args = (len(args), tuple(args)) exp = exp.sort_key(order=order) return expr.class_key(), args, exp, coeff
# *************** # * Arithmetics * # *************** # Expr and its sublcasses use _op_priority to determine which object # passed to a binary special method (__mul__, etc.) will handle the # operation. In general, the 'call_highest_priority' decorator will choose # the object with the highest _op_priority to handle the call. # Custom subclasses that want to define their own binary special methods # should set an _op_priority value that is higher than the default. # # **NOTE**: # This is a temporary fix, and will eventually be replaced with # something better and more powerful. See issue sympy/sympy#5510. _op_priority = 10.0 def __pos__(self): return self def __neg__(self): return Mul(S.NegativeOne, self) def __abs__(self): from ..functions import Abs return Abs(self) @_sympifyit('other', NotImplemented) @call_highest_priority('__radd__') def __add__(self, other): return Add(self, other) @_sympifyit('other', NotImplemented) @call_highest_priority('__add__') def __radd__(self, other): return Add(other, self) @_sympifyit('other', NotImplemented) @call_highest_priority('__rsub__') def __sub__(self, other): return Add(self, -other) @_sympifyit('other', NotImplemented) @call_highest_priority('__sub__') def __rsub__(self, other): return Add(other, -self) @_sympifyit('other', NotImplemented) @call_highest_priority('__rmul__') def __mul__(self, other): return Mul(self, other) @_sympifyit('other', NotImplemented) @call_highest_priority('__mul__') def __rmul__(self, other): return Mul(other, self) @_sympifyit('other', NotImplemented) @call_highest_priority('__rpow__') def __pow__(self, other): return Pow(self, other) @_sympifyit('other', NotImplemented) @call_highest_priority('__pow__') def __rpow__(self, other): return Pow(other, self) @_sympifyit('other', NotImplemented) @call_highest_priority('__rtruediv__') def __truediv__(self, other): return Mul(self, Pow(other, S.NegativeOne)) @_sympifyit('other', NotImplemented) @call_highest_priority('__truediv__') def __rtruediv__(self, other): return Mul(other, Pow(self, S.NegativeOne)) @_sympifyit('other', NotImplemented) @call_highest_priority('__rmod__') def __mod__(self, other): return Mod(self, other) @_sympifyit('other', NotImplemented) @call_highest_priority('__mod__') def __rmod__(self, other): return Mod(other, self) def __int__(self): r = self.round(2) if not r.is_Number: raise TypeError("can't convert complex to int") if r in (nan, oo, -oo): raise TypeError("can't convert %s to int" % r) return int(r) def __floor__(self): from ..functions import floor return floor(self) def __float__(self): # Don't bother testing if it's a number; if it's not this is going # to fail, and if it is we still need to check that it evalf'ed to # a number. result = self.evalf(strict=False) if result.is_Number: return float(result) if result.is_number and result.as_real_imag()[1]: raise TypeError("can't convert complex to float") raise TypeError("can't convert expression to float") def __complex__(self): result = self.evalf(strict=False) re, im = result.as_real_imag() return complex(float(re), float(im)) @_sympifyit('other', NotImplemented) def __ge__(self, other): from .relational import GreaterThan for me in (self, other): if me.is_commutative and me.is_extended_real is False: raise TypeError("Invalid comparison of complex %s" % me) if me is nan: raise TypeError("Invalid NaN comparison") if self.is_extended_real or other.is_extended_real: dif = self - other if dif.is_nonnegative is not None and \ dif.is_nonnegative is not dif.is_negative: return sympify(dif.is_nonnegative) return GreaterThan(self, other, evaluate=False) @_sympifyit('other', NotImplemented) def __le__(self, other): from .relational import LessThan for me in (self, other): if me.is_commutative and me.is_extended_real is False: raise TypeError("Invalid comparison of complex %s" % me) if me is nan: raise TypeError("Invalid NaN comparison") if self.is_extended_real or other.is_extended_real: dif = self - other if dif.is_nonpositive is not None and \ dif.is_nonpositive is not dif.is_positive: return sympify(dif.is_nonpositive) return LessThan(self, other, evaluate=False) @_sympifyit('other', NotImplemented) def __gt__(self, other): from .relational import StrictGreaterThan for me in (self, other): if me.is_commutative and me.is_extended_real is False: raise TypeError("Invalid comparison of complex %s" % me) if me is nan: raise TypeError("Invalid NaN comparison") if self.is_extended_real or other.is_extended_real: dif = self - other if dif.is_positive is not None and \ dif.is_positive is not dif.is_nonpositive: return sympify(dif.is_positive) return StrictGreaterThan(self, other, evaluate=False) @_sympifyit('other', NotImplemented) def __lt__(self, other): from .relational import StrictLessThan for me in (self, other): if me.is_commutative and me.is_extended_real is False: raise TypeError("Invalid comparison of complex %s" % me) if me is nan: raise TypeError("Invalid NaN comparison") if self.is_extended_real or other.is_extended_real: dif = self - other if dif.is_negative is not None and \ dif.is_negative is not dif.is_nonnegative: return sympify(dif.is_negative) return StrictLessThan(self, other, evaluate=False) @staticmethod def _from_mpmath(x, prec): from .numbers import Float if hasattr(x, "_mpf_"): return Float._new(x._mpf_, prec) elif hasattr(x, "_mpc_"): re, im = x._mpc_ re = Float._new(re, prec) im = Float._new(im, prec)*I return re + im else: raise TypeError("expected mpmath number (mpf or mpc)") @property def is_number(self): """Returns True if 'self' has no free symbols. It will be faster than ``if not self.free_symbols``, however, since ``is_number`` will fail as soon as it hits a free symbol. Examples ======== >>> x.is_number False >>> (2*x).is_number False >>> (2 + log(2)).is_number True >>> (2 + Integral(2, x)).is_number False >>> (2 + Integral(2, (x, 1, 2))).is_number True """ return all(obj.is_number for obj in self.args) def _random(self, n=None, re_min=-1, im_min=-1, re_max=1, im_max=1): """Return self evaluated, if possible, replacing free symbols with random complex values, if necessary. The random complex value for each free symbol is generated by the random_complex_number routine giving real and imaginary parts in the range given by the re_min, re_max, im_min, and im_max values. The returned value is evaluated to a precision of n (if given) else the maximum of 15 and the precision needed to get more than 1 digit of precision. If the expression could not be evaluated to a number, or could not be evaluated to more than 1 digit of precision, then None is returned. Examples ======== >>> x._random() # doctest: +SKIP 0.0392918155679172 + 0.916050214307199*I >>> x._random(2) # doctest: +SKIP -0.77 - 0.87*I >>> (x + y/2)._random(2) # doctest: +SKIP -0.57 + 0.16*I >>> sqrt(2)._random(2) 1.4 See Also ======== diofant.utilities.randtest.random_complex_number """ free = self.free_symbols prec = 1 if free: from ..utilities.randtest import random_complex_number a, c, b, d = re_min, re_max, im_min, im_max reps = dict(zip(free, [random_complex_number(a, b, c, d, rational=True) for zi in free])) try: nmag = abs(self.evalf(2, subs=reps, strict=False)) except (ValueError, TypeError): # if an out of range value resulted in evalf problems # then return None -- XXX is there a way to know how to # select a good random number for a given expression? # e.g. when calculating n! negative values for n should not # be used return else: reps = {} nmag = abs(self.evalf(2, strict=False)) if not hasattr(nmag, '_prec'): # e.g. exp_polar(2*I*pi) doesn't evaluate but is_number is True return if nmag._prec != 1: if n is None: n = max(prec, 15) return self.evalf(n, strict=False, subs=reps)
[docs] def is_constant(self, *wrt, **flags): """Return True if self is constant, False if not, or None if the constancy could not be determined conclusively. If an expression has no free symbols then it is a constant. If there are free symbols it is possible that the expression is a constant, perhaps (but not necessarily) zero. To test such expressions, two strategies are tried: 1) numerical evaluation at two random points. If two such evaluations give two different values and the values have a precision greater than 1 then self is not constant. If the evaluations agree or could not be obtained with any precision, no decision is made. The numerical testing is done only if ``wrt`` is different than the free symbols. 2) differentiation with respect to variables in 'wrt' (or all free symbols if omitted) to see if the expression is constant or not. This will not always lead to an expression that is zero even though an expression is constant (see added test in test_expr.py). If all derivatives are zero then self is constant with respect to the given symbols. If neither evaluation nor differentiation can prove the expression is constant, None is returned unless two numerical values happened to be the same and the flag ``failing_number`` is True -- in that case the numerical value will be returned. If flag simplify=False is passed, self will not be simplified; the default is True since self should be simplified before testing. Examples ======== >>> x.is_constant() False >>> Integer(2).is_constant() True >>> Sum(x, (x, 1, 10)).is_constant() True >>> Sum(x, (x, 1, n)).is_constant() False >>> Sum(x, (x, 1, n)).is_constant(y) True >>> Sum(x, (x, 1, n)).is_constant(n) False >>> Sum(x, (x, 1, n)).is_constant(x) True >>> eq = a*cos(x)**2 + a*sin(x)**2 - a >>> eq.is_constant() True >>> eq.subs({x: pi, a: 2}) == eq.subs({x: pi, a: 3}) == 0 True >>> (0**x).is_constant() False >>> x.is_constant() False >>> (x**x).is_constant() False >>> one = cos(x)**2 + sin(x)**2 >>> one.is_constant() True >>> ((one - 1)**(x + 1)).is_constant() in (True, False) # could be 0 or 1 True """ simplify = flags.get('simplify', True) # Except for expressions that contain units, only one of these should # be necessary since if something is # known to be a number it should also know that there are no # free symbols. But is_number quits as soon as it hits a non-number # whereas free_symbols goes until all free symbols have been collected, # thus is_number should be faster. But a double check on free symbols # is made just in case there is a discrepancy between the two. free = self.free_symbols if self.is_number or not free: # if the following assertion fails then that object's free_symbols # method needs attention: if an expression is a number it cannot # have free symbols assert not free return True # if we are only interested in some symbols and they are not in the # free symbols then this expression is constant wrt those symbols wrt = set(wrt) if wrt and not wrt & free: return True wrt = wrt or free # simplify unless this has already been done expr = self if simplify: expr = expr.simplify() # is_zero should be a quick assumptions check; it can be wrong for # numbers (see test_is_not_constant test), giving False when it # shouldn't, but hopefully it will never give True unless it is sure. if expr.is_zero: return True # try numerical evaluation to see if we get two different values failing_number = None if wrt == free: # try 0 (for a) and 1 (for b) try: a = expr.subs(list(zip(free, [0]*len(free))), simultaneous=True).evalf(15, strict=False) if a is nan: # evaluation may succeed when substitution fails a = expr._random(None, 0, 0, 0, 0) if a is None or a is nan: # try random real a = expr._random(None, -1, 0, 1, 0) except ZeroDivisionError: a = None if a is not None and a is not nan: try: b = expr.subs(list(zip(free, [1]*len(free))), simultaneous=True).evalf(15, strict=False) if b is nan: # evaluation may succeed when substitution fails b = expr._random(None, 1, 0, 1, 0) except ZeroDivisionError: b = None if b is not None and b is not nan and b.equals(a) is False: return False # try random real b = expr._random(None, -1, 0, 1, 0) if b is not None and b is not nan and b.equals(a) is False: return False failing_number = a if a.is_number else b # now we will test each wrt symbol (or all free symbols) to see if the # expression depends on them or not using differentiation. This is # not sufficient for all expressions, however, so we don't return # False if we get a derivative other than 0 with free symbols. for w in wrt: deriv = expr.diff(w) if simplify: deriv = deriv.simplify() if deriv != 0: if not (deriv.is_Number or pure_complex(deriv)): if flags.get('failing_number', False): return failing_number else: assert deriv.free_symbols return # dead line provided _random returns None in such cases return False return True
[docs] def equals(self, other, failing_expression=False): """Return True if self == other, False if it doesn't, or None. If failing_expression is True then the expression which did not simplify to a 0 will be returned instead of None. If ``self`` is a Number (or complex number) that is not zero, then the result is False. If ``self`` is a number and has not evaluated to zero, evalf will be used to test whether the expression evaluates to zero. If it does so and the result has significance (i.e. the precision is either -1, for a Rational result, or is greater than 1) then the evalf value will be used to return True or False. """ other = sympify(other) if self == other: return True # they aren't the same so see if we can make the difference 0; # don't worry about doing simplification steps one at a time # because if the expression ever goes to 0 then the subsequent # simplification steps that are done will be very fast. diff = self - other try: diff = factor_terms(diff.simplify(), radical=True) except PrecisionExhausted: pass if not diff: return True if not diff.has(Add, Mod): # if there is no expanding to be done after simplifying # then this can't be a zero return False constant = diff.is_constant(simplify=False, failing_number=True) if constant is False: return False if constant is None: # e.g. unless the right simplification is done, a symbolic # zero is possible (see expression of issue sympy/sympy#6829: without # simplification constant will be None). return ndiff = diff._random() if ndiff: return False if diff.is_zero: return True if failing_expression: return diff
def _eval_is_zero(self): from ..polys.numberfields import minimal_polynomial from .function import count_ops from .symbol import Dummy if self.is_number: try: # check to see that we can get a value n2 = self._eval_evalf(2) if n2 is None or n2._prec == 1: raise AttributeError if n2 == nan: raise AttributeError except (AttributeError, ValueError, ZeroDivisionError): return r, i = self.evalf(2, strict=False).as_real_imag() if r.is_Number and i.is_Number and r._prec != 1 and i._prec != 1: if r != 0 or i != 0: return False elif (r._prec == 1 and (not i or i._prec == 1) and self.is_algebraic and not self.has(Function)): if count_ops(self) > 75: return try: return minimal_polynomial(self)(Dummy()).is_Symbol except NotImplementedError: # pragma: no cover return def _eval_is_positive(self): if self.is_number: if self.is_extended_real is False: return False try: # check to see that we can get a value n2 = self._eval_evalf(2) if n2 is None or n2._prec == 1: raise AttributeError if n2 == nan: raise AttributeError except (AttributeError, ValueError, ZeroDivisionError): return r, i = self.evalf(2, strict=False).as_real_imag() if r.is_Number and i.is_Number and r._prec != 1 and i._prec != 1: return bool(not i and r > 0) def _eval_is_negative(self): if self.is_number: if self.is_extended_real is False: return False try: # check to see that we can get a value n2 = self._eval_evalf(2) if n2 is None or n2._prec == 1: raise AttributeError if n2 == nan: raise AttributeError except (AttributeError, ValueError, ZeroDivisionError): return r, i = self.evalf(2, strict=False).as_real_imag() if r.is_Number and i.is_Number and r._prec != 1 and i._prec != 1: return bool(not i and r < 0) def _eval_interval(self, x, a, b): """Returns evaluation over an interval. For most functions this is: self.subs({x: b}) - self.subs({x: a}), possibly using limit() if NaN is returned from subs. If b or a is None, it only evaluates -self.subs({x: a}) or self.subs({b: x}), respectively. """ from ..logic import false from ..series import limit, Limit if (a is None and b is None): raise ValueError('Both interval ends cannot be None.') if a is None: A = 0 else: A = self.subs({x: a}) if A.has(nan, oo, -oo, zoo): A = limit(self, x, a, '+' if (a < b) is not false else '-') if isinstance(A, Limit): raise NotImplementedError("Could not compute limit") if b is None: B = 0 else: B = self.subs({x: b}) if B.has(nan, oo, -oo, zoo): B = limit(self, x, b) B = limit(self, x, b, '-' if (a < b) is not false else '+') if isinstance(B, Limit): raise NotImplementedError("Could not compute limit") return B - A def _eval_power(self, other): # subclass to compute self**other for cases when # other is not NaN, 0, or 1 return def _eval_conjugate(self): if self.is_extended_real: return self elif self.is_imaginary: return -self
[docs] def conjugate(self): """Returns the complex conjugate of self. See Also ======== diofant.functions.elementary.complexes.conjugate """ from ..functions.elementary.complexes import conjugate as c return c(self)
def _eval_transpose(self): from ..functions.elementary.complexes import conjugate if self.is_complex or self.is_extended_real: return self elif self.is_hermitian: return conjugate(self) elif self.is_antihermitian: return -conjugate(self)
[docs] def transpose(self): """Transpose self. See Also ======== diofant.functions.elementary.complexes.transpose """ from ..functions.elementary.complexes import transpose return transpose(self)
def _eval_adjoint(self): from ..functions.elementary.complexes import conjugate, transpose if self.is_hermitian: return self elif self.is_antihermitian: return -self obj = self._eval_conjugate() if obj is not None: return transpose(obj) obj = self._eval_transpose() if obj is not None: return conjugate(obj)
[docs] def adjoint(self): """Compute conjugate transpose or Hermite conjugation. See Also ======== diofant.functions.elementary.complexes.adjoint """ from ..functions.elementary.complexes import adjoint return adjoint(self)
@classmethod def _parse_order(cls, order): """Parse and configure the ordering of terms.""" from ..polys.orderings import monomial_key try: reverse = order.startswith('rev-') except AttributeError: reverse = False else: if reverse: order = order[4:] monom_key = monomial_key(order) def neg(monom): result = [] for m in monom: if isinstance(m, tuple): result.append(neg(m)) else: result.append(-m) return tuple(result) def key(term): _, ((re, im), monom, ncpart) = term monom = neg(monom_key(monom)) ncpart = tuple(e.sort_key(order=order) for e in ncpart) coeff = ((bool(im), im), (re, im)) return monom, ncpart, coeff return key, reverse
[docs] def as_ordered_factors(self, order=None): """Return list of ordered factors (if Mul) else [self].""" return [self]
[docs] def as_ordered_terms(self, order=None, data=False): """Transform an expression to an ordered list of terms. Examples ======== >>> (sin(x)**2*cos(x) + sin(x)**2 + 1).as_ordered_terms() [sin(x)**2*cos(x), sin(x)**2, 1] """ key, reverse = self._parse_order(order) terms, gens = self.as_terms() if not any(term.is_Order for term, _ in terms): ordered = sorted(terms, key=key, reverse=reverse) else: _terms, _order = [], [] for term, repr in terms: if not term.is_Order: _terms.append((term, repr)) else: _order.append((term, repr)) ordered = sorted(_terms, key=key, reverse=True) \ + sorted(_order, key=key, reverse=True) if data: return ordered, gens else: return [term for term, _ in ordered]
[docs] def as_terms(self): """Transform an expression to a list of terms.""" from . import Add, Mul, S from .exprtools import decompose_power gens, terms = set(), [] for term in Add.make_args(self): coeff, _term = term.as_coeff_Mul() coeff = complex(coeff) cpart, ncpart = {}, [] if _term is not S.One: for factor in Mul.make_args(_term): if factor.is_number: try: coeff *= complex(factor) continue except TypeError: pass if factor.is_commutative: base, exp = decompose_power(factor) cpart[base] = exp gens.add(base) else: ncpart.append(factor) coeff = coeff.real, coeff.imag ncpart = tuple(ncpart) terms.append((term, (coeff, cpart, ncpart))) gens = sorted(gens, key=default_sort_key) k, indices = len(gens), {} for i, g in enumerate(gens): indices[g] = i result = [] for term, (coeff, cpart, ncpart) in terms: monom = [0]*k for base, exp in cpart.items(): monom[indices[base]] = exp result.append((term, (coeff, tuple(monom), ncpart))) return result, gens
[docs] def removeO(self): """Removes the additive O(..) symbol if there is one.""" return self
[docs] def getO(self): """Returns the additive O(..) symbol if there is one, else None.""" return
[docs] def getn(self): """Returns the order of the expression. The order is determined either from the O(...) term. If there is no O(...) term, it returns None. Examples ======== >>> (1 + x + O(x**2)).getn() 2 >>> (1 + x).getn() """ from .symbol import Dummy, Symbol o = self.getO() if o is None: return elif o.is_Order: o = o.expr if o is S.One: return S.Zero elif o.is_Symbol: return S.One elif o.is_Pow: return o.args[1] elif o.is_Mul: # x**n*log(x)**n or x**n/log(x)**n for oi in o.args: if oi.is_Symbol: return S.One elif oi.is_Pow: syms = oi.atoms(Dummy, Symbol) if len(syms) == 1: x = syms.pop() oi = oi.subs({x: Dummy('x', positive=True)}) if oi.base.is_Symbol and oi.exp.is_Rational: return abs(oi.exp) raise NotImplementedError('not sure of order of %s' % o)
[docs] def count_ops(self, visual=None): """wrapper for count_ops that returns the operation count.""" from .function import count_ops return count_ops(self, visual)
[docs] def args_cnc(self, cset=False, warn=True, split_1=True): """Return [commutative factors, non-commutative factors] of self. self is treated as a Mul and the ordering of the factors is maintained. If ``cset`` is True the commutative factors will be returned in a set. If there were repeated factors (as may happen with an unevaluated Mul) then an error will be raised unless it is explicitly suppressed by setting ``warn`` to False. Note: -1 is always separated from a Number unless split_1 is False. >>> A, B = symbols('A B', commutative=0) >>> (-2*x*y).args_cnc() [[-1, 2, x, y], []] >>> (-2.5*x).args_cnc() [[-1, 2.5, x], []] >>> (-2*x*A*B*y).args_cnc() [[-1, 2, x, y], [A, B]] >>> (-2*x*A*B*y).args_cnc(split_1=False) [[-2, x, y], [A, B]] >>> (-2*x*y).args_cnc(cset=True) [{-1, 2, x, y}, []] The arg is always treated as a Mul: >>> (-2 + x + A).args_cnc() [[], [x - 2 + A]] >>> (-oo).args_cnc() # -oo is a singleton [[-1, oo], []] """ if self.is_Mul: args = list(self.args) else: args = [self] for i, mi in enumerate(args): if not mi.is_commutative: c = args[:i] nc = args[i:] break else: c = args nc = [] if c and split_1 and ( c[0].is_Number and c[0].is_negative and c[0] is not S.NegativeOne): c[:1] = [S.NegativeOne, -c[0]] if cset: clen = len(c) c = set(c) if clen and warn and len(c) != clen: raise ValueError('repeated commutative arguments: %s' % [ci for ci in c if list(self.args).count(ci) > 1]) return [c, nc]
[docs] def coeff(self, x, n=1, right=False): """Returns the coefficient from the term(s) containing ``x**n``. If ``n`` is zero then all terms independent of ``x`` will be returned. When ``x`` is noncommutative, the coefficient to the left (default) or right of ``x`` can be returned. The keyword 'right' is ignored when ``x`` is commutative. See Also ======== diofant.core.expr.Expr.as_coefficient diofant.core.expr.Expr.as_coeff_Add diofant.core.expr.Expr.as_coeff_Mul diofant.core.expr.Expr.as_independent diofant.polys.polytools.Poly.coeff_monomial Examples ======== You can select terms that have an explicit negative in front of them: >>> (-x + 2*y).coeff(-1) x >>> (x - 2*y).coeff(-1) 2*y You can select terms with no Rational coefficient: >>> (x + 2*y).coeff(1) x >>> (3 + 2*x + 4*x**2).coeff(1) 0 You can select terms independent of x by making n=0; in this case expr.as_independent(x)[0] is returned (and 0 will be returned instead of None): >>> (3 + 2*x + 4*x**2).coeff(x, 0) 3 >>> eq = ((x + 1)**3).expand() + 1 >>> eq x**3 + 3*x**2 + 3*x + 2 >>> [eq.coeff(x, i) for i in reversed(range(4))] [1, 3, 3, 2] >>> eq -= 2 >>> [eq.coeff(x, i) for i in reversed(range(4))] [1, 3, 3, 0] You can select terms that have a numerical term in front of them: >>> (-x - 2*y).coeff(2) -y >>> (x + sqrt(2)*x).coeff(sqrt(2)) x The matching is exact: >>> (3 + 2*x + 4*x**2).coeff(x) 2 >>> (3 + 2*x + 4*x**2).coeff(x**2) 4 >>> (3 + 2*x + 4*x**2).coeff(x**3) 0 >>> (z*(x + y)**2).coeff((x + y)**2) z >>> (z*(x + y)**2).coeff(x + y) 0 In addition, no factoring is done, so 1 + z*(1 + y) is not obtained from the following: >>> (x + z*(x + x*y)).coeff(x) 1 If such factoring is desired, factor_terms can be used first: >>> factor_terms(x + z*(x + x*y)).coeff(x) z*(y + 1) + 1 >>> n, m, o = symbols('n m o', commutative=False) >>> n.coeff(n) 1 >>> (3*n).coeff(n) 3 >>> (n*m + m*n*m).coeff(n) # = (1 + m)*n*m 1 + m >>> (n*m + m*n*m).coeff(n, right=True) # = (1 + m)*n*m m If there is more than one possible coefficient 0 is returned: >>> (n*m + m*n).coeff(n) 0 If there is only one possible coefficient, it is returned: >>> (n*m + x*m*n).coeff(m*n) x >>> (n*m + x*m*n).coeff(m*n, right=1) 1 """ x = sympify(x) n = as_int(n) if not x: return S.Zero if x == self: if n == 1: return S.One return S.Zero if x is S.One: co = [a for a in Add.make_args(self) if a.as_coeff_Mul()[0] is S.One] if not co: return S.Zero return Add(*co) if n == 0: return self.as_independent(x, as_Add=True)[0] # continue with the full method, looking for this power of x: x = x**n def incommon(l1, l2): if not l1 or not l2: return [] n = min(len(l1), len(l2)) for i in range(n): if l1[i] != l2[i]: return l1[:i] return l1[:] def find(l, sub, first=True): """Find where list sub appears in list l. When ``first`` is True the first occurance from the left is returned, else the last occurance is returned. Return None if sub is not in l. >> l = range(5)*2 >> find(l, [2, 3]) 2 >> find(l, [2, 3], first=0) 7 >> find(l, [2, 4]) None """ if not sub or not l or len(sub) > len(l): return n = len(sub) if not first: l.reverse() sub.reverse() for i in range(len(l) - n + 1): if all(l[i + j] == sub[j] for j in range(n)): break else: i = None if not first: l.reverse() sub.reverse() if i is not None and not first: i = len(l) - (i + n) return i co = [] args = Add.make_args(self) self_c = self.is_commutative x_c = x.is_commutative if self_c and not x_c: return S.Zero if self_c: xargs = x.args_cnc(cset=True, warn=False)[0] for a in args: margs = a.args_cnc(cset=True, warn=False)[0] if len(xargs) > len(margs): continue resid = margs.difference(xargs) if len(resid) + len(xargs) == len(margs): co.append(Mul(*resid)) if co == []: return S.Zero else: return Add(*co) elif x_c: xargs = x.args_cnc(cset=True, warn=False)[0] for a in args: margs, nc = a.args_cnc(cset=True) if len(xargs) > len(margs): continue resid = margs.difference(xargs) if len(resid) + len(xargs) == len(margs): co.append(Mul(*(list(resid) + nc))) if co == []: return S.Zero else: return Add(*co) else: # both nc xargs, nx = x.args_cnc(cset=True) # find the parts that pass the commutative terms for a in args: margs, nc = a.args_cnc(cset=True) if len(xargs) > len(margs): continue resid = margs.difference(xargs) if len(resid) + len(xargs) == len(margs): co.append((resid, nc)) # now check the non-comm parts if not co: return S.Zero if all(n == co[0][1] for r, n in co): ii = find(co[0][1], nx, right) if ii is not None: if not right: return Mul(Add(*[Mul(*r) for r, c in co]), Mul(*co[0][1][:ii])) else: return Mul(*co[0][1][ii + len(nx):]) beg = reduce(incommon, (n[1] for n in co)) if beg: ii = find(beg, nx, right) if ii is not None: if not right: gcdc = co[0][0] for i in range(1, len(co)): gcdc = gcdc.intersection(co[i][0]) if not gcdc: break return Mul(*(list(gcdc) + beg[:ii])) else: m = ii + len(nx) return Add(*[Mul(*(list(r) + n[m:])) for r, n in co]) end = list(reversed( reduce(incommon, (list(reversed(n[1])) for n in co)))) if end: ii = find(end, nx, right) if ii is not None: if not right: return Add(*[Mul(*(list(r) + n[:-len(end) + ii])) for r, n in co]) else: return Mul(*end[ii + len(nx):]) # look for single match hit = None for i, (r, n) in enumerate(co): ii = find(n, nx, right) if ii is not None: if not hit: hit = ii, r, n else: break else: if hit: ii, r, n = hit if not right: return Mul(*(list(r) + n[:ii])) else: return Mul(*n[ii + len(nx):]) return S.Zero
[docs] def as_expr(self, *gens): """Convert a polynomial to a Diofant expression. Examples ======== >>> f = (x**2 + x*y).as_poly(x, y) >>> f.as_expr() x**2 + x*y >>> sin(x).as_expr() sin(x) """ return self
[docs] def as_poly(self, *gens, **args): """Converts ``self`` to a polynomial or returns ``None``. Examples ======== >>> (x**2 + x*y).as_poly() Poly(x**2 + x*y, x, y, domain='ZZ') >>> (x**2 + x*y).as_poly(x, y) Poly(x**2 + x*y, x, y, domain='ZZ') >>> (x**2 + sin(y)).as_poly(x, y) is None True """ from ..polys import Poly, PolynomialError try: return Poly(self, *gens, **args) except PolynomialError: pass
[docs] def as_coefficient(self, expr): """Extracts symbolic coefficient at the given expression. In other words, this functions separates 'self' into the product of 'expr' and 'expr'-free coefficient. If such separation is not possible it will return None. Examples ======== >>> E.as_coefficient(E) 1 >>> (2*E).as_coefficient(E) 2 >>> (2*sin(E)*E).as_coefficient(E) Two terms have E in them so a sum is returned. (If one were desiring the coefficient of the term exactly matching E then the constant from the returned expression could be selected. Or, for greater precision, a method of Poly can be used to indicate the desired term from which the coefficient is desired.) >>> (2*E + x*E).as_coefficient(E) x + 2 >>> _.args[0] # just want the exact match 2 >>> p = Poly(2*E + x*E); p Poly(x*E + 2*E, x, E, domain='ZZ') >>> p.coeff_monomial(E) 2 Since the following cannot be written as a product containing E as a factor, None is returned. (If the coefficient ``2*x`` is desired then the ``coeff`` method should be used.) >>> (2*E*x + x).as_coefficient(E) >>> (2*E*x + x).coeff(E) 2*x >>> (E*(x + 1) + x).as_coefficient(E) >>> (2*pi*I).as_coefficient(pi*I) 2 >>> (2*I).as_coefficient(pi*I) See Also ======== coeff: return sum of terms have a given factor as_coeff_Add: separate the additive constant from an expression as_coeff_Mul: separate the multiplicative constant from an expression as_independent: separate x-dependent terms/factors from others diofant.polys.polytools.Poly.coeff_monomial: efficiently find the single coefficient of a monomial in Poly """ r = self.extract_multiplicatively(expr) if r and not r.has(expr): return r
[docs] def as_independent(self, *deps, **hint): """A mostly naive separation of a Mul or Add into arguments that are not are dependent on deps. To obtain as complete a separation of variables as possible, use a separation method first, e.g.: * separatevars() to change Mul, Add and Pow (including exp) into Mul * .expand(mul=True) to change Add or Mul into Add * .expand(log=True) to change log expr into an Add The only non-naive thing that is done here is to respect noncommutative ordering of variables. The returned tuple (i, d) has the following interpretation: * i will has no variable that appears in deps * d will be 1 or else have terms that contain variables that are in deps * if self is an Add then self = i + d * if self is a Mul then self = i*d * if self is anything else, either tuple (self, S.One) or (S.One, self) is returned. To force the expression to be treated as an Add, use the hint as_Add=True Examples ======== -- self is an Add >>> (x + x*y).as_independent(x) (0, x*y + x) >>> (x + x*y).as_independent(y) (x, x*y) >>> (2*x*sin(x) + y + x + z).as_independent(x) (y + z, 2*x*sin(x) + x) >>> (2*x*sin(x) + y + x + z).as_independent(x, y) (z, 2*x*sin(x) + x + y) -- self is a Mul >>> (x*sin(x)*cos(y)).as_independent(x) (cos(y), x*sin(x)) non-commutative terms cannot always be separated out when self is a Mul >>> n1, n2, n3 = symbols('n1 n2 n3', commutative=False) >>> (n1 + n1*n2).as_independent(n2) (n1, n1*n2) >>> (n2*n1 + n1*n2).as_independent(n2) (0, n1*n2 + n2*n1) >>> (n1*n2*n3).as_independent(n1) (1, n1*n2*n3) >>> (n1*n2*n3).as_independent(n2) (n1, n2*n3) >>> ((x-n1)*(x-y)).as_independent(x) (1, (x - y)*(x - n1)) -- self is anything else: >>> (sin(x)).as_independent(x) (1, sin(x)) >>> (sin(x)).as_independent(y) (sin(x), 1) >>> exp(x+y).as_independent(x) (1, E**(x + y)) -- force self to be treated as an Add: >>> (3*x).as_independent(x, as_Add=True) (0, 3*x) -- force self to be treated as a Mul: >>> (3+x).as_independent(x, as_Add=False) (1, x + 3) >>> (-3+x).as_independent(x, as_Add=False) (1, x - 3) Note how the below differs from the above in making the constant on the dep term positive. >>> (y*(-3+x)).as_independent(x) (y, x - 3) -- use .as_independent() for true independence testing instead of .has(). The former considers only symbols in the free symbols while the latter considers all symbols >>> I = Integral(x, (x, 1, 2)) >>> I.has(x) True >>> x in I.free_symbols False >>> I.as_independent(x) == (I, 1) True >>> (I + x).as_independent(x) == (I, x) True Note: when trying to get independent terms, a separation method might need to be used first. In this case, it is important to keep track of what you send to this routine so you know how to interpret the returned values >>> separatevars(exp(x+y)).as_independent(x) (E**y, E**x) >>> (x + x*y).as_independent(y) (x, x*y) >>> separatevars(x + x*y).as_independent(y) (x, y + 1) >>> (x*(1 + y)).as_independent(y) (x, y + 1) >>> (x*(1 + y)).expand(mul=True).as_independent(y) (x, x*y) >>> a, b=symbols('a b', positive=True) >>> (log(a*b).expand(log=True)).as_independent(b) (log(a), log(b)) See Also ======== diofant.simplify.simplify.separatevars expand diofant.core.add.Add.as_two_terms diofant.core.mul.Mul.as_two_terms as_coeff_add as_coeff_mul """ from .symbol import Dummy, Symbol from ..utilities.iterables import sift func = self.func # sift out deps into symbolic and other and ignore # all symbols but those that are in the free symbols sym = set() other = [] for d in deps: if isinstance(d, (Dummy, Symbol)): # Symbol.is_Symbol is True sym.add(d) else: other.append(d) def has(e): """return the standard has() if there are no literal symbols, else check to see that symbol-deps are in the free symbols. """ has_other = e.has(*other) if not sym: return has_other return has_other or e.has(*(e.free_symbols & sym)) if hint.get('as_Add', func is Add): want = Add else: want = Mul if (want is not func or func is not Add and func is not Mul): if has(self): return want.identity, self else: return self, want.identity else: if func is Add: args = list(self.args) else: args, nc = self.args_cnc() d = sift(args, lambda x: has(x)) depend = d[True] indep = d[False] if func is Add: # all terms were treated as commutative return Add(*indep), Add(*depend) else: # handle noncommutative by stopping at first dependent term for i, n in enumerate(nc): if has(n): depend.extend(nc[i:]) break indep.append(n) return Mul(*indep), Mul(*depend)
[docs] def as_real_imag(self, deep=True, **hints): """Performs complex expansion on 'self' and returns a tuple containing collected both real and imaginary parts. This method can't be confused with re() and im() functions, which does not perform complex expansion at evaluation. However it is possible to expand both re() and im() functions and get exactly the same results as with a single call to this function. >>> x, y = symbols('x y', real=True) >>> (x + y*I).as_real_imag() (x, y) >>> (z + t*I).as_real_imag() (re(z) - im(t), re(t) + im(z)) """ from ..functions import im, re if hints.get('ignore') == self: return else: return re(self), im(self)
[docs] def as_powers_dict(self): """Return self as a dictionary of factors with each factor being treated as a power. The keys are the bases of the factors and the values, the corresponding exponents. The resulting dictionary should be used with caution if the expression is a Mul and contains non- commutative factors since the order that they appeared will be lost in the dictionary. """ d = defaultdict(int) b, e = self.as_base_exp() d[b] = e return d
[docs] def as_coefficients_dict(self): """Return a dictionary mapping terms to their Rational coefficient. Since the dictionary is a defaultdict, inquiries about terms which were not present will return a coefficient of 0. If an expression is not an Add it is considered to have a single term. Examples ======== >>> (3*x + a*x + 4).as_coefficients_dict() {1: 4, x: 3, a*x: 1} >>> _[a] 0 >>> (3*a*x).as_coefficients_dict() {a*x: 3} """ c, m = self.as_coeff_Mul() if not c.is_Rational: c = S.One m = self d = defaultdict(int) d.update({m: c}) return d
[docs] def as_base_exp(self): """Return base and exp of self. See Also ======== diofant.core.power.Pow.as_base_exp """ return self, S.One
[docs] def as_coeff_mul(self, *deps, **kwargs): """Return the tuple (c, args) where self is written as a Mul, ``m``. c should be a Rational multiplied by any terms of the Mul that are independent of deps. args should be a tuple of all other terms of m; args is empty if self is a Number or if self is independent of deps (when given). This should be used when you don't know if self is a Mul or not but you want to treat self as a Mul or if you want to process the individual arguments of the tail of self as a Mul. - if you know self is a Mul and want only the head, use self.args[0]; - if you don't want to process the arguments of the tail but need the tail then use self.as_two_terms() which gives the head and tail; - if you want to split self into an independent and dependent parts use ``self.as_independent(*deps)`` >>> (Integer(3)).as_coeff_mul() (3, ()) >>> (3*x*y).as_coeff_mul() (3, (x, y)) >>> (3*x*y).as_coeff_mul(x) (3*y, (x,)) >>> (3*y).as_coeff_mul(x) (3*y, ()) """ if deps: if not self.has(*deps): return self, () return S.One, (self,)
[docs] def as_coeff_add(self, *deps): """Return the tuple (c, args) where self is written as an Add, ``a``. c should be a Rational added to any terms of the Add that are independent of deps. args should be a tuple of all other terms of ``a``; args is empty if self is a Number or if self is independent of deps (when given). This should be used when you don't know if self is an Add or not but you want to treat self as an Add or if you want to process the individual arguments of the tail of self as an Add. - if you know self is an Add and want only the head, use self.args[0]; - if you don't want to process the arguments of the tail but need the tail then use self.as_two_terms() which gives the head and tail. - if you want to split self into an independent and dependent parts use ``self.as_independent(*deps)`` >>> (Integer(3)).as_coeff_add() (3, ()) >>> (3 + x).as_coeff_add() (3, (x,)) >>> (3 + x + y).as_coeff_add(x) (y + 3, (x,)) >>> (3 + y).as_coeff_add(x) (y + 3, ()) """ if deps: if not self.has(*deps): return self, () return S.Zero, (self,)
[docs] def primitive(self): """Return the positive Rational that can be extracted non-recursively from every term of self (i.e., self is treated like an Add). This is like the as_coeff_Mul() method but primitive always extracts a positive Rational (never a negative or a Float). Examples ======== >>> (3*(x + 1)**2).primitive() (3, (x + 1)**2) >>> a = (6*x + 2); a.primitive() (2, 3*x + 1) >>> b = (x/2 + 3); b.primitive() (1/2, x + 6) >>> (a*b).primitive() (1, (x/2 + 3)*(6*x + 2)) """ if not self: return S.One, S.Zero c, r = self.as_coeff_Mul(rational=True) if c.is_negative: c, r = -c, -r return c, r
[docs] def as_content_primitive(self, radical=False): """This method should recursively remove a Rational from all arguments and return that (content) and the new self (primitive). The content should always be positive and ``Mul(*foo.as_content_primitive()) == foo``. The primitive need no be in canonical form and should try to preserve the underlying structure if possible (i.e. expand_mul should not be applied to self). Examples ======== >>> eq = 2 + 2*x + 2*y*(3 + 3*y) The as_content_primitive function is recursive and retains structure: >>> eq.as_content_primitive() (2, x + 3*y*(y + 1) + 1) Integer powers will have Rationals extracted from the base: >>> ((2 + 6*x)**2).as_content_primitive() (4, (3*x + 1)**2) >>> ((2 + 6*x)**(2*y)).as_content_primitive() (1, (2*(3*x + 1))**(2*y)) Terms may end up joining once their as_content_primitives are added: >>> ((5*(x*(1 + y)) + 2*x*(3 + 3*y))).as_content_primitive() (11, x*(y + 1)) >>> ((3*(x*(1 + y)) + 2*x*(3 + 3*y))).as_content_primitive() (9, x*(y + 1)) >>> ((3*(z*(1 + y)) + 2.0*x*(3 + 3*y))).as_content_primitive() (1, 6.0*x*(y + 1) + 3*z*(y + 1)) >>> ((5*(x*(1 + y)) + 2*x*(3 + 3*y))**2).as_content_primitive() (121, x**2*(y + 1)**2) >>> ((5*(x*(1 + y)) + 2.0*x*(3 + 3*y))**2).as_content_primitive() (1, 121.0*x**2*(y + 1)**2) Radical content can also be factored out of the primitive: >>> (2*sqrt(2) + 4*sqrt(10)).as_content_primitive(radical=True) (2, sqrt(2)*(1 + 2*sqrt(5))) """ return S.One, self
[docs] def as_numer_denom(self): """expression -> a/b -> a, b This is just a stub that should be defined by an object's class methods to get anything else. See Also ======== normal: return a/b instead of a, b """ try: return self._eval_as_numer_denom() except AttributeError: return self, S.One
[docs] def normal(self): """canonicalize ratio, i.e. return numerator if denominator is 1.""" n, d = self.as_numer_denom() if d is S.One: return n return n/d
[docs] def extract_multiplicatively(self, c): """Return None if it's not possible to make self in the form c * something in a nice way, i.e. preserving the properties of arguments of self. >>> x, y = symbols('x y', real=True) >>> ((x*y)**3).extract_multiplicatively(x**2 * y) x*y**2 >>> ((x*y)**3).extract_multiplicatively(x**4 * y) >>> (2*x).extract_multiplicatively(2) x >>> (2*x).extract_multiplicatively(3) >>> (Rational(1, 2)*x).extract_multiplicatively(3) x/6 """ c = sympify(c) if self is nan: return if c is S.One: return self elif c == self: return S.One if c.is_Add: cc, pc = c.primitive() if cc is not S.One: c = Mul(cc, pc, evaluate=False) if c.is_Mul: a, b = c.as_two_terms() x = self.extract_multiplicatively(a) if x is not None: return x.extract_multiplicatively(b) quotient = self / c if self.is_Number: if self is oo: if c.is_positive: return oo elif self == -oo: if c.is_negative: return oo elif c.is_positive: return -oo elif self.is_Integer: if not quotient.is_Integer: return elif self.is_positive and quotient.is_negative: return else: return quotient elif self.is_Rational: if not quotient.is_Rational: return elif self.is_positive and quotient.is_negative: return else: return quotient elif self.is_Float: if not quotient.is_Float: return elif self.is_positive and quotient.is_negative: return else: return quotient else: raise NotImplementedError elif self.is_Add: cs, ps = self.primitive() if cs is not S.One: return Mul(cs, ps, evaluate=False).extract_multiplicatively(c) newargs = [] for arg in self.args: newarg = arg.extract_multiplicatively(c) if newarg is not None: newargs.append(newarg) else: return return Add(*newargs) elif self.is_Mul: args = list(self.args) for i, arg in enumerate(args): newarg = arg.extract_multiplicatively(c) if newarg is not None: args[i] = newarg return Mul(*args) elif self.is_Pow: if c.is_Pow and c.base == self.base: new_exp = self.exp.extract_additively(c.exp) if new_exp is not None: return self.base ** (new_exp) elif c == self.base: new_exp = self.exp.extract_additively(1) if new_exp is not None: return self.base ** (new_exp)
[docs] def extract_additively(self, c): """Return self - c if it's possible to subtract c from self and make all matching coefficients move towards zero, else return None. Examples ======== >>> e = 2*x + 3 >>> e.extract_additively(x + 1) x + 2 >>> e.extract_additively(3*x) >>> e.extract_additively(4) >>> (y*(x + 1)).extract_additively(x + 1) >>> ((x + 1)*(x + 2*y + 1) + 3).extract_additively(x + 1) (x + 1)*(x + 2*y) + 3 Sometimes auto-expansion will return a less simplified result than desired; gcd_terms might be used in such cases: >>> (4*x*(y + 1) + y).extract_additively(x) 4*x*(y + 1) + x*(4*y + 3) - x*(4*y + 4) + y >>> gcd_terms(_) x*(4*y + 3) + y See Also ======== extract_multiplicatively coeff as_coefficient """ c = sympify(c) if self is nan: return if c is S.Zero: return self elif c == self: return S.Zero elif self is S.Zero: return if self.is_Number: if not c.is_Number: return co = self diff = co - c # XXX should we match types? i.e should 3 - .1 succeed? if (co > 0 and diff > 0 and diff < co or co < 0 and diff < 0 and diff > co): return diff return if c.is_Number: co, t = self.as_coeff_Add() xa = co.extract_additively(c) if xa is None: return return xa + t # handle the args[0].is_Number case separately # since we will have trouble looking for the coeff of # a number. if c.is_Add and c.args[0].is_Number: # whole term as a term factor co = self.coeff(c) xa0 = (co.extract_additively(1) or 0)*c if xa0: diff = self - co*c return (xa0 + (diff.extract_additively(c) or diff)) or None # term-wise h, t = c.as_coeff_Add() sh, st = self.as_coeff_Add() xa = sh.extract_additively(h) if xa is None: return xa2 = st.extract_additively(t) if xa2 is None: return return xa + xa2 # whole term as a term factor co = self.coeff(c) xa0 = (co.extract_additively(1) or 0)*c if xa0: diff = self - co*c return (xa0 + (diff.extract_additively(c) or diff)) or None # term-wise coeffs = [] for a in Add.make_args(c): ac, at = a.as_coeff_Mul() co = self.coeff(at) if not co: return coc, cot = co.as_coeff_Add() xa = coc.extract_additively(ac) if xa is None: return self -= co*at coeffs.append((cot + xa)*at) coeffs.append(self) return Add(*coeffs)
[docs] def could_extract_minus_sign(self): """Canonical way to choose an element in the set {e, -e} where e is any expression. If the canonical element is e, we have e.could_extract_minus_sign() == True, else e.could_extract_minus_sign() == False. For any expression, the set ``{e.could_extract_minus_sign(), (-e).could_extract_minus_sign()}`` must be ``{True, False}``. >>> (x-y).could_extract_minus_sign() != (y-x).could_extract_minus_sign() True """ negative_self = -self self_has_minus = (self.extract_multiplicatively(-1) is not None) negative_self_has_minus = ( (negative_self).extract_multiplicatively(-1) is not None) if self_has_minus != negative_self_has_minus: return self_has_minus else: if self.is_Add: # We choose the one with less arguments with minus signs all_args = len(self.args) negative_args = len([False for arg in self.args if arg.could_extract_minus_sign()]) positive_args = all_args - negative_args if positive_args > negative_args: return False elif positive_args < negative_args: return True elif self.is_Mul: # We choose the one with an odd number of minus signs num, den = self.as_numer_denom() args = Mul.make_args(num) + Mul.make_args(den) arg_signs = [arg.could_extract_minus_sign() for arg in args] negative_args = list(filter(None, arg_signs)) return len(negative_args) % 2 == 1 # As a last resort, we choose the one with greater value of .sort_key() return bool(self.sort_key() < negative_self.sort_key())
[docs] def extract_branch_factor(self, allow_half=False): """Try to write self as ``exp_polar(2*pi*I*n)*z`` in a nice way. Return (z, n). >>> exp_polar(I*pi).extract_branch_factor() (exp_polar(I*pi), 0) >>> exp_polar(2*I*pi).extract_branch_factor() (1, 1) >>> exp_polar(-pi*I).extract_branch_factor() (exp_polar(I*pi), -1) >>> exp_polar(3*pi*I + x).extract_branch_factor() (exp_polar(x + I*pi), 1) >>> (y*exp_polar(-5*pi*I)*exp_polar(3*pi*I + 2*pi*x)).extract_branch_factor() (y*exp_polar(2*pi*x), -1) >>> exp_polar(-I*pi/2).extract_branch_factor() (exp_polar(-I*pi/2), 0) If allow_half is True, also extract exp_polar(I*pi): >>> exp_polar(I*pi).extract_branch_factor(allow_half=True) (1, 1/2) >>> exp_polar(2*I*pi).extract_branch_factor(allow_half=True) (1, 1) >>> exp_polar(3*I*pi).extract_branch_factor(allow_half=True) (1, 3/2) >>> exp_polar(-I*pi).extract_branch_factor(allow_half=True) (1, -1/2) """ from .add import Add from .numbers import pi, I from ..functions import exp_polar, ceiling n = Integer(0) res = Integer(1) args = Mul.make_args(self) exps = [] for arg in args: if isinstance(arg, exp_polar): exps += [arg.exp] else: res *= arg piimult = Integer(0) extras = [] while exps: exp = exps.pop() if exp.is_Add: exps += exp.args continue if exp.is_Mul: coeff = exp.as_coefficient(pi*I) if coeff is not None: piimult += coeff continue extras += [exp] if not piimult.free_symbols: coeff = piimult tail = () else: coeff, tail = piimult.as_coeff_add(*piimult.free_symbols) # round down to nearest multiple of 2 branchfact = ceiling(coeff/2 - Rational(1, 2))*2 n += branchfact/2 c = coeff - branchfact if allow_half: nc = c.extract_additively(1) if nc is not None: n += Rational(1, 2) c = nc newexp = pi*I*Add(*((c, ) + tail)) + Add(*extras) if newexp != 0: res *= exp_polar(newexp) return res, n
def _eval_is_polynomial(self, syms): if self.free_symbols.intersection(syms) == set(): return True return False
[docs] def is_polynomial(self, *syms): r"""Return True if self is a polynomial in syms and False otherwise. This checks if self is an exact polynomial in syms. This function returns False for expressions that are "polynomials" with symbolic exponents. Thus, you should be able to apply polynomial algorithms to expressions for which this returns True, and Poly(expr, \*syms) should work if and only if expr.is_polynomial(\*syms) returns True. The polynomial does not have to be in expanded form. If no symbols are given, all free symbols in the expression will be used. This is not part of the assumptions system. You cannot do Symbol('z', polynomial=True). Examples ======== >>> ((x**2 + 1)**4).is_polynomial(x) True >>> ((x**2 + 1)**4).is_polynomial() True >>> (2**x + 1).is_polynomial(x) False >>> n = Symbol('n', nonnegative=True, integer=True) >>> (x**n + 1).is_polynomial(x) False This function does not attempt any nontrivial simplifications that may result in an expression that does not appear to be a polynomial to become one. >>> y = Symbol('y', positive=True) >>> a = sqrt(y**2 + 2*y + 1) >>> a.is_polynomial(y) False >>> factor(a) y + 1 >>> factor(a).is_polynomial(y) True >>> b = (y**2 + 2*y + 1)/(y + 1) >>> b.is_polynomial(y) False >>> cancel(b) y + 1 >>> cancel(b).is_polynomial(y) True See Also ======== is_rational_function """ if syms: syms = set(map(sympify, syms)) else: syms = self.free_symbols if syms.intersection(self.free_symbols) == set(): # constant polynomial return True else: return self._eval_is_polynomial(syms)
def _eval_is_rational_function(self, syms): if self.free_symbols.intersection(syms) == set(): return True return False
[docs] def is_rational_function(self, *syms): """Test whether function is a ratio of two polynomials in the given symbols, syms. When syms is not given, all free symbols will be used. The rational function does not have to be in expanded or in any kind of canonical form. This function returns False for expressions that are "rational functions" with symbolic exponents. Thus, you should be able to call .as_numer_denom() and apply polynomial algorithms to the result for expressions for which this returns True. This is not part of the assumptions system. You cannot do Symbol('z', rational_function=True). Examples ======== >>> (x/y).is_rational_function() True >>> (x**2).is_rational_function() True >>> (x/sin(y)).is_rational_function(y) False >>> n = Symbol('n', integer=True) >>> (x**n + 1).is_rational_function(x) False This function does not attempt any nontrivial simplifications that may result in an expression that does not appear to be a rational function to become one. >>> y = Symbol('y', positive=True) >>> a = sqrt(y**2 + 2*y + 1)/y >>> a.is_rational_function(y) False >>> factor(a) (y + 1)/y >>> factor(a).is_rational_function(y) True See Also ======== is_algebraic_expr """ if self in [nan, oo, -oo, zoo]: return False if syms: syms = set(map(sympify, syms)) else: syms = self.free_symbols if syms.intersection(self.free_symbols) == set(): # constant rational function return True else: return self._eval_is_rational_function(syms)
def _eval_is_algebraic_expr(self, syms): if self.free_symbols.intersection(syms) == set(): return True return False
[docs] def is_algebraic_expr(self, *syms): """This tests whether a given expression is algebraic or not, in the given symbols, syms. When syms is not given, all free symbols will be used. The rational function does not have to be in expanded or in any kind of canonical form. This function returns False for expressions that are "algebraic expressions" with symbolic exponents. This is a simple extension to the is_rational_function, including rational exponentiation. Examples ======== >>> x = Symbol('x', real=True) >>> sqrt(1 + x).is_rational_function() False >>> sqrt(1 + x).is_algebraic_expr() True This function does not attempt any nontrivial simplifications that may result in an expression that does not appear to be an algebraic expression to become one. >>> a = sqrt(exp(x)**2 + 2*exp(x) + 1)/(exp(x) + 1) >>> a.is_algebraic_expr(x) False >>> factor(a).is_algebraic_expr() True See Also ======== is_rational_function References ========== * https://en.wikipedia.org/wiki/Algebraic_expression """ if syms: syms = set(map(sympify, syms)) else: syms = self.free_symbols if syms.intersection(self.free_symbols) == set(): # constant algebraic expression return True else: return self._eval_is_algebraic_expr(syms)
[docs] def is_hypergeometric(self, k): """Test if self is a hypergeometric term in k. See Also ======== diofant.simplify.simplify.hypersimp """ from ..simplify import hypersimp return hypersimp(self, k) is not None
@property def is_comparable(self): """ Test if self can be computed to a real number with precision. Examples ======== >>> (I*exp_polar(I*pi/2)).is_comparable True >>> (I*exp_polar(I*pi*2)).is_comparable False """ is_real = self.is_extended_real if is_real is False: return False is_number = self.is_number if is_number is False: return False n, i = self.evalf(strict=False).as_real_imag() if not i.is_Number or not n.is_Number: return False if i._prec > 1 or i._prec == -1: if i: return False elif not i and (n._prec > 1 or n._prec == -1): return True ################################################################################### # #################### SERIES, LEADING TERM, LIMIT, ORDER METHODS ############### # ###################################################################################
[docs] def series(self, x=None, x0=0, n=6, dir="+", logx=None): """Series expansion of "self" around ``x = x0`` yielding either terms of the series one by one (the lazy series given when n=None), else all the terms at once when n != None. Returns the series expansion of "self" around the point ``x = x0`` with respect to ``x`` up to ``O((x - x0)**n, x, x0)`` (default n is 6). If ``x=None`` and ``self`` is univariate, the univariate symbol will be supplied, otherwise an error will be raised. >>> cos(x).series() 1 - x**2/2 + x**4/24 + O(x**6) >>> cos(x).series(n=4) 1 - x**2/2 + O(x**4) >>> cos(x).series(x, x0=1, n=2) cos(1) - (x - 1)*sin(1) + O((x - 1)**2, (x, 1)) >>> e = cos(x + exp(y)) >>> e.series(y, n=2) cos(x + 1) - y*sin(x + 1) + O(y**2) >>> e.series(x, n=2) cos(E**y) - x*sin(E**y) + O(x**2) If ``n=None`` then a generator of the series terms will be returned. >>> term = cos(x).series(n=None) >>> [next(term) for i in range(2)] [1, -x**2/2] For ``dir=+`` (default) the series is calculated from the right and for ``dir=-`` the series from the left. For smooth functions this flag will not alter the results. >>> abs(x).series(dir="+") x >>> abs(x).series(dir="-") -x For rational expressions this method may return original expression. >>> (1/x).series(x, n=8) 1/x """ from .symbol import Dummy, Symbol from ..series import Order from ..simplify import collect if x is None: syms = self.atoms(Dummy, Symbol) if not syms: return self elif len(syms) > 1: raise ValueError('x must be given for multivariate functions.') x = syms.pop() if not x.is_Symbol: # pragma: no cover raise NotImplementedError("x is not a symbol") if not self.has(x): if n is None: return (s for s in [self]) else: return self if len(dir) != 1 or dir not in '+-': raise ValueError("Dir must be '+' or '-'") if x0 in [oo, -oo]: s = self.aseries(x, n) if x0 == -oo: return s.subs({x: -x}) return s # use rep to shift origin to x0 and change sign (if dir is negative) # and undo the process with rep2 if x0 or dir == '-': if dir == '-': rep = -x + x0 rep2 = -x rep2b = x0 else: rep = x + x0 rep2 = x rep2b = -x0 s = self.subs({x: rep}).series(x, x0=0, n=n, dir='+', logx=logx) if n is None: # lseries... return (si.subs({x: rep2 + rep2b}) for si in s) # pragma: no branch return s.subs({x: rep2 + rep2b}) # from here on it's x0=0 and dir='+' handling if x.is_positive is x.is_negative is None or x.is_Symbol is not True: # replace x with an x that has a positive assumption xpos = Dummy('x', positive=True, finite=True) rv = self.subs({x: xpos}).series(xpos, x0, n, dir, logx=logx) if n is None: return (s.subs({xpos: x}) for s in rv) else: return rv.subs({xpos: x}) if n is not None: # nseries handling s1 = self._eval_nseries(x, n=n, logx=logx) cur_order = s1.getO() or S.Zero # Now make sure the requested order is returned target_order = Order(x**n, x) ndo = n + 1 while not target_order.contains(cur_order): s1 = self._eval_nseries(x, n=ndo, logx=logx) ndo += 1 cur_order = s1.getO() if (s1 + target_order).removeO() == s1: target_order = S.Zero try: return collect(s1.removeO(), x) + target_order except NotImplementedError: # XXX parse_derivative of radsimp.py return s1 + target_order else: # lseries handling def yield_lseries(s): """Return terms of lseries one at a time.""" for si in s: if not si.is_Add: yield si continue # yield terms 1 at a time if possible # by increasing order until all the # terms have been returned yielded = 0 o = Order(si, x)*x if expand_mul(o.expr).is_Add: raise NotImplementedError ndid = 0 ndo = len(si.args) while 1: do = (si - yielded + o).removeO() o *= x if not do or do.is_Order: o # XXX "peephole" optimization, http://bugs.python.org/issue2506 continue if do.is_Add: ndid += len(do.args) else: ndid += 1 yield do if ndid == ndo: break yielded += do return yield_lseries(self.removeO()._eval_lseries(x, logx=logx))
[docs] def taylor_term(self, n, x, *previous_terms): """General method for the taylor term. This method is slow, because it differentiates n-times. Subclasses can redefine it to make it faster by using the "previous_terms". """ from .symbol import Dummy from ..functions import factorial x = sympify(x) _x = Dummy('x') return self.subs({x: _x}).diff(_x, n).subs({_x: x}).subs({x: 0}) * x**n / factorial(n)
[docs] def lseries(self, x=None, x0=0, dir='+', logx=None): """Wrapper for series yielding an iterator of the terms of the series. Note: an infinite series will yield an infinite iterator. The following, for exaxmple, will never terminate. It will just keep printing terms of the sin(x) series:: for term in sin(x).lseries(x): print term The advantage of lseries() over nseries() is that many times you are just interested in the next term in the series (i.e. the first term for example), but you don't know how many you should ask for in nseries() using the "n" parameter. See Also ======== nseries """ return self.series(x, x0, n=None, dir=dir, logx=logx)
def _eval_lseries(self, x, logx=None): # default implementation of lseries is using nseries(), and adaptively # increasing the "n". As you can see, it is not very efficient, because # we are calculating the series over and over again. Subclasses should # override this method and implement much more efficient yielding of # terms. n = 0 series = self._eval_nseries(x, n=n, logx=logx) if not series.is_Order: if series.is_Add: yield series.removeO() else: yield series return while series.is_Order: n += 1 series = self._eval_nseries(x, n=n, logx=logx) e = series.removeO() yield e while 1: while 1: n += 1 series = self._eval_nseries(x, n=n, logx=logx).removeO() if e != series: break yield series - e e = series
[docs] def nseries(self, x, n=6, logx=None): """Calculate "n" terms of series in x around 0 This calculates n terms of series in the innermost expressions and then builds up the final series just by "cross-multiplying" everything out. Advantage -- it's fast, because we don't have to determine how many terms we need to calculate in advance. Disadvantage -- you may end up with less terms than you may have expected, but the O(x**n) term appended will always be correct and so the result, though perhaps shorter, will also be correct. Parameters ========== x : Symbol variable for series expansion (positive and finite symbol) n : Integer, optional number of terms to calculate. Default is 6. logx : Symbol, optional This can be used to replace any log(x) in the returned series with a symbolic value to avoid evaluating log(x) at 0. See Also ======== series lseries Examples ======== >>> sin(x).nseries(x) x - x**3/6 + x**5/120 + O(x**7) >>> log(x + 1).nseries(x, 5) x - x**2/2 + x**3/3 - x**4/4 + O(x**5) Handling of the ``logx`` parameter --- in the following example the expansion fails since ``sin`` does not have an asymptotic expansion at -oo (the limit of log(x) as x approaches 0). >>> e = sin(log(x)) >>> e.nseries(x) Traceback (most recent call last): ... PoleError: ... >>> logx = Symbol('logx') >>> e.nseries(x, logx=logx) sin(logx) Notes ===== This method call the helper method _eval_nseries. Such methods should be implemented in subclasses. The series expansion code is an important part of the gruntz algorithm for determining limits. _eval_nseries has to return a generalized power series with coefficients in C(log(x), log):: c_0*x**e_0 + ... (finitely many terms) where e_i are numbers (not necessarily integers) and c_i involve only numbers, the function log, and log(x). (This also means it must not contain log(x(1 + p)), this *has* to be expanded to log(x) + log(1 + p) if p.is_positive.) """ from .symbol import Dummy from ..simplify import collect if x.is_positive and x.is_finite: series = self._eval_nseries(x, n=n, logx=logx) order = series.getO() or S.Zero return collect(series.removeO(), x) + order else: p = Dummy('x', positive=True, finite=True) e = self.subs({x: p}) e = e.nseries(p, n, logx=logx) return e.subs({p: x})
[docs] def aseries(self, x, n=6, bound=0, hir=False): """Returns asymptotic expansion for "self". This is equivalent to ``self.series(x, oo, n)`` Use the ``hir`` parameter to produce hierarchical series. It stops the recursion at an early level and may provide nicer and more useful results. If the most rapidly varying subexpression of a given expression f is f itself, the algorithm tries to find a normalized representation of the mrv set and rewrites f using this normalized representation. Use the ``bound`` parameter to give limit on rewriting coefficients in its normalized form. If the expansion contains an order term, it will be either ``O(x**(-n))`` or ``O(w**(-n))`` where ``w`` belongs to the most rapidly varying expression of ``self``. Examples ======== >>> e = sin(1/x + exp(-x)) - sin(1/x) >>> e.aseries(x) E**(-x)*(1/(24*x**4) - 1/(2*x**2) + 1 + O(x**(-6), (x, oo))) >>> e.aseries(x, n=3, hir=True) -E**(-2*x)*sin(1/x)/2 + E**(-x)*cos(1/x) + O(E**(-3*x), (x, oo)) >>> e = exp(exp(x)/(1 - 1/x)) >>> e.aseries(x, bound=3) E**(E**x)*E**(E**x/x**2)*E**(E**x/x)*E**(-E**x + E**x/(1 - 1/x) - E**x/x - E**x/x**2) >>> e.aseries(x) E**(E**x/(1 - 1/x)) Notes ===== This algorithm is directly induced from the limit computational algorithm provided by Gruntz :cite:`Gruntz1996limits`, p.90. It majorly uses the mrv and rewrite sub-routines. The overall idea of this algorithm is first to look for the most rapidly varying subexpression w of a given expression f and then expands f in a series in w. Then same thing is recursively done on the leading coefficient till we get constant coefficients. References ========== * https://en.wikipedia.org/wiki/Asymptotic_expansion """ from . import Dummy from ..series.gruntz import mrv, rewrite from ..functions import exp, log from ..series import Order if x.is_positive is x.is_negative is None: xpos = Dummy('x', positive=True, finite=True) return self.subs({x: xpos}).aseries(xpos, n, bound, hir).subs({xpos: x}) omega = mrv(self, x) if x in omega: s = self.subs({x: exp(x)}).aseries(x, n, bound, hir).subs({x: log(x)}) if s.getO(): o = Order(1/x**n, (x, oo)) return s + o return s d = Dummy('d', positive=True) f, logw = rewrite(self, x, d) if self in omega: # Need to find a canonical representative if bound <= 0: return self a = self.exp s = a.aseries(x, n, bound=bound) s = s.func(*[t.removeO() for t in s.args]) rep = exp(s.subs({x: 1/x}).as_leading_term(x).subs({x: 1/x})) f = exp(self.exp - rep.exp)/d logw = log(1/rep) s = f.series(d, 0, n) # Hierarchical series: break after first recursion if hir: return s.subs({d: exp(logw)}) o = s.getO() terms = sorted(Add.make_args(s.removeO()), key=lambda i: int(i.as_coeff_exponent(d)[1])) s = S.Zero gotO = False for t in terms: coeff, expo = t.as_coeff_exponent(d) if coeff.has(x): s1 = coeff.aseries(x, n, bound=bound-1) if gotO and s1.getO(): break elif s1.getO(): gotO = True s += (s1 * d**expo) else: s += t if not o or gotO: return s.subs({d: exp(logw)}) else: return (s + o).subs({d: exp(logw)})
[docs] def limit(self, x, xlim, dir='+'): """Compute limit x->xlim.""" from ..series.limits import limit return limit(self, x, xlim, dir)
[docs] def compute_leading_term(self, x, logx=None): """as_leading_term is only allowed for results of .series() This is a wrapper to compute a series first. """ from .symbol import Dummy from ..functions import log d = logx if logx else Dummy('logx') for t in self.lseries(x, logx=d): t = t.cancel() is_zero = t.equals(0) if is_zero is True: continue elif is_zero is False: break else: raise NotImplementedError("Zero-decision problem for %s" % t) if logx is None: t = t.subs({d: log(x)}) return t.as_leading_term(x)
[docs] @cacheit def as_leading_term(self, *symbols): """Returns the leading (nonzero) term of the series expansion of self. The _eval_as_leading_term routines are used to do this, and they must always return a non-zero value. Examples ======== >>> (1 + x + x**2).as_leading_term(x) 1 >>> (1/x**2 + x + x**2).as_leading_term(x) x**(-2) """ from ..simplify import powsimp if len(symbols) > 1: c = self for x in symbols: c = c.as_leading_term(x) return c elif not symbols: return self x = sympify(symbols[0]) if not x.is_Symbol: raise ValueError('expecting a Symbol but got %s' % x) if x not in self.free_symbols: return self obj = self._eval_as_leading_term(x) if obj is not None: return powsimp(obj, deep=True, combine='exp') raise NotImplementedError('as_leading_term(%s, %s)' % (self, x)) # pragma: no cover
def _eval_as_leading_term(self, x): return self
[docs] def as_coeff_exponent(self, x): """``c*x**e -> c,e`` where x can be any symbolic expression.""" from ..simplify import collect s = collect(self, x) c, p = s.as_coeff_mul(x) if len(p) == 1: b, e = p[0].as_base_exp() if b == x: return c, e if s.has(x): s = s.simplify() return s, S.Zero
[docs] def as_coeff_Mul(self, rational=False): """Efficiently extract the coefficient of a product.""" return S.One, self
[docs] def as_coeff_Add(self, rational=False): """Efficiently extract the coefficient of a summation.""" return S.Zero, self
@property def canonical_variables(self): """Return a dictionary mapping any variable defined in ``self.variables`` as underscore-suffixed numbers corresponding to their position in ``self.variables``. Enough underscores are added to ensure that there will be no clash with existing free symbols. Examples ======== >>> Lambda(x, 2*x).canonical_variables {x: 0_} """ from . import Symbol try: V = self.variables except AttributeError: return {} u = "_" while any(str(s).endswith(u) for s in V): u += "_" name = '%%i%s' % u return {v: Symbol(name % i, **v._assumptions) for i, v in enumerate(V)} ################################################################################### # ################### DERIVATIVE, INTEGRAL, FUNCTIONAL METHODS ################## # ###################################################################################
[docs] def diff(self, *symbols, **kwargs): """Alias for :func:`~diofant.core.function.diff`.""" from .function import diff return diff(self, *symbols, **kwargs)
########################################################################### # #################### EXPRESSION EXPANSION METHODS ##################### # ########################################################################### # Relevant subclasses should override _eval_expand_hint() methods. See # the docstring of expand() for more info. def _eval_expand_complex(self, **hints): real, imag = self.as_real_imag(**hints) return real + I*imag @staticmethod def _expand_hint(expr, hint, deep=True, **hints): """Helper for ``expand()``. Recursively calls ``expr._eval_expand_hint()``. Returns ``(expr, hit)``, where expr is the (possibly) expanded ``expr`` and ``hit`` is ``True`` if ``expr`` was truly expanded and ``False`` otherwise. """ hit = False # XXX: Hack to support non-Basic args # | # V if deep and getattr(expr, 'args', ()) and not expr.is_Atom: sargs = [] for arg in expr.args: arg, arghit = Expr._expand_hint(arg, hint, **hints) hit |= arghit sargs.append(arg) if hit: expr = expr.func(*sargs) if hasattr(expr, hint): newexpr = getattr(expr, hint)(**hints) if newexpr != expr: return newexpr, True return expr, hit
[docs] @cacheit def expand(self, deep=True, modulus=None, power_base=True, power_exp=True, mul=True, log=True, multinomial=True, basic=True, **hints): """Expand an expression using hints. See Also ======== diofant.core.function.expand """ from ..simplify.radsimp import fraction hints.update(power_base=power_base, power_exp=power_exp, mul=mul, log=log, multinomial=multinomial, basic=basic) expr = self if hints.pop('frac', False): n, d = [a.expand(deep=deep, modulus=modulus, **hints) for a in fraction(self)] return n/d elif hints.pop('denom', False): n, d = fraction(self) return n/d.expand(deep=deep, modulus=modulus, **hints) elif hints.pop('numer', False): n, d = fraction(self) return n.expand(deep=deep, modulus=modulus, **hints)/d # Although the hints are sorted here, an earlier hint may get applied # at a given node in the expression tree before another because of how # the hints are applied. e.g. expand(log(x*(y + z))) -> log(x*y + # x*z) because while applying log at the top level, log and mul are # applied at the deeper level in the tree so that when the log at the # upper level gets applied, the mul has already been applied at the # lower level. # Additionally, because hints are only applied once, the expression # may not be expanded all the way. For example, if mul is applied # before multinomial, x*(x + 1)**2 won't be expanded all the way. For # now, we just use a special case to make multinomial run before mul, # so that at least polynomials will be expanded all the way. In the # future, smarter heuristics should be applied. # TODO: Smarter heuristics def _expand_hint_key(hint): """Make multinomial come before mul.""" if hint == 'mul': return 'mulz' return hint for hint in sorted(hints, key=_expand_hint_key): use_hint = hints[hint] if use_hint: hint = '_eval_expand_' + hint expr, hit = Expr._expand_hint(expr, hint, deep=deep, **hints) while True: was = expr if hints.get('multinomial', False): expr, _ = Expr._expand_hint( expr, '_eval_expand_multinomial', deep=deep, **hints) if hints.get('mul', False): expr, _ = Expr._expand_hint( expr, '_eval_expand_mul', deep=deep, **hints) if hints.get('log', False): expr, _ = Expr._expand_hint( expr, '_eval_expand_log', deep=deep, **hints) if expr == was: break if modulus is not None: modulus = sympify(modulus) if not modulus.is_Integer or modulus <= 0: raise ValueError( "modulus must be a positive integer, got %s" % modulus) terms = [] for term in Add.make_args(expr): coeff, tail = term.as_coeff_Mul(rational=True) coeff %= modulus if coeff: terms.append(coeff*tail) expr = Add(*terms) return expr
########################################################################### # ################# GLOBAL ACTION VERB WRAPPER METHODS ################## # ###########################################################################
[docs] def integrate(self, *args, **kwargs): """See the integrate function in diofant.integrals.""" from ..integrals import integrate return integrate(self, *args, **kwargs)
[docs] def simplify(self, ratio=1.7, measure=None): """See the simplify function in diofant.simplify.""" from ..simplify import simplify from .function import count_ops measure = measure or count_ops return simplify(self, ratio, measure)
[docs] def nsimplify(self, constants=[], tolerance=None, full=False): """See the nsimplify function in diofant.simplify.""" from ..simplify import nsimplify return nsimplify(self, constants, tolerance, full)
[docs] def collect(self, syms, func=None, evaluate=True, exact=False, distribute_order_term=True): """See the collect function in diofant.simplify.""" from ..simplify import collect return collect(self, syms, func, evaluate, exact, distribute_order_term)
[docs] def together(self, *args, **kwargs): """See the together function in diofant.polys.""" from ..polys import together return together(self, *args, **kwargs)
[docs] def apart(self, x=None, **args): """See the apart function in diofant.polys.""" from ..polys import apart return apart(self, x, **args)
[docs] def ratsimp(self): """See the ratsimp function in diofant.simplify.""" from ..simplify import ratsimp return ratsimp(self)
[docs] def trigsimp(self, **args): """See the trigsimp function in diofant.simplify.""" from ..simplify import trigsimp return trigsimp(self, **args)
[docs] def radsimp(self, **kwargs): """See the radsimp function in diofant.simplify.""" from ..simplify import radsimp return radsimp(self, **kwargs)
[docs] def powsimp(self, **args): """See the powsimp function in diofant.simplify.""" from ..simplify import powsimp return powsimp(self, **args)
[docs] def combsimp(self): """See the combsimp function in diofant.simplify.""" from ..simplify import combsimp return combsimp(self)
[docs] def factor(self, *gens, **args): """See the factor() function in diofant.polys.polytools.""" from ..polys import factor return factor(self, *gens, **args)
[docs] def cancel(self, *gens, **args): """See the cancel function in diofant.polys.""" from ..polys import cancel return cancel(self, *gens, **args)
[docs] def invert(self, g, *gens, **args): """Return the multiplicative inverse of ``self`` mod ``g`` where ``self`` (and ``g``) may be symbolic expressions). See Also ======== diofant.core.numbers.mod_inverse diofant.polys.polytools.invert """ from ..polys.polytools import invert from .numbers import mod_inverse if self.is_number and getattr(g, 'is_number', True): return mod_inverse(self, g) return invert(self, g, *gens, **args)
[docs] def round(self, p=0): """Return x rounded to the given decimal place. If a complex number would results, apply round to the real and imaginary components of the number. Examples ======== >>> Float(10.5).round() 11. >>> pi.round() 3. >>> pi.round(2) 3.14 >>> (2*pi + E*I).round() 6.0 + 3.0*I The round method has a chopping effect: >>> (2*pi + I/10).round() 6. >>> (pi/10 + 2*I).round() 2.0*I >>> (pi/10 + E*I).round(2) 0.31 + 2.72*I Notes ===== Do not confuse the Python builtin function, round, with the Diofant method of the same name. The former always returns a float (or raises an error if applied to a complex value) while the latter returns either a Number or a complex number: >>> isinstance(round(Integer(123), -2), Number) False >>> isinstance(Integer(123).round(-2), Number) True >>> isinstance((3*I).round(), Mul) True >>> isinstance((1 + 3*I).round(), Add) True """ from .numbers import Float x = self if not x.is_number: raise TypeError('%s is not a number' % type(x)) if x in (nan, oo, -oo, zoo): return x if not x.is_extended_real: i, r = x.as_real_imag() return i.round(p) + I*r.round(p) if not x: return x p = int(p) precs = [f._prec for f in x.atoms(Float)] dps = prec_to_dps(max(precs)) if precs else None mag_first_dig = _mag(x) allow = digits_needed = mag_first_dig + p if dps is not None and allow > dps: allow = dps mag = Pow(10, p) # magnitude needed to bring digit p to units place xwas = x x += 1/(2*mag) # add the half for rounding i10 = 10*mag*x.evalf((dps if dps is not None else digits_needed) + 1, strict=False) if i10.is_negative: x = xwas - 1/(2*mag) # should have gone the other way i10 = 10*mag*x.evalf((dps if dps is not None else digits_needed) + 1, strict=False) rv = -(Integer(-i10)//10) else: rv = Integer(i10)//10 q = 1 if p > 0: q = mag elif p < 0: rv /= mag rv = Rational(rv, q) if rv.is_Integer: # use str or else it won't be a float return Float(str(rv), digits_needed) else: if not allow and rv > self: allow += 1 return Float(rv, allow)
[docs]class AtomicExpr(Atom, Expr): """A parent class for object which are both atoms and Exprs. For example: Symbol, Number, Rational, Integer, ... But not: Add, Mul, Pow, ... """ is_number = False is_Atom = True def _eval_derivative(self, s): if self == s: return S.One return S.Zero def _eval_is_polynomial(self, syms): return True def _eval_is_rational_function(self, syms): return True def _eval_is_algebraic_expr(self, syms): return True def _eval_nseries(self, x, n, logx): return self
def _mag(x): """Return integer ``i`` such that .1 <= x/10**i < 1 Examples ======== >>> _mag(Float(.1)) 0 >>> _mag(Float(.01)) -1 >>> _mag(Float(1234)) 4 """ from math import log10, ceil, log from .numbers import Float xpos = abs(x.evalf(strict=False)) if not xpos: return S.Zero try: mag_first_dig = int(ceil(log10(xpos))) except (ValueError, OverflowError): mag_first_dig = int(ceil(Float(mpf_log(xpos._mpf_, 53))/log(10))) # check that we aren't off by 1 if (xpos/10**mag_first_dig) >= 1: assert 1 <= (xpos/10**mag_first_dig) < 10 mag_first_dig += 1 return mag_first_dig from .add import Add from .mul import Mul from .power import Pow from .function import Function, expand_mul from .mod import Mod from .exprtools import factor_terms from .numbers import I, Integer, Rational, nan, oo, zoo