def _rebuild(expr): if not isinstance(expr, (Basic, Unevaluated)): return expr if not expr.args: return expr if iterable(expr): new_args = [_rebuild(arg) for arg in expr] return expr.func(*new_args) if expr in subs: return subs[expr] orig_expr = expr if expr in opt_subs: expr = opt_subs[expr] # If enabled, parse Muls and Adds arguments by order to ensure # replacement order independent from hashes if order != 'none': if isinstance(expr, (Mul, MatMul)): c, nc = expr.args_cnc() if c == [1]: args = nc else: args = list(ordered(c)) + nc elif isinstance(expr, (Add, MatAdd)): args = list(ordered(expr.args)) else: args = expr.args else: args = expr.args new_args = list(map(_rebuild, args)) if isinstance(expr, Unevaluated) or new_args != args: new_expr = expr.func(*new_args) else: new_expr = expr if orig_expr in to_eliminate: try: sym = next(symbols) except StopIteration: raise ValueError("Symbols iterator ran out of symbols.") if isinstance(orig_expr, MatrixExpr): sym = MatrixSymbol(sym.name, orig_expr.rows, orig_expr.cols) subs[orig_expr] = sym replacements.append((sym, new_expr)) return sym else: return new_expr
def test_ordered(): # Issue 7210 - this had been failing with python2/3 problems assert (list(ordered([{1:3, 2:4, 9:10}, {1:3}])) == \ [{1: 3}, {1: 3, 2: 4, 9: 10}]) # warnings should not be raised for identical items l = [1, 1] assert list(ordered(l, warn=True)) == l l = [[1], [2], [1]] assert list(ordered(l, warn=True)) == [[1], [1], [2]] raises(ValueError, lambda: list(ordered(['a', 'ab'], keys=[lambda x: x[0]], default=False, warn=True)))
def test_free_dynamicsymbols(): A, B, C, D = symbols('A, B, C, D', cls=ReferenceFrame) a, b, c, d, e, f = dynamicsymbols('a, b, c, d, e, f') B.orient_axis(A, a, A.x) C.orient_axis(B, b, B.y) D.orient_axis(C, c, C.x) v = d*D.x + e*D.y + f*D.z assert set(ordered(v.free_dynamicsymbols(A))) == {a, b, c, d, e, f} assert set(ordered(v.free_dynamicsymbols(B))) == {b, c, d, e, f} assert set(ordered(v.free_dynamicsymbols(C))) == {c, d, e, f} assert set(ordered(v.free_dynamicsymbols(D))) == {d, e, f}
def intersection(self, o): """The intersection of the parabola and another geometrical entity `o`. Parameters ========== o : GeometryEntity, LinearEntity Returns ======= intersection : list of GeometryEntity objects Examples ======== >>> from sympy import Parabola, Point, Ellipse, Line, Segment >>> p1 = Point(0,0) >>> l1 = Line(Point(1, -2), Point(-1,-2)) >>> parabola1 = Parabola(p1, l1) >>> parabola1.intersection(Ellipse(Point(0, 0), 2, 5)) [Point2D(-2, 0), Point2D(2, 0)] >>> parabola1.intersection(Line(Point(-7, 3), Point(12, 3))) [Point2D(-4, 3), Point2D(4, 3)] >>> parabola1.intersection(Segment((-12, -65), (14, -68))) [] """ x, y = symbols('x y', real=True) parabola_eq = self.equation() if isinstance(o, Parabola): if o in self: return [o] else: return list(ordered([Point(i) for i in solve([parabola_eq, o.equation()], [x, y])])) elif isinstance(o, Point2D): if simplify(parabola_eq.subs([(x, o._args[0]), (y, o._args[1])])) == 0: return [o] else: return [] elif isinstance(o, (Segment2D, Ray2D)): result = solve([parabola_eq, Line2D(o.points[0], o.points[1]).equation()], [x, y]) return list(ordered([Point2D(i) for i in result if i in o])) elif isinstance(o, (Line2D, Ellipse)): return list(ordered([Point2D(i) for i in solve([parabola_eq, o.equation()], [x, y])])) elif isinstance(o, LinearEntity3D): raise TypeError('Entity must be two dimensional, not three dimensional') else: raise TypeError('Wrong type of argument were put')
def _(a, b): # handle (-oo, oo) infty = S.NegativeInfinity, S.Infinity if a == Interval(*infty): l, r = a.left, a.right if l.is_real or l in infty or r.is_real or r in infty: return b # We can't intersect [0,3] with [x,6] -- we don't know if x>0 or x<0 if not a._is_comparable(b): return None empty = False if a.start <= b.end and b.start <= a.end: # Get topology right. if a.start < b.start: start = b.start left_open = b.left_open elif a.start > b.start: start = a.start left_open = a.left_open else: #this is to ensure that if Eq(a.start,b.start) but #type(a.start) != type(b.start) the order of a and b #does not matter for the result start = list(ordered([a, b]))[0].start left_open = a.left_open or b.left_open if a.end < b.end: end = a.end right_open = a.right_open elif a.end > b.end: end = b.end right_open = b.right_open else: end = list(ordered([a, b]))[0].end right_open = a.right_open or b.right_open if end - start == 0 and (left_open or right_open): empty = True else: empty = True if empty: return S.EmptySet return Interval(start, end, left_open, right_open)
def quantity_simplify(expr): """Return an equivalent expression in which prefixes are replaced with numerical values and all units of a given dimension are the unified in a canonical manner. Examples ======== >>> from sympy.physics.units.util import quantity_simplify >>> from sympy.physics.units.prefixes import kilo >>> from sympy.physics.units import foot, inch >>> quantity_simplify(kilo*foot*inch) 250*foot**2/3 >>> quantity_simplify(foot - 6*inch) foot/2 """ if expr.is_Atom or not expr.has(Prefix, Quantity): return expr # replace all prefixes with numerical values p = expr.atoms(Prefix) expr = expr.xreplace({p: p.scale_factor for p in p}) # replace all quantities of given dimension with a canonical # quantity, chosen from those in the expression d = sift(expr.atoms(Quantity), lambda i: i.dimension) for k in d: if len(d[k]) == 1: continue v = list(ordered(d[k])) ref = v[0] / v[0].scale_factor expr = expr.xreplace({vi: ref * vi.scale_factor for vi in v[1:]}) return expr
def roots_cyclotomic(f, factor=False): """Compute roots of cyclotomic polynomials. """ L, U = _inv_totient_estimate(f.degree()) for n in range(L, U + 1): g = cyclotomic_poly(n, f.gen, polys=True) if f.expr == g.expr: break else: # pragma: no cover raise RuntimeError("failed to find index of a cyclotomic polynomial") roots = [] if not factor: # get the indices in the right order so the computed # roots will be sorted h = n // 2 ks = [i for i in range(1, n + 1) if igcd(i, n) == 1] ks.sort(key=lambda x: (x, -1) if x <= h else (abs(x - n), 1)) d = 2 * I * pi / n for k in reversed(ks): roots.append(exp(k * d).expand(complex=True)) else: g = Poly(f, extension=root(-1, n)) for h, _ in ordered(g.factor_list()[1]): roots.append(-h.TC()) return roots
def _refine_imaginary(cls, complexes): sifted = sift(complexes, lambda c: c[1]) complexes = [] for f in ordered(sifted): nimag = _imag_count_of_factor(f) if nimag == 0: # refine until xbounds are neg or pos for u, f, k in sifted[f]: while u.ax * u.bx <= 0: u = u._inner_refine() complexes.append((u, f, k)) else: # refine until all but nimag xbounds are neg or pos potential_imag = list(range(len(sifted[f]))) while True: assert len(potential_imag) > 1 for i in list(potential_imag): u, f, k = sifted[f][i] if u.ax * u.bx > 0: potential_imag.remove(i) elif u.ax != u.bx: u = u._inner_refine() sifted[f][i] = u, f, k if len(potential_imag) == nimag: break complexes.extend(sifted[f]) return complexes
def test_ternary_quadratic(): # solution with 3 parameters s = diophantine(2 * x**2 + y**2 - 2 * z**2) p, q, r = ordered(S(s).free_symbols) assert s == { (p**2 - 2 * q**2, -2 * p**2 + 4 * p * q - 4 * p * r - 4 * q**2, p**2 - 4 * p * q + 2 * q**2 - 4 * q * r) } # solution with Mul in solution s = diophantine(x**2 + 2 * y**2 - 2 * z**2) assert s == {(4 * p * q, p**2 - 2 * q**2, p**2 + 2 * q**2)} # solution with no Mul in solution s = diophantine(2 * x**2 + 2 * y**2 - z**2) assert s == {(2 * p**2 - q**2, -2 * p**2 + 4 * p * q - q**2, 4 * p**2 - 4 * p * q + 2 * q**2)} # reduced form when parametrized s = diophantine(3 * x**2 + 72 * y**2 - 27 * z**2) assert s == {(24 * p**2 - 9 * q**2, 6 * p * q, 8 * p**2 + 3 * q**2)} assert parametrize_ternary_quadratic(3 * x**2 + 2 * y**2 - z**2 - 2 * x * y + 5 * y * z - 7 * y * z) == (2 * p**2 - 2 * p * q - q**2, 2 * p**2 + 2 * p * q - q**2, 2 * p**2 - 2 * p * q + 3 * q**2) assert parametrize_ternary_quadratic(124 * x**2 - 30 * y**2 - 7729 * z**2) == ( -1410 * p**2 - 363263 * q**2, 2700 * p**2 + 30916 * p * q - 695610 * q**2, -60 * p**2 + 5400 * p * q + 15458 * q**2)
def __new__(cls, *args, **assumptions): evaluate = assumptions.pop('evaluate', True) args = (sympify(arg) for arg in args) # first standard filter, for cls.zero and cls.identity # also reshape Max(a, Max(b, c)) to Max(a, b, c) if evaluate: try: args = frozenset(cls._new_args_filter(args)) except ShortCircuit: return cls.zero else: args = frozenset(args) if evaluate: # remove redundant args that are easily identified args = cls._collapse_arguments(args, **assumptions) # find local zeros args = cls._find_localzeros(args, **assumptions) if not args: return cls.identity if len(args) == 1: return list(args).pop() # base creation _args = frozenset(args) obj = Expr.__new__(cls, *ordered(_args), **assumptions) obj._argset = _args return obj
def __new__(cls, *args, **kwargs): evaluate = kwargs.get('evaluate', global_parameters.evaluate) # flatten inputs args = list(args) # adapted from sympy.sets.sets.Union def _flatten(arg): if isinstance(arg, SeqBase): if isinstance(arg, SeqMul): return sum(map(_flatten, arg.args), []) else: return [arg] elif iterable(arg): return sum(map(_flatten, arg), []) raise TypeError("Input must be Sequences or " " iterables of Sequences") args = _flatten(args) # Multiplication of no sequences is EmptySequence if not args: return S.EmptySequence if Intersection(*(a.interval for a in args)) is S.EmptySet: return S.EmptySequence # reduce using known rules if evaluate: return SeqMul.reduce(args) args = list(ordered(args, SeqBase._start_key)) return Basic.__new__(cls, *args)
def __init__(self, clauses, variables, var_settings, symbols=None, heuristic='vsids', clause_learning='none', INTERVAL=500): self.var_settings = var_settings self.heuristic = heuristic self.is_unsatisfied = False self._unit_prop_queue = [] self.update_functions = [] self.INTERVAL = INTERVAL if symbols is None: self.symbols = list(ordered(variables)) else: self.symbols = symbols self._initialize_variables(variables) self._initialize_clauses(clauses) if 'vsids' == heuristic: self._vsids_init() self.heur_calculate = self._vsids_calculate self.heur_lit_assigned = self._vsids_lit_assigned self.heur_lit_unset = self._vsids_lit_unset self.heur_clause_added = self._vsids_clause_added # Note: Uncomment this if/when clause learning is enabled #self.update_functions.append(self._vsids_decay) else: raise NotImplementedError if 'simple' == clause_learning: self.add_learned_clause = self._simple_add_learned_clause self.compute_conflict = self.simple_compute_conflict self.update_functions.append(self.simple_clean_clauses) elif 'none' == clause_learning: self.add_learned_clause = lambda x: None self.compute_conflict = lambda: None else: raise NotImplementedError # Create the base level self.levels = [Level(0)] self._current_level.varsettings = var_settings # Keep stats self.num_decisions = 0 self.num_learned_clauses = 0 self.original_num_clauses = len(self.clauses)
def _construct_algebraic(coeffs, opt): """We know that coefficients are algebraic so construct the extension. """ from sympy.polys.numberfields import primitive_element exts = set() def build_trees(args): trees = [] for a in args: if a.is_Rational: tree = ('Q', QQ.from_sympy(a)) elif a.is_Add: tree = ('+', build_trees(a.args)) elif a.is_Mul: tree = ('*', build_trees(a.args)) else: tree = ('e', a) exts.add(a) trees.append(tree) return trees trees = build_trees(coeffs) exts = list(ordered(exts)) g, span, H = primitive_element(exts, ex=True, polys=True) root = sum([s * ext for s, ext in zip(span, exts)]) domain, g = QQ.algebraic_field((g, root)), g.rep.rep exts_dom = [domain.dtype.from_list(h, g, QQ) for h in H] exts_map = dict(zip(exts, exts_dom)) def convert_tree(tree): op, args = tree if op == 'Q': return domain.dtype.from_list([args], g, QQ) elif op == '+': return sum((convert_tree(a) for a in args), domain.zero) elif op == '*': # return prod(convert(a) for a in args) t = convert_tree(args[0]) for a in args[1:]: t *= convert_tree(a) return t elif op == 'e': return exts_map[args] else: raise RuntimeError result = [convert_tree(tree) for tree in trees] return domain, result
def _get_complexes(cls, factors, use_cache=True): """Compute complex root isolating intervals for a list of factors. """ complexes = [] for currentfactor, k in ordered(factors): try: if not use_cache: raise KeyError c = _complexes_cache[currentfactor] complexes.extend([(i, currentfactor, k) for i in c]) except KeyError: complex_part = cls._get_complexes_sqf(currentfactor, use_cache) new = [(root, currentfactor, k) for root in complex_part] complexes.extend(new) complexes = cls._complexes_sorted(complexes) return complexes
def test_ordered(): assert list(ordered((x, y), hash, default=False)) in [[x, y], [y, x]] assert list(ordered((x, y), hash, default=False)) == \ list(ordered((y, x), hash, default=False)) assert list(ordered((x, y))) == [x, y] seq, keys = [[[1, 2, 1], [0, 3, 1], [1, 1, 3], [2], [1]], (lambda x: len(x), lambda x: sum(x))] assert list(ordered(seq, keys, default=False, warn=False)) == \ [[1], [2], [1, 2, 1], [0, 3, 1], [1, 1, 3]] raises(ValueError, lambda: list(ordered(seq, keys, default=False, warn=True)))
def root_factors(f, *gens, filter=None, **args): """ Returns all factors of a univariate polynomial. Examples ======== >>> from sympy.abc import x, y >>> from sympy.polys.polyroots import root_factors >>> root_factors(x**2 - y, x) [x - sqrt(y), x + sqrt(y)] """ args = dict(args) F = Poly(f, *gens, **args) if not F.is_Poly: return [f] if F.is_multivariate: raise ValueError('multivariate polynomials are not supported') x = F.gens[0] zeros = roots(F, filter=filter) if not zeros: factors = [F] else: factors, N = [], 0 for r, n in ordered(zeros.items()): factors, N = factors + [Poly(x - r, x)] * n, N + n if N < F.degree(): G = reduce(lambda p, q: p * q, factors) factors.append(F.quo(G)) if not isinstance(f, Poly): factors = [f.as_expr() for f in factors] return factors
def _preprocess(self, args, expr): """Preprocess args, expr to replace arguments that do not map to valid Python identifiers. Returns string form of args, and updated expr. """ from sympy.core.basic import Basic from sympy.core.sorting import ordered from sympy.core.function import (Derivative, Function) from sympy.core.symbol import Dummy, uniquely_named_symbol from sympy.matrices import DeferredVector from sympy.core.expr import Expr # Args of type Dummy can cause name collisions with args # of type Symbol. Force dummify of everything in this # situation. dummify = self._dummify or any( isinstance(arg, Dummy) for arg in flatten(args)) argstrs = [None]*len(args) for arg, i in reversed(list(ordered(zip(args, range(len(args)))))): if iterable(arg): s, expr = self._preprocess(arg, expr) elif isinstance(arg, DeferredVector): s = str(arg) elif isinstance(arg, Basic) and arg.is_symbol: s = self._argrepr(arg) if dummify or not self._is_safe_ident(s): dummy = Dummy() if isinstance(expr, Expr): dummy = uniquely_named_symbol( dummy.name, expr, modify=lambda s: '_' + s) s = self._argrepr(dummy) expr = self._subexpr(expr, {arg: dummy}) elif dummify or isinstance(arg, (Function, Derivative)): dummy = Dummy() s = self._argrepr(dummy) expr = self._subexpr(expr, {arg: dummy}) else: s = str(arg) argstrs[i] = s return argstrs, expr
def _mostfunc(lhs, func, X=None): """Returns the term in lhs which contains the most of the func-type things e.g. log(log(x)) wins over log(x) if both terms appear. ``func`` can be a function (exp, log, etc...) or any other SymPy object, like Pow. If ``X`` is not ``None``, then the function returns the term composed with the most ``func`` having the specified variable. Examples ======== >>> from sympy.solvers.bivariate import _mostfunc >>> from sympy import exp >>> from sympy.abc import x, y >>> _mostfunc(exp(x) + exp(exp(x) + 2), exp) exp(exp(x) + 2) >>> _mostfunc(exp(x) + exp(exp(y) + 2), exp) exp(exp(y) + 2) >>> _mostfunc(exp(x) + exp(exp(y) + 2), exp, x) exp(x) >>> _mostfunc(x, exp, x) is None True >>> _mostfunc(exp(x) + exp(x*y), exp, x) exp(x) """ fterms = [ tmp for tmp in lhs.atoms(func) if (not X or X.is_Symbol and X in tmp.free_symbols or not X.is_Symbol and tmp.has(X)) ] if len(fterms) == 1: return fterms[0] elif fterms: return max(list(ordered(fterms)), key=lambda x: x.count(func)) return None
def heurisch(f, x, rewrite=False, hints=None, mappings=None, retries=3, degree_offset=0, unnecessary_permutations=None, _try_heurisch=None): """ Compute indefinite integral using heuristic Risch algorithm. Explanation =========== This is a heuristic approach to indefinite integration in finite terms using the extended heuristic (parallel) Risch algorithm, based on Manuel Bronstein's "Poor Man's Integrator". The algorithm supports various classes of functions including transcendental elementary or special functions like Airy, Bessel, Whittaker and Lambert. Note that this algorithm is not a decision procedure. If it isn't able to compute the antiderivative for a given function, then this is not a proof that such a functions does not exist. One should use recursive Risch algorithm in such case. It's an open question if this algorithm can be made a full decision procedure. This is an internal integrator procedure. You should use top level 'integrate' function in most cases, as this procedure needs some preprocessing steps and otherwise may fail. Specification ============= heurisch(f, x, rewrite=False, hints=None) where f : expression x : symbol rewrite -> force rewrite 'f' in terms of 'tan' and 'tanh' hints -> a list of functions that may appear in anti-derivate - hints = None --> no suggestions at all - hints = [ ] --> try to figure out - hints = [f1, ..., fn] --> we know better Examples ======== >>> from sympy import tan >>> from sympy.integrals.heurisch import heurisch >>> from sympy.abc import x, y >>> heurisch(y*tan(x), x) y*log(tan(x)**2 + 1)/2 See Manuel Bronstein's "Poor Man's Integrator": References ========== .. [1] http://www-sop.inria.fr/cafe/Manuel.Bronstein/pmint/index.html For more information on the implemented algorithm refer to: .. [2] K. Geddes, L. Stefanus, On the Risch-Norman Integration Method and its Implementation in Maple, Proceedings of ISSAC'89, ACM Press, 212-217. .. [3] J. H. Davenport, On the Parallel Risch Algorithm (I), Proceedings of EUROCAM'82, LNCS 144, Springer, 144-157. .. [4] J. H. Davenport, On the Parallel Risch Algorithm (III): Use of Tangents, SIGSAM Bulletin 16 (1982), 3-6. .. [5] J. H. Davenport, B. M. Trager, On the Parallel Risch Algorithm (II), ACM Transactions on Mathematical Software 11 (1985), 356-362. See Also ======== sympy.integrals.integrals.Integral.doit sympy.integrals.integrals.Integral sympy.integrals.heurisch.components """ f = sympify(f) # There are some functions that Heurisch cannot currently handle, # so do not even try. # Set _try_heurisch=True to skip this check if _try_heurisch is not True: if f.has(Abs, re, im, sign, Heaviside, DiracDelta, floor, ceiling, arg): return if not f.has_free(x): return f*x if not f.is_Add: indep, f = f.as_independent(x) else: indep = S.One rewritables = { (sin, cos, cot): tan, (sinh, cosh, coth): tanh, } if rewrite: for candidates, rule in rewritables.items(): f = f.rewrite(candidates, rule) else: for candidates in rewritables.keys(): if f.has(*candidates): break else: rewrite = True terms = components(f, x) if hints is not None: if not hints: a = Wild('a', exclude=[x]) b = Wild('b', exclude=[x]) c = Wild('c', exclude=[x]) for g in set(terms): # using copy of terms if g.is_Function: if isinstance(g, li): M = g.args[0].match(a*x**b) if M is not None: terms.add( x*(li(M[a]*x**M[b]) - (M[a]*x**M[b])**(-1/M[b])*Ei((M[b]+1)*log(M[a]*x**M[b])/M[b])) ) #terms.add( x*(li(M[a]*x**M[b]) - (x**M[b])**(-1/M[b])*Ei((M[b]+1)*log(M[a]*x**M[b])/M[b])) ) #terms.add( x*(li(M[a]*x**M[b]) - x*Ei((M[b]+1)*log(M[a]*x**M[b])/M[b])) ) #terms.add( li(M[a]*x**M[b]) - Ei((M[b]+1)*log(M[a]*x**M[b])/M[b]) ) elif isinstance(g, exp): M = g.args[0].match(a*x**2) if M is not None: if M[a].is_positive: terms.add(erfi(sqrt(M[a])*x)) else: # M[a].is_negative or unknown terms.add(erf(sqrt(-M[a])*x)) M = g.args[0].match(a*x**2 + b*x + c) if M is not None: if M[a].is_positive: terms.add(sqrt(pi/4*(-M[a]))*exp(M[c] - M[b]**2/(4*M[a]))* erfi(sqrt(M[a])*x + M[b]/(2*sqrt(M[a])))) elif M[a].is_negative: terms.add(sqrt(pi/4*(-M[a]))*exp(M[c] - M[b]**2/(4*M[a]))* erf(sqrt(-M[a])*x - M[b]/(2*sqrt(-M[a])))) M = g.args[0].match(a*log(x)**2) if M is not None: if M[a].is_positive: terms.add(erfi(sqrt(M[a])*log(x) + 1/(2*sqrt(M[a])))) if M[a].is_negative: terms.add(erf(sqrt(-M[a])*log(x) - 1/(2*sqrt(-M[a])))) elif g.is_Pow: if g.exp.is_Rational and g.exp.q == 2: M = g.base.match(a*x**2 + b) if M is not None and M[b].is_positive: if M[a].is_positive: terms.add(asinh(sqrt(M[a]/M[b])*x)) elif M[a].is_negative: terms.add(asin(sqrt(-M[a]/M[b])*x)) M = g.base.match(a*x**2 - b) if M is not None and M[b].is_positive: if M[a].is_positive: terms.add(acosh(sqrt(M[a]/M[b])*x)) elif M[a].is_negative: terms.add(-M[b]/2*sqrt(-M[a])* atan(sqrt(-M[a])*x/sqrt(M[a]*x**2 - M[b]))) else: terms |= set(hints) dcache = DiffCache(x) for g in set(terms): # using copy of terms terms |= components(dcache.get_diff(g), x) # TODO: caching is significant factor for why permutations work at all. Change this. V = _symbols('x', len(terms)) # sort mapping expressions from largest to smallest (last is always x). mapping = list(reversed(list(zip(*ordered( # [(a[0].as_independent(x)[1], a) for a in zip(terms, V)])))[1])) # rev_mapping = {v: k for k, v in mapping} # if mappings is None: # # optimizing the number of permutations of mapping # assert mapping[-1][0] == x # if not, find it and correct this comment unnecessary_permutations = [mapping.pop(-1)] mappings = permutations(mapping) else: unnecessary_permutations = unnecessary_permutations or [] def _substitute(expr): return expr.subs(mapping) for mapping in mappings: mapping = list(mapping) mapping = mapping + unnecessary_permutations diffs = [ _substitute(dcache.get_diff(g)) for g in terms ] denoms = [ g.as_numer_denom()[1] for g in diffs ] if all(h.is_polynomial(*V) for h in denoms) and _substitute(f).is_rational_function(*V): denom = reduce(lambda p, q: lcm(p, q, *V), denoms) break else: if not rewrite: result = heurisch(f, x, rewrite=True, hints=hints, unnecessary_permutations=unnecessary_permutations) if result is not None: return indep*result return None numers = [ cancel(denom*g) for g in diffs ] def _derivation(h): return Add(*[ d * h.diff(v) for d, v in zip(numers, V) ]) def _deflation(p): for y in V: if not p.has(y): continue if _derivation(p) is not S.Zero: c, q = p.as_poly(y).primitive() return _deflation(c)*gcd(q, q.diff(y)).as_expr() return p def _splitter(p): for y in V: if not p.has(y): continue if _derivation(y) is not S.Zero: c, q = p.as_poly(y).primitive() q = q.as_expr() h = gcd(q, _derivation(q), y) s = quo(h, gcd(q, q.diff(y), y), y) c_split = _splitter(c) if s.as_poly(y).degree() == 0: return (c_split[0], q * c_split[1]) q_split = _splitter(cancel(q / s)) return (c_split[0]*q_split[0]*s, c_split[1]*q_split[1]) return (S.One, p) special = {} for term in terms: if term.is_Function: if isinstance(term, tan): special[1 + _substitute(term)**2] = False elif isinstance(term, tanh): special[1 + _substitute(term)] = False special[1 - _substitute(term)] = False elif isinstance(term, LambertW): special[_substitute(term)] = True F = _substitute(f) P, Q = F.as_numer_denom() u_split = _splitter(denom) v_split = _splitter(Q) polys = set(list(v_split) + [ u_split[0] ] + list(special.keys())) s = u_split[0] * Mul(*[ k for k, v in special.items() if v ]) polified = [ p.as_poly(*V) for p in [s, P, Q] ] if None in polified: return None #--- definitions for _integrate a, b, c = [ p.total_degree() for p in polified ] poly_denom = (s * v_split[0] * _deflation(v_split[1])).as_expr() def _exponent(g): if g.is_Pow: if g.exp.is_Rational and g.exp.q != 1: if g.exp.p > 0: return g.exp.p + g.exp.q - 1 else: return abs(g.exp.p + g.exp.q) else: return 1 elif not g.is_Atom and g.args: return max([ _exponent(h) for h in g.args ]) else: return 1 A, B = _exponent(f), a + max(b, c) if A > 1 and B > 1: monoms = tuple(ordered(itermonomials(V, A + B - 1 + degree_offset))) else: monoms = tuple(ordered(itermonomials(V, A + B + degree_offset))) poly_coeffs = _symbols('A', len(monoms)) poly_part = Add(*[ poly_coeffs[i]*monomial for i, monomial in enumerate(monoms) ]) reducibles = set() for poly in ordered(polys): coeff, factors = factor_list(poly, *V) reducibles.add(coeff) for fact, mul in factors: reducibles.add(fact) def _integrate(field=None): atans = set() pairs = set() if field == 'Q': irreducibles = set(reducibles) else: setV = set(V) irreducibles = set() for poly in ordered(reducibles): zV = setV & set(iterfreeargs(poly)) for z in ordered(zV): s = set(root_factors(poly, z, filter=field)) irreducibles |= s break log_part, atan_part = [], [] for poly in ordered(irreducibles): m = collect(poly, I, evaluate=False) y = m.get(I, S.Zero) if y: x = m.get(S.One, S.Zero) if x.has(I) or y.has(I): continue # nontrivial x + I*y pairs.add((x, y)) irreducibles.remove(poly) while pairs: x, y = pairs.pop() if (x, -y) in pairs: pairs.remove((x, -y)) # Choosing b with no minus sign if y.could_extract_minus_sign(): y = -y irreducibles.add(x*x + y*y) atans.add(atan(x/y)) else: irreducibles.add(x + I*y) B = _symbols('B', len(irreducibles)) C = _symbols('C', len(atans)) # Note: the ordering matters here for poly, b in reversed(list(zip(ordered(irreducibles), B))): if poly.has(*V): poly_coeffs.append(b) log_part.append(b * log(poly)) for poly, c in reversed(list(zip(ordered(atans), C))): if poly.has(*V): poly_coeffs.append(c) atan_part.append(c * poly) # TODO: Currently it's better to use symbolic expressions here instead # of rational functions, because it's simpler and FracElement doesn't # give big speed improvement yet. This is because cancellation is slow # due to slow polynomial GCD algorithms. If this gets improved then # revise this code. candidate = poly_part/poly_denom + Add(*log_part) + Add(*atan_part) h = F - _derivation(candidate) / denom raw_numer = h.as_numer_denom()[0] # Rewrite raw_numer as a polynomial in K[coeffs][V] where K is a field # that we have to determine. We can't use simply atoms() because log(3), # sqrt(y) and similar expressions can appear, leading to non-trivial # domains. syms = set(poly_coeffs) | set(V) non_syms = set() def find_non_syms(expr): if expr.is_Integer or expr.is_Rational: pass # ignore trivial numbers elif expr in syms: pass # ignore variables elif not expr.has_free(*syms): non_syms.add(expr) elif expr.is_Add or expr.is_Mul or expr.is_Pow: list(map(find_non_syms, expr.args)) else: # TODO: Non-polynomial expression. This should have been # filtered out at an earlier stage. raise PolynomialError try: find_non_syms(raw_numer) except PolynomialError: return None else: ground, _ = construct_domain(non_syms, field=True) coeff_ring = PolyRing(poly_coeffs, ground) ring = PolyRing(V, coeff_ring) try: numer = ring.from_expr(raw_numer) except ValueError: raise PolynomialError solution = solve_lin_sys(numer.coeffs(), coeff_ring, _raw=False) if solution is None: return None else: return candidate.xreplace(solution).xreplace( dict(zip(poly_coeffs, [S.Zero]*len(poly_coeffs)))) if all(isinstance(_, Symbol) for _ in V): more_free = F.free_symbols - set(V) else: Fd = F.as_dummy() more_free = Fd.xreplace(dict(zip(V, (Dummy() for _ in V))) ).free_symbols & Fd.free_symbols if not more_free: # all free generators are identified in V solution = _integrate('Q') if solution is None: solution = _integrate() else: solution = _integrate() if solution is not None: antideriv = solution.subs(rev_mapping) antideriv = cancel(antideriv).expand() if antideriv.is_Add: antideriv = antideriv.as_independent(x)[1] return indep*antideriv else: if retries >= 0: result = heurisch(f, x, mappings=mappings, rewrite=rewrite, hints=hints, retries=retries - 1, unnecessary_permutations=unnecessary_permutations) if result is not None: return indep*result return None
def unify(K0, K1, symbols=None): """ Construct a minimal domain that contains elements of ``K0`` and ``K1``. Known domains (from smallest to largest): - ``GF(p)`` - ``ZZ`` - ``QQ`` - ``RR(prec, tol)`` - ``CC(prec, tol)`` - ``ALG(a, b, c)`` - ``K[x, y, z]`` - ``K(x, y, z)`` - ``EX`` """ if symbols is not None: return K0.unify_with_symbols(K1, symbols) if K0 == K1: return K0 if K0.is_EXRAW: return K0 if K1.is_EXRAW: return K1 if K0.is_EX: return K0 if K1.is_EX: return K1 if K0.is_FiniteExtension or K1.is_FiniteExtension: if K1.is_FiniteExtension: K0, K1 = K1, K0 if K1.is_FiniteExtension: # Unifying two extensions. # Try to ensure that K0.unify(K1) == K1.unify(K0) if list(ordered([K0.modulus, K1.modulus]))[1] == K0.modulus: K0, K1 = K1, K0 return K1.set_domain(K0) else: # Drop the generator from other and unify with the base domain K1 = K1.drop(K0.symbol) K1 = K0.domain.unify(K1) return K0.set_domain(K1) if K0.is_Composite or K1.is_Composite: K0_ground = K0.dom if K0.is_Composite else K0 K1_ground = K1.dom if K1.is_Composite else K1 K0_symbols = K0.symbols if K0.is_Composite else () K1_symbols = K1.symbols if K1.is_Composite else () domain = K0_ground.unify(K1_ground) symbols = _unify_gens(K0_symbols, K1_symbols) order = K0.order if K0.is_Composite else K1.order if ((K0.is_FractionField and K1.is_PolynomialRing or K1.is_FractionField and K0.is_PolynomialRing) and (not K0_ground.is_Field or not K1_ground.is_Field) and domain.is_Field and domain.has_assoc_Ring): domain = domain.get_ring() if K0.is_Composite and (not K1.is_Composite or K0.is_FractionField or K1.is_PolynomialRing): cls = K0.__class__ else: cls = K1.__class__ from sympy.polys.domains.old_polynomialring import GlobalPolynomialRing if cls == GlobalPolynomialRing: return cls(domain, symbols) return cls(domain, symbols, order) def mkinexact(cls, K0, K1): prec = max(K0.precision, K1.precision) tol = max(K0.tolerance, K1.tolerance) return cls(prec=prec, tol=tol) if K1.is_ComplexField: K0, K1 = K1, K0 if K0.is_ComplexField: if K1.is_ComplexField or K1.is_RealField: return mkinexact(K0.__class__, K0, K1) else: return K0 if K1.is_RealField: K0, K1 = K1, K0 if K0.is_RealField: if K1.is_RealField: return mkinexact(K0.__class__, K0, K1) elif K1.is_GaussianRing or K1.is_GaussianField: from sympy.polys.domains.complexfield import ComplexField return ComplexField(prec=K0.precision, tol=K0.tolerance) else: return K0 if K1.is_AlgebraicField: K0, K1 = K1, K0 if K0.is_AlgebraicField: if K1.is_GaussianRing: K1 = K1.get_field() if K1.is_GaussianField: K1 = K1.as_AlgebraicField() if K1.is_AlgebraicField: return K0.__class__(K0.dom.unify(K1.dom), *_unify_gens(K0.orig_ext, K1.orig_ext)) else: return K0 if K0.is_GaussianField: return K0 if K1.is_GaussianField: return K1 if K0.is_GaussianRing: if K1.is_RationalField: K0 = K0.get_field() return K0 if K1.is_GaussianRing: if K0.is_RationalField: K1 = K1.get_field() return K1 if K0.is_RationalField: return K0 if K1.is_RationalField: return K1 if K0.is_IntegerRing: return K0 if K1.is_IntegerRing: return K1 if K0.is_FiniteField and K1.is_FiniteField: return K0.__class__(max(K0.mod, K1.mod, key=default_sort_key)) from sympy.polys.domains import EX return EX
def gammasimp(expr): r""" Simplify expressions with gamma functions. Explanation =========== This function takes as input an expression containing gamma functions or functions that can be rewritten in terms of gamma functions and tries to minimize the number of those functions and reduce the size of their arguments. The algorithm works by rewriting all gamma functions as expressions involving rising factorials (Pochhammer symbols) and applies recurrence relations and other transformations applicable to rising factorials, to reduce their arguments, possibly letting the resulting rising factorial to cancel. Rising factorials with the second argument being an integer are expanded into polynomial forms and finally all other rising factorial are rewritten in terms of gamma functions. Then the following two steps are performed. 1. Reduce the number of gammas by applying the reflection theorem gamma(x)*gamma(1-x) == pi/sin(pi*x). 2. Reduce the number of gammas by applying the multiplication theorem gamma(x)*gamma(x+1/n)*...*gamma(x+(n-1)/n) == C*gamma(n*x). It then reduces the number of prefactors by absorbing them into gammas where possible and expands gammas with rational argument. All transformation rules can be found (or were derived from) here: .. [1] http://functions.wolfram.com/GammaBetaErf/Pochhammer/17/01/02/ .. [2] http://functions.wolfram.com/GammaBetaErf/Pochhammer/27/01/0005/ Examples ======== >>> from sympy.simplify import gammasimp >>> from sympy import gamma, Symbol >>> from sympy.abc import x >>> n = Symbol('n', integer = True) >>> gammasimp(gamma(x)/gamma(x - 3)) (x - 3)*(x - 2)*(x - 1) >>> gammasimp(gamma(n + 3)) gamma(n + 3) """ expr = expr.rewrite(gamma) # compute_ST will be looking for Functions and we don't want # it looking for non-gamma functions: issue 22606 # so we mask free, non-gamma functions f = expr.atoms(Function) # take out gammas gammas = {i for i in f if isinstance(i, gamma)} if not gammas: return expr # avoid side effects like factoring f -= gammas # keep only those without bound symbols f = f & expr.as_dummy().atoms(Function) if f: dum, fun, simp = zip( *[(Dummy(), fi, fi.func(*[_gammasimp(a, as_comb=False) for a in fi.args])) for fi in ordered(f)]) d = expr.xreplace(dict(zip(fun, dum))) return _gammasimp(d, as_comb=False).xreplace(dict(zip(dum, simp))) return _gammasimp(expr, as_comb=False)
def rule_gamma(expr, level=0): """ Simplify products of gamma functions further. """ if expr.is_Atom: return expr def gamma_rat(x): # helper to simplify ratios of gammas was = x.count(gamma) xx = x.replace( gamma, lambda n: _rf(1, (n - 1).expand()).replace( _rf, lambda a, b: gamma(a + b) / gamma(a))) if xx.count(gamma) < was: x = xx return x def gamma_factor(x): # return True if there is a gamma factor in shallow args if isinstance(x, gamma): return True if x.is_Add or x.is_Mul: return any(gamma_factor(xi) for xi in x.args) if x.is_Pow and (x.exp.is_integer or x.base.is_positive): return gamma_factor(x.base) return False # recursion step if level == 0: expr = expr.func(*[rule_gamma(x, level + 1) for x in expr.args]) level += 1 if not expr.is_Mul: return expr # non-commutative step if level == 1: args, nc = expr.args_cnc() if not args: return expr if nc: return rule_gamma(Mul._from_args(args), level + 1) * Mul._from_args(nc) level += 1 # pure gamma handling, not factor absorption if level == 2: T, F = sift(expr.args, gamma_factor, binary=True) gamma_ind = Mul(*F) d = Mul(*T) nd, dd = d.as_numer_denom() for ipass in range(2): args = list(ordered(Mul.make_args(nd))) for i, ni in enumerate(args): if ni.is_Add: ni, dd = Add(*[ rule_gamma(gamma_rat(a / dd), level + 1) for a in ni.args ]).as_numer_denom() args[i] = ni if not dd.has(gamma): break nd = Mul(*args) if ipass == 0 and not gamma_factor(nd): break nd, dd = dd, nd # now process in reversed order expr = gamma_ind * nd / dd if not (expr.is_Mul and (gamma_factor(dd) or gamma_factor(nd))): return expr level += 1 # iteration until constant if level == 3: while True: was = expr expr = rule_gamma(expr, 4) if expr == was: return expr numer_gammas = [] denom_gammas = [] numer_others = [] denom_others = [] def explicate(p): if p is S.One: return None, [] b, e = p.as_base_exp() if e.is_Integer: if isinstance(b, gamma): return True, [b.args[0]] * e else: return False, [b] * e else: return False, [p] newargs = list(ordered(expr.args)) while newargs: n, d = newargs.pop().as_numer_denom() isg, l = explicate(n) if isg: numer_gammas.extend(l) elif isg is False: numer_others.extend(l) isg, l = explicate(d) if isg: denom_gammas.extend(l) elif isg is False: denom_others.extend(l) # =========== level 2 work: pure gamma manipulation ========= if not as_comb: # Try to reduce the number of gamma factors by applying the # reflection formula gamma(x)*gamma(1-x) = pi/sin(pi*x) for gammas, numer, denom in [ (numer_gammas, numer_others, denom_others), (denom_gammas, denom_others, numer_others) ]: new = [] while gammas: g1 = gammas.pop() if g1.is_integer: new.append(g1) continue for i, g2 in enumerate(gammas): n = g1 + g2 - 1 if not n.is_Integer: continue numer.append(S.Pi) denom.append(sin(S.Pi * g1)) gammas.pop(i) if n > 0: for k in range(n): numer.append(1 - g1 + k) elif n < 0: for k in range(-n): denom.append(-g1 - k) break else: new.append(g1) # /!\ updating IN PLACE gammas[:] = new # Try to reduce the number of gammas by using the duplication # theorem to cancel an upper and lower: gamma(2*s)/gamma(s) = # 2**(2*s + 1)/(4*sqrt(pi))*gamma(s + 1/2). Although this could # be done with higher argument ratios like gamma(3*x)/gamma(x), # this would not reduce the number of gammas as in this case. for ng, dg, no, do in [ (numer_gammas, denom_gammas, numer_others, denom_others), (denom_gammas, numer_gammas, denom_others, numer_others) ]: while True: for x in ng: for y in dg: n = x - 2 * y if n.is_Integer: break else: continue break else: break ng.remove(x) dg.remove(y) if n > 0: for k in range(n): no.append(2 * y + k) elif n < 0: for k in range(-n): do.append(2 * y - 1 - k) ng.append(y + S.Half) no.append(2**(2 * y - 1)) do.append(sqrt(S.Pi)) # Try to reduce the number of gamma factors by applying the # multiplication theorem (used when n gammas with args differing # by 1/n mod 1 are encountered). # # run of 2 with args differing by 1/2 # # >>> gammasimp(gamma(x)*gamma(x+S.Half)) # 2*sqrt(2)*2**(-2*x - 1/2)*sqrt(pi)*gamma(2*x) # # run of 3 args differing by 1/3 (mod 1) # # >>> gammasimp(gamma(x)*gamma(x+S(1)/3)*gamma(x+S(2)/3)) # 6*3**(-3*x - 1/2)*pi*gamma(3*x) # >>> gammasimp(gamma(x)*gamma(x+S(1)/3)*gamma(x+S(5)/3)) # 2*3**(-3*x - 1/2)*pi*(3*x + 2)*gamma(3*x) # def _run(coeffs): # find runs in coeffs such that the difference in terms (mod 1) # of t1, t2, ..., tn is 1/n u = list(uniq(coeffs)) for i in range(len(u)): dj = ([((u[j] - u[i]) % 1, j) for j in range(i + 1, len(u))]) for one, j in dj: if one.p == 1 and one.q != 1: n = one.q got = [i] get = list(range(1, n)) for d, j in dj: m = n * d if m.is_Integer and m in get: get.remove(m) got.append(j) if not get: break else: continue for i, j in enumerate(got): c = u[j] coeffs.remove(c) got[i] = c return one.q, got[0], got[1:] def _mult_thm(gammas, numer, denom): # pull off and analyze the leading coefficient from each gamma arg # looking for runs in those Rationals # expr -> coeff + resid -> rats[resid] = coeff rats = {} for g in gammas: c, resid = g.as_coeff_Add() rats.setdefault(resid, []).append(c) # look for runs in Rationals for each resid keys = sorted(rats, key=default_sort_key) for resid in keys: coeffs = list(sorted(rats[resid])) new = [] while True: run = _run(coeffs) if run is None: break # process the sequence that was found: # 1) convert all the gamma functions to have the right # argument (could be off by an integer) # 2) append the factors corresponding to the theorem # 3) append the new gamma function n, ui, other = run # (1) for u in other: con = resid + u - 1 for k in range(int(u - ui)): numer.append(con - k) con = n * (resid + ui) # for (2) and (3) # (2) numer.append( (2 * S.Pi)**(S(n - 1) / 2) * n**(S.Half - con)) # (3) new.append(con) # restore resid to coeffs rats[resid] = [resid + c for c in coeffs] + new # rebuild the gamma arguments g = [] for resid in keys: g += rats[resid] # /!\ updating IN PLACE gammas[:] = g for l, numer, denom in [(numer_gammas, numer_others, denom_others), (denom_gammas, denom_others, numer_others) ]: _mult_thm(l, numer, denom) # =========== level >= 2 work: factor absorption ========= if level >= 2: # Try to absorb factors into the gammas: x*gamma(x) -> gamma(x + 1) # and gamma(x)/(x - 1) -> gamma(x - 1) # This code (in particular repeated calls to find_fuzzy) can be very # slow. def find_fuzzy(l, x): if not l: return S1, T1 = compute_ST(x) for y in l: S2, T2 = inv[y] if T1 != T2 or (not S1.intersection(S2) and (S1 != set() or S2 != set())): continue # XXX we want some simplification (e.g. cancel or # simplify) but no matter what it's slow. a = len(cancel(x / y).free_symbols) b = len(x.free_symbols) c = len(y.free_symbols) # TODO is there a better heuristic? if a == 0 and (b > 0 or c > 0): return y # We thus try to avoid expensive calls by building the following # "invariants": For every factor or gamma function argument # - the set of free symbols S # - the set of functional components T # We will only try to absorb if T1==T2 and (S1 intersect S2 != emptyset # or S1 == S2 == emptyset) inv = {} def compute_ST(expr): if expr in inv: return inv[expr] return (expr.free_symbols, expr.atoms(Function).union( {e.exp for e in expr.atoms(Pow)})) def update_ST(expr): inv[expr] = compute_ST(expr) for expr in numer_gammas + denom_gammas + numer_others + denom_others: update_ST(expr) for gammas, numer, denom in [ (numer_gammas, numer_others, denom_others), (denom_gammas, denom_others, numer_others) ]: new = [] while gammas: g = gammas.pop() cont = True while cont: cont = False y = find_fuzzy(numer, g) if y is not None: numer.remove(y) if y != g: numer.append(y / g) update_ST(y / g) g += 1 cont = True y = find_fuzzy(denom, g - 1) if y is not None: denom.remove(y) if y != g - 1: numer.append((g - 1) / y) update_ST((g - 1) / y) g -= 1 cont = True new.append(g) # /!\ updating IN PLACE gammas[:] = new # =========== rebuild expr ================================== return Mul(*[gamma(g) for g in numer_gammas]) \ / Mul(*[gamma(g) for g in denom_gammas]) \ * Mul(*numer_others) / Mul(*denom_others)
def intersection(*entities, pairwise=False, **kwargs): """The intersection of a collection of GeometryEntity instances. Parameters ========== entities : sequence of GeometryEntity pairwise (keyword argument) : Can be either True or False Returns ======= intersection : list of GeometryEntity Raises ====== NotImplementedError When unable to calculate intersection. Notes ===== The intersection of any geometrical entity with itself should return a list with one item: the entity in question. An intersection requires two or more entities. If only a single entity is given then the function will return an empty list. It is possible for `intersection` to miss intersections that one knows exists because the required quantities were not fully simplified internally. Reals should be converted to Rationals, e.g. Rational(str(real_num)) or else failures due to floating point issues may result. Case 1: When the keyword argument 'pairwise' is False (default value): In this case, the function returns a list of intersections common to all entities. Case 2: When the keyword argument 'pairwise' is True: In this case, the functions returns a list intersections that occur between any pair of entities. See Also ======== sympy.geometry.entity.GeometryEntity.intersection Examples ======== >>> from sympy.geometry import Ray, Circle, intersection >>> c = Circle((0, 1), 1) >>> intersection(c, c.center) [] >>> right = Ray((0, 0), (1, 0)) >>> up = Ray((0, 0), (0, 1)) >>> intersection(c, right, up) [Point2D(0, 0)] >>> intersection(c, right, up, pairwise=True) [Point2D(0, 0), Point2D(0, 2)] >>> left = Ray((1, 0), (0, 0)) >>> intersection(right, left) [Segment2D(Point2D(0, 0), Point2D(1, 0))] """ from .entity import GeometryEntity from .point import Point if len(entities) <= 1: return [] # entities may be an immutable tuple entities = list(entities) for i, e in enumerate(entities): if not isinstance(e, GeometryEntity): entities[i] = Point(e) if not pairwise: # find the intersection common to all objects res = entities[0].intersection(entities[1]) for entity in entities[2:]: newres = [] for x in res: newres.extend(x.intersection(entity)) res = newres return res # find all pairwise intersections ans = [] for j in range(0, len(entities)): for k in range(j + 1, len(entities)): ans.extend(intersection(entities[j], entities[k])) return list(ordered(set(ans)))
def intersection(self, o): """The intersection of this ellipse and another geometrical entity `o`. Parameters ========== o : GeometryEntity Returns ======= intersection : list of GeometryEntity objects Notes ----- Currently supports intersections with Point, Line, Segment, Ray, Circle and Ellipse types. See Also ======== sympy.geometry.entity.GeometryEntity Examples ======== >>> from sympy import Ellipse, Point, Line >>> e = Ellipse(Point(0, 0), 5, 7) >>> e.intersection(Point(0, 0)) [] >>> e.intersection(Point(5, 0)) [Point2D(5, 0)] >>> e.intersection(Line(Point(0,0), Point(0, 1))) [Point2D(0, -7), Point2D(0, 7)] >>> e.intersection(Line(Point(5,0), Point(5, 1))) [Point2D(5, 0)] >>> e.intersection(Line(Point(6,0), Point(6, 1))) [] >>> e = Ellipse(Point(-1, 0), 4, 3) >>> e.intersection(Ellipse(Point(1, 0), 4, 3)) [Point2D(0, -3*sqrt(15)/4), Point2D(0, 3*sqrt(15)/4)] >>> e.intersection(Ellipse(Point(5, 0), 4, 3)) [Point2D(2, -3*sqrt(7)/4), Point2D(2, 3*sqrt(7)/4)] >>> e.intersection(Ellipse(Point(100500, 0), 4, 3)) [] >>> e.intersection(Ellipse(Point(0, 0), 3, 4)) [Point2D(3, 0), Point2D(-363/175, -48*sqrt(111)/175), Point2D(-363/175, 48*sqrt(111)/175)] >>> e.intersection(Ellipse(Point(-1, 0), 3, 4)) [Point2D(-17/5, -12/5), Point2D(-17/5, 12/5), Point2D(7/5, -12/5), Point2D(7/5, 12/5)] """ # TODO: Replace solve with nonlinsolve, when nonlinsolve will be able to solve in real domain x = Dummy('x', real=True) y = Dummy('y', real=True) if isinstance(o, Point): if o in self: return [o] else: return [] elif isinstance(o, (Segment2D, Ray2D)): ellipse_equation = self.equation(x, y) result = solve([ellipse_equation, Line(o.points[0], o.points[1]).equation(x, y)], [x, y]) return list(ordered([Point(i) for i in result if i in o])) elif isinstance(o, Polygon): return o.intersection(self) elif isinstance(o, (Ellipse, Line2D)): if o == self: return self else: ellipse_equation = self.equation(x, y) return list(ordered([Point(i) for i in solve([ellipse_equation, o.equation(x, y)], [x, y])])) elif isinstance(o, LinearEntity3D): raise TypeError('Entity must be two dimensional, not three dimensional') else: raise TypeError('Intersection not handled for %s' % func_name(o))
def roots(f, *gens, auto=True, cubics=True, trig=False, quartics=True, quintics=False, multiple=False, filter=None, predicate=None, **flags): """ Computes symbolic roots of a univariate polynomial. Given a univariate polynomial f with symbolic coefficients (or a list of the polynomial's coefficients), returns a dictionary with its roots and their multiplicities. Only roots expressible via radicals will be returned. To get a complete set of roots use RootOf class or numerical methods instead. By default cubic and quartic formulas are used in the algorithm. To disable them because of unreadable output set ``cubics=False`` or ``quartics=False`` respectively. If cubic roots are real but are expressed in terms of complex numbers (casus irreducibilis [1]) the ``trig`` flag can be set to True to have the solutions returned in terms of cosine and inverse cosine functions. To get roots from a specific domain set the ``filter`` flag with one of the following specifiers: Z, Q, R, I, C. By default all roots are returned (this is equivalent to setting ``filter='C'``). By default a dictionary is returned giving a compact result in case of multiple roots. However to get a list containing all those roots set the ``multiple`` flag to True; the list will have identical roots appearing next to each other in the result. (For a given Poly, the all_roots method will give the roots in sorted numerical order.) Examples ======== >>> from sympy import Poly, roots >>> from sympy.abc import x, y >>> roots(x**2 - 1, x) {-1: 1, 1: 1} >>> p = Poly(x**2-1, x) >>> roots(p) {-1: 1, 1: 1} >>> p = Poly(x**2-y, x, y) >>> roots(Poly(p, x)) {-sqrt(y): 1, sqrt(y): 1} >>> roots(x**2 - y, x) {-sqrt(y): 1, sqrt(y): 1} >>> roots([1, 0, -1]) {-1: 1, 1: 1} References ========== .. [1] https://en.wikipedia.org/wiki/Cubic_function#Trigonometric_.28and_hyperbolic.29_method """ from sympy.polys.polytools import to_rational_coeffs flags = dict(flags) if isinstance(f, list): if gens: raise ValueError('redundant generators given') x = Dummy('x') poly, i = {}, len(f) - 1 for coeff in f: poly[i], i = sympify(coeff), i - 1 f = Poly(poly, x, field=True) else: try: F = Poly(f, *gens, **flags) if not isinstance(f, Poly) and not F.gen.is_Symbol: raise PolynomialError("generator must be a Symbol") else: f = F if f.length == 2 and f.degree() != 1: # check for foo**n factors in the constant n = f.degree() npow_bases = [] others = [] expr = f.as_expr() con = expr.as_independent(*gens)[0] for p in Mul.make_args(con): if p.is_Pow and not p.exp % n: npow_bases.append(p.base**(p.exp / n)) else: others.append(p) if npow_bases: b = Mul(*npow_bases) B = Dummy() d = roots( Poly(expr - con + B**n * Mul(*others), *gens, **flags), *gens, **flags) rv = {} for k, v in d.items(): rv[k.subs(B, b)] = v return rv except GeneratorsNeeded: if multiple: return [] else: return {} if f.is_multivariate: raise PolynomialError('multivariate polynomials are not supported') def _update_dict(result, zeros, currentroot, k): if currentroot == S.Zero: if S.Zero in zeros: zeros[S.Zero] += k else: zeros[S.Zero] = k if currentroot in result: result[currentroot] += k else: result[currentroot] = k def _try_decompose(f): """Find roots using functional decomposition. """ factors, roots = f.decompose(), [] for currentroot in _try_heuristics(factors[0]): roots.append(currentroot) for currentfactor in factors[1:]: previous, roots = list(roots), [] for currentroot in previous: g = currentfactor - Poly(currentroot, f.gen) for currentroot in _try_heuristics(g): roots.append(currentroot) return roots def _try_heuristics(f): """Find roots using formulas and some tricks. """ if f.is_ground: return [] if f.is_monomial: return [S.Zero] * f.degree() if f.length() == 2: if f.degree() == 1: return list(map(cancel, roots_linear(f))) else: return roots_binomial(f) result = [] for i in [-1, 1]: if not f.eval(i): f = f.quo(Poly(f.gen - i, f.gen)) result.append(i) break n = f.degree() if n == 1: result += list(map(cancel, roots_linear(f))) elif n == 2: result += list(map(cancel, roots_quadratic(f))) elif f.is_cyclotomic: result += roots_cyclotomic(f) elif n == 3 and cubics: result += roots_cubic(f, trig=trig) elif n == 4 and quartics: result += roots_quartic(f) elif n == 5 and quintics: result += roots_quintic(f) return result # Convert the generators to symbols dumgens = symbols('x:%d' % len(f.gens), cls=Dummy) f = f.per(f.rep, dumgens) (k, ), f = f.terms_gcd() if not k: zeros = {} else: zeros = {S.Zero: k} coeff, f = preprocess_roots(f) if auto and f.get_domain().is_Ring: f = f.to_field() # Use EX instead of ZZ_I or QQ_I if f.get_domain().is_QQ_I: f = f.per(f.rep.convert(EX)) rescale_x = None translate_x = None result = {} if not f.is_ground: dom = f.get_domain() if not dom.is_Exact and dom.is_Numerical: for r in f.nroots(): _update_dict(result, zeros, r, 1) elif f.degree() == 1: _update_dict(result, zeros, roots_linear(f)[0], 1) elif f.length() == 2: roots_fun = roots_quadratic if f.degree() == 2 else roots_binomial for r in roots_fun(f): _update_dict(result, zeros, r, 1) else: _, factors = Poly(f.as_expr()).factor_list() if len(factors) == 1 and f.degree() == 2: for r in roots_quadratic(f): _update_dict(result, zeros, r, 1) else: if len(factors) == 1 and factors[0][1] == 1: if f.get_domain().is_EX: res = to_rational_coeffs(f) if res: if res[0] is None: translate_x, f = res[2:] else: rescale_x, f = res[1], res[-1] result = roots(f) if not result: for currentroot in _try_decompose(f): _update_dict(result, zeros, currentroot, 1) else: for r in _try_heuristics(f): _update_dict(result, zeros, r, 1) else: for currentroot in _try_decompose(f): _update_dict(result, zeros, currentroot, 1) else: for currentfactor, k in factors: for r in _try_heuristics( Poly(currentfactor, f.gen, field=True)): _update_dict(result, zeros, r, k) if coeff is not S.One: _result, result, = result, {} for currentroot, k in _result.items(): result[coeff * currentroot] = k if filter not in [None, 'C']: handlers = { 'Z': lambda r: r.is_Integer, 'Q': lambda r: r.is_Rational, 'R': lambda r: all(a.is_real for a in r.as_numer_denom()), 'I': lambda r: r.is_imaginary, } try: query = handlers[filter] except KeyError: raise ValueError("Invalid filter: %s" % filter) for zero in dict(result).keys(): if not query(zero): del result[zero] if predicate is not None: for zero in dict(result).keys(): if not predicate(zero): del result[zero] if rescale_x: result1 = {} for k, v in result.items(): result1[k * rescale_x] = v result = result1 if translate_x: result1 = {} for k, v in result.items(): result1[k + translate_x] = v result = result1 # adding zero roots after non-trivial roots have been translated result.update(zeros) if not multiple: return result else: zeros = [] for zero in ordered(result): zeros.extend([zero] * result[zero]) return zeros
def quantity_simplify(expr, across_dimensions: bool = False, unit_system=None): """Return an equivalent expression in which prefixes are replaced with numerical values and all units of a given dimension are the unified in a canonical manner by default. `across_dimensions` allows for units of different dimensions to be simplified together. `unit_system` must be specified if `across_dimensions` is True. Examples ======== >>> from sympy.physics.units.util import quantity_simplify >>> from sympy.physics.units.prefixes import kilo >>> from sympy.physics.units import foot, inch, joule, coulomb >>> quantity_simplify(kilo*foot*inch) 250*foot**2/3 >>> quantity_simplify(foot - 6*inch) foot/2 >>> quantity_simplify(5*joule/coulomb, across_dimensions=True, unit_system="SI") 5*volt """ if expr.is_Atom or not expr.has(Prefix, Quantity): return expr # replace all prefixes with numerical values p = expr.atoms(Prefix) expr = expr.xreplace({p: p.scale_factor for p in p}) # replace all quantities of given dimension with a canonical # quantity, chosen from those in the expression d = sift(expr.atoms(Quantity), lambda i: i.dimension) for k in d: if len(d[k]) == 1: continue v = list(ordered(d[k])) ref = v[0] / v[0].scale_factor expr = expr.xreplace({vi: ref * vi.scale_factor for vi in v[1:]}) if across_dimensions: # combine quantities of different dimensions into a single # quantity that is equivalent to the original expression if unit_system is None: raise ValueError( "unit_system must be specified if across_dimensions is True") unit_system = UnitSystem.get_unit_system(unit_system) dimension_system: DimensionSystem = unit_system.get_dimension_system() dim_expr = unit_system.get_dimensional_expr(expr) dim_deps = dimension_system.get_dimensional_dependencies( dim_expr, mark_dimensionless=True) target_dimension: Optional[Dimension] = None for ds_dim, ds_dim_deps in dimension_system.dimensional_dependencies.items( ): if ds_dim_deps == dim_deps: target_dimension = ds_dim break if target_dimension is None: # if we can't find a target dimension, we can't do anything. unsure how to handle this case. return expr target_unit = unit_system.derived_units.get(target_dimension) if target_unit: expr = convert_to(expr, target_unit, unit_system) return expr
def test_ordered_partition_9608(): a = Partition([1, 2, 3], [4]) b = Partition([1, 2], [3, 4]) assert list(ordered([a,b], Set._infimum_key))
def _integrate(field=None): atans = set() pairs = set() if field == 'Q': irreducibles = set(reducibles) else: setV = set(V) irreducibles = set() for poly in ordered(reducibles): zV = setV & set(iterfreeargs(poly)) for z in ordered(zV): s = set(root_factors(poly, z, filter=field)) irreducibles |= s break log_part, atan_part = [], [] for poly in ordered(irreducibles): m = collect(poly, I, evaluate=False) y = m.get(I, S.Zero) if y: x = m.get(S.One, S.Zero) if x.has(I) or y.has(I): continue # nontrivial x + I*y pairs.add((x, y)) irreducibles.remove(poly) while pairs: x, y = pairs.pop() if (x, -y) in pairs: pairs.remove((x, -y)) # Choosing b with no minus sign if y.could_extract_minus_sign(): y = -y irreducibles.add(x*x + y*y) atans.add(atan(x/y)) else: irreducibles.add(x + I*y) B = _symbols('B', len(irreducibles)) C = _symbols('C', len(atans)) # Note: the ordering matters here for poly, b in reversed(list(zip(ordered(irreducibles), B))): if poly.has(*V): poly_coeffs.append(b) log_part.append(b * log(poly)) for poly, c in reversed(list(zip(ordered(atans), C))): if poly.has(*V): poly_coeffs.append(c) atan_part.append(c * poly) # TODO: Currently it's better to use symbolic expressions here instead # of rational functions, because it's simpler and FracElement doesn't # give big speed improvement yet. This is because cancellation is slow # due to slow polynomial GCD algorithms. If this gets improved then # revise this code. candidate = poly_part/poly_denom + Add(*log_part) + Add(*atan_part) h = F - _derivation(candidate) / denom raw_numer = h.as_numer_denom()[0] # Rewrite raw_numer as a polynomial in K[coeffs][V] where K is a field # that we have to determine. We can't use simply atoms() because log(3), # sqrt(y) and similar expressions can appear, leading to non-trivial # domains. syms = set(poly_coeffs) | set(V) non_syms = set() def find_non_syms(expr): if expr.is_Integer or expr.is_Rational: pass # ignore trivial numbers elif expr in syms: pass # ignore variables elif not expr.has_free(*syms): non_syms.add(expr) elif expr.is_Add or expr.is_Mul or expr.is_Pow: list(map(find_non_syms, expr.args)) else: # TODO: Non-polynomial expression. This should have been # filtered out at an earlier stage. raise PolynomialError try: find_non_syms(raw_numer) except PolynomialError: return None else: ground, _ = construct_domain(non_syms, field=True) coeff_ring = PolyRing(poly_coeffs, ground) ring = PolyRing(V, coeff_ring) try: numer = ring.from_expr(raw_numer) except ValueError: raise PolynomialError solution = solve_lin_sys(numer.coeffs(), coeff_ring, _raw=False) if solution is None: return None else: return candidate.xreplace(solution).xreplace( dict(zip(poly_coeffs, [S.Zero]*len(poly_coeffs))))
def _collapse_arguments(cls, args, **assumptions): """Remove redundant args. Examples ======== >>> from sympy import Min, Max >>> from sympy.abc import a, b, c, d, e Any arg in parent that appears in any parent-like function in any of the flat args of parent can be removed from that sub-arg: >>> Min(a, Max(b, Min(a, c, d))) Min(a, Max(b, Min(c, d))) If the arg of parent appears in an opposite-than parent function in any of the flat args of parent that function can be replaced with the arg: >>> Min(a, Max(b, Min(c, d, Max(a, e)))) Min(a, Max(b, Min(a, c, d))) """ if not args: return args args = list(ordered(args)) if cls == Min: other = Max else: other = Min # find global comparable max of Max and min of Min if a new # value is being introduced in these args at position 0 of # the ordered args if args[0].is_number: sifted = mins, maxs = [], [] for i in args: for v in walk(i, Min, Max): if v.args[0].is_comparable: sifted[isinstance(v, Max)].append(v) small = Min.identity for i in mins: v = i.args[0] if v.is_number and (v < small) == True: small = v big = Max.identity for i in maxs: v = i.args[0] if v.is_number and (v > big) == True: big = v # at the point when this function is called from __new__, # there may be more than one numeric arg present since # local zeros have not been handled yet, so look through # more than the first arg if cls == Min: for i in range(len(args)): if not args[i].is_number: break if (args[i] < small) == True: small = args[i] elif cls == Max: for i in range(len(args)): if not args[i].is_number: break if (args[i] > big) == True: big = args[i] T = None if cls == Min: if small != Min.identity: other = Max T = small elif big != Max.identity: other = Min T = big if T is not None: # remove numerical redundancy for i in range(len(args)): a = args[i] if isinstance(a, other): a0 = a.args[0] if ((a0 > T) if other == Max else (a0 < T)) == True: args[i] = cls.identity # remove redundant symbolic args def do(ai, a): if not isinstance(ai, (Min, Max)): return ai cond = a in ai.args if not cond: return ai.func(*[do(i, a) for i in ai.args], evaluate=False) if isinstance(ai, cls): return ai.func(*[do(i, a) for i in ai.args if i != a], evaluate=False) return a for i, a in enumerate(args): args[i + 1:] = [do(ai, a) for ai in args[i + 1:]] # factor out common elements as for # Min(Max(x, y), Max(x, z)) -> Max(x, Min(y, z)) # and vice versa when swapping Min/Max -- do this only for the # easy case where all functions contain something in common; # trying to find some optimal subset of args to modify takes # too long def factor_minmax(args): is_other = lambda arg: isinstance(arg, other) other_args, remaining_args = sift(args, is_other, binary=True) if not other_args: return args # Min(Max(x, y, z), Max(x, y, u, v)) -> {x,y}, ({z}, {u,v}) arg_sets = [set(arg.args) for arg in other_args] common = set.intersection(*arg_sets) if not common: return args new_other_args = list(common) arg_sets_diff = [arg_set - common for arg_set in arg_sets] # If any set is empty after removing common then all can be # discarded e.g. Min(Max(a, b, c), Max(a, b)) -> Max(a, b) if all(arg_sets_diff): other_args_diff = [ other(*s, evaluate=False) for s in arg_sets_diff ] new_other_args.append(cls(*other_args_diff, evaluate=False)) other_args_factored = other(*new_other_args, evaluate=False) return remaining_args + [other_args_factored] if len(args) > 1: args = factor_minmax(args) return args
def powsimp(expr, deep=False, combine='all', force=False, measure=count_ops): """ reduces expression by combining powers with similar bases and exponents. Explanation =========== If ``deep`` is ``True`` then powsimp() will also simplify arguments of functions. By default ``deep`` is set to ``False``. If ``force`` is ``True`` then bases will be combined without checking for assumptions, e.g. sqrt(x)*sqrt(y) -> sqrt(x*y) which is not true if x and y are both negative. You can make powsimp() only combine bases or only combine exponents by changing combine='base' or combine='exp'. By default, combine='all', which does both. combine='base' will only combine:: a a a 2x x x * y => (x*y) as well as things like 2 => 4 and combine='exp' will only combine :: a b (a + b) x * x => x combine='exp' will strictly only combine exponents in the way that used to be automatic. Also use deep=True if you need the old behavior. When combine='all', 'exp' is evaluated first. Consider the first example below for when there could be an ambiguity relating to this. This is done so things like the second example can be completely combined. If you want 'base' combined first, do something like powsimp(powsimp(expr, combine='base'), combine='exp'). Examples ======== >>> from sympy import powsimp, exp, log, symbols >>> from sympy.abc import x, y, z, n >>> powsimp(x**y*x**z*y**z, combine='all') x**(y + z)*y**z >>> powsimp(x**y*x**z*y**z, combine='exp') x**(y + z)*y**z >>> powsimp(x**y*x**z*y**z, combine='base', force=True) x**y*(x*y)**z >>> powsimp(x**z*x**y*n**z*n**y, combine='all', force=True) (n*x)**(y + z) >>> powsimp(x**z*x**y*n**z*n**y, combine='exp') n**(y + z)*x**(y + z) >>> powsimp(x**z*x**y*n**z*n**y, combine='base', force=True) (n*x)**y*(n*x)**z >>> x, y = symbols('x y', positive=True) >>> powsimp(log(exp(x)*exp(y))) log(exp(x)*exp(y)) >>> powsimp(log(exp(x)*exp(y)), deep=True) x + y Radicals with Mul bases will be combined if combine='exp' >>> from sympy import sqrt >>> x, y = symbols('x y') Two radicals are automatically joined through Mul: >>> a=sqrt(x*sqrt(y)) >>> a*a**3 == a**4 True But if an integer power of that radical has been autoexpanded then Mul does not join the resulting factors: >>> a**4 # auto expands to a Mul, no longer a Pow x**2*y >>> _*a # so Mul doesn't combine them x**2*y*sqrt(x*sqrt(y)) >>> powsimp(_) # but powsimp will (x*sqrt(y))**(5/2) >>> powsimp(x*y*a) # but won't when doing so would violate assumptions x*y*sqrt(x*sqrt(y)) """ def recurse(arg, **kwargs): _deep = kwargs.get('deep', deep) _combine = kwargs.get('combine', combine) _force = kwargs.get('force', force) _measure = kwargs.get('measure', measure) return powsimp(arg, _deep, _combine, _force, _measure) expr = sympify(expr) if (not isinstance(expr, Basic) or isinstance(expr, MatrixSymbol) or (expr.is_Atom or expr in (exp_polar(0), exp_polar(1)))): return expr if deep or expr.is_Add or expr.is_Mul and _y not in expr.args: expr = expr.func(*[recurse(w) for w in expr.args]) if expr.is_Pow: return recurse(expr * _y, deep=False) / _y if not expr.is_Mul: return expr # handle the Mul if combine in ('exp', 'all'): # Collect base/exp data, while maintaining order in the # non-commutative parts of the product c_powers = defaultdict(list) nc_part = [] newexpr = [] coeff = S.One for term in expr.args: if term.is_Rational: coeff *= term continue if term.is_Pow: term = _denest_pow(term) if term.is_commutative: b, e = term.as_base_exp() if deep: b, e = [recurse(i) for i in [b, e]] if b.is_Pow or isinstance(b, exp): # don't let smthg like sqrt(x**a) split into x**a, 1/2 # or else it will be joined as x**(a/2) later b, e = b**e, S.One c_powers[b].append(e) else: # This is the logic that combines exponents for equal, # but non-commutative bases: A**x*A**y == A**(x+y). if nc_part: b1, e1 = nc_part[-1].as_base_exp() b2, e2 = term.as_base_exp() if (b1 == b2 and e1.is_commutative and e2.is_commutative): nc_part[-1] = Pow(b1, Add(e1, e2)) continue nc_part.append(term) # add up exponents of common bases for b, e in ordered(iter(c_powers.items())): # allow 2**x/4 -> 2**(x - 2); don't do this when b and e are # Numbers since autoevaluation will undo it, e.g. # 2**(1/3)/4 -> 2**(1/3 - 2) -> 2**(1/3)/4 if (b and b.is_Rational and not all(ei.is_Number for ei in e) and \ coeff is not S.One and b not in (S.One, S.NegativeOne)): m = multiplicity(abs(b), abs(coeff)) if m: e.append(m) coeff /= b**m c_powers[b] = Add(*e) if coeff is not S.One: if coeff in c_powers: c_powers[coeff] += S.One else: c_powers[coeff] = S.One # convert to plain dictionary c_powers = dict(c_powers) # check for base and inverted base pairs be = list(c_powers.items()) skip = set() # skip if we already saw them for b, e in be: if b in skip: continue bpos = b.is_positive or b.is_polar if bpos: binv = 1 / b if b != binv and binv in c_powers: if b.as_numer_denom()[0] is S.One: c_powers.pop(b) c_powers[binv] -= e else: skip.add(binv) e = c_powers.pop(binv) c_powers[b] -= e # check for base and negated base pairs be = list(c_powers.items()) _n = S.NegativeOne for b, e in be: if (b.is_Symbol or b.is_Add) and -b in c_powers and b in c_powers: if (b.is_positive is not None or e.is_integer): if e.is_integer or b.is_negative: c_powers[-b] += c_powers.pop(b) else: # (-b).is_positive so use its e e = c_powers.pop(-b) c_powers[b] += e if _n in c_powers: c_powers[_n] += e else: c_powers[_n] = e # filter c_powers and convert to a list c_powers = [(b, e) for b, e in c_powers.items() if e] # ============================================================== # check for Mul bases of Rational powers that can be combined with # separated bases, e.g. x*sqrt(x*y)*sqrt(x*sqrt(x*y)) -> # (x*sqrt(x*y))**(3/2) # ---------------- helper functions def ratq(x): '''Return Rational part of x's exponent as it appears in the bkey. ''' return bkey(x)[0][1] def bkey(b, e=None): '''Return (b**s, c.q), c.p where e -> c*s. If e is not given then it will be taken by using as_base_exp() on the input b. e.g. x**3/2 -> (x, 2), 3 x**y -> (x**y, 1), 1 x**(2*y/3) -> (x**y, 3), 2 exp(x/2) -> (exp(a), 2), 1 ''' if e is not None: # coming from c_powers or from below if e.is_Integer: return (b, S.One), e elif e.is_Rational: return (b, Integer(e.q)), Integer(e.p) else: c, m = e.as_coeff_Mul(rational=True) if c is not S.One: if m.is_integer: return (b, Integer(c.q)), m * Integer(c.p) return (b**m, Integer(c.q)), Integer(c.p) else: return (b**e, S.One), S.One else: return bkey(*b.as_base_exp()) def update(b): '''Decide what to do with base, b. If its exponent is now an integer multiple of the Rational denominator, then remove it and put the factors of its base in the common_b dictionary or update the existing bases if necessary. If it has been zeroed out, simply remove the base. ''' newe, r = divmod(common_b[b], b[1]) if not r: common_b.pop(b) if newe: for m in Mul.make_args(b[0]**newe): b, e = bkey(m) if b not in common_b: common_b[b] = 0 common_b[b] += e if b[1] != 1: bases.append(b) # ---------------- end of helper functions # assemble a dictionary of the factors having a Rational power common_b = {} done = [] bases = [] for b, e in c_powers: b, e = bkey(b, e) if b in common_b: common_b[b] = common_b[b] + e else: common_b[b] = e if b[1] != 1 and b[0].is_Mul: bases.append(b) bases.sort(key=default_sort_key) # this makes tie-breaking canonical bases.sort(key=measure, reverse=True) # handle longest first for base in bases: if base not in common_b: # it may have been removed already continue b, exponent = base last = False # True when no factor of base is a radical qlcm = 1 # the lcm of the radical denominators while True: bstart = b qstart = qlcm bb = [] # list of factors ee = [] # (factor's expo. and it's current value in common_b) for bi in Mul.make_args(b): bib, bie = bkey(bi) if bib not in common_b or common_b[bib] < bie: ee = bb = [] # failed break ee.append([bie, common_b[bib]]) bb.append(bib) if ee: # find the number of integral extractions possible # e.g. [(1, 2), (2, 2)] -> min(2/1, 2/2) -> 1 min1 = ee[0][1] // ee[0][0] for i in range(1, len(ee)): rat = ee[i][1] // ee[i][0] if rat < 1: break min1 = min(min1, rat) else: # update base factor counts # e.g. if ee = [(2, 5), (3, 6)] then min1 = 2 # and the new base counts will be 5-2*2 and 6-2*3 for i in range(len(bb)): common_b[bb[i]] -= min1 * ee[i][0] update(bb[i]) # update the count of the base # e.g. x**2*y*sqrt(x*sqrt(y)) the count of x*sqrt(y) # will increase by 4 to give bkey (x*sqrt(y), 2, 5) common_b[base] += min1 * qstart * exponent if (last # no more radicals in base or len(common_b) == 1 # nothing left to join with or all(k[1] == 1 for k in common_b) # no rad's in common_b ): break # see what we can exponentiate base by to remove any radicals # so we know what to search for # e.g. if base were x**(1/2)*y**(1/3) then we should # exponentiate by 6 and look for powers of x and y in the ratio # of 2 to 3 qlcm = lcm([ratq(bi) for bi in Mul.make_args(bstart)]) if qlcm == 1: break # we are done b = bstart**qlcm qlcm *= qstart if all(ratq(bi) == 1 for bi in Mul.make_args(b)): last = True # we are going to be done after this next pass # this base no longer can find anything to join with and # since it was longer than any other we are done with it b, q = base done.append((b, common_b.pop(base) * Rational(1, q))) # update c_powers and get ready to continue with powsimp c_powers = done # there may be terms still in common_b that were bases that were # identified as needing processing, so remove those, too for (b, q), e in common_b.items(): if (b.is_Pow or isinstance(b, exp)) and \ q is not S.One and not b.exp.is_Rational: b, be = b.as_base_exp() b = b**(be / q) else: b = root(b, q) c_powers.append((b, e)) check = len(c_powers) c_powers = dict(c_powers) assert len(c_powers) == check # there should have been no duplicates # ============================================================== # rebuild the expression newexpr = expr.func(*(newexpr + [Pow(b, e) for b, e in c_powers.items()])) if combine == 'exp': return expr.func(newexpr, expr.func(*nc_part)) else: return recurse(expr.func(*nc_part), combine='base') * \ recurse(newexpr, combine='base') elif combine == 'base': # Build c_powers and nc_part. These must both be lists not # dicts because exp's are not combined. c_powers = [] nc_part = [] for term in expr.args: if term.is_commutative: c_powers.append(list(term.as_base_exp())) else: nc_part.append(term) # Pull out numerical coefficients from exponent if assumptions allow # e.g., 2**(2*x) => 4**x for i in range(len(c_powers)): b, e = c_powers[i] if not (all(x.is_nonnegative for x in b.as_numer_denom()) or e.is_integer or force or b.is_polar): continue exp_c, exp_t = e.as_coeff_Mul(rational=True) if exp_c is not S.One and exp_t is not S.One: c_powers[i] = [Pow(b, exp_c), exp_t] # Combine bases whenever they have the same exponent and # assumptions allow # first gather the potential bases under the common exponent c_exp = defaultdict(list) for b, e in c_powers: if deep: e = recurse(e) if e.is_Add and (b.is_positive or e.is_integer): e = factor_terms(e) if _coeff_isneg(e): e = -e b = 1 / b c_exp[e].append(b) del c_powers # Merge back in the results of the above to form a new product c_powers = defaultdict(list) for e in c_exp: bases = c_exp[e] # calculate the new base for e if len(bases) == 1: new_base = bases[0] elif e.is_integer or force: new_base = expr.func(*bases) else: # see which ones can be joined unk = [] nonneg = [] neg = [] for bi in bases: if bi.is_negative: neg.append(bi) elif bi.is_nonnegative: nonneg.append(bi) elif bi.is_polar: nonneg.append( bi) # polar can be treated like non-negative else: unk.append(bi) if len(unk) == 1 and not neg or len(neg) == 1 and not unk: # a single neg or a single unk can join the rest nonneg.extend(unk + neg) unk = neg = [] elif neg: # their negative signs cancel in groups of 2*q if we know # that e = p/q else we have to treat them as unknown israt = False if e.is_Rational: israt = True else: p, d = e.as_numer_denom() if p.is_integer and d.is_integer: israt = True if israt: neg = [-w for w in neg] unk.extend([S.NegativeOne] * len(neg)) else: unk.extend(neg) neg = [] del israt # these shouldn't be joined for b in unk: c_powers[b].append(e) # here is a new joined base new_base = expr.func(*(nonneg + neg)) # if there are positive parts they will just get separated # again unless some change is made def _terms(e): # return the number of terms of this expression # when multiplied out -- assuming no joining of terms if e.is_Add: return sum([_terms(ai) for ai in e.args]) if e.is_Mul: return prod([_terms(mi) for mi in e.args]) return 1 xnew_base = expand_mul(new_base, deep=False) if len(Add.make_args(xnew_base)) < _terms(new_base): new_base = factor_terms(xnew_base) c_powers[new_base].append(e) # break out the powers from c_powers now c_part = [Pow(b, ei) for b, e in c_powers.items() for ei in e] # we're done return expr.func(*(c_part + nc_part)) else: raise ValueError("combine must be one of ('all', 'exp', 'base').")