Source code for sympy.core.basic

"""Base class for all the objects in SymPy"""

from assumptions import AssumeMeths, make__get_assumption
from cache import cacheit
from core import BasicMeta, BasicType, C
from sympify import _sympify, sympify, SympifyError
from compatibility import callable, reduce, cmp, iterable
from sympy.core.decorators import deprecated

[docs]class Basic(AssumeMeths): """ Base class for all objects in sympy. Conventions: 1) When you want to access parameters of some instance, always use .args: Example: >>> from sympy import symbols, cot >>> from sympy.abc import x, y >>> cot(x).args (x,) >>> cot(x).args[0] x >>> (x*y).args (x, y) >>> (x*y).args[1] y 2) Never use internal methods or variables (the ones prefixed with "_"). Example: >>> cot(x)._args #don't use this, use cot(x).args instead (x,) """ __metaclass__ = BasicMeta __slots__ = ['_mhash', # hash value '_args', # arguments '_assume_type_keys', # assumptions typeinfo keys ] # To be overridden with True in the appropriate subclasses is_Atom = False is_Symbol = False is_Dummy = False is_Wild = False is_Function = False is_Add = False is_Mul = False is_Pow = False is_Number = False is_Float = False is_Rational = False is_Integer = False is_NumberSymbol = False is_Order = False is_Derivative = False is_Piecewise = False is_Poly = False is_AlgebraicNumber = False is_Relational = False is_Equality = False is_Boolean = False is_Not = False @property @deprecated
[docs] def is_Real(self): # pragma: no cover """Deprecated alias for ``is_Float``""" return self.is_Float
def __new__(cls, *args, **assumptions): obj = object.__new__(cls) # FIXME we are slowed a *lot* by Add/Mul passing is_commutative as the # only assumption. # # .is_commutative is not an assumption -- it's like typeinfo!!! # we should remove it. # initially assumptions are shared between instances and class obj._assumptions = cls.default_assumptions obj._a_inprogress = [] # NOTE this could be made lazy -- probably not all instances will need # fully derived assumptions? if assumptions: obj._learn_new_facts(assumptions) # ^ # FIXME this is slow | another NOTE: speeding this up is *not* # | | important. say for %timeit x+y most of # .------' | the time is spent elsewhere # | | # | XXX _learn_new_facts could be asked about what *new* facts have # v XXX been learned -- we'll need this to append to _hashable_content basek = set(cls.default_assumptions.keys()) k2 = set(obj._assumptions.keys()) newk = k2.difference(basek) obj._assume_type_keys = frozenset(newk) else: obj._assume_type_keys = None obj._mhash = None # will be set by __hash__ method. obj._args = args # all items in args must be Basic objects return obj # XXX better name? @property
[docs] def assumptions0(self): """ Return object ``type`` assumptions. For example: Symbol('x', real=True) Symbol('x', integer=True) are different objects. In other words, besides Python type (Symbol in this case), the initial assumptions are also forming their typeinfo. Example: >>> from sympy import Symbol >>> from sympy.abc import x >>> x.assumptions0 {} >>> x = Symbol("x", positive=True) >>> x.assumptions0 {'commutative': True, 'complex': True, 'imaginary': False, 'negative': False, 'nonnegative': True, 'nonpositive': False, 'nonzero': True, 'positive': True, 'real': True, 'zero': False} """ cls = type(self) A = self._assumptions # assumptions shared: if A is cls.default_assumptions or (self._assume_type_keys is None): assumptions0 = {} else: assumptions0 = dict( (k, A[k]) for k in self._assume_type_keys ) return assumptions0 # NOTE NOTE NOTE # -------------- # # new-style classes + __getattr__ is *very* slow! # def __getattr__(self, name): # raise Warning('no way, *all* attribute access will be 2.5x slower') # here is what we do instead:
for k in AssumeMeths._assume_defined: exec "is_%s = property(make__get_assumption('Basic', '%s'))" % (k,k) del k # NB: there is no need in protective __setattr__ def __getnewargs__(self): """ Pickling support. """ return tuple(self.args) def __hash__(self): # hash cannot be cached using cache_it because infinite recurrence # occurs as hash is needed for setting cache dictionary keys h = self._mhash if h is None: h = (type(self).__name__,) + self._hashable_content() if self._assume_type_keys is not None: a = [] kv= self._assumptions for k in sorted(self._assume_type_keys): a.append( (k, kv[k]) ) h = hash( h + tuple(a) ) else: h = hash( h ) self._mhash = h return h else: return h def _hashable_content(self): # If class defines additional attributes, like name in Symbol, # then this method should be updated accordingly to return # relevant attributes as tuple. return self._args
[docs] def compare(self, other): """ Return -1,0,1 if the object is smaller, equal, or greater than other. Not in the mathematical sense. If the object is of a different type from the "other" then their classes are ordered according to the sorted_classes list. Example: >>> from sympy.abc import x, y >>> x.compare(y) -1 >>> x.compare(x) 0 >>> y.compare(x) 1 """ # all redefinitions of __cmp__ method should start with the # following three lines: if self is other: return 0 c = cmp(self.__class__, other.__class__) if c: return c # st = self._hashable_content() ot = other._hashable_content() c = cmp(len(st),len(ot)) if c: return c for l,r in zip(st,ot): if isinstance(l, Basic): c = l.compare(r) else: c = cmp(l, r) if c: return c return 0
@staticmethod def _compare_pretty(a, b): from sympy.series.order import Order if isinstance(a, Order) and not isinstance(b, Order): return 1 if not isinstance(a, Order) and isinstance(b, Order): return -1 if a.is_Rational and b.is_Rational: return cmp(a.p*b.q, b.p*a.q) else: from sympy.core.symbol import Wild p1, p2, p3 = Wild("p1"), Wild("p2"), Wild("p3") r_a = a.match(p1 * p2**p3) if r_a and p3 in r_a: a3 = r_a[p3] r_b = b.match(p1 * p2**p3) if r_b and p3 in r_b: b3 = r_b[p3] c = Basic.compare(a3, b3) if c != 0: return c return Basic.compare(a,b) @staticmethod
[docs] def compare_pretty(a, b): """ Is a > b in the sense of ordering in printing? :: yes ..... return 1 no ...... return -1 equal ... return 0 Strategy: It uses Basic.compare as a fallback, but improves it in many cases, like x**3, x**4, O(x**3) etc. In those simple cases, it just parses the expression and returns the "sane" ordering such as:: 1 < x < x**2 < x**3 < O(x**4) etc. Example: >>> from sympy.abc import x >>> from sympy import Basic, Number >>> Basic._compare_pretty(x, x**2) -1 >>> Basic._compare_pretty(x**2, x**2) 0 >>> Basic._compare_pretty(x**3, x**2) 1 >>> Basic._compare_pretty(Number(1, 2), Number(1, 3)) 1 >>> Basic._compare_pretty(Number(0), Number(-1)) 1 """ try: a = _sympify(a) except SympifyError: pass try: b = _sympify(b) except SympifyError: pass # both objects are non-SymPy if (not isinstance(a, Basic)) and (not isinstance(b, Basic)): return cmp(a,b) if not isinstance(a, Basic): return -1 # other < sympy if not isinstance(b, Basic): return +1 # sympy > other # now both objects are from SymPy, so we can proceed to usual comparison return cmp(a.sort_key(), b.sort_key())
@classmethod
[docs] def fromiter(cls, args, **assumptions): """ Create a new object from an iterable. This is a convenience function that allows one to create objects from any iterable, without having to convert to a list or tuple first. Example: >>> from sympy import Tuple >>> Tuple.fromiter(i for i in xrange(5)) (0, 1, 2, 3, 4) """ return cls(*tuple(args), **assumptions)
@classmethod
[docs] def class_key(cls): """Nice order of classes. """ return 5, 0, cls.__name__
[docs] def sort_key(self, order=None): """ Return a sort key. **Examples** >>> from sympy.core import Basic, S, I >>> from sympy.abc import x >>> sorted([S(1)/2, I, -I], key=lambda x: x.sort_key()) [1/2, -I, I] >>> S("[x, 1/x, 1/x**2, x**2, x**(1/2), x**(1/4), x**(3/2)]") [x, 1/x, x**(-2), x**2, x**(1/2), x**(1/4), x**(3/2)] >>> sorted(_, key=lambda x: x.sort_key()) [x**(-2), 1/x, x**(1/4), x**(1/2), x, x**(3/2), x**2] """ from sympy.core.singleton import S return self.class_key(), (len(self.args), self.args), S.One.sort_key(), S.One
def __eq__(self, other): """a == b -> Compare two symbolic trees and see whether they are equal this is the same as: a.compare(b) == 0 but faster """ if type(self) is not type(other): try: other = _sympify(other) except SympifyError: return False # sympy != other if type(self) is not type(other): return False # type(self) == type(other) st = self._hashable_content() ot = other._hashable_content() return st == ot and self._assume_type_keys == other._assume_type_keys def __ne__(self, other): """a != b -> Compare two symbolic trees and see whether they are different this is the same as: a.compare(b) != 0 but faster """ if type(self) is not type(other): try: other = _sympify(other) except SympifyError: return True # sympy != other if type(self) is not type(other): return True # type(self) == type(other) st = self._hashable_content() ot = other._hashable_content() return (st != ot) or self._assume_type_keys != other._assume_type_keys
[docs] def dummy_eq(self, other, symbol=None): """ Compare two expressions and handle dummy symbols. **Examples** >>> from sympy import Dummy >>> from sympy.abc import x, y >>> u = Dummy('u') >>> (u**2 + 1).dummy_eq(x**2 + 1) True >>> (u**2 + 1) == (x**2 + 1) False >>> (u**2 + y).dummy_eq(x**2 + y, x) True >>> (u**2 + y).dummy_eq(x**2 + y, y) False """ dummy_symbols = [ s for s in self.free_symbols if s.is_Dummy ] if not dummy_symbols: return self == other elif len(dummy_symbols) == 1: dummy = dummy_symbols.pop() else: raise ValueError("only one dummy symbol allowed on the left-hand side") if symbol is None: symbols = other.free_symbols if not symbols: return self == other elif len(symbols) == 1: symbol = symbols.pop() else: raise ValueError("specify a symbol in which expressions should be compared") tmp = dummy.__class__() return self.subs(dummy, tmp) == other.subs(symbol, tmp) # Note, we always use the default ordering (lex) in __str__ and __repr__, # regardless of the global setting. See issue 2388.
def __repr__(self): from sympy.printing import sstr return sstr(self, order=None) def __str__(self): from sympy.printing import sstr return sstr(self, order=None)
[docs] def atoms(self, *types): """Returns the atoms that form the current object. By default, only objects that are truly atomic and can't be divided into smaller pieces are returned: symbols, numbers, and number symbols like I and pi. It is possible to request atoms of any type, however, as demonstrated below. Examples: >>> from sympy import I, pi, sin >>> from sympy.abc import x, y >>> (1 + x + 2*sin(y + I*pi)).atoms() set([1, 2, I, pi, x, y]) If one or more types are given, the results will contain only those types of atoms. Examples: >>> from sympy import Number, NumberSymbol, Symbol >>> (1 + x + 2*sin(y + I*pi)).atoms(Symbol) set([x, y]) >>> (1 + x + 2*sin(y + I*pi)).atoms(Number) set([1, 2]) >>> (1 + x + 2*sin(y + I*pi)).atoms(Number, NumberSymbol) set([1, 2, pi]) >>> (1 + x + 2*sin(y + I*pi)).atoms(Number, NumberSymbol, I) set([1, 2, I, pi]) Note that I (imaginary unit) and zoo (complex infinity) are special types of number symbols and are not part of the NumberSymbol class. The type can be given implicitly, too: >>> (1 + x + 2*sin(y + I*pi)).atoms(x) # x is a Symbol set([x, y]) Be careful to check your assumptions when using the implicit option since ``S(1).is_Integer = True`` but ``type(S(1))`` is ``One``, a special type of sympy atom, while ``type(S(2))`` is type ``Integer`` and will find all integers in an expression: >>> from sympy import S >>> (1 + x + 2*sin(y + I*pi)).atoms(S(1)) set([1]) >>> (1 + x + 2*sin(y + I*pi)).atoms(S(2)) set([1, 2]) Finally, arguments to atoms() can select more than atomic atoms: any sympy type (loaded in core/__init__.py) can be listed as an argument and those types of "atoms" as found in scanning the arguments of the expression recursively: >>> from sympy import Function, Mul >>> (1 + x + 2*sin(y + I*pi)).atoms(Function) set([sin(y + I*pi)]) >>> (1 + x + 2*sin(y + I*pi)).atoms(Mul) set([I*pi, 2*sin(y + I*pi)]) """ def _atoms(expr, typ): """Helper function for recursively denesting atoms""" result = set() if isinstance(expr, Basic): if expr.is_Atom and len(typ) == 0: # if we haven't specified types return set([expr]) else: try: if isinstance(expr, typ): result.add(expr) except TypeError: #one or more types is in implicit form for t in typ: if isinstance(t, type): if isinstance(expr, t): result.add(expr) else: if isinstance(expr, type(t)): result.add(expr) iter = expr.iter_basic_args() elif iterable(expr): iter = expr.__iter__() else: iter = [] for obj in iter: result.update(_atoms(obj, typ)) return result return _atoms(self, typ=types)
@property
[docs] def free_symbols(self): """Return from the atoms of self those which are free symbols. For most expressions, all symbols are free symbols. For some classes this is not true. e.g. Integrals use Symbols for the dummy variables which are bound variables, so Integral has a method to return all symbols except those. Derivative keeps track of symbols with respect to which it will perform a derivative; those are bound variables, too, so it has its own symbols method. Any other method that uses bound variables should implement a symbols method.""" union = set.union return reduce(union, [arg.free_symbols for arg in self.args], set())
def is_hypergeometric(self, k): from sympy.simplify import hypersimp return hypersimp(self, k) is not None @property
[docs] def is_number(self): """Returns ``True`` if 'self' is a number. >>> from sympy import log, Integral >>> from sympy.abc import x, y >>> 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 """ # should be overriden by subclasses return False
@property
[docs] def func(self): """ The top-level function in an expression. The following should hold for all objects:: >> x == x.func(*x.args) Example: >>> from sympy.abc import x >>> a = 2*x >>> a.func <class 'sympy.core.mul.Mul'> >>> a.args (2, x) >>> a.func(*a.args) 2*x >>> a == a.func(*a.args) True """ return self.__class__
@property
[docs] def args(self): """Returns a tuple of arguments of 'self'. Example: >>> from sympy import symbols, cot >>> from sympy.abc import x, y >>> cot(x).args (x,) >>> cot(x).args[0] x >>> (x*y).args (x, y) >>> (x*y).args[1] y Note for developers: Never use self._args, always use self.args. Only when you are creating your own new function, use _args in the __new__. Don't override .args() from Basic (so that it's easy to change the interface in the future if needed). """ return self._args
[docs] def iter_basic_args(self): """ Iterates arguments of 'self'. Example: >>> from sympy.abc import x >>> a = 2*x >>> a.iter_basic_args() <tupleiterator object at 0x...> >>> list(a.iter_basic_args()) [2, x] """ return iter(self.args)
[docs] def as_poly(self, *gens, **args): """Converts ``self`` to a polynomial or returns ``None``. >>> from sympy import Poly, sin >>> from sympy.abc import x, y >>> print (x**2 + x*y).as_poly() Poly(x**2 + x*y, x, y, domain='ZZ') >>> print (x**2 + x*y).as_poly(x, y) Poly(x**2 + x*y, x, y, domain='ZZ') >>> print (x**2 + sin(y)).as_poly(x, y) None """ from sympy.polys import Poly, PolynomialError try: poly = Poly(self, *gens, **args) if not poly.is_Poly: return None else: return poly except PolynomialError: return None
[docs] def subs(self, *args): """ Substitutes an expression. Calls either _subs_old_new, _subs_dict or _subs_list depending if you give it two arguments (old, new), a dictionary or a list. Examples: >>> from sympy import pi >>> from sympy.abc import x, y >>> (1 + x*y).subs(x, pi) pi*y + 1 >>> (1 + x*y).subs({x:pi, y:2}) 1 + 2*pi >>> (1 + x*y).subs([(x,pi), (y,2)]) 1 + 2*pi >>> (x + y).subs([(y,x**2), (x,2)]) 6 >>> (x + y).subs([(x,2), (y,x**2)]) x**2 + 2 """ if len(args) == 1: sequence = args[0] if isinstance(sequence, dict): return self._subs_dict(sequence) elif iterable(sequence): return self._subs_list(sequence) else: raise TypeError("Not an iterable container") elif len(args) == 2: old, new = args return self._subs_old_new(old, new) else: raise TypeError("subs accepts either 1 or 2 arguments")
@cacheit def _subs_old_new(self, old, new): """Substitutes an expression old -> new.""" old = sympify(old) new = sympify(new) return self._eval_subs(old, new) def _eval_subs(self, old, new): if self == old: return new else: return self.func(*[arg._eval_subs(old, new) for arg in self.args]) def _subs_list(self, sequence): """ Performs an order sensitive substitution from the input sequence list. Examples: >>> from sympy.abc import x, y >>> (x+y)._subs_list( [(x, 3), (y, x**2)] ) x**2 + 3 >>> (x+y)._subs_list( [(y, x**2), (x, 3) ] ) 12 """ result = self for old, new in sequence: if hasattr(result, 'subs'): result = result.subs(old, new) return result def _subs_dict(self, sequence): """Performs sequential substitution. Given a collection of key, value pairs, which correspond to old and new expressions respectively, substitute all given pairs handling properly all overlapping keys (according to 'in' relation). We have to use naive O(n**2) sorting algorithm, as 'in' gives only partial order and all asymptotically faster fail (depending on the initial order). >>> from sympy import sqrt, sin, cos, exp >>> from sympy.abc import x, y >>> from sympy.abc import a, b, c, d, e >>> A = (sqrt(sin(2*x)), a) >>> B = (sin(2*x), b) >>> C = (cos(2*x), c) >>> D = (x, d) >>> E = (exp(x), e) >>> expr = sqrt(sin(2*x))*sin(exp(x)*x)*cos(2*x) + sin(2*x) >>> expr._subs_dict([A,B,C,D,E]) a*c*sin(d*e) + b """ if isinstance(sequence, dict): sequence = sequence.items() subst = [] for pattern in sequence: for i, (expr, _) in enumerate(subst): if pattern[0] in expr: subst.insert(i, pattern) break else: subst.append(pattern) subst.reverse() return self._subs_list(subst) def __contains__(self, obj): if self == obj: return True for arg in self.args: try: if obj in arg: return True except TypeError: if obj == arg: return True return False @cacheit
[docs] def has(self, *patterns): """ Test whether any subexpression matches any of the patterns. Examples: >>> from sympy import sin, S >>> from sympy.abc import x, y, z >>> (x**2 + sin(x*y)).has(z) False >>> (x**2 + sin(x*y)).has(x, y, z) True >>> x.has(x) True Note that ``expr.has(*patterns)`` is exactly equivalent to ``any(expr.has(p) for p in patterns)``. In particular, ``False`` is returned when the list of patterns is empty. >>> x.has() False """ def search(expr, test): if not isinstance(expr, Basic): try: return any(search(i, test) for i in expr) except TypeError: return False elif test(expr): return True else: return any(search(i, test) for i in expr.iter_basic_args()) def _match(p): if isinstance(p, BasicType): return lambda w: isinstance(w, p) else: return lambda w: p.matches(w) is not None patterns = map(sympify, patterns) return any(search(self, _match(p)) for p in patterns)
[docs] def replace(self, query, value, map=False): """ Replace matching subexpressions of ``self`` with ``value``. If ``map = True`` then also return the mapping {old: new} where ``old`` was a sub-expression found with query and ``new`` is the replacement value for it. Traverses an expression tree and performs replacement of matching subexpressions from the bottom to the top of the tree. The list of possible combinations of queries and replacement values is listed below: 1.1. type -> type obj.replace(sin, tan) 1.2. type -> func obj.replace(sin, lambda expr, arg: ...) 2.1. expr -> expr obj.replace(sin(a), tan(a)) 2.2. expr -> func obj.replace(sin(a), lambda a: ...) 3.1. func -> func obj.replace(lambda expr: ..., lambda expr: ...) Examples: >>> from sympy import log, sin, cos, tan, Wild >>> from sympy.abc import x >>> f = log(sin(x)) + tan(sin(x**2)) >>> f.replace(sin, cos) log(cos(x)) + tan(cos(x**2)) >>> f.replace(sin, lambda arg: sin(2*arg)) log(sin(2*x)) + tan(sin(2*x**2)) >>> sin(x).replace(sin, cos, map=True) (cos(x), {sin(x): cos(x)}) >>> a = Wild('a') >>> f.replace(sin(a), cos(a)) log(cos(x)) + tan(cos(x**2)) >>> f.replace(sin(a), lambda a: sin(2*a)) log(sin(2*x)) + tan(sin(2*x**2)) >>> g = 2*sin(x**3) >>> g.replace(lambda expr: expr.is_Number, lambda expr: expr**2) 4*sin(x**9) """ if isinstance(query, type): _query = lambda expr: isinstance(expr, query) if isinstance(value, type): _value = lambda expr, result: value(*expr.args) elif callable(value): _value = lambda expr, result: value(*expr.args) else: raise TypeError("given a type, replace() expects another type or a callable") elif isinstance(query, Basic): _query = lambda expr: expr.match(query) if isinstance(value, Basic): _value = lambda expr, result: value.subs(result) elif callable(value): _value = lambda expr, result: value(**dict([ (str(key)[:-1], val) for key, val in result.iteritems() ])) else: raise TypeError("given an expression, replace() expects another expression or a callable") elif callable(query): _query = query if callable(value): _value = lambda expr, result: value(expr) else: raise TypeError("given a callable, replace() expects another callable") else: raise TypeError("first argument to replace() must be a type, an expression or a callable") mapping = {} def rec_replace(expr): args, construct = [], False for arg in expr.args: result = rec_replace(arg) if result is not None: construct = True else: result = arg args.append(result) if construct: return expr.__class__(*args) else: result = _query(expr) if result: value = _value(expr, result) if map: mapping[expr] = value return value else: return None result = rec_replace(self) if result is None: result = self if not map: return result else: return result, mapping
[docs] def find(self, query, group=False): """Find all subexpressions matching a query. """ if isinstance(query, type): _query = lambda expr: isinstance(expr, query) elif isinstance(query, Basic): _query = lambda expr: expr.match(query) else: _query = query results = [] def rec_find(expr): if _query(expr): results.append(expr) for arg in expr.args: rec_find(arg) rec_find(self) if not group: return set(results) else: groups = {} for result in results: if result in groups: groups[result] += 1 else: groups[result] = 1 return groups
[docs] def count(self, query): """Count the number of matching subexpressions. """ return sum(self.find(query, group=True).values())
[docs] def matches(self, expr, repl_dict={}, evaluate=False): """ Helper method for match() - switches the pattern and expr. Can be used to solve linear equations: >>> from sympy import Symbol, Wild, Integer >>> a,b = map(Symbol, 'ab') >>> x = Wild('x') >>> (a+b*x).matches(Integer(0)) {x_: -a/b} """ if evaluate: return self.subs(repl_dict).matches(expr, repl_dict) expr = sympify(expr) if not isinstance(expr, self.__class__): return None if self == expr: return repl_dict if len(self.args) != len(expr.args): return None d = repl_dict.copy() for arg, other_arg in zip(self.args, expr.args): if arg == other_arg: continue d = arg.subs(d).matches(other_arg, d) if d is None: return None return d
[docs] def match(self, pattern): """ Pattern matching. Wild symbols match all. Return ``None`` when expression (self) does not match with pattern. Otherwise return a dictionary such that:: pattern.subs(self.match(pattern)) == self Example: >>> from sympy import symbols, Wild >>> from sympy.abc import x, y >>> p = Wild("p") >>> q = Wild("q") >>> r = Wild("r") >>> e = (x+y)**(x+y) >>> e.match(p**p) {p_: x + y} >>> e.match(p**q) {p_: x + y, q_: x + y} >>> e = (2*x)**2 >>> e.match(p*q**r) {p_: 4, q_: x, r_: 2} >>> (p*q**r).subs(e.match(p*q**r)) 4*x**2 """ pattern = sympify(pattern) return pattern.matches(self)
[docs] def count_ops(self, visual=None): """wrapper for count_ops that returns the operation count.""" from sympy import count_ops return count_ops(self, visual) return sum(a.count_ops(visual) for a in self.args)
[docs] def doit(self, **hints): """Evaluate objects that are not evaluated by default like limits, integrals, sums and products. All objects of this kind will be evaluated recursively, unless some species were excluded via 'hints' or unless the 'deep' hint was set to 'False'. >>> from sympy import Integral >>> from sympy.abc import x, y >>> 2*Integral(x, x) 2*Integral(x, x) >>> (2*Integral(x, x)).doit() x**2 >>> (2*Integral(x, x)).doit(deep = False) 2*Integral(x, x) """ if hints.get('deep', True): terms = [ term.doit(**hints) for term in self.args ] return self.func(*terms) else: return self
def _eval_rewrite(self, pattern, rule, **hints): if self.is_Atom: return self sargs = self.args terms = [ t._eval_rewrite(pattern, rule, **hints) for t in sargs ] return self.func(*terms)
[docs] def rewrite(self, *args, **hints): """Rewrites expression containing applications of functions of one kind in terms of functions of different kind. For example you can rewrite trigonometric functions as complex exponentials or combinatorial functions as gamma function. As a pattern this function accepts a list of functions to to rewrite (instances of DefinedFunction class). As rule you can use string or a destination function instance (in this case rewrite() will use the str() function). There is also possibility to pass hints on how to rewrite the given expressions. For now there is only one such hint defined called 'deep'. When 'deep' is set to False it will forbid functions to rewrite their contents. >>> from sympy import sin, exp, I >>> from sympy.abc import x, y >>> sin(x).rewrite(sin, exp) -I*(exp(I*x) - exp(-I*x))/2 """ if self.is_Atom or not args: return self else: pattern = args[:-1] rule = '_eval_rewrite_as_' + str(args[-1]) if not pattern: return self._eval_rewrite(None, rule, **hints) else: if iterable(pattern[0]): pattern = pattern[0] pattern = [ p.__class__ for p in pattern if self.has(p) ] if pattern: return self._eval_rewrite(tuple(pattern), rule, **hints) else: return self
[docs]class Atom(Basic): """ A parent class for atomic things. An atom is an expression with no subexpressions. Examples: Symbol, Number, Rational, Integer, ... But not: Add, Mul, Pow, ... """ is_Atom = True __slots__ = [] def matches(self, expr, repl_dict={}, evaluate=False): if self == expr: return repl_dict def _eval_subs(self, old, new): if self == old: return new else: return self def doit(self, **hints): return self def __contains__(self, obj): return (self == obj) @classmethod def class_key(cls): return 2, 0, cls.__name__ def sort_key(self, order=None): from sympy.core import S return self.class_key(), (1, (self,)), S.One.sort_key(), S.One