Python compatibility.exec_函数代码示例

def test_issue_5596():
    assert str(S("Q & C", locals=_clash1)) == 'And(C, Q)'
    assert str(S('pi(x)', locals=_clash2)) == 'pi(x)'
    assert str(S('pi(C, Q)', locals=_clash)) == 'pi(C, Q)'
    locals = {}
    exec_("from sympy.abc import Q, C", locals)
    assert str(S('C&Q', locals)) == 'And(C, Q)'

def parse_expr(s, local_dict=None, transformations=standard_transformations,
               global_dict=None):
    """Converts the string ``s`` to a SymPy expression, in ``local_dict``

    Parameters
    ==========

    s : str
        The string to parse.

    local_dict : dict, optional
        A dictionary of local variables to use when parsing.

    global_dict : dict, optional
        A dictionary of global variables. By default, this is initialized
        with ``from sympy import *``; provide this parameter to override
        this behavior (for instance, to parse ``"Q & S"``).

    transformations : tuple, optional
        A tuple of transformation functions used to modify the tokens of the
        parsed expression before evaluation. The default transformations
        convert numeric literals into their SymPy equivalents, convert
        undefined variables into SymPy symbols, and allow the use of standard
        mathematical factorial notation (e.g. ``x!``).


    Examples
    ========

    >>> from sympy.parsing.sympy_parser import parse_expr
    >>> parse_expr("1/2")
    1/2
    >>> type(_)
    <class 'sympy.core.numbers.Half'>
    >>> from sympy.parsing.sympy_parser import standard_transformations,\\
    ... implicit_multiplication_application
    >>> transformations = (standard_transformations +
    ...     (implicit_multiplication_application,))
    >>> parse_expr("2x", transformations=transformations)
    2*x

    See Also
    ========

    stringify_expr, eval_expr, standard_transformations,
    implicit_multiplication_application

    """

    if local_dict is None:
        local_dict = {}

    if global_dict is None:
        global_dict = {}
        exec_('from sympy import *', global_dict)

    code = stringify_expr(s, local_dict, global_dict, transformations)
    return eval_expr(code, local_dict, global_dict)

def _import(module, reload=False):
    """
    Creates a global translation dictionary for module.

    The argument module has to be one of the following strings: "math",
    "mpmath", "numpy", "sympy", "tensorflow".
    These dictionaries map names of python functions to their equivalent in
    other modules.
    """
    # Required despite static analysis claiming it is not used
    from sympy.external import import_module
    try:
        namespace, namespace_default, translations, import_commands = MODULES[
            module]
    except KeyError:
        raise NameError(
            "'%s' module can't be used for lambdification" % module)

    # Clear namespace or exit
    if namespace != namespace_default:
        # The namespace was already generated, don't do it again if not forced.
        if reload:
            namespace.clear()
            namespace.update(namespace_default)
        else:
            return

    for import_command in import_commands:
        if import_command.startswith('import_module'):
            module = eval(import_command)

            if module is not None:
                namespace.update(module.__dict__)
                continue
        else:
            try:
                exec_(import_command, {}, namespace)
                continue
            except ImportError:
                pass

        raise ImportError(
            "can't import '%s' with '%s' command" % (module, import_command))

    # Add translated names to namespace
    for sympyname, translation in translations.items():
        namespace[sympyname] = namespace[translation]

    # For computing the modulus of a sympy expression we use the builtin abs
    # function, instead of the previously used fabs function for all
    # translation modules. This is because the fabs function in the math
    # module does not accept complex valued arguments. (see issue 9474). The
    # only exception, where we don't use the builtin abs function is the
    # mpmath translation module, because mpmath.fabs returns mpf objects in
    # contrast to abs().
    if 'Abs' not in namespace:
        namespace['Abs'] = abs

def test_newtons_method_function__pycode():
    x = sp.Symbol('x', real=True)
    expr = sp.cos(x) - x**3
    func = newtons_method_function(expr, x)
    py_mod = py_module(func)
    namespace = {}
    exec_(py_mod, namespace, namespace)
    res = eval('newton(0.5)', namespace)
    assert abs(res - 0.865474033102) < 1e-12

    def __init__(self, args, expr, print_lambda=False, use_evalf=False,
                 float_wrap_evalf=False, complex_wrap_evalf=False,
                 use_np=False, use_python_math=False, use_python_cmath=False,
                 use_interval=False):

        self.print_lambda = print_lambda
        self.use_evalf = use_evalf
        self.float_wrap_evalf = float_wrap_evalf
        self.complex_wrap_evalf = complex_wrap_evalf
        self.use_np = use_np
        self.use_python_math = use_python_math
        self.use_python_cmath = use_python_cmath
        self.use_interval = use_interval

        # Constructing the argument string
        if not all([isinstance(a, Symbol) for a in args]):
            raise ValueError('The arguments must be Symbols.')
        else:
            argstr = ', '.join([str(a) for a in args])

        # Constructing the translation dictionaries and making the translation
        self.dict_str = self.get_dict_str()
        self.dict_fun = self.get_dict_fun()
        exprstr = str(expr)
        newexpr = self.tree2str_translate(self.str2tree(exprstr))

        # Constructing the namespaces
        namespace = {}
        namespace.update(self.sympy_atoms_namespace(expr))
        namespace.update(self.sympy_expression_namespace(expr))
        # XXX Workaround
        # Ugly workaround because Pow(a,Half) prints as sqrt(a)
        # and sympy_expression_namespace can not catch it.
        from sympy import sqrt
        namespace.update({'sqrt': sqrt})
        # End workaround.
        if use_python_math:
            namespace.update({'math': __import__('math')})
        if use_python_cmath:
            namespace.update({'cmath': __import__('cmath')})
        if use_np:
            try:
                namespace.update({'np': __import__('numpy')})
            except ImportError:
                raise ImportError(
                    'experimental_lambdify failed to import numpy.')
        if use_interval:
            namespace.update({'imath': __import__(
                'sympy.plotting.intervalmath', fromlist=['intervalmath'])})
            namespace.update({'math': __import__('math')})

        # Construct the lambda
        if self.print_lambda:
            print(newexpr)
        eval_str = 'lambda %s : ( %s )' % (argstr, newexpr)
        exec_("from __future__ import division; MYNEWLAMBDA = %s" % eval_str, namespace)
        self.lambda_func = namespace['MYNEWLAMBDA']

 def __init__(self, stmt, setup='pass', timer=timeit.default_timer, globals=globals()):
     # copy of timeit.Timer.__init__
     # similarity index 95%
     self.timer = timer
     stmt = timeit.reindent(stmt, 8)
     setup = timeit.reindent(setup, 4)
     src = timeit.template % {'stmt': stmt, 'setup': setup}
     self.src = src  # Save for traceback display
     code = compile(src, timeit.dummy_src_name, "exec")
     ns = {}
     #exec code in globals(), ns      -- original timeit code
     exec_(code, globals, ns)  # -- we use caller-provided globals instead
     self.inner = ns["inner"]

def parse_expr(s, local_dict):
    """
    Converts the string "s" to a SymPy expression, in local_dict.

    It converts all numbers to Integers before feeding it to Python and
    automatically creates Symbols.
    """
    global_dict = {}
    exec_('from sympy import *', global_dict)
    try:
        a = parse(s.strip(), mode="eval")
    except SyntaxError:
        raise SympifyError("Cannot parse %s." % repr(s))
    a = Transform(local_dict, global_dict).visit(a)
    e = compile(a, "<string>", "eval")
    return eval(e, global_dict, local_dict)

def _import(module, reload="False"):
    """
    Creates a global translation dictionary for module.

    The argument module has to be one of the following strings: "math",
    "mpmath", "numpy", "sympy".
    These dictionaries map names of python functions to their equivalent in
    other modules.
    """
    try:
        namespace, namespace_default, translations, import_commands = MODULES[
            module]
    except KeyError:
        raise NameError(
            "'%s' module can't be used for lambdification" % module)

    # Clear namespace or exit
    if namespace != namespace_default:
        # The namespace was already generated, don't do it again if not forced.
        if reload:
            namespace.clear()
            namespace.update(namespace_default)
        else:
            return

    for import_command in import_commands:
        if import_command.startswith('import_module'):
            module = eval(import_command)

            if module is not None:
                namespace.update(module.__dict__)
                continue
        else:
            try:
                exec_(import_command, {}, namespace)
                continue
            except ImportError:
                pass

        raise ImportError(
            "can't import '%s' with '%s' command" % (module, import_command))

    # Add translated names to namespace
    for sympyname, translation in translations.items():
        namespace[sympyname] = namespace[translation]

def test_names_in_namespace():
    # Every singleton name should be accessible from the 'from sympy import *'
    # namespace in addition to the S object. However, it does not need to be
    # by the same name (e.g., oo instead of S.Infinity).

    # As a general rule, things should only be added to the singleton registry
    # if they are used often enough that code can benefit either from the
    # performance benefit of being able to use 'is' (this only matters in very
    # tight loops), or from the memory savings of having exactly one instance
    # (this matters for the numbers singletons, but very little else). The
    # singleton registry is already a bit overpopulated, and things cannot be
    # removed from it without breaking backwards compatibility. So if you got
    # here by adding something new to the singletons, ask yourself if it
    # really needs to be singletonized. Note that SymPy classes compare to one
    # another just fine, so Class() == Class() will give True even if each
    # Class() returns a new instance. Having unique instances is only
    # necessary for the above noted performance gains. It should not be needed
    # for any behavioral purposes.

    # If you determine that something really should be a singleton, it must be
    # accessible to sympify() without using 'S' (hence this test). Also, its
    # str printer should print a form that does not use S. This is because
    # sympify() disables attribute lookups by default for safety purposes.
    d = {}
    exec_('from sympy import *', d)

    for name in dir(S) + list(S._classes_to_install):
        if name.startswith('_'):
            continue
        if name == 'register':
            continue
        if isinstance(getattr(S, name), Rational):
            continue
        if getattr(S, name).__module__.startswith('sympy.physics'):
            continue
        if name in ['MySingleton', 'MySingleton_sub']:
            # From the test above
            continue
        if name == 'NegativeInfinity':
            # Accessible by -oo
            continue

        # Use is here because of complications with ==
        assert any(getattr(S, name) is i or type(getattr(S, name)) is i for i in d.values()), name

def parse_expr(s, local_dict=None, transformations=standard_transformations,
               global_dict=None, evaluate=True):
    """Converts the string ``s`` to a SymPy expression, in ``local_dict``

    Parameters
    ==========

    s : str
        The string to parse.

    local_dict : dict, optional
        A dictionary of local variables to use when parsing.

    global_dict : dict, optional
        A dictionary of global variables. By default, this is initialized
        with ``from sympy import *``; provide this parameter to override
        this behavior (for instance, to parse ``"Q & S"``).

    transformations : tuple, optional
        A tuple of transformation functions used to modify the tokens of the
        parsed expression before evaluation. The default transformations
        convert numeric literals into their SymPy equivalents, convert
        undefined variables into SymPy symbols, and allow the use of standard
        mathematical factorial notation (e.g. ``x!``).

    evaluate : bool, optional
        When False, the order of the arguments will remain as they were in the
        string and automatic simplification that would normally occur is
        suppressed. (see examples)

    Examples
    ========

    >>> from sympy.parsing.sympy_parser import parse_expr
    >>> parse_expr("1/2")
    1/2
    >>> type(_)
    <class 'sympy.core.numbers.Half'>
    >>> from sympy.parsing.sympy_parser import standard_transformations,\\
    ... implicit_multiplication_application
    >>> transformations = (standard_transformations +
    ...     (implicit_multiplication_application,))
    >>> parse_expr("2x", transformations=transformations)
    2*x

    When evaluate=False, some automatic simplifications will not occur:

    >>> parse_expr("2**3"), parse_expr("2**3", evaluate=False)
    (8, 2**3)

    In addition the order of the arguments will not be made canonical.
    This feature allows one to tell exactly how the expression was entered:

    >>> a = parse_expr('1 + x', evaluate=False)
    >>> b = parse_expr('x + 1', evaluate=0)
    >>> a == b
    False
    >>> a.args
    (1, x)
    >>> b.args
    (x, 1)

    See Also
    ========

    stringify_expr, eval_expr, standard_transformations,
    implicit_multiplication_application

    """

    if local_dict is None:
        local_dict = {}

    if global_dict is None:
        global_dict = {}
        exec_('from sympy import *', global_dict)

    code = stringify_expr(s, local_dict, global_dict, transformations)

    if not evaluate:
        code = compile(evaluateFalse(code), '<string>', 'eval')

    return eval_expr(code, local_dict, global_dict)

 def _build(self, code, name):
     ns = {}
     exec_(code, ns)
     return ns[name]

from sympy import (symbols, Function, Integer, Matrix, Abs,
    Rational, Float, S, WildFunction, ImmutableDenseMatrix, sin, true, false, ones,
    sqrt, root, AlgebraicNumber, Symbol, Dummy, Wild)
from sympy.core.compatibility import exec_
from sympy.geometry import Point, Ellipse
from sympy.printing import srepr
from sympy.polys import ring, field, ZZ, QQ, lex, grlex, Poly
from sympy.polys.polyclasses import DMP
from sympy.polys.agca.extensions import FiniteExtension

x, y = symbols('x,y')

# eval(srepr(expr)) == expr has to succeed in the right environment. The right
# environment is the scope of "from sympy import *" for most cases.
ENV = {}
exec_("from sympy import *", ENV)


def sT(expr, string):
    """
    sT := sreprTest

    Tests that srepr delivers the expected string and that
    the condition eval(srepr(expr))==expr holds.
    """
    assert srepr(expr) == string
    assert eval(string, ENV) == expr


def test_printmethod():
    class R(Abs):

#.........这里部分代码省略.........
    if hasattr(expr, "atoms"):
        #Try if you can extract symbols from the expression.
        #Move on if expr.atoms in not implemented.
        syms = expr.atoms(Symbol)
        for term in syms:
            namespace.update({str(term): term})

    if printer is None:
        if _module_present('mpmath', namespaces):
            from sympy.printing.pycode import MpmathPrinter as Printer
        elif _module_present('scipy', namespaces):
            from sympy.printing.pycode import SciPyPrinter as Printer
        elif _module_present('numpy', namespaces):
            from sympy.printing.pycode import NumPyPrinter as Printer
        elif _module_present('numexpr', namespaces):
            from sympy.printing.lambdarepr import NumExprPrinter as Printer
        elif _module_present('tensorflow', namespaces):
            from sympy.printing.lambdarepr import TensorflowPrinter as Printer
        elif _module_present('sympy', namespaces):
            from sympy.printing.pycode import SymPyPrinter as Printer
        else:
            from sympy.printing.pycode import PythonCodePrinter as Printer
        user_functions = {}
        for m in namespaces[::-1]:
            if isinstance(m, dict):
                for k in m:
                    user_functions[k] = k
        printer = Printer({'fully_qualified_modules': False, 'inline': True,
                           'allow_unknown_functions': True,
                           'user_functions': user_functions})

    # Get the names of the args, for creating a docstring
    if not iterable(args):
        args = (args,)
    names = []
    # Grab the callers frame, for getting the names by inspection (if needed)
    callers_local_vars = inspect.currentframe().f_back.f_locals.items()
    for n, var in enumerate(args):
        if hasattr(var, 'name'):
            names.append(var.name)
        else:
            # It's an iterable. Try to get name by inspection of calling frame.
            name_list = [var_name for var_name, var_val in callers_local_vars
                    if var_val is var]
            if len(name_list) == 1:
                names.append(name_list[0])
            else:
                # Cannot infer name with certainty. arg_# will have to do.
                names.append('arg_' + str(n))

    imp_mod_lines = []
    for mod, keys in (getattr(printer, 'module_imports', None) or {}).items():
        for k in keys:
            if k not in namespace:
                imp_mod_lines.append("from %s import %s" % (mod, k))
    for ln in imp_mod_lines:
        exec_(ln, {}, namespace)

    # Provide lambda expression with builtins, and compatible implementation of range
    namespace.update({'builtins':builtins, 'range':range})

    # Create the function definition code and execute it

    funcname = '_lambdifygenerated'

    if _module_present('tensorflow', namespaces):
        funcprinter = _TensorflowEvaluatorPrinter(printer, dummify)
    else:
        funcprinter = _EvaluatorPrinter(printer, dummify)

    funcstr = funcprinter.doprint(funcname, args, expr)

    funclocals = {}
    global _lambdify_generated_counter
    filename = '<lambdifygenerated-%s>' % _lambdify_generated_counter
    _lambdify_generated_counter += 1
    c = compile(funcstr, filename, 'exec')
    exec_(c, namespace, funclocals)
    # mtime has to be None or else linecache.checkcache will remove it
    linecache.cache[filename] = (len(funcstr), None, funcstr.splitlines(True), filename)

    func = funclocals[funcname]

    # Apply the docstring
    sig = "func({0})".format(", ".join(str(i) for i in names))
    sig = textwrap.fill(sig, subsequent_indent=' '*8)
    expr_str = str(expr)
    if len(expr_str) > 78:
        expr_str = textwrap.wrap(expr_str, 75)[0] + '...'
    func.__doc__ = (
        "Created with lambdify. Signature:\n\n"
        "{sig}\n\n"
        "Expression:\n\n"
        "{expr}\n\n"
        "Source code:\n\n"
        "{src}\n\n"
        "Imported modules:\n\n"
        "{imp_mods}"
        ).format(sig=sig, expr=expr_str, src=funcstr, imp_mods='\n'.join(imp_mod_lines))
    return func

    def __init__(self, args, expr, print_lambda=False, use_evalf=False,
                 float_wrap_evalf=False, complex_wrap_evalf=False,
                 use_np=False, use_python_math=False, use_python_cmath=False,
                 use_interval=False):

        self.print_lambda = print_lambda
        self.use_evalf = use_evalf
        self.float_wrap_evalf = float_wrap_evalf
        self.complex_wrap_evalf = complex_wrap_evalf
        self.use_np = use_np
        self.use_python_math = use_python_math
        self.use_python_cmath = use_python_cmath
        self.use_interval = use_interval

        # Constructing the argument string
        # - check
        if not all([isinstance(a, Symbol) for a in args]):
            raise ValueError('The arguments must be Symbols.')
        # - use numbered symbols
        syms = numbered_symbols(exclude=expr.free_symbols)
        newargs = [next(syms) for i in args]
        expr = expr.xreplace(dict(zip(args, newargs)))
        argstr = ', '.join([str(a) for a in newargs])
        del syms, newargs, args

        # Constructing the translation dictionaries and making the translation
        self.dict_str = self.get_dict_str()
        self.dict_fun = self.get_dict_fun()
        exprstr = str(expr)
        # the & and | operators don't work on tuples, see discussion #12108
        exprstr = exprstr.replace(" & "," and ").replace(" | "," or ")

        newexpr = self.tree2str_translate(self.str2tree(exprstr))

        # Constructing the namespaces
        namespace = {}
        namespace.update(self.sympy_atoms_namespace(expr))
        namespace.update(self.sympy_expression_namespace(expr))
        # XXX Workaround
        # Ugly workaround because Pow(a,Half) prints as sqrt(a)
        # and sympy_expression_namespace can not catch it.
        from sympy import sqrt
        namespace.update({'sqrt': sqrt})
        namespace.update({'Eq': lambda x, y: x == y})
        # End workaround.
        if use_python_math:
            namespace.update({'math': __import__('math')})
        if use_python_cmath:
            namespace.update({'cmath': __import__('cmath')})
        if use_np:
            try:
                namespace.update({'np': __import__('numpy')})
            except ImportError:
                raise ImportError(
                    'experimental_lambdify failed to import numpy.')
        if use_interval:
            namespace.update({'imath': __import__(
                'sympy.plotting.intervalmath', fromlist=['intervalmath'])})
            namespace.update({'math': __import__('math')})

        # Construct the lambda
        if self.print_lambda:
            print(newexpr)
        eval_str = 'lambda %s : ( %s )' % (argstr, newexpr)
        self.eval_str = eval_str
        exec_("from __future__ import division; MYNEWLAMBDA = %s" % eval_str, namespace)
        self.lambda_func = namespace['MYNEWLAMBDA']

#.........这里部分代码省略.........
    if hasattr(expr, "atoms"):
        #Try if you can extract symbols from the expression.
        #Move on if expr.atoms in not implemented.
        syms = expr.atoms(Symbol)
        for term in syms:
            namespace.update({str(term): term})

    if printer is None:
        if _module_present('mpmath', namespaces):
            from sympy.printing.pycode import MpmathPrinter as Printer
        elif _module_present('scipy', namespaces):
            from sympy.printing.pycode import SciPyPrinter as Printer
        elif _module_present('numpy', namespaces):
            from sympy.printing.pycode import NumPyPrinter as Printer
        elif _module_present('numexpr', namespaces):
            from sympy.printing.lambdarepr import NumExprPrinter as Printer
        elif _module_present('tensorflow', namespaces):
            from sympy.printing.tensorflow import TensorflowPrinter as Printer
        elif _module_present('sympy', namespaces):
            from sympy.printing.pycode import SymPyPrinter as Printer
        else:
            from sympy.printing.pycode import PythonCodePrinter as Printer
        user_functions = {}
        for m in namespaces[::-1]:
            if isinstance(m, dict):
                for k in m:
                    user_functions[k] = k
        printer = Printer({'fully_qualified_modules': False, 'inline': True,
                           'allow_unknown_functions': True,
                           'user_functions': user_functions})

    # Get the names of the args, for creating a docstring
    if not iterable(args):
        args = (args,)
    names = []
    # Grab the callers frame, for getting the names by inspection (if needed)
    callers_local_vars = inspect.currentframe().f_back.f_locals.items()
    for n, var in enumerate(args):
        if hasattr(var, 'name'):
            names.append(var.name)
        else:
            # It's an iterable. Try to get name by inspection of calling frame.
            name_list = [var_name for var_name, var_val in callers_local_vars
                    if var_val is var]
            if len(name_list) == 1:
                names.append(name_list[0])
            else:
                # Cannot infer name with certainty. arg_# will have to do.
                names.append('arg_' + str(n))

    imp_mod_lines = []
    for mod, keys in (getattr(printer, 'module_imports', None) or {}).items():
        for k in keys:
            if k not in namespace:
                imp_mod_lines.append("from %s import %s" % (mod, k))
    for ln in imp_mod_lines:
        exec_(ln, {}, namespace)

    # Provide lambda expression with builtins, and compatible implementation of range
    namespace.update({'builtins':builtins, 'range':range})

    # Create the function definition code and execute it

    funcname = '_lambdifygenerated'

    if _module_present('tensorflow', namespaces):
        funcprinter = _TensorflowEvaluatorPrinter(printer, dummify)
    else:
        funcprinter = _EvaluatorPrinter(printer, dummify)

    funcstr = funcprinter.doprint(funcname, args, expr)

    funclocals = {}
    global _lambdify_generated_counter
    filename = '<lambdifygenerated-%s>' % _lambdify_generated_counter
    _lambdify_generated_counter += 1
    c = compile(funcstr, filename, 'exec')
    exec_(c, namespace, funclocals)
    # mtime has to be None or else linecache.checkcache will remove it
    linecache.cache[filename] = (len(funcstr), None, funcstr.splitlines(True), filename)

    func = funclocals[funcname]

    # Apply the docstring
    sig = "func({0})".format(", ".join(str(i) for i in names))
    sig = textwrap.fill(sig, subsequent_indent=' '*8)
    expr_str = str(expr)
    if len(expr_str) > 78:
        expr_str = textwrap.wrap(expr_str, 75)[0] + '...'
    func.__doc__ = (
        "Created with lambdify. Signature:\n\n"
        "{sig}\n\n"
        "Expression:\n\n"
        "{expr}\n\n"
        "Source code:\n\n"
        "{src}\n\n"
        "Imported modules:\n\n"
        "{imp_mods}"
        ).format(sig=sig, expr=expr_str, src=funcstr, imp_mods='\n'.join(imp_mod_lines))
    return func