Source code for sympy.utilities.autowrap

"""Module for compiling codegen output, and wrap the binary for use in

.. note:: To use the autowrap module it must first be imported

   >>> from sympy.utilities.autowrap import autowrap

This module provides a common interface for different external backends, such
as f2py, fwrap, Cython, SWIG(?) etc. (Currently only f2py and Cython are
implemented) The goal is to provide access to compiled binaries of acceptable
performance with a one-button user interface, i.e.

    >>> from sympy.abc import x,y
    >>> expr = ((x - y)**(25)).expand()
    >>> binary_callable = autowrap(expr)
    >>> binary_callable(1, 2)

The callable returned from autowrap() is a binary python function, not a
SymPy object.  If it is desired to use the compiled function in symbolic
expressions, it is better to use binary_function() which returns a SymPy
Function object.  The binary callable is attached as the _imp_ attribute and
invoked when a numerical evaluation is requested with evalf(), or with

    >>> from sympy.utilities.autowrap import binary_function
    >>> f = binary_function('f', expr)
    >>> 2*f(x, y) + y
    y + 2*f(x, y)
    >>> (2*f(x, y) + y).evalf(2, subs={x: 1, y:2})

The idea is that a SymPy user will primarily be interested in working with
mathematical expressions, and should not have to learn details about wrapping
tools in order to evaluate expressions numerically, even if they are
computationally expensive.

When is this useful?

    1) For computations on large arrays, Python iterations may be too slow,
       and depending on the mathematical expression, it may be difficult to
       exploit the advanced index operations provided by NumPy.

    2) For *really* long expressions that will be called repeatedly, the
       compiled binary should be significantly faster than SymPy's .evalf()

    3) If you are generating code with the codegen utility in order to use
       it in another project, the automatic python wrappers let you test the
       binaries immediately from within SymPy.

    4) To create customized ufuncs for use with numpy arrays.
       See *ufuncify*.

When is this module NOT the best approach?

    1) If you are really concerned about speed or memory optimizations,
       you will probably get better results by working directly with the
       wrapper tools and the low level code.  However, the files generated
       by this utility may provide a useful starting point and reference
       code. Temporary files will be left intact if you supply the keyword

    2) If the array computation can be handled easily by numpy, and you
       don't need the binaries for another project.


from __future__ import print_function, division

_doctest_depends_on = { 'exe': ('f2py', 'gfortran'), 'modules': ('numpy',)}

import sys
import os
import shutil
import tempfile
from subprocess import STDOUT, CalledProcessError

from sympy.core.compatibility import check_output
from sympy.utilities.codegen import (
    get_code_generator, Routine, OutputArgument, InOutArgument,
    CodeGenArgumentListError, Result
from sympy.utilities.lambdify import implemented_function
from sympy.utilities.decorator import doctest_depends_on
from sympy import C

class CodeWrapError(Exception):

[docs]class CodeWrapper: """Base Class for code wrappers""" _filename = "wrapped_code" _module_basename = "wrapper_module" _module_counter = 0 @property def filename(self): return "%s_%s" % (self._filename, CodeWrapper._module_counter) @property def module_name(self): return "%s_%s" % (self._module_basename, CodeWrapper._module_counter) def __init__(self, generator, filepath=None, flags=[], verbose=False): """ generator -- the code generator to use """ self.generator = generator self.filepath = filepath self.flags = flags self.quiet = not verbose @property def include_header(self): return bool(self.filepath) @property def include_empty(self): return bool(self.filepath) def _generate_code(self, main_routine, routines): routines.append(main_routine) self.generator.write( routines, self.filename, True, self.include_header, self.include_empty) def wrap_code(self, routine, helpers=[]): workdir = self.filepath or tempfile.mkdtemp("_sympy_compile") if not os.access(workdir, os.F_OK): os.mkdir(workdir) oldwork = os.getcwd() os.chdir(workdir) try: sys.path.append(workdir) self._generate_code(routine, helpers) self._prepare_files(routine) self._process_files(routine) mod = __import__(self.module_name) finally: sys.path.remove(workdir) CodeWrapper._module_counter += 1 os.chdir(oldwork) if not self.filepath: shutil.rmtree(workdir) return self._get_wrapped_function(mod) def _process_files(self, routine): command = self.command command.extend(self.flags) try: retoutput = check_output(command, stderr=STDOUT) except CalledProcessError as e: raise CodeWrapError( "Error while executing command: %s. Command output is:\n%s" % ( " ".join(command), e.output)) if not self.quiet: print(retoutput)
[docs]class DummyWrapper(CodeWrapper): """Class used for testing independent of backends """ template = """# dummy module for testing of SymPy def %(name)s(): return "%(expr)s" %(name)s.args = "%(args)s" %(name)s.returns = "%(retvals)s" """ def _prepare_files(self, routine): return def _generate_code(self, routine, helpers): with open('%s.py' % self.module_name, 'w') as f: printed = ", ".join( [str(res.expr) for res in routine.result_variables]) # convert OutputArguments to return value like f2py inargs = filter(lambda x: not isinstance( x, OutputArgument), routine.arguments) retvals = [] for val in routine.result_variables: if isinstance(val, Result): retvals.append('nameless') else: retvals.append(val.result_var) print(DummyWrapper.template % { 'name': routine.name, 'expr': printed, 'args': ", ".join([str(arg.name) for arg in inargs]), 'retvals': ", ".join([str(val) for val in retvals]) }, end="", file=f) def _process_files(self, routine): return @classmethod def _get_wrapped_function(cls, mod): return mod.autofunc
[docs]class CythonCodeWrapper(CodeWrapper): """Wrapper that uses Cython""" setup_template = """ from distutils.core import setup from distutils.extension import Extension from Cython.Distutils import build_ext setup( cmdclass = {'build_ext': build_ext}, ext_modules = [Extension(%(args)s)] ) """ @property def command(self): command = [sys.executable, "setup.py", "build_ext", "--inplace"] return command def _prepare_files(self, routine): pyxfilename = self.module_name + '.pyx' codefilename = "%s.%s" % (self.filename, self.generator.code_extension) # pyx with open(pyxfilename, 'w') as f: self.dump_pyx([routine], f, self.filename, self.include_header, self.include_empty) # setup.py ext_args = [repr(self.module_name), repr([pyxfilename, codefilename])] with open('setup.py', 'w') as f: print(CythonCodeWrapper.setup_template % { 'args': ", ".join(ext_args)}, file=f) @classmethod def _get_wrapped_function(cls, mod): return mod.autofunc_c
[docs] def dump_pyx(self, routines, f, prefix, header=True, empty=True): """Write a Cython file with python wrappers This file contains all the definitions of the routines in c code and refers to the header file. :Arguments: routines List of Routine instances f File-like object to write the file to prefix The filename prefix, used to refer to the proper header file. Only the basename of the prefix is used. empty Optional. When True, empty lines are included to structure the source files. [DEFAULT=True] """ for routine in routines: prototype = self.generator.get_prototype(routine) # declare print('cdef extern from "%s.h":' % prefix, file=f) print(' %s' % prototype, file=f) if empty: print(file=f) # wrap ret, args_py = self._split_retvals_inargs(routine.arguments) args_c = ", ".join([str(a.name) for a in routine.arguments]) print("def %s_c(%s):" % (routine.name, ", ".join(self._declare_arg(arg) for arg in args_py)), file=f) for r in ret: if not r in args_py: print(" cdef %s" % self._declare_arg(r), file=f) rets = ", ".join([str(r.name) for r in ret]) if routine.results: call = ' return %s(%s)' % (routine.name, args_c) if rets: print(call + ', ' + rets, file=f) else: print(call, file=f) else: print(' %s(%s)' % (routine.name, args_c), file=f) print(' return %s' % rets, file=f) if empty: print(file=f)
dump_pyx.extension = "pyx" def _split_retvals_inargs(self, args): """Determines arguments and return values for python wrapper""" py_args = [] py_returns = [] for arg in args: if isinstance(arg, OutputArgument): py_returns.append(arg) elif isinstance(arg, InOutArgument): py_returns.append(arg) py_args.append(arg) else: py_args.append(arg) return py_returns, py_args def _declare_arg(self, arg): t = arg.get_datatype('c') if arg.dimensions: return "%s *%s" % (t, str(arg.name)) else: return "%s %s" % (t, str(arg.name))
[docs]class F2PyCodeWrapper(CodeWrapper): """Wrapper that uses f2py""" @property def command(self): filename = self.filename + '.' + self.generator.code_extension command = ["f2py", "-m", self.module_name, "-c", filename] return command def _prepare_files(self, routine): pass @classmethod def _get_wrapped_function(cls, mod): return mod.autofunc
def _get_code_wrapper_class(backend): wrappers = { 'F2PY': F2PyCodeWrapper, 'CYTHON': CythonCodeWrapper, 'DUMMY': DummyWrapper} return wrappers[backend.upper()] @doctest_depends_on(exe=('f2py', 'gfortran'), modules=('numpy',))
[docs]def autowrap( expr, language='F95', backend='f2py', tempdir=None, args=None, flags=[], verbose=False, helpers=[]): """Generates python callable binaries based on the math expression. expr The SymPy expression that should be wrapped as a binary routine :Optional arguments: language The programming language to use, currently 'C' or 'F95' backend The wrapper backend to use, currently f2py or Cython tempdir Path to directory for temporary files. If this argument is supplied, the generated code and the wrapper input files are left intact in the specified path. args Sequence of the formal parameters of the generated code, if ommited the function signature is determined by the code generator. flags Additional option flags that will be passed to the backend verbose If True, autowrap will not mute the command line backends. This can be helpful for debugging. helpers Used to define auxillary expressions needed for the main expr. If the main expression need to do call a specialized function it should be put in the ``helpers`` list. Autowrap will then make sure that the compiled main expression can link to the helper routine. Items should be tuples with (<funtion_name>, <sympy_expression>, <arguments>). It is mandatory to supply an argument sequence to helper routines. >>> from sympy.abc import x, y, z >>> from sympy.utilities.autowrap import autowrap >>> expr = ((x - y + z)**(13)).expand() >>> binary_func = autowrap(expr) >>> binary_func(1, 4, 2) -1.0 """ code_generator = get_code_generator(language, "autowrap") CodeWrapperClass = _get_code_wrapper_class(backend) code_wrapper = CodeWrapperClass(code_generator, tempdir, flags, verbose) try: routine = Routine('autofunc', expr, args) except CodeGenArgumentListError as e: # if all missing arguments are for pure output, we simply attach them # at the end and try again, because the wrappers will silently convert # them to return values anyway. new_args = [] for missing in e.missing_args: if not isinstance(missing, OutputArgument): raise new_args.append(missing.name) routine = Routine('autofunc', expr, args + new_args) helps = [] for name, expr, args in helpers: helps.append(Routine(name, expr, args)) return code_wrapper.wrap_code(routine, helpers=helps)
@doctest_depends_on (exe=('f2py', 'gfortran'), modules=('numpy',))
[docs]def binary_function(symfunc, expr, **kwargs): """Returns a sympy function with expr as binary implementation This is a convenience function that automates the steps needed to autowrap the SymPy expression and attaching it to a Function object with implemented_function(). >>> from sympy.abc import x, y >>> from sympy.utilities.autowrap import binary_function >>> expr = ((x - y)**(25)).expand() >>> f = binary_function('f', expr) >>> type(f) <class 'sympy.core.function.UndefinedFunction'> >>> 2*f(x, y) 2*f(x, y) >>> f(x, y).evalf(2, subs={x: 1, y: 2}) -1.0 """ binary = autowrap(expr, **kwargs) return implemented_function(symfunc, binary)
@doctest_depends_on (exe=('f2py', 'gfortran'), modules=('numpy',))
[docs]def ufuncify(args, expr, **kwargs): """ Generates a binary ufunc-like lambda function for numpy arrays ``args`` Either a Symbol or a tuple of symbols. Specifies the argument sequence for the ufunc-like function. ``expr`` A SymPy expression that defines the element wise operation ``kwargs`` Optional keyword arguments are forwarded to autowrap(). The returned function can only act on one array at a time, as only the first argument accept arrays as input. .. Note:: a *proper* numpy ufunc is required to support broadcasting, type casting and more. The function returned here, may not qualify for numpy's definition of a ufunc. That why we use the term ufunc-like. References ========== [1] http://docs.scipy.org/doc/numpy/reference/ufuncs.html Examples ======== >>> from sympy.utilities.autowrap import ufuncify >>> from sympy.abc import x, y >>> import numpy as np >>> f = ufuncify([x, y], y + x**2) >>> f([1, 2, 3], 2) [ 3. 6. 11.] >>> a = f(np.arange(5), 3) >>> isinstance(a, np.ndarray) True >>> print a [ 3. 4. 7. 12. 19.] """ y = C.IndexedBase(C.Dummy('y')) x = C.IndexedBase(C.Dummy('x')) m = C.Dummy('m', integer=True) i = C.Dummy('i', integer=True) i = C.Idx(i, m) l = C.Lambda(args, expr) f = implemented_function('f', l) if isinstance(args, C.Symbol): args = [args] else: args = list(args) # first argument accepts an array args[0] = x[i] return autowrap(C.Equality(y[i], f(*args)), **kwargs)