Source code for sympy.utilities.autowrap

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

.. 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)
    -1.0

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
lambdify().

    >>> 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})
    0.e-110

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
       tempdir="path/to/files/".

    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

import sys
import os
import shutil
import tempfile
from subprocess import STDOUT, CalledProcessError, check_output
from string import Template
from warnings import warn

from sympy.core.cache import cacheit
from sympy.core.compatibility import range, iterable
from sympy.core.function import Lambda
from sympy.core.relational import Eq
from sympy.core.symbol import Dummy, Symbol
from sympy.tensor.indexed import Idx, IndexedBase
from sympy.utilities.codegen import (make_routine, get_code_generator,
                                     OutputArgument, InOutArgument,
                                     InputArgument, CodeGenArgumentListError,
                                     Result, ResultBase, C99CodeGen)
from sympy.utilities.lambdify import implemented_function
from sympy.utilities.decorator import doctest_depends_on

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


class CodeWrapError(Exception):
    pass


[docs]class CodeWrapper(object): """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: try: shutil.rmtree(workdir) except OSError: # Could be some issues on Windows pass return self._get_wrapped_function(mod, routine.name) 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.decode())) 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 args = 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(a.name) for a in args]), 'retvals': ", ".join([str(val) for val in retvals]) }, end="", file=f) def _process_files(self, routine): return @classmethod def _get_wrapped_function(cls, mod, name): return getattr(mod, name)
[docs]class CythonCodeWrapper(CodeWrapper): """Wrapper that uses Cython""" setup_template = """\ try: from setuptools import setup from setuptools import Extension except ImportError: from distutils.core import setup from distutils.extension import Extension from Cython.Build import cythonize cy_opts = {cythonize_options} {np_import} ext_mods = [Extension( {ext_args}, include_dirs={include_dirs}, library_dirs={library_dirs}, libraries={libraries}, extra_compile_args={extra_compile_args}, extra_link_args={extra_link_args} )] setup(ext_modules=cythonize(ext_mods, **cy_opts)) """ pyx_imports = ( "import numpy as np\n" "cimport numpy as np\n\n") pyx_header = ( "cdef extern from '{header_file}.h':\n" " {prototype}\n\n") pyx_func = ( "def {name}_c({arg_string}):\n" "\n" "{declarations}" "{body}") std_compile_flag = '-std=c99' def __init__(self, *args, **kwargs): """Instantiates a Cython code wrapper. The following optional parameters get passed to ``distutils.Extension`` for building the Python extension module. Read its documentation to learn more. Parameters ========== include_dirs : [list of strings] A list of directories to search for C/C++ header files (in Unix form for portability). library_dirs : [list of strings] A list of directories to search for C/C++ libraries at link time. libraries : [list of strings] A list of library names (not filenames or paths) to link against. extra_compile_args : [list of strings] Any extra platform- and compiler-specific information to use when compiling the source files in 'sources'. For platforms and compilers where "command line" makes sense, this is typically a list of command-line arguments, but for other platforms it could be anything. Note that the attribute ``std_compile_flag`` will be appended to this list. extra_link_args : [list of strings] Any extra platform- and compiler-specific information to use when linking object files together to create the extension (or to create a new static Python interpreter). Similar interpretation as for 'extra_compile_args'. cythonize_options : [dictionary] Keyword arguments passed on to cythonize. """ self._include_dirs = kwargs.pop('include_dirs', []) self._library_dirs = kwargs.pop('library_dirs', []) self._libraries = kwargs.pop('libraries', []) self._extra_compile_args = kwargs.pop('extra_compile_args', []) self._extra_compile_args.append(self.std_compile_flag) self._extra_link_args = kwargs.pop('extra_link_args', []) self._cythonize_options = kwargs.pop('cythonize_options', {}) self._need_numpy = False super(CythonCodeWrapper, self).__init__(*args, **kwargs) @property def command(self): command = [sys.executable, "setup.py", "build_ext", "--inplace"] return command def _prepare_files(self, routine, build_dir=os.curdir): # NOTE : build_dir is used for testing purposes. 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) # setup.py ext_args = [repr(self.module_name), repr([pyxfilename, codefilename])] if self._need_numpy: np_import = 'import numpy as np\n' self._include_dirs.append('np.get_include()') else: np_import = '' with open(os.path.join(build_dir, 'setup.py'), 'w') as f: includes = str(self._include_dirs).replace("'np.get_include()'", 'np.get_include()') f.write(self.setup_template.format( ext_args=", ".join(ext_args), np_import=np_import, include_dirs=includes, library_dirs=self._library_dirs, libraries=self._libraries, extra_compile_args=self._extra_compile_args, extra_link_args=self._extra_link_args, cythonize_options=self._cythonize_options )) @classmethod def _get_wrapped_function(cls, mod, name): return getattr(mod, name + '_c')
[docs] def dump_pyx(self, routines, f, prefix): """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. """ headers = [] functions = [] for routine in routines: prototype = self.generator.get_prototype(routine) # C Function Header Import headers.append(self.pyx_header.format(header_file=prefix, prototype=prototype)) # Partition the C function arguments into categories py_rets, py_args, py_loc, py_inf = self._partition_args(routine.arguments) # Function prototype name = routine.name arg_string = ", ".join(self._prototype_arg(arg) for arg in py_args) # Local Declarations local_decs = [] for arg, val in py_inf.items(): proto = self._prototype_arg(arg) mat, ind = val local_decs.append(" cdef {0} = {1}.shape[{2}]".format(proto, mat, ind)) local_decs.extend([" cdef {0}".format(self._declare_arg(a)) for a in py_loc]) declarations = "\n".join(local_decs) if declarations: declarations = declarations + "\n" # Function Body args_c = ", ".join([self._call_arg(a) for a in routine.arguments]) rets = ", ".join([str(r.name) for r in py_rets]) if routine.results: body = ' return %s(%s)' % (routine.name, args_c) if rets: body = body + ', ' + rets else: body = ' %s(%s)\n' % (routine.name, args_c) body = body + ' return ' + rets functions.append(self.pyx_func.format(name=name, arg_string=arg_string, declarations=declarations, body=body)) # Write text to file if self._need_numpy: # Only import numpy if required f.write(self.pyx_imports) f.write('\n'.join(headers)) f.write('\n'.join(functions))
def _partition_args(self, args): """Group function arguments into categories.""" py_args = [] py_returns = [] py_locals = [] py_inferred = {} for arg in args: if isinstance(arg, OutputArgument): py_returns.append(arg) py_locals.append(arg) elif isinstance(arg, InOutArgument): py_returns.append(arg) py_args.append(arg) else: py_args.append(arg) # Find arguments that are array dimensions. These can be inferred # locally in the Cython code. if isinstance(arg, (InputArgument, InOutArgument)) and arg.dimensions: dims = [d[1] + 1 for d in arg.dimensions] sym_dims = [(i, d) for (i, d) in enumerate(dims) if isinstance(d, Symbol)] for (i, d) in sym_dims: py_inferred[d] = (arg.name, i) for arg in args: if arg.name in py_inferred: py_inferred[arg] = py_inferred.pop(arg.name) # Filter inferred arguments from py_args py_args = [a for a in py_args if a not in py_inferred] return py_returns, py_args, py_locals, py_inferred def _prototype_arg(self, arg): mat_dec = "np.ndarray[{mtype}, ndim={ndim}] {name}" np_types = {'double': 'np.double_t', 'int': 'np.int_t'} t = arg.get_datatype('c') if arg.dimensions: self._need_numpy = True ndim = len(arg.dimensions) mtype = np_types[t] return mat_dec.format(mtype=mtype, ndim=ndim, name=arg.name) else: return "%s %s" % (t, str(arg.name)) def _declare_arg(self, arg): proto = self._prototype_arg(arg) if arg.dimensions: shape = '(' + ','.join(str(i[1] + 1) for i in arg.dimensions) + ')' return proto + " = np.empty({shape})".format(shape=shape) else: return proto + " = 0" def _call_arg(self, arg): if arg.dimensions: t = arg.get_datatype('c') return "<{0}*> {1}.data".format(t, arg.name) elif isinstance(arg, ResultBase): return "&{0}".format(arg.name) else: return str(arg.name)
[docs]class F2PyCodeWrapper(CodeWrapper): """Wrapper that uses f2py""" def __init__(self, *args, **kwargs): ext_keys = ['include_dirs', 'library_dirs', 'libraries', 'extra_compile_args', 'extra_link_args'] msg = ('The compilation option kwarg {} is not supported with the f2py ' 'backend.') for k in ext_keys: if k in kwargs.keys(): warn(msg.format(k)) kwargs.pop(k, None) super(F2PyCodeWrapper, self).__init__(*args, **kwargs) @property def command(self): filename = self.filename + '.' + self.generator.code_extension args = ['-c', '-m', self.module_name, filename] command = [sys.executable, "-c", "import numpy.f2py as f2py2e;f2py2e.main()"]+args return command def _prepare_files(self, routine): pass @classmethod def _get_wrapped_function(cls, mod, name): return getattr(mod, name)
# Here we define a lookup of backends -> tuples of languages. For now, each # tuple is of length 1, but if a backend supports more than one language, # the most preferable language is listed first. _lang_lookup = {'CYTHON': ('C99', 'C89', 'C'), 'F2PY': ('F95',), 'NUMPY': ('C99', 'C89', 'C'), 'DUMMY': ('F95',)} # Dummy here just for testing def _infer_language(backend): """For a given backend, return the top choice of language""" langs = _lang_lookup.get(backend.upper(), False) if not langs: raise ValueError("Unrecognized backend: " + backend) return langs[0] def _validate_backend_language(backend, language): """Throws error if backend and language are incompatible""" langs = _lang_lookup.get(backend.upper(), False) if not langs: raise ValueError("Unrecognized backend: " + backend) if language.upper() not in langs: raise ValueError(("Backend {0} and language {1} are " "incompatible").format(backend, language))
[docs]@cacheit @doctest_depends_on(exe=('f2py', 'gfortran'), modules=('numpy',)) def autowrap(expr, language=None, backend='f2py', tempdir=None, args=None, flags=None, verbose=False, helpers=None, code_gen=None, **kwargs): """Generates python callable binaries based on the math expression. Parameters ---------- expr The SymPy expression that should be wrapped as a binary routine. language : string, optional If supplied, (options: 'C' or 'F95'), specifies the language of the generated code. If ``None`` [default], the language is inferred based upon the specified backend. backend : string, optional Backend used to wrap the generated code. Either 'f2py' [default], or 'cython'. tempdir : string, optional 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 : iterable, optional An ordered iterable of symbols. Specifies the argument sequence for the function. flags : iterable, optional Additional option flags that will be passed to the backend. verbose : bool, optional If True, autowrap will not mute the command line backends. This can be helpful for debugging. helpers : iterable, optional Used to define auxillary expressions needed for the main expr. If the main expression needs to call a specialized function it should be put in the ``helpers`` iterable. 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. code_gen : CodeGen instance An instance of a CodeGen subclass. Overrides ``language``. include_dirs : [string] A list of directories to search for C/C++ header files (in Unix form for portability). library_dirs : [string] A list of directories to search for C/C++ libraries at link time. libraries : [string] A list of library names (not filenames or paths) to link against. extra_compile_args : [string] Any extra platform- and compiler-specific information to use when compiling the source files in 'sources'. For platforms and compilers where "command line" makes sense, this is typically a list of command-line arguments, but for other platforms it could be anything. extra_link_args : [string] Any extra platform- and compiler-specific information to use when linking object files together to create the extension (or to create a new static Python interpreter). Similar interpretation as for 'extra_compile_args'. Examples -------- >>> 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 """ if language: if not isinstance(language, type): _validate_backend_language(backend, language) else: language = _infer_language(backend) helpers = [helpers] if helpers else () args = list(args) if iterable(args, exclude=set) else args if code_gen is None: code_gen = get_code_generator(language, "autowrap") CodeWrapperClass = { 'F2PY': F2PyCodeWrapper, 'CYTHON': CythonCodeWrapper, 'DUMMY': DummyWrapper }[backend.upper()] code_wrapper = CodeWrapperClass(code_gen, tempdir, flags if flags else (), verbose, **kwargs) helps = [] for name_h, expr_h, args_h in helpers: helps.append(make_routine(name_h, expr_h, args_h)) for name_h, expr_h, args_h in helpers: if expr.has(expr_h): name_h = binary_function(name_h, expr_h, backend='dummy') expr = expr.subs(expr_h, name_h(*args_h)) try: routine = make_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 = make_routine('autofunc', expr, args + new_args) return code_wrapper.wrap_code(routine, helpers=helps)
[docs]@doctest_depends_on(exe=('f2py', 'gfortran'), modules=('numpy',)) 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(). Parameters ---------- symfunc : sympy Function The function to bind the callable to. expr : sympy Expression The expression used to generate the function. kwargs : dict Any kwargs accepted by autowrap. Examples -------- >>> 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)
################################################################# # UFUNCIFY # ################################################################# _ufunc_top = Template("""\ #include "Python.h" #include "math.h" #include "numpy/ndarraytypes.h" #include "numpy/ufuncobject.h" #include "numpy/halffloat.h" #include ${include_file} static PyMethodDef ${module}Methods[] = { {NULL, NULL, 0, NULL} };""") _ufunc_outcalls = Template("*((double *)out${outnum}) = ${funcname}(${call_args});") _ufunc_body = Template("""\ static void ${funcname}_ufunc(char **args, npy_intp *dimensions, npy_intp* steps, void* data) { npy_intp i; npy_intp n = dimensions[0]; ${declare_args} ${declare_steps} for (i = 0; i < n; i++) { ${outcalls} ${step_increments} } } PyUFuncGenericFunction ${funcname}_funcs[1] = {&${funcname}_ufunc}; static char ${funcname}_types[${n_types}] = ${types} static void *${funcname}_data[1] = {NULL};""") _ufunc_bottom = Template("""\ #if PY_VERSION_HEX >= 0x03000000 static struct PyModuleDef moduledef = { PyModuleDef_HEAD_INIT, "${module}", NULL, -1, ${module}Methods, NULL, NULL, NULL, NULL }; PyMODINIT_FUNC PyInit_${module}(void) { PyObject *m, *d; ${function_creation} m = PyModule_Create(&moduledef); if (!m) { return NULL; } import_array(); import_umath(); d = PyModule_GetDict(m); ${ufunc_init} return m; } #else PyMODINIT_FUNC init${module}(void) { PyObject *m, *d; ${function_creation} m = Py_InitModule("${module}", ${module}Methods); if (m == NULL) { return; } import_array(); import_umath(); d = PyModule_GetDict(m); ${ufunc_init} } #endif\ """) _ufunc_init_form = Template("""\ ufunc${ind} = PyUFunc_FromFuncAndData(${funcname}_funcs, ${funcname}_data, ${funcname}_types, 1, ${n_in}, ${n_out}, PyUFunc_None, "${module}", ${docstring}, 0); PyDict_SetItemString(d, "${funcname}", ufunc${ind}); Py_DECREF(ufunc${ind});""") _ufunc_setup = Template("""\ def configuration(parent_package='', top_path=None): import numpy from numpy.distutils.misc_util import Configuration config = Configuration('', parent_package, top_path) config.add_extension('${module}', sources=['${module}.c', '${filename}.c']) return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration=configuration)""")
[docs]class UfuncifyCodeWrapper(CodeWrapper): """Wrapper for Ufuncify""" def __init__(self, *args, **kwargs): ext_keys = ['include_dirs', 'library_dirs', 'libraries', 'extra_compile_args', 'extra_link_args'] msg = ('The compilation option kwarg {} is not supported with the numpy' ' backend.') for k in ext_keys: if k in kwargs.keys(): warn(msg.format(k)) kwargs.pop(k, None) super(UfuncifyCodeWrapper, self).__init__(*args, **kwargs) @property def command(self): command = [sys.executable, "setup.py", "build_ext", "--inplace"] return command def wrap_code(self, routines, helpers=None): # This routine overrides CodeWrapper because we can't assume funcname == routines[0].name # Therefore we have to break the CodeWrapper private API. # There isn't an obvious way to extend multi-expr support to # the other autowrap backends, so we limit this change to ufuncify. helpers = helpers if helpers is not None else [] # We just need a consistent name funcname = 'wrapped_' + str(id(routines) + id(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(routines, helpers) self._prepare_files(routines, funcname) self._process_files(routines) mod = __import__(self.module_name) finally: sys.path.remove(workdir) CodeWrapper._module_counter += 1 os.chdir(oldwork) if not self.filepath: try: shutil.rmtree(workdir) except OSError: # Could be some issues on Windows pass return self._get_wrapped_function(mod, funcname) def _generate_code(self, main_routines, helper_routines): all_routines = main_routines + helper_routines self.generator.write( all_routines, self.filename, True, self.include_header, self.include_empty) def _prepare_files(self, routines, funcname): # C codefilename = self.module_name + '.c' with open(codefilename, 'w') as f: self.dump_c(routines, f, self.filename, funcname=funcname) # setup.py with open('setup.py', 'w') as f: self.dump_setup(f) @classmethod def _get_wrapped_function(cls, mod, name): return getattr(mod, name) def dump_setup(self, f): setup = _ufunc_setup.substitute(module=self.module_name, filename=self.filename) f.write(setup)
[docs] def dump_c(self, routines, f, prefix, funcname=None): """Write a C file with python wrappers This file contains all the definitions of the routines in c code. Arguments --------- routines List of Routine instances f File-like object to write the file to prefix The filename prefix, used to name the imported module. funcname Name of the main function to be returned. """ if (funcname is None) and (len(routines) == 1): funcname = routines[0].name elif funcname is None: msg = 'funcname must be specified for multiple output routines' raise ValueError(msg) functions = [] function_creation = [] ufunc_init = [] module = self.module_name include_file = "\"{0}.h\"".format(prefix) top = _ufunc_top.substitute(include_file=include_file, module=module) name = funcname # Partition the C function arguments into categories # Here we assume all routines accept the same arguments r_index = 0 py_in, _ = self._partition_args(routines[0].arguments) n_in = len(py_in) n_out = len(routines) # Declare Args form = "char *{0}{1} = args[{2}];" arg_decs = [form.format('in', i, i) for i in range(n_in)] arg_decs.extend([form.format('out', i, i+n_in) for i in range(n_out)]) declare_args = '\n '.join(arg_decs) # Declare Steps form = "npy_intp {0}{1}_step = steps[{2}];" step_decs = [form.format('in', i, i) for i in range(n_in)] step_decs.extend([form.format('out', i, i+n_in) for i in range(n_out)]) declare_steps = '\n '.join(step_decs) # Call Args form = "*(double *)in{0}" call_args = ', '.join([form.format(a) for a in range(n_in)]) # Step Increments form = "{0}{1} += {0}{1}_step;" step_incs = [form.format('in', i) for i in range(n_in)] step_incs.extend([form.format('out', i, i) for i in range(n_out)]) step_increments = '\n '.join(step_incs) # Types n_types = n_in + n_out types = "{" + ', '.join(["NPY_DOUBLE"]*n_types) + "};" # Docstring docstring = '"Created in SymPy with Ufuncify"' # Function Creation function_creation.append("PyObject *ufunc{0};".format(r_index)) # Ufunc initialization init_form = _ufunc_init_form.substitute(module=module, funcname=name, docstring=docstring, n_in=n_in, n_out=n_out, ind=r_index) ufunc_init.append(init_form) outcalls = [_ufunc_outcalls.substitute( outnum=i, call_args=call_args, funcname=routines[i].name) for i in range(n_out)] body = _ufunc_body.substitute(module=module, funcname=name, declare_args=declare_args, declare_steps=declare_steps, call_args=call_args, step_increments=step_increments, n_types=n_types, types=types, outcalls='\n '.join(outcalls)) functions.append(body) body = '\n\n'.join(functions) ufunc_init = '\n '.join(ufunc_init) function_creation = '\n '.join(function_creation) bottom = _ufunc_bottom.substitute(module=module, ufunc_init=ufunc_init, function_creation=function_creation) text = [top, body, bottom] f.write('\n\n'.join(text))
def _partition_args(self, args): """Group function arguments into categories.""" py_in = [] py_out = [] for arg in args: if isinstance(arg, OutputArgument): py_out.append(arg) elif isinstance(arg, InOutArgument): raise ValueError("Ufuncify doesn't support InOutArguments") else: py_in.append(arg) return py_in, py_out
[docs]@cacheit @doctest_depends_on(exe=('f2py', 'gfortran', 'gcc'), modules=('numpy',)) def ufuncify(args, expr, language=None, backend='numpy', tempdir=None, flags=None, verbose=False, helpers=None, **kwargs): """Generates a binary function that supports broadcasting on numpy arrays. Parameters ---------- args : iterable Either a Symbol or an iterable of symbols. Specifies the argument sequence for the function. expr A SymPy expression that defines the element wise operation. language : string, optional If supplied, (options: 'C' or 'F95'), specifies the language of the generated code. If ``None`` [default], the language is inferred based upon the specified backend. backend : string, optional Backend used to wrap the generated code. Either 'numpy' [default], 'cython', or 'f2py'. tempdir : string, optional 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. flags : iterable, optional Additional option flags that will be passed to the backend. verbose : bool, optional If True, autowrap will not mute the command line backends. This can be helpful for debugging. helpers : iterable, optional Used to define auxillary expressions needed for the main expr. If the main expression needs to call a specialized function it should be put in the ``helpers`` iterable. 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. kwargs : dict These kwargs will be passed to autowrap if the `f2py` or `cython` backend is used and ignored if the `numpy` backend is used. Note ---- The default backend ('numpy') will create actual instances of ``numpy.ufunc``. These support ndimensional broadcasting, and implicit type conversion. Use of the other backends will result in a "ufunc-like" function, which requires equal length 1-dimensional arrays for all arguments, and will not perform any type conversions. 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) >>> type(f) <class 'numpy.ufunc'> >>> f([1, 2, 3], 2) array([ 3., 6., 11.]) >>> f(np.arange(5), 3) array([ 3., 4., 7., 12., 19.]) For the 'f2py' and 'cython' backends, inputs are required to be equal length 1-dimensional arrays. The 'f2py' backend will perform type conversion, but the Cython backend will error if the inputs are not of the expected type. >>> f_fortran = ufuncify((x, y), y + x**2, backend='f2py') >>> f_fortran(1, 2) array([ 3.]) >>> f_fortran(np.array([1, 2, 3]), np.array([1.0, 2.0, 3.0])) array([ 2., 6., 12.]) >>> f_cython = ufuncify((x, y), y + x**2, backend='Cython') >>> f_cython(1, 2) # doctest: +ELLIPSIS Traceback (most recent call last): ... TypeError: Argument '_x' has incorrect type (expected numpy.ndarray, got int) >>> f_cython(np.array([1.0]), np.array([2.0])) array([ 3.]) """ if isinstance(args, Symbol): args = (args,) else: args = tuple(args) if language: _validate_backend_language(backend, language) else: language = _infer_language(backend) helpers = helpers if helpers else () flags = flags if flags else () if backend.upper() == 'NUMPY': # maxargs is set by numpy compile-time constant NPY_MAXARGS # If a future version of numpy modifies or removes this restriction # this variable should be changed or removed maxargs = 32 helps = [] for name, expr, args in helpers: helps.append(make_routine(name, expr, args)) code_wrapper = UfuncifyCodeWrapper(C99CodeGen("ufuncify"), tempdir, flags, verbose) if not isinstance(expr, (list, tuple)): expr = [expr] if len(expr) == 0: raise ValueError('Expression iterable has zero length') if (len(expr) + len(args)) > maxargs: msg = ('Cannot create ufunc with more than {0} total arguments: ' 'got {1} in, {2} out') raise ValueError(msg.format(maxargs, len(args), len(expr))) routines = [make_routine('autofunc{}'.format(idx), exprx, args) for idx, exprx in enumerate(expr)] return code_wrapper.wrap_code(routines, helpers=helps) else: # Dummies are used for all added expressions to prevent name clashes # within the original expression. y = IndexedBase(Dummy()) m = Dummy(integer=True) i = Idx(Dummy(integer=True), m) f = implemented_function(Dummy().name, Lambda(args, expr)) # For each of the args create an indexed version. indexed_args = [IndexedBase(Dummy(str(a))) for a in args] # Order the arguments (out, args, dim) args = [y] + indexed_args + [m] args_with_indices = [a[i] for a in indexed_args] return autowrap(Eq(y[i], f(*args_with_indices)), language, backend, tempdir, args, flags, verbose, helpers, **kwargs)