"""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
_doctest_depends_on = {'exe': ('f2py', 'gfortran', 'gcc'), 'modules': ('numpy',)}
import sys
import os
import shutil
import tempfile
from subprocess import STDOUT, CalledProcessError, check_output
from string import Template
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, CCodeGen)
from sympy.utilities.lambdify import implemented_function
from sympy.utilities.decorator import doctest_depends_on
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:\n"
" from setuptools import setup\n"
" from setuptools import Extension\n"
"except ImportError:\n"
" from distutils.core import setup\n"
" from distutils.extension import Extension\n"
"from Cython.Distutils import build_ext\n"
"{np_import}"
"\n"
"setup(\n"
" cmdclass = {{'build_ext': build_ext}},\n"
" ext_modules = [Extension({ext_args},\n"
" extra_compile_args=['-std=c99'])],\n"
"{np_includes}"
" )")
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}")
def __init__(self, *args, **kwargs):
super(CythonCodeWrapper, self).__init__(*args, **kwargs)
self._need_numpy = False
@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)
# setup.py
ext_args = [repr(self.module_name), repr([pyxfilename, codefilename])]
if self._need_numpy:
np_import = 'import numpy as np\n'
np_includes = ' include_dirs = [np.get_include()],\n'
else:
np_import = ''
np_includes = ''
with open('setup.py', 'w') as f:
f.write(self.setup_template.format(ext_args=", ".join(ext_args),
np_import=np_import,
np_includes=np_includes))
@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"""
@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)
def _get_code_wrapper_class(backend):
wrappers = {'F2PY': F2PyCodeWrapper, 'CYTHON': CythonCodeWrapper,
'DUMMY': DummyWrapper}
return wrappers[backend.upper()]
# 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': ('C',),
'F2PY': ('F95',),
'NUMPY': ('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))
@cacheit
@doctest_depends_on(exe=('f2py', 'gfortran'), modules=('numpy',))
[docs]def autowrap(
expr, language=None, backend='f2py', tempdir=None, args=None, flags=None,
verbose=False, helpers=None):
"""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.
>>> 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:
_validate_backend_language(backend, language)
else:
language = _infer_language(backend)
helpers = [helpers] if helpers else ()
flags = flags if flags else ()
args = list(args) if iterable(args, exclude=set) else args
code_generator = get_code_generator(language, "autowrap")
CodeWrapperClass = _get_code_wrapper_class(backend)
code_wrapper = CodeWrapperClass(code_generator, tempdir, flags, verbose)
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)
@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)
#################################################################
# 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_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++) {
*((double *)out1) = ${funcname}(${call_args});
${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"""
@property
def command(self):
command = [sys.executable, "setup.py", "build_ext", "--inplace"]
return command
def _prepare_files(self, routine):
# C
codefilename = self.module_name + '.c'
with open(codefilename, 'w') as f:
self.dump_c([routine], f, self.filename)
# 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):
"""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.
"""
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)
for r_index, routine in enumerate(routines):
name = routine.name
# Partition the C function arguments into categories
py_in, py_out = self._partition_args(routine.arguments)
n_in = len(py_in)
n_out = 1
# Declare Args
form = "char *{0}{1} = args[{2}];"
arg_decs = [form.format('in', i, i) for i in range(n_in)]
arg_decs.append(form.format('out', 1, n_in))
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.append(form.format('out', 1, n_in))
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.append(form.format('out', 1))
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)
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)
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):
if py_out:
msg = "Ufuncify doesn't support multiple OutputArguments"
raise ValueError(msg)
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
@cacheit
@doctest_depends_on(exe=('f2py', 'gfortran', 'gcc'), modules=('numpy',))
[docs]def ufuncify(args, expr, language=None, backend='numpy', tempdir=None,
flags=None, verbose=False, helpers=None):
"""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.
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)
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)
3
>>> f_fortran(numpy.array([1, 2, 3]), numpy.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)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Argument '_x' has incorrect type (expected numpy.ndarray, got int)
>>> f_cython(numpy.array([1.0]), numpy.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':
routine = make_routine('autofunc', expr, args)
helps = []
for name, expr, args in helpers:
helps.append(make_routine(name, expr, args))
code_wrapper = UfuncifyCodeWrapper(CCodeGen("ufuncify"), tempdir,
flags, verbose)
return code_wrapper.wrap_code(routine, 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)
