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# should re-write compiled functions to take a local and global dict
# as input.
from __future__ import absolute_import, print_function
import sys
import os
from . import ext_tools
from . import catalog
from . import common_info
from numpy.core.multiarray import _get_ndarray_c_version
ndarray_api_version = '/* NDARRAY API VERSION %x */' % (_get_ndarray_c_version(),)
# not an easy way for the user_path_list to come in here.
# the PYTHONCOMPILED environment variable offers the most hope.
function_catalog = catalog.catalog()
class inline_ext_function(ext_tools.ext_function):
# Some specialization is needed for inline extension functions
def function_declaration_code(self):
code = 'static PyObject* %s(PyObject*self, PyObject* args)\n{\n'
return code % self.name
def template_declaration_code(self):
code = 'template<class T>\n' \
'static PyObject* %s(PyObject*self, PyObject* args)\n{\n'
return code % self.name
def parse_tuple_code(self):
""" Create code block for PyArg_ParseTuple. Variable declarations
for all PyObjects are done also.
This code got a lot uglier when I added local_dict...
"""
declare_return = 'py::object return_val;\n' \
'int exception_occurred = 0;\n' \
'PyObject *py__locals = NULL;\n' \
'PyObject *py__globals = NULL;\n'
py_objects = ', '.join(self.arg_specs.py_pointers())
if py_objects:
declare_py_objects = 'PyObject ' + py_objects + ';\n'
else:
declare_py_objects = ''
py_vars = ' = '.join(self.arg_specs.py_variables())
if py_vars:
init_values = py_vars + ' = NULL;\n\n'
else:
init_values = ''
parse_tuple = 'if(!PyArg_ParseTuple(args,"OO:compiled_func",'\
'&py__locals,'\
'&py__globals))\n'\
' return NULL;\n'
return declare_return + declare_py_objects + \
init_values + parse_tuple
def arg_declaration_code(self):
"""Return the declaration code as a string."""
arg_strings = [arg.declaration_code(inline=1)
for arg in self.arg_specs]
return "".join(arg_strings)
def arg_cleanup_code(self):
"""Return the cleanup code as a string."""
arg_strings = [arg.cleanup_code() for arg in self.arg_specs]
return "".join(arg_strings)
def arg_local_dict_code(self):
"""Return the code to create the local dict as a string."""
arg_strings = [arg.local_dict_code() for arg in self.arg_specs]
return "".join(arg_strings)
def function_code(self):
from .ext_tools import indent
decl_code = indent(self.arg_declaration_code(),4)
cleanup_code = indent(self.arg_cleanup_code(),4)
function_code = indent(self.code_block,4)
# local_dict_code = indent(self.arg_local_dict_code(),4)
try_code = \
' try \n' \
' { \n' \
'#if defined(__GNUC__) || defined(__ICC)\n' \
' PyObject* raw_locals __attribute__ ((unused));\n' \
' PyObject* raw_globals __attribute__ ((unused));\n' \
'#else\n' \
' PyObject* raw_locals;\n' \
' PyObject* raw_globals;\n' \
'#endif\n' \
' raw_locals = py_to_raw_dict(py__locals,"_locals");\n' \
' raw_globals = py_to_raw_dict(py__globals,"_globals");\n' \
' /* argument conversion code */ \n' \
+ decl_code + \
' /* inline code */ \n' \
+ function_code + \
' /*I would like to fill in changed locals and globals here...*/ \n' \
' }\n'
catch_code = "catch(...) \n" \
"{ \n" + \
" return_val = py::object(); \n" \
" exception_occurred = 1; \n" \
"} \n"
return_code = " /* cleanup code */ \n" + \
cleanup_code + \
" if(!(PyObject*)return_val && !exception_occurred)\n" \
" {\n \n" \
" return_val = Py_None; \n" \
" }\n \n" \
" return return_val.disown(); \n" \
"} \n"
all_code = self.function_declaration_code() + \
indent(self.parse_tuple_code(),4) + \
try_code + \
indent(catch_code,4) + \
return_code
return all_code
def python_function_definition_code(self):
args = (self.name, self.name)
function_decls = '{"%s",(PyCFunction)%s , METH_VARARGS},\n' % args
return function_decls
class inline_ext_module(ext_tools.ext_module):
def __init__(self,name,compiler=''):
ext_tools.ext_module.__init__(self,name,compiler)
self._build_information.append(common_info.inline_info())
function_cache = {}
def inline(code,arg_names=[],local_dict=None, global_dict=None,
force=0,
compiler='',
verbose=0,
support_code=None,
headers=[],
customize=None,
type_converters=None,
auto_downcast=1,
newarr_converter=0,
**kw):
"""
Inline C/C++ code within Python scripts.
``inline()`` compiles and executes C/C++ code on the fly. Variables
in the local and global Python scope are also available in the
C/C++ code. Values are passed to the C/C++ code by assignment
much like variables passed are passed into a standard Python
function. Values are returned from the C/C++ code through a
special argument called return_val. Also, the contents of
mutable objects can be changed within the C/C++ code and the
changes remain after the C code exits and returns to Python.
inline has quite a few options as listed below. Also, the keyword
arguments for distutils extension modules are accepted to
specify extra information needed for compiling.
Parameters
----------
code : string
A string of valid C++ code. It should not specify a return
statement. Instead it should assign results that need to be
returned to Python in the `return_val`.
arg_names : [str], optional
A list of Python variable names that should be transferred from
Python into the C/C++ code. It defaults to an empty string.
local_dict : dict, optional
If specified, it is a dictionary of values that should be used as
the local scope for the C/C++ code. If local_dict is not
specified the local dictionary of the calling function is used.
global_dict : dict, optional
If specified, it is a dictionary of values that should be used as
the global scope for the C/C++ code. If `global_dict` is not
specified, the global dictionary of the calling function is used.
force : {0, 1}, optional
If 1, the C++ code is compiled every time inline is called. This
is really only useful for debugging, and probably only useful if
your editing `support_code` a lot.
compiler : str, optional
The name of compiler to use when compiling. On windows, it
understands 'msvc' and 'gcc' as well as all the compiler names
understood by distutils. On Unix, it'll only understand the
values understood by distutils. (I should add 'gcc' though to
this).
On windows, the compiler defaults to the Microsoft C++ compiler.
If this isn't available, it looks for mingw32 (the gcc compiler).
On Unix, it'll probably use the same compiler that was used when
compiling Python. Cygwin's behavior should be similar.
verbose : {0,1,2}, optional
Speficies how much much information is printed during the compile
phase of inlining code. 0 is silent (except on windows with msvc
where it still prints some garbage). 1 informs you when compiling
starts, finishes, and how long it took. 2 prints out the command
lines for the compilation process and can be useful if your having
problems getting code to work. Its handy for finding the name of
the .cpp file if you need to examine it. verbose has no affect if
the compilation isn't necessary.
support_code : str, optional
A string of valid C++ code declaring extra code that might be
needed by your compiled function. This could be declarations of
functions, classes, or structures.
headers : [str], optional
A list of strings specifying header files to use when compiling
the code. The list might look like ``["<vector>","'my_header'"]``.
Note that the header strings need to be in a form than can be
pasted at the end of a ``#include`` statement in the C++ code.
customize : base_info.custom_info, optional
An alternative way to specify `support_code`, `headers`, etc. needed
by the function. See :mod:`scipy.weave.base_info` for more
details. (not sure this'll be used much).
type_converters : [type converters], optional
These guys are what convert Python data types to C/C++ data types.
If you'd like to use a different set of type conversions than the
default, specify them here. Look in the type conversions section
of the main documentation for examples.
auto_downcast : {1,0}, optional
This only affects functions that have numpy arrays as input
variables. Setting this to 1 will cause all floating point values
to be cast as float instead of double if all the Numeric arrays
are of type float. If even one of the arrays has type double or
double complex, all variables maintain there standard
types.
newarr_converter : int, optional
Unused.
Other Parameters
----------------
Relevant :mod:`distutils` keywords. These are duplicated from Greg Ward's
:class:`distutils.extension.Extension` class for convenience:
sources : [string]
list of source filenames, relative to the distribution root
(where the setup script lives), in Unix form (slash-separated)
for portability. Source files may be C, C++, SWIG (.i),
platform-specific resource files, or whatever else is recognized
by the "build_ext" command as source for a Python extension.
.. note:: The `module_path` file is always appended to the front of
this list
include_dirs : [string]
list of directories to search for C/C++ header files (in Unix
form for portability)
define_macros : [(name : string, value : string|None)]
list of macros to define; each macro is defined using a 2-tuple,
where 'value' is either the string to define it to or None to
define it without a particular value (equivalent of "#define
FOO" in source or -DFOO on Unix C compiler command line)
undef_macros : [string]
list of macros to undefine explicitly
library_dirs : [string]
list of directories to search for C/C++ libraries at link time
libraries : [string]
list of library names (not filenames or paths) to link against
runtime_library_dirs : [string]
list of directories to search for C/C++ libraries at run time
(for shared extensions, this is when the extension is loaded)
extra_objects : [string]
list of extra files to link with (eg. object files not implied
by 'sources', static library that must be explicitly specified,
binary resource files, etc.)
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'.
export_symbols : [string]
list of symbols to be exported from a shared extension. Not
used on all platforms, and not generally necessary for Python
extensions, which typically export exactly one symbol: "init" +
extension_name.
swig_opts : [string]
any extra options to pass to SWIG if a source file has the .i
extension.
depends : [string]
list of files that the extension depends on
language : string
extension language (i.e. "c", "c++", "objc"). Will be detected
from the source extensions if not provided.
See Also
--------
distutils.extension.Extension : Describes additional parameters.
"""
# this grabs the local variables from the *previous* call
# frame -- that is the locals from the function that called
# inline.
global function_catalog
call_frame = sys._getframe().f_back
if local_dict is None:
local_dict = call_frame.f_locals
if global_dict is None:
global_dict = call_frame.f_globals
if force:
module_dir = global_dict.get('__file__',None)
func = compile_function(code,arg_names,local_dict,
global_dict,module_dir,
compiler=compiler,
verbose=verbose,
support_code=support_code,
headers=headers,
customize=customize,
type_converters=type_converters,
auto_downcast=auto_downcast,
**kw)
function_catalog.add_function(code,func,module_dir)
results = attempt_function_call(code,local_dict,global_dict)
else:
# 1. try local cache
try:
results = apply(function_cache[code],(local_dict,global_dict))
return results
except TypeError as msg:
msg = str(msg).strip()
if msg[:16] == "Conversion Error":
pass
else:
raise TypeError(msg)
except NameError as msg:
msg = str(msg).strip()
if msg[:16] == "Conversion Error":
pass
else:
raise NameError(msg)
except KeyError:
pass
# 2. try function catalog
try:
results = attempt_function_call(code,local_dict,global_dict)
# 3. build the function
except ValueError:
# compile the library
module_dir = global_dict.get('__file__',None)
func = compile_function(code,arg_names,local_dict,
global_dict,module_dir,
compiler=compiler,
verbose=verbose,
support_code=support_code,
headers=headers,
customize=customize,
type_converters=type_converters,
auto_downcast=auto_downcast,
**kw)
function_catalog.add_function(code,func,module_dir)
results = attempt_function_call(code,local_dict,global_dict)
return results
def attempt_function_call(code,local_dict,global_dict):
# we try 3 levels here -- a local cache first, then the
# catalog cache, and then persistent catalog.
#
global function_catalog
# 1. try local cache
try:
results = apply(function_cache[code],(local_dict,global_dict))
return results
except TypeError as msg:
msg = str(msg).strip()
if msg[:16] == "Conversion Error":
pass
else:
raise TypeError(msg)
except NameError as msg:
msg = str(msg).strip()
if msg[:16] == "Conversion Error":
pass
else:
raise NameError(msg)
except KeyError:
pass
# 2. try catalog cache.
function_list = function_catalog.get_functions_fast(code)
for func in function_list:
try:
results = apply(func,(local_dict,global_dict))
function_catalog.fast_cache(code,func)
function_cache[code] = func
return results
except TypeError as msg: # should specify argument types here.
# This should really have its own error type, instead of
# checking the beginning of the message, but I don't know
# how to define that yet.
msg = str(msg)
if msg[:16] == "Conversion Error":
pass
else:
raise TypeError(msg)
except NameError as msg:
msg = str(msg).strip()
if msg[:16] == "Conversion Error":
pass
else:
raise NameError(msg)
# 3. try persistent catalog
module_dir = global_dict.get('__file__',None)
function_list = function_catalog.get_functions(code,module_dir)
for func in function_list:
try:
results = apply(func,(local_dict,global_dict))
function_catalog.fast_cache(code,func)
function_cache[code] = func
return results
except: # should specify argument types here.
pass
# if we get here, the function wasn't found
raise ValueError('function with correct signature not found')
def inline_function_code(code,arg_names,local_dict=None,
global_dict=None,auto_downcast=1,
type_converters=None,compiler=''):
call_frame = sys._getframe().f_back
if local_dict is None:
local_dict = call_frame.f_locals
if global_dict is None:
global_dict = call_frame.f_globals
ext_func = inline_ext_function('compiled_func',code,arg_names,
local_dict,global_dict,auto_downcast,
type_converters=type_converters)
from . import build_tools
compiler = build_tools.choose_compiler(compiler)
ext_func.set_compiler(compiler)
return ext_func.function_code()
def compile_function(code,arg_names,local_dict,global_dict,
module_dir,
compiler='',
verbose=1,
support_code=None,
headers=[],
customize=None,
type_converters=None,
auto_downcast=1,
**kw):
# figure out where to store and what to name the extension module
# that will contain the function.
# storage_dir = catalog.intermediate_dir()
code = ndarray_api_version + '\n' + code
module_path = function_catalog.unique_module_name(code, module_dir)
storage_dir, module_name = os.path.split(module_path)
mod = inline_ext_module(module_name,compiler)
# create the function. This relies on the auto_downcast and
# type factories setting
ext_func = inline_ext_function('compiled_func',code,arg_names,
local_dict,global_dict,auto_downcast,
type_converters=type_converters)
mod.add_function(ext_func)
# if customize (a custom_info object), then set the module customization.
if customize:
mod.customize = customize
# add the extra "support code" needed by the function to the module.
if support_code:
mod.customize.add_support_code(support_code)
# add the extra headers needed by the function to the module.
for header in headers:
mod.customize.add_header(header)
# it's nice to let the users know when anything gets compiled, as the
# slowdown is very noticeable.
if verbose > 0:
print('<weave: compiling>')
# compile code in correct location, with the given compiler and verbosity
# setting. All input keywords are passed through to distutils
mod.compile(location=storage_dir,compiler=compiler,
verbose=verbose, **kw)
# import the module and return the function. Make sure
# the directory where it lives is in the python path.
try:
sys.path.insert(0,storage_dir)
exec('import ' + module_name)
func = eval(module_name+'.compiled_func')
finally:
del sys.path[0]
return func
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