Boost Python Exec_file Error
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here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and python exec format error policies of this site About Us Learn more about Stack Overflow the company perl exec format error Business Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users ruby exec format error Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a php exec format error minute: Sign up Python exception text on syntax errors (boost library) up vote 1 down vote favorite 1 I've got this code snnipet (the whole program compiles and links correctly): ... try { boost::python::exec_file( "myscript.py", // this file contains a syntax error my_main_namespace, my_local_namespace ); return true; } catch(const boost::python::error_already_set &) { PyObject *ptype, *pvalue, *ptraceback; PyErr_Fetch(&ptype, &pvalue, &ptraceback); // the
Python Execute File
next line crashes on syntax error std::string error = boost::python::extract
documentation home page. Boost.Python Frequently Asked Questions (FAQs) How can I wrap a function which takes a function pointer as an argument? I'm getting the "attempt to return dangling reference" error. What am I doing wrong? Is
Python Eval File
return_internal_reference efficient? How can I wrap functions which take C++ containers as arguments? fatal error python os file C1204:Compiler limit:internal structure overflow How do I debug my Python extensions? Why doesn't my *= operator work? Does Boost.Python work with Mac OS boost python error_already_set X? How can I find the existing PyObject that holds a C++ object? How can I wrap a function which needs to take ownership of a raw pointer? Compilation takes too much time and eats too much memory! http://stackoverflow.com/questions/5026597/python-exception-text-on-syntax-errors-boost-library What can I do to make it faster? How do I create sub-packages using Boost.Python? error C2064: term does not evaluate to a function taking 2 arguments How can I automatically convert my custom string type to and from a Python string? Why is my automatic to-python conversion not being found? Is Boost.Python thread-aware/compatible with multiple interpreters? How can I wrap a function which takes a function pointer as an argument? If what you're trying to http://www.boost.org/doc/libs/1_43_0/libs/python/doc/v2/faq.html do is something like this: typedef boost::function
C++, one issue you will almost certainly have to deal with is handling exceptions thrown from the Python code. Python exceptions are not exceptions in the C++-language sense. That is, an exception thrown in Python code does https://misspent.wordpress.com/2009/10/11/boost-python-and-handling-python-exceptions/ not start stack unwinding in C++ or trigger catch blocks. Rather, a Python exception http://thejosephturner.com/blog/post/embedding-python-in-c-applications-with-boostpython-part-1/ is generally indicated by an error return value from a C-API function call, and information about the exception can be retrieved by yet more calls to the Python C-API. Clearly, handling Python exceptions from C++ code requires diligence and consistent checking of error codes, and, really, who wants to deal with that? (A: C programmers, apparently.) A exec format more natural system is one in which Python exceptions are somehow converted to C++ exceptions at the Python-C++ boundary, and where exception propagation continues out of Python into C++. Boost.Python makes it much easier deal with Python exceptions in a consistent and uniform manner with the boost::python::error_already_set exception. This C++ exception is thrown whenever a Boost.Python operation results in a Python exception being thrown. Consider the following code: using namespace exec format error boost::python; object obj = ...; try { double length = extract
base. In short, it allows me to leverage all the benefits of the Python language and the Python standard library when approaching tasks that are normally painful or awkward in C++. The underlying subtext, of course, is that I didn't have to port any of the existing C++ code to do so. Today, I'd like to take a look at some first steps at using boost::python to embed Python in C++ and interact with Python objects. I've put all the code from this section in a github repo, so feel free to check the code out and play along. At it's core, embedding Python is very simple, and requires no C++ code whatsoever - the libraries provided with a Python distribution include C bindings. I'm going to skip over all that though, and jump straight into using Python in C++ via boost::python, which provides class wrappers and polymorphic behavior much more consistent with actual Python code than the C bindings would allow. In the later parts of this tutorial, we'll cover a few things that you can't do with boost::python (notably, multithreading and error handling). So anyway, to get started you need to download and build boost, or retrieve a copy from your package manager. If you choose to build it, you can build just the boost::python library (it is unfortunately not header-only), though I would suggest getting familiar with the entire set of libraries if you do a lot of C++ programming. If you are following along with the git repo, make sure you change the path in the Makefile to point to your boost installation directory. And thus concludes the exposition. Let's dive in! First, we need to be able to build an application with Python embedded. With gcc this isn't too difficult; it is simply a matter of including boost::python and libpython as either static or shared libraries. Depending on how you build boost, you may have trouble mixing and matching. In the tutorial code on github, we will use the static boost::python library (libboost_python.a) and the dynamic version of the Python library (libpython.so). One of the soft requirements I had for my development efforts at MiserWare was to make the environment consistent across all of our support operating systems: several Windows and an ever-changing list of Linux distros. As a result, Granola links against a pinned version of Python and the installation packages include the Python library files required to run our code. Not ideal, perhaps, but it results in an environment where I am positive our code will run across all supported operating systems. Let's get some code running. You'll need to include the correct headers, as you might imagine. Py_Initialize(); py::object main_module = py::imp