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1. Top results related to what is python's logging module in c compiler code generator

   • What is the equivalent of Python's list[:x] in C++?

     Top Answer

     Answered Apr 01, 2015 · 3 votes

     There are several ways:

     1) Create a vector v consisting of first x elements as:

     std::vector<T> v { begin(L), begin(L) + x };-

     2) Pass first x elements to a function, as pair of iterators:

     f(begin(L), begin(L) + x);-

     where f accepts two iterators as arguments — explore the standard algorithms from <algorithm>, as almost all of them work on pair of iterators.

     Depending on your use case, you could use any of them.

     Check all answers on Stack Overflow

     1/5

   • What is Python's default logging formatter?

     Top Answer

     Answered Jul 18, 2024 · 91 votes

     The default format is located here which is:

     BASIC_FORMAT = "%(levelname)s:%(name)s:%(message)s" -

     The section on formatting in the python docs will tell you how you can customize it. Here is one example on how you can customize it.

     import sysimport logging-logging.basicConfig( level=logging.DEBUG, format="[%(asctime)s] %(levelname)s [%(name)s.%(funcName)s:%(lineno)d] %(message)s", datefmt="%d/%b/%Y %H:%M:%S", stream=sys.stdout)-logging.info("HEY")-

     Which results in:

     [26/May/2013 06:41:40] INFO [root.<module>:1] HEY-

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     2/5

   • What is the significance of condition.notify() in python's threading module?

     Top Answer

     Answered Aug 06, 2018 · 0 votes

     1) It signals thread waiting for a lock release it can continue execution.

     2) Condition.notify(n) takes up to n threads from the internal queue and calls release on the locks they are waiting for - thus waking them up. If the internal queue is empty, there is no one to wake up and the notify call has no effect. That's why from the beginning, removing notify had no effect, but once consumer threads called wait, there was no one to wake them up and they were waiting for ever.

     Check all answers on Stack Overflow

     3/5

   • What is the C++ equivalent of Python's "in" operator?

     Top Answer

     Answered Oct 06, 2020 · 63 votes

     The time complexity of Python's in operator varies depending on the data structure it is actually called with. When you use it with a list, complexity is linear (as one would expect from an unsorted array without an index). When you use it to look up set membership or presence of a dictionary key complexity is constant on average (as one would expect from a hash table based implementation):

     • https://wiki.python.org/moin/TimeComplexity

     In C++ you can use std::find to determine whether or not an item is contained in a std::vector. Complexity is said to be linear (as one would expect from an unsorted array without an index). If you make sure the vector is sorted, you can also use std::binary_search to achieve the same in logarithmic time.

     • http://en.cppreference.com/w/cpp/algorithm/find
     • Check if element is in the list (contains)
     • Check if element found in array c++
     • http://en.cppreference.com/w/cpp/algorithm/binary_search

     The associative containers provided by the standard library (std::set, std::unordered_set, std::map, ...) provide the member functions find() and count() and contains() (C++20) for this. These will perform better than linear search, i.e., logarithmic or constant time depending on whether you have picked the ordered or the unordered alternative. Which one of these functions to prefer largely depends on what you want to achieve with that info afterwards, but also a bit on personal preference. (Lookup the documentation for details and examples.)

     • How to check that an element is in a std::set?
     • How to check if std::map contains a key without doing insert?
     • https://en.wikipedia.org/wiki/Associative_containers
     • http://en.cppreference.com/w/cpp/container

     If you want to, you can use some template magic to write a wrapper function that picks the correct method for the container at hand, e.g., as presented in this answer.

     Other Answers

     Answered Jun 19, 2017 · 18 votes

     You can approach this in two ways:

     You can use std::find

     from <algorithm>:

     auto it = std::find(container.begin(), container.end(), value);if (it != container.end()) return it; -

     or you can iterate through every element in your containers with for ranged loops:

     for(const auto& it : container){ if(it == value) return it;} -

     Other Answers

     Answered Jun 19, 2017 · 14 votes

     Python does different things for in depending on what kind of container it is. In C++, you'd want the same mechanism. Rule of thumb for the standard containers is that if they provide a find(), it's going to be a better algorithm than std::find() (e.g. find() for std::unordered_map is O(1), but std::find() is always O(N)).

     So we can write something to do that check ourselves. The most concise would be to take advantage of C++17's if constexpr and use something like Yakk's can_apply

     :

     template <class C, class K>using find_t = decltype(std::declval<C const&>().find(std::declval<K const&>()));-template <class Container, class Key>bool in(Container const& c, Key const& key) { if constexpr (can_apply<find_t, Container, Key>{}) { // the specialized case return c.find(key) != c.end(); } else { // the general case using std::begin; using std::end; return std::find(begin(c), end(c), key) != end(c); }}-

     In C++11, we can take advantage of expression SFINAE:

     namespace details { // the specialized case template <class C, class K> auto in_impl(C const& c, K const& key, int ) -> decltype(c.find(key), true) { return c.find(key) != c.end(); }- // the general case template <class C, class K> bool in_impl(C const& c, K const& key, ...) { using std::begin; using std::end; return std::find(begin(c), end(c), key) != end(c); }}-template <class Container, class Key>bool in(Container const& c, Key const& key) { return details::in_impl(c, key, 0);}-

     Note that in both cases we have the using std::begin; using std::end; two-step in order to handle all the standard containers, raw arrays, and any use-provided/adapted containers.

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     4/5

   • What is Python's heapq module?

     Top Answer

     Answered Jan 08, 2019 · 113 votes

     The heapq module maintains the heap invariant, which is not the same thing as maintaining the actual list object in sorted order.

     Quoting from the heapq

     documentation:

     Heaps are binary trees for which every parent node has a value less than or equal to any of its children. This implementation uses arrays for which heap[k] <= heap[2*k+1] and heap[k] <= heap[2*k+2] for all k, counting elements from zero. For the sake of comparison, non-existing elements are considered to be infinite. The interesting property of a heap is that its smallest element is always the root, heap[0].

     This means that it is very efficient to find the smallest element (just take heap[0]), which is great for a priority queue. After that, the next 2 values will be larger (or equal) than the 1st, and the next 4 after that are going to be larger than their 'parent' node, then the next 8 are larger, etc.

     You can read more about the theory behind the datastructure in the Theory section of the documentation. You can also watch this lecture from the MIT OpenCourseWare Introduction to Algorithms course, which explains the algorithm in general terms.

     A heap can be turned back into a sorted list very efficiently:

     def heapsort(heap): return [heapq.heappop(heap) for _ in range(len(heap))]-

     by just popping the next element from the heap. Using sorted(heap) should be faster still, however, as the TimSort algorithm used by Python’s sort will take advantage of the partial ordering already present in a heap.

     You'd use a heap if you are only interested in the smallest value, or the first n smallest values, especially if you are interested in those values on an ongoing basis; adding new items and removing the smallest is very efficient indeed, more so than resorting the list each time you added a value.

     Check all answers on Stack Overflow

     5/5

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2. stackoverflow.com · questions · 19438611Using Python logging module in C - Stack Overflow

   stackoverflow.com · questions · 19438611

   

   Oct 18, 2013 · I am interested in using the Python logging module in a C program. I've tried reading over the documentation at http://docs.python.org/2/extending/embedding.html , but I'm rather stuck. This is what I want to accomplish (Python equivalent): import logging, logging.handlers. logger = logging.getLogger('')

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4. Searches related to what is python's logging module in c compiler code generator

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5. stackoverflow.com · questions · 7826505Usage of python logging module in c-extension - Stack Overflow

   stackoverflow.com · questions · 7826505

   

   Oct 19, 2011 · I'm working on a python script and I'm using the python logging module for writing some information into a log file (see http://docs.python.org/howto/logging.html). Here is a small example (also from the mentioned page): import logging. import logging.config. logging.config.fileConfig('logging.conf')

6. pypi.org · project · csnakecsnake - PyPI

   pypi.org · project · csnake

   

   Jul 13, 2024 · Csnake is a Python 3 package that helps you generate C code from Python. Csnake provides you with a consistent and opinionated API that helps you structure your C-generating Python code. It does so by providing classes and functions for generating every C language construct.

7. github.com · cython · cythoncython/cython: The most widely used Python to C compiler - GitHub

   github.com · cython · cython

   

   • Overview
   • Installation:
   • License:
   • Contributing:
   • Differences to other Python compilers
   • Get the full source history:
   • The following is from Pyrex:

   Cython is a Python compiler that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations.

   Cython translates Python code to C/C++ code, but additionally supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code.

   This makes Cython the ideal language for wrapping external C libraries, and for fast C modules that speed up the execution of Python code.

   •Official website: https://cython.org/

   •Documentation: https://docs.cython.org/

   •Github repository: https://github.com/cython/cython

   See full list on github.com

   If you already have a C compiler, just run following command:

   otherwise, see the installation page.

   See full list on github.com

   The original Pyrex program was licensed "free of restrictions" (see below). Cython itself is licensed under the permissive Apache License.

   See LICENSE.txt.

   See full list on github.com

   Want to contribute to the Cython project? Here is some help to get you started.

   See full list on github.com

   Started as a project in the early 2000s, Cython has outlived most other attempts at producing static compilers for the Python language.

   Similar projects that have a relevance today include:

   •PyPy, a Python implementation with a JIT compiler.

   •Pros: JIT compilation with runtime optimisations, fully language compliant, good integration with external C/C++ code

   •Cons: non-CPython runtime, relatively large resource usage of the runtime, limited compatibility with CPython extensions, non-obvious performance results

   •Numba, a Python extension that features a JIT compiler for a subset of the language, based on the LLVM compiler infrastructure (probably best known for its clang C compiler). It mostly targets numerical code that uses NumPy.

   See full list on github.com

   Note that Cython used to ship the full version control repository in its source distribution, but no longer does so due to space constraints. To get the full source history from a downloaded source archive, make sure you have git installed, then step into the base directory of the Cython source distribution and type:

   See full list on github.com

   This is a development version of Pyrex, a language for writing Python extension modules.

   For more info, take a look at:

   •Doc/About.html for a description of the language

   •INSTALL.txt for installation instructions

   •USAGE.txt for usage instructions

   •Demos for usage examples

   See full list on github.com

8. cython.orgCython: C-Extensions for Python

   cython.org

   

   The Cython language is a superset of the Python language that additionally supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code.

9. stackoverflow.com · questions · 9420673Is it possible to compile c code using python? - Stack Overflow

   stackoverflow.com · questions · 9420673

   

   You can compile C code using only the standard library, and it will work on every platform and with every Python version (assuming you actually have a C compiler available). Check out the distutils.ccompiler module which Python uses to compile C extension modules. A simple example:

10. People also ask

    How can I compile C code using Python?

    • You can compile C code using Python by utilizing the standard library. This method works on every platform and with every Python version, assuming you have a C compiler available.\n\nPython uses the distutils.ccompiler module for compiling C extension modules.

    Is it possible to compile c code using python? - Stack Overflow

    stackoverflow.com/questions/9420673/is-it-possible-to-compile-c-code-using-python

    See all results for this question

    What is Cython programming language?

    • Cython is a programming language that aims to be a superset of the Python language, adding static typing and compiling Python code into C. Cython is an optimising static compiler for both the programming language and the extended Cython programming language…

    Integrate C with Python: A Comprehensive Guide for Developers

    medium.com/@elniak/integrate-c-with-python-a-comprehensive-guide-for-developers-e98a1883fc7b

    See all results for this question

    Is Cython a static compiler?

    • Here is some help to get you started. Started as a project in the early 2000s, Cython has outlived most other attempts at producing static compilers for the Python language. Similar projects that have a relevance today include: PyPy, a Python implementation with a JIT compiler.

    GitHub - cython/cython: The most widely used Python to C compiler

    github.com/cython/cython

    See all results for this question

    Why should I integrate C code into Python?

    • By integrating C code into Python, you can enjoy the best of both worlds: Python’s ease of use and C’s performance. ctypes is a foreign function library for Python that provides C compatible data types and allows calling functions in DLLs or shared libraries. It's a built-in module, so you don't need to install anything extra to use it.

    Integrate C with Python: A Comprehensive Guide for Developers

    medium.com/@elniak/integrate-c-with-python-a-comprehensive-guide-for-developers-e98a1883fc7b

    See all results for this question

11. medium.com · @elniak · integrate-c-with-python-aIntegrate C with Python: A Comprehensive Guide for Developers

    medium.com · @elniak · integrate-c-with-python-a

    

    Apr 5, 2024 · This tutorial delves into how you can seamlessly integrate C code into Python, enhancing performance without losing Python’s readability and ease of use.