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

   • 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.

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     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.

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     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.

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     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 · What I'd recommend in reality is figure out how much of the logging module you actually need to use from C, and wrap that functionality in a simpler subset which you can call from C. See the _bootstrap.py module i linked to above.

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4. 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')

5. docs.python.org · 3 · extending1. Extending Python with C or C++ — Python 3.12.6 documentation

   docs.python.org · 3 · extending

   

   19 hours ago · Extending Python with C or C++¶ It is quite easy to add new built-in modules to Python, if you know how to program in C. Such extension modules can do two things that can’t be done directly in Python: they can implement new built-in object types, and they can call C library functions and system calls.

6. stackoverflow.com · questions · 1153577Integrate Python And C++ - Stack Overflow

   stackoverflow.com · questions · 1153577

   

   Jul 20, 2009 · describes how to write modules in C or C++ to extend the Python interpreter with new modules. Those modules can define new functions but also new object types and their methods. The document also describes how to embed the Python interpreter in another application, for use as an extension language.

7. realpython.com · build-python-c-extension-moduleBuilding a Python C Extension Module

   realpython.com · build-python-c-extension-module

   

   In this tutorial, you’ll discover how to use the Python API to write Python C extension modules. You’ll learn how to: Invoke C functions from within Python; Pass arguments from Python to C and parse them accordingly; Raise exceptions from C code and create custom Python exceptions in C; Define global constants in C and make them accessible ...

8. realpython.com · cpython-source-code-guideYour Guide to the CPython Source Code – Real Python

   realpython.com · cpython-source-code-guide

   

   In this detailed Python tutorial, you'll explore the CPython source code. By following this step-by-step walkthrough, you'll take a deep dive into how the CPython compiler works and how your Python code gets executed.

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

   github.com · cython · cython

   

   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.