GaelVaroquaux

Cython example of exposing C-computed arrays in Python without data copies

The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.

The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy

ddemidov

hello: hello.cpp
	g++ -std=c++0x -o hello hello.cpp -lOpenCL

acrosby

from rtree import index
from random import random
from datetime import datetime

timer = datetime.now()

# Create 10,000,000 random numbers between 0 and 1
rands = [random() for i in range(10000000)] 

# Function required to bulk load the random points into the index

miguelgrinberg

#!flask/bin/python
from flask import Flask, jsonify, abort, request, make_response, url_for
from flask_httpauth import HTTPBasicAuth

app = Flask(__name__, static_url_path = "")
auth = HTTPBasicAuth()

@auth.get_password
def get_password(username):
    if username == 'miguel':

dan-blanchard

For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.

A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s

tonyseek

Run with Python:

pip-2.7 install cffi
PYTHON=python2.7 sh go.sh

Run with PyPy:

pip-pypy install cffi
PYTHON=pypy sh go.sh

clavery

import logging
import time
from functools import wraps

from flask import request, jsonify
import redis

r = redis.StrictRedis(host='localhost', port=6379, db=0)
logger = logging.getLogger(__name__)

timlinux

# coding=utf-8

# A simple demonstration of to generate a PDF using a QGIS project
# and a QGIS layout template.
#
# This code is public domain, use if for any purpose you see fit.
# Tim Sutton 2015

import sys
from qgis.core import (

snorfalorpagus

Using distutils and build_clib to build a C library

This repository demonstrates how to build a C library that is included in the module distribution.

To build the C library:

python setup.py build_clib

alq666

  0. pxname [LFBS]: proxy name
  1. svname [LFBS]: service name (FRONTEND for frontend, BACKEND for backend,
     any name for server/listener)
  2. qcur [..BS]: current queued requests. For the backend this reports the
     number queued without a server assigned.
  3. qmax [..BS]: max value of qcur
  4. scur [LFBS]: current sessions
  5. smax [LFBS]: max sessions
  6. slim [LFBS]: configured session limit
  7. stot [LFBS]: cumulative number of connections