revolunet

import os
from PIL import Image


def extractFrames(inGif, outFolder):
    frame = Image.open(inGif)
    nframes = 0
    while frame:
        frame.save( '%s/%s-%s.gif' % (outFolder, os.path.basename(inGif), nframes ) , 'GIF')
        nframes += 1

phn

# Code moved to http://github.com/phn/angles.

ladyada

#!/usr/bin/env python

# Written by Limor "Ladyada" Fried for Adafruit Industries, (c) 2015
# This code is released into the public domain

import time
import os
import RPi.GPIO as GPIO

GPIO.setmode(GPIO.BCM)

timbaileyjones

#!/usr/bin/python
import os
import sys
import time
import subprocess

def space2_(directory):
    for filename in os.listdir(directory): # parse through file list in the current directory
        #print "checking %s" % filename
        if filename.find(".svn") >= 0:

endolith

ctokheim

Cython

Cython has two major benefits:

1. Making python code faster, particularly things that can't be done in scipy/numpy
2. Wrapping/interfacing with C/C++ code

Cython gains most of it's benefit from statically typing arguments. However, statically typing is not required, in fact, regular python code is valid cython (but don't expect much of a speed up). By incrementally adding more type information, the code can speed up by several factors. This gist just provides a very basic usage of cython.

WarrenWeckesser

from __future__ import division, print_function

import numpy as np
from scipy import signal
import matplotlib.pyplot as plt


def plot_response(fs, w, h, title):
    plt.figure()
    plt.plot(0.5*fs*w/np.pi, 20*np.log10(np.abs(h)))

staticfloat

#!/usr/bin/env python

from numpy import *
from scipy import *
from scipy.signal import *
from pylab import *


# Generate AM-modulated sinusoid
N = 256

mathebox

import math

def rgb_to_hsv(r, g, b):
    r = float(r)
    g = float(g)
    b = float(b)
    high = max(r, g, b)
    low = min(r, g, b)
    h, s, v = high, high, high

DavidYKay

import cv2
import math
import numpy as np
import sys

def apply_mask(matrix, mask, fill_value):
    masked = np.ma.array(matrix, mask=mask, fill_value=fill_value)
    return masked.filled()

def apply_threshold(matrix, low_value, high_value):