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def persist_cache_to_disk(filename):
    def decorator(original_func):
        try:
            cache = pickle.load(open(filename, 'r'))
        except (IOError, ValueError):
            cache = {}

        atexit.register(lambda: pickle.dump(cache, open(filename, "w")))

        def new_func(*args):

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This Gist is about how I use PyAudio, NumPy, and Matplotlib to plot freqency spectrum of system sound or microphone.

You can read this blog post for more detail or watch this video:

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#import necessary libraries
import cv2
import numpy as np

#capture video from the webcam
cap = cv2.VideoCapture(0)

#load the face finder
face_cascade = cv2.CascadeClassifier('/home/sm/Desktop/haarcascade_frontalface_default.xml')

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#!/usr/bin/env python
#
# Extracts email addresses from one or more plain text files.
#
# Notes:
# - Does not save to file (pipe the output to a file if you want it saved).
# - Does not check for duplicates (which can easily be done in the terminal).
#
# (c) 2013  Dennis Ideler <[email protected]>

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Using Selenium and Python to screenshot a javascript-heavy page

As websites become more JavaScript heavy, it's harder to automate things like screenshotting for archival purposes. I've seen examples and suggestions to use PhantomJS for visual testing/archiving of websites, but have run into issues such as the non-rendering of webfonts. I've never tried out Selenium until today...and while I'm not thinking about performance implications yet, Selenium seems far more accurate than PhantomJS...which makes sense since it actually opens a real browser. And it's not too hard to script to do complex interactions: here's an [example of how to log in to Twitter, write a tweet, upload an image, and send a tweet via Selenium and DOM element selection](https://gist.github.com/dannguyen/8a6fa49253c1d6a0eb92

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# see also: http://code.activestate.com/recipes/578150-sending-non-ascii-emails-from-python-3/

import os
import smtplib

from email.utils import formataddr
from email.utils import formatdate
from email.utils import COMMASPACE

from email.header import Header

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import sys
import numpy as np
import scipy.ndimage as nd
from scipy.cluster.vq import vq
from scipy.misc import imsave

def crayola_9():
    """
    Palette of the first 8 crayola colors + white
    """

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"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

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"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

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Text Classification

To demonstrate text classification with Scikit Learn, we'll build a simple spam filter. While the filters in production for services like Gmail will obviously be vastly more sophisticated, the model we'll have by the end of this chapter is effective and surprisingly accurate.

Spam filtering is the "hello world" of document classification, but something to be aware of is that we aren't limited to two classes. The classifier we will