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class KeyAwareDefaultDict(dict):
    """
    Like a standard library defaultdict, but pass the key
    to the default factory.
    """
    def __init__(self, default_factory=None):
        self.default_factory = default_factory

    def __getitem__(self, key):
        if key not in self and self.default_factory is not None:

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"""
A matplotlib-based image viewer utility function.
"""
import matplotlib.pyplot as plt

def image_view(arr, fig=None, frame_on=True, *args, **kwargs):
    """
    Sensibly display an image in a plot window.

    Parameters

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import theano
import functools


def cached_theano_function(fn):
    @functools.wraps(fn)
    def wrapped(self):
        if not hasattr(self, '_function_cache'):
            self._function_cache = {}
        if fn.func_name not in self._function_cache:

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"""
Parallelized k-means module.

By David Warde-Farley, February 2012. Licensed under the 3-clause BSD.
"""
cimport cython
from cython.parallel import prange
import numpy as np
cimport numpy as np
from numpy.random import normal

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"""
Example of how to read a partial NumPy array stored in NPY
format off of disk.
"""
__author__ = "David Warde-Farley"
__copyright__ = "Copyright (c) 2012 by " + __author__
__license__ = "3-clause BSD"
__email__ = "dwf@dwf.name"

import struct

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#!/usr/bin/env python

# Copyright (c) 2009, David Warde-Farley
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# 1. Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.

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"""
L1-penalized minimization using the feature sign search algorithm.
"""

import logging
import numpy as np
log = logging.getLogger("feature_sign")
log.setLevel(logging.INFO)

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import theano.tensor as tensor
from theano import function
import numpy as np
value = np.array([ 0.992827313923019039165751564724,
                   0.118152792712208978831434080803,
                  -0.008531793400639134036800292904,
                   0.016157066691618496290239193058])                           
aa = tensor.vector('aa')
bb = tensor.vector('bb')
ee = tensor.scalar()

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"""Demo of how to pop up plots asynchronously using separate processes."""
from multiprocessing import Process
import time
import sys
import matplotlib.pyplot as plt
import numpy as np

def demo():
    i = 0
    processes = []