endolith

from __future__ import division
from numpy.fft import rfft
from numpy import argmax, mean, diff, log, nonzero
from scipy.signal import blackmanharris, correlate
from time import time
import sys
try:
    import soundfile as sf
except ImportError:
    from scikits.audiolab import flacread

mikeivanov

import math
import primes

def invmod(a, p):
    '''
    http://code.activestate.com/recipes/576737-inverse-modulo-p/
    The multiplicitive inverse of a in the integers modulo p.
    Return b s.t.
    a * b == 1 mod p
    '''

vedant

#!/usr/bin/env python
# -*- coding: ascii -*-
#-----------------------------------------------------------------------------
"""
Time Series Analysis
pytsa (read "pizza") depends on scipy and numpy.

Pytsa is a simple timeseries utility for python.
It is good for pedagogical purposes, such as to understand moving averages,
linear regression, interpolation, and single/double/triple exponential smoothing.

amueller

##########################################################################
# Maximum Response filterbank from
# http://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html
# based on several edge and bar filters.
# Adapted to Python by Andreas Mueller amueller@ais.uni-bonn.de
# Share and enjoy
#

import numpy as np
import matplotlib.pyplot as plt

willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

aldous-rey

#converts all midi files in the current folder

import glob
import os
import music21

#converting everything into the key of C major or A minor

# major conversions
majors = dict([("A-", 4),("A", 3),("B-", 2),("B", 1),("C", 0),("D-", -1),("D", -2),("E-", -3),("E", -4),("F", -5),("G-", 6),("G", 5)])

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

EncodeTS

VGG-Face model for Keras

This is the Keras model of VGG-Face.

It has been obtained through the following method:

• vgg-face-keras:directly convert the vgg-face matconvnet model to keras model
• vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model

Details about the network architecture can be found in the following paper:

JPvRiel

Delete all containers

$ docker ps -q -a | xargs docker rm

• -q prints only the container IDs
• -a prints all containers

Notice that it uses xargs to issue a remove container command for each container ID

angeris

Initial positions

Player A:
[0-5] [1-3] [1-4] [2-2] [3-4] [4-4] [5-6]
Player B:
[0-0] [0-1] [0-6] [1-1] [1-6] [2-3] [2-6]
Player C:
[0-2] [2-4] [2-5] [3-3] [3-6] [4-5] [4-6]
Player D:
[0-3] [0-4] [1-2] [1-5] [3-5] [5-5] [6-6]