jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

oseledets

import sys
import os
from math import exp,sqrt
import numpy as np
from numpy.linalg import svd,norm,qr
from numpy.random import rand
from scipy.linalg import expm
import time
import timestep as ts
import itertools

poolio

#Instructions for installing OpenBLAS on a terminal.com instance. Mostly folows: http://stackoverflow.com/questions/11443302/compiling-numpy-with-openblas-integration

##Run the following:

sudo dpkg -r python-numpy

sudo apt-get install gfortran
sudo pip install cython

alexrudy

How to add custom (or system) fonts to matplotlib on OS X

Matplotlib expects to find .ttf fonts on your system. Newer versions of OS X use .dfont files. Converting them is easy, and putting them in a proper font path is also easy. The user font path on OS X is ~/Library/Fonts/, and matplotlib will find fonts here.

1. First, install what you'll need:

• matplotlib
• fondu (port install fondu, or brew install fondu)

2. Then find your desired font file. For system font files, the easiest way to do this is in the FontBook application. You can open FontBook in your utlities folder. Then select your font of choice, right click on it, and select "Show in Finder". You'll need to know where it is. Many system fonts are in places like /System/Library/Fonts/...
3. Move to your user's font directory ~/Library/Fonts/.
4. Use fondu to convert the OS-X specific font file (ends with .dfont) to .ttf.

kingjr

# Author: Jean-Remi King <[email protected]>
"""
    Illustrate how a hinge loss and a log loss functions 
    typically used in SVM and Logistic Regression 
    respectively focus on a variable number of samples.
    For simplification purposes, we won't consider the
    regularization or penalty (C) factors.
"""
import numpy as np
import matplotlib.animation as animation

jakevdp

# Author: Jake VanderPlas
# LICENSE: MIT

from __future__ import division
import numpy as np


def convolution_matrix(x, N=None, mode='full'):
    """Compute the Convolution Matrix