andrewgiessel

def downsample2d(inputArray, kernelSize):
    """This function downsamples a 2d numpy array by convolving with a flat
    kernel and then sub-sampling the resulting array.

    A kernel size of 2 means convolution with a 2x2 array [[1, 1], [1, 1]] and
    a resulting downsampling of 2-fold.

    :param: inputArray: 2d numpy array
    :param: kernelSize: integer
    """

andrewgiessel

import requests, os, glob, json

you = 'giessel'
data = 'tweets'

try: os.mkdir(data)
except Exception: pass

def run(max_id = False):
    already = glob.glob("%s/*.json" % data)

andrewgiessel

from IPython.display import HTML
import subprocess
import tempfile
from PIL import Image

def playNPArray(array, frameRate = 6):
    # ffmpeg is fussy- images must be in the right format to encode right
    uint8_array = np.uint8(array.astype('float').copy() / float(array.max()) * 2**8-1)
    
    fileName = 'temp'

andrewgiessel

{
 "metadata": {
  "name": "manual inline mode"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

andrewgiessel

#!/usr/bin/env ruby

## disconnect

# ./disconnect.rb -u yourusername [-o /your/path] [-p yourhttpproxyserver]
#
# This is a command-line utility for the bulk-downloading of run data from
# the connect.garmin.com web application, which has lackluster export
# capabilities.
#

andrewgiessel

{
 "metadata": {
  "name": "astro_ml_tutorial"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

andrewgiessel

def bayesian_blocks(t):
    """Bayesian Blocks Implementation

    By Jake Vanderplas.  License: BSD
    Based on algorithm outlined in http://adsabs.harvard.edu/abs/2012arXiv1207.5578S

    Parameters
    ----------
    t : ndarray, length N
        data to be histogrammed

andrewgiessel

andrewgiessel

andrewgiessel