cjbayesian

## Corey Chivers, 2012 ##
sim_bayes<-function(p=0.5,N=100,y_lim=20,a_a=2,a_b=10,b_a=8,b_b=3)
{
   ## Simulate outcomes in advance   
   outcomes<-sample(1:0,N,prob=c(p,1-p),replace=TRUE)
   success<-cumsum(outcomes)

   for(frame in 1:N)
   {
      png(paste("plots/",1000+frame,".png",sep=""))

catawbasam

# -*- coding: utf-8 -*-
"""
LICENSE: BSD (same as pandas)
example use of pandas with oracle mysql postgresql sqlite
    - updated 9/18/2012 with better column name handling; couple of bug fixes.
    - used ~20 times for various ETL jobs.  Mostly MySQL, but some Oracle. 

    to do:  
            save/restore index (how to check table existence? just do select count(*)?), 
            finish odbc, 

gka

Year,Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec,Total
1950,3091.48387097,3266.96428571,3325.90322581,3187.86666667,3160.58064516,3078.7,2958.09677419,2885.80645161,3056.83333333,2943.70967742,2912.8,2864.74193548,3059.45479452
1951,3029.5483871,3181.71428571,3210.74193548,3142.96666667,3196.41935484,3087.1,3017.0,2929.35483871,3022.56666667,2884.61290323,2806.4,2878.16129032,3031.17808219
1952,3072.0,3223.24137931,3268.4516129,3181.03333333,3115.93548387,2994.03333333,2973.5483871,2865.29032258,3070.0,2807.74193548,2860.5,2814.5483871,3027.62739726
1953,3052.90322581,3175.96428571,3248.19354839,3163.06666667,3142.83870968,3039.6,2975.5483871,2856.61290323,3061.5,2774.12903226,2679.63333333,2846.16129032,3000.07945205
1954,3074.06451613,3238.64285714,3172.93548387,3180.3,3243.35483871,3096.2,3021.51612903,2930.4516129,3054.9,2781.06451613,2848.86666667,2862.19354839,3040.39178082
1955,3058.03225806,3235.96428571,3286.93548387,3178.4,3151.77419355,3104.63333333,2988.51612903,2933.41935484,3064.06666667,287

jcreixell

import gspread
from pandas import *
import logging


class GDocError(Exception):
    def __init__(self, value):
        self.parameter = value

    def __str__(self):

mdengler

#!/usr/bin/env python                                                                                                                                                         
                                                                                                                                                                              
# Trivial GARCH implementation in python                                                                                                                                      
#                                                                                                                                                                             
# From Peter Tessin's http://www.petertessin.com/TimeSeries.pdf                                                                                                               
#                                                                                                                                                    

kohlmeier

#!/usr/bin/env python

from pandas import DataFrame, Series

import numpy as np
import math
import random
import copy

NaN_Flag = -1  # pandas uses np.nan, but that coerces ints to floats :(

kohlmeier

#!/usr/bin/env python

from numpy import asmatrix, asarray, ones, zeros, mean, sum, arange, prod, dot, loadtxt
from numpy.random import random, randint
import pickle

MISSING_VALUE = -1 # a constant I will use to denote missing integer values

def impute_hidden_node(E, I, theta, sample_hidden):

flashingpumpkin

def coroutine(func):
    """
    A decorator that turns a function with normal input into a coroutine.
    This decorator takes care of priming the coroutine, doing the looping and
    handling the closing.
    
    The first argument of any decorated function is what was received when 
    data was sent to the coroutine via the ``.send`` method. All the other
    ``*args`` and ``**kwargs`` are what was passed to the decorated function
    when instantiating the coroutine.

avar

# To check if this is up-to-date with the tax rates go to
# http://www.expatax.nl/tax-rates-2016.php and see if there's anything
# newer there.
#
# I make no guarantees that any of this is correct. I calculated this
# at the time and have been updating it when new tax rates come along
# because people keep finding this useful.
#
# There's also an interactive JS version of this created by
# @stevermeister at

j2labs

#!/usr/bin/env python

# Average runs looks like this:
# $ ./serialization.py 
# Starting json...
# done: 5.75292301178
# Starting simplejson...
# done: 6.66689586639
# Starting cjson...
# done: 2.09307098389