agramfort

"""
This module implements the Lowess function for nonparametric regression.

Functions:
lowess Fit a smooth nonparametric regression curve to a scatterplot.

For more information, see

William S. Cleveland: "Robust locally weighted regression and smoothing
scatterplots", Journal of the American Statistical Association, December 1979,

driki

19:40John B.: Hello Matt! How may I help you?
19:41Matt M.: Hi John. I need to lower my bill. Comcast internet is much cheaper. Thinking of switching
19:43Matt M.: They are offering me $34.99/mo my latest RCN bill is $49.99
19:43John B.: Let me see what I can do, sir. I'd be happy to help you. May I please have your address and phone number?
19:44Matt M.: Sure. XXXXXXXXX, MA XXXXX and my phone number is XXX-XXX-XXXX.
19:45John B.: Thank you sir. One moment please while I research what I can do.
19:45Matt M.: Great thanks.
19:48John B.: I tried to put you in the $34.99 plan, however the system knows your an active customer and denies me.
19:49John B.: But we do have another option!
19:49Matt M.: OK. What are you thinking?

yanofsky

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.

In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit

julionc

How to install PhantomJS on Ubuntu

Version: 1.9.8

Platform: x86_64

First, install or update to the latest system software.

sudo apt-get update
sudo apt-get install build-essential chrpath libssl-dev libxft-dev

arnov

#!/usr/bin/python
# -*- coding: utf-8 -*-

import math

def calc_ab(alpha_a, beta_a, alpha_b, beta_b):
    '''
    See http://www.evanmiller.org/bayesian-ab-testing.html

    αA is one plus the number of successes for A

tmalsburg

Predict vs simulate in lme4

For this investigation we are going to use the sleepdata data set from the lme4 package. Here is the head of the data frame:

gigamonkey

Write a program that does what it’s supposed to do
Write idiomatic code
Debug a program that you wrote
Debug a program someone else wrote
Debug the interaction between a system you wrote and one you didn’t
File a good bug report
Modify a program you didn’t write
Test a program you wrote
Test a program you didn’t write
Learn a new programming language

betatim

# Convert your notebook to an interactive webpage
#
#  Attached a notebook (really-interactive-posts.ipynb) and the generated
# output (really-interactive-posts.html). The thebe.tpl template file is
# at the very end of the gist.

$ jupyter nbconvert --template thebe.tpl --to html <notebook.ipynb>


# You can open the generated webpage locally file://... howerver some

dwilkie

Build docker image

$ cd /path/to/Dockerfile
$ sudo docker build .

View running processes

alanwill

import boto3

getCleanTable = taskStatus.query(
    KeyConditionExpression=Key('requestId').eq('a3248585-8ac0-4889-b1fb-976d9113f7f2') & Key('eventTimestamp').gt('1')
)

for i in getCleanTable['Items']:
    cleanupTable = boto3.client('dynamodb').batch_write_item(
        RequestItems={
            'talr-taskStatus': [