korymath

<?php
	// load the API Client library
	$authentication_tokens = array('app_key'  => 'APPKEY',
	                               'user_key' => 'USERKEY');
	$eb_client = new Eventbrite( $authentication_tokens );
	
	$events = $eb_client->user_list_events(array('event_statuses'=>'live,started','display'=>'repeat_schedule'));
	$repeat_events = array();
	foreach ($events->events as $event) {
		if ( 'yes' === $event->event->repeats ) {

korymath

<?php
	$paged = (get_query_var('paged')) ? get_query_var('paged') : 1;
    
    $args = array(
    			'paged' => $paged, 
    			'post_type' => 'review-single',
    			'category_name' => types_render_field("category-slug-to-pull", array()), 
    			'posts_per_page' => '10'
    		));

korymath

<?php 
/*
Template Name: Videos
*/

get_template_part( 'header' ); ?>

<div class="container">
  <div class="header">
  	<a href="<?php bloginfo('url'); ?>"><div class="logo"></div></a>  

korymath

Output = IDEALv3(MultiEchoImage,TEs,[-220,0.75;-244,0.1;-164,0.08;43,0.06],Mode,InitialGuess);

Ray's description/help for IDEALv3 should explain everything, but simply:

MultiEchoImage = X,Y,nEchos
TEs = array of echo times (in ms)
[-220,0.75;-244,0.1;-164,0.08;43,0.06] is the fat profile IDEAL will use
Mode is whether you set an initial phase guess to 0 (Mode = 0), provide an initial guess (Mode = 1) but let IDEAL come to potentially another solution, or 'clamp' to the provided 'initial guess' (Mode = 2)
Initial guess is, as I provide it, the output (X,Y,nEchos) from MRIBackgroundCorrect_new_auto (data_background_r output), when I call it:
[data_corrected_r,data_background_r]=MRIBackgroundCorrect_new_auto(im31/(2*pi*delTE*10^-3),mask,3);

korymath

beerfinder = {

  map: null,
  markers: [],
  windows: [],
  masterBeerList:[],
  defaults: {
    lat: 51.045925999999990000,
    long: -114.057959999999980000,
    style:  

korymath

   var beer_list = {
    'beers': [
    
    {
        'beer': {
            'ID': 9,
            'name': 'Alberta Crude',
            'icon': ''
        }
    },

korymath

from joblib import Parallel, delayed  
import multiprocessing

inputs = range(100000)  

def processInput(i):  
    return i * i

num_cores = multiprocessing.cpu_count()
print str(num_cores)

korymath

Run the following in different tabs in terminal

MDP Constant
/usr/bin/python blueberry_move_and_emg.py -t 40000 -s 0.02 -p 1000 -a 1 -r MDP -o 49234 -g 0.1 -u 0.05 -mean 0.005 -std 1.0

MDP Relative
/usr/bin/python blueberry_move_and_emg.py -t 40000 -s 0.02 -p 1000 -a 1 -r MDP -m relative -o 49234 -g 0.1 -u 0.05 -mean 0.005 -std 1.0

Human
/usr/bin/python blueberry_move_and_emg.py -t 40000 -s 0.02 -p 1000 -a 1 -r Human -o 49234 -g 0.1 -u 0.05 -mean 0.005 -std 1.0

korymath

# Basic Random Cart Pole Performance

run the basic cart pole code in the tutorial

korymath

import gym
env = gym.make('Reacher-v1')
env.reset()
env.render()

env.monitor.start('/tmp/reacher-1', force=True)
for i_episode in xrange(101):
    observation = env.reset()
    for t in xrange(100):
        env.render()