apatil

#cloud-config

coreos:
  units:
    - name: test.service
      content: "[Unit]\nDescription=Test\n\n[Service]\nExecStart=/usr/bin/echo \"hi\"\nType=oneshot\n\n[Install]\nWantedBy=multi-user.target\n"

apatil

#!/bin/bash
# I ran this on an EC2 c1.medium instance with Docker version 0.6.3 on Ubuntu 12.04.
# I ran docker -d on tcp port 4243, and delivered 'stop' via the rest api
# because that's what I do in my production setup, but I don't know if that 
# makes a difference.                                      

# First, run ubuntu:12.04, and in it do                                                                                
# echo -e '#!/bin/bash\nsleep 600' > cmd.sh                                                                            
# chmod +x cmd.sh                                                                                                      
# Then commit it and tag it as img-with-cmd                                                                            

apatil

#!/usr/bin/env node

var shell = require("shelljs")
var n = 100;
var nPorts = 10;

var port = 40000;

var launchCmds = [];
for (var i = 0; i < n; i++) {

apatil

#!/bin/bash

# (C) Copyright Canonical 2011,2012

# What lxc container to clone
LXC_BASE=""
# $2 is a path to bind mount e.g. /tmp/foo.
LXC_BIND=""
LXC_DIR=""
UNION="overlayfs"

apatil

#!/bin/bash

#
# template script for generating container for LXC 
# with username and password 'sense'
# and an ssh key for the root user.
#
# This script consolidates and extends the existing lxc ubuntu scripts
#

apatil

import numpy as np
from theano import tensor as T
import theano

x = T.dvector('x')
z = T.dscalar('z')
y = T.sum(T.cos(x*z))

x_test = np.random.normal(size=10)

apatil

from scipy.sparse import linalg
import numpy as np
import map_utils

def dim2w(dim):
    return np.arange(-np.ceil((dim-1)/2.), np.floor((dim-1)/2.)+1)

class fractional_modified_laplacian(linalg.LinearOperator):
    
    def __init__(self, dims, kappa, alpha):

apatil

from copy import copy
import theano as th
import theano.tensor as T

def isshared(node):
    "Is there a better way to do this?"
    return hasattr(node, 'update')
    
def unpack_nodes(expr):
    """

apatil

import pymc as pm
import numpy as np

# FIXME: Need to store duplicates too, when jumps are rejected. That means some mechanism
# for making sure the history is full-rank needs to be employed.

class HistoryCovarianceStepper(pm.StepMethod):
    
    _state = ['n_points','history','tally','verbose']
    

apatil

#!/usr/bin/env python
import numpy as npy 
from pymc.gp import Mean, Covariance, Realization, observe, plot_envelope, NearlyFullRankCovariance, FullRankCovariance
from pymc.gp.cov_funs import matern #, thinplate1d
import matplotlib
matplotlib.rcParams['axes.facecolor']=[1,1,1]

__all__ = ['surface_mean', 'M', 'C']
def surface_mean(x, val):
    """docstring for parabolic_fun"""