alexpearce

import numpy as np
import matplotlib.pyplot as plt


def normfactor(hist, edges):
    a = np.sum(hist)*(edges[1:] - edges[:-1])
    return a


def gauss(xs, loc=0.0, scale=1.0):

alexpearce

#!/bin/sh

echo 'Provisioning acron VM'

# Install what we'll need
sudo yum install -y cern-config-users cern-get-keytab arc-server

# Make sure we ready for Kerberos-authenticated jobs
sudo cern-get-keytab --force

alexpearce

// Asymmetrical Gaussian with exponential tails
// parameters:
//   mean - position at maximum
//   sigma - sigma of the Gaussian
//   asym - asymmetry of the Gaussian ( sigmaLeft = sigma * (1 - asym), sigmaRight = sigma * (1 + asym) )
//   rhoL - number of sigmas from mean where left tail starts: mean - rhoL * sigmaLeft
//   rhoR - number of sigmas from mean where right tail starts: mean + rhoR * sigmaRight
//
// Allowed values of parameters: sigma > 0, -1 < asym < 1, rhoL > 0, rhoR > 0

alexpearce

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def pdf_probabilities(xs, dependents, yields, pdfs):
    """Return an array of normalised PDF probabilities.

    For a list of N yields, each y_i, and a list of N PDFs f_i, the probability
    for the ith PDF at the (possibly vector) point x is defined as

        p_{i} = \frac{N_{i}f_{i}}{\sum_{i}^{N} N_{i}f_{i}}

    Keyword arguments:
    xs -- List of values at which to evaluate the PDFs

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#!/bin/bash
# Run commands in the Docker container with a particular UID and GID.
# The idea is to run the container like
#   docker run -i \
#     -v `pwd`:/work \
#     -e LOCAL_USER_ID=`id -u $USER` \
#     -e LOCAL_GROUP_ID=`id -g $USER` \
#     image-name bash
# where the -e flags pass the env vars to the container, which are read by this script.
# By setting copying this script to the container and setting it to the