betatim

from Ganga.GPI import *
import sys
import inspect
import os


local_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))

if len(sys.argv) not in (2,3):
    sys.exit("Script requires the ID of a DaVinci (Turbo) or Brunel job to use as inputdata and optionally name of a file containing LFNs to process.")

betatim

def lockstep(a, b):
 i = j = 0
 while i<len(a) and j<len(b):
  if a[i] == b[j]:
   #yield a[i], b[j]
   yield i,j
   i+=1;j+=1

  elif a[i] < b[j]:
   i+=1

betatim

import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_calidation import train_test_split
from root_numpy import root2array


signal = root2array("MySignalFile.root",
                    "MyDecayTree",
                    ["names", "of", "branches", "to", "use",
                     "for", "classifying"])

betatim

# split our development sample into a test and train set
X_train,X_test, y_train,y_test = cross_validation.train_test_split(X_dev,
                                                                   y_dev,
                                                                   test_size=0.33,
                                                                   random_state=7853354)
                                                                   
clf = GradientBoostingClassifier(n_estimators=3000,
                                 max_depth=1,
                                 subsample=0.5,
                                 max_features=0.5,

betatim

import random

import pandas as pd
import numpy as np

import pandas.core.common as com
from pandas.core.index import Index

from pandas.tools import plotting
from pandas.tools.plotting import scatter_matrix

betatim

betatim

import math

import ROOT as R
from ROOT import RooFit as RF
from ROOT import RooStats as RS

def argset__iter__(self):
    start = self.fwdIterator()
    for i in xrange(len(self)):
        yield start.next()

betatim

Nexp = 10000 # how many experiments/searches were performed?
power = 0.5 # power of each search or p(discovery | new physics)
Pval = 1 - 0.997300204 # p-value
NPrate = 0.001 # New physics base rate

print "true positives: %i false positives: %i"%((Nexp * NPrate * power),
                                                ((Nexp-Nexp*NPrate) * Pval))
# true positives: 5 false positives: 26

betatim

#! /bin/sh
whoami
cd /tmp/
wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda-2.2.0-Linux-x86_64.sh
bash Anaconda-2.2.0-Linux-x86_64.sh -b -p /opt/anaconda
yum install -y curl-devel expat-devel gettext-devel \
  openssl-devel zlib-devel
yum install -y asciidoc xmlto docbook2x
yum install -y gcc
wget https://github.com/git/git/archive/v2.4.1.tar.gz

betatim

; AD  : This Kerberos configuration is for CERN's Active Directory realm
; The line above this is magic and is used by cern-config-keytab. Do
; not remove.

; Installed with puppet from a series of
; template fragments.

; /etc/krb5.conf

[libdefaults]