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from collections import namedtuple
from pprint import pprint

import numpy as np
np.random.seed(12345)

import matplotlib.pyplot as plt


Appointment = namedtuple("Appointment", "scheduled late_by emergency")

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import random

random.seed(12345)


class Dice(object):
    def __init__(self, sides=6):
        print "creating a dice with %i sides."%sides
        self.sides = sides

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# All sorts of stuff for setting up the Forward tracking
# itneresting stuff towards the bottom
FstForward = PrForwardTracking("FstForward")
FstForward.InputName = self.getProp("RootInTES") + "Track/VeloUTFst"  
FstForward.OutputName = self.getProp("RootInTES") + "Track/Forward"
FstForward.addTool(PrForwardTool)       
FstForward.PrForwardTool.AddUTHitsToolName = ""
# Now with the momentum estimate                                                                                                                                                             FstForward.PrForwardTool.UseMomentumEstimate = True
# MinPt determines the size of the search window, set                                                                                                                                        # this to the lowest pT you want to be able to find                                                                                                                              

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from collections import namedtuple

Infos = namedtuple('Infos', ["bias"," threshold", "alignment"])

# information about runs 1234 and 1235
run_info = {1234: Infos(12.3, 2., 42.),
            1235: Infos(12.2, 2.1, 42.3)}
            
# retrieve it
print run_info[1234]

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$ git clone https://github.com/scikit-learn/scikit-learn.git
$ git remote add pprett https://github.com/pprett/scikit-learn.git
$ git remote add ndawe https://github.com/ndawe/scikit-learn.git
$ git fetch
$ git checkout -b weighted_scores ndawe/weighted_scores
$ git rebase master
$ git checkout master
$ git merge weighted_scores
$ git checkout -b gbrt-sample-weight pprett/gbrt-sample-weight
$ git rebase master

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# SetupProject DaVinci v35r1
# ipython -i dst-explore.py <inputDST>
import sys

import GaudiPython as GP
from GaudiConf import IOHelper
from Configurables import LHCbApp, ApplicationMgr, DataOnDemandSvc
from Configurables import SimConf, DigiConf, DecodeRawEvent
from Configurables import CondDB, DstConf, PhysConf
from Configurables import LoKiSvc

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def cluster2mc_particles(cluster, event):
    links = event['/Event/Link/Raw/VP/Clusters']
    refs = links.linkReference()
    mc_particles = event['MC/Particles']
    next_idx = -1
    particles = []
    for channel, key in links.keyIndex():
        if channel == cluster.channelID().channelID():
            mc_key = refs[key].objectKey()
            particles.append(mc_particles.object(mc_key))

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def nodes(evt, node=None):
    nodenames = []

    if node is None:
        root = evt.retrieveObject('')
        node = root.registry()

    if node.object():
        nodenames.append(node.identifier())
        for l in evt.leaves(node):

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