Noel Dawe ndawe
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| from numpythia import Pythia | |
| from numpythia import STATUS, HAS_END_VERTEX, ABS_PDG_ID | |
| from numpythia.testcmnd import get_cmnd | |
| from pyjet import cluster | |
| pythia = Pythia(get_cmnd('w'), random_state=1) | |
| selection = ((STATUS == 1) & ~HAS_END_VERTEX & | |
| (ABS_PDG_ID != 12) & (ABS_PDG_ID != 14) & (ABS_PDG_ID != 16)) |
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| from pyjet import cluster | |
| from root_numpy import root2array, stretch | |
| branches = [ | |
| 'recoPFCandidates_particleFlow__RECO.obj.pt_', | |
| 'recoPFCandidates_particleFlow__RECO.obj.eta_', | |
| 'recoPFCandidates_particleFlow__RECO.obj.phi_', | |
| 'recoPFCandidates_particleFlow__RECO.obj.mass_', | |
| ] |
ndawe
/ plot_jet_areas.py
Created
May 27, 2017 14:47
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| import numpy as np | |
| from pyjet import cluster, DTYPE_PTEPM | |
| from pyjet.utils import ep2ptepm | |
| from pyjet.testdata import get_event | |
| import matplotlib.pyplot as plt | |
| from matplotlib.pyplot import cm | |
| from matplotlib.colors import LinearSegmentedColormap | |
| eta_min, eta_max = -4., 4. | |
| extent = eta_min, eta_max, -np.pi, np.pi |
ndawe
/ bootstrap.py
Created
June 13, 2014 00:10
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| from rootpy.tree import Tree, TreeModel, FloatCol | |
| from rootpy.io import root_open | |
| from root_numpy import root2array, array2tree | |
| import numpy as np | |
| from random import gauss | |
| import random | |
| random.seed(0) |
ndawe
/ gist:5187a2fd118719df71a2
Created
May 29, 2014 09:53
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| class BCHCleaning(EventFilter): | |
| """ | |
| https://twiki.cern.ch/twiki/bin/view/AtlasProtected/BCHCleaningTool | |
| """ | |
| def __init__(self, tree, passthrough, datatype, **kwargs): | |
| if not passthrough: | |
| from externaltools import TileTripReader | |
| from externaltools import BCHCleaningTool | |
| from ROOT import Root | |
| from ROOT import BCHTool |
ndawe
/ gist:f72d4ef26d06b79a42bb
Created
May 16, 2014 00:43
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| WARNING:ROOT.Root.TPileupReweighting.GetPrimaryWeight] No mc for weight 'pileup' in channelNumber=1, periodNumber/runNumber=195847,x=44.000000 | |
| WARNING:ROOT.Root.TPileupReweighting.GetPrimaryWeight] No mc for weight 'pileup' in channelNumber=1, periodNumber/runNumber=195847,x=44.000000 | |
| WARNING:ROOT.Root.TPileupReweighting.GetPrimaryWeight] No mc for weight 'pileup' in channelNumber=1, periodNumber/runNumber=195847,x=44.000000 | |
| INFO:higgstautau.pileup] Run: 195848 | |
| INFO:higgstautau.pileup] Channel: 161617 | |
| INFO:higgstautau.pileup] mu: 44.0 | |
| INFO:higgstautau.pileup] Weight: 0.0 | |
| WARNING:ROOT.Root.TPileupReweighting.GetPrimaryWeight] No mc for weight 'pileup' in channelNumber=1, periodNumber/runNumber=195847,x=44.000000 | |
| WARNING:ROOT.Root.TPileupReweighting.GetPrimaryWeight] No mc for weight 'pileup' in channelNumber=1, periodNumber/runNumber=195847,x=44.000000 | |
| WARNING:ROOT.Root.TPileupReweighting.GetPrimaryWeight] No mc for weight 'pileup' in channelNumber=1, periodNumber/runNumber=195847,x=44.000000 |
ndawe
/ gist:10944859
Created
April 17, 2014 00:32
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| """ | |
| ===================================== | |
| Multi-class AdaBoosted Decision Trees | |
| ===================================== | |
| This example reproduces Figure 1 of Zhu et al [1] and shows how boosting can | |
| improve prediction accuracy on a multi-class problem. The classification | |
| dataset is constructed by taking a ten-dimensional standard normal distribution | |
| and defining three classes separated by nested concentric ten-dimensional | |
| spheres such that roughly equal numbers of samples are in each class (quantiles |
ndawe
/ gist:10944617
Created
April 17, 2014 00:26
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| 115 # Check parameters | |
| 116 self._validate_estimator() | |
| 117 | |
| 118 # Clear any previous fit results | |
| 119 self.estimators_ = [] | |
| 120 self.estimator_weights_ = np.zeros(self.n_estimators, dtype=np.float) | |
| 121 self.estimator_errors_ = np.ones(self.n_estimators, dtype=np.float) | |
| 122 | |
| 123 for iboost ∈ xrange(self.n_estimators): | |
| 124 # Boosting step |
ndawe
/ rootpy_matplotlib_mpld3
Created
February 20, 2014 07:15
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| { | |
| "metadata": { | |
| "name": "rootpy_matplotlib_mpl3d" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { |
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| # ttbar 1 lepton filter | |
| mc12_8TeV.117050.PowhegPythia_P2011C_ttbar.merge.NTUP_TAU.e1728_s1581_s1586_r3658_r3549_p1443/ | |
| # ttbar fully hadronic | |
| mc12_8TeV.117049.TTbar_MT1725_allhad_PowHeg_Pythia_P2011C.merge.NTUP_TAU.e2075_s1581_s1586_r3658_r3549_p1344/ | |
| # MC@NLO NOT USED | |
| #mc12_8TeV.105200.McAtNloJimmy_CT10_ttbar_LeptonFilter.merge.NTUP_TAU.e1513_s1499_s1504_r3658_r3549_p1344/ | |
| #mc12_8TeV.105204.McAtNloJimmy_AUET2CT10_ttbar_allhad.merge.NTUP_TAU.e1576_s1499_s1504_r3658_r3549_p1344/ | |
| # Single top Wt | |
| mc12_8TeV.110140.PowhegPythia_P2011C_st_Wtchan_incl_DR.merge.NTUP_TAU.e1743_s1581_s1586_r3658_r3549_p1344/ |
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