lukedeo

import rootpy
from rootpy.io import root_open
from rootpy.tree import Tree, TreeChain

# -- Let me real quick grab a nasty d3pd
f = root_open('user.dguest.050909._00001.skim-output.root')

# -- Yank that tree out of there...
T = f['physics'] 

lukedeo

import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib.tri as tri
from matplotlib.mlab import griddata
from matplotlib import rc
import numpy.ma as ma

def data_load(filename):
    data = np.genfromtxt(filename, delimiter = ',', names = True)

lukedeo

class TrainingHandler(object):
    def __init__(self, sig=signal.SIGINT):
        self.sig = sig
        
    def __enter__(self):
        self.interrupted = False
        self.released = False
        self.original_handler = signal.getsignal(self.sig)
        def handler(signum, frame):
            self.release()

lukedeo

from setuptools import setup
from setuptools import find_packages

setup(
    name='NewPackage',
    version='0.0.1',
    description='Library for doing a thing.',
    author='Luke de Oliveira',
    author_email='[email protected]',
    url='https://github.com/lukedeo/repo',

lukedeo

#!/usr/bin/env bash
# bootstrap.sh 
# author: Luke de Oliveira ([email protected])
# boostrap script for clean (CPU) EC2 instance setup for machine learning.

# -- get a basic build up
sudo apt-get update && sudo apt-get upgrade
sudo apt-get install -y  build-essential

# -- dev builds of key software

lukedeo

'''
hacked out deepdish.io style keras NN saving functionality

[credit] deepdish
'''
from __future__ import division, print_function, absolute_import

import numpy as np
import tables
import warnings

lukedeo

from rootpy.vector import LorentzVector

class Jet(object):
    """
    Better Jet Class!

    Example:

    >>> j = Jet(100.1, 1.2, 1.0, 12)
    >>> j.lv.Pt()

lukedeo

import numpy as np

class NDWeights(object):
    '''
    An n-dimension reweighting object
    '''
    def __init__(self, bins):
        ''' 
        Constructor for weighting
        

lukedeo

import keras.backend as K
import numpy as np

def custom_uniform(shape, range=(-1, 1), name=None):
    min_, max_ = range
    return K.variable(np.random.uniform(low=min_, high=max_, size=shape), name=name)


net.add(Dense(10, input_dim=5, init=lambda shape, name: custom_uniform(shape, (-10, 5), name)))

lukedeo

# import csv
# import os
# import numpy as np

# from cervantes.box import WordVectorBox
# from cervantes.language import OneLevelEmbedding
# from cervantes.nn.models import RNNClassifier
# from cervantes.box import EnglishCharBox

from keras.models import Model, Sequential