ibab

#include <array>
#include <iostream>
#include <vector>

template <typename T,
          // Number of dimensions of tensor.
          int D>
class Tensor {
public:
  template <typename... T2,

ibab

ibab

ibab

import numpy as np
import tensorflow as tf
from scipy.optimize import minimize
import matplotlib
matplotlib.use('PDF')
import matplotlib.pyplot as plt

# Generate dataset
data = np.random.normal(0, 1, 1000)

ibab

import numpy as np
import tensorflow as tf

sess = tf.Session()

TYPE=np.float64

N = 1000000
data = np.random.normal(0, 1, N).astype(TYPE)

ibab

from __future__ import print_function

import gevent
import gevent.queue
import gevent.pool
import gevent.subprocess
import gevent.monkey

gevent.monkey.patch_all()

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merge []         ys                   = ys
merge xs         []                   = xs
merge xs@(x:xt) ys@(y:yt) | x <= y    = x : merge xt ys
                          | otherwise = y : merge xs yt

mergeLists [] = []
mergeLists [x] = [x]
mergeLists (x:y:rest) = mergeLists $ (merge x y) : mergeLists rest

ll = [[1,2,3], [1,3,6], [1, 10], [3,3,3], [1], [1,1,1,5]]

ibab

def get_matching_variables(fname, tree, patterns):
    from fnmatch import fnmatch
    from root_numpy import list_branches

    branches = list_branches(fname, tree)

    selected = []

    for p in patterns:
        for b in branches:

ibab

import numpy as np
# normal ordering values from NuFit 2.0, JHEP 11 (2014) 052 [arXiv:1409.5439]
θ_12 = np.deg2rad(33.48)
θ_23 = np.deg2rad(42.3)
θ_13 = np.deg2rad(8.50)
δ_CP = np.deg2rad(306)

Δm21 = np.sqrt(7.50e-5) # eV^2
Δm31 = np.sqrt(2.449e-3) # eV^2

ibab

#ifndef ENUMS_H
#define ENUMS_H

#include <boost/preprocessor.hpp>                                                
#include <string>

/*
 * The following macros allow you to define an enum and get `toString`
 * and `fromString` functions generated automatically.
 * If you use