# install numpy
#
# sudo apt install python-pip
# python -m pip install --upgrade pip
# pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose
#

import sys
import timeit
import numpy as np

print (sys.version_info)

# fiedler matrix 25 x 25
A = [
  [ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24],
  [ 1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
  [ 2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22],
  [ 3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
  [ 4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
  [ 5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
  [ 6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
  [ 7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17],
  [ 8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16],
  [ 9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15],
  [10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14],
  [11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13],
  [12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12],
  [13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],
  [14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10],
  [15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
  [16, 15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7,  8],
  [17, 16, 15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6,  7],
  [18, 17, 16, 15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5,  6],
  [19, 18, 17, 16, 15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4,  5],
  [20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3,  4],
  [21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2,  3],
  [22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1,  2],
  [23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0,  1],
  [24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0]
]

iterations = 10000
ms = 1000000

def add():
  return np.add(A, A)

def multiply():
  return np.matmul(A, A)

def transpose():
  return np.transpose(A)

def det():
  return np.linalg.det(A)

print('Add       duration {} microseconds'.format(timeit.timeit(add, number=iterations) * ms / (iterations)))
print('Multiply  duration {} microseconds'.format(timeit.timeit(multiply, number=iterations) * ms / iterations))
print('Transpose duration {} microseconds'.format(timeit.timeit(transpose, number=iterations) * ms / iterations))
print('Det       duration {} microseconds'.format(timeit.timeit(det, number=iterations) * ms / iterations))

# run again with more iterations to see whether we get the same sort of durations
iterations2 = iterations * 10
print('')
print('second round...')
print('Add       duration {} microseconds'.format(timeit.timeit(add, number=iterations2) * ms / iterations2))
print('Multiply  duration {} microseconds'.format(timeit.timeit(multiply, number=iterations2) * ms / iterations2))
print('Transpose duration {} microseconds'.format(timeit.timeit(transpose, number=iterations2) * ms / iterations2))
print('Det       duration {} microseconds'.format(timeit.timeit(det, number=iterations2) * ms / iterations2))