import random
import numpy as np
from PythonNumpy.tools import timeit # get time it from tools.py(custom module)
def add_python(Z1, Z2):
return [z1 + z2 for (z1, z2) in zip(Z1, Z2)]
def add_numpy(Z1, Z2):
return np.add(Z1, Z2)
Z1 = random.sample(range(1000), 100)
Z2 = random.sample(range(1000), 100)
timeit("add_python(Z1, Z2)", globals())
timeit("add_numpy(Z1, Z2)", globals())
import numpy as np
from PythonNumpy.tools import timeit # get time it from tools.py(custom module)
Z = np.ones(4 * 1000000,
np.float32) # create an array of size 4*10000000 np.float32
print("np.float16:")
# time required to view array as np.float16
timeit("Z.view(np.float16)[...] = 0", globals())
print("np.int16:")
# time required to view array as np.int16
timeit("Z.view(np.int16)[...] = 0", globals())
print("np.int32:")
# time required to view array as np.int32
timeit("Z.view(np.int32)[...] = 0", globals())
print("np.float32:")
# time required to view array as np.float32
timeit("Z.view(np.float32)[...] = 0", globals())
print("np.int64:")
# time required to view array as np.int64
timeit("Z.view(np.int64)[...] = 0", globals())
print("np.float64:")
# time required to view array as np.float64
timeit("Z.view(np.float64)[...] = 0", globals())
print("np.complex128:")
from PythonNumpy.tools import timeit # get time it from tools.py(custom module)
import numpy as np
def random_walk_fastest(n=1000):
# No 's' in NumPy choice (Python offers choice & choices)
steps = np.random.choice([-1, +1], n)
return np.cumsum(
steps) # return the cumulative sum of the steps along a given axis.
walk = random_walk_fastest(1000)
timeit("random_walk_fastest(n=1000)", globals())
# calculates the total loops and time per loop
from PythonNumpy.tools import timeit # get time it from tools.py(custom module)
from itertools import accumulate # get accumulate function from built accumulate module
import random
def random_walk_faster(n=1000):
steps = random.choices([-1, +1], k=n)
return [0] + list(accumulate(steps)) # get the total number of steps
walk = random_walk_faster(1000)
timeit("random_walk_faster(n=10000)",
globals()) # calculates the total loops and time per loop
Z = Z[I]
Xi, Yi = Xi[I], Yi[I]
C = C[I]
return Z_.T, N_.T
if __name__ == '__main__':
from matplotlib import colors
import matplotlib.pyplot as plt
from PythonNumpy.tools import timeit # get time it from tools.py(custom module)
# Benchmark
xmin, xmax, xn = -2.25, +0.75, int(3000 / 3)
ymin, ymax, yn = -1.25, +1.25, int(2500 / 3)
maxiter = 200
timeit("mandelbrot_numpy2(xmin, xmax, ymin, ymax, xn, yn, maxiter)",
globals())
# Visualization
xmin, xmax, xn = -2.25, +0.75, int(3000 / 2)
ymin, ymax, yn = -1.25, +1.25, int(2500 / 2)
maxiter = 20
horizon = 2.0**40
log_horizon = np.log(np.log(horizon)) / np.log(2)
Z, N = mandelbrot(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon)
# Normalized recount as explained in:
# http://linas.org/art-gallery/escape/smooth.html
M = np.nan_to_num(N + 1 - np.log(np.log(abs(Z))) / np.log(2) + log_horizon)
dpi = 72
width = 10
import itertools as it
from PythonNumpy.tools import timeit
def solution_2():
# Itertools
# 14641 (=11*11*11*11) iterations & tests
return [(i, j, k, l) for i, j, k, l in it.product(range(11), repeat=4)
if i + j + k + l == 10]
timeit("solution_2()", globals())
from PythonNumpy.tools import timeit # get time it from tools.py(custom module)
import random
class RandomWalker:
def __init__(self):
self.position = 0
def walk(self, n): # walk method
self.position = 0
for i in range(n):
yield self.position
self.position += 2 * random.randint(0, 1) - 1
# returns current position after each random step
walker = RandomWalker() # make instance of class walk
walk = [position for position in walker.walk(1000)] # call the walk function
walker = RandomWalker()
timeit("[position for position in walker.walk(n=10000)]", globals())
# calculates the total loops and time per loop
def test(X, Y):
timeit("Z=X + 2.0*Y", globals()) # time taken by Z=X + 2.0*Y
timeit("Z = X + 2*Y", globals()) # time taken by Z=X + 2*Y
timeit("np.add(X, Y, out=X); np.add(X, Y, out=X)", globals())
Z = Z[I]
Xi, Yi = Xi[I], Yi[I]
C = C[I]
return Z_.T, N_.T
if __name__ == '__main__':
from matplotlib import colors
import matplotlib.pyplot as plt
from PythonNumpy.tools import timeit # get time it from tools.py(custom module)
# Benchmark
xmin, xmax, xn = -2.25, +0.75, int(3000 / 3)
ymin, ymax, yn = -1.25, +1.25, int(2500 / 3)
maxiter = 200
timeit("mandelbrot_python(xmin, xmax, ymin, ymax, xn, yn, maxiter)", globals())
# Visualization
xmin, xmax, xn = -2.25, +0.75, int(3000 / 2)
ymin, ymax, yn = -1.25, +1.25, int(2500 / 2)
maxiter = 20
horizon = 2.0 ** 40
log_horizon = np.log(np.log(horizon)) / np.log(2)
Z, N = mandelbrot(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon)
# Normalized recount as explained in:
# http://linas.org/art-gallery/escape/smooth.html
M = np.nan_to_num(N + 1 - np.log(np.log(abs(Z))) / np.log(2) + log_horizon)
dpi = 72
width = 10
