def metropolis_step_removal(X, move_index=None):
'''For a given state vector X, apply MC Metropolis move and update
bookkeeping associated with counting grid'''
global accepted, rejected, counting_grid
# Original point
if move_index == None:
index = fastrand.pcg32bounded(N)
else:
index = move_index
point_old = X[index].copy()
counting_index_old = (
int(point_old[1] * grid_marks_y / Ly),
int(point_old[0] * grid_marks_x / Lx)
) # the index that the particle appears in in the counting grid
# Propose new move
point_new = [0, 0]
point_new[0] = (point_old[0] + step * (fastrand.pcg32() / 2147483647)) % Lx
point_new[1] = (point_old[1] + step * (fastrand.pcg32() / 2147483647)) % Ly
counting_index_new = (int(point_new[1] * grid_marks_y / Ly),
int(point_new[0] * grid_marks_x / Lx))
# Use counting grid to check for nearest neighbours
neighbour_indicies = [
neighbour for neighbours in extract_3X5(counting_grid,
counting_index_new).flatten()
for neighbour in neighbours
]
# Determine whether overlap occurs
# Here is the difference from old step. Move accepted if it's further away
for point_index in neighbour_indicies:
if torus_dist(point_new,
X[point_index]) < 2 * r and point_index != index:
rejected += 1
return X
# Else accept move
accepted += 1
X[index] = point_new
# Determine whether grid counts need to be changed
if counting_index_old != counting_index_new:
counting_grid[counting_index_old].remove(index)
counting_grid[counting_index_new].append(index)
return X
import fastrand
import time
import numpy as np
import matplotlib.pyplot as plt
# Time tests
# Fastrand32 float
t0 = time.time()
for i in range(10000000):
my_random_float = fastrand.pcg32() / 2147483647
print('fastrand32 float: ', time.time() - t0, 's')
# Numpy float
t0 = time.time()
for i in range(10000000):
my_random_float = 2 * (np.random.rand() - 0.5)
print('numpy float: ', time.time() - t0, 's')
# Fastrand int
t0 = time.time()
for i in range(10000000):
my_random_int = fastrand.pcg32bounded(250)
print('fastrand int: ', time.time() - t0, 's')
# Numpy int
t0 = time.time()
for i in range(10000000):
my_random_int = np.random.randint(0, 250)
print('numpy int: ', time.time() - t0, 's')
def reseed():
# seed from system and flush initial output
fastrand.pcg32_seed(random.getrandbits(63))
fastrand.pcg32()
fastrand.pcg32()
import itertools
import fastrand
TARGET_SIZE = 1024 * 1024 * 1000
with tqdm.tqdm(total=TARGET_SIZE) as p:
with io.StringIO() as f:
print("""<?xml version="1.0" encoding="utf-8" ?>""", file=f)
print("<badges>", file=f)
for id in itertools.count():
p.n = f.tell()
p.update(n=0)
if f.tell() >= TARGET_SIZE:
break
userid = fastrand.pcg32bounded(100000) + 1
name = "".join(
(string.ascii_lowercase
)[fastrand.pcg32bounded(len(string.ascii_lowercase))]
for x in range(5, 5 + fastrand.pcg32bounded(8)))
date = datetime.datetime(2020, 1, 1) + datetime.timedelta(
seconds=fastrand.pcg32bounded(86400 * 150))
cls = fastrand.pcg32bounded(10) + 1
tagbased = fastrand.pcg32() & 1
print(
f'<row Id="{id}" UserId="{userid}" Name="{name}" Date="{date.isoformat()}" Class="{cls}" TagBased="{str(tagbased).title()}" />',
file=f,
)
print("</badges>", file=f)
with open("badges.xml", "w") as ff:
ff.write(f.getvalue())
from multiprocessing import Pool
from tqdm import tqdm
PROCESSES = 6
import fastrand
r_number = lambda: fastrand.pcg32() / int(2**32)
def fastmap(function, iterable, display_progress=False):
with Pool(processes=PROCESSES) as p:
max_ = len(iterable)
if display_progress:
with tqdm(total=max_) as pbar:
result = list()
for res in p.imap(func=function, iterable=iterable):
pbar.update()
result.append(res)
return result
else:
return list(p.imap(func=function, iterable=iterable))
#test
if __name__ == '__main__':
def double(x):
return x * 2
print(list(fastmap(double, range(10))))
