NumPy Random Module — Generating and Controlling Random Data

import numpy as np

# Reproducible — same seed gives same numbers every run
rng = np.random.default_rng(seed=42)

print(rng.random(5))          # 5 floats in [0, 1)
print(rng.integers(0, 10, 5)) # 5 ints in [0, 10)
print(rng.normal(0, 1, 5))    # 5 standard normal samples
print(rng.uniform(2.0, 5.0, 3)) # 3 floats in [2, 5)

import numpy as np
rng = np.random.default_rng(0)

# Normal (Gaussian)
print(rng.normal(loc=170, scale=10, size=5))  # heights in cm

# Binomial — n trials, p probability
print(rng.binomial(n=10, p=0.5, size=5))  # coin flips

# Poisson — events per interval
print(rng.poisson(lam=3, size=5))

# Exponential — time between events
print(rng.exponential(scale=1.0, size=5))

import numpy as np
rng = np.random.default_rng(42)

arr = np.arange(10)

# Shuffle in place
rng.shuffle(arr)
print(arr)  # [0 3 7 2 5 1 9 4 6 8] — order varies

# Sample without replacement
print(rng.choice(arr, size=3, replace=False))

# Sample with replacement (bootstrap)
print(rng.choice(arr, size=5, replace=True))

# Permutation — returns a copy, does not modify original
orig = np.arange(5)
shuffled = rng.permutation(orig)
print(orig)     # [0 1 2 3 4] — unchanged
print(shuffled) # shuffled copy

import numpy as np
from hashlib import sha256

# Good: fixed seed
rng = np.random.default_rng(seed=42)

# Better: unique seed per process (e.g., MPI rank)
process_id = 0  # from MPI
seed = int(sha256(b"my_experiment").hexdigest(), 16) + process_id
rng_rank = np.random.default_rng(seed)

# Testing: seed from test name
def my_test_function():
    test_seed = int(sha256(b"my_test_function").hexdigest(), 16) % 2**32
    rng_local = np.random.default_rng(test_seed)
    # ... use rng_local

import numpy as np
import time

rng = np.random.default_rng(0)

# Slow: loop
start = time.time()
for _ in range(100000):
    rng.random()
print("Loop time:", time.time() - start)

# Fast: vectorised
start = time.time()
rng.random(100000)
print("Vectorised time:", time.time() - start)