Seed and Reproduce¶

Setting a random seed ensures reproducible results in simulations and scientific computing.

Why Seeds Matter¶

Random number generators produce deterministic sequences from an initial state.

1. Pseudorandomness¶

NumPy uses pseudorandom number generators that are deterministic given the same seed.

2. Reproducibility¶

Scientific research requires reproducible results for validation and peer review.

np.random.seed¶

Sets the global random state for the legacy random API.

1. Basic Usage¶

import numpy as np

def main():
    np.random.seed(0)

    data = np.random.randn(5)
    print(data)

if __name__ == "__main__":
    main()

2. Consistent Results¶

Running the same seed produces identical sequences every time.

Seed with Histogram¶

Visualize that seeded random samples follow expected distributions.

1. Normal Distribution¶

import numpy as np
import matplotlib.pyplot as plt
from scipy import stats

def main():
    np.random.seed(0)

    n_samples = 10_000
    data = np.random.randn(n_samples)

    fig, ax = plt.subplots()

    _, bins_, _ = ax.hist(data, bins=100, density=True)

    mu = data.mean()
    sigma = data.std()
    pdf_at_bins_ = stats.norm(loc=mu, scale=sigma).pdf(bins_)
    ax.plot(bins_, pdf_at_bins_, '--r', linewidth=5)

    plt.show()

if __name__ == "__main__":
    main()

2. Histogram Fit¶

The red dashed line shows the theoretical PDF matching the sample histogram.

Modern Generator API¶

The recommended approach uses explicit generator objects.

1. default_rng¶

import numpy as np

rng = np.random.default_rng(seed=42)
data = rng.standard_normal(1000)

2. Advantages¶

Explicit generators avoid global state and enable parallel random streams.

Best Practices¶

Follow these guidelines for reproducible random experiments.

1. Set Early¶

Set the seed at the beginning of your script or notebook.

2. Document Seeds¶

Record seed values in logs or comments for future reproduction.

3. Avoid Resetting¶

Resetting seeds mid-experiment can cause subtle statistical biases.