Python生成PDF随机数遇异常：get_brown函数返回十六进制值问题

Hey there! Let's work through this Brownian motion simulation issue you're hitting—since you're new to Python, this is probably a common rookie hiccup we can fix quickly.

First, Diagnose the Hex Output Issue #

That fixed-prefix hex value you're seeing (0x000000A66400AXXX) is almost certainly the memory address of the get_brown function itself, not the output of running the function. This happens when you accidentally print the function object instead of calling it.

For example, if you wrote:

print(get_brown)  # ❌ Prints the function's memory address (hex value)

You need to add parentheses to actually execute the function and get its return value:

print(get_brown())  # ✅ Runs the function and prints its output

Next, Fix the get_brown Function to Generate Valid Random Numbers #

If you're already calling the function correctly but still getting weird output, your function probably isn't properly returning the random numbers generated with SciPy. Let's walk through a working example tailored to Brownian motion (since Brownian motion increments follow a normal distribution):

from scipy.stats import norm
import numpy as np

def get_brown(dt=0.01):
    """Generate a single Brownian motion increment (normal distribution: mean=0, variance=dt)"""
    # Use scipy.stats.norm.rvs to generate random samples matching the target PDF
    increment = norm.rvs(loc=0, scale=np.sqrt(dt))
    # Make sure to RETURN the generated value—this is critical!
    return increment

# Test the function correctly
brown_increment = get_brown()
print(f"Generated Brownian increment: {brown_increment}")

Key things to check in your own code:

• Are you using return to send the generated random number back from the function? Without this, the function returns None by default (though that wouldn't give hex output—so the first issue is more likely).
• Are you using the right SciPy method for your target PDF? For custom PDFs, you might need scipy.stats.rv_continuous to define your distribution first, then use .rvs() to generate samples.

Bonus: Verify Your Random Numbers Match the Target PDF #

Once you're getting valid numbers, you can confirm they fit your desired PDF by generating a large sample and plotting a histogram against the theoretical PDF:

import matplotlib.pyplot as plt

# Generate 10,000 samples
samples = [get_brown() for _ in range(10000)]

# Plot histogram of samples (normalized to match PDF)
plt.hist(samples, bins=50, density=True, alpha=0.6, label='Generated Samples')

# Plot the theoretical normal PDF for comparison
x = np.linspace(-0.2, 0.2, 1000)
plt.plot(x, norm.pdf(x, loc=0, scale=np.sqrt(0.01)), 'r--', label='Theoretical PDF')

plt.xlabel('Brownian Increment')
plt.ylabel('Probability Density')
plt.legend()
plt.show()

内容的提问来源于stack exchange，提问作者user1209014