Coverage for src/causalspyne/implicit_spectrum_histogram.py: 0%

1import numpy as np 

2import matplotlib.pyplot as plt 

3from scipy import stats 

4from causalspyne.implicit_gen_Sigma import gen_spectrum 

5 

6 

7# Generate sample data 

8 

9# samples = np.random.normal(size=10000) 

10samples = [gen_spectrum() for _ in range(10000)] 

11# Create histogram 

12fig, ax = plt.subplots(figsize=(8, 6)) 

13 

14hist, bins, _ = ax.hist(samples, bins=30, density=True, alpha=0.7, 

15 label="Histogram") 

16 

17# Calculate bin centers for PDF plotting 

18bin_centers = 0.5 * (bins[1:] + bins[:-1]) 

19 

20# Compute PDF 

21pdf = stats.norm.pdf(bin_centers) 

22 

23# Plot PDF 

24ax.plot(bin_centers, pdf, 'r-', label="PDF") 

25 

26# Customize plot 

27ax.set_xlabel('eigenvalue') 

28ax.set_ylabel('Density') 

29ax.set_title('Histogram of maximum eigenvalue distribution') 

30ax.legend() 

31 

32plt.show()