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

# Generating non-linear data
np.random.seed(42)
A = np.linspace(-10, 10, 100)
B = A**5 + np.random.normal(0, 4000, size=len(A))

# Calculate Spearman correlation coefficient
spearman_corr, _ = spearmanr(A, B)
# Calculate Pearson correlation coefficient
pearson_corr, _ = pearsonr(A, B)
m, b = np.polyfit(A, B, 1)  # Fit a linear regression line

# Scatter plot
fig, ax = plt.subplots()
ax.scatter(A, B, color=sns.color_palette("hls",24)[14], alpha=.9, label='Data points')
plt.plot(X, m * X + b, color='red', alpha=.6, label='Pearson Correlation Line')
plt.title("A vs. B (Non-linear Relationship)")
plt.xlabel("A")
plt.ylabel("B")
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.xaxis.set_ticks_position('none') 
ax.yaxis.set_ticks_position('none')

plt.legend()