sim_mat1.py

sim_mat1.py

import seaborn as sns
import numpy as np
import pandas as pd
import scipy.stats as stats
from scipy.spatial.distance import squareform
import matplotlib.pyplot as plt

# n is the number of subjects or items that will determine the size of the correlation matrix. 
n = 20 
data_length = 50
nr = .58

# Random data
np.random.seed(0)
m1_u = np.random.normal(loc=0, scale=1, size=(data_length, n))
m2_u = nr*np.random.normal(loc=0, scale=1, size=(data_length, n)) + (1-nr)*m1_u

m1 = pd.DataFrame(m1_u).corr()
m2 = pd.DataFrame(m2_u).corr()

f,axes = plt.subplots(1,2, figsize=(10,5))
sns.set_style("white")
for ix, m in enumerate([m1,m2]):
  sns.heatmap(m, cmap="RdBu_r", center=0, vmin=-1, vmax=1, ax=axes[ix], square=True, cbar_kws={"shrink": .5}, xticklabels=True)
  axes[ix].set(title=f"m{ix+1}")