Apply Joy Division to your timeseries data

joy.py

import numpy as np
from scipy.signal.windows import hamming

class Joy:
    def __truediv__(cls, arr):
        T, N = arr.shape
        spacer = 2 * np.arange(N)
        t = np.arange(T)
        window = hamming(T)
        normed = (arr - arr.mean(0)) / (1e-3 + arr.std(0))
        
        fig = plt.figure(figsize=(6, 8), facecolor='k')
        for n in range(N):
            plt.plot(t, win * normed[:,n] + spacer[n], lw=1, c='0.9', zorder=2*(N-n))
            plt.fill_between(x=t, y1=win * normed[:,n] + spacer[n], y2=(win*normed[:,n]).min() + spacer[n], color='k', lw=0, zorder=2*(N-n)+1)
            
        plt.axis('off')