Time Series Prediction Interval - Normal Distribution Animation Code

2_Normal_Distribution.py

!pip install --upgrade pandas
!pip install --upgrade pandas-datareader
!pip install celluloid

from celluloid import Camera as Cam
from IPython.display import HTML, clear_output

import pandas_datareader as pdr
import pandas as pd
from statsmodels.tsa.ar_model import AutoReg
from statsmodels.tools.eval_measures import rmse
import numpy as np
import seaborn as sb
import matplotlib.pyplot as plt
from matplotlib import rcParams
from cycler import cycler 


import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import math

mu = 100
sigma = 5
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)

fig, ax = plt.subplots(dpi = 400)
cam = Cam(fig)

rcParams['figure.figsize'] = 17,5
rcParams['axes.spines.top'] = False
rcParams['axes.spines.right'] = False
rcParams['lines.linewidth'] = 2.5

plt.title("Normal Distribution")
plt.xlabel("Weights")
plt.ylabel("Proportions")
sb.despine(right = True, top = True)

for i in range(len(x)):
    plt.plot(x[0:i], stats.norm.pdf(x[0:i], mu, sigma), color = '#000080') 
    cam.snap()
    
for i in range(len(x)*5):
    plt.plot(x, stats.norm.pdf(x, mu, sigma), color = '#000080')
    
    plt.axvline(x = mu, ymin = 0, ymax = 1, color = 'black')
    plt.text(mu, 0, 'μ', color = 'black')

    plt.axvline(x = mu+sigma, ymin = 0, ymax = 1, color = 'grey')
    plt.text(mu+sigma, 0, 'μ + 1σ', color = 'black')

    plt.axvline(x = mu-sigma, ymin = 0, ymax = 1, color = 'grey')
    plt.text(mu-sigma, 0, 'μ - 1σ', color = 'black')

    plt.axvline(x = mu+2*sigma, ymin = 0, ymax = 1, color = 'silver')
    plt.text(mu+2*sigma, 0, 'μ + 2σ', color = 'black')

    plt.axvline(x = mu-2*sigma, ymin = 0, ymax = 1, color = 'silver')
    plt.text(mu-2*sigma, 0, 'μ - 2σ', color = 'black')
    
    plt.axvline(x = mu+3*sigma, ymin = 0, ymax = 1, color = 'silver', linestyle = 'dashed')
    plt.text(mu+3*sigma, 0, 'μ + 3σ', color = 'black')

    plt.axvline(x = mu-3*sigma, ymin = 0, ymax = 1, color = 'silver', linestyle = 'dashed')
    plt.text(mu-3*sigma, 0, 'μ - 3σ', color = 'black')
    
    cam.snap()
      
plt.close(fig)

animation = cam.animate(blit=False, interval=15)
HTML(animation.to_html5_video())