normal_plot.dn.py

# normal_plot.dn.py
import nitroplot
import numpy as np

mu, sigma = Cell("mu").value, Cell("sigma").value # mean and standard deviation
s = np.random.normal(mu, sigma, Cell("samples").value)

n = [None] * 5000
Cell("C3").vertical = n   # clear the vertical range
Cell("C3").vertical = s   # fill the range with the samples

# Verify the mean and the variance:

error = Cell("error").value

abs(mu - np.mean(s)) < error

abs(sigma - np.std(s, ddof=1)) < error


count, bins, ignored = nitroplot.hist(s, Cell("bins").value, normed=True)

nitroplot.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (bins - mu)**2 / (2 * sigma**2) ),linewidth=2, color='r')
nitroplot.graph()