In [7]:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.stats import norm
%matplotlib inline
In [8]:
mu = 998.8
sigma = 73.10
x1 = 900
x2 = 1100
In [9]:
# calculate the z-transform
z1 = ( x1 - mu ) / sigma
z2 = ( x2 - mu ) / sigma
In [10]:
x = np.arange(z1, z2, 0.001) # range of x in spec
x_all = np.arange(-10, 10, 0.001) # entire range of x, both in and out of spec
# mean = 0, stddev = 1, since Z-transform was calculated
y = norm.pdf(x,0,1)
y2 = norm.pdf(x_all,0,1)
In [11]:
# build the plot
fig, ax = plt.subplots(figsize=(9,6))
plt.style.use('fivethirtyeight')
ax.plot(x_all,y2)
ax.fill_between(x,y,0, alpha=0.3, color='b')
ax.fill_between(x_all,y2,0, alpha=0.1)
ax.set_xlim([-4,4])
ax.set_xlabel('# of Standard Deviations Outside the Mean')
ax.set_yticklabels([])
ax.set_title('Normal Gaussian Curve')
plt.savefig('normal_curve.png', dpi=72, bbox_inches='tight')
plt.show()
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