El Confuso - 7 months ago 71

Python Question

I have written the below code to fit a Gaussian curve to a histogram. It seems to work, although the Y scaling is different. What am I doing wrong?

`import matplotlib.pyplot as plt`

import numpy as np

import matplotlib.mlab as mlab

list = [0,1,1,2,2,2,3,3,4]

plt.figure(1)

plt.hist(list)

plt.xlim((min(list), max(list)))

mean = np.mean(list)

variance = np.var(list)

sigma = np.sqrt(variance)

x = np.linspace(min(list), max(list),100)

plt.plot(x,mlab.normpdf(x,mean,sigma))

plt.show()

Thanks!

Answer

You need to normalize the histogram, since the distribution you plot is also normalized:

```
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
arr = np.random.randn(100)
plt.figure(1)
plt.hist(arr, normed=True)
plt.xlim((min(arr), max(arr)))
mean = np.mean(arr)
variance = np.var(arr)
sigma = np.sqrt(variance)
x = np.linspace(min(arr), max(arr), 100)
plt.plot(x, mlab.normpdf(x, mean, sigma))
plt.show()
```

Note the `normed=True`

in the call to `plt.hist`

. Note also that I changed your sample data, because the histogram looks weird with too few data points.

If you instead want to keep the original histogram and rather adjust the distribution, you have to scale the distribution such that the integral over the distribution equals the integral of the histogram, i.e. the number of items in the list multiplied by the width of the bars. This can be achieved like

```
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
arr = np.random.randn(1000)
plt.figure(1)
result = plt.hist(arr)
plt.xlim((min(arr), max(arr)))
mean = np.mean(arr)
variance = np.var(arr)
sigma = np.sqrt(variance)
x = np.linspace(min(arr), max(arr), 100)
dx = result[1][1] - result[1][0]
scale = len(arr)*dx
plt.plot(x, mlab.normpdf(x, mean, sigma)*scale)
plt.show()
```

Note the `scale`

factor calculated from the number of items times the width of a single bar.

Source (Stackoverflow)