python, weighted linspace

Question

can anyone show me what the best way is to generate a (numpy) array containig values from 0 to 100, that is weighted by a (for example) normal distribution function with mean 50 and variance 5. So that there are more 50s and less (nearly no) zeros and hundreds. I think the problem should not be too hard to solve, but I'm stucked somehow...

I thought about something with np.linspace but it seems, that there is no weight option.

So just to be clear: I don't wan't a simple normal distribution from 0 to 100, but something like an array from 0 to 100 with higher density of values in the middle.

Thanks

Answer 1

You can use scipy's stats distributions:

import numpy as np
from scipy import stats

# your distribution:
distribution = stats.norm(loc=50, scale=5)

# percentile point, the range for the inverse cumulative distribution function:
bounds_for_range = distribution.cdf([0, 100])

# Linspace for the inverse cdf:
pp = np.linspace(*bounds_for_range, num=1000)

x = distribution.ppf(pp)

# And just to check that it makes sense you can try:
from matplotlib import pyplot as plt
plt.hist(x)
plt.show()

Of course, I admit the start and end point is not quite exact like this due to numerical inaccuracies when going back and forth.