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- How to generate 2D gaussian with Python? - Stack Overflow
We can try just using the numpy method np random normal to generate a 2D gaussian distribution The sample code is np random normal(mean, sigma, (num_samples, 2)) A sample run by taking mean = 0 and sigma 20 is shown below :
- numpy - Fit mixture of two gaussian normal distributions to a histogram . . .
This can be achieved in a clean and simple way using sklearn Python library: import numpy as np from sklearn mixture import GaussianMixture from pylab import concatenate, normal # First normal distribution parameters mu1 = 1 sigma1 = 0 1 # Second normal distribution parameters mu2 = 2 sigma2 = 0 2 w1 = 2 3 # Proportion of samples from first distribution w2 = 1 3 # Proportion of samples from
- adding noise to a signal in python - Stack Overflow
A solution that I also tried was to make test over my variable and find what distribution it was closer numpy random has different distributions that can be used, it can be seen in its documentation: documentation numpy random As an example from a different distribution (example referenced from Noel's answer):
- How to calculate probability in a normal distribution given mean . . .
import scipy stats scipy stats norm(loc=100, scale=12) #where loc is the mean and scale is the std dev #if you wish to pull out a random number from your distribution scipy stats norm rvs(loc=100, scale=12) #To find the probability that the variable has a value LESS than or equal #let's say 113, you'd use CDF cumulative Density Function scipy
- How to generate a random normal distribution of integers
The accepted answer here works, but I tried Will Vousden's solution and it works well too: import numpy as np # Generate Distribution: randomNums = np random normal(scale=3, size=100000) # Round to the nearest integer and convert to integer (NB the latter is not done automatically otherwise!) randomInts = np round(randomNums) astype(int) # Plot: axis = np arange(start=min(randomInts), stop
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