Category : gaussian

I want to calculate Gauss-Hermite moments, such as h1,h2, h3, h4 moments from the values in an array of numbers which represent velocity distribution of stars in a galaxy. How would I fit a curve to these values that would be a Gauss-Hermite curve fitting and get the value of h1, h2, h3 and h4 ..

I am trying to create and plot two sets of Gaussian Data. I have used numpy np.random.multivariate_normal(mu, cov, #points).T format. When I don’t transpose, it gives me a "too many values to unpack (expected 2) error. At any rate, when I do transpose, I can successfully plot one set of data. My goal is to .. I’m trying to split a gaussian shaped curve, to K equal-volume segments, with Python for signal-filtering purposes. I’m seeking for pseudo-code, general idea or a library that performs it. Any help will be much appreciated. Thanks! For example in the image below: for K=6. volumes s1 = s2 = … = s6: Source: Python.. I have this data, I tried to fit by a Gaussian function but I can’t found an appropriate function, I tried using curve_fit from scipy.optimize : time_s = [1.44692600e+09, 1.44692634e+09, 1.44692671e+09, 1.44692707e+09, 1.44692743e+09, 1.44692785e+09, 1.44692826e+09, 1.44692941e+09, 1.44692967e+09, 1.44692997e+09, 1.44693029e+09, 1.44693062e+09, 1.44693096e+09, 1.44693131e+09, 1.44693200e+09, 1.44693227e+09, 1.44693254e+09, 1.44693284e+09, 1.44693313e+09, 1.44693342e+09, 1.44693370e+09, 1.44693398e+09, 1.44693429e+09, 1.44693460e+09, 1.44693492e+09, 1.44693522e+09, 1.44693552e+09, ..

I’m trying to create a dataset suitable for a Gaussian distribution. The x and y values will be the same, and on the z-axis, these values will be in accordance with the gaussian distribution. Taking this site as a resource for myself: https://towardsdatascience.com/a-python-tutorial-on-generating-and-plotting-a-3d-guassian-distribution-8c6ec6c41d03 I wrote the following code. But unfortunately, the output I got was ..

I’m using optimize.curve_fit to fit a function that includes an error function. On the first try I used math.erfc and got TypeError: only size-1 arrays can be converted to Python scalars On the second try I used scipy.special.erf and got [1. 1.] as the fit results, which I don’t believe correctly reflects the given data. ..

I need to generate a random sample of size 200 (n=200) from a normal distribution with variance 1 and true mu (average) I specify; then, I test the draw against a hypothesis: mu <= 1. I need to do this for each of 400 potential true thetas, and for each true theta I need to ..