#### Category : gaussian

I saw that creating a Gaussian window works for 1D tensors with scipy.signal.window.gaussian, but how would it work for 3D tensors? I expect for example a tensor of shape (64, 64, 32) to have a value of 1.0 at the central voxel and 0.0 at the borders. Thank you in advance! Source: Python-3x..

I am trying to fit a gaussian in python and I found some codes on the web. But for some reason,I am a getting a weird line. Can somebody help me fix this? import numpy as np from astropy.io import fits import matplotlib as plt import matplotlib.pyplot as plt from scipy.stats import norm file=fits.open(‘trial.fits’) file.info() ..

i am trying to plot an Gaussian Beam which becomes very thin at some point ( focal plane) my problem is now that as it becomes thinner it cannot be shown properly. What can i do to display it adequate? The Normal Beam The thinner becoming Beam Source: Python.. I am so stuck trying to fit 3D gaussians, and I am hoping someone can see some silly mistake I am making, because I have spent hours debugging to no avail. I have a 3d image stored in an array called "data", where data[x, y, z] gives the grayscale intensity at the point (x, y, ..

Actually i made gaussian map for my data, and i want to merge with map. but after merge with map, there’s no image which is with gaussian data. How can i plot the data on enter image description herethe map? is there any problem? Source: Python..

Based on the Newton Raphson method for multivariable functions, the tolerance or "δ" is calculated as the following: J(x) * δ = -F(x) Where J is a 2×2 matrix (Jacobian matrix). Where δ is the tolerance that is calculated by using the gaussian elimination method. Where F is a 2×1 matrix (Initial given function). Question: ..

I’m just learning about the Gaussian Mixture Model. I found an example online and I’m reviewing it. But the code gave an error. I could not understand the reason for this error. If I ask, can you help me? Erorr: ValueError: The number of folds must be of Integral type. [0 0 0 0 0 ..

I’m trying to make a code that fits a distribution with a Gaussian format, but I can only find solutions on the internet, where the y-axis (which represents the number of events) is like p.d.f. . I’m wanting a Gaussian adjustment that the y-axis keeps the number of events (without density) Source: Python-3x.. 