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How to make a Gaussian filter in Matlab

here’s an alternative: Create the 2D-Gaussian: function f=gaussian2d(N,sigma) % N is grid size, sigma speaks for itself [x y]=meshgrid(round(-N/2):round(N/2), round(-N/2):round(N/2)); f=exp(-x.^2/(2*sigma^2)-y.^2/(2*sigma^2)); f=f./sum(f(:)); Filtered image, given your image is called Im: filtered_signal=conv2(Im,gaussian2d(N,sig),’same’); Here’s some plots: imagesc(gaussian2d(7,2.5)) Im=rand(100);subplot(1,2,1);imagesc(Im) subplot(1,2,2);imagesc(conv2(Im,gaussian2d(7,2.5),’same’));

Python: finding the intersection point of two gaussian curves

You want to find the x’s such that both gaussian functions have the same height.(i.e intersect) You can do so by equating two gaussian functions and solve for x. In the end you will get a quadratic equation with coefficients relating to the gaussian means and variances. Here is the final result: import numpy as … Read more

JavaScript Math.random Normal distribution (Gaussian bell curve)?

Since this is the first Google result for “js gaussian random” in my experience, I feel an obligation to give an actual answer to that query. The Box-Muller transform converts two independent uniform variates on (0, 1) into two standard Gaussian variates (mean 0, variance 1). This probably isn’t very performant because of the sqrt, … Read more

Gaussian fit for Python

Here is corrected code: import pylab as plb import matplotlib.pyplot as plt from scipy.optimize import curve_fit from scipy import asarray as ar,exp x = ar(range(10)) y = ar([0,1,2,3,4,5,4,3,2,1]) n = len(x) #the number of data mean = sum(x*y)/n #note this correction sigma = sum(y*(x-mean)**2)/n #note this correction def gaus(x,a,x0,sigma): return a*exp(-(x-x0)**2/(2*sigma**2)) popt,pcov = curve_fit(gaus,x,y,p0=[1,mean,sigma]) plt.plot(x,y,’b+:’,label=”data”) … Read more

Fastest Gaussian blur implementation

You should use the fact that a Gaussian kernel is separable, i. e. you can express a 2D convolution as a combination of two 1D convolutions. If the filter is large, it may also make sense to use the fact that convolution in the spatial domain is equivalent to multiplication in the frequency (Fourier) domain. … Read more

Gaussian elimination without result for acceleration

When you perform a Gaussian elimination, you swap rows and repeatedly subtract a multiple of one row from another to produce an upper triangular form. When you do this on a system of equations or an “augmented matrix”, you do not use any information from the result column. The decisions about which rows to swap … Read more

How to specify upper and lower limits when using numpy.random.normal

It sounds like you want a truncated normal distribution. Using scipy, you could use scipy.stats.truncnorm to generate random variates from such a distribution: import matplotlib.pyplot as plt import scipy.stats as stats lower, upper = 3.5, 6 mu, sigma = 5, 0.7 X = stats.truncnorm( (lower – mu) / sigma, (upper – mu) / sigma, loc=mu, … Read more

Overlay normal curve to histogram in R

Here’s a nice easy way I found: h <- hist(g, breaks = 10, density = 10, col = “lightgray”, xlab = “Accuracy”, main = “Overall”) xfit <- seq(min(g), max(g), length = 40) yfit <- dnorm(xfit, mean = mean(g), sd = sd(g)) yfit <- yfit * diff(h$mids[1:2]) * length(g) lines(xfit, yfit, col = “black”, lwd = … Read more