I am trying to fit a 2D Gaussian to an image to find the location of the brightest point in it. My code looks like this:
import numpy as np import astropy.io.fits as fits import os from astropy.stats import mad_std from scipy.optimize import curve_fit import matplotlib.pyplot as plt from matplotlib.patches import Circle from lmfit.models import GaussianModel from astropy.modeling import models, fitting def gaussian(xycoor,x0, y0, sigma, amp): '''This Function is the Gaussian Function''' x, y = xycoor # x and y taken from fit function. Stars at 0, increases by 1, goes to length of axis A = 1 / (2*sigma**2) eq = amp*np.exp(-A*((x-x0)**2 + (y-y0)**2)) #Gaussian return eq def fit(image): med = np.median(image) image = image-med image = image[0,0,:,:] max_index = np.where(image >= np.max(image)) x0 = max_index[1] #Middle of X axis y0 = max_index[0] #Middle of Y axis x = np.arange(0, image.shape[1], 1) #Stars at 0, increases by 1, goes to length of axis y = np.arange(0, image.shape[0], 1) #Stars at 0, increases by 1, goes to length of axis xx, yy = np.meshgrid(x, y) #creates a grid to plot the function over sigma = np.std(image) #The standard dev given in the Gaussian amp = np.max(image) #amplitude guess = [x0, y0, sigma, amp] #The initial guess for the gaussian fitting low = [0,0,0,0] #start of data array #Upper Bounds x0: length of x axis, y0: length of y axis, st dev: max value in image, amplitude: 2x the max value upper = [image.shape[0], image.shape[1], np.max(image), np.max(image)*2] bounds = [low, upper] params, pcov = curve_fit(gaussian, (xx.ravel(), yy.ravel()), image.ravel(),p0 = guess, bounds = bounds) #optimal fit. Not sure what pcov is. return params def plotting(image, params): fig, ax = plt.subplots() ax.imshow(image) ax.scatter(params[0], params[1],s = 10, c = 'red', marker = 'x') circle = Circle((params[0], params[1]), params[2], facecolor = 'none', edgecolor = 'red', linewidth = 1) ax.add_patch(circle) plt.show() data = fits.getdata('AzTECC100.fits') #read in file med = np.median(data) data = data - med data = data[0,0,:,:] parameters = fit(data) #generates a gaussian based on the parameters given plotting(data, parameters) The image is plotting and the code is giving no errors but the fitting isn't working. It's just putting an x wherever the x0 and y0 are. The pixel values in my image are very small. The max value is 0.0007 and std dev is 0.0001 and the x and y are a few orders of magnitude larger. So I believe my problem is that because of this my eq is going to zero everywhere so the curve_fit is failing. I'm wondering if there's a better way to construct my gaussian so that it plots correctly?
