Python 2D高斯拟合数据中的NaN值(Python 2D Gaussian Fit with NaN Values in Data)

 我是Python的新手，但我正在尝试为某些数据生成2D高斯拟合。 具体而言，恒星通量与坐标系/网格中的某些位置相关联。 然而，并非我的网格中的所有位置都具有相应的通量值。 我真的不想将这些值设置为零，以防它偏向我，但我似乎无法将它们设置为nan并且仍然可以使我的Gaussian适合工作。 这是我正在使用的代码（从这里略微修改）： 
 
import numpy
import scipy
from numpy import *
from scipy import optimize

def gaussian(height, center_x, center_y, width_x, width_y):
    width_x = float(width_x)
    width_y = float(width_y)
    return lambda x,y: height*exp(-(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)

def moments(data):
    total = nansum(data)
    X, Y = indices(data.shape)
    center_x = nansum(X*data)/total
    center_y = nansum(Y*data)/total
    row = data[int(center_x), :]
    col = data[:, int(center_y)]
    width_x = nansum(sqrt(abs((arange(col.size)-center_y)**2*col))/nansum(col))
    width_y = nansum(sqrt(abs((arange(row.size)-center_x)**2*row))/nansum(row))
    height = nanmax(data)
    return height, center_x, center_y, width_x, width_y

def fitgaussian(data):
    params = moments(data)
    errorfunction = lambda p: ravel(gaussian(*p)(*indices(data.shape)) - data)
    p, success = optimize.leastsq(errorfunction, params)
    return p

parameters = fitgaussian(data)
fit = gaussian(*parameters)
 
 我的通量值位于称为data的2D数组中。 如果我在这个数组中有0而不是nan值，代码可以工作，但是否则我的parameters总是出现为[nan nan nan nan nan] 。 如果有办法解决这个问题，我将非常感谢您的见解！ 解释越详细越好。 提前致谢！ 

I'm very new to Python but I'm trying to produce a 2D Gaussian fit for some data. Specifically, stellar fluxes linked to certain positions in a coordinate system/grid. However not all of the positions in my grid have corresponding flux values. I don't really want to set these values to zero in case it biases my fit, but I can't seem to set them to nan and still get my Gaussian fit to work. This is the code I'm using (modified slightly from here):
 
import numpy
import scipy
from numpy import *
from scipy import optimize

def gaussian(height, center_x, center_y, width_x, width_y):
    width_x = float(width_x)
    width_y = float(width_y)
    return lambda x,y: height*exp(-(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)

def moments(data):
    total = nansum(data)
    X, Y = indices(data.shape)
    center_x = nansum(X*data)/total
    center_y = nansum(Y*data)/total
    row = data[int(center_x), :]
    col = data[:, int(center_y)]
    width_x = nansum(sqrt(abs((arange(col.size)-center_y)**2*col))/nansum(col))
    width_y = nansum(sqrt(abs((arange(row.size)-center_x)**2*row))/nansum(row))
    height = nanmax(data)
    return height, center_x, center_y, width_x, width_y

def fitgaussian(data):
    params = moments(data)
    errorfunction = lambda p: ravel(gaussian(*p)(*indices(data.shape)) - data)
    p, success = optimize.leastsq(errorfunction, params)
    return p

parameters = fitgaussian(data)
fit = gaussian(*parameters)
 
My flux values are in a 2D array called data. The code works if I have 0 instead of nan values in this array, but otherwise my parameters always come out as [nan nan nan nan nan]. If there's a way to fix this, I would really appreciate your insight! The more detailed the explanation, the better. Thanks in advance!

原文：https://stackoverflow.com/questions/30790234