看一下下面的图像。在左边，你可以看到一个2D数组img_roi的plt.imshow图。在右边，我首先确定最大值，然后沿着垂直方向切割，即
centre = np.unravel_index(np.argmax(img_roi, axis=None), img_roi.shape)
x_profile = img_roi[:, centre[1]]然后我用正态分布拟合曲线，可以看出，由于每隔一个像素就有一个特定的因子，所以拟合看起来并不好，尽管曲线明显是高斯的。
为完整起见，数据如下
x_profile = [ 1.50000000e+00 -7.00000000e-01 -2.70000000e+00 2.80000000e+00
3.00000000e-01 5.00000000e-01 2.50000000e+00 1.10000000e+00
-1.00000000e-01 8.10000000e+00 6.90000000e+00 7.53000000e+01
2.14000000e+01 2.31000000e+02 4.68000000e+01 4.84900000e+02
8.91000000e+01 7.47500000e+02 1.16400000e+02 8.62900000e+02
1.13100000e+02 7.01300000e+02 8.74000000e+01 4.46700000e+02
4.70000000e+01 2.01900000e+02 1.69000000e+01 4.68000000e+01
7.50000000e+00 1.60000000e+00 3.50000000e+00 4.20000000e+00
4.00000000e-01 9.10000000e+00 7.10542736e-15 5.50000000e+00
2.60000000e+00 5.00000000e-01 2.50000000e+00 1.40000000e+00]我的目标是仅使用亮像素值拟合高斯，而不丢失它们不是近邻而是次近邻的信息。
我知道有一些“简单”的修复方法，例如：每隔一个像素，拟合，然后重新缩放。
x_profile_squish = x_profile[::2] - Fit to this把“不太亮”的像素设置为零是行不通的。我可以缩放它们来匹配亮的像素，但这没有抓住重点。如何进行高斯拟合是很清楚的。
问题是否有办法将每隔一个像素设置为empty或Python在拟合时直接丢弃的某个值？或者是否有另一种方法告诉scipy在拟合时忽略某些值？
编辑：这里是完整的2D数据（它相当大）
img_roi = [[ 3.10000000e+00 1.60000000e+00 -3.90000000e+00 4.10000000e+00
-4.10000000e+00 3.10000000e+00 4.00000000e-01 -6.00000000e-01
1.60000000e+00 -2.00000000e-01 -3.00000000e-01 3.00000000e-01
3.00000000e-01 1.30000000e+00 9.00000000e-01 2.70000000e+00
-1.80000000e+00 -6.00000000e-01 3.50000000e+00 2.00000000e+00
1.30000000e+00 -7.00000000e-01 9.00000000e-01 7.10542736e-15
-4.00000000e-01 -3.00000000e-01 3.00000000e-01 2.50000000e+00
-2.20000000e+00 7.00000000e-01 2.40000000e+00 -9.00000000e-01
7.00000000e-01 6.00000000e-01 2.70000000e+00 7.00000000e-01
1.50000000e+00 1.40000000e+00 1.30000000e+00 9.00000000e-01]
[ 7.00000000e-01 2.10000000e+00 1.30000000e+00 1.20000000e+00
7.00000000e-01 -1.30000000e+00 3.20000000e+00 -3.90000000e+00
2.50000000e+00 -2.40000000e+00 3.00000000e-01 1.70000000e+00
6.00000000e-01 1.30000000e+00 -9.00000000e-01 1.00000000e-01
1.00000000e+00 3.00000000e+00 2.00000000e-01 2.00000000e+00
1.10000000e+00 2.00000000e-01 -1.00000000e-01 1.20000000e+00
1.20000000e+00 6.00000000e-01 5.00000000e-01 9.00000000e-01
-4.00000000e-01 7.00000000e-01 -1.00000000e-01 -9.00000000e-01
3.10000000e+00 4.00000000e-01 1.10000000e+00 6.00000000e-01
2.00000000e-01 3.00000000e-01 -1.00000000e-01 3.00000000e-01]
[-3.30000000e+00 -3.00000000e-01 -2.00000000e-01 7.00000000e-01
1.40000000e+00 4.00000000e-01 1.40000000e+00 -6.00000000e-01
5.00000000e-01 -5.00000000e-01 2.20000000e+00 2.00000000e-01
1.60000000e+00 3.00000000e-01 -2.60000000e+00 4.00000000e-01
9.00000000e-01 3.10000000e+00 -5.00000000e-01 2.00000000e-01
1.70000000e+00 8.00000000e-01 -1.50000000e+00 7.10542736e-15
6.00000000e-01 -3.00000000e-01 1.70000000e+00 -1.10000000e+00
1.10000000e+00 -1.00000000e-01 1.10000000e+00 2.00000000e-01
2.60000000e+00 1.00000000e-01 -6.00000000e-01 3.00000000e-01
3.50000000e+00 5.30000000e+00 4.10000000e+00 4.90000000e+00]
[-4.00000000e-01 -2.00000000e-01 7.00000000e-01 1.40000000e+00
8.00000000e-01 1.40000000e+00 6.00000000e-01 3.30000000e+00
-4.00000000e-01 1.40000000e+00 -4.00000000e-01 3.20000000e+00
1.10000000e+00 2.70000000e+00 8.00000000e-01 1.50000000e+00
4.00000000e-01 1.50000000e+00 -4.40000000e+00 1.60000000e+00
3.00000000e-01 3.90000000e+00 -4.00000000e-01 2.00000000e+00
-9.00000000e-01 1.00000000e-01 2.00000000e-01 2.80000000e+00
5.00000000e-01 -3.00000000e-01 8.00000000e-01 -3.00000000e-01
5.20000000e+00 2.80000000e+00 5.70000000e+00 3.10000000e+00
4.60000000e+00 -6.00000000e-01 9.60000000e+00 1.30000000e+00]
[ 7.00000000e-01 7.00000000e-01 5.00000000e-01 2.30000000e+00
-2.40000000e+00 7.00000000e-01 8.00000000e-01 2.00000000e-01
3.20000000e+00 1.00000000e-01 1.00000000e-01 -2.00000000e-01
1.40000000e+00 1.00000000e+00 -4.20000000e+00 8.00000000e-01
4.00000000e-01 3.30000000e+00 5.00000000e-01 4.00000000e-01
-4.00000000e-01 -4.00000000e-01 -4.00000000e-01 -3.00000000e-01
1.00000000e+00 8.00000000e-01 3.70000000e+00 7.00000000e-01
9.00000000e-01 -6.00000000e-01 1.20000000e+00 6.20000000e+00
1.10000000e+00 4.70000000e+00 -1.00000000e-01 9.90000000e+00
1.80000000e+00 1.31000000e+01 -5.00000000e-01 8.70000000e+00]
[-6.00000000e-01 1.30000000e+00 9.00000000e-01 8.00000000e-01
0.00000000e+00 -6.00000000e-01 1.40000000e+00 6.00000000e-01
1.80000000e+00 1.60000000e+00 5.20000000e+00 3.70000000e+00
-8.00000000e-01 3.00000000e-01 -3.70000000e+00 -8.00000000e-01
5.00000000e-01 3.10000000e+00 3.00000000e-01 -2.00000000e-01
6.00000000e-01 2.20000000e+00 -9.00000000e-01 1.00000000e+00
-7.00000000e-01 1.20000000e+00 2.00000000e+00 2.00000000e-01
3.60000000e+00 1.90000000e+00 8.50000000e+00 -2.00000000e-01
7.60000000e+00 1.30000000e+00 1.48000000e+01 3.80000000e+00
1.24000000e+01 3.00000000e-01 1.33000000e+01 2.60000000e+00]
[ 1.00000000e-01 3.00000000e-01 1.50000000e+00 6.00000000e-01
3.00000000e-01 5.00000000e-01 1.40000000e+00 3.20000000e+00
-4.60000000e+00 9.00000000e-01 1.10000000e+00 4.00000000e-01
1.60000000e+00 -1.00000000e-01 5.00000000e-01 2.20000000e+00
6.00000000e-01 2.00000000e+00 1.10000000e+00 7.00000000e-01
2.00000000e+00 0.00000000e+00 -2.90000000e+00 -5.00000000e-01
7.00000000e-01 3.20000000e+00 7.00000000e-01 2.60000000e+00
-1.70000000e+00 4.30000000e+00 1.20000000e+00 5.00000000e+00
1.80000000e+00 9.00000000e+00 1.60000000e+00 1.36000000e+01
8.00000000e-01 2.18000000e+01 2.50000000e+00 2.26000000e+01]
[ 5.40000000e+00 -6.00000000e-01 -2.00000000e-01 4.00000000e-01
-2.00000000e-01 -2.00000000e-01 -6.00000000e-01 2.00000000e+00
1.00000000e+00 8.00000000e-01 4.00000000e-01 4.10000000e+00
-4.00000000e-01 3.10000000e+00 -2.70000000e+00 3.00000000e+00
1.10000000e+00 2.80000000e+00 2.40000000e+00 4.00000000e-01
2.90000000e+00 -1.70000000e+00 -7.00000000e-01 9.00000000e-01
1.10000000e+00 7.00000000e-01 2.30000000e+00 1.70000000e+00
5.00000000e-01 -3.10000000e+00 7.00000000e-01 2.70000000e+00
4.90000000e+00 2.20000000e+00 1.17000000e+01 7.00000000e+00
1.53000000e+01 2.60000000e+00 2.80000000e+01 4.40000000e+00]
[ 3.30000000e+00 -1.00000000e-01 5.00000000e-01 -6.00000000e-01
2.80000000e+00 1.90000000e+00 2.10000000e+00 5.00000000e-01
2.30000000e+00 -8.00000000e-01 3.20000000e+00 4.10000000e+00
1.00000000e+00 -9.00000000e-01 1.50000000e+00 8.00000000e-01
1.30000000e+00 3.20000000e+00 -6.00000000e-01 3.00000000e-01
2.30000000e+00 1.00000000e-01 3.60000000e+00 1.20000000e+00
1.00000000e-01 -2.00000000e-01 1.00000000e+00 8.00000000e-01
1.60000000e+00 1.90000000e+00 -1.00000000e-01 6.70000000e+00
-6.00000000e-01 1.37000000e+01 6.00000000e-01 1.55000000e+01
6.80000000e+00 2.90000000e+01 4.10000000e+00 2.77000000e+01]
[-6.00000000e-01 2.00000000e+00 3.00000000e-01 4.00000000e-01
0.00000000e+00 6.00000000e-01 1.50000000e+00 2.80000000e+00
1.40000000e+00 1.70000000e+00 1.00000000e-01 2.50000000e+00
-3.00000000e-01 3.00000000e+00 2.40000000e+00 2.60000000e+00
6.00000000e+00 1.70000000e+00 4.00000000e+00 1.00000000e+00
1.00000000e-01 9.00000000e-01 -3.00000000e-01 -1.40000000e+00
1.03000000e+01 3.10000000e+00 1.11000000e+01 8.00000000e-01
8.20000000e+00 6.00000000e-01 9.20000000e+00 1.00000000e+00
1.29000000e+01 6.00000000e-01 1.79000000e+01 4.90000000e+00
2.35000000e+01 5.30000000e+00 2.31000000e+01 7.40000000e+00]
[ 1.00000000e-01 2.50000000e+00 2.80000000e+00 4.10000000e+00
-3.00000000e+00 -8.00000000e-01 2.90000000e+00 -3.00000000e-01
2.40000000e+00 -1.00000000e-01 3.20000000e+00 2.90000000e+00
-5.00000000e-01 7.00000000e-01 -4.00000000e-01 6.50000000e+00
-3.00000000e-01 4.30000000e+00 1.20000000e+00 4.60000000e+00
-6.00000000e-01 3.20000000e+00 2.50000000e+00 1.97000000e+01
4.40000000e+00 2.58000000e+01 4.80000000e+00 3.30000000e+01
1.00000000e-01 2.34000000e+01 0.00000000e+00 1.72000000e+01
3.60000000e+00 2.74000000e+01 -2.00000000e-01 3.04000000e+01
5.70000000e+00 3.94000000e+01 5.00000000e+00 3.15000000e+01]
[ 2.60000000e+00 3.60000000e+00 -4.00000000e-01 2.60000000e+00
5.00000000e-01 2.30000000e+00 2.30000000e+00 7.00000000e-01
4.00000000e-01 1.20000000e+00 9.00000000e-01 0.00000000e+00
5.20000000e+00 1.60000000e+00 5.30000000e+00 2.90000000e+00
5.90000000e+00 5.00000000e-01 6.00000000e-01 3.50000000e+00
8.00000000e-01 3.40000000e+00 2.31000000e+01 5.10000000e+00
5.82000000e+01 1.01000000e+01 7.56000000e+01 7.70000000e+00
6.34000000e+01 8.50000000e+00 4.76000000e+01 7.50000000e+00
4.03000000e+01 6.00000000e+00 4.40000000e+01 3.90000000e+00
4.30000000e+01 3.40000000e+00 4.10000000e+01 9.70000000e+00]
[ 3.00000000e-01 5.00000000e-01 -4.10000000e+00 1.00000000e-01
-6.00000000e-01 2.90000000e+00 2.10000000e+00 3.00000000e-01
3.10000000e+00 -4.00000000e-01 -1.10000000e+00 4.50000000e+00
1.90000000e+00 2.90000000e+00 1.70000000e+00 4.10000000e+00
-1.00000000e-01 5.60000000e+00 1.50000000e+00 2.50000000e+00
1.30000000e+00 2.47000000e+01 1.17000000e+01 7.51000000e+01
1.50000000e+01 1.15200000e+02 2.28000000e+01 1.30900000e+02
1.29000000e+01 1.09500000e+02 1.21000000e+01 7.60000000e+01
1.13000000e+01 6.12000000e+01 6.30000000e+00 5.38000000e+01
4.20000000e+00 4.44000000e+01 4.70000000e+00 4.59000000e+01]
[ 9.00000000e-01 4.00000000e-01 1.80000000e+00 5.00000000e-01
2.00000000e-01 5.00000000e-01 -3.00000000e-01 5.00000000e-01
8.00000000e-01 2.30000000e+00 1.80000000e+00 2.80000000e+00
1.20000000e+00 3.00000000e+00 4.90000000e+00 -1.00000000e-01
5.20000000e+00 4.00000000e-01 1.30000000e+00 3.70000000e+00
2.45000000e+01 1.48000000e+01 8.45000000e+01 3.01000000e+01
1.60100000e+02 3.88000000e+01 2.24200000e+02 3.32000000e+01
2.21900000e+02 2.77000000e+01 1.59000000e+02 2.22000000e+01
1.14200000e+02 1.60000000e+01 8.95000000e+01 1.01000000e+01
5.86000000e+01 1.03000000e+01 5.06000000e+01 5.00000000e+00]
[ 3.10000000e+00 2.00000000e-01 6.00000000e-01 -1.20000000e+00
3.50000000e+00 4.00000000e-01 2.00000000e+00 3.00000000e-01
-3.00000000e-01 1.10000000e+00 1.50000000e+00 9.00000000e-01
1.00000000e+00 4.40000000e+00 2.50000000e+00 -2.00000000e-01
2.90000000e+00 2.00000000e-01 1.90000000e+00 1.76000000e+01
1.19000000e+01 8.60000000e+01 2.25000000e+01 2.11800000e+02
4.67000000e+01 3.03900000e+02 4.53000000e+01 3.38200000e+02
3.98000000e+01 2.85500000e+02 2.98000000e+01 2.05900000e+02
2.00000000e+01 1.42400000e+02 1.32000000e+01 9.68000000e+01
1.12000000e+01 5.57000000e+01 6.40000000e+00 4.90000000e+01]
[-4.00000000e+00 0.00000000e+00 -2.90000000e+00 3.00000000e+00
4.00000000e-01 3.10000000e+00 1.40000000e+00 8.00000000e-01
-1.10000000e+00 8.00000000e-01 1.30000000e+00 0.00000000e+00
4.10000000e+00 2.10000000e+00 5.30000000e+00 2.80000000e+00
-1.00000000e-01 1.30000000e+00 1.60000000e+01 1.42000000e+01
7.20000000e+01 2.89000000e+01 2.05000000e+02 5.11000000e+01
3.58600000e+02 7.71000000e+01 4.28900000e+02 7.63000000e+01
4.32700000e+02 5.68000000e+01 3.30600000e+02 4.14000000e+01
2.33900000e+02 3.12000000e+01 1.50400000e+02 1.80000000e+01
8.39000000e+01 9.40000000e+00 5.02000000e+01 6.70000000e+00]
[ 1.60000000e+00 5.00000000e-01 6.00000000e-01 5.00000000e-01
6.00000000e-01 1.00000000e+00 2.00000000e+00 6.00000000e-01
-6.00000000e-01 3.00000000e-01 1.20000000e+00 8.00000000e-01
2.30000000e+00 1.00000000e-01 3.40000000e+00 6.00000000e-01
1.90000000e+00 4.60000000e+00 6.70000000e+00 5.37000000e+01
1.62000000e+01 1.82300000e+02 5.04000000e+01 3.82500000e+02
6.97000000e+01 5.40600000e+02 7.82000000e+01 5.44800000e+02
7.65000000e+01 4.89900000e+02 5.67000000e+01 3.57800000e+02
3.81000000e+01 2.19400000e+02 2.04000000e+01 1.48500000e+02
1.32000000e+01 6.97000000e+01 6.10000000e+00 4.51000000e+01]
[ 1.00000000e-01 -2.90000000e+00 -4.00000000e-01 -3.90000000e+00
3.30000000e+00 8.00000000e-01 -9.00000000e-01 4.00000000e-01
5.00000000e-01 1.80000000e+00 2.90000000e+00 1.00000000e-01
9.00000000e-01 -1.60000000e+00 -9.00000000e-01 1.30000000e+00
4.30000000e+00 6.00000000e+00 3.76000000e+01 2.17000000e+01
1.44300000e+02 4.89000000e+01 3.33400000e+02 8.32000000e+01
5.66200000e+02 1.09800000e+02 6.62000000e+02 1.16500000e+02
6.38900000e+02 8.49000000e+01 4.89100000e+02 6.72000000e+01
3.42200000e+02 4.13000000e+01 2.06000000e+02 2.02000000e+01
1.09900000e+02 1.01000000e+01 4.38000000e+01 4.80000000e+00]
[ 1.00000000e-01 7.00000000e-01 -3.30000000e+00 0.00000000e+00
1.90000000e+00 2.20000000e+00 1.30000000e+00 7.10542736e-15
7.00000000e-01 0.00000000e+00 -3.70000000e+00 -9.00000000e-01
1.80000000e+00 3.80000000e+00 2.60000000e+00 2.50000000e+00
3.10000000e+00 1.96000000e+01 9.80000000e+00 9.64000000e+01
2.91000000e+01 2.77500000e+02 6.83000000e+01 5.17200000e+02
8.77000000e+01 6.69000000e+02 1.14300000e+02 7.31000000e+02
7.97000000e+01 6.14700000e+02 6.81000000e+01 4.53100000e+02
4.72000000e+01 2.86100000e+02 3.10000000e+01 1.53000000e+02
1.61000000e+01 6.66000000e+01 6.10000000e+00 3.09000000e+01]
[ 3.30000000e+00 -9.00000000e-01 8.00000000e-01 6.00000000e-01
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1.40000000e+00 3.00000000e-01 1.60000000e+00 1.00000000e-01
0.00000000e+00 -5.00000000e-01 1.20000000e+00 6.00000000e-01
8.40000000e+00 6.40000000e+00 5.73000000e+01 2.63000000e+01
1.93700000e+02 6.28000000e+01 4.26900000e+02 9.50000000e+01
6.18500000e+02 1.27700000e+02 7.38200000e+02 1.29800000e+02
6.68100000e+02 9.69000000e+01 5.17600000e+02 6.49000000e+01
3.69900000e+02 5.00000000e+01 2.18600000e+02 2.18000000e+01
9.12000000e+01 9.20000000e+00 3.50000000e+01 2.50000000e+00]
[ 8.00000000e-01 -2.90000000e+00 1.30000000e+00 2.50000000e+00
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5.00000000e-01 -9.00000000e-01 4.00000000e-01 -6.00000000e-01
1.80000000e+00 3.00000000e-01 2.70000000e+00 7.20000000e+00
3.00000000e-01 2.53000000e+01 1.51000000e+01 1.14600000e+02
3.40000000e+01 2.82100000e+02 6.08000000e+01 5.11400000e+02
9.37000000e+01 6.59200000e+02 1.05600000e+02 7.09900000e+02
9.21000000e+01 5.78900000e+02 6.34000000e+01 4.09200000e+02
4.77000000e+01 2.77700000e+02 2.03000000e+01 1.27500000e+02
1.08000000e+01 5.07000000e+01 7.00000000e+00 1.34000000e+01]
[-3.10000000e+00 5.00000000e-01 -8.00000000e-01 -1.00000000e-01
6.00000000e-01 4.90000000e+00 -1.30000000e+00 5.00000000e-01
-4.00000000e-01 1.60000000e+00 1.00000000e-01 -4.00000000e-01
1.30000000e+00 1.00000000e-01 2.70000000e+00 3.50000000e+00
9.70000000e+00 5.40000000e+00 5.08000000e+01 2.58000000e+01
1.67700000e+02 5.84000000e+01 3.53500000e+02 9.03000000e+01
5.34100000e+02 1.15000000e+02 6.15400000e+02 1.13100000e+02
5.86700000e+02 9.07000000e+01 4.21400000e+02 5.94000000e+01
2.93800000e+02 3.84000000e+01 1.50200000e+02 1.29000000e+01
6.51000000e+01 5.70000000e+00 1.63000000e+01 2.00000000e+00]
[ 1.10000000e+00 9.00000000e-01 -1.00000000e-01 1.40000000e+00
-1.20000000e+00 1.60000000e+00 8.00000000e-01 -4.00000000e-01
4.00000000e-01 -9.00000000e-01 4.00000000e-01 -4.00000000e-01
7.00000000e-01 -3.00000000e-01 9.00000000e-01 4.80000000e+00
4.00000000e-01 2.11000000e+01 1.01000000e+01 8.35000000e+01
2.32000000e+01 2.22400000e+02 4.82000000e+01 3.86300000e+02
7.43000000e+01 4.94700000e+02 7.49000000e+01 5.27900000e+02
6.54000000e+01 4.16900000e+02 4.92000000e+01 2.88200000e+02
3.27000000e+01 1.70000000e+02 1.68000000e+01 7.52000000e+01
7.10000000e+00 2.23000000e+01 2.70000000e+00 5.00000000e+00]
[-4.00000000e-01 1.70000000e+00 1.50000000e+00 1.20000000e+00
-5.00000000e-01 -7.00000000e-01 -1.20000000e+00 1.00000000e+00
1.10000000e+00 1.30000000e+00 4.00000000e-01 2.00000000e-01
1.10000000e+00 6.00000000e-01 1.60000000e+00 5.00000000e-01
6.50000000e+00 5.80000000e+00 2.94000000e+01 1.96000000e+01
1.01200000e+02 3.35000000e+01 2.30600000e+02 5.66000000e+01
3.44300000e+02 6.62000000e+01 3.91000000e+02 7.62000000e+01
3.59600000e+02 5.39000000e+01 2.44500000e+02 3.76000000e+01
1.66200000e+02 1.88000000e+01 7.38000000e+01 9.60000000e+00
2.49000000e+01 -6.00000000e-01 5.70000000e+00 3.70000000e+00]
[-3.90000000e+00 -1.40000000e+00 6.00000000e-01 -4.30000000e+00
3.20000000e+00 1.00000000e-01 -2.20000000e+00 8.00000000e-01
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2.60000000e+00 1.10000000e+00 -7.00000000e-01 6.50000000e+00
-3.00000000e-01 1.62000000e+01 5.70000000e+00 4.84000000e+01
1.29000000e+01 1.05300000e+02 2.76000000e+01 2.04400000e+02
4.00000000e+01 2.86700000e+02 4.98000000e+01 2.74100000e+02
4.12000000e+01 2.00200000e+02 3.19000000e+01 1.44300000e+02
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1条答案
按热度按时间bnlyeluc1#
我发现了一个相关的帖子，描述了一个适用于
scipy.optimize.leastsq例程的解决方案。我首先将不相关的数据点设置为nan，然后使用mask = ~np.isnan(img_roi)屏蔽它们。这对于上述目的是足够的。
答案链接：https://stackoverflow.com/a/30791245/12460839