python 高斯拟合的一个分量不起作用
one component Gaussian fit with python is not working
我有一个橙色的峰，我想对其进行高斯拟合，目的是获得 FWHM 和最高温度的估计值：
函数由：
def Gauss(velo_peak, a, mu0, sigma):
res = a * np.exp(-(velo_peak - mu0)**2 / (2 * sigma**2))
return res
我的代码是：
i1,i2 = 0,len(y)
n = len(x[i1:i2])
mu0 = sum(x[i1:i2] * y[i1:i2])/n
sigma = sum(y[i1:i2]*(x[i1:i2] - mu0)**2)/n
peak = max(y)
p0 = [peak, mu0, sigma] # a = max(spec_peak)
popt,pcov = curve_fit(Gauss, x, y, p0, maxfev=100000)
但拟合不起作用，我尝试使用猜测值，但我找不到它不起作用的任何原因。任何帮助将不胜感激。
x 轴由以下数据给出：
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.791
109.791
和y轴：
-0.0693423
-0.0383312
-0.0130822
0.00771434
-0.00475569
-0.0288578
-0.00323742
0.000307108
-0.0181949
0.00129764
0.00661946
-0.0116734
0.0439911
-0.0189704
0.0134336
0.017783
-0.00059444
0.00129813
-0.0146921
-0.0178051
0.00210355
0.00739107
0.0193562
0.0177199
-0.0115096
-0.0148834
-0.0359211
0.0268527
0.0159948
0.0214348
0.015795
0.00807647
-0.0597478
-0.037623
0.000166686
0.0119881
0.0127355
0.00687692
0.00479245
-0.0207917
0.0627117
0.0133312
0.011981
0.0308865
0.0323675
-0.0353238
0.0498601
0.00484114
-0.00354253
-0.0181545
0.0476038
0.019046
0.0195323
-0.013426
-0.0154619
0.0129866
-0.0158984
0.0126304
0.0269754
-0.00217857
0.0206669
-0.0219605
-0.0224113
0.00217749
-0.0359304
0.0273953
0.0133183
0.0202708
-0.0144499
0.0351752
-0.0202478
-0.0074738
0.0127188
-0.0116596
0.00869577
-0.0234507
0.0373167
0.00263353
0.0166561
-0.0043449
-0.0229105
-0.00741182
0.0467549
-0.0235804
-0.0191783
0.0528504
-0.00901956
0.043926
0.0223436
0.0181945
-0.0400392
0.0220731
-0.0167595
0.0214929
0.028309
-0.0234769
-0.0419024
0.0131882
-0.00421679
0.00359541
-0.055839
-0.0599337
-0.0283572
0.00686772
-0.00965801
0.0164275
0.00458221
-0.00909531
0.138937
0.297971
0.247663
0.124508
0.0365572
-0.00971529
0.0238192
-0.0509615
-0.0101447
-0.0298155
-0.0196555
0.0224242
-0.0329058
-0.00786179
-0.00347346
-0.0102662
0.0111553
0.013002
-0.0375893
0.00996665
-0.0125302
-0.00829957
0.0366645
0.0219919
-0.038467
-0.0260219
-0.0375669
0.00625599
-0.0498297
0.0258702
-0.0217369
-0.0349204
-0.014657
-0.0180611
-0.0420286
-0.000379184
-0.0333805
-0.0551173
-0.0224908
0.0179898
0.020866
0.0288823
-0.0182207
-0.0413725
-0.0162658
0.00223817
0.0243006
-0.0170214
0.0320711
0.0012465
0.00344509
0.00150138
-0.00169928
-0.0139581
0.0552647
0.0229482
-0.00316584
-0.033333
0.000161762
-0.00905961
-0.00685663
-0.0162735
-0.0399026
0.0270222
0.00798811
-0.00408101
-0.0072991
0.0112089
-0.012056
0.0146916
-0.00340297
0.0217221
0.00722562
-0.0203967
-0.0150112
0.00900151
0.0322559
0.00482019
-0.000814166
-0.0225995
-0.00817639
0.0201735
-0.0285309
0.0355886
0.000298672
-0.0129141
0.0428829
-0.028223
0.0183822
-6.62023e-05
0.0358768
-0.0293772
0.0125377
-0.00919312
0.00703798
0.00537255
-0.00413266
0.0505678
-0.00586183
0.0087835
-0.0113064
-0.0198051
0.0477742
0.00607189
-0.0112695
0.0288124
0.0354801
-0.0550288
-0.00167514
-0.0440247
-0.00573284
0.0138037
0.0170393
-0.0350715
0.0333013
失败的典型原因是：启动参数错误。这里使用的公式有点像那些将用于测量统计的公式，而这里的数据必须被认为是相应的直方图。因此，使用 n 没有意义。更像是
## smoothing the data with its own noisy Gaussian
ysmooth = np.convolve( y, y[::-1], mode="same")
mu0 = sum( x * y ) / sum( y )
## the abs() is wrong but the strong noise forces it
sigma = np.sqrt( sum( np.abs( ysmooth ) * ( ( x - mu0 ) )**2 ) / sum( np.abs(ysmooth)) )
peak = max( y )
总规模 n 未知，但 x 值的相对数量作为直方图的值给出，即 y。
sigma 的猜测有点乱，但在这里有效。相当的但是拟合收敛而不增加maxfev
我有一个橙色的峰，我想对其进行高斯拟合，目的是获得 FWHM 和最高温度的估计值：
函数由：
def Gauss(velo_peak, a, mu0, sigma):
res = a * np.exp(-(velo_peak - mu0)**2 / (2 * sigma**2))
return res
我的代码是：
i1,i2 = 0,len(y)
n = len(x[i1:i2])
mu0 = sum(x[i1:i2] * y[i1:i2])/n
sigma = sum(y[i1:i2]*(x[i1:i2] - mu0)**2)/n
peak = max(y)
p0 = [peak, mu0, sigma] # a = max(spec_peak)
popt,pcov = curve_fit(Gauss, x, y, p0, maxfev=100000)
但拟合不起作用，我尝试使用猜测值，但我找不到它不起作用的任何原因。任何帮助将不胜感激。 x 轴由以下数据给出：
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.774
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.775
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.776
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.777
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.778
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.779
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.78
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.781
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.782
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.783
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.784
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.785
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.786
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.787
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.788
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.789
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.79
109.791
109.791
和y轴：
-0.0693423
-0.0383312
-0.0130822
0.00771434
-0.00475569
-0.0288578
-0.00323742
0.000307108
-0.0181949
0.00129764
0.00661946
-0.0116734
0.0439911
-0.0189704
0.0134336
0.017783
-0.00059444
0.00129813
-0.0146921
-0.0178051
0.00210355
0.00739107
0.0193562
0.0177199
-0.0115096
-0.0148834
-0.0359211
0.0268527
0.0159948
0.0214348
0.015795
0.00807647
-0.0597478
-0.037623
0.000166686
0.0119881
0.0127355
0.00687692
0.00479245
-0.0207917
0.0627117
0.0133312
0.011981
0.0308865
0.0323675
-0.0353238
0.0498601
0.00484114
-0.00354253
-0.0181545
0.0476038
0.019046
0.0195323
-0.013426
-0.0154619
0.0129866
-0.0158984
0.0126304
0.0269754
-0.00217857
0.0206669
-0.0219605
-0.0224113
0.00217749
-0.0359304
0.0273953
0.0133183
0.0202708
-0.0144499
0.0351752
-0.0202478
-0.0074738
0.0127188
-0.0116596
0.00869577
-0.0234507
0.0373167
0.00263353
0.0166561
-0.0043449
-0.0229105
-0.00741182
0.0467549
-0.0235804
-0.0191783
0.0528504
-0.00901956
0.043926
0.0223436
0.0181945
-0.0400392
0.0220731
-0.0167595
0.0214929
0.028309
-0.0234769
-0.0419024
0.0131882
-0.00421679
0.00359541
-0.055839
-0.0599337
-0.0283572
0.00686772
-0.00965801
0.0164275
0.00458221
-0.00909531
0.138937
0.297971
0.247663
0.124508
0.0365572
-0.00971529
0.0238192
-0.0509615
-0.0101447
-0.0298155
-0.0196555
0.0224242
-0.0329058
-0.00786179
-0.00347346
-0.0102662
0.0111553
0.013002
-0.0375893
0.00996665
-0.0125302
-0.00829957
0.0366645
0.0219919
-0.038467
-0.0260219
-0.0375669
0.00625599
-0.0498297
0.0258702
-0.0217369
-0.0349204
-0.014657
-0.0180611
-0.0420286
-0.000379184
-0.0333805
-0.0551173
-0.0224908
0.0179898
0.020866
0.0288823
-0.0182207
-0.0413725
-0.0162658
0.00223817
0.0243006
-0.0170214
0.0320711
0.0012465
0.00344509
0.00150138
-0.00169928
-0.0139581
0.0552647
0.0229482
-0.00316584
-0.033333
0.000161762
-0.00905961
-0.00685663
-0.0162735
-0.0399026
0.0270222
0.00798811
-0.00408101
-0.0072991
0.0112089
-0.012056
0.0146916
-0.00340297
0.0217221
0.00722562
-0.0203967
-0.0150112
0.00900151
0.0322559
0.00482019
-0.000814166
-0.0225995
-0.00817639
0.0201735
-0.0285309
0.0355886
0.000298672
-0.0129141
0.0428829
-0.028223
0.0183822
-6.62023e-05
0.0358768
-0.0293772
0.0125377
-0.00919312
0.00703798
0.00537255
-0.00413266
0.0505678
-0.00586183
0.0087835
-0.0113064
-0.0198051
0.0477742
0.00607189
-0.0112695
0.0288124
0.0354801
-0.0550288
-0.00167514
-0.0440247
-0.00573284
0.0138037
0.0170393
-0.0350715
0.0333013
失败的典型原因是：启动参数错误。这里使用的公式有点像那些将用于测量统计的公式，而这里的数据必须被认为是相应的直方图。因此，使用 n 没有意义。更像是
## smoothing the data with its own noisy Gaussian
ysmooth = np.convolve( y, y[::-1], mode="same")
mu0 = sum( x * y ) / sum( y )
## the abs() is wrong but the strong noise forces it
sigma = np.sqrt( sum( np.abs( ysmooth ) * ( ( x - mu0 ) )**2 ) / sum( np.abs(ysmooth)) )
peak = max( y )
总规模 n 未知，但 x 值的相对数量作为直方图的值给出，即 y。
sigma 的猜测有点乱，但在这里有效。相当的但是拟合收敛而不增加maxfev