我在使用 scipy.optimize.curve_fit 对正弦/余弦函数进行回归拟合时遇到问题，但拟合似乎没有我想要的那样优化。我如何更改我的代码以使拟合更好？
我已经尝试更改为数据集尝试参数的方式，并且我生成的拟合的相位偏移似乎总是存在差异，或者拟合函数不适合正确的最小值/最大值。
这是我用来生成回归拟合的代码。可以绘制输出 (fitfunc) 以显示结果。
def sin_regress(data_x, data_y):
"""Function regression fits data to SIN function; does not need guess of freq.
Parameters
----------
data_x :
Data for X values, most likely a set of voltages.
data_y :
Data for Y values, most likely the resulting powers from voltages.
Returns
-------
__ :
Dictionary containing values for amplitude, angular frequency, phase, offset, frequency, period, fit function, max covariance, initial guess.
"""
data_x = np.array(data_x)
data_y = np.array(data_y)
freqz = np.fft.rfftfreq(len(data_x), (data_x[1] - data_x[0])) # uniform spacing
freq_y = abs(np.fft.rfft(data_y))
guess_freq = abs(freqz[np.argmax(freq_y[1:])+1]) # exclude offset peak
guess_amp = np.std(data_y) * 2.**0.5
guess_offset = np.mean(data_y)
guess = np.array([guess_amp, 2.*np.pi*guess_freq, 0., guess_offset])
def sinfunc(t, A, w, p, c):
"""Raw function to be used to fit data.
Parameters
----------
t :
Voltage array
A :
Amplitude
w :
Angular frequency
p :
Phase
c :
Constant value
Returns
-------
__ :
Formed fit function with provided values.
"""
return A * np.sin(w*t + p) + c
popt, pcov = scipy.optimize.curve_fit(sinfunc, data_x, data_y, p0=guess)
A, w, p, c = popt
f = w/(2.*np.pi)
fitfunc = lambda t: A * np.sin(w*t + p) + c
return {"amp": A, "omega": w, "phase": p, "offset": c, "freq": f, "period": 1./f, "fitfunc": fitfunc, "maxcov": np.max(pcov), "rawres": (guess,popt,pcov)}
我的试验数据集是:
x = np.linspace(3.5,9.5,(9.5-3.5)/0.00625 + 1)
pow1 = [1.8262110863, 1.80944546009, 1.7970185646900003, 1.77120336754, 1.7458101235699999, 1.73597098224, 1.7122529922799998, 1.70015674142, 1.68968617429, 1.6989396515, 1.69760676076, 1.6946375613599998, 1.6895321899, 1.68145658386, 1.68581793183, 1.6920468775900002, 1.6865452951599997, 1.68570953338, 1.6922784791700003, 1.70958957412, 1.71683408637, 1.70360183933, 1.6919669752199997, 1.6669487117300001, 1.6351298032300001, 1.6061729066600001, 1.57344333403, 1.54723708217, 1.5277773737599998, 1.5122628414300001, 1.4962354965200002, 1.4873367459, 1.47567715522, 1.4696584634, 1.46159565032, 1.45320592315, 1.4487225244200002, 1.44572887186, 1.44089260198, 1.4367157657399998, 1.4349226211, 1.43614316806, 1.4381950627400002, 1.43947658627, 1.4483572314200002, 1.4504305909200002, 1.44436990692, 1.43367609757, 1.42637295252, 1.41197427963, 1.4067529511399999, 1.39714414185, 1.38309980493, 1.3730701362500004, 1.3693239836499997, 1.3729558979599998, 1.38291189477, 1.3988274622900003, 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以下是 pow 数据集的一些附加值:
(链接到 pastebin 不超过帖子长度限制) https://pastebin.com/5GP8sj4N
此处显示了我从试验数据集 (x, pow1) 得到的拟合结果(橙色)与原始 (pow1) 数据(蓝色)
如前所述，相位如何拟合最小值和最大值存在一个问题。不幸的是，正确获得此拟合函数的应用几乎没有出错的余地。
如果您知道如何更好地拟合数据，请帮忙!
编辑: 我尝试了@Joe 在评论中提到的内容，首先过滤数据。我使用了 Savitzky-Golay 过滤器并收到了以下结果，原始数据(蓝色)、过滤后的数据(绿色)和过滤后数据的拟合(橙色)。同样，最小值和最大值的相同偏移仍然存在于过滤数据的拟合函数中。
最佳答案
这是我的结果，每个数据集的剪裁界限更激进，为 0.5 到 1.75。
对于 pow1:
A = 9.6711505138648990E-01
c = 9.7613787086912507E-01
p = 4.0262076448344617E+00
w = 1.2654001570670070E+00
对于 pow2:
A = 9.4894637490866129E-01
c = 9.6733405789489280E-01
p = 4.0892433833755097E+00
w = 1.2578627414445132E+00
对于 pow3:
A = 9.8595630272060597E-01
c = 9.6749868212694512E-01
p = 4.0859456191316230E+00
w = 1.2598547148182329E+00
对于 pow4:
A = -9.4636707498392481E-01
c = 9.5047597808408602E-01
p = -4.2643913461857056E+02
w = 1.2761107231684055E+00
关于python - 如何使 scipy.optimize.curve_fit 产生更好的正弦回归拟合？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/56496981/