拟合单指数衰减误差 python
Fitting single exponential decay error python
我有两个数组，它们会导致 df_intensity_01 与 df_time 的关系图。
df_time
[[ 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170
180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350
360 370 380 390 400 410 420 430 440 450 460 470 480 490 500 510 520 530
540 550 560 570 580 590 600 610 620 630 640 650 660 670 680 690 700 710
720 730 740 750 760 770 780 790 800]]
df_intensity_01
[1. 0.98666909 0.935487 0.91008815 0.86347009 0.81356788
0.79591582 0.78624289 0.76503846 0.75105705 0.72333501 0.67815733
0.69481674 0.68321344 0.66108185 0.65859392 0.64047511 0.63100282
0.63605049 0.6248548 0.60341172 0.57538132 0.57952294 0.57901395
0.56353725 0.56164702 0.55901125 0.54833934 0.53271058 0.52880127
0.52268282 0.51111965 0.5067436 0.49988595 0.49689326 0.48888879
0.48247889 0.4790469 0.47320723 0.46156169 0.45921527 0.4592913
0.45104607 0.44445031 0.44618426 0.43893589 0.42988811 0.42887013
0.42842872 0.41952032 0.41286965 0.41392143 0.41175663 0.40432874
0.39645523 0.39813004 0.38932936 0.38264912 0.38094263 0.3855869
0.38378537 0.37570065 0.37573022 0.37550635 0.36941113 0.36502241
0.36607629 0.36624103 0.36163477 0.35550154 0.35627875 0.35421111
0.34858053 0.34767026 0.34967665 0.34818347 0.34007975 0.34139552
0.34017057 0.33732993 0.33320098]
我正在尝试将数据拟合到单个指数衰减函数，其中我提供了拟合的初始系数。
def func(x, a, b, c):
return a * np.exp(-b * x) + c
xdata = df_time
guess=[1,0.001,0]
ydata = df_intensity
plt.plot(xdata, ydata, 'b-', label='data')
popt, pcov = curve_fit(func, xdata, ydata,p0=guess)
popt
plt.plot(xdata, func(xdata, *popt), 'r-',
label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
plt.xlabel('x')
plt.ylabel('y')
我收到一个我真的不知道如何解决的错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
ValueError: object too deep for desired array
---------------------------------------------------------------------------
error Traceback (most recent call last)
<ipython-input-62-97bcc77fc6c7> in <module>
5 ydata = df_intensity
6 plt.plot(xdata, ydata, 'b-', label='data')
----> 7 popt, pcov = curve_fit(func, xdata, ydata,p0=guess)
8 popt
9
/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py in curve_fit(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, **kwargs)
749 # Remove full_output from kwargs, otherwise we're passing it in twice.
750 return_full = kwargs.pop('full_output', False)
--> 751 res = leastsq(func, p0, Dfun=jac, full_output=1, **kwargs)
752 popt, pcov, infodict, errmsg, ier = res
753 cost = np.sum(infodict['fvec'] ** 2)
/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py in leastsq(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)
392 with _MINPACK_LOCK:
393 retval = _minpack._lmdif(func, x0, args, full_output, ftol, xtol,
--> 394 gtol, maxfev, epsfcn, factor, diag)
395 else:
396 if col_deriv:
error: Result from function call is not a proper array of floats.
首先，您需要两个输入都是一维数组（只有一组大括号：[ ]）。目前看起来 df_time 是一个二维数组，这似乎是您发布的错误的来源。
然后，当您绘制数据时，请记住您需要为 x 的每个值计算函数 ，以便您的 x 和y 数组长度相同。您可以通过列表理解来做到这一点，记住将您的 x 值转换为 float 以便您可以将它们传递给您的函数：
plt.plot(xdata, [func(float(x), *popt) for x in xdata], 'r-',
label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
整个工作代码如下所示：
df_time = ['0', '10', '20', '30', '40', '50', '60', '70', '80', '90', '100',
'110', '120', '130', '140', '150', '160', '170', '180', '190', '200',
'210', '220', '230', '240', '250', '260', '270', '280', '290', '300',
'310', '320', '330', '340', '350', '360', '370', '380', '390', '400',
'410', '420', '430', '440', '450', '460', '470', '480', '490', '500',
'510', '520', '530', '540', '550', '560', '570', '580', '590', '600',
'610', '620', '630', '640', '650', '660', '670', '680', '690', '700',
'710', '720', '730', '740', '750', '760', '770', '780', '790', '800']
df_intensity = ['1.', '0.98666909', '0.935487', '0.91008815', '0.86347009', '0.81356788',
'0.79591582', '0.78624289', '0.76503846', '0.75105705', '0.72333501', '0.67815733',
'0.69481674', '0.68321344', '0.66108185', '0.65859392', '0.64047511', '0.63100282',
'0.63605049', '0.6248548', '0.60341172', '0.57538132', '0.57952294', '0.57901395',
'0.56353725', '0.56164702', '0.55901125', '0.54833934', '0.53271058', '0.52880127',
'0.52268282', '0.51111965', '0.5067436', '0.49988595', '0.49689326', '0.48888879',
'0.48247889', '0.4790469', '0.47320723', '0.46156169', '0.45921527', '0.4592913',
'0.45104607', '0.44445031', '0.44618426', '0.43893589', '0.42988811', '0.42887013',
'0.42842872', '0.41952032', '0.41286965', '0.41392143', '0.41175663', '0.40432874',
'0.39645523', '0.39813004', '0.38932936', '0.38264912', '0.38094263', '0.3855869',
'0.38378537', '0.37570065', '0.37573022', '0.37550635', '0.36941113', '0.36502241',
'0.36607629', '0.36624103', '0.36163477', '0.35550154', '0.35627875', '0.35421111',
'0.34858053', '0.34767026', '0.34967665', '0.34818347', '0.34007975', '0.34139552',
'0.34017057', '0.33732993', '0.33320098']
from scipy.optimize import curve_fit
def func(x, a, b, c):
return a * np.exp(-b * x) + c
xdata = [float(x) for x in df_time]
guess=[1,0.001,0]
ydata = df_intensity
plt.plot(xdata, ydata, 'b-', label='data')
popt, pcov = curve_fit(func, xdata, ydata,p0=guess)
fig = plt.figure() # created a 2nd figure for 2nd plot
plt.plot(xdata, [func(float(x), *popt) for x in xdata], 'r-',
label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
plt.xlabel('x')
plt.ylabel('y');
我有两个数组，它们会导致 df_intensity_01 与 df_time 的关系图。
df_time
[[ 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170
180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350
360 370 380 390 400 410 420 430 440 450 460 470 480 490 500 510 520 530
540 550 560 570 580 590 600 610 620 630 640 650 660 670 680 690 700 710
720 730 740 750 760 770 780 790 800]]
df_intensity_01
[1. 0.98666909 0.935487 0.91008815 0.86347009 0.81356788
0.79591582 0.78624289 0.76503846 0.75105705 0.72333501 0.67815733
0.69481674 0.68321344 0.66108185 0.65859392 0.64047511 0.63100282
0.63605049 0.6248548 0.60341172 0.57538132 0.57952294 0.57901395
0.56353725 0.56164702 0.55901125 0.54833934 0.53271058 0.52880127
0.52268282 0.51111965 0.5067436 0.49988595 0.49689326 0.48888879
0.48247889 0.4790469 0.47320723 0.46156169 0.45921527 0.4592913
0.45104607 0.44445031 0.44618426 0.43893589 0.42988811 0.42887013
0.42842872 0.41952032 0.41286965 0.41392143 0.41175663 0.40432874
0.39645523 0.39813004 0.38932936 0.38264912 0.38094263 0.3855869
0.38378537 0.37570065 0.37573022 0.37550635 0.36941113 0.36502241
0.36607629 0.36624103 0.36163477 0.35550154 0.35627875 0.35421111
0.34858053 0.34767026 0.34967665 0.34818347 0.34007975 0.34139552
0.34017057 0.33732993 0.33320098]
我正在尝试将数据拟合到单个指数衰减函数，其中我提供了拟合的初始系数。
def func(x, a, b, c):
return a * np.exp(-b * x) + c
xdata = df_time
guess=[1,0.001,0]
ydata = df_intensity
plt.plot(xdata, ydata, 'b-', label='data')
popt, pcov = curve_fit(func, xdata, ydata,p0=guess)
popt
plt.plot(xdata, func(xdata, *popt), 'r-',
label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
plt.xlabel('x')
plt.ylabel('y')
我收到一个我真的不知道如何解决的错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
ValueError: object too deep for desired array
---------------------------------------------------------------------------
error Traceback (most recent call last)
<ipython-input-62-97bcc77fc6c7> in <module>
5 ydata = df_intensity
6 plt.plot(xdata, ydata, 'b-', label='data')
----> 7 popt, pcov = curve_fit(func, xdata, ydata,p0=guess)
8 popt
9
/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py in curve_fit(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, **kwargs)
749 # Remove full_output from kwargs, otherwise we're passing it in twice.
750 return_full = kwargs.pop('full_output', False)
--> 751 res = leastsq(func, p0, Dfun=jac, full_output=1, **kwargs)
752 popt, pcov, infodict, errmsg, ier = res
753 cost = np.sum(infodict['fvec'] ** 2)
/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py in leastsq(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)
392 with _MINPACK_LOCK:
393 retval = _minpack._lmdif(func, x0, args, full_output, ftol, xtol,
--> 394 gtol, maxfev, epsfcn, factor, diag)
395 else:
396 if col_deriv:
error: Result from function call is not a proper array of floats.
首先，您需要两个输入都是一维数组（只有一组大括号：[ ]）。目前看起来 df_time 是一个二维数组，这似乎是您发布的错误的来源。
然后，当您绘制数据时，请记住您需要为 x 的每个值计算函数 ，以便您的 x 和y 数组长度相同。您可以通过列表理解来做到这一点，记住将您的 x 值转换为 float 以便您可以将它们传递给您的函数：
plt.plot(xdata, [func(float(x), *popt) for x in xdata], 'r-',
label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
整个工作代码如下所示：
df_time = ['0', '10', '20', '30', '40', '50', '60', '70', '80', '90', '100',
'110', '120', '130', '140', '150', '160', '170', '180', '190', '200',
'210', '220', '230', '240', '250', '260', '270', '280', '290', '300',
'310', '320', '330', '340', '350', '360', '370', '380', '390', '400',
'410', '420', '430', '440', '450', '460', '470', '480', '490', '500',
'510', '520', '530', '540', '550', '560', '570', '580', '590', '600',
'610', '620', '630', '640', '650', '660', '670', '680', '690', '700',
'710', '720', '730', '740', '750', '760', '770', '780', '790', '800']
df_intensity = ['1.', '0.98666909', '0.935487', '0.91008815', '0.86347009', '0.81356788',
'0.79591582', '0.78624289', '0.76503846', '0.75105705', '0.72333501', '0.67815733',
'0.69481674', '0.68321344', '0.66108185', '0.65859392', '0.64047511', '0.63100282',
'0.63605049', '0.6248548', '0.60341172', '0.57538132', '0.57952294', '0.57901395',
'0.56353725', '0.56164702', '0.55901125', '0.54833934', '0.53271058', '0.52880127',
'0.52268282', '0.51111965', '0.5067436', '0.49988595', '0.49689326', '0.48888879',
'0.48247889', '0.4790469', '0.47320723', '0.46156169', '0.45921527', '0.4592913',
'0.45104607', '0.44445031', '0.44618426', '0.43893589', '0.42988811', '0.42887013',
'0.42842872', '0.41952032', '0.41286965', '0.41392143', '0.41175663', '0.40432874',
'0.39645523', '0.39813004', '0.38932936', '0.38264912', '0.38094263', '0.3855869',
'0.38378537', '0.37570065', '0.37573022', '0.37550635', '0.36941113', '0.36502241',
'0.36607629', '0.36624103', '0.36163477', '0.35550154', '0.35627875', '0.35421111',
'0.34858053', '0.34767026', '0.34967665', '0.34818347', '0.34007975', '0.34139552',
'0.34017057', '0.33732993', '0.33320098']
from scipy.optimize import curve_fit
def func(x, a, b, c):
return a * np.exp(-b * x) + c
xdata = [float(x) for x in df_time]
guess=[1,0.001,0]
ydata = df_intensity
plt.plot(xdata, ydata, 'b-', label='data')
popt, pcov = curve_fit(func, xdata, ydata,p0=guess)
fig = plt.figure() # created a 2nd figure for 2nd plot
plt.plot(xdata, [func(float(x), *popt) for x in xdata], 'r-',
label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
plt.xlabel('x')
plt.ylabel('y');