我一直在对 PdSn4 进行 QMSA 分析，在尝试实施多载波拟合时，我不断收到错误消息
NameError：名称“DiracDelta”未定义
DiracDelta我的输出仅包括一个在查看第二个带拟合 ( n2, ) 时存在该函数的实例mu2。DiracDelta据我了解，根据我的模型设计，输出中不应该有任何功能。
import sympy as sp
from sympy.functions.special.delta_functions import DiracDelta
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import math
import symfit
from symfit import DiracDelta
from symfit import Variable, Parameter, parameters, Fit, Model, cosh
from symfit.contrib.interactive_guess import InteractiveGuess
import numpy as np
plt.ion()
data = np.loadtxt('PdSn4_010K_rhoData.txt')
xdata = np.array(data[:, 0])
rhoxx_exp = np.array(data[:, 1])
rhoxy_exp = np.array(data[:, 2])
B = Variable('B')
y1 = Variable('y1')
y2 = Variable('y2')
n_1 = Parameter('n1', value=n1, min=1.5e27, max=1.55e27)
n_2 = Parameter('n2', value=n2, min=-1.52e27, max=-1.51e27)
n_3 = Parameter('n3', value=n3, min=0.91e27, max=0.92e27)
n_4 = Parameter('n4', value=n4, min=-1.05e27, max=-1.045e27)
mu_1 = Parameter('mu1', value=mu1, min=1.4, max=1.42)
mu_2 = Parameter('mu2', value=mu2, min=0.63, max=0.65)
mu_3 = Parameter('mu3', value=mu3, min=0.085, max=0.09)
mu_4 = Parameter('mu4', value=mu4, min=0.088, max=0.092)
a = 1.1513
f = 0.992
q = 1.6e-19
R1 = -1 / (q*n_1)
R2 = -1 / (q*n_2)
R3 = -1 / (q*n_3)
R4 = -1 / (q*n_4)
rho1 = 1 / (abs(n_1) * q * mu_1)
rho2 = 1 / (abs(n_2) * q * mu_2)
rho3 = 1 / (abs(n_3) * q * mu_3)
rho4 = 1 / (abs(n_4) * q * mu_4)
dictionary = {
y1: (rho1 / (rho1**2 + (R1*B)**2) + rho2 / (rho2**2 + (R2*B)**2)
+ rho3 / (rho3**2 + (R3*B)**2) + rho4 / (rho4**2 + (R4*B)**2))
/ ((rho1 / (rho1**2 + (R1*B)**2) + rho2 / (rho2**2 + (R2*B)**2)
+ rho3 / (rho3**2 + (R3*B)**2) + rho4 / (rho4**2 + (R4*B)**2))**2
+ ((-R1*B)/(rho1**2+(R1*B)**2) + (-R2*B)/(rho2**2+(R2*B)**2)
+ (-R3*B)/(rho3**2+(R3*B)**2) + (-R4*B)/(rho4**2+(R4*B)**2))**2)
+ (a/cosh(B/f))*((rho1 / (rho1**2 + (R1*B)**2) + rho2 / (rho2**2 + (R2*B)**2)
+ rho3 / (rho3**2 + (R3*B)**2) + rho4 / (rho4**2 + (R4*B)**2))
/ ((rho1 / (rho1**2 + (R1*B)**2) + rho2 / (rho2**2 + (R2*B)**2)
+ rho3 / (rho3**2 + (R3*B)**2) + rho4 / (rho4**2 + (R4*B)**2))**2
+ ((-R1*B)/(rho1**2+(R1*B)**2) + (-R2*B)/(rho2**2+(R2*B)**2)
+ (-R3*B)/(rho3**2+(R3*B)**2) + (-R4*B)/(rho4**2+(R4*B)**2))**2))
,
y2: - ((-R1*B)/(rho1**2+(R1*B)**2) + (-R2*B)/(rho2**2+(R2*B)**2)
+ (-R3*B)/(rho3**2+(R3*B)**2) + (-R4*B)/(rho4**2+(R4*B)**2)) /
((rho1 / (rho1**2 + (R1*B)**2) + rho2 / (rho2**2 + (R2*B)**2)
+ rho3 / (rho3**2 + (R3*B)**2) + rho4 / (rho4**2 + (R4*B)**2))**2
+ ((-R1*B)/(rho1**2+(R1*B)**2) + (-R2*B)/(rho2**2+(R2*B)**2)
+ (-R3*B)/(rho3**2+(R3*B)**2) + (-R4*B)/(rho4**2+(R4*B)**2))**2)
}
model = Model(dictionary)
#print(model)
model_sim = model(B=xdata, n1=n1, n2=n2, n3=n3, n4=n4, mu1=mu1, mu2=mu2, mu3=mu3, mu4=mu4)
#print(data)
rho_xx_sim = model_sim.y1
rho_xy_sim = model_sim.y2
f = plt.figure(figsize=(15,15))
ax = f.add_subplot(211)
plt.xlabel("Magnetic Field [T]")
plt.ylabel("Rho$_{xx}$")
plt.plot(xdata, rhoxx_exp, 'k.')
plt.plot(xdata, rho_xx_sim, 'r-')
plt.xlim([0,8])
ax2 = f.add_subplot(212)
plt.xlabel("Magnetic Field [T]")
plt.ylabel("Rho$_{xy}$")
plt.plot(xdata, rhoxy_exp, 'k.')
plt.plot(xdata, rho_xy_sim, 'r-')
plt.xlim([0,8])
plt.show()
guess = InteractiveGuess(dictionary, B=xdata, y1=rho_xx_sim, y2=rho_xy_sim, n_points=250)
guess.execute()
print(guess)
fit = Fit(model, B=xdata, y1=rho_xx_sim, y2=rho_xy_sim)
fit_result = fit.execute()
print(fit_result)
数据链接：Multicarrier Fitting Data 应保存为 PdSn4_010K_rhoData.txt，与文件在同一目录下。
如果有人能弄清楚为什么DiracDelta我的输出中有这个函数或者如何克服这个错误，那就太好了！
错误回溯如下：
NameError Traceback (most recent call last)
<ipython-input-19-68c235066887> in <module>
197
198 fit = Fit(model, B=xdata, y1=rho_xx_sim, y2=rho_xy_sim)
--> 199 fit_result = fit.execute()
200 print(fit_result)
201 #y1result, y2result = fit.model(B=xdata, **fit_result.params)
~\anaconda5\lib\site-packages\symfit\core\fit.py in execute(self, **minimize_options)
579 """
580 minimizer_ans = self.minimizer.execute(**minimize_options)
--> 581 minimizer_ans.covariance_matrix = self.covariance_matrix(
582 dict(zip(self.model.params, minimizer_ans._popt))
583 )
~\anaconda5\lib\site-packages\symfit\core\fit.py in covariance_matrix(self, best_fit_params)
278 :return: covariance matrix.
279 """
--> 280 cov_matrix = self._covariance_matrix(best_fit_params,
281 objective=self.objective)
282 if cov_matrix is None:
~\anaconda5\lib\site-packages\symfit\core\fit.py in _covariance_matrix(self, best_fit_params, objective)
236 # Helper function for self.covariance_matrix.
237 try:
--> 238 hess = objective.eval_hessian(**key2str(best_fit_params))
239 except AttributeError:
240 # Some models do not have an eval_hessian, in which case we give up
~\anaconda5\lib\site-packages\symfit\core\objectives.py in eval_hessian(self, ordered_parameters, **parameters)
367 ordered_parameters, **parameters
368 )
--> 369 evaluated_hess = super(LeastSquares, self).eval_hessian(
370 ordered_parameters, **parameters
371 )
~\anaconda5\lib\site-packages\symfit\core\objectives.py in eval_hessian(self, ordered_parameters, **parameters)
221 parameters.update(dict(zip(self.model.free_params, ordered_parameters)))
222 parameters.update(self._invariant_kwargs)
--> 223 result = self.model.eval_hessian(**key2str(parameters))._asdict()
224 # Return only the components corresponding to the dependent data.
225 return self._shape_of_dependent_data(
~\anaconda5\lib\site-packages\symfit\core\models.py in eval_hessian(self, *args, **kwargs)
897 # Evaluate the hessian model and use the resulting Ans namedtuple as a
898 # dict. From this, take the relevant components.
--> 899 eval_hess_dict = self.hessian_model(*args, **kwargs)._asdict()
900 hess = [[[np.broadcast_to(eval_hess_dict.get(D(var, p1, p2), 0),
901 eval_hess_dict[var].shape)
~\anaconda5\lib\site-packages\symfit\core\models.py in __call__(self, *args, **kwargs)
665 even for scalar valued functions. This is done for consistency.
666 """
--> 667 return ModelOutput(self.keys(), self.eval_components(*args, **kwargs))
668
669
~\anaconda5\lib\site-packages\symfit\core\models.py in eval_components(self, *args, **kwargs)
613 dependencies_kwargs = {d.name: kwargs[d.name]
614 for d in dependencies}
--> 615 kwargs[symbol.name] = components[symbol](**dependencies_kwargs)
616
617 return [np.atleast_1d(kwargs[var.name]) for var in self]
<lambdifygenerated-1024> in _lambdifygenerated(B, mu1, mu2, mu3, mu4, n1, n2, n3, n4)
1 def _lambdifygenerated(B, mu1, mu2, mu3, mu4, n1, n2, n3, n4):
----> 2 return (6.5536e-76*(-3.90625e+37*(1.31072e-75*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(mu2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2*abs(n2)) - 2*sign(n2)/(mu2*n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))))*(1/(mu4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))*abs(n4)) + 1/(mu3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))*abs(n3)) + 1/(mu2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))*abs(n2)) + 1/(mu1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))*abs(n1))) - 3.90625e+37*(1.31072e-75*B*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(n2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) - 2*B/(n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))))*(B/(n4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))) + B/(n3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))) + B/(n2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) + B/(n1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2)))))*(-2*(1.31072e-75*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(mu2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2*abs(n2)) - 2*sign(n2)/(mu2*n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))))*(1/(mu4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))*abs(n4)) + 1/(mu3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))*abs(n3)) + 1/(mu2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))*abs(n2)) + 1/(mu1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))*abs(n1))) - 2*(1.31072e-75*B*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(n2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) - 2*B/(n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))))*(B/(n4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))) + B/(n3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))) + B/(n2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) + B/(n1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2)))))*(-6.25e+18*B/(n4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))) - 6.25e+18*B/(n3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))) - 6.25e+18*B/(n2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) - 6.25e+18*B/(n1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))))/((1/(mu4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))*abs(n4)) + 1/(mu3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))*abs(n3)) + 1/(mu2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))*abs(n2)) + 1/(mu1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))*abs(n1)))**2 + (B/(n4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))) + B/(n3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))) + B/(n2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) + B/(n1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))))**2)**3 + 1.31072e-75*(-3.90625e+37*(1.31072e-75*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(mu2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2*abs(n2)) - 2*sign(n2)/(mu2*n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))))*(1/(mu4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))*abs(n4)) + 1/(mu3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))*abs(n3)) + 1/(mu2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))*abs(n2)) + 1/(mu1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))*abs(n1))) - 3.90625e+37*(1.31072e-75*B*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(n2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) - 2*B/(n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))))*(B/(n4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))) + B/(n3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))) + B/(n2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) + B/(n1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2)))))*(-4.096e-57*B*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(n2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) + 6.25e+18*B/(n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))))/((1/(mu4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))*abs(n4)) + 1/(mu3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))*abs(n3)) + 1/(mu2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))*abs(n2)) + 1/(mu1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))*abs(n1)))**2 + (B/(n4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))) + B/(n3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))) + B/(n2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) + B/(n1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))))**2)**2 + (-4.096e-57*B*(-2.34375e+38*B**2/n2**4 - 2.34375e+38/(mu2**2*n2**4))/(n2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) - 4.096e-57*B*(4*B**2/n2**3 + 4/(mu2**2*n2**3))*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(n2*(B**2/n2**2 + 1/(mu2**2*n2**2))**3) + 8.192e-57*B*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(n2**2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) - 1.25e+19*B/(n2**3*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))))/(3.90625e+37*(1/(mu4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))*abs(n4)) + 1/(mu3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))*abs(n3)) + 1/(mu2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))*abs(n2)) + 1/(mu1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))*abs(n1)))**2 + 3.90625e+37*(B/(n4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))) + B/(n3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))) + B/(n2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) + B/(n1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))))**2) + 6.5536e-76*((-5.12e-38*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(mu2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2*abs(n2)) + 7.8125e+37*sign(n2)/(mu2*n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))))*(6.5536e-76*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(mu2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2*abs(n2)) - sign(n2)/(mu2*n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2)))) + (-5.12e-38*B*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(n2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) + 7.8125e+37*B/(n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))))*(6.5536e-76*B*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(n2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) - B/(n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2)))) + (1/(mu4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))*abs(n4)) + 1/(mu3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))*abs(n3)) + 1/(mu2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))*abs(n2)) + 1/(mu1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))*abs(n1)))*(-5.12e-38*(-2.34375e+38*B**2/n2**4 - 2.34375e+38/(mu2**2*n2**4))/(mu2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2*abs(n2)) - 5.12e-38*(4*B**2/n2**3 + 4/(mu2**2*n2**3))*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(mu2*(B**2/n2**2 + 1/(mu2**2*n2**2))**3*abs(n2)) + 1.024e-37*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))*sign(n2)/(mu2*n2**2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) + 1.5625e+38*DiracDelta(n2)/(mu2*n2**2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) - 1.5625e+38*sign(n2)/(mu2*n2**3*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2)))) + (B/(n4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))) + B/(n3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))) + B/(n2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) + B/(n1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))))*(-5.12e-38*B*(-2.34375e+38*B**2/n2**4 - 2.34375e+38/(mu2**2*n2**4))/(n2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) - 5.12e-38*B*(4*B**2/n2**3 + 4/(mu2**2*n2**3))*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(n2*(B**2/n2**2 + 1/(mu2**2*n2**2))**3) + 1.024e-37*B*(7.8125e+37*B**2/n2**3 + 7.8125e+37/(mu2**2*n2**3))/(n2**2*(B**2/n2**2 + 1/(mu2**2*n2**2))**2) - 1.5625e+38*B/(n2**3*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2)))))*(-6.25e+18*B/(n4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))) - 6.25e+18*B/(n3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))) - 6.25e+18*B/(n2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) - 6.25e+18*B/(n1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))))/((1/(mu4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))*abs(n4)) + 1/(mu3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))*abs(n3)) + 1/(mu2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))*abs(n2)) + 1/(mu1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))*abs(n1)))**2 + (B/(n4*(3.90625e+37*B**2/n4**2 + 3.90625e+37/(mu4**2*n4**2))) + B/(n3*(3.90625e+37*B**2/n3**2 + 3.90625e+37/(mu3**2*n3**2))) + B/(n2*(3.90625e+37*B**2/n2**2 + 3.90625e+37/(mu2**2*n2**2))) + B/(n1*(3.90625e+37*B**2/n1**2 + 3.90625e+37/(mu1**2*n1**2))))**2)**2)
NameError: name 'DiracDelta' is not defined