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Heaviside 函数应该内置到 Sympy 和 Numpy 中,但是下面的代码给出了错误 Name Heaviside not defined
。尝试在将使用它的数值计算(基于 Traceback)之前在代码中自己定义 Heaviside 函数什么也没做——我想它应该在 lambdifygenerated
中定义。有解决方法吗?
from sympy import *
from IPython.display import display
mux, s, Px, Py, Pxe, Pye = symbols("mu_X s P_X P_Y P_X^* P_Y^*", positive=True)
vx, vy, cx, cy = symbols("v_X v_Y c_X c_Y", real=True)
pix = (Px-cx)*( mux*integrate(integrate(1,(vx,Min(1,Max(0,Px+Max(0,vy-Pye-s))),1)),(vy,0,1))
+(1-mux)*integrate(integrate(1,(vx,Min(1,Max(0,Max(Pxe+s,Px)+Max(0,vy-Pye))),1)),(vy,0,1))
)
piy = (Py-cy)*( (1-mux)*integrate(integrate(1,(vy,Min(1,Max(0,Py+Max(0,vx-Pxe-s))),1)),(vx,0,1))
+mux*integrate(integrate(1,(vy,Min(1,Max(0,Max(Pye+s,Py)+Max(0,vx-Pxe))),1)),(vx,0,1))
)
focx =diff(pix,Px)
focy =diff(piy,Py)
focxeq=focx.subs(Px,Pxe)
focyeq=focy.subs(Py,Pye)
import numpy as np
focx_lambda = lambdify((Pxe,Pye), focxeq, modules=['numpy', 'sympy'])
focy_lambda = lambdify((Pxe,Pye), focyeq, modules=['numpy', 'sympy'])
nsolve([focxeq.subs({mux:0.4,s:0.05,cx:0,cy:0.1}).evalf(),focyeq.subs({mux:0.4,s:0.05,cx:0,cy:0.1}).evalf()],(Pxe,Pye),(0.3,0.4))
回溯如下:
--------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-10-b7bc7e96827d> in <module>
26 focx_lambda = lambdify((Pxe,Pye), focxeq, modules=['numpy', 'sympy'])
27 focy_lambda = lambdify((Pxe,Pye), focyeq, modules=['numpy', 'sympy'])
---> 28 nsolve([focxeq.subs({mux:0.4,s:0.05,cx:0,cy:0.1}).evalf(),focyeq.subs({mux:0.4,s:0.05,cx:0,cy:0.1}).evalf()],(Pxe,Pye),(0.3,0.4))
29 mux=0.4
30 s=0.05
~/anaconda3/lib/python3.6/site-packages/sympy/utilities/decorator.py in func_wrapper(*args, **kwargs)
88 dps = mpmath.mp.dps
89 try:
---> 90 return func(*args, **kwargs)
91 finally:
92 mpmath.mp.dps = dps
~/anaconda3/lib/python3.6/site-packages/sympy/solvers/solvers.py in nsolve(*args, **kwargs)
3045 J = lambdify(fargs, J, modules)
3046 # solve the system numerically
-> 3047 x = findroot(f, x0, J=J, **kwargs)
3048 if as_dict:
3049 return [dict(zip(fargs, [sympify(xi) for xi in x]))]
~/anaconda3/lib/python3.6/site-packages/mpmath/calculus/optimization.py in findroot(ctx, f, x0, solver, tol, verbose, verify, **kwargs)
926 # detect multidimensional functions
927 try:
--> 928 fx = f(*x0)
929 multidimensional = isinstance(fx, (list, tuple, ctx.matrix))
930 except TypeError:
<lambdifygenerated-23> in _lambdifygenerated(Dummy_4515, _Dummy_4514)
1 def _lambdifygenerated(Dummy_4515, _Dummy_4514):
----> 2 return (ImmutableDenseMatrix([[Dummy_4515*(mpf((0, 3602879701896397, -53, 52))*((-(_Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Heaviside(1 - Dummy_4515)*Heaviside(1 - Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) + Heaviside(1 - Dummy_4515)*Heaviside(1 - Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53))))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) + Min(mpf((0, 1, 0, 1)), _Dummy_4514 + mpf((0, 3602879701896397, -56, 52))) - Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53))))) if (Dummy_4515 >= 1) else (-(_Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Heaviside(1 - Dummy_4515)*Heaviside(1 - Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) + Heaviside(1 - Dummy_4515)*Heaviside(1 - Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53))))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) - Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))))) + mpf((0, 5404319552844595, -53, 53))*((0) if (Dummy_4515 >= mpf((0, 4278419646001971, -52, 52))) else (-(_Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Heaviside(1 - Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + 1))*Heaviside(_Dummy_4514 - Dummy_4515 - Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52))) + 1) + Heaviside(1 - Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + 1))*Heaviside(_Dummy_4514 - Dummy_4515 - Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52))) + 1)*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + mpf((0, 1, 0, 1))))) if (Dummy_4515 >= 1) else (0))) + mpf((0, 3602879701896397, -53, 52))*((-(_Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Min(mpf((0, 1, 0, 1)), _Dummy_4514 + mpf((0, 3602879701896397, -56, 52))) + (_Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) + mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), _Dummy_4514 + mpf((0, 3602879701896397, -56, 52)))**2 - mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53))))**2) if (Dummy_4515 >= 1) else ((mpf((0, 1, 0, 1)) - Dummy_4515)*Min(mpf((0, 1, 0, 1)), _Dummy_4514 + mpf((0, 3602879701896397, -56, 52))) - (_Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Min(mpf((0, 1, 0, 1)), _Dummy_4514 + mpf((0, 3602879701896397, -56, 52))) + (_Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) + mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), _Dummy_4514 + mpf((0, 3602879701896397, -56, 52)))**2 - mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514 + mpf((0, 3602879701896397, -56, 52)), _Dummy_4514 - Dummy_4515 + mpf((0, 4728779608739021, -52, 53))))**2)) + mpf((0, 5404319552844595, -53, 53))*((-(_Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), _Dummy_4514) + (_Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))) + mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), _Dummy_4514)**2 - mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52))))**2) if (Dummy_4515 >= mpf((0, 4278419646001971, -52, 52))) else (-(_Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), _Dummy_4514) + (_Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + mpf((0, 1, 0, 1)))) + mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), _Dummy_4514)**2 - mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + mpf((0, 1, 0, 1))))**2) if (Dummy_4515 >= 1) else ((mpf((0, 4278419646001971, -52, 52)) - Dummy_4515)*Min(mpf((0, 1, 0, 1)), _Dummy_4514) - (_Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), _Dummy_4514) + (_Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))) + mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), _Dummy_4514)**2 - mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Max(_Dummy_4514, _Dummy_4514 - Dummy_4515 + mpf((0, 4278419646001971, -52, 52))))**2))], [(_Dummy_4514 + mpf((1, 3602879701896397, -55, 52)))*(mpf((0, 5404319552844595, -53, 53))*((-(-_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Heaviside(1 - _Dummy_4514)*Heaviside(1 - Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) + Heaviside(1 - _Dummy_4514)*Heaviside(1 - Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53))))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) + Min(mpf((0, 1, 0, 1)), Dummy_4515 + mpf((0, 3602879701896397, -56, 52))) - Min(mpf((0, 1, 0, 1)), Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53))))) if (_Dummy_4514 >= 1) else (-(-_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Heaviside(1 - _Dummy_4514)*Heaviside(1 - Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) + Heaviside(1 - _Dummy_4514)*Heaviside(1 - Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53))))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) - Min(mpf((0, 1, 0, 1)), Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))))) + mpf((0, 3602879701896397, -53, 52))*((0) if (_Dummy_4514 >= mpf((0, 4278419646001971, -52, 52))) else (-(-_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Heaviside(1 - Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + 1))*Heaviside(-_Dummy_4514 + Dummy_4515 - Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52))) + 1) + Heaviside(1 - Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + 1))*Heaviside(-_Dummy_4514 + Dummy_4515 - Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52))) + 1)*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + mpf((0, 1, 0, 1))))) if (_Dummy_4514 >= 1) else (0))) + mpf((0, 5404319552844595, -53, 53))*((-(-_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Min(mpf((0, 1, 0, 1)), Dummy_4515 + mpf((0, 3602879701896397, -56, 52))) + (-_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) + mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Dummy_4515 + mpf((0, 3602879701896397, -56, 52)))**2 - mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53))))**2) if (_Dummy_4514 >= 1) else ((mpf((0, 1, 0, 1)) - _Dummy_4514)*Min(mpf((0, 1, 0, 1)), Dummy_4515 + mpf((0, 3602879701896397, -56, 52))) - (-_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Min(mpf((0, 1, 0, 1)), Dummy_4515 + mpf((0, 3602879701896397, -56, 52))) + (-_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53)))) + mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Dummy_4515 + mpf((0, 3602879701896397, -56, 52)))**2 - mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515 + mpf((0, 3602879701896397, -56, 52)), -_Dummy_4514 + Dummy_4515 + mpf((0, 4728779608739021, -52, 53))))**2)) + mpf((0, 3602879701896397, -53, 52))*((-(-_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), Dummy_4515) + (-_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))) + mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Dummy_4515)**2 - mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52))))**2) if (_Dummy_4514 >= mpf((0, 4278419646001971, -52, 52))) else (-(-_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), Dummy_4515) + (-_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + mpf((0, 1, 0, 1)))) + mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Dummy_4515)**2 - mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + mpf((0, 1, 0, 1))))**2) if (_Dummy_4514 >= 1) else ((mpf((0, 4278419646001971, -52, 52)) - _Dummy_4514)*Min(mpf((0, 1, 0, 1)), Dummy_4515) - (-_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), Dummy_4515) + (-_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52)))) + mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Dummy_4515)**2 - mpf((0, 1, -1, 1))*Min(mpf((0, 1, 0, 1)), Max(Dummy_4515, -_Dummy_4514 + Dummy_4515 + mpf((0, 4278419646001971, -52, 52))))**2))]]))
NameError: name 'Heaviside' is not defined
我根据对 (Some function) is not defined with SymPy Lambdify 的回答添加了 focx_lambda = lambdify((Pxe,Pye), focxeq, modules=['numpy', 'sympy'])
但这并没有改变任何东西。
我自己定义 Heaviside 的方式是
def Heaviside(x):
if x<0:
out=0
else:
out=1
return out
我还尝试了 from numpy import *
以防万一。这并没有改变任何东西。
最佳答案
lambdify
的几个问题似乎同时发生。我想我可以让事情正常进行,但你应该检查它是否有意义,因为我不熟悉具体的方程式。
一般来说,调用 from sympy import *
和 from numpy import *
会造成很多困惑。两个库中的许多函数都有相同的名称,而且它们真的不喜欢与其他变量一起工作。
另一方面,lambdify
不能很好地与 Heaviside
配合使用。此外,numpy 中的函数是小写的 and 需要两个参数:一个 x
值和一个 x2
来决定 应该发生什么x==0
。作为补救措施,下面的代码将“Heaviside”替换为 lambda x: np.heaviside(x, 1)
。
我无法让 sympy 的 nsolve
使用这些函数,所以我尝试了 scipy 的 fsolve
。fsolve
还需要一些技巧才能处理函数元组。
创建focx_lambda
时,重要的是除了函数参数Pxe
和Pye
之外的所有变量都接收一个固定值。因此,我在执行 lambdify
时替换了它们。
from sympy import symbols, integrate, Min, Max, diff, lambdify
from IPython.display import display
mux, s, Px, Py, Pxe, Pye = symbols("mu_X s P_X P_Y P_X^* P_Y^*", positive=True)
vx, vy, cx, cy = symbols("v_X v_Y c_X c_Y", real=True)
pix = (Px - cx) * (mux * integrate(integrate(1, (vx, Min(1, Max(0, Px + Max(0, vy - Pye - s))), 1)), (vy, 0, 1))
+ (1 - mux) * integrate(integrate(1, (vx, Min(1, Max(0, Max(Pxe + s, Px) + Max(0, vy - Pye))), 1)),
(vy, 0, 1))
)
piy = (Py - cy) * ((1 - mux) * integrate(integrate(1, (vy, Min(1, Max(0, Py + Max(0, vx - Pxe - s))), 1)), (vx, 0, 1))
+ mux * integrate(integrate(1, (vy, Min(1, Max(0, Max(Pye + s, Py) + Max(0, vx - Pxe))), 1)),
(vx, 0, 1))
)
focx = diff(pix, Px)
focy = diff(piy, Py)
focxeq = focx.subs(Px, Pxe)
focyeq = focy.subs(Py, Pye)
import numpy as np
from scipy.optimize import fsolve
modules = [{'Heaviside': lambda x: np.heaviside(x, 1)}, 'numpy']
values_for_parameters = {mux: 0.4, s: 0.05, cx: 0, cy: 0.1}
focx_lambda = lambdify((Pxe, Pye), focxeq.subs(values_for_parameters), modules=modules)
focy_lambda = lambdify((Pxe, Pye), focyeq.subs(values_for_parameters), modules=modules)
print(focx_lambda(0.3, 0.4)) # we need to check that the lambdify works, so this should print a floating point number
print(focy_lambda(0.3, 0.4))
def equations(p):
x, y = p
return focx_lambda(x, y), focy_lambda(x, y)
sol = fsolve(equations, (0.3, 0.4))
print(sol) # [0.64701372 0.61726372]
关于numpy - Sympy 名称 Heaviside 未在 lambdifygenerated 中定义,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/60171926/
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作者: zhuwenzhuang, 2024.05.08. 阅读前假设读者熟悉数据库使用,了解 SQL 的语法和关系算子的大概含义, 能通过 EXPLAIN 命令查看数据库执行计划. 0 前言
我似乎无法找到是否可以声明一个 header 对象以便在响应 header 中重用它,有一些示例定义了响应模式的对象,但它不会转置为响应 header 。我只设法制作了一个可重用的响应对象,如下所示:
css 选择器 * + * 实际上是什么意思?当您执行检查元素时,您可以在谷歌浏览器的控制台中看到它。在我看来,这似乎是对 "Every second child"应用一种风格,但仍然想确定。谁能帮我
我试图弄清楚基本的IO Haskell 函数是定义好的,所以我使用了this reference我到了putChar函数定义: putChar :: Char -> IO () putChar
我得到了一个自动生成的文件,该文件定义了程序集属性,我正在尝试理解内容。 [assembly: global::System.Runtime.Versioning.TargetFrameworkAtt
This文档演示了如何检查变量是否先前已在 gnuplot 脚本中定义。 文档中的示例: a = 10 if (exists("a")) print "a is defined" if (!exist
好吧,这是一个相当基本的问题:我正在关注 SICP 视频,我对 define、let 和 之间的区别有点困惑设置!. 1) 根据 Sussman 在视频中的说法,define 只允许为变量附加一个值一
我一直在尝试定义一个包含只能具有以下三个值之一的字段的 XSD: 绿色 红色 蓝色 本质上,我想在架构级别定义严格的枚举。 我的第一次尝试似乎是错误的,我不确定修复它的“正确”方法。
有人可以定义“POCO”到底是什么意思吗?我越来越频繁地遇到这个术语,我想知道它是否仅与普通类有关还是意味着更多? 最佳答案 “普通旧式 C# 对象” 只是一个普通的类,没有描述基础结构问题或域对象不
在我经常看到的一些django模型中 myfield = models.CharField(_('myfield')) class_name = models.CharField(_('Type'),
每当 BOOL 数据类型不容易预定义时,我都会使用以下定义进行 boolean 运算, typedef unsigned char BOOL; (由于内存使用)。 我意识到出于性能原因,使用本地总线宽
l_ABC_BEANVector = utilRemote.fnGetVector("ABC_COVBEANVector"); 编码的含义是什么?任何帮助,我真的很感激。谢谢 最佳答案 唯一可以肯定地
我正在使用 javacc 开发一个项目,我遇到问题并需要一些帮助,我的文件中有这样的内容: STRING COPYRIGHT (C) 2003, 2004 SYNOPSYS, INC.; 我为单词 S
我想弄清楚基本的 IO定义了 Haskell 函数,所以我使用了 this reference然后我到了 putChar函数定义: putChar :: Char -> IO () putCha
我在具体类中使用 @property 定义 getter 时遇到问题。这是Python代码: from abc import ABCMeta, abstractproperty class abstr
我正在为大学用 C 语言编写一个小游戏,但我陷入了困境。我(在头文件中)有这个结构: typedef struct{ game_element field[MAX_ROWS][MAX_COLU
我一直在 .l 文件中创建标记定义。由于数据集数量庞大,它变得有点乏味。有没有办法读取文件中的所有单词,例如包含所有名词的 noun.txt 并给所有名词一个标记。 基本上,我想自动化这部分: %%
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