如何处理np.fft的输出？
How to deal with the output of np.fft?
我很困惑如何使用 FFD 的输出，这里是
生成的示例输出
result = np.abs(fftpack.fft(targetArray))[0:sample_size/2]
print result 将给出一个 ndarray :
[ 4.21477326e+05 3.03444591e+04 1.80682694e+04 1.05924224e+04
1.98799134e+04 2.82620258e+04 1.39538936e+04 2.40331051e+04
4.57465472e+04 6.41241587e+04 1.88203479e+04 1.88670469e+04
5.42137198e+03 5.97675834e+03 1.23986101e+04 9.70841580e+02
2.07380817e+04 4.52632516e+04 4.49493295e+04 1.03481720e+04
2.09126074e+04 2.07691720e+04 1.22091133e+04 1.85574554e+04
1.70652450e+04 2.53329892e+04 3.33015423e+04 2.80037092e+04
2.22665347e+04 2.08644916e+04 1.76186194e+04 1.14038308e+04
3.10918574e+04 2.97875178e+04 5.85659852e+04 3.77721316e+04
1.78952068e+04 1.74037254e+04 1.80938450e+04 3.05330693e+04
4.24407000e+04 2.74864012e+04 4.41637099e+04 3.31359768e+04
1.74327061e+04 1.06382371e+04 2.49780963e+04 1.66725706e+04
8.29635786e+03 2.58186162e+04 1.16012033e+04 2.63763332e+04
1.57059736e+04 1.70297649e+04 1.03524047e+04 3.25882139e+03
5.26665772e+03 1.15851816e+04 1.28794168e+04 1.01768783e+04
8.62533363e+03 4.42905399e+03 4.23319771e+03 9.19616531e+03
4.25165576e+03 4.96332367e+03 6.89522891e+03 4.04350194e+03
1.44337315e+04 7.78488536e+03 5.85210341e+03 3.56468734e+03
8.35331002e+03 5.41229478e+03 4.55374300e+03 1.01986739e+04
7.04839030e+03 3.78646612e+03 7.07872186e+03 4.06017111e+03
4.63905900e+03 9.84305110e+03 6.24621254e+03 2.76105428e+03
3.23469868e+03 2.31035716e+03 2.15636401e+03 2.60730520e+03
3.77829898e+03 3.40698553e+03 8.72582345e+02 1.72800115e+03
1.83439871e+03 7.95636998e+03 4.69696233e+03 2.50245035e+03
6.50413681e+03 4.75657064e+03 3.18621272e+03 1.38365470e+03
6.25551674e+02 3.51399477e+03 2.96447293e+03 1.88665733e+03
1.63347429e+03 1.92721372e+03 5.34091697e+03 2.86529694e+03
1.80361382e+03 3.65567684e+03 2.08391205e+03 3.77302704e+03
2.61882954e+03 1.17689735e+03 1.10303601e+03 1.46603669e+03
1.67959657e+03 1.90800388e+03 2.35782546e+03 1.61309844e+03
1.36326484e+03 4.06967773e+03 1.40142207e+03 1.32657523e+03
3.17829657e+03 2.48240862e+03 1.84764188e+03 2.46198195e+03
2.44352793e+03 1.29546145e+03 9.34633855e+02 1.42411185e+02
1.11686208e+03 1.61629862e+03 1.82113405e+03 1.26350347e+03
3.63268437e+03 9.33373272e+02 8.45292645e+02 1.03929325e+03
1.65583031e+03 9.54310546e+02 1.95195173e+03 1.91535953e+03
5.61485427e+02 1.98666296e+03 9.88850958e+02 7.80781362e+02
1.16064386e+03 1.08425676e+03 3.95616137e+02 1.25423006e+03
2.12467757e+03 7.12337370e+02 1.44060716e+03 7.73146781e+02
1.05641593e+03 1.19763314e+03 1.59583780e+03 1.23434921e+03
3.33146158e+02 1.75650022e+03 8.81978933e+02 1.28186954e+03
1.47573928e+03 8.07757403e+02 8.84292001e+02 1.64624690e+03
1.29680496e+03 4.76763593e+02 1.14002526e+03 1.88558087e+02
6.21497355e+02 5.30485958e+02 1.14902281e+03 4.16705689e+02
1.46212548e+03 1.32165278e+03 7.72060051e+02 9.39714410e+02
1.09011170e+03 8.90859235e+02 7.67129975e+02 2.72632265e+02
2.71574309e+02 5.28939138e+02 5.04479312e+02 4.53129779e+02
7.42214724e+02 2.61798368e+02 4.98990728e+02 6.02745861e+02
9.87830434e+02 2.97161466e+02 1.08718023e+03 5.87366849e+02
3.00425885e+02 8.33291219e+02 1.31052224e+02 2.31099067e+02
6.64652156e+02 1.32180021e+02 2.92862313e+00 2.39475444e+02
7.71773465e+02 8.34334863e+02 7.92791780e+02 6.70180885e+02
5.73451905e+02 4.66006885e+02 9.48437277e+02 7.04566875e+02
2.54136392e+02 4.29167074e+02 2.69560662e+02 6.08905902e+02
1.04487371e+03 5.70108773e+02 5.03504459e+02 7.67808997e+02
4.38126513e+02 7.56769864e+02 7.36892665e+02 5.61631429e+02
8.44062274e+02 8.30259267e+02 3.41959075e+02 4.06049010e+02
1.68799150e+02 7.98590743e+02 5.24271279e+02 4.96069745e+02
5.49647172e+02 7.41309283e+02 9.07897622e+02 1.04985345e+03
1.00181109e+03 5.42974899e+02 7.35959741e+02 4.04694642e+02
5.81271022e+02 2.01778038e+02 6.00141101e+02 3.80334242e+02
6.44350585e+02 8.54890120e+02 7.12173695e+02 8.64161173e+02
7.57346370e+02 7.92985369e+02 7.39425694e+02 4.64160309e+02
7.04501040e+02 4.39166237e+02 1.01374899e+03 7.39703012e+02
8.22200001e+02 4.71396567e+02 8.06529692e+02 7.18184947e+02
7.04886010e+02 6.71256922e+02 5.19651471e+02 9.20043821e+02
7.69576193e+02 8.78863865e+02 1.09071085e+03 9.10790235e+02
6.99356743e+02 9.75210348e+02 7.42159855e+02 2.94034843e+02
6.98690944e+02 7.64206208e+02 6.88827262e+02 5.81514517e+02
1.00230881e+03 7.13219427e+02 8.59968358e+02 8.52206990e+02
4.52436732e+02 6.05729013e+02 8.60630471e+02 7.77693596e+02
6.06655413e+02 7.24578627e+02 6.57839491e+02 6.72231281e+02
7.01971817e+02 4.12298654e+02 6.04044947e+02 6.71707719e+02
6.30927816e+02 7.82746860e+02 7.94808478e+02 5.94066021e+02
6.51161261e+02 7.95649076e+02 2.92195286e+02 4.08585488e+02
6.10540227e+02 5.15197819e+02 5.67327416e+02 5.21334315e+02
4.52410192e+02 7.44553730e+02 6.98824805e+02 7.93759345e+02
5.97743322e+02 5.74907952e+02 3.85973511e+02 3.58967385e+02
5.79438559e+02 4.50199311e+02 4.60028768e+02 4.84243380e+02
7.86184753e+02 4.04682342e+02 5.55837013e+02 6.36922370e+02
3.40645318e+02 5.57139578e+02 3.69777174e+02 3.78496601e+02
5.39000001e+02 8.90982470e+02 3.24737044e+02 2.77411040e+02
4.87813362e+02 1.67412278e+02 7.61243559e+02 3.58371802e+02
5.23608891e+02 2.89915508e+02 5.71091257e+02 6.46835815e+02
4.49435858e+02]
我想用这个数组来计算 FFT 能量，但是这些数字看起来不像一个复数..
我想写一个这样的函数：
def get_energy(input):
energy = 0
for e in input:
energy = energy + sqrt(pow(e.real,2)+pow(e.imag,2))
return energy
（我对python语言不是很熟悉，抱歉这种非pythonic编码）
非常感谢..
起初，您的 result 看起来不像是复杂的 FFT 输出，因为您计算了 FFT 的绝对值。这一点都不复杂...
因此：
result = fftpack.fft(targetArray)[0:sample_size//2]
(sample_size//2确保切片的上界是一个整数。)
您的 get_energy-函数实际上应该如下所示：
def get_energy(input):
return np.sum(input*input.conj())
信号的能量是幅度平方的总和...或者您可以将其写为
def get_energy(input):
return np.sum(np.abs(input)**2)
请注意，在 numpy 中，数学运算是按元素执行的。因此，您不必对 for 循环中的每个元素求平方。
我很困惑如何使用 FFD 的输出，这里是
生成的示例输出 result = np.abs(fftpack.fft(targetArray))[0:sample_size/2]
print result 将给出一个 ndarray :
[ 4.21477326e+05 3.03444591e+04 1.80682694e+04 1.05924224e+04
1.98799134e+04 2.82620258e+04 1.39538936e+04 2.40331051e+04
4.57465472e+04 6.41241587e+04 1.88203479e+04 1.88670469e+04
5.42137198e+03 5.97675834e+03 1.23986101e+04 9.70841580e+02
2.07380817e+04 4.52632516e+04 4.49493295e+04 1.03481720e+04
2.09126074e+04 2.07691720e+04 1.22091133e+04 1.85574554e+04
1.70652450e+04 2.53329892e+04 3.33015423e+04 2.80037092e+04
2.22665347e+04 2.08644916e+04 1.76186194e+04 1.14038308e+04
3.10918574e+04 2.97875178e+04 5.85659852e+04 3.77721316e+04
1.78952068e+04 1.74037254e+04 1.80938450e+04 3.05330693e+04
4.24407000e+04 2.74864012e+04 4.41637099e+04 3.31359768e+04
1.74327061e+04 1.06382371e+04 2.49780963e+04 1.66725706e+04
8.29635786e+03 2.58186162e+04 1.16012033e+04 2.63763332e+04
1.57059736e+04 1.70297649e+04 1.03524047e+04 3.25882139e+03
5.26665772e+03 1.15851816e+04 1.28794168e+04 1.01768783e+04
8.62533363e+03 4.42905399e+03 4.23319771e+03 9.19616531e+03
4.25165576e+03 4.96332367e+03 6.89522891e+03 4.04350194e+03
1.44337315e+04 7.78488536e+03 5.85210341e+03 3.56468734e+03
8.35331002e+03 5.41229478e+03 4.55374300e+03 1.01986739e+04
7.04839030e+03 3.78646612e+03 7.07872186e+03 4.06017111e+03
4.63905900e+03 9.84305110e+03 6.24621254e+03 2.76105428e+03
3.23469868e+03 2.31035716e+03 2.15636401e+03 2.60730520e+03
3.77829898e+03 3.40698553e+03 8.72582345e+02 1.72800115e+03
1.83439871e+03 7.95636998e+03 4.69696233e+03 2.50245035e+03
6.50413681e+03 4.75657064e+03 3.18621272e+03 1.38365470e+03
6.25551674e+02 3.51399477e+03 2.96447293e+03 1.88665733e+03
1.63347429e+03 1.92721372e+03 5.34091697e+03 2.86529694e+03
1.80361382e+03 3.65567684e+03 2.08391205e+03 3.77302704e+03
2.61882954e+03 1.17689735e+03 1.10303601e+03 1.46603669e+03
1.67959657e+03 1.90800388e+03 2.35782546e+03 1.61309844e+03
1.36326484e+03 4.06967773e+03 1.40142207e+03 1.32657523e+03
3.17829657e+03 2.48240862e+03 1.84764188e+03 2.46198195e+03
2.44352793e+03 1.29546145e+03 9.34633855e+02 1.42411185e+02
1.11686208e+03 1.61629862e+03 1.82113405e+03 1.26350347e+03
3.63268437e+03 9.33373272e+02 8.45292645e+02 1.03929325e+03
1.65583031e+03 9.54310546e+02 1.95195173e+03 1.91535953e+03
5.61485427e+02 1.98666296e+03 9.88850958e+02 7.80781362e+02
1.16064386e+03 1.08425676e+03 3.95616137e+02 1.25423006e+03
2.12467757e+03 7.12337370e+02 1.44060716e+03 7.73146781e+02
1.05641593e+03 1.19763314e+03 1.59583780e+03 1.23434921e+03
3.33146158e+02 1.75650022e+03 8.81978933e+02 1.28186954e+03
1.47573928e+03 8.07757403e+02 8.84292001e+02 1.64624690e+03
1.29680496e+03 4.76763593e+02 1.14002526e+03 1.88558087e+02
6.21497355e+02 5.30485958e+02 1.14902281e+03 4.16705689e+02
1.46212548e+03 1.32165278e+03 7.72060051e+02 9.39714410e+02
1.09011170e+03 8.90859235e+02 7.67129975e+02 2.72632265e+02
2.71574309e+02 5.28939138e+02 5.04479312e+02 4.53129779e+02
7.42214724e+02 2.61798368e+02 4.98990728e+02 6.02745861e+02
9.87830434e+02 2.97161466e+02 1.08718023e+03 5.87366849e+02
3.00425885e+02 8.33291219e+02 1.31052224e+02 2.31099067e+02
6.64652156e+02 1.32180021e+02 2.92862313e+00 2.39475444e+02
7.71773465e+02 8.34334863e+02 7.92791780e+02 6.70180885e+02
5.73451905e+02 4.66006885e+02 9.48437277e+02 7.04566875e+02
2.54136392e+02 4.29167074e+02 2.69560662e+02 6.08905902e+02
1.04487371e+03 5.70108773e+02 5.03504459e+02 7.67808997e+02
4.38126513e+02 7.56769864e+02 7.36892665e+02 5.61631429e+02
8.44062274e+02 8.30259267e+02 3.41959075e+02 4.06049010e+02
1.68799150e+02 7.98590743e+02 5.24271279e+02 4.96069745e+02
5.49647172e+02 7.41309283e+02 9.07897622e+02 1.04985345e+03
1.00181109e+03 5.42974899e+02 7.35959741e+02 4.04694642e+02
5.81271022e+02 2.01778038e+02 6.00141101e+02 3.80334242e+02
6.44350585e+02 8.54890120e+02 7.12173695e+02 8.64161173e+02
7.57346370e+02 7.92985369e+02 7.39425694e+02 4.64160309e+02
7.04501040e+02 4.39166237e+02 1.01374899e+03 7.39703012e+02
8.22200001e+02 4.71396567e+02 8.06529692e+02 7.18184947e+02
7.04886010e+02 6.71256922e+02 5.19651471e+02 9.20043821e+02
7.69576193e+02 8.78863865e+02 1.09071085e+03 9.10790235e+02
6.99356743e+02 9.75210348e+02 7.42159855e+02 2.94034843e+02
6.98690944e+02 7.64206208e+02 6.88827262e+02 5.81514517e+02
1.00230881e+03 7.13219427e+02 8.59968358e+02 8.52206990e+02
4.52436732e+02 6.05729013e+02 8.60630471e+02 7.77693596e+02
6.06655413e+02 7.24578627e+02 6.57839491e+02 6.72231281e+02
7.01971817e+02 4.12298654e+02 6.04044947e+02 6.71707719e+02
6.30927816e+02 7.82746860e+02 7.94808478e+02 5.94066021e+02
6.51161261e+02 7.95649076e+02 2.92195286e+02 4.08585488e+02
6.10540227e+02 5.15197819e+02 5.67327416e+02 5.21334315e+02
4.52410192e+02 7.44553730e+02 6.98824805e+02 7.93759345e+02
5.97743322e+02 5.74907952e+02 3.85973511e+02 3.58967385e+02
5.79438559e+02 4.50199311e+02 4.60028768e+02 4.84243380e+02
7.86184753e+02 4.04682342e+02 5.55837013e+02 6.36922370e+02
3.40645318e+02 5.57139578e+02 3.69777174e+02 3.78496601e+02
5.39000001e+02 8.90982470e+02 3.24737044e+02 2.77411040e+02
4.87813362e+02 1.67412278e+02 7.61243559e+02 3.58371802e+02
5.23608891e+02 2.89915508e+02 5.71091257e+02 6.46835815e+02
4.49435858e+02]
我想用这个数组来计算 FFT 能量，但是这些数字看起来不像一个复数.. 我想写一个这样的函数：
def get_energy(input):
energy = 0
for e in input:
energy = energy + sqrt(pow(e.real,2)+pow(e.imag,2))
return energy
（我对python语言不是很熟悉，抱歉这种非pythonic编码）
非常感谢..
起初，您的 result 看起来不像是复杂的 FFT 输出，因为您计算了 FFT 的绝对值。这一点都不复杂...
因此：
result = fftpack.fft(targetArray)[0:sample_size//2]
(sample_size//2确保切片的上界是一个整数。)
您的 get_energy-函数实际上应该如下所示：
def get_energy(input):
return np.sum(input*input.conj())
信号的能量是幅度平方的总和...或者您可以将其写为
def get_energy(input):
return np.sum(np.abs(input)**2)
请注意，在 numpy 中，数学运算是按元素执行的。因此，您不必对 for 循环中的每个元素求平方。