是否可以使用python库在没有设置任何参数的情况下检测最高峰？我正在开发一个用户界面，我希望该算法能够自动检测最高峰...
我希望它能够检测下图中的这些峰：graph here
数据看起来像这样：
8.60291e-07
-1.5491e-06
5.64568e-07
-9.51195e-07
1.07203e-06
4.6521e-07
6.43967e-07
-9.86092e-07
-9.82323e-07
6.38977e-07
-1.93884e-06
-2.98309e-08
1.33543e-06
1.05064e-06
1.17332e-06
-1.53549e-07
-8.9357e-07
1.59176e-06
-2.17331e-06
1.46756e-06
5.63301e-07
-8.77556e-07
7.47681e-09
-8.30101e-07
-3.6647e-07
5.27046e-07
-1.94983e-06
1.89018e-07
1.22533e-06
8.00735e-07
-8.51166e-07
1.13437e-06
-2.75787e-07
1.79601e-06
-1.67875e-06
1.13529e-06
-1.29865e-06
9.9688e-07
-9.34486e-07
8.89931e-07
-3.88634e-07
1.15124e-06
-4.23569e-07
-1.8029e-07
1.20537e-07
4.10736e-07
-9.99077e-07
-3.62984e-07
2.97916e-06
-1.95828e-06
-1.07398e-06
2.422e-06
-6.33202e-07
-1.36953e-06
1.6694e-06
-4.71764e-07
3.98849e-07
-1.0071e-06
-9.72984e-07
8.13553e-07
2.64193e-06
-3.12365e-06
1.34049e-06
-1.30419e-06
1.48369e-07
1.26033e-06
-2.59872e-07
4.28284e-07
-6.44356e-07
2.99934e-07
8.34335e-07
3.53226e-07
-7.08252e-07
4.1243e-07
2.41525e-06
-8.92159e-07
8.82339e-08
4.31945e-06
3.75152e-06
1.091e-06
3.8204e-06
-1.21356e-06
3.35564e-06
-1.06234e-06
-5.99808e-07
2.18155e-06
5.90652e-07
-1.36728e-06
-4.97017e-07
-7.77283e-08
8.68263e-07
4.37645e-07
-1.26514e-06
2.26413e-06
-8.52966e-07
-7.35596e-07
4.11911e-07
1.7585e-06
-inf
1.10779e-08
-1.49507e-06
9.87305e-07
-3.85296e-06
4.31265e-06
-9.89227e-07
-1.33537e-06
4.1713e-07
1.89362e-07
3.21968e-07
6.80237e-08
2.31636e-07
-2.98523e-07
7.99133e-07
7.36305e-07
6.39862e-07
-1.11932e-06
-1.57262e-06
1.86305e-06
-3.63716e-07
3.83865e-07
-5.23293e-07
1.31812e-06
-1.23608e-06
2.54684e-06
-3.99796e-06
2.90441e-06
-5.20203e-07
1.36295e-06
-1.89317e-06
1.22366e-06
-1.10373e-06
2.71276e-06
9.48181e-07
7.70881e-06
5.17066e-06
6.21254e-06
1.3513e-05
1.47878e-05
8.78543e-06
1.61819e-05
1.68438e-05
1.16082e-05
5.74059e-06
4.92458e-06
1.11884e-06
-1.07419e-06
-1.28517e-06
-2.70949e-06
1.65662e-06
1.42964e-06
3.40604e-06
-5.82825e-07
1.98288e-06
1.42819e-06
1.65517e-06
4.42749e-07
-1.95609e-06
-2.1756e-07
1.69164e-06
8.7204e-08
-5.35324e-07
7.43546e-07
-1.08687e-06
2.07289e-06
2.18529e-06
-2.8161e-06
1.88821e-06
4.07272e-07
1.063e-06
8.47244e-07
1.53879e-06
-9.0799e-07
-1.26709e-07
2.40044e-06
-9.48166e-07
1.41788e-06
3.67615e-07
-1.29199e-06
3.868e-06
9.54654e-06
2.51951e-05
2.2769e-05
7.21716e-06
1.36545e-06
-1.32681e-06
-3.09641e-06
4.90417e-07
2.99335e-06
1.578e-06
6.0025e-07
2.90656e-06
-2.08258e-06
-1.54214e-06
2.19757e-07
3.74982e-06
-1.76944e-06
2.15018e-06
-1.01935e-06
4.37469e-07
1.39078e-06
6.39587e-07
-1.7807e-06
-6.16455e-09
1.61557e-06
1.59644e-06
-2.35217e-06
5.29449e-07
1.9169e-06
-7.54822e-07
2.00342e-06
-3.28452e-06
3.91663e-06
1.66016e-08
-2.65897e-06
-1.4064e-06
4.67987e-07
1.67786e-06
4.69543e-07
-8.90106e-07
-1.4584e-06
1.37915e-06
1.98483e-06
-2.3735e-06
4.45618e-07
1.91504e-06
1.09653e-06
-8.00873e-07
1.32321e-06
2.04846e-06
-1.50656e-06
7.23816e-07
2.06049e-06
-2.43918e-06
1.64417e-06
2.65411e-07
-2.66107e-06
-8.01788e-07
2.05121e-06
-1.74988e-06
1.83594e-06
-8.14026e-07
-2.69342e-06
1.81152e-06
1.11664e-07
-4.21863e-06
-7.20551e-06
-5.92407e-07
-1.44629e-06
-2.08136e-06
2.86105e-06
3.77911e-06
-1.91898e-06
1.41742e-06
2.67914e-07
-8.55835e-07
-9.8584e-07
-2.74115e-06
3.39044e-06
1.39639e-06
-2.4964e-06
8.2486e-07
2.02432e-06
1.65793e-06
-1.43094e-06
-3.36807e-06
-8.96515e-07
5.31323e-06
-8.27209e-07
-1.39221e-06
-3.3754e-06
2.12372e-06
3.08218e-06
-1.42947e-06
-2.36777e-06
3.86218e-06
2.29327e-06
-3.3941e-06
-1.67291e-06
2.63828e-06
2.21008e-07
7.07794e-07
1.8172e-06
-2.00082e-06
1.80664e-06
6.69739e-07
-3.95395e-06
1.92148e-06
-1.07187e-06
-4.04938e-07
-1.76553e-06
2.7099e-06
1.30768e-06
1.41812e-06
-1.55518e-07
-3.78302e-06
4.00137e-06
-8.38623e-07
4.54651e-07
1.00027e-06
1.32196e-06
-2.62717e-06
1.67865e-06
-6.99249e-07
2.8837e-06
-1.00516e-06
-3.68011e-06
1.61847e-06
1.90887e-06
1.59641e-06
4.16779e-07
-1.35245e-06
1.65717e-06
-2.92667e-06
3.6203e-07
2.53528e-06
-2.0578e-07
-3.41919e-07
-1.42154e-06
-2.33322e-06
3.07175e-06
-2.69165e-08
-8.21045e-07
2.3175e-06
-7.22992e-07
1.49069e-06
8.75488e-07
-2.02676e-06
-2.81158e-07
3.6004e-06
-3.94708e-06
4.72983e-06
-1.38873e-06
-6.92139e-08
-1.4678e-06
1.04251e-06
-2.06625e-06
3.10406e-06
-8.13873e-07
7.23694e-07
-9.78912e-07
-8.65967e-07
7.37335e-07
1.52563e-06
-2.33591e-06
1.78265e-06
9.58435e-07
-5.22064e-07
-2.29736e-07
-4.26996e-06
-6.61411e-06
1.14789e-06
-4.32697e-06
-5.32779e-06
2.12241e-06
-1.40726e-06
1.76086e-07
-3.77194e-06
-2.71326e-06
-9.49402e-08
1.70807e-07
-2.495e-06
4.22324e-06
-3.62476e-06
-9.56055e-07
7.16583e-07
3.01447e-06
-1.41229e-06
-1.67694e-06
7.61627e-07
3.55881e-06
2.31015e-06
-9.50378e-07
4.45251e-08
-1.94791e-06
2.27081e-06
-3.34717e-06
3.05688e-06
4.57062e-07
3.87326e-06
-2.39215e-06
-3.52682e-06
-2.05212e-06
5.26495e-06
-3.28613e-07
-5.76569e-07
-7.46338e-07
5.98795e-06
8.80493e-07
-4.82965e-06
2.56839e-06
-1.58792e-06
-2.2294e-06
1.83841e-06
2.65482e-06
-3.10474e-06
-3.46741e-07
2.45557e-06
2.01328e-06
-3.92606e-06
inf
-8.11737e-07
5.72174e-07
1.57245e-06
8.02612e-09
-2.901e-06
1.22079e-06
-6.31714e-07
3.06241e-06
1.20059e-06
-1.80344e-06
4.90784e-07
3.74243e-06
-2.94342e-07
-3.45764e-08
-3.42099e-06
-1.43695e-06
5.91064e-07
3.47308e-06
3.78232e-06
4.01093e-07
-1.58435e-06
-3.47375e-06
1.34943e-06
1.11768e-06
1.95212e-06
-8.28033e-07
1.53705e-06
6.38031e-07
-1.84702e-06
1.34689e-06
-6.98669e-07
1.81653e-06
-2.42355e-06
-1.35257e-06
3.04367e-06
-1.21976e-06
1.61896e-06
-2.69528e-06
1.84601e-06
6.45447e-08
-4.94263e-07
3.47568e-06
-2.00531e-06
3.56693e-06
-3.19446e-06
2.72141e-06
-1.39059e-06
2.20032e-06
-1.76819e-06
2.32727e-07
-3.47382e-07
2.11823e-07
-5.22614e-07
2.69846e-06
-1.47983e-06
2.14554e-06
-6.27594e-07
-8.8501e-10
7.89124e-07
-2.8653e-07
8.30902e-07
-2.12857e-06
-1.90887e-07
1.07593e-06
1.40781e-06
2.41641e-06
-4.52689e-06
2.37207e-06
-2.19479e-06
1.65131e-06
1.2706e-06
-2.18387e-06
-1.72821e-07
5.41687e-07
7.2879e-07
7.56927e-07
1.57739e-06
-3.79395e-07
-1.02887e-06
-1.20987e-06
1.43066e-06
8.96301e-08
5.09766e-07
-2.8812e-06
-2.35944e-06
2.25912e-06
-2.78967e-06
-4.69913e-06
1.60822e-06
6.9342e-07
4.6225e-07
-1.33276e-06
-3.59033e-06
1.11206e-06
1.83521e-06
2.39163e-06
2.3468e-08
5.91431e-07
-8.80249e-07
-2.77405e-08
-1.13184e-06
-1.28036e-06
1.66229e-06
2.81784e-06
-2.97589e-06
8.73413e-08
1.06439e-06
2.39075e-06
-2.76974e-06
1.20862e-06
-5.12817e-07
-5.19104e-07
4.51324e-07
-4.7168e-07
2.35608e-06
5.46906e-07
-1.66748e-06
5.85236e-07
6.42944e-07
2.43164e-07
4.01031e-07
-1.93646e-06
2.07416e-06
-1.16116e-06
4.27155e-07
5.2951e-07
9.09149e-07
-8.71887e-08
-1.5564e-09
1.07266e-06
-9.49402e-08
2.04016e-06
-6.38123e-07
-1.94241e-06
-5.17294e-06
-2.18622e-06
-8.26703e-06
2.54364e-06
4.32614e-06
8.3847e-07
-2.85309e-06
2.72345e-06
-3.42752e-06
-1.36871e-07
2.23346e-06
5.26825e-07
1.3566e-06
-2.17111e-06
2.1463e-07
2.06479e-06
1.76929e-06
-1.2655e-06
-1.3797e-06
3.10706e-06
-4.72189e-06
4.38138e-06
6.41815e-07
-3.25623e-08
-4.93707e-06
5.05743e-06
5.17578e-07
-5.30524e-06
3.62463e-06
5.68909e-07
1.16226e-06
1.10843e-06
-5.00854e-07
9.48761e-07
-2.18701e-06
-3.57635e-07
4.26709e-06
-1.50836e-06
-5.84412e-06
3.5054e-06
3.94019e-06
-4.7623e-06
2.05856e-06
-2.22992e-07
1.64969e-06
2.64694e-06
-8.49487e-07
-3.63562e-06
1.0386e-06
1.69461e-06
-2.05798e-06
3.60349e-06
3.42651e-07
-1.46686e-06
1.19949e-06
-1.60519e-06
2.37793e-07
6.12366e-07
-1.54669e-06
1.43668e-06
1.87009e-06
-2.22626e-06
2.15155e-06
-3.10571e-06
2.05188e-06
-4.40002e-07
2.06683e-06
-1.11362e-06
5.96924e-07
-2.64471e-06
2.4892e-06
1.13083e-06
-3.23181e-07
5.10651e-07
2.73499e-07
-1.24899e-06
1.40564e-06
-9.3158e-07
1.45947e-06
3.70544e-07
-1.62628e-06
-1.70215e-06
1.72098e-06
8.19031e-07
-5.57709e-07
1.10107e-06
-2.81845e-06
1.57654e-07
3.30716e-06
-9.75403e-07
1.73126e-07
1.30447e-06
7.64771e-08
-6.65344e-07
-1.4346e-06
5.03171e-06
-2.84576e-06
2.3212e-06
-2.73373e-06
2.16675e-08
2.24026e-06
-4.11682e-08
-3.36642e-06
1.78775e-06
1.28174e-08
-9.32068e-07
2.97177e-06
-1.05338e-06
9.42505e-07
2.02362e-07
-1.81326e-06
2.16995e-06
2.83722e-07
-1.2648e-06
9.21814e-07
-8.9447e-07
-1.61597e-06
3.5036e-06
-6.79626e-08
1.52823e-06
-2.98682e-06
5.57404e-07
9.5166e-07
7.10419e-07
-1.28528e-06
-3.76038e-07
-1.03845e-06
2.96631e-06
-1.18356e-06
-2.77313e-07
3.24149e-06
-1.85455e-06
-1.27747e-07
3.6264e-07
4.66431e-07
-1.54443e-06
1.38437e-06
-1.53119e-06
7.4231e-07
-1.2388e-06
1.99774e-06
1.15799e-06
1.39478e-06
-2.93527e-06
-2.03012e-06
2.46667e-06
2.16751e-06
-2.50354e-06
3.95905e-07
5.74371e-07
1.33575e-07
-3.98315e-07
4.93927e-07
-5.23987e-07
-1.74713e-07
6.49384e-07
-7.16766e-07
2.35733e-06
-4.91333e-08
-1.88138e-06
1.74722e-06
4.03503e-07
3.5965e-07
1.44836e-07]
您正在描述的任务可以被视为异常/异常检测。
一种可能的解决方案是使用Z分数转换并将z得分高于特定阈值的每个值视为异常值。因为没有明确的离群值定义，所以不设置任何参数（阈值）就无法检测到此类峰。
一种可能的解决方案是：
import numpy as np
def detect_outliers(data):
outliers = []
d_mean = np.mean(data)
d_std = np.std(data)
threshold = 3 # this defines what you would consider a peak (outlier)
for point in data:
z_score = (point - d_mean)/d_std
if np.abs(z_score) > threshold:
outliers.append(point)
return outliers
# create normal data
data = np.random.normal(size=100)
# create outliers
outliers = np.random.normal(100, size=3)
# combine normal data and outliers
full_data = data.tolist() + outliers.tolist()
# print outliers
print(detect_outliers(full_data))
如果只想检测峰，请从代码中删除np.abs函数调用。
此代码段基于Medium Post，它也提供了另一种检测异常值的方法。