我有一些一維數(shù)據(jù),并與樣條擬合.然后我想在其中找到拐點(diǎn)(忽略鞍點(diǎn)).現(xiàn)在我通過(guò)在splev生成的很多值上使用scipy.signal.argrelmin(和argrelmax)來(lái)搜索其第一個(gè)派生的極值.

import scipy.interpolate

import scipy.optimize

import scipy.signal

import numpy as np

import matplotlib.pyplot as plt

import operator

y = [-1, 5, 6, 4, 2, 5, 8, 5, 1]

x = np.arange(0, len(y))

tck = scipy.interpolate.splrep(x, y, s=0)

print 'roots', scipy.interpolate.sproot(tck)

# output:

# [0.11381478]

xnew = np.arange(0, len(y), 0.01)

ynew = scipy.interpolate.splev(xnew, tck, der=0)

ynew_deriv = scipy.interpolate.splev(xnew, tck, der=1)

min_idxs = scipy.signal.argrelmin(ynew_deriv)

max_idxs = scipy.signal.argrelmax(ynew_deriv)

mins = zip(xnew[min_idxs].tolist(), ynew_deriv[min_idxs].tolist())

maxs = zip(xnew[max_idxs].tolist(), ynew_deriv[max_idxs].tolist())

inflection_points = sorted(mins + maxs, key=operator.itemgetter(0))

print 'inflection_points', inflection_points

# output:

# [(3.13, -2.9822449358974357),

# (5.03, 4.3817785256410255)

# (7.13, -4.867132628205128)]

plt.legend(['data','Cubic Spline', '1st deriv'])

plt.plot(x, y, 'o',

xnew, ynew, '-',

xnew, ynew_deriv, '-')

plt.show()

但這感覺非常錯(cuò)誤.我想有可能在沒(méi)有產(chǎn)生這么多價(jià)值的情況下找到我要找的東西.像sproot這樣的東西,但也許適用于二次推導(dǎo)？

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