>>> from scipy.interpolate import interp1d
#interp1d表示1維插值
>>> x = np.linspace(0, 10, num=11, endpoint=True) >>> y = np.cos(-x**2/9.0) >>> f = interp1d(x, y) >>> f2 = interp1d(x, y, kind='cubic')
#kind='cubic' 省去kind參數 默認為線性插值
>>> xnew = np.linspace(0, 10, num=41, endpoint=True)
#xnew表示插值之后，畫圖，選取的畫圖點數，再用線將這些點連起來 >>> import matplotlib.pyplot as plt >>> plt.plot(x, y, 'o', xnew, f(xnew), '-', xnew, f2(xnew), '--') >>> plt.legend(['data', 'linear', 'cubic'], loc='best') >>> plt.show()
插值的方法 通過kind 導入；
f2 = interp1d(x, y, kind='cubic') 返回一個插值后的函數
>>> from scipy.interpolate import interp1d
>>> x = np.linspace(0, 10, num=11, endpoint=True) >>> y = np.cos(-x**2/9.0) >>> f1 = interp1d(x, y, kind='nearest') >>> f2 = interp1d(x, y, kind='previous') >>> f3 = interp1d(x, y, kind='next')
>>> xnew = np.linspace(0, 10, num=1001, endpoint=True) >>> import matplotlib.pyplot as plt >>> plt.plot(x, y, 'o') >>> plt.plot(xnew, f1(xnew), '-', xnew, f2(xnew), '--', xnew, f3(xnew), ':') >>> plt.legend(['data', 'nearest', 'previous', 'next'], loc='best') >>> plt.show()
