>>> from scipy.interpolate import interp1d #interp1d表示1维插值

>>>

>>> 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参数 默认为线性插值

>>>

>>> xnew = np.linspace(0, 10, num=41, endpoint=True) #xnew表示插值之后，画图，选取的画图点数，再用线将这些点连起来 >>> 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 通过kind 导入；

f2 = interp1d(x, y, kind='cubic') 返回一个插值后的函数

>>> 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()