OrdinaryKriging 克里金插值
摘要：x,y,d = 一维数组（散点数据） x0 = np.linspace(x.min, x.max, 150) y0 = np.linspace(y.min, y.max, 100) x1, y1 = np.meshgrid(x0, y0) for i in ['linear', 'power', '
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posted @ 2022-10-26 17:58
随笔分类 - scipy
griddata 插值
摘要：x,y,d = 一维数组（散点数据） # 适用于散点数据插值为格点数据 x1, y1 = np.meshgrid(x, y) for i in ['linear', 'nearest', 'cubic']: DATA = griddata((x, y), d, (x1, y1), method=i)
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posted @ 2022-10-26 17:37
Rbf 插值
摘要：i = ['multiquadric', 'inverse', 'gaussian', 'linear', 'cubic', 'quintic','thin_plate'] # 插值方法有这个几个 interp = Rbf(x, y, d, function=i) DATA = interp(x1,
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posted @ 2022-10-26 17:23
python scipy.interpolate.splrep插值用法
摘要：from scipy.interpolate import splrep, splev X = [1, 9] Y = [20, 25] new_x = [1, 2, 3, 4, 5, 6, 7, 8, 9] # 进行三次样条拟合插值 ipo3 = splrep(X, Y, k=2) # 样本点导入，
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posted @ 2022-05-13 15:55
