make_interp_spline(x, y, k=3, t=None, bc_type=None, axis=0, check_finite=True)# 计算插值 B-spline 的(系数)。 参数 :: x: 数组, 形状 (n,) 横坐标。 y: 数组,形状(n,...) 纵坐标。 k: 整数,可选 B-spline学位。默认为立方体,k = 3。 t: 数组,形状(nt + k + 1,),可...
y = xx[::5]make_interp_spline(x, y, k=1) 开发者ID:alchemyst,项目名称:scipy,代码行数:8,代码来源:test_bsplines.py 示例2: test_quadratic_deriv ▲点赞 6▼ deftest_quadratic_deriv(self):der = [(1,8.)]# order, value: f'(x) = 8.# derivative at right-hand edgeb =make_interp_...
We then use make_interp_spline to create a cubic spline interpolating function spline_function from the data. Finally, we evaluate the interpolating function at 100 evenly spaced x values between 1 and 5 using the linspace function from the numpy module. Conclusion make_interp_spline is a ...
in <module> make_interp_spline([1,2,3,4,5],[6,7,8,9,10],bc_type = ((1,0.0),(1,0.0))) File "/anaconda/lib/python3.6/site-packages/scipy/interpolate/_bsplines.py", line 682, in make_interp_spline deriv_l_ords, deriv_l_vals = zip(*deriv_l) TypeError: zip argument #1 mu...
利用make_interp_spline函数绘制平滑的曲线 import matplotlib.pyplot as plt import numpy as np from scipy.interpolate import make_interp_spline x = np.array([6, 8, 10, 12]) y = np.array([35, 7, 2, 1]) x_smooth = np.linspace(x.min(), x.max(), 100) ...
reduceByKey(_+_)是reduceByKey((x,y) => x+y)的一个 简洁的形式 */ val rdd08 = sc...
利用make_interp_spline函数绘制平滑的曲线 import matplotlib.pyplot as plt import numpy as np from scipy.interpolate import make_interp_spline x = np.array([6, 8, 10, 12]) y = np.array([35, 7, 2, 1]) x_smooth = np.linspace(x.min(), x.max(), 100) ...
利用make_interp_spline函数绘制平滑的曲线 import matplotlib.pyplot as plt import numpy as np from scipy.interpolate import make_interp_spline x = np.array([6, 8, 10, 12]) y = np.array([35, 7, 2, 1]) x_smooth = np.linspace(x.min(), x.max(), 100) ...
利用make_interp_spline函数绘制平滑的曲线 import matplotlib.pyplot as plt import numpy as np from scipy.interpolate import make_interp_spline x = np.array([6, 8, 10, 12]) y = np.array([35, 7, 2, 1]) x_smooth = np.linspace(x.min(), x.max(), 100) ...