plot_line_and_spline.py

import numpy as np
import matplotlib.pyplot as plt

n_dimensions = 2
A = np.random.rand(n_dimensions)  # source
B = np.random.rand(n_dimensions)  # target

# linear function
n_steps = 50
delta = (A-B)/n_steps
delta_array = np.ones((n_dimensions, n_steps))*delta[:,np.newaxis]
trajectory = A[:,np.newaxis]+delta_array.cumsum(1)
# plt.scatter(trajectory[0], trajectory[1], alpha=np.linspace(0.3,1, n_steps))
# plt.show()

import scipy.interpolate as si
import numpy as np
import matplotlib.pyplot as plt


def bspline(cv, n=100, degree=3):
    cv = np.asarray(cv)
    count = cv.shape[0]
    degree = np.clip(degree, 1, count-1)
    kv = np.array([0]*degree + list(range(count-degree+1))
                  + [count-degree]*degree, dtype='int')
    u = np.linspace(0, (count-degree), n)
    return np.array(si.splev(u, (kv, cv.T, degree))).T


cv = np.array([[50., 25.],   # A point
               [57., 2.],    # intermediate point
               [40., 4.],    # intermediate point
               [40., 14.]])  # B Point

p = bspline(cv, n=n_steps, degree=3)
plt.scatter(p[:, 0], p[:, 1], alpha=np.linspace(0.3, 1, n_steps))
plt.show()