Add orthogonal and parallel

networks/curve.py

@@ -1,42 +0,0 @@
-import numpy as np
-from scipy import interpolate
-
-
-class Curve:
-    def __init__(self, target_points):
-        # list of points to [(x1, y1, z1), (...), ...]
-        self.target_points = target_points
-        self.computed_points = []
-
-    def compute_curve(self, resolution=40):
-        """
-        Fill self.computed_points with a list of points that approximate a smooth curve following self.target_points.
-
-        https://stackoverflow.com/questions/18962175/spline-interpolation-coefficients-of-a-line-curve-in-3d-space
-
-        Args:
-            points (np.array): Points where the curve should pass in order.
-            resolution (int, optional): Total number of points to compute. Defaults to 40.
-        """
-        # Remove duplicates. Curve can't intersect itself
-        points = tuple(map(tuple, np.array(self.target_points)))
-        points = sorted(set(points), key=points.index)
-
-        # Change coordinates structure to (x1, x2, x3, ...), (y1, y2, y3, ...) (z1, z2, z3, ...)
-        coords = np.array(points, dtype=np.float32)
-        x = coords[:, 0]
-        y = coords[:, 1]
-        z = coords[:, 2]
-
-        # Compute
-        tck, u = interpolate.splprep([x, y, z], s=2, k=2)
-        x_knots, y_knots, z_knots = interpolate.splev(tck[0], tck)
-        u_fine = np.linspace(0, 1, resolution)
-        x_fine, y_fine, z_fine = interpolate.splev(u_fine, tck)
-
-        x_rounded = np.round(x_fine).astype(int)
-        y_rounded = np.round(y_fine).astype(int)
-        z_rounded = np.round(z_fine).astype(int)
-
-        self.computed_points = [(x, y, z) for x, y, z in zip(
-            x_rounded, y_rounded, z_rounded)]