Optimize LineCollection

src/data_morph/shapes/bases/line_collection.py

@@ -23,7 +23,13 @@ class LineCollection(Shape):
     """
 
     def __init__(self, *lines: Iterable[Iterable[Number]]) -> None:
-        self.lines = lines
+        # check that lines with the same starting and ending points raise an error
+        for line in lines:
+            start, end = line
+            if np.allclose(start, end):
+                raise ValueError(f'Line {line} has the same start and end point')
+
+        self.lines = np.array(lines)
         """Iterable[Iterable[numbers.Number]]: An iterable
         of two (x, y) pairs representing the endpoints of a line."""
 
@@ -45,66 +51,48 @@ def distance(self, x: Number, y: Number) -> float:
         float
             The minimum distance from the lines of this shape to the
             point (x, y).
-        """
-        return min(
-            self._distance_point_to_line(point=(x, y), line=line) for line in self.lines
-        )
 
-    def _distance_point_to_line(
-        self,
-        point: Iterable[Number],
-        line: Iterable[Iterable[Number]],
-    ) -> float:
+        Notes
+        -----
+        Implementation based on `this Stack Overflow answer`_.
+
+        .. _this Stack Overflow answer: https://stackoverflow.com/a/58781995
         """
-        Calculate the minimum distance between a point and a line.
+        point = np.array([x, y])
 
-        Parameters
-        ----------
-        point : Iterable[numbers.Number]
-            Coordinates of a point in 2D space.
-        line : Iterable[Iterable[numbers.Number]]
-            Coordinates of the endpoints of a line in 2D space.
+        start_points = self.lines[:, 0, :]
+        end_points = self.lines[:, 1, :]
 
-        Returns
-        -------
-        float
-            The minimum distance between the point and the line.
+        tangent_vector = end_points - start_points
+        normalized_tangent_vectors = np.divide(
+            tangent_vector,
+            np.hypot(tangent_vector[:, 0], tangent_vector[:, 1]).reshape(-1, 1),
+        )
 
-        Notes
-        -----
-        Implementation based on `this VBA code`_.
+        # row-wise dot products of 2D vectors
+        signed_parallel_distance_start = np.multiply(
+            start_points - point, normalized_tangent_vectors
+        ).sum(axis=1)
+        signed_parallel_distance_end = np.multiply(
+            point - end_points, normalized_tangent_vectors
+        ).sum(axis=1)
+
+        clamped_parallel_distance = np.maximum.reduce(
+            [
+                signed_parallel_distance_start,
+                signed_parallel_distance_end,
+                np.zeros(signed_parallel_distance_start.shape[0]),
+            ]
+        )
 
-        .. _this VBA code: http://local.wasp.uwa.edu.au/~pbourke/geometry/pointline/source.vba
-        """
-        start, end = np.array(line)
-        line_mag = self._euclidean_distance(start, end)
-        point = np.array(point)
-
-        if line_mag < 0.00000001:
-            # Arbitrarily large value
-            return 9999
-
-        px, py = point
-        x1, y1 = start
-        x2, y2 = end
-
-        u1 = ((px - x1) * (x2 - x1)) + ((py - y1) * (y2 - y1))
-        u = u1 / (line_mag * line_mag)
-
-        if (u < 0.00001) or (u > 1):
-            # closest point does not fall within the line segment, take the shorter
-            # distance to an endpoint
-            distance = min(
-                self._euclidean_distance(point, start),
-                self._euclidean_distance(point, end),
-            )
-        else:
-            # Intersecting point is on the line, use the formula
-            ix = x1 + u * (x2 - x1)
-            iy = y1 + u * (y2 - y1)
-            distance = self._euclidean_distance(point, np.array((ix, iy)))
-
-        return distance
+        # row-wise cross products of 2D vectors
+        perpendicular_distance_component = np.cross(
+            point - start_points, normalized_tangent_vectors
+        )
+
+        return np.min(
+            np.hypot(clamped_parallel_distance, perpendicular_distance_component)
+        )
 
     @plot_with_custom_style
     def plot(self, ax: Axes = None) -> Axes:

tests/shapes/bases/test_line_collection.py

@@ -35,20 +35,16 @@ def test_distance_nonzero(self, line_collection, point, expected_distance):
         assert pytest.approx(line_collection.distance(*point)) == expected_distance
 
     @pytest.mark.parametrize('line', [[(0, 0), (0, 0)], [(-1, -1), (-1, -1)]], ids=str)
-    def test_distance_to_small_line_magnitude(self, line_collection, line):
-        """Test _distance_point_to_line() for small line magnitudes."""
-        distance = line_collection._distance_point_to_line((30, 50), line)
-        assert distance == 9999
+    def test_line_as_point(self, line):
+        """Test LineCollection raises a ValueError for small line magnitudes."""
+        with pytest.raises(ValueError):
+            LineCollection(line)
 
     def test_repr(self, line_collection):
         """Test that the __repr__() method is working."""
-        lines = r'\n        '.join(
-            [r'\[\[\d+\.*\d*, \d+\.*\d*\], \[\d+\.*\d*, \d+\.*\d*\]\]']
-            * len(line_collection.lines)
-        )
         assert (
             re.match(
-                (r'^<LineCollection>\n  lines=\n        ' + lines),
+                r"""<LineCollection>\n  lines=\n {8}array\(\[\[\d+""",
                 repr(line_collection),
             )
             is not None