fix: ✨ bounding box object

a new class to store a bounding box, provides useful functions and accessors

Author: lachlangrose

LoopStructural/utils/_bounding_box.py

@@ -0,0 +1,134 @@
+from __future__ import annotations
+from typing import Optional
+from LoopStructural.utils.exceptions import LoopValueError
+import numpy as np
+
+
+class BoundingBox:
+    def __init__(
+        self,
+        dimensions: int = 3,
+        origin: Optional[np.ndarray] = None,
+        maximum: Optional[np.ndarray] = None,
+        nsteps: Optional[np.ndarray] = None,
+    ):
+        self._origin = origin
+        self._maximum = maximum
+        self.dimensions = dimensions
+        if nsteps is None:
+            self.nsteps = np.array([50, 50, 25])
+        self.name_map = {
+            "xmin": (0, 0),
+            "ymin": (0, 1),
+            "zmin": (0, 2),
+            "xmax": (1, 0),
+            "ymax": (1, 1),
+            "zmax": (1, 2),
+            "lower": (0, 2),
+            "upper": (1, 2),
+            "minx": (0, 0),
+            "miny": (0, 1),
+            "minz": (0, 2),
+            "maxx": (1, 0),
+            "maxy": (1, 1),
+            "maxz": (1, 2),
+        }
+
+    @property
+    def origin(self) -> np.ndarray:
+        if self._origin is None:
+            raise LoopValueError("Origin is not set")
+        return self._origin
+
+    @origin.setter
+    def origin(self, origin: np.ndarray):
+        self._origin = origin
+
+    @property
+    def maximum(self) -> np.ndarray:
+        if self._maximum is None:
+            raise LoopValueError("Maximum is not set")
+        return self._maximum
+
+    @maximum.setter
+    def maximum(self, maximum: np.ndarray):
+        self._maximum = maximum
+
+    @property
+    def nelements(self):
+        return self.nsteps.prod()
+
+    @property
+    def volume(self):
+        return np.product(self.maximum - self.origin)
+
+    @nelements.setter
+    def nelements(self, nelements):
+        box_vol = self.volume
+        ele_vol = box_vol / nelements
+        # calculate the step vector of a regular cube
+        step_vector = np.zeros(3)
+        step_vector[:] = ele_vol ** (1.0 / 3.0)
+        # step_vector /= np.array([1,1,2])
+        # number of steps is the length of the box / step vector
+        nsteps = np.ceil((self.maximum - self.origin) / step_vector).astype(int)
+        self.nsteps = nsteps
+
+    def fit(self, locations: np.ndarray):
+        if locations.shape[1] != self.dimensions:
+            raise LoopValueError(
+                f"locations array is {locations.shape[1]}D but bounding box is {self.dimensions}"
+            )
+        self.origin = locations.min(axis=0)
+        self.maximum = locations.max(axis=0)
+        return self
+
+    def with_buffer(self, buffer: float = 0.2) -> BoundingBox:
+        if self.origin is None or self.maximum is None:
+            raise LoopValueError(
+                "Cannot create bounding box with buffer, no origin or maximum"
+            )
+        origin = self.origin - buffer * (self.maximum - self.origin)
+        maximum = self.maximum + buffer * (self.maximum - self.origin)
+        return BoundingBox(origin=origin, maximum=maximum)
+
+    def get_value(self, name):
+        ix, iy = self.name_map.get(name, (-1, -1))
+        if ix == -1 and iy == -1:
+            raise LoopValueError(f"{name} is not a valid bounding box name")
+        if iy == -1:
+            return self.origin[ix]
+
+        return self.bb[ix,]
+
+    def __getitem__(self, name):
+        if isinstance(name, str):
+            return self.get_value(name)
+        elif isinstance(name, tuple):
+            return self.origin
+        return self.get_value(name)
+
+    def is_inside(self, xyz):
+        inside = np.zeros(xyz.shape[0], dtype=bool)
+        inside = np.logical_and(inside, xyz[:, 0] > self.origin[0])
+        inside = np.logical_and(inside, xyz[:, 0] < self.maximum[0])
+        inside = np.logical_and(inside, xyz[:, 1] > self.origin[1])
+        inside = np.logical_and(inside, xyz[:, 1] < self.maximum[1])
+        inside = np.logical_and(inside, xyz[:, 2] > self.origin[2])
+        inside = np.logical_and(inside, xyz[:, 2] < self.maximum[2])
+        return inside
+
+    def regular_grid(self, nsteps=None, shuffle=False, order="C"):
+        if nsteps is None:
+            nsteps = self.nsteps
+        x = np.linspace(self.origin[0], self.maximum[0], nsteps[0])
+        y = np.linspace(self.origin[1], self.maximum[1], nsteps[1])
+        z = np.linspace(self.origin[2], self.maximum[2], nsteps[2])
+        xx, yy, zz = np.meshgrid(x, y, z, indexing="ij")
+        locs = np.array(
+            [xx.flatten(order=order), yy.flatten(order=order), zz.flatten(order=order)]
+        ).T
+        if shuffle:
+            # logger.info("Shuffling points")
+            np.random.shuffle(locs)
+        return locs