refactor to avoid class-scoped imports

gridlock/base.py

@@ -0,0 +1,198 @@
+from typing import ClassVar, Self, Protocol
+from collections.abc import Callable, Sequence
+
+import numpy
+from numpy.typing import NDArray, ArrayLike
+
+from . import GridError
+
+
+class GridBase(Protocol):
+    exyz: list[NDArray]
+    """Cell edges. Monotonically increasing without duplicates."""
+
+    periodic: list[bool]
+    """For each axis, determines how far the rightmost boundary gets shifted. """
+
+    shifts: NDArray
+    """Offsets `[[x0, y0, z0], [x1, y1, z1], ...]` for grid `0,1,...`"""
+
+    @property
+    def dxyz(self) -> list[NDArray]:
+        """
+        Cell sizes for each axis, no shifts applied
+
+        Returns:
+            List of 3 ndarrays of cell sizes
+        """
+        return [numpy.diff(ee) for ee in self.exyz]
+
+    @property
+    def xyz(self) -> list[NDArray]:
+        """
+        Cell centers for each axis, no shifts applied
+
+        Returns:
+            List of 3 ndarrays of cell edges
+        """
+        return [self.exyz[a][:-1] + self.dxyz[a] / 2.0 for a in range(3)]
+
+    @property
+    def shape(self) -> NDArray[numpy.int_]:
+        """
+        The number of cells in x, y, and z
+
+        Returns:
+            ndarray of [x_centers.size, y_centers.size, z_centers.size]
+        """
+        return numpy.array([coord.size - 1 for coord in self.exyz], dtype=int)
+
+    @property
+    def num_grids(self) -> int:
+        """
+        The number of grids (number of shifts)
+        """
+        return self.shifts.shape[0]
+
+    @property
+    def cell_data_shape(self):
+        """
+        The shape of the cell_data ndarray (num_grids, *self.shape).
+        """
+        return numpy.hstack((self.num_grids, self.shape))
+
+    @property
+    def dxyz_with_ghost(self) -> list[NDArray]:
+        """
+        Gives dxyz with an additional 'ghost' cell at the end, whose value depends
+         on whether or not the axis has periodic boundary conditions. See main description
+         above to learn why this is necessary.
+
+         If periodic, final edge shifts same amount as first
+         Otherwise, final edge shifts same amount as second-to-last
+
+        Returns:
+            list of [dxs, dys, dzs] with each element same length as elements of `self.xyz`
+        """
+        el = [0 if p else -1 for p in self.periodic]
+        return [numpy.hstack((self.dxyz[a], self.dxyz[a][e])) for a, e in zip(range(3), el, strict=True)]
+
+    @property
+    def center(self) -> NDArray[numpy.float64]:
+        """
+        Center position of the entire grid, no shifts applied
+
+        Returns:
+            ndarray of [x_center, y_center, z_center]
+        """
+        # center is just average of first and last xyz, which is just the average of the
+        #  first two and last two exyz
+        centers = [(self.exyz[a][:2] + self.exyz[a][-2:]).sum() / 4.0 for a in range(3)]
+        return numpy.array(centers, dtype=float)
+
+    @property
+    def dxyz_limits(self) -> tuple[NDArray, NDArray]:
+        """
+        Returns the minimum and maximum cell size for each axis, as a tuple of two 3-element
+         ndarrays. No shifts are applied, so these are extreme bounds on these values (as a
+         weighted average is performed when shifting).
+
+        Returns:
+            Tuple of 2 ndarrays, `d_min=[min(dx), min(dy), min(dz)]` and `d_max=[...]`
+        """
+        d_min = numpy.array([min(self.dxyz[a]) for a in range(3)], dtype=float)
+        d_max = numpy.array([max(self.dxyz[a]) for a in range(3)], dtype=float)
+        return d_min, d_max
+
+    def shifted_exyz(self, which_shifts: int | None) -> list[NDArray]:
+        """
+        Returns edges for which_shifts.
+
+        Args:
+            which_shifts: Which grid (which shifts) to use, or `None` for unshifted
+
+        Returns:
+            List of 3 ndarrays of cell edges
+        """
+        if which_shifts is None:
+            return self.exyz
+        dxyz = self.dxyz_with_ghost
+        shifts = self.shifts[which_shifts, :]
+
+        # If shift is negative, use left cell's dx to determine shift
+        for a in range(3):
+            if shifts[a] < 0:
+                dxyz[a] = numpy.roll(dxyz[a], 1)
+
+        return [self.exyz[a] + dxyz[a] * shifts[a] for a in range(3)]
+
+    def shifted_dxyz(self, which_shifts: int | None) -> list[NDArray]:
+        """
+        Returns cell sizes for `which_shifts`.
+
+        Args:
+            which_shifts: Which grid (which shifts) to use, or `None` for unshifted
+
+        Returns:
+            List of 3 ndarrays of cell sizes
+        """
+        if which_shifts is None:
+            return self.dxyz
+        shifts = self.shifts[which_shifts, :]
+        dxyz = self.dxyz_with_ghost
+
+        # If shift is negative, use left cell's dx to determine size
+        sdxyz = []
+        for a in range(3):
+            if shifts[a] < 0:
+                roll_dxyz = numpy.roll(dxyz[a], 1)
+                abs_shift = numpy.abs(shifts[a])
+                sdxyz.append(roll_dxyz[:-1] * abs_shift + roll_dxyz[1:] * (1 - abs_shift))
+            else:
+                sdxyz.append(dxyz[a][:-1] * (1 - shifts[a]) + dxyz[a][1:] * shifts[a])
+
+        return sdxyz
+
+    def shifted_xyz(self, which_shifts: int | None) -> list[NDArray[numpy.float64]]:
+        """
+        Returns cell centers for `which_shifts`.
+
+        Args:
+            which_shifts: Which grid (which shifts) to use, or `None` for unshifted
+
+        Returns:
+            List of 3 ndarrays of cell centers
+        """
+        if which_shifts is None:
+            return self.xyz
+        exyz = self.shifted_exyz(which_shifts)
+        dxyz = self.shifted_dxyz(which_shifts)
+        return [exyz[a][:-1] + dxyz[a] / 2.0 for a in range(3)]
+
+    def autoshifted_dxyz(self) -> list[NDArray[numpy.float64]]:
+        """
+        Return cell widths, with each dimension shifted by the corresponding shifts.
+
+        Returns:
+            `[grid.shifted_dxyz(which_shifts=a)[a] for a in range(3)]`
+        """
+        if self.num_grids != 3:
+            raise GridError('Autoshifting requires exactly 3 grids')
+        return [self.shifted_dxyz(which_shifts=a)[a] for a in range(3)]
+
+    def allocate(self, fill_value: float | None = 1.0, dtype=numpy.float32) -> NDArray:
+        """
+        Allocate an ndarray for storing grid data.
+
+        Args:
+            fill_value: Value to initialize the grid to. If None, an
+                uninitialized array is returned.
+            dtype: Numpy dtype for the array. Default is `numpy.float32`.
+
+        Returns:
+            The allocated array
+        """
+        if fill_value is None:
+            return numpy.empty(self.cell_data_shape, dtype=dtype)
+        else:
+            return numpy.full(self.cell_data_shape, fill_value, dtype=dtype)

gridlock/draw.py

@@ -1,13 +1,16 @@
 """
 Drawing-related methods for Grid class
 """
-from typing import Union, Sequence, Callable
+from typing import Union
+from collections.abc import Sequence, Callable
 
 import numpy
 from numpy.typing import NDArray, ArrayLike
 from float_raster import raster
 
 from . import GridError
+from .base import GridBase
+from .position import GridPosMixin
 
 
 # NOTE: Maybe it would make sense to create a GridDrawer class
@@ -20,12 +23,13 @@ foreground_callable_t = Callable[[NDArray, NDArray, NDArray], NDArray]
 foreground_t = Union[float, foreground_callable_t]
 
 
+class GridDrawMixin(GridPosMixin):
     def draw_polygons(
             self,
             cell_data: NDArray,
             surface_normal: int,
             center: ArrayLike,
-        polygons: Sequence[NDArray],
+            polygons: Sequence[ArrayLike],
             thickness: float,
             foreground: Sequence[foreground_t] | foreground_t,
             ) -> None:
@@ -50,13 +54,13 @@ def draw_polygons(
         """
         if surface_normal not in range(3):
             raise GridError('Invalid surface_normal direction')
-
         center = numpy.squeeze(center)
+        poly_list = [numpy.array(poly, copy=False) for poly in polygons]
 
         # Check polygons, and remove redundant coordinates
         surface = numpy.delete(range(3), surface_normal)
 
-    for i, polygon in enumerate(polygons):
+        for i, polygon in enumerate(poly_list):
             malformed = f'Malformed polygon: ({i})'
             if polygon.shape[1] not in (2, 3):
                 raise GridError(malformed + 'must be a Nx2 or Nx3 ndarray')
@@ -82,7 +86,7 @@ def draw_polygons(
         # 1) Compute outer bounds (bd) of polygons
         bd_2d_min = numpy.array([0, 0])
         bd_2d_max = numpy.array([0, 0])
-    for polygon in polygons:
+        for polygon in poly_list:
             bd_2d_min = numpy.minimum(bd_2d_min, polygon.min(axis=0))
             bd_2d_max = numpy.maximum(bd_2d_max, polygon.max(axis=0))
         bd_min = numpy.insert(bd_2d_min, surface_normal, -thickness / 2.0) + center
@@ -100,7 +104,7 @@ def draw_polygons(
         bdi_max = numpy.minimum(numpy.ceil(bdi_max), self.shape - 1).astype(int)
 
         # 3) Adjust polygons for center
-    polygons = [poly + center[surface] for poly in polygons]
+        poly_list = [poly + center[surface] for poly in poly_list]
 
         # ## Generate weighing function
         def to_3d(vector: NDArray, val: float = 0.0) -> NDArray[numpy.float64]:
@@ -108,6 +112,7 @@ def draw_polygons(
             return numpy.insert(v_2d, surface_normal, (val,))
 
         # iterate over grids
+        foreground_val: NDArray | float
         for i, _ in enumerate(cell_data):
             # ## Evaluate or expand foregrounds[i]
             foregrounds_i = foregrounds[i]
@@ -129,7 +134,7 @@ def draw_polygons(
             w_xy = numpy.zeros((bdi_max - bdi_min + 1)[surface].astype(int))
 
             # Draw each polygon separately
-        for polygon in polygons:
+            for polygon in poly_list:
 
                 # Get the boundaries of the polygon
                 pbd_min = polygon.min(axis=0)

gridlock/grid.py

@@ -1,4 +1,5 @@
-from typing import Callable, Sequence, ClassVar, Self
+from typing import ClassVar, Self
+from collections.abc import Callable, Sequence
 
 import numpy
 from numpy.typing import NDArray, ArrayLike
@@ -8,12 +9,16 @@ import warnings
 import copy
 
 from . import GridError
+from .base import GridBase
+from .draw import GridDrawMixin
+from .read import GridReadMixin
+from .position import GridPosMixin
 
 
 foreground_callable_type = Callable[[NDArray, NDArray, NDArray], NDArray]
 
 
-class Grid:
+class Grid(GridDrawMixin, GridReadMixin, GridPosMixin):
     """
     Simulation grid metadata for finite-difference simulations.
 
@@ -70,193 +75,6 @@ class Grid:
         ], dtype=float)
     """Default shifts for Yee grid H-field"""
 
-    from .draw import (
-        draw_polygons, draw_polygon, draw_slab, draw_cuboid,
-        draw_cylinder, draw_extrude_rectangle,
-        )
-    from .read import get_slice, visualize_slice, visualize_isosurface
-    from .position import ind2pos, pos2ind
-
-    @property
-    def dxyz(self) -> list[NDArray]:
-        """
-        Cell sizes for each axis, no shifts applied
-
-        Returns:
-            List of 3 ndarrays of cell sizes
-        """
-        return [numpy.diff(ee) for ee in self.exyz]
-
-    @property
-    def xyz(self) -> list[NDArray]:
-        """
-        Cell centers for each axis, no shifts applied
-
-        Returns:
-            List of 3 ndarrays of cell edges
-        """
-        return [self.exyz[a][:-1] + self.dxyz[a] / 2.0 for a in range(3)]
-
-    @property
-    def shape(self) -> NDArray[numpy.int_]:
-        """
-        The number of cells in x, y, and z
-
-        Returns:
-            ndarray of [x_centers.size, y_centers.size, z_centers.size]
-        """
-        return numpy.array([coord.size - 1 for coord in self.exyz], dtype=int)
-
-    @property
-    def num_grids(self) -> int:
-        """
-        The number of grids (number of shifts)
-        """
-        return self.shifts.shape[0]
-
-    @property
-    def cell_data_shape(self):
-        """
-        The shape of the cell_data ndarray (num_grids, *self.shape).
-        """
-        return numpy.hstack((self.num_grids, self.shape))
-
-    @property
-    def dxyz_with_ghost(self) -> list[NDArray]:
-        """
-        Gives dxyz with an additional 'ghost' cell at the end, whose value depends
-         on whether or not the axis has periodic boundary conditions. See main description
-         above to learn why this is necessary.
-
-         If periodic, final edge shifts same amount as first
-         Otherwise, final edge shifts same amount as second-to-last
-
-        Returns:
-            list of [dxs, dys, dzs] with each element same length as elements of `self.xyz`
-        """
-        el = [0 if p else -1 for p in self.periodic]
-        return [numpy.hstack((self.dxyz[a], self.dxyz[a][e])) for a, e in zip(range(3), el, strict=True)]
-
-    @property
-    def center(self) -> NDArray[numpy.float64]:
-        """
-        Center position of the entire grid, no shifts applied
-
-        Returns:
-            ndarray of [x_center, y_center, z_center]
-        """
-        # center is just average of first and last xyz, which is just the average of the
-        #  first two and last two exyz
-        centers = [(self.exyz[a][:2] + self.exyz[a][-2:]).sum() / 4.0 for a in range(3)]
-        return numpy.array(centers, dtype=float)
-
-    @property
-    def dxyz_limits(self) -> tuple[NDArray, NDArray]:
-        """
-        Returns the minimum and maximum cell size for each axis, as a tuple of two 3-element
-         ndarrays. No shifts are applied, so these are extreme bounds on these values (as a
-         weighted average is performed when shifting).
-
-        Returns:
-            Tuple of 2 ndarrays, `d_min=[min(dx), min(dy), min(dz)]` and `d_max=[...]`
-        """
-        d_min = numpy.array([min(self.dxyz[a]) for a in range(3)], dtype=float)
-        d_max = numpy.array([max(self.dxyz[a]) for a in range(3)], dtype=float)
-        return d_min, d_max
-
-    def shifted_exyz(self, which_shifts: int | None) -> list[NDArray]:
-        """
-        Returns edges for which_shifts.
-
-        Args:
-            which_shifts: Which grid (which shifts) to use, or `None` for unshifted
-
-        Returns:
-            List of 3 ndarrays of cell edges
-        """
-        if which_shifts is None:
-            return self.exyz
-        dxyz = self.dxyz_with_ghost
-        shifts = self.shifts[which_shifts, :]
-
-        # If shift is negative, use left cell's dx to determine shift
-        for a in range(3):
-            if shifts[a] < 0:
-                dxyz[a] = numpy.roll(dxyz[a], 1)
-
-        return [self.exyz[a] + dxyz[a] * shifts[a] for a in range(3)]
-
-    def shifted_dxyz(self, which_shifts: int | None) -> list[NDArray]:
-        """
-        Returns cell sizes for `which_shifts`.
-
-        Args:
-            which_shifts: Which grid (which shifts) to use, or `None` for unshifted
-
-        Returns:
-            List of 3 ndarrays of cell sizes
-        """
-        if which_shifts is None:
-            return self.dxyz
-        shifts = self.shifts[which_shifts, :]
-        dxyz = self.dxyz_with_ghost
-
-        # If shift is negative, use left cell's dx to determine size
-        sdxyz = []
-        for a in range(3):
-            if shifts[a] < 0:
-                roll_dxyz = numpy.roll(dxyz[a], 1)
-                abs_shift = numpy.abs(shifts[a])
-                sdxyz.append(roll_dxyz[:-1] * abs_shift + roll_dxyz[1:] * (1 - abs_shift))
-            else:
-                sdxyz.append(dxyz[a][:-1] * (1 - shifts[a]) + dxyz[a][1:] * shifts[a])
-
-        return sdxyz
-
-    def shifted_xyz(self, which_shifts: int | None) -> list[NDArray[numpy.float64]]:
-        """
-        Returns cell centers for `which_shifts`.
-
-        Args:
-            which_shifts: Which grid (which shifts) to use, or `None` for unshifted
-
-        Returns:
-            List of 3 ndarrays of cell centers
-        """
-        if which_shifts is None:
-            return self.xyz
-        exyz = self.shifted_exyz(which_shifts)
-        dxyz = self.shifted_dxyz(which_shifts)
-        return [exyz[a][:-1] + dxyz[a] / 2.0 for a in range(3)]
-
-    def autoshifted_dxyz(self) -> list[NDArray[numpy.float64]]:
-        """
-        Return cell widths, with each dimension shifted by the corresponding shifts.
-
-        Returns:
-            `[grid.shifted_dxyz(which_shifts=a)[a] for a in range(3)]`
-        """
-        if self.num_grids != 3:
-            raise GridError('Autoshifting requires exactly 3 grids')
-        return [self.shifted_dxyz(which_shifts=a)[a] for a in range(3)]
-
-    def allocate(self, fill_value: float | None = 1.0, dtype=numpy.float32) -> NDArray:
-        """
-        Allocate an ndarray for storing grid data.
-
-        Args:
-            fill_value: Value to initialize the grid to. If None, an
-                uninitialized array is returned.
-            dtype: Numpy dtype for the array. Default is `numpy.float32`.
-
-        Returns:
-            The allocated array
-        """
-        if fill_value is None:
-            return numpy.empty(self.cell_data_shape, dtype=dtype)
-        else:
-            return numpy.full(self.cell_data_shape, fill_value, dtype=dtype)
-
     def __init__(
             self,
             pixel_edge_coordinates: Sequence[ArrayLike],
@@ -277,11 +95,12 @@ class Grid:
         Raises:
             `GridError` on invalid input
         """
-        self.exyz = [numpy.unique(pixel_edge_coordinates[i]) for i in range(3)]
+        edge_arrs = [numpy.array(cc, copy=False) for cc in pixel_edge_coordinates]
+        self.exyz = [numpy.unique(edges) for edges in edge_arrs]
         self.shifts = numpy.array(shifts, dtype=float)
 
         for i in range(3):
-            if len(self.exyz[i]) != len(pixel_edge_coordinates[i]):
+            if self.exyz[i].size != edge_arrs[i].size:
                 warnings.warn(f'Dimension {i} had duplicate edge coordinates', stacklevel=2)
 
         if isinstance(periodic, bool):

gridlock/position.py

@@ -5,8 +5,10 @@ import numpy
 from numpy.typing import NDArray, ArrayLike
 
 from . import GridError
+from .base import GridBase
 
 
+class GridPosMixin(GridBase):
     def ind2pos(
             self,
             ind: NDArray,

gridlock/read.py

@@ -7,6 +7,8 @@ import numpy
 from numpy.typing import NDArray
 
 from . import GridError
+from .base import GridBase
+from .position import GridPosMixin
 
 if TYPE_CHECKING:
     import matplotlib.axes
@@ -18,6 +20,7 @@ if TYPE_CHECKING:
 # .visualize_isosurface uses mpl_toolkits.mplot3d
 
 
+class GridReadMixin(GridPosMixin):
     def get_slice(
             self,
             cell_data: NDArray,