bump version to v1.2

.flake8

@@ -0,0 +1,29 @@
+[flake8]
+ignore =
+    # E501 line too long
+    E501,
+    # W391 newlines at EOF
+    W391,
+    # E241 multiple spaces after comma
+    E241,
+    # E302 expected 2 newlines
+    E302,
+    # W503 line break before binary operator (to be deprecated)
+    W503,
+    # E265 block comment should start with '# '
+    E265,
+    # E123 closing bracket does not match indentation of opening bracket's line
+    E123,
+    # E124 closing bracket does not match visual indentation
+    E124,
+    # E221 multiple spaces before operator
+    E221,
+    # E201 whitespace after '['
+    E201,
+    # E741 ambiguous variable name 'I'
+    E741,
+
+
+per-file-ignores =
+    # F401 import without use
+    */__init__.py: F401,

MANIFEST.in

@@ -1,2 +0,0 @@
-include README.md
-include LICENSE.md

README.md

@@ -14,7 +14,7 @@ the coordinates of the boundary points along each axis).
 ## Installation
 
 Requirements:
-* python 3 (written and tested with 3.9)
+* python >3.11 (written and tested with 3.12)
 * numpy
 * [float_raster](https://mpxd.net/code/jan/float_raster)
 * matplotlib (optional, used for visualization functions)

gridlock/LICENSE.md

@@ -0,0 +1 @@
+../LICENSE.md

gridlock/README.md

@@ -0,0 +1 @@
+../README.md

gridlock/VERSION.py

@@ -1,4 +0,0 @@
-""" VERSION defintion. THIS FILE IS MANUALLY PARSED BY setup.py and REQUIRES A SPECIFIC FORMAT """
-__version__ = '''
-1.0
-'''.strip()

gridlock/__init__.py

@@ -15,10 +15,9 @@ Dependencies:
 - mpl_toolkits.mplot3d  [Grid.visualize_isosurface()]
 - skimage               [Grid.visualize_isosurface()]
 """
-from .error import GridError
+from .error import GridError as GridError
-from .grid import Grid
+from .grid import Grid as Grid
 
 __author__ = 'Jan Petykiewicz'
-
+__version__ = '1.2'
-from .VERSION import __version__
 version = __version__

gridlock/base.py

@@ -0,0 +1,196 @@
+from typing import Protocol
+
+import numpy
+from numpy.typing import NDArray
+
+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.intp]:
+        """
+        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) -> NDArray[numpy.intp]:
+        """
+        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: type[numpy.number] = 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)
+        return numpy.full(self.cell_data_shape, fill_value, dtype=dtype)

gridlock/draw.py

@@ -1,12 +1,14 @@
 """
 Drawing-related methods for Grid class
 """
-from typing import List, Optional, Union, Sequence, Callable
+from collections.abc import Sequence, Callable
 
-import numpy        # type: ignore
+import numpy
+from numpy.typing import NDArray, ArrayLike
 from float_raster import raster
 
 from . import GridError
+from .position import GridPosMixin
 
 
 # NOTE: Maybe it would make sense to create a GridDrawer class
@@ -15,16 +17,19 @@ from . import GridError
 #       without having to pass `cell_data` again each time?
 
 
-foreground_callable_t = Callable[[numpy.ndarray, numpy.ndarray, numpy.ndarray], numpy.ndarray]
+foreground_callable_t = Callable[[NDArray, NDArray, NDArray], NDArray]
+foreground_t = float | foreground_callable_t
 
 
-def draw_polygons(self,
+class GridDrawMixin(GridPosMixin):
-                  cell_data: numpy.ndarray,
+    def draw_polygons(
+            self,
+            cell_data: NDArray,
             surface_normal: int,
-                  center: numpy.ndarray,
+            center: ArrayLike,
-                  polygons: Sequence[numpy.ndarray],
+            polygons: Sequence[ArrayLike],
             thickness: float,
-                  foreground: Union[Sequence[Union[float, foreground_callable_t]], float, foreground_callable_t],
+            foreground: Sequence[foreground_t] | foreground_t,
             ) -> None:
         """
         Draw polygons on an axis-aligned plane.
@@ -47,18 +52,20 @@ def draw_polygons(self,
         """
         if surface_normal not in range(3):
             raise GridError('Invalid surface_normal direction')
-
         center = numpy.squeeze(center)
+        poly_list = [numpy.asarray(poly) for poly in polygons]
 
         # Check polygons, and remove redundant coordinates
         surface = numpy.delete(range(3), surface_normal)
 
-    for i, polygon in enumerate(polygons):
+        for ii in range(len(poly_list)):
-        malformed = f'Malformed polygon: ({i})'
+            polygon = poly_list[ii]
+            malformed = f'Malformed polygon: ({ii})'
             if polygon.shape[1] not in (2, 3):
                 raise GridError(malformed + 'must be a Nx2 or Nx3 ndarray')
             if polygon.shape[1] == 3:
                 polygon = polygon[surface, :]
+                poly_list[ii] = polygon
 
             if not polygon.shape[0] > 2:
                 raise GridError(malformed + 'must consist of more than 2 points')
@@ -67,16 +74,19 @@ def draw_polygons(self,
                                 + 'xyz'[surface_normal])
 
         # Broadcast foreground where necessary
-    if numpy.size(foreground) == 1:
+        foregrounds: Sequence[foreground_callable_t] | Sequence[float]
-        foreground = [foreground] * len(cell_data)
+        if numpy.size(foreground) == 1:     # type: ignore
+            foregrounds = [foreground] * len(cell_data)  # type: ignore
         elif isinstance(foreground, numpy.ndarray):
             raise GridError('ndarray not supported for foreground')
+        else:
+            foregrounds = foreground    # type: ignore
 
         # ## Compute sub-domain of the grid occupied by polygons
         # 1) Compute outer bounds (bd) of polygons
-    bd_2d_min = [0, 0]
+        bd_2d_min = numpy.array([0, 0])
-    bd_2d_max = [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
@@ -94,35 +104,37 @@ def draw_polygons(self,
         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: numpy.ndarray, val: float = 0.0) -> numpy.ndarray:
+        def to_3d(vector: NDArray, val: float = 0.0) -> NDArray[numpy.float64]:
             v_2d = numpy.array(vector, dtype=float)
             return numpy.insert(v_2d, surface_normal, (val,))
 
         # iterate over grids
-    for i, grid in enumerate(cell_data):
+        foreground_val: NDArray | float
-        # ## Evaluate or expand foreground[i]
+        for i, _ in enumerate(cell_data):
-        if callable(foreground[i]):
+            # ## Evaluate or expand foregrounds[i]
+            foregrounds_i = foregrounds[i]
+            if callable(foregrounds_i):
                 # meshgrid over the (shifted) domain
                 domain = [self.shifted_xyz(i)[k][bdi_min[k]:bdi_max[k] + 1] for k in range(3)]
                 (x0, y0, z0) = numpy.meshgrid(*domain, indexing='ij')
 
                 # evaluate on the meshgrid
-            foreground_i = foreground[i](x0, y0, z0)
+                foreground_val = foregrounds_i(x0, y0, z0)
-            if not numpy.isfinite(foreground_i).all():
+                if not numpy.isfinite(foreground_val).all():
                     raise GridError(f'Non-finite values in foreground[{i}]')
-        elif numpy.size(foreground[i]) != 1:
+            elif numpy.size(foregrounds_i) != 1:
-            raise GridError(f'Unsupported foreground[{i}]: {type(foreground[i])}')
+                raise GridError(f'Unsupported foreground[{i}]: {type(foregrounds_i)}')
             else:
                 # foreground[i] is scalar non-callable
-            foreground_i = foreground[i]
+                foreground_val = foregrounds_i
 
             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)
@@ -137,13 +149,13 @@ def draw_polygons(self,
 
                 # Find indices in w_xy which are modified by polygon
                 # First for the edge coordinates (+1 since we're indexing edges)
-            edge_slices = [numpy.s_[i:f + 2] for i, f in zip(corner_min, corner_max)]
+                edge_slices = [numpy.s_[i:f + 2] for i, f in zip(corner_min, corner_max, strict=True)]
                 # Then for the pixel centers (-bdi_min since we're
                 #  calculating weights within a subspace)
                 centers_slice = tuple(numpy.s_[i:f + 1] for i, f in zip(corner_min - bdi_min[surface],
-                                                                    corner_max - bdi_min[surface]))
+                                                                        corner_max - bdi_min[surface], strict=True))
 
-            aa_x, aa_y = (self.shifted_exyz(i)[a][s] for a, s in zip(surface, edge_slices))
+                aa_x, aa_y = (self.shifted_exyz(i)[a][s] for a, s in zip(surface, edge_slices, strict=True))
                 w_xy[centers_slice] += raster(polygon.T, aa_x, aa_y)
 
             # Clamp overlapping polygons to 1
@@ -152,7 +164,7 @@ def draw_polygons(self,
             # 2) Generate weights in z-direction
             w_z = numpy.zeros(((bdi_max - bdi_min + 1)[surface_normal], ))
 
-        def get_zi(offset, i=i, w_z=w_z):
+            def get_zi(offset: float, i=i, w_z=w_z) -> tuple[float, int]:          # noqa: ANN001
                 edges = self.shifted_exyz(i)[surface_normal]
                 point = center[surface_normal] + offset
                 grid_coord = numpy.digitize(point, edges) - 1
@@ -185,16 +197,17 @@ def draw_polygons(self,
 
             # ## Modify the grid
             g_slice = (i,) + tuple(numpy.s_[bdi_min[a]:bdi_max[a] + 1] for a in range(3))
-        cell_data[g_slice] = (1 - w) * cell_data[g_slice] + w * foreground_i
+            cell_data[g_slice] = (1 - w) * cell_data[g_slice] + w * foreground_val
 
 
-def draw_polygon(self,
+    def draw_polygon(
-                 cell_data: numpy.ndarray,
+            self,
+            cell_data: NDArray,
             surface_normal: int,
-                 center: numpy.ndarray,
+            center: ArrayLike,
-                 polygon: numpy.ndarray,
+            polygon: ArrayLike,
             thickness: float,
-                 foreground: Union[Sequence[Union[float, foreground_callable_t]], float, foreground_callable_t],
+            foreground: Sequence[foreground_t] | foreground_t,
             ) -> None:
         """
         Draw a polygon on an axis-aligned plane.
@@ -212,12 +225,13 @@ def draw_polygon(self,
         self.draw_polygons(cell_data, surface_normal, center, [polygon], thickness, foreground)
 
 
-def draw_slab(self,
+    def draw_slab(
-              cell_data: numpy.ndarray,
+            self,
+            cell_data: NDArray,
             surface_normal: int,
-              center: numpy.ndarray,
+            center: ArrayLike,
             thickness: float,
-              foreground: Union[List[Union[float, foreground_callable_t]], float, foreground_callable_t],
+            foreground: Sequence[foreground_t] | foreground_t,
             ) -> None:
         """
         Draw an axis-aligned infinite slab.
@@ -262,11 +276,12 @@ def draw_slab(self,
         self.draw_polygon(cell_data, surface_normal, center_shift, p, thickness, foreground)
 
 
-def draw_cuboid(self,
+    def draw_cuboid(
-                cell_data: numpy.ndarray,
+            self,
-                center: numpy.ndarray,
+            cell_data: NDArray,
-                dimensions: numpy.ndarray,
+            center: ArrayLike,
-                foreground: Union[List[Union[float, foreground_callable_t]], float, foreground_callable_t],
+            dimensions: ArrayLike,
+            foreground: Sequence[foreground_t] | foreground_t,
             ) -> None:
         """
         Draw an axis-aligned cuboid
@@ -278,22 +293,24 @@ def draw_cuboid(self,
                  sizes of the cuboid
             foreground: Value to draw with ('brush color'). See `draw_polygons()` for details.
         """
+        dimensions = numpy.asarray(dimensions)
         p = numpy.array([[-dimensions[0], +dimensions[1]],
                          [+dimensions[0], +dimensions[1]],
                          [+dimensions[0], -dimensions[1]],
-                     [-dimensions[0], -dimensions[1]]], dtype=float) / 2.0
+                         [-dimensions[0], -dimensions[1]]], dtype=float) * 0.5
         thickness = dimensions[2]
         self.draw_polygon(cell_data, 2, center, p, thickness, foreground)
 
 
-def draw_cylinder(self,
+    def draw_cylinder(
-                  cell_data: numpy.ndarray,
+            self,
+            cell_data: NDArray,
             surface_normal: int,
-                  center: numpy.ndarray,
+            center: ArrayLike,
             radius: float,
             thickness: float,
             num_points: int,
-                  foreground: Union[List[Union[float, foreground_callable_t]], float, foreground_callable_t],
+            foreground: Sequence[foreground_t] | foreground_t,
             ) -> None:
         """
         Draw an axis-aligned cylinder. Approximated by a num_points-gon
@@ -314,9 +331,10 @@ def draw_cylinder(self,
         self.draw_polygon(cell_data, surface_normal, center, polygon, thickness, foreground)
 
 
-def draw_extrude_rectangle(self,
+    def draw_extrude_rectangle(
-                           cell_data: numpy.ndarray,
+            self,
-                           rectangle: numpy.ndarray,
+            cell_data: NDArray,
+            rectangle: ArrayLike,
             direction: int,
             polarity: int,
             distance: float,
@@ -347,8 +365,8 @@ def draw_extrude_rectangle(self,
         surface = numpy.delete(range(3), direction)
 
         dim = numpy.fabs(numpy.diff(rectangle, axis=0).T)[surface]
-    p = numpy.vstack((numpy.array([-1, -1, 1, 1], dtype=float) * dim[0]/2.0,
+        p = numpy.vstack((numpy.array([-1, -1, 1, 1], dtype=float) * dim[0] * 0.5,
-                      numpy.array([-1, 1, 1, -1], dtype=float) * dim[1]/2.0)).T
+                          numpy.array([-1, 1, 1, -1], dtype=float) * dim[1] * 0.5)).T
         thickness = distance
 
         foreground_func = []
@@ -361,13 +379,13 @@ def draw_extrude_rectangle(self,
             mult = [1 - fpart, fpart][::s]  # reverses if s negative
 
             foreground = mult[0] * grid[tuple(ind)]
-        ind[direction] += 1
+            ind[direction] += 1                         # type: ignore #(known safe)
             foreground += mult[1] * grid[tuple(ind)]
 
-        def f_foreground(xs, ys, zs, i=i, foreground=foreground) -> numpy.ndarray:
+            def f_foreground(xs, ys, zs, i=i, foreground=foreground) -> NDArray[numpy.int64]:            # noqa: ANN001
                 # transform from natural position to index
                 xyzi = numpy.array([self.pos2ind(qrs, which_shifts=i)
-                                for qrs in zip(xs.flat, ys.flat, zs.flat)], dtype=int)
+                                    for qrs in zip(xs.flat, ys.flat, zs.flat, strict=True)], dtype=numpy.int64)
                 # reshape to original shape and keep only in-plane components
                 qi, ri = (numpy.reshape(xyzi[:, k], xs.shape) for k in surface)
                 return foreground[qi, ri]

gridlock/examples/ex0.py

@@ -1,4 +1,4 @@
-import numpy        # type: ignore
+import numpy
 from gridlock import Grid
 
 
@@ -29,16 +29,16 @@ if __name__ == '__main__':
     #         numpy.linspace(-5.5, 5.5, 10)]
 
     half_x = [.25, .5, 0.75, 1, 1.25, 1.5, 2, 2.5, 3, 3.5]
-    xyz3 = [[-x for x in half_x[::-1]] + [0] + half_x,
+    xyz3 = [numpy.array([-x for x in half_x[::-1]] + [0] + half_x, dtype=float),
-            numpy.linspace(-5.5, 5.5, 10),
+            numpy.linspace(-5.5, 5.5, 10, dtype=float),
-            numpy.linspace(-5.5, 5.5, 10)]
+            numpy.linspace(-5.5, 5.5, 10, dtype=float)]
     eg = Grid(xyz3)
     egc = eg.allocate(0)
     # eg.draw_slab(Direction.z, 0, 10, 2)
     eg.save('/home/jan/Desktop/test.pickle')
     eg.draw_cylinder(egc, surface_normal=2, center=[0, 0, 0], radius=2.0,
-                     thickness=10, num_poitns=1000, foreground=1)
+                     thickness=10, num_points=1000, foreground=1)
     eg.draw_extrude_rectangle(egc, rectangle=[[-2, 1, -1], [0, 1, 1]],
-                              direction=1, poalarity=+1, distance=5)
+                              direction=1, polarity=+1, distance=5)
     eg.visualize_slice(egc, surface_normal=2, center=0, which_shifts=2)
     eg.visualize_isosurface(egc, which_shifts=2)

gridlock/grid.py

@@ -1,20 +1,23 @@
-from typing import List, Tuple, Callable, Dict, Optional, Union, Sequence, ClassVar, TypeVar
+from typing import ClassVar, Self
+from collections.abc import Callable, Sequence
 
-import numpy    # type: ignore
+import numpy
-from numpy import diff, floor, ceil, zeros, hstack, newaxis
+from numpy.typing import NDArray, ArrayLike
 
 import pickle
 import warnings
 import copy
 
 from . import GridError
+from .draw import GridDrawMixin
+from .read import GridReadMixin
+from .position import GridPosMixin
 
 
-foreground_callable_type = Callable[[numpy.ndarray, numpy.ndarray, numpy.ndarray], numpy.ndarray]
+foreground_callable_type = Callable[[NDArray, NDArray, NDArray], NDArray]
-T = TypeVar('T', bound='Grid')
 
 
-class Grid:
+class Grid(GridDrawMixin, GridReadMixin, GridPosMixin):
     """
     Simulation grid metadata for finite-difference simulations.
 
@@ -48,216 +51,34 @@ class Grid:
      Because of this, we either assume this 'ghost' cell is the same size as the last
       real cell, or, if `self.periodic[a]` is set to `True`, the same size as the first cell.
     """
-    exyz: List[numpy.ndarray]
+    exyz: list[NDArray]
     """Cell edges. Monotonically increasing without duplicates."""
 
-    periodic: List[bool]
+    periodic: list[bool]
     """For each axis, determines how far the rightmost boundary gets shifted. """
 
-    shifts: numpy.ndarray
+    shifts: NDArray
     """Offsets `[[x0, y0, z0], [x1, y1, z1], ...]` for grid `0,1,...`"""
 
-    Yee_Shifts_E: ClassVar[numpy.ndarray] = 0.5 * numpy.array([[1, 0, 0],
+    Yee_Shifts_E: ClassVar[NDArray] = 0.5 * numpy.array([
+        [1, 0, 0],
         [0, 1, 0],
-                                                               [0, 0, 1]], dtype=float)
+        [0, 0, 1],
+        ], dtype=float)
     """Default shifts for Yee grid E-field"""
 
-    Yee_Shifts_H: ClassVar[numpy.ndarray] = 0.5 * numpy.array([[0, 1, 1],
+    Yee_Shifts_H: ClassVar[NDArray] = 0.5 * numpy.array([
+        [0, 1, 1],
         [1, 0, 1],
-                                                               [1, 1, 0]], dtype=float)
+        [1, 1, 0],
+        ], dtype=float)
     """Default shifts for Yee grid H-field"""
 
-    from .draw import (
+    def __init__(
-        draw_polygons, draw_polygon, draw_slab, draw_cuboid,
+            self,
-        draw_cylinder, draw_extrude_rectangle,
+            pixel_edge_coordinates: Sequence[ArrayLike],
-        )
+            shifts: ArrayLike = Yee_Shifts_E,
-    from .read import get_slice, visualize_slice, visualize_isosurface
+            periodic: bool | Sequence[bool] = False,
-    from .position import ind2pos, pos2ind
-
-    @property
-    def dxyz(self) -> List[numpy.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[numpy.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) -> numpy.ndarray:
-        """
-        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[numpy.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)]
-
-    @property
-    def center(self) -> numpy.ndarray:
-        """
-        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[numpy.ndarray, numpy.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: Optional[int]) -> List[numpy.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: Optional[int]) -> List[numpy.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: Optional[int]) -> List[numpy.ndarray]:
-        """
-        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[numpy.ndarray]:
-        """
-        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: Optional[float] = 1.0, dtype=numpy.float32) -> numpy.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[numpy.ndarray],
-                 shifts: numpy.ndarray = Yee_Shifts_E,
-                 periodic: Union[bool, Sequence[bool]] = False,
             ) -> None:
         """
         Args:
@@ -273,11 +94,12 @@ 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) 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):
@@ -314,7 +136,7 @@ class Grid:
         g.__dict__.update(tmp_dict)
         return g
 
-    def save(self: T, filename: str) -> T:
+    def save(self, filename: str) -> Self:
         """
         Save to file.
 
@@ -328,7 +150,7 @@ class Grid:
             pickle.dump(self.__dict__, f, protocol=2)
         return self
 
-    def copy(self: T) -> T:
+    def copy(self) -> Self:
         """
         Returns:
             Deep copy of the grid.

gridlock/position.py

@@ -1,19 +1,21 @@
 """
 Position-related methods for Grid class
 """
-from typing import List, Optional
+import numpy
-
+from numpy.typing import NDArray, ArrayLike
-import numpy        # type: ignore
 
 from . import GridError
+from .base import GridBase
 
 
-def ind2pos(self,
+class GridPosMixin(GridBase):
-            ind: numpy.ndarray,
+    def ind2pos(
-            which_shifts: Optional[int] = None,
+            self,
+            ind: NDArray,
+            which_shifts: int | None = None,
             round_ind: bool = True,
             check_bounds: bool = True
-            ) -> numpy.ndarray:
+            ) -> NDArray[numpy.float64]:
         """
         Returns the natural position corresponding to the specified cell center indices.
          The resulting position is clipped to the bounds of the grid
@@ -59,12 +61,13 @@ def ind2pos(self,
         return numpy.array(position, dtype=float)
 
 
-def pos2ind(self,
+    def pos2ind(
-            r: numpy.ndarray,
+            self,
-            which_shifts: Optional[int],
+            r: ArrayLike,
+            which_shifts: int | None,
             round_ind: bool = True,
             check_bounds: bool = True
-            ) -> numpy.ndarray:
+            ) -> NDArray[numpy.float64]:
         """
         Returns the cell-center indices corresponding to the specified natural position.
              The resulting position is clipped to within the outer centers of the grid.

gridlock/read.py

@@ -1,24 +1,33 @@
 """
 Readback and visualization methods for Grid class
 """
-from typing import Dict, Optional, Union, Any
+from typing import Any, TYPE_CHECKING
 
-import numpy        # type: ignore
+import numpy
+from numpy.typing import NDArray
 
 from . import GridError
+from .position import GridPosMixin
+
+if TYPE_CHECKING:
+    import matplotlib.axes
+    import matplotlib.figure
+
 
 # .visualize_* uses matplotlib
 # .visualize_isosurface uses skimage
 # .visualize_isosurface uses mpl_toolkits.mplot3d
 
 
-def get_slice(self,
+class GridReadMixin(GridPosMixin):
-              cell_data: numpy.ndarray,
+    def get_slice(
+            self,
+            cell_data: NDArray,
             surface_normal: int,
             center: float,
             which_shifts: int = 0,
             sample_period: int = 1
-              ) -> numpy.ndarray:
+            ) -> NDArray:
         """
         Retrieve a slice of a grid.
         Interpolates if given a position between two planes.
@@ -65,7 +74,7 @@ def get_slice(self,
 
         # Extract grid values from planes above and below visualized slice
         sliced_grid = numpy.zeros(self.shape[surface])
-    for ci, weight in zip(centers, w):
+        for ci, weight in zip(centers, w, strict=True):
             s = tuple(ci if a == surface_normal else numpy.s_[::sp] for a in range(3))
             sliced_grid += weight * cell_data[which_shifts][tuple(s)]
 
@@ -75,15 +84,16 @@ def get_slice(self,
         return sliced_grid
 
 
-def visualize_slice(self,
+    def visualize_slice(
-                    cell_data: numpy.ndarray,
+            self,
+            cell_data: NDArray,
             surface_normal: int,
             center: float,
             which_shifts: int = 0,
             sample_period: int = 1,
             finalize: bool = True,
-                    pcolormesh_args: Optional[Dict[str, Any]] = None,
+            pcolormesh_args: dict[str, Any] | None = None,
-                    ) -> None:
+            ) -> tuple['matplotlib.axes.Axes', 'matplotlib.figure.Figure']:
         """
         Visualize a slice of a grid.
         Interpolates if given a position between two planes.
@@ -94,6 +104,9 @@ def visualize_slice(self,
             which_shifts: Which grid to display. Default is the first grid (0).
             sample_period: Period for down-sampling the image. Default 1 (disabled)
             finalize: Whether to call `pyplot.show()` after constructing the plot. Default `True`
+
+        Returns:
+            (Figure, Axes)
         """
         from matplotlib import pyplot
 
@@ -112,24 +125,27 @@ def visualize_slice(self,
         xmesh, ymesh = numpy.meshgrid(x, y, indexing='ij')
         x_label, y_label = ('xyz'[a] for a in surface)
 
-    pyplot.figure()
+        fig, ax = pyplot.subplots()
-    pyplot.pcolormesh(xmesh, ymesh, grid_slice, **pcolormesh_args)
+        mappable = ax.pcolormesh(xmesh, ymesh, grid_slice, **pcolormesh_args)
-    pyplot.colorbar()
+        fig.colorbar(mappable)
-    pyplot.gca().set_aspect('equal', adjustable='box')
+        ax.set_aspect('equal', adjustable='box')
-    pyplot.xlabel(x_label)
+        ax.set_xlabel(x_label)
-    pyplot.ylabel(y_label)
+        ax.set_ylabel(y_label)
         if finalize:
             pyplot.show()
 
+        return fig, ax
 
-def visualize_isosurface(self,
+
-                         cell_data: numpy.ndarray,
+    def visualize_isosurface(
-                         level: Optional[float] = None,
+            self,
+            cell_data: NDArray,
+            level: float | None = None,
             which_shifts: int = 0,
             sample_period: int = 1,
             show_edges: bool = True,
             finalize: bool = True,
-                         ) -> None:
+            ) -> tuple['matplotlib.axes.Axes', 'matplotlib.figure.Figure']:
         """
         Draw an isosurface plot of the device.
 
@@ -140,11 +156,15 @@ def visualize_isosurface(self,
             sample_period: Period for down-sampling the image. Default 1 (disabled)
             show_edges: Whether to draw triangle edges. Default `True`
             finalize: Whether to call `pyplot.show()` after constructing the plot. Default `True`
+
+        Returns:
+            (Figure, Axes)
         """
         from matplotlib import pyplot
         import skimage.measure
         # Claims to be unused, but needed for subplot(projection='3d')
         from mpl_toolkits.mplot3d import Axes3D
+        del Axes3D      # imported for side effects only
 
         # Get data from cell_data
         grid = cell_data[which_shifts][::sample_period, ::sample_period, ::sample_period]
@@ -176,8 +196,10 @@ def visualize_isosurface(self,
         ybs = 0.5 * max_range * mg[1].flatten() + 0.5 * (ys.max() + ys.min())
         zbs = 0.5 * max_range * mg[2].flatten() + 0.5 * (zs.max() + zs.min())
         # Comment or uncomment following both lines to test the fake bounding box:
-    for xb, yb, zb in zip(xbs, ybs, zbs):
+        for xb, yb, zb in zip(xbs, ybs, zbs, strict=True):
             ax.plot([xb], [yb], [zb], 'w')
 
         if finalize:
             pyplot.show()
+
+        return fig, ax

gridlock/test/test_grid.py

@@ -1,6 +1,6 @@
-import pytest       # type: ignore
+# import pytest
-import numpy        # type: ignore
+import numpy
-from numpy.testing import assert_allclose, assert_array_equal       # type: ignore
+from numpy.testing import assert_allclose       #, assert_array_equal
 
 from .. import Grid
 

pyproject.toml

@@ -0,0 +1,99 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "gridlock"
+description = "Coupled gridding library"
+readme = "README.md"
+license = { file = "LICENSE.md" }
+authors = [
+    { name="Jan Petykiewicz", email="jan@mpxd.net" },
+    ]
+homepage = "https://mpxd.net/code/jan/gridlock"
+repository = "https://mpxd.net/code/jan/gridlock"
+keywords = [
+    "FDTD",
+    "gridding",
+    "simulation",
+    "nonuniform",
+    "FDFD",
+    "finite",
+    "difference",
+    ]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: GNU Affero General Public License v3",
+    "Topic :: Multimedia :: Graphics :: 3D Rendering",
+    "Topic :: Scientific/Engineering :: Electronic Design Automation (EDA)",
+    "Topic :: Scientific/Engineering :: Physics",
+    "Topic :: Scientific/Engineering :: Visualization",
+    ]
+requires-python = ">=3.11"
+include = [
+    "LICENSE.md"
+    ]
+dynamic = ["version"]
+dependencies = [
+    "numpy>=1.26",
+    "float_raster>=0.8",
+    ]
+
+
+[tool.hatch.version]
+path = "gridlock/__init__.py"
+
+[project.optional-dependencies]
+visualization = ["matplotlib"]
+visualization-isosurface = [
+    "matplotlib",
+    "skimage>=0.13",
+    "mpl_toolkits",
+    ]
+
+
+[tool.ruff]
+exclude = [
+    ".git",
+    "dist",
+    ]
+line-length = 145
+indent-width = 4
+lint.dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+lint.select = [
+    "NPY", "E", "F", "W", "B", "ANN", "UP", "SLOT", "SIM", "LOG",
+    "C4", "ISC", "PIE", "PT", "RET", "TCH", "PTH", "INT",
+    "ARG", "PL", "R", "TRY",
+    "G010", "G101", "G201", "G202",
+    "Q002", "Q003", "Q004",
+    ]
+lint.ignore = [
+    #"ANN001",   # No annotation
+    "ANN002",   # *args
+    "ANN003",   # **kwargs
+    "ANN401",   # Any
+    "ANN101",   # self: Self
+    "SIM108",   # single-line if / else assignment
+    "RET504",   # x=y+z; return x
+    "PIE790",   # unnecessary pass
+    "ISC003",   # non-implicit string concatenation
+    "C408",     # dict(x=y) instead of {'x': y}
+    "PLR09",    # Too many xxx
+    "PLR2004",  # magic number
+    "PLC0414",  # import x as x
+    "TRY003",   # Long exception message
+    "PTH123",   # open()
+    ]
+
+
+[[tool.mypy.overrides]]
+module = [
+    "matplotlib",
+    "matplotlib.axes",
+    "matplotlib.figure",
+    "mpl_toolkits.mplot3d",
+    ]
+ignore_missing_imports = true

setup.py

@@ -1,47 +0,0 @@
-#!/usr/bin/env python3
-
-from setuptools import setup, find_packages
-
-with open('README.md', 'r') as f:
-    long_description = f.read()
-
-with open('gridlock/VERSION.py', 'rt') as f:
-    version = f.readlines()[2].strip()
-
-setup(name='gridlock',
-      version=version,
-      description='Coupled gridding library',
-      long_description=long_description,
-      long_description_content_type='text/markdown',
-      author='Jan Petykiewicz',
-      author_email='jan@mpxd.net',
-      url='https://mpxd.net/code/jan/gridlock',
-      packages=find_packages(),
-      package_data={
-          'gridlock': ['py.typed'],
-      },
-      install_requires=[
-            'numpy',
-            'float_raster',
-      ],
-      extras_require={
-          'visualization': ['matplotlib'],
-          'visualization-isosurface': [
-                'matplotlib',
-                'skimage>=0.13',
-                'mpl_toolkits',
-          ],
-      },
-      classifiers=[
-            'Programming Language :: Python :: 3',
-            'Development Status :: 4 - Beta',
-            'Intended Audience :: Developers',
-            'Intended Audience :: Science/Research',
-            'License :: OSI Approved :: GNU Affero General Public License v3',
-            'Topic :: Multimedia :: Graphics :: 3D Rendering',
-            'Topic :: Scientific/Engineering :: Electronic Design Automation (EDA)',
-            'Topic :: Scientific/Engineering :: Physics',
-            'Topic :: Scientific/Engineering :: Visualization',
-      ],
-      )
-