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9ab97e763c
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9ab97e763c | |||
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3e4e6eead3 | |||
a94c2cae67 |
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.flake8
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[flake8]
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ignore =
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E501,
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# W391 newlines at EOF
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E302,
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# E741 ambiguous variable name 'I'
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E741,
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per-file-ignores =
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# F401 import without use
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*/__init__.py: F401,
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@ -14,7 +14,7 @@ the coordinates of the boundary points along each axis).
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## Installation
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Requirements:
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* python 3 (written and tested with 3.9)
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* python >3.11 (written and tested with 3.12)
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* numpy
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* [float_raster](https://mpxd.net/code/jan/float_raster)
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* matplotlib (optional, used for visualization functions)
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@ -1,7 +1,7 @@
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"""
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Drawing-related methods for Grid class
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"""
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from typing import List, Optional, Union, Sequence, Callable
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from typing import Union, Sequence, Callable
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import numpy
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from numpy.typing import NDArray, ArrayLike
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@ -27,7 +27,7 @@ def draw_polygons(
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center: ArrayLike,
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polygons: Sequence[NDArray],
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thickness: float,
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foreground: Union[Sequence[foreground_t], foreground_t],
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foreground: Sequence[foreground_t] | foreground_t,
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) -> None:
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"""
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Draw polygons on an axis-aligned plane.
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@ -70,7 +70,7 @@ def draw_polygons(
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+ 'xyz'[surface_normal])
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# Broadcast foreground where necessary
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foregrounds: Union[Sequence[foreground_callable_t], Sequence[float]]
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foregrounds: Sequence[foreground_callable_t] | Sequence[float]
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if numpy.size(foreground) == 1: # type: ignore
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foregrounds = [foreground] * len(cell_data) # type: ignore
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elif isinstance(foreground, numpy.ndarray):
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@ -113,7 +113,7 @@ def draw_polygons(
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foregrounds_i = foregrounds[i]
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if callable(foregrounds_i):
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# meshgrid over the (shifted) domain
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domain = [self.shifted_xyz(i)[k][bdi_min[k]:bdi_max[k]+1] for k in range(3)]
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domain = [self.shifted_xyz(i)[k][bdi_min[k]:bdi_max[k] + 1] for k in range(3)]
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(x0, y0, z0) = numpy.meshgrid(*domain, indexing='ij')
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# evaluate on the meshgrid
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@ -202,7 +202,7 @@ def draw_polygon(
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center: ArrayLike,
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polygon: ArrayLike,
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thickness: float,
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foreground: Union[Sequence[foreground_t], foreground_t],
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foreground: Sequence[foreground_t] | foreground_t,
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) -> None:
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"""
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Draw a polygon on an axis-aligned plane.
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@ -226,7 +226,7 @@ def draw_slab(
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surface_normal: int,
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center: ArrayLike,
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thickness: float,
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foreground: Union[Sequence[foreground_t], foreground_t],
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foreground: Sequence[foreground_t] | foreground_t,
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) -> None:
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"""
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Draw an axis-aligned infinite slab.
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@ -276,7 +276,7 @@ def draw_cuboid(
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cell_data: NDArray,
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center: ArrayLike,
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dimensions: ArrayLike,
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foreground: Union[Sequence[foreground_t], foreground_t],
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foreground: Sequence[foreground_t] | foreground_t,
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) -> None:
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"""
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Draw an axis-aligned cuboid
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@ -305,7 +305,7 @@ def draw_cylinder(
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radius: float,
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thickness: float,
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num_points: int,
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foreground: Union[Sequence[foreground_t], foreground_t],
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foreground: Sequence[foreground_t] | foreground_t,
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) -> None:
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"""
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Draw an axis-aligned cylinder. Approximated by a num_points-gon
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@ -319,7 +319,7 @@ def draw_cylinder(
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num_points: The circle is approximated by a polygon with `num_points` vertices
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foreground: Value to draw with ('brush color'). See `draw_polygons()` for details.
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"""
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theta = numpy.linspace(0, 2*numpy.pi, num_points, endpoint=False)
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theta = numpy.linspace(0, 2 * numpy.pi, num_points, endpoint=False)
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x = radius * numpy.sin(theta)
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y = radius * numpy.cos(theta)
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polygon = numpy.hstack((x[:, None], y[:, None]))
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@ -360,8 +360,8 @@ def draw_extrude_rectangle(
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surface = numpy.delete(range(3), direction)
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dim = numpy.fabs(numpy.diff(rectangle, axis=0).T)[surface]
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p = numpy.vstack((numpy.array([-1, -1, 1, 1], dtype=float) * dim[0]/2.0,
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numpy.array([-1, 1, 1, -1], dtype=float) * dim[1]/2.0)).T
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p = numpy.vstack((numpy.array([-1, -1, 1, 1], dtype=float) * dim[0] * 0.5,
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numpy.array([-1, 1, 1, -1], dtype=float) * dim[1] * 0.5)).T
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thickness = distance
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foreground_func = []
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@ -371,7 +371,7 @@ def draw_extrude_rectangle(
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ind = [int(numpy.floor(z)) if i == direction else slice(None) for i in range(3)]
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fpart = z - numpy.floor(z)
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mult = [1-fpart, fpart][::s] # reverses if s negative
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mult = [1 - fpart, fpart][::s] # reverses if s negative
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foreground = mult[0] * grid[tuple(ind)]
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ind[direction] += 1 # type: ignore #(known safe)
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@ -1,8 +1,7 @@
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from typing import List, Tuple, Callable, Dict, Optional, Union, Sequence, ClassVar, TypeVar
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from typing import Callable, Sequence, ClassVar, Self
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import numpy
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from numpy.typing import NDArray, ArrayLike
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from numpy import diff, floor, ceil, zeros, hstack, newaxis
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import pickle
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import warnings
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@ -12,7 +11,6 @@ from . import GridError
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foreground_callable_type = Callable[[NDArray, NDArray, NDArray], NDArray]
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T = TypeVar('T', bound='Grid')
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class Grid:
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@ -49,10 +47,10 @@ class Grid:
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Because of this, we either assume this 'ghost' cell is the same size as the last
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real cell, or, if `self.periodic[a]` is set to `True`, the same size as the first cell.
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"""
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exyz: List[NDArray]
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exyz: list[NDArray]
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"""Cell edges. Monotonically increasing without duplicates."""
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periodic: List[bool]
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periodic: list[bool]
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"""For each axis, determines how far the rightmost boundary gets shifted. """
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shifts: NDArray
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@ -80,7 +78,7 @@ class Grid:
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from .position import ind2pos, pos2ind
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@property
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def dxyz(self) -> List[NDArray]:
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def dxyz(self) -> list[NDArray]:
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"""
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Cell sizes for each axis, no shifts applied
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@ -90,7 +88,7 @@ class Grid:
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return [numpy.diff(ee) for ee in self.exyz]
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@property
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def xyz(self) -> List[NDArray]:
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def xyz(self) -> list[NDArray]:
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"""
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Cell centers for each axis, no shifts applied
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@ -124,7 +122,7 @@ class Grid:
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return numpy.hstack((self.num_grids, self.shape))
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@property
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def dxyz_with_ghost(self) -> List[NDArray]:
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def dxyz_with_ghost(self) -> list[NDArray]:
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"""
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Gives dxyz with an additional 'ghost' cell at the end, whose value depends
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on whether or not the axis has periodic boundary conditions. See main description
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@ -153,7 +151,7 @@ class Grid:
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return numpy.array(centers, dtype=float)
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@property
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def dxyz_limits(self) -> Tuple[NDArray, NDArray]:
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def dxyz_limits(self) -> tuple[NDArray, NDArray]:
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"""
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Returns the minimum and maximum cell size for each axis, as a tuple of two 3-element
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ndarrays. No shifts are applied, so these are extreme bounds on these values (as a
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@ -166,7 +164,7 @@ class Grid:
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d_max = numpy.array([max(self.dxyz[a]) for a in range(3)], dtype=float)
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return d_min, d_max
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def shifted_exyz(self, which_shifts: Optional[int]) -> List[NDArray]:
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def shifted_exyz(self, which_shifts: int | None) -> list[NDArray]:
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"""
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Returns edges for which_shifts.
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@ -188,7 +186,7 @@ class Grid:
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return [self.exyz[a] + dxyz[a] * shifts[a] for a in range(3)]
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def shifted_dxyz(self, which_shifts: Optional[int]) -> List[NDArray]:
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def shifted_dxyz(self, which_shifts: int | None) -> list[NDArray]:
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"""
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Returns cell sizes for `which_shifts`.
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@ -215,7 +213,7 @@ class Grid:
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return sdxyz
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def shifted_xyz(self, which_shifts: Optional[int]) -> List[NDArray[numpy.float64]]:
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def shifted_xyz(self, which_shifts: int | None) -> list[NDArray[numpy.float64]]:
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"""
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Returns cell centers for `which_shifts`.
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@ -231,7 +229,7 @@ class Grid:
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dxyz = self.shifted_dxyz(which_shifts)
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return [exyz[a][:-1] + dxyz[a] / 2.0 for a in range(3)]
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def autoshifted_dxyz(self) -> List[NDArray[numpy.float64]]:
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def autoshifted_dxyz(self) -> list[NDArray[numpy.float64]]:
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"""
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Return cell widths, with each dimension shifted by the corresponding shifts.
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@ -242,7 +240,7 @@ class Grid:
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raise GridError('Autoshifting requires exactly 3 grids')
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return [self.shifted_dxyz(which_shifts=a)[a] for a in range(3)]
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def allocate(self, fill_value: Optional[float] = 1.0, dtype=numpy.float32) -> NDArray:
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def allocate(self, fill_value: float | None = 1.0, dtype=numpy.float32) -> NDArray:
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"""
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Allocate an ndarray for storing grid data.
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@ -263,7 +261,7 @@ class Grid:
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self,
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pixel_edge_coordinates: Sequence[ArrayLike],
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shifts: ArrayLike = Yee_Shifts_E,
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periodic: Union[bool, Sequence[bool]] = False,
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periodic: bool | Sequence[bool] = False,
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) -> None:
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"""
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Args:
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@ -320,7 +318,7 @@ class Grid:
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g.__dict__.update(tmp_dict)
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return g
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def save(self: T, filename: str) -> T:
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def save(self, filename: str) -> Self:
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"""
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Save to file.
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@ -334,7 +332,7 @@ class Grid:
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pickle.dump(self.__dict__, f, protocol=2)
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return self
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def copy(self: T) -> T:
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def copy(self) -> Self:
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"""
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Returns:
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Deep copy of the grid.
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@ -1,8 +1,6 @@
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"""
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Position-related methods for Grid class
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"""
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from typing import List, Optional, Sequence
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import numpy
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from numpy.typing import NDArray, ArrayLike
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@ -12,7 +10,7 @@ from . import GridError
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def ind2pos(
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self,
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ind: NDArray,
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which_shifts: Optional[int] = None,
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which_shifts: int | None = None,
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round_ind: bool = True,
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check_bounds: bool = True
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) -> NDArray[numpy.float64]:
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@ -64,7 +62,7 @@ def ind2pos(
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def pos2ind(
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self,
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r: ArrayLike,
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which_shifts: Optional[int],
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which_shifts: int | None,
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round_ind: bool = True,
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check_bounds: bool = True
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) -> NDArray[numpy.float64]:
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@ -1,13 +1,18 @@
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"""
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Readback and visualization methods for Grid class
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"""
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from typing import Dict, Optional, Union, Any
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from typing import Any, TYPE_CHECKING
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import numpy
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from numpy.typing import NDArray, ArrayLike
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from numpy.typing import NDArray
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from . import GridError
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if TYPE_CHECKING:
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import matplotlib.axes
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import matplotlib.figure
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# .visualize_* uses matplotlib
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# .visualize_isosurface uses skimage
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# .visualize_isosurface uses mpl_toolkits.mplot3d
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@ -85,8 +90,8 @@ def visualize_slice(
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which_shifts: int = 0,
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sample_period: int = 1,
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finalize: bool = True,
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pcolormesh_args: Optional[Dict[str, Any]] = None,
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) -> None:
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pcolormesh_args: dict[str, Any] | None = None,
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) -> tuple['matplotlib.axes.Axes', 'matplotlib.figure.Figure']:
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"""
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Visualize a slice of a grid.
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Interpolates if given a position between two planes.
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@ -97,6 +102,9 @@ def visualize_slice(
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which_shifts: Which grid to display. Default is the first grid (0).
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sample_period: Period for down-sampling the image. Default 1 (disabled)
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finalize: Whether to call `pyplot.show()` after constructing the plot. Default `True`
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Returns:
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(Figure, Axes)
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"""
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from matplotlib import pyplot
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@ -115,25 +123,27 @@ def visualize_slice(
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xmesh, ymesh = numpy.meshgrid(x, y, indexing='ij')
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x_label, y_label = ('xyz'[a] for a in surface)
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pyplot.figure()
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pyplot.pcolormesh(xmesh, ymesh, grid_slice, **pcolormesh_args)
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pyplot.colorbar()
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pyplot.gca().set_aspect('equal', adjustable='box')
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pyplot.xlabel(x_label)
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pyplot.ylabel(y_label)
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fig, ax = pyplot.subplots()
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mappable = ax.pcolormesh(xmesh, ymesh, grid_slice, **pcolormesh_args)
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fig.colorbar(mappable)
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ax.set_aspect('equal', adjustable='box')
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ax.set_xlabel(x_label)
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ax.set_ylabel(y_label)
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if finalize:
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pyplot.show()
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return fig, ax
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def visualize_isosurface(
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self,
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cell_data: NDArray,
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level: Optional[float] = None,
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level: float | None = None,
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which_shifts: int = 0,
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sample_period: int = 1,
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show_edges: bool = True,
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finalize: bool = True,
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) -> None:
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) -> tuple['matplotlib.axes.Axes', 'matplotlib.figure.Figure']:
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"""
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Draw an isosurface plot of the device.
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@ -144,6 +154,9 @@ def visualize_isosurface(
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sample_period: Period for down-sampling the image. Default 1 (disabled)
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show_edges: Whether to draw triangle edges. Default `True`
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finalize: Whether to call `pyplot.show()` after constructing the plot. Default `True`
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Returns:
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(Figure, Axes)
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"""
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from matplotlib import pyplot
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import skimage.measure
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@ -185,3 +198,5 @@ def visualize_isosurface(
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if finalize:
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pyplot.show()
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return fig, ax
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|
@ -32,13 +32,13 @@ classifiers = [
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"Topic :: Scientific/Engineering :: Physics",
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"Topic :: Scientific/Engineering :: Visualization",
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]
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requires-python = ">=3.8"
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requires-python = ">=3.11"
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include = [
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"LICENSE.md"
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]
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dynamic = ["version"]
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dependencies = [
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"numpy~=1.21",
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"numpy~=1.26",
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"float_raster",
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]
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|
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Reference in New Issue
Block a user