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jewettaijfcjewettaijfc
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fixed a bug that broke HeatChargeSimulationData
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‎report.log_BRANCH=develop

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2025-06-09 02:58:24,556 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:58:24,559 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -4.0, best pos: [1.31458373 2.68381999 0.17008842]
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2025-06-09 02:58:24,567 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:58:25,515 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.2173841905076921, best pos: [0.21058041 0.59430447 1.60148914]
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2025-06-09 02:58:25,520 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:58:25,895 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:48,687 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:48,691 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -4.0, best pos: [1.31458373 2.68381999 0.17008842]
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2025-06-09 02:59:48,698 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:48,701 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -4.0, best pos: [1.31458373 2.68381999 0.17008842]
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2025-06-09 02:59:48,708 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:48,713 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:48,716 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -8.0, best pos: [1.31458373 2.68381999 0.17008842]
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2025-06-09 02:59:48,723 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:48,726 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -8.0, best pos: [1.31458373 2.68381999 0.17008842]
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2025-06-09 02:59:48,731 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:48,737 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:49,887 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.2173841905076921, best pos: [0.21058041 0.59430447 1.60148914]
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2025-06-09 02:59:49,892 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:50,882 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.2173841905076921, best pos: [0.21058041 0.59430447 1.60148914]
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2025-06-09 02:59:50,887 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:51,757 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.2173841905076921, best pos: [0.21058041 0.59430447 1.60148914]
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2025-06-09 02:59:51,857 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:52,763 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.2173841905076921, best pos: [0.21058041 0.59430447 1.60148914]
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2025-06-09 02:59:52,768 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:54,346 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.2173841905076921, best pos: [0.21058041 0.59430447 1.60148914]
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2025-06-09 02:59:54,351 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:55,614 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.6521525715230763, best pos: [0.21058041 0.59430447 1.60148914]
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2025-06-09 02:59:55,617 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:57,191 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.8695367620307684, best pos: [0.21058041 0.59430447 1.60148914]
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2025-06-09 02:59:57,197 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:58,257 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.4347683810153842, best pos: [0.21058041 0.59430447 1.60148914]
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2025-06-09 02:59:58,262 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 02:59:59,275 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.4347683810153842, best pos: [0.21058041 0.59430447 1.60148914]
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2025-06-09 02:59:59,367 - pyswarms.single.global_best - INFO - Optimize for 2 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.9}
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2025-06-09 03:00:00,172 - pyswarms.single.global_best - INFO - Optimization finished | best cost: -0.2173841905076921, best pos: [0.21058041 0.59430447 1.60148914]

‎results.prof_BRANCH=develop

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‎tidy3d/components/geometry/polyslab.py

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@@ -89,6 +89,7 @@ class PolySlab(base.Planar):
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@staticmethod
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def make_shapely_polygon(vertices: ArrayLike) -> shapely.Polygon:
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"""Make a shapely polygon from some vertices, first ensures they are untraced."""
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vertices = get_static(vertices)
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return shapely.Polygon(vertices)
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@pydantic.validator("slab_bounds", always=True)

‎tidy3d/components/tcad/data/sim_data.py

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from tidy3d.components.base import Tidy3dBaseModel
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from tidy3d.components.base_sim.data.sim_data import AbstractSimulationData
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from tidy3d.components.geometry.base import Geometry
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from tidy3d.components.data.data_array import (
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SpatialDataArray,
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SteadyVoltageDataArray,
@@ -262,8 +263,8 @@ def plot_field(
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# compute plot bounds
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field_data_bounds = field_data.bounds
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_, min_bounds = self.pop_axis_and_swap(field_data_bounds[0], axis, swap_axes=swap_axes)
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_, max_bounds = self.pop_axis_and_swap(field_data_bounds[1], axis, swap_axes=swap_axes)
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_, min_bounds = Geometry.pop_axis_and_swap(field_data_bounds[0], axis, swap_axes=swap_axes)
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_, max_bounds = Geometry.pop_axis_and_swap(field_data_bounds[1], axis, swap_axes=swap_axes)
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if isinstance(field_data, SpatialDataArray):
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# interp out any monitor.size==0 dimensions
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axis = "xyz".index(planar_coord)
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position = float(field_data.coords[planar_coord])
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_, xy_coord_labels = self.pop_axis_and_swap(list("xyz"), axis=axis, swap_axes=swap_axes)
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_, xy_coord_labels = Geometry.pop_axis_and_swap(list("xyz"), axis=axis, swap_axes=swap_axes)
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x_coord_label, y_coord_label = xy_coord_labels[0], xy_coord_labels[1]
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field_data.plot(

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