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https://github.com/edoverg/modeconverter.git
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modified reshaping to automatically account for correct dimensions
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+5
-5
@@ -109,7 +109,7 @@ def filter_and_project(
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print("Inside filter_and_project function...")
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weights_filtered = mpa.conic_filter(
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weights.reshape(NX_DESIGN_GRID, NY_DESIGN_GRID),
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weights.reshape(-1, NY_DESIGN_GRID), #row major mapping
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filter_radius_um,
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DESIGN_REGION_UM.x,
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DESIGN_REGION_UM.y,
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@@ -127,7 +127,7 @@ def filter_and_project(
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return weights_filtered.flatten()
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else:
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weights_projected = mpa.tanh_projection(
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weights_filtered.reshape(NX_DESIGN_GRID,NY_DESIGN_GRID),
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weights_filtered.reshape(-1,NY_DESIGN_GRID),
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sigmoid_bias,
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sigmoid_threshold,
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)
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@@ -253,7 +253,7 @@ def line_width_and_spacing_constraint(
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gradient[:,0] = -a1
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filter_func = lambda a: mpa.conic_filter(
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a.reshape(NX_DESIGN_GRID,NY_DESIGN_GRID),
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a.reshape(-1,NY_DESIGN_GRID),
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filter_radius_um,
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DESIGN_REGION_UM.x,
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DESIGN_REGION_UM.y,
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@@ -261,7 +261,7 @@ def line_width_and_spacing_constraint(
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)
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threshold_func = lambda a: mpa.tanh_projection(
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a.reshape(NX_DESIGN_GRID,NY_DESIGN_GRID),
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a.reshape(-1,NY_DESIGN_GRID),
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sigmoid_bias,
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SIGMOID_THRESHOLD_INTRINSIC,
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)
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@@ -742,7 +742,7 @@ if __name__ == "__main__":
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epigraph_and_weights[1:],
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SIGMOID_THRESHOLD_INTRINSIC,
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sigmoid_bias
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).reshape(NX_DESIGN_GRID, NY_DESIGN_GRID)
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).reshape(-1, NY_DESIGN_GRID)
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fig, ax = plt.subplots()
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ax.imshow(
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