forked from jan/fdfd_tools
Return real part of the gradient
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@ -400,7 +400,7 @@ def eigsolve(num_modes: int,
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df_dy = scipy_iop @ (AzU - zU @ zTAzU)
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else:
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df_dy = (AzU - zU @ zTAzU)
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return numpy.abs(f), numpy.sign(f) * df_dy.ravel()
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return numpy.abs(f), numpy.sign(f) * numpy.real(df_dy).ravel()
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'''
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Use the conjugate gradient method and the approximate gradient calculation to
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