Fix: align NumPy/PyTorch backends with C++ backend for dI/dV - #69
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…d Si(7x7) examples (#66)
…yTorch backends (#66)
Add py, px, dyz, dxz orbital types to verify consistency between C++ and other backends in dI/dV calculations.
Body: - checks C++ vs alternative dI/dV backends - covers single-orbital tip/sample pairs for the intended sp/spd combinations
Correct bugs in NumPy and PyTorch backends that caused incorrect results when computing dI/dV for px/py tip orbitals with d-orbital samples.
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ondrejkrejci
reviewed
Aug 9, 2026
| WF (float): Work function | ||
| eta (float): Broadening parameter (energy smearing) | ||
| eig (np.ndarray): Eigenvalues array, shape (n_o,) | ||
| eig (np.ndarray): Eigenvalues array, shape (n_e,) |
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Just a comment - n_o -> n_e means from number of orbitals to number of electrons?
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number of eigenvalues or eigenstates
ondrejkrejci
reviewed
Aug 9, 2026
| _N_D = math.sqrt(15) | ||
| _N_D2 = math.sqrt(5) * 0.5 | ||
| _I_3 = 0.3333333 | ||
| _I_3 = 1/3 |
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So you have found a way, how to make it precise and in agreement with previous calculations, which are not as precise on this?
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Using the exact fraction 1/3 lets the numerical backend (NumPy/PyTorch) use its own precision management.
ondrejkrejci
reviewed
Aug 9, 2026
| wf = np.float32(WF) | ||
| eta = np.float32(eta) | ||
| self._tip_coes = self._to_float(tip_coes) # shape (9,) | ||
| eig = self._to_float(eig) # shape (n_e,) |
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Why is here this "_to_float"?
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"_to_float" moves tensors to single-precision floating-point.
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Closes #66