Implements More Tests and Begins Dice Refactor Implementation
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@@ -79,14 +79,13 @@ class Molecule:
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"""
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Updated positions based on the center of mass of the molecule
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"""
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self.center_of_mass()
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for atom in self.atom:
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atom.rx -= self.com[0]
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atom.ry -= self.com[1]
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atom.rz -= self.com[2]
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self.center_of_mass()
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def charges_and_dipole(self) -> List[float]:
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"""
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Calculates the charges and dipole of the molecule atoms
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@@ -119,16 +118,15 @@ class Molecule:
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distances = []
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dim = len(self.atom)
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for atom1 in self.atom:
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if atom1.na != ghost_number:
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for atom2 in self.atom:
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if atom2.na != ghost_number:
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dx = atom1.rx - atom2.rx
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dy = atom1.ry - atom2.ry
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dz = atom1.rz - atom2.rz
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distances.append(math.sqrt(dx ** 2 + dy ** 2 + dz ** 2))
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for index1, atom1 in enumerate(self.atom):
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for index2, atom2 in enumerate(self.atom):
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if index1 != index2:
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dx = atom1.rx - atom2.rx
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dy = atom1.ry - atom2.ry
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dz = atom1.rz - atom2.rz
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distances.append(math.sqrt(dx ** 2 + dy ** 2 + dz ** 2))
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return np.array(distances).reshape(dim, dim)
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return np.array(distances).reshape(dim, dim-1)
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def inertia_tensor(self) -> NDArray[Shape["3, 3"], Float]:
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"""
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@@ -156,40 +154,6 @@ class Molecule:
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return np.array([[Ixx, Ixy, Ixz], [Ixy, Iyy, Iyz], [Ixz, Iyz, Izz]])
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def axes(self) -> NDArray[Shape["3, 3"], Float]:
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"""
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Calculates the axes of the molecule
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Returns:
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NDArray[Shape["3, 3"], Float]: Returns the axes of molecule
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"""
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eixos = np.zeros(3)
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if len(self.atom) == 2:
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position1 = np.array([self.atom[0].rx, self.atom[0].ry, self.atom[0].rz])
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position2 = np.array([self.atom[1].rx, self.atom[1].ry, self.atom[1].rz])
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eixos = position2 - position1
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eixos /= linalg.norm(eixos)
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elif len(self.atom) > 2:
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position1 = np.array([self.atom[0].rx, self.atom[0].ry, self.atom[0].rz])
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position2 = np.array([self.atom[1].rx, self.atom[1].ry, self.atom[1].rz])
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position3 = np.array([self.atom[2].rx, self.atom[2].ry, self.atom[2].rz])
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v1 = position2 - position1
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v2 = position3 - position1
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v3 = np.cross(v1, v2)
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v2 = np.cross(v1, v3)
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v1 /= linalg.norm(v1)
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v2 /= linalg.norm(v2)
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v3 /= linalg.norm(v3)
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eixos = np.array(
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[[v1[0], v1[1], v1[2]], [v2[0], v2[1], v2[2]], [v3[0], v3[1], v3[2]]]
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)
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return eixos
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def principal_axes(self) -> Tuple[np.ndarray, np.ndarray]:
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"""
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Calculates the principal axes of the molecule
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@@ -201,7 +165,7 @@ class Molecule:
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try:
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evals, evecs = linalg.eigh(self.inertia_tensor())
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except:
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except ValueError:
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raise RuntimeError("Error: diagonalization of inertia tensor did not converge")
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return evals, evecs
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@@ -221,38 +185,38 @@ class Molecule:
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return position
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def updateCharges(self, charges: List[float]) -> None:
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def update_charges(self, charges: List[float]) -> None:
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for i, atom in enumerate(self.atom):
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atom.chg = charges[i]
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def update_hessian(
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self,
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step: np.ndarray,
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cur_gradient: np.ndarray,
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old_gradient: np.ndarray,
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hessian: np.ndarray,
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) -> np.ndarray:
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"""
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Updates the Hessian of the molecule based on the current hessian, the current gradient and the previous gradient
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Args:
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step (np.ndarray): step value of the iteration
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cur_gradient (np.ndarray): current gradient
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old_gradient (np.ndarray): previous gradient
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hessian (np.ndarray): current hessian
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Returns:
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np.ndarray: updated hessian of the molecule
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"""
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dif_gradient = cur_gradient - old_gradient
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mat1 = 1 / np.dot(dif_gradient, step) * np.matmul(dif_gradient.T, dif_gradient)
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mat2 = 1 / np.dot(step, np.matmul(hessian, step.T).T)
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mat2 *= np.matmul(np.matmul(hessian, step.T), np.matmul(step, hessian))
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return hessian + mat1 - mat2
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# @staticmethod
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# def update_hessian(
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# step: np.ndarray,
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# cur_gradient: np.ndarray,
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# old_gradient: np.ndarray,
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# hessian: np.ndarray,
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# ) -> np.ndarray:
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# """
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# Updates the Hessian of the molecule based on the current hessian, the current gradient and the previous gradient
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#
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# Args:
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# step (np.ndarray): step value of the iteration
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# cur_gradient (np.ndarray): current gradient
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# old_gradient (np.ndarray): previous gradient
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# hessian (np.ndarray): current hessian
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#
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# Returns:
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# np.ndarray: updated hessian of the molecule
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# """
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#
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# dif_gradient = cur_gradient - old_gradient
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#
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# mat1 = 1 / np.dot(dif_gradient, step) * np.matmul(dif_gradient.T, dif_gradient)
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# mat2 = 1 / np.dot(step, np.matmul(hessian, step.T).T)
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# mat2 *= np.matmul(np.matmul(hessian, step.T), np.matmul(step, hessian))
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#
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# return hessian + mat1 - mat2
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def sizes_of_molecule(self) -> List[float]:
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"""
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@@ -267,10 +231,9 @@ class Molecule:
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z_list = []
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for atom in self.atom:
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if atom.na != ghost_number:
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x_list.append(atom.rx)
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y_list.append(atom.ry)
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z_list.append(atom.rz)
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x_list.append(atom.rx)
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y_list.append(atom.ry)
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z_list.append(atom.rz)
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x_max = max(x_list)
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x_min = min(x_list)
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