示例#1
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    def metric(point):

        # Update molecular configuration based on given configuration
        molecule.set_positions(convert_vector_to_atoms(point))

        # Evaluate the value of sqrt(2(E-V)), replacing E-V with 1E-9 if V > E.
        cf = math.sqrt(max([2*(energy - molecule.get_potential_energy()), 1E-9]))

        # Return sqrt(2(E-V)) and it's gradient
        return [cf, molecule.get_forces().flatten()/cf]
        def integrand(t, x_1, x_2):
            x = np.add(np.subtract(x_2, x_1) * t, x_1)
            curve.configuration['molecule'].set_positions(
                convert_vector_to_atoms(x))
            metric_cf = 1 / math.sqrt(
                2 * (curve.energy -
                     curve.configuration['molecule'].get_potential_energy()))

            return metric_cf * math.sqrt(
                np.inner(np.subtract(x_2, x_1),
                         mass_matrix.dot(np.subtract(x_2, x_1))))
示例#3
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def write_xyz_animation(curve_pickle, filename):
    """ This function takes a curve object that has been pickled and writes out an XYZ animation file to use with JMol.

    Args:
      curve_pickle (str): The location of a pickled curve object.
      filename (str): The location of where to write the XYZ animation.

    """

    # Unpickle the curve object
    curve = pickle.load(open(curve_pickle, "rb"))

    # Extract the trajectories points
    trajectory = curve.get_points()

    # Reparameterise to determine physical times
    times = get_times(curve)

    # Index to determine correct time
    i = 0

    # Extract the curves molecular configuration as described by an ASE atoms object
    molecule = curve.configuration['molecule']

    # Create a new file for the animation to be stored
    animation_file = open(filename, 'w')

    # For each node along the curve...
    for state in trajectory:

        # Determine the molecular configuration in ASE
        molecule.set_positions(convert_vector_to_atoms(state))

        # Produce a snapshot XYZ file of the configuration at that point in time
        write('current_state.xyz',
              molecule,
              format='xyz')

        # Increase index
        i += 1

        # Open the newly produced XYZ file
        current_state = open('current_state.xyz', 'r')

        # Append the snapshot XYZ file into the animation file
        for line in current_state:
            animation_file.write(line)
        current_state.close()

    # Once finished close the file so that other programs can access it
    animation_file.close()

    # Delete the temporary file used to store current snapshots
    os.remove('current_state.xyz')
def write_xyz_animation(curve_pickle, filename):
    """ This function takes a curve object that has been pickled and writes out an XYZ animation file to use with JMol.

    Args:
      curve_pickle (str): The location of a pickled curve object.
      filename (str): The location of where to write the XYZ animation.

    """

    # Unpickle the curve object
    curve = pickle.load(open(curve_pickle, "rb"))

    # Extract the trajectories points
    trajectory = curve.get_points()

    # Reparameterise to determine physical times
    times = get_times(curve)

    # Index to determine correct time
    i = 0

    # Extract the curves molecular configuration as described by an ASE atoms object
    molecule = curve.configuration['molecule']

    # Create a new file for the animation to be stored
    animation_file = open(filename, 'w')

    # For each node along the curve...
    for state in trajectory:

        # Determine the molecular configuration in ASE
        molecule.set_positions(convert_vector_to_atoms(state))

        # Produce a snapshot XYZ file of the configuration at that point in time
        write('current_state.xyz', molecule, format='xyz')

        # Increase index
        i += 1

        # Open the newly produced XYZ file
        current_state = open('current_state.xyz', 'r')

        # Append the snapshot XYZ file into the animation file
        for line in current_state:
            animation_file.write(line)
        current_state.close()

    # Once finished close the file so that other programs can access it
    animation_file.close()

    # Delete the temporary file used to store current snapshots
    os.remove('current_state.xyz')
示例#5
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    def metric(point):

        # Update molecular configuration based on given configuration
        molecule.set_positions(convert_vector_to_atoms(point[:-9]))

        # Compute -grad(V)
        minus_grad_V = molecule.get_forces().flatten()

        # Extract cell information from the point
        cell = np.reshape(convert_vector_to_atoms(point[-9:]), (3, 3))

        # Compute the cell volume scaled by pressure
        cell_volume = pressure * abs(np.linalg.det(cell))

        # Compute gradient of the volume
        grad_cell_volume = -cell_volume * np.linalg.inv(cell).transpose().flatten()

        # Update the molecule's cell
        molecule.set_cell(cell)

        # Evaluate the value of sqrt(2(E-V)), replacing E-V with 1E-9 if V > E.
        cf = math.sqrt(max([2*(energy - molecule.get_potential_energy() - cell_volume), 1E-9]))

        return [cf, np.hstack((minus_grad_V, grad_cell_volume))/cf]
    def metric(point, Q_I_matrix, x_k):

        # Update molecular configuration based on given configuration
        molecule.set_positions(convert_vector_to_atoms(point))

        # Evaluate the value of sqrt(2(E-V)), replacing E-V with 1E-9 if V > E.
        x_k = np.asarray(x_k)

        Q_I_matrix = np.asarray(Q_I_matrix)

        # print Q_I_matrix

        b = Q_I_matrix.dot(x_k)
        # print b

        c = np.absolute(np.inner(x_k, b))

        # print c

        cf = math.sqrt(max([c, 1E-9]))

        # Return sqrt(2(E-V)) and it's gradient
        return [cf, molecule.get_forces().flatten() / cf]
    def metric(point, Q_I_matrix, x_k):

        # Update molecular configuration based on given configuration
        molecule.set_positions(convert_vector_to_atoms(point))

        # Evaluate the value of sqrt(2(E-V)), replacing E-V with 1E-9 if V > E.
        x_k = np.asarray(x_k)

        Q_I_matrix = np.asarray(Q_I_matrix)

        # print Q_I_matrix

        b = Q_I_matrix.dot(x_k)
        # print b

        c = np.absolute(np.inner(x_k, b))

        # print c

        cf = math.sqrt(max([c, 1E-9]))

        # Return sqrt(2(E-V)) and it's gradient
        return [cf, molecule.get_forces().flatten()/cf]
示例#8
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        def integrand(t, x_1, x_2):
            x = np.add(np.subtract(x_2, x_1)*t, x_1)
            curve.configuration['molecule'].set_positions(convert_vector_to_atoms(x))
            metric_cf = 1/math.sqrt(2*(curve.energy - curve.configuration['molecule'].get_potential_energy()))

            return metric_cf * math.sqrt(np.inner(np.subtract(x_2, x_1), mass_matrix.dot(np.subtract(x_2, x_1))))