def test_index_of_grid_at_x(self):
coordinates = np.array([-2.0, -1.0, 0.0, 1.0, 2.0])
with patch('pyscses.grid.closest_index') as mock_closest_index:
mock_closest_index.side_effect = [0, 2, 3]
self.assertEqual(
index_of_grid_at_x(coordinates=coordinates, x=-1.5), 0)
self.assertEqual(
index_of_grid_at_x(coordinates=coordinates, x=0.1), 2)
self.assertEqual(
index_of_grid_at_x(coordinates=coordinates, x=1.5), 3)
def calculate_energies_on_grid( self, grid, phi ):
"""
Returns an array of energies at their points on a one-dimensional grid.
Args:
grid (object): Grid object - contains properties of the grid including the x coordinates and the volumes. Used to access the x coordinates.
phi (array): electrostatic potential on a one-dimensional grid.
Returns:
array: energies at their grid points
"""
energies_on_grid = np.zeros_like( grid )
for site in self.sites:
energies_on_grid[index_of_grid_at_x( grid.x, site.x )] =+ energy_at_x( site.defect_energies, grid.x, site.x )
return energies_on_grid
def calculate_probabilities( self, grid, phi, temp ):
"""
Calculates the probability of a site being occupied by its corresponding defect.
Args:
grid (object): Grid object - contains properties of the grid including the x coordinates and the volumes. Used to access the x coordinates.
phi (array): electrostatic potential on a one-dimensional grid.
temp (float): Absolute temperature.
Returns:
array: probabilities of defects occupying each site using their grid points
"""
probability = np.zeros_like( grid.x )
for site in self.sites:
probability[index_of_grid_at_x( grid.x, site.x )] = np.asarray( site.probabilities( phi_at_x( phi, grid.x, site.x ), temp ) )
return probability
def calculate_defect_density( self, grid, phi, temp ):
"""
Calculates the defect density at each site.
Args:
grid (object): Grid object - contains properties of the grid including the x coordinates and the volumes. Used to access the x coordinates.
phi (array): electrostatic potential on a one-dimensional grid.
temp (float): Absolute temperature.
Returns:
array: defect density for each site using their grid points
"""
defect_density = np.zeros_like( grid.x )
for site in self.sites:
i = index_of_grid_at_x( grid.x, site.x )
defect_density[ i ] += np.asarray( site.probabilities( phi_at_x( phi, grid.x, site.x ), temp ) ) / grid.volumes[ i ]
return defect_density
def subgrid_calculate_defect_density(self,
sub_grid: Grid,
full_grid: Grid,
phi: np.ndarray,
temp: float) -> np.ndarray:
"""
Calculates the defect density at each site for a given subset of sites.
Args:
subgrid (Grid): Grid object - contains properties of the grid including the x coordinates and the volumes. Used to access the x coordinates. For a given subset of sites.
full_grid (Grid): Grid object - contains properties of the grid including the x coordinates and the volumes. Used to access the x coordinates. For all sites.
phi (array): electrostatic potential on a one-dimensional grid.
temp (float): Absolute temperature.
Returns:
array: defect density for each site using their grid points
"""
defect_density = np.zeros_like( sub_grid.x )
for site in self.sites:
i = index_of_grid_at_x( sub_grid.x, site.x )
defect_density[ i ] += np.asarray( site.probabilities_as_list( phi_at_x( phi, full_grid.x, site.x ), temp ) ) / sub_grid.volumes[ i ]
return defect_density