def test_cell_init( self ):
cell_matrix = np.array( [ [ 1.0, 1.0, 0.0 ],
[ 1.0, 0.0, 1.0 ],
[ 0.0, 1.0, 1.0 ] ] )
with patch( 'numpy.linalg.inv' ) as mock_invert:
mock_invert.return_value = np.array( [ [ 0.0, 0.0, 1.0 ],
[ 0.0, 1.0, 0.0 ],
[ 1.0, 0.0, 0.0 ] ] )
cell = Cell( cell_matrix )
mock_invert.assert_called_with( cell_matrix )
np.testing.assert_array_equal( cell.matrix, cell_matrix )
np.testing.assert_array_equal( cell.inv_matrix, mock_invert.return_value )
def poscar_from_pimaim_restart(filename, atom_numbers, atom_labels):
number_of_atoms = sum(atom_numbers)
coordinates, velocities, dipoles, full_cell_matrix = read_restart_file(
filename, number_of_atoms)
poscar = Poscar()
poscar.cell = Cell(
full_cell_matrix) # TODO: possibly this needs transposing
poscar.atoms = atom_labels
poscar.atom_numbers = atom_numbers
poscar.coordinate_type = 'Direct'
poscar.coordinates = poscar.cell.cartesian_to_fractional_coordinates(
coordinates)
return poscar
def main():
filename = 'testout.rst'
restart_file = True
args = parse_command_line_arguments()
coordinates, velocities, dipoles, full_cell_matrix, cell_lengths = read_pimaim_restart( filename )
assert( sum( args.atom_numbers ) == len( coordinates ) )
poscar = Poscar()
full_cell_matrix = full_cell_matrix.transpose()
coordinates = get_cart_coords_from_pimaim_restart(coordinates, full_cell_matrix,cell_lengths)
poscar.cell = Cell( full_cell_matrix)
poscar.atoms = args.labels
poscar.atom_numbers = args.atom_numbers
poscar.coordinate_type = 'Cartesian'
poscar.coordinates = coordinates
poscar.output()
def main():
filename = 'testout.rst'
restart_file = True
args = parse_command_line_arguments()
coordinates, velocities, dipoles, full_cell_matrix = read_pimaim_restart(
filename)
assert (sum(args.atom_numbers) == len(coordinates))
poscar = Poscar()
poscar.cell = Cell(
full_cell_matrix) # TODO: possibly this needs transposing?
poscar.atoms = args.labels
poscar.atom_numbers = args.atom_numbers
poscar.coordinate_type = 'Cartesian'
poscar.coordinates = coordinates
poscar.output()
def poscar_from_pimaim_restart(filename, atom_numbers, atom_labels):
number_of_atoms = sum(atom_numbers)
coordinates, velocities, dipoles, full_cell_matrix, cell_lengths = read_restart_file(
filename, number_of_atoms, cell_lengths)
poscar = Poscar()
full_cell_matrix = full_cell_matrix.transpose()
coordinates = get_cart_coords_from_pimaim_restart(coordinates,
full_cell_matrix,
cell_lengths)
poscar.cell = Cell(full_cell_matrix)
poscar.atoms = atom_labels
poscar.atom_numbers = atom_numbers
poscar.coordinate_type = 'Direct'
poscar.coordinates = poscar.cell.cartesian_to_fractional_coordinates(
coordinates)
return poscar
class Test_Cell( unittest.TestCase ):
def test_cell_init( self ):
cell_matrix = np.array( [ [ 1.0, 1.0, 0.0 ],
[ 1.0, 0.0, 1.0 ],
[ 0.0, 1.0, 1.0 ] ] )
with patch( 'numpy.linalg.inv' ) as mock_invert:
mock_invert.return_value = np.array( [ [ 0.0, 0.0, 1.0 ],
[ 0.0, 1.0, 0.0 ],
[ 1.0, 0.0, 0.0 ] ] )
cell = Cell( cell_matrix )
mock_invert.assert_called_with( cell_matrix )
np.testing.assert_array_equal( cell.matrix, cell_matrix )
np.testing.assert_array_equal( cell.inv_matrix, mock_invert.return_value )
def setUp( self ):
cell_matrix = np.array( [ [ 10.0, 0.0, 0.0 ],
[ 0.0, 10.0, 0.0 ],
[ 0.0, 0.0, 10.0 ] ] )
self.cell = Cell( cell_matrix )
def test_dr( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.1, 0.4, 0.1 ] )
self.assertEqual( self.cell.dr( r1, r2 ), 5.0 )
def test_dr_cutoff( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.1, 0.4, 0.1 ] )
self.assertEqual( self.cell.dr( r1, r2, cutoff=1.0 ), None )
def test_nearest_image( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.1, 0.4, 0.1 ] )
self.cell.minimum_image = Mock( return_value=np.array( [ -0.4, 0.3, 0.0 ] ) )
np.testing.assert_array_almost_equal( self.cell.nearest_image( r1, r2 ), r2 )
def test_minimum_image( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.1, 0.4, 0.3 ] )
np.testing.assert_array_almost_equal( self.cell.minimum_image( r1, r2 ),
np.array( [ -0.4, 0.3, 0.2 ] ) )
def test_minimum_image_2( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.6, 0.8, 0.8 ] )
np.testing.assert_array_almost_equal( self.cell.minimum_image( r1, r2 ),
np.array( [ 0.1, -0.3, -0.3 ] ) )
def test_minimum_image_dr( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.8, 0.7, 0.3 ] )
self.cell.minimum_image = Mock( return_value=np.array( [ 0.3, -0.4, 0.2 ] ) )
self.assertAlmostEqual( self.cell.minimum_image_dr( r1, r2 ), 5.385164807 )
def test_lengths( self ):
np.testing.assert_array_equal( self.cell.lengths(), np.array( [ 10.0, 10.0, 10.0 ] ) )
def test_angles( self ):
self.assertEqual( self.cell.angles(), [ 90.0, 90.0, 90.0 ] )
def test_cartesian_to_fractional_coordinates( self ):
coordinates = np.array( [ [ 1.0, 2.0, 3.0 ],
[ 4.0, 6.0, 2.0 ] ] )
expected_fractional_coordinates = np.array( [ [ 0.1, 0.2, 0.3 ],
[ 0.4, 0.6, 0.2 ] ] )
np.testing.assert_array_almost_equal( self.cell.cartesian_to_fractional_coordinates( coordinates ),
expected_fractional_coordinates )
def test_fractional_to_cartesian_coordinates( self ):
coordinates = np.array( [ [ 0.1, 0.2, 0.3 ],
[ 0.4, 0.6, 0.2 ] ] )
expected_cartesian_coordinates = np.array( [ [ 1.0, 2.0, 3.0 ],
[ 4.0, 6.0, 2.0 ] ] )
np.testing.assert_array_almost_equal( self.cell.fractional_to_cartesian_coordinates( coordinates ),
expected_cartesian_coordinates )
def test_inside_cell( self ):
c = np.array( [ [ [ 0.1, 0.2, 0.3 ], [ 0.1, 0.2, 0.3 ] ],
[ [ 0.3, 1.2, 1.3 ], [ 0.3, 0.2, 0.3 ] ] ] )
for r1, r2 in c:
np.testing.assert_array_almost_equal( self.cell.inside_cell( r1 ), r2 )
def test_volume( self ):
self.assertEqual( self.cell.volume(), 1000.0 )
def test_unit_vectors( self ):
np.testing.assert_array_equal( self.cell.unit_vectors(),
np.array( [ [ 1.0, 0.0, 0.0 ],
[ 0.0, 1.0, 0.0 ],
[ 0.0, 0.0, 1.0 ] ] ) )
def setUp( self ):
cell_matrix = np.array( [ [ 10.0, 0.0, 0.0 ],
[ 0.0, 10.0, 0.0 ],
[ 0.0, 0.0, 10.0 ] ] )
self.cell = Cell( cell_matrix )
number_of_atomic_data_lines = next( index for index, d in enumerate( data_per_line[4:] ) if d == 1 )
number_of_atomic_data_types = sum( [ cr_dump_log, vel_dump_log, chg_dump_log ] )
number_of_atoms = int( number_of_atomic_data_lines / number_of_atomic_data_types )
# this assumes coordinates, velocities, and dipoles are all present.
# not sure what happens if atoms have qudrupoles, etc.
coordinates = lines_to_numpy_array( file_data[ 4 : 4 + number_of_atoms ] )
velocities = lines_to_numpy_array( file_data[ 4 + number_of_atoms : 4 + number_of_atoms * 2 ] )
dipoles = lines_to_numpy_array( file_data[ 4 + number_of_atoms * 2 : 4 + number_of_atoms * 3 ] )
cell_matrix = lines_to_numpy_array( file_data[ -6: -3 ] )
cell_lengths = lines_to_numpy_array( file_data[ -3: ] )
full_cell_matrix = cell_matrix * cell_lengths
# TODO! need to check this with a non-orthorhombic cell
return( coordinates, velocities, dipoles, full_cell_matrix )
if __name__ == '__main__':
filename = 'testout.rst'
restart_file = True
args = parse_command_line_arguments()
coordinates, velocities, dipoles, full_cell_matrix = read_pimaim_restart( filename )
assert( sum( args.atom_numbers ) == len( coordinates ) )
poscar = Poscar()
poscar.cell = Cell( full_cell_matrix ) # TODO: possibly this needs transposing?
poscar.atoms = args.labels
poscar.atom_numbers = args.atom_numbers
poscar.coordinate_type = 'Cartesian'
poscar.coordinates = coordinates
poscar.output()
class Test_Cell( unittest.TestCase ):
def test_cell_init( self ):
cell_matrix = np.array( [ [ 1.0, 1.0, 0.0 ],
[ 1.0, 0.0, 1.0 ],
[ 0.0, 1.0, 1.0 ] ] )
with patch( 'numpy.linalg.inv' ) as mock_invert:
mock_invert.return_value = np.array( [ [ 0.0, 0.0, 1.0 ],
[ 0.0, 1.0, 0.0 ],
[ 1.0, 0.0, 0.0 ] ] )
cell = Cell( cell_matrix )
mock_invert.assert_called_with( cell_matrix )
np.testing.assert_array_equal( cell.matrix, cell_matrix )
np.testing.assert_array_equal( cell.inv_matrix, mock_invert.return_value )
def setUp( self ):
cell_matrix = np.array( [ [ 10.0, 0.0, 0.0 ],
[ 0.0, 10.0, 0.0 ],
[ 0.0, 0.0, 10.0 ] ] )
self.cell = Cell( cell_matrix )
def test_dr( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.1, 0.4, 0.1 ] )
self.assertEqual( self.cell.dr( r1, r2 ), 5.0 )
def test_dr_cutoff( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.1, 0.4, 0.1 ] )
self.assertEqual( self.cell.dr( r1, r2, cutoff=1.0 ), None )
def test_nearest_image( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.1, 0.4, 0.1 ] )
self.cell.minimum_image = Mock( return_value=np.array( [ -0.4, 0.3, 0.0 ] ) )
np.testing.assert_array_almost_equal( self.cell.nearest_image( r1, r2 ), r2 )
def test_minimum_image( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.1, 0.4, 0.3 ] )
np.testing.assert_array_almost_equal( self.cell.minimum_image( r1, r2 ),
np.array( [ -0.4, 0.3, 0.2 ] ) )
def test_minimum_image_2( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.6, 0.8, 0.8 ] )
np.testing.assert_array_almost_equal( self.cell.minimum_image( r1, r2 ),
np.array( [ 0.1, -0.3, -0.3 ] ) )
def test_minimum_image_dr( self ):
r1 = np.array( [ 0.5, 0.1, 0.1 ] )
r2 = np.array( [ 0.8, 0.7, 0.3 ] )
self.cell.minimum_image = Mock( return_value=np.array( [ 0.3, -0.4, 0.2 ] ) )
self.assertAlmostEqual( self.cell.minimum_image_dr( r1, r2 ), 5.385164807 )
def test_lengths( self ):
np.testing.assert_array_equal( self.cell.lengths(), np.array( [ 10.0, 10.0, 10.0 ] ) )
def test_angles( self ):
self.assertEqual( self.cell.angles(), [ 90.0, 90.0, 90.0 ] )
def test_cartesian_to_fractional_coordinates( self ):
coordinates = np.array( [ [ 1.0, 2.0, 3.0 ],
[ 4.0, 6.0, 2.0 ] ] )
expected_fractional_coordinates = np.array( [ [ 0.1, 0.2, 0.3 ],
[ 0.4, 0.6, 0.2 ] ] )
np.testing.assert_array_almost_equal( self.cell.cartesian_to_fractional_coordinates( coordinates ),
expected_fractional_coordinates )
def test_fractional_to_cartesian_coordinates( self ):
coordinates = np.array( [ [ 0.1, 0.2, 0.3 ],
[ 0.4, 0.6, 0.2 ] ] )
expected_cartesian_coordinates = np.array( [ [ 1.0, 2.0, 3.0 ],
[ 4.0, 6.0, 2.0 ] ] )
np.testing.assert_array_almost_equal( self.cell.fractional_to_cartesian_coordinates( coordinates ),
expected_cartesian_coordinates )
def test_inside_cell( self ):
c = np.array( [ [ [ 0.1, 0.2, 0.3 ], [ 0.1, 0.2, 0.3 ] ],
[ [ 0.3, 1.2, 1.3 ], [ 0.3, 0.2, 0.3 ] ] ] )
for r1, r2 in c:
np.testing.assert_array_almost_equal( self.cell.inside_cell( r1 ), r2 )
def test_volume( self ):
self.assertEqual( self.cell.volume(), 1000.0 )
def test_unit_vectors( self ):
np.testing.assert_array_equal( self.cell.unit_vectors(),
np.array( [ [ 1.0, 0.0, 0.0 ],
[ 0.0, 1.0, 0.0 ],
[ 0.0, 0.0, 1.0 ] ] ) )
def setUp( self ):
cell_matrix = np.array( [ [ 10.0, 0.0, 0.0 ],
[ 0.0, 10.0, 0.0 ],
[ 0.0, 0.0, 10.0 ] ] )
self.cell = Cell( cell_matrix )