def _initialize_array(global_grid, result_array):
"Fill in starting values for the energy array."
max_energies = global_grid['GM'].max(dim='points', skipna=False)
len_comps = result_array.dims['component']
if max_energies.isnull().any():
raise ValueError('Input energy surface contains one or more NaNs.')
result_array['GM'] = xray.broadcast_arrays(max_energies, result_array['GM'])[0].copy()
result_array['MU'] = xray.broadcast_arrays(max_energies, result_array['MU'])[0].copy()
result_array['MU'].values[:] = np.nan
result_array['NP'] = xray.broadcast_arrays(xray.DataArray(np.nan), result_array['NP'])[0].copy()
# Initial simplex for each target point in will be
# the fictitious hyperplane
# This hyperplane sits above the system's energy surface
# The reason for this is to guarantee our initial simplex contains
# the target point
# Note: We're assuming that the max energy is in the first few, presumably
# fictitious points instead of more rigorously checking with argmax.
result_array['points'] = xray.broadcast_arrays(xray.DataArray(np.arange(len_comps),
dims='vertex'),
result_array['points'])[0].copy()
def _initialize_array(global_grid, result_array):
"Fill in starting values for the energy array."
max_energies = global_grid['GM'].max(dim='points', skipna=False)
len_comps = result_array.dims['component']
if max_energies.isnull().any():
raise ValueError('Input energy surface contains one or more NaNs.')
result_array['GM'] = xray.broadcast_arrays(max_energies, result_array['GM'])[0].copy()
result_array['MU'] = xray.broadcast_arrays(max_energies, result_array['MU'])[0].copy()
result_array['MU'].values[:] = np.nan
result_array['NP'] = xray.broadcast_arrays(xray.DataArray(np.nan), result_array['NP'])[0].copy()
# Initial simplex for each target point in will be
# the fictitious hyperplane
# This hyperplane sits above the system's energy surface
# The reason for this is to guarantee our initial simplex contains
# the target point
# Note: We're assuming that the max energy is in the first few, presumably
# fictitious points instead of more rigorously checking with argmax.
result_array['points'] = xray.broadcast_arrays(xray.DataArray(np.arange(len_comps),
dims='vertex'),
result_array['points'])[0].copy()
def test_broadcast_arrays(self):
x = DataArray([1, 2], coords=[('a', [-1, -2])], name='x')
y = DataArray([1, 2], coords=[('b', [3, 4])], name='y')
x2, y2 = broadcast_arrays(x, y)
expected_coords = [('a', [-1, -2]), ('b', [3, 4])]
expected_x2 = DataArray([[1, 1], [2, 2]], expected_coords, name='x')
expected_y2 = DataArray([[1, 2], [1, 2]], expected_coords, name='y')
self.assertDataArrayIdentical(expected_x2, x2)
self.assertDataArrayIdentical(expected_y2, y2)
x = DataArray(np.random.randn(2, 3), dims=['a', 'b'])
y = DataArray(np.random.randn(3, 2), dims=['b', 'a'])
x2, y2 = broadcast_arrays(x, y)
expected_x2 = x
expected_y2 = y.T
self.assertDataArrayIdentical(expected_x2, x2)
self.assertDataArrayIdentical(expected_y2, y2)
with self.assertRaisesRegexp(ValueError, 'cannot broadcast'):
z = DataArray([1, 2], coords=[('a', [-10, 20])])
broadcast_arrays(x, z)
def test_broadcast_arrays(self):
x = DataArray([1, 2], coords=[('a', [-1, -2])], name='x')
y = DataArray([1, 2], coords=[('b', [3, 4])], name='y')
x2, y2 = broadcast_arrays(x, y)
expected_coords = [('a', [-1, -2]), ('b', [3, 4])]
expected_x2 = DataArray([[1, 1], [2, 2]], expected_coords, name='x')
expected_y2 = DataArray([[1, 2], [1, 2]], expected_coords, name='y')
self.assertDataArrayIdentical(expected_x2, x2)
self.assertDataArrayIdentical(expected_y2, y2)
x = DataArray(np.random.randn(2, 3), dims=['a', 'b'])
y = DataArray(np.random.randn(3, 2), dims=['b', 'a'])
x2, y2 = broadcast_arrays(x, y)
expected_x2 = x
expected_y2 = y.T
self.assertDataArrayIdentical(expected_x2, x2)
self.assertDataArrayIdentical(expected_y2, y2)
with self.assertRaisesRegexp(ValueError, 'cannot broadcast'):
z = DataArray([1, 2], coords=[('a', [-10, 20])])
broadcast_arrays(x, z)
