from collections import OrderedDict import numpy as np import pytest from typing import List, Tuple, Any from ase.spectrum.dosdata import DOSData, GridDOSData, RawDOSData class MinimalDOSData(DOSData): """Inherit from ABC to test its features""" def get_energies(self): return NotImplementedError() def get_weights(self): return NotImplementedError() def copy(self): return NotImplementedError() class TestDosData: """Test the abstract base class for DOS data""" sample_info = [(None, {}), ({}, {}), ({'symbol': 'C', 'index': '2', 'strangekey': 'isallowed'}, {'symbol': 'C', 'index': '2', 'strangekey': 'isallowed'}), ('notadict', TypeError), (False, TypeError)] # type: List[Tuple[Any,Any]] @pytest.mark.parametrize('info, expected', sample_info) def test_dosdata_init_info(self, info, expected): """Check 'info' parameter is handled properly""" if isinstance(expected, type) and isinstance(expected(), Exception): with pytest.raises(expected): dos_data = MinimalDOSData(info=info) else: dos_data = MinimalDOSData(info=info) assert dos_data.info == expected @pytest.mark.parametrize('info, expected', [({}, ''), ({'key1': 'value1'}, 'key1: value1'), (OrderedDict([('key1', 'value1'), ('key2', 'value2')]), 'key1: value1; key2: value2'), ({'key1': 'value1', 'label': 'xyz'}, 'xyz'), ({'label': 'xyz'}, 'xyz')]) def test_label_from_info(self, info, expected): assert DOSData.label_from_info(info) == expected class TestRawDosData: """Test the raw DOS data container""" @pytest.fixture def sparse_dos(self): return RawDOSData([1.2, 3.4, 5.], [3., 2.1, 0.], info={'symbol': 'H', 'number': '1', 'food': 'egg'}) @pytest.fixture def another_sparse_dos(self): return RawDOSData([8., 2., 2., 5.], [1., 1., 1., 1.], info={'symbol': 'H', 'number': '2'}) def test_init(self): with pytest.raises(ValueError): RawDOSData([1, 2, 3], [4, 5], info={'symbol': 'H'}) def test_access(self, sparse_dos): assert sparse_dos.info == {'symbol': 'H', 'number': '1', 'food': 'egg'} assert np.allclose(sparse_dos.get_energies(), [1.2, 3.4, 5.]) assert np.allclose(sparse_dos.get_weights(), [3., 2.1, 0.]) def test_copy(self, sparse_dos): copy_dos = sparse_dos.copy() assert copy_dos.info == sparse_dos.info sparse_dos.info['symbol'] = 'X' assert sparse_dos.info['symbol'] == 'X' assert copy_dos.info['symbol'] == 'H' @pytest.mark.parametrize('other', [True, 1, 0.5, 'string']) def test_equality_wrongtype(self, sparse_dos, other): assert not sparse_dos._almost_equals(other) equality_data = [(((1., 2.), (3., 4.), {'symbol': 'H'}), ((1., 2.), (3., 4.), {'symbol': 'H'}), True), (((1., 3.), (3., 4.), {'symbol': 'H'}), ((1., 2.), (3., 4.), {'symbol': 'H'}), False), (((1., 2.), (3., 5.), {'symbol': 'H'}), ((1., 2.), (3., 4.), {'symbol': 'H'}), False), (((1., 3.), (3., 5.), {'symbol': 'H'}), ((1., 2.), (3., 4.), {'symbol': 'H'}), False), (((1., 2.), (3., 4.), {'symbol': 'H'}), ((1., 2.), (3., 4.), {'symbol': 'He'}), False), (((1., 3.), (3., 4.), {'symbol': 'H'}), ((1., 2.), (3., 4.), {'symbol': 'He'}), False)] @pytest.mark.parametrize('data_1, data_2, isequal', equality_data) def test_equality(self, data_1, data_2, isequal): assert (RawDOSData(*data_1[:2], info=data_1[2]) ._almost_equals(RawDOSData(*data_2[:2], info=data_2[2])) ) == isequal def test_addition(self, sparse_dos, another_sparse_dos): summed_dos = sparse_dos + another_sparse_dos assert summed_dos.info == {'symbol': 'H'} assert np.allclose(summed_dos.get_energies(), [1.2, 3.4, 5., 8., 2., 2., 5.]) assert np.allclose(summed_dos.get_weights(), [3., 2.1, 0., 1., 1., 1., 1.]) sampling_data_args_results = [ # Special case: peak max at width 1 ([[0.], [1.]], [[0.], {'width': 1}], [1. / (np.sqrt(2. * np.pi))]), # Peak max with different width, position ([[1.], [2.]], [[1.], {'width': 0.5}], [2. / (np.sqrt(2. * np.pi) * 0.5)]), # Peak max for two simultaneous deltas ([[1., 1.], [2., 1.]], [[1.], {'width': 1}], [3. / (np.sqrt(2. * np.pi))]), # Compare with theoretical half-maximum ([[0.], [1.]], [[np.sqrt(2 * np.log(2)) * 3], {'width': 3}], [0.5 / (np.sqrt(2 * np.pi) * 3)]), # And a case with multiple values, generated # using the ASE code (not benchmarked) ([[1.2, 3.4, 5], [3., 2.1, 0.]], [[1., 1.5, 2., 2.4], {'width': 2}], [0.79932418, 0.85848101, 0.88027184, 0.8695055])] @pytest.mark.parametrize('data, args, result', sampling_data_args_results) def test_sampling(self, data, args, result): dos = RawDOSData(data[0], data[1]) weights = dos._sample(*args[:-1], **args[-1]) assert np.allclose(weights, result) with pytest.raises(ValueError): dos._sample([1], smearing="Gauss's spherical cousin") def test_sampling_error(self, sparse_dos): with pytest.raises(ValueError): sparse_dos._sample([1, 2, 3], width=0.) with pytest.raises(ValueError): sparse_dos._sample([1, 2, 3], width=-1) def test_sample_grid(self, sparse_dos): min_dos = sparse_dos.sample_grid(10, xmax=5, padding=3, width=0.1) assert min_dos.get_energies()[0] == pytest.approx(1.2 - 3 * 0.1) max_dos = sparse_dos.sample_grid(10, xmin=0, padding=2, width=0.2) assert max_dos.get_energies()[-1] == pytest.approx(5 + 2 * 0.2) default_dos = sparse_dos.sample_grid(10) assert np.allclose(default_dos.get_energies(), np.linspace(0.9, 5.3, 10)) dos0 = sparse_dos._sample(np.linspace(0.9, 5.3, 10)) assert np.allclose(default_dos.get_weights(), dos0) # Comparing plot outputs is hard, so we # - inspect the line values # - check that a line styling parameter is correctly passed through mplargs # - set a kwarg from self.sample() to check broadening args are recognised linewidths = [1, 5, None] @pytest.mark.usefixtures("figure") @pytest.mark.parametrize('linewidth', linewidths) def test_plot(self, sparse_dos, figure, linewidth): if linewidth is None: mplargs = None else: mplargs = {'linewidth': linewidth} ax = figure.add_subplot(111) ax_out = sparse_dos.plot(npts=5, ax=ax, mplargs=mplargs, smearing='Gauss') assert ax_out == ax line_data = ax.lines[0].get_data() assert np.allclose(line_data[0], np.linspace(0.9, 5.3, 5)) assert np.allclose(line_data[1], [1.32955452e-01, 1.51568133e-13, 9.30688167e-02, 1.06097693e-13, 3.41173568e-78]) @pytest.mark.usefixtures("figure") @pytest.mark.parametrize('linewidth', linewidths) def test_plot_deltas(self, sparse_dos, figure, linewidth): if linewidth is None: mplargs = None else: mplargs = {'linewidth': linewidth} ax = figure.add_subplot(111) ax_out = sparse_dos.plot_deltas(ax=ax, mplargs=mplargs) assert ax_out == ax assert np.allclose(list(map(lambda x: x.vertices, ax.get_children()[0].get_paths())), [[[1.2, 0.], [1.2, 3.]], [[3.4, 0.], [3.4, 2.1]], [[5., 0.], [5., 0.]]]) class TestGridDosData: """Test the grid DOS data container""" def test_init(self): # energies and weights must be equal lengths with pytest.raises(ValueError): GridDOSData(np.linspace(0, 10, 11), np.zeros(10)) # energies must be evenly spaced with pytest.raises(ValueError): GridDOSData(np.linspace(0, 10, 11)**2, np.zeros(11)) @pytest.fixture def dense_dos(self): x = np.linspace(0., 10., 11) y = np.sin(x / 10) return GridDOSData(x, y, info={'symbol': 'C', 'orbital': '2s', 'day': 'Tue'}) @pytest.fixture def denser_dos(self): x = np.linspace(0., 10., 21) y = np.sin(x / 10) return GridDOSData(x, y) @pytest.fixture def another_dense_dos(self): x = np.linspace(0., 10., 11) y = np.sin(x / 10) * 2 return GridDOSData(x, y, info={'symbol': 'C', 'orbital': '2p', 'month': 'Feb'}) def test_access(self, dense_dos): assert dense_dos.info == {'symbol': 'C', 'orbital': '2s', 'day': 'Tue'} assert len(dense_dos.get_energies()) == 11 assert dense_dos.get_energies()[-2] == pytest.approx(9.) assert dense_dos.get_weights()[-1] == pytest.approx(np.sin(1)) def test_copy(self, dense_dos): copy_dos = dense_dos.copy() assert copy_dos.info == dense_dos.info dense_dos.info['symbol'] = 'X' assert dense_dos.info['symbol'] == 'X' assert copy_dos.info['symbol'] == 'C' def test_addition(self, dense_dos, another_dense_dos): sum_dos = dense_dos + another_dense_dos assert np.allclose(sum_dos.get_energies(), dense_dos.get_energies()) assert np.allclose(sum_dos.get_weights(), dense_dos.get_weights() * 3) assert sum_dos.info == {'symbol': 'C'} with pytest.raises(ValueError): dense_dos + GridDOSData(dense_dos.get_energies() + 1., dense_dos.get_weights()) with pytest.raises(ValueError): dense_dos + GridDOSData(dense_dos.get_energies()[1:], dense_dos.get_weights()[1:]) def test_check_spacing(self, dense_dos): """Check a warning is logged when width < 2 * grid spacing""" # In the sample data, grid spacing is 1.0 dense_dos._sample([1], width=2.1) with pytest.warns(UserWarning, match="The broadening width is small"): dense_dos._sample([1], width=1.9) def test_resampling_consistency(self, dense_dos, denser_dos): """Check that resampled spectra are independent of the original density Compare resampling of sample function on two different grids to the same new grid with broadening. We accept a 5% difference because the initial shape is slightly different; what we are checking for is a factor 2 difference from "double-counting" the extra data points. """ sampling_params = dict(npts=500, xmin=0, xmax=10, width=4) from_dense = dense_dos.sample_grid(**sampling_params) from_denser = denser_dos.sample_grid(**sampling_params) assert np.allclose(from_dense.get_energies(), from_denser.get_energies()) assert np.allclose(from_dense.get_weights(), from_denser.get_weights(), rtol=0.05, atol=0.01) linewidths = [1, 5, None] @pytest.mark.usefixtures("figure") @pytest.mark.parametrize('linewidth', linewidths) def test_plot(self, dense_dos, figure, linewidth): if linewidth is None: mplargs = None else: mplargs = {'linewidth': linewidth} ax = figure.add_subplot(111) ax_out = dense_dos.plot(ax=ax, mplargs=mplargs, smearing='Gauss') assert ax_out == ax line_data = ax.lines[0].get_data() # With default settings, plot data should be unchanged from input data; # this is a special feature of "grid" data to avoid repeated broadening assert np.allclose(line_data[0], np.linspace(0., 10., 11)) assert np.allclose(line_data[1], np.sin(np.linspace(0., 1., 11))) @pytest.mark.usefixtures("figure") def test_plot_broad_dos(self, dense_dos, figure): # Check that setting a grid/broadening does not blow up and reproduces # previous results; this result has not been rigorously checked but at # least it should not _change_ unexpectedly ax = figure.add_subplot(111) _ = dense_dos.plot(ax=ax, npts=10, xmin=0, xmax=9, width=4, smearing='Gauss') line_data = ax.lines[0].get_data() assert np.allclose(line_data[0], range(10)) assert np.allclose(line_data[1], [0.14659725, 0.19285644, 0.24345501, 0.29505574, 0.34335948, 0.38356488, 0.41104823, 0.42216901, 0.41503382, 0.39000808]) smearing_args = [(dict(npts=0, width=None), (0, None)), (dict(npts=10, width=None, default_width=5.), (10, 5.)), (dict(npts=0, width=0.5, default_npts=100), (100, 0.5)), (dict(npts=10, width=0.5), (10, 0.5))] @pytest.mark.parametrize('inputs, expected', smearing_args) def test_smearing_args_interpreter(self, inputs, expected): assert GridDOSData._interpret_smearing_args(**inputs) == expected class TestMultiDosData: """Test interaction between DOS data objects""" @pytest.fixture def sparse_dos(self): return RawDOSData([1.2, 3.4, 5.], [3., 2.1, 0.], info={'symbol': 'H', 'number': '1', 'food': 'egg'}) @pytest.fixture def dense_dos(self): x = np.linspace(0., 10., 11) y = np.sin(x / 10) return GridDOSData(x, y, info={'symbol': 'C', 'orbital': '2s', 'day': 'Tue'}) def test_addition(self, sparse_dos, dense_dos): with pytest.raises(TypeError): sparse_dos + dense_dos with pytest.raises(TypeError): dense_dos + sparse_dos