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# fmt: off
from typing import Iterable
import numpy as np
import pytest
from ase.spectrum.doscollection import (
DOSCollection,
GridDOSCollection,
RawDOSCollection,
)
from ase.spectrum.dosdata import DOSData, GridDOSData, RawDOSData
class MinimalDOSCollection(DOSCollection):
"""Inherit from abstract base class to check its features"""
def __init__(self, dos_series: Iterable[DOSData]) -> None:
super().__init__(dos_series)
class YetAnotherDOSCollection(DOSCollection):
"""Inherit from abstract base class to check its features"""
def __init__(self, dos_series: Iterable[DOSData]) -> None:
super().__init__(dos_series)
class TestDOSCollection:
@pytest.fixture()
def rawdos(self):
return RawDOSData([1., 2., 4.], [2., 3., 2.],
info={'my_key': 'my_value'})
@pytest.fixture()
def another_rawdos(self):
return RawDOSData([3., 2., 5.], [1., 0., 2.],
info={'other_key': 'other_value'})
@pytest.fixture()
def mindoscollection(self, rawdos, another_rawdos):
return MinimalDOSCollection([rawdos, another_rawdos])
@pytest.mark.parametrize('n_entries', [0, 1, 3])
def test_sequence(self, rawdos, n_entries):
dc = MinimalDOSCollection([rawdos] * n_entries)
assert len(dc) == n_entries
for i in range(n_entries):
assert dc[i] == rawdos
with pytest.raises(IndexError):
dc[n_entries + 1]
with pytest.raises(TypeError):
dc['hello']
linewidths = [1, 5, None]
@pytest.mark.usefixtures("figure")
@pytest.mark.parametrize('linewidth', linewidths)
def test_plot(self, mindoscollection, figure, linewidth):
npts = 20
if linewidth is None:
mplargs = None
else:
mplargs = {'linewidth': linewidth}
ax = figure.add_subplot(111)
with pytest.warns(UserWarning): # Default width is small for npts=20
ax_out = mindoscollection.plot(npts=npts, ax=ax, mplargs=mplargs)
assert ax_out == ax
assert ([line.get_label() for line in ax.get_legend().get_lines()]
== ['my_key: my_value', 'other_key: other_value'])
def test_slicing(self, rawdos, another_rawdos):
dc = MinimalDOSCollection([rawdos, another_rawdos, rawdos])
assert dc[1:]._almost_equals(
MinimalDOSCollection([another_rawdos, rawdos]))
assert dc[:-1]._almost_equals(
MinimalDOSCollection([rawdos, another_rawdos]))
# It would be much nicer if this test could be done with parameterization,
# but creating equality_data as a parameter list requires the lazy_fixtures
# pytest plugin.
def test_collection_equality(self, rawdos, another_rawdos):
equality_data = [([], [], True),
([rawdos], [rawdos], True),
([rawdos, another_rawdos],
[rawdos, another_rawdos], True),
([], [rawdos], False),
([rawdos], [], False),
([rawdos, another_rawdos], [rawdos], False),
([rawdos, another_rawdos],
[another_rawdos, rawdos], False)]
for series_1, series_2, isequal in equality_data:
assert (MinimalDOSCollection(series_1)
._almost_equals(MinimalDOSCollection(series_2)) == isequal)
@pytest.mark.parametrize('other', [True, 1, 0.5, 'string', rawdos])
def test_equality_wrongtype(self, rawdos, other):
assert not MinimalDOSCollection([rawdos])._almost_equals(other)
def test_addition(self, rawdos, another_rawdos):
dc = MinimalDOSCollection([rawdos])
double_dc = dc + dc
assert len(double_dc) == 2
assert double_dc[0]._almost_equals(rawdos)
assert double_dc[1]._almost_equals(rawdos)
assert (dc + MinimalDOSCollection([another_rawdos])
)._almost_equals(dc + another_rawdos)
with pytest.raises(TypeError):
MinimalDOSCollection([rawdos]) + YetAnotherDOSCollection([rawdos])
with pytest.raises(TypeError):
MinimalDOSCollection([rawdos]) + 'string'
@pytest.mark.parametrize('options', [{'energies': [1., 1.1, 1.2],
'width': 1.3,
'smearing': 'Gauss'},
{'energies': [1.7, 2.1, 2.0],
'width': 3.4,
'smearing': 'Gauss'}])
def test_sample(self, rawdos, another_rawdos, options):
dc = MinimalDOSCollection([rawdos, another_rawdos])
sampled_data = dc._sample(**options)
for i, data in enumerate((rawdos, another_rawdos)):
# Check consistency with individual DOSData objects
newdos_weights = data._sample(**options)
assert np.allclose(sampled_data[i, :], newdos_weights)
# Check we aren't trivially comparing zeros
assert np.all(sampled_data)
sample_grid_options = [{'npts': 10, 'xmin': -2, 'xmax': 10,
'padding': 3, 'width': 1},
{'npts': 12, 'xmin': 0, 'xmax': 4,
'padding': 2.1, 'width': 2.3}]
@pytest.mark.parametrize('options', sample_grid_options)
def test_sample_grid(self, rawdos, another_rawdos, options):
ref_min = min(rawdos.get_energies())
ref_max = max(another_rawdos.get_energies())
# Check auto minimum
dc = MinimalDOSCollection([rawdos, another_rawdos])
dos = dc.sample_grid(10, xmax=options['xmax'],
padding=options['padding'],
width=options['width'])
energies = dos.get_energies()
assert (pytest.approx(energies[0])
== ref_min - options['padding'] * options['width'])
assert pytest.approx(energies[-1]) == options['xmax']
# Check auto maximum
dos = dc.sample_grid(10, xmin=options['xmin'],
padding=options['padding'],
width=options['width'])
energies = dos.get_energies()
assert pytest.approx(energies[0]) == options['xmin']
assert (pytest.approx(energies[-1])
== ref_max + options['padding'] * options['width'])
# Check values
dos = dc.sample_grid(**options)
energies = dos.get_energies()
weights = dos.get_all_weights()
for i, data in enumerate((rawdos, another_rawdos)):
tmp_dos = data.sample_grid(**options)
tmp_weights = tmp_dos.get_weights()
assert np.allclose(weights[i, :], tmp_weights)
def test_sample_empty(self):
empty_dc = MinimalDOSCollection([])
with pytest.raises(IndexError):
empty_dc._sample(10)
with pytest.raises(IndexError):
empty_dc.sample_grid(10)
@pytest.mark.parametrize('x, weights, bad_info',
[([1, 2, 4, 5],
[[0, 1, 1, 0], [2, 1, 2, 1]],
[{'notenough': 'entries'}]),
([3.1, 2.4, 1.1],
[[2, 1., 3.12]],
[{'too': 'many'}, {'entries': 'here'}])
])
def test_from_data(self, x, weights, bad_info):
dc = DOSCollection.from_data(x, weights)
for i, dos_data in enumerate(dc):
assert dos_data.info == {}
assert np.allclose(dos_data.get_energies(), x)
assert np.allclose(dos_data.get_weights(), weights[i])
with pytest.raises(ValueError):
dc = DOSCollection.from_data(x, weights, info=bad_info)
collection_data = [[([1., 2., 3.], [1., 1., 2.])],
[([1., 2., 3.], [1., 1., 2.]),
([2., 3.5], [0.5, 1.])],
[([1., 2., 3.], [1., 1., 2.]),
([2., 3.5], [0.5, 1.]),
([1.], [0.25])]]
collection_info = [[{'el': 'C', 'index': '1'}],
[{'el': 'C', 'index': '1'},
{'el': 'C', 'index': '2'}],
[{'el': 'C', 'index': '1'},
{'el': 'C', 'index': '2'},
{'el': 'C', 'index': '2'}]]
expected_sum = [([1., 2., 3.], [1., 1., 2.],
{'el': 'C', 'index': '1'}),
([1., 2., 3., 2., 3.5], [1., 1., 2., 0.5, 1.],
{'el': 'C'}),
([1., 2., 3., 2., 3.5, 1.], [1., 1., 2., 0.5, 1., 0.25],
{'el': 'C'})]
@pytest.mark.parametrize('collection_data, collection_info, expected',
zip(collection_data, collection_info,
expected_sum))
def test_sum_all(self, collection_data, collection_info, expected):
dc = DOSCollection([RawDOSData(*item, info=info)
for item, info in zip(collection_data,
collection_info)])
summed_dc = dc.sum_all()
energies, weights, ref_info = expected
assert np.allclose(summed_dc.get_energies(), energies)
assert np.allclose(summed_dc.get_weights(), weights)
assert summed_dc.info == ref_info
def test_sum_empty(self):
dc = DOSCollection([])
with pytest.raises(IndexError):
dc.sum_all()
@pytest.mark.parametrize('collection_data, collection_info',
zip(collection_data, collection_info))
def test_total(self, collection_data, collection_info):
dc = DOSCollection([RawDOSData(*item, info=info)
for item, info in zip(collection_data,
collection_info)])
summed = dc.sum_all()
total = dc.total()
assert np.allclose(summed.get_energies(), total.get_energies())
assert np.allclose(summed.get_weights(), total.get_weights())
assert (set(total.info.items()) - set(summed.info.items())
== {('label', 'Total')})
select_info = [[{'a': '1', 'b': '1'}, {'a': '2'}],
[{'a': '1', 'b': '1'}, {'a': '1', 'b': '2'}],
[{'a': '1'}, {'a': '2'}],
[{'a': '1', 'b': '1', 'c': '1'},
{'a': '1', 'b': '1', 'c': '2'},
{'a': '1', 'b': '2', 'c': '3'}]]
select_query = [{'a': '1'},
{'a': '1'},
{'a': '0'},
{'a': '1', 'b': '1'}]
select_result = [[{'a': '1', 'b': '1'}],
[{'a': '1', 'b': '1'}, {'a': '1', 'b': '2'}],
None,
[{'a': '1', 'b': '1', 'c': '1'},
{'a': '1', 'b': '1', 'c': '2'}]]
select_not_result = [[{'a': '2'}],
None,
[{'a': '1'}, {'a': '2'}],
[{'a': '1', 'b': '2', 'c': '3'}]]
sum_by_result = [[{'a': '1', 'b': '1'}, {'a': '2'}],
[{'a': '1'}],
[{'a': '1'}, {'a': '2'}],
[{'a': '1', 'b': '1'}, {'a': '1', 'b': '2', 'c': '3'}]]
@pytest.mark.parametrize(
'select_info, select_query, '
'select_result, select_not_result, sum_by_result',
zip(select_info, select_query,
select_result, select_not_result, sum_by_result))
def test_select(self, select_info, select_query,
select_result, select_not_result, sum_by_result):
dc = DOSCollection([RawDOSData([0.], [0.], info=info)
for info in select_info])
if select_result is None:
assert dc.select(**select_query)._almost_equals(DOSCollection([]))
else:
assert select_result == [data.info for data in
dc.select(**select_query)]
if select_not_result is None:
assert (dc.select_not(**select_query)
._almost_equals(DOSCollection([])))
else:
assert select_not_result == [data.info for data in
dc.select_not(**select_query)]
assert sum_by_result == [data.info for data in
dc.sum_by(*sorted(select_query.keys()))]
class TestRawDOSCollection:
@pytest.fixture()
def griddos(self):
energies = np.linspace(1, 10, 7)
weights = np.sin(energies)
return GridDOSData(energies, weights, info={'my_key': 'my_value'})
def test_init(self, griddos):
with pytest.raises(TypeError):
RawDOSCollection([griddos])
class TestGridDOSCollection:
@pytest.fixture()
def griddos(self):
energies = np.linspace(1, 10, 7)
weights = np.sin(energies)
return GridDOSData(energies, weights, info={'my_key': 'my_value'})
@pytest.fixture()
def another_griddos(self):
energies = np.linspace(1, 10, 7)
weights = np.cos(energies)
return GridDOSData(energies, weights, info={'my_key': 'other_value'})
@pytest.fixture()
def griddoscollection(self, griddos, another_griddos):
return GridDOSCollection([griddos, another_griddos])
def test_init_errors(self, griddos):
with pytest.raises(TypeError):
GridDOSCollection([RawDOSData([1.], [1.])])
with pytest.raises(ValueError):
energies = np.linspace(1, 10, 7) + 1
GridDOSCollection([griddos,
GridDOSData(energies, np.sin(energies))])
with pytest.raises(ValueError):
energies = np.linspace(1, 10, 6)
GridDOSCollection([griddos,
GridDOSData(energies, np.sin(energies))])
with pytest.raises(ValueError):
GridDOSCollection([], energies=None)
with pytest.raises(ValueError):
GridDOSCollection([griddos], energies=np.linspace(1, 10, 6))
def test_select(self, griddos, another_griddos):
gdc = GridDOSCollection([griddos, another_griddos])
assert (gdc.select(my_key='my_value')
._almost_equals(GridDOSCollection([griddos])))
assert (gdc.select(my_key='not_present')._almost_equals(
GridDOSCollection([], energies=griddos.get_energies())))
assert (gdc.select_not(my_key='my_value')
._almost_equals(GridDOSCollection([another_griddos])))
assert (gdc.select(my_key='my_value').select_not(my_key='my_value')
._almost_equals(
GridDOSCollection([], energies=griddos.get_energies())))
def test_sequence(self, griddos, another_griddos):
gdc = GridDOSCollection([griddos, another_griddos])
for i, (coll_dosdata, dosdata) in enumerate(zip(gdc,
[griddos,
another_griddos])):
assert coll_dosdata._almost_equals(dosdata)
assert gdc[i]._almost_equals(dosdata)
def test_slicing(self, griddos, another_griddos):
gdc = GridDOSCollection([griddos, another_griddos, griddos])
assert gdc[1:]._almost_equals(
GridDOSCollection([another_griddos, griddos]))
assert gdc[:-1]._almost_equals(
GridDOSCollection([griddos, another_griddos]))
with pytest.raises(TypeError):
gdc['string']
@pytest.mark.parametrize(
'x, weights, info, error',
[(np.linspace(1, 10, 12), [np.linspace(4, 1, 12), np.sin(range(12))],
[{'entry': '1'}, {'entry': '2'}], None),
(np.linspace(1, 5, 7), [np.sqrt(range(7))], [{'entry': '1'}], None),
(np.linspace(1, 5, 7), [np.ones((3, 3))], None, IndexError),
(np.linspace(1, 5, 7), np.array([]).reshape(0, 7), None, IndexError),
(np.linspace(1, 5, 7), np.ones((2, 6)), None, IndexError)])
def test_from_data(self, x, weights, info, error):
if error is not None:
with pytest.raises(error):
dc = GridDOSCollection.from_data(x, weights, info=info)
else:
dc = GridDOSCollection.from_data(x, weights, info=info)
for i, dos_data in enumerate(dc):
assert dos_data.info == info[i]
assert np.allclose(dos_data.get_energies(), x)
assert np.allclose(dos_data.get_weights(), weights[i])
@pytest.mark.usefixtures("figure")
def test_plot_no_resample(self, griddoscollection, figure):
ax = figure.add_subplot(111)
griddoscollection.plot(ax=ax)
assert np.allclose(ax.get_lines()[0].get_xdata(),
griddoscollection[0].get_energies())
assert np.allclose(ax.get_lines()[1].get_ydata(),
griddoscollection[1].get_weights())
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