test: add tests from ess.reflectometry

Author: jokasimr

tests/amor/__init__.py

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+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright (c) 2023 Scipp contributors (https://github.com/scipp)

tests/amor/tools_test.py

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+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright (c) 2023 Scipp contributors (https://github.com/scipp)
+# @author Andrew R. McCluskey (arm61)
+import pytest
+import scipp as sc
+
+from essreflectometry.amor import tools
+
+
+def test_linlogspace_linear():
+    q_lin = tools.linlogspace(
+        dim='qz', edges=[0.008, 0.08], scale='linear', num=50, unit='1/angstrom'
+    )
+    expected = sc.linspace(dim='qz', start=0.008, stop=0.08, num=50, unit='1/angstrom')
+    assert sc.allclose(q_lin, expected)
+
+
+def test_linlogspace_linear_list_input():
+    q_lin = tools.linlogspace(
+        dim='qz', edges=[0.008, 0.08], unit='1/angstrom', scale=['linear'], num=[50]
+    )
+    expected = sc.linspace(dim='qz', start=0.008, stop=0.08, num=50, unit='1/angstrom')
+    assert sc.allclose(q_lin, expected)
+
+
+def test_linlogspace_log():
+    q_log = tools.linlogspace(
+        dim='qz', edges=[0.008, 0.08], unit='1/angstrom', scale='log', num=50
+    )
+    expected = sc.geomspace(dim='qz', start=0.008, stop=0.08, num=50, unit='1/angstrom')
+    assert sc.allclose(q_log, expected)
+
+
+def test_linlogspace_linear_log():
+    q_linlog = tools.linlogspace(
+        dim='qz',
+        edges=[0.008, 0.03, 0.08],
+        unit='1/angstrom',
+        scale=['linear', 'log'],
+        num=[16, 20],
+    )
+    exp_lin = sc.linspace(dim='qz', start=0.008, stop=0.03, num=16, unit='1/angstrom')
+    exp_log = sc.geomspace(dim='qz', start=0.03, stop=0.08, num=21, unit='1/angstrom')
+    expected = sc.concat([exp_lin, exp_log['qz', 1:]], 'qz')
+    assert sc.allclose(q_linlog, expected)
+
+
+def test_linlogspace_log_linear():
+    q_loglin = tools.linlogspace(
+        dim='qz',
+        edges=[0.008, 0.03, 0.08],
+        unit='1/angstrom',
+        scale=['log', 'linear'],
+        num=[16, 20],
+    )
+    exp_log = sc.geomspace(dim='qz', start=0.008, stop=0.03, num=16, unit='1/angstrom')
+    exp_lin = sc.linspace(dim='qz', start=0.03, stop=0.08, num=21, unit='1/angstrom')
+    expected = sc.concat([exp_log, exp_lin['qz', 1:]], 'qz')
+    assert sc.allclose(q_loglin, expected)
+
+
+def test_linlogspace_linear_log_linear():
+    q_linloglin = tools.linlogspace(
+        dim='qz',
+        edges=[0.008, 0.03, 0.08, 0.12],
+        unit='1/angstrom',
+        scale=['linear', 'log', 'linear'],
+        num=[16, 20, 10],
+    )
+    exp_lin = sc.linspace(dim='qz', start=0.008, stop=0.03, num=16, unit='1/angstrom')
+    exp_log = sc.geomspace(dim='qz', start=0.03, stop=0.08, num=21, unit='1/angstrom')
+    exp_lin2 = sc.linspace(dim='qz', start=0.08, stop=0.12, num=11, unit='1/angstrom')
+    expected = sc.concat([exp_lin, exp_log['qz', 1:], exp_lin2['qz', 1:]], 'qz')
+    assert sc.allclose(q_linloglin, expected)
+
+
+def test_linlogspace_bad_input():
+    with pytest.raises(ValueError):
+        _ = tools.linlogspace(
+            dim='qz',
+            edges=[0.008, 0.03, 0.08, 0.12],
+            unit='1/angstrom',
+            scale=['linear', 'log'],
+            num=[16, 20],
+        )

tests/reflectometry/__init__.py

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+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright (c) 2023 Scipp contributors (https://github.com/scipp)

tests/reflectometry/corrections_test.py

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+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright (c) 2023 Scipp contributors (https://github.com/scipp)
+import pytest
+import scipp as sc
+from orsopy import fileio
+
+from essreflectometry.reflectometry import corrections
+
+
+def test_normalize_by_counts():
+    """
+    Tests the corrections.normalize_by_counts function without
+    a orsopy object present.
+    """
+    N = 50
+    values = [1.0] * N
+    data = sc.Variable(
+        dims=['position'], unit=sc.units.counts, values=values, variances=values
+    )
+    array = sc.DataArray(data=data)
+    array_normalized = corrections.normalize_by_counts(array)
+    result = sc.DataArray(
+        data=sc.Variable(
+            dims=['position'],
+            unit=sc.units.dimensionless,
+            values=[1 / N] * N,
+            variances=[1 / (N * N)] * N,
+        )
+    )
+    assert sc.allclose(array_normalized.data, result.data)
+
+
+def test_normalize_by_counts_fails_when_ncounts_is_too_small():
+    N = 10
+    values = [1.0] * N
+    values[3] = 20.0
+    data = sc.Variable(
+        dims=['position'], unit=sc.units.counts, values=values, variances=values
+    )
+    array = sc.DataArray(data=data)
+    with pytest.raises(
+        ValueError,
+        match='One or more bins contain a number of counts of the same '
+        'order as the total number of counts',
+    ):
+        _ = corrections.normalize_by_counts(array)
+
+
+def test_normalize_by_counts_orso():
+    """
+    Tests the corrections.normalize_by_counts function
+    with a orsopy object present.
+    """
+    N = 50
+    values = [1.0] * N
+    data = sc.Variable(
+        dims=['position'], unit=sc.units.counts, values=values, variances=values
+    )
+    array = sc.DataArray(data=data, attrs={'orso': sc.scalar(fileio.orso.Orso.empty())})
+    array.attrs['orso'].value.reduction.corrections = []
+    array_normalized = corrections.normalize_by_counts(array)
+    result = sc.DataArray(
+        data=sc.Variable(
+            dims=['position'],
+            unit=sc.units.dimensionless,
+            values=[1 / N] * N,
+            variances=[1 / (N * N)] * N,
+        )
+    )
+    assert sc.allclose(array_normalized.data, result.data)
+    assert 'total counts' in array.attrs['orso'].value.reduction.corrections
+
+
+def test_beam_on_sample():
+    """
+    Tests the corrections.beam_on_sample function.
+    """
+    beam_size = sc.scalar(1.0, unit=sc.units.mm)
+    theta = sc.scalar(0.1, unit=sc.units.rad)
+    expected_result = sc.scalar(10.01668613, unit=sc.units.mm)
+    assert sc.allclose(expected_result, corrections.beam_on_sample(beam_size, theta))
+
+
+def test_beam_on_sample_array():
+    """
+    Tests the corrections.beam_on_sample function with an array of theta.
+    """
+    beam_size = sc.scalar(1.0, unit=sc.units.mm)
+    theta = sc.array(dims=['x'], values=[0.1, 0.5], unit=sc.units.rad)
+    expected_result = sc.array(
+        dims=['x'], values=[10.01668613, 2.085829643], unit=sc.units.mm
+    )
+    assert sc.allclose(expected_result, corrections.beam_on_sample(beam_size, theta))