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GuiMacielPereiraGuiMacielPereira
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Add unit test for masking tof range
Added unit test that compares masking tof range with zeros with using a range that has been cut off (i.e. does not include the values with zeros). If the error function is successfully ignoring zeros then the result of both fits should be the same
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‎tests/unit/analysis/test_analysis_reduction.py

Lines changed: 69 additions & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -6,6 +6,8 @@
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from mvesuvio.util.analysis_helpers import load_resolution
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from mantid.simpleapi import CreateWorkspace, DeleteWorkspace
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import inspect
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import scipy
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import re
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np.set_printoptions(suppress=True, precision=6, linewidth=200)
@@ -86,7 +88,6 @@ def test_fit_neutron_compton_profiles_number_of_calls(self):
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def test_fit_neutron_compton_profiles_to_row(self):
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# Test 3 spectra, one nornal, one with masked Tof and another fully masked
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alg = VesuvioAnalysisRoutine()
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alg._workspace_being_fit = MagicMock()
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alg._workspace_being_fit.name.return_value = "test_workspace"
@@ -104,9 +105,9 @@ def test_fit_neutron_compton_profiles_to_row(self):
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[[0.0015, 0.0014, 0.0014, 0.0014, 0.0014, 0.0014, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0014, 0.0014, 0.0014, 0.0014, 0.0014, 0.0014, 0.0014, 0.0013, 0.0013, 0.0013, 0.0013, 0.0012, 0.0012, 0.0012, 0.0012, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.0009, 0.0009, 0.0016],
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[0.0014, 0.0014, 0.0014, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0012, 0.0012, 0.0012, 0.0012, 0.0012, 0.0012, 0.0012, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0013, 0.0012, 0.0012, 0.0012, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.001, 0.001, 0.001, 0.001, 0.001, 0.0011, 0.0011, 0.0011, 0.001, 0.001, 0.001, 0.001, 0.0009, 0.0009, 0.0009, 0.0009, 0.0009, 0.0016]]
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)
107-
alg._instrument_params = np.array(
108-
[[3, 3, 131.12, -0.2, 11.005, 0.6039],
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[4, 4, 132.77, -0.2, 11.005, 0.5789],
108+
alg._instrument_params = np.array([
109+
[144, 144, 54.1686, -0.41, 11.005, 0.720184],
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[145, 145, 52.3407, -0.53, 11.005, 0.717311]
110111
])
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alg._resolution_params = load_resolution(alg._instrument_params)
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alg._masses = np.array([1, 12, 16, 27])
@@ -149,23 +150,76 @@ def pick_ncp_row_result(row):
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alg._fit_neutron_compton_profiles_to_row()
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alg._row_being_fit = 2
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alg._fit_neutron_compton_profiles_to_row()
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# Compare results
154-
expected_total_ncp_fits = np.array(
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[[0.003246, 0.003334, 0.003416, 0.003492, 0.003562, 0.003628, 0.003633, 0.003679, 0.003721, 0.00376, 0.003796, 0.003829, 0.00386, 0.003888, 0.003914, 0.003937, 0.003959, 0.003978, 0.003996, 0.004012, 0.004026, 0.004038, 0.004049, 0.004059, 0.004067, 0.004074, 0.004079, 0.004083, 0.004086, 0.004088, 0.004088, 0.004087, 0.004085, 0.004082, 0.004078, 0.004073, 0.004067, 0.00406, 0.004052, 0.004043, 0.004033, 0.004022, 0.00401, 0.003996, 0.003982, 0.003967, 0.003951, 0.00384, 0.004628, 0.004635, 0.004642, 0.004649, 0.004655, 0.004659],
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[0.003383, 0.003481, 0.003572, 0.003657, 0.003737, 0.003811, 0.003866, 0.003927, 0.003984, 0.004038, 0.004088, 0.004136, 0.00418, 0.004222, 0.004261, 0.004298, 0.004333, 0.004366, 0.004397, 0.004426, 0.004453, 0.004479, 0.004503, 0.004526, 0.004547, 0.004567, 0.004586, 0.004603, 0.00462, 0.004635, 0.004649, 0.004663, 0.004675, 0.004686, 0.004697, 0.004707, 0.004715, 0.004723, 0.004731, 0.004737, 0.004743, 0.004748, 0.004752, 0.004756, 0.004759, 0.004761, 0.004763, 0.004727, 0.005024, 0.005033, 0.005043, 0.005052, 0.00506, 0.005066],
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., ]]
158-
)
154+
expected_total_ncp_fits = np.array([
155+
[0.00004, 0.000059, 0.000089, 0.000138, 0.000215, 0.000335, 0.000647, 0.000943, 0.001347, 0.001888, 0.002593, 0.003486, 0.004589, 0.005913, 0.007453, 0.009182, 0.011037, 0.01292, 0.014694, 0.016196, 0.017254, 0.017722, 0.017509, 0.016606, 0.01509, 0.013115, 0.010885, 0.00861, 0.006475, 0.004615, 0.003101, 0.001949, 0.001128, 0.000583, 0.000251, 0.000068, -0.000016, -0.000041, -0.000005, 0.000119, 0.000355, 0.00105, 0.002667, 0.006238, 0.008899, 0.004131, 0.000602, -0.000026, 0.000111, 0.000078, 0.000061, 0.00005, 0.000043, 0.00004],
156+
[0.000021, 0.000029, 0.000042, 0.000062, 0.000095, 0.000148, 0.000324, 0.000486, 0.00072, 0.001046, 0.001492, 0.002084, 0.002851, 0.003818, 0.005006, 0.006424, 0.008066, 0.009895, 0.011838, 0.013776, 0.015551, 0.016978, 0.017872, 0.018092, 0.017566, 0.016325, 0.014492, 0.012265, 0.009878, 0.007552, 0.005464, 0.003723, 0.002369, 0.001389, 0.00073, 0.000322, 0.000095, -0.000012, -0.000016, 0.000117, 0.000383, 0.001178, 0.003156, 0.006523, 0.009388, 0.00489, 0.00077, -0.000037, 0.000125, 0.000087, 0.000067, 0.000055, 0.000047, 0.000043],
157+
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]
158+
])
159159
np.testing.assert_allclose(ncp_total_array, expected_total_ncp_fits, atol=1e-6)
160160

161-
expected_fit_parameters = np.array(
162-
[[3., 5568.767144, 6., -3., 0., 12.71, 0.999934, 0., 8.76, -3., 0., 13.897, -2.999712, 26.483439, 16.],
163-
[4., 6551.082529, 4.511378, -2.999998, 0., 12.71, 0.85686, 0., 8.76, -2.782142, 0., 13.897, -2.683993, 28.399091, 18.],
164-
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]
165-
)
161+
expected_fit_parameters = np.array([
162+
[144., 7.095403, 5.051053, 0.015996, 0.218192, 12.71, 1., 0., 8.76, -1.091821, 0.29291, 13.897, 0.639245, 0.848425, 21.],
163+
[145., 6.825532, 5.073835, -0.087401, 0.29456, 12.71, 1., 0., 8.76, -0.14256, 0.26776, 13.897, -2.563441, 1.246451, 20.],
164+
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]
165+
])
166166
np.testing.assert_allclose(alg._fit_parameters, expected_fit_parameters, atol=1e-6)
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169+
def test_fit_neutron_compton_profiles_to_row_with_masked_tof(self):
170+
alg = VesuvioAnalysisRoutine()
171+
alg._workspace_being_fit = MagicMock()
172+
alg._workspace_being_fit.name.return_value = "test_workspace"
173+
alg._workspace_being_fit.getNumberHistograms.return_value = 1
174+
dataX = np.arange(113, 430).reshape(1, -1)
175+
dataE = np.full_like(dataX, 0.0015)
176+
dataY = scipy.special.voigt_profile(dataX - 235, 30, 0) + 0.005*(np.random.random_sample(dataX.shape)-0.5)
177+
178+
# Mask TOF range
179+
cut_off_idx = 100
180+
dataY[:, :cut_off_idx] = 0
181+
182+
alg._workspace_being_fit.extractY.return_value = dataY
183+
alg._workspace_being_fit.extractX.return_value = dataX
184+
alg._workspace_being_fit.extractE.return_value = dataE
185+
alg._instrument_params = np.array([[144, 144, 54.1686, -0.41, 11.005, 0.720184]])
186+
alg._resolution_params = load_resolution(alg._instrument_params)
187+
alg._masses = np.array([1])
188+
alg._initial_fit_parameters = np.array([1, 5.2, 0])
189+
alg._initial_fit_bounds = np.array([[0, None], [3, 6], [None, None]])
190+
alg._constraints = ()
191+
192+
# Set up several fit arguments
193+
alg._profiles_table = MagicMock()
194+
alg._profiles_table.column.return_value = ['1']
195+
alg._profiles_table.rowCount.return_value = 1
196+
alg._create_emtpy_ncp_workspace = MagicMock()
197+
alg._update_workspace_data()
198+
199+
# Create mock for storing ncp total result
200+
ncp_array_masked = np.zeros_like(dataY)
201+
alg._fit_profiles_workspaces = {"total": MagicMock(dataY=lambda arg: ncp_array_masked), "1": MagicMock()}
202+
203+
# Fit ncp
204+
alg._row_being_fit = 0
205+
alg._fit_neutron_compton_profiles_to_row()
206+
fit_parameters_masked = alg._fit_parameters.copy()
207+
208+
# Now cut range so that zeros are not part of dataY
209+
# (Still need to leave a padding with 6 zeros due to numerical derivative in ncp)
210+
alg._workspace_being_fit.extractY.return_value = dataY[:, cut_off_idx - 6:].reshape(1, -1)
211+
alg._workspace_being_fit.extractX.return_value = dataX[:, cut_off_idx - 6:].reshape(1, -1)
212+
alg._workspace_being_fit.extractE.return_value = dataE[:, cut_off_idx - 6:].reshape(1, -1)
213+
alg._update_workspace_data()
214+
215+
ncp_array_cut = ncp_array_masked[:, cut_off_idx - 6:].reshape(1, -1)
216+
alg._fit_profiles_workspaces = {"total": MagicMock(dataY=lambda arg: ncp_array_cut), "1": MagicMock()}
217+
218+
alg._fit_neutron_compton_profiles_to_row()
219+
fit_parameters_cut = alg._fit_parameters.copy()
220+
221+
np.testing.assert_allclose(fit_parameters_cut, fit_parameters_masked, atol=1e-6)
222+
np.testing.assert_allclose(ncp_array_masked[:, cut_off_idx-6:], ncp_array_cut, atol=1e-6)
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if __name__ == "__main__":

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