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Add unit test for masking tof range
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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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@GuiMacielPereira
GuiMacielPereira committed Nov 25, 2024
1 parent 76281fa commit ffa62f6
Showing 1 changed file with 69 additions and 15 deletions.
84 changes: 69 additions & 15 deletions tests/unit/analysis/test_analysis_reduction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@
from mvesuvio.util.analysis_helpers import load_resolution
from mantid.simpleapi import CreateWorkspace, DeleteWorkspace
import inspect
import scipy
import re


np.set_printoptions(suppress=True, precision=6, linewidth=200)
Expand Down Expand Up @@ -86,7 +88,6 @@ def test_fit_neutron_compton_profiles_number_of_calls(self):


def test_fit_neutron_compton_profiles_to_row(self):
# Test 3 spectra, one nornal, one with masked Tof and another fully masked
alg = VesuvioAnalysisRoutine()
alg._workspace_being_fit = MagicMock()
alg._workspace_being_fit.name.return_value = "test_workspace"
Expand All @@ -104,9 +105,9 @@ def test_fit_neutron_compton_profiles_to_row(self):
[[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],
[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]]
)
alg._instrument_params = np.array(
[[3, 3, 131.12, -0.2, 11.005, 0.6039],
[4, 4, 132.77, -0.2, 11.005, 0.5789],
alg._instrument_params = np.array([
[144, 144, 54.1686, -0.41, 11.005, 0.720184],
[145, 145, 52.3407, -0.53, 11.005, 0.717311]
])
alg._resolution_params = load_resolution(alg._instrument_params)
alg._masses = np.array([1, 12, 16, 27])
Expand Down Expand Up @@ -149,23 +150,76 @@ def pick_ncp_row_result(row):
alg._fit_neutron_compton_profiles_to_row()
alg._row_being_fit = 2
alg._fit_neutron_compton_profiles_to_row()

# Compare results
expected_total_ncp_fits = np.array(
[[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],
[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],
[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., ]]
)
expected_total_ncp_fits = np.array([
[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],
[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],
[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.]
])
np.testing.assert_allclose(ncp_total_array, expected_total_ncp_fits, atol=1e-6)

expected_fit_parameters = np.array(
[[3., 5568.767144, 6., -3., 0., 12.71, 0.999934, 0., 8.76, -3., 0., 13.897, -2.999712, 26.483439, 16.],
[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.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]
)
expected_fit_parameters = np.array([
[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.],
[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.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]
])
np.testing.assert_allclose(alg._fit_parameters, expected_fit_parameters, atol=1e-6)


def test_fit_neutron_compton_profiles_to_row_with_masked_tof(self):
alg = VesuvioAnalysisRoutine()
alg._workspace_being_fit = MagicMock()
alg._workspace_being_fit.name.return_value = "test_workspace"
alg._workspace_being_fit.getNumberHistograms.return_value = 1
dataX = np.arange(113, 430).reshape(1, -1)
dataE = np.full_like(dataX, 0.0015)
dataY = scipy.special.voigt_profile(dataX - 235, 30, 0) + 0.005*(np.random.random_sample(dataX.shape)-0.5)

# Mask TOF range
cut_off_idx = 100
dataY[:, :cut_off_idx] = 0

alg._workspace_being_fit.extractY.return_value = dataY
alg._workspace_being_fit.extractX.return_value = dataX
alg._workspace_being_fit.extractE.return_value = dataE
alg._instrument_params = np.array([[144, 144, 54.1686, -0.41, 11.005, 0.720184]])
alg._resolution_params = load_resolution(alg._instrument_params)
alg._masses = np.array([1])
alg._initial_fit_parameters = np.array([1, 5.2, 0])
alg._initial_fit_bounds = np.array([[0, None], [3, 6], [None, None]])
alg._constraints = ()

# Set up several fit arguments
alg._profiles_table = MagicMock()
alg._profiles_table.column.return_value = ['1']
alg._profiles_table.rowCount.return_value = 1
alg._create_emtpy_ncp_workspace = MagicMock()
alg._update_workspace_data()

# Create mock for storing ncp total result
ncp_array_masked = np.zeros_like(dataY)
alg._fit_profiles_workspaces = {"total": MagicMock(dataY=lambda arg: ncp_array_masked), "1": MagicMock()}

# Fit ncp
alg._row_being_fit = 0
alg._fit_neutron_compton_profiles_to_row()
fit_parameters_masked = alg._fit_parameters.copy()

# Now cut range so that zeros are not part of dataY
# (Still need to leave a padding with 6 zeros due to numerical derivative in ncp)
alg._workspace_being_fit.extractY.return_value = dataY[:, cut_off_idx - 6:].reshape(1, -1)
alg._workspace_being_fit.extractX.return_value = dataX[:, cut_off_idx - 6:].reshape(1, -1)
alg._workspace_being_fit.extractE.return_value = dataE[:, cut_off_idx - 6:].reshape(1, -1)
alg._update_workspace_data()

ncp_array_cut = ncp_array_masked[:, cut_off_idx - 6:].reshape(1, -1)
alg._fit_profiles_workspaces = {"total": MagicMock(dataY=lambda arg: ncp_array_cut), "1": MagicMock()}

alg._fit_neutron_compton_profiles_to_row()
fit_parameters_cut = alg._fit_parameters.copy()

np.testing.assert_allclose(fit_parameters_cut, fit_parameters_masked, atol=1e-6)
np.testing.assert_allclose(ncp_array_masked[:, cut_off_idx-6:], ncp_array_cut, atol=1e-6)


if __name__ == "__main__":
Expand Down

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