def test_algorithm_with_invalid_spectrum_number(self):
#tests that a float spectrum number throws an error
with self.assertRaises(TypeError):
FindPeaksAutomatic(InputWorkspace=self.data_ws,
PlotPeaks=False,
SpectrumNumber=3.4)
#tests that a negative integer throws an error
with self.assertRaises(ValueError):
FindPeaksAutomatic(InputWorkspace=self.data_ws,
PlotPeaks=False,
SpectrumNumber=-1)
def test_algorithm_creates_all_output_workspaces(self):
ws_name = self.raw_ws.getName()
FindPeaksAutomatic(self.raw_ws)
self.assertIn('{}_with_errors'.format(ws_name), mtd)
self.assertIn('{}_{}'.format(self.raw_ws.getName(), 'properties'), mtd)
self.assertIn('{}_{}'.format(self.raw_ws.getName(), 'refit_properties'), mtd)
def test_output_tables_are_correctly_formatted(self):
FindPeaksAutomatic(self.raw_ws, FitToBaseline=True)
peak_table = mtd['{}_{}'.format(self.raw_ws.getName(), 'properties')]
refit_peak_table = mtd['{}_{}'.format(self.raw_ws.getName(), 'refit_properties')]
self.assertEqual(self.peak_table_header, peak_table.getColumnNames())
self.assertEqual(self.peak_table_header, refit_peak_table.getColumnNames())
self.assertEqual(2, peak_table.rowCount())
self.assertEqual(0, refit_peak_table.rowCount())
def test_algorithm_does_not_create_temporary_workspaces(self):
FindPeaksAutomatic(self.raw_ws)
self.assertNotIn('ret', mtd)
self.assertNotIn('raw_data_ws', mtd)
self.assertNotIn('flat_ws', mtd)
self.assertNotIn('fit_result_NormalisedCovarianceMatrix', mtd)
self.assertNotIn('fit_result_Parameters', mtd)
self.assertNotIn('fit_result_Workspace', mtd)
self.assertNotIn('fit_cost', mtd)
def find_peak_algorithm(workspace, spectrum_number, min_energy, max_energy,
threshold, min_width, estimate_width, max_width):
FindPeaksAutomatic(InputWorkspace=retrieve_ws(workspace),
SpectrumNumber=spectrum_number,
StartXValue=min_energy,
EndXValue=max_energy,
AcceptanceThreshold=threshold,
PeakPropertiesTableName=workspace + PEAKS_WS_SUFFIX,
RefitPeakPropertiesTableName=workspace +
REFITTED_PEAKS_WS_SUFFIX,
MinPeakSigma=min_width,
EstimatePeakSigma=estimate_width,
MaxPeakSigma=max_width)
def test_that_algorithm_finds_peaks_correctly(self):
FindPeaksAutomatic(
InputWorkspace=self.raw_ws,
SmoothWindow=500,
EstimatePeakSigma=5,
MinPeakSigma=3,
MaxPeakSigma=15,
)
peak_table = mtd['{}_{}'.format(self.raw_ws.getName(), 'properties')]
refit_peak_table = mtd['{}_{}'.format(self.raw_ws.getName(), 'refit_properties')]
self.assertEqual(2, peak_table.rowCount())
self.assertEqual(0, refit_peak_table.rowCount())
self.assertPeakFound(peak_table.row(0), self.centre[0], self.height[0], self.width[0], 0.05)
self.assertPeakFound(peak_table.row(1), self.centre[1], self.height[1], self.width[1], 0.05)
def test_algorithm_works_on_specified_spectrum(self):
x_values = np.array(
[np.linspace(0, 100, 1001),
np.linspace(0, 100, 1001)],
dtype=float)
centre = np.array([[25, 75], [10, 60]], dtype=float)
height = np.array([[35, 20], [40, 50]], dtype=float)
width = np.array([[10, 5], [8, 6]], dtype=float)
y_values = np.array([
self.gaussian(x_values[0], centre[0, 0], height[0, 0], width[0,
0]),
self.gaussian(x_values[1], centre[1, 0], height[1, 0], width[1, 0])
])
y_values += np.array([
self.gaussian(x_values[0], centre[0, 1], height[0, 1], width[0,
1]),
self.gaussian(x_values[1], centre[1, 1], height[1, 1], width[1, 1])
])
background = 10 * np.ones(x_values.shape)
y_values += background
raw_ws = CreateWorkspace(DataX=x_values,
DataY=y_values,
OutputWorkspace='raw_ws',
NSpec=2)
FindPeaksAutomatic(
InputWorkspace=raw_ws,
SpectrumNumber=2,
SmoothWindow=500,
EstimatePeakSigma=6,
MinPeakSigma=3,
MaxPeakSigma=15,
)
peak_table = mtd['{}_{}'.format(raw_ws.getName(), 'properties')]
print(peak_table.row(1))
self.assertPeakFound(peak_table.row(0), 10, 40, 8)
self.assertPeakFound(peak_table.row(1), 60, 50, 6)
def test_algorithm_throws_RuntimeError_when_called_with_invalid_spectrum_number(
self):
x_values = np.array(
[np.linspace(0, 100, 1001),
np.linspace(0, 100, 1001)],
dtype=float)
centre = np.array([[25, 75], [10, 60]], dtype=float)
height = np.array([[35, 20], [40, 50]], dtype=float)
width = np.array([[10, 5], [8, 6]], dtype=float)
y_values = np.array([
self.gaussian(x_values[0], centre[0, 0], height[0, 0], width[0,
0]),
self.gaussian(x_values[1], centre[1, 0], height[1, 0], width[1, 0])
])
y_values += np.array([
self.gaussian(x_values[0], centre[0, 1], height[0, 1], width[0,
1]),
self.gaussian(x_values[1], centre[1, 1], height[1, 1], width[1, 1])
])
background = 10 * np.ones(x_values.shape)
y_values += background
raw_ws = CreateWorkspace(DataX=x_values,
DataY=y_values,
OutputWorkspace='raw_ws',
NSpec=2)
with self.assertRaises(RuntimeError):
FindPeaksAutomatic(
InputWorkspace=raw_ws,
SpectrumNumber=3,
SmoothWindow=500,
EstimatePeakSigma=6,
MinPeakSigma=3,
MaxPeakSigma=15,
)
def test_algorithm_with_negative_max_peak_sigma_throws(self):
with self.assertRaises(ValueError):
FindPeaksAutomatic(InputWorkspace=self.data_ws,
MaxPeakSigma=-0.1,
PlotPeaks=False)
def test_algorithm_with_negative_num_bad_peaks_to_consider_throws(self):
with self.assertRaises(ValueError):
FindPeaksAutomatic(InputWorkspace=self.data_ws,
BadPeaksToConsider=-3,
PlotPeaks=False)
def test_algorithm_with_negative_smooth_window_throws(self):
with self.assertRaises(ValueError):
FindPeaksAutomatic(InputWorkspace=self.data_ws,
SmoothWindow=-5,
PlotPeaks=False)
def test_algorithm_with_negative_acceptance_threshold_throws(self):
with self.assertRaises(ValueError):
FindPeaksAutomatic(InputWorkspace=self.data_ws,
AcceptanceThreshold=-0.1,
PlotPeaks=False)
def test_algorithm_with_no_input_workspace_raises_exception(self):
with self.assertRaises(RuntimeError):
FindPeaksAutomatic()
