def _createDiagnosticsReportTable(reportWSName, numberHistograms, algorithmLogging):
"""Return a table workspace for detector diagnostics reporting."""
if mtd.doesExist(reportWSName):
reportWS = mtd[reportWSName]
else:
reportWS = CreateEmptyTableWorkspace(OutputWorkspace=reportWSName,
EnableLogging=algorithmLogging)
existingColumnNames = reportWS.getColumnNames()
if 'WorkspaceIndex' not in existingColumnNames:
reportWS.addColumn('int', 'WorkspaceIndex', _PLOT_TYPE_X)
reportWS.setRowCount(numberHistograms)
for i in range(numberHistograms):
reportWS.setCell('WorkspaceIndex', i, i)
return reportWS
def _createDiagnosticsReportTable(reportWSName, numberHistograms, algorithmLogging):
"""Return a table workspace for detector diagnostics reporting."""
if mtd.doesExist(reportWSName):
reportWS = mtd[reportWSName]
else:
reportWS = CreateEmptyTableWorkspace(OutputWorkspace=reportWSName,
EnableLogging=algorithmLogging)
existingColumnNames = reportWS.getColumnNames()
if 'WorkspaceIndex' not in existingColumnNames:
reportWS.addColumn('int', 'WorkspaceIndex', _PLOT_TYPE_X)
reportWS.setRowCount(numberHistograms)
for i in range(numberHistograms):
reportWS.setCell('WorkspaceIndex', i, i)
return reportWS
def test_parse_fit_table_correctly_formats_the_table(self):
peaks = [(35.2, 0.4), (25.03, 0.1), (10.03, 0.05)]
peaks += [(20.003, 0.004), (75.15, 0.2), (5.2, 0.05)]
fit_table = self.simulate_fit_parameter_output(peaks, 100.034)
data_table = CreateEmptyTableWorkspace()
to_refit = self.alg_instance.parse_fit_table(fit_table,
data_table,
refit=False)
self.assertEqual(to_refit, [])
self.assertEqual(data_table.getColumnNames(), self.peak_table_header)
np.testing.assert_almost_equal(data_table.column(0), [25.03, 75.15], 5)
np.testing.assert_almost_equal(data_table.column(1), [0.1, 0.2], 5)
np.testing.assert_almost_equal(data_table.column(2), [35.2, 20.003], 5)
np.testing.assert_almost_equal(data_table.column(3), [0.4, 0.004], 5)
np.testing.assert_almost_equal(data_table.column(4), [10.03, 5.2], 5)
np.testing.assert_almost_equal(data_table.column(5), [0.05, 0.05], 5)
def test_parse_fit_table_marks_peaks_for_refitting_if_error_larger_than_value(
self):
peaks = [(35.2, 0.4), (25.03, 0.1), (10.03, 0.05)]
peaks += [(20.003, 40.22), (75.15, 0.2), (5.2, np.NaN)]
fit_table = self.simulate_fit_parameter_output(peaks, 100.034)
data_table = CreateEmptyTableWorkspace()
to_refit = self.alg_instance.parse_fit_table(fit_table,
data_table,
refit=True)
self.assertEqual(data_table.getColumnNames(), self.peak_table_header)
np.testing.assert_almost_equal(data_table.column(0), [25.03], 5)
np.testing.assert_almost_equal(data_table.column(1), [0.1], 5)
np.testing.assert_almost_equal(data_table.column(2), [35.2], 5)
np.testing.assert_almost_equal(data_table.column(3), [0.4], 5)
np.testing.assert_almost_equal(data_table.column(4), [10.03], 5)
np.testing.assert_almost_equal(data_table.column(5), [0.05], 5)
np.testing.assert_almost_equal(to_refit, [(75.15, 20.003, 5.2)], 5)