Specview export tof wavelength (#2218)

docs/release_notes/next/feature-2216-export_tof_wavelength_and_energy_to_csv

@@ -0,0 +1 @@
+#2216: Add Wavelength, ToF and Energy unit conversions to Spectrum Viewer CSV Output

mantidimaging/core/io/csv_output.py

@@ -11,18 +11,22 @@ class CSVOutput:
     def __init__(self) -> None:
         self.columns: dict[str, np.ndarray] = {}
         self.num_rows: int | None = None
+        self.units: dict[str, str] = {}
 
-    def add_column(self, name: str, values: np.ndarray) -> None:
+    def add_column(self, name: str, values: np.ndarray, units: str) -> None:
         as_column = values.reshape((-1, 1))
-        if self.num_rows is not None:
-            if as_column.size != self.num_rows:
-                raise ValueError("Column sizes must match")
-        else:
-            self.num_rows = as_column.size
+        if self.num_rows is not None and as_column.size != self.num_rows:
+            raise ValueError('Column sizes must match')
 
+        if units:
+            self.units[name] = units
+
+        self.num_rows = as_column.size if self.num_rows is None else self.num_rows
         self.columns[name] = as_column
 
     def write(self, outstream: IO[str]) -> None:
-        header = ",".join(self.columns.keys())
+        header = ','.join(self.columns.keys())
+        units = ','.join(self.units.values())
+        outstream.write('# ' + header + '\n' + '# ' + units + '\n')
         data = np.hstack(list(self.columns.values()))
-        np.savetxt(outstream, data, header=header, fmt="%s", delimiter=",")
+        np.savetxt(outstream, data, fmt='%s', delimiter=',')

mantidimaging/core/io/test/test_csv_output.py

@@ -19,37 +19,37 @@ def test_add_column(self):
         col_name = "new_col"
         values = np.arange(10)
 
-        self.csv_output.add_column(col_name, values)
+        self.csv_output.add_column(col_name, values, "")
 
         self.assertIn(col_name, self.csv_output.columns)
         np.testing.assert_array_equal(self.csv_output.columns[col_name].flatten(), values.flatten())
 
     def test_column_order(self):
         col_names = "red orange yellow green blue indigo violet".split()
         for col_name in col_names:
-            self.csv_output.add_column(col_name, np.arange(10))
+            self.csv_output.add_column(col_name, np.arange(10), "")
 
         self.assertEqual(col_names, list(self.csv_output.columns.keys()))
 
     def test_write_single(self):
         col_name = "new_col"
         values = np.arange(5, dtype=np.float32)
-        expected_out = "# new_col\n0.0\n1.0\n2.0\n3.0\n4.0\n"
-        self.csv_output.add_column(col_name, values)
+        expected_out = "# new_col\n# \n0.0\n1.0\n2.0\n3.0\n4.0\n"
+        self.csv_output.add_column(col_name, values, "")
 
         self.csv_output.write(self.stream)
         self.assertEqual(self.stream.getvalue(), expected_out)
 
     def test_write_2_cols(self):
-        expected_out = "# col_1,col_2\n0.0,5.0\n1.0,6.0\n2.0,7.0\n3.0,8.0\n4.0,9.0\n"
-        self.csv_output.add_column("col_1", np.arange(5, dtype=np.float32))
-        self.csv_output.add_column("col_2", np.arange(5, 10, dtype=np.float32))
+        expected_out = "# col_1,col_2\n# \n0.0,5.0\n1.0,6.0\n2.0,7.0\n3.0,8.0\n4.0,9.0\n"
+        self.csv_output.add_column("col_1", np.arange(5, dtype=np.float32), "")
+        self.csv_output.add_column("col_2", np.arange(5, 10, dtype=np.float32), "")
 
         self.csv_output.write(self.stream)
         self.assertEqual(self.stream.getvalue(), expected_out)
 
     def test_add_column_wrong_size(self):
-        self.csv_output.add_column("col_1", np.arange(5, dtype=np.float32))
+        self.csv_output.add_column("col_1", np.arange(5, dtype=np.float32), "")
 
         with self.assertRaises(ValueError):
-            self.csv_output.add_column("col_2", np.arange(6, dtype=np.float32))
+            self.csv_output.add_column("col_2", np.arange(6, dtype=np.float32), "")

mantidimaging/gui/windows/spectrum_viewer/model.py

@@ -282,15 +282,21 @@ def save_csv(self, path: Path, normalized: bool) -> None:
             raise ValueError("No stack selected")
 
         csv_output = CSVOutput()
-        csv_output.add_column("tof_index", np.arange(self._stack.data.shape[0]))
+        csv_output.add_column("ToF_index", np.arange(self._stack.data.shape[0]), "Index")
+        self.tof_data = self.get_stack_time_of_flight()
+        if self.tof_data is not None:
+            self.units.set_data_to_convert(self.tof_data)
+            csv_output.add_column("Wavelength", self.units.tof_seconds_to_wavelength_in_angstroms(), "Angstrom")
+            csv_output.add_column("ToF", self.units.tof_seconds_to_us(), "Microseconds")
+            csv_output.add_column("Energy", self.units.tof_seconds_to_energy(), "MeV")
 
         for roi_name in self.get_list_of_roi_names():
-            csv_output.add_column(roi_name, self.get_spectrum(roi_name, SpecType.SAMPLE))
+            csv_output.add_column(roi_name, self.get_spectrum(roi_name, SpecType.SAMPLE), "Counts")
             if normalized:
                 if self._normalise_stack is None:
                     raise RuntimeError("No normalisation stack selected")
-                csv_output.add_column(roi_name + "_open", self.get_spectrum(roi_name, SpecType.OPEN))
-                csv_output.add_column(roi_name + "_norm", self.get_spectrum(roi_name, SpecType.SAMPLE_NORMED))
+                csv_output.add_column(roi_name + "_open", self.get_spectrum(roi_name, SpecType.OPEN), "Counts")
+                csv_output.add_column(roi_name + "_norm", self.get_spectrum(roi_name, SpecType.SAMPLE_NORMED), "Counts")
 
         with path.open("w") as outfile:
             csv_output.write(outfile)

mantidimaging/gui/windows/spectrum_viewer/test/model_test.py

@@ -39,12 +39,12 @@ def setUp(self) -> None:
     def _set_sample_stack(self, with_tof=False):
         spectrum = np.arange(0, 10)
         stack = ImageStack(np.ones([10, 11, 12]) * spectrum.reshape((10, 1, 1)))
-        self.model.set_stack(stack)
-        self.model.set_new_roi("roi")
         if with_tof:
             mock_inst_log = mock.create_autospec(InstrumentLog, source_file="")
             mock_inst_log.get_column.return_value = np.arange(0, 10) * 0.1
             stack.log_file = mock_inst_log
+        self.model.set_stack(stack)
+        self.model.set_new_roi("roi")
         return stack, spectrum
 
     def _make_mock_path_stream(self):
@@ -182,9 +182,10 @@ def test_save_csv(self):
             self.model.save_csv(mock_path, False)
 
         mock_path.open.assert_called_once_with("w")
-        self.assertIn("# tof_index,all,roi", mock_stream.captured[0])
-        self.assertIn("0.0,0.0,0.0", mock_stream.captured[1])
-        self.assertIn("1.0,2.0,2.0", mock_stream.captured[2])
+        self.assertIn("# ToF_index,all,roi", mock_stream.captured[0])
+        self.assertIn("# Index,Counts,Counts", mock_stream.captured[1])
+        self.assertIn("0.0,0.0,0.0", mock_stream.captured[2])
+        self.assertIn("1.0,2.0,2.0", mock_stream.captured[3])
         self.assertTrue(mock_stream.is_closed)
 
     def test_save_rits_dat(self):
@@ -319,9 +320,30 @@ def test_save_csv_norm(self):
             self.model.save_csv(mock_path, True)
 
         mock_path.open.assert_called_once_with("w")
-        self.assertIn("# tof_index,all,all_open,all_norm,roi,roi_open,roi_norm", mock_stream.captured[0])
-        self.assertIn("0.0,0.0,2.0,0.0,0.0,2.0,0.0", mock_stream.captured[1])
-        self.assertIn("1.0,1.0,2.0,0.5,1.0,2.0,0.5", mock_stream.captured[2])
+        self.assertIn("# ToF_index,all,all_open,all_norm,roi,roi_open,roi_norm", mock_stream.captured[0])
+        self.assertIn("# Index,Counts,Counts,Counts,Counts,Counts,Counts", mock_stream.captured[1])
+        self.assertIn("0.0,0.0,2.0,0.0,0.0,2.0,0.0", mock_stream.captured[2])
+        self.assertIn("1.0,1.0,2.0,0.5,1.0,2.0,0.5", mock_stream.captured[3])
+        self.assertTrue(mock_stream.is_closed)
+
+    def test_save_csv_norm_with_tof_loaded(self):
+        stack, _ = self._set_sample_stack(with_tof=True)
+        norm = ImageStack(np.full([10, 11, 12], 2))
+        stack.data[:, :, :5] *= 2
+        self.model.set_normalise_stack(norm)
+
+        mock_stream, mock_path = self._make_mock_path_stream()
+        with mock.patch.object(self.model, "save_roi_coords"):
+            self.model.save_csv(mock_path, True)
+
+        mock_path.open.assert_called_once_with("w")
+        self.assertIn("# ToF_index,Wavelength,ToF,Energy,all,all_open,all_norm,roi,roi_open,roi_norm",
+                      mock_stream.captured[0])
+        self.assertIn("# Index,Angstrom,Microseconds,MeV,Counts,Counts,Counts", mock_stream.captured[1])
+        self.assertIn("0.0,0.0,0.0,inf,0.0,2.0,0.0,0.0,2.0,0.0", mock_stream.captured[2])
+        self.assertIn(
+            "1.0,7.064346392065392,100000.0,2.9271405738026552,1.4166666666666667,2.0,0.7083333333333334,1.4166666666666667,2.0,0.7083333333333334",
+            mock_stream.captured[3])
         self.assertTrue(mock_stream.is_closed)
 
     def test_WHEN_roi_name_generator_called_THEN_correct_names_returned_visible_to_model(self):