FIX: Test

mne/io/snirf/_snirf.py

@@ -304,6 +304,7 @@ def natural_keys(text):
                         # append time delay
                         ch_name = f"{ch_name} bin{fnirs_time_delays[bin_idx - 1]}"
                         ch_type = "fnirs_td_gated_amplitude"
+                        need_data_scale = True
                     else:
                         assert snirf_data_type == SNIRF_TD_MOMENTS_AMPLITUDE
                         moment_idx = int(
@@ -316,6 +317,7 @@ def natural_keys(text):
                             f"{ch_name} moment{fnirs_moment_orders[moment_idx - 1]}"
                         )
                         ch_type = "fnirs_td_moments_amplitude"
+                        need_data_scale = True
 
                 elif snirf_data_type == SNIRF_PROCESSED:
                     dt_id = _correct_shape(
@@ -333,7 +335,14 @@ def natural_keys(text):
                         )
                         suffix = str(fnirs_wavelengths[wve_idx - 1])
                     else:
+                        if dt_id not in ("hbo", "hbr"):
+                            raise RuntimeError(
+                                "read_raw_snirf can only handle processed "
+                                "data in the form of optical density or "
+                                f"HbO/HbR, but got type f{dt_id}"
+                            )
                         suffix = dt_id.lower()
+                        need_data_scale = True
                     ch_name = f"{ch_name} {suffix}"
                     ch_type = dt_id
                 chnames.append(ch_name)
@@ -351,7 +360,7 @@ def natural_keys(text):
                 scale = _get_dataunit_scaling(snirf_data_unit)
                 if scale is not None:
                     for ch in info["chs"]:
-                        ch["cal"] = 1.0 / scale
+                        ch["cal"] = scale
 
             subject_info = {}
             names = np.array(dat.get("nirs/metaDataTags/SubjectID"))

mne/io/snirf/tests/test_snirf.py

@@ -427,14 +427,19 @@ def test_snirf_kernel(kind):
     if kind == "hb":
         # Test data import
         assert raw._data.shape == (180 * 2, 14)
-        assert raw.copy().pick("hbo")._data.shape == (180, 14)
-        assert raw.copy().pick("hbr")._data.shape == (180, 14)
+        hbo_data = raw.get_data("hbo")
+        hbr_data = raw.get_data("hbr")
+        assert hbo_data.shape == hbr_data.shape == (180, 14)
+        hbo_norm = np.nanmedian(np.linalg.norm(hbo_data, axis=-1))
+        hbr_norm = np.nanmedian(np.linalg.norm(hbr_data, axis=-1))
+        assert 1 < hbr_norm < hbo_norm < 3
         n_nan = 20
     else:
         assert raw._data.shape == (1080, 14)
-        assert (
-            raw.copy().pick("fnirs_td_moments_amplitude")._data.shape == raw._data.shape
-        )
+        data = raw.get_data("fnirs_td_moments_amplitude")
+        assert data.shape == raw._data.shape
+        norm = np.nanmedian(np.linalg.norm(data, axis=-1))
+        assert 1e5 < norm < 1e6  # TODO: 429256, is this reasonable Molars!??
         n_nan = 60
 
     assert_allclose(raw.info["sfreq"], 8.257638)