FIX: Tests passing

Author: larsoner

mne/_fiff/constants.py

@@ -931,6 +931,7 @@
 FIFF.FIFF_UNIT_LM = 115  # lumen
 FIFF.FIFF_UNIT_LX = 116  # lux
 FIFF.FIFF_UNIT_V_M2 = 117  # V/m^2
+FIFF.FIFF_UNIT_SEC2 = 118  # second^2
 #
 # Others we need
 #
@@ -972,6 +973,7 @@
         FIFF.FIFF_UNIT_LM,
         FIFF.FIFF_UNIT_LX,
         FIFF.FIFF_UNIT_V_M2,
+        FIFF.FIFF_UNIT_SEC2,
         FIFF.FIFF_UNIT_T_M,
         FIFF.FIFF_UNIT_AM,
         FIFF.FIFF_UNIT_AM_M2,

mne/_fiff/meas_info.py

@@ -444,6 +444,7 @@ def set_montage(
     FIFF.FIFF_UNIT_NONE: "NA",
     FIFF.FIFF_UNIT_CEL: "C",
     FIFF.FIFF_UNIT_S: "S",
+    FIFF.FIFF_UNIT_SEC: "s",
     FIFF.FIFF_UNIT_PX: "px",
 }
 

mne/_fiff/pick.py

@@ -116,12 +116,12 @@ def get_channel_type_constants(include_defaults=False):
         ),
         fnirs_td_moments_mean=dict(
             kind=FIFF.FIFFV_FNIRS_CH,
-            unit=FIFF.FIFF_UNIT_S,
+            unit=FIFF.FIFF_UNIT_SEC,
             coil_type=FIFF.FIFFV_COIL_FNIRS_TD_MOMENTS_MEAN,
         ),
         fnirs_td_moments_variance=dict(
             kind=FIFF.FIFFV_FNIRS_CH,
-            unit=FIFF.FIFF_UNIT_NONE,  # TODO: Maybe someday add s^2
+            unit=FIFF.FIFF_UNIT_SEC2,
             coil_type=FIFF.FIFFV_COIL_FNIRS_TD_MOMENTS_VARIANCE,
         ),
         fnirs_od=dict(kind=FIFF.FIFFV_FNIRS_CH, coil_type=FIFF.FIFFV_COIL_FNIRS_OD),

mne/_fiff/tests/test_constants.py

@@ -28,7 +28,7 @@
 
 # https://github.com/mne-tools/fiff-constants/commits/master
 REPO = "larsoner"  # TODO: Replace with upstream once merged
-COMMIT = "ba2288355b61b00d65d4f1d8a47ef82b83414201"
+COMMIT = "63cdf5d64a7006d2b8d21931bd7c4d898e310df8"
 
 # These are oddities that we won't address:
 iod_dups = (355, 359)  # these are in both MEGIN and MNE files

mne/defaults.py

@@ -66,8 +66,8 @@
         fnirs_od="V",
         fnirs_td_gated_amplitude="AU",  # counts
         fnirs_td_moments_intensity="AU",  # counts
-        fnirs_td_moments_mean="S",
-        fnirs_td_moments_variance="S²",
+        fnirs_td_moments_mean="s",
+        fnirs_td_moments_variance="s²",
         csd="V/m²",
         whitened="Z",
         gsr="S",
@@ -98,16 +98,16 @@
         fnirs_od="V",
         fnirs_td_gated_amplitude="AU",
         fnirs_td_moments_intensity="AU",
-        fnirs_td_moments_mean="S",
-        fnirs_td_moments_variance="S²",
+        fnirs_td_moments_mean="ps",
+        fnirs_td_moments_variance="ps²",
         csd="mV/m²",
         whitened="Z",
         gsr="S",
         temperature="C",
         eyegaze="rad",
         pupil="µm",
     ),
-    # scalings for the units
+    # scalings for the "units" above (must match!)
     scalings=dict(
         mag=1e15,
         grad=1e13,
@@ -131,8 +131,8 @@
         fnirs_od=1.0,
         fnirs_td_gated_amplitude=1.0,
         fnirs_td_moments_intensity=1.0,
-        fnirs_td_moments_mean=1.0,
-        fnirs_td_moments_variance=1.0,
+        fnirs_td_moments_mean=1e12,
+        fnirs_td_moments_variance=1e24,
         csd=1e3,
         whitened=1.0,
         gsr=1.0,
@@ -169,8 +169,8 @@
         fnirs_od=2e-2,
         fnirs_td_gated_amplitude=1.0,
         fnirs_td_moments_intensity=1.0,
-        fnirs_td_moments_mean=1.0,
-        fnirs_td_moments_variance=1.0,
+        fnirs_td_moments_mean=1e-10,
+        fnirs_td_moments_variance=1e-20,
         csd=200e-4,
         dipole=1e-7,
         gof=1e2,

mne/io/snirf/_snirf.py

@@ -288,9 +288,16 @@ def natural_keys(text):
                     "location information"
                 )
 
+            # Uniform scale factor assumed here!
+            snirf_data_unit = np.array(
+                dat.get("nirs/data1/measurementList1/dataUnit", b"M")
+            )
+            snirf_data_unit = snirf_data_unit.item().decode("utf-8")
+            scale = _get_dataunit_scaling(snirf_data_unit)
+
             chnames = []
             ch_types = []
-            need_data_scale = False
+            ch_cals = []
             for chan in channels:
                 ch_root = f"nirs/data1/{chan}"
                 src_idx = int(
@@ -300,6 +307,7 @@ def natural_keys(text):
                     _correct_shape(np.array(dat.get(f"{ch_root}/detectorIndex")))[0]
                 )
                 ch_name = f"{sources[src_idx]}_{detectors[det_idx]}"
+                ch_cal = scale
 
                 if snirf_data_type in (
                     SNIRF_CW_AMPLITUDE,
@@ -324,7 +332,6 @@ 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(
@@ -340,8 +347,13 @@ def natural_keys(text):
                             _TD_MOMENT_ORDER_MAP,
                         )
                         ch_name = f"{ch_name} moment{order}"
-                        ch_type = f"fnirs_td_moments_{_TD_MOMENT_ORDER_MAP[order]}"
-
+                        kind = _TD_MOMENT_ORDER_MAP[order]
+                        ch_type = f"fnirs_td_moments_{kind}"
+                        if kind == "mean":
+                            # Stored in picoseconds
+                            ch_cal = 1e-12
+                        elif kind == "variance":
+                            ch_cal = 1e-24
                 elif snirf_data_type == SNIRF_PROCESSED:
                     dt_id = _correct_shape(
                         np.array(dat.get(f"{ch_root}/dataTypeLabel"))
@@ -365,25 +377,18 @@ def natural_keys(text):
                                 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)
                 ch_types.append(ch_type)
-                del ch_root, ch_name, ch_type
+                ch_cals.append(ch_cal)
+                del ch_root, ch_name, ch_type, ch_cal
+            del scale
 
             # Create mne structure
             info = create_info(chnames, sampling_rate, ch_types=ch_types)
-
-            if need_data_scale:
-                snirf_data_unit = np.array(
-                    dat.get("nirs/data1/measurementList1/dataUnit", b"M")
-                )
-                snirf_data_unit = snirf_data_unit.item().decode("utf-8")
-                scale = _get_dataunit_scaling(snirf_data_unit)  # " " or "M")
-                if scale is not None:
-                    for ch in info["chs"]:
-                        ch["cal"] = scale
+            for ch, ch_cal in zip(info["chs"], ch_cals):
+                ch["cal"] = ch_cal
 
             subject_info = {}
             names = np.array(dat.get("nirs/metaDataTags/SubjectID"))
@@ -417,8 +422,8 @@ def natural_keys(text):
             length_unit = _get_metadata_str(dat, "LengthUnit")
             length_scaling = _get_lengthunit_scaling(length_unit)
 
-            srcPos3D /= length_scaling
-            detPos3D /= length_scaling
+            srcPos3D *= length_scaling
+            detPos3D *= length_scaling
 
             if optode_frame in ["mri", "meg"]:
                 # These are all in MNI or MEG coordinates, so let's transform
@@ -651,7 +656,7 @@ def _get_timeunit_scaling(time_unit):
 
 def _get_lengthunit_scaling(length_unit):
     """MNE expects distance in m, return required scaling."""
-    scalings = {"m": 1, "cm": 100, "mm": 1000}
+    scalings = {"m": 1.0, "cm": 1e-2, "mm": 1e-3}
     if length_unit in scalings:
         return scalings[length_unit]
     else:
@@ -664,7 +669,7 @@ def _get_lengthunit_scaling(length_unit):
 
 def _get_dataunit_scaling(hbx_unit):
     """MNE expects hbo/hbr in M, return required scaling."""
-    scalings = {"M": None, "uM": 1e-6}
+    scalings = {"M": 1.0, "uM": 1e-6, "": 1.0}
     try:
         return scalings[hbx_unit]
     except KeyError:

mne/io/snirf/tests/test_snirf.py

@@ -474,16 +474,15 @@ def test_snirf_kernel_basic(kind, ver, shape, n_nan, fname):
         assert hbo_data.shape == hbr_data.shape == (shape[0] // 2, shape[1])
         hbo_norm = np.nanmedian(np.linalg.norm(hbo_data, axis=-1))
         hbr_norm = np.nanmedian(np.linalg.norm(hbr_data, axis=-1))
-        # TODO: Old file vs new file scaling, one is wrong!
+        # TODO: Old file vs new file scaling, old one is wrong most likely!
         if ver == "new":
             assert 1e-5 < hbr_norm < hbo_norm < 1e-4
         else:
             assert 1 < hbr_norm < 3
     elif kind == "td moments":
         assert raw._data.shape == shape
         n_ch = 0
-        # TODO: Reasonable values here???
-        lims = dict(intensity=(1e4, 1e7), mean=(1e3, 1e4), variance=(1e5, 1e7))
+        lims = dict(intensity=(1e4, 1e7), mean=(1e-9, 1e-8), variance=(1e-19, 1e-16))
         for key, val in lims.items():
             data = raw.get_data(f"fnirs_td_moments_{key}")
             assert data.shape[1] == len(raw.times)
@@ -492,13 +491,17 @@ def test_snirf_kernel_basic(kind, ver, shape, n_nan, fname):
             assert min_ < norm < max_, key
             n_ch += data.shape[0]
         assert raw._data.shape[0] == len(raw.ch_names) == n_ch
+        mean_ch = raw.copy().pick("fnirs_td_moments_mean").info["chs"][0]
+        assert mean_ch["unit"] == FIFF.FIFF_UNIT_SEC
+        var_ch = raw.copy().pick("fnirs_td_moments_variance").info["chs"][0]
+        assert var_ch["unit"] == FIFF.FIFF_UNIT_SEC2
     else:
         pass  # TODO: add some gated tests
     if ver == "old":
-        sfreq = 8.257638
+        sfreq = 8.256495
         n_annot = 2
     else:
-        sfreq = 3.759398
+        sfreq = 3.759351
         n_annot = 8
 
     assert_allclose(raw.info["sfreq"], sfreq, atol=1e-5)
@@ -533,7 +536,7 @@ def test_user_set_sfreq(sfreq, context):
     with context:
         # both sfreqs are far enough from true rate to yield >1% jitter
         with pytest.warns(RuntimeWarning, match=r"jitter of \d+\.\d*% in sample times"):
-            raw = read_raw_snirf(kernel_hb, preload=False, sfreq=sfreq)
+            raw = read_raw_snirf(kernel_hb_old, preload=False, sfreq=sfreq)
     assert raw.info["sfreq"] == sfreq