FIX: Fix constants (mne-tools#5691)

doc/manual/matlab.rst

@@ -696,7 +696,7 @@ The documented structures are:
     | FIFFV_PROJ_ITEM_HOMOG_FIELD   | 5     | This projection item contains the three homogeneous field|
     |                               |       | components as seen by the sensor array.                  |
     +-------------------------------+-------+----------------------------------------------------------+
-    | FIFFV_MNE_PROJ_ITEM_EEG_AVREF | 10    | This projection item corresponds to the average EEG      |
+    | FIFFV_PROJ_ITEM_EEG_AVREF     | 10    | This projection item corresponds to the average EEG      |
     |                               |       | reference.                                               |
     +-------------------------------+-------+----------------------------------------------------------+
 

mne/io/constants.py

@@ -290,7 +290,9 @@ def __setattr__(self, attr, val):  # noqa: D105
 # BEM surface IDs
 #
 FIFF.FIFFV_BEM_SURF_ID_UNKNOWN    = -1
+FIFF.FIFFV_BEM_SURF_ID_NOT_KNOWN  = 0
 FIFF.FIFFV_BEM_SURF_ID_BRAIN      = 1
+FIFF.FIFFV_BEM_SURF_ID_CSF        = 2
 FIFF.FIFFV_BEM_SURF_ID_SKULL      = 3
 FIFF.FIFFV_BEM_SURF_ID_HEAD       = 4
 
@@ -327,6 +329,7 @@ def __setattr__(self, attr, val):  # noqa: D105
 FIFF.FIFFV_POINT_EEG      = 3
 FIFF.FIFFV_POINT_ECG      = FIFF.FIFFV_POINT_EEG
 FIFF.FIFFV_POINT_EXTRA    = 4
+FIFF.FIFFV_POINT_HEAD     = 5  # Point on the surface of the head
 
 FIFF.FIFFV_POINT_LPA = 1
 FIFF.FIFFV_POINT_NASION = 2
@@ -584,7 +587,6 @@ def __setattr__(self, attr, val):  # noqa: D105
 FIFF.FIFFV_MNE_DEPTH_PRIOR_COV      = 5         # The depth weighting prior
 FIFF.FIFFV_MNE_ORIENT_PRIOR_COV     = 6         # The orientation prior
 
-FIFF.FIFFV_MNE_PROJ_ITEM_EEG_AVREF  = 10        # Linear projection related to EEG average reference
 #
 # Output map types
 #
@@ -623,6 +625,30 @@ def __setattr__(self, attr, val):  # noqa: D105
 FIFF.FIFFV_PROJ_ITEM_DIP_ROT        = 3
 FIFF.FIFFV_PROJ_ITEM_HOMOG_GRAD     = 4
 FIFF.FIFFV_PROJ_ITEM_HOMOG_FIELD    = 5
+FIFF.FIFFV_PROJ_ITEM_EEG_AVREF      = 10  # Linear projection related to EEG average reference
+FIFF.FIFFV_MNE_PROJ_ITEM_EEG_AVREF  = FIFF.FIFFV_PROJ_ITEM_EEG_AVREF  # backward compat alias
+#
+# SSS job options
+#
+FIFF.FIFFV_SSS_JOB_NOTHING          = 0   # No SSS, just copy input to output
+FIFF.FIFFV_SSS_JOB_CTC              = 1   # No SSS, only cross-talk correction
+FIFF.FIFFV_SSS_JOB_FILTER           = 2   # Spatial maxwell filtering
+FIFF.FIFFV_SSS_JOB_VIRT             = 3   # Transform data to another sensor array
+FIFF.FIFFV_SSS_JOB_HEAD_POS         = 4   # Estimate head positions, no SSS
+FIFF.FIFFV_SSS_JOB_MOVEC_FIT        = 5   # Estimate and compensate head movement
+FIFF.FIFFV_SSS_JOB_MOVEC_QUA        = 6   # Compensate head movement from previously estimated head positions
+FIFF.FIFFV_SSS_JOB_REC_ALL          = 7   # Reconstruct inside and outside signals
+FIFF.FIFFV_SSS_JOB_REC_IN           = 8   # Reconstruct inside signals
+FIFF.FIFFV_SSS_JOB_REC_OUT          = 9   # Reconstruct outside signals
+FIFF.FIFFV_SSS_JOB_ST               = 10  # Spatio-temporal maxwell filtering
+FIFF.FIFFV_SSS_JOB_TPROJ            = 11  # Temporal projection, no SSS
+FIFF.FIFFV_SSS_JOB_XSSS             = 12  # Cross-validation SSS
+FIFF.FIFFV_SSS_JOB_XSUB             = 13  # Cross-validation subtraction, no SSS
+FIFF.FIFFV_SSS_JOB_XWAV             = 14  # Cross-validation noise waveforms
+FIFF.FIFFV_SSS_JOB_NCOV             = 15  # Noise covariance estimation
+FIFF.FIFFV_SSS_JOB_SCOV             = 16  # SSS sample covariance estimation
+#}
+
 #
 # Additional coordinate frames
 #
@@ -707,6 +733,7 @@ def __setattr__(self, attr, val):  # noqa: D105
 #
 # SI base units
 #
+FIFF.FIFF_UNIT_UNITLESS = 0
 FIFF.FIFF_UNIT_M   = 1  # meter
 FIFF.FIFF_UNIT_KG  = 2  # kilogram
 FIFF.FIFF_UNIT_SEC = 3  # second
@@ -725,6 +752,7 @@ def __setattr__(self, attr, val):  # noqa: D105
 #
 # SI derived units
 #
+FIFF.FIFF_UNIT_MOL_M3 = 10  # mol/m^3
 FIFF.FIFF_UNIT_HZ  = 101  # hertz
 FIFF.FIFF_UNIT_N   = 102  # Newton
 FIFF.FIFF_UNIT_PA  = 103  # pascal
@@ -754,6 +782,7 @@ def __setattr__(self, attr, val):  # noqa: D105
 FIFF.FIFF_UNITM_E    = 18
 FIFF.FIFF_UNITM_PET  = 15
 FIFF.FIFF_UNITM_T    = 12
+FIFF.FIFF_UNITM_GIG  = 9
 FIFF.FIFF_UNITM_MEG  = 6
 FIFF.FIFF_UNITM_K    = 3
 FIFF.FIFF_UNITM_H    = 2

mne/io/proj.py

@@ -611,7 +611,7 @@ def _make_projector(projs, ch_names, bads=(), include_active=True,
                     orig_n = p['data']['data'].any(axis=0).sum()
                     # Average ref still works if channels are removed
                     if len(vecsel) < 0.9 * orig_n and not inplace and \
-                            (p['kind'] != FIFF.FIFFV_MNE_PROJ_ITEM_EEG_AVREF or
+                            (p['kind'] != FIFF.FIFFV_PROJ_ITEM_EEG_AVREF or
                              len(vecsel) == 1):
                         warn('Projection vector "%s" has magnitude %0.2f '
                              '(should be unity), applying projector with '
@@ -794,7 +794,7 @@ def make_eeg_average_ref_proj(info, activate=True, verbose=None):
                          data=vec, nrow=1, ncol=n_eeg)
     eeg_proj = Projection(active=activate, data=eeg_proj_data,
                           desc='Average EEG reference',
-                          kind=FIFF.FIFFV_MNE_PROJ_ITEM_EEG_AVREF,
+                          kind=FIFF.FIFFV_PROJ_ITEM_EEG_AVREF,
                           explained_var=explained_var)
     return eeg_proj
 
@@ -807,7 +807,7 @@ def _has_eeg_average_ref_proj(projs, check_active=False):
     """
     for proj in projs:
         if (proj['desc'] == 'Average EEG reference' or
-                proj['kind'] == FIFF.FIFFV_MNE_PROJ_ITEM_EEG_AVREF):
+                proj['kind'] == FIFF.FIFFV_PROJ_ITEM_EEG_AVREF):
             if not check_active or proj['active']:
                 return True
     return False

mne/io/reference.py

@@ -103,7 +103,7 @@ def _apply_reference(inst, ref_from, ref_to=None):
     for i, proj in enumerate(inst.info['projs']):
         # Remove any average reference projections
         if proj['desc'] == 'Average EEG reference' or \
-                proj['kind'] == FIFF.FIFFV_MNE_PROJ_ITEM_EEG_AVREF:
+                proj['kind'] == FIFF.FIFFV_PROJ_ITEM_EEG_AVREF:
             logger.info('Removing existing average EEG reference '
                         'projection.')
             # Don't remove the projection right away, but do this at the end of

mne/io/tests/test_constants.py

@@ -23,6 +23,13 @@
                      'has_key', 'iteritems', 'iterkeys', 'itervalues',  # Py2
                      'viewitems', 'viewkeys', 'viewvalues',  # Py2
                      )
+_ignore_incomplete_enums = (  # XXX eventually we could complete these
+    'bem_surf_id', 'cardinal_point_cardiac', 'cond_model', 'coord',
+    'dacq_system', 'diffusion_param', 'gantry_type', 'map_surf',
+    'mne_lin_proj', 'mne_ori', 'mri_format', 'mri_pixel', 'proj_by',
+    'tags', 'type', 'iod', 'volume_type', 'vol_type',
+    'coil',  # Especially these!  3015, 3025
+)
 # not in coil_def.dat but in DictionaryTypes:enum(coil)
 _missing_coil_def = (
     0,      # The location info contains no data
@@ -207,6 +214,7 @@ def test_constants(tmpdir):
     unknowns = list()
 
     # Assert that all our constants are in the FIF def
+    assert 'FIFFV_SSS_JOB_NOTHING' in dir(FIFF)
     for name in sorted(dir(FIFF)):
         if name.startswith('_') or name in _dir_ignore_names:
             continue
@@ -245,6 +253,8 @@ def test_constants(tmpdir):
                 else:
                     raise RuntimeError('Could not find %s' % (name,))
             assert check in used_enums, name
+            if 'SSS' in check:
+                raise RuntimeError
         elif name.startswith('FIFF_UNIT'):  # units and multipliers
             check = name.split('_')[1].lower()
         elif name.startswith('FIFF_'):
@@ -262,13 +272,17 @@ def test_constants(tmpdir):
     assert len(unknowns) == 0, 'Unknown types\n\t%s' % unknowns
 
     # Assert that all the FIF defs are in our constants
+    assert set(fif.keys()) == set(con.keys())
     for key in sorted(set(fif.keys()) - set(['defines'])):
         this_fif, this_con = fif[key], con[key]
         assert len(set(this_fif.keys())) == len(this_fif)
         assert len(set(this_con.keys())) == len(this_con)
-        assert set(con.keys()) - set(fif.keys()) == set()
-        missing = sorted(set(fif.keys()) - set(con.keys()))
-        assert missing == [], key
+        missing_from_con = sorted(set(this_con.keys()) - set(this_fif.keys()))
+        assert missing_from_con == [], key
+        if key not in _ignore_incomplete_enums:
+            missing_from_fif = sorted(set(this_fif.keys()) -
+                                      set(this_con.keys()))
+            assert missing_from_fif == [], key
 
     # Assert that `coil_def.dat` has accurate descriptions of all enum(coil)
     coil_def = _read_coil_defs()

mne/preprocessing/maxwell.py

@@ -410,7 +410,11 @@ def maxwell_filter(raw, origin='auto', int_order=8, ext_order=3,
     # Reconstruct raw file object with spatiotemporal processed data
     max_st = dict()
     if st_duration is not None:
-        max_st.update(job=10, subspcorr=st_correlation,
+        if st_only:
+            job = FIFF.FIFFV_SSS_JOB_TPROJ
+        else:
+            job = FIFF.FIFFV_SSS_JOB_ST
+        max_st.update(job=job, subspcorr=st_correlation,
                       buflen=st_duration / info['sfreq'])
         logger.info('    Processing data using tSSS with st_duration=%s'
                     % max_st['buflen'])
@@ -1464,7 +1468,8 @@ def _update_sss_info(raw, origin, int_order, ext_order, nchan, coord_frame,
     components[reg_moments] = 1
     sss_info_dict = dict(in_order=int_order, out_order=ext_order,
                          nchan=nchan, origin=origin.astype('float32'),
-                         job=np.array([2]), nfree=np.sum(components[:n_in]),
+                         job=FIFF.FIFFV_SSS_JOB_FILTER,
+                         nfree=np.sum(components[:n_in]),
                          frame=_str_to_frame[coord_frame],
                          components=components)
     max_info_dict = dict(max_st=max_st)