Merge branch 'doc/test' into r/0.38

doc/publications.bib

@@ -764,3 +764,23 @@ @article{matarmodels
     url = "https://2022.ccneuro.org/proceedings/0000331.pdf",
     google_result_id = "mBZCoKR6Va4J"
 }
+
+@article{gillis2022heard,
+    author = "Gillis, Marlies and Vanthornhout, Jonas and Francart, Tom",
+    title = "Heard or understood? Neural tracking of language features in a comprehensible story, an incomprehensible story and a word list",
+    journal = "bioRxiv",
+    year = "2022",
+    publisher = "Cold Spring Harbor Laboratory",
+    doi = "10.1101/2022.11.22.517477",
+    google_result_id = "7w3_G4qWqWUJ"
+}
+
+@article{schmidt2022age,
+    author = "Schmidt, Fabian and Danboeck, Sarah K and Trinka, Eugen and Demarchi, Gianpaolo and Weisz, Nathan",
+    title = "Age-related changes in'cortical'1/f dynamics are explained by cardiac activity",
+    journal = "bioRxiv",
+    year = "2022",
+    publisher = "Cold Spring Harbor Laboratory",
+    doi = "10.1101/2022.11.07.515423",
+    google_result_id = "ERvT26o8AlUJ"
+}

doc/scripts/cache.json

@@ -115,6 +115,41 @@
             "summary": "VNKC Puvvada - 2017 - search.proquest.com"
         }
     },
+    "4D5RTBi0ub8J": {
+        "title": "Neural tracking as a diagnostic tool to assess the auditory pathway",
+        "result_id": "4D5RTBi0ub8J",
+        "link": "https://www.biorxiv.org/content/10.1101/2021.11.26.470129.abstract",
+        "snippet": "When a person listens to sound, the brain time-locks to specific aspects of the sound. This is called neural tracking and it can be investigated by analysing neural responses (eg, \u2026",
+        "publication_info": {
+            "summary": "M Gillis, J Van Canneyt, T Francart, J Vanthornhout - bioRxiv, 2022 - biorxiv.org",
+            "authors": [
+                {
+                    "name": "M Gillis",
+                    "link": "https://scholar.google.com/citations?user=_3DlZ_EAAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=_3DlZ_EAAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "_3DlZ_EAAAAJ"
+                },
+                {
+                    "name": "J Van Canneyt",
+                    "link": "https://scholar.google.com/citations?user=Nd9x9FIAAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=Nd9x9FIAAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "Nd9x9FIAAAAJ"
+                },
+                {
+                    "name": "T Francart",
+                    "link": "https://scholar.google.com/citations?user=rv2nwCEAAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=rv2nwCEAAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "rv2nwCEAAAAJ"
+                },
+                {
+                    "name": "J Vanthornhout",
+                    "link": "https://scholar.google.com/citations?user=I0EdEu0AAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=I0EdEu0AAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "I0EdEu0AAAAJ"
+                }
+            ]
+        }
+    },
     "4QAnG8imeE4J": {
         "title": "A Python-based Brain-Computer Interface Package for Neural Data Analysis",
         "result_id": "4QAnG8imeE4J",
@@ -264,6 +299,12 @@
         "publication_info": {
             "summary": "M Tulling, R Law, A Cournane, L Pylkk\u00e4nen - Eneuro, 2021 - ncbi.nlm.nih.gov",
             "authors": [
+                {
+                    "name": "M Tulling",
+                    "link": "https://scholar.google.com/citations?user=rXzC82IAAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=rXzC82IAAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "rXzC82IAAAAJ"
+                },
                 {
                     "name": "R Law",
                     "link": "https://scholar.google.com/citations?user=d06kiU8AAAAJ&hl=en&num=20&oi=sra",
@@ -285,6 +326,35 @@
             ]
         }
     },
+    "7w3_G4qWqWUJ": {
+        "title": "Heard or understood? Neural tracking of language features in a comprehensible story, an incomprehensible story and a word list",
+        "result_id": "7w3_G4qWqWUJ",
+        "link": "https://www.biorxiv.org/content/10.1101/2022.11.22.517477.abstract",
+        "snippet": "Speech comprehension is a complex neural process on which relies on activation and integration of multiple brain regions. In the current study, we evaluated whether speech \u2026",
+        "publication_info": {
+            "summary": "M Gillis, J Vanthornhout, T Francart - bioRxiv, 2022 - biorxiv.org",
+            "authors": [
+                {
+                    "name": "M Gillis",
+                    "link": "https://scholar.google.com/citations?user=_3DlZ_EAAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=_3DlZ_EAAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "_3DlZ_EAAAAJ"
+                },
+                {
+                    "name": "J Vanthornhout",
+                    "link": "https://scholar.google.com/citations?user=I0EdEu0AAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=I0EdEu0AAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "I0EdEu0AAAAJ"
+                },
+                {
+                    "name": "T Francart",
+                    "link": "https://scholar.google.com/citations?user=rv2nwCEAAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=rv2nwCEAAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "rv2nwCEAAAAJ"
+                }
+            ]
+        }
+    },
     "8dBIoRFtXiMJ": {
         "title": "Morphological representations are extrapolated from morpho-syntactic rules",
         "result_id": "8dBIoRFtXiMJ",
@@ -436,6 +506,29 @@
             ]
         }
     },
+    "ERvT26o8AlUJ": {
+        "title": "Age-related changes in'cortical'1/f dynamics are explained by cardiac activity",
+        "result_id": "ERvT26o8AlUJ",
+        "link": "https://www.biorxiv.org/content/10.1101/2022.11.07.515423.abstract",
+        "snippet": "The power of electrophysiologically measured cortical activity decays 1/fX. The slope of this decay (ie the spectral exponent) is modulated by various factors such as age, cognitive \u2026",
+        "publication_info": {
+            "summary": "F Schmidt, SK Danboeck, E Trinka, G Demarchi\u2026 - bioRxiv, 2022 - biorxiv.org",
+            "authors": [
+                {
+                    "name": "SK Danboeck",
+                    "link": "https://scholar.google.com/citations?user=6s-AmMwAAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=6s-AmMwAAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "6s-AmMwAAAAJ"
+                },
+                {
+                    "name": "G Demarchi",
+                    "link": "https://scholar.google.com/citations?user=GiarXIUAAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=GiarXIUAAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "GiarXIUAAAAJ"
+                }
+            ]
+        }
+    },
     "F1tmJHAXx5IJ": {
         "title": "Data for: Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions",
         "result_id": "F1tmJHAXx5IJ",
@@ -1533,6 +1626,12 @@
         "publication_info": {
             "summary": "J Li, L Pylkk\u00e4nen - Journal of Neuroscience, 2021 - Soc Neuroscience",
             "authors": [
+                {
+                    "name": "J Li",
+                    "link": "https://scholar.google.com/citations?user=T5EokYYAAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=T5EokYYAAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "T5EokYYAAAAJ"
+                },
                 {
                     "name": "L Pylkk\u00e4nen",
                     "link": "https://scholar.google.com/citations?user=yGIs62YAAAAJ&hl=en&num=20&oi=sra",
@@ -1718,7 +1817,15 @@
         "link": "https://search.proquest.com/openview/bae98a7cd0e7c6bbb436ed364ed00e8e/1.pdf?pq-origsite=gscholar&cbl=18750&diss=y",
         "snippet": "This dissertation investigates the neural bases and development of displacement, which is a language property that allows us to communicate about situations outside the here-and-now\u2026",
         "publication_info": {
-            "summary": "MA Tulling - 2022 - search.proquest.com"
+            "summary": "MA Tulling - 2022 - search.proquest.com",
+            "authors": [
+                {
+                    "name": "MA Tulling",
+                    "link": "https://scholar.google.com/citations?user=rXzC82IAAAAJ&hl=en&num=20&oi=sra",
+                    "serpapi_scholar_link": "https://serpapi.com/search.json?author_id=rXzC82IAAAAJ&engine=google_scholar_author&hl=en",
+                    "author_id": "rXzC82IAAAAJ"
+                }
+            ]
         }
     },
     "t5g817um_mQJ": {

doc/scripts/selection.json

@@ -80,5 +80,8 @@
  "5m5W40xRnIQJ": true,
  "jQmGaEK_518J": true,
  "lC-KuCZxkksJ": true,
- "mBZCoKR6Va4J": true
+ "mBZCoKR6Va4J": true,
+ "4D5RTBi0ub8J": false,
+ "7w3_G4qWqWUJ": true,
+ "ERvT26o8AlUJ": true
 }

eelbrain/_experiment/mne_experiment.py

@@ -6238,8 +6238,7 @@ def run_mne_browse_raw(self, modal=False):
         Sets the current directory to raw-dir, and sets the SUBJECT and
         SUBJECTS_DIR to current values
         """
-        subp.run_mne_browse_raw(self.get('raw-dir'), self.get('mrisubject'),
-                                self.get('mri-sdir'), modal)
+        subp.run_mne_browse_raw(self.get('raw-dir'), self.get('mrisubject'), self.get('mri-sdir'), modal)
 
     def set(self, subject=None, match=True, allow_asterisk=False, **state):
         """

eelbrain/_stats/glm.py

@@ -347,8 +347,7 @@ def map(self, y, perm=None):
             A list with maps of F values (order corresponding to self.effects).
         """
         if y.shape[0] != self._n_obs:
-            raise ValueError("y has wrong number of observations (%i, model "
-                             "has %i)" % (y.shape[0], self._n_obs))
+            raise ValueError(f"y has wrong number of observations ({y.shape[0]}, model has {self._n_obs})")
 
         # find result container
         if self._flat_f_map is None:
@@ -402,7 +401,7 @@ def preallocate(self, y_shape):
             content of this array will change (and map() will not return
             anything)
         """
-        shape = (self.n_effects,) + y_shape
+        shape = (self.n_effects, *y_shape)
         f_map = np.empty(shape)
         self._flat_f_map = f_map.reshape((self.n_effects, -1))
         return f_map

eelbrain/_stats/testnd.py

@@ -90,8 +90,7 @@ class NDTest:
         Map of the test statistic processed with the threshold-free cluster
         enhancement algorithm (or None if no TFCE was performed).
     """
-    _state_common = ('y', 'match', 'sub', 'samples', 'tfce', 'pmin', '_cdist',
-                     'tstart', 'tstop', '_dims')
+    _state_common = ('y', 'match', 'sub', 'samples', 'tfce', 'pmin', '_cdist', 'tstart', 'tstop', '_dims')
     _state_specific = ()
     _statistic = None
     _statistic_tail = 0
@@ -224,21 +223,20 @@ def _assert_has_cdist(self):
                                "with threshold-based clustering and tests with "
                                "a permutation distribution (samples > 0)")
 
-    def masked_parameter_map(self, pmin=0.05, **sub):
-        """Create a copy of the parameter map masked by significance
+    def masked_parameter_map(self, pmin: float = 0.05, **sub) -> NDVar:
+        """Statistical parameter map masked by significance
 
         Parameters
         ----------
-        pmin : scalar
+        pmin
             Threshold p-value for masking (default 0.05). For threshold-based
             cluster tests, ``pmin=1`` includes all clusters regardless of their
             p-value.
 
         Returns
         -------
-        masked_map : NDVar
-            NDVar with data from the original parameter map wherever p <= pmin
-            and 0 everywhere else.
+        NDVar
+            Parameter map with a mask that is ``True`` (masked) where p > pmin.
         """
         self._assert_has_cdist()
         return self._cdist.masked_parameter_map(pmin, **sub)
@@ -1298,26 +1296,26 @@ class TTestRelated(NDMaskedC1Mixin, NDDifferenceTest):
         0: both (two-tailed, default);
         1: upper tail (one-tailed);
         -1: lower tail (one-tailed).
-    samples : int
+    samples
         Number of samples for permutation test (default 10,000).
-    pmin : None | scalar (0 < pmin < 1)
+    pmin
         Threshold for forming clusters:  use a t-value equivalent to an
         uncorrected p-value.
-    tmin : scalar
+    tmin
         Threshold for forming clusters as t-value.
-    tfce : bool | scalar
+    tfce
         Use threshold-free cluster enhancement. Use a scalar to specify the
         step of TFCE levels (for ``tfce is True``, 0.1 is used).
-    tstart : scalar
+    tstart
         Start of the time window for the permutation test (default is the
         beginning of ``y``).
-    tstop : scalar
+    tstop
         Stop of the time window for the permutation test (default is the
         end of ``y``).
-    parc : str
+    parc
         Collect permutation statistics for all regions of the parcellation of
         this dimension. For threshold-based test, the regions are disconnected.
-    force_permutation: bool
+    force_permutation
         Conduct permutations regardless of whether there are any clusters.
     mintime : scalar
         Minimum duration for clusters (in seconds).
@@ -1502,14 +1500,14 @@ class MultiEffectNDTest(NDTest):
     def _repr_test_args(self):
         args = [repr(self.y), repr(self.x)]
         if self.match is not None:
-            args.append('match=%r' % self.match)
+            args.append(f'match={self.match!r}')
         return args
 
     def _repr_cdist(self):
         args = self._cdist[0]._repr_test_args(self.pmin)
         for cdist in self._cdist:
             effect_args = cdist._repr_clusters()
-            args.append("%r: %s" % (cdist.name, ', '.join(effect_args)))
+            args.append(f"{cdist.name!r}: {', '.join(effect_args)}")
         return args
 
     def _asfmtext(self, **_):
@@ -1612,23 +1610,27 @@ def compute_probability_map(self, effect=0, **sub):
         i = self._effect_index(effect)
         return self._cdist[i].compute_probability_map(**sub)
 
-    def masked_parameter_map(self, effect=0, pmin=0.05, **sub):
-        """Create a copy of the parameter map masked by significance
+    def masked_parameter_map(
+            self,
+            effect: Union[str, int] = 0,
+            pmin: float = 0.05,
+            **sub,
+    ) -> NDVar:
+        """Statistical parameter map masked by significance
 
         Parameters
         ----------
-        effect : int | str
-            Index or name of the effect from which to use the parameter map.
-        pmin : scalar
+        effect
+            Index or name of the effect for which to retrieve the parameter map.
+        pmin
             Threshold p-value for masking (default 0.05). For threshold-based
             cluster tests, ``pmin=1`` includes all clusters regardless of their
             p-value.
 
         Returns
         -------
-        masked_map : NDVar
-            NDVar with data from the original parameter map wherever p <= pmin
-            and 0 everywhere else.
+        NDVar
+            Parameter map with a mask that is ``True`` (masked) where p > pmin.
         """
         self._assert_has_cdist()
         i = self._effect_index(effect)
@@ -1805,30 +1807,29 @@ def __init__(
         dfs_denom = lm.dfs_denom
         fmaps = lm.map(y.x)
 
+        # Cluster-based tests
         n_threshold_params = sum((pmin is not None, fmin is not None, bool(tfce)))
         if n_threshold_params == 0 and not samples:
             cdists = None
-            thresholds = tuple(repeat(None, len(effects)))
+            thresholds = repeat(None, len(effects))
         elif n_threshold_params > 1:
             raise ValueError("Only one of pmin, fmin and tfce can be specified")
         else:
             if pmin is not None:
-                thresholds = tuple(stats.ftest_f(pmin, e.df, df_den) for e, df_den in zip(effects, dfs_denom))
+                thresholds = [stats.ftest_f(pmin, e.df, df_den) for e, df_den in zip(effects, dfs_denom)]
             elif fmin is not None:
                 thresholds = tuple(repeat(abs(fmin), len(effects)))
             else:
                 thresholds = tuple(repeat(None, len(effects)))
 
-            cdists = [
-                NDPermutationDistribution( y, samples, thresh, tfce, 1, 'f', e.name, tstart, tstop, criteria, parc, force_permutation)
-                for e, thresh in zip(effects, thresholds)]
+            cdists = [NDPermutationDistribution(y, samples, thresh, tfce, 1, 'f', e.name, tstart, tstop, criteria, parc, force_permutation) for e, thresh in zip(effects, thresholds)]
 
             # Find clusters in the actual data
             do_permutation = 0
             for cdist, fmap in zip(cdists, fmaps):
                 cdist.add_original(fmap)
                 do_permutation += cdist.do_permutation
-
+            # Generate null distribution
             if do_permutation:
                 iterator = permute_order(len(y), samples, unit=match)
                 run_permutation_me(lm, cdists, iterator)