Merge branch 'main' into r/0.38

doc/conf.py

@@ -121,7 +121,7 @@ def section_order(self):
         'colormaps.py',
         'customizing.py',
     ],
-    'deconvolution': [
+    'temporal-response-functions': [
         'trf_intro.py',
         'mtrf.py',
         'partitions.py',

doc/experiment.rst

@@ -30,7 +30,7 @@ Once set up, an :class:`MneExperiment` subclass instance provides access into th
 An :class:`MneExperiment` instance has a state, which determines what data and settings it is currently using. Not all settings are always relevant. For example, :ref:`state-subject` is relevenat for steps applied separately to each subject, like :meth:`~MneExperiment.make_ica_selection`, whereas :ref:`state-group` defines the group of subjects in group level analysis, such as in :meth:`~MneExperiment.load_test`. For more information, see :ref:`state-parameters`.
 
 
-Step by step
+Step by Step
 ============
 
 .. contents:: Contents

doc/reference.rst

@@ -168,9 +168,9 @@ functions. See also :class:`NDVar` methods.
    xhemi
 
 
-^^^^^^^^^^^^^
-Deconvolution
-^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Temporal Response Functions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. autosummary::
    :toctree: generated

eelbrain/_experiment/mne_experiment.py

@@ -3562,7 +3562,7 @@ def _load_result_plotter(self, test, tstart, tstop, pmin, parc=None,
             State parameters.
         """
         if data != 'source':
-            raise NotImplementedError("data=%s" % repr(data))
+            raise NotImplementedError(f"{data=}")
         elif not isinstance(pmin, float):
             raise NotImplementedError("Threshold-free tests")
 
@@ -3573,8 +3573,7 @@ def _load_result_plotter(self, test, tstart, tstop, pmin, parc=None,
         if dst is None:
             dst = self.get('res-plot-dir', mkdir=True)
 
-        return ClusterPlotter(ds, res, colors, dst, vec_fmt, pix_fmt, labels, h,
-                              rc)
+        return ClusterPlotter(ds, res, colors, dst, vec_fmt, pix_fmt, labels, h, rc)
 
     def load_selected_events(self, subjects=None, reject=True, add_bads=True,
                              index=True, data_raw=False, vardef=None, cat=None,
@@ -4627,11 +4626,9 @@ def make_mov_ga_dspm(self, subjects=None, baseline=True, src_baseline=False,
         state['model'] = ''
         subject, group = self._process_subject_arg(subjects, state)
         data = TestDims("source", morph=bool(group))
-        brain_kwargs = self._surfer_plot_kwargs(surf, views, foreground, background,
-                                                smoothing_steps, hemi)
+        brain_kwargs = self._surfer_plot_kwargs(surf, views, foreground, background, smoothing_steps, hemi)
         self._set_analysis_options(data, baseline, src_baseline, None, None, None)
-        self.set(equalize_evoked_count='',
-                 resname="GA dSPM %s %s" % (brain_kwargs['surf'], fmin))
+        self.set(equalize_evoked_count='', resname=f"GA dSPM {brain_kwargs['surf']} {fmin}")
 
         if dst is None:
             if group is None:

eelbrain/_stats/spm.py

@@ -29,7 +29,8 @@ class LM:
         Model parametrization (default is dummy coding). Vars are centered for
         effect coding (but not for dummy coding).
     subject : str
-        Optional information used by :class:`LMGroup`.
+        Optional information used by :class:`LMGroup`; if subject is a column in
+        ``ds`` it will be extracted automatically.
     sub : index
         Only use part of the data.
 
@@ -59,7 +60,7 @@ def __init__(self, y, model, ds=None, coding='dummy', subject=None, sub=None):
         # subject
         if subject is not None:
             if not isinstance(subject, str):
-                raise TypeError(f"subject={subject!r}: needs to be string or None")
+                raise TypeError(f"{subject=}: needs to be string or None")
             variables['subject'] = subject
         self.coding = coding
         self._coeffs_flat = b.reshape((len(b), -1))
@@ -99,7 +100,7 @@ def __getstate__(self):
 
     def __repr__(self):
         y = self._y or '<?>'
-        return f"<LM: {y} ~ {self.model.name}>"
+        return f"<LM: {y} ~ {self.model.name}, {self.subject}>"
 
     def _coefficient(self, term):
         """Regression coefficient for a given term"""

eelbrain/_trf/_predictors.py

@@ -1,4 +1,4 @@
-"""Predictors for deconvolution"""
+"""Predictors for continuous data"""
 # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
 from itertools import repeat
 

eelbrain/datasets/_simple.py

@@ -47,7 +47,7 @@ def _get_continuous(
         seed: int = 0,
         xn: int = 0,
 ) -> Dataset:
-    """Generate continuous data for deconvolution tests
+    """Generate continuous data for temporal response function tests
 
     Parameters
     ----------

examples/mass-univariate-statistics/README.txt

@@ -1,5 +1,5 @@
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
-Mass-univariate statistics
+Mass-Univariate Statistics
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 :class:`NDVar`, n-dimensional variables, allow mass-univariate statistics

examples/temporal-response-functions/README.txt

@@ -0,0 +1,3 @@
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Temporal Response Functions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^

examples/deconvolution/mtrf.py → examples/temporal-response-functions/mtrf.py

@@ -44,7 +44,7 @@
 plot.Array(spectrogram, xlim=5, w=6, h=2)
 
 ###############################################################################
-# Plot the envelope used as stimulus representation for deconvolution:
+# Plot the envelope used as stimulus representation for TRFs:
 plot.UTS(envelope, xlim=5, w=6, h=2)
 
 ###############################################################################
@@ -53,8 +53,8 @@
 p.set_time(1.200)
 
 ###############################################################################
-# Deconvolution
-# -------------
+# TRF estimation
+# --------------
 # TRF for the envelope using boosting:
 #
 # - TRF from -100 to 400 ms