DOC: rebrand reverse correlation -> deconvolution

doc/changes.rst

@@ -294,7 +294,7 @@ New in 0.25
   - The ``model`` parameter for ANOVA tests is now optional (see
     :attr:`MneExperiment.tests`).
 
-* Reverse correlation using :func:`boosting`.
+* Deconvolution using :func:`boosting`.
 * Loading and saving ``*.wav`` files (:func:`load.wav` and :func:`save.wav`).
 
 

doc/conf.py

@@ -117,7 +117,7 @@ def section_order(self):
         'colormaps.py',
         'customizing.py',
     ],
-    'reverse-correlation': [
+    'deconvolution': [
         'trf_intro.py',
         'mtrf.py',
         'epoch_impulse.py',

doc/reference.rst

@@ -166,9 +166,9 @@ functions. See also :class:`NDVar` methods.
    xhemi
 
 
-^^^^^^^^^^^^^^^^^^^
-Reverse Correlation
-^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^
+Deconvolution
+^^^^^^^^^^^^^
 
 .. autosummary::
    :toctree: generated

eelbrain/_trf/_boosting.py

@@ -41,7 +41,7 @@
 from .._utils import LazyProperty, PickleableDataClass, user_activity
 from .._utils.notebooks import tqdm
 from ._boosting_opt import l1, l2, generate_options, update_error
-from .shared import PredictorData, RevCorrData, Split, Splits, merge_segments
+from .shared import PredictorData, DeconvolutionData, Split, Splits, merge_segments
 from ._fit_metrics import get_evaluators
 
 
@@ -499,7 +499,7 @@ class Boosting:
     Standard usage of the object-oriented API for a model with
     cross-validation, comparable to the :func:`boosting` function::
 
-        data = RevCorrData(y, x, ds)
+        data = DeconvolutionData(y, x, ds)
         data.apply_basis(0.05, 'hamming'')
         data.normalize('l1')
         data.initialize_cross_validation(5, test=1)
@@ -527,7 +527,7 @@ class Boosting:
 
     def __init__(
             self,
-            data: RevCorrData,
+            data: DeconvolutionData,
     ):
         self.data = data
 
@@ -958,7 +958,7 @@ def boosting(
     if selective_stopping < 0:
         raise ValueError(f"selective_stopping={selective_stopping}")
 
-    data = RevCorrData(y, x, ds, scale_in_place)
+    data = DeconvolutionData(y, x, ds, scale_in_place)
     data.apply_basis(basis, basis_window)
     if scale_data:
         data.normalize(error)

eelbrain/_trf/_fit_metrics.py

@@ -7,7 +7,7 @@
 from scipy.stats import spearmanr, SpearmanRConstantInputWarning
 
 from ._boosting_opt import l1, l2
-from .shared import RevCorrData
+from .shared import DeconvolutionData
 
 
 class Evaluator:
@@ -18,7 +18,7 @@ class Evaluator:
 
     def __init__(
             self,
-            data: RevCorrData,
+            data: DeconvolutionData,
             segments: List[np.ndarray] = None,  # evaluations for different test-segments
     ):
         n = len(data.y)
@@ -247,7 +247,7 @@ def _r(self, y, y_pred):
 
 def get_evaluators(
         keys: List[str],
-        data: RevCorrData,
+        data: DeconvolutionData,
         segments: List[np.ndarray] = None,  # evaluations for different test-segments
 ) -> (List[Evaluator], List[Evaluator], List[Evaluator]):
     evaluators = [EVALUATORS[key](data, segments) for key in keys]

eelbrain/_trf/_predictors.py

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

eelbrain/_trf/shared.py

@@ -206,7 +206,7 @@ def split_data(
 
 
 class PredictorData:
-    """Restructure model NDVars (like RevCorrData but for x only)"""
+    """Restructure model NDVars (like DeconvolutionData but for x only)"""
 
     def __init__(
             self,
@@ -298,8 +298,8 @@ def __init__(
         self.segments = segments
 
 
-class RevCorrData:
-    """Restructure input NDVars into arrays for reverse correlation
+class DeconvolutionData:
+    """Restructure input NDVars into arrays for deconvolution
     
     Attributes
     ----------

eelbrain/_trf/tests/test_boosting.py

@@ -19,7 +19,7 @@
 )
 
 from eelbrain.testing import assert_dataobj_equal
-from eelbrain._trf._boosting import Boosting, RevCorrData, Split, boost, convolve as boosting_convolve
+from eelbrain._trf._boosting import Boosting, DeconvolutionData, Split, boost, convolve as boosting_convolve
 
 
 def assert_res_equal(res1, res):
@@ -158,7 +158,7 @@ def test_boosting_epochs():
 
 def test_boosting_oo():
     ds = datasets._get_continuous(ynd=True)
-    data = RevCorrData('y', 'x2', ds)
+    data = DeconvolutionData('y', 'x2', ds)
     data.apply_basis(0.2, 'hamming')
     data.normalize('l1')
     data.initialize_cross_validation(4, test=1)

eelbrain/_trf/tests/test_data.py

@@ -4,21 +4,21 @@
 import pytest
 
 from eelbrain import Factor, datasets, epoch_impulse_predictor
-from eelbrain._trf.shared import RevCorrData
+from eelbrain._trf.shared import DeconvolutionData
 
 
-def test_revcorr_data_continuous():
+def test_deconvolution_data_continuous():
     ds = datasets._get_continuous(ynd=True)
 
     # normalizing
-    data = RevCorrData(ds['y'][:8], ds['x1'][:8])
+    data = DeconvolutionData(ds['y'][:8], ds['x1'][:8])
     data.normalize('l1')
     assert data.x[0].mean() == pytest.approx(0, abs=1e-16)
     assert abs(data.x).mean() == pytest.approx(1, abs=1e-10)
     with pytest.raises(ValueError):
         data.apply_basis(0.050, 'hamming')
     # with basis
-    data = RevCorrData(ds['y'][:8], ds['x1'][:8])
+    data = DeconvolutionData(ds['y'][:8], ds['x1'][:8])
     data.apply_basis(0.500, 'hamming')
     data.normalize('l1')
     assert data.x[0].mean() == pytest.approx(0, abs=1e-16)
@@ -65,13 +65,13 @@ def test_revcorr_data_continuous():
     assert_array_equal(data.splits.splits[11].train, [[0, 40]])
 
 
-def test_revcorr_data_trials():
+def test_deconvolution_data_trials():
     ds = datasets.get_uts()
     n_times = len(ds['uts'].time)
     ds['imp'] = epoch_impulse_predictor('uts', ds=ds)
     ds['imp_a'] = epoch_impulse_predictor('uts', "A == 'a1'", ds=ds)
 
-    data = RevCorrData('uts', 'imp', ds)
+    data = DeconvolutionData('uts', 'imp', ds)
     data.normalize('l1')
     assert_array_equal(data.segments, [[i * n_times, (i + 1) * n_times] for i in range(60)])
 

eelbrain/datasets/_simple.py

@@ -46,7 +46,7 @@ def _get_continuous(
         seed: int = 0,
         xn: int = 0,
 ) -> Dataset:
-    """Generate continuous data for reverse correlation
+    """Generate continuous data for deconvolution tests
 
     Parameters
     ----------

examples/reverse-correlation/mtrf.py → examples/deconvolution/mtrf.py

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