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MAINT: Fix doc build failure due to deprecation (#13299)
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doc/sphinxext/mne_doc_utils.py

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@@ -67,6 +67,8 @@ def reset_warnings(gallery_conf, fname):
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"\nImplementing implicit namespace packages",
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# latexcodec
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r"open_text is deprecated\. Use files",
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# dipy etc.
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"The `disp` and `iprint` options of the L-BFGS-B solver",
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):
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warnings.filterwarnings( # deal with other modules having bad imports
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"ignore", message=f".*{key}.*", category=DeprecationWarning

examples/inverse/morph_volume_stc.py

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================================
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This example demonstrates how to morph an individual subject's
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:class:`mne.VolSourceEstimate` to a common reference space. We achieve this
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using :class:`mne.SourceMorph`. Data will be morphed based on
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an affine transformation and a nonlinear registration method
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known as Symmetric Diffeomorphic Registration (SDR) by
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:class:`mne.VolSourceEstimate` to a common reference space. We achieve this using
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:class:`mne.SourceMorph`. Data will be morphed based on an affine transformation and
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a nonlinear registration method known as Symmetric Diffeomorphic Registration (SDR) by
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:footcite:`AvantsEtAl2008`.
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Transformation is estimated from the subject's anatomical T1 weighted MRI
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)
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# Add functional data as overlay
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display.add_overlay(img_fsaverage, alpha=0.75)
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display.add_overlay(img_fsaverage, transparency=0.75)
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# %%

mne/utils/tests/test_config.py

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from mne.utils import (
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ClosingStringIO,
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_get_stim_channel,
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_record_warnings,
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get_config,
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get_config_path,
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get_subjects_dir,
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new_key = f"worker_{worker_id}_{i}"
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new_value = f"value_{worker_id}_{i}"
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# Update the configuration (our set_config holds the lock over the full cycle)
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set_config(new_key, new_value, home_dir=home_dir)
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with _record_warnings(): # ignore non-standard key warning
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set_config(new_key, new_value, home_dir=home_dir)
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time.sleep(random.uniform(0, 0.05))
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return worker_id
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tutorials/inverse/50_beamformer_lcmv.py

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# Frequency-resolved data can be reconstructed with the dynamic imaging of
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# coherent sources (DICS) beamforming method :footcite:`GrossEtAl2001`.
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# As we will see in the following, the spatial filter is computed from two
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# ingredients: the forward model solution and the covariance matrix of the
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# data.
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# ingredients: the forward model solution and the covariance matrix of the data.
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# %%
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# Data processing

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