Repository Analysis

mne-tools/mne-python

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

7.8 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of mne-tools/mne-python, a Python project with 3,497 GitHub stars. SynthScan v2.0 examined 680,007 lines of code across 8710 source files, recording 3772 pattern matches distributed across 20 syntactic categories. The overall adjusted score of 7.8 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

7.8
Adjusted Score
7.8
Raw Score
100%
Time Factor
2026-08-29
Last Push
3.5K
Stars
Python
Language
680.0K
Lines of Code
8.7K
Files
3.8K
Pattern Hits
2026-08-29
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 14HIGH 19MEDIUM 659LOW 3080

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 3772 distinct pattern matches across 20 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Decorative Section Separators483 hits · 1446 pts
SeverityFileLineSnippetContext
MEDIUMmne/io/edf/edf.py915 # -------------------------------------------------------------------------COMMENT
MEDIUMmne/io/edf/edf.py1371 # ---------------------------------------------------------------------COMMENT
MEDIUMmne/io/edf/edf.py1483 # -----------------------------------------------------------------COMMENT
MEDIUMmne/io/edf/edf.py1509 # ---------------------------------------------------------------------COMMENT
MEDIUMmne/io/edf/edf.py1736 # -----------------------------------------------------------------COMMENT
MEDIUMmne/io/kit/constants.py147# -----------------COMMENT
MEDIUMmne/tests/test_annotations.py1510 # -------------------------------------------------------------------------COMMENT
MEDIUMmne/tests/test_annotations.py1522 # -------------------------------------------------------------------------COMMENT
MEDIUMmne/tests/test_annotations.py1536 # -------------------------------------------------------------------------COMMENT
MEDIUMmne/tests/test_annotations.py1550 # -------------------------------------------------------------------------COMMENT
MEDIUMmne/tests/test_annotations.py1564 # -------------------------------------------------------------------------COMMENT
MEDIUMmne/viz/ica.py307 # ----------COMMENT
MEDIUMmne/viz/ica.py548 # -------------------------COMMENT
MEDIUMmne/viz/ica.py591 # ------------COMMENT
MEDIUMmne/viz/backends/_qt.py805# ------------------------------------COMMENT
MEDIUMmne/viz/backends/_qt.py807# ------------------------------------COMMENT
MEDIUMmne/viz/backends/_abstract.py714# -------------------COMMENT
MEDIUMmne/viz/backends/_abstract.py716# -------------------COMMENT
MEDIUMmne/viz/backends/_abstract.py1024# -------------------COMMENT
MEDIUMmne/viz/backends/_abstract.py1026# -------------------COMMENT
MEDIUMmne/viz/backends/_abstract.py1062# ------------------------------------COMMENT
MEDIUMmne/viz/backends/_abstract.py1064# ------------------------------------COMMENT
MEDIUMmne/viz/backends/_notebook.py789# ------------------------------------COMMENT
MEDIUMmne/viz/backends/_notebook.py791# ------------------------------------COMMENT
MEDIUMmne/viz/tests/test_topomap.py395 # ---------------------------------------------------COMMENT
MEDIUMmne/viz/tests/test_topomap.py1174 # -----------COMMENT
MEDIUMmne/viz/tests/test_ica.py130 # ----------------COMMENT
MEDIUMmne/viz/tests/test_ica.py145 # ---------------COMMENT
MEDIUMmne/stats/cluster_level.py887 # -------------------------------------------------------------COMMENT
MEDIUMmne/stats/cluster_level.py999 # -------------------------------------------------------------------COMMENT
MEDIUMexamples/inverse/mixed_norm_inverse.py168# ----------COMMENT
MEDIUMexamples/inverse/psf_ctf_vertices_lcmv.py83# ------------------------------------------------------------COMMENT
MEDIUMexamples/inverse/psf_ctf_vertices_lcmv.py115# --------------------------------------------------------COMMENT
MEDIUMexamples/inverse/rap_music.py60# ----------COMMENT
MEDIUMexamples/inverse/resolution_metrics_eegmeg.py99# -------------COMMENT
MEDIUMexamples/inverse/resolution_metrics_eegmeg.py197# ----------COMMENT
MEDIUMexamples/inverse/resolution_metrics.py90# -----------------COMMENT
MEDIUMexamples/inverse/resolution_metrics.py180# ----------COMMENT
MEDIUMexamples/inverse/psf_ctf_label_leakage.py41# ------------------------------------------COMMENT
MEDIUMexamples/inverse/psf_ctf_label_leakage.py65# --------------------------------------------------COMMENT
MEDIUMexamples/inverse/psf_ctf_label_leakage.py90# ------------------------------------------------------------COMMENT
MEDIUMexamples/inverse/psf_ctf_label_leakage.py119# ---------------------------------------------------------COMMENT
MEDIUMexamples/inverse/psf_ctf_label_leakage.py158# --------------------------COMMENT
MEDIUMexamples/inverse/psf_ctf_label_leakage.py168# -------------------------------COMMENT
MEDIUMexamples/inverse/trap_music.py60# ----------COMMENT
MEDIUMexamples/inverse/mne_cov_power.py41# -----------------------------COMMENT
MEDIUMexamples/inverse/mne_cov_power.py55# --------------COMMENT
MEDIUMexamples/inverse/mne_cov_power.py82# ----------------------------COMMENT
MEDIUMexamples/inverse/mne_cov_power.py124# ------------------------------------COMMENT
MEDIUMexamples/inverse/mixed_source_space_inverse.py44# -----------------------COMMENT
MEDIUMexamples/inverse/mixed_source_space_inverse.py91# ---------------------COMMENT
MEDIUMexamples/inverse/mixed_source_space_inverse.py108# ----------------------COMMENT
MEDIUMexamples/inverse/mixed_source_space_inverse.py136# ------------------------COMMENT
MEDIUMexamples/inverse/mixed_source_space_inverse.py154# ------------------------------COMMENT
MEDIUMexamples/inverse/mixed_source_space_inverse.py173# ----------------COMMENT
MEDIUMexamples/inverse/mixed_source_space_inverse.py179# ---------------COMMENT
MEDIUMexamples/inverse/mixed_source_space_inverse.py185# --------------COMMENT
MEDIUMexamples/inverse/morph_surface_stc.py56# -----------------------------------------COMMENT
MEDIUMexamples/inverse/morph_surface_stc.py100# --------------------------------------COMMENT
MEDIUMexamples/inverse/morph_surface_stc.py109# ------------COMMENT
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Hyper-Verbose Identifiers1002 hits · 990 pts
SeverityFileLineSnippetContext
LOWmne/source_estimate.py468def _verify_source_estimate_compat(a, b):CODE
LOWmne/source_estimate.py597 def extract_label_time_course(CODE
LOWmne/source_estimate.py831 def _remove_kernel_sens_data_(self):CODE
LOWmne/source_estimate.py3104def _spatio_temporal_src_adjacency_vol(src, n_times):CODE
LOWmne/source_estimate.py3113def _spatio_temporal_src_adjacency_surf(src, n_times):CODE
LOWmne/source_estimate.py3157def spatio_temporal_src_adjacency(src, n_times, dist=None, verbose=None):CODE
LOWmne/source_estimate.py3221def spatio_temporal_tris_adjacency(tris, n_times, remap_vertices=False, verbose=None):CODE
LOWmne/source_estimate.py3256def spatio_temporal_dist_adjacency(src, n_times, dist, verbose=None):CODE
LOWmne/source_estimate.py3375def spatial_inter_hemi_adjacency(src, dist, verbose=None):CODE
LOWmne/source_estimate.py3409def _get_adjacency_from_edges(edges, n_times, verbose=None):CODE
LOWmne/source_estimate.py3494def _prepare_label_extraction(stc, labels, src, mode, allow_empty, use_sparse):CODE
LOWmne/source_estimate.py3723def _gen_extract_label_time_course(CODE
LOWmne/source_estimate.py3822def extract_label_time_course(CODE
LOWmne/source_estimate.py2481 def extract_label_time_course(STRING
LOWmne/transforms.py697def get_ras_to_neuromag_trans(nasion, lpa, rpa):CODE
LOWmne/transforms.py750def _get_transforms_to_coord_frame(info, trans, coord_frame="mri"):CODE
LOWmne/transforms.py1332def angle_distance_between_rigid(a, b=None, *, angle_units="rad", distance_units="m"):CODE
LOWmne/transforms.py1661def compute_volume_registration(CODE
LOWmne/transforms.py1710def _compute_volume_registration(CODE
LOWmne/transforms.py1825def apply_volume_registration(CODE
LOWmne/transforms.py1912def apply_volume_registration_points(CODE
LOWmne/conftest.py269def pytest_collection_modifyitems(items: list[pytest.Item]):STRING
LOWmne/conftest.py761def renderer_interactive_pyvistaqt(request, options_3d, qt_windows_closed):CODE
LOWmne/conftest.py954def _all_src_types_inv_evoked(_evoked_cov_sphere, _all_src_types_fwd):CODE
LOWmne/conftest.py1377def pytest_runtest_makereport(item: pytest.Item, call: pytest.CallInfo):CODE
LOWmne/event.py1344 def _categories_from_acq_pars(self):CODE
LOWmne/epochs.py722 def _check_events_outside_data(self, on_outside, raw):CODE
LOWmne/epochs.py3335 def _diff_input_strings_vs_event_id(input_strings, input_name, event_id):CODE
LOWmne/coreg.py1723 def _nearest_transformed_high_res_mri_idx_hsp(self):CODE
LOWmne/coreg.py1774 def _processed_high_res_mri_points(self):CODE
LOWmne/coreg.py1777 def _get_processed_mri_points(self, res):CODE
LOWmne/coreg.py2091 def compute_dig_mri_distances(self):CODE
LOWmne/coreg.py2141 def _get_fiducials_distance_str(self):CODE
LOWmne/_freesurfer.py212def get_volume_labels_from_aseg(mgz_fname, return_colors=False, atlas_ids=None):CODE
LOWmne/_freesurfer.py510def _get_affine_from_lta_info(lines):CODE
LOWmne/bem.py291def _fwd_bem_ip_modify_solution(solution, ip_solution, ip_mult, n_tri):CODE
LOWmne/bem.py329def _fwd_bem_linear_collocation_solution(bem):CODE
LOWmne/bem.py381def _fwd_bem_openmeeg_solution(bem):CODE
LOWmne/bem.py717def _fwd_eeg_get_multi_sphere_model_coeffs(m, n_terms):CODE
LOWmne/bem.py757def _compose_linear_fitting_data(mu, u):CODE
LOWmne/bem.py771def _compute_linear_parameters(mu, u):CODE
LOWmne/bem.py1876def _write_bem_surfaces_block(fid, surfs):CODE
LOWmne/cov.py886def _unpack_covariance_inputs(inst):CODE
LOWmne/evoked.py1669def _check_evokeds_ch_names_times(all_evoked, inplace=False):CODE
LOWmne/chpi.py518def _magnetic_dipole_objective(CODE
LOWmne/chpi.py533def _magnetic_dipole_delta_multi(whitened_fwd_svd, B, B2):CODE
LOWmne/chpi.py594def _fit_coil_order_dev_head_trans(CODE
LOWmne/chpi.py658def _setup_hpi_amplitude_fitting(CODE
LOWmne/cuda.py189def _setup_cuda_fft_multiply_repeated(n_jobs, h, n_fft, kind="FFT FIR filtering"):CODE
LOWmne/label.py1907def _grow_nonoverlapping_labels(CODE
LOWmne/parallel.py157def _running_in_joblib_context():CODE
LOWmne/annotations.py1169def _hed_extras_from_hed_annotations(annot):CODE
LOWmne/annotations.py1642 def get_annotations_per_epoch(self, *, with_extras=False):CODE
LOWmne/annotations.py1728 def add_annotations_to_metadata(self, overwrite=False, *, with_extras=True):CODE
LOWmne/annotations.py1882def _annotations_starts_stops(raw, kinds, name="skip_by_annotation", invert=False):CODE
LOWmne/annotations.py2182def _read_brainstorm_annotations(fname, orig_time=None):STRING
LOWmne/annotations.py2229def _read_annotations_txt_parse_header(fname):STRING
LOWmne/annotations.py2378def _select_annotations_based_on_description(descriptions, event_id, regexp):CODE
LOWmne/annotations.py2413def _select_events_based_on_id(events, event_desc):CODE
LOWmne/rank.py158def _estimate_rank_meeg_signals(CODE
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Over-Commented Block831 hits · 793 pts
SeverityFileLineSnippetContext
LOWmne/transforms.py961COMMENT
LOWmne/transforms.py981#COMMENT
LOWmne/transforms.py1441 assert quats.ndim == 2 and quats.shape[1] in (3, 4)COMMENT
LOWmne/_transforms_numba.py81COMMENT
LOWmne/coreg.py1181 mri_transforms_dirname.format(subjects_dir=subjects_dir, subject=subject_to),COMMENT
LOWmne/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/morph.py801 )COMMENT
LOWmne/_surface_numba.py101 run_all=True,COMMENT
LOWmne/label.py3041 vertices belonging to label ``li``.COMMENT
LOWmne/dipole.py1201def _fit_confidence(*, rd, Q, ori, whitener, fwd_data, sensors):COMMENT
LOWmne/dipole.py1361 # Tested minimizers:COMMENT
LOWmne/dipole.py1681 if "eeg" in ch_types:COMMENT
LOWmne/time_frequency/spectrum.py941 data = _psd_from_mt(data, self.weights)COMMENT
LOWmne/time_frequency/psd.py21 #COMMENT
LOWmne/time_frequency/multitaper.py141 # the variance of the timeseriesCOMMENT
LOWmne/time_frequency/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/beamformer/_compute_beamformer.py381 #COMMENT
LOWmne/beamformer/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/datasets/config.py101MNE_DATASETS = dict()COMMENT
LOWmne/datasets/_infant/base.py61 # Code used to create the lists:COMMENT
LOWmne/datasets/_phantom/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/datasets/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/datasets/_fsaverage/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/ant/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/nirx/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/curry/curry.py821 if isinstance(events, np.ndarray): # if there are eventsCOMMENT
LOWmne/io/curry/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/egi/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/edf/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/boxy/boxy.py241COMMENT
LOWmne/io/boxy/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/bci2k/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/bci2k/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/nicolet/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/artemis123/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/array/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/snirf/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/persyst/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/eximia/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/ctf/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/nedf/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/tests/data/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/mef/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/eeglab/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/eyelink/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/bti/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/cnt/_utils.py61# unsigned char KeyBoard; /* range 0-11 corresponding to fcn keys +1 */COMMENT
LOWmne/io/cnt/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/fieldtrip/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/fiff/raw.py121 "properly and that split and original files were not "COMMENT
LOWmne/io/fiff/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/brainvision/brainvision.py921 # for newer BV files, the unit is specified for every channel separated by aCOMMENT
LOWmne/io/brainvision/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/io/neuralynx/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/tests/test_morph.py601 )COMMENT
LOWmne/simulation/metrics/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/simulation/tests/__init__.py1# Authors: The MNE-Python contributors.COMMENT
LOWmne/utils/check.py141 #COMMENT
LOWmne/decoding/time_delaying_ridge.py81 # it's faster to create the Toeplitz ourselves than useCOMMENT
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Deep Nesting461 hits · 440 pts
SeverityFileLineSnippetContext
LOWmne/source_estimate.py236CODE
LOWmne/source_estimate.py392CODE
LOWmne/source_estimate.py3494CODE
LOWmne/source_estimate.py3723CODE
LOWmne/transforms.py578CODE
LOWmne/transforms.py1591CODE
LOWmne/transforms.py1710CODE
LOWmne/transforms.py2211CODE
LOWmne/misc.py6CODE
LOWmne/event.py36CODE
LOWmne/event.py85CODE
LOWmne/event.py1275CODE
LOWmne/fixes.py656CODE
LOWmne/fixes.py774CODE
LOWmne/baseline.py35CODE
LOWmne/baseline.py149CODE
LOWmne/epochs.py266CODE
LOWmne/epochs.py335CODE
LOWmne/epochs.py3116CODE
LOWmne/epochs.py4095CODE
LOWmne/epochs.py4160CODE
LOWmne/epochs.py4245CODE
LOWmne/epochs.py4727CODE
LOWmne/epochs.py852CODE
LOWmne/epochs.py1720CODE
LOWmne/epochs.py2075CODE
LOWmne/epochs.py2518CODE
LOWmne/coreg.py824CODE
LOWmne/coreg.py1041CODE
LOWmne/coreg.py1341CODE
LOWmne/morph_map.py112CODE
LOWmne/morph.py961CODE
LOWmne/morph.py1263CODE
LOWmne/morph.py605CODE
LOWmne/bem.py215CODE
LOWmne/bem.py1179CODE
LOWmne/bem.py2393CODE
LOWmne/cov.py902CODE
LOWmne/cov.py1311CODE
LOWmne/cov.py1984CODE
LOWmne/cov.py2452CODE
LOWmne/evoked.py1669CODE
LOWmne/evoked.py1886CODE
LOWmne/evoked.py289CODE
LOWmne/evoked.py1083CODE
LOWmne/_surface_numba.py88CODE
LOWmne/chpi.py419CODE
LOWmne/chpi.py905CODE
LOWmne/chpi.py1229CODE
LOWmne/proj.py380CODE
LOWmne/label.py1273CODE
LOWmne/label.py1551CODE
LOWmne/label.py2018CODE
LOWmne/label.py2675CODE
LOWmne/filter.py42CODE
LOWmne/filter.py671CODE
LOWmne/filter.py1067CODE
LOWmne/filter.py1450CODE
LOWmne/filter.py2169CODE
LOWmne/dipole.py692CODE
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AI Structural Patterns333 hits · 300 pts
SeverityFileLineSnippetContext
LOWmne/source_estimate.py152CODE
LOWmne/source_estimate.py3895CODE
LOWmne/source_estimate.py749CODE
LOWmne/source_estimate.py2297CODE
LOWmne/source_estimate.py2373CODE
LOWmne/source_estimate.py2442CODE
LOWmne/source_estimate.py2818CODE
LOWmne/transforms.py798CODE
LOWmne/transforms.py2070CODE
LOWmne/event.py478CODE
LOWmne/event.py592CODE
LOWmne/fixes.py342CODE
LOWmne/epochs.py4966CODE
LOWmne/epochs.py463CODE
LOWmne/epochs.py1364CODE
LOWmne/epochs.py1428CODE
LOWmne/epochs.py1571CODE
LOWmne/epochs.py1720CODE
LOWmne/epochs.py1996CODE
LOWmne/epochs.py2682CODE
LOWmne/epochs.py2747CODE
LOWmne/epochs.py2882CODE
LOWmne/epochs.py3707CODE
LOWmne/epochs.py3845CODE
LOWmne/epochs.py3906CODE
LOWmne/coreg.py328CODE
LOWmne/coreg.py609CODE
LOWmne/coreg.py824CODE
LOWmne/coreg.py1964CODE
LOWmne/morph.py44CODE
LOWmne/bem.py130CODE
LOWmne/bem.py1179CODE
LOWmne/bem.py2393CODE
LOWmne/cov.py587CODE
LOWmne/cov.py902CODE
LOWmne/cov.py1503CODE
LOWmne/cov.py1886CODE
LOWmne/cov.py1984CODE
LOWmne/cov.py2215CODE
LOWmne/cov.py2281CODE
LOWmne/cov.py330CODE
LOWmne/cov.py1590CODE
LOWmne/cov.py1750CODE
LOWmne/evoked.py529CODE
LOWmne/evoked.py584CODE
LOWmne/evoked.py636CODE
LOWmne/evoked.py682CODE
LOWmne/evoked.py753CODE
LOWmne/evoked.py841CODE
LOWmne/evoked.py1083CODE
LOWmne/evoked.py1234CODE
LOWmne/evoked.py1300CODE
LOWmne/evoked.py1362CODE
LOWmne/chpi.py1716CODE
LOWmne/proj.py238CODE
LOWmne/proj.py284CODE
LOWmne/label.py2269CODE
LOWmne/label.py2675CODE
LOWmne/label.py2935CODE
LOWmne/label.py233CODE
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Self-Referential Comments88 hits · 274 pts
SeverityFileLineSnippetContext
MEDIUM.extended_metadata.yaml1# This file contains additional metadata about MNE-Python, beyond what is contained in `pyproject.toml`.COMMENT
MEDIUMmne/cov.py1909 # Create the projection operatorCOMMENT
MEDIUMmne/evoked.py2142 # Create the file and save the essentialsCOMMENT
MEDIUMmne/annotations.py1807 # Create a new Annotations column that is instantiated as an emptyCOMMENT
MEDIUMmne/beamformer/tests/test_dics.py77 # Create an info object that holds information about the sensorsCOMMENT
MEDIUMmne/beamformer/tests/test_dics.py95 # Define a single epoch (weird baseline but shouldn't matter)COMMENT
MEDIUMmne/beamformer/tests/test_dics.py730 # Create a DICS beamformer and convert the EpochsTFR to source space.COMMENT
MEDIUMmne/datasets/utils.py331 # This function is designed primarily to be used by CircleCI, to:COMMENT
MEDIUMmne/io/base.py3226 # Create the file and save the essentialsCOMMENT
MEDIUMmne/io/snirf/tests/test_snirf.py652 # Create a clean copy and ensure it loads without errorCOMMENT
MEDIUMmne/io/ctf/info.py197 # Create the channel position informationCOMMENT
MEDIUMmne/io/ctf/tests/test_ctf.py81 # Create a dummy .eeg file so we can test our reading/application of itCOMMENT
MEDIUMmne/io/ctf/tests/test_ctf.py90 # Create a bad fileCOMMENT
MEDIUMmne/io/ctf/tests/test_ctf.py94 # Create a good fileCOMMENT
MEDIUMmne/io/ctf/tests/test_ctf.py115 # Create a version with no hc, starting out *with* EEG pos (error)COMMENT
MEDIUMmne/io/kit/kit.py227 # Create a synthetic stim channelCOMMENT
MEDIUMmne/tests/test_source_estimate.py2066 # Create a sample _BaseSourceEstimate objectCOMMENT
MEDIUMmne/utils/_bunch.py11# Create a Bunch class that acts like a struct (mybunch.key = val)COMMENT
MEDIUMmne/utils/tests/test_config.py302 # Create a unique key/value pair.COMMENT
MEDIUMmne/decoding/receptive_field.py233 # Define the slice that we should use in the middleCOMMENT
MEDIUMmne/decoding/receptive_field.py553# Create a correlation scikit-learn-style scorerCOMMENT
MEDIUMmne/decoding/tests/test_base.py129 # Define a classifier, an invertible transformer and an non-invertible one.COMMENT
MEDIUMmne/viz/topomap.py789 # Define the outline of the head, ears and noseCOMMENT
MEDIUMmne/viz/evoked.py2153# The following functions are all helpers for plot_compare_evokeds. #COMMENT
MEDIUMmne/viz/ui_events.py291 # Create the event channel if it doesn't exist yetCOMMENT
MEDIUMmne/viz/topo.py243 # Create a PolyCollection for the axis backgroundsCOMMENT
MEDIUMmne/_fiff/proj.py1290 # Create the projectorCOMMENT
MEDIUMmne/_fiff/compensator.py40 # Create the preselectorCOMMENT
MEDIUMmne/_fiff/compensator.py50 # Create the postselector (zero entries for channels not found)COMMENT
MEDIUMmne/_fiff/reference.py438 # Creating an average reference may fail. In this case, makeCOMMENT
MEDIUMmne/gui/_dipolefit.py971 # Create a coordinate system where X and Y are tangential to the helmetCOMMENT
MEDIUMmne/report/report.py3497 # Create the figure once and reuse it for performance reasonsCOMMENT
MEDIUMmne/preprocessing/_regress.py92 # Create an EOGRegression object and load the given betas into it.COMMENT
MEDIUMmne/preprocessing/xdawn.py477 # Define the components to keepCOMMENT
MEDIUMmne/preprocessing/eyetracking/_pupillometry.py98 # Create an empty boolean maskCOMMENT
MEDIUM…e/preprocessing/eyetracking/tests/test_pupillometry.py34 # Create a dummy stim channelCOMMENT
MEDIUMmne/preprocessing/nirs/_tddr.py67 # This function is the reference implementation for the TDDR algorithm forCOMMENT
MEDIUMmne/source_space/_source_space.py2114 # Define the sphere which fits the surfaceCOMMENT
MEDIUMmne/source_space/_source_space.py2902 # Create a list of LabelCOMMENT
MEDIUMmne/source_space/tests/test_source_space.py1047# The following code was used to generate small-src.fif.gz.COMMENT
MEDIUMmne/minimum_norm/inverse.py435 # Create the file and save the essentialsCOMMENT
MEDIUMmne/minimum_norm/inverse.py678 # Create the diagonal matrix for computing the regularized inverseCOMMENT
MEDIUMmne/minimum_norm/inverse.py683 # Create the projection operatorCOMMENT
MEDIUMmne/minimum_norm/inverse.py692 # Create the whitenerCOMMENT
MEDIUMmne/stats/parametric.py12# The following function is a rewriting of scipy.stats.f_onewayCOMMENT
MEDIUMmne/stats/cluster_level.py768 # Create the leftmost column as 0,0,...,1,1,...COMMENT
MEDIUMmne/forward/_compute_forward.py359 # The following code is equivalent to this, but saves memoryCOMMENT
MEDIUMmne/forward/_make_forward.py181 # Create the resultCOMMENT
MEDIUMmne/forward/tests/test_make_forward.py779 # Create a new Dipole object with the dipole fitsCOMMENT
MEDIUMmne/channels/interpolation.py549 # Create a new instance for the interpolated EEG channelsCOMMENT
MEDIUMmne/channels/montage.py1852 # Create a mapping from column name to indexSTRING
MEDIUMmne/channels/tests/test_interpolation.py518 # Create a target montageCOMMENT
MEDIUMmne/channels/tests/test_layout.py335 # Create a grid of points. Box size should be (0.1, 0.1).COMMENT
MEDIUMmne/channels/tests/test_layout.py340 # Create a random set of points. This should never break the function.COMMENT
MEDIUMexamples/visualization/evoked_topomap.py132# Define a threshold and create the maskCOMMENT
MEDIUMexamples/visualization/brain.py127# Create a screenshot for exporting the brain imageCOMMENT
MEDIUMexamples/datasets/hf_sef_data.py31# Create a highpass filtered versionCOMMENT
MEDIUMexamples/datasets/limo_data.py166# Create a dictionary containing the evoked responsesCOMMENT
MEDIUMexamples/simulation/plot_stc_metrics.py62# Define main parameters of sourcesCOMMENT
MEDIUMexamples/simulation/plot_stc_metrics.py95# Define the time course of the activityCOMMENT
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Unused Imports222 hits · 220 pts
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LOWmne/conftest.py1230CODE
LOWmne/conftest.py1232CODE
LOWmne/conftest.py1233CODE
LOWmne/conftest.py1234CODE
LOWmne/epochs.py118CODE
LOWmne/epochs.py119CODE
LOWmne/epochs.py120CODE
LOWmne/epochs.py123CODE
LOWmne/epochs.py124CODE
LOWmne/epochs.py127CODE
LOWmne/epochs.py131CODE
LOWmne/coreg.py26CODE
LOWmne/coreg.py47CODE
LOWmne/coreg.py47CODE
LOWmne/coreg.py47CODE
LOWmne/coreg.py47CODE
LOWmne/evoked.py75CODE
LOWmne/evoked.py76CODE
LOWmne/evoked.py77CODE
LOWmne/evoked.py77CODE
LOWmne/evoked.py78CODE
LOWmne/evoked.py79CODE
LOWmne/evoked.py81CODE
LOWmne/evoked.py82CODE
LOWmne/evoked.py84CODE
LOWmne/evoked.py84CODE
LOWmne/evoked.py84CODE
LOWmne/datasets/limo/__init__.py7CODE
LOWmne/datasets/limo/__init__.py7CODE
LOWmne/datasets/_infant/__init__.py5CODE
LOWmne/datasets/misc/__init__.py7CODE
LOWmne/datasets/misc/__init__.py7CODE
LOWmne/datasets/eegbci/__init__.py7CODE
LOWmne/datasets/eegbci/__init__.py7CODE
LOWmne/datasets/eegbci/__init__.py7CODE
LOWmne/datasets/opm/__init__.py7CODE
LOWmne/datasets/opm/__init__.py7CODE
LOWmne/datasets/sleep_physionet/__init__.py5CODE
LOWmne/datasets/sleep_physionet/__init__.py5CODE
LOWmne/datasets/sleep_physionet/__init__.py5CODE
LOWmne/datasets/refmeg_noise/__init__.py7CODE
LOWmne/datasets/refmeg_noise/__init__.py7CODE
LOWmne/datasets/brainstorm/__init__.py7CODE
LOWmne/datasets/brainstorm/__init__.py7CODE
LOWmne/datasets/brainstorm/__init__.py7CODE
LOWmne/datasets/brainstorm/__init__.py7CODE
LOWmne/datasets/brainstorm/__init__.py7CODE
LOWmne/datasets/phantom_kernel/__init__.py7CODE
LOWmne/datasets/phantom_kernel/__init__.py7CODE
LOWmne/datasets/ucl_opm_auditory/__init__.py7CODE
LOWmne/datasets/ucl_opm_auditory/__init__.py7CODE
LOWmne/datasets/spm_face/__init__.py7CODE
LOWmne/datasets/spm_face/__init__.py7CODE
LOWmne/datasets/spm_face/__init__.py7CODE
LOWmne/datasets/spm_face/__init__.py7CODE
LOWmne/datasets/phantom_kit/__init__.py7CODE
LOWmne/datasets/phantom_kit/__init__.py7CODE
LOWmne/datasets/ssvep/__init__.py7CODE
LOWmne/datasets/ssvep/__init__.py7CODE
LOWmne/datasets/lite_data/__init__.py7CODE
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Hallucination Indicators14 hits · 165 pts
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CRITICALmne/viz/_mpl_figure.py212 self.mne.parent_fig.mne.child_figs.remove(self)CODE
CRITICALmne/viz/_mpl_figure.py360 self.mne.parent_fig.mne.child_figs.remove(self)CODE
CRITICALmne/viz/_mpl_figure.py1443 self.mne.fig_annotation.mne.radio_ax.set_title(CODE
CRITICALmne/viz/utils.py1492 self.cidpress = self.cbar.ax.figure.canvas.mpl_connect(CODE
CRITICALmne/viz/utils.py1495 self.cidrelease = self.cbar.ax.figure.canvas.mpl_connect(CODE
CRITICALmne/viz/utils.py1498 self.cidmotion = self.cbar.ax.figure.canvas.mpl_connect(CODE
CRITICALmne/viz/utils.py1501 self.keypress = self.cbar.ax.figure.canvas.mpl_connect(CODE
CRITICALmne/viz/utils.py1504 self.scroll = self.cbar.ax.figure.canvas.mpl_connect(CODE
CRITICALmne/viz/utils.py1611 self.cbar.ax.figure.canvas.draw()CODE
CRITICALmne/viz/_figure.py217 durations = self.mne.inst.annotations.duration.copy()CODE
CRITICALmne/viz/_brain/tests/test_brain.py941 fp = np.array(brain._renderer.figure.plotter.renderer.ComputeVisiblePropBounds())CODE
CRITICALmne/report/js_and_css/jquery-3.6.0.min.js2!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):fCODE
CRITICAL…d_css/bootstrap-table/bootstrap-table-copy-rows.min.js10!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(require("jquery")):"function"==typeof define&&defiCODE
CRITICAL…_and_css/bootstrap-table/bootstrap-table-export.min.js10!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(require("jquery")):"function"==typeof define&&defiCODE
AI Slop Vocabulary73 hits · 163 pts
SeverityFileLineSnippetContext
MEDIUMmne/transforms.py1453 # But this is not robust to quaternions having sign ambiguity,COMMENT
MEDIUMmne/fixes.py442# sklearn uses np.linalg for this, but ours is more robust to zero eigenvaluesCOMMENT
LOWmne/epochs.py2632 # 2b. for non-tag ids, just pass them directlyCOMMENT
LOWmne/rank.py321 # one, so we cannot just use proc_history[0].COMMENT
MEDIUMmne/time_frequency/tfr.py1287 del self._shape # calculated from self._data henceforthCODE
MEDIUMmne/time_frequency/spectrum.py1224 del self._shape # calculated from self._data henceforthCODE
LOWmne/beamformer/tests/test_lcmv.py172 # just use gradsCOMMENT
LOWmne/io/curry/curry.py141 # of the acquisition system (which is unknown); therefore, just setCOMMENT
MEDIUMmne/io/persyst/persyst.py454 # TODO: utilize state and var_type in code.COMMENT
LOWmne/io/tests/test_raw.py1101 # tmin and tmax just use time_as_index under the hoodCOMMENT
LOWmne/io/fiff/raw.py433 continue # just use zeros for gapsCODE
MEDIUMmne/tests/test_chpi.py652 """Wrap to facilitate change diff."""STRING
MEDIUMmne/tests/test_rank.py135 # Now do some more comprehensive testsCOMMENT
MEDIUMmne/utils/misc.py354 # but this might be more robust to file-like objects not properlyCOMMENT
MEDIUMmne/utils/config.py516 # This is a more robust way of getting the user's home folder onCOMMENT
LOWmne/utils/numerics.py873 # Adapted from sklearn and stripped down to just use linalg.svdCOMMENT
MEDIUMmne/decoding/receptive_field.py527 """Find the slice to be taken in order to remove missing values."""STRING
LOWmne/viz/topomap.py1030 # rare so for now just use the average value of all extrasCOMMENT
LOWmne/viz/evoked.py1959 # simply create a new evoked object with the desired channel selectionCOMMENT
LOWmne/viz/_3d.py755 trans = Transform("head", "mri") # not used so just use identityCODE
LOWmne/_fiff/meas_info.py672 # whatever mapping was provided, now we can just use a dictCOMMENT
LOWmne/_fiff/meas_info.py949 # set does not preserve order but dict does, so let's just use itCOMMENT
MEDIUMmne/_fiff/tests/test_meas_info.py485 # Use assert_object_equal for comprehensive checkCOMMENT
LOWmne/preprocessing/ecg.py512 # Raw has start param, can just use intCOMMENT
LOWmne/preprocessing/ecg.py520 # Raw has stop param, can just use intCOMMENT
LOWmne/preprocessing/ica.py3258 # Here we just use the median rather than the (transformed-back) mean ofCOMMENT
MEDIUMmne/preprocessing/eyetracking/tests/test_calibration.py262 # Calibration reading should be robust to what we are about to do to this fileCOMMENT
MEDIUMmne/preprocessing/nirs/_tddr.py117 # Step 3. Iterative estimation of robust weightsCOMMENT
MEDIUMmne/preprocessing/nirs/_tddr.py143 # Step 4. Apply robust weights to centered derivativeCOMMENT
LOWmne/minimum_norm/spatial_resolution.py148 # can be quite expensive in memory. So let's just use abs() in place below.COMMENT
LOWmne/minimum_norm/_eloreta.py156 # to work. So let's just set to nan for now.COMMENT
LOWmne/stats/cluster_level.py805 # Here we can just use the hash-table (w/collision detection)COMMENT
MEDIUMmne/forward/_lead_dots.py235 # Accumulate the result, a little bit streamlined versionCOMMENT
LOWmne/channels/channels.py86 # Our helmet does not match very well, so let's just create itCOMMENT
LOWmne/channels/tests/test_layout.py365 # Test specifying both width and height. The function should simply returnCOMMENT
MEDIUMexamples/visualization/3d_to_2d.py70# with the electrode positions on that image. We use this in conjunction withCOMMENT
LOWexamples/time_frequency/source_power_spectrum_opm.py99# For speed, let's just use a 1-layer BEMCOMMENT
MEDIUMexamples/datasets/limo_data.py162# Since this is a "visual paradigm" it might be best to look at electrodesCOMMENT
LOWexamples/preprocessing/muscle_detection.py47raw.crop(130, 160).load_data() # just use a fraction of data for speed hereCODE
LOWtutorials/inverse/30_mne_dspm_loreta.py67# Let's just use the MEG channels for simplicity.COMMENT
MEDIUMtutorials/intro/15_inplace.py76# Above we saw an example of using the `~mne.io.Raw.copy` method to facilitateCOMMENT
MEDIUMtutorials/time-freq/20_sensors_time_frequency.py119# bias relative to the mean), which is a more robust measure.COMMENT
MEDIUMtutorials/time-freq/20_sensors_time_frequency.py304# units of baseline standard deviations. This can facilitate comparison acrossCOMMENT
LOW…/stats-source-space/30_cluster_ftest_spatiotemporal.py78# just pass the spatial adjacency matrix (instead of spatio-temporal)COMMENT
LOW…tats-source-space/60_cluster_rmANOVA_spatiotemporal.py225# just pass the spatial adjacency matrix (instead of spatio-temporal).COMMENT
LOW…/stats-source-space/20_cluster_1samp_spatiotemporal.py176# just pass the spatial adjacency matrix (instead of spatio-temporal).COMMENT
LOWtutorials/epochs/50_epochs_to_data_frame.py82# :class:`~pandas.DataFrame` is simple: just call :meth:`epochs.to_data_frame()COMMENT
LOWtutorials/epochs/60_make_fixed_length_epochs.py49# To create fixed length epochs, we simply call the function and provide itCOMMENT
MEDIUMtutorials/epochs/40_autogenerate_metadata.py204# present paradigm stimuli were presented in rapid succession, sometimesCOMMENT
MEDIUMtutorials/epochs/40_autogenerate_metadata.py366# this paradigm; taking the last response (time point zero) and the lastCOMMENT
MEDIUMtutorials/epochs/40_autogenerate_metadata.py468# the ERN nicely in the given paradigm.COMMENT
MEDIUMtutorials/preprocessing/30_filtering_resampling.py284# The following section suggests best practices to mitigate both of theseCOMMENT
MEDIUMtutorials/preprocessing/30_filtering_resampling.py345# If for some reason you cannot follow the above-recommended best practices,COMMENT
MEDIUMtutorials/preprocessing/15_handling_bad_channels.py174# rejection thresholds, which in turn can lead to less robust estimation of theCOMMENT
MEDIUMtutorials/preprocessing/20_rejecting_bad_data.py50# best practices for annotating *bad* data spans so that they will be excludedCOMMENT
LOWtutorials/preprocessing/20_rejecting_bad_data.py190# of events: simply pass the array via the ``events`` parameter. ExistingCOMMENT
LOWtutorials/preprocessing/10_preprocessing_overview.py32raw.crop(0, 60).load_data() # just use a fraction of data for speed hereCODE
MEDIUMtutorials/preprocessing/10_preprocessing_overview.py71# in the sensory response to a stimulus, but the experimental paradigm involvesCOMMENT
MEDIUMtutorials/preprocessing/40_artifact_correction_ica.py101# converge faster than FastICA and Infomax, and is more robust than otherCOMMENT
MEDIUMtutorials/preprocessing/25_background_filtering.py796# Similarly, in a P300 paradigm reported byCOMMENT
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Excessive Try-Catch Wrapping134 hits · 142 pts
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LOWmne/conftest.py133 except Exception:CODE
LOWmne/conftest.py243 except Exception:STRING
LOWmne/conftest.py683 except Exception:CODE
LOWmne/conftest.py1237 except Exception as exc:CODE
LOWmne/conftest.py1274 except Exception:CODE
MEDIUMmne/conftest.py1228def _nbclient():CODE
LOWmne/fixes.py699 except Exception:CODE
LOWmne/fixes.py704 except Exception:CODE
LOWmne/fixes.py788 except Exception:CODE
LOWmne/_numba.py28except Exception: # could be ImportError, SystemError, etc.CODE
LOWmne/coreg.py1259 except Exception:CODE
LOWmne/morph_map.py70 except Exception:CODE
LOWmne/morph_map.py151 except Exception as exp:CODE
LOWmne/__init__.py27except Exception:CODE
MEDIUMmne/bem.py355def _import_openmeeg(what="compute a BEM solution using OpenMEEG"):CODE
LOWmne/bem.py358 except Exception as exc:CODE
LOWmne/bem.py1502 except Exception: # not our formatCODE
LOWmne/cuda.py80 except Exception:CODE
LOWmne/cuda.py137 except Exception as exp:CODE
LOWmne/cuda.py243 except Exception as exp:CODE
LOWmne/cuda.py345 except Exception:CODE
LOWmne/filter.py1812 except Exception:CODE
LOWmne/parallel.py136 except Exception:CODE
LOWmne/time_frequency/tfr.py2043 except Exception as e:CODE
LOWmne/io/_preload_cache.py63 except Exception as exc:CODE
LOWmne/io/_read_raw.py195 except Exception:CODE
LOWmne/io/base.py843 except Exception:CODE
LOWmne/io/base.py1483 except Exception:CODE
LOWmne/io/base.py1574 except Exception:CODE
LOWmne/io/base.py3452 except Exception:CODE
LOWmne/io/curry/curry.py166 except Exception:CODE
LOWmne/io/egi/egimff.py86 except Exception:CODE
LOWmne/io/egi/events.py47 except Exception:CODE
LOWmne/io/egi/events.py61 except Exception as exc:CODE
LOWmne/io/edf/edf.py1128 except Exception:CODE
LOWmne/io/edf/edf.py1138 except Exception:CODE
LOWmne/io/edf/edf.py1145 except Exception:CODE
LOWmne/io/edf/edf.py270 except Exception:CODE
LOWmne/io/edf/edf.py1400 except Exception:CODE
LOWmne/io/artemis123/artemis123.py168 except Exception:CODE
LOWmne/io/mef/_utils.py160 except Exception:CODE
LOWmne/io/eeglab/_eeglab.py124 except Exception:STRING
LOWmne/io/cnt/cnt.py548 except Exception:CODE
LOWmne/io/fiff/raw.py571 except Exception:CODE
LOWmne/io/hitachi/hitachi.py215 except Exception:CODE
LOWmne/io/hitachi/hitachi.py221 except Exception:CODE
LOWmne/tests/test_cov.py316 except Exception as exp:CODE
LOWmne/tests/test_docstring_parameters.py296 except Exception: # e.g., mne.export not having pybvCODE
LOWmne/utils/misc.py250 except Exception:CODE
LOWmne/utils/_logging.py517 except Exception:STRING
LOWmne/utils/config.py668 except Exception as exc:CODE
LOWmne/utils/config.py695 except Exception:CODE
LOWmne/utils/config.py735 except Exception: # pragma: no coverCODE
LOWmne/utils/config.py824 except Exception: # in case someone overrides sys.stdout in an unsafe wayCODE
LOWmne/utils/config.py841 except Exception as exc:CODE
LOWmne/utils/config.py848 except Exception as exc:CODE
LOWmne/utils/config.py1004 except Exception:CODE
LOWmne/utils/check.py309 except Exception:CODE
LOWmne/utils/check.py1069 except Exception:CODE
LOWmne/utils/check.py1082 except Exception:CODE
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Slop Phrases50 hits · 136 pts
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LOWmne/label.py2810 # make sure to add no duplicate colorSTRING
LOWmne/utils/tests/test_check.py218 # make sure to inform the user that 7 channels were droppedCOMMENT
LOWmne/viz/topo.py1251 # make sure to only modify the time courses, not the ticksCOMMENT
MEDIUMmne/preprocessing/tests/test_css.py37 # include test for gh-12373, that you can use MAG+EEG if you wantCOMMENT
LOWmne/channels/channels.py2154 # create new instances and make sure to copy important attributesCOMMENT
MEDIUMexamples/visualization/brain.py75# To highlight a region on the brain you can use the ``add_label`` method.COMMENT
MEDIUMtutorials/inverse/21_interactive_dipole_fit.py53# sources. On the command line there are various options you can use to specify filesCOMMENT
MEDIUMtutorials/inverse/21_interactive_dipole_fit.py146# happy with the selection, you can use the "Fit dipole" button as before to fit aCOMMENT
MEDIUMtutorials/intro/30_info.py181# channels in it, you can use the :func:`mne.pick_info` function to pick theCOMMENT
LOWtutorials/intro/40_sensor_locations.py52# computed on a spherical head model, make sure to check out the `eeg_positions`_COMMENT
MEDIUMtutorials/intro/50_configure_mne.py198# the current Python session, you can use :func:`mne.set_log_level` instead.COMMENT
MEDIUMtutorials/intro/50_configure_mne.py275# Or you can use :func:`mne.sys_info` directly, which prints to ``stdout`` byCOMMENT
MEDIUMtutorials/time-freq/20_sensors_time_frequency.py180# but you can also use :func:`mne.time_frequency.tfr_multitaper`COMMENT
MEDIUMtutorials/time-freq/10_spectrum_class.py104# similar to :class:`~mne.Epochs` objects: you can use string values to selectCOMMENT
MEDIUMtutorials/io/60_ctf_bst_auditory.py223# saccades) you can use keyword ``reject_by_annotation=False``.COMMENT
MEDIUMtutorials/simulation/10_array_objs.py115# To create a `~mne.io.Raw` object from scratch, you can use theCOMMENT
MEDIUMtutorials/simulation/10_array_objs.py137# To create an `~mne.Epochs` object from scratch, you can use theCOMMENT
MEDIUMtutorials/simulation/10_array_objs.py197# If you already have data that was averaged across trials, you can use it toCOMMENT
MEDIUM…/stats-source-space/20_cluster_1samp_spatiotemporal.py9conditions across subjects. Here just for demonstration purposes we simulate data fromSTRING
MEDIUMtutorials/epochs/30_epochs_metadata.py90# Like any :class:`pandas.DataFrame`, you can modify the data or add columns asCOMMENT
MEDIUMtutorials/epochs/10_epochs_overview.py178# constructor, you can add it later by assigning to the ``event_id``COMMENT
MEDIUMtutorials/epochs/10_epochs_overview.py313# To change the temporal extent of the :class:`~mne.Epochs`, you can use theCOMMENT
MEDIUMtutorials/epochs/10_epochs_overview.py386# channel will appear in a separate column. Then you can use any of Pandas'COMMENT
MEDIUMtutorials/epochs/10_epochs_overview.py454# If you want to iterate over :class:`~mne.Epochs` objects, you can use anCOMMENT
MEDIUMtutorials/preprocessing/60_maxwell_filtering_sss.py347# want to doublecheck, you can use :func:`mne.chpi.get_active_chpi` (currentlyCOMMENT
MEDIUMtutorials/preprocessing/40_artifact_correction_ica.py430# you can use as a proxy for EOG. You can use a single channel, or create aCOMMENT
LOWtutorials/preprocessing/40_artifact_correction_ica.py45# the artifacts in your data to make sure you choose the right repair tool.COMMENT
MEDIUMtutorials/preprocessing/45_projectors_background.py315# Python :class:`dict`, so you can use its ``.keys()`` method to see whatCOMMENT
MEDIUMtutorials/preprocessing/55_setting_eeg_reference.py79# earlobe or mastoid channels, so this is just for demonstration purposes:COMMENT
MEDIUMtutorials/preprocessing/55_setting_eeg_reference.py97# the signal at* ``Fp1``. In this situation, you can add back ``Fp1`` as a flatCOMMENT
MEDIUMtutorials/preprocessing/55_setting_eeg_reference.py132# To set a "virtual reference" that is the average of all channels, you can useCOMMENT
MEDIUMtutorials/preprocessing/55_setting_eeg_reference.py220# To create a bipolar reference, you can use :meth:`~mne.set_bipolar_reference`COMMENT
MEDIUMtutorials/preprocessing/25_background_filtering.py1056# were used through the 'auto' setting, you can useCOMMENT
MEDIUMtutorials/preprocessing/50_artifact_correction_ssp.py251# To compute SSP projectors for the heartbeat artifact, you can useCOMMENT
MEDIUMtutorials/preprocessing/50_artifact_correction_ssp.py268# end of the list). If you don't want that, you can change that behavior withCOMMENT
LOWtutorials/preprocessing/50_artifact_correction_ssp.py40# the artifacts in your data to make sure you choose the right repair tool.COMMENT
MEDIUMtutorials/raw/20_event_arrays.py199# you can change that with the ``id`` parameter. It is also possible to specifyCOMMENT
MEDIUMtutorials/raw/10_raw_overview.py293# inaccurate, you can change the type of any channel with theCOMMENT
MEDIUMtutorials/raw/10_raw_overview.py341# or combine two or more separate :class:`~mne.io.Raw` objects — you can useCOMMENT
MEDIUMtutorials/evoked/30_eeg_erp.py108# system, you can add sensor locations to the data withCOMMENT
MEDIUMtutorials/evoked/30_eeg_erp.py583# measures. First, you can use an *a priori* time window based on priorCOMMENT
MEDIUMtutorials/evoked/10_evoked_overview.py128# If you've already created the :class:`~mne.Evoked` object, you can use theCOMMENT
MEDIUMtutorials/evoked/10_evoked_overview.py281# you can use :meth:`~mne.Epochs.equalize_event_counts` prior to averaging.COMMENT
LOWtutorials/forward/25_automated_coreg.py103# Don't forget to save the resulting ``trans`` matrix!COMMENT
MEDIUMtutorials/forward/80_fix_bem_in_blender.py348# :ref:`tut-fix-meshes-smoothing` does not work, you can try fixing the meshCOMMENT
LOWtutorials/forward/80_fix_bem_in_blender.py257# When editing BEM surfaces/meshes in Blender, make sure to useCOMMENT
MEDIUMtutorials/forward/30_forward.py151# you can use the following code.COMMENT
MEDIUMtutorials/forward/30_forward.py169# dipoles inside the brain (requires the :term:`BEM` surfaces) you can use theCOMMENT
MEDIUMtutorials/forward/30_forward.py290# To save to disk a forward solution you can useCOMMENT
MEDIUMtutorials/forward/35_eeg_no_mri.py98# To use an infant head model for M/EEG data, you can useCOMMENT
AI Response Leakage9 hits · 79 pts
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HIGHexamples/visualization/brain.py21# In this example we use the ``sample`` data which is data from a subjectCOMMENT
HIGHexamples/simulation/plot_stc_metrics.py58# In this example we simulate two types of cortical sources: a region andCOMMENT
HIGH…simulation/simulated_raw_data_using_subject_anatomy.py30# In this example, raw data will be simulated for the sample subject, so itsCOMMENT
HIGH…simulation/simulated_raw_data_using_subject_anatomy.py128# Generate source time courses for each region. In this example, we want toCOMMENT
HIGHexamples/preprocessing/eeg_bridging.py398# In this example, we have shown a dataset where electrical bridging occurredCOMMENT
HIGHtutorials/clinical/20_seeg.py230# In this example, it is simply the source with the largest raw signal value.COMMENT
HIGHtutorials/preprocessing/70_fnirs_processing.py106# In this example the data is clean and the coupling is good for allCOMMENT
HIGHtutorials/preprocessing/70_fnirs_processing.py116# In this example we will mark all channels with a SCI less than 0.5 as badCOMMENT
HIGH…rials/stats-sensor-space/40_cluster_1samp_time_freq.py185# In this example, we wish to set the threshold for including data bins inCOMMENT
Redundant / Tautological Comments41 hits · 62 pts
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LOWmne/epochs.py296 # Check if we already have an entry for merged keys of duplicateCOMMENT
LOWmne/epochs.py1672 # Check if indices and reasons are of the same lengthCOMMENT
LOWmne/epochs.py4191 # Check if criterion is a function and apply itCOMMENT
LOWmne/bem.py1482 # Open the file, create directoryCOMMENT
LOWmne/chpi.py1802 # Set bads to empty list here. In theory flux jumps etc. or even flat channelsCOMMENT
LOWmne/time_frequency/tests/test_csd.py363 # Check if the CSD matrices are hermitianCOMMENT
LOWmne/io/nirx/nirx.py149 # Check if required files exist and store names for later useCOMMENT
LOWmne/io/eeglab/eeglab.py468 # Check if data is embedded in the same .set fileSTRING
LOWmne/io/eeglab/eeglab.py515 # Check if data is embedded in .set fileCOMMENT
LOWmne/io/eeglab/eeglab.py518 # Check if we have already loaded and cached the embedded dataCOMMENT
LOWmne/tests/test_epochs.py589 # Check if callable returns a tuple with reasonsCOMMENT
LOWmne/tests/test_morph.py519 # Check if not morphed, but voxel size not boolean, raise ValueError.COMMENT
LOWmne/utils/tests/test_config.py77 # Check if get_config with key=None returns all configCOMMENT
LOWmne/decoding/tests/test_ssd.py316 # Check if the 5 first 5 components are the same for bothCOMMENT
LOWmne/viz/tests/test_raw.py928 # Check if single annotation toggle worksCOMMENT
LOWmne/viz/tests/test_raw.py933 # Check if annotation filtering works - All annotationsCOMMENT
LOWmne/viz/tests/test_raw.py942 # Check if annotation filtering works - filtering annotationsCOMMENT
LOWmne/viz/tests/test_ica.py504 # Check if annotation filtering works - All annotationsCOMMENT
LOWmne/viz/tests/test_ica.py512 # Check if annotation filtering works - filtering annotationsCOMMENT
LOWmne/viz/_brain/_brain.py1605 # Check if limits have changed at all.COMMENT
LOWmne/_fiff/meas_info.py2238 # Check if all elements are numbers - convert to numpy arrayCOMMENT
LOWmne/_fiff/meas_info.py2252 # Check if this is a tagged MNE typeCOMMENT
LOWmne/preprocessing/_peak_finder.py150 # Check if we still need to add the last pointCOMMENT
LOWmne/preprocessing/_lof.py80 # Check if there are different channel typesCOMMENT
LOWmne/preprocessing/eyetracking/tests/test_calibration.py107 # Check if the copied object is an instance of CalibrationCOMMENT
LOWmne/preprocessing/eyetracking/tests/test_calibration.py109 # Check if the an attribute of the copied object is equal to the original objectCOMMENT
LOWmne/minimum_norm/spatial_resolution.py79 # Check if input options are validCOMMENT
LOWmne/minimum_norm/inverse.py207 # Open the file, create directoryCOMMENT
LOWmne/minimum_norm/inverse.py431 # Open the file, create directoryCOMMENT
LOWmne/forward/forward.py462 # Check if a custom reference has been appliedCOMMENT
LOWmne/forward/forward.py571 # Open the file, create directoryCOMMENT
LOWmne/channels/montage.py1917 # Set device to head transform to identity (canonical positions)STRING
LOWmne/channels/channels.py2116 # Check if channel indices are out of boundsCOMMENT
LOWmne/channels/channels.py2119 # Check if heterogeneous sensor type combinationsCOMMENT
LOWmne/channels/channels.py2134 # Check if combining less than 2 channelCOMMENT
LOWtutorials/inverse/35_dipole_orientations.py254# Set loose to 0.2, the default valueCOMMENT
LOWtutorials/evoked/30_eeg_erp.py474# Print output from the good time window that contains the peakCOMMENT
LOWtutorials/evoked/30_eeg_erp.py499# Print outputCOMMENT
LOWtutorials/evoked/30_eeg_erp.py541# Print outputCOMMENT
LOWdoc/sphinxext/directive_formatting.py92 # Check if text resembling directives is presentCOMMENT
LOWdoc/sphinxext/directive_formatting.py140 # Check if previous line is part of another directiveCOMMENT
Cross-File Repetition6 hits · 30 pts
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HIGHmne/epochs.py0%(plot_psd_doc)s. parameters ---------- %(fmin_fmax_psd)s %(tmin_tmax_psd)s %(picks_good_data_noref)s %(proj_psd)s %(rejSTRING
HIGHmne/evoked.py0%(plot_psd_doc)s. parameters ---------- %(fmin_fmax_psd)s %(tmin_tmax_psd)s %(picks_good_data_noref)s %(proj_psd)s %(rejSTRING
HIGHmne/time_frequency/spectrum.py0%(plot_psd_doc)s. parameters ---------- %(fmin_fmax_psd)s %(tmin_tmax_psd)s %(picks_good_data_noref)s %(proj_psd)s %(rejSTRING
HIGHmne/io/tests/test_raw.py0tests resampling doesn't affect data if sfreq is identical.STRING
HIGHmne/tests/test_source_estimate.py0tests resampling doesn't affect data if sfreq is identical.STRING
HIGHmne/tests/test_evoked.py0tests resampling doesn't affect data if sfreq is identical.STRING
Cross-Language Confusion4 hits · 24 pts
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HIGHmne/conftest.py1246 "execution_count": null,STRING
HIGHmne/annotations.py2650 Annotations returned by this function will all have zero (null) duration.STRING
HIGHmne/stats/parametric.py99 # If df is undefined, variances are zero (assumes n1 > 0 & n2 > 0).COMMENT
HIGHtutorials/stats-sensor-space/10_background_stats.py437# the null, no further inference on the cluster (e.g., peak location) can beCOMMENT
Verbosity Indicators6 hits · 13 pts
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LOWmne/bem.py2069 # Step 2 : Create the parameter mapsCOMMENT
LOWmne/bem.py2079 # Step 3 : Synthesize the flash 5 imagesCOMMENT
LOWmne/bem.py2183 # Step 4 : Register with MPRAGECOMMENT
LOWmne/stats/cluster_level.py886 # Step 1: Calculate t-stat for original dataCOMMENT
LOWmne/stats/cluster_level.py998 # Step 2: If we have some clusters, repeat process on permuted dataCOMMENT
LOWmne/stats/cluster_level.py1000 # Step 3: repeat permutations for step-down-in-jumps procedureCOMMENT
Structural Annotation Overuse6 hits · 13 pts
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LOWmne/bem.py2069 # Step 2 : Create the parameter mapsCOMMENT
LOWmne/bem.py2079 # Step 3 : Synthesize the flash 5 imagesCOMMENT
LOWmne/bem.py2183 # Step 4 : Register with MPRAGECOMMENT
LOWmne/stats/cluster_level.py886 # Step 1: Calculate t-stat for original dataCOMMENT
LOWmne/stats/cluster_level.py998 # Step 2: If we have some clusters, repeat process on permuted dataCOMMENT
LOWmne/stats/cluster_level.py1000 # Step 3: repeat permutations for step-down-in-jumps procedureCOMMENT
Modern Structural Boilerplate4 hits · 4 pts
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LOWmne/io/constants.py7__all__ = ["FIFF"]CODE
LOWmne/io/pick.py12__all__ = [CODE
LOWmne/utils/_typing.py16__all__ = ["Color", "FileLike", "Self"]CODE
LOWmne/viz/_brain/__init__.py11__all__ = ["Brain"]CODE
Fake / Example Data4 hits · 4 pts
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LOWmne/viz/backends/tests/test_abstract.py31 text = backend._Text("test", "placeholder", callback)CODE
LOWmne/report/js_and_css/highlightjs/highlight.min.js421;const e=["a","abbr","address","article","aside","audio","b","blockquote","body","button","canvas","caption","cite","codCODE
LOWmne/report/js_and_css/highlightjs/highlight.min.js1035;const e=["a","abbr","address","article","aside","audio","b","blockquote","body","button","canvas","caption","cite","codCODE
LOWmne/report/js_and_css/highlightjs/highlight.min.js1061;const e=["a","abbr","address","article","aside","audio","b","blockquote","body","button","canvas","caption","cite","codCODE
Example Usage Blocks1 hit · 2 pts
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LOWmne/preprocessing/nirs/_tddr.py75 # Usage:COMMENT