Python functions for everything MEG related. Simply to prevent having a copy of those files in all my repositories

The idea is to have this repo as a submodule in your project folder and import functions from there. This way, projects can keep a specific version of the repository as submodules, while the repo is continuously improved.

• fif2edf(fif_file, chs=None, edf_file=None): Convert a FIF file to an EDF file using pyedflib.

• load_events(file, event_ids=None): retrieve event markers in chronological order from a mne readable file
• load_meg(file, sfreq=100, ica=None, filter_func="lambda x:x", verbose="ERROR"): Load MEG data and applies preprocessing to it (resampling, filtering, ICA)
• load_epochs(file, sfreq=100, event_ids=None, event_filter=None, tmin=-0.1, tmax=0.5, ica=None, autoreject=True, filter_func="lambda x:x", picks="meg"): Load data from FIF file and return into epochs given by MEG triggers.
• load_segments(file, sfreq=100, markers=[[10, 11]], picks='meg', filter_func='lambda x:x', ica=None, verbose='ERROR'): Load interval of data between two markers

• save_clf(clf, filename, save_json=True, metadata=None): Saves a scikit-learn classifier to a compressed pickle file, with an optional JSON sidecar containing parameters and training code metadata.
• cross_validation_across_time(data_x, data_y, clf, add_null_data=False, n_jobs=-2, plot_confmat=False, title_add="", ex_per_fold=2, simulate=False, subj="", tmin=-0.1, tmax=0.5, sfreq=100, return_probas=False, verbose=True): Perform cross-validation across time on the given dataset.
• decoding_heatmap_transfer(clf, data_x, data_y, test_x, test_y, range_t=None): create a heatmap of decoding by varying training and testing time of two independent samples
• decoding_heatmap_generalization(clf, data_x, data_y, ex_per_fold=4, n_jobs=8, range_t=None): using cross validation, create a heatmap of decoding by varying training and testing times
• train_predict(train_x, train_y, test_x, clf, neg_x=None, proba=False): Train a classifier with the given data and return predictions on test data.
• get_channel_importances(data_x, data_y, n_folds=5, n_trees=500, **kwargs): returns the importance of features from a RandomForest cross-validation
• to_long_df(arr, columns=None, value_name='value', **col_labels): Convert an N-D numpy array to a long-format DataFrame; include only labeled dims.
• stratify(X, y, strategy='undersample', random_state=None, verbose=False): Balance a dataset by over- or undersampling.
• TimeEnsembleVoting(base_estimator, voting='hard', weights=None, n_jobs=None, flatten_transform=True): TimeVotingClassifier trains one clone of a base estimator on each time slice of a 3D input (n_samples, n_features, n_times).
• LogisticRegressionOvaNegX(base_clf=None, penalty="l1", C=1.0, solver="liblinear", max_iter=1000, neg_x_ratio=1.0): one vs all logistic regression classifier including negative examples.

• list_files(path, exts=None, patterns=None, relative=False, recursive=False, subfolders=None, only_folders=False, max_results=None, case_sensitive=False): will make a list of all files with extention exts (list) found in the path and possibly all subfolders and return a list of all files matching this pattern
• choose_file(default_dir=None, default_file=None, exts='txt', title='Choose file', mode='open', multiple=False): Open a file chooser dialog using tkinter.
• string_to_seed(string): get a reproducible seed from any string, to set a random seed
• hash_array(arr, length=8, dtype=np.int64): create a hash for any array by doing a full hash of the hexdigest
• hash_md5(input_string, length=8): make a persistent md5 hash from a string
• get_ch_neighbours(ch_name, n=9, return_idx=False, layout_name='Vectorview-all', plot=False): retrieve the n neighbours of a given MEG channel location.
• low_priority(): Set the priority of the process to below-normal (cross platform).

• DataPipeline(steps, memory=None, verbose=False): A pipeline of data processing steps for MNE objects, extending scikit-learn's Pipeline class.
• CustomStep(func, **kwargs): A custom processing step that applies a user-defined function.
• LoadRawStep(preload=True, verbose=None, **kwargs): Load a raw data file from disk.
• FilterStep(l_freq, h_freq, picks=None, filter_length='auto', l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=None, method='fir', iir_params=None, phase='zero', fir_window='hamming', fir_design='firwin', skip_by_annotation=('edge', 'bad_acq_skip'), pad='reflect_limited', verbose=None): Apply a band-pass filter to the data using MNE's filtering functions.
• ResampleStep(sfreq, npad='auto', window='auto', stim_picks=None, n_jobs=None, events=None, pad='auto', method='fft', verbose=None): Resample the data to a new sampling frequency.
• CropStep(id_start, id_stop, events=None, min_length_sanity=0, verbose=None): Crop a raw MNE object between two event markers.
• EpochingStep(events=None, event_id=None, tmin=-0.2, tmax=0.5, baseline=(None, 0), picks=None, preload=True, reject=None, flat=None, proj=True, decim=1, reject_tmin=None, reject_tmax=None, detrend=None, on_missing='error', reject_by_annotation=False, metadata=None, verbose=None): Create epochs from the raw data.
• NormalizationStep(norm_func, axis=None, picks=None): Apply a normalization function to the data.
• ToArrayStep(X=True, y=True, picks=None, verbose=None): Convert MNE objects to NumPy arrays.
• ICAStep(n_components=None, method='fastica', random_state=97, max_iter='auto', decim=3, reject_by_annotation=True, tstep=0.02, verbose=None): No docstring available.
• StratifyStep(strategy='undersample', random_state=None, verbose=None): No docstring available.

• plot_sensors(values, layout='auto', positions=None, title="Sensors active", mode="size", color=None, ax=None, vmin=None, vmax=None, cmap="Reds", **kwargs): Plot sensor positions with markers representing various data values.
• make_sensor_importance_gif(output_filename, data_x=None, data_y=None, importances=None, accuracies = None, layout='auto', tmin=None, tmax=None, n_jobs=-1, n_folds=10, fps=0.2): No docstring available.
• circular_hist(ax, x, bins=16, density=True, offset=0, gaps=True, **kwargs): Produce a circular histogram of angles on ax.
• make_fig(n_axs=30, bottom_plots=2, no_ticks=False, suptitle="", xlabel="Timepoint", ylabel="", figsize=None, despine=True): helper function to create a grid space with RxC rows and a large row with two axis on the bottom
• savefig(fig, file, tight=True, despine=True, **kwargs): Save a Matplotlib figure to a specified file with optional adjustments.
• normalize_lims(axs, which='xy'): Synchronize axis and/or color (clim) limits across a collection of Matplotlib Axes.
• highlight_cells(mask, ax, color='r', linewidth=1, linestyle='solid'): Draws borders around the true entries of the mask array on a heatmap plot.

• rescale_meg_transform_outlier(arr): same as rescale_meg, but also removes all values that are above [-1, 1] and rescales them to smaller values
• rescale_meg(arr): this tries to statically re-scale the values from Tesla to Nano-Tesla, such that most sensor values are between -1 and 1
• robust_scale_nd(arr, axis=None, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True, unit_variance=False): Robust-scale an nD array along a specified axis using sklearn's robust_scale.
• sanity_check_ECG(raw, channels=["BIO001", "BIO002", "BIO003"]): Checks that the first channel of channels is actually containing the most ECG events.
• load_events(file, event_ids=None): retrieve event markers in chronological order from a mne readable file
• repair_epochs_autoreject(raw, epochs, ar_file, picks="meg"): runs autorejec with default parameters on chosen picks

• resample(array, o_sfreq, t_sfreq, n_jobs=-1, verbose=False): Resample EEG data using MNE.
• bandpass(data, lfreq=None, ufreq=None, sfreq=100, verbose=False, **kwargs): Apply bandpass filter using MNE.
• notch(data, freqs=None, notch_widths=None, sfreq=100, verbose=False, n_jobs=-1, **kwargs): Apply notch filter using MNE RawArray.
• get_ch_neighbours(ch_name, n=9, return_idx=False, plot=False): retrieve the n neighbours of a given electrode location.
• estimate_peak_alpha_freq(raw_or_fname, picks=None, alpha_band=(7.0, 13.0), spec_range=(2.0, 30.0), bandwidth=2.0, notch=(50,), hp=1.0, lp=40.0, use_specparam=True, specparam_kwargs=None, method="peak", plot=False, verbose=True): Estimate peak-alpha frequency (PAF) from an MEG recording.
• get_alpha_peak(raw_or_data, sfreq: float | None = None, alpha_bounds: tuple[float, float] = (7, 14), return_spectrum: bool = False, plot_spectrum: bool = False): Alpha-peak finder for MNE Raw, (data, sfreq) tuples, or plain ndarrays.
• get_alpha_phase(raw, alpha_peak, bandwidth=1.5): No docstring available.
• create_oscillation(hz, sfreq=100, n_samples=None, n_seconds=None, phi_rad=None, phi_deg=None, amp=1.0): Generate a sinusoidal waveform with amplitude modulation per cycle.
• wave_speed_cm(phases: np.ndarray, idx_source: int, pos: np.ndarray, freq_hz = None): Estimate the phase-velocity (cm s⁻¹) of a travelling cortical wave from MEG phase angles.
• fit_curve(data, data_sfreq=1 / 1.25, *, model=curves.gaussian, curve_sfreq=100, curve_params, plot_fit=False): Fit a parametric curve to 1-D data with L-BFGS-B.
• interpolate(times, data, n_samples=None, kind='linear', axis=-1): interpolate data sampled at times to evenly spaced
• polyfit(times, data, n_samples=None, degree=3, axis=-1): Fit a polynomial to data sampled at times and evaluate it at evenly spaced points.
• find_phase_reversals(phases, threshold=np.pi/2): Find phase reversals in a 1D array of phase values.