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io-raw-data【数据与研究】Read, create, inspect, annotate, concatenate, save, and export MNE-Python Raw/Info data containers.
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Use this sub-skill when the task starts from neurophysiology files or NumPy arrays and needs an MNE-Python Raw/BaseRaw object, Info metadata, channel picks/types, raw-level annotations, concatenation, preloading, FIF save, or external export.
mne.io.read_raw() for simple suffix-based dispatch, or a format-specific read_raw_* function when the format has sidecars, ambiguous suffixes, optional keyword choices, or better error messages. See file-formats and reader workflow.mne.create_info() plus mne.io.RawArray(data, info) with data shaped (n_channels, n_times) and MNE SI units. See RawArray workflow and the bundled helper create_synthetic_raw.py.raw.info, raw.ch_names, raw.n_times, raw.times, raw.get_data(), raw.pick(), raw.drop_channels(), raw.reorder_channels(), raw.rename_channels(), and raw.set_channel_types(). See API reference.preload=True, preload="path.dat", or raw.load_data(memmap="path.dat") only when operations require writable in-memory data. Crop and pick before preloading when possible. See preload workflow.mne.Annotations, call raw.set_annotations(), and remember that descriptions beginning with BAD are respected by many later processing functions. See annotations workflow.mne.io.concatenate_raws() or raw.append() only for compatible recordings; save MNE-native work with raw.save(...raw.fif), and use raw.export() for BrainVision, EDF, or EEGLAB only when optional exporters are installed. See save/export workflow.Route these tasks elsewhere in the MNE-Python skill graph:
Epochs, Evoked, filtering, artifact rejection, ICA/SSP, and cleaning decisions → preprocessing-epochs-evoked.visualization-reporting.source-modeling-inverse.sys_info → cli-datasets-config.BaseRaw directly except for type checks.Raw methods as mostly in-place. Use raw.copy() before experimental channel picking, cropping, type changes, annotation replacement, concatenation, or filtering.