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Internal representation (units)
===============================

.. NOTE: part of this file is included in doc/manual/io.rst and
   doc/overview/implementation.rst. Changes here are reflected there. If you
   want to link to this content, link to :ref:`manual-units` for the manual or
   :ref:`units` for the implementation page. The next line is a target for
   :start-after: so we can omit what's above:
   units-begin-content

Irrespective of the units used in your manufacturer's format, when importing
data, MNE-Python will always convert measurements to the same standard units.
Thus the in-memory representation of data are always in:

- Volts (eeg, eog, seeg, emg, ecg, bio, ecog, dbs)
- Teslas (magnetometers)
- Teslas/meter (gradiometers)
- Amperes*meter (dipole fits, minimum-norm estimates, etc.)
- Moles/liter ("molar"; fNIRS data: oxyhemoglobin (hbo), deoxyhemoglobin (hbr))
- Arbitrary units (various derived unitless quantities)

.. NOTE: this is a target for :end-before: units-end-of-list

Note, however, that most MNE-Python plotting functions will scale the data when
plotted to yield nice-looking axis annotations in a sensible range; for
example, :meth:`mne.io.Raw.plot_psd` will convert teslas to femtoteslas (fT)
and volts to microvolts (µV) when plotting MEG and EEG data.

The units used in internal data representation are particularly important to
remember when extracting data from MNE-Python objects and manipulating it
outside MNE-Python (e.g., when using methods like :meth:`~mne.io.Raw.get_data`
or :meth:`~mne.Epochs.to_data_frame` to convert data to :class:`NumPy arrays
<numpy.ndarray>` or :class:`Pandas DataFrames <pandas.DataFrame>` for analysis
or plotting with other Python modules).
