Clean-up _mne_to_eeglab_locs doc and comments

[mscheltienne]

Hello @jacobf18, when you have the time (super low priority!), could you check and clean up the doc and comments that were left in

mne-icalabel/mne_icalabel/iclabel/utils.py

Lines 8 to 75 in 15679f1

	def _mne_to_eeglab_locs(raw: BaseRaw, picks: List[str]) -> Tuple[NDArray[float], NDArray[float]]:
	"""Obtain EEGLab-like spherical coordinate from EEG channel positions.
	TODO: @JACOB:
	- Where is (0,0,0) defined in MNE vs EEGLab?
	- some text description of how the sphere coordinates differ between MNE
	and EEGLab.
	Parameters
	----------
	raw : mne.io.BaseRaw
	Instance of raw object with a `mne.montage.DigMontage` set with
	``n_channels`` channel positions.
	picks : list of str
	List of channel names to include.
	Returns
	-------
	Rd : np.array of shape (1, n_channels)
	Angle in spherical coordinates of each EEG channel.
	Th : np.array of shape (1, n_channels)
	Degree in spherical coordinates of each EEG channel.
	"""
	def _sph2topo(_theta, _phi):
	"""Convert spherical coordinates to topo."""
	az = _phi
	horiz = _theta
	angle = -1 * horiz
	radius = (np.pi / 2 - az) / np.pi
	return angle, radius
	def _cart2sph(_x, _y, _z):
	"""Convert cartesian coordinates to spherical."""
	azimuth = np.arctan2(_y, _x)
	elevation = np.arctan2(_z, np.sqrt(_x**2 + _y**2))
	r = np.sqrt(_x**2 + _y**2 + _z**2)
	# theta,phi,r
	return azimuth, elevation, r
	# get the channel position dictionary
	montage = raw.copy().pick_channels(picks, ordered=True).get_montage()
	positions = montage.get_positions()
	ch_pos = positions["ch_pos"]
	# get locations as a 2D array
	locs = np.vstack(list(ch_pos.values()))
	# Obtain carthesian coordinates
	x = locs[:, 1]
	# be mindful of the nose orientation in eeglab and mne
	# TODO: @Jacob, please expand on this.
	y = -1 * locs[:, 0]
	# see https://github.com/mne-tools/mne-python/blob/24377ad3200b6099ed47576e9cf8b27578d571ef/mne/io/eeglab/eeglab.py#L105 # noqa
	z = locs[:, 2]
	# Obtain Spherical Coordinates
	sph = np.array([_cart2sph(x[i], y[i], z[i]) for i in range(len(x))])
	theta = sph[:, 0]
	phi = sph[:, 1]
	# Obtain Polar coordinates (as in eeglab)
	topo = np.array([_sph2topo(theta[i], phi[i]) for i in range(len(theta))])
	rd = topo[:, 1]
	th = topo[:, 0]
	return rd.reshape([1, -1]), np.degrees(th).reshape([1, -1])