Apply hilbert stc

[BabaSanfour]

Fixes #12310.

Updating mne.filter.MixinFilter methods to work with Source Estimated data

[BabaSanfour]

working to fix the failed tests

[BabaSanfour]

All tests passed.
I think the functions are decent.
For SourceEstimate.resample the function overwrite the MixinFilter.resample so I did not update the latter. However I think that SourceEstimate.resample can be integrated into MixinFilter.resample. Not sure if this would have any implication on speed but I think it would improve the code.

[larsoner]

Looks like a good start! See some inline comments.

Any new functionality should be accompanied by unit tests, so in mne/tests/test_source_estimate.py we'll need some basic tests of the new functions that have been added for at least the SourceEstimate class.

[BabaSanfour]

I have addressed all of the suggestions. I also added one function to test to stc.apply_function in test_source_estimate.py . For the object.apply_hilbert function, the test_evoked.py has the test function for raw, epochs and evoked objects, I wonder if the tests should be added in the same function. Also, to test object.apply_function there is pre-existing data in ../io/tests/data . Should we use for example raw data to generate sources? or is there a way to get access for stc_test_data ?

[larsoner]

For the object.apply_hilbert function, the test_evoked.py has the test function for raw, epochs and evoked objects, I wonder if the tests should be added in the same function.

Sure!

Should we use for example raw data to generate sources? or is there a way to get access for stc_test_data ?

I would just use np.random.default_rng(0).normal(...) like here:

mne-python/mne/tests/test_source_estimate.py

Lines 373 to 379 in e6b49ea

	def _fake_stc(n_time=10, is_complex=False):
	np.random.seed(7)
	verts = [np.arange(10), np.arange(90)]
	data = np.random.rand(100, n_time)
	if is_complex:
	data.astype(complex)
	return SourceEstimate(data, verts, 0, 1e-1, "foo")

But don't use np.random.seed, that's bad practice -- use class-based methods after instantiating a default_rng instead (modern best practice for NumPy random that we haven't fully adopted yet but should at some point!).

[larsoner]

Just three minor tweaks, marking for merge-when-green, thanks in advance @BabaSanfour !