is is_filtered a manual param?

[carlacodes]

Maybe I am missing something here. I am preprocessing and filtering input neuropixels data, yet in my resulting spikeinterface_recording.json file, all the recordings stored within the recording list are:
"annotations": { "is_filtered": false,

Is the is_filtered parameter a manual parameter that I need to change? Asking as when I then use the waveform extractor in post processing I run into an error where is_filtered needs to be set to True.

example functions below:

import spikeinterface.full as si
import spikeinterface.extractors as se
import spikeinterface.preprocessing as spre
import spikeinterface.sorters as ss
import spikeinterface.core as sc

def spikeglx_preprocessing(recording):
    recording = si.bandpass_filter(recording, freq_min=300, freq_max=6000)
    bad_channel_ids, channel_labels = si.detect_bad_channels(recording)
    recording = recording.remove_channels(bad_channel_ids)
    recording = si.phase_shift(recording)
    recording = si.common_reference(recording, reference='global', operator='median')

    return recording


def spikesorting_pipeline(recording, working_directory, sorter='kilosort4'):
    # working_directory = Path(output_folder) / 'tempDir' / recording.name

    if (working_directory / 'binary.json').exists():
        recording = si.load_extractor(working_directory)
    else:
        recording = spikeglx_preprocessing(recording)
        job_kwargs = dict(n_jobs=-1, chunk_duration='1s', progress_bar=True)
        recording = recording.save(folder = working_directory, format='binary', **job_kwargs)

    sorting = si.run_sorter(
        sorter_name=sorter, 
        recording=recording, 
        output_folder = working_directory / f'{sorter}_output',
        verbose=True,
        )
    
    return sorting

And below is where I extract the waveforms:

def spikesorting_postprocessing(sorting, output_folder, datadir):
    output_folder.mkdir(exist_ok=True, parents=True)
    rec = sorting._recording


    outDir = output_folder/ sorting.name

    jobs_kwargs = dict(n_jobs=-1, chunk_duration='1s', progress_bar=True)
    sorting = si.remove_duplicated_spikes(sorting, censored_period_ms=2)

    if (outDir / 'waveforms_folder').exists():
        we = si.load_waveforms(
            outDir / 'waveforms_folder', 
            sorting=sorting,
            with_recording=True,
            )

    else:
        we = si.extract_waveforms(rec, sorting, outDir / 'waveforms_folder',
            overwrite=False,
            ms_before=2, 
            ms_after=3., 
            max_spikes_per_unit=500,
            sparse=True,
            num_spikes_for_sparsity=100,
            method="radius",
            radius_um=40,
            **jobs_kwargs,
            )

[zm711]

I moved your comment over to an issue instead. We typically use the issues way more than discussions (sorry we just forget this is here). Do you want to move over to the issue I opened for you!

[zm711]

#3244 if you're looking.