How to use the spikeinterface.toolkit.preprocessing.bandpass_filter function in spikeinterface

def test_import():
    import spikeinterface.extractors as se
    import spikeinterface.toolkit as st
    import spikeinterface.sorters as ss
    import spikeinterface.comparison as sc
    import spikeinterface.widgets as sw

    # se
    recording, sorting_true = se.example_datasets.toy_example(duration=60, num_channels=4, seed=0)

    # st
    rec_f = st.preprocessing.bandpass_filter(recording)

    # ss
    print(ss.available_sorters())

    # sc
    sc.compare_two_sorters(sorting_true, sorting_true)

    # sw
    sw.plot_timeseries(rec_f)

recording_prb = recording.load_probe_file('custom_probe.prb')
print('Channel ids:', recording_prb.get_channel_ids())
print('Loaded properties', recording_prb.get_shared_channel_property_names())
print('Label of channel 0:', recording_prb.get_channel_property(channel_id=0, property_name='label'))

# 'group' and 'location' can be returned as lists:
print(recording_prb.get_channel_groups())
print(recording_prb.get_channel_locations())


##############################################################################
# Using the :code:`toolkit`, you can perform pre-processing on the recordings. Each pre-processing function also returns
# a :code:`RecordingExtractor`, which makes it easy to build pipelines. Here, we filter the recording and apply common
# median reference (CMR)

recording_f = st.preprocessing.bandpass_filter(recording, freq_min=300, freq_max=6000)
recording_cmr = st.preprocessing.common_reference(recording_f, reference='median')

##############################################################################
# Now you are ready to spikesort using the :code:`sorters` module!
# Let's first check which sorters are implemented and which are installed

print('Available sorters', ss.available_sorters())
print('Installed sorters', ss.installed_sorter_list)

##############################################################################
# The :code:`ss.installed_sorter_list` will list the sorters installed in the machine. Each spike sorter
# is implemented as a class. We can see we have Klusta and Mountainsort4 installed.
# Spike sorters come with a set of parameters that users can change. The available parameters are dictionaries and
# can be accessed with:

print(ss.get_default_params('mountainsort4'))

import spikeinterface.extractors as se
import spikeinterface.toolkit as st

##############################################################################
# First, let's create a toy example:

recording, sorting = se.example_datasets.toy_example(num_channels=4, duration=10, seed=0)

##############################################################################
# Apply filters
# -----------------
#  
# Now apply a bandpass filter and a notch filter (separately) to the
# recording extractor. Filters are also RecordingExtractor objects.

recording_bp = st.preprocessing.bandpass_filter(recording, freq_min=300, freq_max=6000)
recording_notch = st.preprocessing.notch_filter(recording, freq=1000, q=10)

##############################################################################
# Now let's plot the power spectrum of non-filtered, bandpass filtered,
# and notch filtered recordings.

f_raw, p_raw = scipy.signal.welch(recording.get_traces(), fs=recording.get_sampling_frequency())
f_bp, p_bp = scipy.signal.welch(recording_bp.get_traces(), fs=recording.get_sampling_frequency())
f_notch, p_notch = scipy.signal.welch(recording_notch.get_traces(), fs=recording.get_sampling_frequency())

fig, ax = plt.subplots()
ax.semilogy(f_raw, p_raw[0], f_bp, p_bp[0], f_notch, p_notch[0])

##############################################################################
# Compute LFP and MUA
# --------------------

fig, ax = plt.subplots()
ax.semilogy(f_raw, p_raw[0], f_bp, p_bp[0], f_notch, p_notch[0])

##############################################################################
# Compute LFP and MUA
# --------------------
#  
# Local field potentials (LFP) are low frequency components of the
# extracellular recordings. Multi-unit activity (MUA) are rectified and
# low-pass filtered recordings showing the diffuse spiking activity.
#  
# In :code:`spiketoolkit`, LFP and MUA can be extracted combining the
# :code:`bandpass_filter`, :code:`rectify` and :code:`resample` functions. In this
# example LFP and MUA are resampled at 1000 Hz.

recording_lfp = st.preprocessing.bandpass_filter(recording, freq_min=1, freq_max=300)
recording_lfp = st.preprocessing.resample(recording_lfp, 1000)
recording_mua = st.preprocessing.resample(st.preprocessing.rectify(recording), 1000)

##############################################################################
#  The toy example data are only contain high frequency components, but
#  these lines of code will work on experimental data


##############################################################################
# Change reference
# -------------------
# 
# In many cases, before spike sorting, it is wise to re-reference the
# signals to reduce the common-mode noise from the recordings.
# 
# To re-reference in :code:`spiketoolkit` you can use the :code:`common_reference`