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

##############################################################################
# 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`
# function. Both common average reference (CAR) and common median
# reference (CMR) can be applied. Moreover, the average/median can be
# computed on different groups. Single channels can also be used as
# reference.

recording_car = st.preprocessing.common_reference(recording, reference='average')
recording_cmr = st.preprocessing.common_reference(recording, reference='median')
recording_single = st.preprocessing.common_reference(recording, reference='single', ref_channels=[0])
recording_single_groups = st.preprocessing.common_reference(recording, reference='single',
                                                            groups=[[0, 1], [2, 3]], ref_channels=[0, 2])


fig1, ax1 = plt.subplots()
ax1.plot(recording_car.get_traces()[0])
ax1.plot(recording_cmr.get_traces()[0])

fig2, ax2 = plt.subplots()
ax2.plot(recording_single_groups.get_traces()[1])  # not zero
ax2.plot(recording_single_groups.get_traces()[0])

##############################################################################
# Remove bad channels
# ----------------------

##############################################################################
# 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`
# function. Both common average reference (CAR) and common median
# reference (CMR) can be applied. Moreover, the average/median can be
# computed on different groups. Single channels can also be used as
# reference.

recording_car = st.preprocessing.common_reference(recording, reference='average')
recording_cmr = st.preprocessing.common_reference(recording, reference='median')
recording_single = st.preprocessing.common_reference(recording, reference='single', ref_channels=[0])
recording_single_groups = st.preprocessing.common_reference(recording, reference='single',
                                                            groups=[[0, 1], [2, 3]], ref_channels=[0, 2])


fig1, ax1 = plt.subplots()
ax1.plot(recording_car.get_traces()[0])
ax1.plot(recording_cmr.get_traces()[0])

fig2, ax2 = plt.subplots()
ax2.plot(recording_single_groups.get_traces()[1])  # not zero
ax2.plot(recording_single_groups.get_traces()[0])

##############################################################################
# Remove bad channels
# ----------------------
#  

##############################################################################
# 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`
# function. Both common average reference (CAR) and common median
# reference (CMR) can be applied. Moreover, the average/median can be
# computed on different groups. Single channels can also be used as
# reference.

recording_car = st.preprocessing.common_reference(recording, reference='average')
recording_cmr = st.preprocessing.common_reference(recording, reference='median')
recording_single = st.preprocessing.common_reference(recording, reference='single', ref_channels=[0])
recording_single_groups = st.preprocessing.common_reference(recording, reference='single',
                                                            groups=[[0, 1], [2, 3]], ref_channels=[0, 2])


fig1, ax1 = plt.subplots()
ax1.plot(recording_car.get_traces()[0])
ax1.plot(recording_cmr.get_traces()[0])

fig2, ax2 = plt.subplots()
ax2.plot(recording_single_groups.get_traces()[1])  # not zero
ax2.plot(recording_single_groups.get_traces()[0])

##############################################################################
# Remove bad channels

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'))
print(ss.get_default_params('klusta'))

# 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`
# function. Both common average reference (CAR) and common median
# reference (CMR) can be applied. Moreover, the average/median can be
# computed on different groups. Single channels can also be used as
# reference.

recording_car = st.preprocessing.common_reference(recording, reference='average')
recording_cmr = st.preprocessing.common_reference(recording, reference='median')
recording_single = st.preprocessing.common_reference(recording, reference='single', ref_channels=[0])
recording_single_groups = st.preprocessing.common_reference(recording, reference='single',
                                                            groups=[[0, 1], [2, 3]], ref_channels=[0, 2])


fig1, ax1 = plt.subplots()
ax1.plot(recording_car.get_traces()[0])
ax1.plot(recording_cmr.get_traces()[0])

fig2, ax2 = plt.subplots()
ax2.plot(recording_single_groups.get_traces()[1])  # not zero
ax2.plot(recording_single_groups.get_traces()[0])

##############################################################################
# Remove bad channels
# ----------------------
#  
# In to remove noisy channels from the analysis, the