channel_ids = recording.get_channel_ids()
fs = recording.get_sampling_frequency()
num_chan = recording.get_num_channels()
print('Channel ids:', channel_ids)
print('Sampling frequency:', fs)
print('Number of channels:', num_chan)
unit_ids = sorting_true.get_unit_ids()
spike_train = sorting_true.get_unit_spike_train(unit_id=unit_ids[0])
print('Unit ids:', unit_ids)
print('Spike train of first unit:', spike_train)
print('Available sorters', ss.available_sorters())
print('Installed sorters', ss.installed_sorters())
print(ss.get_default_params('mountainsort4'))
print(ss.get_default_params('klusta'))
recording_f = st.preprocessing.bandpass_filter(recording,
freq_min=300,
freq_max=6000)
recording_cmr = st.preprocessing.common_reference(recording_f,
reference='median')
sorting_KL = ss.run_klusta(recording=recording_cmr)
print('Units found by Klusta:', sorting_KL.get_unit_ids())
snrs = st.validation.compute_snrs(sorting_KL, recording_cmr)
# First, let's create a toy example: recording, sorting_true = se.example_datasets.toy_example(duration=10, seed=0) ############################################################################## # Check available sorters # -------------------------- # print(ss.available_sorters()) ############################################################################## # This will list the sorters available through SpikeInterface. To see which sorters are installed on the machine # you can run: print(ss.installed_sorters()) ############################################################################## # Change sorter parameters # ----------------------------------- # default_ms4_params = ss.Mountainsort4Sorter.default_params() print(default_ms4_params) ############################################################################## # Parameters can be changed either by passing a full dictionary, or by # passing single arguments. # Mountainsort4 spike sorting default_ms4_params['detect_threshold'] = 4