References for simplecochlea classes
Simple-Cochlea Main Package
cochlea.BandPassFilterbank(order, wn, fs[, …]) |
Band-pass Filterbank Class Defines a bank of band-pass filters. |
cochlea.RectifierBank(n_channels, fs[, …]) |
RectifierBank Class Parralel association of rectifier units. |
cochlea.CompressionBank(n_channels, fs, …) |
CompressionBank Class Parralel association of compression units. |
cochlea.LIFBank(n_channels, fs, tau[, …]) |
Leaky Integrate and Fire (LIF) Bank Parralel association of LIF neuron model. |
simplecochlea.cochlea.Cochlea:
process_input(input_sig[, do_plot]) |
Process input signal through the Cochlea |
process_input_block_ver |
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process_one_channel(input_sig, channel_pos) |
Process a signal with 1 channel of the cochlea |
process_test_signal(signal_type[, …]) |
Run a test signal through the cochlea. |
plot_channel_evolution(input_sig[, channel_pos]) |
Plot the processing of input_sig signal through the cochlea, for channels specified by channel_pos. |
plot_filterbank_frequency_response |
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save(dirpath[, filename]) |
Save the cochlea. |
simplecochlea.generate_signals:
generate_sinus(fs, f_sin[, t_offset, t_max, …]) |
Generate a signal containing one or more sinusoides. | ||
generate_dirac(fs[, t_offset, t_max, amplitude]) |
Generate a impulse signal (mathematically defined by the Dirac delta function) | ||
generate_step(fs[, t_offset, t_max, amplitude]) |
Generate a impulse signal (mathematically defined by the Dirac delta function) | ||
merge_wav_sound_from_dir(dirpath, …[, …]) |
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generate_abs_stim(dirpath, chunk_duration, …) |
Generate a stimulus used for Audio Brain Spotting (ABS). | ||
get_abs_stim_params(chunk_duration_s, …) |
For a sequence when a target segment is repeating n_repeat_target timesand interleaved by n_noise_iter noise segments, returns the pattern of each segments | ||
delete_zero_signal(dirpath) |
There are some null signals (only zero amplitude) in the ABS directory. |
simplecochlea.spikes.spikelist:
sort(field) |
Sort the spikelist by increasing attribute, selected by field |
select_spike([time_min, time_max, …]) |
Select a subset of the spikelist. |
epoch(t_start, t_end) |
Epoch the spikelist given the time periods defined by t_start and t_end. |
epoch_on_triggers(t_triggers, time_pre, …) |
Apply the epoch function on each trigger whose time is defined by t_triggers. |
export([export_path, export_name]) |
Export the spikelist as a .mat file |
to_dataframe() |
Convert the spikelist to a dataframe containing 3 fields : ‘time’, ‘channel’ and ‘pattern_id’ |
plot([bin_duration, potential_thresh, …]) |
Plot the spikelist. |
plot_channel_selectivity([title_str]) |
Plot channel selectivity. |
get_median_isi() |
Compute and return the median ISI (Inter-Spike-Interval) for each channel. |
get_mean_isi() |
Compute and return the mean ISI (Inter-Spike-Interval) for each channel. |
set_pattern_id_from_time_limits(t_start, …) |
Given time limits given by t_start and t_end, set the pattern_id of spikes in this time interval. |
get_pattern_results(t_start, t_end, pattern_id) |
Compute the number of spikes per segment. |
get_channel_selectivity() |
Compute channel selectivity. |
add_time_offset(t_offset) |
Add a time offset to the spikes time. |