neoStructures module¶

class neoStructures.NeoAll(spikesorting_dirpath, spikesorting_filename, probe_filepath, signal_dirpath=[], event_filepath=[], event_csv_sep=';', fig_dirpath=[], save_fig=0, srate=30000, t_max=-1)¶

Contain spike sorting data from Spyking-Circus results file. Create one Neo Segment per unit.

spikesorting_dirpath : str: Path of the directory containng the spyking circus result file
spikesorting_filename : str: Name of the Spiking Circus result file
probe_filepath : str: Path to the Spiking Circus probe file
signal_dirpath :str | none (default): Path of the directory containing the LFP signals files (must be in EDF format)
event_filepath : str | none (default): Path to the event file
event_csv_sep : str (default ‘;’): Separator used in the event file
fig_dirpath : str | none (default): Path to the directory where figures will be saved. If none and save_fig is true, then save the figures in the current directory.
save_fig : bool: If True, save the figures
srate : float: Sampling rate (Hz)
t_max : float: Maximum time of spike. If not provided will be computed from the spiketrains.
n_units : int: Number of units
unit_names : array: Name of each unit
unit_shapes : array: Shape/Template of each unit
unit_preferred_chan : array: Preferred channel for each unit
channel_names : array: Name of the channels of the LFP files
electrode_names : array: Name of the electrode for each channel
electrode_names_unique : array: Unique electrode names
fig_dirpath : str: Path where the figure are saved
segments : List of Neo Segments: Neo Segment, 1 per unit
channel_indexes : Neo ChannelIndex: Neo ChannelIndex, 1 per tetrode
signal_provided : bool: True if LFP signal was provided, False otherwise

Methods

`channelpos2elec`(channel_pos)	From the channel (micro-wire) position, return the corresponding electrode’s number and name
`channelpos2tetrode`(channel_pos)	From the channel (micro-wire) position, return the information about the tetrode of this channel
`create_epochs_around_events`(event_name, …)	Create Neo Epochs objects around events specified by event name.
`create_event_list`(event_times, event_names)	Create Neo Event objects for each event specified by event_times and event_names.
`elecnum2channelpos`(elec_num)	From the electrode’s number return the electrode’s channels position and name
`get_spikerate_change_ratio_on_epochs`(…)	Compute the mean spikerate on all target and reference epochs, calcul the ratio of these two, for each unit
`getunitpreferredchannelname`(unit_name)	Get the preferred channel of a unit from the unit’s name
`plot_crosscorrelogram`(unit_pos_a, unit_pos_b)	Plot the cross-correlogram between spiketrains of units specified by `unit_pos_a` and `unit_pos_b`
`plot_isi`([unit_pos, bin_duration_ms, …])	Plot the Inter-Spike Intervals histogram for unit specified by unit_pos
`plot_raw_unit_shape`(unit_pos, …[, sep, …])	Get the raw signal of each action potential for the unit specified by `unit_pos`.
`plot_spikerate_change_ratio_on_epochs`(…[, …])	Plot the mean spikerate ratio between 2 epochs.
`plot_spikerate_evolution`([unit_pos, …])	Plot the spikerate evolution across all the segment duration for units specified by unit_pos
`plot_spikerate_evolution_group`(grp_index[, …])	Plot the spike rate evolution across all the length of the segment of all units of the channel index specified by grp_index.
`plot_unit_shape`([unit_pos, plot_mean, ax])	Plot the shape of unit specified by unit_pos
`read_event_file`(event_filepath[, event_csv_sep])	Read the event file, specified by event_filepath and add the event to each segments using the `neoStructures.NeoAll.create_event_list()` method.
`reject_spikes_around_events`(event_name, …)	Reject spikes around events specified by `event_name`.
`save`(dirpath[, filename])	Save the instance as a pickle (.p) file The filename is appended with the currendt date and time
`subfun_plot_unit_shape`(unit_pos[, …])	Sub-routine for plotting shape of the unit

export_spike_file_time

channelpos2elec(channel_pos)¶

From the channel (micro-wire) position, return the corresponding electrode’s number and name

Parameters:	channel_pos : int Channel’s (micro-wire) position
Returns:	el_num : int Corresponding electrode’s number el_name : str Corresponding electrode’s name

channelpos2tetrode(channel_pos)¶

From the channel (micro-wire) position, return the information about the tetrode of this channel

Parameters:	channel_pos : int Channel’s (micro-wire) position
Returns:	tetrode_num : int Corresponding tetrode number tetrode_channels_pos : array Position of the 4 channels of the tetrode wire_num_in_tetrode : int Position of the input channel in the tetrode (between 0 and 3) tetrode_channels_pos_glob : array ??? same as tetrode_channels_pos ???

create_epochs_around_events(event_name, time_offset, epoch_duration, epoch_names)¶

Create Neo Epochs objects around events specified by event name. The epoch times start at the event time plus an possible offset time_offset, the duration is specified by epoch_duration. Epochs are added to all segments

Parameters:	event_name : str Event name from where to create epochs time_offset : float Time offset from the event time epoch_duration : float Duration of the epoch epoch_names : str Name of the epoch

create_event_list(event_times, event_names, segment_pos=-1)¶

Create Neo Event objects for each event specified by event_times and event_names. Add these events to all segments specified by segment_pos

Parameters:	event_times : array Time of each event (s) event_names : array Name of each event segment_pos : int \| array Position of the segments in which events must be added

elecnum2channelpos(elec_num)¶

From the electrode’s number return the electrode’s channels position and name

Parameters:	elec_num : int Electrode position
Returns:	channel_pos : array Position of the input electrode’s channels channel_names : array Name of the input electrode’s channels

export_spike_file_time(artefact_free_periods_filepath, output_path, unit_pos=[], sep=';')¶

get_spikerate_change_ratio_on_epochs(epoch_name_target, epoch_name_ref)¶

Compute the mean spikerate on all target and reference epochs, calcul the ratio of these two, for each unit

Parameters:	epoch_name_target : str Name of the target epochs epoch_name_ref : str Name of the reference epochs
Returns:	spikerate_ratio : list spikerate_target : list spikerate_ref : list

getunitpreferredchannelname(unit_name)¶

Get the preferred channel of a unit from the unit’s name

Parameters:	unit_name : str Name of the unit
Returns:	channel_name : str Name of the preferred channel (micro-wire) of the input unit

plot_crosscorrelogram(unit_pos_a, unit_pos_b, bin_time=array(1.) * ms, max_lag_time=array(80.) * ms, smooth_time=array(10.) * ms, merge_plots=0, same_yscale=0, fill_under_plot=0, do_stat=0, n_surrogates=100, normal_dist_sd=array(10.) * ms, do_plot=1)¶

Plot the cross-correlogram between spiketrains of units specified by unit_pos_a and unit_pos_b

Parameters:

Parameters:	unit_pos_a : int Position of first unit unit_pos_b : int Position of second unit bin_time : float \| duration quantity (default: 1ms) Bin duration (in ms) max_lag_time* : float \| duration quantity (default: 80ms) Maximal lag time (in ms) smooth_time* : float \| duration quantity (default: 10ms) Smoothing time (in ms) merge_plots* : bool (default: False) If True, merge all the cross-correlogram in one figure same_yscale : bool (default: False) If True, the y-axis limits are the same fill_under_plot : bool (default: False) If True, fill the area under the plot do_stat : bool (default: False) If True, do some wrong statistics n_surrogates : int (default: 100) Number of jittered spike-trains when doing the wrong stats normal_dist_sd : float \| duration quantity (default: 50*ms) Parameter for the stats
Returns:	cch : array Cross-correlation histogram between the spike-trains of input units bins : array Contains the IDs of the individual histogram bins

unit_pos_a : int: Position of first unit
unit_pos_b : int: Position of second unit
bin_time : float | duration quantity (default: 1*ms): Bin duration (in ms)
max_lag_time : float | duration quantity (default: 80*ms): Maximal lag time (in ms)
smooth_time : float | duration quantity (default: 10*ms): Smoothing time (in ms)
merge_plots : bool (default: False): If True, merge all the cross-correlogram in one figure
same_yscale : bool (default: False): If True, the y-axis limits are the same
fill_under_plot : bool (default: False): If True, fill the area under the plot
do_stat : bool (default: False): If True, do some wrong statistics
n_surrogates : int (default: 100): Number of jittered spike-trains when doing the wrong stats
normal_dist_sd : float | duration quantity (default: 50*ms): Parameter for the stats

Returns:

cch : array: Cross-correlation histogram between the spike-trains of input units
bins : array: Contains the IDs of the individual histogram bins

plot_isi(unit_pos=-1, bin_duration_ms=1, tmax_ms=100, logscale=0)¶

Plot the Inter-Spike Intervals histogram for unit specified by unit_pos

Parameters:	unit_pos : int Unit position bin_duration_ms : float (default: 1) Bin duration for the histogram in ms tmax_ms : float (default: 100) Maximal time interval logscale : bool If True, the scale of the x-axis is logarithmic

plot_raw_unit_shape(unit_pos, signal_dirpath, artefact_free_periods_filepath, sep=';', fir_order=400, iir_type='bessel', iir_order=2, fn_hz=[300, 3000], half_duration=0.0018, realign=False, plot_mean_shape=True, plot_density_plot=True)¶

Get the raw signal of each action potential for the unit specified by unit_pos. This allows to investigate the filtering effects on the shape of the unit.

Parameters:

Parameters:	unit_pos : int Unit’s position signal_dirpath : str Path to the directory containing the EEG files artefact_free_periods_filepath : str Path to the file containing the periods free of artefacts. Must be a CSV file with 3 columns in the order {‘Filename’, ‘t_start’, ‘t_end’}, with a 1 row header. sep : str (default: ‘;’) CSV separator fir_order : int (default FIR filter order iir_type : str (default: ‘bessel’) IIR filter type - can be ‘bessel’, ‘butter’ iir_order : int (default IIR filter order fn_hz : float \| array Cut-off frequency in Hertz. If a scalar, filter will be a high-pass. If contains 2 frequencies, filter will be a band-pass filter. Default : [300, 3000] half_duration : float (default Half-duration of the observed spikes realign : bool (default If true, tries to realign the spike traces based on the extremum. plot_mean_shape : bool (default If true, plot the mean unit shape plot_density_plot : bool (default If true, plot the density plot

unit_pos : int: Unit’s position
signal_dirpath : str: Path to the directory containing the EEG files
artefact_free_periods_filepath : str: Path to the file containing the periods free of artefacts. Must be a CSV file with 3 columns in the order {‘Filename’, ‘t_start’, ‘t_end’}, with a 1 row header.
sep : str (default: ‘;’): CSV separator
fir_order : int (default: FIR filter order
iir_type : str (default: ‘bessel’): IIR filter type - can be ‘bessel’, ‘butter’
iir_order : int (default: IIR filter order
fn_hz : float | array: Cut-off frequency in Hertz. If a scalar, filter will be a high-pass. If contains 2 frequencies, filter will be a band-pass filter. Default : [300, 3000]
half_duration : float (default: Half-duration of the observed spikes
realign : bool (default: If true, tries to realign the spike traces based on the extremum.
plot_mean_shape : bool (default: If true, plot the mean unit shape
plot_density_plot : bool (default: If true, plot the density plot

plot_spikerate_change_ratio_on_epochs(epoch_name_target, epoch_name_ref, plot_ratio_only=0, single_plot=1, merge_plot=0)¶

Plot the mean spikerate ratio between 2 epochs. Call the method get_spikerate_change_ratio_on_epochs.

Parameters:	epoch_name_target : str Name of the target epochs epoch_name_ref : str Name of the reference epochs plot_ratio_only : bool (default: False) If True, plot only the ratio and not the target and reference spiking rate single_plot : bool (default: True) If True, plot individual figures merge_plot : bool (default: False) If True, merge the figures
Returns:	spikerate_ratio : array

plot_spikerate_evolution(unit_pos=-1, sigma_gauss_kernel=array(30.) * s, merge_plots=0, norm_spikerate=0, plot_events=1)¶

Plot the spikerate evolution across all the segment duration for units specified by unit_pos

Parameters:

Parameters:	unit_pos : int (default: -1) Position of the unit. If equal to -1 sigma_gauss_kernel : float \| duration quantity (default: 150s) Duration of the smoothing gaussian kernel merge_plots : bool (default: False) If True, merge the plots norm_spikerate : bool (default: False) If True, normalize the spiking rate between 0 and 1 plot_events : bool (default: True) If True, plot a vertical line for each event

unit_pos : int (default: -1): Position of the unit. If equal to -1
sigma_gauss_kernel : float | duration quantity (default: 150s): Duration of the smoothing gaussian kernel
merge_plots : bool (default: False): If True, merge the plots
norm_spikerate : bool (default: False): If True, normalize the spiking rate between 0 and 1
plot_events : bool (default: True): If True, plot a vertical line for each event

plot_spikerate_evolution_group(grp_index, sigma_gauss_kernel=array(30.) * s, merge_plots=0, norm_spikerate=0, mean_plot=0, plot_events=1)¶

Plot the spike rate evolution across all the length of the segment of all units of the channel index specified by grp_index. The spike rate is computed using the Elephant module

Parameters:

Parameters:	grp_index : int \| str Either the position of the channel index, or its name sigma_gauss_kernel : float \| duration quantity (default: 100s) Time of the smoothing gaussian kernel merge_plots* : bool (default: False) If True, merge the plots norm_spikerate : bool (default: False) If True, normalize the spiking rate between 0 and 1 mean_plot : bool (default: False) If True, only the mean spikerate across all units of the group/channel_index is plot plot_events : bool (default: True) If True, plot a vertical line for each event

grp_index : int | str: Either the position of the channel index, or its name
sigma_gauss_kernel : float | duration quantity (default: 100*s): Time of the smoothing gaussian kernel
merge_plots : bool (default: False): If True, merge the plots
norm_spikerate : bool (default: False): If True, normalize the spiking rate between 0 and 1
mean_plot : bool (default: False): If True, only the mean spikerate across all units of the group/channel_index is plot
plot_events : bool (default: True): If True, plot a vertical line for each event

plot_unit_shape(unit_pos=-1, plot_mean=0, ax=[])¶

Plot the shape of unit specified by unit_pos

Parameters:	unit_pos : int Position of the unit plot_mean : bool If True, plot the mean of all tetrode templates/shapes ax : axis \| None Axis to plot on. If none create a new figure and an axis

read_event_file(event_filepath, event_csv_sep=';')¶

Read the event file, specified by event_filepath and add the event to each segments using the neoStructures.NeoAll.create_event_list() method. This can be done directly when instanciating the NeoAll instance by providin the event_filepath parameter or by calling this function.

Parameters:	event_filepath : str Path to the event file event_csv_sep : str CSV file separator (default: ‘;’)

reject_spikes_around_events(event_name, time_pre, time_post)¶

Reject spikes around events specified by event_name. All spikes occuring in the window [t_event-time_pre < t < t_event+time_post] will be rejected, for each event. Modify the spiketrains of the segments in which an event is defined.

Parameters:	event_name : str Name of the events to select time_pre : float Beginning of the window, relative to the event time time_post : float End of the window, relative to the event time

save(dirpath, filename=[])¶

Save the instance as a pickle (.p) file The filename is appended with the currendt date and time

Parameters:	dirpath : str Directory path filename : str \| None Output filename. If none is ‘neoall_{currentdate}.p’

subfun_plot_unit_shape(unit_pos, plot_mean=0, ax=[], raster_plot_call=0)¶

Sub-routine for plotting shape of the unit

Parameters:	unit_pos : int Unit’s position plot_mean : bool (default: False) If True, plot the mean of the unit’s shape over the 4 micro-wires of the tetrode ax : axis handle \| None If provided, plot the figure on this axis raster_plot_call : bool (default: False) Should be true, when this method is called from the `plot_rasterplot` method

class neoStructures.NeoEpoch(neoAll, event_filepath, event_csv_sep=';', time_pre=1, time_post=1)¶

NeoEpoch instances are created from a NeoAll instance. The spiketrains contained in the NeoAll instance are divided into epochs from an event file. Spikes occuring around each event are kept ([time_pre, ``time_post]). NeoEpoch contains a list of Neo Segments. Each segment represent one epoch. Each segment contains as many Neo SpikeTrain as there are units. Each segment also contain a Neo AnalogSignal instance representing the LFP signal associated with the spiketrains.

Methods

`channelpos2elec`(channel_pos)	From the channel (micro-wire) position, return the corresponding electrode’s number and name
`channelpos2tetrode`(channel_pos)	From the channel (micro-wire) position, return the information about the tetrode of this channel
`elecnum2channelpos`(elec_num)	From the electrode’s number return the electrode’s channels position and name
`getunitpreferredchannelname`(unit_name)	Get the preferred channel of a unit from the unit’s name
`plot_crosscorrelogram_around_events`(…[, …])	Plot the cross-correlogram around the events of neoEpoch. Compute the shift-predictor to give a significance
`plot_rasterplot`([unit_pos, bin_duration, …])	Plot the PSTH (peri/post stimulus time histogram) for selected units.
`save`(dirpath[, filename])	Save the instance as a pickle (.p) file The filename is appended with the currendt date and time
`subfun_plot_unit_shape`(unit_pos[, …])	Sub-routine for plotting shape of the unit

channelpos2elec(channel_pos)¶

From the channel (micro-wire) position, return the corresponding electrode’s number and name

Parameters:	channel_pos : int Channel’s (micro-wire) position
Returns:	el_num : int Corresponding electrode’s number el_name : str Corresponding electrode’s name

channelpos2tetrode(channel_pos)¶

From the channel (micro-wire) position, return the information about the tetrode of this channel

Parameters:	channel_pos : int Channel’s (micro-wire) position
Returns:	tetrode_num : int Corresponding tetrode number tetrode_channels_pos : array Position of the 4 channels of the tetrode wire_num_in_tetrode : int Position of the input channel in the tetrode (between 0 and 3) tetrode_channels_pos_glob : array ??? same as tetrode_channels_pos ???

elecnum2channelpos(elec_num)¶

From the electrode’s number return the electrode’s channels position and name

Parameters:	elec_num : int Electrode position
Returns:	channel_pos : array Position of the input electrode’s channels channel_names : array Name of the input electrode’s channels

getunitpreferredchannelname(unit_name)¶

Get the preferred channel of a unit from the unit’s name

Parameters:	unit_name : str Name of the unit
Returns:	channel_name : str Name of the preferred channel (micro-wire) of the input unit

plot_crosscorrelogram_around_events(unit_pos_a, unit_pos_b, bin_time=array(1.) * ms, max_lag_time=array(80.) * ms, n_repets=100, plot_superpose=1)¶

Plot the cross-correlogram around the events of neoEpoch. Compute the shift-predictor to give a significance: level. The shift-predictor is computed by shuffling the trials position n_repets times. The interval plotted is equal to the mean +/- 2 times the standard deviation.

Parameters:

Parameters:	unit_pos_a : int \| array \| list Position of first unit(s) unit_pos_b : int \| array \| list Position of second unit(s) bin_time : time (quantity) (default: 1ms) Bin duration max_lag_time* : time (quantity) (default: 80ms) Max lagging time when computing the cross-corelogram n_repets* : int (default: 100) Number of repetitions when computing the sampling distribution plot_superpose : bool (default: True) If True, superimpose the plots

unit_pos_a : int | array | list: Position of first unit(s)
unit_pos_b : int | array | list: Position of second unit(s)
bin_time : time (quantity) (default: 1*ms): Bin duration
max_lag_time : time (quantity) (default: 80*ms): Max lagging time when computing the cross-corelogram
n_repets : int (default: 100): Number of repetitions when computing the sampling distribution
plot_superpose : bool (default: True): If True, superimpose the plots

plot_rasterplot(unit_pos=-1, bin_duration=0.01, sigma_gauss_kernel=array(20.) * ms, plot_unit_shape=1, plot_event_signal=1)¶

Plot the PSTH (peri/post stimulus time histogram) for selected units. A gaussian kernel is used for estimating the instantaneous firing rate. Plot the figure if save_fig is True, in fig_dirpath_raster

Parameters:

Parameters:	unit_pos : int \| array \|list (default: all) Unit position. bin_duration : float (default: 0.01 s) Bin duration when plotting the rasterplot sigma_gauss_kernel : quantity (default: 20ms) Duration of the gaussian kernel used for estimating the instantaneous firing rate. See elephant.statistics.instantaneous_rate plot_unit_shape* : bool (default: True) If True, plot the shape of the selected units in the bottom-right corner plot_event_signal : bool (default: True) If True, plot the mean of the events signals (i.e. mean over the different events)

unit_pos : int | array |list (default: all): Unit position.
bin_duration : float (default: 0.01 s): Bin duration when plotting the rasterplot
sigma_gauss_kernel : quantity (default: 20*ms): Duration of the gaussian kernel used for estimating the instantaneous firing rate. See elephant.statistics.instantaneous_rate
plot_unit_shape : bool (default: True): If True, plot the shape of the selected units in the bottom-right corner
plot_event_signal : bool (default: True): If True, plot the mean of the events signals (i.e. mean over the different events)

save(dirpath, filename=[])¶

Save the instance as a pickle (.p) file The filename is appended with the currendt date and time

Parameters:	dirpath : str Directory path filename : str \| None Output filename. If none is ‘neoall_{currentdate}.p’

subfun_plot_unit_shape(unit_pos, plot_mean=0, ax=[], raster_plot_call=0)¶

Sub-routine for plotting shape of the unit

Parameters:	unit_pos : int Unit’s position plot_mean : bool (default: False) If True, plot the mean of the unit’s shape over the 4 micro-wires of the tetrode ax : axis handle \| None If provided, plot the figure on this axis raster_plot_call : bool (default: False) Should be true, when this method is called from the `plot_rasterplot` method

class neoStructures.NeoMother¶

Superclass of NeoAll and NeoEpoch (both NeoAll and NeoEpoch inherits from NeoMother). Used to define methods for both NeoAll and NeoEpochs

Methods

`channelpos2elec`(channel_pos)	From the channel (micro-wire) position, return the corresponding electrode’s number and name
`channelpos2tetrode`(channel_pos)	From the channel (micro-wire) position, return the information about the tetrode of this channel
`elecnum2channelpos`(elec_num)	From the electrode’s number return the electrode’s channels position and name
`getunitpreferredchannelname`(unit_name)	Get the preferred channel of a unit from the unit’s name
`subfun_plot_unit_shape`(unit_pos[, …])	Sub-routine for plotting shape of the unit

channelpos2elec(channel_pos)¶

From the channel (micro-wire) position, return the corresponding electrode’s number and name

Parameters:	channel_pos : int Channel’s (micro-wire) position
Returns:	el_num : int Corresponding electrode’s number el_name : str Corresponding electrode’s name

channelpos2tetrode(channel_pos)¶

From the channel (micro-wire) position, return the information about the tetrode of this channel

Parameters:	channel_pos : int Channel’s (micro-wire) position
Returns:	tetrode_num : int Corresponding tetrode number tetrode_channels_pos : array Position of the 4 channels of the tetrode wire_num_in_tetrode : int Position of the input channel in the tetrode (between 0 and 3) tetrode_channels_pos_glob : array ??? same as tetrode_channels_pos ???

elecnum2channelpos(elec_num)¶

From the electrode’s number return the electrode’s channels position and name

Parameters:	elec_num : int Electrode position
Returns:	channel_pos : array Position of the input electrode’s channels channel_names : array Name of the input electrode’s channels

getunitpreferredchannelname(unit_name)¶

Get the preferred channel of a unit from the unit’s name

Parameters:	unit_name : str Name of the unit
Returns:	channel_name : str Name of the preferred channel (micro-wire) of the input unit

subfun_plot_unit_shape(unit_pos, plot_mean=0, ax=[], raster_plot_call=0)¶

Sub-routine for plotting shape of the unit

Parameters:	unit_pos : int Unit’s position plot_mean : bool (default: False) If True, plot the mean of the unit’s shape over the 4 micro-wires of the tetrode ax : axis handle \| None If provided, plot the figure on this axis raster_plot_call : bool (default: False) Should be true, when this method is called from the `plot_rasterplot` method

neoStructures.realign_spike_trace(spike_trace, spike_trace_filtered=[], resample_factor=5, interp_method='cubic', half_extremum_win=15)¶

Re-align the spike trace so that the extremum in on the center of the window. The extremum position is computed on the filtered spike trace. Before taking the extremum, the filtered spike trace is upsampled and interpolated. The returned spike trace is the upsampled and interpolated trace, aligned on the center of the window.

Parameters:

Parameters:	spike_trace : array Spike trace spike_trace_filtered : array \| none (default) Filtered spike trace. If empty is equal to `spike_trace` resample_factor : int (default Upsampling factor interp_method : str (default Interpolation method used half_extremum_win : int The extremum will be search only around the center of the window. In a sub-window around the center : [center-half_extremum_win, center+half_extremum_win] Spikes should already be more or less centered.
Returns:	spike_trace_realigned : array Spike trace aligned and upsampled

spike_trace : array: Spike trace
spike_trace_filtered : array | none (default): Filtered spike trace. If empty is equal to spike_trace
resample_factor : int (default: Upsampling factor
interp_method : str (default: Interpolation method used
half_extremum_win : int: The extremum will be search only around the center of the window. In a sub-window around the center : [center-half_extremum_win, center+half_extremum_win] Spikes should already be more or less centered.

Returns:

spike_trace_realigned : array: Spike trace aligned and upsampled