API Documentation

API reference for the module.

Table of Contents

Measures

Spike related measures and conversions, available in the measures sub-module.

Spikes

Measures for spike data.

compute_firing_rate(spikes[, time_range])	Estimate firing rate from a vector of spike times, in seconds.
compute_isis(spikes)	Compute inter-spike intervals.
compute_cv(isis)	Compute coefficient of variation.
compute_fano_factor(spike_train)	Compute the fano factor of a spike train.
compute_spike_presence(spikes, bins[, ...])	Compute the spike presence across time bins.
compute_presence_ratio(spikes, bins[, ...])	Compute the presence ratio for a set of spike times.

Conversions

Conversions between spike representations.

convert_times_to_train(spikes[, fs, time_range])	Convert spike times into a binary spike train.
convert_train_to_times(spike_train[, fs, ...])	Convert a spike train representation into spike times, in seconds.
convert_isis_to_times(isis[, add_initial, ...])	Convert a sequence of inter-spike intervals to spike times.
convert_times_to_counts(spikes, bins[, ...])	Convert spikes times to counts of spikes per time bin.
convert_times_to_rates(spikes, bins[, ...])	Convert spike times to continuous firing rates.

Circular

Measures for working with circular data.

bin_circular(data[, bin_width])

Bin circular data.

Trials

Measures related to trial-epoched data.

compute_trial_frs(trial_spikes, bins[, ...])	Compute continuous binned firing rates for a set of epoched spike times.
compute_segment_frs(spikes, segments)	Compute firing rate across trial segments.
compute_pre_post_rates(trial_spikes, ...)	Compute the firing rates in pre and post event windows.
compute_pre_post_averages(frs_pre, frs_post)	Compute the average firing rate across pre & post event windows.
compute_pre_post_diffs(frs_pre, frs_post[, ...])	Compute the difference in firing rates between pre & post event windows.

Collections

Measures that can be applied to collections (groups) of neurons.

detect_empty_time_ranges(all_spikes, bins[, ...])	Compute the empty time ranges that are common across a collection of recorded neurons.
find_empty_bins(all_spikes, bins[, time_range])	Find empty time bins across a collection of recorded neurons.
find_empty_ranges(bin_emptiness, bins[, ...])	Find empty time ranges based on time bin emptiness for a collection of recorded neurons.

Objects

Objects for managing spike-related data, available in the objects sub-module.

Unit

Unit([uid, spikes, channel, region, cluster])

A Unit object.

Session

Session([subject, session, task, units])

A Session object.

Spatial

Functionality for processing spatial data, available in the spatial sub-module.

Distance

Measures and methods related to distances.

compute_distance(p1, p2)	Compute the distance between two positions.
compute_distances(position)	Compute distances across a sequence of positions.
compute_cumulative_distances(position[, ...])	Compute cumulative distance across a sequence of positions.
compute_distances_to_location(position, location)	Compute distances between a sequence of positions and a specified location.
get_closest_position(position, location[, ...])	Get the index of the closest position value to a specified location.

Speed

Measures and methods related to speed.

compute_speed(position, timestamps[, ...])

Compute speeds across a sequence of positions.

Occupancy

Measures and methods related to computing spatial occupancy.

compute_bin_edges(position, bins[, area_range])	Compute spatial bin edges.
compute_bin_assignment(position, x_edges[, ...])	Compute spatial bin assignment.
compute_bin_counts_pos(position, bins[, ...])	Compute counts per bin, from position data.
compute_bin_counts_assgn(bins, xbins[, ...])	Compute number of counts per bin, from bin assignments.
normalize_bin_counts(bin_counts, occupancy)	Normalize bin counts by occupancy.
create_position_df(position, timestamps, bins)	Create a dataframe that stores information about position bins.
compute_occupancy_df(bindf, bins[, minimum, ...])	Compute the bin occupancy from bin-position dataframe.
compute_occupancy(position, timestamps, bins)	Compute occupancy across spatial bin positions.
compute_trial_occupancy(position, ...[, ...])	Compute trial-level occupancy across spatial bin positions.

Place

Measures and methods related to place cell analyses.

compute_place_bins(spikes, position, ...[, ...])	Compute the spatially binned spiking activity.
compute_trial_place_bins(spikes, position, ...)	Compute the spatially binned spiking activity, across trials.

Target

Measures and methods related to spatial target cell analyses.

compute_target_bins(target_frs, bins, xbins)

Compute binned firing based on spatial target.

Information

Measures and methods related to computing spatial information.

compute_spatial_information(bin_firing, ...)

Compute spatial information.

Utilities

Utilities related to spatial data.

get_position_xy(position[, orientation])	Get the x & y data vectors from a 2d position data array.
compute_nbins(bins)	Compute the number of bins for a given bin definition.
compute_bin_width(bin_edges)	Compute bin width from a set of bin edges.
compute_pos_ranges(position)	Compute the range of positions.
convert_2dindices(xbins, ybins, bins)	Convert a set of 2d bin indices into the equivalent 1d indices.
convert_1dindices(indices, bins)	Convert a set of 1d bin indices into the equivalent 2d indices.

Checks

Functions to check data and arguments to spatial related processes.

check_position(position)	Check a position array.
check_bin_definition(bins[, position])	Check a bin definition for binning spatial data.
check_bin_widths(bin_widths)	Check computed bin widths for a set of spatial bins.

Statistics

Statistical analyses, available in the stats sub-module.

Generators

Statistical generators.

poisson_generator(rate, duration[, start_time])

Generator function for a Homogeneous Poisson distribution.

Trials

Statistical measures for trial-epoched data.

compute_pre_post_ttest(frs_pre, frs_post)	Compute a related samples t-test between the pre & post event firing rates.
compare_pre_post_activity(trial_spikes, ...)	Compare pre & post activity, computing the average firing rates and a t-test comparison.
compare_trial_frs(trials1, trials2)	Compare binned firing rates between two sets of trials with independent samples t-tests.

Shuffle

Methods for shuffling data.

shuffle_spikes(spikes, approach[, ...])	Shuffle spikes.
shuffle_isis(spikes[, n_shuffles, start_time])	Create shuffled spike times using permuted inter-spike intervals.
shuffle_bins(spikes[, bin_width_range, ...])	Shuffle data with circular shuffles of randomly sized bins of the spike train.
shuffle_poisson(spikes[, n_shuffles, start_time])	Shuffle spikes based on generating new spike trains from a Poisson distribution.
shuffle_circular(spikes[, shuffle_min, ...])	Shuffle spikes based on circularly shifting the spike train.

Permutations

Methods and measures related to permutation statistics.

compute_surrogate_pvalue(value, surrogates)	Compute the empirical p-value from a distribution of surrogates.
compute_surrogate_zscore(value, surrogates)	Compute the z-score of a real data value compared to a distribution of surrogates.
compute_surrogate_stats(data_value, surrogates)	Compute surrogate statistics.

ANOVA

Methods and measures related to computing ANOVAs.

create_dataframe(data[, columns, dropna, dtypes])	Create a dataframe from an array of data.
create_dataframe_bins(bin_data[, ...])	Create a dataframe from an array of binned data.
fit_anova(df, formula[, feature, ...])	Fit an ANOVA.

Simulations

Functionality for simulating spiking data, available in the sim sub-module.

General

General simulation functions.

sim_spiketimes(spike_param, duration, method)	Simulate spike times.
sim_spiketrain(spike_param, n_samples, method)	Simulate a spike train.
sim_trials(n_trials[, method, time_pre, ...])	Simulate a collection of trials.

Spike Times

Simulate spike times.

sim_spiketimes_poisson(rate, duration[, ...])

Simulate spike times based on a Poisson distribution.

Spike Trains

Simulate spike trains.

sim_spiketrain_prob(p_spiking[, n_samples, ...])	Simulate spikes based on a probability of spiking per sample.
sim_spiketrain_binom(p_spiking[, n_samples, ...])	Simulate spike train from a binomial probability distribution.
sim_spiketrain_poisson(rate, n_samples[, ...])	Simulate spike train from a Poisson distribution.

Trials

Simulate trial-structured data.

sim_trials_poisson(n_trials, rate_pre, rate_post)

Simulate a collection of trials, based on a Poisson spike time simulation.

Utilities

Simulation related utilities.

apply_refractory_times(spike_times, ...)	Apply a refractory period to simulated spike times.
apply_refractory_train(spike_train, ...)	Apply a refractory period to a simulated spike train.

Plots

Functions in the plts sub-module for visualizing spiking data and related measures.

Spikes

Plots for spike data.

plot_waveform(waveform[, timestamps, ...])	Plot a spike waveform.
plot_waveforms3d(waveforms[, timestamps])	Plot waveforms on a 3D axis.
plot_waveform_density(waveforms[, ...])	Plot a heatmap of waveform density, created as a 2d histogram of spike waveforms.
plot_isis(isis[, bins, range, density, ax])	Plot a distribution of ISIs.
plot_firing_rates(rates[, ax])	Plot firing rates for a group of neurons.

Spatial

Plots for spatial data.

plot_positions(position[, spike_positions, ...])	Plot positions.
plot_position_1d(position[, events, colors, ...])	Position 1d position data, with annotated events.
plot_position_by_time(timestamps, position)	Plot position across time for a single dimension.
plot_heatmap(data[, smooth, ...])	Plot a spatial heat map.
plot_trial_heatmaps(trial_data, **plt_kwargs)	Plot spatial heat maps for a set of trials.
create_heatmap_title(label, data[, stat, p_val])	Create a standardized title for an heatmap, listing the data range.

Task

Plots for visualizing task structure.

plot_task_structure([task_ranges, ...])	Plot task structure with shaded ranges of event durations and lines for point events.
plot_task_events(events[, ax])	Plot task events.

Trials

Plots for trial-epoched data.

plot_rasters(spikes[, vline, colors, ...])	Plot rasters across multiple trials.
plot_rate_by_time(bin_times, trial_cfrs[, ...])	Plot continuous firing rates across time.
create_raster_title(label, avg_pre, avg_post)	Create a standardized title for an event-related raster plot.

Stats

Plots for statistical measures and methods.

plot_surrogates(surrogates[, data_value, ...])

Plot a distribution of surrogate data.

Data

Basic plot function for different data types.

plot_lines(x_values, y_values[, vline, ax])	Plot data as a line.
plot_scatter(x_values, y_values[, ax])	Plot 2d data as a scatter plot.
plot_points(data[, label, ax])	Plot 1d data as points.
plot_hist(data[, bins, range, density, ...])	Plot data as a histogram.
plot_bar(data[, labels, add_text, ax])	Plot data in a bar graph.
plot_barh(data[, labels, add_text, ax])	Plot a horizontal bar plot.
plot_polar_hist(data[, bin_width, ax])	Plot a polar histogram.
plot_text(text[, xpos, ypos, show_axis, ax])	Plot text.

Annotate

Helper functions to annotate plot axes.

color_pvalue(p_value[, alpha, ...])	Select a color based on the significance of a p-value.
add_vlines(vline[, ax])	Add vertical line(s) to a plot axis.
add_hlines(hline[, ax])	Add horizontal line(s) to a plot axis.
add_gridlines(x_bins, y_bins[, ax])	Add gridlines to a plot axis.
add_vshades(vshades[, ax])	Add vertical shade region(s) to a plot axis.
add_hshades(hshades[, ax])	Add horizontal shade region(s) to a plot axis.
add_box_shade(x1, x2, y_val[, y_range, ax])	Add a shaded box to a plot axis.
add_box_shades(x_values[, y_values, ...])	Add multiple shaded boxes to a plot axis.
add_dots(dots[, ax])	Add dots to a plot axis.
add_significance(stats[, sig_level, x_vals, ax])	Add markers to a plot axis to indicate statistical significance.
add_text_labels(texts[, location, axis, ...])	Add text to the side of a plot.

Style

Helper functions for managing plot style.

drop_spines(sides[, ax])	Drop spines from a plot axis.
invert_axes(invert[, ax])	Invert plot axes.

Utils

Plot related utilities.

check_ax(ax[, figsize, return_current])	Check whether a figure axes object is defined, and define or return current axis if not.
save_figure(file_name[, file_path, close])	Save out a figure.
make_axes(n_axes[, n_cols, figsize, ...])	Make a subplot with multiple axes.
make_grid(nrows, ncols[, title])	Create a plot grid.
get_grid_subplot(grid, row, col, **plt_kwargs)	Get a subplot section from a grid layout.

Utilities

Utility functions, in the utils sub-module.

Base

General utilities for basic data types.

flatten(lst)	Flatten a list of lists into a single list.
lower_list(lst)	Convert all strings in a list to lowercase.
select_from_list(lst, select)	Select elements from a list based on a boolean mask.
count_elements(data[, labels, sort])	Count elements within a collection object.
combine_dicts(dicts)	Combine a list of dictionaries together.
add_key_prefix(indict, prefix)	Update keys of a dictionary by appending a prefix.
drop_key_prefix(indict, prefix)	Update keys of a dictionary by dropping a prefix.
relabel_keys(indict, new_keys)	Relabel keys in a dictionary.
subset_dict(indict, label)	Subset a dictionary based on key labels.
check_keys(indict, lst)	Check a dictionary for a set of keys.
listify(param[, index])	Check and embed a parameter into a list, if is not already in a list.

Data

Utilities for working with arrays of data.

make_orientation(arr, to_orientation[, ...])	Check and make sure a 2d array is in a specified orientation.
compute_range(data)	Compute the range of an array of data.
smooth_data(data, sigma)	Smooth an array of data, using a gaussian kernel.
drop_nans(data)	Drop any NaNs values from an array.
permute_vector(data[, n_permutations])	Create permutations of a vector of data.
assign_data_to_bins(data, edges[, ...])	Assign data values to data bins, based on given edges.

Extract

Utilities for extracting data segments of interest.

create_mask(data[, min_value, max_value])	Create a mask to select data points based on value.
create_nan_mask(data)	Create a mask for non-nan array elements.
select_from_arrays(mask, *arrays)	Select the same set of elements from a collection of arrays.
get_range(data[, min_value, max_value, reset])	Get a specified range from a vector of data.
get_value_range(timestamps, data[, ...])	Extract data and associated timestamps for a desired value range.
get_ind_by_value(values, value[, threshold])	Get the index for a set of values closest to a specified value.
get_inds_by_values(values, select[, ...])	Get indices for a set of specified time points.
get_ind_by_time(timestamps, timepoint[, ...])	Get the index for a set of timepoints closest to a specified timepoint.
get_inds_by_times(timestamps, timepoints[, ...])	Get indices for a set of specified time points.
get_value_by_time(timestamps, values, timepoint)	Get the value from a data array at a specific time point.
get_values_by_times(timestamps, values, ...)	Get values from a data array for a set of specified time points.
get_values_by_time_range(timestamps, values, ...)	Extract data for a requested time range.
threshold_spikes_by_times(spikes, ...)	Threshold spikes by sub-selecting those that are temporally close to a set of time values.
threshold_spikes_by_values(spikes, ...[, ...])	Threshold spikes by sub-selecting based on thresholding values on another data stream.
drop_range(spikes, time_range[, check_empty])	Drop a specified time range from a vector spike times.
reinstate_range(spikes, time_range)	Reinstate a dropped time range into an array of spike times.

Timestamps

Utilities for working with timestamps.

compute_sample_durations(timestamps[, ...])	Compute the time duration of each sample.
infer_time_unit(time_values)	Infer the time unit of given time values.
convert_ms_to_sec(ms)	Convert time value(s) from milliseconds to seconds.
convert_sec_to_min(sec)	Convert time value(s) from seconds to minutes.
convert_min_to_hour(mins)	Convert time value(s) from minutes to hours.
convert_ms_to_min(ms)	Convert time value(s) from milliseconds to minutes.
convert_nsamples_to_time(n_samples, fs)	Convert a number of samples into the corresponding time length.
convert_time_to_nsamples(time, fs)	Convert a time length into the corresponding number of samples.
sum_time_ranges(ranges)	Sum the total amount of time defined by time range(s).
create_bin_times(bins)	Create a timepoints definition for a set of time bins.
split_time_value(sec)	Split a time value from seconds to hours / minutes / seconds.
format_time_string(hours, minutes, seconds)	Format a time value into a string.

Epoch

Utilities for epoching data.

epoch_spikes_by_event(spikes, events, window)	Epoch spiking data into trials, based on events of interest.
epoch_spikes_by_range(spikes, start_times, ...)	Epoch spiking data into trials, based on time ranges of interest.
epoch_spikes_by_segment(spikes, segments)	Epoch spikes by segments.
epoch_data_by_time(timestamps, values, ...)	Epoch data into trials, based on individual timepoints of interest.
epoch_data_by_event(timestamps, values, ...)	Epoch data into trials, based on events of interest.
epoch_data_by_range(timestamps, values, ...)	Epoch data into trials, based on time ranges of interest.
epoch_data_by_segment(timestamps, values, ...)	Epoch data by segments.

Trials

Utilities for working with trial-level data.

split_trials_by_condition(trials, conditions)	Split trial data by condition label.
split_trials_by_condition_list(trials, ...)	Split trial data by condition label, for trial data as a list.
split_trials_by_condition_array(trials, ...)	Split trial data by condition label, for trial data as an array.
recombine_trial_data(times_trials, values_trials)	Recombine data across trials.

Run

Utilities for helping with running analyses.

create_methods_list(methods)

Create a list of all methods.

Checks

Utilities to check for basic properties of data / inputs.

check_param_range(param, label, bounds)	Check a parameter value is within an acceptable range.
check_param_options(param, label, options[, ...])	Check a parameter value is one of the acceptable options.
check_param_lengths(params, labels[, ...])	Check that a set of parameters have the same length.
check_param_type(param, label, types)	Check that a parameter has an acceptable type.
check_list_options(contents, label, options)	Check a list of values that each element is one of a set of acceptable options.
check_array_orientation(arr[, expected])	Check the orientation of an array of data.
check_array_lst_orientation(arr_lst[, expected])	Check the orientation of arrays in a list.
check_axis(axis, arr)	Check axis argument, and infer from array orientation if not defined.
check_bin_range(values, bin_area)	Checks data values against given bin edges, warning if values exceed bin range.
check_time_bins(bins[, time_range, values, ...])	Check a given time bin definition, and define if only given a time resolution.

Random

Utilities for managing random state.

set_random_seed([seed_value])	Set the random seed value.
set_random_state([seed_value])	Set the random state in a RandomState object.