Plotting

NeuroVis

class spykes.plot.neurovis.NeuroVis(spiketimes, name='neuron')

This class is used to visualize firing activity of single neurons.

This class implements several conveniences for visualizing firing activity of single neurons.

Parameters:

• spiketimes (Numpy array) – Array of spike times.
• name (str) – The name of the visualization.

get_psth(event=None, df=None, conditions=None, cond_id=None, window=[-100, 500], binsize=10, plot=True, event_name=None, conditions_names=None, ylim=None, colors=['#F5A21E', '#134B64', '#EF3E34', '#02A68E', '#FF07CD'])

Compute the PSTH and plot it.

Parameters:

• event (str) – Column/key name of DataFrame/dictionary data which contains event times in milliseconds (e.g. stimulus/trial/fixation onset, etc.)
• conditions (str) – Column/key name of DataFrame/dictionary data which contains the conditions by which the trials must be grouped.
• cond_id (list) – Which psth to plot indicated by the key in all_psth['data']`. If None then all are plotted.
• df (DataFrame or dictionary) – The dataframe containing the data.
• window (list of 2 elements) – Time interval to consider, in milliseconds.
• binsize (int) – Bin size in milliseconds.
• plot (bool) – If True then plot.
• event_name (string) – Legend name for event. Default is the actual event name
• conditions_names (TODO) – Legend names for conditions. Default are the unique values in df['conditions'].
• ylim (list) – The lower and upper limits for Y.
• colors (list) – The colors for the plot.

Returns:

rasters with keys event, conditions, binsize, window, and data. rasters['data'] is a dictionary where each value is a raster for each unique entry of df['conditions'].

Return type:

dict

get_raster(event=None, conditions=None, df=None, window=[-100, 500], binsize=10, plot=True, sortby=None, sortorder='descend')

Computes the raster and plots it.

Parameters:

• event (str) – Column/key name of DataFrame/dictionary “data” which contains event times in milliseconds (e.g. stimulus/trial/fixation onset, etc.)
• conditions (str) – Column/key name of DataFrame/dictionary data which contains the conditions by which the trials must be grouped.
• df (DataFrame or dictionary) – The dataframe containing the data, or a dictionary with the equivalent structure.
• window (list of 2 elements) – Time interval to consider, in milliseconds.
• binsize (int) – Bin size in milliseconds
• plot (bool) – If True then plot
• sortby (str or list) – If rate, sort by firing rate. If latency, sort by peak latency. If a list, integers to be used as sorting indices.
• sortorder (str) – Direction to sort, either descend or ascend.

Returns:

rasters with keys event, conditions, binsize, window, and data. rasters['data'] is a dictionary where each value is a raster for each unique entry of df['conditions'].

Return type:

dict

get_spikecounts(event=None, df=None, window=array([ 50., 100.]))

Counts spikes in the dataframe.

Parameters:

• event (str) – Column/key name of DataFrame/dictionary data which contains event times in milliseconds (e.g. stimulus/trial/fixation onset, etc.)
• window (list of 2 elements) – Time interval to consider, in milliseconds.

Returns:

An n x 1 array of spike counts.

Return type:

array

plot_psth(psth, event_name='event_onset', conditions_names=None, cond_id=None, ylim=None, colors=['#F5A21E', '#134B64', '#EF3E34', '#02A68E', '#FF07CD'])

Plots PSTH.

Parameters:

• psth (dict) – Output of get_psth().
• event_name (string) – Legend name for event. Default is the actual event name.
• conditions_names (list of str) – Legend names for conditions. Default are the keys in psth['data'].
• cond_id (list) – Which psth to plot indicated by the key in all_psth['data']`. If None then all are plotted.
• ylim (list) – The lower and upper limits for Y.
• colors (list) – The colors for the plot.

plot_raster(rasters, cond_id=None, cond_name=None, sortby=None, sortorder='descend', cmap='Greys', has_title=True)

Plot a single raster.

Parameters:

• rasters (dict) – Output of get_raster method
• cond_id (str) – Which raster to plot indicated by the key in rasters['data']. If None then all are plotted.
• cond_name (str) – Name to appear in the title.
• sortby (str or list) – If rate, sort by firing rate. If latency, sort by peak latency. If a list, integers to be used as sorting indices.
• sortorder (str) – Direction to sort in, either descend or ascend.
• cmap (str) – Colormap for raster.
• has_title (bool) – If True then adds title.

PopVis

class spykes.plot.popvis.PopVis(neuron_list, name='PopVis')

Facilitates visualization of neuron population firing activity.

Parameters:neuron_list (list of NeuroVis objects) – The list of neurons to visualize (see the NeuroVis class in spykes.plot.neurovis).

n_neurons

int – The number of neurons in the visualization.

name

str – The name of this visualization.

get_all_psth(event=None, df=None, conditions=None, window=[-100, 500], binsize=10, conditions_names=None, plot=True, colors=['Blues', 'Reds', 'Greens'])

Iterates through all neurons and computes their PSTH’s.

Parameters:

• event (str) – Column/key name of DataFrame/dictionary “data” which contains event times in milliseconds (e.g. stimulus/trial/ fixation onset, etc.).
• df (DataFrame or dictionary) – The data to use.
• conditions (str) – Column/key name of DataFrame/dictionary df which contains the conditions by which the trials must be grouped
• window (list of 2 elements) – Time interval to consider, in milliseconds.
• binsize (int) – Bin size, in milliseconds.
• conditions_names (list of str) – Legend names for conditions. Default are the unique values in df['conditions'].
• plot (bool) – If set, automatically plot; otherwise, don’t.
• colors (list) – List of colors for heatmap (only if plot is True).

Returns:

With keys event, conditions, binsize, window, and data. Each entry in psth['data'] is itself a dictionary with keys of each cond_id that correspond to the means for that condition.

Return type:

dict

plot_heat_map(psth_dict, cond_id=None, conditions_names=None, sortby=None, sortorder='descend', normalize=None, neuron_names=True, colors=None, show=False)

Plots heat map for neuron population

Parameters:

• psth_dict (dict) – With keys event, conditions, binsize, window, and data. Each entry in psth['data'] is itself a dictionary with keys of each cond_id that correspond to the means for that condition.
• cond_id (str) – Which psth to plot indicated by the key in all_psth['data']. If None then all are plotted.
• conditions_names (str or list of str) – Name(s) to appear in the title.
• sortby (str or list) – If rate, sort by firing rate. If latency, sort by peak latency. If a list or array is provided, it must correspond to integer indices to be used as sorting indices. If no sort order is provided, the data is returned as-is.
• sortorder (str) – The direction to sort in, either descend or ascend.
• normalize (str) – If all, divide all PSTHs by highest peak firing rate in all neurons. If each, divide each PSTH by its own peak firing rate. If None, do not normalize.
• neuron_names (bool) – Whether or not to list the names of neurons on the side.
• colors (list of str) – List of colors for the heatmap (as strings).
• show (bool) – If set, show the plot once finished.

plot_population_psth(all_psth=None, event=None, df=None, conditions=None, cond_id=None, window=[-100, 500], binsize=10, conditions_names=None, event_name='event_onset', ylim=None, colors=['#F5A21E', '#134B64', '#EF3E34', '#02A68E', '#FF07CD'], show=False)

Plots population PSTH’s.

This involves two steps. First, it normalizes each neuron’s PSTH across the conditions. Second, it averages out and plots population PSTH.

Parameters:

• all_psth (dict) – With keys event, conditions, binsize, window, and data. Each entry in psth['data'] is itself a dictionary with keys of each cond_id that correspond to the means for that condition.
• event (str) – Column/key name of the df DataFrame/dictionary which contains event times in milliseconds (stimulus/trial/fixation onset, etc.).
• df (DataFrame or dictionary) – DataFrame containing the data to plot, or a dictionary corresponding to such a DataFrame.
• conditions (str) – Column/key name of DataFrame/dictionary df which contains the conditions by which the trials must be grouped.
• cond_id (list) – Which psth to plot indicated by the key in all_psth['data']`. If None then all are plotted.
• window (list of 2 elements) – Time interval to consider, in milliseconds.
• binsize (int) – Bin size, in milliseconds.
• conditions_names (list) – Legend names for conditions. Default are the unique values in df['conditions'].
• event_name (string) – Legend name for event. Default is the actual event name
• ylim (list) – The minimum and maximum values for Y.
• colors (list) – The colors to use for the plot (as strings).
• show (bool) – If set, show the plot once it has been created.