allensdk.brain_observatory.stimulus_analysis module

class allensdk.brain_observatory.stimulus_analysis.StimulusAnalysis(data_set)[source]

Bases: object

Base class for all response analysis code. Subclasses are responsible for computing metrics and traces relevant to a particular stimulus. The base class contains methods for organizing sweep responses row of a stimulus stable (get_sweep_response). Subclasses implement the get_response method, computes the mean sweep response to all sweeps for a each stimulus condition.

Parameters:
data_set: BrainObservatoryNwbDataSet instance
speed_tuning: boolean, deprecated

Whether or not to compute speed tuning histograms

property acquisition_rate
property binned_cells_sp
property binned_cells_vis
property binned_dx_sp
property binned_dx_vis
property cell_id
property celltraces
property dfftraces
property dxcm
property dxtime
get_fluorescence()[source]
get_peak()[source]

Implemented by subclasses.

get_response()[source]

Implemented by subclasses.

get_speed_tuning(binsize)[source]

Calculates speed tuning, spontaneous versus visually driven. The return is a 5-tuple of speed and dF/F histograms.

binned_dx_sp: (bins,2) np.ndarray of running speeds binned during spontaneous activity stimulus. The first bin contains all speeds below 1 cm/s. Dimension 0 is mean running speed in the bin. Dimension 1 is the standard error of the mean.

binned_cells_sp: (bins,2) np.ndarray of fluorescence during spontaneous activity stimulus. First bin contains all data for speeds below 1 cm/s. Dimension 0 is mean fluorescence in the bin. Dimension 1 is the standard error of the mean.

binned_dx_vis: (bins,2) np.ndarray of running speeds outside of spontaneous activity stimulus. The first bin contains all speeds below 1 cm/s. Dimension 0 is mean running speed in the bin. Dimension 1 is the standard error of the mean.

binned_cells_vis: np.ndarray of fluorescence outside of spontaneous activity stimulu. First bin contains all data for speeds below 1 cm/s. Dimension 0 is mean fluorescence in the bin. Dimension 1 is the standard error of the mean.

peak_run: pd.DataFrame of speed-related properties of a cell.

Returns:
tuple: binned_dx_sp, binned_cells_sp, binned_dx_vis,
binned_cells_vis, peak_run
get_sweep_response()[source]

Calculates the response to each sweep in the stimulus table for each cell and the mean response. The return is a 3-tuple of:

  • sweep_response: pd.DataFrame of response dF/F traces organized

by cell (column) and sweep (row)

  • mean_sweep_response: mean values of the traces returned in

sweep_response

  • pval: p value from 1-way ANOVA comparing response during sweep

to response prior to sweep

Returns:
3-tuple: sweep_response, mean_sweep_response, pval
property mean_sweep_response
property numbercells
property peak
property peak_run
plot_representational_similarity(repsim, stimulus=False)[source]
plot_running_speed_histogram(xlim=None, nbins=None)[source]
plot_speed_tuning(cell_specimen_id=None, cell_index=None, evoked_color='#b30000', spontaneous_color='#0000b3')[source]
populate_stimulus_table()[source]

Implemented by subclasses.

property pval
property response
property roi_id
row_from_cell_id(csid=None, idx=None)[source]
property stim_table
property sweep_response
property timestamps
allensdk.brain_observatory.stimulus_analysis.nonraising_ks_2samp(data1, data2, **kwargs)[source]

scipy.stats.ks_2samp now raises a ValueError if one of the input arrays is of length 0. Previously it signaled this case by returning nans. This function restores the prior behavior.