NWB File I/O

Initialize

• NWB File Member

  • Acquisition

    • TwoPhotonSeries

  • Device object

  • Imaging plane

    • Optical Channel

    • Device object

Suite2p

suite2p_file_convert

NWB Output

No access to NWB file

Function Output

info = { ‘meanImg’: ImageData(ops[‘meanImg’], file_name=’meanImg’), ‘ops’: Suite2pData(ops, file_name=’ops’) }

suite2p_registration

NWB Output

No access to NWB file

Function Output

info = { ‘refImg’: ImageData(ops[‘refImg’], file_name=’refImg’), ‘meanImgE’: ImageData(ops[‘meanImgE’], file_name=’meanImgE’), ‘ops’: Suite2pData(ops, file_name=’ops’), }

suite2p_roi

NWB Output

• ophys

  • ImageSegmentation

    • PlaneSegmentation

      • pixel_mask

      • columns: ‘iscell’

        • [description]: ‘two columns - iscell & probcell’

        • data

  • Fluorescence

    • Fluorescence

      • data:F

      • rois

  • Neuropil

    • Neuropil

      • data:F_neu

      • rois

Function Output

info = { ‘ops’: Suite2pData(ops), ‘max_proj’: ImageData(ops[‘max_proj’], file_name=’max_proj’), ‘Vcorr’: ImageData(ops[‘Vcorr’], file_name=’Vcorr’), ‘fluorescence’: FluoData(F, file_name=’fluorescence’), ‘iscell’: IscellData(iscell, file_name=’iscell’), ‘all_roi’: RoiData(np.nanmax(im, axis=0), file_name=’all_roi’), ‘non_cell_roi’: RoiData(np.nanmax(im[~iscell], axis=0), file_name=’noncell_roi’), ‘cell_roi’: RoiData(np.nanmax(im[iscell], axis=0), file_name=’cell_roi’), ‘nwbfile’: nwbfile, }

suite2p_spike_deconv

NWB Output

• ophys

  • ImageSegmentation

    • PlaneSegmentation

      • [pixel_mask](https://suite2p.readthedocs.io/en/latest/api/suite2p.extraction.h

  • Deconvolved

    • Deconvolved

      • data

Function Output

info = { ‘ops’: Suite2pData(ops), ‘spks’: FluoData(spks, file_name=’spks’), ‘nwbfile’: nwbfile }

CaImAn

caiman_mc

NWB Output

• CorrectedImageStack

  • corrected

    • external_file

    • format: ‘external’

  • xy_translation

    • data

  • [original] : Acquisition

Function Output

info = { ‘mc_images’: mc_images, ‘meanImg’: ImageData(meanImg, file_name=’meanImg’), ‘rois’: RoiData(rois, file_name=’rois’), ‘nwbfile’: nwbfile, }

caiman_cnmf

NWB Output

• ophys

  • ImageSegmentation

    • PlaneSegmentation

      • image_mask

      • columns: ‘iscell’

        • [description]: ‘two columns - iscell & probcell’

        • data

  • RoiResponseSeries

    • RoiResponseSeries

      • data: estimates.C

      • rois

  • Background_Fluorescence_Response

    • Background_Fluorescence_Response

      • data: estimates.f

      • rois

Function Output

info = { ‘images’: ImageData(np.array(Cn * 255, dtype=np.uint8), file_name=’images’), ‘fluorescence’: FluoData(fluorescence, file_name=’fluorescence’), ‘iscell’: IscellData(iscell, file_name=’iscell’), ‘all_roi’: RoiData(all_roi, file_name=’all_roi’), ‘cell_roi’: RoiData(cell_roi, file_name=’cell_roi’), ‘non_cell_roi’: RoiData(non_cell_roi, file_name=’non_cell_roi’), ‘nwbfile’: nwbfile }

LCCD

lccd_detect

NWB Output

No access to NWB file

Function Output

info = { ‘rois’: RoiData(np.nanmax(roi_list, axis=0), file_name=’cell_roi’), ‘fluorescence’: FluoData(timeseries, file_name=’fluorescence’), ‘dff’: FluoData(timeseries_dff, file_name=’dff’), }

Edit ROI

NWB Output

No access to NWB file

Function Output

save_json_data(ops, im, save_path=node_dirpath, save_data=[‘ops’, ‘fluorescence’, ‘all_roi’, ‘non_cell_roi’, ‘cell_roi’, ‘nwbfile’] )

Add ROI

NWB Output

• optinist

  • add_roi

  • delete_roi

  • merge_roi

Function Output

save_json_data(ops, im, save_path=node_dirpath, save_data=[‘ops’, ‘fluorescence’, ‘all_roi’, ‘non_cell_roi’, ‘cell_roi’, ‘nwbfile’])

Delete ROI

NWB Output

• optinist

  • add_roi

  • delete_roi

  • merge_roi

Function Output

save_json_data(ops, im, save_path=node_dirpath, save_data=[‘ops’, ‘fluorescence’, ‘all_roi’, ‘non_cell_roi’, ‘cell_roi’, ‘nwbfile’])

Merge ROI

NWB Output

• optinist

  • add_roi

  • delete_roi

  • merge_roi

Function Output

save_json_data(ops, im, save_path=node_dirpath, save_data=[‘ops’, ‘fluorescence’, ‘all_roi’, ‘non_cell_roi’, ‘cell_roi’], )

OptiNiSt

Basic Neural Analysis

eta

NWB Output

• optinist

  • mean

  • sem

  • num_sample

Function Output

info[‘mean’] = TimeSeriesData( mean, std=sem, index=list(np.arange(params[‘pre_event’], params[‘post_event’])), cell_numbers=cell_numbers if iscell is not None else None, file_name=’mean’ ) info[‘mean_heatmap’] = HeatMapData( norm_mean, columns=list(np.arange(params[‘pre_event’], params[‘post_event’])), file_name=’mean_heatmap’ ) info[‘nwbfile’] = nwbfile

Dimension Reduction

cca

NWB Output

• optinist

  • projectedNd

  • x_weights

  • y_weights

  • x_loadings_

  • y_loadings_

  • coef

  • n_iter_

Function Output

info = { ‘projectedNd’: ScatterData(proj, file_name=’projectedNd’), ‘coef’: BarData(cca.coef_.flatten(), file_name=’coef’), ‘nwbfile’: nwbfile, }

pca

NWB Output

• optinist

  • pca_projectedNd

  • components

  • explained_variance

  • explained_variance_ratio

  • singular_values

  • mean

  • n_components

  • n_samples

  • noise_variance

  • n_features_in

Function Output

info = { ‘explained_variance’: BarData(pca.explained_variance_ratio_, file_name=’evr’), ‘projectedNd’: ScatterData(proj_X, file_name=’projectedNd’), ‘contribution’: BarData( pca.components_, index=[f’pca: {i}’ for i in range(len(pca.components_))], file_name=’contribution’ ), ‘cumsum_contribution’: BarData( np.cumsum(pca.components_, axis=0), index=[f’pca: {i}’ for i in range(len(pca.components_))], file_name=’cumsum_contribution’ ), ‘nwbfile’: nwbfile, }

tsne

NWB Output

• optinist

  • projectedNd

Function Output

info = { ‘projectedNd’: ScatterData(proj_X, file_name=’projectedNd’), ‘nwbfile’: nwbfile, }

Neural Decoding

glm

NWB Output

• optinist

  • actual_predicted

  • params

  • pvalues

  • tvalues

  • aic

  • bic_llf

  • llf

  • pearson_chi2

  • df_model

  • df_resid

  • scale

  • mu

  • endog

Function Output

info = { ‘actual_predicted’: ScatterData( np.array([Res._endog, Res.mu]).transpose(), file_name=’actual_predicted’ ), ‘params’: BarData(Res.params.values, file_name=’params’), ‘textout’: HTMLData(Res.summary().as_html(), file_name=’textout’), ‘nwbfile’: nwbfile, }

lda

NWB Output

• optinist

  • score

Function Output

info = { ‘score’: BarData(score, file_name=’score’), ‘nwbfile’: nwbfile }

svm

NWB Output

• optinist

  • score

Function Output

info = { ‘score’: BarData(score, file_name=’score’), ‘nwbfile’: nwbfile }

Neural Population Analysis

correlation

NWB Output

• optinist

  • corr

Function Output

info = { ‘corr’: HeatMapData(corr, file_name=’corr’), ‘nwbfile’: nwbfile, }

cross_correlation

NWB Output

• optinist

  • mat

  • baseline

  • base_confint

Function Output

info = { ‘nwbfile’: nwbfile } info[name] = TimeSeriesData(arr1.T, file_name=name) info[name] = TimeSeriesData(arr2.T, file_name=name)

granger

NWB Output

• optinist

  • Granger_fval_mat

  • gc_combinations

  • gc_ssr_ftest

  • gc_ssr_chi2test

  • gc_lrtest

  • gc_params_ftest

  • cit_pvalue

  • adf_pvalue

Function Output

info[‘Granger_fval_mat_heatmap’] = HeatMapData( GC[‘Granger_fval_mat’][0], file_name=’gfm_heatmap’ ) info[‘Granger_fval_mat_scatter’] = ScatterData( GC[‘Granger_fval_mat’][0], file_name=’gfm’ ) info[‘nwbfile’] = nwbfile