Input and output

Input 

`settings.json`

See how to prepare it here.

See how to adjust the settings here.

Output 

The output will be saved in the output_folder specified in settings.json, which are mostly .npy and .npz files.

Results 

This part is the main output of pyDANT, which contains the estimated cluster IDs for each unit across sessions.

IdxCluster.npy assigns a unique cluster ID for each unit (-1 for non-matched units). You can use it to extract the matched units across sessions.

ClusterMatrix.npy is a cluster assignment matrix. ClusterMatrix(i,j) = 1 means unit i and j are in the same cluster.

MatchedPairs.npy contains the unit index for all matched pairs.

RunTimeSec.npy contains the total run time in seconds.

Output.npz is a compressed file that contains all the above files and some additional information, such as the number of units, sessions, and clusters. It is designed to mimic the Output.mat file which is the main output of the MATLAB version of DANT.

Features 

The feature-related files include auto_corr.npy, isi.npy, peth.npy. These files store the computed features for each unit, which are used in the motion estimation and clustering processes.

Waveforms 

If you choose to center the waveforms, the centered waveforms will be saved in waveforms_corrected.npy. And the corrected waveforms will be saved in waveforms_corrected.npy.

Spike location 

The estimated locations for each unit will be saved in locations.npy, with the amplitudes and the peak channels saved in amplitude.npy and peak_channels.npy, respectively.

Similarity matrix 

Four similarity matrices will be saved to waveform_similarity_matrix.npy, ISI_similarity_matrix.npy, AutoCorr_similarity_matrix.npy and PETH_similarity_matrix.npy. If some features are not used, the corresponding matrices will be filled with zeros. The final weighted similarity matrix will be saved in SimilarityMatrix.npy.

Motion correction 

The estimated probe motion will be saved in motion_linear_scale.npy, motion_linear.npy and motion_constant.npy, which can be loaded via Motion.load(). If rigid motion correction is used, only motion_constant.npy is meaningful. See here for details about non-rigid motion correction.

Clustering 

The clustering-related files include DistanceMatrix.npy, SimilarityPairs.npy, SimilarityThreshold.npy and SimilarityWeights.npy. These files store the intermediate results of the final clustering process after motion correction. The DistanceMatrix.npy contains the pairwise distances between units. The SimilarityPairs.npy contains the unit index for each pair of units within the max_distance. See Clustering for details about the meaning of these files.

ClusteringResults.npz is a compressed file that contains the results of the final clustering process after motion correction, which includes more details about the clustering process, like the clustering results before curation.

Curation 

The curation-related information will be saved in CurationTypeNames.npy, CurationTypes.npy, and CurationPairs.npy. It stores the deleted unit pairs and why they are deleted (types). See curation for details.