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Loaders of neurophysiological data into the DataJoint Elements

Project description

DataJoint Elements Interface for external analysis packages

  • This repository serves a few purposes:

    • Load neurophysiological data into the DataJoint Elements.
    • Trigger packages used for neurophysiological data processing.
    • Functions common to the DataJoint Elements (e.g. search directory tree for data files).
  • See DataJoint Elements for descriptions of the elements and workflows developed as part of this initiative.


  • The functions for each acquisition and analysis package are stored within a separate module.

  • Acquisition packages

  • Analysis packages



  • Install element-interface:

    pip install element-interface
  • This package is to be used in combination with the other DataJoint Elements (e.g. element-calcium-imaging). The installation of packages used for data processing (e.g. Suite2p) will be included within the respective DataJoint Element (e.g. element-calcium-imaging).


  • See the workflow-calcium-imaging and element-calcium-imaging repositories for example usage of element-interface.

  • ScanImage

    import scanreader
    from element_interface import scanimage_utils
    # ScanImage file path
    scan_filepath = '<imaging_root_data_dir>/subject1/session0/<scan_filename>.tif'
    loaded_scan = scanreader.read_scan(scan_filepath)
    recording_time = scanimage_utils.get_scanimage_acq_time(loaded_scan)
    header = scanimage_utils.parse_scanimage_header(loaded_scan)
  • Suite2p

    from element_interface import suite2p_loader
    # Directory containing Suite2p output
    output_dir = '<imaging_root_data_dir>/subject1/session0/suite2p'
    loaded_dataset = suite2p_loader.Suite2p(output_dir)
  • Suite2p wrapper functions for triggering analysis

    • Functions to independently run Suite2p's motion correction, segmentation, and deconvolution steps. These functions currently work for single plane tiff files. If running all Suite2p pre-processing steps concurrently, these functions are not required and one can run suite2p.run_s2p().

    • These wrapper functions were developed primarily because run_s2p cannot individually run deconvolution using the spikedetect flag (Suite2p Issue #718).

    • Requirements

    • Note that the ops dictionary returned from the motion_correction_suite2p and segmentation_suite2p functions is only a subset of the keys generated with the suite2p.default_ops() function.

      import element_interface
      import suite2p
      ops = dict(suite2p.default_ops(), nonrigid=False, two_step_registration=False)
      db = {
           'h5py': [], # single h5 file path
           'h5py_key': 'data',
           'look_one_level_down': False, # search for TIFFs in all subfolders 
           'data_path': ['/test_data'], # list of folders with tiffs                                    
           'subfolders': [], # choose subfolders of 'data_path'
           'fast-disk': '/test_data' # string path for storing binary file 
      ops.update(do_registration=1, roidetect=False, spikedetect=False)
      motion_correction_ops = element_interface.suite2p_trigger.motion_correction_suite2p(ops, db)
      motion_correction_ops.update(do_registration=0, roidetect=True, spikedetect=False)
      segmentation_ops = element_interface.suite2p_trigger.segmentation_suite2p(motion_correction_ops, db)
      segmentation_ops.update(do_registration=0, roidetect=False, spikedetect=True)
      spikes = element_interface.suite2p_trigger.deconvolution_suite2p(segmentation_ops, db)
  • CaImAn

    from element_interface import caiman_loader
    # Directory containing CaImAn output
    output_dir = '<imaging_root_data_dir>/subject1/session0/caiman'
    loaded_dataset = caiman_loader.CaImAn(output_dir)

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