axel-lab-to-nwb

NWB conversion scripts and tutorials. A collaboration with Axel Lab.

Install

To clone the repository and set up a conda environment, do:

$ git clone https://github.com/ben-dichter-consulting/axel-lab-to-nwb.git
$ conda env create -f axel-lab-to-nwb/make_env.yml
$ source activate convert_to_nwb

Alternatively, to install directly in an existing environment:

$ pip install axel-lab-to-nwb

Use

After activating the correct environment, the conversion function can be used in different forms:

1. Imported and run from a python script:
Here's an example: we'll grab the data from the same experiment but stored in different .npz and .mat files and save it to a single .nwb file.

from axel_lab_to_nwb import conversion_function
import yaml

# Nwb file
f_nwb = 'output.nwb'

# Source files
source_paths = {}
source_paths['raw data'] = {'type': 'file', 'path': PATH_TO_FILE}
source_paths['raw info'] = {'type': 'file', 'path': PATH_TO_FILE}
source_paths['processed data'] = {'type': 'file', 'path': PATH_TO_FILE}
source_paths['sparse matrix'] = {'type': 'file', 'path': PATH_TO_FILE}
source_paths['ref image'] = {'type': 'file', 'path': PATH_TO_FILE}

# Load metadata from YAML file
metafile = 'metafile.yml'
with open(metafile) as f:
   metadata = yaml.safe_load(f)

# Other options
kwargs = {
   'raw': False, 
   'processed': True, 
   'behavior': True
   'plot_rois': False
}

conversion_function(source_paths=source_paths,
                   f_nwb=f_nwb,
                   metadata=metadata,
                   **kwargs)

2. Command line:
Similarly, the conversion function can be called from the command line in terminal:

$ python conversion_module.py [processed_data_file] [sparse_matrix_file] [ref_image_file]
  [output_file] [metadata_file]

3. Graphical User Interface:
To use the GUI, just run the auxiliary function nwb_gui.py from terminal:

$ python nwb_gui.py

The GUI eases the task of editing the metadata of the resulting .nwb file, it is integrated with the conversion module (conversion on-click) and allows for visually exploring the data in the end file with nwb-jupyter-widgets.

4. Tutorial:
At tutorials you can also find Jupyter notebooks with the step-by-step process of conversion.