- !!! Windows and the forward slash... :\
- !!! Clean up the neurotransmitter table.
- !!! Make sure all citations are correct: neurotransmitter citations are currently incomplete.
- !!! Add reading and writing to and from NWB format. Add that to the paper also.
- !!! Write about csv/json integration.
- !!! Add note section to each class. Add data_source (publication + location) from where data is taken in a note section.
- !!! Redo the neuropeptide receptor connectivity. It has been redone in the git repo for the paper.
- !! An adaptor connecting the CATMAID API directly into CeDNe objects.
- !!! Integrate behavioral components. Since there are limited behavioral outputs for the worm, having a framework for each might be useful.
- !! Other graph elements (Map another graph to an object)
- ! Mapping functions.
- !! Write down paths from one neuron to another at different levels of depth (direct, 1 path away, etc.)
- !!! Formalizing connecting the StimResponse and Trial classes.
- !!! Jax based optimization.
- !!! Latin hypercube sampling for hyperparameter optimization.
- !! Easy Loading functions for loading custom data.
- !! Make movies for time series data.
- !! Export graph for plotting elsewhere.
- !! Dealing with missing (MISS), null (NOT) and alternative values (OR and AND) values for a given attribute.
- !!! Run the neuropeptide example with the L4 connectivity.
- !!! Example on Louvain modularity and structural analysis.
- !!! Neurotransmitter check if paper uses connectivity information and do a shuffle to see correlation between connectivity and neurotransmitter identity.
- !!! Example for gap junction subunits and check with Bhattacharya et al paper.
- !!! Time series make movies.
- ! HDF5 storage of data.
- !! neo4j+json+hdf5 for data persistance instead of pickles. neo4j for storing relationships, json for metadata and hdf5 for large matrices.
- !! Pickle warning
- ! Add some tests for sanitizing the pickle files
- !! Plugin development
- !! Dataset uploads
- !! Contribution guidelines
- !! Evolving models framework (as a network): Setup for root models and submodels, etc.