* Essentially, the goal is to turn the **main** datasets into powerful python objects that obviate the need for users to dig into ancillary datasets to slice, understand the datasets. Pycroscopy chooses to use a rather generalized representation of data at the cost of simplictiy. This object should bring back the simplicity of accessing the data.
* In the process of enabling greater insight into a dataset, this class would read and analyze ancillary datasets once and reuse this knowledge when the user requests another operation (that most likely also requires references to ancillary datasets etc. anyway).
* Nearly all the functionality has been implemented in hdf_utils and some in io_utils. This class can simply reuse these general functions.
* Generic visualizer in plot.lly / dash? that can use the pycrodata class
* Simplify and demystify analyis / optimize. Use parallel_compute (joblib instead of multiprocessing)
* multi-node computing capability in parallel_compute
