Add generalizable introspectors

[evamaxfield]

Generalizable Introspectors

Allow custom validators and parsers for any python object to Dataset object. This would allow for any data type to be consumed and processed by DSDB. Meaning, if a person wants to track a single object as it is process instead of a dataframe like dataset you can.

Example: Dataset(pytorch_model), allows for tracking of epochs/ cycles. Introspector could then read the state of the model to create Iota based off it's attributes.

Default: assumed DataFrame like.

introspectors.init

from .object import ObjectIntrospector
import dataframe
import ometif

introspector_map = {
   pd.DataFrame = dataframe.DataFrameIntrospector,
   OmeTifReader = ometif.OmeTifIntrospector
}

core

from .introspectors import introspector_map, ObjectIntrospector

class Dataset(object):
   def __init__(self, obj):
      try:
         self._introspector = introspector_map[type(obj)]
      except KeyError:
         self._introspector = ObjectIntrospector

      self._validated = self.introspector.validated

   def validate(self):
      self.introspector.validate()

   def generate(self):
      ds_id_map = self.introspector.generate()

[evamaxfield]

General model is complete so closing the issue.

Custom structures must have the following:

__init__(self, obj):
# initialize the basic parameters of your introspector
# at a minimum requires `_obj` and `_validated`.
self._obj = obj
self._validated = False

@property
def obj(self):
# so users cannot edit the object

@property
def validated(self):
# so users cannot edit the validated props

def validate(self):
# single call to validate that can call other validation methods

def deconstruct(self):
# take object and create Iota, Group, and IotaGroup lists

def reconstruct(self):
# take lists of Iota, Group, and IotaGroup and create object

def package(self):
# take object and form into Quilt package