An easy-to-use data library for developing mathematical engines
ticdat is an easy-to-use, lightweight, relational, data library. It provides a simple interface for defining a data schema, and a factory class for creating TicDat data objects that confirm to this schema.
It is primarily intended to simplify the process of developing proof-of-concept mathematical engines that read from one schema and write to another. It provides easy routines for reading and/or writing an entire data set for a range of stand-alone file types (Excel, .csv, Access or SQLite). For Access or SQLite, it can be used as a very condensed representation of the database schema.
For archiving test suites, ticdat is a useful way to convert data instances into .sql text files that can be archived in source code control systems.
When primary keys are specified, each table is a dictionary of dictionaries. Otherwise, each table is an enumerable of dictionaries. The inner dictionaries are data rows indexed by field names (as in csv.DictReader/csv.DictWriter).
When default values are provided, unfrozen TicDat objects will use them during the addition of new rows. In general, unfrozen TicDat data tables behave like a defaultdict. There are a variety of other overrides to facilitate the addition of new data rows.
Alternately, TicDat data objects can be frozen. This facilitates good software development by insuring that code that is supposed to read from a data set without editing it behaves properly.
Finally, the “dict-of-dicts” representation of a table can be eschewed entirely in favor of pandas.DataFrame. In this case, ticdat can be used as a shim library that facilitates schema level definitions and query abstraction for pandas developers.
Although ticdat was specifically designed with Mixed Integer Programming data sets in mind, it can be used for rapidly developing a wide variety of mathematical engines. It facilitates creating one definition of your input data schema and one solve module, and reusing this same code, unchanged, on data from different sources. This “separation of model from data” enables a user to move easily from small, testing data sets to larger, more realistic examples. In addition, Opalytics Inc (the developer of ticdat) can support cloud deployments of solve engines that use ticdat data objects.