feardameow / undaqtools

Unofficial NADS DAQ Toolkit

undaqTools [uhn-dak-tools]

undaqTools is the unofficial pythonic interface to the National Advanced Driving Simulator (NADS) Data AcQuisition (DAQ) files (i.e. NADS miniSim (TM) datafiles).

undaqTools (unofficial) vs. ndaqTools (official)

Approach:

ndaqTools focuses on ease-of-use (via GUIs) and templating. In contrast, the focus of undaqTools is on data abstraction and simplification of data extraction.

Programming Language:

ndaqTools is built on MATLAB. Whereas, undaqTools utilizes the opensource Python programming language (as well as Python's opensource scientific stack (NumPy, SciPy, Matplotlib, and h5py).

Data Storage:

ndaqTools stores and loads DAQ data as .mat files. undaqTools stores and loads data in HDF5 containers.

Summary

undaqTools converts the native DAQ data into the HDF5 high performance binary data format. From there data can be quickly accessed in Python using the Daq class of undaqTools. Daq objects are dict subclasses. The keys are the elements of the daq and the values are stored in a numpy array subclass called Element. Each Element contain measure relevent metadata stored as instance attributes so these metadata does not have to be accessed across datastructures.

Each Element object also has a frames attribute that keeps track of the frames cooresponding to the element. This means that when they are sliced the resulting views keeps the frames in sync without any additional book-keeping to the end user! The element data can also be sliced and indexed using frame indices. These abstractions simplify the complexity of managing these complex datasets (see class hierarchy diagram below).

undaqTools can also unpack and process dynamic objects. Each dynamic object becomes a DynObj instance in the Daq's dynobj dict attribute. The keys to the dynamic objects are the dynamic object names. The DynObj containers store the measure that you would expect (position, frames, speed, heading, etc.) as numpy.array objects as well as a running relative distance measure to the Own Vehicle.

These abstractions from the original DAQ representation are intended to provide the end user the convenience and capability to decide how to best utilize their data.

Simplified Class Hierarchy Diagram:

Daq(dict)
  + [elemName] -> Element (ndarray)
  |                 + data
  |                 + frames (ndarray)
  |                 + rate
  |                 + type
  |                 + ...
  |               --------------------
  |                 + isCSSDC()
  |
  + dynobjs (dict)
  |   + [doName] -> DynObj (object)
  |                   + pos (Element)
  |                   + heading (Element)
  |                   + speed (Element)
  |                   + ...
  |                 ---------------------
  |                   + process()
  |                   + read_hd5()
  |                   + write_hd5()
  |                   + ...
  |
  + info (numedTuple)
  |   + run
  |   + subject
  |   + date
  |   + ...
  |
  | + etc (dict)
  | + ...
--------------------------------------------
  | + read_daq() <==> read()
  | + read_hd5py()
  | + write_hd5py()
  | + write_mat()
  | ...

Source

https://github.com/rogerlew/undaqTools

Release

https://pypi.python.org/pypi/undaqTools/

Documentation

http://pythonhosted.org/undaqTools/

Acknowledgments

Daq.read_daq() method based on Chris Schwarz and others of NADS convert_daq.py script.