DataProcessor

Description

This program is setup to process the raw data signals collected from the instrumented bicycle's data acquisition system (i.e. the output of BicycleDAQ)

Dependencies

These are the versions that I tested the code with, but the code will most likely work with other versions.

- Python 2.7.1__ .. __: http://www.python.org - Scipy 0.10.0__ .. __: http://www.scipy.org - Numpy 1.5.1__ .. __: http://numpy.scipy.org - Matplotlib 0.99.3__ .. __: http://matplotlib.sourceforge.net - PyTables 2.1.2__ .. __: http://www.pytables.org - BicycleParameters 0.1.3__ .. __: http://pypi.python.org/pypi/BicycleParameters - DynamicistToolKit 0.1.0dev__ .. __: https://github.com/moorepants/DynamicistToolKit

Usage

Load prebuilt database file

The simplest way to get started with the data is to download the latest database file from:

http://mae.ucdavis.edu/~biosport/InstrumentedBicycleData/InstrumentedBicycleData.h5.bz2

Uncompress the file into your BicycleDataProcessor directory, the file is ready for use. Open a python command prompt and import the module:

>>> import bicycledataprocessor as bdp

First load the database as read-only:

>>> dataset = bdp.DataSet()

Now load a run:

>>> run = bdp.Run('00105', dataset, <pathToParameterData>, filterSigs=True)

The <pathToParameterData> needs to point to the data directory associated with the BicycleParameters module and should contain Jason, Luke, Charlie and the Rigid and Rigidcl bicycles. The filterSigs will apply a filter to the signals to remove some of the noise, it is optional.

Check to make sure the data was properly time synchronized:

>>> run.verify_time_sync()

The graph that appears shows the mostly downward acceleration signals from the two accelerometers. These signals are used to synchronize the NI USB-2008 and the VN-100 data. If these do not match, then the synchronization algorithm didn't not work and the data may be unusable.

The run has a lot of data associated with it. Firstly, you can print a subset of the metadata with:

>>> print run

The complete metadata is stored in a dictionary:

>>> run.metadata

The raw data for each sensor is stored in a dictionary and can be accessed by:

>>> run.rawSignals

The data for each sensor with calibration scaling can be accessed by:

>>> run.calibratedSignals

The data for each sensor after truncation based on the time synchronization can be accessed with:

>>> run.truncatedSignals

The data for each computed signal is also stored in a dictionary:

>>> run.computedSignals

The data for each task signal is also stored in a dictionary:

>>> run.taskSignals

The taskSignals can be plotted:

>>> run.taskSignals.keys() # see a list of options
>>> run.plot('SteerAngle', 'RollAngle', 'PullForce')

Export the computed signals as a mat file with:

>>> run.export('mat')

Build the PyTables HDF5 File from raw data

The second option would be to build the database with the raw data from BicycleDAQ. BicycleDAQ stores the raw data as Matlab mat files. Then use this module to create the database and fill it with the data. First create an empty database file in the current directory.:

>>> import bicycledataprocessor as bdp
>>> dataset = bdp.DataSet()
>>> dataset.create_database()

Now, fill the database with the data.:

>>> dataset.fill_all_tables()

The path to all of the raw data must be specififed in the defaults.cfg or as arguments to DataSet().

Warnings

• The roll angle is not guaranteed to be calibrated in some of the early pavillion runs. Check this.
• The first set of pavilion runs with Luke and Charlie are mostly corrupt, beware.
• The yaw angle and lateral deviation values depend on integrating the yaw rate. This seems to work for runs that have signals centered around zero, but are definitely wrong for others. (There are plans to fix this for all runs.)