BicycleDataProcessor 0.1.0

Load the prebuilt database file

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

$ wget http://files.figshare.com/1710608/instrumented_bicycle_raw_data_h5.tar.bz2
$ tar -jxvf instrumented_bicycle_raw_data_h5.tar.bz2

And also the bicycle parameter data:

$ wget http://files.figshare.com/1710525/bicycle_parameters.tar.gz
$ tar -zxvf bicycle_parameters.tar.gz
$ rm bicycle_parameters.tar.gz

In your working directory, create a bdp-defaults.cfg and change pathToDatabase and pathToParameters to point to the downloaded and unzipped database file and the bicycle-parameters data directory, respectively. See the example-bdp-defaults.cfg for reference. This file follows the standard Python configuration file format.

Interact with the data

Open a Python command prompt and import the module:

>>> import bicycledataprocessor as bdp

First load the database:

>>> dataset = bdp.DataSet()

Now load a run:

>>> run = bdp.Run('00105', dataset)

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 meta data with:

>>> print(run)

The complete meta data 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 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 from trials and calibrations as Matlab mat files. Then use this module to create the database and fill it with the data.

The raw trial data can downloaded as so:

$ wget -O raw-trial-data.zip http://downloads.figshare.com/article/public/1164632
$ unzip -d raw-trial-data raw-trial-data.zip
$ rm raw-trial-data.zip

The raw calibration files:

$ wget -O raw-calibration-data.zip http://downloads.figshare.com/article/public/1164630
$ unzip -d raw-calibration-data raw-calibration-data.zip
$ rm raw-calibration-data.zip

And the additional corrupt trial file:

$ wget -O data-corruption.csv http://files.figshare.com/1696860/data_corruption.csv

Make sure your bdp-defaults.cfg paths point to the correct directories for the run mat files (pathToRunMat), calibration mat files (pathToCalibMat), the corrupt data file (data-corruption.csv). Optionally the paths can be set as arguments to DataSet().

Now create an empty database file in the current directory (or to the path specified in bdp-defaults.cfg if you’ve done that).:

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

Now, fill the database with the data.:

>>> dataset.fill_all_tables()

The will take a little time to populate the database.