Python API for accessing IDE data recordings
idelib is a Python API for accessing enDAQ's IDE recordings. The IDE format is an EBML encoded file using a custom schemata. This library utilizes our ebmlite to parse the files, and provides classes that make reading data simple.
IDE File Basics
What's an IDE file?
An IDE file is a read-only hierarchical data format that stores recording information generated by an enDAQ sensor device. It contains both time-indexed data from several different kinds of sensors (like acceleration, pressure, temperature, etc.), as well as metadata about the recording device (like device serial number, model number, device name, etc.) and recording settings.
Accessing an IDE file
The top-level interface for an IDE file is the
Dataset object, through which
one can access all of the above-listed information. When you open a file for
reading, for example, this is the type of object that is returned.
Opening an IDE File
You can open an IDE file like so:
filename = "your_file.IDE" with idelib.importFile(filename) as ds: print(type(ds))
Dataset object perfoms lazy-loading, meaning that it only loads
information as is needed. As a result, it internally retains a handle to the
source file which after use needs to be closed. This can be accomplished by
Dataset as a context manager (as seen above; this is the
recommended method), or by using
Dataset as a normal value and calling the
close() method manually:
filename = "your_file.IDE" ds = idelib.importFile(filename) # use `ds` here ds.close() # remember to close the file after use!
Getting recording data
Channels and Subchannels
IDE files organize recording data into channels and subchannels. A channel encapsulates data recorded by a particular individual sensor on the device (e.g., XYZ acceleration from the ADXL375 DC Accelerometer); a subchannel, if present, specifies a particular data stream within a channel (e.g., the X-coordinate acceleration from the ADXL375 DC Accelerometer).
At the top-level, a
Dataset object has a
channels member, which is a dict
of all channels recorded in the file. The dict is keyed by channel id numbers,
Channel objects in the values.
Channel object has a
subchannels member, which is a list of
Subchannel objects. If the channel has no subchannels, this member will be
The below table lists current conventions for channels across all enDAQ sensors:
|(Abbreviated) Product No.||Description||Example Product Nos.|
|S-D||enDAQ S-series devices with single digital accelerometers||S3-D16, S4-D40|
|S-DD||enDAQ S-series devices with dual digital accelerometers||S1-D100D40, S2-D25D16|
|S-ED||enDAQ S-series devices with an analog electrocapacitive and digital accelerometer||S5-E25D40, S4-E100D40|
|S-RD||enDAQ S-series devices with an analog piezoresistive and digital accelerometer||S4-R500D40, S5-R2000D40|
|SSX||Midé Slam Stick X data recorders||SSX|
|SSC||Midé Slam Stick C data recorders||SSC|
|SSS||Midé Slam Stick S data recorders||SSS|
The below table lists channel ID numbers used in a recording file based on the recording device’s product number (device series numbers and accelerometer sensitivity ranges are omitted when applicable to all such devices):
|Main Accelerometer||8||S-RD, S-ED, SSS, SSX||X-, Y-, Z-axis Acceleration|
|16/200g Accelerometer||32||S-DD, SSX, SSS, SSC, S-D16, S-D200||X-, Y-, Z-axis Acceleration|
|8/40g Accelerometer||80||S-RD, S-DD, S-ED, S-D40, S-D8||X-, Y-, Z-axis Acceleration|
|IMU Gyroscope||47||All1||X-, Y-, Z-axis Rotation|
|Absolute Orientation||65||All1||X-, Y-, Z-, W-axis Quaternion; Acc|
|Relative Orientation||70||All1||X-, Y-, Z-, W-axis Quaternion|
|MPL3115||36||All1||Pressure, Temperature 2|
|MS8607||59||All1||Pressure, Temperature, Humidity 3|
1 excluding early SSC/SSS/SSX models
2 1 Hz Internal Measurements
3 10 Hz Control Pad Measurements
To simply use all recording data, we can iterate through each subchannel in a dataset like so:
for ch in ds.channels.values(): for sch in ch.subchannels: print(sch)
EventArrays and raw data
Subchannel object has a
getSession() method, which
EventArray is a wrapper around a channel's
underlying recording data that loads data on demand from the source file. You
can index an
eventarray[i] for some index
i) to get a
ndarray of data. Data is organized in an n-dimensional array.
For subchannels, this will always be a 2-by-n array, where n is the number of
eventarray indexes the samples,
the respective timestamps.
For channels, this will be a (c+1)-by-n array, where n is the number of samples
recorded and c is the number of subchannels;
eventarray[1:] indexes the
eventarray indexes the respective timestamps.
Dataset makes available some basic metadata. Some useful pieces of information
are stored directly as members:
>>> ds.filename 'C:\\Users\\Public\\SSX09546_019.IDE'
Other data is stored in the dict member recorderInfo:
>>> ds.recorderInfo['RecorderSerial'] 9546 >>> ds.recorderInfo['PartNumber'] 'S3-E500D40'
EventArray also stores some sample-specific metadata, like the data's units:
>>> eventarray.units ('Acceleration', u'g')
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