Eyetracking data tools based on pandas
Cili is meant to reduce the overhead of basic eyetracking data processing. While it sets the stage for more advanced work by providing data in the form of pandas DataFrames, manipulating the contents of those DataFrames works like manipulating any other pandas DataFrame, so that’s where cili stops - we leave it to the user to learn to work with pandas. If you’re going to be dealing with eyetracking data in python, you’ll be glad you did.
At the moment, we support EyeLink data only. We’d be happy to support other manufacturers, but don’t have the data on hand to do so. If you have the data, and would like us to support something in particular, please drop us a message at https://github.com/beOn/cili/issues, or add it yourself and submit a pull request.
Please note that this is an alpha release.
Before installing cili, you’ll need to install numpy. The rest of the dependencies should install when you install cili, but numpy is special. Then you can install cili using pip or easy_install:
pip install numpy pip install cili
Or grab the latest development version from https://github.com/beOn/cili, and install using setup.py.
If you look closely at EyeLink data, you’ll probably notice a little high frequency noise. This can be a little problematic in several circumstances. To deal with it, cili provides a butterworth filter function with default settings based on previously published pupillometry studies. You can modify the order and cutoff frequency of the filter if you like, but the basic usage looks like this:
samps = butterworth_series(samps, fields=["pup_l"])
FMI, check out the documentation on butterworth_series.
Events from a List of Dicts
Sometimes you are interested in events recorded using something other than EyeLink software. For those crazy times, if you can turn that data into a list of dicts, each containing a name, onset and duration, then it’s pretty easy to create a cili Events object. Assuming you already have your list of dicts, and that it’s called list_o_dicts, then all you do is:
from cili.models import Events events = Events.from_list_of_dicts(list_o_dicts)
Extracting Event-based Ranges
To my mind, this is where things start to get interesting. In many eye tracking and pupillometry studies, the goal is to examine a collection of sample ranges surrounding certain events. So cili provides a method for extracting sample ranges based on event timing, returning a DataFrame with a MultiIndex (event #, sample #).
Suppose you were interested in the 10 seconds following every event in some Events object, called “events,” and you have a 1kHz Samples object, samps. To extract this range for every event in events, you would:
from cili.extract import extract_event_ranges ranges = extract_event_ranges(samps, events, end_offset=10000)
Often, pupillometric sample ranges will be transformed into a % deviation from baseline measure, where the baseline is an average of some small range immediately preceding the range of interest. Continuing the example above, let’s extract baseline measures for each of the events, then divide the ranges of interest by the baselines for the field “pup_r”:
baselines = extract_event_ranges(samps, events, start_offset=-100, end_offset=-1).mean(level=0) ranges.pup_r = (ranges.pup_r / baselines.pup_r - 1).values
Not so painful! For more info on range extraction, check out the documentation on extract_event_ranges. To work with the returned data effectively, You’ll probably also want to take a minute to learn about pandas MultiIndex objects.
Borrowing Event Attributes
If your events each have a field, let’s say “subject”, and you’d like to insert each event’s value for that field into every row of the corrisponding range under a column of the same name, you can “borrow” event attributes using borrow_attributes, like so:
ranges = extract_event_ranges(samps, events, end_offset=10000, borrow_attributes=["subject"])
Saving and Loading
If you keep reading and writing large .txt files, you’ll die young. Or at least having spent too much of your time waiting for .txt files to be read or written. So cili uses hdf5 to speed things up. To use this, you’ll need to install h5py and its dependencies, as documented at http://docs.h5py.org/en/latest/build.html.
Once that’s done, saving and loading Samples and Events objects is pretty easy. It works the same way in both cases, so we’ll just work with samples below:
from cili.models import Samples samps.save("some_filename.hdf") samps_2 = Samples.load_saved("some_filename.hdf")
Exporting to .txt
If you have to export samples or extracted ranges to a .txt file, fine. Ok. We understand.
Luckily, pandas datasets already include a function for writing csv files, any several other formats as well (check their documentation for the complete list: http://pandas.pydata.org/pandas-docs/stable/io.html). For example, to create a tab delimited .txt file:
To create a Zamboni delimited .txt file, just set sep to “Zamboni”.
Reporting Bugs, Requesting Features
Submit all bug reports and feature requests using the github ticketing system: https://github.com/beOn/cili/issues
Please make an effort to provide high quality bug reports. If we get one that just says, “sample range extraction is broken,” we’ll probably trash it without a second look, because the submitter is probably the kind of person who saps energy from everything they touch.
A good bug report should include three things:
- Steps to reproduce the bug
- Expected result
- Actual result
The goal is to give the developers the ability to recreate the bug before their own eyes. If you can give us that, we’ll take a very close look.
Because, like the mighty ciliary muscles, it brings your eye data into focus.
TODO: Thanks, credit to CCP Lab
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
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|cili-0.5.2-py2-none-any.whl (20.6 kB) Copy SHA256 hash SHA256||Wheel||2.7||May 15, 2014|
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