PyOKR
A Python-based optokinetic reflex analysis tool to measure and quantify eye tracking motion in three dimensions. Video-oculography data can be modeled computationally to quantify specific tracking speeds and ability in horizontal and vertical space.
Requirements:
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Python >= 3.8
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Spyder IDE via Anaconda (suggested for interactive graphs)
Imports:
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PyQT5
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Pandas
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Matplotlib
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Numpy
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Sklearn.neighbors (from scikit)
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Scipy
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SymPy
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Pandasgui
1 maintainer
Metadata
1 maintainer
Release files for PyOKR 1.1.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pyokr-1.1.4.tar.gz | 29.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pyokr-1.1.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 58.7 kB
Release files / pyokr-1.1.4.tar.gz
| Download URL | pyokr-1.1.4.tar.gz |
|---|---|
| Size | 29.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.3
|
Release files / pyokr-1.1.4-py3-none-any.whl
| Download URL | pyokr-1.1.4-py3-none-any.whl |
|---|---|
| Size | 29.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
1c552e841b69e64adcddd22d788f7a53e4ef34f359981dbfa276280c0ae03627
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BLAKE2b-256 checksum How to use checksums |
e594a692d4825137da16a8a62183e4c6750ed5ff06ff93e3bcaff65abdef0b53
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.3
|
