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"""K coefficient and K-modified coefficient module.
This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with
this program. If not, see <https://www.gnu.org/licenses/>.
"""
__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "GPLv3"
from argaze import GazeFeatures, DataFeatures
import numpy
class ScanPathAnalyzer(GazeFeatures.ScanPathAnalyzer):
"""Implementation of the K coefficient algorithm as described in:
**Krejtz K., Duchowski A., Krejtz I., Szarkowska A., & Kopacz A. (2016).**
*Discerning ambient/focal attention with coefficient K.*
ACM Transactions on Applied Perception (TAP, 1–20).
[https://doi.org/10.1145/2896452](https://doi.org/10.1145/2896452)
"""
@DataFeatures.PipelineStepInit
def __init__(self, **kwargs):
# Init ScanPathAnalyzer class
super().__init__()
self.__K = 0
@DataFeatures.PipelineStepMethod
def analyze(self, scan_path: GazeFeatures.ScanPath):
assert(len(scan_path) > 1)
durations = []
amplitudes = []
for scan_step in scan_path:
durations.append(scan_step.duration)
amplitudes.append(scan_step.last_saccade.amplitude)
durations = numpy.array(durations)
amplitudes = numpy.array(amplitudes)
duration_mean = numpy.mean(durations)
amplitude_mean = numpy.mean(amplitudes)
duration_std = numpy.std(durations)
amplitude_std = numpy.std(amplitudes)
if duration_std > 0. and amplitude_std > 0.:
Ks = []
for scan_step in scan_path:
Ks.append((abs(scan_step.duration - duration_mean) / duration_std) - (abs(scan_step.last_saccade.amplitude - amplitude_mean) / amplitude_std))
self.__K = numpy.array(Ks).mean()
else:
self.__K = 0.
@property
def K(self) -> float:
"""K coefficient."""
return self.__K
class AOIScanPathAnalyzer(GazeFeatures.AOIScanPathAnalyzer):
"""Implementation of the K-modified coefficient algorithm as described in:
**Lounis, C. A., Hassoumi, A., Lefrancois, O., Peysakhovich, V., & Causse, M. (2020, June).**
*Detecting ambient/focal visual attention in professional airline pilots with a modified Coefficient K: a full flight simulator study.*
ACM Symposium on Eye Tracking Research and Applications (ETRA'20, 1-6).
[https://doi.org/10.1145/3379157.3391412](https://doi.org/10.1145/3379157.3391412)
"""
@DataFeatures.PipelineStepInit
def __init__(self, **kwargs):
# Init AOIScanPathAnalyzer class
super().__init__()
self.__K = 0
@DataFeatures.PipelineStepMethod
def analyze(self, aoi_scan_path: GazeFeatures.AOIScanPath):
assert(len(aoi_scan_path) > 1)
durations = []
amplitudes = []
for aoi_scan_step in aoi_scan_path:
durations.append(aoi_scan_step.duration)
amplitudes.append(aoi_scan_step.last_saccade.amplitude)
durations = numpy.array(durations)
amplitudes = numpy.array(amplitudes)
duration_mean = numpy.mean(durations)
amplitude_mean = numpy.mean(amplitudes)
duration_std = numpy.std(durations)
amplitude_std = numpy.std(amplitudes)
if duration_std > 0. and amplitude_std > 0.:
Ks = []
for aoi_scan_step in aoi_scan_path:
Ks.append((abs(aoi_scan_step.duration - duration_mean) / duration_std) - (abs(aoi_scan_step.last_saccade.amplitude - amplitude_mean) / amplitude_std))
self.__K = numpy.array(Ks).mean()
else:
self.__K = 0.
@property
def K(self) -> float:
"""K coefficient."""
return self.__K
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