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Scan path
=========
[GazeFeatures](/argaze/#argaze.GazeFeatures) defines classes to handle successive fixations/saccades and analyse their spatial or temporal properties.
## Fixation based scan path
### Definition
The [ScanPath](/argaze/#argaze.GazeFeatures.ScanPath) class is defined as a list of [ScanSteps](/argaze/#argaze.GazeFeatures.ScanStep) which are defined as a fixation and a consecutive saccade.
As fixations and saccades are identified, the scan path is built by calling respectively [append_fixation](/argaze/#argaze.GazeFeatures.ScanPath.append_fixation) and [append_saccade](/argaze/#argaze.GazeFeatures.ScanPath.append_saccade) methods.
### Analysis
[GazeFeatures](/argaze/#argaze.GazeFeatures) defines abstract [ScanPathAnalyzer](/argaze/#argaze.GazeFeatures.ScanPathAnalyzer) classe to let add various analysis algorithms.
Some scan path analysis are available thanks to [GazeAnalysis](/argaze/#argaze.GazeAnalysis) submodule:
* [K-Coefficient](/argaze/#argaze.GazeAnalysis.KCoefficient)
* [Nearest Neighbor Index](/argaze/#argaze.GazeAnalysis.NearestNeighborIndex)
### Example
Here is a sample of code to illustrate how to built a scan path and analyze it:
``` python
from argaze import GazeFeatures
from argaze.GazeAnalysis import KCoefficient
# Create a empty scan path
scan_path = GazeFeatures.ScanPath()
# Create a K coefficient analyzer
kc_analyzer = KCoefficient.ScanPathAnalyzer()
# Assuming a gaze movement is identified at ts time
...:
# Fixation identified
if GazeFeatures.is_fixation(gaze_movement):
# Append fixation to scan path : no step is created
scan_path.append_fixation(ts, gaze_movement)
# Saccade identified
elif GazeFeatures.is_saccade(gaze_movement):
# Append saccade to scan path : a new step should be created
new_step = scan_path.append_saccade(data_ts, gaze_movement)
# Analyse scan path
if new_step:
K = kc_analyzer.analyze(scan_path)
# Do something with K metric
...
```
## AOI based scan path
### Definition
The [AOIScanPath](/argaze/#argaze.GazeFeatures.AOIScanPath) class is defined as a list of [AOIScanSteps](/argaze/#argaze.GazeFeatures.AOIScanStep) which are defined as set of consecutives fixations looking at a same Area Of Interest (AOI) and a consecutive saccade.
As fixations and saccades are identified, the scan path is built by calling respectively [append_fixation](/argaze/#argaze.GazeFeatures.AOIScanPath.append_fixation) and [append_saccade](/argaze/#argaze.GazeFeatures.AOIScanPath.append_saccade) methods.
### Analysis
[GazeFeatures](/argaze/#argaze.GazeFeatures) defines abstract [AOIScanPathAnalyzer](/argaze/#argaze.GazeFeatures.AOIScanPathAnalyzer) classe to let add various analysis algorithms.
Some scan path analysis are available thanks to [GazeAnalysis](/argaze/#argaze.GazeAnalysis) submodule:
* [Transition matrix](/argaze/#argaze.GazeAnalysis.TransitionMatrix)
* [Entropy](/argaze/#argaze.GazeAnalysis.Entropy)
* [Lempel-Ziv complexity](/argaze/#argaze.GazeAnalysis.LempelZivComplexity)
* [N-Gram](/argaze/#argaze.GazeAnalysis.NGram)
* [K-modified coefficient](/argaze/#argaze.GazeAnalysis.KCoefficient)
### Example
Here is a sample of code to illustrate how to built a AOI scan path and analyze it:
``` python
from argaze import GazeFeatures
from argaze.GazeAnalysis import LempelZivComplexity
# Assuming all AOI names are listed
...
# Create a empty AOI scan path
aoi_scan_path = GazeFeatures.AOIScanPath(aoi_names)
# Create a Lempel-Ziv complexity analyzer
lzc_analyzer = LempelZivComplexity.AOIScanPathAnalyzer()
# Assuming a gaze movement is identified at ts time
...:
# Fixation identified
if GazeFeatures.is_fixation(gaze_movement):
# Assuming fixation is detected as inside an AOI
...
# Append fixation to AOI scan path : a new step should be created
new_step = aoi_scan_path.append_fixation(ts, gaze_movement, looked_aoi_name)
# Analyse AOI scan path
if new_step:
LZC = kc_analyzer.analyze(aoi_scan_path)
# Do something with LZC metric
...
# Saccade identified
elif GazeFeatures.is_saccade(gaze_movement):
# Append saccade to scan path : no step is created
aoi_scan_path.append_saccade(data_ts, gaze_movement)
```
### Advanced
The [AOIScanPath](/argaze/#argaze.GazeFeatures.AOIScanPath) class provides some advanced features to analyse it.
#### String representation
When a new [AOIScanStep](/argaze/#argaze.GazeFeatures.AOIScanStep) is created, the [AOIScanPath](/argaze/#argaze.GazeFeatures.AOIScanPath) internally affects a unique letter index related to its AOI to ease pattern analysis.
Then, the [AOIScanPath str](/argaze/#argaze.GazeFeatures.AOIScanPath.__str__) representation returns the concatenation of each [AOIScanStep](/argaze/#argaze.GazeFeatures.AOIScanStep) letter.
The [AOIScanPath get_letter_aoi](/argaze/#argaze.GazeFeatures.AOIScanPath.get_letter_aoi) method helps to get back the AOI related to a letter index.
``` python
# Assuming the following AOI scan path is built: Foo > Bar > Shu > Foo
aoi_scan_path = ...
# String representation should be: 'ABCA'
print(str(aoi_scan_path))
# Output should be: 'Bar'
print(aoi_scan_path.get_letter_aoi('B'))
```
#### Transition matrix
When a new [AOIScanStep](/argaze/#argaze.GazeFeatures.AOIScanStep) is created, the [AOIScanPath](/argaze/#argaze.GazeFeatures.AOIScanPath) internally counts the number of transitions from an AOI to another AOI to ease Markov chain analysis.
Then, the [AOIScanPath transition_matrix](/argaze/#argaze.GazeFeatures.AOIScanPath.transition_matrix) property returns a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) where indexes are transition departures and columns are transition destinations.
Here is an exemple of transition matrix for the following [AOIScanPath](/argaze/#argaze.GazeFeatures.AOIScanPath): Foo > Bar > Shu > Foo > Bar
| |Foo|Bar|Shu|
|:--|:--|:--|:--|
|Foo|0 |2 |0 |
|Bar|0 |0 |1 |
|Shu|1 |0 |0 |
#### Fixations count
The [AOIScanPath fixations_count](/argaze/#argaze.GazeFeatures.AOIScanPath.fixations_count) method returns the total number of fixations in the whole scan path and a dictionary to get the fixations count per AOI.
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