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.

![Fixation based scan path](../../img/scan_path.png)

As fixations and saccades are identified, the scan path is built by calling respectively **append_fixation** and **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.

![AOI based scan path](../../img/aoi_scan_path.png)

As fixations and saccades are identified, the scan path is built by calling respectively **append_fixation** and **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* 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.