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diff --git a/docs/user_guide/gaze_analysis/scan_path.md b/docs/user_guide/gaze_analysis/scan_path.md new file mode 100644 index 0000000..e00682f --- /dev/null +++ b/docs/user_guide/gaze_analysis/scan_path.md @@ -0,0 +1,168 @@ +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** 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. + + + +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. |