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- Segment.Segment
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- Scene
class Scene(Segment.Segment) |
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A Scene is a class that represent one scene in the experiment.
A Scene is a class that represent one scene in the experiment. The Scene is designed to capture all "Datapoint"s related to a target
conceptual entity in the experiment. A Scene should have at least one Segment assigned to it. The Scene class is also used to
combine multiple "Segment"s and calculate the aggregated statistics for this new entity as a whole. This class is the equivalent
of scenes as defined in Tobii studio.
Attributes:
scid: a string containing the Scene ids
segments: a list of "Segment"s for this Scene
Attributes inherited from Segment:
alldata: A list of "Datapoint"s for this Scene
features: A dict with feature names as its keys and feature values as its values
completion_time: An integer indicating total duration of the Scene in milliseconds
minimum is 16 ms (ength of one sample with 60Hz sampling rate (ms)
start:An integer indicating the Scene's start time in milliseconds
end: An integer indicating the Scene's end time in milliseconds
sample_start_ind: An integer indicating the index of the first Datapoint for this Scene in the Participant's list of all "Datapoint"s (all_data)
sample_end_ind: An integer indicating the index of the last Datapoint for this Scene in the Participant's list of all "Datapoint"s (all_data)
fixation_start_ind: An integer indicating the index of the first Fixation for this Scene in the Participant's list of all "Fixation"s (fixation_data)
fixation_end_ind: An integer indicating the index of the first Fixation for this Scene in the Participant's list of all "Fixation"s (fixation_data)
numfixations: An integer indicating the number of "Fixation"s in this Scene
time_gaps: a list of tuples of the form (start, end) indicating the start and end of the gaps of invalid samples in the Segement's samples
largest_data_gap: An integer indicating the length of largest invalid gap for this Scene in milliseconds
proportion_valid: A float indicating the proportion of valid samples over all the samples in this Scene
proportion_valid_fix: A float indicating the proportion of (valid + restored) samples over all the samples in this Scene
validity1: a boolean indicating whether this Scene is valid using proportion of valid samples threshold
validity2: a boolean indicating whether this Scene is valid using largest acceptable gap threshold
validity3: a boolean indicating whether this Scene is valid using proportion of (valid + restored) samples threshold
is_valid: a boolean indicating whether this Scene is considered valid by the validity method indicated by params.VALIDITY_METHOD
length: An integer indicating total duration of the Scene in milliseconds
numsamples: An integer indicating total number of samples in the Scene
fixation_data: A list of "Fixation"s for this Scene
fixation_start = fixation_data[0].timestamp
fixation_end = fixation_data[-1].timestamp
aoi_data: A list of AOI_Stat objects for relevants "AOI"s for this Scene
has_aois: A boolean indicating if this Scene has AOI features calculated for it
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Methods defined here:
- __init__(self, scid, seglist, all_data, fixation_data, Segments=None, aoilist=None, prune_length=None, require_valid=True, auto_partition=False)
- Args:
scid: A string containing the id of the Scene.
seglist: a list of tuples of the form (segid, start, end) defining the segments
*Note: this method of defining segments is implemented to make batch processing of
files defining segments easier
all_data: a list of "Datapoint"s which make up this Scene.
fixation_data: a list of "Fixation"s which make up this Scene.
Segments: a list of "Segment"s which belong to this Scene.
aoilist: If not None, a list of "AOI"s.
prune_length: If not None, an integer that specifies the time
interval (in ms) from the begining of each Segment of this Scene
which samples are considered in calculations. This can be used if,
for example, you only wish to consider data in the first
1000 ms of each Segment. In this case (prune_length = 1000),
all data beyond the first 1000ms of the start of the "Segment"s
will be disregarded.
require_valid: a boolean determining whether invalid "Segment"s
will be ignored when calculating the features or not. default = True
auto_partition_low_quality_segments: a boolean flag determining whether
EMDAT should automatically split the "Segment"s which have low sample quality
into two new ssub "Segment"s discarding the largest invalid sample gap in
the "Segment". default = False
Yields:
a Scene object
- calc_abs_angles(self, fixdatalists)
- returns the absolute angles between a sequence of "Fixation"s that build a scan path.
Abosolute angle for each saccade is the angle between that saccade and the horizental axis
Args:
fixdatalists: a list of lists of "Fixation"s
Returns:
a list of absolute angles for the saccades formed by the given sequence of "Fixation"s in Radiant
- calc_distances(self, fixdatalists)
- returns the Euclidean distances between a sequence of "Fixation"s
Args:
fixdatalists: a list of lists of "Fixation"s
- calc_rel_angles(self, fixdatalists)
- returns the relative angles between a sequence of "Fixation"s that build a scan path in Radiant
Relative angle for each saccade is the angle between that saccade and the previous saccade.
Args:
fixdatalists: a list of lists of "Fixation"s
Returns:
a list of relative angles for the saccades formed by the given sequence of "Fixation"s in Radiant
- getid(self)
- Returns the scid for this Scene
Returns: a string conataining the scid for this Scene
- set_aois(self, segments, aois)
- Sets the "AOI"s relevant to this Scene
Args:
segments: a list of "Segment"s which belong to this Scene.
aois: a list of "AOI"s relevant to this Scene
Methods inherited from Segment.Segment:
- calc_largest_validity_gap(self, all_data)
- Calculates the largest gap of invalid samples in the "Datapoint"s for this Segment.
Args:
all_data: The list of "Datapoint"s which make up this Segement
Returns:
An integer indicating the length of largest invalid gap for this Segment in milliseconds
- calc_num_samples(self, all_data)
- Returns the number of samples in the Segment
Args:
all_data: a list of "Datapoint"s which make up this Segment.
Returns:
An integer determining the number of samples in the Segment
- calc_validity1(self, threshold=0.84999999999999998)
- Returns a boolean indicating whether this Segment is valid using proportion of valid samples threshold
Args:
threshold:
$$$ Daria: threshold: the minimum proportion of valid samples for a Segment or Scene to be considered valid.
$$$ By default set to value VALID_PROP_THRESH from module params.py
- calc_validity2(self, threshold=3000)
- Returns a boolean indicating whether this Segment is valid using largest acceptable gap threshold
- calc_validity3(self, threshold=0.84999999999999998)
- Returns a boolean indicating whether this Segment is valid using proportion of (valid + restored) samples threshold
- calc_validity_fixation(self, all_data)
- Calculates the proportion of (valid + restored) "Datapoint"s over all "Datapoint"s of the Segment.
Restored samples are the samples which are not valid but they are part of a Fixation.
The idea is that if the user was looking at a certain point and then we loose the eye data for
a short period of time and afterwards the user is looking at the same point we can assume that user
was looking at that same point during that period.
Args:
all_data: The list of "Datapoint"s which make up this Segement
Returns:
A float indicating the proportion of (valid + restored) samples over all the samples in this Segment
- calc_validity_proportion(self, all_data)
- Calculates the proportion of "Datapoint"s which are valid.
Args:
all_data: The list of "Datapoint"s which make up this Segement
Returns:
A float indicating the proportion of valid samples over all the samples in this Segment
- get_features(self, featurelist=None, aoifeaturelist=None, aoifeaturelabels=None)
- Returns feature names and their values for this Segment
Args:
featurelist: if not None, a list containing the name of features to be returned. If this is None all features will be returned
aoifeaturelist: if not None, a list of features to be returned for each of the "AOI"s relevant to this Segment.
aoifeaturelabels: if not None, a list of AOI related features to be returned.
*Note: while aoifeaturelist is a subset of features that will be returned for all relevant "AOI"s, aoifeaturelabels contains
the exact AOI feature name, i.e., a feature of the form: [AOI name]_[feature name]
For example if an AOI called 'graph' is releveant to this Segment, aoifeaturelabels may contain 'graph_fixationrate'
Returns:
featnames: a list of feature names sorted alphabetically
featvals: a corrsponding list of feature values
e.g.
featnames = ['fixationrate', 'length', 'meanabspathangles']
featvals = [0.00268522882294', '1529851', '1.60354714212']
- get_indices(self)
- Returns the index features
Returns:
An integer indicating the index of the first Datapoint for this Segment in the Participant's list of all "Datapoint"s (all_data)
An integer indicating the index of the last Datapoint for this Segment in the Participant's list of all "Datapoint"s (all_data)
An integer indicating the index of the first Fixation for this Segment in the Participant's list of all "Fixation"s (fixation_data)
An integer indicating the index of the first Fixation for this Segment in the Participant's list of all "Fixation"s (fixation_data)
Raises:
Exception: An exception is thrown if the values are read before initialization
- get_validity(self)
- Determines if this Segment is valid with the given validity method set in params.VALIDITY_METHOD
Returns:
A boolean indicating whether this Segment is valid
- getgaps(self)
- Returns the list of invalid gaps for this Segment
Args:
a list of invalid gaps for this Segment
- print_(self)
- Ourputs all feature names and their values for this Segment on the console
- set_indices(self, sample_st, sample_end, fix_st, fix_end)
- Sets the index features
Args:
sample_st: An integer indicating the index of the first Datapoint for this Segment in the Participant's list of all "Datapoint"s (all_data)
sample_end: An integer indicating the index of the last Datapoint for this Segment in the Participant's list of all "Datapoint"s (all_data)
fix_st: An integer indicating the index of the first Fixation for this Segment in the Participant's list of all "Fixation"s (fixation_data)
fix_st: An integer indicating the index of the first Fixation for this Segment in the Participant's list of all "Fixation"s (fixation_data)
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