Update dataset_config.py

pysaliency/dataset_config.py

@@ -1,13 +1,11 @@
-import numpy as np
-from .datasets import read_hdf5, clip_out_of_stimulus_fixations, remove_out_of_stimulus_fixations, FixationTrains, Fixations, Stimuli, create_subset
+from .datasets import read_hdf5, clip_out_of_stimulus_fixations, remove_out_of_stimulus_fixations
 from .filter_datasets import (
     filter_fixations_by_number,
     filter_stimuli_by_number,
     filter_stimuli_by_size,
     train_split,
     validation_split,
     test_split,
-    _check_intervals
 )
 
 from schema import Schema, Optional
@@ -53,53 +51,7 @@ def apply_dataset_filter_config(stimuli, fixations, filter_config):
 
     return filter_fn(stimuli, fixations, **filter_config['parameters'])
 
-
-def filter_scanpaths_by_attribute(scanpaths:  FixationTrains, attribute_name, attribute_value, invert_match=False):
-    """Filter Scanpaths by values of scanpath attribute (fixation_trains.scanpath_attributes)"""
-
-    mask = np.array([element == attribute_value for element in getattr(scanpaths, attribute_name)])
-    if invert_match is True:
-        mask = ~mask
-    indices = list(np.nonzero(mask)[0])
-    return scanpaths.filter_fixation_trains(indices)
-
-
-def filter_fixations_by_attribute(fixations: Fixations, attribute_name, attribute_value, invert_match=False):
-    """Filter Fixations by values of attribute (fixations.__attributes__)"""
-
-    mask = np.array([element == attribute_value for element in getattr(fixations, attribute_name)])
-    if invert_match is True:
-        mask = ~mask
-    indices = list(np.nonzero(mask)[0])
-    return fixations.filter(indices)
-
-
-def filter_stimuli_by_attribute(stimuli: Stimuli, fixations: Fixations, attribute_name, attribute_value, invert_match=False):
-    """Filter stimuli by values of attribute"""
-
-    mask = np.array([element == attribute_value for element in getattr(stimuli, attribute_name)])
-    if invert_match is True:
-        mask = ~mask
-    indices = list(np.nonzero(mask)[0])
-    return create_subset(stimuli, fixations, indices)
-
-
-def filter_scanpaths_by_lengths(scanpaths: FixationTrains, intervals: list):
-    """Filter Scanpaths by number of fixations"""
-
-    intervals = _check_intervals(intervals, type=int)
-    mask = np.zeros(len(scanpaths.train_lengths), dtype=bool)
-
-    for n1, n2 in intervals:
-        temp_mask = np.logical_and(scanpaths.train_lengths>=n1,scanpaths.train_lengths<=n2)
-        mask = np.logical_or(mask, temp_mask)
-
-    indices = list(np.nonzero(mask)[0])
-    scanpaths = scanpaths.filter_fixation_trains(indices)
-
-    return scanpaths
-
-
+
 def _clip_out_of_stimulus_fixations(stimuli, fixations):
     clipped_fixations = clip_out_of_stimulus_fixations(fixations, stimuli=stimuli)
     return stimuli, clipped_fixations