-
Notifications
You must be signed in to change notification settings - Fork 0
/
visualization_tool.py
968 lines (776 loc) · 44.5 KB
/
visualization_tool.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
# library imports
import os
import numpy as np
import pandas as pd
import pickle
import keyboard
import networkx as nx
import copy
import pyqtgraph as pg
import pyqtgraph.exporters
from glob import glob
from PyQt5 import QtWidgets, QtGui, QtCore
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QColor
from PyQt5.QtWidgets import QPushButton, QSlider
from pyqtgraph import mkPen
from matplotlib import cm
from matplotlib.colors import LinearSegmentedColormap
from functools import partial
# script imports
import utils
import constants
from visualizations.graph_3d import Graph3D
from visualizations.graph_2d import Graph2D
from visualizations.projec_2d import Scatter2D
from visualizations.quality_sphere import QualitySphere
from visualizations.parallel_bar_plot import parallelBarPlot
class Tool(pg.GraphicsWindow):
def __init__(self, dataset_name = "3elt", default_layout_technique = "FA2", default_proj_choice = 'global norm', analysis_data = None):
super(Tool, self).__init__()
keyboard.on_press(self.keyboard_event)
# data
self.dataset_name = dataset_name
self.default_layout_technique = default_layout_technique
self.proj_choice = default_proj_choice
self.view_locked = False
# grid initialization of the window
self.setBackground((0, 0, 0, 60))
self.layoutgb = QtWidgets.QGridLayout()
self.layoutgb.setHorizontalSpacing(1)
self.layoutgb.setVerticalSpacing(1)
self.layoutgb.setContentsMargins(1, 1, 1, 1)
self.setLayout(self.layoutgb)
self.layoutgb.setColumnStretch(0, 3)
self.layoutgb.setColumnStretch(1, 10)
self.layoutgb.setColumnStretch(2, 10)
self.layoutgb.setColumnStretch(3, 10)
self.layoutgb.setRowStretch(0, 10)
self.layoutgb.setRowStretch(1, 10)
self.sphere_widgets = []
self.analysis_data = analysis_data
# not used, may be used in the future for user study
if constants.user_mode != 'free':
self.layout_index = 0
self.dataset_name, self.default_layout_technique = constants.evaluation_set[self.layout_index]
self.evaluation_data = []
# get the view points and initialize the starting metric and some variables
self.view_points = np.load(f'spheres/sphere{constants.samples}_points.npy')
# get the minima and maxima of all the quality metrics
bounds_dict = constants.bounds_dict
self.mins_global = np.array(list(bounds_dict.values()))[:, 0]
self.maxs_global = np.array(list(bounds_dict.values()))[:, 1]
# get the minimum and maximum of the averages of all the 9 quality metrics combined
self.global_average_min = constants.glob_averages_min
self.global_average_max = constants.glob_averages_max
# create the default colors for the heatmap
self.heatmap_colors = ["darkred", "red", "yellow", "green", "lightblue"]
# create the default color mapping for the projection
self.proj_cmap = LinearSegmentedColormap.from_list("", self.heatmap_colors)
self.D_P_dict = self.available_datasets_layouts()
self.current_metric = 'crossing_number'
self.curr_viewpoint = None
self.initialize_menu()
self.graph_2d = None
self.graph_3d = None
self.metric_proj = None
self.stats = None
self.curr_proj_idx = None
self.set_data(self.dataset_name, self.default_layout_technique)
self.highlight()
# get all the datasets listed in the metrics file into a dictionary
def available_datasets_layouts(self):
consolid_metrics = os.path.join(constants.metrics_dir, 'metrics.pkl')
data_frame = pd.read_pickle(consolid_metrics)
D_P_dict = {}
datasets = set(data_frame['dataset_name'].to_list())
for dataset in datasets:
D_P_dict[dataset.split('.')[0]] = set(data_frame[data_frame['dataset_name'] == dataset]['layout_technique'].to_list())
return D_P_dict
# initialize the menu, add buttons, sliders, labels etc.
def initialize_menu(self):
self.menu = pg.LayoutWidget()
# width_policy = QtWidgets.QSizePolicy()
# width_policy.setHorizontalPolicy(QtWidgets.QSizePolicy.MinimumExpanding)
# Set background white:
palette = QtGui.QPalette()
palette.setColor(QtGui.QPalette.Window, QColor(255, 255, 255, 255))
self.menu.setPalette(palette)
self.menu.setAutoFillBackground(True)
# get the graph file and create a graph object
input_file = glob('data/{0}/*-src.csv'.format(self.dataset_name))[0]
df = pd.read_csv(input_file, sep=';', header=0)
graph = nx.from_pandas_edgelist(df, 'from', 'to', edge_attr='strength')
graph = nx.convert_node_labels_to_integers(graph)
self.G = graph
# default user mode, can be switched when doing future user study
if constants.user_mode == 'free':
keys = list(self.D_P_dict.keys())
# create a drop down list to pick datasets
datasets = list(self.D_P_dict.keys())
datasets.sort()
self.dataset_picker = pg.ComboBox(items=datasets, default=self.dataset_name)
self.dataset_picker.currentIndexChanged.connect(self.data_selected)
# create a drop down list to pick the different layout techniques
layouts = list(self.D_P_dict[keys[0]])
layouts.sort()
self.layout_technique_picker = pg.ComboBox(items=layouts, default=self.default_layout_technique)
self.layout_technique_picker.currentIndexChanged.connect(self.data_selected)
# create a drop down list to pick the different metrics
self.metric_picker = pg.ComboBox(items = constants.metrics + ['Weighted sum of metrics (norm)', 'Weighted sum of metrics (raw)'], default = self.current_metric)
self.metric_picker.currentIndexChanged.connect(self.data_selected)
# create a drop down list to pick different versions of the quality metrics projection plot
# self.projection_picker = pg.ComboBox(items = ['local norm', 'global norm'], default = 'global norm')
# self.projection_picker.currentIndexChanged.connect(self.change_proj_type)
# add a few labels indicating what each drop down list does
self.menu.addLabel(text="Dataset:", row=len(self.menu.rows), col=0)
self.menu.addWidget(self.dataset_picker, len(self.menu.rows), 0)
# self.menu.addLabel(text="Projection type:", row=len(self.menu.rows), col=1)
# self.menu.addWidget(self.projection_picker, len(self.menu.rows), 1)
self.menu.addLabel(text="Layout technique:", row=len(self.menu.rows), col=0)
self.menu.addWidget(self.layout_technique_picker, len(self.menu.rows), 0)
self.menu.addLabel(text="Quality Metric:", row=len(self.menu.rows), col=0)
self.menu.addWidget(self.metric_picker, len(self.menu.rows), 0)
# change the text of the dataset label when drop down list for dataset is used
self.dataset_picker.currentIndexChanged.connect(self.change_label_text)
# more labels indicating some graph stats
self.label_n = QtWidgets.QLabel('Number of nodes (n): ' + str(self.G.number_of_nodes()))
self.label_m = QtWidgets.QLabel('Number of edges (m): ' + str(self.G.number_of_edges()))
self.menu.addWidget(self.label_n, row=len(self.menu.rows), col=0)
self.menu.addWidget(self.label_m, row=len(self.menu.rows), col=0)
# button that freezes some widgets so that it doesn't change when hovering
self.select_button = QPushButton('Select view')
self.select_button.pressed.connect(self.select_view)
self.menu.addWidget(self.select_button, len(self.menu.rows), 0)
self.select_button.setVisible(True)
# adds a button that finds the best solution when scores are normalized
self.best_solution_button = QPushButton('Find best solution (norm)')
self.best_solution_button.pressed.connect(self.get_best_solution)
self.menu.addWidget(self.best_solution_button, len(self.menu.rows), 0)
# adds a button that finds the best solution with raw values
self.best_solution_button_raw = QPushButton('Find best solution')
self.best_solution_button_raw.pressed.connect(self.get_best_solution_raw)
self.menu.addWidget(self.best_solution_button_raw, len(self.menu.rows), 0)
# sliders for qms
self.n_metrics = len(constants.metrics)
# map the name of the metrics to the integer identifiers
self.metrics_name_dict = dict(zip(range(len(constants.metrics)), constants.metrics))
# initialization of sliders, labels etc
self.sliders = {}
self.sliders_labels = {}
self.weights = {}
self.slider_buttons_min = {}
self.slider_buttons_max = {}
# create sliders for the quality metrics
for i in range(self.n_metrics):
# add labels to the sliders and give them an object name
self.sliders_labels[i] = QtWidgets.QLabel(self.metrics_name_dict[i] + ' 1')
self.sliders_labels[i].setObjectName(str(i))
self.menu.addWidget(self.sliders_labels[i], len(self.menu.rows), 0)
# add buttons next to the sliders that set weights to 0 or 100(max)
self.slider_buttons_min[i] = QPushButton('Min')
self.slider_buttons_min[i].setObjectName(str(i))
self.slider_buttons_min[i].pressed.connect(lambda: self.update_weights_button(value = 0))
self.slider_buttons_max[i] = QPushButton('Max')
self.slider_buttons_max[i].setObjectName(str(i))
self.slider_buttons_max[i].pressed.connect(lambda: self.update_weights_button(value = 100))
self.menu.addWidget(self.slider_buttons_min[i], len(self.menu.rows), 1)
self.menu.addWidget(self.slider_buttons_max[i], len(self.menu.rows), 1)
# set all weights to the max 100 by default
self.weights[i] = 100
# create the sliders
self.sliders[i] = QSlider(Qt.Horizontal)
# self.sliders[i].setSizePolicy(width_policy)
self.sliders[i].setMinimum(0)
self.sliders[i].setMaximum(100)
self.sliders[i].setValue(100)
self.sliders[i].setSingleStep(5)
self.sliders[i].setTickInterval(10)
self.sliders[i].setTickPosition(QSlider.TicksBelow)
self.sliders[i].valueChanged.connect(self.update_weights)
self.sliders[i].setObjectName(str(i))
self.menu.addWidget(self.sliders[i], len(self.menu.rows), 0)
# deprecated, may be used in the future this entire else statement
else:
self.evaluation_started = False #Keep track of when the tutorial is over
self.next_button = QPushButton('Begin survey')
self.next_button.pressed.connect(self.next_layout)
self.menu.addWidget(self.next_button, len(self.menu.rows), 0)
self.select_button = QPushButton('Select view')
self.select_button.pressed.connect(self.select_view)
self.menu.addWidget(self.select_button, len(self.menu.rows), 0)
self.selected_counter = self.menu.addLabel(text=f"0/{constants.required_view_count} views selected", row=len(self.menu.rows), col=0)
self.select_button.setVisible(False)
self.selected_counter.setVisible(False)
self.preference_label = self.menu.addLabel(text="Preference:", row=len(self.menu.rows), col=0)
self.prefer_3d = QPushButton('3D Preference')
self.prefer_3d.pressed.connect(partial(self.select_preference, '3D'))
self.menu.addWidget(self.prefer_3d, len(self.menu.rows), 0)
self.prefer_2d = QPushButton('2D Preference')
self.prefer_2d.pressed.connect(partial(self.select_preference, '2D'))
self.menu.addWidget(self.prefer_2d, len(self.menu.rows), 0)
self.preference_label.setVisible(False)
self.prefer_3d.setVisible(False)
self.prefer_2d.setVisible(False)
for i in range(len(self.menu.rows) - 1):
self.menu.layout.setRowStretch(i, 0)
self.menu.layout.setRowStretch(len(self.menu.rows), 1)
self.layoutgb.addWidget(self.menu, 0, 0, 2, 1)
# updating the weights according to the values given in the sliders
def update_weights(self, value):
# get the name of the object
sender = self.sender()
name = sender.objectName()
# adjust text of the slider and then the weights according to slider value
self.sliders_labels[int(name)].setText(self.metrics_name_dict[int(name)] + ' ' + str(round(value / 100, 2)))
self.weights[int(name)] = value
# updating the weights according to a button
def update_weights_button(self, value):
# get the name of the object
sender = self.sender()
name = sender.objectName()
# adjust text of the slider and the slider value and then the weights according to slider value
self.sliders_labels[int(name)].setText(self.metrics_name_dict[int(name)] + ' ' + str(round(value / 100, 2)))
self.sliders[int(name)].setValue(value)
self.weights[int(name)] = value
# setting the text of the textboxes according to the graph details
def change_label_text(self):
self.label_n.setText('Number of nodes (n): ' + str(self.G.number_of_nodes()))
self.label_m.setText('Number of edges (m): ' + str(self.G.number_of_edges()))
# deprecated, may be used in the future for user study
def next_layout(self):
if not self.evaluation_started:
self.evaluation_started = True
self.select_button.setVisible(True)
self.selected_counter.setVisible(True)
self.next_button.setText('Next layout')
constants.user_mode = 'eval_half'
self.sphere_widget.setVisible(False)
self.hist.setVisible(False)
self.sphere_widget.setVisible(False)
if constants.user_mode != 'free':
self.layout_index += 1
if self.layout_index < len(constants.evaluation_set):
if self.layout_index >= 4:
constants.user_mode = 'eval_full'
config = constants.evaluation_set[self.layout_index]
self.set_data(config[0], config[1])
self.set_tool_lock(False)
self.next_button.setDisabled(True)
self.update_selected_count_text()
else:
with open(constants.output_file, 'wb') as file:
pickle.dump(self.evaluation_data, file)
self.close()
# function attached to button or keypress, freezes the screen so hovering will not interact
def select_view(self):
self.set_tool_lock(not self.view_locked)
pass
# deprecated, may be used in the future for user study
def selected_view_count(self):
count = 0
for data in self.evaluation_data:
if data['dataset'] == self.dataset_name and data['layout_technique'] == self.default_layout_technique:
count += 1
return count
# deprecated, may be used in the future for user study
def update_selected_count_text(self):
self.selected_counter.setText(f"{self.selected_view_count()}/{constants.required_view_count} views selected")
# deprecated, may be used in the future for user study
def select_preference(self, preference):
self.preference_label.setVisible(False)
self.prefer_3d.setVisible(False)
self.prefer_2d.setVisible(False)
self.set_tool_lock(False)
self.evaluation_data.append({
'dataset': self.dataset_name,
'layout_technique': self.default_layout_technique,
'viewpoint': np.array(self.graph_3d.cameraPosition()),
'view_quality': self.current_quality(),
'2D_quality': self.metrics_2d,
'3D_quality': self.metrics_3d,
'preference': preference,
'mode': constants.user_mode,
})
if self.selected_view_count() >= 3:
self.next_button.setDisabled(False)
self.check_select_available()
self.update_selected_count_text()
# freezes the screen so hovering will not interact
def set_tool_lock(self, lock):
self.view_locked = lock
if self.view_locked:
self.select_button.setText('Deselect view')
else:
self.select_button.setText('Select view')
self.hist.lock = self.view_locked
self.graph_3d.lock = self.view_locked
self.sphere.lock = self.view_locked
# self.preference_label.setVisible(self.view_locked)
# self.prefer_3d.setVisible(self.view_locked)
# self.prefer_2d.setVisible(self.view_locked)
# function for displaying the viewpoint with the best weighted solution with normalized metric values
def get_best_solution(self):
curr_views_metrics = copy.deepcopy(self.views_metrics)
for i in range(self.n_metrics):
curr_views_metrics[:, i] *= (self.weights[i] / 100)
best_view = np.argmax(np.sum(curr_views_metrics, axis = 1))
view = self.view_points[best_view]
self.move_to_viewpoint(view)
# function for displaying the viewpoint with the best weighted solution with raw values
def get_best_solution_raw(self):
curr_views_metrics = copy.deepcopy(self.original_metrics_views)
for i in range(self.n_metrics):
curr_views_metrics[:, i] *= (self.weights[i] / 100)
best_view = np.argmax(np.sum(curr_views_metrics, axis = 1))
view = self.view_points[best_view]
self.move_to_viewpoint(view)
# initializes the 3d graph layout
def initialize_3d_layout(self):
# get the data
layout_file_3d = F"{constants.output_dir}/{self.dataset_name}-{self.default_layout_technique}-3d.csv"
input_file = glob('data/{0}/*-src.csv'.format(self.dataset_name))[0]
data = pd.read_csv(layout_file_3d, sep=';').to_numpy()
# get the graph object and edges
df = pd.read_csv(input_file, sep=';', header=0)
graph = nx.from_pandas_edgelist(df, 'from', 'to', edge_attr='strength')
graph = nx.convert_node_labels_to_integers(graph)
edges = list(graph.edges())
# will overwrite the existing graph (in case a new dataset is chosen in the widget)
self.G = graph
# create a 3d graph drawing widget if there is none, if there is then adjust the data
if self.graph_3d is None:
self.graph_3d = Graph3D(data, self.cmap, edges, parent=self, title="3D Layout")
self.graph_3d.setBackgroundColor('w')
self.layoutgb.addWidget(self.graph_3d, 0, 1)
else:
self.graph_3d.set_data(data, self.cmap, edges)
# initializes the 2d graph layout
def initialize_2d_layout(self):
# deprecated may be used for userstudy
if constants.user_mode == 'evalimage':
return
# get the graph data
input_file = glob('data/{0}/*-src.csv'.format(self.dataset_name))[0]
df = pd.read_csv(input_file, sep=';', header=0)
graph = nx.from_pandas_edgelist(df, 'from', 'to', edge_attr='strength')
graph = nx.convert_node_labels_to_integers(graph)
edges = list(graph.edges())
# get the layout data
layout_file_2d = F"{constants.output_dir}/{self.dataset_name}-{self.default_layout_technique}-2d.csv"
df_2d = pd.read_csv(layout_file_2d, sep=';').to_numpy()
# create a 2d graph drawing widget if there is none, if there is then adjust the data
if self.graph_2d is None:
self.graph_2d = Graph2D(df_2d, self.cmap, edges, title ="2D Layout")
self.layoutgb.addWidget(self.graph_2d, 0, 2)
self.graph_2d.setBackground('w')
else:
self.graph_2d.set_data(df_2d, edges)
# initialize the qualitymetric sphere
def initialize_sphere(self, sphere_sum = None):
double_bar = False
# # add the viewpoint quality metric data to the sphere
if sphere_sum == 'Weighted sum of metrics (raw)':
# weight the raw metric values
curr_views_metrics = copy.deepcopy(self.original_metrics_views)
for i in range(self.n_metrics):
curr_views_metrics[:, i] *= (self.weights[i] / 100)
weight_sum = np.sum(curr_views_metrics, axis = 1)
# scale weight sum again
mins, maxs = np.min(weight_sum), np.max(weight_sum)
self.sphere_data = (weight_sum - mins) / (maxs - mins)
elif sphere_sum == 'Weighted sum of metrics (norm)':
# weight the normalized metric values
curr_views_metrics = copy.deepcopy(self.views_metrics)
for i in range(self.n_metrics):
curr_views_metrics[:, i] *= (self.weights[i] / 100)
weight_sum = np.sum(curr_views_metrics, axis=1)
mins, maxs = np.min(weight_sum), np.max(weight_sum)
self.sphere_data = (weight_sum - mins) / (maxs - mins)
else:
double_bar = True
self.sphere_data = np.copy(self.views_metrics[:, constants.metrics.index(self.current_metric)])
min_color_bar = np.min(self.original_metrics_views[:, constants.metrics.index(self.current_metric)])
max_color_bar = np.max(self.original_metrics_views[:, constants.metrics.index(self.current_metric)])
c = self.heatmap_colors
v = [0, 0.25, 0.5, 0.75, 1]
self.heatmap = pg.ColorMap(v, c)
if sphere_sum:
title_add = sphere_sum
else:
title_add = self.current_metric
self.sphere = QualitySphere(self.sphere_data, self.heatmap, parent=self, title=F"Viewpoint quality ({title_add})")
self.sphere.setBackgroundColor('w')
# add the sphere to a widget
self.sphere_widget = pg.LayoutWidget()
self.sphere_widget.addWidget(self.sphere, 0, 0)
self.sphere_widget.layout.setContentsMargins(0, 0, 0, 0)
self.sphere_widget.layout.setHorizontalSpacing(0)
self.cbw = pg.GraphicsLayoutWidget()
self.color_bar = pg.ColorBarItem(colorMap=self.heatmap, interactive=False, values=(0, 1))
# display max, min and current metric value with a horizontal line
self.color_bar.addLine(y=np.max(self.sphere_data) * 255, pen=mkPen(255, 255, 255, width=2))
self.color_bar.addLine(y=np.min(self.sphere_data) * 255, pen=mkPen(255, 255, 255, width=2))
self.color_bar_line = self.color_bar.addLine(y=255, pen=mkPen(0,0,0,255))
# if double_bar:
# # cbw2 = pg.GraphicsLayoutWidget()
# start, stop, step = min_color_bar, max_color_bar, (max_color_bar - min_color_bar) / 4
# v2 = list(np.arange(start, stop + step / 2, step))
# heatmap2 = pg.ColorMap(v2, c)
#
# color_bar2 = pg.ColorBarItem(colorMap=heatmap2, interactive=False, values=(min_color_bar, max_color_bar))
#
# # display max, min and current metric value with a horizontal line
# color_bar2.addLine(y=max_color_bar * 255, pen=mkPen(255, 255, 255, width=2))
# color_bar2.addLine(y=min_color_bar * 255, pen=mkPen(255, 255, 255, width=2))
# color_bar_line2 = color_bar2.addLine(y=255, pen=mkPen(0, 0, 0, 255))
# self.cbw.addItem(color_bar2, row = 0, col = 0)
# # cbw.setBackground('w')
# # self.sphere_widget.addWidget(cbw2, 0, 2)
# # cbw.setSizePolicy(self.sphere.sizePolicy())
# add the color bar to the side
self.cbw.addItem(self.color_bar, row = 0, col = 1)
self.cbw.setBackground('w')
self.sphere_widget.addWidget(self.cbw, 0, 1)
self.sphere_widget.layout.setColumnStretch(1, 1)
self.sphere_widget.layout.setColumnStretch(0, 12)
self.cbw.setSizePolicy(self.sphere.sizePolicy())
self.layoutgb.addWidget(self.sphere_widget, 1, 1)
# synchronize the sphere and 3d graph rotation with each other
self.graph_3d.sync_camera_with(self.sphere)
self.sphere.sync_camera_with(self.graph_3d)
self.sphere_widgets.append(self.sphere_widget)
# deprecrated but may be used in the future for user study
if constants.user_mode == 'eval_half':
for widget in self.sphere_widgets:
widget.setVisible(False)
# initialize the histogram, uses the parallelBarPlot class from parallel_bar_plot script
def initialize_histogram(self):
self.hist = parallelBarPlot(self.views_metrics, self.metrics_2d, self.metrics_3d, self.view_points, parent=self)
self.hist.setBackground('w')
# deprecated but may be used in the future for user study
if constants.user_mode == 'evalimage':
self.layoutgb.addWidget(self.hist, 0, 2, 2, 1)
else:
self.layoutgb.addWidget(self.hist, 1, 2)
# initialize the projection of the metric space
def initialize_metric_proj(self, proj_choice):
# get the data, local normalization projection or global normalization projection
name = F"{constants.metrics_projects_dir}/{self.dataset_name}_projcs_global.pkl"
if proj_choice[0:5] == 'local':
name = F"{constants.metrics_projects_dir}/{self.dataset_name}_projcs_local.pkl"
data = pd.read_pickle(name)
curr_row = data[data['layout_technique'] == self.default_layout_technique]
proj_data = curr_row['projection'].to_numpy()[0]
# get the min and max value (minus the min)
curr_min = np.min(proj_data)
scale_val = np.max(proj_data - curr_min)
# extra check, if we only have values of 0 then we set the scale to 1, to avoid / 0
if scale_val == 0.0:
scale_val = 1
# normalize the data (the points on the scatterplot so that they're between 0 and 1)
proj_data -= curr_min
proj_data /= scale_val
self.proj_data = proj_data
coloring_norm = self.avg_met_vals_global
if proj_choice[0:5] == 'local':
coloring_norm = self.avg_met_vals_local
self.coloring_norm_proj = coloring_norm
if self.metric_proj is None:
self.metric_proj = Scatter2D(proj_data, coloring_norm, self.proj_cmap, parent=self)
self.metric_proj.setBackground('w')
self.layoutgb.addWidget(self.metric_proj, 0, 3)
else:
self.metric_proj.set_data(proj_data, coloring_norm, self.proj_cmap, self.nearest_viewpoint_idx)
def initialize_stats(self):
self.stats = pg.LayoutWidget()
# Set background white:
palette = QtGui.QPalette()
palette.setColor(QtGui.QPalette.Window, QColor(255, 255, 255, 255))
self.stats.setPalette(palette)
self.stats.setAutoFillBackground(True)
# go over all the metrics
labels_stats = {}
for i in list(reversed(range(len(constants.metrics)))):
# compute how many viewpoints are better than the 2d layout, get the best viewpoint and comute how much better it is
better_raw_viewpoints_perc = round(np.sum((self.original_metrics_views[:, i] > self.original_qms[i])) / len(self.original_metrics_views[:, i]) * 100, 3)
best_res = np.max(self.original_metrics_views[:, i])
best_res_how_much_better = best_res - self.original_qms[i]
# add a label for each metric, its 2d value and how much better it is
labels_stats[constants.metrics[i]] = self.stats.addLabel(text=constants.metrics[i], row=len(self.stats.rows), col=0)
labels_stats[constants.metrics[i]].setFont(QtGui.QFont("Times", weight=QtGui.QFont.Bold))
labels_stats[constants.metrics[i] + '_h'] = self.stats.addLabel(text=': 2d value ' + str(
round(self.original_qms[i], 5)) + ' | best 3d value ' + str(round(best_res, 5)),
row=len(self.stats.rows), col=0)
labels_stats[constants.metrics[i] + '_j'] = self.stats.addLabel(text = str(round(better_raw_viewpoints_perc, 3)) + '% of viewpoints are better than 2d value | best viewpoint is ' + str(round(best_res_how_much_better, 3)) + ' better', row = len(self.stats.rows), col = 0)
self.layoutgb.addWidget(self.stats, 1, 3)
# since we have a fixed number of viewpoints, rotating the sphere might select a viewpoint that has not been computed
# simply compute the distance to the nearest viewpoint and return that
def current_quality(self):
eye = self.sphere.cameraPosition()
eye.normalize()
# Find the viewpoint for which me have metrics. that is closest to the current viewpoint
distances = np.sum((self.view_points - np.array(eye)) ** 2, axis=1)
nearest = np.argmin(distances)
self.nearest_viewpoint_idx = nearest
# Get the metric values, and highlight the corresponding histogram bars
nearest_values = self.views_metrics[nearest]
return nearest_values
# deprecated but may be used in the future for user study
# returns the unit vector of the vector
def unit_vector(self, vector):
return vector / np.linalg.norm(vector)
# deprecated but may be used in the future for user study
def angle(self, v1, v2):
n_v1 = self.unit_vector(np.array(v1))
n_v2 = self.unit_vector(np.array(v2))
dot = np.dot(n_v1, n_v2)
angle = np.arccos(np.clip(dot, -1.0, 1.0))
return angle
# deprecated but may be used in the future for user study
def check_select_available(self):
""" Test whether the current viewpoint is close to a previously selected viewpoint, in which case we disable the select button"""
if self.selected_view_count() >= constants.required_view_count:
self.select_button.setDisabled(True)
return
self.select_button.setDisabled(False)
self.select_button.setText('Select view')
for data in self.evaluation_data:
if data['dataset'] == self.dataset_name and data['layout_technique'] == self.default_layout_technique:
if self.angle(data['viewpoint'], self.graph_3d.cameraPosition()) < 0.4:
self.select_button.setDisabled(True)
self.select_button.setText("Find a different viewpoint")
# highlights the values of the viewpoint metrics on the histogram
def highlight(self):
if not self.view_locked:
# get the closest viewpoint
nearest_values = self.current_quality()
self.hist.highlight_bar_with_values(nearest_values, self.original_metrics_views[self.nearest_viewpoint_idx])
# update the line in the sphere colorbar
metric_score = nearest_values[constants.metrics.index(self.current_metric)]
self.color_bar_line.setValue(255 * metric_score)
self.metric_proj.set_data(self.proj_data, self.coloring_norm_proj, self.proj_cmap, self.nearest_viewpoint_idx)
# deprecated but may be used in the future for user study
if constants.user_mode != 'free':
self.check_select_available()
# deprecated but may be used in the future for user study
if constants.debug_mode:
print('here')
eye = self.sphere.cameraPosition()
eye.normalize()
# Find the viewpoint for which me have metrics. that is closest to the current viewpoint
distances = np.sum((self.view_points - np.array(eye)) ** 2, axis=1)
nearest = np.argmin(distances)
df = pd.read_pickle(f"layouts/{self.dataset_name}-{self.default_layout_technique}-views.pkl")
view = df['views'][nearest]
self.graph_2d.set_data(view)
# move to a certain view based on the parallel bar plot
def move_to_view(self, metric_index, bin_index, metric_value_l, metric_value_r, percentage):
if not self.view_locked:
a = self.views_metrics[:, metric_index]
indices = np.argwhere(np.logical_and(a >= metric_value_l, a <= metric_value_r)).flatten()
indices = indices[np.argsort(self.views_metrics[indices, metric_index])]
index = indices[round((len(indices) - 1) * percentage)]
viewpoint = np.array(self.view_points[index])
self.move_to_viewpoint(viewpoint)
# move to a certain viewpoint based on the sphere rotation, updates the 3d graph layout and sphere
def move_to_viewpoint(self, viewpoint):
self.curr_viewpoint = viewpoint
viewpoint_spherical = utils.rectangular_to_spherical(np.array([viewpoint]))[0]
self.sphere.setCameraPosition(azimuth=viewpoint_spherical[1], elevation=viewpoint_spherical[0],
distance=self.sphere.cameraParams()['distance'])
self.graph_3d.setCameraPosition(azimuth=viewpoint_spherical[1], elevation=viewpoint_spherical[0],
distance=self.graph_3d.cameraParams()['distance'])
self.sphere.update_views()
self.graph_3d.update_order()
# attached to the data selection drop down menu, selects dataset
def data_selected(self):
dataset_name = self.dataset_picker.value()
layout_technique = self.layout_technique_picker.value()
self.set_data(dataset_name, layout_technique)
self.metric_selected(self.metric_picker.value())
def change_proj_type(self):
self.proj_choice = self.projection_picker.value()
self.initialize_metric_proj(self.proj_choice)
# attached to the metric selection drop down menu, selects the metric
def metric_selected(self, metric):
if self.metric_picker.value() in constants.metrics:
self.current_metric = metric
self.initialize_sphere()
else:
self.initialize_sphere(sphere_sum= metric)
self.graph_3d.update_views()
self.sphere.update_views()
# sets the data based on the dataset and layout technique and metric
def set_data(self, dataset_name, layout_technique):
"""
Update the data of all the widgets inside the tool to a new dataset and layout technique combination
"""
self.dataset_name = dataset_name
self.default_layout_technique = layout_technique
metrics_file = constants.metrics_dir + '/metrics_' + self.dataset_name + '.pkl'
metrics_file = r""+metrics_file
#metrics_file = os.path.join(constants.metrics_dir, F'metrics_{self.dataset_name}.pkl')
#df = pd.read_pickle(metrics_file)
try:
df = pd.read_pickle(metrics_file)
except Exception as e:
print(e)
print(metrics_file)
select = df.loc[df['layout_technique'] == self.default_layout_technique]
# deprecated but may be used in the future for user study
self.iscategorical = self.dataset_name in constants.categorical_datasets
if self.iscategorical:
self.cmap = cm.get_cmap('tab10')
else:
self.cmap = cm.get_cmap('rainbow')
# get the views and the metrics of the views and the 2d layout
self.views_metrics = select.iloc[1]['views_metrics']
self.metrics_2d = select.iloc[0][constants.metrics].to_numpy()
self.metrics_3d = select.iloc[1][constants.metrics].to_numpy()
self.n_metrics = np.shape(self.views_metrics)[1]
# normalization step: for each quality metric, the lowest seen value of all views is set to be the lowest point (0), then the highest
# seen value of all views is set to the highest point (1), local normalization
# global normalization is when we normalize according to all seen values from all views from all datasets and techniques
self.original_qms = copy.deepcopy(self.metrics_2d)
self.original_metrics_views = copy.deepcopy(np.array(self.views_metrics))
self.views_metrics_global = copy.deepcopy(np.array(self.views_metrics))
self.metrics_2d_global = copy.deepcopy(self.metrics_2d)
qm_idx = dict(zip(range(len(self.metrics_2d)), self.metrics_2d))
for key, val in qm_idx.items():
temp_array = np.append(self.views_metrics[:, key], val)
curr_min_local = np.min(temp_array)
scale_val_local = np.max(temp_array - curr_min_local)
# extra check, if we only have values of 0 then we set the scale to 1, to avoid / 0
if scale_val_local == 0.0:
scale_val_local = 1
curr_min_global = self.mins_global[key]
curr_max_global = self.maxs_global[key]
scale_val_global = curr_max_global - curr_min_global
self.views_metrics[:, key] = (self.views_metrics[:, key] - curr_min_local) / scale_val_local
self.views_metrics_global[:, key] = (self.views_metrics_global[:, key] - curr_min_global) / scale_val_global
self.metrics_2d[key] = (self.metrics_2d[key] - curr_min_local) / scale_val_local
self.metrics_2d_global[key] = (self.metrics_2d_global[key] - curr_min_global) / scale_val_global
self.metrics_3d[key] = (self.metrics_3d[key] - curr_min_local) / scale_val_local
# now all of our metric values are scaled locally or globally
# we want the averages of these metrics across all viewpoints in order to color them
self.avg_met_vals_local = np.mean(np.vstack((self.views_metrics, self.metrics_2d)), axis = 1)
# to compare the global averages we want the distribution of the global averages to be the same, so we normalize these again
# using the minimum and maximum average seen of all layouts (global min and max of all averages)
self.avg_met_vals_global = np.mean(np.vstack((self.views_metrics_global, self.metrics_2d_global)), axis = 1)
self.avg_met_vals_global = (self.avg_met_vals_global - self.global_average_min) / (self.global_average_max - self.global_average_min)
# initialize all the layouts, plots etc.
self.initialize_3d_layout()
self.initialize_2d_layout()
self.initialize_histogram()
self.initialize_sphere()
self.initialize_metric_proj(self.proj_choice)
self.initialize_stats()
self.graph_3d.update_views()
self.highlight()
# captures the downpress of the f button, attached to locking the tool
def keyboard_event(self, event):
pass
if event.event_type == 'down':
if event.name == 'f':
self.set_tool_lock(True)
def indices(self, di, pi):
pi += 1
if pi == 5:
di += 1
pi = 0
return di, pi
def save_image(self, dataset, layout, name):
exporter = pg.exporters.ImageExporter(self.hist.scene())
exporter.export(f'{constants.analysis_dir}/{dataset}-{layout}-{name}.png')
if constants.user_mode == 'image':
for metric in constants.metrics:
self.metric_selected(metric)
self.sphere.readQImage().save(f"{constants.analysis_dir}/{dataset}-{layout}-{metric}-sphere1.png")
self.move_to_viewpoint(-np.array(self.sphere.cameraPosition()))
self.sphere.readQImage().save(f"{constants.analysis_dir}/{dataset}-{layout}-{metric}-sphere2.png")
# QtCore.QTimer.singleShot(100, lambda: self.sphere.readQImage().save(
# f"{constants.analysis_dir}/{dataset}-{layout}-sphere1.png"))
# QtCore.QTimer.singleShot(200, lambda: self.move_to_viewpoint(-np.array(self.sphere.cameraPosition())))
# QtCore.QTimer.singleShot(300, lambda: self.sphere.readQImage().save(
# f"{constants.analysis_dir}/{dataset}-{layout}-sphere2.png"))
# deprecated but may be used in the future for user study
def save_images(self, tuple):
di, pi = tuple
configs = self.available_datasets_layouts()
dataset = list(configs.keys())[di]
layout = list(configs[dataset])[pi]
QtCore.QTimer.singleShot(100, lambda: self.set_data(dataset, layout))
QtCore.QTimer.singleShot(200, lambda: self.save_image(dataset, layout, 'histograms'))
QtCore.QTimer.singleShot(300, lambda: self.save_images(self.indices(di, pi)))
# deprecated but may be used in the future for user study
def get_boxplot_data(self):
data_with_tools = self.analysis_data[(self.analysis_data['dataset'] == self.dataset_name) &
(self.analysis_data['layout_technique'] == self.default_layout_technique) &
(self.analysis_data['mode'] == 'eval_full')]
data_without_tools = self.analysis_data[(self.analysis_data['dataset'] == self.dataset_name) &
(self.analysis_data['layout_technique'] == self.default_layout_technique) &
(self.analysis_data['mode'] == 'eval_half')]
qualities_with_tools = np.array([l for l in data_with_tools['view_quality']])
data_without_tools = np.array([l for l in data_without_tools['view_quality']])
box_plot_data = []
for quality_lists in [self.views_metrics, data_without_tools, qualities_with_tools]:
box_plot_data.append([
quality_lists.mean(axis=0),
np.quantile(quality_lists, 0.25, axis=0),
np.quantile(quality_lists, 0.75, axis=0),
quality_lists.min(axis=0),
quality_lists.max(axis=0)
])
return np.array(box_plot_data)
# deprecated but may be used in the future for user study
def box_plot_images(self, index=0):
dataset, layout = constants.evaluation_set[1:][index]
self.set_data(dataset, layout)
self.hist.draw_box_plots()
QtCore.QTimer.singleShot(200, lambda: self.save_image(dataset, layout, 'boxplots2'))
QtCore.QTimer.singleShot(300, lambda: self.box_plot_images(index + 1))
# deprecated but may be used in the future for user study
def get_user_selected_viewpoints(self):
"""
Return a list of all viewpoint sets from the evaluation data.
Order: Guided 2D preference, Guided 3D preference, Blind 2D preference, Blind 3D preference
"""
data = self.analysis_data.where((self.analysis_data['layout_technique'] == self.default_layout_technique) &
(self.analysis_data['dataset'] == self.dataset_name))
viewpoints = []
for mode in ['eval_full', 'eval_half']:
for preference in ['2D', '3D']:
viewpoints_sub = data.loc[(data['mode'] == mode) & (data['preference'] == preference)]['viewpoint'].to_numpy()
viewpoints_sub = np.array([p for p in viewpoints_sub])
viewpoints.append(viewpoints_sub)
return viewpoints
# deprecated but may be used in the future for user study
def save_snapshot(self, dataset, layout, viewpoints, i, type, preference):
if len(viewpoints) == i:
return
QtCore.QTimer.singleShot(10, lambda: self.move_to_viewpoint(viewpoints[i]))
path = f"{constants.analysis_dir}/snapshots/{type}/{preference}/{dataset}-{layout}-{i}.png"
utils.create_folder_for_path(path)
QtCore.QTimer.singleShot(20, lambda: self.graph_3d.readQImage().save(path))
QtCore.QTimer.singleShot(30, lambda: self.save_snapshot(dataset, layout, viewpoints, i + 1, type, preference))
# deprecated but may be used in the future for user study
def save_user_selected_view_snapshots(self, index, set = 0):
dataset, layout = constants.evaluation_set[1:][index]
self.set_data(dataset, layout)
#Get the right viewpoint set, and save all snapshots
viewpoint_sets = self.get_user_selected_viewpoints()
type = 'guided' if set <= 1 else 'blind'
preference = '2D_preference' if set in [0, 2] else '3D_preference'
self.save_snapshot(dataset, layout, viewpoint_sets[set], 0, type, preference)
#Save snapshots of the 2D layout
path = f"{constants.analysis_dir}/snapshots/2D/{dataset}-{layout}.png"
utils.create_folder_for_path(path)
exporter = pg.exporters.ImageExporter(self.graph_2d.scene())
exporter.export(path)
#Recursive call for the next dataset
if index == len(constants.evaluation_set[1:]) - 1:
set = set + 1
if set >= 4:
return
QtCore.QTimer.singleShot(5000, lambda: self.save_user_selected_view_snapshots(index + 1, set = set))