def test_label_mutability(): dummy_video = Video(backend=MediaVideo) dummy_skeleton = Skeleton() dummy_instance = Instance(dummy_skeleton) dummy_frame = LabeledFrame(dummy_video, frame_idx=0, instances=[dummy_instance]) labels = Labels() labels.append(dummy_frame) assert dummy_video in labels.videos assert dummy_video in labels assert dummy_skeleton in labels.skeletons assert dummy_skeleton in labels assert dummy_frame in labels.labeled_frames assert dummy_frame in labels assert (dummy_video, 0) in labels assert (dummy_video, 1) not in labels dummy_video2 = Video(backend=MediaVideo) dummy_skeleton2 = Skeleton(name="dummy2") dummy_instance2 = Instance(dummy_skeleton2) dummy_frame2 = LabeledFrame(dummy_video2, frame_idx=0, instances=[dummy_instance2]) assert dummy_video2 not in labels assert dummy_skeleton2 not in labels assert dummy_frame2 not in labels labels.append(dummy_frame2) assert dummy_video2 in labels assert dummy_frame2 in labels labels.remove_video(dummy_video2) assert dummy_video2 not in labels assert dummy_frame2 not in labels assert len(labels.find(dummy_video2)) == 0 assert len(labels) == 1 labels.append(LabeledFrame(dummy_video, frame_idx=0)) assert len(labels) == 1 dummy_frames = [LabeledFrame(dummy_video, frame_idx=i) for i in range(10)] dummy_frames2 = [LabeledFrame(dummy_video2, frame_idx=i) for i in range(10)] for f in dummy_frames + dummy_frames2: labels.append(f) assert len(labels) == 20 labels.remove_video(dummy_video2) assert len(labels) == 10 assert len(labels.find(dummy_video)) == 10 assert dummy_frame in labels assert all([label in labels for label in dummy_frames[1:]]) assert dummy_video2 not in labels assert len(labels.find(dummy_video2)) == 0 assert all([label not in labels for label in dummy_frames2]) labels.remove_video(dummy_video) assert len(labels.find(dummy_video)) == 0
def test_scalar_properties(): # Scalar dummy_video = Video(backend=MediaVideo) dummy_skeleton = Skeleton() dummy_instance = Instance(dummy_skeleton) dummy_frame = LabeledFrame(dummy_video, frame_idx=0, instances=[dummy_instance]) labels = Labels() labels.append(dummy_frame) assert labels.video == dummy_video assert labels.skeleton == dummy_skeleton # Empty labels = Labels() with pytest.raises(ValueError): labels.video with pytest.raises(ValueError): labels.skeleton # More than one video dummy_skeleton = Skeleton() labels = Labels() labels.append( LabeledFrame(Video(backend=MediaVideo), frame_idx=0, instances=[Instance(dummy_skeleton)])) labels.append( LabeledFrame(Video(backend=MediaVideo), frame_idx=0, instances=[Instance(dummy_skeleton)])) assert labels.skeleton == dummy_skeleton with pytest.raises(ValueError): labels.video # More than one skeleton dummy_video = Video(backend=MediaVideo) labels = Labels() labels.append( LabeledFrame(dummy_video, frame_idx=0, instances=[Instance(Skeleton())])) labels.append( LabeledFrame(dummy_video, frame_idx=1, instances=[Instance(Skeleton())])) assert labels.video == dummy_video with pytest.raises(ValueError): labels.skeleton
def test_eq(): s1 = Skeleton("s1") s1.add_nodes(["1", "2", "3", "4", "5", "6"]) s1.add_edge("1", "2") s1.add_edge("3", "4") s1.add_edge("5", "6") s1.add_symmetry("3", "6") # Make a copy check that they are equal s2 = copy.deepcopy(s1) assert s1.matches(s2) # Add an edge, check that they are not equal s2 = copy.deepcopy(s1) s2.add_edge("5", "1") assert not s1.matches(s2) # Add a symmetry edge, not equal s2 = copy.deepcopy(s1) s2.add_symmetry("5", "1") assert not s1.matches(s2) # Delete a node s2 = copy.deepcopy(s1) s2.delete_node("5") assert not s1.matches(s2) # Delete and edge, not equal s2 = copy.deepcopy(s1) s2.delete_edge("1", "2") assert not s1.matches(s2)
def test_symmetry(): s1 = Skeleton("s1") s1.add_nodes(["1", "2", "3", "4", "5", "6"]) s1.add_edge("1", "2") s1.add_edge("3", "4") s1.add_edge("5", "6") s1.add_symmetry("1", "5") s1.add_symmetry("3", "6") assert s1.get_symmetry("1").name == "5" assert s1.get_symmetry("5").name == "1" assert s1.get_symmetry("3").name == "6" # Cannot add more than one symmetry to a node with pytest.raises(ValueError): s1.add_symmetry("1", "6") with pytest.raises(ValueError): s1.add_symmetry("6", "1") s1.delete_symmetry("1", "5") assert s1.get_symmetry("1") is None with pytest.raises(ValueError): s1.delete_symmetry("1", "5")
def test_symmetry(): s1 = Skeleton("s1") s1.add_nodes(["1", "2", "3", "4", "5", "6"]) s1.add_edge("1", "2") s1.add_edge("3", "4") s1.add_edge("5", "6") s1.add_symmetry("1", "5") s1.add_symmetry("3", "6") assert (s1.nodes[0], s1.nodes[4]) in s1.symmetries assert (s1.nodes[2], s1.nodes[5]) in s1.symmetries assert len(s1.symmetries) == 2 assert (0, 4) in s1.symmetric_inds assert (2, 5) in s1.symmetric_inds assert len(s1.symmetric_inds) == 2 assert s1.get_symmetry("1").name == "5" assert s1.get_symmetry("5").name == "1" assert s1.get_symmetry("3").name == "6" # Cannot add more than one symmetry to a node with pytest.raises(ValueError): s1.add_symmetry("1", "6") with pytest.raises(ValueError): s1.add_symmetry("6", "1") s1.delete_symmetry("1", "5") assert s1.get_symmetry("1") is None with pytest.raises(ValueError): s1.delete_symmetry("1", "5")
def test_instance_access(): labels = Labels() dummy_skeleton = Skeleton() dummy_video = Video(backend=MediaVideo) dummy_video2 = Video(backend=MediaVideo) for i in range(10): labels.append( LabeledFrame( dummy_video, frame_idx=i, instances=[Instance(dummy_skeleton), Instance(dummy_skeleton)], )) for i in range(10): labels.append( LabeledFrame( dummy_video2, frame_idx=i, instances=[ Instance(dummy_skeleton), Instance(dummy_skeleton), Instance(dummy_skeleton), ], )) assert len(labels.all_instances) == 50 assert len(list(labels.instances(video=dummy_video))) == 20 assert len(list(labels.instances(video=dummy_video2))) == 30
def test_merge_predictions(): dummy_video_a = Video.from_filename("foo.mp4") dummy_video_b = Video.from_filename("foo.mp4") dummy_skeleton_a = Skeleton() dummy_skeleton_a.add_node("node") dummy_skeleton_b = Skeleton() dummy_skeleton_b.add_node("node") dummy_instances_a = [] dummy_instances_a.append( Instance(skeleton=dummy_skeleton_a, points=dict(node=Point(1, 1))) ) dummy_instances_a.append( Instance(skeleton=dummy_skeleton_a, points=dict(node=Point(2, 2))) ) labels_a = Labels() labels_a.append( LabeledFrame(dummy_video_a, frame_idx=0, instances=dummy_instances_a) ) dummy_instances_b = [] dummy_instances_b.append( Instance(skeleton=dummy_skeleton_b, points=dict(node=Point(1, 1))) ) dummy_instances_b.append( PredictedInstance( skeleton=dummy_skeleton_b, points=dict(node=Point(3, 3)), score=1 ) ) labels_b = Labels() labels_b.append( LabeledFrame(dummy_video_b, frame_idx=0, instances=dummy_instances_b) ) # Frames have one redundant instance (perfect match) and all the # non-matching instances are different types (one predicted, one not). merged, extra_a, extra_b = Labels.complex_merge_between(labels_a, labels_b) assert len(merged[dummy_video_a]) == 1 assert len(merged[dummy_video_a][0]) == 1 # the predicted instance was merged assert not extra_a assert not extra_b
def simple_predictions(): video = Video.from_filename("video.mp4") skeleton = Skeleton() skeleton.add_node("a") skeleton.add_node("b") track_a = Track(0, "a") track_b = Track(0, "b") labels = Labels() instances = [] instances.append( PredictedInstance( skeleton=skeleton, score=2, track=track_a, points=dict(a=PredictedPoint(1, 1, score=0.5), b=PredictedPoint(1, 1, score=0.5)), )) instances.append( PredictedInstance( skeleton=skeleton, score=5, track=track_b, points=dict(a=PredictedPoint(1, 1, score=0.7), b=PredictedPoint(1, 1, score=0.7)), )) labeled_frame = LabeledFrame(video, frame_idx=0, instances=instances) labels.append(labeled_frame) instances = [] instances.append( PredictedInstance( skeleton=skeleton, score=3, track=track_a, points=dict(a=PredictedPoint(4, 5, score=1.5), b=PredictedPoint(1, 1, score=1.0)), )) instances.append( PredictedInstance( skeleton=skeleton, score=6, track=track_b, points=dict(a=PredictedPoint(6, 13, score=1.7), b=PredictedPoint(1, 1, score=1.0)), )) labeled_frame = LabeledFrame(video, frame_idx=1, instances=instances) labels.append(labeled_frame) return labels
def test_basic_suggestions(small_robot_mp4_vid): dummy_video = small_robot_mp4_vid dummy_skeleton = Skeleton() dummy_instance = Instance(dummy_skeleton) dummy_frame = LabeledFrame(dummy_video, frame_idx=0, instances=[dummy_instance]) labels = Labels() labels.append(dummy_frame) suggestions = VideoFrameSuggestions.suggest( labels=labels, params=dict(method="sample", per_video=13) ) labels.set_suggestions(suggestions) assert len(labels.get_video_suggestions(dummy_video)) == 13
def skeleton(): # Create a simple skeleton object skeleton = Skeleton("Fly") skeleton.add_node("head") skeleton.add_node("thorax") skeleton.add_node("abdomen") skeleton.add_node("left-wing") skeleton.add_node("right-wing") skeleton.add_edge(source="head", destination="thorax") skeleton.add_edge(source="thorax", destination="abdomen") skeleton.add_edge(source="thorax", destination="left-wing") skeleton.add_edge(source="thorax", destination="right-wing") skeleton.add_symmetry(node1="left-wing", node2="right-wing") return skeleton
def removal_test_labels(): skeleton = Skeleton() video = Video(backend=MediaVideo) lf_user_only = LabeledFrame( video=video, frame_idx=0, instances=[Instance(skeleton=skeleton)] ) lf_pred_only = LabeledFrame( video=video, frame_idx=1, instances=[PredictedInstance(skeleton=skeleton)] ) lf_both = LabeledFrame( video=video, frame_idx=2, instances=[Instance(skeleton=skeleton), PredictedInstance(skeleton=skeleton)], ) labels = Labels([lf_user_only, lf_pred_only, lf_both]) return labels
def stickman(): # Make a skeleton with a space in its name to test things. stickman = Skeleton("Stick man") stickman.add_nodes( ["head", "neck", "body", "right-arm", "left-arm", "right-leg", "left-leg"] ) stickman.add_edge("neck", "head") stickman.add_edge("body", "neck") stickman.add_edge("body", "right-arm") stickman.add_edge("body", "left-arm") stickman.add_edge("body", "right-leg") stickman.add_edge("body", "left-leg") stickman.add_symmetry(node1="left-arm", node2="right-arm") stickman.add_symmetry(node1="left-leg", node2="right-leg") return stickman
def test_nms_instances_to_remove(): skeleton = Skeleton() skeleton.add_nodes(("a", "b")) instances = [] inst = PredictedInstance(skeleton=skeleton) inst["a"].x = 10 inst["a"].y = 10 inst["b"].x = 20 inst["b"].y = 20 inst.score = 1 instances.append(inst) inst = PredictedInstance(skeleton=skeleton) inst["a"].x = 10 inst["a"].y = 10 inst["b"].x = 15 inst["b"].y = 15 inst.score = 0.3 instances.append(inst) inst = PredictedInstance(skeleton=skeleton) inst["a"].x = 30 inst["a"].y = 30 inst["b"].x = 40 inst["b"].y = 40 inst.score = 1 instances.append(inst) inst = PredictedInstance(skeleton=skeleton) inst["a"].x = 32 inst["a"].y = 32 inst["b"].x = 42 inst["b"].y = 42 inst.score = 0.5 instances.append(inst) to_keep, to_remove = nms_instances(instances, iou_threshold=0.5, target_count=3) assert len(to_remove) == 1 assert to_remove[0].matches(instances[1])
def test_labels_merge(): dummy_video = Video(backend=MediaVideo) dummy_skeleton = Skeleton() dummy_skeleton.add_node("node") labels = Labels() dummy_frames = [] # Add 10 instances with different points (so they aren't "redundant") for i in range(10): instance = Instance(skeleton=dummy_skeleton, points=dict(node=Point(i, i))) dummy_frame = LabeledFrame(dummy_video, frame_idx=0, instances=[instance]) dummy_frames.append(dummy_frame) labels.labeled_frames.extend(dummy_frames) assert len(labels) == 10 assert len(labels.labeled_frames[0].instances) == 1 labels.merge_matching_frames() assert len(labels) == 1 assert len(labels.labeled_frames[0].instances) == 10
def test_hdf5(skeleton, stickman, tmpdir): filename = os.path.join(tmpdir, "skeleton.h5") if os.path.isfile(filename): os.remove(filename) # Save both skeletons to the HDF5 filename skeleton.save_hdf5(filename) stickman.save_hdf5(filename) # Load the all the skeletons as a list sk_list = Skeleton.load_all_hdf5(filename) # Lets check that they are equal to what we saved, this checks the order too. assert skeleton.matches(sk_list[0]) assert stickman.matches(sk_list[1]) # Check load to dict as well sk_dict = Skeleton.load_all_hdf5(filename, return_dict=True) assert skeleton.matches(sk_dict[skeleton.name]) assert stickman.matches(sk_dict[stickman.name]) # Check individual load assert Skeleton.load_hdf5(filename, skeleton.name).matches(skeleton) assert Skeleton.load_hdf5(filename, stickman.name).matches(stickman) # Check overwrite save and save list Skeleton.save_all_hdf5(filename, [skeleton, stickman]) assert Skeleton.load_hdf5(filename, skeleton.name).matches(skeleton) assert Skeleton.load_hdf5(filename, stickman.name).matches(stickman) # Make sure we can't load a non-existent skeleton with pytest.raises(KeyError): Skeleton.load_hdf5(filename, "BadName") # Make sure we can't save skeletons with the same name with pytest.raises(ValueError): Skeleton.save_all_hdf5( filename, [skeleton, Skeleton(name=skeleton.name)])
def test_deserialize_suggestions(small_robot_mp4_vid, tmpdir): dummy_video = small_robot_mp4_vid dummy_skeleton = Skeleton() dummy_instance = Instance(dummy_skeleton) dummy_frame = LabeledFrame(dummy_video, frame_idx=0, instances=[dummy_instance]) labels = Labels() labels.append(dummy_frame) suggestions = VideoFrameSuggestions.suggest( labels=labels, params=dict(method="sample", per_video=13) ) labels.set_suggestions(suggestions) filename = os.path.join(tmpdir, "new_suggestions.h5") Labels.save_file(filename=filename, labels=labels) new_suggestion_labels = Labels.load_file(filename) assert len(suggestions) == len(new_suggestion_labels.suggestions) assert [frame.frame_idx for frame in suggestions] == [ frame.frame_idx for frame in new_suggestion_labels.suggestions ]
def __init__(self, labels_path: Optional[str] = None, *args, **kwargs): """Initialize the app. Args: labels_path: Path to saved :class:`Labels` dataset. Returns: None. """ super(MainWindow, self).__init__(*args, **kwargs) self.state = GuiState() self.labels = Labels() self.commands = CommandContext(state=self.state, app=self, update_callback=self.on_data_update) self._menu_actions = dict() self._buttons = dict() self._child_windows = dict() self.overlays = dict() self.state.connect("filename", self.setWindowTitle) self.state["skeleton"] = Skeleton() self.state["labeled_frame"] = None self.state["filename"] = None self.state["show labels"] = True self.state["show edges"] = True self.state["edge style"] = "Line" self.state["fit"] = False self.state["color predicted"] = prefs["color predicted"] self._initialize_gui() if labels_path: self.loadProjectFile(labels_path)
def test_skeleton_node_name_change(): """ Test that and instance is not broken after a node on the skeleton has its name changed. """ s = Skeleton("Test") s.add_nodes(["a", "b", "c", "d", "e"]) s.add_edge("a", "b") instance = Instance(s) instance["a"] = Point(1, 2) instance["b"] = Point(3, 4) # Rename the node s.relabel_nodes({"a": "A"}) # Reference to the old node name should raise a KeyError with pytest.raises(KeyError): instance["a"].x = 2 # Make sure the A now references the same point on the instance assert instance["A"] == Point(1, 2) assert instance["b"] == Point(3, 4)
def load_predicted_labels_json_old( data_path: str, parsed_json: dict = None, adjust_matlab_indexing: bool = True, fix_rel_paths: bool = True, ) -> List[LabeledFrame]: """ Load predicted instances from Talmo's old JSON format. Args: data_path: The path to the JSON file. parsed_json: The parsed json if already loaded, so we can save some time if already parsed. adjust_matlab_indexing: Whether to adjust indexing from MATLAB. fix_rel_paths: Whether to fix paths to videos to absolute paths. Returns: List of :class:`LabeledFrame` objects. """ if parsed_json is None: data = json.loads(open(data_path).read()) else: data = parsed_json videos = pd.DataFrame(data["videos"]) predicted_instances = pd.DataFrame(data["predicted_instances"]) predicted_points = pd.DataFrame(data["predicted_points"]) if adjust_matlab_indexing: predicted_instances.frameIdx -= 1 predicted_points.frameIdx -= 1 predicted_points.node -= 1 predicted_points.x -= 1 predicted_points.y -= 1 skeleton = Skeleton() skeleton.add_nodes(data["skeleton"]["nodeNames"]) edges = data["skeleton"]["edges"] if adjust_matlab_indexing: edges = np.array(edges) - 1 for (src_idx, dst_idx) in edges: skeleton.add_edge( data["skeleton"]["nodeNames"][src_idx], data["skeleton"]["nodeNames"][dst_idx], ) if fix_rel_paths: for i, row in videos.iterrows(): p = row.filepath if not os.path.exists(p): p = os.path.join(os.path.dirname(data_path), p) if os.path.exists(p): videos.at[i, "filepath"] = p # Make the video objects video_objects = {} for i, row in videos.iterrows(): if videos.at[i, "format"] == "media": vid = Video.from_media(videos.at[i, "filepath"]) else: vid = Video.from_hdf5( filename=videos.at[i, "filepath"], dataset=videos.at[i, "dataset"] ) video_objects[videos.at[i, "id"]] = vid track_ids = predicted_instances["trackId"].values unique_track_ids = np.unique(track_ids) spawned_on = { track_id: predicted_instances.loc[predicted_instances["trackId"] == track_id][ "frameIdx" ].values[0] for track_id in unique_track_ids } tracks = { i: Track(name=str(i), spawned_on=spawned_on[i]) for i in np.unique(predicted_instances["trackId"].values).tolist() } # A function to get all the instances for a particular video frame def get_frame_predicted_instances(video_id, frame_idx): points = predicted_points is_in_frame = (points["videoId"] == video_id) & ( points["frameIdx"] == frame_idx ) if not is_in_frame.any(): return [] instances = [] frame_instance_ids = np.unique(points["instanceId"][is_in_frame]) for i, instance_id in enumerate(frame_instance_ids): is_instance = is_in_frame & (points["instanceId"] == instance_id) track_id = predicted_instances.loc[ predicted_instances["id"] == instance_id ]["trackId"].values[0] match_score = predicted_instances.loc[ predicted_instances["id"] == instance_id ]["matching_score"].values[0] track_score = predicted_instances.loc[ predicted_instances["id"] == instance_id ]["tracking_score"].values[0] instance_points = { data["skeleton"]["nodeNames"][n]: PredictedPoint( x, y, visible=v, score=confidence ) for x, y, n, v, confidence in zip( *[ points[k][is_instance] for k in ["x", "y", "node", "visible", "confidence"] ] ) } instance = PredictedInstance( skeleton=skeleton, points=instance_points, track=tracks[track_id], score=match_score, ) instances.append(instance) return instances # Get the unique labeled frames and construct a list of LabeledFrame objects for them. frame_keys = list( { (videoId, frameIdx) for videoId, frameIdx in zip( predicted_points["videoId"], predicted_points["frameIdx"] ) } ) frame_keys.sort() labels = [] for videoId, frameIdx in frame_keys: label = LabeledFrame( video=video_objects[videoId], frame_idx=frameIdx, instances=get_frame_predicted_instances(videoId, frameIdx), ) labels.append(label) return labels
def test_complex_merge(): dummy_video_a = Video.from_filename("foo.mp4") dummy_video_b = Video.from_filename("foo.mp4") dummy_skeleton_a = Skeleton() dummy_skeleton_a.add_node("node") dummy_skeleton_b = Skeleton() dummy_skeleton_b.add_node("node") dummy_instances_a = [] dummy_instances_a.append( Instance(skeleton=dummy_skeleton_a, points=dict(node=Point(1, 1)))) dummy_instances_a.append( Instance(skeleton=dummy_skeleton_a, points=dict(node=Point(2, 2)))) labels_a = Labels() labels_a.append( LabeledFrame(dummy_video_a, frame_idx=0, instances=dummy_instances_a)) dummy_instances_b = [] dummy_instances_b.append( Instance(skeleton=dummy_skeleton_b, points=dict(node=Point(1, 1)))) dummy_instances_b.append( Instance(skeleton=dummy_skeleton_b, points=dict(node=Point(3, 3)))) labels_b = Labels() labels_b.append( LabeledFrame(dummy_video_b, frame_idx=0, instances=dummy_instances_b)) # conflict labels_b.append( LabeledFrame(dummy_video_b, frame_idx=1, instances=dummy_instances_b)) # clean merged, extra_a, extra_b = Labels.complex_merge_between(labels_a, labels_b) # Check that we have the cleanly merged frame assert dummy_video_a in merged assert len(merged[dummy_video_a]) == 1 # one merged frame assert len(merged[dummy_video_a][1]) == 2 # with two instances # Check that labels_a includes redundant and clean assert len(labels_a.labeled_frames) == 2 assert len(labels_a.labeled_frames[0].instances) == 1 assert labels_a.labeled_frames[0].instances[0].points[0].x == 1 assert len(labels_a.labeled_frames[1].instances) == 2 assert labels_a.labeled_frames[1].instances[0].points[0].x == 1 assert labels_a.labeled_frames[1].instances[1].points[0].x == 3 # Check that extra_a/b includes the appropriate conflicting instance assert len(extra_a) == 1 assert len(extra_b) == 1 assert len(extra_a[0].instances) == 1 assert len(extra_b[0].instances) == 1 assert extra_a[0].instances[0].points[0].x == 2 assert extra_b[0].instances[0].points[0].x == 3 # Check that objects were unified assert extra_a[0].video == extra_b[0].video # Check resolving the conflict using new Labels.finish_complex_merge(labels_a, extra_b) assert len(labels_a.labeled_frames) == 2 assert len(labels_a.labeled_frames[0].instances) == 2 assert labels_a.labeled_frames[0].instances[1].points[0].x == 3
def test_arborescence(): skeleton = Skeleton() skeleton.add_node("a") skeleton.add_node("b") skeleton.add_node("c") # linear: a -> b -> c skeleton.add_edge("a", "b") skeleton.add_edge("b", "c") assert skeleton.is_arborescence skeleton = Skeleton() skeleton.add_node("a") skeleton.add_node("b") skeleton.add_node("c") # two branches from a: a -> b and a -> c skeleton.add_edge("a", "b") skeleton.add_edge("a", "c") assert skeleton.is_arborescence skeleton = Skeleton() skeleton.add_node("a") skeleton.add_node("b") skeleton.add_node("c") # no edges so too many roots assert not skeleton.is_arborescence assert sorted((n.name for n in skeleton.root_nodes)) == ["a", "b", "c"] # still too many roots: a and c skeleton.add_edge("a", "b") assert not skeleton.is_arborescence assert sorted((n.name for n in skeleton.root_nodes)) == ["a", "c"] skeleton = Skeleton() skeleton.add_node("a") skeleton.add_node("b") skeleton.add_node("c") # cycle skeleton.add_edge("a", "b") skeleton.add_edge("b", "c") skeleton.add_edge("c", "a") assert not skeleton.is_arborescence assert len(skeleton.cycles) == 1 assert len(skeleton.root_nodes) == 0 skeleton = Skeleton() skeleton.add_node("a") skeleton.add_node("b") skeleton.add_node("c") skeleton.add_node("d") # diamond, too many sources leading to d skeleton.add_edge("a", "b") skeleton.add_edge("a", "c") skeleton.add_edge("b", "d") skeleton.add_edge("c", "d") assert not skeleton.is_arborescence assert len(skeleton.cycles) == 0 assert len(skeleton.root_nodes) == 1 assert len(skeleton.in_degree_over_one) == 1
def test_edge_order(): """Test is edge list order is maintained upon insertion""" skeleton = Skeleton("Test")