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Add code language to anomaly detection reports and plots
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-76
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4 files changed

+112
-76
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‎domains/anomaly-detection/anomalyDetectionCsv.sh‎

Lines changed: 25 additions & 19 deletions
Original file line numberDiff line numberDiff line change
@@ -69,17 +69,20 @@ anomaly_detection_queries() {
6969
local nodeLabel
7070
nodeLabel=$( extractQueryParameter "projection_node_label" "${@}" )
7171

72+
local language
73+
language=$( extractQueryParameter "projection_language" "${@}" )
74+
7275
echo "anomalyDetectionCsv: $(date +'%Y-%m-%dT%H:%M:%S%z') Executing Queries for ${nodeLabel} nodes..."
73-
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionPotentialImbalancedRoles.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${nodeLabel}AnomalyDetection_PotentialImbalancedRoles.csv"
74-
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionPotentialOverEngineerOrIsolated.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${nodeLabel}AnomalyDetection_PotentialOverEngineerOrIsolated.csv"
76+
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionPotentialImbalancedRoles.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${language}_${nodeLabel}AnomalyDetection_PotentialImbalancedRoles.csv"
77+
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionPotentialOverEngineerOrIsolated.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${language}_${nodeLabel}AnomalyDetection_PotentialOverEngineerOrIsolated.csv"
7578

76-
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionHiddenBridgeNodes.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${nodeLabel}AnomalyDetection_HiddenBridgeNodes.csv"
77-
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionPopularBottlenecks.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${nodeLabel}AnomalyDetection_PopularBottlenecks.csv"
78-
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionSilentCoordinators.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${nodeLabel}AnomalyDetection_SilentCoordinators.csv"
79-
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionOverReferencesUtilities.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${nodeLabel}AnomalyDetection_OverReferencesUtilities.csv"
80-
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionFragileStructuralBridges.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${nodeLabel}AnomalyDetection_FragileStructuralBridges.csv"
81-
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionDependencyHungryOrchestrators.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${nodeLabel}AnomalyDetection_DependencyHungryOrchestrators.csv"
82-
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionUnexpectedCentralNodes.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${nodeLabel}AnomalyDetection_UnexpectedCentralNodes.csv"
79+
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionHiddenBridgeNodes.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${language}_${nodeLabel}AnomalyDetection_HiddenBridgeNodes.csv"
80+
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionPopularBottlenecks.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${language}_${nodeLabel}AnomalyDetection_PopularBottlenecks.csv"
81+
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionSilentCoordinators.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${language}_${nodeLabel}AnomalyDetection_SilentCoordinators.csv"
82+
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionOverReferencesUtilities.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${language}_${nodeLabel}AnomalyDetection_OverReferencesUtilities.csv"
83+
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionFragileStructuralBridges.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${language}_${nodeLabel}AnomalyDetection_FragileStructuralBridges.csv"
84+
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionDependencyHungryOrchestrators.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${language}_${nodeLabel}AnomalyDetection_DependencyHungryOrchestrators.csv"
85+
execute_cypher "${ANOMALY_DETECTION_QUERY_CYPHER_DIR}/AnomalyDetectionUnexpectedCentralNodes.cypher" "${@}" > "${FULL_REPORT_DIRECTORY}/${language}_${nodeLabel}AnomalyDetection_UnexpectedCentralNodes.csv"
8386
}
8487

8588
# Run the anomaly detection pipeline.
@@ -111,36 +114,39 @@ ALGORITHM_NODE="projection_node_label"
111114
PROJECTION_WEIGHT="dependencies_projection_weight_property"
112115
ALGORITHM_WEIGHT="projection_weight_property"
113116

117+
PROJECTION_LANGUAGE="dependencies_projection_language"
118+
ALGORITHM_LANGUAGE="projection_language"
119+
114120
# Code independent algorithm parameters
115121
COMMUNITY_PROPERTY="community_property=communityLeidenIdTuned"
116122
EMBEDDING_PROPERTY="embedding_property=embeddingsFastRandomProjectionTunedForClustering"
117123

118124
# -- Java Artifact Node Embeddings -------------------------------
119125

120-
if createUndirectedDependencyProjection "${PROJECTION_NAME}=artifact-anomaly-detection" "${PROJECTION_NODE}=Artifact" "${PROJECTION_WEIGHT}=weight"; then
121-
createDirectedDependencyProjection "${PROJECTION_NAME}=artifact-anomaly-detection-directed" "${PROJECTION_NODE}=Artifact" "${PROJECTION_WEIGHT}=weight"
122-
anomaly_detection_csv_reports "${ALGORITHM_PROJECTION}=artifact-anomaly-detection" "${ALGORITHM_NODE}=Artifact" "${ALGORITHM_WEIGHT}=weight" "${COMMUNITY_PROPERTY}" "${EMBEDDING_PROPERTY}"
126+
if createUndirectedDependencyProjection "${PROJECTION_NAME}=artifact-anomaly-detection" "${PROJECTION_NODE}=Artifact" "${PROJECTION_WEIGHT}=weight" "${PROJECTION_LANGUAGE}=Java"; then
127+
createDirectedDependencyProjection "${PROJECTION_NAME}=artifact-anomaly-detection-directed" "${PROJECTION_NODE}=Artifact" "${PROJECTION_WEIGHT}=weight" "${PROJECTION_LANGUAGE}=Java"
128+
anomaly_detection_csv_reports "${ALGORITHM_PROJECTION}=artifact-anomaly-detection" "${ALGORITHM_NODE}=Artifact" "${ALGORITHM_WEIGHT}=weight" "${ALGORITHM_LANGUAGE}=Java" "${COMMUNITY_PROPERTY}" "${EMBEDDING_PROPERTY}"
123129
fi
124130

125131
# -- Java Package Node Embeddings --------------------------------
126132

127-
if createUndirectedDependencyProjection "${PROJECTION_NAME}=package-anomaly-detection" "${PROJECTION_NODE}=Package" "${PROJECTION_WEIGHT}=weight25PercentInterfaces"; then
128-
createDirectedDependencyProjection "${PROJECTION_NAME}=package-anomaly-detection-directed" "${PROJECTION_NODE}=Package" "${PROJECTION_WEIGHT}=weight25PercentInterfaces"
129-
anomaly_detection_csv_reports "${ALGORITHM_PROJECTION}=package-anomaly-detection" "${ALGORITHM_NODE}=Package" "${ALGORITHM_WEIGHT}=weight25PercentInterfaces" "${COMMUNITY_PROPERTY}" "${EMBEDDING_PROPERTY}"
133+
if createUndirectedDependencyProjection "${PROJECTION_NAME}=package-anomaly-detection" "${PROJECTION_NODE}=Package" "${PROJECTION_WEIGHT}=weight25PercentInterfaces" "${PROJECTION_LANGUAGE}=Java"; then
134+
createDirectedDependencyProjection "${PROJECTION_NAME}=package-anomaly-detection-directed" "${PROJECTION_NODE}=Package" "${PROJECTION_WEIGHT}=weight25PercentInterfaces" "${PROJECTION_LANGUAGE}=Java"
135+
anomaly_detection_csv_reports "${ALGORITHM_PROJECTION}=package-anomaly-detection" "${ALGORITHM_NODE}=Package" "${ALGORITHM_WEIGHT}=weight25PercentInterfaces" "${ALGORITHM_LANGUAGE}=Java" "${COMMUNITY_PROPERTY}" "${EMBEDDING_PROPERTY}"
130136
fi
131137

132138
# -- Java Type Node Embeddings -----------------------------------
133139

134140
if createUndirectedJavaTypeDependencyProjection "${PROJECTION_NAME}=type-anomaly-detection"; then
135141
createDirectedJavaTypeDependencyProjection "${PROJECTION_NAME}=type-anomaly-detection-directed"
136-
anomaly_detection_csv_reports "${ALGORITHM_PROJECTION}=type-anomaly-detection" "${ALGORITHM_NODE}=Type" "${ALGORITHM_WEIGHT}=weight" "${COMMUNITY_PROPERTY}" "${EMBEDDING_PROPERTY}"
142+
anomaly_detection_csv_reports "${ALGORITHM_PROJECTION}=type-anomaly-detection" "${ALGORITHM_NODE}=Type" "${ALGORITHM_WEIGHT}=weight" "${ALGORITHM_LANGUAGE}=Java" "${COMMUNITY_PROPERTY}" "${EMBEDDING_PROPERTY}"
137143
fi
138144

139145
# -- Typescript Module Node Embeddings ---------------------------
140146

141-
if createUndirectedDependencyProjection "${PROJECTION_NAME}=typescript-module-embedding" "${PROJECTION_NODE}=Module" "${PROJECTION_WEIGHT}=lowCouplingElement25PercentWeight"; then
142-
createDirectedDependencyProjection "${PROJECTION_NAME}=typescript-module-embedding-directed" "${PROJECTION_NODE}=Module" "${PROJECTION_WEIGHT}=lowCouplingElement25PercentWeight"
143-
anomaly_detection_csv_reports "${ALGORITHM_PROJECTION}=typescript-module-embedding" "${ALGORITHM_NODE}=Module" "${ALGORITHM_WEIGHT}=lowCouplingElement25PercentWeight" "${COMMUNITY_PROPERTY}" "${EMBEDDING_PROPERTY}"
147+
if createUndirectedDependencyProjection "${PROJECTION_NAME}=typescript-module-embedding" "${PROJECTION_NODE}=Module" "${PROJECTION_WEIGHT}=lowCouplingElement25PercentWeight" "${PROJECTION_LANGUAGE}=Typescript"; then
148+
createDirectedDependencyProjection "${PROJECTION_NAME}=typescript-module-embedding-directed" "${PROJECTION_NODE}=Module" "${PROJECTION_WEIGHT}=lowCouplingElement25PercentWeight" "${PROJECTION_LANGUAGE}=Typescript"
149+
anomaly_detection_csv_reports "${ALGORITHM_PROJECTION}=typescript-module-embedding" "${ALGORITHM_NODE}=Module" "${ALGORITHM_WEIGHT}=lowCouplingElement25PercentWeight" "${ALGORITHM_LANGUAGE}=Typescript" "${COMMUNITY_PROPERTY}" "${EMBEDDING_PROPERTY}"
144150
fi
145151

146152
# ---------------------------------------------------------------

‎domains/anomaly-detection/anomalyDetectionFeaturePlots.py‎

Lines changed: 50 additions & 38 deletions
Original file line numberDiff line numberDiff line change
@@ -30,6 +30,7 @@
3030

3131
from visualization import plot_annotation_style, annotate_each, annotate_each_with_index, scale_marker_sizes, zoom_into_center, zoom_into_center_while_preserving_scores_above_threshold, zoom_into_center_while_preserving_top_scores
3232

33+
3334
class Parameters:
3435
required_parameters_ = ["projection_node_label"]
3536

@@ -88,9 +89,20 @@ def example(cls):
8889
def get_query_parameters(self) -> typing.Dict[str, str]:
8990
return self.query_parameters_.copy() # copy enforces immutability
9091

91-
def get_projection_node_label(self) -> str:
92+
def __get_projection_node_label(self) -> str:
9293
return self.query_parameters_["projection_node_label"]
9394

95+
def __is_code_language_available(self) -> bool:
96+
return "projection_language" in self.query_parameters_
97+
98+
def __get_projection_language(self) -> str:
99+
return self.query_parameters_["projection_language"] if self.__is_code_language_available() else ""
100+
101+
def get_plot_prefix(self) -> str:
102+
if self.__is_code_language_available():
103+
return self.__get_projection_language() + " " + self.__get_projection_node_label()
104+
return self.__get_projection_node_label()
105+
94106
def get_report_directory(self) -> str:
95107
return self.report_directory
96108

@@ -451,7 +463,7 @@ def plot_clustering_coefficient_vs_page_rank(
451463

452464
common_column_names_for_annotations = {
453465
"name_column": 'shortName',
454-
"x_position_column": 'clusteringCoefficient',
466+
"x_position_column": 'clusteringCoefficient',
455467
"y_position_column": 'pageRank'
456468
}
457469

@@ -461,8 +473,8 @@ def plot_clustering_coefficient_vs_page_rank(
461473
threshold_page_rank = mean_page_rank + 1.5 * standard_deviation_page_rank
462474
significant_points = combined_data[combined_data['pageRank'] > threshold_page_rank].sort_values(by='pageRank', ascending=False).reset_index(drop=True).head(10)
463475
annotate_each_with_index(
464-
significant_points,
465-
using=plot.annotate,
476+
significant_points,
477+
using=plot.annotate,
466478
value_column='pageRank',
467479
**common_column_names_for_annotations
468480
)
@@ -473,8 +485,8 @@ def plot_clustering_coefficient_vs_page_rank(
473485
top_clustering_coefficients = combined_data.sort_values(by='clusteringCoefficient', ascending=False).reset_index(drop=True).head(20)
474486
top_clustering_coefficients = top_clustering_coefficients.sort_values(by='pageRank', ascending=True).reset_index(drop=True).head(5)
475487
annotate_each_with_index(
476-
top_clustering_coefficients,
477-
using=plot.annotate,
488+
top_clustering_coefficients,
489+
using=plot.annotate,
478490
value_column='clusteringCoefficient',
479491
**common_column_names_for_annotations
480492
)
@@ -508,9 +520,9 @@ def plot_clusters(
508520
# Setup columns
509521
node_size_column = centrality_column_name
510522

511-
clustering_visualization_dataframe_zoomed=zoom_into_center(
512-
clustering_visualization_dataframe,
513-
x_position_column,
523+
clustering_visualization_dataframe_zoomed = zoom_into_center(
524+
clustering_visualization_dataframe,
525+
x_position_column,
514526
y_position_column
515527
)
516528

@@ -617,10 +629,10 @@ def plot_clusters_probabilities(
617629
print("No projected data to plot available")
618630
return
619631

620-
clustering_visualization_dataframe_zoomed=zoom_into_center_while_preserving_top_scores(
621-
clustering_visualization_dataframe,
622-
x_position_column,
623-
y_position_column,
632+
clustering_visualization_dataframe_zoomed = zoom_into_center_while_preserving_top_scores(
633+
clustering_visualization_dataframe,
634+
x_position_column,
635+
y_position_column,
624636
cluster_probability_column,
625637
annotate_n_lowest_probabilities,
626638
lowest_scores=True
@@ -636,7 +648,7 @@ def get_common_plot_parameters(data: pd.DataFrame) -> dict:
636648
"y": data[y_position_column],
637649
"s": data[size_column + '_scaled'],
638650
}
639-
651+
640652
cluster_noise = clustering_visualization_dataframe_zoomed[clustering_visualization_dataframe_zoomed[cluster_label_column] == -1]
641653
cluster_non_noise = clustering_visualization_dataframe_zoomed[clustering_visualization_dataframe_zoomed[cluster_label_column] != -1]
642654
cluster_even_labels = clustering_visualization_dataframe_zoomed[clustering_visualization_dataframe_zoomed[cluster_label_column] % 2 == 0]
@@ -800,7 +812,7 @@ def get_common_plot_parameters(data: pd.DataFrame) -> dict:
800812
# ------------------------------------------------------------------------------------------------------------
801813

802814
parameters = parse_input_parameters()
803-
plot_type = parameters.get_projection_node_label()
815+
plot_prefix = parameters.get_plot_prefix()
804816
report_directory = parameters.get_report_directory()
805817

806818
driver = get_graph_database_driver()
@@ -813,31 +825,31 @@ def get_common_plot_parameters(data: pd.DataFrame) -> dict:
813825
data['pageRank'],
814826
data['articleRank'],
815827
data['shortCodeUnitName'],
816-
title=f"{plot_type} distribution of PageRank - ArticleRank differences",
817-
plot_file_path=get_file_path(f"{plot_type}_PageRank_Minus_ArticleRank_Distribution", parameters)
828+
title=f"{plot_prefix} distribution of PageRank - ArticleRank differences",
829+
plot_file_path=get_file_path(f"{plot_prefix}_PageRank_Minus_ArticleRank_Distribution", parameters)
818830
)
819831

820832
plot_clustering_coefficient_distribution(
821833
data['clusteringCoefficient'],
822-
title=f"{plot_type} distribution of clustering coefficients",
823-
plot_file_path=get_file_path(f"{plot_type}_ClusteringCoefficient_distribution", parameters)
834+
title=f"{plot_prefix} distribution of clustering coefficients",
835+
plot_file_path=get_file_path(f"{plot_prefix}_ClusteringCoefficient_distribution", parameters)
824836
)
825837

826838
plot_clustering_coefficient_vs_page_rank(
827839
data['clusteringCoefficient'],
828840
data['pageRank'],
829841
data['shortCodeUnitName'],
830842
data['clusterNoise'],
831-
title=f"{plot_type} clustering coefficient versus PageRank",
832-
plot_file_path=get_file_path(f"{plot_type}_ClusteringCoefficient_versus_PageRank", parameters)
843+
title=f"{plot_prefix} clustering coefficient versus PageRank",
844+
plot_file_path=get_file_path(f"{plot_prefix}_ClusteringCoefficient_versus_PageRank", parameters)
833845
)
834846

835847
if (overall_cluster_count < 20):
836848
print(f"anomalyDetectionFeaturePlots: Less than 20 clusters: {overall_cluster_count}. Only one plot containing all clusters will be created.")
837849
plot_clusters(
838850
clustering_visualization_dataframe=data,
839-
title=f"{plot_type} all clusters overall (less than 20)",
840-
plot_file_path=get_file_path(f"{plot_type}_Clusters_Overall", parameters)
851+
title=f"{plot_prefix} all clusters overall (less than 20)",
852+
plot_file_path=get_file_path(f"{plot_prefix}_Clusters_Overall", parameters)
841853
)
842854
else:
843855
print(f"anomalyDetectionFeaturePlots: More than 20 clusters: {overall_cluster_count}. Different plots focussing on different features like cluster size will be created.")
@@ -846,57 +858,57 @@ def get_common_plot_parameters(data: pd.DataFrame) -> dict:
846858
)
847859
plot_clusters(
848860
clustering_visualization_dataframe=clusters_by_largest_size,
849-
title=f"{plot_type} clusters with the largest size",
850-
plot_file_path=get_file_path(f"{plot_type}_Clusters_largest_size", parameters)
861+
title=f"{plot_prefix} clusters with the largest size",
862+
plot_file_path=get_file_path(f"{plot_prefix}_Clusters_largest_size", parameters)
851863
)
852864

853865
clusters_by_largest_max_radius = get_clusters_by_criteria(
854866
data, by='clusterRadiusMax', ascending=False, cluster_count=20
855867
)
856868
plot_clusters(
857869
clustering_visualization_dataframe=clusters_by_largest_max_radius,
858-
title=f"{plot_type} clusters with the largest max radius",
859-
plot_file_path=get_file_path(f"{plot_type}_Clusters_largest_max_radius", parameters)
870+
title=f"{plot_prefix} clusters with the largest max radius",
871+
plot_file_path=get_file_path(f"{plot_prefix}_Clusters_largest_max_radius", parameters)
860872
)
861873

862874
clusters_by_largest_average_radius = get_clusters_by_criteria(
863875
data, by='clusterRadiusAverage', ascending=False, cluster_count=20
864876
)
865877
plot_clusters(
866878
clustering_visualization_dataframe=clusters_by_largest_average_radius,
867-
title=f"{plot_type} clusters with the largest average radius",
868-
plot_file_path=get_file_path(f"{plot_type}_Clusters_largest_average_radius", parameters)
879+
title=f"{plot_prefix} clusters with the largest average radius",
880+
plot_file_path=get_file_path(f"{plot_prefix}_Clusters_largest_average_radius", parameters)
869881
)
870882

871883
plot_clusters_probabilities(
872-
clustering_visualization_dataframe=data,
873-
title=f"{plot_type} clustering probabilities (red=high uncertainty)",
874-
plot_file_path=get_file_path(f"{plot_type}_Cluster_probabilities", parameters)
884+
clustering_visualization_dataframe=data,
885+
title=f"{plot_prefix} clustering probabilities (red=high uncertainty)",
886+
plot_file_path=get_file_path(f"{plot_prefix}_Cluster_probabilities", parameters)
875887
)
876888

877889
plot_cluster_noise(
878890
clustering_visualization_dataframe=data,
879-
title=f"{plot_type} clustering noise points that are surprisingly central (red) or popular (size)",
891+
title=f"{plot_prefix} clustering noise points that are surprisingly central (red) or popular (size)",
880892
size_column_name='degree',
881893
color_column_name='pageRank',
882-
plot_file_path=get_file_path(f"{plot_type}_ClusterNoise_highly_central_and_popular", parameters)
894+
plot_file_path=get_file_path(f"{plot_prefix}_ClusterNoise_highly_central_and_popular", parameters)
883895
)
884896

885897
plot_cluster_noise(
886898
clustering_visualization_dataframe=data,
887-
title=f"{plot_type} clustering noise points that bridge flow (red) and are poorly integrated (size)",
899+
title=f"{plot_prefix} clustering noise points that bridge flow (red) and are poorly integrated (size)",
888900
size_column_name='inverseClusteringCoefficient',
889901
color_column_name='betweenness',
890-
plot_file_path=get_file_path(f"{plot_type}_ClusterNoise_poorly_integrated_bridges", parameters),
902+
plot_file_path=get_file_path(f"{plot_prefix}_ClusterNoise_poorly_integrated_bridges", parameters),
891903
downscale_normal_sizes=0.4
892904
)
893905

894906
plot_cluster_noise(
895907
clustering_visualization_dataframe=data,
896-
title=f"{plot_type} clustering noise points with role inversion (size) possibly violating layering or dependency direction (red)",
908+
title=f"{plot_prefix} clustering noise points with role inversion (size) possibly violating layering or dependency direction (red)",
897909
size_column_name='pageToArticleRankDifference',
898910
color_column_name='betweenness',
899-
plot_file_path=get_file_path(f"{plot_type}_ClusterNoise_role_inverted_bridges", parameters)
911+
plot_file_path=get_file_path(f"{plot_prefix}_ClusterNoise_role_inverted_bridges", parameters)
900912
)
901913

902914
driver.close()

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