Merge commit 'cbdcf9dffcc2d2f56b5e01c1dd4def8b6dd8ddee' into backport

  refactor references to new top-level package nemere
  init package nemere

src/check_parse-pcap.py

@@ -10,8 +10,8 @@
 from sys import exit
 import IPython
 
-from validation.messageParser import ParsedMessage
-from utils.loader import SpecimenLoader
+from nemere.validation.messageParser import ParsedMessage
+from nemere.utils.loader import SpecimenLoader
 
 if __name__ == '__main__':
     parser = ArgumentParser(

src/check_pcap-info.py

@@ -6,7 +6,7 @@
 from os.path import isfile, basename
 from tabulate import tabulate
 
-from utils.loader import SpecimenLoader
+from nemere.utils.loader import SpecimenLoader
 
 
 def countByteFrequency():

src/alignment/alignMessages.py → src/nemere/alignment/alignMessages.py

@@ -6,10 +6,10 @@
 from sklearn.cluster import DBSCAN
 from scipy.special import comb
 
-from alignment.hirschbergAlignSegments import HirschbergOnSegmentSimilarity
-from inference.segmentHandler import matrixFromTpairs
-from inference.segments import MessageSegment
-from inference.templates import DistanceCalculator, MemmapDC
+from nemere.alignment.hirschbergAlignSegments import HirschbergOnSegmentSimilarity
+from nemere.inference.segmentHandler import matrixFromTpairs
+from nemere.inference.segments import MessageSegment
+from nemere.inference.templates import DistanceCalculator, MemmapDC
 
 
 class SegmentedMessages(object):
@@ -71,8 +71,8 @@ def _calcSimilarityMatrix(self):
         """
         Calculate a similarity matrix of messages from nwscores yielded by HirschbergOnSegmentSimilarity.
 
-        >>> from utils.baseAlgorithms import generateTestSegments
-        >>> from inference.templates import DistanceCalculator
+        >>> from nemere.utils.baseAlgorithms import generateTestSegments
+        >>> from nemere.inference.templates import DistanceCalculator
         >>> segments = generateTestSegments()
         >>> DistanceCalculator.debug = False
         >>> dc = DistanceCalculator(segments)
@@ -104,8 +104,8 @@ def _calcDistanceMatrix(self, similarityMatrix: numpy.ndarray):
         """
         For clustering, convert the nwscores-based similarity matrix to a distance measure.
 
-        >>> from utils.baseAlgorithms import generateTestSegments
-        >>> from inference.templates import DistanceCalculator
+        >>> from nemere.utils.baseAlgorithms import generateTestSegments
+        >>> from nemere.inference.templates import DistanceCalculator
         >>> segments = generateTestSegments()
         >>> DistanceCalculator.debug = False
         >>> dc = DistanceCalculator(segments)
@@ -213,8 +213,8 @@ def alignMessageType(self, msgcluster: List[Tuple[MessageSegment]]):
         Messages segments of one cluster aligned to the medoid ("segments that is most similar too all segments")
         of the cluster.
 
-        >>> from utils.baseAlgorithms import generateTestSegments
-        >>> from inference.templates import DistanceCalculator
+        >>> from nemere.utils.baseAlgorithms import generateTestSegments
+        >>> from nemere.inference.templates import DistanceCalculator
         >>> segments = generateTestSegments()
         >>> DistanceCalculator.debug = False
         >>> dc = DistanceCalculator(segments)
@@ -292,7 +292,7 @@ def autoconfigureDBSCAN(self):
 
         :return: minpts, epsilon
         """
-        from utils.baseAlgorithms import autoconfigureDBSCAN
+        from nemere.utils.baseAlgorithms import autoconfigureDBSCAN
         # can we omit k = 0 ?
         # No - recall and even more so precision deteriorates for dns and dhcp (1000s)
         epsilon, min_samples, k = autoconfigureDBSCAN(self.neighbors())

src/alignment/clusterMerging.py → src/nemere/alignment/clusterMerging.py

@@ -5,9 +5,9 @@
 from networkx import Graph
 from networkx.algorithms.components.connected import connected_components
 
-from inference.templates import DistanceCalculator, Template
-from alignment.hirschbergAlignSegments import HirschbergOnSegmentSimilarity
-from inference.analyzers import *
+from nemere.inference.templates import DistanceCalculator, Template
+from nemere.alignment.hirschbergAlignSegments import HirschbergOnSegmentSimilarity
+from nemere.inference.analyzers import *
 
 
 
@@ -399,8 +399,8 @@ def mergeClusters(self, messageClusters, clusterStats, alignedFieldClasses,
                       matchingClusters, matchingConditions):
         import IPython
         from tabulate import tabulate
-        from utils.evaluationHelpers import printClusterMergeConditions
-        from inference.templates import Template
+        from nemere.utils.evaluationHelpers import printClusterMergeConditions
+        from nemere.inference.templates import Template
 
         remDue2gaps = [
             clunuAB for clunuAB in matchingClusters
@@ -588,7 +588,7 @@ def __init__(self, alignedClusters: Dict[int, List], dc: DistanceCalculator):
 
 
     def calcClusterDistances(self, mmg=(0, -1, 5)):
-        from inference.segmentHandler import matrixFromTpairs
+        from nemere.inference.segmentHandler import matrixFromTpairs
 
         fclassHirsch, statDynFields, statDynValues = self.generateHirsch(mmg)
 

src/alignment/clusterSplitting.py → src/nemere/alignment/clusterSplitting.py

@@ -8,8 +8,8 @@
 from collections import Counter
 from tabulate import tabulate
 
-from inference.segments import MessageSegment
-from alignment.alignMessages import SegmentedMessages
+from nemere.inference.segments import MessageSegment
+from nemere.alignment.alignMessages import SegmentedMessages
 
 
 debug = True