@@ -63,10 +63,10 @@ def name(self):
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return 'knearestconcavehull'
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def displayName (self ):
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- return self .tr ('Concave hull (k-nearest neighbour )' )
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+ return self .tr ('Concave hull (k-nearest neighbor )' )
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def shortDescription (self ):
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- return self .tr ('Creates a concave hull using the k-nearest neighbour algorithm.' )
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+ return self .tr ('Creates a concave hull using the k-nearest neighbor algorithm.' )
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def icon (self ):
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return QgsApplication .getThemeIcon ("/algorithms/mAlgorithmConcaveHull.svg" )
@@ -168,7 +168,6 @@ def processAlgorithm(self, parameters, context, feedback):
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else :
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# Not grouped by field
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-
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# Initialize writer
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(sink , dest_id ) = self .parameterAsSink (parameters , self .OUTPUT , context ,
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fields , QgsWkbTypes .Polygon , source .sourceCrs ())
@@ -440,7 +439,7 @@ def concave_hull(points_list, k):
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without intersecting any previous line segments.
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This is an implementation of the algorithm described by Adriano Moreira and Maribel Yasmina Santos:
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- CONCAVE HULL: A neighborhood_k-NEAREST NEIGHBOURS APPROACH FOR THE COMPUTATION OF THE REGION OCCUPIED BY A SET OF POINTS.
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+ CONCAVE HULL: A neighborhood_k-NEAREST NEIGHBORS APPROACH FOR THE COMPUTATION OF THE REGION OCCUPIED BY A SET OF POINTS.
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GRAPP 2007 - International Conference on Computer Graphics Theory and Applications; pp 61-68.
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:param points_list: list of tuples (x, y)
@@ -466,7 +465,7 @@ def concave_hull(points_list, k):
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if len (point_set ) == 3 :
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return add_point (point_set , point_set [0 ])
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- # make sure that k neighbours can be found
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+ # make sure that k neighbors can be found
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kk = min (kk , len (point_set ))
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# start with the point having the smallest y-coordinate (most southern point)
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