There is a small overhead of using multiple Pyprocessing is already included in Python's standard library as the It has an API similar to Python's threading and Queue standard modules,īut work with processes instead of threads. However, we can use multiple processes (multiple interpreters). Is, Python threads can be used for asynchrony but not concurrency. Threads cannot be used to conduct multiple searches in parallel. Due to Python's dreaded "Global Interpreter Lock" (GIL), While creating a kd-tree is very fast, searching it can be timeĬonsuming. append ( tree ) if intersect ( right_hrect, radius, datapoint ): stack. where ( distance <= radius ) if len ( near ): idx = leaf_idx distance = distance inside += ( zip ( distance, idx )) else : if intersect ( left_hrect, radius, datapoint ): stack. pop () # leaf if leaf_idx is not None : param = leaf_data. append ( _knn ) return knn def radius_search ( tree, datapoint, radius ): """ find all points within radius of datapoint """ stack = ] inside = while stack : leaf_idx, leaf_data, left_hrect, \ repeat ( leafsize, axis = 1 ) _knn = search_kdtree ( tree, _data, K + 1 ) knn. copy (), leafsize = leafsize ) # search kdtree knn = for i in numpy. shape # build kdtree tree = kdtree ( data. append ( tree ) return knn def knn_search ( data, K, leafsize = 2048 ): """ find the K nearest neighbours for data points in data, using an O(n log n) kd-tree """ ndata = data. append ( tree ) # chech right branch if intersect ( right_hrect, knn, _datapt ): stack. pop () # leaf if leaf_idx is not None : _knn = quadratic_knn_search ( datapoint, leaf_idx, leaf_data, K ) if _knn < knn : knn = sorted ( knn + _knn ) # not a leaf else : # check left branch if intersect ( left_hrect, knn, _datapt ): stack. argsort ( sqd, kind = 'mergesort' ) idx = idx return zip ( sqd, lidx ) def search_kdtree ( tree, datapoint, K ): """ find the k nearest neighbours of datapoint in a kdtree """ stack = ] knn = * K _datapt = datapoint while stack : leaf_idx, leaf_data, left_hrect, \ sum ( axis = 0 ) # data.reshape((param,1)).repeat(ndata, axis=1) idx = numpy. shape K = K if K < ndata else ndata retval = sqd = (( ldata - data ) ** 2 ). sum () < r2 def quadratic_knn_search ( data, lidx, ldata, K ): """ find K nearest neighbours of data among ldata """ ndata = ldata. copy () idx = p maxval p = maxval return (( p - centroid ) ** 2 ). #!python numbers=disable def intersect ( hrect, r2, centroid ): """ checks if the hyperrectangle hrect intersects with the hypersphere defined by centroid and r2 """ maxval = hrect minval = hrect p = centroid. append (( None, None, left_hrect, right_hrect, None, None )) return tree copy () left_hrect = splitval right_hrect = splitval # append node to tree tree. ![]() copy () else : left_hrect = _right_hrect. append (( data, didx, depth + 1, nodeptr, False )) splitval = data if leftbranch : left_hrect = _left_hrect. append (( data, didx, depth + 1, nodeptr, True )) stack. ![]() argsort ( data, kind = 'mergesort' ) data = data didx = didx nodeptr = len ( tree ) stack. append ( leaf ) # not a leaf, split the data in two else : splitdim = depth % ndim idx = numpy. copy () leaf = ( _didx, _data, None, None, 0, 0 ) tree. ![]() shape nodeptr = len ( tree ) # update parent node _didx, _data, _left_hrect, _right_hrect, left, right = tree tree = ( _didx, _data, _left_hrect, _right_hrect, nodeptr, right ) if leftbranch \Įlse ( _didx, _data, _left_hrect, _right_hrect, left, nodeptr ) # insert node in kd-tree # leaf node? if ndata <= leafsize : _didx = didx. copy () left_hrect = splitval right_hrect = splitval tree = stack =, idx, 1, 0, True ), ( data, idx, 1, 0, False )] # recursively split data in halves using hyper-rectangles: while stack : # pop data off stack data, didx, depth, parent, leftbranch = stack. argsort ( data, kind = 'mergesort' ) data = data splitval = data left_hrect = hrect. max ( axis = 1 ) # create root of kd-tree idx = numpy. shape # find bounding hyper-rectangle hrect = numpy. ![]() number of data points to leave in a leaf output: kd-tree: list of tuples """ ndim = data. #!python numbers=disable # Copyleft 2008 Sturla Molden # University of Oslo #import psyco #psyco.full() import numpy def kdtree ( data, leafsize = 10 ): """ build a kd-tree for O(n log n) nearest neighbour search input: data: 2D ndarray, shape =(ndim,ndata), preferentially C order leafsize: max.
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