def findWonkyVectors(x, y, dx, dy, tol=100):
from PYME.Analysis.LMVis.visHelpers import genEdgeDB
T = delaunay.Triangulation(x, y)
edb = genEdgeDB(T)
wonkyVecs = np.zeros(len(x))
#remove any shifts which are markedly different from their neighbours
for i in range(len(x)):
incidentEdges = T.edge_db[edb[i][0]]
# d_x = np.diff(T.x[incidentEdges])
# d_y = np.diff(T.y[incidentEdges])
#
# dist = (d_x**2 + d_y**2)
#
# di = np.mean(np.sqrt(dist))
neighb = incidentEdges.ravel()
neighb = neighb[(neighb == i) < .5]
if (abs(dx[i] - np.median(dx[neighb])) >
tol) or (abs(dy[i] - np.median(dy[neighb])) > tol):
wonkyVecs[i] = 1
return wonkyVecs > .5
def findWonkyVectors(x, y,dx,dy, tol=100):
from PYME.Analysis.LMVis.visHelpers import genEdgeDB
T = delaunay.Triangulation(x,y)
edb = genEdgeDB(T)
wonkyVecs = np.zeros(len(x))
#remove any shifts which are markedly different from their neighbours
for i in range(len(x)):
incidentEdges = T.edge_db[edb[i][0]]
# d_x = np.diff(T.x[incidentEdges])
# d_y = np.diff(T.y[incidentEdges])
#
# dist = (d_x**2 + d_y**2)
#
# di = np.mean(np.sqrt(dist))
neighb = incidentEdges.ravel()
neighb = neighb[(neighb == i) < .5]
if (abs(dx[i] - np.median(dx[neighb])) > tol) or (abs(dy[i] - np.median(dy[neighb])) > tol):
wonkyVecs[i] = 1
return wonkyVecs > .5
def cluster(T):
edb = genEdgeDB(T)
nodeNum = -1*np.ones(T.x.shape)
nodeInfo = np.iinfo('i').max*np.ones((len(T.x), 2))
curNode = 0
for i in range(len(T.x)):
incidentEdges = T.edge_db[edb[i][0]]
neighbourPoints = edb[i][1]
dx = np.diff(T.x[incidentEdges])
dy = np.diff(T.y[incidentEdges])
dist = (dx**2 + dy**2)
sI = argsort(dist)[::-1]
#Do nearest neighbour - this MUST generate a new node
iMin = np.argmin(np.sqrt(dist))
d = dist[iMin]
n = neighbourPoints[iMin]
nodeInfo[curNode, 1] = d
nodeNum[i] = curNode
if n < i: #have already visited other point
nn = nodeNum[n]
if nodeInfo[nn, 1] >= d:
nodeNum[n] = curNode
else:
nnp = nn
while nodeInfo[nn, 1] < d: #traverse upwards until distance is larger
nnp = nn
nn = nodeInfo[nn, 0] #parent
nodeInfo[nnp, 0] = curNode
nodeInfo[curNode, 0] = nn
else:
nodeInfo[curNode,0] = len(T.x)-1 #last node
nodeNum[n] = curNode
curNode += 1
return nodeNum, nodeInfo
def cluster(T):
edb = genEdgeDB(T)
nodeNum = -1 * np.ones(T.x.shape)
nodeInfo = np.iinfo('i').max * np.ones((len(T.x), 2))
curNode = 0
for i in range(len(T.x)):
incidentEdges = T.edge_db[edb[i][0]]
neighbourPoints = edb[i][1]
dx = np.diff(T.x[incidentEdges])
dy = np.diff(T.y[incidentEdges])
dist = (dx**2 + dy**2)
sI = argsort(dist)[::-1]
#Do nearest neighbour - this MUST generate a new node
iMin = np.argmin(np.sqrt(dist))
d = dist[iMin]
n = neighbourPoints[iMin]
nodeInfo[curNode, 1] = d
nodeNum[i] = curNode
if n < i: #have already visited other point
nn = nodeNum[n]
if nodeInfo[nn, 1] >= d:
nodeNum[n] = curNode
else:
nnp = nn
while nodeInfo[
nn, 1] < d: #traverse upwards until distance is larger
nnp = nn
nn = nodeInfo[nn, 0] #parent
nodeInfo[nnp, 0] = curNode
nodeInfo[curNode, 0] = nn
else:
nodeInfo[curNode, 0] = len(T.x) - 1 #last node
nodeNum[n] = curNode
curNode += 1
return nodeNum, nodeInfo
