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comparison python/moving.py @ 881:8ba82b371eea

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work on storing PET
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 14 Mar 2017 17:48:40 -0400
parents 000555430b28
children e2452abba0e7
comparison
equal deleted inserted replaced
880:000555430b28 881:8ba82b371eea
338 if delta >= 0: 338 if delta >= 0:
339 deltaRoot = sqrt(delta) 339 deltaRoot = sqrt(delta)
340 ttc1 = (-b + deltaRoot)/(2*a) 340 ttc1 = (-b + deltaRoot)/(2*a)
341 ttc2 = (-b - deltaRoot)/(2*a) 341 ttc2 = (-b - deltaRoot)/(2*a)
342 if ttc1 >= 0 and ttc2 >= 0: 342 if ttc1 >= 0 and ttc2 >= 0:
343 ttc = min(ttc1,ttc2) 343 return min(ttc1,ttc2)
344 elif ttc1 >= 0: 344 elif ttc1 >= 0:
345 ttc = ttc1 345 return ttc1
346 elif ttc2 >= 0: 346 elif ttc2 >= 0:
347 ttc = ttc2 347 return ttc2
348 else: # ttc1 < 0 and ttc2 < 0: 348 else: # ttc1 < 0 and ttc2 < 0:
349 ttc = None 349 return None
350 else: 350 else:
351 ttc = None 351 return None
352 return ttc
353 352
354 @staticmethod 353 @staticmethod
355 def midPoint(p1, p2): 354 def midPoint(p1, p2):
356 'Returns the middle of the segment [p1, p2]' 355 'Returns the middle of the segment [p1, p2]'
357 return Point(0.5*p1.x+0.5*p2.x, 0.5*p1.y+0.5*p2.y) 356 return Point(0.5*p1.x+0.5*p2.x, 0.5*p1.y+0.5*p2.y)
1426 @staticmethod 1425 @staticmethod
1427 def computePET(obj1, obj2, collisionDistanceThreshold): 1426 def computePET(obj1, obj2, collisionDistanceThreshold):
1428 '''Post-encroachment time based on distance threshold 1427 '''Post-encroachment time based on distance threshold
1429 1428
1430 Returns the smallest time difference when the object positions are within collisionDistanceThreshold''' 1429 Returns the smallest time difference when the object positions are within collisionDistanceThreshold'''
1431 #for i in xrange(int(obj1.length())-1):
1432 # for j in xrange(int(obj2.length())-1):
1433 # inter = segmentIntersection(obj1.getPositionAt(i), obj1.getPositionAt(i+1), obj2.getPositionAt(i), obj2.getPositionAt(i+1))
1434 positions1 = [p.astuple() for p in obj1.getPositions()] 1430 positions1 = [p.astuple() for p in obj1.getPositions()]
1435 positions2 = [p.astuple() for p in obj2.getPositions()] 1431 positions2 = [p.astuple() for p in obj2.getPositions()]
1436 pets = zeros((int(obj1.length()), int(obj2.length()))) 1432 pets = zeros((int(obj1.length()), int(obj2.length())))
1437 for i,t1 in enumerate(obj1.getTimeInterval()): 1433 for i,t1 in enumerate(obj1.getTimeInterval()):
1438 for j,t2 in enumerate(obj2.getTimeInterval()): 1434 for j,t2 in enumerate(obj2.getTimeInterval()):
1439 pets[i,j] = abs(t1-t2) 1435 pets[i,j] = abs(t1-t2)
1440 distances = cdist(positions1, positions2, metric = 'euclidean') 1436 distances = cdist(positions1, positions2, metric = 'euclidean')
1441 if distances.min() <= collisionDistanceThreshold: 1437 if distances.min() <= collisionDistanceThreshold:
1438 #idx = distances.argmin()
1442 return pets[distances <= collisionDistanceThreshold].min() 1439 return pets[distances <= collisionDistanceThreshold].min()
1443 else: 1440 else:
1444 return None 1441 return None
1445 1442
1446 def predictPosition(self, instant, nTimeSteps, externalAcceleration = Point(0,0)): 1443 def predictPosition(self, instant, nTimeSteps, externalAcceleration = Point(0,0)):