def process_picking(self,near,far):
print('process picking')
min_dist=[cll.mindistance_segment2track(near,far,xyz) for xyz in self.data]
min_dist=np.array(min_dist)
#print min_dist
self.picked_track=min_dist.argmin()
print 'min index',self.picked_track
min_dist_info=[cll.mindistance_segment2track_info(near,far,xyz) for xyz in self.data]
A = np.array(min_dist_info)
dist=10**(-3)
iA=np.where(A[:,0]<dist)
minA=A[iA]
print 'minA ', minA
miniA=minA[:,1].argmin()
print 'final min index ',iA[0][miniA]
self.picked_track=iA[0][miniA]
def process_pickray(self,near,far):
if self.centered:
shift=np.array(self.position)-self.mean
print shift
else:
shift=np.array([0,0,0])
min_dist_info=[ \
cll.mindistance_segment2track_info(near,far,xyz+shift) \
for xyz in self.curves]
#print'Min distance info'
#print min_dist_info
A = np.array(min_dist_info)
#print A
dist=10**(-3)
np.where(A[:,0]<dist)
iA=np.where(A[:,0]<dist)
minA=A[iA]
if len(minA)==0:
#print 'IN'
iA=np.where(A[:,0]==A[:,0].min())
minA = A[iA]
#print 'A','minA next',minA, iA
miniA=minA[:,1].argmin()
print 'track_center',self.position, 'selected',iA[0][miniA]
self.current=iA[0][miniA]
def process_pickray(self,near,far):
if self.centered:
shift=np.array(self.position)-self.mean
print shift
else:
shift=np.array([0,0,0])
min_dist_info=[ \
cll.mindistance_segment2track_info(near,far,xyz+shift) \
for xyz in self.curves]
#print'Min distance info'
#print min_dist_info
A = np.array(min_dist_info)
#print A
dist=10**(-3)
np.where(A[:,0]<dist)
iA=np.where(A[:,0]<dist)
minA=A[iA]
if len(minA)==0:
#print 'IN'
iA=np.where(A[:,0]==A[:,0].min())
minA = A[iA]
#print 'A','minA next',minA, iA
miniA=minA[:,1].argmin()
print 'track_center',self.position, 'selected',iA[0][miniA]
self.current=iA[0][miniA]
def picking_virtuals(self,symbol,modifiers):
x,y=self.mouse_x,self.mouse_y
nx,ny,nz=screen_to_model(x,y,0)
fx,fy,fz=screen_to_model(x,y,1)
near=(nx,ny,nz)
far=(fx,fy,fz)
min_dist_info=[ cll.mindistance_segment2track_info(near,far,xyz) \
for xyz in self.virtuals]
A = np.array(min_dist_info)
dist=10**(-3)
np.where(A[:,0]<dist)
iA=np.where(A[:,0]<dist)
minA=A[iA]
if len(minA)==0:
iA=np.where(A[:,0]==A[:,0].min())
minA = A[iA]
miniA=minA[:,1].argmin()
return iA[0][miniA]
def process_pickray(self,near,far):
min_dist_info=[ cll.mindistance_segment2track_info(near,far,xyz) \
for xyz in self.curves]
A = np.array(min_dist_info)
dist=10**(-3)
np.where(A[:,0]<dist)
iA=np.where(A[:,0]<dist)
minA=A[iA]
if len(minA)==0:
iA=np.where(A[:,0]==A[:,0].min())
minA = A[iA]
miniA=minA[:,1].argmin()
print 'track_id',iA[0][miniA]
self.current=iA[0][miniA]
if self.selected.count(self.current)==0:
self.selected.append(self.current)
if self.selected.count(self.current)>0:
self.selected.remove(self.current)
def picking_virtuals(self, symbol,modifiers, min_dist=1e-3):
"""Compute the id of the closest track to the mouse pointer.
"""
x, y = self.mouse_x, self.mouse_y
# Define two points in model space from mouse+screen(=0) position and mouse+horizon(=1) position
near = screen_to_model(x, y, 0)
far = screen_to_model(x, y, 1)
# Compute distance of virtuals from screen and from the line defined by the two points above
tmp = np.array([cll.mindistance_segment2track_info(near, far, xyz) \
for xyz in self.virtuals])
line_distance, screen_distance = tmp[:,0], tmp[:,1]
if False: # basic algoritm:
# Among the virtuals within a range to the line (i.e. < min_dist) return the closest to the screen:
closest_to_line_idx = np.argsort(line_distance)
closest_to_line_thresholded_bool = line_distance[closest_to_line_idx] < min_dist
if (closest_to_line_thresholded_bool).any():
return closest_to_line_idx[np.argmin(screen_distance[closest_to_line_thresholded_bool])]
else:
return closest_to_line_idx[0]
else: # simpler and apparently more effective algorithm:
return np.argmin(line_distance + screen_distance)
def process_picking(self, near, far):
shift = np.array(self.position) - self.mean
min_dist_info=[ \
cll.mindistance_segment2track_info(near,far,xyz+shift) \
for xyz in self.data]
A = np.array(min_dist_info)
dist = 10**(-3)
np.where(A[:, 0] < dist)
iA = np.where(A[:, 0] < dist)
minA = A[iA]
if len(minA) == 0:
#print 'IN'
iA = np.where(A[:, 0] == A[:, 0].min())
minA = A[iA]
#print 'A','minA next',minA, iA
miniA = minA[:, 1].argmin()
print 'track_center', self.position, 'selected index ', iA[0][miniA]
self.picked_track = iA[0][miniA]
self.picked_tracks.append(self.picked_track)
def process_picking(self,near,far):
shift=np.array(self.position)-self.mean
min_dist_info=[ \
cll.mindistance_segment2track_info(near,far,xyz+shift) \
for xyz in self.data]
A = np.array(min_dist_info)
dist=10**(-3)
np.where(A[:,0]<dist)
iA=np.where(A[:,0]<dist)
minA=A[iA]
if len(minA)==0:
#print 'IN'
iA=np.where(A[:,0]==A[:,0].min())
minA = A[iA]
#print 'A','minA next',minA, iA
miniA=minA[:,1].argmin()
print 'track_center',self.position, 'selected index ',iA[0][miniA]
self.picked_track=iA[0][miniA]
self.picked_tracks.append(self.picked_track)