def domain_length(self,face_1,face_2):
r'''
Calculate the distance between two faces of the network
Parameters
----------
face_1 and face_2 : array_like
Lists of pores belonging to opposite faces of the network
Returns
-------
The length of the domain in the specified direction
Notes
-----
- Does not yet check if input faces are perpendicular to each other
'''
#Ensure given points are coplanar before proceeding
if misc.iscoplanar(self['pore.coords'][face_1]) and misc.iscoplanar(self['pore.coords'][face_2]):
#Find distance between given faces
x = self['pore.coords'][face_1]
y = self['pore.coords'][face_2]
Ds = misc.dist(x,y)
L = sp.median(sp.amin(Ds,axis=0))
else:
self._logger.warning('The supplied pores are not coplanar. Length will be approximate.')
f1 = self['pore.coords'][face_1]
f2 = self['pore.coords'][face_2]
distavg = [0,0,0]
distavg[0] = sp.absolute(sp.average(f1[:,0]) - sp.average(f2[:,0]))
distavg[1] = sp.absolute(sp.average(f1[:,1]) - sp.average(f2[:,1]))
distavg[2] = sp.absolute(sp.average(f1[:,2]) - sp.average(f2[:,2]))
L = max(distavg)
return L
def domain_length(self,face_1,face_2):
r'''
Calculate the distance between two faces of the network
Parameters
----------
face_1 and face_2 : array_like
Lists of pores belonging to opposite faces of the network
Returns
-------
The length of the domain in the specified direction
Notes
-----
- Does not yet check if input faces are perpendicular to each other
'''
#Ensure given points are coplanar before proceeding
if misc.iscoplanar(self['pore.coords'][face_1]) and misc.iscoplanar(self['pore.coords'][face_2]):
#Find distance between given faces
x = self['pore.coords'][face_1]
y = self['pore.coords'][face_2]
Ds = misc.dist(x,y)
L = sp.median(sp.amin(Ds,axis=0))
else:
logger.warning('The supplied pores are not coplanar. Length will be approximate.')
f1 = self['pore.coords'][face_1]
f2 = self['pore.coords'][face_2]
distavg = [0,0,0]
distavg[0] = sp.absolute(sp.average(f1[:,0]) - sp.average(f2[:,0]))
distavg[1] = sp.absolute(sp.average(f1[:,1]) - sp.average(f2[:,1]))
distavg[2] = sp.absolute(sp.average(f1[:,2]) - sp.average(f2[:,2]))
L = max(distavg)
return L