def setup_Q(self):
self.Q = scipy.zeros((self.nperiods, self.ndists, self.ndists))
#D = self.D * self.Dmask
for p in range(self.nperiods):
for i, d1 in enumerate(self.dists):
s1 = sum(d1)
if s1 > 0:
for j, d2 in enumerate(self.dists):
s2 = sum(d2)
xor = scipy.logical_xor(d1, d2)
# only consider transitions between dists that are
# 1 step (dispersal or extinction) apart
if sum(xor) == 1:
dest = scipy.nonzero(xor)[0]
#prior = self.dist_priors[i]
if s1 < s2: # dispersal
rate = 0.0
for src in scipy.nonzero(d1)[0]:
rate += self.D[p,src,dest] * \
self.Dmask[p,src,dest]
# for each area in d1, add rate of
# dispersal to dest
else: # extinction
rate = self.E[p,dest]
#self.Q[i][j] = (prior * rate)
self.Q[p,i,j] = rate
self.set_Qdiag(p)
def alg(ring):
# fill holes in binary objects and then see if anything got filled
xor = scipy.logical_xor(ring, ndimage.binary_fill_holes(ring))
if xor.any():
return True
else:
return False
def alg2(ring):
# fill holes in binary objects
filled = ndimage.binary_fill_holes(ring)
# substract original image with object \w holes from the filled-holes image to obtain holes only
holes = scipy.logical_xor(ring, filled)
# label all objects in the image to obtain the number of holes
_, hole_count = ndimage.measurements.label(holes)
return hole_count
def iter_dist_splits(dist):
if sum(dist) == 1:
yield (dist, dist)
else:
for i in scipy.nonzero(dist)[0]:
x = scipy.zeros((len(dist),), dtype="i")
x[i] = 1
x = tuple(x)
yield (x, dist)
yield (dist, x)
y = tuple(scipy.array(scipy.logical_xor(dist, x), dtype="i"))
yield (x, y)
if sum(y) > 1:
yield (y, x)
def iter_dist_splits(dist):
if sum(dist) == 1:
yield (dist, dist)
else:
for i in scipy.nonzero(dist)[0]:
x = scipy.zeros((len(dist), ), dtype="i")
x[i] = 1
x = tuple(x)
yield (x, dist)
yield (dist, x)
y = tuple(scipy.array(scipy.logical_xor(dist, x), dtype="i"))
yield (x, y)
if sum(y) > 1:
yield (y, x)
def extract_holes(ring):
"""
Check if an object in an image forms a ring with one single hole.
@note if the number of holes equals 1, the supplied objects is either a ring (in 2D)
or another shape with a single hole somewhere (low probability). If the number of
holes equals 0, the supplied object is of a shape containing no holes (e.g. an not
completely closed ring). If the number is higher than 1, the object contains multiple
holes.
@param ring a 2D array of type bool_ containing a single object
@type ring ndarray
@return an 2D array containing all holes labeled consecutively from 1 as first and
the number of found holes as second arguments
@rtype (ndarray, int)
"""
# fill the holes in the binary array (using ndim*2 connectedness) and xor with original image
holes = scipy.logical_xor(ring, ndimage.binary_fill_holes(ring))
# find all objects (= holes in the original object) in the image
return ndimage.measurements.label(holes)
#center=(100,100);
radius1=200;
radius2=205;
siz=np.round(2*radius2*1.40);
img1=np.zeros((siz,siz));
img2=np.zeros((siz,siz));
center=np.round([siz/2,siz/2]);
rr1, cc1 = circle(center[0],center[1], radius1)
img1[rr1, cc1] = 1
rr2, cc2 = circle(center[0],center[1], radius2)
img2[rr2, cc2] = 1
circ=sc.logical_xor(img2,img1)
circ.astype(int)
plt.imshow(img1)
plt.show()
io.imshow(img2)
io.imshow(circ)
io.imsave('Circle_radius'+str(radius1)+'.png',circ)
# #angles on the circle
# indices=np.where(xCenterLine==1)
## randperm=np.random.permutation(len(indices[0]))
# randperm=range(len(indices[0]))
#
# #consider 2% random points on the circle
#center=(100,100);
radius1 = 200
radius2 = 205
siz = np.round(2 * radius2 * 1.40)
img1 = np.zeros((siz, siz))
img2 = np.zeros((siz, siz))
center = np.round([siz / 2, siz / 2])
rr1, cc1 = circle(center[0], center[1], radius1)
img1[rr1, cc1] = 1
rr2, cc2 = circle(center[0], center[1], radius2)
img2[rr2, cc2] = 1
circ = sc.logical_xor(img2, img1)
circ.astype(int)
plt.imshow(img1)
plt.show()
io.imshow(img2)
io.imshow(circ)
io.imsave('Circle_radius' + str(radius1) + '.png', circ)
# #angles on the circle
# indices=np.where(xCenterLine==1)
## randperm=np.random.permutation(len(indices[0]))
# randperm=range(len(indices[0]))
#
# #consider 2% random points on the circle
# numPts=np.ceil(1.00*len(randperm))
def doTestEdtWithHalo(self, haloSz=0):
if (isinstance(haloSz, int) or ((sys.version_info.major < 3) and isinstance(haloSz, long))):
if (haloSz < 0):
haloSz = 0
haloSz = sp.array((haloSz,)*3)
subDirName = "doTestEdtWithHalo_%s" % ("x".join(map(str, haloSz)), )
outDir = self.createTmpDir(subDirName)
#outDir = subDirName
if (mpi.world != None):
if ((mpi.world.Get_rank() == 0) and (not os.path.exists(outDir))):
os.makedirs(subDirName)
mpi.world.barrier()
segDds = mango.zeros(self.imgShape, mtype="segmented", halo=haloSz)
segDds.setAllToValue(segDds.mtype.maskValue())
mango.data.fill_ellipsoid(segDds, centre=self.centre, radius=(self.radius*1.05,)*3, fill=0)
mango.data.fill_ellipsoid(segDds, centre=self.centre, radius=(self.radius,)*3, fill=1)
mango.io.writeDds(os.path.join(outDir,"segmentedSphere.nc"), segDds)
dtDds = mango.image.distance_transform_edt(segDds, 1)
mango.io.writeDds(os.path.join(outDir,"distance_mapSphereEdt.nc"), dtDds)
segDds.updateHaloRegions()
dtDds.updateHaloRegions()
segDds.setBorderToValue(segDds.mtype.maskValue())
dtDds.setBorderToValue(dtDds.mtype.maskValue())
slc = []
for d in range(len(haloSz)):
slc.append(slice(haloSz[d], segDds.asarray().shape[d]-haloSz[d]))
slc = tuple(slc)
arr = dtDds.subd_h.asarray()
sbeg = dtDds.subd_h.origin
send = sbeg + dtDds.subd_h.shape
coords = np.ogrid[sbeg[0]:send[0],sbeg[1]:send[1],sbeg[2]:send[2]]
distDds = mango.copy(dtDds)
distArr = distDds.subd_h.asarray()
distArr[...] = \
sp.where(
sp.logical_and(dtDds.subd_h.asarray() != dtDds.mtype.maskValue(), dtDds.subd_h.asarray() >= 0),
self.radius
-
sp.sqrt(
((coords[0]-self.centre[0])**2)
+
((coords[1]-self.centre[1])**2)
+
((coords[2]-self.centre[2])**2)
),
dtDds.subd_h.asarray()
)
mango.io.writeDds(os.path.join(outDir,"distance_mapSphereDistRef.nc"), distDds)
rootLogger.info("max diff = %s" % (np.max(sp.absolute(distArr - dtDds.subd_h.asarray())),))
self.assertTrue(
sp.all(
sp.absolute(distArr - dtDds.subd_h.asarray())
<=
1.0
)
)
self.assertTrue(
sp.all(
sp.logical_not(sp.logical_xor(
segDds.asarray() == 1,
dtDds.asarray() >= 0
))
)
)
self.assertTrue(
sp.all(
sp.logical_not(sp.logical_xor(
segDds.asarray() == segDds.mtype.maskValue(),
dtDds.asarray() == dtDds.mtype.maskValue()
))
)
)
self.assertTrue(
sp.all(
sp.logical_not(sp.logical_xor(
segDds.asarray() == 0,
dtDds.asarray() == -1
))
)
)
self.assertTrue(sp.all(segDds.halo == dtDds.halo))
self.assertTrue(sp.all(segDds.shape == dtDds.shape))
self.assertTrue(sp.all(segDds.origin == dtDds.origin), "%s != %s" % (segDds.origin, dtDds.origin))
self.assertTrue(sp.all(segDds.mpi.shape == dtDds.mpi.shape))
