def local(self, ind, r): '''returns a space of the same type as self, in which all the points within radius r of point ind have value 1.0, and all other points are not defined.''' allpts=combinations([arange(0,r+1) for x in range(len(self.dims))]) allpts=allpts[(allpts**2).sum(1)<=r**2] allpts= quadrants(allpts) allpts+=ind dat=(allpts, ones((allpts.shape[0], len(self.rules)))) dat=self.inspace(dat) return self.new(dat,False)
def boundary(self, ind, r): '''like local, but return only the points on the boundary of the local''' allpts=combinations([arange(0,r+1) for x in range(len(self.dims))]) allpts=allpts[(allpts**2).sum(1)<=r**2] allpts=allpts[(allpts**2).sum(1)>(r-1)**2] allpts= quadrants(allpts) allpts+=ind dat=(allpts, ones((allpts.shape[0], len(self.rules)))) dat=self.inspace(dat) return self.new(dat,False)
def quadrants(pts): '''renders the locus pts symmetric around the origin (pts should contain strictly non-negative values)''' z=[pts] quad=combinations([arange(-1,2,2) for x in range(pts.shape[1])]) for t in quad: if all(t>0): continue z.append(pts*t) z=row_stack(z) return uniqueND(z)
def local(self, a=None): ls = self.space() xmin = max(self.location[0]-self.localsize, 0) xmax = min(self.location[0]+self.localsize+1, ls.shape[0]) ymin = max(self.location[1]-self.localsize, 0) ymax = min(self.location[1]+self.localsize+1, ls.shape[1]) lpts = combinations([arange(xmin, xmax), arange(ymin, ymax)]) ls[lpts[:,0], lpts[:,1]]=1.0 if a!=None: ls = ls*a return ls
def gmmToSpatialField(doc, upathAbstract="/AbstractModel:class1LLdensity", edge = 5.0, minvalue=.01, maxvalue=.1):
am = doc.getInstance(upathAbstract)
bb = _getGMMBB(am, minvalue)
origin = bb[0]
rang = bb[1]-bb[0]
rang = ceil(rang/edge).astype(int32)
indexes = combinations([arange(rang[0]), arange(rang[1]), arange(rang[2])])
dat = indexes*edge + array(origin)
ds = createElement("Data", {"Name":'points', "SampleType":"generic"})
ds.datinit(dat)
am.run(ds)
sfs = ds.getSubData('gmmout').getData()
md = sfs.max()
keep = nonzero(sfs>=minvalue*sfs.max())[0]
if keep.shape[0]<indexes.shape[0]/2.0:
indexes = indexes[keep, :]
values = sfs[keep, 0]
mindex = indexes.min(0)
indexes -= mindex
maxdex = indexes.max(0)+1
origin = tuple(array(origin) + edge*mindex)
sfd = zeros(maxdex)
sfd[indexes[:,0], indexes[:,1], indexes[:,2]]=values
else:
sfd = zeros((rang[0], rang[1], rang[2]))
sfd[indexes[:,0], indexes[:,1], indexes[:,2]]=sfs[:,0]
name = am.name()+"_sf"
sf = doc.getElements('SpatialField', name)
if sf:
sf[0].sever()
sf = createElement('SpatialField', {"Name":name})
ds = createElement('Data', {"Name":"sfd"})
sfd = sfd/sfd.max()
sfd = sfd*am.attributes.get('total_weight', .07)/.07
ds.datinit(sfd, {"SampleType":"locus"})
sf.newElement(ds)
for an in am.attributes:
if an.startswith("meta_"):
sf.setAttrib(an, am.attrib(an))
sf.setAttrib("Origin", origin)
sf.setAttrib("mindensity", minvalue)
sf.setAttrib("maxdensity", maxvalue)
ang = sf.attrib('meta_directional_tuning') % 360
ang = ang *pi/180
c = ccbcv.dircolors._getAngleColor(ang)
pycol=ccbcv.dircolors.convertColor(c, 'py')
sf.setAttrib('color', pycol)
sf.setAttrib("Edge", edge)
doc.newElement(sf)
def gmmToSpatialField(doc, upathAbstract='/AbstractModel:MixtureModel/', npts = 30, minvalue=.01, maxvalue=.1):
am = doc.getInstance(upathAbstract)
bb = _getGMMBB(am, minvalue)
origin = bb[0][:3]
rang = bb[1][:3] - origin
edge = rang.max()/npts
pts = [arange(origin[i], bb[1][i], edge) for i in range(3)]
pts = combinations(pts)
ds = createElement("Data", {"Name":'points', "SampleType":"generic"})
ds.datinit(pts)
am.run(ds)
sfs = ds.getSubData('gmmout').getData()
pts = ((pts-origin)/edge).round().astype(int32)
md = sfs[:,0].max()
keep = nonzero(sfs[:,0]>=minvalue*md)[0]
if keep.shape[0]<pts.shape[0]/2.0:
pts = pts[keep, :]
sfs = sfs[keep, :]
mindex = pts.min(0)
pts -= mindex
origin = tuple(array(origin) + edge*mindex)
maxdex = pts.max(0)+1
fshape = list(maxdex) + [sfs.shape[1]]
sfd = zeros(fshape )
sfd[ pts[:,0], pts[:,1], pts[:,2], : ] = sfs
sfd[...,0] = maxvalue*sfd[...,0]/md
if sfd.shape[3] == 1:
sfd = sfd[...,0]
name = am.name()+"_sf"
sf = doc.getElements('SpatialField', name)
if sf:
sf[0].sever()
sf = createElement('SpatialField', {"Name":name})
ds = createElement('Data', {"Name":"sfd"})
sfd = sfd*am.attributes.get('total_weight', 1.0)
ds.datinit(sfd, {"SampleType":"sfield"})
sf.newElement(ds)
sf.setAttrib("Origin", origin)
sf.setAttrib("colormap", 'hot')
sf.setAttrib("colordimension", 1)
sf.setAttrib("colorrange", (minvalue, maxvalue))
sf.setAttrib("mindensity", minvalue)
sf.setAttrib("Edge", edge)
doc.newElement(sf)
def gmmOverlapNumerical(doc, upathGMM1="/AbstractModel:class1LLdensity", upathGMM2="/AbstractModel:class2LLdensity", edge = 5.0, minvalue=.01):
am1 = doc.getInstance(upathGMM1)
am2 = doc.getInstance(upathGMM2)
bb1 = _getGMMBB(am1, minvalue)
bb2 = _getGMMBB(am2, minvalue)
bb = (minimum(bb1[0], bb2[0]), maximum(bb1[1], bb2[1]))
origin = bb[0]
rang = bb[1]-bb[0]
rang = ceil(rang/edge).astype(int32)
indexes = combinations([arange(rang[0]), arange(rang[1]), arange(rang[2])])
dat = indexes*edge + array(origin)
ds = createElement("Data", {"Name":'points', "SampleType":"generic"})
ds.datinit(dat)
am1.run(ds)
sfs1 = ds.getSubData('gmmout').getData()
am2.run(ds)
sfs2 = ds.getSubData('gmmout').getData()
sfs1 = sfs1 / sfs1.sum()
sfs2 = sfs2 / sfs2.sum()
sfs = minimum(sfs2 ,sfs1)
val = sfs.sum()
print "Overlap is %.2g" % val
def allPoints(self): '''Return an array of indexes that could be used as self.ind if every point in the space was specified''' l=[] for d in self.dims: l.append(arange(d)) return combinations(l)
