def compare_vector(self, mean=True, revers=True):
"""
comparison of vectors
mean: compute vector mean?
revers: compare observations with revers id from and to?
"""
self.revers =revers
# find vectors in cluster list
self.obsToComp1 = ObsClusterList() # cluster list with vectors
self.obsToComp2 = ObsClusterList()
for cl in self.data1.obsClusterList:
if isinstance(cl, ObsClusterVector):
self.obsToComp1.append_cluster(cl)
for cl in self.data2.obsClusterList:
if isinstance(cl, ObsClusterVector):
self.obsToComp2.append_cluster(cl)
if sum([len(c) for c in self.obsToComp1]) is 0:
return
#raise CompareObsError, "No vectors found in data #1"
if sum([len(c) for c in self.obsToComp2]) is 0:
return
#raise CompareObsError, "No vectors found in data #2"
if mean:
self.obsToComp1, self.meanList1, self.obsMeanList1, self.resList1 =\
self._mean_vector(self.obsToComp1)
self.obsToComp2, self.meanList2, self.obsMeanList2, self.resList2 =\
self._mean_vector(self.obsToComp2)
# cluster for compared vectors
# covariance matrix - initial zero dimension
self._cluster = ObsClusterVector(covmat=CovMat(dim=0, band=2))
self.compObsClList.append_cluster(self._cluster)
# find vectors with same fromid and toid and compare
for c1 in self.obsToComp1:
for vec1 in c1:
found, vec2, reversed = self._find_obs(vec1, self.obsToComp2)
if found:
self._cmp_vector(vec1, vec2, reversed=reversed)
# find uncompared vectors
self._uncompared_obs()
def _mean_vector(self, data):
"returns mean of vector with same fromid and toid"
# add vectors to list
lvec = [] # helper list of vectors
for c in data:
for v in c:
lvec.append(v)
datamean = ObsClusterList()
dmcluster = ObsClusterVector(covmat=CovMat(dim=0, band=2))
datamean.append_cluster(dmcluster)
# list of vector means and single vectors (without mean)
cll_mean = ObsClusterList()
cll_obs = ObsClusterList()
cll_res = ObsClusterList()
if len(lvec) <= 1:
return # nothink to compare
while len(lvec) > 1:
# find vectors with same fromid and toid
v1 = lvec.pop()
fromid = v1.fromid
toid = v1.toid
lvecm = [v1] # list of vectors for mean
lsign = [1.0] # list of signs: for revers vectors -1.0
for v2 in lvec:
if v2.fromid == fromid and v2.toid == toid:
lvecm.append(v2)
lvec.remove(v2)
lsign.append(1.0)
continue
if self.revers and\
v2.fromid == toid and v2.toid == fromid:
lvecm.append(v2)
lvec.remove(v2)
lsign.append(-1.0)
# compute mean
if float(len(lvecm)) == 1:
# nothink to compute
vec = lvecm[0]
covmat = vec.covmat
dmcluster.append_obs(vec)
dmcluster.covmat.extend_dim(3)
vec.covmat = covmat
continue
from numpy import mat, mean
ll = [mat([i.dx, i.dy, i.dz])*sign for i,sign in zip(lvecm,lsign)]
# row vectors of observ.
#lm = mean(ll, axis=0)
#print "Mean 0:", lm
#print "covmat0:", lvecm[0].covmat.mat
# compute covariance matrix
# covariances between vectors not handled
sil = [i.covmat.mat.I for i in lvecm]
# covariance matrice inverses inv(Sigma)
sill = [si*l.T for si,l in zip(sil,ll)]
# product inv(Sigma)*l
cmm = sum(sil).I # covariance matrix of mean
mn = cmm * sum(sill) # mean value
#print "Mean 1:", mn
#print "covmat1:", cmm
# append clusters
cl_mean = ObsClusterVector(covmat=CovMat(dim=3, band=2))
cl_obs = ObsClusterVector(covmat=CovMat(dim=0, band=2))
cl_res = ObsClusterVector(covmat=None)
cll_mean.append_cluster(cl_mean)
cll_obs.append_cluster(cl_obs)
cll_res.append_cluster(cl_res)
# append vector mean
vec1 = ObsVector(fromid=lvecm[0].fromid, toid=lvecm[0].toid,
dx=float(mn[0]), dy=float(mn[1]), dz=float(mn[2]))
vec2 = ObsVector(fromid=lvecm[0].fromid, toid=lvecm[0].toid,
dx=float(mn[0]), dy=float(mn[1]), dz=float(mn[2]))
# vec1 == vec2
cl_mean.append_obs(vec1)
dmcluster.append_obs(vec2)
# set covmat of mean
covmat = CovMat(dim=3, band=2)
covmat.mat = cmm
vec1.covmat = covmat
dmcluster.covmat.extend_dim(3)
vec2.covmat = covmat
# append observations with covmat
for v in lvecm:
vcovmat = v.covmat
cl_obs.append_obs(v)
cl_obs.covmat.extend_dim(3)
v.covmat = vcovmat
# append residuals
for l,v,s in zip(ll,lvecm,lsign):
res = (mn - l.T) * s
vec = ObsVector(fromid=v.fromid, toid=v.toid,
dx=float(res[0]),
dy=float(res[1]),
dz=float(res[2]))
cl_res.append_obs(vec)
return datamean, cll_mean, cll_obs, cll_res
class CompareObs(object):
"""
compares measured data
"""
def __init__(self, data1, data2, revers=True):
"""
data1, data2: observation data - GamaLocalDataObs instance
revers: handle fromid_1 == toid_2 and toid_1 == fromid_2
"""
self.data1 = data1
self.data2 = data2
self.revers = revers
self.compObsClList = ObsClusterList() # list of differences from comparison
# data 1
self.meanList1 = None # list of means
self.obsMeanList1 = None # list of observations for mean
self.resList1 = None # list of residuals
self.obsToComp1 = None # observations in cluster list to be compared
# data 2
self.meanList2 = None
self.obsMeanList2 = None
self.resList2 = None
self.obsToComp2 = None
# make list of uncompared observations
self.uncompObsClList1 = ObsClusterList() # cluster list with observations uncompared
self.uncompObsClList2 = ObsClusterList()
self._cluster = None # actual cluster
def _mean_vector(self, data):
"returns mean of vector with same fromid and toid"
# add vectors to list
lvec = [] # helper list of vectors
for c in data:
for v in c:
lvec.append(v)
datamean = ObsClusterList()
dmcluster = ObsClusterVector(covmat=CovMat(dim=0, band=2))
datamean.append_cluster(dmcluster)
# list of vector means and single vectors (without mean)
cll_mean = ObsClusterList()
cll_obs = ObsClusterList()
cll_res = ObsClusterList()
if len(lvec) <= 1:
return # nothink to compare
while len(lvec) > 1:
# find vectors with same fromid and toid
v1 = lvec.pop()
fromid = v1.fromid
toid = v1.toid
lvecm = [v1] # list of vectors for mean
lsign = [1.0] # list of signs: for revers vectors -1.0
for v2 in lvec:
if v2.fromid == fromid and v2.toid == toid:
lvecm.append(v2)
lvec.remove(v2)
lsign.append(1.0)
continue
if self.revers and\
v2.fromid == toid and v2.toid == fromid:
lvecm.append(v2)
lvec.remove(v2)
lsign.append(-1.0)
# compute mean
if float(len(lvecm)) == 1:
# nothink to compute
vec = lvecm[0]
covmat = vec.covmat
dmcluster.append_obs(vec)
dmcluster.covmat.extend_dim(3)
vec.covmat = covmat
continue
from numpy import mat, mean
ll = [mat([i.dx, i.dy, i.dz])*sign for i,sign in zip(lvecm,lsign)]
# row vectors of observ.
#lm = mean(ll, axis=0)
#print "Mean 0:", lm
#print "covmat0:", lvecm[0].covmat.mat
# compute covariance matrix
# covariances between vectors not handled
sil = [i.covmat.mat.I for i in lvecm]
# covariance matrice inverses inv(Sigma)
sill = [si*l.T for si,l in zip(sil,ll)]
# product inv(Sigma)*l
cmm = sum(sil).I # covariance matrix of mean
mn = cmm * sum(sill) # mean value
#print "Mean 1:", mn
#print "covmat1:", cmm
# append clusters
cl_mean = ObsClusterVector(covmat=CovMat(dim=3, band=2))
cl_obs = ObsClusterVector(covmat=CovMat(dim=0, band=2))
cl_res = ObsClusterVector(covmat=None)
cll_mean.append_cluster(cl_mean)
cll_obs.append_cluster(cl_obs)
cll_res.append_cluster(cl_res)
# append vector mean
vec1 = ObsVector(fromid=lvecm[0].fromid, toid=lvecm[0].toid,
dx=float(mn[0]), dy=float(mn[1]), dz=float(mn[2]))
vec2 = ObsVector(fromid=lvecm[0].fromid, toid=lvecm[0].toid,
dx=float(mn[0]), dy=float(mn[1]), dz=float(mn[2]))
# vec1 == vec2
cl_mean.append_obs(vec1)
dmcluster.append_obs(vec2)
# set covmat of mean
covmat = CovMat(dim=3, band=2)
covmat.mat = cmm
vec1.covmat = covmat
dmcluster.covmat.extend_dim(3)
vec2.covmat = covmat
# append observations with covmat
for v in lvecm:
vcovmat = v.covmat
cl_obs.append_obs(v)
cl_obs.covmat.extend_dim(3)
v.covmat = vcovmat
# append residuals
for l,v,s in zip(ll,lvecm,lsign):
res = (mn - l.T) * s
vec = ObsVector(fromid=v.fromid, toid=v.toid,
dx=float(res[0]),
dy=float(res[1]),
dz=float(res[2]))
cl_res.append_obs(vec)
return datamean, cll_mean, cll_obs, cll_res
def _find_obs(self, obs, cl):
"""
finds observation in cluster list
returns found, observation, reversed
"""
fromid = obs.fromid
toid = obs.toid
for c in cl:
for o in c:
if o.fromid == fromid and o.toid == toid:
return True, o, False
if self.revers and \
o.fromid == toid and o.toid == fromid:
return True, o, True
return False, None, False
def compare_vector(self, mean=True, revers=True):
"""
comparison of vectors
mean: compute vector mean?
revers: compare observations with revers id from and to?
"""
self.revers =revers
# find vectors in cluster list
self.obsToComp1 = ObsClusterList() # cluster list with vectors
self.obsToComp2 = ObsClusterList()
for cl in self.data1.obsClusterList:
if isinstance(cl, ObsClusterVector):
self.obsToComp1.append_cluster(cl)
for cl in self.data2.obsClusterList:
if isinstance(cl, ObsClusterVector):
self.obsToComp2.append_cluster(cl)
if sum([len(c) for c in self.obsToComp1]) is 0:
return
#raise CompareObsError, "No vectors found in data #1"
if sum([len(c) for c in self.obsToComp2]) is 0:
return
#raise CompareObsError, "No vectors found in data #2"
if mean:
self.obsToComp1, self.meanList1, self.obsMeanList1, self.resList1 =\
self._mean_vector(self.obsToComp1)
self.obsToComp2, self.meanList2, self.obsMeanList2, self.resList2 =\
self._mean_vector(self.obsToComp2)
# cluster for compared vectors
# covariance matrix - initial zero dimension
self._cluster = ObsClusterVector(covmat=CovMat(dim=0, band=2))
self.compObsClList.append_cluster(self._cluster)
# find vectors with same fromid and toid and compare
for c1 in self.obsToComp1:
for vec1 in c1:
found, vec2, reversed = self._find_obs(vec1, self.obsToComp2)
if found:
self._cmp_vector(vec1, vec2, reversed=reversed)
# find uncompared vectors
self._uncompared_obs()
def _uncompared_obs(self):
for cl in self.obsToComp1:
# add cluster to cluster list
uc1 = cl.__class__()
added = False # added some observation
for obs in cl:
found, x, x = self._find_obs(obs, self.compObsClList)
if not found:
uc1.append_obs(obs)
added = True
if added:
self.uncompObsClList1.append_cluster(uc1)
for cl in self.obsToComp2:
# add cluster to cluster list
uc2 = cl.__class__()
added = False # added some observation
for obs in cl:
found, x ,x = self._find_obs(obs, self.compObsClList)
if not found:
uc2.append_obs(obs)
added = True
if added:
self.uncompObsClList2.append_cluster(uc2)
def _cmp_vector(self, vec1, vec2, reversed=False):
"comparison of two vectors - dx, dy, dz, var_dx, var_dy, var_dz"
if reversed:
ddx = vec1.dx + vec2.dx
ddy = vec1.dy + vec2.dy
ddz = vec1.dz + vec2.dz
else:
ddx = vec1.dx - vec2.dx
ddy = vec1.dy - vec2.dy
ddz = vec1.dz - vec2.dz
# variances
var1 = vec1.var
var2 = vec2.var
var_ddx = var1[0] + var2[0]
var_ddy = var1[1] + var2[1]
var_ddz = var1[2] + var2[2]
# covariances
cov1 = vec1.cov
cov2 = vec2.cov
cov_xy = cov1[0] + cov2[0]
cov_xz = cov1[1] + cov2[1]
cov_yz = cov1[2] + cov2[2]
#if excludeZero:
# if (ddx, ddy, ddz) == (0, 0, 0):
# return
# append vector
vec = ObsVector(fromid=vec1.fromid, toid=vec1.toid,
dx=ddx, dy=ddy, dz=ddz)
self._cluster.append_obs(vec)
# extend covariance matrix and add values
self._cluster.covmat.extend_dim(3)
vec.var = (var_ddx, var_ddy, var_ddz)
vec.cov = (cov_xy, cov_xz, cov_yz)
def str_mean(self):
str = ["\nObservations 1: mean and residuals",
"=================================="]
str.extend(["\nmean:%s\n\nobservations:%s\n\nresiduals:%s"\
% (mean, obs, res) \
for mean, obs, res in zip(self.meanList1,
self.obsMeanList1,
self.resList1)])
str.extend(["\nObservations 2: mean and residuals",
"=================================="])
str.extend(["\nmean:%s\n\nobservations:%s\n\nresiduals:%s"\
% (mean, obs, res) \
for mean, obs, res in zip(self.meanList2,
self.obsMeanList2,
self.resList2)])
return "\n".join(str)
def str_summary(self):
tt = TextTable([("", "%10s"),
("ep 0", "%4i"),
("ep 1", "%4i")])
str = [tt.make_table_head()]
sum1 = sum([len(cl) for cl in self.obsToComp1])
sum2 = sum([len(cl) for cl in self.obsToComp2])
sumComp = sum([len(cl) for cl in self.compObsClList])
sumu1 = sum([len(cl) for cl in self.uncompObsClList1])
sumu2 = sum([len(cl) for cl in self.uncompObsClList2])
str.append(tt.make_table_row("sum obs", sum1, sum2))
str.append(tt.make_table_row("obs comp", sumComp, sumComp))
str.append(tt.make_table_row("obs uncomp", sumu1, sumu2))
str.append(tt.make_table_foot())
return "".join(str)
def str_uncompared(self):
str = ["\nUncompared observations 1:",
"=========================="]
str.append(self.uncompObsClList1.__str__())
str.extend(["\nUncompared observations 2:",
"=========================="])
str.append(self.uncompObsClList2.__str__())
return "\n".join(str)
def str_compared(self):
str = ["\nObservations compared:",
"======================"]
str.append(self.compObsClList.__str__())
return "\n".join(str)
def __str__(self):
return self.str_compared()
