def getL(self,provis,obs,control):
# print "================================================================================="
# print "====== Calculate Misclosure L ==========================================="
count=obsCount(obs)
L=numpy.zeros(shape=(count,1))
Lnames=Points("L vector name pairs")
i=0
for sn1,station in obs.iteritems():
for tn1,target in station.iteritems():
tn2=tn1
tn1=tn1[0:-2]
if target.type=="direction":
if control.has_key(tn1):
if provis.has_key(sn1):
calc=provis[sn1].join(control[tn1])
elif control.has_key(sn1):
calc=control[sn1].join(control[tn1])
elif provis.has_key(tn1):
if provis.has_key(sn1):
calc=provis[sn1].join(provis[tn1])
elif control.has_key(sn1):
calc=control[sn1].join(provis[tn1])
else:
print "error"
calc=0
observed=obs[sn1][tn2].direction
target.setMisclosure(observed-calc)
L[i][0]=(observed-calc)
Lnames[sn1 + " to "+ tn1 + " direc"]=L[i][0]
i+=1
#
continue
elif target.type == "distance":
if control.has_key(tn1):
if provis.has_key(sn1):
calc=provis[sn1].joinS(control[tn1])
elif control.has_key(sn1):
calc=control[sn1].joinS(control[tn1])
elif provis.has_key(tn1):
if provis.has_key(sn1):
calc=provis[sn1].joinS(provis[tn1])
elif control.has_key(sn1):
calc=control[sn1].joinS(provis[tn1])
else: print "error"
observed=obs[sn1][tn2].distance
target.setMisclosure(observed-calc)
L[i][0]=round((observed-calc),3)
Lnames[sn1 + " to "+ tn1 + " distance"]=L[i][0]
i+=1
return L
def getL(self, provis, obs, control):
# print "================================================================================="
# print "====== Calculate Misclosure L ==========================================="
count = obsCount(obs)
L = numpy.zeros(shape=(count, 1))
Lnames = Points("L vector name pairs")
i = 0
for sn1, station in obs.iteritems():
for tn1, target in station.iteritems():
tn2 = tn1
tn1 = tn1[0:-2]
if target.type == "direction":
if control.has_key(tn1):
if provis.has_key(sn1):
calc = provis[sn1].join(control[tn1])
elif control.has_key(sn1):
calc = control[sn1].join(control[tn1])
elif provis.has_key(tn1):
if provis.has_key(sn1):
calc = provis[sn1].join(provis[tn1])
elif control.has_key(sn1):
calc = control[sn1].join(provis[tn1])
else:
print "error"
calc = 0
observed = obs[sn1][tn2].direction
target.setMisclosure(observed - calc)
L[i][0] = (observed - calc)
Lnames[sn1 + " to " + tn1 + " direc"] = L[i][0]
i += 1
#
continue
elif target.type == "distance":
if control.has_key(tn1):
if provis.has_key(sn1):
calc = provis[sn1].joinS(control[tn1])
elif control.has_key(sn1):
calc = control[sn1].joinS(control[tn1])
elif provis.has_key(tn1):
if provis.has_key(sn1):
calc = provis[sn1].joinS(provis[tn1])
elif control.has_key(sn1):
calc = control[sn1].joinS(provis[tn1])
else:
print "error"
observed = obs[sn1][tn2].distance
target.setMisclosure(observed - calc)
L[i][0] = round((observed - calc), 3)
Lnames[sn1 + " to " + tn1 + " distance"] = L[i][0]
i += 1
return L
def Iterate(self,provis,OBS,control,unknowns):
count=obsCount(OBS)
L=self.getL(provis, OBS, control)
# print "============== A Matrix ========================================="
A = numpy.zeros(shape=(count,len(unknowns)))
A[0][0]=3
i=0
j=0
for station_name,station in OBS.iteritems():
if control.has_key(station_name):
current_station=control[station_name]
elif provis.has_key(station_name):
current_station=provis[station_name]
for tname,target in station.iteritems():
tname=tname[0:-2]
j=0
# tname=tname[0:-2]
if target.type=='direction' or target.type=='distance' :
for diff_wrt in unknowns:
if diff_wrt[-1]=='o':
if station_name==diff_wrt[0:-2]:
A[i][j]=-1.
j+=1
continue
else:
A[i][j]=0
j+=1
continue
if not tname==diff_wrt[0:-2] and not station_name==diff_wrt[0:-2]:
A[i][j]=0
j+=1
continue
if control.has_key(tname):#if observing control
if target.type=="direction":
A[i][j]=equations(current_station,control[tname],diff_wrt,'direction')
else :
A[i][j]=equations(current_station,control[tname],diff_wrt,'distance')
j+=1
continue
else:#if observing provisional
if target.type=="direction":
A[i][j]=equations(current_station,provis[tname],diff_wrt,'direction')
else:
A[i][j]=equations(current_station,provis[tname],diff_wrt,'distance')
j+=1
continue
else:
for diff_wrt in unknowns:
if diff_wrt[-1]=='o':
if station_name==diff_wrt[0:-2]:
A[i][j]=-1.
j+=1
continue
else:
A[i][j]=0
j+=1
continue
if not tname==diff_wrt[0:-2] and not station_name==diff_wrt[0:-2]:
A[i][j]=0
j+=1
continue
if control.has_key(tname):#if observing control
A[i][j]=equations(current_station,control[tname],diff_wrt,target.type)
j+=1
else:#if observing provisional
A[i][j]=equations(current_station,provis[tname],diff_wrt,target.type)
j+=1
i+=1
names=[]
for name,ob in OBS.iteritems():
for na,tar in ob.iteritems():
if tar.type=="both":
names.append(name + "-" + na + "D")
names.append(name + "-" + na + "d")
else:
names.append(name + "-" + na + "D")
row_labels = ['SUR09-T013', 'Y', 'N', 'W']
# for row_label, row in zip(names, A):
# print '%s [%s]' % (row_label, ' '.join('%01f' % i for i in row))
# print "============== Calculating Least squares ========================================="
Pob= Weights()
Pob.setDirectionWeight(1)
Pob.setDistanceWeight(1)
P=Pob.matrix(OBS,A)
A=numpy.asmatrix(A)
N= (((A.T)*A)**(-1))*A.T*L
provUpdate=self.Provisional(N, provis, unknowns)
Xtemp=Points("N correction pairs")
i=0
for name in unknowns:
Xtemp[name]=float64(N[i])
i+=1
V=A*N-L
# print A.T*V
posteriori=float((V.T*P*V)/(count-size(unknowns)))
# print posteriori
covarience_mat= posteriori*(A.T*A)**(-1)
precisions=Points("Precisions of Unknowns")
i=0
for name,value in Xtemp.iteritems():
precisions[name]=sqrt(float(covarience_mat[i,i]))
i+=1
# print precisions
#
# for i,j in finalX.iteritems():
# print i + ": "
# print("Y: %.2f" % j.y)
# print("N: %.2f" % j.x)
# print "Orientations :\n"
# for i,ob in orientations.iteritems():
#
# print str.format("{0} {1}", i, round(ob,1))
#
return A,Xtemp , provUpdate , OBS , control , unknowns, V,P
# if __name__ == '__main__':
# Leas= LeastSqr()
# A,Xdict,provis,OBS,control,unknowns,V,P=Leas.Read('control.csv','observations.csv')
def Iterate(self, provis, OBS, control, unknowns):
count = obsCount(OBS)
L = self.getL(provis, OBS, control)
# print "============== A Matrix ========================================="
A = numpy.zeros(shape=(count, len(unknowns)))
A[0][0] = 3
i = 0
j = 0
for station_name, station in OBS.iteritems():
if control.has_key(station_name):
current_station = control[station_name]
elif provis.has_key(station_name):
current_station = provis[station_name]
for tname, target in station.iteritems():
tname = tname[0:-2]
j = 0
# tname=tname[0:-2]
if target.type == 'direction' or target.type == 'distance':
for diff_wrt in unknowns:
if diff_wrt[-1] == 'o':
if station_name == diff_wrt[0:-2]:
A[i][j] = -1.
j += 1
continue
else:
A[i][j] = 0
j += 1
continue
if not tname == diff_wrt[
0:-2] and not station_name == diff_wrt[0:-2]:
A[i][j] = 0
j += 1
continue
if control.has_key(tname): #if observing control
if target.type == "direction":
A[i][j] = equations(current_station,
control[tname], diff_wrt,
'direction')
else:
A[i][j] = equations(current_station,
control[tname], diff_wrt,
'distance')
j += 1
continue
else: #if observing provisional
if target.type == "direction":
A[i][j] = equations(current_station,
provis[tname], diff_wrt,
'direction')
else:
A[i][j] = equations(current_station,
provis[tname], diff_wrt,
'distance')
j += 1
continue
else:
for diff_wrt in unknowns:
if diff_wrt[-1] == 'o':
if station_name == diff_wrt[0:-2]:
A[i][j] = -1.
j += 1
continue
else:
A[i][j] = 0
j += 1
continue
if not tname == diff_wrt[
0:-2] and not station_name == diff_wrt[0:-2]:
A[i][j] = 0
j += 1
continue
if control.has_key(tname): #if observing control
A[i][j] = equations(current_station,
control[tname], diff_wrt,
target.type)
j += 1
else: #if observing provisional
A[i][j] = equations(current_station, provis[tname],
diff_wrt, target.type)
j += 1
i += 1
names = []
for name, ob in OBS.iteritems():
for na, tar in ob.iteritems():
if tar.type == "both":
names.append(name + "-" + na + "D")
names.append(name + "-" + na + "d")
else:
names.append(name + "-" + na + "D")
row_labels = ['SUR09-T013', 'Y', 'N', 'W']
# for row_label, row in zip(names, A):
# print '%s [%s]' % (row_label, ' '.join('%01f' % i for i in row))
# print "============== Calculating Least squares ========================================="
Pob = Weights()
Pob.setDirectionWeight(1)
Pob.setDistanceWeight(1)
P = Pob.matrix(OBS, A)
A = numpy.asmatrix(A)
N = (((A.T) * A)**(-1)) * A.T * L
provUpdate = self.Provisional(N, provis, unknowns)
Xtemp = Points("N correction pairs")
i = 0
for name in unknowns:
Xtemp[name] = float64(N[i])
i += 1
V = A * N - L
# print A.T*V
posteriori = float((V.T * P * V) / (count - size(unknowns)))
# print posteriori
covarience_mat = posteriori * (A.T * A)**(-1)
precisions = Points("Precisions of Unknowns")
i = 0
for name, value in Xtemp.iteritems():
precisions[name] = sqrt(float(covarience_mat[i, i]))
i += 1
# print precisions
#
# for i,j in finalX.iteritems():
# print i + ": "
# print("Y: %.2f" % j.y)
# print("N: %.2f" % j.x)
# print "Orientations :\n"
# for i,ob in orientations.iteritems():
#
# print str.format("{0} {1}", i, round(ob,1))
#
return A, Xtemp, provUpdate, OBS, control, unknowns, V, P
# if __name__ == '__main__':
# Leas= LeastSqr()
# A,Xdict,provis,OBS,control,unknowns,V,P=Leas.Read('control.csv','observations.csv')