def setUpCalcSiteModelPostFit(models,site_residuals, info, zen_grid, az_grid, cpus) :
nZen = int(90.0/zen_grid) + 1
nAz = int(360./az_grid)
print("nAz",nAz)
#numParamsPerSite = nZen * nAz
nModels = info['numModels']
#tSite = numParamsPerSite*params['numModels']
#numParams = tSat + tSite
prefit = np.zeros(nModels)
prefit_sums = np.zeros((nModels,nAz,nZen))
prefit_res = np.zeros((nModels,nAz,nZen))
postfit = np.zeros(nModels)
postfit_sums = np.zeros((nModels,nAz,nZen))
postfit_res = np.zeros((nModels,nAz,nZen))
numObs = np.zeros(nModels)
numObs_sums = np.zeros((nModels,nAz,nZen))
change = info['changes']
print("Changes for site",info['site'],change)
# keep track of how may observations are in each bin
#SiteFreq = np.zeros((nModels,nAz,nZen))
#Models = np.zeros((nModels,nAz,nZen))
#model_stdev = np.zeros((int(params['numModels']),nAz,nZen))
# create a new model everythime there has been a change of antenna
for m in range(0,nModels):
print(info['site'],"----> creating model",m+1,"of",info['numModels'])
# start_yyyy and start_ddd should always be defind, however stop_dd may be absent
#ie no changes have ocured since the last setup
minVal_dt = gt.ydhms2dt(change['start_yyyy'][m],change['start_ddd'][m],0,0,0)
if np.size(change['stop_ddd']) > m :
maxVal_dt = gt.ydhms2dt(change['stop_yyyy'][m],change['stop_ddd'][m],23,59,59)
print("Min:",minVal_dt,"Max:",maxVal_dt,m,np.size(change['stop_ddd']))
criterion = ( ( site_residuals[:,0] >= calendar.timegm(minVal_dt.utctimetuple()) ) &
( site_residuals[:,0] < calendar.timegm(maxVal_dt.utctimetuple()) ) )
else:
criterion = ( site_residuals[:,0] >= calendar.timegm(minVal_dt.utctimetuple()) )
maxVal_dt = gt.unix2dt(site_residuals[-1,0])
# get the residuals for this model time period
mind = np.array(np.where(criterion))[0]
model_residuals = site_residuals[mind,:]
diff_dt = maxVal_dt - minVal_dt
numDays = diff_dt.days + 1
print("Have a total of",numDays,"days",np.shape(models))
prefit[m], prefit_sums[m,:,:], prefit_res[m,:,:], postfit[m], postfit_sums[m,:,:], postfit_res[m,:,:],numObs[m],numObs_sums[m,:,:] = setUpPostFitTasks(models,model_residuals,args.cpu,zen_grid,az_grid,params,numDays,minVal_dt)
print("FINISHED Post fit RUN for model",m)
return prefit, prefit_sums, prefit_res, postfit, postfit_sums, postfit_res,numObs,numObs_sums
def processAzSlice(model_residuals,svs,args,params,numDays,minVal_dt,az) :
tSat = np.size(svs) * (int(14./args.nadir_grid) + 2)
tSite = int(90./args.zen) + 1
numParams = tSat + tSite
Neq = np.zeros((numParams,numParams))
AtWb = np.zeros(numParams)
SiteFreq = np.zeros(tSite)
# set up a lookup dictionary
lookup_svs = {}
lctr = 0
for sv in svs:
lookup_svs[str(sv)] = lctr
lctr+=1
site_geocentric_distance = np.linalg.norm(params['sitepos'])
for d in range(0,numDays):
minDTO = minVal_dt + dt.timedelta(days = d)
maxDTO = minVal_dt + dt.timedelta(days = d+1)
#print(d,"Stacking residuals on:",minDTO,maxDTO)
criterion = ( ( model_residuals[:,0] >= calendar.timegm(minDTO.utctimetuple()) ) &
( model_residuals[:,0] < calendar.timegm(maxDTO.utctimetuple()) ) )
tind = np.array(np.where(criterion))[0]
# if there are less than 300 obs, then skip to the next day
if np.size(tind) < 300:
continue
#print("rejecting any residuals greater than 100mm",np.shape(site_residuals))
tdata = res.reject_absVal(model_residuals[tind,:],100.)
#print("rejecting any residuals greater than 5 sigma",np.shape(tdata))
data = res.reject_outliers_elevation(tdata,5,0.5)
#print("finished outlier detection",np.shape(data))
del tdata
# determine the elevation dependent weighting
a,b = res.gamitWeight(data)
if(az - args.az/2. < 0) :
criterion = (data[:,1] < (az + args.az/2.)) | (data[:,1] > (360. - args.az/2.) )
else:
criterion = (data[:,1] < (az + args.az/2.)) & (data[:,1] > (az - args.az/2.) )
azind = np.array(np.where(criterion))[0]
#print("Size of data before azimuth search",np.size(data))
data = data[azind,:]
#print("Size of data after azimuth search",np.size(data))
# parse the broadcast navigation file for this day to get an accurate
# nadir angle
yy = minDTO.strftime("%y")
doy = minDTO.strftime("%j")
navfile = args.brdc_dir + 'brdc'+ doy +'0.'+ yy +'n'
#print("Will read in the broadcast navigation file:",navfile)
nav = rnxN.parseFile(navfile)
# Get the total number of observations for this site
numd = np.shape(data)[0]
#print("Have:",numd,"observations")
for i in range(0,numd):
# work out the svn number
svndto = gt.unix2dt(data[i,0])
svn = svnav.findSV_DTO(svdat,data[i,4],svndto)
svn_search = 'G{:03d}'.format(svn)
#print("Looking for:",svn_search,lookup_svs)
ctr = lookup_svs[str(svn_search)]
#print("Position is CTR:",ctr,data[i,4])
try:
# get the satellite position
svnpos = rnxN.satpos(data[i,4],svndto,nav)
#print("SVNPOS:",svnpos[0])
satnorm = np.linalg.norm(svnpos[0])
#print("NORM:",np.linalg.norm(svnpos[0]))
except:
print("Error calculation satelite position for",svndto,data[i,:])
continue
# work out the nadir angle
nadir = NADIR.calcNadirAngle(data[i,2],site_geocentric_distance,satnorm)
#print("Ele {:.2f} Old: {:.2f} New:{:.2f}".format(data[i,2],oldnadir,nadir))
#print("Ele {:.2f} New:{:.2f}".format(data[i,2],nadir))
w = a**2 + b**2/np.sin(np.radians(90.-data[i,2]))**2
w = 1./w
# Work out the indices for the satellite parameters
niz = int(np.floor(nadir/args.nadir_grid))
iz = int((numParamsPerSat * ctr) + niz)
pco_iz = numParamsPerSat * (ctr+1) - 1
# work out the location of site parameters
nsiz = int(np.floor(data[i,2]/args.zen))
#aiz = int(np.floor(data[i,1]/args.az))
#siz = int( tSat + (m*numParamsPerSite) + (aiz * nZen) + nsiz)
siz = int( tSat + nsiz)
# check that the indices are not overlapping
if iz+1 >= pco_iz or iz >= pco_iz:
#print("WARNING in indices iz+1 = pco_iz skipping obs",nadir,iz,pco_iz)
continue
#NadirFreq[ctr,niz] = NadirFreq[ctr,niz] +1
SiteFreq[nsiz] = SiteFreq[nsiz] +1
#
# R = SITE_PCV_ERR + SAT_PCV_ERR + SAT_PCO_ERR * cos(nadir)
#
# dR/dSITE_PCV_ERR = 1
# dR/dSAT_PCV_ERR = 1
# dR/dSAT_PCO_ERR = cos(nadir)
#
# nice partial derivative tool:
# http://www.symbolab.com/solver/partial-derivative-calculator
#
# Nadir partials..
Apart_1 = (1.-(nadir-niz*args.nadir_grid)/args.nadir_grid)
Apart_2 = (nadir-niz*args.nadir_grid)/args.nadir_grid
#
# PCO partial ...
Apart_3 = np.cos(np.radians(nadir))
# Site partials
Apart_4 = (1.-(data[i,2]-nsiz*args.zen)/args.zen)
Apart_5 = (data[i,2]-nsiz*args.zen)/args.zen
#print("Finished forming Design matrix")
#print("Starting AtWb",np.shape(AtWb),iz,pco_iz,siz)
AtWb[iz] = AtWb[iz] + Apart_1 * data[i,3] * w
AtWb[iz+1] = AtWb[iz+1] + Apart_2 * data[i,3] * w
AtWb[pco_iz] = AtWb[pco_iz] + Apart_3 * data[i,3] * w
AtWb[siz] = AtWb[siz] + Apart_4 * data[i,3] * w
AtWb[siz+1] = AtWb[siz+1] + Apart_5 * data[i,3] * w
#print("Finished forming b vector")
Neq[iz,iz] = Neq[iz,iz] + (Apart_1 * Apart_1 * w)
Neq[iz,iz+1] = Neq[iz,iz+1] + (Apart_1 * Apart_2 * w)
Neq[iz,pco_iz] = Neq[iz,pco_iz] + (Apart_1 * Apart_3 * w)
Neq[iz,siz] = Neq[iz,siz] + (Apart_1 * Apart_4 * w)
Neq[iz,siz+1] = Neq[iz,siz+1] + (Apart_1 * Apart_5 * w)
Neq[iz+1,iz] = Neq[iz+1,iz] + (Apart_2 * Apart_1 * w)
Neq[iz+1,iz+1] = Neq[iz+1,iz+1] + (Apart_2 * Apart_2 * w)
Neq[iz+1,pco_iz] = Neq[iz+1,pco_iz] + (Apart_2 * Apart_3 * w)
Neq[iz+1,siz] = Neq[iz+1,siz] + (Apart_2 * Apart_4 * w)
Neq[iz+1,siz+1] = Neq[iz+1,siz+1] + (Apart_2 * Apart_5 * w)
#print("Finished NEQ Nadir estimates")
Neq[pco_iz,iz] = Neq[pco_iz,iz] + (Apart_3 * Apart_1 * w)
Neq[pco_iz,iz+1] = Neq[pco_iz,iz+1] + (Apart_3 * Apart_2 * w)
Neq[pco_iz,pco_iz] = Neq[pco_iz,pco_iz] + (Apart_3 * Apart_3 * w)
Neq[pco_iz,siz] = Neq[pco_iz,siz] + (Apart_3 * Apart_4 * w)
Neq[pco_iz,siz+1] = Neq[pco_iz,siz+1] + (Apart_3 * Apart_5 * w)
#print("Finished NEQ PCO estimates")
Neq[siz,iz] = Neq[siz,iz] + (Apart_4 * Apart_1 * w)
Neq[siz,iz+1] = Neq[siz,iz+1] + (Apart_4 * Apart_2 * w)
Neq[siz,pco_iz] = Neq[siz,pco_iz] + (Apart_4 * Apart_3 * w)
Neq[siz,siz] = Neq[siz,siz] + (Apart_4 * Apart_4 * w)
Neq[siz,siz+1] = Neq[siz,siz+1] + (Apart_4 * Apart_5 * w)
Neq[siz+1,iz] = Neq[siz+1,iz] + (Apart_5 * Apart_1 * w)
Neq[siz+1,iz+1] = Neq[siz+1,iz+1] + (Apart_5 * Apart_2 * w)
Neq[siz+1,pco_iz] = Neq[siz+1,pco_iz] + (Apart_5 * Apart_3 * w)
Neq[siz+1,siz] = Neq[siz+1,siz] + (Apart_5 * Apart_4 * w)
Neq[siz+1,siz+1] = Neq[siz+1,siz+1] + (Apart_5 * Apart_5 * w)
#print("Finished NEQ Site estimates")
if siz == pco_iz:
print("ERROR in indices siz = pco_iz")
# Add the parameter constraints to the Neq
#Neq = np.add(Neq,C_inv)
C_inv = formConstraints(args,tSat,tSite,1,numParams)
Neq = np.add(Neq,C_inv)
#print("Inverting")
Cov = np.linalg.pinv(Neq)
Sol = np.dot(Cov,AtWb)
stdev = np.sqrt(np.diag(Cov))
return Sol, stdev, SiteFreq, az
def solveSiteModel(site_residuals, svs, params, apr, nadSpacing=0.1, zenSpacing=0.5, azSpacing=0.5, brdc_dir="./"):
"""
Create a model for the satellites and sites at the same time.
PWL piece-wise-linear interpolation fit of phase residuals
-construct a PWL fit for each azimuth bin, and then paste them all together to get
the full model
-inversion is done within each bin
site_residuals = the one-way L3 post-fit, ambiguity fixed phase residuals
svs = an array of satellite SVN numbers that are spacebourne/operating
for the period of this residual stack
params = meta data about the solution bein attempted
['site'] = 4 char site id
['changes'] = dictionary of when model changes need to be applied
apr = satellite apriori data
"""
#prechi = 0
#NUMD = 0
# add one to make sure we have a linspace which includes 0.0 and 14.0
# add another parameter for the zenith PCO estimate
numNADS = int(14.0/nadSpacing) + 1
PCOEstimates = 1
numSVS = np.size(svs)
numParamsPerSat = numNADS + PCOEstimates
tSat = numParamsPerSat * numSVS
nZen = int(90.0/zenSpacing) + 1
nAz = int(360./azSpacing)
print("nAz",nAz)
numParamsPerSite = nZen * nAz
tSite = numParamsPerSite*params['numModels']
numParams = tSat + tSite
print("------------------------------------------------")
print("Processing Site: ",params['site'])
print("------------------------------------------------")
print("Sat Params:----------------",numParamsPerSat)
print("Number of Sats:------------",np.size(svs))
print("Total satellite parameters:-------------",tSat)
print("Site Params:---------------",numParamsPerSite)
print("Number of Models:----------",params['numModels'])
print("Total Site Params:----------------------",tSite)
print("------------------------------------------------")
print("Total Params:---------------------------",numParams)
print("------------------------------------------------")
# Creating matrices
#Neq = np.zeros((numParams,numParams))
#AtWb = np.zeros(numParams)
change = params['changes']
print("Changes for site",params['site'],change)
# keep track of how may observations are in each bin
#NadirFreq = np.zeros((numSVS,numNADS))
SiteFreq = np.zeros((int(params['numModels']),nAz,nZen))
Models = np.zeros((int(params['numModels']),nAz,nZen))
model_stdev = np.zeros((int(params['numModels']),nAz,nZen))
# create a new model everythime there has been a change of antenna
for m in range(0,int(params['numModels'])):
print(params['site'],"----> creating model",m+1,"of",params['numModels'])
# start_yyyy and start_ddd should always be defind, however stop_dd may be absent
#ie no changes have ocured since the last setup
minVal_dt = gt.ydhms2dt(change['start_yyyy'][m],change['start_ddd'][m],0,0,0)
if np.size(change['stop_ddd']) > m :
maxVal_dt = gt.ydhms2dt(change['stop_yyyy'][m],change['stop_ddd'][m],23,59,59)
print("Min:",minVal_dt,"Max:",maxVal_dt,m,np.size(change['stop_ddd']))
criterion = ( ( site_residuals[:,0] >= calendar.timegm(minVal_dt.utctimetuple()) ) &
( site_residuals[:,0] < calendar.timegm(maxVal_dt.utctimetuple()) ) )
else:
criterion = ( site_residuals[:,0] >= calendar.timegm(minVal_dt.utctimetuple()) )
maxVal_dt = gt.unix2dt(site_residuals[-1,0])
# get the residuals for this model time period
mind = np.array(np.where(criterion))[0]
model_residuals = site_residuals[mind,:]
diff_dt = maxVal_dt - minVal_dt
numDays = diff_dt.days + 1
print("Have a total of",numDays,"days")
Models[m,:,:], model_stdev[m,:,:],SiteFreq[m,:,:] = setUpAzTasks(model_residuals,svs,args,params,numDays,minVal_dt,nAz)
print("FINISHED AZ RUN for model",m)
print("Normal finish of pwl")
return Models, model_stdev, SiteFreq
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(8)
plt.tight_layout()
plt.show()
# Calculate the block median
zz = np.linspace(0,90,181)
if args.consolidatedFile :
cdata = parseConsolidated(args.consolidatedFile)
if args.daily:
dt_start = gt.unix2dt(cdata[0,0])
startDTO = dt_start
res_start = int(dt_start.strftime("%Y") + dt_start.strftime("%j"))
dt_stop = gt.unix2dt(cdata[-1,0])
res_stop = int(dt_stop.strftime("%Y") + dt_stop.strftime("%j"))
total_time = dt_stop - dt_start
days = total_time.days + 1
print("Residuals start from:",res_start," and end at ",res_stop,"total_time:",total_time,"in days:",total_time.days)
eleMedians = np.zeros((days,181))
d = 0
while d < days:
minDTO = startDTO + dt.timedelta(days = d)
maxDTO = startDTO + dt.timedelta(days = d+1)
#===================================================================
# Work out the time scale of observations, and number of parameters
# that will be solved for.
#===================================================================
if args.syyyy and args.eyyyy:
dt_start = dt.datetime(int(args.syyyy),01,01) + dt.timedelta(days=int(args.sdoy)-1)
dt_stop = dt.datetime(int(args.eyyyy),01,01) + dt.timedelta(days=int(args.edoy)-1)
else:
print("")
print("Warning:")
print("\tusing:",args.resfile,"to work out the time period to determine how many satellites were operating.")
print("")
dt_start = gt.unix2dt(site_residuals[0,0])
res_start = int(dt_start.strftime("%Y") + dt_start.strftime("%j")-1)
dt_stop = gt.unix2dt(site_residuals[-1,0])
res_stop = int(dt_stop.strftime("%Y") + dt_stop.strftime("%j")-1)
print("\tResiduals run from:",res_start,"to:",res_stop)
filename = os.path.basename(args.resfile)
siteID = filename[0:4]
sdata = gsf.parseSite(args.station_file,siteID.upper())
changes = gsf.determineESMChanges(dt_start,dt_stop,sdata)
sitepos = gapr.getStationPos(args.apr_file,siteID)
numModels = np.size(changes['ind']) + 1
info = {}
info['filename'] = args.resfile
info['basename'] = filename
def setUpCalcSiteModelPostFit(models, site_residuals, info, zen_grid, az_grid,
cpus):
nZen = int(90.0 / zen_grid) + 1
nAz = int(360. / az_grid)
print("nAz", nAz)
#numParamsPerSite = nZen * nAz
nModels = info['numModels']
#tSite = numParamsPerSite*params['numModels']
#numParams = tSat + tSite
prefit = np.zeros(nModels)
prefit_sums = np.zeros((nModels, nAz, nZen))
prefit_res = np.zeros((nModels, nAz, nZen))
postfit = np.zeros(nModels)
postfit_sums = np.zeros((nModels, nAz, nZen))
postfit_res = np.zeros((nModels, nAz, nZen))
numObs = np.zeros(nModels)
numObs_sums = np.zeros((nModels, nAz, nZen))
change = info['changes']
print("Changes for site", info['site'], change)
# keep track of how may observations are in each bin
#SiteFreq = np.zeros((nModels,nAz,nZen))
#Models = np.zeros((nModels,nAz,nZen))
#model_stdev = np.zeros((int(params['numModels']),nAz,nZen))
# create a new model everythime there has been a change of antenna
for m in range(0, nModels):
print(info['site'], "----> creating model", m + 1, "of",
info['numModels'])
# start_yyyy and start_ddd should always be defind, however stop_dd may be absent
#ie no changes have ocured since the last setup
minVal_dt = gt.ydhms2dt(change['start_yyyy'][m],
change['start_ddd'][m], 0, 0, 0)
if np.size(change['stop_ddd']) > m:
maxVal_dt = gt.ydhms2dt(change['stop_yyyy'][m],
change['stop_ddd'][m], 23, 59, 59)
print("Min:", minVal_dt, "Max:", maxVal_dt, m,
np.size(change['stop_ddd']))
criterion = ((site_residuals[:, 0] >= calendar.timegm(
minVal_dt.utctimetuple())) &
(site_residuals[:, 0] < calendar.timegm(
maxVal_dt.utctimetuple())))
else:
criterion = (site_residuals[:, 0] >= calendar.timegm(
minVal_dt.utctimetuple()))
maxVal_dt = gt.unix2dt(site_residuals[-1, 0])
# get the residuals for this model time period
mind = np.array(np.where(criterion))[0]
model_residuals = site_residuals[mind, :]
diff_dt = maxVal_dt - minVal_dt
numDays = diff_dt.days + 1
print("Have a total of", numDays, "days", np.shape(models))
prefit[m], prefit_sums[m, :, :], prefit_res[
m, :, :], postfit[m], postfit_sums[m, :, :], postfit_res[
m, :, :], numObs[m], numObs_sums[m, :, :] = setUpPostFitTasks(
models, model_residuals, args.cpu, zen_grid, az_grid,
params, numDays, minVal_dt)
print("FINISHED Post fit RUN for model", m)
return prefit, prefit_sums, prefit_res, postfit, postfit_sums, postfit_res, numObs, numObs_sums
def solveSiteModel(site_residuals,
svs,
params,
apr,
nadSpacing=0.1,
zenSpacing=0.5,
azSpacing=0.5,
brdc_dir="./"):
"""
Create a model for the satellites and sites at the same time.
PWL piece-wise-linear interpolation fit of phase residuals
-construct a PWL fit for each azimuth bin, and then paste them all together to get
the full model
-inversion is done within each bin
site_residuals = the one-way L3 post-fit, ambiguity fixed phase residuals
svs = an array of satellite SVN numbers that are spacebourne/operating
for the period of this residual stack
params = meta data about the solution bein attempted
['site'] = 4 char site id
['changes'] = dictionary of when model changes need to be applied
apr = satellite apriori data
"""
#prechi = 0
#NUMD = 0
# add one to make sure we have a linspace which includes 0.0 and 14.0
# add another parameter for the zenith PCO estimate
numNADS = int(14.0 / nadSpacing) + 1
PCOEstimates = 1
numSVS = np.size(svs)
numParamsPerSat = numNADS + PCOEstimates
tSat = numParamsPerSat * numSVS
nZen = int(90.0 / zenSpacing) + 1
nAz = int(360. / azSpacing)
print("nAz", nAz)
numParamsPerSite = nZen * nAz
tSite = numParamsPerSite * params['numModels']
numParams = tSat + tSite
print("------------------------------------------------")
print("Processing Site: ", params['site'])
print("------------------------------------------------")
print("Sat Params:----------------", numParamsPerSat)
print("Number of Sats:------------", np.size(svs))
print("Total satellite parameters:-------------", tSat)
print("Site Params:---------------", numParamsPerSite)
print("Number of Models:----------", params['numModels'])
print("Total Site Params:----------------------", tSite)
print("------------------------------------------------")
print("Total Params:---------------------------", numParams)
print("------------------------------------------------")
# Creating matrices
#Neq = np.zeros((numParams,numParams))
#AtWb = np.zeros(numParams)
change = params['changes']
print("Changes for site", params['site'], change)
# keep track of how may observations are in each bin
#NadirFreq = np.zeros((numSVS,numNADS))
SiteFreq = np.zeros((int(params['numModels']), nAz, nZen))
Models = np.zeros((int(params['numModels']), nAz, nZen))
model_stdev = np.zeros((int(params['numModels']), nAz, nZen))
# create a new model everythime there has been a change of antenna
for m in range(0, int(params['numModels'])):
print(params['site'], "----> creating model", m + 1, "of",
params['numModels'])
# start_yyyy and start_ddd should always be defind, however stop_dd may be absent
#ie no changes have ocured since the last setup
minVal_dt = gt.ydhms2dt(change['start_yyyy'][m],
change['start_ddd'][m], 0, 0, 0)
if np.size(change['stop_ddd']) > m:
maxVal_dt = gt.ydhms2dt(change['stop_yyyy'][m],
change['stop_ddd'][m], 23, 59, 59)
print("Min:", minVal_dt, "Max:", maxVal_dt, m,
np.size(change['stop_ddd']))
criterion = ((site_residuals[:, 0] >= calendar.timegm(
minVal_dt.utctimetuple())) &
(site_residuals[:, 0] < calendar.timegm(
maxVal_dt.utctimetuple())))
else:
criterion = (site_residuals[:, 0] >= calendar.timegm(
minVal_dt.utctimetuple()))
maxVal_dt = gt.unix2dt(site_residuals[-1, 0])
# get the residuals for this model time period
mind = np.array(np.where(criterion))[0]
model_residuals = site_residuals[mind, :]
diff_dt = maxVal_dt - minVal_dt
numDays = diff_dt.days + 1
print("Have a total of", numDays, "days")
Models[m, :, :], model_stdev[m, :, :], SiteFreq[
m, :, :] = setUpAzTasks(model_residuals, svs, args, params,
numDays, minVal_dt, nAz)
print("FINISHED AZ RUN for model", m)
print("Normal finish of pwl")
return Models, model_stdev, SiteFreq
def processAzSlice(model_residuals, svs, args, params, numDays, minVal_dt, az):
tSat = np.size(svs) * (int(14. / args.nadir_grid) + 2)
tSite = int(90. / args.zen) + 1
numParams = tSat + tSite
Neq = np.zeros((numParams, numParams))
AtWb = np.zeros(numParams)
SiteFreq = np.zeros(tSite)
# set up a lookup dictionary
lookup_svs = {}
lctr = 0
for sv in svs:
lookup_svs[str(sv)] = lctr
lctr += 1
site_geocentric_distance = np.linalg.norm(params['sitepos'])
for d in range(0, numDays):
minDTO = minVal_dt + dt.timedelta(days=d)
maxDTO = minVal_dt + dt.timedelta(days=d + 1)
#print(d,"Stacking residuals on:",minDTO,maxDTO)
criterion = (
(model_residuals[:, 0] >= calendar.timegm(minDTO.utctimetuple())) &
(model_residuals[:, 0] < calendar.timegm(maxDTO.utctimetuple())))
tind = np.array(np.where(criterion))[0]
# if there are less than 300 obs, then skip to the next day
if np.size(tind) < 300:
continue
#print("rejecting any residuals greater than 100mm",np.shape(site_residuals))
tdata = res.reject_absVal(model_residuals[tind, :], 100.)
#print("rejecting any residuals greater than 5 sigma",np.shape(tdata))
data = res.reject_outliers_elevation(tdata, 5, 0.5)
#print("finished outlier detection",np.shape(data))
del tdata
# determine the elevation dependent weighting
a, b = res.gamitWeight(data)
if (az - args.az / 2. < 0):
criterion = (data[:, 1] <
(az + args.az / 2.)) | (data[:, 1] >
(360. - args.az / 2.))
else:
criterion = (data[:, 1] <
(az + args.az / 2.)) & (data[:, 1] >
(az - args.az / 2.))
azind = np.array(np.where(criterion))[0]
#print("Size of data before azimuth search",np.size(data))
data = data[azind, :]
#print("Size of data after azimuth search",np.size(data))
# parse the broadcast navigation file for this day to get an accurate
# nadir angle
yy = minDTO.strftime("%y")
doy = minDTO.strftime("%j")
navfile = args.brdc_dir + 'brdc' + doy + '0.' + yy + 'n'
#print("Will read in the broadcast navigation file:",navfile)
nav = rnxN.parseFile(navfile)
# Get the total number of observations for this site
numd = np.shape(data)[0]
#print("Have:",numd,"observations")
for i in range(0, numd):
# work out the svn number
svndto = gt.unix2dt(data[i, 0])
svn = svnav.findSV_DTO(svdat, data[i, 4], svndto)
svn_search = 'G{:03d}'.format(svn)
#print("Looking for:",svn_search,lookup_svs)
ctr = lookup_svs[str(svn_search)]
#print("Position is CTR:",ctr,data[i,4])
try:
# get the satellite position
svnpos = rnxN.satpos(data[i, 4], svndto, nav)
#print("SVNPOS:",svnpos[0])
satnorm = np.linalg.norm(svnpos[0])
#print("NORM:",np.linalg.norm(svnpos[0]))
except:
print("Error calculation satelite position for", svndto,
data[i, :])
continue
# work out the nadir angle
nadir = NADIR.calcNadirAngle(data[i, 2], site_geocentric_distance,
satnorm)
#print("Ele {:.2f} Old: {:.2f} New:{:.2f}".format(data[i,2],oldnadir,nadir))
#print("Ele {:.2f} New:{:.2f}".format(data[i,2],nadir))
w = a**2 + b**2 / np.sin(np.radians(90. - data[i, 2]))**2
w = 1. / w
# Work out the indices for the satellite parameters
niz = int(np.floor(nadir / args.nadir_grid))
iz = int((numParamsPerSat * ctr) + niz)
pco_iz = numParamsPerSat * (ctr + 1) - 1
# work out the location of site parameters
nsiz = int(np.floor(data[i, 2] / args.zen))
#aiz = int(np.floor(data[i,1]/args.az))
#siz = int( tSat + (m*numParamsPerSite) + (aiz * nZen) + nsiz)
siz = int(tSat + nsiz)
# check that the indices are not overlapping
if iz + 1 >= pco_iz or iz >= pco_iz:
#print("WARNING in indices iz+1 = pco_iz skipping obs",nadir,iz,pco_iz)
continue
#NadirFreq[ctr,niz] = NadirFreq[ctr,niz] +1
SiteFreq[nsiz] = SiteFreq[nsiz] + 1
#
# R = SITE_PCV_ERR + SAT_PCV_ERR + SAT_PCO_ERR * cos(nadir)
#
# dR/dSITE_PCV_ERR = 1
# dR/dSAT_PCV_ERR = 1
# dR/dSAT_PCO_ERR = cos(nadir)
#
# nice partial derivative tool:
# http://www.symbolab.com/solver/partial-derivative-calculator
#
# Nadir partials..
Apart_1 = (1. - (nadir - niz * args.nadir_grid) / args.nadir_grid)
Apart_2 = (nadir - niz * args.nadir_grid) / args.nadir_grid
#
# PCO partial ...
Apart_3 = np.cos(np.radians(nadir))
# Site partials
Apart_4 = (1. - (data[i, 2] - nsiz * args.zen) / args.zen)
Apart_5 = (data[i, 2] - nsiz * args.zen) / args.zen
#print("Finished forming Design matrix")
#print("Starting AtWb",np.shape(AtWb),iz,pco_iz,siz)
AtWb[iz] = AtWb[iz] + Apart_1 * data[i, 3] * w
AtWb[iz + 1] = AtWb[iz + 1] + Apart_2 * data[i, 3] * w
AtWb[pco_iz] = AtWb[pco_iz] + Apart_3 * data[i, 3] * w
AtWb[siz] = AtWb[siz] + Apart_4 * data[i, 3] * w
AtWb[siz + 1] = AtWb[siz + 1] + Apart_5 * data[i, 3] * w
#print("Finished forming b vector")
Neq[iz, iz] = Neq[iz, iz] + (Apart_1 * Apart_1 * w)
Neq[iz, iz + 1] = Neq[iz, iz + 1] + (Apart_1 * Apart_2 * w)
Neq[iz, pco_iz] = Neq[iz, pco_iz] + (Apart_1 * Apart_3 * w)
Neq[iz, siz] = Neq[iz, siz] + (Apart_1 * Apart_4 * w)
Neq[iz, siz + 1] = Neq[iz, siz + 1] + (Apart_1 * Apart_5 * w)
Neq[iz + 1, iz] = Neq[iz + 1, iz] + (Apart_2 * Apart_1 * w)
Neq[iz + 1, iz + 1] = Neq[iz + 1, iz + 1] + (Apart_2 * Apart_2 * w)
Neq[iz + 1, pco_iz] = Neq[iz + 1, pco_iz] + (Apart_2 * Apart_3 * w)
Neq[iz + 1, siz] = Neq[iz + 1, siz] + (Apart_2 * Apart_4 * w)
Neq[iz + 1,
siz + 1] = Neq[iz + 1, siz + 1] + (Apart_2 * Apart_5 * w)
#print("Finished NEQ Nadir estimates")
Neq[pco_iz, iz] = Neq[pco_iz, iz] + (Apart_3 * Apart_1 * w)
Neq[pco_iz, iz + 1] = Neq[pco_iz, iz + 1] + (Apart_3 * Apart_2 * w)
Neq[pco_iz, pco_iz] = Neq[pco_iz, pco_iz] + (Apart_3 * Apart_3 * w)
Neq[pco_iz, siz] = Neq[pco_iz, siz] + (Apart_3 * Apart_4 * w)
Neq[pco_iz,
siz + 1] = Neq[pco_iz, siz + 1] + (Apart_3 * Apart_5 * w)
#print("Finished NEQ PCO estimates")
Neq[siz, iz] = Neq[siz, iz] + (Apart_4 * Apart_1 * w)
Neq[siz, iz + 1] = Neq[siz, iz + 1] + (Apart_4 * Apart_2 * w)
Neq[siz, pco_iz] = Neq[siz, pco_iz] + (Apart_4 * Apart_3 * w)
Neq[siz, siz] = Neq[siz, siz] + (Apart_4 * Apart_4 * w)
Neq[siz, siz + 1] = Neq[siz, siz + 1] + (Apart_4 * Apart_5 * w)
Neq[siz + 1, iz] = Neq[siz + 1, iz] + (Apart_5 * Apart_1 * w)
Neq[siz + 1,
iz + 1] = Neq[siz + 1, iz + 1] + (Apart_5 * Apart_2 * w)
Neq[siz + 1,
pco_iz] = Neq[siz + 1, pco_iz] + (Apart_5 * Apart_3 * w)
Neq[siz + 1, siz] = Neq[siz + 1, siz] + (Apart_5 * Apart_4 * w)
Neq[siz + 1,
siz + 1] = Neq[siz + 1, siz + 1] + (Apart_5 * Apart_5 * w)
#print("Finished NEQ Site estimates")
if siz == pco_iz:
print("ERROR in indices siz = pco_iz")
# Add the parameter constraints to the Neq
#Neq = np.add(Neq,C_inv)
C_inv = formConstraints(args, tSat, tSite, 1, numParams)
Neq = np.add(Neq, C_inv)
#print("Inverting")
Cov = np.linalg.pinv(Neq)
Sol = np.dot(Cov, AtWb)
stdev = np.sqrt(np.diag(Cov))
return Sol, stdev, SiteFreq, az
if args.syyyy and args.eyyyy:
dt_start = dt.datetime(int(args.syyyy), 01,
01) + dt.timedelta(days=int(args.sdoy) - 1)
dt_stop = dt.datetime(int(args.eyyyy), 01,
01) + dt.timedelta(days=int(args.edoy) - 1)
else:
print("")
print("Warning:")
print(
"\tusing:", args.resfile,
"to work out the time period to determine how many satellites were operating."
)
print("")
dt_start = gt.unix2dt(site_residuals[0, 0])
res_start = int(
dt_start.strftime("%Y") + dt_start.strftime("%j") - 1)
dt_stop = gt.unix2dt(site_residuals[-1, 0])
res_stop = int(dt_stop.strftime("%Y") + dt_stop.strftime("%j") - 1)
print("\tResiduals run from:", res_start, "to:", res_stop)
filename = os.path.basename(args.resfile)
siteID = filename[0:4]
sdata = gsf.parseSite(args.station_file, siteID.upper())
changes = gsf.determineESMChanges(dt_start, dt_stop, sdata)
sitepos = gapr.getStationPos(args.apr_file, siteID)
numModels = np.size(changes['ind']) + 1
info = {}
info['filename'] = args.resfile
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(8)
plt.tight_layout()
plt.show()
# Calculate the block median
zz = np.linspace(0, 90, 181)
if args.consolidatedFile:
cdata = parseConsolidated(args.consolidatedFile)
if args.daily:
dt_start = gt.unix2dt(cdata[0, 0])
startDTO = dt_start
res_start = int(dt_start.strftime("%Y") + dt_start.strftime("%j"))
dt_stop = gt.unix2dt(cdata[-1, 0])
res_stop = int(dt_stop.strftime("%Y") + dt_stop.strftime("%j"))
total_time = dt_stop - dt_start
days = total_time.days + 1
print("Residuals start from:", res_start, " and end at ", res_stop,
"total_time:", total_time, "in days:", total_time.days)
eleMedians = np.zeros((days, 181))
d = 0
while d < days:
minDTO = startDTO + dt.timedelta(days=d)
