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nadir.py
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nadir.py
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#!/usr/bin/env python
from __future__ import division, print_function, absolute_import
#import matplotlib
#matplotlib.use('Agg')
import numpy as np
import re
import calendar
import os, sys
import datetime as dt
import pickle
import multiprocessing
import antenna as ant
import residuals as res
import gpsTime as gt
import GamitStationFile as gsf
import GamitAprioriFile as gapr
import svnav
#import broadcastNavigation as brdc
import Navigation as rnxN
def satelliteModel(antenna,nadirData):
#assuming a 14 model at 1 deg intervals
ctr = 0
# from the Nadir model force the value at 13.8 to be equal to 14.0
for val in antenna['noazi'] :
if ctr == 13:
antenna['noazi'][ctr] = (val + nadirData[ctr*5 -1])
elif ctr > 13:
antenna['noazi'][ctr] = val
else:
antenna['noazi'][ctr] = val + nadirData[ctr*5]
ctr +=1
return antenna
def calcNadirAngle(zen,R=6378.0,r=26378.0):
"""
Calculate the NADIR angle based on the station's elevation angle
nadiar_angle = calNadirAngle(elevation,R,r)
zen = zenith angle of satellite being observed
R = geocentric disatnce of station (default = 6378.0)
r = geocentric distance of satellite (default = 26378.0)
"""
nadeg = np.degrees(np.arcsin(R/r * np.sin(np.radians(zen)))) # * 180./np.pi
return nadeg
def pwlNadirSiteDailyStack(site_residuals, svs, params, nadSpacing=0.1,zenSpacing=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
cdata -> compressed 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
numParamsPerSite = int(90.0/zenSpacing) + 1
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))
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")
# 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)
#print("Gamit Weighting:",minDTO,a,b)
# parse the broadcast navigation file for this day to get an accurate
# nadir angle
yy = minDTO.strftime("%y")
doy = minDTO.strftime("%j")
navfile = 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
#oldnadir = calcNadirAngle(data[i,2])
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
niz = int(np.floor(nadir/nadSpacing))
iz = int((numParamsPerSat * ctr) + niz)
pco_iz = numParamsPerSat * (ctr+1) - 1
nsiz = int(np.floor(data[i,2]/zenSpacing))
siz = int( tSat + m*numParamsPerSite + 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
# Nadir partials..
Apart_1 = (1.-(nadir-niz*nadSpacing)/nadSpacing)
Apart_2 = (nadir-niz*nadSpacing)/nadSpacing
#
# PCO partial ...
#Apart_3 = -np.sin(np.radians(nadir))
Apart_3 = np.cos(np.radians(nadir))
# Site partials
Apart_4 = (1.-(data[i,2]-nsiz*zenSpacing)/zenSpacing)
Apart_5 = (data[i,2]-nsiz*zenSpacing)/zenSpacing
#print("Finished forming Design matrix")
#
# 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
#
#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")
prechi = prechi + np.dot(data[:,3].T,data[:,3])
NUMD = NUMD + numd
print("Normal finish of pwlNadirSiteDailyStack",prechi,NUMD)
#return Neq, AtWb, prechi, NUMD, NadirFreq, prefit, prefit_sums
return Neq, AtWb, prechi, NUMD, NadirFreq
#==============================================================================
def pwlNadirSiteAZDailyStack(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)
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((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")
# 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)
#print("Gamit Weighting:",minDTO,a,b)
# parse the broadcast navigation file for this day to get an accurate
# nadir angle
yy = minDTO.strftime("%y")
doy = minDTO.strftime("%j")
navfile = 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
#oldnadir = calcNadirAngle(data[i,2])
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/nadSpacing))
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]/zenSpacing))
aiz = int(np.floor(data[i,1]/azSpacing))
siz = int( tSat + (m*numParamsPerSite) + (aiz * nZen) + 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[aiz,nsiz] = SiteFreq[aiz,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*nadSpacing)/nadSpacing)
Apart_2 = (nadir-niz*nadSpacing)/nadSpacing
#
# PCO partial ...
#Apart_3 = -np.sin(np.radians(nadir))
Apart_3 = np.cos(np.radians(nadir))
# Site partials
Apart_4 = (1.-(data[i,2]-nsiz*zenSpacing)/zenSpacing)
Apart_5 = (data[i,2]-nsiz*zenSpacing)/zenSpacing
#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")
prechi = prechi + np.dot(data[:,3].T,data[:,3])
NUMD = NUMD + numd
print("Normal finish of pwlNadirSiteDailyStack",prechi,NUMD)
#return Neq, AtWb, prechi, NUMD, NadirFreq, prefit, prefit_sums
return Neq, AtWb, prechi, NUMD, NadirFreq, SiteFreq
def neqBySite(params,svs,args):
print("\t Reading in file:",params['filename'])
site_residuals = res.parseConsolidatedNumpy(params['filename'])
# if args.model == 'pwl':
# Neq_tmp,AtWb_tmp = pwl(site_residuals,svs,args.nadir_grid)
# elif args.model == 'pwlSite':
# Neq_tmp,AtWb_tmp,prechi_tmp,numd_tmp = pwlNadirSite(site_residuals,svs,params,args.nadir_grid,0.5)
if args.model == 'pwlSiteDaily':
print("Attempting a stack on each day")
#Neq_tmp,AtWb_tmp,prechi_tmp,numd_tmp, nadir_freq, prefit_sum, prefit_sums = pwlNadirSiteDailyStack(site_residuals,svs,params,args.nadir_grid,0.5,args.brdc_dir)
Neq_tmp,AtWb_tmp,prechi_tmp,numd_tmp, nadir_freq, obs_freq = pwlNadirSiteDailyStack(site_residuals,svs,params,args.nadir_grid,args.zen,args.brdc_dir)
elif args.model == 'pwlSiteAzDaily':
# check to see if we are adding in the satellite info first
numNADS = int(14.0/args.nadir_grid) + 1
PCOEstimates = 1
numSVS = np.size(svs)
numParamsPerSat = numNADS + PCOEstimates
tSat = numParamsPerSat * numSVS
nZen = int(90.0/args.zen) + 1
nAz = int(360./args.az)
numParamsPerSite = nZen * nAz
tSite = numParamsPerSite*params['numModels']
numParams = tSat + tSite
apr = np.zeros(numParams)
if args.sol_apriori:
npzfile = np.load(args.solutionfile2)
Sol = npzfile['Sol']
for s in range(0,np.size(Sol)):
apr[s] = Sol[s]
del Sol, npzfile
nadir_freq = np.add(nadir_freq,npzfile['nadirfreq'])
Neq_tmp,AtWb_tmp,prechi_tmp,numd_tmp, nadir_freq,obs_freq = pwlNadirSiteDailyStack(site_residuals,svs,params,apr,args.nadir_grid,args.zen,args.az,args.brdc_dir)
print("Returned Neq, AtWb:",np.shape(Neq_tmp),np.shape(AtWb_tmp),prechi_tmp,numd_tmp,np.shape(nadir_freq))
sf = params['filename']+".npz"
prechis = [prechi_tmp]
numds = [numd_tmp]
if args.model == 'pwlSiteDaily':
np.savez_compressed(sf,neq=Neq_tmp,atwb=AtWb_tmp,svs=svs,prechi=prechis,numd=numds,nadirfreq=nadir_freq)
elif args.model == 'pwlSiteAzDaily':
np.savez_compressed(sf,neq=Neq_tmp,atwb=AtWb_tmp,svs=svs,prechi=prechis,numd=numds,nadirfreq=nadir_freq,obsfreq=obs_freq)
return prechi_tmp, numd_tmp
def setUpTasks(cl3files,svs,opts,params):
prechi = 0
numd = 0
print('cpu_count() = {:d}\n'.format(multiprocessing.cpu_count()))
NUMBER_OF_PROCESSES = multiprocessing.cpu_count()
if opts.cpu < NUMBER_OF_PROCESSES:
NUMBER_OF_PROCESSES = int(opts.cpu)
pool = multiprocessing.Pool(NUMBER_OF_PROCESSES)
# Submit the tasks
results = []
for i in range(0,np.size(cl3files)) :
print("Submitting job:",params[i]['site'])
results.append(pool.apply_async(neqBySite,(params[i],svs,opts)))
# Wait for all of them to finish before moving on
for r in results:
#print("\t Waiting:",r.wait())
r.wait()
prechi_tmp, numd_tmp = r.get()
prechi = prechi + prechi_tmp
numd = numd + numd_tmp
print("RGET:", prechi,numd)
return prechi,numd
def compressNeq(Neq,AtWb,svs,numParamsPerSat,nadir_freq):
# check for any rows/ columns without any observations, if they are empty remove the parameters
satCtr = 0
starts = []
ends = []
remove = []
for sv in svs:
non_zero = 0
start = satCtr * numParamsPerSat
end = (satCtr+1) * numParamsPerSat
## Check how many elements have values..
for d in range(start,end):
criterion = (np.abs(Neq[d,:]) > 0.00001)
non_zero = non_zero + np.size(np.array(np.where(criterion))[0])
#print(sv,"Non_zero:",non_zero,d)
if non_zero < 1 :
print("No observations for:",sv)
starts.append(start)
ends.append(end)
remove.append(satCtr)
satCtr = satCtr + 1
# remove the satellites without any observations from the svs array
# Need to do it in reverse order
remove = np.array(remove[::-1])
for i in remove:
svs = np.delete(svs,i)
nadir_freq = np.delete(nadir_freq,i,0)
ends = np.array(ends[::-1])
starts = np.array(starts[::-1])
print("BEFORE Neq shape:",np.shape(Neq),np.shape(nadir_freq))
# Axis 0 ---> (row)
# Axis 1 | (column)
# |
# v
#
for i in range(0,np.size(ends)):
del_ind = range(starts[i],ends[i])
Neq = np.delete(Neq,del_ind,0)
Neq = np.delete(Neq,del_ind,1)
AtWb = np.delete(AtWb,del_ind)
#prefit_sums = np.delete(prefit_sums,del_ind)
print("AFTER Neq shape:",np.shape(Neq),np.shape(nadir_freq))
#return Neq, AtWb, svs, nadir_freq, prefit_sums
return Neq, AtWb, svs, nadir_freq
def AzStripNeq(Neq,AtWb,svs,args,azimuth):
# check for any rows/ columns without any observations, if they are empty remove the parameters
numNADS = int(14.0/args.nadir_grid) + 1
PCOEstimates = 1
numSVS = np.size(svs)
numParamsPerSat = numNADS + PCOEstimates
tSat = numParamsPerSat * numSVS
nZen = int(90.0/args.zen) + 1
nAz = int(360./args.az)
numParamsPerSite = nZen * nAz
tSite = numParamsPerSite*params['numModels']
numParams = tSat + tSite
satCtr = 0
starts = []
ends = []
remove = []
# First remove any azimuth not in the requested strip
nsiz = int(np.floor(data[i,2]/zenSpacing))
aiz = int(np.floor(data[i,1]/azSpacing))
siz = int( tSat + (m*numParamsPerSite) + (aiz * nZen) + nsiz)
for sv in svs:
non_zero = 0
start = satCtr * numParamsPerSat
end = (satCtr+1) * numParamsPerSat
## Check how many elements have values..
for d in range(start,end):
criterion = (np.abs(Neq[d,:]) > 0.00001)
non_zero = non_zero + np.size(np.array(np.where(criterion))[0])
#print(sv,"Non_zero:",non_zero,d)
if non_zero < 1 :
print("No observations for:",sv)
starts.append(start)
ends.append(end)
remove.append(satCtr)
satCtr = satCtr + 1
# remove the satellites without any observations from the svs array
# Need to do it in reverse order
remove = np.array(remove[::-1])
for i in remove:
svs = np.delete(svs,i)
nadir_freq = np.delete(nadir_freq,i,0)
ends = np.array(ends[::-1])
starts = np.array(starts[::-1])
print("BEFORE Neq shape:",np.shape(Neq),np.shape(nadir_freq))
# Axis 0 ---> (row)
# Axis 1 | (column)
# |
# v
#
for i in range(0,np.size(ends)):
del_ind = range(starts[i],ends[i])
Neq = np.delete(Neq,del_ind,0)
Neq = np.delete(Neq,del_ind,1)
AtWb = np.delete(AtWb,del_ind)
#prefit_sums = np.delete(prefit_sums,del_ind)
print("AFTER Neq shape:",np.shape(Neq),np.shape(nadir_freq))
#return Neq, AtWb, svs, nadir_freq, prefit_sums
return Neq, AtWb, svs, nadir_freq
def calcPostFitBySite(cl3file,svs,Sol,params,svdat,args,modelNum):
"""
calcPostFitBySite()
"""
# add one to make sure we have a linspace which includes 0.0 and 14.0
# add another parameter for the zenith PCO estimate
nadSpacing = args.nadir_grid
numNADS = int(14.0/nadSpacing) + 1
PCOEstimates = 1
numSVS = np.size(svs)
numParamsPerSat = numNADS + PCOEstimates
tSat = numParamsPerSat * numSVS
zenSpacing = args.zen
numParamsPerSite = int(90.0/zenSpacing) + 1
tSite = numParamsPerSite*params['numModels']
numParams = tSat + tSite
brdc_dir = args.brdc_dir
postfit = 0.0
postfit_sums = np.zeros(numParams)
postfit_res = np.zeros(numParams)
prefit = 0.0
prefit_sums = np.zeros(numParams)
prefit_res = np.zeros(numParams)
prefit_rms = 0.0
postfit_rms = 0.0
mod_rms = 0.0
numObs = 0
numObs_sums = np.zeros(numParams)
change = params['changes']
site_residuals = res.parseConsolidatedNumpy(cl3file)
for m in range(0,int(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 in",cl3file)
# set up a lookup dictionary
lookup_svs = {}
lctr = 0
for sv in svs:
lookup_svs[str(sv)] = lctr
lctr+=1
#print("The lookup_svs is:",lookup_svs)
# get the distance from the centre of earth for nadir calculation
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 3 sigma",np.shape(tdata))
data = res.reject_outliers_elevation(tdata,3,0.5)
del tdata
# parse the broadcast navigation file for this day to get an accurate
# nadir angle
yy = minDTO.strftime("%y")
doy = minDTO.strftime("%j")
navfile = 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]
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)
ctr = lookup_svs[str(svn_search)]
# get the satellite position
try:
svnpos = rnxN.satpos(data[i,4],svndto,nav)
satnorm = np.linalg.norm(svnpos[0])
except:
print("Error caclulating sat pos for:",svndto,data[i,:])
continue
# work out the nadir angle
nadir = calcNadirAngle(data[i,2],site_geocentric_distance,satnorm)
niz = int(np.floor(nadir/nadSpacing))
iz = int((numParamsPerSat * ctr) + niz)
pco_iz = numParamsPerSat * (ctr+1) - 1
nsiz = int(np.floor(data[i,2]/zenSpacing))
siz = int( tSat + m*numParamsPerSite + nsiz)
sol_site = int( tSat + (m+modelNum)*numParamsPerSite + nsiz)
# check that the indices are not overlapping
if iz+1 >= pco_iz or iz >= pco_iz:
continue
if sol_site+1 >= np.size(Sol) :
continue
if nsiz+1 >= numParamsPerSite :
print("nsiz+1 >= numParamsPerSite",nsiz+1,numParamsPerSite)
continue
factor = (nadir/args.nadir_grid-(np.floor(nadir/args.nadir_grid)))
dNad = Sol[iz] + (Sol[iz+1] - Sol[iz]) * factor
dPCO = np.cos(np.radians(nadir))*Sol[pco_iz]
factor = (data[i,2]/args.zen-(np.floor(data[i,2]/args.zen)))
dSit = Sol[sol_site] + (Sol[sol_site+1] - Sol[sol_site]) * factor
prefit_tmp = data[i,3]**2
prefit = prefit + prefit_tmp
postfit_tmp = (data[i,3] - dNad-dPCO-dSit)**2
postfit = postfit + postfit_tmp
#postfit_all[iz] = data[i,3] - dNad+dPCO-dSit
mod_rms += (dNad+dPCO+dSit)**2
post_res = data[i,3] - dNad-dPCO-dSit # 1.02
pre_res = data[i,3]
numObs += 1
#print("Obs pre post:",i,numObs, np.sqrt(data[i,3]**2), np.sqrt(postfit_rms))
postfit_sums[iz] = postfit_sums[iz] + postfit_tmp
postfit_sums[iz+1] = postfit_sums[iz+1] + postfit_tmp
postfit_sums[pco_iz] = postfit_sums[pco_iz] + postfit_tmp
postfit_sums[siz] = postfit_sums[siz] + postfit_tmp
postfit_sums[siz+1] = postfit_sums[siz+1] + postfit_tmp
postfit_res[iz] = postfit_res[iz] + post_res
postfit_res[iz+1] = postfit_res[iz+1] + post_res
postfit_res[pco_iz] = postfit_res[pco_iz] + post_res
postfit_res[siz] = postfit_res[siz] + post_res
postfit_res[siz+1] = postfit_res[siz+1] + post_res
prefit_sums[iz] = prefit_sums[iz] + prefit_tmp
prefit_sums[iz+1] = prefit_sums[iz+1] + prefit_tmp
prefit_sums[pco_iz] = prefit_sums[pco_iz] + prefit_tmp
prefit_sums[siz] = prefit_sums[siz] + prefit_tmp
prefit_sums[siz+1] = prefit_sums[siz+1] + prefit_tmp
prefit_res[iz] = prefit_res[iz] + pre_res
prefit_res[iz+1] = prefit_res[iz+1] + pre_res
prefit_res[pco_iz] = prefit_res[pco_iz] + pre_res
prefit_res[siz] = prefit_res[siz] + pre_res
prefit_res[siz+1] = prefit_res[siz+1] + pre_res
numObs_sums[iz] = numObs_sums[iz] + 1
numObs_sums[iz+1] = numObs_sums[iz+1] + 1
numObs_sums[pco_iz] = numObs_sums[pco_iz] + 1
numObs_sums[siz] = numObs_sums[siz] + 1
numObs_sums[siz+1] = numObs_sums[siz+1] + 1
prefit_rms = np.sqrt(prefit/numObs)
postfit_rms = np.sqrt(postfit/numObs)
mod_rms = np.sqrt(mod_rms/numObs)
print("PREFIT rms :",prefit_rms,"Postfit rms:",postfit_rms,"Model rms:",mod_rms)
print("post/pre:",postfit_rms/prefit_rms, "diff:", np.sqrt(prefit_rms**2 - postfit_rms**2))
print("NumObs:",numObs,np.size(numObs_sums))
#np.savez_compressed(params['site']+'residuals.npz',residuals=np.array(res_all))
return prefit,prefit_sums,prefit_res, postfit, postfit_sums, postfit_res, numObs, numObs_sums, params, modelNum
def setUpPostFitTasks(cl3files,svs,Sol,params,svdat,args,tSat,numParamsPerSite,tParams):
prefit = 0
prefit_sums = np.zeros(tParams)
prefit_res = np.zeros(tParams)
postfit = 0
postfit_res = np.zeros(tParams)
numObs = 0
numObs_sums = np.zeros(tParams)
print('cpu_count() = {:d}\n'.format(multiprocessing.cpu_count()))
NUMBER_OF_PROCESSES = multiprocessing.cpu_count()
if args.cpu < NUMBER_OF_PROCESSES:
NUMBER_OF_PROCESSES = int(args.cpu)
pool = multiprocessing.Pool(NUMBER_OF_PROCESSES)
# Submit the tasks
results = []
mdlCtr = 0
for i in range(0,np.size(cl3files)) :
print("Submitting job:",params[i]['site'])
info = params[i]
results.append(pool.apply_async(calcPostFitBySite,(cl3files[i],svs,Sol,params[i],svdat,args,mdlCtr)))
mdlCtr = mdlCtr + params[i]['numModels']
# Wait for all of them to finish before moving on
for r in results:
r.wait()
print("Waiting for results")
prefit_tmp, prefit_sums_tmp,prefit_res_tmp, postfit_tmp, postfit_sums_tmp,postfit_res_tmp, numObs_tmp, numObs_sums_tmp, info, mdlCtr = r.get()
print("Received results back")
prefit = prefit + prefit_tmp
prefit_sums[0:tSat] = prefit_sums[0:tSat] + prefit_sums_tmp[0:tSat]
prefit_res[0:tSat] = prefit_res[0:tSat] + prefit_res_tmp[0:tSat]
postfit = postfit + postfit_tmp
postfit_sums[0:tSat] = postfit_sums[0:tSat] + postfit_sums_tmp[0:tSat]
postfit_res[0:tSat] = postfit_res[0:tSat] + postfit_res_tmp[0:tSat]
numObs = numObs + numObs_tmp
numObs_sums[0:tSat] = numObs_sums[0:tSat] + numObs_sums_tmp[0:tSat]
#print("RGET:",info['site'],info['numModels'],postfit,mdlCtr,numParamsPerSite)
ctr = 0
for m in range(mdlCtr,(info['numModels']+mdlCtr)) :
# Add in the station dependent models
start = tSat + m * numParamsPerSite
end = tSat + (m+1) * numParamsPerSite
tmp_start = tSat + numParamsPerSite * ctr
tmp_end = tSat + numParamsPerSite * (ctr+1) # + numParamsPerSite
prefit_sums[start:end] = prefit_sums[start:end] + prefit_sums_tmp[tmp_start:tmp_end]
prefit_res[start:end] = prefit_res[start:end] + prefit_res_tmp[tmp_start:tmp_end]
postfit_sums[start:end] = postfit_sums[start:end] + postfit_sums_tmp[tmp_start:tmp_end]
postfit_res[start:end] = postfit_res[start:end] + postfit_res_tmp[tmp_start:tmp_end]
numObs_sums[start:end] = numObs_sums[start:end] + numObs_sums_tmp[tmp_start:tmp_end]
ctr += 1
return prefit, prefit_sums,prefit_res, postfit, postfit_sums,postfit_res, numObs, numObs_sums
def prepareSites(cl3files,dt_start,dt_end,args,siteIDList):
#=====================================================================
# Work out how many station models need to be created
#=====================================================================
numModels = 0
params = []
for f in range(0,np.size(cl3files)):