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techdemosat.wind.speed.triple.paired.jl
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techdemosat.wind.speed.triple.paired.jl
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#=
= Perform a pair of analysis calibration and performance estimates, followed by
= recalibration using the OLS slope and intercept from the opposing collocations,
= followed by another calibration and performance estimate. The important issue
= of data outlier detection and removal is treated using the DetMCD algorithm in
= R. For morphing experiments, the linearity of the bijective mapping into and
= out of Gaussian-anamorphosis space is also checked (given that the in situ
= slope and intercept remain the reference) - RD June, July, October, November
= 2016.
=#
using My, Morph, RCall ; R"library(DetMCD)"
const ODAT = 1 # identify indecies of the input data:
const OLAT = 2 # date/lat/lon on the collocation grid
const OLON = 3
const OCUR = 4 # plus the GNSS-R retrieval and three
const TOTB = 5 # ECMWF Interim indecies (b/n/a)
const TOTN = 6
const TOTA = 7
const MISS = -9999.0 # generic missing value
const MORPH = false # perform Gaussian anamorphosis
const EXTRA = true # recalibrate the extrapolated data using extra collocations
const MCDTRIM = 0.5 # Minimum Covariance Determinant trimming (nonoutlier percent)
if (argc = length(ARGS)) != 2
print("\nUsage: jjj $(basename(@__FILE__)) gnss.tds1.txt.all.locate_1.0_calib_obs.comb 1.0\n\n")
exit(1)
end
rescale = float(ARGS[2])
#=
= Function returning triple collocation cal/val measures for a group of analyses, following McColl
= et al. (2014). Inputs are an array of collocated values and stats are returned for a collocation
= set, where it is assumed that extrapolation from before and after is done using the same analysis,
= so no consideration of relative effective resolution is necessary (cf. Vogelzang et al. 2011)
=#
function triple(curr::Array{Float64,3})
#=mask = masquepourcent(curr[1, :,2], MCDTRIM) & # get the parametric center of mass
masquepourcent(curr[1, :,1], MCDTRIM) & # after trimming extreme values first
masquepourcent(curr[2, :,1], MCDTRIM)
sampsitu = curr[1,mask,2]
samprefa = curr[1,mask,1]
samprefb = curr[2,mask,1]
mass = mean(curr[2,mask,2])
# @show length(mask) length(mask[mask])
mass = mean(curr[2,:,2])
sampsitu = curr[1,:,2] #- mass
samprefa = curr[1,:,1] #- mass
samprefb = curr[2,:,1] #- mass
avg1 = mean(sampsitu) # and use a robust calculation of covariance
avg2 = mean(samprefa) # (two-pass here, but more algorithms are at
avg3 = mean(samprefb) # en.wikipedia.org/wiki/Algorithms_for_calculating_variance)
cv11 = mean((sampsitu - avg1) .* (sampsitu - avg1))
cv12 = mean((sampsitu - avg1) .* (samprefa - avg2))
cv13 = mean((sampsitu - avg1) .* (samprefb - avg3))
cv22 = mean((samprefa - avg2) .* (samprefa - avg2))
cv23 = mean((samprefa - avg2) .* (samprefb - avg3))
cv33 = mean((samprefb - avg3) .* (samprefb - avg3))
=#
mass = mean(curr[2,:,2])
# curr[1,:,2] -= mass
# curr[1,:,1] -= mass
# curr[2,:,1] -= mass
temp = [curr[1,:,2]' curr[1,:,1]' curr[2,:,1]']
remp = rcopy(R"DetMCD($temp, alpha = $MCDTRIM)")
mask = falses(length(temp[:,1])) ; for a in remp[:Hsubsets] mask[a] = true end
# mass = mean(curr[2,mask,2])
avg1 = remp[:center][1]
avg2 = remp[:center][2]
avg3 = remp[:center][3]
cv11 = remp[:cov][1,1]
cv12 = remp[:cov][1,2]
cv13 = remp[:cov][1,3]
cv22 = remp[:cov][2,2]
cv23 = remp[:cov][2,3]
cv33 = remp[:cov][3,3]
bet1 = 0.5 * (cv12 + cv13) / cv23
alp1 = avg1 - bet1 * 0.5 * (avg2 + avg3) #+ mass * (1.0 - bet1)
# bet1 = cv13 / cv23
# alp1 = avg1 - bet1 * avg3
bet2 = bet3 = 1.0 ; alp2 = alp3 = 0.0
# bet2 = cv23 / cv13
# bet3 = cv23 / cv12
# alp2 = avg2 - bet2 * avg1 + mass * (1.0 - bet2)
# alp3 = avg3 - bet3 * avg1 + mass * (1.0 - bet3)
tmpval = cv11 - cv12 * cv13 / cv23 ; sig1 = tmpval > 0 ? sqrt(tmpval) : 0.0
tmpval = cv22 - cv12 * cv23 / cv13 ; sig2 = tmpval > 0 ? sqrt(tmpval) : 0.0
tmpval = cv33 - cv13 * cv23 / cv12 ; sig3 = tmpval > 0 ? sqrt(tmpval) : 0.0
tmpval = cv12 * cv13 / cv11 / cv23 ; cor1 = tmpval > 0 ? sqrt(tmpval) : 0.0
tmpval = cv12 * cv23 / cv22 / cv13 ; cor2 = tmpval > 0 ? sqrt(tmpval) : 0.0
tmpval = cv13 * cv23 / cv33 / cv12 ; cor3 = tmpval > 0 ? sqrt(tmpval) : 0.0
return(mass, alp1, bet1, sig1, cor1, alp2, bet2, sig2, cor2, alp3, bet3, sig3, cor3) # then return all statistics
end
#=
= main program
=#
const MEMO = 1 # center-of-mass parameter
const MEMB = 2 # error model x = ALPH + BETA * truth + error
const MEMA = 3 # error model x = ALPH + BETA * truth + error
const MEMS = 3 # number of triple collocation members
const MASS = 1 # center-of-mass parameter (again...)
const ALPH = 2 # error model x = ALPH + BETA * truth + error
const BETA = 3 # error model x = ALPH + BETA * truth + error
const SIGM = 4 # triple coll RMSE
const CORR = 5 # triple coll correlation coefficient
const PARS = 5 # number of triple collocation parameters
const CUTOFF = 170.0 # gain cutoff
ARGS222 = replace(ARGS[1], "calib", "valid") # read both sets of collocations
fpa = My.ouvre(ARGS[1], "r") ; minea = readlines(fpa) ; close(fpa) # dat, lat, lon, spd, bef, now,
fpb = My.ouvre(ARGS222, "r") ; mineb = readlines(fpb) ; close(fpb) # aft, inc, elo, azo, ela, aza, gai
minuma = length(minea) ; print("$(ARGS[1]) has $minuma lines\n")
minumb = length(mineb) ; print("$ARGS222 has $minumb lines\n")
maska = Array(Bool, minuma) # filter data by passing high gain
maskb = Array(Bool, minumb)
#for a = 1:minuma vala = float(split(minea[a])) ; maska[a] = true ; vala[8] > CUTOFF && (maska[a] = false) end
#for a = 1:minumb valb = float(split(mineb[a])) ; maskb[a] = true ; valb[8] > CUTOFF && (maskb[a] = false) end
#for a = 1:minuma vala = float(split(minea[a])) ; maska[a] = false ; abs(vala[4] - vala[6]) < 1 && (maska[a] = true) end
#for a = 1:minumb valb = float(split(mineb[a])) ; maskb[a] = false ; abs(valb[4] - valb[6]) < 1 && (maskb[a] = true) end
maska = fill(true, minuma) ; maskb = fill(true, minumb)
linea = minea[maska] ; linuma = length(linea) ; cura = zeros(2, linuma, 2) ; print("$(ARGS[1]) has $linuma good lines\n")
lineb = mineb[maskb] ; linumb = length(lineb) ; curb = zeros(2, linumb, 2) ; print("$ARGS222 has $linumb good lines\n")
if rescale > 0 # rescale the first set of input data values
print("rescaling $(ARGS[1])\n")
for a = 1:linuma
tmpbef = float(linea[a][46:56]) + rescale * randn()
tmpnow = float(linea[a][56:66])
tmpaft = float(linea[a][66:76]) + rescale * randn()
out = @sprintf("%s %9.3f %9.3f %9.3f %s", linea[a][1:44], tmpbef, tmpnow, tmpaft, linea[a][76:end])
linea[a] = out
end
print("rescaling $ARGS222\n") # rescale the second set of input data values
for a = 1:linumb
tmpbef = float(lineb[a][46:56]) + rescale * randn()
tmpnow = float(lineb[a][56:66])
tmpaft = float(lineb[a][66:76]) + rescale * randn()
out = @sprintf("%s %9.3f %9.3f %9.3f %s", lineb[a][1:44], tmpbef, tmpnow, tmpaft, lineb[a][76:end])
lineb[a] = out
end
end
if EXTRA
fname = replace(ARGS[1], "calib", "extra") * ".extra.reg" # also read the regression coefficient pairs
fpa = My.ouvre(fname, "r") ; line = readline(fpa) ; close(fpa) # for calibrating the extrapolations relative
(intb, slob, inta, sloa) = float(split(line)) # to the extra collocation target (TOTN)
# intz = 0.5 * (intb + inta) ; intb = inta = intz
# sloz = 0.5 * (slob + sloa) ; slob = sloa = sloz
end
refa = Array(Float64, linuma) # and calculate a pair of reference variables
refb = Array(Float64, linumb) # (either from observations or from analyses)
for a = 1:linuma
vala = float(split(linea[a]))
EXTRA && (vala[TOTB] = (vala[TOTB] - intb) / slob ;
vala[TOTA] = (vala[TOTA] - inta) / sloa)
# refa[a] = vala[OCUR]
# refa[a] = vala[TOTN]
# refa[a] = 0.9 * vala[OCUR] + 0.1 * vala[TOTN]
refa[a] = 0.5 * (vala[OCUR] + vala[TOTN])
end
for a = 1:linumb
vala = float(split(lineb[a]))
EXTRA && (vala[TOTB] = (vala[TOTB] - intb) / slob ;
vala[TOTA] = (vala[TOTA] - inta) / sloa)
# refb[a] = vala[OCUR]
# refb[a] = vala[TOTN]
# refb[a] = 0.9 * vala[OCUR] + 0.1 * vala[TOTN]
refb[a] = 0.5 * (vala[OCUR] + vala[TOTN])
end
statis = [MISS for a = 1:4, b = 1:MEMS, c = 1:PARS] # allocate a set of global cal/val arrays
allmas = [MISS for a = 1:4]
allalp = [MISS for a = 1:4]
allbet = [MISS for a = 1:4]
allsig = [MISS for a = 1:4]
allcor = [MISS for a = 1:4]
if MORPH # define for both collocation sets a mapping
tmpa = Array(Float64, linuma) # (Gaussian anamorphosis) based on the in situ
tmpb = Array(Float64, linumb) # observations (which are taken to be unbiased)
for a = 1:linuma vals = float(split(linea[a])) ; tmpa[a] = vals[OCUR] end # and apply each map to the opposite set
for a = 1:linumb vals = float(split(lineb[a])) ; tmpb[a] = vals[OCUR] end
shfa = minimum(tmpa) < 0 ? -2.0 * minimum(tmpa) : 0.0
shfb = minimum(tmpb) < 0 ? -2.0 * minimum(tmpb) : 0.0
rawa = sort(tmpa) + shfa
rawb = sort(tmpb) + shfb
R"library(RGeostats)"
R"dba = db.create(aa = c($rawa))"
R"fia = anam.fit(dba, name='aa', type='emp')"
R"ana <- anam.z2y(fia, dba, 'aa')"
cdfa = unique(rcopy(R"db.extract(ana, names = c('Gaussian.aa'))"))
R"rm(dba, fia, ana)"
rawa = unique(rawa - shfa)
if length(rawa) != length(cdfa)
print("\nERROR: length(rawa) $(length(rawa)) != $(length(cdfa)) length(cdfa)\n\n")
exit(1)
end
R"dbb = db.create(bb = c($rawb))"
R"fib = anam.fit(dbb, name='bb', type='emp')"
R"anb <- anam.z2y(fib, dbb, 'bb')"
cdfb = unique(rcopy(R"db.extract(anb, names = c('Gaussian.bb'))"))
R"rm(dbb, fib, anb)"
rawb = unique(rawb - shfb)
if length(rawb) != length(cdfb)
print("\nERROR: length(rawb) $(length(rawb)) != $(length(cdfb)) length(cdfb)\n\n")
exit(1)
end # finally extend and equate the tails
rawa[ 1] > rawb[ 1] && (rawa[ 1] = rawb[ 1]) ; rawb[ 1] > rawa[ 1] && (rawb[ 1] = rawa[ 1])
rawa[end] < rawb[end] && (rawa[end] = rawb[end]) ; rawb[end] < rawa[end] && (rawb[end] = rawa[end])
cdfa[ 1] > cdfb[ 1] && (cdfa[ 1] = cdfb[ 1]) ; cdfb[ 1] > cdfa[ 1] && (cdfb[ 1] = cdfa[ 1])
cdfa[end] < cdfb[end] && (cdfa[end] = cdfb[end]) ; cdfb[end] < cdfa[end] && (cdfb[end] = cdfa[end])
cdfa[ 1] > -9 && (cdfa[ 1] = -9) ; cdfb[ 1] > -9 && (cdfb[ 1] = -9)
cdfa[end] < 9 && (cdfa[end] = 9) ; cdfb[end] < 9 && (cdfb[end] = 9)
# tmp = Winston.FramedPlot(title="Empirical anamorphosis", xlabel="Gaussian Current", ylabel="Actual Current")
# ppp = Winston.add(tmp)
# tmp = Winston.Curve(cdfa, rawa, "color", parse(Winston.Colorant, "black"))
# Winston.add(ppp, tmp)
# tmp = Winston.Curve(cdfb, rawb, "color", parse(Winston.Colorant, "red"))
# Winston.add(ppp, tmp)
# xyzzy = "gaussian_anamorphosis_test.png"
# print("writing $xyzzy\n")
# Winston.savefig(ppp, xyzzy, "width", 1700, "height", 1000)
end
for a = 1:linuma # report cal/val parameters for the first set
vals = float(split(linea[a])) # (in original units regardless of morphing)
EXTRA && (vals[TOTB] = (vals[TOTB] - intb) / slob ;
vals[TOTA] = (vals[TOTA] - inta) / sloa)
cura[1,a,:] = [vals[TOTB] vals[OCUR]]
cura[2,a,:] = [vals[TOTA] refa[a] ]
end
a = 1 ; (mass, alp1, bet1, sig1, cor1, alp2, bet2, sig2, cor2, alp3, bet3, sig3, cor3) = triple(cura)
statis[a,MEMO,MASS] = statis[a,MEMB,MASS] = statis[a,MEMA,MASS] = allmas[a] = mass
statis[a,MEMO,ALPH] = alp1 ; statis[a,MEMB,ALPH] = alp2 ; statis[a,MEMA,ALPH] = alp3 ; allalp[a] = alp1 #0.5 * (alp2 + alp3)
statis[a,MEMO,BETA] = bet1 ; statis[a,MEMB,BETA] = bet2 ; statis[a,MEMA,BETA] = bet3 ; allbet[a] = bet1 #0.5 * (bet2 + bet3)
statis[a,MEMO,SIGM] = sig1 ; statis[a,MEMB,SIGM] = sig2 ; statis[a,MEMA,SIGM] = sig3 ; allsig[a] = sig1 #0.5 * (sig2 + sig3)
statis[a,MEMO,CORR] = cor1 ; statis[a,MEMB,CORR] = cor2 ; statis[a,MEMA,CORR] = cor3 ; allcor[a] = cor1 #0.5 * (cor2 + cor3)
#a = 1 ; (allmas[a], allalp[a], allbet[a], allsig[a], allcor[a]) = triple(cura)
@printf("\nnumb = %15d for %s\n", linuma, ARGS[1])
@printf("cala = %15.8f mean(vals[TOTB]) = %15.8f\n", allalp[a], mean(cura[1,:,1]))
@printf("calb = %15.8f mean(vals[TOTA]) = %15.8f\n", allbet[a], mean(cura[2,:,1]))
@printf("mean = %15.8f mean(vals[OCUR]) = %15.8f\n", mean(allmas[a]), mean(cura[1,:,2]))
@printf("%33s %8s %8s %8s %8s\n", " ", "allalp", "allbet", "allsig", "allcor")
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", allalp[a], allbet[a], allsig[a], allcor[a])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMO,ALPH], statis[a,MEMO,BETA], statis[a,MEMO,SIGM], statis[a,MEMO,CORR])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMB,ALPH], statis[a,MEMB,BETA], statis[a,MEMB,SIGM], statis[a,MEMB,CORR])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMA,ALPH], statis[a,MEMA,BETA], statis[a,MEMA,SIGM], statis[a,MEMA,CORR])
for a = 1:linumb # report cal/val parameters for the second set
vals = float(split(lineb[a])) # (in original units regardless of morphing)
EXTRA && (vals[TOTB] = (vals[TOTB] - intb) / slob ;
vals[TOTA] = (vals[TOTA] - inta) / sloa)
curb[1,a,:] = [vals[TOTB] vals[OCUR]]
curb[2,a,:] = [vals[TOTA] refb[a] ]
end
a = 2 ; (mass, alp1, bet1, sig1, cor1, alp2, bet2, sig2, cor2, alp3, bet3, sig3, cor3) = triple(curb)
statis[a,MEMO,MASS] = statis[a,MEMB,MASS] = statis[a,MEMA,MASS] = allmas[a] = mass
statis[a,MEMO,ALPH] = alp1 ; statis[a,MEMB,ALPH] = alp2 ; statis[a,MEMA,ALPH] = alp3 ; allalp[a] = alp1 #0.5 * (alp2 + alp3)
statis[a,MEMO,BETA] = bet1 ; statis[a,MEMB,BETA] = bet2 ; statis[a,MEMA,BETA] = bet3 ; allbet[a] = bet1 #0.5 * (bet2 + bet3)
statis[a,MEMO,SIGM] = sig1 ; statis[a,MEMB,SIGM] = sig2 ; statis[a,MEMA,SIGM] = sig3 ; allsig[a] = sig1 #0.5 * (sig2 + sig3)
statis[a,MEMO,CORR] = cor1 ; statis[a,MEMB,CORR] = cor2 ; statis[a,MEMA,CORR] = cor3 ; allcor[a] = cor1 #0.5 * (cor2 + cor3)
#a = 2 ; (allmas[a], allalp[a], allbet[a], allsig[a], allcor[a]) = triple(curb)
@printf("\nnumb = %15d for %s\n", linumb, ARGS222)
@printf("cala = %15.8f mean(vals[TOTB]) = %15.8f\n", allalp[a], mean(curb[1,:,1]))
@printf("calb = %15.8f mean(vals[TOTA]) = %15.8f\n", allbet[a], mean(curb[2,:,1]))
@printf("mean = %15.8f mean(vals[OCUR]) = %15.8f\n", mean(allmas[a]), mean(curb[1,:,2]))
@printf("%33s %8s %8s %8s %8s\n", " ", "allalp", "allbet", "allsig", "allcor")
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", allalp[a], allbet[a], allsig[a], allcor[a])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMO,ALPH], statis[a,MEMO,BETA], statis[a,MEMO,SIGM], statis[a,MEMO,CORR])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMB,ALPH], statis[a,MEMB,BETA], statis[a,MEMB,SIGM], statis[a,MEMB,CORR])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMA,ALPH], statis[a,MEMA,BETA], statis[a,MEMA,SIGM], statis[a,MEMA,CORR])
if MORPH # save linear mapping metrics
rawavga11 = mean(cura[1,:,1]) ; rawavga21 = mean(cura[2,:,1]) ; rawavga22 = mean(cura[2,:,2])
rawvara11 = var(cura[1,:,1]) ; rawvara21 = var(cura[2,:,1]) ; rawvara22 = var(cura[2,:,2])
rawavgb11 = mean(curb[1,:,1]) ; rawavgb21 = mean(curb[2,:,1]) ; rawavgb22 = mean(curb[2,:,2])
rawvarb11 = var(curb[1,:,1]) ; rawvarb21 = var(curb[2,:,1]) ; rawvarb22 = var(curb[2,:,2])
end
MORPH && (fpb = My.ouvre(ARGS[1] * ".cali.pair.morph", "w"))
MORPH || (fpb = My.ouvre(ARGS[1] * ".cali.pair", "w"))
form = @sprintf(" mean param MASS is %6.2f\n", mean(allmas[1]))
write(fpb, form)
form = @sprintf(" mean param MASS is %6.2f\n", mean(allmas[2]))
write(fpb, form)
form = @sprintf("%77s %8s %8s %8s %8s\n", " ", "allalp", "allbet", "allsig", "allcor")
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", ARGS[1], allalp[1], allbet[1], allsig[1], allcor[1])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", ARGS222, allalp[2], allbet[2], allsig[2], allcor[2])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "obs", statis[1,MEMO,ALPH], statis[1,MEMO,BETA], statis[1,MEMO,SIGM], statis[1,MEMO,CORR])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "obs", statis[2,MEMO,ALPH], statis[2,MEMO,BETA], statis[2,MEMO,SIGM], statis[2,MEMO,CORR])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "bef", statis[1,MEMB,ALPH], statis[1,MEMB,BETA], statis[1,MEMB,SIGM], statis[1,MEMB,CORR])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "bef", statis[2,MEMB,ALPH], statis[2,MEMB,BETA], statis[2,MEMB,SIGM], statis[2,MEMB,CORR])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "aft", statis[1,MEMA,ALPH], statis[1,MEMA,BETA], statis[1,MEMA,SIGM], statis[1,MEMA,CORR])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "aft", statis[2,MEMA,ALPH], statis[2,MEMA,BETA], statis[2,MEMA,SIGM], statis[2,MEMA,CORR])
write(fpb, form)
close(fpb)
fpb = My.ouvre(ARGS[1] * ".recalibrate", "w") # and save these bias corrections
@printf(fpb, "%33.11f %33.11f %33.11f %33.11f\n",
allalp[1], allbet[1], allalp[2], allbet[2])
close(fpb)
if MORPH
for a = 1:linuma # compute cal/val parameters for the first set
vals = float(split(linea[a])) # (in Gaussian units if morphing)
EXTRA && (vals[TOTB] = (vals[TOTB] - intb) / slob ;
vals[TOTA] = (vals[TOTA] - inta) / sloa)
MORPH && (vals[TOTB] = rawtonorm(cdfb, rawb, vals[TOTB]) ;
vals[TOTA] = rawtonorm(cdfb, rawb, vals[TOTA]) ;
vals[OCUR] = rawtonorm(cdfb, rawb, vals[OCUR]) ;
refa[a] = rawtonorm(cdfb, rawb, refa[a]))
cura[1,a,:] = [vals[TOTB] vals[OCUR]]
cura[2,a,:] = [vals[TOTA] refa[a] ]
end
a = 1 ; (mass, alp1, bet1, sig1, cor1, alp2, bet2, sig2, cor2, alp3, bet3, sig3, cor3) = triple(cura)
statis[a,MEMO,MASS] = statis[a,MEMB,MASS] = statis[a,MEMA,MASS] = allmas[a] = mass
statis[a,MEMO,ALPH] = alp1 ; statis[a,MEMB,ALPH] = alp2 ; statis[a,MEMA,ALPH] = alp3 ; allalp[a] = alp1 #0.5 * (alp2 + alp3)
statis[a,MEMO,BETA] = bet1 ; statis[a,MEMB,BETA] = bet2 ; statis[a,MEMA,BETA] = bet3 ; allbet[a] = bet1 #0.5 * (bet2 + bet3)
statis[a,MEMO,SIGM] = sig1 ; statis[a,MEMB,SIGM] = sig2 ; statis[a,MEMA,SIGM] = sig3 ; allsig[a] = sig1 #0.5 * (sig2 + sig3)
statis[a,MEMO,CORR] = cor1 ; statis[a,MEMB,CORR] = cor2 ; statis[a,MEMA,CORR] = cor3 ; allcor[a] = cor1 #0.5 * (cor2 + cor3)
# a = 1 ; (allmas[a], allalp[a], allbet[a], allsig[a], allcor[a]) = triple(cura)
for a = 1:linumb # compute cal/val parameters for the second set
vals = float(split(lineb[a])) # (in Gaussian units if morphing)
EXTRA && (vals[TOTB] = (vals[TOTB] - intb) / slob ;
vals[TOTA] = (vals[TOTA] - inta) / sloa)
MORPH && (vals[TOTB] = rawtonorm(cdfa, rawa, vals[TOTB]) ;
vals[TOTA] = rawtonorm(cdfa, rawa, vals[TOTA]) ;
vals[OCUR] = rawtonorm(cdfa, rawa, vals[OCUR]) ;
refb[a] = rawtonorm(cdfb, rawb, refb[a]))
curb[1,a,:] = [vals[TOTB] vals[OCUR]]
curb[2,a,:] = [vals[TOTA] refb[a] ]
end
a = 2 ; (mass, alp1, bet1, sig1, cor1, alp2, bet2, sig2, cor2, alp3, bet3, sig3, cor3) = triple(curb)
statis[a,MEMO,MASS] = statis[a,MEMB,MASS] = statis[a,MEMA,MASS] = allmas[a] = mass
statis[a,MEMO,ALPH] = alp1 ; statis[a,MEMB,ALPH] = alp2 ; statis[a,MEMA,ALPH] = alp3 ; allalp[a] = alp1 #0.5 * (alp2 + alp3)
statis[a,MEMO,BETA] = bet1 ; statis[a,MEMB,BETA] = bet2 ; statis[a,MEMA,BETA] = bet3 ; allbet[a] = bet1 #0.5 * (bet2 + bet3)
statis[a,MEMO,SIGM] = sig1 ; statis[a,MEMB,SIGM] = sig2 ; statis[a,MEMA,SIGM] = sig3 ; allsig[a] = cor1 #0.5 * (sig2 + sig3)
statis[a,MEMO,CORR] = cor1 ; statis[a,MEMB,CORR] = cor2 ; statis[a,MEMA,CORR] = cor3 ; allcor[a] = sig1 #0.5 * (cor2 + cor3)
# a = 2 ; (allmas[a], allalp[a], allbet[a], allsig[a], allcor[a]) = triple(curb)
# save linear mapping metrics
gauavga11 = mean(cura[1,:,1]) ; gauavga21 = mean(cura[2,:,1]) ; gauavga22 = mean(cura[2,:,2])
gauvara11 = var(cura[1,:,1]) ; gauvara21 = var(cura[2,:,1]) ; gauvara22 = var(cura[2,:,2])
gauavgb11 = mean(curb[1,:,1]) ; gauavgb21 = mean(curb[2,:,1]) ; gauavgb22 = mean(curb[2,:,2])
gauvarb11 = var(curb[1,:,1]) ; gauvarb21 = var(curb[2,:,1]) ; gauvarb22 = var(curb[2,:,2])
end
for a = 1:linuma # apply recalibration either in Gaussian or original units
vals = float(split(linea[a])) # using calibration parameters (and anamorphosis) from the
EXTRA && (vals[TOTB] = (vals[TOTB] - intb) / slob ; # other set and report new cal/val parameters in original
vals[TOTA] = (vals[TOTA] - inta) / sloa) # units
MORPH && (vals[TOTB] = rawtonorm(cdfb, rawb, vals[TOTB]) ;
vals[TOTA] = rawtonorm(cdfb, rawb, vals[TOTA]))
# vals[TOTB] = (vals[TOTB] - allalp[2]) / allbet[2]
# vals[TOTA] = (vals[TOTA] - allalp[2]) / allbet[2]
vals[OCUR] = (vals[OCUR] - allalp[2]) / allbet[2]#; if vals[OCUR] < 0 vals[OCUR] = 0.0 end
# vals[OCUR] = (vals[OCUR] - 2.0) / 0.5
##vals[TOTB] = (vals[TOTB] - statis[2,MEMB,ALPH]) / statis[2,MEMB,ALPH]
##vals[TOTA] = (vals[TOTA] - statis[2,MEMA,ALPH]) / statis[2,MEMA,BETA]
MORPH && (vals[TOTB] = normtoraw(cdfb, rawb, vals[TOTB]) ;
vals[TOTA] = normtoraw(cdfb, rawb, vals[TOTA]))
cura[1,a,:] = [vals[TOTB] vals[OCUR]]
cura[2,a,:] = [vals[TOTA] refa[a] ]
end
a = 3 ; (mass, alp1, bet1, sig1, cor1, alp2, bet2, sig2, cor2, alp3, bet3, sig3, cor3) = triple(cura)
statis[a,MEMO,MASS] = statis[a,MEMB,MASS] = statis[a,MEMA,MASS] = allmas[a] = mass
statis[a,MEMO,ALPH] = alp1 ; statis[a,MEMB,ALPH] = alp2 ; statis[a,MEMA,ALPH] = alp3 ; allalp[a] = alp1 #0.5 * (alp2 + alp3)
statis[a,MEMO,BETA] = bet1 ; statis[a,MEMB,BETA] = bet2 ; statis[a,MEMA,BETA] = bet3 ; allbet[a] = bet1 #0.5 * (bet2 + bet3)
statis[a,MEMO,SIGM] = sig1 ; statis[a,MEMB,SIGM] = sig2 ; statis[a,MEMA,SIGM] = sig3 ; allsig[a] = sig1 #0.5 * (sig2 + sig3)
statis[a,MEMO,CORR] = cor1 ; statis[a,MEMB,CORR] = cor2 ; statis[a,MEMA,CORR] = cor3 ; allcor[a] = cor1 #0.5 * (cor2 + cor3)
#a = 3 ; (allmas[a], allalp[a], allbet[a], allsig[a], allcor[a]) = triple(cura)
tmpstr = "after recalibration only (using alpha and beta from the opposite set of collocations)"
MORPH && (tmpstr = "after applying Gaussian anamorphosis, recalibration, and inverse Gaussian anamorphosis")
@printf("\nnumb = %15.0f for %s\n", linuma, ARGS[1])
@printf("cala = %15.8f %s\n", allalp[a], tmpstr)
@printf("calb = %15.8f %s\n", allbet[a], tmpstr)
@printf("mean = %15.8f %s\n", mean(allmas[a]), tmpstr)
@printf("%33s %8s %8s %8s %8s\n", " ", "allalp", "allbet", "allsig", "allcor")
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", allalp[a], allbet[a], allsig[a], allcor[a])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMO,ALPH], statis[a,MEMO,BETA], statis[a,MEMO,SIGM], statis[a,MEMO,CORR])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMB,ALPH], statis[a,MEMB,BETA], statis[a,MEMB,SIGM], statis[a,MEMB,CORR])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMA,ALPH], statis[a,MEMA,BETA], statis[a,MEMA,SIGM], statis[a,MEMA,CORR])
for a = 1:linumb # apply recalibration either in Gaussian or original units
vals = float(split(lineb[a])) # using calibration parameters (and anamorphosis) from the
EXTRA && (vals[TOTB] = (vals[TOTB] - intb) / slob ; # other set and report new cal/val parameters in original
vals[TOTA] = (vals[TOTA] - inta) / sloa) # units
MORPH && (vals[TOTB] = rawtonorm(cdfa, rawa, vals[TOTB]) ;
vals[TOTA] = rawtonorm(cdfa, rawa, vals[TOTA]))
# vals[TOTB] = (vals[TOTB] - allalp[1]) / allbet[1]
# vals[TOTA] = (vals[TOTA] - allalp[1]) / allbet[1]
vals[OCUR] = (vals[OCUR] - allalp[1]) / allbet[1]#; if vals[OCUR] < 0 vals[OCUR] = 0.0 end
##vals[TOTB] = (vals[TOTB] - statis[1,MEMB,ALPH]) / statis[1,MEMB,ALPH]
##vals[TOTA] = (vals[TOTA] - statis[1,MEMA,ALPH]) / statis[1,MEMA,BETA]
MORPH && (vals[TOTB] = normtoraw(cdfa, rawa, vals[TOTB]) ;
vals[TOTA] = normtoraw(cdfa, rawa, vals[TOTA]))
curb[1,a,:] = [vals[TOTB] vals[OCUR]]
curb[2,a,:] = [vals[TOTA] refb[a] ]
end
a = 4 ; (mass, alp1, bet1, sig1, cor1, alp2, bet2, sig2, cor2, alp3, bet3, sig3, cor3) = triple(curb)
statis[a,MEMO,MASS] = statis[a,MEMB,MASS] = statis[a,MEMA,MASS] = allmas[a] = mass
statis[a,MEMO,ALPH] = alp1 ; statis[a,MEMB,ALPH] = alp2 ; statis[a,MEMA,ALPH] = alp3 ; allalp[a] = alp1 #0.5 * (alp2 + alp3)
statis[a,MEMO,BETA] = bet1 ; statis[a,MEMB,BETA] = bet2 ; statis[a,MEMA,BETA] = bet3 ; allbet[a] = bet1 #0.5 * (bet2 + bet3)
statis[a,MEMO,SIGM] = sig1 ; statis[a,MEMB,SIGM] = sig2 ; statis[a,MEMA,SIGM] = sig3 ; allsig[a] = sig1 #0.5 * (sig2 + sig3)
statis[a,MEMO,CORR] = cor1 ; statis[a,MEMB,CORR] = cor2 ; statis[a,MEMA,CORR] = cor3 ; allcor[a] = cor1 #0.5 * (cor2 + cor3)
#a = 4 ; (allmas[a], allalp[a], allbet[a], allsig[a], allcor[a]) = triple(curb)
@printf("\nnumb = %15.0f for %s\n", linumb, ARGS222)
@printf("cala = %15.8f %s\n", allalp[a], tmpstr)
@printf("calb = %15.8f %s\n", allbet[a], tmpstr)
@printf("mean = %15.8f %s\n", mean(allmas[a]), tmpstr)
@printf("%33s %8s %8s %8s %8s\n", " ", "allalp", "allbet", "allsig", "allcor")
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", allalp[a], allbet[a], allsig[a], allcor[a])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMO,ALPH], statis[a,MEMO,BETA], statis[a,MEMO,SIGM], statis[a,MEMO,CORR])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMB,ALPH], statis[a,MEMB,BETA], statis[a,MEMB,SIGM], statis[a,MEMB,CORR])
@printf("%33s %8.3f %8.3f %8.3f %8.3f\n", " ", statis[a,MEMA,ALPH], statis[a,MEMA,BETA], statis[a,MEMA,SIGM], statis[a,MEMA,CORR])
if MORPH # get average linear mapping metrics (as
linrawa = (abs(rawavga11 - normtoraw(cdfb, rawb, gauavga11)) / rawvara11 + # mean difference normalized by variance)
abs(rawavga21 - normtoraw(cdfb, rawb, gauavga21)) / rawvara21 +
abs(rawavga22 - normtoraw(cdfb, rawb, gauavga22)) / rawvara22) * 100 / 3
linrawb = (abs(rawavgb11 - normtoraw(cdfb, rawb, gauavgb11)) / rawvarb11 +
abs(rawavgb21 - normtoraw(cdfb, rawb, gauavgb21)) / rawvarb21 +
abs(rawavgb22 - normtoraw(cdfb, rawb, gauavgb22)) / rawvarb22) * 100 / 3
lingaua = (abs(gauavga11 - rawtonorm(cdfb, rawb, rawavga11)) / gauvara11 +
abs(gauavga21 - rawtonorm(cdfb, rawb, rawavga21)) / gauvara21 +
abs(gauavga22 - rawtonorm(cdfb, rawb, rawavga22)) / gauvara22) * 100 / 3
lingaub = (abs(gauavgb11 - rawtonorm(cdfb, rawb, rawavgb11)) / gauvarb11 +
abs(gauavgb21 - rawtonorm(cdfb, rawb, rawavgb21)) / gauvarb21 +
abs(gauavgb22 - rawtonorm(cdfb, rawb, rawavgb22)) / gauvarb22) * 100 / 3
linstr = @sprintf("linrawa = %5.1f linrawb = %5.1f lingaua = %5.1f lingaub = %5.1f", linrawa, linrawb, lingaua, lingaub)
println("\n$linstr\n")
tmpstr *= " $linstr"
end
MORPH && (fpb = My.ouvre(ARGS[1] * ".cali.pair.morph", "a"))
MORPH || (fpb = My.ouvre(ARGS[1] * ".cali.pair", "a"))
form = @sprintf(" mean param MASS is %6.2f %s\n", mean(allmas[3]), tmpstr)
write(fpb, form)
form = @sprintf(" mean param MASS is %6.2f %s\n", mean(allmas[4]), tmpstr)
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", ARGS[1], allalp[3], allbet[3], allsig[3], allcor[3])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", ARGS222, allalp[4], allbet[4], allsig[4], allcor[4])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "obs", statis[3,MEMO,ALPH], statis[3,MEMO,BETA], statis[3,MEMO,SIGM], statis[3,MEMO,CORR])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "obs", statis[4,MEMO,ALPH], statis[4,MEMO,BETA], statis[4,MEMO,SIGM], statis[4,MEMO,CORR])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "bef", statis[3,MEMB,ALPH], statis[3,MEMB,BETA], statis[3,MEMB,SIGM], statis[3,MEMB,CORR])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "bef", statis[4,MEMB,ALPH], statis[4,MEMB,BETA], statis[4,MEMB,SIGM], statis[4,MEMB,CORR])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "aft", statis[3,MEMA,ALPH], statis[3,MEMA,BETA], statis[3,MEMA,SIGM], statis[3,MEMA,CORR])
write(fpb, form)
form = @sprintf("%77s %8.3f %8.3f %8.3f %8.3f\n", "aft", statis[4,MEMA,ALPH], statis[4,MEMA,BETA], statis[4,MEMA,SIGM], statis[4,MEMA,CORR])
write(fpb, form)
close(fpb)
exit(0)
#=
date 16 lat 26 lon 36 gnss 46 bef 56 now 66 aft 76 inc 87 elo 99 azo ela aza gai
20150604000000 -52.500 253.750 20.250 10.005 10.442 9.874 165.8281 77.2194 -126.5719 77.6264 -128.6019 12.4070
=#