From 05f61c712ea1cd6589c2c6b39bc27334b17e5cb3 Mon Sep 17 00:00:00 2001 From: Catherine Ledna Date: Thu, 14 Jun 2018 15:28:02 -0700 Subject: [PATCH 1/7] Altered Storm No Adapt function to fix bug with negative sea levels --- src/ciam.jl | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/ciam.jl b/src/ciam.jl index 93df490..938f059 100644 --- a/src/ciam.jl +++ b/src/ciam.jl @@ -277,7 +277,9 @@ function run_timestep(s::ciam, t::Int) # ** Calculate No Adaptation Costs ** for i in t_range - v.StormNoAdapt[m, i] = p.tstep * (1 - v.ρ[rgn_ind , i]) * (p.rsig0[m] / (1 + p.rsigA[m] )) * + R_NoAdapt = max(0, p.lslr[m,i]) + + v.StormNoAdapt[m, i] = p.tstep * (1 - v.ρ[rgn_ind , i]) * (p.rsig0[m] / (1 + p.rsigA[m] * exp(p.rsigB[m] * max(0, R_NoAdapt - p.lslr[m,i])))) * (v.capital[m, i] + v.popdens_seg[m, i] * v.vsl[rgn_ind, i] * p.floodmortality) v.WetlandNoAdapt[m,i] = p.tstep * v.wetlandservice[rgn_ind, i] * v.wetlandloss[m, i] * From bbf6e1cfe4b2f72494585aec8f7bcdefad07366e Mon Sep 17 00:00:00 2001 From: Catherine Ledna Date: Thu, 14 Jun 2018 16:04:31 -0700 Subject: [PATCH 2/7] Fixed bug in Protect Storms with negative sea level rise --- src/ciam.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/ciam.jl b/src/ciam.jl index 938f059..3dcc187 100644 --- a/src/ciam.jl +++ b/src/ciam.jl @@ -374,7 +374,7 @@ function run_timestep(s::ciam, t::Int) v.WetlandProtect[m,j] = p.tstep * p.wetland[m] .* v.wetlandservice[rgn_ind, j] - v.StormProtect[m,j,i-1] = p.tstep * (1 - v.ρ[rgn_ind, j]) * (p.psig0[m] + p.psig0coef[m] * p.lslr[m, j]) / + v.StormProtect[m,j,i-1] = p.tstep * (1 - v.ρ[rgn_ind, j]) * (p.psig0[m] + p.psig0coef[m] * max(0,p.lslr[m, j])) / (1. + p.psigA[m] * exp(p.psigB[m] * max(0,(v.H[m, j, i-1] - p.lslr[m,j])))) * (v.capital[m,j] + v.popdens_seg[m,j] * v.vsl[rgn_ind, j] * p.floodmortality) From 882013526515d2a9d488685335ede56d55d4f739 Mon Sep 17 00:00:00 2001 From: Catherine Ledna Date: Tue, 19 Jun 2018 20:59:12 -0700 Subject: [PATCH 3/7] Updated code to take into account special treatment of Greenland segments --- data/input-data/globalparams.csv | 4 +- data/input-data/xsc.csv | 24296 ++++++++++++++--------------- src/ciam.jl | 84 +- src/ciamhelper.jl | 57 +- test/test.jl | 25 +- 5 files changed, 12254 insertions(+), 12212 deletions(-) diff --git a/data/input-data/globalparams.csv b/data/input-data/globalparams.csv index deb8683..547f0ab 100644 --- a/data/input-data/globalparams.csv +++ b/data/input-data/globalparams.csv @@ -16,4 +16,6 @@ wmaxrate,0.01 tstep,10 ntsteps,20 fixed,TRUE -landinput,TRUE \ No newline at end of file +landinput,TRUE +fundland_canada,0.162801546 +gtapland_canada,0.131428128 \ No newline at end of file diff --git a/data/input-data/xsc.csv b/data/input-data/xsc.csv index 1d8db27..899fb6e 100644 --- a/data/input-data/xsc.csv +++ b/data/input-data/xsc.csv @@ -1,12148 +1,12148 @@ -Canada5106,CAN -Canada5115,CAN -Canada5120,CAN -Canada5132,CAN -Canada5166,CAN -Canada5167,CAN 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b/src/ciam.jl index 3dcc187..273ea8c 100644 --- a/src/ciam.jl +++ b/src/ciam.jl @@ -36,7 +36,7 @@ using Mimi popdens = Parameter( index = [segments]) # Pop density of segment in time t = 1 (people/km^2) ypcc = Parameter(index = [regions, time]) # GDP per capita per region ($2010 per capita) ypc_usa = Parameter(index = [time]) # GDP per capita in USA; used as benchmark ($2010 per capita) - + popdens_seg = Variable(index = [segments, time]) # Population density of segment extrapolated forward in time (people / km^2) ypc_seg = Variable(index = [segments, time]) # GDP per capita by segment ($2010 per capita) (multiplied by scaling factor) @@ -45,6 +45,8 @@ using Mimi landinput::Bool = Parameter() # Set to T for FUND or F for GTAP gtapland = Parameter( index = [regions]) # GTAP land value in 2007 (million 2010$ / km^2) + gtapland_canada = Parameter() # GTAP land value in 2007 for Canada (million 2010$ / km^2) + fundland_canada = Parameter() # FUND land value in 1995 for Canada (million 2010$ / km^2) dvbm = Parameter() # FUND value of OECD dryland per Darwin et al 1995 converted from $1995 ($2010M per sqkm) (5.376) kgdp = Parameter() # Capital output ratio (per MERGE) (3 by default) discountrate = Parameter() # Discount rate (0.04 by default) @@ -52,8 +54,11 @@ using Mimi landdata = Variable( index = [regions]) # Takes on value of either fundland or gtapland + landdata_canada = Variable() fundland = Variable( index = [regions]) # FUND land value in 1995 (calculated in run_timestep) (million 2010$ / km^2), # Q maybe import directly? + + land_appr_canada = Parameter(index = [time]) # Canada land appreciation rate (used for Greenland) land_appr = Variable(index = [regions, time]) # Land appreciation rate (calculated as regression by Yohe ref Abraham and Hendershott) coastland = Variable(index = [segments, time]) # Coastal land value (function of interior land value * scaling factor) ($2010M per sqkm) landvalue = Variable(index = [segments, time]) # Total endowment value of land ($2010M per sqkm) @@ -94,7 +99,7 @@ using Mimi # ---Storm damage parameters--- floodmortality = Parameter() # Flood deaths as percent of exposed population; (Jonkman Vrijling 2008) (0.01) - vsl = Variable(index = [regions, time]) # Value of statistica life (million 2010$) + vsl = Variable(index = [segments, time]) # Value of statistica life (million 2010$) # ---Wetland Loss Parameters--- wbvm = Parameter() # Annual value of wetland services (million 2010$ / km^2 / yr); (Brander et al 2006) (0.376) @@ -196,21 +201,20 @@ function run_timestep(s::ciam, t::Int) if p.landinput v.fundland[r] = min(p.dvbm, max(0.005, p.dvbm * p.ypcc[r,1] * p.refpopdens[r] / (p.ypc_usa[1] * p.refpopdens_usa))) v.landdata[r] = v.fundland[r] + v.landdata_canada = p.fundland_canada else v.landdata[r] = p.gtapland[r] + v.landdata_canada = p.gtapland_canada end - v.land_appr[r, t] = 1. v.wetlandservice[r, t] = p.wbvm * ((p.ypcc[r,t] / p.ypc_usa[1])^1.16 * (p.refpopdens[r] /27.59)^0.47) v.ρ[r, t] = p.ypcc[r,t] / (p.ypcc[r,t] + p.ypc_usa[1]) - v.vsl[r, t] = 1e-6 * 216 * p.ypc_usa[t] * (p.ypcc[r,t]/p.ypc_usa[t])^0.5 + v.land_appr[r, t] = 1. - for i in collect(2:Int(p.ntsteps)) - v.land_appr[r, i] = v.land_appr[r, i-1] * exp(0.565 * growthrate(p.ypcc[r,i-1], p.ypcc[r,i]) + 0.313 * growthrate(p.pop[r,i-1], p.pop[r,i])) v.wetlandservice[r,i] = v.land_appr[r,i] * v.wetlandservice[r,1] v.ρ[r, i] = p.ypcc[r,i] / (p.ypcc[r,i] + p.ypc_usa[1]) - v.vsl[r, i] = 1e-6 * 216 * p.ypc_usa[i] * (p.ypcc[r,i]/p.ypc_usa[i])^0.5 + v.land_appr[r, i] = v.land_appr[r, i-1] * exp(0.565 * growthrate(p.ypcc[r,i-1], p.ypcc[r,i]) + 0.313 * growthrate(p.pop[r,i-1], p.pop[r,i])) end end @@ -219,21 +223,47 @@ function run_timestep(s::ciam, t::Int) rgn_ind = getregion(m, p.xsc) v.popdens_seg[m, t] = p.popdens[m] - v.ypc_seg[m, t] = p.ypcc[rgn_ind,t] * max(0.9, (p.popdens[m]/250.)^0.05) + + # Greenland segments treated differently + if isgreenland(m,p.xsc)==1 + v.ypc_seg[m,t] =22642*1.01^t # FLAG: assumes t is an index (1-20) + v.vsl[m, t] = 1e-6 * 216 * p.ypc_usa[t] * (v.ypc_seg[m,t]/p.ypc_usa[t])^0.5 + v.coastland[m, t] = (p.land_appr_canada[t] * v.landdata_canada) * max(0.5, log(1+v.popdens_seg[m, t])/log(25)) + v.landvalue[m, t] = min(v.coastland[m, t], (p.land_appr_canada[t] * v.landdata_canada)) + else + v.ypc_seg[m, t] = p.ypcc[rgn_ind,t] * max(0.9, (p.popdens[m]/250.)^0.05) + v.vsl[m, t] = 1e-6 * 216 * p.ypc_usa[t] * (p.ypcc[rgn_ind,t]/p.ypc_usa[t])^0.5 + v.coastland[m, t] = max(0.5, log(1+v.popdens_seg[m, t])/log(25)) * (v.land_appr[rgn_ind, t] * v.landdata[rgn_ind]) # Interior * scaling factor + v.landvalue[m, t] = min(v.coastland[m, t], (v.land_appr[rgn_ind, t] * v.landdata[rgn_ind])) + end + v.capital[m, t] = p.kgdp * v.ypc_seg[m, t] * v.popdens_seg[m, t] * 1e-6 - v.coastland[m, t] = max(0.5, log(1+v.popdens_seg[m, t])/log(25)) * (v.land_appr[rgn_ind, t] * v.landdata[rgn_ind]) # Interior * scaling factor - v.landvalue[m, t] = min(v.coastland[m, t], (v.land_appr[rgn_ind, t] * v.landdata[rgn_ind])) v.coastArea[m, t] = calcCoastArea(v.areaparams[m,:], p.lslr[m, t]) - - for i in collect(2:Int(p.ntsteps)) + + for i in 2:Int(p.ntsteps) v.popdens_seg[m,i] = v.popdens_seg[m, i-1] * (1 + growthrate(p.pop[rgn_ind, i-1], p.pop[rgn_ind,i])) - v.ypc_seg[m, i] = p.ypcc[rgn_ind,i] * max(0.9, (p.popdens[m]/250.)^0.05) # ypcc * popdens scaling factor + + # Special treatment for Greenland segments + if isgreenland(m,p.xsc)==1 + v.ypc_seg[m,i] =22642*1.01^i # FLAG: assumes i is an index (1-20) + v.vsl[m, i] = 1e-6 * 216 * p.ypc_usa[i] * (v.ypc_seg[m,i]/p.ypc_usa[i])^0.5 + v.coastland[m,i] = (p.land_appr_canada[i] * v.landdata_canada) * max(0.5, log(1+v.popdens_seg[m, i])/log(25)) + v.landvalue[m, i] = min(v.coastland[m, i], (p.land_appr_canada[i] * v.landdata_canada)) + + else + v.ypc_seg[m, i] = p.ypcc[rgn_ind,i] * max(0.9, (p.popdens[m]/250.)^0.05) # ypcc * popdens scaling factor + v.coastland[m, i] = max(0.5, log(1+v.popdens_seg[m, i])/log(25)) * (v.land_appr[rgn_ind, i] * v.landdata[rgn_ind]) + v.vsl[m, i] = 1e-6 * 216 * p.ypc_usa[i] * (p.ypcc[rgn_ind,i]/p.ypc_usa[i])^0.5 + v.landvalue[m, i] = min(v.coastland[m, i], (v.land_appr[rgn_ind, i] * v.landdata[rgn_ind])) + + end + v.capital[m, i] = p.kgdp * v.ypc_seg[m, i] * v.popdens_seg[m, i] * 1e-6 - v.coastland[m, i] = max(0.5, log(1+v.popdens_seg[m, i])/log(25)) * (v.land_appr[rgn_ind, i] * v.landdata[rgn_ind]) - v.landvalue[m, i] = min(v.coastland[m, i], (v.land_appr[rgn_ind, i] * v.landdata[rgn_ind])) v.coastArea[m, i] = calcCoastArea(v.areaparams[m,:], p.lslr[m, i]) v.wetlandloss[m, i-1] = min(1, (localrate(p.lslr[m, i-1], p.lslr[m, i], p.tstep)/p.wmaxrate)^2) + + end v.wetlandloss[m, p.ntsteps] = min(1, (localrate(p.lslr[m, p.ntsteps-1], p.lslr[m, p.ntsteps], p.tstep)/p.wmaxrate)^2) @@ -280,7 +310,7 @@ function run_timestep(s::ciam, t::Int) R_NoAdapt = max(0, p.lslr[m,i]) v.StormNoAdapt[m, i] = p.tstep * (1 - v.ρ[rgn_ind , i]) * (p.rsig0[m] / (1 + p.rsigA[m] * exp(p.rsigB[m] * max(0, R_NoAdapt - p.lslr[m,i])))) * - (v.capital[m, i] + v.popdens_seg[m, i] * v.vsl[rgn_ind, i] * p.floodmortality) + (v.capital[m, i] + v.popdens_seg[m, i] * v.vsl[m, i] * p.floodmortality) v.WetlandNoAdapt[m,i] = p.tstep * v.wetlandservice[rgn_ind, i] * v.wetlandloss[m, i] * min(v.coastArea[m, i], p.wetland[m]) @@ -345,6 +375,8 @@ function run_timestep(s::ciam, t::Int) Hprev = v.H[m, convert(Int,p.at[at_index_prev]),i-1] end + + v.Construct[m,t,i-1] = (p.tstep/atstep) * (p.length[m] * p.pc0 * p.cci[rgn_ind] * (p.pcfixed + (1- p.pcfixed)*(v.H[m, t, i-1]^2 - Hprev^2) + p.mc*atstep*v.H[m, t, i-1]) + p.length[m] * 1.7 * v.H[m, t, i-1] * v.landvalue[m,t]*.04/2*atstep) * 1e-4 @@ -358,7 +390,7 @@ function run_timestep(s::ciam, t::Int) v.StormRetreat[m,j,i] = p.tstep * (1 - v.ρ[rgn_ind, j]) * (p.rsig0[m] / (1 + p.rsigA[m] * exp(p.rsigB[m] * max(0, v.R[m,j,i] - p.lslr[m, j])))) * - (v.capital[m, j] + v.popdens_seg[m, j] * v.vsl[rgn_ind, j] * p.floodmortality) + (v.capital[m, j] + v.popdens_seg[m, j] * v.vsl[m, j] * p.floodmortality) v.FloodRetreat[m, j, i] = v.FloodRetreat[m, t, i] v.RelocateRetreat[m,j,i] = v.RelocateRetreat[m,t,i] @@ -376,7 +408,7 @@ function run_timestep(s::ciam, t::Int) v.StormProtect[m,j,i-1] = p.tstep * (1 - v.ρ[rgn_ind, j]) * (p.psig0[m] + p.psig0coef[m] * max(0,p.lslr[m, j])) / (1. + p.psigA[m] * exp(p.psigB[m] * max(0,(v.H[m, j, i-1] - p.lslr[m,j])))) * - (v.capital[m,j] + v.popdens_seg[m,j] * v.vsl[rgn_ind, j] * p.floodmortality) + (v.capital[m,j] + v.popdens_seg[m,j] * v.vsl[m, j] * p.floodmortality) v.Construct[m,j,i-1] = v.Construct[m, t, i-1] @@ -461,7 +493,7 @@ end function getsegments(rgn_name, xsc) - segs = collect(keys(filter( (k,v) -> v==rgn_name,xsc))) + segs = collect(keys(filter( (k,v) -> v[1]==rgn_name,xsc))) return segs end @@ -476,17 +508,17 @@ function findind(val, vec) end -function getregion(seg_ind, xsc, index=true) - rgn = xsc[seg_ind] +function getregion(seg_ind, xsc) + rgn = xsc[seg_ind][1] - # if index - # rgn_ind = index_lookup(rgn, rgnms) - # return rgn_ind - # end - return rgn end +function isgreenland(seg_ind, xsc) + greenland = xsc[seg_ind][2] + return greenland +end + function calcHorR(option, level, lslrPlan, surgeExpLevels, adaptOptions) ind = findind(level, adaptOptions) diff --git a/src/ciamhelper.jl b/src/ciamhelper.jl index 7f80440..d0ab23b 100644 --- a/src/ciamhelper.jl +++ b/src/ciamhelper.jl @@ -171,51 +171,46 @@ end # Function to process the segment-country mapping file (xsc) in CIAM # Reads from CSV, outputs list of dictionaries and arrays # Filters xsc file to desired segments/regions -function prepxsc(data_dir, xscfile,subset) - # Returns regions (rgns), segments (segs), dictionary translating segment index - # to region index (xsc_ind), and dictionary translating segment name to region name (xsc_out) +function prepxsc(data_dir, xscfile, subset) - # Create segment-region mapping (string version) - xsc_params = Dict{Any, Any}(lowercase(splitext(xscfile)[1]) => readdlm(joinpath(data_dir,xscfile), ',' )) - xsc_char = Dict{Any,Any}( xsc_params["xsc"][i,1] => xsc_params["xsc"][i,2] for i in 1:size(xsc_params["xsc"],1)) - - # Subset dictionary according to list of desired segments or regions + xsc_name = replace(xscfile, ".csv","") # Strip ".csv" from file name + + # Read in csv and convert to dictionary format + xsc_params = Dict{Any, Any}(lowercase(splitext(xscfile)[1]) => readdlm(joinpath(data_dir, xscfile), ',' )) + xsc_char = Dict{Any,Any}( xsc_params[xsc_name][i,1] => (xsc_params[xsc_name][i,2],xsc_params[xsc_name][i,3]) for i in 1:size(xsc_params[xsc_name],1)) + + # If only a subset of segments is used, filter down to relevant segments if subset!=false - subset_sorted = sort(subset) - xsc_out = Dict{Any,Any}() - for k in keys(xsc_char) - if k in subset - xsc_out[k] = xsc_char[k] - end - end - else - xsc_out = xsc_char + filter!((k,v)-> k in subset, xsc_char) end # Create region and segment indices - rgns = sort(unique(collect(values(xsc_out)))) - segs = sort(unique(collect(keys(xsc_out)))) - - # # Map segment index to region index - # # Also create seg -> number, rgn -> number dictionary - xsc_ind = Dict{Any,Any}() # numeric seg -> numeric rgn - xsc_segmap = Dict{Any,Any}() # Numeric seg/rgn -> char seg/rgn + rgns = sort(unique([i[1] for i in collect(values(xsc_char))])) + segs = sort(unique(collect(keys(xsc_char)))) + + xsc_ind = Dict{Any,Any}() # numeric seg -> (numeric rgn, greenland bool) + xsc_segmap = Dict{Any,Any}() # Numeric seg/rgn -> char seg/rgn xsc_rgnmap = Dict{Any,Any}() + for i in 1:length(segs) - r = xsc_char[segs[i]] - r_ind = findind(r, rgns) + r = xsc_char[segs[i]][1] # Region character + grn = xsc_char[segs[i]][2] # 0 = non-Greenland, 1 = greenland bool + r_ind = findind(r, rgns) # Region index + + new_val = (r_ind, grn) # New tuple w/ region index instead of character # Build XSC Seg/rgn Maps - r2 = rgns[r_ind] - s = segs[i] + r2 = rgns[r_ind] # New region char + s = segs[i] xsc_segmap[i] = s if !(r2 in values(xsc_rgnmap)) xsc_rgnmap[r_ind] = r2 end - - xsc_ind[i] = r_ind + + xsc_ind[i] = new_val end - return (xsc_ind, rgns, segs, xsc_out, xsc_rgnmap, xsc_segmap) + + return (xsc_ind, rgns, segs, xsc_char, xsc_rgnmap, xsc_segmap) end diff --git a/test/test.jl b/test/test.jl index 8069e45..5befa96 100644 --- a/test/test.jl +++ b/test/test.jl @@ -257,7 +257,7 @@ end # resultsdir - output directory # gamsdata,jldata - location of comparison data from GAMS/Julia (relative to datadir) # rcp - string rcp value to test -function run_tests(datadir, gamsfile, resultsdir, lslfile, subset, rcp, tag, model=true) +function run_tests(datadir, gamsfile, resultsdir, lslfile, subset, rcp, tag, model=true, sum = false) # Import model data modelparams = import_model_data(datadir, lslfile,"xsc.csv", subset) params = modelparams[1] @@ -274,11 +274,11 @@ function run_tests(datadir, gamsfile, resultsdir, lslfile, subset, rcp, tag, mod m = run_model(params, xsc) - if (length(subset)>5 || subset==false) - sum = true - else - sum = false - end + # if (length(subset)>5 || subset==false) + # sum = true + # else + # sum = false + # end write_results(m, rcp, resultsdir, xsc, tag, sum) end @@ -337,3 +337,16 @@ function run_tests(datadir, gamsfile, resultsdir, lslfile, subset, rcp, tag, mod end +function run_model_only(datadir, lslfile, subset) + # Import model data + modelparams = import_model_data(datadir, lslfile,"xsc.csv", subset) + params = modelparams[1] + xsc = modelparams[2] + + # return modelparams + m = run_model(params, xsc) + + return m + +end + From a7950a1a5d5a0edf3bd7f91accb5eb88071636da Mon Sep 17 00:00:00 2001 From: Catherine Ledna Date: Sat, 30 Jun 2018 21:17:54 -0700 Subject: [PATCH 4/7] Created main.jl, moved some functions from test.jl to ciamhelper.jl to work better with main; modified some ciam.jl documentation (no code changes) --- src/ciam.jl | 24 +++++++---- src/ciamhelper.jl | 80 +++++++++++++++++++++++++++++-------- src/main.jl | 6 +++ test/test.jl | 100 +++++++++++++++++++--------------------------- 4 files changed, 126 insertions(+), 84 deletions(-) create mode 100644 src/main.jl diff --git a/src/ciam.jl b/src/ciam.jl index 273ea8c..0774afd 100644 --- a/src/ciam.jl +++ b/src/ciam.jl @@ -1,12 +1,10 @@ # Catherine Ledna -# April 20, 2018 +# June 30, 2018 ##------------------------------------------------------------------------ # CIAM Model #------------------------------------------------------------------------ # Implements CIAM model adapted from Diaz, 2016. #------------------------------------------------------------------------ -# TODO optimalFixed variable write-outs -# TODO multi-segment bugs # TODO performance optimization using Mimi @@ -277,14 +275,14 @@ function run_timestep(s::ciam, t::Int) # Determine length of adaptation period ("atstep") g(c) = c == t - at_index = find(g, p.at)[1] # WEIRD - changing to g solved issue - at_index_next = at_index + 1 + at_index = find(g, p.at)[1] # Find index corresponding to adaptation period in p.at + at_index_next = at_index + 1 # Find index corresponding to next adaptation period at_index_prev = max(1,at_index - 1) at_prev = Int(p.at[at_index_prev]) # TODO pick one - either index into vector or use the period, it's confusing if at_index_next <= length(p.at) - atstep = (p.at[at_index_next] - p.at[at_index])*p.tstep # years + atstep = (p.at[at_index_next] - p.at[at_index])*p.tstep # In years at_next = Int(p.at[at_index_next]) last_t = at_next-1 last = 0 @@ -375,10 +373,15 @@ function run_timestep(s::ciam, t::Int) Hprev = v.H[m, convert(Int,p.at[at_index_prev]),i-1] end - + # Island protection costs are higher + if isisland(m,p.xsc)==1 + pc = 2*p.pc0*p.cci[rgn_ind] + else + pc = p.pc0 * p.cci[rgn_ind] + end v.Construct[m,t,i-1] = (p.tstep/atstep) * - (p.length[m] * p.pc0 * p.cci[rgn_ind] * (p.pcfixed + (1- p.pcfixed)*(v.H[m, t, i-1]^2 - Hprev^2) + + (p.length[m] * pc * (p.pcfixed + (1- p.pcfixed)*(v.H[m, t, i-1]^2 - Hprev^2) + p.mc*atstep*v.H[m, t, i-1]) + p.length[m] * 1.7 * v.H[m, t, i-1] * v.landvalue[m,t]*.04/2*atstep) * 1e-4 end @@ -519,6 +522,11 @@ function isgreenland(seg_ind, xsc) return greenland end +function isisland(seg_ind, xsc) + island = xsc[seg_ind][3] + return island +end + function calcHorR(option, level, lslrPlan, surgeExpLevels, adaptOptions) ind = findind(level, adaptOptions) diff --git a/src/ciamhelper.jl b/src/ciamhelper.jl index d0ab23b..565b531 100644 --- a/src/ciamhelper.jl +++ b/src/ciamhelper.jl @@ -5,12 +5,15 @@ #------------------------------------------------------------------------ # Assorted functions to process CIAM data and run CIAM model #------------------------------------------------------------------------ +include("ciam.jl") +using Mimi using Distributions # Function to load CIAM parameters from CSV to dictionary # data_dir = relative path to data location -function load_ciam_params(data_dir) +function load_ciam_params() + data_dir = "../data/input-data" files = readdir(data_dir) filter!(i->(i!="desktop.ini" && i!=".DS_Store" && i!="xsc.csv"), files) params = Dict{Any, Any}(lowercase(splitext(m)[1]) => readdlm(joinpath(data_dir,m), ',' ) for m in files) @@ -20,18 +23,20 @@ end # Function to read in LSLR from file and filter to desired set of segments # Modifies input parameter dictionary +# lslfile - string; name of lslr file to use; location relative to data/input-data directory # subset - list of segments you want # params - parameter dictionary you want to add lslr to -function preplsl!(data_dir,lslfile,subset, params) +function preplsl!(lslfile,subset, params) + data_dir = "../data/input-data" lsl_params = Dict{Any, Any}("lslr" => readdlm(joinpath(data_dir,lslfile), ',' )) # Filter LSL according to subset segments p = lsl_params["lslr"] - p_new = p[2:end,:] + p_new = p[2:end,:] # Chomp off first row row_order = sortperm(p_new[:,1]) p_new = p_new[row_order,1:end] - if subset != false + if subset != false # TODO use filter! function instead like in prepxsc s = p_new[:,1] ind_s = filter_index(s,subset) p_new = p_new[ind_s,2:end] @@ -158,26 +163,18 @@ function filter_index(v1, v2) return(out) end -function transpose_string_matrix(mat) - # Utility function to manually transpose a matrix composed of non-numeric types - nmat = vec(mat[1,:]) - for i in collect(2:size(mat,1)) - col = mat[i,:] - nmat = hcat(nmat,col) - end - return nmat -end - # Function to process the segment-country mapping file (xsc) in CIAM # Reads from CSV, outputs list of dictionaries and arrays # Filters xsc file to desired segments/regions -function prepxsc(data_dir, xscfile, subset) +function prepxsc(subset) + data_dir = "../data/input-data" + xscfile = "xsc.csv" xsc_name = replace(xscfile, ".csv","") # Strip ".csv" from file name # Read in csv and convert to dictionary format xsc_params = Dict{Any, Any}(lowercase(splitext(xscfile)[1]) => readdlm(joinpath(data_dir, xscfile), ',' )) - xsc_char = Dict{Any,Any}( xsc_params[xsc_name][i,1] => (xsc_params[xsc_name][i,2],xsc_params[xsc_name][i,3]) for i in 1:size(xsc_params[xsc_name],1)) + xsc_char = Dict{Any,Any}( xsc_params[xsc_name][i,1] => (xsc_params[xsc_name][i,2],xsc_params[xsc_name][i,3], xsc_params[xsc_name][i,4]) for i in 1:size(xsc_params[xsc_name],1)) # If only a subset of segments is used, filter down to relevant segments if subset!=false @@ -195,9 +192,10 @@ function prepxsc(data_dir, xscfile, subset) for i in 1:length(segs) r = xsc_char[segs[i]][1] # Region character grn = xsc_char[segs[i]][2] # 0 = non-Greenland, 1 = greenland bool + isl = xsc_char[segs[i]][3] # 0 = non-island, 1 = island bool r_ind = findind(r, rgns) # Region index - new_val = (r_ind, grn) # New tuple w/ region index instead of character + new_val = (r_ind, grn, isl) # New tuple w/ region index instead of character # Build XSC Seg/rgn Maps r2 = rgns[r_ind] # New region char @@ -224,6 +222,54 @@ function findind(val, vec) end +# Function to run CIAM for given parameters and segment-country mapping +# Some params are currently hard-coded +function run_model(params, xsc) + m = Model() + setindex(m, :time, 20) + setindex(m, :adaptPers, 5) # Todo figure out way not to hardcode these + setindex(m, :regions, xsc[2]) + setindex(m, :segments, xsc[3]) + + addcomponent(m, ciam) + setparameter(m, :ciam, :xsc, xsc[1]) + setleftoverparameters(m, params) + + run(m) + + return m +end + + +# Wrapper for importing model data. +# lslfile - filename for lsl (string) +# subset - filename with names of segments to use (string) or false (bool) to run all segments +function import_model_data(lslfile, subset) + # Process main and lsl params + params = load_ciam_params() + + # Process XSC (segment-country mapping dictionary) + xsc = prepxsc(subset) + + # Process params using xsc and format lsl file + parse_ciam_params!(params, xsc[2], xsc[3]) + preplsl!(lslfile, subset, params) + + return(params, xsc) + +end + +function get_ciam(lslfile, subset) + # Import model data + modelparams = import_model_data(lslfile,subset) + params = modelparams[1] + xsc = modelparams[2] + + m = run_model(params, xsc) + + return m + +end diff --git a/src/main.jl b/src/main.jl new file mode 100644 index 0000000..b6652df --- /dev/null +++ b/src/main.jl @@ -0,0 +1,6 @@ +include("ciamhelper.jl") + +lslfile = "lsl_rcp0_p50.csv" +subset = false # use string of subset file location relative to main.jl to run for a subset of coastal segments + +m = get_ciam() # Run ciam for SLR of rcp0p50 and all 12148 coastal segments \ No newline at end of file diff --git a/test/test.jl b/test/test.jl index 5befa96..e1f65db 100644 --- a/test/test.jl +++ b/test/test.jl @@ -52,24 +52,6 @@ function compare_outputs(A, B, header, metadata, file, tol=1e-5) end -# Function to run CIAM for given parameters and segment-country mapping -# Some params are currently hard-coded -function run_model(params, xsc) - m = Model() - setindex(m, :time, 20) - setindex(m, :adaptPers, 5) # Todo figure out way not to hardcode these - setindex(m, :regions, xsc[2]) - setindex(m, :segments, xsc[3]) - - addcomponent(m, ciam) - setparameter(m, :ciam, :xsc, xsc[1]) - setleftoverparameters(m, params) - - run(m) - - return m -end - # Wrapper for importing model data. Not generalizable; hard-coded names; WIP # datadir - data directory # lslfile - filename for lsl (string) @@ -284,49 +266,49 @@ function run_tests(datadir, gamsfile, resultsdir, lslfile, subset, rcp, tag, mod end # Compare and ouptut results - jldata = import_comparison_data(resultsdir, "results_$(rcp)_$(tag).csv") # TODO - distinct outputs for each model run - levels = readlines(open("../data/meta/levels.csv")) - variables = unique([v[2] for v in values(metavars[2])]) - segments = unique(jldata[:seg]) - - for j in 1:length(levels) - plotlist = [] - for k in 1:length(variables) - - # Skip incompatible combinations - if (contains(levels[j], "retreat")|| levels[j]=="noAdaptation") && variables[k]=="protection" - continue - elseif contains(levels[j], "protect") && (variables[k]=="inundation" || variables[k]=="relocation") - continue - elseif (levels[j]=="optimalFixed") && (variables[k] != "total") # TODO update this - continue - else + # jldata = import_comparison_data(resultsdir, "results_$(rcp)_$(tag).csv") # TODO - distinct outputs for each model run + # levels = readlines(open("../data/meta/levels.csv")) + # variables = unique([v[2] for v in values(metavars[2])]) + # segments = unique(jldata[:seg]) + + # for j in 1:length(levels) + # plotlist = [] + # for k in 1:length(variables) + + # # Skip incompatible combinations + # if (contains(levels[j], "retreat")|| levels[j]=="noAdaptation") && variables[k]=="protection" + # continue + # elseif contains(levels[j], "protect") && (variables[k]=="inundation" || variables[k]=="relocation") + # continue + # elseif (levels[j]=="optimalFixed") && (variables[k] != "total") # TODO update this + # continue + # else - if sum - A = jldata[ (jldata[:level] .== levels[j]) .& (jldata[:costtype] .== variables[k]), :value] - B = gamsdata[ (gamsdata[:level] .== levels[j]) .& (gamsdata[:costtype] .== variables[k]), :value] - - # Make plots - title = string(variables[k]) - p = line_plot(B, A, title) - push!(plotlist, p) - else - for s in segments - A = jldata[ (jldata[:level] .== levels[j]) .& (jldata[:costtype] .== variables[k]) .& (jldata[:seg] .== s), :value] - B = gamsdata[ (gamsdata[:level] .== levels[j]) .& (gamsdata[:costtype] .== variables[k]) .& (gamsdata[:seg].==s), :value] - - # Make plots - title = string(s,variables[k]) - p = line_plot(B, A, title) - push!(plotlist, p) - end - - end - end + # if sum + # A = jldata[ (jldata[:level] .== levels[j]) .& (jldata[:costtype] .== variables[k]), :value] + # B = gamsdata[ (gamsdata[:level] .== levels[j]) .& (gamsdata[:costtype] .== variables[k]), :value] + + # # Make plots + # title = string(variables[k]) + # p = line_plot(B, A, title) + # push!(plotlist, p) + # else + # for s in segments + # A = jldata[ (jldata[:level] .== levels[j]) .& (jldata[:costtype] .== variables[k]) .& (jldata[:seg] .== s), :value] + # B = gamsdata[ (gamsdata[:level] .== levels[j]) .& (gamsdata[:costtype] .== variables[k]) .& (gamsdata[:seg].==s), :value] + + # # Make plots + # title = string(s,variables[k]) + # p = line_plot(B, A, title) + # push!(plotlist, p) + # end + + # end + # end - end - make_plots(plotlist,"$(rcp)_$(levels[j])_$(tag)", resultsdir) - end + # end + # make_plots(plotlist,"$(rcp)_$(levels[j])_$(tag)", resultsdir) + # end if model return m From eb0127c77fa18bc6239049188989f1f6bc5d6862 Mon Sep 17 00:00:00 2001 From: Catherine Ledna Date: Sat, 30 Jun 2018 21:21:57 -0700 Subject: [PATCH 5/7] Fixed data/input path in ciamhelper.jl; renamed input-data to input; deleted old single-segment test folders --- data/input-data/xsc.csv | 12148 --------------- data/input-data/ypcc.csv | 161 - data/{input-data => input}/adaptOptions.csv | 0 data/{input-data => input}/at.csv | 0 data/{input-data => input}/cci.csv | 0 data/{input-data => input}/data.csv | 110 +- data/{input-data => input}/globalparams.csv | 0 data/{input-data => input}/gtapland.csv | 0 data/input/land_appr_canada.csv | 20 + data/{input-data => input}/lsl_rcp0_p50.csv | 4 +- data/{input-data => input}/lsl_rcp85_p5.csv | 0 data/{input-data => input}/lsl_rcp85_p50.csv | 0 data/{input-data => input}/lsl_rcp85_p95.csv | 0 data/{input-data => input}/pop.csv | 0 data/{input-data => input}/refpopdens.csv | 0 data/input/xsc.csv | 12148 +++++++++++++++ data/{input-data => input}/ypc_usa.csv | 0 .../input-data => data/input}/ypcc.csv | 0 src/ciamhelper.jl | 6 +- test/test_phil/input-data/cci.csv | 160 - test/test_phil/input-data/data.csv | 2 - test/test_phil/input-data/globalparams.csv | 19 - test/test_phil/input-data/gtapland.csv | 160 - test/test_phil/input-data/lsl_rcp0_p50.csv | 12149 ---------------- test/test_phil/input-data/pop.csv | 161 - test/test_phil/input-data/refpopdens.csv | 160 - test/test_phil/input-data/xsc.csv | 12148 --------------- test/test_phil/input-data/ypc_usa.csv | 20 - test/test_phil/results-gams/test.csv | 1303 -- 29 files changed, 12228 insertions(+), 38651 deletions(-) delete mode 100644 data/input-data/xsc.csv delete mode 100644 data/input-data/ypcc.csv rename data/{input-data => input}/adaptOptions.csv (100%) rename data/{input-data => input}/at.csv (100%) rename data/{input-data => input}/cci.csv (100%) rename data/{input-data => input}/data.csv (99%) rename data/{input-data => input}/globalparams.csv (100%) rename data/{input-data => input}/gtapland.csv (100%) create mode 100644 data/input/land_appr_canada.csv rename data/{input-data => input}/lsl_rcp0_p50.csv (99%) rename data/{input-data => input}/lsl_rcp85_p5.csv (100%) rename data/{input-data => input}/lsl_rcp85_p50.csv (100%) rename data/{input-data => input}/lsl_rcp85_p95.csv (100%) rename data/{input-data => input}/pop.csv (100%) rename data/{input-data => input}/refpopdens.csv (100%) create mode 100644 data/input/xsc.csv rename data/{input-data => input}/ypc_usa.csv (100%) rename {test/test_phil/input-data => data/input}/ypcc.csv (100%) delete mode 100644 test/test_phil/input-data/cci.csv delete mode 100644 test/test_phil/input-data/data.csv delete mode 100644 test/test_phil/input-data/globalparams.csv delete mode 100644 test/test_phil/input-data/gtapland.csv delete mode 100644 test/test_phil/input-data/lsl_rcp0_p50.csv delete mode 100644 test/test_phil/input-data/pop.csv delete mode 100644 test/test_phil/input-data/refpopdens.csv delete mode 100644 test/test_phil/input-data/xsc.csv delete mode 100644 test/test_phil/input-data/ypc_usa.csv delete mode 100644 test/test_phil/results-gams/test.csv diff --git a/data/input-data/xsc.csv b/data/input-data/xsc.csv deleted file mode 100644 index 899fb6e..0000000 --- a/data/input-data/xsc.csv +++ /dev/null @@ -1,12148 +0,0 @@ -Canada5106,CAN,0 -Canada5115,CAN,0 -Canada5120,CAN,0 -Canada5132,CAN,0 -Canada5166,CAN,0 -Canada5167,CAN,0 -Canada5202,CAN,0 -Canada5168,CAN,0 -Canada5169,CAN,0 -Canada5171,CAN,0 -Canada5172,CAN,0 -Canada5173,CAN,0 -Canada5175,CAN,0 -Canada5177,CAN,0 -Canada5178,CAN,0 -Canada5179,CAN,0 -Canada5180,CAN,0 -Canada5181,CAN,0 -Canada5203,CAN,0 -Canada5204,CAN,0 -Canada5218,CAN,0 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-"ZAF",5751.314178872,8118.161645672,11547.218434299,16687.435011761,24389.500019053,32568.95113517,39732.379100561,46554.009061509,52291.719346259,58947.900736872,65147.505576001,71999.128547767,79571.342997782,87939.934193371,97188.657808,107410.07818,118706.49471,131190.96574,144988.44005,160237.0074 diff --git a/data/input-data/adaptOptions.csv b/data/input/adaptOptions.csv similarity index 100% rename from data/input-data/adaptOptions.csv rename to data/input/adaptOptions.csv diff --git a/data/input-data/at.csv b/data/input/at.csv similarity index 100% rename from data/input-data/at.csv rename to data/input/at.csv diff --git a/data/input-data/cci.csv b/data/input/cci.csv similarity index 100% rename from data/input-data/cci.csv rename to data/input/cci.csv diff --git a/data/input-data/data.csv b/data/input/data.csv similarity index 99% rename from data/input-data/data.csv rename to data/input/data.csv index 58a97e6..a745e2c 100644 --- a/data/input-data/data.csv +++ b/data/input/data.csv @@ -3072,7 +3072,7 @@ Greece9219,0.11,0.221,0.335,3.335,81,1,1,1,1,0.333333333,0.333333333,0.333333333 Greece9221,0.13,0.265,0.404,3.404,39,1,1,1,1,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25,0.25,56.369,37.688,0,0.384978716,1.845756179,0.05873604,6.809393514,0.384978716,0.201783444,8.723881892 Greece9222,0.109,0.22,0.331,3.331,106,1,1,1,1,0.333333333,0.333333333,0.333333333,0.25,0.25,0.25,0.25,0,0,0,159.049,37.688,0,1.122662025,9.476498212,0.063893727,11.69977726,1.122662025,0.189667518,16.11687507 Greece9224,0.108,0.219,0.33,3.33,70,1,1,1,1,1,1,1,3.25,3.25,3.25,3.25,4,4,4,115.439,81.605,0.663,0.278906659,3.266087091,0.065033039,16.27803175,0.278906659,0.184846021,22.37395465 -KoreaDemPeople'sRep2491,0.125,0.255,0.388,3.388,121,65,56,72,83,72.66666667,72.66666667,72.66666667,70,70,70,70,52,52,52,324.647,120.158,2.13,0.496515639,5.447612885,0.061565771,15.50408633,0.496515639,0.186931592,20.88643895 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filter!(i->(i!="desktop.ini" && i!=".DS_Store" && i!="xsc.csv"), files) params = Dict{Any, Any}(lowercase(splitext(m)[1]) => readdlm(joinpath(data_dir,m), ',' ) for m in files) @@ -27,7 +27,7 @@ end # subset - list of segments you want # params - parameter dictionary you want to add lslr to function preplsl!(lslfile,subset, params) - data_dir = "../data/input-data" + data_dir = "../data/input" lsl_params = Dict{Any, Any}("lslr" => readdlm(joinpath(data_dir,lslfile), ',' )) # Filter LSL according to subset segments @@ -168,7 +168,7 @@ end # Filters xsc file to desired segments/regions function prepxsc(subset) - data_dir = "../data/input-data" + data_dir = "../data/input" xscfile = "xsc.csv" xsc_name = replace(xscfile, ".csv","") # Strip ".csv" from file name diff --git a/test/test_phil/input-data/cci.csv b/test/test_phil/input-data/cci.csv deleted file mode 100644 index cef0d66..0000000 --- a/test/test_phil/input-data/cci.csv +++ /dev/null @@ -1,160 +0,0 @@ -MAR,0.5 -ABW,0.756 -AGO,0.661 -ALB,0.704 -ANT,0.756 -ARE,0.659 -ARG,1.128 -ATG,0.731 -AUS,2.5 -BEL,1.67 -BEN,0.754 -BGD,0.5 -BGR,0.78 -BHR,0.604 -BHS,1.161 -BIH,0.649 -BLZ,1.484 -BMU,2.09 -BRA,0.88 -BRB,1.06 -CAN,2.261 -CHL,1.128 -CHN,0.689 -CIV,0.638 -CMR,0.885 -COD,0.912 -COG,1.028 -COL,0.975 -COM,0.798 -CPV,0.961 -CRI,0.94 -CUB,0.641 -CYP,1.279 -DEU,2.179 -DJI,0.567 -DMA,1.051 -DNK,2.213 -DOM,0.794 -DZA,0.639 -ECU,0.63 -EGY,0.5 -ERI,0.5 -ESP,1.338 -EST,1.09 -FIN,1.982 -FJI,0.645 -FRA,2.084 -FSM,0.645 -GAB,1.032 -GBR,1.782 -GEO,1.65 -GHA,0.5 -GIN,0.65 -GLP,1.051 -GMB,0.65 -GNB,0.649 -GNQ,0.602 -GRC,1.548 -GRD,1.033 -GTM,0.653 -GUY,0.763 -GYF,0.763 -HKG,1.051 -HND,0.721 -HRV,0.968 -HTI,0.578 -IDN,0.5 -IND,0.5 -IRL,1.289 -IRQ,0.837 -ISL,2.5 -ISR,1.742 -ITA,1.472 -JAM,0.873 -JOR,0.684 -JPN,2.5 -KEN,0.522 -KHM,0.5 -KIR,0.645 -KOR,1.276 -KWT,0.754 -LBN,0.684 -LBR,0.54 -LBY,0.639 -LKA,0.5 -LTU,1.084 -LVA,1.142 -MAC,0.816 -MCO,1.743 -MDG,0.514 -MDV,0.764 -MEX,1.058 -MHL,0.645 -MLT,1.097 -MMR,0.5 -MNE,0.931 -MOZ,0.829 -MRT,0.644 -MTQ,0.645 -MUS,0.642 -MYS,0.619 -NAM,0.585 -NCL,0.645 -NGA,0.642 -NIC,0.774 -NLD,1.955 -NOR,2.5 -NRU,0.645 -NZL,2.5 -OMN,0.5 -PAK,0.5 -PAN,0.936 -PER,0.84 -PHL,0.5 -PLW,0.5 -PNG,2.5 -POL,1.24 -PRI,0.794 -PRK,1.276 -PRT,1.038 -PYF,0.645 -QAT,0.515 -REU,0.514 -ROU,0.717 -RUS,1.594 -SAU,0.5 -SDN,1.009 -SEN,0.709 -SGP,0.875 -SLB,0.645 -SLE,0.5 -SLV,0.776 -SOM,0.5 -STP,0.5 -SUR,0.763 -SVN,1.366 -SWE,2.5 -SYC,0.892 -SYR,0.837 -TGO,0.647 -THA,0.5 -TLS,0.5 -TON,0.645 -TTO,0.76 -TUN,0.5 -TUR,0.835 -TUV,0.645 -TWN,0.689 -TZA,0.5 -UKR,1.401 -URY,1.063 -USA,2.039 -VCT,0.794 -VEN,0.642 -VNM,0.5 -VUT,0.645 -WLF,1.742 -WSM,0.645 -YEM,0.687 -ZAF,0.781 \ No newline at end of file diff --git a/test/test_phil/input-data/data.csv b/test/test_phil/input-data/data.csv deleted file mode 100644 index fda3b82..0000000 --- a/test/test_phil/input-data/data.csv +++ /dev/null @@ -1,2 +0,0 @@ -NA,s10,s100,s1000,smax,uplift,area1,area2,area3,area4,area5,area6,area7,area8,area9,area10,area11,area12,area13,area14,area15,length,popdens,psig0,psig0coef,psigA,psigB,rsig0,rsigA,rsigB,s10000,countryarea,wetland -Philippines10615,0.122,0.247,0.375,3.375,-0.313,54,6,14,10,4,9.333333333,9.333333333,9.333333333,8,8,8,8,5.75,5.75,5.75,52.367,203.868,8.723557429,47.10421508,0.063414675,7.574134976,8.723557429,0.195666739,10.26001382,0.732682488,3.00E+05,0 \ No newline at end of file diff --git a/test/test_phil/input-data/globalparams.csv b/test/test_phil/input-data/globalparams.csv deleted file mode 100644 index deb8683..0000000 --- a/test/test_phil/input-data/globalparams.csv +++ /dev/null @@ -1,19 +0,0 @@ -refpopdens_usa,51.42127388 -dvbm,5.376 -kgdp,3 -discountrate,0.04 -depr,1 -mobcapfrac,0.25 -movefactor,1 -capmovefactor,0.1 -democost,0.05 -pcfixed,0.3 -mc,0.02 -pc0,6.02 -floodmortality,0.01 -wbvm,0.376 -wmaxrate,0.01 -tstep,10 -ntsteps,20 -fixed,TRUE -landinput,TRUE \ No newline at end of file diff --git a/test/test_phil/input-data/gtapland.csv b/test/test_phil/input-data/gtapland.csv deleted file mode 100644 index 8944f3a..0000000 --- a/test/test_phil/input-data/gtapland.csv +++ /dev/null @@ -1,160 +0,0 @@ -MAR,0.121323567 -ABW,0.440486765 -AGO,0.051589866 -ALB,1.047302357 -ANT,0.440486765 -ARE,1.648043475 -ARG,0.093868681 -ATG,0.440486765 -AUS,0.041341934 -BEL,0.996932775 -BEN,0.093513711 -BGD,0.843191031 -BGR,0.335370644 -BHR,0.020193614 -BHS,0.440486765 -BIH,0.158932308 -BLZ,0.72946281 -BMU,0.440486765 -BRA,0.118039866 -BRB,0.440486765 -CAN,0.131428128 -CHL,0.202986104 -CHN,1.047227591 -CIV,0.086306751 -CMR,0.120798296 -COD,0.011499708 -COG,0.011499708 -COL,0.251767113 -COM,0.009295121 -CPV,0.011499708 -CRI,0.474624285 -CUB,0.440486765 -CYP,0.37975142 -DEU,0.996779229 -DJI,0.009295121 -DMA,0.440486765 -DNK,0.544261104 -DOM,0.440486765 -DZA,0.061312374 -ECU,0.313753737 -EGY,0.943160878 -ERI,0.009295121 -ESP,0.609953234 -EST,0.556837421 -FIN,1.840896387 -FJI,0.397166248 -FRA,0.734037883 -FSM,0.397166248 -GAB,0.011499708 -GBR,0.548329555 -GEO,0.435877071 -GHA,0.148463991 -GIN,0.022755107 -GLP,0.440486765 -GMB,0.011499708 -GNB,0.011499708 -GNQ,0.011499708 -GRC,2.012859735 -GRD,0.440486765 -GTM,0.529667699 -GUY,0.124061806 -GYF,0.124061806 -HKG,1.499009663 -HND,0.31138133 -HRV,1.798927164 -HTI,0.440486765 -IDN,0.921928218 -IND,1.077680391 -IRL,0.644267625 -IRQ,0.061312374 -ISL,0.158932308 -ISR,1.233157211 -ITA,1.745444672 -JAM,0.440486765 -JOR,0.061312374 -JPN,4.953002492 -KEN,0.066628553 -KHM,0.281461845 -KIR,0.397166248 -KOR,5.197505198 -KWT,0.57278012 -LBN,0.061312374 -LBR,0.011499708 -LBY,0.061312374 -LKA,2.313510952 -LTU,0.457781683 -LVA,0.251866712 -MAC,0.397166248 -MCO,0.158932308 -MDG,0.014591787 -MDV,0.397166248 -MEX,0.206350182 -MHL,0.397166248 -MLT,0.37975142 -MMR,0.335599572 -MNE,0.158932308 -MOZ,0.012193845 -MRT,0.397166248 -MTQ,0.440486765 -MUS,0.014591787 -MYS,1.252260647 -NAM,0.004572551 -NCL,0.397166248 -NGA,0.269859676 -NIC,0.084021094 -NLD,2.751196671 -NOR,5.197505198 -NRU,0.397166248 -NZL,0.250103266 -OMN,0.020193614 -PAK,0.967639785 -PAN,0.332050805 -PER,0.379712696 -PHL,1.266034758 -PLW,0.397166248 -PNG,0.397166248 -POL,0.719884731 -PRI,0.440486765 -PRK,0.943472311 -PRT,0.488296144 -PYF,0.397166248 -QAT,0.5377926 -REU,0.009295121 -ROU,0.797618381 -RUS,0.225490205 -SAU,0.206113804 -SDN,0.061312374 -SEN,0.06070062 -SGP,0.482669356 -SLB,0.397166248 -SLE,0.009295121 -SLV,0.650274113 -SOM,0.061312374 -STP,0.011499708 -SUR,0.011499708 -SVN,1.683038863 -SWE,0.643208761 -SYC,0.009295121 -SYR,0.124061806 -TGO,0.037006344 -THA,0.965975983 -TLS,0.011499708 -TON,0.061312374 -TTO,0.009295121 -TUN,0.195152748 -TUR,0.37975142 -TUV,0.397166248 -TWN,1.499009663 -TZA,0.033780071 -UKR,0.185624599 -URY,0.071472454 -USA,0.236364421 -VCT,0.397166248 -VEN,0.289846841 -VNM,1.086906236 -VUT,0.397166248 -WLF,0.440486765 -WSM,0.440486765 -YEM,0.397166248 -ZAF,0.023922215 \ No newline at end of file diff --git a/test/test_phil/input-data/lsl_rcp0_p50.csv b/test/test_phil/input-data/lsl_rcp0_p50.csv deleted file mode 100644 index 74e6e8d..0000000 --- a/test/test_phil/input-data/lsl_rcp0_p50.csv +++ /dev/null @@ -1,12149 +0,0 @@ -segment,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 -Albania489,0.0048953,0.0097906,0.014686,0.019581,0.024476,0.029372,0.034267,0.039162,0.044058,0.048953,0.053848,0.058744,0.063639,0.068534,0.073429,0.078325,0.08322,0.088115,0.093011,0.097906 -Albania490,0.0048953,0.0097906,0.014686,0.019581,0.024476,0.029372,0.034267,0.039162,0.044058,0.048953,0.053848,0.058744,0.063639,0.068534,0.073429,0.078325,0.08322,0.088115,0.093011,0.097906 -Albania491,0.0048953,0.0097906,0.014686,0.019581,0.024476,0.029372,0.034267,0.039162,0.044058,0.048953,0.053848,0.058744,0.063639,0.068534,0.073429,0.078325,0.08322,0.088115,0.093011,0.097906 -Albania492,0.0048953,0.0097906,0.014686,0.019581,0.024476,0.029372,0.034267,0.039162,0.044058,0.048953,0.053848,0.058744,0.063639,0.068534,0.073429,0.078325,0.08322,0.088115,0.093011,0.097906 -Albania493,0.0048953,0.0097906,0.014686,0.019581,0.024476,0.029372,0.034267,0.039162,0.044058,0.048953,0.053848,0.058744,0.063639,0.068534,0.073429,0.078325,0.08322,0.088115,0.093011,0.097906 -Albania494,0.0048953,0.0097906,0.014686,0.019581,0.024476,0.029372,0.034267,0.039162,0.044058,0.048953,0.053848,0.058744,0.063639,0.068534,0.073429,0.078325,0.08322,0.088115,0.093011,0.097906 -Albania495,0.0048953,0.0097906,0.014686,0.019581,0.024476,0.029372,0.034267,0.039162,0.044058,0.048953,0.053848,0.058744,0.063639,0.068534,0.073429,0.078325,0.08322,0.088115,0.093011,0.097906 -Albania496,0.0048953,0.0097906,0.014686,0.019581,0.024476,0.029372,0.034267,0.039162,0.044058,0.048953,0.053848,0.058744,0.063639,0.068534,0.073429,0.078325,0.08322,0.088115,0.093011,0.097906 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-EquatorialGuinea1198,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -EquatorialGuinea1199,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -EquatorialGuinea9213,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -EquatorialGuinea9214,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -EquatorialGuinea9215,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Eritrea1415,0.0093,0.0186,0.0279,0.0372,0.0465,0.0558,0.0651,0.0744,0.0837,0.093,0.102,0.112,0.121,0.13,0.14,0.149,0.158,0.167,0.177,0.186 -Eritrea1416,0.0093,0.0186,0.0279,0.0372,0.0465,0.0558,0.0651,0.0744,0.0837,0.093,0.102,0.112,0.121,0.13,0.14,0.149,0.158,0.167,0.177,0.186 -Eritrea1417,0.0093,0.0186,0.0279,0.0372,0.0465,0.0558,0.0651,0.0744,0.0837,0.093,0.102,0.112,0.121,0.13,0.14,0.149,0.158,0.167,0.177,0.186 -Eritrea1418,0.0093,0.0186,0.0279,0.0372,0.0465,0.0558,0.0651,0.0744,0.0837,0.093,0.102,0.112,0.121,0.13,0.14,0.149,0.158,0.167,0.177,0.186 -Eritrea1419,0.0093,0.0186,0.0279,0.0372,0.0465,0.0558,0.0651,0.0744,0.0837,0.093,0.102,0.112,0.121,0.13,0.14,0.149,0.158,0.167,0.177,0.186 -Eritrea1420,0.0093,0.0186,0.0279,0.0372,0.0465,0.0558,0.0651,0.0744,0.0837,0.093,0.102,0.112,0.121,0.13,0.14,0.149,0.158,0.167,0.177,0.186 -Eritrea1421,0.0093,0.0186,0.0279,0.0372,0.0465,0.0558,0.0651,0.0744,0.0837,0.093,0.102,0.112,0.121,0.13,0.14,0.149,0.158,0.167,0.177,0.186 -Eritrea1422,0.0093,0.0186,0.0279,0.0372,0.0465,0.0558,0.0651,0.0744,0.0837,0.093,0.102,0.112,0.121,0.13,0.14,0.149,0.158,0.167,0.177,0.186 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-Indonesia11309,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11310,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11311,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11312,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11313,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11314,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11315,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11316,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11317,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11318,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11319,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11320,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11321,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11322,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11323,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11324,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11325,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11326,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11327,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11328,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11329,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11330,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11331,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11332,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11337,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11338,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11339,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11340,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11341,0.000657,0.00131,0.00197,0.00263,0.00329,0.00394,0.0046,0.00526,0.00592,0.00657,0.00723,0.00789,0.00854,0.0092,0.00986,0.0105,0.0112,0.0118,0.0125,0.0131 -Indonesia11342,0.000657,0.00131,0.00197,0.00263,0.00329,0.00394,0.0046,0.00526,0.00592,0.00657,0.00723,0.00789,0.00854,0.0092,0.00986,0.0105,0.0112,0.0118,0.0125,0.0131 -Indonesia11343,0.000657,0.00131,0.00197,0.00263,0.00329,0.00394,0.0046,0.00526,0.00592,0.00657,0.00723,0.00789,0.00854,0.0092,0.00986,0.0105,0.0112,0.0118,0.0125,0.0131 -Indonesia11344,0.000657,0.00131,0.00197,0.00263,0.00329,0.00394,0.0046,0.00526,0.00592,0.00657,0.00723,0.00789,0.00854,0.0092,0.00986,0.0105,0.0112,0.0118,0.0125,0.0131 -Indonesia11346,0.000657,0.00131,0.00197,0.00263,0.00329,0.00394,0.0046,0.00526,0.00592,0.00657,0.00723,0.00789,0.00854,0.0092,0.00986,0.0105,0.0112,0.0118,0.0125,0.0131 -Indonesia11347,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11348,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11353,0.000657,0.00131,0.00197,0.00263,0.00329,0.00394,0.0046,0.00526,0.00592,0.00657,0.00723,0.00789,0.00854,0.0092,0.00986,0.0105,0.0112,0.0118,0.0125,0.0131 -Indonesia11354,0.000657,0.00131,0.00197,0.00263,0.00329,0.00394,0.0046,0.00526,0.00592,0.00657,0.00723,0.00789,0.00854,0.0092,0.00986,0.0105,0.0112,0.0118,0.0125,0.0131 -Indonesia11355,0.000657,0.00131,0.00197,0.00263,0.00329,0.00394,0.0046,0.00526,0.00592,0.00657,0.00723,0.00789,0.00854,0.0092,0.00986,0.0105,0.0112,0.0118,0.0125,0.0131 -Indonesia11357,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11358,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11360,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11424,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11426,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11428,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11430,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11431,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11433,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11437,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11438,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11439,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11440,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11441,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11453,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11454,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11455,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11456,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11458,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11459,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11466,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11467,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11468,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11473,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11474,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11475,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11476,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11477,0.000444,0.000887,0.00133,0.00177,0.00222,0.00266,0.0031,0.00355,0.00399,0.00444,0.00488,0.00532,0.00577,0.00621,0.00665,0.0071,0.00754,0.00798,0.00843,0.00887 -Indonesia11479,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11480,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11481,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11482,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11483,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11484,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11486,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11487,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11488,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11489,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11490,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11491,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11492,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11493,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11494,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11495,0.000825,0.00165,0.00248,0.0033,0.00413,0.00495,0.00578,0.0066,0.00743,0.00825,0.00908,0.0099,0.0107,0.0116,0.0124,0.0132,0.014,0.0149,0.0157,0.0165 -Indonesia11496,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11497,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11498,-0.0171,-0.0343,-0.0514,-0.0686,-0.0857,-0.103,-0.12,-0.137,-0.154,-0.171,-0.189,-0.206,-0.223,-0.24,-0.257,-0.274,-0.291,-0.309,-0.326,-0.343 -Indonesia11499,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11500,-0.0171,-0.0343,-0.0514,-0.0686,-0.0857,-0.103,-0.12,-0.137,-0.154,-0.171,-0.189,-0.206,-0.223,-0.24,-0.257,-0.274,-0.291,-0.309,-0.326,-0.343 -Indonesia11501,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 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-Indonesia11529,-0.0171,-0.0343,-0.0514,-0.0686,-0.0857,-0.103,-0.12,-0.137,-0.154,-0.171,-0.189,-0.206,-0.223,-0.24,-0.257,-0.274,-0.291,-0.309,-0.326,-0.343 -Indonesia11530,-0.0171,-0.0343,-0.0514,-0.0686,-0.0857,-0.103,-0.12,-0.137,-0.154,-0.171,-0.189,-0.206,-0.223,-0.24,-0.257,-0.274,-0.291,-0.309,-0.326,-0.343 -Indonesia11562,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11582,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11631,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11632,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11633,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11634,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11635,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11636,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11637,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11638,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11639,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11640,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11641,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 -Indonesia11646,0.000988,0.00198,0.00296,0.00395,0.00494,0.00593,0.00692,0.00791,0.00889,0.00988,0.0109,0.0119,0.0128,0.0138,0.0148,0.0158,0.0168,0.0178,0.0188,0.0198 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-Nicaragua7691,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7692,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7693,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7694,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7695,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7696,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7697,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7698,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7699,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7700,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7701,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7702,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7703,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7704,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7705,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7709,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7711,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7713,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nicaragua7714,0.00804,0.0161,0.0241,0.0322,0.0402,0.0482,0.0563,0.0643,0.0723,0.0804,0.0884,0.0965,0.104,0.113,0.121,0.129,0.137,0.145,0.153,0.161 -Nigeria1140,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1141,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1142,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1143,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1144,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1145,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1146,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1147,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1148,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1149,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1150,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1151,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1152,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1153,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1154,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1155,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1156,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1157,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1158,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1159,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1160,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1161,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1162,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1163,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1164,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1165,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1166,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1167,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1168,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1169,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1170,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1171,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1172,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 -Nigeria1173,0.00835,0.0167,0.025,0.0334,0.0417,0.0501,0.0584,0.0668,0.0751,0.0835,0.0918,0.1,0.109,0.117,0.125,0.134,0.142,0.15,0.159,0.167 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-COD,65.965795,85.05427,105.956285,127.440681,148.522753,167.979654,184.539067,197.494991,206.632626,212.112822,217.7383607,223.5130969,229.4409874,235.5260941,241.7725866,248.1847452,254.7669635,261.5237517,268.4597397,275.5796802 -COG,4.042899,5.002953,6.169093,7.46227,8.801176,10.124697,11.389317,12.52658,13.481304,14.224263,15.00816671,15.8352716,16.70795852,17.62873948,18.60026499,19.62533158,20.70688991,21.84805328,23.05210652,24.32251552 -COL,46.294841,52.185377,56.85609,60.058209,61.764353,62.366519,62.08975,61.118901,59.701431,58.136886,58.136886,58.136886,58.136886,58.136886,58.136886,58.136886,58.136886,58.136886,58.136886,58.136886 -COM,0.73475,0.933326,1.160261,1.425967,1.70013,2.001749,2.305455,2.58787,2.837466,3.046901,3.271794518,3.513287556,3.772605334,4.051063507,4.350074839,4.671156368,5.015937111,5.386166318,5.783722357,6.210622236 -CPV,0.495999,0.544419,0.588297,0.61814,0.632094,0.629878,0.611923,0.581637,0.548489,0.519725,0.519725,0.519725,0.519725,0.519725,0.519725,0.519725,0.519725,0.519725,0.519725,0.519725 -CRI,4.658887,5.272365,5.693562,5.928596,6.001065,5.932118,5.759707,5.514009,5.248166,5.019237,5.019237,5.019237,5.019237,5.019237,5.019237,5.019237,5.019237,5.019237,5.019237,5.019237 -CUB,11.257979,11.172971,10.983124,10.555973,9.898393,9.111195,8.388159,7.824182,7.360055,7.022025,7.022025,7.022025,7.022025,7.022025,7.022025,7.022025,7.022025,7.022025,7.022025,7.022025 -CYP,1.103647,1.21816,1.301376,1.34399,1.347448,1.326693,1.287674,1.244067,1.21063,1.192062,1.192062,1.192062,1.192062,1.192062,1.192062,1.192062,1.192062,1.192062,1.192062,1.192062 -DEU,82.302465,80.987688,79.469449,77.304593,74.781359,72.371401,70.88608,70.228268,70.144993,70.391881,70.63963797,70.88826696,71.13777105,71.38815331,71.63941684,71.89156473,72.14460011,72.39852609,72.6533458,72.90906241 -DJI,0.888716,1.065935,1.262971,1.447373,1.619732,1.743471,1.831558,1.88947,1.91944,1.923161,1.926889213,1.930624654,1.934367337,1.938117275,1.941874482,1.945638973,1.949410762,1.953189863,1.95697629,1.960770058 -DMA,0.067757,0.067879,0.068815,0.06738,0.064847,0.062585,0.060263,0.057702,0.055179,0.053437,0.053437,0.053437,0.053437,0.053437,0.053437,0.053437,0.053437,0.053437,0.053437,0.053437 -DNK,5.550142,5.735559,5.885446,5.938192,5.919903,5.910413,5.926231,5.954021,5.986421,6.032061,6.078048956,6.12438752,6.171079366,6.218127187,6.265533696,6.313301629,6.361433741,6.409932809,6.458801629,6.508043022 -DOM,9.92732,11.121404,12.059752,12.67864,12.942018,13.075309,13.069641,12.897183,12.596645,12.230609,12.230609,12.230609,12.230609,12.230609,12.230609,12.230609,12.230609,12.230609,12.230609,12.230609 -DZA,35.468208,40.180358,43.475065,45.490264,46.52156,46.165906,44.743307,43.06509,41.492436,39.983254,39.983254,39.983254,39.983254,39.983254,39.983254,39.983254,39.983254,39.983254,39.983254,39.983254 -ECU,14.464739,16.354684,17.893008,18.973267,19.54939,19.810437,19.75566,19.42287,18.913106,18.319238,18.319238,18.319238,18.319238,18.319238,18.319238,18.319238,18.319238,18.319238,18.319238,18.319238 -EGY,81.121077,94.809938,106.497969,116.232003,123.451644,127.730393,129.251105,128.440901,126.276996,123.227316,123.227316,123.227316,123.227316,123.227316,123.227316,123.227316,123.227316,123.227316,123.227316,123.227316 -ERI,5.253676,6.848254,8.394403,10.032701,11.567912,12.859289,13.933993,14.738584,15.244932,15.495786,15.75076778,16.00994526,16.27338749,16.54116463,16.81334802,17.09001017,17.37122477,17.65706673,17.94761219,18.24293855 -ESP,46.076989,48.661277,49.998071,50.937767,51.353635,49.856601,47.738213,46.280329,45.531836,45.010763,45.010763,45.010763,45.010763,45.010763,45.010763,45.010763,45.010763,45.010763,45.010763,45.010763 -EST,1.34114,1.32948,1.296158,1.25788,1.232821,1.201049,1.167219,1.150032,1.146092,1.144681,1.144681,1.144681,1.144681,1.144681,1.144681,1.144681,1.144681,1.144681,1.144681,1.144681 -FIN,5.364546,5.52586,5.619448,5.619135,5.610851,5.635736,5.674355,5.722781,5.781215,5.841647,5.902710706,5.964412721,6.026759717,6.089758436,6.153415692,6.217738368,6.282733419,6.348407874,6.414768835,6.481823479 -FJI,0.860623,0.922797,0.958253,0.991666,1.017284,1.032134,1.036523,1.032899,1.022269,1.004845,1.004845,1.004845,1.004845,1.004845,1.004845,1.004845,1.004845,1.004845,1.004845,1.004845 -FRA,62.787427,65.874337,68.466942,70.680517,72.44216,73.952842,75.554419,77.286724,78.886226,80.287658,81.71398676,83.16565458,84.64311162,86.14681602,87.67723408,89.23484036,90.82011789,92.43355823,94.07566171,95.74693754 -FSM,0.105681,0.10990824,0.11430457,0.118876752,0.123631822,0.128577095,0.133720179,0.139068986,0.144631746,0.150417016,0.156433696,0.162691044,0.169198686,0.175966633,0.183005299,0.190325511,0.197938531,0.205856072,0.214090315,0.222653928 -GAB,1.505463,1.818034,2.145528,2.467631,2.783767,3.071461,3.316382,3.512454,3.664984,3.775931,3.890236606,4.00800249,4.129333402,4.254337264,4.383125263,4.515811954,4.652515358,4.79335707,4.938462366,5.087960313 -GBR,62.03557,65.801945,69.314279,71.525039,72.817388,73.537784,74.09725,74.620435,75.081863,75.676046,76.27493125,76.87855595,77.48695762,78.10017407,78.71824338,79.34120398,79.96909456,80.60195414,81.23982205,81.88273792 -GEO,4.352244,4.079722,3.759689,3.45628,3.185945,2.958374,2.753529,2.577,2.452089,2.367024,2.367024,2.367024,2.367024,2.367024,2.367024,2.367024,2.367024,2.367024,2.367024,2.367024 -GHA,24.391823,30.324996,36.5374,42.940982,49.10674,54.647953,59.371391,63.077459,65.685964,67.230301,68.81094677,70.42875497,72.08459932,73.77937408,75.51399454,77.28939751,79.10654183,80.96640887,82.87000309,84.81835254 -GIN,9.98159,12.765081,15.945604,19.414264,23.006011,26.533422,29.813621,32.66384,34.962733,36.663664,38.447345,40.31780178,42.27925597,44.33613456,46.49307995,48.75496036,51.12688087,53.61419491,56.22251635,58.95773219 -GLP,0.4,0.416,0.43264,0.4499456,0.467943424,0.486661161,0.506127607,0.526372712,0.54742762,0.569324725,0.592097714,0.615781623,0.640412887,0.666029403,0.692670579,0.720377402,0.749192498,0.779160198,0.810326606,0.84273967 -GMB,1.728394,2.242102,2.817867,3.427274,4.036275,4.611863,5.122667,5.543952,5.864625,6.083984,6.31154785,6.547623443,6.79252915,7.04659525,7.310164377,7.583591978,7.867246799,8.161511375,8.466782552,8.783472017 -GNB,1.515224,1.86329,2.262638,2.705848,3.185172,3.682001,4.178121,4.660169,5.112026,5.518389,5.957054435,6.430590077,6.941767812,7.493579871,8.08925634,8.73228407,9.426427088,10.17574863,10.98463492,11.85782085 -GNQ,0.700401,0.905012,1.101505,1.298391,1.492645,1.663617,1.807822,1.923121,2.005111,2.054226,2.104544067,2.156094671,2.208908001,2.263014989,2.318447322,2.375237465,2.433418677,2.493025032,2.554091439,2.616653662 -GRC,11.359346,11.569033,11.62142,11.660985,11.646763,11.475521,11.277179,11.188502,11.16155,11.1085,11.1085,11.1085,11.1085,11.1085,11.1085,11.1085,11.1085,11.1085,11.1085,11.1085 -GRD,0.104487,0.108127,0.107563,0.102819,0.094921,0.089907,0.085786,0.081259,0.077619,0.07518,0.07518,0.07518,0.07518,0.07518,0.07518,0.07518,0.07518,0.07518,0.07518,0.07518 -GTM,14.388929,18.382163,22.725553,27.192297,31.594785,35.765404,39.422234,42.377002,44.569343,46.036067,47.55105914,49.11590787,50.7322539,52.40179196,54.12627253,55.9075037,57.74735306,59.64774968,61.61068609,63.63822039 -GUY,0.754493,0.773172,0.794743,0.790578,0.766143,0.768297,0.758939,0.739087,0.714934,0.693465,0.693465,0.693465,0.693465,0.693465,0.693465,0.693465,0.693465,0.693465,0.693465,0.693465 -GYF,0.25,0.26,0.2704,0.281216,0.29246464,0.304163226,0.316329755,0.328982945,0.342142263,0.355827953,0.370061071,0.384863514,0.400258055,0.416268377,0.432919112,0.450235876,0.468245311,0.486975124,0.506454129,0.526712294 -HKG,7.053189,7.803144,8.482956,8.947243,9.305325,9.567076,9.73834,9.892424,10.105249,10.35548,10.61190734,10.87468446,11.14396858,11.41992085,11.70270639,11.99249439,12.28945826,12.59377569,12.90562878,13.22520412 -HND,7.600524,9.178731,10.656972,11.933636,12.938753,13.682023,14.108941,14.219713,14.082663,13.788784,13.788784,13.788784,13.788784,13.788784,13.788784,13.788784,13.788784,13.788784,13.788784,13.788784 -HRV,4.40333,4.311246,4.185278,4.024073,3.858902,3.699071,3.55033,3.435064,3.361096,3.31701,3.31701,3.31701,3.31701,3.31701,3.31701,3.31701,3.31701,3.31701,3.31701,3.31701 -HTI,9.993247,11.311185,12.528107,13.503872,14.178411,14.721754,15.004238,15.018881,14.851864,14.566009,14.566009,14.566009,14.566009,14.566009,14.566009,14.566009,14.566009,14.566009,14.566009,14.566009 -IDN,239.870937,262.569461,279.658936,290.22305,293.455578,290.256945,282.799442,273.362656,263.48237,254.17815,254.17815,254.17815,254.17815,254.17815,254.17815,254.17815,254.17815,254.17815,254.17815,254.17815 -IND,1224.614327,1386.909072,1523.482335,1627.029386,1692.007631,1717.9688,1708.200393,1670.461509,1614.97423,1550.899337,1550.899337,1550.899337,1550.899337,1550.899337,1550.899337,1550.899337,1550.899337,1550.899337,1550.899337,1550.899337 -IRL,4.4699,4.968032,5.35623,5.707234,6.037716,6.273861,6.455261,6.658139,6.870417,7.045809,7.225678509,7.410139831,7.59931019,7.793309799,7.992261943,8.196293053,8.405532788,8.620114116,8.840173401,9.065850488 -IRQ,31.671591,42.68429,55.256976,68.950081,83.357221,97.894639,111.940533,124.854165,136.098077,145.275556,155.0718984,165.528836,176.6909144,188.6056835,201.3238992,214.8997402,229.3910387,244.8595264,261.3710982,278.996092 -ISL,0.320136,0.357745,0.389985,0.41385,0.431005,0.443736,0.453785,0.46287,0.47159,0.480095,0.488753385,0.497567922,0.506541427,0.515676767,0.52497686,0.534444678,0.544083245,0.553895642,0.563885002,0.574054518 -ISR,7.4184,8.666465,9.816146,10.955079,12.029449,12.960848,13.762554,14.435357,14.957353,15.311846,15.67474057,16.04623584,16.42653563,16.81584863,17.21438844,17.62237374,18.0400284,18.46758157,18.90526787,19.35332744 -ITA,60.550848,61.29034,60.850507,60.18236,59.158108,57.399172,55.768153,55.137356,55.161049,55.618984,56.08072068,56.54629059,57.01572556,57.48905768,57.9663193,58.44754305,58.9327618,59.42200874,59.9153173,60.4127212 -JAM,2.741052,2.827975,2.842494,2.750629,2.569228,2.454284,2.378322,2.305403,2.229621,2.165762,2.165762,2.165762,2.165762,2.165762,2.165762,2.165762,2.165762,2.165762,2.165762,2.165762 -JOR,6.187227,7.365712,8.414929,9.289244,9.881728,10.191617,10.249024,10.103762,9.829656,9.494687,9.494687,9.494687,9.494687,9.494687,9.494687,9.494687,9.494687,9.494687,9.494687,9.494687 -JPN,126.53592,124.803628,120.217549,114.340034,108.548677,103.241312,98.12575,94.365146,92.345439,91.329789,91.329789,91.329789,91.329789,91.329789,91.329789,91.329789,91.329789,91.329789,91.329789,91.329789 -KEN,40.512682,52.563909,65.928221,80.975369,96.887193,112.54016,127.319357,140.52447,151.551182,160.009016,168.9388685,178.367082,188.3214694,198.8313955,209.9278641,221.6436093,234.0131918,247.0731014,260.861864,275.4201559 -KHM,14.138255,15.893014,17.362741,18.361038,18.964892,19.117534,18.78821,18.134946,17.392423,16.660936,16.660936,16.660936,16.660936,16.660936,16.660936,16.660936,16.660936,16.660936,16.660936,16.660936 -KIR,0.099546,0.115828,0.132178,0.145273,0.155721,0.164044,0.168314,0.168816,0.167459,0.164374,0.164374,0.164374,0.164374,0.164374,0.164374,0.164374,0.164374,0.164374,0.164374,0.164374 -KOR,48.183584,49.809727,50.335041,49.353734,47.049782,44.253748,41.61361,39.483423,38.039574,37.221235,37.221235,37.221235,37.221235,37.221235,37.221235,37.221235,37.221235,37.221235,37.221235,37.221235 -KWT,2.736732,3.394217,4.012089,4.633113,5.163994,5.559047,5.84473,6.072184,6.258508,6.37062,6.484740322,6.600904943,6.719150482,6.839514217,6.962034092,7.086748731,7.21369745,7.34292027,7.474457927,7.608351888 -LBN,4.227597,4.516091,4.700974,4.749348,4.677987,4.508442,4.269991,4.015352,3.787724,3.61217,3.61217,3.61217,3.61217,3.61217,3.61217,3.61217,3.61217,3.61217,3.61217,3.61217 -LBR,3.994122,5.166102,6.532817,8.064668,9.660352,11.271451,12.839627,14.279926,15.524015,16.534577,17.61092324,18.75733606,19.97837656,21.27890276,22.66408891,24.13944609,25.71084414,27.384535,29.1671776,31.06586434 -LBY,6.355112,7.082535,7.7829,8.359938,8.773331,8.892874,8.804202,8.643086,8.395336,8.073008,8.073008,8.073008,8.073008,8.073008,8.073008,8.073008,8.073008,8.073008,8.073008,8.073008 -LKA,20.859949,22.343623,23.094473,23.432924,23.192808,22.551854,21.836617,21.108788,20.450911,19.888432,19.888432,19.888432,19.888432,19.888432,19.888432,19.888432,19.888432,19.888432,19.888432,19.888432 -LTU,3.323611,3.18992,3.067621,2.930591,2.812659,2.704982,2.592363,2.507835,2.471255,2.452531,2.452531,2.452531,2.452531,2.452531,2.452531,2.452531,2.452531,2.452531,2.452531,2.452531 -LVA,2.25206,2.168864,2.073405,1.976504,1.902467,1.823091,1.738797,1.684703,1.662837,1.649538,1.649538,1.649538,1.649538,1.649538,1.649538,1.649538,1.649538,1.649538,1.649538,1.649538 -MAC,0.543656,0.654407,0.742303,0.79685,0.82443,0.830503,0.821443,0.808403,0.803462,0.810426,0.81745036,0.824535604,0.83168226,0.838890859,0.846161938,0.853496039,0.860893709,0.868355498,0.875881962,0.883473661 -MAR,31.951412,35.077821,37.501586,38.80575,39.199971,38.750318,37.516009,35.947908,34.436664,33.068165,33.068165,33.068165,33.068165,33.068165,33.068165,33.068165,33.068165,33.068165,33.068165,33.068165 -MCO,0.030508,0.03172832,0.032997453,0.034317351,0.035690045,0.037117647,0.038602353,0.040146447,0.041752305,0.043422397,0.045159293,0.046965664,0.048844291,0.050798063,0.052829985,0.054943184,0.057140912,0.059426548,0.06180361,0.064275755 -MDG,20.713819,27.365838,35.333076,44.132032,53.561173,63.178813,72.472034,80.989158,88.295978,94.222345,100.5464858,107.2950987,114.496674,122.1816141,130.3823624,139.1335394,148.4720895,158.4374369,169.0716517,180.4196279 -MDV,0.315885,0.356029,0.383231,0.398413,0.405237,0.39858,0.378818,0.34994,0.320973,0.299545,0.299545,0.299545,0.299545,0.299545,0.299545,0.299545,0.299545,0.299545,0.299545,0.299545 -MEX,113.423047,125.927829,135.398031,141.523297,143.924837,142.774662,140.174856,136.420918,131.834955,127.08145,127.08145,127.08145,127.08145,127.08145,127.08145,127.08145,127.08145,127.08145,127.08145,127.08145 -MHL,0.070983,0.07382232,0.076775213,0.079846221,0.08304007,0.086361673,0.08981614,0.093408785,0.097145137,0.101030942,0.10507218,0.109275067,0.11364607,0.118191913,0.122919589,0.127836373,0.132949828,0.138267821,0.143798534,0.149550475 -MLT,0.416515,0.427706,0.430652,0.424299,0.415034,0.407443,0.399374,0.391454,0.389121,0.394285,0.399517531,0.404819503,0.410191837,0.415635466,0.421151338,0.426740411,0.432403656,0.438142057,0.443956612,0.449848332 -MMR,47.963012,51.687782,54.3308,55.410022,55.295866,54.223252,52.400998,50.349965,48.503621,46.941148,46.941148,46.941148,46.941148,46.941148,46.941148,46.941148,46.941148,46.941148,46.941148,46.941148 -MNE,0.63149,0.635906,0.632543,0.621232,0.60361,0.582507,0.558515,0.534636,0.514721,0.499201,0.499201,0.499201,0.499201,0.499201,0.499201,0.499201,0.499201,0.499201,0.499201,0.499201 -MOZ,23.390765,29.177268,35.906545,43.020685,50.191707,57.280451,63.743409,69.33505,73.891655,77.347349,80.96465558,84.75113288,88.71469252,92.86361611,97.20657258,101.7526363,106.5113059,111.4925244,116.7066998,122.1647267 -MRT,3.459773,4.298114,5.199643,6.146278,7.0847,7.975749,8.788545,9.483997,10.03396,10.434362,10.85074192,11.28373734,11.73401131,12.20225332,12.68918038,13.19553811,13.72210188,14.26967802,14.839105,15.4312548 -MTQ,0.38,0.3952,0.411008,0.42744832,0.444546253,0.462328103,0.480821227,0.500054076,0.520056239,0.540858489,0.562492828,0.584992541,0.608392243,0.632727933,0.65803705,0.684358532,0.711732873,0.740202188,0.769810276,0.800602687 -MUS,1.299172,1.361465,1.394492,1.394919,1.366915,1.323385,1.274906,1.226998,1.185598,1.156751,1.156751,1.156751,1.156751,1.156751,1.156751,1.156751,1.156751,1.156751,1.156751,1.156751 -MYS,28.401017,32.985544,37.265632,40.799652,43.454751,45.437418,46.701332,47.239257,47.247076,46.945953,46.945953,46.945953,46.945953,46.945953,46.945953,46.945953,46.945953,46.945953,46.945953,46.945953 -NAM,2.283289,2.671995,3.042334,3.353627,3.599432,3.7702,3.854319,3.86027,3.811516,3.728459,3.728459,3.728459,3.728459,3.728459,3.728459,3.728459,3.728459,3.728459,3.728459,3.728459 -NCL,0.26784,0.2785536,0.289695744,0.301283574,0.313334917,0.325868313,0.338903046,0.352459168,0.366557534,0.381219836,0.396468629,0.412327374,0.428820469,0.445973288,0.46381222,0.482364709,0.501659297,0.521725669,0.542594695,0.564298483 -NGA,158.423182,203.868897,257.81481,320.341187,389.614656,462.1029,535.498117,606.865493,672.52182,729.885435,792.1419534,859.7087218,933.0386847,1012.623421,1098.996441,1192.736758,1294.472776,1404.886499,1524.718103,1654.770897 -NIC,5.788163,6.602877,7.239865,7.661275,7.845543,7.953592,7.938282,7.78254,7.53645,7.260845,7.260845,7.260845,7.260845,7.260845,7.260845,7.260845,7.260845,7.260845,7.260845,7.260845 -NLD,16.612988,17.039498,17.31084,17.344211,17.151142,16.983981,17.000888,17.112215,17.229594,17.381251,17.5342429,17.68858146,17.84427852,18.00134605,18.15979611,18.31964086,18.48089259,18.64356367,18.80766661,18.973214 -NOR,4.883111,5.230052,5.574094,5.841453,6.063492,6.269099,6.46133,6.651864,6.822676,6.964166,7.108590248,7.256009594,7.406486151,7.560083321,7.716865819,7.876899704,8.040252403,8.206992743,8.377190976,8.550918814 -NRU,0.010134,0.01053936,0.010960934,0.011399372,0.011855347,0.012329561,0.012822743,0.013335653,0.013869079,0.014423842,0.015000796,0.015600827,0.016224861,0.016873855,0.017548809,0.018250761,0.018980792,0.019740024,0.020529625,0.02135081 -NZL,4.368136,4.824223,5.210754,5.483441,5.677801,5.827112,5.960733,6.093398,6.212499,6.323442,6.436366224,6.551307051,6.668300496,6.787383213,6.908592513,7.031966373,7.157543447,7.28536308,7.41546532,7.54789093 -OMN,2.782435,3.289964,3.602727,3.749317,3.739585,3.611043,3.430226,3.248604,3.051505,2.839368,2.839368,2.839368,2.839368,2.839368,2.839368,2.839368,2.839368,2.839368,2.839368,2.839368 -PAK,173.593383,205.364462,234.432014,257.777902,274.875396,283.959432,285.191258,280.414852,271.821514,261.270532,261.270532,261.270532,261.270532,261.270532,261.270532,261.270532,261.270532,261.270532,261.270532,261.270532 -PAN,3.51682,4.038009,4.50154,4.869788,5.127706,5.271127,5.326996,5.31995,5.261516,5.170454,5.170454,5.170454,5.170454,5.170454,5.170454,5.170454,5.170454,5.170454,5.170454,5.170454 -PER,29.076512,32.434902,35.491535,37.668225,38.832436,39.292812,39.07578,38.296869,37.162279,35.910704,35.910704,35.910704,35.910704,35.910704,35.910704,35.910704,35.910704,35.910704,35.910704,35.910704 -PHL,93.260798,109.7417,126.320712,141.675245,154.938579,165.506964,172.893227,177.081631,178.485197,177.803317,177.803317,177.803317,177.803317,177.803317,177.803317,177.803317,177.803317,177.803317,177.803317,177.803317 -PLW,0.02,0.0208,0.021632,0.02249728,0.023397171,0.024333058,0.02530638,0.026318636,0.027371381,0.028466236,0.029604886,0.030789081,0.032020644,0.03330147,0.034633529,0.03601887,0.037459625,0.03895801,0.04051633,0.042136984 -PNG,6.858266,8.463812,10.184988,11.918525,13.549364,15.007767,16.224495,17.150019,17.76967,18.112632,18.46221331,18.81854168,19.18174734,19.55196302,19.92932401,20.31396823,20.70603624,21.10567133,21.51301954,21.92822974 -POL,38.27666,38.374741,37.834926,36.372464,34.905868,33.553719,31.873705,30.458658,29.810289,29.453612,29.453612,29.453612,29.453612,29.453612,29.453612,29.453612,29.453612,29.453612,29.453612,29.453612 -PRI,3.749009,3.747304,3.753534,3.726037,3.657439,3.561287,3.43401,3.290503,3.147632,3.0242,3.0242,3.0242,3.0242,3.0242,3.0242,3.0242,3.0242,3.0242,3.0242,3.0242 -PRK,24.346229,25.355354,26.180166,26.444855,26.381631,26.161175,25.764258,25.334033,24.924742,24.552338,24.552338,24.552338,24.552338,24.552338,24.552338,24.552338,24.552338,24.552338,24.552338,24.552338 -PRT,10.675572,10.623191,10.309201,9.917503,9.378684,8.676315,7.994099,7.446725,7.044813,6.753632,6.753632,6.753632,6.753632,6.753632,6.753632,6.753632,6.753632,6.753632,6.753632,6.753632 -PYF,0.280026,0.29122704,0.302876122,0.314991166,0.327590813,0.340694446,0.354322223,0.368495112,0.383234917,0.398564314,0.414506886,0.431087162,0.448330648,0.466263874,0.484914429,0.504311006,0.524483446,0.545462784,0.567281296,0.589972547 -QAT,1.758793,2.198666,2.370905,2.523783,2.611601,2.59686,2.507417,2.387501,2.275095,2.162322,2.162322,2.162322,2.162322,2.162322,2.162322,2.162322,2.162322,2.162322,2.162322,2.162322 -REU,0.840974,0.87461296,0.909597478,0.945981378,0.983820633,1.023173458,1.064100396,1.106664412,1.150930989,1.196968228,1.244846957,1.294640836,1.346426469,1.400283528,1.456294869,1.514546664,1.57512853,1.638133671,1.703659018,1.771805379 -ROU,21.486371,20.969678,20.29063,19.458138,18.534956,17.430174,16.345455,15.567695,15.111365,14.838941,14.838941,14.838941,14.838941,14.838941,14.838941,14.838941,14.838941,14.838941,14.838941,14.838941 -RUS,142.958164,141.02197,136.429151,131.279651,126.188341,120.761349,116.247879,113.329574,111.785005,111.057451,111.057451,111.057451,111.057451,111.057451,111.057451,111.057451,111.057451,111.057451,111.057451,111.057451 -SAU,27.448086,33.535044,38.481078,42.18332,44.93769,46.291134,46.213689,45.291472,44.012973,42.427274,42.427274,42.427274,42.427274,42.427274,42.427274,42.427274,42.427274,42.427274,42.427274,42.427274 -SDN,43.551941,54.919306,66.856417,79.055628,90.9616,102.055432,111.525257,119.024817,124.36027,127.620961,130.9671464,134.4010678,137.9250257,141.5413807,145.2525556,149.0610364,152.9693745,156.9801881,161.0961641,165.3200598 -SEN,12.433728,15.997622,19.963185,24.268608,28.606957,32.761688,36.557829,39.76836,42.275753,44.074588,45.9499635,47.90513631,49.94350179,52.06859982,54.28412084,56.59391238,59.00198564,61.51252252,64.12988284,66.85861195 -SGP,5.086418,5.596754,5.978458,6.144768,6.105946,6.006127,5.886725,5.757996,5.680539,5.659392,5.659392,5.659392,5.659392,5.659392,5.659392,5.659392,5.659392,5.659392,5.659392,5.659392 -SLB,0.538148,0.684041,0.841161,1.004916,1.162515,1.304919,1.426435,1.522356,1.590078,1.629871,1.670659852,1.712469479,1.755325426,1.799253877,1.844281674,1.890436329,1.937746041,1.986239718,2.035946988,2.086898224 -SLE,5.867536,7.178034,8.531598,9.872389,11.088208,12.143673,12.995032,13.604387,13.977856,14.153773,14.33190399,14.51227682,14.69491973,14.87986127,15.06713037,15.25675633,15.44876881,15.64319785,15.84007385,16.03942762 -SLV,6.192993,6.61033,7.092623,7.438052,7.607108,7.667396,7.594192,7.389146,7.09251,6.783031,6.783031,6.783031,6.783031,6.783031,6.783031,6.783031,6.783031,6.783031,6.783031,6.783031 -SOM,9.330872,12.237223,16.359849,21.668851,28.217081,35.954398,44.651787,54.003694,63.596229,72.976499,83.74033319,96.09180352,110.265082,126.5288803,145.1915443,166.6069004,191.180963,219.3796326,251.7375289,288.8681266 -STP,0.165397,0.199765,0.23463,0.268618,0.298618,0.323775,0.341685,0.352391,0.356861,0.356185,0.356185,0.356185,0.356185,0.356185,0.356185,0.356185,0.356185,0.356185,0.356185,0.356185 -SUR,0.524636,0.568758,0.601679,0.616608,0.613801,0.610532,0.600714,0.585762,0.568414,0.551373,0.551373,0.551373,0.551373,0.551373,0.551373,0.551373,0.551373,0.551373,0.551373,0.551373 -SVN,2.02968,2.066004,2.059279,2.028944,1.993561,1.946388,1.898102,1.868481,1.856083,1.846372,1.846372,1.846372,1.846372,1.846372,1.846372,1.846372,1.846372,1.846372,1.846372,1.846372 -SWE,9.379687,9.923586,10.379451,10.65622,10.915885,11.138128,11.315823,11.510605,11.692436,11.859475,12.02890033,12.20074609,12.37504684,12.55183767,12.73115414,12.91303235,13.09750888,13.28462085,13.47440592,13.66690228 -SYC,0.086518,0.089841,0.092431,0.093273,0.091324,0.087711,0.083601,0.079598,0.076039,0.072922,0.072922,0.072922,0.072922,0.072922,0.072922,0.072922,0.072922,0.072922,0.072922,0.072922 -SYR,20.410606,24.079119,27.859412,30.921425,33.051312,34.464404,35.01646,34.701145,33.7849,32.623493,32.623493,32.623493,32.623493,32.623493,32.623493,32.623493,32.623493,32.623493,32.623493,32.623493 -TGO,6.027798,7.342518,8.684298,9.969991,11.130448,12.089147,12.800016,13.26557,13.507903,13.558407,13.60909983,13.65998219,13.71105479,13.76231834,13.81377356,13.86542117,13.91726187,13.9692964,14.02152548,14.07394984 -THA,69.122234,72.090999,73.321394,72.994033,71.03671,68.261936,65.340268,62.547492,60.092722,58.165506,58.165506,58.165506,58.165506,58.165506,58.165506,58.165506,58.165506,58.165506,58.165506,58.165506 -TLS,1.201542,1.24960368,1.299587827,1.35157134,1.405634194,1.461859562,1.520333944,1.581147302,1.644393194,1.710168922,1.778575679,1.849718706,1.923707454,2.000655752,2.080681982,2.163909261,2.250465632,2.340484257,2.434103628,2.531467773 -TON,0.104058,0.110881,0.12054,0.131031,0.138176,0.14938,0.163941,0.176366,0.185557,0.193252,0.20126611,0.209612562,0.21830514,0.227358197,0.236786682,0.246606163,0.256832856,0.267483647,0.278576123,0.290128602 -TTO,1.341465,1.372817,1.354311,1.329033,1.288356,1.230482,1.167007,1.108365,1.064017,1.031314,1.031314,1.031314,1.031314,1.031314,1.031314,1.031314,1.031314,1.031314,1.031314,1.031314 -TUN,10.480934,11.51823,12.212398,12.532759,12.648966,12.474644,12.018241,11.550835,11.208331,10.891401,10.891401,10.891401,10.891401,10.891401,10.891401,10.891401,10.891401,10.891401,10.891401,10.891401 -TUR,72.752325,80.75289,86.665132,90.30157,91.616772,90.800304,88.477803,85.426068,82.226946,79.200061,79.200061,79.200061,79.200061,79.200061,79.200061,79.200061,79.200061,79.200061,79.200061,79.200061 -TUV,0.010782,0.01121328,0.011661811,0.012128284,0.012613415,0.013117952,0.01364267,0.014188376,0.014755912,0.015346148,0.015959994,0.016598394,0.017262329,0.017952823,0.018670935,0.019417773,0.020194484,0.021002263,0.021842354,0.022716048 -TWN,23.162123,23.96432961,24.05558311,23.50008022,22.37243516,20.92078778,19.44204261,18.09909464,17.00111659,16.24987651,16.24987651,16.24987651,16.24987651,16.24987651,16.24987651,16.24987651,16.24987651,16.24987651,16.24987651,16.24987651 -TZA,44.841226,61.080809,81.852016,107.737069,138.31214,172.232145,208.563118,245.779732,282.218517,316.337831,354.5820607,397.4498951,445.5003134,499.3598731,559.730881,627.4005502,703.2512654,788.272089,883.5716576,990.3926386 -UKR,45.448329,43.04655,40.515498,38.10035,36.074469,34.086038,32.356625,31.210464,30.575598,30.253908,30.253908,30.253908,30.253908,30.253908,30.253908,30.253908,30.253908,30.253908,30.253908,30.253908 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-rcp0_p50,optimalfixed,Philippines10615,stormPopulation,4,3.84E-16 -rcp0_p50,optimalfixed,Philippines10615,total,4,0.003815405 -rcp0_p50,optimalfixed,Philippines10615,inundation,5,0.014845891 -rcp0_p50,optimalfixed,Philippines10615,relocation,5,4.28E-05 -rcp0_p50,optimalfixed,Philippines10615,storms,5,4.12E-16 -rcp0_p50,optimalfixed,Philippines10615,stormCapital,5,1.27E-16 -rcp0_p50,optimalfixed,Philippines10615,stormPopulation,5,2.85E-16 -rcp0_p50,optimalfixed,Philippines10615,total,5,0.014888722 -rcp0_p50,optimalfixed,Philippines10615,inundation,6,0.014845891 -rcp0_p50,optimalfixed,Philippines10615,relocation,6,4.28E-05 -rcp0_p50,optimalfixed,Philippines10615,storms,6,6.14E-16 -rcp0_p50,optimalfixed,Philippines10615,stormCapital,6,2.05E-16 -rcp0_p50,optimalfixed,Philippines10615,stormPopulation,6,4.09E-16 -rcp0_p50,optimalfixed,Philippines10615,total,6,0.014888722 -rcp0_p50,optimalfixed,Philippines10615,inundation,7,0.014845891 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-rcp0_p50,optimalfixed,Philippines10615,stormPopulation,9,8.82E-16 -rcp0_p50,optimalfixed,Philippines10615,total,9,0.014888722 -rcp0_p50,optimalfixed,Philippines10615,inundation,10,0.029522968 -rcp0_p50,optimalfixed,Philippines10615,relocation,10,0.000133157 -rcp0_p50,optimalfixed,Philippines10615,storms,10,8.31E-16 -rcp0_p50,optimalfixed,Philippines10615,stormCapital,10,3.12E-16 -rcp0_p50,optimalfixed,Philippines10615,stormPopulation,10,5.18E-16 -rcp0_p50,optimalfixed,Philippines10615,total,10,0.029656124 -rcp0_p50,optimalfixed,Philippines10615,inundation,11,0.029522968 -rcp0_p50,optimalfixed,Philippines10615,relocation,11,0.000133157 -rcp0_p50,optimalfixed,Philippines10615,storms,11,1.02E-15 -rcp0_p50,optimalfixed,Philippines10615,stormCapital,11,3.86E-16 -rcp0_p50,optimalfixed,Philippines10615,stormPopulation,11,6.34E-16 -rcp0_p50,optimalfixed,Philippines10615,total,11,0.029656124 -rcp0_p50,optimalfixed,Philippines10615,inundation,12,0.029522968 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-rcp0_p50,optimalfixed,Philippines10615,stormPopulation,14,1.14E-15 -rcp0_p50,optimalfixed,Philippines10615,total,14,0.029656124 -rcp0_p50,optimalfixed,Philippines10615,inundation,15,0.040020173 -rcp0_p50,optimalfixed,Philippines10615,relocation,15,0.000219539 -rcp0_p50,optimalfixed,Philippines10615,storms,15,1.26E-15 -rcp0_p50,optimalfixed,Philippines10615,stormCapital,15,4.92E-16 -rcp0_p50,optimalfixed,Philippines10615,stormPopulation,15,7.71E-16 -rcp0_p50,optimalfixed,Philippines10615,total,15,0.040239712 -rcp0_p50,optimalfixed,Philippines10615,inundation,16,0.040020173 -rcp0_p50,optimalfixed,Philippines10615,relocation,16,0.000219539 -rcp0_p50,optimalfixed,Philippines10615,storms,16,1.53E-15 -rcp0_p50,optimalfixed,Philippines10615,stormCapital,16,6.01E-16 -rcp0_p50,optimalfixed,Philippines10615,stormPopulation,16,9.31E-16 -rcp0_p50,optimalfixed,Philippines10615,total,16,0.040239712 -rcp0_p50,optimalfixed,Philippines10615,inundation,17,0.040020173 -rcp0_p50,optimalfixed,Philippines10615,relocation,17,0.000219539 -rcp0_p50,optimalfixed,Philippines10615,storms,17,1.85E-15 -rcp0_p50,optimalfixed,Philippines10615,stormCapital,17,7.33E-16 -rcp0_p50,optimalfixed,Philippines10615,stormPopulation,17,1.12E-15 -rcp0_p50,optimalfixed,Philippines10615,total,17,0.040239712 -rcp0_p50,optimalfixed,Philippines10615,inundation,18,0.040020173 -rcp0_p50,optimalfixed,Philippines10615,relocation,18,0.000219539 -rcp0_p50,optimalfixed,Philippines10615,storms,18,2.24E-15 -rcp0_p50,optimalfixed,Philippines10615,stormCapital,18,8.91E-16 -rcp0_p50,optimalfixed,Philippines10615,stormPopulation,18,1.35E-15 -rcp0_p50,optimalfixed,Philippines10615,total,18,0.040239712 -rcp0_p50,optimalfixed,Philippines10615,inundation,19,0.050847871 -rcp0_p50,optimalfixed,Philippines10615,relocation,19,0.000163699 -rcp0_p50,optimalfixed,Philippines10615,storms,19,2.33E-15 -rcp0_p50,optimalfixed,Philippines10615,stormCapital,19,9.33E-16 -rcp0_p50,optimalfixed,Philippines10615,stormPopulation,19,1.40E-15 -rcp0_p50,optimalfixed,Philippines10615,total,19,0.05101157 -rcp0_p50,optimalfixed,Philippines10615,inundation,20,0.050847871 -rcp0_p50,optimalfixed,Philippines10615,relocation,20,0.000163699 -rcp0_p50,optimalfixed,Philippines10615,storms,20,2.80E-15 -rcp0_p50,optimalfixed,Philippines10615,stormCapital,20,1.13E-15 -rcp0_p50,optimalfixed,Philippines10615,stormPopulation,20,1.67E-15 -rcp0_p50,optimalfixed,Philippines10615,total,20,0.05101157 \ No newline at end of file From d94487fc7aab2304ce4523e5c572178c3f868d39 Mon Sep 17 00:00:00 2001 From: Catherine Ledna Date: Mon, 2 Jul 2018 11:27:32 -0700 Subject: [PATCH 6/7] Updated main.jl to write out results and run all segments. Moved some functions from test.jl to ciamhelper.jl (to write results and run the model). Fixed some paths for run_tests.jl. Added segment subsets to data directory; removed data/results directory --- data/results/results-gams/Phl_test_comp.csv | 1241 ------- data/results/results-gams/globalCostB.csv | 280 -- data/results/results-gams/globalCostBcomp.csv | 281 -- data/results/results-gams/total_mockup.csv | 1241 ------- data/results/results-jl/tst.txt | 8 - data/subsets/greenland.csv | 431 +++ data/subsets/island.csv | 2974 +++++++++++++++++ data/subsets/sub10names.csv | 2148 ++++++++++++ .../sub1names.csv} | 0 data/subsets/sub2names.csv | 1000 ++++++ data/subsets/sub3names.csv | 1000 ++++++ data/subsets/sub4names.csv | 1000 ++++++ data/subsets/sub5names.csv | 1000 ++++++ data/subsets/sub6names.csv | 1000 ++++++ data/subsets/sub7names.csv | 1000 ++++++ data/subsets/sub8names.csv | 1000 ++++++ data/subsets/sub9names.csv | 2000 +++++++++++ src/ciamhelper.jl | 134 +- src/main.jl | 4 +- test/run_tests.jl | 60 +- test/test.jl | 257 +- 21 files changed, 14770 insertions(+), 3289 deletions(-) delete mode 100644 data/results/results-gams/Phl_test_comp.csv delete mode 100644 data/results/results-gams/globalCostB.csv delete mode 100644 data/results/results-gams/globalCostBcomp.csv delete mode 100644 data/results/results-gams/total_mockup.csv delete mode 100644 data/results/results-jl/tst.txt create mode 100644 data/subsets/greenland.csv create mode 100644 data/subsets/island.csv create mode 100644 data/subsets/sub10names.csv rename data/{segmentnames.csv => subsets/sub1names.csv} (100%) create mode 100644 data/subsets/sub2names.csv create mode 100644 data/subsets/sub3names.csv create mode 100644 data/subsets/sub4names.csv create mode 100644 data/subsets/sub5names.csv create mode 100644 data/subsets/sub6names.csv create mode 100644 data/subsets/sub7names.csv create mode 100644 data/subsets/sub8names.csv create mode 100644 data/subsets/sub9names.csv diff --git a/data/results/results-gams/Phl_test_comp.csv b/data/results/results-gams/Phl_test_comp.csv deleted file mode 100644 index dde56dd..0000000 --- a/data/results/results-gams/Phl_test_comp.csv +++ /dev/null @@ -1,1241 +0,0 @@ -rcp,level,seg,costtype,time,value -rcp0_p50,retreat1,Philippines10615,inundation,1,0.00018679 -rcp0_p50,retreat1,Philippines10615,relocation,1,2.59E-05 -rcp0_p50,retreat1,Philippines10615,storms,1,0.026506185 -rcp0_p50,retreat1,Philippines10615,stormCapital,1,0.005319103 -rcp0_p50,retreat1,Philippines10615,stormPopulation,1,0.021187082 -rcp0_p50,retreat1,Philippines10615,total,1,0.026718895 -rcp0_p50,retreat1,Philippines10615,inundation,2,0.00018679 -rcp0_p50,retreat1,Philippines10615,relocation,2,2.59E-05 -rcp0_p50,retreat1,Philippines10615,storms,2,0.043585598 -rcp0_p50,retreat1,Philippines10615,stormCapital,2,0.009711028 -rcp0_p50,retreat1,Philippines10615,stormPopulation,2,0.03387457 -rcp0_p50,retreat1,Philippines10615,total,2,0.043798308 -rcp0_p50,retreat1,Philippines10615,inundation,3,0.00018679 -rcp0_p50,retreat1,Philippines10615,relocation,3,2.59E-05 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-rcp0_p50,optimalfixed,Philippines10615,stormPopulation,20,1.67E-15 -rcp0_p50,optimalfixed,Philippines10615,total,20,0.05101157 \ No newline at end of file diff --git a/data/results/results-gams/globalCostB.csv b/data/results/results-gams/globalCostB.csv deleted file mode 100644 index ea7ee59..0000000 --- a/data/results/results-gams/globalCostB.csv +++ /dev/null @@ -1,280 +0,0 @@ -fixed,slron,rcp85,50,ref,noAdaptation,1,wetland,0.029431138 -fixed,slron,rcp85,50,ref,noAdaptation,1,inundation,0.778473095 -fixed,slron,rcp85,50,ref,noAdaptation,1,relocation,0.320070481 -fixed,slron,rcp85,50,ref,noAdaptation,1,storms,0.129657616 -fixed,slron,rcp85,50,ref,noAdaptation,1,stormCapital,0.071755779 -fixed,slron,rcp85,50,ref,noAdaptation,1,stormPopulation,0.057901837 -fixed,slron,rcp85,50,ref,noAdaptation,1,total,1.25763233 -fixed,slron,rcp85,50,ref,noAdaptation,2,wetland,0.086259236 -fixed,slron,rcp85,50,ref,noAdaptation,2,inundation,1.358313689 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-fixed,slron,rcp85,50,ref,optimalfixed,10,wetland,2.575444631 -fixed,slron,rcp85,50,ref,optimalfixed,10,inundation,7.849785486 -fixed,slron,rcp85,50,ref,optimalfixed,10,relocation,0.114704757 -fixed,slron,rcp85,50,ref,optimalfixed,10,protection,0.81260485 -fixed,slron,rcp85,50,ref,optimalfixed,10,storms,0.079675764 -fixed,slron,rcp85,50,ref,optimalfixed,10,stormCapital,0.043682872 -fixed,slron,rcp85,50,ref,optimalfixed,10,stormPopulation,0.035992892 -fixed,slron,rcp85,50,ref,optimalfixed,10,total,11.43221549 \ No newline at end of file diff --git a/data/results/results-gams/globalCostBcomp.csv b/data/results/results-gams/globalCostBcomp.csv deleted file mode 100644 index 133c5c9..0000000 --- a/data/results/results-gams/globalCostBcomp.csv +++ /dev/null @@ -1,281 +0,0 @@ -rcp,level,seg,costtype,time,value -rcp85_p50,noAdaptation,1000segs,wetland,1,0.029431138 -rcp85_p50,noAdaptation,1000segs,inundation,1,0.778473095 -rcp85_p50,noAdaptation,1000segs,relocation,1,0.320070481 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deleted file mode 100644 index cab1829..0000000 --- a/data/results/results-gams/total_mockup.csv +++ /dev/null @@ -1,1241 +0,0 @@ -rcp,level,seg,costtype,time,value -rcp85_p50,retreat1,NA,inundation,1,0 -rcp85_p51,retreat1,NA,relocation,1,0 -rcp85_p52,retreat1,NA,storms,1,0 -rcp85_p53,retreat1,NA,stormCapital,1,0 -rcp85_p54,retreat1,NA,stormPopulation,1,0 -rcp85_p55,retreat1,NA,total,1,0 -rcp85_p56,retreat1,NA,inundation,2,0 -rcp85_p57,retreat1,NA,relocation,2,0 -rcp85_p58,retreat1,NA,storms,2,0 -rcp85_p59,retreat1,NA,stormCapital,2,0 -rcp85_p60,retreat1,NA,stormPopulation,2,0 -rcp85_p61,retreat1,NA,total,2,0 -rcp85_p62,retreat1,NA,inundation,3,0 -rcp85_p63,retreat1,NA,relocation,3,0 -rcp85_p64,retreat1,NA,storms,3,0 -rcp85_p65,retreat1,NA,stormCapital,3,0 -rcp85_p66,retreat1,NA,stormPopulation,3,0 -rcp85_p67,retreat1,NA,total,3,0 -rcp85_p68,retreat1,NA,inundation,4,0 -rcp85_p69,retreat1,NA,relocation,4,0 -rcp85_p70,retreat1,NA,storms,4,0 -rcp85_p71,retreat1,NA,stormCapital,4,0 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+VietNam2160 +VietNam2161 +VietNam2162 +VietNam2163 +VietNam2164 +VietNam2165 +VietNam2166 +VietNam2167 +VietNam2168 +VietNam2169 +VietNam2170 +VietNam2171 +VietNam2172 +VietNam2173 +VietNam2174 +VietNam2175 +VietNam2176 +VietNam2177 +VietNam2178 +VietNam2179 +VietNam2180 +VietNam2181 +VietNam2182 +VietNam2183 +VietNam2184 +VietNam2185 +VietNam2186 +VietNam2187 +VietNam2188 +VietNam2189 +VietNam2190 +VietNam2191 +VietNam2192 +VietNam2193 +VietNam2194 +VietNam2195 +VietNam2196 +VietNam2197 +VietNam2198 +VietNam2199 +VietNam2200 +VietNam2201 +VietNam2202 +VietNam2203 +VietNam2204 +VietNam2205 +VietNam2206 +VietNam2207 +VietNam2208 +VietNam2209 +VietNam2210 +VietNam2211 +VietNam2212 +VietNam2213 +VietNam2214 +VietNam2215 +VietNam2216 +VietNam2217 +VietNam2218 +VietNam2219 +VietNam2220 +VietNam2221 +VietNam2222 +VietNam2223 +VietNam2224 +VietNam2225 +VietNam2226 +VietNam2227 +VietNam2228 +VietNam2229 +VietNam2230 +VietNam2231 +VietNam2232 +VietNam2233 +VietNam2234 +VietNam2235 +VietNam2236 +VietNam2237 +VietNam2238 +VietNam2239 +VietNam2240 +VietNam2241 +VietNam2242 +VietNam2243 +VietNam2244 +VietNam2245 +VietNam2246 +VietNam2247 +VietNam2248 +VietNam2249 +VietNam2250 +VietNam2251 +VietNam2252 +VietNam2253 +VietNam2254 +VietNam2255 +VietNam2256 +VietNam2257 +VietNam2258 +VietNam2259 +VietNam2260 +VietNam2261 +VietNam2262 \ No newline at end of file diff --git a/src/ciamhelper.jl b/src/ciamhelper.jl index 2abebb0..fbb659f 100644 --- a/src/ciamhelper.jl +++ b/src/ciamhelper.jl @@ -11,7 +11,6 @@ using Mimi using Distributions # Function to load CIAM parameters from CSV to dictionary -# data_dir = relative path to data location function load_ciam_params() data_dir = "../data/input" files = readdir(data_dir) @@ -267,8 +266,139 @@ function get_ciam(lslfile, subset) m = run_model(params, xsc) - return m + return (m, xsc) + +end + +function load_meta() + metadir = "../data/meta" + + # Read header and mappings + header = readstring(open(joinpath(metadir,"header.txt"))) + + varnames = readlines(open(joinpath(metadir,"variablenames.csv"))) + vardict = Dict{Any,Any}(split(varnames[i],',')[1] => (split(varnames[i],',')[2],split(varnames[i],',')[3]) for i in 1:length(varnames)) + protect = readlines(open(joinpath(metadir, "protectlevels.csv"))) + protectdict = Dict{Any,Any}(parse(Int,split(protect[i],',')[1]) => split(protect[i],',')[2] for i in 1:length(protect)) + + retreat = readlines(open(joinpath(metadir, "retreatlevels.csv"))) + retreatdict = Dict{Any,Any}(parse(Int,split(retreat[i],',')[1]) => split(retreat[i],',')[2] for i in 1:length(retreat)) + + return(header,vardict, protectdict, retreatdict) +end + +# Function to write out model results to CSV file +# m - an instance of the model +# RCP - string for RCP we're using; todo make automatic from lsl file +# xsc - segment-region dictionaries +# outputdir, outfile - where to write results to, relative to test folder +# sumsegs - whether to sum across segments +function write_ciam(m, xsc; rcp="na", tag="untagged", sumsegs=false) + outputdir = "../output/results-jl" + meta_output = load_meta() + + outfile = "results_$(rcp)_$(tag).csv" + header = meta_output[1] + vardict = meta_output[2] + protectdict = meta_output[3] + retreatdict = meta_output[4] + + segmap = xsc[6] + + vars = [k for k in keys(vardict)] + + open(joinpath(outputdir,outfile),"w") do g + write(g, "$(header)\n") + end + + for v in vars # Iterate variables + d = m[:ciam, parse(v)] + + level = vardict[v][1] + costtype = vardict[v][2] + + s_arr = [segmap[i] for i in 1:size(d,1)] # List of segments + + if sumsegs + func = x -> sum(x) + n = 1 + s_arr = ["sum$(size(d,1))segs"] + else + func = x -> identity(x) + n = size(d,1) + end + + for i in 1:size(d,2) # Iterate times (t = 1, 2, ...) + t = repeat([i], outer=n) + rcp_arr = repeat([rcp], outer= n) + cost_arr = repeat([costtype], outer = n) + + if typeof(d)==Array{Float64,2} + val = d[:,i] + v_arr = func(val) + + if level=="protect" + for j in 1:4 + p = protectdict[j] + lev_arr = repeat([p], outer = n) + + outarr= [rcp_arr lev_arr s_arr cost_arr t v_arr] + # Write results to csv + + open(joinpath(outputdir,outfile),"a") do g + writecsv(g, outarr) + end + + end + elseif level=="retreat" + for j in 1:5 + q = retreatdict[j] + lev_arr = repeat([q], outer = n) + + outarr= [rcp_arr lev_arr s_arr cost_arr t v_arr] + + open(joinpath(outputdir,outfile),"a") do g + writecsv(g, outarr) + end + end + + else + lev_arr = repeat([level], outer = n) + outarr= [rcp_arr lev_arr s_arr cost_arr t v_arr] + + open(joinpath(outputdir,outfile),"a") do g + writecsv(g, outarr) + end + + end + + elseif typeof(d)==Array{Float64,3} + for j in 1:size(d,3) + val = d[:,i,j] + v_arr = func(val) + + if level=="protect" + p = protectdict[j] + lev_arr = repeat([p], outer = n) + + elseif level=="retreat" + q = retreatdict[j] + lev_arr = repeat([q], outer = n) + else + lev_arr = repeat([level], outer = n) + end + + outarr= [rcp_arr lev_arr s_arr cost_arr t v_arr] + + open(joinpath(outputdir,outfile),"a") do g + writecsv(g, outarr) + end + + end + end + end + end end diff --git a/src/main.jl b/src/main.jl index b6652df..6f7ed46 100644 --- a/src/main.jl +++ b/src/main.jl @@ -3,4 +3,6 @@ include("ciamhelper.jl") lslfile = "lsl_rcp0_p50.csv" subset = false # use string of subset file location relative to main.jl to run for a subset of coastal segments -m = get_ciam() # Run ciam for SLR of rcp0p50 and all 12148 coastal segments \ No newline at end of file +m = get_ciam(lslfile, subset) # Run ciam for SLR of rcp0p50 and all 12148 coastal segments + +write_ciam(m[1], m[2], rcp="rcp0p50", tag="allsegs", sumsegs=false) # Write ciam results to output/results-jl directory diff --git a/test/run_tests.jl b/test/run_tests.jl index 90784b8..4c594c1 100644 --- a/test/run_tests.jl +++ b/test/run_tests.jl @@ -15,46 +15,54 @@ include("test.jl") # CIAM - Single-Segment version (Philippines10615 segment) #------------------------------------------------------------------------ -data_dir_phl = "../data/input-data" -gamsfilephl = "../results/results-gams/Phl_test_comp.csv" +gamsfilephl = "../output/results-gams/Phl_test_comp.csv" lslfilephl = "lsl_rcp0_p50.csv" -resultsdirphl = "../data/results/results-jl" segnames_phl = ["Philippines10615"] rcp_phl = "rcp0_p50" -test1seg = run_tests(data_dir_phl, gamsfilephl, resultsdirphl, lslfilephl, segnames_phl, rcp_phl,"Phl") +test1seg = run_tests(gamsfilephl, lslfilephl, segnames_phl, rcp_phl,"Phl") #------------------------------------------------------------------------ # CIAM - Multisegment version (1000 segments) #------------------------------------------------------------------------ -data_dir_1000 = "../data/input-data" -resultsdir_1000 = "../data/results/results-jl" -lslfile_1000 = "lsl_rcp85_p50.csv" -gamsfile_1000 = "../results/results-gams/globalCostBcomp.csv" - -segnames_1000 = readlines(open("../data/segmentnames.csv")) -rcp_1000 = "rcp85_p50" - -test1000segs = run_tests(data_dir_1000, gamsfile_1000, resultsdir_1000, lslfile_1000, segnames_1000, rcp_1000,"1000") - -# Averages 1.9-2.2 seconds, 21.83 M allocations; 530.734 MiB; 22-31% gc time per run +lslfile_1000_ref = "lsl_rcp0_p50.csv" +gamsfile_1000 = "../output/results-gams/globalCostBcomp.csv" +rcp_1000_ref = "rcp0_p50" + +seg1 = readlines(open("../data/subsets/sub1.csv")) +seg2 = readlines(open("../data/subsets/sub2names.csv")) +seg3 = readlines(open("../data/subsets/sub3names.csv")) +seg4 = readlines(open("../data/subsets/sub4names.csv")) +seg5 = readlines(open("../data/subsets/sub5names.csv")) +seg6 = readlines(open("../data/subsets/sub6names.csv")) +seg7 = readlines(open("../data/subsets/sub7names.csv")) +seg8 = readlines(open("../data/subsets/sub8names.csv")) +seg9 = readlines(open("../data/subsets/sub9names.csv")) +seg10 = readlines(open("../data/subsets/sub10names.csv")) + + +# tsub1 = run_tests(gamsfile_1000, lslfile_1000_ref, seg1, rcp_1000_ref,"sub1") +# tsub2 = run_tests(gamsfile_1000, lslfile_1000_ref, seg2, rcp_1000_ref,"sub2") +# tsub3 = run_tests(gamsfile_1000, lslfile_1000_ref, seg3, rcp_1000_ref,"sub3") +# tsub4 = run_tests(gamsfile_1000, lslfile_1000_ref, seg4, rcp_1000_ref,"sub4") +# tsub5 = run_tests(gamsfile_1000, lslfile_1000_ref, seg5, rcp_1000_ref,"sub5") +# tsub6 = run_tests(gamsfile_1000, lslfile_1000_ref, seg6, rcp_1000_ref,"sub6") +# tsub7 = run_tests(gamsfile_1000, lslfile_1000_ref, seg7, rcp_1000_ref,"sub7") +# tsub8 = run_tests(gamsfile_1000, lslfile_1000_ref, seg8, rcp_1000_ref,"sub8") +# tsub9 = run_tests(gamsfile_1000, lslfile_1000_ref, seg9, rcp_1000_ref,"sub9") +# tsub10 = run_tests(gamsfile_1000, lslfile_1000_ref, seg10, rcp_1000_ref,"sub10") + +# Averages 1.9-2.2 seconds, 21.83 M allocations; 530.734 MiB; 22-31% gc time per runt #------------------------------------------------------------------------ # CIAM - Full version (~12000 segments) #------------------------------------------------------------------------ -data_dir_full = "../data/input-data" -resultsdir_full = "../data/results/results-jl" -lslfile_full = "lsl_rcp85_p50.csv" -gamsfile_full = "../results/results-gams/total_mockup.csv" # Don't have comparison data -rcp_full = "rcp85_p50" - -testAllsegs = run_tests(data_dir_full, gamsfile_full, resultsdir_full, lslfile_full, false, rcp_full,"full") - +lslfile_full = "lsl_rcp0_p50.csv" +gamsfile_full = "../output/results-gams/total_mockup.csv" # Don't have comparison data +rcp_full = "rcp0_p50" -#for i in 1:10 -# @time model_driver(data_dir, lslfile, false) # TODO re-write this function -#end +testAllsegs = run_tests(gamsfile_full,lslfile_full, false, rcp_full,"full", sum=true) diff --git a/test/test.jl b/test/test.jl index e1f65db..102615e 100644 --- a/test/test.jl +++ b/test/test.jl @@ -7,6 +7,7 @@ using DataFrames using Plots using StatPlots +include("../src/ciamhelper.jl") gr() function compare_outputs(A, B, header, metadata, file, tol=1e-5) @@ -52,28 +53,10 @@ function compare_outputs(A, B, header, metadata, file, tol=1e-5) end -# Wrapper for importing model data. Not generalizable; hard-coded names; WIP -# datadir - data directory -# lslfile - filename for lsl (string) -# xscfile - filename for segment-country mapping (string) -function import_model_data(datadir, lslfile, xscfile, subset) - # Process main and lsl params - params = load_ciam_params(datadir) - - # Process XSC - xsc = prepxsc(datadir, xscfile, subset) - - parse_ciam_params!(params, xsc[2], xsc[3]) - preplsl!(datadir, lslfile, subset, params) - - return(params, xsc) - -end - # Function to import comparison data and store in DataFrame # WIP - to do -- unique header or detect header automatically -function import_comparison_data(datadir, file) - data = readtable(abspath(datadir,file)) +function load_data(file) + data = readtable(file) data = DataFrame(data) return(data) end @@ -100,138 +83,6 @@ function line_plot(gams, jl, title) end -# Function to write out model results to CSV file -# m - an instance of the model -# RCP - string for RCP we're using; todo make automatic from lsl file -# xsc - segment-region dictionaries -# outputdir, outfile - where to write results to, relative to test folder -# sumsegs - whether to sum across segments -function write_results(m, rcp, outputdir, xsc, tag, sumsegs = true) - - meta_output = load_meta() - outfile = "results_$(rcp)_$(tag).csv" - header = meta_output[1] - vardict = meta_output[2] - protectdict = meta_output[3] - retreatdict = meta_output[4] - - segmap = xsc[6] - - vars = [k for k in keys(vardict)] - - open(joinpath(outputdir,outfile),"w") do g - write(g, "$(header)\n") - end - - for v in vars # Iterate variables - d = m[:ciam, parse(v)] - - level = vardict[v][1] - costtype = vardict[v][2] - - s_arr = [segmap[i] for i in 1:size(d,1)] # List of segments - - if sumsegs - func = x -> sum(x) - n = 1 - s_arr = ["sum$(size(d,1))segs"] - else - func = x -> identity(x) - n = size(d,1) - end - - for i in 1:size(d,2) # Iterate times (t = 1, 2, ...) - t = repeat([i], outer=n) - rcp_arr = repeat([rcp], outer= n) - cost_arr = repeat([costtype], outer = n) - - if typeof(d)==Array{Float64,2} - val = d[:,i] - v_arr = func(val) - - if level=="protect" - for j in 1:4 - p = protectdict[j] - lev_arr = repeat([p], outer = n) - - outarr= [rcp_arr lev_arr s_arr cost_arr t v_arr] - # Write results to csv - - open(joinpath(outputdir,outfile),"a") do g - writecsv(g, outarr) - end - - end - elseif level=="retreat" - for j in 1:5 - q = retreatdict[j] - lev_arr = repeat([q], outer = n) - - outarr= [rcp_arr lev_arr s_arr cost_arr t v_arr] - - open(joinpath(outputdir,outfile),"a") do g - writecsv(g, outarr) - end - end - - else - lev_arr = repeat([level], outer = n) - outarr= [rcp_arr lev_arr s_arr cost_arr t v_arr] - - open(joinpath(outputdir,outfile),"a") do g - writecsv(g, outarr) - end - - end - - elseif typeof(d)==Array{Float64,3} - for j in 1:size(d,3) - val = d[:,i,j] - v_arr = func(val) - - if level=="protect" - p = protectdict[j] - lev_arr = repeat([p], outer = n) - - elseif level=="retreat" - q = retreatdict[j] - lev_arr = repeat([q], outer = n) - else - lev_arr = repeat([level], outer = n) - end - - outarr= [rcp_arr lev_arr s_arr cost_arr t v_arr] - - open(joinpath(outputdir,outfile),"a") do g - writecsv(g, outarr) - end - - end - end - - end - end - -end - -function load_meta() - metadir = "../data/meta" - - # Read header and mappings - header = readstring(open(joinpath(metadir,"header.txt"))) - - varnames = readlines(open(joinpath(metadir,"variablenames.csv"))) - vardict = Dict{Any,Any}(split(varnames[i],',')[1] => (split(varnames[i],',')[2],split(varnames[i],',')[3]) for i in 1:length(varnames)) - - protect = readlines(open(joinpath(metadir, "protectlevels.csv"))) - protectdict = Dict{Any,Any}(parse(Int,split(protect[i],',')[1]) => split(protect[i],',')[2] for i in 1:length(protect)) - - retreat = readlines(open(joinpath(metadir, "retreatlevels.csv"))) - retreatdict = Dict{Any,Any}(parse(Int,split(retreat[i],',')[1]) => split(retreat[i],',')[2] for i in 1:length(retreat)) - - return(header,vardict, protectdict, retreatdict) -end - # Function to run tests for model and make plots # WIP - hardcoded strings @@ -239,14 +90,14 @@ end # resultsdir - output directory # gamsdata,jldata - location of comparison data from GAMS/Julia (relative to datadir) # rcp - string rcp value to test -function run_tests(datadir, gamsfile, resultsdir, lslfile, subset, rcp, tag, model=true, sum = false) +function run_tests(gamsfile, lslfile, subset, rcp, tag, model=true, sum = false) # Import model data - modelparams = import_model_data(datadir, lslfile,"xsc.csv", subset) + modelparams = import_model_data(lslfile,subset) params = modelparams[1] xsc = modelparams[2] # Import GAMS Data - gamsdata = import_comparison_data(datadir, gamsfile) + gamsdata = load_data(gamsfile) # Load Metadata metavars = load_meta() @@ -262,53 +113,53 @@ function run_tests(datadir, gamsfile, resultsdir, lslfile, subset, rcp, tag, mod # sum = false # end - write_results(m, rcp, resultsdir, xsc, tag, sum) + write_ciam(m, xsc, rcp=rcp, tag=tag, sumsegs=sum) end # Compare and ouptut results - # jldata = import_comparison_data(resultsdir, "results_$(rcp)_$(tag).csv") # TODO - distinct outputs for each model run - # levels = readlines(open("../data/meta/levels.csv")) - # variables = unique([v[2] for v in values(metavars[2])]) - # segments = unique(jldata[:seg]) - - # for j in 1:length(levels) - # plotlist = [] - # for k in 1:length(variables) - - # # Skip incompatible combinations - # if (contains(levels[j], "retreat")|| levels[j]=="noAdaptation") && variables[k]=="protection" - # continue - # elseif contains(levels[j], "protect") && (variables[k]=="inundation" || variables[k]=="relocation") - # continue - # elseif (levels[j]=="optimalFixed") && (variables[k] != "total") # TODO update this - # continue - # else + jldata = load_data("../output/results-jl/results_$(rcp)_$(tag).csv") + levels = readlines(open("../data/meta/levels.csv")) + variables = unique([v[2] for v in values(metavars[2])]) + segments = unique(jldata[:seg]) + + for j in 1:length(levels) + plotlist = [] + for k in 1:length(variables) + + # Skip incompatible combinations + if (contains(levels[j], "retreat")|| levels[j]=="noAdaptation") && variables[k]=="protection" + continue + elseif contains(levels[j], "protect") && (variables[k]=="inundation" || variables[k]=="relocation") + continue + elseif (levels[j]=="optimalFixed") && (variables[k] != "total") # TODO update this + continue + else - # if sum - # A = jldata[ (jldata[:level] .== levels[j]) .& (jldata[:costtype] .== variables[k]), :value] - # B = gamsdata[ (gamsdata[:level] .== levels[j]) .& (gamsdata[:costtype] .== variables[k]), :value] - - # # Make plots - # title = string(variables[k]) - # p = line_plot(B, A, title) - # push!(plotlist, p) - # else - # for s in segments - # A = jldata[ (jldata[:level] .== levels[j]) .& (jldata[:costtype] .== variables[k]) .& (jldata[:seg] .== s), :value] - # B = gamsdata[ (gamsdata[:level] .== levels[j]) .& (gamsdata[:costtype] .== variables[k]) .& (gamsdata[:seg].==s), :value] - - # # Make plots - # title = string(s,variables[k]) - # p = line_plot(B, A, title) - # push!(plotlist, p) - # end - - # end - # end + if sum + A = jldata[ (jldata[:level] .== levels[j]) .& (jldata[:costtype] .== variables[k]), :value] + B = gamsdata[ (gamsdata[:level] .== levels[j]) .& (gamsdata[:costtype] .== variables[k]), :value] + + # Make plots + title = string(variables[k]) + p = line_plot(B, A, title) + push!(plotlist, p) + else + for s in segments + A = jldata[ (jldata[:level] .== levels[j]) .& (jldata[:costtype] .== variables[k]) .& (jldata[:seg] .== s), :value] + B = gamsdata[ (gamsdata[:level] .== levels[j]) .& (gamsdata[:costtype] .== variables[k]) .& (gamsdata[:seg].==s), :value] + + # Make plots + title = string(s,variables[k]) + p = line_plot(B, A, title) + push!(plotlist, p) + end + + end + end - # end - # make_plots(plotlist,"$(rcp)_$(levels[j])_$(tag)", resultsdir) - # end + end + make_plots(plotlist,"$(rcp)_$(levels[j])_$(tag)", resultsdir) + end if model return m @@ -319,16 +170,4 @@ function run_tests(datadir, gamsfile, resultsdir, lslfile, subset, rcp, tag, mod end -function run_model_only(datadir, lslfile, subset) - # Import model data - modelparams = import_model_data(datadir, lslfile,"xsc.csv", subset) - params = modelparams[1] - xsc = modelparams[2] - - # return modelparams - m = run_model(params, xsc) - - return m - -end From d284a43ef1d9a50011a958737889a9b9319f0dea Mon Sep 17 00:00:00 2001 From: Catherine Ledna Date: Mon, 2 Jul 2018 11:30:18 -0700 Subject: [PATCH 7/7] Adding empty output folder --- output/placeholder.txt | 6 ++++++ output/plots/placeholder.txt | 6 ++++++ output/results-gams/placeholder.txt | 6 ++++++ output/results-jl/placeholder.txt | 6 ++++++ 4 files changed, 24 insertions(+) create mode 100644 output/placeholder.txt create mode 100644 output/plots/placeholder.txt create mode 100644 output/results-gams/placeholder.txt create mode 100644 output/results-jl/placeholder.txt diff --git a/output/placeholder.txt b/output/placeholder.txt new file mode 100644 index 0000000..4fe891b --- /dev/null +++ b/output/placeholder.txt @@ -0,0 +1,6 @@ +{\rtf1\ansi\ansicpg1252\cocoartf1561\cocoasubrtf200 +{\fonttbl} +{\colortbl;\red255\green255\blue255;} +{\*\expandedcolortbl;;} +\margl1440\margr1440\vieww10800\viewh8400\viewkind0 +} \ No newline at end of file diff --git a/output/plots/placeholder.txt b/output/plots/placeholder.txt new file mode 100644 index 0000000..4fe891b --- /dev/null +++ b/output/plots/placeholder.txt @@ -0,0 +1,6 @@ +{\rtf1\ansi\ansicpg1252\cocoartf1561\cocoasubrtf200 +{\fonttbl} +{\colortbl;\red255\green255\blue255;} +{\*\expandedcolortbl;;} +\margl1440\margr1440\vieww10800\viewh8400\viewkind0 +} \ No newline at end of file diff --git a/output/results-gams/placeholder.txt b/output/results-gams/placeholder.txt new file mode 100644 index 0000000..4fe891b --- /dev/null +++ b/output/results-gams/placeholder.txt @@ -0,0 +1,6 @@ +{\rtf1\ansi\ansicpg1252\cocoartf1561\cocoasubrtf200 +{\fonttbl} +{\colortbl;\red255\green255\blue255;} +{\*\expandedcolortbl;;} +\margl1440\margr1440\vieww10800\viewh8400\viewkind0 +} \ No newline at end of file diff --git a/output/results-jl/placeholder.txt b/output/results-jl/placeholder.txt new file mode 100644 index 0000000..4fe891b --- /dev/null +++ b/output/results-jl/placeholder.txt @@ -0,0 +1,6 @@ +{\rtf1\ansi\ansicpg1252\cocoartf1561\cocoasubrtf200 +{\fonttbl} +{\colortbl;\red255\green255\blue255;} +{\*\expandedcolortbl;;} +\margl1440\margr1440\vieww10800\viewh8400\viewkind0 +} \ No newline at end of file