Karl Pettersson klpn

## pdfa2a.diff
*** default.context	2019-06-22 08:40:19.015364224 +0200
--- default-pdfa2a.context	2019-06-22 08:43:07.763916183 +0200
***************
*** 27,33 ****
  % make chapter, section bookmarks visible when opening document
  \placebookmarks[chapter, section, subsection, subsubsection, subsubsubsection, subsubsubsubsection][chapter, section]
  \setupinteractionscreen[option=bookmark]
- \setuptagging[state=start]

  $if(papersize)$

## WordToPDFA.psm1
function ConvertTo-WordPDFA
{
	Param(
		[Parameter(Mandatory=$true,Position=0)][String]$dpath
		)
	$word_app = New-Object -ComObject Word.Application
	$doc = $word_app.Documents.Open($dpath)
	$dpath_dir = [System.IO.Path]::GetDirectoryName($dpath)
	$dpath_base = [System.IO.Path]::GetFileNameWithoutExtension($dpath)
	$pdf_fname = $dpath_base + ".pdf"

## sedor.jl
using DataFrames
deaths = readtable(normpath("data", "2017-9-10-4Amod.csv"))
ages = DataFrame(age = collect(0:20),
	agest = [0;0;1;collect(5:5:90)],
	age2 =[0;1;fill(2,3);fill(3,6);fill(4,4);fill(5,2);fill(6,4)])
ages2 = by(ages[:,[:agest, :age2]], :age2,
df -> DataFrame(agest=minimum(df[:agest])))

pop = DataFrame()
pop1 = readtable(normpath("data", "mfsv16.csv"))

## meni.jl
using DataFrames, PyPlot
m = readtable("meni9815.csv")
magg = aggregate(m, :Ålder, sum)
bar(magg[:Ålder], magg[:Kvinnor_sum], -2, align="edge", label = "Kvinnor")
bar(magg[:Ålder], magg[:Män_sum], 2, align="edge", label = "Män")
grid(1)
legend()
xlabel("Ålder")
ylabel("Antal (summa för ålder över år)")
title("Sjukhusvårdade meningokockinfektion Sverige 1998\u20132015")

## allskinsv15.csv

          
            age
            fall
            mall
            fskin
            mskin

            
              0
              0.0002006
              0.0002459
              0.0
              0.0

            
              5
              9.22e-5
              0.0001006
              0.0
              0.0

            
              10
              0.0001424
              0.0001568
              0.0
              0.0

            
              15
              0.00022940000000000002
              0.0002389
              0.0
              0.0

            
              20
              0.0003875
              0.00036030000000000003
              9.3e-6
              0.0

            
              25
              0.0007676
              0.0004501
              9.2e-6
              0.0

            
              30
              0.0011704
              0.0007615
              1.34e-5
              0.0

            
              35
              0.0023091
              0.0009373000000000001
              2.02e-5
              9.7e-6

            
              40
              0.0032355
              0.0013991
              3.74e-5
              3.0299999999999998e-5

## amplt.jl
using LifeTable, DataFrames

function AmpLt(inframe, sex, rate = "inc")
	age = inframe[1]
	pop = inframe[2]
	acd = inframe[3]
	cd = inframe[4]
	cc = inframe[5]
	if rate == "inc"
		ncol = cc

## cervc4290rate2.csv
Year,Pop1,Pop2,Pop3,Pop4,Pop5,Pop6,Pop7,Pop8,Pop9,Pop10,Pop11,Pop12,Pop13,Pop14,Pop15,Pop16,Pop17,Pop18,Pop19,Pop20,Pop21,Pop22,Pop23,Pop24,Pop25,Pop26,Pop36sum,Pop222sum,Pop2325sum,Pop38mean,Pop914mean,Pop1518mean,Pop1920mean,Pop2122mean,List
1951,3.268342161613885e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.923816852635629e-05,2.2123893805309735e-05,2.982848620432513e-05,5.200594353640416e-05,8.46091861402095e-05,4.908522980812137e-05,8.060453400503778e-05,6.872852233676975e-05,6.93000693000693e-05,3.5842293906810036e-05,5.46448087431694e-05,0.00010416666666666667,4.464285714285714e-05,,,,0.0,3.260653831977091e-05,4.464285714285714e-05,0.0,2.053274867873255e-05,7.07568680725346e-05,5.2571181603439665e-05,7.940573770491804e-05,07A
1952,3.8315247790580604e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.42282176028306e-06,0.0,3.637686431429611e-05,4.514672686230248e-05,5.167958656330749e-05,5.966587112171838e-05,0.00010048055919615553,8.91089108910891e-05,7.369614512471656e-05,7.407407407407407e-05,9.70873786407767e-05,6.

## Masironi70.csv

          
            ctryname
            country
            totfat
            satfat
            suc
            m55mort
            f55mort

            
              Jordan
              3170
              18.5
              3.7
              11
              49
              19

            
              El Salvador
              2190
              19.5
              5.5
              
              51
              28

            
              Taiwan
              3070
              15.3
              4.8
              4.2
              58
              53

            
              Philippines
              3300
              9.6
              3.7
              5.5
              89
              40

            
              Guatemala
              2250
              15.1
              4.1
              
              115
              84

            
              Mexico
              2310
              24.8
              7.9
              
              122
              65

            
              Greece
              4140
              26.5
              5.4
              4.9
              162
              61

            
              Japan
              3160
              14.5
              3.4
              7.3
              165
              92

            
              Panama
              2350
              22.6
              6.8
              8.7
              170
              76

## svpred.jl
using MortParamsPlot, PyPlot
import YAML

obsyrs = 1999:2013
predyrs = 2018:5:2033
af = 0
ages = 35:5:95
country = 4290
circcomb = ["ihd";"circnonihd"]
predcauses = ["tum";circcomb]

## statswe.json
{
 "response": {
  "format": "json-stat"
 },
 "query": [
  {
   "code": "Region",
   "selection": {
    "values": [
     "25"