File:North killeak lake alaska pollen diagram.svg

1. require(car);

1. 1. R neotoma explorer .csv file to simpler csv
   2. output is semicolon separated
   3. alfa wip 0003 2017.2
   4. sample usage

neotoma_reader<-function(sitename, outfilename)

{
  data= read.table(file = sitename, sep=",");

  mata=as.matrix(data);

  kolu=ncol(mata);
  rovu=nrow(mata);

1. data 6:s sarake

  names0=mata[8:rovu,1]; 

1. print(names0);

  ages0=mata[7,6:kolu]; 

1. print(ages0);

  ages1=gsub("--/", "", ages0);

  ages2=gsub("/--", "", ages1);

1. print(ages2);

# names0, ages2

1. 1. f8

 counts0=mata[8:rovu,6:(kolu)];

 counts1=transpose(counts0);

 makolu=ncol(counts1);
 marovu=nrow(counts1);

 counts2=matrix(, nrow=marovu, ncol = makolu)

for (i in 1:nrow(counts1) ) 
  {

    for (j in 1:ncol(counts1) )
       {       

        valo=as.numeric(counts1[i,j]);
     #   print (valo);    

        if ( is.na(valo) ) 
              {
               #   counts2[i,j]="0.001";
                counts2[i,j]=NA;
              }
         else { 
                  counts2[i,j]=counts1[i,j]; 
              }
             
     }
 }

1. print (counts2);

1. print (counts2);

ekarivi=c(c("Age BP"), names0);
tokarivi=cbind(ages2, counts2);
kontti=rbind(ekarivi, tokarivi);

1. kontti2=as.character(kontti);

write.table(kontti, file = outfilename, sep=";", na="0", quote=FALSE,row.names=FALSE, col.names=FALSE);

}

1. neotoma_reader("dataset17345.csv", "killeak_p15.pxt");

args <- commandArgs(trailingOnly = TRUE)

neotoma_reader(args[1], args[2]);

1. 1. nilab pollen datafile
   2. beatifizer test
   3. select 15 most abundant pollen types
   4. wip 0005 alfa

1. 1. sample usage

1. Rscript nilab2.r titaluk2.txt titaluk2_p15.txt

1. require("neotoma")
2. require("analogue")

to_p15<-function(sitename, outfilename)

{

  data= read.table(file = sitename, sep=";");

  mata=as.matrix(data);

  kolu=ncol(mata);
  rovu=nrow(mata);

  taxes0=mata[1,];  

  ages0=mata[,1];

  taxes=taxes0[2:length(taxes0)];
  ages=ages0[2:length(ages0)];

  kounts=mata[2:(rovu), 2:(kolu)];

#  print (kounts);

  kounts2=as.numeric(kounts);
  dim(kounts2) <- c((rovu-1), (kolu-1)) 

 # stop();

  #  kounts[,1:15]
      
  maksimit=apply(kounts2, 2, max);
  indeksit=c(1:(kolu-1));

  kols=length(maksimit);

  #length(maksimit);
  #length(indeksit);

  #footeri=rbind( indeksit, maksimit)
  footeri=rbind( as.numeric(maksimit), as.numeric(indeksit));

  footeri1=rbind( as.numeric(maksimit), as.numeric(indeksit));

  #footeri

  footeri2=footeri1[, order(footeri1[1,])];
  footeri3=footeri2[, ncol(footeri2):1];

  jindeksit=footeri3[2, 1:kols ];

 # print(jindeksit);

1. print(footeri3);
2. footeri3

1. kounts2

 paketti=rbind(footeri, kounts2);

 paketti2=paketti[, order(paketti[1,])];
 paketti3=paketti2[, ncol(paketti2):1];

# print(paketti3[,3]);

 summat0=rowSums(paketti3);
  summat=summat0[3:rovu];

1. print (sum(paketti3[1,]));

 osio0=paketti3[, 1:15];
 jaannos0=paketti3[, 16:kols ];

 osio=osio0[-2,];
 jaannos=jaannos0[-2,];

 osiosummat0=rowSums(osio);
 osiosummat=osiosummat0[2:rovu];

 jaannossummat0=rowSums(jaannos);
 jaannossummat=jaannossummat0[2:rovu];

# print (summat);
#print (osiosummat);

1. print (jaannossummat);

1. print(osio);

1. print(osio[,2]);

  muut=jaannossummat;

1. muut=summat-osiosummat;

1. print (muut);

 taksb <- vector(mode="character", length=32)

 for (i in 1:kols){
   indeksi=jindeksit[i];
   taksi=taxes[indeksi];
   taksj=taksi[1];

1. print (taksj);

   taksb[i]=taksi;
 }

 taksc=taksb[1:15];

1. print(taksc);

 ekarivi=c(c("Age"), taksc,c( "Others"));

1. print (ekarivi);

 lohko=cbind(ages, osio, muut);

 kontti=rbind(ekarivi, lohko);

 konttirows=nrow(kontti);

kontti2=kontti[1:konttirows-1, ];

1. print (kontti);

write.table(kontti2, file = outfilename, sep=";", quote=FALSE,row.names=FALSE, col.names=FALSE);

}

2. to_p15("vaara.txt", "vaara_p15.pxt");

args <- commandArgs(trailingOnly = TRUE)

to_p15(args[1], args[2]);

1. 1. python pollen diagram draw
   2. staclead area percentage plot
   3. test program
   4. WIP 0005 2017.2
   5. input ; separated csv

1. 1. in ubuntu needs matplotlib numpy tk-inter

import numpy as np import matplotlib.pyplot as plt import matplotlib.patches as mpatches

1. 1. pypol 1 python csw pollen percentage drawer
   2. version wip 0005

1. 1. print size

basefontsize=80 xsiz=240 ysiz=60

1. time, x axis

alkut=0 lopput=14000 tstep=1000

kaptioni="North Killeak Lake, near Kotzebue, Alaska" xlaabeli= 'Age BP, Neotoma' ylaabeli='Pollen percent \n '

1. infiili_2="berelekh.csv"

infiili_2='killeak_p15.txt'

colorado=['#00005f',

             '#004000', 
            '#008000',
            '#377EB8',  

            '#F00000', 
            '#FF7F00',
            '#7F7F00', 
            '#008000',

            '#00F000', 
            '#FFFF00',
            '#FFFF80',
            '#800000',

            '#8000F0',
            '#F00000', 
            '#FF7F00',
            '#3f3f3f', 
            '#008000',
         ]

1. 1. pollen data

indata_2 = np.genfromtxt(infiili_2, delimiter=';', dtype=None) muoto_2=np.shape(indata_2) rowis_2=muoto_2[0] colis_2=muoto_2[1] data_2=np.zeros((rowis_2, colis_2))

for i in xrange(1,rowis_2):

     for j in xrange(0,colis_2):
           data_2[i-1][j]=np.float(indata_2[i][j])

rowis_2=rowis_2-1

pdata_2=data_2.transpose() pdata_2[0][rowis_2]=pdata_2[0][rowis_2-1] pdata_2[1][rowis_2]=pdata_2[1][rowis_2-1]

takki=[] plants2=[] varjy=[]

for i in xrange(1,colis_2):

    takki.append(pdata_2[i])
    plants2.append(indata_2[0][i])
    varjy.append(mpatches.Patch(color=colorado[i-1]) )

1. 1. 1. plottingi

fig = plt.figure()

ax2=plt.subplot()

x = pdata_2[0] y = np.row_stack(( takki) ) percent = y / y.sum(axis=0).astype(float) * 100

ax2.stackplot(x,percent, colors=colorado, edgecolors=['#377EB8'], linewidth=0)

ax2.set_ylim([0, 100]) ax2.set_xlim([alkut, lopput])

1. Shrink current axis's height by 10% on the bottom

box = ax2.get_position()

ax2.set_position([box.x0, box.y0, box.width * 0.80, box.height])

ax2.legend( varjy, plants2,loc='center left', bbox_to_anchor=(1.05, 0.5), fontsize=16)

for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):

   label.set_fontname('Arial')
   label.set_fontsize(15)

ax2.set_xlabel(xlaabeli, fontsize=15)

plt.title(kaptioni, fontsize=18)

ax2.set_ylabel(ylaabeli , color='#004000', fontsize=15) for tl in ax2.get_yticklabels():

   tl.set_color('#004000')

start, end = ax2.get_xlim() ax2.xaxis.set_ticks(np.arange(start, end, tstep))

ax2.xaxis.set_tick_params(width=4) ax2.yaxis.set_tick_params(width=4)

plt.gca().invert_xaxis()

plt.show() plt.draw()

1. plt.get_current_fig_manager().window.setGeometry(600,400,1000,800)

plt.get_current_fig_manager().window.showMaximized()

fig.set_size_inches(21, 6) fig.savefig('out.svg', dpi=80)