From 502722ddc6a2f848c1597e2809164f20c576b480 Mon Sep 17 00:00:00 2001 From: Mark Saleh Date: Mon, 18 Apr 2022 18:40:41 +0200 Subject: [PATCH 1/2] Delete "Part 1" folder "Part 1" folder was deleted because it is a duplicate of "Part 3" folder --- Part 1/ESSdata.csv | 8595 ---------------------------- Part 1/Section-2.ipynb | 2906 ---------- Part 1/curse_of_dimensionality.png | Bin 94166 -> 0 bytes Part 1/pca_pic.png | Bin 11521 -> 0 bytes Part 1/pca_vector.png | Bin 27662 -> 0 bytes Part 1/svm.png | Bin 23826 -> 0 bytes 6 files changed, 11501 deletions(-) delete mode 100755 Part 1/ESSdata.csv delete mode 100755 Part 1/Section-2.ipynb delete mode 100755 Part 1/curse_of_dimensionality.png delete mode 100755 Part 1/pca_pic.png delete mode 100755 Part 1/pca_vector.png delete mode 100755 Part 1/svm.png diff --git a/Part 1/ESSdata.csv b/Part 1/ESSdata.csv deleted file mode 100755 index 1f2a4a7..0000000 --- a/Part 1/ESSdata.csv +++ /dev/null @@ -1,8595 +0,0 @@ -cntry,idno,year,tvtot,ppltrst,pplfair,pplhlp,happy,sclmeet,sclact,gndr,agea,partner -CH,5.0,6,3.0,3.0,10.0,5.0,8.0,5.0,4.0,2.0,60.0,1.0 -CH,25.0,6,6.0,5.0,7.0,5.0,9.0,3.0,2.0,2.0,59.0,1.0 -CH,26.0,6,1.0,8.0,8.0,8.0,7.0,6.0,3.0,1.0,24.0,2.0 -CH,28.0,6,4.0,6.0,6.0,7.0,10.0,6.0,2.0,2.0,64.0,1.0 -CH,29.0,6,5.0,6.0,7.0,5.0,8.0,7.0,2.0,2.0,55.0,1.0 -CH,36.0,6,4.0,6.0,7.0,3.0,8.0,7.0,4.0,2.0,,2.0 -CH,40.0,6,3.0,0.0,5.0,2.0,0.0,2.0,2.0,1.0,76.0,1.0 -CH,41.0,6,2.0,4.0,5.0,3.0,10.0,5.0,2.0,2.0,30.0,1.0 -CH,51.0,6,2.0,8.0,8.0,8.0,9.0,6.0,4.0,2.0,84.0,2.0 -CH,53.0,6,4.0,4.0,4.0,8.0,7.0,4.0,2.0,2.0,62.0,1.0 -CH,55.0,6,1.0,6.0,7.0,7.0,9.0,5.0,2.0,2.0,33.0,1.0 -CH,56.0,6,4.0,5.0,7.0,7.0,7.0,5.0,3.0,1.0,40.0,1.0 -CH,57.0,6,4.0,7.0,7.0,4.0,9.0,6.0,2.0,1.0,69.0,1.0 -CH,58.0,6,2.0,1.0,9.0,7.0,8.0,4.0,3.0,1.0,59.0,2.0 -CH,68.0,6,4.0,4.0,5.0,3.0,8.0,1.0,2.0,2.0,32.0,1.0 -CH,73.0,6,5.0,4.0,5.0,5.0,9.0,3.0,3.0,2.0,70.0,1.0 -CH,81.0,6,4.0,5.0,7.0,7.0,8.0,3.0,3.0,1.0,61.0,1.0 -CH,82.0,6,2.0,7.0,7.0,7.0,6.0,6.0,3.0,1.0,30.0,1.0 -CH,84.0,6,0.0,9.0,7.0,8.0,9.0,5.0,4.0,1.0,21.0,2.0 -CH,86.0,6,2.0,6.0,6.0,6.0,8.0,4.0,3.0,2.0,36.0,1.0 -CH,87.0,6,7.0,3.0,4.0,4.0,7.0,6.0,4.0,1.0,51.0,1.0 -CH,96.0,6,1.0,8.0,8.0,6.0,8.0,7.0,4.0,1.0,25.0,2.0 -CH,98.0,6,2.0,7.0,7.0,8.0,9.0,6.0,3.0,1.0,62.0,1.0 -CH,104.0,6,2.0,4.0,8.0,8.0,9.0,6.0,2.0,2.0,20.0,2.0 -CH,108.0,6,0.0,7.0,7.0,4.0,9.0,4.0,2.0,2.0,22.0,1.0 -CH,117.0,6,0.0,6.0,7.0,5.0,7.0,6.0,2.0,2.0,32.0,1.0 -CH,118.0,6,2.0,7.0,6.0,5.0,8.0,4.0,3.0,2.0,35.0,1.0 -CH,119.0,6,0.0,5.0,8.0,6.0,9.0,4.0,2.0,2.0,26.0,1.0 -CH,121.0,6,2.0,8.0,8.0,8.0,10.0,6.0,3.0,2.0,35.0,1.0 -CH,133.0,6,7.0,8.0,5.0,5.0,8.0,5.0,3.0,2.0,54.0,1.0 -CH,135.0,6,6.0,8.0,8.0,3.0,5.0,6.0,1.0,2.0,38.0,2.0 -CH,137.0,6,3.0,2.0,3.0,2.0,9.0,5.0,2.0,1.0,38.0,1.0 -CH,148.0,6,0.0,5.0,6.0,10.0,10.0,6.0,2.0,2.0,82.0,2.0 -CH,149.0,6,5.0,8.0,3.0,2.0,6.0,6.0,3.0,1.0,69.0,2.0 -CH,161.0,6,1.0,7.0,7.0,8.0,6.0,4.0,3.0,2.0,24.0,2.0 -CH,162.0,6,7.0,7.0,6.0,8.0,8.0,6.0,2.0,2.0,53.0,1.0 -CH,168.0,6,2.0,6.0,6.0,7.0,8.0,6.0,3.0,2.0,46.0,2.0 -CH,177.0,6,4.0,5.0,8.0,8.0,8.0,4.0,3.0,1.0,38.0,1.0 -CH,179.0,6,4.0,7.0,7.0,3.0,9.0,6.0,3.0,1.0,47.0,1.0 -CH,180.0,6,6.0,5.0,7.0,4.0,7.0,4.0,2.0,1.0,72.0,1.0 -CH,185.0,6,2.0,4.0,4.0,5.0,9.0,7.0,3.0,1.0,22.0,2.0 -CH,193.0,6,5.0,3.0,5.0,5.0,8.0,3.0,4.0,2.0,,1.0 -CH,200.0,6,2.0,9.0,7.0,7.0,9.0,7.0,2.0,1.0,16.0,2.0 -CH,216.0,6,1.0,7.0,5.0,7.0,7.0,7.0,3.0,1.0,20.0,2.0 -CH,218.0,6,0.0,8.0,8.0,7.0,9.0,5.0,2.0,1.0,42.0,1.0 -CH,219.0,6,5.0,7.0,7.0,7.0,8.0,7.0,3.0,2.0,16.0,2.0 -CH,220.0,6,4.0,7.0,7.0,7.0,8.0,6.0,3.0,1.0,57.0,2.0 -CH,231.0,6,2.0,8.0,8.0,7.0,8.0,6.0,4.0,2.0,45.0,1.0 -CH,232.0,6,1.0,8.0,8.0,6.0,9.0,4.0,4.0,1.0,28.0,1.0 -CH,251.0,6,3.0,5.0,5.0,4.0,6.0,3.0,2.0,1.0,85.0,1.0 -CH,257.0,6,2.0,7.0,5.0,6.0,8.0,6.0,3.0,1.0,48.0,1.0 -CH,258.0,6,2.0,9.0,9.0,8.0,9.0,5.0,2.0,1.0,85.0,2.0 -CH,260.0,6,6.0,0.0,1.0,5.0,6.0,2.0,1.0,2.0,52.0,2.0 -CH,261.0,6,2.0,3.0,5.0,5.0,8.0,5.0,3.0,2.0,30.0,2.0 -CH,270.0,6,2.0,6.0,8.0,8.0,10.0,5.0,3.0,2.0,51.0,1.0 -CH,271.0,6,1.0,10.0,9.0,9.0,7.0,6.0,2.0,2.0,29.0,1.0 -CH,273.0,6,2.0,7.0,8.0,7.0,8.0,5.0,3.0,1.0,51.0,1.0 -CH,279.0,6,1.0,8.0,8.0,5.0,8.0,5.0,3.0,1.0,62.0,2.0 -CH,295.0,6,0.0,6.0,9.0,6.0,10.0,4.0,3.0,2.0,27.0,1.0 -CH,297.0,6,3.0,8.0,9.0,4.0,8.0,6.0,3.0,1.0,42.0,2.0 -CH,303.0,6,3.0,6.0,3.0,7.0,9.0,7.0,2.0,2.0,47.0,1.0 -CH,308.0,6,0.0,8.0,6.0,6.0,9.0,4.0,2.0,2.0,24.0,2.0 -CH,318.0,6,4.0,8.0,8.0,5.0,10.0,5.0,3.0,1.0,79.0,1.0 -CH,322.0,6,3.0,5.0,7.0,4.0,9.0,5.0,3.0,2.0,70.0,1.0 -CH,323.0,6,3.0,3.0,3.0,3.0,9.0,3.0,2.0,2.0,44.0,1.0 -CH,332.0,6,7.0,6.0,7.0,5.0,8.0,4.0,2.0,2.0,70.0,1.0 -CH,333.0,6,2.0,3.0,3.0,3.0,8.0,6.0,3.0,1.0,30.0,1.0 -CH,339.0,6,4.0,4.0,7.0,8.0,6.0,7.0,5.0,2.0,92.0,2.0 -CH,345.0,6,2.0,7.0,7.0,3.0,9.0,5.0,3.0,1.0,45.0,1.0 -CH,354.0,6,5.0,4.0,7.0,6.0,7.0,3.0,2.0,1.0,50.0,1.0 -CH,357.0,6,2.0,7.0,7.0,8.0,8.0,5.0,3.0,2.0,37.0,1.0 -CH,359.0,6,1.0,7.0,6.0,7.0,8.0,6.0,3.0,1.0,48.0,1.0 -CH,362.0,6,3.0,1.0,6.0,5.0,9.0,4.0,3.0,1.0,64.0,1.0 -CH,369.0,6,5.0,8.0,5.0,4.0,10.0,5.0,3.0,1.0,75.0,1.0 -CH,380.0,6,1.0,5.0,7.0,3.0,10.0,4.0,5.0,1.0,20.0,2.0 -CH,400.0,6,0.0,7.0,8.0,8.0,9.0,6.0,3.0,2.0,45.0,1.0 -CH,402.0,6,6.0,4.0,6.0,2.0,8.0,4.0,2.0,2.0,79.0,2.0 -CH,404.0,6,2.0,4.0,5.0,3.0,8.0,5.0,3.0,1.0,47.0,2.0 -CH,405.0,6,2.0,8.0,7.0,9.0,10.0,3.0,3.0,1.0,19.0,2.0 -CH,407.0,6,2.0,8.0,8.0,5.0,9.0,5.0,2.0,1.0,52.0,1.0 -CH,412.0,6,2.0,5.0,5.0,8.0,6.0,4.0,2.0,1.0,60.0,1.0 -CH,417.0,6,4.0,2.0,5.0,4.0,9.0,4.0,3.0,2.0,31.0,2.0 -CH,420.0,6,2.0,4.0,5.0,5.0,9.0,7.0,3.0,2.0,35.0,2.0 -CH,422.0,6,1.0,7.0,8.0,4.0,6.0,2.0,2.0,1.0,48.0,1.0 -CH,425.0,6,3.0,0.0,2.0,0.0,8.0,6.0,3.0,2.0,37.0,2.0 -CH,427.0,6,4.0,6.0,5.0,4.0,10.0,6.0,3.0,2.0,65.0,1.0 -CH,430.0,6,3.0,6.0,3.0,3.0,7.0,7.0,5.0,1.0,68.0,1.0 -CH,434.0,6,3.0,5.0,5.0,2.0,1.0,5.0,2.0,1.0,27.0,2.0 -CH,441.0,6,5.0,5.0,8.0,8.0,5.0,6.0,2.0,2.0,84.0,2.0 -CH,443.0,6,7.0,4.0,3.0,1.0,8.0,4.0,2.0,2.0,40.0,2.0 -CH,444.0,6,2.0,8.0,7.0,5.0,9.0,6.0,4.0,1.0,28.0,1.0 -CH,447.0,6,5.0,3.0,5.0,4.0,7.0,3.0,3.0,1.0,58.0,1.0 -CH,448.0,6,2.0,7.0,8.0,6.0,7.0,2.0,2.0,1.0,58.0,1.0 -CH,456.0,6,2.0,4.0,7.0,5.0,7.0,2.0,2.0,2.0,39.0,1.0 -CH,457.0,6,0.0,8.0,8.0,8.0,8.0,4.0,2.0,2.0,20.0,2.0 -CH,459.0,6,3.0,8.0,5.0,7.0,7.0,6.0,2.0,1.0,32.0,1.0 -CH,461.0,6,3.0,6.0,7.0,7.0,8.0,6.0,3.0,1.0,16.0,2.0 -CH,471.0,6,0.0,8.0,8.0,7.0,10.0,5.0,3.0,2.0,36.0,1.0 -CH,476.0,6,2.0,5.0,3.0,2.0,8.0,6.0,3.0,1.0,67.0,2.0 -CH,481.0,6,5.0,7.0,5.0,4.0,7.0,7.0,3.0,1.0,23.0,2.0 -CH,491.0,6,5.0,7.0,6.0,6.0,7.0,6.0,3.0,2.0,69.0,1.0 -CH,494.0,6,1.0,5.0,7.0,7.0,10.0,6.0,3.0,2.0,45.0,1.0 -CH,496.0,6,5.0,4.0,6.0,6.0,7.0,3.0,2.0,1.0,45.0,1.0 -CH,497.0,6,2.0,10.0,8.0,8.0,10.0,7.0,3.0,1.0,56.0,1.0 -CH,498.0,6,6.0,3.0,3.0,5.0,7.0,4.0,2.0,1.0,39.0,1.0 -CH,501.0,6,4.0,9.0,9.0,9.0,10.0,5.0,2.0,1.0,59.0,1.0 -CH,502.0,6,1.0,4.0,5.0,10.0,10.0,6.0,3.0,1.0,42.0,1.0 -CH,503.0,6,1.0,9.0,8.0,8.0,9.0,7.0,3.0,1.0,23.0,2.0 -CH,504.0,6,6.0,7.0,5.0,5.0,9.0,6.0,2.0,2.0,75.0,1.0 -CH,506.0,6,3.0,3.0,7.0,5.0,8.0,2.0,3.0,2.0,78.0,2.0 -CH,508.0,6,3.0,6.0,4.0,3.0,8.0,3.0,1.0,1.0,57.0,1.0 -CH,509.0,6,0.0,5.0,5.0,3.0,9.0,6.0,3.0,2.0,33.0,1.0 -CH,512.0,6,4.0,5.0,5.0,9.0,8.0,6.0,3.0,2.0,55.0,1.0 -CH,516.0,6,2.0,8.0,7.0,8.0,9.0,5.0,3.0,2.0,32.0,1.0 -CH,520.0,6,7.0,5.0,8.0,6.0,8.0,6.0,2.0,2.0,83.0,2.0 -CH,522.0,6,2.0,5.0,5.0,6.0,10.0,4.0,3.0,1.0,24.0,2.0 -CH,524.0,6,3.0,6.0,5.0,8.0,9.0,5.0,3.0,1.0,74.0,1.0 -CH,526.0,6,0.0,7.0,6.0,8.0,9.0,6.0,2.0,2.0,25.0,2.0 -CH,539.0,6,6.0,6.0,5.0,6.0,9.0,6.0,3.0,1.0,46.0,1.0 -CH,546.0,6,2.0,7.0,7.0,7.0,8.0,4.0,4.0,2.0,49.0,1.0 -CH,551.0,6,3.0,7.0,5.0,3.0,7.0,4.0,1.0,1.0,68.0,1.0 -CH,563.0,6,1.0,6.0,7.0,6.0,9.0,6.0,3.0,2.0,37.0,1.0 -CH,568.0,6,5.0,9.0,9.0,10.0,10.0,4.0,,2.0,39.0,1.0 -CH,569.0,6,4.0,7.0,8.0,5.0,8.0,6.0,4.0,2.0,58.0,1.0 -CH,570.0,6,3.0,7.0,8.0,7.0,8.0,6.0,3.0,2.0,19.0,2.0 -CH,575.0,6,3.0,7.0,4.0,4.0,9.0,7.0,3.0,1.0,21.0,2.0 -CH,576.0,6,3.0,4.0,3.0,3.0,7.0,2.0,1.0,2.0,45.0,2.0 -CH,578.0,6,5.0,5.0,7.0,7.0,10.0,4.0,2.0,2.0,43.0,1.0 -CH,581.0,6,2.0,5.0,5.0,3.0,8.0,7.0,5.0,1.0,32.0,2.0 -CH,586.0,6,5.0,7.0,6.0,4.0,8.0,4.0,3.0,1.0,68.0,1.0 -CH,587.0,6,3.0,5.0,8.0,8.0,9.0,5.0,1.0,2.0,86.0,2.0 -CH,590.0,6,4.0,5.0,4.0,3.0,7.0,4.0,3.0,2.0,50.0,1.0 -CH,597.0,6,6.0,4.0,2.0,5.0,8.0,4.0,3.0,1.0,65.0,1.0 -CH,600.0,6,4.0,5.0,6.0,3.0,8.0,6.0,3.0,2.0,61.0,1.0 -CH,605.0,6,4.0,3.0,6.0,5.0,7.0,6.0,3.0,1.0,60.0,1.0 -CH,606.0,6,1.0,4.0,4.0,3.0,8.0,3.0,3.0,2.0,62.0,1.0 -CH,607.0,6,6.0,4.0,6.0,8.0,8.0,5.0,3.0,2.0,74.0,1.0 -CH,609.0,6,6.0,4.0,8.0,5.0,7.0,6.0,2.0,2.0,42.0,1.0 -CH,610.0,6,2.0,7.0,5.0,5.0,9.0,5.0,2.0,2.0,51.0,1.0 -CH,613.0,6,2.0,7.0,8.0,5.0,7.0,7.0,3.0,1.0,24.0,2.0 -CH,618.0,6,3.0,5.0,7.0,8.0,8.0,6.0,3.0,2.0,50.0,1.0 -CH,625.0,6,5.0,3.0,4.0,4.0,10.0,6.0,3.0,2.0,67.0,1.0 -CH,630.0,6,1.0,8.0,10.0,7.0,8.0,6.0,3.0,1.0,55.0,1.0 -CH,633.0,6,1.0,6.0,8.0,6.0,8.0,6.0,3.0,2.0,23.0,1.0 -CH,637.0,6,4.0,6.0,8.0,9.0,9.0,6.0,2.0,2.0,60.0,1.0 -CH,643.0,6,1.0,8.0,7.0,7.0,8.0,6.0,2.0,1.0,44.0,1.0 -CH,646.0,6,6.0,8.0,7.0,6.0,9.0,6.0,2.0,2.0,42.0,1.0 -CH,648.0,6,1.0,5.0,5.0,5.0,7.0,4.0,3.0,2.0,37.0,2.0 -CH,655.0,6,3.0,1.0,4.0,5.0,5.0,6.0,2.0,2.0,45.0,2.0 -CH,658.0,6,1.0,7.0,8.0,6.0,10.0,4.0,3.0,1.0,27.0,1.0 -CH,660.0,6,3.0,5.0,5.0,4.0,8.0,4.0,5.0,1.0,59.0,1.0 -CH,663.0,6,0.0,6.0,8.0,4.0,9.0,4.0,4.0,2.0,57.0,1.0 -CH,666.0,6,1.0,8.0,10.0,3.0,8.0,6.0,3.0,1.0,58.0,2.0 -CH,675.0,6,2.0,7.0,7.0,5.0,7.0,4.0,4.0,2.0,47.0,1.0 -CH,676.0,6,5.0,6.0,0.0,5.0,10.0,3.0,2.0,2.0,91.0,2.0 -CH,680.0,6,3.0,7.0,8.0,7.0,8.0,6.0,2.0,2.0,16.0,2.0 -CH,684.0,6,1.0,8.0,8.0,7.0,9.0,6.0,3.0,2.0,34.0,1.0 -CH,685.0,6,7.0,7.0,7.0,2.0,5.0,6.0,1.0,1.0,60.0,2.0 -CH,686.0,6,2.0,3.0,10.0,5.0,10.0,7.0,,2.0,,1.0 -CH,690.0,6,1.0,5.0,7.0,3.0,9.0,5.0,3.0,2.0,51.0,1.0 -CH,692.0,6,7.0,4.0,7.0,4.0,6.0,3.0,4.0,2.0,68.0,2.0 -CH,693.0,6,4.0,3.0,6.0,4.0,4.0,2.0,1.0,1.0,36.0,1.0 -CH,696.0,6,4.0,7.0,4.0,5.0,7.0,5.0,3.0,2.0,51.0,1.0 -CH,697.0,6,6.0,3.0,4.0,5.0,6.0,5.0,2.0,1.0,65.0,1.0 -CH,698.0,6,0.0,7.0,7.0,4.0,9.0,6.0,1.0,2.0,37.0,1.0 -CH,701.0,6,1.0,5.0,6.0,8.0,5.0,4.0,2.0,2.0,58.0,2.0 -CH,703.0,6,7.0,6.0,8.0,7.0,8.0,7.0,3.0,1.0,22.0,2.0 -CH,706.0,6,5.0,4.0,5.0,4.0,6.0,2.0,1.0,1.0,39.0,1.0 -CH,707.0,6,4.0,2.0,5.0,4.0,9.0,6.0,4.0,2.0,23.0,2.0 -CH,708.0,6,1.0,7.0,2.0,3.0,8.0,6.0,3.0,1.0,68.0,1.0 -CH,709.0,6,6.0,6.0,7.0,8.0,8.0,7.0,2.0,1.0,65.0,1.0 -CH,711.0,6,3.0,3.0,3.0,3.0,9.0,6.0,4.0,2.0,40.0,1.0 -CH,713.0,6,2.0,0.0,0.0,5.0,10.0,5.0,1.0,2.0,28.0,1.0 -CH,714.0,6,3.0,6.0,8.0,6.0,9.0,6.0,4.0,1.0,17.0,2.0 -CH,715.0,6,2.0,8.0,8.0,5.0,,6.0,4.0,1.0,70.0,1.0 -CH,716.0,6,1.0,0.0,5.0,5.0,10.0,6.0,2.0,2.0,52.0,1.0 -CH,717.0,6,3.0,6.0,4.0,10.0,10.0,2.0,2.0,2.0,32.0,1.0 -CH,720.0,6,1.0,5.0,7.0,7.0,7.0,6.0,4.0,1.0,54.0,2.0 -CH,722.0,6,1.0,4.0,9.0,8.0,8.0,6.0,3.0,2.0,31.0,1.0 -CH,724.0,6,0.0,8.0,8.0,7.0,10.0,5.0,3.0,2.0,55.0,1.0 -CH,728.0,6,3.0,5.0,5.0,3.0,5.0,3.0,2.0,1.0,76.0,1.0 -CH,729.0,6,3.0,4.0,8.0,5.0,7.0,5.0,3.0,2.0,69.0,2.0 -CH,730.0,6,6.0,3.0,6.0,4.0,9.0,6.0,4.0,2.0,23.0,2.0 -CH,733.0,6,7.0,5.0,0.0,3.0,8.0,7.0,3.0,1.0,41.0,1.0 -CH,739.0,6,3.0,9.0,10.0,7.0,9.0,7.0,2.0,2.0,,2.0 -CH,740.0,6,3.0,2.0,4.0,4.0,7.0,4.0,2.0,1.0,39.0,1.0 -CH,741.0,6,3.0,7.0,7.0,7.0,8.0,3.0,3.0,1.0,63.0,1.0 -CH,743.0,6,5.0,5.0,7.0,5.0,8.0,7.0,3.0,2.0,44.0,1.0 -CH,747.0,6,2.0,5.0,8.0,8.0,10.0,6.0,3.0,2.0,69.0,1.0 -CH,748.0,6,3.0,9.0,7.0,7.0,9.0,6.0,3.0,2.0,45.0,2.0 -CH,757.0,6,5.0,4.0,4.0,5.0,8.0,6.0,2.0,2.0,51.0,1.0 -CH,759.0,6,4.0,5.0,5.0,4.0,9.0,6.0,3.0,1.0,43.0,1.0 -CH,760.0,6,1.0,6.0,8.0,8.0,9.0,5.0,3.0,1.0,48.0,1.0 -CH,761.0,6,6.0,7.0,8.0,5.0,10.0,6.0,4.0,1.0,62.0,1.0 -CH,762.0,6,1.0,7.0,5.0,8.0,8.0,7.0,4.0,1.0,16.0,2.0 -CH,764.0,6,0.0,6.0,7.0,7.0,9.0,6.0,3.0,2.0,36.0,1.0 -CH,770.0,6,3.0,9.0,9.0,9.0,8.0,6.0,4.0,2.0,74.0,1.0 -CH,771.0,6,2.0,7.0,8.0,6.0,9.0,4.0,2.0,2.0,56.0,1.0 -CH,774.0,6,2.0,5.0,6.0,5.0,9.0,5.0,3.0,2.0,70.0,1.0 -CH,778.0,6,3.0,6.0,7.0,7.0,7.0,3.0,1.0,2.0,35.0,1.0 -CH,779.0,6,3.0,5.0,8.0,4.0,8.0,4.0,3.0,1.0,41.0,1.0 -CH,781.0,6,4.0,4.0,4.0,5.0,3.0,5.0,2.0,1.0,78.0,1.0 -CH,788.0,6,1.0,5.0,5.0,7.0,8.0,6.0,3.0,1.0,41.0,1.0 -CH,789.0,6,0.0,7.0,3.0,5.0,,3.0,1.0,2.0,35.0,2.0 -CH,790.0,6,3.0,4.0,8.0,6.0,8.0,3.0,4.0,1.0,62.0,1.0 -CH,793.0,6,2.0,7.0,9.0,8.0,10.0,5.0,3.0,1.0,67.0,1.0 -CH,796.0,6,3.0,9.0,8.0,8.0,8.0,3.0,2.0,1.0,53.0,1.0 -CH,797.0,6,1.0,5.0,6.0,5.0,7.0,7.0,2.0,1.0,59.0,1.0 -CH,800.0,6,0.0,8.0,6.0,5.0,8.0,6.0,1.0,2.0,35.0,1.0 -CH,801.0,6,0.0,8.0,8.0,3.0,7.0,6.0,2.0,1.0,53.0,2.0 -CH,803.0,6,0.0,9.0,6.0,6.0,8.0,7.0,4.0,2.0,64.0,2.0 -CH,809.0,6,3.0,7.0,7.0,7.0,9.0,6.0,3.0,1.0,56.0,1.0 -CH,810.0,6,7.0,2.0,3.0,6.0,10.0,6.0,3.0,2.0,38.0,1.0 -CH,814.0,6,3.0,0.0,0.0,3.0,8.0,7.0,3.0,2.0,45.0,1.0 -CH,817.0,6,7.0,2.0,3.0,7.0,9.0,5.0,2.0,1.0,75.0,1.0 -CH,819.0,6,5.0,5.0,4.0,4.0,6.0,7.0,4.0,1.0,28.0,2.0 -CH,821.0,6,4.0,1.0,7.0,3.0,9.0,6.0,4.0,1.0,57.0,1.0 -CH,822.0,6,4.0,5.0,5.0,6.0,9.0,4.0,2.0,1.0,,1.0 -CH,824.0,6,2.0,6.0,7.0,3.0,9.0,3.0,2.0,1.0,25.0,1.0 -CH,827.0,6,3.0,4.0,6.0,4.0,10.0,6.0,4.0,2.0,52.0,1.0 -CH,834.0,6,5.0,6.0,6.0,5.0,8.0,6.0,3.0,2.0,50.0,1.0 -CH,838.0,6,5.0,7.0,7.0,5.0,9.0,4.0,3.0,2.0,31.0,1.0 -CH,842.0,6,2.0,5.0,9.0,6.0,6.0,6.0,4.0,2.0,31.0,1.0 -CH,844.0,6,2.0,6.0,6.0,5.0,8.0,6.0,2.0,1.0,73.0,2.0 -CH,847.0,6,4.0,6.0,7.0,7.0,7.0,6.0,3.0,2.0,43.0,1.0 -CH,850.0,6,1.0,7.0,8.0,8.0,9.0,6.0,1.0,1.0,60.0,1.0 -CH,852.0,6,3.0,6.0,5.0,6.0,6.0,4.0,2.0,1.0,72.0,2.0 -CH,857.0,6,3.0,4.0,7.0,9.0,9.0,6.0,3.0,1.0,25.0,1.0 -CH,858.0,6,3.0,7.0,6.0,5.0,8.0,5.0,3.0,2.0,35.0,1.0 -CH,860.0,6,3.0,5.0,5.0,7.0,9.0,6.0,2.0,2.0,16.0,2.0 -CH,861.0,6,2.0,7.0,4.0,7.0,9.0,5.0,3.0,2.0,29.0,1.0 -CH,862.0,6,3.0,4.0,3.0,3.0,10.0,2.0,2.0,1.0,68.0,1.0 -CH,875.0,6,1.0,9.0,10.0,9.0,9.0,6.0,3.0,1.0,33.0,1.0 -CH,877.0,6,4.0,6.0,4.0,6.0,8.0,2.0,1.0,1.0,26.0,1.0 -CH,880.0,6,2.0,8.0,8.0,9.0,8.0,6.0,3.0,2.0,36.0,2.0 -CH,881.0,6,4.0,4.0,8.0,8.0,8.0,6.0,2.0,2.0,16.0,2.0 -CH,883.0,6,5.0,8.0,8.0,4.0,9.0,5.0,3.0,1.0,67.0,1.0 -CH,893.0,6,2.0,9.0,9.0,9.0,10.0,7.0,3.0,1.0,23.0,2.0 -CH,898.0,6,5.0,6.0,8.0,6.0,8.0,4.0,1.0,2.0,79.0,1.0 -CH,900.0,6,1.0,8.0,7.0,6.0,8.0,5.0,2.0,1.0,57.0,1.0 -CH,903.0,6,5.0,5.0,4.0,5.0,8.0,4.0,3.0,2.0,57.0,2.0 -CH,908.0,6,3.0,2.0,2.0,3.0,8.0,4.0,2.0,1.0,80.0,2.0 -CH,910.0,6,4.0,4.0,5.0,7.0,8.0,6.0,1.0,1.0,81.0,2.0 -CH,911.0,6,3.0,7.0,7.0,5.0,7.0,6.0,4.0,1.0,35.0,1.0 -CH,912.0,6,2.0,6.0,8.0,6.0,8.0,5.0,1.0,1.0,47.0,1.0 -CH,920.0,6,2.0,3.0,7.0,6.0,2.0,4.0,2.0,2.0,68.0,1.0 -CH,921.0,6,5.0,6.0,5.0,6.0,8.0,5.0,3.0,1.0,,1.0 -CH,923.0,6,3.0,8.0,8.0,8.0,10.0,7.0,4.0,2.0,63.0,1.0 -CH,924.0,6,0.0,8.0,8.0,7.0,8.0,3.0,1.0,1.0,52.0,1.0 -CH,927.0,6,2.0,3.0,4.0,4.0,5.0,6.0,2.0,1.0,51.0,1.0 -CH,931.0,6,0.0,8.0,7.0,5.0,7.0,6.0,2.0,1.0,19.0,2.0 -CH,934.0,6,7.0,2.0,7.0,7.0,5.0,5.0,2.0,2.0,71.0,2.0 -CH,937.0,6,0.0,7.0,7.0,5.0,9.0,7.0,3.0,2.0,42.0,1.0 -CH,939.0,6,4.0,6.0,8.0,8.0,7.0,6.0,3.0,2.0,21.0,2.0 -CH,941.0,6,5.0,6.0,8.0,9.0,8.0,4.0,1.0,1.0,71.0,1.0 -CH,944.0,6,4.0,7.0,8.0,5.0,8.0,7.0,2.0,2.0,17.0,2.0 -CH,947.0,6,3.0,7.0,5.0,2.0,7.0,5.0,4.0,1.0,74.0,1.0 -CH,951.0,6,2.0,1.0,6.0,3.0,3.0,7.0,3.0,1.0,23.0,2.0 -CH,953.0,6,1.0,5.0,7.0,7.0,7.0,6.0,3.0,2.0,63.0,1.0 -CH,955.0,6,5.0,5.0,5.0,7.0,8.0,4.0,3.0,2.0,81.0,1.0 -CH,961.0,6,7.0,5.0,10.0,10.0,10.0,5.0,3.0,2.0,82.0,1.0 -CH,962.0,6,5.0,3.0,3.0,3.0,7.0,4.0,2.0,1.0,34.0,1.0 -CH,963.0,6,2.0,3.0,4.0,7.0,5.0,6.0,3.0,1.0,22.0,2.0 -CH,966.0,6,6.0,4.0,6.0,6.0,4.0,3.0,2.0,2.0,30.0,1.0 -CH,967.0,6,3.0,5.0,10.0,6.0,10.0,4.0,1.0,2.0,54.0,1.0 -CH,976.0,6,2.0,5.0,8.0,8.0,10.0,6.0,5.0,2.0,52.0,1.0 -CH,979.0,6,6.0,4.0,5.0,4.0,8.0,4.0,3.0,1.0,80.0,1.0 -CH,980.0,6,3.0,5.0,5.0,5.0,7.0,6.0,3.0,2.0,26.0,1.0 -CH,987.0,6,4.0,6.0,8.0,6.0,8.0,6.0,3.0,1.0,52.0,2.0 -CH,990.0,6,3.0,8.0,7.0,9.0,6.0,3.0,2.0,2.0,74.0,1.0 -CH,1001.0,6,5.0,4.0,7.0,6.0,9.0,3.0,3.0,1.0,76.0,1.0 -CH,1004.0,6,3.0,8.0,8.0,2.0,9.0,,3.0,2.0,68.0,1.0 -CH,1009.0,6,2.0,7.0,8.0,6.0,9.0,5.0,3.0,1.0,58.0,2.0 -CH,1016.0,6,6.0,4.0,5.0,5.0,9.0,5.0,3.0,2.0,42.0,1.0 -CH,1017.0,6,7.0,4.0,8.0,3.0,10.0,1.0,1.0,2.0,70.0,1.0 -CH,1027.0,6,2.0,5.0,5.0,7.0,8.0,5.0,5.0,2.0,48.0,2.0 -CH,1029.0,6,4.0,2.0,1.0,3.0,10.0,6.0,5.0,1.0,79.0,2.0 -CH,1031.0,6,0.0,9.0,9.0,6.0,10.0,6.0,5.0,1.0,42.0,1.0 -CH,1033.0,6,7.0,3.0,3.0,2.0,10.0,6.0,3.0,2.0,28.0,1.0 -CH,1035.0,6,3.0,8.0,7.0,7.0,8.0,6.0,4.0,1.0,43.0,1.0 -CH,1036.0,6,5.0,5.0,9.0,10.0,10.0,5.0,3.0,1.0,40.0,1.0 -CH,1037.0,6,6.0,3.0,4.0,4.0,9.0,6.0,2.0,1.0,24.0,2.0 -CH,1041.0,6,5.0,5.0,7.0,5.0,9.0,6.0,2.0,1.0,69.0,1.0 -CH,1043.0,6,5.0,5.0,8.0,5.0,10.0,2.0,4.0,1.0,72.0,1.0 -CH,1044.0,6,7.0,2.0,5.0,8.0,8.0,6.0,1.0,1.0,84.0,2.0 -CH,1046.0,6,2.0,1.0,8.0,8.0,9.0,3.0,3.0,1.0,48.0,1.0 -CH,1049.0,6,1.0,5.0,7.0,6.0,10.0,4.0,4.0,2.0,85.0,1.0 -CH,1050.0,6,3.0,7.0,7.0,10.0,10.0,6.0,2.0,1.0,51.0,1.0 -CH,1051.0,6,0.0,4.0,8.0,3.0,8.0,5.0,2.0,2.0,49.0,2.0 -CH,1052.0,6,2.0,5.0,8.0,7.0,8.0,6.0,3.0,2.0,19.0,2.0 -CH,1060.0,6,7.0,2.0,4.0,1.0,7.0,6.0,2.0,2.0,44.0,1.0 -CH,1061.0,6,4.0,1.0,9.0,10.0,8.0,6.0,4.0,2.0,68.0,1.0 -CH,1062.0,6,5.0,8.0,7.0,5.0,9.0,6.0,4.0,2.0,39.0,1.0 -CH,1063.0,6,1.0,4.0,5.0,7.0,9.0,4.0,2.0,2.0,27.0,1.0 -CH,1066.0,6,6.0,3.0,7.0,9.0,6.0,4.0,3.0,1.0,51.0,1.0 -CH,1067.0,6,2.0,8.0,8.0,7.0,9.0,6.0,3.0,1.0,24.0,1.0 -CH,1072.0,6,3.0,2.0,4.0,4.0,8.0,4.0,2.0,2.0,35.0,1.0 -CH,1076.0,6,0.0,7.0,7.0,6.0,9.0,5.0,4.0,1.0,48.0,2.0 -CH,1083.0,6,5.0,6.0,6.0,4.0,6.0,2.0,3.0,2.0,43.0,1.0 -CH,1086.0,6,1.0,7.0,8.0,6.0,8.0,4.0,2.0,1.0,23.0,2.0 -CH,1088.0,6,3.0,5.0,6.0,7.0,8.0,7.0,2.0,1.0,27.0,2.0 -CH,1090.0,6,7.0,7.0,8.0,6.0,8.0,7.0,4.0,2.0,51.0,1.0 -CH,1096.0,6,5.0,5.0,5.0,6.0,8.0,4.0,3.0,2.0,,1.0 -CH,1106.0,6,1.0,8.0,9.0,5.0,8.0,6.0,1.0,2.0,,1.0 -CH,1107.0,6,5.0,3.0,6.0,5.0,8.0,6.0,4.0,2.0,38.0,2.0 -CH,1112.0,6,0.0,8.0,8.0,6.0,10.0,3.0,,2.0,52.0,1.0 -CH,1117.0,6,7.0,5.0,8.0,5.0,7.0,5.0,2.0,1.0,79.0,2.0 -CH,1118.0,6,0.0,7.0,7.0,5.0,8.0,4.0,2.0,2.0,34.0,1.0 -CH,1127.0,6,7.0,3.0,8.0,9.0,10.0,6.0,2.0,2.0,89.0,2.0 -CH,1133.0,6,3.0,8.0,8.0,8.0,9.0,6.0,3.0,2.0,48.0,1.0 -CH,1134.0,6,6.0,7.0,5.0,0.0,5.0,4.0,2.0,1.0,86.0,1.0 -CH,1135.0,6,2.0,6.0,7.0,7.0,8.0,6.0,3.0,1.0,41.0,1.0 -CH,1137.0,6,1.0,6.0,8.0,6.0,8.0,6.0,3.0,2.0,16.0,2.0 -CH,1138.0,6,2.0,5.0,6.0,4.0,7.0,4.0,3.0,1.0,34.0,1.0 -CH,1141.0,6,6.0,5.0,7.0,8.0,9.0,5.0,3.0,2.0,58.0,1.0 -CH,1144.0,6,0.0,8.0,8.0,7.0,8.0,6.0,3.0,2.0,42.0,1.0 -CH,1148.0,6,2.0,3.0,4.0,3.0,6.0,5.0,3.0,2.0,31.0,2.0 -CH,1152.0,6,2.0,5.0,8.0,7.0,10.0,6.0,3.0,1.0,60.0,1.0 -CH,1154.0,6,4.0,4.0,5.0,6.0,8.0,5.0,2.0,1.0,70.0,1.0 -CH,1159.0,6,5.0,5.0,5.0,3.0,6.0,5.0,2.0,1.0,46.0,1.0 -CH,1164.0,6,1.0,9.0,9.0,7.0,9.0,6.0,2.0,2.0,34.0,1.0 -CH,1168.0,6,6.0,5.0,5.0,2.0,8.0,7.0,3.0,2.0,41.0,1.0 -CH,1173.0,6,2.0,3.0,5.0,4.0,9.0,5.0,2.0,2.0,43.0,1.0 -CH,1174.0,6,3.0,8.0,7.0,5.0,9.0,3.0,3.0,1.0,48.0,1.0 -CH,1182.0,6,2.0,6.0,8.0,8.0,9.0,2.0,2.0,2.0,47.0,1.0 -CH,1183.0,6,4.0,4.0,4.0,3.0,9.0,6.0,3.0,2.0,42.0,1.0 -CH,1188.0,6,2.0,4.0,5.0,,5.0,3.0,1.0,2.0,45.0,1.0 -CH,1189.0,6,2.0,7.0,9.0,7.0,9.0,5.0,3.0,2.0,56.0,1.0 -CH,1194.0,6,4.0,8.0,8.0,6.0,8.0,6.0,3.0,2.0,58.0,1.0 -CH,1198.0,6,0.0,8.0,8.0,7.0,8.0,4.0,2.0,2.0,42.0,2.0 -CH,1201.0,6,1.0,8.0,8.0,7.0,9.0,6.0,3.0,1.0,40.0,1.0 -CH,1208.0,6,0.0,8.0,8.0,4.0,9.0,6.0,4.0,2.0,38.0,1.0 -CH,1209.0,6,1.0,4.0,7.0,7.0,8.0,4.0,2.0,1.0,62.0,1.0 -CH,1217.0,6,2.0,5.0,7.0,4.0,10.0,6.0,2.0,2.0,19.0,2.0 -CH,1222.0,6,2.0,5.0,5.0,7.0,9.0,5.0,2.0,2.0,41.0,1.0 -CH,1223.0,6,4.0,8.0,4.0,7.0,10.0,6.0,3.0,2.0,61.0,1.0 -CH,1229.0,6,2.0,5.0,7.0,8.0,10.0,6.0,2.0,1.0,54.0,1.0 -CH,1230.0,6,0.0,8.0,2.0,5.0,8.0,2.0,3.0,1.0,37.0,1.0 -CH,1233.0,6,5.0,10.0,8.0,8.0,9.0,7.0,3.0,1.0,70.0,1.0 -CH,1236.0,6,2.0,5.0,8.0,7.0,10.0,4.0,2.0,1.0,74.0,1.0 -CH,1237.0,6,4.0,8.0,8.0,8.0,8.0,6.0,3.0,1.0,23.0,2.0 -CH,1240.0,6,0.0,8.0,8.0,8.0,9.0,5.0,3.0,2.0,56.0,1.0 -CH,1242.0,6,2.0,8.0,9.0,6.0,9.0,7.0,3.0,1.0,46.0,1.0 -CH,1245.0,6,5.0,9.0,8.0,8.0,10.0,5.0,2.0,1.0,61.0,1.0 -CH,1247.0,6,2.0,2.0,4.0,2.0,3.0,1.0,1.0,2.0,41.0,2.0 -CH,1254.0,6,2.0,6.0,4.0,4.0,8.0,6.0,2.0,1.0,27.0,1.0 -CH,1264.0,6,3.0,8.0,8.0,7.0,8.0,7.0,3.0,1.0,16.0,2.0 -CH,1265.0,6,5.0,8.0,8.0,8.0,9.0,5.0,3.0,2.0,23.0,2.0 -CH,1266.0,6,4.0,2.0,2.0,3.0,8.0,5.0,3.0,2.0,17.0,2.0 -CH,1269.0,6,1.0,8.0,7.0,7.0,7.0,3.0,3.0,1.0,56.0,1.0 -CH,1278.0,6,4.0,2.0,4.0,3.0,6.0,2.0,1.0,1.0,69.0,1.0 -CH,1282.0,6,2.0,8.0,8.0,8.0,8.0,3.0,3.0,2.0,32.0,1.0 -CH,1288.0,6,3.0,4.0,5.0,7.0,9.0,6.0,2.0,2.0,51.0,2.0 -CH,1290.0,6,2.0,7.0,8.0,7.0,9.0,6.0,3.0,1.0,24.0,1.0 -CH,1293.0,6,1.0,4.0,8.0,4.0,9.0,6.0,3.0,2.0,58.0,1.0 -CH,1303.0,6,4.0,7.0,4.0,8.0,10.0,4.0,3.0,1.0,41.0,1.0 -CH,1306.0,6,3.0,7.0,8.0,7.0,8.0,6.0,2.0,1.0,34.0,2.0 -CH,1310.0,6,2.0,10.0,10.0,10.0,10.0,6.0,3.0,2.0,68.0,1.0 -CH,1311.0,6,4.0,8.0,7.0,10.0,9.0,3.0,2.0,1.0,49.0,1.0 -CH,1312.0,6,3.0,7.0,8.0,6.0,9.0,6.0,3.0,1.0,61.0,1.0 -CH,1319.0,6,3.0,6.0,7.0,7.0,8.0,5.0,3.0,2.0,60.0,2.0 -CH,1322.0,6,1.0,5.0,8.0,7.0,10.0,6.0,1.0,2.0,16.0,2.0 -CH,1328.0,6,1.0,5.0,4.0,4.0,9.0,6.0,4.0,1.0,56.0,2.0 -CH,1333.0,6,6.0,9.0,8.0,9.0,8.0,4.0,2.0,2.0,69.0,1.0 -CH,1347.0,6,4.0,3.0,6.0,3.0,9.0,4.0,3.0,1.0,36.0,1.0 -CH,1349.0,6,4.0,6.0,7.0,4.0,7.0,6.0,3.0,2.0,21.0,2.0 -CH,1353.0,6,0.0,8.0,8.0,7.0,9.0,5.0,2.0,1.0,56.0,1.0 -CH,1356.0,6,5.0,6.0,8.0,7.0,8.0,6.0,2.0,1.0,61.0,1.0 -CH,1361.0,6,4.0,4.0,8.0,8.0,8.0,5.0,3.0,1.0,17.0,2.0 -CH,1362.0,6,4.0,6.0,8.0,4.0,7.0,5.0,3.0,2.0,73.0,2.0 -CH,1367.0,6,3.0,8.0,2.0,5.0,9.0,5.0,3.0,2.0,51.0,1.0 -CH,1369.0,6,2.0,5.0,7.0,5.0,7.0,4.0,2.0,1.0,67.0,1.0 -CH,1370.0,6,1.0,9.0,9.0,8.0,7.0,3.0,2.0,2.0,54.0,1.0 -CH,1371.0,6,5.0,0.0,6.0,5.0,9.0,4.0,3.0,1.0,60.0,1.0 -CH,1375.0,6,7.0,4.0,5.0,2.0,7.0,1.0,1.0,1.0,57.0,2.0 -CH,1378.0,6,4.0,6.0,6.0,6.0,7.0,6.0,2.0,1.0,31.0,2.0 -CH,1379.0,6,2.0,5.0,6.0,7.0,9.0,4.0,2.0,2.0,45.0,2.0 -CH,1382.0,6,5.0,2.0,5.0,0.0,10.0,3.0,2.0,2.0,50.0,2.0 -CH,1390.0,6,3.0,0.0,0.0,0.0,5.0,2.0,1.0,2.0,61.0,2.0 -CH,1402.0,6,1.0,4.0,7.0,7.0,10.0,6.0,3.0,2.0,19.0,2.0 -CH,1404.0,6,6.0,8.0,8.0,6.0,8.0,6.0,3.0,2.0,56.0,2.0 -CH,1405.0,6,4.0,0.0,7.0,6.0,7.0,4.0,3.0,2.0,,1.0 -CH,1409.0,6,2.0,8.0,8.0,4.0,10.0,4.0,2.0,1.0,36.0,1.0 -CH,1410.0,6,7.0,8.0,8.0,8.0,10.0,5.0,2.0,2.0,,2.0 -CH,1418.0,6,2.0,6.0,6.0,4.0,10.0,7.0,3.0,2.0,45.0,1.0 -CH,1431.0,6,3.0,7.0,10.0,5.0,7.0,7.0,4.0,2.0,23.0,2.0 -CH,1432.0,6,4.0,3.0,5.0,3.0,9.0,6.0,1.0,1.0,69.0,1.0 -CH,1436.0,6,3.0,8.0,8.0,7.0,8.0,6.0,3.0,2.0,40.0,1.0 -CH,1439.0,6,0.0,5.0,5.0,3.0,8.0,6.0,2.0,1.0,30.0,2.0 -CH,1447.0,6,4.0,5.0,7.0,4.0,8.0,6.0,3.0,1.0,20.0,2.0 -CH,1448.0,6,2.0,7.0,7.0,7.0,10.0,6.0,3.0,2.0,62.0,1.0 -CH,1449.0,6,4.0,5.0,5.0,5.0,10.0,7.0,3.0,2.0,23.0,1.0 -CH,1450.0,6,6.0,5.0,3.0,8.0,8.0,4.0,2.0,2.0,61.0,2.0 -CH,1451.0,6,3.0,7.0,9.0,8.0,7.0,7.0,2.0,1.0,49.0,1.0 -CH,1453.0,6,5.0,7.0,7.0,4.0,8.0,6.0,1.0,2.0,78.0,2.0 -CH,1454.0,6,4.0,5.0,4.0,2.0,5.0,4.0,3.0,1.0,76.0,2.0 -CH,1457.0,6,2.0,7.0,9.0,9.0,9.0,6.0,3.0,2.0,31.0,2.0 -CH,1462.0,6,3.0,5.0,8.0,5.0,5.0,3.0,3.0,1.0,,2.0 -CH,1463.0,6,1.0,7.0,7.0,7.0,9.0,6.0,3.0,2.0,35.0,1.0 -CH,1468.0,6,5.0,9.0,8.0,7.0,10.0,6.0,3.0,1.0,30.0,1.0 -CH,1471.0,6,3.0,10.0,,10.0,8.0,3.0,3.0,2.0,47.0,1.0 -CH,1477.0,6,2.0,9.0,9.0,10.0,9.0,6.0,3.0,2.0,26.0,1.0 -CH,1478.0,6,4.0,3.0,3.0,3.0,7.0,5.0,2.0,1.0,65.0,1.0 -CH,1484.0,6,5.0,5.0,5.0,6.0,6.0,3.0,2.0,1.0,61.0,1.0 -CH,1490.0,6,3.0,7.0,5.0,3.0,8.0,6.0,2.0,1.0,33.0,1.0 -CH,1495.0,6,1.0,5.0,10.0,4.0,8.0,6.0,4.0,2.0,58.0,1.0 -CH,1502.0,6,3.0,6.0,7.0,7.0,7.0,6.0,2.0,1.0,74.0,1.0 -CH,1504.0,6,2.0,7.0,8.0,7.0,4.0,6.0,4.0,2.0,46.0,2.0 -CH,1505.0,6,5.0,6.0,7.0,8.0,8.0,6.0,4.0,2.0,35.0,2.0 -CH,1507.0,6,2.0,7.0,8.0,6.0,8.0,5.0,3.0,2.0,51.0,1.0 -CH,1508.0,6,0.0,7.0,9.0,8.0,8.0,5.0,3.0,2.0,27.0,2.0 -CH,1510.0,6,0.0,6.0,7.0,7.0,8.0,7.0,4.0,2.0,22.0,2.0 -CH,1513.0,6,6.0,5.0,5.0,1.0,9.0,6.0,3.0,1.0,34.0,1.0 -CH,1527.0,6,5.0,7.0,7.0,7.0,7.0,6.0,1.0,2.0,44.0,1.0 -CH,1530.0,6,2.0,8.0,10.0,5.0,8.0,3.0,2.0,2.0,57.0,1.0 -CH,1535.0,6,1.0,9.0,5.0,6.0,7.0,3.0,2.0,2.0,43.0,2.0 -CH,1536.0,6,5.0,6.0,4.0,5.0,7.0,4.0,2.0,1.0,62.0,1.0 -CH,1542.0,6,3.0,8.0,2.0,8.0,9.0,6.0,3.0,2.0,79.0,1.0 -CH,1547.0,6,2.0,7.0,7.0,6.0,7.0,6.0,3.0,1.0,16.0,2.0 -CH,1553.0,6,6.0,5.0,9.0,5.0,10.0,7.0,2.0,1.0,19.0,2.0 -CH,1554.0,6,5.0,5.0,6.0,6.0,8.0,5.0,3.0,2.0,48.0,1.0 -CH,1562.0,6,7.0,5.0,8.0,5.0,5.0,7.0,1.0,1.0,65.0,2.0 -CH,1573.0,6,4.0,3.0,5.0,3.0,10.0,6.0,2.0,1.0,46.0,2.0 -CH,1577.0,6,6.0,4.0,5.0,6.0,7.0,6.0,4.0,2.0,48.0,1.0 -CH,1579.0,6,5.0,3.0,5.0,5.0,8.0,5.0,2.0,1.0,41.0,1.0 -CH,1583.0,6,5.0,3.0,5.0,6.0,9.0,3.0,,2.0,46.0,2.0 -CH,1588.0,6,6.0,0.0,10.0,10.0,9.0,4.0,2.0,1.0,74.0,1.0 -CH,1594.0,6,7.0,4.0,2.0,7.0,7.0,4.0,2.0,1.0,79.0,1.0 -CH,1597.0,6,0.0,4.0,5.0,2.0,8.0,5.0,2.0,2.0,41.0,2.0 -CH,1600.0,6,2.0,0.0,10.0,10.0,10.0,4.0,3.0,1.0,69.0,1.0 -CH,1603.0,6,4.0,3.0,6.0,3.0,7.0,4.0,3.0,2.0,22.0,2.0 -CH,1604.0,6,4.0,4.0,1.0,5.0,8.0,6.0,1.0,1.0,18.0,2.0 -CH,1605.0,6,0.0,6.0,7.0,7.0,8.0,7.0,3.0,1.0,21.0,2.0 -CH,1615.0,6,4.0,2.0,8.0,4.0,9.0,5.0,2.0,2.0,61.0,1.0 -CH,1620.0,6,4.0,8.0,9.0,5.0,7.0,4.0,3.0,2.0,64.0,1.0 -CH,1624.0,6,0.0,7.0,8.0,7.0,8.0,6.0,3.0,2.0,40.0,1.0 -CH,1637.0,6,3.0,6.0,7.0,6.0,10.0,6.0,4.0,2.0,22.0,2.0 -CH,1638.0,6,3.0,7.0,7.0,6.0,8.0,5.0,3.0,2.0,46.0,1.0 -CH,1639.0,6,3.0,6.0,7.0,5.0,9.0,4.0,3.0,1.0,56.0,2.0 -CH,1643.0,6,5.0,3.0,5.0,6.0,9.0,7.0,4.0,1.0,19.0,2.0 -CH,1653.0,6,2.0,8.0,8.0,7.0,8.0,6.0,2.0,2.0,31.0,2.0 -CH,1654.0,6,2.0,5.0,6.0,5.0,7.0,5.0,4.0,2.0,48.0,2.0 -CH,1656.0,6,6.0,7.0,7.0,6.0,10.0,3.0,,1.0,78.0,1.0 -CH,1657.0,6,2.0,3.0,3.0,4.0,8.0,6.0,3.0,2.0,16.0,2.0 -CH,1662.0,6,7.0,7.0,8.0,8.0,8.0,6.0,2.0,1.0,19.0,2.0 -CH,1667.0,6,2.0,6.0,5.0,4.0,10.0,6.0,3.0,1.0,22.0,2.0 -CH,1668.0,6,1.0,8.0,8.0,6.0,8.0,6.0,3.0,1.0,35.0,2.0 -CH,1671.0,6,3.0,6.0,5.0,8.0,8.0,6.0,3.0,1.0,16.0,2.0 -CH,1683.0,6,6.0,8.0,9.0,8.0,9.0,7.0,4.0,2.0,16.0,2.0 -CH,1686.0,6,4.0,7.0,8.0,10.0,9.0,6.0,4.0,1.0,71.0,1.0 -CH,1689.0,6,4.0,8.0,6.0,8.0,9.0,6.0,2.0,2.0,40.0,1.0 -CH,1691.0,6,6.0,6.0,6.0,8.0,10.0,7.0,3.0,2.0,59.0,1.0 -CH,1693.0,6,2.0,4.0,5.0,4.0,8.0,6.0,3.0,1.0,18.0,2.0 -CH,1701.0,6,2.0,5.0,4.0,3.0,8.0,,2.0,2.0,50.0,1.0 -CH,1707.0,6,5.0,7.0,10.0,5.0,5.0,6.0,4.0,2.0,92.0,2.0 -CH,1709.0,6,3.0,4.0,4.0,5.0,10.0,3.0,3.0,2.0,38.0,1.0 -CH,1718.0,6,3.0,5.0,3.0,5.0,8.0,6.0,3.0,2.0,18.0,2.0 -CH,1719.0,6,3.0,6.0,6.0,7.0,8.0,5.0,4.0,1.0,20.0,2.0 -CH,1720.0,6,2.0,4.0,6.0,6.0,8.0,5.0,3.0,1.0,32.0,2.0 -CH,1723.0,6,3.0,8.0,7.0,4.0,10.0,5.0,2.0,1.0,31.0,1.0 -CH,1726.0,6,3.0,7.0,4.0,7.0,9.0,2.0,4.0,1.0,57.0,1.0 -CH,1738.0,6,3.0,3.0,6.0,4.0,8.0,7.0,2.0,2.0,17.0,2.0 -CH,1743.0,6,0.0,7.0,6.0,8.0,10.0,2.0,2.0,2.0,47.0,1.0 -CH,1748.0,6,2.0,4.0,5.0,7.0,6.0,2.0,1.0,2.0,48.0,1.0 -CH,1750.0,6,3.0,7.0,7.0,5.0,5.0,4.0,2.0,1.0,51.0,1.0 -CH,1753.0,6,1.0,3.0,3.0,3.0,7.0,4.0,2.0,1.0,42.0,2.0 -CH,1755.0,6,4.0,8.0,8.0,6.0,9.0,4.0,2.0,2.0,38.0,1.0 -CH,1756.0,6,0.0,9.0,9.0,7.0,7.0,4.0,3.0,2.0,40.0,2.0 -CH,1757.0,6,3.0,6.0,7.0,8.0,8.0,5.0,3.0,1.0,48.0,1.0 -CH,1765.0,6,2.0,0.0,5.0,5.0,3.0,3.0,2.0,2.0,86.0,1.0 -CH,1766.0,6,6.0,7.0,8.0,4.0,8.0,7.0,3.0,2.0,24.0,2.0 -CH,1769.0,6,6.0,7.0,8.0,7.0,10.0,4.0,2.0,2.0,58.0,1.0 -CH,1776.0,6,1.0,6.0,5.0,7.0,9.0,6.0,3.0,1.0,33.0,1.0 -CH,1788.0,6,4.0,8.0,7.0,7.0,8.0,5.0,3.0,1.0,63.0,1.0 -CH,1790.0,6,4.0,5.0,7.0,5.0,7.0,4.0,2.0,1.0,28.0,2.0 -CH,1791.0,6,1.0,8.0,9.0,9.0,8.0,6.0,3.0,2.0,39.0,1.0 -CH,1792.0,6,3.0,8.0,8.0,6.0,8.0,4.0,2.0,1.0,55.0,1.0 -CH,1797.0,6,3.0,5.0,5.0,5.0,7.0,4.0,2.0,2.0,39.0,1.0 -CH,1801.0,6,5.0,5.0,4.0,7.0,9.0,6.0,3.0,1.0,82.0,1.0 -CH,1802.0,6,7.0,5.0,5.0,2.0,7.0,6.0,3.0,2.0,50.0,2.0 -CH,1803.0,6,0.0,5.0,9.0,8.0,8.0,4.0,1.0,2.0,27.0,2.0 -CH,1805.0,6,3.0,1.0,5.0,5.0,6.0,6.0,4.0,1.0,81.0,1.0 -CH,1809.0,6,4.0,2.0,8.0,6.0,7.0,5.0,3.0,2.0,38.0,1.0 -CH,1815.0,6,5.0,6.0,5.0,6.0,8.0,5.0,2.0,2.0,58.0,1.0 -CH,1816.0,6,2.0,8.0,7.0,7.0,9.0,6.0,3.0,2.0,32.0,2.0 -CH,1817.0,6,0.0,7.0,7.0,7.0,8.0,5.0,3.0,2.0,61.0,1.0 -CH,1819.0,6,4.0,6.0,4.0,5.0,8.0,5.0,3.0,2.0,55.0,1.0 -CH,1829.0,6,4.0,8.0,8.0,7.0,9.0,4.0,5.0,1.0,89.0,1.0 -CH,1832.0,6,3.0,7.0,7.0,7.0,9.0,7.0,4.0,2.0,36.0,1.0 -CH,1844.0,6,5.0,7.0,8.0,6.0,8.0,4.0,3.0,1.0,,1.0 -CH,1849.0,6,3.0,6.0,8.0,6.0,9.0,6.0,3.0,2.0,71.0,1.0 -CH,1850.0,6,4.0,5.0,7.0,5.0,8.0,7.0,3.0,2.0,56.0,1.0 -CH,1852.0,6,1.0,5.0,5.0,3.0,8.0,4.0,2.0,2.0,70.0,1.0 -CH,1854.0,6,4.0,5.0,7.0,6.0,9.0,2.0,2.0,1.0,55.0,1.0 -CH,1869.0,6,2.0,5.0,7.0,7.0,8.0,6.0,5.0,2.0,45.0,2.0 -CH,1877.0,6,1.0,3.0,5.0,4.0,8.0,6.0,3.0,1.0,56.0,2.0 -CH,1888.0,6,3.0,5.0,4.0,4.0,7.0,6.0,3.0,2.0,24.0,2.0 -CH,1897.0,6,5.0,0.0,4.0,8.0,8.0,7.0,3.0,2.0,33.0,1.0 -CH,1906.0,6,5.0,6.0,7.0,4.0,8.0,3.0,3.0,1.0,41.0,1.0 -CH,1914.0,6,4.0,5.0,2.0,2.0,8.0,3.0,3.0,1.0,39.0,1.0 -CH,1917.0,6,5.0,6.0,5.0,4.0,7.0,5.0,3.0,1.0,46.0,2.0 -CH,1921.0,6,3.0,5.0,7.0,4.0,8.0,3.0,3.0,1.0,49.0,1.0 -CH,1924.0,6,2.0,3.0,5.0,5.0,8.0,6.0,3.0,1.0,49.0,1.0 -CH,1929.0,6,2.0,7.0,7.0,6.0,8.0,5.0,4.0,1.0,,1.0 -CH,1940.0,6,3.0,1.0,3.0,5.0,8.0,6.0,3.0,2.0,19.0,2.0 -CH,1941.0,6,4.0,7.0,7.0,7.0,8.0,3.0,3.0,1.0,61.0,1.0 -CH,1943.0,6,3.0,9.0,7.0,9.0,9.0,7.0,4.0,2.0,17.0,2.0 -CH,1955.0,6,0.0,0.0,,,8.0,4.0,,2.0,50.0,2.0 -CH,1968.0,6,3.0,6.0,4.0,5.0,9.0,6.0,3.0,2.0,59.0,1.0 -CH,1969.0,6,2.0,7.0,8.0,8.0,8.0,5.0,3.0,2.0,58.0,2.0 -CH,1971.0,6,5.0,5.0,6.0,8.0,8.0,6.0,3.0,2.0,74.0,1.0 -CH,1978.0,6,1.0,4.0,4.0,4.0,7.0,4.0,3.0,1.0,42.0,1.0 -CH,1982.0,6,3.0,5.0,7.0,8.0,8.0,4.0,4.0,1.0,61.0,2.0 -CH,1991.0,6,1.0,6.0,7.0,7.0,9.0,6.0,2.0,2.0,22.0,1.0 -CH,1992.0,6,7.0,5.0,2.0,7.0,9.0,5.0,2.0,2.0,61.0,2.0 -CH,1998.0,6,4.0,6.0,6.0,8.0,8.0,6.0,3.0,2.0,49.0,1.0 -CH,2000.0,6,6.0,3.0,5.0,7.0,7.0,6.0,3.0,2.0,17.0,2.0 -CH,2004.0,6,1.0,5.0,3.0,5.0,10.0,2.0,1.0,2.0,,1.0 -CH,2005.0,6,2.0,7.0,10.0,9.0,9.0,6.0,2.0,1.0,37.0,1.0 -CH,2007.0,6,5.0,7.0,7.0,7.0,8.0,7.0,2.0,2.0,43.0,1.0 -CH,2008.0,6,7.0,3.0,1.0,1.0,8.0,5.0,1.0,1.0,67.0,2.0 -CH,2011.0,6,0.0,8.0,7.0,7.0,8.0,5.0,2.0,1.0,43.0,1.0 -CH,2017.0,6,2.0,8.0,9.0,9.0,9.0,5.0,3.0,1.0,16.0,2.0 -CH,2020.0,6,2.0,3.0,6.0,7.0,7.0,6.0,3.0,1.0,55.0,1.0 -CH,2021.0,6,1.0,7.0,8.0,5.0,6.0,3.0,2.0,2.0,56.0,1.0 -CH,2027.0,6,2.0,7.0,9.0,5.0,7.0,4.0,1.0,1.0,57.0,1.0 -CH,2028.0,6,1.0,4.0,5.0,6.0,8.0,5.0,2.0,1.0,36.0,1.0 -CH,2036.0,6,4.0,0.0,5.0,7.0,10.0,4.0,3.0,2.0,,1.0 -CH,2041.0,6,2.0,9.0,9.0,8.0,7.0,6.0,3.0,1.0,40.0,2.0 -CH,2049.0,6,3.0,3.0,5.0,7.0,10.0,5.0,2.0,2.0,47.0,1.0 -CH,2052.0,6,2.0,4.0,4.0,5.0,10.0,6.0,2.0,2.0,56.0,2.0 -CH,2060.0,6,2.0,5.0,8.0,6.0,8.0,5.0,3.0,1.0,53.0,1.0 -CH,2062.0,6,5.0,4.0,6.0,7.0,7.0,4.0,2.0,1.0,28.0,1.0 -CH,2063.0,6,3.0,6.0,8.0,8.0,10.0,4.0,4.0,2.0,57.0,1.0 -CH,2077.0,6,3.0,7.0,9.0,5.0,7.0,7.0,3.0,1.0,39.0,1.0 -CH,2078.0,6,2.0,9.0,7.0,5.0,8.0,5.0,3.0,2.0,56.0,1.0 -CH,2084.0,6,2.0,7.0,9.0,10.0,10.0,6.0,2.0,2.0,46.0,1.0 -CH,2090.0,6,1.0,8.0,8.0,5.0,7.0,6.0,3.0,1.0,40.0,1.0 -CH,2092.0,6,5.0,8.0,8.0,1.0,9.0,5.0,2.0,2.0,54.0,1.0 -CH,2095.0,6,7.0,9.0,9.0,5.0,10.0,5.0,4.0,1.0,35.0,1.0 -CH,2102.0,6,5.0,5.0,7.0,5.0,8.0,5.0,3.0,1.0,55.0,1.0 -CH,2106.0,6,3.0,3.0,5.0,6.0,7.0,4.0,2.0,1.0,39.0,1.0 -CH,2111.0,6,7.0,7.0,2.0,3.0,7.0,6.0,3.0,2.0,27.0,1.0 -CH,2112.0,6,3.0,8.0,7.0,6.0,10.0,5.0,2.0,1.0,33.0,1.0 -CH,2113.0,6,3.0,7.0,8.0,8.0,8.0,5.0,2.0,2.0,54.0,2.0 -CH,2115.0,6,1.0,8.0,8.0,7.0,8.0,5.0,2.0,1.0,52.0,1.0 -CH,2124.0,6,0.0,3.0,3.0,3.0,9.0,5.0,2.0,2.0,52.0,1.0 -CH,2128.0,6,2.0,8.0,7.0,7.0,9.0,5.0,2.0,1.0,34.0,1.0 -CH,2129.0,6,2.0,2.0,5.0,6.0,8.0,5.0,2.0,1.0,75.0,2.0 -CH,2131.0,6,3.0,5.0,7.0,8.0,8.0,3.0,4.0,2.0,79.0,1.0 -CH,2133.0,6,3.0,7.0,7.0,7.0,7.0,7.0,4.0,1.0,23.0,2.0 -CH,2135.0,6,3.0,8.0,7.0,6.0,9.0,6.0,5.0,2.0,25.0,2.0 -CH,2154.0,6,5.0,8.0,5.0,8.0,8.0,6.0,3.0,2.0,69.0,2.0 -CH,2159.0,6,0.0,5.0,8.0,5.0,9.0,5.0,3.0,1.0,39.0,1.0 -CH,2160.0,6,7.0,4.0,3.0,5.0,9.0,2.0,2.0,1.0,80.0,1.0 -CH,2161.0,6,5.0,6.0,8.0,4.0,7.0,2.0,2.0,2.0,69.0,1.0 -CH,2167.0,6,4.0,2.0,2.0,2.0,5.0,4.0,3.0,2.0,19.0,2.0 -CH,2169.0,6,3.0,7.0,7.0,8.0,7.0,6.0,3.0,1.0,69.0,1.0 -CH,2180.0,6,5.0,5.0,8.0,5.0,10.0,3.0,1.0,1.0,78.0,1.0 -CH,2182.0,6,7.0,7.0,5.0,2.0,4.0,4.0,1.0,1.0,42.0,2.0 -CH,2183.0,6,0.0,7.0,6.0,5.0,9.0,6.0,,2.0,68.0,2.0 -CH,2190.0,6,2.0,3.0,5.0,6.0,8.0,6.0,3.0,2.0,26.0,2.0 -CH,2192.0,6,3.0,5.0,6.0,4.0,10.0,5.0,3.0,1.0,63.0,1.0 -CH,2193.0,6,6.0,3.0,4.0,4.0,9.0,7.0,2.0,2.0,21.0,2.0 -CH,2196.0,6,2.0,7.0,8.0,8.0,8.0,4.0,2.0,1.0,59.0,1.0 -CH,2197.0,6,0.0,8.0,8.0,5.0,8.0,6.0,3.0,2.0,63.0,1.0 -CH,2201.0,6,3.0,8.0,8.0,8.0,8.0,5.0,3.0,1.0,59.0,1.0 -CH,2203.0,6,5.0,7.0,5.0,7.0,8.0,5.0,2.0,1.0,57.0,2.0 -CH,2205.0,6,1.0,8.0,10.0,8.0,10.0,5.0,3.0,1.0,46.0,1.0 -CH,2209.0,6,0.0,7.0,8.0,7.0,7.0,5.0,2.0,2.0,50.0,2.0 -CH,2211.0,6,2.0,7.0,6.0,5.0,10.0,6.0,2.0,2.0,36.0,1.0 -CH,2217.0,6,4.0,10.0,10.0,9.0,10.0,5.0,2.0,2.0,41.0,1.0 -CH,2221.0,6,7.0,7.0,7.0,8.0,8.0,4.0,2.0,2.0,75.0,1.0 -CH,2228.0,6,3.0,6.0,8.0,7.0,7.0,6.0,3.0,1.0,22.0,2.0 -CH,2229.0,6,0.0,7.0,7.0,4.0,8.0,6.0,3.0,2.0,45.0,2.0 -CH,2236.0,6,4.0,3.0,3.0,4.0,7.0,6.0,2.0,2.0,37.0,1.0 -CH,2237.0,6,2.0,10.0,10.0,9.0,10.0,4.0,3.0,2.0,79.0,1.0 -CH,2239.0,6,3.0,7.0,7.0,6.0,9.0,4.0,3.0,1.0,47.0,1.0 -CH,2241.0,6,2.0,6.0,4.0,3.0,10.0,6.0,4.0,1.0,48.0,1.0 -CH,2243.0,6,4.0,5.0,7.0,4.0,9.0,6.0,2.0,1.0,55.0,1.0 -CH,2244.0,6,7.0,2.0,5.0,7.0,5.0,1.0,1.0,2.0,,2.0 -CH,2247.0,6,3.0,3.0,3.0,5.0,8.0,6.0,3.0,1.0,23.0,2.0 -CH,2248.0,6,3.0,5.0,8.0,2.0,10.0,2.0,3.0,1.0,59.0,1.0 -CH,2249.0,6,1.0,6.0,7.0,4.0,7.0,6.0,4.0,1.0,30.0,2.0 -CH,2251.0,6,3.0,6.0,7.0,5.0,7.0,6.0,3.0,1.0,36.0,1.0 -CH,2256.0,6,3.0,7.0,7.0,5.0,8.0,5.0,3.0,2.0,47.0,1.0 -CH,2257.0,6,3.0,8.0,6.0,5.0,7.0,6.0,2.0,2.0,40.0,1.0 -CH,2262.0,6,5.0,7.0,7.0,4.0,8.0,6.0,4.0,1.0,33.0,1.0 -CH,2265.0,6,1.0,7.0,4.0,8.0,7.0,6.0,3.0,1.0,21.0,2.0 -CH,2267.0,6,1.0,7.0,9.0,5.0,8.0,6.0,2.0,2.0,53.0,2.0 -CH,2270.0,6,5.0,6.0,4.0,6.0,6.0,6.0,2.0,1.0,44.0,2.0 -CH,2272.0,6,0.0,8.0,7.0,5.0,5.0,3.0,1.0,1.0,64.0,1.0 -CH,2273.0,6,1.0,7.0,7.0,5.0,8.0,7.0,3.0,1.0,21.0,2.0 -CH,2284.0,6,3.0,7.0,7.0,7.0,9.0,7.0,3.0,1.0,23.0,2.0 -CH,2289.0,6,1.0,7.0,7.0,5.0,8.0,6.0,3.0,1.0,23.0,2.0 -CH,2290.0,6,7.0,7.0,7.0,7.0,8.0,4.0,3.0,1.0,71.0,1.0 -CH,2294.0,6,3.0,5.0,9.0,7.0,7.0,6.0,4.0,2.0,86.0,2.0 -CH,2295.0,6,6.0,7.0,5.0,5.0,5.0,5.0,2.0,2.0,60.0,1.0 -CH,2298.0,6,3.0,3.0,8.0,5.0,8.0,5.0,2.0,1.0,20.0,2.0 -CH,2315.0,6,4.0,7.0,8.0,3.0,3.0,5.0,3.0,1.0,32.0,1.0 -CH,2318.0,6,2.0,4.0,5.0,5.0,8.0,6.0,3.0,1.0,23.0,1.0 -CH,2319.0,6,2.0,9.0,8.0,8.0,9.0,6.0,4.0,1.0,18.0,2.0 -CH,2322.0,6,2.0,3.0,7.0,4.0,9.0,6.0,4.0,1.0,30.0,1.0 -CH,2326.0,6,2.0,6.0,5.0,5.0,7.0,6.0,3.0,2.0,17.0,2.0 -CH,2329.0,6,3.0,8.0,8.0,5.0,10.0,6.0,2.0,2.0,74.0,1.0 -CH,2330.0,6,6.0,7.0,5.0,4.0,8.0,4.0,3.0,2.0,71.0,2.0 -CH,2333.0,6,4.0,3.0,6.0,4.0,6.0,3.0,1.0,2.0,51.0,1.0 -CH,2334.0,6,2.0,4.0,4.0,5.0,8.0,6.0,3.0,2.0,37.0,1.0 -CH,2335.0,6,6.0,5.0,5.0,3.0,8.0,6.0,4.0,1.0,61.0,2.0 -CH,2338.0,6,2.0,5.0,8.0,8.0,7.0,6.0,,1.0,38.0,2.0 -CH,2343.0,6,3.0,8.0,3.0,2.0,9.0,4.0,3.0,1.0,79.0,1.0 -CH,2345.0,6,3.0,6.0,7.0,4.0,9.0,4.0,3.0,2.0,16.0,2.0 -CH,2346.0,6,4.0,5.0,7.0,7.0,8.0,7.0,3.0,2.0,27.0,2.0 -CH,2347.0,6,4.0,10.0,10.0,10.0,10.0,5.0,,1.0,43.0,1.0 -CH,2350.0,6,0.0,7.0,8.0,8.0,7.0,2.0,1.0,2.0,63.0,1.0 -CH,2352.0,6,0.0,7.0,8.0,5.0,8.0,6.0,4.0,2.0,28.0,1.0 -CH,2356.0,6,1.0,7.0,7.0,4.0,9.0,6.0,3.0,1.0,28.0,1.0 -CH,2358.0,6,3.0,8.0,10.0,7.0,10.0,7.0,4.0,2.0,60.0,1.0 -CH,2366.0,6,2.0,4.0,4.0,5.0,7.0,6.0,4.0,1.0,47.0,2.0 -CH,2376.0,6,2.0,8.0,7.0,7.0,7.0,4.0,3.0,2.0,45.0,1.0 -CH,2382.0,6,3.0,7.0,6.0,5.0,5.0,6.0,3.0,1.0,54.0,1.0 -CH,2383.0,6,6.0,7.0,8.0,5.0,9.0,3.0,2.0,1.0,76.0,1.0 -CH,2388.0,6,4.0,7.0,7.0,7.0,7.0,4.0,3.0,2.0,70.0,1.0 -CH,2392.0,6,4.0,5.0,6.0,6.0,7.0,6.0,3.0,1.0,53.0,1.0 -CH,2397.0,6,2.0,5.0,5.0,5.0,,4.0,1.0,1.0,48.0,2.0 -CH,2403.0,6,7.0,5.0,5.0,5.0,8.0,4.0,2.0,2.0,69.0,1.0 -CH,2419.0,6,0.0,2.0,4.0,4.0,8.0,4.0,4.0,2.0,94.0,2.0 -CH,2425.0,6,6.0,2.0,7.0,9.0,8.0,7.0,3.0,1.0,42.0,1.0 -CH,2427.0,6,4.0,8.0,8.0,6.0,9.0,7.0,2.0,2.0,36.0,1.0 -CH,2429.0,6,2.0,8.0,5.0,3.0,8.0,4.0,4.0,1.0,54.0,1.0 -CH,2430.0,6,1.0,2.0,8.0,3.0,8.0,6.0,3.0,2.0,42.0,1.0 -CH,2431.0,6,4.0,5.0,7.0,6.0,5.0,7.0,3.0,1.0,25.0,1.0 -CH,2433.0,6,3.0,5.0,5.0,7.0,8.0,6.0,2.0,2.0,20.0,1.0 -CH,2434.0,6,4.0,6.0,7.0,9.0,10.0,5.0,2.0,2.0,29.0,1.0 -CH,2435.0,6,2.0,2.0,3.0,0.0,8.0,6.0,2.0,1.0,30.0,2.0 -CH,2439.0,6,3.0,4.0,5.0,5.0,6.0,4.0,2.0,1.0,39.0,1.0 -CH,2448.0,6,2.0,5.0,0.0,5.0,2.0,3.0,2.0,2.0,84.0,2.0 -CH,2452.0,6,3.0,3.0,5.0,8.0,8.0,5.0,3.0,2.0,48.0,1.0 -CH,2454.0,6,4.0,8.0,8.0,6.0,9.0,5.0,1.0,2.0,30.0,1.0 -CH,2456.0,6,1.0,5.0,,7.0,5.0,5.0,4.0,2.0,20.0,2.0 -CH,2457.0,6,3.0,3.0,7.0,7.0,8.0,6.0,2.0,2.0,39.0,1.0 -CH,2459.0,6,2.0,8.0,8.0,4.0,10.0,3.0,1.0,1.0,51.0,1.0 -CH,2462.0,6,3.0,5.0,5.0,6.0,8.0,3.0,3.0,1.0,54.0,1.0 -CH,2464.0,6,3.0,8.0,8.0,8.0,10.0,4.0,3.0,2.0,24.0,1.0 -CH,2465.0,6,5.0,7.0,5.0,3.0,9.0,7.0,4.0,1.0,19.0,2.0 -CH,2466.0,6,2.0,6.0,7.0,7.0,9.0,6.0,3.0,2.0,37.0,1.0 -CH,2474.0,6,7.0,8.0,2.0,8.0,10.0,5.0,2.0,2.0,70.0,1.0 -CH,2477.0,6,2.0,2.0,4.0,4.0,5.0,3.0,3.0,2.0,41.0,1.0 -CH,2482.0,6,3.0,6.0,8.0,8.0,8.0,6.0,2.0,1.0,31.0,1.0 -CH,2485.0,6,3.0,3.0,7.0,5.0,6.0,6.0,2.0,1.0,49.0,1.0 -CH,2486.0,6,3.0,7.0,7.0,7.0,8.0,6.0,3.0,2.0,20.0,2.0 -CH,2487.0,6,1.0,6.0,3.0,8.0,6.0,4.0,4.0,1.0,52.0,1.0 -CH,2488.0,6,5.0,3.0,4.0,3.0,9.0,6.0,4.0,1.0,69.0,1.0 -CH,2489.0,6,1.0,5.0,3.0,5.0,7.0,4.0,2.0,2.0,54.0,2.0 -CH,2491.0,6,5.0,7.0,7.0,6.0,8.0,4.0,3.0,1.0,,1.0 -CH,2498.0,6,7.0,6.0,6.0,3.0,8.0,6.0,5.0,2.0,80.0,1.0 -CH,2500.0,6,1.0,5.0,5.0,5.0,8.0,6.0,3.0,1.0,34.0,2.0 -CH,2514.0,6,5.0,10.0,8.0,6.0,9.0,5.0,2.0,1.0,67.0,1.0 -CH,2515.0,6,4.0,0.0,7.0,5.0,10.0,7.0,4.0,2.0,59.0,1.0 -CH,2528.0,6,1.0,6.0,7.0,6.0,9.0,5.0,3.0,2.0,67.0,1.0 -CH,2539.0,6,4.0,7.0,5.0,8.0,8.0,4.0,4.0,1.0,53.0,2.0 -CH,2540.0,6,1.0,6.0,7.0,7.0,10.0,6.0,4.0,1.0,31.0,2.0 -CH,2545.0,6,2.0,4.0,8.0,3.0,10.0,5.0,1.0,1.0,30.0,1.0 -CH,2555.0,6,0.0,8.0,8.0,2.0,10.0,6.0,3.0,1.0,44.0,1.0 -CH,2557.0,6,2.0,7.0,5.0,4.0,9.0,6.0,3.0,1.0,22.0,2.0 -CH,2558.0,6,6.0,3.0,5.0,5.0,8.0,3.0,2.0,1.0,70.0,1.0 -CH,2560.0,6,4.0,6.0,7.0,6.0,6.0,5.0,2.0,1.0,25.0,2.0 -CH,2566.0,6,0.0,3.0,3.0,4.0,7.0,6.0,2.0,2.0,28.0,2.0 -CH,2567.0,6,3.0,2.0,5.0,2.0,6.0,4.0,2.0,2.0,29.0,1.0 -CH,2568.0,6,0.0,8.0,8.0,7.0,9.0,7.0,5.0,2.0,54.0,2.0 -CH,2569.0,6,2.0,9.0,6.0,5.0,8.0,5.0,3.0,1.0,46.0,1.0 -CH,2571.0,6,2.0,8.0,8.0,3.0,8.0,6.0,2.0,1.0,32.0,2.0 -CH,2573.0,6,3.0,8.0,10.0,9.0,9.0,5.0,3.0,1.0,59.0,1.0 -CH,2581.0,6,3.0,1.0,1.0,1.0,10.0,7.0,3.0,1.0,64.0,1.0 -CH,2582.0,6,1.0,3.0,6.0,4.0,7.0,5.0,3.0,2.0,20.0,2.0 -CH,2584.0,6,5.0,2.0,3.0,9.0,8.0,3.0,2.0,2.0,24.0,2.0 -CH,2589.0,6,4.0,5.0,5.0,5.0,9.0,6.0,5.0,2.0,48.0,1.0 -CH,2601.0,6,4.0,2.0,2.0,2.0,8.0,3.0,1.0,2.0,61.0,2.0 -CH,2620.0,6,7.0,5.0,5.0,5.0,6.0,6.0,4.0,1.0,71.0,1.0 -CH,2621.0,6,5.0,6.0,7.0,7.0,7.0,5.0,2.0,2.0,54.0,2.0 -CH,2625.0,6,2.0,6.0,9.0,5.0,8.0,7.0,3.0,2.0,16.0,2.0 -CH,2626.0,6,1.0,7.0,8.0,4.0,10.0,6.0,3.0,1.0,35.0,1.0 -CH,2627.0,6,5.0,5.0,8.0,8.0,8.0,4.0,3.0,1.0,58.0,1.0 -CH,2630.0,6,1.0,7.0,7.0,5.0,8.0,6.0,4.0,1.0,51.0,2.0 -CH,2631.0,6,6.0,5.0,8.0,5.0,6.0,6.0,2.0,1.0,69.0,1.0 -CH,2634.0,6,1.0,5.0,5.0,5.0,10.0,6.0,4.0,1.0,52.0,1.0 -CH,2639.0,6,2.0,7.0,7.0,8.0,8.0,4.0,2.0,2.0,44.0,1.0 -CH,2640.0,6,0.0,7.0,7.0,7.0,9.0,7.0,5.0,1.0,40.0,2.0 -CH,2642.0,6,6.0,8.0,8.0,5.0,8.0,7.0,2.0,1.0,57.0,1.0 -CH,2643.0,6,3.0,8.0,7.0,3.0,9.0,6.0,2.0,1.0,59.0,1.0 -CH,2644.0,6,2.0,8.0,4.0,6.0,9.0,4.0,4.0,2.0,49.0,1.0 -CH,2648.0,6,3.0,6.0,5.0,7.0,10.0,5.0,3.0,1.0,54.0,1.0 -CH,2651.0,6,1.0,7.0,10.0,10.0,10.0,7.0,4.0,2.0,57.0,1.0 -CH,2652.0,6,6.0,8.0,8.0,5.0,8.0,5.0,2.0,1.0,44.0,1.0 -CH,2666.0,6,2.0,5.0,5.0,4.0,9.0,6.0,1.0,1.0,80.0,2.0 -CH,2668.0,6,7.0,8.0,8.0,7.0,9.0,6.0,2.0,2.0,29.0,1.0 -CH,2670.0,6,5.0,5.0,5.0,5.0,8.0,5.0,3.0,2.0,72.0,2.0 -CH,2672.0,6,6.0,9.0,3.0,8.0,7.0,7.0,3.0,2.0,82.0,2.0 -CH,2674.0,6,4.0,5.0,4.0,3.0,10.0,5.0,4.0,1.0,62.0,1.0 -CH,2675.0,6,6.0,6.0,4.0,6.0,9.0,6.0,2.0,1.0,16.0,2.0 -CH,2676.0,6,3.0,4.0,7.0,6.0,5.0,6.0,2.0,2.0,55.0,2.0 -CH,2683.0,6,3.0,6.0,10.0,0.0,10.0,7.0,2.0,2.0,69.0,2.0 -CH,2684.0,6,2.0,8.0,7.0,5.0,9.0,7.0,3.0,2.0,22.0,2.0 -CH,2687.0,6,0.0,5.0,5.0,6.0,9.0,5.0,3.0,2.0,33.0,1.0 -CH,2689.0,6,2.0,7.0,5.0,6.0,6.0,5.0,3.0,2.0,83.0,2.0 -CH,2693.0,6,2.0,0.0,5.0,7.0,10.0,5.0,2.0,2.0,49.0,1.0 -CH,2699.0,6,5.0,7.0,3.0,7.0,9.0,6.0,2.0,1.0,44.0,1.0 -CH,2702.0,6,4.0,2.0,5.0,7.0,9.0,3.0,2.0,2.0,19.0,2.0 -CH,2711.0,6,3.0,5.0,5.0,5.0,10.0,6.0,3.0,1.0,81.0,1.0 -CH,2713.0,6,1.0,7.0,8.0,5.0,10.0,4.0,4.0,1.0,57.0,1.0 -CH,2714.0,6,3.0,5.0,8.0,5.0,9.0,6.0,3.0,2.0,21.0,1.0 -CH,2716.0,6,5.0,8.0,8.0,7.0,6.0,6.0,3.0,1.0,32.0,2.0 -CH,2720.0,6,6.0,2.0,6.0,6.0,8.0,4.0,3.0,2.0,,2.0 -CH,2728.0,6,6.0,6.0,7.0,4.0,7.0,6.0,4.0,2.0,49.0,1.0 -CH,2729.0,6,1.0,7.0,9.0,9.0,8.0,6.0,3.0,2.0,16.0,2.0 -CH,2732.0,6,1.0,3.0,5.0,3.0,8.0,5.0,2.0,1.0,27.0,2.0 -CH,2738.0,6,2.0,6.0,7.0,4.0,7.0,5.0,3.0,2.0,51.0,1.0 -CH,2741.0,6,7.0,8.0,9.0,6.0,10.0,5.0,,2.0,64.0,2.0 -CH,2745.0,6,3.0,7.0,6.0,7.0,8.0,4.0,3.0,2.0,35.0,1.0 -CH,2749.0,6,2.0,4.0,9.0,10.0,5.0,3.0,1.0,2.0,26.0,2.0 -CH,2752.0,6,1.0,8.0,8.0,9.0,9.0,6.0,4.0,1.0,53.0,1.0 -CH,2757.0,6,3.0,3.0,5.0,5.0,7.0,5.0,3.0,2.0,32.0,2.0 -CH,2765.0,6,2.0,6.0,5.0,5.0,9.0,4.0,2.0,1.0,60.0,1.0 -CH,2772.0,6,4.0,0.0,5.0,5.0,7.0,2.0,3.0,1.0,76.0,1.0 -CH,2773.0,6,2.0,6.0,5.0,7.0,6.0,6.0,4.0,1.0,85.0,1.0 -CH,2774.0,6,0.0,7.0,9.0,7.0,8.0,5.0,5.0,2.0,40.0,1.0 -CH,2775.0,6,7.0,3.0,5.0,1.0,5.0,1.0,1.0,1.0,50.0,1.0 -CH,2776.0,6,7.0,6.0,6.0,5.0,8.0,4.0,2.0,1.0,81.0,2.0 -CH,2777.0,6,4.0,7.0,7.0,4.0,8.0,6.0,4.0,1.0,24.0,1.0 -CH,2782.0,6,7.0,5.0,7.0,8.0,5.0,3.0,1.0,1.0,61.0,2.0 -CH,2784.0,6,1.0,7.0,8.0,9.0,9.0,4.0,1.0,2.0,37.0,1.0 -CH,2787.0,6,5.0,5.0,6.0,5.0,8.0,4.0,3.0,1.0,69.0,1.0 -CH,2788.0,6,2.0,5.0,5.0,6.0,8.0,5.0,2.0,1.0,,1.0 -CH,2793.0,6,3.0,6.0,6.0,5.0,8.0,4.0,3.0,1.0,42.0,1.0 -CH,2795.0,6,6.0,7.0,7.0,7.0,9.0,4.0,3.0,2.0,56.0,1.0 -CH,2797.0,6,3.0,5.0,8.0,7.0,8.0,4.0,3.0,2.0,70.0,1.0 -CH,2800.0,6,3.0,5.0,5.0,4.0,8.0,7.0,3.0,1.0,25.0,2.0 -CH,2803.0,6,4.0,5.0,10.0,0.0,10.0,4.0,2.0,2.0,26.0,1.0 -CH,2805.0,6,2.0,8.0,7.0,7.0,8.0,5.0,3.0,1.0,53.0,2.0 -CH,2806.0,6,6.0,8.0,5.0,8.0,10.0,2.0,2.0,1.0,52.0,1.0 -CH,2807.0,6,1.0,8.0,7.0,9.0,8.0,6.0,3.0,2.0,70.0,1.0 -CH,2809.0,6,4.0,7.0,7.0,6.0,8.0,4.0,,2.0,49.0,1.0 -CH,2811.0,6,7.0,10.0,10.0,5.0,7.0,5.0,1.0,1.0,74.0,1.0 -CH,2812.0,6,6.0,7.0,5.0,6.0,8.0,5.0,1.0,1.0,64.0,1.0 -CH,2813.0,6,6.0,3.0,2.0,1.0,8.0,6.0,4.0,1.0,63.0,2.0 -CH,2814.0,6,2.0,3.0,7.0,7.0,8.0,6.0,3.0,1.0,55.0,1.0 -CH,2817.0,6,3.0,7.0,5.0,5.0,8.0,6.0,4.0,1.0,19.0,2.0 -CH,2821.0,6,2.0,8.0,8.0,8.0,9.0,6.0,2.0,2.0,42.0,1.0 -CH,2826.0,6,0.0,5.0,5.0,2.0,5.0,6.0,3.0,2.0,45.0,1.0 -CH,2828.0,6,2.0,6.0,4.0,6.0,8.0,5.0,4.0,2.0,52.0,1.0 -CH,2835.0,6,3.0,5.0,5.0,5.0,9.0,5.0,3.0,2.0,35.0,1.0 -CH,2839.0,6,1.0,3.0,3.0,4.0,7.0,6.0,2.0,1.0,30.0,2.0 -CH,2842.0,6,5.0,8.0,8.0,7.0,8.0,4.0,3.0,1.0,68.0,1.0 -CH,2845.0,6,4.0,6.0,5.0,3.0,7.0,6.0,3.0,1.0,40.0,1.0 -CH,2847.0,6,5.0,6.0,6.0,5.0,8.0,6.0,4.0,2.0,72.0,2.0 -CH,2851.0,6,,5.0,5.0,5.0,7.0,4.0,4.0,1.0,52.0,1.0 -CH,2853.0,6,6.0,7.0,5.0,7.0,8.0,6.0,2.0,1.0,45.0,1.0 -CH,2855.0,6,5.0,5.0,9.0,7.0,10.0,3.0,2.0,2.0,22.0,2.0 -CH,2858.0,6,6.0,3.0,10.0,8.0,8.0,3.0,2.0,2.0,,1.0 -CH,2861.0,6,4.0,8.0,9.0,7.0,8.0,6.0,4.0,2.0,52.0,1.0 -CH,2872.0,6,3.0,6.0,3.0,4.0,9.0,6.0,2.0,1.0,29.0,1.0 -CH,2876.0,6,4.0,3.0,5.0,5.0,5.0,,2.0,2.0,60.0,1.0 -CH,2878.0,6,2.0,8.0,7.0,7.0,8.0,7.0,3.0,2.0,29.0,2.0 -CH,2879.0,6,3.0,1.0,3.0,2.0,9.0,4.0,3.0,1.0,44.0,1.0 -CH,2882.0,6,4.0,8.0,8.0,8.0,8.0,5.0,2.0,2.0,18.0,2.0 -CH,2884.0,6,5.0,0.0,7.0,7.0,8.0,4.0,2.0,2.0,73.0,2.0 -CH,2885.0,6,4.0,2.0,3.0,1.0,3.0,4.0,3.0,2.0,34.0,2.0 -CH,2888.0,6,2.0,7.0,8.0,7.0,8.0,6.0,4.0,2.0,69.0,1.0 -CH,2890.0,6,5.0,7.0,7.0,5.0,8.0,4.0,2.0,1.0,75.0,1.0 -CH,2893.0,6,5.0,7.0,7.0,8.0,10.0,3.0,3.0,1.0,,1.0 -CH,2894.0,6,3.0,5.0,7.0,5.0,9.0,6.0,4.0,1.0,24.0,1.0 -CH,2900.0,6,4.0,7.0,7.0,6.0,10.0,2.0,2.0,2.0,59.0,1.0 -CH,2905.0,6,7.0,5.0,4.0,7.0,7.0,6.0,3.0,2.0,67.0,1.0 -CH,5.0,7,2.0,8.0,9.0,7.0,9.0,6.0,4.0,1.0,69.0,1.0 -CH,25.0,7,4.0,4.0,2.0,5.0,6.0,5.0,3.0,1.0,30.0,1.0 -CH,26.0,7,3.0,8.0,8.0,6.0,8.0,7.0,5.0,2.0,49.0,2.0 -CH,28.0,7,2.0,7.0,7.0,5.0,9.0,6.0,2.0,1.0,51.0,1.0 -CH,29.0,7,2.0,4.0,7.0,0.0,8.0,6.0,2.0,2.0,32.0,2.0 -CH,36.0,7,4.0,5.0,6.0,4.0,9.0,7.0,3.0,2.0,31.0,2.0 -CH,40.0,7,7.0,7.0,5.0,4.0,10.0,6.0,4.0,1.0,75.0,1.0 -CH,41.0,7,7.0,5.0,5.0,5.0,10.0,6.0,3.0,1.0,30.0,1.0 -CH,51.0,7,7.0,7.0,6.0,4.0,8.0,7.0,4.0,2.0,65.0,2.0 -CH,53.0,7,4.0,6.0,4.0,5.0,8.0,6.0,3.0,1.0,67.0,1.0 -CH,55.0,7,1.0,8.0,8.0,6.0,6.0,4.0,3.0,1.0,49.0,1.0 -CH,56.0,7,6.0,10.0,2.0,0.0,8.0,6.0,3.0,1.0,43.0,1.0 -CH,57.0,7,5.0,7.0,7.0,7.0,8.0,4.0,3.0,2.0,50.0,1.0 -CH,58.0,7,5.0,8.0,8.0,5.0,9.0,5.0,2.0,1.0,85.0,2.0 -CH,68.0,7,7.0,4.0,2.0,6.0,10.0,6.0,3.0,2.0,26.0,1.0 -CH,73.0,7,4.0,6.0,6.0,6.0,8.0,4.0,2.0,1.0,60.0,1.0 -CH,81.0,7,6.0,9.0,8.0,7.0,9.0,6.0,4.0,2.0,35.0,1.0 -CH,82.0,7,5.0,6.0,8.0,5.0,8.0,7.0,5.0,1.0,29.0,1.0 -CH,84.0,7,2.0,8.0,5.0,3.0,10.0,5.0,3.0,1.0,33.0,1.0 -CH,86.0,7,1.0,7.0,5.0,8.0,9.0,7.0,2.0,1.0,17.0,2.0 -CH,87.0,7,3.0,5.0,5.0,2.0,7.0,4.0,3.0,2.0,44.0,1.0 -CH,96.0,7,2.0,6.0,6.0,6.0,9.0,6.0,3.0,2.0,35.0,1.0 -CH,98.0,7,2.0,5.0,5.0,1.0,7.0,3.0,2.0,1.0,57.0,1.0 -CH,104.0,7,1.0,8.0,7.0,7.0,9.0,5.0,2.0,1.0,47.0,1.0 -CH,108.0,7,6.0,6.0,6.0,5.0,7.0,6.0,5.0,1.0,24.0,2.0 -CH,117.0,7,3.0,7.0,7.0,6.0,8.0,6.0,3.0,2.0,32.0,1.0 -CH,118.0,7,2.0,3.0,6.0,7.0,9.0,6.0,3.0,1.0,52.0,1.0 -CH,119.0,7,6.0,8.0,3.0,7.0,7.0,2.0,2.0,1.0,69.0,1.0 -CH,121.0,7,2.0,2.0,2.0,10.0,9.0,5.0,1.0,2.0,84.0,2.0 -CH,133.0,7,4.0,6.0,6.0,7.0,7.0,7.0,2.0,1.0,63.0,1.0 -CH,135.0,7,2.0,6.0,4.0,6.0,5.0,6.0,1.0,2.0,46.0,2.0 -CH,137.0,7,7.0,5.0,6.0,5.0,8.0,6.0,3.0,1.0,19.0,2.0 -CH,148.0,7,6.0,7.0,5.0,4.0,8.0,6.0,1.0,2.0,36.0,2.0 -CH,149.0,7,3.0,8.0,9.0,8.0,9.0,5.0,3.0,2.0,29.0,2.0 -CH,161.0,7,4.0,3.0,3.0,5.0,5.0,6.0,4.0,2.0,18.0,2.0 -CH,162.0,7,1.0,5.0,5.0,4.0,7.0,6.0,2.0,2.0,53.0,1.0 -CH,168.0,7,2.0,7.0,7.0,8.0,8.0,7.0,3.0,2.0,25.0,2.0 -CH,177.0,7,3.0,5.0,5.0,7.0,10.0,6.0,3.0,2.0,30.0,1.0 -CH,179.0,7,0.0,6.0,4.0,7.0,10.0,6.0,2.0,2.0,72.0,1.0 -CH,180.0,7,3.0,6.0,6.0,2.0,9.0,7.0,4.0,1.0,36.0,2.0 -CH,185.0,7,3.0,8.0,5.0,7.0,8.0,6.0,4.0,1.0,31.0,2.0 -CH,193.0,7,7.0,4.0,6.0,5.0,8.0,6.0,1.0,2.0,19.0,2.0 -CH,200.0,7,7.0,0.0,10.0,4.0,10.0,6.0,2.0,2.0,78.0,2.0 -CH,216.0,7,2.0,8.0,8.0,4.0,8.0,4.0,2.0,1.0,49.0,1.0 -CH,218.0,7,2.0,4.0,8.0,5.0,6.0,6.0,2.0,1.0,75.0,1.0 -CH,219.0,7,7.0,5.0,10.0,10.0,10.0,7.0,5.0,2.0,89.0,2.0 -CH,220.0,7,3.0,3.0,6.0,2.0,9.0,6.0,2.0,1.0,41.0,1.0 -CH,231.0,7,4.0,7.0,8.0,8.0,8.0,7.0,4.0,2.0,55.0,2.0 -CH,232.0,7,3.0,4.0,3.0,5.0,7.0,2.0,2.0,2.0,53.0,1.0 -CH,251.0,7,7.0,4.0,5.0,0.0,8.0,7.0,3.0,1.0,33.0,2.0 -CH,257.0,7,7.0,7.0,6.0,5.0,8.0,5.0,1.0,2.0,69.0,2.0 -CH,258.0,7,5.0,4.0,7.0,3.0,10.0,5.0,3.0,2.0,65.0,1.0 -CH,260.0,7,1.0,5.0,5.0,5.0,6.0,5.0,3.0,1.0,22.0,2.0 -CH,261.0,7,0.0,9.0,8.0,8.0,8.0,7.0,4.0,2.0,18.0,2.0 -CH,270.0,7,3.0,5.0,5.0,3.0,10.0,7.0,5.0,1.0,80.0,1.0 -CH,271.0,7,6.0,0.0,5.0,4.0,7.0,4.0,3.0,1.0,67.0,1.0 -CH,273.0,7,3.0,5.0,7.0,5.0,10.0,6.0,4.0,2.0,69.0,1.0 -CH,279.0,7,2.0,5.0,5.0,4.0,9.0,5.0,3.0,1.0,56.0,1.0 -CH,295.0,7,7.0,5.0,7.0,7.0,8.0,4.0,3.0,1.0,49.0,1.0 -CH,297.0,7,1.0,5.0,4.0,5.0,10.0,7.0,5.0,1.0,71.0,2.0 -CH,303.0,7,3.0,4.0,0.0,3.0,7.0,6.0,3.0,2.0,28.0,2.0 -CH,308.0,7,0.0,8.0,8.0,7.0,9.0,7.0,3.0,1.0,25.0,2.0 -CH,318.0,7,1.0,3.0,3.0,3.0,10.0,7.0,5.0,2.0,55.0,1.0 -CH,322.0,7,0.0,7.0,8.0,5.0,9.0,7.0,3.0,1.0,20.0,2.0 -CH,323.0,7,2.0,7.0,5.0,3.0,8.0,4.0,3.0,1.0,46.0,2.0 -CH,332.0,7,3.0,1.0,5.0,6.0,6.0,3.0,1.0,2.0,27.0,1.0 -CH,333.0,7,5.0,6.0,9.0,8.0,9.0,4.0,2.0,1.0,34.0,1.0 -CH,339.0,7,4.0,8.0,8.0,7.0,10.0,5.0,3.0,1.0,43.0,1.0 -CH,345.0,7,2.0,5.0,8.0,8.0,8.0,5.0,1.0,2.0,58.0,1.0 -CH,354.0,7,2.0,8.0,8.0,7.0,7.0,6.0,3.0,2.0,55.0,2.0 -CH,357.0,7,5.0,7.0,7.0,6.0,9.0,6.0,3.0,2.0,48.0,1.0 -CH,359.0,7,5.0,4.0,7.0,7.0,7.0,6.0,3.0,1.0,23.0,2.0 -CH,362.0,7,4.0,5.0,5.0,5.0,7.0,4.0,3.0,2.0,30.0,1.0 -CH,369.0,7,6.0,7.0,7.0,6.0,9.0,7.0,4.0,1.0,41.0,1.0 -CH,380.0,7,3.0,5.0,5.0,6.0,8.0,6.0,2.0,1.0,67.0,1.0 -CH,400.0,7,5.0,5.0,5.0,6.0,8.0,4.0,3.0,1.0,72.0,1.0 -CH,402.0,7,6.0,5.0,2.0,7.0,7.0,4.0,4.0,1.0,71.0,1.0 -CH,404.0,7,2.0,9.0,9.0,6.0,9.0,5.0,2.0,2.0,39.0,1.0 -CH,405.0,7,3.0,7.0,5.0,8.0,9.0,7.0,2.0,1.0,23.0,2.0 -CH,407.0,7,1.0,7.0,8.0,6.0,9.0,6.0,4.0,1.0,22.0,2.0 -CH,412.0,7,2.0,9.0,7.0,7.0,10.0,6.0,3.0,1.0,27.0,1.0 -CH,417.0,7,6.0,7.0,8.0,7.0,9.0,6.0,4.0,2.0,22.0,2.0 -CH,420.0,7,7.0,9.0,8.0,4.0,6.0,6.0,2.0,2.0,,2.0 -CH,422.0,7,2.0,5.0,7.0,5.0,9.0,7.0,3.0,1.0,56.0,1.0 -CH,425.0,7,2.0,6.0,4.0,6.0,10.0,7.0,2.0,1.0,65.0,1.0 -CH,427.0,7,4.0,6.0,6.0,3.0,8.0,6.0,3.0,2.0,53.0,1.0 -CH,430.0,7,4.0,3.0,5.0,2.0,9.0,7.0,3.0,1.0,25.0,2.0 -CH,434.0,7,1.0,5.0,2.0,4.0,10.0,6.0,3.0,1.0,18.0,2.0 -CH,441.0,7,3.0,4.0,5.0,4.0,7.0,6.0,4.0,1.0,16.0,2.0 -CH,443.0,7,1.0,1.0,3.0,4.0,8.0,4.0,1.0,1.0,54.0,1.0 -CH,444.0,7,2.0,3.0,7.0,3.0,4.0,6.0,3.0,2.0,54.0,2.0 -CH,447.0,7,3.0,6.0,8.0,6.0,10.0,6.0,4.0,2.0,59.0,1.0 -CH,448.0,7,3.0,4.0,8.0,7.0,9.0,7.0,4.0,2.0,54.0,1.0 -CH,456.0,7,1.0,7.0,7.0,7.0,8.0,4.0,2.0,1.0,48.0,1.0 -CH,457.0,7,3.0,5.0,4.0,4.0,8.0,6.0,3.0,2.0,56.0,1.0 -CH,459.0,7,4.0,8.0,8.0,6.0,8.0,3.0,3.0,1.0,40.0,1.0 -CH,461.0,7,7.0,1.0,0.0,0.0,5.0,6.0,3.0,2.0,75.0,1.0 -CH,471.0,7,5.0,8.0,7.0,7.0,9.0,7.0,1.0,2.0,63.0,2.0 -CH,476.0,7,2.0,4.0,7.0,5.0,7.0,5.0,1.0,1.0,51.0,2.0 -CH,481.0,7,7.0,5.0,0.0,5.0,5.0,7.0,2.0,2.0,36.0,2.0 -CH,491.0,7,5.0,10.0,8.0,5.0,10.0,6.0,1.0,1.0,39.0,1.0 -CH,494.0,7,4.0,5.0,7.0,5.0,8.0,5.0,2.0,1.0,58.0,1.0 -CH,496.0,7,0.0,7.0,8.0,8.0,9.0,7.0,1.0,2.0,29.0,2.0 -CH,497.0,7,2.0,8.0,8.0,5.0,10.0,6.0,4.0,1.0,75.0,1.0 -CH,498.0,7,5.0,6.0,7.0,4.0,8.0,7.0,2.0,1.0,83.0,2.0 -CH,501.0,7,5.0,5.0,7.0,5.0,10.0,3.0,3.0,2.0,53.0,1.0 -CH,502.0,7,2.0,6.0,4.0,5.0,10.0,7.0,5.0,2.0,22.0,2.0 -CH,503.0,7,2.0,5.0,7.0,8.0,8.0,6.0,4.0,2.0,51.0,2.0 -CH,504.0,7,4.0,6.0,8.0,7.0,7.0,5.0,2.0,1.0,64.0,1.0 -CH,506.0,7,7.0,3.0,3.0,5.0,7.0,4.0,2.0,2.0,65.0,1.0 -CH,508.0,7,3.0,2.0,1.0,4.0,9.0,6.0,2.0,2.0,20.0,2.0 -CH,509.0,7,3.0,5.0,5.0,3.0,10.0,4.0,3.0,1.0,32.0,1.0 -CH,512.0,7,2.0,5.0,7.0,6.0,8.0,4.0,4.0,2.0,51.0,1.0 -CH,516.0,7,0.0,7.0,6.0,4.0,5.0,7.0,1.0,2.0,60.0,1.0 -CH,520.0,7,5.0,4.0,5.0,3.0,6.0,7.0,3.0,2.0,23.0,2.0 -CH,522.0,7,3.0,7.0,2.0,7.0,9.0,3.0,3.0,1.0,53.0,2.0 -CH,524.0,7,5.0,6.0,6.0,7.0,7.0,6.0,4.0,2.0,71.0,2.0 -CH,526.0,7,7.0,7.0,7.0,6.0,9.0,5.0,2.0,1.0,42.0,1.0 -CH,539.0,7,4.0,5.0,4.0,5.0,7.0,7.0,3.0,1.0,19.0,2.0 -CH,546.0,7,3.0,6.0,8.0,5.0,8.0,4.0,5.0,1.0,73.0,1.0 -CH,551.0,7,5.0,5.0,6.0,6.0,8.0,4.0,1.0,2.0,61.0,2.0 -CH,563.0,7,4.0,8.0,7.0,5.0,8.0,4.0,3.0,1.0,47.0,1.0 -CH,568.0,7,3.0,8.0,8.0,4.0,9.0,4.0,3.0,2.0,35.0,1.0 -CH,569.0,7,5.0,3.0,5.0,3.0,6.0,3.0,2.0,2.0,39.0,1.0 -CH,570.0,7,2.0,7.0,6.0,5.0,10.0,6.0,3.0,2.0,18.0,2.0 -CH,575.0,7,6.0,7.0,7.0,4.0,8.0,7.0,4.0,2.0,50.0,2.0 -CH,576.0,7,0.0,10.0,10.0,0.0,10.0,1.0,3.0,1.0,19.0,2.0 -CH,578.0,7,2.0,7.0,7.0,6.0,8.0,5.0,2.0,1.0,36.0,2.0 -CH,581.0,7,5.0,7.0,9.0,6.0,10.0,6.0,3.0,2.0,30.0,1.0 -CH,586.0,7,6.0,7.0,3.0,7.0,7.0,6.0,4.0,1.0,39.0,1.0 -CH,587.0,7,4.0,5.0,5.0,7.0,8.0,6.0,2.0,2.0,52.0,2.0 -CH,590.0,7,2.0,6.0,7.0,3.0,8.0,4.0,2.0,2.0,37.0,1.0 -CH,597.0,7,7.0,7.0,5.0,2.0,7.0,6.0,5.0,1.0,68.0,1.0 -CH,600.0,7,1.0,5.0,7.0,6.0,9.0,7.0,4.0,2.0,34.0,1.0 -CH,605.0,7,1.0,7.0,6.0,8.0,10.0,7.0,3.0,2.0,49.0,1.0 -CH,606.0,7,3.0,5.0,6.0,5.0,7.0,4.0,4.0,1.0,56.0,1.0 -CH,607.0,7,3.0,7.0,9.0,8.0,9.0,6.0,2.0,1.0,67.0,1.0 -CH,609.0,7,3.0,3.0,7.0,3.0,8.0,5.0,5.0,1.0,44.0,1.0 -CH,610.0,7,4.0,5.0,6.0,5.0,8.0,6.0,3.0,2.0,16.0,2.0 -CH,613.0,7,4.0,6.0,7.0,5.0,10.0,5.0,3.0,1.0,32.0,1.0 -CH,618.0,7,1.0,6.0,6.0,6.0,9.0,4.0,5.0,1.0,18.0,2.0 -CH,625.0,7,5.0,0.0,0.0,3.0,9.0,6.0,2.0,1.0,33.0,2.0 -CH,630.0,7,7.0,8.0,7.0,7.0,8.0,5.0,3.0,2.0,39.0,2.0 -CH,633.0,7,3.0,4.0,8.0,8.0,9.0,5.0,3.0,2.0,78.0,1.0 -CH,637.0,7,7.0,1.0,1.0,1.0,10.0,5.0,3.0,1.0,56.0,2.0 -CH,643.0,7,0.0,8.0,7.0,8.0,8.0,5.0,3.0,2.0,37.0,2.0 -CH,646.0,7,3.0,5.0,6.0,5.0,6.0,4.0,2.0,2.0,39.0,1.0 -CH,648.0,7,0.0,7.0,7.0,8.0,8.0,5.0,3.0,1.0,34.0,2.0 -CH,655.0,7,1.0,8.0,6.0,6.0,9.0,6.0,3.0,2.0,21.0,2.0 -CH,658.0,7,1.0,3.0,5.0,5.0,9.0,5.0,2.0,1.0,19.0,2.0 -CH,660.0,7,0.0,5.0,8.0,5.0,8.0,7.0,3.0,1.0,27.0,2.0 -CH,663.0,7,3.0,5.0,8.0,6.0,9.0,6.0,3.0,2.0,31.0,1.0 -CH,666.0,7,2.0,0.0,0.0,8.0,4.0,5.0,3.0,2.0,34.0,1.0 -CH,675.0,7,6.0,8.0,5.0,3.0,8.0,6.0,3.0,1.0,35.0,2.0 -CH,676.0,7,5.0,8.0,4.0,7.0,9.0,5.0,3.0,1.0,79.0,1.0 -CH,680.0,7,6.0,4.0,6.0,3.0,9.0,6.0,3.0,1.0,21.0,2.0 -CH,684.0,7,2.0,7.0,7.0,5.0,8.0,6.0,3.0,1.0,39.0,1.0 -CH,685.0,7,1.0,7.0,7.0,6.0,10.0,6.0,2.0,1.0,18.0,2.0 -CH,686.0,7,7.0,5.0,5.0,5.0,9.0,5.0,3.0,1.0,44.0,1.0 -CH,690.0,7,5.0,5.0,8.0,7.0,7.0,6.0,3.0,2.0,32.0,1.0 -CH,692.0,7,7.0,5.0,7.0,5.0,8.0,5.0,4.0,2.0,59.0,1.0 -CH,693.0,7,3.0,7.0,7.0,4.0,9.0,6.0,3.0,2.0,39.0,1.0 -CH,696.0,7,7.0,5.0,5.0,7.0,8.0,5.0,1.0,1.0,,1.0 -CH,697.0,7,2.0,3.0,5.0,4.0,10.0,5.0,2.0,1.0,32.0,1.0 -CH,698.0,7,7.0,5.0,5.0,3.0,8.0,5.0,2.0,1.0,63.0,1.0 -CH,701.0,7,4.0,5.0,8.0,6.0,9.0,4.0,2.0,1.0,57.0,1.0 -CH,703.0,7,3.0,9.0,6.0,7.0,8.0,2.0,5.0,2.0,63.0,1.0 -CH,706.0,7,0.0,6.0,6.0,6.0,10.0,5.0,3.0,2.0,60.0,1.0 -CH,707.0,7,4.0,6.0,6.0,4.0,6.0,3.0,3.0,2.0,49.0,1.0 -CH,708.0,7,2.0,4.0,8.0,7.0,10.0,7.0,4.0,2.0,71.0,1.0 -CH,709.0,7,6.0,4.0,4.0,4.0,8.0,6.0,3.0,2.0,33.0,1.0 -CH,711.0,7,4.0,6.0,6.0,3.0,8.0,3.0,2.0,1.0,67.0,1.0 -CH,713.0,7,0.0,7.0,7.0,4.0,7.0,4.0,1.0,2.0,21.0,2.0 -CH,714.0,7,4.0,5.0,5.0,5.0,6.0,5.0,3.0,2.0,33.0,1.0 -CH,715.0,7,4.0,6.0,6.0,6.0,9.0,4.0,2.0,2.0,72.0,1.0 -CH,716.0,7,2.0,3.0,3.0,3.0,8.0,4.0,3.0,2.0,44.0,1.0 -CH,717.0,7,3.0,6.0,6.0,5.0,9.0,6.0,4.0,1.0,21.0,2.0 -CH,720.0,7,7.0,7.0,7.0,6.0,8.0,6.0,3.0,2.0,42.0,1.0 -CH,722.0,7,6.0,7.0,6.0,6.0,7.0,4.0,4.0,1.0,,1.0 -CH,724.0,7,0.0,4.0,4.0,4.0,8.0,4.0,3.0,1.0,17.0,2.0 -CH,728.0,7,3.0,5.0,6.0,6.0,8.0,4.0,3.0,2.0,28.0,1.0 -CH,729.0,7,5.0,7.0,7.0,6.0,8.0,7.0,3.0,2.0,60.0,1.0 -CH,730.0,7,5.0,5.0,5.0,5.0,8.0,6.0,3.0,2.0,72.0,2.0 -CH,733.0,7,2.0,5.0,3.0,3.0,9.0,3.0,3.0,1.0,59.0,1.0 -CH,739.0,7,2.0,8.0,8.0,5.0,10.0,6.0,3.0,1.0,35.0,1.0 -CH,740.0,7,3.0,7.0,8.0,7.0,7.0,4.0,3.0,1.0,46.0,1.0 -CH,741.0,7,2.0,8.0,7.0,7.0,8.0,6.0,3.0,1.0,38.0,2.0 -CH,743.0,7,0.0,9.0,9.0,6.0,9.0,4.0,2.0,1.0,,1.0 -CH,747.0,7,2.0,7.0,5.0,5.0,8.0,5.0,2.0,1.0,70.0,2.0 -CH,748.0,7,2.0,4.0,6.0,6.0,7.0,6.0,3.0,2.0,25.0,1.0 -CH,757.0,7,2.0,5.0,5.0,7.0,7.0,6.0,2.0,2.0,20.0,2.0 -CH,759.0,7,4.0,7.0,5.0,7.0,7.0,6.0,3.0,2.0,19.0,2.0 -CH,760.0,7,4.0,5.0,5.0,2.0,7.0,6.0,3.0,2.0,52.0,2.0 -CH,761.0,7,6.0,8.0,6.0,5.0,9.0,4.0,3.0,1.0,63.0,1.0 -CH,762.0,7,1.0,8.0,8.0,2.0,8.0,3.0,1.0,2.0,62.0,1.0 -CH,764.0,7,5.0,5.0,8.0,5.0,8.0,5.0,3.0,1.0,47.0,1.0 -CH,770.0,7,2.0,8.0,3.0,9.0,10.0,6.0,3.0,2.0,30.0,1.0 -CH,771.0,7,2.0,3.0,5.0,5.0,7.0,4.0,3.0,1.0,78.0,1.0 -CH,774.0,7,3.0,5.0,8.0,8.0,8.0,6.0,3.0,2.0,64.0,1.0 -CH,778.0,7,2.0,3.0,4.0,7.0,10.0,6.0,3.0,1.0,69.0,1.0 -CH,779.0,7,5.0,7.0,9.0,4.0,7.0,5.0,2.0,1.0,33.0,2.0 -CH,781.0,7,3.0,3.0,8.0,4.0,10.0,6.0,4.0,2.0,32.0,1.0 -CH,788.0,7,4.0,6.0,5.0,6.0,8.0,6.0,1.0,2.0,73.0,1.0 -CH,789.0,7,1.0,8.0,8.0,3.0,10.0,4.0,4.0,1.0,,1.0 -CH,790.0,7,2.0,7.0,7.0,6.0,8.0,5.0,3.0,1.0,49.0,1.0 -CH,793.0,7,5.0,4.0,8.0,8.0,7.0,4.0,3.0,2.0,45.0,1.0 -CH,796.0,7,7.0,5.0,7.0,3.0,5.0,5.0,2.0,2.0,50.0,2.0 -CH,797.0,7,3.0,5.0,5.0,6.0,7.0,2.0,2.0,2.0,64.0,2.0 -CH,800.0,7,3.0,6.0,6.0,7.0,7.0,5.0,3.0,2.0,45.0,1.0 -CH,801.0,7,1.0,8.0,5.0,4.0,10.0,3.0,2.0,1.0,62.0,1.0 -CH,803.0,7,2.0,8.0,7.0,5.0,8.0,7.0,2.0,1.0,55.0,2.0 -CH,809.0,7,2.0,7.0,7.0,8.0,5.0,4.0,2.0,2.0,56.0,2.0 -CH,810.0,7,1.0,4.0,2.0,6.0,9.0,6.0,3.0,2.0,28.0,1.0 -CH,814.0,7,2.0,5.0,5.0,8.0,9.0,5.0,2.0,1.0,38.0,1.0 -CH,817.0,7,0.0,5.0,4.0,4.0,8.0,5.0,2.0,1.0,35.0,2.0 -CH,819.0,7,1.0,6.0,7.0,4.0,9.0,4.0,3.0,1.0,43.0,1.0 -CH,821.0,7,3.0,5.0,8.0,9.0,8.0,4.0,3.0,1.0,89.0,2.0 -CH,822.0,7,1.0,5.0,7.0,4.0,9.0,6.0,2.0,2.0,55.0,1.0 -CH,824.0,7,3.0,7.0,2.0,10.0,10.0,6.0,3.0,1.0,19.0,2.0 -CH,827.0,7,3.0,5.0,5.0,7.0,9.0,5.0,3.0,1.0,74.0,1.0 -CH,834.0,7,1.0,8.0,8.0,7.0,8.0,6.0,3.0,1.0,48.0,1.0 -CH,838.0,7,5.0,2.0,3.0,3.0,8.0,4.0,2.0,1.0,49.0,1.0 -CH,842.0,7,1.0,2.0,8.0,5.0,6.0,6.0,1.0,1.0,41.0,2.0 -CH,844.0,7,2.0,1.0,5.0,4.0,9.0,4.0,1.0,2.0,21.0,2.0 -CH,847.0,7,3.0,6.0,5.0,10.0,8.0,5.0,3.0,2.0,82.0,2.0 -CH,850.0,7,0.0,6.0,8.0,8.0,9.0,6.0,3.0,2.0,35.0,1.0 -CH,852.0,7,5.0,10.0,5.0,3.0,10.0,7.0,5.0,1.0,42.0,1.0 -CH,857.0,7,4.0,5.0,7.0,7.0,10.0,7.0,3.0,1.0,18.0,2.0 -CH,858.0,7,1.0,6.0,7.0,3.0,9.0,5.0,2.0,2.0,23.0,1.0 -CH,860.0,7,3.0,0.0,0.0,3.0,9.0,5.0,4.0,2.0,43.0,1.0 -CH,861.0,7,2.0,0.0,10.0,5.0,10.0,4.0,3.0,2.0,70.0,1.0 -CH,862.0,7,1.0,7.0,8.0,5.0,9.0,4.0,3.0,1.0,76.0,2.0 -CH,875.0,7,0.0,5.0,8.0,7.0,8.0,6.0,2.0,2.0,38.0,1.0 -CH,877.0,7,0.0,8.0,6.0,6.0,9.0,6.0,3.0,1.0,31.0,1.0 -CH,880.0,7,2.0,5.0,8.0,8.0,9.0,6.0,3.0,2.0,62.0,1.0 -CH,881.0,7,1.0,3.0,7.0,7.0,9.0,6.0,3.0,2.0,31.0,1.0 -CH,883.0,7,3.0,7.0,9.0,7.0,9.0,6.0,4.0,2.0,36.0,1.0 -CH,893.0,7,3.0,4.0,8.0,5.0,8.0,4.0,3.0,2.0,56.0,1.0 -CH,898.0,7,5.0,8.0,8.0,6.0,5.0,4.0,2.0,1.0,39.0,2.0 -CH,900.0,7,4.0,5.0,7.0,8.0,9.0,4.0,2.0,2.0,65.0,1.0 -CH,903.0,7,1.0,8.0,8.0,7.0,9.0,6.0,3.0,1.0,46.0,2.0 -CH,908.0,7,2.0,6.0,7.0,8.0,8.0,5.0,2.0,2.0,18.0,2.0 -CH,910.0,7,0.0,7.0,8.0,7.0,8.0,6.0,3.0,2.0,51.0,1.0 -CH,911.0,7,7.0,2.0,8.0,7.0,8.0,5.0,3.0,2.0,79.0,2.0 -CH,912.0,7,6.0,6.0,6.0,7.0,7.0,6.0,3.0,1.0,45.0,2.0 -CH,920.0,7,5.0,7.0,3.0,8.0,8.0,6.0,2.0,1.0,35.0,2.0 -CH,921.0,7,2.0,8.0,8.0,7.0,9.0,4.0,2.0,1.0,55.0,1.0 -CH,923.0,7,5.0,7.0,6.0,5.0,9.0,7.0,3.0,1.0,51.0,1.0 -CH,924.0,7,0.0,2.0,5.0,3.0,9.0,5.0,4.0,1.0,35.0,2.0 -CH,927.0,7,5.0,5.0,7.0,3.0,7.0,4.0,4.0,1.0,53.0,1.0 -CH,931.0,7,3.0,9.0,9.0,6.0,10.0,6.0,3.0,2.0,60.0,1.0 -CH,934.0,7,4.0,10.0,8.0,8.0,9.0,6.0,3.0,1.0,22.0,2.0 -CH,937.0,7,3.0,5.0,6.0,6.0,10.0,7.0,3.0,2.0,31.0,1.0 -CH,939.0,7,6.0,7.0,8.0,6.0,7.0,5.0,2.0,1.0,35.0,1.0 -CH,941.0,7,5.0,8.0,9.0,6.0,9.0,6.0,4.0,1.0,72.0,1.0 -CH,944.0,7,2.0,5.0,8.0,8.0,10.0,6.0,3.0,2.0,71.0,2.0 -CH,947.0,7,6.0,5.0,8.0,8.0,9.0,4.0,2.0,1.0,75.0,1.0 -CH,951.0,7,2.0,8.0,8.0,7.0,7.0,4.0,3.0,2.0,60.0,1.0 -CH,953.0,7,4.0,5.0,5.0,3.0,10.0,4.0,2.0,1.0,70.0,1.0 -CH,955.0,7,7.0,8.0,7.0,5.0,9.0,4.0,4.0,1.0,75.0,1.0 -CH,961.0,7,3.0,7.0,8.0,5.0,8.0,3.0,3.0,1.0,48.0,1.0 -CH,962.0,7,4.0,2.0,5.0,8.0,8.0,4.0,2.0,1.0,62.0,1.0 -CH,963.0,7,4.0,7.0,5.0,7.0,9.0,4.0,3.0,1.0,44.0,1.0 -CH,966.0,7,0.0,4.0,4.0,5.0,5.0,3.0,1.0,1.0,44.0,1.0 -CH,967.0,7,2.0,8.0,8.0,5.0,10.0,4.0,3.0,2.0,37.0,2.0 -CH,976.0,7,2.0,7.0,7.0,6.0,9.0,4.0,3.0,2.0,45.0,1.0 -CH,979.0,7,7.0,10.0,10.0,10.0,5.0,6.0,3.0,2.0,65.0,2.0 -CH,980.0,7,0.0,8.0,7.0,6.0,8.0,6.0,2.0,2.0,48.0,1.0 -CH,987.0,7,3.0,8.0,8.0,8.0,8.0,6.0,2.0,2.0,38.0,1.0 -CH,990.0,7,5.0,2.0,1.0,2.0,4.0,3.0,4.0,2.0,82.0,1.0 -CH,1001.0,7,3.0,7.0,7.0,7.0,9.0,6.0,3.0,2.0,53.0,1.0 -CH,1004.0,7,0.0,10.0,7.0,3.0,10.0,5.0,2.0,2.0,61.0,1.0 -CH,1009.0,7,2.0,8.0,9.0,10.0,10.0,7.0,3.0,2.0,29.0,1.0 -CH,1016.0,7,0.0,6.0,8.0,8.0,10.0,3.0,2.0,2.0,75.0,1.0 -CH,1017.0,7,0.0,5.0,6.0,7.0,7.0,5.0,3.0,2.0,38.0,1.0 -CH,1027.0,7,3.0,5.0,8.0,5.0,8.0,4.0,3.0,2.0,62.0,1.0 -CH,1029.0,7,3.0,6.0,7.0,7.0,9.0,6.0,3.0,1.0,16.0,2.0 -CH,1031.0,7,1.0,2.0,4.0,4.0,5.0,6.0,1.0,2.0,27.0,2.0 -CH,1033.0,7,0.0,7.0,5.0,6.0,8.0,4.0,3.0,2.0,51.0,1.0 -CH,1035.0,7,2.0,9.0,3.0,5.0,7.0,7.0,3.0,1.0,59.0,1.0 -CH,1036.0,7,3.0,5.0,4.0,3.0,4.0,2.0,2.0,1.0,49.0,1.0 -CH,1037.0,7,3.0,7.0,7.0,5.0,10.0,6.0,2.0,1.0,41.0,1.0 -CH,1041.0,7,7.0,5.0,6.0,7.0,6.0,4.0,2.0,1.0,,2.0 -CH,1043.0,7,3.0,6.0,6.0,5.0,8.0,4.0,2.0,1.0,48.0,1.0 -CH,1044.0,7,1.0,7.0,6.0,7.0,8.0,6.0,2.0,1.0,22.0,1.0 -CH,1046.0,7,6.0,8.0,7.0,6.0,9.0,2.0,2.0,1.0,68.0,1.0 -CH,1049.0,7,6.0,8.0,7.0,9.0,8.0,6.0,4.0,1.0,67.0,1.0 -CH,1050.0,7,3.0,5.0,4.0,4.0,7.0,5.0,3.0,1.0,57.0,1.0 -CH,1051.0,7,4.0,5.0,6.0,3.0,8.0,4.0,2.0,1.0,40.0,1.0 -CH,1052.0,7,3.0,5.0,3.0,3.0,7.0,6.0,5.0,2.0,,2.0 -CH,1060.0,7,4.0,8.0,7.0,5.0,8.0,6.0,3.0,2.0,39.0,1.0 -CH,1061.0,7,7.0,4.0,8.0,9.0,8.0,5.0,1.0,2.0,84.0,1.0 -CH,1062.0,7,3.0,5.0,8.0,6.0,8.0,6.0,4.0,1.0,,1.0 -CH,1063.0,7,7.0,2.0,4.0,7.0,9.0,6.0,3.0,1.0,21.0,2.0 -CH,1066.0,7,1.0,7.0,6.0,5.0,8.0,6.0,3.0,1.0,39.0,2.0 -CH,1067.0,7,0.0,6.0,8.0,8.0,8.0,6.0,3.0,2.0,55.0,1.0 -CH,1072.0,7,0.0,7.0,9.0,7.0,9.0,4.0,4.0,2.0,50.0,2.0 -CH,1076.0,7,5.0,6.0,6.0,8.0,7.0,4.0,3.0,1.0,54.0,1.0 -CH,1083.0,7,2.0,5.0,5.0,5.0,8.0,4.0,2.0,2.0,38.0,1.0 -CH,1086.0,7,1.0,9.0,9.0,6.0,8.0,7.0,4.0,2.0,61.0,1.0 -CH,1088.0,7,4.0,4.0,3.0,4.0,5.0,4.0,4.0,1.0,39.0,1.0 -CH,1090.0,7,2.0,8.0,8.0,7.0,8.0,7.0,3.0,1.0,34.0,2.0 -CH,1096.0,7,2.0,8.0,7.0,6.0,8.0,6.0,2.0,1.0,17.0,2.0 -CH,1106.0,7,5.0,7.0,7.0,8.0,6.0,4.0,3.0,2.0,43.0,1.0 -CH,1107.0,7,2.0,3.0,5.0,3.0,7.0,4.0,2.0,1.0,27.0,2.0 -CH,1112.0,7,5.0,0.0,10.0,7.0,9.0,5.0,3.0,2.0,,2.0 -CH,1117.0,7,4.0,9.0,6.0,6.0,8.0,6.0,4.0,1.0,70.0,1.0 -CH,1118.0,7,7.0,4.0,7.0,4.0,9.0,5.0,4.0,1.0,69.0,1.0 -CH,1127.0,7,1.0,6.0,8.0,5.0,9.0,6.0,3.0,1.0,25.0,2.0 -CH,1133.0,7,2.0,5.0,9.0,6.0,9.0,3.0,2.0,2.0,17.0,2.0 -CH,1134.0,7,6.0,7.0,7.0,5.0,10.0,6.0,2.0,2.0,64.0,2.0 -CH,1135.0,7,4.0,6.0,6.0,5.0,8.0,6.0,3.0,2.0,41.0,1.0 -CH,1137.0,7,2.0,7.0,7.0,5.0,8.0,6.0,5.0,1.0,79.0,2.0 -CH,1138.0,7,1.0,8.0,8.0,8.0,9.0,7.0,4.0,1.0,60.0,1.0 -CH,1141.0,7,3.0,5.0,7.0,6.0,9.0,3.0,2.0,2.0,57.0,1.0 -CH,1144.0,7,4.0,5.0,7.0,7.0,9.0,3.0,5.0,2.0,56.0,1.0 -CH,1148.0,7,3.0,8.0,8.0,8.0,9.0,5.0,4.0,1.0,49.0,1.0 -CH,1152.0,7,1.0,5.0,7.0,7.0,9.0,6.0,4.0,2.0,20.0,2.0 -CH,1154.0,7,2.0,10.0,10.0,10.0,9.0,4.0,3.0,2.0,68.0,1.0 -CH,1159.0,7,1.0,7.0,9.0,3.0,10.0,7.0,4.0,1.0,22.0,2.0 -CH,1164.0,7,7.0,6.0,6.0,1.0,9.0,6.0,4.0,1.0,68.0,1.0 -CH,1168.0,7,1.0,2.0,6.0,3.0,8.0,4.0,2.0,2.0,21.0,2.0 -CH,1173.0,7,3.0,7.0,8.0,7.0,9.0,6.0,4.0,1.0,16.0,2.0 -CH,1174.0,7,2.0,5.0,3.0,6.0,7.0,3.0,1.0,2.0,34.0,1.0 -CH,1182.0,7,1.0,4.0,7.0,5.0,8.0,6.0,4.0,2.0,17.0,2.0 -CH,1183.0,7,2.0,5.0,5.0,7.0,8.0,5.0,2.0,1.0,64.0,2.0 -CH,1188.0,7,1.0,7.0,8.0,8.0,10.0,7.0,5.0,2.0,68.0,2.0 -CH,1189.0,7,1.0,6.0,4.0,3.0,8.0,7.0,3.0,2.0,54.0,2.0 -CH,1194.0,7,1.0,7.0,4.0,5.0,9.0,6.0,3.0,1.0,43.0,1.0 -CH,1198.0,7,5.0,2.0,3.0,2.0,9.0,6.0,5.0,1.0,30.0,2.0 -CH,1201.0,7,7.0,6.0,7.0,7.0,6.0,4.0,2.0,1.0,78.0,1.0 -CH,1208.0,7,2.0,7.0,6.0,4.0,10.0,7.0,2.0,1.0,52.0,1.0 -CH,1209.0,7,5.0,6.0,7.0,7.0,8.0,4.0,3.0,2.0,18.0,2.0 -CH,1217.0,7,5.0,5.0,9.0,7.0,8.0,7.0,3.0,1.0,17.0,2.0 -CH,1222.0,7,3.0,5.0,8.0,2.0,8.0,6.0,4.0,2.0,39.0,1.0 -CH,1223.0,7,1.0,0.0,8.0,5.0,7.0,4.0,1.0,2.0,21.0,2.0 -CH,1229.0,7,4.0,3.0,5.0,3.0,6.0,2.0,1.0,2.0,50.0,2.0 -CH,1230.0,7,3.0,4.0,7.0,3.0,9.0,6.0,3.0,1.0,33.0,1.0 -CH,1233.0,7,4.0,1.0,1.0,2.0,10.0,4.0,2.0,2.0,41.0,1.0 -CH,1236.0,7,3.0,7.0,8.0,6.0,8.0,6.0,2.0,2.0,,2.0 -CH,1237.0,7,2.0,9.0,8.0,6.0,10.0,6.0,4.0,1.0,36.0,1.0 -CH,1240.0,7,1.0,7.0,8.0,8.0,9.0,2.0,1.0,1.0,31.0,1.0 -CH,1242.0,7,5.0,6.0,7.0,7.0,8.0,5.0,2.0,2.0,44.0,1.0 -CH,1245.0,7,6.0,2.0,3.0,3.0,6.0,6.0,3.0,1.0,,1.0 -CH,1247.0,7,2.0,4.0,7.0,4.0,7.0,5.0,2.0,1.0,23.0,2.0 -CH,1254.0,7,1.0,7.0,8.0,9.0,7.0,6.0,4.0,2.0,82.0,2.0 -CH,1264.0,7,1.0,7.0,8.0,9.0,9.0,6.0,2.0,2.0,23.0,2.0 -CH,1265.0,7,5.0,4.0,5.0,6.0,9.0,5.0,3.0,2.0,39.0,1.0 -CH,1266.0,7,3.0,7.0,6.0,6.0,9.0,3.0,2.0,1.0,,1.0 -CH,1269.0,7,3.0,6.0,8.0,7.0,9.0,6.0,5.0,2.0,47.0,2.0 -CH,1278.0,7,5.0,8.0,8.0,8.0,6.0,4.0,4.0,2.0,65.0,1.0 -CH,1282.0,7,0.0,5.0,8.0,5.0,7.0,5.0,3.0,1.0,69.0,2.0 -CH,1288.0,7,2.0,5.0,7.0,5.0,10.0,6.0,3.0,2.0,85.0,1.0 -CH,1290.0,7,0.0,7.0,8.0,7.0,8.0,6.0,3.0,2.0,25.0,2.0 -CH,1293.0,7,3.0,5.0,7.0,7.0,9.0,5.0,2.0,2.0,52.0,1.0 -CH,1303.0,7,2.0,4.0,6.0,4.0,9.0,6.0,4.0,1.0,24.0,2.0 -CH,1306.0,7,5.0,8.0,9.0,9.0,9.0,4.0,3.0,2.0,68.0,1.0 -CH,1310.0,7,2.0,7.0,8.0,4.0,8.0,6.0,1.0,2.0,31.0,2.0 -CH,1311.0,7,6.0,6.0,10.0,7.0,9.0,6.0,2.0,2.0,83.0,2.0 -CH,1312.0,7,3.0,3.0,5.0,5.0,6.0,6.0,3.0,1.0,40.0,1.0 -CH,1319.0,7,0.0,0.0,4.0,4.0,8.0,3.0,2.0,2.0,25.0,1.0 -CH,1322.0,7,1.0,5.0,4.0,6.0,6.0,4.0,2.0,1.0,16.0,2.0 -CH,1328.0,7,3.0,5.0,5.0,4.0,7.0,2.0,1.0,2.0,73.0,2.0 -CH,1333.0,7,2.0,5.0,5.0,4.0,9.0,7.0,3.0,2.0,48.0,1.0 -CH,1347.0,7,5.0,6.0,4.0,4.0,7.0,4.0,3.0,2.0,73.0,1.0 -CH,1349.0,7,3.0,5.0,8.0,5.0,8.0,6.0,4.0,2.0,52.0,1.0 -CH,1353.0,7,4.0,5.0,7.0,7.0,8.0,4.0,1.0,2.0,20.0,2.0 -CH,1356.0,7,2.0,9.0,9.0,7.0,8.0,4.0,2.0,2.0,18.0,2.0 -CH,1361.0,7,2.0,5.0,7.0,8.0,9.0,3.0,2.0,2.0,18.0,2.0 -CH,1362.0,7,5.0,8.0,10.0,6.0,8.0,4.0,4.0,1.0,89.0,1.0 -CH,1367.0,7,2.0,5.0,8.0,8.0,7.0,5.0,1.0,1.0,52.0,1.0 -CH,1369.0,7,6.0,9.0,8.0,7.0,8.0,5.0,3.0,2.0,27.0,2.0 -CH,1370.0,7,1.0,6.0,5.0,8.0,10.0,4.0,3.0,2.0,34.0,2.0 -CH,1371.0,7,2.0,9.0,0.0,9.0,8.0,5.0,1.0,2.0,70.0,1.0 -CH,1375.0,7,5.0,8.0,10.0,9.0,10.0,3.0,3.0,2.0,81.0,1.0 -CH,1378.0,7,4.0,3.0,6.0,8.0,8.0,5.0,3.0,2.0,49.0,1.0 -CH,1379.0,7,3.0,8.0,5.0,4.0,9.0,5.0,4.0,1.0,46.0,1.0 -CH,1382.0,7,3.0,5.0,6.0,4.0,8.0,6.0,4.0,2.0,33.0,1.0 -CH,1390.0,7,2.0,2.0,2.0,0.0,7.0,2.0,1.0,2.0,31.0,1.0 -CH,1402.0,7,0.0,9.0,8.0,5.0,10.0,6.0,2.0,2.0,34.0,1.0 -CH,1404.0,7,1.0,8.0,8.0,8.0,8.0,6.0,4.0,2.0,59.0,1.0 -CH,1405.0,7,3.0,5.0,6.0,4.0,9.0,5.0,1.0,2.0,63.0,1.0 -CH,1409.0,7,2.0,7.0,7.0,7.0,7.0,4.0,2.0,1.0,49.0,1.0 -CH,1410.0,7,3.0,5.0,6.0,6.0,6.0,6.0,3.0,2.0,36.0,1.0 -CH,1418.0,7,7.0,5.0,8.0,6.0,8.0,2.0,2.0,1.0,73.0,1.0 -CH,1431.0,7,2.0,5.0,5.0,7.0,10.0,6.0,3.0,2.0,42.0,1.0 -CH,1432.0,7,4.0,3.0,3.0,7.0,8.0,6.0,3.0,2.0,64.0,1.0 -CH,1436.0,7,3.0,3.0,7.0,0.0,9.0,6.0,4.0,1.0,25.0,2.0 -CH,1439.0,7,3.0,1.0,3.0,3.0,7.0,6.0,4.0,1.0,30.0,2.0 -CH,1447.0,7,5.0,9.0,6.0,7.0,9.0,5.0,2.0,1.0,28.0,2.0 -CH,1448.0,7,2.0,6.0,6.0,7.0,8.0,4.0,2.0,2.0,21.0,2.0 -CH,1449.0,7,3.0,5.0,3.0,5.0,4.0,5.0,1.0,1.0,42.0,1.0 -CH,1450.0,7,7.0,4.0,5.0,2.0,8.0,2.0,2.0,1.0,45.0,1.0 -CH,1451.0,7,4.0,5.0,7.0,7.0,9.0,7.0,3.0,2.0,23.0,2.0 -CH,1453.0,7,2.0,7.0,8.0,8.0,9.0,6.0,4.0,2.0,,1.0 -CH,1454.0,7,0.0,8.0,8.0,8.0,9.0,6.0,4.0,1.0,47.0,1.0 -CH,1457.0,7,3.0,4.0,2.0,5.0,7.0,3.0,2.0,1.0,86.0,1.0 -CH,1462.0,7,5.0,10.0,5.0,5.0,8.0,6.0,3.0,1.0,,1.0 -CH,1463.0,7,4.0,4.0,5.0,3.0,9.0,5.0,4.0,2.0,53.0,1.0 -CH,1468.0,7,2.0,7.0,8.0,9.0,10.0,6.0,3.0,2.0,18.0,2.0 -CH,1471.0,7,3.0,7.0,8.0,6.0,9.0,5.0,3.0,1.0,40.0,1.0 -CH,1477.0,7,5.0,5.0,1.0,10.0,8.0,2.0,2.0,2.0,51.0,1.0 -CH,1478.0,7,2.0,9.0,7.0,6.0,10.0,5.0,3.0,1.0,51.0,1.0 -CH,1484.0,7,3.0,7.0,7.0,8.0,10.0,4.0,3.0,1.0,84.0,1.0 -CH,1490.0,7,5.0,5.0,8.0,9.0,8.0,4.0,2.0,1.0,33.0,1.0 -CH,1495.0,7,3.0,6.0,8.0,7.0,6.0,4.0,3.0,1.0,55.0,1.0 -CH,1502.0,7,2.0,2.0,5.0,3.0,8.0,7.0,2.0,1.0,18.0,2.0 -CH,1504.0,7,0.0,0.0,8.0,5.0,8.0,6.0,3.0,2.0,40.0,1.0 -CH,1505.0,7,0.0,4.0,2.0,5.0,10.0,6.0,3.0,2.0,38.0,1.0 -CH,1507.0,7,2.0,5.0,7.0,4.0,10.0,6.0,3.0,2.0,70.0,1.0 -CH,1508.0,7,2.0,8.0,8.0,7.0,8.0,6.0,3.0,1.0,51.0,1.0 -CH,1510.0,7,3.0,5.0,7.0,5.0,8.0,5.0,2.0,2.0,36.0,1.0 -CH,1513.0,7,0.0,5.0,7.0,7.0,9.0,6.0,3.0,2.0,52.0,2.0 -CH,1527.0,7,2.0,6.0,4.0,3.0,9.0,7.0,4.0,1.0,36.0,1.0 -CH,1530.0,7,3.0,6.0,7.0,5.0,9.0,6.0,4.0,1.0,73.0,1.0 -CH,1535.0,7,3.0,5.0,5.0,5.0,10.0,4.0,4.0,1.0,46.0,1.0 -CH,1536.0,7,7.0,5.0,7.0,8.0,8.0,6.0,2.0,1.0,69.0,1.0 -CH,1542.0,7,2.0,4.0,8.0,8.0,7.0,6.0,4.0,1.0,48.0,1.0 -CH,1547.0,7,1.0,5.0,7.0,7.0,9.0,5.0,2.0,1.0,18.0,2.0 -CH,1553.0,7,2.0,7.0,5.0,4.0,9.0,6.0,3.0,2.0,19.0,2.0 -CH,1554.0,7,2.0,4.0,4.0,7.0,7.0,5.0,3.0,2.0,43.0,1.0 -CH,1562.0,7,0.0,5.0,8.0,7.0,7.0,5.0,2.0,1.0,55.0,1.0 -CH,1573.0,7,3.0,3.0,4.0,3.0,9.0,6.0,3.0,2.0,34.0,1.0 -CH,1577.0,7,5.0,5.0,5.0,8.0,8.0,7.0,3.0,2.0,53.0,2.0 -CH,1579.0,7,4.0,7.0,7.0,8.0,9.0,6.0,3.0,2.0,56.0,1.0 -CH,1583.0,7,1.0,4.0,6.0,4.0,10.0,6.0,3.0,1.0,17.0,2.0 -CH,1588.0,7,3.0,4.0,4.0,5.0,6.0,2.0,1.0,1.0,21.0,2.0 -CH,1594.0,7,2.0,7.0,4.0,4.0,9.0,6.0,2.0,2.0,18.0,2.0 -CH,1597.0,7,3.0,8.0,8.0,7.0,9.0,6.0,3.0,1.0,71.0,1.0 -CH,1600.0,7,5.0,7.0,5.0,7.0,8.0,3.0,2.0,1.0,62.0,2.0 -CH,1603.0,7,6.0,5.0,5.0,5.0,8.0,2.0,1.0,1.0,68.0,2.0 -CH,1604.0,7,3.0,7.0,6.0,6.0,9.0,4.0,2.0,2.0,42.0,1.0 -CH,1605.0,7,4.0,7.0,7.0,7.0,8.0,5.0,3.0,2.0,67.0,1.0 -CH,1615.0,7,3.0,2.0,0.0,2.0,8.0,4.0,4.0,1.0,43.0,1.0 -CH,1620.0,7,3.0,5.0,5.0,7.0,8.0,4.0,2.0,1.0,42.0,2.0 -CH,1624.0,7,0.0,6.0,8.0,5.0,8.0,5.0,2.0,2.0,54.0,1.0 -CH,1637.0,7,5.0,6.0,3.0,8.0,8.0,4.0,3.0,2.0,69.0,1.0 -CH,1638.0,7,2.0,5.0,8.0,6.0,10.0,7.0,5.0,2.0,16.0,2.0 -CH,1639.0,7,1.0,7.0,4.0,8.0,7.0,3.0,2.0,2.0,92.0,2.0 -CH,1643.0,7,4.0,5.0,6.0,7.0,8.0,5.0,3.0,2.0,16.0,2.0 -CH,1653.0,7,1.0,5.0,5.0,7.0,10.0,6.0,3.0,2.0,18.0,2.0 -CH,1654.0,7,4.0,7.0,7.0,6.0,8.0,4.0,3.0,2.0,51.0,1.0 -CH,1656.0,7,2.0,9.0,9.0,7.0,8.0,6.0,3.0,2.0,36.0,1.0 -CH,1657.0,7,2.0,8.0,8.0,7.0,10.0,4.0,2.0,2.0,54.0,2.0 -CH,1662.0,7,1.0,5.0,8.0,8.0,9.0,5.0,2.0,2.0,,1.0 -CH,1667.0,7,2.0,5.0,6.0,6.0,7.0,5.0,3.0,1.0,47.0,2.0 -CH,1668.0,7,2.0,8.0,7.0,8.0,7.0,6.0,4.0,2.0,20.0,2.0 -CH,1671.0,7,3.0,8.0,8.0,8.0,8.0,6.0,3.0,1.0,74.0,1.0 -CH,1683.0,7,4.0,8.0,8.0,8.0,8.0,5.0,3.0,1.0,28.0,1.0 -CH,1686.0,7,4.0,5.0,7.0,6.0,8.0,4.0,2.0,1.0,48.0,2.0 -CH,1689.0,7,0.0,4.0,5.0,8.0,9.0,7.0,2.0,2.0,16.0,2.0 -CH,1691.0,7,3.0,1.0,1.0,4.0,10.0,6.0,3.0,1.0,29.0,1.0 -CH,1693.0,7,7.0,5.0,4.0,9.0,10.0,2.0,1.0,2.0,31.0,1.0 -CH,1701.0,7,1.0,7.0,8.0,10.0,9.0,7.0,4.0,2.0,18.0,2.0 -CH,1707.0,7,2.0,9.0,9.0,7.0,8.0,6.0,3.0,2.0,41.0,1.0 -CH,1709.0,7,3.0,7.0,8.0,6.0,7.0,4.0,3.0,1.0,57.0,1.0 -CH,1718.0,7,2.0,0.0,0.0,0.0,10.0,6.0,2.0,2.0,53.0,1.0 -CH,1719.0,7,4.0,6.0,6.0,6.0,7.0,6.0,3.0,1.0,64.0,1.0 -CH,1720.0,7,4.0,3.0,5.0,4.0,8.0,5.0,2.0,1.0,19.0,2.0 -CH,1723.0,7,2.0,8.0,8.0,7.0,9.0,6.0,4.0,1.0,21.0,2.0 -CH,1726.0,7,5.0,3.0,5.0,5.0,8.0,6.0,3.0,2.0,48.0,1.0 -CH,1738.0,7,3.0,7.0,7.0,7.0,8.0,5.0,3.0,1.0,46.0,1.0 -CH,1743.0,7,4.0,3.0,4.0,5.0,9.0,7.0,4.0,1.0,43.0,1.0 -CH,1748.0,7,1.0,8.0,7.0,4.0,7.0,6.0,3.0,2.0,46.0,2.0 -CH,1750.0,7,4.0,4.0,7.0,7.0,8.0,6.0,2.0,1.0,72.0,2.0 -CH,1753.0,7,1.0,8.0,8.0,6.0,9.0,2.0,2.0,2.0,39.0,1.0 -CH,1755.0,7,4.0,6.0,7.0,8.0,8.0,3.0,3.0,1.0,71.0,1.0 -CH,1756.0,7,5.0,10.0,7.0,7.0,10.0,4.0,3.0,1.0,42.0,1.0 -CH,1757.0,7,4.0,4.0,3.0,7.0,10.0,4.0,2.0,2.0,63.0,1.0 -CH,1765.0,7,1.0,5.0,7.0,5.0,8.0,6.0,2.0,1.0,29.0,1.0 -CH,1766.0,7,2.0,3.0,4.0,6.0,4.0,3.0,2.0,2.0,41.0,1.0 -CH,1769.0,7,3.0,7.0,7.0,8.0,7.0,4.0,3.0,1.0,38.0,1.0 -CH,1776.0,7,2.0,3.0,9.0,3.0,7.0,4.0,2.0,2.0,58.0,1.0 -CH,1788.0,7,2.0,4.0,8.0,8.0,9.0,7.0,3.0,1.0,22.0,2.0 -CH,1790.0,7,3.0,7.0,6.0,8.0,7.0,6.0,4.0,2.0,20.0,2.0 -CH,1791.0,7,3.0,5.0,5.0,6.0,8.0,4.0,2.0,2.0,69.0,1.0 -CH,1792.0,7,2.0,9.0,9.0,9.0,9.0,4.0,3.0,1.0,49.0,1.0 -CH,1797.0,7,3.0,7.0,7.0,8.0,6.0,5.0,2.0,1.0,61.0,2.0 -CH,1801.0,7,5.0,0.0,9.0,0.0,8.0,1.0,1.0,2.0,,2.0 -CH,1802.0,7,3.0,4.0,5.0,3.0,7.0,6.0,3.0,2.0,47.0,2.0 -CH,1803.0,7,6.0,0.0,5.0,5.0,8.0,6.0,3.0,1.0,71.0,1.0 -CH,1805.0,7,0.0,8.0,7.0,3.0,9.0,5.0,3.0,1.0,34.0,2.0 -CH,1809.0,7,2.0,2.0,3.0,5.0,9.0,6.0,1.0,2.0,59.0,1.0 -CH,1815.0,7,2.0,7.0,7.0,5.0,7.0,7.0,3.0,2.0,21.0,2.0 -CH,1816.0,7,5.0,0.0,7.0,5.0,8.0,4.0,3.0,2.0,40.0,1.0 -CH,1817.0,7,3.0,7.0,6.0,3.0,5.0,2.0,2.0,1.0,81.0,1.0 -CH,1819.0,7,3.0,5.0,5.0,3.0,3.0,5.0,2.0,1.0,29.0,1.0 -CH,1829.0,7,3.0,0.0,5.0,2.0,7.0,4.0,2.0,1.0,52.0,1.0 -CH,1832.0,7,1.0,5.0,7.0,5.0,6.0,4.0,2.0,2.0,19.0,2.0 -CH,1844.0,7,0.0,4.0,7.0,6.0,8.0,6.0,3.0,2.0,23.0,2.0 -CH,1849.0,7,1.0,5.0,5.0,4.0,8.0,6.0,2.0,2.0,17.0,2.0 -CH,1850.0,7,7.0,3.0,3.0,3.0,8.0,5.0,3.0,2.0,53.0,2.0 -CH,1852.0,7,3.0,7.0,5.0,8.0,8.0,6.0,1.0,1.0,29.0,2.0 -CH,1854.0,7,3.0,4.0,5.0,5.0,8.0,6.0,3.0,1.0,21.0,2.0 -CH,1869.0,7,0.0,4.0,5.0,3.0,8.0,5.0,3.0,1.0,26.0,2.0 -CH,1877.0,7,7.0,4.0,5.0,5.0,8.0,6.0,4.0,2.0,56.0,1.0 -CH,1888.0,7,4.0,4.0,5.0,3.0,9.0,5.0,2.0,1.0,35.0,1.0 -CH,1897.0,7,3.0,7.0,7.0,7.0,8.0,4.0,3.0,1.0,45.0,2.0 -CH,1906.0,7,4.0,2.0,8.0,2.0,8.0,3.0,1.0,1.0,33.0,1.0 -CH,1914.0,7,2.0,5.0,3.0,3.0,7.0,7.0,4.0,1.0,68.0,2.0 -CH,1917.0,7,2.0,7.0,7.0,7.0,7.0,6.0,3.0,1.0,55.0,1.0 -CH,1921.0,7,4.0,4.0,3.0,3.0,5.0,6.0,3.0,2.0,38.0,2.0 -CH,1924.0,7,4.0,5.0,3.0,2.0,7.0,6.0,2.0,1.0,21.0,2.0 -CH,1929.0,7,2.0,6.0,5.0,6.0,9.0,7.0,4.0,2.0,17.0,2.0 -CH,1940.0,7,6.0,4.0,3.0,5.0,8.0,6.0,4.0,2.0,63.0,1.0 -CH,1941.0,7,2.0,8.0,5.0,4.0,5.0,,,2.0,45.0,1.0 -CH,1943.0,7,3.0,7.0,7.0,6.0,9.0,7.0,4.0,2.0,54.0,1.0 -CH,1955.0,7,4.0,7.0,5.0,5.0,10.0,6.0,3.0,1.0,37.0,1.0 -CH,1968.0,7,3.0,8.0,7.0,8.0,9.0,7.0,2.0,2.0,44.0,1.0 -CH,1969.0,7,0.0,9.0,8.0,4.0,10.0,6.0,1.0,2.0,49.0,2.0 -CH,1971.0,7,3.0,7.0,7.0,5.0,6.0,5.0,3.0,1.0,74.0,1.0 -CH,1978.0,7,2.0,6.0,8.0,3.0,9.0,7.0,3.0,2.0,25.0,1.0 -CH,1982.0,7,2.0,8.0,8.0,6.0,9.0,6.0,4.0,1.0,27.0,1.0 -CH,1991.0,7,4.0,6.0,7.0,8.0,10.0,7.0,3.0,2.0,82.0,2.0 -CH,1992.0,7,6.0,7.0,7.0,8.0,8.0,4.0,2.0,2.0,76.0,1.0 -CH,1998.0,7,3.0,5.0,5.0,5.0,9.0,5.0,3.0,1.0,16.0,2.0 -CH,2000.0,7,5.0,4.0,4.0,4.0,9.0,5.0,3.0,1.0,52.0,2.0 -CH,2004.0,7,6.0,6.0,7.0,6.0,8.0,4.0,2.0,1.0,42.0,1.0 -CH,2005.0,7,4.0,6.0,5.0,5.0,9.0,6.0,3.0,1.0,36.0,1.0 -CH,2007.0,7,5.0,2.0,8.0,7.0,5.0,5.0,3.0,1.0,40.0,1.0 -CH,2008.0,7,3.0,4.0,6.0,7.0,9.0,6.0,4.0,2.0,39.0,1.0 -CH,2011.0,7,4.0,5.0,5.0,5.0,8.0,3.0,2.0,2.0,21.0,2.0 -CH,2017.0,7,6.0,8.0,8.0,9.0,10.0,4.0,3.0,1.0,72.0,2.0 -CH,2020.0,7,0.0,8.0,8.0,8.0,10.0,7.0,3.0,1.0,43.0,1.0 -CH,2021.0,7,3.0,7.0,7.0,3.0,7.0,6.0,3.0,2.0,81.0,2.0 -CH,2027.0,7,1.0,9.0,8.0,8.0,7.0,6.0,3.0,2.0,54.0,1.0 -CH,2028.0,7,3.0,8.0,10.0,5.0,10.0,6.0,4.0,2.0,52.0,2.0 -CH,2036.0,7,0.0,10.0,8.0,8.0,7.0,6.0,2.0,1.0,39.0,1.0 -CH,2041.0,7,1.0,7.0,7.0,6.0,8.0,6.0,3.0,2.0,35.0,1.0 -CH,2049.0,7,2.0,0.0,3.0,3.0,9.0,7.0,2.0,2.0,48.0,1.0 -CH,2052.0,7,1.0,8.0,5.0,4.0,9.0,6.0,2.0,1.0,37.0,1.0 -CH,2060.0,7,0.0,8.0,8.0,5.0,9.0,6.0,3.0,2.0,36.0,2.0 -CH,2062.0,7,2.0,3.0,8.0,8.0,9.0,6.0,3.0,2.0,45.0,1.0 -CH,2063.0,7,2.0,7.0,7.0,7.0,8.0,6.0,3.0,2.0,36.0,1.0 -CH,2077.0,7,4.0,8.0,7.0,7.0,8.0,2.0,2.0,1.0,53.0,1.0 -CH,2078.0,7,6.0,4.0,4.0,4.0,7.0,5.0,1.0,1.0,28.0,2.0 -CH,2084.0,7,7.0,5.0,4.0,6.0,5.0,4.0,3.0,1.0,45.0,2.0 -CH,2090.0,7,2.0,5.0,6.0,6.0,8.0,6.0,3.0,1.0,52.0,1.0 -CH,2092.0,7,2.0,6.0,5.0,3.0,5.0,3.0,2.0,2.0,49.0,1.0 -CH,2095.0,7,2.0,7.0,7.0,6.0,9.0,7.0,3.0,2.0,17.0,2.0 -CH,2102.0,7,0.0,7.0,7.0,8.0,8.0,3.0,2.0,2.0,68.0,2.0 -CH,2106.0,7,3.0,4.0,3.0,5.0,8.0,6.0,4.0,2.0,32.0,1.0 -CH,2111.0,7,0.0,6.0,8.0,9.0,7.0,2.0,2.0,2.0,71.0,1.0 -CH,2112.0,7,3.0,7.0,8.0,7.0,9.0,6.0,1.0,1.0,20.0,2.0 -CH,2113.0,7,2.0,8.0,8.0,7.0,9.0,6.0,3.0,1.0,27.0,2.0 -CH,2115.0,7,2.0,5.0,5.0,5.0,8.0,4.0,2.0,1.0,44.0,2.0 -CH,2124.0,7,1.0,6.0,7.0,3.0,8.0,4.0,2.0,1.0,40.0,1.0 -CH,2128.0,7,5.0,9.0,8.0,5.0,10.0,7.0,3.0,1.0,60.0,1.0 -CH,2129.0,7,2.0,4.0,8.0,4.0,8.0,4.0,4.0,1.0,86.0,2.0 -CH,2131.0,7,6.0,8.0,8.0,9.0,5.0,2.0,2.0,2.0,70.0,2.0 -CH,2133.0,7,4.0,2.0,5.0,5.0,10.0,6.0,2.0,1.0,29.0,1.0 -CH,2135.0,7,3.0,2.0,8.0,3.0,7.0,5.0,3.0,1.0,,1.0 -CH,2154.0,7,5.0,9.0,8.0,9.0,10.0,3.0,2.0,2.0,84.0,1.0 -CH,2159.0,7,4.0,6.0,6.0,6.0,6.0,6.0,2.0,2.0,79.0,1.0 -CH,2160.0,7,4.0,8.0,8.0,5.0,9.0,6.0,3.0,2.0,36.0,1.0 -CH,2161.0,7,2.0,6.0,7.0,7.0,8.0,4.0,3.0,2.0,80.0,2.0 -CH,2167.0,7,2.0,5.0,6.0,7.0,6.0,3.0,2.0,2.0,78.0,1.0 -CH,2169.0,7,5.0,7.0,6.0,7.0,9.0,5.0,3.0,1.0,64.0,1.0 -CH,2180.0,7,5.0,5.0,7.0,5.0,6.0,3.0,3.0,1.0,56.0,1.0 -CH,2182.0,7,4.0,5.0,5.0,5.0,5.0,5.0,3.0,2.0,52.0,2.0 -CH,2183.0,7,1.0,5.0,10.0,6.0,9.0,5.0,3.0,1.0,53.0,1.0 -CH,2190.0,7,0.0,4.0,7.0,4.0,10.0,7.0,3.0,2.0,27.0,2.0 -CH,2192.0,7,0.0,8.0,7.0,8.0,8.0,6.0,3.0,2.0,34.0,1.0 -CH,2193.0,7,4.0,7.0,7.0,6.0,8.0,5.0,2.0,2.0,36.0,1.0 -CH,2196.0,7,4.0,5.0,8.0,4.0,3.0,5.0,1.0,2.0,53.0,1.0 -CH,2197.0,7,1.0,5.0,7.0,7.0,9.0,6.0,4.0,2.0,50.0,1.0 -CH,2201.0,7,0.0,8.0,8.0,9.0,9.0,6.0,2.0,2.0,42.0,1.0 -CH,2203.0,7,6.0,2.0,7.0,8.0,6.0,2.0,1.0,2.0,84.0,2.0 -CH,2205.0,7,2.0,4.0,7.0,7.0,9.0,5.0,2.0,2.0,41.0,2.0 -CH,2209.0,7,3.0,7.0,7.0,6.0,8.0,6.0,3.0,1.0,57.0,1.0 -CH,2211.0,7,2.0,5.0,9.0,5.0,7.0,6.0,2.0,2.0,46.0,1.0 -CH,2217.0,7,2.0,0.0,0.0,0.0,6.0,6.0,3.0,1.0,21.0,2.0 -CH,2221.0,7,2.0,9.0,8.0,6.0,8.0,7.0,4.0,1.0,42.0,1.0 -CH,2228.0,7,2.0,6.0,5.0,7.0,4.0,4.0,2.0,1.0,35.0,2.0 -CH,2229.0,7,6.0,4.0,5.0,4.0,8.0,3.0,2.0,1.0,68.0,1.0 -CH,2236.0,7,1.0,6.0,4.0,7.0,9.0,4.0,2.0,1.0,38.0,1.0 -CH,2237.0,7,5.0,5.0,8.0,9.0,9.0,5.0,3.0,1.0,18.0,2.0 -CH,2239.0,7,3.0,5.0,7.0,4.0,9.0,5.0,4.0,1.0,42.0,1.0 -CH,2241.0,7,3.0,7.0,6.0,8.0,8.0,6.0,2.0,2.0,35.0,1.0 -CH,2243.0,7,4.0,5.0,6.0,8.0,8.0,4.0,2.0,2.0,19.0,2.0 -CH,2244.0,7,2.0,4.0,3.0,4.0,9.0,6.0,2.0,2.0,23.0,1.0 -CH,2247.0,7,5.0,4.0,6.0,3.0,9.0,7.0,3.0,1.0,27.0,2.0 -CH,2248.0,7,2.0,0.0,5.0,9.0,7.0,6.0,4.0,1.0,45.0,1.0 -CH,2249.0,7,4.0,7.0,8.0,9.0,9.0,6.0,2.0,2.0,63.0,1.0 -CH,2251.0,7,2.0,7.0,3.0,5.0,9.0,7.0,4.0,1.0,17.0,2.0 -CH,2256.0,7,7.0,7.0,8.0,5.0,7.0,2.0,1.0,1.0,82.0,2.0 -CH,2257.0,7,4.0,0.0,2.0,6.0,7.0,6.0,2.0,2.0,63.0,2.0 -CH,2262.0,7,2.0,6.0,6.0,7.0,9.0,6.0,3.0,1.0,23.0,2.0 -CH,2265.0,7,3.0,5.0,9.0,5.0,7.0,6.0,3.0,2.0,49.0,1.0 -CH,2267.0,7,5.0,3.0,3.0,5.0,8.0,4.0,3.0,1.0,46.0,1.0 -CH,2270.0,7,5.0,3.0,5.0,5.0,7.0,4.0,3.0,1.0,52.0,1.0 -CH,2272.0,7,3.0,8.0,9.0,5.0,8.0,4.0,3.0,1.0,78.0,1.0 -CH,2273.0,7,3.0,0.0,0.0,1.0,3.0,2.0,1.0,1.0,62.0,2.0 -CH,2284.0,7,0.0,2.0,2.0,2.0,8.0,4.0,2.0,1.0,50.0,1.0 -CH,2289.0,7,3.0,8.0,6.0,5.0,9.0,6.0,3.0,1.0,72.0,1.0 -CH,2290.0,7,0.0,8.0,2.0,0.0,9.0,4.0,2.0,2.0,87.0,1.0 -CH,2294.0,7,2.0,7.0,4.0,4.0,8.0,6.0,3.0,2.0,23.0,2.0 -CH,2295.0,7,2.0,5.0,6.0,5.0,6.0,4.0,3.0,1.0,46.0,1.0 -CH,2298.0,7,3.0,8.0,7.0,7.0,8.0,6.0,3.0,2.0,33.0,2.0 -CH,2315.0,7,5.0,5.0,8.0,7.0,8.0,5.0,2.0,2.0,42.0,1.0 -CH,2318.0,7,1.0,7.0,3.0,7.0,9.0,6.0,3.0,1.0,30.0,2.0 -CH,2319.0,7,2.0,3.0,3.0,2.0,5.0,6.0,4.0,1.0,53.0,2.0 -CH,2322.0,7,5.0,8.0,8.0,8.0,10.0,6.0,4.0,2.0,37.0,1.0 -CH,2326.0,7,7.0,5.0,5.0,6.0,8.0,3.0,1.0,2.0,,1.0 -CH,2329.0,7,3.0,5.0,5.0,6.0,9.0,5.0,2.0,2.0,76.0,1.0 -CH,2330.0,7,2.0,8.0,8.0,3.0,9.0,6.0,3.0,2.0,50.0,1.0 -CH,2333.0,7,7.0,10.0,9.0,6.0,10.0,7.0,4.0,2.0,50.0,1.0 -CH,2334.0,7,2.0,6.0,6.0,4.0,10.0,6.0,3.0,2.0,20.0,2.0 -CH,2335.0,7,1.0,2.0,4.0,6.0,7.0,4.0,2.0,1.0,33.0,1.0 -CH,2338.0,7,2.0,8.0,7.0,7.0,9.0,5.0,1.0,1.0,48.0,1.0 -CH,2343.0,7,1.0,2.0,2.0,7.0,9.0,6.0,2.0,2.0,36.0,1.0 -CH,2345.0,7,0.0,8.0,8.0,7.0,10.0,6.0,3.0,2.0,39.0,1.0 -CH,2346.0,7,0.0,3.0,2.0,4.0,6.0,6.0,2.0,1.0,19.0,2.0 -CH,2347.0,7,3.0,8.0,8.0,5.0,8.0,6.0,3.0,2.0,74.0,2.0 -CH,2350.0,7,5.0,3.0,7.0,4.0,7.0,3.0,2.0,1.0,,2.0 -CH,2352.0,7,6.0,5.0,7.0,6.0,10.0,7.0,3.0,2.0,44.0,2.0 -CH,2356.0,7,6.0,5.0,3.0,7.0,8.0,6.0,,1.0,32.0,1.0 -CH,2358.0,7,4.0,5.0,4.0,3.0,7.0,5.0,3.0,1.0,36.0,2.0 -CH,2366.0,7,3.0,7.0,6.0,7.0,8.0,3.0,3.0,2.0,37.0,1.0 -CH,2376.0,7,1.0,8.0,8.0,7.0,9.0,6.0,4.0,2.0,69.0,1.0 -CH,2382.0,7,1.0,2.0,8.0,2.0,6.0,6.0,4.0,2.0,49.0,1.0 -CH,2383.0,7,3.0,3.0,5.0,5.0,5.0,6.0,3.0,2.0,54.0,1.0 -CH,2388.0,7,4.0,8.0,8.0,5.0,8.0,6.0,2.0,2.0,75.0,2.0 -CH,2392.0,7,3.0,7.0,3.0,7.0,6.0,6.0,3.0,1.0,54.0,1.0 -CH,2397.0,7,2.0,7.0,8.0,8.0,9.0,6.0,3.0,2.0,52.0,1.0 -CH,2403.0,7,4.0,5.0,6.0,6.0,8.0,4.0,2.0,1.0,54.0,1.0 -CH,2419.0,7,0.0,8.0,8.0,8.0,9.0,4.0,3.0,1.0,39.0,1.0 -CH,2425.0,7,1.0,8.0,8.0,7.0,7.0,4.0,3.0,1.0,63.0,1.0 -CH,2427.0,7,3.0,5.0,8.0,5.0,10.0,3.0,3.0,2.0,91.0,2.0 -CH,2429.0,7,2.0,3.0,5.0,4.0,8.0,6.0,4.0,2.0,36.0,1.0 -CH,2430.0,7,3.0,7.0,5.0,6.0,8.0,6.0,3.0,1.0,18.0,2.0 -CH,2431.0,7,4.0,9.0,10.0,10.0,9.0,4.0,3.0,2.0,64.0,1.0 -CH,2433.0,7,2.0,7.0,7.0,5.0,9.0,5.0,3.0,2.0,59.0,1.0 -CH,2434.0,7,2.0,3.0,5.0,5.0,8.0,5.0,2.0,1.0,27.0,1.0 -CH,2435.0,7,2.0,6.0,8.0,5.0,7.0,5.0,2.0,1.0,16.0,2.0 -CH,2439.0,7,5.0,8.0,8.0,7.0,8.0,6.0,,1.0,73.0,1.0 -CH,2448.0,7,2.0,7.0,7.0,7.0,8.0,4.0,3.0,1.0,20.0,2.0 -CH,2452.0,7,4.0,7.0,7.0,5.0,8.0,6.0,3.0,1.0,27.0,1.0 -CH,2454.0,7,7.0,8.0,2.0,5.0,9.0,5.0,3.0,2.0,67.0,1.0 -CH,2456.0,7,3.0,1.0,8.0,9.0,7.0,4.0,,1.0,55.0,1.0 -CH,2457.0,7,0.0,5.0,8.0,8.0,5.0,4.0,,2.0,35.0,2.0 -CH,2459.0,7,2.0,8.0,5.0,5.0,8.0,7.0,3.0,2.0,33.0,1.0 -CH,2462.0,7,3.0,5.0,7.0,6.0,8.0,4.0,3.0,1.0,27.0,2.0 -CH,2464.0,7,7.0,7.0,8.0,10.0,8.0,6.0,,1.0,69.0,1.0 -CH,2465.0,7,3.0,3.0,7.0,5.0,8.0,6.0,3.0,1.0,18.0,2.0 -CH,2466.0,7,3.0,8.0,8.0,8.0,9.0,6.0,3.0,2.0,64.0,1.0 -CH,2474.0,7,6.0,3.0,8.0,7.0,9.0,4.0,3.0,1.0,72.0,1.0 -CH,2477.0,7,3.0,6.0,8.0,6.0,9.0,3.0,3.0,2.0,50.0,1.0 -CH,2482.0,7,1.0,4.0,6.0,7.0,8.0,6.0,3.0,1.0,23.0,2.0 -CH,2485.0,7,2.0,5.0,7.0,6.0,10.0,6.0,,1.0,47.0,1.0 -CH,2486.0,7,0.0,6.0,7.0,7.0,6.0,3.0,2.0,1.0,56.0,1.0 -CH,2487.0,7,1.0,8.0,1.0,1.0,10.0,6.0,3.0,1.0,69.0,1.0 -CH,2488.0,7,5.0,3.0,3.0,3.0,4.0,5.0,3.0,1.0,63.0,1.0 -CH,2489.0,7,1.0,5.0,7.0,2.0,7.0,7.0,3.0,2.0,39.0,1.0 -CH,2491.0,7,5.0,8.0,8.0,6.0,6.0,7.0,3.0,1.0,,2.0 -CH,2498.0,7,4.0,5.0,5.0,3.0,10.0,5.0,4.0,1.0,70.0,1.0 -CH,2500.0,7,5.0,2.0,3.0,8.0,0.0,2.0,4.0,2.0,,1.0 -CH,2514.0,7,1.0,10.0,9.0,8.0,10.0,6.0,3.0,2.0,50.0,2.0 -CH,2515.0,7,2.0,5.0,7.0,7.0,9.0,1.0,3.0,1.0,29.0,1.0 -CH,2528.0,7,1.0,8.0,7.0,4.0,8.0,4.0,3.0,1.0,40.0,1.0 -CH,2539.0,7,5.0,1.0,5.0,4.0,8.0,5.0,3.0,1.0,39.0,1.0 -CH,2540.0,7,6.0,6.0,9.0,6.0,5.0,4.0,2.0,2.0,68.0,1.0 -CH,2545.0,7,0.0,5.0,5.0,5.0,5.0,3.0,3.0,2.0,71.0,1.0 -CH,2555.0,7,1.0,10.0,8.0,8.0,10.0,5.0,3.0,2.0,52.0,1.0 -CH,2557.0,7,5.0,8.0,8.0,7.0,8.0,6.0,4.0,2.0,35.0,1.0 -CH,2558.0,7,4.0,6.0,8.0,8.0,8.0,6.0,3.0,2.0,60.0,1.0 -CH,2560.0,7,0.0,8.0,9.0,2.0,8.0,7.0,1.0,2.0,25.0,2.0 -CH,2566.0,7,2.0,6.0,8.0,7.0,8.0,6.0,2.0,2.0,32.0,1.0 -CH,2567.0,7,1.0,8.0,2.0,10.0,9.0,4.0,2.0,1.0,51.0,1.0 -CH,2568.0,7,2.0,8.0,5.0,4.0,8.0,3.0,1.0,1.0,23.0,2.0 -CH,2569.0,7,4.0,4.0,5.0,4.0,6.0,6.0,2.0,2.0,36.0,1.0 -CH,2571.0,7,4.0,8.0,7.0,8.0,7.0,7.0,3.0,1.0,64.0,1.0 -CH,2573.0,7,3.0,7.0,5.0,7.0,7.0,6.0,3.0,2.0,31.0,2.0 -CH,2581.0,7,2.0,7.0,6.0,5.0,7.0,6.0,2.0,1.0,54.0,2.0 -CH,2582.0,7,0.0,7.0,3.0,8.0,8.0,7.0,3.0,2.0,22.0,2.0 -CH,2584.0,7,4.0,5.0,3.0,7.0,9.0,7.0,4.0,2.0,61.0,1.0 -CH,2589.0,7,5.0,6.0,8.0,6.0,7.0,4.0,4.0,2.0,83.0,1.0 -CH,2601.0,7,4.0,6.0,7.0,7.0,8.0,4.0,3.0,2.0,36.0,1.0 -CH,2620.0,7,0.0,3.0,4.0,3.0,9.0,4.0,2.0,1.0,80.0,1.0 -CH,2621.0,7,0.0,8.0,3.0,2.0,5.0,4.0,2.0,1.0,60.0,1.0 -CH,2625.0,7,2.0,6.0,7.0,7.0,6.0,5.0,2.0,2.0,91.0,2.0 -CH,2626.0,7,4.0,8.0,6.0,5.0,8.0,4.0,3.0,2.0,78.0,1.0 -CH,2627.0,7,4.0,7.0,6.0,5.0,8.0,6.0,3.0,2.0,62.0,1.0 -CH,2630.0,7,4.0,6.0,3.0,5.0,8.0,5.0,3.0,1.0,35.0,1.0 -CH,2631.0,7,4.0,8.0,7.0,7.0,9.0,4.0,1.0,2.0,62.0,1.0 -CH,2634.0,7,5.0,7.0,8.0,7.0,9.0,7.0,5.0,2.0,16.0,2.0 -CH,2639.0,7,3.0,3.0,4.0,5.0,7.0,7.0,2.0,2.0,60.0,1.0 -CH,2640.0,7,0.0,7.0,8.0,7.0,8.0,6.0,3.0,2.0,18.0,2.0 -CH,2642.0,7,4.0,9.0,9.0,7.0,10.0,6.0,4.0,1.0,60.0,1.0 -CH,2643.0,7,7.0,8.0,8.0,5.0,9.0,5.0,3.0,2.0,63.0,1.0 -CH,2644.0,7,7.0,3.0,5.0,2.0,10.0,3.0,1.0,1.0,78.0,1.0 -CH,2648.0,7,4.0,7.0,7.0,5.0,8.0,7.0,3.0,2.0,32.0,1.0 -CH,2651.0,7,2.0,8.0,10.0,9.0,9.0,4.0,4.0,2.0,62.0,1.0 -CH,2652.0,7,6.0,7.0,5.0,7.0,9.0,5.0,2.0,1.0,42.0,1.0 -CH,2666.0,7,4.0,9.0,8.0,6.0,10.0,6.0,3.0,2.0,74.0,1.0 -CH,2668.0,7,6.0,10.0,3.0,10.0,9.0,6.0,3.0,1.0,60.0,2.0 -CH,2670.0,7,2.0,5.0,4.0,5.0,7.0,5.0,3.0,2.0,29.0,1.0 -CH,2672.0,7,4.0,6.0,4.0,5.0,7.0,6.0,2.0,2.0,68.0,2.0 -CH,2674.0,7,7.0,3.0,6.0,3.0,8.0,4.0,2.0,2.0,58.0,1.0 -CH,2675.0,7,3.0,7.0,8.0,2.0,9.0,5.0,3.0,2.0,38.0,1.0 -CH,2676.0,7,0.0,2.0,6.0,6.0,5.0,7.0,3.0,1.0,47.0,2.0 -CH,2683.0,7,2.0,9.0,5.0,8.0,8.0,6.0,3.0,1.0,20.0,2.0 -CH,2684.0,7,2.0,4.0,8.0,8.0,8.0,4.0,3.0,2.0,75.0,2.0 -CH,2687.0,7,2.0,9.0,9.0,9.0,9.0,6.0,4.0,2.0,41.0,1.0 -CH,2689.0,7,3.0,8.0,8.0,7.0,10.0,6.0,4.0,2.0,75.0,1.0 -CH,2693.0,7,1.0,4.0,9.0,7.0,8.0,3.0,1.0,1.0,35.0,1.0 -CH,2699.0,7,3.0,5.0,2.0,4.0,7.0,5.0,3.0,2.0,25.0,2.0 -CH,2702.0,7,6.0,5.0,3.0,6.0,10.0,4.0,2.0,2.0,,1.0 -CH,2711.0,7,0.0,8.0,8.0,7.0,8.0,6.0,,2.0,42.0,1.0 -CH,2713.0,7,1.0,5.0,8.0,5.0,7.0,4.0,3.0,2.0,28.0,1.0 -CH,2714.0,7,4.0,5.0,5.0,2.0,10.0,6.0,1.0,2.0,25.0,1.0 -CH,2716.0,7,0.0,4.0,6.0,4.0,9.0,6.0,5.0,1.0,18.0,2.0 -CH,2720.0,7,0.0,8.0,7.0,5.0,8.0,6.0,4.0,1.0,20.0,2.0 -CH,2728.0,7,4.0,6.0,2.0,4.0,8.0,6.0,3.0,1.0,28.0,2.0 -CH,2729.0,7,2.0,6.0,7.0,7.0,8.0,5.0,3.0,1.0,31.0,1.0 -CH,2732.0,7,4.0,8.0,7.0,4.0,9.0,3.0,3.0,1.0,84.0,2.0 -CH,2738.0,7,5.0,5.0,5.0,5.0,10.0,5.0,2.0,1.0,49.0,2.0 -CH,2741.0,7,2.0,7.0,7.0,7.0,6.0,7.0,2.0,1.0,39.0,2.0 -CH,2745.0,7,5.0,5.0,6.0,10.0,9.0,5.0,3.0,1.0,34.0,1.0 -CH,2749.0,7,1.0,10.0,10.0,8.0,9.0,6.0,4.0,2.0,47.0,2.0 -CH,2752.0,7,0.0,8.0,9.0,9.0,9.0,6.0,3.0,2.0,61.0,1.0 -CH,2757.0,7,2.0,6.0,7.0,8.0,9.0,6.0,3.0,1.0,37.0,1.0 -CH,2765.0,7,2.0,8.0,7.0,7.0,7.0,6.0,3.0,2.0,39.0,2.0 -CH,2772.0,7,2.0,0.0,7.0,8.0,10.0,6.0,3.0,1.0,21.0,2.0 -CH,2773.0,7,0.0,7.0,8.0,6.0,10.0,5.0,2.0,2.0,78.0,1.0 -CH,2774.0,7,4.0,3.0,7.0,3.0,3.0,5.0,1.0,2.0,56.0,2.0 -CH,2775.0,7,2.0,5.0,5.0,7.0,6.0,5.0,2.0,1.0,34.0,1.0 -CH,2776.0,7,4.0,7.0,7.0,5.0,8.0,6.0,2.0,1.0,28.0,2.0 -CH,2777.0,7,0.0,8.0,7.0,7.0,8.0,6.0,3.0,2.0,37.0,1.0 -CH,2782.0,7,2.0,7.0,8.0,8.0,8.0,4.0,2.0,2.0,61.0,1.0 -CH,2784.0,7,0.0,7.0,7.0,6.0,8.0,6.0,4.0,1.0,48.0,2.0 -CH,2787.0,7,1.0,8.0,8.0,7.0,9.0,6.0,4.0,2.0,49.0,1.0 -CH,2788.0,7,2.0,8.0,8.0,3.0,10.0,7.0,5.0,1.0,61.0,2.0 -CH,2793.0,7,5.0,7.0,7.0,8.0,8.0,5.0,3.0,2.0,54.0,1.0 -CH,2795.0,7,4.0,7.0,5.0,4.0,8.0,6.0,1.0,2.0,20.0,2.0 -CH,2797.0,7,4.0,4.0,5.0,7.0,10.0,6.0,4.0,1.0,60.0,1.0 -CH,2800.0,7,3.0,7.0,5.0,4.0,5.0,2.0,3.0,1.0,58.0,1.0 -CH,2803.0,7,5.0,7.0,8.0,6.0,9.0,7.0,4.0,2.0,34.0,1.0 -CH,2805.0,7,3.0,5.0,10.0,7.0,10.0,4.0,3.0,2.0,76.0,2.0 -CH,2806.0,7,2.0,4.0,4.0,5.0,7.0,5.0,2.0,2.0,17.0,2.0 -CH,2807.0,7,2.0,5.0,6.0,3.0,8.0,6.0,2.0,2.0,47.0,1.0 -CH,2809.0,7,1.0,3.0,2.0,4.0,8.0,3.0,2.0,1.0,53.0,1.0 -CH,2811.0,7,5.0,7.0,8.0,9.0,8.0,3.0,1.0,1.0,82.0,1.0 -CH,2812.0,7,6.0,8.0,8.0,5.0,10.0,6.0,3.0,1.0,68.0,1.0 -CH,2813.0,7,1.0,9.0,9.0,6.0,8.0,6.0,2.0,2.0,22.0,1.0 -CH,2814.0,7,7.0,6.0,4.0,5.0,6.0,6.0,3.0,1.0,21.0,2.0 -CH,2817.0,7,2.0,9.0,10.0,8.0,10.0,6.0,4.0,1.0,33.0,2.0 -CH,2821.0,7,2.0,3.0,4.0,4.0,8.0,4.0,3.0,1.0,43.0,1.0 -CH,2826.0,7,3.0,7.0,8.0,7.0,9.0,6.0,4.0,1.0,17.0,2.0 -CH,2828.0,7,3.0,4.0,10.0,3.0,2.0,5.0,1.0,2.0,40.0,1.0 -CH,2835.0,7,3.0,7.0,7.0,6.0,8.0,3.0,3.0,2.0,48.0,1.0 -CH,2839.0,7,2.0,2.0,3.0,3.0,8.0,3.0,3.0,1.0,42.0,1.0 -CH,2842.0,7,0.0,7.0,8.0,6.0,9.0,3.0,3.0,2.0,61.0,1.0 -CH,2845.0,7,3.0,7.0,7.0,6.0,7.0,3.0,3.0,2.0,24.0,2.0 -CH,2847.0,7,4.0,3.0,3.0,3.0,6.0,2.0,2.0,1.0,68.0,2.0 -CH,2851.0,7,4.0,7.0,6.0,6.0,9.0,4.0,3.0,2.0,59.0,1.0 -CH,2853.0,7,4.0,7.0,7.0,6.0,7.0,4.0,3.0,2.0,51.0,1.0 -CH,2855.0,7,3.0,8.0,8.0,5.0,9.0,5.0,4.0,1.0,33.0,1.0 -CH,2858.0,7,5.0,5.0,5.0,5.0,5.0,5.0,3.0,2.0,72.0,1.0 -CH,2861.0,7,7.0,5.0,7.0,7.0,9.0,5.0,3.0,2.0,62.0,1.0 -CH,2872.0,7,3.0,5.0,4.0,5.0,8.0,4.0,2.0,1.0,46.0,1.0 -CH,2876.0,7,2.0,10.0,8.0,3.0,9.0,5.0,4.0,1.0,59.0,2.0 -CH,2878.0,7,6.0,5.0,6.0,5.0,7.0,6.0,3.0,1.0,23.0,2.0 -CH,2879.0,7,7.0,6.0,8.0,8.0,9.0,3.0,2.0,2.0,67.0,1.0 -CH,2882.0,7,1.0,7.0,6.0,7.0,8.0,6.0,3.0,1.0,50.0,1.0 -CH,2884.0,7,2.0,8.0,7.0,7.0,8.0,5.0,3.0,1.0,52.0,1.0 -CH,2885.0,7,3.0,7.0,8.0,7.0,8.0,6.0,3.0,2.0,25.0,1.0 -CH,2888.0,7,2.0,7.0,7.0,6.0,8.0,6.0,4.0,2.0,17.0,2.0 -CH,2890.0,7,5.0,4.0,6.0,2.0,10.0,6.0,4.0,1.0,38.0,1.0 -CH,2893.0,7,2.0,5.0,5.0,5.0,9.0,3.0,2.0,2.0,61.0,1.0 -CH,2894.0,7,5.0,7.0,6.0,5.0,7.0,3.0,2.0,2.0,56.0,1.0 -CH,2900.0,7,3.0,3.0,7.0,7.0,8.0,6.0,4.0,2.0,20.0,1.0 -CH,2905.0,7,4.0,5.0,6.0,6.0,8.0,4.0,3.0,1.0,19.0,2.0 -CZ,1001.0,6,2.0,10.0,9.0,4.0,9.0,6.0,3.0,1.0,39.0,1.0 -CZ,1002.0,6,4.0,5.0,4.0,6.0,8.0,6.0,3.0,1.0,29.0,1.0 -CZ,1003.0,6,1.0,9.0,8.0,6.0,9.0,5.0,5.0,1.0,33.0,2.0 -CZ,1004.0,6,3.0,2.0,1.0,2.0,7.0,4.0,3.0,1.0,24.0,2.0 -CZ,1005.0,6,7.0,2.0,10.0,6.0,4.0,5.0,3.0,2.0,63.0,2.0 -CZ,1006.0,6,7.0,3.0,10.0,3.0,3.0,,1.0,2.0,58.0,2.0 -CZ,1007.0,6,4.0,3.0,5.0,3.0,6.0,6.0,1.0,1.0,41.0,1.0 -CZ,1008.0,6,7.0,4.0,4.0,3.0,8.0,6.0,3.0,2.0,43.0,1.0 -CZ,1009.0,6,7.0,4.0,6.0,4.0,7.0,2.0,2.0,1.0,57.0,1.0 -CZ,1010.0,6,6.0,4.0,6.0,6.0,7.0,4.0,3.0,2.0,37.0,1.0 -CZ,1011.0,6,4.0,3.0,3.0,3.0,6.0,6.0,3.0,1.0,27.0,2.0 -CZ,1012.0,6,5.0,6.0,7.0,3.0,7.0,3.0,2.0,2.0,48.0,1.0 -CZ,1013.0,6,6.0,5.0,5.0,5.0,5.0,5.0,1.0,2.0,64.0,1.0 -CZ,1014.0,6,6.0,5.0,5.0,3.0,7.0,6.0,3.0,1.0,31.0,2.0 -CZ,1015.0,6,6.0,3.0,5.0,5.0,6.0,2.0,2.0,1.0,52.0,1.0 -CZ,1016.0,6,3.0,3.0,4.0,2.0,7.0,7.0,3.0,2.0,64.0,1.0 -CZ,1017.0,6,5.0,4.0,4.0,4.0,3.0,2.0,3.0,2.0,38.0,1.0 -CZ,1018.0,6,7.0,1.0,0.0,0.0,10.0,1.0,1.0,1.0,47.0,2.0 -CZ,1019.0,6,7.0,2.0,4.0,2.0,4.0,5.0,1.0,2.0,57.0,1.0 -CZ,1020.0,6,6.0,3.0,5.0,4.0,6.0,4.0,3.0,2.0,74.0,1.0 -CZ,1021.0,6,4.0,7.0,5.0,5.0,5.0,4.0,3.0,1.0,36.0,2.0 -CZ,1022.0,6,7.0,2.0,3.0,4.0,7.0,7.0,4.0,2.0,71.0,1.0 -CZ,1023.0,6,7.0,4.0,3.0,5.0,7.0,6.0,3.0,2.0,65.0,2.0 -CZ,1024.0,6,6.0,7.0,7.0,4.0,6.0,7.0,2.0,2.0,51.0,1.0 -CZ,1025.0,6,4.0,6.0,8.0,4.0,8.0,5.0,3.0,1.0,68.0,2.0 -CZ,1026.0,6,7.0,5.0,6.0,4.0,7.0,4.0,3.0,2.0,69.0,2.0 -CZ,1028.0,6,6.0,3.0,6.0,3.0,6.0,2.0,2.0,2.0,48.0,1.0 -CZ,1029.0,6,5.0,8.0,8.0,8.0,9.0,2.0,1.0,1.0,38.0,1.0 -CZ,1030.0,6,6.0,3.0,3.0,3.0,5.0,4.0,1.0,2.0,72.0,1.0 -CZ,1031.0,6,6.0,5.0,3.0,4.0,7.0,5.0,1.0,1.0,42.0,1.0 -CZ,1032.0,6,7.0,4.0,2.0,5.0,7.0,4.0,4.0,2.0,53.0,1.0 -CZ,1033.0,6,7.0,8.0,7.0,10.0,4.0,2.0,5.0,2.0,54.0,2.0 -CZ,1034.0,6,5.0,2.0,3.0,3.0,2.0,4.0,1.0,1.0,54.0,2.0 -CZ,1035.0,6,4.0,4.0,7.0,5.0,6.0,2.0,2.0,2.0,64.0,1.0 -CZ,1036.0,6,3.0,7.0,7.0,5.0,5.0,6.0,3.0,1.0,23.0,2.0 -CZ,1037.0,6,4.0,5.0,3.0,6.0,8.0,6.0,1.0,2.0,52.0,1.0 -CZ,1038.0,6,7.0,2.0,2.0,4.0,6.0,2.0,,2.0,69.0,1.0 -CZ,1039.0,6,0.0,2.0,3.0,0.0,,5.0,,2.0,59.0,2.0 -CZ,1040.0,6,4.0,4.0,8.0,0.0,9.0,5.0,2.0,2.0,31.0,1.0 -CZ,1041.0,6,7.0,4.0,5.0,5.0,7.0,4.0,1.0,1.0,71.0,2.0 -CZ,1042.0,6,5.0,9.0,7.0,9.0,8.0,5.0,2.0,2.0,31.0,1.0 -CZ,1043.0,6,4.0,7.0,4.0,7.0,7.0,6.0,3.0,2.0,27.0,1.0 -CZ,1044.0,6,7.0,0.0,4.0,4.0,5.0,6.0,1.0,2.0,41.0,2.0 -CZ,1045.0,6,7.0,3.0,3.0,3.0,7.0,7.0,3.0,1.0,67.0,2.0 -CZ,1046.0,6,6.0,5.0,5.0,5.0,8.0,4.0,3.0,1.0,69.0,1.0 -CZ,1047.0,6,2.0,4.0,4.0,4.0,9.0,6.0,4.0,1.0,18.0,2.0 -CZ,1048.0,6,6.0,6.0,6.0,6.0,8.0,6.0,3.0,1.0,40.0,2.0 -CZ,1049.0,6,4.0,8.0,7.0,7.0,9.0,6.0,3.0,1.0,19.0,2.0 -CZ,1050.0,6,6.0,7.0,8.0,7.0,8.0,4.0,2.0,1.0,32.0,1.0 -CZ,1051.0,6,7.0,6.0,6.0,8.0,9.0,5.0,3.0,2.0,32.0,2.0 -CZ,1052.0,6,7.0,4.0,6.0,4.0,4.0,6.0,3.0,2.0,56.0,2.0 -CZ,1053.0,6,2.0,1.0,7.0,7.0,9.0,3.0,3.0,1.0,54.0,1.0 -CZ,1054.0,6,6.0,3.0,4.0,4.0,4.0,2.0,2.0,1.0,47.0,2.0 -CZ,1055.0,6,7.0,1.0,2.0,2.0,5.0,5.0,3.0,1.0,65.0,1.0 -CZ,1056.0,6,7.0,6.0,8.0,6.0,7.0,4.0,3.0,1.0,63.0,1.0 -CZ,1057.0,6,7.0,5.0,5.0,2.0,6.0,6.0,3.0,2.0,64.0,2.0 -CZ,1058.0,6,7.0,5.0,10.0,5.0,,,,1.0,63.0,1.0 -CZ,1059.0,6,7.0,4.0,,3.0,9.0,5.0,3.0,2.0,61.0,1.0 -CZ,1060.0,6,2.0,5.0,7.0,6.0,9.0,3.0,3.0,1.0,60.0,1.0 -CZ,1061.0,6,3.0,4.0,4.0,7.0,8.0,4.0,3.0,2.0,33.0,1.0 -CZ,1062.0,6,2.0,2.0,5.0,0.0,7.0,4.0,2.0,1.0,51.0,1.0 -CZ,1063.0,6,7.0,9.0,9.0,9.0,8.0,5.0,2.0,2.0,59.0,2.0 -CZ,1064.0,6,6.0,2.0,4.0,6.0,6.0,4.0,3.0,2.0,28.0,1.0 -CZ,1065.0,6,6.0,3.0,3.0,4.0,9.0,6.0,5.0,1.0,23.0,2.0 -CZ,1066.0,6,7.0,9.0,10.0,1.0,,1.0,3.0,1.0,,1.0 -CZ,1067.0,6,4.0,4.0,5.0,4.0,5.0,2.0,1.0,1.0,59.0,1.0 -CZ,1068.0,6,3.0,3.0,4.0,4.0,5.0,4.0,2.0,1.0,63.0,1.0 -CZ,1069.0,6,6.0,7.0,7.0,4.0,8.0,6.0,3.0,2.0,18.0,2.0 -CZ,1070.0,6,3.0,6.0,6.0,5.0,7.0,7.0,3.0,1.0,19.0,2.0 -CZ,1071.0,6,6.0,3.0,4.0,4.0,7.0,3.0,1.0,2.0,60.0,2.0 -CZ,1072.0,6,6.0,4.0,6.0,7.0,8.0,4.0,3.0,1.0,50.0,1.0 -CZ,1073.0,6,,5.0,6.0,3.0,8.0,6.0,5.0,1.0,42.0,1.0 -CZ,1074.0,6,6.0,9.0,0.0,6.0,10.0,7.0,4.0,1.0,18.0,2.0 -CZ,1075.0,6,3.0,8.0,7.0,6.0,7.0,4.0,3.0,2.0,57.0,1.0 -CZ,1076.0,6,5.0,5.0,5.0,3.0,9.0,7.0,3.0,2.0,62.0,2.0 -CZ,1077.0,6,6.0,2.0,5.0,,5.0,3.0,2.0,1.0,55.0,1.0 -CZ,1078.0,6,4.0,7.0,7.0,8.0,8.0,6.0,3.0,2.0,17.0,2.0 -CZ,1079.0,6,5.0,4.0,5.0,6.0,6.0,2.0,1.0,1.0,43.0,2.0 -CZ,1080.0,6,1.0,2.0,4.0,4.0,7.0,3.0,3.0,1.0,48.0,2.0 -CZ,1081.0,6,7.0,4.0,3.0,5.0,5.0,2.0,,1.0,,1.0 -CZ,1082.0,6,5.0,1.0,4.0,1.0,4.0,7.0,2.0,1.0,55.0,1.0 -CZ,1083.0,6,4.0,4.0,5.0,7.0,4.0,4.0,2.0,2.0,45.0,1.0 -CZ,1084.0,6,7.0,7.0,8.0,8.0,7.0,5.0,4.0,2.0,16.0,2.0 -CZ,1085.0,6,3.0,5.0,5.0,4.0,8.0,4.0,4.0,1.0,16.0,2.0 -CZ,1086.0,6,7.0,6.0,8.0,8.0,8.0,6.0,1.0,2.0,76.0,2.0 -CZ,1087.0,6,1.0,5.0,6.0,6.0,9.0,5.0,3.0,1.0,46.0,1.0 -CZ,1088.0,6,5.0,1.0,3.0,3.0,7.0,3.0,2.0,1.0,34.0,1.0 -CZ,1089.0,6,6.0,6.0,7.0,6.0,4.0,5.0,3.0,1.0,18.0,2.0 -CZ,1090.0,6,5.0,9.0,7.0,10.0,3.0,2.0,2.0,2.0,25.0,2.0 -CZ,1091.0,6,3.0,10.0,8.0,10.0,9.0,3.0,3.0,2.0,39.0,1.0 -CZ,1092.0,6,4.0,3.0,4.0,1.0,7.0,4.0,2.0,1.0,48.0,2.0 -CZ,1093.0,6,4.0,9.0,9.0,7.0,9.0,6.0,3.0,2.0,16.0,2.0 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-CZ,1114.0,6,5.0,7.0,7.0,,7.0,6.0,3.0,1.0,20.0,2.0 -CZ,1116.0,6,7.0,7.0,5.0,4.0,6.0,4.0,5.0,1.0,51.0,1.0 -CZ,1117.0,6,6.0,4.0,5.0,5.0,5.0,5.0,5.0,1.0,51.0,1.0 -CZ,1118.0,6,7.0,5.0,7.0,3.0,5.0,6.0,3.0,1.0,71.0,1.0 -CZ,1119.0,6,5.0,5.0,5.0,2.0,8.0,5.0,2.0,1.0,61.0,2.0 -CZ,1120.0,6,4.0,5.0,5.0,0.0,5.0,5.0,2.0,1.0,60.0,1.0 -CZ,1121.0,6,5.0,5.0,5.0,4.0,5.0,4.0,3.0,2.0,59.0,1.0 -CZ,1122.0,6,7.0,2.0,2.0,2.0,1.0,3.0,1.0,2.0,64.0,1.0 -CZ,1123.0,6,7.0,4.0,7.0,3.0,3.0,5.0,2.0,1.0,79.0,2.0 -CZ,1124.0,6,4.0,5.0,6.0,2.0,9.0,7.0,3.0,1.0,29.0,1.0 -CZ,1125.0,6,5.0,6.0,6.0,6.0,8.0,6.0,3.0,1.0,45.0,1.0 -CZ,1126.0,6,7.0,7.0,5.0,7.0,6.0,2.0,1.0,2.0,51.0,1.0 -CZ,1127.0,6,5.0,9.0,9.0,3.0,10.0,6.0,3.0,1.0,19.0,2.0 -CZ,1128.0,6,7.0,2.0,4.0,0.0,8.0,4.0,3.0,1.0,53.0,1.0 -CZ,1129.0,6,2.0,5.0,6.0,8.0,1.0,6.0,3.0,1.0,19.0,2.0 -CZ,1130.0,6,4.0,7.0,7.0,5.0,7.0,6.0,3.0,1.0,72.0,2.0 -CZ,1131.0,6,6.0,5.0,5.0,2.0,9.0,6.0,3.0,2.0,16.0,2.0 -CZ,1132.0,6,7.0,5.0,5.0,4.0,5.0,2.0,1.0,1.0,50.0,2.0 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-CZ,1477.0,6,5.0,3.0,1.0,2.0,9.0,7.0,3.0,1.0,33.0,1.0 -CZ,1478.0,6,3.0,2.0,3.0,4.0,10.0,7.0,1.0,1.0,16.0,2.0 -CZ,1479.0,6,3.0,1.0,1.0,1.0,2.0,7.0,4.0,2.0,32.0,2.0 -CZ,1480.0,6,3.0,6.0,6.0,5.0,7.0,7.0,3.0,2.0,17.0,2.0 -CZ,1481.0,6,3.0,6.0,6.0,6.0,7.0,6.0,3.0,1.0,36.0,2.0 -CZ,1482.0,6,3.0,9.0,8.0,10.0,5.0,7.0,4.0,1.0,17.0,2.0 -CZ,1483.0,6,0.0,7.0,7.0,8.0,7.0,7.0,3.0,1.0,37.0,1.0 -CZ,1485.0,6,7.0,2.0,2.0,5.0,6.0,4.0,2.0,2.0,76.0,2.0 -CZ,1486.0,6,5.0,4.0,4.0,5.0,5.0,2.0,1.0,1.0,55.0,2.0 -CZ,1487.0,6,6.0,2.0,3.0,4.0,9.0,7.0,3.0,1.0,54.0,1.0 -CZ,1488.0,6,6.0,4.0,4.0,9.0,10.0,7.0,2.0,1.0,18.0,2.0 -CZ,1489.0,6,7.0,4.0,4.0,4.0,10.0,7.0,2.0,2.0,17.0,2.0 -CZ,1490.0,6,5.0,2.0,4.0,3.0,10.0,7.0,3.0,1.0,40.0,1.0 -CZ,1491.0,6,5.0,3.0,4.0,6.0,9.0,7.0,3.0,1.0,31.0,1.0 -CZ,1492.0,6,7.0,3.0,3.0,4.0,10.0,7.0,3.0,2.0,57.0,1.0 -CZ,1493.0,6,6.0,3.0,2.0,5.0,9.0,7.0,3.0,1.0,54.0,2.0 -CZ,1494.0,6,7.0,8.0,8.0,8.0,7.0,6.0,3.0,1.0,31.0,1.0 -CZ,1495.0,6,7.0,4.0,5.0,5.0,5.0,6.0,3.0,2.0,55.0,1.0 -CZ,1496.0,6,6.0,3.0,3.0,5.0,10.0,4.0,1.0,2.0,70.0,2.0 -CZ,1497.0,6,4.0,3.0,3.0,2.0,9.0,7.0,3.0,1.0,54.0,1.0 -CZ,1498.0,6,5.0,4.0,8.0,4.0,5.0,4.0,3.0,2.0,53.0,1.0 -CZ,1499.0,6,7.0,0.0,5.0,6.0,5.0,4.0,2.0,2.0,53.0,2.0 -CZ,1500.0,6,4.0,7.0,7.0,3.0,5.0,3.0,1.0,2.0,53.0,1.0 -CZ,1501.0,6,4.0,3.0,5.0,4.0,3.0,6.0,3.0,2.0,,2.0 -CZ,1502.0,6,7.0,0.0,3.0,4.0,10.0,2.0,2.0,2.0,24.0,1.0 -CZ,1503.0,6,6.0,2.0,4.0,3.0,5.0,2.0,2.0,1.0,31.0,1.0 -CZ,1504.0,6,5.0,8.0,5.0,9.0,9.0,7.0,3.0,1.0,33.0,1.0 -CZ,1505.0,6,6.0,4.0,3.0,3.0,9.0,7.0,3.0,1.0,50.0,1.0 -CZ,1506.0,6,5.0,3.0,5.0,5.0,9.0,7.0,3.0,1.0,42.0,1.0 -CZ,1507.0,6,6.0,4.0,3.0,4.0,8.0,3.0,2.0,1.0,73.0,1.0 -CZ,1508.0,6,7.0,0.0,2.0,2.0,0.0,1.0,1.0,2.0,36.0,2.0 -CZ,1509.0,6,5.0,9.0,8.0,8.0,7.0,3.0,3.0,1.0,50.0,1.0 -CZ,1510.0,6,4.0,8.0,8.0,7.0,8.0,4.0,3.0,1.0,44.0,1.0 -CZ,1511.0,6,2.0,4.0,5.0,3.0,7.0,5.0,2.0,1.0,35.0,1.0 -CZ,1512.0,6,7.0,5.0,5.0,5.0,5.0,5.0,2.0,1.0,34.0,1.0 -CZ,1550.0,6,5.0,0.0,5.0,4.0,2.0,4.0,4.0,1.0,78.0,1.0 -CZ,1551.0,6,5.0,2.0,7.0,8.0,9.0,2.0,3.0,2.0,64.0,1.0 -CZ,2001.0,6,7.0,5.0,4.0,4.0,3.0,4.0,2.0,2.0,61.0,1.0 -CZ,2002.0,6,7.0,5.0,6.0,6.0,9.0,2.0,2.0,2.0,50.0,1.0 -CZ,2003.0,6,3.0,3.0,1.0,1.0,7.0,4.0,3.0,2.0,17.0,2.0 -CZ,2004.0,6,3.0,1.0,0.0,0.0,8.0,3.0,3.0,2.0,26.0,1.0 -CZ,2005.0,6,7.0,3.0,5.0,2.0,8.0,6.0,2.0,2.0,83.0,2.0 -CZ,2006.0,6,7.0,3.0,3.0,5.0,7.0,6.0,4.0,1.0,67.0,1.0 -CZ,2007.0,6,5.0,0.0,5.0,0.0,10.0,3.0,1.0,2.0,70.0,2.0 -CZ,2008.0,6,7.0,4.0,4.0,6.0,7.0,4.0,3.0,2.0,32.0,1.0 -CZ,2009.0,6,6.0,3.0,4.0,5.0,7.0,4.0,3.0,1.0,45.0,1.0 -CZ,2010.0,6,7.0,4.0,5.0,3.0,3.0,6.0,2.0,2.0,74.0,2.0 -CZ,2011.0,6,6.0,5.0,5.0,5.0,9.0,3.0,3.0,1.0,63.0,1.0 -CZ,2012.0,6,4.0,4.0,4.0,5.0,3.0,2.0,2.0,1.0,40.0,1.0 -CZ,2013.0,6,7.0,7.0,5.0,5.0,8.0,3.0,4.0,2.0,60.0,1.0 -CZ,2014.0,6,6.0,8.0,8.0,7.0,9.0,4.0,3.0,2.0,38.0,1.0 -CZ,2015.0,6,7.0,0.0,5.0,0.0,3.0,7.0,1.0,2.0,59.0,2.0 -CZ,2016.0,6,6.0,4.0,3.0,8.0,8.0,6.0,3.0,1.0,33.0,2.0 -CZ,2017.0,6,4.0,5.0,5.0,5.0,6.0,4.0,3.0,2.0,75.0,2.0 -CZ,2018.0,6,7.0,3.0,3.0,1.0,5.0,5.0,1.0,1.0,70.0,1.0 -CZ,2019.0,6,4.0,3.0,5.0,2.0,7.0,6.0,3.0,1.0,64.0,1.0 -CZ,2020.0,6,1.0,8.0,1.0,4.0,9.0,7.0,4.0,1.0,19.0,2.0 -CZ,2021.0,6,4.0,8.0,7.0,6.0,9.0,5.0,2.0,2.0,35.0,1.0 -CZ,2022.0,6,6.0,3.0,3.0,4.0,8.0,3.0,2.0,2.0,56.0,1.0 -CZ,2023.0,6,4.0,3.0,4.0,3.0,8.0,1.0,1.0,2.0,75.0,2.0 -CZ,2024.0,6,6.0,5.0,7.0,5.0,4.0,6.0,3.0,2.0,32.0,2.0 -CZ,2025.0,6,7.0,2.0,5.0,4.0,7.0,6.0,3.0,2.0,21.0,2.0 -CZ,2026.0,6,6.0,2.0,6.0,7.0,8.0,5.0,2.0,2.0,63.0,1.0 -CZ,2027.0,6,6.0,8.0,8.0,8.0,7.0,5.0,4.0,2.0,47.0,1.0 -CZ,2028.0,6,2.0,4.0,2.0,4.0,10.0,2.0,3.0,2.0,37.0,2.0 -CZ,2029.0,6,7.0,7.0,7.0,5.0,7.0,4.0,2.0,1.0,43.0,1.0 -CZ,2030.0,6,7.0,8.0,8.0,3.0,5.0,4.0,1.0,1.0,54.0,1.0 -CZ,2031.0,6,7.0,3.0,5.0,3.0,6.0,4.0,1.0,2.0,54.0,1.0 -CZ,2032.0,6,3.0,2.0,3.0,3.0,5.0,3.0,1.0,2.0,79.0,2.0 -CZ,2033.0,6,5.0,4.0,4.0,5.0,9.0,4.0,1.0,1.0,37.0,1.0 -CZ,2034.0,6,5.0,0.0,5.0,5.0,5.0,3.0,3.0,2.0,33.0,1.0 -CZ,2035.0,6,3.0,8.0,8.0,8.0,8.0,6.0,3.0,1.0,23.0,2.0 -CZ,2036.0,6,4.0,6.0,2.0,2.0,7.0,3.0,,2.0,,1.0 -CZ,2037.0,6,7.0,5.0,5.0,8.0,9.0,5.0,3.0,2.0,36.0,1.0 -CZ,2038.0,6,6.0,5.0,6.0,3.0,6.0,5.0,3.0,1.0,39.0,1.0 -CZ,2039.0,6,7.0,3.0,3.0,4.0,4.0,5.0,3.0,2.0,48.0,1.0 -CZ,2040.0,6,3.0,4.0,5.0,8.0,3.0,2.0,2.0,1.0,30.0,2.0 -CZ,2041.0,6,5.0,3.0,7.0,7.0,7.0,6.0,2.0,1.0,37.0,1.0 -CZ,2042.0,6,6.0,7.0,9.0,10.0,6.0,5.0,3.0,1.0,18.0,2.0 -CZ,2043.0,6,5.0,0.0,9.0,7.0,7.0,7.0,5.0,2.0,16.0,2.0 -CZ,2044.0,6,7.0,0.0,2.0,2.0,5.0,7.0,2.0,2.0,55.0,1.0 -CZ,2045.0,6,5.0,6.0,7.0,6.0,6.0,3.0,3.0,2.0,91.0,2.0 -CZ,2046.0,6,7.0,3.0,4.0,4.0,8.0,4.0,3.0,1.0,71.0,1.0 -CZ,2047.0,6,7.0,6.0,6.0,6.0,5.0,5.0,2.0,2.0,67.0,2.0 -CZ,2048.0,6,7.0,4.0,4.0,7.0,7.0,2.0,1.0,1.0,75.0,2.0 -CZ,2049.0,6,4.0,3.0,4.0,3.0,7.0,6.0,2.0,1.0,48.0,1.0 -CZ,2050.0,6,7.0,6.0,8.0,7.0,7.0,4.0,3.0,1.0,58.0,1.0 -CZ,2051.0,6,7.0,7.0,8.0,7.0,5.0,6.0,3.0,2.0,70.0,1.0 -CZ,2052.0,6,7.0,5.0,5.0,6.0,5.0,6.0,4.0,2.0,37.0,2.0 -CZ,2053.0,6,7.0,2.0,5.0,3.0,4.0,4.0,2.0,1.0,65.0,1.0 -CZ,2054.0,6,7.0,6.0,6.0,7.0,8.0,6.0,2.0,1.0,29.0,1.0 -CZ,2055.0,6,4.0,7.0,5.0,5.0,7.0,6.0,3.0,1.0,29.0,2.0 -CZ,2056.0,6,0.0,4.0,5.0,2.0,3.0,4.0,1.0,1.0,74.0,1.0 -CZ,2057.0,6,7.0,4.0,5.0,4.0,6.0,6.0,3.0,2.0,39.0,1.0 -CZ,2058.0,6,7.0,4.0,5.0,5.0,8.0,3.0,3.0,1.0,38.0,1.0 -CZ,2059.0,6,4.0,8.0,9.0,3.0,8.0,6.0,3.0,2.0,28.0,2.0 -CZ,2060.0,6,5.0,7.0,7.0,3.0,7.0,6.0,3.0,1.0,64.0,1.0 -CZ,2061.0,6,2.0,4.0,5.0,5.0,5.0,6.0,3.0,2.0,38.0,1.0 -CZ,2062.0,6,7.0,3.0,3.0,5.0,6.0,3.0,3.0,1.0,68.0,1.0 -CZ,2063.0,6,6.0,3.0,6.0,8.0,7.0,5.0,3.0,2.0,59.0,2.0 -CZ,2064.0,6,7.0,5.0,7.0,0.0,7.0,4.0,2.0,1.0,50.0,2.0 -CZ,2065.0,6,7.0,4.0,5.0,2.0,7.0,6.0,3.0,2.0,,2.0 -CZ,2066.0,6,5.0,7.0,7.0,6.0,5.0,5.0,2.0,1.0,43.0,1.0 -CZ,2067.0,6,7.0,7.0,7.0,5.0,5.0,5.0,2.0,1.0,39.0,1.0 -CZ,2068.0,6,7.0,5.0,5.0,4.0,5.0,3.0,2.0,1.0,50.0,1.0 -CZ,2069.0,6,6.0,5.0,5.0,5.0,7.0,6.0,3.0,2.0,17.0,2.0 -CZ,2070.0,6,7.0,4.0,5.0,4.0,6.0,4.0,2.0,2.0,32.0,2.0 -CZ,2071.0,6,7.0,0.0,6.0,3.0,5.0,4.0,4.0,1.0,,1.0 -CZ,2072.0,6,5.0,3.0,6.0,5.0,4.0,6.0,2.0,1.0,59.0,1.0 -CZ,2073.0,6,5.0,3.0,7.0,4.0,7.0,4.0,3.0,1.0,40.0,1.0 -CZ,2074.0,6,7.0,3.0,7.0,8.0,7.0,6.0,4.0,1.0,68.0,1.0 -CZ,2075.0,6,4.0,8.0,2.0,6.0,8.0,5.0,3.0,1.0,40.0,1.0 -CZ,2076.0,6,7.0,7.0,7.0,4.0,7.0,4.0,2.0,2.0,43.0,2.0 -CZ,2077.0,6,3.0,5.0,9.0,8.0,8.0,7.0,1.0,1.0,35.0,1.0 -CZ,2078.0,6,5.0,6.0,7.0,7.0,8.0,6.0,2.0,2.0,79.0,2.0 -CZ,2079.0,6,6.0,2.0,1.0,5.0,10.0,3.0,4.0,2.0,62.0,1.0 -CZ,2080.0,6,7.0,5.0,5.0,5.0,8.0,6.0,3.0,1.0,19.0,2.0 -CZ,2081.0,6,7.0,4.0,6.0,3.0,7.0,6.0,2.0,2.0,33.0,1.0 -CZ,2082.0,6,6.0,6.0,7.0,7.0,8.0,4.0,3.0,2.0,27.0,1.0 -CZ,2083.0,6,6.0,5.0,7.0,7.0,7.0,4.0,3.0,2.0,36.0,2.0 -CZ,2084.0,6,3.0,5.0,5.0,5.0,5.0,6.0,4.0,1.0,27.0,2.0 -CZ,2085.0,6,5.0,5.0,5.0,6.0,5.0,5.0,3.0,2.0,49.0,2.0 -CZ,2086.0,6,6.0,1.0,1.0,2.0,4.0,2.0,2.0,1.0,53.0,1.0 -CZ,2087.0,6,7.0,3.0,4.0,3.0,5.0,6.0,2.0,2.0,37.0,2.0 -CZ,2088.0,6,7.0,3.0,6.0,6.0,6.0,7.0,2.0,2.0,44.0,2.0 -CZ,2089.0,6,5.0,1.0,1.0,0.0,7.0,1.0,,1.0,,1.0 -CZ,2090.0,6,3.0,2.0,2.0,3.0,5.0,2.0,3.0,2.0,19.0,2.0 -CZ,2091.0,6,7.0,8.0,10.0,10.0,7.0,5.0,3.0,2.0,18.0,2.0 -CZ,2092.0,6,6.0,8.0,8.0,6.0,8.0,3.0,3.0,1.0,50.0,1.0 -CZ,2093.0,6,7.0,7.0,5.0,3.0,5.0,6.0,4.0,1.0,48.0,1.0 -CZ,2094.0,6,7.0,9.0,10.0,10.0,8.0,6.0,2.0,2.0,68.0,1.0 -CZ,2095.0,6,0.0,5.0,5.0,3.0,5.0,2.0,3.0,2.0,40.0,2.0 -CZ,2096.0,6,4.0,7.0,7.0,4.0,9.0,7.0,3.0,1.0,18.0,2.0 -CZ,2097.0,6,6.0,,,,5.0,1.0,3.0,1.0,16.0,2.0 -CZ,2098.0,6,5.0,6.0,7.0,3.0,10.0,2.0,2.0,2.0,52.0,1.0 -CZ,2099.0,6,4.0,9.0,7.0,8.0,4.0,3.0,1.0,2.0,40.0,2.0 -CZ,2100.0,6,5.0,3.0,3.0,2.0,7.0,3.0,2.0,1.0,51.0,2.0 -CZ,2101.0,6,0.0,9.0,9.0,5.0,9.0,5.0,3.0,2.0,43.0,1.0 -CZ,2102.0,6,5.0,4.0,7.0,3.0,8.0,4.0,3.0,1.0,43.0,1.0 -CZ,2103.0,6,4.0,10.0,10.0,5.0,9.0,6.0,3.0,1.0,44.0,1.0 -CZ,2104.0,6,7.0,9.0,9.0,9.0,5.0,4.0,2.0,1.0,74.0,2.0 -CZ,2105.0,6,4.0,4.0,5.0,3.0,5.0,2.0,1.0,2.0,45.0,1.0 -CZ,2106.0,6,4.0,2.0,5.0,4.0,7.0,6.0,3.0,2.0,,2.0 -CZ,2107.0,6,7.0,6.0,7.0,3.0,7.0,5.0,2.0,1.0,28.0,2.0 -CZ,2108.0,6,7.0,9.0,9.0,9.0,8.0,5.0,4.0,1.0,73.0,1.0 -CZ,2109.0,6,5.0,8.0,8.0,4.0,9.0,4.0,3.0,2.0,47.0,1.0 -CZ,2110.0,6,5.0,5.0,5.0,6.0,8.0,5.0,3.0,1.0,54.0,1.0 -CZ,2111.0,6,7.0,5.0,5.0,5.0,5.0,6.0,1.0,1.0,56.0,1.0 -CZ,2112.0,6,0.0,3.0,9.0,0.0,9.0,4.0,3.0,2.0,58.0,1.0 -CZ,2113.0,6,5.0,3.0,3.0,3.0,8.0,4.0,4.0,1.0,37.0,2.0 -CZ,2114.0,6,6.0,0.0,0.0,2.0,6.0,3.0,4.0,2.0,,1.0 -CZ,2115.0,6,6.0,7.0,7.0,5.0,7.0,4.0,4.0,1.0,57.0,1.0 -CZ,2116.0,6,5.0,0.0,1.0,1.0,6.0,1.0,,1.0,,1.0 -CZ,2117.0,6,6.0,9.0,9.0,8.0,8.0,2.0,2.0,2.0,53.0, -CZ,2118.0,6,7.0,,8.0,8.0,7.0,3.0,2.0,2.0,56.0,2.0 -CZ,2119.0,6,,6.0,6.0,6.0,,4.0,3.0,2.0,75.0,2.0 -CZ,2120.0,6,6.0,3.0,4.0,5.0,5.0,5.0,3.0,1.0,73.0,1.0 -CZ,2121.0,6,7.0,2.0,4.0,3.0,3.0,4.0,2.0,1.0,76.0,2.0 -CZ,2122.0,6,4.0,8.0,8.0,2.0,7.0,5.0,3.0,1.0,36.0,1.0 -CZ,2123.0,6,4.0,8.0,8.0,7.0,7.0,3.0,3.0,2.0,51.0,1.0 -CZ,2124.0,6,0.0,5.0,1.0,0.0,7.0,3.0,3.0,2.0,34.0,1.0 -CZ,2125.0,6,0.0,7.0,8.0,1.0,8.0,6.0,3.0,1.0,19.0,2.0 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-CZ,1448.0,7,7.0,8.0,3.0,6.0,4.0,5.0,2.0,1.0,74.0,2.0 -CZ,1449.0,7,2.0,4.0,5.0,5.0,6.0,3.0,3.0,1.0,49.0,1.0 -CZ,1450.0,7,3.0,4.0,5.0,5.0,7.0,6.0,3.0,1.0,47.0,1.0 -CZ,1451.0,7,7.0,3.0,3.0,3.0,5.0,4.0,3.0,2.0,69.0,1.0 -CZ,1452.0,7,4.0,2.0,4.0,3.0,8.0,4.0,2.0,2.0,36.0,1.0 -CZ,1453.0,7,4.0,5.0,5.0,5.0,5.0,3.0,1.0,1.0,61.0,1.0 -CZ,1454.0,7,5.0,7.0,5.0,6.0,7.0,4.0,4.0,2.0,35.0,1.0 -CZ,1455.0,7,3.0,2.0,5.0,2.0,5.0,3.0,1.0,1.0,40.0,1.0 -CZ,1456.0,7,4.0,3.0,4.0,3.0,5.0,5.0,4.0,2.0,41.0,1.0 -CZ,1457.0,7,3.0,5.0,5.0,5.0,7.0,3.0,3.0,2.0,56.0,1.0 -CZ,1458.0,7,6.0,4.0,3.0,4.0,6.0,5.0,3.0,1.0,54.0,1.0 -CZ,1459.0,7,6.0,6.0,6.0,6.0,8.0,3.0,3.0,1.0,56.0,1.0 -CZ,1460.0,7,7.0,9.0,8.0,6.0,10.0,6.0,3.0,1.0,23.0,2.0 -CZ,1461.0,7,7.0,6.0,6.0,5.0,8.0,4.0,3.0,2.0,55.0,2.0 -CZ,1462.0,7,5.0,5.0,5.0,4.0,5.0,3.0,3.0,2.0,63.0,1.0 -CZ,1463.0,7,4.0,5.0,5.0,5.0,5.0,3.0,3.0,2.0,42.0,1.0 -CZ,1464.0,7,6.0,5.0,5.0,5.0,5.0,3.0,3.0,2.0,33.0,2.0 -CZ,1465.0,7,5.0,5.0,5.0,4.0,4.0,3.0,2.0,2.0,64.0,1.0 -CZ,1466.0,7,6.0,4.0,3.0,2.0,5.0,4.0,3.0,1.0,41.0,1.0 -CZ,1468.0,7,7.0,4.0,4.0,4.0,6.0,6.0,3.0,2.0,49.0,1.0 -CZ,1469.0,7,5.0,3.0,4.0,5.0,5.0,4.0,3.0,2.0,80.0,2.0 -CZ,1470.0,7,4.0,5.0,5.0,5.0,5.0,3.0,3.0,2.0,52.0,2.0 -CZ,1471.0,7,5.0,0.0,0.0,0.0,4.0,3.0,2.0,1.0,41.0,2.0 -CZ,1472.0,7,3.0,0.0,5.0,2.0,5.0,6.0,5.0,1.0,27.0,2.0 -CZ,1473.0,7,1.0,0.0,0.0,0.0,4.0,2.0,3.0,1.0,59.0,1.0 -CZ,1474.0,7,1.0,0.0,0.0,0.0,5.0,2.0,3.0,2.0,30.0,2.0 -CZ,1475.0,7,2.0,0.0,0.0,0.0,5.0,5.0,3.0,1.0,21.0,2.0 -CZ,1476.0,7,3.0,0.0,0.0,0.0,6.0,3.0,4.0,2.0,36.0,2.0 -CZ,1477.0,7,6.0,4.0,4.0,3.0,8.0,7.0,3.0,2.0,20.0,2.0 -CZ,1478.0,7,5.0,2.0,4.0,4.0,10.0,3.0,1.0,1.0,43.0,1.0 -CZ,1479.0,7,6.0,3.0,6.0,3.0,10.0,6.0,4.0,1.0,22.0,2.0 -CZ,1480.0,7,6.0,1.0,3.0,2.0,5.0,2.0,1.0,2.0,51.0,1.0 -CZ,1481.0,7,5.0,2.0,5.0,0.0,8.0,6.0,3.0,1.0,24.0,2.0 -CZ,1482.0,7,6.0,7.0,4.0,5.0,8.0,6.0,3.0,1.0,25.0,2.0 -CZ,1483.0,7,7.0,4.0,7.0,7.0,7.0,4.0,3.0,1.0,70.0,1.0 -CZ,1485.0,7,7.0,7.0,5.0,7.0,9.0,6.0,4.0,1.0,20.0,2.0 -CZ,1486.0,7,4.0,8.0,9.0,9.0,9.0,1.0,1.0,1.0,17.0,2.0 -CZ,1487.0,7,7.0,8.0,7.0,7.0,8.0,4.0,3.0,2.0,27.0,2.0 -CZ,1488.0,7,4.0,8.0,3.0,8.0,8.0,4.0,3.0,1.0,39.0,1.0 -CZ,1489.0,7,7.0,3.0,6.0,4.0,8.0,4.0,3.0,2.0,53.0,1.0 -CZ,1490.0,7,0.0,6.0,3.0,7.0,8.0,4.0,3.0,1.0,57.0,1.0 -CZ,1491.0,7,6.0,3.0,4.0,2.0,8.0,2.0,1.0,2.0,78.0,2.0 -CZ,1492.0,7,4.0,5.0,5.0,5.0,5.0,4.0,2.0,2.0,36.0,1.0 -CZ,1493.0,7,4.0,9.0,8.0,10.0,7.0,6.0,2.0,2.0,35.0,1.0 -CZ,1494.0,7,2.0,5.0,5.0,5.0,8.0,4.0,3.0,1.0,46.0,2.0 -CZ,1495.0,7,5.0,2.0,1.0,7.0,8.0,6.0,4.0,1.0,41.0,1.0 -CZ,1496.0,7,5.0,0.0,3.0,5.0,8.0,5.0,4.0,2.0,52.0,1.0 -CZ,1497.0,7,1.0,1.0,4.0,0.0,8.0,3.0,2.0,1.0,53.0,1.0 -CZ,1498.0,7,3.0,6.0,8.0,9.0,8.0,7.0,5.0,2.0,19.0,2.0 -CZ,1499.0,7,5.0,8.0,8.0,8.0,7.0,1.0,1.0,1.0,68.0,1.0 -CZ,1500.0,7,5.0,0.0,0.0,1.0,10.0,6.0,4.0,2.0,19.0,2.0 -CZ,1501.0,7,3.0,8.0,5.0,7.0,8.0,7.0,4.0,1.0,17.0,2.0 -CZ,1502.0,7,5.0,8.0,9.0,9.0,10.0,6.0,4.0,1.0,17.0,2.0 -CZ,1503.0,7,5.0,7.0,7.0,8.0,10.0,3.0,3.0,2.0,42.0, -CZ,1504.0,7,7.0,0.0,5.0,5.0,7.0,4.0,1.0,1.0,75.0,2.0 -CZ,1505.0,7,7.0,5.0,8.0,8.0,8.0,5.0,4.0,2.0,55.0,2.0 -CZ,1506.0,7,7.0,5.0,5.0,7.0,7.0,4.0,1.0,2.0,33.0,1.0 -CZ,1507.0,7,3.0,7.0,6.0,4.0,5.0,4.0,1.0,1.0,57.0,1.0 -CZ,1508.0,7,7.0,3.0,7.0,8.0,6.0,3.0,1.0,2.0,78.0,2.0 -CZ,1509.0,7,7.0,7.0,7.0,8.0,8.0,7.0,4.0,1.0,19.0,2.0 -CZ,1510.0,7,7.0,7.0,8.0,7.0,6.0,4.0,3.0,2.0,55.0,1.0 -CZ,1511.0,7,7.0,5.0,5.0,5.0,8.0,2.0,2.0,2.0,43.0,1.0 -CZ,1512.0,7,2.0,6.0,7.0,4.0,4.0,3.0,3.0,1.0,42.0,1.0 -CZ,1550.0,7,0.0,10.0,4.0,7.0,8.0,6.0,2.0,1.0,20.0,2.0 -CZ,1551.0,7,1.0,8.0,4.0,8.0,3.0,5.0,,1.0,21.0,2.0 -CZ,2001.0,7,5.0,5.0,5.0,5.0,9.0,4.0,3.0,1.0,43.0,1.0 -CZ,2002.0,7,7.0,7.0,7.0,7.0,8.0,4.0,3.0,,65.0,2.0 -CZ,2003.0,7,7.0,5.0,5.0,5.0,7.0,4.0,3.0,1.0,55.0,1.0 -CZ,2004.0,7,7.0,5.0,5.0,5.0,8.0,4.0,3.0,,69.0,2.0 -CZ,2005.0,7,5.0,5.0,5.0,5.0,10.0,5.0,3.0,2.0,21.0,2.0 -CZ,2006.0,7,7.0,5.0,6.0,6.0,9.0,4.0,3.0,1.0,,1.0 -CZ,2007.0,7,7.0,5.0,5.0,5.0,8.0,5.0,3.0,2.0,24.0,2.0 -CZ,2008.0,7,5.0,7.0,7.0,7.0,8.0,4.0,3.0,2.0,33.0,2.0 -CZ,2009.0,7,5.0,5.0,5.0,5.0,9.0,5.0,3.0,1.0,50.0,2.0 -CZ,2010.0,7,6.0,7.0,7.0,7.0,9.0,5.0,3.0,2.0,25.0,2.0 -CZ,2011.0,7,7.0,5.0,5.0,5.0,7.0,4.0,3.0,1.0,51.0,1.0 -CZ,2012.0,7,7.0,6.0,6.0,6.0,8.0,4.0,3.0,,82.0,2.0 -CZ,2013.0,7,3.0,6.0,5.0,5.0,8.0,3.0,3.0,,54.0,2.0 -CZ,2014.0,7,7.0,5.0,5.0,5.0,8.0,4.0,3.0,,74.0,2.0 -CZ,2015.0,7,7.0,8.0,8.0,8.0,8.0,4.0,3.0,2.0,64.0,2.0 -CZ,2016.0,7,7.0,6.0,6.0,6.0,8.0,4.0,3.0,1.0,62.0,2.0 -CZ,2017.0,7,7.0,5.0,5.0,5.0,8.0,4.0,3.0,,87.0,2.0 -CZ,2018.0,7,7.0,5.0,5.0,5.0,8.0,4.0,3.0,2.0,74.0,1.0 -CZ,2019.0,7,7.0,8.0,8.0,7.0,8.0,4.0,3.0,2.0,64.0,2.0 -CZ,2020.0,7,7.0,5.0,7.0,7.0,7.0,4.0,3.0,,84.0,2.0 -CZ,2021.0,7,7.0,6.0,7.0,7.0,8.0,4.0,3.0,1.0,59.0,1.0 -CZ,2022.0,7,7.0,6.0,7.0,7.0,8.0,4.0,3.0,1.0,54.0,1.0 -CZ,2023.0,7,7.0,4.0,5.0,5.0,8.0,5.0,3.0,1.0,26.0,1.0 -CZ,2024.0,7,7.0,5.0,5.0,5.0,7.0,4.0,3.0,1.0,24.0,2.0 -CZ,2025.0,7,7.0,5.0,5.0,6.0,8.0,6.0,2.0,2.0,37.0,1.0 -CZ,2026.0,7,4.0,3.0,2.0,2.0,6.0,7.0,2.0,2.0,43.0,1.0 -CZ,2027.0,7,5.0,7.0,6.0,4.0,8.0,7.0,3.0,2.0,60.0,2.0 -CZ,2028.0,7,7.0,2.0,5.0,3.0,5.0,2.0,1.0,2.0,67.0,2.0 -CZ,2029.0,7,7.0,1.0,1.0,5.0,5.0,7.0,4.0,1.0,65.0,1.0 -CZ,2030.0,7,7.0,3.0,3.0,3.0,6.0,7.0,3.0,1.0,41.0,2.0 -CZ,2031.0,7,2.0,4.0,3.0,5.0,7.0,6.0,3.0,2.0,55.0,1.0 -CZ,2032.0,7,6.0,5.0,5.0,5.0,8.0,3.0,3.0,2.0,45.0,1.0 -CZ,2033.0,7,4.0,0.0,5.0,2.0,5.0,2.0,3.0,1.0,56.0,1.0 -CZ,2034.0,7,5.0,2.0,3.0,3.0,8.0,4.0,3.0,2.0,35.0,1.0 -CZ,2035.0,7,6.0,5.0,4.0,6.0,7.0,5.0,3.0,2.0,50.0,1.0 -CZ,2036.0,7,4.0,5.0,10.0,5.0,5.0,6.0,2.0,2.0,58.0,1.0 -CZ,2037.0,7,3.0,0.0,5.0,5.0,9.0,4.0,3.0,2.0,37.0,1.0 -CZ,2038.0,7,3.0,1.0,6.0,3.0,6.0,7.0,2.0,1.0,39.0,2.0 -CZ,2039.0,7,7.0,0.0,0.0,0.0,4.0,7.0,3.0,2.0,60.0,1.0 -CZ,2040.0,7,5.0,5.0,5.0,5.0,8.0,6.0,4.0,2.0,52.0,1.0 -CZ,2041.0,7,6.0,2.0,2.0,2.0,8.0,4.0,3.0,1.0,39.0,2.0 -CZ,2042.0,7,6.0,5.0,5.0,5.0,9.0,4.0,3.0,1.0,25.0,1.0 -CZ,2043.0,7,7.0,5.0,6.0,7.0,9.0,4.0,3.0,1.0,71.0,1.0 -CZ,2044.0,7,7.0,6.0,6.0,6.0,8.0,4.0,3.0,1.0,64.0,2.0 -CZ,2045.0,7,4.0,5.0,5.0,5.0,9.0,4.0,3.0,2.0,29.0,1.0 -CZ,2046.0,7,3.0,5.0,5.0,6.0,10.0,5.0,3.0,1.0,22.0,2.0 -CZ,2047.0,7,6.0,5.0,5.0,5.0,8.0,4.0,3.0,1.0,45.0,2.0 -CZ,2048.0,7,5.0,8.0,8.0,8.0,9.0,5.0,3.0,2.0,24.0,2.0 -CZ,2049.0,7,6.0,5.0,5.0,6.0,8.0,4.0,3.0,1.0,50.0,2.0 -CZ,2050.0,7,7.0,6.0,6.0,6.0,8.0,4.0,3.0,1.0,65.0,1.0 -CZ,2051.0,7,5.0,5.0,5.0,5.0,9.0,5.0,3.0,1.0,22.0,2.0 -CZ,2052.0,7,6.0,4.0,4.0,4.0,8.0,4.0,3.0,1.0,40.0,2.0 -CZ,2053.0,7,5.0,6.0,6.0,6.0,8.0,4.0,3.0,1.0,41.0,1.0 -CZ,2054.0,7,5.0,7.0,7.0,7.0,9.0,4.0,3.0,2.0,45.0,1.0 -CZ,2055.0,7,2.0,5.0,5.0,3.0,10.0,5.0,3.0,2.0,22.0,2.0 -CZ,2056.0,7,6.0,5.0,5.0,5.0,7.0,4.0,3.0,2.0,40.0,1.0 -CZ,2057.0,7,7.0,4.0,4.0,4.0,7.0,4.0,3.0,1.0,51.0,2.0 -CZ,2058.0,7,6.0,5.0,5.0,5.0,8.0,4.0,3.0,1.0,53.0,1.0 -CZ,2059.0,7,7.0,5.0,5.0,5.0,8.0,4.0,3.0,,50.0,2.0 -CZ,2060.0,7,5.0,4.0,5.0,5.0,9.0,4.0,3.0,2.0,24.0,1.0 -CZ,2061.0,7,2.0,4.0,4.0,5.0,6.0,4.0,2.0,2.0,73.0,2.0 -CZ,2062.0,7,7.0,5.0,5.0,5.0,8.0,4.0,3.0,1.0,65.0,2.0 -CZ,2063.0,7,7.0,7.0,7.0,6.0,8.0,6.0,3.0,2.0,69.0,2.0 -CZ,2064.0,7,7.0,7.0,7.0,7.0,8.0,5.0,3.0,1.0,26.0,2.0 -CZ,2065.0,7,4.0,2.0,6.0,6.0,6.0,2.0,,1.0,55.0,1.0 -CZ,2066.0,7,1.0,3.0,7.0,2.0,7.0,5.0,1.0,1.0,31.0,2.0 -CZ,2067.0,7,2.0,6.0,5.0,3.0,7.0,6.0,2.0,1.0,52.0,1.0 -CZ,2068.0,7,2.0,5.0,5.0,5.0,10.0,3.0,2.0,2.0,35.0,2.0 -CZ,2069.0,7,6.0,7.0,5.0,7.0,7.0,4.0,3.0,2.0,52.0,2.0 -CZ,2070.0,7,6.0,0.0,0.0,2.0,3.0,3.0,3.0,2.0,45.0,1.0 -CZ,2071.0,7,7.0,5.0,5.0,5.0,7.0,6.0,1.0,2.0,59.0,1.0 -CZ,2072.0,7,3.0,3.0,2.0,2.0,9.0,6.0,2.0,2.0,40.0,1.0 -CZ,2073.0,7,3.0,5.0,2.0,2.0,5.0,6.0,3.0,1.0,48.0,2.0 -CZ,2074.0,7,3.0,1.0,1.0,3.0,10.0,6.0,2.0,1.0,28.0,1.0 -CZ,2075.0,7,7.0,8.0,8.0,7.0,7.0,4.0,1.0,1.0,36.0,2.0 -CZ,2076.0,7,5.0,5.0,5.0,5.0,8.0,5.0,3.0,2.0,27.0,2.0 -CZ,2077.0,7,5.0,3.0,3.0,4.0,7.0,3.0,4.0,2.0,62.0,1.0 -CZ,2078.0,7,4.0,6.0,5.0,5.0,7.0,3.0,1.0,2.0,54.0,1.0 -CZ,2079.0,7,3.0,10.0,9.0,7.0,9.0,6.0,4.0,2.0,51.0,1.0 -CZ,2080.0,7,6.0,3.0,5.0,5.0,6.0,5.0,4.0,2.0,58.0,1.0 -CZ,2081.0,7,2.0,7.0,7.0,5.0,8.0,7.0,4.0,2.0,28.0,2.0 -CZ,2082.0,7,5.0,4.0,4.0,3.0,6.0,4.0,2.0,1.0,46.0,1.0 -CZ,2083.0,7,3.0,7.0,7.0,4.0,9.0,6.0,4.0,1.0,59.0,1.0 -CZ,2084.0,7,4.0,6.0,6.0,7.0,8.0,7.0,2.0,2.0,53.0,1.0 -CZ,2085.0,7,4.0,5.0,6.0,5.0,10.0,3.0,3.0,2.0,57.0,1.0 -CZ,2086.0,7,6.0,3.0,6.0,5.0,8.0,7.0,5.0,2.0,55.0,1.0 -CZ,2087.0,7,3.0,6.0,8.0,4.0,9.0,2.0,3.0,2.0,55.0,2.0 -CZ,2088.0,7,1.0,7.0,5.0,3.0,9.0,6.0,1.0,1.0,42.0,1.0 -CZ,2089.0,7,4.0,3.0,3.0,1.0,5.0,6.0,1.0,2.0,68.0,2.0 -CZ,2090.0,7,4.0,3.0,3.0,2.0,9.0,6.0,4.0,2.0,58.0,2.0 -CZ,2091.0,7,4.0,5.0,3.0,2.0,9.0,7.0,2.0,2.0,20.0,2.0 -CZ,2092.0,7,7.0,2.0,1.0,1.0,3.0,4.0,1.0,2.0,,2.0 -CZ,2093.0,7,7.0,5.0,5.0,2.0,7.0,5.0,3.0,2.0,39.0,2.0 -CZ,2094.0,7,7.0,7.0,6.0,3.0,9.0,6.0,2.0,2.0,60.0,2.0 -CZ,2095.0,7,3.0,5.0,6.0,3.0,9.0,6.0,3.0,2.0,53.0,2.0 -CZ,2096.0,7,7.0,3.0,4.0,1.0,5.0,4.0,2.0,1.0,23.0,2.0 -CZ,2097.0,7,7.0,2.0,3.0,2.0,7.0,4.0,3.0,2.0,59.0,2.0 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-NO,9846.0,6,7.0,2.0,5.0,2.0,,6.0,2.0,2.0,22.0,1.0 -NO,9852.0,6,4.0,5.0,4.0,4.0,8.0,6.0,4.0,1.0,33.0,1.0 -NO,9858.0,6,5.0,7.0,8.0,5.0,8.0,5.0,4.0,2.0,76.0,1.0 -NO,9876.0,6,4.0,5.0,5.0,7.0,8.0,7.0,4.0,1.0,22.0,2.0 -NO,9966.0,6,6.0,9.0,8.0,7.0,8.0,6.0,3.0,2.0,21.0,2.0 -NO,9996.0,6,6.0,8.0,7.0,6.0,8.0,6.0,4.0,2.0,71.0,1.0 -NO,10020.0,6,2.0,7.0,6.0,6.0,8.0,7.0,2.0,2.0,56.0,1.0 -NO,10032.0,6,5.0,7.0,7.0,7.0,8.0,6.0,3.0,2.0,31.0,1.0 -NO,10050.0,6,4.0,8.0,9.0,4.0,9.0,4.0,3.0,1.0,45.0,1.0 -NO,10128.0,6,4.0,7.0,9.0,8.0,9.0,7.0,3.0,2.0,54.0,1.0 -NO,10134.0,6,7.0,7.0,7.0,5.0,6.0,5.0,2.0,1.0,72.0,2.0 -NO,10188.0,6,3.0,7.0,7.0,7.0,9.0,6.0,4.0,1.0,58.0,1.0 -NO,10200.0,6,7.0,7.0,6.0,3.0,3.0,6.0,3.0,1.0,49.0,2.0 -NO,10206.0,6,4.0,8.0,9.0,8.0,9.0,6.0,2.0,1.0,86.0,1.0 -NO,10212.0,6,4.0,9.0,9.0,5.0,8.0,4.0,3.0,2.0,59.0,2.0 -NO,10218.0,6,6.0,7.0,8.0,6.0,7.0,6.0,3.0,2.0,49.0,1.0 -NO,10266.0,6,4.0,6.0,6.0,6.0,8.0,7.0,4.0,1.0,68.0,2.0 -NO,10284.0,6,4.0,8.0,7.0,6.0,9.0,5.0,3.0,1.0,48.0,1.0 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-NO,11718.0,6,2.0,6.0,7.0,5.0,8.0,6.0,3.0,1.0,18.0,2.0 -NO,11724.0,6,3.0,8.0,8.0,5.0,9.0,6.0,4.0,1.0,61.0,2.0 -NO,11730.0,6,6.0,7.0,9.0,7.0,9.0,6.0,3.0,2.0,57.0,2.0 -NO,11742.0,6,3.0,8.0,8.0,8.0,9.0,6.0,3.0,2.0,19.0,2.0 -NO,11790.0,6,7.0,6.0,5.0,3.0,8.0,6.0,3.0,2.0,25.0,1.0 -NO,11820.0,6,7.0,7.0,9.0,5.0,7.0,7.0,4.0,2.0,34.0,2.0 -NO,11832.0,6,3.0,7.0,5.0,5.0,9.0,6.0,3.0,1.0,46.0,1.0 -NO,11850.0,6,6.0,7.0,7.0,6.0,9.0,4.0,3.0,1.0,57.0,1.0 -NO,11874.0,6,4.0,8.0,8.0,9.0,9.0,5.0,3.0,2.0,65.0,1.0 -NO,11898.0,6,2.0,7.0,7.0,5.0,8.0,7.0,4.0,1.0,42.0,1.0 -NO,11940.0,6,4.0,6.0,6.0,9.0,9.0,4.0,3.0,2.0,93.0,2.0 -NO,11958.0,6,3.0,9.0,9.0,5.0,10.0,6.0,3.0,1.0,16.0,2.0 -NO,11964.0,6,3.0,8.0,8.0,4.0,10.0,3.0,3.0,1.0,62.0,1.0 -NO,11982.0,6,6.0,7.0,6.0,3.0,8.0,5.0,3.0,2.0,24.0,1.0 -NO,12006.0,6,4.0,5.0,5.0,6.0,8.0,6.0,3.0,2.0,69.0,2.0 -NO,12024.0,6,4.0,7.0,6.0,7.0,8.0,5.0,3.0,2.0,33.0,1.0 -NO,12060.0,6,1.0,5.0,5.0,2.0,9.0,3.0,3.0,1.0,67.0,2.0 -NO,12096.0,6,7.0,8.0,8.0,8.0,10.0,6.0,3.0,2.0,62.0,1.0 -NO,12102.0,6,7.0,5.0,8.0,3.0,8.0,6.0,3.0,2.0,53.0,1.0 -NO,12132.0,6,4.0,8.0,8.0,6.0,10.0,5.0,5.0,2.0,73.0,1.0 -NO,12156.0,6,3.0,3.0,6.0,5.0,10.0,6.0,3.0,2.0,27.0,1.0 -NO,12198.0,6,4.0,7.0,8.0,6.0,6.0,6.0,2.0,2.0,65.0,1.0 -NO,12228.0,6,6.0,8.0,9.0,5.0,5.0,4.0,2.0,2.0,80.0, -NO,12240.0,6,7.0,7.0,7.0,5.0,5.0,3.0,1.0,1.0,44.0,2.0 -NO,12264.0,6,4.0,8.0,6.0,7.0,8.0,3.0,3.0,1.0,49.0,1.0 -NO,12270.0,6,3.0,7.0,7.0,8.0,8.0,6.0,3.0,2.0,24.0,2.0 -NO,12300.0,6,3.0,8.0,8.0,5.0,10.0,6.0,3.0,2.0,46.0,1.0 -NO,12306.0,6,2.0,8.0,8.0,6.0,9.0,7.0,4.0,1.0,61.0,1.0 -NO,12318.0,6,7.0,7.0,7.0,5.0,7.0,4.0,2.0,2.0,37.0,2.0 -NO,12324.0,6,6.0,10.0,6.0,7.0,7.0,7.0,5.0,2.0,60.0,2.0 -NO,12330.0,6,5.0,9.0,8.0,6.0,10.0,7.0,3.0,1.0,19.0,1.0 -NO,12372.0,6,3.0,8.0,8.0,5.0,8.0,6.0,2.0,2.0,37.0,1.0 -NO,12402.0,6,7.0,8.0,6.0,5.0,6.0,6.0,3.0,2.0,34.0,2.0 -NO,12426.0,6,3.0,5.0,6.0,7.0,8.0,6.0,3.0,1.0,28.0,2.0 -NO,12516.0,6,2.0,8.0,8.0,7.0,9.0,6.0,4.0,1.0,61.0,1.0 -NO,12570.0,6,4.0,7.0,7.0,5.0,8.0,6.0,3.0,2.0,40.0,1.0 -NO,12594.0,6,7.0,7.0,7.0,8.0,6.0,4.0,3.0,1.0,40.0,2.0 -NO,12606.0,6,4.0,5.0,5.0,7.0,7.0,6.0,1.0,2.0,55.0,1.0 -NO,12612.0,6,5.0,8.0,8.0,5.0,7.0,5.0,2.0,1.0,40.0,1.0 -NO,12618.0,6,2.0,6.0,5.0,7.0,9.0,7.0,4.0,2.0,18.0,2.0 -NO,12648.0,6,7.0,8.0,8.0,7.0,9.0,6.0,3.0,2.0,76.0,1.0 -NO,12654.0,6,1.0,9.0,9.0,8.0,9.0,7.0,5.0,1.0,51.0,1.0 -NO,12678.0,6,2.0,9.0,8.0,7.0,8.0,5.0,3.0,1.0,44.0,1.0 -NO,12690.0,6,3.0,7.0,7.0,6.0,8.0,6.0,3.0,1.0,57.0,1.0 -NO,12714.0,6,1.0,8.0,8.0,7.0,9.0,6.0,3.0,2.0,45.0,1.0 -NO,12762.0,6,1.0,8.0,7.0,5.0,8.0,6.0,2.0,2.0,26.0,1.0 -NO,12774.0,6,5.0,8.0,8.0,7.0,9.0,4.0,3.0,2.0,63.0,1.0 -NO,12798.0,6,3.0,5.0,6.0,8.0,8.0,4.0,2.0,1.0,75.0,1.0 -NO,12810.0,6,4.0,7.0,7.0,6.0,8.0,6.0,3.0,1.0,,1.0 -NO,12822.0,6,2.0,5.0,5.0,4.0,10.0,7.0,3.0,2.0,42.0,1.0 -NO,12828.0,6,7.0,5.0,7.0,7.0,9.0,3.0,3.0,2.0,74.0,2.0 -NO,12840.0,6,3.0,8.0,8.0,7.0,9.0,6.0,3.0,1.0,50.0,1.0 -NO,12846.0,6,3.0,8.0,8.0,8.0,7.0,6.0,3.0,2.0,49.0,2.0 -NO,12924.0,6,5.0,8.0,7.0,8.0,9.0,6.0,4.0,2.0,80.0,1.0 -NO,12984.0,6,5.0,0.0,0.0,0.0,9.0,7.0,4.0,1.0,34.0,1.0 -NO,13008.0,6,5.0,6.0,7.0,6.0,7.0,6.0,3.0,2.0,67.0,2.0 -NO,13044.0,6,5.0,10.0,10.0,5.0,10.0,6.0,3.0,2.0,51.0,1.0 -NO,13128.0,6,0.0,9.0,9.0,6.0,9.0,7.0,3.0,1.0,18.0,2.0 -NO,13146.0,6,4.0,2.0,2.0,4.0,8.0,7.0,5.0,1.0,48.0,1.0 -NO,13158.0,6,3.0,8.0,7.0,5.0,10.0,4.0,3.0,1.0,76.0,1.0 -NO,13218.0,6,5.0,8.0,8.0,7.0,9.0,6.0,4.0,1.0,35.0,1.0 -NO,13230.0,6,3.0,7.0,6.0,7.0,9.0,6.0,4.0,1.0,64.0,1.0 -NO,13248.0,6,4.0,5.0,5.0,6.0,8.0,5.0,2.0,2.0,38.0,1.0 -NO,13338.0,6,6.0,8.0,7.0,8.0,8.0,6.0,2.0,1.0,67.0,1.0 -NO,13344.0,6,4.0,3.0,7.0,6.0,9.0,7.0,3.0,1.0,53.0,2.0 -NO,13422.0,6,4.0,8.0,8.0,8.0,10.0,6.0,3.0,2.0,73.0,1.0 -NO,13440.0,6,4.0,7.0,8.0,5.0,9.0,5.0,3.0,1.0,28.0,2.0 -NO,13464.0,6,4.0,6.0,5.0,3.0,8.0,7.0,3.0,1.0,32.0,1.0 -NO,13470.0,6,3.0,7.0,8.0,7.0,8.0,6.0,4.0,2.0,60.0,1.0 -NO,13482.0,6,2.0,7.0,7.0,7.0,7.0,6.0,2.0,2.0,43.0,1.0 -NO,13488.0,6,5.0,8.0,8.0,7.0,8.0,7.0,3.0,2.0,24.0,2.0 -NO,13524.0,6,3.0,7.0,7.0,7.0,8.0,5.0,3.0,1.0,35.0,1.0 -NO,13542.0,6,3.0,8.0,8.0,7.0,10.0,6.0,4.0,2.0,29.0,1.0 -NO,13548.0,6,1.0,5.0,6.0,3.0,8.0,7.0,4.0,1.0,26.0,1.0 -NO,13584.0,6,7.0,6.0,6.0,2.0,9.0,4.0,4.0,1.0,,2.0 -NO,13638.0,6,0.0,7.0,8.0,7.0,9.0,4.0,3.0,2.0,37.0,2.0 -NO,13650.0,6,6.0,7.0,6.0,6.0,10.0,7.0,3.0,2.0,24.0,2.0 -NO,13680.0,6,7.0,8.0,8.0,7.0,8.0,6.0,3.0,1.0,26.0,1.0 -NO,13704.0,6,2.0,3.0,4.0,7.0,10.0,6.0,4.0,1.0,51.0,1.0 -NO,13734.0,6,6.0,3.0,6.0,3.0,8.0,6.0,3.0,1.0,68.0,1.0 -NO,13746.0,6,4.0,8.0,5.0,6.0,8.0,7.0,4.0,1.0,62.0,1.0 -NO,13752.0,6,3.0,8.0,8.0,6.0,8.0,5.0,4.0,2.0,80.0,2.0 -NO,13758.0,6,1.0,8.0,8.0,6.0,7.0,6.0,3.0,2.0,57.0,1.0 -NO,13782.0,6,3.0,9.0,9.0,9.0,10.0,6.0,3.0,2.0,70.0,1.0 -NO,13788.0,6,4.0,8.0,7.0,7.0,8.0,4.0,3.0,2.0,49.0,1.0 -NO,13794.0,6,4.0,8.0,8.0,5.0,8.0,7.0,4.0,1.0,55.0,1.0 -NO,13800.0,6,2.0,7.0,4.0,4.0,9.0,7.0,3.0,1.0,24.0,1.0 -NO,13812.0,6,5.0,5.0,6.0,3.0,8.0,6.0,4.0,2.0,67.0,2.0 -NO,13824.0,6,7.0,7.0,7.0,4.0,8.0,6.0,3.0,2.0,69.0,1.0 -NO,13830.0,6,2.0,9.0,8.0,6.0,8.0,6.0,3.0,1.0,46.0,1.0 -NO,13842.0,6,4.0,6.0,7.0,4.0,9.0,5.0,3.0,2.0,18.0,2.0 -NO,13848.0,6,1.0,6.0,10.0,3.0,7.0,6.0,3.0,1.0,64.0,1.0 -NO,13866.0,6,7.0,4.0,3.0,5.0,6.0,5.0,2.0,1.0,67.0,1.0 -NO,13902.0,6,3.0,5.0,5.0,8.0,6.0,4.0,3.0,1.0,87.0,2.0 -NO,13908.0,6,2.0,7.0,7.0,3.0,8.0,3.0,2.0,1.0,44.0,1.0 -NO,13920.0,6,2.0,6.0,8.0,,7.0,5.0,,1.0,43.0,2.0 -NO,13926.0,6,0.0,4.0,6.0,3.0,7.0,5.0,2.0,1.0,33.0,1.0 -NO,13932.0,6,3.0,7.0,8.0,7.0,9.0,5.0,3.0,1.0,33.0,1.0 -NO,13944.0,6,3.0,8.0,9.0,9.0,8.0,6.0,3.0,1.0,76.0,1.0 -NO,13950.0,6,3.0,8.0,8.0,8.0,9.0,4.0,4.0,2.0,90.0,2.0 -NO,13962.0,6,4.0,7.0,7.0,8.0,9.0,5.0,3.0,1.0,49.0,1.0 -NO,13968.0,6,4.0,4.0,6.0,6.0,6.0,6.0,3.0,1.0,75.0,1.0 -NO,13980.0,6,4.0,5.0,5.0,4.0,10.0,6.0,4.0,1.0,32.0,2.0 -NO,13986.0,6,4.0,7.0,3.0,5.0,9.0,6.0,2.0,2.0,27.0,1.0 -NO,14004.0,6,5.0,7.0,7.0,8.0,9.0,6.0,3.0,2.0,45.0,1.0 -NO,14076.0,6,3.0,5.0,6.0,7.0,7.0,4.0,3.0,2.0,51.0,1.0 -NO,14124.0,6,7.0,9.0,5.0,5.0,9.0,7.0,2.0,2.0,20.0,2.0 -NO,14148.0,6,1.0,9.0,9.0,9.0,9.0,4.0,3.0,1.0,40.0,1.0 -NO,14160.0,6,7.0,8.0,8.0,8.0,7.0,5.0,4.0,1.0,48.0,2.0 -NO,14166.0,6,6.0,8.0,9.0,8.0,10.0,3.0,2.0,2.0,86.0,2.0 -NO,14214.0,6,4.0,7.0,8.0,9.0,9.0,7.0,3.0,2.0,16.0,2.0 -NO,14310.0,6,2.0,8.0,7.0,7.0,10.0,7.0,4.0,2.0,33.0,1.0 -NO,14346.0,6,3.0,7.0,8.0,2.0,9.0,3.0,2.0,1.0,61.0,1.0 -NO,14412.0,6,5.0,8.0,10.0,10.0,8.0,3.0,3.0,1.0,35.0,1.0 -NO,14418.0,6,4.0,9.0,8.0,6.0,8.0,6.0,1.0,2.0,39.0,1.0 -NO,14424.0,6,3.0,7.0,6.0,5.0,7.0,7.0,3.0,2.0,51.0,1.0 -NO,14436.0,6,2.0,8.0,8.0,9.0,10.0,2.0,2.0,1.0,39.0,1.0 -NO,14448.0,6,1.0,8.0,8.0,5.0,8.0,6.0,4.0,1.0,46.0,1.0 -NO,14460.0,6,3.0,7.0,8.0,8.0,9.0,6.0,3.0,1.0,,1.0 -NO,14466.0,6,4.0,8.0,8.0,9.0,8.0,7.0,3.0,2.0,38.0,1.0 -NO,14472.0,6,4.0,8.0,7.0,6.0,8.0,7.0,5.0,1.0,27.0,1.0 -NO,14484.0,6,3.0,7.0,7.0,6.0,9.0,6.0,3.0,1.0,54.0,1.0 -NO,14508.0,6,7.0,8.0,8.0,9.0,9.0,4.0,3.0,2.0,47.0,1.0 -NO,14514.0,6,6.0,7.0,7.0,5.0,10.0,6.0,3.0,1.0,27.0,1.0 -NO,14556.0,6,6.0,7.0,7.0,7.0,8.0,6.0,3.0,1.0,60.0,1.0 -NO,14586.0,6,2.0,7.0,9.0,7.0,8.0,6.0,3.0,1.0,18.0,2.0 -NO,14646.0,6,4.0,5.0,6.0,6.0,10.0,7.0,2.0,2.0,26.0,1.0 -NO,14652.0,6,5.0,7.0,7.0,7.0,8.0,6.0,3.0,1.0,73.0,1.0 -NO,14658.0,6,5.0,7.0,5.0,6.0,9.0,4.0,3.0,2.0,42.0,1.0 -NO,14682.0,6,4.0,8.0,9.0,8.0,8.0,4.0,3.0,1.0,25.0,1.0 -NO,14730.0,6,5.0,6.0,6.0,5.0,9.0,7.0,3.0,1.0,19.0,2.0 -NO,14754.0,6,4.0,7.0,7.0,8.0,8.0,6.0,3.0,1.0,63.0,1.0 -NO,14766.0,6,4.0,9.0,8.0,7.0,10.0,7.0,2.0,2.0,39.0,1.0 -NO,14784.0,6,6.0,8.0,7.0,8.0,9.0,4.0,3.0,1.0,71.0,1.0 -NO,14808.0,6,5.0,8.0,9.0,8.0,8.0,7.0,3.0,2.0,56.0,1.0 -NO,14814.0,6,6.0,7.0,7.0,5.0,8.0,4.0,3.0,2.0,,2.0 -NO,14838.0,6,3.0,7.0,8.0,8.0,9.0,4.0,3.0,1.0,37.0,1.0 -NO,14862.0,6,7.0,9.0,7.0,5.0,,7.0,3.0,2.0,76.0,1.0 -NO,14904.0,6,3.0,8.0,8.0,3.0,5.0,4.0,2.0,2.0,49.0,1.0 -NO,14994.0,6,2.0,9.0,8.0,8.0,9.0,6.0,3.0,1.0,62.0,1.0 -NO,15012.0,6,1.0,8.0,6.0,6.0,7.0,4.0,2.0,1.0,37.0,1.0 -NO,15024.0,6,7.0,10.0,10.0,9.0,10.0,6.0,3.0,2.0,53.0,1.0 -NO,15030.0,6,0.0,9.0,8.0,8.0,9.0,6.0,3.0,1.0,33.0,1.0 -NO,15072.0,6,5.0,8.0,8.0,7.0,7.0,6.0,4.0,1.0,51.0,1.0 -NO,15078.0,6,1.0,8.0,8.0,7.0,8.0,6.0,3.0,1.0,40.0,1.0 -NO,15084.0,6,5.0,8.0,8.0,7.0,8.0,5.0,,1.0,62.0,1.0 -NO,15096.0,6,4.0,6.0,5.0,5.0,10.0,6.0,3.0,2.0,24.0,1.0 -NO,15108.0,6,2.0,6.0,7.0,8.0,9.0,7.0,3.0,1.0,15.0,2.0 -NO,15144.0,6,2.0,3.0,3.0,3.0,4.0,5.0,3.0,2.0,57.0,2.0 -NO,15150.0,6,4.0,8.0,6.0,4.0,9.0,5.0,2.0,1.0,47.0,1.0 -NO,15168.0,6,2.0,3.0,6.0,7.0,10.0,2.0,2.0,1.0,37.0,1.0 -NO,15210.0,6,2.0,9.0,8.0,6.0,8.0,7.0,3.0,2.0,51.0,1.0 -NO,15282.0,6,5.0,8.0,5.0,7.0,8.0,7.0,3.0,1.0,19.0,2.0 -NO,15318.0,6,7.0,5.0,8.0,5.0,7.0,7.0,2.0,2.0,78.0,2.0 -NO,15330.0,6,5.0,7.0,7.0,5.0,6.0,5.0,3.0,2.0,48.0,2.0 -NO,15372.0,6,3.0,7.0,7.0,5.0,8.0,5.0,3.0,1.0,62.0,1.0 -NO,15402.0,6,4.0,5.0,6.0,6.0,6.0,6.0,4.0,2.0,73.0,2.0 -NO,15456.0,6,7.0,7.0,7.0,5.0,8.0,6.0,4.0,1.0,38.0,1.0 -NO,15474.0,6,7.0,4.0,8.0,8.0,6.0,7.0,3.0,2.0,30.0,1.0 -NO,15480.0,6,4.0,6.0,6.0,5.0,7.0,6.0,4.0,1.0,49.0,1.0 -NO,15516.0,6,2.0,7.0,6.0,7.0,8.0,4.0,4.0,2.0,56.0,2.0 -NO,15534.0,6,6.0,5.0,8.0,8.0,8.0,6.0,4.0,1.0,74.0,1.0 -NO,15558.0,6,2.0,7.0,9.0,8.0,10.0,7.0,2.0,1.0,22.0,1.0 -NO,15588.0,6,3.0,6.0,4.0,6.0,10.0,7.0,4.0,1.0,17.0,2.0 -NO,15630.0,6,4.0,8.0,7.0,6.0,8.0,7.0,3.0,1.0,38.0,1.0 -NO,15636.0,6,3.0,5.0,7.0,4.0,9.0,7.0,3.0,2.0,17.0,2.0 -NO,15642.0,6,3.0,5.0,10.0,6.0,10.0,7.0,5.0,2.0,22.0,2.0 -NO,15660.0,6,7.0,2.0,4.0,5.0,10.0,6.0,2.0,1.0,50.0,1.0 -NO,15672.0,6,3.0,7.0,5.0,7.0,7.0,6.0,3.0,1.0,38.0,1.0 -NO,15684.0,6,3.0,7.0,6.0,6.0,10.0,7.0,3.0,2.0,37.0,1.0 -NO,15696.0,6,5.0,8.0,6.0,3.0,7.0,5.0,4.0,1.0,57.0,1.0 -NO,15702.0,6,2.0,7.0,7.0,7.0,8.0,5.0,2.0,2.0,63.0,1.0 -NO,15720.0,6,2.0,8.0,7.0,7.0,8.0,6.0,3.0,2.0,17.0,2.0 -NO,15738.0,6,1.0,0.0,0.0,1.0,4.0,1.0,4.0,1.0,41.0,2.0 -NO,15768.0,6,6.0,7.0,5.0,6.0,9.0,6.0,3.0,1.0,23.0,1.0 -NO,15804.0,6,6.0,6.0,7.0,5.0,5.0,6.0,2.0,1.0,33.0,2.0 -NO,15810.0,6,2.0,7.0,9.0,5.0,9.0,4.0,3.0,1.0,63.0,1.0 -NO,15816.0,6,2.0,6.0,9.0,5.0,8.0,3.0,3.0,2.0,87.0,1.0 -NO,15876.0,6,7.0,9.0,8.0,7.0,8.0,6.0,1.0,1.0,68.0,1.0 -NO,15906.0,6,3.0,3.0,6.0,8.0,9.0,6.0,1.0,1.0,19.0,1.0 -NO,15912.0,6,7.0,5.0,5.0,5.0,8.0,2.0,3.0,2.0,45.0,1.0 -NO,15936.0,6,4.0,7.0,7.0,8.0,8.0,6.0,3.0,2.0,49.0,1.0 -NO,15996.0,6,7.0,8.0,7.0,5.0,7.0,6.0,2.0,1.0,59.0,1.0 -NO,16002.0,6,3.0,7.0,7.0,2.0,8.0,6.0,2.0,1.0,41.0,1.0 -NO,16044.0,6,2.0,7.0,8.0,6.0,8.0,6.0,3.0,1.0,49.0,1.0 -NO,16068.0,6,4.0,6.0,6.0,7.0,7.0,6.0,3.0,1.0,65.0,1.0 -NO,16092.0,6,3.0,9.0,6.0,8.0,10.0,4.0,3.0,1.0,62.0,1.0 -NO,16098.0,6,2.0,4.0,6.0,6.0,8.0,7.0,3.0,1.0,30.0,2.0 -NO,16200.0,6,2.0,8.0,7.0,8.0,9.0,6.0,3.0,1.0,46.0,1.0 -NO,16206.0,6,2.0,8.0,9.0,8.0,9.0,6.0,3.0,1.0,39.0,1.0 -NO,16284.0,6,6.0,5.0,8.0,7.0,10.0,3.0,4.0,2.0,71.0,1.0 -NO,16290.0,6,7.0,1.0,1.0,5.0,9.0,6.0,2.0,1.0,24.0,1.0 -NO,16308.0,6,5.0,8.0,5.0,7.0,7.0,4.0,2.0,2.0,62.0,1.0 -NO,16320.0,6,2.0,7.0,7.0,6.0,8.0,6.0,3.0,2.0,37.0,1.0 -NO,16350.0,6,2.0,8.0,8.0,7.0,8.0,4.0,3.0,2.0,45.0,1.0 -NO,16356.0,6,4.0,6.0,7.0,7.0,8.0,6.0,3.0,1.0,38.0,1.0 -NO,16374.0,6,4.0,7.0,7.0,3.0,9.0,6.0,3.0,2.0,48.0,1.0 -NO,16422.0,6,6.0,8.0,7.0,4.0,3.0,5.0,3.0,1.0,64.0,2.0 -NO,16470.0,6,5.0,7.0,9.0,8.0,9.0,3.0,5.0,1.0,,1.0 -NO,16488.0,6,2.0,9.0,8.0,8.0,10.0,7.0,4.0,1.0,31.0,1.0 -NO,16500.0,6,3.0,7.0,8.0,7.0,8.0,7.0,3.0,2.0,54.0,1.0 -NO,16506.0,6,2.0,6.0,6.0,7.0,9.0,6.0,3.0,1.0,30.0,1.0 -NO,16512.0,6,2.0,8.0,8.0,7.0,10.0,6.0,4.0,1.0,31.0,1.0 -NO,16518.0,6,3.0,7.0,7.0,6.0,8.0,6.0,4.0,1.0,70.0,1.0 -NO,16524.0,6,1.0,6.0,8.0,6.0,10.0,4.0,3.0,2.0,31.0,1.0 -NO,16548.0,6,3.0,7.0,9.0,6.0,8.0,6.0,3.0,1.0,46.0,2.0 -NO,16566.0,6,2.0,6.0,6.0,6.0,8.0,7.0,3.0,2.0,63.0,1.0 -NO,16572.0,6,3.0,8.0,7.0,8.0,4.0,4.0,2.0,1.0,49.0,2.0 -NO,16584.0,6,4.0,7.0,7.0,8.0,7.0,4.0,3.0,2.0,45.0,1.0 -NO,16638.0,6,2.0,8.0,8.0,4.0,8.0,6.0,2.0,1.0,50.0,1.0 -NO,16644.0,6,2.0,8.0,9.0,7.0,8.0,7.0,4.0,2.0,17.0,2.0 -NO,16680.0,6,2.0,4.0,6.0,8.0,5.0,7.0,2.0,1.0,23.0,2.0 -NO,16692.0,6,7.0,8.0,7.0,8.0,5.0,5.0,2.0,1.0,71.0,1.0 -NO,16698.0,6,5.0,6.0,6.0,3.0,10.0,4.0,1.0,1.0,59.0,1.0 -NO,16740.0,6,1.0,7.0,9.0,4.0,8.0,6.0,3.0,1.0,37.0,1.0 -NO,16746.0,6,4.0,4.0,8.0,5.0,6.0,5.0,3.0,1.0,48.0,1.0 -NO,16770.0,6,2.0,6.0,5.0,7.0,8.0,5.0,4.0,2.0,48.0,2.0 -NO,16776.0,6,5.0,6.0,5.0,5.0,5.0,5.0,3.0,1.0,63.0,1.0 -NO,16824.0,6,2.0,6.0,5.0,4.0,8.0,4.0,3.0,1.0,59.0,1.0 -NO,16836.0,6,4.0,7.0,7.0,5.0,9.0,5.0,3.0,1.0,35.0,1.0 -NO,16860.0,6,5.0,5.0,6.0,10.0,10.0,7.0,3.0,2.0,16.0,2.0 -NO,16950.0,6,1.0,5.0,6.0,6.0,10.0,5.0,3.0,2.0,40.0,1.0 -NO,16968.0,6,5.0,5.0,4.0,2.0,10.0,5.0,3.0,1.0,79.0,1.0 -NO,16974.0,6,2.0,7.0,7.0,8.0,8.0,6.0,3.0,1.0,49.0,1.0 -NO,16980.0,6,2.0,8.0,8.0,7.0,9.0,6.0,3.0,1.0,41.0,1.0 -NO,16986.0,6,5.0,7.0,8.0,5.0,10.0,7.0,4.0,1.0,61.0,1.0 -NO,17064.0,6,3.0,7.0,7.0,6.0,9.0,6.0,3.0,2.0,41.0,1.0 -NO,17070.0,6,3.0,8.0,7.0,5.0,8.0,4.0,3.0,1.0,54.0,2.0 -NO,17082.0,6,4.0,7.0,7.0,8.0,6.0,5.0,3.0,2.0,65.0,1.0 -NO,17118.0,6,4.0,7.0,6.0,5.0,9.0,6.0,2.0,1.0,29.0,1.0 -NO,17142.0,6,6.0,7.0,7.0,4.0,10.0,6.0,3.0,1.0,70.0,1.0 -NO,17154.0,6,4.0,4.0,3.0,7.0,8.0,7.0,4.0,1.0,38.0,1.0 -NO,17190.0,6,2.0,9.0,9.0,8.0,9.0,6.0,4.0,2.0,73.0,1.0 -NO,17208.0,6,4.0,7.0,7.0,4.0,9.0,4.0,3.0,1.0,49.0,1.0 -NO,17214.0,6,3.0,7.0,6.0,5.0,8.0,4.0,3.0,1.0,46.0,1.0 -NO,17220.0,6,2.0,9.0,9.0,8.0,9.0,5.0,3.0,2.0,34.0,1.0 -NO,17286.0,6,3.0,2.0,2.0,2.0,9.0,4.0,3.0,2.0,56.0,1.0 -NO,17310.0,6,4.0,3.0,6.0,8.0,8.0,6.0,4.0,1.0,60.0,1.0 -NO,17328.0,6,2.0,8.0,8.0,4.0,9.0,6.0,3.0,1.0,47.0,2.0 -NO,17334.0,6,2.0,8.0,9.0,8.0,9.0,7.0,3.0,2.0,43.0,1.0 -NO,17412.0,6,2.0,6.0,8.0,10.0,7.0,5.0,3.0,2.0,75.0,2.0 -NO,17436.0,6,3.0,8.0,8.0,7.0,10.0,7.0,3.0,2.0,43.0,1.0 -NO,17448.0,6,5.0,8.0,8.0,4.0,9.0,6.0,3.0,1.0,43.0,1.0 -NO,17460.0,6,7.0,8.0,,8.0,10.0,7.0,,1.0,55.0, -NO,17466.0,6,2.0,7.0,5.0,8.0,7.0,6.0,2.0,1.0,47.0,2.0 -NO,17484.0,6,5.0,7.0,10.0,5.0,8.0,3.0,3.0,1.0,74.0,1.0 -NO,17508.0,6,3.0,8.0,7.0,5.0,7.0,5.0,3.0,1.0,31.0,2.0 -NO,17520.0,6,7.0,6.0,8.0,5.0,5.0,3.0,2.0,2.0,59.0,2.0 -NO,17544.0,6,4.0,6.0,6.0,4.0,8.0,7.0,2.0,1.0,38.0,2.0 -NO,17550.0,6,4.0,7.0,7.0,7.0,9.0,7.0,2.0,2.0,37.0,1.0 -NO,17556.0,6,5.0,6.0,8.0,8.0,8.0,6.0,2.0,1.0,42.0,1.0 -NO,17634.0,6,3.0,1.0,1.0,2.0,10.0,6.0,5.0,1.0,47.0,2.0 -NO,17688.0,6,2.0,7.0,8.0,5.0,10.0,6.0,4.0,1.0,38.0,2.0 -NO,17742.0,6,2.0,9.0,9.0,8.0,9.0,5.0,3.0,2.0,46.0,1.0 -NO,17754.0,6,1.0,8.0,8.0,9.0,8.0,6.0,3.0,2.0,41.0,1.0 -NO,17766.0,6,3.0,5.0,4.0,7.0,7.0,7.0,3.0,1.0,26.0,1.0 -NO,17790.0,6,3.0,6.0,6.0,5.0,8.0,4.0,3.0,1.0,32.0,1.0 -NO,17802.0,6,4.0,7.0,9.0,7.0,9.0,3.0,3.0,2.0,60.0,2.0 -NO,17808.0,6,4.0,2.0,5.0,5.0,10.0,4.0,3.0,1.0,51.0,1.0 -NO,17826.0,6,2.0,8.0,8.0,7.0,9.0,4.0,3.0,2.0,64.0,1.0 -NO,17832.0,6,5.0,3.0,4.0,5.0,7.0,3.0,2.0,1.0,23.0,1.0 -NO,17856.0,6,6.0,8.0,8.0,6.0,8.0,6.0,3.0,1.0,30.0,1.0 -NO,17874.0,6,5.0,8.0,8.0,7.0,8.0,5.0,2.0,1.0,42.0,2.0 -NO,17880.0,6,7.0,7.0,7.0,6.0,8.0,5.0,3.0,1.0,61.0,1.0 -NO,17886.0,6,4.0,5.0,5.0,5.0,9.0,6.0,3.0,2.0,60.0,1.0 -NO,17916.0,6,1.0,5.0,5.0,8.0,5.0,7.0,3.0,2.0,29.0,2.0 -NO,17922.0,6,5.0,7.0,7.0,5.0,8.0,6.0,3.0,2.0,44.0,1.0 -NO,17940.0,6,2.0,5.0,5.0,5.0,10.0,3.0,,1.0,81.0,1.0 -NO,17976.0,6,5.0,10.0,10.0,8.0,8.0,7.0,3.0,2.0,41.0,1.0 -NO,18000.0,6,3.0,7.0,5.0,6.0,5.0,6.0,2.0,1.0,45.0,2.0 -NO,18006.0,6,5.0,5.0,4.0,8.0,9.0,4.0,3.0,1.0,72.0,1.0 -NO,18024.0,6,7.0,7.0,7.0,5.0,9.0,5.0,2.0,2.0,61.0,1.0 -NO,18066.0,6,2.0,5.0,2.0,4.0,9.0,6.0,3.0,2.0,31.0,1.0 -NO,18090.0,6,1.0,9.0,9.0,8.0,9.0,6.0,3.0,2.0,43.0,1.0 -NO,18096.0,6,3.0,8.0,8.0,7.0,10.0,7.0,3.0,1.0,19.0,2.0 -NO,18120.0,6,1.0,8.0,8.0,6.0,8.0,7.0,3.0,1.0,18.0,2.0 -NO,18162.0,6,2.0,6.0,6.0,8.0,10.0,7.0,3.0,2.0,46.0,1.0 -NO,18198.0,6,6.0,1.0,5.0,0.0,10.0,6.0,3.0,2.0,75.0,2.0 -NO,18204.0,6,2.0,7.0,7.0,7.0,7.0,6.0,3.0,1.0,36.0,1.0 -NO,18210.0,6,5.0,8.0,10.0,8.0,7.0,6.0,3.0,2.0,52.0,2.0 -NO,18216.0,6,7.0,8.0,9.0,5.0,7.0,2.0,,1.0,86.0,2.0 -NO,18222.0,6,3.0,5.0,8.0,7.0,9.0,6.0,3.0,2.0,23.0,1.0 -NO,18240.0,6,7.0,7.0,10.0,10.0,10.0,4.0,5.0,1.0,46.0,1.0 -NO,18270.0,6,5.0,7.0,7.0,8.0,9.0,6.0,3.0,2.0,32.0,1.0 -NO,18306.0,6,7.0,5.0,5.0,8.0,8.0,7.0,3.0,2.0,58.0,2.0 -NO,18336.0,6,2.0,3.0,5.0,7.0,9.0,7.0,3.0,1.0,16.0,2.0 -NO,18360.0,6,6.0,7.0,8.0,6.0,9.0,4.0,3.0,1.0,21.0,2.0 -NO,18390.0,6,4.0,5.0,7.0,6.0,7.0,6.0,3.0,2.0,54.0,2.0 -NO,18396.0,6,2.0,9.0,9.0,8.0,9.0,6.0,4.0,2.0,83.0,1.0 -NO,18438.0,6,3.0,8.0,7.0,8.0,8.0,6.0,3.0,2.0,79.0,1.0 -NO,18480.0,6,3.0,10.0,8.0,4.0,10.0,3.0,3.0,1.0,52.0,1.0 -NO,18486.0,6,4.0,5.0,7.0,6.0,9.0,6.0,3.0,2.0,35.0,1.0 -NO,18498.0,6,2.0,7.0,7.0,5.0,7.0,6.0,2.0,1.0,67.0,1.0 -NO,18540.0,6,4.0,6.0,9.0,5.0,8.0,6.0,2.0,2.0,39.0,1.0 -NO,18546.0,6,1.0,5.0,4.0,2.0,10.0,6.0,4.0,2.0,19.0,1.0 -NO,18570.0,6,7.0,10.0,10.0,9.0,9.0,6.0,3.0,1.0,82.0,2.0 -NO,18576.0,6,2.0,9.0,9.0,7.0,10.0,7.0,3.0,2.0,47.0,1.0 -NO,18600.0,6,4.0,7.0,7.0,7.0,8.0,6.0,2.0,1.0,32.0,1.0 -NO,18606.0,6,3.0,10.0,10.0,6.0,10.0,6.0,5.0,2.0,79.0,2.0 -NO,18618.0,6,4.0,9.0,9.0,9.0,10.0,7.0,3.0,1.0,65.0,1.0 -NO,18630.0,6,3.0,6.0,7.0,6.0,8.0,7.0,3.0,2.0,51.0,1.0 -NO,18648.0,6,4.0,8.0,8.0,8.0,9.0,6.0,3.0,2.0,61.0,1.0 -NO,18678.0,6,3.0,8.0,8.0,9.0,8.0,6.0,3.0,2.0,26.0,1.0 -NO,18684.0,6,7.0,7.0,7.0,5.0,9.0,7.0,3.0,1.0,70.0,2.0 -NO,18696.0,6,6.0,6.0,7.0,6.0,8.0,6.0,2.0,1.0,28.0,1.0 -NO,18720.0,6,2.0,8.0,8.0,6.0,10.0,6.0,3.0,2.0,59.0,1.0 -NO,18732.0,6,5.0,4.0,3.0,6.0,9.0,7.0,3.0,2.0,20.0,2.0 -NO,18744.0,6,6.0,10.0,5.0,10.0,10.0,6.0,2.0,2.0,24.0,1.0 -NO,18750.0,6,1.0,8.0,7.0,7.0,8.0,7.0,4.0,1.0,20.0,2.0 -NO,18762.0,6,5.0,6.0,8.0,6.0,8.0,6.0,3.0,2.0,74.0,1.0 -NO,18774.0,6,0.0,5.0,9.0,6.0,9.0,4.0,3.0,1.0,43.0,2.0 -NO,18786.0,6,2.0,3.0,3.0,2.0,8.0,7.0,3.0,1.0,50.0,1.0 -NO,18792.0,6,0.0,10.0,8.0,7.0,10.0,7.0,3.0,1.0,24.0,2.0 -NO,18798.0,6,7.0,1.0,4.0,5.0,8.0,6.0,3.0,1.0,46.0,1.0 -NO,18822.0,6,2.0,8.0,8.0,8.0,10.0,6.0,3.0,2.0,45.0,1.0 -NO,18834.0,6,2.0,8.0,8.0,7.0,8.0,6.0,3.0,2.0,27.0,1.0 -NO,18846.0,6,2.0,5.0,6.0,8.0,7.0,5.0,3.0,1.0,18.0,2.0 -NO,18858.0,6,7.0,10.0,9.0,10.0,9.0,6.0,3.0,1.0,82.0,2.0 -NO,18876.0,6,5.0,3.0,1.0,5.0,9.0,6.0,3.0,1.0,26.0,2.0 -NO,18894.0,6,4.0,7.0,7.0,6.0,5.0,5.0,2.0,1.0,42.0,2.0 -NO,18900.0,6,3.0,4.0,5.0,6.0,9.0,6.0,3.0,2.0,36.0,1.0 -NO,18912.0,6,4.0,7.0,8.0,7.0,8.0,7.0,3.0,1.0,23.0,2.0 -NO,18918.0,6,6.0,8.0,6.0,7.0,8.0,6.0,2.0,1.0,59.0,1.0 -NO,18942.0,6,2.0,8.0,8.0,9.0,9.0,6.0,3.0,1.0,40.0,1.0 -NO,19014.0,6,5.0,2.0,5.0,7.0,7.0,7.0,4.0,2.0,18.0,2.0 -NO,19020.0,6,2.0,8.0,8.0,7.0,8.0,5.0,2.0,1.0,51.0,1.0 -NO,19038.0,6,3.0,7.0,7.0,8.0,10.0,7.0,3.0,1.0,22.0,2.0 -NO,19056.0,6,5.0,6.0,4.0,7.0,8.0,4.0,3.0,1.0,67.0,1.0 -NO,19068.0,6,4.0,5.0,5.0,6.0,8.0,7.0,1.0,2.0,48.0,1.0 -NO,19092.0,6,6.0,6.0,4.0,8.0,10.0,4.0,3.0,2.0,,1.0 -NO,19104.0,6,5.0,7.0,8.0,5.0,9.0,4.0,4.0,1.0,41.0,1.0 -NO,19122.0,6,4.0,8.0,7.0,7.0,7.0,4.0,3.0,1.0,61.0,1.0 -NO,19140.0,6,2.0,6.0,8.0,7.0,8.0,7.0,3.0,2.0,68.0,1.0 -NO,19146.0,6,1.0,9.0,10.0,8.0,10.0,3.0,2.0,1.0,64.0,1.0 -NO,19152.0,6,1.0,6.0,7.0,6.0,7.0,6.0,3.0,2.0,34.0,1.0 -NO,19170.0,6,4.0,7.0,9.0,8.0,9.0,7.0,3.0,2.0,16.0,2.0 -NO,19218.0,6,2.0,7.0,5.0,4.0,8.0,7.0,3.0,2.0,17.0,2.0 -NO,19254.0,6,6.0,8.0,6.0,4.0,8.0,6.0,2.0,1.0,82.0,1.0 -NO,19278.0,6,2.0,3.0,7.0,7.0,10.0,3.0,3.0,2.0,49.0,1.0 -NO,19284.0,6,7.0,7.0,10.0,10.0,10.0,6.0,4.0,1.0,47.0,1.0 -NO,19308.0,6,3.0,5.0,7.0,3.0,9.0,6.0,3.0,1.0,73.0,1.0 -NO,19320.0,6,5.0,8.0,8.0,8.0,8.0,4.0,3.0,1.0,63.0,1.0 -NO,19326.0,6,2.0,8.0,9.0,9.0,9.0,2.0,3.0,2.0,29.0,1.0 -NO,19332.0,6,6.0,8.0,8.0,8.0,10.0,4.0,2.0,1.0,64.0,1.0 -NO,19404.0,6,7.0,8.0,8.0,6.0,10.0,6.0,3.0,2.0,32.0,1.0 -NO,19416.0,6,5.0,7.0,7.0,7.0,10.0,7.0,4.0,1.0,22.0,2.0 -NO,19422.0,6,6.0,6.0,8.0,6.0,9.0,3.0,3.0,1.0,57.0,1.0 -NO,19464.0,6,1.0,7.0,7.0,6.0,8.0,4.0,4.0,1.0,50.0,1.0 -NO,19500.0,6,5.0,8.0,7.0,7.0,7.0,6.0,3.0,2.0,63.0,1.0 -NO,19524.0,6,2.0,7.0,8.0,6.0,9.0,6.0,3.0,1.0,61.0,1.0 -NO,19548.0,6,2.0,5.0,8.0,4.0,8.0,4.0,4.0,2.0,68.0,1.0 -NO,19596.0,6,5.0,7.0,8.0,5.0,9.0,7.0,3.0,1.0,46.0,1.0 -NO,19626.0,6,3.0,7.0,7.0,7.0,7.0,4.0,2.0,2.0,58.0,1.0 -NO,19638.0,6,4.0,8.0,9.0,9.0,10.0,7.0,3.0,1.0,15.0,2.0 -NO,19656.0,6,7.0,3.0,5.0,6.0,8.0,4.0,3.0,1.0,72.0,1.0 -NO,19662.0,6,3.0,5.0,9.0,9.0,10.0,6.0,3.0,1.0,48.0,1.0 -NO,19680.0,6,1.0,5.0,7.0,5.0,4.0,3.0,2.0,2.0,58.0,1.0 -NO,19686.0,6,6.0,8.0,6.0,5.0,10.0,7.0,3.0,1.0,56.0,1.0 -NO,19704.0,6,2.0,5.0,8.0,8.0,9.0,6.0,3.0,1.0,48.0,1.0 -NO,19710.0,6,4.0,7.0,7.0,5.0,8.0,4.0,3.0,2.0,69.0,1.0 -NO,19716.0,6,6.0,5.0,7.0,4.0,7.0,5.0,2.0,2.0,40.0,1.0 -NO,19734.0,6,6.0,7.0,9.0,5.0,9.0,4.0,3.0,1.0,62.0,1.0 -NO,19752.0,6,1.0,8.0,10.0,7.0,10.0,3.0,1.0,2.0,54.0,2.0 -NO,19758.0,6,0.0,8.0,5.0,5.0,9.0,6.0,4.0,1.0,52.0,1.0 -NO,19764.0,6,6.0,5.0,6.0,3.0,8.0,5.0,2.0,2.0,69.0,1.0 -NO,19776.0,6,7.0,5.0,6.0,6.0,8.0,4.0,2.0,2.0,52.0,2.0 -NO,19824.0,6,5.0,8.0,6.0,3.0,8.0,6.0,2.0,1.0,33.0,2.0 -NO,19836.0,6,4.0,8.0,8.0,7.0,8.0,6.0,3.0,2.0,70.0,2.0 -NO,19842.0,6,4.0,4.0,5.0,4.0,9.0,7.0,3.0,2.0,18.0,2.0 -NO,19872.0,6,3.0,7.0,5.0,5.0,9.0,7.0,3.0,2.0,58.0,1.0 -NO,19884.0,6,7.0,5.0,5.0,6.0,6.0,2.0,2.0,1.0,73.0,1.0 -NO,19890.0,6,4.0,6.0,8.0,8.0,9.0,4.0,4.0,1.0,51.0,1.0 -NO,19896.0,6,6.0,8.0,8.0,7.0,8.0,4.0,2.0,2.0,46.0,2.0 -NO,19902.0,6,4.0,7.0,8.0,6.0,9.0,5.0,3.0,2.0,58.0,1.0 -NO,19908.0,6,7.0,8.0,7.0,8.0,10.0,4.0,1.0,2.0,84.0,1.0 -NO,19950.0,6,2.0,1.0,10.0,0.0,8.0,7.0,2.0,1.0,21.0,2.0 -NO,19968.0,6,5.0,9.0,9.0,8.0,8.0,6.0,3.0,1.0,46.0,2.0 -NO,19980.0,6,2.0,5.0,8.0,8.0,8.0,6.0,3.0,2.0,44.0,1.0 -NO,19998.0,6,4.0,8.0,7.0,9.0,8.0,7.0,3.0,1.0,54.0,1.0 -NO,20022.0,6,3.0,6.0,7.0,6.0,8.0,4.0,3.0,1.0,40.0,1.0 -NO,20034.0,6,4.0,9.0,8.0,7.0,8.0,6.0,4.0,2.0,47.0,2.0 -NO,20076.0,6,4.0,7.0,6.0,6.0,,1.0,3.0,1.0,76.0,2.0 -NO,20088.0,6,5.0,4.0,7.0,5.0,8.0,7.0,2.0,2.0,33.0,1.0 -NO,20142.0,6,5.0,7.0,7.0,7.0,9.0,7.0,3.0,2.0,23.0,1.0 -NO,20154.0,6,4.0,5.0,7.0,4.0,10.0,7.0,5.0,1.0,16.0,2.0 -NO,20166.0,6,4.0,9.0,9.0,7.0,8.0,6.0,2.0,1.0,36.0,2.0 -NO,20262.0,6,3.0,5.0,7.0,7.0,9.0,4.0,2.0,2.0,42.0,1.0 -NO,20286.0,6,5.0,7.0,7.0,8.0,7.0,5.0,3.0,1.0,43.0,1.0 -NO,20316.0,6,5.0,8.0,8.0,6.0,8.0,5.0,3.0,1.0,35.0,2.0 -NO,20328.0,6,0.0,10.0,10.0,10.0,10.0,6.0,3.0,1.0,70.0,1.0 -NO,20352.0,6,2.0,3.0,4.0,2.0,9.0,6.0,4.0,2.0,44.0,1.0 -NO,20358.0,6,5.0,7.0,8.0,9.0,8.0,5.0,4.0,1.0,37.0,1.0 -NO,20370.0,6,7.0,9.0,9.0,8.0,9.0,6.0,3.0,1.0,,2.0 -NO,20394.0,6,5.0,5.0,6.0,5.0,7.0,6.0,3.0,2.0,17.0,2.0 -NO,20400.0,6,5.0,8.0,8.0,7.0,9.0,7.0,3.0,1.0,23.0,2.0 -NO,20412.0,6,5.0,7.0,8.0,8.0,10.0,4.0,2.0,2.0,58.0,1.0 -NO,20418.0,6,4.0,7.0,8.0,8.0,8.0,4.0,3.0,2.0,52.0,1.0 -NO,20424.0,6,5.0,8.0,8.0,6.0,10.0,6.0,3.0,1.0,58.0,1.0 -NO,20436.0,6,2.0,5.0,5.0,7.0,10.0,3.0,2.0,1.0,44.0,2.0 -NO,20442.0,6,2.0,2.0,4.0,0.0,8.0,6.0,3.0,1.0,43.0,2.0 -NO,20448.0,6,0.0,8.0,7.0,9.0,10.0,6.0,4.0,1.0,26.0,2.0 -NO,20460.0,6,1.0,6.0,8.0,7.0,9.0,7.0,3.0,2.0,63.0,1.0 -NO,20466.0,6,5.0,5.0,8.0,6.0,10.0,4.0,2.0,2.0,59.0,1.0 -NO,20478.0,6,5.0,7.0,7.0,5.0,8.0,7.0,3.0,1.0,46.0,1.0 -NO,20484.0,6,6.0,8.0,6.0,6.0,2.0,5.0,2.0,2.0,68.0,2.0 -NO,20496.0,6,3.0,8.0,7.0,7.0,10.0,6.0,3.0,2.0,39.0,1.0 -NO,20508.0,6,2.0,10.0,10.0,10.0,9.0,6.0,3.0,1.0,52.0,1.0 -NO,20514.0,6,7.0,5.0,3.0,5.0,9.0,4.0,3.0,2.0,62.0,1.0 -NO,20526.0,6,5.0,3.0,3.0,2.0,3.0,4.0,2.0,2.0,61.0,2.0 -NO,20568.0,6,1.0,5.0,5.0,5.0,10.0,7.0,2.0,1.0,55.0,1.0 -NO,20580.0,6,4.0,5.0,3.0,5.0,7.0,5.0,3.0,2.0,78.0,2.0 -NO,20586.0,6,3.0,4.0,5.0,4.0,9.0,6.0,3.0,1.0,47.0,1.0 -NO,20640.0,6,3.0,4.0,9.0,4.0,9.0,7.0,2.0,2.0,18.0,2.0 -NO,20646.0,6,7.0,5.0,7.0,6.0,9.0,7.0,3.0,2.0,69.0,1.0 -NO,20688.0,6,3.0,5.0,6.0,8.0,10.0,7.0,3.0,1.0,21.0,2.0 -NO,20730.0,6,6.0,8.0,8.0,5.0,10.0,6.0,3.0,1.0,44.0,1.0 -NO,20742.0,6,6.0,8.0,8.0,6.0,8.0,6.0,3.0,2.0,62.0,1.0 -NO,20772.0,6,4.0,6.0,8.0,8.0,9.0,4.0,2.0,1.0,32.0,1.0 -NO,20778.0,6,7.0,6.0,5.0,3.0,8.0,6.0,3.0,1.0,18.0,2.0 -NO,20808.0,6,7.0,7.0,9.0,5.0,6.0,6.0,3.0,2.0,18.0,2.0 -NO,20862.0,6,4.0,8.0,7.0,7.0,9.0,4.0,2.0,2.0,55.0,1.0 -NO,20892.0,6,2.0,9.0,8.0,8.0,9.0,7.0,5.0,1.0,25.0,2.0 -NO,20910.0,6,4.0,8.0,8.0,8.0,9.0,6.0,3.0,1.0,45.0,1.0 -NO,20922.0,6,5.0,8.0,8.0,8.0,10.0,7.0,4.0,1.0,67.0,2.0 -NO,20928.0,6,5.0,5.0,7.0,5.0,8.0,5.0,3.0,2.0,47.0,1.0 -NO,20934.0,6,6.0,7.0,7.0,4.0,8.0,4.0,3.0,2.0,58.0,1.0 -NO,20970.0,6,2.0,7.0,7.0,3.0,10.0,2.0,3.0,1.0,42.0,1.0 -NO,20988.0,6,1.0,9.0,9.0,5.0,10.0,6.0,5.0,1.0,54.0,1.0 -NO,20994.0,6,5.0,7.0,7.0,4.0,7.0,4.0,3.0,2.0,43.0,1.0 -NO,21042.0,6,0.0,7.0,7.0,6.0,9.0,6.0,3.0,2.0,35.0,1.0 -NO,21114.0,6,3.0,5.0,5.0,3.0,7.0,5.0,2.0,1.0,76.0,1.0 -NO,21168.0,6,6.0,7.0,7.0,6.0,8.0,6.0,4.0,2.0,73.0,1.0 -NO,21210.0,6,3.0,6.0,6.0,7.0,9.0,4.0,4.0,2.0,54.0,1.0 -NO,21228.0,6,5.0,7.0,5.0,5.0,7.0,6.0,4.0,1.0,16.0,2.0 -NO,21234.0,6,3.0,7.0,7.0,7.0,7.0,3.0,3.0,2.0,48.0,2.0 -NO,21282.0,6,3.0,8.0,8.0,7.0,9.0,4.0,3.0,2.0,47.0,2.0 -NO,21306.0,6,4.0,9.0,9.0,9.0,7.0,7.0,4.0,2.0,45.0,2.0 -NO,21312.0,6,2.0,7.0,7.0,8.0,8.0,6.0,3.0,2.0,50.0,2.0 -NO,21318.0,6,2.0,8.0,7.0,6.0,9.0,5.0,4.0,1.0,48.0,1.0 -NO,21402.0,6,6.0,5.0,7.0,7.0,8.0,6.0,3.0,2.0,67.0,1.0 -NO,21414.0,6,5.0,7.0,8.0,8.0,9.0,6.0,4.0,1.0,62.0,2.0 -NO,21462.0,6,2.0,5.0,5.0,5.0,10.0,7.0,3.0,1.0,75.0,1.0 -NO,21468.0,6,4.0,7.0,5.0,4.0,8.0,6.0,3.0,1.0,47.0,2.0 -NO,21492.0,6,5.0,8.0,8.0,7.0,1.0,7.0,3.0,2.0,57.0,1.0 -NO,21540.0,6,1.0,5.0,8.0,7.0,9.0,7.0,2.0,2.0,52.0,2.0 -NO,21564.0,6,0.0,6.0,9.0,8.0,10.0,6.0,4.0,2.0,54.0,1.0 -NO,21576.0,6,7.0,4.0,7.0,5.0,8.0,6.0,2.0,2.0,45.0,1.0 -NO,21582.0,6,6.0,3.0,3.0,3.0,6.0,5.0,2.0,1.0,64.0,1.0 -NO,21594.0,6,4.0,8.0,8.0,8.0,10.0,6.0,3.0,2.0,54.0,2.0 -NO,21606.0,6,3.0,7.0,7.0,8.0,9.0,5.0,3.0,2.0,40.0,1.0 -NO,21696.0,6,2.0,5.0,7.0,3.0,7.0,7.0,3.0,2.0,20.0,2.0 -NO,21726.0,6,3.0,5.0,5.0,5.0,10.0,7.0,3.0,2.0,22.0,2.0 -NO,21738.0,6,4.0,9.0,9.0,8.0,9.0,4.0,4.0,2.0,71.0,1.0 -NO,21768.0,6,5.0,5.0,4.0,5.0,9.0,6.0,3.0,2.0,43.0,1.0 -NO,21774.0,6,2.0,6.0,7.0,6.0,8.0,6.0,3.0,1.0,63.0,1.0 -NO,21798.0,6,7.0,5.0,6.0,5.0,8.0,6.0,2.0,1.0,23.0,1.0 -NO,21804.0,6,4.0,4.0,7.0,5.0,8.0,6.0,2.0,2.0,35.0,1.0 -NO,21816.0,6,4.0,7.0,9.0,10.0,10.0,6.0,3.0,2.0,17.0,2.0 -NO,21822.0,6,6.0,5.0,4.0,5.0,9.0,6.0,3.0,1.0,51.0,1.0 -NO,21840.0,6,4.0,5.0,7.0,8.0,8.0,6.0,3.0,2.0,17.0,2.0 -NO,21852.0,6,4.0,7.0,7.0,8.0,8.0,4.0,3.0,1.0,52.0,1.0 -NO,21882.0,6,5.0,7.0,7.0,8.0,9.0,6.0,3.0,2.0,55.0,1.0 -NO,21930.0,6,0.0,9.0,5.0,4.0,7.0,3.0,1.0,2.0,18.0,2.0 -NO,21978.0,6,4.0,8.0,8.0,4.0,9.0,7.0,3.0,2.0,70.0,2.0 -NO,21984.0,6,3.0,5.0,7.0,6.0,10.0,6.0,3.0,2.0,26.0,1.0 -NO,22038.0,6,5.0,4.0,5.0,4.0,9.0,6.0,2.0,2.0,21.0,2.0 -NO,22068.0,6,5.0,6.0,4.0,4.0,10.0,6.0,3.0,1.0,73.0,1.0 -NO,22074.0,6,6.0,8.0,5.0,7.0,8.0,6.0,4.0,1.0,67.0,1.0 -NO,22158.0,6,5.0,5.0,6.0,6.0,8.0,3.0,2.0,2.0,49.0,1.0 -NO,22170.0,6,4.0,6.0,7.0,5.0,7.0,4.0,3.0,2.0,39.0,1.0 -NO,22176.0,6,0.0,6.0,5.0,7.0,8.0,6.0,1.0,1.0,22.0,2.0 -NO,22224.0,6,5.0,8.0,9.0,8.0,9.0,4.0,2.0,1.0,,1.0 -NO,22260.0,6,6.0,7.0,10.0,7.0,9.0,4.0,1.0,2.0,23.0,2.0 -NO,22266.0,6,7.0,6.0,8.0,3.0,6.0,6.0,2.0,1.0,81.0,2.0 -NO,22290.0,6,6.0,6.0,8.0,7.0,8.0,6.0,2.0,2.0,34.0,1.0 -NO,22362.0,6,6.0,6.0,7.0,5.0,8.0,5.0,3.0,1.0,59.0,1.0 -NO,22386.0,6,4.0,7.0,5.0,6.0,9.0,6.0,2.0,1.0,68.0,1.0 -NO,22428.0,6,5.0,6.0,5.0,5.0,7.0,6.0,2.0,2.0,38.0,1.0 -NO,6312.0,7,4.0,5.0,7.0,7.0,5.0,6.0,4.0,2.0,46.0,2.0 -NO,6342.0,7,7.0,9.0,8.0,4.0,7.0,2.0,1.0,1.0,74.0,1.0 -NO,6360.0,7,5.0,8.0,7.0,7.0,8.0,4.0,2.0,2.0,82.0,1.0 -NO,6390.0,7,6.0,3.0,5.0,5.0,7.0,3.0,2.0,2.0,64.0,1.0 -NO,6396.0,7,2.0,5.0,8.0,6.0,10.0,4.0,3.0,2.0,43.0,1.0 -NO,6420.0,7,7.0,4.0,7.0,4.0,9.0,4.0,3.0,1.0,59.0,1.0 -NO,6444.0,7,2.0,5.0,5.0,6.0,10.0,6.0,3.0,2.0,21.0,2.0 -NO,6468.0,7,4.0,7.0,5.0,5.0,8.0,7.0,3.0,2.0,52.0,2.0 -NO,6474.0,7,4.0,6.0,7.0,8.0,10.0,4.0,3.0,2.0,63.0,1.0 -NO,6540.0,7,2.0,7.0,7.0,6.0,7.0,2.0,2.0,1.0,44.0,1.0 -NO,6576.0,7,1.0,8.0,6.0,4.0,8.0,7.0,3.0,1.0,26.0,2.0 -NO,6582.0,7,1.0,8.0,8.0,6.0,8.0,6.0,4.0,1.0,33.0,1.0 -NO,6594.0,7,0.0,8.0,7.0,8.0,8.0,5.0,3.0,2.0,58.0,1.0 -NO,6636.0,7,7.0,8.0,7.0,6.0,7.0,4.0,3.0,2.0,73.0,1.0 -NO,6648.0,7,5.0,8.0,8.0,8.0,9.0,6.0,3.0,2.0,58.0,2.0 -NO,6672.0,7,2.0,3.0,7.0,4.0,7.0,6.0,3.0,1.0,23.0,2.0 -NO,6684.0,7,3.0,8.0,7.0,6.0,8.0,5.0,3.0,1.0,56.0,1.0 -NO,6708.0,7,2.0,8.0,8.0,9.0,7.0,6.0,4.0,2.0,25.0,2.0 -NO,6732.0,7,2.0,2.0,7.0,6.0,9.0,6.0,3.0,1.0,23.0,2.0 -NO,6750.0,7,2.0,7.0,6.0,6.0,5.0,7.0,2.0,2.0,19.0,2.0 -NO,6756.0,7,2.0,5.0,6.0,6.0,5.0,7.0,3.0,2.0,20.0,2.0 -NO,6774.0,7,1.0,7.0,5.0,4.0,10.0,4.0,1.0,1.0,33.0,1.0 -NO,6792.0,7,4.0,7.0,7.0,6.0,9.0,6.0,3.0,2.0,22.0,2.0 -NO,6798.0,7,6.0,7.0,6.0,5.0,8.0,5.0,3.0,2.0,54.0,1.0 -NO,6816.0,7,6.0,5.0,5.0,6.0,8.0,2.0,2.0,1.0,69.0,2.0 -NO,6846.0,7,0.0,7.0,6.0,5.0,4.0,6.0,2.0,1.0,38.0,2.0 -NO,6870.0,7,5.0,5.0,7.0,3.0,9.0,4.0,4.0,2.0,70.0,1.0 -NO,6882.0,7,3.0,5.0,8.0,4.0,8.0,6.0,4.0,1.0,52.0,1.0 -NO,6888.0,7,3.0,6.0,5.0,8.0,10.0,4.0,3.0,2.0,39.0,1.0 -NO,6918.0,7,5.0,5.0,3.0,4.0,8.0,6.0,4.0,1.0,53.0,1.0 -NO,6936.0,7,0.0,5.0,5.0,3.0,8.0,6.0,5.0,1.0,21.0,2.0 -NO,6966.0,7,7.0,3.0,5.0,5.0,5.0,4.0,2.0,2.0,30.0,1.0 -NO,6984.0,7,3.0,7.0,7.0,8.0,8.0,4.0,3.0,1.0,81.0,2.0 -NO,7002.0,7,4.0,7.0,7.0,6.0,9.0,6.0,3.0,2.0,62.0,1.0 -NO,7020.0,7,4.0,8.0,8.0,8.0,5.0,5.0,2.0,2.0,68.0,1.0 -NO,7062.0,7,5.0,4.0,7.0,3.0,9.0,6.0,3.0,1.0,85.0,1.0 -NO,7104.0,7,2.0,9.0,9.0,5.0,9.0,7.0,3.0,1.0,37.0,1.0 -NO,7116.0,7,7.0,5.0,7.0,5.0,8.0,6.0,2.0,1.0,67.0,2.0 -NO,7134.0,7,4.0,7.0,6.0,4.0,10.0,5.0,3.0,1.0,17.0,2.0 -NO,7170.0,7,5.0,8.0,8.0,8.0,8.0,4.0,3.0,1.0,58.0,1.0 -NO,7182.0,7,6.0,5.0,8.0,6.0,8.0,4.0,3.0,2.0,64.0,1.0 -NO,7188.0,7,7.0,6.0,6.0,3.0,7.0,7.0,5.0,1.0,17.0,2.0 -NO,7200.0,7,3.0,7.0,7.0,7.0,8.0,4.0,3.0,2.0,25.0,2.0 -NO,7206.0,7,1.0,10.0,10.0,5.0,8.0,5.0,3.0,2.0,60.0,1.0 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-NO,9702.0,7,3.0,6.0,7.0,5.0,7.0,6.0,3.0,2.0,47.0,2.0 -NO,9714.0,7,2.0,7.0,8.0,8.0,8.0,3.0,3.0,1.0,64.0,1.0 -NO,9726.0,7,7.0,8.0,8.0,10.0,8.0,6.0,4.0,1.0,67.0,2.0 -NO,9732.0,7,1.0,8.0,7.0,5.0,6.0,7.0,4.0,1.0,27.0,2.0 -NO,9738.0,7,3.0,8.0,7.0,7.0,8.0,6.0,3.0,1.0,37.0,2.0 -NO,9750.0,7,2.0,5.0,8.0,6.0,7.0,6.0,3.0,1.0,67.0,1.0 -NO,9762.0,7,4.0,5.0,7.0,2.0,7.0,2.0,2.0,1.0,24.0,2.0 -NO,9768.0,7,1.0,10.0,8.0,3.0,8.0,4.0,2.0,1.0,34.0,2.0 -NO,9792.0,7,2.0,9.0,7.0,8.0,8.0,7.0,4.0,2.0,31.0,1.0 -NO,9798.0,7,7.0,6.0,6.0,7.0,9.0,6.0,2.0,1.0,34.0,1.0 -NO,9840.0,7,2.0,8.0,6.0,4.0,10.0,6.0,3.0,2.0,30.0,1.0 -NO,9846.0,7,2.0,5.0,5.0,0.0,10.0,6.0,4.0,1.0,24.0,2.0 -NO,9852.0,7,3.0,9.0,8.0,4.0,10.0,4.0,4.0,1.0,35.0,1.0 -NO,9858.0,7,2.0,8.0,7.0,8.0,7.0,6.0,4.0,2.0,32.0,2.0 -NO,9876.0,7,6.0,8.0,7.0,7.0,7.0,7.0,3.0,1.0,42.0,2.0 -NO,9966.0,7,3.0,5.0,7.0,5.0,8.0,4.0,2.0,2.0,67.0,1.0 -NO,9996.0,7,4.0,6.0,7.0,6.0,7.0,4.0,3.0,1.0,31.0,1.0 -NO,10020.0,7,2.0,8.0,9.0,7.0,8.0,6.0,2.0,2.0,46.0,1.0 -NO,10032.0,7,1.0,6.0,7.0,5.0,7.0,6.0,3.0,2.0,19.0,2.0 -NO,10050.0,7,2.0,5.0,4.0,4.0,8.0,6.0,2.0,2.0,41.0,1.0 -NO,10128.0,7,5.0,6.0,8.0,5.0,9.0,6.0,4.0,1.0,41.0,2.0 -NO,10134.0,7,5.0,4.0,6.0,4.0,6.0,2.0,2.0,2.0,32.0,1.0 -NO,10188.0,7,5.0,7.0,7.0,7.0,9.0,5.0,3.0,1.0,26.0,1.0 -NO,10200.0,7,5.0,7.0,7.0,8.0,8.0,5.0,3.0,2.0,30.0,1.0 -NO,10206.0,7,2.0,8.0,7.0,8.0,8.0,5.0,2.0,2.0,53.0,1.0 -NO,10212.0,7,2.0,8.0,8.0,5.0,9.0,7.0,3.0,2.0,20.0,2.0 -NO,10218.0,7,5.0,7.0,8.0,7.0,8.0,4.0,3.0,2.0,58.0,1.0 -NO,10266.0,7,5.0,6.0,6.0,3.0,9.0,4.0,3.0,2.0,33.0,1.0 -NO,10284.0,7,2.0,3.0,5.0,2.0,4.0,4.0,2.0,1.0,36.0,1.0 -NO,10314.0,7,5.0,10.0,10.0,10.0,10.0,7.0,3.0,2.0,46.0,1.0 -NO,10332.0,7,1.0,8.0,9.0,7.0,9.0,4.0,3.0,1.0,37.0,1.0 -NO,10362.0,7,4.0,7.0,7.0,6.0,8.0,6.0,3.0,1.0,52.0,2.0 -NO,10368.0,7,1.0,7.0,7.0,5.0,7.0,6.0,3.0,1.0,45.0,1.0 -NO,10386.0,7,0.0,9.0,8.0,7.0,8.0,6.0,2.0,2.0,33.0,2.0 -NO,10416.0,7,4.0,9.0,8.0,7.0,8.0,7.0,2.0,1.0,44.0,1.0 -NO,10440.0,7,3.0,6.0,6.0,6.0,8.0,6.0,3.0,2.0,71.0,2.0 -NO,10464.0,7,3.0,7.0,3.0,6.0,7.0,6.0,3.0,1.0,28.0,2.0 -NO,10470.0,7,5.0,1.0,5.0,5.0,7.0,4.0,2.0,1.0,67.0,1.0 -NO,10512.0,7,4.0,4.0,5.0,3.0,8.0,6.0,3.0,1.0,59.0,2.0 -NO,10530.0,7,0.0,8.0,8.0,5.0,9.0,7.0,3.0,1.0,22.0,2.0 -NO,10536.0,7,4.0,6.0,8.0,5.0,9.0,6.0,5.0,2.0,83.0,2.0 -NO,10590.0,7,1.0,5.0,3.0,7.0,8.0,3.0,3.0,2.0,56.0,1.0 -NO,10596.0,7,4.0,7.0,8.0,5.0,7.0,6.0,3.0,2.0,28.0,2.0 -NO,10638.0,7,2.0,4.0,5.0,3.0,5.0,4.0,3.0,1.0,45.0,2.0 -NO,10644.0,7,7.0,8.0,7.0,8.0,8.0,7.0,4.0,1.0,36.0,2.0 -NO,10674.0,7,2.0,7.0,6.0,5.0,8.0,3.0,3.0,1.0,47.0,1.0 -NO,10698.0,7,3.0,6.0,7.0,7.0,7.0,4.0,3.0,2.0,61.0,2.0 -NO,10716.0,7,6.0,7.0,8.0,4.0,7.0,7.0,3.0,1.0,19.0,2.0 -NO,10788.0,7,1.0,8.0,4.0,7.0,5.0,3.0,1.0,1.0,53.0,2.0 -NO,10794.0,7,6.0,6.0,7.0,7.0,8.0,6.0,3.0,2.0,44.0,2.0 -NO,10800.0,7,1.0,7.0,5.0,8.0,6.0,6.0,2.0,1.0,18.0,2.0 -NO,10824.0,7,3.0,6.0,8.0,9.0,9.0,5.0,3.0,2.0,61.0,1.0 -NO,10830.0,7,6.0,8.0,7.0,7.0,9.0,6.0,3.0,2.0,63.0,1.0 -NO,10836.0,7,2.0,6.0,5.0,4.0,6.0,6.0,3.0,2.0,19.0,2.0 -NO,10872.0,7,3.0,7.0,7.0,6.0,7.0,6.0,3.0,1.0,48.0,1.0 -NO,10884.0,7,5.0,7.0,6.0,8.0,8.0,4.0,3.0,1.0,70.0,1.0 -NO,10890.0,7,2.0,9.0,9.0,6.0,9.0,7.0,3.0,1.0,21.0,2.0 -NO,10896.0,7,3.0,10.0,8.0,8.0,10.0,6.0,2.0,1.0,52.0,1.0 -NO,10914.0,7,3.0,7.0,7.0,3.0,9.0,7.0,4.0,1.0,33.0,1.0 -NO,10932.0,7,2.0,7.0,7.0,8.0,8.0,3.0,2.0,1.0,44.0,1.0 -NO,10938.0,7,2.0,10.0,7.0,4.0,0.0,7.0,5.0,2.0,41.0,1.0 -NO,10950.0,7,3.0,3.0,4.0,1.0,5.0,6.0,3.0,2.0,19.0,2.0 -NO,10974.0,7,2.0,9.0,8.0,7.0,9.0,6.0,3.0,2.0,56.0,1.0 -NO,10986.0,7,3.0,7.0,8.0,4.0,10.0,6.0,4.0,2.0,22.0,2.0 -NO,11016.0,7,4.0,6.0,6.0,6.0,9.0,4.0,3.0,2.0,56.0,1.0 -NO,11052.0,7,7.0,7.0,7.0,7.0,9.0,6.0,1.0,1.0,68.0,1.0 -NO,11058.0,7,2.0,7.0,7.0,7.0,8.0,7.0,3.0,2.0,46.0,1.0 -NO,11064.0,7,6.0,3.0,8.0,6.0,4.0,6.0,3.0,2.0,19.0,2.0 -NO,11070.0,7,3.0,8.0,8.0,8.0,8.0,6.0,3.0,2.0,44.0,1.0 -NO,11118.0,7,7.0,7.0,8.0,3.0,9.0,6.0,4.0,1.0,69.0,1.0 -NO,11130.0,7,4.0,8.0,8.0,6.0,8.0,7.0,3.0,1.0,67.0,1.0 -NO,11160.0,7,3.0,6.0,7.0,7.0,8.0,5.0,2.0,2.0,15.0,2.0 -NO,11178.0,7,5.0,7.0,8.0,5.0,9.0,5.0,3.0,2.0,28.0,1.0 -NO,11184.0,7,4.0,7.0,7.0,8.0,10.0,6.0,3.0,1.0,37.0,1.0 -NO,11190.0,7,2.0,5.0,5.0,4.0,5.0,4.0,3.0,2.0,71.0,1.0 -NO,11196.0,7,7.0,8.0,7.0,8.0,10.0,5.0,3.0,2.0,55.0,1.0 -NO,11298.0,7,1.0,6.0,5.0,5.0,7.0,7.0,2.0,2.0,51.0,1.0 -NO,11304.0,7,7.0,5.0,8.0,7.0,7.0,6.0,2.0,1.0,71.0,2.0 -NO,11316.0,7,1.0,6.0,6.0,2.0,8.0,6.0,3.0,1.0,55.0,1.0 -NO,11322.0,7,6.0,9.0,8.0,6.0,9.0,7.0,4.0,2.0,23.0,2.0 -NO,11340.0,7,2.0,4.0,8.0,3.0,9.0,6.0,3.0,2.0,53.0,1.0 -NO,11346.0,7,2.0,7.0,7.0,3.0,8.0,7.0,3.0,1.0,47.0,1.0 -NO,11352.0,7,2.0,7.0,9.0,7.0,8.0,5.0,3.0,2.0,43.0,1.0 -NO,11382.0,7,7.0,7.0,3.0,1.0,8.0,6.0,3.0,1.0,49.0,1.0 -NO,11388.0,7,6.0,6.0,7.0,8.0,8.0,7.0,3.0,1.0,40.0,1.0 -NO,11412.0,7,4.0,8.0,9.0,8.0,9.0,7.0,3.0,2.0,58.0,1.0 -NO,11430.0,7,3.0,7.0,5.0,6.0,8.0,6.0,3.0,1.0,22.0,2.0 -NO,11454.0,7,5.0,7.0,,7.0,8.0,6.0,3.0,2.0,90.0,2.0 -NO,11466.0,7,5.0,7.0,8.0,6.0,9.0,6.0,3.0,1.0,35.0,1.0 -NO,11478.0,7,4.0,5.0,10.0,3.0,10.0,5.0,4.0,1.0,48.0,1.0 -NO,11484.0,7,7.0,7.0,8.0,10.0,9.0,6.0,3.0,2.0,52.0,2.0 -NO,11562.0,7,7.0,7.0,7.0,5.0,9.0,7.0,3.0,1.0,46.0,2.0 -NO,11568.0,7,4.0,8.0,8.0,7.0,9.0,7.0,3.0,1.0,37.0,1.0 -NO,11586.0,7,3.0,7.0,4.0,5.0,9.0,7.0,3.0,2.0,17.0,2.0 -NO,11598.0,7,4.0,6.0,7.0,3.0,9.0,4.0,3.0,1.0,52.0,1.0 -NO,11604.0,7,4.0,3.0,8.0,8.0,8.0,7.0,3.0,1.0,47.0,1.0 -NO,11622.0,7,1.0,9.0,6.0,8.0,6.0,7.0,3.0,1.0,78.0,2.0 -NO,11628.0,7,5.0,5.0,8.0,2.0,7.0,5.0,4.0,1.0,62.0,1.0 -NO,11640.0,7,2.0,8.0,9.0,5.0,7.0,7.0,3.0,2.0,36.0,1.0 -NO,11676.0,7,4.0,9.0,8.0,7.0,8.0,7.0,3.0,2.0,56.0,1.0 -NO,11694.0,7,5.0,8.0,10.0,9.0,8.0,6.0,4.0,2.0,69.0,2.0 -NO,11718.0,7,3.0,7.0,7.0,6.0,8.0,7.0,3.0,1.0,54.0,2.0 -NO,11724.0,7,3.0,6.0,7.0,7.0,7.0,6.0,3.0,2.0,16.0,2.0 -NO,11730.0,7,5.0,8.0,8.0,5.0,8.0,6.0,3.0,2.0,34.0,1.0 -NO,11742.0,7,1.0,7.0,6.0,8.0,8.0,6.0,4.0,1.0,44.0,1.0 -NO,11790.0,7,4.0,7.0,6.0,7.0,9.0,5.0,3.0,1.0,56.0,1.0 -NO,11820.0,7,2.0,10.0,10.0,7.0,8.0,6.0,4.0,1.0,74.0,2.0 -NO,11832.0,7,1.0,8.0,8.0,4.0,7.0,7.0,4.0,1.0,22.0,2.0 -NO,11850.0,7,7.0,7.0,7.0,8.0,8.0,6.0,2.0,2.0,64.0,1.0 -NO,11874.0,7,3.0,1.0,4.0,4.0,8.0,6.0,3.0,2.0,68.0,2.0 -NO,11898.0,7,4.0,6.0,8.0,7.0,9.0,4.0,3.0,2.0,58.0,1.0 -NO,11940.0,7,5.0,6.0,3.0,4.0,7.0,6.0,3.0,1.0,64.0,2.0 -NO,11958.0,7,2.0,6.0,8.0,4.0,8.0,7.0,4.0,1.0,16.0,2.0 -NO,11964.0,7,5.0,7.0,7.0,7.0,5.0,5.0,3.0,1.0,73.0,2.0 -NO,11982.0,7,2.0,7.0,6.0,9.0,10.0,6.0,3.0,1.0,48.0,1.0 -NO,12006.0,7,4.0,8.0,5.0,7.0,6.0,4.0,3.0,1.0,43.0,1.0 -NO,12024.0,7,3.0,8.0,7.0,6.0,8.0,6.0,3.0,2.0,40.0,2.0 -NO,12060.0,7,3.0,7.0,8.0,8.0,7.0,6.0,4.0,2.0,80.0,2.0 -NO,12096.0,7,1.0,8.0,9.0,8.0,9.0,7.0,3.0,1.0,16.0,2.0 -NO,12102.0,7,7.0,6.0,6.0,6.0,9.0,4.0,2.0,2.0,70.0,1.0 -NO,12132.0,7,6.0,7.0,7.0,5.0,5.0,4.0,2.0,2.0,56.0,1.0 -NO,12156.0,7,3.0,9.0,8.0,5.0,10.0,4.0,2.0,1.0,71.0,2.0 -NO,12198.0,7,4.0,5.0,9.0,3.0,10.0,6.0,2.0,2.0,70.0,1.0 -NO,12228.0,7,5.0,7.0,7.0,8.0,9.0,6.0,4.0,1.0,43.0,1.0 -NO,12240.0,7,2.0,5.0,7.0,5.0,8.0,7.0,3.0,1.0,53.0,1.0 -NO,12264.0,7,2.0,9.0,8.0,4.0,8.0,4.0,3.0,2.0,56.0,1.0 -NO,12270.0,7,7.0,10.0,10.0,6.0,10.0,6.0,4.0,1.0,87.0,1.0 -NO,12300.0,7,1.0,7.0,8.0,7.0,8.0,4.0,3.0,2.0,34.0,1.0 -NO,12306.0,7,6.0,5.0,5.0,3.0,8.0,6.0,3.0,2.0,24.0,2.0 -NO,12318.0,7,6.0,8.0,8.0,5.0,9.0,6.0,3.0,2.0,67.0,1.0 -NO,12324.0,7,4.0,3.0,3.0,6.0,7.0,5.0,2.0,1.0,39.0,2.0 -NO,12330.0,7,4.0,8.0,9.0,6.0,9.0,6.0,3.0,1.0,46.0,1.0 -NO,12372.0,7,1.0,7.0,8.0,6.0,8.0,7.0,3.0,1.0,47.0,1.0 -NO,12402.0,7,3.0,5.0,5.0,8.0,8.0,7.0,3.0,1.0,41.0,1.0 -NO,12426.0,7,6.0,5.0,9.0,3.0,8.0,4.0,2.0,2.0,32.0,1.0 -NO,12516.0,7,4.0,7.0,7.0,8.0,9.0,4.0,2.0,2.0,41.0,2.0 -NO,12570.0,7,2.0,8.0,8.0,7.0,10.0,7.0,4.0,1.0,69.0,1.0 -NO,12594.0,7,0.0,10.0,10.0,8.0,10.0,7.0,5.0,1.0,65.0,2.0 -NO,12606.0,7,6.0,8.0,7.0,9.0,8.0,6.0,3.0,1.0,48.0,1.0 -NO,12612.0,7,5.0,8.0,8.0,9.0,8.0,4.0,3.0,2.0,47.0,2.0 -NO,12618.0,7,5.0,5.0,4.0,6.0,9.0,2.0,2.0,2.0,35.0,2.0 -NO,12648.0,7,4.0,5.0,10.0,8.0,6.0,2.0,2.0,2.0,43.0,1.0 -NO,12654.0,7,2.0,10.0,9.0,7.0,10.0,4.0,2.0,1.0,49.0,1.0 -NO,12678.0,7,3.0,5.0,8.0,8.0,7.0,4.0,2.0,1.0,78.0,2.0 -NO,12690.0,7,5.0,6.0,6.0,6.0,5.0,2.0,2.0,2.0,52.0,2.0 -NO,12714.0,7,1.0,9.0,9.0,5.0,8.0,3.0,3.0,1.0,57.0,1.0 -NO,12762.0,7,1.0,7.0,9.0,7.0,8.0,1.0,3.0,1.0,45.0,1.0 -NO,12774.0,7,4.0,8.0,7.0,6.0,9.0,6.0,2.0,1.0,60.0,1.0 -NO,12798.0,7,6.0,2.0,0.0,10.0,3.0,4.0,3.0,2.0,74.0,2.0 -NO,12810.0,7,2.0,10.0,8.0,8.0,9.0,6.0,4.0,2.0,69.0,2.0 -NO,12822.0,7,2.0,5.0,4.0,6.0,7.0,7.0,3.0,2.0,47.0,1.0 -NO,12828.0,7,0.0,5.0,5.0,3.0,10.0,6.0,2.0,1.0,41.0,1.0 -NO,12840.0,7,7.0,6.0,5.0,3.0,4.0,4.0,2.0,2.0,59.0,1.0 -NO,12846.0,7,4.0,8.0,6.0,8.0,9.0,7.0,3.0,2.0,18.0,2.0 -NO,12924.0,7,3.0,5.0,7.0,7.0,8.0,4.0,3.0,1.0,44.0,2.0 -NO,12984.0,7,4.0,5.0,6.0,7.0,8.0,7.0,4.0,1.0,64.0,1.0 -NO,13008.0,7,2.0,8.0,8.0,9.0,7.0,5.0,2.0,1.0,53.0,2.0 -NO,13044.0,7,2.0,8.0,8.0,7.0,10.0,6.0,3.0,1.0,59.0,1.0 -NO,13128.0,7,4.0,7.0,5.0,6.0,7.0,4.0,2.0,1.0,34.0,1.0 -NO,13146.0,7,1.0,5.0,7.0,8.0,10.0,7.0,2.0,2.0,50.0,2.0 -NO,13158.0,7,5.0,5.0,6.0,6.0,8.0,6.0,3.0,2.0,53.0,2.0 -NO,13218.0,7,2.0,8.0,7.0,7.0,6.0,7.0,3.0,2.0,23.0,1.0 -NO,13230.0,7,5.0,7.0,5.0,6.0,5.0,5.0,2.0,1.0,63.0,1.0 -NO,13248.0,7,4.0,6.0,6.0,8.0,8.0,7.0,4.0,2.0,56.0,2.0 -NO,13338.0,7,3.0,7.0,9.0,7.0,9.0,6.0,2.0,1.0,38.0,1.0 -NO,13344.0,7,4.0,7.0,7.0,6.0,9.0,6.0,3.0,2.0,42.0,1.0 -NO,13422.0,7,5.0,7.0,6.0,4.0,10.0,5.0,3.0,1.0,67.0,1.0 -NO,13440.0,7,5.0,7.0,7.0,4.0,9.0,5.0,3.0,2.0,19.0,2.0 -NO,13464.0,7,6.0,10.0,10.0,3.0,8.0,6.0,4.0,1.0,47.0,1.0 -NO,13470.0,7,1.0,7.0,7.0,7.0,5.0,7.0,3.0,1.0,16.0,2.0 -NO,13482.0,7,3.0,7.0,8.0,10.0,7.0,6.0,3.0,1.0,46.0,2.0 -NO,13488.0,7,2.0,7.0,7.0,3.0,10.0,7.0,3.0,1.0,21.0,2.0 -NO,13524.0,7,5.0,5.0,7.0,7.0,8.0,7.0,3.0,2.0,16.0,2.0 -NO,13542.0,7,3.0,8.0,8.0,4.0,8.0,3.0,2.0,1.0,52.0,1.0 -NO,13548.0,7,0.0,3.0,7.0,5.0,7.0,3.0,2.0,1.0,31.0,1.0 -NO,13584.0,7,2.0,9.0,8.0,9.0,10.0,7.0,3.0,1.0,18.0,2.0 -NO,13638.0,7,2.0,8.0,8.0,5.0,5.0,5.0,2.0,1.0,21.0,2.0 -NO,13650.0,7,3.0,4.0,2.0,6.0,7.0,3.0,1.0,2.0,60.0,1.0 -NO,13680.0,7,2.0,5.0,2.0,6.0,9.0,6.0,4.0,1.0,75.0,1.0 -NO,13704.0,7,4.0,7.0,8.0,7.0,5.0,6.0,3.0,2.0,17.0,2.0 -NO,13734.0,7,6.0,5.0,3.0,4.0,7.0,6.0,2.0,1.0,19.0,2.0 -NO,13746.0,7,5.0,7.0,9.0,8.0,9.0,6.0,2.0,2.0,68.0,2.0 -NO,13752.0,7,2.0,9.0,9.0,9.0,10.0,7.0,3.0,2.0,62.0,1.0 -NO,13758.0,7,2.0,8.0,8.0,7.0,6.0,4.0,3.0,2.0,39.0,1.0 -NO,13782.0,7,7.0,6.0,8.0,4.0,6.0,6.0,4.0,1.0,18.0,2.0 -NO,13788.0,7,3.0,8.0,8.0,8.0,7.0,6.0,5.0,2.0,52.0,1.0 -NO,13794.0,7,5.0,8.0,6.0,7.0,8.0,7.0,3.0,2.0,20.0,2.0 -NO,13800.0,7,4.0,4.0,4.0,3.0,6.0,4.0,4.0,1.0,44.0,2.0 -NO,13812.0,7,4.0,8.0,7.0,3.0,9.0,6.0,3.0,2.0,71.0,1.0 -NO,13824.0,7,6.0,8.0,10.0,5.0,10.0,4.0,1.0,2.0,47.0,2.0 -NO,13830.0,7,3.0,7.0,7.0,6.0,7.0,6.0,3.0,1.0,49.0,1.0 -NO,13842.0,7,1.0,8.0,9.0,8.0,8.0,4.0,3.0,1.0,72.0,1.0 -NO,13848.0,7,1.0,9.0,9.0,6.0,9.0,6.0,4.0,2.0,53.0,2.0 -NO,13866.0,7,3.0,8.0,9.0,8.0,7.0,4.0,3.0,2.0,62.0,1.0 -NO,13902.0,7,0.0,9.0,9.0,10.0,10.0,7.0,3.0,1.0,21.0,2.0 -NO,13908.0,7,4.0,8.0,8.0,9.0,9.0,6.0,3.0,2.0,48.0,1.0 -NO,13920.0,7,5.0,7.0,7.0,8.0,8.0,4.0,3.0,2.0,42.0,1.0 -NO,13926.0,7,1.0,7.0,7.0,7.0,9.0,4.0,2.0,1.0,44.0,1.0 -NO,13932.0,7,0.0,7.0,8.0,6.0,6.0,7.0,4.0,1.0,24.0,2.0 -NO,13944.0,7,2.0,9.0,8.0,8.0,10.0,6.0,3.0,2.0,44.0,1.0 -NO,13950.0,7,3.0,9.0,9.0,9.0,8.0,6.0,3.0,1.0,58.0,1.0 -NO,13962.0,7,0.0,6.0,6.0,7.0,10.0,3.0,2.0,2.0,36.0,2.0 -NO,13968.0,7,6.0,9.0,7.0,8.0,7.0,7.0,4.0,2.0,67.0,2.0 -NO,13980.0,7,5.0,6.0,9.0,5.0,7.0,6.0,3.0,2.0,28.0,1.0 -NO,13986.0,7,7.0,6.0,6.0,7.0,8.0,7.0,4.0,1.0,17.0,2.0 -NO,14004.0,7,7.0,4.0,6.0,5.0,3.0,6.0,2.0,2.0,19.0,2.0 -NO,14076.0,7,4.0,5.0,8.0,6.0,8.0,6.0,3.0,2.0,21.0,2.0 -NO,14124.0,7,4.0,8.0,8.0,6.0,8.0,4.0,3.0,2.0,78.0,2.0 -NO,14148.0,7,2.0,7.0,8.0,5.0,7.0,5.0,3.0,2.0,43.0,1.0 -NO,14160.0,7,3.0,5.0,4.0,6.0,7.0,6.0,3.0,1.0,38.0,1.0 -NO,14166.0,7,4.0,10.0,9.0,7.0,9.0,7.0,3.0,1.0,44.0,1.0 -NO,14214.0,7,5.0,7.0,7.0,5.0,7.0,5.0,3.0,1.0,57.0,1.0 -NO,14310.0,7,2.0,7.0,7.0,5.0,8.0,6.0,3.0,2.0,71.0,1.0 -NO,14346.0,7,1.0,9.0,7.0,8.0,7.0,4.0,2.0,2.0,50.0,2.0 -NO,14412.0,7,4.0,8.0,8.0,8.0,7.0,6.0,5.0,1.0,63.0,1.0 -NO,14418.0,7,1.0,6.0,10.0,5.0,7.0,4.0,2.0,1.0,55.0,1.0 -NO,14424.0,7,3.0,5.0,7.0,7.0,10.0,7.0,3.0,1.0,49.0,1.0 -NO,14436.0,7,5.0,10.0,9.0,9.0,9.0,5.0,2.0,2.0,71.0,1.0 -NO,14448.0,7,1.0,5.0,3.0,3.0,8.0,7.0,4.0,2.0,43.0,1.0 -NO,14460.0,7,1.0,9.0,7.0,9.0,9.0,5.0,2.0,1.0,54.0,1.0 -NO,14466.0,7,2.0,8.0,7.0,8.0,9.0,7.0,3.0,1.0,42.0,1.0 -NO,14472.0,7,6.0,7.0,7.0,6.0,7.0,4.0,2.0,1.0,55.0,1.0 -NO,14484.0,7,7.0,7.0,7.0,8.0,8.0,4.0,3.0,2.0,69.0,2.0 -NO,14508.0,7,3.0,6.0,9.0,7.0,7.0,7.0,3.0,2.0,17.0,2.0 -NO,14514.0,7,4.0,5.0,8.0,5.0,7.0,3.0,2.0,1.0,56.0,1.0 -NO,14556.0,7,4.0,7.0,3.0,7.0,10.0,6.0,1.0,1.0,48.0,1.0 -NO,14586.0,7,7.0,9.0,10.0,7.0,8.0,5.0,2.0,2.0,67.0,1.0 -NO,14646.0,7,3.0,8.0,8.0,5.0,4.0,5.0,3.0,2.0,35.0,1.0 -NO,14652.0,7,4.0,5.0,3.0,5.0,5.0,4.0,3.0,1.0,,1.0 -NO,14658.0,7,6.0,8.0,8.0,7.0,9.0,5.0,3.0,2.0,47.0,1.0 -NO,14682.0,7,3.0,8.0,8.0,9.0,9.0,4.0,3.0,2.0,41.0,1.0 -NO,14730.0,7,4.0,4.0,3.0,3.0,4.0,6.0,1.0,2.0,46.0,1.0 -NO,14754.0,7,2.0,7.0,8.0,4.0,8.0,6.0,2.0,1.0,27.0,1.0 -NO,14766.0,7,0.0,5.0,8.0,9.0,9.0,5.0,3.0,1.0,49.0,1.0 -NO,14784.0,7,0.0,8.0,8.0,8.0,8.0,6.0,3.0,1.0,93.0,1.0 -NO,14808.0,7,2.0,7.0,5.0,8.0,9.0,6.0,3.0,1.0,21.0,2.0 -NO,14814.0,7,6.0,8.0,8.0,8.0,8.0,3.0,2.0,2.0,63.0,1.0 -NO,14838.0,7,3.0,5.0,6.0,6.0,10.0,5.0,4.0,1.0,40.0,1.0 -NO,14862.0,7,0.0,7.0,10.0,3.0,10.0,4.0,4.0,2.0,74.0,2.0 -NO,14904.0,7,1.0,5.0,5.0,7.0,6.0,6.0,2.0,1.0,43.0,1.0 -NO,14994.0,7,7.0,6.0,5.0,7.0,8.0,6.0,1.0,2.0,,2.0 -NO,15012.0,7,6.0,0.0,2.0,2.0,8.0,6.0,4.0,2.0,61.0,2.0 -NO,15024.0,7,3.0,4.0,7.0,5.0,9.0,7.0,3.0,1.0,33.0,2.0 -NO,15030.0,7,0.0,6.0,6.0,5.0,8.0,6.0,4.0,1.0,56.0,1.0 -NO,15072.0,7,4.0,8.0,8.0,7.0,5.0,6.0,4.0,2.0,,1.0 -NO,15078.0,7,5.0,9.0,9.0,5.0,8.0,7.0,3.0,2.0,61.0,2.0 -NO,15084.0,7,3.0,9.0,9.0,6.0,9.0,6.0,4.0,1.0,65.0,1.0 -NO,15096.0,7,5.0,8.0,8.0,6.0,7.0,7.0,2.0,2.0,40.0,1.0 -NO,15108.0,7,4.0,7.0,7.0,7.0,8.0,3.0,2.0,1.0,55.0,1.0 -NO,15144.0,7,1.0,6.0,0.0,3.0,10.0,7.0,5.0,2.0,48.0,2.0 -NO,15150.0,7,7.0,7.0,8.0,7.0,10.0,4.0,2.0,1.0,72.0,1.0 -NO,15168.0,7,5.0,8.0,7.0,8.0,8.0,6.0,3.0,1.0,43.0,1.0 -NO,15210.0,7,2.0,7.0,7.0,6.0,8.0,6.0,3.0,1.0,40.0,1.0 -NO,15282.0,7,1.0,6.0,7.0,5.0,6.0,6.0,3.0,1.0,48.0,1.0 -NO,15318.0,7,6.0,7.0,7.0,7.0,9.0,4.0,2.0,1.0,50.0,2.0 -NO,15330.0,7,3.0,8.0,8.0,8.0,9.0,6.0,3.0,1.0,22.0,2.0 -NO,15372.0,7,2.0,8.0,9.0,9.0,8.0,7.0,3.0,1.0,53.0,1.0 -NO,15402.0,7,2.0,5.0,6.0,6.0,9.0,4.0,3.0,1.0,73.0,1.0 -NO,15456.0,7,4.0,8.0,9.0,5.0,7.0,4.0,3.0,1.0,54.0,2.0 -NO,15474.0,7,6.0,7.0,5.0,8.0,8.0,5.0,3.0,1.0,39.0,1.0 -NO,15480.0,7,3.0,8.0,8.0,7.0,8.0,6.0,3.0,2.0,34.0,1.0 -NO,15516.0,7,4.0,3.0,6.0,6.0,8.0,6.0,3.0,1.0,50.0,2.0 -NO,15534.0,7,5.0,7.0,7.0,4.0,8.0,6.0,3.0,2.0,59.0,1.0 -NO,15558.0,7,5.0,8.0,7.0,5.0,8.0,6.0,3.0,2.0,26.0,2.0 -NO,15588.0,7,1.0,6.0,7.0,6.0,9.0,6.0,3.0,2.0,29.0,1.0 -NO,15630.0,7,7.0,1.0,5.0,2.0,9.0,3.0,5.0,1.0,69.0,1.0 -NO,15636.0,7,6.0,5.0,5.0,3.0,8.0,7.0,4.0,2.0,29.0,1.0 -NO,15642.0,7,3.0,7.0,5.0,5.0,8.0,7.0,5.0,2.0,19.0,2.0 -NO,15660.0,7,3.0,7.0,7.0,7.0,8.0,5.0,3.0,1.0,42.0,1.0 -NO,15672.0,7,6.0,8.0,8.0,5.0,8.0,5.0,3.0,1.0,63.0,1.0 -NO,15684.0,7,3.0,7.0,6.0,6.0,8.0,7.0,4.0,2.0,18.0,2.0 -NO,15696.0,7,7.0,9.0,6.0,8.0,7.0,6.0,1.0,2.0,47.0, -NO,15702.0,7,2.0,4.0,4.0,3.0,6.0,4.0,3.0,1.0,31.0,1.0 -NO,15720.0,7,4.0,5.0,7.0,6.0,9.0,5.0,3.0,2.0,53.0,1.0 -NO,15738.0,7,5.0,8.0,9.0,8.0,8.0,6.0,3.0,1.0,67.0,2.0 -NO,15768.0,7,7.0,9.0,7.0,5.0,9.0,7.0,3.0,1.0,24.0,2.0 -NO,15804.0,7,3.0,6.0,5.0,4.0,8.0,6.0,4.0,1.0,39.0,1.0 -NO,15810.0,7,1.0,4.0,6.0,4.0,7.0,5.0,2.0,2.0,59.0,1.0 -NO,15816.0,7,1.0,9.0,9.0,7.0,9.0,6.0,4.0,2.0,33.0,1.0 -NO,15876.0,7,3.0,3.0,5.0,7.0,7.0,2.0,1.0,1.0,49.0,1.0 -NO,15906.0,7,0.0,5.0,5.0,3.0,9.0,6.0,2.0,1.0,22.0,2.0 -NO,15912.0,7,3.0,7.0,7.0,,7.0,4.0,2.0,1.0,51.0,1.0 -NO,15936.0,7,0.0,8.0,8.0,9.0,8.0,6.0,2.0,2.0,40.0,1.0 -NO,15996.0,7,7.0,5.0,3.0,7.0,1.0,7.0,3.0,2.0,17.0,2.0 -NO,16002.0,7,1.0,7.0,7.0,5.0,8.0,4.0,2.0,2.0,42.0,1.0 -NO,16044.0,7,4.0,7.0,6.0,6.0,8.0,7.0,2.0,1.0,31.0,2.0 -NO,16068.0,7,2.0,6.0,6.0,5.0,8.0,6.0,4.0,1.0,42.0,1.0 -NO,16092.0,7,4.0,8.0,7.0,3.0,8.0,4.0,4.0,1.0,53.0,1.0 -NO,16098.0,7,5.0,6.0,6.0,5.0,8.0,3.0,3.0,1.0,73.0,1.0 -NO,16200.0,7,7.0,3.0,5.0,5.0,10.0,6.0,2.0,2.0,15.0,2.0 -NO,16206.0,7,4.0,8.0,8.0,4.0,8.0,5.0,2.0,2.0,84.0,1.0 -NO,16284.0,7,1.0,5.0,6.0,4.0,6.0,7.0,3.0,1.0,17.0,2.0 -NO,16290.0,7,7.0,9.0,9.0,9.0,9.0,7.0,3.0,1.0,56.0,1.0 -NO,16308.0,7,7.0,8.0,8.0,6.0,8.0,6.0,2.0,2.0,33.0,1.0 -NO,16320.0,7,2.0,1.0,7.0,5.0,6.0,7.0,3.0,1.0,25.0,2.0 -NO,16350.0,7,5.0,5.0,6.0,3.0,7.0,4.0,2.0,1.0,42.0,1.0 -NO,16356.0,7,3.0,10.0,10.0,0.0,10.0,6.0,2.0,1.0,26.0,1.0 -NO,16374.0,7,4.0,8.0,8.0,7.0,7.0,6.0,3.0,1.0,60.0,1.0 -NO,16422.0,7,5.0,0.0,0.0,5.0,0.0,1.0,1.0,1.0,47.0,2.0 -NO,16470.0,7,3.0,7.0,8.0,5.0,8.0,4.0,4.0,1.0,68.0,1.0 -NO,16488.0,7,2.0,5.0,7.0,,7.0,5.0,2.0,1.0,40.0,1.0 -NO,16500.0,7,5.0,8.0,9.0,3.0,9.0,6.0,3.0,2.0,24.0,1.0 -NO,16506.0,7,6.0,5.0,8.0,5.0,7.0,5.0,2.0,1.0,55.0,1.0 -NO,16512.0,7,6.0,8.0,8.0,5.0,8.0,3.0,2.0,1.0,39.0,1.0 -NO,16518.0,7,3.0,7.0,7.0,8.0,9.0,6.0,3.0,1.0,47.0,1.0 -NO,16524.0,7,3.0,9.0,9.0,9.0,8.0,4.0,2.0,2.0,51.0,1.0 -NO,16548.0,7,2.0,8.0,8.0,7.0,7.0,6.0,4.0,2.0,57.0,1.0 -NO,16566.0,7,6.0,8.0,5.0,7.0,6.0,6.0,3.0,1.0,52.0,1.0 -NO,16572.0,7,6.0,7.0,8.0,7.0,8.0,6.0,2.0,2.0,60.0,1.0 -NO,16584.0,7,4.0,8.0,9.0,8.0,7.0,6.0,4.0,1.0,74.0,1.0 -NO,16638.0,7,4.0,5.0,6.0,4.0,8.0,6.0,2.0,2.0,63.0,2.0 -NO,16644.0,7,5.0,7.0,8.0,6.0,8.0,6.0,5.0,1.0,26.0,1.0 -NO,16680.0,7,4.0,5.0,5.0,6.0,7.0,6.0,3.0,1.0,52.0,1.0 -NO,16692.0,7,3.0,8.0,8.0,5.0,7.0,7.0,3.0,2.0,42.0,1.0 -NO,16698.0,7,5.0,7.0,7.0,7.0,10.0,6.0,3.0,2.0,57.0,1.0 -NO,16740.0,7,5.0,7.0,7.0,7.0,10.0,4.0,5.0,1.0,74.0,1.0 -NO,16746.0,7,3.0,8.0,7.0,6.0,8.0,5.0,2.0,1.0,26.0,2.0 -NO,16770.0,7,6.0,5.0,6.0,3.0,8.0,4.0,2.0,1.0,35.0,1.0 -NO,16776.0,7,2.0,7.0,6.0,6.0,7.0,6.0,3.0,1.0,44.0,1.0 -NO,16824.0,7,7.0,7.0,7.0,7.0,9.0,5.0,2.0,2.0,81.0,2.0 -NO,16836.0,7,5.0,7.0,8.0,5.0,10.0,4.0,3.0,2.0,82.0,1.0 -NO,16860.0,7,1.0,8.0,9.0,5.0,9.0,7.0,3.0,1.0,27.0,2.0 -NO,16950.0,7,7.0,4.0,6.0,3.0,7.0,7.0,4.0,2.0,26.0,1.0 -NO,16968.0,7,4.0,7.0,5.0,7.0,9.0,5.0,1.0,1.0,62.0,1.0 -NO,16974.0,7,2.0,8.0,9.0,6.0,8.0,4.0,3.0,2.0,50.0,1.0 -NO,16980.0,7,7.0,8.0,8.0,5.0,8.0,4.0,3.0,1.0,57.0,1.0 -NO,16986.0,7,5.0,10.0,10.0,8.0,8.0,5.0,3.0,2.0,63.0,2.0 -NO,17064.0,7,4.0,8.0,8.0,5.0,9.0,5.0,3.0,1.0,19.0,2.0 -NO,17070.0,7,2.0,9.0,8.0,6.0,9.0,4.0,3.0,1.0,64.0,1.0 -NO,17082.0,7,2.0,4.0,7.0,5.0,5.0,6.0,3.0,2.0,17.0,2.0 -NO,17118.0,7,3.0,7.0,7.0,8.0,9.0,5.0,3.0,1.0,63.0,1.0 -NO,17142.0,7,2.0,8.0,6.0,6.0,8.0,4.0,2.0,2.0,41.0,1.0 -NO,17154.0,7,3.0,4.0,4.0,5.0,6.0,7.0,5.0,2.0,17.0,2.0 -NO,17190.0,7,5.0,8.0,7.0,5.0,8.0,3.0,2.0,2.0,60.0,2.0 -NO,17208.0,7,4.0,7.0,8.0,7.0,9.0,4.0,4.0,1.0,49.0,1.0 -NO,17214.0,7,2.0,7.0,5.0,5.0,9.0,4.0,3.0,1.0,19.0,2.0 -NO,17220.0,7,5.0,7.0,7.0,7.0,9.0,7.0,3.0,2.0,68.0,1.0 -NO,17286.0,7,2.0,9.0,9.0,9.0,7.0,5.0,4.0,1.0,40.0,1.0 -NO,17310.0,7,7.0,5.0,7.0,9.0,8.0,4.0,2.0,1.0,55.0,1.0 -NO,17328.0,7,7.0,5.0,8.0,5.0,6.0,6.0,3.0,2.0,94.0,2.0 -NO,17334.0,7,1.0,4.0,2.0,3.0,7.0,6.0,3.0,2.0,20.0,2.0 -NO,17412.0,7,5.0,6.0,8.0,3.0,3.0,2.0,1.0,2.0,20.0,1.0 -NO,17436.0,7,6.0,8.0,7.0,8.0,8.0,6.0,2.0,2.0,47.0,1.0 -NO,17448.0,7,7.0,8.0,6.0,7.0,8.0,6.0,3.0,1.0,70.0,1.0 -NO,17460.0,7,5.0,7.0,8.0,7.0,8.0,5.0,2.0,2.0,61.0,1.0 -NO,17466.0,7,4.0,8.0,7.0,8.0,9.0,6.0,4.0,2.0,,1.0 -NO,17484.0,7,5.0,7.0,7.0,7.0,7.0,6.0,3.0,1.0,51.0,1.0 -NO,17508.0,7,3.0,7.0,7.0,6.0,10.0,4.0,3.0,1.0,65.0,1.0 -NO,17520.0,7,4.0,7.0,7.0,6.0,8.0,4.0,3.0,1.0,46.0,1.0 -NO,17544.0,7,3.0,9.0,9.0,8.0,8.0,6.0,3.0,2.0,20.0,2.0 -NO,17550.0,7,5.0,5.0,5.0,3.0,8.0,2.0,2.0,1.0,53.0,1.0 -NO,17556.0,7,6.0,8.0,10.0,9.0,10.0,6.0,4.0,1.0,80.0,1.0 -NO,17634.0,7,5.0,5.0,8.0,7.0,10.0,5.0,3.0,2.0,65.0,1.0 -NO,17688.0,7,5.0,8.0,7.0,8.0,9.0,7.0,4.0,1.0,17.0,2.0 -NO,17742.0,7,7.0,8.0,7.0,7.0,9.0,6.0,2.0,2.0,62.0,1.0 -NO,17754.0,7,1.0,9.0,7.0,4.0,10.0,4.0,2.0,2.0,38.0,1.0 -NO,17766.0,7,5.0,7.0,7.0,8.0,8.0,4.0,2.0,1.0,63.0,2.0 -NO,17790.0,7,1.0,7.0,5.0,7.0,10.0,6.0,3.0,1.0,23.0,1.0 -NO,17802.0,7,1.0,6.0,9.0,4.0,9.0,7.0,3.0,1.0,54.0,1.0 -NO,17808.0,7,5.0,5.0,7.0,7.0,7.0,4.0,3.0,1.0,54.0,1.0 -NO,17826.0,7,5.0,7.0,7.0,5.0,8.0,3.0,2.0,1.0,35.0,1.0 -NO,17832.0,7,3.0,8.0,8.0,7.0,9.0,7.0,5.0,2.0,44.0,1.0 -NO,17856.0,7,6.0,7.0,5.0,6.0,9.0,6.0,3.0,2.0,76.0,1.0 -NO,17874.0,7,4.0,5.0,9.0,8.0,8.0,2.0,2.0,1.0,44.0,2.0 -NO,17880.0,7,7.0,3.0,5.0,8.0,7.0,6.0,3.0,2.0,73.0,2.0 -NO,17886.0,7,6.0,5.0,8.0,10.0,8.0,4.0,3.0,1.0,92.0,1.0 -NO,17916.0,7,2.0,6.0,6.0,3.0,10.0,5.0,3.0,1.0,28.0,2.0 -NO,17922.0,7,4.0,7.0,7.0,8.0,10.0,4.0,2.0,2.0,42.0,1.0 -NO,17940.0,7,7.0,8.0,10.0,9.0,8.0,6.0,3.0,2.0,32.0,2.0 -NO,17976.0,7,3.0,7.0,7.0,6.0,9.0,6.0,3.0,2.0,64.0,1.0 -NO,18000.0,7,6.0,7.0,7.0,4.0,4.0,4.0,2.0,2.0,44.0,1.0 -NO,18006.0,7,3.0,7.0,7.0,7.0,8.0,3.0,3.0,1.0,43.0,1.0 -NO,18024.0,7,2.0,8.0,7.0,9.0,9.0,6.0,3.0,1.0,17.0,2.0 -NO,18066.0,7,5.0,7.0,7.0,7.0,8.0,5.0,3.0,1.0,62.0,2.0 -NO,18090.0,7,5.0,5.0,5.0,8.0,8.0,6.0,4.0,2.0,38.0,2.0 -NO,18096.0,7,2.0,9.0,9.0,7.0,7.0,5.0,3.0,2.0,45.0,1.0 -NO,18120.0,7,3.0,9.0,9.0,8.0,9.0,6.0,3.0,1.0,16.0,2.0 -NO,18162.0,7,7.0,6.0,7.0,5.0,9.0,5.0,4.0,1.0,69.0,1.0 -NO,18198.0,7,2.0,5.0,8.0,5.0,9.0,7.0,3.0,1.0,17.0,2.0 -NO,18204.0,7,3.0,7.0,8.0,6.0,8.0,6.0,4.0,1.0,73.0,2.0 -NO,18210.0,7,3.0,9.0,7.0,8.0,7.0,7.0,3.0,1.0,34.0,2.0 -NO,18216.0,7,2.0,6.0,7.0,3.0,9.0,6.0,3.0,1.0,15.0,2.0 -NO,18222.0,7,1.0,8.0,5.0,8.0,7.0,7.0,2.0,2.0,49.0,1.0 -NO,18240.0,7,2.0,6.0,8.0,5.0,7.0,3.0,1.0,1.0,75.0,2.0 -NO,18270.0,7,1.0,7.0,5.0,3.0,8.0,5.0,2.0,1.0,45.0,2.0 -NO,18306.0,7,4.0,3.0,5.0,5.0,7.0,4.0,2.0,1.0,22.0,2.0 -NO,18336.0,7,2.0,5.0,5.0,5.0,9.0,6.0,3.0,1.0,62.0,1.0 -NO,18360.0,7,5.0,5.0,9.0,7.0,8.0,5.0,3.0,1.0,40.0,1.0 -NO,18390.0,7,3.0,10.0,10.0,10.0,9.0,6.0,3.0,1.0,45.0,1.0 -NO,18396.0,7,1.0,8.0,9.0,8.0,8.0,5.0,3.0,2.0,45.0,1.0 -NO,18438.0,7,7.0,8.0,7.0,7.0,6.0,3.0,1.0,1.0,58.0,1.0 -NO,18480.0,7,3.0,8.0,8.0,8.0,8.0,6.0,4.0,2.0,63.0,1.0 -NO,18486.0,7,6.0,5.0,7.0,4.0,10.0,6.0,2.0,2.0,21.0,2.0 -NO,18498.0,7,3.0,7.0,8.0,8.0,7.0,4.0,3.0,1.0,60.0,2.0 -NO,18540.0,7,3.0,5.0,5.0,5.0,5.0,2.0,1.0,1.0,45.0,1.0 -NO,18546.0,7,5.0,5.0,7.0,6.0,6.0,6.0,2.0,1.0,45.0,1.0 -NO,18570.0,7,4.0,4.0,4.0,5.0,9.0,4.0,3.0,1.0,50.0,2.0 -NO,18576.0,7,3.0,8.0,8.0,8.0,7.0,7.0,3.0,1.0,55.0,1.0 -NO,18600.0,7,5.0,7.0,7.0,7.0,6.0,3.0,2.0,1.0,60.0,2.0 -NO,18606.0,7,4.0,7.0,7.0,5.0,10.0,5.0,2.0,1.0,87.0,1.0 -NO,18618.0,7,2.0,5.0,5.0,5.0,9.0,6.0,3.0,2.0,42.0,1.0 -NO,18630.0,7,6.0,7.0,4.0,6.0,8.0,7.0,2.0,1.0,18.0,2.0 -NO,18648.0,7,3.0,7.0,8.0,7.0,8.0,6.0,4.0,1.0,48.0,1.0 -NO,18678.0,7,4.0,,10.0,,9.0,7.0,4.0,2.0,90.0,2.0 -NO,18684.0,7,3.0,10.0,8.0,10.0,10.0,4.0,2.0,2.0,45.0,1.0 -NO,18696.0,7,4.0,9.0,9.0,9.0,8.0,6.0,3.0,1.0,67.0,2.0 -NO,18720.0,7,2.0,5.0,8.0,5.0,8.0,7.0,3.0,1.0,18.0,2.0 -NO,18732.0,7,5.0,8.0,8.0,7.0,9.0,5.0,3.0,2.0,,2.0 -NO,18744.0,7,4.0,8.0,9.0,7.0,7.0,7.0,3.0,2.0,23.0,2.0 -NO,18750.0,7,5.0,7.0,8.0,7.0,9.0,6.0,4.0,1.0,25.0,1.0 -NO,18762.0,7,4.0,5.0,5.0,3.0,8.0,7.0,3.0,2.0,42.0,1.0 -NO,18774.0,7,6.0,8.0,9.0,6.0,10.0,7.0,3.0,2.0,59.0,1.0 -NO,18786.0,7,0.0,6.0,9.0,8.0,10.0,7.0,3.0,2.0,20.0,2.0 -NO,18792.0,7,6.0,4.0,6.0,5.0,4.0,6.0,2.0,1.0,21.0,2.0 -NO,18798.0,7,1.0,8.0,5.0,6.0,9.0,4.0,3.0,1.0,27.0,1.0 -NO,18822.0,7,7.0,4.0,5.0,6.0,8.0,2.0,1.0,1.0,,2.0 -NO,18834.0,7,2.0,8.0,6.0,6.0,9.0,6.0,3.0,2.0,46.0,1.0 -NO,18846.0,7,2.0,7.0,5.0,4.0,8.0,7.0,4.0,2.0,18.0,2.0 -NO,18858.0,7,2.0,5.0,6.0,5.0,10.0,7.0,3.0,1.0,15.0,2.0 -NO,18876.0,7,4.0,5.0,4.0,3.0,10.0,6.0,2.0,2.0,22.0,1.0 -NO,18894.0,7,4.0,5.0,7.0,4.0,7.0,5.0,2.0,1.0,22.0,2.0 -NO,18900.0,7,5.0,2.0,3.0,2.0,10.0,6.0,3.0,1.0,27.0,1.0 -NO,18912.0,7,1.0,7.0,7.0,6.0,8.0,7.0,4.0,1.0,49.0,2.0 -NO,18918.0,7,3.0,4.0,7.0,4.0,6.0,7.0,3.0,2.0,19.0,2.0 -NO,18942.0,7,3.0,5.0,6.0,6.0,9.0,7.0,3.0,1.0,45.0,1.0 -NO,19014.0,7,7.0,6.0,8.0,8.0,10.0,5.0,3.0,2.0,89.0,1.0 -NO,19020.0,7,7.0,4.0,5.0,4.0,9.0,7.0,4.0,1.0,23.0,1.0 -NO,19038.0,7,4.0,6.0,6.0,6.0,8.0,5.0,2.0,1.0,32.0,1.0 -NO,19056.0,7,2.0,8.0,9.0,8.0,10.0,6.0,3.0,2.0,75.0,1.0 -NO,19068.0,7,4.0,3.0,4.0,3.0,5.0,4.0,3.0,2.0,63.0,1.0 -NO,19092.0,7,2.0,7.0,4.0,7.0,7.0,3.0,3.0,1.0,52.0,1.0 -NO,19104.0,7,2.0,5.0,8.0,6.0,7.0,6.0,3.0,1.0,38.0,1.0 -NO,19122.0,7,4.0,5.0,7.0,5.0,6.0,4.0,3.0,2.0,49.0,1.0 -NO,19140.0,7,0.0,8.0,5.0,4.0,8.0,4.0,3.0,1.0,43.0,1.0 -NO,19146.0,7,2.0,0.0,0.0,3.0,10.0,2.0,2.0,1.0,37.0,1.0 -NO,19152.0,7,2.0,8.0,8.0,8.0,8.0,6.0,3.0,1.0,60.0,1.0 -NO,19170.0,7,5.0,5.0,7.0,5.0,8.0,6.0,3.0,1.0,16.0,2.0 -NO,19218.0,7,4.0,9.0,8.0,8.0,6.0,4.0,4.0,2.0,51.0,1.0 -NO,19254.0,7,5.0,5.0,6.0,8.0,9.0,6.0,4.0,2.0,84.0,2.0 -NO,19278.0,7,2.0,8.0,6.0,8.0,8.0,7.0,3.0,1.0,55.0,2.0 -NO,19284.0,7,3.0,4.0,5.0,0.0,7.0,6.0,3.0,1.0,82.0,2.0 -NO,19308.0,7,6.0,7.0,10.0,8.0,10.0,4.0,3.0,2.0,29.0,1.0 -NO,19320.0,7,5.0,8.0,7.0,7.0,10.0,6.0,3.0,1.0,47.0,1.0 -NO,19326.0,7,3.0,3.0,5.0,5.0,9.0,7.0,1.0,2.0,38.0,1.0 -NO,19332.0,7,5.0,8.0,8.0,6.0,8.0,5.0,2.0,1.0,,2.0 -NO,19404.0,7,1.0,5.0,4.0,6.0,8.0,6.0,2.0,1.0,15.0,2.0 -NO,19416.0,7,2.0,7.0,7.0,6.0,7.0,4.0,3.0,1.0,62.0,1.0 -NO,19422.0,7,2.0,8.0,7.0,5.0,8.0,3.0,3.0,1.0,33.0,1.0 -NO,19464.0,7,4.0,8.0,9.0,4.0,9.0,7.0,5.0,1.0,18.0,2.0 -NO,19500.0,7,5.0,7.0,10.0,9.0,10.0,7.0,3.0,1.0,55.0,2.0 -NO,19524.0,7,7.0,8.0,8.0,1.0,7.0,4.0,2.0,2.0,83.0,1.0 -NO,19548.0,7,5.0,7.0,7.0,6.0,7.0,2.0,3.0,1.0,53.0,1.0 -NO,19596.0,7,7.0,8.0,7.0,6.0,5.0,4.0,2.0,1.0,56.0,2.0 -NO,19626.0,7,3.0,7.0,5.0,3.0,9.0,4.0,3.0,1.0,55.0,1.0 -NO,19638.0,7,0.0,9.0,8.0,8.0,9.0,7.0,3.0,2.0,55.0,1.0 -NO,19656.0,7,5.0,6.0,6.0,8.0,7.0,4.0,2.0,2.0,74.0,1.0 -NO,19662.0,7,2.0,6.0,5.0,6.0,8.0,4.0,2.0,1.0,23.0,2.0 -NO,19680.0,7,2.0,7.0,5.0,8.0,9.0,6.0,5.0,1.0,36.0,2.0 -NO,19686.0,7,5.0,7.0,8.0,7.0,4.0,6.0,5.0,1.0,40.0,1.0 -NO,19704.0,7,5.0,7.0,7.0,5.0,6.0,6.0,4.0,1.0,25.0,2.0 -NO,19710.0,7,4.0,8.0,7.0,6.0,7.0,4.0,3.0,1.0,76.0,1.0 -NO,19716.0,7,3.0,7.0,5.0,6.0,8.0,6.0,2.0,1.0,62.0,1.0 -NO,19734.0,7,4.0,3.0,4.0,3.0,7.0,7.0,3.0,2.0,22.0,2.0 -NO,19752.0,7,2.0,6.0,5.0,6.0,10.0,2.0,1.0,2.0,60.0,2.0 -NO,19758.0,7,7.0,5.0,5.0,4.0,2.0,5.0,1.0,2.0,45.0,2.0 -NO,19764.0,7,6.0,7.0,9.0,9.0,10.0,6.0,4.0,2.0,71.0,2.0 -NO,19776.0,7,5.0,5.0,8.0,8.0,9.0,3.0,1.0,1.0,84.0,1.0 -NO,19824.0,7,2.0,5.0,4.0,6.0,8.0,7.0,3.0,2.0,21.0,2.0 -NO,19836.0,7,2.0,9.0,8.0,8.0,9.0,6.0,3.0,2.0,55.0,1.0 -NO,19842.0,7,4.0,6.0,8.0,8.0,9.0,4.0,2.0,1.0,52.0,1.0 -NO,19872.0,7,0.0,4.0,5.0,4.0,7.0,4.0,3.0,2.0,18.0,2.0 -NO,19884.0,7,4.0,3.0,5.0,5.0,3.0,5.0,2.0,2.0,49.0,2.0 -NO,19890.0,7,6.0,8.0,6.0,4.0,8.0,6.0,4.0,2.0,86.0,2.0 -NO,19896.0,7,6.0,6.0,5.0,7.0,6.0,4.0,3.0,1.0,73.0,2.0 -NO,19902.0,7,2.0,7.0,7.0,5.0,10.0,4.0,4.0,1.0,57.0,1.0 -NO,19908.0,7,1.0,8.0,8.0,7.0,9.0,6.0,2.0,1.0,53.0,2.0 -NO,19950.0,7,4.0,8.0,9.0,9.0,10.0,5.0,3.0,1.0,65.0,1.0 -NO,19968.0,7,7.0,3.0,5.0,5.0,7.0,6.0,3.0,2.0,65.0,2.0 -NO,19980.0,7,5.0,8.0,6.0,7.0,9.0,6.0,3.0,1.0,37.0,1.0 -NO,19998.0,7,2.0,8.0,9.0,7.0,9.0,6.0,3.0,1.0,26.0,1.0 -NO,20022.0,7,3.0,6.0,4.0,6.0,7.0,6.0,1.0,2.0,22.0,1.0 -NO,20034.0,7,4.0,8.0,8.0,7.0,9.0,6.0,3.0,2.0,47.0,1.0 -NO,20076.0,7,7.0,8.0,6.0,6.0,8.0,4.0,2.0,1.0,61.0,1.0 -NO,20088.0,7,7.0,3.0,6.0,5.0,10.0,6.0,3.0,1.0,74.0,1.0 -NO,20142.0,7,5.0,7.0,8.0,5.0,9.0,6.0,3.0,2.0,30.0,1.0 -NO,20154.0,7,7.0,3.0,5.0,5.0,5.0,2.0,1.0,2.0,69.0,2.0 -NO,20166.0,7,7.0,5.0,,6.0,7.0,2.0,2.0,1.0,52.0,1.0 -NO,20262.0,7,2.0,5.0,6.0,0.0,9.0,4.0,3.0,1.0,57.0,1.0 -NO,20286.0,7,3.0,3.0,3.0,5.0,4.0,2.0,1.0,2.0,16.0,2.0 -NO,20316.0,7,5.0,7.0,7.0,5.0,7.0,5.0,4.0,2.0,,1.0 -NO,20328.0,7,5.0,7.0,7.0,6.0,8.0,5.0,2.0,1.0,52.0,1.0 -NO,20352.0,7,5.0,5.0,9.0,5.0,6.0,4.0,3.0,1.0,72.0,1.0 -NO,20358.0,7,3.0,7.0,9.0,7.0,9.0,5.0,3.0,2.0,65.0,1.0 -NO,20370.0,7,1.0,8.0,8.0,8.0,10.0,3.0,3.0,2.0,44.0,1.0 -NO,20394.0,7,1.0,7.0,7.0,7.0,7.0,7.0,3.0,1.0,24.0,2.0 -NO,20400.0,7,7.0,7.0,8.0,5.0,8.0,4.0,1.0,2.0,70.0,2.0 -NO,20412.0,7,5.0,8.0,8.0,8.0,8.0,2.0,1.0,1.0,78.0,1.0 -NO,20418.0,7,3.0,6.0,5.0,4.0,8.0,6.0,3.0,2.0,18.0,2.0 -NO,20424.0,7,4.0,2.0,10.0,6.0,8.0,2.0,3.0,2.0,64.0,2.0 -NO,20436.0,7,5.0,8.0,7.0,8.0,9.0,6.0,4.0,2.0,75.0,2.0 -NO,20442.0,7,7.0,8.0,5.0,4.0,8.0,6.0,2.0,1.0,56.0,1.0 -NO,20448.0,7,3.0,4.0,5.0,7.0,6.0,6.0,3.0,2.0,29.0,1.0 -NO,20460.0,7,5.0,8.0,8.0,5.0,10.0,6.0,2.0,2.0,44.0,1.0 -NO,20466.0,7,2.0,5.0,5.0,5.0,10.0,6.0,3.0,1.0,53.0,1.0 -NO,20478.0,7,7.0,8.0,5.0,7.0,8.0,6.0,2.0,1.0,72.0,2.0 -NO,20484.0,7,6.0,6.0,8.0,6.0,7.0,7.0,3.0,1.0,27.0,1.0 -NO,20496.0,7,5.0,4.0,5.0,6.0,10.0,4.0,3.0,2.0,62.0,1.0 -NO,20508.0,7,0.0,7.0,7.0,7.0,7.0,3.0,2.0,1.0,84.0,2.0 -NO,20514.0,7,3.0,7.0,8.0,9.0,6.0,5.0,2.0,1.0,39.0,1.0 -NO,20526.0,7,2.0,9.0,4.0,2.0,8.0,5.0,2.0,1.0,21.0,2.0 -NO,20568.0,7,5.0,7.0,7.0,6.0,8.0,4.0,3.0,2.0,61.0,1.0 -NO,20580.0,7,3.0,7.0,7.0,7.0,9.0,6.0,2.0,1.0,48.0,1.0 -NO,20586.0,7,6.0,7.0,7.0,3.0,10.0,4.0,3.0,1.0,78.0,1.0 -NO,20640.0,7,4.0,7.0,8.0,7.0,8.0,4.0,3.0,1.0,65.0,1.0 -NO,20646.0,7,3.0,8.0,7.0,4.0,8.0,6.0,3.0,1.0,23.0,1.0 -NO,20688.0,7,3.0,7.0,6.0,7.0,6.0,6.0,4.0,1.0,35.0,2.0 -NO,20730.0,7,7.0,6.0,6.0,5.0,6.0,4.0,2.0,1.0,46.0,1.0 -NO,20742.0,7,2.0,5.0,7.0,5.0,5.0,3.0,2.0,2.0,37.0,2.0 -NO,20772.0,7,2.0,7.0,8.0,7.0,9.0,6.0,3.0,2.0,65.0,1.0 -NO,20778.0,7,4.0,8.0,8.0,8.0,7.0,6.0,3.0,1.0,67.0,1.0 -NO,20808.0,7,4.0,7.0,8.0,8.0,9.0,5.0,3.0,2.0,51.0,2.0 -NO,20862.0,7,3.0,9.0,8.0,8.0,8.0,6.0,2.0,2.0,24.0,2.0 -NO,20892.0,7,5.0,7.0,9.0,7.0,9.0,4.0,4.0,1.0,70.0,1.0 -NO,20910.0,7,6.0,5.0,6.0,3.0,8.0,7.0,2.0,2.0,39.0,1.0 -NO,20922.0,7,5.0,3.0,3.0,4.0,9.0,6.0,4.0,2.0,72.0,2.0 -NO,20928.0,7,4.0,8.0,8.0,7.0,7.0,7.0,4.0,1.0,29.0,2.0 -NO,20934.0,7,7.0,0.0,4.0,5.0,9.0,6.0,3.0,2.0,44.0,1.0 -NO,20970.0,7,5.0,7.0,8.0,7.0,7.0,6.0,3.0,1.0,82.0,1.0 -NO,20988.0,7,4.0,7.0,6.0,4.0,9.0,3.0,2.0,1.0,39.0,1.0 -NO,20994.0,7,3.0,3.0,4.0,6.0,10.0,6.0,3.0,1.0,60.0,1.0 -NO,21042.0,7,6.0,7.0,7.0,6.0,8.0,7.0,3.0,2.0,25.0,1.0 -NO,21114.0,7,2.0,1.0,4.0,9.0,10.0,7.0,3.0,1.0,21.0,2.0 -NO,21168.0,7,1.0,8.0,8.0,7.0,8.0,6.0,3.0,1.0,36.0,2.0 -NO,21210.0,7,3.0,8.0,7.0,9.0,8.0,3.0,2.0,1.0,46.0,1.0 -NO,21228.0,7,4.0,6.0,6.0,4.0,7.0,4.0,3.0,2.0,43.0,1.0 -NO,21234.0,7,5.0,7.0,7.0,5.0,7.0,6.0,2.0,1.0,51.0,1.0 -NO,21282.0,7,7.0,8.0,10.0,10.0,10.0,7.0,4.0,2.0,81.0,2.0 -NO,21306.0,7,0.0,8.0,7.0,7.0,5.0,6.0,2.0,2.0,22.0,2.0 -NO,21312.0,7,3.0,6.0,8.0,5.0,9.0,7.0,3.0,2.0,,1.0 -NO,21318.0,7,2.0,7.0,9.0,7.0,8.0,6.0,4.0,2.0,51.0,1.0 -NO,21402.0,7,2.0,10.0,8.0,7.0,9.0,4.0,3.0,1.0,47.0,1.0 -NO,21414.0,7,7.0,7.0,10.0,9.0,10.0,6.0,2.0,1.0,82.0,1.0 -NO,21462.0,7,3.0,6.0,7.0,7.0,3.0,4.0,2.0,2.0,48.0,2.0 -NO,21468.0,7,4.0,6.0,8.0,5.0,3.0,4.0,2.0,2.0,40.0,2.0 -NO,21492.0,7,2.0,9.0,9.0,6.0,9.0,6.0,3.0,2.0,53.0,1.0 -NO,21540.0,7,6.0,4.0,7.0,3.0,4.0,7.0,3.0,2.0,18.0,1.0 -NO,21564.0,7,6.0,3.0,2.0,7.0,8.0,3.0,2.0,1.0,70.0,1.0 -NO,21576.0,7,4.0,7.0,7.0,6.0,7.0,7.0,3.0,2.0,58.0,1.0 -NO,21582.0,7,0.0,5.0,5.0,7.0,5.0,3.0,2.0,1.0,69.0,2.0 -NO,21594.0,7,3.0,5.0,7.0,6.0,9.0,7.0,2.0,1.0,16.0,2.0 -NO,21606.0,7,3.0,5.0,5.0,5.0,10.0,6.0,3.0,1.0,34.0,1.0 -NO,21696.0,7,7.0,6.0,6.0,5.0,10.0,4.0,3.0,2.0,31.0,1.0 -NO,21726.0,7,1.0,5.0,7.0,6.0,9.0,6.0,2.0,1.0,32.0,1.0 -NO,21738.0,7,7.0,4.0,6.0,2.0,5.0,3.0,2.0,1.0,42.0,2.0 -NO,21768.0,7,3.0,7.0,7.0,5.0,7.0,5.0,4.0,1.0,41.0,1.0 -NO,21774.0,7,2.0,3.0,5.0,5.0,9.0,7.0,2.0,2.0,51.0,2.0 -NO,21798.0,7,7.0,8.0,8.0,8.0,9.0,4.0,2.0,1.0,54.0,1.0 -NO,21804.0,7,1.0,7.0,5.0,5.0,9.0,7.0,1.0,2.0,25.0,2.0 -NO,21816.0,7,5.0,4.0,5.0,7.0,3.0,7.0,3.0,2.0,39.0,2.0 -NO,21822.0,7,4.0,7.0,7.0,6.0,8.0,6.0,2.0,1.0,19.0,2.0 -NO,21840.0,7,6.0,5.0,6.0,9.0,10.0,6.0,3.0,2.0,67.0,1.0 -NO,21852.0,7,5.0,5.0,7.0,5.0,6.0,6.0,3.0,1.0,20.0,1.0 -NO,21882.0,7,4.0,8.0,8.0,4.0,8.0,6.0,3.0,1.0,18.0,2.0 -NO,21930.0,7,4.0,7.0,7.0,7.0,9.0,4.0,3.0,2.0,55.0,1.0 -NO,21978.0,7,5.0,7.0,8.0,5.0,7.0,5.0,2.0,2.0,80.0,1.0 -NO,21984.0,7,3.0,6.0,7.0,6.0,10.0,6.0,3.0,2.0,63.0,1.0 -NO,22038.0,7,7.0,8.0,8.0,5.0,7.0,5.0,2.0,1.0,55.0,2.0 -NO,22068.0,7,4.0,3.0,3.0,4.0,8.0,4.0,3.0,2.0,48.0,1.0 -NO,22074.0,7,7.0,1.0,6.0,5.0,9.0,6.0,1.0,1.0,55.0,1.0 -NO,22158.0,7,2.0,7.0,5.0,5.0,5.0,4.0,1.0,2.0,32.0,1.0 -NO,22170.0,7,3.0,3.0,3.0,5.0,1.0,7.0,3.0,2.0,61.0,2.0 -NO,22176.0,7,3.0,6.0,6.0,6.0,7.0,6.0,3.0,2.0,54.0,1.0 -NO,22224.0,7,4.0,9.0,8.0,6.0,8.0,6.0,4.0,1.0,51.0,2.0 -NO,22260.0,7,1.0,8.0,8.0,5.0,8.0,4.0,2.0,1.0,15.0,2.0 -NO,22266.0,7,4.0,5.0,4.0,4.0,10.0,5.0,3.0,2.0,51.0,1.0 -NO,22290.0,7,5.0,8.0,7.0,8.0,7.0,6.0,2.0,2.0,68.0,2.0 -NO,22362.0,7,3.0,8.0,9.0,6.0,7.0,3.0,2.0,2.0,61.0,1.0 -NO,22386.0,7,4.0,5.0,4.0,6.0,9.0,6.0,4.0,2.0,70.0,1.0 -NO,22428.0,7,2.0,8.0,7.0,3.0,8.0,6.0,3.0,1.0,16.0,2.0 -SE,7.0,6,3.0,7.0,7.0,10.0,5.0,6.0,2.0,2.0,96.0,2.0 -SE,13.0,6,1.0,10.0,10.0,10.0,10.0,5.0,3.0,1.0,92.0,1.0 -SE,22.0,6,2.0,6.0,6.0,5.0,9.0,4.0,4.0,1.0,92.0,1.0 -SE,30.0,6,7.0,6.0,8.0,8.0,9.0,7.0,4.0,1.0,90.0,2.0 -SE,39.0,6,6.0,6.0,8.0,8.0,9.0,5.0,2.0,1.0,90.0,2.0 -SE,58.0,6,4.0,6.0,8.0,8.0,7.0,6.0,2.0,1.0,,1.0 -SE,79.0,6,7.0,2.0,2.0,5.0,10.0,7.0,4.0,2.0,87.0,2.0 -SE,97.0,6,7.0,5.0,5.0,4.0,8.0,5.0,1.0,1.0,86.0,1.0 -SE,102.0,6,4.0,7.0,5.0,5.0,8.0,6.0,1.0,2.0,85.0,2.0 -SE,105.0,6,3.0,0.0,,0.0,4.0,2.0,,2.0,86.0,2.0 -SE,106.0,6,5.0,8.0,5.0,8.0,6.0,3.0,2.0,2.0,85.0,2.0 -SE,107.0,6,4.0,8.0,8.0,6.0,9.0,6.0,2.0,1.0,85.0,1.0 -SE,109.0,6,3.0,5.0,3.0,4.0,8.0,5.0,1.0,1.0,86.0,1.0 -SE,112.0,6,6.0,7.0,8.0,6.0,3.0,4.0,2.0,1.0,85.0,1.0 -SE,116.0,6,5.0,0.0,0.0,10.0,10.0,7.0,5.0,1.0,85.0,2.0 -SE,120.0,6,3.0,9.0,9.0,8.0,6.0,5.0,4.0,1.0,84.0,1.0 -SE,122.0,6,2.0,10.0,8.0,5.0,4.0,2.0,3.0,1.0,84.0,1.0 -SE,130.0,6,3.0,6.0,7.0,6.0,9.0,7.0,4.0,1.0,84.0,2.0 -SE,134.0,6,7.0,5.0,7.0,5.0,10.0,2.0,2.0,1.0,84.0,1.0 -SE,148.0,6,6.0,4.0,9.0,7.0,9.0,6.0,3.0,2.0,83.0,2.0 -SE,154.0,6,6.0,5.0,7.0,8.0,8.0,6.0,5.0,2.0,83.0,2.0 -SE,160.0,6,0.0,5.0,7.0,8.0,9.0,2.0,1.0,1.0,82.0,2.0 -SE,165.0,6,7.0,5.0,8.0,9.0,7.0,5.0,3.0,2.0,82.0,2.0 -SE,170.0,6,5.0,5.0,5.0,5.0,5.0,5.0,2.0,1.0,81.0,2.0 -SE,177.0,6,2.0,8.0,8.0,8.0,8.0,4.0,3.0,2.0,81.0,2.0 -SE,178.0,6,7.0,8.0,5.0,5.0,5.0,1.0,1.0,1.0,81.0,1.0 -SE,180.0,6,7.0,8.0,10.0,6.0,8.0,6.0,2.0,1.0,81.0,2.0 -SE,192.0,6,0.0,5.0,8.0,10.0,8.0,6.0,4.0,1.0,80.0,2.0 -SE,198.0,6,6.0,5.0,8.0,6.0,5.0,6.0,4.0,2.0,80.0,2.0 -SE,199.0,6,5.0,4.0,8.0,5.0,10.0,4.0,3.0,2.0,81.0,2.0 -SE,201.0,6,3.0,3.0,6.0,6.0,9.0,6.0,2.0,1.0,80.0,1.0 -SE,206.0,6,4.0,5.0,7.0,7.0,10.0,4.0,2.0,1.0,81.0,1.0 -SE,216.0,6,3.0,2.0,2.0,4.0,7.0,5.0,3.0,1.0,80.0,1.0 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-SE,1047.0,6,5.0,7.0,8.0,6.0,8.0,6.0,3.0,1.0,61.0,1.0 -SE,1050.0,6,2.0,8.0,7.0,7.0,8.0,6.0,3.0,1.0,61.0,2.0 -SE,1051.0,6,1.0,7.0,8.0,6.0,8.0,7.0,4.0,2.0,61.0,1.0 -SE,1053.0,6,5.0,8.0,7.0,7.0,9.0,5.0,2.0,1.0,61.0,1.0 -SE,1057.0,6,6.0,4.0,8.0,8.0,10.0,3.0,5.0,1.0,61.0,2.0 -SE,1067.0,6,6.0,7.0,6.0,4.0,5.0,4.0,1.0,1.0,61.0,1.0 -SE,1069.0,6,7.0,7.0,2.0,10.0,8.0,3.0,1.0,2.0,62.0,1.0 -SE,1075.0,6,3.0,5.0,8.0,4.0,7.0,7.0,3.0,2.0,61.0,1.0 -SE,1078.0,6,4.0,9.0,10.0,10.0,7.0,6.0,3.0,2.0,60.0,1.0 -SE,1085.0,6,5.0,3.0,5.0,7.0,8.0,4.0,3.0,1.0,60.0,2.0 -SE,1086.0,6,3.0,7.0,8.0,8.0,7.0,4.0,4.0,1.0,60.0,1.0 -SE,1090.0,6,4.0,3.0,5.0,5.0,6.0,7.0,3.0,2.0,61.0,2.0 -SE,1092.0,6,7.0,5.0,4.0,6.0,7.0,4.0,4.0,1.0,60.0,1.0 -SE,1095.0,6,2.0,8.0,8.0,7.0,8.0,7.0,3.0,2.0,61.0,1.0 -SE,1096.0,6,4.0,5.0,7.0,8.0,7.0,3.0,3.0,1.0,60.0,1.0 -SE,1103.0,6,4.0,8.0,8.0,7.0,7.0,5.0,3.0,1.0,60.0,2.0 -SE,1106.0,6,4.0,5.0,7.0,6.0,7.0,7.0,3.0,2.0,60.0,2.0 -SE,1107.0,6,4.0,7.0,7.0,5.0,8.0,4.0,3.0,2.0,60.0,2.0 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-SE,1197.0,6,4.0,8.0,8.0,7.0,9.0,7.0,2.0,1.0,58.0,1.0 -SE,1208.0,6,2.0,8.0,8.0,7.0,8.0,7.0,4.0,2.0,58.0,1.0 -SE,1211.0,6,1.0,10.0,10.0,10.0,9.0,6.0,3.0,2.0,,1.0 -SE,1214.0,6,2.0,7.0,8.0,5.0,9.0,4.0,3.0,2.0,59.0,1.0 -SE,1215.0,6,3.0,7.0,7.0,5.0,8.0,5.0,3.0,2.0,58.0,2.0 -SE,1217.0,6,3.0,7.0,7.0,7.0,7.0,7.0,2.0,1.0,58.0,1.0 -SE,1220.0,6,6.0,8.0,5.0,9.0,8.0,7.0,3.0,1.0,58.0,1.0 -SE,1221.0,6,4.0,8.0,7.0,4.0,8.0,7.0,4.0,2.0,58.0,2.0 -SE,1225.0,6,2.0,7.0,10.0,8.0,7.0,5.0,3.0,2.0,57.0,1.0 -SE,1228.0,6,2.0,5.0,8.0,5.0,9.0,4.0,3.0,2.0,57.0,1.0 -SE,1231.0,6,3.0,9.0,7.0,9.0,10.0,7.0,3.0,2.0,58.0,1.0 -SE,1236.0,6,2.0,5.0,5.0,6.0,7.0,6.0,3.0,1.0,58.0,1.0 -SE,1238.0,6,2.0,7.0,7.0,5.0,8.0,6.0,3.0,1.0,57.0,1.0 -SE,1240.0,6,4.0,7.0,7.0,7.0,8.0,6.0,3.0,2.0,57.0,1.0 -SE,1243.0,6,4.0,1.0,5.0,5.0,9.0,4.0,3.0,1.0,58.0,1.0 -SE,1244.0,6,5.0,2.0,5.0,3.0,9.0,6.0,1.0,2.0,57.0,1.0 -SE,1246.0,6,3.0,6.0,7.0,7.0,10.0,7.0,4.0,1.0,57.0,1.0 -SE,1247.0,6,7.0,2.0,5.0,0.0,10.0,7.0,2.0,2.0,57.0,1.0 -SE,1248.0,6,3.0,7.0,7.0,7.0,8.0,3.0,3.0,1.0,57.0,1.0 -SE,1251.0,6,4.0,6.0,6.0,3.0,7.0,6.0,3.0,1.0,57.0,1.0 -SE,1252.0,6,3.0,8.0,7.0,3.0,7.0,7.0,2.0,2.0,58.0,2.0 -SE,1253.0,6,4.0,5.0,5.0,2.0,8.0,4.0,2.0,1.0,58.0,1.0 -SE,1256.0,6,6.0,5.0,7.0,4.0,10.0,5.0,5.0,1.0,57.0,1.0 -SE,1258.0,6,3.0,7.0,5.0,2.0,9.0,6.0,3.0,2.0,57.0,1.0 -SE,1259.0,6,3.0,7.0,7.0,8.0,8.0,6.0,2.0,2.0,58.0,2.0 -SE,1263.0,6,4.0,7.0,6.0,5.0,7.0,4.0,2.0,2.0,57.0,1.0 -SE,1264.0,6,3.0,7.0,9.0,5.0,9.0,5.0,3.0,1.0,57.0,1.0 -SE,1271.0,6,2.0,9.0,9.0,7.0,8.0,6.0,3.0,2.0,57.0,1.0 -SE,1274.0,6,4.0,8.0,8.0,7.0,9.0,6.0,3.0,2.0,56.0,2.0 -SE,1279.0,6,3.0,6.0,6.0,4.0,5.0,4.0,3.0,2.0,57.0,1.0 -SE,1286.0,6,5.0,6.0,6.0,6.0,5.0,4.0,2.0,2.0,57.0,1.0 -SE,1287.0,6,2.0,8.0,8.0,5.0,7.0,6.0,4.0,1.0,56.0,1.0 -SE,1298.0,6,6.0,4.0,6.0,7.0,9.0,7.0,3.0,1.0,56.0,1.0 -SE,1302.0,6,4.0,6.0,4.0,3.0,8.0,7.0,4.0,1.0,56.0,1.0 -SE,1314.0,6,2.0,6.0,5.0,7.0,6.0,7.0,3.0,2.0,57.0,1.0 -SE,1315.0,6,1.0,7.0,5.0,5.0,6.0,3.0,2.0,1.0,56.0,1.0 -SE,1316.0,6,1.0,7.0,8.0,5.0,7.0,4.0,3.0,2.0,56.0,1.0 -SE,1318.0,6,2.0,3.0,3.0,3.0,7.0,4.0,2.0,2.0,57.0,2.0 -SE,1321.0,6,2.0,8.0,8.0,7.0,9.0,6.0,2.0,2.0,56.0,2.0 -SE,1325.0,6,5.0,4.0,6.0,6.0,6.0,6.0,3.0,1.0,56.0,1.0 -SE,1334.0,6,5.0,6.0,7.0,7.0,7.0,6.0,3.0,2.0,55.0,2.0 -SE,1336.0,6,3.0,3.0,3.0,3.0,10.0,7.0,3.0,2.0,55.0,1.0 -SE,1341.0,6,4.0,8.0,6.0,8.0,7.0,7.0,3.0,1.0,55.0,2.0 -SE,1343.0,6,2.0,7.0,8.0,5.0,7.0,6.0,2.0,1.0,55.0,1.0 -SE,1344.0,6,4.0,8.0,8.0,8.0,8.0,6.0,3.0,2.0,55.0,1.0 -SE,1345.0,6,1.0,8.0,7.0,5.0,7.0,4.0,3.0,1.0,55.0,1.0 -SE,1348.0,6,2.0,8.0,7.0,7.0,8.0,6.0,3.0,1.0,56.0,1.0 -SE,1353.0,6,2.0,8.0,9.0,9.0,9.0,2.0,2.0,2.0,55.0,1.0 -SE,1358.0,6,6.0,5.0,7.0,4.0,5.0,6.0,2.0,1.0,55.0,1.0 -SE,1360.0,6,2.0,8.0,8.0,5.0,8.0,6.0,3.0,1.0,56.0,1.0 -SE,1361.0,6,3.0,7.0,7.0,10.0,10.0,6.0,3.0,2.0,55.0,1.0 -SE,1365.0,6,7.0,5.0,5.0,7.0,8.0,7.0,2.0,1.0,56.0,1.0 -SE,1366.0,6,5.0,5.0,5.0,5.0,8.0,6.0,2.0,1.0,55.0,1.0 -SE,1370.0,6,2.0,8.0,8.0,5.0,7.0,5.0,3.0,1.0,55.0,1.0 -SE,1371.0,6,2.0,3.0,7.0,7.0,8.0,4.0,2.0,1.0,56.0,1.0 -SE,1385.0,6,4.0,8.0,8.0,6.0,8.0,4.0,2.0,1.0,54.0,1.0 -SE,1394.0,6,3.0,2.0,5.0,3.0,7.0,6.0,2.0,1.0,54.0,2.0 -SE,1400.0,6,1.0,7.0,7.0,7.0,9.0,7.0,5.0,2.0,55.0,2.0 -SE,1405.0,6,4.0,8.0,8.0,8.0,9.0,6.0,3.0,2.0,54.0,1.0 -SE,1408.0,6,7.0,6.0,4.0,8.0,10.0,5.0,3.0,2.0,54.0,2.0 -SE,1412.0,6,2.0,3.0,3.0,5.0,5.0,7.0,2.0,1.0,54.0,1.0 -SE,1413.0,6,3.0,8.0,7.0,7.0,9.0,7.0,2.0,1.0,54.0,1.0 -SE,1416.0,6,1.0,9.0,8.0,6.0,10.0,7.0,4.0,1.0,54.0,1.0 -SE,1418.0,6,5.0,2.0,4.0,4.0,7.0,7.0,3.0,1.0,55.0,2.0 -SE,1425.0,6,3.0,8.0,8.0,8.0,8.0,6.0,3.0,1.0,54.0,1.0 -SE,1429.0,6,2.0,7.0,7.0,6.0,6.0,6.0,3.0,2.0,54.0,2.0 -SE,1430.0,6,2.0,3.0,3.0,2.0,10.0,2.0,1.0,1.0,55.0,1.0 -SE,1431.0,6,7.0,7.0,7.0,7.0,8.0,6.0,3.0,2.0,54.0,1.0 -SE,1432.0,6,3.0,5.0,3.0,2.0,7.0,4.0,3.0,2.0,54.0,2.0 -SE,1435.0,6,3.0,3.0,4.0,6.0,5.0,6.0,2.0,2.0,54.0,2.0 -SE,1439.0,6,5.0,4.0,7.0,7.0,8.0,7.0,3.0,2.0,53.0,1.0 -SE,1440.0,6,3.0,9.0,9.0,9.0,8.0,6.0,3.0,1.0,53.0,1.0 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-SE,1664.0,6,7.0,7.0,7.0,7.0,8.0,7.0,2.0,2.0,50.0,1.0 -SE,1671.0,6,1.0,8.0,7.0,6.0,7.0,4.0,2.0,1.0,51.0,1.0 -SE,1677.0,6,2.0,9.0,9.0,7.0,8.0,7.0,3.0,2.0,50.0,1.0 -SE,1682.0,6,3.0,4.0,6.0,4.0,8.0,6.0,3.0,1.0,50.0,1.0 -SE,1705.0,6,4.0,8.0,6.0,5.0,9.0,6.0,3.0,2.0,50.0,1.0 -SE,1706.0,6,7.0,5.0,4.0,7.0,10.0,7.0,1.0,2.0,49.0,1.0 -SE,1710.0,6,2.0,5.0,6.0,5.0,8.0,6.0,,1.0,50.0,1.0 -SE,1714.0,6,4.0,3.0,5.0,4.0,6.0,6.0,4.0,2.0,50.0,1.0 -SE,1719.0,6,2.0,6.0,6.0,4.0,8.0,7.0,3.0,1.0,49.0,1.0 -SE,1727.0,6,5.0,6.0,6.0,7.0,7.0,7.0,4.0,1.0,49.0,1.0 -SE,1733.0,6,4.0,5.0,3.0,7.0,9.0,5.0,2.0,1.0,49.0,1.0 -SE,1738.0,6,4.0,8.0,8.0,7.0,7.0,5.0,3.0,2.0,50.0,1.0 -SE,1739.0,6,2.0,4.0,5.0,7.0,7.0,6.0,2.0,2.0,50.0,2.0 -SE,1740.0,6,4.0,3.0,7.0,7.0,8.0,7.0,3.0,2.0,49.0,2.0 -SE,1743.0,6,2.0,7.0,7.0,5.0,9.0,6.0,3.0,2.0,48.0,2.0 -SE,1746.0,6,5.0,6.0,8.0,8.0,5.0,6.0,4.0,1.0,48.0,2.0 -SE,1748.0,6,0.0,3.0,6.0,8.0,8.0,7.0,3.0,2.0,49.0,1.0 -SE,1749.0,6,7.0,7.0,7.0,0.0,5.0,7.0,3.0,1.0,49.0,2.0 -SE,1750.0,6,2.0,4.0,5.0,5.0,8.0,7.0,3.0,2.0,49.0,2.0 -SE,1759.0,6,3.0,4.0,7.0,4.0,7.0,6.0,3.0,2.0,49.0,1.0 -SE,1763.0,6,7.0,8.0,8.0,7.0,8.0,7.0,3.0,2.0,48.0,2.0 -SE,1764.0,6,2.0,7.0,8.0,7.0,8.0,6.0,3.0,2.0,49.0,1.0 -SE,1770.0,6,6.0,3.0,8.0,9.0,6.0,2.0,5.0,1.0,49.0,1.0 -SE,1772.0,6,3.0,7.0,1.0,7.0,7.0,3.0,4.0,2.0,48.0,2.0 -SE,1780.0,6,3.0,7.0,6.0,5.0,7.0,6.0,3.0,1.0,49.0,1.0 -SE,1787.0,6,1.0,8.0,8.0,7.0,8.0,6.0,4.0,1.0,49.0,1.0 -SE,1789.0,6,2.0,5.0,9.0,7.0,8.0,4.0,2.0,2.0,49.0,1.0 -SE,1799.0,6,3.0,5.0,6.0,5.0,7.0,5.0,3.0,1.0,48.0,1.0 -SE,1801.0,6,5.0,4.0,7.0,8.0,8.0,6.0,3.0,2.0,48.0,1.0 -SE,1808.0,6,3.0,8.0,8.0,5.0,9.0,7.0,2.0,2.0,48.0,1.0 -SE,1809.0,6,1.0,7.0,5.0,5.0,8.0,6.0,4.0,2.0,47.0,2.0 -SE,1812.0,6,3.0,7.0,7.0,7.0,8.0,6.0,3.0,2.0,48.0,1.0 -SE,1817.0,6,5.0,7.0,7.0,6.0,7.0,6.0,2.0,2.0,48.0,1.0 -SE,1820.0,6,1.0,5.0,5.0,5.0,9.0,4.0,3.0,1.0,48.0,1.0 -SE,1823.0,6,3.0,7.0,8.0,8.0,10.0,7.0,5.0,1.0,47.0,1.0 -SE,1826.0,6,2.0,9.0,8.0,8.0,10.0,6.0,3.0,2.0,48.0,1.0 -SE,1827.0,6,1.0,8.0,8.0,7.0,7.0,4.0,3.0,1.0,48.0,1.0 -SE,1828.0,6,2.0,8.0,8.0,8.0,8.0,7.0,3.0,1.0,47.0,1.0 -SE,1831.0,6,5.0,8.0,9.0,5.0,8.0,6.0,4.0,2.0,47.0,1.0 -SE,1833.0,6,3.0,7.0,6.0,7.0,10.0,4.0,3.0,1.0,47.0,1.0 -SE,1834.0,6,3.0,5.0,5.0,5.0,5.0,7.0,3.0,1.0,48.0,1.0 -SE,1845.0,6,3.0,3.0,3.0,5.0,7.0,5.0,1.0,2.0,48.0,1.0 -SE,1856.0,6,1.0,8.0,8.0,7.0,8.0,5.0,4.0,1.0,47.0,1.0 -SE,1860.0,6,0.0,6.0,6.0,6.0,5.0,3.0,4.0,2.0,47.0,2.0 -SE,1870.0,6,3.0,6.0,6.0,3.0,5.0,2.0,2.0,1.0,46.0,2.0 -SE,1873.0,6,0.0,8.0,7.0,5.0,2.0,7.0,2.0,2.0,47.0,2.0 -SE,1874.0,6,2.0,2.0,4.0,5.0,8.0,6.0,4.0,1.0,46.0,2.0 -SE,1881.0,6,2.0,7.0,7.0,7.0,9.0,7.0,3.0,2.0,46.0,1.0 -SE,1883.0,6,2.0,9.0,8.0,8.0,8.0,7.0,3.0,2.0,47.0,1.0 -SE,1886.0,6,5.0,7.0,5.0,8.0,7.0,7.0,3.0,1.0,47.0,2.0 -SE,1890.0,6,3.0,7.0,7.0,8.0,8.0,5.0,3.0,2.0,47.0,1.0 -SE,1892.0,6,2.0,7.0,7.0,7.0,8.0,6.0,4.0,1.0,47.0,1.0 -SE,1899.0,6,4.0,3.0,6.0,3.0,5.0,6.0,3.0,1.0,46.0,2.0 -SE,1900.0,6,5.0,6.0,8.0,8.0,7.0,6.0,3.0,2.0,47.0,1.0 -SE,1905.0,6,1.0,5.0,5.0,2.0,9.0,7.0,3.0,2.0,46.0,2.0 -SE,1906.0,6,1.0,8.0,8.0,9.0,9.0,7.0,3.0,2.0,46.0,1.0 -SE,1914.0,6,2.0,8.0,7.0,7.0,9.0,6.0,3.0,2.0,47.0,1.0 -SE,1927.0,6,5.0,5.0,8.0,8.0,8.0,3.0,2.0,1.0,46.0,1.0 -SE,1933.0,6,5.0,7.0,7.0,8.0,6.0,4.0,3.0,2.0,46.0,1.0 -SE,1935.0,6,7.0,8.0,8.0,5.0,7.0,7.0,3.0,1.0,46.0,2.0 -SE,1936.0,6,2.0,7.0,7.0,7.0,8.0,7.0,5.0,2.0,47.0,1.0 -SE,1939.0,6,2.0,8.0,8.0,8.0,8.0,5.0,3.0,2.0,45.0,1.0 -SE,1953.0,6,3.0,6.0,7.0,7.0,8.0,7.0,4.0,2.0,45.0,1.0 -SE,1955.0,6,5.0,6.0,6.0,6.0,7.0,4.0,2.0,2.0,46.0,1.0 -SE,1956.0,6,6.0,7.0,8.0,6.0,6.0,3.0,2.0,1.0,46.0,2.0 -SE,1957.0,6,3.0,0.0,0.0,0.0,8.0,6.0,3.0,1.0,46.0,1.0 -SE,1960.0,6,4.0,5.0,3.0,7.0,8.0,7.0,3.0,1.0,46.0,1.0 -SE,1961.0,6,5.0,7.0,8.0,6.0,8.0,4.0,3.0,1.0,46.0,1.0 -SE,1966.0,6,1.0,9.0,9.0,9.0,8.0,7.0,3.0,2.0,46.0,1.0 -SE,1969.0,6,4.0,8.0,8.0,8.0,9.0,3.0,2.0,2.0,46.0,1.0 -SE,1972.0,6,4.0,8.0,8.0,5.0,8.0,6.0,3.0,1.0,45.0,1.0 -SE,1977.0,6,2.0,7.0,6.0,8.0,9.0,4.0,3.0,2.0,46.0,2.0 -SE,1982.0,6,4.0,8.0,8.0,8.0,9.0,6.0,3.0,1.0,45.0,1.0 -SE,1984.0,6,6.0,5.0,5.0,5.0,8.0,6.0,3.0,1.0,46.0,2.0 -SE,1988.0,6,5.0,3.0,8.0,4.0,9.0,7.0,3.0,1.0,46.0,1.0 -SE,1991.0,6,1.0,5.0,6.0,7.0,8.0,3.0,2.0,1.0,45.0,1.0 -SE,2009.0,6,4.0,5.0,7.0,3.0,8.0,3.0,1.0,1.0,44.0,1.0 -SE,2010.0,6,5.0,8.0,8.0,4.0,9.0,4.0,4.0,1.0,44.0,1.0 -SE,2016.0,6,3.0,7.0,8.0,8.0,9.0,5.0,3.0,2.0,45.0,1.0 -SE,2017.0,6,1.0,10.0,0.0,10.0,7.0,4.0,2.0,1.0,45.0,2.0 -SE,2039.0,6,5.0,5.0,5.0,1.0,5.0,4.0,2.0,1.0,45.0,2.0 -SE,2042.0,6,0.0,8.0,8.0,6.0,7.0,6.0,4.0,2.0,44.0,1.0 -SE,2046.0,6,3.0,9.0,9.0,6.0,9.0,4.0,2.0,2.0,44.0,1.0 -SE,2061.0,6,2.0,2.0,4.0,4.0,9.0,6.0,2.0,2.0,44.0,1.0 -SE,2077.0,6,4.0,8.0,8.0,9.0,10.0,7.0,4.0,1.0,43.0,1.0 -SE,2079.0,6,3.0,6.0,7.0,7.0,7.0,4.0,3.0,2.0,44.0,1.0 -SE,2102.0,6,5.0,7.0,7.0,6.0,7.0,6.0,3.0,1.0,44.0,2.0 -SE,2104.0,6,3.0,7.0,7.0,8.0,8.0,6.0,4.0,1.0,43.0,1.0 -SE,2105.0,6,4.0,8.0,7.0,7.0,7.0,7.0,4.0,1.0,43.0,2.0 -SE,2119.0,6,2.0,5.0,7.0,5.0,9.0,5.0,1.0,2.0,43.0,2.0 -SE,2126.0,6,5.0,8.0,9.0,0.0,6.0,7.0,3.0,1.0,42.0,2.0 -SE,2141.0,6,3.0,8.0,8.0,9.0,8.0,7.0,3.0,2.0,42.0,1.0 -SE,2146.0,6,1.0,5.0,5.0,5.0,8.0,4.0,1.0,2.0,42.0,1.0 -SE,2153.0,6,4.0,6.0,6.0,4.0,7.0,4.0,2.0,1.0,42.0,1.0 -SE,2161.0,6,0.0,7.0,7.0,5.0,8.0,4.0,2.0,2.0,43.0,2.0 -SE,2164.0,6,1.0,7.0,7.0,7.0,9.0,4.0,3.0,2.0,42.0,1.0 -SE,2166.0,6,4.0,8.0,8.0,8.0,8.0,7.0,2.0,2.0,42.0,1.0 -SE,2169.0,6,5.0,8.0,8.0,6.0,10.0,6.0,5.0,1.0,43.0,1.0 -SE,2175.0,6,3.0,3.0,4.0,5.0,7.0,7.0,3.0,1.0,42.0,1.0 -SE,2187.0,6,1.0,5.0,8.0,7.0,8.0,5.0,2.0,2.0,41.0,2.0 -SE,2188.0,6,2.0,8.0,8.0,8.0,8.0,4.0,4.0,1.0,42.0,1.0 -SE,2191.0,6,2.0,7.0,7.0,6.0,8.0,6.0,3.0,2.0,41.0,1.0 -SE,2196.0,6,3.0,4.0,4.0,4.0,7.0,7.0,3.0,2.0,42.0,1.0 -SE,2197.0,6,2.0,4.0,7.0,6.0,9.0,7.0,4.0,1.0,42.0,1.0 -SE,2200.0,6,3.0,6.0,6.0,8.0,9.0,3.0,2.0,2.0,41.0,1.0 -SE,2201.0,6,1.0,8.0,9.0,7.0,7.0,7.0,2.0,1.0,42.0,1.0 -SE,2207.0,6,7.0,5.0,5.0,5.0,10.0,4.0,3.0,1.0,42.0,1.0 -SE,2210.0,6,5.0,4.0,8.0,5.0,7.0,7.0,4.0,1.0,42.0,1.0 -SE,2216.0,6,1.0,5.0,6.0,5.0,7.0,6.0,2.0,1.0,42.0,2.0 -SE,2219.0,6,4.0,7.0,6.0,7.0,6.0,7.0,4.0,1.0,42.0,2.0 -SE,2221.0,6,0.0,6.0,7.0,6.0,3.0,4.0,2.0,1.0,42.0,2.0 -SE,2225.0,6,1.0,5.0,7.0,3.0,9.0,7.0,3.0,2.0,41.0,2.0 -SE,2227.0,6,3.0,4.0,5.0,6.0,7.0,7.0,3.0,2.0,42.0,1.0 -SE,2232.0,6,3.0,6.0,8.0,7.0,8.0,6.0,2.0,1.0,41.0,2.0 -SE,2236.0,6,1.0,8.0,7.0,6.0,8.0,4.0,3.0,1.0,40.0,1.0 -SE,2238.0,6,4.0,10.0,9.0,10.0,8.0,6.0,3.0,1.0,40.0,2.0 -SE,2244.0,6,4.0,5.0,8.0,7.0,10.0,6.0,2.0,2.0,40.0,2.0 -SE,2245.0,6,4.0,6.0,7.0,4.0,7.0,4.0,3.0,2.0,41.0,1.0 -SE,2250.0,6,4.0,7.0,6.0,6.0,7.0,7.0,4.0,1.0,41.0,2.0 -SE,2251.0,6,2.0,0.0,5.0,8.0,9.0,7.0,3.0,1.0,41.0,1.0 -SE,2252.0,6,7.0,3.0,0.0,4.0,10.0,6.0,2.0,1.0,40.0,1.0 -SE,2260.0,6,4.0,7.0,10.0,9.0,9.0,7.0,2.0,1.0,41.0,1.0 -SE,2265.0,6,2.0,8.0,9.0,8.0,9.0,7.0,3.0,1.0,41.0,1.0 -SE,2268.0,6,5.0,8.0,7.0,6.0,8.0,5.0,3.0,1.0,41.0,1.0 -SE,2274.0,6,2.0,7.0,7.0,7.0,10.0,6.0,3.0,1.0,40.0,1.0 -SE,2275.0,6,5.0,7.0,7.0,5.0,7.0,4.0,2.0,1.0,41.0,1.0 -SE,2282.0,6,1.0,8.0,7.0,5.0,6.0,7.0,5.0,2.0,40.0,1.0 -SE,2288.0,6,2.0,3.0,4.0,4.0,7.0,7.0,4.0,2.0,41.0,1.0 -SE,2294.0,6,2.0,5.0,8.0,7.0,7.0,6.0,1.0,2.0,40.0,1.0 -SE,2301.0,6,2.0,7.0,6.0,6.0,9.0,7.0,4.0,2.0,40.0,1.0 -SE,2313.0,6,3.0,7.0,7.0,3.0,8.0,5.0,2.0,1.0,40.0,1.0 -SE,2315.0,6,3.0,8.0,8.0,8.0,9.0,7.0,4.0,2.0,40.0,1.0 -SE,2317.0,6,4.0,6.0,4.0,3.0,8.0,4.0,3.0,1.0,39.0,1.0 -SE,2320.0,6,2.0,6.0,6.0,7.0,4.0,6.0,3.0,2.0,39.0,1.0 -SE,2329.0,6,4.0,4.0,5.0,7.0,4.0,7.0,2.0,2.0,40.0,2.0 -SE,2336.0,6,5.0,7.0,7.0,6.0,6.0,5.0,3.0,2.0,39.0,1.0 -SE,2346.0,6,3.0,4.0,6.0,7.0,7.0,7.0,3.0,2.0,39.0,2.0 -SE,2350.0,6,4.0,7.0,7.0,4.0,8.0,6.0,3.0,1.0,38.0,1.0 -SE,2357.0,6,2.0,5.0,8.0,7.0,7.0,4.0,2.0,2.0,39.0,1.0 -SE,2359.0,6,3.0,6.0,8.0,4.0,6.0,5.0,3.0,1.0,39.0,1.0 -SE,2363.0,6,7.0,10.0,8.0,8.0,10.0,4.0,1.0,2.0,38.0,2.0 -SE,2365.0,6,2.0,4.0,7.0,7.0,8.0,6.0,3.0,1.0,38.0,2.0 -SE,2368.0,6,4.0,8.0,8.0,7.0,7.0,4.0,3.0,1.0,38.0,1.0 -SE,2372.0,6,6.0,5.0,5.0,5.0,7.0,5.0,1.0,2.0,39.0,1.0 -SE,2374.0,6,4.0,5.0,7.0,7.0,10.0,5.0,3.0,1.0,39.0,2.0 -SE,2380.0,6,5.0,3.0,2.0,3.0,8.0,2.0,3.0,1.0,38.0,1.0 -SE,2383.0,6,5.0,6.0,7.0,7.0,7.0,5.0,2.0,2.0,38.0,1.0 -SE,2393.0,6,1.0,8.0,7.0,8.0,8.0,7.0,4.0,2.0,38.0,1.0 -SE,2415.0,6,2.0,9.0,8.0,6.0,8.0,6.0,3.0,2.0,38.0,1.0 -SE,2422.0,6,5.0,5.0,5.0,5.0,3.0,5.0,3.0,2.0,37.0,2.0 -SE,2423.0,6,3.0,7.0,7.0,5.0,6.0,7.0,3.0,1.0,37.0,2.0 -SE,2432.0,6,2.0,5.0,3.0,8.0,7.0,3.0,4.0,2.0,38.0,1.0 -SE,2433.0,6,2.0,7.0,5.0,7.0,9.0,7.0,3.0,1.0,38.0,1.0 -SE,2435.0,6,0.0,8.0,7.0,6.0,10.0,6.0,3.0,1.0,37.0,1.0 -SE,2437.0,6,2.0,8.0,7.0,8.0,9.0,6.0,3.0,2.0,37.0,1.0 -SE,2438.0,6,1.0,7.0,5.0,5.0,10.0,7.0,3.0,2.0,38.0,1.0 -SE,2440.0,6,3.0,4.0,5.0,5.0,5.0,5.0,3.0,1.0,37.0,1.0 -SE,2441.0,6,3.0,8.0,7.0,9.0,8.0,7.0,2.0,1.0,38.0,1.0 -SE,2452.0,6,5.0,9.0,3.0,7.0,9.0,7.0,4.0,1.0,38.0,1.0 -SE,2459.0,6,2.0,2.0,2.0,0.0,8.0,7.0,,1.0,38.0,2.0 -SE,2461.0,6,1.0,8.0,8.0,7.0,7.0,6.0,5.0,2.0,37.0,1.0 -SE,2466.0,6,3.0,3.0,4.0,3.0,7.0,2.0,1.0,2.0,38.0,1.0 -SE,2467.0,6,0.0,8.0,5.0,4.0,9.0,4.0,2.0,1.0,37.0,1.0 -SE,2469.0,6,1.0,7.0,7.0,9.0,9.0,7.0,4.0,2.0,37.0,1.0 -SE,2471.0,6,4.0,3.0,4.0,4.0,7.0,4.0,3.0,1.0,38.0,1.0 -SE,2472.0,6,5.0,8.0,8.0,7.0,8.0,5.0,4.0,2.0,38.0,1.0 -SE,2473.0,6,2.0,7.0,8.0,6.0,9.0,6.0,3.0,2.0,38.0,1.0 -SE,2476.0,6,2.0,7.0,8.0,7.0,7.0,6.0,3.0,1.0,37.0,1.0 -SE,2484.0,6,2.0,8.0,8.0,9.0,7.0,3.0,3.0,1.0,36.0,1.0 -SE,2485.0,6,4.0,7.0,7.0,5.0,8.0,5.0,3.0,2.0,36.0,1.0 -SE,2486.0,6,3.0,7.0,7.0,4.0,8.0,6.0,3.0,2.0,36.0,1.0 -SE,2488.0,6,4.0,5.0,6.0,7.0,10.0,6.0,3.0,2.0,37.0,1.0 -SE,2493.0,6,4.0,3.0,5.0,5.0,7.0,7.0,4.0,2.0,37.0,1.0 -SE,2500.0,6,2.0,7.0,7.0,7.0,8.0,7.0,4.0,1.0,37.0,1.0 -SE,2508.0,6,4.0,5.0,5.0,6.0,9.0,2.0,3.0,2.0,36.0,1.0 -SE,2511.0,6,5.0,7.0,4.0,7.0,7.0,5.0,3.0,1.0,37.0,1.0 -SE,2515.0,6,0.0,9.0,9.0,3.0,9.0,5.0,1.0,1.0,36.0,2.0 -SE,2526.0,6,3.0,7.0,6.0,6.0,6.0,4.0,2.0,1.0,37.0,1.0 -SE,2528.0,6,4.0,8.0,8.0,3.0,9.0,7.0,3.0,2.0,36.0,1.0 -SE,2549.0,6,3.0,3.0,7.0,8.0,8.0,7.0,3.0,2.0,36.0,1.0 -SE,2561.0,6,6.0,6.0,6.0,7.0,9.0,5.0,2.0,1.0,35.0,1.0 -SE,2564.0,6,5.0,7.0,8.0,7.0,10.0,4.0,3.0,1.0,35.0,1.0 -SE,2567.0,6,5.0,5.0,8.0,5.0,10.0,6.0,3.0,2.0,36.0,1.0 -SE,2587.0,6,2.0,5.0,1.0,2.0,6.0,3.0,1.0,2.0,36.0,1.0 -SE,2590.0,6,3.0,7.0,5.0,7.0,9.0,6.0,2.0,1.0,35.0,1.0 -SE,2591.0,6,6.0,6.0,4.0,3.0,8.0,5.0,3.0,2.0,34.0,1.0 -SE,2593.0,6,7.0,5.0,8.0,7.0,9.0,6.0,2.0,1.0,35.0,1.0 -SE,2597.0,6,3.0,3.0,6.0,6.0,8.0,7.0,4.0,2.0,35.0,2.0 -SE,2602.0,6,4.0,5.0,6.0,6.0,8.0,6.0,2.0,2.0,34.0,1.0 -SE,2605.0,6,1.0,7.0,8.0,7.0,8.0,4.0,2.0,1.0,35.0,1.0 -SE,2622.0,6,1.0,3.0,5.0,4.0,6.0,6.0,3.0,1.0,35.0,2.0 -SE,2624.0,6,3.0,8.0,8.0,8.0,9.0,5.0,2.0,1.0,35.0,1.0 -SE,2625.0,6,4.0,4.0,4.0,5.0,7.0,4.0,3.0,2.0,35.0,1.0 -SE,2628.0,6,5.0,6.0,5.0,7.0,8.0,6.0,4.0,2.0,34.0,1.0 -SE,2632.0,6,3.0,6.0,7.0,5.0,7.0,6.0,5.0,2.0,34.0,1.0 -SE,2633.0,6,5.0,5.0,8.0,6.0,9.0,5.0,2.0,1.0,34.0,1.0 -SE,2637.0,6,6.0,7.0,5.0,6.0,8.0,4.0,4.0,2.0,34.0,1.0 -SE,2641.0,6,4.0,4.0,6.0,6.0,7.0,7.0,4.0,1.0,35.0,2.0 -SE,2644.0,6,6.0,7.0,5.0,3.0,9.0,6.0,4.0,1.0,35.0,1.0 -SE,2659.0,6,6.0,8.0,7.0,6.0,8.0,5.0,2.0,1.0,34.0,1.0 -SE,2663.0,6,3.0,6.0,6.0,5.0,7.0,7.0,4.0,1.0,34.0,2.0 -SE,2679.0,6,4.0,5.0,5.0,7.0,8.0,7.0,3.0,2.0,34.0,2.0 -SE,2683.0,6,4.0,5.0,7.0,8.0,6.0,5.0,2.0,2.0,33.0,1.0 -SE,2689.0,6,3.0,8.0,8.0,7.0,7.0,7.0,3.0,1.0,33.0,2.0 -SE,2697.0,6,4.0,7.0,5.0,4.0,8.0,5.0,4.0,1.0,34.0,1.0 -SE,2700.0,6,5.0,5.0,4.0,3.0,8.0,5.0,2.0,1.0,34.0,2.0 -SE,2714.0,6,5.0,5.0,7.0,3.0,5.0,2.0,3.0,2.0,32.0,1.0 -SE,2716.0,6,4.0,7.0,7.0,6.0,7.0,5.0,3.0,1.0,33.0,1.0 -SE,2724.0,6,3.0,7.0,7.0,6.0,8.0,3.0,3.0,1.0,32.0,1.0 -SE,2729.0,6,5.0,4.0,5.0,6.0,7.0,6.0,2.0,1.0,33.0,2.0 -SE,2732.0,6,1.0,7.0,4.0,3.0,9.0,7.0,3.0,2.0,33.0,1.0 -SE,2739.0,6,4.0,3.0,6.0,3.0,8.0,7.0,4.0,1.0,33.0,1.0 -SE,2743.0,6,3.0,7.0,7.0,8.0,6.0,6.0,3.0,2.0,32.0,2.0 -SE,2748.0,6,2.0,6.0,5.0,4.0,8.0,6.0,2.0,2.0,33.0,2.0 -SE,2749.0,6,4.0,6.0,7.0,7.0,8.0,7.0,3.0,2.0,33.0,1.0 -SE,2768.0,6,5.0,7.0,7.0,8.0,9.0,6.0,3.0,2.0,31.0,1.0 -SE,2777.0,6,5.0,7.0,9.0,8.0,9.0,5.0,2.0,1.0,32.0,1.0 -SE,2780.0,6,2.0,7.0,7.0,4.0,8.0,4.0,4.0,1.0,31.0,1.0 -SE,2788.0,6,3.0,10.0,10.0,7.0,9.0,6.0,3.0,2.0,31.0,2.0 -SE,2790.0,6,3.0,8.0,7.0,7.0,9.0,7.0,4.0,1.0,32.0,1.0 -SE,2794.0,6,4.0,8.0,8.0,5.0,7.0,5.0,3.0,1.0,31.0,2.0 -SE,2796.0,6,0.0,4.0,4.0,5.0,5.0,7.0,2.0,1.0,31.0,2.0 -SE,2799.0,6,5.0,4.0,6.0,6.0,7.0,7.0,4.0,2.0,32.0,1.0 -SE,2800.0,6,2.0,7.0,4.0,3.0,6.0,6.0,2.0,1.0,32.0,2.0 -SE,2801.0,6,4.0,6.0,8.0,5.0,8.0,6.0,4.0,1.0,32.0,2.0 -SE,2813.0,6,4.0,7.0,5.0,2.0,5.0,6.0,2.0,2.0,32.0,2.0 -SE,2816.0,6,5.0,7.0,7.0,7.0,9.0,7.0,3.0,1.0,32.0,1.0 -SE,2832.0,6,7.0,7.0,7.0,7.0,10.0,6.0,3.0,1.0,31.0,1.0 -SE,2836.0,6,7.0,5.0,8.0,8.0,10.0,6.0,3.0,2.0,31.0,1.0 -SE,2838.0,6,2.0,8.0,7.0,7.0,10.0,7.0,3.0,1.0,31.0,1.0 -SE,2839.0,6,2.0,6.0,7.0,6.0,7.0,6.0,4.0,1.0,32.0,1.0 -SE,2840.0,6,2.0,2.0,5.0,3.0,7.0,4.0,2.0,1.0,30.0,2.0 -SE,2841.0,6,3.0,7.0,7.0,6.0,10.0,6.0,3.0,2.0,31.0,1.0 -SE,2844.0,6,5.0,7.0,7.0,5.0,10.0,6.0,2.0,2.0,31.0,1.0 -SE,2847.0,6,4.0,6.0,8.0,8.0,10.0,4.0,4.0,2.0,30.0,1.0 -SE,2851.0,6,7.0,5.0,5.0,6.0,10.0,3.0,1.0,2.0,31.0,1.0 -SE,2868.0,6,3.0,5.0,8.0,8.0,7.0,5.0,3.0,1.0,31.0,2.0 -SE,2875.0,6,3.0,7.0,7.0,5.0,7.0,6.0,3.0,1.0,31.0,1.0 -SE,2879.0,6,4.0,7.0,7.0,6.0,8.0,7.0,3.0,1.0,31.0,1.0 -SE,2880.0,6,1.0,6.0,6.0,6.0,7.0,6.0,2.0,2.0,31.0,2.0 -SE,2893.0,6,2.0,7.0,7.0,6.0,8.0,5.0,2.0,1.0,29.0,1.0 -SE,2903.0,6,1.0,3.0,3.0,5.0,7.0,6.0,2.0,2.0,30.0,2.0 -SE,2905.0,6,5.0,6.0,5.0,5.0,5.0,3.0,3.0,2.0,30.0,1.0 -SE,2912.0,6,1.0,7.0,7.0,7.0,10.0,5.0,2.0,1.0,29.0,1.0 -SE,2929.0,6,1.0,8.0,5.0,8.0,10.0,7.0,4.0,1.0,30.0,2.0 -SE,2931.0,6,1.0,7.0,7.0,6.0,6.0,7.0,3.0,2.0,30.0,1.0 -SE,2939.0,6,3.0,7.0,6.0,4.0,10.0,6.0,5.0,2.0,29.0,2.0 -SE,2948.0,6,3.0,4.0,4.0,3.0,10.0,4.0,2.0,1.0,29.0,1.0 -SE,2951.0,6,4.0,6.0,6.0,5.0,8.0,6.0,3.0,1.0,29.0,1.0 -SE,2956.0,6,3.0,7.0,7.0,7.0,9.0,6.0,3.0,2.0,28.0,1.0 -SE,2957.0,6,3.0,10.0,9.0,8.0,9.0,6.0,3.0,2.0,29.0,1.0 -SE,2960.0,6,4.0,7.0,7.0,6.0,8.0,6.0,3.0,2.0,28.0,1.0 -SE,2968.0,6,1.0,10.0,8.0,8.0,9.0,6.0,2.0,1.0,28.0,1.0 -SE,2969.0,6,3.0,8.0,8.0,8.0,10.0,6.0,3.0,2.0,28.0,1.0 -SE,2970.0,6,6.0,3.0,4.0,4.0,3.0,7.0,3.0,1.0,29.0,2.0 -SE,2974.0,6,3.0,7.0,6.0,3.0,9.0,6.0,4.0,1.0,28.0,1.0 -SE,2992.0,6,3.0,4.0,8.0,7.0,7.0,6.0,3.0,1.0,29.0,2.0 -SE,2994.0,6,4.0,5.0,5.0,3.0,8.0,6.0,3.0,2.0,29.0,1.0 -SE,3004.0,6,3.0,5.0,4.0,4.0,9.0,7.0,2.0,1.0,27.0,1.0 -SE,3008.0,6,4.0,4.0,4.0,4.0,8.0,7.0,2.0,2.0,28.0,1.0 -SE,3012.0,6,4.0,4.0,7.0,6.0,7.0,7.0,3.0,2.0,28.0,1.0 -SE,3016.0,6,1.0,4.0,6.0,8.0,9.0,6.0,3.0,1.0,28.0,1.0 -SE,3017.0,6,4.0,4.0,5.0,6.0,6.0,6.0,4.0,1.0,28.0,2.0 -SE,3022.0,6,2.0,3.0,4.0,4.0,5.0,6.0,3.0,2.0,28.0,2.0 -SE,3031.0,6,7.0,5.0,5.0,2.0,2.0,3.0,1.0,1.0,27.0,2.0 -SE,3033.0,6,2.0,7.0,8.0,5.0,5.0,7.0,4.0,1.0,27.0,2.0 -SE,3034.0,6,2.0,8.0,8.0,6.0,7.0,5.0,3.0,2.0,27.0,1.0 -SE,3041.0,6,2.0,8.0,7.0,6.0,9.0,6.0,3.0,1.0,27.0,1.0 -SE,3046.0,6,0.0,4.0,7.0,7.0,9.0,7.0,4.0,1.0,27.0,2.0 -SE,3047.0,6,1.0,5.0,5.0,4.0,8.0,6.0,3.0,1.0,28.0,1.0 -SE,3054.0,6,2.0,2.0,1.0,4.0,8.0,7.0,3.0,1.0,28.0,2.0 -SE,3059.0,6,1.0,8.0,8.0,5.0,6.0,5.0,3.0,1.0,27.0,2.0 -SE,3080.0,6,1.0,2.0,5.0,7.0,7.0,7.0,1.0,1.0,26.0,2.0 -SE,3089.0,6,2.0,8.0,8.0,7.0,9.0,7.0,2.0,2.0,27.0,2.0 -SE,3090.0,6,5.0,9.0,9.0,7.0,7.0,7.0,3.0,1.0,26.0,1.0 -SE,3096.0,6,6.0,0.0,5.0,0.0,8.0,7.0,1.0,1.0,26.0,2.0 -SE,3098.0,6,4.0,6.0,3.0,2.0,8.0,7.0,3.0,2.0,26.0,2.0 -SE,3099.0,6,6.0,7.0,4.0,6.0,6.0,5.0,4.0,2.0,26.0,1.0 -SE,3114.0,6,5.0,8.0,7.0,5.0,9.0,6.0,2.0,2.0,25.0,1.0 -SE,3116.0,6,4.0,2.0,6.0,7.0,7.0,7.0,3.0,2.0,26.0,2.0 -SE,3120.0,6,3.0,4.0,6.0,6.0,7.0,7.0,4.0,1.0,26.0,1.0 -SE,3124.0,6,5.0,7.0,8.0,8.0,8.0,4.0,2.0,1.0,25.0,1.0 -SE,3133.0,6,2.0,7.0,9.0,7.0,8.0,6.0,3.0,2.0,25.0,2.0 -SE,3140.0,6,3.0,6.0,6.0,7.0,6.0,7.0,2.0,2.0,26.0,1.0 -SE,3143.0,6,5.0,7.0,8.0,5.0,9.0,6.0,2.0,2.0,25.0,1.0 -SE,3144.0,6,1.0,5.0,8.0,5.0,8.0,7.0,5.0,1.0,26.0,2.0 -SE,3145.0,6,7.0,8.0,6.0,6.0,9.0,7.0,3.0,2.0,25.0,1.0 -SE,3148.0,6,5.0,5.0,5.0,3.0,8.0,5.0,4.0,2.0,26.0,2.0 -SE,3151.0,6,4.0,8.0,8.0,7.0,7.0,7.0,4.0,1.0,26.0,2.0 -SE,3153.0,6,4.0,6.0,6.0,3.0,8.0,6.0,3.0,2.0,26.0,2.0 -SE,3155.0,6,6.0,5.0,7.0,3.0,6.0,4.0,4.0,2.0,25.0,2.0 -SE,3156.0,6,1.0,7.0,6.0,7.0,8.0,6.0,3.0,2.0,26.0,2.0 -SE,3157.0,6,2.0,4.0,7.0,7.0,8.0,7.0,3.0,2.0,26.0,1.0 -SE,3163.0,6,5.0,8.0,1.0,5.0,10.0,6.0,3.0,2.0,25.0,1.0 -SE,3170.0,6,2.0,4.0,7.0,7.0,10.0,4.0,2.0,1.0,25.0,1.0 -SE,3172.0,6,5.0,3.0,5.0,2.0,8.0,1.0,2.0,2.0,25.0,1.0 -SE,3176.0,6,4.0,0.0,5.0,2.0,7.0,7.0,3.0,1.0,25.0,2.0 -SE,3183.0,6,0.0,6.0,6.0,2.0,8.0,6.0,2.0,1.0,25.0,2.0 -SE,3185.0,6,3.0,5.0,5.0,6.0,7.0,4.0,2.0,2.0,24.0,1.0 -SE,3186.0,6,4.0,6.0,6.0,3.0,7.0,7.0,3.0,2.0,24.0,2.0 -SE,3189.0,6,2.0,2.0,1.0,5.0,8.0,6.0,4.0,1.0,25.0,2.0 -SE,3197.0,6,2.0,5.0,5.0,8.0,8.0,6.0,4.0,1.0,25.0,2.0 -SE,3205.0,6,4.0,6.0,7.0,9.0,10.0,2.0,2.0,1.0,25.0,1.0 -SE,3206.0,6,4.0,7.0,7.0,4.0,8.0,7.0,3.0,2.0,25.0,2.0 -SE,3214.0,6,5.0,6.0,6.0,7.0,7.0,5.0,3.0,2.0,25.0,1.0 -SE,3216.0,6,5.0,6.0,6.0,7.0,5.0,7.0,3.0,1.0,25.0,2.0 -SE,3220.0,6,2.0,7.0,6.0,3.0,10.0,7.0,3.0,1.0,24.0,1.0 -SE,3226.0,6,3.0,7.0,7.0,6.0,8.0,7.0,3.0,2.0,25.0,1.0 -SE,3235.0,6,2.0,6.0,6.0,7.0,9.0,5.0,2.0,2.0,25.0,1.0 -SE,3237.0,6,4.0,8.0,6.0,6.0,9.0,6.0,3.0,1.0,25.0,2.0 -SE,3238.0,6,4.0,7.0,7.0,8.0,7.0,6.0,3.0,2.0,24.0,2.0 -SE,3245.0,6,4.0,8.0,1.0,5.0,9.0,7.0,5.0,1.0,24.0,1.0 -SE,3246.0,6,3.0,7.0,7.0,8.0,8.0,6.0,3.0,1.0,24.0,1.0 -SE,3251.0,6,3.0,7.0,8.0,6.0,8.0,7.0,4.0,2.0,23.0,2.0 -SE,3252.0,6,2.0,7.0,7.0,5.0,8.0,6.0,3.0,2.0,24.0,2.0 -SE,3255.0,6,4.0,7.0,7.0,8.0,9.0,6.0,4.0,1.0,23.0,1.0 -SE,3256.0,6,1.0,8.0,7.0,6.0,8.0,7.0,3.0,2.0,24.0,2.0 -SE,3265.0,6,3.0,8.0,8.0,7.0,8.0,7.0,2.0,2.0,23.0,2.0 -SE,3272.0,6,4.0,4.0,6.0,6.0,6.0,7.0,3.0,2.0,23.0,1.0 -SE,3275.0,6,4.0,5.0,5.0,5.0,9.0,6.0,2.0,2.0,24.0,2.0 -SE,3277.0,6,5.0,6.0,4.0,5.0,10.0,6.0,3.0,2.0,24.0,1.0 -SE,3280.0,6,5.0,8.0,4.0,2.0,8.0,7.0,4.0,1.0,24.0,2.0 -SE,3287.0,6,7.0,4.0,5.0,8.0,8.0,6.0,3.0,1.0,23.0,1.0 -SE,3295.0,6,1.0,7.0,9.0,7.0,6.0,4.0,1.0,1.0,22.0,2.0 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-SE,269.0,7,5.0,5.0,8.0,8.0,9.0,6.0,3.0,2.0,79.0,2.0 -SE,270.0,7,3.0,5.0,6.0,5.0,7.0,6.0,3.0,1.0,83.0,1.0 -SE,273.0,7,5.0,3.0,7.0,8.0,8.0,6.0,4.0,1.0,79.0,1.0 -SE,282.0,7,4.0,8.0,7.0,7.0,9.0,6.0,4.0,2.0,79.0,1.0 -SE,290.0,7,5.0,8.0,8.0,5.0,8.0,6.0,3.0,2.0,78.0,1.0 -SE,292.0,7,5.0,7.0,7.0,7.0,8.0,6.0,3.0,1.0,78.0,1.0 -SE,293.0,7,7.0,9.0,9.0,8.0,6.0,5.0,1.0,1.0,78.0,2.0 -SE,295.0,7,3.0,7.0,7.0,5.0,8.0,6.0,4.0,1.0,78.0,1.0 -SE,298.0,7,7.0,0.0,5.0,8.0,10.0,4.0,2.0,1.0,78.0,1.0 -SE,302.0,7,5.0,7.0,7.0,5.0,8.0,5.0,3.0,1.0,78.0,1.0 -SE,303.0,7,5.0,6.0,8.0,7.0,7.0,5.0,3.0,2.0,78.0,1.0 -SE,305.0,7,6.0,3.0,5.0,6.0,9.0,4.0,2.0,2.0,78.0,1.0 -SE,312.0,7,6.0,9.0,9.0,9.0,7.0,4.0,3.0,2.0,78.0,2.0 -SE,321.0,7,5.0,8.0,10.0,9.0,8.0,6.0,5.0,2.0,,2.0 -SE,323.0,7,0.0,8.0,8.0,8.0,8.0,4.0,4.0,1.0,,1.0 -SE,325.0,7,2.0,7.0,7.0,6.0,8.0,5.0,3.0,2.0,,1.0 -SE,328.0,7,6.0,7.0,7.0,7.0,10.0,7.0,3.0,2.0,,1.0 -SE,331.0,7,4.0,9.0,7.0,5.0,6.0,5.0,3.0,1.0,,1.0 -SE,332.0,7,2.0,5.0,7.0,9.0,10.0,5.0,3.0,2.0,,2.0 -SE,334.0,7,5.0,6.0,8.0,8.0,8.0,4.0,1.0,2.0,,1.0 -SE,335.0,7,5.0,7.0,7.0,6.0,7.0,7.0,4.0,2.0,,2.0 -SE,336.0,7,4.0,10.0,10.0,5.0,10.0,6.0,4.0,1.0,,1.0 -SE,346.0,7,3.0,6.0,7.0,7.0,8.0,7.0,4.0,2.0,76.0,1.0 -SE,347.0,7,7.0,8.0,8.0,10.0,10.0,6.0,4.0,2.0,76.0,2.0 -SE,349.0,7,7.0,7.0,9.0,7.0,10.0,3.0,3.0,2.0,76.0,1.0 -SE,350.0,7,5.0,8.0,7.0,5.0,9.0,6.0,4.0,2.0,76.0,1.0 -SE,351.0,7,4.0,5.0,5.0,6.0,7.0,4.0,3.0,1.0,76.0,1.0 -SE,353.0,7,2.0,9.0,8.0,9.0,10.0,2.0,3.0,1.0,76.0,1.0 -SE,357.0,7,3.0,6.0,7.0,5.0,8.0,5.0,3.0,1.0,76.0,1.0 -SE,360.0,7,7.0,8.0,5.0,8.0,8.0,3.0,3.0,1.0,76.0,1.0 -SE,369.0,7,5.0,7.0,8.0,8.0,10.0,5.0,3.0,2.0,76.0,1.0 -SE,371.0,7,6.0,8.0,8.0,7.0,7.0,6.0,3.0,2.0,76.0,2.0 -SE,377.0,7,7.0,8.0,9.0,6.0,10.0,4.0,2.0,1.0,75.0,1.0 -SE,379.0,7,3.0,6.0,8.0,8.0,8.0,4.0,3.0,1.0,75.0,1.0 -SE,381.0,7,6.0,3.0,6.0,6.0,5.0,5.0,1.0,2.0,75.0,1.0 -SE,383.0,7,4.0,6.0,7.0,6.0,7.0,5.0,4.0,2.0,75.0,2.0 -SE,385.0,7,6.0,8.0,8.0,7.0,10.0,6.0,3.0,1.0,75.0,1.0 -SE,386.0,7,5.0,6.0,6.0,6.0,5.0,5.0,3.0,1.0,75.0,2.0 -SE,389.0,7,4.0,3.0,2.0,5.0,9.0,5.0,3.0,1.0,75.0,1.0 -SE,390.0,7,7.0,5.0,5.0,4.0,6.0,4.0,2.0,2.0,75.0,2.0 -SE,396.0,7,7.0,7.0,8.0,7.0,8.0,6.0,2.0,1.0,75.0,1.0 -SE,399.0,7,6.0,5.0,8.0,7.0,8.0,6.0,3.0,2.0,75.0,1.0 -SE,405.0,7,6.0,6.0,7.0,8.0,5.0,4.0,2.0,1.0,75.0,1.0 -SE,409.0,7,4.0,5.0,6.0,7.0,7.0,5.0,3.0,1.0,74.0,1.0 -SE,410.0,7,5.0,8.0,9.0,9.0,7.0,7.0,5.0,2.0,74.0,1.0 -SE,412.0,7,7.0,5.0,5.0,5.0,8.0,6.0,3.0,2.0,74.0,1.0 -SE,413.0,7,5.0,7.0,8.0,8.0,9.0,5.0,2.0,2.0,74.0,1.0 -SE,414.0,7,3.0,2.0,3.0,5.0,8.0,4.0,4.0,2.0,74.0,1.0 -SE,415.0,7,7.0,3.0,5.0,7.0,5.0,4.0,3.0,2.0,74.0,1.0 -SE,416.0,7,4.0,5.0,5.0,5.0,8.0,6.0,4.0,1.0,74.0,2.0 -SE,418.0,7,2.0,8.0,9.0,9.0,9.0,6.0,4.0,2.0,74.0,2.0 -SE,422.0,7,6.0,9.0,9.0,8.0,8.0,3.0,2.0,2.0,74.0,2.0 -SE,423.0,7,5.0,6.0,7.0,7.0,8.0,3.0,5.0,1.0,74.0,1.0 -SE,433.0,7,4.0,9.0,9.0,5.0,6.0,4.0,3.0,1.0,74.0,1.0 -SE,434.0,7,5.0,9.0,9.0,9.0,10.0,6.0,4.0,1.0,74.0,1.0 -SE,438.0,7,3.0,5.0,3.0,2.0,10.0,3.0,3.0,2.0,,1.0 -SE,439.0,7,5.0,5.0,5.0,5.0,8.0,7.0,3.0,2.0,74.0,2.0 -SE,447.0,7,3.0,7.0,8.0,6.0,4.0,6.0,4.0,1.0,73.0,2.0 -SE,450.0,7,5.0,6.0,7.0,5.0,8.0,4.0,3.0,1.0,73.0,2.0 -SE,452.0,7,6.0,7.0,8.0,7.0,8.0,5.0,3.0,2.0,73.0,1.0 -SE,457.0,7,7.0,2.0,5.0,7.0,9.0,6.0,3.0,2.0,73.0,2.0 -SE,458.0,7,6.0,5.0,5.0,5.0,8.0,2.0,3.0,1.0,73.0,1.0 -SE,459.0,7,7.0,6.0,10.0,10.0,10.0,6.0,3.0,2.0,73.0,1.0 -SE,463.0,7,4.0,8.0,9.0,6.0,10.0,6.0,4.0,1.0,73.0,1.0 -SE,465.0,7,4.0,7.0,7.0,5.0,6.0,6.0,3.0,1.0,73.0,2.0 -SE,469.0,7,4.0,8.0,7.0,7.0,8.0,5.0,4.0,1.0,73.0,1.0 -SE,470.0,7,2.0,9.0,9.0,8.0,10.0,6.0,4.0,1.0,73.0,1.0 -SE,471.0,7,4.0,7.0,7.0,6.0,7.0,7.0,2.0,1.0,73.0,1.0 -SE,474.0,7,7.0,0.0,5.0,5.0,4.0,5.0,3.0,1.0,73.0,1.0 -SE,475.0,7,7.0,5.0,7.0,5.0,9.0,6.0,3.0,1.0,73.0,1.0 -SE,479.0,7,4.0,5.0,7.0,3.0,6.0,4.0,2.0,2.0,73.0,2.0 -SE,480.0,7,5.0,7.0,10.0,8.0,8.0,6.0,3.0,2.0,73.0,2.0 -SE,481.0,7,2.0,5.0,5.0,7.0,8.0,5.0,3.0,1.0,73.0,1.0 -SE,494.0,7,5.0,7.0,7.0,7.0,9.0,4.0,3.0,1.0,72.0,1.0 -SE,495.0,7,2.0,6.0,5.0,7.0,6.0,5.0,2.0,1.0,72.0,1.0 -SE,498.0,7,4.0,4.0,8.0,3.0,9.0,4.0,3.0,2.0,72.0,1.0 -SE,499.0,7,2.0,8.0,7.0,7.0,9.0,6.0,3.0,2.0,72.0,1.0 -SE,502.0,7,6.0,6.0,4.0,7.0,7.0,4.0,3.0,1.0,72.0,2.0 -SE,504.0,7,2.0,3.0,5.0,5.0,9.0,5.0,3.0,2.0,72.0,1.0 -SE,505.0,7,5.0,0.0,3.0,1.0,5.0,5.0,2.0,1.0,72.0,2.0 -SE,507.0,7,7.0,6.0,4.0,2.0,9.0,6.0,2.0,2.0,72.0,1.0 -SE,510.0,7,4.0,5.0,4.0,5.0,8.0,3.0,3.0,1.0,83.0,1.0 -SE,513.0,7,4.0,8.0,7.0,8.0,8.0,5.0,4.0,2.0,72.0,1.0 -SE,518.0,7,5.0,7.0,8.0,8.0,8.0,5.0,3.0,2.0,72.0,1.0 -SE,519.0,7,4.0,7.0,7.0,5.0,7.0,7.0,4.0,1.0,72.0,1.0 -SE,520.0,7,2.0,8.0,8.0,5.0,7.0,4.0,4.0,1.0,72.0,1.0 -SE,522.0,7,6.0,7.0,7.0,3.0,6.0,2.0,3.0,1.0,72.0,2.0 -SE,524.0,7,4.0,9.0,9.0,8.0,9.0,6.0,3.0,2.0,72.0,1.0 -SE,527.0,7,7.0,10.0,10.0,7.0,8.0,6.0,3.0,1.0,72.0,1.0 -SE,530.0,7,7.0,5.0,8.0,8.0,9.0,6.0,3.0,1.0,71.0,1.0 -SE,535.0,7,7.0,10.0,6.0,8.0,10.0,4.0,5.0,2.0,71.0,1.0 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-SE,1035.0,7,5.0,6.0,8.0,8.0,8.0,5.0,2.0,1.0,62.0,1.0 -SE,1037.0,7,3.0,9.0,8.0,6.0,8.0,4.0,4.0,1.0,62.0,1.0 -SE,1038.0,7,4.0,8.0,3.0,3.0,8.0,6.0,2.0,2.0,62.0,2.0 -SE,1041.0,7,0.0,8.0,8.0,6.0,9.0,6.0,4.0,2.0,62.0,1.0 -SE,1047.0,7,1.0,9.0,9.0,8.0,9.0,7.0,3.0,2.0,62.0,1.0 -SE,1050.0,7,7.0,8.0,9.0,9.0,8.0,4.0,1.0,1.0,62.0,1.0 -SE,1051.0,7,2.0,5.0,5.0,4.0,7.0,6.0,3.0,2.0,62.0,2.0 -SE,1053.0,7,0.0,0.0,8.0,10.0,5.0,5.0,3.0,2.0,62.0,2.0 -SE,1057.0,7,3.0,5.0,8.0,5.0,10.0,7.0,3.0,1.0,62.0,1.0 -SE,1067.0,7,3.0,9.0,8.0,5.0,8.0,7.0,3.0,1.0,61.0,2.0 -SE,1069.0,7,4.0,8.0,8.0,8.0,9.0,4.0,3.0,2.0,61.0,1.0 -SE,1075.0,7,5.0,6.0,4.0,6.0,9.0,2.0,2.0,1.0,61.0,1.0 -SE,1078.0,7,4.0,5.0,5.0,5.0,6.0,4.0,3.0,1.0,61.0,2.0 -SE,1085.0,7,2.0,4.0,6.0,6.0,3.0,4.0,3.0,2.0,61.0,2.0 -SE,1086.0,7,6.0,2.0,6.0,4.0,10.0,6.0,3.0,2.0,61.0,1.0 -SE,1090.0,7,3.0,8.0,8.0,7.0,8.0,4.0,3.0,2.0,61.0,1.0 -SE,1092.0,7,2.0,7.0,9.0,8.0,9.0,7.0,3.0,1.0,61.0,2.0 -SE,1095.0,7,2.0,5.0,5.0,7.0,7.0,5.0,3.0,1.0,61.0,1.0 -SE,1096.0,7,2.0,5.0,8.0,8.0,7.0,7.0,3.0,1.0,61.0,2.0 -SE,1103.0,7,6.0,6.0,6.0,5.0,5.0,5.0,3.0,1.0,61.0,1.0 -SE,1106.0,7,4.0,8.0,8.0,7.0,7.0,7.0,3.0,1.0,61.0,2.0 -SE,1107.0,7,3.0,5.0,8.0,8.0,9.0,6.0,3.0,2.0,61.0,1.0 -SE,1110.0,7,3.0,7.0,3.0,5.0,10.0,6.0,4.0,1.0,61.0,1.0 -SE,1113.0,7,6.0,10.0,4.0,4.0,5.0,5.0,2.0,2.0,60.0,2.0 -SE,1114.0,7,2.0,2.0,8.0,6.0,9.0,6.0,3.0,1.0,60.0,1.0 -SE,1115.0,7,6.0,8.0,7.0,7.0,9.0,6.0,3.0,1.0,60.0,1.0 -SE,1118.0,7,2.0,7.0,6.0,7.0,7.0,5.0,4.0,1.0,60.0,1.0 -SE,1119.0,7,5.0,8.0,8.0,9.0,10.0,2.0,2.0,1.0,60.0,2.0 -SE,1120.0,7,1.0,5.0,8.0,2.0,10.0,7.0,3.0,1.0,60.0,1.0 -SE,1131.0,7,0.0,7.0,8.0,7.0,9.0,7.0,4.0,2.0,60.0,1.0 -SE,1134.0,7,4.0,6.0,5.0,3.0,7.0,5.0,3.0,2.0,60.0,1.0 -SE,1140.0,7,5.0,8.0,9.0,7.0,10.0,5.0,3.0,1.0,62.0,1.0 -SE,1143.0,7,0.0,10.0,10.0,5.0,6.0,4.0,1.0,2.0,60.0,1.0 -SE,1155.0,7,5.0,6.0,6.0,7.0,7.0,6.0,3.0,2.0,60.0,1.0 -SE,1162.0,7,2.0,7.0,8.0,5.0,8.0,6.0,4.0,2.0,60.0,1.0 -SE,1167.0,7,1.0,8.0,8.0,5.0,10.0,5.0,3.0,2.0,59.0,1.0 -SE,1175.0,7,7.0,5.0,4.0,5.0,9.0,4.0,3.0,2.0,59.0,1.0 -SE,1183.0,7,4.0,9.0,9.0,9.0,8.0,7.0,3.0,1.0,59.0,1.0 -SE,1187.0,7,5.0,7.0,8.0,5.0,5.0,7.0,2.0,1.0,59.0,1.0 -SE,1196.0,7,2.0,7.0,7.0,5.0,7.0,4.0,3.0,2.0,59.0,1.0 -SE,1197.0,7,7.0,5.0,8.0,10.0,9.0,7.0,5.0,2.0,59.0,2.0 -SE,1208.0,7,7.0,8.0,8.0,8.0,7.0,4.0,2.0,2.0,59.0,2.0 -SE,1211.0,7,1.0,5.0,7.0,4.0,7.0,4.0,2.0,1.0,59.0,2.0 -SE,1214.0,7,7.0,4.0,8.0,5.0,8.0,5.0,2.0,1.0,59.0,1.0 -SE,1215.0,7,1.0,8.0,8.0,7.0,9.0,4.0,3.0,1.0,61.0,1.0 -SE,1217.0,7,5.0,9.0,8.0,9.0,10.0,7.0,1.0,2.0,59.0,1.0 -SE,1220.0,7,2.0,7.0,7.0,6.0,9.0,6.0,4.0,1.0,59.0,2.0 -SE,1221.0,7,5.0,7.0,7.0,5.0,8.0,5.0,2.0,1.0,59.0,1.0 -SE,1225.0,7,6.0,7.0,8.0,5.0,8.0,6.0,2.0,1.0,58.0,2.0 -SE,1228.0,7,2.0,5.0,6.0,4.0,8.0,5.0,2.0,1.0,58.0,2.0 -SE,1231.0,7,4.0,8.0,8.0,8.0,9.0,7.0,4.0,2.0,58.0,1.0 -SE,1236.0,7,3.0,4.0,6.0,6.0,10.0,7.0,3.0,1.0,58.0,1.0 -SE,1238.0,7,4.0,1.0,8.0,3.0,9.0,7.0,3.0,1.0,58.0,1.0 -SE,1240.0,7,1.0,4.0,5.0,4.0,6.0,2.0,2.0,1.0,58.0,1.0 -SE,1243.0,7,6.0,8.0,8.0,6.0,4.0,2.0,2.0,2.0,58.0,2.0 -SE,1244.0,7,2.0,8.0,10.0,5.0,10.0,7.0,1.0,2.0,58.0,2.0 -SE,1246.0,7,3.0,5.0,5.0,5.0,6.0,6.0,4.0,1.0,58.0,2.0 -SE,1247.0,7,4.0,6.0,6.0,5.0,8.0,5.0,2.0,2.0,58.0,1.0 -SE,1248.0,7,4.0,0.0,3.0,7.0,8.0,2.0,1.0,1.0,58.0,2.0 -SE,1251.0,7,2.0,7.0,7.0,8.0,7.0,7.0,4.0,2.0,58.0,1.0 -SE,1252.0,7,5.0,4.0,7.0,7.0,7.0,6.0,3.0,2.0,58.0,2.0 -SE,1253.0,7,2.0,7.0,3.0,5.0,9.0,7.0,4.0,2.0,58.0,1.0 -SE,1256.0,7,4.0,4.0,3.0,3.0,6.0,6.0,2.0,2.0,58.0,2.0 -SE,1258.0,7,7.0,7.0,8.0,6.0,8.0,7.0,2.0,2.0,58.0,2.0 -SE,1259.0,7,7.0,3.0,5.0,5.0,4.0,5.0,2.0,1.0,58.0,2.0 -SE,1263.0,7,2.0,8.0,3.0,9.0,7.0,7.0,5.0,2.0,58.0,2.0 -SE,1264.0,7,1.0,7.0,8.0,8.0,10.0,2.0,1.0,1.0,58.0,2.0 -SE,1271.0,7,5.0,6.0,5.0,6.0,7.0,6.0,2.0,2.0,57.0,1.0 -SE,1274.0,7,5.0,8.0,8.0,9.0,9.0,6.0,2.0,2.0,57.0,1.0 -SE,1279.0,7,6.0,8.0,8.0,4.0,10.0,4.0,2.0,2.0,57.0,1.0 -SE,1286.0,7,2.0,9.0,9.0,7.0,8.0,4.0,3.0,2.0,57.0,1.0 -SE,1287.0,7,5.0,7.0,7.0,7.0,7.0,7.0,3.0,1.0,57.0,1.0 -SE,1298.0,7,5.0,6.0,5.0,4.0,7.0,7.0,3.0,1.0,57.0,1.0 -SE,1302.0,7,4.0,3.0,4.0,3.0,7.0,7.0,4.0,2.0,58.0,2.0 -SE,1314.0,7,2.0,4.0,7.0,7.0,8.0,4.0,2.0,2.0,56.0,1.0 -SE,1315.0,7,2.0,7.0,9.0,8.0,7.0,6.0,3.0,2.0,56.0,2.0 -SE,1316.0,7,7.0,2.0,8.0,7.0,7.0,6.0,2.0,1.0,56.0,2.0 -SE,1318.0,7,7.0,7.0,5.0,8.0,6.0,7.0,1.0,1.0,56.0,2.0 -SE,1321.0,7,2.0,7.0,10.0,7.0,8.0,5.0,2.0,2.0,56.0,2.0 -SE,1325.0,7,4.0,10.0,9.0,7.0,8.0,7.0,3.0,2.0,56.0,1.0 -SE,1334.0,7,4.0,10.0,8.0,8.0,9.0,6.0,4.0,1.0,56.0,1.0 -SE,1336.0,7,7.0,2.0,2.0,5.0,6.0,7.0,2.0,2.0,56.0,2.0 -SE,1341.0,7,4.0,7.0,7.0,7.0,9.0,6.0,3.0,2.0,56.0,1.0 -SE,1343.0,7,5.0,3.0,3.0,5.0,5.0,6.0,3.0,2.0,56.0,2.0 -SE,1344.0,7,3.0,0.0,0.0,0.0,5.0,2.0,4.0,1.0,56.0,1.0 -SE,1345.0,7,2.0,0.0,6.0,4.0,7.0,6.0,2.0,1.0,56.0,1.0 -SE,1348.0,7,6.0,7.0,7.0,3.0,8.0,5.0,3.0,1.0,56.0,1.0 -SE,1353.0,7,4.0,8.0,8.0,8.0,9.0,7.0,3.0,2.0,56.0,1.0 -SE,1358.0,7,2.0,6.0,7.0,3.0,9.0,4.0,3.0,1.0,56.0,1.0 -SE,1360.0,7,5.0,5.0,7.0,8.0,10.0,4.0,1.0,2.0,56.0,2.0 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-SE,1432.0,7,4.0,8.0,8.0,7.0,9.0,5.0,3.0,1.0,55.0,1.0 -SE,1435.0,7,3.0,5.0,5.0,4.0,7.0,7.0,3.0,2.0,54.0,1.0 -SE,1439.0,7,2.0,4.0,5.0,5.0,8.0,6.0,2.0,1.0,54.0,1.0 -SE,1440.0,7,6.0,4.0,4.0,6.0,8.0,6.0,4.0,2.0,54.0,2.0 -SE,1442.0,7,3.0,5.0,6.0,7.0,8.0,7.0,3.0,2.0,54.0,1.0 -SE,1444.0,7,3.0,8.0,8.0,5.0,8.0,7.0,4.0,1.0,54.0,2.0 -SE,1445.0,7,5.0,6.0,10.0,7.0,9.0,6.0,3.0,2.0,54.0,2.0 -SE,1448.0,7,3.0,5.0,6.0,5.0,8.0,6.0,3.0,2.0,54.0,1.0 -SE,1453.0,7,4.0,8.0,7.0,8.0,9.0,6.0,4.0,2.0,54.0,1.0 -SE,1455.0,7,6.0,5.0,7.0,7.0,9.0,6.0,2.0,2.0,54.0,1.0 -SE,1463.0,7,1.0,5.0,7.0,4.0,8.0,7.0,4.0,1.0,54.0,2.0 -SE,1468.0,7,4.0,5.0,5.0,7.0,8.0,4.0,2.0,2.0,54.0,1.0 -SE,1473.0,7,7.0,7.0,7.0,7.0,7.0,6.0,3.0,1.0,54.0,1.0 -SE,1479.0,7,4.0,8.0,8.0,8.0,9.0,7.0,2.0,2.0,54.0,1.0 -SE,1484.0,7,4.0,8.0,8.0,7.0,8.0,5.0,4.0,1.0,54.0,1.0 -SE,1487.0,7,2.0,8.0,8.0,8.0,9.0,4.0,3.0,1.0,54.0,2.0 -SE,1488.0,7,3.0,7.0,7.0,7.0,6.0,3.0,3.0,2.0,54.0,1.0 -SE,1491.0,7,1.0,7.0,6.0,4.0,6.0,5.0,3.0,1.0,53.0,1.0 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-SE,1644.0,7,2.0,8.0,8.0,7.0,6.0,4.0,3.0,2.0,51.0,2.0 -SE,1658.0,7,4.0,8.0,8.0,7.0,9.0,7.0,3.0,1.0,50.0,1.0 -SE,1660.0,7,2.0,7.0,7.0,6.0,7.0,3.0,2.0,1.0,50.0,1.0 -SE,1663.0,7,4.0,8.0,8.0,7.0,5.0,3.0,2.0,2.0,50.0,1.0 -SE,1664.0,7,6.0,8.0,7.0,3.0,9.0,4.0,2.0,1.0,50.0,1.0 -SE,1671.0,7,3.0,7.0,7.0,6.0,9.0,5.0,4.0,1.0,50.0,1.0 -SE,1677.0,7,2.0,4.0,3.0,5.0,7.0,4.0,2.0,2.0,50.0,1.0 -SE,1682.0,7,5.0,7.0,7.0,7.0,8.0,4.0,2.0,1.0,50.0,1.0 -SE,1705.0,7,4.0,5.0,5.0,5.0,8.0,7.0,3.0,1.0,50.0,1.0 -SE,1706.0,7,7.0,6.0,9.0,7.0,9.0,3.0,2.0,2.0,50.0,1.0 -SE,1710.0,7,1.0,8.0,7.0,7.0,9.0,7.0,4.0,2.0,50.0,1.0 -SE,1714.0,7,1.0,9.0,9.0,9.0,6.0,3.0,2.0,1.0,49.0,1.0 -SE,1719.0,7,2.0,4.0,8.0,8.0,8.0,7.0,3.0,2.0,49.0,1.0 -SE,1727.0,7,1.0,7.0,7.0,3.0,9.0,6.0,4.0,1.0,50.0,1.0 -SE,1733.0,7,3.0,8.0,9.0,7.0,9.0,5.0,4.0,1.0,49.0,1.0 -SE,1738.0,7,6.0,8.0,7.0,8.0,10.0,4.0,3.0,2.0,49.0,1.0 -SE,1739.0,7,2.0,3.0,,6.0,7.0,6.0,,1.0,49.0,1.0 -SE,1740.0,7,4.0,9.0,9.0,6.0,9.0,6.0,4.0,1.0,49.0,2.0 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-SE,1890.0,7,6.0,5.0,0.0,5.0,9.0,5.0,3.0,2.0,47.0,1.0 -SE,1892.0,7,4.0,9.0,9.0,4.0,7.0,6.0,3.0,2.0,47.0,1.0 -SE,1899.0,7,1.0,8.0,8.0,9.0,9.0,5.0,3.0,1.0,47.0,1.0 -SE,1900.0,7,2.0,9.0,10.0,9.0,10.0,6.0,2.0,2.0,47.0,1.0 -SE,1905.0,7,4.0,8.0,5.0,4.0,8.0,4.0,4.0,1.0,47.0,1.0 -SE,1906.0,7,2.0,8.0,8.0,8.0,6.0,4.0,2.0,1.0,47.0,1.0 -SE,1914.0,7,1.0,9.0,9.0,9.0,9.0,6.0,3.0,2.0,47.0,1.0 -SE,1927.0,7,2.0,9.0,9.0,8.0,8.0,6.0,4.0,1.0,46.0,1.0 -SE,1933.0,7,0.0,5.0,5.0,3.0,7.0,5.0,2.0,1.0,46.0,2.0 -SE,1935.0,7,4.0,5.0,8.0,3.0,10.0,4.0,4.0,1.0,46.0,1.0 -SE,1936.0,7,1.0,7.0,7.0,6.0,8.0,7.0,3.0,1.0,46.0,2.0 -SE,1939.0,7,5.0,4.0,7.0,7.0,4.0,6.0,3.0,1.0,46.0,1.0 -SE,1953.0,7,4.0,7.0,7.0,4.0,9.0,7.0,3.0,1.0,46.0,1.0 -SE,1955.0,7,4.0,7.0,6.0,7.0,8.0,3.0,1.0,2.0,46.0,1.0 -SE,1956.0,7,2.0,8.0,8.0,6.0,8.0,6.0,4.0,1.0,46.0,1.0 -SE,1957.0,7,3.0,4.0,7.0,4.0,8.0,3.0,3.0,2.0,46.0,2.0 -SE,1960.0,7,4.0,4.0,6.0,5.0,5.0,7.0,2.0,2.0,46.0,2.0 -SE,1961.0,7,2.0,8.0,8.0,7.0,8.0,7.0,3.0,2.0,46.0,2.0 -SE,1966.0,7,3.0,5.0,5.0,5.0,9.0,7.0,4.0,2.0,46.0,1.0 -SE,1969.0,7,2.0,6.0,3.0,5.0,8.0,4.0,3.0,2.0,46.0,1.0 -SE,1972.0,7,1.0,6.0,7.0,7.0,10.0,7.0,3.0,2.0,46.0,1.0 -SE,1977.0,7,2.0,6.0,7.0,8.0,4.0,3.0,2.0,2.0,46.0,2.0 -SE,1982.0,7,6.0,8.0,7.0,5.0,9.0,6.0,3.0,1.0,45.0,1.0 -SE,1984.0,7,2.0,8.0,9.0,8.0,10.0,5.0,3.0,2.0,45.0,1.0 -SE,1988.0,7,4.0,6.0,7.0,4.0,8.0,6.0,5.0,2.0,45.0,1.0 -SE,1991.0,7,6.0,7.0,7.0,8.0,7.0,6.0,3.0,1.0,45.0,2.0 -SE,2009.0,7,1.0,8.0,8.0,8.0,9.0,6.0,1.0,2.0,45.0,1.0 -SE,2010.0,7,5.0,7.0,7.0,7.0,7.0,7.0,2.0,1.0,45.0,2.0 -SE,2016.0,7,4.0,10.0,8.0,7.0,8.0,6.0,4.0,2.0,45.0,1.0 -SE,2017.0,7,2.0,7.0,5.0,8.0,7.0,4.0,3.0,2.0,45.0,1.0 -SE,2039.0,7,4.0,6.0,8.0,6.0,8.0,7.0,2.0,2.0,44.0,2.0 -SE,2042.0,7,3.0,9.0,8.0,9.0,9.0,7.0,3.0,2.0,44.0,1.0 -SE,2046.0,7,4.0,5.0,7.0,5.0,7.0,7.0,3.0,2.0,44.0,2.0 -SE,2061.0,7,2.0,7.0,8.0,7.0,7.0,6.0,4.0,2.0,44.0,1.0 -SE,2077.0,7,1.0,10.0,10.0,7.0,8.0,5.0,2.0,2.0,44.0,2.0 -SE,2079.0,7,5.0,7.0,7.0,7.0,9.0,6.0,3.0,1.0,44.0,1.0 -SE,2102.0,7,2.0,9.0,9.0,5.0,9.0,5.0,3.0,2.0,43.0,1.0 -SE,2104.0,7,2.0,1.0,1.0,5.0,8.0,7.0,2.0,1.0,43.0,1.0 -SE,2105.0,7,4.0,6.0,4.0,4.0,5.0,5.0,4.0,2.0,43.0,2.0 -SE,2119.0,7,2.0,7.0,7.0,8.0,8.0,6.0,3.0,1.0,43.0,1.0 -SE,2126.0,7,2.0,8.0,9.0,5.0,9.0,7.0,4.0,2.0,43.0,1.0 -SE,2141.0,7,5.0,7.0,8.0,5.0,10.0,2.0,2.0,1.0,43.0,1.0 -SE,2146.0,7,4.0,5.0,5.0,4.0,7.0,4.0,3.0,2.0,43.0,1.0 -SE,2153.0,7,4.0,8.0,8.0,8.0,7.0,7.0,4.0,1.0,42.0,1.0 -SE,2161.0,7,6.0,8.0,4.0,3.0,10.0,2.0,5.0,2.0,42.0,1.0 -SE,2164.0,7,2.0,7.0,7.0,7.0,7.0,7.0,2.0,1.0,42.0,2.0 -SE,2166.0,7,5.0,7.0,8.0,6.0,5.0,6.0,3.0,2.0,42.0,2.0 -SE,2169.0,7,5.0,5.0,7.0,6.0,8.0,3.0,2.0,2.0,42.0,1.0 -SE,2175.0,7,6.0,7.0,4.0,8.0,8.0,7.0,4.0,1.0,42.0,1.0 -SE,2187.0,7,1.0,6.0,8.0,2.0,5.0,7.0,3.0,2.0,42.0,1.0 -SE,2188.0,7,3.0,8.0,7.0,3.0,6.0,7.0,3.0,2.0,42.0,1.0 -SE,2191.0,7,2.0,7.0,7.0,7.0,9.0,7.0,4.0,1.0,42.0,1.0 -SE,2196.0,7,1.0,7.0,7.0,7.0,9.0,7.0,3.0,2.0,42.0,1.0 -SE,2197.0,7,5.0,7.0,9.0,8.0,10.0,6.0,3.0,1.0,42.0,1.0 -SE,2200.0,7,2.0,7.0,7.0,7.0,7.0,4.0,2.0,2.0,42.0,1.0 -SE,2201.0,7,1.0,8.0,7.0,6.0,10.0,7.0,4.0,1.0,42.0,1.0 -SE,2207.0,7,6.0,5.0,5.0,5.0,3.0,2.0,1.0,2.0,42.0,2.0 -SE,2210.0,7,3.0,6.0,7.0,7.0,8.0,5.0,2.0,2.0,47.0,1.0 -SE,2216.0,7,6.0,8.0,7.0,7.0,9.0,4.0,3.0,1.0,42.0,1.0 -SE,2219.0,7,3.0,5.0,5.0,5.0,7.0,6.0,2.0,2.0,42.0,2.0 -SE,2221.0,7,2.0,8.0,7.0,5.0,8.0,6.0,3.0,1.0,42.0,1.0 -SE,2225.0,7,3.0,8.0,7.0,7.0,8.0,4.0,4.0,2.0,41.0,1.0 -SE,2227.0,7,4.0,9.0,10.0,8.0,8.0,6.0,3.0,2.0,41.0,1.0 -SE,2232.0,7,2.0,8.0,8.0,8.0,9.0,5.0,3.0,2.0,41.0,1.0 -SE,2236.0,7,1.0,9.0,8.0,6.0,9.0,4.0,3.0,2.0,41.0,1.0 -SE,2238.0,7,2.0,8.0,9.0,6.0,9.0,5.0,4.0,2.0,41.0,1.0 -SE,2244.0,7,4.0,5.0,8.0,4.0,7.0,4.0,3.0,2.0,41.0,1.0 -SE,2245.0,7,2.0,7.0,7.0,3.0,9.0,7.0,5.0,2.0,41.0,1.0 -SE,2250.0,7,5.0,5.0,6.0,6.0,7.0,6.0,2.0,1.0,41.0,2.0 -SE,2251.0,7,3.0,7.0,8.0,5.0,8.0,2.0,3.0,1.0,41.0,1.0 -SE,2252.0,7,3.0,8.0,8.0,4.0,7.0,4.0,3.0,1.0,41.0,1.0 -SE,2260.0,7,3.0,7.0,6.0,6.0,8.0,6.0,3.0,1.0,41.0,1.0 -SE,2265.0,7,0.0,7.0,8.0,5.0,8.0,3.0,1.0,2.0,41.0,1.0 -SE,2268.0,7,2.0,8.0,7.0,7.0,8.0,6.0,2.0,2.0,41.0,1.0 -SE,2274.0,7,7.0,5.0,7.0,7.0,7.0,4.0,2.0,1.0,41.0,2.0 -SE,2275.0,7,2.0,7.0,7.0,5.0,7.0,4.0,3.0,2.0,41.0,1.0 -SE,2282.0,7,4.0,7.0,7.0,6.0,8.0,4.0,3.0,1.0,41.0,1.0 -SE,2288.0,7,3.0,1.0,9.0,4.0,10.0,3.0,3.0,1.0,40.0,1.0 -SE,2294.0,7,2.0,7.0,8.0,6.0,8.0,4.0,2.0,1.0,40.0,1.0 -SE,2301.0,7,4.0,7.0,9.0,8.0,8.0,6.0,4.0,1.0,40.0,2.0 -SE,2313.0,7,4.0,8.0,7.0,6.0,8.0,6.0,3.0,1.0,40.0,2.0 -SE,2315.0,7,5.0,3.0,3.0,2.0,5.0,6.0,3.0,1.0,40.0,2.0 -SE,2317.0,7,2.0,8.0,8.0,7.0,9.0,7.0,3.0,1.0,40.0,1.0 -SE,2320.0,7,3.0,6.0,6.0,6.0,5.0,6.0,3.0,1.0,40.0,2.0 -SE,2329.0,7,3.0,8.0,8.0,7.0,9.0,5.0,3.0,1.0,40.0,1.0 -SE,2336.0,7,1.0,8.0,8.0,8.0,9.0,6.0,4.0,2.0,40.0,1.0 -SE,2346.0,7,5.0,8.0,8.0,7.0,8.0,6.0,3.0,2.0,39.0,1.0 -SE,2350.0,7,2.0,7.0,7.0,6.0,8.0,4.0,3.0,2.0,39.0,1.0 -SE,2357.0,7,4.0,10.0,8.0,7.0,9.0,7.0,3.0,2.0,39.0,2.0 -SE,2359.0,7,2.0,7.0,9.0,8.0,8.0,6.0,3.0,2.0,39.0,1.0 -SE,2363.0,7,2.0,4.0,5.0,8.0,8.0,7.0,3.0,2.0,39.0,1.0 -SE,2365.0,7,0.0,8.0,9.0,8.0,8.0,6.0,3.0,2.0,39.0,1.0 -SE,2368.0,7,7.0,2.0,0.0,2.0,10.0,6.0,2.0,1.0,39.0,1.0 -SE,2372.0,7,2.0,2.0,5.0,2.0,7.0,6.0,1.0,1.0,39.0,1.0 -SE,2374.0,7,2.0,5.0,8.0,5.0,10.0,6.0,3.0,2.0,39.0,1.0 -SE,2380.0,7,6.0,3.0,5.0,7.0,5.0,4.0,2.0,1.0,39.0,1.0 -SE,2383.0,7,1.0,1.0,5.0,6.0,9.0,6.0,3.0,2.0,38.0,1.0 -SE,2393.0,7,2.0,8.0,8.0,8.0,9.0,6.0,3.0,1.0,38.0,1.0 -SE,2415.0,7,3.0,6.0,7.0,6.0,9.0,7.0,4.0,1.0,38.0,1.0 -SE,2422.0,7,1.0,3.0,0.0,0.0,8.0,6.0,3.0,1.0,38.0,2.0 -SE,2423.0,7,3.0,8.0,10.0,7.0,7.0,6.0,2.0,1.0,38.0,1.0 -SE,2432.0,7,3.0,8.0,7.0,8.0,9.0,5.0,1.0,2.0,38.0,2.0 -SE,2433.0,7,3.0,6.0,9.0,8.0,6.0,7.0,2.0,2.0,38.0,1.0 -SE,2435.0,7,4.0,8.0,8.0,8.0,8.0,6.0,2.0,1.0,38.0,2.0 -SE,2437.0,7,2.0,8.0,7.0,3.0,7.0,7.0,3.0,2.0,38.0,1.0 -SE,2438.0,7,2.0,4.0,6.0,5.0,8.0,7.0,3.0,2.0,38.0,1.0 -SE,2440.0,7,4.0,8.0,8.0,4.0,6.0,7.0,4.0,1.0,38.0,1.0 -SE,2441.0,7,3.0,8.0,7.0,5.0,9.0,6.0,3.0,1.0,38.0,1.0 -SE,2452.0,7,0.0,5.0,5.0,6.0,8.0,5.0,3.0,2.0,37.0,2.0 -SE,2459.0,7,4.0,0.0,8.0,6.0,7.0,7.0,2.0,2.0,37.0,2.0 -SE,2461.0,7,1.0,10.0,9.0,9.0,8.0,6.0,4.0,2.0,37.0,1.0 -SE,2466.0,7,2.0,5.0,5.0,4.0,10.0,5.0,2.0,2.0,37.0,1.0 -SE,2467.0,7,2.0,9.0,7.0,9.0,9.0,7.0,2.0,2.0,37.0,1.0 -SE,2469.0,7,4.0,7.0,7.0,7.0,7.0,7.0,4.0,1.0,37.0,1.0 -SE,2471.0,7,4.0,6.0,6.0,7.0,9.0,5.0,2.0,1.0,37.0,1.0 -SE,2472.0,7,3.0,8.0,7.0,7.0,9.0,6.0,3.0,2.0,37.0,1.0 -SE,2473.0,7,5.0,5.0,7.0,6.0,7.0,5.0,2.0,2.0,37.0,2.0 -SE,2476.0,7,2.0,10.0,10.0,9.0,10.0,7.0,3.0,2.0,37.0,1.0 -SE,2484.0,7,1.0,7.0,7.0,6.0,7.0,7.0,4.0,2.0,37.0,1.0 -SE,2485.0,7,1.0,8.0,8.0,8.0,9.0,6.0,2.0,2.0,37.0,1.0 -SE,2486.0,7,2.0,7.0,8.0,5.0,8.0,6.0,3.0,2.0,37.0,1.0 -SE,2488.0,7,4.0,9.0,9.0,7.0,10.0,6.0,3.0,2.0,37.0,1.0 -SE,2493.0,7,0.0,7.0,7.0,6.0,9.0,7.0,3.0,2.0,37.0,1.0 -SE,2500.0,7,0.0,6.0,7.0,3.0,6.0,7.0,2.0,1.0,37.0,2.0 -SE,2508.0,7,1.0,6.0,7.0,4.0,7.0,4.0,3.0,2.0,36.0,2.0 -SE,2511.0,7,4.0,6.0,5.0,6.0,8.0,6.0,3.0,2.0,36.0,1.0 -SE,2515.0,7,1.0,5.0,4.0,6.0,6.0,2.0,2.0,1.0,36.0,1.0 -SE,2526.0,7,0.0,6.0,3.0,4.0,7.0,2.0,2.0,2.0,36.0,1.0 -SE,2528.0,7,3.0,7.0,7.0,7.0,8.0,6.0,3.0,2.0,36.0,1.0 -SE,2549.0,7,2.0,5.0,4.0,6.0,10.0,6.0,3.0,1.0,36.0,1.0 -SE,2561.0,7,0.0,0.0,5.0,3.0,9.0,7.0,5.0,1.0,35.0,1.0 -SE,2564.0,7,1.0,4.0,5.0,5.0,5.0,7.0,3.0,1.0,35.0,1.0 -SE,2567.0,7,2.0,6.0,7.0,6.0,8.0,7.0,4.0,2.0,35.0,1.0 -SE,2587.0,7,6.0,7.0,6.0,7.0,10.0,7.0,,1.0,35.0,1.0 -SE,2590.0,7,2.0,7.0,8.0,5.0,8.0,7.0,3.0,1.0,35.0,1.0 -SE,2591.0,7,2.0,2.0,3.0,1.0,8.0,7.0,2.0,2.0,35.0,2.0 -SE,2593.0,7,5.0,4.0,10.0,5.0,10.0,7.0,2.0,2.0,35.0,2.0 -SE,2597.0,7,4.0,7.0,7.0,7.0,8.0,7.0,2.0,1.0,35.0,1.0 -SE,2602.0,7,2.0,8.0,9.0,8.0,8.0,2.0,4.0,1.0,35.0,1.0 -SE,2605.0,7,2.0,3.0,5.0,3.0,7.0,6.0,3.0,1.0,35.0,1.0 -SE,2622.0,7,1.0,8.0,9.0,7.0,7.0,4.0,4.0,2.0,34.0,1.0 -SE,2624.0,7,3.0,6.0,6.0,6.0,8.0,6.0,2.0,1.0,34.0,1.0 -SE,2625.0,7,1.0,4.0,5.0,4.0,7.0,6.0,2.0,1.0,34.0,2.0 -SE,2628.0,7,2.0,7.0,7.0,8.0,10.0,6.0,4.0,2.0,34.0,1.0 -SE,2632.0,7,0.0,8.0,8.0,6.0,8.0,4.0,3.0,1.0,34.0,2.0 -SE,2633.0,7,2.0,8.0,8.0,7.0,9.0,7.0,1.0,1.0,34.0,1.0 -SE,2637.0,7,4.0,8.0,6.0,8.0,7.0,6.0,4.0,1.0,34.0,1.0 -SE,2641.0,7,2.0,6.0,7.0,7.0,9.0,4.0,5.0,1.0,34.0,1.0 -SE,2644.0,7,5.0,3.0,4.0,3.0,7.0,4.0,3.0,1.0,34.0,1.0 -SE,2659.0,7,2.0,8.0,7.0,6.0,7.0,6.0,3.0,1.0,34.0,1.0 -SE,2663.0,7,3.0,8.0,8.0,7.0,8.0,7.0,3.0,1.0,34.0,1.0 -SE,2679.0,7,4.0,4.0,5.0,3.0,6.0,5.0,2.0,2.0,33.0,2.0 -SE,2683.0,7,5.0,7.0,7.0,2.0,9.0,7.0,3.0,1.0,33.0,1.0 -SE,2689.0,7,3.0,7.0,7.0,7.0,8.0,7.0,4.0,1.0,33.0,1.0 -SE,2697.0,7,7.0,8.0,8.0,8.0,8.0,6.0,4.0,2.0,33.0,2.0 -SE,2700.0,7,2.0,7.0,7.0,6.0,8.0,6.0,4.0,2.0,33.0,2.0 -SE,2714.0,7,5.0,10.0,8.0,4.0,8.0,6.0,3.0,2.0,33.0,1.0 -SE,2716.0,7,3.0,10.0,10.0,9.0,10.0,7.0,3.0,1.0,33.0,1.0 -SE,2724.0,7,1.0,7.0,7.0,6.0,8.0,4.0,3.0,1.0,33.0,1.0 -SE,2729.0,7,0.0,5.0,7.0,3.0,9.0,7.0,4.0,2.0,33.0,1.0 -SE,2732.0,7,6.0,3.0,8.0,9.0,7.0,3.0,1.0,1.0,33.0,1.0 -SE,2739.0,7,6.0,8.0,8.0,6.0,5.0,5.0,2.0,1.0,32.0,2.0 -SE,2743.0,7,2.0,8.0,6.0,9.0,8.0,6.0,2.0,1.0,32.0,1.0 -SE,2748.0,7,3.0,7.0,7.0,5.0,9.0,4.0,3.0,2.0,32.0,1.0 -SE,2749.0,7,7.0,5.0,5.0,5.0,10.0,6.0,3.0,2.0,32.0,1.0 -SE,2768.0,7,4.0,6.0,7.0,6.0,9.0,7.0,4.0,1.0,32.0,1.0 -SE,2777.0,7,3.0,7.0,6.0,4.0,8.0,7.0,3.0,2.0,32.0,1.0 -SE,2780.0,7,2.0,10.0,10.0,8.0,9.0,4.0,1.0,1.0,32.0,1.0 -SE,2788.0,7,3.0,7.0,6.0,6.0,8.0,6.0,2.0,1.0,32.0,2.0 -SE,2790.0,7,2.0,8.0,9.0,8.0,9.0,6.0,3.0,1.0,32.0,1.0 -SE,2794.0,7,3.0,7.0,5.0,6.0,9.0,7.0,3.0,2.0,32.0,2.0 -SE,2796.0,7,1.0,5.0,5.0,7.0,5.0,7.0,2.0,2.0,32.0,1.0 -SE,2799.0,7,0.0,9.0,8.0,6.0,8.0,6.0,3.0,1.0,31.0,2.0 -SE,2800.0,7,3.0,7.0,9.0,5.0,8.0,4.0,3.0,1.0,31.0,1.0 -SE,2801.0,7,1.0,7.0,8.0,8.0,9.0,6.0,3.0,2.0,31.0,1.0 -SE,2813.0,7,7.0,7.0,8.0,4.0,9.0,6.0,3.0,2.0,31.0,1.0 -SE,2816.0,7,3.0,7.0,6.0,4.0,7.0,6.0,3.0,1.0,31.0,1.0 -SE,2832.0,7,4.0,5.0,6.0,6.0,10.0,7.0,4.0,1.0,31.0,1.0 -SE,2836.0,7,,4.0,6.0,8.0,8.0,7.0,1.0,2.0,31.0,2.0 -SE,2838.0,7,5.0,8.0,8.0,8.0,7.0,5.0,2.0,1.0,31.0,1.0 -SE,2839.0,7,3.0,8.0,8.0,7.0,8.0,6.0,4.0,2.0,31.0,1.0 -SE,2840.0,7,4.0,7.0,6.0,7.0,8.0,5.0,3.0,1.0,31.0,2.0 -SE,2841.0,7,0.0,6.0,6.0,7.0,9.0,6.0,4.0,2.0,31.0,2.0 -SE,2844.0,7,1.0,8.0,3.0,7.0,9.0,7.0,3.0,1.0,31.0,1.0 -SE,2847.0,7,3.0,8.0,5.0,7.0,9.0,7.0,4.0,2.0,31.0,2.0 -SE,2851.0,7,2.0,8.0,7.0,7.0,9.0,7.0,3.0,2.0,31.0,1.0 -SE,2868.0,7,4.0,8.0,7.0,1.0,8.0,7.0,3.0,1.0,30.0,1.0 -SE,2875.0,7,0.0,8.0,10.0,10.0,10.0,7.0,,1.0,30.0,2.0 -SE,2879.0,7,4.0,8.0,7.0,8.0,7.0,4.0,2.0,1.0,30.0,2.0 -SE,2880.0,7,2.0,5.0,7.0,3.0,8.0,7.0,4.0,1.0,30.0,2.0 -SE,2893.0,7,2.0,7.0,7.0,8.0,8.0,4.0,4.0,1.0,30.0,1.0 -SE,2903.0,7,0.0,2.0,6.0,4.0,10.0,7.0,3.0,2.0,30.0,2.0 -SE,2905.0,7,2.0,7.0,7.0,7.0,7.0,5.0,3.0,2.0,30.0,1.0 -SE,2912.0,7,3.0,7.0,7.0,4.0,10.0,6.0,3.0,1.0,29.0,1.0 -SE,2929.0,7,7.0,8.0,7.0,8.0,8.0,3.0,2.0,2.0,29.0,2.0 -SE,2931.0,7,5.0,7.0,7.0,5.0,7.0,5.0,2.0,1.0,29.0,2.0 -SE,2939.0,7,2.0,6.0,6.0,5.0,7.0,6.0,3.0,1.0,29.0,1.0 -SE,2948.0,7,4.0,6.0,7.0,7.0,8.0,7.0,3.0,2.0,29.0,1.0 -SE,2951.0,7,1.0,6.0,5.0,3.0,8.0,7.0,4.0,1.0,29.0,1.0 -SE,2956.0,7,4.0,7.0,9.0,9.0,8.0,6.0,4.0,2.0,29.0,1.0 -SE,2957.0,7,3.0,6.0,9.0,7.0,3.0,6.0,2.0,2.0,29.0,2.0 -SE,2960.0,7,3.0,8.0,8.0,7.0,8.0,5.0,4.0,2.0,29.0,1.0 -SE,2968.0,7,5.0,5.0,7.0,5.0,6.0,3.0,2.0,2.0,29.0,1.0 -SE,2969.0,7,3.0,4.0,7.0,3.0,7.0,6.0,3.0,1.0,29.0,2.0 -SE,2970.0,7,3.0,7.0,7.0,7.0,6.0,7.0,4.0,2.0,29.0,2.0 -SE,2974.0,7,2.0,3.0,5.0,6.0,9.0,7.0,4.0,2.0,29.0,1.0 -SE,2992.0,7,4.0,8.0,8.0,8.0,8.0,7.0,3.0,2.0,28.0,2.0 -SE,2994.0,7,2.0,7.0,7.0,5.0,7.0,6.0,3.0,1.0,28.0,1.0 -SE,3004.0,7,1.0,10.0,10.0,5.0,9.0,6.0,2.0,1.0,28.0,2.0 -SE,3008.0,7,7.0,8.0,8.0,7.0,7.0,7.0,3.0,2.0,28.0,2.0 -SE,3012.0,7,4.0,3.0,7.0,7.0,7.0,6.0,2.0,1.0,28.0,2.0 -SE,3016.0,7,4.0,7.0,6.0,6.0,6.0,7.0,3.0,2.0,28.0,2.0 -SE,3017.0,7,3.0,5.0,7.0,6.0,8.0,7.0,3.0,1.0,28.0,1.0 -SE,3022.0,7,4.0,7.0,9.0,5.0,8.0,6.0,2.0,1.0,28.0,2.0 -SE,3031.0,7,2.0,6.0,4.0,4.0,8.0,6.0,3.0,2.0,27.0,2.0 -SE,3033.0,7,4.0,8.0,8.0,8.0,8.0,6.0,3.0,1.0,27.0,1.0 -SE,3034.0,7,0.0,7.0,7.0,6.0,9.0,7.0,3.0,1.0,27.0,1.0 -SE,3041.0,7,3.0,4.0,4.0,4.0,7.0,7.0,2.0,1.0,27.0,2.0 -SE,3046.0,7,5.0,8.0,6.0,3.0,6.0,6.0,3.0,1.0,27.0,2.0 -SE,3047.0,7,5.0,7.0,8.0,7.0,8.0,4.0,3.0,1.0,27.0,1.0 -SE,3054.0,7,6.0,7.0,6.0,6.0,8.0,7.0,4.0,2.0,27.0,2.0 -SE,3059.0,7,2.0,8.0,7.0,7.0,9.0,6.0,3.0,1.0,27.0,1.0 -SE,3080.0,7,3.0,7.0,8.0,6.0,9.0,3.0,3.0,1.0,27.0,2.0 -SE,3089.0,7,5.0,4.0,5.0,6.0,8.0,6.0,4.0,2.0,26.0,2.0 -SE,3090.0,7,3.0,8.0,7.0,5.0,7.0,5.0,3.0,2.0,26.0,1.0 -SE,3096.0,7,1.0,8.0,10.0,8.0,10.0,7.0,4.0,2.0,26.0,1.0 -SE,3098.0,7,2.0,3.0,5.0,6.0,8.0,4.0,2.0,2.0,26.0,1.0 -SE,3099.0,7,0.0,4.0,5.0,3.0,7.0,4.0,3.0,1.0,26.0,2.0 -SE,3114.0,7,0.0,6.0,8.0,8.0,9.0,6.0,3.0,2.0,26.0,2.0 -SE,3116.0,7,3.0,7.0,4.0,7.0,7.0,4.0,2.0,1.0,26.0,2.0 -SE,3120.0,7,2.0,7.0,7.0,8.0,10.0,4.0,3.0,2.0,26.0,2.0 -SE,3124.0,7,5.0,6.0,6.0,3.0,9.0,7.0,4.0,2.0,26.0,1.0 -SE,3133.0,7,2.0,7.0,7.0,8.0,9.0,7.0,4.0,1.0,26.0,2.0 -SE,3140.0,7,1.0,10.0,10.0,9.0,9.0,6.0,2.0,2.0,26.0,2.0 -SE,3143.0,7,2.0,7.0,8.0,5.0,8.0,6.0,4.0,2.0,26.0,1.0 -SE,3144.0,7,0.0,6.0,5.0,5.0,6.0,6.0,3.0,1.0,26.0,2.0 -SE,3145.0,7,3.0,6.0,7.0,7.0,4.0,4.0,2.0,2.0,26.0,1.0 -SE,3148.0,7,1.0,8.0,8.0,8.0,10.0,6.0,3.0,2.0,26.0,1.0 -SE,3151.0,7,4.0,7.0,7.0,6.0,5.0,7.0,3.0,1.0,26.0,2.0 -SE,3153.0,7,1.0,6.0,7.0,9.0,7.0,6.0,3.0,1.0,26.0,1.0 -SE,3155.0,7,3.0,7.0,7.0,8.0,9.0,6.0,2.0,1.0,26.0,1.0 -SE,3156.0,7,0.0,7.0,7.0,5.0,8.0,6.0,4.0,1.0,25.0,2.0 -SE,3157.0,7,5.0,7.0,8.0,6.0,8.0,7.0,3.0,2.0,25.0,1.0 -SE,3163.0,7,1.0,5.0,5.0,5.0,6.0,6.0,2.0,2.0,25.0,2.0 -SE,3170.0,7,2.0,7.0,6.0,6.0,7.0,6.0,3.0,1.0,25.0,1.0 -SE,3172.0,7,4.0,5.0,8.0,8.0,8.0,3.0,3.0,2.0,25.0,1.0 -SE,3176.0,7,4.0,3.0,4.0,5.0,9.0,7.0,4.0,2.0,25.0,2.0 -SE,3183.0,7,4.0,7.0,7.0,5.0,8.0,6.0,3.0,1.0,25.0,1.0 -SE,3185.0,7,4.0,9.0,8.0,8.0,10.0,6.0,3.0,1.0,25.0,1.0 -SE,3186.0,7,1.0,5.0,5.0,4.0,6.0,4.0,4.0,1.0,25.0,2.0 -SE,3189.0,7,2.0,7.0,9.0,7.0,8.0,4.0,3.0,1.0,25.0,2.0 -SE,3197.0,7,4.0,6.0,8.0,4.0,9.0,6.0,3.0,2.0,25.0,1.0 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analysis\n", - "## Classification and regression\n", - "### Logistic regression, k-nn classifier, and svm\n", - "## Association and correlation\n", - "### Outlier\n", - "## Dimensionality reduction\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Section 2.1 Cluster analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd \n", - "import seaborn as sns \n", - "import numpy as np \n", - "import matplotlib.pyplot as plt \n", - "from sklearn.cluster import KMeans \n", - "%matplotlib inline " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "df = pd.read_csv('single_family_home_values.csv') # zillow " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idaddresscitystatezipcodelatitudelongitudebedroomsbathroomsroomssquareFootagelotSizeyearBuiltlastSaleDatelastSaleAmountpriorSaleDatepriorSaleAmountestimated_value
0395257498171 E 84th AveDenverCO8002239.84916-104.89346832.06137899682003.02009-12-17750002004-05-13165700.0239753
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" - ], - "text/plain": [ - " bedrooms bathrooms rooms squareFootage lotSize yearBuilt \\\n", - "cluster \n", - "0 3.0 4.0 8.0 2582.0 6250.0 1927.0 \n", - "1 2.0 2.0 5.0 1133.0 6238.0 1929.0 \n", - "2 3.0 4.5 9.0 3748.0 8597.5 1998.0 \n", - "3 4.0 6.0 10.0 4424.0 8580.0 1989.0 \n", - "4 3.0 2.0 6.0 1327.0 5210.0 1923.0 \n", - "\n", - " priorSaleAmount \n", - "cluster \n", - "0 651500.0 \n", - "1 0.0 \n", - "2 13750055.0 \n", - "3 2200000.0 \n", - "4 279900.0 " - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.groupby('cluster').median()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "X= X.drop('cluster', axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn.metrics import silhouette_score" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.614559572342\n", - "0.616747726506\n", - "0.632865622992\n", - "0.64350268351\n", - "0.657962789235\n", - "0.661299799128\n", - "0.655707743822\n" - ] - } - ], - "source": [ - "for i in range(3, 10):\n", - " kmeans=KMeans(n_clusters=i).fit(X)\n", - " labels = kmeans.labels_\n", - " print silhouette_score(X, labels)\n", - " #print kmeans.cluster_centers_ " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Automatically created module for IPython interactive environment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/koyuki.nakamori/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", - " \"matplotlib is currently using a non-GUI backend, \"\n" - ] - }, - { - "data": { - "image/png": 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5u7qvH7fkY4cT3Y69vSKbzZkEVHwMx7HWWqqqWk/uD3/4FgCwercqTf8snAsA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\nlow4rEokEzRjb3sgL4IeTeTg02VJbOkTuSMVLxVrzzEiEWIqpkOp7Jh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q6gZKIVazfjejotXdHSIY9FVxaAYGBsqS4ZIkm+c0YIRjuschtT3jXRAE8nkj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\nUWmfTaJ9JOPTrUheFctEpCxa1bC96e/DVb2KnhYXN8V2tnwO8d3iHe8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YqHkuuZtSvQTD4qpdboLr0IiKes4qvwUk++4AFUH4bPoQ0tVXX132j9Fosvwf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\neyU7m+bm1GZ5JBKhq6sTjUZbtHYzpH4XotFUNpi5ee9L7Gp9ccbnpUA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G43a7mTx5EooiU15ejtVqpby8jMbGRurq9uLxlBXNcF64cCEvvvii4S2dTCZ5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\nmckRR0zm0kuvoKysouuDHCDoYzWfVGzduoWKisrEaLhQC5KnYiIEZnf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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(__doc__)\n", - "\n", - "\n", - "# Code source: Gaël Varoquaux\n", - "# Modified for documentation by Jaques Grobler\n", - "# License: BSD 3 clause\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "# Though the following import is not directly being used, it is required\n", - "# for 3D projection to work\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "\n", - "from sklearn.cluster import KMeans\n", - "from sklearn import datasets\n", - "\n", - "np.random.seed(5)\n", - "\n", - "iris = datasets.load_iris()\n", - "X = iris.data\n", - "y = iris.target\n", - "\n", - "estimators = [('k_means_iris_8', KMeans(n_clusters=8)),\n", - " ('k_means_iris_3', KMeans(n_clusters=3)),\n", - " ('k_means_iris_bad_init', KMeans(n_clusters=3, n_init=1,\n", - " init='random'))]\n", - "\n", - "fignum = 1\n", - "titles = ['8 clusters', '3 clusters', '3 clusters, bad initialization']\n", - "for name, est in estimators:\n", - " fig = plt.figure(fignum, figsize=(4, 3))\n", - " ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n", - " est.fit(X)\n", - " labels = est.labels_\n", - "\n", - " ax.scatter(X[:, 3], X[:, 0], X[:, 2],\n", - " c=labels.astype(np.float), edgecolor='k')\n", - "\n", - " ax.w_xaxis.set_ticklabels([])\n", - " ax.w_yaxis.set_ticklabels([])\n", - " ax.w_zaxis.set_ticklabels([])\n", - " ax.set_xlabel('Petal width')\n", - " ax.set_ylabel('Sepal length')\n", - " ax.set_zlabel('Petal length')\n", - " ax.set_title(titles[fignum - 1])\n", - " ax.dist = 12\n", - " fignum = fignum + 1\n", - "\n", - "# Plot the ground truth\n", - "fig = plt.figure(fignum, figsize=(4, 3))\n", - "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n", - "\n", - "for name, label in [('Setosa', 0),\n", - " ('Versicolour', 1),\n", - " ('Virginica', 2)]:\n", - " ax.text3D(X[y == label, 3].mean(),\n", - " X[y == label, 0].mean(),\n", - " X[y == label, 2].mean() + 2, name,\n", - " horizontalalignment='center',\n", - " bbox=dict(alpha=.2, edgecolor='w', facecolor='w'))\n", - "# Reorder the labels to have colors matching the cluster results\n", - "y = np.choose(y, [1, 2, 0]).astype(np.float)\n", - "ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y, edgecolor='k')\n", - "\n", - "ax.w_xaxis.set_ticklabels([])\n", - "ax.w_yaxis.set_ticklabels([])\n", - "ax.w_zaxis.set_ticklabels([])\n", - "ax.set_xlabel('Petal width')\n", - "ax.set_ylabel('Sepal length')\n", - "ax.set_zlabel('Petal length')\n", - "ax.set_title('Ground Truth')\n", - "ax.dist = 12\n", - "\n", - "fig.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Section 2.2 Classification and regression" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn.linear_model import LinearRegression, LogisticRegression \n", - "\n", - "from sklearn.svm import SVC, SVR\n", - "\n", - "from sklearn.neighbors import KNeighborsClassifier , KNeighborsRegressor\n", - "from sklearn.cross_validation import train_test_split\n", - "from sklearn.metrics import confusion_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
bedroomsbathroomsroomssquareFootagelotSizeyearBuiltpriorSaleAmount
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" - ], - "text/plain": [ - " bedrooms bathrooms rooms squareFootage lotSize yearBuilt \\\n", - "0 3 2.0 6 1378 9968 2003.0 \n", - "1 2 2.0 6 1653 6970 2004.0 \n", - "\n", - " priorSaleAmount \n", - "0 165700.0 \n", - "1 0.0 " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "y=df.estimated_value " - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7648477834199694" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lg = LinearRegression()\n", - "lg.fit(X,y) #training \n", - "lg.score(X,y) " - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test= train_test_split(X,y)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((11250, 7), (11250,))" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train.shape, y_train.shape " - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.80307010330903539" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lg = LinearRegression()\n", - "lg.fit(X_train,y_train) # training , fit \n", - "lg.score(X_test,y_test) # evaluate , score, R2 " - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "df['estimated_value_bins']=df.estimated_value.apply(lambda x: 'high' if x> 500000 else 'low')" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "high 7963\n", - "low 7037\n", - "Name: estimated_value_bins, dtype: int64" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.estimated_value_bins.value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "y2= df.estimated_value_bins" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "log = LogisticRegression() " - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "X_train, X_test, y2_train, y2_test= train_test_split(X,y2)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", - " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "log.fit(X_train, y2_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.81679999999999997" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "log.score(X_test, y2_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "y_pred = log.predict(X_test) " - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['low', 'low', 'high', ..., 'high', 'low', 'high'], dtype=object)" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array(y2_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1568, 377],\n", - " [ 310, 1495]])" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion_matrix(y2_test,y_pred )" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "## Section 2.2 Continued\n", - "from IPython.display import Image" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Support Vector Machine\n" - ] - } - ], - "source": [ - "Image('svm.png')\n", - "print 'Support Vector Machine'" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.056414465159077887" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "svr= SVR() \n", - "svr.fit(X_train, y_train)\n", - "svr.score(X_test, y_test) \n", - "# not so great " - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.55386666666666662" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "svc= SVC() \n", - "svc.fit(X_train, y2_train)\n", - "svc.score(X_test, y2_test) " - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "y2_pred=svc.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1937, 8],\n", - " [1665, 140]])" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion_matrix(y2_test, y2_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "## KNN: \n", - "xxxxxx\n", - "unknown=x\n", - "xxxxx \n", - "\n", - " ooooooo\n", - " unknown= o \n", - " ooooooo" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "knn= KNeighborsRegressor() " - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n", - " weights='uniform')" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "knn.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.2227615288381741" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "knn.score(X_test, y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "knn= KNeighborsClassifier()" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n", - " weights='uniform')" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "knn.fit(X_train, y2_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.81999999999999995" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "knn.score(X_test,y2_test) " - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "y2_pred=knn.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1564, 381],\n", - " [ 294, 1511]])" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion_matrix(y2_test, y2_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Section 2.3 Association and correlation" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 148, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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RjyR9EZ92O6zWbQkhhHoVI6J+ZGZ/Bv5c63aEEEI9i44ohBBCTc2cPafWTeiT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\niJrLpPTzGPwPv7SI+2N8FFPEv1ISyFf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"text/plain": [ - "Index([u'bedrooms', u'bathrooms', u'rooms', u'squareFootage', u'lotSize',\n", - " u'yearBuilt', u'priorSaleAmount'],\n", - " dtype='object')" - ] - }, - "execution_count": 141, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====\n", - "bedrooms\n", - "mean: 2.7084\n", - "std: 0.897230799854\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for i in X.columns:\n", - " print ('====')\n", - " print i\n", - " X.loc[:, i].hist()\n", - " print 'mean:' , X.loc[:, i].mean()\n", - " print 'std:' , X.loc[:, i].std()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "X2=X[(X.bedrooms<7) & (X.bathrooms< 7) ] #drop " - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 157, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X2.bathrooms.hist()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Outliers \n", - "\n", - "# 1. Three Sigma Rule: \n", - "# 2. Boxplot Rule:\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.boxplot(X[['lotSize']])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#Mahalanobis Rule " - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def MahalanobisDist(x, y):\n", - " covariance_xy = np.cov(x,y, rowvar=0)\n", - " inv_covariance_xy = np.linalg.inv(covariance_xy)\n", - " xy_mean = np.mean(x),np.mean(y)\n", - " x_diff = np.array([x_i - xy_mean[0] for x_i in x])\n", - " y_diff = np.array([y_i - xy_mean[1] for y_i in y])\n", - " diff_xy = np.transpose([x_diff, y_diff])\n", - " \n", - " md = []\n", - " for i in range(len(diff_xy)):\n", - " md.append(np.sqrt(np.dot(np.dot(np.transpose(diff_xy[i]),inv_covariance_xy),diff_xy[i])))\n", - " return md" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.29907509139050276,\n", - " 0.46486017984700306,\n", - " 1.4436650807702445,\n", - " 1.1015699997639492,\n", - " 2.0068484330525269,\n", - " 1.6628011461732402,\n", - " 2.0775848572441347,\n", - " 0.46486017984700306,\n", - " 1.2045428522263557,\n", - " 1.2732274657026237]" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# as column vectors\n", - "x = np.random.poisson(5,10)\n", - "y = np.random.poisson(5,10)\n", - "MahalanobisDist(x,y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Section 2.4 Dimensionality reduction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Curse of dimensionality: \"As the number of features or dimensions grows, the amount of data we need to generalize accurately grows exponentially\" " - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn.decomposition import PCA " - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "pca = PCA(4) " - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(15000, 7)" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.shape " - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "X_transformed = pca.fit_transform(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(15000, 4)" - ] - }, - "execution_count": 169, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_transformed.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 4.34835866e-07, 1.39033126e-06, 1.76645671e-06,\n", - " 9.91884229e-04, 1.22556479e-03, 8.13159056e-06,\n", - " 9.99998757e-01])" - ] - }, - "execution_count": 171, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca.components_[0] " - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "lg=LinearRegression()" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test= train_test_split(X_transformed, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lg.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.70664730600167636" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lg.score(X_test, y_test) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Seciton 2.5 Dimensionality reduction Continued : PCA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is Principal Components Analysis (PCA)?\n", - "\n", - "PCA is a complexity-reduction technique that tries to reduce a set of variables down to a smaller set of components that represent most of the information in the variables. At a conceptual level, PCA works by identifying sets of variables that share variance, and creating a component to represent that variance. For example, the two images below represent the two different ways of sharing variance across three variables. In each, overlapping areas represent shared variance. \n", - "\n", - "![PCA example](pca_pic.png)\n", - "\n", - "A PCA of set A would probably result in one component representing the variance shared by all three, discarding the rest of the information in the circles. A PCA of set B, on the other hand, would probably result in two components, one representing the overlapping area shared by magenta and cyan, and one representing the variance in yellow not already included in the magenta/cyan component. In both cases, some variance is lost.\n", - "\n", - "## Why PCA?\n", - "Losing variance in exchange for a smaller set of features can be worthwhile. Some model types (such as regression) assume that features will be uncorrelated with each other, and high levels of inter-feature correlation create unstable solutions. Solutions with fewer features are easier to understand and are more computationally efficient. Solutions with fewer features are also less vulnerable to overfitting.\n", - "\n", - "Curse of dimensionality: \n", - "Working with data becomes more demanding as the number of dimensions increases. \n", - "![CurseofDimensionality](curse_of_dimensionality.png)\n", - "\n", - "With n = 1, there are only 5 boxes to search. With n = 2, there are now 25 boxes; and with n = 3, there are 125 boxes to search. As n gets bigger, it becomes difficult to sample all the boxes. This makes the treasure harder to find — especially as many of the boxes are likely to be empty!\n", - "In general, with n dimensions each allowing for m states, we will have m^n possible combinations. Try plugging in a few different values and you will be convinced that this presents a workload-versus-sampling challenge to machines tasked with repeatedly sampling different combinations of variables.\n", - "With high-dimensional data, we simply cannot comprehensively sample all the possible combinations, leaving vast regions of feature space in the dark." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import math\n", - "from matplotlib.mlab import PCA as mlabPCA\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.decomposition import PCA " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " cntry idno year tvtot ppltrst pplfair pplhlp happy sclmeet sclact \\\n", - "0 CH 5.0 6 3.0 3.0 10.0 5.0 8.0 5.0 4.0 \n", - "1 CH 25.0 6 6.0 5.0 7.0 5.0 9.0 3.0 2.0 \n", - "\n", - " gndr agea partner \n", - "0 2.0 60.0 1.0 \n", - "1 2.0 59.0 1.0 \n" - ] - }, - { - "data": { - "image/png": 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N7a4d/dizc5AZ/IB8M0/NVnoP5DQiX2UdeOSlkrTuSnqXmmr0H3/8cViWhQce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C+29RyDKBYbqLq0qVt//nhWNQFAmyTGC7tnHH4vn58RVCRJMR1WrXfFJKce17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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Make the scatterplot matrix.\n", - "\n", - "# Setting the default plot aesthetics to be prettier.\n", - "sns.set_style(\"white\")\n", - "\n", - "df = pd.read_csv('ESSdata.csv')\n", - "\n", - "print df.head(2)\n", - "\n", - "# Take a subset of the data for PCA. This limits to Swiss and Czech data from 2012\n", - "# and keeps only specific columns.\n", - "df_pca = df.loc[\n", - " ((df['cntry'] == 'CZ') | (df['cntry'] == 'CH')) & (df['year'] == 6),\n", - " ['tvtot','ppltrst','pplfair','pplhlp']\n", - "].dropna()\n", - "\n", - "t = sns.regplot(\n", - " 'ppltrst',\n", - " 'pplfair',\n", - " df_pca,\n", - " x_jitter=.49,\n", - " y_jitter=.49,\n", - " fit_reg=False\n", - ")\n", - "t.set(xlim=(-1, 11), ylim=(-1, 11))\n", - "t.axhline(0, color='k', linestyle='-', linewidth=2)\n", - "t.axvline(0, color='k', linestyle='-', linewidth=2)\n", - "t.axes.set_title('Raw data')\n", - "plt.show()\n", - "\n", - "# Standardizing variables by subtracting the mean and dividing by the standard\n", - "# deviation. Now both variables are on the same scale.\n", - "df_pca['ppltrst_z'] = (df_pca['ppltrst'] - df_pca['ppltrst'].mean()) / df_pca['ppltrst'].std()\n", - "df_pca['pplfair_z'] = (df_pca['pplfair'] - df_pca['pplfair'].mean()) / df_pca['pplfair'].std()\n", - "\n", - "t = sns.regplot(\n", - " 'ppltrst_z',\n", - " 'pplfair_z',\n", - " df_pca,\n", - " x_jitter=.49,\n", - " y_jitter=.49,\n", - " fit_reg=False\n", - ")\n", - "t.axhline(0, color='k', linestyle='-', linewidth=2)\n", - "t.axvline(0, color='k', linestyle='-', linewidth=2)\n", - "t.axes.set_title('Standardized data')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the plot above, both axes describe equal variance- they both run from about -3 to 2.5. If we were to drop one of the axes and describe the data using only the information from the other axis, we would lose roughly 50% of our information. \n", - "\n", - "However, if we fit a line through the origin that minimizes the distance between the line and each point and then rotate the data, our axes and their information value will change.\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Math Behind PCA\n", - "The \"rotation\" pictured above is based on calculating the covariance matrix of the data and applying a linear transformation (rotation) and deriving from that the eigenvalues and eigenvectors that express the amount of variance in the data explained by the new axes. To explore this further, let's delve into some linear algebra.\n", - "\n", - "## Covariance matrix\n", - "A correlation matrix, which we have discussed before, is a covariance matrix where the covariances have been divided by the variances. This standardizes the covariances so that they are all on the same scale (-1 to 1) and can be compared. Covariance matrices, like correlation matrices, contain information about the amount of variance shared between pairs of variables.\n", - "\n", - "The variance of x is the sum of the squared differences between each value in x ($x_i$) and the mean of x ($\\bar{x}$), divided by the sample size (*n*).\n", - "\n", - "$$var(x)=\\frac{\\sum(x_i-\\bar{x})^2}n$$\n", - "\n", - "(Note that the standard deviation is $\\sqrt{var(x)}$)\n", - "\n", - "The covariance between two variables x and y is the product of the differences of each variable value and its mean, divided by the sample size.\n", - "\n", - "$$cov(A)=\\sum\\frac{(x_i-\\bar{x})(y_i-\\bar{y})}n$$\n", - "\n", - "Here is the covariance matrix for the ESS data:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance Matrix:\n", - "[[ 1.00071174 -0.24535312 -0.23531159 -0.17820482]\n", - " [-0.24535312 1.00071174 0.60528939 0.49609931]\n", - " [-0.23531159 0.60528939 1.00071174 0.53193085]\n", - " [-0.17820482 0.49609931 0.53193085 1.00071174]]\n" - ] - } - ], - "source": [ - "# Take a subset of the data for PCA and drop missing values because PCA cannot\n", - "# handle them. We could also impute, but missingness is quite low so dropping\n", - "# missing rows is unlikely to create bias.\n", - "df_pca = df.loc[\n", - " ((df['cntry'] == 'CZ') | (df['cntry'] == 'CH')) & (df['year']==6),\n", - " ['tvtot','ppltrst','pplfair','pplhlp']\n", - "].dropna()\n", - "\n", - "# Normalize the data so that all variables have a mean of 0 and standard deviation\n", - "# of 1.\n", - "X = StandardScaler().fit_transform(df_pca)\n", - "\n", - "# The NumPy covariance function assumes that variables are represented by rows,\n", - "# not columns, so we transpose X.\n", - "Xt = X.T\n", - "Cx = np.cov(Xt)\n", - "print 'Covariance Matrix:\\n', Cx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Eigenvectors\n", - "An Eigenvector is the directional aspect of a component – it is the red line in the graph earlier. During PCA, the eigenvectors are chosen to be orthogonal, that is, to have a correlation of 0 with one another. In fact, this is done sequentially. First, a vector is found that minimizes the distance between that vector and the datapoints. This vector is the first component. Next, a second vector is found that also minimizes the distance between that vector and the datapoints, the catch being that this second vector must be perpendicular to the first in one of the n dimensions of the space. This procedure continues until there are n vectors.\n", - "\n", - "## Eigenvalues\n", - "Eigenvalues represent the length of the Eigenvectors – each eigenvector has an eigenvalue. The length of the eigenvector encodes the proportion of total variance explained by a component. The total variance is equal to the number of variables in the PCA. Thus, an Eigenvalue of 1 means that the component explains the same amount of variance as one variable. An eigenvalue greater than 1 is desirable, since a component with an eigenvalue of 1 adds no value beyond the information contained in any individual variable, and an eigenvalue of less than 1 is actually less efficient at conveying information than a variable by itself. An eigenvalue of 2 means that the component contains an amount of information equal to that of two variables. Of course, it doesn’t mean that only two variables load on that component.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eigenvector 1: \n", - "[[-0.30084526]\n", - " [ 0.55945823]\n", - " [ 0.5681188 ]\n", - " [ 0.52320135]]\n", - "Eigenvalue 1: 2.2112568632\n", - "----------------------------------------\n", - "Eigenvector 2: \n", - "[[ 0.94786152]\n", - " [ 0.11600843]\n", - " [ 0.15403897]\n", - " [ 0.25371751]]\n", - "Eigenvalue 2: 0.884741357071\n", - "----------------------------------------\n", - "Eigenvector 3: \n", - "[[ 0.10491426]\n", - " [ 0.51597659]\n", - " [ 0.28505945]\n", - " [-0.80093836]]\n", - "Eigenvalue 3: 0.515142138924\n", - "----------------------------------------\n", - "Eigenvector 4: \n", - "[[ 0.00660771]\n", - " [ 0.63821367]\n", - " [-0.75647481]\n", - " [ 0.14277786]]\n", - "Eigenvalue 4: 0.391706615894\n", - "----------------------------------------\n", - "The percentage of total variance in the dataset explained by each component calculated by hand \n", - "[ 0.55242103 0.22102802 0.12869394 0.097857 ]\n" - ] - } - ], - "source": [ - "# Calculating eigenvalues and eigenvectors.\n", - "eig_val_cov, eig_vec_cov = np.linalg.eig(Cx)\n", - "\n", - "# Inspecting the eigenvalues and eigenvectors.\n", - "for i in range(len(eig_val_cov)):\n", - " eigvec_cov = eig_vec_cov[:, i].reshape(1, 4).T\n", - " print('Eigenvector {}: \\n{}'.format(i + 1, eigvec_cov))\n", - " print('Eigenvalue {}: {}'.format(i + 1, eig_val_cov[i]))\n", - " print(40 * '-')\n", - "\n", - "print 'The percentage of total variance in the dataset explained by each component calculated by hand \\n', eig_val_cov / sum(eig_val_cov)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## How many components?\n", - "The biggest decision to make when running a PCA is how many components to keep. PCA will actually give us back as many components as there are variables in the correlation matrix. If we have n variables and choose to keep n components, we will be able to reproduce 100% of the information in the original data. On the other hand, we won’t have simplified our situation at all – we’ll still be dealing with the same number of separate pieces of information, just expressed as components instead of as variables.\n", - "\n", - "There are a number of rules to guide us in choosing the number of components to keep. The most straightforward is to keep components with eigenvalues greater than 1, as they add value (because they contain more information than a single variable). This rule tends to keep more components than is ideal.\n", - "\n", - "Another rule is to visualize the eigenvalues in order from highest to lowest, connecting them with a line. This is called a \"scree\" plot because it supposedly resembles the loose rock that accumulates at the foot of a mountain. Upon visual inspection, the analyst will keep all the components whose eigenvalue falls above the point where the slope of the line changes the most drastically, also called the \"elbow\". \n", - "\n", - "Many other rules also exist, including variance cutoffs where we only keep components that explain at least x% of the variance in the data, and programmatic ones involving simulating the PCA solution on equivalent randomized data. Ultimately, the choice of how many components to keep comes down to your reasons for doing PCA.\n", - "\n", - "Let's see how many " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(eig_val_cov)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scree plot and the eigenvalues >1 rule agree that we should keep only the first component. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "PCA is a series of linear transformations applied to a data frame to yield a smaller number of columns that explain a large proportion of the total variance contained in the data frame." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "# Python Installations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1) Install Anaconda \n", - "https://www.anaconda.com/download/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2) Install Jupyter Notebook \n", - "https://jupyter.org/install" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3) To launch Notebook \n", - "### In your terminal, enter\n", - "### jupyter notebook " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4) Install Python Libraries\n", - "### Conda install pandas \n", - "### Conda install numpy " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Part 1/curse_of_dimensionality.png b/Part 1/curse_of_dimensionality.png deleted file mode 100755 index 3e1585859785f5d9293347c2a2c9c85fa0ae9e9a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 94166 zcmaI7Q*@&c8 zYs`T@dJmKTDS-fs0}BEIf*>U+st5uCP6Pr1iU|$&b!GLY2^<8(7eq=_NZEDu?ENrB zYblOCR3g)IHYtP}=~76ZNN74NpQ?O8v8mOgW6SpM?d#g#K2O&U)taWi6L*eH>G>R2 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zH^046qWA60VJHs|rUBVo->oc$Kd7O@DOZNDcpwHs?G1hDjgevlod+YF^l)Qes9#G* zpm@vrIoyQIuqgwui&f(+-8#7PHYzGg$xaIOn)m-SRoguDf0^CQpm9#qW=SX0*ku$2 QMHE9$TKQp#r0I+Q0sDUvD*ylh From 540ba520e8988c2b4edfce039fb2133450e6cd1e Mon Sep 17 00:00:00 2001 From: Mark Saleh Date: Mon, 18 Apr 2022 18:43:33 +0200 Subject: [PATCH 2/2] Rename the folders The folders were renamed according to their section order respectively --- {Part 2 => Part 1}/Data_Wrangling.ipynb | 0 {Part 2 => Part 1}/single_family_home_values.csv | 0 {Part 3 => Part 2}/ESSdata.csv | 0 {Part 3 => Part 2}/Section-2.ipynb | 0 {Part 3 => Part 2}/curse_of_dimensionality.png | Bin {Part 3 => Part 2}/pca_pic.png | Bin {Part 3 => Part 2}/pca_vector.png | Bin {Part 3 => Part 2}/svm.png | Bin {Part 4 => Part 3}/Section 3.3_3.4.ipynb | 0 {Part 4 => Part 3}/Section_3.1_3.2.ipynb | 0 {Part 4 => Part 3}/assets/spark_ecosystem.png | Bin {Part 4 => Part 3}/assets/spark_execution.png | Bin {Part 4 => Part 3}/data/AAPL.csv | 0 {Part 4 => Part 3}/spark-install/README.md | 0 .../spark-install/images/spark-install-21.png | Bin {Part 4 => Part 3}/spark-install/setup_aws.md | 0 {Part 4 => Part 3}/spark-install/spark-install.sh | 0 {Part 5 => Part 4}/Section-4.ipynb | 0 {Part 5 => Part 4}/test.db | Bin {Part 6 => Part 5}/Section-5 .ipynb | 0 {Part 6 => Part 5}/data/SMSSpamCollection | 0 21 files changed, 0 insertions(+), 0 deletions(-) rename {Part 2 => Part 1}/Data_Wrangling.ipynb (100%) mode change 100755 => 100644 rename {Part 2 => Part 1}/single_family_home_values.csv (100%) mode change 100755 => 100644 rename {Part 3 => Part 2}/ESSdata.csv (100%) mode change 100755 => 100644 rename {Part 3 => Part 2}/Section-2.ipynb (100%) mode change 100755 => 100644 rename {Part 3 => Part 2}/curse_of_dimensionality.png (100%) mode change 100755 => 100644 rename {Part 3 => Part 2}/pca_pic.png (100%) mode change 100755 => 100644 rename {Part 3 => Part 2}/pca_vector.png (100%) mode change 100755 => 100644 rename {Part 3 => Part 2}/svm.png (100%) mode change 100755 => 100644 rename {Part 4 => Part 3}/Section 3.3_3.4.ipynb (100%) mode change 100755 => 100644 rename {Part 4 => Part 3}/Section_3.1_3.2.ipynb (100%) mode change 100755 => 100644 rename {Part 4 => Part 3}/assets/spark_ecosystem.png (100%) mode change 100755 => 100644 rename {Part 4 => Part 3}/assets/spark_execution.png (100%) mode change 100755 => 100644 rename {Part 4 => Part 3}/data/AAPL.csv (100%) mode change 100755 => 100644 rename {Part 4 => Part 3}/spark-install/README.md (100%) mode change 100755 => 100644 rename {Part 4 => Part 3}/spark-install/images/spark-install-21.png (100%) mode change 100755 => 100644 rename {Part 4 => Part 3}/spark-install/setup_aws.md (100%) mode change 100755 => 100644 rename {Part 4 => Part 3}/spark-install/spark-install.sh (100%) mode change 100755 => 100644 rename {Part 5 => Part 4}/Section-4.ipynb (100%) mode change 100755 => 100644 rename {Part 5 => Part 4}/test.db (100%) mode change 100755 => 100644 rename {Part 6 => Part 5}/Section-5 .ipynb (100%) mode change 100755 => 100644 rename {Part 6 => Part 5}/data/SMSSpamCollection (100%) mode change 100755 => 100644 diff --git a/Part 2/Data_Wrangling.ipynb b/Part 1/Data_Wrangling.ipynb old mode 100755 new mode 100644 similarity index 100% rename from Part 2/Data_Wrangling.ipynb rename to Part 1/Data_Wrangling.ipynb diff --git a/Part 2/single_family_home_values.csv b/Part 1/single_family_home_values.csv old mode 100755 new mode 100644 similarity index 100% rename from Part 2/single_family_home_values.csv rename to Part 1/single_family_home_values.csv diff --git a/Part 3/ESSdata.csv b/Part 2/ESSdata.csv old mode 100755 new mode 100644 similarity index 100% rename from Part 3/ESSdata.csv rename to Part 2/ESSdata.csv diff --git a/Part 3/Section-2.ipynb b/Part 2/Section-2.ipynb old mode 100755 new mode 100644 similarity index 100% rename from Part 3/Section-2.ipynb rename to Part 2/Section-2.ipynb diff --git a/Part 3/curse_of_dimensionality.png b/Part 2/curse_of_dimensionality.png old mode 100755 new mode 100644 similarity index 100% rename from Part 3/curse_of_dimensionality.png rename to Part 2/curse_of_dimensionality.png diff --git a/Part 3/pca_pic.png b/Part 2/pca_pic.png old mode 100755 new mode 100644 similarity index 100% rename from Part 3/pca_pic.png rename to Part 2/pca_pic.png diff --git a/Part 3/pca_vector.png b/Part 2/pca_vector.png old mode 100755 new mode 100644 similarity index 100% rename from Part 3/pca_vector.png rename to Part 2/pca_vector.png diff --git a/Part 3/svm.png b/Part 2/svm.png old mode 100755 new mode 100644 similarity index 100% rename from Part 3/svm.png rename to Part 2/svm.png diff --git a/Part 4/Section 3.3_3.4.ipynb b/Part 3/Section 3.3_3.4.ipynb old mode 100755 new mode 100644 similarity index 100% rename from Part 4/Section 3.3_3.4.ipynb rename to Part 3/Section 3.3_3.4.ipynb diff --git a/Part 4/Section_3.1_3.2.ipynb b/Part 3/Section_3.1_3.2.ipynb old mode 100755 new mode 100644 similarity index 100% rename from Part 4/Section_3.1_3.2.ipynb rename to Part 3/Section_3.1_3.2.ipynb diff --git a/Part 4/assets/spark_ecosystem.png b/Part 3/assets/spark_ecosystem.png old mode 100755 new mode 100644 similarity index 100% rename from Part 4/assets/spark_ecosystem.png rename to Part 3/assets/spark_ecosystem.png diff --git a/Part 4/assets/spark_execution.png b/Part 3/assets/spark_execution.png old mode 100755 new mode 100644 similarity index 100% rename from Part 4/assets/spark_execution.png rename to Part 3/assets/spark_execution.png diff --git a/Part 4/data/AAPL.csv b/Part 3/data/AAPL.csv old mode 100755 new mode 100644 similarity index 100% rename from Part 4/data/AAPL.csv rename to Part 3/data/AAPL.csv diff --git a/Part 4/spark-install/README.md b/Part 3/spark-install/README.md old mode 100755 new mode 100644 similarity index 100% rename from Part 4/spark-install/README.md rename to Part 3/spark-install/README.md diff --git a/Part 4/spark-install/images/spark-install-21.png b/Part 3/spark-install/images/spark-install-21.png old mode 100755 new mode 100644 similarity index 100% rename from Part 4/spark-install/images/spark-install-21.png rename to Part 3/spark-install/images/spark-install-21.png diff --git a/Part 4/spark-install/setup_aws.md b/Part 3/spark-install/setup_aws.md old mode 100755 new mode 100644 similarity index 100% rename from Part 4/spark-install/setup_aws.md rename to Part 3/spark-install/setup_aws.md diff --git a/Part 4/spark-install/spark-install.sh b/Part 3/spark-install/spark-install.sh old mode 100755 new mode 100644 similarity index 100% rename from Part 4/spark-install/spark-install.sh rename to Part 3/spark-install/spark-install.sh diff --git a/Part 5/Section-4.ipynb b/Part 4/Section-4.ipynb old mode 100755 new mode 100644 similarity index 100% rename from Part 5/Section-4.ipynb rename to Part 4/Section-4.ipynb diff --git a/Part 5/test.db b/Part 4/test.db old mode 100755 new mode 100644 similarity index 100% rename from Part 5/test.db rename to Part 4/test.db diff --git a/Part 6/Section-5 .ipynb b/Part 5/Section-5 .ipynb old mode 100755 new mode 100644 similarity index 100% rename from Part 6/Section-5 .ipynb rename to Part 5/Section-5 .ipynb diff --git a/Part 6/data/SMSSpamCollection b/Part 5/data/SMSSpamCollection old mode 100755 new mode 100644 similarity index 100% rename from Part 6/data/SMSSpamCollection rename to Part 5/data/SMSSpamCollection